diff --git a/.gitignore b/.gitignore
index de5be7a..f6d25fe 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,7 @@
 data/
+benchmarks/data
+benchmarks/models
+*.pkl
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
diff --git a/benchmarks/benchmark/function-recognition-hard.json b/benchmarks/benchmark/function-recognition-hard.json
new file mode 100644
index 0000000..cbb2ec1
--- /dev/null
+++ b/benchmarks/benchmark/function-recognition-hard.json
@@ -0,0 +1,122 @@
+[
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.99, -9.87)\": -1.497, \"(-9.87, -9.71)\": -1.461, \"(-9.71, -9.58)\": -1.423, \"(-9.58, -9.45)\": -1.393, \"(-9.45, -9.32)\": -1.358, \"(-9.32, -9.17)\": -1.326, \"(-9.17, -9.05)\": -1.287, \"(-9.05, -8.9)\": -1.257, \"(-8.9, -8.79)\": -1.223, \"(-8.79, -8.67)\": -1.193, \"(-8.67, -8.54)\": -1.163, \"(-8.54, -8.38)\": -1.126, \"(-8.38, -8.25)\": -1.095, \"(-8.25, -8.13)\": -1.054, \"(-8.13, -7.97)\": -1.023, \"(-7.97, -7.84)\": -0.989, \"(-7.84, -7.72)\": -0.958, \"(-7.72, -7.6)\": -0.927, \"(-7.6, -7.49)\": -0.896, \"(-7.49, -7.35)\": -0.866, \"(-7.35, -7.24)\": -0.836, \"(-7.24, -7.13)\": -0.806, \"(-7.13, -7.0)\": -0.776, \"(-7.0, -6.84)\": -0.746, \"(-6.84, -6.72)\": -0.707, \"(-6.72, -6.59)\": -0.676, \"(-6.59, -6.44)\": -0.643, \"(-6.44, -6.29)\": -0.608, \"(-6.29, -6.12)\": -0.574, \"(-6.12, -5.97)\": -0.521, \"(-5.97, -5.82)\": -0.485, \"(-5.82, -5.69)\": -0.453, \"(-5.69, -5.57)\": -0.422, \"(-5.57, -5.44)\": -0.39, \"(-5.44, -5.31)\": -0.356, \"(-5.31, -5.17)\": -0.322, \"(-5.17, -5.05)\": -0.291, \"(-5.05, -4.92)\": -0.26, \"(-4.92, -4.8)\": -0.228, \"(-4.8, -4.67)\": -0.196, \"(-4.67, -4.54)\": -0.162, \"(-4.54, -4.4)\": -0.131, \"(-4.4, -4.25)\": -0.097, \"(-4.25, -4.14)\": -0.066, \"(-4.14, -4.02)\": -0.035, \"(-4.02, -3.89)\": -0.002, \"(-3.89, -3.73)\": 0.034, \"(-3.73, -3.59)\": 0.071, \"(-3.59, -3.46)\": 0.103, \"(-3.46, -3.33)\": 0.137, \"(-3.33, -3.19)\": 0.168, \"(-3.19, -3.05)\": 0.203, \"(-3.05, -2.94)\": 0.235, \"(-2.94, -2.79)\": 0.265, \"(-2.79, -2.67)\": 0.296, \"(-2.67, -2.52)\": 0.328, \"(-2.52, -2.37)\": 0.36, \"(-2.37, -2.19)\": 0.395, \"(-2.19, -2.01)\": 0.431, \"(-2.01, -1.83)\": 0.463, \"(-1.83, -1.56)\": 0.494, \"(-1.56, -0.93)\": 0.527, \"(-0.93, -0.81)\": 0.497, \"(-0.81, -0.66)\": 0.462, \"(-0.66, -0.52)\": 0.385, \"(-0.52, -0.44)\": 0.336, \"(-0.44, -0.37)\": 0.299, \"(-0.37, -0.31)\": 0.248, \"(-0.31, -0.26)\": 0.217, \"(-0.26, -0.2)\": 0.182, \"(-0.2, -0.16)\": 0.144, \"(-0.16, -0.1)\": 0.111, \"(-0.1, -0.04)\": 0.06, \"(-0.04, -0.01)\": 0.026, \"(-0.01, 0.09)\": -0.014, \"(0.09, 0.17)\": -0.087, \"(0.17, 0.26)\": -0.143, \"(0.26, 0.33)\": -0.206, \"(0.33, 0.39)\": -0.244, \"(0.39, 0.46)\": -0.278, \"(0.46, 0.53)\": -0.325, \"(0.53, 0.59)\": -0.357, \"(0.59, 0.68)\": -0.393, \"(0.68, 0.78)\": -0.427, \"(0.78, 0.96)\": -0.472, \"(0.96, 1.89)\": -0.509, \"(1.89, 2.1)\": -0.476, \"(2.1, 2.27)\": -0.443, \"(2.27, 2.42)\": -0.412, \"(2.42, 2.57)\": -0.379, \"(2.57, 2.71)\": -0.346, \"(2.71, 2.85)\": -0.312, \"(2.85, 2.99)\": -0.268, \"(2.99, 3.16)\": -0.236, \"(3.16, 3.29)\": -0.201, \"(3.29, 3.45)\": -0.167, \"(3.45, 3.57)\": -0.134, \"(3.57, 3.72)\": -0.103, \"(3.72, 3.88)\": -0.061, \"(3.88, 3.99)\": -0.029, \"(3.99, 4.12)\": 0.002, \"(4.12, 4.24)\": 0.034, \"(4.24, 4.39)\": 0.068, \"(4.39, 4.52)\": 0.1, \"(4.52, 4.65)\": 0.132, \"(4.65, 4.79)\": 0.167, \"(4.79, 4.94)\": 0.199, \"(4.94, 5.08)\": 0.241, \"(5.08, 5.24)\": 0.275, \"(5.24, 5.41)\": 0.32, \"(5.41, 5.52)\": 0.352, \"(5.52, 5.65)\": 0.384, \"(5.65, 5.79)\": 0.415, \"(5.79, 5.92)\": 0.449, \"(5.92, 6.03)\": 0.482, \"(6.03, 6.17)\": 0.515, \"(6.17, 6.3)\": 0.546, \"(6.3, 6.43)\": 0.577, \"(6.43, 6.55)\": 0.608, \"(6.55, 6.65)\": 0.639, \"(6.65, 6.82)\": 0.67, \"(6.82, 6.96)\": 0.71, \"(6.96, 7.09)\": 0.743, \"(7.09, 7.23)\": 0.776, \"(7.23, 7.35)\": 0.808, \"(7.35, 7.48)\": 0.842, \"(7.48, 7.6)\": 0.872, \"(7.6, 7.73)\": 0.903, \"(7.73, 7.85)\": 0.935, \"(7.85, 8.01)\": 0.972, \"(8.01, 8.14)\": 1.007, \"(8.14, 8.28)\": 1.038, \"(8.28, 8.42)\": 1.073, \"(8.42, 8.55)\": 1.106, \"(8.55, 8.67)\": 1.139, \"(8.67, 8.81)\": 1.172, \"(8.81, 8.93)\": 1.202, \"(8.93, 9.06)\": 1.236, \"(9.06, 9.2)\": 1.269, \"(9.2, 9.33)\": 1.303, \"(9.33, 9.47)\": 1.335, \"(9.47, 9.59)\": 1.368, \"(9.59, 9.73)\": 1.4, \"(9.73, 9.86)\": 1.436, \"(9.86, 10.0)\": 1.471}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = -tanh(x) + 1/4 * x\nb) f(x) = abs(x ** 2 - 20)\nc) f(x) = 1/2 * (x+1) ** 4 - 5 * (x+2) **3 + 3 * x ** 2 + 2 * x + 1\nd) f(x) = exp(-x+1)+ 2000 * abs(x+1)\ne) f(x) = x ** 3 + 250 * sin(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "a)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-10.0, -9.95)\": -0.937, \"(-9.95, -9.89)\": -0.986, \"(-9.89, -9.82)\": -1.035, \"(-9.82, -9.77)\": -1.088, \"(-9.77, -9.69)\": -1.157, \"(-9.69, -9.62)\": -1.211, \"(-9.62, -9.54)\": -1.282, \"(-9.54, -9.46)\": -1.374, \"(-9.46, -9.41)\": -1.434, \"(-9.41, -9.34)\": -1.493, \"(-9.34, -9.29)\": -1.55, \"(-9.29, -9.23)\": -1.603, \"(-9.23, -9.16)\": -1.671, \"(-9.16, -9.1)\": -1.729, \"(-9.1, -9.05)\": -1.79, \"(-9.05, -8.95)\": -1.854, \"(-8.95, -8.88)\": -1.932, \"(-8.88, -8.78)\": -2.008, \"(-8.78, -8.72)\": -2.07, \"(-8.72, -8.63)\": -2.119, \"(-8.63, -8.59)\": -2.172, \"(-8.59, -8.46)\": -2.221, \"(-8.46, -8.36)\": -2.284, \"(-8.36, -8.22)\": -2.334, \"(-8.22, -7.43)\": -2.403, \"(-7.43, -7.32)\": -2.339, \"(-7.32, -7.22)\": -2.288, \"(-7.22, -7.15)\": -2.231, \"(-7.15, -7.06)\": -2.178, \"(-7.06, -6.98)\": -2.126, \"(-6.98, -6.92)\": -2.068, \"(-6.92, -6.85)\": -2.018, \"(-6.85, -6.78)\": -1.935, \"(-6.78, -6.72)\": -1.883, \"(-6.72, -6.66)\": -1.832, \"(-6.66, -6.61)\": -1.78, \"(-6.61, -6.56)\": -1.73, \"(-6.56, -6.52)\": -1.68, \"(-6.52, -6.45)\": -1.63, \"(-6.45, -6.38)\": -1.569, \"(-6.38, -6.32)\": -1.506, \"(-6.32, -6.27)\": -1.445, \"(-6.27, -6.2)\": -1.381, \"(-6.2, -6.12)\": -1.301, \"(-6.12, -6.07)\": -1.246, \"(-6.07, -6.01)\": -1.187, \"(-6.01, -5.95)\": -1.121, \"(-5.95, -5.89)\": -1.066, \"(-5.89, -5.83)\": -1.002, \"(-5.83, -5.77)\": -0.948, \"(-5.77, -5.7)\": -0.886, \"(-5.7, -5.61)\": -0.819, \"(-5.61, -5.53)\": -0.764, \"(-5.53, -5.45)\": -0.698, \"(-5.45, -5.38)\": -0.647, \"(-5.38, -5.27)\": -0.592, \"(-5.27, -5.16)\": -0.529, \"(-5.16, -4.97)\": -0.463, \"(-4.97, -4.21)\": -0.406, \"(-4.21, -4.08)\": -0.477, \"(-4.08, -4.0)\": -0.526, \"(-4.0, -3.92)\": -0.576, \"(-3.92, -3.84)\": -0.629, \"(-3.84, -3.76)\": -0.681, \"(-3.76, -3.68)\": -0.74, \"(-3.68, -3.62)\": -0.798, \"(-3.62, -3.52)\": -0.853, \"(-3.52, -3.44)\": -0.945, \"(-3.44, -3.36)\": -1.011, \"(-3.36, -3.29)\": -1.081, \"(-3.29, -3.17)\": -1.174, \"(-3.17, -3.11)\": -1.246, \"(-3.11, -3.04)\": -1.298, \"(-3.04, -2.97)\": -1.365, \"(-2.97, -2.89)\": -1.443, \"(-2.89, -2.83)\": -1.493, \"(-2.83, -2.77)\": -1.545, \"(-2.77, -2.67)\": -1.622, \"(-2.67, -2.59)\": -1.683, \"(-2.59, -2.5)\": -1.738, \"(-2.5, -2.42)\": -1.795, \"(-2.42, -2.33)\": -1.846, \"(-2.33, -2.22)\": -1.9, \"(-2.22, -2.07)\": -1.95, \"(-2.07, -1.45)\": -2.0, \"(-1.45, -1.33)\": -1.95, \"(-1.33, -1.22)\": -1.884, \"(-1.22, -1.09)\": -1.771, \"(-1.09, -1.0)\": -1.691, \"(-1.0, -0.93)\": -1.59, \"(-0.93, -0.86)\": -1.537, \"(-0.86, -0.81)\": -1.462, \"(-0.81, -0.77)\": -1.395, \"(-0.77, -0.74)\": -1.341, \"(-0.74, -0.67)\": -1.286, \"(-0.67, -0.56)\": -1.135, \"(-0.56, -0.51)\": -1.01, \"(-0.51, -0.47)\": -0.948, \"(-0.47, -0.43)\": -0.888, \"(-0.43, -0.37)\": -0.768, \"(-0.37, -0.35)\": -0.712, \"(-0.35, -0.3)\": -0.65, \"(-0.3, -0.25)\": -0.544, \"(-0.25, -0.22)\": -0.482, \"(-0.22, -0.18)\": -0.407, \"(-0.18, -0.16)\": -0.348, \"(-0.16, -0.12)\": -0.274, \"(-0.12, -0.09)\": -0.221, \"(-0.09, -0.04)\": -0.115, \"(-0.04, -0.01)\": -0.059, \"(-0.01, 0.04)\": 0.029, \"(0.04, 0.09)\": 0.083, \"(0.09, 0.19)\": 0.268, \"(0.19, 0.27)\": 0.47, \"(0.27, 0.32)\": 0.557, \"(0.32, 0.35)\": 0.627, \"(0.35, 0.39)\": 0.707, \"(0.39, 0.43)\": 0.774, \"(0.43, 0.5)\": 0.867, \"(0.5, 0.56)\": 0.994, \"(0.56, 0.6)\": 1.053, \"(0.6, 0.66)\": 1.143, \"(0.66, 0.7)\": 1.228, \"(0.7, 0.75)\": 1.277, \"(0.75, 0.8)\": 1.339, \"(0.8, 0.83)\": 1.4, \"(0.83, 0.9)\": 1.472, \"(0.9, 0.95)\": 1.53, \"(0.95, 1.0)\": 1.587, \"(1.0, 1.06)\": 1.648, \"(1.06, 1.12)\": 1.704, \"(1.12, 1.2)\": 1.753, \"(1.2, 1.28)\": 1.814, \"(1.28, 1.41)\": 1.884, \"(1.41, 1.55)\": 1.949, \"(1.55, 2.2)\": 1.998, \"(2.2, 2.32)\": 1.945, \"(2.32, 2.41)\": 1.895, \"(2.41, 2.5)\": 1.841, \"(2.5, 2.63)\": 1.76, \"(2.63, 2.72)\": 1.661, \"(2.72, 2.83)\": 1.591, \"(2.83, 2.9)\": 1.516, \"(2.9, 2.95)\": 1.465, \"(2.95, 3.04)\": 1.401, \"(3.04, 3.1)\": 1.333, \"(3.1, 3.18)\": 1.274, \"(3.18, 3.24)\": 1.211, \"(3.24, 3.31)\": 1.158, \"(3.31, 3.36)\": 1.105, \"(3.36, 3.43)\": 1.054, \"(3.43, 3.49)\": 1.002, \"(3.49, 3.55)\": 0.939, \"(3.55, 3.64)\": 0.887, \"(3.64, 3.72)\": 0.808, \"(3.72, 3.81)\": 0.756, \"(3.81, 3.92)\": 0.665, \"(3.92, 3.99)\": 0.615, \"(3.99, 4.1)\": 0.565, \"(4.1, 4.22)\": 0.51, \"(4.22, 4.39)\": 0.45, \"(4.39, 5.06)\": 0.395, \"(5.06, 5.2)\": 0.45, \"(5.2, 5.32)\": 0.505, \"(5.32, 5.4)\": 0.57, \"(5.4, 5.48)\": 0.62, \"(5.48, 5.53)\": 0.678, \"(5.53, 5.62)\": 0.731, \"(5.62, 5.7)\": 0.783, \"(5.7, 5.78)\": 0.863, \"(5.78, 5.84)\": 0.919, \"(5.84, 5.89)\": 0.981, \"(5.89, 5.97)\": 1.038, \"(5.97, 6.03)\": 1.102, \"(6.03, 6.08)\": 1.16, \"(6.08, 6.14)\": 1.22, \"(6.14, 6.18)\": 1.278, \"(6.18, 6.27)\": 1.331, \"(6.27, 6.34)\": 1.431, \"(6.34, 6.4)\": 1.482, \"(6.4, 6.45)\": 1.54, \"(6.45, 6.51)\": 1.601, \"(6.51, 6.56)\": 1.651, \"(6.56, 6.62)\": 1.705, \"(6.62, 6.65)\": 1.758, \"(6.65, 6.74)\": 1.811, \"(6.74, 6.81)\": 1.865, \"(6.81, 6.87)\": 1.928, \"(6.87, 6.93)\": 1.989, \"(6.93, 7.0)\": 2.042, \"(7.0, 7.09)\": 2.094, \"(7.09, 7.22)\": 2.181, \"(7.22, 7.31)\": 2.24, \"(7.31, 7.44)\": 2.296, \"(7.44, 7.62)\": 2.36, \"(7.62, 8.29)\": 2.413, \"(8.29, 8.42)\": 2.352, \"(8.42, 8.51)\": 2.289, \"(8.51, 8.6)\": 2.24, \"(8.6, 8.69)\": 2.184, \"(8.69, 8.8)\": 2.117, \"(8.8, 8.89)\": 2.022, \"(8.89, 8.95)\": 1.97, \"(8.95, 9.04)\": 1.902, \"(9.04, 9.14)\": 1.786, \"(9.14, 9.2)\": 1.734, \"(9.2, 9.24)\": 1.677, \"(9.24, 9.32)\": 1.619, \"(9.32, 9.39)\": 1.558, \"(9.39, 9.46)\": 1.483, \"(9.46, 9.54)\": 1.413, \"(9.54, 9.63)\": 1.325, \"(9.63, 9.69)\": 1.253, \"(9.69, 9.74)\": 1.188, \"(9.74, 9.83)\": 1.127, \"(9.83, 9.9)\": 1.054, \"(9.9, 9.97)\": 0.996}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = x ** 3 + 250 * sin(x)\nb) f(x) = sin(x) + sin(0.5 * x)\nc) f(x) = exp(x)+ 2000 * abs(x)\nd) f(x) = log(x+10) + 1/3 * x \ne) f(x) = arctan(x) + sin(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "e)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.98, -9.87)\": 3.314, \"(-9.87, -9.79)\": 3.286, \"(-9.79, -9.7)\": 3.26, \"(-9.7, -9.59)\": 3.228, \"(-9.59, -9.49)\": 3.187, \"(-9.49, -9.4)\": 3.159, \"(-9.4, -9.31)\": 3.131, \"(-9.31, -9.23)\": 3.099, \"(-9.23, -9.14)\": 3.072, \"(-9.14, -9.04)\": 3.044, \"(-9.04, -8.97)\": 3.012, \"(-8.97, -8.86)\": 2.985, \"(-8.86, -8.77)\": 2.949, \"(-8.77, -8.7)\": 2.916, \"(-8.7, -8.58)\": 2.887, \"(-8.58, -8.47)\": 2.855, \"(-8.47, -8.39)\": 2.825, \"(-8.39, -8.3)\": 2.795, \"(-8.3, -8.22)\": 2.762, \"(-8.22, -8.14)\": 2.735, \"(-8.14, -8.05)\": 2.708, \"(-8.05, -7.97)\": 2.681, \"(-7.97, -7.89)\": 2.653, \"(-7.89, -7.8)\": 2.624, \"(-7.8, -7.7)\": 2.591, \"(-7.7, -7.61)\": 2.564, \"(-7.61, -7.52)\": 2.532, \"(-7.52, -7.42)\": 2.504, \"(-7.42, -7.33)\": 2.473, \"(-7.33, -7.26)\": 2.445, \"(-7.26, -7.18)\": 2.415, \"(-7.18, -7.06)\": 2.387, \"(-7.06, -6.97)\": 2.347, \"(-6.97, -6.86)\": 2.314, \"(-6.86, -6.76)\": 2.279, \"(-6.76, -6.68)\": 2.252, \"(-6.68, -6.54)\": 2.222, \"(-6.54, -6.4)\": 2.16, \"(-6.4, -6.29)\": 2.128, \"(-6.29, -6.21)\": 2.096, \"(-6.21, -6.13)\": 2.067, \"(-6.13, -6.04)\": 2.04, \"(-6.04, -5.94)\": 2.01, \"(-5.94, -5.85)\": 1.977, \"(-5.85, -5.75)\": 1.943, \"(-5.75, -5.63)\": 1.907, \"(-5.63, -5.51)\": 1.87, \"(-5.51, -5.39)\": 1.824, \"(-5.39, -5.3)\": 1.787, \"(-5.3, -5.22)\": 1.761, \"(-5.22, -5.12)\": 1.733, \"(-5.12, -5.04)\": 1.705, \"(-5.04, -4.96)\": 1.676, \"(-4.96, -4.85)\": 1.648, \"(-4.85, -4.76)\": 1.616, \"(-4.76, -4.67)\": 1.581, \"(-4.67, -4.59)\": 1.554, \"(-4.59, -4.49)\": 1.525, \"(-4.49, -4.4)\": 1.493, \"(-4.4, -4.31)\": 1.463, \"(-4.31, -4.24)\": 1.436, \"(-4.24, -4.15)\": 1.408, \"(-4.15, -4.06)\": 1.377, \"(-4.06, -3.97)\": 1.35, \"(-3.97, -3.9)\": 1.323, \"(-3.9, -3.79)\": 1.295, \"(-3.79, -3.7)\": 1.259, \"(-3.7, -3.59)\": 1.231, \"(-3.59, -3.49)\": 1.185, \"(-3.49, -3.39)\": 1.156, \"(-3.39, -3.3)\": 1.129, \"(-3.3, -3.21)\": 1.098, \"(-3.21, -3.12)\": 1.068, \"(-3.12, -3.05)\": 1.037, \"(-3.05, -2.95)\": 1.008, \"(-2.95, -2.86)\": 0.981, \"(-2.86, -2.77)\": 0.95, \"(-2.77, -2.68)\": 0.923, \"(-2.68, -2.59)\": 0.894, \"(-2.59, -2.49)\": 0.86, \"(-2.49, -2.36)\": 0.824, \"(-2.36, -2.26)\": 0.796, \"(-2.26, -2.15)\": 0.767, \"(-2.15, -1.99)\": 0.74, \"(-1.99, -1.62)\": 0.714, \"(-1.62, -1.54)\": 0.743, \"(-1.54, -1.49)\": 0.773, \"(-1.49, -1.42)\": 0.805, \"(-1.42, -1.38)\": 0.845, \"(-1.38, -1.33)\": 0.89, \"(-1.33, -1.3)\": 0.92, \"(-1.3, -1.26)\": 0.953, \"(-1.26, -1.22)\": 0.999, \"(-1.22, -1.16)\": 1.05, \"(-1.16, -1.1)\": 1.15, \"(-1.1, -1.07)\": 1.205, \"(-1.07, -1.04)\": 1.246, \"(-1.04, -1.02)\": 1.278, \"(-1.02, -0.98)\": 1.312, \"(-0.98, -0.94)\": 1.42, \"(-0.94, -0.92)\": 1.45, \"(-0.92, -0.9)\": 1.484, \"(-0.9, -0.87)\": 1.528, \"(-0.87, -0.84)\": 1.609, \"(-0.84, -0.81)\": 1.64, \"(-0.81, -0.77)\": 1.724, \"(-0.77, -0.75)\": 1.763, \"(-0.75, -0.72)\": 1.8, \"(-0.72, -0.7)\": 1.875, \"(-0.7, -0.69)\": 1.906, \"(-0.69, -0.67)\": 1.94, \"(-0.67, -0.65)\": 1.982, \"(-0.65, -0.61)\": 2.029, \"(-0.61, -0.58)\": 2.12, \"(-0.58, -0.56)\": 2.153, \"(-0.56, -0.54)\": 2.189, \"(-0.54, -0.51)\": 2.248, \"(-0.51, -0.48)\": 2.284, \"(-0.48, -0.45)\": 2.359, \"(-0.45, -0.4)\": 2.392, \"(-0.4, -0.37)\": 2.475, \"(-0.37, -0.33)\": 2.517, \"(-0.33, -0.26)\": 2.578, \"(-0.26, -0.19)\": 2.652, \"(-0.19, 0.19)\": 2.699, \"(0.19, 0.26)\": 2.672, \"(0.26, 0.31)\": 2.592, \"(0.31, 0.35)\": 2.55, \"(0.35, 0.37)\": 2.502, \"(0.37, 0.45)\": 2.452, \"(0.45, 0.53)\": 2.255, \"(0.53, 0.55)\": 2.226, \"(0.55, 0.57)\": 2.174, \"(0.57, 0.59)\": 2.129, \"(0.59, 0.61)\": 2.088, \"(0.61, 0.63)\": 2.059, \"(0.63, 0.66)\": 2.033, \"(0.66, 0.69)\": 1.939, \"(0.69, 0.72)\": 1.91, \"(0.72, 0.77)\": 1.796, \"(0.77, 0.8)\": 1.746, \"(0.8, 0.81)\": 1.692, \"(0.81, 0.83)\": 1.666, \"(0.83, 0.85)\": 1.637, \"(0.85, 0.89)\": 1.592, \"(0.89, 0.94)\": 1.482, \"(0.94, 0.98)\": 1.405, \"(0.98, 1.02)\": 1.321, \"(1.02, 1.05)\": 1.284, \"(1.05, 1.11)\": 1.23, \"(1.11, 1.16)\": 1.115, \"(1.16, 1.19)\": 1.082, \"(1.19, 1.22)\": 1.054, \"(1.22, 1.26)\": 1.009, \"(1.26, 1.32)\": 0.952, \"(1.32, 1.38)\": 0.893, \"(1.38, 1.42)\": 0.862, \"(1.42, 1.48)\": 0.829, \"(1.48, 1.54)\": 0.791, \"(1.54, 1.63)\": 0.761, \"(1.63, 2.22)\": 0.733, \"(2.22, 2.33)\": 0.759, \"(2.33, 2.44)\": 0.791, \"(2.44, 2.55)\": 0.824, \"(2.55, 2.67)\": 0.867, \"(2.67, 2.76)\": 0.896, \"(2.76, 2.83)\": 0.923, \"(2.83, 2.98)\": 0.967, \"(2.98, 3.09)\": 0.993, \"(3.09, 3.21)\": 1.042, \"(3.21, 3.29)\": 1.071, \"(3.29, 3.34)\": 1.098, \"(3.34, 3.45)\": 1.124, \"(3.45, 3.58)\": 1.157, \"(3.58, 3.69)\": 1.202, \"(3.69, 3.78)\": 1.234, \"(3.78, 3.86)\": 1.262, \"(3.86, 3.95)\": 1.289, \"(3.95, 4.03)\": 1.318, \"(4.03, 4.13)\": 1.346, \"(4.13, 4.27)\": 1.395, \"(4.27, 4.36)\": 1.427, \"(4.36, 4.46)\": 1.456, \"(4.46, 4.53)\": 1.49, \"(4.53, 4.64)\": 1.52, \"(4.64, 4.72)\": 1.55, \"(4.72, 4.81)\": 1.578, \"(4.81, 4.91)\": 1.611, \"(4.91, 4.99)\": 1.638, \"(4.99, 5.08)\": 1.669, \"(5.08, 5.16)\": 1.695, \"(5.16, 5.25)\": 1.723, \"(5.25, 5.33)\": 1.757, \"(5.33, 5.46)\": 1.792, \"(5.46, 5.53)\": 1.819, \"(5.53, 5.62)\": 1.845, \"(5.62, 5.73)\": 1.883, \"(5.73, 5.82)\": 1.916, \"(5.82, 5.92)\": 1.942, \"(5.92, 6.01)\": 1.976, \"(6.01, 6.08)\": 2.004, \"(6.08, 6.17)\": 2.033, \"(6.17, 6.27)\": 2.063, \"(6.27, 6.36)\": 2.093, \"(6.36, 6.43)\": 2.121, \"(6.43, 6.52)\": 2.147, \"(6.52, 6.61)\": 2.176, \"(6.61, 6.7)\": 2.205, \"(6.7, 6.81)\": 2.241, \"(6.81, 6.91)\": 2.277, \"(6.91, 7.01)\": 2.305, \"(7.01, 7.09)\": 2.341, \"(7.09, 7.18)\": 2.373, \"(7.18, 7.28)\": 2.399, \"(7.28, 7.37)\": 2.437, \"(7.37, 7.48)\": 2.466, \"(7.48, 7.58)\": 2.501, \"(7.58, 7.65)\": 2.527, \"(7.65, 7.75)\": 2.555, \"(7.75, 7.83)\": 2.585, \"(7.83, 7.92)\": 2.613, \"(7.92, 8.03)\": 2.642, \"(8.03, 8.1)\": 2.675, \"(8.1, 8.2)\": 2.705, \"(8.2, 8.29)\": 2.734, \"(8.29, 8.37)\": 2.765, \"(8.37, 8.45)\": 2.792, \"(8.45, 8.53)\": 2.82, \"(8.53, 8.63)\": 2.846, \"(8.63, 8.71)\": 2.88, \"(8.71, 8.8)\": 2.906, \"(8.8, 8.89)\": 2.938, \"(8.89, 8.97)\": 2.969, \"(8.97, 9.07)\": 2.996, \"(9.07, 9.15)\": 3.025, \"(9.15, 9.24)\": 3.055, \"(9.24, 9.33)\": 3.083, \"(9.33, 9.42)\": 3.11, \"(9.42, 9.51)\": 3.143, \"(9.51, 9.6)\": 3.175, \"(9.6, 9.69)\": 3.202, \"(9.69, 9.8)\": 3.24, \"(9.8, 9.88)\": 3.27, \"(9.88, 9.98)\": 3.297, \"(9.98, 10.0)\": 3.329}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = log(x+10) + 1/3 * x \nb) f(x) = sin(x)+cos(x)\nc) f(x) = |sin(x/2)|\nd) f(x) = arctan(x) + sin(x)\ne) f(x) = exp(-x^2+1)+ 1/3 * |x|\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "e)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-10.0, -9.96)\": 76762.2, \"(-9.96, -9.95)\": 75826.3, \"(-9.95, -9.92)\": 74501.1, \"(-9.92, -9.9)\": 72352.3, \"(-9.9, -9.89)\": 71545.1, \"(-9.89, -9.86)\": 70589.9, \"(-9.86, -9.83)\": 68904.8, \"(-9.83, -9.8)\": 67406.0, \"(-9.8, -9.78)\": 66299.7, \"(-9.78, -9.76)\": 65251.4, \"(-9.76, -9.74)\": 64255.3, \"(-9.74, -9.71)\": 63153.3, \"(-9.71, -9.67)\": 61481.3, \"(-9.67, -9.64)\": 59819.7, \"(-9.64, -9.62)\": 58864.1, \"(-9.62, -9.6)\": 58094.2, \"(-9.6, -9.56)\": 56863.7, \"(-9.56, -9.52)\": 54777.6, \"(-9.52, -9.48)\": 53718.5, \"(-9.48, -9.45)\": 51928.8, \"(-9.45, -9.42)\": 51156.2, \"(-9.42, -9.39)\": 50275.9, \"(-9.39, -9.36)\": 48774.3, \"(-9.36, -9.31)\": 47849.6, \"(-9.31, -9.27)\": 46019.8, \"(-9.27, -9.24)\": 45048.1, \"(-9.24, -9.2)\": 44105.6, \"(-9.2, -9.18)\": 43202.1, \"(-9.18, -9.12)\": 42291.4, \"(-9.12, -9.07)\": 40595.1, \"(-9.07, -9.01)\": 39726.1, \"(-9.01, -8.94)\": 37459.5, \"(-8.94, -8.9)\": 36449.7, \"(-8.9, -8.86)\": 35523.8, \"(-8.86, -8.82)\": 34709.8, \"(-8.82, -8.77)\": 33793.2, \"(-8.77, -8.71)\": 32831.1, \"(-8.71, -8.62)\": 31553.8, \"(-8.62, -8.53)\": 29741.5, \"(-8.53, -8.45)\": 28577.6, \"(-8.45, -8.39)\": 27344.1, \"(-8.39, -8.31)\": 26410.3, \"(-8.31, -8.25)\": 25638.1, \"(-8.25, -8.18)\": 24769.7, \"(-8.18, -8.09)\": 23915.2, \"(-8.09, -8.03)\": 23144.7, \"(-8.03, -7.95)\": 22297.7, \"(-7.95, -7.85)\": 21471.0, \"(-7.85, -7.78)\": 20459.8, \"(-7.78, -7.61)\": 19638.1, \"(-7.61, -7.51)\": 18780.6, \"(-7.51, -7.38)\": 17823.0, \"(-7.38, -7.21)\": 16937.3, \"(-7.21, -7.07)\": 16031.2, \"(-7.07, -6.89)\": 15191.4, \"(-6.89, -6.71)\": 14370.6, \"(-6.71, -6.5)\": 13563.6, \"(-6.5, -6.27)\": 12769.0, \"(-6.27, -6.04)\": 11991.5, \"(-6.04, -5.75)\": 11223.4, \"(-5.75, -5.47)\": 10376.6, \"(-5.47, -5.18)\": 9580.6, \"(-5.18, -4.84)\": 8777.4, \"(-4.84, -4.49)\": 7970.2, \"(-4.49, -4.12)\": 7193.5, \"(-4.12, -3.75)\": 6409.5, \"(-3.75, -3.37)\": 5614.4, \"(-3.37, -3.0)\": 4824.7, \"(-3.0, -2.63)\": 4041.4, \"(-2.63, -2.22)\": 3260.8, \"(-2.22, -1.84)\": 2459.3, \"(-1.84, -1.44)\": 1663.4, \"(-1.44, -0.21)\": 852.4, \"(-0.21, 0.19)\": 1621.4, \"(0.19, 0.61)\": 2438.7, \"(0.61, 1.04)\": 3335.7, \"(1.04, 1.43)\": 4109.2, \"(1.43, 1.83)\": 4884.6, \"(1.83, 2.22)\": 5680.0, \"(2.22, 2.62)\": 6478.2, \"(2.62, 3.0)\": 7255.7, \"(3.0, 3.4)\": 8037.9, \"(3.4, 3.79)\": 8809.3, \"(3.79, 4.18)\": 9617.6, \"(4.18, 4.58)\": 10397.9, \"(4.58, 4.96)\": 11169.9, \"(4.96, 5.37)\": 11961.8, \"(5.37, 5.75)\": 12757.4, \"(5.75, 6.14)\": 13523.9, \"(6.14, 6.57)\": 14321.3, \"(6.57, 6.95)\": 15120.8, \"(6.95, 7.35)\": 15925.7, \"(7.35, 7.73)\": 16706.7, \"(7.73, 8.12)\": 17484.6, \"(8.12, 8.56)\": 18305.9, \"(8.56, 8.92)\": 19074.1, \"(8.92, 9.32)\": 19849.3, \"(9.32, 9.72)\": 20660.9, \"(9.72, 10.0)\": 21449.9}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = exp(x) + 4000* sin(x)\nb) f(x) = sign(x) + cos(x)\nc) f(x) = -1/2 * (x+1) ** 4 + 5 * (x+2) **3 + 3 * x ** 2 + 2 * x + 1\nd) f(x) = exp(-x+1)+ 2000 * abs(x+1)\ne) f(x) = sign(sin(x))\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "d)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.97, -9.79)\": 19943.2, \"(-9.79, -9.55)\": 19525.0, \"(-9.55, -9.33)\": 19072.4, \"(-9.33, -9.12)\": 18627.6, \"(-9.12, -8.89)\": 18205.0, \"(-8.89, -8.7)\": 17779.6, \"(-8.7, -8.48)\": 17356.8, \"(-8.48, -8.28)\": 16942.0, \"(-8.28, -8.04)\": 16477.6, \"(-8.04, -7.82)\": 16049.9, \"(-7.82, -7.6)\": 15621.4, \"(-7.6, -7.38)\": 15192.4, \"(-7.38, -7.16)\": 14732.2, \"(-7.16, -6.93)\": 14296.5, \"(-6.93, -6.74)\": 13856.0, \"(-6.74, -6.53)\": 13437.0, \"(-6.53, -6.28)\": 13015.5, \"(-6.28, -6.06)\": 12536.5, \"(-6.06, -5.83)\": 12099.9, \"(-5.83, -5.62)\": 11652.4, \"(-5.62, -5.39)\": 11214.6, \"(-5.39, -5.18)\": 10783.3, \"(-5.18, -4.96)\": 10352.6, \"(-4.96, -4.75)\": 9916.8, \"(-4.75, -4.55)\": 9495.8, \"(-4.55, -4.32)\": 9079.7, \"(-4.32, -4.11)\": 8630.5, \"(-4.11, -3.89)\": 8201.0, \"(-3.89, -3.67)\": 7765.7, \"(-3.67, -3.46)\": 7315.9, \"(-3.46, -3.23)\": 6875.0, \"(-3.23, -2.99)\": 6444.4, \"(-2.99, -2.75)\": 5902.6, \"(-2.75, -2.56)\": 5484.6, \"(-2.56, -2.34)\": 5071.4, \"(-2.34, -2.11)\": 4652.1, \"(-2.11, -1.88)\": 4195.9, \"(-1.88, -1.67)\": 3743.9, \"(-1.67, -1.46)\": 3302.3, \"(-1.46, -1.23)\": 2877.7, \"(-1.23, -1.02)\": 2460.6, \"(-1.02, -0.83)\": 2047.3, \"(-0.83, -0.61)\": 1631.7, \"(-0.61, -0.4)\": 1194.5, \"(-0.4, -0.19)\": 780.1, \"(-0.19, 0.37)\": 319.4, \"(0.37, 0.59)\": 773.4, \"(0.59, 0.8)\": 1189.8, \"(0.8, 1.03)\": 1663.9, \"(1.03, 1.25)\": 2097.3, \"(1.25, 1.45)\": 2524.0, \"(1.45, 1.69)\": 2954.7, \"(1.69, 1.92)\": 3425.7, \"(1.92, 2.07)\": 3862.6, \"(2.07, 2.33)\": 4276.8, \"(2.33, 2.55)\": 4695.1, \"(2.55, 2.78)\": 5160.4, \"(2.78, 3.0)\": 5623.5, \"(3.0, 3.22)\": 6049.0, \"(3.22, 3.44)\": 6495.7, \"(3.44, 3.67)\": 6926.6, \"(3.67, 3.88)\": 7397.5, \"(3.88, 4.08)\": 7811.8, \"(4.08, 4.31)\": 8268.0, \"(4.31, 4.53)\": 8717.7, \"(4.53, 4.72)\": 9157.9, \"(4.72, 4.93)\": 9580.7, \"(4.93, 5.15)\": 10041.3, \"(5.15, 5.36)\": 10516.5, \"(5.36, 5.54)\": 10946.1, \"(5.54, 5.77)\": 11370.6, \"(5.77, 5.98)\": 11875.1, \"(5.98, 6.12)\": 12304.2, \"(6.12, 6.31)\": 12737.9, \"(6.31, 6.45)\": 13188.8, \"(6.45, 6.66)\": 13637.1, \"(6.66, 6.85)\": 14255.1, \"(6.85, 7.0)\": 14689.1, \"(7.0, 7.15)\": 15159.9, \"(7.15, 7.3)\": 15621.2, \"(7.3, 7.44)\": 16109.8, \"(7.44, 7.56)\": 16578.8, \"(7.56, 7.68)\": 17072.3, \"(7.68, 7.79)\": 17506.9, \"(7.79, 7.92)\": 18125.2, \"(7.92, 8.05)\": 18734.7, \"(8.05, 8.15)\": 19312.0, \"(8.15, 8.24)\": 19802.0, \"(8.24, 8.32)\": 20325.4, \"(8.32, 8.39)\": 20784.6, \"(8.39, 8.46)\": 21244.1, \"(8.46, 8.53)\": 21724.2, \"(8.53, 8.6)\": 22156.7, \"(8.6, 8.67)\": 22692.3, \"(8.67, 8.75)\": 23318.1, \"(8.75, 8.81)\": 23900.3, \"(8.81, 8.87)\": 24381.7, \"(8.87, 8.94)\": 24995.0, \"(8.94, 8.99)\": 25580.7, \"(8.99, 9.03)\": 26210.1, \"(9.03, 9.1)\": 26765.2, \"(9.1, 9.16)\": 27537.1, \"(9.16, 9.21)\": 27964.1, \"(9.21, 9.27)\": 28635.3, \"(9.27, 9.32)\": 29290.4, \"(9.32, 9.35)\": 29858.8, \"(9.35, 9.39)\": 30350.9, \"(9.39, 9.44)\": 31205.7, \"(9.44, 9.51)\": 31788.0, \"(9.51, 9.62)\": 33696.6, \"(9.62, 9.68)\": 35037.2, \"(9.68, 9.77)\": 35679.5, \"(9.77, 9.86)\": 38369.7, \"(9.86, 9.89)\": 39147.1, \"(9.89, 9.93)\": 39935.2, \"(9.93, 9.96)\": 40570.9, \"(9.96, 10.0)\": 41304.6}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = exp(-x+1)+ 2000 * abs(x+1)\nb) f(x) = -tanh(x) + 1/4 * x\nc) f(x) = sin(x) + sin(0.5 * x)\nd) f(x) = abs(x ** 2 - 20)\ne) f(x) = exp(x)+ 2000 * abs(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "e)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.99, -0.02)\": -4014.4, \"(-0.02, 0.02)\": -3307.1, \"(0.02, 0.04)\": 3335.1, \"(0.04, 4.81)\": 3842.4, \"(4.81, 6.03)\": 4130.5, \"(6.03, 6.55)\": 4430.9, \"(6.55, 6.89)\": 4727.1, \"(6.89, 7.18)\": 5022.4, \"(7.18, 7.38)\": 5326.1, \"(7.38, 7.56)\": 5627.0, \"(7.56, 7.72)\": 5942.1, \"(7.72, 7.82)\": 6233.0, \"(7.82, 7.97)\": 6564.7, \"(7.97, 8.06)\": 6904.3, \"(8.06, 8.15)\": 7214.2, \"(8.15, 8.23)\": 7521.4, \"(8.23, 8.33)\": 7820.8, \"(8.33, 8.4)\": 8153.8, \"(8.4, 8.47)\": 8474.6, \"(8.47, 8.58)\": 8903.5, \"(8.58, 8.66)\": 9470.1, \"(8.66, 8.73)\": 9804.3, \"(8.73, 8.8)\": 10280.5, \"(8.8, 8.84)\": 10671.8, \"(8.84, 8.91)\": 11120.7, \"(8.91, 8.97)\": 11411.5, \"(8.97, 9.06)\": 12273.1, \"(9.06, 9.1)\": 12727.8, \"(9.1, 9.14)\": 13109.7, \"(9.14, 9.21)\": 13492.2, \"(9.21, 9.26)\": 14207.0, \"(9.26, 9.31)\": 14778.9, \"(9.31, 9.36)\": 15225.2, \"(9.36, 9.39)\": 15772.3, \"(9.39, 9.43)\": 16119.3, \"(9.43, 9.47)\": 16597.3, \"(9.47, 9.51)\": 17255.8, \"(9.51, 9.53)\": 17569.4, \"(9.53, 9.59)\": 17960.8, \"(9.59, 9.66)\": 19289.1, \"(9.66, 9.69)\": 19820.4, \"(9.69, 9.7)\": 20206.3, \"(9.7, 9.74)\": 20599.1, \"(9.74, 9.75)\": 20999.3, \"(9.75, 9.78)\": 21450.9, \"(9.78, 9.81)\": 21859.2, \"(9.81, 9.85)\": 22513.0, \"(9.85, 9.9)\": 23652.1, \"(9.9, 9.99)\": 24677.9}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = exp(x)+ 4000 * sign(x)\nb) f(x) = sign(x ** 2 - 15)\nc) f(x) = |sin(x/2)|\nd) f(x) = -tanh(x) + 1/4 * x\ne) f(x) = abs(x) + sin(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "a)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.98, -9.95)\": -0.338, \"(-9.95, -9.93)\": -0.373, \"(-9.93, -9.9)\": -0.406, \"(-9.9, -9.88)\": -0.439, \"(-9.88, -9.86)\": -0.468, \"(-9.86, -9.83)\": -0.499, \"(-9.83, -9.79)\": -0.553, \"(-9.79, -9.76)\": -0.587, \"(-9.76, -9.71)\": -0.641, \"(-9.71, -9.67)\": -0.704, \"(-9.67, -9.63)\": -0.752, \"(-9.63, -9.59)\": -0.795, \"(-9.59, -9.55)\": -0.835, \"(-9.55, -9.5)\": -0.887, \"(-9.5, -9.46)\": -0.939, \"(-9.46, -9.43)\": -0.976, \"(-9.43, -9.4)\": -1.008, \"(-9.4, -9.36)\": -1.037, \"(-9.36, -9.31)\": -1.071, \"(-9.31, -9.23)\": -1.138, \"(-9.23, -9.17)\": -1.189, \"(-9.17, -9.12)\": -1.22, \"(-9.12, -9.06)\": -1.256, \"(-9.06, -9.0)\": -1.301, \"(-9.0, -8.92)\": -1.331, \"(-8.92, -8.84)\": -1.361, \"(-8.84, -8.36)\": -1.389, \"(-8.36, -8.27)\": -1.351, \"(-8.27, -8.2)\": -1.304, \"(-8.2, -8.14)\": -1.27, \"(-8.14, -8.07)\": -1.226, \"(-8.07, -8.03)\": -1.193, \"(-8.03, -7.96)\": -1.151, \"(-7.96, -7.9)\": -1.066, \"(-7.9, -7.85)\": -1.023, \"(-7.85, -7.82)\": -0.994, \"(-7.82, -7.8)\": -0.957, \"(-7.8, -7.76)\": -0.927, \"(-7.76, -7.72)\": -0.886, \"(-7.72, -7.67)\": -0.831, \"(-7.67, -7.62)\": -0.787, \"(-7.62, -7.56)\": -0.713, \"(-7.56, -7.51)\": -0.649, \"(-7.51, -7.47)\": -0.585, \"(-7.47, -7.42)\": -0.538, \"(-7.42, -7.38)\": -0.465, \"(-7.38, -7.34)\": -0.422, \"(-7.34, -7.32)\": -0.372, \"(-7.32, -7.28)\": -0.326, \"(-7.28, -7.22)\": -0.241, \"(-7.22, -7.19)\": -0.197, \"(-7.19, -7.14)\": -0.141, \"(-7.14, -7.13)\": -0.104, \"(-7.13, -7.09)\": -0.073, \"(-7.09, -7.05)\": -0.008, \"(-7.05, -7.01)\": 0.04, \"(-7.01, -6.98)\": 0.108, \"(-6.98, -6.95)\": 0.14, \"(-6.95, -6.93)\": 0.171, \"(-6.93, -6.88)\": 0.224, \"(-6.88, -6.83)\": 0.293, \"(-6.83, -6.81)\": 0.345, \"(-6.81, -6.77)\": 0.375, \"(-6.77, -6.73)\": 0.415, \"(-6.73, -6.7)\": 0.49, \"(-6.7, -6.66)\": 0.529, \"(-6.66, -6.62)\": 0.579, \"(-6.62, -6.6)\": 0.619, \"(-6.6, -6.56)\": 0.652, \"(-6.56, -6.51)\": 0.73, \"(-6.51, -6.47)\": 0.769, \"(-6.47, -6.42)\": 0.83, \"(-6.42, -6.39)\": 0.862, \"(-6.39, -6.34)\": 0.909, \"(-6.34, -6.32)\": 0.953, \"(-6.32, -6.28)\": 0.983, \"(-6.28, -6.24)\": 1.013, \"(-6.24, -6.2)\": 1.05, \"(-6.2, -6.15)\": 1.088, \"(-6.15, -6.11)\": 1.135, \"(-6.11, -6.03)\": 1.185, \"(-6.03, -5.99)\": 1.22, \"(-5.99, -5.93)\": 1.256, \"(-5.93, -5.87)\": 1.289, \"(-5.87, -5.8)\": 1.32, \"(-5.8, -5.7)\": 1.358, \"(-5.7, -5.21)\": 1.386, \"(-5.21, -5.13)\": 1.35, \"(-5.13, -5.05)\": 1.303, \"(-5.05, -4.98)\": 1.26, \"(-4.98, -4.94)\": 1.219, \"(-4.94, -4.9)\": 1.19, \"(-4.9, -4.84)\": 1.158, \"(-4.84, -4.78)\": 1.092, \"(-4.78, -4.74)\": 1.059, \"(-4.74, -4.69)\": 1.018, \"(-4.69, -4.64)\": 0.958, \"(-4.64, -4.6)\": 0.904, \"(-4.6, -4.56)\": 0.868, \"(-4.56, -4.52)\": 0.835, \"(-4.52, -4.46)\": 0.759, \"(-4.46, -4.42)\": 0.69, \"(-4.42, -4.38)\": 0.648, \"(-4.38, -4.35)\": 0.616, \"(-4.35, -4.32)\": 0.581, \"(-4.32, -4.29)\": 0.541, \"(-4.29, -4.27)\": 0.495, \"(-4.27, -4.24)\": 0.457, \"(-4.24, -4.21)\": 0.421, \"(-4.21, -4.16)\": 0.375, \"(-4.16, -4.12)\": 0.295, \"(-4.12, -4.08)\": 0.267, \"(-4.08, -4.04)\": 0.174, \"(-4.04, -4.0)\": 0.139, \"(-4.0, -3.97)\": 0.095, \"(-3.97, -3.95)\": 0.06, \"(-3.95, -3.92)\": 0.016, \"(-3.92, -3.89)\": -0.02, \"(-3.89, -3.86)\": -0.066, \"(-3.86, -3.83)\": -0.098, \"(-3.83, -3.78)\": -0.16, \"(-3.78, -3.75)\": -0.229, \"(-3.75, -3.73)\": -0.259, \"(-3.73, -3.68)\": -0.291, \"(-3.68, -3.63)\": -0.391, \"(-3.63, -3.57)\": -0.435, \"(-3.57, -3.53)\": -0.521, \"(-3.53, -3.5)\": -0.559, \"(-3.5, -3.46)\": -0.599, \"(-3.46, -3.39)\": -0.676, \"(-3.39, -3.34)\": -0.76, \"(-3.34, -3.32)\": -0.789, \"(-3.32, -3.27)\": -0.821, \"(-3.27, -3.2)\": -0.915, \"(-3.2, -3.18)\": -0.946, \"(-3.18, -3.14)\": -0.982, \"(-3.14, -3.09)\": -1.013, \"(-3.09, -3.04)\": -1.07, \"(-3.04, -2.99)\": -1.11, \"(-2.99, -2.95)\": -1.15, \"(-2.95, -2.9)\": -1.182, \"(-2.9, -2.82)\": -1.23, \"(-2.82, -2.76)\": -1.272, \"(-2.76, -2.69)\": -1.309, \"(-2.69, -2.58)\": -1.337, \"(-2.58, -2.09)\": -1.391, \"(-2.09, -2.02)\": -1.356, \"(-2.02, -1.95)\": -1.326, \"(-1.95, -1.9)\": -1.295, \"(-1.9, -1.85)\": -1.264, \"(-1.85, -1.78)\": -1.229, \"(-1.78, -1.72)\": -1.171, \"(-1.72, -1.68)\": -1.121, \"(-1.68, -1.62)\": -1.074, \"(-1.62, -1.54)\": -1.035, \"(-1.54, -1.48)\": -0.943, \"(-1.48, -1.46)\": -0.901, \"(-1.46, -1.44)\": -0.873, \"(-1.44, -1.4)\": -0.844, \"(-1.4, -1.37)\": -0.804, \"(-1.37, -1.32)\": -0.773, \"(-1.32, -1.27)\": -0.684, \"(-1.27, -1.23)\": -0.638, \"(-1.23, -1.2)\": -0.59, \"(-1.2, -1.12)\": -0.553, \"(-1.12, -1.05)\": -0.408, \"(-1.05, -1.03)\": -0.373, \"(-1.03, -0.97)\": -0.317, \"(-0.97, -0.9)\": -0.183, \"(-0.9, -0.88)\": -0.152, \"(-0.88, -0.86)\": -0.122, \"(-0.86, -0.83)\": -0.083, \"(-0.83, -0.81)\": -0.052, \"(-0.81, -0.78)\": -0.021, \"(-0.78, -0.75)\": 0.034, \"(-0.75, -0.72)\": 0.072, \"(-0.72, -0.68)\": 0.111, \"(-0.68, -0.64)\": 0.176, \"(-0.64, -0.6)\": 0.217, \"(-0.6, -0.55)\": 0.286, \"(-0.55, -0.5)\": 0.366, \"(-0.5, -0.46)\": 0.419, \"(-0.46, -0.44)\": 0.456, \"(-0.44, -0.41)\": 0.497, \"(-0.41, -0.38)\": 0.53, \"(-0.38, -0.33)\": 0.576, \"(-0.33, -0.29)\": 0.655, \"(-0.29, -0.25)\": 0.69, \"(-0.25, -0.19)\": 0.757, \"(-0.19, -0.15)\": 0.795, \"(-0.15, -0.1)\": 0.868, \"(-0.1, -0.06)\": 0.917, \"(-0.06, -0.02)\": 0.957, \"(-0.02, 0.01)\": 0.986, \"(0.01, 0.05)\": 1.024, \"(0.05, 0.14)\": 1.072, \"(0.14, 0.23)\": 1.158, \"(0.23, 0.34)\": 1.23, \"(0.34, 0.41)\": 1.293, \"(0.41, 0.48)\": 1.323, \"(0.48, 0.58)\": 1.356, \"(0.58, 1.07)\": 1.386, \"(1.07, 1.13)\": 1.347, \"(1.13, 1.23)\": 1.305, \"(1.23, 1.27)\": 1.276, \"(1.27, 1.34)\": 1.231, \"(1.34, 1.38)\": 1.198, \"(1.38, 1.41)\": 1.169, \"(1.41, 1.46)\": 1.136, \"(1.46, 1.5)\": 1.102, \"(1.5, 1.52)\": 1.07, \"(1.52, 1.59)\": 1.041, \"(1.59, 1.64)\": 0.956, \"(1.64, 1.66)\": 0.923, \"(1.66, 1.7)\": 0.885, \"(1.7, 1.74)\": 0.836, \"(1.74, 1.77)\": 0.806, \"(1.77, 1.8)\": 0.768, \"(1.8, 1.83)\": 0.725, \"(1.83, 1.86)\": 0.689, \"(1.86, 1.89)\": 0.657, \"(1.89, 1.92)\": 0.628, \"(1.92, 1.96)\": 0.579, \"(1.96, 1.97)\": 0.541, \"(1.97, 2.02)\": 0.511, \"(2.02, 2.07)\": 0.454, \"(2.07, 2.13)\": 0.352, \"(2.13, 2.18)\": 0.274, \"(2.18, 2.22)\": 0.24, \"(2.22, 2.28)\": 0.153, \"(2.28, 2.33)\": 0.092, \"(2.33, 2.38)\": 0.004, \"(2.38, 2.4)\": -0.043, \"(2.4, 2.45)\": -0.079, \"(2.45, 2.48)\": -0.154, \"(2.48, 2.5)\": -0.187, \"(2.5, 2.53)\": -0.226, \"(2.53, 2.56)\": -0.262, \"(2.56, 2.58)\": -0.297, \"(2.58, 2.61)\": -0.328, \"(2.61, 2.64)\": -0.379, \"(2.64, 2.68)\": -0.413, \"(2.68, 2.74)\": -0.5, \"(2.74, 2.76)\": -0.532, \"(2.76, 2.8)\": -0.578, \"(2.8, 2.82)\": -0.612, \"(2.82, 2.86)\": -0.656, \"(2.86, 2.89)\": -0.699, \"(2.89, 2.93)\": -0.751, \"(2.93, 2.98)\": -0.786, \"(2.98, 3.04)\": -0.861, \"(3.04, 3.09)\": -0.902, \"(3.09, 3.12)\": -0.96, \"(3.12, 3.15)\": -0.992, \"(3.15, 3.2)\": -1.027, \"(3.2, 3.24)\": -1.057, \"(3.24, 3.28)\": -1.097, \"(3.28, 3.31)\": -1.128, \"(3.31, 3.37)\": -1.158, \"(3.37, 3.44)\": -1.221, \"(3.44, 3.5)\": -1.259, \"(3.5, 3.59)\": -1.305, \"(3.59, 3.66)\": -1.337, \"(3.66, 3.79)\": -1.37, \"(3.79, 4.17)\": -1.402, \"(4.17, 4.24)\": -1.372, \"(4.24, 4.3)\": -1.342, \"(4.3, 4.36)\": -1.307, \"(4.36, 4.42)\": -1.278, \"(4.42, 4.5)\": -1.233, \"(4.5, 4.54)\": -1.176, \"(4.54, 4.59)\": -1.147, \"(4.59, 4.62)\": -1.108, \"(4.62, 4.65)\": -1.076, \"(4.65, 4.7)\": -1.045, \"(4.7, 4.74)\": -0.994, \"(4.74, 4.77)\": -0.961, \"(4.77, 4.8)\": -0.926, \"(4.8, 4.85)\": -0.886, \"(4.85, 4.88)\": -0.85, \"(4.88, 4.92)\": -0.806, \"(4.92, 4.97)\": -0.748, \"(4.97, 4.99)\": -0.703, \"(4.99, 5.04)\": -0.668, \"(5.04, 5.08)\": -0.606, \"(5.08, 5.1)\": -0.561, \"(5.1, 5.13)\": -0.532, \"(5.13, 5.16)\": -0.496, \"(5.16, 5.19)\": -0.452, \"(5.19, 5.22)\": -0.415, \"(5.22, 5.27)\": -0.373, \"(5.27, 5.32)\": -0.277, \"(5.32, 5.34)\": -0.248, \"(5.34, 5.36)\": -0.207, \"(5.36, 5.4)\": -0.174, \"(5.4, 5.44)\": -0.117, \"(5.44, 5.47)\": -0.074, \"(5.47, 5.49)\": -0.04, \"(5.49, 5.53)\": 0.018, \"(5.53, 5.56)\": 0.063, \"(5.56, 5.61)\": 0.116, \"(5.61, 5.66)\": 0.2, \"(5.66, 5.69)\": 0.243, \"(5.69, 5.72)\": 0.282, \"(5.72, 5.74)\": 0.324, \"(5.74, 5.78)\": 0.359, \"(5.78, 5.83)\": 0.417, \"(5.83, 5.87)\": 0.481, \"(5.87, 5.91)\": 0.539, \"(5.91, 5.94)\": 0.582, \"(5.94, 5.98)\": 0.629, \"(5.98, 6.03)\": 0.684, \"(6.03, 6.08)\": 0.752, \"(6.08, 6.12)\": 0.804, \"(6.12, 6.15)\": 0.836, \"(6.15, 6.18)\": 0.872, \"(6.18, 6.22)\": 0.918, \"(6.22, 6.25)\": 0.95, \"(6.25, 6.31)\": 0.992, \"(6.31, 6.34)\": 1.039, \"(6.34, 6.37)\": 1.068, \"(6.37, 6.43)\": 1.105, \"(6.43, 6.49)\": 1.145, \"(6.49, 6.54)\": 1.192, \"(6.54, 6.63)\": 1.244, \"(6.63, 6.7)\": 1.287, \"(6.7, 6.78)\": 1.332, \"(6.78, 6.91)\": 1.369, \"(6.91, 7.32)\": 1.398, \"(7.32, 7.43)\": 1.367, \"(7.43, 7.5)\": 1.308, \"(7.5, 7.57)\": 1.278, \"(7.57, 7.6)\": 1.241, \"(7.6, 7.66)\": 1.209, \"(7.66, 7.72)\": 1.158, \"(7.72, 7.76)\": 1.117, \"(7.76, 7.79)\": 1.085, \"(7.79, 7.86)\": 1.031, \"(7.86, 7.91)\": 0.966, \"(7.91, 7.96)\": 0.915, \"(7.96, 8.01)\": 0.844, \"(8.01, 8.07)\": 0.804, \"(8.07, 8.14)\": 0.715, \"(8.14, 8.18)\": 0.66, \"(8.18, 8.22)\": 0.589, \"(8.22, 8.27)\": 0.551, \"(8.27, 8.29)\": 0.51, \"(8.29, 8.33)\": 0.476, \"(8.33, 8.38)\": 0.382, \"(8.38, 8.41)\": 0.346, \"(8.41, 8.44)\": 0.31, \"(8.44, 8.49)\": 0.243, \"(8.49, 8.52)\": 0.189, \"(8.52, 8.55)\": 0.154, \"(8.55, 8.58)\": 0.109, \"(8.58, 8.62)\": 0.071, \"(8.62, 8.67)\": 0.006, \"(8.67, 8.72)\": -0.088, \"(8.72, 8.75)\": -0.139, \"(8.75, 8.79)\": -0.176, \"(8.79, 8.85)\": -0.274, \"(8.85, 8.9)\": -0.325, \"(8.9, 8.93)\": -0.387, \"(8.93, 8.97)\": -0.428, \"(8.97, 9.02)\": -0.491, \"(9.02, 9.04)\": -0.536, \"(9.04, 9.07)\": -0.568, \"(9.07, 9.12)\": -0.611, \"(9.12, 9.18)\": -0.68, \"(9.18, 9.22)\": -0.764, \"(9.22, 9.26)\": -0.797, \"(9.26, 9.31)\": -0.853, \"(9.31, 9.37)\": -0.91, \"(9.37, 9.44)\": -0.958, \"(9.44, 9.49)\": -1.041, \"(9.49, 9.54)\": -1.072, \"(9.54, 9.58)\": -1.122, \"(9.58, 9.62)\": -1.152, \"(9.62, 9.66)\": -1.187, \"(9.66, 9.75)\": -1.221, \"(9.75, 9.84)\": -1.289, \"(9.84, 9.9)\": -1.32, \"(9.9, 9.96)\": -1.349}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = sin(x)+cos(x)\nb) f(x) = -tanh(x) + 1/4 * x\nc) f(x) = log(x+10) + 1/3 * x \nd) f(x) = sign(cos(x))\ne) f(x) = sign(x) + cos(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "a)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-10.0, -9.91)\": 0.9613, \"(-9.91, -9.81)\": 0.9715, \"(-9.81, -9.67)\": 0.9825, \"(-9.67, -9.07)\": 0.993, \"(-9.07, -8.96)\": 0.9825, \"(-8.96, -8.86)\": 0.9714, \"(-8.86, -8.79)\": 0.9597, \"(-8.79, -8.72)\": 0.9488, \"(-8.72, -8.65)\": 0.9355, \"(-8.65, -8.59)\": 0.9241, \"(-8.59, -8.54)\": 0.912, \"(-8.54, -8.48)\": 0.9006, \"(-8.48, -8.44)\": 0.8902, \"(-8.44, -8.39)\": 0.8794, \"(-8.39, -8.36)\": 0.8675, \"(-8.36, -8.3)\": 0.8567, \"(-8.3, -8.23)\": 0.8373, \"(-8.23, -8.17)\": 0.8218, \"(-8.17, -8.1)\": 0.7993, \"(-8.1, -8.05)\": 0.783, \"(-8.05, -8.02)\": 0.7717, \"(-8.02, -7.99)\": 0.7602, \"(-7.99, -7.92)\": 0.7426, \"(-7.92, -7.87)\": 0.7256, \"(-7.87, -7.84)\": 0.712, \"(-7.84, -7.81)\": 0.6988, \"(-7.81, -7.75)\": 0.6841, \"(-7.75, -7.71)\": 0.6618, \"(-7.71, -7.68)\": 0.65, \"(-7.68, -7.63)\": 0.6373, \"(-7.63, -7.6)\": 0.6228, \"(-7.6, -7.57)\": 0.6064, \"(-7.57, -7.52)\": 0.5889, \"(-7.52, -7.49)\": 0.5781, \"(-7.49, -7.46)\": 0.5656, \"(-7.46, -7.44)\": 0.553, \"(-7.44, -7.41)\": 0.5399, \"(-7.41, -7.37)\": 0.526, \"(-7.37, -7.34)\": 0.5132, \"(-7.34, -7.3)\": 0.4974, \"(-7.3, -7.25)\": 0.4742, \"(-7.25, -7.21)\": 0.4603, \"(-7.21, -7.18)\": 0.438, \"(-7.18, -7.14)\": 0.4279, \"(-7.14, -7.1)\": 0.4058, \"(-7.1, -7.07)\": 0.3933, \"(-7.07, -7.04)\": 0.3764, \"(-7.04, -7.0)\": 0.3664, \"(-7.0, -6.96)\": 0.3449, \"(-6.96, -6.94)\": 0.3268, \"(-6.94, -6.9)\": 0.3148, \"(-6.9, -6.86)\": 0.2923, \"(-6.86, -6.83)\": 0.2778, \"(-6.83, -6.79)\": 0.259, \"(-6.79, -6.76)\": 0.2453, \"(-6.76, -6.71)\": 0.2208, \"(-6.71, -6.69)\": 0.2087, \"(-6.69, -6.67)\": 0.1979, \"(-6.67, -6.63)\": 0.1867, \"(-6.63, -6.59)\": 0.1625, \"(-6.59, -6.56)\": 0.1467, \"(-6.56, -6.52)\": 0.1353, \"(-6.52, -6.48)\": 0.1099, \"(-6.48, -6.42)\": 0.0876, \"(-6.42, -6.37)\": 0.0549, \"(-6.37, -6.35)\": 0.0394, \"(-6.35, -6.2)\": 0.0251, \"(-6.2, -6.17)\": 0.0462, \"(-6.17, -6.13)\": 0.0626, \"(-6.13, -6.07)\": 0.0828, \"(-6.07, -6.02)\": 0.1217, \"(-6.02, -6.0)\": 0.1348, \"(-6.0, -5.96)\": 0.1499, \"(-5.96, -5.94)\": 0.166, \"(-5.94, -5.92)\": 0.1761, \"(-5.92, -5.89)\": 0.1863, \"(-5.89, -5.87)\": 0.1975, \"(-5.87, -5.84)\": 0.2086, \"(-5.84, -5.8)\": 0.2266, \"(-5.8, -5.77)\": 0.2419, \"(-5.77, -5.72)\": 0.2654, \"(-5.72, -5.67)\": 0.2814, \"(-5.67, -5.62)\": 0.3167, \"(-5.62, -5.6)\": 0.3277, \"(-5.6, -5.56)\": 0.3417, \"(-5.56, -5.5)\": 0.3724, \"(-5.5, -5.48)\": 0.3866, \"(-5.48, -5.44)\": 0.399, \"(-5.44, -5.42)\": 0.4093, \"(-5.42, -5.39)\": 0.4237, \"(-5.39, -5.34)\": 0.4442, \"(-5.34, -5.29)\": 0.4652, \"(-5.29, -5.22)\": 0.4891, \"(-5.22, -5.18)\": 0.5173, \"(-5.18, -5.14)\": 0.5292, \"(-5.14, -5.09)\": 0.5528, \"(-5.09, -5.05)\": 0.571, \"(-5.05, -5.02)\": 0.5854, \"(-5.02, -4.91)\": 0.6085, \"(-4.91, -4.81)\": 0.6597, \"(-4.81, -4.78)\": 0.6765, \"(-4.78, -4.73)\": 0.6865, \"(-4.73, -4.7)\": 0.703, \"(-4.7, -4.67)\": 0.717, \"(-4.67, -4.61)\": 0.7298, \"(-4.61, -4.54)\": 0.756, \"(-4.54, -4.5)\": 0.7683, \"(-4.5, -4.45)\": 0.789, \"(-4.45, -4.4)\": 0.7994, \"(-4.4, -4.33)\": 0.8129, \"(-4.33, -4.26)\": 0.8407, \"(-4.26, -4.19)\": 0.8511, \"(-4.19, -4.12)\": 0.8741, \"(-4.12, -4.04)\": 0.8894, \"(-4.04, -3.99)\": 0.9025, \"(-3.99, -3.95)\": 0.9142, \"(-3.95, -3.86)\": 0.9243, \"(-3.86, -3.78)\": 0.9386, \"(-3.78, -3.69)\": 0.9526, \"(-3.69, -3.61)\": 0.9633, \"(-3.61, -3.51)\": 0.9736, \"(-3.51, -3.35)\": 0.9861, \"(-3.35, -2.8)\": 0.9961, \"(-2.8, -2.68)\": 0.9838, \"(-2.68, -2.6)\": 0.9733, \"(-2.6, -2.51)\": 0.962, \"(-2.51, -2.44)\": 0.9488, \"(-2.44, -2.33)\": 0.938, \"(-2.33, -2.23)\": 0.9085, \"(-2.23, -2.19)\": 0.8977, \"(-2.19, -2.14)\": 0.8867, \"(-2.14, -2.09)\": 0.8756, \"(-2.09, -2.04)\": 0.8636, \"(-2.04, -2.0)\": 0.8512, \"(-2.0, -1.94)\": 0.8385, \"(-1.94, -1.87)\": 0.816, \"(-1.87, -1.82)\": 0.7977, \"(-1.82, -1.78)\": 0.7852, \"(-1.78, -1.74)\": 0.7744, \"(-1.74, -1.7)\": 0.7614, \"(-1.7, -1.66)\": 0.7468, \"(-1.66, -1.62)\": 0.7323, \"(-1.62, -1.57)\": 0.7203, \"(-1.57, -1.53)\": 0.7012, \"(-1.53, -1.49)\": 0.6912, \"(-1.49, -1.46)\": 0.675, \"(-1.46, -1.42)\": 0.662, \"(-1.42, -1.38)\": 0.649, \"(-1.38, -1.35)\": 0.6298, \"(-1.35, -1.31)\": 0.6163, \"(-1.31, -1.26)\": 0.6036, \"(-1.26, -1.22)\": 0.5849, \"(-1.22, -1.19)\": 0.5692, \"(-1.19, -1.15)\": 0.5529, \"(-1.15, -1.12)\": 0.5389, \"(-1.12, -1.07)\": 0.5204, \"(-1.07, -1.03)\": 0.507, \"(-1.03, -0.95)\": 0.4801, \"(-0.95, -0.89)\": 0.4394, \"(-0.89, -0.87)\": 0.4275, \"(-0.87, -0.83)\": 0.4122, \"(-0.83, -0.78)\": 0.3982, \"(-0.78, -0.74)\": 0.371, \"(-0.74, -0.72)\": 0.3591, \"(-0.72, -0.7)\": 0.3479, \"(-0.7, -0.67)\": 0.3377, \"(-0.67, -0.65)\": 0.3271, \"(-0.65, -0.6)\": 0.3135, \"(-0.6, -0.55)\": 0.2802, \"(-0.55, -0.51)\": 0.2636, \"(-0.51, -0.48)\": 0.2451, \"(-0.48, -0.45)\": 0.2345, \"(-0.45, -0.42)\": 0.2179, \"(-0.42, -0.37)\": 0.2052, \"(-0.37, -0.33)\": 0.1755, \"(-0.33, -0.3)\": 0.1612, \"(-0.3, -0.25)\": 0.1408, \"(-0.25, -0.22)\": 0.1225, \"(-0.22, -0.19)\": 0.1029, \"(-0.19, -0.16)\": 0.0928, \"(-0.16, -0.1)\": 0.0767, \"(-0.1, -0.04)\": 0.0322, \"(-0.04, 0.06)\": 0.0168, \"(0.06, 0.09)\": 0.0336, \"(0.09, 0.13)\": 0.0499, \"(0.13, 0.16)\": 0.0721, \"(0.16, 0.18)\": 0.0836, \"(0.18, 0.22)\": 0.0977, \"(0.22, 0.28)\": 0.1119, \"(0.28, 0.32)\": 0.1525, \"(0.32, 0.35)\": 0.1644, \"(0.35, 0.38)\": 0.1747, \"(0.38, 0.4)\": 0.1919, \"(0.4, 0.43)\": 0.2057, \"(0.43, 0.45)\": 0.2207, \"(0.45, 0.5)\": 0.2312, \"(0.5, 0.55)\": 0.2594, \"(0.55, 0.59)\": 0.2801, \"(0.59, 0.62)\": 0.2972, \"(0.62, 0.64)\": 0.3081, \"(0.64, 0.67)\": 0.3225, \"(0.67, 0.7)\": 0.333, \"(0.7, 0.75)\": 0.3581, \"(0.75, 0.79)\": 0.3707, \"(0.79, 0.84)\": 0.3833, \"(0.84, 0.9)\": 0.4269, \"(0.9, 0.94)\": 0.4411, \"(0.94, 0.98)\": 0.4595, \"(0.98, 1.01)\": 0.4748, \"(1.01, 1.04)\": 0.4857, \"(1.04, 1.08)\": 0.5024, \"(1.08, 1.12)\": 0.5238, \"(1.12, 1.14)\": 0.5346, \"(1.14, 1.21)\": 0.5494, \"(1.21, 1.29)\": 0.5848, \"(1.29, 1.32)\": 0.6075, \"(1.32, 1.36)\": 0.6186, \"(1.36, 1.39)\": 0.6313, \"(1.39, 1.42)\": 0.6412, \"(1.42, 1.46)\": 0.6575, \"(1.46, 1.49)\": 0.6708, \"(1.49, 1.53)\": 0.6832, \"(1.53, 1.59)\": 0.7004, \"(1.59, 1.64)\": 0.7209, \"(1.64, 1.69)\": 0.7349, \"(1.69, 1.74)\": 0.753, \"(1.74, 1.79)\": 0.7688, \"(1.79, 1.83)\": 0.7861, \"(1.83, 1.87)\": 0.7964, \"(1.87, 1.9)\": 0.8079, \"(1.9, 1.95)\": 0.8182, \"(1.95, 2.01)\": 0.8287, \"(2.01, 2.08)\": 0.8524, \"(2.08, 2.13)\": 0.8649, \"(2.13, 2.18)\": 0.8763, \"(2.18, 2.25)\": 0.8919, \"(2.25, 2.3)\": 0.9019, \"(2.3, 2.36)\": 0.9137, \"(2.36, 2.43)\": 0.9261, \"(2.43, 2.49)\": 0.9398, \"(2.49, 2.58)\": 0.9501, \"(2.58, 2.68)\": 0.9633, \"(2.68, 2.78)\": 0.9737, \"(2.78, 2.93)\": 0.9841, \"(2.93, 3.47)\": 0.9946, \"(3.47, 3.59)\": 0.9846, \"(3.59, 3.67)\": 0.9741, \"(3.67, 3.77)\": 0.9641, \"(3.77, 3.85)\": 0.9489, \"(3.85, 3.9)\": 0.9376, \"(3.9, 3.96)\": 0.9259, \"(3.96, 4.02)\": 0.9153, \"(4.02, 4.06)\": 0.9047, \"(4.06, 4.11)\": 0.8912, \"(4.11, 4.18)\": 0.8794, \"(4.18, 4.24)\": 0.8653, \"(4.24, 4.27)\": 0.8503, \"(4.27, 4.34)\": 0.8393, \"(4.34, 4.39)\": 0.8207, \"(4.39, 4.43)\": 0.8104, \"(4.43, 4.49)\": 0.7912, \"(4.49, 4.53)\": 0.7756, \"(4.53, 4.59)\": 0.7616, \"(4.59, 4.67)\": 0.7429, \"(4.67, 4.72)\": 0.7118, \"(4.72, 4.75)\": 0.7016, \"(4.75, 4.79)\": 0.6897, \"(4.79, 4.82)\": 0.6738, \"(4.82, 4.86)\": 0.6591, \"(4.86, 4.92)\": 0.6458, \"(4.92, 4.96)\": 0.623, \"(4.96, 5.0)\": 0.611, \"(5.0, 5.04)\": 0.5949, \"(5.04, 5.1)\": 0.5694, \"(5.1, 5.14)\": 0.5503, \"(5.14, 5.17)\": 0.5379, \"(5.17, 5.19)\": 0.5265, \"(5.19, 5.22)\": 0.5162, \"(5.22, 5.26)\": 0.5022, \"(5.26, 5.28)\": 0.4887, \"(5.28, 5.31)\": 0.477, \"(5.31, 5.33)\": 0.4632, \"(5.33, 5.38)\": 0.4495, \"(5.38, 5.44)\": 0.4246, \"(5.44, 5.47)\": 0.406, \"(5.47, 5.51)\": 0.3882, \"(5.51, 5.56)\": 0.3655, \"(5.56, 5.59)\": 0.3458, \"(5.59, 5.63)\": 0.334, \"(5.63, 5.67)\": 0.3111, \"(5.67, 5.71)\": 0.2975, \"(5.71, 5.74)\": 0.2766, \"(5.74, 5.77)\": 0.2625, \"(5.77, 5.81)\": 0.249, \"(5.81, 5.85)\": 0.2204, \"(5.85, 5.91)\": 0.2078, \"(5.91, 5.96)\": 0.1718, \"(5.96, 6.02)\": 0.1467, \"(6.02, 6.05)\": 0.123, \"(6.05, 6.07)\": 0.1124, \"(6.07, 6.09)\": 0.1023, \"(6.09, 6.12)\": 0.0888, \"(6.12, 6.16)\": 0.0774, \"(6.16, 6.2)\": 0.054, \"(6.2, 6.21)\": 0.0401, \"(6.21, 6.24)\": 0.0296, \"(6.24, 6.34)\": 0.0193, \"(6.34, 6.38)\": 0.0384, \"(6.38, 6.44)\": 0.0607, \"(6.44, 6.48)\": 0.0831, \"(6.48, 6.51)\": 0.1047, \"(6.51, 6.55)\": 0.119, \"(6.55, 6.59)\": 0.139, \"(6.59, 6.65)\": 0.1659, \"(6.65, 6.69)\": 0.1946, \"(6.69, 6.73)\": 0.2134, \"(6.73, 6.77)\": 0.2272, \"(6.77, 6.8)\": 0.246, \"(6.8, 6.83)\": 0.2562, \"(6.83, 6.86)\": 0.2794, \"(6.86, 6.9)\": 0.2898, \"(6.9, 6.92)\": 0.3074, \"(6.92, 6.97)\": 0.3201, \"(6.97, 6.99)\": 0.3415, \"(6.99, 7.02)\": 0.3525, \"(7.02, 7.06)\": 0.3676, \"(7.06, 7.1)\": 0.3896, \"(7.1, 7.13)\": 0.4029, \"(7.13, 7.17)\": 0.4129, \"(7.17, 7.25)\": 0.4366, \"(7.25, 7.29)\": 0.4792, \"(7.29, 7.35)\": 0.4904, \"(7.35, 7.43)\": 0.5296, \"(7.43, 7.47)\": 0.5502, \"(7.47, 7.51)\": 0.5662, \"(7.51, 7.54)\": 0.5844, \"(7.54, 7.59)\": 0.5953, \"(7.59, 7.64)\": 0.6168, \"(7.64, 7.68)\": 0.6344, \"(7.68, 7.73)\": 0.6511, \"(7.73, 7.77)\": 0.6659, \"(7.77, 7.81)\": 0.6795, \"(7.81, 7.85)\": 0.6966, \"(7.85, 7.88)\": 0.7066, \"(7.88, 7.91)\": 0.7194, \"(7.91, 7.97)\": 0.7295, \"(7.97, 8.02)\": 0.7507, \"(8.02, 8.08)\": 0.7715, \"(8.08, 8.13)\": 0.7875, \"(8.13, 8.18)\": 0.7995, \"(8.18, 8.22)\": 0.8135, \"(8.22, 8.26)\": 0.8268, \"(8.26, 8.31)\": 0.8387, \"(8.31, 8.35)\": 0.8513, \"(8.35, 8.4)\": 0.8615, \"(8.4, 8.46)\": 0.8736, \"(8.46, 8.51)\": 0.8861, \"(8.51, 8.56)\": 0.897, \"(8.56, 8.63)\": 0.9089, \"(8.63, 8.71)\": 0.9278, \"(8.71, 8.78)\": 0.9386, \"(8.78, 8.85)\": 0.9501, \"(8.85, 8.93)\": 0.9603, \"(8.93, 9.05)\": 0.9722, \"(9.05, 9.2)\": 0.9828, \"(9.2, 9.78)\": 0.9936, \"(9.78, 9.91)\": 0.9818, \"(9.91, 9.99)\": 0.9682}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = -tanh(x) + 1/4 * x\nb) f(x) = exp(-x^2+1)+ 1/3 * |x|\nc) f(x) = -x ** 2 + 20 * tanh(5*x)\nd) f(x) = exp(x)+ 2000 * abs(x)\ne) f(x) = |sin(x/2)|\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "e)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.99, -9.9)\": 2087.2, \"(-9.9, -9.82)\": 1818.5, \"(-9.82, -9.73)\": 1469.3, \"(-9.73, -9.67)\": 1182.0, \"(-9.67, -9.6)\": 932.1, \"(-9.6, -9.52)\": 657.2, \"(-9.52, -9.45)\": 310.8, \"(-9.45, -9.35)\": 11.0, \"(-9.35, -9.24)\": -400.9, \"(-9.24, -9.14)\": -957.3, \"(-9.14, -9.04)\": -1253.3, \"(-9.04, -8.98)\": -1501.7, \"(-8.98, -8.91)\": -1744.7, \"(-8.91, -8.8)\": -2052.9, \"(-8.8, -8.71)\": -2345.5, \"(-8.71, -8.61)\": -2681.5, \"(-8.61, -8.51)\": -2929.4, \"(-8.51, -8.38)\": -3210.3, \"(-8.38, -8.22)\": -3471.7, \"(-8.22, -7.9)\": -3758.0, \"(-7.9, -7.49)\": -3997.2, \"(-7.49, -7.32)\": -3689.3, \"(-7.32, -7.2)\": -3445.9, \"(-7.2, -7.1)\": -3150.4, \"(-7.1, -7.01)\": -2889.1, \"(-7.01, -6.93)\": -2625.1, \"(-6.93, -6.86)\": -2389.3, \"(-6.86, -6.78)\": -2144.2, \"(-6.78, -6.72)\": -1879.5, \"(-6.72, -6.63)\": -1556.4, \"(-6.63, -6.56)\": -1306.4, \"(-6.56, -6.49)\": -1060.1, \"(-6.49, -6.43)\": -822.0, \"(-6.43, -6.36)\": -522.6, \"(-6.36, -6.29)\": -266.1, \"(-6.29, -6.23)\": -24.5, \"(-6.23, -6.15)\": 318.1, \"(-6.15, -6.09)\": 557.3, \"(-6.09, -5.99)\": 926.1, \"(-5.99, -5.93)\": 1189.7, \"(-5.93, -5.83)\": 1538.0, \"(-5.83, -5.75)\": 1773.4, \"(-5.75, -5.67)\": 2103.6, \"(-5.67, -5.59)\": 2343.9, \"(-5.59, -5.49)\": 2592.2, \"(-5.49, -5.37)\": 2933.4, \"(-5.37, -5.24)\": 3228.4, \"(-5.24, -5.09)\": 3496.3, \"(-5.09, -4.22)\": 3754.5, \"(-4.22, -4.09)\": 3500.8, \"(-4.09, -3.97)\": 3210.1, \"(-3.97, -3.88)\": 2927.8, \"(-3.88, -3.79)\": 2665.4, \"(-3.79, -3.72)\": 2422.7, \"(-3.72, -3.66)\": 2158.1, \"(-3.66, -3.57)\": 1888.9, \"(-3.57, -3.47)\": 1551.7, \"(-3.47, -3.38)\": 1189.4, \"(-3.38, -3.27)\": 810.6, \"(-3.27, -3.17)\": 360.8, \"(-3.17, -3.08)\": -54.6, \"(-3.08, -3.0)\": -346.7, \"(-3.0, -2.93)\": -601.8, \"(-2.93, -2.83)\": -949.7, \"(-2.83, -2.76)\": -1275.4, \"(-2.76, -2.69)\": -1536.5, \"(-2.69, -2.61)\": -1795.7, \"(-2.61, -2.52)\": -2042.4, \"(-2.52, -2.41)\": -2420.6, \"(-2.41, -2.31)\": -2695.7, \"(-2.31, -2.19)\": -3013.4, \"(-2.19, -2.04)\": -3344.8, \"(-2.04, -1.89)\": -3587.7, \"(-1.89, -1.1)\": -3824.0, \"(-1.1, -0.96)\": -3524.3, \"(-0.96, -0.86)\": -3260.6, \"(-0.86, -0.77)\": -3003.0, \"(-0.77, -0.68)\": -2753.8, \"(-0.68, -0.6)\": -2488.9, \"(-0.6, -0.53)\": -2242.9, \"(-0.53, -0.47)\": -1991.7, \"(-0.47, -0.36)\": -1750.9, \"(-0.36, -0.27)\": -1335.0, \"(-0.27, -0.19)\": -1035.7, \"(-0.19, -0.08)\": -587.4, \"(-0.08, -0.02)\": -306.2, \"(-0.02, 0.06)\": 39.8, \"(0.06, 0.13)\": 279.8, \"(0.13, 0.24)\": 600.1, \"(0.24, 0.33)\": 1041.6, \"(0.33, 0.4)\": 1338.9, \"(0.4, 0.46)\": 1588.5, \"(0.46, 0.54)\": 1854.6, \"(0.54, 0.61)\": 2094.9, \"(0.61, 0.7)\": 2350.4, \"(0.7, 0.79)\": 2594.8, \"(0.79, 0.91)\": 2902.9, \"(0.91, 1.05)\": 3202.9, \"(1.05, 1.22)\": 3518.1, \"(1.22, 2.06)\": 3769.4, \"(2.06, 2.19)\": 3530.7, \"(2.19, 2.29)\": 3230.7, \"(2.29, 2.38)\": 2986.0, \"(2.38, 2.45)\": 2744.5, \"(2.45, 2.53)\": 2509.7, \"(2.53, 2.61)\": 2246.9, \"(2.61, 2.71)\": 1924.9, \"(2.71, 2.78)\": 1617.4, \"(2.78, 2.88)\": 1329.2, \"(2.88, 2.96)\": 1030.2, \"(2.96, 3.05)\": 628.7, \"(3.05, 3.13)\": 309.3, \"(3.13, 3.19)\": -15.3, \"(3.19, 3.28)\": -278.7, \"(3.28, 3.38)\": -657.0, \"(3.38, 3.45)\": -966.8, \"(3.45, 3.53)\": -1215.7, \"(3.53, 3.61)\": -1535.5, \"(3.61, 3.69)\": -1821.9, \"(3.69, 3.78)\": -2058.7, \"(3.78, 3.85)\": -2374.2, \"(3.85, 3.96)\": -2643.8, \"(3.96, 4.08)\": -2923.7, \"(4.08, 4.25)\": -3277.9, \"(4.25, 4.46)\": -3538.7, \"(4.46, 5.08)\": -3796.2, \"(5.08, 5.22)\": -3557.3, \"(5.22, 5.35)\": -3281.7, \"(5.35, 5.46)\": -2924.1, \"(5.46, 5.54)\": -2645.9, \"(5.54, 5.64)\": -2358.3, \"(5.64, 5.72)\": -2088.7, \"(5.72, 5.82)\": -1710.9, \"(5.82, 5.88)\": -1435.5, \"(5.88, 5.94)\": -1149.7, \"(5.94, 6.02)\": -890.6, \"(6.02, 6.07)\": -602.8, \"(6.07, 6.13)\": -315.4, \"(6.13, 6.2)\": -70.8, \"(6.2, 6.28)\": 173.8, \"(6.28, 6.34)\": 644.6, \"(6.34, 6.44)\": 918.7, \"(6.44, 6.5)\": 1374.8, \"(6.5, 6.6)\": 1652.3, \"(6.6, 6.69)\": 2174.5, \"(6.69, 6.77)\": 2421.3, \"(6.77, 6.85)\": 2817.5, \"(6.85, 6.91)\": 3098.7, \"(6.91, 6.96)\": 3370.3, \"(6.96, 7.04)\": 3620.0, \"(7.04, 7.12)\": 3988.6, \"(7.12, 7.19)\": 4257.5, \"(7.19, 7.26)\": 4494.7, \"(7.26, 7.34)\": 4764.4, \"(7.34, 7.41)\": 5077.1, \"(7.41, 7.5)\": 5321.5, \"(7.5, 7.58)\": 5599.6, \"(7.58, 7.68)\": 5875.1, \"(7.68, 7.77)\": 6148.5, \"(7.77, 7.87)\": 6383.3, \"(7.87, 7.98)\": 6634.6, \"(7.98, 8.09)\": 6918.1, \"(8.09, 8.2)\": 7161.5, \"(8.2, 8.32)\": 7418.5, \"(8.32, 8.43)\": 7697.6, \"(8.43, 8.53)\": 7960.8, \"(8.53, 8.63)\": 8205.8, \"(8.63, 8.7)\": 8450.0, \"(8.7, 8.79)\": 8703.8, \"(8.79, 8.85)\": 8939.0, \"(8.85, 8.92)\": 9214.7, \"(8.92, 9.0)\": 9489.5, \"(9.0, 9.07)\": 9827.2, \"(9.07, 9.16)\": 10102.4, \"(9.16, 9.26)\": 10848.5, \"(9.26, 9.32)\": 11287.3, \"(9.32, 9.35)\": 11566.3, \"(9.35, 9.39)\": 11830.1, \"(9.39, 9.42)\": 12144.9, \"(9.42, 9.46)\": 12510.8, \"(9.46, 9.51)\": 12770.6, \"(9.51, 9.55)\": 13282.2, \"(9.55, 9.6)\": 13646.1, \"(9.6, 9.64)\": 14267.8, \"(9.64, 9.66)\": 14645.0, \"(9.66, 9.7)\": 14940.3, \"(9.7, 9.74)\": 15620.7, \"(9.74, 9.77)\": 15878.4, \"(9.77, 9.82)\": 16336.3, \"(9.82, 9.88)\": 17300.7, \"(9.88, 9.93)\": 18341.1, \"(9.93, 9.96)\": 18879.0, \"(9.96, 10.0)\": 19420.4}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = x + 1/3 * sin(5*x) + 3\nb) f(x) = sign(sin(x))\nc) f(x) = sign(cos(x))\nd) f(x) = exp(x)+ 2000 * abs(x)\ne) f(x) = exp(x) + 4000* sin(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "e)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.97, -9.8)\": 1.023, \"(-9.8, -9.54)\": 1.002, \"(-9.54, -9.48)\": 0.98, \"(-9.48, -9.46)\": 0.953, \"(-9.46, -9.43)\": 0.889, \"(-9.43, -9.4)\": -0.897, \"(-9.4, -9.23)\": -0.968, \"(-9.23, -8.85)\": -0.99, \"(-8.85, -6.78)\": -1.01, \"(-6.78, -6.55)\": -0.989, \"(-6.55, -6.36)\": -0.968, \"(-6.36, -6.32)\": -0.947, \"(-6.32, -6.29)\": -0.877, \"(-6.29, -6.26)\": 0.86, \"(-6.26, -6.23)\": 0.903, \"(-6.23, -6.09)\": 0.947, \"(-6.09, -5.78)\": 0.97, \"(-5.78, -4.76)\": 0.991, \"(-4.76, -3.95)\": 1.013, \"(-3.95, -3.38)\": 0.992, \"(-3.38, -3.16)\": 0.971, \"(-3.16, -3.13)\": 0.637, \"(-3.13, -3.1)\": -0.905, \"(-3.1, -2.7)\": -0.955, \"(-2.7, -2.27)\": -0.978, \"(-2.27, -0.45)\": -1.0, \"(-0.45, -0.13)\": -0.978, \"(-0.13, -0.03)\": -0.957, \"(-0.03, -0.02)\": -0.832, \"(-0.02, -0.0)\": -0.639, \"(-0.0, 0.01)\": 0.816, \"(0.01, 0.05)\": 0.882, \"(0.05, 0.08)\": 0.913, \"(0.08, 0.34)\": 0.949, \"(0.34, 0.8)\": 0.97, \"(0.8, 3.05)\": 0.993, \"(3.05, 3.12)\": 0.97, \"(3.12, 3.15)\": 0.648, \"(3.15, 3.16)\": -0.793, \"(3.16, 3.69)\": -0.972, \"(3.69, 5.94)\": -0.994, \"(5.94, 6.15)\": -0.973, \"(6.15, 6.26)\": -0.949, \"(6.26, 6.29)\": -0.807, \"(6.29, 6.31)\": 0.818, \"(6.31, 6.56)\": 0.949, \"(6.56, 6.82)\": 0.972, \"(6.82, 9.3)\": 0.994, \"(9.3, 9.38)\": 0.968, \"(9.38, 9.39)\": 0.871, \"(9.39, 9.41)\": 0.738, \"(9.41, 9.43)\": -0.845, \"(9.43, 9.67)\": -0.963, \"(9.67, 9.89)\": -0.986, \"(9.89, 9.99)\": -1.01}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = sign(sin(x))\nb) f(x) = tanh(x+10) - 1/3 * x + 1/8 * sin(5*x)\nc) f(x) = log(x+10) + 1/3 * x \nd) f(x) = exp(-x+1)+ 2000 * abs(x+1)\ne) f(x) = sin(x)+cos(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "a)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-10.0, -8.6)\": -1.018, \"(-8.6, -8.0)\": -0.998, \"(-8.0, -7.93)\": -0.977, \"(-7.93, -7.9)\": -0.956, \"(-7.9, -7.89)\": -0.918, \"(-7.89, -7.85)\": -0.692, \"(-7.85, -7.8)\": 0.905, \"(-7.8, -7.71)\": 0.948, \"(-7.71, -7.62)\": 0.969, \"(-7.62, -6.42)\": 0.99, \"(-6.42, -5.31)\": 1.011, \"(-5.31, -4.77)\": 0.99, \"(-4.77, -4.73)\": 0.97, \"(-4.73, -4.72)\": 0.578, \"(-4.72, -4.71)\": -0.913, \"(-4.71, -4.61)\": -0.962, \"(-4.61, -3.77)\": -0.982, \"(-3.77, -1.82)\": -1.002, \"(-1.82, -1.62)\": -0.982, \"(-1.62, -1.59)\": -0.958, \"(-1.59, -1.57)\": -0.814, \"(-1.57, -1.56)\": 0.894, \"(-1.56, -1.35)\": 0.962, \"(-1.35, -0.29)\": 0.983, \"(-0.29, 1.28)\": 1.003, \"(1.28, 1.55)\": 0.982, \"(1.55, 1.57)\": 0.959, \"(1.57, 1.6)\": -0.652, \"(1.6, 1.62)\": -0.897, \"(1.62, 1.64)\": -0.933, \"(1.64, 1.96)\": -0.967, \"(1.96, 2.9)\": -0.987, \"(2.9, 4.3)\": -1.008, \"(4.3, 4.68)\": -0.987, \"(4.68, 4.7)\": -0.966, \"(4.7, 4.71)\": -0.903, \"(4.71, 4.74)\": 0.686, \"(4.74, 5.14)\": 0.966, \"(5.14, 5.81)\": 0.986, \"(5.81, 7.46)\": 1.007, \"(7.46, 7.68)\": 0.984, \"(7.68, 7.81)\": 0.959, \"(7.81, 7.85)\": 0.709, \"(7.85, 7.9)\": -0.833, \"(7.9, 7.99)\": -0.952, \"(7.99, 8.26)\": -0.972, \"(8.26, 9.98)\": -0.993, \"(9.98, 10.0)\": -1.017}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = exp(-x+1)+ 2000 * abs(x+1)\nb) f(x) = sign(cos(x))\nc) f(x) = sign(x ** 2 - 15)\nd) f(x) = -tanh(x) + 1/4 * x\ne) f(x) = x ** 3 + 250 * sin(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "b)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.94, -9.83)\": -0.36, \"(-9.83, -9.75)\": -0.322, \"(-9.75, -9.7)\": -0.286, \"(-9.7, -9.64)\": -0.249, \"(-9.64, -9.58)\": -0.192, \"(-9.58, -9.53)\": -0.137, \"(-9.53, -9.47)\": -0.094, \"(-9.47, -9.44)\": -0.037, \"(-9.44, -9.38)\": 0.009, \"(-9.38, -9.35)\": 0.049, \"(-9.35, -9.29)\": 0.092, \"(-9.29, -9.26)\": 0.136, \"(-9.26, -9.19)\": 0.177, \"(-9.19, -9.12)\": 0.226, \"(-9.12, -9.05)\": 0.282, \"(-9.05, -8.96)\": 0.318, \"(-8.96, -8.69)\": 0.36, \"(-8.69, -8.63)\": 0.324, \"(-8.63, -8.58)\": 0.271, \"(-8.58, -8.52)\": 0.225, \"(-8.52, -8.47)\": 0.158, \"(-8.47, -8.43)\": 0.103, \"(-8.43, -8.39)\": 0.046, \"(-8.39, -8.35)\": -0.005, \"(-8.35, -8.33)\": -0.055, \"(-8.33, -8.31)\": -0.093, \"(-8.31, -8.27)\": -0.132, \"(-8.27, -8.23)\": -0.21, \"(-8.23, -8.21)\": -0.263, \"(-8.21, -8.18)\": -0.309, \"(-8.18, -8.16)\": -0.345, \"(-8.16, -8.12)\": -0.416, \"(-8.12, -8.07)\": -0.5, \"(-8.07, -8.05)\": -0.559, \"(-8.05, -8.03)\": -0.601, \"(-8.03, -7.99)\": -0.666, \"(-7.99, -7.97)\": -0.745, \"(-7.97, -7.95)\": -0.787, \"(-7.95, -7.93)\": -0.825, \"(-7.93, -7.9)\": -0.869, \"(-7.9, -7.87)\": -0.918, \"(-7.87, -7.85)\": -0.979, \"(-7.85, -7.82)\": -1.022, \"(-7.82, -7.79)\": -1.102, \"(-7.79, -7.76)\": -1.144, \"(-7.76, -7.74)\": -1.205, \"(-7.74, -7.71)\": -1.245, \"(-7.71, -7.67)\": -1.289, \"(-7.67, -7.6)\": -1.402, \"(-7.6, -7.56)\": -1.478, \"(-7.56, -7.52)\": -1.525, \"(-7.52, -7.48)\": -1.573, \"(-7.48, -7.41)\": -1.648, \"(-7.41, -7.34)\": -1.688, \"(-7.34, -7.04)\": -1.734, \"(-7.04, -6.96)\": -1.643, \"(-6.96, -6.91)\": -1.575, \"(-6.91, -6.88)\": -1.519, \"(-6.88, -6.85)\": -1.483, \"(-6.85, -6.82)\": -1.443, \"(-6.82, -6.78)\": -1.372, \"(-6.78, -6.75)\": -1.304, \"(-6.75, -6.72)\": -1.249, \"(-6.72, -6.7)\": -1.175, \"(-6.7, -6.67)\": -1.125, \"(-6.67, -6.65)\": -1.057, \"(-6.65, -6.63)\": -1.009, \"(-6.63, -6.61)\": -0.953, \"(-6.61, -6.58)\": -0.899, \"(-6.58, -6.55)\": -0.829, \"(-6.55, -6.52)\": -0.741, \"(-6.52, -6.51)\": -0.689, \"(-6.51, -6.49)\": -0.638, \"(-6.49, -6.48)\": -0.592, \"(-6.48, -6.45)\": -0.548, \"(-6.45, -6.42)\": -0.461, \"(-6.42, -6.37)\": -0.333, \"(-6.37, -6.33)\": -0.181, \"(-6.33, -6.3)\": -0.1, \"(-6.3, -6.27)\": 0.008, \"(-6.27, -6.26)\": 0.047, \"(-6.26, -6.23)\": 0.114, \"(-6.23, -6.22)\": 0.168, \"(-6.22, -6.19)\": 0.217, \"(-6.19, -6.16)\": 0.356, \"(-6.16, -6.13)\": 0.392, \"(-6.13, -6.12)\": 0.47, \"(-6.12, -6.09)\": 0.523, \"(-6.09, -6.06)\": 0.583, \"(-6.06, -6.03)\": 0.669, \"(-6.03, -6.01)\": 0.771, \"(-6.01, -5.98)\": 0.826, \"(-5.98, -5.96)\": 0.9, \"(-5.96, -5.93)\": 0.937, \"(-5.93, -5.91)\": 0.998, \"(-5.91, -5.88)\": 1.045, \"(-5.88, -5.85)\": 1.149, \"(-5.85, -5.83)\": 1.195, \"(-5.83, -5.78)\": 1.269, \"(-5.78, -5.72)\": 1.368, \"(-5.72, -5.66)\": 1.491, \"(-5.66, -5.63)\": 1.549, \"(-5.63, -5.58)\": 1.593, \"(-5.58, -5.56)\": 1.639, \"(-5.56, -5.5)\": 1.675, \"(-5.5, -5.43)\": 1.711, \"(-5.43, -5.2)\": 1.747, \"(-5.2, -5.15)\": 1.704, \"(-5.15, -5.12)\": 1.666, \"(-5.12, -5.06)\": 1.628, \"(-5.06, -5.02)\": 1.567, \"(-5.02, -4.99)\": 1.51, \"(-4.99, -4.95)\": 1.465, \"(-4.95, -4.9)\": 1.422, \"(-4.9, -4.85)\": 1.299, \"(-4.85, -4.82)\": 1.254, \"(-4.82, -4.78)\": 1.182, \"(-4.78, -4.75)\": 1.123, \"(-4.75, -4.74)\": 1.082, \"(-4.74, -4.69)\": 1.016, \"(-4.69, -4.63)\": 0.894, \"(-4.63, -4.59)\": 0.799, \"(-4.59, -4.57)\": 0.751, \"(-4.57, -4.55)\": 0.701, \"(-4.55, -4.53)\": 0.661, \"(-4.53, -4.5)\": 0.596, \"(-4.5, -4.47)\": 0.531, \"(-4.47, -4.43)\": 0.474, \"(-4.43, -4.39)\": 0.388, \"(-4.39, -4.36)\": 0.35, \"(-4.36, -4.33)\": 0.257, \"(-4.33, -4.3)\": 0.217, \"(-4.3, -4.27)\": 0.163, \"(-4.27, -4.23)\": 0.112, \"(-4.23, -4.17)\": 0.017, \"(-4.17, -4.13)\": -0.033, \"(-4.13, -4.07)\": -0.127, \"(-4.07, -4.04)\": -0.163, \"(-4.04, -3.97)\": -0.204, \"(-3.97, -3.86)\": -0.287, \"(-3.86, -3.52)\": -0.352, \"(-3.52, -3.46)\": -0.315, \"(-3.46, -3.38)\": -0.256, \"(-3.38, -3.31)\": -0.192, \"(-3.31, -3.26)\": -0.157, \"(-3.26, -3.21)\": -0.106, \"(-3.21, -3.14)\": -0.037, \"(-3.14, -3.07)\": 0.039, \"(-3.07, -3.03)\": 0.082, \"(-3.03, -2.97)\": 0.119, \"(-2.97, -2.93)\": 0.181, \"(-2.93, -2.85)\": 0.217, \"(-2.85, -2.78)\": 0.275, \"(-2.78, -2.7)\": 0.311, \"(-2.7, -2.38)\": 0.348, \"(-2.38, -2.3)\": 0.295, \"(-2.3, -2.24)\": 0.218, \"(-2.24, -2.2)\": 0.182, \"(-2.2, -2.16)\": 0.127, \"(-2.16, -2.1)\": 0.05, \"(-2.1, -2.06)\": -0.022, \"(-2.06, -2.02)\": -0.081, \"(-2.02, -2.0)\": -0.117, \"(-2.0, -1.97)\": -0.163, \"(-1.97, -1.92)\": -0.233, \"(-1.92, -1.87)\": -0.334, \"(-1.87, -1.83)\": -0.413, \"(-1.83, -1.79)\": -0.518, \"(-1.79, -1.77)\": -0.572, \"(-1.77, -1.75)\": -0.613, \"(-1.75, -1.72)\": -0.653, \"(-1.72, -1.69)\": -0.72, \"(-1.69, -1.68)\": -0.762, \"(-1.68, -1.65)\": -0.801, \"(-1.65, -1.62)\": -0.871, \"(-1.62, -1.6)\": -0.921, \"(-1.6, -1.59)\": -0.964, \"(-1.59, -1.54)\": -1.001, \"(-1.54, -1.48)\": -1.136, \"(-1.48, -1.43)\": -1.211, \"(-1.43, -1.37)\": -1.327, \"(-1.37, -1.34)\": -1.394, \"(-1.34, -1.31)\": -1.435, \"(-1.31, -1.27)\": -1.476, \"(-1.27, -1.22)\": -1.53, \"(-1.22, -1.17)\": -1.607, \"(-1.17, -1.13)\": -1.647, \"(-1.13, -1.07)\": -1.69, \"(-1.07, -0.77)\": -1.733, \"(-0.77, -0.72)\": -1.674, \"(-0.72, -0.67)\": -1.623, \"(-0.67, -0.63)\": -1.578, \"(-0.63, -0.58)\": -1.516, \"(-0.58, -0.54)\": -1.431, \"(-0.54, -0.51)\": -1.372, \"(-0.51, -0.48)\": -1.325, \"(-0.48, -0.44)\": -1.23, \"(-0.44, -0.41)\": -1.178, \"(-0.41, -0.39)\": -1.117, \"(-0.39, -0.37)\": -1.051, \"(-0.37, -0.35)\": -1.005, \"(-0.35, -0.31)\": -0.951, \"(-0.31, -0.25)\": -0.8, \"(-0.25, -0.21)\": -0.657, \"(-0.21, -0.2)\": -0.607, \"(-0.2, -0.16)\": -0.545, \"(-0.16, -0.12)\": -0.396, \"(-0.12, -0.11)\": -0.335, \"(-0.11, -0.08)\": -0.299, \"(-0.08, -0.05)\": -0.2, \"(-0.05, -0.03)\": -0.139, \"(-0.03, -0.02)\": -0.092, \"(-0.02, -0.01)\": -0.057, \"(-0.01, 0.01)\": -0.021, \"(0.01, 0.04)\": 0.038, \"(0.04, 0.07)\": 0.17, \"(0.07, 0.1)\": 0.253, \"(0.1, 0.11)\": 0.32, \"(0.11, 0.14)\": 0.362, \"(0.14, 0.17)\": 0.461, \"(0.17, 0.19)\": 0.507, \"(0.19, 0.21)\": 0.587, \"(0.21, 0.24)\": 0.65, \"(0.24, 0.27)\": 0.736, \"(0.27, 0.3)\": 0.819, \"(0.3, 0.32)\": 0.868, \"(0.32, 0.36)\": 0.965, \"(0.36, 0.39)\": 1.054, \"(0.39, 0.43)\": 1.122, \"(0.43, 0.46)\": 1.207, \"(0.46, 0.51)\": 1.272, \"(0.51, 0.58)\": 1.378, \"(0.58, 0.65)\": 1.52, \"(0.65, 0.7)\": 1.594, \"(0.7, 0.73)\": 1.642, \"(0.73, 0.8)\": 1.684, \"(0.8, 1.09)\": 1.739, \"(1.09, 1.14)\": 1.703, \"(1.14, 1.21)\": 1.654, \"(1.21, 1.26)\": 1.565, \"(1.26, 1.33)\": 1.494, \"(1.33, 1.37)\": 1.424, \"(1.37, 1.42)\": 1.326, \"(1.42, 1.45)\": 1.262, \"(1.45, 1.49)\": 1.197, \"(1.49, 1.5)\": 1.157, \"(1.5, 1.55)\": 1.107, \"(1.55, 1.59)\": 0.988, \"(1.59, 1.62)\": 0.933, \"(1.62, 1.65)\": 0.873, \"(1.65, 1.68)\": 0.787, \"(1.68, 1.71)\": 0.751, \"(1.71, 1.73)\": 0.709, \"(1.73, 1.76)\": 0.649, \"(1.76, 1.78)\": 0.597, \"(1.78, 1.83)\": 0.542, \"(1.83, 1.89)\": 0.384, \"(1.89, 1.91)\": 0.338, \"(1.91, 1.96)\": 0.281, \"(1.96, 1.99)\": 0.195, \"(1.99, 2.01)\": 0.156, \"(2.01, 2.03)\": 0.114, \"(2.03, 2.07)\": 0.069, \"(2.07, 2.12)\": 0.006, \"(2.12, 2.19)\": -0.068, \"(2.19, 2.24)\": -0.156, \"(2.24, 2.28)\": -0.195, \"(2.28, 2.34)\": -0.232, \"(2.34, 2.44)\": -0.289, \"(2.44, 2.76)\": -0.351, \"(2.76, 2.82)\": -0.316, \"(2.82, 2.88)\": -0.278, \"(2.88, 2.93)\": -0.238, \"(2.93, 2.98)\": -0.193, \"(2.98, 3.03)\": -0.151, \"(3.03, 3.09)\": -0.098, \"(3.09, 3.16)\": -0.022, \"(3.16, 3.23)\": 0.036, \"(3.23, 3.3)\": 0.117, \"(3.3, 3.38)\": 0.191, \"(3.38, 3.43)\": 0.229, \"(3.43, 3.49)\": 0.27, \"(3.49, 3.6)\": 0.31, \"(3.6, 3.86)\": 0.361, \"(3.86, 3.93)\": 0.323, \"(3.93, 4.01)\": 0.28, \"(4.01, 4.05)\": 0.203, \"(4.05, 4.11)\": 0.161, \"(4.11, 4.19)\": 0.037, \"(4.19, 4.23)\": -0.022, \"(4.23, 4.25)\": -0.075, \"(4.25, 4.29)\": -0.117, \"(4.29, 4.34)\": -0.208, \"(4.34, 4.39)\": -0.299, \"(4.39, 4.41)\": -0.365, \"(4.41, 4.44)\": -0.404, \"(4.44, 4.48)\": -0.467, \"(4.48, 4.53)\": -0.553, \"(4.53, 4.57)\": -0.689, \"(4.57, 4.59)\": -0.725, \"(4.59, 4.62)\": -0.762, \"(4.62, 4.65)\": -0.85, \"(4.65, 4.68)\": -0.892, \"(4.68, 4.71)\": -0.945, \"(4.71, 4.75)\": -1.045, \"(4.75, 4.78)\": -1.092, \"(4.78, 4.81)\": -1.15, \"(4.81, 4.85)\": -1.216, \"(4.85, 4.89)\": -1.304, \"(4.89, 4.95)\": -1.372, \"(4.95, 5.0)\": -1.464, \"(5.0, 5.05)\": -1.515, \"(5.05, 5.12)\": -1.608, \"(5.12, 5.16)\": -1.649, \"(5.16, 5.21)\": -1.686, \"(5.21, 5.32)\": -1.722, \"(5.32, 5.48)\": -1.759, \"(5.48, 5.53)\": -1.715, \"(5.53, 5.62)\": -1.658, \"(5.62, 5.7)\": -1.513, \"(5.7, 5.74)\": -1.445, \"(5.74, 5.78)\": -1.35, \"(5.78, 5.82)\": -1.297, \"(5.82, 5.84)\": -1.208, \"(5.84, 5.89)\": -1.17, \"(5.89, 5.98)\": -0.975, \"(5.98, 6.02)\": -0.785, \"(6.02, 6.04)\": -0.747, \"(6.04, 6.09)\": -0.686, \"(6.09, 6.19)\": -0.4, \"(6.19, 6.23)\": -0.204, \"(6.23, 6.26)\": -0.146, \"(6.26, 6.3)\": -0.005, \"(6.3, 6.32)\": 0.068, \"(6.32, 6.33)\": 0.119, \"(6.33, 6.35)\": 0.185, \"(6.35, 6.37)\": 0.22, \"(6.37, 6.39)\": 0.287, \"(6.39, 6.42)\": 0.369, \"(6.42, 6.46)\": 0.464, \"(6.46, 6.49)\": 0.533, \"(6.49, 6.53)\": 0.674, \"(6.53, 6.56)\": 0.749, \"(6.56, 6.61)\": 0.848, \"(6.61, 6.65)\": 0.967, \"(6.65, 6.67)\": 1.048, \"(6.67, 6.69)\": 1.106, \"(6.69, 6.75)\": 1.185, \"(6.75, 6.8)\": 1.329, \"(6.8, 6.82)\": 1.366, \"(6.82, 6.85)\": 1.405, \"(6.85, 6.89)\": 1.47, \"(6.89, 6.93)\": 1.54, \"(6.93, 6.96)\": 1.58, \"(6.96, 7.03)\": 1.627, \"(7.03, 7.09)\": 1.691, \"(7.09, 7.4)\": 1.729, \"(7.4, 7.47)\": 1.678, \"(7.47, 7.53)\": 1.594, \"(7.53, 7.58)\": 1.534, \"(7.58, 7.61)\": 1.474, \"(7.61, 7.63)\": 1.431, \"(7.63, 7.65)\": 1.392, \"(7.65, 7.68)\": 1.352, \"(7.68, 7.73)\": 1.296, \"(7.73, 7.78)\": 1.178, \"(7.78, 7.82)\": 1.131, \"(7.82, 7.87)\": 1.009, \"(7.87, 7.9)\": 0.952, \"(7.9, 7.93)\": 0.869, \"(7.93, 7.96)\": 0.816, \"(7.96, 8.01)\": 0.75, \"(8.01, 8.05)\": 0.641, \"(8.05, 8.07)\": 0.603, \"(8.07, 8.1)\": 0.535, \"(8.1, 8.15)\": 0.493, \"(8.15, 8.21)\": 0.324, \"(8.21, 8.24)\": 0.279, \"(8.24, 8.26)\": 0.225, \"(8.26, 8.3)\": 0.189, \"(8.3, 8.34)\": 0.103, \"(8.34, 8.36)\": 0.062, \"(8.36, 8.4)\": -0.001, \"(8.4, 8.44)\": -0.056, \"(8.44, 8.52)\": -0.138, \"(8.52, 8.57)\": -0.189, \"(8.57, 8.62)\": -0.242, \"(8.62, 8.66)\": -0.282, \"(8.66, 8.77)\": -0.321, \"(8.77, 9.04)\": -0.359, \"(9.04, 9.11)\": -0.316, \"(9.11, 9.15)\": -0.27, \"(9.15, 9.23)\": -0.228, \"(9.23, 9.31)\": -0.161, \"(9.31, 9.37)\": -0.092, \"(9.37, 9.4)\": -0.048, \"(9.4, 9.45)\": -0.013, \"(9.45, 9.51)\": 0.035, \"(9.51, 9.58)\": 0.108, \"(9.58, 9.66)\": 0.172, \"(9.66, 9.73)\": 0.243, \"(9.73, 9.79)\": 0.284, \"(9.79, 9.91)\": 0.328, \"(9.91, 9.97)\": 0.363}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = exp(x)+ 2000 * abs(x)\nb) f(x) = sin(x)+cos(x)\nc) f(x) = sign(x) + cos(x)\nd) f(x) = sin(x)+sin(2*x)\ne) f(x) = abs(x ** 2 - 20)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "d)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.99, -9.97)\": 6104.5, \"(-9.97, -9.93)\": 6007.9, \"(-9.93, -9.88)\": 5921.3, \"(-9.88, -9.83)\": 5827.6, \"(-9.83, -9.78)\": 5678.9, \"(-9.78, -9.73)\": 5535.0, \"(-9.73, -9.67)\": 5427.1, \"(-9.67, -9.63)\": 5335.7, \"(-9.63, -9.59)\": 5229.9, \"(-9.59, -9.53)\": 5125.3, \"(-9.53, -9.49)\": 5024.0, \"(-9.49, -9.46)\": 4944.5, \"(-9.46, -9.41)\": 4862.1, \"(-9.41, -9.36)\": 4768.2, \"(-9.36, -9.3)\": 4635.8, \"(-9.3, -9.24)\": 4521.1, \"(-9.24, -9.17)\": 4398.8, \"(-9.17, -9.13)\": 4311.9, \"(-9.13, -9.09)\": 4223.6, \"(-9.09, -9.04)\": 4138.0, \"(-9.04, -9.01)\": 4055.5, \"(-9.01, -8.95)\": 3971.1, \"(-8.95, -8.9)\": 3885.3, \"(-8.9, -8.84)\": 3778.7, \"(-8.84, -8.78)\": 3690.5, \"(-8.78, -8.72)\": 3572.9, \"(-8.72, -8.67)\": 3466.6, \"(-8.67, -8.6)\": 3385.1, \"(-8.6, -8.53)\": 3290.3, \"(-8.53, -8.48)\": 3202.0, \"(-8.48, -8.43)\": 3122.9, \"(-8.43, -8.37)\": 3040.9, \"(-8.37, -8.29)\": 2949.2, \"(-8.29, -8.21)\": 2814.2, \"(-8.21, -8.14)\": 2727.3, \"(-8.14, -8.08)\": 2635.6, \"(-8.08, -8.03)\": 2557.8, \"(-8.03, -7.95)\": 2473.0, \"(-7.95, -7.88)\": 2386.9, \"(-7.88, -7.82)\": 2292.1, \"(-7.82, -7.71)\": 2188.1, \"(-7.71, -7.63)\": 2109.8, \"(-7.63, -7.56)\": 2016.7, \"(-7.56, -7.48)\": 1937.0, \"(-7.48, -7.39)\": 1841.3, \"(-7.39, -7.3)\": 1757.9, \"(-7.3, -7.22)\": 1677.9, \"(-7.22, -7.14)\": 1597.3, \"(-7.14, -7.04)\": 1506.4, \"(-7.04, -6.94)\": 1429.4, \"(-6.94, -6.85)\": 1347.1, \"(-6.85, -6.74)\": 1264.2, \"(-6.74, -6.61)\": 1176.5, \"(-6.61, -6.49)\": 1092.9, \"(-6.49, -6.34)\": 1003.2, \"(-6.34, -6.24)\": 920.9, \"(-6.24, -6.06)\": 824.5, \"(-6.06, -5.86)\": 732.1, \"(-5.86, -5.7)\": 654.8, \"(-5.7, -5.47)\": 573.8, \"(-5.47, -5.27)\": 491.6, \"(-5.27, -4.99)\": 404.9, \"(-4.99, -4.65)\": 316.4, \"(-4.65, -4.24)\": 233.2, \"(-4.24, -3.64)\": 156.6, \"(-3.64, -1.2)\": 76.2, \"(-1.2, 0.56)\": -0.1, \"(0.56, 1.24)\": -76.6, \"(1.24, 1.81)\": -154.5, \"(1.81, 2.24)\": -232.1, \"(2.24, 2.64)\": -310.3, \"(2.64, 3.02)\": -388.7, \"(3.02, 3.37)\": -470.7, \"(3.37, 3.7)\": -551.4, \"(3.7, 4.0)\": -634.6, \"(4.0, 4.31)\": -716.3, \"(4.31, 4.59)\": -796.6, \"(4.59, 4.9)\": -873.2, \"(4.9, 5.17)\": -953.7, \"(5.17, 5.51)\": -1038.2, \"(5.51, 5.81)\": -1116.8, \"(5.81, 6.14)\": -1194.3, \"(6.14, 6.54)\": -1276.7, \"(6.54, 7.0)\": -1356.0, \"(7.0, 9.12)\": -1436.6, \"(9.12, 9.38)\": -1358.4, \"(9.38, 9.58)\": -1276.9, \"(9.58, 9.77)\": -1195.7, \"(9.77, 9.94)\": -1115.2, \"(9.94, 9.98)\": -1021.5}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = 1/2 * (x+1) ** 4 - 5 * (x+2) **3 + 3 * x ** 2 + 2 * x + 1\nb) f(x) = sign(x) + cos(x)\nc) f(x) = exp(x) + 4000* sin(x)\nd) f(x) = sign(x ** 2 - 15)\ne) f(x) = abs(x ** 2 - 20)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "a)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.98, -9.92)\": -5507.9, \"(-9.92, -9.85)\": -5293.4, \"(-9.85, -9.81)\": -5177.9, \"(-9.81, -9.76)\": -5098.0, \"(-9.76, -9.71)\": -4999.9, \"(-9.71, -9.65)\": -4874.7, \"(-9.65, -9.61)\": -4764.6, \"(-9.61, -9.57)\": -4672.9, \"(-9.57, -9.52)\": -4598.0, \"(-9.52, -9.47)\": -4482.5, \"(-9.47, -9.43)\": -4384.3, \"(-9.43, -9.38)\": -4303.4, \"(-9.38, -9.34)\": -4219.4, \"(-9.34, -9.28)\": -4097.4, \"(-9.28, -9.22)\": -4016.0, \"(-9.22, -9.17)\": -3903.1, \"(-9.17, -9.11)\": -3794.3, \"(-9.11, -9.05)\": -3697.8, \"(-9.05, -8.98)\": -3584.8, \"(-8.98, -8.93)\": -3480.1, \"(-8.93, -8.86)\": -3402.0, \"(-8.86, -8.8)\": -3283.9, \"(-8.8, -8.75)\": -3193.9, \"(-8.75, -8.69)\": -3102.0, \"(-8.69, -8.63)\": -3024.1, \"(-8.63, -8.57)\": -2940.6, \"(-8.57, -8.51)\": -2845.5, \"(-8.51, -8.47)\": -2770.0, \"(-8.47, -8.42)\": -2689.2, \"(-8.42, -8.28)\": -2580.3, \"(-8.28, -8.18)\": -2397.9, \"(-8.18, -8.1)\": -2303.3, \"(-8.1, -8.02)\": -2203.3, \"(-8.02, -7.93)\": -2085.9, \"(-7.93, -7.85)\": -2005.0, \"(-7.85, -7.77)\": -1929.9, \"(-7.77, -7.68)\": -1844.4, \"(-7.68, -7.58)\": -1714.5, \"(-7.58, -7.5)\": -1636.8, \"(-7.5, -7.38)\": -1556.3, \"(-7.38, -7.27)\": -1416.3, \"(-7.27, -7.16)\": -1338.4, \"(-7.16, -7.07)\": -1260.9, \"(-7.07, -6.99)\": -1184.5, \"(-6.99, -6.84)\": -1103.4, \"(-6.84, -6.71)\": -1003.1, \"(-6.71, -6.56)\": -922.2, \"(-6.56, -6.41)\": -816.2, \"(-6.41, -6.27)\": -740.9, \"(-6.27, -6.13)\": -664.3, \"(-6.13, -5.95)\": -589.0, \"(-5.95, -5.78)\": -513.4, \"(-5.78, -5.53)\": -435.8, \"(-5.53, -5.26)\": -340.0, \"(-5.26, -4.95)\": -265.1, \"(-4.95, -4.59)\": -184.9, \"(-4.59, -3.94)\": -110.0, \"(-3.94, -0.04)\": -33.3, \"(-0.04, 0.86)\": 41.7, \"(0.86, 1.4)\": 116.7, \"(1.4, 1.85)\": 191.8, \"(1.85, 2.23)\": 269.3, \"(2.23, 2.56)\": 344.9, \"(2.56, 2.88)\": 422.1, \"(2.88, 3.18)\": 504.8, \"(3.18, 3.45)\": 580.1, \"(3.45, 3.71)\": 658.3, \"(3.71, 3.96)\": 739.7, \"(3.96, 4.22)\": 814.7, \"(4.22, 4.43)\": 891.1, \"(4.43, 4.69)\": 969.6, \"(4.69, 4.9)\": 1050.8, \"(4.9, 5.14)\": 1125.5, \"(5.14, 5.4)\": 1202.4, \"(5.4, 5.62)\": 1288.8, \"(5.62, 5.87)\": 1364.8, \"(5.87, 6.13)\": 1444.7, \"(6.13, 6.41)\": 1525.7, \"(6.41, 6.71)\": 1609.8, \"(6.71, 7.01)\": 1689.6, \"(7.01, 7.38)\": 1764.4, \"(7.38, 7.86)\": 1841.7, \"(7.86, 9.44)\": 1916.9, \"(9.44, 9.71)\": 1826.9, \"(9.71, 9.92)\": 1751.2}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = abs(x) ** (1/10)\nb) f(x) = exp(x)+ 4000 * sign(x)\nc) f(x) = -1/2 * (x+1) ** 4 + 5 * (x+2) **3 + 3 * x ** 2 + 2 * x + 1\nd) f(x) = sin(x) + sin(3*x)\ne) f(x) = x ** 3 + 250 * sin(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "c)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-10.0, -4.08)\": 1.006, \"(-4.08, -3.97)\": 0.986, \"(-3.97, -3.94)\": 0.917, \"(-3.94, -3.87)\": 0.808, \"(-3.87, -3.82)\": -0.767, \"(-3.82, -3.74)\": -0.964, \"(-3.74, 0.09)\": -0.986, \"(0.09, 3.67)\": -1.006, \"(3.67, 3.86)\": -0.98, \"(3.86, 3.87)\": -0.96, \"(3.87, 3.9)\": -0.755, \"(3.9, 3.94)\": 0.835, \"(3.94, 4.22)\": 0.967, \"(4.22, 10.0)\": 0.988}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = sin(x) + sin(3*x)\nb) f(x) = exp(x)+ 4000 * sign(x)\nc) f(x) = sign(x ** 2 - 15)\nd) f(x) = -tanh(x) + 1/4 * x\ne) f(x) = sin(x) + sin(0.5 * x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "c)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.96, -9.86)\": 78.59, \"(-9.86, -9.81)\": 76.87, \"(-9.81, -9.76)\": 76.02, \"(-9.76, -9.71)\": 75.17, \"(-9.71, -9.65)\": 74.14, \"(-9.65, -9.6)\": 72.89, \"(-9.6, -9.56)\": 71.88, \"(-9.56, -9.49)\": 71.08, \"(-9.49, -9.45)\": 70.17, \"(-9.45, -9.39)\": 69.02, \"(-9.39, -9.34)\": 67.86, \"(-9.34, -9.27)\": 66.82, \"(-9.27, -9.21)\": 65.68, \"(-9.21, -9.13)\": 64.23, \"(-9.13, -9.07)\": 63.29, \"(-9.07, -9.0)\": 62.01, \"(-9.0, -8.91)\": 60.27, \"(-8.91, -8.83)\": 58.93, \"(-8.83, -8.76)\": 57.42, \"(-8.76, -8.7)\": 56.59, \"(-8.7, -8.65)\": 55.66, \"(-8.65, -8.59)\": 54.8, \"(-8.59, -8.52)\": 53.38, \"(-8.52, -8.46)\": 52.43, \"(-8.46, -8.4)\": 51.26, \"(-8.4, -8.33)\": 50.4, \"(-8.33, -8.27)\": 49.12, \"(-8.27, -8.21)\": 48.31, \"(-8.21, -8.17)\": 47.51, \"(-8.17, -8.13)\": 46.7, \"(-8.13, -8.07)\": 45.74, \"(-8.07, -7.99)\": 44.74, \"(-7.99, -7.92)\": 43.68, \"(-7.92, -7.86)\": 42.57, \"(-7.86, -7.8)\": 41.6, \"(-7.8, -7.74)\": 40.8, \"(-7.74, -7.66)\": 39.58, \"(-7.66, -7.59)\": 38.46, \"(-7.59, -7.54)\": 37.5, \"(-7.54, -7.47)\": 36.42, \"(-7.47, -7.38)\": 35.6, \"(-7.38, -7.33)\": 34.39, \"(-7.33, -7.28)\": 33.53, \"(-7.28, -7.2)\": 32.64, \"(-7.2, -7.16)\": 31.76, \"(-7.16, -7.09)\": 30.77, \"(-7.09, -6.99)\": 29.75, \"(-6.99, -6.94)\": 28.94, \"(-6.94, -6.88)\": 28.12, \"(-6.88, -6.82)\": 27.23, \"(-6.82, -6.76)\": 26.3, \"(-6.76, -6.68)\": 25.51, \"(-6.68, -6.64)\": 24.51, \"(-6.64, -6.55)\": 23.62, \"(-6.55, -6.47)\": 22.71, \"(-6.47, -6.4)\": 21.71, \"(-6.4, -6.33)\": 20.88, \"(-6.33, -6.26)\": 20.04, \"(-6.26, -6.2)\": 19.17, \"(-6.2, -6.13)\": 18.28, \"(-6.13, -6.03)\": 17.16, \"(-6.03, -5.95)\": 16.28, \"(-5.95, -5.88)\": 15.18, \"(-5.88, -5.78)\": 14.31, \"(-5.78, -5.67)\": 13.12, \"(-5.67, -5.6)\": 12.13, \"(-5.6, -5.52)\": 11.16, \"(-5.52, -5.42)\": 10.16, \"(-5.42, -5.32)\": 9.12, \"(-5.32, -5.23)\": 8.18, \"(-5.23, -5.15)\": 7.32, \"(-5.15, -5.06)\": 6.51, \"(-5.06, -4.95)\": 5.33, \"(-4.95, -4.86)\": 4.33, \"(-4.86, -4.75)\": 3.4, \"(-4.75, -4.66)\": 2.44, \"(-4.66, -4.57)\": 1.65, \"(-4.57, -4.3)\": 0.74, \"(-4.3, -4.21)\": 1.63, \"(-4.21, -4.1)\": 2.48, \"(-4.1, -4.0)\": 3.27, \"(-4.0, -3.89)\": 4.12, \"(-3.89, -3.77)\": 4.95, \"(-3.77, -3.66)\": 5.8, \"(-3.66, -3.53)\": 6.72, \"(-3.53, -3.43)\": 7.62, \"(-3.43, -3.3)\": 8.47, \"(-3.3, -3.15)\": 9.29, \"(-3.15, -3.01)\": 10.11, \"(-3.01, -2.84)\": 11.12, \"(-2.84, -2.68)\": 12.0, \"(-2.68, -2.52)\": 12.84, \"(-2.52, -2.34)\": 13.72, \"(-2.34, -2.15)\": 14.58, \"(-2.15, -1.96)\": 15.38, \"(-1.96, -1.73)\": 16.24, \"(-1.73, -1.46)\": 17.03, \"(-1.46, -1.15)\": 17.87, \"(-1.15, -0.67)\": 18.72, \"(-0.67, 1.13)\": 19.52, \"(1.13, 1.44)\": 18.7, \"(1.44, 1.71)\": 17.9, \"(1.71, 1.94)\": 17.03, \"(1.94, 2.14)\": 16.17, \"(2.14, 2.38)\": 15.33, \"(2.38, 2.59)\": 14.04, \"(2.59, 2.75)\": 13.22, \"(2.75, 2.91)\": 12.35, \"(2.91, 3.04)\": 11.45, \"(3.04, 3.2)\": 10.55, \"(3.2, 3.35)\": 9.43, \"(3.35, 3.49)\": 8.61, \"(3.49, 3.61)\": 7.73, \"(3.61, 3.73)\": 6.93, \"(3.73, 3.88)\": 5.86, \"(3.88, 3.98)\": 4.9, \"(3.98, 4.15)\": 3.8, \"(4.15, 4.28)\": 2.54, \"(4.28, 4.38)\": 1.69, \"(4.38, 4.65)\": 0.77, \"(4.65, 4.72)\": 1.57, \"(4.72, 4.81)\": 2.39, \"(4.81, 4.9)\": 3.24, \"(4.9, 5.0)\": 4.11, \"(5.0, 5.12)\": 5.08, \"(5.12, 5.25)\": 6.68, \"(5.25, 5.37)\": 7.9, \"(5.37, 5.45)\": 8.87, \"(5.45, 5.54)\": 9.81, \"(5.54, 5.62)\": 10.78, \"(5.62, 5.67)\": 11.58, \"(5.67, 5.74)\": 12.38, \"(5.74, 5.82)\": 13.19, \"(5.82, 5.9)\": 14.03, \"(5.9, 6.01)\": 15.18, \"(6.01, 6.09)\": 16.26, \"(6.09, 6.14)\": 17.27, \"(6.14, 6.24)\": 18.14, \"(6.24, 6.3)\": 19.12, \"(6.3, 6.4)\": 20.04, \"(6.4, 6.46)\": 21.01, \"(6.46, 6.52)\": 21.87, \"(6.52, 6.61)\": 22.72, \"(6.61, 6.68)\": 23.8, \"(6.68, 6.77)\": 24.83, \"(6.77, 6.85)\": 25.95, \"(6.85, 6.93)\": 27.26, \"(6.93, 7.02)\": 28.1, \"(7.02, 7.11)\": 29.67, \"(7.11, 7.18)\": 30.64, \"(7.18, 7.24)\": 31.58, \"(7.24, 7.31)\": 32.67, \"(7.31, 7.38)\": 33.5, \"(7.38, 7.43)\": 34.47, \"(7.43, 7.48)\": 35.5, \"(7.48, 7.56)\": 36.33, \"(7.56, 7.62)\": 37.22, \"(7.62, 7.68)\": 38.19, \"(7.68, 7.76)\": 39.25, \"(7.76, 7.81)\": 40.42, \"(7.81, 7.89)\": 41.25, \"(7.89, 7.95)\": 42.41, \"(7.95, 8.01)\": 43.58, \"(8.01, 8.08)\": 44.45, \"(8.08, 8.14)\": 45.46, \"(8.14, 8.18)\": 46.38, \"(8.18, 8.25)\": 47.25, \"(8.25, 8.3)\": 48.21, \"(8.3, 8.35)\": 49.05, \"(8.35, 8.43)\": 49.87, \"(8.43, 8.5)\": 51.53, \"(8.5, 8.55)\": 52.35, \"(8.55, 8.61)\": 53.31, \"(8.61, 8.65)\": 54.37, \"(8.65, 8.71)\": 55.16, \"(8.71, 8.78)\": 56.14, \"(8.78, 8.85)\": 57.32, \"(8.85, 8.9)\": 58.59, \"(8.9, 8.96)\": 59.39, \"(8.96, 9.02)\": 60.45, \"(9.02, 9.06)\": 61.43, \"(9.06, 9.11)\": 62.27, \"(9.11, 9.14)\": 63.11, \"(9.14, 9.2)\": 63.97, \"(9.2, 9.27)\": 65.46, \"(9.27, 9.32)\": 66.36, \"(9.32, 9.38)\": 67.23, \"(9.38, 9.45)\": 68.22, \"(9.45, 9.5)\": 69.41, \"(9.5, 9.56)\": 70.79, \"(9.56, 9.6)\": 71.77, \"(9.6, 9.66)\": 72.65, \"(9.66, 9.71)\": 73.52, \"(9.71, 9.75)\": 74.38, \"(9.75, 9.81)\": 75.25, \"(9.81, 9.86)\": 76.51, \"(9.86, 9.91)\": 77.32, \"(9.91, 9.96)\": 78.4, \"(9.96, 10.0)\": 79.44}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = -1/2 * (x+1) ** 4 + 5 * (x+2) **3 + 3 * x ** 2 + 2 * x + 1\nb) f(x) = exp(-x^2+1)+ 1/3 * |x|\nc) f(x) = exp(-x+1)+ 2000 * abs(x+1)\nd) f(x) = sign(cos(x))\ne) f(x) = abs(x ** 2 - 20)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "e)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.98, -9.39)\": 1.2574, \"(-9.39, -8.92)\": 1.251, \"(-8.92, -8.48)\": 1.2444, \"(-8.48, -8.05)\": 1.238, \"(-8.05, -7.65)\": 1.2316, \"(-7.65, -7.22)\": 1.2251, \"(-7.22, -6.84)\": 1.2184, \"(-6.84, -6.5)\": 1.212, \"(-6.5, -6.16)\": 1.2055, \"(-6.16, -5.86)\": 1.1991, \"(-5.86, -5.49)\": 1.1925, \"(-5.49, -5.16)\": 1.1846, \"(-5.16, -4.86)\": 1.1779, \"(-4.86, -4.61)\": 1.1713, \"(-4.61, -4.36)\": 1.1645, \"(-4.36, -4.1)\": 1.158, \"(-4.1, -3.88)\": 1.1511, \"(-3.88, -3.65)\": 1.1447, \"(-3.65, -3.42)\": 1.1372, \"(-3.42, -3.23)\": 1.1305, \"(-3.23, -3.06)\": 1.1241, \"(-3.06, -2.88)\": 1.1177, \"(-2.88, -2.71)\": 1.111, \"(-2.71, -2.54)\": 1.1041, \"(-2.54, -2.38)\": 1.0972, \"(-2.38, -2.24)\": 1.0901, \"(-2.24, -2.08)\": 1.0827, \"(-2.08, -1.95)\": 1.0757, \"(-1.95, -1.82)\": 1.0686, \"(-1.82, -1.69)\": 1.06, \"(-1.69, -1.59)\": 1.0536, \"(-1.59, -1.46)\": 1.046, \"(-1.46, -1.36)\": 1.0365, \"(-1.36, -1.27)\": 1.03, \"(-1.27, -1.19)\": 1.0235, \"(-1.19, -1.12)\": 1.0167, \"(-1.12, -1.03)\": 1.0092, \"(-1.03, -0.94)\": 1.0014, \"(-0.94, -0.87)\": 0.9927, \"(-0.87, -0.76)\": 0.9854, \"(-0.76, -0.67)\": 0.9665, \"(-0.67, -0.62)\": 0.9598, \"(-0.62, -0.56)\": 0.9504, \"(-0.56, -0.51)\": 0.9413, \"(-0.51, -0.46)\": 0.9293, \"(-0.46, -0.42)\": 0.9208, \"(-0.42, -0.35)\": 0.9118, \"(-0.35, -0.3)\": 0.8963, \"(-0.3, -0.26)\": 0.8824, \"(-0.26, -0.24)\": 0.869, \"(-0.24, -0.21)\": 0.8624, \"(-0.21, -0.18)\": 0.848, \"(-0.18, -0.16)\": 0.8415, \"(-0.16, -0.14)\": 0.8275, \"(-0.14, -0.1)\": 0.8156, \"(-0.1, -0.04)\": 0.7609, \"(-0.04, -0.01)\": 0.658, \"(-0.01, -0.0)\": 0.6308, \"(-0.0, 0.01)\": 0.6212, \"(0.01, 0.05)\": 0.6801, \"(0.05, 0.09)\": 0.7643, \"(0.09, 0.13)\": 0.8057, \"(0.13, 0.16)\": 0.8262, \"(0.16, 0.18)\": 0.8329, \"(0.18, 0.19)\": 0.8417, \"(0.19, 0.21)\": 0.8499, \"(0.21, 0.24)\": 0.8601, \"(0.24, 0.28)\": 0.8737, \"(0.28, 0.31)\": 0.882, \"(0.31, 0.33)\": 0.8922, \"(0.33, 0.38)\": 0.9003, \"(0.38, 0.43)\": 0.9125, \"(0.43, 0.48)\": 0.9218, \"(0.48, 0.52)\": 0.9325, \"(0.52, 0.58)\": 0.9409, \"(0.58, 0.64)\": 0.9484, \"(0.64, 0.72)\": 0.9614, \"(0.72, 0.77)\": 0.9689, \"(0.77, 0.84)\": 0.9756, \"(0.84, 0.91)\": 0.9832, \"(0.91, 0.99)\": 0.9928, \"(0.99, 1.03)\": 0.9998, \"(1.03, 1.13)\": 1.0064, \"(1.13, 1.22)\": 1.0131, \"(1.22, 1.3)\": 1.0205, \"(1.3, 1.41)\": 1.0283, \"(1.41, 1.52)\": 1.0363, \"(1.52, 1.65)\": 1.0446, \"(1.65, 1.78)\": 1.0526, \"(1.78, 1.9)\": 1.0592, \"(1.9, 2.01)\": 1.0664, \"(2.01, 2.14)\": 1.073, \"(2.14, 2.29)\": 1.0798, \"(2.29, 2.47)\": 1.0876, \"(2.47, 2.63)\": 1.0959, \"(2.63, 2.81)\": 1.1025, \"(2.81, 2.99)\": 1.1098, \"(2.99, 3.21)\": 1.1172, \"(3.21, 3.4)\": 1.1237, \"(3.4, 3.6)\": 1.1302, \"(3.6, 3.85)\": 1.1368, \"(3.85, 4.05)\": 1.1439, \"(4.05, 4.28)\": 1.1505, \"(4.28, 4.54)\": 1.1569, \"(4.54, 4.77)\": 1.1634, \"(4.77, 5.08)\": 1.1699, \"(5.08, 5.39)\": 1.177, \"(5.39, 5.71)\": 1.184, \"(5.71, 6.04)\": 1.1906, \"(6.04, 6.37)\": 1.197, \"(6.37, 6.75)\": 1.2036, \"(6.75, 7.1)\": 1.2101, \"(7.1, 7.48)\": 1.2166, \"(7.48, 7.92)\": 1.2233, \"(7.92, 8.3)\": 1.2298, \"(8.3, 8.79)\": 1.2363, \"(8.79, 9.24)\": 1.2427, \"(9.24, 9.74)\": 1.2493, \"(9.74, 9.99)\": 1.2557}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = abs(x) ** (1/10)\nb) f(x) = -tanh(x) + 1/4 * x\nc) f(x) = sin(x)+sin(2*x)\nd) f(x) = sin(x) + sin(0.5 * x)\ne) f(x) = sign(cos(x))\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "a)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.98, -9.95)\": 1.524, \"(-9.95, -9.91)\": 1.492, \"(-9.91, -9.88)\": 1.436, \"(-9.88, -9.86)\": 1.404, \"(-9.86, -9.83)\": 1.368, \"(-9.83, -9.81)\": 1.334, \"(-9.81, -9.79)\": 1.281, \"(-9.79, -9.77)\": 1.225, \"(-9.77, -9.74)\": 1.138, \"(-9.74, -9.69)\": 1.102, \"(-9.69, -9.63)\": 0.829, \"(-9.63, -9.63)\": 0.784, \"(-9.63, -9.62)\": 0.752, \"(-9.62, -9.58)\": 0.709, \"(-9.58, -9.55)\": 0.542, \"(-9.55, -9.53)\": 0.448, \"(-9.53, -9.52)\": 0.381, \"(-9.52, -9.5)\": 0.349, \"(-9.5, -9.48)\": 0.254, \"(-9.48, -9.46)\": 0.212, \"(-9.46, -9.45)\": 0.133, \"(-9.45, -9.43)\": 0.067, \"(-9.43, -9.43)\": 0.021, \"(-9.43, -9.4)\": -0.018, \"(-9.4, -9.38)\": -0.142, \"(-9.38, -9.36)\": -0.229, \"(-9.36, -9.35)\": -0.281, \"(-9.35, -9.33)\": -0.342, \"(-9.33, -9.33)\": -0.373, \"(-9.33, -9.3)\": -0.417, \"(-9.3, -9.28)\": -0.557, \"(-9.28, -9.27)\": -0.588, \"(-9.27, -9.26)\": -0.629, \"(-9.26, -9.23)\": -0.671, \"(-9.23, -9.18)\": -0.823, \"(-9.18, -9.14)\": -0.998, \"(-9.14, -9.12)\": -1.031, \"(-9.12, -9.1)\": -1.108, \"(-9.1, -9.07)\": -1.183, \"(-9.07, -9.06)\": -1.236, \"(-9.06, -9.03)\": -1.269, \"(-9.03, -9.0)\": -1.329, \"(-9.0, -8.97)\": -1.398, \"(-8.97, -8.94)\": -1.44, \"(-8.94, -8.89)\": -1.484, \"(-8.89, -8.69)\": -1.519, \"(-8.69, -8.66)\": -1.467, \"(-8.66, -8.62)\": -1.419, \"(-8.62, -8.58)\": -1.354, \"(-8.58, -8.56)\": -1.303, \"(-8.56, -8.51)\": -1.236, \"(-8.51, -8.46)\": -1.116, \"(-8.46, -8.42)\": -1.05, \"(-8.42, -8.38)\": -0.909, \"(-8.38, -8.35)\": -0.833, \"(-8.35, -8.31)\": -0.766, \"(-8.31, -8.27)\": -0.637, \"(-8.27, -8.24)\": -0.566, \"(-8.24, -8.22)\": -0.512, \"(-8.22, -8.21)\": -0.475, \"(-8.21, -8.19)\": -0.419, \"(-8.19, -8.17)\": -0.385, \"(-8.17, -8.14)\": -0.35, \"(-8.14, -8.1)\": -0.264, \"(-8.1, -8.08)\": -0.231, \"(-8.08, -8.04)\": -0.166, \"(-8.04, -7.99)\": -0.118, \"(-7.99, -7.92)\": -0.052, \"(-7.92, -7.76)\": -0.012, \"(-7.76, -7.69)\": -0.058, \"(-7.69, -7.65)\": -0.14, \"(-7.65, -7.62)\": -0.178, \"(-7.62, -7.6)\": -0.224, \"(-7.6, -7.58)\": -0.259, \"(-7.58, -7.54)\": -0.294, \"(-7.54, -7.5)\": -0.406, \"(-7.5, -7.47)\": -0.492, \"(-7.47, -7.44)\": -0.546, \"(-7.44, -7.42)\": -0.598, \"(-7.42, -7.39)\": -0.683, \"(-7.39, -7.37)\": -0.728, \"(-7.37, -7.35)\": -0.781, \"(-7.35, -7.33)\": -0.845, \"(-7.33, -7.3)\": -0.876, \"(-7.3, -7.26)\": -1.001, \"(-7.26, -7.22)\": -1.081, \"(-7.22, -7.19)\": -1.16, \"(-7.19, -7.16)\": -1.23, \"(-7.16, -7.14)\": -1.272, \"(-7.14, -7.11)\": -1.317, \"(-7.11, -7.08)\": -1.366, \"(-7.08, -7.05)\": -1.402, \"(-7.05, -7.0)\": -1.458, \"(-7.0, -6.95)\": -1.5, \"(-6.95, -6.81)\": -1.535, \"(-6.81, -6.76)\": -1.495, \"(-6.76, -6.73)\": -1.434, \"(-6.73, -6.7)\": -1.398, \"(-6.7, -6.68)\": -1.334, \"(-6.68, -6.62)\": -1.281, \"(-6.62, -6.57)\": -1.048, \"(-6.57, -6.52)\": -0.978, \"(-6.52, -6.47)\": -0.745, \"(-6.47, -6.45)\": -0.701, \"(-6.45, -6.44)\": -0.631, \"(-6.44, -6.43)\": -0.6, \"(-6.43, -6.39)\": -0.494, \"(-6.39, -6.34)\": -0.342, \"(-6.34, -6.31)\": -0.132, \"(-6.31, -6.3)\": -0.072, \"(-6.3, -6.28)\": -0.019, \"(-6.28, -6.26)\": 0.083, \"(-6.26, -6.25)\": 0.118, \"(-6.25, -6.23)\": 0.181, \"(-6.23, -6.22)\": 0.25, \"(-6.22, -6.2)\": 0.293, \"(-6.2, -6.19)\": 0.349, \"(-6.19, -6.15)\": 0.409, \"(-6.15, -6.12)\": 0.609, \"(-6.12, -6.1)\": 0.656, \"(-6.1, -6.07)\": 0.769, \"(-6.07, -6.06)\": 0.81, \"(-6.06, -6.05)\": 0.849, \"(-6.05, -6.02)\": 0.917, \"(-6.02, -5.98)\": 1.037, \"(-5.98, -5.92)\": 1.146, \"(-5.92, -5.84)\": 1.34, \"(-5.84, -5.78)\": 1.419, \"(-5.78, -5.55)\": 1.507, \"(-5.55, -5.51)\": 1.463, \"(-5.51, -5.48)\": 1.427, \"(-5.48, -5.42)\": 1.365, \"(-5.42, -5.37)\": 1.219, \"(-5.37, -5.34)\": 1.177, \"(-5.34, -5.31)\": 1.084, \"(-5.31, -5.28)\": 1.023, \"(-5.28, -5.26)\": 0.951, \"(-5.26, -5.23)\": 0.894, \"(-5.23, -5.21)\": 0.83, \"(-5.21, -5.19)\": 0.778, \"(-5.19, -5.17)\": 0.726, \"(-5.17, -5.14)\": 0.673, \"(-5.14, -5.07)\": 0.626, \"(-5.07, -4.97)\": 0.323, \"(-4.97, -4.9)\": 0.195, \"(-4.9, -4.84)\": 0.102, \"(-4.84, -4.79)\": 0.058, \"(-4.79, -4.61)\": 0.009, \"(-4.61, -4.54)\": 0.068, \"(-4.54, -4.5)\": 0.144, \"(-4.5, -4.47)\": 0.197, \"(-4.47, -4.44)\": 0.236, \"(-4.44, -4.42)\": 0.298, \"(-4.42, -4.4)\": 0.33, \"(-4.4, -4.38)\": 0.371, \"(-4.38, -4.36)\": 0.425, \"(-4.36, -4.34)\": 0.461, \"(-4.34, -4.32)\": 0.514, \"(-4.32, -4.31)\": 0.547, \"(-4.31, -4.29)\": 0.591, \"(-4.29, -4.25)\": 0.632, \"(-4.25, -4.21)\": 0.765, \"(-4.21, -4.18)\": 0.82, \"(-4.18, -4.15)\": 0.958, \"(-4.15, -4.1)\": 0.994, \"(-4.1, -4.05)\": 1.168, \"(-4.05, -4.03)\": 1.215, \"(-4.03, -3.99)\": 1.259, \"(-3.99, -3.95)\": 1.331, \"(-3.95, -3.91)\": 1.398, \"(-3.91, -3.86)\": 1.451, \"(-3.86, -3.64)\": 1.51, \"(-3.64, -3.6)\": 1.464, \"(-3.6, -3.58)\": 1.416, \"(-3.58, -3.55)\": 1.365, \"(-3.55, -3.53)\": 1.328, \"(-3.53, -3.48)\": 1.24, \"(-3.48, -3.43)\": 1.119, \"(-3.43, -3.39)\": 0.961, \"(-3.39, -3.36)\": 0.887, \"(-3.36, -3.31)\": 0.731, \"(-3.31, -3.27)\": 0.591, \"(-3.27, -3.24)\": 0.441, \"(-3.24, -3.23)\": 0.361, \"(-3.23, -3.2)\": 0.323, \"(-3.2, -3.17)\": 0.15, \"(-3.17, -3.15)\": 0.074, \"(-3.15, -3.11)\": -0.044, \"(-3.11, -3.09)\": -0.205, \"(-3.09, -3.07)\": -0.238, \"(-3.07, -3.03)\": -0.344, \"(-3.03, -3.0)\": -0.489, \"(-3.0, -2.98)\": -0.6, \"(-2.98, -2.96)\": -0.648, \"(-2.96, -2.93)\": -0.73, \"(-2.93, -2.9)\": -0.868, \"(-2.9, -2.88)\": -0.942, \"(-2.88, -2.84)\": -1.011, \"(-2.84, -2.78)\": -1.152, \"(-2.78, -2.72)\": -1.305, \"(-2.72, -2.69)\": -1.389, \"(-2.69, -2.64)\": -1.442, \"(-2.64, -2.61)\": -1.477, \"(-2.61, -2.41)\": -1.511, \"(-2.41, -2.38)\": -1.473, \"(-2.38, -2.35)\": -1.428, \"(-2.35, -2.32)\": -1.371, \"(-2.32, -2.3)\": -1.339, \"(-2.3, -2.27)\": -1.291, \"(-2.27, -2.24)\": -1.229, \"(-2.24, -2.22)\": -1.17, \"(-2.22, -2.18)\": -1.121, \"(-2.18, -2.13)\": -1.014, \"(-2.13, -2.1)\": -0.894, \"(-2.1, -2.08)\": -0.859, \"(-2.08, -2.07)\": -0.827, \"(-2.07, -2.04)\": -0.773, \"(-2.04, -2.03)\": -0.735, \"(-2.03, -1.99)\": -0.683, \"(-1.99, -1.96)\": -0.567, \"(-1.96, -1.93)\": -0.498, \"(-1.93, -1.9)\": -0.434, \"(-1.9, -1.85)\": -0.359, \"(-1.85, -1.79)\": -0.263, \"(-1.79, -1.72)\": -0.135, \"(-1.72, -1.69)\": -0.076, \"(-1.69, -1.63)\": -0.044, \"(-1.63, -1.46)\": -0.009, \"(-1.46, -1.42)\": -0.054, \"(-1.42, -1.38)\": -0.115, \"(-1.38, -1.36)\": -0.147, \"(-1.36, -1.33)\": -0.178, \"(-1.33, -1.31)\": -0.217, \"(-1.31, -1.28)\": -0.271, \"(-1.28, -1.26)\": -0.321, \"(-1.26, -1.23)\": -0.381, \"(-1.23, -1.2)\": -0.463, \"(-1.2, -1.17)\": -0.515, \"(-1.17, -1.15)\": -0.566, \"(-1.15, -1.11)\": -0.631, \"(-1.11, -1.06)\": -0.794, \"(-1.06, -1.03)\": -0.885, \"(-1.03, -1.02)\": -0.92, \"(-1.02, -0.99)\": -0.964, \"(-0.99, -0.95)\": -1.082, \"(-0.95, -0.92)\": -1.138, \"(-0.92, -0.89)\": -1.185, \"(-0.89, -0.86)\": -1.261, \"(-0.86, -0.85)\": -1.296, \"(-0.85, -0.82)\": -1.328, \"(-0.82, -0.8)\": -1.378, \"(-0.8, -0.77)\": -1.409, \"(-0.77, -0.72)\": -1.447, \"(-0.72, -0.5)\": -1.522, \"(-0.5, -0.46)\": -1.454, \"(-0.46, -0.44)\": -1.418, \"(-0.44, -0.42)\": -1.386, \"(-0.42, -0.39)\": -1.336, \"(-0.39, -0.36)\": -1.268, \"(-0.36, -0.29)\": -1.195, \"(-0.29, -0.21)\": -0.905, \"(-0.21, -0.19)\": -0.78, \"(-0.19, -0.18)\": -0.722, \"(-0.18, -0.15)\": -0.655, \"(-0.15, -0.13)\": -0.57, \"(-0.13, -0.12)\": -0.487, \"(-0.12, -0.1)\": -0.428, \"(-0.1, -0.09)\": -0.387, \"(-0.09, -0.07)\": -0.308, \"(-0.07, -0.05)\": -0.261, \"(-0.05, -0.02)\": -0.119, \"(-0.02, -0.01)\": -0.077, \"(-0.01, 0.01)\": -0.006, \"(0.01, 0.04)\": 0.104, \"(0.04, 0.06)\": 0.186, \"(0.06, 0.07)\": 0.263, \"(0.07, 0.08)\": 0.308, \"(0.08, 0.11)\": 0.356, \"(0.11, 0.14)\": 0.516, \"(0.14, 0.17)\": 0.581, \"(0.17, 0.21)\": 0.765, \"(0.21, 0.22)\": 0.805, \"(0.22, 0.25)\": 0.838, \"(0.25, 0.3)\": 1.038, \"(0.3, 0.35)\": 1.111, \"(0.35, 0.4)\": 1.282, \"(0.4, 0.44)\": 1.365, \"(0.44, 0.48)\": 1.418, \"(0.48, 0.53)\": 1.476, \"(0.53, 0.74)\": 1.513, \"(0.74, 0.77)\": 1.468, \"(0.77, 0.8)\": 1.412, \"(0.8, 0.82)\": 1.377, \"(0.82, 0.85)\": 1.337, \"(0.85, 0.86)\": 1.306, \"(0.86, 0.9)\": 1.259, \"(0.9, 0.91)\": 1.212, \"(0.91, 0.93)\": 1.171, \"(0.93, 0.95)\": 1.126, \"(0.95, 0.99)\": 1.056, \"(0.99, 1.02)\": 0.972, \"(1.02, 1.04)\": 0.929, \"(1.04, 1.05)\": 0.886, \"(1.05, 1.06)\": 0.853, \"(1.06, 1.08)\": 0.815, \"(1.08, 1.11)\": 0.741, \"(1.11, 1.16)\": 0.675, \"(1.16, 1.19)\": 0.533, \"(1.19, 1.22)\": 0.484, \"(1.22, 1.24)\": 0.446, \"(1.24, 1.26)\": 0.371, \"(1.26, 1.28)\": 0.333, \"(1.28, 1.3)\": 0.292, \"(1.3, 1.35)\": 0.244, \"(1.35, 1.42)\": 0.121, \"(1.42, 1.48)\": 0.068, \"(1.48, 1.69)\": 0.031, \"(1.69, 1.75)\": 0.068, \"(1.75, 1.79)\": 0.165, \"(1.79, 1.82)\": 0.205, \"(1.82, 1.85)\": 0.262, \"(1.85, 1.86)\": 0.295, \"(1.86, 1.88)\": 0.335, \"(1.88, 1.9)\": 0.369, \"(1.9, 1.92)\": 0.406, \"(1.92, 1.93)\": 0.445, \"(1.93, 1.95)\": 0.477, \"(1.95, 1.99)\": 0.53, \"(1.99, 2.03)\": 0.665, \"(2.03, 2.05)\": 0.739, \"(2.05, 2.07)\": 0.777, \"(2.07, 2.1)\": 0.816, \"(2.1, 2.16)\": 0.91, \"(2.16, 2.21)\": 1.086, \"(2.21, 2.24)\": 1.17, \"(2.24, 2.29)\": 1.245, \"(2.29, 2.32)\": 1.333, \"(2.32, 2.35)\": 1.364, \"(2.35, 2.36)\": 1.405, \"(2.36, 2.39)\": 1.439, \"(2.39, 2.44)\": 1.473, \"(2.44, 2.63)\": 1.51, \"(2.63, 2.67)\": 1.475, \"(2.67, 2.71)\": 1.394, \"(2.71, 2.73)\": 1.355, \"(2.73, 2.79)\": 1.304, \"(2.79, 2.86)\": 1.103, \"(2.86, 2.89)\": 0.977, \"(2.89, 2.91)\": 0.897, \"(2.91, 2.93)\": 0.842, \"(2.93, 2.97)\": 0.781, \"(2.97, 3.01)\": 0.573, \"(3.01, 3.04)\": 0.451, \"(3.04, 3.06)\": 0.349, \"(3.06, 3.08)\": 0.304, \"(3.08, 3.11)\": 0.154, \"(3.11, 3.13)\": 0.082, \"(3.13, 3.15)\": 0.006, \"(3.15, 3.18)\": -0.109, \"(3.18, 3.19)\": -0.173, \"(3.19, 3.2)\": -0.208, \"(3.2, 3.22)\": -0.273, \"(3.22, 3.23)\": -0.317, \"(3.23, 3.24)\": -0.379, \"(3.24, 3.29)\": -0.426, \"(3.29, 3.36)\": -0.747, \"(3.36, 3.38)\": -0.878, \"(3.38, 3.4)\": -0.932, \"(3.4, 3.43)\": -0.979, \"(3.43, 3.46)\": -1.069, \"(3.46, 3.47)\": -1.141, \"(3.47, 3.48)\": -1.175, \"(3.48, 3.51)\": -1.207, \"(3.51, 3.54)\": -1.281, \"(3.54, 3.57)\": -1.335, \"(3.57, 3.62)\": -1.398, \"(3.62, 3.91)\": -1.483, \"(3.91, 3.96)\": -1.408, \"(3.96, 3.99)\": -1.346, \"(3.99, 4.04)\": -1.282, \"(4.04, 4.09)\": -1.154, \"(4.09, 4.14)\": -1.045, \"(4.14, 4.2)\": -0.927, \"(4.2, 4.25)\": -0.724, \"(4.25, 4.29)\": -0.68, \"(4.29, 4.34)\": -0.563, \"(4.34, 4.37)\": -0.463, \"(4.37, 4.39)\": -0.403, \"(4.39, 4.42)\": -0.354, \"(4.42, 4.44)\": -0.302, \"(4.44, 4.47)\": -0.27, \"(4.47, 4.51)\": -0.188, \"(4.51, 4.55)\": -0.142, \"(4.55, 4.59)\": -0.083, \"(4.59, 4.67)\": -0.045, \"(4.67, 4.81)\": -0.006, \"(4.81, 4.85)\": -0.043, \"(4.85, 4.87)\": -0.08, \"(4.87, 4.91)\": -0.117, \"(4.91, 4.95)\": -0.195, \"(4.95, 4.97)\": -0.226, \"(4.97, 5.01)\": -0.284, \"(5.01, 5.05)\": -0.389, \"(5.05, 5.08)\": -0.431, \"(5.08, 5.1)\": -0.507, \"(5.1, 5.12)\": -0.548, \"(5.12, 5.15)\": -0.591, \"(5.15, 5.17)\": -0.693, \"(5.17, 5.21)\": -0.733, \"(5.21, 5.25)\": -0.864, \"(5.25, 5.27)\": -0.933, \"(5.27, 5.32)\": -0.987, \"(5.32, 5.37)\": -1.149, \"(5.37, 5.4)\": -1.19, \"(5.4, 5.42)\": -1.263, \"(5.42, 5.45)\": -1.297, \"(5.45, 5.49)\": -1.376, \"(5.49, 5.52)\": -1.415, \"(5.52, 5.57)\": -1.466, \"(5.57, 5.81)\": -1.503, \"(5.81, 5.86)\": -1.418, \"(5.86, 5.88)\": -1.357, \"(5.88, 5.9)\": -1.324, \"(5.9, 5.94)\": -1.253, \"(5.94, 5.99)\": -1.155, \"(5.99, 6.04)\": -0.939, \"(6.04, 6.06)\": -0.877, \"(6.06, 6.08)\": -0.806, \"(6.08, 6.08)\": -0.759, \"(6.08, 6.12)\": -0.718, \"(6.12, 6.16)\": -0.51, \"(6.16, 6.19)\": -0.442, \"(6.19, 6.23)\": -0.271, \"(6.23, 6.27)\": -0.131, \"(6.27, 6.29)\": -0.017, \"(6.29, 6.33)\": 0.094, \"(6.33, 6.36)\": 0.289, \"(6.36, 6.37)\": 0.324, \"(6.37, 6.4)\": 0.402, \"(6.4, 6.42)\": 0.508, \"(6.42, 6.45)\": 0.592, \"(6.45, 6.47)\": 0.669, \"(6.47, 6.48)\": 0.733, \"(6.48, 6.5)\": 0.786, \"(6.5, 6.51)\": 0.853, \"(6.51, 6.54)\": 0.886, \"(6.54, 6.56)\": 0.989, \"(6.56, 6.58)\": 1.039, \"(6.58, 6.59)\": 1.073, \"(6.59, 6.6)\": 1.104, \"(6.6, 6.62)\": 1.147, \"(6.62, 6.63)\": 1.181, \"(6.63, 6.65)\": 1.225, \"(6.65, 6.67)\": 1.26, \"(6.67, 6.71)\": 1.331, \"(6.71, 6.78)\": 1.414, \"(6.78, 7.01)\": 1.506, \"(7.01, 7.05)\": 1.468, \"(7.05, 7.08)\": 1.43, \"(7.08, 7.11)\": 1.385, \"(7.11, 7.15)\": 1.323, \"(7.15, 7.17)\": 1.276, \"(7.17, 7.19)\": 1.231, \"(7.19, 7.2)\": 1.197, \"(7.2, 7.21)\": 1.165, \"(7.21, 7.24)\": 1.125, \"(7.24, 7.27)\": 1.057, \"(7.27, 7.32)\": 0.98, \"(7.32, 7.36)\": 0.852, \"(7.36, 7.37)\": 0.778, \"(7.37, 7.39)\": 0.738, \"(7.39, 7.42)\": 0.707, \"(7.42, 7.43)\": 0.624, \"(7.43, 7.45)\": 0.589, \"(7.45, 7.47)\": 0.542, \"(7.47, 7.52)\": 0.499, \"(7.52, 7.57)\": 0.344, \"(7.57, 7.59)\": 0.284, \"(7.59, 7.61)\": 0.252, \"(7.61, 7.64)\": 0.203, \"(7.64, 7.67)\": 0.165, \"(7.67, 7.72)\": 0.113, \"(7.72, 7.79)\": 0.053, \"(7.79, 7.94)\": 0.01, \"(7.94, 7.98)\": 0.046, \"(7.98, 8.02)\": 0.081, \"(8.02, 8.06)\": 0.122, \"(8.06, 8.13)\": 0.198, \"(8.13, 8.18)\": 0.348, \"(8.18, 8.21)\": 0.395, \"(8.21, 8.26)\": 0.478, \"(8.26, 8.29)\": 0.615, \"(8.29, 8.3)\": 0.654, \"(8.3, 8.32)\": 0.692, \"(8.32, 8.34)\": 0.753, \"(8.34, 8.37)\": 0.805, \"(8.37, 8.39)\": 0.864, \"(8.39, 8.41)\": 0.9, \"(8.41, 8.44)\": 0.977, \"(8.44, 8.47)\": 1.065, \"(8.47, 8.48)\": 1.102, \"(8.48, 8.51)\": 1.136, \"(8.51, 8.53)\": 1.181, \"(8.53, 8.55)\": 1.247, \"(8.55, 8.58)\": 1.293, \"(8.58, 8.61)\": 1.337, \"(8.61, 8.63)\": 1.374, \"(8.63, 8.65)\": 1.411, \"(8.65, 8.68)\": 1.45, \"(8.68, 8.74)\": 1.482, \"(8.74, 8.91)\": 1.528, \"(8.91, 8.97)\": 1.445, \"(8.97, 9.01)\": 1.38, \"(9.01, 9.04)\": 1.336, \"(9.04, 9.06)\": 1.282, \"(9.06, 9.08)\": 1.236, \"(9.08, 9.1)\": 1.18, \"(9.1, 9.14)\": 1.114, \"(9.14, 9.17)\": 0.987, \"(9.17, 9.19)\": 0.902, \"(9.19, 9.22)\": 0.845, \"(9.22, 9.25)\": 0.696, \"(9.25, 9.27)\": 0.629, \"(9.27, 9.3)\": 0.554, \"(9.3, 9.35)\": 0.421, \"(9.35, 9.39)\": 0.181, \"(9.39, 9.39)\": 0.15, \"(9.39, 9.4)\": 0.102, \"(9.4, 9.42)\": 0.046, \"(9.42, 9.46)\": -0.047, \"(9.46, 9.5)\": -0.22, \"(9.5, 9.54)\": -0.388, \"(9.54, 9.56)\": -0.476, \"(9.56, 9.57)\": -0.547, \"(9.57, 9.58)\": -0.591, \"(9.58, 9.59)\": -0.625, \"(9.59, 9.61)\": -0.661, \"(9.61, 9.65)\": -0.763, \"(9.65, 9.7)\": -0.945, \"(9.7, 9.72)\": -1.026, \"(9.72, 9.74)\": -1.078, \"(9.74, 9.76)\": -1.142, \"(9.76, 9.79)\": -1.201, \"(9.79, 9.82)\": -1.27, \"(9.82, 9.86)\": -1.327, \"(9.86, 9.91)\": -1.428, \"(9.91, 9.95)\": -1.468, \"(9.95, 10.0)\": -1.513}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = exp(-x+1)+ 2000 * abs(x+1)\nb) f(x) = sin(x) + sin(0.5 * x)\nc) f(x) = exp(-x^2+1)+ 1/3 * |x|\nd) f(x) = sin(x) + sin(3*x)\ne) f(x) = -x ** 2 + 2 * cos(5*x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "d)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.97, -9.93)\": 1.486, \"(-9.93, -9.88)\": 1.449, \"(-9.88, -9.84)\": 1.404, \"(-9.84, -9.77)\": 1.362, \"(-9.77, -9.73)\": 1.324, \"(-9.73, -9.67)\": 1.277, \"(-9.67, -9.63)\": 1.236, \"(-9.63, -9.59)\": 1.198, \"(-9.59, -9.49)\": 1.138, \"(-9.49, -9.41)\": 1.034, \"(-9.41, -9.36)\": 0.96, \"(-9.36, -9.32)\": 0.919, \"(-9.32, -9.27)\": 0.875, \"(-9.27, -9.23)\": 0.839, \"(-9.23, -9.19)\": 0.8, \"(-9.19, -9.15)\": 0.755, \"(-9.15, -9.12)\": 0.716, \"(-9.12, -9.08)\": 0.679, \"(-9.08, -9.03)\": 0.626, \"(-9.03, -8.98)\": 0.57, \"(-8.98, -8.94)\": 0.535, \"(-8.94, -8.9)\": 0.491, \"(-8.9, -8.83)\": 0.455, \"(-8.83, -8.77)\": 0.369, \"(-8.77, -8.72)\": 0.328, \"(-8.72, -8.66)\": 0.277, \"(-8.66, -8.62)\": 0.232, \"(-8.62, -8.58)\": 0.186, \"(-8.58, -8.51)\": 0.142, \"(-8.51, -8.44)\": 0.1, \"(-8.44, -8.36)\": 0.018, \"(-8.36, -8.28)\": -0.032, \"(-8.28, -8.22)\": -0.079, \"(-8.22, -8.12)\": -0.119, \"(-8.12, -8.0)\": -0.192, \"(-8.0, -7.9)\": -0.241, \"(-7.9, -7.78)\": -0.285, \"(-7.78, -7.57)\": -0.321, \"(-7.57, -7.05)\": -0.361, \"(-7.05, -6.92)\": -0.315, \"(-6.92, -6.83)\": -0.278, \"(-6.83, -6.71)\": -0.238, \"(-6.71, -6.61)\": -0.191, \"(-6.61, -6.51)\": -0.148, \"(-6.51, -6.45)\": -0.112, \"(-6.45, -6.37)\": -0.076, \"(-6.37, -6.26)\": -0.03, \"(-6.26, -6.2)\": 0.012, \"(-6.2, -6.11)\": 0.049, \"(-6.11, -5.99)\": 0.105, \"(-5.99, -5.91)\": 0.145, \"(-5.91, -5.82)\": 0.183, \"(-5.82, -5.69)\": 0.224, \"(-5.69, -5.54)\": 0.277, \"(-5.54, -5.32)\": 0.321, \"(-5.32, -4.8)\": 0.36, \"(-4.8, -4.65)\": 0.307, \"(-4.65, -4.56)\": 0.262, \"(-4.56, -4.48)\": 0.222, \"(-4.48, -4.42)\": 0.186, \"(-4.42, -4.33)\": 0.15, \"(-4.33, -4.25)\": 0.084, \"(-4.25, -4.19)\": 0.042, \"(-4.19, -4.13)\": -0.005, \"(-4.13, -4.09)\": -0.051, \"(-4.09, -4.05)\": -0.086, \"(-4.05, -3.99)\": -0.13, \"(-3.99, -3.93)\": -0.174, \"(-3.93, -3.86)\": -0.232, \"(-3.86, -3.81)\": -0.289, \"(-3.81, -3.75)\": -0.332, \"(-3.75, -3.71)\": -0.394, \"(-3.71, -3.66)\": -0.447, \"(-3.66, -3.61)\": -0.485, \"(-3.61, -3.59)\": -0.52, \"(-3.59, -3.53)\": -0.56, \"(-3.53, -3.49)\": -0.612, \"(-3.49, -3.45)\": -0.651, \"(-3.45, -3.42)\": -0.686, \"(-3.42, -3.37)\": -0.729, \"(-3.37, -3.28)\": -0.787, \"(-3.28, -3.19)\": -0.899, \"(-3.19, -3.13)\": -0.971, \"(-3.13, -3.11)\": -1.019, \"(-3.11, -3.04)\": -1.059, \"(-3.04, -2.98)\": -1.119, \"(-2.98, -2.93)\": -1.167, \"(-2.93, -2.87)\": -1.212, \"(-2.87, -2.81)\": -1.28, \"(-2.81, -2.77)\": -1.327, \"(-2.77, -2.7)\": -1.369, \"(-2.7, -2.64)\": -1.412, \"(-2.64, -2.59)\": -1.462, \"(-2.59, -2.49)\": -1.515, \"(-2.49, -2.43)\": -1.555, \"(-2.43, -2.32)\": -1.607, \"(-2.32, -2.22)\": -1.665, \"(-2.22, -2.01)\": -1.71, \"(-2.01, -1.6)\": -1.751, \"(-1.6, -1.51)\": -1.715, \"(-1.51, -1.46)\": -1.678, \"(-1.46, -1.36)\": -1.639, \"(-1.36, -1.3)\": -1.602, \"(-1.3, -1.25)\": -1.566, \"(-1.25, -1.19)\": -1.523, \"(-1.19, -1.14)\": -1.478, \"(-1.14, -1.05)\": -1.422, \"(-1.05, -0.96)\": -1.318, \"(-0.96, -0.94)\": -1.281, \"(-0.94, -0.9)\": -1.24, \"(-0.9, -0.86)\": -1.201, \"(-0.86, -0.82)\": -1.163, \"(-0.82, -0.76)\": -1.11, \"(-0.76, -0.71)\": -1.026, \"(-0.71, -0.68)\": -0.985, \"(-0.68, -0.63)\": -0.941, \"(-0.63, -0.59)\": -0.885, \"(-0.59, -0.54)\": -0.816, \"(-0.54, -0.5)\": -0.755, \"(-0.5, -0.46)\": -0.707, \"(-0.46, -0.42)\": -0.664, \"(-0.42, -0.37)\": -0.602, \"(-0.37, -0.32)\": -0.507, \"(-0.32, -0.25)\": -0.431, \"(-0.25, -0.21)\": -0.323, \"(-0.21, -0.17)\": -0.284, \"(-0.17, -0.11)\": -0.221, \"(-0.11, -0.06)\": -0.143, \"(-0.06, -0.01)\": -0.045, \"(-0.01, 0.02)\": -0.006, \"(0.02, 0.06)\": 0.036, \"(0.06, 0.12)\": 0.121, \"(0.12, 0.19)\": 0.243, \"(0.19, 0.21)\": 0.284, \"(0.21, 0.23)\": 0.326, \"(0.23, 0.27)\": 0.363, \"(0.27, 0.32)\": 0.431, \"(0.32, 0.36)\": 0.508, \"(0.36, 0.41)\": 0.566, \"(0.41, 0.47)\": 0.627, \"(0.47, 0.51)\": 0.702, \"(0.51, 0.53)\": 0.74, \"(0.53, 0.57)\": 0.786, \"(0.57, 0.61)\": 0.843, \"(0.61, 0.65)\": 0.879, \"(0.65, 0.68)\": 0.939, \"(0.68, 0.71)\": 0.982, \"(0.71, 0.79)\": 1.033, \"(0.79, 0.87)\": 1.148, \"(0.87, 0.91)\": 1.204, \"(0.91, 1.01)\": 1.242, \"(1.01, 1.11)\": 1.384, \"(1.11, 1.18)\": 1.426, \"(1.18, 1.26)\": 1.499, \"(1.26, 1.36)\": 1.559, \"(1.36, 1.45)\": 1.614, \"(1.45, 1.55)\": 1.667, \"(1.55, 1.69)\": 1.704, \"(1.69, 2.18)\": 1.74, \"(2.18, 2.29)\": 1.699, \"(2.29, 2.38)\": 1.656, \"(2.38, 2.49)\": 1.598, \"(2.49, 2.54)\": 1.548, \"(2.54, 2.61)\": 1.505, \"(2.61, 2.67)\": 1.465, \"(2.67, 2.78)\": 1.402, \"(2.78, 2.86)\": 1.312, \"(2.86, 2.9)\": 1.263, \"(2.9, 2.94)\": 1.227, \"(2.94, 2.99)\": 1.187, \"(2.99, 3.02)\": 1.145, \"(3.02, 3.07)\": 1.091, \"(3.07, 3.12)\": 1.053, \"(3.12, 3.17)\": 0.998, \"(3.17, 3.22)\": 0.957, \"(3.22, 3.24)\": 0.916, \"(3.24, 3.28)\": 0.877, \"(3.28, 3.32)\": 0.842, \"(3.32, 3.37)\": 0.804, \"(3.37, 3.43)\": 0.75, \"(3.43, 3.48)\": 0.69, \"(3.48, 3.51)\": 0.648, \"(3.51, 3.55)\": 0.603, \"(3.55, 3.59)\": 0.566, \"(3.59, 3.7)\": 0.508, \"(3.7, 3.79)\": 0.387, \"(3.79, 3.83)\": 0.34, \"(3.83, 3.87)\": 0.299, \"(3.87, 3.92)\": 0.255, \"(3.92, 3.98)\": 0.21, \"(3.98, 4.05)\": 0.155, \"(4.05, 4.13)\": 0.086, \"(4.13, 4.19)\": 0.035, \"(4.19, 4.26)\": -0.002, \"(4.26, 4.34)\": -0.076, \"(4.34, 4.39)\": -0.112, \"(4.39, 4.49)\": -0.158, \"(4.49, 4.56)\": -0.193, \"(4.56, 4.65)\": -0.235, \"(4.65, 4.75)\": -0.271, \"(4.75, 4.88)\": -0.307, \"(4.88, 5.51)\": -0.344, \"(5.51, 5.68)\": -0.307, \"(5.68, 5.78)\": -0.268, \"(5.78, 5.87)\": -0.232, \"(5.87, 5.98)\": -0.187, \"(5.98, 6.08)\": -0.142, \"(6.08, 6.16)\": -0.094, \"(6.16, 6.25)\": -0.058, \"(6.25, 6.33)\": -0.006, \"(6.33, 6.42)\": 0.03, \"(6.42, 6.5)\": 0.077, \"(6.5, 6.59)\": 0.114, \"(6.59, 6.7)\": 0.161, \"(6.7, 6.81)\": 0.204, \"(6.81, 6.95)\": 0.256, \"(6.95, 7.08)\": 0.292, \"(7.08, 7.84)\": 0.332, \"(7.84, 7.94)\": 0.294, \"(7.94, 8.03)\": 0.256, \"(8.03, 8.11)\": 0.213, \"(8.11, 8.18)\": 0.174, \"(8.18, 8.26)\": 0.13, \"(8.26, 8.33)\": 0.074, \"(8.33, 8.38)\": 0.033, \"(8.38, 8.45)\": -0.022, \"(8.45, 8.51)\": -0.064, \"(8.51, 8.55)\": -0.107, \"(8.55, 8.59)\": -0.145, \"(8.59, 8.64)\": -0.181, \"(8.64, 8.69)\": -0.233, \"(8.69, 8.74)\": -0.275, \"(8.74, 8.77)\": -0.319, \"(8.77, 8.83)\": -0.356, \"(8.83, 8.87)\": -0.403, \"(8.87, 8.91)\": -0.442, \"(8.91, 8.97)\": -0.501, \"(8.97, 9.0)\": -0.54, \"(9.0, 9.04)\": -0.575, \"(9.04, 9.1)\": -0.612, \"(9.1, 9.15)\": -0.692, \"(9.15, 9.21)\": -0.746, \"(9.21, 9.26)\": -0.793, \"(9.26, 9.3)\": -0.839, \"(9.3, 9.37)\": -0.896, \"(9.37, 9.42)\": -0.948, \"(9.42, 9.49)\": -1.023, \"(9.49, 9.56)\": -1.08, \"(9.56, 9.62)\": -1.159, \"(9.62, 9.68)\": -1.212, \"(9.68, 9.73)\": -1.25, \"(9.73, 9.79)\": -1.304, \"(9.79, 9.83)\": -1.341, \"(9.83, 9.92)\": -1.4, \"(9.92, 9.97)\": -1.45}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = x + 1/3 * sin(5*x) + 3\nb) f(x) = sign(sin(x))\nc) f(x) = sign(x ** 2 - 15)\nd) f(x) = sin(x) + sin(0.5 * x)\ne) f(x) = -x ** 2 + 2 * cos(5*x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "d)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-10.0, -9.92)\": 10.52, \"(-9.92, -9.86)\": 10.39, \"(-9.86, -9.81)\": 10.28, \"(-9.81, -9.75)\": 10.17, \"(-9.75, -9.68)\": 10.06, \"(-9.68, -9.6)\": 9.93, \"(-9.6, -9.5)\": 9.68, \"(-9.5, -9.44)\": 9.55, \"(-9.44, -9.37)\": 9.44, \"(-9.37, -9.32)\": 9.31, \"(-9.32, -9.27)\": 9.21, \"(-9.27, -9.2)\": 9.09, \"(-9.2, -9.09)\": 8.93, \"(-9.09, -9.0)\": 8.7, \"(-9.0, -8.92)\": 8.54, \"(-8.92, -8.85)\": 8.42, \"(-8.85, -8.8)\": 8.31, \"(-8.8, -8.74)\": 8.19, \"(-8.74, -8.66)\": 8.09, \"(-8.66, -8.59)\": 7.94, \"(-8.59, -8.5)\": 7.82, \"(-8.5, -8.38)\": 7.66, \"(-8.38, -8.29)\": 7.45, \"(-8.29, -8.17)\": 7.33, \"(-8.17, -8.07)\": 7.2, \"(-8.07, -7.98)\": 7.07, \"(-7.98, -7.82)\": 6.93, \"(-7.82, -7.69)\": 6.81, \"(-7.69, -7.55)\": 6.69, \"(-7.55, -7.34)\": 6.57, \"(-7.34, -7.1)\": 6.47, \"(-7.1, -5.73)\": 6.36, \"(-5.73, -5.38)\": 6.25, \"(-5.38, -5.12)\": 6.15, \"(-5.12, -4.97)\": 6.03, \"(-4.97, -4.8)\": 5.91, \"(-4.8, -4.68)\": 5.79, \"(-4.68, -4.56)\": 5.67, \"(-4.56, -4.48)\": 5.54, \"(-4.48, -4.34)\": 5.39, \"(-4.34, -4.25)\": 5.26, \"(-4.25, -4.18)\": 5.12, \"(-4.18, -4.11)\": 5.01, \"(-4.11, -4.01)\": 4.87, \"(-4.01, -3.94)\": 4.77, \"(-3.94, -3.83)\": 4.64, \"(-3.83, -3.72)\": 4.37, \"(-3.72, -3.66)\": 4.26, \"(-3.66, -3.6)\": 4.13, \"(-3.6, -3.56)\": 4.01, \"(-3.56, -3.48)\": 3.91, \"(-3.48, -3.39)\": 3.79, \"(-3.39, -3.32)\": 3.58, \"(-3.32, -3.21)\": 3.43, \"(-3.21, -3.12)\": 3.2, \"(-3.12, -3.02)\": 3.08, \"(-3.02, -2.91)\": 2.81, \"(-2.91, -2.83)\": 2.63, \"(-2.83, -2.77)\": 2.52, \"(-2.77, -2.71)\": 2.39, \"(-2.71, -2.64)\": 2.28, \"(-2.64, -2.51)\": 2.05, \"(-2.51, -2.43)\": 1.88, \"(-2.43, -2.34)\": 1.77, \"(-2.34, -2.26)\": 1.6, \"(-2.26, -2.18)\": 1.48, \"(-2.18, -2.1)\": 1.33, \"(-2.1, -2.01)\": 1.21, \"(-2.01, -1.93)\": 1.09, \"(-1.93, -1.84)\": 0.97, \"(-1.84, -1.74)\": 0.86, \"(-1.74, -1.63)\": 0.74, \"(-1.63, -1.51)\": 0.63, \"(-1.51, -1.36)\": 0.49, \"(-1.36, -1.21)\": 0.38, \"(-1.21, -1.0)\": 0.26, \"(-1.0, -0.69)\": 0.16, \"(-0.69, 0.07)\": 0.05, \"(0.07, 0.12)\": 0.16, \"(0.12, 0.18)\": 0.26, \"(0.18, 0.24)\": 0.42, \"(0.24, 0.33)\": 0.53, \"(0.33, 0.42)\": 0.74, \"(0.42, 0.48)\": 0.85, \"(0.48, 0.54)\": 0.96, \"(0.54, 0.6)\": 1.07, \"(0.6, 0.67)\": 1.2, \"(0.67, 0.73)\": 1.3, \"(0.73, 0.8)\": 1.42, \"(0.8, 0.88)\": 1.54, \"(0.88, 0.98)\": 1.67, \"(0.98, 1.11)\": 1.87, \"(1.11, 1.21)\": 2.03, \"(1.21, 1.3)\": 2.15, \"(1.3, 1.4)\": 2.27, \"(1.4, 1.51)\": 2.4, \"(1.51, 1.64)\": 2.53, \"(1.64, 1.77)\": 2.64, \"(1.77, 1.92)\": 2.75, \"(1.92, 2.1)\": 2.86, \"(2.1, 2.38)\": 2.96, \"(2.38, 3.74)\": 3.07, \"(3.74, 4.09)\": 3.18, \"(4.09, 4.31)\": 3.29, \"(4.31, 4.48)\": 3.4, \"(4.48, 4.61)\": 3.52, \"(4.61, 4.74)\": 3.63, \"(4.74, 4.85)\": 3.75, \"(4.85, 4.92)\": 3.86, \"(4.92, 5.02)\": 3.97, \"(5.02, 5.1)\": 4.1, \"(5.1, 5.21)\": 4.21, \"(5.21, 5.28)\": 4.33, \"(5.28, 5.32)\": 4.44, \"(5.32, 5.42)\": 4.55, \"(5.42, 5.49)\": 4.68, \"(5.49, 5.55)\": 4.78, \"(5.55, 5.62)\": 4.89, \"(5.62, 5.69)\": 5.02, \"(5.69, 5.76)\": 5.16, \"(5.76, 5.83)\": 5.29, \"(5.83, 5.91)\": 5.43, \"(5.91, 5.98)\": 5.58, \"(5.98, 6.05)\": 5.73, \"(6.05, 6.16)\": 5.9, \"(6.16, 6.22)\": 6.05, \"(6.22, 6.31)\": 6.2, \"(6.31, 6.36)\": 6.33, \"(6.36, 6.45)\": 6.5, \"(6.45, 6.52)\": 6.64, \"(6.52, 6.58)\": 6.77, \"(6.58, 6.65)\": 6.89, \"(6.65, 6.71)\": 7.03, \"(6.71, 6.78)\": 7.15, \"(6.78, 6.83)\": 7.26, \"(6.83, 6.9)\": 7.38, \"(6.9, 6.97)\": 7.51, \"(6.97, 7.06)\": 7.63, \"(7.06, 7.17)\": 7.8, \"(7.17, 7.25)\": 7.97, \"(7.25, 7.33)\": 8.1, \"(7.33, 7.42)\": 8.21, \"(7.42, 7.51)\": 8.33, \"(7.51, 7.62)\": 8.47, \"(7.62, 7.7)\": 8.59, \"(7.7, 7.8)\": 8.7, \"(7.8, 7.93)\": 8.81, \"(7.93, 8.05)\": 8.93, \"(8.05, 8.23)\": 9.04, \"(8.23, 8.41)\": 9.16, \"(8.41, 8.74)\": 9.26, \"(8.74, 9.99)\": 9.37}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = sin(x)+sin(2*x)\nb) f(x) = exp(-x^2+1)+ 1/3 * |x|\nc) f(x) = abs(x) ** (1/10)\nd) f(x) = exp(x) + 4000* sin(x)\ne) f(x) = abs(x) + sin(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "e)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.97, -9.86)\": -1.868, \"(-9.86, -9.73)\": -1.913, \"(-9.73, -9.53)\": -1.955, \"(-9.53, -9.12)\": -1.995, \"(-9.12, -9.01)\": -1.952, \"(-9.01, -8.91)\": -1.909, \"(-8.91, -8.84)\": -1.863, \"(-8.84, -8.74)\": -1.813, \"(-8.74, -8.67)\": -1.77, \"(-8.67, -8.55)\": -1.712, \"(-8.55, -8.46)\": -1.606, \"(-8.46, -8.41)\": -1.565, \"(-8.41, -8.35)\": -1.524, \"(-8.35, -8.27)\": -1.446, \"(-8.27, -8.22)\": -1.404, \"(-8.22, -8.18)\": -1.351, \"(-8.18, -8.13)\": -1.296, \"(-8.13, -8.07)\": -1.254, \"(-8.07, -8.02)\": -1.205, \"(-8.02, -7.94)\": -1.129, \"(-7.94, -7.87)\": -1.056, \"(-7.87, -7.82)\": -1.008, \"(-7.82, -7.76)\": -0.953, \"(-7.76, -7.72)\": -0.908, \"(-7.72, -7.66)\": -0.851, \"(-7.66, -7.61)\": -0.805, \"(-7.61, -7.56)\": -0.753, \"(-7.56, -7.49)\": -0.703, \"(-7.49, -7.4)\": -0.602, \"(-7.4, -7.34)\": -0.556, \"(-7.34, -7.25)\": -0.489, \"(-7.25, -7.16)\": -0.398, \"(-7.16, -7.1)\": -0.356, \"(-7.1, -7.03)\": -0.309, \"(-7.03, -6.96)\": -0.256, \"(-6.96, -6.88)\": -0.215, \"(-6.88, -6.79)\": -0.169, \"(-6.79, -6.68)\": -0.117, \"(-6.68, -6.51)\": -0.077, \"(-6.51, -5.9)\": -0.035, \"(-5.9, -5.8)\": -0.075, \"(-5.8, -5.71)\": -0.119, \"(-5.71, -5.64)\": -0.165, \"(-5.64, -5.55)\": -0.21, \"(-5.55, -5.48)\": -0.27, \"(-5.48, -5.42)\": -0.31, \"(-5.42, -5.35)\": -0.377, \"(-5.35, -5.26)\": -0.422, \"(-5.26, -5.17)\": -0.523, \"(-5.17, -5.12)\": -0.567, \"(-5.12, -5.05)\": -0.607, \"(-5.05, -4.99)\": -0.688, \"(-4.99, -4.92)\": -0.752, \"(-4.92, -4.88)\": -0.806, \"(-4.88, -4.78)\": -0.861, \"(-4.78, -4.69)\": -0.972, \"(-4.69, -4.65)\": -1.032, \"(-4.65, -4.6)\": -1.088, \"(-4.6, -4.53)\": -1.137, \"(-4.53, -4.47)\": -1.198, \"(-4.47, -4.39)\": -1.271, \"(-4.39, -4.35)\": -1.322, \"(-4.35, -4.29)\": -1.371, \"(-4.29, -4.24)\": -1.414, \"(-4.24, -4.17)\": -1.476, \"(-4.17, -4.12)\": -1.523, \"(-4.12, -4.05)\": -1.563, \"(-4.05, -4.0)\": -1.621, \"(-4.0, -3.9)\": -1.669, \"(-3.9, -3.81)\": -1.742, \"(-3.81, -3.72)\": -1.795, \"(-3.72, -3.61)\": -1.849, \"(-3.61, -3.5)\": -1.899, \"(-3.5, -3.28)\": -1.945, \"(-3.28, -2.85)\": -1.988, \"(-2.85, -2.7)\": -1.944, \"(-2.7, -2.61)\": -1.897, \"(-2.61, -2.52)\": -1.855, \"(-2.52, -2.44)\": -1.805, \"(-2.44, -2.29)\": -1.731, \"(-2.29, -2.2)\": -1.627, \"(-2.2, -2.13)\": -1.576, \"(-2.13, -2.07)\": -1.531, \"(-2.07, -2.01)\": -1.458, \"(-2.01, -1.96)\": -1.414, \"(-1.96, -1.89)\": -1.337, \"(-1.89, -1.83)\": -1.294, \"(-1.83, -1.76)\": -1.235, \"(-1.76, -1.71)\": -1.174, \"(-1.71, -1.66)\": -1.129, \"(-1.66, -1.61)\": -1.083, \"(-1.61, -1.57)\": -1.036, \"(-1.57, -1.51)\": -0.978, \"(-1.51, -1.46)\": -0.931, \"(-1.46, -1.42)\": -0.873, \"(-1.42, -1.36)\": -0.827, \"(-1.36, -1.29)\": -0.775, \"(-1.29, -1.23)\": -0.703, \"(-1.23, -1.17)\": -0.654, \"(-1.17, -1.11)\": -0.609, \"(-1.11, -1.06)\": -0.542, \"(-1.06, -1.0)\": -0.502, \"(-1.0, -0.96)\": -0.453, \"(-0.96, -0.88)\": -0.403, \"(-0.88, -0.82)\": -0.358, \"(-0.82, -0.77)\": -0.317, \"(-0.77, -0.69)\": -0.266, \"(-0.69, -0.62)\": -0.221, \"(-0.62, -0.49)\": -0.177, \"(-0.49, -0.36)\": -0.104, \"(-0.36, -0.18)\": -0.059, \"(-0.18, -0.02)\": -0.019, \"(-0.02, 0.02)\": 0.153, \"(0.02, 0.07)\": 1.683, \"(0.07, 0.37)\": 1.967, \"(0.37, 0.47)\": 1.924, \"(0.47, 0.56)\": 1.883, \"(0.56, 0.67)\": 1.827, \"(0.67, 0.74)\": 1.779, \"(0.74, 0.8)\": 1.729, \"(0.8, 0.88)\": 1.678, \"(0.88, 0.94)\": 1.636, \"(0.94, 1.0)\": 1.586, \"(1.0, 1.05)\": 1.53, \"(1.05, 1.12)\": 1.484, \"(1.12, 1.17)\": 1.43, \"(1.17, 1.22)\": 1.381, \"(1.22, 1.26)\": 1.34, \"(1.26, 1.32)\": 1.294, \"(1.32, 1.38)\": 1.233, \"(1.38, 1.44)\": 1.175, \"(1.44, 1.51)\": 1.11, \"(1.51, 1.57)\": 1.041, \"(1.57, 1.62)\": 0.989, \"(1.62, 1.67)\": 0.932, \"(1.67, 1.75)\": 0.885, \"(1.75, 1.84)\": 0.781, \"(1.84, 1.89)\": 0.728, \"(1.89, 1.96)\": 0.674, \"(1.96, 2.03)\": 0.599, \"(2.03, 2.08)\": 0.558, \"(2.08, 2.13)\": 0.491, \"(2.13, 2.2)\": 0.449, \"(2.2, 2.29)\": 0.397, \"(2.29, 2.35)\": 0.333, \"(2.35, 2.42)\": 0.291, \"(2.42, 2.46)\": 0.246, \"(2.46, 2.58)\": 0.204, \"(2.58, 2.72)\": 0.134, \"(2.72, 2.89)\": 0.08, \"(2.89, 3.49)\": 0.027, \"(3.49, 3.61)\": 0.068, \"(3.61, 3.71)\": 0.116, \"(3.71, 3.79)\": 0.164, \"(3.79, 3.85)\": 0.205, \"(3.85, 3.95)\": 0.254, \"(3.95, 4.01)\": 0.318, \"(4.01, 4.08)\": 0.367, \"(4.08, 4.15)\": 0.425, \"(4.15, 4.19)\": 0.469, \"(4.19, 4.25)\": 0.512, \"(4.25, 4.29)\": 0.554, \"(4.29, 4.35)\": 0.612, \"(4.35, 4.42)\": 0.664, \"(4.42, 4.49)\": 0.732, \"(4.49, 4.55)\": 0.791, \"(4.55, 4.61)\": 0.855, \"(4.61, 4.65)\": 0.902, \"(4.65, 4.68)\": 0.949, \"(4.68, 4.74)\": 0.996, \"(4.74, 4.8)\": 1.051, \"(4.8, 4.84)\": 1.091, \"(4.84, 4.87)\": 1.133, \"(4.87, 4.95)\": 1.173, \"(4.95, 5.01)\": 1.247, \"(5.01, 5.07)\": 1.311, \"(5.07, 5.12)\": 1.364, \"(5.12, 5.2)\": 1.433, \"(5.2, 5.25)\": 1.475, \"(5.25, 5.32)\": 1.53, \"(5.32, 5.37)\": 1.577, \"(5.37, 5.44)\": 1.628, \"(5.44, 5.5)\": 1.669, \"(5.5, 5.59)\": 1.717, \"(5.59, 5.66)\": 1.777, \"(5.66, 5.74)\": 1.819, \"(5.74, 5.86)\": 1.862, \"(5.86, 6.08)\": 1.931, \"(6.08, 6.64)\": 1.974, \"(6.64, 6.74)\": 1.934, \"(6.74, 6.85)\": 1.89, \"(6.85, 6.93)\": 1.834, \"(6.93, 7.0)\": 1.794, \"(7.0, 7.08)\": 1.742, \"(7.08, 7.15)\": 1.698, \"(7.15, 7.22)\": 1.627, \"(7.22, 7.31)\": 1.586, \"(7.31, 7.42)\": 1.476, \"(7.42, 7.49)\": 1.398, \"(7.49, 7.53)\": 1.358, \"(7.53, 7.57)\": 1.316, \"(7.57, 7.62)\": 1.265, \"(7.62, 7.66)\": 1.224, \"(7.66, 7.69)\": 1.183, \"(7.69, 7.75)\": 1.142, \"(7.75, 7.8)\": 1.092, \"(7.8, 7.86)\": 1.041, \"(7.86, 7.91)\": 0.972, \"(7.91, 7.96)\": 0.924, \"(7.96, 8.01)\": 0.876, \"(8.01, 8.09)\": 0.81, \"(8.09, 8.11)\": 0.769, \"(8.11, 8.18)\": 0.727, \"(8.18, 8.24)\": 0.662, \"(8.24, 8.28)\": 0.62, \"(8.28, 8.35)\": 0.576, \"(8.35, 8.41)\": 0.52, \"(8.41, 8.48)\": 0.46, \"(8.48, 8.55)\": 0.408, \"(8.55, 8.6)\": 0.355, \"(8.6, 8.67)\": 0.315, \"(8.67, 8.76)\": 0.253, \"(8.76, 8.84)\": 0.207, \"(8.84, 8.92)\": 0.164, \"(8.92, 9.04)\": 0.12, \"(9.04, 9.18)\": 0.071, \"(9.18, 9.81)\": 0.031, \"(9.81, 9.94)\": 0.078, \"(9.94, 9.99)\": 0.138}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = sign(x ** 2 - 15)\nb) f(x) = sign(cos(x))\nc) f(x) = abs(x) + sin(x)\nd) f(x) = sign(x) + cos(x)\ne) f(x) = tanh(x+10) - 1/3 * x \n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "d)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.96, -9.64)\": -859.8, \"(-9.64, -8.69)\": -842.0, \"(-8.69, -8.43)\": -823.5, \"(-8.43, -8.2)\": -805.8, \"(-8.2, -8.06)\": -786.0, \"(-8.06, -7.95)\": -764.6, \"(-7.95, -7.84)\": -746.1, \"(-7.84, -7.74)\": -728.3, \"(-7.74, -7.64)\": -710.2, \"(-7.64, -7.57)\": -687.1, \"(-7.57, -7.48)\": -666.7, \"(-7.48, -7.41)\": -649.5, \"(-7.41, -7.33)\": -627.9, \"(-7.33, -7.24)\": -603.7, \"(-7.24, -7.17)\": -578.1, \"(-7.17, -7.11)\": -559.4, \"(-7.11, -7.06)\": -540.3, \"(-7.06, -6.99)\": -522.5, \"(-6.99, -6.93)\": -503.8, \"(-6.93, -6.88)\": -477.3, \"(-6.88, -6.81)\": -453.9, \"(-6.81, -6.73)\": -433.8, \"(-6.73, -6.67)\": -409.2, \"(-6.67, -6.63)\": -391.3, \"(-6.63, -6.54)\": -369.9, \"(-6.54, -6.49)\": -334.9, \"(-6.49, -6.42)\": -315.2, \"(-6.42, -6.34)\": -290.3, \"(-6.34, -6.31)\": -267.9, \"(-6.31, -6.23)\": -243.7, \"(-6.23, -6.16)\": -223.6, \"(-6.16, -6.1)\": -196.9, \"(-6.1, -6.04)\": -175.3, \"(-6.04, -5.98)\": -156.4, \"(-5.98, -5.93)\": -138.0, \"(-5.93, -5.87)\": -120.6, \"(-5.87, -5.8)\": -95.5, \"(-5.8, -5.73)\": -74.8, \"(-5.73, -5.66)\": -54.0, \"(-5.66, -5.59)\": -32.9, \"(-5.59, -5.53)\": -13.9, \"(-5.53, -5.46)\": 4.8, \"(-5.46, -5.36)\": 24.8, \"(-5.36, -5.3)\": 44.8, \"(-5.3, -5.22)\": 62.7, \"(-5.22, -5.1)\": 81.8, \"(-5.1, -4.97)\": 100.3, \"(-4.97, -4.81)\": 119.8, \"(-4.81, -3.96)\": 137.6, \"(-3.96, -3.84)\": 119.4, \"(-3.84, -3.71)\": 101.0, \"(-3.71, -3.61)\": 82.7, \"(-3.61, -3.48)\": 63.0, \"(-3.48, -3.37)\": 33.2, \"(-3.37, -3.27)\": 15.6, \"(-3.27, -3.19)\": -7.1, \"(-3.19, -3.08)\": -26.9, \"(-3.08, -2.97)\": -51.4, \"(-2.97, -2.88)\": -74.9, \"(-2.88, -2.77)\": -93.8, \"(-2.77, -2.67)\": -118.1, \"(-2.67, -2.56)\": -137.4, \"(-2.56, -2.46)\": -155.5, \"(-2.46, -2.34)\": -175.7, \"(-2.34, -2.22)\": -193.3, \"(-2.22, -2.06)\": -212.4, \"(-2.06, -1.8)\": -231.3, \"(-1.8, -1.18)\": -250.6, \"(-1.18, -1.01)\": -232.2, \"(-1.01, -0.86)\": -209.9, \"(-0.86, -0.75)\": -188.0, \"(-0.75, -0.64)\": -168.6, \"(-0.64, -0.56)\": -146.2, \"(-0.56, -0.46)\": -127.0, \"(-0.46, -0.38)\": -107.8, \"(-0.38, -0.29)\": -86.6, \"(-0.29, -0.21)\": -68.8, \"(-0.21, -0.13)\": -50.7, \"(-0.13, -0.06)\": -29.0, \"(-0.06, 0.02)\": -10.7, \"(0.02, 0.1)\": 8.5, \"(0.1, 0.17)\": 27.2, \"(0.17, 0.24)\": 47.1, \"(0.24, 0.36)\": 72.3, \"(0.36, 0.44)\": 91.5, \"(0.44, 0.53)\": 108.8, \"(0.53, 0.63)\": 129.5, \"(0.63, 0.77)\": 149.8, \"(0.77, 0.93)\": 182.1, \"(0.93, 1.05)\": 200.9, \"(1.05, 1.25)\": 222.6, \"(1.25, 2.12)\": 239.9, \"(2.12, 2.25)\": 221.4, \"(2.25, 2.39)\": 202.5, \"(2.39, 2.48)\": 183.5, \"(2.48, 2.56)\": 165.7, \"(2.56, 2.73)\": 140.6, \"(2.73, 2.82)\": 118.1, \"(2.82, 2.92)\": 98.4, \"(2.92, 3.02)\": 75.6, \"(3.02, 3.14)\": 51.2, \"(3.14, 3.24)\": 28.7, \"(3.24, 3.31)\": 7.6, \"(3.31, 3.4)\": -10.7, \"(3.4, 3.51)\": -29.6, \"(3.51, 3.61)\": -50.5, \"(3.61, 3.74)\": -71.8, \"(3.74, 3.87)\": -91.5, \"(3.87, 4.02)\": -109.7, \"(4.02, 4.24)\": -128.8, \"(4.24, 4.87)\": -147.0, \"(4.87, 4.99)\": -129.7, \"(4.99, 5.14)\": -111.1, \"(5.14, 5.27)\": -83.8, \"(5.27, 5.36)\": -64.6, \"(5.36, 5.43)\": -45.0, \"(5.43, 5.5)\": -26.6, \"(5.5, 5.56)\": -7.1, \"(5.56, 5.67)\": 19.9, \"(5.67, 5.73)\": 39.0, \"(5.73, 5.82)\": 58.2, \"(5.82, 5.92)\": 98.6, \"(5.92, 5.97)\": 118.4, \"(5.97, 6.02)\": 141.0, \"(6.02, 6.1)\": 158.6, \"(6.1, 6.15)\": 188.5, \"(6.15, 6.24)\": 207.5, \"(6.24, 6.31)\": 246.8, \"(6.31, 6.39)\": 272.7, \"(6.39, 6.46)\": 290.1, \"(6.46, 6.54)\": 322.4, \"(6.54, 6.6)\": 346.0, \"(6.6, 6.66)\": 368.0, \"(6.66, 6.72)\": 389.7, \"(6.72, 6.79)\": 416.7, \"(6.79, 6.86)\": 438.6, \"(6.86, 6.92)\": 460.0, \"(6.92, 6.98)\": 489.1, \"(6.98, 7.07)\": 506.5, \"(7.07, 7.19)\": 552.4, \"(7.19, 7.27)\": 571.9, \"(7.27, 7.37)\": 598.3, \"(7.37, 7.45)\": 628.4, \"(7.45, 7.55)\": 647.4, \"(7.55, 7.64)\": 671.7, \"(7.64, 7.74)\": 696.5, \"(7.74, 7.84)\": 716.9, \"(7.84, 7.96)\": 735.1, \"(7.96, 8.08)\": 754.5, \"(8.08, 8.23)\": 774.6, \"(8.23, 8.45)\": 792.5, \"(8.45, 8.83)\": 811.4, \"(8.83, 9.75)\": 829.2, \"(9.75, 10.0)\": 847.7}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = exp(-x+1)+ 2000 * abs(x+1)\nb) f(x) = x ** 3 + 250 * sin(x)\nc) f(x) = tanh(x+10) - 1/3 * x \nd) f(x) = exp(-x^2+1)+ 1/3 * |x|\ne) f(x) = -1/10 * x ** 3 + 20 * tanh(2*x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "b)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.98, -9.94)\": -3.18, \"(-9.94, -9.89)\": -3.06, \"(-9.89, -9.81)\": -2.93, \"(-9.81, -9.7)\": -2.81, \"(-9.7, -9.59)\": -2.67, \"(-9.59, -9.47)\": -2.54, \"(-9.47, -9.35)\": -2.42, \"(-9.35, -9.21)\": -2.29, \"(-9.21, -9.07)\": -2.18, \"(-9.07, -8.92)\": -2.05, \"(-8.92, -8.79)\": -1.94, \"(-8.79, -8.64)\": -1.82, \"(-8.64, -8.47)\": -1.71, \"(-8.47, -8.3)\": -1.57, \"(-8.3, -8.14)\": -1.45, \"(-8.14, -7.96)\": -1.34, \"(-7.96, -7.77)\": -1.21, \"(-7.77, -7.55)\": -1.07, \"(-7.55, -7.4)\": -0.95, \"(-7.4, -7.21)\": -0.84, \"(-7.21, -7.02)\": -0.72, \"(-7.02, -6.83)\": -0.61, \"(-6.83, -6.64)\": -0.49, \"(-6.64, -6.45)\": -0.38, \"(-6.45, -6.27)\": -0.26, \"(-6.27, -6.05)\": -0.15, \"(-6.05, -5.84)\": -0.02, \"(-5.84, -5.65)\": 0.09, \"(-5.65, -5.43)\": 0.21, \"(-5.43, -5.21)\": 0.33, \"(-5.21, -5.02)\": 0.44, \"(-5.02, -4.83)\": 0.56, \"(-4.83, -4.61)\": 0.67, \"(-4.61, -4.41)\": 0.78, \"(-4.41, -4.21)\": 0.89, \"(-4.21, -3.99)\": 1.01, \"(-3.99, -3.79)\": 1.13, \"(-3.79, -3.57)\": 1.24, \"(-3.57, -3.35)\": 1.36, \"(-3.35, -3.14)\": 1.47, \"(-3.14, -2.92)\": 1.58, \"(-2.92, -2.68)\": 1.69, \"(-2.68, -2.46)\": 1.82, \"(-2.46, -2.24)\": 1.93, \"(-2.24, -2.0)\": 2.05, \"(-2.0, -1.79)\": 2.16, \"(-1.79, -1.56)\": 2.28, \"(-1.56, -1.32)\": 2.4, \"(-1.32, -1.09)\": 2.51, \"(-1.09, -0.85)\": 2.63, \"(-0.85, -0.56)\": 2.76, \"(-0.56, -0.3)\": 2.9, \"(-0.3, -0.06)\": 3.02, \"(-0.06, 0.18)\": 3.13, \"(0.18, 0.43)\": 3.26, \"(0.43, 0.67)\": 3.38, \"(0.67, 0.94)\": 3.5, \"(0.94, 1.17)\": 3.62, \"(1.17, 1.41)\": 3.74, \"(1.41, 1.63)\": 3.85, \"(1.63, 1.88)\": 3.96, \"(1.88, 2.09)\": 4.07, \"(2.09, 2.35)\": 4.19, \"(2.35, 2.57)\": 4.3, \"(2.57, 2.82)\": 4.42, \"(2.82, 3.09)\": 4.53, \"(3.09, 3.34)\": 4.65, \"(3.34, 3.6)\": 4.78, \"(3.6, 3.86)\": 4.89, \"(3.86, 4.06)\": 5.01, \"(4.06, 4.33)\": 5.12, \"(4.33, 4.59)\": 5.23, \"(4.59, 4.81)\": 5.35, \"(4.81, 5.07)\": 5.47, \"(5.07, 5.32)\": 5.58, \"(5.32, 5.57)\": 5.69, \"(5.57, 5.81)\": 5.81, \"(5.81, 6.06)\": 5.92, \"(6.06, 6.32)\": 6.03, \"(6.32, 6.56)\": 6.15, \"(6.56, 6.85)\": 6.26, \"(6.85, 7.16)\": 6.41, \"(7.16, 7.44)\": 6.54, \"(7.44, 7.7)\": 6.66, \"(7.7, 7.93)\": 6.78, \"(7.93, 8.2)\": 6.89, \"(8.2, 8.49)\": 7.01, \"(8.49, 8.71)\": 7.12, \"(8.71, 8.99)\": 7.24, \"(8.99, 9.25)\": 7.35, \"(9.25, 9.55)\": 7.49, \"(9.55, 9.78)\": 7.6, \"(9.78, 9.94)\": 7.71}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = abs(x) + sin(x)\nb) f(x) = exp(x) + 4000* sin(x)\nc) f(x) = x + 1/3 * sin(5*x) + 3\nd) f(x) = exp(-x^2+1)+ 1/3 * |x|\ne) f(x) = sqrt(x+10) + 1/3 * x \n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "e)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.98, -9.97)\": -7.32, \"(-9.97, -9.95)\": -6.48, \"(-9.95, -9.94)\": -6.27, \"(-9.94, -9.93)\": -6.03, \"(-9.93, -9.9)\": -5.86, \"(-9.9, -9.87)\": -5.49, \"(-9.87, -9.84)\": -5.3, \"(-9.84, -9.79)\": -4.94, \"(-9.79, -9.73)\": -4.79, \"(-9.73, -9.67)\": -4.46, \"(-9.67, -9.62)\": -4.28, \"(-9.62, -9.55)\": -4.13, \"(-9.55, -9.47)\": -3.95, \"(-9.47, -9.41)\": -3.76, \"(-9.41, -9.32)\": -3.63, \"(-9.32, -9.22)\": -3.48, \"(-9.22, -9.11)\": -3.3, \"(-9.11, -9.01)\": -3.16, \"(-9.01, -8.89)\": -3.0, \"(-8.89, -8.77)\": -2.84, \"(-8.77, -8.61)\": -2.67, \"(-8.61, -8.47)\": -2.53, \"(-8.47, -8.31)\": -2.38, \"(-8.31, -8.13)\": -2.24, \"(-8.13, -7.95)\": -2.07, \"(-7.95, -7.77)\": -1.93, \"(-7.77, -7.58)\": -1.78, \"(-7.58, -7.37)\": -1.63, \"(-7.37, -7.15)\": -1.49, \"(-7.15, -6.96)\": -1.33, \"(-6.96, -6.74)\": -1.19, \"(-6.74, -6.5)\": -1.05, \"(-6.5, -6.27)\": -0.91, \"(-6.27, -6.03)\": -0.77, \"(-6.03, -5.81)\": -0.63, \"(-5.81, -5.53)\": -0.49, \"(-5.53, -5.28)\": -0.35, \"(-5.28, -5.01)\": -0.21, \"(-5.01, -4.74)\": -0.06, \"(-4.74, -4.48)\": 0.09, \"(-4.48, -4.17)\": 0.23, \"(-4.17, -3.9)\": 0.37, \"(-3.9, -3.61)\": 0.52, \"(-3.61, -3.34)\": 0.66, \"(-3.34, -3.05)\": 0.8, \"(-3.05, -2.73)\": 0.94, \"(-2.73, -2.42)\": 1.08, \"(-2.42, -2.1)\": 1.24, \"(-2.1, -1.8)\": 1.38, \"(-1.8, -1.43)\": 1.52, \"(-1.43, -1.08)\": 1.69, \"(-1.08, -0.76)\": 1.83, \"(-0.76, -0.43)\": 1.98, \"(-0.43, -0.09)\": 2.12, \"(-0.09, 0.24)\": 2.27, \"(0.24, 0.56)\": 2.41, \"(0.56, 0.86)\": 2.55, \"(0.86, 1.22)\": 2.69, \"(1.22, 1.54)\": 2.83, \"(1.54, 1.88)\": 2.97, \"(1.88, 2.21)\": 3.11, \"(2.21, 2.55)\": 3.25, \"(2.55, 2.9)\": 3.39, \"(2.9, 3.25)\": 3.53, \"(3.25, 3.57)\": 3.67, \"(3.57, 3.94)\": 3.81, \"(3.94, 4.28)\": 3.95, \"(4.28, 4.68)\": 4.09, \"(4.68, 5.04)\": 4.25, \"(5.04, 5.39)\": 4.39, \"(5.39, 5.71)\": 4.53, \"(5.71, 6.07)\": 4.67, \"(6.07, 6.43)\": 4.8, \"(6.43, 6.8)\": 4.96, \"(6.8, 7.18)\": 5.09, \"(7.18, 7.5)\": 5.23, \"(7.5, 7.87)\": 5.37, \"(7.87, 8.25)\": 5.51, \"(8.25, 8.62)\": 5.65, \"(8.62, 8.97)\": 5.79, \"(8.97, 9.33)\": 5.93, \"(9.33, 9.71)\": 6.08, \"(9.71, 9.99)\": 6.22}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = exp(-x+1)+ 2000 * abs(x+1)\nb) f(x) = sign(x ** 2 - 15)\nc) f(x) = -tanh(x) + 1/4 * x\nd) f(x) = log(x+10) + 1/3 * x \ne) f(x) = exp(x)+ 2000 * abs(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "d)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.99, -9.9)\": 3.341, \"(-9.9, -9.81)\": 3.405, \"(-9.81, -9.69)\": 3.474, \"(-9.69, -9.56)\": 3.538, \"(-9.56, -9.42)\": 3.603, \"(-9.42, -9.2)\": 3.67, \"(-9.2, -8.16)\": 3.734, \"(-8.16, -7.91)\": 3.668, \"(-7.91, -7.7)\": 3.606, \"(-7.7, -7.48)\": 3.543, \"(-7.48, -7.27)\": 3.477, \"(-7.27, -7.08)\": 3.414, \"(-7.08, -6.89)\": 3.352, \"(-6.89, -6.67)\": 3.279, \"(-6.67, -6.47)\": 3.216, \"(-6.47, -6.29)\": 3.152, \"(-6.29, -6.08)\": 3.089, \"(-6.08, -5.88)\": 3.026, \"(-5.88, -5.69)\": 2.959, \"(-5.69, -5.52)\": 2.898, \"(-5.52, -5.32)\": 2.829, \"(-5.32, -5.09)\": 2.761, \"(-5.09, -4.9)\": 2.696, \"(-4.9, -4.69)\": 2.629, \"(-4.69, -4.46)\": 2.552, \"(-4.46, -4.25)\": 2.487, \"(-4.25, -4.04)\": 2.408, \"(-4.04, -3.85)\": 2.343, \"(-3.85, -3.65)\": 2.279, \"(-3.65, -3.46)\": 2.215, \"(-3.46, -3.25)\": 2.149, \"(-3.25, -3.04)\": 2.073, \"(-3.04, -2.85)\": 2.006, \"(-2.85, -2.65)\": 1.942, \"(-2.65, -2.46)\": 1.881, \"(-2.46, -2.26)\": 1.816, \"(-2.26, -2.07)\": 1.748, \"(-2.07, -1.87)\": 1.685, \"(-1.87, -1.68)\": 1.621, \"(-1.68, -1.47)\": 1.553, \"(-1.47, -1.29)\": 1.488, \"(-1.29, -1.09)\": 1.421, \"(-1.09, -0.9)\": 1.357, \"(-0.9, -0.71)\": 1.295, \"(-0.71, -0.52)\": 1.232, \"(-0.52, -0.33)\": 1.167, \"(-0.33, -0.12)\": 1.104, \"(-0.12, 0.07)\": 1.041, \"(0.07, 0.24)\": 0.973, \"(0.24, 0.46)\": 0.911, \"(0.46, 0.66)\": 0.843, \"(0.66, 0.84)\": 0.777, \"(0.84, 1.04)\": 0.716, \"(1.04, 1.24)\": 0.654, \"(1.24, 1.42)\": 0.588, \"(1.42, 1.6)\": 0.524, \"(1.6, 1.79)\": 0.463, \"(1.79, 1.99)\": 0.397, \"(1.99, 2.19)\": 0.334, \"(2.19, 2.39)\": 0.268, \"(2.39, 2.61)\": 0.199, \"(2.61, 2.79)\": 0.131, \"(2.79, 2.98)\": 0.067, \"(2.98, 3.2)\": -0.003, \"(3.2, 3.39)\": -0.068, \"(3.39, 3.58)\": -0.133, \"(3.58, 3.77)\": -0.195, \"(3.77, 3.96)\": -0.259, \"(3.96, 4.14)\": -0.32, \"(4.14, 4.37)\": -0.391, \"(4.37, 4.55)\": -0.457, \"(4.55, 4.78)\": -0.534, \"(4.78, 4.98)\": -0.598, \"(4.98, 5.21)\": -0.671, \"(5.21, 5.41)\": -0.734, \"(5.41, 5.59)\": -0.802, \"(5.59, 5.78)\": -0.867, \"(5.78, 5.99)\": -0.935, \"(5.99, 6.18)\": -0.997, \"(6.18, 6.36)\": -1.066, \"(6.36, 6.56)\": -1.127, \"(6.56, 6.76)\": -1.189, \"(6.76, 6.94)\": -1.255, \"(6.94, 7.13)\": -1.317, \"(7.13, 7.32)\": -1.38, \"(7.32, 7.49)\": -1.444, \"(7.49, 7.69)\": -1.508, \"(7.69, 7.91)\": -1.571, \"(7.91, 8.09)\": -1.636, \"(8.09, 8.3)\": -1.698, \"(8.3, 8.54)\": -1.777, \"(8.54, 8.75)\": -1.855, \"(8.75, 8.94)\": -1.921, \"(8.94, 9.12)\": -1.983, \"(9.12, 9.32)\": -2.047, \"(9.32, 9.51)\": -2.111, \"(9.51, 9.7)\": -2.173, \"(9.7, 9.89)\": -2.234, \"(9.89, 9.97)\": -2.3}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = -1/2 * (x+1) ** 4 + 5 * (x+2) **3 + 3 * x ** 2 + 2 * x + 1\nb) f(x) = abs(x) ** (1/10)\nc) f(x) = sin(x)+sin(2*x)\nd) f(x) = tanh(x+10) - 1/3 * x + 1/8 * sin(5*x)\ne) f(x) = tanh(x+10) - 1/3 * x \n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "e)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.96, -9.89)\": 3.433, \"(-9.89, -9.83)\": 3.497, \"(-9.83, -9.73)\": 3.571, \"(-9.73, -8.89)\": 3.642, \"(-8.89, -8.74)\": 3.719, \"(-8.74, -8.29)\": 3.829, \"(-8.29, -8.21)\": 3.767, \"(-8.21, -8.13)\": 3.699, \"(-8.13, -8.04)\": 3.629, \"(-8.04, -7.94)\": 3.562, \"(-7.94, -7.05)\": 3.496, \"(-7.05, -6.94)\": 3.413, \"(-6.94, -6.86)\": 3.298, \"(-6.86, -6.76)\": 3.227, \"(-6.76, -6.66)\": 3.161, \"(-6.66, -5.82)\": 3.094, \"(-5.82, -5.75)\": 3.024, \"(-5.75, -5.67)\": 2.946, \"(-5.67, -5.56)\": 2.874, \"(-5.56, -5.43)\": 2.767, \"(-5.43, -4.59)\": 2.686, \"(-4.59, -4.49)\": 2.618, \"(-4.49, -4.42)\": 2.546, \"(-4.42, -4.38)\": 2.483, \"(-4.38, -4.29)\": 2.42, \"(-4.29, -4.2)\": 2.356, \"(-4.2, -4.02)\": 2.288, \"(-4.02, -3.63)\": 2.225, \"(-3.63, -3.36)\": 2.293, \"(-3.36, -3.25)\": 2.225, \"(-3.25, -3.16)\": 2.126, \"(-3.16, -3.08)\": 2.056, \"(-3.08, -3.02)\": 1.985, \"(-3.02, -2.9)\": 1.914, \"(-2.9, -2.06)\": 1.839, \"(-2.06, -1.98)\": 1.774, \"(-1.98, -1.91)\": 1.71, \"(-1.91, -1.82)\": 1.634, \"(-1.82, -1.74)\": 1.549, \"(-1.74, -1.59)\": 1.485, \"(-1.59, -0.77)\": 1.401, \"(-0.77, -0.7)\": 1.331, \"(-0.7, -0.62)\": 1.26, \"(-0.62, -0.57)\": 1.198, \"(-0.57, -0.46)\": 1.124, \"(-0.46, -0.25)\": 1.032, \"(-0.25, 0.12)\": 0.968, \"(0.12, 0.42)\": 1.031, \"(0.42, 0.5)\": 0.962, \"(0.5, 0.61)\": 0.88, \"(0.61, 0.68)\": 0.802, \"(0.68, 0.76)\": 0.738, \"(0.76, 0.87)\": 0.657, \"(0.87, 1.69)\": 0.591, \"(1.69, 1.78)\": 0.528, \"(1.78, 1.86)\": 0.453, \"(1.86, 1.98)\": 0.344, \"(1.98, 2.08)\": 0.278, \"(2.08, 2.93)\": 0.19, \"(2.93, 3.02)\": 0.12, \"(3.02, 3.1)\": 0.052, \"(3.1, 3.17)\": -0.03, \"(3.17, 3.25)\": -0.098, \"(3.25, 3.37)\": -0.178, \"(3.37, 4.23)\": -0.245, \"(4.23, 4.31)\": -0.325, \"(4.31, 4.37)\": -0.391, \"(4.37, 4.48)\": -0.476, \"(4.48, 4.57)\": -0.562, \"(4.57, 4.74)\": -0.624, \"(4.74, 5.53)\": -0.7, \"(5.53, 5.61)\": -0.78, \"(5.61, 5.66)\": -0.846, \"(5.66, 5.77)\": -0.92, \"(5.77, 5.87)\": -1.014, \"(5.87, 6.72)\": -1.078, \"(6.72, 6.82)\": -1.148, \"(6.82, 6.92)\": -1.227, \"(6.92, 7.01)\": -1.336, \"(7.01, 7.12)\": -1.404, \"(7.12, 7.98)\": -1.498, \"(7.98, 8.06)\": -1.563, \"(8.06, 8.13)\": -1.626, \"(8.13, 8.19)\": -1.692, \"(8.19, 8.29)\": -1.772, \"(8.29, 8.38)\": -1.835, \"(8.38, 9.24)\": -1.916, \"(9.24, 9.32)\": -1.984, \"(9.32, 9.39)\": -2.047, \"(9.39, 9.45)\": -2.118, \"(9.45, 9.56)\": -2.21, \"(9.56, 9.66)\": -2.276, \"(9.66, 9.99)\": -2.345}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = x ** 3 + 250 * sin(x)\nb) f(x) = -x ** 2 + 20 * tanh(5*x)\nc) f(x) = tanh(x+10) - 1/3 * x + 1/8 * sin(5*x)\nd) f(x) = abs(x) ** (1/10)\ne) f(x) = sqrt(x+10) + 1/3 * x \n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "c)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.97, -9.89)\": -6.85, \"(-9.89, -9.78)\": -6.64, \"(-9.78, -8.97)\": -6.42, \"(-8.97, -8.87)\": -6.19, \"(-8.87, -8.78)\": -5.94, \"(-8.78, -8.69)\": -5.72, \"(-8.69, -8.62)\": -5.52, \"(-8.62, -8.38)\": -5.3, \"(-8.38, -7.68)\": -5.1, \"(-7.68, -7.59)\": -4.87, \"(-7.59, -7.48)\": -4.62, \"(-7.48, -7.39)\": -4.36, \"(-7.39, -7.23)\": -4.1, \"(-7.23, -6.45)\": -3.9, \"(-6.45, -6.36)\": -3.66, \"(-6.36, -6.28)\": -3.45, \"(-6.28, -6.18)\": -3.23, \"(-6.18, -6.08)\": -2.99, \"(-6.08, -5.23)\": -2.75, \"(-5.23, -5.12)\": -2.47, \"(-5.12, -5.04)\": -2.22, \"(-5.04, -4.98)\": -1.99, \"(-4.98, -4.83)\": -1.77, \"(-4.83, -4.67)\": -1.54, \"(-4.67, -3.91)\": -1.35, \"(-3.91, -3.8)\": -1.08, \"(-3.8, -3.72)\": -0.83, \"(-3.72, -3.61)\": -0.59, \"(-3.61, -3.5)\": -0.38, \"(-3.5, -2.71)\": -0.17, \"(-2.71, -2.6)\": 0.04, \"(-2.6, -2.51)\": 0.34, \"(-2.51, -2.4)\": 0.57, \"(-2.4, -2.31)\": 0.79, \"(-2.31, -2.06)\": 0.99, \"(-2.06, -1.39)\": 1.19, \"(-1.39, -1.31)\": 1.42, \"(-1.31, -1.21)\": 1.62, \"(-1.21, -1.09)\": 1.96, \"(-1.09, -0.93)\": 2.19, \"(-0.93, -0.16)\": 2.4, \"(-0.16, -0.04)\": 2.69, \"(-0.04, 0.06)\": 2.96, \"(0.06, 0.17)\": 3.21, \"(0.17, 0.32)\": 3.43, \"(0.32, 1.09)\": 3.65, \"(1.09, 1.21)\": 3.87, \"(1.21, 1.32)\": 4.23, \"(1.32, 1.4)\": 4.44, \"(1.4, 1.52)\": 4.66, \"(1.52, 2.32)\": 4.86, \"(2.32, 2.42)\": 5.09, \"(2.42, 2.5)\": 5.29, \"(2.5, 2.58)\": 5.52, \"(2.58, 2.67)\": 5.74, \"(2.67, 2.86)\": 5.95, \"(2.86, 3.61)\": 6.18, \"(3.61, 3.7)\": 6.4, \"(3.7, 3.81)\": 6.63, \"(3.81, 3.89)\": 6.92, \"(3.89, 4.02)\": 7.13, \"(4.02, 4.83)\": 7.34, \"(4.83, 4.92)\": 7.56, \"(4.92, 5.02)\": 7.76, \"(5.02, 5.14)\": 8.12, \"(5.14, 5.24)\": 8.34, \"(5.24, 6.06)\": 8.55, \"(6.06, 6.17)\": 8.78, \"(6.17, 6.25)\": 9.02, \"(6.25, 6.34)\": 9.25, \"(6.34, 6.44)\": 9.46, \"(6.44, 6.57)\": 9.68, \"(6.57, 7.36)\": 9.9, \"(7.36, 7.45)\": 10.12, \"(7.45, 7.53)\": 10.34, \"(7.53, 7.61)\": 10.59, \"(7.61, 7.72)\": 10.79, \"(7.72, 8.56)\": 11.06, \"(8.56, 8.66)\": 11.27, \"(8.66, 8.76)\": 11.5, \"(8.76, 8.84)\": 11.72, \"(8.84, 8.92)\": 11.94, \"(8.92, 9.03)\": 12.15, \"(9.03, 9.85)\": 12.37, \"(9.85, 9.96)\": 12.6, \"(9.96, 9.99)\": 12.84}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = -x ** 2 + 2 * cos(5*x)\nb) f(x) = tanh(x+10) - 1/3 * x + 1/8 * sin(5*x)\nc) f(x) = exp(x)+ 2000 * abs(x)\nd) f(x) = sin(x)+cos(x)\ne) f(x) = x + 1/3 * sin(5*x) + 3\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "e)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.95, -9.85)\": -96.98, \"(-9.85, -9.74)\": -95.94, \"(-9.74, -9.63)\": -94.8, \"(-9.63, -9.56)\": -93.65, \"(-9.56, -9.47)\": -92.65, \"(-9.47, -9.41)\": -91.44, \"(-9.41, -9.35)\": -90.41, \"(-9.35, -9.29)\": -88.54, \"(-9.29, -9.23)\": -87.51, \"(-9.23, -9.17)\": -86.02, \"(-9.17, -9.13)\": -84.45, \"(-9.13, -9.09)\": -83.41, \"(-9.09, -9.05)\": -82.37, \"(-9.05, -9.01)\": -81.27, \"(-9.01, -8.97)\": -80.15, \"(-8.97, -8.91)\": -78.95, \"(-8.91, -8.85)\": -77.18, \"(-8.85, -8.77)\": -76.12, \"(-8.77, -8.71)\": -74.93, \"(-8.71, -8.59)\": -73.81, \"(-8.59, -8.43)\": -72.61, \"(-8.43, -8.3)\": -71.49, \"(-8.3, -8.24)\": -70.38, \"(-8.24, -8.14)\": -69.26, \"(-8.14, -8.06)\": -67.86, \"(-8.06, -7.96)\": -66.05, \"(-7.96, -7.9)\": -63.79, \"(-7.9, -7.86)\": -62.77, \"(-7.86, -7.8)\": -61.37, \"(-7.8, -7.74)\": -59.93, \"(-7.74, -7.68)\": -58.47, \"(-7.68, -7.61)\": -57.05, \"(-7.61, -7.54)\": -55.93, \"(-7.54, -7.46)\": -54.73, \"(-7.46, -7.3)\": -53.6, \"(-7.3, -7.12)\": -52.57, \"(-7.12, -6.98)\": -51.57, \"(-6.98, -6.89)\": -50.52, \"(-6.89, -6.83)\": -48.97, \"(-6.83, -6.73)\": -47.74, \"(-6.73, -6.67)\": -45.99, \"(-6.67, -6.6)\": -44.77, \"(-6.6, -6.55)\": -43.47, \"(-6.55, -6.5)\": -42.24, \"(-6.5, -6.44)\": -41.21, \"(-6.44, -6.37)\": -39.75, \"(-6.37, -6.29)\": -38.54, \"(-6.29, -6.17)\": -37.43, \"(-6.17, -5.89)\": -36.3, \"(-5.89, -5.67)\": -35.29, \"(-5.67, -5.58)\": -33.99, \"(-5.58, -5.51)\": -32.85, \"(-5.51, -5.45)\": -31.78, \"(-5.45, -5.41)\": -30.53, \"(-5.41, -5.33)\": -29.17, \"(-5.33, -5.28)\": -28.01, \"(-5.28, -5.21)\": -26.99, \"(-5.21, -5.14)\": -25.71, \"(-5.14, -5.06)\": -24.57, \"(-5.06, -4.9)\": -23.55, \"(-4.9, -4.42)\": -22.46, \"(-4.42, -4.31)\": -21.33, \"(-4.31, -4.24)\": -20.29, \"(-4.24, -4.18)\": -19.11, \"(-4.18, -4.11)\": -18.08, \"(-4.11, -4.05)\": -16.94, \"(-4.05, -3.97)\": -15.81, \"(-3.97, -3.93)\": -14.65, \"(-3.93, -3.8)\": -13.56, \"(-3.8, -3.08)\": -12.39, \"(-3.08, -3.0)\": -11.33, \"(-3.0, -2.9)\": -10.23, \"(-2.9, -2.81)\": -8.88, \"(-2.81, -2.75)\": -7.5, \"(-2.75, -2.64)\": -6.39, \"(-2.64, -2.43)\": -5.24, \"(-2.43, -2.12)\": -4.21, \"(-2.12, -1.73)\": -5.32, \"(-1.73, -1.65)\": -4.3, \"(-1.65, -1.5)\": -3.09, \"(-1.5, -1.36)\": -1.18, \"(-1.36, -0.93)\": -0.05, \"(-0.93, -0.75)\": -1.09, \"(-0.75, -0.41)\": -2.13, \"(-0.41, -0.32)\": -1.04, \"(-0.32, -0.18)\": 0.09, \"(-0.18, 0.27)\": 1.33, \"(0.27, 0.39)\": 0.24, \"(0.39, 0.57)\": -1.15, \"(0.57, 0.91)\": -2.18, \"(0.91, 1.03)\": -1.13, \"(1.03, 1.46)\": -0.12, \"(1.46, 1.55)\": -1.15, \"(1.55, 1.63)\": -2.15, \"(1.63, 1.73)\": -3.38, \"(1.73, 1.94)\": -4.53, \"(1.94, 2.23)\": -5.55, \"(2.23, 2.67)\": -4.53, \"(2.67, 2.75)\": -5.81, \"(2.75, 2.82)\": -6.89, \"(2.82, 2.89)\": -8.04, \"(2.89, 2.99)\": -9.27, \"(2.99, 3.12)\": -10.58, \"(3.12, 3.84)\": -11.76, \"(3.84, 3.94)\": -13.15, \"(3.94, 4.0)\": -14.39, \"(4.0, 4.08)\": -15.6, \"(4.08, 4.13)\": -16.82, \"(4.13, 4.23)\": -18.17, \"(4.23, 4.3)\": -19.3, \"(4.3, 4.4)\": -20.35, \"(4.4, 4.82)\": -21.42, \"(4.82, 5.05)\": -22.45, \"(5.05, 5.15)\": -23.97, \"(5.15, 5.21)\": -25.05, \"(5.21, 5.27)\": -26.11, \"(5.27, 5.33)\": -27.31, \"(5.33, 5.41)\": -28.78, \"(5.41, 5.46)\": -30.19, \"(5.46, 5.56)\": -31.24, \"(5.56, 5.66)\": -33.01, \"(5.66, 5.8)\": -34.05, \"(5.8, 6.13)\": -35.09, \"(6.13, 6.27)\": -36.25, \"(6.27, 6.33)\": -37.35, \"(6.33, 6.4)\": -38.37, \"(6.4, 6.48)\": -39.44, \"(6.48, 6.54)\": -41.22, \"(6.54, 6.6)\": -42.3, \"(6.6, 6.66)\": -44.11, \"(6.66, 6.73)\": -45.14, \"(6.73, 6.8)\": -46.96, \"(6.8, 6.86)\": -47.98, \"(6.86, 6.93)\": -49.08, \"(6.93, 7.05)\": -50.11, \"(7.05, 7.24)\": -51.35, \"(7.24, 7.43)\": -52.38, \"(7.43, 7.54)\": -53.78, \"(7.54, 7.59)\": -54.8, \"(7.59, 7.69)\": -56.4, \"(7.69, 7.76)\": -57.89, \"(7.76, 7.83)\": -59.98, \"(7.83, 7.89)\": -61.57, \"(7.89, 7.91)\": -62.59, \"(7.91, 7.97)\": -63.66, \"(7.97, 8.02)\": -64.94, \"(8.02, 8.08)\": -66.03, \"(8.08, 8.16)\": -67.24, \"(8.16, 8.25)\": -68.84, \"(8.25, 8.39)\": -70.1, \"(8.39, 8.51)\": -71.25, \"(8.51, 8.65)\": -72.26, \"(8.65, 8.73)\": -73.48, \"(8.73, 8.83)\": -74.78, \"(8.83, 8.87)\": -76.13, \"(8.87, 8.95)\": -77.27, \"(8.95, 9.0)\": -79.2, \"(9.0, 9.08)\": -80.73, \"(9.08, 9.18)\": -83.81, \"(9.18, 9.22)\": -85.13, \"(9.22, 9.28)\": -86.23, \"(9.28, 9.34)\": -87.95, \"(9.34, 9.39)\": -89.12, \"(9.39, 9.45)\": -90.15, \"(9.45, 9.53)\": -91.48, \"(9.53, 9.61)\": -92.6, \"(9.61, 9.71)\": -93.6, \"(9.71, 9.83)\": -94.73, \"(9.83, 9.92)\": -95.84, \"(9.92, 10.0)\": -97.07}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = abs(x ** 2 - 20)\nb) f(x) = -x ** 2 + 2 * cos(5*x)\nc) f(x) = sqrt(x+10) + 1/3 * x \nd) f(x) = sin(x)+sin(2*x)\ne) f(x) = exp(x) + 4000* sin(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "b)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.98, -9.92)\": -119.8, \"(-9.92, -9.86)\": -118.3, \"(-9.86, -9.69)\": -116.7, \"(-9.69, -9.51)\": -112.0, \"(-9.51, -9.42)\": -109.8, \"(-9.42, -9.33)\": -108.4, \"(-9.33, -9.27)\": -106.7, \"(-9.27, -9.15)\": -105.3, \"(-9.15, -9.07)\": -103.4, \"(-9.07, -8.96)\": -101.8, \"(-8.96, -8.87)\": -99.9, \"(-8.87, -8.78)\": -98.3, \"(-8.78, -8.68)\": -96.9, \"(-8.68, -8.58)\": -95.1, \"(-8.58, -8.48)\": -93.4, \"(-8.48, -8.39)\": -91.9, \"(-8.39, -8.29)\": -90.1, \"(-8.29, -8.2)\": -88.5, \"(-8.2, -8.1)\": -87.1, \"(-8.1, -8.02)\": -85.7, \"(-8.02, -7.92)\": -84.2, \"(-7.92, -7.8)\": -82.6, \"(-7.8, -7.66)\": -80.6, \"(-7.66, -7.51)\": -77.8, \"(-7.51, -7.4)\": -76.3, \"(-7.4, -7.29)\": -74.8, \"(-7.29, -7.15)\": -72.7, \"(-7.15, -7.05)\": -71.0, \"(-7.05, -6.92)\": -69.3, \"(-6.92, -6.81)\": -67.7, \"(-6.81, -6.69)\": -66.2, \"(-6.69, -6.56)\": -64.5, \"(-6.56, -6.43)\": -62.6, \"(-6.43, -6.31)\": -61.2, \"(-6.31, -6.19)\": -59.8, \"(-6.19, -6.05)\": -58.3, \"(-6.05, -5.91)\": -56.4, \"(-5.91, -5.79)\": -54.9, \"(-5.79, -5.65)\": -53.4, \"(-5.65, -5.53)\": -51.9, \"(-5.53, -5.4)\": -50.5, \"(-5.4, -5.24)\": -48.9, \"(-5.24, -5.11)\": -47.5, \"(-5.11, -4.96)\": -46.0, \"(-4.96, -4.79)\": -44.5, \"(-4.79, -4.63)\": -42.8, \"(-4.63, -4.42)\": -41.0, \"(-4.42, -4.25)\": -39.5, \"(-4.25, -4.03)\": -37.7, \"(-4.03, -3.83)\": -36.1, \"(-3.83, -3.63)\": -34.7, \"(-3.63, -3.43)\": -33.0, \"(-3.43, -3.18)\": -31.6, \"(-3.18, -2.99)\": -30.2, \"(-2.99, -2.68)\": -28.7, \"(-2.68, -2.39)\": -27.1, \"(-2.39, -2.05)\": -25.6, \"(-2.05, -1.69)\": -24.2, \"(-1.69, -1.15)\": -22.7, \"(-1.15, -0.52)\": -21.3, \"(-0.52, -0.32)\": -19.9, \"(-0.32, -0.25)\": -18.4, \"(-0.25, -0.21)\": -16.9, \"(-0.21, -0.15)\": -15.3, \"(-0.15, -0.09)\": -11.4, \"(-0.09, -0.06)\": -6.5, \"(-0.06, -0.03)\": -5.0, \"(-0.03, -0.01)\": -3.0, \"(-0.01, 0.02)\": 0.8, \"(0.02, 0.04)\": 2.7, \"(0.04, 0.06)\": 4.6, \"(0.06, 0.08)\": 6.6, \"(0.08, 0.11)\": 9.0, \"(0.11, 0.13)\": 10.4, \"(0.13, 0.17)\": 12.5, \"(0.17, 0.23)\": 14.6, \"(0.23, 0.29)\": 16.2, \"(0.29, 0.42)\": 17.7, \"(0.42, 1.51)\": 19.2, \"(1.51, 1.95)\": 17.6, \"(1.95, 2.3)\": 16.2, \"(2.3, 2.59)\": 14.7, \"(2.59, 2.87)\": 13.2, \"(2.87, 3.12)\": 11.7, \"(3.12, 3.34)\": 10.1, \"(3.34, 3.57)\": 8.7, \"(3.57, 3.76)\": 7.2, \"(3.76, 3.98)\": 5.7, \"(3.98, 4.17)\": 4.0, \"(4.17, 4.34)\": 2.4, \"(4.34, 4.52)\": 1.0, \"(4.52, 4.65)\": -0.4, \"(4.65, 4.82)\": -1.9, \"(4.82, 4.97)\": -3.3, \"(4.97, 5.12)\": -4.8, \"(5.12, 5.28)\": -6.4, \"(5.28, 5.43)\": -8.0, \"(5.43, 5.55)\": -9.5, \"(5.55, 5.71)\": -11.0, \"(5.71, 5.85)\": -12.8, \"(5.85, 5.97)\": -14.3, \"(5.97, 6.09)\": -15.9, \"(6.09, 6.21)\": -17.4, \"(6.21, 6.33)\": -18.9, \"(6.33, 6.46)\": -20.3, \"(6.46, 6.59)\": -21.9, \"(6.59, 6.71)\": -23.4, \"(6.71, 6.81)\": -25.1, \"(6.81, 6.92)\": -26.6, \"(6.92, 7.04)\": -28.0, \"(7.04, 7.16)\": -29.8, \"(7.16, 7.25)\": -31.4, \"(7.25, 7.37)\": -32.8, \"(7.37, 7.47)\": -34.3, \"(7.47, 7.6)\": -36.1, \"(7.6, 7.72)\": -38.0, \"(7.72, 7.83)\": -39.7, \"(7.83, 7.93)\": -41.4, \"(7.93, 8.02)\": -43.2, \"(8.02, 8.13)\": -44.6, \"(8.13, 8.23)\": -46.2, \"(8.23, 8.33)\": -48.1, \"(8.33, 8.43)\": -49.6, \"(8.43, 8.5)\": -51.0, \"(8.5, 8.6)\": -52.5, \"(8.6, 8.69)\": -54.0, \"(8.69, 8.75)\": -55.5, \"(8.75, 8.84)\": -56.9, \"(8.84, 8.96)\": -58.7, \"(8.96, 9.06)\": -60.5, \"(9.06, 9.16)\": -62.3, \"(9.16, 9.25)\": -64.1, \"(9.25, 9.33)\": -65.7, \"(9.33, 9.41)\": -67.3, \"(9.41, 9.48)\": -68.7, \"(9.48, 9.59)\": -70.3, \"(9.59, 9.69)\": -72.7, \"(9.69, 9.78)\": -74.2, \"(9.78, 9.83)\": -75.7, \"(9.83, 9.9)\": -77.2, \"(9.9, 9.99)\": -78.6}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = tanh(x+10) - 1/3 * x \nb) f(x) = sin(x) + sin(0.5 * x)\nc) f(x) = -x ** 2 + 20 * tanh(5*x)\nd) f(x) = -1/10 * x ** 3 + 20 * tanh(2*x)\ne) f(x) = exp(-x+1)+ 2000 * abs(x+1)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "c)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.98, -9.91)\": 78.7, \"(-9.91, -9.85)\": 77.0, \"(-9.85, -9.79)\": 75.3, \"(-9.79, -9.72)\": 73.5, \"(-9.72, -9.65)\": 71.4, \"(-9.65, -9.58)\": 69.7, \"(-9.58, -9.51)\": 67.6, \"(-9.51, -9.44)\": 65.8, \"(-9.44, -9.38)\": 63.9, \"(-9.38, -9.3)\": 62.1, \"(-9.3, -9.23)\": 60.1, \"(-9.23, -9.14)\": 58.1, \"(-9.14, -9.07)\": 56.1, \"(-9.07, -8.97)\": 53.9, \"(-8.97, -8.89)\": 51.7, \"(-8.89, -8.82)\": 50.1, \"(-8.82, -8.73)\": 48.4, \"(-8.73, -8.64)\": 46.3, \"(-8.64, -8.56)\": 44.2, \"(-8.56, -8.48)\": 42.3, \"(-8.48, -8.38)\": 40.7, \"(-8.38, -8.27)\": 38.3, \"(-8.27, -8.17)\": 36.4, \"(-8.17, -8.06)\": 34.2, \"(-8.06, -7.97)\": 32.1, \"(-7.97, -7.86)\": 30.3, \"(-7.86, -7.76)\": 28.5, \"(-7.76, -7.62)\": 26.3, \"(-7.62, -7.48)\": 23.5, \"(-7.48, -7.37)\": 21.6, \"(-7.37, -7.29)\": 19.8, \"(-7.29, -7.18)\": 18.2, \"(-7.18, -7.05)\": 16.6, \"(-7.05, -6.95)\": 14.9, \"(-6.95, -6.83)\": 13.2, \"(-6.83, -6.68)\": 11.5, \"(-6.68, -6.57)\": 9.9, \"(-6.57, -6.43)\": 8.2, \"(-6.43, -6.31)\": 6.5, \"(-6.31, -6.13)\": 4.9, \"(-6.13, -5.94)\": 2.5, \"(-5.94, -5.76)\": 0.8, \"(-5.76, -5.58)\": -1.0, \"(-5.58, -5.39)\": -2.7, \"(-5.39, -5.15)\": -4.4, \"(-5.15, -4.91)\": -6.6, \"(-4.91, -4.67)\": -8.2, \"(-4.67, -4.41)\": -9.8, \"(-4.41, -4.12)\": -11.5, \"(-4.12, -3.77)\": -13.1, \"(-3.77, -3.36)\": -14.7, \"(-3.36, -2.78)\": -16.3, \"(-2.78, -1.56)\": -18.0, \"(-1.56, -0.74)\": -19.6, \"(-0.74, -0.57)\": -17.9, \"(-0.57, -0.46)\": -16.2, \"(-0.46, -0.36)\": -14.1, \"(-0.36, -0.29)\": -11.7, \"(-0.29, -0.23)\": -10.0, \"(-0.23, -0.17)\": -8.3, \"(-0.17, -0.11)\": -6.3, \"(-0.11, -0.06)\": -4.0, \"(-0.06, -0.03)\": -2.3, \"(-0.03, 0.03)\": -0.3, \"(0.03, 0.07)\": 1.5, \"(0.07, 0.13)\": 3.1, \"(0.13, 0.2)\": 5.8, \"(0.2, 0.24)\": 7.5, \"(0.24, 0.31)\": 9.2, \"(0.31, 0.38)\": 11.5, \"(0.38, 0.51)\": 13.4, \"(0.51, 0.68)\": 15.8, \"(0.68, 1.03)\": 17.6, \"(1.03, 2.83)\": 19.3, \"(2.83, 3.41)\": 17.7, \"(3.41, 3.85)\": 16.0, \"(3.85, 4.19)\": 14.2, \"(4.19, 4.48)\": 12.6, \"(4.48, 4.73)\": 11.0, \"(4.73, 4.96)\": 9.4, \"(4.96, 5.18)\": 7.8, \"(5.18, 5.36)\": 6.1, \"(5.36, 5.55)\": 4.5, \"(5.55, 5.71)\": 2.9, \"(5.71, 5.87)\": 1.3, \"(5.87, 6.03)\": -0.3, \"(6.03, 6.18)\": -2.1, \"(6.18, 6.32)\": -3.7, \"(6.32, 6.46)\": -5.5, \"(6.46, 6.6)\": -7.2, \"(6.6, 6.75)\": -8.9, \"(6.75, 6.88)\": -11.1, \"(6.88, 7.0)\": -12.7, \"(7.0, 7.1)\": -14.4, \"(7.1, 7.23)\": -16.1, \"(7.23, 7.34)\": -17.7, \"(7.34, 7.45)\": -20.0, \"(7.45, 7.55)\": -21.7, \"(7.55, 7.67)\": -23.5, \"(7.67, 7.77)\": -25.4, \"(7.77, 7.88)\": -27.3, \"(7.88, 7.98)\": -29.4, \"(7.98, 8.05)\": -31.1, \"(8.05, 8.17)\": -33.1, \"(8.17, 8.26)\": -34.8, \"(8.26, 8.39)\": -37.0, \"(8.39, 8.47)\": -39.6, \"(8.47, 8.56)\": -41.2, \"(8.56, 8.63)\": -42.8, \"(8.63, 8.72)\": -44.6, \"(8.72, 8.79)\": -46.4, \"(8.79, 8.87)\": -48.2, \"(8.87, 8.93)\": -50.4, \"(8.93, 9.03)\": -52.1, \"(9.03, 9.11)\": -54.1, \"(9.11, 9.19)\": -56.0, \"(9.19, 9.27)\": -57.9, \"(9.27, 9.32)\": -59.7, \"(9.32, 9.38)\": -61.3, \"(9.38, 9.46)\": -63.1, \"(9.46, 9.53)\": -64.9, \"(9.53, 9.57)\": -66.6, \"(9.57, 9.65)\": -68.2, \"(9.65, 9.74)\": -70.9, \"(9.74, 9.83)\": -72.7, \"(9.83, 9.9)\": -75.5, \"(9.9, 9.95)\": -77.2, \"(9.95, 10.0)\": -79.5}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = exp(-x+1)+ 2000 * abs(x+1)\nb) f(x) = sqrt(x+10) + 1/3 * x \nc) f(x) = log(x+10) + 1/3 * x \nd) f(x) = -1/10 * x ** 3 + 20 * tanh(2*x)\ne) f(x) = exp(x)+ 4000 * sign(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "d)"
+  ]
+]
\ No newline at end of file
diff --git a/benchmarks/benchmark/function-recognition.json b/benchmarks/benchmark/function-recognition.json
new file mode 100644
index 0000000..876bb45
--- /dev/null
+++ b/benchmarks/benchmark/function-recognition.json
@@ -0,0 +1,202 @@
+[
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.99, -9.79)\": -9.99, \"(-9.79, -9.59)\": -9.78, \"(-9.59, -9.38)\": -9.58, \"(-9.38, -9.19)\": -9.38, \"(-9.19, -8.97)\": -9.16, \"(-8.97, -8.76)\": -8.96, \"(-8.76, -8.53)\": -8.74, \"(-8.53, -8.29)\": -8.51, \"(-8.29, -8.07)\": -8.27, \"(-8.07, -7.84)\": -8.04, \"(-7.84, -7.65)\": -7.84, \"(-7.65, -7.43)\": -7.62, \"(-7.43, -7.19)\": -7.41, \"(-7.19, -6.99)\": -7.19, \"(-6.99, -6.79)\": -6.98, \"(-6.79, -6.58)\": -6.78, \"(-6.58, -6.38)\": -6.58, \"(-6.38, -6.15)\": -6.36, \"(-6.15, -5.94)\": -6.14, \"(-5.94, -5.74)\": -5.94, \"(-5.74, -5.53)\": -5.73, \"(-5.53, -5.31)\": -5.5, \"(-5.31, -5.1)\": -5.3, \"(-5.1, -4.91)\": -5.09, \"(-4.91, -4.69)\": -4.88, \"(-4.69, -4.48)\": -4.66, \"(-4.48, -4.28)\": -4.46, \"(-4.28, -4.08)\": -4.25, \"(-4.08, -3.86)\": -4.05, \"(-3.86, -3.63)\": -3.85, \"(-3.63, -3.43)\": -3.63, \"(-3.43, -3.2)\": -3.42, \"(-3.2, -3.0)\": -3.2, \"(-3.0, -2.78)\": -2.99, \"(-2.78, -2.52)\": -2.71, \"(-2.52, -2.32)\": -2.5, \"(-2.32, -2.07)\": -2.28, \"(-2.07, -1.84)\": -2.05, \"(-1.84, -1.63)\": -1.83, \"(-1.63, -1.43)\": -1.62, \"(-1.43, -1.25)\": -1.42, \"(-1.25, -1.02)\": -1.21, \"(-1.02, -0.84)\": -1.01, \"(-0.84, -0.62)\": -0.81, \"(-0.62, -0.4)\": -0.61, \"(-0.4, -0.16)\": -0.36, \"(-0.16, 0.03)\": -0.14, \"(0.03, 0.26)\": 0.06, \"(0.26, 0.46)\": 0.27, \"(0.46, 0.68)\": 0.48, \"(0.68, 0.88)\": 0.69, \"(0.88, 1.11)\": 0.9, \"(1.11, 1.33)\": 1.13, \"(1.33, 1.55)\": 1.35, \"(1.55, 1.76)\": 1.56, \"(1.76, 1.97)\": 1.77, \"(1.97, 2.16)\": 1.98, \"(2.16, 2.37)\": 2.18, \"(2.37, 2.59)\": 2.39, \"(2.59, 2.83)\": 2.61, \"(2.83, 3.06)\": 2.86, \"(3.06, 3.31)\": 3.09, \"(3.31, 3.53)\": 3.33, \"(3.53, 3.77)\": 3.56, \"(3.77, 3.95)\": 3.77, \"(3.95, 4.16)\": 3.97, \"(4.16, 4.38)\": 4.18, \"(4.38, 4.62)\": 4.42, \"(4.62, 4.85)\": 4.63, \"(4.85, 5.06)\": 4.88, \"(5.06, 5.29)\": 5.09, \"(5.29, 5.49)\": 5.31, \"(5.49, 5.7)\": 5.51, \"(5.7, 5.91)\": 5.71, \"(5.91, 6.1)\": 5.92, \"(6.1, 6.32)\": 6.12, \"(6.32, 6.53)\": 6.33, \"(6.53, 6.7)\": 6.53, \"(6.7, 6.91)\": 6.73, \"(6.91, 7.14)\": 6.94, \"(7.14, 7.37)\": 7.14, \"(7.37, 7.58)\": 7.38, \"(7.58, 7.79)\": 7.59, \"(7.79, 7.97)\": 7.79, \"(7.97, 8.2)\": 8.0, \"(8.2, 8.42)\": 8.21, \"(8.42, 8.66)\": 8.45, \"(8.66, 8.87)\": 8.67, \"(8.87, 9.09)\": 8.88, \"(9.09, 9.31)\": 9.12, \"(9.31, 9.52)\": 9.33, \"(9.52, 9.72)\": 9.53, \"(9.72, 9.94)\": 9.73, \"(9.94, 9.95)\": 9.93}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = x\nb) f(x) = -sinh(x)\nc) f(x) = -3*x^3\nd) f(x) = -x^5\ne) f(x) = -sin(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "a)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.98, -9.78)\": 24.95, \"(-9.78, -9.55)\": 24.53, \"(-9.55, -9.29)\": 23.98, \"(-9.29, -9.07)\": 23.53, \"(-9.07, -8.84)\": 23.11, \"(-8.84, -8.63)\": 22.67, \"(-8.63, -8.4)\": 22.22, \"(-8.4, -8.21)\": 21.79, \"(-8.21, -7.98)\": 21.39, \"(-7.98, -7.78)\": 20.95, \"(-7.78, -7.57)\": 20.52, \"(-7.57, -7.35)\": 20.1, \"(-7.35, -7.17)\": 19.7, \"(-7.17, -6.95)\": 19.28, \"(-6.95, -6.73)\": 18.83, \"(-6.73, -6.5)\": 18.38, \"(-6.5, -6.3)\": 17.97, \"(-6.3, -6.08)\": 17.57, \"(-6.08, -5.84)\": 17.14, \"(-5.84, -5.6)\": 16.6, \"(-5.6, -5.38)\": 16.16, \"(-5.38, -5.19)\": 15.76, \"(-5.19, -4.97)\": 15.36, \"(-4.97, -4.79)\": 14.94, \"(-4.79, -4.56)\": 14.53, \"(-4.56, -4.35)\": 14.11, \"(-4.35, -4.17)\": 13.71, \"(-4.17, -3.93)\": 13.3, \"(-3.93, -3.73)\": 12.84, \"(-3.73, -3.52)\": 12.42, \"(-3.52, -3.31)\": 11.99, \"(-3.31, -3.08)\": 11.59, \"(-3.08, -2.86)\": 11.13, \"(-2.86, -2.62)\": 10.68, \"(-2.62, -2.4)\": 10.18, \"(-2.4, -2.18)\": 9.74, \"(-2.18, -1.96)\": 9.33, \"(-1.96, -1.7)\": 8.85, \"(-1.7, -1.5)\": 8.39, \"(-1.5, -1.25)\": 7.97, \"(-1.25, -1.05)\": 7.48, \"(-1.05, -0.81)\": 7.07, \"(-0.81, -0.63)\": 6.62, \"(-0.63, -0.41)\": 6.2, \"(-0.41, -0.19)\": 5.79, \"(-0.19, 0.01)\": 5.38, \"(0.01, 0.23)\": 4.96, \"(0.23, 0.43)\": 4.53, \"(0.43, 0.65)\": 4.09, \"(0.65, 0.87)\": 3.68, \"(0.87, 1.08)\": 3.24, \"(1.08, 1.27)\": 2.83, \"(1.27, 1.49)\": 2.41, \"(1.49, 1.72)\": 1.99, \"(1.72, 1.96)\": 1.51, \"(1.96, 2.14)\": 1.08, \"(2.14, 2.38)\": 0.67, \"(2.38, 2.56)\": 0.26, \"(2.56, 2.77)\": -0.14, \"(2.77, 2.98)\": -0.54, \"(2.98, 3.19)\": -0.96, \"(3.19, 3.4)\": -1.39, \"(3.4, 3.62)\": -1.81, \"(3.62, 3.81)\": -2.23, \"(3.81, 4.03)\": -2.65, \"(4.03, 4.24)\": -3.07, \"(4.24, 4.46)\": -3.53, \"(4.46, 4.66)\": -3.93, \"(4.66, 4.86)\": -4.33, \"(4.86, 5.08)\": -4.75, \"(5.08, 5.29)\": -5.18, \"(5.29, 5.49)\": -5.58, \"(5.49, 5.68)\": -6.03, \"(5.68, 5.95)\": -6.46, \"(5.95, 6.15)\": -6.91, \"(6.15, 6.37)\": -7.31, \"(6.37, 6.59)\": -7.77, \"(6.59, 6.82)\": -8.22, \"(6.82, 7.01)\": -8.66, \"(7.01, 7.23)\": -9.05, \"(7.23, 7.45)\": -9.48, \"(7.45, 7.66)\": -9.93, \"(7.66, 7.9)\": -10.38, \"(7.9, 8.09)\": -10.79, \"(8.09, 8.3)\": -11.2, \"(8.3, 8.53)\": -11.64, \"(8.53, 8.73)\": -12.08, \"(8.73, 8.96)\": -12.51, \"(8.96, 9.17)\": -12.95, \"(9.17, 9.37)\": -13.35, \"(9.37, 9.58)\": -13.77, \"(9.58, 9.8)\": -14.19, \"(9.8, 9.97)\": -14.61}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = -x^5\nb) f(x) = -2*x+5\nc) f(x) = x\nd) f(x) = sign(x+3)\ne) f(x) = x^3\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "b)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.98, -9.93)\": 99.4, \"(-9.93, -9.87)\": 98.31, \"(-9.87, -9.81)\": 97.19, \"(-9.81, -9.76)\": 96.11, \"(-9.76, -9.7)\": 95.04, \"(-9.7, -9.65)\": 94.01, \"(-9.65, -9.58)\": 92.73, \"(-9.58, -9.51)\": 91.67, \"(-9.51, -9.47)\": 90.48, \"(-9.47, -9.43)\": 89.37, \"(-9.43, -9.34)\": 88.16, \"(-9.34, -9.28)\": 87.09, \"(-9.28, -9.23)\": 86.04, \"(-9.23, -9.15)\": 84.82, \"(-9.15, -9.1)\": 83.54, \"(-9.1, -9.02)\": 82.31, \"(-9.02, -8.92)\": 80.93, \"(-8.92, -8.87)\": 79.41, \"(-8.87, -8.78)\": 78.31, \"(-8.78, -8.71)\": 76.47, \"(-8.71, -8.62)\": 75.46, \"(-8.62, -8.54)\": 74.01, \"(-8.54, -8.48)\": 72.62, \"(-8.48, -8.42)\": 71.53, \"(-8.42, -8.32)\": 70.24, \"(-8.32, -8.27)\": 69.21, \"(-8.27, -8.2)\": 68.18, \"(-8.2, -8.12)\": 66.92, \"(-8.12, -8.05)\": 65.63, \"(-8.05, -7.97)\": 64.56, \"(-7.97, -7.89)\": 63.45, \"(-7.89, -7.83)\": 62.36, \"(-7.83, -7.77)\": 61.32, \"(-7.77, -7.71)\": 60.21, \"(-7.71, -7.62)\": 59.18, \"(-7.62, -7.56)\": 58.13, \"(-7.56, -7.47)\": 57.06, \"(-7.47, -7.41)\": 55.65, \"(-7.41, -7.33)\": 54.58, \"(-7.33, -7.25)\": 53.55, \"(-7.25, -7.18)\": 52.43, \"(-7.18, -7.12)\": 51.43, \"(-7.12, -7.04)\": 50.41, \"(-7.04, -6.95)\": 49.31, \"(-6.95, -6.88)\": 48.23, \"(-6.88, -6.79)\": 47.12, \"(-6.79, -6.72)\": 46.02, \"(-6.72, -6.63)\": 44.95, \"(-6.63, -6.55)\": 43.75, \"(-6.55, -6.45)\": 42.74, \"(-6.45, -6.35)\": 41.39, \"(-6.35, -6.24)\": 40.11, \"(-6.24, -6.17)\": 38.97, \"(-6.17, -6.06)\": 37.89, \"(-6.06, -5.95)\": 36.45, \"(-5.95, -5.85)\": 35.25, \"(-5.85, -5.77)\": 34.13, \"(-5.77, -5.66)\": 33.06, \"(-5.66, -5.56)\": 31.88, \"(-5.56, -5.45)\": 30.75, \"(-5.45, -5.34)\": 29.55, \"(-5.34, -5.24)\": 28.49, \"(-5.24, -5.15)\": 27.39, \"(-5.15, -5.01)\": 26.33, \"(-5.01, -4.92)\": 25.2, \"(-4.92, -4.79)\": 23.89, \"(-4.79, -4.67)\": 22.84, \"(-4.67, -4.54)\": 21.69, \"(-4.54, -4.43)\": 20.6, \"(-4.43, -4.31)\": 19.6, \"(-4.31, -4.2)\": 18.5, \"(-4.2, -4.07)\": 17.46, \"(-4.07, -3.93)\": 16.43, \"(-3.93, -3.8)\": 15.33, \"(-3.8, -3.64)\": 14.3, \"(-3.64, -3.51)\": 13.27, \"(-3.51, -3.34)\": 12.22, \"(-3.34, -3.15)\": 10.98, \"(-3.15, -2.98)\": 9.9, \"(-2.98, -2.79)\": 8.85, \"(-2.79, -2.58)\": 7.72, \"(-2.58, -2.37)\": 6.53, \"(-2.37, -2.14)\": 5.51, \"(-2.14, -1.86)\": 4.5, \"(-1.86, -1.54)\": 3.39, \"(-1.54, -1.11)\": 2.25, \"(-1.11, -0.39)\": 1.18, \"(-0.39, 1.09)\": 0.16, \"(1.09, 1.47)\": 1.17, \"(1.47, 1.76)\": 2.17, \"(1.76, 2.03)\": 3.19, \"(2.03, 2.27)\": 4.2, \"(2.27, 2.49)\": 5.22, \"(2.49, 2.7)\": 6.26, \"(2.7, 2.92)\": 7.5, \"(2.92, 3.08)\": 8.6, \"(3.08, 3.27)\": 9.65, \"(3.27, 3.43)\": 10.71, \"(3.43, 3.62)\": 12.0, \"(3.62, 3.78)\": 13.22, \"(3.78, 3.91)\": 14.3, \"(3.91, 4.04)\": 15.43, \"(4.04, 4.17)\": 16.43, \"(4.17, 4.3)\": 17.43, \"(4.3, 4.45)\": 18.5, \"(4.45, 4.62)\": 20.3, \"(4.62, 4.74)\": 21.54, \"(4.74, 4.85)\": 22.57, \"(4.85, 4.95)\": 23.58, \"(4.95, 5.06)\": 24.58, \"(5.06, 5.14)\": 25.64, \"(5.14, 5.26)\": 26.66, \"(5.26, 5.39)\": 27.87, \"(5.39, 5.48)\": 29.18, \"(5.48, 5.62)\": 30.42, \"(5.62, 5.73)\": 31.7, \"(5.73, 5.8)\": 32.8, \"(5.8, 5.9)\": 33.81, \"(5.9, 6.02)\": 34.97, \"(6.02, 6.14)\": 36.63, \"(6.14, 6.2)\": 37.66, \"(6.2, 6.29)\": 38.67, \"(6.29, 6.41)\": 39.71, \"(6.41, 6.55)\": 41.78, \"(6.55, 6.65)\": 42.94, \"(6.65, 6.75)\": 44.48, \"(6.75, 6.83)\": 45.59, \"(6.83, 6.91)\": 46.74, \"(6.91, 6.99)\": 47.92, \"(6.99, 7.08)\": 49.12, \"(7.08, 7.16)\": 50.33, \"(7.16, 7.23)\": 51.38, \"(7.23, 7.31)\": 52.48, \"(7.31, 7.38)\": 53.62, \"(7.38, 7.47)\": 54.74, \"(7.47, 7.55)\": 55.87, \"(7.55, 7.63)\": 57.06, \"(7.63, 7.68)\": 58.15, \"(7.68, 7.75)\": 59.17, \"(7.75, 7.84)\": 60.33, \"(7.84, 7.93)\": 61.95, \"(7.93, 8.05)\": 63.71, \"(8.05, 8.11)\": 64.79, \"(8.11, 8.18)\": 65.93, \"(8.18, 8.27)\": 67.71, \"(8.27, 8.37)\": 68.96, \"(8.37, 8.45)\": 70.29, \"(8.45, 8.51)\": 71.47, \"(8.51, 8.59)\": 72.6, \"(8.59, 8.62)\": 73.67, \"(8.62, 8.72)\": 74.69, \"(8.72, 8.78)\": 76.09, \"(8.78, 8.85)\": 77.31, \"(8.85, 8.89)\": 78.33, \"(8.89, 8.99)\": 79.44, \"(8.99, 9.06)\": 81.18, \"(9.06, 9.16)\": 82.62, \"(9.16, 9.24)\": 84.01, \"(9.24, 9.3)\": 85.56, \"(9.3, 9.38)\": 86.58, \"(9.38, 9.45)\": 88.34, \"(9.45, 9.53)\": 89.65, \"(9.53, 9.59)\": 91.14, \"(9.59, 9.64)\": 92.15, \"(9.64, 9.72)\": 93.3, \"(9.72, 9.78)\": 94.6, \"(9.78, 9.82)\": 95.66, \"(9.82, 9.89)\": 97.11, \"(9.89, 10.0)\": 98.32}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = |x|\nb) f(x) = -|-x|\nc) f(x) = x^2\nd) f(x) = -sin(x)\ne) f(x) = x^5\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "c)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-10.0, -9.95)\": -200.0, \"(-9.95, -9.88)\": -197.6, \"(-9.88, -9.84)\": -195.0, \"(-9.84, -9.76)\": -192.9, \"(-9.76, -9.7)\": -189.9, \"(-9.7, -9.64)\": -187.2, \"(-9.64, -9.54)\": -184.0, \"(-9.54, -9.45)\": -181.6, \"(-9.45, -9.35)\": -176.4, \"(-9.35, -9.26)\": -173.7, \"(-9.26, -9.2)\": -171.5, \"(-9.2, -9.14)\": -169.0, \"(-9.14, -9.07)\": -166.8, \"(-9.07, -9.01)\": -164.7, \"(-9.01, -8.96)\": -162.0, \"(-8.96, -8.88)\": -159.7, \"(-8.88, -8.81)\": -157.6, \"(-8.81, -8.74)\": -154.6, \"(-8.74, -8.67)\": -152.6, \"(-8.67, -8.61)\": -149.9, \"(-8.61, -8.55)\": -147.7, \"(-8.55, -8.47)\": -145.6, \"(-8.47, -8.41)\": -143.1, \"(-8.41, -8.33)\": -140.6, \"(-8.33, -8.26)\": -138.6, \"(-8.26, -8.19)\": -136.4, \"(-8.19, -8.12)\": -133.7, \"(-8.12, -8.04)\": -131.5, \"(-8.04, -7.97)\": -129.3, \"(-7.97, -7.9)\": -126.9, \"(-7.9, -7.82)\": -124.1, \"(-7.82, -7.73)\": -122.0, \"(-7.73, -7.65)\": -118.8, \"(-7.65, -7.55)\": -116.1, \"(-7.55, -7.48)\": -113.6, \"(-7.48, -7.36)\": -110.7, \"(-7.36, -7.26)\": -107.9, \"(-7.26, -7.15)\": -104.8, \"(-7.15, -7.07)\": -102.1, \"(-7.07, -6.99)\": -99.7, \"(-6.99, -6.93)\": -97.5, \"(-6.93, -6.81)\": -95.2, \"(-6.81, -6.75)\": -92.7, \"(-6.75, -6.64)\": -90.1, \"(-6.64, -6.55)\": -87.6, \"(-6.55, -6.43)\": -85.4, \"(-6.43, -6.34)\": -82.2, \"(-6.34, -6.25)\": -80.0, \"(-6.25, -6.17)\": -77.9, \"(-6.17, -6.07)\": -75.7, \"(-6.07, -5.99)\": -73.6, \"(-5.99, -5.89)\": -71.5, \"(-5.89, -5.81)\": -69.4, \"(-5.81, -5.72)\": -67.2, \"(-5.72, -5.63)\": -65.1, \"(-5.63, -5.51)\": -63.1, \"(-5.51, -5.43)\": -60.8, \"(-5.43, -5.31)\": -58.5, \"(-5.31, -5.21)\": -56.2, \"(-5.21, -5.09)\": -53.9, \"(-5.09, -4.99)\": -51.9, \"(-4.99, -4.85)\": -49.4, \"(-4.85, -4.74)\": -46.8, \"(-4.74, -4.63)\": -44.7, \"(-4.63, -4.52)\": -42.7, \"(-4.52, -4.41)\": -40.6, \"(-4.41, -4.27)\": -38.3, \"(-4.27, -4.13)\": -36.3, \"(-4.13, -4.03)\": -34.2, \"(-4.03, -3.87)\": -32.2, \"(-3.87, -3.73)\": -29.9, \"(-3.73, -3.58)\": -27.7, \"(-3.58, -3.41)\": -25.6, \"(-3.41, -3.25)\": -23.3, \"(-3.25, -3.06)\": -20.7, \"(-3.06, -2.88)\": -18.4, \"(-2.88, -2.65)\": -16.2, \"(-2.65, -2.48)\": -14.1, \"(-2.48, -2.28)\": -12.0, \"(-2.28, -1.99)\": -10.0, \"(-1.99, -1.71)\": -7.9, \"(-1.71, -1.38)\": -5.8, \"(-1.38, -0.92)\": -3.8, \"(-0.92, 1.35)\": -1.7, \"(1.35, 1.68)\": -3.7, \"(1.68, 1.98)\": -5.8, \"(1.98, 2.2)\": -7.9, \"(2.2, 2.44)\": -10.0, \"(2.44, 2.65)\": -12.0, \"(2.65, 2.83)\": -14.1, \"(2.83, 3.03)\": -16.4, \"(3.03, 3.22)\": -18.5, \"(3.22, 3.38)\": -20.7, \"(3.38, 3.51)\": -23.0, \"(3.51, 3.68)\": -25.1, \"(3.68, 3.83)\": -27.2, \"(3.83, 3.99)\": -29.5, \"(3.99, 4.16)\": -32.3, \"(4.16, 4.27)\": -34.5, \"(4.27, 4.39)\": -36.6, \"(4.39, 4.53)\": -38.6, \"(4.53, 4.65)\": -41.2, \"(4.65, 4.79)\": -43.3, \"(4.79, 4.91)\": -46.4, \"(4.91, 5.02)\": -48.5, \"(5.02, 5.12)\": -50.7, \"(5.12, 5.25)\": -52.7, \"(5.25, 5.34)\": -54.9, \"(5.34, 5.44)\": -57.3, \"(5.44, 5.54)\": -59.5, \"(5.54, 5.64)\": -61.6, \"(5.64, 5.72)\": -63.8, \"(5.72, 5.83)\": -66.1, \"(5.83, 5.92)\": -68.2, \"(5.92, 6.03)\": -70.4, \"(6.03, 6.15)\": -73.7, \"(6.15, 6.24)\": -75.7, \"(6.24, 6.32)\": -77.9, \"(6.32, 6.41)\": -80.1, \"(6.41, 6.49)\": -82.6, \"(6.49, 6.6)\": -84.9, \"(6.6, 6.7)\": -87.7, \"(6.7, 6.78)\": -89.7, \"(6.78, 6.88)\": -92.4, \"(6.88, 6.95)\": -94.9, \"(6.95, 7.03)\": -97.0, \"(7.03, 7.1)\": -99.1, \"(7.1, 7.2)\": -101.6, \"(7.2, 7.28)\": -104.0, \"(7.28, 7.35)\": -106.2, \"(7.35, 7.43)\": -108.4, \"(7.43, 7.51)\": -110.9, \"(7.51, 7.58)\": -113.1, \"(7.58, 7.65)\": -115.2, \"(7.65, 7.71)\": -117.3, \"(7.71, 7.79)\": -119.4, \"(7.79, 7.87)\": -121.7, \"(7.87, 7.95)\": -124.2, \"(7.95, 8.04)\": -127.3, \"(8.04, 8.1)\": -129.5, \"(8.1, 8.17)\": -131.6, \"(8.17, 8.26)\": -134.0, \"(8.26, 8.33)\": -136.8, \"(8.33, 8.37)\": -139.0, \"(8.37, 8.46)\": -141.4, \"(8.46, 8.52)\": -143.8, \"(8.52, 8.6)\": -146.5, \"(8.6, 8.68)\": -148.5, \"(8.68, 8.75)\": -150.8, \"(8.75, 8.8)\": -154.1, \"(8.8, 8.92)\": -156.8, \"(8.92, 9.06)\": -161.9, \"(9.06, 9.15)\": -165.8, \"(9.15, 9.23)\": -168.3, \"(9.23, 9.29)\": -170.8, \"(9.29, 9.36)\": -173.4, \"(9.36, 9.42)\": -175.5, \"(9.42, 9.49)\": -178.0, \"(9.49, 9.54)\": -180.4, \"(9.54, 9.61)\": -182.9, \"(9.61, 9.66)\": -184.9, \"(9.66, 9.74)\": -187.4, \"(9.74, 9.83)\": -191.4, \"(9.83, 9.89)\": -193.6, \"(9.89, 9.97)\": -196.6}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = 1/2*cos(x-2)\nb) f(x) = -2*x^2\nc) f(x) = (x-2)^2\nd) f(x) = sqrt(x+10)\ne) f(x) = -sign(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "b)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-10.0, -9.94)\": 144.0, \"(-9.94, -9.85)\": 142.0, \"(-9.85, -9.77)\": 140.4, \"(-9.77, -9.66)\": 137.5, \"(-9.66, -9.58)\": 135.8, \"(-9.58, -9.51)\": 134.0, \"(-9.51, -9.41)\": 131.7, \"(-9.41, -9.35)\": 130.1, \"(-9.35, -9.27)\": 128.6, \"(-9.27, -9.18)\": 126.7, \"(-9.18, -9.09)\": 124.5, \"(-9.09, -9.01)\": 122.7, \"(-9.01, -8.93)\": 121.1, \"(-8.93, -8.84)\": 119.2, \"(-8.84, -8.76)\": 117.3, \"(-8.76, -8.69)\": 115.5, \"(-8.69, -8.59)\": 114.0, \"(-8.59, -8.52)\": 112.2, \"(-8.52, -8.42)\": 110.3, \"(-8.42, -8.35)\": 108.6, \"(-8.35, -8.29)\": 106.9, \"(-8.29, -8.22)\": 105.4, \"(-8.22, -8.11)\": 103.7, \"(-8.11, -8.03)\": 102.1, \"(-8.03, -7.92)\": 99.8, \"(-7.92, -7.86)\": 98.4, \"(-7.86, -7.76)\": 96.8, \"(-7.76, -7.67)\": 95.1, \"(-7.67, -7.56)\": 92.8, \"(-7.56, -7.48)\": 91.2, \"(-7.48, -7.4)\": 89.8, \"(-7.4, -7.32)\": 88.2, \"(-7.32, -7.23)\": 86.7, \"(-7.23, -7.13)\": 84.9, \"(-7.13, -7.06)\": 83.1, \"(-7.06, -6.96)\": 81.6, \"(-6.96, -6.88)\": 80.1, \"(-6.88, -6.78)\": 78.6, \"(-6.78, -6.71)\": 76.9, \"(-6.71, -6.6)\": 75.4, \"(-6.6, -6.53)\": 73.8, \"(-6.53, -6.42)\": 72.3, \"(-6.42, -6.32)\": 70.8, \"(-6.32, -6.25)\": 69.2, \"(-6.25, -6.12)\": 67.6, \"(-6.12, -6.03)\": 66.0, \"(-6.03, -5.94)\": 64.4, \"(-5.94, -5.82)\": 62.8, \"(-5.82, -5.71)\": 60.8, \"(-5.71, -5.61)\": 59.1, \"(-5.61, -5.48)\": 57.6, \"(-5.48, -5.41)\": 56.1, \"(-5.41, -5.29)\": 54.6, \"(-5.29, -5.18)\": 53.1, \"(-5.18, -5.08)\": 51.5, \"(-5.08, -4.96)\": 49.8, \"(-4.96, -4.87)\": 48.4, \"(-4.87, -4.76)\": 46.9, \"(-4.76, -4.62)\": 45.4, \"(-4.62, -4.47)\": 43.4, \"(-4.47, -4.36)\": 41.9, \"(-4.36, -4.23)\": 40.4, \"(-4.23, -4.14)\": 38.8, \"(-4.14, -3.99)\": 37.3, \"(-3.99, -3.85)\": 35.7, \"(-3.85, -3.68)\": 33.8, \"(-3.68, -3.54)\": 32.1, \"(-3.54, -3.42)\": 30.6, \"(-3.42, -3.26)\": 29.2, \"(-3.26, -3.12)\": 27.6, \"(-3.12, -2.97)\": 26.0, \"(-2.97, -2.79)\": 24.5, \"(-2.79, -2.62)\": 22.9, \"(-2.62, -2.45)\": 21.3, \"(-2.45, -2.29)\": 19.9, \"(-2.29, -2.12)\": 18.3, \"(-2.12, -1.93)\": 16.8, \"(-1.93, -1.72)\": 15.3, \"(-1.72, -1.52)\": 13.8, \"(-1.52, -1.29)\": 12.3, \"(-1.29, -1.05)\": 10.7, \"(-1.05, -0.8)\": 9.3, \"(-0.8, -0.51)\": 7.8, \"(-0.51, -0.22)\": 6.3, \"(-0.22, 0.2)\": 4.8, \"(0.2, 0.65)\": 3.3, \"(0.65, 1.31)\": 1.9, \"(1.31, 3.39)\": 0.4, \"(3.39, 3.82)\": 1.9, \"(3.82, 4.19)\": 3.4, \"(4.19, 4.51)\": 4.8, \"(4.51, 4.79)\": 6.3, \"(4.79, 5.04)\": 7.8, \"(5.04, 5.28)\": 9.3, \"(5.28, 5.5)\": 10.8, \"(5.5, 5.71)\": 12.4, \"(5.71, 5.92)\": 13.9, \"(5.92, 6.12)\": 15.4, \"(6.12, 6.29)\": 17.1, \"(6.29, 6.47)\": 18.5, \"(6.47, 6.63)\": 20.1, \"(6.63, 6.8)\": 21.5, \"(6.8, 6.96)\": 23.0, \"(6.96, 7.14)\": 24.8, \"(7.14, 7.3)\": 26.7, \"(7.3, 7.46)\": 28.3, \"(7.46, 7.6)\": 29.8, \"(7.6, 7.74)\": 31.5, \"(7.74, 7.87)\": 33.1, \"(7.87, 8.0)\": 34.5, \"(8.0, 8.13)\": 36.1, \"(8.13, 8.23)\": 37.7, \"(8.23, 8.37)\": 39.1, \"(8.37, 8.48)\": 40.7, \"(8.48, 8.6)\": 42.2, \"(8.6, 8.73)\": 43.7, \"(8.73, 8.83)\": 45.2, \"(8.83, 8.94)\": 46.9, \"(8.94, 9.06)\": 48.4, \"(9.06, 9.2)\": 50.2, \"(9.2, 9.28)\": 51.9, \"(9.28, 9.37)\": 53.3, \"(9.37, 9.52)\": 54.9, \"(9.52, 9.62)\": 56.6, \"(9.62, 9.72)\": 58.1, \"(9.72, 9.82)\": 59.7, \"(9.82, 9.92)\": 61.2, \"(9.92, 9.97)\": 62.9}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = 1/2*cos(x-2)\nb) f(x) = sign(x)\nc) f(x) = (x-2)^2\nd) f(x) = x^4\ne) f(x) = x\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "c)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.97, -9.92)\": 98.39, \"(-9.92, -9.85)\": 97.15, \"(-9.85, -9.81)\": 95.88, \"(-9.81, -9.74)\": 94.86, \"(-9.74, -9.68)\": 93.59, \"(-9.68, -9.62)\": 92.55, \"(-9.62, -9.55)\": 91.55, \"(-9.55, -9.46)\": 89.64, \"(-9.46, -9.41)\": 88.35, \"(-9.41, -9.33)\": 87.32, \"(-9.33, -9.25)\": 85.49, \"(-9.25, -9.17)\": 84.3, \"(-9.17, -9.13)\": 83.01, \"(-9.13, -9.05)\": 81.89, \"(-9.05, -8.97)\": 80.48, \"(-8.97, -8.93)\": 79.36, \"(-8.93, -8.86)\": 78.35, \"(-8.86, -8.79)\": 77.27, \"(-8.79, -8.73)\": 76.13, \"(-8.73, -8.65)\": 74.9, \"(-8.65, -8.59)\": 73.6, \"(-8.59, -8.51)\": 72.49, \"(-8.51, -8.43)\": 71.2, \"(-8.43, -8.36)\": 69.95, \"(-8.36, -8.3)\": 68.88, \"(-8.3, -8.24)\": 67.85, \"(-8.24, -8.2)\": 66.82, \"(-8.2, -8.09)\": 65.74, \"(-8.09, -7.99)\": 64.03, \"(-7.99, -7.92)\": 62.6, \"(-7.92, -7.84)\": 61.47, \"(-7.84, -7.77)\": 60.46, \"(-7.77, -7.7)\": 59.21, \"(-7.7, -7.64)\": 58.1, \"(-7.64, -7.56)\": 57.06, \"(-7.56, -7.47)\": 55.86, \"(-7.47, -7.38)\": 54.54, \"(-7.38, -7.3)\": 53.23, \"(-7.3, -7.2)\": 52.18, \"(-7.2, -7.12)\": 50.56, \"(-7.12, -7.05)\": 49.46, \"(-7.05, -6.96)\": 48.46, \"(-6.96, -6.86)\": 47.23, \"(-6.86, -6.78)\": 45.85, \"(-6.78, -6.69)\": 44.83, \"(-6.69, -6.61)\": 43.72, \"(-6.61, -6.48)\": 42.57, \"(-6.48, -6.39)\": 40.79, \"(-6.39, -6.31)\": 39.61, \"(-6.31, -6.19)\": 38.35, \"(-6.19, -6.11)\": 37.14, \"(-6.11, -6.01)\": 36.08, \"(-6.01, -5.91)\": 35.08, \"(-5.91, -5.8)\": 33.79, \"(-5.8, -5.71)\": 32.62, \"(-5.71, -5.62)\": 31.54, \"(-5.62, -5.54)\": 30.51, \"(-5.54, -5.42)\": 29.34, \"(-5.42, -5.32)\": 28.19, \"(-5.32, -5.21)\": 27.1, \"(-5.21, -5.13)\": 26.08, \"(-5.13, -4.99)\": 24.98, \"(-4.99, -4.88)\": 23.88, \"(-4.88, -4.78)\": 22.76, \"(-4.78, -4.66)\": 21.64, \"(-4.66, -4.55)\": 20.58, \"(-4.55, -4.4)\": 19.56, \"(-4.4, -4.27)\": 18.08, \"(-4.27, -4.1)\": 17.07, \"(-4.1, -3.94)\": 15.39, \"(-3.94, -3.78)\": 14.35, \"(-3.78, -3.67)\": 13.2, \"(-3.67, -3.51)\": 12.18, \"(-3.51, -3.34)\": 11.09, \"(-3.34, -3.19)\": 10.02, \"(-3.19, -3.01)\": 8.96, \"(-3.01, -2.81)\": 7.91, \"(-2.81, -2.64)\": 6.89, \"(-2.64, -2.43)\": 5.88, \"(-2.43, -2.18)\": 4.83, \"(-2.18, -1.92)\": 3.8, \"(-1.92, -1.68)\": 2.8, \"(-1.68, -1.35)\": 1.78, \"(-1.35, -0.86)\": 0.78, \"(-0.86, 1.35)\": -0.23, \"(1.35, 1.69)\": 0.83, \"(1.69, 1.95)\": 1.89, \"(1.95, 2.22)\": 2.9, \"(2.22, 2.45)\": 3.93, \"(2.45, 2.64)\": 4.99, \"(2.64, 2.82)\": 6.0, \"(2.82, 3.01)\": 7.09, \"(3.01, 3.17)\": 8.1, \"(3.17, 3.33)\": 9.18, \"(3.33, 3.48)\": 10.2, \"(3.48, 3.63)\": 11.21, \"(3.63, 3.76)\": 12.21, \"(3.76, 3.9)\": 13.23, \"(3.9, 4.02)\": 14.25, \"(4.02, 4.14)\": 15.25, \"(4.14, 4.33)\": 16.51, \"(4.33, 4.43)\": 18.04, \"(4.43, 4.59)\": 19.11, \"(4.59, 4.72)\": 20.23, \"(4.72, 4.83)\": 21.31, \"(4.83, 4.94)\": 22.41, \"(4.94, 5.06)\": 23.55, \"(5.06, 5.21)\": 24.74, \"(5.21, 5.33)\": 26.5, \"(5.33, 5.44)\": 27.56, \"(5.44, 5.56)\": 28.81, \"(5.56, 5.7)\": 30.24, \"(5.7, 5.79)\": 31.47, \"(5.79, 5.9)\": 32.75, \"(5.9, 5.97)\": 33.84, \"(5.97, 6.06)\": 34.84, \"(6.06, 6.17)\": 35.97, \"(6.17, 6.26)\": 37.13, \"(6.26, 6.36)\": 38.33, \"(6.36, 6.44)\": 39.56, \"(6.44, 6.53)\": 40.65, \"(6.53, 6.64)\": 42.19, \"(6.64, 6.72)\": 43.31, \"(6.72, 6.79)\": 44.31, \"(6.79, 6.87)\": 45.37, \"(6.87, 6.95)\": 46.4, \"(6.95, 7.04)\": 47.51, \"(7.04, 7.12)\": 48.74, \"(7.12, 7.21)\": 49.81, \"(7.21, 7.3)\": 51.11, \"(7.3, 7.37)\": 52.36, \"(7.37, 7.46)\": 53.51, \"(7.46, 7.55)\": 54.83, \"(7.55, 7.64)\": 56.16, \"(7.64, 7.75)\": 57.7, \"(7.75, 7.82)\": 59.25, \"(7.82, 7.88)\": 60.47, \"(7.88, 7.98)\": 61.58, \"(7.98, 8.07)\": 62.96, \"(8.07, 8.13)\": 64.28, \"(8.13, 8.22)\": 65.68, \"(8.22, 8.3)\": 66.71, \"(8.3, 8.37)\": 68.12, \"(8.37, 8.43)\": 69.16, \"(8.43, 8.52)\": 70.63, \"(8.52, 8.59)\": 71.64, \"(8.59, 8.63)\": 72.97, \"(8.63, 8.72)\": 74.06, \"(8.72, 8.78)\": 75.16, \"(8.78, 8.82)\": 76.16, \"(8.82, 8.89)\": 77.2, \"(8.89, 8.96)\": 78.23, \"(8.96, 9.02)\": 79.31, \"(9.02, 9.07)\": 80.46, \"(9.07, 9.14)\": 81.5, \"(9.14, 9.2)\": 82.54, \"(9.2, 9.26)\": 84.28, \"(9.26, 9.34)\": 85.34, \"(9.34, 9.43)\": 86.69, \"(9.43, 9.51)\": 88.17, \"(9.51, 9.59)\": 89.86, \"(9.59, 9.66)\": 91.37, \"(9.66, 9.73)\": 92.47, \"(9.73, 9.81)\": 94.14, \"(9.81, 9.86)\": 95.26, \"(9.86, 9.92)\": 96.46, \"(9.92, 9.96)\": 97.73}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = (x-1)*(x+1)\nb) f(x) = sign(x-1)\nc) f(x) = -3*x^3\nd) f(x) = sign(x)\ne) f(x) = -sqrt(x+10)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "a)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.94, -9.85)\": 67.7, \"(-9.85, -9.78)\": 66.3, \"(-9.78, -9.67)\": 64.9, \"(-9.67, -9.56)\": 63.0, \"(-9.56, -9.45)\": 61.7, \"(-9.45, -9.35)\": 59.6, \"(-9.35, -9.26)\": 58.2, \"(-9.26, -9.17)\": 56.9, \"(-9.17, -9.07)\": 55.2, \"(-9.07, -8.96)\": 53.9, \"(-8.96, -8.86)\": 52.3, \"(-8.86, -8.79)\": 50.8, \"(-8.79, -8.66)\": 49.3, \"(-8.66, -8.56)\": 47.8, \"(-8.56, -8.46)\": 46.5, \"(-8.46, -8.35)\": 45.0, \"(-8.35, -8.26)\": 43.6, \"(-8.26, -8.14)\": 42.2, \"(-8.14, -8.02)\": 40.7, \"(-8.02, -7.91)\": 39.2, \"(-7.91, -7.81)\": 37.8, \"(-7.81, -7.7)\": 36.4, \"(-7.7, -7.59)\": 35.1, \"(-7.59, -7.47)\": 33.7, \"(-7.47, -7.36)\": 32.4, \"(-7.36, -7.24)\": 31.0, \"(-7.24, -7.1)\": 29.6, \"(-7.1, -6.95)\": 27.8, \"(-6.95, -6.81)\": 26.4, \"(-6.81, -6.68)\": 24.9, \"(-6.68, -6.55)\": 23.5, \"(-6.55, -6.4)\": 22.1, \"(-6.4, -6.23)\": 20.7, \"(-6.23, -6.05)\": 18.9, \"(-6.05, -5.9)\": 17.5, \"(-5.9, -5.74)\": 16.1, \"(-5.74, -5.59)\": 14.7, \"(-5.59, -5.41)\": 13.4, \"(-5.41, -5.22)\": 12.0, \"(-5.22, -5.03)\": 10.5, \"(-5.03, -4.81)\": 9.1, \"(-4.81, -4.6)\": 7.7, \"(-4.6, -4.36)\": 6.3, \"(-4.36, -4.11)\": 4.9, \"(-4.11, -3.81)\": 3.4, \"(-3.81, -3.51)\": 2.1, \"(-3.51, -3.11)\": 0.7, \"(-3.11, -2.62)\": -0.6, \"(-2.62, 0.11)\": -2.0, \"(0.11, 0.48)\": -0.6, \"(0.48, 0.78)\": 0.7, \"(0.78, 1.08)\": 2.1, \"(1.08, 1.33)\": 3.5, \"(1.33, 1.59)\": 4.9, \"(1.59, 1.8)\": 6.4, \"(1.8, 2.0)\": 7.7, \"(2.0, 2.18)\": 9.1, \"(2.18, 2.37)\": 10.4, \"(2.37, 2.55)\": 11.8, \"(2.55, 2.72)\": 13.2, \"(2.72, 2.89)\": 14.7, \"(2.89, 3.05)\": 16.2, \"(3.05, 3.2)\": 17.5, \"(3.2, 3.33)\": 18.9, \"(3.33, 3.49)\": 20.2, \"(3.49, 3.63)\": 21.7, \"(3.63, 3.74)\": 23.1, \"(3.74, 3.89)\": 24.4, \"(3.89, 4.06)\": 26.2, \"(4.06, 4.16)\": 27.5, \"(4.16, 4.29)\": 28.9, \"(4.29, 4.41)\": 30.3, \"(4.41, 4.53)\": 31.8, \"(4.53, 4.63)\": 33.1, \"(4.63, 4.77)\": 34.6, \"(4.77, 4.89)\": 36.0, \"(4.89, 4.97)\": 37.6, \"(4.97, 5.07)\": 38.9, \"(5.07, 5.19)\": 40.3, \"(5.19, 5.28)\": 41.7, \"(5.28, 5.42)\": 43.2, \"(5.42, 5.57)\": 45.3, \"(5.57, 5.65)\": 46.7, \"(5.65, 5.74)\": 48.0, \"(5.74, 5.83)\": 49.4, \"(5.83, 5.93)\": 50.8, \"(5.93, 6.04)\": 52.1, \"(6.04, 6.15)\": 53.8, \"(6.15, 6.26)\": 55.4, \"(6.26, 6.36)\": 57.4, \"(6.36, 6.46)\": 58.9, \"(6.46, 6.56)\": 60.3, \"(6.56, 6.65)\": 62.0, \"(6.65, 6.74)\": 63.3, \"(6.74, 6.82)\": 64.8, \"(6.82, 6.9)\": 66.1, \"(6.9, 7.01)\": 67.6, \"(7.01, 7.13)\": 69.7, \"(7.13, 7.24)\": 71.5, \"(7.24, 7.3)\": 73.1, \"(7.3, 7.39)\": 74.5, \"(7.39, 7.47)\": 76.0, \"(7.47, 7.57)\": 77.6, \"(7.57, 7.65)\": 79.2, \"(7.65, 7.72)\": 80.7, \"(7.72, 7.82)\": 82.2, \"(7.82, 7.9)\": 83.8, \"(7.9, 7.99)\": 85.6, \"(7.99, 8.07)\": 87.1, \"(8.07, 8.15)\": 88.5, \"(8.15, 8.28)\": 90.7, \"(8.28, 8.35)\": 92.5, \"(8.35, 8.44)\": 94.1, \"(8.44, 8.53)\": 96.2, \"(8.53, 8.61)\": 97.5, \"(8.61, 8.67)\": 99.3, \"(8.67, 8.75)\": 100.7, \"(8.75, 8.82)\": 102.0, \"(8.82, 8.92)\": 103.8, \"(8.92, 8.99)\": 105.6, \"(8.99, 9.06)\": 107.1, \"(9.06, 9.14)\": 108.6, \"(9.14, 9.19)\": 110.3, \"(9.19, 9.27)\": 111.7, \"(9.27, 9.37)\": 113.7, \"(9.37, 9.43)\": 115.2, \"(9.43, 9.52)\": 116.7, \"(9.52, 9.6)\": 118.6, \"(9.6, 9.66)\": 120.1, \"(9.66, 9.71)\": 121.4, \"(9.71, 9.77)\": 122.8, \"(9.77, 9.86)\": 124.2, \"(9.86, 9.96)\": 126.8, \"(9.96, 9.99)\": 128.5}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = x^3\nb) f(x) = 2^x\nc) f(x) = x^2+3*x-1\nd) f(x) = -2*x^2\ne) f(x) = -3*x^3\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "c)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.96, -9.87)\": -981.6, \"(-9.87, -9.79)\": -955.1, \"(-9.79, -9.69)\": -932.7, \"(-9.69, -9.57)\": -904.2, \"(-9.57, -9.46)\": -866.0, \"(-9.46, -9.38)\": -845.6, \"(-9.38, -9.3)\": -819.6, \"(-9.3, -9.2)\": -796.3, \"(-9.2, -9.11)\": -774.0, \"(-9.11, -9.01)\": -753.7, \"(-9.01, -8.91)\": -727.4, \"(-8.91, -8.82)\": -705.4, \"(-8.82, -8.73)\": -684.2, \"(-8.73, -8.63)\": -662.3, \"(-8.63, -8.53)\": -642.0, \"(-8.53, -8.41)\": -618.4, \"(-8.41, -8.29)\": -590.8, \"(-8.29, -8.19)\": -567.9, \"(-8.19, -8.08)\": -548.0, \"(-8.08, -7.96)\": -523.1, \"(-7.96, -7.86)\": -503.0, \"(-7.86, -7.75)\": -482.3, \"(-7.75, -7.63)\": -462.2, \"(-7.63, -7.5)\": -441.6, \"(-7.5, -7.36)\": -420.2, \"(-7.36, -7.23)\": -393.7, \"(-7.23, -7.05)\": -371.0, \"(-7.05, -6.93)\": -348.8, \"(-6.93, -6.77)\": -328.8, \"(-6.77, -6.62)\": -309.0, \"(-6.62, -6.47)\": -288.8, \"(-6.47, -6.28)\": -268.8, \"(-6.28, -6.11)\": -247.8, \"(-6.11, -5.89)\": -226.5, \"(-5.89, -5.68)\": -203.7, \"(-5.68, -5.44)\": -183.1, \"(-5.44, -5.23)\": -162.3, \"(-5.23, -4.98)\": -141.7, \"(-4.98, -4.66)\": -121.6, \"(-4.66, -4.39)\": -100.7, \"(-4.39, -3.95)\": -80.5, \"(-3.95, -3.46)\": -60.5, \"(-3.46, -2.73)\": -40.7, \"(-2.73, -0.89)\": -20.4, \"(-0.89, 2.65)\": -0.7, \"(2.65, 3.4)\": 19.2, \"(3.4, 3.88)\": 39.1, \"(3.88, 4.26)\": 59.0, \"(4.26, 4.62)\": 79.1, \"(4.62, 4.95)\": 101.5, \"(4.95, 5.24)\": 124.1, \"(5.24, 5.48)\": 144.9, \"(5.48, 5.72)\": 166.2, \"(5.72, 5.91)\": 188.2, \"(5.91, 6.11)\": 208.0, \"(6.11, 6.29)\": 228.8, \"(6.29, 6.46)\": 249.7, \"(6.46, 6.62)\": 273.2, \"(6.62, 6.8)\": 293.0, \"(6.8, 6.95)\": 316.3, \"(6.95, 7.06)\": 336.2, \"(7.06, 7.22)\": 356.6, \"(7.22, 7.37)\": 378.8, \"(7.37, 7.46)\": 398.8, \"(7.46, 7.6)\": 418.8, \"(7.6, 7.72)\": 440.3, \"(7.72, 7.83)\": 461.9, \"(7.83, 7.95)\": 482.2, \"(7.95, 8.09)\": 510.1, \"(8.09, 8.2)\": 531.8, \"(8.2, 8.3)\": 553.7, \"(8.3, 8.41)\": 574.4, \"(8.41, 8.5)\": 594.9, \"(8.5, 8.62)\": 617.7, \"(8.62, 8.72)\": 642.2, \"(8.72, 8.81)\": 665.0, \"(8.81, 8.9)\": 689.1, \"(8.9, 9.0)\": 708.9, \"(9.0, 9.08)\": 728.8, \"(9.08, 9.17)\": 753.1, \"(9.17, 9.26)\": 778.1, \"(9.26, 9.36)\": 797.9, \"(9.36, 9.45)\": 824.2, \"(9.45, 9.55)\": 848.0, \"(9.55, 9.65)\": 878.4, \"(9.65, 9.7)\": 900.2, \"(9.7, 9.8)\": 921.8, \"(9.8, 9.89)\": 943.0, \"(9.89, 9.97)\": 972.2}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = -x^5\nb) f(x) = -sinh(x)\nc) f(x) = x\nd) f(x) = x^3\ne) f(x) = exp(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "d)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.97, -9.89)\": 2958.3, \"(-9.89, -9.81)\": 2897.4, \"(-9.81, -9.68)\": 2812.0, \"(-9.68, -9.56)\": 2682.0, \"(-9.56, -9.47)\": 2614.1, \"(-9.47, -9.38)\": 2537.9, \"(-9.38, -9.29)\": 2468.1, \"(-9.29, -9.2)\": 2393.1, \"(-9.2, -9.1)\": 2330.5, \"(-9.1, -9.0)\": 2246.2, \"(-9.0, -8.91)\": 2181.9, \"(-8.91, -8.82)\": 2120.3, \"(-8.82, -8.73)\": 2055.4, \"(-8.73, -8.62)\": 1989.1, \"(-8.62, -8.51)\": 1908.4, \"(-8.51, -8.4)\": 1848.1, \"(-8.4, -8.3)\": 1768.3, \"(-8.3, -8.2)\": 1703.2, \"(-8.2, -8.08)\": 1642.7, \"(-8.08, -7.97)\": 1575.7, \"(-7.97, -7.86)\": 1511.5, \"(-7.86, -7.73)\": 1449.0, \"(-7.73, -7.62)\": 1385.1, \"(-7.62, -7.49)\": 1322.4, \"(-7.49, -7.38)\": 1260.6, \"(-7.38, -7.23)\": 1200.2, \"(-7.23, -7.12)\": 1133.8, \"(-7.12, -6.97)\": 1073.0, \"(-6.97, -6.82)\": 1012.6, \"(-6.82, -6.67)\": 950.1, \"(-6.67, -6.51)\": 884.4, \"(-6.51, -6.35)\": 824.5, \"(-6.35, -6.17)\": 764.7, \"(-6.17, -5.98)\": 702.6, \"(-5.98, -5.77)\": 639.3, \"(-5.77, -5.54)\": 574.1, \"(-5.54, -5.3)\": 507.0, \"(-5.3, -5.02)\": 442.9, \"(-5.02, -4.74)\": 377.2, \"(-4.74, -4.39)\": 316.1, \"(-4.39, -4.05)\": 255.3, \"(-4.05, -3.55)\": 194.5, \"(-3.55, -2.86)\": 130.4, \"(-2.86, -1.5)\": 70.2, \"(-1.5, 2.49)\": 9.8, \"(2.49, 3.34)\": -51.9, \"(3.34, 3.83)\": -113.0, \"(3.83, 4.27)\": -173.2, \"(4.27, 4.63)\": -242.4, \"(4.63, 4.96)\": -303.0, \"(4.96, 5.23)\": -370.0, \"(5.23, 5.5)\": -431.9, \"(5.5, 5.75)\": -511.6, \"(5.75, 5.96)\": -574.8, \"(5.96, 6.17)\": -641.3, \"(6.17, 6.3)\": -702.0, \"(6.3, 6.49)\": -763.0, \"(6.49, 6.64)\": -827.7, \"(6.64, 6.84)\": -888.6, \"(6.84, 6.96)\": -956.7, \"(6.96, 7.11)\": -1018.6, \"(7.11, 7.26)\": -1082.9, \"(7.26, 7.4)\": -1158.2, \"(7.4, 7.53)\": -1219.9, \"(7.53, 7.65)\": -1286.5, \"(7.65, 7.78)\": -1349.6, \"(7.78, 7.91)\": -1429.7, \"(7.91, 8.01)\": -1491.5, \"(8.01, 8.16)\": -1579.4, \"(8.16, 8.28)\": -1640.6, \"(8.28, 8.38)\": -1703.9, \"(8.38, 8.48)\": -1766.3, \"(8.48, 8.57)\": -1832.3, \"(8.57, 8.65)\": -1894.9, \"(8.65, 8.75)\": -1958.6, \"(8.75, 8.87)\": -2021.5, \"(8.87, 8.97)\": -2108.5, \"(8.97, 9.05)\": -2172.1, \"(9.05, 9.13)\": -2237.2, \"(9.13, 9.25)\": -2314.5, \"(9.25, 9.32)\": -2383.5, \"(9.32, 9.41)\": -2450.4, \"(9.41, 9.49)\": -2510.6, \"(9.49, 9.58)\": -2579.4, \"(9.58, 9.64)\": -2642.5, \"(9.64, 9.74)\": -2709.6, \"(9.74, 9.82)\": -2777.4, \"(9.82, 9.92)\": -2841.5, \"(9.92, 10.0)\": -2946.7}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = -3*x^3\nb) f(x) = -sign(-x)\nc) f(x) = |x|\nd) f(x) = 1/2*cos(x-2)\ne) f(x) = x\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "a)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.99, -9.96)\": 9925.4, \"(-9.96, -9.93)\": 9796.5, \"(-9.93, -9.9)\": 9661.4, \"(-9.9, -9.87)\": 9554.9, \"(-9.87, -9.84)\": 9418.4, \"(-9.84, -9.76)\": 9288.8, \"(-9.76, -9.7)\": 8939.3, \"(-9.7, -9.64)\": 8838.8, \"(-9.64, -9.58)\": 8517.3, \"(-9.58, -9.52)\": 8394.2, \"(-9.52, -9.47)\": 8158.8, \"(-9.47, -9.4)\": 7950.1, \"(-9.4, -9.35)\": 7749.0, \"(-9.35, -9.31)\": 7601.3, \"(-9.31, -9.27)\": 7473.3, \"(-9.27, -9.24)\": 7343.3, \"(-9.24, -9.16)\": 7209.1, \"(-9.16, -9.09)\": 6937.9, \"(-9.09, -9.05)\": 6809.9, \"(-9.05, -9.01)\": 6682.3, \"(-9.01, -8.96)\": 6567.6, \"(-8.96, -8.91)\": 6439.4, \"(-8.91, -8.87)\": 6272.3, \"(-8.87, -8.82)\": 6151.3, \"(-8.82, -8.78)\": 6018.8, \"(-8.78, -8.72)\": 5918.4, \"(-8.72, -8.65)\": 5727.7, \"(-8.65, -8.6)\": 5568.2, \"(-8.6, -8.54)\": 5452.7, \"(-8.54, -8.49)\": 5307.5, \"(-8.49, -8.45)\": 5201.6, \"(-8.45, -8.4)\": 5061.1, \"(-8.4, -8.36)\": 4937.8, \"(-8.36, -8.29)\": 4834.7, \"(-8.29, -8.23)\": 4689.8, \"(-8.23, -8.17)\": 4539.8, \"(-8.17, -8.13)\": 4433.5, \"(-8.13, -8.05)\": 4290.6, \"(-8.05, -8.0)\": 4179.2, \"(-8.0, -7.94)\": 4070.6, \"(-7.94, -7.87)\": 3950.9, \"(-7.87, -7.82)\": 3832.5, \"(-7.82, -7.76)\": 3714.3, \"(-7.76, -7.69)\": 3608.4, \"(-7.69, -7.64)\": 3476.8, \"(-7.64, -7.55)\": 3372.5, \"(-7.55, -7.49)\": 3240.1, \"(-7.49, -7.4)\": 3114.9, \"(-7.4, -7.32)\": 2986.2, \"(-7.32, -7.26)\": 2873.7, \"(-7.26, -7.19)\": 2745.2, \"(-7.19, -7.12)\": 2636.4, \"(-7.12, -7.03)\": 2518.3, \"(-7.03, -6.92)\": 2412.6, \"(-6.92, -6.83)\": 2281.7, \"(-6.83, -6.73)\": 2140.2, \"(-6.73, -6.64)\": 2038.3, \"(-6.64, -6.51)\": 1923.8, \"(-6.51, -6.4)\": 1777.9, \"(-6.4, -6.27)\": 1661.1, \"(-6.27, -6.1)\": 1499.1, \"(-6.1, -6.0)\": 1381.6, \"(-6.0, -5.84)\": 1271.8, \"(-5.84, -5.7)\": 1158.4, \"(-5.7, -5.55)\": 1055.8, \"(-5.55, -5.38)\": 937.1, \"(-5.38, -5.2)\": 828.2, \"(-5.2, -5.0)\": 723.0, \"(-5.0, -4.78)\": 622.8, \"(-4.78, -4.51)\": 517.8, \"(-4.51, -4.23)\": 416.4, \"(-4.23, -3.86)\": 312.5, \"(-3.86, -3.27)\": 211.5, \"(-3.27, -1.87)\": 111.0, \"(-1.87, 3.23)\": 11.3, \"(3.23, 3.81)\": 111.0, \"(3.81, 4.19)\": 210.8, \"(4.19, 4.47)\": 313.0, \"(4.47, 4.76)\": 412.7, \"(4.76, 5.0)\": 526.3, \"(5.0, 5.21)\": 628.6, \"(5.21, 5.39)\": 741.5, \"(5.39, 5.56)\": 855.7, \"(5.56, 5.71)\": 966.2, \"(5.71, 5.87)\": 1080.9, \"(5.87, 5.98)\": 1194.9, \"(5.98, 6.11)\": 1296.9, \"(6.11, 6.22)\": 1396.9, \"(6.22, 6.35)\": 1500.1, \"(6.35, 6.47)\": 1646.1, \"(6.47, 6.56)\": 1750.7, \"(6.56, 6.69)\": 1915.2, \"(6.69, 6.76)\": 2024.1, \"(6.76, 6.86)\": 2128.4, \"(6.86, 6.93)\": 2230.8, \"(6.93, 7.04)\": 2336.2, \"(7.04, 7.12)\": 2474.0, \"(7.12, 7.19)\": 2586.1, \"(7.19, 7.27)\": 2691.0, \"(7.27, 7.33)\": 2792.1, \"(7.33, 7.4)\": 2899.9, \"(7.4, 7.47)\": 3006.4, \"(7.47, 7.52)\": 3106.3, \"(7.52, 7.58)\": 3209.3, \"(7.58, 7.66)\": 3325.2, \"(7.66, 7.71)\": 3443.2, \"(7.71, 7.79)\": 3554.1, \"(7.79, 7.86)\": 3717.2, \"(7.86, 7.91)\": 3824.6, \"(7.91, 7.97)\": 3924.9, \"(7.97, 8.05)\": 4041.8, \"(8.05, 8.14)\": 4277.8, \"(8.14, 8.2)\": 4406.8, \"(8.2, 8.25)\": 4519.3, \"(8.25, 8.3)\": 4648.7, \"(8.3, 8.36)\": 4783.7, \"(8.36, 8.41)\": 4889.3, \"(8.41, 8.47)\": 5066.2, \"(8.47, 8.53)\": 5208.2, \"(8.53, 8.6)\": 5321.3, \"(8.6, 8.67)\": 5533.3, \"(8.67, 8.72)\": 5675.8, \"(8.72, 8.77)\": 5787.3, \"(8.77, 8.81)\": 5946.8, \"(8.81, 8.85)\": 6055.1, \"(8.85, 8.88)\": 6163.1, \"(8.88, 8.92)\": 6268.4, \"(8.92, 9.01)\": 6460.5, \"(9.01, 9.1)\": 6730.9, \"(9.1, 9.13)\": 6901.6, \"(9.13, 9.19)\": 7004.2, \"(9.19, 9.25)\": 7189.6, \"(9.25, 9.31)\": 7384.8, \"(9.31, 9.36)\": 7605.4, \"(9.36, 9.44)\": 7732.9, \"(9.44, 9.53)\": 8100.3, \"(9.53, 9.57)\": 8299.2, \"(9.57, 9.6)\": 8408.9, \"(9.6, 9.64)\": 8511.4, \"(9.64, 9.71)\": 8735.4, \"(9.71, 9.75)\": 8946.3, \"(9.75, 9.8)\": 9104.9, \"(9.8, 9.84)\": 9250.9, \"(9.84, 9.88)\": 9427.2, \"(9.88, 9.91)\": 9555.6, \"(9.91, 9.93)\": 9676.0}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = x^4\nb) f(x) = cos(x)\nc) f(x) = exp(x)\nd) f(x) = x\ne) f(x) = x^3\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "a)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-10.0, -9.46)\": 1274.2, \"(-9.46, -8.75)\": 886.7, \"(-8.75, -7.28)\": 497.8, \"(-7.28, 0.69)\": 111.5, \"(0.69, 1.45)\": 492.6, \"(1.45, 1.97)\": 885.5, \"(1.97, 2.35)\": 1270.5, \"(2.35, 2.71)\": 1652.0, \"(2.71, 3.05)\": 2053.5, \"(3.05, 3.29)\": 2485.8, \"(3.29, 3.56)\": 2876.7, \"(3.56, 3.77)\": 3263.2, \"(3.77, 3.98)\": 3674.7, \"(3.98, 4.16)\": 4070.3, \"(4.16, 4.34)\": 4463.6, \"(4.34, 4.54)\": 4887.1, \"(4.54, 4.67)\": 5300.3, \"(4.67, 4.82)\": 5691.3, \"(4.82, 4.98)\": 6079.9, \"(4.98, 5.11)\": 6499.4, \"(5.11, 5.21)\": 6898.9, \"(5.21, 5.38)\": 7293.8, \"(5.38, 5.56)\": 7946.6, \"(5.56, 5.65)\": 8361.8, \"(5.65, 5.78)\": 8749.0, \"(5.78, 5.89)\": 9184.1, \"(5.89, 5.98)\": 9633.8, \"(5.98, 6.1)\": 10024.0, \"(6.1, 6.2)\": 10461.3, \"(6.2, 6.32)\": 10919.6, \"(6.32, 6.44)\": 11527.8, \"(6.44, 6.54)\": 11952.1, \"(6.54, 6.64)\": 12413.7, \"(6.64, 6.72)\": 12900.0, \"(6.72, 6.79)\": 13284.0, \"(6.79, 6.89)\": 13742.0, \"(6.89, 6.98)\": 14148.6, \"(6.98, 7.08)\": 14617.6, \"(7.08, 7.17)\": 15182.3, \"(7.17, 7.24)\": 15610.5, \"(7.24, 7.31)\": 16008.4, \"(7.31, 7.39)\": 16442.2, \"(7.39, 7.46)\": 16860.6, \"(7.46, 7.53)\": 17308.5, \"(7.53, 7.59)\": 17854.2, \"(7.59, 7.69)\": 18245.9, \"(7.69, 7.76)\": 18739.1, \"(7.76, 7.85)\": 19394.4, \"(7.85, 7.92)\": 19808.5, \"(7.92, 8.02)\": 20237.5, \"(8.02, 8.12)\": 21073.7, \"(8.12, 8.18)\": 21752.6, \"(8.18, 8.26)\": 22200.1, \"(8.26, 8.33)\": 22682.2, \"(8.33, 8.37)\": 23133.9, \"(8.37, 8.41)\": 23516.2, \"(8.41, 8.49)\": 23945.3, \"(8.49, 8.54)\": 24356.6, \"(8.54, 8.6)\": 24814.4, \"(8.6, 8.65)\": 25251.2, \"(8.65, 8.72)\": 25658.7, \"(8.72, 8.77)\": 26235.9, \"(8.77, 8.85)\": 26774.0, \"(8.85, 8.92)\": 27450.7, \"(8.92, 8.98)\": 27930.6, \"(8.98, 9.06)\": 28553.9, \"(9.06, 9.12)\": 29333.3, \"(9.12, 9.17)\": 29758.9, \"(9.17, 9.21)\": 30151.3, \"(9.21, 9.25)\": 30544.4, \"(9.25, 9.32)\": 31134.0, \"(9.32, 9.39)\": 31752.0, \"(9.39, 9.45)\": 32191.1, \"(9.45, 9.51)\": 32826.5, \"(9.51, 9.58)\": 33580.9, \"(9.58, 9.64)\": 34169.5, \"(9.64, 9.69)\": 34833.3, \"(9.69, 9.76)\": 35471.4, \"(9.76, 9.82)\": 36008.6, \"(9.82, 9.86)\": 36565.4, \"(9.86, 9.92)\": 37131.8, \"(9.92, 9.98)\": 37787.8}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = sin(x)\nb) f(x) = -sin(x)\nc) f(x) = -exp(x)\nd) f(x) = -|x|\ne) f(x) = -(x + 4)^4\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "e)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-10.0, -9.94)\": -99557.1, \"(-9.94, -9.87)\": -96593.9, \"(-9.87, -9.8)\": -92323.8, \"(-9.8, -9.75)\": -90239.8, \"(-9.75, -9.69)\": -87419.5, \"(-9.69, -9.66)\": -85244.4, \"(-9.66, -9.59)\": -82995.3, \"(-9.59, -9.53)\": -79941.3, \"(-9.53, -9.47)\": -77943.9, \"(-9.47, -9.42)\": -75872.4, \"(-9.42, -9.34)\": -73673.1, \"(-9.34, -9.27)\": -70761.4, \"(-9.27, -9.2)\": -67965.4, \"(-9.2, -9.13)\": -65606.9, \"(-9.13, -9.08)\": -63123.1, \"(-9.08, -8.99)\": -61011.9, \"(-8.99, -8.92)\": -58173.1, \"(-8.92, -8.85)\": -56091.4, \"(-8.85, -8.79)\": -54078.6, \"(-8.79, -8.69)\": -51785.0, \"(-8.69, -8.64)\": -49625.6, \"(-8.64, -8.54)\": -47329.4, \"(-8.54, -8.47)\": -45334.5, \"(-8.47, -8.37)\": -43209.7, \"(-8.37, -8.27)\": -40830.7, \"(-8.27, -8.21)\": -38709.7, \"(-8.21, -8.11)\": -36509.5, \"(-8.11, -7.98)\": -34502.1, \"(-7.98, -7.86)\": -31966.0, \"(-7.86, -7.74)\": -29902.9, \"(-7.74, -7.62)\": -27722.2, \"(-7.62, -7.49)\": -25582.8, \"(-7.49, -7.35)\": -23520.0, \"(-7.35, -7.22)\": -21271.2, \"(-7.22, -7.06)\": -19262.1, \"(-7.06, -6.85)\": -17069.6, \"(-6.85, -6.62)\": -14927.7, \"(-6.62, -6.36)\": -12436.0, \"(-6.36, -6.1)\": -10319.6, \"(-6.1, -5.75)\": -8248.6, \"(-5.75, -5.29)\": -6131.1, \"(-5.29, -4.62)\": -4096.1, \"(-4.62, -2.29)\": -2062.3, \"(-2.29, 4.53)\": -62.6, \"(4.53, 5.25)\": 1949.1, \"(5.25, 5.69)\": 3975.1, \"(5.69, 6.05)\": 6004.5, \"(6.05, 6.32)\": 8199.9, \"(6.32, 6.57)\": 10215.0, \"(6.57, 6.78)\": 12239.7, \"(6.78, 6.94)\": 14276.3, \"(6.94, 7.1)\": 16300.8, \"(7.1, 7.29)\": 18314.3, \"(7.29, 7.41)\": 20523.8, \"(7.41, 7.56)\": 22578.8, \"(7.56, 7.71)\": 25162.1, \"(7.71, 7.82)\": 27395.1, \"(7.82, 7.94)\": 29501.6, \"(7.94, 8.05)\": 31955.0, \"(8.05, 8.18)\": 33949.1, \"(8.18, 8.32)\": 37824.2, \"(8.32, 8.42)\": 40198.4, \"(8.42, 8.48)\": 42348.1, \"(8.48, 8.57)\": 44454.7, \"(8.57, 8.64)\": 46718.3, \"(8.64, 8.74)\": 48729.4, \"(8.74, 8.81)\": 51162.2, \"(8.81, 8.88)\": 53381.5, \"(8.88, 8.95)\": 55474.6, \"(8.95, 9.02)\": 57580.5, \"(9.02, 9.09)\": 59897.9, \"(9.09, 9.16)\": 62683.8, \"(9.16, 9.23)\": 64958.2, \"(9.23, 9.29)\": 67087.1, \"(9.29, 9.35)\": 69474.3, \"(9.35, 9.41)\": 71754.7, \"(9.41, 9.45)\": 74257.3, \"(9.45, 9.53)\": 76720.0, \"(9.53, 9.57)\": 79235.1, \"(9.57, 9.68)\": 81360.8, \"(9.68, 9.76)\": 86671.1, \"(9.76, 9.8)\": 88741.6, \"(9.8, 9.88)\": 91245.8, \"(9.88, 9.94)\": 95023.7, \"(9.94, 9.99)\": 97534.0}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = sqrt(x+10)\nb) f(x) = sqrt(x ** 2 + 3*x +5)\nc) f(x) = -sign(-x)\nd) f(x) = x^5\ne) f(x) = x\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "d)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.98, -9.92)\": 97596.5, \"(-9.92, -9.87)\": 95386.4, \"(-9.87, -9.82)\": 92906.9, \"(-9.82, -9.77)\": 90789.5, \"(-9.77, -9.73)\": 88739.1, \"(-9.73, -9.67)\": 86372.3, \"(-9.67, -9.61)\": 83417.2, \"(-9.61, -9.55)\": 81399.7, \"(-9.55, -9.47)\": 78651.7, \"(-9.47, -9.43)\": 76343.1, \"(-9.43, -9.37)\": 74281.1, \"(-9.37, -9.31)\": 71960.0, \"(-9.31, -9.25)\": 69716.9, \"(-9.25, -9.18)\": 67306.3, \"(-9.18, -9.11)\": 64721.0, \"(-9.11, -9.05)\": 62448.9, \"(-9.05, -8.98)\": 60066.7, \"(-8.98, -8.84)\": 56569.9, \"(-8.84, -8.77)\": 53375.2, \"(-8.77, -8.67)\": 51396.9, \"(-8.67, -8.6)\": 48788.7, \"(-8.6, -8.52)\": 46641.7, \"(-8.52, -8.44)\": 44610.9, \"(-8.44, -8.4)\": 42620.4, \"(-8.4, -8.27)\": 40658.6, \"(-8.27, -8.18)\": 38372.2, \"(-8.18, -8.07)\": 36396.0, \"(-8.07, -7.97)\": 33978.4, \"(-7.97, -7.86)\": 31832.1, \"(-7.86, -7.74)\": 29861.4, \"(-7.74, -7.61)\": 27695.1, \"(-7.61, -7.44)\": 24874.1, \"(-7.44, -7.31)\": 22722.9, \"(-7.31, -7.14)\": 20565.6, \"(-7.14, -6.95)\": 18333.0, \"(-6.95, -6.74)\": 15934.8, \"(-6.74, -6.54)\": 13902.4, \"(-6.54, -6.31)\": 11943.2, \"(-6.31, -6.02)\": 9987.1, \"(-6.02, -5.7)\": 7940.7, \"(-5.7, -5.23)\": 5925.0, \"(-5.23, -4.53)\": 3864.7, \"(-4.53, 2.3)\": 1883.6, \"(2.3, 4.57)\": -76.3, \"(4.57, 5.27)\": -2037.1, \"(5.27, 5.71)\": -4056.9, \"(5.71, 6.04)\": -6043.2, \"(6.04, 6.34)\": -8145.1, \"(6.34, 6.56)\": -10131.9, \"(6.56, 6.76)\": -12232.2, \"(6.76, 6.97)\": -14359.1, \"(6.97, 7.14)\": -16496.5, \"(7.14, 7.29)\": -18634.4, \"(7.29, 7.44)\": -20792.0, \"(7.44, 7.56)\": -22972.9, \"(7.56, 7.7)\": -24955.7, \"(7.7, 7.81)\": -27051.9, \"(7.81, 7.92)\": -29257.6, \"(7.92, 8.03)\": -31248.9, \"(8.03, 8.11)\": -33285.0, \"(8.11, 8.22)\": -35417.7, \"(8.22, 8.32)\": -37847.2, \"(8.32, 8.41)\": -39947.8, \"(8.41, 8.51)\": -42702.9, \"(8.51, 8.59)\": -44771.4, \"(8.59, 8.66)\": -46756.9, \"(8.66, 8.73)\": -48792.3, \"(8.73, 8.8)\": -51013.6, \"(8.8, 8.88)\": -53144.3, \"(8.88, 8.96)\": -55753.0, \"(8.96, 9.03)\": -57851.1, \"(9.03, 9.09)\": -60222.0, \"(9.09, 9.16)\": -62299.4, \"(9.16, 9.23)\": -64844.5, \"(9.23, 9.29)\": -67171.3, \"(9.29, 9.37)\": -69567.5, \"(9.37, 9.42)\": -73029.5, \"(9.42, 9.5)\": -75308.4, \"(9.5, 9.58)\": -79080.1, \"(9.58, 9.64)\": -81046.9, \"(9.64, 9.7)\": -83741.1, \"(9.7, 9.74)\": -85870.7, \"(9.74, 9.81)\": -88266.1, \"(9.81, 9.86)\": -91495.1, \"(9.86, 9.93)\": -94432.6, \"(9.93, 9.97)\": -97692.9}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = -x^5\nb) f(x) = 2^(x-5)\nc) f(x) = -sqrt(x+10)\nd) f(x) = sqrt(x+10)\ne) f(x) = tanh(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "a)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.98, -0.1)\": -1.003, \"(-0.1, -0.01)\": -0.981, \"(-0.01, -0.0)\": -0.903, \"(-0.0, 0.01)\": 0.828, \"(0.01, 0.02)\": 0.938, \"(0.02, 0.55)\": 0.979, \"(0.55, 9.97)\": 0.999}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = sign(x)\nb) f(x) = x^2+3*x-1\nc) f(x) = x\nd) f(x) = |2*x+4|\ne) f(x) = x^2\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "a)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.99, -0.24)\": 1.004, \"(-0.24, -0.02)\": 0.984, \"(-0.02, -0.0)\": 0.924, \"(-0.0, 0.02)\": -0.664, \"(0.02, 0.3)\": -0.974, \"(0.3, 9.97)\": -0.996}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = x^3\nb) f(x) = sign(x)\nc) f(x) = -sign(x)\nd) f(x) = -3*x^3\ne) f(x) = x^5\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "c)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.96, -0.21)\": -1.003, \"(-0.21, -0.13)\": -0.982, \"(-0.13, -0.06)\": -0.929, \"(-0.06, -0.0)\": -0.883, \"(-0.0, 0.03)\": 0.704, \"(0.03, 0.34)\": 0.978, \"(0.34, 9.99)\": 0.998}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = x^3\nb) f(x) = sin(x+2)+2\nc) f(x) = -sign(-x)\nd) f(x) = sin(x)\ne) f(x) = -cosh(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "c)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.96, -3.18)\": -1.007, \"(-3.18, -3.09)\": -0.986, \"(-3.09, -3.05)\": -0.941, \"(-3.05, -2.99)\": -0.839, \"(-2.99, -2.56)\": 0.969, \"(-2.56, 9.97)\": 0.99}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = 2^(x-5)\nb) f(x) = sign(x+3)\nc) f(x) = x\nd) f(x) = |x|\ne) f(x) = -(x + 4)^4\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "b)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-10.0, 0.81)\": -1.002, \"(0.81, 0.97)\": -0.981, \"(0.97, 0.98)\": -0.928, \"(0.98, 1.0)\": -0.816, \"(1.0, 1.01)\": 0.877, \"(1.01, 1.01)\": 0.914, \"(1.01, 1.04)\": 0.944, \"(1.04, 1.4)\": 0.98, \"(1.4, 9.88)\": 1.001}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = -|-x|\nb) f(x) = sign(x-1)\nc) f(x) = exp(-x)\nd) f(x) = -(x + 4)^4\ne) f(x) = 2^(x-5)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "b)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.98, -9.87)\": 9.962, \"(-9.87, -9.75)\": 9.851, \"(-9.75, -9.63)\": 9.743, \"(-9.63, -9.5)\": 9.604, \"(-9.5, -9.38)\": 9.485, \"(-9.38, -9.26)\": 9.362, \"(-9.26, -9.16)\": 9.262, \"(-9.16, -9.02)\": 9.122, \"(-9.02, -8.91)\": 9.01, \"(-8.91, -8.79)\": 8.905, \"(-8.79, -8.66)\": 8.773, \"(-8.66, -8.55)\": 8.655, \"(-8.55, -8.45)\": 8.54, \"(-8.45, -8.34)\": 8.439, \"(-8.34, -8.2)\": 8.319, \"(-8.2, -8.11)\": 8.204, \"(-8.11, -8.0)\": 8.1, \"(-8.0, -7.89)\": 7.986, \"(-7.89, -7.77)\": 7.881, \"(-7.77, -7.66)\": 7.768, \"(-7.66, -7.54)\": 7.645, \"(-7.54, -7.43)\": 7.538, \"(-7.43, -7.34)\": 7.424, \"(-7.34, -7.22)\": 7.322, \"(-7.22, -7.11)\": 7.207, \"(-7.11, -7.01)\": 7.103, \"(-7.01, -6.89)\": 6.987, \"(-6.89, -6.75)\": 6.856, \"(-6.75, -6.66)\": 6.727, \"(-6.66, -6.52)\": 6.618, \"(-6.52, -6.42)\": 6.511, \"(-6.42, -6.3)\": 6.406, \"(-6.3, -6.18)\": 6.284, \"(-6.18, -6.06)\": 6.168, \"(-6.06, -5.94)\": 6.037, \"(-5.94, -5.81)\": 5.919, \"(-5.81, -5.7)\": 5.806, \"(-5.7, -5.58)\": 5.661, \"(-5.58, -5.47)\": 5.561, \"(-5.47, -5.35)\": 5.456, \"(-5.35, -5.25)\": 5.345, \"(-5.25, -5.14)\": 5.226, \"(-5.14, -5.01)\": 5.122, \"(-5.01, -4.92)\": 5.013, \"(-4.92, -4.81)\": 4.897, \"(-4.81, -4.7)\": 4.792, \"(-4.7, -4.58)\": 4.684, \"(-4.58, -4.46)\": 4.555, \"(-4.46, -4.33)\": 4.434, \"(-4.33, -4.2)\": 4.318, \"(-4.2, -4.07)\": 4.17, \"(-4.07, -3.97)\": 4.058, \"(-3.97, -3.85)\": 3.956, \"(-3.85, -3.72)\": 3.826, \"(-3.72, -3.6)\": 3.702, \"(-3.6, -3.5)\": 3.6, \"(-3.5, -3.39)\": 3.492, \"(-3.39, -3.32)\": 3.393, \"(-3.32, -3.2)\": 3.289, \"(-3.2, -3.07)\": 3.167, \"(-3.07, -2.95)\": 3.056, \"(-2.95, -2.85)\": 2.945, \"(-2.85, -2.74)\": 2.824, \"(-2.74, -2.64)\": 2.717, \"(-2.64, -2.5)\": 2.602, \"(-2.5, -2.4)\": 2.493, \"(-2.4, -2.29)\": 2.388, \"(-2.29, -2.19)\": 2.288, \"(-2.19, -2.02)\": 2.161, \"(-2.02, -1.89)\": 1.976, \"(-1.89, -1.77)\": 1.858, \"(-1.77, -1.61)\": 1.719, \"(-1.61, -1.52)\": 1.611, \"(-1.52, -1.39)\": 1.503, \"(-1.39, -1.28)\": 1.385, \"(-1.28, -1.15)\": 1.272, \"(-1.15, -1.02)\": 1.126, \"(-1.02, -0.92)\": 1.008, \"(-0.92, -0.79)\": 0.89, \"(-0.79, -0.66)\": 0.767, \"(-0.66, -0.54)\": 0.643, \"(-0.54, -0.43)\": 0.533, \"(-0.43, -0.3)\": 0.412, \"(-0.3, -0.17)\": 0.287, \"(-0.17, -0.06)\": 0.151, \"(-0.06, 0.15)\": 0.048, \"(0.15, 0.26)\": 0.161, \"(0.26, 0.39)\": 0.266, \"(0.39, 0.51)\": 0.403, \"(0.51, 0.63)\": 0.524, \"(0.63, 0.75)\": 0.654, \"(0.75, 0.86)\": 0.757, \"(0.86, 0.96)\": 0.857, \"(0.96, 1.07)\": 0.974, \"(1.07, 1.16)\": 1.079, \"(1.16, 1.29)\": 1.193, \"(1.29, 1.4)\": 1.316, \"(1.4, 1.53)\": 1.416, \"(1.53, 1.65)\": 1.541, \"(1.65, 1.78)\": 1.672, \"(1.78, 1.9)\": 1.793, \"(1.9, 2.0)\": 1.899, \"(2.0, 2.12)\": 2.006, \"(2.12, 2.25)\": 2.149, \"(2.25, 2.37)\": 2.26, \"(2.37, 2.47)\": 2.366, \"(2.47, 2.6)\": 2.492, \"(2.6, 2.71)\": 2.609, \"(2.71, 2.82)\": 2.717, \"(2.82, 2.91)\": 2.823, \"(2.91, 3.03)\": 2.933, \"(3.03, 3.14)\": 3.055, \"(3.14, 3.29)\": 3.166, \"(3.29, 3.42)\": 3.304, \"(3.42, 3.53)\": 3.431, \"(3.53, 3.64)\": 3.541, \"(3.64, 3.74)\": 3.643, \"(3.74, 3.86)\": 3.753, \"(3.86, 3.99)\": 3.888, \"(3.99, 4.09)\": 3.993, \"(4.09, 4.21)\": 4.097, \"(4.21, 4.3)\": 4.215, \"(4.3, 4.41)\": 4.319, \"(4.41, 4.53)\": 4.427, \"(4.53, 4.63)\": 4.537, \"(4.63, 4.74)\": 4.644, \"(4.74, 4.86)\": 4.756, \"(4.86, 5.01)\": 4.895, \"(5.01, 5.14)\": 5.032, \"(5.14, 5.26)\": 5.152, \"(5.26, 5.36)\": 5.264, \"(5.36, 5.48)\": 5.371, \"(5.48, 5.59)\": 5.488, \"(5.59, 5.72)\": 5.6, \"(5.72, 5.81)\": 5.713, \"(5.81, 5.9)\": 5.817, \"(5.9, 6.04)\": 5.943, \"(6.04, 6.14)\": 6.044, \"(6.14, 6.27)\": 6.147, \"(6.27, 6.4)\": 6.286, \"(6.4, 6.5)\": 6.413, \"(6.5, 6.62)\": 6.516, \"(6.62, 6.72)\": 6.622, \"(6.72, 6.86)\": 6.75, \"(6.86, 6.97)\": 6.867, \"(6.97, 7.08)\": 6.977, \"(7.08, 7.19)\": 7.099, \"(7.19, 7.3)\": 7.203, \"(7.3, 7.42)\": 7.304, \"(7.42, 7.52)\": 7.42, \"(7.52, 7.62)\": 7.53, \"(7.62, 7.75)\": 7.633, \"(7.75, 7.84)\": 7.749, \"(7.84, 7.98)\": 7.858, \"(7.98, 8.11)\": 8.019, \"(8.11, 8.21)\": 8.124, \"(8.21, 8.32)\": 8.224, \"(8.32, 8.44)\": 8.348, \"(8.44, 8.56)\": 8.457, \"(8.56, 8.7)\": 8.588, \"(8.7, 8.81)\": 8.705, \"(8.81, 8.92)\": 8.821, \"(8.92, 9.02)\": 8.928, \"(9.02, 9.15)\": 9.039, \"(9.15, 9.3)\": 9.182, \"(9.3, 9.43)\": 9.322, \"(9.43, 9.53)\": 9.446, \"(9.53, 9.66)\": 9.551, \"(9.66, 9.75)\": 9.664, \"(9.75, 9.87)\": 9.774, \"(9.87, 9.99)\": 9.882, \"(9.99, 10.0)\": 9.993}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = 1/2*cos(x-2)\nb) f(x) = log(x+10)\nc) f(x) = -sqrt(x+10)\nd) f(x) = |x|\ne) f(x) = x\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "d)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.99, -9.9)\": -9.994, \"(-9.9, -9.81)\": -9.891, \"(-9.81, -9.69)\": -9.775, \"(-9.69, -9.57)\": -9.656, \"(-9.57, -9.47)\": -9.557, \"(-9.47, -9.33)\": -9.429, \"(-9.33, -9.23)\": -9.326, \"(-9.23, -9.12)\": -9.221, \"(-9.12, -9.02)\": -9.112, \"(-9.02, -8.92)\": -9.012, \"(-8.92, -8.8)\": -8.894, \"(-8.8, -8.64)\": -8.769, \"(-8.64, -8.55)\": -8.638, \"(-8.55, -8.41)\": -8.53, \"(-8.41, -8.3)\": -8.383, \"(-8.3, -8.16)\": -8.27, \"(-8.16, -8.05)\": -8.15, \"(-8.05, -7.93)\": -8.045, \"(-7.93, -7.82)\": -7.91, \"(-7.82, -7.71)\": -7.807, \"(-7.71, -7.6)\": -7.703, \"(-7.6, -7.47)\": -7.569, \"(-7.47, -7.36)\": -7.455, \"(-7.36, -7.27)\": -7.355, \"(-7.27, -7.16)\": -7.247, \"(-7.16, -7.05)\": -7.144, \"(-7.05, -6.92)\": -7.032, \"(-6.92, -6.84)\": -6.922, \"(-6.84, -6.72)\": -6.819, \"(-6.72, -6.62)\": -6.715, \"(-6.62, -6.52)\": -6.612, \"(-6.52, -6.43)\": -6.504, \"(-6.43, -6.31)\": -6.397, \"(-6.31, -6.2)\": -6.294, \"(-6.2, -6.09)\": -6.188, \"(-6.09, -5.99)\": -6.085, \"(-5.99, -5.9)\": -5.985, \"(-5.9, -5.76)\": -5.878, \"(-5.76, -5.6)\": -5.697, \"(-5.6, -5.48)\": -5.578, \"(-5.48, -5.36)\": -5.478, \"(-5.36, -5.26)\": -5.367, \"(-5.26, -5.15)\": -5.245, \"(-5.15, -5.06)\": -5.131, \"(-5.06, -4.91)\": -5.019, \"(-4.91, -4.81)\": -4.916, \"(-4.81, -4.7)\": -4.804, \"(-4.7, -4.6)\": -4.701, \"(-4.6, -4.49)\": -4.589, \"(-4.49, -4.39)\": -4.473, \"(-4.39, -4.26)\": -4.367, \"(-4.26, -4.13)\": -4.239, \"(-4.13, -4.04)\": -4.13, \"(-4.04, -3.91)\": -4.014, \"(-3.91, -3.81)\": -3.905, \"(-3.81, -3.71)\": -3.803, \"(-3.71, -3.59)\": -3.691, \"(-3.59, -3.49)\": -3.58, \"(-3.49, -3.37)\": -3.466, \"(-3.37, -3.26)\": -3.356, \"(-3.26, -3.15)\": -3.233, \"(-3.15, -3.04)\": -3.12, \"(-3.04, -2.93)\": -3.015, \"(-2.93, -2.82)\": -2.912, \"(-2.82, -2.71)\": -2.802, \"(-2.71, -2.59)\": -2.687, \"(-2.59, -2.47)\": -2.576, \"(-2.47, -2.37)\": -2.454, \"(-2.37, -2.21)\": -2.347, \"(-2.21, -2.04)\": -2.139, \"(-2.04, -1.93)\": -2.038, \"(-1.93, -1.82)\": -1.91, \"(-1.82, -1.71)\": -1.806, \"(-1.71, -1.62)\": -1.697, \"(-1.62, -1.5)\": -1.596, \"(-1.5, -1.4)\": -1.496, \"(-1.4, -1.28)\": -1.378, \"(-1.28, -1.18)\": -1.273, \"(-1.18, -1.06)\": -1.159, \"(-1.06, -0.95)\": -1.047, \"(-0.95, -0.84)\": -0.937, \"(-0.84, -0.73)\": -0.833, \"(-0.73, -0.6)\": -0.713, \"(-0.6, -0.45)\": -0.561, \"(-0.45, -0.33)\": -0.426, \"(-0.33, -0.22)\": -0.327, \"(-0.22, -0.1)\": -0.201, \"(-0.1, 0.17)\": -0.067, \"(0.17, 0.28)\": -0.179, \"(0.28, 0.41)\": -0.307, \"(0.41, 0.52)\": -0.415, \"(0.52, 0.63)\": -0.545, \"(0.63, 0.74)\": -0.645, \"(0.74, 0.86)\": -0.751, \"(0.86, 0.99)\": -0.891, \"(0.99, 1.11)\": -1.006, \"(1.11, 1.21)\": -1.125, \"(1.21, 1.32)\": -1.226, \"(1.32, 1.44)\": -1.35, \"(1.44, 1.56)\": -1.459, \"(1.56, 1.68)\": -1.566, \"(1.68, 1.8)\": -1.703, \"(1.8, 1.95)\": -1.827, \"(1.95, 2.08)\": -1.985, \"(2.08, 2.17)\": -2.088, \"(2.17, 2.29)\": -2.19, \"(2.29, 2.43)\": -2.307, \"(2.43, 2.56)\": -2.464, \"(2.56, 2.69)\": -2.592, \"(2.69, 2.79)\": -2.7, \"(2.79, 2.9)\": -2.803, \"(2.9, 3.0)\": -2.905, \"(3.0, 3.12)\": -3.01, \"(3.12, 3.23)\": -3.118, \"(3.23, 3.34)\": -3.237, \"(3.34, 3.44)\": -3.344, \"(3.44, 3.56)\": -3.457, \"(3.56, 3.68)\": -3.582, \"(3.68, 3.8)\": -3.686, \"(3.8, 3.89)\": -3.8, \"(3.89, 4.01)\": -3.903, \"(4.01, 4.11)\": -4.011, \"(4.11, 4.23)\": -4.124, \"(4.23, 4.31)\": -4.228, \"(4.31, 4.43)\": -4.328, \"(4.43, 4.53)\": -4.43, \"(4.53, 4.62)\": -4.535, \"(4.62, 4.75)\": -4.644, \"(4.75, 4.85)\": -4.76, \"(4.85, 4.98)\": -4.865, \"(4.98, 5.1)\": -4.993, \"(5.1, 5.2)\": -5.11, \"(5.2, 5.3)\": -5.216, \"(5.3, 5.43)\": -5.315, \"(5.43, 5.52)\": -5.441, \"(5.52, 5.63)\": -5.543, \"(5.63, 5.77)\": -5.671, \"(5.77, 5.86)\": -5.785, \"(5.86, 5.97)\": -5.885, \"(5.97, 6.09)\": -5.984, \"(6.09, 6.19)\": -6.086, \"(6.19, 6.3)\": -6.21, \"(6.3, 6.43)\": -6.33, \"(6.43, 6.55)\": -6.443, \"(6.55, 6.64)\": -6.566, \"(6.64, 6.76)\": -6.668, \"(6.76, 6.88)\": -6.79, \"(6.88, 6.99)\": -6.893, \"(6.99, 7.1)\": -7.004, \"(7.1, 7.22)\": -7.124, \"(7.22, 7.37)\": -7.255, \"(7.37, 7.48)\": -7.375, \"(7.48, 7.62)\": -7.5, \"(7.62, 7.74)\": -7.64, \"(7.74, 7.85)\": -7.747, \"(7.85, 7.96)\": -7.858, \"(7.96, 8.08)\": -7.99, \"(8.08, 8.19)\": -8.092, \"(8.19, 8.3)\": -8.193, \"(8.3, 8.43)\": -8.318, \"(8.43, 8.56)\": -8.468, \"(8.56, 8.66)\": -8.568, \"(8.66, 8.8)\": -8.687, \"(8.8, 8.91)\": -8.807, \"(8.91, 8.99)\": -8.91, \"(8.99, 9.13)\": -9.02, \"(9.13, 9.24)\": -9.147, \"(9.24, 9.34)\": -9.255, \"(9.34, 9.44)\": -9.357, \"(9.44, 9.58)\": -9.459, \"(9.58, 9.7)\": -9.609, \"(9.7, 9.83)\": -9.719, \"(9.83, 9.94)\": -9.848, \"(9.94, 10.0)\": -9.95}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = 2^(x-5)\nb) f(x) = |x^3|\nc) f(x) = -|x|\nd) f(x) = |x|\ne) f(x) = 1/2*cos(x-2)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "c)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.97, -9.88)\": -9.968, \"(-9.88, -9.77)\": -9.86, \"(-9.77, -9.64)\": -9.756, \"(-9.64, -9.51)\": -9.62, \"(-9.51, -9.41)\": -9.508, \"(-9.41, -9.29)\": -9.395, \"(-9.29, -9.19)\": -9.286, \"(-9.19, -9.11)\": -9.182, \"(-9.11, -8.98)\": -9.076, \"(-8.98, -8.85)\": -8.956, \"(-8.85, -8.73)\": -8.835, \"(-8.73, -8.62)\": -8.723, \"(-8.62, -8.49)\": -8.579, \"(-8.49, -8.38)\": -8.473, \"(-8.38, -8.27)\": -8.369, \"(-8.27, -8.16)\": -8.265, \"(-8.16, -8.03)\": -8.125, \"(-8.03, -7.91)\": -8.011, \"(-7.91, -7.8)\": -7.899, \"(-7.8, -7.69)\": -7.795, \"(-7.69, -7.6)\": -7.678, \"(-7.6, -7.45)\": -7.569, \"(-7.45, -7.36)\": -7.449, \"(-7.36, -7.25)\": -7.349, \"(-7.25, -7.15)\": -7.239, \"(-7.15, -7.0)\": -7.099, \"(-7.0, -6.88)\": -6.983, \"(-6.88, -6.77)\": -6.866, \"(-6.77, -6.69)\": -6.76, \"(-6.69, -6.56)\": -6.66, \"(-6.56, -6.44)\": -6.535, \"(-6.44, -6.34)\": -6.431, \"(-6.34, -6.23)\": -6.329, \"(-6.23, -6.12)\": -6.225, \"(-6.12, -6.02)\": -6.123, \"(-6.02, -5.91)\": -6.009, \"(-5.91, -5.77)\": -5.888, \"(-5.77, -5.68)\": -5.771, \"(-5.68, -5.57)\": -5.667, \"(-5.57, -5.46)\": -5.562, \"(-5.46, -5.37)\": -5.459, \"(-5.37, -5.26)\": -5.357, \"(-5.26, -5.13)\": -5.246, \"(-5.13, -5.0)\": -5.111, \"(-5.0, -4.93)\": -5.005, \"(-4.93, -4.8)\": -4.9, \"(-4.8, -4.67)\": -4.787, \"(-4.67, -4.57)\": -4.669, \"(-4.57, -4.48)\": -4.564, \"(-4.48, -4.36)\": -4.464, \"(-4.36, -4.24)\": -4.336, \"(-4.24, -4.13)\": -4.235, \"(-4.13, -4.02)\": -4.122, \"(-4.02, -3.88)\": -3.98, \"(-3.88, -3.73)\": -3.847, \"(-3.73, -3.61)\": -3.692, \"(-3.61, -3.48)\": -3.579, \"(-3.48, -3.38)\": -3.476, \"(-3.38, -3.26)\": -3.373, \"(-3.26, -3.15)\": -3.237, \"(-3.15, -3.02)\": -3.122, \"(-3.02, -2.91)\": -3.016, \"(-2.91, -2.79)\": -2.898, \"(-2.79, -2.67)\": -2.765, \"(-2.67, -2.57)\": -2.664, \"(-2.57, -2.47)\": -2.561, \"(-2.47, -2.34)\": -2.455, \"(-2.34, -2.25)\": -2.34, \"(-2.25, -2.14)\": -2.235, \"(-2.14, -2.02)\": -2.134, \"(-2.02, -1.92)\": -2.004, \"(-1.92, -1.81)\": -1.903, \"(-1.81, -1.71)\": -1.798, \"(-1.71, -1.6)\": -1.697, \"(-1.6, -1.48)\": -1.581, \"(-1.48, -1.36)\": -1.468, \"(-1.36, -1.26)\": -1.362, \"(-1.26, -1.14)\": -1.25, \"(-1.14, -1.03)\": -1.125, \"(-1.03, -0.92)\": -1.022, \"(-0.92, -0.83)\": -0.917, \"(-0.83, -0.72)\": -0.816, \"(-0.72, -0.58)\": -0.69, \"(-0.58, -0.49)\": -0.562, \"(-0.49, -0.37)\": -0.455, \"(-0.37, -0.25)\": -0.352, \"(-0.25, -0.13)\": -0.23, \"(-0.13, 0.21)\": -0.112, \"(0.21, 0.33)\": -0.219, \"(0.33, 0.47)\": -0.372, \"(0.47, 0.57)\": -0.474, \"(0.57, 0.69)\": -0.577, \"(0.69, 0.8)\": -0.694, \"(0.8, 0.91)\": -0.813, \"(0.91, 1.02)\": -0.921, \"(1.02, 1.13)\": -1.035, \"(1.13, 1.24)\": -1.138, \"(1.24, 1.37)\": -1.267, \"(1.37, 1.5)\": -1.374, \"(1.5, 1.64)\": -1.535, \"(1.64, 1.78)\": -1.649, \"(1.78, 1.91)\": -1.819, \"(1.91, 2.03)\": -1.925, \"(2.03, 2.11)\": -2.031, \"(2.11, 2.22)\": -2.132, \"(2.22, 2.33)\": -2.234, \"(2.33, 2.45)\": -2.337, \"(2.45, 2.59)\": -2.493, \"(2.59, 2.72)\": -2.603, \"(2.72, 2.82)\": -2.714, \"(2.82, 2.92)\": -2.827, \"(2.92, 3.05)\": -2.94, \"(3.05, 3.21)\": -3.091, \"(3.21, 3.31)\": -3.209, \"(3.31, 3.41)\": -3.311, \"(3.41, 3.51)\": -3.414, \"(3.51, 3.63)\": -3.523, \"(3.63, 3.73)\": -3.636, \"(3.73, 3.86)\": -3.741, \"(3.86, 3.97)\": -3.878, \"(3.97, 4.09)\": -3.997, \"(4.09, 4.2)\": -4.099, \"(4.2, 4.31)\": -4.211, \"(4.31, 4.41)\": -4.323, \"(4.41, 4.53)\": -4.429, \"(4.53, 4.65)\": -4.573, \"(4.65, 4.79)\": -4.685, \"(4.79, 4.88)\": -4.798, \"(4.88, 5.01)\": -4.899, \"(5.01, 5.12)\": -5.015, \"(5.12, 5.24)\": -5.121, \"(5.24, 5.34)\": -5.246, \"(5.34, 5.42)\": -5.354, \"(5.42, 5.56)\": -5.456, \"(5.56, 5.66)\": -5.562, \"(5.66, 5.77)\": -5.663, \"(5.77, 5.88)\": -5.774, \"(5.88, 5.97)\": -5.881, \"(5.97, 6.09)\": -5.99, \"(6.09, 6.2)\": -6.1, \"(6.2, 6.31)\": -6.22, \"(6.31, 6.46)\": -6.33, \"(6.46, 6.59)\": -6.484, \"(6.59, 6.7)\": -6.585, \"(6.7, 6.79)\": -6.706, \"(6.79, 6.93)\": -6.813, \"(6.93, 7.03)\": -6.945, \"(7.03, 7.14)\": -7.051, \"(7.14, 7.24)\": -7.151, \"(7.24, 7.33)\": -7.255, \"(7.33, 7.45)\": -7.36, \"(7.45, 7.57)\": -7.469, \"(7.57, 7.68)\": -7.596, \"(7.68, 7.8)\": -7.698, \"(7.8, 7.91)\": -7.801, \"(7.91, 8.03)\": -7.931, \"(8.03, 8.13)\": -8.041, \"(8.13, 8.27)\": -8.164, \"(8.27, 8.38)\": -8.28, \"(8.38, 8.49)\": -8.383, \"(8.49, 8.61)\": -8.507, \"(8.61, 8.73)\": -8.627, \"(8.73, 8.83)\": -8.746, \"(8.83, 8.96)\": -8.855, \"(8.96, 9.05)\": -8.957, \"(9.05, 9.17)\": -9.072, \"(9.17, 9.28)\": -9.189, \"(9.28, 9.39)\": -9.302, \"(9.39, 9.5)\": -9.402, \"(9.5, 9.62)\": -9.505, \"(9.62, 9.74)\": -9.638, \"(9.74, 9.85)\": -9.742, \"(9.85, 9.94)\": -9.849, \"(9.94, 9.99)\": -9.957}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = -sign(-x)\nb) f(x) = sign(x-1)\nc) f(x) = -|-x|\nd) f(x) = (x-1)*(x+1)\ne) f(x) = sinh(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "c)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.98, -9.86)\": 14.94, \"(-9.86, -9.72)\": 14.69, \"(-9.72, -9.58)\": 14.43, \"(-9.58, -9.42)\": 14.12, \"(-9.42, -9.28)\": 13.8, \"(-9.28, -9.16)\": 13.53, \"(-9.16, -9.0)\": 13.25, \"(-9.0, -8.87)\": 12.97, \"(-8.87, -8.73)\": 12.72, \"(-8.73, -8.61)\": 12.44, \"(-8.61, -8.47)\": 12.17, \"(-8.47, -8.33)\": 11.92, \"(-8.33, -8.2)\": 11.65, \"(-8.2, -8.08)\": 11.38, \"(-8.08, -7.91)\": 11.13, \"(-7.91, -7.76)\": 10.78, \"(-7.76, -7.66)\": 10.52, \"(-7.66, -7.51)\": 10.26, \"(-7.51, -7.41)\": 10.0, \"(-7.41, -7.23)\": 9.73, \"(-7.23, -7.09)\": 9.42, \"(-7.09, -6.95)\": 9.13, \"(-6.95, -6.79)\": 8.86, \"(-6.79, -6.63)\": 8.49, \"(-6.63, -6.49)\": 8.24, \"(-6.49, -6.36)\": 7.96, \"(-6.36, -6.25)\": 7.7, \"(-6.25, -6.09)\": 7.44, \"(-6.09, -5.96)\": 7.18, \"(-5.96, -5.82)\": 6.89, \"(-5.82, -5.68)\": 6.6, \"(-5.68, -5.53)\": 6.32, \"(-5.53, -5.39)\": 6.03, \"(-5.39, -5.25)\": 5.77, \"(-5.25, -5.1)\": 5.49, \"(-5.1, -5.0)\": 5.21, \"(-5.0, -4.84)\": 4.92, \"(-4.84, -4.69)\": 4.64, \"(-4.69, -4.57)\": 4.36, \"(-4.57, -4.43)\": 4.1, \"(-4.43, -4.31)\": 3.84, \"(-4.31, -4.16)\": 3.59, \"(-4.16, -4.03)\": 3.33, \"(-4.03, -3.91)\": 3.04, \"(-3.91, -3.77)\": 2.78, \"(-3.77, -3.64)\": 2.51, \"(-3.64, -3.49)\": 2.24, \"(-3.49, -3.36)\": 1.97, \"(-3.36, -3.23)\": 1.71, \"(-3.23, -3.09)\": 1.43, \"(-3.09, -2.96)\": 1.17, \"(-2.96, -2.83)\": 0.89, \"(-2.83, -2.69)\": 0.62, \"(-2.69, -2.55)\": 0.37, \"(-2.55, -2.31)\": 0.1, \"(-2.31, -2.2)\": 0.38, \"(-2.2, -2.05)\": 0.63, \"(-2.05, -1.93)\": 0.92, \"(-1.93, -1.77)\": 1.18, \"(-1.77, -1.62)\": 1.51, \"(-1.62, -1.48)\": 1.78, \"(-1.48, -1.33)\": 2.03, \"(-1.33, -1.18)\": 2.38, \"(-1.18, -1.04)\": 2.66, \"(-1.04, -0.89)\": 2.92, \"(-0.89, -0.77)\": 3.25, \"(-0.77, -0.6)\": 3.54, \"(-0.6, -0.46)\": 3.83, \"(-0.46, -0.33)\": 4.1, \"(-0.33, -0.21)\": 4.36, \"(-0.21, -0.06)\": 4.61, \"(-0.06, 0.1)\": 4.94, \"(0.1, 0.23)\": 5.22, \"(0.23, 0.38)\": 5.49, \"(0.38, 0.5)\": 5.76, \"(0.5, 0.65)\": 6.06, \"(0.65, 0.81)\": 6.35, \"(0.81, 0.94)\": 6.64, \"(0.94, 1.08)\": 6.89, \"(1.08, 1.21)\": 7.19, \"(1.21, 1.36)\": 7.46, \"(1.36, 1.5)\": 7.76, \"(1.5, 1.64)\": 8.01, \"(1.64, 1.78)\": 8.29, \"(1.78, 1.91)\": 8.57, \"(1.91, 2.04)\": 8.83, \"(2.04, 2.17)\": 9.08, \"(2.17, 2.31)\": 9.36, \"(2.31, 2.44)\": 9.62, \"(2.44, 2.58)\": 9.9, \"(2.58, 2.7)\": 10.17, \"(2.7, 2.82)\": 10.42, \"(2.82, 2.94)\": 10.67, \"(2.94, 3.05)\": 10.93, \"(3.05, 3.21)\": 11.18, \"(3.21, 3.35)\": 11.47, \"(3.35, 3.48)\": 11.72, \"(3.48, 3.63)\": 11.99, \"(3.63, 3.78)\": 12.28, \"(3.78, 3.91)\": 12.57, \"(3.91, 4.04)\": 12.84, \"(4.04, 4.16)\": 13.11, \"(4.16, 4.31)\": 13.37, \"(4.31, 4.45)\": 13.66, \"(4.45, 4.59)\": 13.91, \"(4.59, 4.73)\": 14.21, \"(4.73, 4.86)\": 14.49, \"(4.86, 5.0)\": 14.76, \"(5.0, 5.15)\": 15.05, \"(5.15, 5.29)\": 15.32, \"(5.29, 5.42)\": 15.6, \"(5.42, 5.55)\": 15.86, \"(5.55, 5.69)\": 16.14, \"(5.69, 5.85)\": 16.45, \"(5.85, 6.01)\": 16.75, \"(6.01, 6.13)\": 17.02, \"(6.13, 6.27)\": 17.3, \"(6.27, 6.42)\": 17.59, \"(6.42, 6.54)\": 17.85, \"(6.54, 6.69)\": 18.12, \"(6.69, 6.79)\": 18.38, \"(6.79, 6.93)\": 18.64, \"(6.93, 7.06)\": 18.89, \"(7.06, 7.19)\": 19.14, \"(7.19, 7.33)\": 19.4, \"(7.33, 7.46)\": 19.66, \"(7.46, 7.6)\": 19.91, \"(7.6, 7.75)\": 20.25, \"(7.75, 7.89)\": 20.52, \"(7.89, 8.02)\": 20.81, \"(8.02, 8.17)\": 21.08, \"(8.17, 8.32)\": 21.38, \"(8.32, 8.45)\": 21.64, \"(8.45, 8.57)\": 21.91, \"(8.57, 8.7)\": 22.17, \"(8.7, 8.84)\": 22.43, \"(8.84, 8.97)\": 22.68, \"(8.97, 9.08)\": 22.94, \"(9.08, 9.2)\": 23.19, \"(9.2, 9.34)\": 23.45, \"(9.34, 9.48)\": 23.71, \"(9.48, 9.62)\": 23.99, \"(9.62, 9.76)\": 24.26, \"(9.76, 9.89)\": 24.53, \"(9.89, 9.99)\": 24.82}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = 1/(1+exp(-x))\nb) f(x) = -sin(x)\nc) f(x) = |2*x+4|\nd) f(x) = sign(x)\ne) f(x) = log(x+10)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "c)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.98, -9.94)\": 992.1, \"(-9.94, -9.91)\": 981.3, \"(-9.91, -9.84)\": 966.9, \"(-9.84, -9.75)\": 941.2, \"(-9.75, -9.68)\": 921.0, \"(-9.68, -9.62)\": 903.2, \"(-9.62, -9.58)\": 889.3, \"(-9.58, -9.52)\": 871.0, \"(-9.52, -9.46)\": 859.2, \"(-9.46, -9.42)\": 840.9, \"(-9.42, -9.34)\": 830.0, \"(-9.34, -9.29)\": 810.8, \"(-9.29, -9.24)\": 798.9, \"(-9.24, -9.16)\": 786.8, \"(-9.16, -9.07)\": 759.6, \"(-9.07, -9.01)\": 742.5, \"(-9.01, -8.97)\": 731.2, \"(-8.97, -8.88)\": 712.4, \"(-8.88, -8.83)\": 700.8, \"(-8.83, -8.78)\": 687.3, \"(-8.78, -8.75)\": 675.9, \"(-8.75, -8.71)\": 665.8, \"(-8.71, -8.64)\": 655.7, \"(-8.64, -8.57)\": 641.1, \"(-8.57, -8.5)\": 623.1, \"(-8.5, -8.42)\": 610.7, \"(-8.42, -8.37)\": 592.9, \"(-8.37, -8.31)\": 581.4, \"(-8.31, -8.23)\": 570.8, \"(-8.23, -8.17)\": 554.0, \"(-8.17, -8.11)\": 544.1, \"(-8.11, -8.05)\": 531.6, \"(-8.05, -8.0)\": 520.7, \"(-8.0, -7.94)\": 510.0, \"(-7.94, -7.88)\": 498.9, \"(-7.88, -7.82)\": 487.5, \"(-7.82, -7.75)\": 474.8, \"(-7.75, -7.67)\": 462.0, \"(-7.67, -7.59)\": 448.4, \"(-7.59, -7.54)\": 435.9, \"(-7.54, -7.47)\": 426.0, \"(-7.47, -7.39)\": 415.3, \"(-7.39, -7.35)\": 404.8, \"(-7.35, -7.25)\": 393.2, \"(-7.25, -7.2)\": 380.0, \"(-7.2, -7.11)\": 369.5, \"(-7.11, -7.05)\": 358.8, \"(-7.05, -6.96)\": 346.5, \"(-6.96, -6.89)\": 336.4, \"(-6.89, -6.79)\": 323.9, \"(-6.79, -6.72)\": 311.4, \"(-6.72, -6.63)\": 301.3, \"(-6.63, -6.55)\": 291.2, \"(-6.55, -6.44)\": 277.2, \"(-6.44, -6.34)\": 265.9, \"(-6.34, -6.25)\": 253.9, \"(-6.25, -6.15)\": 242.7, \"(-6.15, -6.04)\": 231.3, \"(-6.04, -5.95)\": 219.6, \"(-5.95, -5.84)\": 208.6, \"(-5.84, -5.73)\": 198.7, \"(-5.73, -5.58)\": 185.3, \"(-5.58, -5.46)\": 173.0, \"(-5.46, -5.33)\": 161.3, \"(-5.33, -5.19)\": 150.9, \"(-5.19, -5.07)\": 140.7, \"(-5.07, -4.89)\": 127.5, \"(-4.89, -4.75)\": 116.6, \"(-4.75, -4.6)\": 106.2, \"(-4.6, -4.42)\": 96.0, \"(-4.42, -4.21)\": 84.8, \"(-4.21, -3.99)\": 74.2, \"(-3.99, -3.81)\": 64.2, \"(-3.81, -3.55)\": 54.3, \"(-3.55, -3.23)\": 43.7, \"(-3.23, -2.89)\": 33.6, \"(-2.89, -2.38)\": 23.5, \"(-2.38, -1.4)\": 13.1, \"(-1.4, 2.35)\": 3.1, \"(2.35, 2.82)\": 13.3, \"(2.82, 3.22)\": 23.3, \"(3.22, 3.5)\": 33.7, \"(3.5, 3.76)\": 43.8, \"(3.76, 3.99)\": 53.9, \"(3.99, 4.19)\": 64.0, \"(4.19, 4.39)\": 74.4, \"(4.39, 4.56)\": 85.5, \"(4.56, 4.74)\": 97.1, \"(4.74, 4.9)\": 107.4, \"(4.9, 5.08)\": 120.9, \"(5.08, 5.21)\": 131.0, \"(5.21, 5.33)\": 141.9, \"(5.33, 5.47)\": 152.6, \"(5.47, 5.6)\": 165.5, \"(5.6, 5.75)\": 178.6, \"(5.75, 5.86)\": 191.8, \"(5.86, 5.96)\": 202.0, \"(5.96, 6.09)\": 214.1, \"(6.09, 6.16)\": 225.7, \"(6.16, 6.27)\": 236.9, \"(6.27, 6.38)\": 249.6, \"(6.38, 6.48)\": 261.3, \"(6.48, 6.57)\": 273.0, \"(6.57, 6.64)\": 284.2, \"(6.64, 6.74)\": 295.6, \"(6.74, 6.85)\": 309.3, \"(6.85, 6.92)\": 321.3, \"(6.92, 6.99)\": 333.9, \"(6.99, 7.09)\": 346.5, \"(7.09, 7.16)\": 356.9, \"(7.16, 7.24)\": 368.1, \"(7.24, 7.3)\": 380.0, \"(7.3, 7.4)\": 391.5, \"(7.4, 7.48)\": 408.3, \"(7.48, 7.55)\": 418.4, \"(7.55, 7.64)\": 433.7, \"(7.64, 7.72)\": 449.1, \"(7.72, 7.78)\": 461.9, \"(7.78, 7.85)\": 474.0, \"(7.85, 7.9)\": 484.7, \"(7.9, 7.96)\": 495.7, \"(7.96, 8.03)\": 507.8, \"(8.03, 8.08)\": 519.1, \"(8.08, 8.17)\": 531.7, \"(8.17, 8.24)\": 546.6, \"(8.24, 8.28)\": 558.9, \"(8.28, 8.35)\": 568.9, \"(8.35, 8.42)\": 587.4, \"(8.42, 8.47)\": 599.5, \"(8.47, 8.52)\": 611.3, \"(8.52, 8.61)\": 628.5, \"(8.61, 8.64)\": 638.9, \"(8.64, 8.68)\": 648.9, \"(8.68, 8.74)\": 662.5, \"(8.74, 8.82)\": 673.4, \"(8.82, 8.88)\": 688.1, \"(8.88, 8.97)\": 701.6, \"(8.97, 9.06)\": 733.9, \"(9.06, 9.11)\": 745.8, \"(9.11, 9.14)\": 757.9, \"(9.14, 9.19)\": 769.2, \"(9.19, 9.25)\": 780.0, \"(9.25, 9.31)\": 796.1, \"(9.31, 9.35)\": 810.4, \"(9.35, 9.4)\": 825.3, \"(9.4, 9.47)\": 835.8, \"(9.47, 9.55)\": 858.9, \"(9.55, 9.6)\": 877.5, \"(9.6, 9.63)\": 887.9, \"(9.63, 9.69)\": 900.6, \"(9.69, 9.75)\": 913.3, \"(9.75, 9.8)\": 930.6, \"(9.8, 9.84)\": 941.0, \"(9.84, 9.88)\": 954.0, \"(9.88, 9.91)\": 968.1}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = |x^3|\nb) f(x) = -2*x+5\nc) f(x) = 2^(x-5)\nd) f(x) = sin(x+2)+2\ne) f(x) = x\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "a)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.96, -9.93)\": 0.217, \"(-9.93, -9.91)\": 0.272, \"(-9.91, -9.87)\": 0.331, \"(-9.87, -9.82)\": 0.383, \"(-9.82, -9.77)\": 0.433, \"(-9.77, -9.72)\": 0.501, \"(-9.72, -9.63)\": 0.57, \"(-9.63, -9.57)\": 0.617, \"(-9.57, -9.52)\": 0.666, \"(-9.52, -9.42)\": 0.711, \"(-9.42, -9.29)\": 0.796, \"(-9.29, -9.18)\": 0.854, \"(-9.18, -9.06)\": 0.921, \"(-9.06, -8.96)\": 0.977, \"(-8.96, -8.86)\": 1.027, \"(-8.86, -8.75)\": 1.073, \"(-8.75, -8.63)\": 1.131, \"(-8.63, -8.53)\": 1.175, \"(-8.53, -8.42)\": 1.218, \"(-8.42, -8.27)\": 1.269, \"(-8.27, -8.17)\": 1.316, \"(-8.17, -8.03)\": 1.36, \"(-8.03, -7.89)\": 1.405, \"(-7.89, -7.74)\": 1.453, \"(-7.74, -7.54)\": 1.515, \"(-7.54, -7.36)\": 1.583, \"(-7.36, -7.21)\": 1.627, \"(-7.21, -7.06)\": 1.671, \"(-7.06, -6.91)\": 1.715, \"(-6.91, -6.74)\": 1.763, \"(-6.74, -6.54)\": 1.81, \"(-6.54, -6.37)\": 1.865, \"(-6.37, -6.19)\": 1.909, \"(-6.19, -5.99)\": 1.958, \"(-5.99, -5.78)\": 2.01, \"(-5.78, -5.61)\": 2.056, \"(-5.61, -5.38)\": 2.106, \"(-5.38, -5.18)\": 2.154, \"(-5.18, -4.99)\": 2.197, \"(-4.99, -4.78)\": 2.246, \"(-4.78, -4.55)\": 2.29, \"(-4.55, -4.33)\": 2.336, \"(-4.33, -4.13)\": 2.38, \"(-4.13, -3.92)\": 2.425, \"(-3.92, -3.67)\": 2.469, \"(-3.67, -3.4)\": 2.52, \"(-3.4, -3.15)\": 2.576, \"(-3.15, -2.9)\": 2.619, \"(-2.9, -2.67)\": 2.665, \"(-2.67, -2.46)\": 2.708, \"(-2.46, -2.19)\": 2.752, \"(-2.19, -1.92)\": 2.799, \"(-1.92, -1.64)\": 2.846, \"(-1.64, -1.4)\": 2.891, \"(-1.4, -1.11)\": 2.936, \"(-1.11, -0.84)\": 2.982, \"(-0.84, -0.59)\": 3.026, \"(-0.59, -0.32)\": 3.069, \"(-0.32, -0.04)\": 3.113, \"(-0.04, 0.22)\": 3.155, \"(0.22, 0.49)\": 3.198, \"(0.49, 0.77)\": 3.242, \"(0.77, 1.1)\": 3.289, \"(1.1, 1.39)\": 3.333, \"(1.39, 1.68)\": 3.376, \"(1.68, 2.01)\": 3.424, \"(2.01, 2.32)\": 3.468, \"(2.32, 2.62)\": 3.511, \"(2.62, 2.93)\": 3.554, \"(2.93, 3.25)\": 3.597, \"(3.25, 3.58)\": 3.644, \"(3.58, 3.95)\": 3.688, \"(3.95, 4.22)\": 3.731, \"(4.22, 4.59)\": 3.775, \"(4.59, 4.88)\": 3.818, \"(4.88, 5.19)\": 3.861, \"(5.19, 5.54)\": 3.904, \"(5.54, 5.9)\": 3.947, \"(5.9, 6.28)\": 3.99, \"(6.28, 6.63)\": 4.033, \"(6.63, 6.96)\": 4.077, \"(6.96, 7.32)\": 4.121, \"(7.32, 7.71)\": 4.165, \"(7.71, 8.05)\": 4.209, \"(8.05, 8.44)\": 4.253, \"(8.44, 8.82)\": 4.296, \"(8.82, 9.21)\": 4.339, \"(9.21, 9.6)\": 4.383, \"(9.6, 9.97)\": 4.427, \"(9.97, 10.0)\": 4.47}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = sign(x)\nb) f(x) = -sign(x)\nc) f(x) = -sinh(x)\nd) f(x) = -|-x|\ne) f(x) = sqrt(x+10)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "e)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-10.0, -9.96)\": -0.111, \"(-9.96, -9.94)\": -0.206, \"(-9.94, -9.89)\": -0.256, \"(-9.89, -9.81)\": -0.391, \"(-9.81, -9.76)\": -0.462, \"(-9.76, -9.67)\": -0.528, \"(-9.67, -9.6)\": -0.577, \"(-9.6, -9.53)\": -0.647, \"(-9.53, -9.46)\": -0.694, \"(-9.46, -9.37)\": -0.739, \"(-9.37, -9.28)\": -0.806, \"(-9.28, -9.21)\": -0.851, \"(-9.21, -9.12)\": -0.895, \"(-9.12, -9.05)\": -0.943, \"(-9.05, -8.94)\": -0.991, \"(-8.94, -8.83)\": -1.036, \"(-8.83, -8.74)\": -1.083, \"(-8.74, -8.64)\": -1.132, \"(-8.64, -8.52)\": -1.177, \"(-8.52, -8.39)\": -1.225, \"(-8.39, -8.26)\": -1.272, \"(-8.26, -8.12)\": -1.322, \"(-8.12, -7.98)\": -1.382, \"(-7.98, -7.83)\": -1.43, \"(-7.83, -7.71)\": -1.475, \"(-7.71, -7.57)\": -1.519, \"(-7.57, -7.41)\": -1.566, \"(-7.41, -7.25)\": -1.614, \"(-7.25, -7.1)\": -1.66, \"(-7.1, -6.97)\": -1.705, \"(-6.97, -6.8)\": -1.748, \"(-6.8, -6.62)\": -1.797, \"(-6.62, -6.41)\": -1.844, \"(-6.41, -6.24)\": -1.896, \"(-6.24, -6.05)\": -1.941, \"(-6.05, -5.86)\": -1.99, \"(-5.86, -5.66)\": -2.036, \"(-5.66, -5.48)\": -2.084, \"(-5.48, -5.27)\": -2.128, \"(-5.27, -5.07)\": -2.18, \"(-5.07, -4.87)\": -2.224, \"(-4.87, -4.64)\": -2.271, \"(-4.64, -4.45)\": -2.315, \"(-4.45, -4.23)\": -2.359, \"(-4.23, -4.0)\": -2.403, \"(-4.0, -3.78)\": -2.451, \"(-3.78, -3.53)\": -2.497, \"(-3.53, -3.28)\": -2.546, \"(-3.28, -3.09)\": -2.59, \"(-3.09, -2.81)\": -2.634, \"(-2.81, -2.59)\": -2.68, \"(-2.59, -2.36)\": -2.725, \"(-2.36, -2.11)\": -2.77, \"(-2.11, -1.8)\": -2.818, \"(-1.8, -1.56)\": -2.862, \"(-1.56, -1.28)\": -2.906, \"(-1.28, -1.02)\": -2.953, \"(-1.02, -0.74)\": -2.998, \"(-0.74, -0.47)\": -3.045, \"(-0.47, -0.16)\": -3.091, \"(-0.16, 0.13)\": -3.135, \"(0.13, 0.4)\": -3.181, \"(0.4, 0.69)\": -3.225, \"(0.69, 0.99)\": -3.27, \"(0.99, 1.3)\": -3.315, \"(1.3, 1.62)\": -3.364, \"(1.62, 1.92)\": -3.41, \"(1.92, 2.21)\": -3.454, \"(2.21, 2.54)\": -3.501, \"(2.54, 2.89)\": -3.546, \"(2.89, 3.2)\": -3.59, \"(3.2, 3.53)\": -3.634, \"(3.53, 3.86)\": -3.681, \"(3.86, 4.21)\": -3.725, \"(4.21, 4.55)\": -3.769, \"(4.55, 4.89)\": -3.813, \"(4.89, 5.22)\": -3.859, \"(5.22, 5.57)\": -3.903, \"(5.57, 5.91)\": -3.948, \"(5.91, 6.29)\": -3.993, \"(6.29, 6.65)\": -4.037, \"(6.65, 7.03)\": -4.081, \"(7.03, 7.42)\": -4.13, \"(7.42, 7.81)\": -4.175, \"(7.81, 8.18)\": -4.22, \"(8.18, 8.58)\": -4.267, \"(8.58, 8.97)\": -4.312, \"(8.97, 9.38)\": -4.357, \"(9.38, 9.77)\": -4.403, \"(9.77, 9.91)\": -4.448}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = x^2\nb) f(x) = x^3\nc) f(x) = -sqrt(x+10)\nd) f(x) = x^4\ne) f(x) = (x-1)*(x+1)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "c)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.99, -9.89)\": 8.651, \"(-9.89, -9.78)\": 8.549, \"(-9.78, -9.64)\": 8.41, \"(-9.64, -9.55)\": 8.31, \"(-9.55, -9.43)\": 8.18, \"(-9.43, -9.31)\": 8.075, \"(-9.31, -9.18)\": 7.957, \"(-9.18, -9.06)\": 7.849, \"(-9.06, -8.95)\": 7.743, \"(-8.95, -8.84)\": 7.611, \"(-8.84, -8.72)\": 7.508, \"(-8.72, -8.61)\": 7.402, \"(-8.61, -8.52)\": 7.301, \"(-8.52, -8.41)\": 7.198, \"(-8.41, -8.31)\": 7.098, \"(-8.31, -8.2)\": 6.988, \"(-8.2, -8.07)\": 6.879, \"(-8.07, -7.95)\": 6.771, \"(-7.95, -7.82)\": 6.652, \"(-7.82, -7.69)\": 6.505, \"(-7.69, -7.57)\": 6.405, \"(-7.57, -7.44)\": 6.279, \"(-7.44, -7.33)\": 6.156, \"(-7.33, -7.22)\": 6.053, \"(-7.22, -7.09)\": 5.942, \"(-7.09, -6.98)\": 5.828, \"(-6.98, -6.87)\": 5.725, \"(-6.87, -6.76)\": 5.614, \"(-6.76, -6.65)\": 5.51, \"(-6.65, -6.52)\": 5.4, \"(-6.52, -6.41)\": 5.285, \"(-6.41, -6.29)\": 5.171, \"(-6.29, -6.16)\": 5.052, \"(-6.16, -6.07)\": 4.948, \"(-6.07, -5.97)\": 4.847, \"(-5.97, -5.83)\": 4.742, \"(-5.83, -5.73)\": 4.642, \"(-5.73, -5.61)\": 4.521, \"(-5.61, -5.49)\": 4.403, \"(-5.49, -5.37)\": 4.304, \"(-5.37, -5.25)\": 4.202, \"(-5.25, -5.13)\": 4.097, \"(-5.13, -5.02)\": 3.975, \"(-5.02, -4.88)\": 3.863, \"(-4.88, -4.74)\": 3.734, \"(-4.74, -4.62)\": 3.629, \"(-4.62, -4.5)\": 3.526, \"(-4.5, -4.39)\": 3.42, \"(-4.39, -4.24)\": 3.309, \"(-4.24, -4.12)\": 3.199, \"(-4.12, -4.0)\": 3.095, \"(-4.0, -3.86)\": 2.988, \"(-3.86, -3.74)\": 2.876, \"(-3.74, -3.6)\": 2.769, \"(-3.6, -3.45)\": 2.66, \"(-3.45, -3.31)\": 2.553, \"(-3.31, -3.18)\": 2.449, \"(-3.18, -3.02)\": 2.348, \"(-3.02, -2.86)\": 2.241, \"(-2.86, -2.68)\": 2.137, \"(-2.68, -2.46)\": 2.018, \"(-2.46, -2.22)\": 1.912, \"(-2.22, -1.91)\": 1.808, \"(-1.91, -0.78)\": 1.706, \"(-0.78, -0.56)\": 1.81, \"(-0.56, -0.36)\": 1.915, \"(-0.36, -0.19)\": 2.018, \"(-0.19, -0.02)\": 2.132, \"(-0.02, 0.16)\": 2.234, \"(0.16, 0.31)\": 2.349, \"(0.31, 0.48)\": 2.473, \"(0.48, 0.64)\": 2.605, \"(0.64, 0.78)\": 2.716, \"(0.78, 0.92)\": 2.837, \"(0.92, 1.04)\": 2.939, \"(1.04, 1.19)\": 3.042, \"(1.19, 1.28)\": 3.154, \"(1.28, 1.42)\": 3.267, \"(1.42, 1.55)\": 3.367, \"(1.55, 1.68)\": 3.481, \"(1.68, 1.79)\": 3.587, \"(1.79, 1.9)\": 3.693, \"(1.9, 2.03)\": 3.794, \"(2.03, 2.12)\": 3.898, \"(2.12, 2.24)\": 4.008, \"(2.24, 2.36)\": 4.117, \"(2.36, 2.51)\": 4.22, \"(2.51, 2.63)\": 4.349, \"(2.63, 2.76)\": 4.464, \"(2.76, 2.87)\": 4.59, \"(2.87, 3.0)\": 4.693, \"(3.0, 3.09)\": 4.796, \"(3.09, 3.23)\": 4.908, \"(3.23, 3.36)\": 5.036, \"(3.36, 3.45)\": 5.141, \"(3.45, 3.58)\": 5.242, \"(3.58, 3.78)\": 5.417, \"(3.78, 3.87)\": 5.518, \"(3.87, 4.0)\": 5.643, \"(4.0, 4.12)\": 5.769, \"(4.12, 4.24)\": 5.872, \"(4.24, 4.37)\": 5.987, \"(4.37, 4.5)\": 6.12, \"(4.5, 4.6)\": 6.243, \"(4.6, 4.74)\": 6.347, \"(4.74, 4.83)\": 6.463, \"(4.83, 4.93)\": 6.564, \"(4.93, 5.05)\": 6.665, \"(5.05, 5.17)\": 6.764, \"(5.17, 5.31)\": 6.898, \"(5.31, 5.42)\": 7.001, \"(5.42, 5.54)\": 7.104, \"(5.54, 5.69)\": 7.27, \"(5.69, 5.81)\": 7.402, \"(5.81, 5.92)\": 7.508, \"(5.92, 6.05)\": 7.618, \"(6.05, 6.2)\": 7.73, \"(6.2, 6.35)\": 7.928, \"(6.35, 6.49)\": 8.041, \"(6.49, 6.62)\": 8.178, \"(6.62, 6.7)\": 8.29, \"(6.7, 6.83)\": 8.39, \"(6.83, 6.94)\": 8.504, \"(6.94, 7.1)\": 8.621, \"(7.1, 7.25)\": 8.799, \"(7.25, 7.37)\": 8.923, \"(7.37, 7.45)\": 9.023, \"(7.45, 7.57)\": 9.123, \"(7.57, 7.69)\": 9.239, \"(7.69, 7.83)\": 9.365, \"(7.83, 7.94)\": 9.478, \"(7.94, 8.05)\": 9.597, \"(8.05, 8.17)\": 9.698, \"(8.17, 8.3)\": 9.828, \"(8.3, 8.4)\": 9.944, \"(8.4, 8.51)\": 10.044, \"(8.51, 8.62)\": 10.159, \"(8.62, 8.74)\": 10.265, \"(8.74, 8.84)\": 10.374, \"(8.84, 8.96)\": 10.476, \"(8.96, 9.06)\": 10.607, \"(9.06, 9.18)\": 10.708, \"(9.18, 9.27)\": 10.81, \"(9.27, 9.4)\": 10.917, \"(9.4, 9.52)\": 11.038, \"(9.52, 9.63)\": 11.143, \"(9.63, 9.74)\": 11.26, \"(9.74, 9.86)\": 11.387, \"(9.86, 9.97)\": 11.49}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = sign(x)\nb) f(x) = sqrt(x ** 2 + 3*x +5)\nc) f(x) = 1/(1+exp(-x))\nd) f(x) = -exp(x)\ne) f(x) = cos(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "b)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.92, 5.37)\": 4.3, \"(5.37, 6.09)\": 234.0, \"(6.09, 6.48)\": 456.0, \"(6.48, 6.81)\": 688.8, \"(6.81, 7.06)\": 942.6, \"(7.06, 7.28)\": 1193.3, \"(7.28, 7.44)\": 1461.9, \"(7.44, 7.54)\": 1696.0, \"(7.54, 7.69)\": 1940.0, \"(7.69, 7.8)\": 2211.7, \"(7.8, 7.88)\": 2441.0, \"(7.88, 7.95)\": 2683.6, \"(7.95, 8.08)\": 2965.2, \"(8.08, 8.16)\": 3238.9, \"(8.16, 8.22)\": 3541.2, \"(8.22, 8.29)\": 3764.4, \"(8.29, 8.33)\": 4004.2, \"(8.33, 8.4)\": 4269.6, \"(8.4, 8.47)\": 4534.7, \"(8.47, 8.53)\": 4827.5, \"(8.53, 8.57)\": 5123.2, \"(8.57, 8.61)\": 5359.8, \"(8.61, 8.68)\": 5638.4, \"(8.68, 8.74)\": 6028.9, \"(8.74, 8.78)\": 6247.5, \"(8.78, 8.82)\": 6565.8, \"(8.82, 8.87)\": 6889.8, \"(8.87, 8.9)\": 7107.1, \"(8.9, 8.92)\": 7332.2, \"(8.92, 8.98)\": 7638.2, \"(8.98, 9.02)\": 8117.9, \"(9.02, 9.07)\": 8410.3, \"(9.07, 9.11)\": 8826.7, \"(9.11, 9.14)\": 9087.4, \"(9.14, 9.17)\": 9402.8, \"(9.17, 9.23)\": 9787.4, \"(9.23, 9.29)\": 10570.9, \"(9.29, 9.33)\": 10985.0, \"(9.33, 9.35)\": 11341.6, \"(9.35, 9.37)\": 11560.2, \"(9.37, 9.43)\": 11991.0, \"(9.43, 9.47)\": 12768.0, \"(9.47, 9.5)\": 13029.1, \"(9.5, 9.51)\": 13360.9, \"(9.51, 9.53)\": 13583.9, \"(9.53, 9.54)\": 13805.6, \"(9.54, 9.56)\": 14063.1, \"(9.56, 9.62)\": 14605.5, \"(9.62, 9.68)\": 15592.6, \"(9.68, 9.7)\": 16137.3, \"(9.7, 9.71)\": 16354.8, \"(9.71, 9.73)\": 16572.7, \"(9.73, 9.75)\": 16897.0, \"(9.75, 9.77)\": 17239.0, \"(9.77, 9.78)\": 17590.2, \"(9.78, 9.8)\": 17883.3, \"(9.8, 9.87)\": 18547.0, \"(9.87, 9.93)\": 20089.7, \"(9.93, 9.95)\": 20757.9, \"(9.95, 9.98)\": 21173.1, \"(9.98, 9.99)\": 21618.4}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = exp(x)\nb) f(x) = sqrt(x+10)\nc) f(x) = -sign(x)\nd) f(x) = -cosh(x)\ne) f(x) = sign(x+3)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "a)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-10.0, 5.31)\": -4.2, \"(5.31, 6.02)\": -212.5, \"(6.02, 6.43)\": -425.5, \"(6.43, 6.73)\": -640.0, \"(6.73, 6.97)\": -850.4, \"(6.97, 7.17)\": -1091.7, \"(7.17, 7.33)\": -1321.2, \"(7.33, 7.49)\": -1567.9, \"(7.49, 7.61)\": -1795.0, \"(7.61, 7.72)\": -2030.9, \"(7.72, 7.8)\": -2255.3, \"(7.8, 7.91)\": -2524.8, \"(7.91, 8.0)\": -2771.5, \"(8.0, 8.09)\": -3046.1, \"(8.09, 8.15)\": -3266.0, \"(8.15, 8.2)\": -3493.6, \"(8.2, 8.26)\": -3707.6, \"(8.26, 8.33)\": -3924.9, \"(8.33, 8.39)\": -4154.9, \"(8.39, 8.46)\": -4548.1, \"(8.46, 8.54)\": -4912.7, \"(8.54, 8.62)\": -5180.9, \"(8.62, 8.69)\": -5690.2, \"(8.69, 8.73)\": -5989.8, \"(8.73, 8.78)\": -6231.5, \"(8.78, 8.83)\": -6601.6, \"(8.83, 8.88)\": -6946.1, \"(8.88, 8.91)\": -7228.2, \"(8.91, 8.95)\": -7475.3, \"(8.95, 8.97)\": -7722.1, \"(8.97, 9.02)\": -7931.8, \"(9.02, 9.07)\": -8415.8, \"(9.07, 9.13)\": -8841.2, \"(9.13, 9.18)\": -9485.9, \"(9.18, 9.21)\": -9773.0, \"(9.21, 9.24)\": -10155.2, \"(9.24, 9.27)\": -10389.6, \"(9.27, 9.29)\": -10742.6, \"(9.29, 9.33)\": -10965.8, \"(9.33, 9.35)\": -11364.7, \"(9.35, 9.38)\": -11582.5, \"(9.38, 9.42)\": -12121.5, \"(9.42, 9.48)\": -12641.3, \"(9.48, 9.51)\": -13360.2, \"(9.51, 9.54)\": -13584.8, \"(9.54, 9.56)\": -13987.8, \"(9.56, 9.59)\": -14417.8, \"(9.59, 9.62)\": -14773.8, \"(9.62, 9.65)\": -15250.4, \"(9.65, 9.68)\": -15889.2, \"(9.68, 9.71)\": -16182.7, \"(9.71, 9.72)\": -16531.4, \"(9.72, 9.75)\": -16740.4, \"(9.75, 9.78)\": -17196.9, \"(9.78, 9.83)\": -18129.0, \"(9.83, 9.85)\": -18727.3, \"(9.85, 9.86)\": -19042.0, \"(9.86, 9.88)\": -19253.8, \"(9.88, 9.89)\": -19531.4, \"(9.89, 9.9)\": -19784.7, \"(9.9, 9.91)\": -20010.2, \"(9.91, 10.0)\": -20747.7}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = -exp(x)\nb) f(x) = -sign(x)\nc) f(x) = x^2\nd) f(x) = x\ne) f(x) = tanh(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "a)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.97, -9.94)\": 20907.1, \"(-9.94, -9.92)\": 20591.2, \"(-9.92, -9.89)\": 19998.3, \"(-9.89, -9.88)\": 19729.3, \"(-9.88, -9.85)\": 19300.1, \"(-9.85, -9.84)\": 18815.0, \"(-9.84, -9.82)\": 18587.4, \"(-9.82, -9.81)\": 18333.4, \"(-9.81, -9.78)\": 17992.0, \"(-9.78, -9.76)\": 17483.5, \"(-9.76, -9.74)\": 17270.1, \"(-9.74, -9.72)\": 16805.7, \"(-9.72, -9.7)\": 16434.0, \"(-9.7, -9.68)\": 16129.0, \"(-9.68, -9.66)\": 15916.5, \"(-9.66, -9.64)\": 15671.4, \"(-9.64, -9.63)\": 15344.9, \"(-9.63, -9.61)\": 15054.1, \"(-9.61, -9.57)\": 14704.3, \"(-9.57, -9.55)\": 14204.5, \"(-9.55, -9.54)\": 13938.9, \"(-9.54, -9.52)\": 13715.7, \"(-9.52, -9.49)\": 13436.7, \"(-9.49, -9.46)\": 12979.0, \"(-9.46, -9.45)\": 12758.0, \"(-9.45, -9.42)\": 12522.7, \"(-9.42, -9.39)\": 12294.8, \"(-9.39, -9.35)\": 11679.6, \"(-9.35, -9.31)\": 11364.2, \"(-9.31, -9.25)\": 10742.8, \"(-9.25, -9.23)\": 10328.0, \"(-9.23, -9.2)\": 10075.0, \"(-9.2, -9.17)\": 9791.6, \"(-9.17, -9.13)\": 9419.0, \"(-9.13, -9.09)\": 9096.2, \"(-9.09, -9.05)\": 8779.3, \"(-9.05, -9.03)\": 8447.6, \"(-9.03, -9.0)\": 8198.9, \"(-9.0, -8.91)\": 7981.2, \"(-8.91, -8.83)\": 7068.6, \"(-8.83, -8.78)\": 6727.3, \"(-8.78, -8.75)\": 6419.2, \"(-8.75, -8.7)\": 6187.2, \"(-8.7, -8.66)\": 5943.3, \"(-8.66, -8.56)\": 5697.0, \"(-8.56, -8.45)\": 4912.8, \"(-8.45, -8.39)\": 4618.4, \"(-8.39, -8.34)\": 4374.8, \"(-8.34, -8.28)\": 4136.7, \"(-8.28, -8.19)\": 3881.0, \"(-8.19, -8.12)\": 3479.6, \"(-8.12, -8.03)\": 3263.3, \"(-8.03, -7.95)\": 3038.3, \"(-7.95, -7.88)\": 2820.9, \"(-7.88, -7.79)\": 2599.6, \"(-7.79, -7.68)\": 2373.0, \"(-7.68, -7.55)\": 2126.9, \"(-7.55, -7.43)\": 1895.5, \"(-7.43, -7.29)\": 1675.3, \"(-7.29, -7.14)\": 1460.9, \"(-7.14, -6.94)\": 1249.3, \"(-6.94, -6.71)\": 1032.8, \"(-6.71, -6.42)\": 822.1, \"(-6.42, -5.98)\": 606.4, \"(-5.98, -5.21)\": 394.3, \"(-5.21, 9.98)\": 182.8}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = -|-x|\nb) f(x) = log(x+10)\nc) f(x) = -|x|\nd) f(x) = x^2\ne) f(x) = exp(-x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "e)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.97, -2.16)\": -0.0003, \"(-2.16, -1.98)\": 0.0101, \"(-1.98, -1.84)\": 0.022, \"(-1.84, -1.76)\": 0.0357, \"(-1.76, -1.66)\": 0.0474, \"(-1.66, -1.58)\": 0.0719, \"(-1.58, -1.53)\": 0.0849, \"(-1.53, -1.48)\": 0.1008, \"(-1.48, -1.45)\": 0.1136, \"(-1.45, -1.42)\": 0.1254, \"(-1.42, -1.38)\": 0.1378, \"(-1.38, -1.36)\": 0.152, \"(-1.36, -1.33)\": 0.1632, \"(-1.33, -1.28)\": 0.1764, \"(-1.28, -1.25)\": 0.1996, \"(-1.25, -1.19)\": 0.2172, \"(-1.19, -1.12)\": 0.2664, \"(-1.12, -1.06)\": 0.3084, \"(-1.06, -1.01)\": 0.3489, \"(-1.01, -0.96)\": 0.3671, \"(-0.96, -0.93)\": 0.4151, \"(-0.93, -0.9)\": 0.427, \"(-0.9, -0.87)\": 0.4621, \"(-0.87, -0.83)\": 0.4739, \"(-0.83, -0.78)\": 0.5263, \"(-0.78, -0.76)\": 0.5535, \"(-0.76, -0.73)\": 0.5709, \"(-0.73, -0.68)\": 0.5946, \"(-0.68, -0.62)\": 0.6504, \"(-0.62, -0.58)\": 0.7061, \"(-0.58, -0.57)\": 0.7207, \"(-0.57, -0.53)\": 0.7308, \"(-0.53, -0.49)\": 0.7719, \"(-0.49, -0.46)\": 0.7993, \"(-0.46, -0.44)\": 0.8154, \"(-0.44, -0.42)\": 0.8257, \"(-0.42, -0.39)\": 0.8406, \"(-0.39, -0.35)\": 0.8746, \"(-0.35, -0.32)\": 0.8868, \"(-0.32, -0.26)\": 0.9201, \"(-0.26, -0.23)\": 0.9387, \"(-0.23, -0.19)\": 0.9507, \"(-0.19, -0.11)\": 0.9755, \"(-0.11, 0.14)\": 0.9886, \"(0.14, 0.19)\": 0.9774, \"(0.19, 0.23)\": 0.9604, \"(0.23, 0.27)\": 0.9395, \"(0.27, 0.3)\": 0.9291, \"(0.3, 0.32)\": 0.9083, \"(0.32, 0.35)\": 0.8929, \"(0.35, 0.37)\": 0.8803, \"(0.37, 0.39)\": 0.8683, \"(0.39, 0.41)\": 0.8496, \"(0.41, 0.44)\": 0.8348, \"(0.44, 0.49)\": 0.8056, \"(0.49, 0.52)\": 0.7727, \"(0.52, 0.54)\": 0.7565, \"(0.54, 0.56)\": 0.7393, \"(0.56, 0.59)\": 0.7197, \"(0.59, 0.61)\": 0.6971, \"(0.61, 0.63)\": 0.6811, \"(0.63, 0.67)\": 0.656, \"(0.67, 0.7)\": 0.625, \"(0.7, 0.73)\": 0.6018, \"(0.73, 0.79)\": 0.571, \"(0.79, 0.83)\": 0.5127, \"(0.83, 0.84)\": 0.498, \"(0.84, 0.86)\": 0.4866, \"(0.86, 0.88)\": 0.4756, \"(0.88, 0.91)\": 0.4479, \"(0.91, 0.95)\": 0.432, \"(0.95, 0.99)\": 0.3882, \"(0.99, 1.02)\": 0.3662, \"(1.02, 1.04)\": 0.3542, \"(1.04, 1.08)\": 0.3302, \"(1.08, 1.09)\": 0.3086, \"(1.09, 1.11)\": 0.2978, \"(1.11, 1.13)\": 0.2839, \"(1.13, 1.16)\": 0.2714, \"(1.16, 1.19)\": 0.2542, \"(1.19, 1.23)\": 0.2335, \"(1.23, 1.26)\": 0.2153, \"(1.26, 1.28)\": 0.2039, \"(1.28, 1.29)\": 0.1938, \"(1.29, 1.33)\": 0.1837, \"(1.33, 1.38)\": 0.1638, \"(1.38, 1.43)\": 0.1391, \"(1.43, 1.49)\": 0.1222, \"(1.49, 1.54)\": 0.1023, \"(1.54, 1.6)\": 0.0885, \"(1.6, 1.64)\": 0.0766, \"(1.64, 1.69)\": 0.0655, \"(1.69, 1.77)\": 0.0533, \"(1.77, 1.85)\": 0.0423, \"(1.85, 1.96)\": 0.0318, \"(1.96, 2.13)\": 0.0212, \"(2.13, 2.83)\": 0.0103, \"(2.83, 9.99)\": 0.0003}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = 3^x+1\nb) f(x) = 2^x\nc) f(x) = exp(-x)\nd) f(x) = exp(-x^2)\ne) f(x) = |x|\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "d)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.97, 3.27)\": 0.2, \"(3.27, 4.28)\": 10.0, \"(4.28, 4.89)\": 19.9, \"(4.89, 5.3)\": 30.0, \"(5.3, 5.61)\": 39.9, \"(5.61, 5.89)\": 50.0, \"(5.89, 6.12)\": 60.1, \"(6.12, 6.31)\": 70.8, \"(6.31, 6.52)\": 82.8, \"(6.52, 6.69)\": 94.0, \"(6.69, 6.83)\": 104.5, \"(6.83, 6.98)\": 115.5, \"(6.98, 7.08)\": 127.1, \"(7.08, 7.19)\": 137.3, \"(7.19, 7.32)\": 149.1, \"(7.32, 7.41)\": 160.2, \"(7.41, 7.5)\": 170.9, \"(7.5, 7.58)\": 182.2, \"(7.58, 7.66)\": 192.4, \"(7.66, 7.73)\": 203.5, \"(7.73, 7.79)\": 213.3, \"(7.79, 7.87)\": 223.7, \"(7.87, 7.95)\": 234.3, \"(7.95, 8.06)\": 255.8, \"(8.06, 8.12)\": 268.7, \"(8.12, 8.18)\": 280.7, \"(8.18, 8.23)\": 290.9, \"(8.23, 8.28)\": 301.1, \"(8.28, 8.33)\": 311.4, \"(8.33, 8.37)\": 323.2, \"(8.37, 8.43)\": 333.0, \"(8.43, 8.47)\": 347.0, \"(8.47, 8.52)\": 360.5, \"(8.52, 8.58)\": 373.9, \"(8.58, 8.65)\": 389.5, \"(8.65, 8.73)\": 415.6, \"(8.73, 8.76)\": 428.8, \"(8.76, 8.83)\": 438.9, \"(8.83, 8.88)\": 460.3, \"(8.88, 8.91)\": 470.6, \"(8.91, 8.95)\": 484.2, \"(8.95, 8.98)\": 497.9, \"(8.98, 9.03)\": 514.7, \"(9.03, 9.07)\": 530.5, \"(9.07, 9.12)\": 546.4, \"(9.12, 9.14)\": 558.5, \"(9.14, 9.18)\": 569.2, \"(9.18, 9.22)\": 582.5, \"(9.22, 9.24)\": 596.9, \"(9.24, 9.28)\": 611.6, \"(9.28, 9.3)\": 623.1, \"(9.3, 9.33)\": 636.5, \"(9.33, 9.37)\": 649.2, \"(9.37, 9.42)\": 672.2, \"(9.42, 9.45)\": 689.2, \"(9.45, 9.48)\": 705.5, \"(9.48, 9.51)\": 721.4, \"(9.51, 9.54)\": 734.3, \"(9.54, 9.57)\": 751.8, \"(9.57, 9.59)\": 761.6, \"(9.59, 9.63)\": 779.3, \"(9.63, 9.65)\": 793.6, \"(9.65, 9.69)\": 808.3, \"(9.69, 9.75)\": 852.2, \"(9.75, 9.78)\": 866.3, \"(9.78, 9.81)\": 889.7, \"(9.81, 9.83)\": 901.1, \"(9.83, 9.86)\": 911.4, \"(9.86, 9.89)\": 941.1, \"(9.89, 9.99)\": 976.5}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = sign(x)\nb) f(x) = 2^x\nc) f(x) = x\nd) f(x) = -|-x|\ne) f(x) = -3*x^3\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "b)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-10.0, 5.82)\": 16.0, \"(5.82, 6.4)\": 621.9, \"(6.4, 6.81)\": 1222.7, \"(6.81, 7.08)\": 1847.7, \"(7.08, 7.25)\": 2425.8, \"(7.25, 7.43)\": 3015.5, \"(7.43, 7.62)\": 3623.8, \"(7.62, 7.71)\": 4284.3, \"(7.71, 7.84)\": 4888.2, \"(7.84, 7.93)\": 5568.5, \"(7.93, 8.03)\": 6301.5, \"(8.03, 8.14)\": 6899.3, \"(8.14, 8.23)\": 7834.9, \"(8.23, 8.31)\": 8656.9, \"(8.31, 8.38)\": 9331.4, \"(8.38, 8.42)\": 9934.1, \"(8.42, 8.48)\": 10537.7, \"(8.48, 8.52)\": 11199.3, \"(8.52, 8.6)\": 11940.5, \"(8.6, 8.64)\": 12767.8, \"(8.64, 8.69)\": 13392.6, \"(8.69, 8.73)\": 14034.4, \"(8.73, 8.77)\": 14804.7, \"(8.77, 8.81)\": 15461.8, \"(8.81, 8.89)\": 16179.1, \"(8.89, 8.95)\": 18128.8, \"(8.95, 9.0)\": 19011.0, \"(9.0, 9.05)\": 20245.5, \"(9.05, 9.1)\": 21005.3, \"(9.1, 9.15)\": 22690.0, \"(9.15, 9.19)\": 23617.7, \"(9.19, 9.24)\": 24893.2, \"(9.24, 9.27)\": 25730.4, \"(9.27, 9.29)\": 26566.2, \"(9.29, 9.32)\": 27300.8, \"(9.32, 9.36)\": 28775.0, \"(9.36, 9.37)\": 29406.4, \"(9.37, 9.41)\": 30172.3, \"(9.41, 9.45)\": 31796.4, \"(9.45, 9.47)\": 32627.7, \"(9.47, 9.5)\": 33364.2, \"(9.5, 9.54)\": 34915.4, \"(9.54, 9.56)\": 35811.1, \"(9.56, 9.6)\": 37508.8, \"(9.6, 9.61)\": 38120.9, \"(9.61, 9.63)\": 38835.5, \"(9.63, 9.64)\": 39570.0, \"(9.64, 9.68)\": 40172.9, \"(9.68, 9.72)\": 42652.6, \"(9.72, 9.73)\": 43418.2, \"(9.73, 9.75)\": 44275.9, \"(9.75, 9.76)\": 45130.5, \"(9.76, 9.79)\": 45921.4, \"(9.79, 9.82)\": 47788.2, \"(9.82, 9.84)\": 49180.7, \"(9.84, 9.86)\": 49786.5, \"(9.86, 9.92)\": 51790.1, \"(9.92, 9.96)\": 55473.7, \"(9.96, 9.97)\": 56813.4, \"(9.97, 9.98)\": 57606.1}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = 1/(1+exp(-x))\nb) f(x) = cosh(x)\nc) f(x) = -sqrt(x+10)\nd) f(x) = 3^x+1\ne) f(x) = -|x|\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "d)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.99, 3.32)\": 0.01, \"(3.32, 4.34)\": 0.32, \"(4.34, 4.91)\": 0.64, \"(4.91, 5.34)\": 0.96, \"(5.34, 5.68)\": 1.3, \"(5.68, 5.96)\": 1.61, \"(5.96, 6.17)\": 1.93, \"(6.17, 6.36)\": 2.25, \"(6.36, 6.54)\": 2.59, \"(6.54, 6.69)\": 2.94, \"(6.69, 6.84)\": 3.26, \"(6.84, 6.96)\": 3.61, \"(6.96, 7.08)\": 3.94, \"(7.08, 7.2)\": 4.25, \"(7.2, 7.3)\": 4.63, \"(7.3, 7.4)\": 4.94, \"(7.4, 7.48)\": 5.26, \"(7.48, 7.56)\": 5.61, \"(7.56, 7.65)\": 5.96, \"(7.65, 7.73)\": 6.32, \"(7.73, 7.8)\": 6.66, \"(7.8, 7.87)\": 7.0, \"(7.87, 7.95)\": 7.35, \"(7.95, 8.04)\": 7.89, \"(8.04, 8.1)\": 8.24, \"(8.1, 8.16)\": 8.61, \"(8.16, 8.21)\": 8.97, \"(8.21, 8.27)\": 9.45, \"(8.27, 8.33)\": 9.77, \"(8.33, 8.39)\": 10.12, \"(8.39, 8.45)\": 10.5, \"(8.45, 8.54)\": 11.23, \"(8.54, 8.58)\": 11.65, \"(8.58, 8.62)\": 12.09, \"(8.62, 8.68)\": 12.43, \"(8.68, 8.73)\": 12.95, \"(8.73, 8.78)\": 13.37, \"(8.78, 8.86)\": 13.89, \"(8.86, 8.94)\": 14.92, \"(8.94, 8.98)\": 15.34, \"(8.98, 9.03)\": 15.91, \"(9.03, 9.06)\": 16.49, \"(9.06, 9.12)\": 16.95, \"(9.12, 9.18)\": 17.91, \"(9.18, 9.21)\": 18.27, \"(9.21, 9.24)\": 18.68, \"(9.24, 9.28)\": 19.22, \"(9.28, 9.34)\": 19.92, \"(9.34, 9.37)\": 20.54, \"(9.37, 9.41)\": 20.92, \"(9.41, 9.45)\": 21.51, \"(9.45, 9.48)\": 22.04, \"(9.48, 9.51)\": 22.41, \"(9.51, 9.54)\": 22.96, \"(9.54, 9.57)\": 23.37, \"(9.57, 9.59)\": 23.88, \"(9.59, 9.63)\": 24.45, \"(9.63, 9.65)\": 24.85, \"(9.65, 9.69)\": 25.21, \"(9.69, 9.72)\": 26.18, \"(9.72, 9.78)\": 26.65, \"(9.78, 9.83)\": 28.13, \"(9.83, 9.85)\": 28.46, \"(9.85, 9.9)\": 29.34, \"(9.9, 9.94)\": 30.32, \"(9.94, 9.98)\": 30.89}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = 2^(x-5)\nb) f(x) = x\nc) f(x) = -|x|\nd) f(x) = -exp(x)\ne) f(x) = -sign(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "a)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.97, -9.95)\": -3.425, \"(-9.95, -9.94)\": -2.832, \"(-9.94, -9.89)\": -2.687, \"(-9.89, -9.84)\": -1.935, \"(-9.84, -9.83)\": -1.798, \"(-9.83, -9.81)\": -1.724, \"(-9.81, -9.79)\": -1.599, \"(-9.79, -9.78)\": -1.522, \"(-9.78, -9.76)\": -1.446, \"(-9.76, -9.72)\": -1.366, \"(-9.72, -9.68)\": -1.174, \"(-9.68, -9.65)\": -1.106, \"(-9.65, -9.63)\": -1.04, \"(-9.63, -9.6)\": -0.974, \"(-9.6, -9.57)\": -0.896, \"(-9.57, -9.55)\": -0.827, \"(-9.55, -9.49)\": -0.759, \"(-9.49, -9.42)\": -0.599, \"(-9.42, -9.38)\": -0.515, \"(-9.38, -9.3)\": -0.43, \"(-9.3, -9.24)\": -0.346, \"(-9.24, -9.19)\": -0.269, \"(-9.19, -9.09)\": -0.171, \"(-9.09, -9.02)\": -0.078, \"(-9.02, -8.92)\": -0.007, \"(-8.92, -8.83)\": 0.098, \"(-8.83, -8.74)\": 0.165, \"(-8.74, -8.64)\": 0.242, \"(-8.64, -8.55)\": 0.318, \"(-8.55, -8.43)\": 0.389, \"(-8.43, -8.31)\": 0.459, \"(-8.31, -8.19)\": 0.54, \"(-8.19, -8.04)\": 0.606, \"(-8.04, -7.87)\": 0.686, \"(-7.87, -7.71)\": 0.765, \"(-7.71, -7.54)\": 0.833, \"(-7.54, -7.38)\": 0.902, \"(-7.38, -7.2)\": 0.977, \"(-7.2, -6.98)\": 1.045, \"(-6.98, -6.77)\": 1.11, \"(-6.77, -6.54)\": 1.183, \"(-6.54, -6.28)\": 1.247, \"(-6.28, -6.05)\": 1.315, \"(-6.05, -5.75)\": 1.379, \"(-5.75, -5.48)\": 1.447, \"(-5.48, -5.16)\": 1.512, \"(-5.16, -4.82)\": 1.579, \"(-4.82, -4.46)\": 1.648, \"(-4.46, -4.1)\": 1.713, \"(-4.1, -3.7)\": 1.78, \"(-3.7, -3.25)\": 1.844, \"(-3.25, -2.77)\": 1.91, \"(-2.77, -2.31)\": 1.978, \"(-2.31, -1.75)\": 2.043, \"(-1.75, -1.19)\": 2.108, \"(-1.19, -0.6)\": 2.176, \"(-0.6, 0.08)\": 2.241, \"(0.08, 0.73)\": 2.308, \"(0.73, 1.48)\": 2.374, \"(1.48, 2.26)\": 2.439, \"(2.26, 3.05)\": 2.505, \"(3.05, 3.92)\": 2.569, \"(3.92, 4.89)\": 2.634, \"(4.89, 5.95)\": 2.701, \"(5.95, 7.02)\": 2.767, \"(7.02, 8.16)\": 2.832, \"(8.16, 9.38)\": 2.897, \"(9.38, 9.98)\": 2.962}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = arcsinh(x)\nb) f(x) = log(exp(x))\nc) f(x) = tanh(x)\nd) f(x) = log(x+10)\ne) f(x) = -log(x+10)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "d)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-10.0, -9.96)\": 4.102, \"(-9.96, -9.96)\": 3.251, \"(-9.96, -9.95)\": 3.037, \"(-9.95, -9.94)\": 2.848, \"(-9.94, -9.91)\": 2.769, \"(-9.91, -9.86)\": 2.133, \"(-9.86, -9.83)\": 1.845, \"(-9.83, -9.82)\": 1.727, \"(-9.82, -9.79)\": 1.602, \"(-9.79, -9.76)\": 1.477, \"(-9.76, -9.73)\": 1.362, \"(-9.73, -9.69)\": 1.269, \"(-9.69, -9.65)\": 1.095, \"(-9.65, -9.61)\": 0.989, \"(-9.61, -9.56)\": 0.897, \"(-9.56, -9.49)\": 0.73, \"(-9.49, -9.46)\": 0.628, \"(-9.46, -9.39)\": 0.549, \"(-9.39, -9.32)\": 0.462, \"(-9.32, -9.24)\": 0.318, \"(-9.24, -9.18)\": 0.242, \"(-9.18, -9.09)\": 0.17, \"(-9.09, -8.96)\": 0.022, \"(-8.96, -8.87)\": -0.053, \"(-8.87, -8.76)\": -0.156, \"(-8.76, -8.66)\": -0.229, \"(-8.66, -8.55)\": -0.316, \"(-8.55, -8.42)\": -0.393, \"(-8.42, -8.3)\": -0.464, \"(-8.3, -8.17)\": -0.544, \"(-8.17, -8.03)\": -0.615, \"(-8.03, -7.9)\": -0.686, \"(-7.9, -7.7)\": -0.757, \"(-7.7, -7.5)\": -0.849, \"(-7.5, -7.29)\": -0.93, \"(-7.29, -7.08)\": -1.002, \"(-7.08, -6.88)\": -1.076, \"(-6.88, -6.65)\": -1.147, \"(-6.65, -6.38)\": -1.22, \"(-6.38, -6.08)\": -1.293, \"(-6.08, -5.81)\": -1.367, \"(-5.81, -5.45)\": -1.44, \"(-5.45, -5.16)\": -1.513, \"(-5.16, -4.79)\": -1.583, \"(-4.79, -4.35)\": -1.656, \"(-4.35, -3.97)\": -1.727, \"(-3.97, -3.49)\": -1.799, \"(-3.49, -2.98)\": -1.87, \"(-2.98, -2.41)\": -1.947, \"(-2.41, -1.97)\": -2.019, \"(-1.97, -1.35)\": -2.091, \"(-1.35, -0.63)\": -2.162, \"(-0.63, 0.07)\": -2.234, \"(0.07, 0.85)\": -2.306, \"(0.85, 1.65)\": -2.378, \"(1.65, 2.47)\": -2.45, \"(2.47, 3.43)\": -2.521, \"(3.43, 4.44)\": -2.593, \"(4.44, 5.59)\": -2.665, \"(5.59, 6.74)\": -2.736, \"(6.74, 8.11)\": -2.809, \"(8.11, 9.51)\": -2.881, \"(9.51, 9.98)\": -2.952}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = -sin(x)\nb) f(x) = x\nc) f(x) = -log(x+10)\nd) f(x) = -|x|\ne) f(x) = -2*x^2\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "c)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-10.0, -9.79)\": -9.99, \"(-9.79, -9.58)\": -9.79, \"(-9.58, -9.39)\": -9.57, \"(-9.39, -9.16)\": -9.36, \"(-9.16, -8.96)\": -9.15, \"(-8.96, -8.74)\": -8.94, \"(-8.74, -8.55)\": -8.74, \"(-8.55, -8.32)\": -8.52, \"(-8.32, -8.11)\": -8.32, \"(-8.11, -7.91)\": -8.1, \"(-7.91, -7.67)\": -7.88, \"(-7.67, -7.44)\": -7.66, \"(-7.44, -7.23)\": -7.43, \"(-7.23, -7.06)\": -7.23, \"(-7.06, -6.83)\": -7.02, \"(-6.83, -6.54)\": -6.8, \"(-6.54, -6.26)\": -6.47, \"(-6.26, -6.07)\": -6.25, \"(-6.07, -5.83)\": -6.05, \"(-5.83, -5.59)\": -5.82, \"(-5.59, -5.34)\": -5.55, \"(-5.34, -5.14)\": -5.35, \"(-5.14, -4.93)\": -5.14, \"(-4.93, -4.73)\": -4.93, \"(-4.73, -4.51)\": -4.71, \"(-4.51, -4.31)\": -4.51, \"(-4.31, -4.07)\": -4.3, \"(-4.07, -3.84)\": -4.06, \"(-3.84, -3.64)\": -3.84, \"(-3.64, -3.45)\": -3.63, \"(-3.45, -3.23)\": -3.43, \"(-3.23, -3.04)\": -3.23, \"(-3.04, -2.79)\": -3.01, \"(-2.79, -2.59)\": -2.79, \"(-2.59, -2.39)\": -2.58, \"(-2.39, -2.17)\": -2.37, \"(-2.17, -1.93)\": -2.15, \"(-1.93, -1.72)\": -1.93, \"(-1.72, -1.52)\": -1.73, \"(-1.52, -1.3)\": -1.5, \"(-1.3, -1.09)\": -1.28, \"(-1.09, -0.86)\": -1.08, \"(-0.86, -0.67)\": -0.87, \"(-0.67, -0.47)\": -0.65, \"(-0.47, -0.23)\": -0.44, \"(-0.23, -0.02)\": -0.22, \"(-0.02, 0.19)\": 0.01, \"(0.19, 0.42)\": 0.21, \"(0.42, 0.68)\": 0.47, \"(0.68, 0.89)\": 0.7, \"(0.89, 1.11)\": 0.91, \"(1.11, 1.3)\": 1.12, \"(1.3, 1.53)\": 1.33, \"(1.53, 1.76)\": 1.55, \"(1.76, 1.95)\": 1.76, \"(1.95, 2.17)\": 1.97, \"(2.17, 2.36)\": 2.18, \"(2.36, 2.58)\": 2.39, \"(2.58, 2.8)\": 2.6, \"(2.8, 3.0)\": 2.82, \"(3.0, 3.24)\": 3.02, \"(3.24, 3.43)\": 3.25, \"(3.43, 3.66)\": 3.45, \"(3.66, 3.86)\": 3.67, \"(3.86, 4.07)\": 3.88, \"(4.07, 4.27)\": 4.08, \"(4.27, 4.49)\": 4.29, \"(4.49, 4.73)\": 4.51, \"(4.73, 4.91)\": 4.73, \"(4.91, 5.14)\": 4.96, \"(5.14, 5.39)\": 5.17, \"(5.39, 5.61)\": 5.41, \"(5.61, 5.82)\": 5.61, \"(5.82, 6.01)\": 5.82, \"(6.01, 6.24)\": 6.04, \"(6.24, 6.48)\": 6.26, \"(6.48, 6.69)\": 6.48, \"(6.69, 6.89)\": 6.69, \"(6.89, 7.11)\": 6.9, \"(7.11, 7.31)\": 7.11, \"(7.31, 7.5)\": 7.32, \"(7.5, 7.7)\": 7.52, \"(7.7, 7.91)\": 7.74, \"(7.91, 8.14)\": 7.94, \"(8.14, 8.37)\": 8.16, \"(8.37, 8.58)\": 8.37, \"(8.58, 8.83)\": 8.63, \"(8.83, 9.05)\": 8.84, \"(9.05, 9.25)\": 9.06, \"(9.25, 9.47)\": 9.27, \"(9.47, 9.7)\": 9.5, \"(9.7, 9.91)\": 9.72, \"(9.91, 9.98)\": 9.92}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = -log(x+10)\nb) f(x) = x^2\nc) f(x) = -sqrt(x+10)\nd) f(x) = log(exp(x))\ne) f(x) = -(x + 4)^4\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "d)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.99, -9.95)\": 0.527, \"(-9.95, -9.9)\": 0.487, \"(-9.9, -9.83)\": 0.44, \"(-9.83, -9.76)\": 0.347, \"(-9.76, -9.72)\": 0.316, \"(-9.72, -9.69)\": 0.279, \"(-9.69, -9.66)\": 0.253, \"(-9.66, -9.64)\": 0.229, \"(-9.64, -9.58)\": 0.193, \"(-9.58, -9.52)\": 0.119, \"(-9.52, -9.5)\": 0.091, \"(-9.5, -9.47)\": 0.063, \"(-9.47, -9.44)\": 0.037, \"(-9.44, -9.42)\": 0.003, \"(-9.42, -9.37)\": -0.023, \"(-9.37, -9.34)\": -0.064, \"(-9.34, -9.3)\": -0.107, \"(-9.3, -9.27)\": -0.138, \"(-9.27, -9.24)\": -0.169, \"(-9.24, -9.2)\": -0.205, \"(-9.2, -9.17)\": -0.231, \"(-9.17, -9.14)\": -0.266, \"(-9.14, -9.1)\": -0.299, \"(-9.1, -9.06)\": -0.344, \"(-9.06, -9.03)\": -0.367, \"(-9.03, -9.0)\": -0.393, \"(-9.0, -8.97)\": -0.424, \"(-8.97, -8.92)\": -0.451, \"(-8.92, -8.87)\": -0.503, \"(-8.87, -8.85)\": -0.528, \"(-8.85, -8.81)\": -0.554, \"(-8.81, -8.78)\": -0.581, \"(-8.78, -8.75)\": -0.605, \"(-8.75, -8.7)\": -0.647, \"(-8.7, -8.68)\": -0.668, \"(-8.68, -8.62)\": -0.689, \"(-8.62, -8.55)\": -0.736, \"(-8.55, -8.49)\": -0.789, \"(-8.49, -8.45)\": -0.811, \"(-8.45, -8.39)\": -0.839, \"(-8.39, -8.31)\": -0.862, \"(-8.31, -8.23)\": -0.911, \"(-8.23, -8.14)\": -0.939, \"(-8.14, -8.04)\": -0.96, \"(-8.04, -7.59)\": -0.985, \"(-7.59, -7.51)\": -0.963, \"(-7.51, -7.46)\": -0.941, \"(-7.46, -7.4)\": -0.92, \"(-7.4, -7.33)\": -0.886, \"(-7.33, -7.28)\": -0.859, \"(-7.28, -7.25)\": -0.836, \"(-7.25, -7.2)\": -0.813, \"(-7.2, -7.15)\": -0.788, \"(-7.15, -7.08)\": -0.745, \"(-7.08, -7.01)\": -0.683, \"(-7.01, -6.98)\": -0.655, \"(-6.98, -6.94)\": -0.633, \"(-6.94, -6.91)\": -0.596, \"(-6.91, -6.88)\": -0.575, \"(-6.88, -6.84)\": -0.549, \"(-6.84, -6.81)\": -0.526, \"(-6.81, -6.76)\": -0.498, \"(-6.76, -6.71)\": -0.427, \"(-6.71, -6.68)\": -0.402, \"(-6.68, -6.63)\": -0.377, \"(-6.63, -6.6)\": -0.33, \"(-6.6, -6.57)\": -0.298, \"(-6.57, -6.55)\": -0.277, \"(-6.55, -6.49)\": -0.254, \"(-6.49, -6.42)\": -0.176, \"(-6.42, -6.37)\": -0.105, \"(-6.37, -6.34)\": -0.076, \"(-6.34, -6.32)\": -0.053, \"(-6.32, -6.28)\": -0.013, \"(-6.28, -6.26)\": 0.012, \"(-6.26, -6.24)\": 0.032, \"(-6.24, -6.22)\": 0.056, \"(-6.22, -6.19)\": 0.077, \"(-6.19, -6.13)\": 0.097, \"(-6.13, -6.08)\": 0.193, \"(-6.08, -6.06)\": 0.214, \"(-6.06, -6.03)\": 0.239, \"(-6.03, -6.0)\": 0.266, \"(-6.0, -5.95)\": 0.3, \"(-5.95, -5.92)\": 0.335, \"(-5.92, -5.89)\": 0.358, \"(-5.89, -5.85)\": 0.403, \"(-5.85, -5.79)\": 0.439, \"(-5.79, -5.72)\": 0.517, \"(-5.72, -5.68)\": 0.546, \"(-5.68, -5.66)\": 0.567, \"(-5.66, -5.61)\": 0.589, \"(-5.61, -5.58)\": 0.629, \"(-5.58, -5.54)\": 0.654, \"(-5.54, -5.52)\": 0.678, \"(-5.52, -5.47)\": 0.708, \"(-5.47, -5.43)\": 0.735, \"(-5.43, -5.39)\": 0.759, \"(-5.39, -5.36)\": 0.779, \"(-5.36, -5.32)\": 0.802, \"(-5.32, -5.26)\": 0.828, \"(-5.26, -5.19)\": 0.87, \"(-5.19, -5.13)\": 0.895, \"(-5.13, -5.06)\": 0.918, \"(-5.06, -4.98)\": 0.944, \"(-4.98, -4.88)\": 0.968, \"(-4.88, -4.46)\": 0.988, \"(-4.46, -4.34)\": 0.964, \"(-4.34, -4.25)\": 0.916, \"(-4.25, -4.2)\": 0.894, \"(-4.2, -4.13)\": 0.869, \"(-4.13, -4.05)\": 0.809, \"(-4.05, -4.02)\": 0.783, \"(-4.02, -3.98)\": 0.759, \"(-3.98, -3.92)\": 0.721, \"(-3.92, -3.9)\": 0.697, \"(-3.9, -3.86)\": 0.677, \"(-3.86, -3.83)\": 0.656, \"(-3.83, -3.78)\": 0.632, \"(-3.78, -3.74)\": 0.579, \"(-3.74, -3.72)\": 0.558, \"(-3.72, -3.65)\": 0.53, \"(-3.65, -3.57)\": 0.436, \"(-3.57, -3.54)\": 0.406, \"(-3.54, -3.51)\": 0.372, \"(-3.51, -3.48)\": 0.348, \"(-3.48, -3.44)\": 0.315, \"(-3.44, -3.41)\": 0.276, \"(-3.41, -3.36)\": 0.244, \"(-3.36, -3.33)\": 0.199, \"(-3.33, -3.28)\": 0.165, \"(-3.28, -3.24)\": 0.125, \"(-3.24, -3.21)\": 0.083, \"(-3.21, -3.18)\": 0.057, \"(-3.18, -3.14)\": 0.016, \"(-3.14, -3.11)\": -0.015, \"(-3.11, -3.08)\": -0.037, \"(-3.08, -3.03)\": -0.074, \"(-3.03, -2.97)\": -0.147, \"(-2.97, -2.95)\": -0.171, \"(-2.95, -2.91)\": -0.201, \"(-2.91, -2.87)\": -0.24, \"(-2.87, -2.83)\": -0.267, \"(-2.83, -2.8)\": -0.321, \"(-2.8, -2.74)\": -0.345, \"(-2.74, -2.69)\": -0.414, \"(-2.69, -2.64)\": -0.453, \"(-2.64, -2.58)\": -0.501, \"(-2.58, -2.56)\": -0.539, \"(-2.56, -2.52)\": -0.562, \"(-2.52, -2.49)\": -0.597, \"(-2.49, -2.46)\": -0.622, \"(-2.46, -2.41)\": -0.647, \"(-2.41, -2.34)\": -0.695, \"(-2.34, -2.31)\": -0.718, \"(-2.31, -2.29)\": -0.741, \"(-2.29, -2.24)\": -0.767, \"(-2.24, -2.17)\": -0.808, \"(-2.17, -2.11)\": -0.829, \"(-2.11, -2.06)\": -0.867, \"(-2.06, -2.0)\": -0.89, \"(-2.0, -1.95)\": -0.912, \"(-1.95, -1.88)\": -0.934, \"(-1.88, -1.78)\": -0.956, \"(-1.78, -1.32)\": -0.984, \"(-1.32, -1.23)\": -0.962, \"(-1.23, -1.17)\": -0.938, \"(-1.17, -1.1)\": -0.913, \"(-1.1, -1.03)\": -0.885, \"(-1.03, -0.92)\": -0.85, \"(-0.92, -0.82)\": -0.745, \"(-0.82, -0.77)\": -0.722, \"(-0.77, -0.72)\": -0.674, \"(-0.72, -0.68)\": -0.654, \"(-0.68, -0.66)\": -0.631, \"(-0.66, -0.63)\": -0.609, \"(-0.63, -0.6)\": -0.582, \"(-0.6, -0.57)\": -0.557, \"(-0.57, -0.54)\": -0.532, \"(-0.54, -0.49)\": -0.494, \"(-0.49, -0.45)\": -0.469, \"(-0.45, -0.39)\": -0.404, \"(-0.39, -0.36)\": -0.379, \"(-0.36, -0.34)\": -0.347, \"(-0.34, -0.3)\": -0.324, \"(-0.3, -0.26)\": -0.27, \"(-0.26, -0.24)\": -0.249, \"(-0.24, -0.2)\": -0.229, \"(-0.2, -0.17)\": -0.187, \"(-0.17, -0.14)\": -0.162, \"(-0.14, -0.1)\": -0.126, \"(-0.1, -0.05)\": -0.07, \"(-0.05, -0.02)\": -0.041, \"(-0.02, 0.01)\": -0.015, \"(0.01, 0.03)\": 0.017, \"(0.03, 0.06)\": 0.04, \"(0.06, 0.08)\": 0.062, \"(0.08, 0.11)\": 0.092, \"(0.11, 0.13)\": 0.117, \"(0.13, 0.18)\": 0.138, \"(0.18, 0.22)\": 0.193, \"(0.22, 0.29)\": 0.238, \"(0.29, 0.34)\": 0.31, \"(0.34, 0.37)\": 0.346, \"(0.37, 0.41)\": 0.384, \"(0.41, 0.46)\": 0.409, \"(0.46, 0.5)\": 0.466, \"(0.5, 0.53)\": 0.493, \"(0.53, 0.6)\": 0.514, \"(0.6, 0.69)\": 0.616, \"(0.69, 0.72)\": 0.637, \"(0.72, 0.77)\": 0.673, \"(0.77, 0.83)\": 0.716, \"(0.83, 0.87)\": 0.741, \"(0.87, 0.93)\": 0.773, \"(0.93, 1.0)\": 0.815, \"(1.0, 1.06)\": 0.85, \"(1.06, 1.1)\": 0.874, \"(1.1, 1.19)\": 0.906, \"(1.19, 1.27)\": 0.93, \"(1.27, 1.37)\": 0.96, \"(1.37, 1.84)\": 0.981, \"(1.84, 1.9)\": 0.96, \"(1.9, 1.99)\": 0.933, \"(1.99, 2.05)\": 0.912, \"(2.05, 2.1)\": 0.88, \"(2.1, 2.15)\": 0.859, \"(2.15, 2.2)\": 0.822, \"(2.2, 2.25)\": 0.795, \"(2.25, 2.3)\": 0.774, \"(2.3, 2.35)\": 0.737, \"(2.35, 2.39)\": 0.697, \"(2.39, 2.45)\": 0.67, \"(2.45, 2.5)\": 0.617, \"(2.5, 2.54)\": 0.59, \"(2.54, 2.57)\": 0.567, \"(2.57, 2.62)\": 0.529, \"(2.62, 2.66)\": 0.484, \"(2.66, 2.68)\": 0.456, \"(2.68, 2.72)\": 0.434, \"(2.72, 2.76)\": 0.391, \"(2.76, 2.79)\": 0.364, \"(2.79, 2.82)\": 0.333, \"(2.82, 2.85)\": 0.308, \"(2.85, 2.89)\": 0.285, \"(2.89, 2.92)\": 0.244, \"(2.92, 2.94)\": 0.218, \"(2.94, 2.96)\": 0.197, \"(2.96, 2.99)\": 0.175, \"(2.99, 3.03)\": 0.137, \"(3.03, 3.07)\": 0.096, \"(3.07, 3.16)\": 0.055, \"(3.16, 3.23)\": -0.064, \"(3.23, 3.25)\": -0.088, \"(3.25, 3.27)\": -0.114, \"(3.27, 3.31)\": -0.145, \"(3.31, 3.36)\": -0.192, \"(3.36, 3.4)\": -0.232, \"(3.4, 3.43)\": -0.269, \"(3.43, 3.45)\": -0.292, \"(3.45, 3.5)\": -0.319, \"(3.5, 3.56)\": -0.379, \"(3.56, 3.59)\": -0.42, \"(3.59, 3.63)\": -0.452, \"(3.63, 3.66)\": -0.477, \"(3.66, 3.71)\": -0.504, \"(3.71, 3.75)\": -0.552, \"(3.75, 3.81)\": -0.596, \"(3.81, 3.88)\": -0.628, \"(3.88, 3.96)\": -0.707, \"(3.96, 3.99)\": -0.739, \"(3.99, 4.05)\": -0.761, \"(4.05, 4.13)\": -0.816, \"(4.13, 4.17)\": -0.838, \"(4.17, 4.22)\": -0.86, \"(4.22, 4.28)\": -0.894, \"(4.28, 4.37)\": -0.915, \"(4.37, 4.46)\": -0.949, \"(4.46, 4.56)\": -0.969, \"(4.56, 4.98)\": -0.99, \"(4.98, 5.06)\": -0.951, \"(5.06, 5.12)\": -0.929, \"(5.12, 5.19)\": -0.907, \"(5.19, 5.28)\": -0.859, \"(5.28, 5.32)\": -0.839, \"(5.32, 5.35)\": -0.813, \"(5.35, 5.39)\": -0.787, \"(5.39, 5.46)\": -0.763, \"(5.46, 5.53)\": -0.708, \"(5.53, 5.57)\": -0.677, \"(5.57, 5.61)\": -0.641, \"(5.61, 5.65)\": -0.615, \"(5.65, 5.68)\": -0.59, \"(5.68, 5.71)\": -0.568, \"(5.71, 5.74)\": -0.53, \"(5.74, 5.77)\": -0.503, \"(5.77, 5.82)\": -0.471, \"(5.82, 5.89)\": -0.417, \"(5.89, 5.95)\": -0.365, \"(5.95, 6.01)\": -0.28, \"(6.01, 6.04)\": -0.259, \"(6.04, 6.11)\": -0.201, \"(6.11, 6.18)\": -0.14, \"(6.18, 6.22)\": -0.089, \"(6.22, 6.27)\": -0.059, \"(6.27, 6.31)\": 0.013, \"(6.31, 6.33)\": 0.035, \"(6.33, 6.36)\": 0.062, \"(6.36, 6.39)\": 0.086, \"(6.39, 6.41)\": 0.106, \"(6.41, 6.46)\": 0.146, \"(6.46, 6.5)\": 0.201, \"(6.5, 6.52)\": 0.224, \"(6.52, 6.56)\": 0.253, \"(6.56, 6.59)\": 0.278, \"(6.59, 6.62)\": 0.31, \"(6.62, 6.66)\": 0.347, \"(6.66, 6.69)\": 0.374, \"(6.69, 6.7)\": 0.398, \"(6.7, 6.74)\": 0.419, \"(6.74, 6.77)\": 0.45, \"(6.77, 6.82)\": 0.485, \"(6.82, 6.87)\": 0.53, \"(6.87, 6.91)\": 0.566, \"(6.91, 6.95)\": 0.599, \"(6.95, 7.02)\": 0.64, \"(7.02, 7.09)\": 0.692, \"(7.09, 7.14)\": 0.734, \"(7.14, 7.18)\": 0.757, \"(7.18, 7.24)\": 0.789, \"(7.24, 7.28)\": 0.824, \"(7.28, 7.35)\": 0.851, \"(7.35, 7.41)\": 0.885, \"(7.41, 7.47)\": 0.907, \"(7.47, 7.54)\": 0.929, \"(7.54, 7.59)\": 0.951, \"(7.59, 7.74)\": 0.972, \"(7.74, 8.09)\": 0.993, \"(8.09, 8.21)\": 0.966, \"(8.21, 8.28)\": 0.927, \"(8.28, 8.32)\": 0.905, \"(8.32, 8.37)\": 0.885, \"(8.37, 8.42)\": 0.86, \"(8.42, 8.47)\": 0.837, \"(8.47, 8.51)\": 0.815, \"(8.51, 8.55)\": 0.779, \"(8.55, 8.59)\": 0.758, \"(8.59, 8.62)\": 0.738, \"(8.62, 8.67)\": 0.709, \"(8.67, 8.71)\": 0.667, \"(8.71, 8.76)\": 0.637, \"(8.76, 8.81)\": 0.596, \"(8.81, 8.86)\": 0.575, \"(8.86, 8.92)\": 0.495, \"(8.92, 8.96)\": 0.47, \"(8.96, 8.98)\": 0.446, \"(8.98, 9.0)\": 0.425, \"(9.0, 9.03)\": 0.405, \"(9.03, 9.06)\": 0.37, \"(9.06, 9.09)\": 0.341, \"(9.09, 9.14)\": 0.32, \"(9.14, 9.19)\": 0.248, \"(9.19, 9.22)\": 0.218, \"(9.22, 9.26)\": 0.195, \"(9.26, 9.29)\": 0.159, \"(9.29, 9.33)\": 0.113, \"(9.33, 9.35)\": 0.088, \"(9.35, 9.39)\": 0.044, \"(9.39, 9.45)\": 0.004, \"(9.45, 9.49)\": -0.041, \"(9.49, 9.51)\": -0.064, \"(9.51, 9.54)\": -0.096, \"(9.54, 9.58)\": -0.129, \"(9.58, 9.6)\": -0.154, \"(9.6, 9.62)\": -0.182, \"(9.62, 9.66)\": -0.21, \"(9.66, 9.68)\": -0.244, \"(9.68, 9.73)\": -0.27, \"(9.73, 9.76)\": -0.309, \"(9.76, 9.79)\": -0.344, \"(9.79, 9.84)\": -0.373, \"(9.84, 9.87)\": -0.41, \"(9.87, 9.9)\": -0.441, \"(9.9, 9.95)\": -0.473, \"(9.95, 10.0)\": -0.526}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = log(exp(x))\nb) f(x) = x\nc) f(x) = sign(x-1)\nd) f(x) = sin(x)\ne) f(x) = -sign(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "d)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.98, -9.93)\": -0.518, \"(-9.93, -9.88)\": -0.47, \"(-9.88, -9.86)\": -0.436, \"(-9.86, -9.83)\": -0.415, \"(-9.83, -9.81)\": -0.389, \"(-9.81, -9.78)\": -0.363, \"(-9.78, -9.74)\": -0.326, \"(-9.74, -9.72)\": -0.304, \"(-9.72, -9.69)\": -0.28, \"(-9.69, -9.66)\": -0.254, \"(-9.66, -9.63)\": -0.225, \"(-9.63, -9.59)\": -0.183, \"(-9.59, -9.56)\": -0.15, \"(-9.56, -9.51)\": -0.122, \"(-9.51, -9.48)\": -0.079, \"(-9.48, -9.46)\": -0.055, \"(-9.46, -9.44)\": -0.027, \"(-9.44, -9.41)\": -0.006, \"(-9.41, -9.39)\": 0.021, \"(-9.39, -9.36)\": 0.044, \"(-9.36, -9.32)\": 0.072, \"(-9.32, -9.29)\": 0.12, \"(-9.29, -9.24)\": 0.157, \"(-9.24, -9.2)\": 0.206, \"(-9.2, -9.17)\": 0.233, \"(-9.17, -9.14)\": 0.257, \"(-9.14, -9.11)\": 0.291, \"(-9.11, -9.08)\": 0.32, \"(-9.08, -9.04)\": 0.346, \"(-9.04, -8.99)\": 0.4, \"(-8.99, -8.93)\": 0.436, \"(-8.93, -8.87)\": 0.503, \"(-8.87, -8.84)\": 0.527, \"(-8.84, -8.82)\": 0.554, \"(-8.82, -8.78)\": 0.575, \"(-8.78, -8.73)\": 0.615, \"(-8.73, -8.69)\": 0.651, \"(-8.69, -8.65)\": 0.677, \"(-8.65, -8.61)\": 0.712, \"(-8.61, -8.58)\": 0.733, \"(-8.58, -8.53)\": 0.755, \"(-8.53, -8.48)\": 0.788, \"(-8.48, -8.42)\": 0.821, \"(-8.42, -8.38)\": 0.846, \"(-8.38, -8.33)\": 0.869, \"(-8.33, -8.27)\": 0.896, \"(-8.27, -8.22)\": 0.916, \"(-8.22, -8.13)\": 0.938, \"(-8.13, -7.98)\": 0.972, \"(-7.98, -7.64)\": 0.994, \"(-7.64, -7.55)\": 0.974, \"(-7.55, -7.48)\": 0.951, \"(-7.48, -7.41)\": 0.922, \"(-7.41, -7.35)\": 0.898, \"(-7.35, -7.33)\": 0.872, \"(-7.33, -7.28)\": 0.852, \"(-7.28, -7.23)\": 0.824, \"(-7.23, -7.18)\": 0.798, \"(-7.18, -7.15)\": 0.776, \"(-7.15, -7.11)\": 0.753, \"(-7.11, -7.05)\": 0.72, \"(-7.05, -7.01)\": 0.684, \"(-7.01, -6.98)\": 0.658, \"(-6.98, -6.95)\": 0.632, \"(-6.95, -6.9)\": 0.601, \"(-6.9, -6.87)\": 0.563, \"(-6.87, -6.84)\": 0.539, \"(-6.84, -6.8)\": 0.515, \"(-6.8, -6.76)\": 0.483, \"(-6.76, -6.73)\": 0.459, \"(-6.73, -6.69)\": 0.424, \"(-6.69, -6.63)\": 0.382, \"(-6.63, -6.58)\": 0.308, \"(-6.58, -6.55)\": 0.284, \"(-6.55, -6.51)\": 0.243, \"(-6.51, -6.48)\": 0.213, \"(-6.48, -6.45)\": 0.184, \"(-6.45, -6.43)\": 0.155, \"(-6.43, -6.4)\": 0.131, \"(-6.4, -6.37)\": 0.105, \"(-6.37, -6.34)\": 0.081, \"(-6.34, -6.31)\": 0.048, \"(-6.31, -6.28)\": 0.014, \"(-6.28, -6.25)\": -0.01, \"(-6.25, -6.24)\": -0.036, \"(-6.24, -6.2)\": -0.057, \"(-6.2, -6.18)\": -0.086, \"(-6.18, -6.16)\": -0.116, \"(-6.16, -6.13)\": -0.139, \"(-6.13, -6.11)\": -0.165, \"(-6.11, -6.08)\": -0.185, \"(-6.08, -6.04)\": -0.217, \"(-6.04, -6.02)\": -0.242, \"(-6.02, -5.99)\": -0.271, \"(-5.99, -5.96)\": -0.304, \"(-5.96, -5.92)\": -0.337, \"(-5.92, -5.89)\": -0.359, \"(-5.89, -5.85)\": -0.397, \"(-5.85, -5.83)\": -0.43, \"(-5.83, -5.8)\": -0.452, \"(-5.8, -5.76)\": -0.476, \"(-5.76, -5.74)\": -0.5, \"(-5.74, -5.7)\": -0.527, \"(-5.7, -5.64)\": -0.571, \"(-5.64, -5.6)\": -0.608, \"(-5.6, -5.55)\": -0.64, \"(-5.55, -5.5)\": -0.682, \"(-5.5, -5.47)\": -0.706, \"(-5.47, -5.41)\": -0.738, \"(-5.41, -5.33)\": -0.789, \"(-5.33, -5.3)\": -0.811, \"(-5.3, -5.27)\": -0.836, \"(-5.27, -5.21)\": -0.859, \"(-5.21, -5.15)\": -0.881, \"(-5.15, -5.09)\": -0.908, \"(-5.09, -5.0)\": -0.933, \"(-5.0, -4.88)\": -0.965, \"(-4.88, -4.45)\": -0.986, \"(-4.45, -4.39)\": -0.961, \"(-4.39, -4.29)\": -0.932, \"(-4.29, -4.24)\": -0.91, \"(-4.24, -4.18)\": -0.886, \"(-4.18, -4.13)\": -0.851, \"(-4.13, -4.1)\": -0.83, \"(-4.1, -4.04)\": -0.809, \"(-4.04, -4.0)\": -0.782, \"(-4.0, -3.95)\": -0.747, \"(-3.95, -3.89)\": -0.702, \"(-3.89, -3.84)\": -0.657, \"(-3.84, -3.78)\": -0.633, \"(-3.78, -3.72)\": -0.567, \"(-3.72, -3.69)\": -0.538, \"(-3.69, -3.64)\": -0.505, \"(-3.64, -3.59)\": -0.458, \"(-3.59, -3.55)\": -0.416, \"(-3.55, -3.51)\": -0.388, \"(-3.51, -3.44)\": -0.338, \"(-3.44, -3.4)\": -0.27, \"(-3.4, -3.37)\": -0.248, \"(-3.37, -3.34)\": -0.213, \"(-3.34, -3.3)\": -0.184, \"(-3.3, -3.27)\": -0.154, \"(-3.27, -3.24)\": -0.117, \"(-3.24, -3.2)\": -0.094, \"(-3.2, -3.15)\": -0.043, \"(-3.15, -3.11)\": 0.008, \"(-3.11, -3.06)\": 0.048, \"(-3.06, -3.01)\": 0.102, \"(-3.01, -2.96)\": 0.166, \"(-2.96, -2.94)\": 0.191, \"(-2.94, -2.91)\": 0.215, \"(-2.91, -2.88)\": 0.236, \"(-2.88, -2.85)\": 0.272, \"(-2.85, -2.82)\": 0.308, \"(-2.82, -2.78)\": 0.329, \"(-2.78, -2.74)\": 0.375, \"(-2.74, -2.72)\": 0.397, \"(-2.72, -2.69)\": 0.417, \"(-2.69, -2.65)\": 0.458, \"(-2.65, -2.6)\": 0.489, \"(-2.6, -2.56)\": 0.54, \"(-2.56, -2.51)\": 0.565, \"(-2.51, -2.43)\": 0.631, \"(-2.43, -2.39)\": 0.667, \"(-2.39, -2.35)\": 0.689, \"(-2.35, -2.29)\": 0.722, \"(-2.29, -2.25)\": 0.761, \"(-2.25, -2.21)\": 0.783, \"(-2.21, -2.18)\": 0.803, \"(-2.18, -2.13)\": 0.829, \"(-2.13, -2.09)\": 0.851, \"(-2.09, -2.04)\": 0.872, \"(-2.04, -1.99)\": 0.895, \"(-1.99, -1.9)\": 0.923, \"(-1.9, -1.8)\": 0.952, \"(-1.8, -1.68)\": 0.973, \"(-1.68, -1.36)\": 0.995, \"(-1.36, -1.22)\": 0.97, \"(-1.22, -1.12)\": 0.925, \"(-1.12, -1.06)\": 0.891, \"(-1.06, -1.02)\": 0.868, \"(-1.02, -0.96)\": 0.84, \"(-0.96, -0.91)\": 0.807, \"(-0.91, -0.87)\": 0.784, \"(-0.87, -0.83)\": 0.762, \"(-0.83, -0.79)\": 0.726, \"(-0.79, -0.75)\": 0.697, \"(-0.75, -0.71)\": 0.677, \"(-0.71, -0.68)\": 0.645, \"(-0.68, -0.64)\": 0.619, \"(-0.64, -0.62)\": 0.595, \"(-0.62, -0.59)\": 0.575, \"(-0.59, -0.57)\": 0.545, \"(-0.57, -0.52)\": 0.523, \"(-0.52, -0.48)\": 0.492, \"(-0.48, -0.42)\": 0.438, \"(-0.42, -0.37)\": 0.382, \"(-0.37, -0.33)\": 0.355, \"(-0.33, -0.3)\": 0.314, \"(-0.3, -0.27)\": 0.292, \"(-0.27, -0.23)\": 0.256, \"(-0.23, -0.19)\": 0.207, \"(-0.19, -0.16)\": 0.181, \"(-0.16, -0.13)\": 0.146, \"(-0.13, -0.08)\": 0.113, \"(-0.08, -0.05)\": 0.078, \"(-0.05, -0.01)\": 0.024, \"(-0.01, 0.02)\": -0.003, \"(0.02, 0.05)\": -0.036, \"(0.05, 0.08)\": -0.065, \"(0.08, 0.11)\": -0.086, \"(0.11, 0.15)\": -0.124, \"(0.15, 0.18)\": -0.171, \"(0.18, 0.21)\": -0.192, \"(0.21, 0.26)\": -0.226, \"(0.26, 0.31)\": -0.288, \"(0.31, 0.34)\": -0.317, \"(0.34, 0.37)\": -0.347, \"(0.37, 0.41)\": -0.372, \"(0.41, 0.46)\": -0.415, \"(0.46, 0.5)\": -0.46, \"(0.5, 0.53)\": -0.493, \"(0.53, 0.56)\": -0.516, \"(0.56, 0.58)\": -0.538, \"(0.58, 0.62)\": -0.56, \"(0.62, 0.65)\": -0.586, \"(0.65, 0.68)\": -0.611, \"(0.68, 0.72)\": -0.638, \"(0.72, 0.76)\": -0.659, \"(0.76, 0.83)\": -0.717, \"(0.83, 0.89)\": -0.739, \"(0.89, 0.96)\": -0.8, \"(0.96, 1.01)\": -0.822, \"(1.01, 1.05)\": -0.849, \"(1.05, 1.12)\": -0.874, \"(1.12, 1.2)\": -0.908, \"(1.2, 1.27)\": -0.935, \"(1.27, 1.37)\": -0.959, \"(1.37, 1.86)\": -0.981, \"(1.86, 1.94)\": -0.952, \"(1.94, 2.02)\": -0.931, \"(2.02, 2.1)\": -0.885, \"(2.1, 2.13)\": -0.863, \"(2.13, 2.19)\": -0.842, \"(2.19, 2.24)\": -0.807, \"(2.24, 2.27)\": -0.781, \"(2.27, 2.33)\": -0.758, \"(2.33, 2.37)\": -0.712, \"(2.37, 2.41)\": -0.683, \"(2.41, 2.46)\": -0.661, \"(2.46, 2.51)\": -0.624, \"(2.51, 2.57)\": -0.56, \"(2.57, 2.61)\": -0.528, \"(2.61, 2.63)\": -0.503, \"(2.63, 2.67)\": -0.475, \"(2.67, 2.71)\": -0.432, \"(2.71, 2.75)\": -0.408, \"(2.75, 2.79)\": -0.369, \"(2.79, 2.82)\": -0.336, \"(2.82, 2.85)\": -0.305, \"(2.85, 2.88)\": -0.273, \"(2.88, 2.9)\": -0.251, \"(2.9, 2.92)\": -0.23, \"(2.92, 2.96)\": -0.205, \"(2.96, 3.04)\": -0.16, \"(3.04, 3.13)\": -0.05, \"(3.13, 3.17)\": 0.01, \"(3.17, 3.22)\": 0.053, \"(3.22, 3.26)\": 0.105, \"(3.26, 3.29)\": 0.138, \"(3.29, 3.33)\": 0.166, \"(3.33, 3.35)\": 0.191, \"(3.35, 3.38)\": 0.216, \"(3.38, 3.42)\": 0.249, \"(3.42, 3.45)\": 0.283, \"(3.45, 3.51)\": 0.336, \"(3.51, 3.53)\": 0.367, \"(3.53, 3.56)\": 0.394, \"(3.56, 3.59)\": 0.417, \"(3.59, 3.63)\": 0.45, \"(3.63, 3.66)\": 0.483, \"(3.66, 3.71)\": 0.511, \"(3.71, 3.77)\": 0.561, \"(3.77, 3.83)\": 0.605, \"(3.83, 3.89)\": 0.646, \"(3.89, 3.96)\": 0.707, \"(3.96, 4.03)\": 0.749, \"(4.03, 4.08)\": 0.788, \"(4.08, 4.11)\": 0.81, \"(4.11, 4.17)\": 0.833, \"(4.17, 4.29)\": 0.876, \"(4.29, 4.36)\": 0.927, \"(4.36, 4.47)\": 0.947, \"(4.47, 4.58)\": 0.973, \"(4.58, 4.93)\": 0.993, \"(4.93, 5.04)\": 0.973, \"(5.04, 5.11)\": 0.938, \"(5.11, 5.17)\": 0.917, \"(5.17, 5.22)\": 0.894, \"(5.22, 5.3)\": 0.862, \"(5.3, 5.34)\": 0.824, \"(5.34, 5.39)\": 0.804, \"(5.39, 5.43)\": 0.779, \"(5.43, 5.46)\": 0.75, \"(5.46, 5.48)\": 0.73, \"(5.48, 5.54)\": 0.704, \"(5.54, 5.57)\": 0.667, \"(5.57, 5.64)\": 0.631, \"(5.64, 5.68)\": 0.588, \"(5.68, 5.69)\": 0.567, \"(5.69, 5.73)\": 0.542, \"(5.73, 5.77)\": 0.511, \"(5.77, 5.81)\": 0.475, \"(5.81, 5.82)\": 0.454, \"(5.82, 5.86)\": 0.431, \"(5.86, 5.91)\": 0.398, \"(5.91, 5.96)\": 0.337, \"(5.96, 6.0)\": 0.298, \"(6.0, 6.03)\": 0.272, \"(6.03, 6.08)\": 0.23, \"(6.08, 6.11)\": 0.189, \"(6.11, 6.14)\": 0.165, \"(6.14, 6.17)\": 0.125, \"(6.17, 6.19)\": 0.102, \"(6.19, 6.23)\": 0.077, \"(6.23, 6.25)\": 0.048, \"(6.25, 6.28)\": 0.025, \"(6.28, 6.31)\": -0.01, \"(6.31, 6.34)\": -0.031, \"(6.34, 6.37)\": -0.074, \"(6.37, 6.39)\": -0.095, \"(6.39, 6.43)\": -0.123, \"(6.43, 6.49)\": -0.179, \"(6.49, 6.53)\": -0.228, \"(6.53, 6.56)\": -0.251, \"(6.56, 6.58)\": -0.281, \"(6.58, 6.62)\": -0.309, \"(6.62, 6.65)\": -0.331, \"(6.65, 6.68)\": -0.361, \"(6.68, 6.71)\": -0.404, \"(6.71, 6.75)\": -0.432, \"(6.75, 6.79)\": -0.463, \"(6.79, 6.83)\": -0.485, \"(6.83, 6.89)\": -0.541, \"(6.89, 6.92)\": -0.58, \"(6.92, 6.95)\": -0.601, \"(6.95, 6.99)\": -0.624, \"(6.99, 7.05)\": -0.662, \"(7.05, 7.11)\": -0.708, \"(7.11, 7.15)\": -0.747, \"(7.15, 7.19)\": -0.77, \"(7.19, 7.23)\": -0.795, \"(7.23, 7.27)\": -0.818, \"(7.27, 7.33)\": -0.848, \"(7.33, 7.38)\": -0.872, \"(7.38, 7.42)\": -0.894, \"(7.42, 7.51)\": -0.915, \"(7.51, 7.56)\": -0.944, \"(7.56, 7.68)\": -0.965, \"(7.68, 8.12)\": -0.985, \"(8.12, 8.23)\": -0.96, \"(8.23, 8.33)\": -0.909, \"(8.33, 8.38)\": -0.882, \"(8.38, 8.43)\": -0.853, \"(8.43, 8.47)\": -0.826, \"(8.47, 8.51)\": -0.805, \"(8.51, 8.55)\": -0.784, \"(8.55, 8.59)\": -0.753, \"(8.59, 8.63)\": -0.73, \"(8.63, 8.69)\": -0.707, \"(8.69, 8.74)\": -0.659, \"(8.74, 8.81)\": -0.605, \"(8.81, 8.88)\": -0.546, \"(8.88, 8.92)\": -0.502, \"(8.92, 8.97)\": -0.455, \"(8.97, 9.0)\": -0.431, \"(9.0, 9.03)\": -0.404, \"(9.03, 9.06)\": -0.378, \"(9.06, 9.11)\": -0.336, \"(9.11, 9.18)\": -0.285, \"(9.18, 9.24)\": -0.208, \"(9.24, 9.29)\": -0.177, \"(9.29, 9.33)\": -0.112, \"(9.33, 9.37)\": -0.085, \"(9.37, 9.42)\": -0.034, \"(9.42, 9.45)\": -0.004, \"(9.45, 9.49)\": 0.034, \"(9.49, 9.56)\": 0.071, \"(9.56, 9.62)\": 0.177, \"(9.62, 9.65)\": 0.201, \"(9.65, 9.68)\": 0.241, \"(9.68, 9.72)\": 0.265, \"(9.72, 9.78)\": 0.322, \"(9.78, 9.82)\": 0.356, \"(9.82, 9.87)\": 0.408, \"(9.87, 9.9)\": 0.434, \"(9.9, 9.96)\": 0.489, \"(9.96, 9.98)\": 0.517}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = x^2+3*x-1\nb) f(x) = sign(x)\nc) f(x) = -sin(x)\nd) f(x) = -(x + 4)^4\ne) f(x) = x\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "c)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.97, -9.93)\": 0.507, \"(-9.93, -9.88)\": 0.479, \"(-9.88, -9.81)\": 0.402, \"(-9.81, -9.77)\": 0.365, \"(-9.77, -9.75)\": 0.337, \"(-9.75, -9.73)\": 0.312, \"(-9.73, -9.7)\": 0.285, \"(-9.7, -9.68)\": 0.264, \"(-9.68, -9.65)\": 0.24, \"(-9.65, -9.63)\": 0.219, \"(-9.63, -9.6)\": 0.198, \"(-9.6, -9.57)\": 0.169, \"(-9.57, -9.53)\": 0.132, \"(-9.53, -9.49)\": 0.093, \"(-9.49, -9.45)\": 0.052, \"(-9.45, -9.42)\": 0.018, \"(-9.42, -9.39)\": -0.015, \"(-9.39, -9.36)\": -0.04, \"(-9.36, -9.35)\": -0.063, \"(-9.35, -9.31)\": -0.086, \"(-9.31, -9.28)\": -0.124, \"(-9.28, -9.26)\": -0.147, \"(-9.26, -9.22)\": -0.184, \"(-9.22, -9.19)\": -0.218, \"(-9.19, -9.15)\": -0.241, \"(-9.15, -9.12)\": -0.29, \"(-9.12, -9.08)\": -0.31, \"(-9.08, -9.04)\": -0.362, \"(-9.04, -9.01)\": -0.392, \"(-9.01, -8.97)\": -0.412, \"(-8.97, -8.92)\": -0.469, \"(-8.92, -8.89)\": -0.498, \"(-8.89, -8.85)\": -0.529, \"(-8.85, -8.78)\": -0.567, \"(-8.78, -8.71)\": -0.638, \"(-8.71, -8.66)\": -0.661, \"(-8.66, -8.62)\": -0.703, \"(-8.62, -8.59)\": -0.724, \"(-8.59, -8.56)\": -0.746, \"(-8.56, -8.51)\": -0.771, \"(-8.51, -8.46)\": -0.796, \"(-8.46, -8.43)\": -0.826, \"(-8.43, -8.35)\": -0.851, \"(-8.35, -8.26)\": -0.898, \"(-8.26, -8.19)\": -0.922, \"(-8.19, -8.12)\": -0.947, \"(-8.12, -7.94)\": -0.975, \"(-7.94, -7.64)\": -0.998, \"(-7.64, -7.56)\": -0.974, \"(-7.56, -7.45)\": -0.949, \"(-7.45, -7.37)\": -0.908, \"(-7.37, -7.32)\": -0.88, \"(-7.32, -7.27)\": -0.855, \"(-7.27, -7.22)\": -0.829, \"(-7.22, -7.17)\": -0.797, \"(-7.17, -7.15)\": -0.773, \"(-7.15, -7.12)\": -0.753, \"(-7.12, -7.07)\": -0.73, \"(-7.07, -7.01)\": -0.689, \"(-7.01, -6.98)\": -0.658, \"(-6.98, -6.94)\": -0.63, \"(-6.94, -6.92)\": -0.606, \"(-6.92, -6.87)\": -0.584, \"(-6.87, -6.84)\": -0.546, \"(-6.84, -6.81)\": -0.522, \"(-6.81, -6.77)\": -0.487, \"(-6.77, -6.74)\": -0.458, \"(-6.74, -6.71)\": -0.425, \"(-6.71, -6.66)\": -0.404, \"(-6.66, -6.59)\": -0.333, \"(-6.59, -6.55)\": -0.281, \"(-6.55, -6.52)\": -0.249, \"(-6.52, -6.49)\": -0.22, \"(-6.49, -6.45)\": -0.187, \"(-6.45, -6.41)\": -0.146, \"(-6.41, -6.37)\": -0.107, \"(-6.37, -6.35)\": -0.08, \"(-6.35, -6.33)\": -0.057, \"(-6.33, -6.3)\": -0.035, \"(-6.3, -6.27)\": -0.009, \"(-6.27, -6.25)\": 0.016, \"(-6.25, -6.21)\": 0.046, \"(-6.21, -6.19)\": 0.07, \"(-6.19, -6.15)\": 0.108, \"(-6.15, -6.09)\": 0.156, \"(-6.09, -6.04)\": 0.218, \"(-6.04, -6.0)\": 0.257, \"(-6.0, -5.95)\": 0.296, \"(-5.95, -5.91)\": 0.345, \"(-5.91, -5.87)\": 0.385, \"(-5.87, -5.83)\": 0.414, \"(-5.83, -5.8)\": 0.45, \"(-5.8, -5.76)\": 0.47, \"(-5.76, -5.71)\": 0.523, \"(-5.71, -5.66)\": 0.562, \"(-5.66, -5.63)\": 0.587, \"(-5.63, -5.59)\": 0.622, \"(-5.59, -5.56)\": 0.646, \"(-5.56, -5.5)\": 0.671, \"(-5.5, -5.44)\": 0.727, \"(-5.44, -5.4)\": 0.752, \"(-5.4, -5.35)\": 0.778, \"(-5.35, -5.31)\": 0.807, \"(-5.31, -5.26)\": 0.83, \"(-5.26, -5.2)\": 0.866, \"(-5.2, -5.15)\": 0.886, \"(-5.15, -5.1)\": 0.908, \"(-5.1, -5.0)\": 0.939, \"(-5.0, -4.93)\": 0.961, \"(-4.93, -4.44)\": 0.982, \"(-4.44, -4.37)\": 0.961, \"(-4.37, -4.3)\": 0.937, \"(-4.3, -4.23)\": 0.906, \"(-4.23, -4.19)\": 0.881, \"(-4.19, -4.11)\": 0.859, \"(-4.11, -4.05)\": 0.811, \"(-4.05, -4.0)\": 0.788, \"(-4.0, -3.95)\": 0.737, \"(-3.95, -3.92)\": 0.714, \"(-3.92, -3.88)\": 0.692, \"(-3.88, -3.85)\": 0.671, \"(-3.85, -3.82)\": 0.644, \"(-3.82, -3.79)\": 0.616, \"(-3.79, -3.76)\": 0.596, \"(-3.76, -3.73)\": 0.575, \"(-3.73, -3.7)\": 0.546, \"(-3.7, -3.68)\": 0.526, \"(-3.68, -3.65)\": 0.504, \"(-3.65, -3.6)\": 0.466, \"(-3.6, -3.57)\": 0.435, \"(-3.57, -3.54)\": 0.406, \"(-3.54, -3.52)\": 0.384, \"(-3.52, -3.49)\": 0.362, \"(-3.49, -3.46)\": 0.338, \"(-3.46, -3.44)\": 0.317, \"(-3.44, -3.4)\": 0.29, \"(-3.4, -3.35)\": 0.226, \"(-3.35, -3.33)\": 0.206, \"(-3.33, -3.29)\": 0.179, \"(-3.29, -3.23)\": 0.117, \"(-3.23, -3.21)\": 0.085, \"(-3.21, -3.19)\": 0.058, \"(-3.19, -3.16)\": 0.035, \"(-3.16, -3.12)\": 0.014, \"(-3.12, -3.09)\": -0.036, \"(-3.09, -3.05)\": -0.062, \"(-3.05, -3.0)\": -0.091, \"(-3.0, -2.93)\": -0.187, \"(-2.93, -2.9)\": -0.222, \"(-2.9, -2.87)\": -0.253, \"(-2.87, -2.82)\": -0.275, \"(-2.82, -2.77)\": -0.345, \"(-2.77, -2.74)\": -0.367, \"(-2.74, -2.7)\": -0.409, \"(-2.7, -2.68)\": -0.431, \"(-2.68, -2.63)\": -0.47, \"(-2.63, -2.6)\": -0.5, \"(-2.6, -2.56)\": -0.524, \"(-2.56, -2.53)\": -0.554, \"(-2.53, -2.5)\": -0.575, \"(-2.5, -2.47)\": -0.605, \"(-2.47, -2.4)\": -0.639, \"(-2.4, -2.33)\": -0.703, \"(-2.33, -2.29)\": -0.734, \"(-2.29, -2.25)\": -0.757, \"(-2.25, -2.18)\": -0.799, \"(-2.18, -2.11)\": -0.84, \"(-2.11, -2.07)\": -0.861, \"(-2.07, -2.01)\": -0.888, \"(-2.01, -1.94)\": -0.912, \"(-1.94, -1.86)\": -0.936, \"(-1.86, -1.76)\": -0.964, \"(-1.76, -1.32)\": -0.985, \"(-1.32, -1.2)\": -0.956, \"(-1.2, -1.14)\": -0.922, \"(-1.14, -1.07)\": -0.901, \"(-1.07, -1.03)\": -0.875, \"(-1.03, -0.99)\": -0.85, \"(-0.99, -0.94)\": -0.819, \"(-0.94, -0.88)\": -0.791, \"(-0.88, -0.84)\": -0.766, \"(-0.84, -0.79)\": -0.725, \"(-0.79, -0.77)\": -0.704, \"(-0.77, -0.73)\": -0.679, \"(-0.73, -0.68)\": -0.654, \"(-0.68, -0.66)\": -0.628, \"(-0.66, -0.63)\": -0.607, \"(-0.63, -0.58)\": -0.57, \"(-0.58, -0.55)\": -0.542, \"(-0.55, -0.52)\": -0.511, \"(-0.52, -0.49)\": -0.489, \"(-0.49, -0.46)\": -0.469, \"(-0.46, -0.42)\": -0.43, \"(-0.42, -0.4)\": -0.406, \"(-0.4, -0.37)\": -0.376, \"(-0.37, -0.33)\": -0.349, \"(-0.33, -0.29)\": -0.308, \"(-0.29, -0.26)\": -0.284, \"(-0.26, -0.25)\": -0.261, \"(-0.25, -0.22)\": -0.235, \"(-0.22, -0.18)\": -0.207, \"(-0.18, -0.15)\": -0.175, \"(-0.15, -0.13)\": -0.14, \"(-0.13, -0.09)\": -0.116, \"(-0.09, -0.04)\": -0.072, \"(-0.04, 0.02)\": -0.015, \"(0.02, 0.06)\": 0.04, \"(0.06, 0.09)\": 0.075, \"(0.09, 0.12)\": 0.106, \"(0.12, 0.15)\": 0.136, \"(0.15, 0.19)\": 0.169, \"(0.19, 0.24)\": 0.2, \"(0.24, 0.29)\": 0.265, \"(0.29, 0.32)\": 0.29, \"(0.32, 0.35)\": 0.326, \"(0.35, 0.39)\": 0.349, \"(0.39, 0.44)\": 0.404, \"(0.44, 0.47)\": 0.432, \"(0.47, 0.49)\": 0.455, \"(0.49, 0.53)\": 0.478, \"(0.53, 0.56)\": 0.517, \"(0.56, 0.6)\": 0.553, \"(0.6, 0.63)\": 0.579, \"(0.63, 0.71)\": 0.6, \"(0.71, 0.8)\": 0.695, \"(0.8, 0.87)\": 0.734, \"(0.87, 0.93)\": 0.776, \"(0.93, 0.98)\": 0.812, \"(0.98, 1.03)\": 0.833, \"(1.03, 1.09)\": 0.861, \"(1.09, 1.14)\": 0.888, \"(1.14, 1.18)\": 0.911, \"(1.18, 1.26)\": 0.932, \"(1.26, 1.34)\": 0.953, \"(1.34, 1.48)\": 0.976, \"(1.48, 1.78)\": 0.996, \"(1.78, 1.85)\": 0.975, \"(1.85, 1.94)\": 0.952, \"(1.94, 1.99)\": 0.926, \"(1.99, 2.08)\": 0.904, \"(2.08, 2.15)\": 0.856, \"(2.15, 2.2)\": 0.835, \"(2.2, 2.24)\": 0.809, \"(2.24, 2.27)\": 0.778, \"(2.27, 2.33)\": 0.753, \"(2.33, 2.4)\": 0.707, \"(2.4, 2.45)\": 0.661, \"(2.45, 2.48)\": 0.626, \"(2.48, 2.53)\": 0.6, \"(2.53, 2.57)\": 0.553, \"(2.57, 2.6)\": 0.531, \"(2.6, 2.65)\": 0.503, \"(2.65, 2.69)\": 0.466, \"(2.69, 2.73)\": 0.418, \"(2.73, 2.76)\": 0.387, \"(2.76, 2.8)\": 0.363, \"(2.8, 2.84)\": 0.313, \"(2.84, 2.87)\": 0.29, \"(2.87, 2.89)\": 0.266, \"(2.89, 2.93)\": 0.232, \"(2.93, 2.95)\": 0.205, \"(2.95, 2.97)\": 0.185, \"(2.97, 2.99)\": 0.159, \"(2.99, 3.02)\": 0.134, \"(3.02, 3.05)\": 0.103, \"(3.05, 3.08)\": 0.077, \"(3.08, 3.1)\": 0.054, \"(3.1, 3.13)\": 0.026, \"(3.13, 3.17)\": -0.008, \"(3.17, 3.22)\": -0.056, \"(3.22, 3.26)\": -0.103, \"(3.26, 3.29)\": -0.124, \"(3.29, 3.32)\": -0.158, \"(3.32, 3.35)\": -0.196, \"(3.35, 3.38)\": -0.216, \"(3.38, 3.42)\": -0.236, \"(3.42, 3.46)\": -0.292, \"(3.46, 3.49)\": -0.326, \"(3.49, 3.52)\": -0.352, \"(3.52, 3.55)\": -0.381, \"(3.55, 3.59)\": -0.412, \"(3.59, 3.65)\": -0.464, \"(3.65, 3.71)\": -0.51, \"(3.71, 3.75)\": -0.555, \"(3.75, 3.79)\": -0.587, \"(3.79, 3.82)\": -0.613, \"(3.82, 3.86)\": -0.634, \"(3.86, 3.9)\": -0.662, \"(3.9, 3.95)\": -0.699, \"(3.95, 4.01)\": -0.728, \"(4.01, 4.09)\": -0.789, \"(4.09, 4.12)\": -0.818, \"(4.12, 4.19)\": -0.847, \"(4.19, 4.23)\": -0.869, \"(4.23, 4.3)\": -0.894, \"(4.3, 4.37)\": -0.92, \"(4.37, 4.49)\": -0.949, \"(4.49, 4.98)\": -0.983, \"(4.98, 5.08)\": -0.963, \"(5.08, 5.17)\": -0.917, \"(5.17, 5.22)\": -0.896, \"(5.22, 5.28)\": -0.866, \"(5.28, 5.33)\": -0.836, \"(5.33, 5.36)\": -0.813, \"(5.36, 5.4)\": -0.789, \"(5.4, 5.44)\": -0.768, \"(5.44, 5.5)\": -0.732, \"(5.5, 5.55)\": -0.696, \"(5.55, 5.62)\": -0.662, \"(5.62, 5.71)\": -0.565, \"(5.71, 5.76)\": -0.523, \"(5.76, 5.8)\": -0.479, \"(5.8, 5.83)\": -0.448, \"(5.83, 5.89)\": -0.425, \"(5.89, 5.96)\": -0.344, \"(5.96, 6.02)\": -0.294, \"(6.02, 6.06)\": -0.234, \"(6.06, 6.13)\": -0.204, \"(6.13, 6.19)\": -0.115, \"(6.19, 6.21)\": -0.091, \"(6.21, 6.23)\": -0.069, \"(6.23, 6.27)\": -0.035, \"(6.27, 6.31)\": 0.003, \"(6.31, 6.34)\": 0.035, \"(6.34, 6.39)\": 0.069, \"(6.39, 6.44)\": 0.135, \"(6.44, 6.48)\": 0.169, \"(6.48, 6.5)\": 0.204, \"(6.5, 6.52)\": 0.225, \"(6.52, 6.55)\": 0.248, \"(6.55, 6.59)\": 0.275, \"(6.59, 6.61)\": 0.301, \"(6.61, 6.66)\": 0.328, \"(6.66, 6.7)\": 0.383, \"(6.7, 6.74)\": 0.42, \"(6.74, 6.79)\": 0.461, \"(6.79, 6.83)\": 0.496, \"(6.83, 6.87)\": 0.534, \"(6.87, 6.91)\": 0.564, \"(6.91, 6.93)\": 0.587, \"(6.93, 6.97)\": 0.614, \"(6.97, 7.0)\": 0.642, \"(7.0, 7.03)\": 0.663, \"(7.03, 7.08)\": 0.687, \"(7.08, 7.13)\": 0.728, \"(7.13, 7.17)\": 0.756, \"(7.17, 7.2)\": 0.78, \"(7.2, 7.25)\": 0.802, \"(7.25, 7.28)\": 0.823, \"(7.28, 7.33)\": 0.847, \"(7.33, 7.44)\": 0.89, \"(7.44, 7.53)\": 0.933, \"(7.53, 7.62)\": 0.954, \"(7.62, 7.76)\": 0.975, \"(7.76, 8.07)\": 0.996, \"(8.07, 8.18)\": 0.975, \"(8.18, 8.25)\": 0.94, \"(8.25, 8.29)\": 0.919, \"(8.29, 8.36)\": 0.897, \"(8.36, 8.4)\": 0.875, \"(8.4, 8.46)\": 0.855, \"(8.46, 8.5)\": 0.811, \"(8.5, 8.57)\": 0.786, \"(8.57, 8.63)\": 0.75, \"(8.63, 8.66)\": 0.703, \"(8.66, 8.7)\": 0.681, \"(8.7, 8.74)\": 0.648, \"(8.74, 8.79)\": 0.627, \"(8.79, 8.84)\": 0.579, \"(8.84, 8.87)\": 0.547, \"(8.87, 8.9)\": 0.525, \"(8.9, 8.93)\": 0.497, \"(8.93, 8.96)\": 0.465, \"(8.96, 8.99)\": 0.441, \"(8.99, 9.04)\": 0.406, \"(9.04, 9.08)\": 0.362, \"(9.08, 9.12)\": 0.318, \"(9.12, 9.15)\": 0.29, \"(9.15, 9.2)\": 0.264, \"(9.2, 9.25)\": 0.195, \"(9.25, 9.28)\": 0.159, \"(9.28, 9.34)\": 0.123, \"(9.34, 9.39)\": 0.056, \"(9.39, 9.41)\": 0.029, \"(9.41, 9.46)\": -0.004, \"(9.46, 9.51)\": -0.065, \"(9.51, 9.52)\": -0.086, \"(9.52, 9.55)\": -0.107, \"(9.55, 9.6)\": -0.143, \"(9.6, 9.65)\": -0.192, \"(9.65, 9.69)\": -0.24, \"(9.69, 9.72)\": -0.271, \"(9.72, 9.75)\": -0.293, \"(9.75, 9.76)\": -0.322, \"(9.76, 9.81)\": -0.344, \"(9.81, 9.85)\": -0.397, \"(9.85, 9.89)\": -0.418, \"(9.89, 9.94)\": -0.475, \"(9.94, 9.96)\": -0.496, \"(9.96, 10.0)\": -0.517}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = x\nb) f(x) = |x^3|\nc) f(x) = cosh(x)\nd) f(x) = x\ne) f(x) = -sin(-x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "e)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.96, -9.66)\": 1.0, \"(-9.66, -9.56)\": 1.022, \"(-9.56, -9.48)\": 1.047, \"(-9.48, -9.42)\": 1.068, \"(-9.42, -9.34)\": 1.108, \"(-9.34, -9.29)\": 1.129, \"(-9.29, -9.23)\": 1.167, \"(-9.23, -9.19)\": 1.19, \"(-9.19, -9.14)\": 1.225, \"(-9.14, -9.09)\": 1.256, \"(-9.09, -9.04)\": 1.299, \"(-9.04, -8.99)\": 1.329, \"(-8.99, -8.97)\": 1.352, \"(-8.97, -8.93)\": 1.386, \"(-8.93, -8.9)\": 1.408, \"(-8.9, -8.86)\": 1.434, \"(-8.86, -8.83)\": 1.458, \"(-8.83, -8.8)\": 1.483, \"(-8.8, -8.78)\": 1.507, \"(-8.78, -8.76)\": 1.528, \"(-8.76, -8.73)\": 1.554, \"(-8.73, -8.69)\": 1.584, \"(-8.69, -8.67)\": 1.606, \"(-8.67, -8.63)\": 1.636, \"(-8.63, -8.6)\": 1.663, \"(-8.6, -8.58)\": 1.693, \"(-8.58, -8.55)\": 1.715, \"(-8.55, -8.52)\": 1.739, \"(-8.52, -8.47)\": 1.791, \"(-8.47, -8.44)\": 1.828, \"(-8.44, -8.42)\": 1.849, \"(-8.42, -8.4)\": 1.876, \"(-8.4, -8.36)\": 1.9, \"(-8.36, -8.34)\": 1.933, \"(-8.34, -8.32)\": 1.954, \"(-8.32, -8.25)\": 1.985, \"(-8.25, -8.19)\": 2.072, \"(-8.19, -8.17)\": 2.105, \"(-8.17, -8.13)\": 2.129, \"(-8.13, -8.03)\": 2.19, \"(-8.03, -7.96)\": 2.298, \"(-7.96, -7.91)\": 2.332, \"(-7.91, -7.86)\": 2.386, \"(-7.86, -7.83)\": 2.415, \"(-7.83, -7.8)\": 2.443, \"(-7.8, -7.77)\": 2.471, \"(-7.77, -7.73)\": 2.509, \"(-7.73, -7.69)\": 2.535, \"(-7.69, -7.66)\": 2.573, \"(-7.66, -7.57)\": 2.609, \"(-7.57, -7.46)\": 2.708, \"(-7.46, -7.41)\": 2.752, \"(-7.41, -7.38)\": 2.774, \"(-7.38, -7.33)\": 2.797, \"(-7.33, -7.3)\": 2.821, \"(-7.3, -7.24)\": 2.843, \"(-7.24, -7.19)\": 2.873, \"(-7.19, -7.1)\": 2.9, \"(-7.1, -7.02)\": 2.934, \"(-7.02, -6.92)\": 2.955, \"(-6.92, -6.77)\": 2.977, \"(-6.77, -6.49)\": 2.998, \"(-6.49, -6.4)\": 2.971, \"(-6.4, -6.34)\": 2.951, \"(-6.34, -6.27)\": 2.924, \"(-6.27, -6.21)\": 2.903, \"(-6.21, -6.15)\": 2.863, \"(-6.15, -6.1)\": 2.843, \"(-6.1, -6.06)\": 2.814, \"(-6.06, -6.01)\": 2.788, \"(-6.01, -5.95)\": 2.743, \"(-5.95, -5.91)\": 2.72, \"(-5.91, -5.88)\": 2.688, \"(-5.88, -5.84)\": 2.666, \"(-5.84, -5.82)\": 2.643, \"(-5.82, -5.79)\": 2.621, \"(-5.79, -5.77)\": 2.6, \"(-5.77, -5.74)\": 2.575, \"(-5.74, -5.7)\": 2.551, \"(-5.7, -5.66)\": 2.516, \"(-5.66, -5.62)\": 2.483, \"(-5.62, -5.59)\": 2.45, \"(-5.59, -5.56)\": 2.428, \"(-5.56, -5.48)\": 2.406, \"(-5.48, -5.4)\": 2.275, \"(-5.4, -5.36)\": 2.234, \"(-5.36, -5.33)\": 2.205, \"(-5.33, -5.29)\": 2.169, \"(-5.29, -5.27)\": 2.145, \"(-5.27, -5.2)\": 2.115, \"(-5.2, -5.12)\": 1.993, \"(-5.12, -5.09)\": 1.97, \"(-5.09, -5.05)\": 1.926, \"(-5.05, -5.02)\": 1.899, \"(-5.02, -4.99)\": 1.872, \"(-4.99, -4.96)\": 1.832, \"(-4.96, -4.92)\": 1.803, \"(-4.92, -4.86)\": 1.76, \"(-4.86, -4.84)\": 1.715, \"(-4.84, -4.81)\": 1.69, \"(-4.81, -4.79)\": 1.667, \"(-4.79, -4.77)\": 1.647, \"(-4.77, -4.71)\": 1.625, \"(-4.71, -4.64)\": 1.538, \"(-4.64, -4.59)\": 1.501, \"(-4.59, -4.56)\": 1.475, \"(-4.56, -4.53)\": 1.443, \"(-4.53, -4.48)\": 1.417, \"(-4.48, -4.44)\": 1.371, \"(-4.44, -4.42)\": 1.342, \"(-4.42, -4.36)\": 1.319, \"(-4.36, -4.31)\": 1.279, \"(-4.31, -4.27)\": 1.257, \"(-4.27, -4.23)\": 1.232, \"(-4.23, -4.17)\": 1.201, \"(-4.17, -4.12)\": 1.168, \"(-4.12, -4.06)\": 1.146, \"(-4.06, -3.98)\": 1.102, \"(-3.98, -3.91)\": 1.08, \"(-3.91, -3.83)\": 1.052, \"(-3.83, -3.73)\": 1.032, \"(-3.73, -3.32)\": 1.01, \"(-3.32, -3.22)\": 1.043, \"(-3.22, -3.17)\": 1.07, \"(-3.17, -3.08)\": 1.097, \"(-3.08, -3.02)\": 1.125, \"(-3.02, -2.97)\": 1.149, \"(-2.97, -2.9)\": 1.2, \"(-2.9, -2.85)\": 1.221, \"(-2.85, -2.82)\": 1.258, \"(-2.82, -2.79)\": 1.28, \"(-2.79, -2.69)\": 1.302, \"(-2.69, -2.61)\": 1.407, \"(-2.61, -2.58)\": 1.433, \"(-2.58, -2.54)\": 1.462, \"(-2.54, -2.52)\": 1.494, \"(-2.52, -2.48)\": 1.515, \"(-2.48, -2.45)\": 1.54, \"(-2.45, -2.39)\": 1.581, \"(-2.39, -2.34)\": 1.635, \"(-2.34, -2.3)\": 1.682, \"(-2.3, -2.28)\": 1.717, \"(-2.28, -2.24)\": 1.737, \"(-2.24, -2.2)\": 1.771, \"(-2.2, -2.17)\": 1.816, \"(-2.17, -2.14)\": 1.84, \"(-2.14, -2.11)\": 1.869, \"(-2.11, -2.07)\": 1.907, \"(-2.07, -2.03)\": 1.951, \"(-2.03, -1.99)\": 1.979, \"(-1.99, -1.95)\": 2.021, \"(-1.95, -1.9)\": 2.081, \"(-1.9, -1.87)\": 2.105, \"(-1.87, -1.83)\": 2.137, \"(-1.83, -1.8)\": 2.184, \"(-1.8, -1.73)\": 2.213, \"(-1.73, -1.69)\": 2.298, \"(-1.69, -1.65)\": 2.32, \"(-1.65, -1.59)\": 2.374, \"(-1.59, -1.51)\": 2.411, \"(-1.51, -1.45)\": 2.499, \"(-1.45, -1.42)\": 2.53, \"(-1.42, -1.39)\": 2.558, \"(-1.39, -1.33)\": 2.591, \"(-1.33, -1.3)\": 2.626, \"(-1.3, -1.26)\": 2.653, \"(-1.26, -1.22)\": 2.678, \"(-1.22, -1.18)\": 2.707, \"(-1.18, -1.14)\": 2.74, \"(-1.14, -1.09)\": 2.764, \"(-1.09, -1.06)\": 2.792, \"(-1.06, -1.02)\": 2.82, \"(-1.02, -0.97)\": 2.841, \"(-0.97, -0.92)\": 2.867, \"(-0.92, -0.85)\": 2.888, \"(-0.85, -0.78)\": 2.92, \"(-0.78, -0.68)\": 2.944, \"(-0.68, -0.51)\": 2.976, \"(-0.51, -0.23)\": 2.997, \"(-0.23, -0.11)\": 2.968, \"(-0.11, -0.03)\": 2.942, \"(-0.03, 0.02)\": 2.912, \"(0.02, 0.07)\": 2.891, \"(0.07, 0.14)\": 2.868, \"(0.14, 0.19)\": 2.84, \"(0.19, 0.23)\": 2.808, \"(0.23, 0.28)\": 2.782, \"(0.28, 0.34)\": 2.754, \"(0.34, 0.39)\": 2.701, \"(0.39, 0.42)\": 2.681, \"(0.42, 0.45)\": 2.66, \"(0.45, 0.5)\": 2.633, \"(0.5, 0.56)\": 2.583, \"(0.56, 0.59)\": 2.541, \"(0.59, 0.63)\": 2.511, \"(0.63, 0.66)\": 2.483, \"(0.66, 0.69)\": 2.456, \"(0.69, 0.73)\": 2.43, \"(0.73, 0.77)\": 2.38, \"(0.77, 0.78)\": 2.36, \"(0.78, 0.82)\": 2.339, \"(0.82, 0.86)\": 2.297, \"(0.86, 0.9)\": 2.263, \"(0.9, 0.94)\": 2.222, \"(0.94, 0.96)\": 2.19, \"(0.96, 0.99)\": 2.166, \"(0.99, 1.03)\": 2.143, \"(1.03, 1.09)\": 2.075, \"(1.09, 1.12)\": 2.046, \"(1.12, 1.14)\": 2.015, \"(1.14, 1.2)\": 1.989, \"(1.2, 1.26)\": 1.903, \"(1.26, 1.29)\": 1.871, \"(1.29, 1.3)\": 1.85, \"(1.3, 1.37)\": 1.822, \"(1.37, 1.43)\": 1.744, \"(1.43, 1.46)\": 1.705, \"(1.46, 1.49)\": 1.683, \"(1.49, 1.54)\": 1.653, \"(1.54, 1.57)\": 1.597, \"(1.57, 1.61)\": 1.573, \"(1.61, 1.62)\": 1.548, \"(1.62, 1.66)\": 1.526, \"(1.66, 1.69)\": 1.5, \"(1.69, 1.71)\": 1.476, \"(1.71, 1.76)\": 1.454, \"(1.76, 1.81)\": 1.401, \"(1.81, 1.84)\": 1.376, \"(1.84, 1.87)\": 1.352, \"(1.87, 1.92)\": 1.325, \"(1.92, 1.96)\": 1.291, \"(1.96, 2.01)\": 1.262, \"(2.01, 2.05)\": 1.233, \"(2.05, 2.08)\": 1.211, \"(2.08, 2.12)\": 1.186, \"(2.12, 2.18)\": 1.164, \"(2.18, 2.25)\": 1.125, \"(2.25, 2.33)\": 1.1, \"(2.33, 2.41)\": 1.07, \"(2.41, 2.52)\": 1.041, \"(2.52, 2.99)\": 1.017, \"(2.99, 3.07)\": 1.046, \"(3.07, 3.13)\": 1.068, \"(3.13, 3.19)\": 1.09, \"(3.19, 3.24)\": 1.113, \"(3.24, 3.28)\": 1.138, \"(3.28, 3.32)\": 1.164, \"(3.32, 3.41)\": 1.201, \"(3.41, 3.46)\": 1.251, \"(3.46, 3.49)\": 1.282, \"(3.49, 3.55)\": 1.302, \"(3.55, 3.62)\": 1.365, \"(3.62, 3.66)\": 1.397, \"(3.66, 3.68)\": 1.42, \"(3.68, 3.74)\": 1.441, \"(3.74, 3.8)\": 1.504, \"(3.8, 3.84)\": 1.553, \"(3.84, 3.87)\": 1.574, \"(3.87, 3.9)\": 1.609, \"(3.9, 3.93)\": 1.635, \"(3.93, 3.97)\": 1.67, \"(3.97, 4.01)\": 1.702, \"(4.01, 4.04)\": 1.739, \"(4.04, 4.07)\": 1.771, \"(4.07, 4.1)\": 1.794, \"(4.1, 4.12)\": 1.825, \"(4.12, 4.16)\": 1.855, \"(4.16, 4.2)\": 1.892, \"(4.2, 4.23)\": 1.934, \"(4.23, 4.26)\": 1.957, \"(4.26, 4.3)\": 1.99, \"(4.3, 4.36)\": 2.031, \"(4.36, 4.4)\": 2.101, \"(4.4, 4.45)\": 2.135, \"(4.45, 4.49)\": 2.176, \"(4.49, 4.51)\": 2.208, \"(4.51, 4.57)\": 2.234, \"(4.57, 4.63)\": 2.322, \"(4.63, 4.65)\": 2.351, \"(4.65, 4.71)\": 2.373, \"(4.71, 4.78)\": 2.456, \"(4.78, 4.81)\": 2.482, \"(4.81, 4.84)\": 2.512, \"(4.84, 4.87)\": 2.536, \"(4.87, 4.92)\": 2.557, \"(4.92, 4.98)\": 2.62, \"(4.98, 5.01)\": 2.645, \"(5.01, 5.05)\": 2.674, \"(5.05, 5.07)\": 2.695, \"(5.07, 5.14)\": 2.718, \"(5.14, 5.2)\": 2.779, \"(5.2, 5.25)\": 2.8, \"(5.25, 5.3)\": 2.837, \"(5.3, 5.35)\": 2.858, \"(5.35, 5.41)\": 2.879, \"(5.41, 5.48)\": 2.911, \"(5.48, 5.58)\": 2.939, \"(5.58, 5.69)\": 2.967, \"(5.69, 6.1)\": 2.989, \"(6.1, 6.18)\": 2.964, \"(6.18, 6.27)\": 2.936, \"(6.27, 6.32)\": 2.912, \"(6.32, 6.38)\": 2.884, \"(6.38, 6.45)\": 2.848, \"(6.45, 6.53)\": 2.806, \"(6.53, 6.56)\": 2.777, \"(6.56, 6.6)\": 2.754, \"(6.6, 6.63)\": 2.73, \"(6.63, 6.68)\": 2.691, \"(6.68, 6.72)\": 2.668, \"(6.72, 6.74)\": 2.644, \"(6.74, 6.79)\": 2.619, \"(6.79, 6.82)\": 2.587, \"(6.82, 6.84)\": 2.564, \"(6.84, 6.89)\": 2.535, \"(6.89, 6.91)\": 2.509, \"(6.91, 6.94)\": 2.481, \"(6.94, 6.98)\": 2.46, \"(6.98, 7.02)\": 2.417, \"(7.02, 7.05)\": 2.388, \"(7.05, 7.09)\": 2.358, \"(7.09, 7.12)\": 2.317, \"(7.12, 7.15)\": 2.293, \"(7.15, 7.18)\": 2.264, \"(7.18, 7.2)\": 2.233, \"(7.2, 7.23)\": 2.209, \"(7.23, 7.26)\": 2.187, \"(7.26, 7.27)\": 2.164, \"(7.27, 7.31)\": 2.141, \"(7.31, 7.34)\": 2.101, \"(7.34, 7.36)\": 2.072, \"(7.36, 7.4)\": 2.047, \"(7.4, 7.43)\": 2.015, \"(7.43, 7.47)\": 1.969, \"(7.47, 7.51)\": 1.938, \"(7.51, 7.54)\": 1.913, \"(7.54, 7.56)\": 1.879, \"(7.56, 7.6)\": 1.853, \"(7.6, 7.63)\": 1.818, \"(7.63, 7.66)\": 1.789, \"(7.66, 7.7)\": 1.748, \"(7.7, 7.74)\": 1.719, \"(7.74, 7.77)\": 1.675, \"(7.77, 7.82)\": 1.645, \"(7.82, 7.85)\": 1.608, \"(7.85, 7.89)\": 1.577, \"(7.89, 7.92)\": 1.543, \"(7.92, 7.93)\": 1.522, \"(7.93, 7.98)\": 1.5, \"(7.98, 8.05)\": 1.442, \"(8.05, 8.08)\": 1.401, \"(8.08, 8.16)\": 1.369, \"(8.16, 8.21)\": 1.313, \"(8.21, 8.27)\": 1.29, \"(8.27, 8.34)\": 1.223, \"(8.34, 8.38)\": 1.202, \"(8.38, 8.43)\": 1.175, \"(8.43, 8.48)\": 1.152, \"(8.48, 8.52)\": 1.128, \"(8.52, 8.57)\": 1.107, \"(8.57, 8.63)\": 1.086, \"(8.63, 8.68)\": 1.064, \"(8.68, 8.75)\": 1.043, \"(8.75, 8.94)\": 1.022, \"(8.94, 9.21)\": 1.002, \"(9.21, 9.29)\": 1.024, \"(9.29, 9.36)\": 1.049, \"(9.36, 9.41)\": 1.071, \"(9.41, 9.48)\": 1.094, \"(9.48, 9.53)\": 1.123, \"(9.53, 9.57)\": 1.15, \"(9.57, 9.61)\": 1.171, \"(9.61, 9.65)\": 1.192, \"(9.65, 9.7)\": 1.216, \"(9.7, 9.75)\": 1.247, \"(9.75, 9.79)\": 1.28, \"(9.79, 9.82)\": 1.306, \"(9.82, 9.86)\": 1.332, \"(9.86, 9.92)\": 1.362, \"(9.92, 9.96)\": 1.412, \"(9.96, 9.99)\": 1.442}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = exp(-x)\nb) f(x) = arcsinh(x)\nc) f(x) = sin(x+2)+2\nd) f(x) = -(x + 4)^4\ne) f(x) = (x-2)^2\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "c)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-10.0, -9.95)\": -0.847, \"(-9.95, -9.88)\": -0.876, \"(-9.88, -9.82)\": -0.9, \"(-9.82, -9.76)\": -0.93, \"(-9.76, -9.66)\": -0.951, \"(-9.66, -9.53)\": -0.975, \"(-9.53, -9.21)\": -0.995, \"(-9.21, -9.12)\": -0.975, \"(-9.12, -9.06)\": -0.951, \"(-9.06, -8.99)\": -0.924, \"(-8.99, -8.92)\": -0.898, \"(-8.92, -8.84)\": -0.864, \"(-8.84, -8.79)\": -0.826, \"(-8.79, -8.75)\": -0.804, \"(-8.75, -8.68)\": -0.762, \"(-8.68, -8.61)\": -0.715, \"(-8.61, -8.55)\": -0.662, \"(-8.55, -8.52)\": -0.637, \"(-8.52, -8.48)\": -0.61, \"(-8.48, -8.46)\": -0.58, \"(-8.46, -8.42)\": -0.556, \"(-8.42, -8.36)\": -0.527, \"(-8.36, -8.31)\": -0.463, \"(-8.31, -8.27)\": -0.429, \"(-8.27, -8.23)\": -0.381, \"(-8.23, -8.2)\": -0.352, \"(-8.2, -8.17)\": -0.331, \"(-8.17, -8.12)\": -0.297, \"(-8.12, -8.08)\": -0.243, \"(-8.08, -8.05)\": -0.212, \"(-8.05, -8.01)\": -0.178, \"(-8.01, -8.0)\": -0.156, \"(-8.0, -7.97)\": -0.131, \"(-7.97, -7.92)\": -0.105, \"(-7.92, -7.88)\": -0.045, \"(-7.88, -7.86)\": -0.018, \"(-7.86, -7.8)\": 0.013, \"(-7.8, -7.75)\": 0.068, \"(-7.75, -7.71)\": 0.13, \"(-7.71, -7.68)\": 0.152, \"(-7.68, -7.65)\": 0.179, \"(-7.65, -7.61)\": 0.222, \"(-7.61, -7.57)\": 0.264, \"(-7.57, -7.53)\": 0.297, \"(-7.53, -7.5)\": 0.328, \"(-7.5, -7.47)\": 0.352, \"(-7.47, -7.44)\": 0.391, \"(-7.44, -7.39)\": 0.414, \"(-7.39, -7.37)\": 0.448, \"(-7.37, -7.34)\": 0.469, \"(-7.34, -7.32)\": 0.496, \"(-7.32, -7.26)\": 0.526, \"(-7.26, -7.19)\": 0.58, \"(-7.19, -7.14)\": 0.635, \"(-7.14, -7.1)\": 0.663, \"(-7.1, -7.03)\": 0.703, \"(-7.03, -6.96)\": 0.757, \"(-6.96, -6.92)\": 0.781, \"(-6.92, -6.87)\": 0.813, \"(-6.87, -6.83)\": 0.835, \"(-6.83, -6.78)\": 0.858, \"(-6.78, -6.74)\": 0.88, \"(-6.74, -6.67)\": 0.905, \"(-6.67, -6.62)\": 0.925, \"(-6.62, -6.54)\": 0.947, \"(-6.54, -6.42)\": 0.969, \"(-6.42, -6.04)\": 0.991, \"(-6.04, -5.96)\": 0.97, \"(-5.96, -5.9)\": 0.947, \"(-5.9, -5.84)\": 0.923, \"(-5.84, -5.79)\": 0.902, \"(-5.79, -5.74)\": 0.871, \"(-5.74, -5.68)\": 0.846, \"(-5.68, -5.63)\": 0.821, \"(-5.63, -5.57)\": 0.787, \"(-5.57, -5.52)\": 0.736, \"(-5.52, -5.47)\": 0.707, \"(-5.47, -5.42)\": 0.676, \"(-5.42, -5.38)\": 0.645, \"(-5.38, -5.35)\": 0.609, \"(-5.35, -5.31)\": 0.585, \"(-5.31, -5.25)\": 0.551, \"(-5.25, -5.22)\": 0.502, \"(-5.22, -5.19)\": 0.48, \"(-5.19, -5.17)\": 0.458, \"(-5.17, -5.14)\": 0.433, \"(-5.14, -5.12)\": 0.41, \"(-5.12, -5.09)\": 0.388, \"(-5.09, -5.06)\": 0.362, \"(-5.06, -5.01)\": 0.325, \"(-5.01, -4.97)\": 0.266, \"(-4.97, -4.94)\": 0.245, \"(-4.94, -4.91)\": 0.219, \"(-4.91, -4.87)\": 0.176, \"(-4.87, -4.83)\": 0.137, \"(-4.83, -4.8)\": 0.106, \"(-4.8, -4.78)\": 0.08, \"(-4.78, -4.73)\": 0.051, \"(-4.73, -4.66)\": -0.018, \"(-4.66, -4.62)\": -0.071, \"(-4.62, -4.6)\": -0.097, \"(-4.6, -4.58)\": -0.121, \"(-4.58, -4.56)\": -0.142, \"(-4.56, -4.53)\": -0.171, \"(-4.53, -4.49)\": -0.198, \"(-4.49, -4.46)\": -0.238, \"(-4.46, -4.42)\": -0.262, \"(-4.42, -4.4)\": -0.298, \"(-4.4, -4.36)\": -0.325, \"(-4.36, -4.31)\": -0.363, \"(-4.31, -4.28)\": -0.404, \"(-4.28, -4.24)\": -0.431, \"(-4.24, -4.2)\": -0.47, \"(-4.2, -4.17)\": -0.496, \"(-4.17, -4.13)\": -0.534, \"(-4.13, -4.11)\": -0.555, \"(-4.11, -4.07)\": -0.587, \"(-4.07, -4.04)\": -0.608, \"(-4.04, -4.01)\": -0.636, \"(-4.01, -3.96)\": -0.66, \"(-3.96, -3.91)\": -0.683, \"(-3.91, -3.85)\": -0.733, \"(-3.85, -3.82)\": -0.766, \"(-3.82, -3.77)\": -0.789, \"(-3.77, -3.74)\": -0.815, \"(-3.74, -3.65)\": -0.836, \"(-3.65, -3.57)\": -0.894, \"(-3.57, -3.5)\": -0.917, \"(-3.5, -3.44)\": -0.938, \"(-3.44, -3.34)\": -0.959, \"(-3.34, -2.85)\": -0.981, \"(-2.85, -2.76)\": -0.948, \"(-2.76, -2.69)\": -0.921, \"(-2.69, -2.64)\": -0.896, \"(-2.64, -2.58)\": -0.872, \"(-2.58, -2.53)\": -0.831, \"(-2.53, -2.48)\": -0.807, \"(-2.48, -2.45)\": -0.787, \"(-2.45, -2.41)\": -0.757, \"(-2.41, -2.37)\": -0.736, \"(-2.37, -2.33)\": -0.712, \"(-2.33, -2.29)\": -0.673, \"(-2.29, -2.25)\": -0.649, \"(-2.25, -2.2)\": -0.608, \"(-2.2, -2.13)\": -0.56, \"(-2.13, -2.08)\": -0.514, \"(-2.08, -2.03)\": -0.468, \"(-2.03, -1.98)\": -0.424, \"(-1.98, -1.96)\": -0.395, \"(-1.96, -1.94)\": -0.375, \"(-1.94, -1.91)\": -0.355, \"(-1.91, -1.87)\": -0.318, \"(-1.87, -1.82)\": -0.28, \"(-1.82, -1.77)\": -0.23, \"(-1.77, -1.73)\": -0.188, \"(-1.73, -1.7)\": -0.149, \"(-1.7, -1.66)\": -0.113, \"(-1.66, -1.62)\": -0.071, \"(-1.62, -1.6)\": -0.033, \"(-1.6, -1.55)\": -0.006, \"(-1.55, -1.5)\": 0.056, \"(-1.5, -1.48)\": 0.083, \"(-1.48, -1.45)\": 0.104, \"(-1.45, -1.41)\": 0.134, \"(-1.41, -1.39)\": 0.167, \"(-1.39, -1.36)\": 0.194, \"(-1.36, -1.34)\": 0.215, \"(-1.34, -1.31)\": 0.236, \"(-1.31, -1.29)\": 0.266, \"(-1.29, -1.27)\": 0.287, \"(-1.27, -1.23)\": 0.31, \"(-1.23, -1.19)\": 0.35, \"(-1.19, -1.15)\": 0.387, \"(-1.15, -1.11)\": 0.428, \"(-1.11, -1.07)\": 0.459, \"(-1.07, -1.03)\": 0.494, \"(-1.03, -1.0)\": 0.517, \"(-1.0, -0.97)\": 0.548, \"(-0.97, -0.94)\": 0.569, \"(-0.94, -0.91)\": 0.593, \"(-0.91, -0.87)\": 0.617, \"(-0.87, -0.81)\": 0.65, \"(-0.81, -0.76)\": 0.706, \"(-0.76, -0.72)\": 0.728, \"(-0.72, -0.69)\": 0.763, \"(-0.69, -0.64)\": 0.785, \"(-0.64, -0.57)\": 0.816, \"(-0.57, -0.53)\": 0.844, \"(-0.53, -0.45)\": 0.878, \"(-0.45, -0.38)\": 0.902, \"(-0.38, -0.29)\": 0.937, \"(-0.29, -0.21)\": 0.961, \"(-0.21, 0.27)\": 0.984, \"(0.27, 0.35)\": 0.961, \"(0.35, 0.41)\": 0.938, \"(0.41, 0.47)\": 0.915, \"(0.47, 0.57)\": 0.886, \"(0.57, 0.67)\": 0.801, \"(0.67, 0.73)\": 0.766, \"(0.73, 0.77)\": 0.738, \"(0.77, 0.83)\": 0.697, \"(0.83, 0.86)\": 0.675, \"(0.86, 0.9)\": 0.65, \"(0.9, 0.94)\": 0.605, \"(0.94, 0.98)\": 0.583, \"(0.98, 1.01)\": 0.552, \"(1.01, 1.03)\": 0.526, \"(1.03, 1.06)\": 0.504, \"(1.06, 1.07)\": 0.483, \"(1.07, 1.13)\": 0.46, \"(1.13, 1.2)\": 0.384, \"(1.2, 1.24)\": 0.353, \"(1.24, 1.28)\": 0.304, \"(1.28, 1.32)\": 0.274, \"(1.32, 1.37)\": 0.237, \"(1.37, 1.41)\": 0.171, \"(1.41, 1.44)\": 0.147, \"(1.44, 1.47)\": 0.117, \"(1.47, 1.5)\": 0.095, \"(1.5, 1.52)\": 0.065, \"(1.52, 1.56)\": 0.041, \"(1.56, 1.6)\": -0.001, \"(1.6, 1.64)\": -0.047, \"(1.64, 1.67)\": -0.073, \"(1.67, 1.69)\": -0.11, \"(1.69, 1.72)\": -0.131, \"(1.72, 1.76)\": -0.161, \"(1.76, 1.8)\": -0.203, \"(1.8, 1.83)\": -0.232, \"(1.83, 1.86)\": -0.26, \"(1.86, 1.9)\": -0.308, \"(1.9, 1.94)\": -0.332, \"(1.94, 1.98)\": -0.371, \"(1.98, 2.01)\": -0.403, \"(2.01, 2.04)\": -0.427, \"(2.04, 2.06)\": -0.457, \"(2.06, 2.09)\": -0.477, \"(2.09, 2.12)\": -0.504, \"(2.12, 2.15)\": -0.527, \"(2.15, 2.19)\": -0.557, \"(2.19, 2.22)\": -0.588, \"(2.22, 2.26)\": -0.618, \"(2.26, 2.3)\": -0.648, \"(2.3, 2.36)\": -0.675, \"(2.36, 2.42)\": -0.727, \"(2.42, 2.46)\": -0.752, \"(2.46, 2.49)\": -0.787, \"(2.49, 2.54)\": -0.809, \"(2.54, 2.61)\": -0.839, \"(2.61, 2.64)\": -0.865, \"(2.64, 2.72)\": -0.887, \"(2.72, 2.8)\": -0.922, \"(2.8, 2.91)\": -0.951, \"(2.91, 3.03)\": -0.974, \"(3.03, 3.37)\": -0.994, \"(3.37, 3.47)\": -0.974, \"(3.47, 3.54)\": -0.934, \"(3.54, 3.64)\": -0.913, \"(3.64, 3.7)\": -0.865, \"(3.7, 3.75)\": -0.844, \"(3.75, 3.79)\": -0.819, \"(3.79, 3.83)\": -0.79, \"(3.83, 3.87)\": -0.759, \"(3.87, 3.92)\": -0.729, \"(3.92, 3.98)\": -0.693, \"(3.98, 4.01)\": -0.665, \"(4.01, 4.04)\": -0.642, \"(4.04, 4.08)\": -0.611, \"(4.08, 4.11)\": -0.588, \"(4.11, 4.13)\": -0.558, \"(4.13, 4.18)\": -0.531, \"(4.18, 4.24)\": -0.471, \"(4.24, 4.27)\": -0.446, \"(4.27, 4.3)\": -0.414, \"(4.3, 4.33)\": -0.388, \"(4.33, 4.36)\": -0.364, \"(4.36, 4.39)\": -0.337, \"(4.39, 4.42)\": -0.308, \"(4.42, 4.45)\": -0.284, \"(4.45, 4.46)\": -0.257, \"(4.46, 4.5)\": -0.235, \"(4.5, 4.56)\": -0.211, \"(4.56, 4.65)\": -0.1, \"(4.65, 4.69)\": -0.039, \"(4.69, 4.71)\": -0.017, \"(4.71, 4.73)\": 0.012, \"(4.73, 4.77)\": 0.034, \"(4.77, 4.8)\": 0.074, \"(4.8, 4.84)\": 0.101, \"(4.84, 4.86)\": 0.131, \"(4.86, 4.88)\": 0.159, \"(4.88, 4.92)\": 0.183, \"(4.92, 4.95)\": 0.208, \"(4.95, 5.0)\": 0.246, \"(5.0, 5.05)\": 0.306, \"(5.05, 5.09)\": 0.35, \"(5.09, 5.13)\": 0.386, \"(5.13, 5.17)\": 0.415, \"(5.17, 5.21)\": 0.446, \"(5.21, 5.24)\": 0.486, \"(5.24, 5.27)\": 0.51, \"(5.27, 5.3)\": 0.534, \"(5.3, 5.32)\": 0.554, \"(5.32, 5.36)\": 0.577, \"(5.36, 5.41)\": 0.62, \"(5.41, 5.45)\": 0.647, \"(5.45, 5.5)\": 0.692, \"(5.5, 5.56)\": 0.716, \"(5.56, 5.65)\": 0.766, \"(5.65, 5.72)\": 0.823, \"(5.72, 5.78)\": 0.856, \"(5.78, 5.87)\": 0.884, \"(5.87, 5.95)\": 0.929, \"(5.95, 6.04)\": 0.95, \"(6.04, 6.17)\": 0.971, \"(6.17, 6.5)\": 0.995, \"(6.5, 6.59)\": 0.974, \"(6.59, 6.66)\": 0.946, \"(6.66, 6.74)\": 0.909, \"(6.74, 6.81)\": 0.888, \"(6.81, 6.85)\": 0.863, \"(6.85, 6.89)\": 0.836, \"(6.89, 6.94)\": 0.809, \"(6.94, 6.98)\": 0.787, \"(6.98, 7.01)\": 0.76, \"(7.01, 7.05)\": 0.734, \"(7.05, 7.13)\": 0.7, \"(7.13, 7.2)\": 0.63, \"(7.2, 7.23)\": 0.606, \"(7.23, 7.26)\": 0.576, \"(7.26, 7.3)\": 0.55, \"(7.3, 7.34)\": 0.525, \"(7.34, 7.39)\": 0.473, \"(7.39, 7.42)\": 0.425, \"(7.42, 7.5)\": 0.395, \"(7.5, 7.58)\": 0.295, \"(7.58, 7.61)\": 0.258, \"(7.61, 7.64)\": 0.233, \"(7.64, 7.66)\": 0.205, \"(7.66, 7.69)\": 0.182, \"(7.69, 7.73)\": 0.14, \"(7.73, 7.75)\": 0.112, \"(7.75, 7.78)\": 0.091, \"(7.78, 7.8)\": 0.067, \"(7.8, 7.84)\": 0.043, \"(7.84, 7.88)\": -0.017, \"(7.88, 7.91)\": -0.039, \"(7.91, 7.94)\": -0.064, \"(7.94, 7.96)\": -0.085, \"(7.96, 7.98)\": -0.107, \"(7.98, 8.03)\": -0.147, \"(8.03, 8.05)\": -0.18, \"(8.05, 8.07)\": -0.202, \"(8.07, 8.12)\": -0.231, \"(8.12, 8.18)\": -0.296, \"(8.18, 8.22)\": -0.34, \"(8.22, 8.27)\": -0.363, \"(8.27, 8.3)\": -0.418, \"(8.3, 8.35)\": -0.441, \"(8.35, 8.38)\": -0.492, \"(8.38, 8.42)\": -0.518, \"(8.42, 8.46)\": -0.551, \"(8.46, 8.51)\": -0.585, \"(8.51, 8.55)\": -0.621, \"(8.55, 8.59)\": -0.658, \"(8.59, 8.62)\": -0.679, \"(8.62, 8.67)\": -0.713, \"(8.67, 8.71)\": -0.734, \"(8.71, 8.79)\": -0.774, \"(8.79, 8.85)\": -0.816, \"(8.85, 8.91)\": -0.846, \"(8.91, 8.96)\": -0.876, \"(8.96, 9.02)\": -0.903, \"(9.02, 9.11)\": -0.931, \"(9.11, 9.18)\": -0.952, \"(9.18, 9.32)\": -0.973, \"(9.32, 9.64)\": -0.995, \"(9.64, 9.75)\": -0.972, \"(9.75, 9.82)\": -0.94, \"(9.82, 9.88)\": -0.919, \"(9.88, 9.94)\": -0.895, \"(9.94, 10.0)\": -0.867}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = cos(x)\nb) f(x) = -sign(-x)\nc) f(x) = sign(x+3)\nd) f(x) = tanh(x)\ne) f(x) = x^3\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "a)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.98, -9.93)\": 0.4132, \"(-9.93, -9.89)\": 0.3973, \"(-9.89, -9.85)\": 0.3867, \"(-9.85, -9.82)\": 0.374, \"(-9.82, -9.76)\": 0.3576, \"(-9.76, -9.72)\": 0.3444, \"(-9.72, -9.65)\": 0.3244, \"(-9.65, -9.59)\": 0.2878, \"(-9.59, -9.53)\": 0.2758, \"(-9.53, -9.47)\": 0.2435, \"(-9.47, -9.42)\": 0.2197, \"(-9.42, -9.39)\": 0.2017, \"(-9.39, -9.36)\": 0.1904, \"(-9.36, -9.34)\": 0.1776, \"(-9.34, -9.32)\": 0.1635, \"(-9.32, -9.28)\": 0.1523, \"(-9.28, -9.25)\": 0.1344, \"(-9.25, -9.21)\": 0.1201, \"(-9.21, -9.18)\": 0.1011, \"(-9.18, -9.14)\": 0.0842, \"(-9.14, -9.12)\": 0.0721, \"(-9.12, -9.1)\": 0.06, \"(-9.1, -9.07)\": 0.0456, \"(-9.07, -9.03)\": 0.033, \"(-9.03, -9.01)\": 0.0126, \"(-9.01, -8.98)\": 0.0021, \"(-8.98, -8.96)\": -0.0157, \"(-8.96, -8.91)\": -0.0275, \"(-8.91, -8.84)\": -0.0682, \"(-8.84, -8.8)\": -0.0885, \"(-8.8, -8.78)\": -0.0992, \"(-8.78, -8.74)\": -0.1148, \"(-8.74, -8.71)\": -0.1303, \"(-8.71, -8.69)\": -0.1408, \"(-8.69, -8.67)\": -0.1515, \"(-8.67, -8.64)\": -0.1629, \"(-8.64, -8.62)\": -0.1772, \"(-8.62, -8.59)\": -0.1885, \"(-8.59, -8.54)\": -0.2108, \"(-8.54, -8.5)\": -0.2235, \"(-8.5, -8.45)\": -0.249, \"(-8.45, -8.4)\": -0.2642, \"(-8.4, -8.36)\": -0.2885, \"(-8.36, -8.31)\": -0.3003, \"(-8.31, -8.27)\": -0.3208, \"(-8.27, -8.24)\": -0.3331, \"(-8.24, -8.21)\": -0.346, \"(-8.21, -8.16)\": -0.3603, \"(-8.16, -8.13)\": -0.3734, \"(-8.13, -8.09)\": -0.3842, \"(-8.09, -8.01)\": -0.4048, \"(-8.01, -7.98)\": -0.417, \"(-7.98, -7.91)\": -0.4299, \"(-7.91, -7.85)\": -0.4479, \"(-7.85, -7.78)\": -0.4599, \"(-7.78, -7.67)\": -0.4738, \"(-7.67, -7.5)\": -0.4885, \"(-7.5, -7.23)\": -0.4986, \"(-7.23, -7.14)\": -0.4885, \"(-7.14, -7.09)\": -0.4776, \"(-7.09, -6.99)\": -0.465, \"(-6.99, -6.95)\": -0.4532, \"(-6.95, -6.91)\": -0.443, \"(-6.91, -6.86)\": -0.4293, \"(-6.86, -6.8)\": -0.4157, \"(-6.8, -6.76)\": -0.4036, \"(-6.76, -6.71)\": -0.3887, \"(-6.71, -6.67)\": -0.375, \"(-6.67, -6.64)\": -0.3607, \"(-6.64, -6.57)\": -0.3473, \"(-6.57, -6.51)\": -0.3167, \"(-6.51, -6.48)\": -0.2981, \"(-6.48, -6.45)\": -0.288, \"(-6.45, -6.42)\": -0.2748, \"(-6.42, -6.38)\": -0.2634, \"(-6.38, -6.31)\": -0.2378, \"(-6.31, -6.28)\": -0.2148, \"(-6.28, -6.26)\": -0.2013, \"(-6.26, -6.2)\": -0.1876, \"(-6.2, -6.1)\": -0.1472, \"(-6.1, -6.05)\": -0.1053, \"(-6.05, -6.01)\": -0.0951, \"(-6.01, -5.97)\": -0.0754, \"(-5.97, -5.95)\": -0.0518, \"(-5.95, -5.92)\": -0.0396, \"(-5.92, -5.88)\": -0.0243, \"(-5.88, -5.83)\": -0.0011, \"(-5.83, -5.78)\": 0.0256, \"(-5.78, -5.74)\": 0.0436, \"(-5.74, -5.7)\": 0.0645, \"(-5.7, -5.67)\": 0.0761, \"(-5.67, -5.63)\": 0.1011, \"(-5.63, -5.58)\": 0.1213, \"(-5.58, -5.54)\": 0.1406, \"(-5.54, -5.52)\": 0.1547, \"(-5.52, -5.49)\": 0.1652, \"(-5.49, -5.42)\": 0.1849, \"(-5.42, -5.35)\": 0.227, \"(-5.35, -5.31)\": 0.253, \"(-5.31, -5.27)\": 0.2648, \"(-5.27, -5.22)\": 0.2884, \"(-5.22, -5.18)\": 0.2993, \"(-5.18, -5.13)\": 0.3222, \"(-5.13, -5.09)\": 0.3353, \"(-5.09, -5.04)\": 0.3551, \"(-5.04, -4.98)\": 0.3696, \"(-4.98, -4.93)\": 0.3875, \"(-4.93, -4.9)\": 0.401, \"(-4.9, -4.85)\": 0.4122, \"(-4.85, -4.77)\": 0.4288, \"(-4.77, -4.69)\": 0.447, \"(-4.69, -4.61)\": 0.4632, \"(-4.61, -4.53)\": 0.4748, \"(-4.53, -4.42)\": 0.4852, \"(-4.42, -4.03)\": 0.4953, \"(-4.03, -3.96)\": 0.4833, \"(-3.96, -3.9)\": 0.4729, \"(-3.9, -3.83)\": 0.4605, \"(-3.83, -3.78)\": 0.4493, \"(-3.78, -3.74)\": 0.4358, \"(-3.74, -3.69)\": 0.4252, \"(-3.69, -3.65)\": 0.4127, \"(-3.65, -3.59)\": 0.394, \"(-3.59, -3.53)\": 0.3754, \"(-3.53, -3.49)\": 0.3565, \"(-3.49, -3.42)\": 0.345, \"(-3.42, -3.37)\": 0.3165, \"(-3.37, -3.32)\": 0.2971, \"(-3.32, -3.29)\": 0.2786, \"(-3.29, -3.24)\": 0.2643, \"(-3.24, -3.2)\": 0.2499, \"(-3.2, -3.17)\": 0.2281, \"(-3.17, -3.13)\": 0.2155, \"(-3.13, -3.1)\": 0.2001, \"(-3.1, -3.07)\": 0.1818, \"(-3.07, -3.04)\": 0.1718, \"(-3.04, -2.99)\": 0.1491, \"(-2.99, -2.96)\": 0.1301, \"(-2.96, -2.92)\": 0.1129, \"(-2.92, -2.88)\": 0.0967, \"(-2.88, -2.87)\": 0.085, \"(-2.87, -2.84)\": 0.0736, \"(-2.84, -2.82)\": 0.0615, \"(-2.82, -2.79)\": 0.0499, \"(-2.79, -2.76)\": 0.0358, \"(-2.76, -2.74)\": 0.0212, \"(-2.74, -2.7)\": 0.0101, \"(-2.7, -2.65)\": -0.0234, \"(-2.65, -2.63)\": -0.034, \"(-2.63, -2.6)\": -0.0461, \"(-2.6, -2.58)\": -0.0569, \"(-2.58, -2.54)\": -0.0707, \"(-2.54, -2.51)\": -0.0887, \"(-2.51, -2.47)\": -0.1083, \"(-2.47, -2.44)\": -0.127, \"(-2.44, -2.42)\": -0.1388, \"(-2.42, -2.37)\": -0.1492, \"(-2.37, -2.34)\": -0.1732, \"(-2.34, -2.3)\": -0.1866, \"(-2.3, -2.28)\": -0.2053, \"(-2.28, -2.22)\": -0.2206, \"(-2.22, -2.17)\": -0.249, \"(-2.17, -2.13)\": -0.2598, \"(-2.13, -2.07)\": -0.2871, \"(-2.07, -2.02)\": -0.3072, \"(-2.02, -1.96)\": -0.3323, \"(-1.96, -1.91)\": -0.3458, \"(-1.91, -1.85)\": -0.3716, \"(-1.85, -1.79)\": -0.3851, \"(-1.79, -1.73)\": -0.4108, \"(-1.73, -1.68)\": -0.4234, \"(-1.68, -1.62)\": -0.4342, \"(-1.62, -1.53)\": -0.4534, \"(-1.53, -1.47)\": -0.4654, \"(-1.47, -1.39)\": -0.4757, \"(-1.39, -1.17)\": -0.4883, \"(-1.17, -0.92)\": -0.4983, \"(-0.92, -0.83)\": -0.4836, \"(-0.83, -0.77)\": -0.4729, \"(-0.77, -0.68)\": -0.4612, \"(-0.68, -0.63)\": -0.4445, \"(-0.63, -0.57)\": -0.4343, \"(-0.57, -0.51)\": -0.4105, \"(-0.51, -0.46)\": -0.3991, \"(-0.46, -0.4)\": -0.3813, \"(-0.4, -0.33)\": -0.3579, \"(-0.33, -0.29)\": -0.3408, \"(-0.29, -0.27)\": -0.3275, \"(-0.27, -0.23)\": -0.3161, \"(-0.23, -0.19)\": -0.3006, \"(-0.19, -0.14)\": -0.2813, \"(-0.14, -0.09)\": -0.259, \"(-0.09, -0.07)\": -0.2477, \"(-0.07, -0.0)\": -0.2273, \"(-0.0, 0.04)\": -0.1997, \"(0.04, 0.09)\": -0.1817, \"(0.09, 0.13)\": -0.158, \"(0.13, 0.15)\": -0.1468, \"(0.15, 0.17)\": -0.1338, \"(0.17, 0.21)\": -0.1218, \"(0.21, 0.23)\": -0.106, \"(0.23, 0.28)\": -0.0917, \"(0.28, 0.33)\": -0.0619, \"(0.33, 0.36)\": -0.0501, \"(0.36, 0.4)\": -0.0273, \"(0.4, 0.43)\": -0.0097, \"(0.43, 0.45)\": 0.0047, \"(0.45, 0.48)\": 0.0167, \"(0.48, 0.5)\": 0.0335, \"(0.5, 0.55)\": 0.0464, \"(0.55, 0.6)\": 0.0779, \"(0.6, 0.63)\": 0.089, \"(0.63, 0.7)\": 0.1151, \"(0.7, 0.74)\": 0.1415, \"(0.74, 0.77)\": 0.1537, \"(0.77, 0.8)\": 0.1746, \"(0.8, 0.83)\": 0.1869, \"(0.83, 0.89)\": 0.1975, \"(0.89, 0.96)\": 0.2352, \"(0.96, 1.02)\": 0.2629, \"(1.02, 1.07)\": 0.2879, \"(1.07, 1.1)\": 0.3049, \"(1.1, 1.14)\": 0.3159, \"(1.14, 1.17)\": 0.3293, \"(1.17, 1.2)\": 0.3427, \"(1.2, 1.26)\": 0.3539, \"(1.26, 1.31)\": 0.3753, \"(1.31, 1.39)\": 0.3905, \"(1.39, 1.45)\": 0.4185, \"(1.45, 1.5)\": 0.4304, \"(1.5, 1.57)\": 0.4413, \"(1.57, 1.63)\": 0.4571, \"(1.63, 1.7)\": 0.4689, \"(1.7, 1.81)\": 0.4804, \"(1.81, 2.26)\": 0.4931, \"(2.26, 2.35)\": 0.4828, \"(2.35, 2.43)\": 0.462, \"(2.43, 2.5)\": 0.4515, \"(2.5, 2.57)\": 0.4303, \"(2.57, 2.63)\": 0.4153, \"(2.63, 2.67)\": 0.4007, \"(2.67, 2.71)\": 0.3874, \"(2.71, 2.77)\": 0.3731, \"(2.77, 2.83)\": 0.3464, \"(2.83, 2.87)\": 0.334, \"(2.87, 2.91)\": 0.3155, \"(2.91, 2.94)\": 0.305, \"(2.94, 2.96)\": 0.2938, \"(2.96, 3.0)\": 0.2803, \"(3.0, 3.05)\": 0.2599, \"(3.05, 3.11)\": 0.2445, \"(3.11, 3.19)\": 0.2037, \"(3.19, 3.24)\": 0.1768, \"(3.24, 3.29)\": 0.1503, \"(3.29, 3.32)\": 0.1313, \"(3.32, 3.36)\": 0.1166, \"(3.36, 3.39)\": 0.0985, \"(3.39, 3.42)\": 0.0871, \"(3.42, 3.45)\": 0.0701, \"(3.45, 3.48)\": 0.0586, \"(3.48, 3.5)\": 0.0445, \"(3.5, 3.54)\": 0.0209, \"(3.54, 3.58)\": 0.0082, \"(3.58, 3.61)\": -0.0042, \"(3.61, 3.64)\": -0.0273, \"(3.64, 3.66)\": -0.0374, \"(3.66, 3.69)\": -0.0488, \"(3.69, 3.75)\": -0.0747, \"(3.75, 3.79)\": -0.0973, \"(3.79, 3.83)\": -0.1177, \"(3.83, 3.87)\": -0.1339, \"(3.87, 3.89)\": -0.148, \"(3.89, 3.92)\": -0.1605, \"(3.92, 3.97)\": -0.1834, \"(3.97, 3.99)\": -0.1984, \"(3.99, 4.02)\": -0.2086, \"(4.02, 4.04)\": -0.2216, \"(4.04, 4.08)\": -0.2326, \"(4.08, 4.12)\": -0.2441, \"(4.12, 4.16)\": -0.2681, \"(4.16, 4.21)\": -0.2784, \"(4.21, 4.26)\": -0.3107, \"(4.26, 4.32)\": -0.3247, \"(4.32, 4.35)\": -0.3454, \"(4.35, 4.4)\": -0.3586, \"(4.4, 4.44)\": -0.3732, \"(4.44, 4.48)\": -0.3848, \"(4.48, 4.54)\": -0.402, \"(4.54, 4.58)\": -0.4125, \"(4.58, 4.65)\": -0.431, \"(4.65, 4.7)\": -0.4423, \"(4.7, 4.8)\": -0.4572, \"(4.8, 4.89)\": -0.4741, \"(4.89, 5.0)\": -0.4851, \"(5.0, 5.39)\": -0.4956, \"(5.39, 5.48)\": -0.4845, \"(5.48, 5.6)\": -0.4573, \"(5.6, 5.66)\": -0.4468, \"(5.66, 5.73)\": -0.4298, \"(5.73, 5.83)\": -0.4082, \"(5.83, 5.89)\": -0.3738, \"(5.89, 5.93)\": -0.3619, \"(5.93, 5.97)\": -0.3495, \"(5.97, 6.02)\": -0.3378, \"(6.02, 6.08)\": -0.312, \"(6.08, 6.11)\": -0.2887, \"(6.11, 6.17)\": -0.2745, \"(6.17, 6.22)\": -0.2522, \"(6.22, 6.28)\": -0.2184, \"(6.28, 6.33)\": -0.2018, \"(6.33, 6.41)\": -0.1736, \"(6.41, 6.49)\": -0.1297, \"(6.49, 6.55)\": -0.0984, \"(6.55, 6.58)\": -0.0759, \"(6.58, 6.6)\": -0.0629, \"(6.6, 6.63)\": -0.048, \"(6.63, 6.67)\": -0.0339, \"(6.67, 6.7)\": -0.0179, \"(6.7, 6.73)\": -0.0008, \"(6.73, 6.76)\": 0.0168, \"(6.76, 6.79)\": 0.0308, \"(6.79, 6.82)\": 0.0441, \"(6.82, 6.86)\": 0.0622, \"(6.86, 6.9)\": 0.0746, \"(6.9, 6.95)\": 0.1025, \"(6.95, 6.99)\": 0.1289, \"(6.99, 7.01)\": 0.1404, \"(7.01, 7.05)\": 0.1564, \"(7.05, 7.09)\": 0.1723, \"(7.09, 7.14)\": 0.1898, \"(7.14, 7.22)\": 0.2246, \"(7.22, 7.29)\": 0.2639, \"(7.29, 7.31)\": 0.2765, \"(7.31, 7.36)\": 0.2867, \"(7.36, 7.42)\": 0.3149, \"(7.42, 7.48)\": 0.3298, \"(7.48, 7.53)\": 0.3553, \"(7.53, 7.57)\": 0.3656, \"(7.57, 7.6)\": 0.3787, \"(7.6, 7.64)\": 0.3912, \"(7.64, 7.71)\": 0.4061, \"(7.71, 7.77)\": 0.4227, \"(7.77, 7.81)\": 0.4373, \"(7.81, 7.88)\": 0.4492, \"(7.88, 7.97)\": 0.466, \"(7.97, 8.05)\": 0.4764, \"(8.05, 8.2)\": 0.488, \"(8.2, 8.51)\": 0.4982, \"(8.51, 8.58)\": 0.4862, \"(8.58, 8.65)\": 0.4752, \"(8.65, 8.7)\": 0.4645, \"(8.7, 8.77)\": 0.4523, \"(8.77, 8.81)\": 0.4404, \"(8.81, 8.88)\": 0.4227, \"(8.88, 8.95)\": 0.4081, \"(8.95, 9.02)\": 0.3841, \"(9.02, 9.08)\": 0.3674, \"(9.08, 9.15)\": 0.3428, \"(9.15, 9.19)\": 0.3178, \"(9.19, 9.22)\": 0.3058, \"(9.22, 9.27)\": 0.2893, \"(9.27, 9.31)\": 0.266, \"(9.31, 9.36)\": 0.2484, \"(9.36, 9.39)\": 0.2301, \"(9.39, 9.41)\": 0.2195, \"(9.41, 9.45)\": 0.2083, \"(9.45, 9.48)\": 0.1963, \"(9.48, 9.52)\": 0.1719, \"(9.52, 9.55)\": 0.1589, \"(9.55, 9.58)\": 0.1455, \"(9.58, 9.59)\": 0.1348, \"(9.59, 9.63)\": 0.1229, \"(9.63, 9.7)\": 0.0987, \"(9.7, 9.77)\": 0.0544, \"(9.77, 9.81)\": 0.0351, \"(9.81, 9.84)\": 0.0124, \"(9.84, 9.87)\": 0.0002, \"(9.87, 9.89)\": -0.0116, \"(9.89, 9.94)\": -0.0273, \"(9.94, 9.96)\": -0.0465, \"(9.96, 9.99)\": -0.0585}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = 1/2*cos(x-2)\nb) f(x) = |2*x+4|\nc) f(x) = -|-x|\nd) f(x) = sqrt(x ** 2 + 3*x +5)\ne) f(x) = tanh(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "a)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.9, -9.8)\": -9943.0, \"(-9.8, -9.69)\": -8277.0, \"(-9.69, -9.66)\": -7967.0, \"(-9.66, -9.62)\": -7757.9, \"(-9.62, -9.59)\": -7475.9, \"(-9.59, -9.53)\": -7162.3, \"(-9.53, -9.49)\": -6726.9, \"(-9.49, -9.43)\": -6466.9, \"(-9.43, -9.39)\": -6171.1, \"(-9.39, -9.36)\": -5940.2, \"(-9.36, -9.3)\": -5632.6, \"(-9.3, -9.26)\": -5411.0, \"(-9.26, -9.2)\": -5169.0, \"(-9.2, -9.13)\": -4768.2, \"(-9.13, -9.08)\": -4506.7, \"(-9.08, -8.99)\": -4225.3, \"(-8.99, -8.92)\": -3991.9, \"(-8.92, -8.86)\": -3730.8, \"(-8.86, -8.76)\": -3376.6, \"(-8.76, -8.69)\": -3164.3, \"(-8.69, -8.61)\": -2941.5, \"(-8.61, -8.54)\": -2726.5, \"(-8.54, -8.43)\": -2469.3, \"(-8.43, -8.3)\": -2245.4, \"(-8.3, -8.19)\": -1995.6, \"(-8.19, -8.04)\": -1771.6, \"(-8.04, -7.88)\": -1522.1, \"(-7.88, -7.69)\": -1284.3, \"(-7.69, -7.45)\": -1068.7, \"(-7.45, -7.2)\": -861.8, \"(-7.2, -6.78)\": -650.5, \"(-6.78, -6.16)\": -444.2, \"(-6.16, -4.16)\": -237.4, \"(-4.16, 5.83)\": -32.2, \"(5.83, 6.64)\": 174.1, \"(6.64, 7.09)\": 396.5, \"(7.09, 7.39)\": 608.6, \"(7.39, 7.62)\": 820.2, \"(7.62, 7.82)\": 1028.0, \"(7.82, 7.99)\": 1275.7, \"(7.99, 8.12)\": 1484.8, \"(8.12, 8.24)\": 1693.4, \"(8.24, 8.36)\": 1909.4, \"(8.36, 8.46)\": 2140.7, \"(8.46, 8.54)\": 2349.1, \"(8.54, 8.66)\": 2650.1, \"(8.66, 8.73)\": 2912.7, \"(8.73, 8.8)\": 3190.7, \"(8.8, 8.9)\": 3419.1, \"(8.9, 8.97)\": 3678.2, \"(8.97, 9.06)\": 4049.3, \"(9.06, 9.15)\": 4369.4, \"(9.15, 9.23)\": 4858.2, \"(9.23, 9.26)\": 5111.3, \"(9.26, 9.32)\": 5331.8, \"(9.32, 9.36)\": 5612.1, \"(9.36, 9.41)\": 5826.7, \"(9.41, 9.45)\": 6262.7, \"(9.45, 9.54)\": 6520.0, \"(9.54, 9.6)\": 7187.3, \"(9.6, 9.66)\": 7439.4, \"(9.66, 9.7)\": 7942.5, \"(9.7, 9.72)\": 8198.8, \"(9.72, 9.76)\": 8499.6, \"(9.76, 9.79)\": 8766.2, \"(9.79, 9.82)\": 9034.5, \"(9.82, 9.85)\": 9252.5, \"(9.85, 9.89)\": 9647.8, \"(9.89, 9.91)\": 9885.1, \"(9.91, 9.94)\": 10115.0, \"(9.94, 9.98)\": 10540.9}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = -x^5\nb) f(x) = sqrt(x+10)\nc) f(x) = exp(x)\nd) f(x) = sinh(x)\ne) f(x) = x\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "d)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.99, -9.97)\": 10826.1, \"(-9.97, -9.94)\": 10590.0, \"(-9.94, -9.91)\": 10245.7, \"(-9.91, -9.89)\": 9981.2, \"(-9.89, -9.83)\": 9757.9, \"(-9.83, -9.75)\": 8942.9, \"(-9.75, -9.68)\": 8308.2, \"(-9.68, -9.64)\": 7859.4, \"(-9.64, -9.6)\": 7624.3, \"(-9.6, -9.58)\": 7392.2, \"(-9.58, -9.56)\": 7170.8, \"(-9.56, -9.52)\": 6956.0, \"(-9.52, -9.47)\": 6716.8, \"(-9.47, -9.43)\": 6407.5, \"(-9.43, -9.35)\": 6053.3, \"(-9.35, -9.27)\": 5569.4, \"(-9.27, -9.21)\": 5245.7, \"(-9.21, -9.17)\": 4947.0, \"(-9.17, -9.13)\": 4729.1, \"(-9.13, -9.05)\": 4484.4, \"(-9.05, -9.0)\": 4253.9, \"(-9.0, -8.9)\": 3964.3, \"(-8.9, -8.79)\": 3560.2, \"(-8.79, -8.7)\": 3185.2, \"(-8.7, -8.6)\": 2950.1, \"(-8.6, -8.51)\": 2693.6, \"(-8.51, -8.39)\": 2470.4, \"(-8.39, -8.28)\": 2173.3, \"(-8.28, -8.15)\": 1951.5, \"(-8.15, -8.0)\": 1692.2, \"(-8.0, -7.84)\": 1479.0, \"(-7.84, -7.64)\": 1250.9, \"(-7.64, -7.4)\": 1025.7, \"(-7.4, -7.09)\": 807.9, \"(-7.09, -6.63)\": 593.7, \"(-6.63, -5.77)\": 379.8, \"(-5.77, 4.55)\": 164.1, \"(4.55, 6.28)\": -49.2, \"(6.28, 6.86)\": -268.1, \"(6.86, 7.24)\": -487.9, \"(7.24, 7.52)\": -704.2, \"(7.52, 7.75)\": -931.7, \"(7.75, 7.91)\": -1160.4, \"(7.91, 8.06)\": -1381.0, \"(8.06, 8.2)\": -1607.4, \"(8.2, 8.33)\": -1879.5, \"(8.33, 8.44)\": -2095.6, \"(8.44, 8.56)\": -2351.0, \"(8.56, 8.66)\": -2663.1, \"(8.66, 8.74)\": -2912.8, \"(8.74, 8.82)\": -3126.9, \"(8.82, 8.9)\": -3457.8, \"(8.9, 8.97)\": -3695.9, \"(8.97, 9.05)\": -4046.4, \"(9.05, 9.08)\": -4288.6, \"(9.08, 9.16)\": -4532.5, \"(9.16, 9.21)\": -4773.1, \"(9.21, 9.26)\": -5104.8, \"(9.26, 9.34)\": -5447.6, \"(9.34, 9.4)\": -5800.4, \"(9.4, 9.45)\": -6103.2, \"(9.45, 9.51)\": -6454.8, \"(9.51, 9.55)\": -6891.7, \"(9.55, 9.59)\": -7123.5, \"(9.59, 9.64)\": -7501.0, \"(9.64, 9.7)\": -8012.7, \"(9.7, 9.75)\": -8240.7, \"(9.75, 9.8)\": -8717.9, \"(9.8, 9.86)\": -9274.7, \"(9.86, 9.89)\": -9610.1, \"(9.89, 9.92)\": -9902.3, \"(9.92, 9.93)\": -10240.2, \"(9.93, 9.98)\": -10480.6}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = -log(x+10)\nb) f(x) = -sinh(x)\nc) f(x) = arctan(x)\nd) f(x) = (x-1)*(x+1)\ne) f(x) = -exp(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "b)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.98, -9.95)\": 10655.8, \"(-9.95, -9.95)\": 10525.1, \"(-9.95, -9.93)\": 10392.5, \"(-9.93, -9.92)\": 10223.9, \"(-9.92, -9.9)\": 10102.8, \"(-9.9, -9.88)\": 9809.9, \"(-9.88, -9.86)\": 9685.1, \"(-9.86, -9.84)\": 9494.3, \"(-9.84, -9.82)\": 9283.6, \"(-9.82, -9.81)\": 9159.7, \"(-9.81, -9.79)\": 9046.5, \"(-9.79, -9.78)\": 8906.3, \"(-9.78, -9.76)\": 8774.1, \"(-9.76, -9.75)\": 8631.5, \"(-9.75, -9.73)\": 8497.3, \"(-9.73, -9.7)\": 8319.5, \"(-9.7, -9.68)\": 8052.3, \"(-9.68, -9.64)\": 7909.7, \"(-9.64, -9.61)\": 7602.0, \"(-9.61, -9.54)\": 7340.8, \"(-9.54, -9.49)\": 6717.4, \"(-9.49, -9.47)\": 6589.9, \"(-9.47, -9.46)\": 6434.6, \"(-9.46, -9.41)\": 6322.6, \"(-9.41, -9.36)\": 5939.2, \"(-9.36, -9.34)\": 5777.1, \"(-9.34, -9.3)\": 5657.0, \"(-9.3, -9.26)\": 5351.5, \"(-9.26, -9.24)\": 5225.7, \"(-9.24, -9.22)\": 5083.4, \"(-9.22, -9.18)\": 4958.9, \"(-9.18, -9.15)\": 4790.2, \"(-9.15, -9.11)\": 4676.8, \"(-9.11, -9.08)\": 4526.4, \"(-9.08, -9.04)\": 4343.9, \"(-9.04, -8.98)\": 4177.3, \"(-8.98, -8.94)\": 3884.2, \"(-8.94, -8.89)\": 3747.1, \"(-8.89, -8.85)\": 3590.7, \"(-8.85, -8.79)\": 3416.8, \"(-8.79, -8.75)\": 3246.1, \"(-8.75, -8.71)\": 3127.2, \"(-8.71, -8.67)\": 3015.2, \"(-8.67, -8.64)\": 2882.0, \"(-8.64, -8.57)\": 2746.4, \"(-8.57, -8.51)\": 2594.9, \"(-8.51, -8.46)\": 2469.6, \"(-8.46, -8.4)\": 2334.7, \"(-8.4, -8.34)\": 2201.2, \"(-8.34, -8.27)\": 2082.8, \"(-8.27, -8.23)\": 1959.0, \"(-8.23, -8.15)\": 1832.0, \"(-8.15, -8.09)\": 1718.6, \"(-8.09, -8.02)\": 1604.9, \"(-8.02, -7.89)\": 1475.3, \"(-7.89, -7.77)\": 1304.5, \"(-7.77, -7.68)\": 1188.9, \"(-7.68, -7.56)\": 1072.0, \"(-7.56, -7.45)\": 960.8, \"(-7.45, -7.29)\": 838.4, \"(-7.29, -7.12)\": 726.7, \"(-7.12, -6.9)\": 605.2, \"(-6.9, -6.63)\": 491.2, \"(-6.63, -6.29)\": 373.9, \"(-6.29, -5.72)\": 265.0, \"(-5.72, -4.44)\": 154.2, \"(-4.44, 5.69)\": 44.8, \"(5.69, 6.25)\": 154.4, \"(6.25, 6.62)\": 266.9, \"(6.62, 6.88)\": 381.9, \"(6.88, 7.08)\": 493.0, \"(7.08, 7.27)\": 608.6, \"(7.27, 7.42)\": 727.0, \"(7.42, 7.55)\": 844.7, \"(7.55, 7.66)\": 960.5, \"(7.66, 7.75)\": 1071.6, \"(7.75, 7.86)\": 1182.3, \"(7.86, 7.94)\": 1299.1, \"(7.94, 8.01)\": 1419.2, \"(8.01, 8.07)\": 1545.5, \"(8.07, 8.18)\": 1661.4, \"(8.18, 8.25)\": 1803.5, \"(8.25, 8.32)\": 1942.2, \"(8.32, 8.43)\": 2064.1, \"(8.43, 8.53)\": 2469.3, \"(8.53, 8.6)\": 2605.6, \"(8.6, 8.63)\": 2728.5, \"(8.63, 8.7)\": 2864.2, \"(8.7, 8.74)\": 3006.2, \"(8.74, 8.79)\": 3178.0, \"(8.79, 8.83)\": 3322.3, \"(8.83, 8.89)\": 3523.4, \"(8.89, 8.92)\": 3644.9, \"(8.92, 8.96)\": 3789.2, \"(8.96, 8.99)\": 3916.2, \"(8.99, 9.04)\": 4119.5, \"(9.04, 9.1)\": 4330.1, \"(9.1, 9.13)\": 4490.0, \"(9.13, 9.2)\": 4781.8, \"(9.2, 9.23)\": 4990.1, \"(9.23, 9.26)\": 5117.9, \"(9.26, 9.28)\": 5246.7, \"(9.28, 9.31)\": 5428.0, \"(9.31, 9.36)\": 5590.8, \"(9.36, 9.4)\": 5899.2, \"(9.4, 9.43)\": 6092.1, \"(9.43, 9.44)\": 6245.1, \"(9.44, 9.49)\": 6425.1, \"(9.49, 9.55)\": 6865.9, \"(9.55, 9.56)\": 7049.2, \"(9.56, 9.58)\": 7167.9, \"(9.58, 9.61)\": 7311.0, \"(9.61, 9.64)\": 7624.9, \"(9.64, 9.66)\": 7753.2, \"(9.66, 9.68)\": 7897.0, \"(9.68, 9.72)\": 8114.0, \"(9.72, 9.76)\": 8526.9, \"(9.76, 9.78)\": 8741.5, \"(9.78, 9.8)\": 8896.9, \"(9.8, 9.83)\": 9110.8, \"(9.83, 9.87)\": 9405.4, \"(9.87, 9.9)\": 9899.9, \"(9.9, 9.91)\": 10024.6, \"(9.91, 9.95)\": 10165.3, \"(9.95, 9.98)\": 10696.4, \"(9.98, 9.99)\": 10855.0}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = cosh(x)\nb) f(x) = -exp(x)\nc) f(x) = x\nd) f(x) = -2*x+5\ne) f(x) = x^2\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "a)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-10.0, -9.98)\": -10906.4, \"(-9.98, -9.97)\": -10783.2, \"(-9.97, -9.95)\": -10642.3, \"(-9.95, -9.92)\": -10362.8, \"(-9.92, -9.88)\": -9907.9, \"(-9.88, -9.86)\": -9712.8, \"(-9.86, -9.85)\": -9540.4, \"(-9.85, -9.84)\": -9427.7, \"(-9.84, -9.82)\": -9291.0, \"(-9.82, -9.78)\": -9085.6, \"(-9.78, -9.75)\": -8679.0, \"(-9.75, -9.73)\": -8563.9, \"(-9.73, -9.7)\": -8288.6, \"(-9.7, -9.68)\": -8157.7, \"(-9.68, -9.66)\": -7952.6, \"(-9.66, -9.64)\": -7829.1, \"(-9.64, -9.62)\": -7622.8, \"(-9.62, -9.59)\": -7393.1, \"(-9.59, -9.56)\": -7215.7, \"(-9.56, -9.53)\": -7017.9, \"(-9.53, -9.5)\": -6817.7, \"(-9.5, -9.47)\": -6549.1, \"(-9.47, -9.45)\": -6435.4, \"(-9.45, -9.43)\": -6324.2, \"(-9.43, -9.4)\": -6154.0, \"(-9.4, -9.37)\": -6026.0, \"(-9.37, -9.34)\": -5780.8, \"(-9.34, -9.32)\": -5606.5, \"(-9.32, -9.26)\": -5449.5, \"(-9.26, -9.2)\": -5077.2, \"(-9.2, -9.18)\": -4929.0, \"(-9.18, -9.16)\": -4808.3, \"(-9.16, -9.11)\": -4664.5, \"(-9.11, -9.08)\": -4512.7, \"(-9.08, -9.05)\": -4362.5, \"(-9.05, -9.02)\": -4245.6, \"(-9.02, -8.97)\": -4079.6, \"(-8.97, -8.91)\": -3841.0, \"(-8.91, -8.89)\": -3699.5, \"(-8.89, -8.84)\": -3557.0, \"(-8.84, -8.8)\": -3436.0, \"(-8.8, -8.77)\": -3304.3, \"(-8.77, -8.72)\": -3173.7, \"(-8.72, -8.68)\": -3008.7, \"(-8.68, -8.6)\": -2881.3, \"(-8.6, -8.52)\": -2650.8, \"(-8.52, -8.42)\": -2416.1, \"(-8.42, -8.31)\": -2183.5, \"(-8.31, -8.23)\": -1962.0, \"(-8.23, -8.14)\": -1845.2, \"(-8.14, -8.07)\": -1716.4, \"(-8.07, -7.98)\": -1586.0, \"(-7.98, -7.88)\": -1437.9, \"(-7.88, -7.8)\": -1317.5, \"(-7.8, -7.69)\": -1196.3, \"(-7.69, -7.56)\": -1071.9, \"(-7.56, -7.43)\": -944.0, \"(-7.43, -7.26)\": -830.6, \"(-7.26, -7.02)\": -677.6, \"(-7.02, -6.8)\": -563.2, \"(-6.8, -6.51)\": -448.2, \"(-6.51, -6.1)\": -336.9, \"(-6.1, -5.5)\": -226.7, \"(-5.5, -2.68)\": -116.0, \"(-2.68, 5.44)\": -6.4, \"(5.44, 6.12)\": -116.7, \"(6.12, 6.52)\": -232.7, \"(6.52, 6.81)\": -342.1, \"(6.81, 7.02)\": -453.1, \"(7.02, 7.22)\": -575.8, \"(7.22, 7.35)\": -687.1, \"(7.35, 7.5)\": -799.0, \"(7.5, 7.62)\": -912.6, \"(7.62, 7.73)\": -1026.6, \"(7.73, 7.81)\": -1141.0, \"(7.81, 7.9)\": -1252.1, \"(7.9, 8.0)\": -1371.8, \"(8.0, 8.09)\": -1503.4, \"(8.09, 8.16)\": -1638.8, \"(8.16, 8.24)\": -1763.0, \"(8.24, 8.3)\": -1906.6, \"(8.3, 8.35)\": -2019.0, \"(8.35, 8.42)\": -2144.3, \"(8.42, 8.49)\": -2315.8, \"(8.49, 8.56)\": -2492.5, \"(8.56, 8.61)\": -2603.7, \"(8.61, 8.68)\": -2787.0, \"(8.68, 8.74)\": -3022.9, \"(8.74, 8.78)\": -3180.3, \"(8.78, 8.83)\": -3349.1, \"(8.83, 8.87)\": -3475.5, \"(8.87, 8.91)\": -3608.4, \"(8.91, 8.98)\": -3779.1, \"(8.98, 9.05)\": -4070.1, \"(9.05, 9.09)\": -4337.6, \"(9.09, 9.11)\": -4452.7, \"(9.11, 9.14)\": -4570.0, \"(9.14, 9.16)\": -4698.8, \"(9.16, 9.2)\": -4833.2, \"(9.2, 9.23)\": -4996.9, \"(9.23, 9.26)\": -5109.8, \"(9.26, 9.27)\": -5267.8, \"(9.27, 9.35)\": -5383.8, \"(9.35, 9.44)\": -6194.5, \"(9.44, 9.46)\": -6328.7, \"(9.46, 9.48)\": -6473.0, \"(9.48, 9.51)\": -6611.3, \"(9.51, 9.54)\": -6869.6, \"(9.54, 9.56)\": -7035.0, \"(9.56, 9.59)\": -7148.6, \"(9.59, 9.6)\": -7312.6, \"(9.6, 9.62)\": -7425.6, \"(9.62, 9.65)\": -7685.2, \"(9.65, 9.69)\": -7804.7, \"(9.69, 9.73)\": -8271.5, \"(9.73, 9.76)\": -8465.3, \"(9.76, 9.78)\": -8773.1, \"(9.78, 9.8)\": -8890.8, \"(9.8, 9.83)\": -9152.5, \"(9.83, 9.87)\": -9433.3, \"(9.87, 9.9)\": -9902.0}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = 2^x\nb) f(x) = -|-x|\nc) f(x) = exp(x)\nd) f(x) = 2^(x-5)\ne) f(x) = -cosh(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "e)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.94, -2.33)\": -1.0, \"(-2.33, -1.95)\": -0.98, \"(-1.95, -1.71)\": -0.959, \"(-1.71, -1.58)\": -0.936, \"(-1.58, -1.46)\": -0.914, \"(-1.46, -1.35)\": -0.893, \"(-1.35, -1.28)\": -0.872, \"(-1.28, -1.19)\": -0.849, \"(-1.19, -1.13)\": -0.824, \"(-1.13, -1.05)\": -0.801, \"(-1.05, -1.0)\": -0.781, \"(-1.0, -0.93)\": -0.752, \"(-0.93, -0.89)\": -0.728, \"(-0.89, -0.82)\": -0.696, \"(-0.82, -0.78)\": -0.674, \"(-0.78, -0.74)\": -0.649, \"(-0.74, -0.7)\": -0.625, \"(-0.7, -0.67)\": -0.596, \"(-0.67, -0.61)\": -0.572, \"(-0.61, -0.58)\": -0.535, \"(-0.58, -0.54)\": -0.509, \"(-0.54, -0.49)\": -0.486, \"(-0.49, -0.46)\": -0.446, \"(-0.46, -0.41)\": -0.421, \"(-0.41, -0.36)\": -0.373, \"(-0.36, -0.34)\": -0.343, \"(-0.34, -0.31)\": -0.32, \"(-0.31, -0.29)\": -0.292, \"(-0.29, -0.25)\": -0.261, \"(-0.25, -0.23)\": -0.234, \"(-0.23, -0.19)\": -0.213, \"(-0.19, -0.17)\": -0.189, \"(-0.17, -0.14)\": -0.158, \"(-0.14, -0.1)\": -0.136, \"(-0.1, -0.05)\": -0.068, \"(-0.05, -0.02)\": -0.041, \"(-0.02, 0.02)\": -0.006, \"(0.02, 0.04)\": 0.026, \"(0.04, 0.06)\": 0.047, \"(0.06, 0.09)\": 0.067, \"(0.09, 0.11)\": 0.092, \"(0.11, 0.13)\": 0.113, \"(0.13, 0.15)\": 0.136, \"(0.15, 0.18)\": 0.161, \"(0.18, 0.22)\": 0.197, \"(0.22, 0.24)\": 0.224, \"(0.24, 0.26)\": 0.245, \"(0.26, 0.29)\": 0.268, \"(0.29, 0.34)\": 0.293, \"(0.34, 0.39)\": 0.342, \"(0.39, 0.42)\": 0.378, \"(0.42, 0.45)\": 0.404, \"(0.45, 0.48)\": 0.427, \"(0.48, 0.52)\": 0.455, \"(0.52, 0.57)\": 0.494, \"(0.57, 0.6)\": 0.521, \"(0.6, 0.64)\": 0.55, \"(0.64, 0.68)\": 0.575, \"(0.68, 0.74)\": 0.609, \"(0.74, 0.8)\": 0.636, \"(0.8, 0.85)\": 0.671, \"(0.85, 0.92)\": 0.693, \"(0.92, 0.99)\": 0.74, \"(0.99, 1.06)\": 0.762, \"(1.06, 1.1)\": 0.784, \"(1.1, 1.18)\": 0.806, \"(1.18, 1.26)\": 0.829, \"(1.26, 1.34)\": 0.852, \"(1.34, 1.43)\": 0.877, \"(1.43, 1.56)\": 0.897, \"(1.56, 1.72)\": 0.919, \"(1.72, 1.92)\": 0.939, \"(1.92, 2.33)\": 0.961, \"(2.33, 9.97)\": 0.983}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = x^2+3*x-1\nb) f(x) = -2*x^2\nc) f(x) = tanh(x)\nd) f(x) = sin(x+2)+2\ne) f(x) = -x^5\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "c)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-10.0, -9.44)\": -3.0, \"(-9.44, -8.89)\": -2.939, \"(-8.89, -8.34)\": -2.879, \"(-8.34, -7.86)\": -2.817, \"(-7.86, -7.38)\": -2.756, \"(-7.38, -6.91)\": -2.695, \"(-6.91, -6.5)\": -2.628, \"(-6.5, -6.09)\": -2.568, \"(-6.09, -5.72)\": -2.505, \"(-5.72, -5.38)\": -2.444, \"(-5.38, -5.07)\": -2.384, \"(-5.07, -4.73)\": -2.32, \"(-4.73, -4.45)\": -2.257, \"(-4.45, -4.17)\": -2.196, \"(-4.17, -3.89)\": -2.131, \"(-3.89, -3.65)\": -2.067, \"(-3.65, -3.42)\": -2.005, \"(-3.42, -3.22)\": -1.942, \"(-3.22, -3.01)\": -1.881, \"(-3.01, -2.81)\": -1.817, \"(-2.81, -2.63)\": -1.751, \"(-2.63, -2.44)\": -1.686, \"(-2.44, -2.29)\": -1.625, \"(-2.29, -2.14)\": -1.559, \"(-2.14, -1.98)\": -1.494, \"(-1.98, -1.83)\": -1.423, \"(-1.83, -1.69)\": -1.36, \"(-1.69, -1.57)\": -1.294, \"(-1.57, -1.45)\": -1.227, \"(-1.45, -1.35)\": -1.166, \"(-1.35, -1.23)\": -1.105, \"(-1.23, -1.13)\": -1.035, \"(-1.13, -1.02)\": -0.959, \"(-1.02, -0.95)\": -0.889, \"(-0.95, -0.81)\": -0.811, \"(-0.81, -0.7)\": -0.719, \"(-0.7, -0.63)\": -0.635, \"(-0.63, -0.53)\": -0.571, \"(-0.53, -0.46)\": -0.49, \"(-0.46, -0.4)\": -0.43, \"(-0.4, -0.3)\": -0.365, \"(-0.3, -0.2)\": -0.265, \"(-0.2, -0.14)\": -0.179, \"(-0.14, -0.05)\": -0.109, \"(-0.05, 0.02)\": -0.042, \"(0.02, 0.09)\": 0.024, \"(0.09, 0.18)\": 0.115, \"(0.18, 0.26)\": 0.186, \"(0.26, 0.31)\": 0.264, \"(0.31, 0.41)\": 0.328, \"(0.41, 0.46)\": 0.404, \"(0.46, 0.55)\": 0.467, \"(0.55, 0.64)\": 0.534, \"(0.64, 0.72)\": 0.6, \"(0.72, 0.81)\": 0.691, \"(0.81, 0.88)\": 0.76, \"(0.88, 1.0)\": 0.822, \"(1.0, 1.11)\": 0.887, \"(1.11, 1.21)\": 0.964, \"(1.21, 1.32)\": 1.03, \"(1.32, 1.42)\": 1.098, \"(1.42, 1.56)\": 1.162, \"(1.56, 1.66)\": 1.23, \"(1.66, 1.8)\": 1.29, \"(1.8, 1.95)\": 1.358, \"(1.95, 2.08)\": 1.426, \"(2.08, 2.22)\": 1.487, \"(2.22, 2.41)\": 1.549, \"(2.41, 2.59)\": 1.614, \"(2.59, 2.77)\": 1.684, \"(2.77, 2.98)\": 1.745, \"(2.98, 3.17)\": 1.811, \"(3.17, 3.41)\": 1.88, \"(3.41, 3.62)\": 1.944, \"(3.62, 3.85)\": 2.005, \"(3.85, 4.15)\": 2.066, \"(4.15, 4.45)\": 2.137, \"(4.45, 4.74)\": 2.2, \"(4.74, 5.06)\": 2.262, \"(5.06, 5.39)\": 2.326, \"(5.39, 5.73)\": 2.387, \"(5.73, 6.14)\": 2.451, \"(6.14, 6.49)\": 2.513, \"(6.49, 6.93)\": 2.573, \"(6.93, 7.37)\": 2.637, \"(7.37, 7.83)\": 2.697, \"(7.83, 8.34)\": 2.758, \"(8.34, 8.88)\": 2.818, \"(8.88, 9.45)\": 2.879, \"(9.45, 9.99)\": 2.943}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = sin(x)\nb) f(x) = x^2+3*x-1\nc) f(x) = arcsinh(x)\nd) f(x) = x\ne) f(x) = sign(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "c)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-10.0, -7.71)\": -1.472, \"(-7.71, -6.33)\": -1.442, \"(-6.33, -5.19)\": -1.412, \"(-5.19, -4.51)\": -1.382, \"(-4.51, -3.93)\": -1.352, \"(-3.93, -3.5)\": -1.322, \"(-3.5, -3.14)\": -1.292, \"(-3.14, -2.83)\": -1.26, \"(-2.83, -2.53)\": -1.229, \"(-2.53, -2.33)\": -1.195, \"(-2.33, -2.16)\": -1.165, \"(-2.16, -1.98)\": -1.132, \"(-1.98, -1.85)\": -1.101, \"(-1.85, -1.71)\": -1.071, \"(-1.71, -1.59)\": -1.04, \"(-1.59, -1.44)\": -0.998, \"(-1.44, -1.33)\": -0.955, \"(-1.33, -1.24)\": -0.92, \"(-1.24, -1.18)\": -0.891, \"(-1.18, -1.08)\": -0.861, \"(-1.08, -1.02)\": -0.814, \"(-1.02, -0.94)\": -0.782, \"(-0.94, -0.89)\": -0.749, \"(-0.89, -0.81)\": -0.718, \"(-0.81, -0.76)\": -0.682, \"(-0.76, -0.73)\": -0.652, \"(-0.73, -0.64)\": -0.619, \"(-0.64, -0.59)\": -0.557, \"(-0.59, -0.53)\": -0.525, \"(-0.53, -0.46)\": -0.46, \"(-0.46, -0.42)\": -0.429, \"(-0.42, -0.37)\": -0.388, \"(-0.37, -0.31)\": -0.335, \"(-0.31, -0.25)\": -0.275, \"(-0.25, -0.21)\": -0.233, \"(-0.21, -0.17)\": -0.193, \"(-0.17, -0.12)\": -0.146, \"(-0.12, -0.07)\": -0.097, \"(-0.07, -0.01)\": -0.061, \"(-0.01, 0.07)\": 0.039, \"(0.07, 0.12)\": 0.101, \"(0.12, 0.15)\": 0.134, \"(0.15, 0.18)\": 0.165, \"(0.18, 0.27)\": 0.208, \"(0.27, 0.39)\": 0.33, \"(0.39, 0.44)\": 0.385, \"(0.44, 0.48)\": 0.416, \"(0.48, 0.52)\": 0.455, \"(0.52, 0.58)\": 0.485, \"(0.58, 0.66)\": 0.533, \"(0.66, 0.73)\": 0.605, \"(0.73, 0.79)\": 0.639, \"(0.79, 0.85)\": 0.673, \"(0.85, 0.91)\": 0.713, \"(0.91, 0.95)\": 0.744, \"(0.95, 1.04)\": 0.774, \"(1.04, 1.12)\": 0.809, \"(1.12, 1.21)\": 0.852, \"(1.21, 1.29)\": 0.882, \"(1.29, 1.41)\": 0.918, \"(1.41, 1.49)\": 0.95, \"(1.49, 1.57)\": 0.98, \"(1.57, 1.71)\": 1.01, \"(1.71, 1.84)\": 1.043, \"(1.84, 2.0)\": 1.074, \"(2.0, 2.18)\": 1.11, \"(2.18, 2.38)\": 1.144, \"(2.38, 2.6)\": 1.176, \"(2.6, 2.84)\": 1.206, \"(2.84, 3.18)\": 1.237, \"(3.18, 3.55)\": 1.268, \"(3.55, 4.04)\": 1.298, \"(4.04, 4.55)\": 1.329, \"(4.55, 5.41)\": 1.359, \"(5.41, 6.54)\": 1.389, \"(6.54, 8.14)\": 1.419, \"(8.14, 9.96)\": 1.449}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = 3^x+1\nb) f(x) = x^4\nc) f(x) = 1/2*cos(x-2)\nd) f(x) = arctan(x)\ne) f(x) = sinh(x)\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "d)"
+  ],
+  [
+    "Consider the following graph. The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. The keys are intervals that represent ranges where the function takes the same value.\n\nFeature Name: x\nFeature Type: continuous\nGraph: {\"(-9.97, -4.69)\": -0.0005, \"(-4.69, -3.92)\": 0.0096, \"(-3.92, -3.49)\": 0.0198, \"(-3.49, -3.19)\": 0.0304, \"(-3.19, -2.96)\": 0.0409, \"(-2.96, -2.74)\": 0.0513, \"(-2.74, -2.56)\": 0.0618, \"(-2.56, -2.4)\": 0.0727, \"(-2.4, -2.3)\": 0.0851, \"(-2.3, -2.12)\": 0.0956, \"(-2.12, -2.03)\": 0.1066, \"(-2.03, -1.92)\": 0.1174, \"(-1.92, -1.81)\": 0.1301, \"(-1.81, -1.73)\": 0.1413, \"(-1.73, -1.63)\": 0.1519, \"(-1.63, -1.55)\": 0.1655, \"(-1.55, -1.46)\": 0.1774, \"(-1.46, -1.39)\": 0.1892, \"(-1.39, -1.35)\": 0.1992, \"(-1.35, -1.27)\": 0.2093, \"(-1.27, -1.22)\": 0.22, \"(-1.22, -1.15)\": 0.2303, \"(-1.15, -1.08)\": 0.2415, \"(-1.08, -1.03)\": 0.254, \"(-1.03, -0.98)\": 0.2643, \"(-0.98, -0.93)\": 0.2746, \"(-0.93, -0.86)\": 0.2857, \"(-0.86, -0.79)\": 0.2989, \"(-0.79, -0.71)\": 0.3188, \"(-0.71, -0.65)\": 0.3316, \"(-0.65, -0.59)\": 0.3449, \"(-0.59, -0.53)\": 0.3564, \"(-0.53, -0.46)\": 0.3767, \"(-0.46, -0.42)\": 0.3894, \"(-0.42, -0.36)\": 0.4009, \"(-0.36, -0.31)\": 0.4127, \"(-0.31, -0.28)\": 0.4227, \"(-0.28, -0.23)\": 0.4336, \"(-0.23, -0.19)\": 0.445, \"(-0.19, -0.13)\": 0.4561, \"(-0.13, -0.08)\": 0.468, \"(-0.08, -0.04)\": 0.4839, \"(-0.04, 0.02)\": 0.4969, \"(0.02, 0.07)\": 0.5102, \"(0.07, 0.14)\": 0.5217, \"(0.14, 0.18)\": 0.5362, \"(0.18, 0.22)\": 0.5482, \"(0.22, 0.32)\": 0.5631, \"(0.32, 0.38)\": 0.587, \"(0.38, 0.45)\": 0.5999, \"(0.45, 0.5)\": 0.6125, \"(0.5, 0.56)\": 0.6245, \"(0.56, 0.6)\": 0.635, \"(0.6, 0.66)\": 0.6491, \"(0.66, 0.75)\": 0.6643, \"(0.75, 0.84)\": 0.6859, \"(0.84, 0.89)\": 0.6986, \"(0.89, 0.95)\": 0.7102, \"(0.95, 1.01)\": 0.723, \"(1.01, 1.07)\": 0.7347, \"(1.07, 1.14)\": 0.7481, \"(1.14, 1.21)\": 0.7598, \"(1.21, 1.29)\": 0.772, \"(1.29, 1.37)\": 0.7875, \"(1.37, 1.46)\": 0.8009, \"(1.46, 1.52)\": 0.8116, \"(1.52, 1.59)\": 0.8233, \"(1.59, 1.69)\": 0.8344, \"(1.69, 1.78)\": 0.8459, \"(1.78, 1.89)\": 0.8569, \"(1.89, 2.02)\": 0.8713, \"(2.02, 2.1)\": 0.8817, \"(2.1, 2.27)\": 0.8939, \"(2.27, 2.41)\": 0.9076, \"(2.41, 2.55)\": 0.918, \"(2.55, 2.72)\": 0.9287, \"(2.72, 2.92)\": 0.9391, \"(2.92, 3.16)\": 0.95, \"(3.16, 3.51)\": 0.9601, \"(3.51, 3.93)\": 0.9706, \"(3.93, 4.73)\": 0.9809, \"(4.73, 9.99)\": 0.9914}\n\nThe graph approximately depicts one of the following functions:\n\na) f(x) = sinh(x)\nb) f(x) = -x^5\nc) f(x) = 1/(1+exp(-x))\nd) f(x) = exp(-x^2)\ne) f(x) = |x|\n\nWhich of these functions is depicted in the graph? Think step by step.",
+    "c)"
+  ]
+]
\ No newline at end of file
diff --git a/benchmarks/benchmark/jumps.json b/benchmarks/benchmark/jumps.json
new file mode 100644
index 0000000..16da108
--- /dev/null
+++ b/benchmarks/benchmark/jumps.json
@@ -0,0 +1,402 @@
+[
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Pclass\nFeature Type: continuous\nMeans: {\"(1.0, 1.5)\": -0.009, \"(1.5, 2.5)\": 0.534, \"(2.5, 3.0)\": -0.532}\nLower Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -0.053, \"(1.5, 2.5)\": 0.174, \"(2.5, 3.0)\": -1.011}\nUpper Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": 0.035, \"(1.5, 2.5)\": 0.893, \"(2.5, 3.0)\": -0.052}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "2.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: smoking\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": 0.01522, \"(0.5, 1.0)\": -0.03391}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0422, \"(0.5, 1.0)\": -0.16186}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.07264, \"(0.5, 1.0)\": 0.09404}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "0.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: area_mean\nFeature Type: continuous\nMeans: {\"(143.5, 259.35)\": -0.759, \"(259.35, 289.4)\": -0.662, \"(289.4, 319.15)\": -0.567, \"(319.15, 348.3)\": -0.464, \"(348.3, 496.5)\": -0.368, \"(496.5, 548.75)\": -0.271, \"(548.75, 606.0)\": -0.173, \"(606.0, 696.25)\": -0.076, \"(696.25, 806.1500000000001)\": 0.309, \"(806.1500000000001, 901.8)\": 0.405, \"(901.8, 959.4000000000001)\": 0.51, \"(959.4000000000001, 1054.0)\": 0.607, \"(1054.0, 1150.0)\": 0.707, \"(1150.0, 1248.5)\": 0.806, \"(1248.5, 1341.0)\": 0.911, \"(1341.0, 1801.0)\": 1.01, \"(1801.0, 2501.0)\": 1.109}\nLower Bounds (95%-Confidence Interval): {\"(143.5, 259.35)\": -1.038, \"(259.35, 289.4)\": -0.892, \"(289.4, 319.15)\": -0.754, \"(319.15, 348.3)\": -0.634, \"(348.3, 496.5)\": -0.559, \"(496.5, 548.75)\": -0.436, \"(548.75, 606.0)\": -0.338, \"(606.0, 696.25)\": -0.727, \"(696.25, 806.1500000000001)\": -0.252, \"(806.1500000000001, 901.8)\": -0.022, \"(901.8, 959.4000000000001)\": 0.058, \"(959.4000000000001, 1054.0)\": 0.141, \"(1054.0, 1150.0)\": 0.243, \"(1150.0, 1248.5)\": 0.328, \"(1248.5, 1341.0)\": 0.393, \"(1341.0, 1801.0)\": 0.475, \"(1801.0, 2501.0)\": 0.574}\nUpper Bounds (95%-Confidence Interval): {\"(143.5, 259.35)\": -0.48, \"(259.35, 289.4)\": -0.432, \"(289.4, 319.15)\": -0.38, \"(319.15, 348.3)\": -0.294, \"(348.3, 496.5)\": -0.177, \"(496.5, 548.75)\": -0.106, \"(548.75, 606.0)\": -0.007, \"(606.0, 696.25)\": 0.575, \"(696.25, 806.1500000000001)\": 0.871, \"(806.1500000000001, 901.8)\": 0.831, \"(901.8, 959.4000000000001)\": 0.962, \"(959.4000000000001, 1054.0)\": 1.074, \"(1054.0, 1150.0)\": 1.171, \"(1150.0, 1248.5)\": 1.285, \"(1248.5, 1341.0)\": 1.428, \"(1341.0, 1801.0)\": 1.544, \"(1801.0, 2501.0)\": 1.644}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "696.25"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: TopographyDrainage\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.0274, \"(0.5, 1.5)\": -0.02381, \"(1.5, 2.5)\": -0.01602, \"(2.5, 3.5)\": -0.01049, \"(3.5, 4.5)\": -0.00528, \"(4.5, 5.5)\": -0.00022, \"(5.5, 6.5)\": 0.00517, \"(6.5, 7.5)\": 0.01282, \"(7.5, 8.5)\": 0.01628, \"(8.5, 9.5)\": 0.02454, \"(9.5, 10.5)\": 0.02883, \"(10.5, 11.5)\": 0.03213, \"(11.5, 17.0)\": 0.03564}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.03013, \"(0.5, 1.5)\": -0.02484, \"(1.5, 2.5)\": -0.01655, \"(2.5, 3.5)\": -0.01088, \"(3.5, 4.5)\": -0.00574, \"(4.5, 5.5)\": -0.00046, \"(5.5, 6.5)\": 0.00473, \"(6.5, 7.5)\": 0.01242, \"(7.5, 8.5)\": 0.01574, \"(8.5, 9.5)\": 0.02354, \"(9.5, 10.5)\": 0.0277, \"(10.5, 11.5)\": 0.03039, \"(11.5, 17.0)\": 0.02281}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02466, \"(0.5, 1.5)\": -0.02278, \"(1.5, 2.5)\": -0.0155, \"(2.5, 3.5)\": -0.0101, \"(3.5, 4.5)\": -0.00482, \"(4.5, 5.5)\": 2e-05, \"(5.5, 6.5)\": 0.00561, \"(6.5, 7.5)\": 0.01323, \"(7.5, 8.5)\": 0.01683, \"(8.5, 9.5)\": 0.02554, \"(9.5, 10.5)\": 0.02996, \"(10.5, 11.5)\": 0.03386, \"(11.5, 17.0)\": 0.04848}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "8.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: area_worst\nFeature Type: continuous\nMeans: {\"(185.2, 357.5)\": -1.345, \"(357.5, 413.15)\": -1.192, \"(413.15, 471.9)\": -1.038, \"(471.9, 508.5)\": -0.878, \"(508.5, 633.9)\": -0.723, \"(633.9, 653.45)\": -0.565, \"(653.45, 710.2)\": -0.348, \"(710.2, 727.0999999999999)\": -0.165, \"(727.0999999999999, 805.95)\": 0.096, \"(805.95, 874.85)\": 0.253, \"(874.85, 928.5)\": 0.48, \"(928.5, 1033.5)\": 0.761, \"(1033.5, 1222.5)\": 0.932, \"(1222.5, 1346.5)\": 1.092, \"(1346.5, 1645.5)\": 1.245, \"(1645.5, 1979.0)\": 1.404, \"(1979.0, 4254.0)\": 1.557}\nLower Bounds (95%-Confidence Interval): {\"(185.2, 357.5)\": -2.413, \"(357.5, 413.15)\": -2.22, \"(413.15, 471.9)\": -2.004, \"(471.9, 508.5)\": -1.818, \"(508.5, 633.9)\": -1.868, \"(633.9, 653.45)\": -1.645, \"(653.45, 710.2)\": -0.767, \"(710.2, 727.0999999999999)\": -0.501, \"(727.0999999999999, 805.95)\": -0.573, \"(805.95, 874.85)\": -0.187, \"(874.85, 928.5)\": -0.49, \"(928.5, 1033.5)\": -0.484, \"(1033.5, 1222.5)\": -0.455, \"(1222.5, 1346.5)\": -0.298, \"(1346.5, 1645.5)\": -0.182, \"(1645.5, 1979.0)\": -0.049, \"(1979.0, 4254.0)\": 0.071}\nUpper Bounds (95%-Confidence Interval): {\"(185.2, 357.5)\": -0.278, \"(357.5, 413.15)\": -0.164, \"(413.15, 471.9)\": -0.073, \"(471.9, 508.5)\": 0.062, \"(508.5, 633.9)\": 0.423, \"(633.9, 653.45)\": 0.516, \"(653.45, 710.2)\": 0.071, \"(710.2, 727.0999999999999)\": 0.17, \"(727.0999999999999, 805.95)\": 0.764, \"(805.95, 874.85)\": 0.693, \"(874.85, 928.5)\": 1.449, \"(928.5, 1033.5)\": 2.006, \"(1033.5, 1222.5)\": 2.319, \"(1222.5, 1346.5)\": 2.482, \"(1346.5, 1645.5)\": 2.672, \"(1645.5, 1979.0)\": 2.857, \"(1979.0, 4254.0)\": 3.043}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "928.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Age\nFeature Type: continuous\nMeans: {\"(2.0, 2.5)\": -0.503, \"(2.5, 5.0)\": 1.062, \"(5.0, 17.5)\": 1.188, \"(17.5, 24.5)\": 0.305, \"(24.5, 28.5)\": 0.438, \"(28.5, 31.5)\": 0.03, \"(31.5, 35.5)\": 0.337, \"(35.5, 36.25)\": 0.047, \"(36.25, 43.5)\": -0.09, \"(43.5, 44.5)\": -0.293, \"(44.5, 47.5)\": -0.611, \"(47.5, 49.5)\": -0.32, \"(49.5, 59.0)\": -0.561, \"(59.0, 60.5)\": -0.283, \"(60.5, 63.5)\": -0.939, \"(63.5, 70.5)\": -1.095, \"(70.5, 75.5)\": -0.598, \"(75.5, 80.0)\": -0.406}\nLower Bounds (95%-Confidence Interval): {\"(2.0, 2.5)\": -2.047, \"(2.5, 5.0)\": -0.63, \"(5.0, 17.5)\": -0.496, \"(17.5, 24.5)\": -0.053, \"(24.5, 28.5)\": -0.121, \"(28.5, 31.5)\": -0.759, \"(31.5, 35.5)\": -0.296, \"(35.5, 36.25)\": -0.141, \"(36.25, 43.5)\": -0.547, \"(43.5, 44.5)\": -0.684, \"(44.5, 47.5)\": -1.551, \"(47.5, 49.5)\": -0.563, \"(49.5, 59.0)\": -1.187, \"(59.0, 60.5)\": -1.123, \"(60.5, 63.5)\": -2.149, \"(63.5, 70.5)\": -2.327, \"(70.5, 75.5)\": -0.924, \"(75.5, 80.0)\": -0.696}\nUpper Bounds (95%-Confidence Interval): {\"(2.0, 2.5)\": 1.042, \"(2.5, 5.0)\": 2.754, \"(5.0, 17.5)\": 2.872, \"(17.5, 24.5)\": 0.662, \"(24.5, 28.5)\": 0.998, \"(28.5, 31.5)\": 0.819, \"(31.5, 35.5)\": 0.969, \"(35.5, 36.25)\": 0.234, \"(36.25, 43.5)\": 0.367, \"(43.5, 44.5)\": 0.098, \"(44.5, 47.5)\": 0.329, \"(47.5, 49.5)\": -0.077, \"(49.5, 59.0)\": 0.064, \"(59.0, 60.5)\": 0.557, \"(60.5, 63.5)\": 0.271, \"(63.5, 70.5)\": 0.137, \"(70.5, 75.5)\": -0.271, \"(75.5, 80.0)\": -0.116}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "2.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: concavity_se\nFeature Type: continuous\nMeans: {\"(0.0, 0.001156)\": -0.6445, \"(0.001156, 0.002325)\": -0.6016, \"(0.002325, 0.0037635)\": -0.5599, \"(0.0037635, 0.0053165)\": -0.5149, \"(0.0053165, 0.0058905)\": -0.4651, \"(0.0058905, 0.006987999999999999)\": -0.4227, \"(0.006987999999999999, 0.0077405)\": -0.3808, \"(0.0077405, 0.008344500000000001)\": -0.3373, \"(0.008344500000000001, 0.009263500000000001)\": -0.2906, \"(0.009263500000000001, 0.010215)\": -0.246, \"(0.010215, 0.010705)\": -0.2028, \"(0.010705, 0.01122)\": -0.1484, \"(0.01122, 0.011625)\": -0.1022, \"(0.011625, 0.01191)\": -0.0592, \"(0.01191, 0.012455)\": -0.0118, \"(0.012455, 0.0203)\": 0.0471, \"(0.0203, 0.022565)\": 0.0914, \"(0.022565, 0.02983)\": 0.1347, \"(0.02983, 0.032535)\": 0.0347, \"(0.032535, 0.0338)\": -0.0071, \"(0.0338, 0.038565)\": 0.0604, \"(0.038565, 0.04418)\": 0.1065, \"(0.04418, 0.059305)\": 0.1494, \"(0.059305, 0.065775)\": 0.1044, \"(0.065775, 0.07794000000000001)\": 0.0533, \"(0.07794000000000001, 0.08089)\": 0.0097, \"(0.08089, 0.096205)\": -0.0573, \"(0.096205, 0.22865000000000002)\": -0.1001, \"(0.22865000000000002, 0.396)\": -0.1471}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.001156)\": -0.9396, \"(0.001156, 0.002325)\": -0.8658, \"(0.002325, 0.0037635)\": -0.8192, \"(0.0037635, 0.0053165)\": -0.7681, \"(0.0053165, 0.0058905)\": -0.716, \"(0.0058905, 0.006987999999999999)\": -0.6675, \"(0.006987999999999999, 0.0077405)\": -0.6156, \"(0.0077405, 0.008344500000000001)\": -0.5789, \"(0.008344500000000001, 0.009263500000000001)\": -0.5182, \"(0.009263500000000001, 0.010215)\": -0.4535, \"(0.010215, 0.010705)\": -0.4164, \"(0.010705, 0.01122)\": -0.3446, \"(0.01122, 0.011625)\": -0.2792, \"(0.011625, 0.01191)\": -0.2184, \"(0.01191, 0.012455)\": -0.172, \"(0.012455, 0.0203)\": -0.1411, \"(0.0203, 0.022565)\": -0.0146, \"(0.022565, 0.02983)\": 0.021, \"(0.02983, 0.032535)\": -0.4149, \"(0.032535, 0.0338)\": -0.4515, \"(0.0338, 0.038565)\": -0.0686, \"(0.038565, 0.04418)\": -0.0006, \"(0.04418, 0.059305)\": -0.0422, \"(0.059305, 0.065775)\": -0.0685, \"(0.065775, 0.07794000000000001)\": -0.1246, \"(0.07794000000000001, 0.08089)\": -0.1713, \"(0.08089, 0.096205)\": -0.2715, \"(0.096205, 0.22865000000000002)\": -0.3432, \"(0.22865000000000002, 0.396)\": -0.3958}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.001156)\": -0.3494, \"(0.001156, 0.002325)\": -0.3374, \"(0.002325, 0.0037635)\": -0.3006, \"(0.0037635, 0.0053165)\": -0.2618, \"(0.0053165, 0.0058905)\": -0.2142, \"(0.0058905, 0.006987999999999999)\": -0.1779, \"(0.006987999999999999, 0.0077405)\": -0.1459, \"(0.0077405, 0.008344500000000001)\": -0.0957, \"(0.008344500000000001, 0.009263500000000001)\": -0.063, \"(0.009263500000000001, 0.010215)\": -0.0385, \"(0.010215, 0.010705)\": 0.0109, \"(0.010705, 0.01122)\": 0.0478, \"(0.01122, 0.011625)\": 0.0749, \"(0.011625, 0.01191)\": 0.0999, \"(0.01191, 0.012455)\": 0.1484, \"(0.012455, 0.0203)\": 0.2352, \"(0.0203, 0.022565)\": 0.1973, \"(0.022565, 0.02983)\": 0.2485, \"(0.02983, 0.032535)\": 0.4843, \"(0.032535, 0.0338)\": 0.4374, \"(0.0338, 0.038565)\": 0.1895, \"(0.038565, 0.04418)\": 0.2135, \"(0.04418, 0.059305)\": 0.3411, \"(0.059305, 0.065775)\": 0.2772, \"(0.065775, 0.07794000000000001)\": 0.2312, \"(0.07794000000000001, 0.08089)\": 0.1907, \"(0.08089, 0.096205)\": 0.1569, \"(0.096205, 0.22865000000000002)\": 0.1429, \"(0.22865000000000002, 0.396)\": 0.1015}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "0.02983"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: total_rooms\nFeature Type: continuous\nMeans: {\"(2.0, 23.0)\": -70808.9, \"(23.0, 38.5)\": -78966.6, \"(38.5, 48.5)\": -28602.1, \"(48.5, 119.0)\": -47079.6, \"(119.0, 163.0)\": -52692.3, \"(163.0, 186.5)\": -60093.0, \"(186.5, 223.5)\": -51150.5, \"(223.5, 239.5)\": -39728.1, \"(239.5, 248.5)\": -7038.8, \"(248.5, 265.5)\": -691.1, \"(265.5, 280.5)\": -14052.2, \"(280.5, 342.5)\": -35705.6, \"(342.5, 364.5)\": -24578.4, \"(364.5, 385.5)\": -34007.7, \"(385.5, 406.5)\": -46655.0, \"(406.5, 413.5)\": -17805.2, \"(413.5, 443.5)\": -12192.7, \"(443.5, 452.5)\": -22779.7, \"(452.5, 502.5)\": -30652.6, \"(502.5, 508.5)\": -25165.4, \"(508.5, 515.5)\": -12943.4, \"(515.5, 1152.5)\": -21645.3, \"(1152.5, 1239.5)\": -16264.4, \"(1239.5, 1245.5)\": -7023.2, \"(1245.5, 1619.5)\": -12855.2, \"(1619.5, 1944.5)\": -7415.6, \"(1944.5, 2330.5)\": -1233.9, \"(2330.5, 2710.5)\": 4370.8, \"(2710.5, 2834.5)\": 9739.0, \"(2834.5, 2838.5)\": 16667.1, \"(2838.5, 3577.5)\": 10096.4, \"(3577.5, 5401.0)\": 15549.4, \"(5401.0, 5535.5)\": 24928.2, \"(5535.5, 9961.0)\": 19069.3, \"(9961.0, 18662.0)\": 26262.6, \"(18662.0, 39320.0)\": 20736.3}\nLower Bounds (95%-Confidence Interval): {\"(2.0, 23.0)\": -91545.9, \"(23.0, 38.5)\": -102966.4, \"(38.5, 48.5)\": -57179.9, \"(48.5, 119.0)\": -64507.9, \"(119.0, 163.0)\": -67051.1, \"(163.0, 186.5)\": -74986.7, \"(186.5, 223.5)\": -62447.2, \"(223.5, 239.5)\": -55573.0, \"(239.5, 248.5)\": -34485.5, \"(248.5, 265.5)\": -18815.6, \"(265.5, 280.5)\": -35576.3, \"(280.5, 342.5)\": -44957.9, \"(342.5, 364.5)\": -36592.4, \"(364.5, 385.5)\": -39620.4, \"(385.5, 406.5)\": -54434.9, \"(406.5, 413.5)\": -28898.3, \"(413.5, 443.5)\": -21926.2, \"(443.5, 452.5)\": -34828.5, \"(452.5, 502.5)\": -40304.3, \"(502.5, 508.5)\": -35649.5, \"(508.5, 515.5)\": -27403.5, \"(515.5, 1152.5)\": -28456.5, \"(1152.5, 1239.5)\": -20918.2, \"(1239.5, 1245.5)\": -15907.4, \"(1245.5, 1619.5)\": -19943.7, \"(1619.5, 1944.5)\": -13063.6, \"(1944.5, 2330.5)\": -8595.8, \"(2330.5, 2710.5)\": 2936.6, \"(2710.5, 2834.5)\": 7069.8, \"(2834.5, 2838.5)\": 1263.0, \"(2838.5, 3577.5)\": 7025.1, \"(3577.5, 5401.0)\": 10287.4, \"(5401.0, 5535.5)\": 10519.1, \"(5535.5, 9961.0)\": 12536.6, \"(9961.0, 18662.0)\": 16596.5, \"(18662.0, 39320.0)\": 17189.5}\nUpper Bounds (95%-Confidence Interval): {\"(2.0, 23.0)\": -50072.0, \"(23.0, 38.5)\": -54966.9, \"(38.5, 48.5)\": -24.3, \"(48.5, 119.0)\": -29651.4, \"(119.0, 163.0)\": -38333.5, \"(163.0, 186.5)\": -45199.3, \"(186.5, 223.5)\": -39853.9, \"(223.5, 239.5)\": -23883.2, \"(239.5, 248.5)\": 20408.0, \"(248.5, 265.5)\": 17433.4, \"(265.5, 280.5)\": 7471.9, \"(280.5, 342.5)\": -26453.2, \"(342.5, 364.5)\": -12564.3, \"(364.5, 385.5)\": -28395.1, \"(385.5, 406.5)\": -38875.1, \"(406.5, 413.5)\": -6712.1, \"(413.5, 443.5)\": -2459.1, \"(443.5, 452.5)\": -10730.8, \"(452.5, 502.5)\": -21000.9, \"(502.5, 508.5)\": -14681.3, \"(508.5, 515.5)\": 1516.8, \"(515.5, 1152.5)\": -14834.1, \"(1152.5, 1239.5)\": -11610.6, \"(1239.5, 1245.5)\": 1860.9, \"(1245.5, 1619.5)\": -5766.8, \"(1619.5, 1944.5)\": -1767.7, \"(1944.5, 2330.5)\": 6128.1, \"(2330.5, 2710.5)\": 5805.0, \"(2710.5, 2834.5)\": 12408.3, \"(2834.5, 2838.5)\": 32071.2, \"(2838.5, 3577.5)\": 13167.8, \"(3577.5, 5401.0)\": 20811.4, \"(5401.0, 5535.5)\": 39337.3, \"(5535.5, 9961.0)\": 25602.1, \"(9961.0, 18662.0)\": 35928.6, \"(18662.0, 39320.0)\": 24283.0}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "38.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: EstimatedSalary\nFeature Type: continuous\nMeans: {\"(106.67, 780.2149999999999)\": 0.3865, \"(780.2149999999999, 4627.98)\": 0.3462, \"(4627.98, 6842.475)\": 0.0858, \"(6842.475, 7401.88)\": 0.157, \"(7401.88, 27330.43)\": 0.2048, \"(27330.43, 38816.375)\": 0.1737, \"(38816.375, 40348.645000000004)\": 0.1063, \"(40348.645000000004, 42807.509999999995)\": 0.0512, \"(42807.509999999995, 48226.81)\": 0.1098, \"(48226.81, 48498.15)\": -0.0771, \"(48498.15, 58535.68)\": 0.0187, \"(58535.68, 94498.98999999999)\": 0.0512, \"(94498.98999999999, 120892.955)\": 0.0186, \"(120892.955, 121151.28)\": -0.0263, \"(121151.28, 121482.61499999999)\": -0.0801, \"(121482.61499999999, 148569.97)\": -0.0388, \"(148569.97, 184522.325)\": -0.0796, \"(184522.325, 187947.635)\": -0.1332, \"(187947.635, 187985.865)\": -0.2342, \"(187985.865, 188452.565)\": -0.0632, \"(188452.565, 189006.61)\": -0.0053, \"(189006.61, 196418.97999999998)\": 0.0291, \"(196418.97999999998, 199505.41)\": -0.0098, \"(199505.41, 199992.48)\": 0.214}\nLower Bounds (95%-Confidence Interval): {\"(106.67, 780.2149999999999)\": 0.0871, \"(780.2149999999999, 4627.98)\": 0.1468, \"(4627.98, 6842.475)\": -0.2734, \"(6842.475, 7401.88)\": -0.01, \"(7401.88, 27330.43)\": 0.0941, \"(27330.43, 38816.375)\": 0.065, \"(38816.375, 40348.645000000004)\": -0.0568, \"(40348.645000000004, 42807.509999999995)\": -0.1427, \"(42807.509999999995, 48226.81)\": 0.0015, \"(48226.81, 48498.15)\": -0.404, \"(48498.15, 58535.68)\": -0.1286, \"(58535.68, 94498.98999999999)\": -0.003, \"(94498.98999999999, 120892.955)\": -0.0541, \"(120892.955, 121151.28)\": -0.186, \"(121151.28, 121482.61499999999)\": -0.2842, \"(121482.61499999999, 148569.97)\": -0.1593, \"(148569.97, 184522.325)\": -0.1401, \"(184522.325, 187947.635)\": -0.216, \"(187947.635, 187985.865)\": -0.7523, \"(187985.865, 188452.565)\": -0.2404, \"(188452.565, 189006.61)\": -0.1779, \"(189006.61, 196418.97999999998)\": -0.1285, \"(196418.97999999998, 199505.41)\": -0.2064, \"(199505.41, 199992.48)\": -0.3318}\nUpper Bounds (95%-Confidence Interval): {\"(106.67, 780.2149999999999)\": 0.6859, \"(780.2149999999999, 4627.98)\": 0.5457, \"(4627.98, 6842.475)\": 0.445, \"(6842.475, 7401.88)\": 0.3239, \"(7401.88, 27330.43)\": 0.3154, \"(27330.43, 38816.375)\": 0.2823, \"(38816.375, 40348.645000000004)\": 0.2695, \"(40348.645000000004, 42807.509999999995)\": 0.2451, \"(42807.509999999995, 48226.81)\": 0.2181, \"(48226.81, 48498.15)\": 0.2497, \"(48498.15, 58535.68)\": 0.166, \"(58535.68, 94498.98999999999)\": 0.1054, \"(94498.98999999999, 120892.955)\": 0.0913, \"(120892.955, 121151.28)\": 0.1335, \"(121151.28, 121482.61499999999)\": 0.1239, \"(121482.61499999999, 148569.97)\": 0.0817, \"(148569.97, 184522.325)\": -0.019, \"(184522.325, 187947.635)\": -0.0504, \"(187947.635, 187985.865)\": 0.2839, \"(187985.865, 188452.565)\": 0.1139, \"(188452.565, 189006.61)\": 0.1673, \"(189006.61, 196418.97999999998)\": 0.1867, \"(196418.97999999998, 199505.41)\": 0.1868, \"(199505.41, 199992.48)\": 0.7597}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "4627.98"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: PopulationScore\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02443, \"(0.5, 1.5)\": -0.02088, \"(1.5, 2.5)\": -0.01613, \"(2.5, 3.5)\": -0.01086, \"(3.5, 4.5)\": -0.00583, \"(4.5, 5.5)\": 0.00139, \"(5.5, 6.5)\": 0.00556, \"(6.5, 7.5)\": 0.01145, \"(7.5, 8.5)\": 0.01748, \"(8.5, 10.5)\": 0.0242, \"(10.5, 11.5)\": 0.03351, \"(11.5, 13.5)\": 0.03691, \"(13.5, 15.0)\": 0.03345, \"(15.0, 16.0)\": 0.02926}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02582, \"(0.5, 1.5)\": -0.02181, \"(1.5, 2.5)\": -0.01706, \"(2.5, 3.5)\": -0.01143, \"(3.5, 4.5)\": -0.00626, \"(4.5, 5.5)\": 0.00099, \"(5.5, 6.5)\": 0.00524, \"(6.5, 7.5)\": 0.01084, \"(7.5, 8.5)\": 0.0167, \"(8.5, 10.5)\": 0.02302, \"(10.5, 11.5)\": 0.03159, \"(11.5, 13.5)\": 0.03427, \"(13.5, 15.0)\": 0.02849, \"(15.0, 16.0)\": 0.02539}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02304, \"(0.5, 1.5)\": -0.01995, \"(1.5, 2.5)\": -0.01521, \"(2.5, 3.5)\": -0.01028, \"(3.5, 4.5)\": -0.00541, \"(4.5, 5.5)\": 0.00178, \"(5.5, 6.5)\": 0.00588, \"(6.5, 7.5)\": 0.01205, \"(7.5, 8.5)\": 0.01826, \"(8.5, 10.5)\": 0.02538, \"(10.5, 11.5)\": 0.03543, \"(11.5, 13.5)\": 0.03955, \"(13.5, 15.0)\": 0.03841, \"(15.0, 16.0)\": 0.03313}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "10.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: housing_median_age\nFeature Type: continuous\nMeans: {\"(1.0, 4.5)\": -19998.0, \"(4.5, 7.5)\": -7788.2, \"(7.5, 16.5)\": -10680.2, \"(16.5, 18.5)\": -6304.4, \"(18.5, 27.5)\": -1760.6, \"(27.5, 34.5)\": 2164.8, \"(34.5, 38.5)\": -912.5, \"(38.5, 41.5)\": 4199.6, \"(41.5, 45.5)\": -497.4, \"(45.5, 47.5)\": -5189.8, \"(47.5, 48.5)\": 5201.0, \"(48.5, 49.5)\": 2159.0, \"(49.5, 50.5)\": 6135.7, \"(50.5, 51.5)\": 11513.8, \"(51.5, 52.0)\": 27549.7}\nLower Bounds (95%-Confidence Interval): {\"(1.0, 4.5)\": -26905.5, \"(4.5, 7.5)\": -11566.0, \"(7.5, 16.5)\": -12538.5, \"(16.5, 18.5)\": -7756.2, \"(18.5, 27.5)\": -3361.1, \"(27.5, 34.5)\": 124.5, \"(34.5, 38.5)\": -1933.4, \"(38.5, 41.5)\": 2260.6, \"(41.5, 45.5)\": -4429.7, \"(45.5, 47.5)\": -8697.7, \"(47.5, 48.5)\": 2180.3, \"(48.5, 49.5)\": -1981.1, \"(49.5, 50.5)\": 1581.5, \"(50.5, 51.5)\": 5647.5, \"(51.5, 52.0)\": 25827.1}\nUpper Bounds (95%-Confidence Interval): {\"(1.0, 4.5)\": -13090.4, \"(4.5, 7.5)\": -4010.4, \"(7.5, 16.5)\": -8821.8, \"(16.5, 18.5)\": -4852.5, \"(18.5, 27.5)\": -160.0, \"(27.5, 34.5)\": 4205.0, \"(34.5, 38.5)\": 108.5, \"(38.5, 41.5)\": 6138.7, \"(41.5, 45.5)\": 3434.9, \"(45.5, 47.5)\": -1682.0, \"(47.5, 48.5)\": 8221.7, \"(48.5, 49.5)\": 6299.1, \"(49.5, 50.5)\": 10689.9, \"(50.5, 51.5)\": 17380.1, \"(51.5, 52.0)\": 29272.3}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "51.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: InadequatePlanning\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02553, \"(0.5, 2.5)\": -0.02038, \"(2.5, 4.5)\": -0.0099, \"(4.5, 6.5)\": 0.00082, \"(6.5, 7.5)\": 0.01088, \"(7.5, 9.5)\": 0.0178, \"(9.5, 10.5)\": 0.02657, \"(10.5, 12.5)\": 0.0329, \"(12.5, 13.5)\": 0.03982, \"(13.5, 15.0)\": 0.05043, \"(15.0, 16.0)\": 0.06084}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02806, \"(0.5, 2.5)\": -0.02117, \"(2.5, 4.5)\": -0.01033, \"(4.5, 6.5)\": 0.00032, \"(6.5, 7.5)\": 0.01025, \"(7.5, 9.5)\": 0.01687, \"(9.5, 10.5)\": 0.02522, \"(10.5, 12.5)\": 0.02998, \"(12.5, 13.5)\": 0.03567, \"(13.5, 15.0)\": 0.03659, \"(15.0, 16.0)\": 0.04096}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.023, \"(0.5, 2.5)\": -0.01959, \"(2.5, 4.5)\": -0.00946, \"(4.5, 6.5)\": 0.00132, \"(6.5, 7.5)\": 0.0115, \"(7.5, 9.5)\": 0.01874, \"(9.5, 10.5)\": 0.02792, \"(10.5, 12.5)\": 0.03583, \"(12.5, 13.5)\": 0.04397, \"(13.5, 15.0)\": 0.06426, \"(15.0, 16.0)\": 0.08071}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "4.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: SkinThickness\nFeature Type: continuous\nMeans: {\"(0.0, 3.5)\": 0.0121, \"(3.5, 7.5)\": -0.0407, \"(7.5, 9.0)\": -0.0873, \"(9.0, 11.5)\": -0.1192, \"(11.5, 13.5)\": -0.1587, \"(13.5, 20.5)\": -0.1856, \"(20.5, 22.5)\": -0.1532, \"(22.5, 24.5)\": -0.1123, \"(24.5, 26.5)\": -0.0708, \"(26.5, 28.5)\": -0.036, \"(28.5, 30.5)\": -0.0039, \"(30.5, 32.5)\": 0.0343, \"(32.5, 34.5)\": 0.0703, \"(34.5, 39.5)\": 0.1069, \"(39.5, 40.5)\": 0.143, \"(40.5, 41.5)\": 0.1769, \"(41.5, 43.5)\": 0.2279, \"(43.5, 47.5)\": 0.2859, \"(47.5, 49.5)\": 0.2453, \"(49.5, 51.0)\": -0.0169, \"(51.0, 55.0)\": -0.0754, \"(55.0, 77.5)\": 0.2174, \"(77.5, 99.0)\": 0.3109}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 3.5)\": -0.071, \"(3.5, 7.5)\": -0.1199, \"(7.5, 9.0)\": -0.1639, \"(9.0, 11.5)\": -0.1953, \"(11.5, 13.5)\": -0.2382, \"(13.5, 20.5)\": -0.2707, \"(20.5, 22.5)\": -0.2184, \"(22.5, 24.5)\": -0.1699, \"(24.5, 26.5)\": -0.1255, \"(26.5, 28.5)\": -0.0953, \"(28.5, 30.5)\": -0.0714, \"(30.5, 32.5)\": -0.0304, \"(32.5, 34.5)\": 0.0205, \"(34.5, 39.5)\": 0.0292, \"(39.5, 40.5)\": 0.0607, \"(40.5, 41.5)\": 0.0987, \"(41.5, 43.5)\": 0.0904, \"(43.5, 47.5)\": 0.0985, \"(47.5, 49.5)\": 0.0202, \"(49.5, 51.0)\": -0.3346, \"(51.0, 55.0)\": -0.5656, \"(55.0, 77.5)\": -0.4718, \"(77.5, 99.0)\": -0.4467}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 3.5)\": 0.0953, \"(3.5, 7.5)\": 0.0385, \"(7.5, 9.0)\": -0.0106, \"(9.0, 11.5)\": -0.0431, \"(11.5, 13.5)\": -0.0792, \"(13.5, 20.5)\": -0.1005, \"(20.5, 22.5)\": -0.088, \"(22.5, 24.5)\": -0.0547, \"(24.5, 26.5)\": -0.0161, \"(26.5, 28.5)\": 0.0233, \"(28.5, 30.5)\": 0.0636, \"(30.5, 32.5)\": 0.099, \"(32.5, 34.5)\": 0.12, \"(34.5, 39.5)\": 0.1847, \"(39.5, 40.5)\": 0.2253, \"(40.5, 41.5)\": 0.255, \"(41.5, 43.5)\": 0.3653, \"(43.5, 47.5)\": 0.4732, \"(47.5, 49.5)\": 0.4704, \"(49.5, 51.0)\": 0.3009, \"(51.0, 55.0)\": 0.4148, \"(55.0, 77.5)\": 0.9065, \"(77.5, 99.0)\": 1.0684}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "55.0"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: MaritalStatus\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.368, \"(0.5, 1.5)\": 0.724, \"(1.5, 2.5)\": 0.587, \"(2.5, 3.5)\": -0.221, \"(3.5, 4.5)\": -0.631, \"(4.5, 5.5)\": -0.545, \"(5.5, 6.0)\": 0.179}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.418, \"(0.5, 1.5)\": 0.02, \"(1.5, 2.5)\": 0.545, \"(2.5, 3.5)\": -0.336, \"(3.5, 4.5)\": -0.676, \"(4.5, 5.5)\": -0.688, \"(5.5, 6.0)\": 0.067}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.318, \"(0.5, 1.5)\": 1.428, \"(1.5, 2.5)\": 0.629, \"(2.5, 3.5)\": -0.106, \"(3.5, 4.5)\": -0.585, \"(4.5, 5.5)\": -0.403, \"(5.5, 6.0)\": 0.291}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "0.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: AgriculturalPractices\nFeature Type: continuous\nMeans: {\"(0.0, 1.5)\": -0.02463, \"(1.5, 2.5)\": -0.01694, \"(2.5, 3.5)\": -0.01147, \"(3.5, 4.5)\": -0.00533, \"(4.5, 5.5)\": 0.00036, \"(5.5, 6.5)\": 0.00641, \"(6.5, 7.5)\": 0.01086, \"(7.5, 8.5)\": 0.01753, \"(8.5, 9.5)\": 0.02391, \"(9.5, 11.5)\": 0.03162, \"(11.5, 14.0)\": 0.0391, \"(14.0, 15.0)\": 0.05506}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02721, \"(1.5, 2.5)\": -0.01778, \"(2.5, 3.5)\": -0.01182, \"(3.5, 4.5)\": -0.00574, \"(4.5, 5.5)\": -9e-05, \"(5.5, 6.5)\": 0.00587, \"(6.5, 7.5)\": 0.01028, \"(7.5, 8.5)\": 0.01669, \"(8.5, 9.5)\": 0.02306, \"(9.5, 11.5)\": 0.02986, \"(11.5, 14.0)\": 0.03465, \"(14.0, 15.0)\": 0.03109}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02205, \"(1.5, 2.5)\": -0.0161, \"(2.5, 3.5)\": -0.01113, \"(3.5, 4.5)\": -0.00492, \"(4.5, 5.5)\": 0.0008, \"(5.5, 6.5)\": 0.00696, \"(6.5, 7.5)\": 0.01144, \"(7.5, 8.5)\": 0.01837, \"(8.5, 9.5)\": 0.02477, \"(9.5, 11.5)\": 0.03339, \"(11.5, 14.0)\": 0.04355, \"(14.0, 15.0)\": 0.07902}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "14.0"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: FoodCourt\nFeature Type: continuous\nMeans: {\"(0.0, 593.5)\": -0.177, \"(593.5, 779.5)\": 0.043, \"(779.5, 1341.5)\": 0.27, \"(1341.5, 2175.5)\": 0.543, \"(2175.5, 3125.0)\": 0.863, \"(3125.0, 3637.0)\": 1.13, \"(3637.0, 4078.5)\": 1.479, \"(4078.5, 5218.5)\": 2.076, \"(5218.5, 6031.5)\": 1.81, \"(6031.5, 6171.5)\": 1.439, \"(6171.5, 8753.0)\": 2.236, \"(8753.0, 8824.0)\": 2.746, \"(8824.0, 10094.5)\": 3.43, \"(10094.5, 12683.5)\": 3.888, \"(12683.5, 27723.0)\": 4.131}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 593.5)\": -0.307, \"(593.5, 779.5)\": -0.11, \"(779.5, 1341.5)\": -0.04, \"(1341.5, 2175.5)\": -0.06, \"(2175.5, 3125.0)\": 0.404, \"(3125.0, 3637.0)\": 0.707, \"(3637.0, 4078.5)\": 0.742, \"(4078.5, 5218.5)\": 1.52, \"(5218.5, 6031.5)\": 1.485, \"(6031.5, 6171.5)\": 0.477, \"(6171.5, 8753.0)\": 1.548, \"(8753.0, 8824.0)\": 1.95, \"(8824.0, 10094.5)\": 2.626, \"(10094.5, 12683.5)\": 2.361, \"(12683.5, 27723.0)\": 2.558}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 593.5)\": -0.047, \"(593.5, 779.5)\": 0.196, \"(779.5, 1341.5)\": 0.58, \"(1341.5, 2175.5)\": 1.145, \"(2175.5, 3125.0)\": 1.322, \"(3125.0, 3637.0)\": 1.554, \"(3637.0, 4078.5)\": 2.216, \"(4078.5, 5218.5)\": 2.631, \"(5218.5, 6031.5)\": 2.135, \"(6031.5, 6171.5)\": 2.4, \"(6171.5, 8753.0)\": 2.925, \"(8753.0, 8824.0)\": 3.543, \"(8824.0, 10094.5)\": 4.234, \"(10094.5, 12683.5)\": 5.416, \"(12683.5, 27723.0)\": 5.705}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "6171.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Age\nFeature Type: continuous\nMeans: {\"(17.0, 18.5)\": -3.326, \"(18.5, 19.5)\": -2.358, \"(19.5, 20.5)\": -2.799, \"(20.5, 21.5)\": -2.354, \"(21.5, 22.5)\": -1.405, \"(22.5, 23.5)\": -1.633, \"(23.5, 24.5)\": -1.214, \"(24.5, 26.5)\": -0.789, \"(26.5, 27.5)\": -0.473, \"(27.5, 29.5)\": -0.216, \"(29.5, 33.5)\": 0.042, \"(33.5, 36.5)\": 0.351, \"(36.5, 44.5)\": 0.658, \"(44.5, 61.5)\": 0.897, \"(61.5, 66.5)\": 0.574, \"(66.5, 73.5)\": 0.099, \"(73.5, 74.5)\": 0.763, \"(74.5, 77.5)\": 0.502, \"(77.5, 79.5)\": 0.875, \"(79.5, 84.5)\": 0.065, \"(84.5, 90.0)\": -1.08}\nLower Bounds (95%-Confidence Interval): {\"(17.0, 18.5)\": -4.677, \"(18.5, 19.5)\": -3.672, \"(19.5, 20.5)\": -3.928, \"(20.5, 21.5)\": -2.706, \"(21.5, 22.5)\": -1.741, \"(22.5, 23.5)\": -1.856, \"(23.5, 24.5)\": -1.407, \"(24.5, 26.5)\": -0.941, \"(26.5, 27.5)\": -0.561, \"(27.5, 29.5)\": -0.322, \"(29.5, 33.5)\": -0.079, \"(33.5, 36.5)\": 0.229, \"(36.5, 44.5)\": 0.5, \"(44.5, 61.5)\": 0.753, \"(61.5, 66.5)\": 0.434, \"(66.5, 73.5)\": -0.37, \"(73.5, 74.5)\": 0.229, \"(74.5, 77.5)\": -0.136, \"(77.5, 79.5)\": 0.35, \"(79.5, 84.5)\": -0.573, \"(84.5, 90.0)\": -2.041}\nUpper Bounds (95%-Confidence Interval): {\"(17.0, 18.5)\": -1.975, \"(18.5, 19.5)\": -1.044, \"(19.5, 20.5)\": -1.669, \"(20.5, 21.5)\": -2.002, \"(21.5, 22.5)\": -1.069, \"(22.5, 23.5)\": -1.41, \"(23.5, 24.5)\": -1.021, \"(24.5, 26.5)\": -0.637, \"(26.5, 27.5)\": -0.385, \"(27.5, 29.5)\": -0.11, \"(29.5, 33.5)\": 0.164, \"(33.5, 36.5)\": 0.473, \"(36.5, 44.5)\": 0.816, \"(44.5, 61.5)\": 1.04, \"(61.5, 66.5)\": 0.714, \"(66.5, 73.5)\": 0.567, \"(73.5, 74.5)\": 1.297, \"(74.5, 77.5)\": 1.141, \"(77.5, 79.5)\": 1.401, \"(79.5, 84.5)\": 0.702, \"(84.5, 90.0)\": -0.119}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "84.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: PoliticalFactors\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.0263, \"(0.5, 1.5)\": -0.02126, \"(1.5, 2.5)\": -0.01709, \"(2.5, 3.5)\": -0.01038, \"(3.5, 4.5)\": -0.00633, \"(4.5, 5.5)\": 0.00068, \"(5.5, 6.5)\": 0.00618, \"(6.5, 7.5)\": 0.01223, \"(7.5, 8.5)\": 0.01761, \"(8.5, 9.5)\": 0.02318, \"(9.5, 10.5)\": 0.02782, \"(10.5, 11.5)\": 0.03238, \"(11.5, 13.5)\": 0.03978, \"(13.5, 15.0)\": 0.04468, \"(15.0, 16.0)\": 0.0529}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02939, \"(0.5, 1.5)\": -0.02258, \"(1.5, 2.5)\": -0.01777, \"(2.5, 3.5)\": -0.01075, \"(3.5, 4.5)\": -0.00677, \"(4.5, 5.5)\": 0.00038, \"(5.5, 6.5)\": 0.00571, \"(6.5, 7.5)\": 0.01182, \"(7.5, 8.5)\": 0.01718, \"(8.5, 9.5)\": 0.02223, \"(9.5, 10.5)\": 0.02645, \"(10.5, 11.5)\": 0.02946, \"(11.5, 13.5)\": 0.03697, \"(13.5, 15.0)\": 0.03459, \"(15.0, 16.0)\": 0.03844}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02321, \"(0.5, 1.5)\": -0.01993, \"(1.5, 2.5)\": -0.01641, \"(2.5, 3.5)\": -0.01001, \"(3.5, 4.5)\": -0.00589, \"(4.5, 5.5)\": 0.00098, \"(5.5, 6.5)\": 0.00665, \"(6.5, 7.5)\": 0.01264, \"(7.5, 8.5)\": 0.01804, \"(8.5, 9.5)\": 0.02414, \"(9.5, 10.5)\": 0.02919, \"(10.5, 11.5)\": 0.0353, \"(11.5, 13.5)\": 0.04259, \"(13.5, 15.0)\": 0.05476, \"(15.0, 16.0)\": 0.06736}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "15.0"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: VRDeck\nFeature Type: continuous\nMeans: {\"(0.0, 135.5)\": 0.445, \"(135.5, 215.5)\": 0.073, \"(215.5, 500.5)\": -0.294, \"(500.5, 727.5)\": -0.661, \"(727.5, 799.5)\": -1.026, \"(799.5, 831.5)\": -0.601, \"(831.5, 872.5)\": -1.156, \"(872.5, 993.5)\": -1.633, \"(993.5, 1430.5)\": -2.012, \"(1430.5, 1514.5)\": -1.512, \"(1514.5, 1796.0)\": -2.212, \"(1796.0, 1909.5)\": -1.699, \"(1909.5, 1970.0)\": -2.568, \"(1970.0, 2571.5)\": -3.006, \"(2571.5, 2582.0)\": -2.375, \"(2582.0, 2657.0)\": -2.964, \"(2657.0, 3710.5)\": -3.98, \"(3710.5, 4089.0)\": -4.347, \"(4089.0, 5089.5)\": -5.923, \"(5089.5, 24133.0)\": -6.634}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 135.5)\": -0.055, \"(135.5, 215.5)\": -0.275, \"(215.5, 500.5)\": -1.359, \"(500.5, 727.5)\": -0.968, \"(727.5, 799.5)\": -1.273, \"(799.5, 831.5)\": -1.285, \"(831.5, 872.5)\": -1.782, \"(872.5, 993.5)\": -2.358, \"(993.5, 1430.5)\": -2.589, \"(1430.5, 1514.5)\": -2.382, \"(1514.5, 1796.0)\": -2.87, \"(1796.0, 1909.5)\": -3.449, \"(1909.5, 1970.0)\": -3.46, \"(1970.0, 2571.5)\": -4.009, \"(2571.5, 2582.0)\": -4.195, \"(2582.0, 2657.0)\": -4.898, \"(2657.0, 3710.5)\": -5.152, \"(3710.5, 4089.0)\": -5.79, \"(4089.0, 5089.5)\": -7.804, \"(5089.5, 24133.0)\": -8.247}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 135.5)\": 0.945, \"(135.5, 215.5)\": 0.422, \"(215.5, 500.5)\": 0.772, \"(500.5, 727.5)\": -0.354, \"(727.5, 799.5)\": -0.779, \"(799.5, 831.5)\": 0.083, \"(831.5, 872.5)\": -0.529, \"(872.5, 993.5)\": -0.908, \"(993.5, 1430.5)\": -1.435, \"(1430.5, 1514.5)\": -0.643, \"(1514.5, 1796.0)\": -1.555, \"(1796.0, 1909.5)\": 0.051, \"(1909.5, 1970.0)\": -1.677, \"(1970.0, 2571.5)\": -2.002, \"(2571.5, 2582.0)\": -0.555, \"(2582.0, 2657.0)\": -1.03, \"(2657.0, 3710.5)\": -2.808, \"(3710.5, 4089.0)\": -2.905, \"(4089.0, 5089.5)\": -4.042, \"(5089.5, 24133.0)\": -5.02}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "4089.0"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Spa\nFeature Type: continuous\nMeans: {\"(0.0, 130.5)\": 0.521, \"(130.5, 278.5)\": 0.118, \"(278.5, 452.5)\": -0.285, \"(452.5, 754.5)\": -0.907, \"(754.5, 1209.5)\": -1.309, \"(1209.5, 1808.0)\": -1.712, \"(1808.0, 2204.5)\": -3.029, \"(2204.5, 2207.5)\": -2.456, \"(2207.5, 2428.0)\": -2.956, \"(2428.0, 2462.5)\": -2.512, \"(2462.5, 2714.5)\": -3.402, \"(2714.5, 2745.0)\": -2.902, \"(2745.0, 2993.5)\": -4.077, \"(2993.5, 3132.0)\": -4.481, \"(3132.0, 3705.5)\": -5.377, \"(3705.5, 3747.0)\": -4.36, \"(3747.0, 22408.0)\": -7.183}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 130.5)\": 0.36, \"(130.5, 278.5)\": -1.599, \"(278.5, 452.5)\": -1.362, \"(452.5, 754.5)\": -1.291, \"(754.5, 1209.5)\": -2.117, \"(1209.5, 1808.0)\": -2.592, \"(1808.0, 2204.5)\": -3.856, \"(2204.5, 2207.5)\": -3.562, \"(2207.5, 2428.0)\": -3.549, \"(2428.0, 2462.5)\": -3.455, \"(2462.5, 2714.5)\": -4.525, \"(2714.5, 2745.0)\": -4.721, \"(2745.0, 2993.5)\": -5.493, \"(2993.5, 3132.0)\": -6.214, \"(3132.0, 3705.5)\": -6.767, \"(3705.5, 3747.0)\": -6.498, \"(3747.0, 22408.0)\": -9.024}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 130.5)\": 0.682, \"(130.5, 278.5)\": 1.834, \"(278.5, 452.5)\": 0.791, \"(452.5, 754.5)\": -0.524, \"(754.5, 1209.5)\": -0.502, \"(1209.5, 1808.0)\": -0.831, \"(1808.0, 2204.5)\": -2.202, \"(2204.5, 2207.5)\": -1.35, \"(2207.5, 2428.0)\": -2.364, \"(2428.0, 2462.5)\": -1.569, \"(2462.5, 2714.5)\": -2.28, \"(2714.5, 2745.0)\": -1.083, \"(2745.0, 2993.5)\": -2.661, \"(2993.5, 3132.0)\": -2.749, \"(3132.0, 3705.5)\": -3.986, \"(3705.5, 3747.0)\": -2.222, \"(3747.0, 22408.0)\": -5.342}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "3747.0"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: texture_worst\nFeature Type: continuous\nMeans: {\"(12.02, 16.935000000000002)\": -1.885, \"(16.935000000000002, 18.335)\": -1.717, \"(18.335, 19.505)\": -1.55, \"(19.505, 20.225)\": -0.851, \"(20.225, 21.955)\": -0.612, \"(21.955, 23.59)\": -0.44, \"(23.59, 24.795)\": -0.272, \"(24.795, 25.18)\": -0.1, \"(25.18, 25.83)\": 0.078, \"(25.83, 26.855)\": 0.279, \"(26.855, 27.994999999999997)\": 0.451, \"(27.994999999999997, 29.225)\": 0.619, \"(29.225, 31.515)\": 0.878, \"(31.515, 32.485)\": 1.044, \"(32.485, 35.05)\": 1.256, \"(35.05, 49.54)\": 1.423}\nLower Bounds (95%-Confidence Interval): {\"(12.02, 16.935000000000002)\": -4.342, \"(16.935000000000002, 18.335)\": -4.128, \"(18.335, 19.505)\": -3.934, \"(19.505, 20.225)\": -1.264, \"(20.225, 21.955)\": -0.945, \"(21.955, 23.59)\": -0.663, \"(23.59, 24.795)\": -0.468, \"(24.795, 25.18)\": -0.274, \"(25.18, 25.83)\": -0.503, \"(25.83, 26.855)\": -0.327, \"(26.855, 27.994999999999997)\": -0.163, \"(27.994999999999997, 29.225)\": -0.01, \"(29.225, 31.515)\": -0.206, \"(31.515, 32.485)\": -0.081, \"(32.485, 35.05)\": -0.18, \"(35.05, 49.54)\": -0.014}\nUpper Bounds (95%-Confidence Interval): {\"(12.02, 16.935000000000002)\": 0.572, \"(16.935000000000002, 18.335)\": 0.695, \"(18.335, 19.505)\": 0.835, \"(19.505, 20.225)\": -0.437, \"(20.225, 21.955)\": -0.279, \"(21.955, 23.59)\": -0.218, \"(23.59, 24.795)\": -0.076, \"(24.795, 25.18)\": 0.073, \"(25.18, 25.83)\": 0.66, \"(25.83, 26.855)\": 0.884, \"(26.855, 27.994999999999997)\": 1.065, \"(27.994999999999997, 29.225)\": 1.248, \"(29.225, 31.515)\": 1.961, \"(31.515, 32.485)\": 2.17, \"(32.485, 35.05)\": 2.691, \"(35.05, 49.54)\": 2.861}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "19.505"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: high_blood_pressure\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.1077, \"(0.5, 1.0)\": 0.1864}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.1574, \"(0.5, 1.0)\": 0.1003}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.058, \"(0.5, 1.0)\": 0.2724}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "0.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: NumOfProducts\nFeature Type: continuous\nMeans: {\"(1.0, 1.5)\": -0.918, \"(1.5, 2.5)\": 0.96, \"(2.5, 3.5)\": -3.104, \"(3.5, 4.0)\": -2.768}\nLower Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -0.985, \"(1.5, 2.5)\": 0.893, \"(2.5, 3.5)\": -3.482, \"(3.5, 4.0)\": -3.159}\nUpper Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -0.852, \"(1.5, 2.5)\": 1.028, \"(2.5, 3.5)\": -2.727, \"(3.5, 4.0)\": -2.376}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "2.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: compactness_worst\nFeature Type: continuous\nMeans: {\"(0.02729, 0.049945)\": -0.0578, \"(0.049945, 0.06971)\": -0.0099, \"(0.06971, 0.099305)\": -0.0565, \"(0.099305, 0.10635)\": -0.1408, \"(0.10635, 0.1243)\": -0.1882, \"(0.1243, 0.14795)\": -0.2357, \"(0.14795, 0.1507)\": -0.1883, \"(0.1507, 0.1861)\": -0.1381, \"(0.1861, 0.20124999999999998)\": -0.0918, \"(0.20124999999999998, 0.3358)\": -0.0443, \"(0.3358, 0.3456)\": 0.0027, \"(0.3456, 0.35755000000000003)\": 0.0649, \"(0.35755000000000003, 0.3703)\": 0.1151, \"(0.3703, 0.39235)\": 0.1642, \"(0.39235, 0.4087)\": 0.2124, \"(0.4087, 0.4229)\": 0.2605, \"(0.4229, 0.4486)\": 0.3109, \"(0.4486, 0.48865000000000003)\": 0.3586, \"(0.48865000000000003, 0.54825)\": 0.4132, \"(0.54825, 0.5892999999999999)\": 0.4651, \"(0.5892999999999999, 0.65835)\": 0.5154, \"(0.65835, 0.7680499999999999)\": 0.572, \"(0.7680499999999999, 0.99795)\": 0.6264, \"(0.99795, 1.058)\": 0.6748}\nLower Bounds (95%-Confidence Interval): {\"(0.02729, 0.049945)\": -0.8125, \"(0.049945, 0.06971)\": -0.7624, \"(0.06971, 0.099305)\": -0.6001, \"(0.099305, 0.10635)\": -0.4033, \"(0.10635, 0.1243)\": -0.4448, \"(0.1243, 0.14795)\": -0.4969, \"(0.14795, 0.1507)\": -0.4446, \"(0.1507, 0.1861)\": -0.2722, \"(0.1861, 0.20124999999999998)\": -0.1924, \"(0.20124999999999998, 0.3358)\": -0.2305, \"(0.3358, 0.3456)\": -0.1741, \"(0.3456, 0.35755000000000003)\": -0.068, \"(0.35755000000000003, 0.3703)\": 0.0047, \"(0.3703, 0.39235)\": 0.0473, \"(0.39235, 0.4087)\": 0.1107, \"(0.4087, 0.4229)\": 0.1686, \"(0.4229, 0.4486)\": 0.2243, \"(0.4486, 0.48865000000000003)\": 0.2736, \"(0.48865000000000003, 0.54825)\": 0.2405, \"(0.54825, 0.5892999999999999)\": 0.2819, \"(0.5892999999999999, 0.65835)\": 0.3155, \"(0.65835, 0.7680499999999999)\": 0.3513, \"(0.7680499999999999, 0.99795)\": 0.3892, \"(0.99795, 1.058)\": 0.4487}\nUpper Bounds (95%-Confidence Interval): {\"(0.02729, 0.049945)\": 0.6969, \"(0.049945, 0.06971)\": 0.7425, \"(0.06971, 0.099305)\": 0.487, \"(0.099305, 0.10635)\": 0.1218, \"(0.10635, 0.1243)\": 0.0684, \"(0.1243, 0.14795)\": 0.0254, \"(0.14795, 0.1507)\": 0.068, \"(0.1507, 0.1861)\": -0.0039, \"(0.1861, 0.20124999999999998)\": 0.0087, \"(0.20124999999999998, 0.3358)\": 0.1418, \"(0.3358, 0.3456)\": 0.1794, \"(0.3456, 0.35755000000000003)\": 0.1979, \"(0.35755000000000003, 0.3703)\": 0.2255, \"(0.3703, 0.39235)\": 0.2811, \"(0.39235, 0.4087)\": 0.314, \"(0.4087, 0.4229)\": 0.3524, \"(0.4229, 0.4486)\": 0.3975, \"(0.4486, 0.48865000000000003)\": 0.4436, \"(0.48865000000000003, 0.54825)\": 0.5859, \"(0.54825, 0.5892999999999999)\": 0.6484, \"(0.5892999999999999, 0.65835)\": 0.7153, \"(0.65835, 0.7680499999999999)\": 0.7927, \"(0.7680499999999999, 0.99795)\": 0.8637, \"(0.99795, 1.058)\": 0.9008}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "0.099305"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: CustomerId\nFeature Type: continuous\nMeans: {\"(15565796.0, 15566519.0)\": -0.8769, \"(15566519.0, 15567333.5)\": -0.8241, \"(15567333.5, 15567844.5)\": -0.1763, \"(15567844.5, 15568343.5)\": 0.0021, \"(15568343.5, 15571612.0)\": -0.2283, \"(15571612.0, 15571858.5)\": -0.0522, \"(15571858.5, 15591260.5)\": -0.1299, \"(15591260.5, 15598058.0)\": -0.0821, \"(15598058.0, 15602525.5)\": -0.1509, \"(15602525.5, 15607288.0)\": -0.0818, \"(15607288.0, 15664896.0)\": -0.0316, \"(15664896.0, 15772587.0)\": 0.0162, \"(15772587.0, 15797097.0)\": 0.0757, \"(15797097.0, 15799214.0)\": 0.0081, \"(15799214.0, 15807559.5)\": 0.0581, \"(15807559.5, 15812616.5)\": -0.0049, \"(15812616.5, 15814479.0)\": -0.0569, \"(15814479.0, 15815247.5)\": -0.111, \"(15815247.5, 15815626.0)\": -0.0335}\nLower Bounds (95%-Confidence Interval): {\"(15565796.0, 15566519.0)\": -1.3796, \"(15566519.0, 15567333.5)\": -1.4199, \"(15567333.5, 15567844.5)\": -0.741, \"(15567844.5, 15568343.5)\": -0.4552, \"(15568343.5, 15571612.0)\": -0.4861, \"(15571612.0, 15571858.5)\": -0.3268, \"(15571858.5, 15591260.5)\": -0.2064, \"(15591260.5, 15598058.0)\": -0.1582, \"(15598058.0, 15602525.5)\": -0.5056, \"(15602525.5, 15607288.0)\": -0.1812, \"(15607288.0, 15664896.0)\": -0.056, \"(15664896.0, 15772587.0)\": -0.142, \"(15772587.0, 15797097.0)\": -0.0689, \"(15797097.0, 15799214.0)\": -0.206, \"(15799214.0, 15807559.5)\": -0.0544, \"(15807559.5, 15812616.5)\": -0.1396, \"(15812616.5, 15814479.0)\": -0.2475, \"(15814479.0, 15815247.5)\": -0.4076, \"(15815247.5, 15815626.0)\": -0.3716}\nUpper Bounds (95%-Confidence Interval): {\"(15565796.0, 15566519.0)\": -0.3742, \"(15566519.0, 15567333.5)\": -0.2283, \"(15567333.5, 15567844.5)\": 0.3884, \"(15567844.5, 15568343.5)\": 0.4594, \"(15568343.5, 15571612.0)\": 0.0295, \"(15571612.0, 15571858.5)\": 0.2223, \"(15571858.5, 15591260.5)\": -0.0535, \"(15591260.5, 15598058.0)\": -0.0061, \"(15598058.0, 15602525.5)\": 0.2038, \"(15602525.5, 15607288.0)\": 0.0176, \"(15607288.0, 15664896.0)\": -0.0071, \"(15664896.0, 15772587.0)\": 0.1744, \"(15772587.0, 15797097.0)\": 0.2202, \"(15797097.0, 15799214.0)\": 0.2223, \"(15799214.0, 15807559.5)\": 0.1706, \"(15807559.5, 15812616.5)\": 0.1298, \"(15812616.5, 15814479.0)\": 0.1336, \"(15814479.0, 15815247.5)\": 0.1855, \"(15815247.5, 15815626.0)\": 0.3046}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "15567333.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: radius_se\nFeature Type: continuous\nMeans: {\"(0.1115, 0.15015)\": -0.773, \"(0.15015, 0.16904999999999998)\": -0.686, \"(0.16904999999999998, 0.1795)\": -0.589, \"(0.1795, 0.18535000000000001)\": -0.499, \"(0.18535000000000001, 0.19345)\": -0.412, \"(0.19345, 0.2103)\": -0.275, \"(0.2103, 0.2329)\": -0.187, \"(0.2329, 0.2939)\": -0.102, \"(0.2939, 0.368)\": -0.186, \"(0.368, 0.38585)\": -0.066, \"(0.38585, 0.42025)\": 0.064, \"(0.42025, 0.46775)\": 0.15, \"(0.46775, 0.54785)\": 0.239, \"(0.54785, 0.5881000000000001)\": 0.334, \"(0.5881000000000001, 0.66425)\": 0.422, \"(0.66425, 0.7562)\": 0.51, \"(0.7562, 0.9131)\": 0.594, \"(0.9131, 1.065)\": 0.683, \"(1.065, 1.2915)\": 0.774, \"(1.2915, 2.873)\": 0.866}\nLower Bounds (95%-Confidence Interval): {\"(0.1115, 0.15015)\": -1.244, \"(0.15015, 0.16904999999999998)\": -1.125, \"(0.16904999999999998, 0.1795)\": -1.008, \"(0.1795, 0.18535000000000001)\": -0.904, \"(0.18535000000000001, 0.19345)\": -0.8, \"(0.19345, 0.2103)\": -0.449, \"(0.2103, 0.2329)\": -0.273, \"(0.2329, 0.2939)\": -0.492, \"(0.2939, 0.368)\": -0.769, \"(0.368, 0.38585)\": -0.437, \"(0.38585, 0.42025)\": -0.188, \"(0.42025, 0.46775)\": -0.119, \"(0.46775, 0.54785)\": -0.037, \"(0.54785, 0.5881000000000001)\": -0.09, \"(0.5881000000000001, 0.66425)\": -0.016, \"(0.66425, 0.7562)\": 0.051, \"(0.7562, 0.9131)\": 0.051, \"(0.9131, 1.065)\": 0.113, \"(1.065, 1.2915)\": 0.123, \"(1.2915, 2.873)\": 0.198}\nUpper Bounds (95%-Confidence Interval): {\"(0.1115, 0.15015)\": -0.302, \"(0.15015, 0.16904999999999998)\": -0.247, \"(0.16904999999999998, 0.1795)\": -0.169, \"(0.1795, 0.18535000000000001)\": -0.094, \"(0.18535000000000001, 0.19345)\": -0.024, \"(0.19345, 0.2103)\": -0.1, \"(0.2103, 0.2329)\": -0.101, \"(0.2329, 0.2939)\": 0.289, \"(0.2939, 0.368)\": 0.396, \"(0.368, 0.38585)\": 0.304, \"(0.38585, 0.42025)\": 0.315, \"(0.42025, 0.46775)\": 0.42, \"(0.46775, 0.54785)\": 0.514, \"(0.54785, 0.5881000000000001)\": 0.758, \"(0.5881000000000001, 0.66425)\": 0.86, \"(0.66425, 0.7562)\": 0.968, \"(0.7562, 0.9131)\": 1.137, \"(0.9131, 1.065)\": 1.253, \"(1.065, 1.2915)\": 1.425, \"(1.2915, 2.873)\": 1.533}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "0.19345"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: EducationNum\nFeature Type: continuous\nMeans: {\"(1.0, 1.5)\": -4.746, \"(1.5, 4.5)\": -1.252, \"(4.5, 6.5)\": -0.882, \"(6.5, 9.5)\": -0.483, \"(9.5, 11.5)\": -0.093, \"(11.5, 13.5)\": 0.276, \"(13.5, 14.5)\": 0.863, \"(14.5, 16.0)\": 1.487}\nLower Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -6.411, \"(1.5, 4.5)\": -1.52, \"(4.5, 6.5)\": -0.99, \"(6.5, 9.5)\": -0.541, \"(9.5, 11.5)\": -0.138, \"(11.5, 13.5)\": 0.205, \"(13.5, 14.5)\": 0.788, \"(14.5, 16.0)\": 1.332}\nUpper Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -3.082, \"(1.5, 4.5)\": -0.984, \"(4.5, 6.5)\": -0.775, \"(6.5, 9.5)\": -0.425, \"(9.5, 11.5)\": -0.049, \"(11.5, 13.5)\": 0.347, \"(13.5, 14.5)\": 0.938, \"(14.5, 16.0)\": 1.641}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "1.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: sepal_length\nFeature Type: continuous\nMeans: {\"(4.3, 4.55)\": 3.328, \"(4.55, 4.75)\": 2.995, \"(4.75, 4.85)\": 2.698, \"(4.85, 5.05)\": 1.665, \"(5.05, 5.25)\": 1.371, \"(5.25, 5.45)\": 1.085, \"(5.45, 5.55)\": 0.339, \"(5.55, 5.75)\": -0.057, \"(5.75, 5.85)\": -0.39, \"(5.85, 6.15)\": -0.757, \"(6.15, 6.45)\": -1.149, \"(6.45, 6.85)\": -1.436, \"(6.85, 7.7)\": -1.718}\nLower Bounds (95%-Confidence Interval): {\"(4.3, 4.55)\": 3.22, \"(4.55, 4.75)\": 2.846, \"(4.75, 4.85)\": 2.54, \"(4.85, 5.05)\": 1.185, \"(5.05, 5.25)\": 1.214, \"(5.25, 5.45)\": 0.892, \"(5.45, 5.55)\": -0.164, \"(5.55, 5.75)\": -0.32, \"(5.75, 5.85)\": -0.665, \"(5.85, 6.15)\": -0.888, \"(6.15, 6.45)\": -1.29, \"(6.45, 6.85)\": -1.575, \"(6.85, 7.7)\": -1.814}\nUpper Bounds (95%-Confidence Interval): {\"(4.3, 4.55)\": 3.437, \"(4.55, 4.75)\": 3.144, \"(4.75, 4.85)\": 2.857, \"(4.85, 5.05)\": 2.145, \"(5.05, 5.25)\": 1.528, \"(5.25, 5.45)\": 1.277, \"(5.45, 5.55)\": 0.843, \"(5.55, 5.75)\": 0.206, \"(5.75, 5.85)\": -0.116, \"(5.85, 6.15)\": -0.627, \"(6.15, 6.45)\": -1.009, \"(6.45, 6.85)\": -1.298, \"(6.85, 7.7)\": -1.623}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "4.85"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: latitude\nFeature Type: continuous\nMeans: {\"(32.54, 32.565)\": 23234.8, \"(32.565, 32.685)\": -3182.4, \"(32.685, 32.715)\": 7727.3, \"(32.715, 32.915)\": 17670.3, \"(32.915, 33.275000000000006)\": 34030.3, \"(33.275000000000006, 33.355000000000004)\": 55000.2, \"(33.355000000000004, 33.465)\": 64326.4, \"(33.465, 33.504999999999995)\": 81519.1, \"(33.504999999999995, 33.555)\": 94496.7, \"(33.555, 33.565)\": 63293.1, \"(33.565, 33.575)\": 51665.3, \"(33.575, 33.635000000000005)\": 66563.2, \"(33.635000000000005, 33.655)\": 47304.3, \"(33.655, 33.765)\": 29789.1, \"(33.765, 33.894999999999996)\": 15892.8, \"(33.894999999999996, 33.985)\": 2769.6, \"(33.985, 33.995000000000005)\": 17775.7, \"(33.995000000000005, 34.045)\": 28884.5, \"(34.045, 34.085)\": 55702.3, \"(34.085, 34.165)\": 46322.8, \"(34.165, 34.175)\": 33820.1, \"(34.175, 34.195)\": 7500.1, \"(34.195, 34.215)\": -4126.2, \"(34.215, 34.254999999999995)\": -16649.8, \"(34.254999999999995, 34.325)\": -27636.8, \"(34.325, 34.345)\": 17113.4, \"(34.345, 34.375)\": 28769.5, \"(34.375, 34.455)\": 43828.3, \"(34.455, 34.474999999999994)\": 57774.8, \"(34.474999999999994, 34.504999999999995)\": 33279.2, \"(34.504999999999995, 34.545)\": 19368.1, \"(34.545, 34.625)\": 5698.9, \"(34.625, 34.635000000000005)\": -19637.8, \"(34.635000000000005, 34.644999999999996)\": -39271.0, \"(34.644999999999996, 34.715)\": -26993.1, \"(34.715, 35.325)\": -17344.4, \"(35.325, 36.375)\": -37699.6, \"(36.375, 36.535)\": -27730.8, \"(36.535, 36.635000000000005)\": -14690.6, \"(36.635000000000005, 36.845)\": -25070.6, \"(36.845, 37.275000000000006)\": -15387.7, \"(37.275000000000006, 37.335)\": -3329.1, \"(37.335, 37.425)\": 7953.5, \"(37.425, 37.445)\": 34546.2, \"(37.445, 37.465)\": 45097.3, \"(37.465, 37.495000000000005)\": 30019.5, \"(37.495000000000005, 37.585)\": 16643.2, \"(37.585, 37.595)\": -3057.8, \"(37.595, 37.605000000000004)\": -32379.8, \"(37.605000000000004, 37.754999999999995)\": -42729.0, \"(37.754999999999995, 37.775000000000006)\": -17898.2, \"(37.775000000000006, 37.795)\": -3229.6, \"(37.795, 37.805)\": 8902.6, \"(37.805, 37.855000000000004)\": -13456.8, \"(37.855000000000004, 37.915)\": -1362.3, \"(37.915, 37.925)\": -19143.7, \"(37.925, 37.945)\": -38768.9, \"(37.945, 38.355000000000004)\": -48247.9, \"(38.355000000000004, 39.085)\": -38467.7, \"(39.085, 39.474999999999994)\": -47690.5, \"(39.474999999999994, 40.135000000000005)\": -56986.6, \"(40.135000000000005, 40.665)\": -66271.5, \"(40.665, 41.775000000000006)\": -75627.3, \"(41.775000000000006, 41.95)\": -85116.1}\nLower Bounds (95%-Confidence Interval): {\"(32.54, 32.565)\": 964.8, \"(32.565, 32.685)\": -13385.7, \"(32.685, 32.715)\": -6553.6, \"(32.715, 32.915)\": 6526.7, \"(32.915, 33.275000000000006)\": 15999.9, \"(33.275000000000006, 33.355000000000004)\": 42157.4, \"(33.355000000000004, 33.465)\": 51350.1, \"(33.465, 33.504999999999995)\": 60415.4, \"(33.504999999999995, 33.555)\": 76698.7, \"(33.555, 33.565)\": 39537.3, \"(33.565, 33.575)\": 41623.9, \"(33.575, 33.635000000000005)\": 54208.1, \"(33.635000000000005, 33.655)\": 37976.2, \"(33.655, 33.765)\": 22371.1, \"(33.765, 33.894999999999996)\": 10058.4, \"(33.894999999999996, 33.985)\": -876.4, \"(33.985, 33.995000000000005)\": 13047.1, \"(33.995000000000005, 34.045)\": 23199.9, \"(34.045, 34.085)\": 50112.0, \"(34.085, 34.165)\": 40162.5, \"(34.165, 34.175)\": 28146.4, \"(34.175, 34.195)\": 2466.5, \"(34.195, 34.215)\": -9561.1, \"(34.215, 34.254999999999995)\": -23153.3, \"(34.254999999999995, 34.325)\": -37515.4, \"(34.325, 34.345)\": -3758.1, \"(34.345, 34.375)\": 15207.7, \"(34.375, 34.455)\": 33875.3, \"(34.455, 34.474999999999994)\": 41591.3, \"(34.474999999999994, 34.504999999999995)\": 20304.7, \"(34.504999999999995, 34.545)\": 13245.7, \"(34.545, 34.625)\": -12771.5, \"(34.625, 34.635000000000005)\": -37375.2, \"(34.635000000000005, 34.644999999999996)\": -49797.4, \"(34.644999999999996, 34.715)\": -34913.5, \"(34.715, 35.325)\": -47411.7, \"(35.325, 36.375)\": -46798.2, \"(36.375, 36.535)\": -34852.7, \"(36.535, 36.635000000000005)\": -23680.1, \"(36.635000000000005, 36.845)\": -34287.5, \"(36.845, 37.275000000000006)\": -23625.3, \"(37.275000000000006, 37.335)\": -9268.7, \"(37.335, 37.425)\": -4329.5, \"(37.425, 37.445)\": 29053.6, \"(37.445, 37.465)\": 29188.3, \"(37.465, 37.495000000000005)\": 21566.7, \"(37.495000000000005, 37.585)\": 8469.3, \"(37.585, 37.595)\": -16791.2, \"(37.595, 37.605000000000004)\": -38739.3, \"(37.605000000000004, 37.754999999999995)\": -51675.5, \"(37.754999999999995, 37.775000000000006)\": -25033.4, \"(37.775000000000006, 37.795)\": -8688.7, \"(37.795, 37.805)\": 36.5, \"(37.805, 37.855000000000004)\": -20482.2, \"(37.855000000000004, 37.915)\": -9472.3, \"(37.915, 37.925)\": -25360.4, \"(37.925, 37.945)\": -46246.0, \"(37.945, 38.355000000000004)\": -55734.3, \"(38.355000000000004, 39.085)\": -48831.7, \"(39.085, 39.474999999999994)\": -57243.4, \"(39.474999999999994, 40.135000000000005)\": -65954.6, \"(40.135000000000005, 40.665)\": -75283.4, \"(40.665, 41.775000000000006)\": -84501.7, \"(41.775000000000006, 41.95)\": -93657.9}\nUpper Bounds (95%-Confidence Interval): {\"(32.54, 32.565)\": 45504.8, \"(32.565, 32.685)\": 7020.9, \"(32.685, 32.715)\": 22008.3, \"(32.715, 32.915)\": 28813.8, \"(32.915, 33.275000000000006)\": 52060.7, \"(33.275000000000006, 33.355000000000004)\": 67843.0, \"(33.355000000000004, 33.465)\": 77302.7, \"(33.465, 33.504999999999995)\": 102622.7, \"(33.504999999999995, 33.555)\": 112294.7, \"(33.555, 33.565)\": 87049.0, \"(33.565, 33.575)\": 61706.7, \"(33.575, 33.635000000000005)\": 78918.4, \"(33.635000000000005, 33.655)\": 56632.4, \"(33.655, 33.765)\": 37207.2, \"(33.765, 33.894999999999996)\": 21727.2, \"(33.894999999999996, 33.985)\": 6415.7, \"(33.985, 33.995000000000005)\": 22504.3, \"(33.995000000000005, 34.045)\": 34569.1, \"(34.045, 34.085)\": 61292.7, \"(34.085, 34.165)\": 52483.1, \"(34.165, 34.175)\": 39493.7, \"(34.175, 34.195)\": 12533.7, \"(34.195, 34.215)\": 1308.7, \"(34.215, 34.254999999999995)\": -10146.2, \"(34.254999999999995, 34.325)\": -17758.2, \"(34.325, 34.345)\": 37984.8, \"(34.345, 34.375)\": 42331.3, \"(34.375, 34.455)\": 53781.2, \"(34.455, 34.474999999999994)\": 73958.2, \"(34.474999999999994, 34.504999999999995)\": 46253.8, \"(34.504999999999995, 34.545)\": 25490.4, \"(34.545, 34.625)\": 24169.2, \"(34.625, 34.635000000000005)\": -1900.3, \"(34.635000000000005, 34.644999999999996)\": -28744.6, \"(34.644999999999996, 34.715)\": -19072.7, \"(34.715, 35.325)\": 12722.9, \"(35.325, 36.375)\": -28601.0, \"(36.375, 36.535)\": -20608.8, \"(36.535, 36.635000000000005)\": -5701.1, \"(36.635000000000005, 36.845)\": -15853.6, \"(36.845, 37.275000000000006)\": -7150.2, \"(37.275000000000006, 37.335)\": 2610.4, \"(37.335, 37.425)\": 20236.5, \"(37.425, 37.445)\": 40038.7, \"(37.445, 37.465)\": 61006.3, \"(37.465, 37.495000000000005)\": 38472.2, \"(37.495000000000005, 37.585)\": 24817.1, \"(37.585, 37.595)\": 10675.7, \"(37.595, 37.605000000000004)\": -26020.2, \"(37.605000000000004, 37.754999999999995)\": -33782.5, \"(37.754999999999995, 37.775000000000006)\": -10763.1, \"(37.775000000000006, 37.795)\": 2229.6, \"(37.795, 37.805)\": 17768.7, \"(37.805, 37.855000000000004)\": -6431.3, \"(37.855000000000004, 37.915)\": 6747.6, \"(37.915, 37.925)\": -12927.0, \"(37.925, 37.945)\": -31291.8, \"(37.945, 38.355000000000004)\": -40761.5, \"(38.355000000000004, 39.085)\": -28103.6, \"(39.085, 39.474999999999994)\": -38137.5, \"(39.474999999999994, 40.135000000000005)\": -48018.6, \"(40.135000000000005, 40.665)\": -57259.6, \"(40.665, 41.775000000000006)\": -66753.0, \"(41.775000000000006, 41.95)\": -76574.4}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "34.325"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: radius_mean\nFeature Type: continuous\nMeans: {\"(6.981, 9.281500000000001)\": -0.762, \"(9.281500000000001, 9.7015)\": -0.659, \"(9.7015, 10.165)\": -0.56, \"(10.165, 10.655000000000001)\": -0.461, \"(10.655000000000001, 12.465)\": -0.36, \"(12.465, 13.39)\": -0.262, \"(13.39, 14.43)\": -0.163, \"(14.43, 14.934999999999999)\": -0.065, \"(14.934999999999999, 15.08)\": 0.037, \"(15.08, 15.815)\": 0.137, \"(15.815, 16.925)\": 0.235, \"(16.925, 17.385)\": 0.394, \"(17.385, 18.0)\": 0.494, \"(18.0, 18.735)\": 0.599, \"(18.735, 19.240000000000002)\": 0.695, \"(19.240000000000002, 19.990000000000002)\": 0.793, \"(19.990000000000002, 20.595)\": 0.891, \"(20.595, 23.240000000000002)\": 0.99, \"(23.240000000000002, 28.11)\": 1.093}\nLower Bounds (95%-Confidence Interval): {\"(6.981, 9.281500000000001)\": -1.01, \"(9.281500000000001, 9.7015)\": -0.884, \"(9.7015, 10.165)\": -0.748, \"(10.165, 10.655000000000001)\": -0.611, \"(10.655000000000001, 12.465)\": -0.536, \"(12.465, 13.39)\": -0.396, \"(13.39, 14.43)\": -0.269, \"(14.43, 14.934999999999999)\": -0.226, \"(14.934999999999999, 15.08)\": -0.156, \"(15.08, 15.815)\": -0.059, \"(15.815, 16.925)\": -0.127, \"(16.925, 17.385)\": 0.041, \"(17.385, 18.0)\": 0.136, \"(18.0, 18.735)\": 0.205, \"(18.735, 19.240000000000002)\": 0.283, \"(19.240000000000002, 19.990000000000002)\": 0.385, \"(19.990000000000002, 20.595)\": 0.462, \"(20.595, 23.240000000000002)\": 0.519, \"(23.240000000000002, 28.11)\": 0.611}\nUpper Bounds (95%-Confidence Interval): {\"(6.981, 9.281500000000001)\": -0.515, \"(9.281500000000001, 9.7015)\": -0.435, \"(9.7015, 10.165)\": -0.373, \"(10.165, 10.655000000000001)\": -0.311, \"(10.655000000000001, 12.465)\": -0.184, \"(12.465, 13.39)\": -0.128, \"(13.39, 14.43)\": -0.057, \"(14.43, 14.934999999999999)\": 0.097, \"(14.934999999999999, 15.08)\": 0.231, \"(15.08, 15.815)\": 0.333, \"(15.815, 16.925)\": 0.597, \"(16.925, 17.385)\": 0.748, \"(17.385, 18.0)\": 0.853, \"(18.0, 18.735)\": 0.993, \"(18.735, 19.240000000000002)\": 1.107, \"(19.240000000000002, 19.990000000000002)\": 1.202, \"(19.990000000000002, 20.595)\": 1.32, \"(20.595, 23.240000000000002)\": 1.461, \"(23.240000000000002, 28.11)\": 1.575}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "16.925"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: smoothness_mean\nFeature Type: continuous\nMeans: {\"(0.05263, 0.0706)\": -0.835, \"(0.0706, 0.07455500000000001)\": -0.769, \"(0.07455500000000001, 0.07589499999999999)\": -0.697, \"(0.07589499999999999, 0.07727500000000001)\": -0.632, \"(0.07727500000000001, 0.078275)\": -0.569, \"(0.078275, 0.07952000000000001)\": -0.506, \"(0.07952000000000001, 0.080315)\": -0.437, \"(0.080315, 0.081035)\": -0.368, \"(0.081035, 0.08308499999999999)\": -0.304, \"(0.08308499999999999, 0.085165)\": -0.242, \"(0.085165, 0.086795)\": -0.177, \"(0.086795, 0.087785)\": -0.111, \"(0.087785, 0.088615)\": -0.047, \"(0.088615, 0.08918999999999999)\": 0.065, \"(0.08918999999999999, 0.090335)\": 0.142, \"(0.090335, 0.09454)\": 0.211, \"(0.09454, 0.11525)\": 0.107, \"(0.11525, 0.11765)\": 0.171, \"(0.11765, 0.12455)\": 0.267, \"(0.12455, 0.13845000000000002)\": 0.334, \"(0.13845000000000002, 0.1634)\": 0.396}\nLower Bounds (95%-Confidence Interval): {\"(0.05263, 0.0706)\": -1.454, \"(0.0706, 0.07455500000000001)\": -1.359, \"(0.07455500000000001, 0.07589499999999999)\": -1.244, \"(0.07589499999999999, 0.07727500000000001)\": -1.162, \"(0.07727500000000001, 0.078275)\": -1.087, \"(0.078275, 0.07952000000000001)\": -1.006, \"(0.07952000000000001, 0.080315)\": -0.882, \"(0.080315, 0.081035)\": -0.622, \"(0.081035, 0.08308499999999999)\": -0.547, \"(0.08308499999999999, 0.085165)\": -0.444, \"(0.085165, 0.086795)\": -0.357, \"(0.086795, 0.087785)\": -0.296, \"(0.087785, 0.088615)\": -0.23, \"(0.088615, 0.08918999999999999)\": -0.16, \"(0.08918999999999999, 0.090335)\": -0.309, \"(0.090335, 0.09454)\": -0.264, \"(0.09454, 0.11525)\": -0.005, \"(0.11525, 0.11765)\": 0.07, \"(0.11765, 0.12455)\": 0.022, \"(0.12455, 0.13845000000000002)\": 0.077, \"(0.13845000000000002, 0.1634)\": 0.127}\nUpper Bounds (95%-Confidence Interval): {\"(0.05263, 0.0706)\": -0.216, \"(0.0706, 0.07455500000000001)\": -0.178, \"(0.07455500000000001, 0.07589499999999999)\": -0.151, \"(0.07589499999999999, 0.07727500000000001)\": -0.102, \"(0.07727500000000001, 0.078275)\": -0.052, \"(0.078275, 0.07952000000000001)\": -0.006, \"(0.07952000000000001, 0.080315)\": 0.008, \"(0.080315, 0.081035)\": -0.113, \"(0.081035, 0.08308499999999999)\": -0.062, \"(0.08308499999999999, 0.085165)\": -0.04, \"(0.085165, 0.086795)\": 0.004, \"(0.086795, 0.087785)\": 0.075, \"(0.087785, 0.088615)\": 0.136, \"(0.088615, 0.08918999999999999)\": 0.291, \"(0.08918999999999999, 0.090335)\": 0.594, \"(0.090335, 0.09454)\": 0.685, \"(0.09454, 0.11525)\": 0.22, \"(0.11525, 0.11765)\": 0.273, \"(0.11765, 0.12455)\": 0.512, \"(0.12455, 0.13845000000000002)\": 0.591, \"(0.13845000000000002, 0.1634)\": 0.664}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "0.088615"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: perimeter_se\nFeature Type: continuous\nMeans: {\"(0.7714, 1.0579999999999998)\": -0.698, \"(1.0579999999999998, 1.1345)\": -0.618, \"(1.1345, 1.197)\": -0.539, \"(1.197, 1.2365)\": -0.461, \"(1.2365, 1.326)\": -0.384, \"(1.326, 1.4435)\": -0.256, \"(1.4435, 1.5314999999999999)\": -0.176, \"(1.5314999999999999, 1.807)\": -0.099, \"(1.807, 2.107)\": -0.023, \"(2.107, 2.593)\": -0.098, \"(2.593, 2.878)\": -0.018, \"(2.878, 3.292)\": 0.065, \"(3.292, 4.095000000000001)\": 0.14, \"(4.095000000000001, 4.714)\": 0.219, \"(4.714, 4.885999999999999)\": 0.296, \"(4.885999999999999, 5.2844999999999995)\": 0.372, \"(5.2844999999999995, 5.8425)\": 0.451, \"(5.8425, 7.104)\": 0.536, \"(7.104, 7.7765)\": 0.611, \"(7.7765, 10.594999999999999)\": 0.701, \"(10.594999999999999, 21.98)\": 0.786}\nLower Bounds (95%-Confidence Interval): {\"(0.7714, 1.0579999999999998)\": -1.131, \"(1.0579999999999998, 1.1345)\": -1.029, \"(1.1345, 1.197)\": -0.923, \"(1.197, 1.2365)\": -0.835, \"(1.2365, 1.326)\": -0.754, \"(1.326, 1.4435)\": -0.43, \"(1.4435, 1.5314999999999999)\": -0.378, \"(1.5314999999999999, 1.807)\": -0.215, \"(1.807, 2.107)\": -0.131, \"(2.107, 2.593)\": -0.215, \"(2.593, 2.878)\": -0.124, \"(2.878, 3.292)\": -0.022, \"(3.292, 4.095000000000001)\": 0.04, \"(4.095000000000001, 4.714)\": 0.049, \"(4.714, 4.885999999999999)\": -0.063, \"(4.885999999999999, 5.2844999999999995)\": 0.007, \"(5.2844999999999995, 5.8425)\": 0.088, \"(5.8425, 7.104)\": 0.151, \"(7.104, 7.7765)\": 0.21, \"(7.7765, 10.594999999999999)\": 0.257, \"(10.594999999999999, 21.98)\": 0.341}\nUpper Bounds (95%-Confidence Interval): {\"(0.7714, 1.0579999999999998)\": -0.265, \"(1.0579999999999998, 1.1345)\": -0.208, \"(1.1345, 1.197)\": -0.155, \"(1.197, 1.2365)\": -0.087, \"(1.2365, 1.326)\": -0.015, \"(1.326, 1.4435)\": -0.081, \"(1.4435, 1.5314999999999999)\": 0.026, \"(1.5314999999999999, 1.807)\": 0.016, \"(1.807, 2.107)\": 0.085, \"(2.107, 2.593)\": 0.019, \"(2.593, 2.878)\": 0.088, \"(2.878, 3.292)\": 0.152, \"(3.292, 4.095000000000001)\": 0.24, \"(4.095000000000001, 4.714)\": 0.388, \"(4.714, 4.885999999999999)\": 0.655, \"(4.885999999999999, 5.2844999999999995)\": 0.737, \"(5.2844999999999995, 5.8425)\": 0.814, \"(5.8425, 7.104)\": 0.921, \"(7.104, 7.7765)\": 1.012, \"(7.7765, 10.594999999999999)\": 1.146, \"(10.594999999999999, 21.98)\": 1.231}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "1.326"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: compactness_se\nFeature Type: continuous\nMeans: {\"(0.002252, 0.0046765)\": -0.0693, \"(0.0046765, 0.005634)\": -0.0214, \"(0.005634, 0.006059500000000001)\": 0.0214, \"(0.006059500000000001, 0.006774499999999999)\": 0.0648, \"(0.006774499999999999, 0.0072375)\": 0.1132, \"(0.0072375, 0.008034)\": 0.1583, \"(0.008034, 0.0082145)\": 0.2045, \"(0.0082145, 0.0085705)\": 0.2482, \"(0.0085705, 0.0089915)\": 0.2969, \"(0.0089915, 0.01089)\": 0.3467, \"(0.01089, 0.011715)\": 0.3948, \"(0.011715, 0.012025000000000001)\": 0.3506, \"(0.012025000000000001, 0.012535000000000001)\": 0.2891, \"(0.012535000000000001, 0.013225)\": 0.244, \"(0.013225, 0.014275)\": 0.2001, \"(0.014275, 0.015615)\": 0.1571, \"(0.015615, 0.017669999999999998)\": 0.1142, \"(0.017669999999999998, 0.020155)\": 0.0681, \"(0.020155, 0.022855)\": 0.0256, \"(0.022855, 0.02586)\": -0.0272, \"(0.02586, 0.027540000000000002)\": -0.098, \"(0.027540000000000002, 0.038220000000000004)\": -0.1414, \"(0.038220000000000004, 0.039245)\": -0.1853, \"(0.039245, 0.040514999999999995)\": -0.2301, \"(0.040514999999999995, 0.04309)\": -0.2754, \"(0.04309, 0.04922)\": -0.3233, \"(0.04922, 0.068925)\": -0.3675, \"(0.068925, 0.1354)\": -0.4112}\nLower Bounds (95%-Confidence Interval): {\"(0.002252, 0.0046765)\": -0.2881, \"(0.0046765, 0.005634)\": -0.2345, \"(0.005634, 0.006059500000000001)\": -0.1933, \"(0.006059500000000001, 0.006774499999999999)\": -0.1451, \"(0.006774499999999999, 0.0072375)\": -0.0877, \"(0.0072375, 0.008034)\": -0.0418, \"(0.008034, 0.0082145)\": -0.0092, \"(0.0082145, 0.0085705)\": 0.0302, \"(0.0085705, 0.0089915)\": 0.0617, \"(0.0089915, 0.01089)\": 0.105, \"(0.01089, 0.011715)\": 0.1231, \"(0.011715, 0.012025000000000001)\": 0.0874, \"(0.012025000000000001, 0.012535000000000001)\": 0.097, \"(0.012535000000000001, 0.013225)\": 0.063, \"(0.013225, 0.014275)\": 0.031, \"(0.014275, 0.015615)\": 0.0018, \"(0.015615, 0.017669999999999998)\": -0.0333, \"(0.017669999999999998, 0.020155)\": -0.0326, \"(0.020155, 0.022855)\": -0.0543, \"(0.022855, 0.02586)\": -0.1745, \"(0.02586, 0.027540000000000002)\": -0.258, \"(0.027540000000000002, 0.038220000000000004)\": -0.3097, \"(0.038220000000000004, 0.039245)\": -0.326, \"(0.039245, 0.040514999999999995)\": -0.3788, \"(0.040514999999999995, 0.04309)\": -0.4514, \"(0.04309, 0.04922)\": -0.5306, \"(0.04922, 0.068925)\": -0.5903, \"(0.068925, 0.1354)\": -0.6732}\nUpper Bounds (95%-Confidence Interval): {\"(0.002252, 0.0046765)\": 0.1496, \"(0.0046765, 0.005634)\": 0.1917, \"(0.005634, 0.006059500000000001)\": 0.2361, \"(0.006059500000000001, 0.006774499999999999)\": 0.2747, \"(0.006774499999999999, 0.0072375)\": 0.3141, \"(0.0072375, 0.008034)\": 0.3584, \"(0.008034, 0.0082145)\": 0.4182, \"(0.0082145, 0.0085705)\": 0.4662, \"(0.0085705, 0.0089915)\": 0.5321, \"(0.0089915, 0.01089)\": 0.5884, \"(0.01089, 0.011715)\": 0.6664, \"(0.011715, 0.012025000000000001)\": 0.6138, \"(0.012025000000000001, 0.012535000000000001)\": 0.4812, \"(0.012535000000000001, 0.013225)\": 0.4251, \"(0.013225, 0.014275)\": 0.3692, \"(0.014275, 0.015615)\": 0.3124, \"(0.015615, 0.017669999999999998)\": 0.2617, \"(0.017669999999999998, 0.020155)\": 0.1689, \"(0.020155, 0.022855)\": 0.1055, \"(0.022855, 0.02586)\": 0.1202, \"(0.02586, 0.027540000000000002)\": 0.062, \"(0.027540000000000002, 0.038220000000000004)\": 0.027, \"(0.038220000000000004, 0.039245)\": -0.0446, \"(0.039245, 0.040514999999999995)\": -0.0815, \"(0.040514999999999995, 0.04309)\": -0.0993, \"(0.04309, 0.04922)\": -0.1161, \"(0.04922, 0.068925)\": -0.1448, \"(0.068925, 0.1354)\": -0.1492}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "0.02586"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Education\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.4028, \"(0.5, 1.5)\": -0.5397, \"(1.5, 3.5)\": -0.4851, \"(3.5, 4.5)\": -0.4021, \"(4.5, 5.5)\": -0.457, \"(5.5, 6.5)\": -0.2537, \"(6.5, 7.5)\": -0.0494, \"(7.5, 8.5)\": 0.0457, \"(8.5, 9.5)\": 0.1831, \"(9.5, 10.5)\": 0.1392, \"(10.5, 11.5)\": -0.0652, \"(11.5, 14.5)\": 0.1954, \"(14.5, 15.0)\": 0.1393}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.5596, \"(0.5, 1.5)\": -0.6499, \"(1.5, 3.5)\": -0.618, \"(3.5, 4.5)\": -0.5693, \"(4.5, 5.5)\": -0.5278, \"(5.5, 6.5)\": -0.3342, \"(6.5, 7.5)\": -0.0948, \"(7.5, 8.5)\": -0.0062, \"(8.5, 9.5)\": 0.1525, \"(9.5, 10.5)\": 0.1072, \"(10.5, 11.5)\": -0.0869, \"(11.5, 14.5)\": 0.1476, \"(14.5, 15.0)\": 0.1012}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.2459, \"(0.5, 1.5)\": -0.4295, \"(1.5, 3.5)\": -0.3523, \"(3.5, 4.5)\": -0.235, \"(4.5, 5.5)\": -0.3862, \"(5.5, 6.5)\": -0.1733, \"(6.5, 7.5)\": -0.0039, \"(7.5, 8.5)\": 0.0977, \"(8.5, 9.5)\": 0.2137, \"(9.5, 10.5)\": 0.1711, \"(10.5, 11.5)\": -0.0435, \"(11.5, 14.5)\": 0.2431, \"(14.5, 15.0)\": 0.1775}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "11.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: households\nFeature Type: continuous\nMeans: {\"(2.0, 4.5)\": -5401.6, \"(4.5, 6.5)\": -23687.9, \"(6.5, 8.5)\": -53732.5, \"(8.5, 9.5)\": -14617.2, \"(9.5, 12.5)\": 16225.5, \"(12.5, 13.5)\": 21846.0, \"(13.5, 14.5)\": 29456.0, \"(14.5, 15.5)\": 14293.2, \"(15.5, 20.5)\": -21670.3, \"(20.5, 21.5)\": 3195.8, \"(21.5, 55.5)\": -12458.9, \"(55.5, 155.5)\": -20063.6, \"(155.5, 156.5)\": -15642.0, \"(156.5, 157.5)\": -6390.8, \"(157.5, 186.5)\": -19320.2, \"(186.5, 196.5)\": -23743.0, \"(196.5, 198.5)\": -18377.6, \"(198.5, 223.5)\": -12744.1, \"(223.5, 230.5)\": -6336.7, \"(230.5, 295.5)\": -10855.3, \"(295.5, 394.5)\": -6355.5, \"(394.5, 535.5)\": -443.1, \"(535.5, 561.5)\": 3934.9, \"(561.5, 599.5)\": 9004.1, \"(599.5, 600.5)\": 13667.2, \"(600.5, 634.5)\": 8706.3, \"(634.5, 635.5)\": 25959.4, \"(635.5, 824.5)\": 13815.1, \"(824.5, 864.5)\": 18503.2, \"(864.5, 962.5)\": 26367.0, \"(962.5, 964.5)\": 14554.6, \"(964.5, 976.5)\": 23227.2, \"(976.5, 978.5)\": 18664.6, \"(978.5, 990.5)\": 26114.1, \"(990.5, 1000.5)\": 30854.6, \"(1000.5, 1088.5)\": 25473.5, \"(1088.5, 1092.5)\": 21095.0, \"(1092.5, 1130.5)\": 26497.2, \"(1130.5, 1272.5)\": 33562.7, \"(1272.5, 3516.0)\": 28522.2, \"(3516.0, 6082.0)\": 21556.0}\nLower Bounds (95%-Confidence Interval): {\"(2.0, 4.5)\": -30426.4, \"(4.5, 6.5)\": -41560.8, \"(6.5, 8.5)\": -83483.7, \"(8.5, 9.5)\": -68637.5, \"(9.5, 12.5)\": -15018.5, \"(12.5, 13.5)\": -5488.2, \"(13.5, 14.5)\": 1721.7, \"(14.5, 15.5)\": -25117.7, \"(15.5, 20.5)\": -41734.0, \"(20.5, 21.5)\": -26800.7, \"(21.5, 55.5)\": -26732.7, \"(55.5, 155.5)\": -27250.3, \"(155.5, 156.5)\": -25256.4, \"(156.5, 157.5)\": -28521.9, \"(157.5, 186.5)\": -26383.4, \"(186.5, 196.5)\": -29250.8, \"(196.5, 198.5)\": -25752.9, \"(198.5, 223.5)\": -20683.5, \"(223.5, 230.5)\": -15595.3, \"(230.5, 295.5)\": -18207.8, \"(295.5, 394.5)\": -15406.0, \"(394.5, 535.5)\": -9211.1, \"(535.5, 561.5)\": -5668.7, \"(561.5, 599.5)\": 904.9, \"(599.5, 600.5)\": -3740.6, \"(600.5, 634.5)\": 3782.4, \"(634.5, 635.5)\": 139.1, \"(635.5, 824.5)\": 6137.4, \"(824.5, 864.5)\": 11294.8, \"(864.5, 962.5)\": 17755.5, \"(962.5, 964.5)\": -5105.1, \"(964.5, 976.5)\": 14837.4, \"(976.5, 978.5)\": 5892.7, \"(978.5, 990.5)\": 18169.8, \"(990.5, 1000.5)\": 15738.6, \"(1000.5, 1088.5)\": 19888.5, \"(1088.5, 1092.5)\": 9478.6, \"(1092.5, 1130.5)\": 20925.9, \"(1130.5, 1272.5)\": 24768.1, \"(1272.5, 3516.0)\": 19419.3, \"(3516.0, 6082.0)\": 8532.3}\nUpper Bounds (95%-Confidence Interval): {\"(2.0, 4.5)\": 19623.3, \"(4.5, 6.5)\": -5814.9, \"(6.5, 8.5)\": -23981.3, \"(8.5, 9.5)\": 39403.2, \"(9.5, 12.5)\": 47469.5, \"(12.5, 13.5)\": 49180.3, \"(13.5, 14.5)\": 57190.3, \"(14.5, 15.5)\": 53704.2, \"(15.5, 20.5)\": -1606.7, \"(20.5, 21.5)\": 33192.3, \"(21.5, 55.5)\": 1814.9, \"(55.5, 155.5)\": -12877.0, \"(155.5, 156.5)\": -6027.7, \"(156.5, 157.5)\": 15740.2, \"(157.5, 186.5)\": -12257.0, \"(186.5, 196.5)\": -18235.2, \"(196.5, 198.5)\": -11002.4, \"(198.5, 223.5)\": -4804.8, \"(223.5, 230.5)\": 2921.9, \"(230.5, 295.5)\": -3502.7, \"(295.5, 394.5)\": 2695.1, \"(394.5, 535.5)\": 8324.9, \"(535.5, 561.5)\": 13538.5, \"(561.5, 599.5)\": 17103.2, \"(599.5, 600.5)\": 31074.9, \"(600.5, 634.5)\": 13630.1, \"(634.5, 635.5)\": 51779.7, \"(635.5, 824.5)\": 21492.8, \"(824.5, 864.5)\": 25711.7, \"(864.5, 962.5)\": 34978.6, \"(962.5, 964.5)\": 34214.4, \"(964.5, 976.5)\": 31616.9, \"(976.5, 978.5)\": 31436.4, \"(978.5, 990.5)\": 34058.4, \"(990.5, 1000.5)\": 45970.6, \"(1000.5, 1088.5)\": 31058.5, \"(1088.5, 1092.5)\": 32711.5, \"(1092.5, 1130.5)\": 32068.4, \"(1130.5, 1272.5)\": 42357.3, \"(1272.5, 3516.0)\": 37625.1, \"(3516.0, 6082.0)\": 34579.6}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "8.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: id\nFeature Type: continuous\nMeans: {\"(8.0, 349.5)\": -0.1954, \"(349.5, 1899.5)\": -0.1448, \"(1899.5, 4908.5)\": -0.18, \"(4908.5, 5578.5)\": -0.2082, \"(5578.5, 5813.5)\": -0.25, \"(5813.5, 6004.5)\": -0.345, \"(6004.5, 7170.5)\": -0.1246, \"(7170.5, 7335.5)\": 0.0378, \"(7335.5, 8083.0)\": 0.1773, \"(8083.0, 8604.0)\": 0.1221, \"(8604.0, 8759.0)\": -0.0027, \"(8759.0, 45049.5)\": -0.0395, \"(45049.5, 45346.5)\": -0.3688, \"(45346.5, 46184.5)\": -0.0125, \"(46184.5, 54575.0)\": 0.0215, \"(54575.0, 55661.5)\": -0.0521, \"(55661.5, 66954.0)\": 0.0101, \"(66954.0, 67057.0)\": -0.0227, \"(67057.0, 68275.0)\": 0.0595, \"(68275.0, 97577.5)\": 0.0244, \"(97577.5, 110643.5)\": 0.0529, \"(110643.5, 146554.5)\": 0.0211, \"(146554.5, 146921.5)\": -0.0139, \"(146921.5, 147131.5)\": -0.0861, \"(147131.5, 161901.5)\": -0.0139, \"(161901.5, 162437.5)\": -0.0745, \"(162437.5, 164212.5)\": -0.0061, \"(164212.5, 164569.5)\": -0.057, \"(164569.5, 164786.5)\": 0.0766, \"(164786.5, 165030.0)\": 0.1394}\nLower Bounds (95%-Confidence Interval): {\"(8.0, 349.5)\": -0.513, \"(349.5, 1899.5)\": -0.2797, \"(1899.5, 4908.5)\": -0.4617, \"(4908.5, 5578.5)\": -0.3743, \"(5578.5, 5813.5)\": -0.5275, \"(5813.5, 6004.5)\": -0.9752, \"(6004.5, 7170.5)\": -0.3878, \"(7170.5, 7335.5)\": -0.2528, \"(7335.5, 8083.0)\": -0.2278, \"(8083.0, 8604.0)\": -0.157, \"(8604.0, 8759.0)\": -0.2655, \"(8759.0, 45049.5)\": -0.1283, \"(45049.5, 45346.5)\": -1.0587, \"(45346.5, 46184.5)\": -0.1339, \"(46184.5, 54575.0)\": -0.1154, \"(54575.0, 55661.5)\": -0.241, \"(55661.5, 66954.0)\": -0.0113, \"(66954.0, 67057.0)\": -0.3245, \"(67057.0, 68275.0)\": -0.2172, \"(68275.0, 97577.5)\": -0.0853, \"(97577.5, 110643.5)\": -0.0258, \"(110643.5, 146554.5)\": -0.1398, \"(146554.5, 146921.5)\": -0.0713, \"(146921.5, 147131.5)\": -0.6452, \"(147131.5, 161901.5)\": -0.1222, \"(161901.5, 162437.5)\": -0.3435, \"(162437.5, 164212.5)\": -0.1164, \"(164212.5, 164569.5)\": -0.2376, \"(164569.5, 164786.5)\": -0.3913, \"(164786.5, 165030.0)\": -0.3304}\nUpper Bounds (95%-Confidence Interval): {\"(8.0, 349.5)\": 0.1221, \"(349.5, 1899.5)\": -0.0099, \"(1899.5, 4908.5)\": 0.1016, \"(4908.5, 5578.5)\": -0.0421, \"(5578.5, 5813.5)\": 0.0274, \"(5813.5, 6004.5)\": 0.2852, \"(6004.5, 7170.5)\": 0.1385, \"(7170.5, 7335.5)\": 0.3284, \"(7335.5, 8083.0)\": 0.5824, \"(8083.0, 8604.0)\": 0.4011, \"(8604.0, 8759.0)\": 0.2602, \"(8759.0, 45049.5)\": 0.0493, \"(45049.5, 45346.5)\": 0.321, \"(45346.5, 46184.5)\": 0.1088, \"(46184.5, 54575.0)\": 0.1583, \"(54575.0, 55661.5)\": 0.1369, \"(55661.5, 66954.0)\": 0.0316, \"(66954.0, 67057.0)\": 0.2791, \"(67057.0, 68275.0)\": 0.3361, \"(68275.0, 97577.5)\": 0.1341, \"(97577.5, 110643.5)\": 0.1316, \"(110643.5, 146554.5)\": 0.182, \"(146554.5, 146921.5)\": 0.0435, \"(146921.5, 147131.5)\": 0.4731, \"(147131.5, 161901.5)\": 0.0945, \"(161901.5, 162437.5)\": 0.1945, \"(162437.5, 164212.5)\": 0.1042, \"(164212.5, 164569.5)\": 0.1235, \"(164569.5, 164786.5)\": 0.5445, \"(164786.5, 165030.0)\": 0.6091}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "45346.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: IneffectiveDisasterPreparedness\nFeature Type: continuous\nMeans: {\"(0.0, 1.5)\": -0.02526, \"(1.5, 2.5)\": -0.01738, \"(2.5, 3.5)\": -0.01172, \"(3.5, 4.5)\": -0.00537, \"(4.5, 5.5)\": -0.00021, \"(5.5, 6.5)\": 0.0066, \"(6.5, 7.5)\": 0.01026, \"(7.5, 8.5)\": 0.01717, \"(8.5, 9.5)\": 0.02426, \"(9.5, 10.5)\": 0.02823, \"(10.5, 11.5)\": 0.03325, \"(11.5, 13.5)\": 0.03915, \"(13.5, 15.0)\": 0.03572}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02806, \"(1.5, 2.5)\": -0.01811, \"(2.5, 3.5)\": -0.01241, \"(3.5, 4.5)\": -0.0056, \"(4.5, 5.5)\": -0.00057, \"(5.5, 6.5)\": 0.00621, \"(6.5, 7.5)\": 0.00967, \"(7.5, 8.5)\": 0.01672, \"(8.5, 9.5)\": 0.02334, \"(9.5, 10.5)\": 0.02687, \"(10.5, 11.5)\": 0.03182, \"(11.5, 13.5)\": 0.03364, \"(13.5, 15.0)\": 0.0307}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02245, \"(1.5, 2.5)\": -0.01664, \"(2.5, 3.5)\": -0.01102, \"(3.5, 4.5)\": -0.00514, \"(4.5, 5.5)\": 0.00016, \"(5.5, 6.5)\": 0.00699, \"(6.5, 7.5)\": 0.01085, \"(7.5, 8.5)\": 0.01761, \"(8.5, 9.5)\": 0.02519, \"(9.5, 10.5)\": 0.02958, \"(10.5, 11.5)\": 0.03468, \"(11.5, 13.5)\": 0.04466, \"(13.5, 15.0)\": 0.04073}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "1.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Insulin\nFeature Type: continuous\nMeans: {\"(0.0, 20.0)\": 0.0422, \"(20.0, 36.5)\": -0.0027, \"(36.5, 40.5)\": -0.0554, \"(40.5, 45.5)\": -0.0967, \"(45.5, 48.5)\": -0.0409, \"(48.5, 55.5)\": -0.2263, \"(55.5, 80.5)\": -0.2661, \"(80.5, 87.5)\": -0.227, \"(87.5, 97.5)\": -0.1794, \"(97.5, 111.0)\": -0.1356, \"(111.0, 123.5)\": -0.0968, \"(123.5, 137.5)\": -0.0561, \"(137.5, 144.5)\": -0.0187, \"(144.5, 157.0)\": 0.0208, \"(157.0, 170.5)\": 0.0623, \"(170.5, 186.5)\": 0.0999, \"(186.5, 190.5)\": 0.0538, \"(190.5, 192.5)\": 0.1059, \"(192.5, 271.0)\": -0.0027, \"(271.0, 277.5)\": 0.035, \"(277.5, 292.0)\": 0.0732, \"(292.0, 311.0)\": 0.1129, \"(311.0, 365.0)\": 0.1551, \"(365.0, 397.0)\": 0.196, \"(397.0, 452.5)\": 0.2331, \"(452.5, 476.0)\": 0.2839, \"(476.0, 487.5)\": 0.346, \"(487.5, 526.5)\": 0.3915, \"(526.5, 680.0)\": 0.4346}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 20.0)\": -0.0556, \"(20.0, 36.5)\": -0.2244, \"(36.5, 40.5)\": -0.2184, \"(40.5, 45.5)\": -0.2543, \"(45.5, 48.5)\": -0.7961, \"(48.5, 55.5)\": -0.5056, \"(55.5, 80.5)\": -0.551, \"(80.5, 87.5)\": -0.3117, \"(87.5, 97.5)\": -0.251, \"(97.5, 111.0)\": -0.2086, \"(111.0, 123.5)\": -0.1731, \"(123.5, 137.5)\": -0.137, \"(137.5, 144.5)\": -0.1027, \"(144.5, 157.0)\": -0.0751, \"(157.0, 170.5)\": -0.0506, \"(170.5, 186.5)\": -0.0163, \"(186.5, 190.5)\": -0.2256, \"(190.5, 192.5)\": -0.2869, \"(192.5, 271.0)\": -0.3659, \"(271.0, 277.5)\": -0.245, \"(277.5, 292.0)\": -0.1491, \"(292.0, 311.0)\": -0.0995, \"(311.0, 365.0)\": -0.0355, \"(365.0, 397.0)\": -0.0134, \"(397.0, 452.5)\": 0.0212, \"(452.5, 476.0)\": 0.0711, \"(476.0, 487.5)\": 0.1139, \"(487.5, 526.5)\": 0.1534, \"(526.5, 680.0)\": 0.0241}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 20.0)\": 0.14, \"(20.0, 36.5)\": 0.2189, \"(36.5, 40.5)\": 0.1076, \"(40.5, 45.5)\": 0.0609, \"(45.5, 48.5)\": 0.7143, \"(48.5, 55.5)\": 0.053, \"(55.5, 80.5)\": 0.0187, \"(80.5, 87.5)\": -0.1422, \"(87.5, 97.5)\": -0.1078, \"(97.5, 111.0)\": -0.0625, \"(111.0, 123.5)\": -0.0206, \"(123.5, 137.5)\": 0.0247, \"(137.5, 144.5)\": 0.0654, \"(144.5, 157.0)\": 0.1166, \"(157.0, 170.5)\": 0.1751, \"(170.5, 186.5)\": 0.2162, \"(186.5, 190.5)\": 0.3332, \"(190.5, 192.5)\": 0.4987, \"(192.5, 271.0)\": 0.3605, \"(271.0, 277.5)\": 0.315, \"(277.5, 292.0)\": 0.2956, \"(292.0, 311.0)\": 0.3253, \"(311.0, 365.0)\": 0.3457, \"(365.0, 397.0)\": 0.4055, \"(397.0, 452.5)\": 0.445, \"(452.5, 476.0)\": 0.4967, \"(476.0, 487.5)\": 0.5782, \"(487.5, 526.5)\": 0.6295, \"(526.5, 680.0)\": 0.8452}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "48.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Fare\nFeature Type: continuous\nMeans: {\"(0.0, 6.325)\": -1.425, \"(6.325, 7.8500000000000005)\": -1.303, \"(7.8500000000000005, 9.256250000000001)\": -0.472, \"(9.256250000000001, 10.48125)\": -0.602, \"(10.48125, 12.9375)\": -0.14, \"(12.9375, 25.79375)\": 0.225, \"(25.79375, 26.46875)\": 0.355, \"(26.46875, 27.7354)\": 0.207, \"(27.7354, 29.85)\": -0.238, \"(29.85, 31.6604)\": 0.051, \"(31.6604, 55.22085)\": -0.075, \"(55.22085, 89.5521)\": 0.041, \"(89.5521, 149.0354)\": 0.152, \"(149.0354, 387.6646)\": -0.029, \"(387.6646, 512.3292)\": 0.808}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 6.325)\": -3.39, \"(6.325, 7.8500000000000005)\": -3.252, \"(7.8500000000000005, 9.256250000000001)\": -1.321, \"(9.256250000000001, 10.48125)\": -1.756, \"(10.48125, 12.9375)\": -0.444, \"(12.9375, 25.79375)\": -0.464, \"(25.79375, 26.46875)\": -0.48, \"(26.46875, 27.7354)\": -0.42, \"(27.7354, 29.85)\": -1.008, \"(29.85, 31.6604)\": -0.616, \"(31.6604, 55.22085)\": -0.278, \"(55.22085, 89.5521)\": -0.095, \"(89.5521, 149.0354)\": -0.062, \"(149.0354, 387.6646)\": -0.493, \"(387.6646, 512.3292)\": -0.839}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 6.325)\": 0.54, \"(6.325, 7.8500000000000005)\": 0.645, \"(7.8500000000000005, 9.256250000000001)\": 0.377, \"(9.256250000000001, 10.48125)\": 0.553, \"(10.48125, 12.9375)\": 0.163, \"(12.9375, 25.79375)\": 0.913, \"(25.79375, 26.46875)\": 1.191, \"(26.46875, 27.7354)\": 0.833, \"(27.7354, 29.85)\": 0.533, \"(29.85, 31.6604)\": 0.718, \"(31.6604, 55.22085)\": 0.127, \"(55.22085, 89.5521)\": 0.176, \"(89.5521, 149.0354)\": 0.367, \"(149.0354, 387.6646)\": 0.436, \"(387.6646, 512.3292)\": 2.455}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "387.6646"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: CoastalVulnerability\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.03259, \"(0.5, 1.5)\": -0.02272, \"(1.5, 2.5)\": -0.0157, \"(2.5, 3.5)\": -0.00983, \"(3.5, 4.5)\": -0.00444, \"(4.5, 5.5)\": -0.00035, \"(5.5, 6.5)\": 0.00575, \"(6.5, 7.5)\": 0.01126, \"(7.5, 8.5)\": 0.01651, \"(8.5, 9.5)\": 0.02143, \"(9.5, 12.5)\": 0.02903, \"(12.5, 13.5)\": 0.03437, \"(13.5, 15.0)\": 0.04826}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0359, \"(0.5, 1.5)\": -0.02356, \"(1.5, 2.5)\": -0.01657, \"(2.5, 3.5)\": -0.01034, \"(3.5, 4.5)\": -0.0048, \"(4.5, 5.5)\": -0.00077, \"(5.5, 6.5)\": 0.00528, \"(6.5, 7.5)\": 0.01081, \"(7.5, 8.5)\": 0.01566, \"(8.5, 9.5)\": 0.02049, \"(9.5, 12.5)\": 0.02706, \"(12.5, 13.5)\": 0.0298, \"(13.5, 15.0)\": 0.0329}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02927, \"(0.5, 1.5)\": -0.02189, \"(1.5, 2.5)\": -0.01482, \"(2.5, 3.5)\": -0.00931, \"(3.5, 4.5)\": -0.00409, \"(4.5, 5.5)\": 7e-05, \"(5.5, 6.5)\": 0.00622, \"(6.5, 7.5)\": 0.0117, \"(7.5, 8.5)\": 0.01736, \"(8.5, 9.5)\": 0.02236, \"(9.5, 12.5)\": 0.031, \"(12.5, 13.5)\": 0.03893, \"(13.5, 15.0)\": 0.06363}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "13.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: creatinine_phosphokinase\nFeature Type: continuous\nMeans: {\"(23.0, 32.0)\": -0.48, \"(32.0, 49.5)\": 0.68, \"(49.5, 56.5)\": -4.31, \"(56.5, 59.5)\": -2.44, \"(59.5, 64.5)\": -1.82, \"(64.5, 85.0)\": -1.1, \"(85.0, 87.0)\": 0.42, \"(87.0, 93.5)\": -0.75, \"(93.5, 94.5)\": 0.47, \"(94.5, 103.5)\": -0.53, \"(103.5, 107.5)\": 0.12, \"(107.5, 120.0)\": -0.5, \"(120.0, 121.5)\": 0.24, \"(121.5, 126.0)\": 1.25, \"(126.0, 127.5)\": -3.14, \"(127.5, 145.5)\": 1.51, \"(145.5, 147.0)\": 0.91, \"(147.0, 150.0)\": -0.15, \"(150.0, 160.5)\": -1.08, \"(160.5, 189.5)\": -0.45, \"(189.5, 232.5)\": -1.26, \"(232.5, 254.5)\": -0.16, \"(254.5, 258.5)\": 2.88, \"(258.5, 280.5)\": 1.68, \"(280.5, 331.5)\": 1.11, \"(331.5, 370.0)\": 0.44, \"(370.0, 462.0)\": 1.1, \"(462.0, 597.5)\": 0.53, \"(597.5, 751.0)\": -1.87, \"(751.0, 766.5)\": 0.06, \"(766.5, 806.0)\": 2.64, \"(806.0, 873.5)\": 2.05, \"(873.5, 1036.0)\": 0.28, \"(1036.0, 1415.0)\": 0.85, \"(1415.0, 1649.0)\": 0.18, \"(1649.0, 1726.0)\": 2.26, \"(1726.0, 1886.0)\": 0.04, \"(1886.0, 2038.5)\": 7.0, \"(2038.5, 2307.5)\": 2.26, \"(2307.5, 2444.0)\": 5.81, \"(2444.0, 3440.5)\": -2.71, \"(3440.5, 4253.0)\": -1.47, \"(4253.0, 5548.5)\": 1.68, \"(5548.5, 7861.0)\": 3.47}\nLower Bounds (95%-Confidence Interval): {\"(23.0, 32.0)\": -1.51, \"(32.0, 49.5)\": -0.87, \"(49.5, 56.5)\": -5.69, \"(56.5, 59.5)\": -3.58, \"(59.5, 64.5)\": -2.64, \"(64.5, 85.0)\": -2.07, \"(85.0, 87.0)\": -2.37, \"(87.0, 93.5)\": -1.85, \"(93.5, 94.5)\": -0.56, \"(94.5, 103.5)\": -0.85, \"(103.5, 107.5)\": -0.45, \"(107.5, 120.0)\": -1.09, \"(120.0, 121.5)\": -0.48, \"(121.5, 126.0)\": 0.88, \"(126.0, 127.5)\": -5.59, \"(127.5, 145.5)\": 0.93, \"(145.5, 147.0)\": 0.57, \"(147.0, 150.0)\": -0.64, \"(150.0, 160.5)\": -2.38, \"(160.5, 189.5)\": -1.47, \"(189.5, 232.5)\": -2.02, \"(232.5, 254.5)\": -1.04, \"(254.5, 258.5)\": 1.73, \"(258.5, 280.5)\": 0.55, \"(280.5, 331.5)\": 0.09, \"(331.5, 370.0)\": -0.26, \"(370.0, 462.0)\": 0.18, \"(462.0, 597.5)\": 0.4, \"(597.5, 751.0)\": -3.59, \"(751.0, 766.5)\": -2.06, \"(766.5, 806.0)\": 1.02, \"(806.0, 873.5)\": 0.45, \"(873.5, 1036.0)\": -0.52, \"(1036.0, 1415.0)\": 0.33, \"(1415.0, 1649.0)\": -0.68, \"(1649.0, 1726.0)\": -0.23, \"(1726.0, 1886.0)\": -1.16, \"(1886.0, 2038.5)\": 5.88, \"(2038.5, 2307.5)\": 1.8, \"(2307.5, 2444.0)\": 4.43, \"(2444.0, 3440.5)\": -5.48, \"(3440.5, 4253.0)\": -2.15, \"(4253.0, 5548.5)\": 0.41, \"(5548.5, 7861.0)\": 2.17}\nUpper Bounds (95%-Confidence Interval): {\"(23.0, 32.0)\": 0.54, \"(32.0, 49.5)\": 2.24, \"(49.5, 56.5)\": -2.93, \"(56.5, 59.5)\": -1.31, \"(59.5, 64.5)\": -1.0, \"(64.5, 85.0)\": -0.13, \"(85.0, 87.0)\": 3.22, \"(87.0, 93.5)\": 0.35, \"(93.5, 94.5)\": 1.51, \"(94.5, 103.5)\": -0.2, \"(103.5, 107.5)\": 0.69, \"(107.5, 120.0)\": 0.09, \"(120.0, 121.5)\": 0.97, \"(121.5, 126.0)\": 1.61, \"(126.0, 127.5)\": -0.68, \"(127.5, 145.5)\": 2.09, \"(145.5, 147.0)\": 1.25, \"(147.0, 150.0)\": 0.33, \"(150.0, 160.5)\": 0.22, \"(160.5, 189.5)\": 0.57, \"(189.5, 232.5)\": -0.49, \"(232.5, 254.5)\": 0.72, \"(254.5, 258.5)\": 4.03, \"(258.5, 280.5)\": 2.81, \"(280.5, 331.5)\": 2.12, \"(331.5, 370.0)\": 1.15, \"(370.0, 462.0)\": 2.02, \"(462.0, 597.5)\": 0.67, \"(597.5, 751.0)\": -0.15, \"(751.0, 766.5)\": 2.18, \"(766.5, 806.0)\": 4.25, \"(806.0, 873.5)\": 3.65, \"(873.5, 1036.0)\": 1.09, \"(1036.0, 1415.0)\": 1.38, \"(1415.0, 1649.0)\": 1.04, \"(1649.0, 1726.0)\": 4.75, \"(1726.0, 1886.0)\": 1.24, \"(1886.0, 2038.5)\": 8.11, \"(2038.5, 2307.5)\": 2.72, \"(2307.5, 2444.0)\": 7.19, \"(2444.0, 3440.5)\": 0.06, \"(3440.5, 4253.0)\": -0.79, \"(4253.0, 5548.5)\": 2.95, \"(5548.5, 7861.0)\": 4.78}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "2444.0"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: anaemia\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.0818, \"(0.5, 1.0)\": 0.0917}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.1464, \"(0.5, 1.0)\": 0.0194}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0173, \"(0.5, 1.0)\": 0.1641}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "0.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: platelets\nFeature Type: continuous\nMeans: {\"(25100.0, 27700.0)\": -1.004, \"(27700.0, 34450.0)\": -0.687, \"(34450.0, 42200.0)\": 0.328, \"(42200.0, 56500.0)\": 1.717, \"(56500.0, 66050.0)\": 2.769, \"(66050.0, 74000.0)\": 2.195, \"(74000.0, 95500.0)\": 2.956, \"(95500.0, 104500.0)\": -0.265, \"(104500.0, 144000.0)\": -0.585, \"(144000.0, 150500.0)\": -0.895, \"(150500.0, 154000.0)\": 2.322, \"(154000.0, 169000.0)\": 0.469, \"(169000.0, 184500.0)\": -1.612, \"(184500.0, 195000.0)\": 1.111, \"(195000.0, 199000.0)\": 3.01, \"(199000.0, 200500.0)\": 1.837, \"(200500.0, 214000.0)\": 0.403, \"(214000.0, 217500.0)\": -0.825, \"(217500.0, 218500.0)\": -1.399, \"(218500.0, 220500.0)\": 0.341, \"(220500.0, 222500.0)\": 0.978, \"(222500.0, 226500.0)\": 1.584, \"(226500.0, 241500.0)\": 0.175, \"(241500.0, 242500.0)\": 0.642, \"(242500.0, 243500.0)\": 1.107, \"(243500.0, 244500.0)\": 1.516, \"(244500.0, 252500.0)\": -2.19, \"(252500.0, 261000.0)\": -0.878, \"(261000.0, 274500.0)\": -0.145, \"(274500.0, 283500.0)\": -0.968, \"(283500.0, 287500.0)\": 0.203, \"(287500.0, 289500.0)\": 1.032, \"(289500.0, 302500.0)\": -1.296, \"(302500.0, 305500.0)\": -2.984, \"(305500.0, 307000.0)\": 0.876, \"(307000.0, 332000.0)\": 0.368, \"(332000.0, 335000.0)\": 1.21, \"(335000.0, 343000.0)\": 0.8, \"(343000.0, 350500.0)\": -0.573, \"(350500.0, 354500.0)\": 3.0, \"(354500.0, 383500.0)\": -0.119, \"(383500.0, 449500.0)\": 0.655, \"(449500.0, 471000.0)\": 1.527, \"(471000.0, 500500.0)\": -2.247, \"(500500.0, 582000.0)\": -0.442, \"(582000.0, 675500.0)\": 2.645, \"(675500.0, 796000.0)\": 2.314, \"(796000.0, 850000.0)\": -0.709}\nLower Bounds (95%-Confidence Interval): {\"(25100.0, 27700.0)\": -1.75, \"(27700.0, 34450.0)\": -1.54, \"(34450.0, 42200.0)\": -0.532, \"(42200.0, 56500.0)\": 0.992, \"(56500.0, 66050.0)\": 1.538, \"(66050.0, 74000.0)\": 1.537, \"(74000.0, 95500.0)\": 1.91, \"(95500.0, 104500.0)\": -1.642, \"(104500.0, 144000.0)\": -1.428, \"(144000.0, 150500.0)\": -1.74, \"(150500.0, 154000.0)\": 1.125, \"(154000.0, 169000.0)\": 0.027, \"(169000.0, 184500.0)\": -2.523, \"(184500.0, 195000.0)\": 0.214, \"(195000.0, 199000.0)\": 0.239, \"(199000.0, 200500.0)\": 0.581, \"(200500.0, 214000.0)\": -0.252, \"(214000.0, 217500.0)\": -2.007, \"(217500.0, 218500.0)\": -3.583, \"(218500.0, 220500.0)\": 0.076, \"(220500.0, 222500.0)\": 0.244, \"(222500.0, 226500.0)\": -0.038, \"(226500.0, 241500.0)\": -0.123, \"(241500.0, 242500.0)\": 0.22, \"(242500.0, 243500.0)\": 0.116, \"(243500.0, 244500.0)\": 0.265, \"(244500.0, 252500.0)\": -4.008, \"(252500.0, 261000.0)\": -1.287, \"(261000.0, 274500.0)\": -0.465, \"(274500.0, 283500.0)\": -1.829, \"(283500.0, 287500.0)\": -1.587, \"(287500.0, 289500.0)\": -0.951, \"(289500.0, 302500.0)\": -1.857, \"(302500.0, 305500.0)\": -4.201, \"(305500.0, 307000.0)\": 0.125, \"(307000.0, 332000.0)\": -0.181, \"(332000.0, 335000.0)\": -0.179, \"(335000.0, 343000.0)\": 0.105, \"(343000.0, 350500.0)\": -1.469, \"(350500.0, 354500.0)\": 1.748, \"(354500.0, 383500.0)\": -0.848, \"(383500.0, 449500.0)\": 0.242, \"(449500.0, 471000.0)\": -2.033, \"(471000.0, 500500.0)\": -5.177, \"(500500.0, 582000.0)\": -1.795, \"(582000.0, 675500.0)\": 1.501, \"(675500.0, 796000.0)\": 0.104, \"(796000.0, 850000.0)\": -1.557}\nUpper Bounds (95%-Confidence Interval): {\"(25100.0, 27700.0)\": -0.258, \"(27700.0, 34450.0)\": 0.165, \"(34450.0, 42200.0)\": 1.188, \"(42200.0, 56500.0)\": 2.441, \"(56500.0, 66050.0)\": 4.0, \"(66050.0, 74000.0)\": 2.853, \"(74000.0, 95500.0)\": 4.001, \"(95500.0, 104500.0)\": 1.113, \"(104500.0, 144000.0)\": 0.258, \"(144000.0, 150500.0)\": -0.049, \"(150500.0, 154000.0)\": 3.518, \"(154000.0, 169000.0)\": 0.911, \"(169000.0, 184500.0)\": -0.702, \"(184500.0, 195000.0)\": 2.008, \"(195000.0, 199000.0)\": 5.781, \"(199000.0, 200500.0)\": 3.093, \"(200500.0, 214000.0)\": 1.058, \"(214000.0, 217500.0)\": 0.356, \"(217500.0, 218500.0)\": 0.785, \"(218500.0, 220500.0)\": 0.606, \"(220500.0, 222500.0)\": 1.711, \"(222500.0, 226500.0)\": 3.206, \"(226500.0, 241500.0)\": 0.472, \"(241500.0, 242500.0)\": 1.064, \"(242500.0, 243500.0)\": 2.099, \"(243500.0, 244500.0)\": 2.766, \"(244500.0, 252500.0)\": -0.372, \"(252500.0, 261000.0)\": -0.468, \"(261000.0, 274500.0)\": 0.176, \"(274500.0, 283500.0)\": -0.106, \"(283500.0, 287500.0)\": 1.993, \"(287500.0, 289500.0)\": 3.014, \"(289500.0, 302500.0)\": -0.734, \"(302500.0, 305500.0)\": -1.767, \"(305500.0, 307000.0)\": 1.626, \"(307000.0, 332000.0)\": 0.917, \"(332000.0, 335000.0)\": 2.599, \"(335000.0, 343000.0)\": 1.496, \"(343000.0, 350500.0)\": 0.324, \"(350500.0, 354500.0)\": 4.251, \"(354500.0, 383500.0)\": 0.609, \"(383500.0, 449500.0)\": 1.068, \"(449500.0, 471000.0)\": 5.088, \"(471000.0, 500500.0)\": 0.684, \"(500500.0, 582000.0)\": 0.912, \"(582000.0, 675500.0)\": 3.789, \"(675500.0, 796000.0)\": 4.525, \"(796000.0, 850000.0)\": 0.138}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "305500.0"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Balance\nFeature Type: continuous\nMeans: {\"(0.0, 50418.515)\": -0.132, \"(50418.515, 53570.93)\": -0.285, \"(53570.93, 54249.445)\": -0.826, \"(54249.445, 57428.56)\": -0.404, \"(57428.56, 60041.265)\": -0.005, \"(60041.265, 64897.8)\": 0.215, \"(64897.8, 72985.875)\": 0.086, \"(72985.875, 74989.08499999999)\": -0.012, \"(74989.08499999999, 76596.815)\": 0.247, \"(76596.815, 79953.185)\": 0.829, \"(79953.185, 83348.07)\": 0.564, \"(83348.07, 101890.23999999999)\": 0.414, \"(101890.23999999999, 114327.485)\": 0.248, \"(114327.485, 123946.3)\": 0.164, \"(123946.3, 141661.24)\": 0.075, \"(141661.24, 174920.08000000002)\": 0.173, \"(174920.08000000002, 181813.135)\": 0.059, \"(181813.135, 191993.675)\": -0.349, \"(191993.675, 200829.925)\": -0.459, \"(200829.925, 206951.87)\": -0.616, \"(206951.87, 216109.88)\": -0.256}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 50418.515)\": -0.192, \"(50418.515, 53570.93)\": -0.628, \"(53570.93, 54249.445)\": -1.999, \"(54249.445, 57428.56)\": -0.798, \"(57428.56, 60041.265)\": -0.322, \"(60041.265, 64897.8)\": -0.105, \"(64897.8, 72985.875)\": -0.195, \"(72985.875, 74989.08499999999)\": -0.418, \"(74989.08499999999, 76596.815)\": -0.231, \"(76596.815, 79953.185)\": 0.338, \"(79953.185, 83348.07)\": 0.321, \"(83348.07, 101890.23999999999)\": 0.247, \"(101890.23999999999, 114327.485)\": 0.097, \"(114327.485, 123946.3)\": 0.069, \"(123946.3, 141661.24)\": -0.23, \"(141661.24, 174920.08000000002)\": -0.272, \"(174920.08000000002, 181813.135)\": -0.147, \"(181813.135, 191993.675)\": -0.864, \"(191993.675, 200829.925)\": -0.991, \"(200829.925, 206951.87)\": -1.401, \"(206951.87, 216109.88)\": -0.862}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 50418.515)\": -0.072, \"(50418.515, 53570.93)\": 0.057, \"(53570.93, 54249.445)\": 0.347, \"(54249.445, 57428.56)\": -0.011, \"(57428.56, 60041.265)\": 0.312, \"(60041.265, 64897.8)\": 0.534, \"(64897.8, 72985.875)\": 0.367, \"(72985.875, 74989.08499999999)\": 0.395, \"(74989.08499999999, 76596.815)\": 0.725, \"(76596.815, 79953.185)\": 1.32, \"(79953.185, 83348.07)\": 0.806, \"(83348.07, 101890.23999999999)\": 0.582, \"(101890.23999999999, 114327.485)\": 0.398, \"(114327.485, 123946.3)\": 0.259, \"(123946.3, 141661.24)\": 0.379, \"(141661.24, 174920.08000000002)\": 0.618, \"(174920.08000000002, 181813.135)\": 0.264, \"(181813.135, 191993.675)\": 0.166, \"(191993.675, 200829.925)\": 0.073, \"(200829.925, 206951.87)\": 0.169, \"(206951.87, 216109.88)\": 0.35}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "76596.815"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: DamsQuality\nFeature Type: continuous\nMeans: {\"(0.0, 1.5)\": -0.02325, \"(1.5, 2.5)\": -0.01532, \"(2.5, 3.5)\": -0.01073, \"(3.5, 4.5)\": -0.00482, \"(4.5, 5.5)\": -0.00032, \"(5.5, 6.5)\": 0.0063, \"(6.5, 7.5)\": 0.01228, \"(7.5, 8.5)\": 0.01637, \"(8.5, 10.5)\": 0.02537, \"(10.5, 12.5)\": 0.03189, \"(12.5, 13.5)\": 0.03961, \"(13.5, 14.0)\": 0.01644}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02598, \"(1.5, 2.5)\": -0.01586, \"(2.5, 3.5)\": -0.01113, \"(3.5, 4.5)\": -0.00525, \"(4.5, 5.5)\": -0.00072, \"(5.5, 6.5)\": 0.00574, \"(6.5, 7.5)\": 0.01173, \"(7.5, 8.5)\": 0.01585, \"(8.5, 10.5)\": 0.02412, \"(10.5, 12.5)\": 0.02908, \"(12.5, 13.5)\": 0.03687, \"(13.5, 14.0)\": 0.00331}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02052, \"(1.5, 2.5)\": -0.01477, \"(2.5, 3.5)\": -0.01034, \"(3.5, 4.5)\": -0.00438, \"(4.5, 5.5)\": 9e-05, \"(5.5, 6.5)\": 0.00686, \"(6.5, 7.5)\": 0.01282, \"(7.5, 8.5)\": 0.01689, \"(8.5, 10.5)\": 0.02662, \"(10.5, 12.5)\": 0.0347, \"(12.5, 13.5)\": 0.04234, \"(13.5, 14.0)\": 0.02957}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "13.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: MonsoonIntensity\nFeature Type: continuous\nMeans: {\"(0.0, 1.5)\": -0.02446, \"(1.5, 2.5)\": -0.01712, \"(2.5, 3.5)\": -0.00908, \"(3.5, 4.5)\": -0.00529, \"(4.5, 5.5)\": 0.0003, \"(5.5, 6.5)\": 0.00497, \"(6.5, 7.5)\": 0.01093, \"(7.5, 8.5)\": 0.01787, \"(8.5, 9.5)\": 0.02262, \"(9.5, 11.5)\": 0.02707, \"(11.5, 12.5)\": 0.03735, \"(12.5, 13.5)\": 0.043, \"(13.5, 15.0)\": 0.01734}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02705, \"(1.5, 2.5)\": -0.01788, \"(2.5, 3.5)\": -0.00955, \"(3.5, 4.5)\": -0.00566, \"(4.5, 5.5)\": 4e-05, \"(5.5, 6.5)\": 0.00451, \"(6.5, 7.5)\": 0.01051, \"(7.5, 8.5)\": 0.01741, \"(8.5, 9.5)\": 0.02167, \"(9.5, 11.5)\": 0.02561, \"(11.5, 12.5)\": 0.03439, \"(12.5, 13.5)\": 0.03822, \"(13.5, 15.0)\": -0.00028}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02187, \"(1.5, 2.5)\": -0.01637, \"(2.5, 3.5)\": -0.00861, \"(3.5, 4.5)\": -0.00492, \"(4.5, 5.5)\": 0.00056, \"(5.5, 6.5)\": 0.00544, \"(6.5, 7.5)\": 0.01134, \"(7.5, 8.5)\": 0.01833, \"(8.5, 9.5)\": 0.02358, \"(9.5, 11.5)\": 0.02853, \"(11.5, 12.5)\": 0.04032, \"(12.5, 13.5)\": 0.04778, \"(13.5, 15.0)\": 0.03495}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "13.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Parch\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": 0.085, \"(0.5, 1.5)\": -0.055, \"(1.5, 3.0)\": -0.299, \"(3.0, 4.0)\": -1.704}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02, \"(0.5, 1.5)\": -0.269, \"(1.5, 3.0)\": -0.62, \"(3.0, 4.0)\": -3.014}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.19, \"(0.5, 1.5)\": 0.158, \"(1.5, 3.0)\": 0.022, \"(3.0, 4.0)\": -0.395}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "3.0"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: serum_sodium\nFeature Type: continuous\nMeans: {\"(113.0, 114.5)\": -1.269, \"(114.5, 118.5)\": 0.283, \"(118.5, 124.5)\": 3.539, \"(124.5, 126.5)\": 2.46, \"(126.5, 127.5)\": 4.042, \"(127.5, 129.5)\": 3.553, \"(129.5, 130.5)\": 0.953, \"(130.5, 132.5)\": 1.22, \"(132.5, 133.5)\": -1.094, \"(133.5, 135.5)\": 0.587, \"(135.5, 138.5)\": -0.629, \"(138.5, 144.5)\": -0.233, \"(144.5, 148.0)\": 0.113}\nLower Bounds (95%-Confidence Interval): {\"(113.0, 114.5)\": -3.483, \"(114.5, 118.5)\": -4.768, \"(118.5, 124.5)\": 2.536, \"(124.5, 126.5)\": 1.699, \"(126.5, 127.5)\": 3.034, \"(127.5, 129.5)\": 2.614, \"(129.5, 130.5)\": 0.389, \"(130.5, 132.5)\": 0.304, \"(132.5, 133.5)\": -2.269, \"(133.5, 135.5)\": 0.366, \"(135.5, 138.5)\": -0.879, \"(138.5, 144.5)\": -0.845, \"(144.5, 148.0)\": -0.129}\nUpper Bounds (95%-Confidence Interval): {\"(113.0, 114.5)\": 0.944, \"(114.5, 118.5)\": 5.334, \"(118.5, 124.5)\": 4.542, \"(124.5, 126.5)\": 3.222, \"(126.5, 127.5)\": 5.05, \"(127.5, 129.5)\": 4.492, \"(129.5, 130.5)\": 1.517, \"(130.5, 132.5)\": 2.136, \"(132.5, 133.5)\": 0.08, \"(133.5, 135.5)\": 0.808, \"(135.5, 138.5)\": -0.38, \"(138.5, 144.5)\": 0.38, \"(144.5, 148.0)\": 0.354}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "118.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: radius_worst\nFeature Type: continuous\nMeans: {\"(7.93, 10.585)\": -1.149, \"(10.585, 11.305)\": -1.016, \"(11.305, 11.965)\": -0.883, \"(11.965, 12.54)\": -0.747, \"(12.54, 13.315000000000001)\": -0.616, \"(13.315000000000001, 14.184999999999999)\": -0.485, \"(14.184999999999999, 14.875)\": -0.349, \"(14.875, 15.485)\": -0.212, \"(15.485, 15.955)\": -0.078, \"(15.955, 16.54)\": 0.055, \"(16.54, 17.22)\": 0.19, \"(17.22, 17.78)\": 0.335, \"(17.78, 18.655)\": 0.469, \"(18.655, 19.785)\": 0.601, \"(19.785, 20.445)\": 0.734, \"(20.445, 21.935000000000002)\": 0.866, \"(21.935000000000002, 23.625)\": 0.997, \"(23.625, 25.335)\": 1.132, \"(25.335, 30.71)\": 1.274, \"(30.71, 36.04)\": 1.406}\nLower Bounds (95%-Confidence Interval): {\"(7.93, 10.585)\": -1.554, \"(10.585, 11.305)\": -1.397, \"(11.305, 11.965)\": -1.223, \"(11.965, 12.54)\": -1.048, \"(12.54, 13.315000000000001)\": -0.881, \"(13.315000000000001, 14.184999999999999)\": -0.698, \"(14.184999999999999, 14.875)\": -0.522, \"(14.875, 15.485)\": -0.332, \"(15.485, 15.955)\": -0.179, \"(15.955, 16.54)\": -0.216, \"(16.54, 17.22)\": -0.079, \"(17.22, 17.78)\": 0.047, \"(17.78, 18.655)\": 0.152, \"(18.655, 19.785)\": 0.262, \"(19.785, 20.445)\": 0.384, \"(20.445, 21.935000000000002)\": 0.494, \"(21.935000000000002, 23.625)\": 0.572, \"(23.625, 25.335)\": 0.664, \"(25.335, 30.71)\": 0.756, \"(30.71, 36.04)\": 0.872}\nUpper Bounds (95%-Confidence Interval): {\"(7.93, 10.585)\": -0.745, \"(10.585, 11.305)\": -0.635, \"(11.305, 11.965)\": -0.542, \"(11.965, 12.54)\": -0.446, \"(12.54, 13.315000000000001)\": -0.351, \"(13.315000000000001, 14.184999999999999)\": -0.271, \"(14.184999999999999, 14.875)\": -0.176, \"(14.875, 15.485)\": -0.091, \"(15.485, 15.955)\": 0.022, \"(15.955, 16.54)\": 0.326, \"(16.54, 17.22)\": 0.459, \"(17.22, 17.78)\": 0.624, \"(17.78, 18.655)\": 0.785, \"(18.655, 19.785)\": 0.94, \"(19.785, 20.445)\": 1.085, \"(20.445, 21.935000000000002)\": 1.239, \"(21.935000000000002, 23.625)\": 1.422, \"(23.625, 25.335)\": 1.6, \"(25.335, 30.71)\": 1.792, \"(30.71, 36.04)\": 1.941}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "17.22"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: compactness_mean\nFeature Type: continuous\nMeans: {\"(0.01938, 0.03164)\": 0.0135, \"(0.03164, 0.035445000000000004)\": 0.0558, \"(0.035445000000000004, 0.03732)\": 0.0934, \"(0.03732, 0.038529999999999995)\": 0.1327, \"(0.038529999999999995, 0.040694999999999995)\": 0.1725, \"(0.040694999999999995, 0.042550000000000004)\": 0.2126, \"(0.042550000000000004, 0.044355000000000006)\": 0.2504, \"(0.044355000000000006, 0.045645000000000005)\": 0.299, \"(0.045645000000000005, 0.0498)\": 0.3373, \"(0.0498, 0.059495)\": 0.2969, \"(0.059495, 0.06042)\": 0.2605, \"(0.06042, 0.0618)\": 0.2247, \"(0.0618, 0.06289)\": 0.1851, \"(0.06289, 0.062985)\": 0.1459, \"(0.062985, 0.06375)\": 0.0823, \"(0.06375, 0.06615499999999999)\": 0.0446, \"(0.06615499999999999, 0.066575)\": 0.0084, \"(0.066575, 0.067345)\": -0.1354, \"(0.067345, 0.06788)\": -0.1923, \"(0.06788, 0.068945)\": -0.232, \"(0.068945, 0.07211999999999999)\": -0.2724, \"(0.07211999999999999, 0.07482)\": -0.309, \"(0.07482, 0.0785)\": -0.3463, \"(0.0785, 0.085875)\": -0.2755, \"(0.085875, 0.095275)\": -0.2297, \"(0.095275, 0.10439999999999999)\": -0.1927, \"(0.10439999999999999, 0.11305000000000001)\": -0.1576, \"(0.11305000000000001, 0.11465)\": -0.121, \"(0.11465, 0.1153)\": -0.0859, \"(0.1153, 0.119)\": -0.0125, \"(0.119, 0.12375)\": 0.024, \"(0.12375, 0.16655)\": 0.0599, \"(0.16655, 0.1923)\": 0.0956, \"(0.1923, 0.23235)\": 0.1316, \"(0.23235, 0.27165)\": 0.1705, \"(0.27165, 0.28075)\": 0.2103, \"(0.28075, 0.3114)\": 0.2453}\nLower Bounds (95%-Confidence Interval): {\"(0.01938, 0.03164)\": -0.4016, \"(0.03164, 0.035445000000000004)\": -0.3995, \"(0.035445000000000004, 0.03732)\": -0.3599, \"(0.03732, 0.038529999999999995)\": -0.3178, \"(0.038529999999999995, 0.040694999999999995)\": -0.2802, \"(0.040694999999999995, 0.042550000000000004)\": -0.2633, \"(0.042550000000000004, 0.044355000000000006)\": -0.2559, \"(0.044355000000000006, 0.045645000000000005)\": -0.2259, \"(0.045645000000000005, 0.0498)\": -0.1947, \"(0.0498, 0.059495)\": -0.2119, \"(0.059495, 0.06042)\": -0.1651, \"(0.06042, 0.0618)\": -0.1904, \"(0.0618, 0.06289)\": -0.2009, \"(0.06289, 0.062985)\": -0.2409, \"(0.062985, 0.06375)\": -0.1808, \"(0.06375, 0.06615499999999999)\": -0.2262, \"(0.06615499999999999, 0.066575)\": -0.2509, \"(0.066575, 0.067345)\": -0.7938, \"(0.067345, 0.06788)\": -0.7983, \"(0.06788, 0.068945)\": -0.838, \"(0.068945, 0.07211999999999999)\": -0.9135, \"(0.07211999999999999, 0.07482)\": -0.9538, \"(0.07482, 0.0785)\": -1.0103, \"(0.0785, 0.085875)\": -0.5241, \"(0.085875, 0.095275)\": -0.4606, \"(0.095275, 0.10439999999999999)\": -0.4301, \"(0.10439999999999999, 0.11305000000000001)\": -0.3863, \"(0.11305000000000001, 0.11465)\": -0.331, \"(0.11465, 0.1153)\": -0.2716, \"(0.1153, 0.119)\": -0.1247, \"(0.119, 0.12375)\": -0.0694, \"(0.12375, 0.16655)\": -0.0509, \"(0.16655, 0.1923)\": -0.025, \"(0.1923, 0.23235)\": 0.0179, \"(0.23235, 0.27165)\": 0.0681, \"(0.27165, 0.28075)\": 0.121, \"(0.28075, 0.3114)\": 0.148}\nUpper Bounds (95%-Confidence Interval): {\"(0.01938, 0.03164)\": 0.4286, \"(0.03164, 0.035445000000000004)\": 0.5111, \"(0.035445000000000004, 0.03732)\": 0.5468, \"(0.03732, 0.038529999999999995)\": 0.5831, \"(0.038529999999999995, 0.040694999999999995)\": 0.6252, \"(0.040694999999999995, 0.042550000000000004)\": 0.6885, \"(0.042550000000000004, 0.044355000000000006)\": 0.7567, \"(0.044355000000000006, 0.045645000000000005)\": 0.8238, \"(0.045645000000000005, 0.0498)\": 0.8693, \"(0.0498, 0.059495)\": 0.8057, \"(0.059495, 0.06042)\": 0.6861, \"(0.06042, 0.0618)\": 0.6397, \"(0.0618, 0.06289)\": 0.5712, \"(0.06289, 0.062985)\": 0.5328, \"(0.062985, 0.06375)\": 0.3454, \"(0.06375, 0.06615499999999999)\": 0.3154, \"(0.06615499999999999, 0.066575)\": 0.2677, \"(0.066575, 0.067345)\": 0.5229, \"(0.067345, 0.06788)\": 0.4136, \"(0.06788, 0.068945)\": 0.3741, \"(0.068945, 0.07211999999999999)\": 0.3686, \"(0.07211999999999999, 0.07482)\": 0.3358, \"(0.07482, 0.0785)\": 0.3178, \"(0.0785, 0.085875)\": -0.0269, \"(0.085875, 0.095275)\": 0.0012, \"(0.095275, 0.10439999999999999)\": 0.0448, \"(0.10439999999999999, 0.11305000000000001)\": 0.0711, \"(0.11305000000000001, 0.11465)\": 0.0889, \"(0.11465, 0.1153)\": 0.0998, \"(0.1153, 0.119)\": 0.0997, \"(0.119, 0.12375)\": 0.1174, \"(0.12375, 0.16655)\": 0.1706, \"(0.16655, 0.1923)\": 0.2162, \"(0.1923, 0.23235)\": 0.2452, \"(0.23235, 0.27165)\": 0.273, \"(0.27165, 0.28075)\": 0.2996, \"(0.28075, 0.3114)\": 0.3427}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "0.066575"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Age\nFeature Type: continuous\nMeans: {\"(18.0, 32.5)\": 0.83, \"(32.5, 34.5)\": 0.681, \"(34.5, 37.5)\": 0.423, \"(37.5, 38.5)\": 0.281, \"(38.5, 39.5)\": 0.054, \"(39.5, 40.5)\": -0.193, \"(40.5, 41.5)\": -0.354, \"(41.5, 42.5)\": -0.494, \"(42.5, 44.5)\": -0.781, \"(44.5, 46.5)\": -1.075, \"(46.5, 48.5)\": -1.546, \"(48.5, 54.5)\": -1.717, \"(54.5, 56.5)\": -1.858, \"(56.5, 64.5)\": -1.707, \"(64.5, 66.5)\": -1.27, \"(66.5, 69.5)\": -1.118, \"(69.5, 70.5)\": -0.888, \"(70.5, 72.5)\": -0.587, \"(72.5, 74.5)\": -0.31, \"(74.5, 81.0)\": -0.157}\nLower Bounds (95%-Confidence Interval): {\"(18.0, 32.5)\": 0.581, \"(32.5, 34.5)\": 0.529, \"(34.5, 37.5)\": 0.367, \"(37.5, 38.5)\": 0.229, \"(38.5, 39.5)\": -0.051, \"(39.5, 40.5)\": -0.305, \"(40.5, 41.5)\": -0.462, \"(41.5, 42.5)\": -0.607, \"(42.5, 44.5)\": -0.855, \"(44.5, 46.5)\": -1.16, \"(46.5, 48.5)\": -1.704, \"(48.5, 54.5)\": -1.885, \"(54.5, 56.5)\": -2.031, \"(56.5, 64.5)\": -1.913, \"(64.5, 66.5)\": -1.66, \"(66.5, 69.5)\": -1.33, \"(69.5, 70.5)\": -1.222, \"(70.5, 72.5)\": -1.257, \"(72.5, 74.5)\": -1.055, \"(74.5, 81.0)\": -0.939}\nUpper Bounds (95%-Confidence Interval): {\"(18.0, 32.5)\": 1.079, \"(32.5, 34.5)\": 0.833, \"(34.5, 37.5)\": 0.48, \"(37.5, 38.5)\": 0.332, \"(38.5, 39.5)\": 0.159, \"(39.5, 40.5)\": -0.08, \"(40.5, 41.5)\": -0.246, \"(41.5, 42.5)\": -0.382, \"(42.5, 44.5)\": -0.706, \"(44.5, 46.5)\": -0.991, \"(46.5, 48.5)\": -1.387, \"(48.5, 54.5)\": -1.548, \"(54.5, 56.5)\": -1.684, \"(56.5, 64.5)\": -1.501, \"(64.5, 66.5)\": -0.88, \"(66.5, 69.5)\": -0.906, \"(69.5, 70.5)\": -0.554, \"(70.5, 72.5)\": 0.082, \"(72.5, 74.5)\": 0.436, \"(74.5, 81.0)\": 0.625}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "46.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: ClimateChange\nFeature Type: continuous\nMeans: {\"(0.0, 1.5)\": -0.02549, \"(1.5, 2.5)\": -0.01575, \"(2.5, 3.5)\": -0.01061, \"(3.5, 4.5)\": -0.0046, \"(4.5, 5.5)\": 0.00059, \"(5.5, 6.5)\": 0.00567, \"(6.5, 7.5)\": 0.01201, \"(7.5, 9.5)\": 0.01601, \"(9.5, 10.5)\": 0.02531, \"(10.5, 11.5)\": 0.02956, \"(11.5, 12.5)\": 0.04031, \"(12.5, 14.0)\": 0.04423}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02735, \"(1.5, 2.5)\": -0.01647, \"(2.5, 3.5)\": -0.01101, \"(3.5, 4.5)\": -0.00502, \"(4.5, 5.5)\": 0.00018, \"(5.5, 6.5)\": 0.00528, \"(6.5, 7.5)\": 0.01139, \"(7.5, 9.5)\": 0.01505, \"(9.5, 10.5)\": 0.0236, \"(10.5, 11.5)\": 0.02677, \"(11.5, 12.5)\": 0.03846, \"(12.5, 14.0)\": 0.03359}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02363, \"(1.5, 2.5)\": -0.01503, \"(2.5, 3.5)\": -0.0102, \"(3.5, 4.5)\": -0.00418, \"(4.5, 5.5)\": 0.00101, \"(5.5, 6.5)\": 0.00607, \"(6.5, 7.5)\": 0.01263, \"(7.5, 9.5)\": 0.01697, \"(9.5, 10.5)\": 0.02702, \"(10.5, 11.5)\": 0.03236, \"(11.5, 12.5)\": 0.04216, \"(12.5, 14.0)\": 0.05488}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "11.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: ShoppingMall\nFeature Type: continuous\nMeans: {\"(0.0, 125.5)\": -0.032, \"(125.5, 541.5)\": -0.211, \"(541.5, 808.5)\": 0.034, \"(808.5, 1082.0)\": 0.213, \"(1082.0, 1187.0)\": -0.042, \"(1187.0, 1434.5)\": 0.401, \"(1434.5, 1658.5)\": 0.585, \"(1658.5, 1968.5)\": 0.948, \"(1968.5, 3394.5)\": 1.235, \"(3394.5, 3460.0)\": 0.871, \"(3460.0, 3741.5)\": 1.066, \"(3741.5, 4803.5)\": 2.339, \"(4803.5, 5204.0)\": 2.909, \"(5204.0, 12253.0)\": 3.236}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 125.5)\": -0.092, \"(125.5, 541.5)\": -0.495, \"(541.5, 808.5)\": -0.379, \"(808.5, 1082.0)\": -0.05, \"(1082.0, 1187.0)\": -0.484, \"(1187.0, 1434.5)\": 0.131, \"(1434.5, 1658.5)\": 0.238, \"(1658.5, 1968.5)\": 0.465, \"(1968.5, 3394.5)\": 0.864, \"(3394.5, 3460.0)\": 0.262, \"(3460.0, 3741.5)\": -0.052, \"(3741.5, 4803.5)\": 1.632, \"(4803.5, 5204.0)\": 1.913, \"(5204.0, 12253.0)\": 2.114}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 125.5)\": 0.028, \"(125.5, 541.5)\": 0.073, \"(541.5, 808.5)\": 0.447, \"(808.5, 1082.0)\": 0.477, \"(1082.0, 1187.0)\": 0.4, \"(1187.0, 1434.5)\": 0.671, \"(1434.5, 1658.5)\": 0.931, \"(1658.5, 1968.5)\": 1.43, \"(1968.5, 3394.5)\": 1.605, \"(3394.5, 3460.0)\": 1.481, \"(3460.0, 3741.5)\": 2.183, \"(3741.5, 4803.5)\": 3.046, \"(4803.5, 5204.0)\": 3.906, \"(5204.0, 12253.0)\": 4.358}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "3741.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: population\nFeature Type: continuous\nMeans: {\"(3.0, 14.5)\": 125210.2, \"(14.5, 25.5)\": 92452.9, \"(25.5, 65.5)\": 80407.9, \"(65.5, 138.5)\": 91917.4, \"(138.5, 151.5)\": 103409.9, \"(151.5, 301.5)\": 85121.7, \"(301.5, 490.5)\": 73106.0, \"(490.5, 657.5)\": 57994.5, \"(657.5, 761.5)\": 44760.8, \"(761.5, 837.5)\": 32058.9, \"(837.5, 1019.5)\": 20715.6, \"(1019.5, 1220.5)\": 6507.2, \"(1220.5, 1267.5)\": -6199.6, \"(1267.5, 1269.5)\": 9858.1, \"(1269.5, 1497.5)\": -9812.8, \"(1497.5, 1886.5)\": -25776.4, \"(1886.5, 2129.5)\": -36953.6, \"(2129.5, 2425.5)\": -48605.9, \"(2425.5, 2686.0)\": -59914.9, \"(2686.0, 2718.5)\": -46231.6, \"(2718.5, 3175.5)\": -61061.6, \"(3175.5, 3965.0)\": -76216.0, \"(3965.0, 35682.0)\": -91117.9}\nLower Bounds (95%-Confidence Interval): {\"(3.0, 14.5)\": 103123.1, \"(14.5, 25.5)\": 58681.0, \"(25.5, 65.5)\": 62309.7, \"(65.5, 138.5)\": 75243.8, \"(138.5, 151.5)\": 78950.4, \"(151.5, 301.5)\": 69535.1, \"(301.5, 490.5)\": 60924.6, \"(490.5, 657.5)\": 45395.6, \"(657.5, 761.5)\": 35273.5, \"(761.5, 837.5)\": 26626.5, \"(837.5, 1019.5)\": 8057.5, \"(1019.5, 1220.5)\": -10609.9, \"(1220.5, 1267.5)\": -14462.5, \"(1267.5, 1269.5)\": -5022.3, \"(1269.5, 1497.5)\": -22884.3, \"(1497.5, 1886.5)\": -37619.7, \"(1886.5, 2129.5)\": -51088.1, \"(2129.5, 2425.5)\": -56504.4, \"(2425.5, 2686.0)\": -64158.2, \"(2686.0, 2718.5)\": -69408.6, \"(2718.5, 3175.5)\": -68643.2, \"(3175.5, 3965.0)\": -84318.8, \"(3965.0, 35682.0)\": -101928.5}\nUpper Bounds (95%-Confidence Interval): {\"(3.0, 14.5)\": 147297.2, \"(14.5, 25.5)\": 126224.8, \"(25.5, 65.5)\": 98506.2, \"(65.5, 138.5)\": 108591.1, \"(138.5, 151.5)\": 127869.3, \"(151.5, 301.5)\": 100708.2, \"(301.5, 490.5)\": 85287.5, \"(490.5, 657.5)\": 70593.3, \"(657.5, 761.5)\": 54248.0, \"(761.5, 837.5)\": 37491.3, \"(837.5, 1019.5)\": 33373.7, \"(1019.5, 1220.5)\": 23624.4, \"(1220.5, 1267.5)\": 2063.4, \"(1267.5, 1269.5)\": 24738.4, \"(1269.5, 1497.5)\": 3258.7, \"(1497.5, 1886.5)\": -13933.2, \"(1886.5, 2129.5)\": -22819.1, \"(2129.5, 2425.5)\": -40707.4, \"(2425.5, 2686.0)\": -55671.5, \"(2686.0, 2718.5)\": -23054.7, \"(2718.5, 3175.5)\": -53480.1, \"(3175.5, 3965.0)\": -68113.2, \"(3965.0, 35682.0)\": -80307.2}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "14.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: concave points_mean\nFeature Type: continuous\nMeans: {\"(0.0, 0.0074145)\": -1.054, \"(0.0074145, 0.011665)\": -0.937, \"(0.011665, 0.01503)\": -0.821, \"(0.01503, 0.017865)\": -0.705, \"(0.017865, 0.019315)\": -0.582, \"(0.019315, 0.023185)\": -0.466, \"(0.023185, 0.026115)\": -0.352, \"(0.026115, 0.042455)\": -0.235, \"(0.042455, 0.048235)\": -0.115, \"(0.048235, 0.048865)\": 0.04, \"(0.048865, 0.059615)\": 0.233, \"(0.059615, 0.070395)\": 0.35, \"(0.070395, 0.08221500000000001)\": 0.474, \"(0.08221500000000001, 0.087175)\": 0.592, \"(0.087175, 0.091445)\": 0.711, \"(0.091445, 0.1006)\": 0.832, \"(0.1006, 0.122)\": 0.949, \"(0.122, 0.16544999999999999)\": 1.068, \"(0.16544999999999999, 0.2012)\": 1.187}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.0074145)\": -1.411, \"(0.0074145, 0.011665)\": -1.253, \"(0.011665, 0.01503)\": -1.095, \"(0.01503, 0.017865)\": -0.965, \"(0.017865, 0.019315)\": -0.823, \"(0.019315, 0.023185)\": -0.72, \"(0.023185, 0.026115)\": -0.517, \"(0.026115, 0.042455)\": -0.743, \"(0.042455, 0.048235)\": -0.628, \"(0.048235, 0.048865)\": -0.409, \"(0.048865, 0.059615)\": -0.151, \"(0.059615, 0.070395)\": 0.09, \"(0.070395, 0.08221500000000001)\": 0.219, \"(0.08221500000000001, 0.087175)\": 0.306, \"(0.087175, 0.091445)\": 0.39, \"(0.091445, 0.1006)\": 0.481, \"(0.1006, 0.122)\": 0.562, \"(0.122, 0.16544999999999999)\": 0.634, \"(0.16544999999999999, 0.2012)\": 0.74}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.0074145)\": -0.697, \"(0.0074145, 0.011665)\": -0.62, \"(0.011665, 0.01503)\": -0.546, \"(0.01503, 0.017865)\": -0.445, \"(0.017865, 0.019315)\": -0.34, \"(0.019315, 0.023185)\": -0.212, \"(0.023185, 0.026115)\": -0.188, \"(0.026115, 0.042455)\": 0.274, \"(0.042455, 0.048235)\": 0.398, \"(0.048235, 0.048865)\": 0.489, \"(0.048865, 0.059615)\": 0.617, \"(0.059615, 0.070395)\": 0.611, \"(0.070395, 0.08221500000000001)\": 0.728, \"(0.08221500000000001, 0.087175)\": 0.878, \"(0.087175, 0.091445)\": 1.032, \"(0.091445, 0.1006)\": 1.182, \"(0.1006, 0.122)\": 1.336, \"(0.122, 0.16544999999999999)\": 1.503, \"(0.16544999999999999, 0.2012)\": 1.634}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "0.048865"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Relationship\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": 0.511, \"(0.5, 1.5)\": -0.233, \"(1.5, 2.5)\": -0.666, \"(2.5, 3.5)\": -1.006, \"(3.5, 4.5)\": -0.529, \"(4.5, 5.0)\": 1.753}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.453, \"(0.5, 1.5)\": -0.278, \"(1.5, 2.5)\": -0.789, \"(2.5, 3.5)\": -1.092, \"(3.5, 4.5)\": -0.6, \"(4.5, 5.0)\": 1.664}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.568, \"(0.5, 1.5)\": -0.188, \"(1.5, 2.5)\": -0.543, \"(2.5, 3.5)\": -0.921, \"(3.5, 4.5)\": -0.458, \"(4.5, 5.0)\": 1.842}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "4.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: total_bedrooms\nFeature Type: continuous\nMeans: {\"(2.0, 4.5)\": -10633.3, \"(4.5, 9.5)\": -19829.1, \"(9.5, 12.5)\": -33356.0, \"(12.5, 14.5)\": -27510.0, \"(14.5, 17.5)\": -34141.4, \"(17.5, 20.5)\": -50740.7, \"(20.5, 22.5)\": -59049.5, \"(22.5, 25.5)\": -37177.7, \"(25.5, 29.5)\": -30710.5, \"(29.5, 111.5)\": -36287.1, \"(111.5, 112.5)\": -22540.1, \"(112.5, 176.5)\": -33870.1, \"(176.5, 245.5)\": -27701.3, \"(245.5, 265.5)\": -20526.0, \"(265.5, 268.5)\": -26170.7, \"(268.5, 317.5)\": -17267.5, \"(317.5, 424.5)\": -8013.2, \"(424.5, 463.5)\": -1894.5, \"(463.5, 512.5)\": 5095.6, \"(512.5, 513.5)\": 17024.1, \"(513.5, 655.5)\": 9371.5, \"(655.5, 697.5)\": 15515.9, \"(697.5, 776.5)\": 22859.4, \"(776.5, 779.5)\": 13774.7, \"(779.5, 1008.5)\": 22608.4, \"(1008.5, 1012.5)\": 37458.5, \"(1012.5, 1081.5)\": 30023.9, \"(1081.5, 1449.5)\": 37066.8, \"(1449.5, 1490.5)\": 51601.0, \"(1490.5, 1616.0)\": 42837.8, \"(1616.0, 2714.5)\": 49023.6, \"(2714.5, 2865.5)\": 40592.1, \"(2865.5, 6445.0)\": 51586.1}\nLower Bounds (95%-Confidence Interval): {\"(2.0, 4.5)\": -43650.4, \"(4.5, 9.5)\": -54645.6, \"(9.5, 12.5)\": -52929.5, \"(12.5, 14.5)\": -57181.8, \"(14.5, 17.5)\": -49207.2, \"(17.5, 20.5)\": -72519.5, \"(20.5, 22.5)\": -82934.2, \"(22.5, 25.5)\": -50942.7, \"(25.5, 29.5)\": -45748.1, \"(29.5, 111.5)\": -47452.5, \"(111.5, 112.5)\": -42457.2, \"(112.5, 176.5)\": -41599.3, \"(176.5, 245.5)\": -35478.0, \"(245.5, 265.5)\": -27520.5, \"(265.5, 268.5)\": -32234.3, \"(268.5, 317.5)\": -23732.7, \"(317.5, 424.5)\": -13237.9, \"(424.5, 463.5)\": -7023.7, \"(463.5, 512.5)\": -1510.7, \"(512.5, 513.5)\": 6820.8, \"(513.5, 655.5)\": 341.5, \"(655.5, 697.5)\": 12634.4, \"(697.5, 776.5)\": 15982.1, \"(776.5, 779.5)\": 221.5, \"(779.5, 1008.5)\": 18345.9, \"(1008.5, 1012.5)\": 20622.3, \"(1012.5, 1081.5)\": 21931.2, \"(1081.5, 1449.5)\": 22140.8, \"(1449.5, 1490.5)\": 39761.7, \"(1490.5, 1616.0)\": 35441.7, \"(1616.0, 2714.5)\": 37135.8, \"(2714.5, 2865.5)\": 32716.4, \"(2865.5, 6445.0)\": 42203.8}\nUpper Bounds (95%-Confidence Interval): {\"(2.0, 4.5)\": 22383.8, \"(4.5, 9.5)\": 14987.3, \"(9.5, 12.5)\": -13782.5, \"(12.5, 14.5)\": 2161.9, \"(14.5, 17.5)\": -19075.5, \"(17.5, 20.5)\": -28961.9, \"(20.5, 22.5)\": -35164.8, \"(22.5, 25.5)\": -23412.7, \"(25.5, 29.5)\": -15672.9, \"(29.5, 111.5)\": -25121.6, \"(111.5, 112.5)\": -2622.9, \"(112.5, 176.5)\": -26141.0, \"(176.5, 245.5)\": -19924.6, \"(245.5, 265.5)\": -13531.5, \"(265.5, 268.5)\": -20107.0, \"(268.5, 317.5)\": -10802.3, \"(317.5, 424.5)\": -2788.6, \"(424.5, 463.5)\": 3234.7, \"(463.5, 512.5)\": 11701.8, \"(512.5, 513.5)\": 27227.4, \"(513.5, 655.5)\": 18401.4, \"(655.5, 697.5)\": 18397.4, \"(697.5, 776.5)\": 29736.8, \"(776.5, 779.5)\": 27327.8, \"(779.5, 1008.5)\": 26870.8, \"(1008.5, 1012.5)\": 54294.7, \"(1012.5, 1081.5)\": 38116.5, \"(1081.5, 1449.5)\": 51992.8, \"(1449.5, 1490.5)\": 63440.2, \"(1490.5, 1616.0)\": 50233.9, \"(1616.0, 2714.5)\": 60911.5, \"(2714.5, 2865.5)\": 48467.9, \"(2865.5, 6445.0)\": 60968.4}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "22.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: BloodPressure\nFeature Type: continuous\nMeans: {\"(0.0, 15.0)\": 0.236, \"(15.0, 37.0)\": 0.1532, \"(37.0, 45.0)\": -0.0296, \"(45.0, 47.0)\": -0.0891, \"(47.0, 54.5)\": -0.1348, \"(54.5, 60.5)\": -0.1774, \"(60.5, 61.5)\": -0.11, \"(61.5, 64.5)\": -0.0541, \"(64.5, 74.5)\": -0.0119, \"(74.5, 75.5)\": -0.058, \"(75.5, 83.0)\": -0.004, \"(83.0, 93.0)\": 0.0343, \"(93.0, 95.0)\": 0.0889, \"(95.0, 97.0)\": 0.1461, \"(97.0, 101.0)\": 0.183, \"(101.0, 103.0)\": 0.2699, \"(103.0, 107.0)\": 0.3158, \"(107.0, 109.0)\": 0.3837, \"(109.0, 110.0)\": 0.5269}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 15.0)\": -0.0274, \"(15.0, 37.0)\": -0.1145, \"(37.0, 45.0)\": -0.2191, \"(45.0, 47.0)\": -0.2854, \"(47.0, 54.5)\": -0.313, \"(54.5, 60.5)\": -0.2953, \"(60.5, 61.5)\": -0.1759, \"(61.5, 64.5)\": -0.1789, \"(64.5, 74.5)\": -0.1212, \"(74.5, 75.5)\": -0.3075, \"(75.5, 83.0)\": -0.0727, \"(83.0, 93.0)\": -0.1515, \"(93.0, 95.0)\": -0.0624, \"(95.0, 97.0)\": -0.0006, \"(97.0, 101.0)\": 0.0092, \"(101.0, 103.0)\": 0.085, \"(103.0, 107.0)\": 0.1217, \"(107.0, 109.0)\": 0.1853, \"(109.0, 110.0)\": 0.2653}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 15.0)\": 0.4994, \"(15.0, 37.0)\": 0.4208, \"(37.0, 45.0)\": 0.16, \"(45.0, 47.0)\": 0.1073, \"(47.0, 54.5)\": 0.0433, \"(54.5, 60.5)\": -0.0595, \"(60.5, 61.5)\": -0.0441, \"(61.5, 64.5)\": 0.0708, \"(64.5, 74.5)\": 0.0974, \"(74.5, 75.5)\": 0.1914, \"(75.5, 83.0)\": 0.0647, \"(83.0, 93.0)\": 0.2201, \"(93.0, 95.0)\": 0.2402, \"(95.0, 97.0)\": 0.2929, \"(97.0, 101.0)\": 0.3567, \"(101.0, 103.0)\": 0.4548, \"(103.0, 107.0)\": 0.51, \"(107.0, 109.0)\": 0.582, \"(109.0, 110.0)\": 0.7884}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "37.0"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: RoomService\nFeature Type: continuous\nMeans: {\"(0.0, 105.5)\": 0.328, \"(105.5, 296.5)\": 0.028, \"(296.5, 335.5)\": -0.208, \"(335.5, 340.0)\": 0.165, \"(340.0, 343.0)\": -0.1, \"(343.0, 596.5)\": -0.741, \"(596.5, 712.5)\": -0.978, \"(712.5, 734.0)\": -1.212, \"(734.0, 800.0)\": -1.446, \"(800.0, 816.0)\": -1.136, \"(816.0, 997.5)\": -1.454, \"(997.5, 1031.0)\": -1.106, \"(1031.0, 1041.0)\": -1.368, \"(1041.0, 2172.5)\": -1.866, \"(2172.5, 2283.5)\": -1.455, \"(2283.5, 2313.5)\": -1.171, \"(2313.5, 2336.5)\": -0.66, \"(2336.5, 2420.0)\": -2.559, \"(2420.0, 2992.5)\": -3.229, \"(2992.5, 3006.0)\": -2.708, \"(3006.0, 3196.5)\": -2.984, \"(3196.5, 3249.5)\": -2.709, \"(3249.5, 14327.0)\": -4.146}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 105.5)\": -0.06, \"(105.5, 296.5)\": -0.369, \"(296.5, 335.5)\": -1.022, \"(335.5, 340.0)\": -0.184, \"(340.0, 343.0)\": -1.038, \"(343.0, 596.5)\": -1.323, \"(596.5, 712.5)\": -1.547, \"(712.5, 734.0)\": -1.555, \"(734.0, 800.0)\": -1.8, \"(800.0, 816.0)\": -2.191, \"(816.0, 997.5)\": -1.824, \"(997.5, 1031.0)\": -1.706, \"(1031.0, 1041.0)\": -2.147, \"(1041.0, 2172.5)\": -2.244, \"(2172.5, 2283.5)\": -2.248, \"(2283.5, 2313.5)\": -1.568, \"(2313.5, 2336.5)\": -2.21, \"(2336.5, 2420.0)\": -3.537, \"(2420.0, 2992.5)\": -3.89, \"(2992.5, 3006.0)\": -3.955, \"(3006.0, 3196.5)\": -4.24, \"(3196.5, 3249.5)\": -3.98, \"(3249.5, 14327.0)\": -5.248}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 105.5)\": 0.716, \"(105.5, 296.5)\": 0.425, \"(296.5, 335.5)\": 0.607, \"(335.5, 340.0)\": 0.513, \"(340.0, 343.0)\": 0.837, \"(343.0, 596.5)\": -0.16, \"(596.5, 712.5)\": -0.409, \"(712.5, 734.0)\": -0.869, \"(734.0, 800.0)\": -1.092, \"(800.0, 816.0)\": -0.082, \"(816.0, 997.5)\": -1.083, \"(997.5, 1031.0)\": -0.506, \"(1031.0, 1041.0)\": -0.589, \"(1041.0, 2172.5)\": -1.488, \"(2172.5, 2283.5)\": -0.661, \"(2283.5, 2313.5)\": -0.774, \"(2313.5, 2336.5)\": 0.89, \"(2336.5, 2420.0)\": -1.582, \"(2420.0, 2992.5)\": -2.569, \"(2992.5, 3006.0)\": -1.461, \"(3006.0, 3196.5)\": -1.727, \"(3196.5, 3249.5)\": -1.438, \"(3249.5, 14327.0)\": -3.043}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "2336.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: ejection_fraction\nFeature Type: continuous\nMeans: {\"(14.0, 16.0)\": 4.55, \"(16.0, 22.5)\": 3.26, \"(22.5, 27.5)\": 1.89, \"(27.5, 32.5)\": -0.42, \"(32.5, 36.5)\": -1.76, \"(36.5, 39.0)\": 0.48, \"(39.0, 61.0)\": -0.83, \"(61.0, 67.5)\": 0.08, \"(67.5, 75.0)\": 0.8, \"(75.0, 80.0)\": -5.67}\nLower Bounds (95%-Confidence Interval): {\"(14.0, 16.0)\": 2.65, \"(16.0, 22.5)\": 2.42, \"(22.5, 27.5)\": 1.26, \"(27.5, 32.5)\": -0.83, \"(32.5, 36.5)\": -2.57, \"(36.5, 39.0)\": 0.17, \"(39.0, 61.0)\": -1.16, \"(61.0, 67.5)\": -0.39, \"(67.5, 75.0)\": 0.32, \"(75.0, 80.0)\": -8.05}\nUpper Bounds (95%-Confidence Interval): {\"(14.0, 16.0)\": 6.45, \"(16.0, 22.5)\": 4.1, \"(22.5, 27.5)\": 2.51, \"(27.5, 32.5)\": -0.01, \"(32.5, 36.5)\": -0.95, \"(36.5, 39.0)\": 0.79, \"(39.0, 61.0)\": -0.49, \"(61.0, 67.5)\": 0.55, \"(67.5, 75.0)\": 1.28, \"(75.0, 80.0)\": -3.29}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "75.0"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: texture_mean\nFeature Type: continuous\nMeans: {\"(9.71, 13.24)\": -1.121, \"(13.24, 14.075)\": -1.023, \"(14.075, 14.665)\": -0.921, \"(14.665, 15.010000000000002)\": -0.82, \"(15.010000000000002, 15.485)\": -0.718, \"(15.485, 15.774999999999999)\": -0.623, \"(15.774999999999999, 16.445)\": -0.523, \"(16.445, 17.045)\": -0.422, \"(17.045, 17.665)\": -0.324, \"(17.665, 18.335)\": -0.225, \"(18.335, 18.725)\": -0.129, \"(18.725, 19.075)\": -0.032, \"(19.075, 19.549999999999997)\": 0.063, \"(19.549999999999997, 19.915)\": 0.161, \"(19.915, 20.235)\": 0.26, \"(20.235, 20.8)\": 0.445, \"(20.8, 21.285)\": 0.549, \"(21.285, 33.81)\": 0.68}\nLower Bounds (95%-Confidence Interval): {\"(9.71, 13.24)\": -1.583, \"(13.24, 14.075)\": -1.428, \"(14.075, 14.665)\": -1.292, \"(14.665, 15.010000000000002)\": -1.127, \"(15.010000000000002, 15.485)\": -1.018, \"(15.485, 15.774999999999999)\": -0.932, \"(15.774999999999999, 16.445)\": -0.765, \"(16.445, 17.045)\": -0.657, \"(17.045, 17.665)\": -0.537, \"(17.665, 18.335)\": -0.404, \"(18.335, 18.725)\": -0.289, \"(18.725, 19.075)\": -0.203, \"(19.075, 19.549999999999997)\": -0.094, \"(19.549999999999997, 19.915)\": 0.017, \"(19.915, 20.235)\": 0.108, \"(20.235, 20.8)\": -0.11, \"(20.8, 21.285)\": -0.011, \"(21.285, 33.81)\": -0.0}\nUpper Bounds (95%-Confidence Interval): {\"(9.71, 13.24)\": -0.658, \"(13.24, 14.075)\": -0.619, \"(14.075, 14.665)\": -0.55, \"(14.665, 15.010000000000002)\": -0.512, \"(15.010000000000002, 15.485)\": -0.417, \"(15.485, 15.774999999999999)\": -0.314, \"(15.774999999999999, 16.445)\": -0.282, \"(16.445, 17.045)\": -0.187, \"(17.045, 17.665)\": -0.112, \"(17.665, 18.335)\": -0.045, \"(18.335, 18.725)\": 0.031, \"(18.725, 19.075)\": 0.139, \"(19.075, 19.549999999999997)\": 0.22, \"(19.549999999999997, 19.915)\": 0.306, \"(19.915, 20.235)\": 0.412, \"(20.235, 20.8)\": 0.999, \"(20.8, 21.285)\": 1.109, \"(21.285, 33.81)\": 1.36}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "20.235"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Encroachments\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02484, \"(0.5, 1.5)\": -0.02089, \"(1.5, 2.5)\": -0.01739, \"(2.5, 3.5)\": -0.01124, \"(3.5, 4.5)\": -0.00474, \"(4.5, 5.5)\": 0.00077, \"(5.5, 6.5)\": 0.00574, \"(6.5, 7.5)\": 0.01068, \"(7.5, 8.5)\": 0.01599, \"(8.5, 9.5)\": 0.02231, \"(9.5, 10.5)\": 0.02667, \"(10.5, 13.5)\": 0.03305, \"(13.5, 16.0)\": 0.02016}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02637, \"(0.5, 1.5)\": -0.02217, \"(1.5, 2.5)\": -0.0179, \"(2.5, 3.5)\": -0.01163, \"(3.5, 4.5)\": -0.00519, \"(4.5, 5.5)\": 0.00046, \"(5.5, 6.5)\": 0.00525, \"(6.5, 7.5)\": 0.00992, \"(7.5, 8.5)\": 0.01538, \"(8.5, 9.5)\": 0.02115, \"(9.5, 10.5)\": 0.02528, \"(10.5, 13.5)\": 0.02547, \"(13.5, 16.0)\": 0.01297}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0233, \"(0.5, 1.5)\": -0.01962, \"(1.5, 2.5)\": -0.01689, \"(2.5, 3.5)\": -0.01085, \"(3.5, 4.5)\": -0.0043, \"(4.5, 5.5)\": 0.00109, \"(5.5, 6.5)\": 0.00623, \"(6.5, 7.5)\": 0.01144, \"(7.5, 8.5)\": 0.0166, \"(8.5, 9.5)\": 0.02348, \"(9.5, 10.5)\": 0.02807, \"(10.5, 13.5)\": 0.04062, \"(13.5, 16.0)\": 0.02734}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "13.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: BMI\nFeature Type: continuous\nMeans: {\"(0.0, 9.1)\": -0.7, \"(9.1, 22.55)\": -0.961, \"(22.55, 23.65)\": -0.856, \"(23.65, 25.55)\": -0.762, \"(25.55, 26.35)\": -0.661, \"(26.35, 27.65)\": -0.24, \"(27.65, 28.45)\": -0.144, \"(28.45, 29.65)\": -0.051, \"(29.65, 30.45)\": 0.049, \"(30.45, 32.150000000000006)\": 0.153, \"(32.150000000000006, 37.650000000000006)\": 0.246, \"(37.650000000000006, 41.75)\": 0.34, \"(41.75, 42.849999999999994)\": 0.434, \"(42.849999999999994, 45.650000000000006)\": 0.529, \"(45.650000000000006, 48.349999999999994)\": 0.626, \"(48.349999999999994, 67.1)\": 0.784}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 9.1)\": -1.139, \"(9.1, 22.55)\": -1.349, \"(22.55, 23.65)\": -1.219, \"(23.65, 25.55)\": -1.281, \"(25.55, 26.35)\": -1.231, \"(26.35, 27.65)\": -0.568, \"(27.65, 28.45)\": -0.258, \"(28.45, 29.65)\": -0.157, \"(29.65, 30.45)\": -0.11, \"(30.45, 32.150000000000006)\": -0.086, \"(32.150000000000006, 37.650000000000006)\": 0.084, \"(37.650000000000006, 41.75)\": 0.189, \"(41.75, 42.849999999999994)\": 0.28, \"(42.849999999999994, 45.650000000000006)\": 0.348, \"(45.650000000000006, 48.349999999999994)\": 0.256, \"(48.349999999999994, 67.1)\": 0.265}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 9.1)\": -0.262, \"(9.1, 22.55)\": -0.573, \"(22.55, 23.65)\": -0.493, \"(23.65, 25.55)\": -0.243, \"(25.55, 26.35)\": -0.09, \"(26.35, 27.65)\": 0.088, \"(27.65, 28.45)\": -0.03, \"(28.45, 29.65)\": 0.054, \"(29.65, 30.45)\": 0.208, \"(30.45, 32.150000000000006)\": 0.392, \"(32.150000000000006, 37.650000000000006)\": 0.409, \"(37.650000000000006, 41.75)\": 0.491, \"(41.75, 42.849999999999994)\": 0.588, \"(42.849999999999994, 45.650000000000006)\": 0.709, \"(45.650000000000006, 48.349999999999994)\": 0.996, \"(48.349999999999994, 67.1)\": 1.303}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "26.35"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Urbanization\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02565, \"(0.5, 1.5)\": -0.02133, \"(1.5, 2.5)\": -0.01683, \"(2.5, 3.5)\": -0.00993, \"(3.5, 4.5)\": -0.00473, \"(4.5, 5.5)\": -1e-05, \"(5.5, 6.5)\": 0.00511, \"(6.5, 7.5)\": 0.01148, \"(7.5, 8.5)\": 0.01621, \"(8.5, 9.5)\": 0.02476, \"(9.5, 11.5)\": 0.02962, \"(11.5, 12.5)\": 0.03469, \"(12.5, 13.5)\": 0.04866, \"(13.5, 16.0)\": 0.05902}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02758, \"(0.5, 1.5)\": -0.02272, \"(1.5, 2.5)\": -0.01769, \"(2.5, 3.5)\": -0.01036, \"(3.5, 4.5)\": -0.00514, \"(4.5, 5.5)\": -0.0004, \"(5.5, 6.5)\": 0.00453, \"(6.5, 7.5)\": 0.01098, \"(7.5, 8.5)\": 0.01535, \"(8.5, 9.5)\": 0.0239, \"(9.5, 11.5)\": 0.02772, \"(11.5, 12.5)\": 0.03206, \"(12.5, 13.5)\": 0.04307, \"(13.5, 16.0)\": 0.0546}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02372, \"(0.5, 1.5)\": -0.01994, \"(1.5, 2.5)\": -0.01596, \"(2.5, 3.5)\": -0.00951, \"(3.5, 4.5)\": -0.00432, \"(4.5, 5.5)\": 0.00037, \"(5.5, 6.5)\": 0.00568, \"(6.5, 7.5)\": 0.01199, \"(7.5, 8.5)\": 0.01706, \"(8.5, 9.5)\": 0.02562, \"(9.5, 11.5)\": 0.03152, \"(11.5, 12.5)\": 0.03732, \"(12.5, 13.5)\": 0.05424, \"(13.5, 16.0)\": 0.06343}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "12.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: HoursPerWeek\nFeature Type: continuous\nMeans: {\"(1.0, 1.5)\": -0.765, \"(1.5, 2.5)\": -0.375, \"(2.5, 4.5)\": -1.909, \"(4.5, 6.5)\": -1.117, \"(6.5, 7.5)\": -0.618, \"(7.5, 14.5)\": -0.822, \"(14.5, 19.5)\": -1.132, \"(19.5, 29.5)\": -0.765, \"(29.5, 33.5)\": -0.6, \"(33.5, 34.5)\": -0.921, \"(34.5, 39.5)\": -0.155, \"(39.5, 41.5)\": 0.03, \"(41.5, 50.5)\": 0.392, \"(50.5, 51.5)\": 0.131, \"(51.5, 55.5)\": 0.457, \"(55.5, 59.5)\": 0.676, \"(59.5, 63.5)\": 0.416, \"(63.5, 64.5)\": 0.952, \"(64.5, 65.5)\": 0.516, \"(65.5, 71.0)\": 0.071, \"(71.0, 75.5)\": 0.43, \"(75.5, 77.5)\": 0.235, \"(77.5, 79.0)\": 0.742, \"(79.0, 83.0)\": 0.977, \"(83.0, 85.5)\": 1.287, \"(85.5, 90.5)\": 0.192, \"(90.5, 97.5)\": -0.071, \"(97.5, 98.5)\": 0.119, \"(98.5, 99.0)\": -0.139}\nLower Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -2.672, \"(1.5, 2.5)\": -0.773, \"(2.5, 4.5)\": -2.709, \"(4.5, 6.5)\": -1.566, \"(6.5, 7.5)\": -1.241, \"(7.5, 14.5)\": -1.098, \"(14.5, 19.5)\": -1.535, \"(19.5, 29.5)\": -1.357, \"(29.5, 33.5)\": -1.248, \"(33.5, 34.5)\": -1.815, \"(34.5, 39.5)\": -0.223, \"(39.5, 41.5)\": -0.129, \"(41.5, 50.5)\": 0.212, \"(50.5, 51.5)\": -0.867, \"(51.5, 55.5)\": 0.357, \"(55.5, 59.5)\": 0.304, \"(59.5, 63.5)\": 0.014, \"(63.5, 64.5)\": 0.009, \"(64.5, 65.5)\": 0.379, \"(65.5, 71.0)\": -0.113, \"(71.0, 75.5)\": 0.054, \"(75.5, 77.5)\": -0.57, \"(77.5, 79.0)\": 0.234, \"(79.0, 83.0)\": 0.788, \"(83.0, 85.5)\": 0.721, \"(85.5, 90.5)\": -0.289, \"(90.5, 97.5)\": -0.504, \"(97.5, 98.5)\": -0.527, \"(98.5, 99.0)\": -0.548}\nUpper Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": 1.142, \"(1.5, 2.5)\": 0.023, \"(2.5, 4.5)\": -1.109, \"(4.5, 6.5)\": -0.668, \"(6.5, 7.5)\": 0.005, \"(7.5, 14.5)\": -0.546, \"(14.5, 19.5)\": -0.729, \"(19.5, 29.5)\": -0.172, \"(29.5, 33.5)\": 0.047, \"(33.5, 34.5)\": -0.027, \"(34.5, 39.5)\": -0.087, \"(39.5, 41.5)\": 0.19, \"(41.5, 50.5)\": 0.571, \"(50.5, 51.5)\": 1.13, \"(51.5, 55.5)\": 0.557, \"(55.5, 59.5)\": 1.048, \"(59.5, 63.5)\": 0.818, \"(63.5, 64.5)\": 1.896, \"(64.5, 65.5)\": 0.653, \"(65.5, 71.0)\": 0.254, \"(71.0, 75.5)\": 0.806, \"(75.5, 77.5)\": 1.04, \"(77.5, 79.0)\": 1.25, \"(79.0, 83.0)\": 1.166, \"(83.0, 85.5)\": 1.852, \"(85.5, 90.5)\": 0.673, \"(90.5, 97.5)\": 0.361, \"(97.5, 98.5)\": 0.765, \"(98.5, 99.0)\": 0.271}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "2.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Deforestation\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02956, \"(0.5, 2.5)\": -0.02081, \"(2.5, 3.5)\": -0.00998, \"(3.5, 4.5)\": -0.00524, \"(4.5, 5.5)\": 0.00043, \"(5.5, 6.5)\": 0.00515, \"(6.5, 8.5)\": 0.01107, \"(8.5, 10.5)\": 0.02102, \"(10.5, 11.5)\": 0.02728, \"(11.5, 13.5)\": 0.0456, \"(13.5, 14.5)\": 0.05244, \"(14.5, 17.0)\": 0.06161}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.03241, \"(0.5, 2.5)\": -0.02172, \"(2.5, 3.5)\": -0.01056, \"(3.5, 4.5)\": -0.0057, \"(4.5, 5.5)\": 1e-05, \"(5.5, 6.5)\": 0.00474, \"(6.5, 8.5)\": 0.01043, \"(8.5, 10.5)\": 0.01957, \"(10.5, 11.5)\": 0.02542, \"(11.5, 13.5)\": 0.04264, \"(13.5, 14.5)\": 0.04883, \"(14.5, 17.0)\": 0.05758}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02672, \"(0.5, 2.5)\": -0.0199, \"(2.5, 3.5)\": -0.0094, \"(3.5, 4.5)\": -0.00479, \"(4.5, 5.5)\": 0.00085, \"(5.5, 6.5)\": 0.00557, \"(6.5, 8.5)\": 0.01172, \"(8.5, 10.5)\": 0.02247, \"(10.5, 11.5)\": 0.02915, \"(11.5, 13.5)\": 0.04855, \"(13.5, 14.5)\": 0.05605, \"(14.5, 17.0)\": 0.06565}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "11.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Siltation\nFeature Type: continuous\nMeans: {\"(0.0, 1.5)\": -0.02643, \"(1.5, 2.5)\": -0.01529, \"(2.5, 3.5)\": -0.01037, \"(3.5, 4.5)\": -0.00562, \"(4.5, 5.5)\": 0.00068, \"(5.5, 6.5)\": 0.00591, \"(6.5, 7.5)\": 0.01127, \"(7.5, 8.5)\": 0.01553, \"(8.5, 10.5)\": 0.02363, \"(10.5, 11.5)\": 0.03038, \"(11.5, 12.5)\": 0.03607, \"(12.5, 13.5)\": 0.04087, \"(13.5, 15.0)\": 0.04477}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02798, \"(1.5, 2.5)\": -0.01578, \"(2.5, 3.5)\": -0.01088, \"(3.5, 4.5)\": -0.00595, \"(4.5, 5.5)\": 0.0002, \"(5.5, 6.5)\": 0.0054, \"(6.5, 7.5)\": 0.0105, \"(7.5, 8.5)\": 0.01459, \"(8.5, 10.5)\": 0.02243, \"(10.5, 11.5)\": 0.0283, \"(11.5, 12.5)\": 0.03438, \"(12.5, 13.5)\": 0.03775, \"(13.5, 15.0)\": 0.03258}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02487, \"(1.5, 2.5)\": -0.0148, \"(2.5, 3.5)\": -0.00987, \"(3.5, 4.5)\": -0.00529, \"(4.5, 5.5)\": 0.00116, \"(5.5, 6.5)\": 0.00643, \"(6.5, 7.5)\": 0.01205, \"(7.5, 8.5)\": 0.01648, \"(8.5, 10.5)\": 0.02483, \"(10.5, 11.5)\": 0.03246, \"(11.5, 12.5)\": 0.03776, \"(12.5, 13.5)\": 0.044, \"(13.5, 15.0)\": 0.05697}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "1.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: fractal_dimension_se\nFeature Type: continuous\nMeans: {\"(0.0008948, 0.001092)\": 0.2818, \"(0.001092, 0.0014135)\": 0.3286, \"(0.0014135, 0.0015165)\": 0.2713, \"(0.0015165, 0.0017545)\": 0.2283, \"(0.0017545, 0.0017905)\": 0.144, \"(0.0017905, 0.0019039999999999999)\": 0.0956, \"(0.0019039999999999999, 0.0021525)\": 0.0526, \"(0.0021525, 0.002572)\": 0.0073, \"(0.002572, 0.002761)\": 0.1543, \"(0.002761, 0.003308)\": 0.1971, \"(0.003308, 0.0033604999999999998)\": 0.1525, \"(0.0033604999999999998, 0.0035329999999999997)\": 0.1049, \"(0.0035329999999999997, 0.003736)\": 0.0586, \"(0.003736, 0.003907)\": 0.0157, \"(0.003907, 0.004092500000000001)\": -0.029, \"(0.004092500000000001, 0.0045775)\": -0.0717, \"(0.0045775, 0.0045935)\": -0.1177, \"(0.0045935, 0.004644499999999999)\": -0.1739, \"(0.004644499999999999, 0.004809)\": -0.2208, \"(0.004809, 0.005856500000000001)\": -0.2666, \"(0.005856500000000001, 0.007497500000000001)\": -0.31, \"(0.007497500000000001, 0.009717)\": -0.356, \"(0.009717, 0.0127)\": -0.4, \"(0.0127, 0.02984)\": -0.4439}\nLower Bounds (95%-Confidence Interval): {\"(0.0008948, 0.001092)\": 0.0382, \"(0.001092, 0.0014135)\": 0.0989, \"(0.0014135, 0.0015165)\": 0.1004, \"(0.0015165, 0.0017545)\": 0.0661, \"(0.0017545, 0.0017905)\": -0.1939, \"(0.0017905, 0.0019039999999999999)\": -0.2485, \"(0.0019039999999999999, 0.0021525)\": -0.2947, \"(0.0021525, 0.002572)\": -0.3301, \"(0.002572, 0.002761)\": -0.1655, \"(0.002761, 0.003308)\": -0.1517, \"(0.003308, 0.0033604999999999998)\": -0.1674, \"(0.0033604999999999998, 0.0035329999999999997)\": -0.1413, \"(0.0035329999999999997, 0.003736)\": -0.1763, \"(0.003736, 0.003907)\": -0.2066, \"(0.003907, 0.004092500000000001)\": -0.2479, \"(0.004092500000000001, 0.0045775)\": -0.2863, \"(0.0045775, 0.0045935)\": -0.3224, \"(0.0045935, 0.004644499999999999)\": -0.372, \"(0.004644499999999999, 0.004809)\": -0.418, \"(0.004809, 0.005856500000000001)\": -0.4726, \"(0.005856500000000001, 0.007497500000000001)\": -0.5133, \"(0.007497500000000001, 0.009717)\": -0.5704, \"(0.009717, 0.0127)\": -0.6199, \"(0.0127, 0.02984)\": -0.6593}\nUpper Bounds (95%-Confidence Interval): {\"(0.0008948, 0.001092)\": 0.5254, \"(0.001092, 0.0014135)\": 0.5584, \"(0.0014135, 0.0015165)\": 0.4422, \"(0.0015165, 0.0017545)\": 0.3904, \"(0.0017545, 0.0017905)\": 0.482, \"(0.0017905, 0.0019039999999999999)\": 0.4397, \"(0.0019039999999999999, 0.0021525)\": 0.3999, \"(0.0021525, 0.002572)\": 0.3448, \"(0.002572, 0.002761)\": 0.4742, \"(0.002761, 0.003308)\": 0.5459, \"(0.003308, 0.0033604999999999998)\": 0.4723, \"(0.0033604999999999998, 0.0035329999999999997)\": 0.3511, \"(0.0035329999999999997, 0.003736)\": 0.2936, \"(0.003736, 0.003907)\": 0.238, \"(0.003907, 0.004092500000000001)\": 0.1899, \"(0.004092500000000001, 0.0045775)\": 0.1429, \"(0.0045775, 0.0045935)\": 0.087, \"(0.0045935, 0.004644499999999999)\": 0.0243, \"(0.004644499999999999, 0.004809)\": -0.0237, \"(0.004809, 0.005856500000000001)\": -0.0605, \"(0.005856500000000001, 0.007497500000000001)\": -0.1068, \"(0.007497500000000001, 0.009717)\": -0.1417, \"(0.009717, 0.0127)\": -0.1801, \"(0.0127, 0.02984)\": -0.2284}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "0.002572"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: symmetry_worst\nFeature Type: continuous\nMeans: {\"(0.1565, 0.165)\": -0.295, \"(0.165, 0.19055)\": -0.472, \"(0.19055, 0.24485)\": -0.549, \"(0.24485, 0.25225)\": -0.469, \"(0.25225, 0.2583)\": -0.392, \"(0.2583, 0.26635)\": -0.31, \"(0.26635, 0.26959999999999995)\": -0.23, \"(0.26959999999999995, 0.27495)\": -0.112, \"(0.27495, 0.28035)\": -0.034, \"(0.28035, 0.28815)\": 0.046, \"(0.28815, 0.2986)\": 0.125, \"(0.2986, 0.31745)\": 0.202, \"(0.31745, 0.32125000000000004)\": 0.281, \"(0.32125000000000004, 0.33065)\": 0.363, \"(0.33065, 0.35335)\": 0.444, \"(0.35335, 0.36085)\": 0.526, \"(0.36085, 0.3702)\": 0.624, \"(0.3702, 0.4223)\": 0.705, \"(0.4223, 0.4697)\": 0.785, \"(0.4697, 0.6638)\": 0.867}\nLower Bounds (95%-Confidence Interval): {\"(0.1565, 0.165)\": -0.839, \"(0.165, 0.19055)\": -0.743, \"(0.19055, 0.24485)\": -0.802, \"(0.24485, 0.25225)\": -0.663, \"(0.25225, 0.2583)\": -0.595, \"(0.2583, 0.26635)\": -0.479, \"(0.26635, 0.26959999999999995)\": -0.388, \"(0.26959999999999995, 0.27495)\": -0.253, \"(0.27495, 0.28035)\": -0.172, \"(0.28035, 0.28815)\": -0.1, \"(0.28815, 0.2986)\": -0.015, \"(0.2986, 0.31745)\": 0.043, \"(0.31745, 0.32125000000000004)\": 0.141, \"(0.32125000000000004, 0.33065)\": 0.21, \"(0.33065, 0.35335)\": 0.276, \"(0.35335, 0.36085)\": 0.357, \"(0.36085, 0.3702)\": 0.348, \"(0.3702, 0.4223)\": 0.395, \"(0.4223, 0.4697)\": 0.478, \"(0.4697, 0.6638)\": 0.538}\nUpper Bounds (95%-Confidence Interval): {\"(0.1565, 0.165)\": 0.249, \"(0.165, 0.19055)\": -0.201, \"(0.19055, 0.24485)\": -0.296, \"(0.24485, 0.25225)\": -0.276, \"(0.25225, 0.2583)\": -0.188, \"(0.2583, 0.26635)\": -0.14, \"(0.26635, 0.26959999999999995)\": -0.072, \"(0.26959999999999995, 0.27495)\": 0.03, \"(0.27495, 0.28035)\": 0.104, \"(0.28035, 0.28815)\": 0.191, \"(0.28815, 0.2986)\": 0.264, \"(0.2986, 0.31745)\": 0.361, \"(0.31745, 0.32125000000000004)\": 0.42, \"(0.32125000000000004, 0.33065)\": 0.516, \"(0.33065, 0.35335)\": 0.612, \"(0.35335, 0.36085)\": 0.694, \"(0.36085, 0.3702)\": 0.901, \"(0.3702, 0.4223)\": 1.015, \"(0.4223, 0.4697)\": 1.092, \"(0.4697, 0.6638)\": 1.197}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "0.165"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: longitude\nFeature Type: continuous\nMeans: {\"(-124.35, -124.10499999999999)\": -50430.1, \"(-124.10499999999999, -124.08500000000001)\": -38925.6, \"(-124.08500000000001, -124.07499999999999)\": -23742.3, \"(-124.07499999999999, -123.3)\": -12526.0, \"(-123.3, -122.955)\": -1690.2, \"(-122.955, -122.66499999999999)\": 19040.8, \"(-122.66499999999999, -122.60499999999999)\": 29856.3, \"(-122.60499999999999, -122.58500000000001)\": 44315.6, \"(-122.58500000000001, -122.555)\": 75515.2, \"(-122.555, -122.455)\": 86444.1, \"(-122.455, -122.445)\": 99533.8, \"(-122.445, -122.42500000000001)\": 112351.5, \"(-122.42500000000001, -122.405)\": 89733.4, \"(-122.405, -122.39500000000001)\": 78586.0, \"(-122.39500000000001, -122.375)\": 46429.6, \"(-122.375, -122.36500000000001)\": 35622.6, \"(-122.36500000000001, -122.305)\": 20538.8, \"(-122.305, -122.155)\": 6386.6, \"(-122.155, -120.92500000000001)\": 24722.9, \"(-120.92500000000001, -120.91499999999999)\": 54457.6, \"(-120.91499999999999, -120.89500000000001)\": 34017.2, \"(-120.89500000000001, -120.86500000000001)\": 18216.5, \"(-120.86500000000001, -120.725)\": 6143.7, \"(-120.725, -120.63499999999999)\": 17429.8, \"(-120.63499999999999, -120.485)\": 407.5, \"(-120.485, -120.405)\": -15764.1, \"(-120.405, -120.10499999999999)\": 1041.6, \"(-120.10499999999999, -120.095)\": 24030.2, \"(-120.095, -119.91499999999999)\": -2161.3, \"(-119.91499999999999, -119.85499999999999)\": -20610.8, \"(-119.85499999999999, -119.795)\": -31705.4, \"(-119.795, -119.755)\": -20112.3, \"(-119.755, -119.525)\": -3774.8, \"(-119.525, -119.505)\": 10442.2, \"(-119.505, -119.295)\": -10555.7, \"(-119.295, -119.215)\": 3582.5, \"(-119.215, -118.905)\": -15819.3, \"(-118.905, -118.695)\": -2790.4, \"(-118.695, -118.57499999999999)\": 13581.3, \"(-118.57499999999999, -118.525)\": 26358.2, \"(-118.525, -118.495)\": 44919.9, \"(-118.495, -118.375)\": 60453.4, \"(-118.375, -118.35499999999999)\": 38572.6, \"(-118.35499999999999, -118.305)\": 21183.4, \"(-118.305, -118.265)\": -6755.4, \"(-118.265, -118.14500000000001)\": -17830.0, \"(-118.14500000000001, -117.985)\": -7071.0, \"(-117.985, -117.755)\": -26435.2, \"(-117.755, -117.725)\": -50667.3, \"(-117.725, -117.64500000000001)\": -63305.2, \"(-117.64500000000001, -117.57499999999999)\": -50999.4, \"(-117.57499999999999, -117.35499999999999)\": -38880.5, \"(-117.35499999999999, -117.285)\": -64800.9, \"(-117.285, -117.155)\": -47182.2, \"(-117.155, -117.13499999999999)\": -65749.6, \"(-117.13499999999999, -116.995)\": -77340.8, \"(-116.995, -116.795)\": -64524.7, \"(-116.795, -116.205)\": -53643.1, \"(-116.205, -116.1)\": -64388.1, \"(-116.1, -115.525)\": -75181.7, \"(-115.525, -115.1)\": -57014.4, \"(-115.1, -114.595)\": -74654.1, \"(-114.595, -114.31)\": -100620.1}\nLower Bounds (95%-Confidence Interval): {\"(-124.35, -124.10499999999999)\": -57749.3, \"(-124.10499999999999, -124.08500000000001)\": -46703.5, \"(-124.08500000000001, -124.07499999999999)\": -34644.5, \"(-124.07499999999999, -123.3)\": -20263.6, \"(-123.3, -122.955)\": -10198.1, \"(-122.955, -122.66499999999999)\": 10561.3, \"(-122.66499999999999, -122.60499999999999)\": 24254.2, \"(-122.60499999999999, -122.58500000000001)\": 34301.3, \"(-122.58500000000001, -122.555)\": 63992.2, \"(-122.555, -122.455)\": 76785.0, \"(-122.455, -122.445)\": 91169.4, \"(-122.445, -122.42500000000001)\": 103719.6, \"(-122.42500000000001, -122.405)\": 81277.9, \"(-122.405, -122.39500000000001)\": 66955.5, \"(-122.39500000000001, -122.375)\": 35631.1, \"(-122.375, -122.36500000000001)\": 24396.3, \"(-122.36500000000001, -122.305)\": 14520.6, \"(-122.305, -122.155)\": -1221.8, \"(-122.155, -120.92500000000001)\": 11630.1, \"(-120.92500000000001, -120.91499999999999)\": 11031.4, \"(-120.91499999999999, -120.89500000000001)\": 19105.3, \"(-120.89500000000001, -120.86500000000001)\": 4469.7, \"(-120.86500000000001, -120.725)\": -1198.1, \"(-120.725, -120.63499999999999)\": 7919.4, \"(-120.63499999999999, -120.485)\": -11276.9, \"(-120.485, -120.405)\": -23035.0, \"(-120.405, -120.10499999999999)\": -4074.0, \"(-120.10499999999999, -120.095)\": -10802.7, \"(-120.095, -119.91499999999999)\": -13216.2, \"(-119.91499999999999, -119.85499999999999)\": -33171.4, \"(-119.85499999999999, -119.795)\": -38315.5, \"(-119.795, -119.755)\": -24784.5, \"(-119.755, -119.525)\": -13160.2, \"(-119.525, -119.505)\": -4048.0, \"(-119.505, -119.295)\": -18789.9, \"(-119.295, -119.215)\": -8493.9, \"(-119.215, -118.905)\": -20485.4, \"(-118.905, -118.695)\": -7647.9, \"(-118.695, -118.57499999999999)\": 4745.1, \"(-118.57499999999999, -118.525)\": 17156.2, \"(-118.525, -118.495)\": 33913.3, \"(-118.495, -118.375)\": 52480.8, \"(-118.375, -118.35499999999999)\": 34068.9, \"(-118.35499999999999, -118.305)\": 14693.0, \"(-118.305, -118.265)\": -11878.1, \"(-118.265, -118.14500000000001)\": -21370.7, \"(-118.14500000000001, -117.985)\": -11803.1, \"(-117.985, -117.755)\": -35281.9, \"(-117.755, -117.725)\": -58041.5, \"(-117.725, -117.64500000000001)\": -72526.8, \"(-117.64500000000001, -117.57499999999999)\": -61627.3, \"(-117.57499999999999, -117.35499999999999)\": -45444.2, \"(-117.35499999999999, -117.285)\": -74287.3, \"(-117.285, -117.155)\": -55258.2, \"(-117.155, -117.13499999999999)\": -74456.9, \"(-117.13499999999999, -116.995)\": -86582.5, \"(-116.995, -116.795)\": -73433.2, \"(-116.795, -116.205)\": -69635.5, \"(-116.205, -116.1)\": -75131.9, \"(-116.1, -115.525)\": -97151.1, \"(-115.525, -115.1)\": -73988.5, \"(-115.1, -114.595)\": -91086.2, \"(-114.595, -114.31)\": -120109.7}\nUpper Bounds (95%-Confidence Interval): {\"(-124.35, -124.10499999999999)\": -43110.8, \"(-124.10499999999999, -124.08500000000001)\": -31147.7, \"(-124.08500000000001, -124.07499999999999)\": -12840.0, \"(-124.07499999999999, -123.3)\": -4788.4, \"(-123.3, -122.955)\": 6817.8, \"(-122.955, -122.66499999999999)\": 27520.3, \"(-122.66499999999999, -122.60499999999999)\": 35458.4, \"(-122.60499999999999, -122.58500000000001)\": 54329.8, \"(-122.58500000000001, -122.555)\": 87038.2, \"(-122.555, -122.455)\": 96103.2, \"(-122.455, -122.445)\": 107898.1, \"(-122.445, -122.42500000000001)\": 120983.4, \"(-122.42500000000001, -122.405)\": 98188.9, \"(-122.405, -122.39500000000001)\": 90216.5, \"(-122.39500000000001, -122.375)\": 57228.1, \"(-122.375, -122.36500000000001)\": 46849.0, \"(-122.36500000000001, -122.305)\": 26556.9, \"(-122.305, -122.155)\": 13995.0, \"(-122.155, -120.92500000000001)\": 37815.7, \"(-120.92500000000001, -120.91499999999999)\": 97883.7, \"(-120.91499999999999, -120.89500000000001)\": 48929.0, \"(-120.89500000000001, -120.86500000000001)\": 31963.3, \"(-120.86500000000001, -120.725)\": 13485.5, \"(-120.725, -120.63499999999999)\": 26940.3, \"(-120.63499999999999, -120.485)\": 12092.0, \"(-120.485, -120.405)\": -8493.1, \"(-120.405, -120.10499999999999)\": 6157.2, \"(-120.10499999999999, -120.095)\": 58863.0, \"(-120.095, -119.91499999999999)\": 8893.5, \"(-119.91499999999999, -119.85499999999999)\": -8050.3, \"(-119.85499999999999, -119.795)\": -25095.3, \"(-119.795, -119.755)\": -15440.1, \"(-119.755, -119.525)\": 5610.6, \"(-119.525, -119.505)\": 24932.3, \"(-119.505, -119.295)\": -2321.4, \"(-119.295, -119.215)\": 15659.0, \"(-119.215, -118.905)\": -11153.1, \"(-118.905, -118.695)\": 2067.1, \"(-118.695, -118.57499999999999)\": 22417.4, \"(-118.57499999999999, -118.525)\": 35560.1, \"(-118.525, -118.495)\": 55926.4, \"(-118.495, -118.375)\": 68426.1, \"(-118.375, -118.35499999999999)\": 43076.4, \"(-118.35499999999999, -118.305)\": 27673.7, \"(-118.305, -118.265)\": -1632.7, \"(-118.265, -118.14500000000001)\": -14289.3, \"(-118.14500000000001, -117.985)\": -2338.9, \"(-117.985, -117.755)\": -17588.5, \"(-117.755, -117.725)\": -43293.2, \"(-117.725, -117.64500000000001)\": -54083.7, \"(-117.64500000000001, -117.57499999999999)\": -40371.5, \"(-117.57499999999999, -117.35499999999999)\": -32316.9, \"(-117.35499999999999, -117.285)\": -55314.6, \"(-117.285, -117.155)\": -39106.3, \"(-117.155, -117.13499999999999)\": -57042.4, \"(-117.13499999999999, -116.995)\": -68099.0, \"(-116.995, -116.795)\": -55616.1, \"(-116.795, -116.205)\": -37650.7, \"(-116.205, -116.1)\": -53644.2, \"(-116.1, -115.525)\": -53212.4, \"(-115.525, -115.1)\": -40040.3, \"(-115.1, -114.595)\": -58221.9, \"(-114.595, -114.31)\": -81130.6}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "-122.39500000000001"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: serum_creatinine\nFeature Type: continuous\nMeans: {\"(0.5, 0.6499999999999999)\": -0.26, \"(0.6499999999999999, 0.725)\": -1.08, \"(0.725, 0.875)\": -3.77, \"(0.875, 0.95)\": -0.9, \"(0.95, 1.1400000000000001)\": -0.15, \"(1.1400000000000001, 1.35)\": -0.88, \"(1.35, 1.45)\": 0.2, \"(1.45, 1.55)\": 1.18, \"(1.55, 1.815)\": 2.18, \"(1.815, 2.05)\": 4.74, \"(2.05, 2.45)\": 1.14, \"(2.45, 2.6)\": 3.63, \"(2.6, 2.95)\": -0.36, \"(2.95, 3.1)\": 2.57, \"(3.1, 3.45)\": 0.36, \"(3.45, 3.6)\": 3.06, \"(3.6, 3.75)\": 6.76, \"(3.75, 3.9)\": 2.31, \"(3.9, 4.7)\": 2.92, \"(4.7, 5.949999999999999)\": 0.76, \"(5.949999999999999, 6.199999999999999)\": -0.43, \"(6.199999999999999, 6.55)\": 0.23, \"(6.55, 9.4)\": 6.97}\nLower Bounds (95%-Confidence Interval): {\"(0.5, 0.6499999999999999)\": -1.13, \"(0.6499999999999999, 0.725)\": -1.45, \"(0.725, 0.875)\": -5.7, \"(0.875, 0.95)\": -1.31, \"(0.95, 1.1400000000000001)\": -0.41, \"(1.1400000000000001, 1.35)\": -1.92, \"(1.35, 1.45)\": -0.14, \"(1.45, 1.55)\": 0.46, \"(1.55, 1.815)\": 1.68, \"(1.815, 2.05)\": 2.75, \"(2.05, 2.45)\": 0.72, \"(2.45, 2.6)\": 1.94, \"(2.6, 2.95)\": -2.5, \"(2.95, 3.1)\": 0.3, \"(3.1, 3.45)\": -0.49, \"(3.45, 3.6)\": 1.58, \"(3.6, 3.75)\": 4.55, \"(3.75, 3.9)\": 0.4, \"(3.9, 4.7)\": 0.8, \"(4.7, 5.949999999999999)\": -0.63, \"(5.949999999999999, 6.199999999999999)\": -1.75, \"(6.199999999999999, 6.55)\": -2.74, \"(6.55, 9.4)\": 5.07}\nUpper Bounds (95%-Confidence Interval): {\"(0.5, 0.6499999999999999)\": 0.62, \"(0.6499999999999999, 0.725)\": -0.72, \"(0.725, 0.875)\": -1.84, \"(0.875, 0.95)\": -0.48, \"(0.95, 1.1400000000000001)\": 0.12, \"(1.1400000000000001, 1.35)\": 0.16, \"(1.35, 1.45)\": 0.53, \"(1.45, 1.55)\": 1.89, \"(1.55, 1.815)\": 2.68, \"(1.815, 2.05)\": 6.73, \"(2.05, 2.45)\": 1.56, \"(2.45, 2.6)\": 5.32, \"(2.6, 2.95)\": 1.77, \"(2.95, 3.1)\": 4.84, \"(3.1, 3.45)\": 1.2, \"(3.45, 3.6)\": 4.53, \"(3.6, 3.75)\": 8.97, \"(3.75, 3.9)\": 4.21, \"(3.9, 4.7)\": 5.04, \"(4.7, 5.949999999999999)\": 2.14, \"(5.949999999999999, 6.199999999999999)\": 0.9, \"(6.199999999999999, 6.55)\": 3.21, \"(6.55, 9.4)\": 8.88}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "6.55"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Landslides\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02593, \"(0.5, 1.5)\": -0.02172, \"(1.5, 2.5)\": -0.01544, \"(2.5, 3.5)\": -0.0098, \"(3.5, 4.5)\": -0.00541, \"(4.5, 5.5)\": 0.00066, \"(5.5, 6.5)\": 0.00575, \"(6.5, 7.5)\": 0.01201, \"(7.5, 8.5)\": 0.01649, \"(8.5, 9.5)\": 0.0215, \"(9.5, 10.5)\": 0.0267, \"(10.5, 11.5)\": 0.03057, \"(11.5, 13.5)\": 0.0366, \"(13.5, 14.0)\": 0.03003}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02743, \"(0.5, 1.5)\": -0.02261, \"(1.5, 2.5)\": -0.01616, \"(2.5, 3.5)\": -0.0102, \"(3.5, 4.5)\": -0.00579, \"(4.5, 5.5)\": 0.00027, \"(5.5, 6.5)\": 0.00544, \"(6.5, 7.5)\": 0.01146, \"(7.5, 8.5)\": 0.01601, \"(8.5, 9.5)\": 0.02065, \"(9.5, 10.5)\": 0.02512, \"(10.5, 11.5)\": 0.0285, \"(11.5, 13.5)\": 0.02931, \"(13.5, 14.0)\": 0.02233}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02443, \"(0.5, 1.5)\": -0.02083, \"(1.5, 2.5)\": -0.01472, \"(2.5, 3.5)\": -0.0094, \"(3.5, 4.5)\": -0.00504, \"(4.5, 5.5)\": 0.00105, \"(5.5, 6.5)\": 0.00606, \"(6.5, 7.5)\": 0.01257, \"(7.5, 8.5)\": 0.01698, \"(8.5, 9.5)\": 0.02234, \"(9.5, 10.5)\": 0.02828, \"(10.5, 11.5)\": 0.03265, \"(11.5, 13.5)\": 0.04389, \"(13.5, 14.0)\": 0.03772}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "13.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: perimeter_mean\nFeature Type: continuous\nMeans: {\"(43.79, 60.035)\": -0.884, \"(60.035, 63.379999999999995)\": -0.783, \"(63.379999999999995, 66.67)\": -0.681, \"(66.67, 68.965)\": -0.581, \"(68.965, 71.275)\": -0.476, \"(71.275, 78.28)\": -0.369, \"(78.28, 84.015)\": -0.267, \"(84.015, 88.70500000000001)\": -0.166, \"(88.70500000000001, 94.68)\": -0.064, \"(94.68, 100.75)\": 0.035, \"(100.75, 106.75)\": 0.14, \"(106.75, 108.6)\": 0.249, \"(108.6, 112.6)\": 0.407, \"(112.6, 117.45)\": 0.518, \"(117.45, 121.7)\": 0.626, \"(121.7, 128.15)\": 0.73, \"(128.15, 133.25)\": 0.835, \"(133.25, 145.85000000000002)\": 0.936, \"(145.85000000000002, 188.5)\": 1.038}\nLower Bounds (95%-Confidence Interval): {\"(43.79, 60.035)\": -1.177, \"(60.035, 63.379999999999995)\": -1.04, \"(63.379999999999995, 66.67)\": -0.892, \"(66.67, 68.965)\": -0.75, \"(68.965, 71.275)\": -0.646, \"(71.275, 78.28)\": -0.532, \"(78.28, 84.015)\": -0.417, \"(84.015, 88.70500000000001)\": -0.316, \"(88.70500000000001, 94.68)\": -0.178, \"(94.68, 100.75)\": -0.201, \"(100.75, 106.75)\": -0.091, \"(106.75, 108.6)\": -0.055, \"(108.6, 112.6)\": 0.049, \"(112.6, 117.45)\": 0.15, \"(117.45, 121.7)\": 0.222, \"(121.7, 128.15)\": 0.282, \"(128.15, 133.25)\": 0.343, \"(133.25, 145.85000000000002)\": 0.427, \"(145.85000000000002, 188.5)\": 0.514}\nUpper Bounds (95%-Confidence Interval): {\"(43.79, 60.035)\": -0.59, \"(60.035, 63.379999999999995)\": -0.526, \"(63.379999999999995, 66.67)\": -0.471, \"(66.67, 68.965)\": -0.411, \"(68.965, 71.275)\": -0.306, \"(71.275, 78.28)\": -0.206, \"(78.28, 84.015)\": -0.118, \"(84.015, 88.70500000000001)\": -0.017, \"(88.70500000000001, 94.68)\": 0.05, \"(94.68, 100.75)\": 0.271, \"(100.75, 106.75)\": 0.371, \"(106.75, 108.6)\": 0.553, \"(108.6, 112.6)\": 0.766, \"(112.6, 117.45)\": 0.887, \"(117.45, 121.7)\": 1.03, \"(121.7, 128.15)\": 1.179, \"(128.15, 133.25)\": 1.327, \"(133.25, 145.85000000000002)\": 1.444, \"(145.85000000000002, 188.5)\": 1.562}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "108.6"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: RiverManagement\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.0273, \"(0.5, 1.5)\": -0.02345, \"(1.5, 2.5)\": -0.01571, \"(2.5, 3.5)\": -0.01174, \"(3.5, 4.5)\": -0.00519, \"(4.5, 5.5)\": 0.00111, \"(5.5, 6.5)\": 0.00506, \"(6.5, 7.5)\": 0.01056, \"(7.5, 8.5)\": 0.01706, \"(8.5, 9.5)\": 0.02398, \"(9.5, 11.5)\": 0.02821, \"(11.5, 12.5)\": 0.03673, \"(12.5, 13.5)\": 0.01311, \"(13.5, 16.0)\": 0.03206}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02945, \"(0.5, 1.5)\": -0.02501, \"(1.5, 2.5)\": -0.01619, \"(2.5, 3.5)\": -0.0121, \"(3.5, 4.5)\": -0.00549, \"(4.5, 5.5)\": 0.00069, \"(5.5, 6.5)\": 0.00469, \"(6.5, 7.5)\": 0.00991, \"(7.5, 8.5)\": 0.01638, \"(8.5, 9.5)\": 0.02306, \"(9.5, 11.5)\": 0.0266, \"(11.5, 12.5)\": 0.02982, \"(12.5, 13.5)\": -0.01689, \"(13.5, 16.0)\": 0.01715}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02515, \"(0.5, 1.5)\": -0.0219, \"(1.5, 2.5)\": -0.01524, \"(2.5, 3.5)\": -0.01139, \"(3.5, 4.5)\": -0.0049, \"(4.5, 5.5)\": 0.00152, \"(5.5, 6.5)\": 0.00544, \"(6.5, 7.5)\": 0.01121, \"(7.5, 8.5)\": 0.01774, \"(8.5, 9.5)\": 0.0249, \"(9.5, 11.5)\": 0.02981, \"(11.5, 12.5)\": 0.04363, \"(12.5, 13.5)\": 0.04312, \"(13.5, 16.0)\": 0.04696}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "12.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: median_income\nFeature Type: continuous\nMeans: {\"(0.4999, 0.5427500000000001)\": -16067.6, \"(0.5427500000000001, 1.4808)\": -55539.5, \"(1.4808, 2.1658999999999997)\": -71376.5, \"(2.1658999999999997, 2.6096)\": -56399.7, \"(2.6096, 3.2433)\": -40762.6, \"(3.2433, 3.66575)\": -25586.1, \"(3.66575, 4.3197)\": -8084.4, \"(4.3197, 4.691000000000001)\": 7391.3, \"(4.691000000000001, 5.1358)\": 22375.3, \"(5.1358, 5.59195)\": 40032.8, \"(5.59195, 5.8294)\": 56900.2, \"(5.8294, 6.29665)\": 75092.3, \"(6.29665, 6.3704)\": 96400.5, \"(6.3704, 6.874750000000001)\": 111491.7, \"(6.874750000000001, 7.6996)\": 135841.6, \"(7.6996, 7.8141)\": 151586.9, \"(7.8141, 8.3976)\": 170219.6, \"(8.3976, 9.046949999999999)\": 192482.3, \"(9.046949999999999, 15.00005)\": 214375.9, \"(15.00005, 15.0001)\": 193753.6}\nLower Bounds (95%-Confidence Interval): {\"(0.4999, 0.5427500000000001)\": -48216.1, \"(0.5427500000000001, 1.4808)\": -68098.8, \"(1.4808, 2.1658999999999997)\": -81907.3, \"(2.1658999999999997, 2.6096)\": -60824.9, \"(2.6096, 3.2433)\": -49299.1, \"(3.2433, 3.66575)\": -32546.2, \"(3.66575, 4.3197)\": -17048.9, \"(4.3197, 4.691000000000001)\": 1621.1, \"(4.691000000000001, 5.1358)\": 13670.4, \"(5.1358, 5.59195)\": 33628.4, \"(5.59195, 5.8294)\": 48173.8, \"(5.8294, 6.29665)\": 69358.1, \"(6.29665, 6.3704)\": 88897.2, \"(6.3704, 6.874750000000001)\": 105607.5, \"(6.874750000000001, 7.6996)\": 129446.9, \"(7.6996, 7.8141)\": 139775.0, \"(7.8141, 8.3976)\": 162646.8, \"(8.3976, 9.046949999999999)\": 184114.0, \"(9.046949999999999, 15.00005)\": 203670.8, \"(15.00005, 15.0001)\": 178950.1}\nUpper Bounds (95%-Confidence Interval): {\"(0.4999, 0.5427500000000001)\": 16080.9, \"(0.5427500000000001, 1.4808)\": -42980.1, \"(1.4808, 2.1658999999999997)\": -60845.8, \"(2.1658999999999997, 2.6096)\": -51974.6, \"(2.6096, 3.2433)\": -32226.2, \"(3.2433, 3.66575)\": -18626.1, \"(3.66575, 4.3197)\": 880.1, \"(4.3197, 4.691000000000001)\": 13161.4, \"(4.691000000000001, 5.1358)\": 31080.1, \"(5.1358, 5.59195)\": 46437.2, \"(5.59195, 5.8294)\": 65626.7, \"(5.8294, 6.29665)\": 80826.5, \"(6.29665, 6.3704)\": 103903.7, \"(6.3704, 6.874750000000001)\": 117376.0, \"(6.874750000000001, 7.6996)\": 142236.4, \"(7.6996, 7.8141)\": 163398.8, \"(7.8141, 8.3976)\": 177792.4, \"(8.3976, 9.046949999999999)\": 200850.6, \"(9.046949999999999, 15.00005)\": 225081.0, \"(15.00005, 15.0001)\": 208557.1}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "0.5427500000000001"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: diabetes\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": 0.3225, \"(0.5, 1.0)\": -0.415}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.1807, \"(0.5, 1.0)\": -0.5976}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.4643, \"(0.5, 1.0)\": -0.2325}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "0.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: concavity_mean\nFeature Type: continuous\nMeans: {\"(0.0, 0.005855)\": -0.897, \"(0.005855, 0.011885)\": -0.811, \"(0.011885, 0.016545)\": -0.719, \"(0.016545, 0.02046)\": -0.631, \"(0.02046, 0.02373)\": -0.543, \"(0.02373, 0.02711)\": -0.458, \"(0.02711, 0.038885)\": -0.374, \"(0.038885, 0.044705)\": -0.29, \"(0.044705, 0.059585)\": -0.205, \"(0.059585, 0.06851)\": -0.121, \"(0.06851, 0.072265)\": -0.032, \"(0.072265, 0.092725)\": 0.14, \"(0.092725, 0.1015)\": 0.224, \"(0.1015, 0.11415)\": 0.309, \"(0.11415, 0.13)\": 0.397, \"(0.13, 0.14534999999999998)\": 0.486, \"(0.14534999999999998, 0.1525)\": 0.581, \"(0.1525, 0.1686)\": 0.665, \"(0.1686, 0.24280000000000002)\": 0.749, \"(0.24280000000000002, 0.29359999999999997)\": 0.657, \"(0.29359999999999997, 0.32699999999999996)\": 0.566, \"(0.32699999999999996, 0.4268)\": 0.48}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.005855)\": -1.183, \"(0.005855, 0.011885)\": -1.062, \"(0.011885, 0.016545)\": -0.961, \"(0.016545, 0.02046)\": -0.861, \"(0.02046, 0.02373)\": -0.749, \"(0.02373, 0.02711)\": -0.665, \"(0.02711, 0.038885)\": -0.545, \"(0.038885, 0.044705)\": -0.442, \"(0.044705, 0.059585)\": -0.43, \"(0.059585, 0.06851)\": -0.344, \"(0.06851, 0.072265)\": -0.246, \"(0.072265, 0.092725)\": -0.128, \"(0.092725, 0.1015)\": 0.093, \"(0.1015, 0.11415)\": 0.166, \"(0.11415, 0.13)\": 0.205, \"(0.13, 0.14534999999999998)\": 0.246, \"(0.14534999999999998, 0.1525)\": 0.264, \"(0.1525, 0.1686)\": 0.346, \"(0.1686, 0.24280000000000002)\": 0.435, \"(0.24280000000000002, 0.29359999999999997)\": 0.402, \"(0.29359999999999997, 0.32699999999999996)\": 0.316, \"(0.32699999999999996, 0.4268)\": 0.208}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.005855)\": -0.612, \"(0.005855, 0.011885)\": -0.559, \"(0.011885, 0.016545)\": -0.477, \"(0.016545, 0.02046)\": -0.4, \"(0.02046, 0.02373)\": -0.338, \"(0.02373, 0.02711)\": -0.252, \"(0.02711, 0.038885)\": -0.203, \"(0.038885, 0.044705)\": -0.138, \"(0.044705, 0.059585)\": 0.021, \"(0.059585, 0.06851)\": 0.103, \"(0.06851, 0.072265)\": 0.183, \"(0.072265, 0.092725)\": 0.409, \"(0.092725, 0.1015)\": 0.355, \"(0.1015, 0.11415)\": 0.452, \"(0.11415, 0.13)\": 0.589, \"(0.13, 0.14534999999999998)\": 0.726, \"(0.14534999999999998, 0.1525)\": 0.898, \"(0.1525, 0.1686)\": 0.984, \"(0.1686, 0.24280000000000002)\": 1.063, \"(0.24280000000000002, 0.29359999999999997)\": 0.912, \"(0.29359999999999997, 0.32699999999999996)\": 0.815, \"(0.32699999999999996, 0.4268)\": 0.752}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "0.072265"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: DrainageSystems\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02593, \"(0.5, 1.5)\": -0.02211, \"(1.5, 2.5)\": -0.01611, \"(2.5, 3.5)\": -0.01125, \"(3.5, 4.5)\": -0.0047, \"(4.5, 5.5)\": 9e-05, \"(5.5, 6.5)\": 0.00652, \"(6.5, 8.5)\": 0.01219, \"(8.5, 10.5)\": 0.02253, \"(10.5, 11.5)\": 0.03412, \"(11.5, 12.5)\": 0.04015, \"(12.5, 14.0)\": 0.04564}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02795, \"(0.5, 1.5)\": -0.02324, \"(1.5, 2.5)\": -0.01672, \"(2.5, 3.5)\": -0.01177, \"(3.5, 4.5)\": -0.00504, \"(4.5, 5.5)\": -0.00021, \"(5.5, 6.5)\": 0.00613, \"(6.5, 8.5)\": 0.01137, \"(8.5, 10.5)\": 0.02139, \"(10.5, 11.5)\": 0.03184, \"(11.5, 12.5)\": 0.03703, \"(12.5, 14.0)\": 0.04222}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02391, \"(0.5, 1.5)\": -0.02097, \"(1.5, 2.5)\": -0.0155, \"(2.5, 3.5)\": -0.01073, \"(3.5, 4.5)\": -0.00435, \"(4.5, 5.5)\": 0.00039, \"(5.5, 6.5)\": 0.00691, \"(6.5, 8.5)\": 0.01301, \"(8.5, 10.5)\": 0.02367, \"(10.5, 11.5)\": 0.0364, \"(11.5, 12.5)\": 0.04328, \"(12.5, 14.0)\": 0.04907}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "10.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Glucose\nFeature Type: continuous\nMeans: {\"(0.0, 22.0)\": -0.728, \"(22.0, 86.5)\": -1.069, \"(86.5, 94.5)\": -0.907, \"(94.5, 99.5)\": -0.729, \"(99.5, 105.5)\": -0.491, \"(105.5, 114.5)\": -0.326, \"(114.5, 123.5)\": -0.157, \"(123.5, 130.5)\": 0.045, \"(130.5, 139.5)\": 0.208, \"(139.5, 147.5)\": 0.37, \"(147.5, 154.5)\": 0.535, \"(154.5, 159.5)\": 0.724, \"(159.5, 165.5)\": 0.984, \"(165.5, 169.5)\": 1.342, \"(169.5, 178.5)\": 1.502, \"(178.5, 187.5)\": 1.691, \"(187.5, 198.5)\": 1.853, \"(198.5, 199.0)\": 2.022}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 22.0)\": -1.316, \"(22.0, 86.5)\": -1.535, \"(86.5, 94.5)\": -1.3, \"(94.5, 99.5)\": -1.042, \"(99.5, 105.5)\": -0.722, \"(105.5, 114.5)\": -0.428, \"(114.5, 123.5)\": -0.249, \"(123.5, 130.5)\": -0.151, \"(130.5, 139.5)\": 0.044, \"(139.5, 147.5)\": 0.215, \"(147.5, 154.5)\": 0.135, \"(154.5, 159.5)\": 0.451, \"(159.5, 165.5)\": 0.509, \"(165.5, 169.5)\": 0.633, \"(169.5, 178.5)\": 0.768, \"(178.5, 187.5)\": 0.987, \"(187.5, 198.5)\": 1.135, \"(198.5, 199.0)\": 1.3}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 22.0)\": -0.14, \"(22.0, 86.5)\": -0.602, \"(86.5, 94.5)\": -0.514, \"(94.5, 99.5)\": -0.417, \"(99.5, 105.5)\": -0.26, \"(105.5, 114.5)\": -0.223, \"(114.5, 123.5)\": -0.064, \"(123.5, 130.5)\": 0.242, \"(130.5, 139.5)\": 0.373, \"(139.5, 147.5)\": 0.525, \"(147.5, 154.5)\": 0.936, \"(154.5, 159.5)\": 0.997, \"(159.5, 165.5)\": 1.458, \"(165.5, 169.5)\": 2.051, \"(169.5, 178.5)\": 2.237, \"(178.5, 187.5)\": 2.394, \"(187.5, 198.5)\": 2.571, \"(198.5, 199.0)\": 2.744}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "165.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: WetlandLoss\nFeature Type: continuous\nMeans: {\"(0.0, 1.5)\": -0.02419, \"(1.5, 2.5)\": -0.01693, \"(2.5, 3.5)\": -0.01069, \"(3.5, 4.5)\": -0.00585, \"(4.5, 5.5)\": 0.00051, \"(5.5, 6.5)\": 0.00676, \"(6.5, 8.5)\": 0.01245, \"(8.5, 10.5)\": 0.02257, \"(10.5, 11.5)\": 0.03265, \"(11.5, 13.5)\": 0.03889, \"(13.5, 14.5)\": 0.04912, \"(14.5, 16.0)\": 0.0585}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02604, \"(1.5, 2.5)\": -0.01758, \"(2.5, 3.5)\": -0.01104, \"(3.5, 4.5)\": -0.00622, \"(4.5, 5.5)\": 0.00022, \"(5.5, 6.5)\": 0.0063, \"(6.5, 8.5)\": 0.01194, \"(8.5, 10.5)\": 0.0215, \"(10.5, 11.5)\": 0.03022, \"(11.5, 13.5)\": 0.03581, \"(13.5, 14.5)\": 0.04439, \"(14.5, 16.0)\": 0.04645}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02235, \"(1.5, 2.5)\": -0.01628, \"(2.5, 3.5)\": -0.01034, \"(3.5, 4.5)\": -0.00547, \"(4.5, 5.5)\": 0.0008, \"(5.5, 6.5)\": 0.00723, \"(6.5, 8.5)\": 0.01295, \"(8.5, 10.5)\": 0.02363, \"(10.5, 11.5)\": 0.03508, \"(11.5, 13.5)\": 0.04198, \"(13.5, 14.5)\": 0.05386, \"(14.5, 16.0)\": 0.07055}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "13.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: symmetry_mean\nFeature Type: continuous\nMeans: {\"(0.1167, 0.1384)\": -0.604, \"(0.1384, 0.14229999999999998)\": -0.55, \"(0.14229999999999998, 0.14565)\": -0.489, \"(0.14565, 0.1488)\": -0.428, \"(0.1488, 0.1507)\": -0.372, \"(0.1507, 0.15245)\": -0.316, \"(0.15245, 0.15375)\": -0.258, \"(0.15375, 0.15410000000000001)\": -0.087, \"(0.15410000000000001, 0.1545)\": -0.03, \"(0.1545, 0.15765)\": 0.279, \"(0.15765, 0.16625)\": 0.335, \"(0.16625, 0.16635)\": 0.258, \"(0.16635, 0.1684)\": 0.048, \"(0.1684, 0.17915)\": -0.007, \"(0.17915, 0.20355)\": -0.062, \"(0.20355, 0.20855)\": -0.005, \"(0.20855, 0.21105000000000002)\": 0.052, \"(0.21105000000000002, 0.21315)\": 0.107, \"(0.21315, 0.21705)\": 0.17, \"(0.21705, 0.22110000000000002)\": 0.234, \"(0.22110000000000002, 0.23020000000000002)\": 0.289, \"(0.23020000000000002, 0.2544)\": 0.347, \"(0.2544, 0.2626)\": 0.408, \"(0.2626, 0.304)\": 0.466}\nLower Bounds (95%-Confidence Interval): {\"(0.1167, 0.1384)\": -1.532, \"(0.1384, 0.14229999999999998)\": -1.437, \"(0.14229999999999998, 0.14565)\": -1.372, \"(0.14565, 0.1488)\": -1.313, \"(0.1488, 0.1507)\": -1.251, \"(0.1507, 0.15245)\": -1.195, \"(0.15245, 0.15375)\": -1.119, \"(0.15375, 0.15410000000000001)\": -0.701, \"(0.15410000000000001, 0.1545)\": -0.663, \"(0.1545, 0.15765)\": -0.664, \"(0.15765, 0.16625)\": -0.597, \"(0.16625, 0.16635)\": -0.613, \"(0.16635, 0.1684)\": -0.077, \"(0.1684, 0.17915)\": -0.11, \"(0.17915, 0.20355)\": -0.175, \"(0.20355, 0.20855)\": -0.112, \"(0.20855, 0.21105000000000002)\": -0.047, \"(0.21105000000000002, 0.21315)\": 0.003, \"(0.21315, 0.21705)\": 0.061, \"(0.21705, 0.22110000000000002)\": 0.118, \"(0.22110000000000002, 0.23020000000000002)\": 0.169, \"(0.23020000000000002, 0.2544)\": 0.191, \"(0.2544, 0.2626)\": 0.21, \"(0.2626, 0.304)\": 0.25}\nUpper Bounds (95%-Confidence Interval): {\"(0.1167, 0.1384)\": 0.323, \"(0.1384, 0.14229999999999998)\": 0.338, \"(0.14229999999999998, 0.14565)\": 0.394, \"(0.14565, 0.1488)\": 0.457, \"(0.1488, 0.1507)\": 0.507, \"(0.1507, 0.15245)\": 0.564, \"(0.15245, 0.15375)\": 0.603, \"(0.15375, 0.15410000000000001)\": 0.527, \"(0.15410000000000001, 0.1545)\": 0.602, \"(0.1545, 0.15765)\": 1.221, \"(0.15765, 0.16625)\": 1.266, \"(0.16625, 0.16635)\": 1.129, \"(0.16635, 0.1684)\": 0.174, \"(0.1684, 0.17915)\": 0.095, \"(0.17915, 0.20355)\": 0.05, \"(0.20355, 0.20855)\": 0.102, \"(0.20855, 0.21105000000000002)\": 0.151, \"(0.21105000000000002, 0.21315)\": 0.211, \"(0.21315, 0.21705)\": 0.279, \"(0.21705, 0.22110000000000002)\": 0.351, \"(0.22110000000000002, 0.23020000000000002)\": 0.408, \"(0.23020000000000002, 0.2544)\": 0.503, \"(0.2544, 0.2626)\": 0.606, \"(0.2626, 0.304)\": 0.681}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "0.1545"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: perimeter_worst\nFeature Type: continuous\nMeans: {\"(50.41, 71.06)\": -1.379, \"(71.06, 76.52000000000001)\": -1.223, \"(76.52000000000001, 80.9)\": -1.069, \"(80.9, 84.035)\": -0.914, \"(84.035, 86.48500000000001)\": -0.755, \"(86.48500000000001, 87.3)\": -0.599, \"(87.3, 91.49000000000001)\": -0.447, \"(91.49000000000001, 95.66)\": -0.292, \"(95.66, 101.15)\": -0.446, \"(101.15, 102.05000000000001)\": -0.294, \"(102.05000000000001, 109.6)\": 0.197, \"(109.6, 116.25)\": 0.351, \"(116.25, 120.35)\": 0.507, \"(120.35, 127.0)\": 0.748, \"(127.0, 133.10000000000002)\": 0.902, \"(133.10000000000002, 145.10000000000002)\": 1.059, \"(145.10000000000002, 160.0)\": 1.215, \"(160.0, 178.85)\": 1.368, \"(178.85, 251.2)\": 1.523}\nLower Bounds (95%-Confidence Interval): {\"(50.41, 71.06)\": -2.45, \"(71.06, 76.52000000000001)\": -2.257, \"(76.52000000000001, 80.9)\": -2.023, \"(80.9, 84.035)\": -1.85, \"(84.035, 86.48500000000001)\": -1.682, \"(86.48500000000001, 87.3)\": -1.531, \"(87.3, 91.49000000000001)\": -1.053, \"(91.49000000000001, 95.66)\": -0.915, \"(95.66, 101.15)\": -1.829, \"(101.15, 102.05000000000001)\": -1.642, \"(102.05000000000001, 109.6)\": -0.387, \"(109.6, 116.25)\": -0.238, \"(116.25, 120.35)\": -0.074, \"(120.35, 127.0)\": -0.761, \"(127.0, 133.10000000000002)\": -0.623, \"(133.10000000000002, 145.10000000000002)\": -0.494, \"(145.10000000000002, 160.0)\": -0.379, \"(160.0, 178.85)\": -0.29, \"(178.85, 251.2)\": -0.162}\nUpper Bounds (95%-Confidence Interval): {\"(50.41, 71.06)\": -0.307, \"(71.06, 76.52000000000001)\": -0.189, \"(76.52000000000001, 80.9)\": -0.114, \"(80.9, 84.035)\": 0.021, \"(84.035, 86.48500000000001)\": 0.172, \"(86.48500000000001, 87.3)\": 0.332, \"(87.3, 91.49000000000001)\": 0.159, \"(91.49000000000001, 95.66)\": 0.331, \"(95.66, 101.15)\": 0.936, \"(101.15, 102.05000000000001)\": 1.054, \"(102.05000000000001, 109.6)\": 0.782, \"(109.6, 116.25)\": 0.94, \"(116.25, 120.35)\": 1.088, \"(120.35, 127.0)\": 2.256, \"(127.0, 133.10000000000002)\": 2.428, \"(133.10000000000002, 145.10000000000002)\": 2.611, \"(145.10000000000002, 160.0)\": 2.809, \"(160.0, 178.85)\": 3.027, \"(178.85, 251.2)\": 3.208}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "102.05000000000001"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: area_se\nFeature Type: continuous\nMeans: {\"(6.802, 11.184999999999999)\": -0.919, \"(11.184999999999999, 12.765)\": -0.814, \"(12.765, 13.350000000000001)\": -0.704, \"(13.350000000000001, 15.3)\": -0.596, \"(15.3, 16.955)\": -0.49, \"(16.955, 18.515)\": -0.367, \"(18.515, 20.905)\": -0.256, \"(20.905, 32.985)\": -0.151, \"(32.985, 34.730000000000004)\": 0.081, \"(34.730000000000004, 41.21)\": 0.188, \"(41.21, 50.405)\": 0.292, \"(50.405, 56.915)\": 0.417, \"(56.915, 67.5)\": 0.53, \"(67.5, 81.56)\": 0.638, \"(81.56, 94.00999999999999)\": 0.751, \"(94.00999999999999, 106.2)\": 0.862, \"(106.2, 153.25)\": 0.974, \"(153.25, 542.2)\": 1.082}\nLower Bounds (95%-Confidence Interval): {\"(6.802, 11.184999999999999)\": -1.305, \"(11.184999999999999, 12.765)\": -1.176, \"(12.765, 13.350000000000001)\": -1.036, \"(13.350000000000001, 15.3)\": -0.901, \"(15.3, 16.955)\": -0.696, \"(16.955, 18.515)\": -0.504, \"(18.515, 20.905)\": -0.392, \"(20.905, 32.985)\": -0.922, \"(32.985, 34.730000000000004)\": -0.261, \"(34.730000000000004, 41.21)\": -0.102, \"(41.21, 50.405)\": 0.02, \"(50.405, 56.915)\": 0.072, \"(56.915, 67.5)\": 0.147, \"(67.5, 81.56)\": 0.223, \"(81.56, 94.00999999999999)\": 0.326, \"(94.00999999999999, 106.2)\": 0.402, \"(106.2, 153.25)\": 0.501, \"(153.25, 542.2)\": 0.571}\nUpper Bounds (95%-Confidence Interval): {\"(6.802, 11.184999999999999)\": -0.532, \"(11.184999999999999, 12.765)\": -0.452, \"(12.765, 13.350000000000001)\": -0.371, \"(13.350000000000001, 15.3)\": -0.291, \"(15.3, 16.955)\": -0.284, \"(16.955, 18.515)\": -0.23, \"(18.515, 20.905)\": -0.121, \"(20.905, 32.985)\": 0.62, \"(32.985, 34.730000000000004)\": 0.424, \"(34.730000000000004, 41.21)\": 0.479, \"(41.21, 50.405)\": 0.563, \"(50.405, 56.915)\": 0.762, \"(56.915, 67.5)\": 0.913, \"(67.5, 81.56)\": 1.052, \"(81.56, 94.00999999999999)\": 1.176, \"(94.00999999999999, 106.2)\": 1.323, \"(106.2, 153.25)\": 1.448, \"(153.25, 542.2)\": 1.593}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "32.985"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: age\nFeature Type: continuous\nMeans: {\"(40.0, 41.5)\": -1.489, \"(41.5, 43.5)\": -0.895, \"(43.5, 44.5)\": -0.02, \"(44.5, 47.5)\": 0.701, \"(47.5, 48.5)\": 1.245, \"(48.5, 58.5)\": -0.923, \"(58.5, 59.5)\": 0.647, \"(59.5, 60.8335)\": -0.288, \"(60.8335, 64.5)\": -1.035, \"(64.5, 65.5)\": 0.0, \"(65.5, 67.5)\": -0.73, \"(67.5, 68.5)\": 0.19, \"(68.5, 70.5)\": 0.784, \"(70.5, 80.5)\": 1.169, \"(80.5, 81.5)\": 0.839, \"(81.5, 85.5)\": 2.112, \"(85.5, 86.5)\": 3.884, \"(86.5, 95.0)\": 4.517}\nLower Bounds (95%-Confidence Interval): {\"(40.0, 41.5)\": -2.719, \"(41.5, 43.5)\": -2.486, \"(43.5, 44.5)\": -0.761, \"(44.5, 47.5)\": 0.297, \"(47.5, 48.5)\": 0.199, \"(48.5, 58.5)\": -1.235, \"(58.5, 59.5)\": 0.291, \"(59.5, 60.8335)\": -0.805, \"(60.8335, 64.5)\": -1.655, \"(64.5, 65.5)\": -0.281, \"(65.5, 67.5)\": -2.122, \"(67.5, 68.5)\": -0.059, \"(68.5, 70.5)\": 0.513, \"(70.5, 80.5)\": 0.404, \"(80.5, 81.5)\": 0.173, \"(81.5, 85.5)\": 1.308, \"(85.5, 86.5)\": 2.758, \"(86.5, 95.0)\": 3.244}\nUpper Bounds (95%-Confidence Interval): {\"(40.0, 41.5)\": -0.259, \"(41.5, 43.5)\": 0.696, \"(43.5, 44.5)\": 0.722, \"(44.5, 47.5)\": 1.105, \"(47.5, 48.5)\": 2.291, \"(48.5, 58.5)\": -0.612, \"(58.5, 59.5)\": 1.004, \"(59.5, 60.8335)\": 0.228, \"(60.8335, 64.5)\": -0.414, \"(64.5, 65.5)\": 0.281, \"(65.5, 67.5)\": 0.662, \"(67.5, 68.5)\": 0.44, \"(68.5, 70.5)\": 1.056, \"(70.5, 80.5)\": 1.934, \"(80.5, 81.5)\": 1.505, \"(81.5, 85.5)\": 2.916, \"(85.5, 86.5)\": 5.009, \"(86.5, 95.0)\": 5.79}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "48.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Occupation\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.297, \"(0.5, 3.5)\": -0.074, \"(3.5, 4.5)\": 0.644, \"(4.5, 6.5)\": -0.723, \"(6.5, 7.5)\": -0.542, \"(7.5, 8.5)\": -0.665, \"(8.5, 9.5)\": -0.926, \"(9.5, 10.5)\": 0.423, \"(10.5, 11.5)\": 0.59, \"(11.5, 12.5)\": 0.27, \"(12.5, 13.5)\": 0.534, \"(13.5, 14.0)\": -0.133}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.409, \"(0.5, 3.5)\": -0.139, \"(3.5, 4.5)\": 0.592, \"(4.5, 6.5)\": -0.847, \"(6.5, 7.5)\": -0.624, \"(7.5, 8.5)\": -0.749, \"(8.5, 9.5)\": -1.549, \"(9.5, 10.5)\": 0.366, \"(10.5, 11.5)\": 0.452, \"(11.5, 12.5)\": 0.225, \"(12.5, 13.5)\": 0.445, \"(13.5, 14.0)\": -0.202}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.185, \"(0.5, 3.5)\": -0.01, \"(3.5, 4.5)\": 0.695, \"(4.5, 6.5)\": -0.598, \"(6.5, 7.5)\": -0.461, \"(7.5, 8.5)\": -0.581, \"(8.5, 9.5)\": -0.302, \"(9.5, 10.5)\": 0.48, \"(10.5, 11.5)\": 0.727, \"(11.5, 12.5)\": 0.315, \"(12.5, 13.5)\": 0.622, \"(13.5, 14.0)\": -0.064}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "4.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: NativeCountry\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.195, \"(0.5, 1.5)\": 1.333, \"(1.5, 2.5)\": -0.02, \"(2.5, 3.5)\": -0.402, \"(3.5, 4.5)\": -1.423, \"(4.5, 5.5)\": 0.086, \"(5.5, 7.5)\": -0.843, \"(7.5, 8.5)\": -0.246, \"(8.5, 11.5)\": 0.062, \"(11.5, 20.5)\": -0.315, \"(20.5, 21.5)\": 0.109, \"(21.5, 22.5)\": 0.476, \"(22.5, 24.5)\": 0.133, \"(24.5, 26.5)\": -0.35, \"(26.5, 29.5)\": -0.489, \"(29.5, 32.5)\": -0.108, \"(32.5, 33.5)\": -0.483, \"(33.5, 35.5)\": -0.664, \"(35.5, 38.5)\": -0.396, \"(38.5, 39.5)\": 0.028, \"(39.5, 40.5)\": -0.596, \"(40.5, 41.0)\": 1.112}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.344, \"(0.5, 1.5)\": 0.452, \"(1.5, 2.5)\": -0.269, \"(2.5, 3.5)\": -0.76, \"(3.5, 4.5)\": -2.688, \"(4.5, 5.5)\": -0.257, \"(5.5, 7.5)\": -1.727, \"(7.5, 8.5)\": -0.488, \"(8.5, 11.5)\": -0.121, \"(11.5, 20.5)\": -0.631, \"(20.5, 21.5)\": -0.319, \"(21.5, 22.5)\": 0.048, \"(22.5, 24.5)\": -0.066, \"(24.5, 26.5)\": -0.66, \"(26.5, 29.5)\": -1.067, \"(29.5, 32.5)\": -0.254, \"(32.5, 33.5)\": -0.844, \"(33.5, 35.5)\": -1.156, \"(35.5, 38.5)\": -0.997, \"(38.5, 39.5)\": 0.02, \"(39.5, 40.5)\": -1.452, \"(40.5, 41.0)\": 0.408}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.045, \"(0.5, 1.5)\": 2.213, \"(1.5, 2.5)\": 0.228, \"(2.5, 3.5)\": -0.043, \"(3.5, 4.5)\": -0.158, \"(4.5, 5.5)\": 0.429, \"(5.5, 7.5)\": 0.04, \"(7.5, 8.5)\": -0.004, \"(8.5, 11.5)\": 0.245, \"(11.5, 20.5)\": 0.001, \"(20.5, 21.5)\": 0.537, \"(21.5, 22.5)\": 0.904, \"(22.5, 24.5)\": 0.331, \"(24.5, 26.5)\": -0.04, \"(26.5, 29.5)\": 0.089, \"(29.5, 32.5)\": 0.038, \"(32.5, 33.5)\": -0.121, \"(33.5, 35.5)\": -0.172, \"(35.5, 38.5)\": 0.204, \"(38.5, 39.5)\": 0.036, \"(39.5, 40.5)\": 0.26, \"(40.5, 41.0)\": 1.816}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "40.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: SibSp\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.0751, \"(0.5, 2.5)\": 0.1633, \"(2.5, 3.0)\": -0.7301}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.1303, \"(0.5, 2.5)\": -0.2711, \"(2.5, 3.0)\": -2.435}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0198, \"(0.5, 2.5)\": 0.5976, \"(2.5, 3.0)\": 0.9748}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "2.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: petal_width\nFeature Type: continuous\nMeans: {\"(0.1, 0.35)\": 8.07, \"(0.35, 0.45)\": 7.27, \"(0.45, 0.75)\": 6.18, \"(0.75, 1.25)\": -2.64, \"(1.25, 1.75)\": -3.46, \"(1.75, 2.5)\": -4.19}\nLower Bounds (95%-Confidence Interval): {\"(0.1, 0.35)\": 7.9, \"(0.35, 0.45)\": 7.05, \"(0.45, 0.75)\": 3.08, \"(0.75, 1.25)\": -2.81, \"(1.25, 1.75)\": -3.62, \"(1.75, 2.5)\": -4.29}\nUpper Bounds (95%-Confidence Interval): {\"(0.1, 0.35)\": 8.23, \"(0.35, 0.45)\": 7.49, \"(0.45, 0.75)\": 9.28, \"(0.75, 1.25)\": -2.47, \"(1.25, 1.75)\": -3.3, \"(1.75, 2.5)\": -4.08}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "0.75"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: HasCrCard\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.004421, \"(0.5, 1.0)\": 0.001379}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.037941, \"(0.5, 1.0)\": -0.009076}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.0291, \"(0.5, 1.0)\": 0.011834}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "0.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: petal_length\nFeature Type: continuous\nMeans: {\"(1.1, 1.65)\": 8.05, \"(1.65, 2.45)\": 7.28, \"(2.45, 3.15)\": -1.17, \"(3.15, 3.8)\": -2.4, \"(3.8, 4.45)\": -3.03, \"(4.45, 5.65)\": -3.73, \"(5.65, 6.9)\": -4.38}\nLower Bounds (95%-Confidence Interval): {\"(1.1, 1.65)\": 7.87, \"(1.65, 2.45)\": 7.08, \"(2.45, 3.15)\": -4.92, \"(3.15, 3.8)\": -2.6, \"(3.8, 4.45)\": -3.19, \"(4.45, 5.65)\": -3.87, \"(5.65, 6.9)\": -4.55}\nUpper Bounds (95%-Confidence Interval): {\"(1.1, 1.65)\": 8.24, \"(1.65, 2.45)\": 7.48, \"(2.45, 3.15)\": 2.57, \"(3.15, 3.8)\": -2.2, \"(3.8, 4.45)\": -2.86, \"(4.45, 5.65)\": -3.58, \"(5.65, 6.9)\": -4.2}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "2.45"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: DiabetesPedigreeFunction\nFeature Type: continuous\nMeans: {\"(0.078, 0.1265)\": -0.528, \"(0.1265, 0.128)\": -0.218, \"(0.128, 0.2185)\": -0.342, \"(0.2185, 0.3375)\": -0.168, \"(0.3375, 0.4215)\": -0.077, \"(0.4215, 0.4955)\": 0.015, \"(0.4955, 0.5874999999999999)\": 0.131, \"(0.5874999999999999, 0.7215)\": 0.223, \"(0.7215, 0.889)\": 0.316, \"(0.889, 1.0865)\": 0.407, \"(1.0865, 1.178)\": 0.498, \"(1.178, 1.275)\": 1.018, \"(1.275, 1.3925)\": 1.283, \"(1.3925, 1.4175)\": 1.168, \"(1.4175, 1.451)\": 0.065, \"(1.451, 1.837)\": -0.193, \"(1.837, 2.137)\": -0.092}\nLower Bounds (95%-Confidence Interval): {\"(0.078, 0.1265)\": -0.817, \"(0.1265, 0.128)\": -0.817, \"(0.128, 0.2185)\": -0.618, \"(0.2185, 0.3375)\": -0.533, \"(0.3375, 0.4215)\": -0.266, \"(0.4215, 0.4955)\": -0.104, \"(0.4955, 0.5874999999999999)\": -0.054, \"(0.5874999999999999, 0.7215)\": 0.138, \"(0.7215, 0.889)\": 0.186, \"(0.889, 1.0865)\": 0.263, \"(1.0865, 1.178)\": 0.35, \"(1.178, 1.275)\": 0.124, \"(1.275, 1.3925)\": 0.133, \"(1.3925, 1.4175)\": -0.063, \"(1.4175, 1.451)\": -1.163, \"(1.451, 1.837)\": -1.466, \"(1.837, 2.137)\": -1.112}\nUpper Bounds (95%-Confidence Interval): {\"(0.078, 0.1265)\": -0.238, \"(0.1265, 0.128)\": 0.381, \"(0.128, 0.2185)\": -0.067, \"(0.2185, 0.3375)\": 0.197, \"(0.3375, 0.4215)\": 0.113, \"(0.4215, 0.4955)\": 0.135, \"(0.4955, 0.5874999999999999)\": 0.316, \"(0.5874999999999999, 0.7215)\": 0.308, \"(0.7215, 0.889)\": 0.445, \"(0.889, 1.0865)\": 0.552, \"(1.0865, 1.178)\": 0.646, \"(1.178, 1.275)\": 1.912, \"(1.275, 1.3925)\": 2.433, \"(1.3925, 1.4175)\": 2.398, \"(1.4175, 1.451)\": 1.293, \"(1.451, 1.837)\": 1.08, \"(1.837, 2.137)\": 0.928}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "1.4175"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: CapitalGain\nFeature Type: continuous\nMeans: {\"(0.0, 57.0)\": -0.25, \"(57.0, 3048.0)\": -4.83, \"(3048.0, 3120.0)\": 2.57, \"(3120.0, 4243.5)\": -4.43, \"(4243.5, 4401.0)\": 1.45, \"(4401.0, 4668.5)\": -1.82, \"(4668.5, 4826.0)\": 3.79, \"(4826.0, 4898.0)\": 0.57, \"(4898.0, 4973.5)\": 2.25, \"(4973.5, 5119.0)\": -3.52, \"(5119.0, 5316.5)\": 4.26, \"(5316.5, 5505.5)\": 0.43, \"(5505.5, 6457.5)\": 2.15, \"(6457.5, 6505.5)\": -0.16, \"(6505.5, 6745.0)\": 0.81, \"(6745.0, 7073.5)\": -1.33, \"(7073.5, 7436.5)\": 5.76, \"(7436.5, 7565.5)\": 2.02, \"(7565.5, 7792.0)\": 6.56, \"(7792.0, 7937.0)\": 4.88, \"(7937.0, 8296.0)\": 3.84, \"(8296.0, 10543.0)\": 7.18, \"(10543.0, 10585.5)\": -1.48, \"(10585.5, 30961.5)\": 8.61, \"(30961.5, 70654.5)\": -0.66, \"(70654.5, 99999.0)\": 9.72}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 57.0)\": -0.27, \"(57.0, 3048.0)\": -6.42, \"(3048.0, 3120.0)\": 2.14, \"(3120.0, 4243.5)\": -5.31, \"(4243.5, 4401.0)\": 1.09, \"(4401.0, 4668.5)\": -2.65, \"(4668.5, 4826.0)\": 2.87, \"(4826.0, 4898.0)\": -0.25, \"(4898.0, 4973.5)\": 1.55, \"(4973.5, 5119.0)\": -6.13, \"(5119.0, 5316.5)\": 3.51, \"(5316.5, 5505.5)\": -0.29, \"(5505.5, 6457.5)\": 1.3, \"(6457.5, 6505.5)\": -0.94, \"(6505.5, 6745.0)\": 0.19, \"(6745.0, 7073.5)\": -2.33, \"(7073.5, 7436.5)\": 4.95, \"(7436.5, 7565.5)\": 0.42, \"(7565.5, 7792.0)\": 5.41, \"(7792.0, 7937.0)\": 2.59, \"(7937.0, 8296.0)\": 1.32, \"(8296.0, 10543.0)\": 6.05, \"(10543.0, 10585.5)\": -2.73, \"(10585.5, 30961.5)\": 7.51, \"(30961.5, 70654.5)\": -3.56, \"(70654.5, 99999.0)\": 8.19}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 57.0)\": -0.23, \"(57.0, 3048.0)\": -3.24, \"(3048.0, 3120.0)\": 3.0, \"(3120.0, 4243.5)\": -3.54, \"(4243.5, 4401.0)\": 1.81, \"(4401.0, 4668.5)\": -1.0, \"(4668.5, 4826.0)\": 4.71, \"(4826.0, 4898.0)\": 1.38, \"(4898.0, 4973.5)\": 2.95, \"(4973.5, 5119.0)\": -0.92, \"(5119.0, 5316.5)\": 5.0, \"(5316.5, 5505.5)\": 1.16, \"(5505.5, 6457.5)\": 3.0, \"(6457.5, 6505.5)\": 0.62, \"(6505.5, 6745.0)\": 1.44, \"(6745.0, 7073.5)\": -0.34, \"(7073.5, 7436.5)\": 6.58, \"(7436.5, 7565.5)\": 3.62, \"(7565.5, 7792.0)\": 7.72, \"(7792.0, 7937.0)\": 7.16, \"(7937.0, 8296.0)\": 6.36, \"(8296.0, 10543.0)\": 8.31, \"(10543.0, 10585.5)\": -0.22, \"(10585.5, 30961.5)\": 9.71, \"(30961.5, 70654.5)\": 2.23, \"(70654.5, 99999.0)\": 11.26}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "70654.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: sepal_width\nFeature Type: continuous\nMeans: {\"(2.0, 2.25)\": -2.473, \"(2.25, 2.6500000000000004)\": -2.179, \"(2.6500000000000004, 2.8499999999999996)\": -1.736, \"(2.8499999999999996, 2.95)\": -0.945, \"(2.95, 3.05)\": 0.062, \"(3.05, 3.25)\": 0.509, \"(3.25, 3.3499999999999996)\": 1.373, \"(3.3499999999999996, 3.55)\": 1.669, \"(3.55, 3.75)\": 2.097, \"(3.75, 3.95)\": 2.489, \"(3.95, 4.1)\": 2.778}\nLower Bounds (95%-Confidence Interval): {\"(2.0, 2.25)\": -2.841, \"(2.25, 2.6500000000000004)\": -2.509, \"(2.6500000000000004, 2.8499999999999996)\": -1.936, \"(2.8499999999999996, 2.95)\": -1.506, \"(2.95, 3.05)\": -0.172, \"(3.05, 3.25)\": 0.23, \"(3.25, 3.3499999999999996)\": 1.091, \"(3.3499999999999996, 3.55)\": 1.492, \"(3.55, 3.75)\": 1.921, \"(3.75, 3.95)\": 2.293, \"(3.95, 4.1)\": 2.546}\nUpper Bounds (95%-Confidence Interval): {\"(2.0, 2.25)\": -2.105, \"(2.25, 2.6500000000000004)\": -1.849, \"(2.6500000000000004, 2.8499999999999996)\": -1.537, \"(2.8499999999999996, 2.95)\": -0.384, \"(2.95, 3.05)\": 0.295, \"(3.05, 3.25)\": 0.789, \"(3.25, 3.3499999999999996)\": 1.656, \"(3.3499999999999996, 3.55)\": 1.846, \"(3.55, 3.75)\": 2.274, \"(3.75, 3.95)\": 2.685, \"(3.95, 4.1)\": 3.01}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "2.95"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: fractal_dimension_mean\nFeature Type: continuous\nMeans: {\"(0.04996, 0.05075)\": 0.5962, \"(0.05075, 0.052285)\": 0.5519, \"(0.052285, 0.05393)\": 0.5087, \"(0.05393, 0.05455)\": 0.4681, \"(0.05455, 0.05505)\": 0.4248, \"(0.05505, 0.055349999999999996)\": 0.3799, \"(0.055349999999999996, 0.055665)\": 0.337, \"(0.055665, 0.055895)\": 0.2922, \"(0.055895, 0.055935)\": 0.2475, \"(0.055935, 0.056365)\": 0.2007, \"(0.056365, 0.05655)\": 0.1163, \"(0.05655, 0.056720000000000007)\": 0.0704, \"(0.056720000000000007, 0.056995000000000004)\": 0.0288, \"(0.056995000000000004, 0.058145)\": -0.0168, \"(0.058145, 0.059715)\": -0.0575, \"(0.059715, 0.06078)\": -0.0163, \"(0.06078, 0.061385)\": -0.0618, \"(0.061385, 0.0622)\": -0.102, \"(0.0622, 0.063145)\": -0.1453, \"(0.063145, 0.065105)\": -0.1865, \"(0.065105, 0.06564)\": -0.1448, \"(0.06564, 0.067575)\": -0.1025, \"(0.067575, 0.09744)\": -0.0621}\nLower Bounds (95%-Confidence Interval): {\"(0.04996, 0.05075)\": 0.3734, \"(0.05075, 0.052285)\": 0.3548, \"(0.052285, 0.05393)\": 0.2212, \"(0.05393, 0.05455)\": 0.2141, \"(0.05455, 0.05505)\": 0.1669, \"(0.05505, 0.055349999999999996)\": 0.1178, \"(0.055349999999999996, 0.055665)\": 0.0712, \"(0.055665, 0.055895)\": 0.0309, \"(0.055895, 0.055935)\": -0.0117, \"(0.055935, 0.056365)\": -0.072, \"(0.056365, 0.05655)\": -0.0136, \"(0.05655, 0.056720000000000007)\": -0.0905, \"(0.056720000000000007, 0.056995000000000004)\": -0.1461, \"(0.056995000000000004, 0.058145)\": -0.1995, \"(0.058145, 0.059715)\": -0.2455, \"(0.059715, 0.06078)\": -0.1126, \"(0.06078, 0.061385)\": -0.1738, \"(0.061385, 0.0622)\": -0.1944, \"(0.0622, 0.063145)\": -0.2238, \"(0.063145, 0.065105)\": -0.2687, \"(0.065105, 0.06564)\": -0.2312, \"(0.06564, 0.067575)\": -0.191, \"(0.067575, 0.09744)\": -0.1891}\nUpper Bounds (95%-Confidence Interval): {\"(0.04996, 0.05075)\": 0.819, \"(0.05075, 0.052285)\": 0.749, \"(0.052285, 0.05393)\": 0.7962, \"(0.05393, 0.05455)\": 0.722, \"(0.05455, 0.05505)\": 0.6828, \"(0.05505, 0.055349999999999996)\": 0.642, \"(0.055349999999999996, 0.055665)\": 0.6028, \"(0.055665, 0.055895)\": 0.5535, \"(0.055895, 0.055935)\": 0.5067, \"(0.055935, 0.056365)\": 0.4734, \"(0.056365, 0.05655)\": 0.2462, \"(0.05655, 0.056720000000000007)\": 0.2312, \"(0.056720000000000007, 0.056995000000000004)\": 0.2038, \"(0.056995000000000004, 0.058145)\": 0.1658, \"(0.058145, 0.059715)\": 0.1306, \"(0.059715, 0.06078)\": 0.0801, \"(0.06078, 0.061385)\": 0.0502, \"(0.061385, 0.0622)\": -0.0097, \"(0.0622, 0.063145)\": -0.0668, \"(0.063145, 0.065105)\": -0.1044, \"(0.065105, 0.06564)\": -0.0583, \"(0.06564, 0.067575)\": -0.0139, \"(0.067575, 0.09744)\": 0.0649}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "0.056365"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: time\nFeature Type: continuous\nMeans: {\"(4.0, 11.5)\": 10.73, \"(11.5, 12.5)\": 1.29, \"(12.5, 15.5)\": 3.88, \"(15.5, 18.0)\": 2.22, \"(18.0, 28.5)\": 6.17, \"(28.5, 30.5)\": 4.47, \"(30.5, 52.0)\": 5.56, \"(52.0, 54.5)\": 3.38, \"(54.5, 67.5)\": 4.79, \"(67.5, 73.5)\": 2.76, \"(73.5, 76.5)\": -3.15, \"(76.5, 78.5)\": 2.29, \"(78.5, 82.5)\": -0.16, \"(82.5, 87.5)\": -2.8, \"(87.5, 90.5)\": 0.19, \"(90.5, 92.5)\": -1.08, \"(92.5, 95.5)\": -2.7, \"(95.5, 108.5)\": -0.98, \"(108.5, 117.5)\": 0.02, \"(117.5, 124.5)\": -3.44, \"(124.5, 137.5)\": 0.64, \"(137.5, 149.0)\": -0.8, \"(149.0, 171.5)\": 5.06, \"(171.5, 173.0)\": 2.66, \"(173.0, 182.5)\": -0.84, \"(182.5, 192.5)\": -3.42, \"(192.5, 193.5)\": -1.01, \"(193.5, 253.0)\": -2.58, \"(253.0, 285.0)\": -8.42}\nLower Bounds (95%-Confidence Interval): {\"(4.0, 11.5)\": 8.45, \"(11.5, 12.5)\": 0.25, \"(12.5, 15.5)\": 2.94, \"(15.5, 18.0)\": -0.25, \"(18.0, 28.5)\": 4.04, \"(28.5, 30.5)\": 3.69, \"(30.5, 52.0)\": 4.21, \"(52.0, 54.5)\": 1.74, \"(54.5, 67.5)\": 3.17, \"(67.5, 73.5)\": 1.96, \"(73.5, 76.5)\": -4.69, \"(76.5, 78.5)\": 1.19, \"(78.5, 82.5)\": -1.25, \"(82.5, 87.5)\": -3.84, \"(87.5, 90.5)\": -0.35, \"(90.5, 92.5)\": -2.75, \"(92.5, 95.5)\": -4.6, \"(95.5, 108.5)\": -1.62, \"(108.5, 117.5)\": -0.66, \"(117.5, 124.5)\": -4.94, \"(124.5, 137.5)\": -0.24, \"(137.5, 149.0)\": -1.83, \"(149.0, 171.5)\": 3.59, \"(171.5, 173.0)\": 1.61, \"(173.0, 182.5)\": -1.86, \"(182.5, 192.5)\": -4.51, \"(192.5, 193.5)\": -1.89, \"(193.5, 253.0)\": -4.11, \"(253.0, 285.0)\": -10.7}\nUpper Bounds (95%-Confidence Interval): {\"(4.0, 11.5)\": 13.0, \"(11.5, 12.5)\": 2.32, \"(12.5, 15.5)\": 4.82, \"(15.5, 18.0)\": 4.68, \"(18.0, 28.5)\": 8.31, \"(28.5, 30.5)\": 5.26, \"(30.5, 52.0)\": 6.91, \"(52.0, 54.5)\": 5.03, \"(54.5, 67.5)\": 6.41, \"(67.5, 73.5)\": 3.57, \"(73.5, 76.5)\": -1.61, \"(76.5, 78.5)\": 3.39, \"(78.5, 82.5)\": 0.92, \"(82.5, 87.5)\": -1.75, \"(87.5, 90.5)\": 0.72, \"(90.5, 92.5)\": 0.6, \"(92.5, 95.5)\": -0.81, \"(95.5, 108.5)\": -0.34, \"(108.5, 117.5)\": 0.7, \"(117.5, 124.5)\": -1.93, \"(124.5, 137.5)\": 1.53, \"(137.5, 149.0)\": 0.22, \"(149.0, 171.5)\": 6.52, \"(171.5, 173.0)\": 3.72, \"(173.0, 182.5)\": 0.18, \"(182.5, 192.5)\": -2.33, \"(192.5, 193.5)\": -0.13, \"(193.5, 253.0)\": -1.06, \"(253.0, 285.0)\": -6.14}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "11.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: smoothness_se\nFeature Type: continuous\nMeans: {\"(0.001713, 0.0031539999999999997)\": 0.2958, \"(0.0031539999999999997, 0.003299)\": 0.2615, \"(0.003299, 0.003384)\": 0.185, \"(0.003384, 0.0034675)\": -0.1523, \"(0.0034675, 0.0036699999999999997)\": -0.1838, \"(0.0036699999999999997, 0.0041069999999999995)\": -0.2174, \"(0.0041069999999999995, 0.004215)\": -0.2532, \"(0.004215, 0.004436)\": -0.2879, \"(0.004436, 0.0045775)\": -0.3223, \"(0.0045775, 0.004612)\": -0.2905, \"(0.004612, 0.0048915)\": -0.2425, \"(0.0048915, 0.0053335)\": -0.2106, \"(0.0053335, 0.005443)\": -0.1771, \"(0.005443, 0.00554)\": -0.1453, \"(0.00554, 0.005729)\": -0.1136, \"(0.005729, 0.0058625)\": -0.0812, \"(0.0058625, 0.0058955)\": -0.0495, \"(0.0058955, 0.0067525)\": 0.0229, \"(0.0067525, 0.00682)\": 0.0562, \"(0.00682, 0.007338)\": 0.1146, \"(0.007338, 0.0074805)\": 0.1474, \"(0.0074805, 0.007967)\": 0.1839, \"(0.007967, 0.009857000000000001)\": 0.219, \"(0.009857000000000001, 0.010665000000000001)\": 0.1863, \"(0.010665000000000001, 0.011054999999999999)\": 0.1538, \"(0.011054999999999999, 0.011915)\": 0.1219, \"(0.011915, 0.012885)\": 0.0873, \"(0.012885, 0.03113)\": 0.0542}\nLower Bounds (95%-Confidence Interval): {\"(0.001713, 0.0031539999999999997)\": -0.864, \"(0.0031539999999999997, 0.003299)\": -0.919, \"(0.003299, 0.003384)\": -1.0196, \"(0.003384, 0.0034675)\": -0.6905, \"(0.0034675, 0.0036699999999999997)\": -0.7233, \"(0.0036699999999999997, 0.0041069999999999995)\": -0.7618, \"(0.0041069999999999995, 0.004215)\": -0.7976, \"(0.004215, 0.004436)\": -0.8492, \"(0.004436, 0.0045775)\": -0.8863, \"(0.0045775, 0.004612)\": -0.8426, \"(0.004612, 0.0048915)\": -0.7021, \"(0.0048915, 0.0053335)\": -0.6905, \"(0.0053335, 0.005443)\": -0.6659, \"(0.005443, 0.00554)\": -0.624, \"(0.00554, 0.005729)\": -0.5761, \"(0.005729, 0.0058625)\": -0.538, \"(0.0058625, 0.0058955)\": -0.5073, \"(0.0058955, 0.0067525)\": -0.1186, \"(0.0067525, 0.00682)\": -0.0928, \"(0.00682, 0.007338)\": -0.288, \"(0.007338, 0.0074805)\": -0.2553, \"(0.0074805, 0.007967)\": -0.2176, \"(0.007967, 0.009857000000000001)\": -0.1787, \"(0.009857000000000001, 0.010665000000000001)\": -0.2012, \"(0.010665000000000001, 0.011054999999999999)\": -0.2344, \"(0.011054999999999999, 0.011915)\": -0.2614, \"(0.011915, 0.012885)\": -0.2838, \"(0.012885, 0.03113)\": -0.4136}\nUpper Bounds (95%-Confidence Interval): {\"(0.001713, 0.0031539999999999997)\": 1.4555, \"(0.0031539999999999997, 0.003299)\": 1.442, \"(0.003299, 0.003384)\": 1.3896, \"(0.003384, 0.0034675)\": 0.386, \"(0.0034675, 0.0036699999999999997)\": 0.3557, \"(0.0036699999999999997, 0.0041069999999999995)\": 0.327, \"(0.0041069999999999995, 0.004215)\": 0.2913, \"(0.004215, 0.004436)\": 0.2734, \"(0.004436, 0.0045775)\": 0.2417, \"(0.0045775, 0.004612)\": 0.2615, \"(0.004612, 0.0048915)\": 0.2171, \"(0.0048915, 0.0053335)\": 0.2692, \"(0.0053335, 0.005443)\": 0.3117, \"(0.005443, 0.00554)\": 0.3335, \"(0.00554, 0.005729)\": 0.349, \"(0.005729, 0.0058625)\": 0.3757, \"(0.0058625, 0.0058955)\": 0.4082, \"(0.0058955, 0.0067525)\": 0.1644, \"(0.0067525, 0.00682)\": 0.2053, \"(0.00682, 0.007338)\": 0.5173, \"(0.007338, 0.0074805)\": 0.5502, \"(0.0074805, 0.007967)\": 0.5854, \"(0.007967, 0.009857000000000001)\": 0.6167, \"(0.009857000000000001, 0.010665000000000001)\": 0.5738, \"(0.010665000000000001, 0.011054999999999999)\": 0.5419, \"(0.011054999999999999, 0.011915)\": 0.5053, \"(0.011915, 0.012885)\": 0.4585, \"(0.012885, 0.03113)\": 0.522}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "0.003384"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: concavity_worst\nFeature Type: continuous\nMeans: {\"(0.0, 0.022775)\": -0.769, \"(0.022775, 0.024655)\": -0.671, \"(0.024655, 0.052095)\": -0.846, \"(0.052095, 0.10575)\": -0.943, \"(0.10575, 0.1313)\": -0.843, \"(0.1313, 0.14545000000000002)\": -0.745, \"(0.14545000000000002, 0.1694)\": -0.646, \"(0.1694, 0.1843)\": -0.54, \"(0.1843, 0.19235000000000002)\": -0.438, \"(0.19235000000000002, 0.1996)\": -0.332, \"(0.1996, 0.20695)\": -0.234, \"(0.20695, 0.20795)\": -0.081, \"(0.20795, 0.2539)\": 0.187, \"(0.2539, 0.273)\": 0.284, \"(0.273, 0.33975)\": 0.385, \"(0.33975, 0.3663)\": 0.486, \"(0.3663, 0.37695)\": 0.586, \"(0.37695, 0.39765)\": 0.698, \"(0.39765, 0.41025)\": 0.797, \"(0.41025, 1.252)\": 0.897}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.022775)\": -1.429, \"(0.022775, 0.024655)\": -1.337, \"(0.024655, 0.052095)\": -1.568, \"(0.052095, 0.10575)\": -1.701, \"(0.10575, 0.1313)\": -1.62, \"(0.1313, 0.14545000000000002)\": -1.521, \"(0.14545000000000002, 0.1694)\": -1.427, \"(0.1694, 0.1843)\": -1.324, \"(0.1843, 0.19235000000000002)\": -1.207, \"(0.19235000000000002, 0.1996)\": -1.093, \"(0.1996, 0.20695)\": -0.982, \"(0.20695, 0.20795)\": -0.814, \"(0.20795, 0.2539)\": -0.518, \"(0.2539, 0.273)\": -0.08, \"(0.273, 0.33975)\": 0.033, \"(0.33975, 0.3663)\": 0.265, \"(0.3663, 0.37695)\": 0.365, \"(0.37695, 0.39765)\": -0.026, \"(0.39765, 0.41025)\": -0.308, \"(0.41025, 1.252)\": -0.23}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.022775)\": -0.109, \"(0.022775, 0.024655)\": -0.005, \"(0.024655, 0.052095)\": -0.123, \"(0.052095, 0.10575)\": -0.186, \"(0.10575, 0.1313)\": -0.065, \"(0.1313, 0.14545000000000002)\": 0.031, \"(0.14545000000000002, 0.1694)\": 0.135, \"(0.1694, 0.1843)\": 0.244, \"(0.1843, 0.19235000000000002)\": 0.332, \"(0.19235000000000002, 0.1996)\": 0.428, \"(0.1996, 0.20695)\": 0.514, \"(0.20695, 0.20795)\": 0.653, \"(0.20795, 0.2539)\": 0.891, \"(0.2539, 0.273)\": 0.648, \"(0.273, 0.33975)\": 0.737, \"(0.33975, 0.3663)\": 0.708, \"(0.3663, 0.37695)\": 0.807, \"(0.37695, 0.39765)\": 1.423, \"(0.39765, 0.41025)\": 1.902, \"(0.41025, 1.252)\": 2.024}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "0.20795"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: sex\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": 0.01719, \"(0.5, 1.0)\": -0.00954}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.08236, \"(0.5, 1.0)\": -0.06482}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.11675, \"(0.5, 1.0)\": 0.04573}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "0.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Tenure\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.3765, \"(0.5, 1.5)\": -0.0692, \"(1.5, 4.5)\": -0.016, \"(4.5, 5.5)\": 0.0109, \"(5.5, 6.5)\": 0.0432, \"(6.5, 7.5)\": 0.0871, \"(7.5, 9.5)\": 0.0554, \"(9.5, 10.0)\": -0.0599}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.4596, \"(0.5, 1.5)\": -0.1046, \"(1.5, 4.5)\": -0.0506, \"(4.5, 5.5)\": -0.017, \"(5.5, 6.5)\": 0.014, \"(6.5, 7.5)\": 0.0581, \"(7.5, 9.5)\": 0.004, \"(9.5, 10.0)\": -0.1542}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.2933, \"(0.5, 1.5)\": -0.0338, \"(1.5, 4.5)\": 0.0185, \"(4.5, 5.5)\": 0.0387, \"(5.5, 6.5)\": 0.0724, \"(6.5, 7.5)\": 0.1161, \"(7.5, 9.5)\": 0.1067, \"(9.5, 10.0)\": 0.0343}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "0.5"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: CapitalLoss\nFeature Type: continuous\nMeans: {\"(0.0, 845.0)\": -0.044, \"(845.0, 1448.0)\": -1.147, \"(1448.0, 1551.5)\": 0.416, \"(1551.5, 1568.5)\": 3.928, \"(1568.5, 1748.0)\": -3.752, \"(1748.0, 1846.0)\": 1.139, \"(1846.0, 1862.0)\": 3.823, \"(1862.0, 1881.5)\": -1.36, \"(1881.5, 1894.5)\": 4.781, \"(1894.5, 1938.0)\": 3.172, \"(1938.0, 1975.5)\": 0.294, \"(1975.5, 1978.5)\": 4.013, \"(1978.5, 2139.0)\": -2.74, \"(2139.0, 2176.5)\": 0.361, \"(2176.5, 2190.0)\": -1.098, \"(2190.0, 2205.5)\": 1.259, \"(2205.5, 2262.5)\": 2.644, \"(2262.5, 2310.5)\": -0.616, \"(2310.5, 2364.5)\": -1.139, \"(2364.5, 2384.5)\": 1.07, \"(2384.5, 2450.5)\": 4.377, \"(2450.5, 2480.5)\": 1.517, \"(2480.5, 2553.0)\": 3.296, \"(2553.0, 2581.0)\": 5.5, \"(2581.0, 2678.5)\": -0.191, \"(2678.5, 2789.0)\": 0.326, \"(2789.0, 3343.5)\": 5.958, \"(3343.5, 3835.0)\": 2.152, \"(3835.0, 4356.0)\": -0.334}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 845.0)\": -0.934, \"(845.0, 1448.0)\": -2.192, \"(1448.0, 1551.5)\": 0.083, \"(1551.5, 1568.5)\": 2.921, \"(1568.5, 1748.0)\": -4.443, \"(1748.0, 1846.0)\": 0.39, \"(1846.0, 1862.0)\": 2.886, \"(1862.0, 1881.5)\": -2.359, \"(1881.5, 1894.5)\": 3.819, \"(1894.5, 1938.0)\": 2.82, \"(1938.0, 1975.5)\": -0.436, \"(1975.5, 1978.5)\": 3.487, \"(1978.5, 2139.0)\": -3.34, \"(2139.0, 2176.5)\": -0.308, \"(2176.5, 2190.0)\": -2.441, \"(2190.0, 2205.5)\": 0.899, \"(2205.5, 2262.5)\": 1.633, \"(2262.5, 2310.5)\": -2.272, \"(2310.5, 2364.5)\": -2.819, \"(2364.5, 2384.5)\": 0.659, \"(2384.5, 2450.5)\": 3.333, \"(2450.5, 2480.5)\": 0.001, \"(2480.5, 2553.0)\": 1.926, \"(2553.0, 2581.0)\": 4.074, \"(2581.0, 2678.5)\": -1.869, \"(2678.5, 2789.0)\": -1.325, \"(2789.0, 3343.5)\": 4.42, \"(3343.5, 3835.0)\": 0.138, \"(3835.0, 4356.0)\": -1.587}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 845.0)\": 0.845, \"(845.0, 1448.0)\": -0.101, \"(1448.0, 1551.5)\": 0.748, \"(1551.5, 1568.5)\": 4.935, \"(1568.5, 1748.0)\": -3.061, \"(1748.0, 1846.0)\": 1.889, \"(1846.0, 1862.0)\": 4.761, \"(1862.0, 1881.5)\": -0.361, \"(1881.5, 1894.5)\": 5.742, \"(1894.5, 1938.0)\": 3.524, \"(1938.0, 1975.5)\": 1.024, \"(1975.5, 1978.5)\": 4.539, \"(1978.5, 2139.0)\": -2.139, \"(2139.0, 2176.5)\": 1.029, \"(2176.5, 2190.0)\": 0.245, \"(2190.0, 2205.5)\": 1.619, \"(2205.5, 2262.5)\": 3.655, \"(2262.5, 2310.5)\": 1.041, \"(2310.5, 2364.5)\": 0.541, \"(2364.5, 2384.5)\": 1.481, \"(2384.5, 2450.5)\": 5.42, \"(2450.5, 2480.5)\": 3.034, \"(2480.5, 2553.0)\": 4.666, \"(2553.0, 2581.0)\": 6.926, \"(2581.0, 2678.5)\": 1.487, \"(2678.5, 2789.0)\": 1.978, \"(2789.0, 3343.5)\": 7.496, \"(3343.5, 3835.0)\": 4.167, \"(3835.0, 4356.0)\": 0.92}\n\nWithin the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n\nWe are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n\nWhat is the x-axis position of the largest jump in the graph? Think step by step.",
+    "1568.5"
+  ]
+]
\ No newline at end of file
diff --git a/benchmarks/benchmark/monotonicity.json b/benchmarks/benchmark/monotonicity.json
new file mode 100644
index 0000000..778fad4
--- /dev/null
+++ b/benchmarks/benchmark/monotonicity.json
@@ -0,0 +1,402 @@
+[
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: FoodCourt\nFeature Type: continuous\nMeans: {\"(0.0, 593.5)\": -0.177, \"(593.5, 779.5)\": 0.043, \"(779.5, 1341.5)\": 0.27, \"(1341.5, 2175.5)\": 0.543, \"(2175.5, 3125.0)\": 0.863, \"(3125.0, 3637.0)\": 1.13, \"(3637.0, 4078.5)\": 1.479, \"(4078.5, 5218.5)\": 2.076, \"(5218.5, 6031.5)\": 1.81, \"(6031.5, 6171.5)\": 1.439, \"(6171.5, 8753.0)\": 2.236, \"(8753.0, 8824.0)\": 2.746, \"(8824.0, 10094.5)\": 3.43, \"(10094.5, 12683.5)\": 3.888, \"(12683.5, 27723.0)\": 4.131}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 593.5)\": -0.307, \"(593.5, 779.5)\": -0.11, \"(779.5, 1341.5)\": -0.04, \"(1341.5, 2175.5)\": -0.06, \"(2175.5, 3125.0)\": 0.404, \"(3125.0, 3637.0)\": 0.707, \"(3637.0, 4078.5)\": 0.742, \"(4078.5, 5218.5)\": 1.52, \"(5218.5, 6031.5)\": 1.485, \"(6031.5, 6171.5)\": 0.477, \"(6171.5, 8753.0)\": 1.548, \"(8753.0, 8824.0)\": 1.95, \"(8824.0, 10094.5)\": 2.626, \"(10094.5, 12683.5)\": 2.361, \"(12683.5, 27723.0)\": 2.558}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 593.5)\": -0.047, \"(593.5, 779.5)\": 0.196, \"(779.5, 1341.5)\": 0.58, \"(1341.5, 2175.5)\": 1.145, \"(2175.5, 3125.0)\": 1.322, \"(3125.0, 3637.0)\": 1.554, \"(3637.0, 4078.5)\": 2.216, \"(4078.5, 5218.5)\": 2.631, \"(5218.5, 6031.5)\": 2.135, \"(6031.5, 6171.5)\": 2.4, \"(6171.5, 8753.0)\": 2.925, \"(8753.0, 8824.0)\": 3.543, \"(8824.0, 10094.5)\": 4.234, \"(10094.5, 12683.5)\": 5.416, \"(12683.5, 27723.0)\": 5.705}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: age\nFeature Type: continuous\nMeans: {\"(40.0, 41.5)\": -1.489, \"(41.5, 43.5)\": -0.895, \"(43.5, 44.5)\": -0.02, \"(44.5, 47.5)\": 0.701, \"(47.5, 48.5)\": 1.245, \"(48.5, 58.5)\": -0.923, \"(58.5, 59.5)\": 0.647, \"(59.5, 60.8335)\": -0.288, \"(60.8335, 64.5)\": -1.035, \"(64.5, 65.5)\": 0.0, \"(65.5, 67.5)\": -0.73, \"(67.5, 68.5)\": 0.19, \"(68.5, 70.5)\": 0.784, \"(70.5, 80.5)\": 1.169, \"(80.5, 81.5)\": 0.839, \"(81.5, 85.5)\": 2.112, \"(85.5, 86.5)\": 3.884, \"(86.5, 95.0)\": 4.517}\nLower Bounds (95%-Confidence Interval): {\"(40.0, 41.5)\": -2.719, \"(41.5, 43.5)\": -2.486, \"(43.5, 44.5)\": -0.761, \"(44.5, 47.5)\": 0.297, \"(47.5, 48.5)\": 0.199, \"(48.5, 58.5)\": -1.235, \"(58.5, 59.5)\": 0.291, \"(59.5, 60.8335)\": -0.805, \"(60.8335, 64.5)\": -1.655, \"(64.5, 65.5)\": -0.281, \"(65.5, 67.5)\": -2.122, \"(67.5, 68.5)\": -0.059, \"(68.5, 70.5)\": 0.513, \"(70.5, 80.5)\": 0.404, \"(80.5, 81.5)\": 0.173, \"(81.5, 85.5)\": 1.308, \"(85.5, 86.5)\": 2.758, \"(86.5, 95.0)\": 3.244}\nUpper Bounds (95%-Confidence Interval): {\"(40.0, 41.5)\": -0.259, \"(41.5, 43.5)\": 0.696, \"(43.5, 44.5)\": 0.722, \"(44.5, 47.5)\": 1.105, \"(47.5, 48.5)\": 2.291, \"(48.5, 58.5)\": -0.612, \"(58.5, 59.5)\": 1.004, \"(59.5, 60.8335)\": 0.228, \"(60.8335, 64.5)\": -0.414, \"(64.5, 65.5)\": 0.281, \"(65.5, 67.5)\": 0.662, \"(67.5, 68.5)\": 0.44, \"(68.5, 70.5)\": 1.056, \"(70.5, 80.5)\": 1.934, \"(80.5, 81.5)\": 1.505, \"(81.5, 85.5)\": 2.916, \"(85.5, 86.5)\": 5.009, \"(86.5, 95.0)\": 5.79}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Spa\nFeature Type: continuous\nMeans: {\"(0.0, 130.5)\": 0.521, \"(130.5, 278.5)\": 0.118, \"(278.5, 452.5)\": -0.285, \"(452.5, 754.5)\": -0.907, \"(754.5, 1209.5)\": -1.309, \"(1209.5, 1808.0)\": -1.712, \"(1808.0, 2204.5)\": -3.029, \"(2204.5, 2207.5)\": -2.456, \"(2207.5, 2428.0)\": -2.956, \"(2428.0, 2462.5)\": -2.512, \"(2462.5, 2714.5)\": -3.402, \"(2714.5, 2745.0)\": -2.902, \"(2745.0, 2993.5)\": -4.077, \"(2993.5, 3132.0)\": -4.481, \"(3132.0, 3705.5)\": -5.377, \"(3705.5, 3747.0)\": -4.36, \"(3747.0, 22408.0)\": -7.183}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 130.5)\": 0.36, \"(130.5, 278.5)\": -1.599, \"(278.5, 452.5)\": -1.362, \"(452.5, 754.5)\": -1.291, \"(754.5, 1209.5)\": -2.117, \"(1209.5, 1808.0)\": -2.592, \"(1808.0, 2204.5)\": -3.856, \"(2204.5, 2207.5)\": -3.562, \"(2207.5, 2428.0)\": -3.549, \"(2428.0, 2462.5)\": -3.455, \"(2462.5, 2714.5)\": -4.525, \"(2714.5, 2745.0)\": -4.721, \"(2745.0, 2993.5)\": -5.493, \"(2993.5, 3132.0)\": -6.214, \"(3132.0, 3705.5)\": -6.767, \"(3705.5, 3747.0)\": -6.498, \"(3747.0, 22408.0)\": -9.024}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 130.5)\": 0.682, \"(130.5, 278.5)\": 1.834, \"(278.5, 452.5)\": 0.791, \"(452.5, 754.5)\": -0.524, \"(754.5, 1209.5)\": -0.502, \"(1209.5, 1808.0)\": -0.831, \"(1808.0, 2204.5)\": -2.202, \"(2204.5, 2207.5)\": -1.35, \"(2207.5, 2428.0)\": -2.364, \"(2428.0, 2462.5)\": -1.569, \"(2462.5, 2714.5)\": -2.28, \"(2714.5, 2745.0)\": -1.083, \"(2745.0, 2993.5)\": -2.661, \"(2993.5, 3132.0)\": -2.749, \"(3132.0, 3705.5)\": -3.986, \"(3705.5, 3747.0)\": -2.222, \"(3747.0, 22408.0)\": -5.342}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: id\nFeature Type: continuous\nMeans: {\"(8670.0, 90271.0)\": 0.342, \"(90271.0, 467526.5)\": 0.574, \"(467526.5, 853506.5)\": 0.657, \"(853506.5, 859643.0)\": 0.719, \"(859643.0, 864727.5)\": 0.655, \"(864727.5, 871421.0)\": 0.593, \"(871421.0, 874848.5)\": 0.528, \"(874848.5, 880845.5)\": 0.464, \"(880845.5, 882230.0)\": 0.399, \"(882230.0, 883266.5)\": 0.319, \"(883266.5, 889561.0)\": 0.171, \"(889561.0, 892521.0)\": 0.103, \"(892521.0, 894330.5)\": 0.039, \"(894330.5, 896851.5)\": -0.023, \"(896851.5, 899167.0)\": -0.107, \"(899167.0, 902138.0)\": -0.176, \"(902138.0, 905080.5)\": -0.241, \"(905080.5, 906551.5)\": -0.305, \"(906551.5, 911540.5)\": -0.368, \"(911540.5, 917896.5)\": -0.431, \"(917896.5, 8810615.5)\": -0.493, \"(8810615.5, 9112480.5)\": -0.386, \"(9112480.5, 89803401.5)\": -0.323, \"(89803401.5, 91544001.5)\": -0.259, \"(91544001.5, 91903901.5)\": -0.191, \"(91903901.5, 911320502.0)\": -0.121}\nLower Bounds (95%-Confidence Interval): {\"(8670.0, 90271.0)\": -0.06, \"(90271.0, 467526.5)\": 0.079, \"(467526.5, 853506.5)\": 0.101, \"(853506.5, 859643.0)\": 0.139, \"(859643.0, 864727.5)\": 0.076, \"(864727.5, 871421.0)\": 0.038, \"(871421.0, 874848.5)\": 0.005, \"(874848.5, 880845.5)\": -0.028, \"(880845.5, 882230.0)\": -0.061, \"(882230.0, 883266.5)\": -0.137, \"(883266.5, 889561.0)\": -0.14, \"(889561.0, 892521.0)\": -0.196, \"(892521.0, 894330.5)\": -0.267, \"(894330.5, 896851.5)\": -0.331, \"(896851.5, 899167.0)\": -0.426, \"(899167.0, 902138.0)\": -0.541, \"(902138.0, 905080.5)\": -0.608, \"(905080.5, 906551.5)\": -0.738, \"(906551.5, 911540.5)\": -0.807, \"(911540.5, 917896.5)\": -0.885, \"(917896.5, 8810615.5)\": -0.946, \"(8810615.5, 9112480.5)\": -0.664, \"(9112480.5, 89803401.5)\": -0.626, \"(89803401.5, 91544001.5)\": -0.554, \"(91544001.5, 91903901.5)\": -0.463, \"(91903901.5, 911320502.0)\": -0.414}\nUpper Bounds (95%-Confidence Interval): {\"(8670.0, 90271.0)\": 0.744, \"(90271.0, 467526.5)\": 1.07, \"(467526.5, 853506.5)\": 1.212, \"(853506.5, 859643.0)\": 1.299, \"(859643.0, 864727.5)\": 1.234, \"(864727.5, 871421.0)\": 1.148, \"(871421.0, 874848.5)\": 1.051, \"(874848.5, 880845.5)\": 0.956, \"(880845.5, 882230.0)\": 0.86, \"(882230.0, 883266.5)\": 0.774, \"(883266.5, 889561.0)\": 0.482, \"(889561.0, 892521.0)\": 0.402, \"(892521.0, 894330.5)\": 0.345, \"(894330.5, 896851.5)\": 0.286, \"(896851.5, 899167.0)\": 0.212, \"(899167.0, 902138.0)\": 0.188, \"(902138.0, 905080.5)\": 0.127, \"(905080.5, 906551.5)\": 0.128, \"(906551.5, 911540.5)\": 0.07, \"(911540.5, 917896.5)\": 0.023, \"(917896.5, 8810615.5)\": -0.04, \"(8810615.5, 9112480.5)\": -0.107, \"(9112480.5, 89803401.5)\": -0.021, \"(89803401.5, 91544001.5)\": 0.036, \"(91544001.5, 91903901.5)\": 0.081, \"(91903901.5, 911320502.0)\": 0.171}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Watersheds\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02526, \"(0.5, 1.5)\": -0.02147, \"(1.5, 2.5)\": -0.01542, \"(2.5, 3.5)\": -0.01026, \"(3.5, 4.5)\": -0.00466, \"(4.5, 5.5)\": 0.00049, \"(5.5, 6.5)\": 0.00555, \"(6.5, 8.5)\": 0.01133, \"(8.5, 10.5)\": 0.02234, \"(10.5, 11.5)\": 0.03241, \"(11.5, 12.5)\": 0.03775, \"(12.5, 13.5)\": 0.04216, \"(13.5, 14.0)\": 0.04656}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0274, \"(0.5, 1.5)\": -0.02237, \"(1.5, 2.5)\": -0.01633, \"(2.5, 3.5)\": -0.01068, \"(3.5, 4.5)\": -0.005, \"(4.5, 5.5)\": 0.00014, \"(5.5, 6.5)\": 0.00514, \"(6.5, 8.5)\": 0.01068, \"(8.5, 10.5)\": 0.02129, \"(10.5, 11.5)\": 0.03073, \"(11.5, 12.5)\": 0.03466, \"(12.5, 13.5)\": 0.038, \"(13.5, 14.0)\": 0.043}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02312, \"(0.5, 1.5)\": -0.02056, \"(1.5, 2.5)\": -0.01451, \"(2.5, 3.5)\": -0.00985, \"(3.5, 4.5)\": -0.00431, \"(4.5, 5.5)\": 0.00084, \"(5.5, 6.5)\": 0.00596, \"(6.5, 8.5)\": 0.01197, \"(8.5, 10.5)\": 0.0234, \"(10.5, 11.5)\": 0.03409, \"(11.5, 12.5)\": 0.04085, \"(12.5, 13.5)\": 0.04633, \"(13.5, 14.0)\": 0.05012}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Deforestation\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02956, \"(0.5, 2.5)\": -0.02081, \"(2.5, 3.5)\": -0.00998, \"(3.5, 4.5)\": -0.00524, \"(4.5, 5.5)\": 0.00043, \"(5.5, 6.5)\": 0.00515, \"(6.5, 8.5)\": 0.01107, \"(8.5, 10.5)\": 0.02102, \"(10.5, 11.5)\": 0.02728, \"(11.5, 13.5)\": 0.0456, \"(13.5, 14.5)\": 0.05244, \"(14.5, 17.0)\": 0.06161}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.03241, \"(0.5, 2.5)\": -0.02172, \"(2.5, 3.5)\": -0.01056, \"(3.5, 4.5)\": -0.0057, \"(4.5, 5.5)\": 1e-05, \"(5.5, 6.5)\": 0.00474, \"(6.5, 8.5)\": 0.01043, \"(8.5, 10.5)\": 0.01957, \"(10.5, 11.5)\": 0.02542, \"(11.5, 13.5)\": 0.04264, \"(13.5, 14.5)\": 0.04883, \"(14.5, 17.0)\": 0.05758}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02672, \"(0.5, 2.5)\": -0.0199, \"(2.5, 3.5)\": -0.0094, \"(3.5, 4.5)\": -0.00479, \"(4.5, 5.5)\": 0.00085, \"(5.5, 6.5)\": 0.00557, \"(6.5, 8.5)\": 0.01172, \"(8.5, 10.5)\": 0.02247, \"(10.5, 11.5)\": 0.02915, \"(11.5, 13.5)\": 0.04855, \"(13.5, 14.5)\": 0.05605, \"(14.5, 17.0)\": 0.06565}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: area_se\nFeature Type: continuous\nMeans: {\"(6.802, 11.184999999999999)\": -0.919, \"(11.184999999999999, 12.765)\": -0.814, \"(12.765, 13.350000000000001)\": -0.704, \"(13.350000000000001, 15.3)\": -0.596, \"(15.3, 16.955)\": -0.49, \"(16.955, 18.515)\": -0.367, \"(18.515, 20.905)\": -0.256, \"(20.905, 32.985)\": -0.151, \"(32.985, 34.730000000000004)\": 0.081, \"(34.730000000000004, 41.21)\": 0.188, \"(41.21, 50.405)\": 0.292, \"(50.405, 56.915)\": 0.417, \"(56.915, 67.5)\": 0.53, \"(67.5, 81.56)\": 0.638, \"(81.56, 94.00999999999999)\": 0.751, \"(94.00999999999999, 106.2)\": 0.862, \"(106.2, 153.25)\": 0.974, \"(153.25, 542.2)\": 1.082}\nLower Bounds (95%-Confidence Interval): {\"(6.802, 11.184999999999999)\": -1.305, \"(11.184999999999999, 12.765)\": -1.176, \"(12.765, 13.350000000000001)\": -1.036, \"(13.350000000000001, 15.3)\": -0.901, \"(15.3, 16.955)\": -0.696, \"(16.955, 18.515)\": -0.504, \"(18.515, 20.905)\": -0.392, \"(20.905, 32.985)\": -0.922, \"(32.985, 34.730000000000004)\": -0.261, \"(34.730000000000004, 41.21)\": -0.102, \"(41.21, 50.405)\": 0.02, \"(50.405, 56.915)\": 0.072, \"(56.915, 67.5)\": 0.147, \"(67.5, 81.56)\": 0.223, \"(81.56, 94.00999999999999)\": 0.326, \"(94.00999999999999, 106.2)\": 0.402, \"(106.2, 153.25)\": 0.501, \"(153.25, 542.2)\": 0.571}\nUpper Bounds (95%-Confidence Interval): {\"(6.802, 11.184999999999999)\": -0.532, \"(11.184999999999999, 12.765)\": -0.452, \"(12.765, 13.350000000000001)\": -0.371, \"(13.350000000000001, 15.3)\": -0.291, \"(15.3, 16.955)\": -0.284, \"(16.955, 18.515)\": -0.23, \"(18.515, 20.905)\": -0.121, \"(20.905, 32.985)\": 0.62, \"(32.985, 34.730000000000004)\": 0.424, \"(34.730000000000004, 41.21)\": 0.479, \"(41.21, 50.405)\": 0.563, \"(50.405, 56.915)\": 0.762, \"(56.915, 67.5)\": 0.913, \"(67.5, 81.56)\": 1.052, \"(81.56, 94.00999999999999)\": 1.176, \"(94.00999999999999, 106.2)\": 1.323, \"(106.2, 153.25)\": 1.448, \"(153.25, 542.2)\": 1.593}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: IsActiveMember\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.555, \"(0.5, 1.0)\": 0.568}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.593, \"(0.5, 1.0)\": 0.529}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.518, \"(0.5, 1.0)\": 0.606}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Relationship\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": 0.511, \"(0.5, 1.5)\": -0.233, \"(1.5, 2.5)\": -0.666, \"(2.5, 3.5)\": -1.006, \"(3.5, 4.5)\": -0.529, \"(4.5, 5.0)\": 1.753}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.453, \"(0.5, 1.5)\": -0.278, \"(1.5, 2.5)\": -0.789, \"(2.5, 3.5)\": -1.092, \"(3.5, 4.5)\": -0.6, \"(4.5, 5.0)\": 1.664}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.568, \"(0.5, 1.5)\": -0.188, \"(1.5, 2.5)\": -0.543, \"(2.5, 3.5)\": -0.921, \"(3.5, 4.5)\": -0.458, \"(4.5, 5.0)\": 1.842}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: ShoppingMall\nFeature Type: continuous\nMeans: {\"(0.0, 125.5)\": -0.032, \"(125.5, 541.5)\": -0.211, \"(541.5, 808.5)\": 0.034, \"(808.5, 1082.0)\": 0.213, \"(1082.0, 1187.0)\": -0.042, \"(1187.0, 1434.5)\": 0.401, \"(1434.5, 1658.5)\": 0.585, \"(1658.5, 1968.5)\": 0.948, \"(1968.5, 3394.5)\": 1.235, \"(3394.5, 3460.0)\": 0.871, \"(3460.0, 3741.5)\": 1.066, \"(3741.5, 4803.5)\": 2.339, \"(4803.5, 5204.0)\": 2.909, \"(5204.0, 12253.0)\": 3.236}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 125.5)\": -0.092, \"(125.5, 541.5)\": -0.495, \"(541.5, 808.5)\": -0.379, \"(808.5, 1082.0)\": -0.05, \"(1082.0, 1187.0)\": -0.484, \"(1187.0, 1434.5)\": 0.131, \"(1434.5, 1658.5)\": 0.238, \"(1658.5, 1968.5)\": 0.465, \"(1968.5, 3394.5)\": 0.864, \"(3394.5, 3460.0)\": 0.262, \"(3460.0, 3741.5)\": -0.052, \"(3741.5, 4803.5)\": 1.632, \"(4803.5, 5204.0)\": 1.913, \"(5204.0, 12253.0)\": 2.114}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 125.5)\": 0.028, \"(125.5, 541.5)\": 0.073, \"(541.5, 808.5)\": 0.447, \"(808.5, 1082.0)\": 0.477, \"(1082.0, 1187.0)\": 0.4, \"(1187.0, 1434.5)\": 0.671, \"(1434.5, 1658.5)\": 0.931, \"(1658.5, 1968.5)\": 1.43, \"(1968.5, 3394.5)\": 1.605, \"(3394.5, 3460.0)\": 1.481, \"(3460.0, 3741.5)\": 2.183, \"(3741.5, 4803.5)\": 3.046, \"(4803.5, 5204.0)\": 3.906, \"(5204.0, 12253.0)\": 4.358}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Siltation\nFeature Type: continuous\nMeans: {\"(0.0, 1.5)\": -0.02643, \"(1.5, 2.5)\": -0.01529, \"(2.5, 3.5)\": -0.01037, \"(3.5, 4.5)\": -0.00562, \"(4.5, 5.5)\": 0.00068, \"(5.5, 6.5)\": 0.00591, \"(6.5, 7.5)\": 0.01127, \"(7.5, 8.5)\": 0.01553, \"(8.5, 10.5)\": 0.02363, \"(10.5, 11.5)\": 0.03038, \"(11.5, 12.5)\": 0.03607, \"(12.5, 13.5)\": 0.04087, \"(13.5, 15.0)\": 0.04477}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02798, \"(1.5, 2.5)\": -0.01578, \"(2.5, 3.5)\": -0.01088, \"(3.5, 4.5)\": -0.00595, \"(4.5, 5.5)\": 0.0002, \"(5.5, 6.5)\": 0.0054, \"(6.5, 7.5)\": 0.0105, \"(7.5, 8.5)\": 0.01459, \"(8.5, 10.5)\": 0.02243, \"(10.5, 11.5)\": 0.0283, \"(11.5, 12.5)\": 0.03438, \"(12.5, 13.5)\": 0.03775, \"(13.5, 15.0)\": 0.03258}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02487, \"(1.5, 2.5)\": -0.0148, \"(2.5, 3.5)\": -0.00987, \"(3.5, 4.5)\": -0.00529, \"(4.5, 5.5)\": 0.00116, \"(5.5, 6.5)\": 0.00643, \"(6.5, 7.5)\": 0.01205, \"(7.5, 8.5)\": 0.01648, \"(8.5, 10.5)\": 0.02483, \"(10.5, 11.5)\": 0.03246, \"(11.5, 12.5)\": 0.03776, \"(12.5, 13.5)\": 0.044, \"(13.5, 15.0)\": 0.05697}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: area_worst\nFeature Type: continuous\nMeans: {\"(185.2, 357.5)\": -1.345, \"(357.5, 413.15)\": -1.192, \"(413.15, 471.9)\": -1.038, \"(471.9, 508.5)\": -0.878, \"(508.5, 633.9)\": -0.723, \"(633.9, 653.45)\": -0.565, \"(653.45, 710.2)\": -0.348, \"(710.2, 727.0999999999999)\": -0.165, \"(727.0999999999999, 805.95)\": 0.096, \"(805.95, 874.85)\": 0.253, \"(874.85, 928.5)\": 0.48, \"(928.5, 1033.5)\": 0.761, \"(1033.5, 1222.5)\": 0.932, \"(1222.5, 1346.5)\": 1.092, \"(1346.5, 1645.5)\": 1.245, \"(1645.5, 1979.0)\": 1.404, \"(1979.0, 4254.0)\": 1.557}\nLower Bounds (95%-Confidence Interval): {\"(185.2, 357.5)\": -2.413, \"(357.5, 413.15)\": -2.22, \"(413.15, 471.9)\": -2.004, \"(471.9, 508.5)\": -1.818, \"(508.5, 633.9)\": -1.868, \"(633.9, 653.45)\": -1.645, \"(653.45, 710.2)\": -0.767, \"(710.2, 727.0999999999999)\": -0.501, \"(727.0999999999999, 805.95)\": -0.573, \"(805.95, 874.85)\": -0.187, \"(874.85, 928.5)\": -0.49, \"(928.5, 1033.5)\": -0.484, \"(1033.5, 1222.5)\": -0.455, \"(1222.5, 1346.5)\": -0.298, \"(1346.5, 1645.5)\": -0.182, \"(1645.5, 1979.0)\": -0.049, \"(1979.0, 4254.0)\": 0.071}\nUpper Bounds (95%-Confidence Interval): {\"(185.2, 357.5)\": -0.278, \"(357.5, 413.15)\": -0.164, \"(413.15, 471.9)\": -0.073, \"(471.9, 508.5)\": 0.062, \"(508.5, 633.9)\": 0.423, \"(633.9, 653.45)\": 0.516, \"(653.45, 710.2)\": 0.071, \"(710.2, 727.0999999999999)\": 0.17, \"(727.0999999999999, 805.95)\": 0.764, \"(805.95, 874.85)\": 0.693, \"(874.85, 928.5)\": 1.449, \"(928.5, 1033.5)\": 2.006, \"(1033.5, 1222.5)\": 2.319, \"(1222.5, 1346.5)\": 2.482, \"(1346.5, 1645.5)\": 2.672, \"(1645.5, 1979.0)\": 2.857, \"(1979.0, 4254.0)\": 3.043}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Encroachments\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02484, \"(0.5, 1.5)\": -0.02089, \"(1.5, 2.5)\": -0.01739, \"(2.5, 3.5)\": -0.01124, \"(3.5, 4.5)\": -0.00474, \"(4.5, 5.5)\": 0.00077, \"(5.5, 6.5)\": 0.00574, \"(6.5, 7.5)\": 0.01068, \"(7.5, 8.5)\": 0.01599, \"(8.5, 9.5)\": 0.02231, \"(9.5, 10.5)\": 0.02667, \"(10.5, 13.5)\": 0.03305, \"(13.5, 16.0)\": 0.02016}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02637, \"(0.5, 1.5)\": -0.02217, \"(1.5, 2.5)\": -0.0179, \"(2.5, 3.5)\": -0.01163, \"(3.5, 4.5)\": -0.00519, \"(4.5, 5.5)\": 0.00046, \"(5.5, 6.5)\": 0.00525, \"(6.5, 7.5)\": 0.00992, \"(7.5, 8.5)\": 0.01538, \"(8.5, 9.5)\": 0.02115, \"(9.5, 10.5)\": 0.02528, \"(10.5, 13.5)\": 0.02547, \"(13.5, 16.0)\": 0.01297}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0233, \"(0.5, 1.5)\": -0.01962, \"(1.5, 2.5)\": -0.01689, \"(2.5, 3.5)\": -0.01085, \"(3.5, 4.5)\": -0.0043, \"(4.5, 5.5)\": 0.00109, \"(5.5, 6.5)\": 0.00623, \"(6.5, 7.5)\": 0.01144, \"(7.5, 8.5)\": 0.0166, \"(8.5, 9.5)\": 0.02348, \"(9.5, 10.5)\": 0.02807, \"(10.5, 13.5)\": 0.04062, \"(13.5, 16.0)\": 0.02734}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: RiverManagement\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.0273, \"(0.5, 1.5)\": -0.02345, \"(1.5, 2.5)\": -0.01571, \"(2.5, 3.5)\": -0.01174, \"(3.5, 4.5)\": -0.00519, \"(4.5, 5.5)\": 0.00111, \"(5.5, 6.5)\": 0.00506, \"(6.5, 7.5)\": 0.01056, \"(7.5, 8.5)\": 0.01706, \"(8.5, 9.5)\": 0.02398, \"(9.5, 11.5)\": 0.02821, \"(11.5, 12.5)\": 0.03673, \"(12.5, 13.5)\": 0.01311, \"(13.5, 16.0)\": 0.03206}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02945, \"(0.5, 1.5)\": -0.02501, \"(1.5, 2.5)\": -0.01619, \"(2.5, 3.5)\": -0.0121, \"(3.5, 4.5)\": -0.00549, \"(4.5, 5.5)\": 0.00069, \"(5.5, 6.5)\": 0.00469, \"(6.5, 7.5)\": 0.00991, \"(7.5, 8.5)\": 0.01638, \"(8.5, 9.5)\": 0.02306, \"(9.5, 11.5)\": 0.0266, \"(11.5, 12.5)\": 0.02982, \"(12.5, 13.5)\": -0.01689, \"(13.5, 16.0)\": 0.01715}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02515, \"(0.5, 1.5)\": -0.0219, \"(1.5, 2.5)\": -0.01524, \"(2.5, 3.5)\": -0.01139, \"(3.5, 4.5)\": -0.0049, \"(4.5, 5.5)\": 0.00152, \"(5.5, 6.5)\": 0.00544, \"(6.5, 7.5)\": 0.01121, \"(7.5, 8.5)\": 0.01774, \"(8.5, 9.5)\": 0.0249, \"(9.5, 11.5)\": 0.02981, \"(11.5, 12.5)\": 0.04363, \"(12.5, 13.5)\": 0.04312, \"(13.5, 16.0)\": 0.04696}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: BMI\nFeature Type: continuous\nMeans: {\"(0.0, 9.1)\": -0.7, \"(9.1, 22.55)\": -0.961, \"(22.55, 23.65)\": -0.856, \"(23.65, 25.55)\": -0.762, \"(25.55, 26.35)\": -0.661, \"(26.35, 27.65)\": -0.24, \"(27.65, 28.45)\": -0.144, \"(28.45, 29.65)\": -0.051, \"(29.65, 30.45)\": 0.049, \"(30.45, 32.150000000000006)\": 0.153, \"(32.150000000000006, 37.650000000000006)\": 0.246, \"(37.650000000000006, 41.75)\": 0.34, \"(41.75, 42.849999999999994)\": 0.434, \"(42.849999999999994, 45.650000000000006)\": 0.529, \"(45.650000000000006, 48.349999999999994)\": 0.626, \"(48.349999999999994, 67.1)\": 0.784}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 9.1)\": -1.139, \"(9.1, 22.55)\": -1.349, \"(22.55, 23.65)\": -1.219, \"(23.65, 25.55)\": -1.281, \"(25.55, 26.35)\": -1.231, \"(26.35, 27.65)\": -0.568, \"(27.65, 28.45)\": -0.258, \"(28.45, 29.65)\": -0.157, \"(29.65, 30.45)\": -0.11, \"(30.45, 32.150000000000006)\": -0.086, \"(32.150000000000006, 37.650000000000006)\": 0.084, \"(37.650000000000006, 41.75)\": 0.189, \"(41.75, 42.849999999999994)\": 0.28, \"(42.849999999999994, 45.650000000000006)\": 0.348, \"(45.650000000000006, 48.349999999999994)\": 0.256, \"(48.349999999999994, 67.1)\": 0.265}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 9.1)\": -0.262, \"(9.1, 22.55)\": -0.573, \"(22.55, 23.65)\": -0.493, \"(23.65, 25.55)\": -0.243, \"(25.55, 26.35)\": -0.09, \"(26.35, 27.65)\": 0.088, \"(27.65, 28.45)\": -0.03, \"(28.45, 29.65)\": 0.054, \"(29.65, 30.45)\": 0.208, \"(30.45, 32.150000000000006)\": 0.392, \"(32.150000000000006, 37.650000000000006)\": 0.409, \"(37.650000000000006, 41.75)\": 0.491, \"(41.75, 42.849999999999994)\": 0.588, \"(42.849999999999994, 45.650000000000006)\": 0.709, \"(45.650000000000006, 48.349999999999994)\": 0.996, \"(48.349999999999994, 67.1)\": 1.303}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: texture_worst\nFeature Type: continuous\nMeans: {\"(12.02, 16.935000000000002)\": -1.885, \"(16.935000000000002, 18.335)\": -1.717, \"(18.335, 19.505)\": -1.55, \"(19.505, 20.225)\": -0.851, \"(20.225, 21.955)\": -0.612, \"(21.955, 23.59)\": -0.44, \"(23.59, 24.795)\": -0.272, \"(24.795, 25.18)\": -0.1, \"(25.18, 25.83)\": 0.078, \"(25.83, 26.855)\": 0.279, \"(26.855, 27.994999999999997)\": 0.451, \"(27.994999999999997, 29.225)\": 0.619, \"(29.225, 31.515)\": 0.878, \"(31.515, 32.485)\": 1.044, \"(32.485, 35.05)\": 1.256, \"(35.05, 49.54)\": 1.423}\nLower Bounds (95%-Confidence Interval): {\"(12.02, 16.935000000000002)\": -4.342, \"(16.935000000000002, 18.335)\": -4.128, \"(18.335, 19.505)\": -3.934, \"(19.505, 20.225)\": -1.264, \"(20.225, 21.955)\": -0.945, \"(21.955, 23.59)\": -0.663, \"(23.59, 24.795)\": -0.468, \"(24.795, 25.18)\": -0.274, \"(25.18, 25.83)\": -0.503, \"(25.83, 26.855)\": -0.327, \"(26.855, 27.994999999999997)\": -0.163, \"(27.994999999999997, 29.225)\": -0.01, \"(29.225, 31.515)\": -0.206, \"(31.515, 32.485)\": -0.081, \"(32.485, 35.05)\": -0.18, \"(35.05, 49.54)\": -0.014}\nUpper Bounds (95%-Confidence Interval): {\"(12.02, 16.935000000000002)\": 0.572, \"(16.935000000000002, 18.335)\": 0.695, \"(18.335, 19.505)\": 0.835, \"(19.505, 20.225)\": -0.437, \"(20.225, 21.955)\": -0.279, \"(21.955, 23.59)\": -0.218, \"(23.59, 24.795)\": -0.076, \"(24.795, 25.18)\": 0.073, \"(25.18, 25.83)\": 0.66, \"(25.83, 26.855)\": 0.884, \"(26.855, 27.994999999999997)\": 1.065, \"(27.994999999999997, 29.225)\": 1.248, \"(29.225, 31.515)\": 1.961, \"(31.515, 32.485)\": 2.17, \"(32.485, 35.05)\": 2.691, \"(35.05, 49.54)\": 2.861}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Age\nFeature Type: continuous\nMeans: {\"(18.0, 32.5)\": 0.83, \"(32.5, 34.5)\": 0.681, \"(34.5, 37.5)\": 0.423, \"(37.5, 38.5)\": 0.281, \"(38.5, 39.5)\": 0.054, \"(39.5, 40.5)\": -0.193, \"(40.5, 41.5)\": -0.354, \"(41.5, 42.5)\": -0.494, \"(42.5, 44.5)\": -0.781, \"(44.5, 46.5)\": -1.075, \"(46.5, 48.5)\": -1.546, \"(48.5, 54.5)\": -1.717, \"(54.5, 56.5)\": -1.858, \"(56.5, 64.5)\": -1.707, \"(64.5, 66.5)\": -1.27, \"(66.5, 69.5)\": -1.118, \"(69.5, 70.5)\": -0.888, \"(70.5, 72.5)\": -0.587, \"(72.5, 74.5)\": -0.31, \"(74.5, 81.0)\": -0.157}\nLower Bounds (95%-Confidence Interval): {\"(18.0, 32.5)\": 0.581, \"(32.5, 34.5)\": 0.529, \"(34.5, 37.5)\": 0.367, \"(37.5, 38.5)\": 0.229, \"(38.5, 39.5)\": -0.051, \"(39.5, 40.5)\": -0.305, \"(40.5, 41.5)\": -0.462, \"(41.5, 42.5)\": -0.607, \"(42.5, 44.5)\": -0.855, \"(44.5, 46.5)\": -1.16, \"(46.5, 48.5)\": -1.704, \"(48.5, 54.5)\": -1.885, \"(54.5, 56.5)\": -2.031, \"(56.5, 64.5)\": -1.913, \"(64.5, 66.5)\": -1.66, \"(66.5, 69.5)\": -1.33, \"(69.5, 70.5)\": -1.222, \"(70.5, 72.5)\": -1.257, \"(72.5, 74.5)\": -1.055, \"(74.5, 81.0)\": -0.939}\nUpper Bounds (95%-Confidence Interval): {\"(18.0, 32.5)\": 1.079, \"(32.5, 34.5)\": 0.833, \"(34.5, 37.5)\": 0.48, \"(37.5, 38.5)\": 0.332, \"(38.5, 39.5)\": 0.159, \"(39.5, 40.5)\": -0.08, \"(40.5, 41.5)\": -0.246, \"(41.5, 42.5)\": -0.382, \"(42.5, 44.5)\": -0.706, \"(44.5, 46.5)\": -0.991, \"(46.5, 48.5)\": -1.387, \"(48.5, 54.5)\": -1.548, \"(54.5, 56.5)\": -1.684, \"(56.5, 64.5)\": -1.501, \"(64.5, 66.5)\": -0.88, \"(66.5, 69.5)\": -0.906, \"(69.5, 70.5)\": -0.554, \"(70.5, 72.5)\": 0.082, \"(72.5, 74.5)\": 0.436, \"(74.5, 81.0)\": 0.625}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: petal_width\nFeature Type: continuous\nMeans: {\"(0.1, 0.35)\": 8.07, \"(0.35, 0.45)\": 7.27, \"(0.45, 0.75)\": 6.18, \"(0.75, 1.25)\": -2.64, \"(1.25, 1.75)\": -3.46, \"(1.75, 2.5)\": -4.19}\nLower Bounds (95%-Confidence Interval): {\"(0.1, 0.35)\": 7.9, \"(0.35, 0.45)\": 7.05, \"(0.45, 0.75)\": 3.08, \"(0.75, 1.25)\": -2.81, \"(1.25, 1.75)\": -3.62, \"(1.75, 2.5)\": -4.29}\nUpper Bounds (95%-Confidence Interval): {\"(0.1, 0.35)\": 8.23, \"(0.35, 0.45)\": 7.49, \"(0.45, 0.75)\": 9.28, \"(0.75, 1.25)\": -2.47, \"(1.25, 1.75)\": -3.3, \"(1.75, 2.5)\": -4.08}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Decreasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: SibSp\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.0751, \"(0.5, 2.5)\": 0.1633, \"(2.5, 3.0)\": -0.7301}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.1303, \"(0.5, 2.5)\": -0.2711, \"(2.5, 3.0)\": -2.435}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0198, \"(0.5, 2.5)\": 0.5976, \"(2.5, 3.0)\": 0.9748}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: SkinThickness\nFeature Type: continuous\nMeans: {\"(0.0, 3.5)\": 0.0121, \"(3.5, 7.5)\": -0.0407, \"(7.5, 9.0)\": -0.0873, \"(9.0, 11.5)\": -0.1192, \"(11.5, 13.5)\": -0.1587, \"(13.5, 20.5)\": -0.1856, \"(20.5, 22.5)\": -0.1532, \"(22.5, 24.5)\": -0.1123, \"(24.5, 26.5)\": -0.0708, \"(26.5, 28.5)\": -0.036, \"(28.5, 30.5)\": -0.0039, \"(30.5, 32.5)\": 0.0343, \"(32.5, 34.5)\": 0.0703, \"(34.5, 39.5)\": 0.1069, \"(39.5, 40.5)\": 0.143, \"(40.5, 41.5)\": 0.1769, \"(41.5, 43.5)\": 0.2279, \"(43.5, 47.5)\": 0.2859, \"(47.5, 49.5)\": 0.2453, \"(49.5, 51.0)\": -0.0169, \"(51.0, 55.0)\": -0.0754, \"(55.0, 77.5)\": 0.2174, \"(77.5, 99.0)\": 0.3109}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 3.5)\": -0.071, \"(3.5, 7.5)\": -0.1199, \"(7.5, 9.0)\": -0.1639, \"(9.0, 11.5)\": -0.1953, \"(11.5, 13.5)\": -0.2382, \"(13.5, 20.5)\": -0.2707, \"(20.5, 22.5)\": -0.2184, \"(22.5, 24.5)\": -0.1699, \"(24.5, 26.5)\": -0.1255, \"(26.5, 28.5)\": -0.0953, \"(28.5, 30.5)\": -0.0714, \"(30.5, 32.5)\": -0.0304, \"(32.5, 34.5)\": 0.0205, \"(34.5, 39.5)\": 0.0292, \"(39.5, 40.5)\": 0.0607, \"(40.5, 41.5)\": 0.0987, \"(41.5, 43.5)\": 0.0904, \"(43.5, 47.5)\": 0.0985, \"(47.5, 49.5)\": 0.0202, \"(49.5, 51.0)\": -0.3346, \"(51.0, 55.0)\": -0.5656, \"(55.0, 77.5)\": -0.4718, \"(77.5, 99.0)\": -0.4467}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 3.5)\": 0.0953, \"(3.5, 7.5)\": 0.0385, \"(7.5, 9.0)\": -0.0106, \"(9.0, 11.5)\": -0.0431, \"(11.5, 13.5)\": -0.0792, \"(13.5, 20.5)\": -0.1005, \"(20.5, 22.5)\": -0.088, \"(22.5, 24.5)\": -0.0547, \"(24.5, 26.5)\": -0.0161, \"(26.5, 28.5)\": 0.0233, \"(28.5, 30.5)\": 0.0636, \"(30.5, 32.5)\": 0.099, \"(32.5, 34.5)\": 0.12, \"(34.5, 39.5)\": 0.1847, \"(39.5, 40.5)\": 0.2253, \"(40.5, 41.5)\": 0.255, \"(41.5, 43.5)\": 0.3653, \"(43.5, 47.5)\": 0.4732, \"(47.5, 49.5)\": 0.4704, \"(49.5, 51.0)\": 0.3009, \"(51.0, 55.0)\": 0.4148, \"(55.0, 77.5)\": 0.9065, \"(77.5, 99.0)\": 1.0684}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: DiabetesPedigreeFunction\nFeature Type: continuous\nMeans: {\"(0.078, 0.1265)\": -0.528, \"(0.1265, 0.128)\": -0.218, \"(0.128, 0.2185)\": -0.342, \"(0.2185, 0.3375)\": -0.168, \"(0.3375, 0.4215)\": -0.077, \"(0.4215, 0.4955)\": 0.015, \"(0.4955, 0.5874999999999999)\": 0.131, \"(0.5874999999999999, 0.7215)\": 0.223, \"(0.7215, 0.889)\": 0.316, \"(0.889, 1.0865)\": 0.407, \"(1.0865, 1.178)\": 0.498, \"(1.178, 1.275)\": 1.018, \"(1.275, 1.3925)\": 1.283, \"(1.3925, 1.4175)\": 1.168, \"(1.4175, 1.451)\": 0.065, \"(1.451, 1.837)\": -0.193, \"(1.837, 2.137)\": -0.092}\nLower Bounds (95%-Confidence Interval): {\"(0.078, 0.1265)\": -0.817, \"(0.1265, 0.128)\": -0.817, \"(0.128, 0.2185)\": -0.618, \"(0.2185, 0.3375)\": -0.533, \"(0.3375, 0.4215)\": -0.266, \"(0.4215, 0.4955)\": -0.104, \"(0.4955, 0.5874999999999999)\": -0.054, \"(0.5874999999999999, 0.7215)\": 0.138, \"(0.7215, 0.889)\": 0.186, \"(0.889, 1.0865)\": 0.263, \"(1.0865, 1.178)\": 0.35, \"(1.178, 1.275)\": 0.124, \"(1.275, 1.3925)\": 0.133, \"(1.3925, 1.4175)\": -0.063, \"(1.4175, 1.451)\": -1.163, \"(1.451, 1.837)\": -1.466, \"(1.837, 2.137)\": -1.112}\nUpper Bounds (95%-Confidence Interval): {\"(0.078, 0.1265)\": -0.238, \"(0.1265, 0.128)\": 0.381, \"(0.128, 0.2185)\": -0.067, \"(0.2185, 0.3375)\": 0.197, \"(0.3375, 0.4215)\": 0.113, \"(0.4215, 0.4955)\": 0.135, \"(0.4955, 0.5874999999999999)\": 0.316, \"(0.5874999999999999, 0.7215)\": 0.308, \"(0.7215, 0.889)\": 0.445, \"(0.889, 1.0865)\": 0.552, \"(1.0865, 1.178)\": 0.646, \"(1.178, 1.275)\": 1.912, \"(1.275, 1.3925)\": 2.433, \"(1.3925, 1.4175)\": 2.398, \"(1.4175, 1.451)\": 1.293, \"(1.451, 1.837)\": 1.08, \"(1.837, 2.137)\": 0.928}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Occupation\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.297, \"(0.5, 3.5)\": -0.074, \"(3.5, 4.5)\": 0.644, \"(4.5, 6.5)\": -0.723, \"(6.5, 7.5)\": -0.542, \"(7.5, 8.5)\": -0.665, \"(8.5, 9.5)\": -0.926, \"(9.5, 10.5)\": 0.423, \"(10.5, 11.5)\": 0.59, \"(11.5, 12.5)\": 0.27, \"(12.5, 13.5)\": 0.534, \"(13.5, 14.0)\": -0.133}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.409, \"(0.5, 3.5)\": -0.139, \"(3.5, 4.5)\": 0.592, \"(4.5, 6.5)\": -0.847, \"(6.5, 7.5)\": -0.624, \"(7.5, 8.5)\": -0.749, \"(8.5, 9.5)\": -1.549, \"(9.5, 10.5)\": 0.366, \"(10.5, 11.5)\": 0.452, \"(11.5, 12.5)\": 0.225, \"(12.5, 13.5)\": 0.445, \"(13.5, 14.0)\": -0.202}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.185, \"(0.5, 3.5)\": -0.01, \"(3.5, 4.5)\": 0.695, \"(4.5, 6.5)\": -0.598, \"(6.5, 7.5)\": -0.461, \"(7.5, 8.5)\": -0.581, \"(8.5, 9.5)\": -0.302, \"(9.5, 10.5)\": 0.48, \"(10.5, 11.5)\": 0.727, \"(11.5, 12.5)\": 0.315, \"(12.5, 13.5)\": 0.622, \"(13.5, 14.0)\": -0.064}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: ejection_fraction\nFeature Type: continuous\nMeans: {\"(14.0, 16.0)\": 4.55, \"(16.0, 22.5)\": 3.26, \"(22.5, 27.5)\": 1.89, \"(27.5, 32.5)\": -0.42, \"(32.5, 36.5)\": -1.76, \"(36.5, 39.0)\": 0.48, \"(39.0, 61.0)\": -0.83, \"(61.0, 67.5)\": 0.08, \"(67.5, 75.0)\": 0.8, \"(75.0, 80.0)\": -5.67}\nLower Bounds (95%-Confidence Interval): {\"(14.0, 16.0)\": 2.65, \"(16.0, 22.5)\": 2.42, \"(22.5, 27.5)\": 1.26, \"(27.5, 32.5)\": -0.83, \"(32.5, 36.5)\": -2.57, \"(36.5, 39.0)\": 0.17, \"(39.0, 61.0)\": -1.16, \"(61.0, 67.5)\": -0.39, \"(67.5, 75.0)\": 0.32, \"(75.0, 80.0)\": -8.05}\nUpper Bounds (95%-Confidence Interval): {\"(14.0, 16.0)\": 6.45, \"(16.0, 22.5)\": 4.1, \"(22.5, 27.5)\": 2.51, \"(27.5, 32.5)\": -0.01, \"(32.5, 36.5)\": -0.95, \"(36.5, 39.0)\": 0.79, \"(39.0, 61.0)\": -0.49, \"(61.0, 67.5)\": 0.55, \"(67.5, 75.0)\": 1.28, \"(75.0, 80.0)\": -3.29}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: CustomerId\nFeature Type: continuous\nMeans: {\"(15565796.0, 15566519.0)\": -0.8769, \"(15566519.0, 15567333.5)\": -0.8241, \"(15567333.5, 15567844.5)\": -0.1763, \"(15567844.5, 15568343.5)\": 0.0021, \"(15568343.5, 15571612.0)\": -0.2283, \"(15571612.0, 15571858.5)\": -0.0522, \"(15571858.5, 15591260.5)\": -0.1299, \"(15591260.5, 15598058.0)\": -0.0821, \"(15598058.0, 15602525.5)\": -0.1509, \"(15602525.5, 15607288.0)\": -0.0818, \"(15607288.0, 15664896.0)\": -0.0316, \"(15664896.0, 15772587.0)\": 0.0162, \"(15772587.0, 15797097.0)\": 0.0757, \"(15797097.0, 15799214.0)\": 0.0081, \"(15799214.0, 15807559.5)\": 0.0581, \"(15807559.5, 15812616.5)\": -0.0049, \"(15812616.5, 15814479.0)\": -0.0569, \"(15814479.0, 15815247.5)\": -0.111, \"(15815247.5, 15815626.0)\": -0.0335}\nLower Bounds (95%-Confidence Interval): {\"(15565796.0, 15566519.0)\": -1.3796, \"(15566519.0, 15567333.5)\": -1.4199, \"(15567333.5, 15567844.5)\": -0.741, \"(15567844.5, 15568343.5)\": -0.4552, \"(15568343.5, 15571612.0)\": -0.4861, \"(15571612.0, 15571858.5)\": -0.3268, \"(15571858.5, 15591260.5)\": -0.2064, \"(15591260.5, 15598058.0)\": -0.1582, \"(15598058.0, 15602525.5)\": -0.5056, \"(15602525.5, 15607288.0)\": -0.1812, \"(15607288.0, 15664896.0)\": -0.056, \"(15664896.0, 15772587.0)\": -0.142, \"(15772587.0, 15797097.0)\": -0.0689, \"(15797097.0, 15799214.0)\": -0.206, \"(15799214.0, 15807559.5)\": -0.0544, \"(15807559.5, 15812616.5)\": -0.1396, \"(15812616.5, 15814479.0)\": -0.2475, \"(15814479.0, 15815247.5)\": -0.4076, \"(15815247.5, 15815626.0)\": -0.3716}\nUpper Bounds (95%-Confidence Interval): {\"(15565796.0, 15566519.0)\": -0.3742, \"(15566519.0, 15567333.5)\": -0.2283, \"(15567333.5, 15567844.5)\": 0.3884, \"(15567844.5, 15568343.5)\": 0.4594, \"(15568343.5, 15571612.0)\": 0.0295, \"(15571612.0, 15571858.5)\": 0.2223, \"(15571858.5, 15591260.5)\": -0.0535, \"(15591260.5, 15598058.0)\": -0.0061, \"(15598058.0, 15602525.5)\": 0.2038, \"(15602525.5, 15607288.0)\": 0.0176, \"(15607288.0, 15664896.0)\": -0.0071, \"(15664896.0, 15772587.0)\": 0.1744, \"(15772587.0, 15797097.0)\": 0.2202, \"(15797097.0, 15799214.0)\": 0.2223, \"(15799214.0, 15807559.5)\": 0.1706, \"(15807559.5, 15812616.5)\": 0.1298, \"(15812616.5, 15814479.0)\": 0.1336, \"(15814479.0, 15815247.5)\": 0.1855, \"(15815247.5, 15815626.0)\": 0.3046}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: WetlandLoss\nFeature Type: continuous\nMeans: {\"(0.0, 1.5)\": -0.02419, \"(1.5, 2.5)\": -0.01693, \"(2.5, 3.5)\": -0.01069, \"(3.5, 4.5)\": -0.00585, \"(4.5, 5.5)\": 0.00051, \"(5.5, 6.5)\": 0.00676, \"(6.5, 8.5)\": 0.01245, \"(8.5, 10.5)\": 0.02257, \"(10.5, 11.5)\": 0.03265, \"(11.5, 13.5)\": 0.03889, \"(13.5, 14.5)\": 0.04912, \"(14.5, 16.0)\": 0.0585}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02604, \"(1.5, 2.5)\": -0.01758, \"(2.5, 3.5)\": -0.01104, \"(3.5, 4.5)\": -0.00622, \"(4.5, 5.5)\": 0.00022, \"(5.5, 6.5)\": 0.0063, \"(6.5, 8.5)\": 0.01194, \"(8.5, 10.5)\": 0.0215, \"(10.5, 11.5)\": 0.03022, \"(11.5, 13.5)\": 0.03581, \"(13.5, 14.5)\": 0.04439, \"(14.5, 16.0)\": 0.04645}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02235, \"(1.5, 2.5)\": -0.01628, \"(2.5, 3.5)\": -0.01034, \"(3.5, 4.5)\": -0.00547, \"(4.5, 5.5)\": 0.0008, \"(5.5, 6.5)\": 0.00723, \"(6.5, 8.5)\": 0.01295, \"(8.5, 10.5)\": 0.02363, \"(10.5, 11.5)\": 0.03508, \"(11.5, 13.5)\": 0.04198, \"(13.5, 14.5)\": 0.05386, \"(14.5, 16.0)\": 0.07055}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Glucose\nFeature Type: continuous\nMeans: {\"(0.0, 22.0)\": -0.728, \"(22.0, 86.5)\": -1.069, \"(86.5, 94.5)\": -0.907, \"(94.5, 99.5)\": -0.729, \"(99.5, 105.5)\": -0.491, \"(105.5, 114.5)\": -0.326, \"(114.5, 123.5)\": -0.157, \"(123.5, 130.5)\": 0.045, \"(130.5, 139.5)\": 0.208, \"(139.5, 147.5)\": 0.37, \"(147.5, 154.5)\": 0.535, \"(154.5, 159.5)\": 0.724, \"(159.5, 165.5)\": 0.984, \"(165.5, 169.5)\": 1.342, \"(169.5, 178.5)\": 1.502, \"(178.5, 187.5)\": 1.691, \"(187.5, 198.5)\": 1.853, \"(198.5, 199.0)\": 2.022}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 22.0)\": -1.316, \"(22.0, 86.5)\": -1.535, \"(86.5, 94.5)\": -1.3, \"(94.5, 99.5)\": -1.042, \"(99.5, 105.5)\": -0.722, \"(105.5, 114.5)\": -0.428, \"(114.5, 123.5)\": -0.249, \"(123.5, 130.5)\": -0.151, \"(130.5, 139.5)\": 0.044, \"(139.5, 147.5)\": 0.215, \"(147.5, 154.5)\": 0.135, \"(154.5, 159.5)\": 0.451, \"(159.5, 165.5)\": 0.509, \"(165.5, 169.5)\": 0.633, \"(169.5, 178.5)\": 0.768, \"(178.5, 187.5)\": 0.987, \"(187.5, 198.5)\": 1.135, \"(198.5, 199.0)\": 1.3}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 22.0)\": -0.14, \"(22.0, 86.5)\": -0.602, \"(86.5, 94.5)\": -0.514, \"(94.5, 99.5)\": -0.417, \"(99.5, 105.5)\": -0.26, \"(105.5, 114.5)\": -0.223, \"(114.5, 123.5)\": -0.064, \"(123.5, 130.5)\": 0.242, \"(130.5, 139.5)\": 0.373, \"(139.5, 147.5)\": 0.525, \"(147.5, 154.5)\": 0.936, \"(154.5, 159.5)\": 0.997, \"(159.5, 165.5)\": 1.458, \"(165.5, 169.5)\": 2.051, \"(169.5, 178.5)\": 2.237, \"(178.5, 187.5)\": 2.394, \"(187.5, 198.5)\": 2.571, \"(198.5, 199.0)\": 2.744}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: ClimateChange\nFeature Type: continuous\nMeans: {\"(0.0, 1.5)\": -0.02549, \"(1.5, 2.5)\": -0.01575, \"(2.5, 3.5)\": -0.01061, \"(3.5, 4.5)\": -0.0046, \"(4.5, 5.5)\": 0.00059, \"(5.5, 6.5)\": 0.00567, \"(6.5, 7.5)\": 0.01201, \"(7.5, 9.5)\": 0.01601, \"(9.5, 10.5)\": 0.02531, \"(10.5, 11.5)\": 0.02956, \"(11.5, 12.5)\": 0.04031, \"(12.5, 14.0)\": 0.04423}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02735, \"(1.5, 2.5)\": -0.01647, \"(2.5, 3.5)\": -0.01101, \"(3.5, 4.5)\": -0.00502, \"(4.5, 5.5)\": 0.00018, \"(5.5, 6.5)\": 0.00528, \"(6.5, 7.5)\": 0.01139, \"(7.5, 9.5)\": 0.01505, \"(9.5, 10.5)\": 0.0236, \"(10.5, 11.5)\": 0.02677, \"(11.5, 12.5)\": 0.03846, \"(12.5, 14.0)\": 0.03359}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02363, \"(1.5, 2.5)\": -0.01503, \"(2.5, 3.5)\": -0.0102, \"(3.5, 4.5)\": -0.00418, \"(4.5, 5.5)\": 0.00101, \"(5.5, 6.5)\": 0.00607, \"(6.5, 7.5)\": 0.01263, \"(7.5, 9.5)\": 0.01697, \"(9.5, 10.5)\": 0.02702, \"(10.5, 11.5)\": 0.03236, \"(11.5, 12.5)\": 0.04216, \"(12.5, 14.0)\": 0.05488}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Pregnancies\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.1506, \"(0.5, 1.5)\": -0.2484, \"(1.5, 2.5)\": -0.1873, \"(2.5, 3.5)\": -0.0302, \"(3.5, 4.5)\": 0.0211, \"(4.5, 5.5)\": 0.1013, \"(5.5, 6.5)\": 0.1489, \"(6.5, 7.5)\": 0.264, \"(7.5, 8.5)\": 0.3553, \"(8.5, 9.5)\": 0.4117, \"(9.5, 13.5)\": 0.2996, \"(13.5, 14.0)\": 0.6729}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.2406, \"(0.5, 1.5)\": -0.3636, \"(1.5, 2.5)\": -0.242, \"(2.5, 3.5)\": -0.093, \"(3.5, 4.5)\": -0.038, \"(4.5, 5.5)\": 0.0314, \"(5.5, 6.5)\": 0.0909, \"(6.5, 7.5)\": 0.1609, \"(7.5, 8.5)\": 0.2075, \"(8.5, 9.5)\": 0.248, \"(9.5, 13.5)\": 0.0671, \"(13.5, 14.0)\": 0.084}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0606, \"(0.5, 1.5)\": -0.1333, \"(1.5, 2.5)\": -0.1326, \"(2.5, 3.5)\": 0.0326, \"(3.5, 4.5)\": 0.0802, \"(4.5, 5.5)\": 0.1712, \"(5.5, 6.5)\": 0.207, \"(6.5, 7.5)\": 0.3671, \"(7.5, 8.5)\": 0.5032, \"(8.5, 9.5)\": 0.5755, \"(9.5, 13.5)\": 0.5321, \"(13.5, 14.0)\": 1.2617}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: creatinine_phosphokinase\nFeature Type: continuous\nMeans: {\"(23.0, 32.0)\": -0.48, \"(32.0, 49.5)\": 0.68, \"(49.5, 56.5)\": -4.31, \"(56.5, 59.5)\": -2.44, \"(59.5, 64.5)\": -1.82, \"(64.5, 85.0)\": -1.1, \"(85.0, 87.0)\": 0.42, \"(87.0, 93.5)\": -0.75, \"(93.5, 94.5)\": 0.47, \"(94.5, 103.5)\": -0.53, \"(103.5, 107.5)\": 0.12, \"(107.5, 120.0)\": -0.5, \"(120.0, 121.5)\": 0.24, \"(121.5, 126.0)\": 1.25, \"(126.0, 127.5)\": -3.14, \"(127.5, 145.5)\": 1.51, \"(145.5, 147.0)\": 0.91, \"(147.0, 150.0)\": -0.15, \"(150.0, 160.5)\": -1.08, \"(160.5, 189.5)\": -0.45, \"(189.5, 232.5)\": -1.26, \"(232.5, 254.5)\": -0.16, \"(254.5, 258.5)\": 2.88, \"(258.5, 280.5)\": 1.68, \"(280.5, 331.5)\": 1.11, \"(331.5, 370.0)\": 0.44, \"(370.0, 462.0)\": 1.1, \"(462.0, 597.5)\": 0.53, \"(597.5, 751.0)\": -1.87, \"(751.0, 766.5)\": 0.06, \"(766.5, 806.0)\": 2.64, \"(806.0, 873.5)\": 2.05, \"(873.5, 1036.0)\": 0.28, \"(1036.0, 1415.0)\": 0.85, \"(1415.0, 1649.0)\": 0.18, \"(1649.0, 1726.0)\": 2.26, \"(1726.0, 1886.0)\": 0.04, \"(1886.0, 2038.5)\": 7.0, \"(2038.5, 2307.5)\": 2.26, \"(2307.5, 2444.0)\": 5.81, \"(2444.0, 3440.5)\": -2.71, \"(3440.5, 4253.0)\": -1.47, \"(4253.0, 5548.5)\": 1.68, \"(5548.5, 7861.0)\": 3.47}\nLower Bounds (95%-Confidence Interval): {\"(23.0, 32.0)\": -1.51, \"(32.0, 49.5)\": -0.87, \"(49.5, 56.5)\": -5.69, \"(56.5, 59.5)\": -3.58, \"(59.5, 64.5)\": -2.64, \"(64.5, 85.0)\": -2.07, \"(85.0, 87.0)\": -2.37, \"(87.0, 93.5)\": -1.85, \"(93.5, 94.5)\": -0.56, \"(94.5, 103.5)\": -0.85, \"(103.5, 107.5)\": -0.45, \"(107.5, 120.0)\": -1.09, \"(120.0, 121.5)\": -0.48, \"(121.5, 126.0)\": 0.88, \"(126.0, 127.5)\": -5.59, \"(127.5, 145.5)\": 0.93, \"(145.5, 147.0)\": 0.57, \"(147.0, 150.0)\": -0.64, \"(150.0, 160.5)\": -2.38, \"(160.5, 189.5)\": -1.47, \"(189.5, 232.5)\": -2.02, \"(232.5, 254.5)\": -1.04, \"(254.5, 258.5)\": 1.73, \"(258.5, 280.5)\": 0.55, \"(280.5, 331.5)\": 0.09, \"(331.5, 370.0)\": -0.26, \"(370.0, 462.0)\": 0.18, \"(462.0, 597.5)\": 0.4, \"(597.5, 751.0)\": -3.59, \"(751.0, 766.5)\": -2.06, \"(766.5, 806.0)\": 1.02, \"(806.0, 873.5)\": 0.45, \"(873.5, 1036.0)\": -0.52, \"(1036.0, 1415.0)\": 0.33, \"(1415.0, 1649.0)\": -0.68, \"(1649.0, 1726.0)\": -0.23, \"(1726.0, 1886.0)\": -1.16, \"(1886.0, 2038.5)\": 5.88, \"(2038.5, 2307.5)\": 1.8, \"(2307.5, 2444.0)\": 4.43, \"(2444.0, 3440.5)\": -5.48, \"(3440.5, 4253.0)\": -2.15, \"(4253.0, 5548.5)\": 0.41, \"(5548.5, 7861.0)\": 2.17}\nUpper Bounds (95%-Confidence Interval): {\"(23.0, 32.0)\": 0.54, \"(32.0, 49.5)\": 2.24, \"(49.5, 56.5)\": -2.93, \"(56.5, 59.5)\": -1.31, \"(59.5, 64.5)\": -1.0, \"(64.5, 85.0)\": -0.13, \"(85.0, 87.0)\": 3.22, \"(87.0, 93.5)\": 0.35, \"(93.5, 94.5)\": 1.51, \"(94.5, 103.5)\": -0.2, \"(103.5, 107.5)\": 0.69, \"(107.5, 120.0)\": 0.09, \"(120.0, 121.5)\": 0.97, \"(121.5, 126.0)\": 1.61, \"(126.0, 127.5)\": -0.68, \"(127.5, 145.5)\": 2.09, \"(145.5, 147.0)\": 1.25, \"(147.0, 150.0)\": 0.33, \"(150.0, 160.5)\": 0.22, \"(160.5, 189.5)\": 0.57, \"(189.5, 232.5)\": -0.49, \"(232.5, 254.5)\": 0.72, \"(254.5, 258.5)\": 4.03, \"(258.5, 280.5)\": 2.81, \"(280.5, 331.5)\": 2.12, \"(331.5, 370.0)\": 1.15, \"(370.0, 462.0)\": 2.02, \"(462.0, 597.5)\": 0.67, \"(597.5, 751.0)\": -0.15, \"(751.0, 766.5)\": 2.18, \"(766.5, 806.0)\": 4.25, \"(806.0, 873.5)\": 3.65, \"(873.5, 1036.0)\": 1.09, \"(1036.0, 1415.0)\": 1.38, \"(1415.0, 1649.0)\": 1.04, \"(1649.0, 1726.0)\": 4.75, \"(1726.0, 1886.0)\": 1.24, \"(1886.0, 2038.5)\": 8.11, \"(2038.5, 2307.5)\": 2.72, \"(2307.5, 2444.0)\": 7.19, \"(2444.0, 3440.5)\": 0.06, \"(3440.5, 4253.0)\": -0.79, \"(4253.0, 5548.5)\": 2.95, \"(5548.5, 7861.0)\": 4.78}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: EducationNum\nFeature Type: continuous\nMeans: {\"(1.0, 1.5)\": -4.746, \"(1.5, 4.5)\": -1.252, \"(4.5, 6.5)\": -0.882, \"(6.5, 9.5)\": -0.483, \"(9.5, 11.5)\": -0.093, \"(11.5, 13.5)\": 0.276, \"(13.5, 14.5)\": 0.863, \"(14.5, 16.0)\": 1.487}\nLower Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -6.411, \"(1.5, 4.5)\": -1.52, \"(4.5, 6.5)\": -0.99, \"(6.5, 9.5)\": -0.541, \"(9.5, 11.5)\": -0.138, \"(11.5, 13.5)\": 0.205, \"(13.5, 14.5)\": 0.788, \"(14.5, 16.0)\": 1.332}\nUpper Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -3.082, \"(1.5, 4.5)\": -0.984, \"(4.5, 6.5)\": -0.775, \"(6.5, 9.5)\": -0.425, \"(9.5, 11.5)\": -0.049, \"(11.5, 13.5)\": 0.347, \"(13.5, 14.5)\": 0.938, \"(14.5, 16.0)\": 1.641}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: NativeCountry\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.195, \"(0.5, 1.5)\": 1.333, \"(1.5, 2.5)\": -0.02, \"(2.5, 3.5)\": -0.402, \"(3.5, 4.5)\": -1.423, \"(4.5, 5.5)\": 0.086, \"(5.5, 7.5)\": -0.843, \"(7.5, 8.5)\": -0.246, \"(8.5, 11.5)\": 0.062, \"(11.5, 20.5)\": -0.315, \"(20.5, 21.5)\": 0.109, \"(21.5, 22.5)\": 0.476, \"(22.5, 24.5)\": 0.133, \"(24.5, 26.5)\": -0.35, \"(26.5, 29.5)\": -0.489, \"(29.5, 32.5)\": -0.108, \"(32.5, 33.5)\": -0.483, \"(33.5, 35.5)\": -0.664, \"(35.5, 38.5)\": -0.396, \"(38.5, 39.5)\": 0.028, \"(39.5, 40.5)\": -0.596, \"(40.5, 41.0)\": 1.112}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.344, \"(0.5, 1.5)\": 0.452, \"(1.5, 2.5)\": -0.269, \"(2.5, 3.5)\": -0.76, \"(3.5, 4.5)\": -2.688, \"(4.5, 5.5)\": -0.257, \"(5.5, 7.5)\": -1.727, \"(7.5, 8.5)\": -0.488, \"(8.5, 11.5)\": -0.121, \"(11.5, 20.5)\": -0.631, \"(20.5, 21.5)\": -0.319, \"(21.5, 22.5)\": 0.048, \"(22.5, 24.5)\": -0.066, \"(24.5, 26.5)\": -0.66, \"(26.5, 29.5)\": -1.067, \"(29.5, 32.5)\": -0.254, \"(32.5, 33.5)\": -0.844, \"(33.5, 35.5)\": -1.156, \"(35.5, 38.5)\": -0.997, \"(38.5, 39.5)\": 0.02, \"(39.5, 40.5)\": -1.452, \"(40.5, 41.0)\": 0.408}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.045, \"(0.5, 1.5)\": 2.213, \"(1.5, 2.5)\": 0.228, \"(2.5, 3.5)\": -0.043, \"(3.5, 4.5)\": -0.158, \"(4.5, 5.5)\": 0.429, \"(5.5, 7.5)\": 0.04, \"(7.5, 8.5)\": -0.004, \"(8.5, 11.5)\": 0.245, \"(11.5, 20.5)\": 0.001, \"(20.5, 21.5)\": 0.537, \"(21.5, 22.5)\": 0.904, \"(22.5, 24.5)\": 0.331, \"(24.5, 26.5)\": -0.04, \"(26.5, 29.5)\": 0.089, \"(29.5, 32.5)\": 0.038, \"(32.5, 33.5)\": -0.121, \"(33.5, 35.5)\": -0.172, \"(35.5, 38.5)\": 0.204, \"(38.5, 39.5)\": 0.036, \"(39.5, 40.5)\": 0.26, \"(40.5, 41.0)\": 1.816}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: PoliticalFactors\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.0263, \"(0.5, 1.5)\": -0.02126, \"(1.5, 2.5)\": -0.01709, \"(2.5, 3.5)\": -0.01038, \"(3.5, 4.5)\": -0.00633, \"(4.5, 5.5)\": 0.00068, \"(5.5, 6.5)\": 0.00618, \"(6.5, 7.5)\": 0.01223, \"(7.5, 8.5)\": 0.01761, \"(8.5, 9.5)\": 0.02318, \"(9.5, 10.5)\": 0.02782, \"(10.5, 11.5)\": 0.03238, \"(11.5, 13.5)\": 0.03978, \"(13.5, 15.0)\": 0.04468, \"(15.0, 16.0)\": 0.0529}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02939, \"(0.5, 1.5)\": -0.02258, \"(1.5, 2.5)\": -0.01777, \"(2.5, 3.5)\": -0.01075, \"(3.5, 4.5)\": -0.00677, \"(4.5, 5.5)\": 0.00038, \"(5.5, 6.5)\": 0.00571, \"(6.5, 7.5)\": 0.01182, \"(7.5, 8.5)\": 0.01718, \"(8.5, 9.5)\": 0.02223, \"(9.5, 10.5)\": 0.02645, \"(10.5, 11.5)\": 0.02946, \"(11.5, 13.5)\": 0.03697, \"(13.5, 15.0)\": 0.03459, \"(15.0, 16.0)\": 0.03844}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02321, \"(0.5, 1.5)\": -0.01993, \"(1.5, 2.5)\": -0.01641, \"(2.5, 3.5)\": -0.01001, \"(3.5, 4.5)\": -0.00589, \"(4.5, 5.5)\": 0.00098, \"(5.5, 6.5)\": 0.00665, \"(6.5, 7.5)\": 0.01264, \"(7.5, 8.5)\": 0.01804, \"(8.5, 9.5)\": 0.02414, \"(9.5, 10.5)\": 0.02919, \"(10.5, 11.5)\": 0.0353, \"(11.5, 13.5)\": 0.04259, \"(13.5, 15.0)\": 0.05476, \"(15.0, 16.0)\": 0.06736}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Balance\nFeature Type: continuous\nMeans: {\"(0.0, 50418.515)\": -0.132, \"(50418.515, 53570.93)\": -0.285, \"(53570.93, 54249.445)\": -0.826, \"(54249.445, 57428.56)\": -0.404, \"(57428.56, 60041.265)\": -0.005, \"(60041.265, 64897.8)\": 0.215, \"(64897.8, 72985.875)\": 0.086, \"(72985.875, 74989.08499999999)\": -0.012, \"(74989.08499999999, 76596.815)\": 0.247, \"(76596.815, 79953.185)\": 0.829, \"(79953.185, 83348.07)\": 0.564, \"(83348.07, 101890.23999999999)\": 0.414, \"(101890.23999999999, 114327.485)\": 0.248, \"(114327.485, 123946.3)\": 0.164, \"(123946.3, 141661.24)\": 0.075, \"(141661.24, 174920.08000000002)\": 0.173, \"(174920.08000000002, 181813.135)\": 0.059, \"(181813.135, 191993.675)\": -0.349, \"(191993.675, 200829.925)\": -0.459, \"(200829.925, 206951.87)\": -0.616, \"(206951.87, 216109.88)\": -0.256}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 50418.515)\": -0.192, \"(50418.515, 53570.93)\": -0.628, \"(53570.93, 54249.445)\": -1.999, \"(54249.445, 57428.56)\": -0.798, \"(57428.56, 60041.265)\": -0.322, \"(60041.265, 64897.8)\": -0.105, \"(64897.8, 72985.875)\": -0.195, \"(72985.875, 74989.08499999999)\": -0.418, \"(74989.08499999999, 76596.815)\": -0.231, \"(76596.815, 79953.185)\": 0.338, \"(79953.185, 83348.07)\": 0.321, \"(83348.07, 101890.23999999999)\": 0.247, \"(101890.23999999999, 114327.485)\": 0.097, \"(114327.485, 123946.3)\": 0.069, \"(123946.3, 141661.24)\": -0.23, \"(141661.24, 174920.08000000002)\": -0.272, \"(174920.08000000002, 181813.135)\": -0.147, \"(181813.135, 191993.675)\": -0.864, \"(191993.675, 200829.925)\": -0.991, \"(200829.925, 206951.87)\": -1.401, \"(206951.87, 216109.88)\": -0.862}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 50418.515)\": -0.072, \"(50418.515, 53570.93)\": 0.057, \"(53570.93, 54249.445)\": 0.347, \"(54249.445, 57428.56)\": -0.011, \"(57428.56, 60041.265)\": 0.312, \"(60041.265, 64897.8)\": 0.534, \"(64897.8, 72985.875)\": 0.367, \"(72985.875, 74989.08499999999)\": 0.395, \"(74989.08499999999, 76596.815)\": 0.725, \"(76596.815, 79953.185)\": 1.32, \"(79953.185, 83348.07)\": 0.806, \"(83348.07, 101890.23999999999)\": 0.582, \"(101890.23999999999, 114327.485)\": 0.398, \"(114327.485, 123946.3)\": 0.259, \"(123946.3, 141661.24)\": 0.379, \"(141661.24, 174920.08000000002)\": 0.618, \"(174920.08000000002, 181813.135)\": 0.264, \"(181813.135, 191993.675)\": 0.166, \"(191993.675, 200829.925)\": 0.073, \"(200829.925, 206951.87)\": 0.169, \"(206951.87, 216109.88)\": 0.35}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: DrainageSystems\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02593, \"(0.5, 1.5)\": -0.02211, \"(1.5, 2.5)\": -0.01611, \"(2.5, 3.5)\": -0.01125, \"(3.5, 4.5)\": -0.0047, \"(4.5, 5.5)\": 9e-05, \"(5.5, 6.5)\": 0.00652, \"(6.5, 8.5)\": 0.01219, \"(8.5, 10.5)\": 0.02253, \"(10.5, 11.5)\": 0.03412, \"(11.5, 12.5)\": 0.04015, \"(12.5, 14.0)\": 0.04564}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02795, \"(0.5, 1.5)\": -0.02324, \"(1.5, 2.5)\": -0.01672, \"(2.5, 3.5)\": -0.01177, \"(3.5, 4.5)\": -0.00504, \"(4.5, 5.5)\": -0.00021, \"(5.5, 6.5)\": 0.00613, \"(6.5, 8.5)\": 0.01137, \"(8.5, 10.5)\": 0.02139, \"(10.5, 11.5)\": 0.03184, \"(11.5, 12.5)\": 0.03703, \"(12.5, 14.0)\": 0.04222}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02391, \"(0.5, 1.5)\": -0.02097, \"(1.5, 2.5)\": -0.0155, \"(2.5, 3.5)\": -0.01073, \"(3.5, 4.5)\": -0.00435, \"(4.5, 5.5)\": 0.00039, \"(5.5, 6.5)\": 0.00691, \"(6.5, 8.5)\": 0.01301, \"(8.5, 10.5)\": 0.02367, \"(10.5, 11.5)\": 0.0364, \"(11.5, 12.5)\": 0.04328, \"(12.5, 14.0)\": 0.04907}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Urbanization\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02565, \"(0.5, 1.5)\": -0.02133, \"(1.5, 2.5)\": -0.01683, \"(2.5, 3.5)\": -0.00993, \"(3.5, 4.5)\": -0.00473, \"(4.5, 5.5)\": -1e-05, \"(5.5, 6.5)\": 0.00511, \"(6.5, 7.5)\": 0.01148, \"(7.5, 8.5)\": 0.01621, \"(8.5, 9.5)\": 0.02476, \"(9.5, 11.5)\": 0.02962, \"(11.5, 12.5)\": 0.03469, \"(12.5, 13.5)\": 0.04866, \"(13.5, 16.0)\": 0.05902}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02758, \"(0.5, 1.5)\": -0.02272, \"(1.5, 2.5)\": -0.01769, \"(2.5, 3.5)\": -0.01036, \"(3.5, 4.5)\": -0.00514, \"(4.5, 5.5)\": -0.0004, \"(5.5, 6.5)\": 0.00453, \"(6.5, 7.5)\": 0.01098, \"(7.5, 8.5)\": 0.01535, \"(8.5, 9.5)\": 0.0239, \"(9.5, 11.5)\": 0.02772, \"(11.5, 12.5)\": 0.03206, \"(12.5, 13.5)\": 0.04307, \"(13.5, 16.0)\": 0.0546}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02372, \"(0.5, 1.5)\": -0.01994, \"(1.5, 2.5)\": -0.01596, \"(2.5, 3.5)\": -0.00951, \"(3.5, 4.5)\": -0.00432, \"(4.5, 5.5)\": 0.00037, \"(5.5, 6.5)\": 0.00568, \"(6.5, 7.5)\": 0.01199, \"(7.5, 8.5)\": 0.01706, \"(8.5, 9.5)\": 0.02562, \"(9.5, 11.5)\": 0.03152, \"(11.5, 12.5)\": 0.03732, \"(12.5, 13.5)\": 0.05424, \"(13.5, 16.0)\": 0.06343}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Landslides\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02593, \"(0.5, 1.5)\": -0.02172, \"(1.5, 2.5)\": -0.01544, \"(2.5, 3.5)\": -0.0098, \"(3.5, 4.5)\": -0.00541, \"(4.5, 5.5)\": 0.00066, \"(5.5, 6.5)\": 0.00575, \"(6.5, 7.5)\": 0.01201, \"(7.5, 8.5)\": 0.01649, \"(8.5, 9.5)\": 0.0215, \"(9.5, 10.5)\": 0.0267, \"(10.5, 11.5)\": 0.03057, \"(11.5, 13.5)\": 0.0366, \"(13.5, 14.0)\": 0.03003}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02743, \"(0.5, 1.5)\": -0.02261, \"(1.5, 2.5)\": -0.01616, \"(2.5, 3.5)\": -0.0102, \"(3.5, 4.5)\": -0.00579, \"(4.5, 5.5)\": 0.00027, \"(5.5, 6.5)\": 0.00544, \"(6.5, 7.5)\": 0.01146, \"(7.5, 8.5)\": 0.01601, \"(8.5, 9.5)\": 0.02065, \"(9.5, 10.5)\": 0.02512, \"(10.5, 11.5)\": 0.0285, \"(11.5, 13.5)\": 0.02931, \"(13.5, 14.0)\": 0.02233}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02443, \"(0.5, 1.5)\": -0.02083, \"(1.5, 2.5)\": -0.01472, \"(2.5, 3.5)\": -0.0094, \"(3.5, 4.5)\": -0.00504, \"(4.5, 5.5)\": 0.00105, \"(5.5, 6.5)\": 0.00606, \"(6.5, 7.5)\": 0.01257, \"(7.5, 8.5)\": 0.01698, \"(8.5, 9.5)\": 0.02234, \"(9.5, 10.5)\": 0.02828, \"(10.5, 11.5)\": 0.03265, \"(11.5, 13.5)\": 0.04389, \"(13.5, 14.0)\": 0.03772}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: AgriculturalPractices\nFeature Type: continuous\nMeans: {\"(0.0, 1.5)\": -0.02463, \"(1.5, 2.5)\": -0.01694, \"(2.5, 3.5)\": -0.01147, \"(3.5, 4.5)\": -0.00533, \"(4.5, 5.5)\": 0.00036, \"(5.5, 6.5)\": 0.00641, \"(6.5, 7.5)\": 0.01086, \"(7.5, 8.5)\": 0.01753, \"(8.5, 9.5)\": 0.02391, \"(9.5, 11.5)\": 0.03162, \"(11.5, 14.0)\": 0.0391, \"(14.0, 15.0)\": 0.05506}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02721, \"(1.5, 2.5)\": -0.01778, \"(2.5, 3.5)\": -0.01182, \"(3.5, 4.5)\": -0.00574, \"(4.5, 5.5)\": -9e-05, \"(5.5, 6.5)\": 0.00587, \"(6.5, 7.5)\": 0.01028, \"(7.5, 8.5)\": 0.01669, \"(8.5, 9.5)\": 0.02306, \"(9.5, 11.5)\": 0.02986, \"(11.5, 14.0)\": 0.03465, \"(14.0, 15.0)\": 0.03109}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02205, \"(1.5, 2.5)\": -0.0161, \"(2.5, 3.5)\": -0.01113, \"(3.5, 4.5)\": -0.00492, \"(4.5, 5.5)\": 0.0008, \"(5.5, 6.5)\": 0.00696, \"(6.5, 7.5)\": 0.01144, \"(7.5, 8.5)\": 0.01837, \"(8.5, 9.5)\": 0.02477, \"(9.5, 11.5)\": 0.03339, \"(11.5, 14.0)\": 0.04355, \"(14.0, 15.0)\": 0.07902}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: diabetes\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": 0.3225, \"(0.5, 1.0)\": -0.415}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.1807, \"(0.5, 1.0)\": -0.5976}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.4643, \"(0.5, 1.0)\": -0.2325}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Decreasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: id\nFeature Type: continuous\nMeans: {\"(8.0, 349.5)\": -0.1954, \"(349.5, 1899.5)\": -0.1448, \"(1899.5, 4908.5)\": -0.18, \"(4908.5, 5578.5)\": -0.2082, \"(5578.5, 5813.5)\": -0.25, \"(5813.5, 6004.5)\": -0.345, \"(6004.5, 7170.5)\": -0.1246, \"(7170.5, 7335.5)\": 0.0378, \"(7335.5, 8083.0)\": 0.1773, \"(8083.0, 8604.0)\": 0.1221, \"(8604.0, 8759.0)\": -0.0027, \"(8759.0, 45049.5)\": -0.0395, \"(45049.5, 45346.5)\": -0.3688, \"(45346.5, 46184.5)\": -0.0125, \"(46184.5, 54575.0)\": 0.0215, \"(54575.0, 55661.5)\": -0.0521, \"(55661.5, 66954.0)\": 0.0101, \"(66954.0, 67057.0)\": -0.0227, \"(67057.0, 68275.0)\": 0.0595, \"(68275.0, 97577.5)\": 0.0244, \"(97577.5, 110643.5)\": 0.0529, \"(110643.5, 146554.5)\": 0.0211, \"(146554.5, 146921.5)\": -0.0139, \"(146921.5, 147131.5)\": -0.0861, \"(147131.5, 161901.5)\": -0.0139, \"(161901.5, 162437.5)\": -0.0745, \"(162437.5, 164212.5)\": -0.0061, \"(164212.5, 164569.5)\": -0.057, \"(164569.5, 164786.5)\": 0.0766, \"(164786.5, 165030.0)\": 0.1394}\nLower Bounds (95%-Confidence Interval): {\"(8.0, 349.5)\": -0.513, \"(349.5, 1899.5)\": -0.2797, \"(1899.5, 4908.5)\": -0.4617, \"(4908.5, 5578.5)\": -0.3743, \"(5578.5, 5813.5)\": -0.5275, \"(5813.5, 6004.5)\": -0.9752, \"(6004.5, 7170.5)\": -0.3878, \"(7170.5, 7335.5)\": -0.2528, \"(7335.5, 8083.0)\": -0.2278, \"(8083.0, 8604.0)\": -0.157, \"(8604.0, 8759.0)\": -0.2655, \"(8759.0, 45049.5)\": -0.1283, \"(45049.5, 45346.5)\": -1.0587, \"(45346.5, 46184.5)\": -0.1339, \"(46184.5, 54575.0)\": -0.1154, \"(54575.0, 55661.5)\": -0.241, \"(55661.5, 66954.0)\": -0.0113, \"(66954.0, 67057.0)\": -0.3245, \"(67057.0, 68275.0)\": -0.2172, \"(68275.0, 97577.5)\": -0.0853, \"(97577.5, 110643.5)\": -0.0258, \"(110643.5, 146554.5)\": -0.1398, \"(146554.5, 146921.5)\": -0.0713, \"(146921.5, 147131.5)\": -0.6452, \"(147131.5, 161901.5)\": -0.1222, \"(161901.5, 162437.5)\": -0.3435, \"(162437.5, 164212.5)\": -0.1164, \"(164212.5, 164569.5)\": -0.2376, \"(164569.5, 164786.5)\": -0.3913, \"(164786.5, 165030.0)\": -0.3304}\nUpper Bounds (95%-Confidence Interval): {\"(8.0, 349.5)\": 0.1221, \"(349.5, 1899.5)\": -0.0099, \"(1899.5, 4908.5)\": 0.1016, \"(4908.5, 5578.5)\": -0.0421, \"(5578.5, 5813.5)\": 0.0274, \"(5813.5, 6004.5)\": 0.2852, \"(6004.5, 7170.5)\": 0.1385, \"(7170.5, 7335.5)\": 0.3284, \"(7335.5, 8083.0)\": 0.5824, \"(8083.0, 8604.0)\": 0.4011, \"(8604.0, 8759.0)\": 0.2602, \"(8759.0, 45049.5)\": 0.0493, \"(45049.5, 45346.5)\": 0.321, \"(45346.5, 46184.5)\": 0.1088, \"(46184.5, 54575.0)\": 0.1583, \"(54575.0, 55661.5)\": 0.1369, \"(55661.5, 66954.0)\": 0.0316, \"(66954.0, 67057.0)\": 0.2791, \"(67057.0, 68275.0)\": 0.3361, \"(68275.0, 97577.5)\": 0.1341, \"(97577.5, 110643.5)\": 0.1316, \"(110643.5, 146554.5)\": 0.182, \"(146554.5, 146921.5)\": 0.0435, \"(146921.5, 147131.5)\": 0.4731, \"(147131.5, 161901.5)\": 0.0945, \"(161901.5, 162437.5)\": 0.1945, \"(162437.5, 164212.5)\": 0.1042, \"(164212.5, 164569.5)\": 0.1235, \"(164569.5, 164786.5)\": 0.5445, \"(164786.5, 165030.0)\": 0.6091}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: smoothness_mean\nFeature Type: continuous\nMeans: {\"(0.05263, 0.0706)\": -0.835, \"(0.0706, 0.07455500000000001)\": -0.769, \"(0.07455500000000001, 0.07589499999999999)\": -0.697, \"(0.07589499999999999, 0.07727500000000001)\": -0.632, \"(0.07727500000000001, 0.078275)\": -0.569, \"(0.078275, 0.07952000000000001)\": -0.506, \"(0.07952000000000001, 0.080315)\": -0.437, \"(0.080315, 0.081035)\": -0.368, \"(0.081035, 0.08308499999999999)\": -0.304, \"(0.08308499999999999, 0.085165)\": -0.242, \"(0.085165, 0.086795)\": -0.177, \"(0.086795, 0.087785)\": -0.111, \"(0.087785, 0.088615)\": -0.047, \"(0.088615, 0.08918999999999999)\": 0.065, \"(0.08918999999999999, 0.090335)\": 0.142, \"(0.090335, 0.09454)\": 0.211, \"(0.09454, 0.11525)\": 0.107, \"(0.11525, 0.11765)\": 0.171, \"(0.11765, 0.12455)\": 0.267, \"(0.12455, 0.13845000000000002)\": 0.334, \"(0.13845000000000002, 0.1634)\": 0.396}\nLower Bounds (95%-Confidence Interval): {\"(0.05263, 0.0706)\": -1.454, \"(0.0706, 0.07455500000000001)\": -1.359, \"(0.07455500000000001, 0.07589499999999999)\": -1.244, \"(0.07589499999999999, 0.07727500000000001)\": -1.162, \"(0.07727500000000001, 0.078275)\": -1.087, \"(0.078275, 0.07952000000000001)\": -1.006, \"(0.07952000000000001, 0.080315)\": -0.882, \"(0.080315, 0.081035)\": -0.622, \"(0.081035, 0.08308499999999999)\": -0.547, \"(0.08308499999999999, 0.085165)\": -0.444, \"(0.085165, 0.086795)\": -0.357, \"(0.086795, 0.087785)\": -0.296, \"(0.087785, 0.088615)\": -0.23, \"(0.088615, 0.08918999999999999)\": -0.16, \"(0.08918999999999999, 0.090335)\": -0.309, \"(0.090335, 0.09454)\": -0.264, \"(0.09454, 0.11525)\": -0.005, \"(0.11525, 0.11765)\": 0.07, \"(0.11765, 0.12455)\": 0.022, \"(0.12455, 0.13845000000000002)\": 0.077, \"(0.13845000000000002, 0.1634)\": 0.127}\nUpper Bounds (95%-Confidence Interval): {\"(0.05263, 0.0706)\": -0.216, \"(0.0706, 0.07455500000000001)\": -0.178, \"(0.07455500000000001, 0.07589499999999999)\": -0.151, \"(0.07589499999999999, 0.07727500000000001)\": -0.102, \"(0.07727500000000001, 0.078275)\": -0.052, \"(0.078275, 0.07952000000000001)\": -0.006, \"(0.07952000000000001, 0.080315)\": 0.008, \"(0.080315, 0.081035)\": -0.113, \"(0.081035, 0.08308499999999999)\": -0.062, \"(0.08308499999999999, 0.085165)\": -0.04, \"(0.085165, 0.086795)\": 0.004, \"(0.086795, 0.087785)\": 0.075, \"(0.087785, 0.088615)\": 0.136, \"(0.088615, 0.08918999999999999)\": 0.291, \"(0.08918999999999999, 0.090335)\": 0.594, \"(0.090335, 0.09454)\": 0.685, \"(0.09454, 0.11525)\": 0.22, \"(0.11525, 0.11765)\": 0.273, \"(0.11765, 0.12455)\": 0.512, \"(0.12455, 0.13845000000000002)\": 0.591, \"(0.13845000000000002, 0.1634)\": 0.664}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Race\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.8604, \"(0.5, 1.5)\": -0.0173, \"(1.5, 2.5)\": -0.2499, \"(2.5, 3.5)\": -0.3026, \"(3.5, 4.0)\": 0.0414}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -1.0291, \"(0.5, 1.5)\": -0.1456, \"(1.5, 2.5)\": -0.3118, \"(2.5, 3.5)\": -0.4557, \"(3.5, 4.0)\": 0.0349}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.6918, \"(0.5, 1.5)\": 0.1111, \"(1.5, 2.5)\": -0.1879, \"(2.5, 3.5)\": -0.1496, \"(3.5, 4.0)\": 0.048}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: WorkClass\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.013, \"(0.5, 1.5)\": 0.434, \"(1.5, 4.5)\": -0.066, \"(4.5, 5.5)\": 0.167, \"(5.5, 7.5)\": -0.464, \"(7.5, 8.0)\": -2.54}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.099, \"(0.5, 1.5)\": 0.319, \"(1.5, 4.5)\": -0.192, \"(4.5, 5.5)\": 0.106, \"(5.5, 7.5)\": -0.567, \"(7.5, 8.0)\": -4.038}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.074, \"(0.5, 1.5)\": 0.549, \"(1.5, 4.5)\": 0.059, \"(4.5, 5.5)\": 0.228, \"(5.5, 7.5)\": -0.362, \"(7.5, 8.0)\": -1.042}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Age\nFeature Type: continuous\nMeans: {\"(2.0, 2.5)\": -0.503, \"(2.5, 5.0)\": 1.062, \"(5.0, 17.5)\": 1.188, \"(17.5, 24.5)\": 0.305, \"(24.5, 28.5)\": 0.438, \"(28.5, 31.5)\": 0.03, \"(31.5, 35.5)\": 0.337, \"(35.5, 36.25)\": 0.047, \"(36.25, 43.5)\": -0.09, \"(43.5, 44.5)\": -0.293, \"(44.5, 47.5)\": -0.611, \"(47.5, 49.5)\": -0.32, \"(49.5, 59.0)\": -0.561, \"(59.0, 60.5)\": -0.283, \"(60.5, 63.5)\": -0.939, \"(63.5, 70.5)\": -1.095, \"(70.5, 75.5)\": -0.598, \"(75.5, 80.0)\": -0.406}\nLower Bounds (95%-Confidence Interval): {\"(2.0, 2.5)\": -2.047, \"(2.5, 5.0)\": -0.63, \"(5.0, 17.5)\": -0.496, \"(17.5, 24.5)\": -0.053, \"(24.5, 28.5)\": -0.121, \"(28.5, 31.5)\": -0.759, \"(31.5, 35.5)\": -0.296, \"(35.5, 36.25)\": -0.141, \"(36.25, 43.5)\": -0.547, \"(43.5, 44.5)\": -0.684, \"(44.5, 47.5)\": -1.551, \"(47.5, 49.5)\": -0.563, \"(49.5, 59.0)\": -1.187, \"(59.0, 60.5)\": -1.123, \"(60.5, 63.5)\": -2.149, \"(63.5, 70.5)\": -2.327, \"(70.5, 75.5)\": -0.924, \"(75.5, 80.0)\": -0.696}\nUpper Bounds (95%-Confidence Interval): {\"(2.0, 2.5)\": 1.042, \"(2.5, 5.0)\": 2.754, \"(5.0, 17.5)\": 2.872, \"(17.5, 24.5)\": 0.662, \"(24.5, 28.5)\": 0.998, \"(28.5, 31.5)\": 0.819, \"(31.5, 35.5)\": 0.969, \"(35.5, 36.25)\": 0.234, \"(36.25, 43.5)\": 0.367, \"(43.5, 44.5)\": 0.098, \"(44.5, 47.5)\": 0.329, \"(47.5, 49.5)\": -0.077, \"(49.5, 59.0)\": 0.064, \"(59.0, 60.5)\": 0.557, \"(60.5, 63.5)\": 0.271, \"(63.5, 70.5)\": 0.137, \"(70.5, 75.5)\": -0.271, \"(75.5, 80.0)\": -0.116}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: households\nFeature Type: continuous\nMeans: {\"(2.0, 4.5)\": -5401.6, \"(4.5, 6.5)\": -23687.9, \"(6.5, 8.5)\": -53732.5, \"(8.5, 9.5)\": -14617.2, \"(9.5, 12.5)\": 16225.5, \"(12.5, 13.5)\": 21846.0, \"(13.5, 14.5)\": 29456.0, \"(14.5, 15.5)\": 14293.2, \"(15.5, 20.5)\": -21670.3, \"(20.5, 21.5)\": 3195.8, \"(21.5, 55.5)\": -12458.9, \"(55.5, 155.5)\": -20063.6, \"(155.5, 156.5)\": -15642.0, \"(156.5, 157.5)\": -6390.8, \"(157.5, 186.5)\": -19320.2, \"(186.5, 196.5)\": -23743.0, \"(196.5, 198.5)\": -18377.6, \"(198.5, 223.5)\": -12744.1, \"(223.5, 230.5)\": -6336.7, \"(230.5, 295.5)\": -10855.3, \"(295.5, 394.5)\": -6355.5, \"(394.5, 535.5)\": -443.1, \"(535.5, 561.5)\": 3934.9, \"(561.5, 599.5)\": 9004.1, \"(599.5, 600.5)\": 13667.2, \"(600.5, 634.5)\": 8706.3, \"(634.5, 635.5)\": 25959.4, \"(635.5, 824.5)\": 13815.1, \"(824.5, 864.5)\": 18503.2, \"(864.5, 962.5)\": 26367.0, \"(962.5, 964.5)\": 14554.6, \"(964.5, 976.5)\": 23227.2, \"(976.5, 978.5)\": 18664.6, \"(978.5, 990.5)\": 26114.1, \"(990.5, 1000.5)\": 30854.6, \"(1000.5, 1088.5)\": 25473.5, \"(1088.5, 1092.5)\": 21095.0, \"(1092.5, 1130.5)\": 26497.2, \"(1130.5, 1272.5)\": 33562.7, \"(1272.5, 3516.0)\": 28522.2, \"(3516.0, 6082.0)\": 21556.0}\nLower Bounds (95%-Confidence Interval): {\"(2.0, 4.5)\": -30426.4, \"(4.5, 6.5)\": -41560.8, \"(6.5, 8.5)\": -83483.7, \"(8.5, 9.5)\": -68637.5, \"(9.5, 12.5)\": -15018.5, \"(12.5, 13.5)\": -5488.2, \"(13.5, 14.5)\": 1721.7, \"(14.5, 15.5)\": -25117.7, \"(15.5, 20.5)\": -41734.0, \"(20.5, 21.5)\": -26800.7, \"(21.5, 55.5)\": -26732.7, \"(55.5, 155.5)\": -27250.3, \"(155.5, 156.5)\": -25256.4, \"(156.5, 157.5)\": -28521.9, \"(157.5, 186.5)\": -26383.4, \"(186.5, 196.5)\": -29250.8, \"(196.5, 198.5)\": -25752.9, \"(198.5, 223.5)\": -20683.5, \"(223.5, 230.5)\": -15595.3, \"(230.5, 295.5)\": -18207.8, \"(295.5, 394.5)\": -15406.0, \"(394.5, 535.5)\": -9211.1, \"(535.5, 561.5)\": -5668.7, \"(561.5, 599.5)\": 904.9, \"(599.5, 600.5)\": -3740.6, \"(600.5, 634.5)\": 3782.4, \"(634.5, 635.5)\": 139.1, \"(635.5, 824.5)\": 6137.4, \"(824.5, 864.5)\": 11294.8, \"(864.5, 962.5)\": 17755.5, \"(962.5, 964.5)\": -5105.1, \"(964.5, 976.5)\": 14837.4, \"(976.5, 978.5)\": 5892.7, \"(978.5, 990.5)\": 18169.8, \"(990.5, 1000.5)\": 15738.6, \"(1000.5, 1088.5)\": 19888.5, \"(1088.5, 1092.5)\": 9478.6, \"(1092.5, 1130.5)\": 20925.9, \"(1130.5, 1272.5)\": 24768.1, \"(1272.5, 3516.0)\": 19419.3, \"(3516.0, 6082.0)\": 8532.3}\nUpper Bounds (95%-Confidence Interval): {\"(2.0, 4.5)\": 19623.3, \"(4.5, 6.5)\": -5814.9, \"(6.5, 8.5)\": -23981.3, \"(8.5, 9.5)\": 39403.2, \"(9.5, 12.5)\": 47469.5, \"(12.5, 13.5)\": 49180.3, \"(13.5, 14.5)\": 57190.3, \"(14.5, 15.5)\": 53704.2, \"(15.5, 20.5)\": -1606.7, \"(20.5, 21.5)\": 33192.3, \"(21.5, 55.5)\": 1814.9, \"(55.5, 155.5)\": -12877.0, \"(155.5, 156.5)\": -6027.7, \"(156.5, 157.5)\": 15740.2, \"(157.5, 186.5)\": -12257.0, \"(186.5, 196.5)\": -18235.2, \"(196.5, 198.5)\": -11002.4, \"(198.5, 223.5)\": -4804.8, \"(223.5, 230.5)\": 2921.9, \"(230.5, 295.5)\": -3502.7, \"(295.5, 394.5)\": 2695.1, \"(394.5, 535.5)\": 8324.9, \"(535.5, 561.5)\": 13538.5, \"(561.5, 599.5)\": 17103.2, \"(599.5, 600.5)\": 31074.9, \"(600.5, 634.5)\": 13630.1, \"(634.5, 635.5)\": 51779.7, \"(635.5, 824.5)\": 21492.8, \"(824.5, 864.5)\": 25711.7, \"(864.5, 962.5)\": 34978.6, \"(962.5, 964.5)\": 34214.4, \"(964.5, 976.5)\": 31616.9, \"(976.5, 978.5)\": 31436.4, \"(978.5, 990.5)\": 34058.4, \"(990.5, 1000.5)\": 45970.6, \"(1000.5, 1088.5)\": 31058.5, \"(1088.5, 1092.5)\": 32711.5, \"(1092.5, 1130.5)\": 32068.4, \"(1130.5, 1272.5)\": 42357.3, \"(1272.5, 3516.0)\": 37625.1, \"(3516.0, 6082.0)\": 34579.6}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: anaemia\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.0818, \"(0.5, 1.0)\": 0.0917}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.1464, \"(0.5, 1.0)\": 0.0194}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0173, \"(0.5, 1.0)\": 0.1641}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: smoking\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": 0.01522, \"(0.5, 1.0)\": -0.03391}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0422, \"(0.5, 1.0)\": -0.16186}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.07264, \"(0.5, 1.0)\": 0.09404}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Decreasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: platelets\nFeature Type: continuous\nMeans: {\"(25100.0, 27700.0)\": -1.004, \"(27700.0, 34450.0)\": -0.687, \"(34450.0, 42200.0)\": 0.328, \"(42200.0, 56500.0)\": 1.717, \"(56500.0, 66050.0)\": 2.769, \"(66050.0, 74000.0)\": 2.195, \"(74000.0, 95500.0)\": 2.956, \"(95500.0, 104500.0)\": -0.265, \"(104500.0, 144000.0)\": -0.585, \"(144000.0, 150500.0)\": -0.895, \"(150500.0, 154000.0)\": 2.322, \"(154000.0, 169000.0)\": 0.469, \"(169000.0, 184500.0)\": -1.612, \"(184500.0, 195000.0)\": 1.111, \"(195000.0, 199000.0)\": 3.01, \"(199000.0, 200500.0)\": 1.837, \"(200500.0, 214000.0)\": 0.403, \"(214000.0, 217500.0)\": -0.825, \"(217500.0, 218500.0)\": -1.399, \"(218500.0, 220500.0)\": 0.341, \"(220500.0, 222500.0)\": 0.978, \"(222500.0, 226500.0)\": 1.584, \"(226500.0, 241500.0)\": 0.175, \"(241500.0, 242500.0)\": 0.642, \"(242500.0, 243500.0)\": 1.107, \"(243500.0, 244500.0)\": 1.516, \"(244500.0, 252500.0)\": -2.19, \"(252500.0, 261000.0)\": -0.878, \"(261000.0, 274500.0)\": -0.145, \"(274500.0, 283500.0)\": -0.968, \"(283500.0, 287500.0)\": 0.203, \"(287500.0, 289500.0)\": 1.032, \"(289500.0, 302500.0)\": -1.296, \"(302500.0, 305500.0)\": -2.984, \"(305500.0, 307000.0)\": 0.876, \"(307000.0, 332000.0)\": 0.368, \"(332000.0, 335000.0)\": 1.21, \"(335000.0, 343000.0)\": 0.8, \"(343000.0, 350500.0)\": -0.573, \"(350500.0, 354500.0)\": 3.0, \"(354500.0, 383500.0)\": -0.119, \"(383500.0, 449500.0)\": 0.655, \"(449500.0, 471000.0)\": 1.527, \"(471000.0, 500500.0)\": -2.247, \"(500500.0, 582000.0)\": -0.442, \"(582000.0, 675500.0)\": 2.645, \"(675500.0, 796000.0)\": 2.314, \"(796000.0, 850000.0)\": -0.709}\nLower Bounds (95%-Confidence Interval): {\"(25100.0, 27700.0)\": -1.75, \"(27700.0, 34450.0)\": -1.54, \"(34450.0, 42200.0)\": -0.532, \"(42200.0, 56500.0)\": 0.992, \"(56500.0, 66050.0)\": 1.538, \"(66050.0, 74000.0)\": 1.537, \"(74000.0, 95500.0)\": 1.91, \"(95500.0, 104500.0)\": -1.642, \"(104500.0, 144000.0)\": -1.428, \"(144000.0, 150500.0)\": -1.74, \"(150500.0, 154000.0)\": 1.125, \"(154000.0, 169000.0)\": 0.027, \"(169000.0, 184500.0)\": -2.523, \"(184500.0, 195000.0)\": 0.214, \"(195000.0, 199000.0)\": 0.239, \"(199000.0, 200500.0)\": 0.581, \"(200500.0, 214000.0)\": -0.252, \"(214000.0, 217500.0)\": -2.007, \"(217500.0, 218500.0)\": -3.583, \"(218500.0, 220500.0)\": 0.076, \"(220500.0, 222500.0)\": 0.244, \"(222500.0, 226500.0)\": -0.038, \"(226500.0, 241500.0)\": -0.123, \"(241500.0, 242500.0)\": 0.22, \"(242500.0, 243500.0)\": 0.116, \"(243500.0, 244500.0)\": 0.265, \"(244500.0, 252500.0)\": -4.008, \"(252500.0, 261000.0)\": -1.287, \"(261000.0, 274500.0)\": -0.465, \"(274500.0, 283500.0)\": -1.829, \"(283500.0, 287500.0)\": -1.587, \"(287500.0, 289500.0)\": -0.951, \"(289500.0, 302500.0)\": -1.857, \"(302500.0, 305500.0)\": -4.201, \"(305500.0, 307000.0)\": 0.125, \"(307000.0, 332000.0)\": -0.181, \"(332000.0, 335000.0)\": -0.179, \"(335000.0, 343000.0)\": 0.105, \"(343000.0, 350500.0)\": -1.469, \"(350500.0, 354500.0)\": 1.748, \"(354500.0, 383500.0)\": -0.848, \"(383500.0, 449500.0)\": 0.242, \"(449500.0, 471000.0)\": -2.033, \"(471000.0, 500500.0)\": -5.177, \"(500500.0, 582000.0)\": -1.795, \"(582000.0, 675500.0)\": 1.501, \"(675500.0, 796000.0)\": 0.104, \"(796000.0, 850000.0)\": -1.557}\nUpper Bounds (95%-Confidence Interval): {\"(25100.0, 27700.0)\": -0.258, \"(27700.0, 34450.0)\": 0.165, \"(34450.0, 42200.0)\": 1.188, \"(42200.0, 56500.0)\": 2.441, \"(56500.0, 66050.0)\": 4.0, \"(66050.0, 74000.0)\": 2.853, \"(74000.0, 95500.0)\": 4.001, \"(95500.0, 104500.0)\": 1.113, \"(104500.0, 144000.0)\": 0.258, \"(144000.0, 150500.0)\": -0.049, \"(150500.0, 154000.0)\": 3.518, \"(154000.0, 169000.0)\": 0.911, \"(169000.0, 184500.0)\": -0.702, \"(184500.0, 195000.0)\": 2.008, \"(195000.0, 199000.0)\": 5.781, \"(199000.0, 200500.0)\": 3.093, \"(200500.0, 214000.0)\": 1.058, \"(214000.0, 217500.0)\": 0.356, \"(217500.0, 218500.0)\": 0.785, \"(218500.0, 220500.0)\": 0.606, \"(220500.0, 222500.0)\": 1.711, \"(222500.0, 226500.0)\": 3.206, \"(226500.0, 241500.0)\": 0.472, \"(241500.0, 242500.0)\": 1.064, \"(242500.0, 243500.0)\": 2.099, \"(243500.0, 244500.0)\": 2.766, \"(244500.0, 252500.0)\": -0.372, \"(252500.0, 261000.0)\": -0.468, \"(261000.0, 274500.0)\": 0.176, \"(274500.0, 283500.0)\": -0.106, \"(283500.0, 287500.0)\": 1.993, \"(287500.0, 289500.0)\": 3.014, \"(289500.0, 302500.0)\": -0.734, \"(302500.0, 305500.0)\": -1.767, \"(305500.0, 307000.0)\": 1.626, \"(307000.0, 332000.0)\": 0.917, \"(332000.0, 335000.0)\": 2.599, \"(335000.0, 343000.0)\": 1.496, \"(343000.0, 350500.0)\": 0.324, \"(350500.0, 354500.0)\": 4.251, \"(354500.0, 383500.0)\": 0.609, \"(383500.0, 449500.0)\": 1.068, \"(449500.0, 471000.0)\": 5.088, \"(471000.0, 500500.0)\": 0.684, \"(500500.0, 582000.0)\": 0.912, \"(582000.0, 675500.0)\": 3.789, \"(675500.0, 796000.0)\": 4.525, \"(796000.0, 850000.0)\": 0.138}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: PopulationScore\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02443, \"(0.5, 1.5)\": -0.02088, \"(1.5, 2.5)\": -0.01613, \"(2.5, 3.5)\": -0.01086, \"(3.5, 4.5)\": -0.00583, \"(4.5, 5.5)\": 0.00139, \"(5.5, 6.5)\": 0.00556, \"(6.5, 7.5)\": 0.01145, \"(7.5, 8.5)\": 0.01748, \"(8.5, 10.5)\": 0.0242, \"(10.5, 11.5)\": 0.03351, \"(11.5, 13.5)\": 0.03691, \"(13.5, 15.0)\": 0.03345, \"(15.0, 16.0)\": 0.02926}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02582, \"(0.5, 1.5)\": -0.02181, \"(1.5, 2.5)\": -0.01706, \"(2.5, 3.5)\": -0.01143, \"(3.5, 4.5)\": -0.00626, \"(4.5, 5.5)\": 0.00099, \"(5.5, 6.5)\": 0.00524, \"(6.5, 7.5)\": 0.01084, \"(7.5, 8.5)\": 0.0167, \"(8.5, 10.5)\": 0.02302, \"(10.5, 11.5)\": 0.03159, \"(11.5, 13.5)\": 0.03427, \"(13.5, 15.0)\": 0.02849, \"(15.0, 16.0)\": 0.02539}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02304, \"(0.5, 1.5)\": -0.01995, \"(1.5, 2.5)\": -0.01521, \"(2.5, 3.5)\": -0.01028, \"(3.5, 4.5)\": -0.00541, \"(4.5, 5.5)\": 0.00178, \"(5.5, 6.5)\": 0.00588, \"(6.5, 7.5)\": 0.01205, \"(7.5, 8.5)\": 0.01826, \"(8.5, 10.5)\": 0.02538, \"(10.5, 11.5)\": 0.03543, \"(11.5, 13.5)\": 0.03955, \"(13.5, 15.0)\": 0.03841, \"(15.0, 16.0)\": 0.03313}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: concave points_worst\nFeature Type: continuous\nMeans: {\"(0.0, 0.02814)\": -0.771, \"(0.02814, 0.08293)\": -0.653, \"(0.08293, 0.08555)\": -0.533, \"(0.08555, 0.093225)\": -0.403, \"(0.093225, 0.1055)\": -0.234, \"(0.1055, 0.11510000000000001)\": -0.117, \"(0.11510000000000001, 0.1346)\": 0.002, \"(0.1346, 0.14545000000000002)\": 0.121, \"(0.14545000000000002, 0.15175)\": 0.241, \"(0.15175, 0.1603)\": 0.365, \"(0.1603, 0.1722)\": 0.539, \"(0.1722, 0.17695)\": 0.661, \"(0.17695, 0.18359999999999999)\": 0.781, \"(0.18359999999999999, 0.194)\": 0.9, \"(0.194, 0.2019)\": 1.022, \"(0.2019, 0.21275)\": 1.14, \"(0.21275, 0.2383)\": 1.259, \"(0.2383, 0.26865)\": 1.378, \"(0.26865, 0.291)\": 1.494}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.02814)\": -1.316, \"(0.02814, 0.08293)\": -1.204, \"(0.08293, 0.08555)\": -1.003, \"(0.08555, 0.093225)\": -0.715, \"(0.093225, 0.1055)\": -0.419, \"(0.1055, 0.11510000000000001)\": -0.299, \"(0.11510000000000001, 0.1346)\": -0.172, \"(0.1346, 0.14545000000000002)\": -0.125, \"(0.14545000000000002, 0.15175)\": 0.077, \"(0.15175, 0.1603)\": 0.185, \"(0.1603, 0.1722)\": 0.112, \"(0.1722, 0.17695)\": 0.052, \"(0.17695, 0.18359999999999999)\": 0.152, \"(0.18359999999999999, 0.194)\": 0.267, \"(0.194, 0.2019)\": 0.392, \"(0.2019, 0.21275)\": 0.477, \"(0.21275, 0.2383)\": 0.593, \"(0.2383, 0.26865)\": 0.693, \"(0.26865, 0.291)\": 0.803}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.02814)\": -0.226, \"(0.02814, 0.08293)\": -0.101, \"(0.08293, 0.08555)\": -0.064, \"(0.08555, 0.093225)\": -0.091, \"(0.093225, 0.1055)\": -0.049, \"(0.1055, 0.11510000000000001)\": 0.065, \"(0.11510000000000001, 0.1346)\": 0.176, \"(0.1346, 0.14545000000000002)\": 0.367, \"(0.14545000000000002, 0.15175)\": 0.406, \"(0.15175, 0.1603)\": 0.545, \"(0.1603, 0.1722)\": 0.966, \"(0.1722, 0.17695)\": 1.27, \"(0.17695, 0.18359999999999999)\": 1.41, \"(0.18359999999999999, 0.194)\": 1.533, \"(0.194, 0.2019)\": 1.653, \"(0.2019, 0.21275)\": 1.803, \"(0.21275, 0.2383)\": 1.926, \"(0.2383, 0.26865)\": 2.063, \"(0.26865, 0.291)\": 2.186}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: CapitalLoss\nFeature Type: continuous\nMeans: {\"(0.0, 845.0)\": -0.044, \"(845.0, 1448.0)\": -1.147, \"(1448.0, 1551.5)\": 0.416, \"(1551.5, 1568.5)\": 3.928, \"(1568.5, 1748.0)\": -3.752, \"(1748.0, 1846.0)\": 1.139, \"(1846.0, 1862.0)\": 3.823, \"(1862.0, 1881.5)\": -1.36, \"(1881.5, 1894.5)\": 4.781, \"(1894.5, 1938.0)\": 3.172, \"(1938.0, 1975.5)\": 0.294, \"(1975.5, 1978.5)\": 4.013, \"(1978.5, 2139.0)\": -2.74, \"(2139.0, 2176.5)\": 0.361, \"(2176.5, 2190.0)\": -1.098, \"(2190.0, 2205.5)\": 1.259, \"(2205.5, 2262.5)\": 2.644, \"(2262.5, 2310.5)\": -0.616, \"(2310.5, 2364.5)\": -1.139, \"(2364.5, 2384.5)\": 1.07, \"(2384.5, 2450.5)\": 4.377, \"(2450.5, 2480.5)\": 1.517, \"(2480.5, 2553.0)\": 3.296, \"(2553.0, 2581.0)\": 5.5, \"(2581.0, 2678.5)\": -0.191, \"(2678.5, 2789.0)\": 0.326, \"(2789.0, 3343.5)\": 5.958, \"(3343.5, 3835.0)\": 2.152, \"(3835.0, 4356.0)\": -0.334}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 845.0)\": -0.934, \"(845.0, 1448.0)\": -2.192, \"(1448.0, 1551.5)\": 0.083, \"(1551.5, 1568.5)\": 2.921, \"(1568.5, 1748.0)\": -4.443, \"(1748.0, 1846.0)\": 0.39, \"(1846.0, 1862.0)\": 2.886, \"(1862.0, 1881.5)\": -2.359, \"(1881.5, 1894.5)\": 3.819, \"(1894.5, 1938.0)\": 2.82, \"(1938.0, 1975.5)\": -0.436, \"(1975.5, 1978.5)\": 3.487, \"(1978.5, 2139.0)\": -3.34, \"(2139.0, 2176.5)\": -0.308, \"(2176.5, 2190.0)\": -2.441, \"(2190.0, 2205.5)\": 0.899, \"(2205.5, 2262.5)\": 1.633, \"(2262.5, 2310.5)\": -2.272, \"(2310.5, 2364.5)\": -2.819, \"(2364.5, 2384.5)\": 0.659, \"(2384.5, 2450.5)\": 3.333, \"(2450.5, 2480.5)\": 0.001, \"(2480.5, 2553.0)\": 1.926, \"(2553.0, 2581.0)\": 4.074, \"(2581.0, 2678.5)\": -1.869, \"(2678.5, 2789.0)\": -1.325, \"(2789.0, 3343.5)\": 4.42, \"(3343.5, 3835.0)\": 0.138, \"(3835.0, 4356.0)\": -1.587}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 845.0)\": 0.845, \"(845.0, 1448.0)\": -0.101, \"(1448.0, 1551.5)\": 0.748, \"(1551.5, 1568.5)\": 4.935, \"(1568.5, 1748.0)\": -3.061, \"(1748.0, 1846.0)\": 1.889, \"(1846.0, 1862.0)\": 4.761, \"(1862.0, 1881.5)\": -0.361, \"(1881.5, 1894.5)\": 5.742, \"(1894.5, 1938.0)\": 3.524, \"(1938.0, 1975.5)\": 1.024, \"(1975.5, 1978.5)\": 4.539, \"(1978.5, 2139.0)\": -2.139, \"(2139.0, 2176.5)\": 1.029, \"(2176.5, 2190.0)\": 0.245, \"(2190.0, 2205.5)\": 1.619, \"(2205.5, 2262.5)\": 3.655, \"(2262.5, 2310.5)\": 1.041, \"(2310.5, 2364.5)\": 0.541, \"(2364.5, 2384.5)\": 1.481, \"(2384.5, 2450.5)\": 5.42, \"(2450.5, 2480.5)\": 3.034, \"(2480.5, 2553.0)\": 4.666, \"(2553.0, 2581.0)\": 6.926, \"(2581.0, 2678.5)\": 1.487, \"(2678.5, 2789.0)\": 1.978, \"(2789.0, 3343.5)\": 7.496, \"(3343.5, 3835.0)\": 4.167, \"(3835.0, 4356.0)\": 0.92}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: MonsoonIntensity\nFeature Type: continuous\nMeans: {\"(0.0, 1.5)\": -0.02446, \"(1.5, 2.5)\": -0.01712, \"(2.5, 3.5)\": -0.00908, \"(3.5, 4.5)\": -0.00529, \"(4.5, 5.5)\": 0.0003, \"(5.5, 6.5)\": 0.00497, \"(6.5, 7.5)\": 0.01093, \"(7.5, 8.5)\": 0.01787, \"(8.5, 9.5)\": 0.02262, \"(9.5, 11.5)\": 0.02707, \"(11.5, 12.5)\": 0.03735, \"(12.5, 13.5)\": 0.043, \"(13.5, 15.0)\": 0.01734}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02705, \"(1.5, 2.5)\": -0.01788, \"(2.5, 3.5)\": -0.00955, \"(3.5, 4.5)\": -0.00566, \"(4.5, 5.5)\": 4e-05, \"(5.5, 6.5)\": 0.00451, \"(6.5, 7.5)\": 0.01051, \"(7.5, 8.5)\": 0.01741, \"(8.5, 9.5)\": 0.02167, \"(9.5, 11.5)\": 0.02561, \"(11.5, 12.5)\": 0.03439, \"(12.5, 13.5)\": 0.03822, \"(13.5, 15.0)\": -0.00028}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02187, \"(1.5, 2.5)\": -0.01637, \"(2.5, 3.5)\": -0.00861, \"(3.5, 4.5)\": -0.00492, \"(4.5, 5.5)\": 0.00056, \"(5.5, 6.5)\": 0.00544, \"(6.5, 7.5)\": 0.01134, \"(7.5, 8.5)\": 0.01833, \"(8.5, 9.5)\": 0.02358, \"(9.5, 11.5)\": 0.02853, \"(11.5, 12.5)\": 0.04032, \"(12.5, 13.5)\": 0.04778, \"(13.5, 15.0)\": 0.03495}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Pclass\nFeature Type: continuous\nMeans: {\"(1.0, 1.5)\": -0.009, \"(1.5, 2.5)\": 0.534, \"(2.5, 3.0)\": -0.532}\nLower Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -0.053, \"(1.5, 2.5)\": 0.174, \"(2.5, 3.0)\": -1.011}\nUpper Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": 0.035, \"(1.5, 2.5)\": 0.893, \"(2.5, 3.0)\": -0.052}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: sex\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": 0.01719, \"(0.5, 1.0)\": -0.00954}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.08236, \"(0.5, 1.0)\": -0.06482}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.11675, \"(0.5, 1.0)\": 0.04573}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Decreasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: NumOfProducts\nFeature Type: continuous\nMeans: {\"(1.0, 1.5)\": -0.918, \"(1.5, 2.5)\": 0.96, \"(2.5, 3.5)\": -3.104, \"(3.5, 4.0)\": -2.768}\nLower Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -0.985, \"(1.5, 2.5)\": 0.893, \"(2.5, 3.5)\": -3.482, \"(3.5, 4.0)\": -3.159}\nUpper Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -0.852, \"(1.5, 2.5)\": 1.028, \"(2.5, 3.5)\": -2.727, \"(3.5, 4.0)\": -2.376}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: total_bedrooms\nFeature Type: continuous\nMeans: {\"(2.0, 4.5)\": -10633.3, \"(4.5, 9.5)\": -19829.1, \"(9.5, 12.5)\": -33356.0, \"(12.5, 14.5)\": -27510.0, \"(14.5, 17.5)\": -34141.4, \"(17.5, 20.5)\": -50740.7, \"(20.5, 22.5)\": -59049.5, \"(22.5, 25.5)\": -37177.7, \"(25.5, 29.5)\": -30710.5, \"(29.5, 111.5)\": -36287.1, \"(111.5, 112.5)\": -22540.1, \"(112.5, 176.5)\": -33870.1, \"(176.5, 245.5)\": -27701.3, \"(245.5, 265.5)\": -20526.0, \"(265.5, 268.5)\": -26170.7, \"(268.5, 317.5)\": -17267.5, \"(317.5, 424.5)\": -8013.2, \"(424.5, 463.5)\": -1894.5, \"(463.5, 512.5)\": 5095.6, \"(512.5, 513.5)\": 17024.1, \"(513.5, 655.5)\": 9371.5, \"(655.5, 697.5)\": 15515.9, \"(697.5, 776.5)\": 22859.4, \"(776.5, 779.5)\": 13774.7, \"(779.5, 1008.5)\": 22608.4, \"(1008.5, 1012.5)\": 37458.5, \"(1012.5, 1081.5)\": 30023.9, \"(1081.5, 1449.5)\": 37066.8, \"(1449.5, 1490.5)\": 51601.0, \"(1490.5, 1616.0)\": 42837.8, \"(1616.0, 2714.5)\": 49023.6, \"(2714.5, 2865.5)\": 40592.1, \"(2865.5, 6445.0)\": 51586.1}\nLower Bounds (95%-Confidence Interval): {\"(2.0, 4.5)\": -43650.4, \"(4.5, 9.5)\": -54645.6, \"(9.5, 12.5)\": -52929.5, \"(12.5, 14.5)\": -57181.8, \"(14.5, 17.5)\": -49207.2, \"(17.5, 20.5)\": -72519.5, \"(20.5, 22.5)\": -82934.2, \"(22.5, 25.5)\": -50942.7, \"(25.5, 29.5)\": -45748.1, \"(29.5, 111.5)\": -47452.5, \"(111.5, 112.5)\": -42457.2, \"(112.5, 176.5)\": -41599.3, \"(176.5, 245.5)\": -35478.0, \"(245.5, 265.5)\": -27520.5, \"(265.5, 268.5)\": -32234.3, \"(268.5, 317.5)\": -23732.7, \"(317.5, 424.5)\": -13237.9, \"(424.5, 463.5)\": -7023.7, \"(463.5, 512.5)\": -1510.7, \"(512.5, 513.5)\": 6820.8, \"(513.5, 655.5)\": 341.5, \"(655.5, 697.5)\": 12634.4, \"(697.5, 776.5)\": 15982.1, \"(776.5, 779.5)\": 221.5, \"(779.5, 1008.5)\": 18345.9, \"(1008.5, 1012.5)\": 20622.3, \"(1012.5, 1081.5)\": 21931.2, \"(1081.5, 1449.5)\": 22140.8, \"(1449.5, 1490.5)\": 39761.7, \"(1490.5, 1616.0)\": 35441.7, \"(1616.0, 2714.5)\": 37135.8, \"(2714.5, 2865.5)\": 32716.4, \"(2865.5, 6445.0)\": 42203.8}\nUpper Bounds (95%-Confidence Interval): {\"(2.0, 4.5)\": 22383.8, \"(4.5, 9.5)\": 14987.3, \"(9.5, 12.5)\": -13782.5, \"(12.5, 14.5)\": 2161.9, \"(14.5, 17.5)\": -19075.5, \"(17.5, 20.5)\": -28961.9, \"(20.5, 22.5)\": -35164.8, \"(22.5, 25.5)\": -23412.7, \"(25.5, 29.5)\": -15672.9, \"(29.5, 111.5)\": -25121.6, \"(111.5, 112.5)\": -2622.9, \"(112.5, 176.5)\": -26141.0, \"(176.5, 245.5)\": -19924.6, \"(245.5, 265.5)\": -13531.5, \"(265.5, 268.5)\": -20107.0, \"(268.5, 317.5)\": -10802.3, \"(317.5, 424.5)\": -2788.6, \"(424.5, 463.5)\": 3234.7, \"(463.5, 512.5)\": 11701.8, \"(512.5, 513.5)\": 27227.4, \"(513.5, 655.5)\": 18401.4, \"(655.5, 697.5)\": 18397.4, \"(697.5, 776.5)\": 29736.8, \"(776.5, 779.5)\": 27327.8, \"(779.5, 1008.5)\": 26870.8, \"(1008.5, 1012.5)\": 54294.7, \"(1012.5, 1081.5)\": 38116.5, \"(1081.5, 1449.5)\": 51992.8, \"(1449.5, 1490.5)\": 63440.2, \"(1490.5, 1616.0)\": 50233.9, \"(1616.0, 2714.5)\": 60911.5, \"(2714.5, 2865.5)\": 48467.9, \"(2865.5, 6445.0)\": 60968.4}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: high_blood_pressure\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.1077, \"(0.5, 1.0)\": 0.1864}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.1574, \"(0.5, 1.0)\": 0.1003}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.058, \"(0.5, 1.0)\": 0.2724}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: sepal_length\nFeature Type: continuous\nMeans: {\"(4.3, 4.55)\": 3.328, \"(4.55, 4.75)\": 2.995, \"(4.75, 4.85)\": 2.698, \"(4.85, 5.05)\": 1.665, \"(5.05, 5.25)\": 1.371, \"(5.25, 5.45)\": 1.085, \"(5.45, 5.55)\": 0.339, \"(5.55, 5.75)\": -0.057, \"(5.75, 5.85)\": -0.39, \"(5.85, 6.15)\": -0.757, \"(6.15, 6.45)\": -1.149, \"(6.45, 6.85)\": -1.436, \"(6.85, 7.7)\": -1.718}\nLower Bounds (95%-Confidence Interval): {\"(4.3, 4.55)\": 3.22, \"(4.55, 4.75)\": 2.846, \"(4.75, 4.85)\": 2.54, \"(4.85, 5.05)\": 1.185, \"(5.05, 5.25)\": 1.214, \"(5.25, 5.45)\": 0.892, \"(5.45, 5.55)\": -0.164, \"(5.55, 5.75)\": -0.32, \"(5.75, 5.85)\": -0.665, \"(5.85, 6.15)\": -0.888, \"(6.15, 6.45)\": -1.29, \"(6.45, 6.85)\": -1.575, \"(6.85, 7.7)\": -1.814}\nUpper Bounds (95%-Confidence Interval): {\"(4.3, 4.55)\": 3.437, \"(4.55, 4.75)\": 3.144, \"(4.75, 4.85)\": 2.857, \"(4.85, 5.05)\": 2.145, \"(5.05, 5.25)\": 1.528, \"(5.25, 5.45)\": 1.277, \"(5.45, 5.55)\": 0.843, \"(5.55, 5.75)\": 0.206, \"(5.75, 5.85)\": -0.116, \"(5.85, 6.15)\": -0.627, \"(6.15, 6.45)\": -1.009, \"(6.45, 6.85)\": -1.298, \"(6.85, 7.7)\": -1.623}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Decreasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: area_mean\nFeature Type: continuous\nMeans: {\"(143.5, 259.35)\": -0.759, \"(259.35, 289.4)\": -0.662, \"(289.4, 319.15)\": -0.567, \"(319.15, 348.3)\": -0.464, \"(348.3, 496.5)\": -0.368, \"(496.5, 548.75)\": -0.271, \"(548.75, 606.0)\": -0.173, \"(606.0, 696.25)\": -0.076, \"(696.25, 806.1500000000001)\": 0.309, \"(806.1500000000001, 901.8)\": 0.405, \"(901.8, 959.4000000000001)\": 0.51, \"(959.4000000000001, 1054.0)\": 0.607, \"(1054.0, 1150.0)\": 0.707, \"(1150.0, 1248.5)\": 0.806, \"(1248.5, 1341.0)\": 0.911, \"(1341.0, 1801.0)\": 1.01, \"(1801.0, 2501.0)\": 1.109}\nLower Bounds (95%-Confidence Interval): {\"(143.5, 259.35)\": -1.038, \"(259.35, 289.4)\": -0.892, \"(289.4, 319.15)\": -0.754, \"(319.15, 348.3)\": -0.634, \"(348.3, 496.5)\": -0.559, \"(496.5, 548.75)\": -0.436, \"(548.75, 606.0)\": -0.338, \"(606.0, 696.25)\": -0.727, \"(696.25, 806.1500000000001)\": -0.252, \"(806.1500000000001, 901.8)\": -0.022, \"(901.8, 959.4000000000001)\": 0.058, \"(959.4000000000001, 1054.0)\": 0.141, \"(1054.0, 1150.0)\": 0.243, \"(1150.0, 1248.5)\": 0.328, \"(1248.5, 1341.0)\": 0.393, \"(1341.0, 1801.0)\": 0.475, \"(1801.0, 2501.0)\": 0.574}\nUpper Bounds (95%-Confidence Interval): {\"(143.5, 259.35)\": -0.48, \"(259.35, 289.4)\": -0.432, \"(289.4, 319.15)\": -0.38, \"(319.15, 348.3)\": -0.294, \"(348.3, 496.5)\": -0.177, \"(496.5, 548.75)\": -0.106, \"(548.75, 606.0)\": -0.007, \"(606.0, 696.25)\": 0.575, \"(696.25, 806.1500000000001)\": 0.871, \"(806.1500000000001, 901.8)\": 0.831, \"(901.8, 959.4000000000001)\": 0.962, \"(959.4000000000001, 1054.0)\": 1.074, \"(1054.0, 1150.0)\": 1.171, \"(1150.0, 1248.5)\": 1.285, \"(1248.5, 1341.0)\": 1.428, \"(1341.0, 1801.0)\": 1.544, \"(1801.0, 2501.0)\": 1.644}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: serum_sodium\nFeature Type: continuous\nMeans: {\"(113.0, 114.5)\": -1.269, \"(114.5, 118.5)\": 0.283, \"(118.5, 124.5)\": 3.539, \"(124.5, 126.5)\": 2.46, \"(126.5, 127.5)\": 4.042, \"(127.5, 129.5)\": 3.553, \"(129.5, 130.5)\": 0.953, \"(130.5, 132.5)\": 1.22, \"(132.5, 133.5)\": -1.094, \"(133.5, 135.5)\": 0.587, \"(135.5, 138.5)\": -0.629, \"(138.5, 144.5)\": -0.233, \"(144.5, 148.0)\": 0.113}\nLower Bounds (95%-Confidence Interval): {\"(113.0, 114.5)\": -3.483, \"(114.5, 118.5)\": -4.768, \"(118.5, 124.5)\": 2.536, \"(124.5, 126.5)\": 1.699, \"(126.5, 127.5)\": 3.034, \"(127.5, 129.5)\": 2.614, \"(129.5, 130.5)\": 0.389, \"(130.5, 132.5)\": 0.304, \"(132.5, 133.5)\": -2.269, \"(133.5, 135.5)\": 0.366, \"(135.5, 138.5)\": -0.879, \"(138.5, 144.5)\": -0.845, \"(144.5, 148.0)\": -0.129}\nUpper Bounds (95%-Confidence Interval): {\"(113.0, 114.5)\": 0.944, \"(114.5, 118.5)\": 5.334, \"(118.5, 124.5)\": 4.542, \"(124.5, 126.5)\": 3.222, \"(126.5, 127.5)\": 5.05, \"(127.5, 129.5)\": 4.492, \"(129.5, 130.5)\": 1.517, \"(130.5, 132.5)\": 2.136, \"(132.5, 133.5)\": 0.08, \"(133.5, 135.5)\": 0.808, \"(135.5, 138.5)\": -0.38, \"(138.5, 144.5)\": 0.38, \"(144.5, 148.0)\": 0.354}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: MaritalStatus\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.368, \"(0.5, 1.5)\": 0.724, \"(1.5, 2.5)\": 0.587, \"(2.5, 3.5)\": -0.221, \"(3.5, 4.5)\": -0.631, \"(4.5, 5.5)\": -0.545, \"(5.5, 6.0)\": 0.179}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.418, \"(0.5, 1.5)\": 0.02, \"(1.5, 2.5)\": 0.545, \"(2.5, 3.5)\": -0.336, \"(3.5, 4.5)\": -0.676, \"(4.5, 5.5)\": -0.688, \"(5.5, 6.0)\": 0.067}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.318, \"(0.5, 1.5)\": 1.428, \"(1.5, 2.5)\": 0.629, \"(2.5, 3.5)\": -0.106, \"(3.5, 4.5)\": -0.585, \"(4.5, 5.5)\": -0.403, \"(5.5, 6.0)\": 0.291}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: population\nFeature Type: continuous\nMeans: {\"(3.0, 14.5)\": 125210.2, \"(14.5, 25.5)\": 92452.9, \"(25.5, 65.5)\": 80407.9, \"(65.5, 138.5)\": 91917.4, \"(138.5, 151.5)\": 103409.9, \"(151.5, 301.5)\": 85121.7, \"(301.5, 490.5)\": 73106.0, \"(490.5, 657.5)\": 57994.5, \"(657.5, 761.5)\": 44760.8, \"(761.5, 837.5)\": 32058.9, \"(837.5, 1019.5)\": 20715.6, \"(1019.5, 1220.5)\": 6507.2, \"(1220.5, 1267.5)\": -6199.6, \"(1267.5, 1269.5)\": 9858.1, \"(1269.5, 1497.5)\": -9812.8, \"(1497.5, 1886.5)\": -25776.4, \"(1886.5, 2129.5)\": -36953.6, \"(2129.5, 2425.5)\": -48605.9, \"(2425.5, 2686.0)\": -59914.9, \"(2686.0, 2718.5)\": -46231.6, \"(2718.5, 3175.5)\": -61061.6, \"(3175.5, 3965.0)\": -76216.0, \"(3965.0, 35682.0)\": -91117.9}\nLower Bounds (95%-Confidence Interval): {\"(3.0, 14.5)\": 103123.1, \"(14.5, 25.5)\": 58681.0, \"(25.5, 65.5)\": 62309.7, \"(65.5, 138.5)\": 75243.8, \"(138.5, 151.5)\": 78950.4, \"(151.5, 301.5)\": 69535.1, \"(301.5, 490.5)\": 60924.6, \"(490.5, 657.5)\": 45395.6, \"(657.5, 761.5)\": 35273.5, \"(761.5, 837.5)\": 26626.5, \"(837.5, 1019.5)\": 8057.5, \"(1019.5, 1220.5)\": -10609.9, \"(1220.5, 1267.5)\": -14462.5, \"(1267.5, 1269.5)\": -5022.3, \"(1269.5, 1497.5)\": -22884.3, \"(1497.5, 1886.5)\": -37619.7, \"(1886.5, 2129.5)\": -51088.1, \"(2129.5, 2425.5)\": -56504.4, \"(2425.5, 2686.0)\": -64158.2, \"(2686.0, 2718.5)\": -69408.6, \"(2718.5, 3175.5)\": -68643.2, \"(3175.5, 3965.0)\": -84318.8, \"(3965.0, 35682.0)\": -101928.5}\nUpper Bounds (95%-Confidence Interval): {\"(3.0, 14.5)\": 147297.2, \"(14.5, 25.5)\": 126224.8, \"(25.5, 65.5)\": 98506.2, \"(65.5, 138.5)\": 108591.1, \"(138.5, 151.5)\": 127869.3, \"(151.5, 301.5)\": 100708.2, \"(301.5, 490.5)\": 85287.5, \"(490.5, 657.5)\": 70593.3, \"(657.5, 761.5)\": 54248.0, \"(761.5, 837.5)\": 37491.3, \"(837.5, 1019.5)\": 33373.7, \"(1019.5, 1220.5)\": 23624.4, \"(1220.5, 1267.5)\": 2063.4, \"(1267.5, 1269.5)\": 24738.4, \"(1269.5, 1497.5)\": 3258.7, \"(1497.5, 1886.5)\": -13933.2, \"(1886.5, 2129.5)\": -22819.1, \"(2129.5, 2425.5)\": -40707.4, \"(2425.5, 2686.0)\": -55671.5, \"(2686.0, 2718.5)\": -23054.7, \"(2718.5, 3175.5)\": -53480.1, \"(3175.5, 3965.0)\": -68113.2, \"(3965.0, 35682.0)\": -80307.2}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: perimeter_mean\nFeature Type: continuous\nMeans: {\"(43.79, 60.035)\": -0.884, \"(60.035, 63.379999999999995)\": -0.783, \"(63.379999999999995, 66.67)\": -0.681, \"(66.67, 68.965)\": -0.581, \"(68.965, 71.275)\": -0.476, \"(71.275, 78.28)\": -0.369, \"(78.28, 84.015)\": -0.267, \"(84.015, 88.70500000000001)\": -0.166, \"(88.70500000000001, 94.68)\": -0.064, \"(94.68, 100.75)\": 0.035, \"(100.75, 106.75)\": 0.14, \"(106.75, 108.6)\": 0.249, \"(108.6, 112.6)\": 0.407, \"(112.6, 117.45)\": 0.518, \"(117.45, 121.7)\": 0.626, \"(121.7, 128.15)\": 0.73, \"(128.15, 133.25)\": 0.835, \"(133.25, 145.85000000000002)\": 0.936, \"(145.85000000000002, 188.5)\": 1.038}\nLower Bounds (95%-Confidence Interval): {\"(43.79, 60.035)\": -1.177, \"(60.035, 63.379999999999995)\": -1.04, \"(63.379999999999995, 66.67)\": -0.892, \"(66.67, 68.965)\": -0.75, \"(68.965, 71.275)\": -0.646, \"(71.275, 78.28)\": -0.532, \"(78.28, 84.015)\": -0.417, \"(84.015, 88.70500000000001)\": -0.316, \"(88.70500000000001, 94.68)\": -0.178, \"(94.68, 100.75)\": -0.201, \"(100.75, 106.75)\": -0.091, \"(106.75, 108.6)\": -0.055, \"(108.6, 112.6)\": 0.049, \"(112.6, 117.45)\": 0.15, \"(117.45, 121.7)\": 0.222, \"(121.7, 128.15)\": 0.282, \"(128.15, 133.25)\": 0.343, \"(133.25, 145.85000000000002)\": 0.427, \"(145.85000000000002, 188.5)\": 0.514}\nUpper Bounds (95%-Confidence Interval): {\"(43.79, 60.035)\": -0.59, \"(60.035, 63.379999999999995)\": -0.526, \"(63.379999999999995, 66.67)\": -0.471, \"(66.67, 68.965)\": -0.411, \"(68.965, 71.275)\": -0.306, \"(71.275, 78.28)\": -0.206, \"(78.28, 84.015)\": -0.118, \"(84.015, 88.70500000000001)\": -0.017, \"(88.70500000000001, 94.68)\": 0.05, \"(94.68, 100.75)\": 0.271, \"(100.75, 106.75)\": 0.371, \"(106.75, 108.6)\": 0.553, \"(108.6, 112.6)\": 0.766, \"(112.6, 117.45)\": 0.887, \"(117.45, 121.7)\": 1.03, \"(121.7, 128.15)\": 1.179, \"(128.15, 133.25)\": 1.327, \"(133.25, 145.85000000000002)\": 1.444, \"(145.85000000000002, 188.5)\": 1.562}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: serum_creatinine\nFeature Type: continuous\nMeans: {\"(0.5, 0.6499999999999999)\": -0.26, \"(0.6499999999999999, 0.725)\": -1.08, \"(0.725, 0.875)\": -3.77, \"(0.875, 0.95)\": -0.9, \"(0.95, 1.1400000000000001)\": -0.15, \"(1.1400000000000001, 1.35)\": -0.88, \"(1.35, 1.45)\": 0.2, \"(1.45, 1.55)\": 1.18, \"(1.55, 1.815)\": 2.18, \"(1.815, 2.05)\": 4.74, \"(2.05, 2.45)\": 1.14, \"(2.45, 2.6)\": 3.63, \"(2.6, 2.95)\": -0.36, \"(2.95, 3.1)\": 2.57, \"(3.1, 3.45)\": 0.36, \"(3.45, 3.6)\": 3.06, \"(3.6, 3.75)\": 6.76, \"(3.75, 3.9)\": 2.31, \"(3.9, 4.7)\": 2.92, \"(4.7, 5.949999999999999)\": 0.76, \"(5.949999999999999, 6.199999999999999)\": -0.43, \"(6.199999999999999, 6.55)\": 0.23, \"(6.55, 9.4)\": 6.97}\nLower Bounds (95%-Confidence Interval): {\"(0.5, 0.6499999999999999)\": -1.13, \"(0.6499999999999999, 0.725)\": -1.45, \"(0.725, 0.875)\": -5.7, \"(0.875, 0.95)\": -1.31, \"(0.95, 1.1400000000000001)\": -0.41, \"(1.1400000000000001, 1.35)\": -1.92, \"(1.35, 1.45)\": -0.14, \"(1.45, 1.55)\": 0.46, \"(1.55, 1.815)\": 1.68, \"(1.815, 2.05)\": 2.75, \"(2.05, 2.45)\": 0.72, \"(2.45, 2.6)\": 1.94, \"(2.6, 2.95)\": -2.5, \"(2.95, 3.1)\": 0.3, \"(3.1, 3.45)\": -0.49, \"(3.45, 3.6)\": 1.58, \"(3.6, 3.75)\": 4.55, \"(3.75, 3.9)\": 0.4, \"(3.9, 4.7)\": 0.8, \"(4.7, 5.949999999999999)\": -0.63, \"(5.949999999999999, 6.199999999999999)\": -1.75, \"(6.199999999999999, 6.55)\": -2.74, \"(6.55, 9.4)\": 5.07}\nUpper Bounds (95%-Confidence Interval): {\"(0.5, 0.6499999999999999)\": 0.62, \"(0.6499999999999999, 0.725)\": -0.72, \"(0.725, 0.875)\": -1.84, \"(0.875, 0.95)\": -0.48, \"(0.95, 1.1400000000000001)\": 0.12, \"(1.1400000000000001, 1.35)\": 0.16, \"(1.35, 1.45)\": 0.53, \"(1.45, 1.55)\": 1.89, \"(1.55, 1.815)\": 2.68, \"(1.815, 2.05)\": 6.73, \"(2.05, 2.45)\": 1.56, \"(2.45, 2.6)\": 5.32, \"(2.6, 2.95)\": 1.77, \"(2.95, 3.1)\": 4.84, \"(3.1, 3.45)\": 1.2, \"(3.45, 3.6)\": 4.53, \"(3.6, 3.75)\": 8.97, \"(3.75, 3.9)\": 4.21, \"(3.9, 4.7)\": 5.04, \"(4.7, 5.949999999999999)\": 2.14, \"(5.949999999999999, 6.199999999999999)\": 0.9, \"(6.199999999999999, 6.55)\": 3.21, \"(6.55, 9.4)\": 8.88}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: CoastalVulnerability\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.03259, \"(0.5, 1.5)\": -0.02272, \"(1.5, 2.5)\": -0.0157, \"(2.5, 3.5)\": -0.00983, \"(3.5, 4.5)\": -0.00444, \"(4.5, 5.5)\": -0.00035, \"(5.5, 6.5)\": 0.00575, \"(6.5, 7.5)\": 0.01126, \"(7.5, 8.5)\": 0.01651, \"(8.5, 9.5)\": 0.02143, \"(9.5, 12.5)\": 0.02903, \"(12.5, 13.5)\": 0.03437, \"(13.5, 15.0)\": 0.04826}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0359, \"(0.5, 1.5)\": -0.02356, \"(1.5, 2.5)\": -0.01657, \"(2.5, 3.5)\": -0.01034, \"(3.5, 4.5)\": -0.0048, \"(4.5, 5.5)\": -0.00077, \"(5.5, 6.5)\": 0.00528, \"(6.5, 7.5)\": 0.01081, \"(7.5, 8.5)\": 0.01566, \"(8.5, 9.5)\": 0.02049, \"(9.5, 12.5)\": 0.02706, \"(12.5, 13.5)\": 0.0298, \"(13.5, 15.0)\": 0.0329}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02927, \"(0.5, 1.5)\": -0.02189, \"(1.5, 2.5)\": -0.01482, \"(2.5, 3.5)\": -0.00931, \"(3.5, 4.5)\": -0.00409, \"(4.5, 5.5)\": 7e-05, \"(5.5, 6.5)\": 0.00622, \"(6.5, 7.5)\": 0.0117, \"(7.5, 8.5)\": 0.01736, \"(8.5, 9.5)\": 0.02236, \"(9.5, 12.5)\": 0.031, \"(12.5, 13.5)\": 0.03893, \"(13.5, 15.0)\": 0.06363}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: CapitalGain\nFeature Type: continuous\nMeans: {\"(0.0, 57.0)\": -0.25, \"(57.0, 3048.0)\": -4.83, \"(3048.0, 3120.0)\": 2.57, \"(3120.0, 4243.5)\": -4.43, \"(4243.5, 4401.0)\": 1.45, \"(4401.0, 4668.5)\": -1.82, \"(4668.5, 4826.0)\": 3.79, \"(4826.0, 4898.0)\": 0.57, \"(4898.0, 4973.5)\": 2.25, \"(4973.5, 5119.0)\": -3.52, \"(5119.0, 5316.5)\": 4.26, \"(5316.5, 5505.5)\": 0.43, \"(5505.5, 6457.5)\": 2.15, \"(6457.5, 6505.5)\": -0.16, \"(6505.5, 6745.0)\": 0.81, \"(6745.0, 7073.5)\": -1.33, \"(7073.5, 7436.5)\": 5.76, \"(7436.5, 7565.5)\": 2.02, \"(7565.5, 7792.0)\": 6.56, \"(7792.0, 7937.0)\": 4.88, \"(7937.0, 8296.0)\": 3.84, \"(8296.0, 10543.0)\": 7.18, \"(10543.0, 10585.5)\": -1.48, \"(10585.5, 30961.5)\": 8.61, \"(30961.5, 70654.5)\": -0.66, \"(70654.5, 99999.0)\": 9.72}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 57.0)\": -0.27, \"(57.0, 3048.0)\": -6.42, \"(3048.0, 3120.0)\": 2.14, \"(3120.0, 4243.5)\": -5.31, \"(4243.5, 4401.0)\": 1.09, \"(4401.0, 4668.5)\": -2.65, \"(4668.5, 4826.0)\": 2.87, \"(4826.0, 4898.0)\": -0.25, \"(4898.0, 4973.5)\": 1.55, \"(4973.5, 5119.0)\": -6.13, \"(5119.0, 5316.5)\": 3.51, \"(5316.5, 5505.5)\": -0.29, \"(5505.5, 6457.5)\": 1.3, \"(6457.5, 6505.5)\": -0.94, \"(6505.5, 6745.0)\": 0.19, \"(6745.0, 7073.5)\": -2.33, \"(7073.5, 7436.5)\": 4.95, \"(7436.5, 7565.5)\": 0.42, \"(7565.5, 7792.0)\": 5.41, \"(7792.0, 7937.0)\": 2.59, \"(7937.0, 8296.0)\": 1.32, \"(8296.0, 10543.0)\": 6.05, \"(10543.0, 10585.5)\": -2.73, \"(10585.5, 30961.5)\": 7.51, \"(30961.5, 70654.5)\": -3.56, \"(70654.5, 99999.0)\": 8.19}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 57.0)\": -0.23, \"(57.0, 3048.0)\": -3.24, \"(3048.0, 3120.0)\": 3.0, \"(3120.0, 4243.5)\": -3.54, \"(4243.5, 4401.0)\": 1.81, \"(4401.0, 4668.5)\": -1.0, \"(4668.5, 4826.0)\": 4.71, \"(4826.0, 4898.0)\": 1.38, \"(4898.0, 4973.5)\": 2.95, \"(4973.5, 5119.0)\": -0.92, \"(5119.0, 5316.5)\": 5.0, \"(5316.5, 5505.5)\": 1.16, \"(5505.5, 6457.5)\": 3.0, \"(6457.5, 6505.5)\": 0.62, \"(6505.5, 6745.0)\": 1.44, \"(6745.0, 7073.5)\": -0.34, \"(7073.5, 7436.5)\": 6.58, \"(7436.5, 7565.5)\": 3.62, \"(7565.5, 7792.0)\": 7.72, \"(7792.0, 7937.0)\": 7.16, \"(7937.0, 8296.0)\": 6.36, \"(8296.0, 10543.0)\": 8.31, \"(10543.0, 10585.5)\": -0.22, \"(10585.5, 30961.5)\": 9.71, \"(30961.5, 70654.5)\": 2.23, \"(70654.5, 99999.0)\": 11.26}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Tenure\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.3765, \"(0.5, 1.5)\": -0.0692, \"(1.5, 4.5)\": -0.016, \"(4.5, 5.5)\": 0.0109, \"(5.5, 6.5)\": 0.0432, \"(6.5, 7.5)\": 0.0871, \"(7.5, 9.5)\": 0.0554, \"(9.5, 10.0)\": -0.0599}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.4596, \"(0.5, 1.5)\": -0.1046, \"(1.5, 4.5)\": -0.0506, \"(4.5, 5.5)\": -0.017, \"(5.5, 6.5)\": 0.014, \"(6.5, 7.5)\": 0.0581, \"(7.5, 9.5)\": 0.004, \"(9.5, 10.0)\": -0.1542}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.2933, \"(0.5, 1.5)\": -0.0338, \"(1.5, 4.5)\": 0.0185, \"(4.5, 5.5)\": 0.0387, \"(5.5, 6.5)\": 0.0724, \"(6.5, 7.5)\": 0.1161, \"(7.5, 9.5)\": 0.1067, \"(9.5, 10.0)\": 0.0343}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: texture_mean\nFeature Type: continuous\nMeans: {\"(9.71, 13.24)\": -1.121, \"(13.24, 14.075)\": -1.023, \"(14.075, 14.665)\": -0.921, \"(14.665, 15.010000000000002)\": -0.82, \"(15.010000000000002, 15.485)\": -0.718, \"(15.485, 15.774999999999999)\": -0.623, \"(15.774999999999999, 16.445)\": -0.523, \"(16.445, 17.045)\": -0.422, \"(17.045, 17.665)\": -0.324, \"(17.665, 18.335)\": -0.225, \"(18.335, 18.725)\": -0.129, \"(18.725, 19.075)\": -0.032, \"(19.075, 19.549999999999997)\": 0.063, \"(19.549999999999997, 19.915)\": 0.161, \"(19.915, 20.235)\": 0.26, \"(20.235, 20.8)\": 0.445, \"(20.8, 21.285)\": 0.549, \"(21.285, 33.81)\": 0.68}\nLower Bounds (95%-Confidence Interval): {\"(9.71, 13.24)\": -1.583, \"(13.24, 14.075)\": -1.428, \"(14.075, 14.665)\": -1.292, \"(14.665, 15.010000000000002)\": -1.127, \"(15.010000000000002, 15.485)\": -1.018, \"(15.485, 15.774999999999999)\": -0.932, \"(15.774999999999999, 16.445)\": -0.765, \"(16.445, 17.045)\": -0.657, \"(17.045, 17.665)\": -0.537, \"(17.665, 18.335)\": -0.404, \"(18.335, 18.725)\": -0.289, \"(18.725, 19.075)\": -0.203, \"(19.075, 19.549999999999997)\": -0.094, \"(19.549999999999997, 19.915)\": 0.017, \"(19.915, 20.235)\": 0.108, \"(20.235, 20.8)\": -0.11, \"(20.8, 21.285)\": -0.011, \"(21.285, 33.81)\": -0.0}\nUpper Bounds (95%-Confidence Interval): {\"(9.71, 13.24)\": -0.658, \"(13.24, 14.075)\": -0.619, \"(14.075, 14.665)\": -0.55, \"(14.665, 15.010000000000002)\": -0.512, \"(15.010000000000002, 15.485)\": -0.417, \"(15.485, 15.774999999999999)\": -0.314, \"(15.774999999999999, 16.445)\": -0.282, \"(16.445, 17.045)\": -0.187, \"(17.045, 17.665)\": -0.112, \"(17.665, 18.335)\": -0.045, \"(18.335, 18.725)\": 0.031, \"(18.725, 19.075)\": 0.139, \"(19.075, 19.549999999999997)\": 0.22, \"(19.549999999999997, 19.915)\": 0.306, \"(19.915, 20.235)\": 0.412, \"(20.235, 20.8)\": 0.999, \"(20.8, 21.285)\": 1.109, \"(21.285, 33.81)\": 1.36}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: latitude\nFeature Type: continuous\nMeans: {\"(32.54, 32.565)\": 23234.8, \"(32.565, 32.685)\": -3182.4, \"(32.685, 32.715)\": 7727.3, \"(32.715, 32.915)\": 17670.3, \"(32.915, 33.275000000000006)\": 34030.3, \"(33.275000000000006, 33.355000000000004)\": 55000.2, \"(33.355000000000004, 33.465)\": 64326.4, \"(33.465, 33.504999999999995)\": 81519.1, \"(33.504999999999995, 33.555)\": 94496.7, \"(33.555, 33.565)\": 63293.1, \"(33.565, 33.575)\": 51665.3, \"(33.575, 33.635000000000005)\": 66563.2, \"(33.635000000000005, 33.655)\": 47304.3, \"(33.655, 33.765)\": 29789.1, \"(33.765, 33.894999999999996)\": 15892.8, \"(33.894999999999996, 33.985)\": 2769.6, \"(33.985, 33.995000000000005)\": 17775.7, \"(33.995000000000005, 34.045)\": 28884.5, \"(34.045, 34.085)\": 55702.3, \"(34.085, 34.165)\": 46322.8, \"(34.165, 34.175)\": 33820.1, \"(34.175, 34.195)\": 7500.1, \"(34.195, 34.215)\": -4126.2, \"(34.215, 34.254999999999995)\": -16649.8, \"(34.254999999999995, 34.325)\": -27636.8, \"(34.325, 34.345)\": 17113.4, \"(34.345, 34.375)\": 28769.5, \"(34.375, 34.455)\": 43828.3, \"(34.455, 34.474999999999994)\": 57774.8, \"(34.474999999999994, 34.504999999999995)\": 33279.2, \"(34.504999999999995, 34.545)\": 19368.1, \"(34.545, 34.625)\": 5698.9, \"(34.625, 34.635000000000005)\": -19637.8, \"(34.635000000000005, 34.644999999999996)\": -39271.0, \"(34.644999999999996, 34.715)\": -26993.1, \"(34.715, 35.325)\": -17344.4, \"(35.325, 36.375)\": -37699.6, \"(36.375, 36.535)\": -27730.8, \"(36.535, 36.635000000000005)\": -14690.6, \"(36.635000000000005, 36.845)\": -25070.6, \"(36.845, 37.275000000000006)\": -15387.7, \"(37.275000000000006, 37.335)\": -3329.1, \"(37.335, 37.425)\": 7953.5, \"(37.425, 37.445)\": 34546.2, \"(37.445, 37.465)\": 45097.3, \"(37.465, 37.495000000000005)\": 30019.5, \"(37.495000000000005, 37.585)\": 16643.2, \"(37.585, 37.595)\": -3057.8, \"(37.595, 37.605000000000004)\": -32379.8, \"(37.605000000000004, 37.754999999999995)\": -42729.0, \"(37.754999999999995, 37.775000000000006)\": -17898.2, \"(37.775000000000006, 37.795)\": -3229.6, \"(37.795, 37.805)\": 8902.6, \"(37.805, 37.855000000000004)\": -13456.8, \"(37.855000000000004, 37.915)\": -1362.3, \"(37.915, 37.925)\": -19143.7, \"(37.925, 37.945)\": -38768.9, \"(37.945, 38.355000000000004)\": -48247.9, \"(38.355000000000004, 39.085)\": -38467.7, \"(39.085, 39.474999999999994)\": -47690.5, \"(39.474999999999994, 40.135000000000005)\": -56986.6, \"(40.135000000000005, 40.665)\": -66271.5, \"(40.665, 41.775000000000006)\": -75627.3, \"(41.775000000000006, 41.95)\": -85116.1}\nLower Bounds (95%-Confidence Interval): {\"(32.54, 32.565)\": 964.8, \"(32.565, 32.685)\": -13385.7, \"(32.685, 32.715)\": -6553.6, \"(32.715, 32.915)\": 6526.7, \"(32.915, 33.275000000000006)\": 15999.9, \"(33.275000000000006, 33.355000000000004)\": 42157.4, \"(33.355000000000004, 33.465)\": 51350.1, \"(33.465, 33.504999999999995)\": 60415.4, \"(33.504999999999995, 33.555)\": 76698.7, \"(33.555, 33.565)\": 39537.3, \"(33.565, 33.575)\": 41623.9, \"(33.575, 33.635000000000005)\": 54208.1, \"(33.635000000000005, 33.655)\": 37976.2, \"(33.655, 33.765)\": 22371.1, \"(33.765, 33.894999999999996)\": 10058.4, \"(33.894999999999996, 33.985)\": -876.4, \"(33.985, 33.995000000000005)\": 13047.1, \"(33.995000000000005, 34.045)\": 23199.9, \"(34.045, 34.085)\": 50112.0, \"(34.085, 34.165)\": 40162.5, \"(34.165, 34.175)\": 28146.4, \"(34.175, 34.195)\": 2466.5, \"(34.195, 34.215)\": -9561.1, \"(34.215, 34.254999999999995)\": -23153.3, \"(34.254999999999995, 34.325)\": -37515.4, \"(34.325, 34.345)\": -3758.1, \"(34.345, 34.375)\": 15207.7, \"(34.375, 34.455)\": 33875.3, \"(34.455, 34.474999999999994)\": 41591.3, \"(34.474999999999994, 34.504999999999995)\": 20304.7, \"(34.504999999999995, 34.545)\": 13245.7, \"(34.545, 34.625)\": -12771.5, \"(34.625, 34.635000000000005)\": -37375.2, \"(34.635000000000005, 34.644999999999996)\": -49797.4, \"(34.644999999999996, 34.715)\": -34913.5, \"(34.715, 35.325)\": -47411.7, \"(35.325, 36.375)\": -46798.2, \"(36.375, 36.535)\": -34852.7, \"(36.535, 36.635000000000005)\": -23680.1, \"(36.635000000000005, 36.845)\": -34287.5, \"(36.845, 37.275000000000006)\": -23625.3, \"(37.275000000000006, 37.335)\": -9268.7, \"(37.335, 37.425)\": -4329.5, \"(37.425, 37.445)\": 29053.6, \"(37.445, 37.465)\": 29188.3, \"(37.465, 37.495000000000005)\": 21566.7, \"(37.495000000000005, 37.585)\": 8469.3, \"(37.585, 37.595)\": -16791.2, \"(37.595, 37.605000000000004)\": -38739.3, \"(37.605000000000004, 37.754999999999995)\": -51675.5, \"(37.754999999999995, 37.775000000000006)\": -25033.4, \"(37.775000000000006, 37.795)\": -8688.7, \"(37.795, 37.805)\": 36.5, \"(37.805, 37.855000000000004)\": -20482.2, \"(37.855000000000004, 37.915)\": -9472.3, \"(37.915, 37.925)\": -25360.4, \"(37.925, 37.945)\": -46246.0, \"(37.945, 38.355000000000004)\": -55734.3, \"(38.355000000000004, 39.085)\": -48831.7, \"(39.085, 39.474999999999994)\": -57243.4, \"(39.474999999999994, 40.135000000000005)\": -65954.6, \"(40.135000000000005, 40.665)\": -75283.4, \"(40.665, 41.775000000000006)\": -84501.7, \"(41.775000000000006, 41.95)\": -93657.9}\nUpper Bounds (95%-Confidence Interval): {\"(32.54, 32.565)\": 45504.8, \"(32.565, 32.685)\": 7020.9, \"(32.685, 32.715)\": 22008.3, \"(32.715, 32.915)\": 28813.8, \"(32.915, 33.275000000000006)\": 52060.7, \"(33.275000000000006, 33.355000000000004)\": 67843.0, \"(33.355000000000004, 33.465)\": 77302.7, \"(33.465, 33.504999999999995)\": 102622.7, \"(33.504999999999995, 33.555)\": 112294.7, \"(33.555, 33.565)\": 87049.0, \"(33.565, 33.575)\": 61706.7, \"(33.575, 33.635000000000005)\": 78918.4, \"(33.635000000000005, 33.655)\": 56632.4, \"(33.655, 33.765)\": 37207.2, \"(33.765, 33.894999999999996)\": 21727.2, \"(33.894999999999996, 33.985)\": 6415.7, \"(33.985, 33.995000000000005)\": 22504.3, \"(33.995000000000005, 34.045)\": 34569.1, \"(34.045, 34.085)\": 61292.7, \"(34.085, 34.165)\": 52483.1, \"(34.165, 34.175)\": 39493.7, \"(34.175, 34.195)\": 12533.7, \"(34.195, 34.215)\": 1308.7, \"(34.215, 34.254999999999995)\": -10146.2, \"(34.254999999999995, 34.325)\": -17758.2, \"(34.325, 34.345)\": 37984.8, \"(34.345, 34.375)\": 42331.3, \"(34.375, 34.455)\": 53781.2, \"(34.455, 34.474999999999994)\": 73958.2, \"(34.474999999999994, 34.504999999999995)\": 46253.8, \"(34.504999999999995, 34.545)\": 25490.4, \"(34.545, 34.625)\": 24169.2, \"(34.625, 34.635000000000005)\": -1900.3, \"(34.635000000000005, 34.644999999999996)\": -28744.6, \"(34.644999999999996, 34.715)\": -19072.7, \"(34.715, 35.325)\": 12722.9, \"(35.325, 36.375)\": -28601.0, \"(36.375, 36.535)\": -20608.8, \"(36.535, 36.635000000000005)\": -5701.1, \"(36.635000000000005, 36.845)\": -15853.6, \"(36.845, 37.275000000000006)\": -7150.2, \"(37.275000000000006, 37.335)\": 2610.4, \"(37.335, 37.425)\": 20236.5, \"(37.425, 37.445)\": 40038.7, \"(37.445, 37.465)\": 61006.3, \"(37.465, 37.495000000000005)\": 38472.2, \"(37.495000000000005, 37.585)\": 24817.1, \"(37.585, 37.595)\": 10675.7, \"(37.595, 37.605000000000004)\": -26020.2, \"(37.605000000000004, 37.754999999999995)\": -33782.5, \"(37.754999999999995, 37.775000000000006)\": -10763.1, \"(37.775000000000006, 37.795)\": 2229.6, \"(37.795, 37.805)\": 17768.7, \"(37.805, 37.855000000000004)\": -6431.3, \"(37.855000000000004, 37.915)\": 6747.6, \"(37.915, 37.925)\": -12927.0, \"(37.925, 37.945)\": -31291.8, \"(37.945, 38.355000000000004)\": -40761.5, \"(38.355000000000004, 39.085)\": -28103.6, \"(39.085, 39.474999999999994)\": -38137.5, \"(39.474999999999994, 40.135000000000005)\": -48018.6, \"(40.135000000000005, 40.665)\": -57259.6, \"(40.665, 41.775000000000006)\": -66753.0, \"(41.775000000000006, 41.95)\": -76574.4}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: DamsQuality\nFeature Type: continuous\nMeans: {\"(0.0, 1.5)\": -0.02325, \"(1.5, 2.5)\": -0.01532, \"(2.5, 3.5)\": -0.01073, \"(3.5, 4.5)\": -0.00482, \"(4.5, 5.5)\": -0.00032, \"(5.5, 6.5)\": 0.0063, \"(6.5, 7.5)\": 0.01228, \"(7.5, 8.5)\": 0.01637, \"(8.5, 10.5)\": 0.02537, \"(10.5, 12.5)\": 0.03189, \"(12.5, 13.5)\": 0.03961, \"(13.5, 14.0)\": 0.01644}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02598, \"(1.5, 2.5)\": -0.01586, \"(2.5, 3.5)\": -0.01113, \"(3.5, 4.5)\": -0.00525, \"(4.5, 5.5)\": -0.00072, \"(5.5, 6.5)\": 0.00574, \"(6.5, 7.5)\": 0.01173, \"(7.5, 8.5)\": 0.01585, \"(8.5, 10.5)\": 0.02412, \"(10.5, 12.5)\": 0.02908, \"(12.5, 13.5)\": 0.03687, \"(13.5, 14.0)\": 0.00331}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02052, \"(1.5, 2.5)\": -0.01477, \"(2.5, 3.5)\": -0.01034, \"(3.5, 4.5)\": -0.00438, \"(4.5, 5.5)\": 9e-05, \"(5.5, 6.5)\": 0.00686, \"(6.5, 7.5)\": 0.01282, \"(7.5, 8.5)\": 0.01689, \"(8.5, 10.5)\": 0.02662, \"(10.5, 12.5)\": 0.0347, \"(12.5, 13.5)\": 0.04234, \"(13.5, 14.0)\": 0.02957}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: IneffectiveDisasterPreparedness\nFeature Type: continuous\nMeans: {\"(0.0, 1.5)\": -0.02526, \"(1.5, 2.5)\": -0.01738, \"(2.5, 3.5)\": -0.01172, \"(3.5, 4.5)\": -0.00537, \"(4.5, 5.5)\": -0.00021, \"(5.5, 6.5)\": 0.0066, \"(6.5, 7.5)\": 0.01026, \"(7.5, 8.5)\": 0.01717, \"(8.5, 9.5)\": 0.02426, \"(9.5, 10.5)\": 0.02823, \"(10.5, 11.5)\": 0.03325, \"(11.5, 13.5)\": 0.03915, \"(13.5, 15.0)\": 0.03572}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02806, \"(1.5, 2.5)\": -0.01811, \"(2.5, 3.5)\": -0.01241, \"(3.5, 4.5)\": -0.0056, \"(4.5, 5.5)\": -0.00057, \"(5.5, 6.5)\": 0.00621, \"(6.5, 7.5)\": 0.00967, \"(7.5, 8.5)\": 0.01672, \"(8.5, 9.5)\": 0.02334, \"(9.5, 10.5)\": 0.02687, \"(10.5, 11.5)\": 0.03182, \"(11.5, 13.5)\": 0.03364, \"(13.5, 15.0)\": 0.0307}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02245, \"(1.5, 2.5)\": -0.01664, \"(2.5, 3.5)\": -0.01102, \"(3.5, 4.5)\": -0.00514, \"(4.5, 5.5)\": 0.00016, \"(5.5, 6.5)\": 0.00699, \"(6.5, 7.5)\": 0.01085, \"(7.5, 8.5)\": 0.01761, \"(8.5, 9.5)\": 0.02519, \"(9.5, 10.5)\": 0.02958, \"(10.5, 11.5)\": 0.03468, \"(11.5, 13.5)\": 0.04466, \"(13.5, 15.0)\": 0.04073}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: HasCrCard\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.004421, \"(0.5, 1.0)\": 0.001379}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.037941, \"(0.5, 1.0)\": -0.009076}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.0291, \"(0.5, 1.0)\": 0.011834}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: sepal_width\nFeature Type: continuous\nMeans: {\"(2.0, 2.25)\": -2.473, \"(2.25, 2.6500000000000004)\": -2.179, \"(2.6500000000000004, 2.8499999999999996)\": -1.736, \"(2.8499999999999996, 2.95)\": -0.945, \"(2.95, 3.05)\": 0.062, \"(3.05, 3.25)\": 0.509, \"(3.25, 3.3499999999999996)\": 1.373, \"(3.3499999999999996, 3.55)\": 1.669, \"(3.55, 3.75)\": 2.097, \"(3.75, 3.95)\": 2.489, \"(3.95, 4.1)\": 2.778}\nLower Bounds (95%-Confidence Interval): {\"(2.0, 2.25)\": -2.841, \"(2.25, 2.6500000000000004)\": -2.509, \"(2.6500000000000004, 2.8499999999999996)\": -1.936, \"(2.8499999999999996, 2.95)\": -1.506, \"(2.95, 3.05)\": -0.172, \"(3.05, 3.25)\": 0.23, \"(3.25, 3.3499999999999996)\": 1.091, \"(3.3499999999999996, 3.55)\": 1.492, \"(3.55, 3.75)\": 1.921, \"(3.75, 3.95)\": 2.293, \"(3.95, 4.1)\": 2.546}\nUpper Bounds (95%-Confidence Interval): {\"(2.0, 2.25)\": -2.105, \"(2.25, 2.6500000000000004)\": -1.849, \"(2.6500000000000004, 2.8499999999999996)\": -1.537, \"(2.8499999999999996, 2.95)\": -0.384, \"(2.95, 3.05)\": 0.295, \"(3.05, 3.25)\": 0.789, \"(3.25, 3.3499999999999996)\": 1.656, \"(3.3499999999999996, 3.55)\": 1.846, \"(3.55, 3.75)\": 2.274, \"(3.75, 3.95)\": 2.685, \"(3.95, 4.1)\": 3.01}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: time\nFeature Type: continuous\nMeans: {\"(4.0, 11.5)\": 10.73, \"(11.5, 12.5)\": 1.29, \"(12.5, 15.5)\": 3.88, \"(15.5, 18.0)\": 2.22, \"(18.0, 28.5)\": 6.17, \"(28.5, 30.5)\": 4.47, \"(30.5, 52.0)\": 5.56, \"(52.0, 54.5)\": 3.38, \"(54.5, 67.5)\": 4.79, \"(67.5, 73.5)\": 2.76, \"(73.5, 76.5)\": -3.15, \"(76.5, 78.5)\": 2.29, \"(78.5, 82.5)\": -0.16, \"(82.5, 87.5)\": -2.8, \"(87.5, 90.5)\": 0.19, \"(90.5, 92.5)\": -1.08, \"(92.5, 95.5)\": -2.7, \"(95.5, 108.5)\": -0.98, \"(108.5, 117.5)\": 0.02, \"(117.5, 124.5)\": -3.44, \"(124.5, 137.5)\": 0.64, \"(137.5, 149.0)\": -0.8, \"(149.0, 171.5)\": 5.06, \"(171.5, 173.0)\": 2.66, \"(173.0, 182.5)\": -0.84, \"(182.5, 192.5)\": -3.42, \"(192.5, 193.5)\": -1.01, \"(193.5, 253.0)\": -2.58, \"(253.0, 285.0)\": -8.42}\nLower Bounds (95%-Confidence Interval): {\"(4.0, 11.5)\": 8.45, \"(11.5, 12.5)\": 0.25, \"(12.5, 15.5)\": 2.94, \"(15.5, 18.0)\": -0.25, \"(18.0, 28.5)\": 4.04, \"(28.5, 30.5)\": 3.69, \"(30.5, 52.0)\": 4.21, \"(52.0, 54.5)\": 1.74, \"(54.5, 67.5)\": 3.17, \"(67.5, 73.5)\": 1.96, \"(73.5, 76.5)\": -4.69, \"(76.5, 78.5)\": 1.19, \"(78.5, 82.5)\": -1.25, \"(82.5, 87.5)\": -3.84, \"(87.5, 90.5)\": -0.35, \"(90.5, 92.5)\": -2.75, \"(92.5, 95.5)\": -4.6, \"(95.5, 108.5)\": -1.62, \"(108.5, 117.5)\": -0.66, \"(117.5, 124.5)\": -4.94, \"(124.5, 137.5)\": -0.24, \"(137.5, 149.0)\": -1.83, \"(149.0, 171.5)\": 3.59, \"(171.5, 173.0)\": 1.61, \"(173.0, 182.5)\": -1.86, \"(182.5, 192.5)\": -4.51, \"(192.5, 193.5)\": -1.89, \"(193.5, 253.0)\": -4.11, \"(253.0, 285.0)\": -10.7}\nUpper Bounds (95%-Confidence Interval): {\"(4.0, 11.5)\": 13.0, \"(11.5, 12.5)\": 2.32, \"(12.5, 15.5)\": 4.82, \"(15.5, 18.0)\": 4.68, \"(18.0, 28.5)\": 8.31, \"(28.5, 30.5)\": 5.26, \"(30.5, 52.0)\": 6.91, \"(52.0, 54.5)\": 5.03, \"(54.5, 67.5)\": 6.41, \"(67.5, 73.5)\": 3.57, \"(73.5, 76.5)\": -1.61, \"(76.5, 78.5)\": 3.39, \"(78.5, 82.5)\": 0.92, \"(82.5, 87.5)\": -1.75, \"(87.5, 90.5)\": 0.72, \"(90.5, 92.5)\": 0.6, \"(92.5, 95.5)\": -0.81, \"(95.5, 108.5)\": -0.34, \"(108.5, 117.5)\": 0.7, \"(117.5, 124.5)\": -1.93, \"(124.5, 137.5)\": 1.53, \"(137.5, 149.0)\": 0.22, \"(149.0, 171.5)\": 6.52, \"(171.5, 173.0)\": 3.72, \"(173.0, 182.5)\": 0.18, \"(182.5, 192.5)\": -2.33, \"(192.5, 193.5)\": -0.13, \"(193.5, 253.0)\": -1.06, \"(253.0, 285.0)\": -6.14}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: median_income\nFeature Type: continuous\nMeans: {\"(0.4999, 0.5427500000000001)\": -16067.6, \"(0.5427500000000001, 1.4808)\": -55539.5, \"(1.4808, 2.1658999999999997)\": -71376.5, \"(2.1658999999999997, 2.6096)\": -56399.7, \"(2.6096, 3.2433)\": -40762.6, \"(3.2433, 3.66575)\": -25586.1, \"(3.66575, 4.3197)\": -8084.4, \"(4.3197, 4.691000000000001)\": 7391.3, \"(4.691000000000001, 5.1358)\": 22375.3, \"(5.1358, 5.59195)\": 40032.8, \"(5.59195, 5.8294)\": 56900.2, \"(5.8294, 6.29665)\": 75092.3, \"(6.29665, 6.3704)\": 96400.5, \"(6.3704, 6.874750000000001)\": 111491.7, \"(6.874750000000001, 7.6996)\": 135841.6, \"(7.6996, 7.8141)\": 151586.9, \"(7.8141, 8.3976)\": 170219.6, \"(8.3976, 9.046949999999999)\": 192482.3, \"(9.046949999999999, 15.00005)\": 214375.9, \"(15.00005, 15.0001)\": 193753.6}\nLower Bounds (95%-Confidence Interval): {\"(0.4999, 0.5427500000000001)\": -48216.1, \"(0.5427500000000001, 1.4808)\": -68098.8, \"(1.4808, 2.1658999999999997)\": -81907.3, \"(2.1658999999999997, 2.6096)\": -60824.9, \"(2.6096, 3.2433)\": -49299.1, \"(3.2433, 3.66575)\": -32546.2, \"(3.66575, 4.3197)\": -17048.9, \"(4.3197, 4.691000000000001)\": 1621.1, \"(4.691000000000001, 5.1358)\": 13670.4, \"(5.1358, 5.59195)\": 33628.4, \"(5.59195, 5.8294)\": 48173.8, \"(5.8294, 6.29665)\": 69358.1, \"(6.29665, 6.3704)\": 88897.2, \"(6.3704, 6.874750000000001)\": 105607.5, \"(6.874750000000001, 7.6996)\": 129446.9, \"(7.6996, 7.8141)\": 139775.0, \"(7.8141, 8.3976)\": 162646.8, \"(8.3976, 9.046949999999999)\": 184114.0, \"(9.046949999999999, 15.00005)\": 203670.8, \"(15.00005, 15.0001)\": 178950.1}\nUpper Bounds (95%-Confidence Interval): {\"(0.4999, 0.5427500000000001)\": 16080.9, \"(0.5427500000000001, 1.4808)\": -42980.1, \"(1.4808, 2.1658999999999997)\": -60845.8, \"(2.1658999999999997, 2.6096)\": -51974.6, \"(2.6096, 3.2433)\": -32226.2, \"(3.2433, 3.66575)\": -18626.1, \"(3.66575, 4.3197)\": 880.1, \"(4.3197, 4.691000000000001)\": 13161.4, \"(4.691000000000001, 5.1358)\": 31080.1, \"(5.1358, 5.59195)\": 46437.2, \"(5.59195, 5.8294)\": 65626.7, \"(5.8294, 6.29665)\": 80826.5, \"(6.29665, 6.3704)\": 103903.7, \"(6.3704, 6.874750000000001)\": 117376.0, \"(6.874750000000001, 7.6996)\": 142236.4, \"(7.6996, 7.8141)\": 163398.8, \"(7.8141, 8.3976)\": 177792.4, \"(8.3976, 9.046949999999999)\": 200850.6, \"(9.046949999999999, 15.00005)\": 225081.0, \"(15.00005, 15.0001)\": 208557.1}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Gender\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.4751, \"(0.5, 1.0)\": 0.2339}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.5571, \"(0.5, 1.0)\": 0.1936}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.3931, \"(0.5, 1.0)\": 0.2743}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Fare\nFeature Type: continuous\nMeans: {\"(0.0, 6.325)\": -1.425, \"(6.325, 7.8500000000000005)\": -1.303, \"(7.8500000000000005, 9.256250000000001)\": -0.472, \"(9.256250000000001, 10.48125)\": -0.602, \"(10.48125, 12.9375)\": -0.14, \"(12.9375, 25.79375)\": 0.225, \"(25.79375, 26.46875)\": 0.355, \"(26.46875, 27.7354)\": 0.207, \"(27.7354, 29.85)\": -0.238, \"(29.85, 31.6604)\": 0.051, \"(31.6604, 55.22085)\": -0.075, \"(55.22085, 89.5521)\": 0.041, \"(89.5521, 149.0354)\": 0.152, \"(149.0354, 387.6646)\": -0.029, \"(387.6646, 512.3292)\": 0.808}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 6.325)\": -3.39, \"(6.325, 7.8500000000000005)\": -3.252, \"(7.8500000000000005, 9.256250000000001)\": -1.321, \"(9.256250000000001, 10.48125)\": -1.756, \"(10.48125, 12.9375)\": -0.444, \"(12.9375, 25.79375)\": -0.464, \"(25.79375, 26.46875)\": -0.48, \"(26.46875, 27.7354)\": -0.42, \"(27.7354, 29.85)\": -1.008, \"(29.85, 31.6604)\": -0.616, \"(31.6604, 55.22085)\": -0.278, \"(55.22085, 89.5521)\": -0.095, \"(89.5521, 149.0354)\": -0.062, \"(149.0354, 387.6646)\": -0.493, \"(387.6646, 512.3292)\": -0.839}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 6.325)\": 0.54, \"(6.325, 7.8500000000000005)\": 0.645, \"(7.8500000000000005, 9.256250000000001)\": 0.377, \"(9.256250000000001, 10.48125)\": 0.553, \"(10.48125, 12.9375)\": 0.163, \"(12.9375, 25.79375)\": 0.913, \"(25.79375, 26.46875)\": 1.191, \"(26.46875, 27.7354)\": 0.833, \"(27.7354, 29.85)\": 0.533, \"(29.85, 31.6604)\": 0.718, \"(31.6604, 55.22085)\": 0.127, \"(55.22085, 89.5521)\": 0.176, \"(89.5521, 149.0354)\": 0.367, \"(149.0354, 387.6646)\": 0.436, \"(387.6646, 512.3292)\": 2.455}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: EstimatedSalary\nFeature Type: continuous\nMeans: {\"(106.67, 780.2149999999999)\": 0.3865, \"(780.2149999999999, 4627.98)\": 0.3462, \"(4627.98, 6842.475)\": 0.0858, \"(6842.475, 7401.88)\": 0.157, \"(7401.88, 27330.43)\": 0.2048, \"(27330.43, 38816.375)\": 0.1737, \"(38816.375, 40348.645000000004)\": 0.1063, \"(40348.645000000004, 42807.509999999995)\": 0.0512, \"(42807.509999999995, 48226.81)\": 0.1098, \"(48226.81, 48498.15)\": -0.0771, \"(48498.15, 58535.68)\": 0.0187, \"(58535.68, 94498.98999999999)\": 0.0512, \"(94498.98999999999, 120892.955)\": 0.0186, \"(120892.955, 121151.28)\": -0.0263, \"(121151.28, 121482.61499999999)\": -0.0801, \"(121482.61499999999, 148569.97)\": -0.0388, \"(148569.97, 184522.325)\": -0.0796, \"(184522.325, 187947.635)\": -0.1332, \"(187947.635, 187985.865)\": -0.2342, \"(187985.865, 188452.565)\": -0.0632, \"(188452.565, 189006.61)\": -0.0053, \"(189006.61, 196418.97999999998)\": 0.0291, \"(196418.97999999998, 199505.41)\": -0.0098, \"(199505.41, 199992.48)\": 0.214}\nLower Bounds (95%-Confidence Interval): {\"(106.67, 780.2149999999999)\": 0.0871, \"(780.2149999999999, 4627.98)\": 0.1468, \"(4627.98, 6842.475)\": -0.2734, \"(6842.475, 7401.88)\": -0.01, \"(7401.88, 27330.43)\": 0.0941, \"(27330.43, 38816.375)\": 0.065, \"(38816.375, 40348.645000000004)\": -0.0568, \"(40348.645000000004, 42807.509999999995)\": -0.1427, \"(42807.509999999995, 48226.81)\": 0.0015, \"(48226.81, 48498.15)\": -0.404, \"(48498.15, 58535.68)\": -0.1286, \"(58535.68, 94498.98999999999)\": -0.003, \"(94498.98999999999, 120892.955)\": -0.0541, \"(120892.955, 121151.28)\": -0.186, \"(121151.28, 121482.61499999999)\": -0.2842, \"(121482.61499999999, 148569.97)\": -0.1593, \"(148569.97, 184522.325)\": -0.1401, \"(184522.325, 187947.635)\": -0.216, \"(187947.635, 187985.865)\": -0.7523, \"(187985.865, 188452.565)\": -0.2404, \"(188452.565, 189006.61)\": -0.1779, \"(189006.61, 196418.97999999998)\": -0.1285, \"(196418.97999999998, 199505.41)\": -0.2064, \"(199505.41, 199992.48)\": -0.3318}\nUpper Bounds (95%-Confidence Interval): {\"(106.67, 780.2149999999999)\": 0.6859, \"(780.2149999999999, 4627.98)\": 0.5457, \"(4627.98, 6842.475)\": 0.445, \"(6842.475, 7401.88)\": 0.3239, \"(7401.88, 27330.43)\": 0.3154, \"(27330.43, 38816.375)\": 0.2823, \"(38816.375, 40348.645000000004)\": 0.2695, \"(40348.645000000004, 42807.509999999995)\": 0.2451, \"(42807.509999999995, 48226.81)\": 0.2181, \"(48226.81, 48498.15)\": 0.2497, \"(48498.15, 58535.68)\": 0.166, \"(58535.68, 94498.98999999999)\": 0.1054, \"(94498.98999999999, 120892.955)\": 0.0913, \"(120892.955, 121151.28)\": 0.1335, \"(121151.28, 121482.61499999999)\": 0.1239, \"(121482.61499999999, 148569.97)\": 0.0817, \"(148569.97, 184522.325)\": -0.019, \"(184522.325, 187947.635)\": -0.0504, \"(187947.635, 187985.865)\": 0.2839, \"(187985.865, 188452.565)\": 0.1139, \"(188452.565, 189006.61)\": 0.1673, \"(189006.61, 196418.97999999998)\": 0.1867, \"(196418.97999999998, 199505.41)\": 0.1868, \"(199505.41, 199992.48)\": 0.7597}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: radius_worst\nFeature Type: continuous\nMeans: {\"(7.93, 10.585)\": -1.149, \"(10.585, 11.305)\": -1.016, \"(11.305, 11.965)\": -0.883, \"(11.965, 12.54)\": -0.747, \"(12.54, 13.315000000000001)\": -0.616, \"(13.315000000000001, 14.184999999999999)\": -0.485, \"(14.184999999999999, 14.875)\": -0.349, \"(14.875, 15.485)\": -0.212, \"(15.485, 15.955)\": -0.078, \"(15.955, 16.54)\": 0.055, \"(16.54, 17.22)\": 0.19, \"(17.22, 17.78)\": 0.335, \"(17.78, 18.655)\": 0.469, \"(18.655, 19.785)\": 0.601, \"(19.785, 20.445)\": 0.734, \"(20.445, 21.935000000000002)\": 0.866, \"(21.935000000000002, 23.625)\": 0.997, \"(23.625, 25.335)\": 1.132, \"(25.335, 30.71)\": 1.274, \"(30.71, 36.04)\": 1.406}\nLower Bounds (95%-Confidence Interval): {\"(7.93, 10.585)\": -1.554, \"(10.585, 11.305)\": -1.397, \"(11.305, 11.965)\": -1.223, \"(11.965, 12.54)\": -1.048, \"(12.54, 13.315000000000001)\": -0.881, \"(13.315000000000001, 14.184999999999999)\": -0.698, \"(14.184999999999999, 14.875)\": -0.522, \"(14.875, 15.485)\": -0.332, \"(15.485, 15.955)\": -0.179, \"(15.955, 16.54)\": -0.216, \"(16.54, 17.22)\": -0.079, \"(17.22, 17.78)\": 0.047, \"(17.78, 18.655)\": 0.152, \"(18.655, 19.785)\": 0.262, \"(19.785, 20.445)\": 0.384, \"(20.445, 21.935000000000002)\": 0.494, \"(21.935000000000002, 23.625)\": 0.572, \"(23.625, 25.335)\": 0.664, \"(25.335, 30.71)\": 0.756, \"(30.71, 36.04)\": 0.872}\nUpper Bounds (95%-Confidence Interval): {\"(7.93, 10.585)\": -0.745, \"(10.585, 11.305)\": -0.635, \"(11.305, 11.965)\": -0.542, \"(11.965, 12.54)\": -0.446, \"(12.54, 13.315000000000001)\": -0.351, \"(13.315000000000001, 14.184999999999999)\": -0.271, \"(14.184999999999999, 14.875)\": -0.176, \"(14.875, 15.485)\": -0.091, \"(15.485, 15.955)\": 0.022, \"(15.955, 16.54)\": 0.326, \"(16.54, 17.22)\": 0.459, \"(17.22, 17.78)\": 0.624, \"(17.78, 18.655)\": 0.785, \"(18.655, 19.785)\": 0.94, \"(19.785, 20.445)\": 1.085, \"(20.445, 21.935000000000002)\": 1.239, \"(21.935000000000002, 23.625)\": 1.422, \"(23.625, 25.335)\": 1.6, \"(25.335, 30.71)\": 1.792, \"(30.71, 36.04)\": 1.941}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Education\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.4028, \"(0.5, 1.5)\": -0.5397, \"(1.5, 3.5)\": -0.4851, \"(3.5, 4.5)\": -0.4021, \"(4.5, 5.5)\": -0.457, \"(5.5, 6.5)\": -0.2537, \"(6.5, 7.5)\": -0.0494, \"(7.5, 8.5)\": 0.0457, \"(8.5, 9.5)\": 0.1831, \"(9.5, 10.5)\": 0.1392, \"(10.5, 11.5)\": -0.0652, \"(11.5, 14.5)\": 0.1954, \"(14.5, 15.0)\": 0.1393}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.5596, \"(0.5, 1.5)\": -0.6499, \"(1.5, 3.5)\": -0.618, \"(3.5, 4.5)\": -0.5693, \"(4.5, 5.5)\": -0.5278, \"(5.5, 6.5)\": -0.3342, \"(6.5, 7.5)\": -0.0948, \"(7.5, 8.5)\": -0.0062, \"(8.5, 9.5)\": 0.1525, \"(9.5, 10.5)\": 0.1072, \"(10.5, 11.5)\": -0.0869, \"(11.5, 14.5)\": 0.1476, \"(14.5, 15.0)\": 0.1012}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.2459, \"(0.5, 1.5)\": -0.4295, \"(1.5, 3.5)\": -0.3523, \"(3.5, 4.5)\": -0.235, \"(4.5, 5.5)\": -0.3862, \"(5.5, 6.5)\": -0.1733, \"(6.5, 7.5)\": -0.0039, \"(7.5, 8.5)\": 0.0977, \"(8.5, 9.5)\": 0.2137, \"(9.5, 10.5)\": 0.1711, \"(10.5, 11.5)\": -0.0435, \"(11.5, 14.5)\": 0.2431, \"(14.5, 15.0)\": 0.1775}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: longitude\nFeature Type: continuous\nMeans: {\"(-124.35, -124.10499999999999)\": -50430.1, \"(-124.10499999999999, -124.08500000000001)\": -38925.6, \"(-124.08500000000001, -124.07499999999999)\": -23742.3, \"(-124.07499999999999, -123.3)\": -12526.0, \"(-123.3, -122.955)\": -1690.2, \"(-122.955, -122.66499999999999)\": 19040.8, \"(-122.66499999999999, -122.60499999999999)\": 29856.3, \"(-122.60499999999999, -122.58500000000001)\": 44315.6, \"(-122.58500000000001, -122.555)\": 75515.2, \"(-122.555, -122.455)\": 86444.1, \"(-122.455, -122.445)\": 99533.8, \"(-122.445, -122.42500000000001)\": 112351.5, \"(-122.42500000000001, -122.405)\": 89733.4, \"(-122.405, -122.39500000000001)\": 78586.0, \"(-122.39500000000001, -122.375)\": 46429.6, \"(-122.375, -122.36500000000001)\": 35622.6, \"(-122.36500000000001, -122.305)\": 20538.8, \"(-122.305, -122.155)\": 6386.6, \"(-122.155, -120.92500000000001)\": 24722.9, \"(-120.92500000000001, -120.91499999999999)\": 54457.6, \"(-120.91499999999999, -120.89500000000001)\": 34017.2, \"(-120.89500000000001, -120.86500000000001)\": 18216.5, \"(-120.86500000000001, -120.725)\": 6143.7, \"(-120.725, -120.63499999999999)\": 17429.8, \"(-120.63499999999999, -120.485)\": 407.5, \"(-120.485, -120.405)\": -15764.1, \"(-120.405, -120.10499999999999)\": 1041.6, \"(-120.10499999999999, -120.095)\": 24030.2, \"(-120.095, -119.91499999999999)\": -2161.3, \"(-119.91499999999999, -119.85499999999999)\": -20610.8, \"(-119.85499999999999, -119.795)\": -31705.4, \"(-119.795, -119.755)\": -20112.3, \"(-119.755, -119.525)\": -3774.8, \"(-119.525, -119.505)\": 10442.2, \"(-119.505, -119.295)\": -10555.7, \"(-119.295, -119.215)\": 3582.5, \"(-119.215, -118.905)\": -15819.3, \"(-118.905, -118.695)\": -2790.4, \"(-118.695, -118.57499999999999)\": 13581.3, \"(-118.57499999999999, -118.525)\": 26358.2, \"(-118.525, -118.495)\": 44919.9, \"(-118.495, -118.375)\": 60453.4, \"(-118.375, -118.35499999999999)\": 38572.6, \"(-118.35499999999999, -118.305)\": 21183.4, \"(-118.305, -118.265)\": -6755.4, \"(-118.265, -118.14500000000001)\": -17830.0, \"(-118.14500000000001, -117.985)\": -7071.0, \"(-117.985, -117.755)\": -26435.2, \"(-117.755, -117.725)\": -50667.3, \"(-117.725, -117.64500000000001)\": -63305.2, \"(-117.64500000000001, -117.57499999999999)\": -50999.4, \"(-117.57499999999999, -117.35499999999999)\": -38880.5, \"(-117.35499999999999, -117.285)\": -64800.9, \"(-117.285, -117.155)\": -47182.2, \"(-117.155, -117.13499999999999)\": -65749.6, \"(-117.13499999999999, -116.995)\": -77340.8, \"(-116.995, -116.795)\": -64524.7, \"(-116.795, -116.205)\": -53643.1, \"(-116.205, -116.1)\": -64388.1, \"(-116.1, -115.525)\": -75181.7, \"(-115.525, -115.1)\": -57014.4, \"(-115.1, -114.595)\": -74654.1, \"(-114.595, -114.31)\": -100620.1}\nLower Bounds (95%-Confidence Interval): {\"(-124.35, -124.10499999999999)\": -57749.3, \"(-124.10499999999999, -124.08500000000001)\": -46703.5, \"(-124.08500000000001, -124.07499999999999)\": -34644.5, \"(-124.07499999999999, -123.3)\": -20263.6, \"(-123.3, -122.955)\": -10198.1, \"(-122.955, -122.66499999999999)\": 10561.3, \"(-122.66499999999999, -122.60499999999999)\": 24254.2, \"(-122.60499999999999, -122.58500000000001)\": 34301.3, \"(-122.58500000000001, -122.555)\": 63992.2, \"(-122.555, -122.455)\": 76785.0, \"(-122.455, -122.445)\": 91169.4, \"(-122.445, -122.42500000000001)\": 103719.6, \"(-122.42500000000001, -122.405)\": 81277.9, \"(-122.405, -122.39500000000001)\": 66955.5, \"(-122.39500000000001, -122.375)\": 35631.1, \"(-122.375, -122.36500000000001)\": 24396.3, \"(-122.36500000000001, -122.305)\": 14520.6, \"(-122.305, -122.155)\": -1221.8, \"(-122.155, -120.92500000000001)\": 11630.1, \"(-120.92500000000001, -120.91499999999999)\": 11031.4, \"(-120.91499999999999, -120.89500000000001)\": 19105.3, \"(-120.89500000000001, -120.86500000000001)\": 4469.7, \"(-120.86500000000001, -120.725)\": -1198.1, \"(-120.725, -120.63499999999999)\": 7919.4, \"(-120.63499999999999, -120.485)\": -11276.9, \"(-120.485, -120.405)\": -23035.0, \"(-120.405, -120.10499999999999)\": -4074.0, \"(-120.10499999999999, -120.095)\": -10802.7, \"(-120.095, -119.91499999999999)\": -13216.2, \"(-119.91499999999999, -119.85499999999999)\": -33171.4, \"(-119.85499999999999, -119.795)\": -38315.5, \"(-119.795, -119.755)\": -24784.5, \"(-119.755, -119.525)\": -13160.2, \"(-119.525, -119.505)\": -4048.0, \"(-119.505, -119.295)\": -18789.9, \"(-119.295, -119.215)\": -8493.9, \"(-119.215, -118.905)\": -20485.4, \"(-118.905, -118.695)\": -7647.9, \"(-118.695, -118.57499999999999)\": 4745.1, \"(-118.57499999999999, -118.525)\": 17156.2, \"(-118.525, -118.495)\": 33913.3, \"(-118.495, -118.375)\": 52480.8, \"(-118.375, -118.35499999999999)\": 34068.9, \"(-118.35499999999999, -118.305)\": 14693.0, \"(-118.305, -118.265)\": -11878.1, \"(-118.265, -118.14500000000001)\": -21370.7, \"(-118.14500000000001, -117.985)\": -11803.1, \"(-117.985, -117.755)\": -35281.9, \"(-117.755, -117.725)\": -58041.5, \"(-117.725, -117.64500000000001)\": -72526.8, \"(-117.64500000000001, -117.57499999999999)\": -61627.3, \"(-117.57499999999999, -117.35499999999999)\": -45444.2, \"(-117.35499999999999, -117.285)\": -74287.3, \"(-117.285, -117.155)\": -55258.2, \"(-117.155, -117.13499999999999)\": -74456.9, \"(-117.13499999999999, -116.995)\": -86582.5, \"(-116.995, -116.795)\": -73433.2, \"(-116.795, -116.205)\": -69635.5, \"(-116.205, -116.1)\": -75131.9, \"(-116.1, -115.525)\": -97151.1, \"(-115.525, -115.1)\": -73988.5, \"(-115.1, -114.595)\": -91086.2, \"(-114.595, -114.31)\": -120109.7}\nUpper Bounds (95%-Confidence Interval): {\"(-124.35, -124.10499999999999)\": -43110.8, \"(-124.10499999999999, -124.08500000000001)\": -31147.7, \"(-124.08500000000001, -124.07499999999999)\": -12840.0, \"(-124.07499999999999, -123.3)\": -4788.4, \"(-123.3, -122.955)\": 6817.8, \"(-122.955, -122.66499999999999)\": 27520.3, \"(-122.66499999999999, -122.60499999999999)\": 35458.4, \"(-122.60499999999999, -122.58500000000001)\": 54329.8, \"(-122.58500000000001, -122.555)\": 87038.2, \"(-122.555, -122.455)\": 96103.2, \"(-122.455, -122.445)\": 107898.1, \"(-122.445, -122.42500000000001)\": 120983.4, \"(-122.42500000000001, -122.405)\": 98188.9, \"(-122.405, -122.39500000000001)\": 90216.5, \"(-122.39500000000001, -122.375)\": 57228.1, \"(-122.375, -122.36500000000001)\": 46849.0, \"(-122.36500000000001, -122.305)\": 26556.9, \"(-122.305, -122.155)\": 13995.0, \"(-122.155, -120.92500000000001)\": 37815.7, \"(-120.92500000000001, -120.91499999999999)\": 97883.7, \"(-120.91499999999999, -120.89500000000001)\": 48929.0, \"(-120.89500000000001, -120.86500000000001)\": 31963.3, \"(-120.86500000000001, -120.725)\": 13485.5, \"(-120.725, -120.63499999999999)\": 26940.3, \"(-120.63499999999999, -120.485)\": 12092.0, \"(-120.485, -120.405)\": -8493.1, \"(-120.405, -120.10499999999999)\": 6157.2, \"(-120.10499999999999, -120.095)\": 58863.0, \"(-120.095, -119.91499999999999)\": 8893.5, \"(-119.91499999999999, -119.85499999999999)\": -8050.3, \"(-119.85499999999999, -119.795)\": -25095.3, \"(-119.795, -119.755)\": -15440.1, \"(-119.755, -119.525)\": 5610.6, \"(-119.525, -119.505)\": 24932.3, \"(-119.505, -119.295)\": -2321.4, \"(-119.295, -119.215)\": 15659.0, \"(-119.215, -118.905)\": -11153.1, \"(-118.905, -118.695)\": 2067.1, \"(-118.695, -118.57499999999999)\": 22417.4, \"(-118.57499999999999, -118.525)\": 35560.1, \"(-118.525, -118.495)\": 55926.4, \"(-118.495, -118.375)\": 68426.1, \"(-118.375, -118.35499999999999)\": 43076.4, \"(-118.35499999999999, -118.305)\": 27673.7, \"(-118.305, -118.265)\": -1632.7, \"(-118.265, -118.14500000000001)\": -14289.3, \"(-118.14500000000001, -117.985)\": -2338.9, \"(-117.985, -117.755)\": -17588.5, \"(-117.755, -117.725)\": -43293.2, \"(-117.725, -117.64500000000001)\": -54083.7, \"(-117.64500000000001, -117.57499999999999)\": -40371.5, \"(-117.57499999999999, -117.35499999999999)\": -32316.9, \"(-117.35499999999999, -117.285)\": -55314.6, \"(-117.285, -117.155)\": -39106.3, \"(-117.155, -117.13499999999999)\": -57042.4, \"(-117.13499999999999, -116.995)\": -68099.0, \"(-116.995, -116.795)\": -55616.1, \"(-116.795, -116.205)\": -37650.7, \"(-116.205, -116.1)\": -53644.2, \"(-116.1, -115.525)\": -53212.4, \"(-115.525, -115.1)\": -40040.3, \"(-115.1, -114.595)\": -58221.9, \"(-114.595, -114.31)\": -81130.6}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Age\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": 0.68, \"(0.5, 3.5)\": 0.36, \"(3.5, 4.5)\": 0.254, \"(4.5, 14.5)\": 0.09, \"(14.5, 23.5)\": 0.028, \"(23.5, 24.5)\": -0.027, \"(24.5, 25.5)\": -0.135, \"(25.5, 39.5)\": -0.05, \"(39.5, 44.5)\": 0.042, \"(44.5, 48.5)\": -0.025, \"(48.5, 54.5)\": -0.102, \"(54.5, 56.5)\": -0.012, \"(56.5, 63.5)\": 0.078, \"(63.5, 64.5)\": -0.028, \"(64.5, 65.5)\": -0.141, \"(65.5, 68.5)\": 0.058, \"(68.5, 69.5)\": -0.021, \"(69.5, 71.5)\": 0.037, \"(71.5, 73.5)\": -0.022, \"(73.5, 74.5)\": 0.413, \"(74.5, 77.5)\": 0.211, \"(77.5, 79.0)\": -0.412}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.461, \"(0.5, 3.5)\": 0.228, \"(3.5, 4.5)\": 0.097, \"(4.5, 14.5)\": -0.111, \"(14.5, 23.5)\": -0.031, \"(23.5, 24.5)\": -0.079, \"(24.5, 25.5)\": -0.32, \"(25.5, 39.5)\": -0.113, \"(39.5, 44.5)\": -0.088, \"(44.5, 48.5)\": -0.081, \"(48.5, 54.5)\": -0.336, \"(54.5, 56.5)\": -0.102, \"(56.5, 63.5)\": -0.123, \"(63.5, 64.5)\": -0.219, \"(64.5, 65.5)\": -0.706, \"(65.5, 68.5)\": -0.265, \"(68.5, 69.5)\": -0.416, \"(69.5, 71.5)\": -0.213, \"(71.5, 73.5)\": -0.172, \"(73.5, 74.5)\": -0.439, \"(74.5, 77.5)\": -0.317, \"(77.5, 79.0)\": -1.348}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.9, \"(0.5, 3.5)\": 0.491, \"(3.5, 4.5)\": 0.411, \"(4.5, 14.5)\": 0.29, \"(14.5, 23.5)\": 0.087, \"(23.5, 24.5)\": 0.024, \"(24.5, 25.5)\": 0.05, \"(25.5, 39.5)\": 0.012, \"(39.5, 44.5)\": 0.172, \"(44.5, 48.5)\": 0.031, \"(48.5, 54.5)\": 0.132, \"(54.5, 56.5)\": 0.077, \"(56.5, 63.5)\": 0.278, \"(63.5, 64.5)\": 0.163, \"(64.5, 65.5)\": 0.424, \"(65.5, 68.5)\": 0.382, \"(68.5, 69.5)\": 0.373, \"(69.5, 71.5)\": 0.287, \"(71.5, 73.5)\": 0.129, \"(73.5, 74.5)\": 1.265, \"(74.5, 77.5)\": 0.739, \"(77.5, 79.0)\": 0.524}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: radius_mean\nFeature Type: continuous\nMeans: {\"(6.981, 9.281500000000001)\": -0.762, \"(9.281500000000001, 9.7015)\": -0.659, \"(9.7015, 10.165)\": -0.56, \"(10.165, 10.655000000000001)\": -0.461, \"(10.655000000000001, 12.465)\": -0.36, \"(12.465, 13.39)\": -0.262, \"(13.39, 14.43)\": -0.163, \"(14.43, 14.934999999999999)\": -0.065, \"(14.934999999999999, 15.08)\": 0.037, \"(15.08, 15.815)\": 0.137, \"(15.815, 16.925)\": 0.235, \"(16.925, 17.385)\": 0.394, \"(17.385, 18.0)\": 0.494, \"(18.0, 18.735)\": 0.599, \"(18.735, 19.240000000000002)\": 0.695, \"(19.240000000000002, 19.990000000000002)\": 0.793, \"(19.990000000000002, 20.595)\": 0.891, \"(20.595, 23.240000000000002)\": 0.99, \"(23.240000000000002, 28.11)\": 1.093}\nLower Bounds (95%-Confidence Interval): {\"(6.981, 9.281500000000001)\": -1.01, \"(9.281500000000001, 9.7015)\": -0.884, \"(9.7015, 10.165)\": -0.748, \"(10.165, 10.655000000000001)\": -0.611, \"(10.655000000000001, 12.465)\": -0.536, \"(12.465, 13.39)\": -0.396, \"(13.39, 14.43)\": -0.269, \"(14.43, 14.934999999999999)\": -0.226, \"(14.934999999999999, 15.08)\": -0.156, \"(15.08, 15.815)\": -0.059, \"(15.815, 16.925)\": -0.127, \"(16.925, 17.385)\": 0.041, \"(17.385, 18.0)\": 0.136, \"(18.0, 18.735)\": 0.205, \"(18.735, 19.240000000000002)\": 0.283, \"(19.240000000000002, 19.990000000000002)\": 0.385, \"(19.990000000000002, 20.595)\": 0.462, \"(20.595, 23.240000000000002)\": 0.519, \"(23.240000000000002, 28.11)\": 0.611}\nUpper Bounds (95%-Confidence Interval): {\"(6.981, 9.281500000000001)\": -0.515, \"(9.281500000000001, 9.7015)\": -0.435, \"(9.7015, 10.165)\": -0.373, \"(10.165, 10.655000000000001)\": -0.311, \"(10.655000000000001, 12.465)\": -0.184, \"(12.465, 13.39)\": -0.128, \"(13.39, 14.43)\": -0.057, \"(14.43, 14.934999999999999)\": 0.097, \"(14.934999999999999, 15.08)\": 0.231, \"(15.08, 15.815)\": 0.333, \"(15.815, 16.925)\": 0.597, \"(16.925, 17.385)\": 0.748, \"(17.385, 18.0)\": 0.853, \"(18.0, 18.735)\": 0.993, \"(18.735, 19.240000000000002)\": 1.107, \"(19.240000000000002, 19.990000000000002)\": 1.202, \"(19.990000000000002, 20.595)\": 1.32, \"(20.595, 23.240000000000002)\": 1.461, \"(23.240000000000002, 28.11)\": 1.575}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: total_rooms\nFeature Type: continuous\nMeans: {\"(2.0, 23.0)\": -70808.9, \"(23.0, 38.5)\": -78966.6, \"(38.5, 48.5)\": -28602.1, \"(48.5, 119.0)\": -47079.6, \"(119.0, 163.0)\": -52692.3, \"(163.0, 186.5)\": -60093.0, \"(186.5, 223.5)\": -51150.5, \"(223.5, 239.5)\": -39728.1, \"(239.5, 248.5)\": -7038.8, \"(248.5, 265.5)\": -691.1, \"(265.5, 280.5)\": -14052.2, \"(280.5, 342.5)\": -35705.6, \"(342.5, 364.5)\": -24578.4, \"(364.5, 385.5)\": -34007.7, \"(385.5, 406.5)\": -46655.0, \"(406.5, 413.5)\": -17805.2, \"(413.5, 443.5)\": -12192.7, \"(443.5, 452.5)\": -22779.7, \"(452.5, 502.5)\": -30652.6, \"(502.5, 508.5)\": -25165.4, \"(508.5, 515.5)\": -12943.4, \"(515.5, 1152.5)\": -21645.3, \"(1152.5, 1239.5)\": -16264.4, \"(1239.5, 1245.5)\": -7023.2, \"(1245.5, 1619.5)\": -12855.2, \"(1619.5, 1944.5)\": -7415.6, \"(1944.5, 2330.5)\": -1233.9, \"(2330.5, 2710.5)\": 4370.8, \"(2710.5, 2834.5)\": 9739.0, \"(2834.5, 2838.5)\": 16667.1, \"(2838.5, 3577.5)\": 10096.4, \"(3577.5, 5401.0)\": 15549.4, \"(5401.0, 5535.5)\": 24928.2, \"(5535.5, 9961.0)\": 19069.3, \"(9961.0, 18662.0)\": 26262.6, \"(18662.0, 39320.0)\": 20736.3}\nLower Bounds (95%-Confidence Interval): {\"(2.0, 23.0)\": -91545.9, \"(23.0, 38.5)\": -102966.4, \"(38.5, 48.5)\": -57179.9, \"(48.5, 119.0)\": -64507.9, \"(119.0, 163.0)\": -67051.1, \"(163.0, 186.5)\": -74986.7, \"(186.5, 223.5)\": -62447.2, \"(223.5, 239.5)\": -55573.0, \"(239.5, 248.5)\": -34485.5, \"(248.5, 265.5)\": -18815.6, \"(265.5, 280.5)\": -35576.3, \"(280.5, 342.5)\": -44957.9, \"(342.5, 364.5)\": -36592.4, \"(364.5, 385.5)\": -39620.4, \"(385.5, 406.5)\": -54434.9, \"(406.5, 413.5)\": -28898.3, \"(413.5, 443.5)\": -21926.2, \"(443.5, 452.5)\": -34828.5, \"(452.5, 502.5)\": -40304.3, \"(502.5, 508.5)\": -35649.5, \"(508.5, 515.5)\": -27403.5, \"(515.5, 1152.5)\": -28456.5, \"(1152.5, 1239.5)\": -20918.2, \"(1239.5, 1245.5)\": -15907.4, \"(1245.5, 1619.5)\": -19943.7, \"(1619.5, 1944.5)\": -13063.6, \"(1944.5, 2330.5)\": -8595.8, \"(2330.5, 2710.5)\": 2936.6, \"(2710.5, 2834.5)\": 7069.8, \"(2834.5, 2838.5)\": 1263.0, \"(2838.5, 3577.5)\": 7025.1, \"(3577.5, 5401.0)\": 10287.4, \"(5401.0, 5535.5)\": 10519.1, \"(5535.5, 9961.0)\": 12536.6, \"(9961.0, 18662.0)\": 16596.5, \"(18662.0, 39320.0)\": 17189.5}\nUpper Bounds (95%-Confidence Interval): {\"(2.0, 23.0)\": -50072.0, \"(23.0, 38.5)\": -54966.9, \"(38.5, 48.5)\": -24.3, \"(48.5, 119.0)\": -29651.4, \"(119.0, 163.0)\": -38333.5, \"(163.0, 186.5)\": -45199.3, \"(186.5, 223.5)\": -39853.9, \"(223.5, 239.5)\": -23883.2, \"(239.5, 248.5)\": 20408.0, \"(248.5, 265.5)\": 17433.4, \"(265.5, 280.5)\": 7471.9, \"(280.5, 342.5)\": -26453.2, \"(342.5, 364.5)\": -12564.3, \"(364.5, 385.5)\": -28395.1, \"(385.5, 406.5)\": -38875.1, \"(406.5, 413.5)\": -6712.1, \"(413.5, 443.5)\": -2459.1, \"(443.5, 452.5)\": -10730.8, \"(452.5, 502.5)\": -21000.9, \"(502.5, 508.5)\": -14681.3, \"(508.5, 515.5)\": 1516.8, \"(515.5, 1152.5)\": -14834.1, \"(1152.5, 1239.5)\": -11610.6, \"(1239.5, 1245.5)\": 1860.9, \"(1245.5, 1619.5)\": -5766.8, \"(1619.5, 1944.5)\": -1767.7, \"(1944.5, 2330.5)\": 6128.1, \"(2330.5, 2710.5)\": 5805.0, \"(2710.5, 2834.5)\": 12408.3, \"(2834.5, 2838.5)\": 32071.2, \"(2838.5, 3577.5)\": 13167.8, \"(3577.5, 5401.0)\": 20811.4, \"(5401.0, 5535.5)\": 39337.3, \"(5535.5, 9961.0)\": 25602.1, \"(9961.0, 18662.0)\": 35928.6, \"(18662.0, 39320.0)\": 24283.0}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Parch\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": 0.085, \"(0.5, 1.5)\": -0.055, \"(1.5, 3.0)\": -0.299, \"(3.0, 4.0)\": -1.704}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02, \"(0.5, 1.5)\": -0.269, \"(1.5, 3.0)\": -0.62, \"(3.0, 4.0)\": -3.014}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.19, \"(0.5, 1.5)\": 0.158, \"(1.5, 3.0)\": 0.022, \"(3.0, 4.0)\": -0.395}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Decreasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: InadequatePlanning\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02553, \"(0.5, 2.5)\": -0.02038, \"(2.5, 4.5)\": -0.0099, \"(4.5, 6.5)\": 0.00082, \"(6.5, 7.5)\": 0.01088, \"(7.5, 9.5)\": 0.0178, \"(9.5, 10.5)\": 0.02657, \"(10.5, 12.5)\": 0.0329, \"(12.5, 13.5)\": 0.03982, \"(13.5, 15.0)\": 0.05043, \"(15.0, 16.0)\": 0.06084}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02806, \"(0.5, 2.5)\": -0.02117, \"(2.5, 4.5)\": -0.01033, \"(4.5, 6.5)\": 0.00032, \"(6.5, 7.5)\": 0.01025, \"(7.5, 9.5)\": 0.01687, \"(9.5, 10.5)\": 0.02522, \"(10.5, 12.5)\": 0.02998, \"(12.5, 13.5)\": 0.03567, \"(13.5, 15.0)\": 0.03659, \"(15.0, 16.0)\": 0.04096}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.023, \"(0.5, 2.5)\": -0.01959, \"(2.5, 4.5)\": -0.00946, \"(4.5, 6.5)\": 0.00132, \"(6.5, 7.5)\": 0.0115, \"(7.5, 9.5)\": 0.01874, \"(9.5, 10.5)\": 0.02792, \"(10.5, 12.5)\": 0.03583, \"(12.5, 13.5)\": 0.04397, \"(13.5, 15.0)\": 0.06426, \"(15.0, 16.0)\": 0.08071}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: HoursPerWeek\nFeature Type: continuous\nMeans: {\"(1.0, 1.5)\": -0.765, \"(1.5, 2.5)\": -0.375, \"(2.5, 4.5)\": -1.909, \"(4.5, 6.5)\": -1.117, \"(6.5, 7.5)\": -0.618, \"(7.5, 14.5)\": -0.822, \"(14.5, 19.5)\": -1.132, \"(19.5, 29.5)\": -0.765, \"(29.5, 33.5)\": -0.6, \"(33.5, 34.5)\": -0.921, \"(34.5, 39.5)\": -0.155, \"(39.5, 41.5)\": 0.03, \"(41.5, 50.5)\": 0.392, \"(50.5, 51.5)\": 0.131, \"(51.5, 55.5)\": 0.457, \"(55.5, 59.5)\": 0.676, \"(59.5, 63.5)\": 0.416, \"(63.5, 64.5)\": 0.952, \"(64.5, 65.5)\": 0.516, \"(65.5, 71.0)\": 0.071, \"(71.0, 75.5)\": 0.43, \"(75.5, 77.5)\": 0.235, \"(77.5, 79.0)\": 0.742, \"(79.0, 83.0)\": 0.977, \"(83.0, 85.5)\": 1.287, \"(85.5, 90.5)\": 0.192, \"(90.5, 97.5)\": -0.071, \"(97.5, 98.5)\": 0.119, \"(98.5, 99.0)\": -0.139}\nLower Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -2.672, \"(1.5, 2.5)\": -0.773, \"(2.5, 4.5)\": -2.709, \"(4.5, 6.5)\": -1.566, \"(6.5, 7.5)\": -1.241, \"(7.5, 14.5)\": -1.098, \"(14.5, 19.5)\": -1.535, \"(19.5, 29.5)\": -1.357, \"(29.5, 33.5)\": -1.248, \"(33.5, 34.5)\": -1.815, \"(34.5, 39.5)\": -0.223, \"(39.5, 41.5)\": -0.129, \"(41.5, 50.5)\": 0.212, \"(50.5, 51.5)\": -0.867, \"(51.5, 55.5)\": 0.357, \"(55.5, 59.5)\": 0.304, \"(59.5, 63.5)\": 0.014, \"(63.5, 64.5)\": 0.009, \"(64.5, 65.5)\": 0.379, \"(65.5, 71.0)\": -0.113, \"(71.0, 75.5)\": 0.054, \"(75.5, 77.5)\": -0.57, \"(77.5, 79.0)\": 0.234, \"(79.0, 83.0)\": 0.788, \"(83.0, 85.5)\": 0.721, \"(85.5, 90.5)\": -0.289, \"(90.5, 97.5)\": -0.504, \"(97.5, 98.5)\": -0.527, \"(98.5, 99.0)\": -0.548}\nUpper Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": 1.142, \"(1.5, 2.5)\": 0.023, \"(2.5, 4.5)\": -1.109, \"(4.5, 6.5)\": -0.668, \"(6.5, 7.5)\": 0.005, \"(7.5, 14.5)\": -0.546, \"(14.5, 19.5)\": -0.729, \"(19.5, 29.5)\": -0.172, \"(29.5, 33.5)\": 0.047, \"(33.5, 34.5)\": -0.027, \"(34.5, 39.5)\": -0.087, \"(39.5, 41.5)\": 0.19, \"(41.5, 50.5)\": 0.571, \"(50.5, 51.5)\": 1.13, \"(51.5, 55.5)\": 0.557, \"(55.5, 59.5)\": 1.048, \"(59.5, 63.5)\": 0.818, \"(63.5, 64.5)\": 1.896, \"(64.5, 65.5)\": 0.653, \"(65.5, 71.0)\": 0.254, \"(71.0, 75.5)\": 0.806, \"(75.5, 77.5)\": 1.04, \"(77.5, 79.0)\": 1.25, \"(79.0, 83.0)\": 1.166, \"(83.0, 85.5)\": 1.852, \"(85.5, 90.5)\": 0.673, \"(90.5, 97.5)\": 0.361, \"(97.5, 98.5)\": 0.765, \"(98.5, 99.0)\": 0.271}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Insulin\nFeature Type: continuous\nMeans: {\"(0.0, 20.0)\": 0.0422, \"(20.0, 36.5)\": -0.0027, \"(36.5, 40.5)\": -0.0554, \"(40.5, 45.5)\": -0.0967, \"(45.5, 48.5)\": -0.0409, \"(48.5, 55.5)\": -0.2263, \"(55.5, 80.5)\": -0.2661, \"(80.5, 87.5)\": -0.227, \"(87.5, 97.5)\": -0.1794, \"(97.5, 111.0)\": -0.1356, \"(111.0, 123.5)\": -0.0968, \"(123.5, 137.5)\": -0.0561, \"(137.5, 144.5)\": -0.0187, \"(144.5, 157.0)\": 0.0208, \"(157.0, 170.5)\": 0.0623, \"(170.5, 186.5)\": 0.0999, \"(186.5, 190.5)\": 0.0538, \"(190.5, 192.5)\": 0.1059, \"(192.5, 271.0)\": -0.0027, \"(271.0, 277.5)\": 0.035, \"(277.5, 292.0)\": 0.0732, \"(292.0, 311.0)\": 0.1129, \"(311.0, 365.0)\": 0.1551, \"(365.0, 397.0)\": 0.196, \"(397.0, 452.5)\": 0.2331, \"(452.5, 476.0)\": 0.2839, \"(476.0, 487.5)\": 0.346, \"(487.5, 526.5)\": 0.3915, \"(526.5, 680.0)\": 0.4346}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 20.0)\": -0.0556, \"(20.0, 36.5)\": -0.2244, \"(36.5, 40.5)\": -0.2184, \"(40.5, 45.5)\": -0.2543, \"(45.5, 48.5)\": -0.7961, \"(48.5, 55.5)\": -0.5056, \"(55.5, 80.5)\": -0.551, \"(80.5, 87.5)\": -0.3117, \"(87.5, 97.5)\": -0.251, \"(97.5, 111.0)\": -0.2086, \"(111.0, 123.5)\": -0.1731, \"(123.5, 137.5)\": -0.137, \"(137.5, 144.5)\": -0.1027, \"(144.5, 157.0)\": -0.0751, \"(157.0, 170.5)\": -0.0506, \"(170.5, 186.5)\": -0.0163, \"(186.5, 190.5)\": -0.2256, \"(190.5, 192.5)\": -0.2869, \"(192.5, 271.0)\": -0.3659, \"(271.0, 277.5)\": -0.245, \"(277.5, 292.0)\": -0.1491, \"(292.0, 311.0)\": -0.0995, \"(311.0, 365.0)\": -0.0355, \"(365.0, 397.0)\": -0.0134, \"(397.0, 452.5)\": 0.0212, \"(452.5, 476.0)\": 0.0711, \"(476.0, 487.5)\": 0.1139, \"(487.5, 526.5)\": 0.1534, \"(526.5, 680.0)\": 0.0241}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 20.0)\": 0.14, \"(20.0, 36.5)\": 0.2189, \"(36.5, 40.5)\": 0.1076, \"(40.5, 45.5)\": 0.0609, \"(45.5, 48.5)\": 0.7143, \"(48.5, 55.5)\": 0.053, \"(55.5, 80.5)\": 0.0187, \"(80.5, 87.5)\": -0.1422, \"(87.5, 97.5)\": -0.1078, \"(97.5, 111.0)\": -0.0625, \"(111.0, 123.5)\": -0.0206, \"(123.5, 137.5)\": 0.0247, \"(137.5, 144.5)\": 0.0654, \"(144.5, 157.0)\": 0.1166, \"(157.0, 170.5)\": 0.1751, \"(170.5, 186.5)\": 0.2162, \"(186.5, 190.5)\": 0.3332, \"(190.5, 192.5)\": 0.4987, \"(192.5, 271.0)\": 0.3605, \"(271.0, 277.5)\": 0.315, \"(277.5, 292.0)\": 0.2956, \"(292.0, 311.0)\": 0.3253, \"(311.0, 365.0)\": 0.3457, \"(365.0, 397.0)\": 0.4055, \"(397.0, 452.5)\": 0.445, \"(452.5, 476.0)\": 0.4967, \"(476.0, 487.5)\": 0.5782, \"(487.5, 526.5)\": 0.6295, \"(526.5, 680.0)\": 0.8452}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: housing_median_age\nFeature Type: continuous\nMeans: {\"(1.0, 4.5)\": -19998.0, \"(4.5, 7.5)\": -7788.2, \"(7.5, 16.5)\": -10680.2, \"(16.5, 18.5)\": -6304.4, \"(18.5, 27.5)\": -1760.6, \"(27.5, 34.5)\": 2164.8, \"(34.5, 38.5)\": -912.5, \"(38.5, 41.5)\": 4199.6, \"(41.5, 45.5)\": -497.4, \"(45.5, 47.5)\": -5189.8, \"(47.5, 48.5)\": 5201.0, \"(48.5, 49.5)\": 2159.0, \"(49.5, 50.5)\": 6135.7, \"(50.5, 51.5)\": 11513.8, \"(51.5, 52.0)\": 27549.7}\nLower Bounds (95%-Confidence Interval): {\"(1.0, 4.5)\": -26905.5, \"(4.5, 7.5)\": -11566.0, \"(7.5, 16.5)\": -12538.5, \"(16.5, 18.5)\": -7756.2, \"(18.5, 27.5)\": -3361.1, \"(27.5, 34.5)\": 124.5, \"(34.5, 38.5)\": -1933.4, \"(38.5, 41.5)\": 2260.6, \"(41.5, 45.5)\": -4429.7, \"(45.5, 47.5)\": -8697.7, \"(47.5, 48.5)\": 2180.3, \"(48.5, 49.5)\": -1981.1, \"(49.5, 50.5)\": 1581.5, \"(50.5, 51.5)\": 5647.5, \"(51.5, 52.0)\": 25827.1}\nUpper Bounds (95%-Confidence Interval): {\"(1.0, 4.5)\": -13090.4, \"(4.5, 7.5)\": -4010.4, \"(7.5, 16.5)\": -8821.8, \"(16.5, 18.5)\": -4852.5, \"(18.5, 27.5)\": -160.0, \"(27.5, 34.5)\": 4205.0, \"(34.5, 38.5)\": 108.5, \"(38.5, 41.5)\": 6138.7, \"(41.5, 45.5)\": 3434.9, \"(45.5, 47.5)\": -1682.0, \"(47.5, 48.5)\": 8221.7, \"(48.5, 49.5)\": 6299.1, \"(49.5, 50.5)\": 10689.9, \"(50.5, 51.5)\": 17380.1, \"(51.5, 52.0)\": 29272.3}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: BloodPressure\nFeature Type: continuous\nMeans: {\"(0.0, 15.0)\": 0.236, \"(15.0, 37.0)\": 0.1532, \"(37.0, 45.0)\": -0.0296, \"(45.0, 47.0)\": -0.0891, \"(47.0, 54.5)\": -0.1348, \"(54.5, 60.5)\": -0.1774, \"(60.5, 61.5)\": -0.11, \"(61.5, 64.5)\": -0.0541, \"(64.5, 74.5)\": -0.0119, \"(74.5, 75.5)\": -0.058, \"(75.5, 83.0)\": -0.004, \"(83.0, 93.0)\": 0.0343, \"(93.0, 95.0)\": 0.0889, \"(95.0, 97.0)\": 0.1461, \"(97.0, 101.0)\": 0.183, \"(101.0, 103.0)\": 0.2699, \"(103.0, 107.0)\": 0.3158, \"(107.0, 109.0)\": 0.3837, \"(109.0, 110.0)\": 0.5269}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 15.0)\": -0.0274, \"(15.0, 37.0)\": -0.1145, \"(37.0, 45.0)\": -0.2191, \"(45.0, 47.0)\": -0.2854, \"(47.0, 54.5)\": -0.313, \"(54.5, 60.5)\": -0.2953, \"(60.5, 61.5)\": -0.1759, \"(61.5, 64.5)\": -0.1789, \"(64.5, 74.5)\": -0.1212, \"(74.5, 75.5)\": -0.3075, \"(75.5, 83.0)\": -0.0727, \"(83.0, 93.0)\": -0.1515, \"(93.0, 95.0)\": -0.0624, \"(95.0, 97.0)\": -0.0006, \"(97.0, 101.0)\": 0.0092, \"(101.0, 103.0)\": 0.085, \"(103.0, 107.0)\": 0.1217, \"(107.0, 109.0)\": 0.1853, \"(109.0, 110.0)\": 0.2653}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 15.0)\": 0.4994, \"(15.0, 37.0)\": 0.4208, \"(37.0, 45.0)\": 0.16, \"(45.0, 47.0)\": 0.1073, \"(47.0, 54.5)\": 0.0433, \"(54.5, 60.5)\": -0.0595, \"(60.5, 61.5)\": -0.0441, \"(61.5, 64.5)\": 0.0708, \"(64.5, 74.5)\": 0.0974, \"(74.5, 75.5)\": 0.1914, \"(75.5, 83.0)\": 0.0647, \"(83.0, 93.0)\": 0.2201, \"(93.0, 95.0)\": 0.2402, \"(95.0, 97.0)\": 0.2929, \"(97.0, 101.0)\": 0.3567, \"(101.0, 103.0)\": 0.4548, \"(103.0, 107.0)\": 0.51, \"(107.0, 109.0)\": 0.582, \"(109.0, 110.0)\": 0.7884}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: petal_length\nFeature Type: continuous\nMeans: {\"(1.1, 1.65)\": 8.05, \"(1.65, 2.45)\": 7.28, \"(2.45, 3.15)\": -1.17, \"(3.15, 3.8)\": -2.4, \"(3.8, 4.45)\": -3.03, \"(4.45, 5.65)\": -3.73, \"(5.65, 6.9)\": -4.38}\nLower Bounds (95%-Confidence Interval): {\"(1.1, 1.65)\": 7.87, \"(1.65, 2.45)\": 7.08, \"(2.45, 3.15)\": -4.92, \"(3.15, 3.8)\": -2.6, \"(3.8, 4.45)\": -3.19, \"(4.45, 5.65)\": -3.87, \"(5.65, 6.9)\": -4.55}\nUpper Bounds (95%-Confidence Interval): {\"(1.1, 1.65)\": 8.24, \"(1.65, 2.45)\": 7.48, \"(2.45, 3.15)\": 2.57, \"(3.15, 3.8)\": -2.2, \"(3.8, 4.45)\": -2.86, \"(4.45, 5.65)\": -3.58, \"(5.65, 6.9)\": -4.2}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Decreasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: smoothness_worst\nFeature Type: continuous\nMeans: {\"(0.07117, 0.09376000000000001)\": -1.298, \"(0.09376000000000001, 0.099705)\": -1.161, \"(0.099705, 0.10519999999999999)\": -1.024, \"(0.10519999999999999, 0.10825)\": -0.889, \"(0.10825, 0.11549999999999999)\": -0.527, \"(0.11549999999999999, 0.12345)\": -0.394, \"(0.12345, 0.13074999999999998)\": -0.26, \"(0.13074999999999998, 0.13585)\": -0.124, \"(0.13585, 0.13640000000000002)\": 0.011, \"(0.13640000000000002, 0.13845000000000002)\": 0.154, \"(0.13845000000000002, 0.14065)\": 0.288, \"(0.14065, 0.14635)\": 0.439, \"(0.14635, 0.15585)\": 0.574, \"(0.15585, 0.16885)\": 0.708, \"(0.16885, 0.17825)\": 0.846, \"(0.17825, 0.19574999999999998)\": 1.17, \"(0.19574999999999998, 0.2226)\": 1.304}\nLower Bounds (95%-Confidence Interval): {\"(0.07117, 0.09376000000000001)\": -2.26, \"(0.09376000000000001, 0.099705)\": -2.132, \"(0.099705, 0.10519999999999999)\": -2.009, \"(0.10519999999999999, 0.10825)\": -1.875, \"(0.10825, 0.11549999999999999)\": -0.765, \"(0.11549999999999999, 0.12345)\": -0.589, \"(0.12345, 0.13074999999999998)\": -0.435, \"(0.13074999999999998, 0.13585)\": -0.384, \"(0.13585, 0.13640000000000002)\": -0.325, \"(0.13640000000000002, 0.13845000000000002)\": -0.247, \"(0.13845000000000002, 0.14065)\": -0.076, \"(0.14065, 0.14635)\": -0.12, \"(0.14635, 0.15585)\": 0.165, \"(0.15585, 0.16885)\": 0.27, \"(0.16885, 0.17825)\": 0.402, \"(0.17825, 0.19574999999999998)\": -0.484, \"(0.19574999999999998, 0.2226)\": -0.349}\nUpper Bounds (95%-Confidence Interval): {\"(0.07117, 0.09376000000000001)\": -0.336, \"(0.09376000000000001, 0.099705)\": -0.19, \"(0.099705, 0.10519999999999999)\": -0.039, \"(0.10519999999999999, 0.10825)\": 0.096, \"(0.10825, 0.11549999999999999)\": -0.289, \"(0.11549999999999999, 0.12345)\": -0.2, \"(0.12345, 0.13074999999999998)\": -0.086, \"(0.13074999999999998, 0.13585)\": 0.136, \"(0.13585, 0.13640000000000002)\": 0.348, \"(0.13640000000000002, 0.13845000000000002)\": 0.556, \"(0.13845000000000002, 0.14065)\": 0.652, \"(0.14065, 0.14635)\": 0.997, \"(0.14635, 0.15585)\": 0.983, \"(0.15585, 0.16885)\": 1.146, \"(0.16885, 0.17825)\": 1.289, \"(0.17825, 0.19574999999999998)\": 2.823, \"(0.19574999999999998, 0.2226)\": 2.957}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: concave points_mean\nFeature Type: continuous\nMeans: {\"(0.0, 0.0074145)\": -1.054, \"(0.0074145, 0.011665)\": -0.937, \"(0.011665, 0.01503)\": -0.821, \"(0.01503, 0.017865)\": -0.705, \"(0.017865, 0.019315)\": -0.582, \"(0.019315, 0.023185)\": -0.466, \"(0.023185, 0.026115)\": -0.352, \"(0.026115, 0.042455)\": -0.235, \"(0.042455, 0.048235)\": -0.115, \"(0.048235, 0.048865)\": 0.04, \"(0.048865, 0.059615)\": 0.233, \"(0.059615, 0.070395)\": 0.35, \"(0.070395, 0.08221500000000001)\": 0.474, \"(0.08221500000000001, 0.087175)\": 0.592, \"(0.087175, 0.091445)\": 0.711, \"(0.091445, 0.1006)\": 0.832, \"(0.1006, 0.122)\": 0.949, \"(0.122, 0.16544999999999999)\": 1.068, \"(0.16544999999999999, 0.2012)\": 1.187}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.0074145)\": -1.411, \"(0.0074145, 0.011665)\": -1.253, \"(0.011665, 0.01503)\": -1.095, \"(0.01503, 0.017865)\": -0.965, \"(0.017865, 0.019315)\": -0.823, \"(0.019315, 0.023185)\": -0.72, \"(0.023185, 0.026115)\": -0.517, \"(0.026115, 0.042455)\": -0.743, \"(0.042455, 0.048235)\": -0.628, \"(0.048235, 0.048865)\": -0.409, \"(0.048865, 0.059615)\": -0.151, \"(0.059615, 0.070395)\": 0.09, \"(0.070395, 0.08221500000000001)\": 0.219, \"(0.08221500000000001, 0.087175)\": 0.306, \"(0.087175, 0.091445)\": 0.39, \"(0.091445, 0.1006)\": 0.481, \"(0.1006, 0.122)\": 0.562, \"(0.122, 0.16544999999999999)\": 0.634, \"(0.16544999999999999, 0.2012)\": 0.74}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.0074145)\": -0.697, \"(0.0074145, 0.011665)\": -0.62, \"(0.011665, 0.01503)\": -0.546, \"(0.01503, 0.017865)\": -0.445, \"(0.017865, 0.019315)\": -0.34, \"(0.019315, 0.023185)\": -0.212, \"(0.023185, 0.026115)\": -0.188, \"(0.026115, 0.042455)\": 0.274, \"(0.042455, 0.048235)\": 0.398, \"(0.048235, 0.048865)\": 0.489, \"(0.048865, 0.059615)\": 0.617, \"(0.059615, 0.070395)\": 0.611, \"(0.070395, 0.08221500000000001)\": 0.728, \"(0.08221500000000001, 0.087175)\": 0.878, \"(0.087175, 0.091445)\": 1.032, \"(0.091445, 0.1006)\": 1.182, \"(0.1006, 0.122)\": 1.336, \"(0.122, 0.16544999999999999)\": 1.503, \"(0.16544999999999999, 0.2012)\": 1.634}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: RoomService\nFeature Type: continuous\nMeans: {\"(0.0, 105.5)\": 0.328, \"(105.5, 296.5)\": 0.028, \"(296.5, 335.5)\": -0.208, \"(335.5, 340.0)\": 0.165, \"(340.0, 343.0)\": -0.1, \"(343.0, 596.5)\": -0.741, \"(596.5, 712.5)\": -0.978, \"(712.5, 734.0)\": -1.212, \"(734.0, 800.0)\": -1.446, \"(800.0, 816.0)\": -1.136, \"(816.0, 997.5)\": -1.454, \"(997.5, 1031.0)\": -1.106, \"(1031.0, 1041.0)\": -1.368, \"(1041.0, 2172.5)\": -1.866, \"(2172.5, 2283.5)\": -1.455, \"(2283.5, 2313.5)\": -1.171, \"(2313.5, 2336.5)\": -0.66, \"(2336.5, 2420.0)\": -2.559, \"(2420.0, 2992.5)\": -3.229, \"(2992.5, 3006.0)\": -2.708, \"(3006.0, 3196.5)\": -2.984, \"(3196.5, 3249.5)\": -2.709, \"(3249.5, 14327.0)\": -4.146}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 105.5)\": -0.06, \"(105.5, 296.5)\": -0.369, \"(296.5, 335.5)\": -1.022, \"(335.5, 340.0)\": -0.184, \"(340.0, 343.0)\": -1.038, \"(343.0, 596.5)\": -1.323, \"(596.5, 712.5)\": -1.547, \"(712.5, 734.0)\": -1.555, \"(734.0, 800.0)\": -1.8, \"(800.0, 816.0)\": -2.191, \"(816.0, 997.5)\": -1.824, \"(997.5, 1031.0)\": -1.706, \"(1031.0, 1041.0)\": -2.147, \"(1041.0, 2172.5)\": -2.244, \"(2172.5, 2283.5)\": -2.248, \"(2283.5, 2313.5)\": -1.568, \"(2313.5, 2336.5)\": -2.21, \"(2336.5, 2420.0)\": -3.537, \"(2420.0, 2992.5)\": -3.89, \"(2992.5, 3006.0)\": -3.955, \"(3006.0, 3196.5)\": -4.24, \"(3196.5, 3249.5)\": -3.98, \"(3249.5, 14327.0)\": -5.248}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 105.5)\": 0.716, \"(105.5, 296.5)\": 0.425, \"(296.5, 335.5)\": 0.607, \"(335.5, 340.0)\": 0.513, \"(340.0, 343.0)\": 0.837, \"(343.0, 596.5)\": -0.16, \"(596.5, 712.5)\": -0.409, \"(712.5, 734.0)\": -0.869, \"(734.0, 800.0)\": -1.092, \"(800.0, 816.0)\": -0.082, \"(816.0, 997.5)\": -1.083, \"(997.5, 1031.0)\": -0.506, \"(1031.0, 1041.0)\": -0.589, \"(1041.0, 2172.5)\": -1.488, \"(2172.5, 2283.5)\": -0.661, \"(2283.5, 2313.5)\": -0.774, \"(2313.5, 2336.5)\": 0.89, \"(2336.5, 2420.0)\": -1.582, \"(2420.0, 2992.5)\": -2.569, \"(2992.5, 3006.0)\": -1.461, \"(3006.0, 3196.5)\": -1.727, \"(3196.5, 3249.5)\": -1.438, \"(3249.5, 14327.0)\": -3.043}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: id\nFeature Type: continuous\nMeans: {\"(91.0, 2307.0)\": 0.00838, \"(2307.0, 4713.5)\": 0.00964, \"(4713.5, 6928.5)\": 0.0038, \"(6928.5, 9761.5)\": 0.00118, \"(9761.5, 13120.0)\": -0.00051, \"(13120.0, 14826.0)\": -0.00127, \"(14826.0, 20043.5)\": 5e-05, \"(20043.5, 22448.0)\": 0.00075, \"(22448.0, 23794.0)\": -0.00133, \"(23794.0, 28014.5)\": -0.00281, \"(28014.5, 28671.0)\": -0.00155, \"(28671.0, 37439.5)\": -0.00049, \"(37439.5, 40007.0)\": 0.00015, \"(40007.0, 41128.5)\": 0.00473, \"(41128.5, 50305.5)\": -0.0009, \"(50305.5, 51818.5)\": -0.00193, \"(51818.5, 66668.0)\": -0.00104, \"(66668.0, 67776.0)\": 0.0019, \"(67776.0, 75664.5)\": 1e-05, \"(75664.5, 76606.0)\": 0.0007, \"(76606.0, 89235.5)\": 0.00161, \"(89235.5, 227800.5)\": -0.00038, \"(227800.5, 231707.5)\": 0.00024, \"(231707.5, 257871.0)\": -0.00045, \"(257871.0, 503283.0)\": 0.00017, \"(503283.0, 507804.5)\": -0.00061, \"(507804.5, 517795.0)\": -0.00125, \"(517795.0, 616121.0)\": -0.00038, \"(616121.0, 622616.5)\": 0.00042, \"(622616.5, 647046.0)\": -0.00022, \"(647046.0, 662956.5)\": 0.00117, \"(662956.5, 667208.5)\": -0.00102, \"(667208.5, 689123.0)\": 0.00021, \"(689123.0, 872554.5)\": -0.00065, \"(872554.5, 942666.5)\": 0.00032, \"(942666.5, 983736.5)\": -0.00052, \"(983736.5, 1025442.0)\": 0.00017, \"(1025442.0, 1029281.5)\": -0.00099, \"(1029281.5, 1040563.0)\": -0.00029, \"(1040563.0, 1103097.0)\": 0.00069, \"(1103097.0, 1103695.0)\": 0.00289, \"(1103695.0, 1104610.5)\": -0.00013, \"(1104610.5, 1109548.0)\": 0.00181, \"(1109548.0, 1113474.5)\": 1e-05, \"(1113474.5, 1114673.5)\": -0.00091, \"(1114673.5, 1116159.5)\": 0.00326, \"(1116159.5, 1117955.0)\": 0.00422}\nLower Bounds (95%-Confidence Interval): {\"(91.0, 2307.0)\": 0.00427, \"(2307.0, 4713.5)\": 0.00579, \"(4713.5, 6928.5)\": 0.00041, \"(6928.5, 9761.5)\": -0.0007, \"(9761.5, 13120.0)\": -0.00221, \"(13120.0, 14826.0)\": -0.00399, \"(14826.0, 20043.5)\": -0.00257, \"(20043.5, 22448.0)\": -0.00208, \"(22448.0, 23794.0)\": -0.00391, \"(23794.0, 28014.5)\": -0.0066, \"(28014.5, 28671.0)\": -0.00305, \"(28671.0, 37439.5)\": -0.00164, \"(37439.5, 40007.0)\": -0.00259, \"(40007.0, 41128.5)\": -0.00544, \"(41128.5, 50305.5)\": -0.00224, \"(50305.5, 51818.5)\": -0.00485, \"(51818.5, 66668.0)\": -0.00248, \"(66668.0, 67776.0)\": -0.00461, \"(67776.0, 75664.5)\": -0.0014, \"(75664.5, 76606.0)\": -0.0013, \"(76606.0, 89235.5)\": -0.00066, \"(89235.5, 227800.5)\": -0.00254, \"(227800.5, 231707.5)\": -0.00057, \"(231707.5, 257871.0)\": -0.00369, \"(257871.0, 503283.0)\": -0.0015, \"(503283.0, 507804.5)\": -0.00235, \"(507804.5, 517795.0)\": -0.00383, \"(517795.0, 616121.0)\": -0.00166, \"(616121.0, 622616.5)\": -0.00158, \"(622616.5, 647046.0)\": -0.00171, \"(647046.0, 662956.5)\": -0.00058, \"(662956.5, 667208.5)\": -0.00336, \"(667208.5, 689123.0)\": -0.00275, \"(689123.0, 872554.5)\": -0.00294, \"(872554.5, 942666.5)\": -0.00097, \"(942666.5, 983736.5)\": -0.00265, \"(983736.5, 1025442.0)\": -0.00252, \"(1025442.0, 1029281.5)\": -0.00403, \"(1029281.5, 1040563.0)\": -0.00272, \"(1040563.0, 1103097.0)\": -0.00112, \"(1103097.0, 1103695.0)\": -0.00196, \"(1103695.0, 1104610.5)\": -0.00237, \"(1104610.5, 1109548.0)\": -0.00097, \"(1109548.0, 1113474.5)\": -0.00207, \"(1113474.5, 1114673.5)\": -0.00422, \"(1114673.5, 1116159.5)\": -0.00105, \"(1116159.5, 1117955.0)\": 0.00066}\nUpper Bounds (95%-Confidence Interval): {\"(91.0, 2307.0)\": 0.01249, \"(2307.0, 4713.5)\": 0.01349, \"(4713.5, 6928.5)\": 0.00719, \"(6928.5, 9761.5)\": 0.00306, \"(9761.5, 13120.0)\": 0.0012, \"(13120.0, 14826.0)\": 0.00144, \"(14826.0, 20043.5)\": 0.00267, \"(20043.5, 22448.0)\": 0.00358, \"(22448.0, 23794.0)\": 0.00124, \"(23794.0, 28014.5)\": 0.00098, \"(28014.5, 28671.0)\": -5e-05, \"(28671.0, 37439.5)\": 0.00065, \"(37439.5, 40007.0)\": 0.00288, \"(40007.0, 41128.5)\": 0.0149, \"(41128.5, 50305.5)\": 0.00044, \"(50305.5, 51818.5)\": 0.00099, \"(51818.5, 66668.0)\": 0.0004, \"(66668.0, 67776.0)\": 0.00841, \"(67776.0, 75664.5)\": 0.00142, \"(75664.5, 76606.0)\": 0.00269, \"(76606.0, 89235.5)\": 0.00388, \"(89235.5, 227800.5)\": 0.00178, \"(227800.5, 231707.5)\": 0.00106, \"(231707.5, 257871.0)\": 0.00278, \"(257871.0, 503283.0)\": 0.00185, \"(503283.0, 507804.5)\": 0.00113, \"(507804.5, 517795.0)\": 0.00134, \"(517795.0, 616121.0)\": 0.0009, \"(616121.0, 622616.5)\": 0.00241, \"(622616.5, 647046.0)\": 0.00128, \"(647046.0, 662956.5)\": 0.00293, \"(662956.5, 667208.5)\": 0.00132, \"(667208.5, 689123.0)\": 0.00317, \"(689123.0, 872554.5)\": 0.00165, \"(872554.5, 942666.5)\": 0.0016, \"(942666.5, 983736.5)\": 0.00161, \"(983736.5, 1025442.0)\": 0.00285, \"(1025442.0, 1029281.5)\": 0.00205, \"(1029281.5, 1040563.0)\": 0.00215, \"(1040563.0, 1103097.0)\": 0.0025, \"(1103097.0, 1103695.0)\": 0.00774, \"(1103695.0, 1104610.5)\": 0.00212, \"(1104610.5, 1109548.0)\": 0.00458, \"(1109548.0, 1113474.5)\": 0.00208, \"(1113474.5, 1114673.5)\": 0.00241, \"(1114673.5, 1116159.5)\": 0.00758, \"(1116159.5, 1117955.0)\": 0.00778}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: TopographyDrainage\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.0274, \"(0.5, 1.5)\": -0.02381, \"(1.5, 2.5)\": -0.01602, \"(2.5, 3.5)\": -0.01049, \"(3.5, 4.5)\": -0.00528, \"(4.5, 5.5)\": -0.00022, \"(5.5, 6.5)\": 0.00517, \"(6.5, 7.5)\": 0.01282, \"(7.5, 8.5)\": 0.01628, \"(8.5, 9.5)\": 0.02454, \"(9.5, 10.5)\": 0.02883, \"(10.5, 11.5)\": 0.03213, \"(11.5, 17.0)\": 0.03564}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.03013, \"(0.5, 1.5)\": -0.02484, \"(1.5, 2.5)\": -0.01655, \"(2.5, 3.5)\": -0.01088, \"(3.5, 4.5)\": -0.00574, \"(4.5, 5.5)\": -0.00046, \"(5.5, 6.5)\": 0.00473, \"(6.5, 7.5)\": 0.01242, \"(7.5, 8.5)\": 0.01574, \"(8.5, 9.5)\": 0.02354, \"(9.5, 10.5)\": 0.0277, \"(10.5, 11.5)\": 0.03039, \"(11.5, 17.0)\": 0.02281}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02466, \"(0.5, 1.5)\": -0.02278, \"(1.5, 2.5)\": -0.0155, \"(2.5, 3.5)\": -0.0101, \"(3.5, 4.5)\": -0.00482, \"(4.5, 5.5)\": 2e-05, \"(5.5, 6.5)\": 0.00561, \"(6.5, 7.5)\": 0.01323, \"(7.5, 8.5)\": 0.01683, \"(8.5, 9.5)\": 0.02554, \"(9.5, 10.5)\": 0.02996, \"(10.5, 11.5)\": 0.03386, \"(11.5, 17.0)\": 0.04848}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: DeterioratingInfrastructure\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02508, \"(0.5, 1.5)\": -0.01897, \"(1.5, 2.5)\": -0.01452, \"(2.5, 3.5)\": -0.01085, \"(3.5, 4.5)\": -0.00475, \"(4.5, 5.5)\": 0.00054, \"(5.5, 6.5)\": 0.00555, \"(6.5, 7.5)\": 0.01137, \"(7.5, 8.5)\": 0.01653, \"(8.5, 9.5)\": 0.0237, \"(9.5, 10.5)\": 0.02782, \"(10.5, 11.5)\": 0.03175, \"(11.5, 12.5)\": 0.03686, \"(12.5, 15.0)\": 0.04451}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02677, \"(0.5, 1.5)\": -0.01971, \"(1.5, 2.5)\": -0.01507, \"(2.5, 3.5)\": -0.0113, \"(3.5, 4.5)\": -0.00523, \"(4.5, 5.5)\": 0.00016, \"(5.5, 6.5)\": 0.00517, \"(6.5, 7.5)\": 0.01094, \"(7.5, 8.5)\": 0.01606, \"(8.5, 9.5)\": 0.02309, \"(9.5, 10.5)\": 0.02666, \"(10.5, 11.5)\": 0.03007, \"(11.5, 12.5)\": 0.03455, \"(12.5, 15.0)\": 0.03295}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02339, \"(0.5, 1.5)\": -0.01822, \"(1.5, 2.5)\": -0.01398, \"(2.5, 3.5)\": -0.0104, \"(3.5, 4.5)\": -0.00428, \"(4.5, 5.5)\": 0.00091, \"(5.5, 6.5)\": 0.00594, \"(6.5, 7.5)\": 0.0118, \"(7.5, 8.5)\": 0.017, \"(8.5, 9.5)\": 0.0243, \"(9.5, 10.5)\": 0.02899, \"(10.5, 11.5)\": 0.03343, \"(11.5, 12.5)\": 0.03916, \"(12.5, 15.0)\": 0.05607}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Increasing"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Age\nFeature Type: continuous\nMeans: {\"(17.0, 18.5)\": -3.326, \"(18.5, 19.5)\": -2.358, \"(19.5, 20.5)\": -2.799, \"(20.5, 21.5)\": -2.354, \"(21.5, 22.5)\": -1.405, \"(22.5, 23.5)\": -1.633, \"(23.5, 24.5)\": -1.214, \"(24.5, 26.5)\": -0.789, \"(26.5, 27.5)\": -0.473, \"(27.5, 29.5)\": -0.216, \"(29.5, 33.5)\": 0.042, \"(33.5, 36.5)\": 0.351, \"(36.5, 44.5)\": 0.658, \"(44.5, 61.5)\": 0.897, \"(61.5, 66.5)\": 0.574, \"(66.5, 73.5)\": 0.099, \"(73.5, 74.5)\": 0.763, \"(74.5, 77.5)\": 0.502, \"(77.5, 79.5)\": 0.875, \"(79.5, 84.5)\": 0.065, \"(84.5, 90.0)\": -1.08}\nLower Bounds (95%-Confidence Interval): {\"(17.0, 18.5)\": -4.677, \"(18.5, 19.5)\": -3.672, \"(19.5, 20.5)\": -3.928, \"(20.5, 21.5)\": -2.706, \"(21.5, 22.5)\": -1.741, \"(22.5, 23.5)\": -1.856, \"(23.5, 24.5)\": -1.407, \"(24.5, 26.5)\": -0.941, \"(26.5, 27.5)\": -0.561, \"(27.5, 29.5)\": -0.322, \"(29.5, 33.5)\": -0.079, \"(33.5, 36.5)\": 0.229, \"(36.5, 44.5)\": 0.5, \"(44.5, 61.5)\": 0.753, \"(61.5, 66.5)\": 0.434, \"(66.5, 73.5)\": -0.37, \"(73.5, 74.5)\": 0.229, \"(74.5, 77.5)\": -0.136, \"(77.5, 79.5)\": 0.35, \"(79.5, 84.5)\": -0.573, \"(84.5, 90.0)\": -2.041}\nUpper Bounds (95%-Confidence Interval): {\"(17.0, 18.5)\": -1.975, \"(18.5, 19.5)\": -1.044, \"(19.5, 20.5)\": -1.669, \"(20.5, 21.5)\": -2.002, \"(21.5, 22.5)\": -1.069, \"(22.5, 23.5)\": -1.41, \"(23.5, 24.5)\": -1.021, \"(24.5, 26.5)\": -0.637, \"(26.5, 27.5)\": -0.385, \"(27.5, 29.5)\": -0.11, \"(29.5, 33.5)\": 0.164, \"(33.5, 36.5)\": 0.473, \"(36.5, 44.5)\": 0.816, \"(44.5, 61.5)\": 1.04, \"(61.5, 66.5)\": 0.714, \"(66.5, 73.5)\": 0.567, \"(73.5, 74.5)\": 1.297, \"(74.5, 77.5)\": 1.141, \"(77.5, 79.5)\": 1.401, \"(79.5, 84.5)\": 0.702, \"(84.5, 90.0)\": -0.119}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Age\nFeature Type: continuous\nMeans: {\"(21.0, 21.5)\": -0.481, \"(21.5, 23.5)\": -0.377, \"(23.5, 24.5)\": -0.294, \"(24.5, 26.5)\": -0.205, \"(26.5, 28.5)\": -0.106, \"(28.5, 30.5)\": 0.056, \"(30.5, 34.5)\": 0.184, \"(34.5, 39.5)\": 0.286, \"(39.5, 44.5)\": 0.389, \"(44.5, 54.5)\": 0.476, \"(54.5, 56.5)\": 0.374, \"(56.5, 58.5)\": 0.224, \"(58.5, 60.5)\": 0.121, \"(60.5, 61.5)\": -0.053, \"(61.5, 62.5)\": -0.314, \"(62.5, 64.5)\": -0.437, \"(64.5, 66.5)\": -0.598, \"(66.5, 67.5)\": -0.714, \"(67.5, 68.5)\": -0.823, \"(68.5, 76.5)\": -0.922, \"(76.5, 81.0)\": -1.102}\nLower Bounds (95%-Confidence Interval): {\"(21.0, 21.5)\": -0.733, \"(21.5, 23.5)\": -0.545, \"(23.5, 24.5)\": -0.449, \"(24.5, 26.5)\": -0.316, \"(26.5, 28.5)\": -0.204, \"(28.5, 30.5)\": -0.094, \"(30.5, 34.5)\": 0.033, \"(34.5, 39.5)\": 0.131, \"(39.5, 44.5)\": 0.234, \"(44.5, 54.5)\": 0.292, \"(54.5, 56.5)\": 0.179, \"(56.5, 58.5)\": 0.067, \"(58.5, 60.5)\": -0.026, \"(60.5, 61.5)\": -0.314, \"(61.5, 62.5)\": -0.776, \"(62.5, 64.5)\": -0.923, \"(64.5, 66.5)\": -1.089, \"(66.5, 67.5)\": -1.205, \"(67.5, 68.5)\": -1.322, \"(68.5, 76.5)\": -1.445, \"(76.5, 81.0)\": -1.674}\nUpper Bounds (95%-Confidence Interval): {\"(21.0, 21.5)\": -0.228, \"(21.5, 23.5)\": -0.21, \"(23.5, 24.5)\": -0.139, \"(24.5, 26.5)\": -0.094, \"(26.5, 28.5)\": -0.008, \"(28.5, 30.5)\": 0.206, \"(30.5, 34.5)\": 0.335, \"(34.5, 39.5)\": 0.441, \"(39.5, 44.5)\": 0.544, \"(44.5, 54.5)\": 0.66, \"(54.5, 56.5)\": 0.569, \"(56.5, 58.5)\": 0.382, \"(58.5, 60.5)\": 0.267, \"(60.5, 61.5)\": 0.208, \"(61.5, 62.5)\": 0.149, \"(62.5, 64.5)\": 0.05, \"(64.5, 66.5)\": -0.107, \"(66.5, 67.5)\": -0.222, \"(67.5, 68.5)\": -0.325, \"(68.5, 76.5)\": -0.399, \"(76.5, 81.0)\": -0.529}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: CreditScore\nFeature Type: continuous\nMeans: {\"(350.0, 416.5)\": 0.62, \"(416.5, 421.5)\": 0.5698, \"(421.5, 427.5)\": 0.3799, \"(427.5, 437.5)\": 0.2757, \"(437.5, 464.5)\": 0.3274, \"(464.5, 470.5)\": 0.2778, \"(470.5, 477.5)\": 0.4561, \"(477.5, 478.5)\": 0.0595, \"(478.5, 494.5)\": 0.1431, \"(494.5, 515.5)\": 0.0909, \"(515.5, 523.5)\": -0.3342, \"(523.5, 539.5)\": -0.2192, \"(539.5, 566.5)\": -0.1337, \"(566.5, 598.5)\": -0.0838, \"(598.5, 661.5)\": -0.0327, \"(661.5, 684.5)\": 0.0186, \"(684.5, 741.5)\": 0.0696, \"(741.5, 769.5)\": 0.0206, \"(769.5, 792.5)\": 0.0691, \"(792.5, 805.5)\": 0.2231, \"(805.5, 806.5)\": 0.1131, \"(806.5, 850.0)\": -0.1138}\nLower Bounds (95%-Confidence Interval): {\"(350.0, 416.5)\": -0.0945, \"(416.5, 421.5)\": -0.1033, \"(421.5, 427.5)\": -0.0186, \"(427.5, 437.5)\": -0.1491, \"(437.5, 464.5)\": 0.0705, \"(464.5, 470.5)\": -0.0008, \"(470.5, 477.5)\": -0.0519, \"(477.5, 478.5)\": -0.3016, \"(478.5, 494.5)\": 0.0126, \"(494.5, 515.5)\": -0.1354, \"(515.5, 523.5)\": -0.5637, \"(523.5, 539.5)\": -0.3225, \"(539.5, 566.5)\": -0.2064, \"(566.5, 598.5)\": -0.1252, \"(598.5, 661.5)\": -0.1126, \"(661.5, 684.5)\": -0.0289, \"(684.5, 741.5)\": -0.0156, \"(741.5, 769.5)\": -0.0756, \"(769.5, 792.5)\": -0.016, \"(792.5, 805.5)\": -0.0471, \"(805.5, 806.5)\": -0.2533, \"(806.5, 850.0)\": -0.3888}\nUpper Bounds (95%-Confidence Interval): {\"(350.0, 416.5)\": 1.3346, \"(416.5, 421.5)\": 1.243, \"(421.5, 427.5)\": 0.7784, \"(427.5, 437.5)\": 0.7005, \"(437.5, 464.5)\": 0.5843, \"(464.5, 470.5)\": 0.5564, \"(470.5, 477.5)\": 0.9641, \"(477.5, 478.5)\": 0.4206, \"(478.5, 494.5)\": 0.2736, \"(494.5, 515.5)\": 0.3173, \"(515.5, 523.5)\": -0.1047, \"(523.5, 539.5)\": -0.1159, \"(539.5, 566.5)\": -0.061, \"(566.5, 598.5)\": -0.0424, \"(598.5, 661.5)\": 0.0472, \"(661.5, 684.5)\": 0.066, \"(684.5, 741.5)\": 0.1548, \"(741.5, 769.5)\": 0.1168, \"(769.5, 792.5)\": 0.1542, \"(792.5, 805.5)\": 0.4932, \"(805.5, 806.5)\": 0.4795, \"(806.5, 850.0)\": 0.1611}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: VRDeck\nFeature Type: continuous\nMeans: {\"(0.0, 135.5)\": 0.445, \"(135.5, 215.5)\": 0.073, \"(215.5, 500.5)\": -0.294, \"(500.5, 727.5)\": -0.661, \"(727.5, 799.5)\": -1.026, \"(799.5, 831.5)\": -0.601, \"(831.5, 872.5)\": -1.156, \"(872.5, 993.5)\": -1.633, \"(993.5, 1430.5)\": -2.012, \"(1430.5, 1514.5)\": -1.512, \"(1514.5, 1796.0)\": -2.212, \"(1796.0, 1909.5)\": -1.699, \"(1909.5, 1970.0)\": -2.568, \"(1970.0, 2571.5)\": -3.006, \"(2571.5, 2582.0)\": -2.375, \"(2582.0, 2657.0)\": -2.964, \"(2657.0, 3710.5)\": -3.98, \"(3710.5, 4089.0)\": -4.347, \"(4089.0, 5089.5)\": -5.923, \"(5089.5, 24133.0)\": -6.634}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 135.5)\": -0.055, \"(135.5, 215.5)\": -0.275, \"(215.5, 500.5)\": -1.359, \"(500.5, 727.5)\": -0.968, \"(727.5, 799.5)\": -1.273, \"(799.5, 831.5)\": -1.285, \"(831.5, 872.5)\": -1.782, \"(872.5, 993.5)\": -2.358, \"(993.5, 1430.5)\": -2.589, \"(1430.5, 1514.5)\": -2.382, \"(1514.5, 1796.0)\": -2.87, \"(1796.0, 1909.5)\": -3.449, \"(1909.5, 1970.0)\": -3.46, \"(1970.0, 2571.5)\": -4.009, \"(2571.5, 2582.0)\": -4.195, \"(2582.0, 2657.0)\": -4.898, \"(2657.0, 3710.5)\": -5.152, \"(3710.5, 4089.0)\": -5.79, \"(4089.0, 5089.5)\": -7.804, \"(5089.5, 24133.0)\": -8.247}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 135.5)\": 0.945, \"(135.5, 215.5)\": 0.422, \"(215.5, 500.5)\": 0.772, \"(500.5, 727.5)\": -0.354, \"(727.5, 799.5)\": -0.779, \"(799.5, 831.5)\": 0.083, \"(831.5, 872.5)\": -0.529, \"(872.5, 993.5)\": -0.908, \"(993.5, 1430.5)\": -1.435, \"(1430.5, 1514.5)\": -0.643, \"(1514.5, 1796.0)\": -1.555, \"(1796.0, 1909.5)\": 0.051, \"(1909.5, 1970.0)\": -1.677, \"(1970.0, 2571.5)\": -2.002, \"(2571.5, 2582.0)\": -0.555, \"(2582.0, 2657.0)\": -1.03, \"(2657.0, 3710.5)\": -2.808, \"(3710.5, 4089.0)\": -2.905, \"(4089.0, 5089.5)\": -4.042, \"(5089.5, 24133.0)\": -5.02}\n\n\nYour task is to determine if the graph is\na) monotone increasing\nb) monotone decreasing\nc) not monotone.\n\nWhat is the correct answer? Think step by step.",
+    "Not monotone"
+  ]
+]
\ No newline at end of file
diff --git a/benchmarks/benchmark/read-value.json b/benchmarks/benchmark/read-value.json
new file mode 100644
index 0000000..9497a91
--- /dev/null
+++ b/benchmarks/benchmark/read-value.json
@@ -0,0 +1,402 @@
+[
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: total_rooms\nFeature Type: continuous\nMeans: {\"(2.0, 23.0)\": -70808.9, \"(23.0, 38.5)\": -78966.6, \"(38.5, 48.5)\": -28602.1, \"(48.5, 119.0)\": -47079.6, \"(119.0, 163.0)\": -52692.3, \"(163.0, 186.5)\": -60093.0, \"(186.5, 223.5)\": -51150.5, \"(223.5, 239.5)\": -39728.1, \"(239.5, 248.5)\": -7038.8, \"(248.5, 265.5)\": -691.1, \"(265.5, 280.5)\": -14052.2, \"(280.5, 342.5)\": -35705.6, \"(342.5, 364.5)\": -24578.4, \"(364.5, 385.5)\": -34007.7, \"(385.5, 406.5)\": -46655.0, \"(406.5, 413.5)\": -17805.2, \"(413.5, 443.5)\": -12192.7, \"(443.5, 452.5)\": -22779.7, \"(452.5, 502.5)\": -30652.6, \"(502.5, 508.5)\": -25165.4, \"(508.5, 515.5)\": -12943.4, \"(515.5, 1152.5)\": -21645.3, \"(1152.5, 1239.5)\": -16264.4, \"(1239.5, 1245.5)\": -7023.2, \"(1245.5, 1619.5)\": -12855.2, \"(1619.5, 1944.5)\": -7415.6, \"(1944.5, 2330.5)\": -1233.9, \"(2330.5, 2710.5)\": 4370.8, \"(2710.5, 2834.5)\": 9739.0, \"(2834.5, 2838.5)\": 16667.1, \"(2838.5, 3577.5)\": 10096.4, \"(3577.5, 5401.0)\": 15549.4, \"(5401.0, 5535.5)\": 24928.2, \"(5535.5, 9961.0)\": 19069.3, \"(9961.0, 18662.0)\": 26262.6, \"(18662.0, 39320.0)\": 20736.3}\nLower Bounds (95%-Confidence Interval): {\"(2.0, 23.0)\": -91545.9, \"(23.0, 38.5)\": -102966.4, \"(38.5, 48.5)\": -57179.9, \"(48.5, 119.0)\": -64507.9, \"(119.0, 163.0)\": -67051.1, \"(163.0, 186.5)\": -74986.7, \"(186.5, 223.5)\": -62447.2, \"(223.5, 239.5)\": -55573.0, \"(239.5, 248.5)\": -34485.5, \"(248.5, 265.5)\": -18815.6, \"(265.5, 280.5)\": -35576.3, \"(280.5, 342.5)\": -44957.9, \"(342.5, 364.5)\": -36592.4, \"(364.5, 385.5)\": -39620.4, \"(385.5, 406.5)\": -54434.9, \"(406.5, 413.5)\": -28898.3, \"(413.5, 443.5)\": -21926.2, \"(443.5, 452.5)\": -34828.5, \"(452.5, 502.5)\": -40304.3, \"(502.5, 508.5)\": -35649.5, \"(508.5, 515.5)\": -27403.5, \"(515.5, 1152.5)\": -28456.5, \"(1152.5, 1239.5)\": -20918.2, \"(1239.5, 1245.5)\": -15907.4, \"(1245.5, 1619.5)\": -19943.7, \"(1619.5, 1944.5)\": -13063.6, \"(1944.5, 2330.5)\": -8595.8, \"(2330.5, 2710.5)\": 2936.6, \"(2710.5, 2834.5)\": 7069.8, \"(2834.5, 2838.5)\": 1263.0, \"(2838.5, 3577.5)\": 7025.1, \"(3577.5, 5401.0)\": 10287.4, \"(5401.0, 5535.5)\": 10519.1, \"(5535.5, 9961.0)\": 12536.6, \"(9961.0, 18662.0)\": 16596.5, \"(18662.0, 39320.0)\": 17189.5}\nUpper Bounds (95%-Confidence Interval): {\"(2.0, 23.0)\": -50072.0, \"(23.0, 38.5)\": -54966.9, \"(38.5, 48.5)\": -24.3, \"(48.5, 119.0)\": -29651.4, \"(119.0, 163.0)\": -38333.5, \"(163.0, 186.5)\": -45199.3, \"(186.5, 223.5)\": -39853.9, \"(223.5, 239.5)\": -23883.2, \"(239.5, 248.5)\": 20408.0, \"(248.5, 265.5)\": 17433.4, \"(265.5, 280.5)\": 7471.9, \"(280.5, 342.5)\": -26453.2, \"(342.5, 364.5)\": -12564.3, \"(364.5, 385.5)\": -28395.1, \"(385.5, 406.5)\": -38875.1, \"(406.5, 413.5)\": -6712.1, \"(413.5, 443.5)\": -2459.1, \"(443.5, 452.5)\": -10730.8, \"(452.5, 502.5)\": -21000.9, \"(502.5, 508.5)\": -14681.3, \"(508.5, 515.5)\": 1516.8, \"(515.5, 1152.5)\": -14834.1, \"(1152.5, 1239.5)\": -11610.6, \"(1239.5, 1245.5)\": 1860.9, \"(1245.5, 1619.5)\": -5766.8, \"(1619.5, 1944.5)\": -1767.7, \"(1944.5, 2330.5)\": 6128.1, \"(2330.5, 2710.5)\": 5805.0, \"(2710.5, 2834.5)\": 12408.3, \"(2834.5, 2838.5)\": 32071.2, \"(2838.5, 3577.5)\": 13167.8, \"(3577.5, 5401.0)\": 20811.4, \"(5401.0, 5535.5)\": 39337.3, \"(5535.5, 9961.0)\": 25602.1, \"(9961.0, 18662.0)\": 35928.6, \"(18662.0, 39320.0)\": 24283.0}\n\n\nYour task is to provide the mean value of the graph at 400.75. What is the mean value of the graph at 400.75?",
+    "-46655.0"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: DrainageSystems\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02593, \"(0.5, 1.5)\": -0.02211, \"(1.5, 2.5)\": -0.01611, \"(2.5, 3.5)\": -0.01125, \"(3.5, 4.5)\": -0.0047, \"(4.5, 5.5)\": 9e-05, \"(5.5, 6.5)\": 0.00652, \"(6.5, 8.5)\": 0.01219, \"(8.5, 10.5)\": 0.02253, \"(10.5, 11.5)\": 0.03412, \"(11.5, 12.5)\": 0.04015, \"(12.5, 14.0)\": 0.04564}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02795, \"(0.5, 1.5)\": -0.02324, \"(1.5, 2.5)\": -0.01672, \"(2.5, 3.5)\": -0.01177, \"(3.5, 4.5)\": -0.00504, \"(4.5, 5.5)\": -0.00021, \"(5.5, 6.5)\": 0.00613, \"(6.5, 8.5)\": 0.01137, \"(8.5, 10.5)\": 0.02139, \"(10.5, 11.5)\": 0.03184, \"(11.5, 12.5)\": 0.03703, \"(12.5, 14.0)\": 0.04222}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02391, \"(0.5, 1.5)\": -0.02097, \"(1.5, 2.5)\": -0.0155, \"(2.5, 3.5)\": -0.01073, \"(3.5, 4.5)\": -0.00435, \"(4.5, 5.5)\": 0.00039, \"(5.5, 6.5)\": 0.00691, \"(6.5, 8.5)\": 0.01301, \"(8.5, 10.5)\": 0.02367, \"(10.5, 11.5)\": 0.0364, \"(11.5, 12.5)\": 0.04328, \"(12.5, 14.0)\": 0.04907}\n\n\nYour task is to provide the mean value of the graph at 8.93. What is the mean value of the graph at 8.93?",
+    "0.02253"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: MonsoonIntensity\nFeature Type: continuous\nMeans: {\"(0.0, 1.5)\": -0.02446, \"(1.5, 2.5)\": -0.01712, \"(2.5, 3.5)\": -0.00908, \"(3.5, 4.5)\": -0.00529, \"(4.5, 5.5)\": 0.0003, \"(5.5, 6.5)\": 0.00497, \"(6.5, 7.5)\": 0.01093, \"(7.5, 8.5)\": 0.01787, \"(8.5, 9.5)\": 0.02262, \"(9.5, 11.5)\": 0.02707, \"(11.5, 12.5)\": 0.03735, \"(12.5, 13.5)\": 0.043, \"(13.5, 15.0)\": 0.01734}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02705, \"(1.5, 2.5)\": -0.01788, \"(2.5, 3.5)\": -0.00955, \"(3.5, 4.5)\": -0.00566, \"(4.5, 5.5)\": 4e-05, \"(5.5, 6.5)\": 0.00451, \"(6.5, 7.5)\": 0.01051, \"(7.5, 8.5)\": 0.01741, \"(8.5, 9.5)\": 0.02167, \"(9.5, 11.5)\": 0.02561, \"(11.5, 12.5)\": 0.03439, \"(12.5, 13.5)\": 0.03822, \"(13.5, 15.0)\": -0.00028}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02187, \"(1.5, 2.5)\": -0.01637, \"(2.5, 3.5)\": -0.00861, \"(3.5, 4.5)\": -0.00492, \"(4.5, 5.5)\": 0.00056, \"(5.5, 6.5)\": 0.00544, \"(6.5, 7.5)\": 0.01134, \"(7.5, 8.5)\": 0.01833, \"(8.5, 9.5)\": 0.02358, \"(9.5, 11.5)\": 0.02853, \"(11.5, 12.5)\": 0.04032, \"(12.5, 13.5)\": 0.04778, \"(13.5, 15.0)\": 0.03495}\n\n\nYour task is to provide the mean value of the graph at 14.15. What is the mean value of the graph at 14.15?",
+    "0.01734"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Fare\nFeature Type: continuous\nMeans: {\"(0.0, 6.325)\": -1.425, \"(6.325, 7.8500000000000005)\": -1.303, \"(7.8500000000000005, 9.256250000000001)\": -0.472, \"(9.256250000000001, 10.48125)\": -0.602, \"(10.48125, 12.9375)\": -0.14, \"(12.9375, 25.79375)\": 0.225, \"(25.79375, 26.46875)\": 0.355, \"(26.46875, 27.7354)\": 0.207, \"(27.7354, 29.85)\": -0.238, \"(29.85, 31.6604)\": 0.051, \"(31.6604, 55.22085)\": -0.075, \"(55.22085, 89.5521)\": 0.041, \"(89.5521, 149.0354)\": 0.152, \"(149.0354, 387.6646)\": -0.029, \"(387.6646, 512.3292)\": 0.808}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 6.325)\": -3.39, \"(6.325, 7.8500000000000005)\": -3.252, \"(7.8500000000000005, 9.256250000000001)\": -1.321, \"(9.256250000000001, 10.48125)\": -1.756, \"(10.48125, 12.9375)\": -0.444, \"(12.9375, 25.79375)\": -0.464, \"(25.79375, 26.46875)\": -0.48, \"(26.46875, 27.7354)\": -0.42, \"(27.7354, 29.85)\": -1.008, \"(29.85, 31.6604)\": -0.616, \"(31.6604, 55.22085)\": -0.278, \"(55.22085, 89.5521)\": -0.095, \"(89.5521, 149.0354)\": -0.062, \"(149.0354, 387.6646)\": -0.493, \"(387.6646, 512.3292)\": -0.839}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 6.325)\": 0.54, \"(6.325, 7.8500000000000005)\": 0.645, \"(7.8500000000000005, 9.256250000000001)\": 0.377, \"(9.256250000000001, 10.48125)\": 0.553, \"(10.48125, 12.9375)\": 0.163, \"(12.9375, 25.79375)\": 0.913, \"(25.79375, 26.46875)\": 1.191, \"(26.46875, 27.7354)\": 0.833, \"(27.7354, 29.85)\": 0.533, \"(29.85, 31.6604)\": 0.718, \"(31.6604, 55.22085)\": 0.127, \"(55.22085, 89.5521)\": 0.176, \"(89.5521, 149.0354)\": 0.367, \"(149.0354, 387.6646)\": 0.436, \"(387.6646, 512.3292)\": 2.455}\n\n\nYour task is to provide the mean value of the graph at 26.19. What is the mean value of the graph at 26.19?",
+    "0.355"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: HasCrCard\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.004421, \"(0.5, 1.0)\": 0.001379}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.037941, \"(0.5, 1.0)\": -0.009076}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.0291, \"(0.5, 1.0)\": 0.011834}\n\n\nYour task is to provide the mean value of the graph at 0.32. What is the mean value of the graph at 0.32?",
+    "-0.004421"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: perimeter_mean\nFeature Type: continuous\nMeans: {\"(43.79, 60.035)\": -0.884, \"(60.035, 63.379999999999995)\": -0.783, \"(63.379999999999995, 66.67)\": -0.681, \"(66.67, 68.965)\": -0.581, \"(68.965, 71.275)\": -0.476, \"(71.275, 78.28)\": -0.369, \"(78.28, 84.015)\": -0.267, \"(84.015, 88.70500000000001)\": -0.166, \"(88.70500000000001, 94.68)\": -0.064, \"(94.68, 100.75)\": 0.035, \"(100.75, 106.75)\": 0.14, \"(106.75, 108.6)\": 0.249, \"(108.6, 112.6)\": 0.407, \"(112.6, 117.45)\": 0.518, \"(117.45, 121.7)\": 0.626, \"(121.7, 128.15)\": 0.73, \"(128.15, 133.25)\": 0.835, \"(133.25, 145.85000000000002)\": 0.936, \"(145.85000000000002, 188.5)\": 1.038}\nLower Bounds (95%-Confidence Interval): {\"(43.79, 60.035)\": -1.177, \"(60.035, 63.379999999999995)\": -1.04, \"(63.379999999999995, 66.67)\": -0.892, \"(66.67, 68.965)\": -0.75, \"(68.965, 71.275)\": -0.646, \"(71.275, 78.28)\": -0.532, \"(78.28, 84.015)\": -0.417, \"(84.015, 88.70500000000001)\": -0.316, \"(88.70500000000001, 94.68)\": -0.178, \"(94.68, 100.75)\": -0.201, \"(100.75, 106.75)\": -0.091, \"(106.75, 108.6)\": -0.055, \"(108.6, 112.6)\": 0.049, \"(112.6, 117.45)\": 0.15, \"(117.45, 121.7)\": 0.222, \"(121.7, 128.15)\": 0.282, \"(128.15, 133.25)\": 0.343, \"(133.25, 145.85000000000002)\": 0.427, \"(145.85000000000002, 188.5)\": 0.514}\nUpper Bounds (95%-Confidence Interval): {\"(43.79, 60.035)\": -0.59, \"(60.035, 63.379999999999995)\": -0.526, \"(63.379999999999995, 66.67)\": -0.471, \"(66.67, 68.965)\": -0.411, \"(68.965, 71.275)\": -0.306, \"(71.275, 78.28)\": -0.206, \"(78.28, 84.015)\": -0.118, \"(84.015, 88.70500000000001)\": -0.017, \"(88.70500000000001, 94.68)\": 0.05, \"(94.68, 100.75)\": 0.271, \"(100.75, 106.75)\": 0.371, \"(106.75, 108.6)\": 0.553, \"(108.6, 112.6)\": 0.766, \"(112.6, 117.45)\": 0.887, \"(117.45, 121.7)\": 1.03, \"(121.7, 128.15)\": 1.179, \"(128.15, 133.25)\": 1.327, \"(133.25, 145.85000000000002)\": 1.444, \"(145.85000000000002, 188.5)\": 1.562}\n\n\nYour task is to provide the mean value of the graph at 76.3. What is the mean value of the graph at 76.3?",
+    "-0.369"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: ShoppingMall\nFeature Type: continuous\nMeans: {\"(0.0, 125.5)\": -0.032, \"(125.5, 541.5)\": -0.211, \"(541.5, 808.5)\": 0.034, \"(808.5, 1082.0)\": 0.213, \"(1082.0, 1187.0)\": -0.042, \"(1187.0, 1434.5)\": 0.401, \"(1434.5, 1658.5)\": 0.585, \"(1658.5, 1968.5)\": 0.948, \"(1968.5, 3394.5)\": 1.235, \"(3394.5, 3460.0)\": 0.871, \"(3460.0, 3741.5)\": 1.066, \"(3741.5, 4803.5)\": 2.339, \"(4803.5, 5204.0)\": 2.909, \"(5204.0, 12253.0)\": 3.236}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 125.5)\": -0.092, \"(125.5, 541.5)\": -0.495, \"(541.5, 808.5)\": -0.379, \"(808.5, 1082.0)\": -0.05, \"(1082.0, 1187.0)\": -0.484, \"(1187.0, 1434.5)\": 0.131, \"(1434.5, 1658.5)\": 0.238, \"(1658.5, 1968.5)\": 0.465, \"(1968.5, 3394.5)\": 0.864, \"(3394.5, 3460.0)\": 0.262, \"(3460.0, 3741.5)\": -0.052, \"(3741.5, 4803.5)\": 1.632, \"(4803.5, 5204.0)\": 1.913, \"(5204.0, 12253.0)\": 2.114}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 125.5)\": 0.028, \"(125.5, 541.5)\": 0.073, \"(541.5, 808.5)\": 0.447, \"(808.5, 1082.0)\": 0.477, \"(1082.0, 1187.0)\": 0.4, \"(1187.0, 1434.5)\": 0.671, \"(1434.5, 1658.5)\": 0.931, \"(1658.5, 1968.5)\": 1.43, \"(1968.5, 3394.5)\": 1.605, \"(3394.5, 3460.0)\": 1.481, \"(3460.0, 3741.5)\": 2.183, \"(3741.5, 4803.5)\": 3.046, \"(4803.5, 5204.0)\": 3.906, \"(5204.0, 12253.0)\": 4.358}\n\n\nYour task is to provide the mean value of the graph at 1617.97. What is the mean value of the graph at 1617.97?",
+    "0.585"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: latitude\nFeature Type: continuous\nMeans: {\"(32.54, 32.565)\": 23234.8, \"(32.565, 32.685)\": -3182.4, \"(32.685, 32.715)\": 7727.3, \"(32.715, 32.915)\": 17670.3, \"(32.915, 33.275000000000006)\": 34030.3, \"(33.275000000000006, 33.355000000000004)\": 55000.2, \"(33.355000000000004, 33.465)\": 64326.4, \"(33.465, 33.504999999999995)\": 81519.1, \"(33.504999999999995, 33.555)\": 94496.7, \"(33.555, 33.565)\": 63293.1, \"(33.565, 33.575)\": 51665.3, \"(33.575, 33.635000000000005)\": 66563.2, \"(33.635000000000005, 33.655)\": 47304.3, \"(33.655, 33.765)\": 29789.1, \"(33.765, 33.894999999999996)\": 15892.8, \"(33.894999999999996, 33.985)\": 2769.6, \"(33.985, 33.995000000000005)\": 17775.7, \"(33.995000000000005, 34.045)\": 28884.5, \"(34.045, 34.085)\": 55702.3, \"(34.085, 34.165)\": 46322.8, \"(34.165, 34.175)\": 33820.1, \"(34.175, 34.195)\": 7500.1, \"(34.195, 34.215)\": -4126.2, \"(34.215, 34.254999999999995)\": -16649.8, \"(34.254999999999995, 34.325)\": -27636.8, \"(34.325, 34.345)\": 17113.4, \"(34.345, 34.375)\": 28769.5, \"(34.375, 34.455)\": 43828.3, \"(34.455, 34.474999999999994)\": 57774.8, \"(34.474999999999994, 34.504999999999995)\": 33279.2, \"(34.504999999999995, 34.545)\": 19368.1, \"(34.545, 34.625)\": 5698.9, \"(34.625, 34.635000000000005)\": -19637.8, \"(34.635000000000005, 34.644999999999996)\": -39271.0, \"(34.644999999999996, 34.715)\": -26993.1, \"(34.715, 35.325)\": -17344.4, \"(35.325, 36.375)\": -37699.6, \"(36.375, 36.535)\": -27730.8, \"(36.535, 36.635000000000005)\": -14690.6, \"(36.635000000000005, 36.845)\": -25070.6, \"(36.845, 37.275000000000006)\": -15387.7, \"(37.275000000000006, 37.335)\": -3329.1, \"(37.335, 37.425)\": 7953.5, \"(37.425, 37.445)\": 34546.2, \"(37.445, 37.465)\": 45097.3, \"(37.465, 37.495000000000005)\": 30019.5, \"(37.495000000000005, 37.585)\": 16643.2, \"(37.585, 37.595)\": -3057.8, \"(37.595, 37.605000000000004)\": -32379.8, \"(37.605000000000004, 37.754999999999995)\": -42729.0, \"(37.754999999999995, 37.775000000000006)\": -17898.2, \"(37.775000000000006, 37.795)\": -3229.6, \"(37.795, 37.805)\": 8902.6, \"(37.805, 37.855000000000004)\": -13456.8, \"(37.855000000000004, 37.915)\": -1362.3, \"(37.915, 37.925)\": -19143.7, \"(37.925, 37.945)\": -38768.9, \"(37.945, 38.355000000000004)\": -48247.9, \"(38.355000000000004, 39.085)\": -38467.7, \"(39.085, 39.474999999999994)\": -47690.5, \"(39.474999999999994, 40.135000000000005)\": -56986.6, \"(40.135000000000005, 40.665)\": -66271.5, \"(40.665, 41.775000000000006)\": -75627.3, \"(41.775000000000006, 41.95)\": -85116.1}\nLower Bounds (95%-Confidence Interval): {\"(32.54, 32.565)\": 964.8, \"(32.565, 32.685)\": -13385.7, \"(32.685, 32.715)\": -6553.6, \"(32.715, 32.915)\": 6526.7, \"(32.915, 33.275000000000006)\": 15999.9, \"(33.275000000000006, 33.355000000000004)\": 42157.4, \"(33.355000000000004, 33.465)\": 51350.1, \"(33.465, 33.504999999999995)\": 60415.4, \"(33.504999999999995, 33.555)\": 76698.7, \"(33.555, 33.565)\": 39537.3, \"(33.565, 33.575)\": 41623.9, \"(33.575, 33.635000000000005)\": 54208.1, \"(33.635000000000005, 33.655)\": 37976.2, \"(33.655, 33.765)\": 22371.1, \"(33.765, 33.894999999999996)\": 10058.4, \"(33.894999999999996, 33.985)\": -876.4, \"(33.985, 33.995000000000005)\": 13047.1, \"(33.995000000000005, 34.045)\": 23199.9, \"(34.045, 34.085)\": 50112.0, \"(34.085, 34.165)\": 40162.5, \"(34.165, 34.175)\": 28146.4, \"(34.175, 34.195)\": 2466.5, \"(34.195, 34.215)\": -9561.1, \"(34.215, 34.254999999999995)\": -23153.3, \"(34.254999999999995, 34.325)\": -37515.4, \"(34.325, 34.345)\": -3758.1, \"(34.345, 34.375)\": 15207.7, \"(34.375, 34.455)\": 33875.3, \"(34.455, 34.474999999999994)\": 41591.3, \"(34.474999999999994, 34.504999999999995)\": 20304.7, \"(34.504999999999995, 34.545)\": 13245.7, \"(34.545, 34.625)\": -12771.5, \"(34.625, 34.635000000000005)\": -37375.2, \"(34.635000000000005, 34.644999999999996)\": -49797.4, \"(34.644999999999996, 34.715)\": -34913.5, \"(34.715, 35.325)\": -47411.7, \"(35.325, 36.375)\": -46798.2, \"(36.375, 36.535)\": -34852.7, \"(36.535, 36.635000000000005)\": -23680.1, \"(36.635000000000005, 36.845)\": -34287.5, \"(36.845, 37.275000000000006)\": -23625.3, \"(37.275000000000006, 37.335)\": -9268.7, \"(37.335, 37.425)\": -4329.5, \"(37.425, 37.445)\": 29053.6, \"(37.445, 37.465)\": 29188.3, \"(37.465, 37.495000000000005)\": 21566.7, \"(37.495000000000005, 37.585)\": 8469.3, \"(37.585, 37.595)\": -16791.2, \"(37.595, 37.605000000000004)\": -38739.3, \"(37.605000000000004, 37.754999999999995)\": -51675.5, \"(37.754999999999995, 37.775000000000006)\": -25033.4, \"(37.775000000000006, 37.795)\": -8688.7, \"(37.795, 37.805)\": 36.5, \"(37.805, 37.855000000000004)\": -20482.2, \"(37.855000000000004, 37.915)\": -9472.3, \"(37.915, 37.925)\": -25360.4, \"(37.925, 37.945)\": -46246.0, \"(37.945, 38.355000000000004)\": -55734.3, \"(38.355000000000004, 39.085)\": -48831.7, \"(39.085, 39.474999999999994)\": -57243.4, \"(39.474999999999994, 40.135000000000005)\": -65954.6, \"(40.135000000000005, 40.665)\": -75283.4, \"(40.665, 41.775000000000006)\": -84501.7, \"(41.775000000000006, 41.95)\": -93657.9}\nUpper Bounds (95%-Confidence Interval): {\"(32.54, 32.565)\": 45504.8, \"(32.565, 32.685)\": 7020.9, \"(32.685, 32.715)\": 22008.3, \"(32.715, 32.915)\": 28813.8, \"(32.915, 33.275000000000006)\": 52060.7, \"(33.275000000000006, 33.355000000000004)\": 67843.0, \"(33.355000000000004, 33.465)\": 77302.7, \"(33.465, 33.504999999999995)\": 102622.7, \"(33.504999999999995, 33.555)\": 112294.7, \"(33.555, 33.565)\": 87049.0, \"(33.565, 33.575)\": 61706.7, \"(33.575, 33.635000000000005)\": 78918.4, \"(33.635000000000005, 33.655)\": 56632.4, \"(33.655, 33.765)\": 37207.2, \"(33.765, 33.894999999999996)\": 21727.2, \"(33.894999999999996, 33.985)\": 6415.7, \"(33.985, 33.995000000000005)\": 22504.3, \"(33.995000000000005, 34.045)\": 34569.1, \"(34.045, 34.085)\": 61292.7, \"(34.085, 34.165)\": 52483.1, \"(34.165, 34.175)\": 39493.7, \"(34.175, 34.195)\": 12533.7, \"(34.195, 34.215)\": 1308.7, \"(34.215, 34.254999999999995)\": -10146.2, \"(34.254999999999995, 34.325)\": -17758.2, \"(34.325, 34.345)\": 37984.8, \"(34.345, 34.375)\": 42331.3, \"(34.375, 34.455)\": 53781.2, \"(34.455, 34.474999999999994)\": 73958.2, \"(34.474999999999994, 34.504999999999995)\": 46253.8, \"(34.504999999999995, 34.545)\": 25490.4, \"(34.545, 34.625)\": 24169.2, \"(34.625, 34.635000000000005)\": -1900.3, \"(34.635000000000005, 34.644999999999996)\": -28744.6, \"(34.644999999999996, 34.715)\": -19072.7, \"(34.715, 35.325)\": 12722.9, \"(35.325, 36.375)\": -28601.0, \"(36.375, 36.535)\": -20608.8, \"(36.535, 36.635000000000005)\": -5701.1, \"(36.635000000000005, 36.845)\": -15853.6, \"(36.845, 37.275000000000006)\": -7150.2, \"(37.275000000000006, 37.335)\": 2610.4, \"(37.335, 37.425)\": 20236.5, \"(37.425, 37.445)\": 40038.7, \"(37.445, 37.465)\": 61006.3, \"(37.465, 37.495000000000005)\": 38472.2, \"(37.495000000000005, 37.585)\": 24817.1, \"(37.585, 37.595)\": 10675.7, \"(37.595, 37.605000000000004)\": -26020.2, \"(37.605000000000004, 37.754999999999995)\": -33782.5, \"(37.754999999999995, 37.775000000000006)\": -10763.1, \"(37.775000000000006, 37.795)\": 2229.6, \"(37.795, 37.805)\": 17768.7, \"(37.805, 37.855000000000004)\": -6431.3, \"(37.855000000000004, 37.915)\": 6747.6, \"(37.915, 37.925)\": -12927.0, \"(37.925, 37.945)\": -31291.8, \"(37.945, 38.355000000000004)\": -40761.5, \"(38.355000000000004, 39.085)\": -28103.6, \"(39.085, 39.474999999999994)\": -38137.5, \"(39.474999999999994, 40.135000000000005)\": -48018.6, \"(40.135000000000005, 40.665)\": -57259.6, \"(40.665, 41.775000000000006)\": -66753.0, \"(41.775000000000006, 41.95)\": -76574.4}\n\n\nYour task is to provide the mean value of the graph at 34.34. What is the mean value of the graph at 34.34?",
+    "17113.4"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: anaemia\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.0818, \"(0.5, 1.0)\": 0.0917}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.1464, \"(0.5, 1.0)\": 0.0194}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0173, \"(0.5, 1.0)\": 0.1641}\n\n\nYour task is to provide the mean value of the graph at 0.93. What is the mean value of the graph at 0.93?",
+    "0.0917"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Insulin\nFeature Type: continuous\nMeans: {\"(0.0, 20.0)\": 0.0422, \"(20.0, 36.5)\": -0.0027, \"(36.5, 40.5)\": -0.0554, \"(40.5, 45.5)\": -0.0967, \"(45.5, 48.5)\": -0.0409, \"(48.5, 55.5)\": -0.2263, \"(55.5, 80.5)\": -0.2661, \"(80.5, 87.5)\": -0.227, \"(87.5, 97.5)\": -0.1794, \"(97.5, 111.0)\": -0.1356, \"(111.0, 123.5)\": -0.0968, \"(123.5, 137.5)\": -0.0561, \"(137.5, 144.5)\": -0.0187, \"(144.5, 157.0)\": 0.0208, \"(157.0, 170.5)\": 0.0623, \"(170.5, 186.5)\": 0.0999, \"(186.5, 190.5)\": 0.0538, \"(190.5, 192.5)\": 0.1059, \"(192.5, 271.0)\": -0.0027, \"(271.0, 277.5)\": 0.035, \"(277.5, 292.0)\": 0.0732, \"(292.0, 311.0)\": 0.1129, \"(311.0, 365.0)\": 0.1551, \"(365.0, 397.0)\": 0.196, \"(397.0, 452.5)\": 0.2331, \"(452.5, 476.0)\": 0.2839, \"(476.0, 487.5)\": 0.346, \"(487.5, 526.5)\": 0.3915, \"(526.5, 680.0)\": 0.4346}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 20.0)\": -0.0556, \"(20.0, 36.5)\": -0.2244, \"(36.5, 40.5)\": -0.2184, \"(40.5, 45.5)\": -0.2543, \"(45.5, 48.5)\": -0.7961, \"(48.5, 55.5)\": -0.5056, \"(55.5, 80.5)\": -0.551, \"(80.5, 87.5)\": -0.3117, \"(87.5, 97.5)\": -0.251, \"(97.5, 111.0)\": -0.2086, \"(111.0, 123.5)\": -0.1731, \"(123.5, 137.5)\": -0.137, \"(137.5, 144.5)\": -0.1027, \"(144.5, 157.0)\": -0.0751, \"(157.0, 170.5)\": -0.0506, \"(170.5, 186.5)\": -0.0163, \"(186.5, 190.5)\": -0.2256, \"(190.5, 192.5)\": -0.2869, \"(192.5, 271.0)\": -0.3659, \"(271.0, 277.5)\": -0.245, \"(277.5, 292.0)\": -0.1491, \"(292.0, 311.0)\": -0.0995, \"(311.0, 365.0)\": -0.0355, \"(365.0, 397.0)\": -0.0134, \"(397.0, 452.5)\": 0.0212, \"(452.5, 476.0)\": 0.0711, \"(476.0, 487.5)\": 0.1139, \"(487.5, 526.5)\": 0.1534, \"(526.5, 680.0)\": 0.0241}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 20.0)\": 0.14, \"(20.0, 36.5)\": 0.2189, \"(36.5, 40.5)\": 0.1076, \"(40.5, 45.5)\": 0.0609, \"(45.5, 48.5)\": 0.7143, \"(48.5, 55.5)\": 0.053, \"(55.5, 80.5)\": 0.0187, \"(80.5, 87.5)\": -0.1422, \"(87.5, 97.5)\": -0.1078, \"(97.5, 111.0)\": -0.0625, \"(111.0, 123.5)\": -0.0206, \"(123.5, 137.5)\": 0.0247, \"(137.5, 144.5)\": 0.0654, \"(144.5, 157.0)\": 0.1166, \"(157.0, 170.5)\": 0.1751, \"(170.5, 186.5)\": 0.2162, \"(186.5, 190.5)\": 0.3332, \"(190.5, 192.5)\": 0.4987, \"(192.5, 271.0)\": 0.3605, \"(271.0, 277.5)\": 0.315, \"(277.5, 292.0)\": 0.2956, \"(292.0, 311.0)\": 0.3253, \"(311.0, 365.0)\": 0.3457, \"(365.0, 397.0)\": 0.4055, \"(397.0, 452.5)\": 0.445, \"(452.5, 476.0)\": 0.4967, \"(476.0, 487.5)\": 0.5782, \"(487.5, 526.5)\": 0.6295, \"(526.5, 680.0)\": 0.8452}\n\n\nYour task is to provide the mean value of the graph at 150.77. What is the mean value of the graph at 150.77?",
+    "0.0208"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: sepal_length\nFeature Type: continuous\nMeans: {\"(4.3, 4.55)\": 3.328, \"(4.55, 4.75)\": 2.995, \"(4.75, 4.85)\": 2.698, \"(4.85, 5.05)\": 1.665, \"(5.05, 5.25)\": 1.371, \"(5.25, 5.45)\": 1.085, \"(5.45, 5.55)\": 0.339, \"(5.55, 5.75)\": -0.057, \"(5.75, 5.85)\": -0.39, \"(5.85, 6.15)\": -0.757, \"(6.15, 6.45)\": -1.149, \"(6.45, 6.85)\": -1.436, \"(6.85, 7.7)\": -1.718}\nLower Bounds (95%-Confidence Interval): {\"(4.3, 4.55)\": 3.22, \"(4.55, 4.75)\": 2.846, \"(4.75, 4.85)\": 2.54, \"(4.85, 5.05)\": 1.185, \"(5.05, 5.25)\": 1.214, \"(5.25, 5.45)\": 0.892, \"(5.45, 5.55)\": -0.164, \"(5.55, 5.75)\": -0.32, \"(5.75, 5.85)\": -0.665, \"(5.85, 6.15)\": -0.888, \"(6.15, 6.45)\": -1.29, \"(6.45, 6.85)\": -1.575, \"(6.85, 7.7)\": -1.814}\nUpper Bounds (95%-Confidence Interval): {\"(4.3, 4.55)\": 3.437, \"(4.55, 4.75)\": 3.144, \"(4.75, 4.85)\": 2.857, \"(4.85, 5.05)\": 2.145, \"(5.05, 5.25)\": 1.528, \"(5.25, 5.45)\": 1.277, \"(5.45, 5.55)\": 0.843, \"(5.55, 5.75)\": 0.206, \"(5.75, 5.85)\": -0.116, \"(5.85, 6.15)\": -0.627, \"(6.15, 6.45)\": -1.009, \"(6.45, 6.85)\": -1.298, \"(6.85, 7.7)\": -1.623}\n\n\nYour task is to provide the mean value of the graph at 4.58. What is the mean value of the graph at 4.58?",
+    "2.995"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: DamsQuality\nFeature Type: continuous\nMeans: {\"(0.0, 1.5)\": -0.02325, \"(1.5, 2.5)\": -0.01532, \"(2.5, 3.5)\": -0.01073, \"(3.5, 4.5)\": -0.00482, \"(4.5, 5.5)\": -0.00032, \"(5.5, 6.5)\": 0.0063, \"(6.5, 7.5)\": 0.01228, \"(7.5, 8.5)\": 0.01637, \"(8.5, 10.5)\": 0.02537, \"(10.5, 12.5)\": 0.03189, \"(12.5, 13.5)\": 0.03961, \"(13.5, 14.0)\": 0.01644}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02598, \"(1.5, 2.5)\": -0.01586, \"(2.5, 3.5)\": -0.01113, \"(3.5, 4.5)\": -0.00525, \"(4.5, 5.5)\": -0.00072, \"(5.5, 6.5)\": 0.00574, \"(6.5, 7.5)\": 0.01173, \"(7.5, 8.5)\": 0.01585, \"(8.5, 10.5)\": 0.02412, \"(10.5, 12.5)\": 0.02908, \"(12.5, 13.5)\": 0.03687, \"(13.5, 14.0)\": 0.00331}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02052, \"(1.5, 2.5)\": -0.01477, \"(2.5, 3.5)\": -0.01034, \"(3.5, 4.5)\": -0.00438, \"(4.5, 5.5)\": 9e-05, \"(5.5, 6.5)\": 0.00686, \"(6.5, 7.5)\": 0.01282, \"(7.5, 8.5)\": 0.01689, \"(8.5, 10.5)\": 0.02662, \"(10.5, 12.5)\": 0.0347, \"(12.5, 13.5)\": 0.04234, \"(13.5, 14.0)\": 0.02957}\n\n\nYour task is to provide the mean value of the graph at 2.78. What is the mean value of the graph at 2.78?",
+    "-0.01073"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Age\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": 0.68, \"(0.5, 3.5)\": 0.36, \"(3.5, 4.5)\": 0.254, \"(4.5, 14.5)\": 0.09, \"(14.5, 23.5)\": 0.028, \"(23.5, 24.5)\": -0.027, \"(24.5, 25.5)\": -0.135, \"(25.5, 39.5)\": -0.05, \"(39.5, 44.5)\": 0.042, \"(44.5, 48.5)\": -0.025, \"(48.5, 54.5)\": -0.102, \"(54.5, 56.5)\": -0.012, \"(56.5, 63.5)\": 0.078, \"(63.5, 64.5)\": -0.028, \"(64.5, 65.5)\": -0.141, \"(65.5, 68.5)\": 0.058, \"(68.5, 69.5)\": -0.021, \"(69.5, 71.5)\": 0.037, \"(71.5, 73.5)\": -0.022, \"(73.5, 74.5)\": 0.413, \"(74.5, 77.5)\": 0.211, \"(77.5, 79.0)\": -0.412}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.461, \"(0.5, 3.5)\": 0.228, \"(3.5, 4.5)\": 0.097, \"(4.5, 14.5)\": -0.111, \"(14.5, 23.5)\": -0.031, \"(23.5, 24.5)\": -0.079, \"(24.5, 25.5)\": -0.32, \"(25.5, 39.5)\": -0.113, \"(39.5, 44.5)\": -0.088, \"(44.5, 48.5)\": -0.081, \"(48.5, 54.5)\": -0.336, \"(54.5, 56.5)\": -0.102, \"(56.5, 63.5)\": -0.123, \"(63.5, 64.5)\": -0.219, \"(64.5, 65.5)\": -0.706, \"(65.5, 68.5)\": -0.265, \"(68.5, 69.5)\": -0.416, \"(69.5, 71.5)\": -0.213, \"(71.5, 73.5)\": -0.172, \"(73.5, 74.5)\": -0.439, \"(74.5, 77.5)\": -0.317, \"(77.5, 79.0)\": -1.348}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.9, \"(0.5, 3.5)\": 0.491, \"(3.5, 4.5)\": 0.411, \"(4.5, 14.5)\": 0.29, \"(14.5, 23.5)\": 0.087, \"(23.5, 24.5)\": 0.024, \"(24.5, 25.5)\": 0.05, \"(25.5, 39.5)\": 0.012, \"(39.5, 44.5)\": 0.172, \"(44.5, 48.5)\": 0.031, \"(48.5, 54.5)\": 0.132, \"(54.5, 56.5)\": 0.077, \"(56.5, 63.5)\": 0.278, \"(63.5, 64.5)\": 0.163, \"(64.5, 65.5)\": 0.424, \"(65.5, 68.5)\": 0.382, \"(68.5, 69.5)\": 0.373, \"(69.5, 71.5)\": 0.287, \"(71.5, 73.5)\": 0.129, \"(73.5, 74.5)\": 1.265, \"(74.5, 77.5)\": 0.739, \"(77.5, 79.0)\": 0.524}\n\n\nYour task is to provide the mean value of the graph at 3.86. What is the mean value of the graph at 3.86?",
+    "0.254"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Parch\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": 0.085, \"(0.5, 1.5)\": -0.055, \"(1.5, 3.0)\": -0.299, \"(3.0, 4.0)\": -1.704}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02, \"(0.5, 1.5)\": -0.269, \"(1.5, 3.0)\": -0.62, \"(3.0, 4.0)\": -3.014}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.19, \"(0.5, 1.5)\": 0.158, \"(1.5, 3.0)\": 0.022, \"(3.0, 4.0)\": -0.395}\n\n\nYour task is to provide the mean value of the graph at 0.45. What is the mean value of the graph at 0.45?",
+    "0.085"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: BloodPressure\nFeature Type: continuous\nMeans: {\"(0.0, 15.0)\": 0.236, \"(15.0, 37.0)\": 0.1532, \"(37.0, 45.0)\": -0.0296, \"(45.0, 47.0)\": -0.0891, \"(47.0, 54.5)\": -0.1348, \"(54.5, 60.5)\": -0.1774, \"(60.5, 61.5)\": -0.11, \"(61.5, 64.5)\": -0.0541, \"(64.5, 74.5)\": -0.0119, \"(74.5, 75.5)\": -0.058, \"(75.5, 83.0)\": -0.004, \"(83.0, 93.0)\": 0.0343, \"(93.0, 95.0)\": 0.0889, \"(95.0, 97.0)\": 0.1461, \"(97.0, 101.0)\": 0.183, \"(101.0, 103.0)\": 0.2699, \"(103.0, 107.0)\": 0.3158, \"(107.0, 109.0)\": 0.3837, \"(109.0, 110.0)\": 0.5269}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 15.0)\": -0.0274, \"(15.0, 37.0)\": -0.1145, \"(37.0, 45.0)\": -0.2191, \"(45.0, 47.0)\": -0.2854, \"(47.0, 54.5)\": -0.313, \"(54.5, 60.5)\": -0.2953, \"(60.5, 61.5)\": -0.1759, \"(61.5, 64.5)\": -0.1789, \"(64.5, 74.5)\": -0.1212, \"(74.5, 75.5)\": -0.3075, \"(75.5, 83.0)\": -0.0727, \"(83.0, 93.0)\": -0.1515, \"(93.0, 95.0)\": -0.0624, \"(95.0, 97.0)\": -0.0006, \"(97.0, 101.0)\": 0.0092, \"(101.0, 103.0)\": 0.085, \"(103.0, 107.0)\": 0.1217, \"(107.0, 109.0)\": 0.1853, \"(109.0, 110.0)\": 0.2653}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 15.0)\": 0.4994, \"(15.0, 37.0)\": 0.4208, \"(37.0, 45.0)\": 0.16, \"(45.0, 47.0)\": 0.1073, \"(47.0, 54.5)\": 0.0433, \"(54.5, 60.5)\": -0.0595, \"(60.5, 61.5)\": -0.0441, \"(61.5, 64.5)\": 0.0708, \"(64.5, 74.5)\": 0.0974, \"(74.5, 75.5)\": 0.1914, \"(75.5, 83.0)\": 0.0647, \"(83.0, 93.0)\": 0.2201, \"(93.0, 95.0)\": 0.2402, \"(95.0, 97.0)\": 0.2929, \"(97.0, 101.0)\": 0.3567, \"(101.0, 103.0)\": 0.4548, \"(103.0, 107.0)\": 0.51, \"(107.0, 109.0)\": 0.582, \"(109.0, 110.0)\": 0.7884}\n\n\nYour task is to provide the mean value of the graph at 109.98. What is the mean value of the graph at 109.98?",
+    "0.5269"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Race\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.8604, \"(0.5, 1.5)\": -0.0173, \"(1.5, 2.5)\": -0.2499, \"(2.5, 3.5)\": -0.3026, \"(3.5, 4.0)\": 0.0414}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -1.0291, \"(0.5, 1.5)\": -0.1456, \"(1.5, 2.5)\": -0.3118, \"(2.5, 3.5)\": -0.4557, \"(3.5, 4.0)\": 0.0349}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.6918, \"(0.5, 1.5)\": 0.1111, \"(1.5, 2.5)\": -0.1879, \"(2.5, 3.5)\": -0.1496, \"(3.5, 4.0)\": 0.048}\n\n\nYour task is to provide the mean value of the graph at 2.52. What is the mean value of the graph at 2.52?",
+    "-0.3026"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Landslides\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02593, \"(0.5, 1.5)\": -0.02172, \"(1.5, 2.5)\": -0.01544, \"(2.5, 3.5)\": -0.0098, \"(3.5, 4.5)\": -0.00541, \"(4.5, 5.5)\": 0.00066, \"(5.5, 6.5)\": 0.00575, \"(6.5, 7.5)\": 0.01201, \"(7.5, 8.5)\": 0.01649, \"(8.5, 9.5)\": 0.0215, \"(9.5, 10.5)\": 0.0267, \"(10.5, 11.5)\": 0.03057, \"(11.5, 13.5)\": 0.0366, \"(13.5, 14.0)\": 0.03003}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02743, \"(0.5, 1.5)\": -0.02261, \"(1.5, 2.5)\": -0.01616, \"(2.5, 3.5)\": -0.0102, \"(3.5, 4.5)\": -0.00579, \"(4.5, 5.5)\": 0.00027, \"(5.5, 6.5)\": 0.00544, \"(6.5, 7.5)\": 0.01146, \"(7.5, 8.5)\": 0.01601, \"(8.5, 9.5)\": 0.02065, \"(9.5, 10.5)\": 0.02512, \"(10.5, 11.5)\": 0.0285, \"(11.5, 13.5)\": 0.02931, \"(13.5, 14.0)\": 0.02233}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02443, \"(0.5, 1.5)\": -0.02083, \"(1.5, 2.5)\": -0.01472, \"(2.5, 3.5)\": -0.0094, \"(3.5, 4.5)\": -0.00504, \"(4.5, 5.5)\": 0.00105, \"(5.5, 6.5)\": 0.00606, \"(6.5, 7.5)\": 0.01257, \"(7.5, 8.5)\": 0.01698, \"(8.5, 9.5)\": 0.02234, \"(9.5, 10.5)\": 0.02828, \"(10.5, 11.5)\": 0.03265, \"(11.5, 13.5)\": 0.04389, \"(13.5, 14.0)\": 0.03772}\n\n\nYour task is to provide the mean value of the graph at 6.11. What is the mean value of the graph at 6.11?",
+    "0.00575"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: area_worst\nFeature Type: continuous\nMeans: {\"(185.2, 357.5)\": -1.345, \"(357.5, 413.15)\": -1.192, \"(413.15, 471.9)\": -1.038, \"(471.9, 508.5)\": -0.878, \"(508.5, 633.9)\": -0.723, \"(633.9, 653.45)\": -0.565, \"(653.45, 710.2)\": -0.348, \"(710.2, 727.0999999999999)\": -0.165, \"(727.0999999999999, 805.95)\": 0.096, \"(805.95, 874.85)\": 0.253, \"(874.85, 928.5)\": 0.48, \"(928.5, 1033.5)\": 0.761, \"(1033.5, 1222.5)\": 0.932, \"(1222.5, 1346.5)\": 1.092, \"(1346.5, 1645.5)\": 1.245, \"(1645.5, 1979.0)\": 1.404, \"(1979.0, 4254.0)\": 1.557}\nLower Bounds (95%-Confidence Interval): {\"(185.2, 357.5)\": -2.413, \"(357.5, 413.15)\": -2.22, \"(413.15, 471.9)\": -2.004, \"(471.9, 508.5)\": -1.818, \"(508.5, 633.9)\": -1.868, \"(633.9, 653.45)\": -1.645, \"(653.45, 710.2)\": -0.767, \"(710.2, 727.0999999999999)\": -0.501, \"(727.0999999999999, 805.95)\": -0.573, \"(805.95, 874.85)\": -0.187, \"(874.85, 928.5)\": -0.49, \"(928.5, 1033.5)\": -0.484, \"(1033.5, 1222.5)\": -0.455, \"(1222.5, 1346.5)\": -0.298, \"(1346.5, 1645.5)\": -0.182, \"(1645.5, 1979.0)\": -0.049, \"(1979.0, 4254.0)\": 0.071}\nUpper Bounds (95%-Confidence Interval): {\"(185.2, 357.5)\": -0.278, \"(357.5, 413.15)\": -0.164, \"(413.15, 471.9)\": -0.073, \"(471.9, 508.5)\": 0.062, \"(508.5, 633.9)\": 0.423, \"(633.9, 653.45)\": 0.516, \"(653.45, 710.2)\": 0.071, \"(710.2, 727.0999999999999)\": 0.17, \"(727.0999999999999, 805.95)\": 0.764, \"(805.95, 874.85)\": 0.693, \"(874.85, 928.5)\": 1.449, \"(928.5, 1033.5)\": 2.006, \"(1033.5, 1222.5)\": 2.319, \"(1222.5, 1346.5)\": 2.482, \"(1346.5, 1645.5)\": 2.672, \"(1645.5, 1979.0)\": 2.857, \"(1979.0, 4254.0)\": 3.043}\n\n\nYour task is to provide the mean value of the graph at 1152.44. What is the mean value of the graph at 1152.44?",
+    "0.932"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: high_blood_pressure\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.1077, \"(0.5, 1.0)\": 0.1864}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.1574, \"(0.5, 1.0)\": 0.1003}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.058, \"(0.5, 1.0)\": 0.2724}\n\n\nYour task is to provide the mean value of the graph at 0.07. What is the mean value of the graph at 0.07?",
+    "-0.1077"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Pregnancies\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.1506, \"(0.5, 1.5)\": -0.2484, \"(1.5, 2.5)\": -0.1873, \"(2.5, 3.5)\": -0.0302, \"(3.5, 4.5)\": 0.0211, \"(4.5, 5.5)\": 0.1013, \"(5.5, 6.5)\": 0.1489, \"(6.5, 7.5)\": 0.264, \"(7.5, 8.5)\": 0.3553, \"(8.5, 9.5)\": 0.4117, \"(9.5, 13.5)\": 0.2996, \"(13.5, 14.0)\": 0.6729}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.2406, \"(0.5, 1.5)\": -0.3636, \"(1.5, 2.5)\": -0.242, \"(2.5, 3.5)\": -0.093, \"(3.5, 4.5)\": -0.038, \"(4.5, 5.5)\": 0.0314, \"(5.5, 6.5)\": 0.0909, \"(6.5, 7.5)\": 0.1609, \"(7.5, 8.5)\": 0.2075, \"(8.5, 9.5)\": 0.248, \"(9.5, 13.5)\": 0.0671, \"(13.5, 14.0)\": 0.084}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0606, \"(0.5, 1.5)\": -0.1333, \"(1.5, 2.5)\": -0.1326, \"(2.5, 3.5)\": 0.0326, \"(3.5, 4.5)\": 0.0802, \"(4.5, 5.5)\": 0.1712, \"(5.5, 6.5)\": 0.207, \"(6.5, 7.5)\": 0.3671, \"(7.5, 8.5)\": 0.5032, \"(8.5, 9.5)\": 0.5755, \"(9.5, 13.5)\": 0.5321, \"(13.5, 14.0)\": 1.2617}\n\n\nYour task is to provide the mean value of the graph at 2.3. What is the mean value of the graph at 2.3?",
+    "-0.1873"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: concavity_worst\nFeature Type: continuous\nMeans: {\"(0.0, 0.022775)\": -0.769, \"(0.022775, 0.024655)\": -0.671, \"(0.024655, 0.052095)\": -0.846, \"(0.052095, 0.10575)\": -0.943, \"(0.10575, 0.1313)\": -0.843, \"(0.1313, 0.14545000000000002)\": -0.745, \"(0.14545000000000002, 0.1694)\": -0.646, \"(0.1694, 0.1843)\": -0.54, \"(0.1843, 0.19235000000000002)\": -0.438, \"(0.19235000000000002, 0.1996)\": -0.332, \"(0.1996, 0.20695)\": -0.234, \"(0.20695, 0.20795)\": -0.081, \"(0.20795, 0.2539)\": 0.187, \"(0.2539, 0.273)\": 0.284, \"(0.273, 0.33975)\": 0.385, \"(0.33975, 0.3663)\": 0.486, \"(0.3663, 0.37695)\": 0.586, \"(0.37695, 0.39765)\": 0.698, \"(0.39765, 0.41025)\": 0.797, \"(0.41025, 1.252)\": 0.897}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.022775)\": -1.429, \"(0.022775, 0.024655)\": -1.337, \"(0.024655, 0.052095)\": -1.568, \"(0.052095, 0.10575)\": -1.701, \"(0.10575, 0.1313)\": -1.62, \"(0.1313, 0.14545000000000002)\": -1.521, \"(0.14545000000000002, 0.1694)\": -1.427, \"(0.1694, 0.1843)\": -1.324, \"(0.1843, 0.19235000000000002)\": -1.207, \"(0.19235000000000002, 0.1996)\": -1.093, \"(0.1996, 0.20695)\": -0.982, \"(0.20695, 0.20795)\": -0.814, \"(0.20795, 0.2539)\": -0.518, \"(0.2539, 0.273)\": -0.08, \"(0.273, 0.33975)\": 0.033, \"(0.33975, 0.3663)\": 0.265, \"(0.3663, 0.37695)\": 0.365, \"(0.37695, 0.39765)\": -0.026, \"(0.39765, 0.41025)\": -0.308, \"(0.41025, 1.252)\": -0.23}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.022775)\": -0.109, \"(0.022775, 0.024655)\": -0.005, \"(0.024655, 0.052095)\": -0.123, \"(0.052095, 0.10575)\": -0.186, \"(0.10575, 0.1313)\": -0.065, \"(0.1313, 0.14545000000000002)\": 0.031, \"(0.14545000000000002, 0.1694)\": 0.135, \"(0.1694, 0.1843)\": 0.244, \"(0.1843, 0.19235000000000002)\": 0.332, \"(0.19235000000000002, 0.1996)\": 0.428, \"(0.1996, 0.20695)\": 0.514, \"(0.20695, 0.20795)\": 0.653, \"(0.20795, 0.2539)\": 0.891, \"(0.2539, 0.273)\": 0.648, \"(0.273, 0.33975)\": 0.737, \"(0.33975, 0.3663)\": 0.708, \"(0.3663, 0.37695)\": 0.807, \"(0.37695, 0.39765)\": 1.423, \"(0.39765, 0.41025)\": 1.902, \"(0.41025, 1.252)\": 2.024}\n\n\nYour task is to provide the mean value of the graph at 0.29. What is the mean value of the graph at 0.29?",
+    "0.385"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: ClimateChange\nFeature Type: continuous\nMeans: {\"(0.0, 1.5)\": -0.02549, \"(1.5, 2.5)\": -0.01575, \"(2.5, 3.5)\": -0.01061, \"(3.5, 4.5)\": -0.0046, \"(4.5, 5.5)\": 0.00059, \"(5.5, 6.5)\": 0.00567, \"(6.5, 7.5)\": 0.01201, \"(7.5, 9.5)\": 0.01601, \"(9.5, 10.5)\": 0.02531, \"(10.5, 11.5)\": 0.02956, \"(11.5, 12.5)\": 0.04031, \"(12.5, 14.0)\": 0.04423}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02735, \"(1.5, 2.5)\": -0.01647, \"(2.5, 3.5)\": -0.01101, \"(3.5, 4.5)\": -0.00502, \"(4.5, 5.5)\": 0.00018, \"(5.5, 6.5)\": 0.00528, \"(6.5, 7.5)\": 0.01139, \"(7.5, 9.5)\": 0.01505, \"(9.5, 10.5)\": 0.0236, \"(10.5, 11.5)\": 0.02677, \"(11.5, 12.5)\": 0.03846, \"(12.5, 14.0)\": 0.03359}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02363, \"(1.5, 2.5)\": -0.01503, \"(2.5, 3.5)\": -0.0102, \"(3.5, 4.5)\": -0.00418, \"(4.5, 5.5)\": 0.00101, \"(5.5, 6.5)\": 0.00607, \"(6.5, 7.5)\": 0.01263, \"(7.5, 9.5)\": 0.01697, \"(9.5, 10.5)\": 0.02702, \"(10.5, 11.5)\": 0.03236, \"(11.5, 12.5)\": 0.04216, \"(12.5, 14.0)\": 0.05488}\n\n\nYour task is to provide the mean value of the graph at 5.94. What is the mean value of the graph at 5.94?",
+    "0.00567"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: EstimatedSalary\nFeature Type: continuous\nMeans: {\"(106.67, 780.2149999999999)\": 0.3865, \"(780.2149999999999, 4627.98)\": 0.3462, \"(4627.98, 6842.475)\": 0.0858, \"(6842.475, 7401.88)\": 0.157, \"(7401.88, 27330.43)\": 0.2048, \"(27330.43, 38816.375)\": 0.1737, \"(38816.375, 40348.645000000004)\": 0.1063, \"(40348.645000000004, 42807.509999999995)\": 0.0512, \"(42807.509999999995, 48226.81)\": 0.1098, \"(48226.81, 48498.15)\": -0.0771, \"(48498.15, 58535.68)\": 0.0187, \"(58535.68, 94498.98999999999)\": 0.0512, \"(94498.98999999999, 120892.955)\": 0.0186, \"(120892.955, 121151.28)\": -0.0263, \"(121151.28, 121482.61499999999)\": -0.0801, \"(121482.61499999999, 148569.97)\": -0.0388, \"(148569.97, 184522.325)\": -0.0796, \"(184522.325, 187947.635)\": -0.1332, \"(187947.635, 187985.865)\": -0.2342, \"(187985.865, 188452.565)\": -0.0632, \"(188452.565, 189006.61)\": -0.0053, \"(189006.61, 196418.97999999998)\": 0.0291, \"(196418.97999999998, 199505.41)\": -0.0098, \"(199505.41, 199992.48)\": 0.214}\nLower Bounds (95%-Confidence Interval): {\"(106.67, 780.2149999999999)\": 0.0871, \"(780.2149999999999, 4627.98)\": 0.1468, \"(4627.98, 6842.475)\": -0.2734, \"(6842.475, 7401.88)\": -0.01, \"(7401.88, 27330.43)\": 0.0941, \"(27330.43, 38816.375)\": 0.065, \"(38816.375, 40348.645000000004)\": -0.0568, \"(40348.645000000004, 42807.509999999995)\": -0.1427, \"(42807.509999999995, 48226.81)\": 0.0015, \"(48226.81, 48498.15)\": -0.404, \"(48498.15, 58535.68)\": -0.1286, \"(58535.68, 94498.98999999999)\": -0.003, \"(94498.98999999999, 120892.955)\": -0.0541, \"(120892.955, 121151.28)\": -0.186, \"(121151.28, 121482.61499999999)\": -0.2842, \"(121482.61499999999, 148569.97)\": -0.1593, \"(148569.97, 184522.325)\": -0.1401, \"(184522.325, 187947.635)\": -0.216, \"(187947.635, 187985.865)\": -0.7523, \"(187985.865, 188452.565)\": -0.2404, \"(188452.565, 189006.61)\": -0.1779, \"(189006.61, 196418.97999999998)\": -0.1285, \"(196418.97999999998, 199505.41)\": -0.2064, \"(199505.41, 199992.48)\": -0.3318}\nUpper Bounds (95%-Confidence Interval): {\"(106.67, 780.2149999999999)\": 0.6859, \"(780.2149999999999, 4627.98)\": 0.5457, \"(4627.98, 6842.475)\": 0.445, \"(6842.475, 7401.88)\": 0.3239, \"(7401.88, 27330.43)\": 0.3154, \"(27330.43, 38816.375)\": 0.2823, \"(38816.375, 40348.645000000004)\": 0.2695, \"(40348.645000000004, 42807.509999999995)\": 0.2451, \"(42807.509999999995, 48226.81)\": 0.2181, \"(48226.81, 48498.15)\": 0.2497, \"(48498.15, 58535.68)\": 0.166, \"(58535.68, 94498.98999999999)\": 0.1054, \"(94498.98999999999, 120892.955)\": 0.0913, \"(120892.955, 121151.28)\": 0.1335, \"(121151.28, 121482.61499999999)\": 0.1239, \"(121482.61499999999, 148569.97)\": 0.0817, \"(148569.97, 184522.325)\": -0.019, \"(184522.325, 187947.635)\": -0.0504, \"(187947.635, 187985.865)\": 0.2839, \"(187985.865, 188452.565)\": 0.1139, \"(188452.565, 189006.61)\": 0.1673, \"(189006.61, 196418.97999999998)\": 0.1867, \"(196418.97999999998, 199505.41)\": 0.1868, \"(199505.41, 199992.48)\": 0.7597}\n\n\nYour task is to provide the mean value of the graph at 141941.44. What is the mean value of the graph at 141941.44?",
+    "-0.0388"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: IsActiveMember\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.555, \"(0.5, 1.0)\": 0.568}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.593, \"(0.5, 1.0)\": 0.529}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.518, \"(0.5, 1.0)\": 0.606}\n\n\nYour task is to provide the mean value of the graph at 0.48. What is the mean value of the graph at 0.48?",
+    "-0.555"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: perimeter_worst\nFeature Type: continuous\nMeans: {\"(50.41, 71.06)\": -1.379, \"(71.06, 76.52000000000001)\": -1.223, \"(76.52000000000001, 80.9)\": -1.069, \"(80.9, 84.035)\": -0.914, \"(84.035, 86.48500000000001)\": -0.755, \"(86.48500000000001, 87.3)\": -0.599, \"(87.3, 91.49000000000001)\": -0.447, \"(91.49000000000001, 95.66)\": -0.292, \"(95.66, 101.15)\": -0.446, \"(101.15, 102.05000000000001)\": -0.294, \"(102.05000000000001, 109.6)\": 0.197, \"(109.6, 116.25)\": 0.351, \"(116.25, 120.35)\": 0.507, \"(120.35, 127.0)\": 0.748, \"(127.0, 133.10000000000002)\": 0.902, \"(133.10000000000002, 145.10000000000002)\": 1.059, \"(145.10000000000002, 160.0)\": 1.215, \"(160.0, 178.85)\": 1.368, \"(178.85, 251.2)\": 1.523}\nLower Bounds (95%-Confidence Interval): {\"(50.41, 71.06)\": -2.45, \"(71.06, 76.52000000000001)\": -2.257, \"(76.52000000000001, 80.9)\": -2.023, \"(80.9, 84.035)\": -1.85, \"(84.035, 86.48500000000001)\": -1.682, \"(86.48500000000001, 87.3)\": -1.531, \"(87.3, 91.49000000000001)\": -1.053, \"(91.49000000000001, 95.66)\": -0.915, \"(95.66, 101.15)\": -1.829, \"(101.15, 102.05000000000001)\": -1.642, \"(102.05000000000001, 109.6)\": -0.387, \"(109.6, 116.25)\": -0.238, \"(116.25, 120.35)\": -0.074, \"(120.35, 127.0)\": -0.761, \"(127.0, 133.10000000000002)\": -0.623, \"(133.10000000000002, 145.10000000000002)\": -0.494, \"(145.10000000000002, 160.0)\": -0.379, \"(160.0, 178.85)\": -0.29, \"(178.85, 251.2)\": -0.162}\nUpper Bounds (95%-Confidence Interval): {\"(50.41, 71.06)\": -0.307, \"(71.06, 76.52000000000001)\": -0.189, \"(76.52000000000001, 80.9)\": -0.114, \"(80.9, 84.035)\": 0.021, \"(84.035, 86.48500000000001)\": 0.172, \"(86.48500000000001, 87.3)\": 0.332, \"(87.3, 91.49000000000001)\": 0.159, \"(91.49000000000001, 95.66)\": 0.331, \"(95.66, 101.15)\": 0.936, \"(101.15, 102.05000000000001)\": 1.054, \"(102.05000000000001, 109.6)\": 0.782, \"(109.6, 116.25)\": 0.94, \"(116.25, 120.35)\": 1.088, \"(120.35, 127.0)\": 2.256, \"(127.0, 133.10000000000002)\": 2.428, \"(133.10000000000002, 145.10000000000002)\": 2.611, \"(145.10000000000002, 160.0)\": 2.809, \"(160.0, 178.85)\": 3.027, \"(178.85, 251.2)\": 3.208}\n\n\nYour task is to provide the mean value of the graph at 75.26. What is the mean value of the graph at 75.26?",
+    "-1.223"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Occupation\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.297, \"(0.5, 3.5)\": -0.074, \"(3.5, 4.5)\": 0.644, \"(4.5, 6.5)\": -0.723, \"(6.5, 7.5)\": -0.542, \"(7.5, 8.5)\": -0.665, \"(8.5, 9.5)\": -0.926, \"(9.5, 10.5)\": 0.423, \"(10.5, 11.5)\": 0.59, \"(11.5, 12.5)\": 0.27, \"(12.5, 13.5)\": 0.534, \"(13.5, 14.0)\": -0.133}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.409, \"(0.5, 3.5)\": -0.139, \"(3.5, 4.5)\": 0.592, \"(4.5, 6.5)\": -0.847, \"(6.5, 7.5)\": -0.624, \"(7.5, 8.5)\": -0.749, \"(8.5, 9.5)\": -1.549, \"(9.5, 10.5)\": 0.366, \"(10.5, 11.5)\": 0.452, \"(11.5, 12.5)\": 0.225, \"(12.5, 13.5)\": 0.445, \"(13.5, 14.0)\": -0.202}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.185, \"(0.5, 3.5)\": -0.01, \"(3.5, 4.5)\": 0.695, \"(4.5, 6.5)\": -0.598, \"(6.5, 7.5)\": -0.461, \"(7.5, 8.5)\": -0.581, \"(8.5, 9.5)\": -0.302, \"(9.5, 10.5)\": 0.48, \"(10.5, 11.5)\": 0.727, \"(11.5, 12.5)\": 0.315, \"(12.5, 13.5)\": 0.622, \"(13.5, 14.0)\": -0.064}\n\n\nYour task is to provide the mean value of the graph at 13.73. What is the mean value of the graph at 13.73?",
+    "-0.133"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: smoking\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": 0.01522, \"(0.5, 1.0)\": -0.03391}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0422, \"(0.5, 1.0)\": -0.16186}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.07264, \"(0.5, 1.0)\": 0.09404}\n\n\nYour task is to provide the mean value of the graph at 0.85. What is the mean value of the graph at 0.85?",
+    "-0.03391"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: compactness_se\nFeature Type: continuous\nMeans: {\"(0.002252, 0.0046765)\": -0.0693, \"(0.0046765, 0.005634)\": -0.0214, \"(0.005634, 0.006059500000000001)\": 0.0214, \"(0.006059500000000001, 0.006774499999999999)\": 0.0648, \"(0.006774499999999999, 0.0072375)\": 0.1132, \"(0.0072375, 0.008034)\": 0.1583, \"(0.008034, 0.0082145)\": 0.2045, \"(0.0082145, 0.0085705)\": 0.2482, \"(0.0085705, 0.0089915)\": 0.2969, \"(0.0089915, 0.01089)\": 0.3467, \"(0.01089, 0.011715)\": 0.3948, \"(0.011715, 0.012025000000000001)\": 0.3506, \"(0.012025000000000001, 0.012535000000000001)\": 0.2891, \"(0.012535000000000001, 0.013225)\": 0.244, \"(0.013225, 0.014275)\": 0.2001, \"(0.014275, 0.015615)\": 0.1571, \"(0.015615, 0.017669999999999998)\": 0.1142, \"(0.017669999999999998, 0.020155)\": 0.0681, \"(0.020155, 0.022855)\": 0.0256, \"(0.022855, 0.02586)\": -0.0272, \"(0.02586, 0.027540000000000002)\": -0.098, \"(0.027540000000000002, 0.038220000000000004)\": -0.1414, \"(0.038220000000000004, 0.039245)\": -0.1853, \"(0.039245, 0.040514999999999995)\": -0.2301, \"(0.040514999999999995, 0.04309)\": -0.2754, \"(0.04309, 0.04922)\": -0.3233, \"(0.04922, 0.068925)\": -0.3675, \"(0.068925, 0.1354)\": -0.4112}\nLower Bounds (95%-Confidence Interval): {\"(0.002252, 0.0046765)\": -0.2881, \"(0.0046765, 0.005634)\": -0.2345, \"(0.005634, 0.006059500000000001)\": -0.1933, \"(0.006059500000000001, 0.006774499999999999)\": -0.1451, \"(0.006774499999999999, 0.0072375)\": -0.0877, \"(0.0072375, 0.008034)\": -0.0418, \"(0.008034, 0.0082145)\": -0.0092, \"(0.0082145, 0.0085705)\": 0.0302, \"(0.0085705, 0.0089915)\": 0.0617, \"(0.0089915, 0.01089)\": 0.105, \"(0.01089, 0.011715)\": 0.1231, \"(0.011715, 0.012025000000000001)\": 0.0874, \"(0.012025000000000001, 0.012535000000000001)\": 0.097, \"(0.012535000000000001, 0.013225)\": 0.063, \"(0.013225, 0.014275)\": 0.031, \"(0.014275, 0.015615)\": 0.0018, \"(0.015615, 0.017669999999999998)\": -0.0333, \"(0.017669999999999998, 0.020155)\": -0.0326, \"(0.020155, 0.022855)\": -0.0543, \"(0.022855, 0.02586)\": -0.1745, \"(0.02586, 0.027540000000000002)\": -0.258, \"(0.027540000000000002, 0.038220000000000004)\": -0.3097, \"(0.038220000000000004, 0.039245)\": -0.326, \"(0.039245, 0.040514999999999995)\": -0.3788, \"(0.040514999999999995, 0.04309)\": -0.4514, \"(0.04309, 0.04922)\": -0.5306, \"(0.04922, 0.068925)\": -0.5903, \"(0.068925, 0.1354)\": -0.6732}\nUpper Bounds (95%-Confidence Interval): {\"(0.002252, 0.0046765)\": 0.1496, \"(0.0046765, 0.005634)\": 0.1917, \"(0.005634, 0.006059500000000001)\": 0.2361, \"(0.006059500000000001, 0.006774499999999999)\": 0.2747, \"(0.006774499999999999, 0.0072375)\": 0.3141, \"(0.0072375, 0.008034)\": 0.3584, \"(0.008034, 0.0082145)\": 0.4182, \"(0.0082145, 0.0085705)\": 0.4662, \"(0.0085705, 0.0089915)\": 0.5321, \"(0.0089915, 0.01089)\": 0.5884, \"(0.01089, 0.011715)\": 0.6664, \"(0.011715, 0.012025000000000001)\": 0.6138, \"(0.012025000000000001, 0.012535000000000001)\": 0.4812, \"(0.012535000000000001, 0.013225)\": 0.4251, \"(0.013225, 0.014275)\": 0.3692, \"(0.014275, 0.015615)\": 0.3124, \"(0.015615, 0.017669999999999998)\": 0.2617, \"(0.017669999999999998, 0.020155)\": 0.1689, \"(0.020155, 0.022855)\": 0.1055, \"(0.022855, 0.02586)\": 0.1202, \"(0.02586, 0.027540000000000002)\": 0.062, \"(0.027540000000000002, 0.038220000000000004)\": 0.027, \"(0.038220000000000004, 0.039245)\": -0.0446, \"(0.039245, 0.040514999999999995)\": -0.0815, \"(0.040514999999999995, 0.04309)\": -0.0993, \"(0.04309, 0.04922)\": -0.1161, \"(0.04922, 0.068925)\": -0.1448, \"(0.068925, 0.1354)\": -0.1492}\n\n\nYour task is to provide the mean value of the graph at 0.01. What is the mean value of the graph at 0.01?",
+    "0.3467"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Watersheds\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02526, \"(0.5, 1.5)\": -0.02147, \"(1.5, 2.5)\": -0.01542, \"(2.5, 3.5)\": -0.01026, \"(3.5, 4.5)\": -0.00466, \"(4.5, 5.5)\": 0.00049, \"(5.5, 6.5)\": 0.00555, \"(6.5, 8.5)\": 0.01133, \"(8.5, 10.5)\": 0.02234, \"(10.5, 11.5)\": 0.03241, \"(11.5, 12.5)\": 0.03775, \"(12.5, 13.5)\": 0.04216, \"(13.5, 14.0)\": 0.04656}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0274, \"(0.5, 1.5)\": -0.02237, \"(1.5, 2.5)\": -0.01633, \"(2.5, 3.5)\": -0.01068, \"(3.5, 4.5)\": -0.005, \"(4.5, 5.5)\": 0.00014, \"(5.5, 6.5)\": 0.00514, \"(6.5, 8.5)\": 0.01068, \"(8.5, 10.5)\": 0.02129, \"(10.5, 11.5)\": 0.03073, \"(11.5, 12.5)\": 0.03466, \"(12.5, 13.5)\": 0.038, \"(13.5, 14.0)\": 0.043}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02312, \"(0.5, 1.5)\": -0.02056, \"(1.5, 2.5)\": -0.01451, \"(2.5, 3.5)\": -0.00985, \"(3.5, 4.5)\": -0.00431, \"(4.5, 5.5)\": 0.00084, \"(5.5, 6.5)\": 0.00596, \"(6.5, 8.5)\": 0.01197, \"(8.5, 10.5)\": 0.0234, \"(10.5, 11.5)\": 0.03409, \"(11.5, 12.5)\": 0.04085, \"(12.5, 13.5)\": 0.04633, \"(13.5, 14.0)\": 0.05012}\n\n\nYour task is to provide the mean value of the graph at 0.09. What is the mean value of the graph at 0.09?",
+    "-0.02526"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: FoodCourt\nFeature Type: continuous\nMeans: {\"(0.0, 593.5)\": -0.177, \"(593.5, 779.5)\": 0.043, \"(779.5, 1341.5)\": 0.27, \"(1341.5, 2175.5)\": 0.543, \"(2175.5, 3125.0)\": 0.863, \"(3125.0, 3637.0)\": 1.13, \"(3637.0, 4078.5)\": 1.479, \"(4078.5, 5218.5)\": 2.076, \"(5218.5, 6031.5)\": 1.81, \"(6031.5, 6171.5)\": 1.439, \"(6171.5, 8753.0)\": 2.236, \"(8753.0, 8824.0)\": 2.746, \"(8824.0, 10094.5)\": 3.43, \"(10094.5, 12683.5)\": 3.888, \"(12683.5, 27723.0)\": 4.131}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 593.5)\": -0.307, \"(593.5, 779.5)\": -0.11, \"(779.5, 1341.5)\": -0.04, \"(1341.5, 2175.5)\": -0.06, \"(2175.5, 3125.0)\": 0.404, \"(3125.0, 3637.0)\": 0.707, \"(3637.0, 4078.5)\": 0.742, \"(4078.5, 5218.5)\": 1.52, \"(5218.5, 6031.5)\": 1.485, \"(6031.5, 6171.5)\": 0.477, \"(6171.5, 8753.0)\": 1.548, \"(8753.0, 8824.0)\": 1.95, \"(8824.0, 10094.5)\": 2.626, \"(10094.5, 12683.5)\": 2.361, \"(12683.5, 27723.0)\": 2.558}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 593.5)\": -0.047, \"(593.5, 779.5)\": 0.196, \"(779.5, 1341.5)\": 0.58, \"(1341.5, 2175.5)\": 1.145, \"(2175.5, 3125.0)\": 1.322, \"(3125.0, 3637.0)\": 1.554, \"(3637.0, 4078.5)\": 2.216, \"(4078.5, 5218.5)\": 2.631, \"(5218.5, 6031.5)\": 2.135, \"(6031.5, 6171.5)\": 2.4, \"(6171.5, 8753.0)\": 2.925, \"(8753.0, 8824.0)\": 3.543, \"(8824.0, 10094.5)\": 4.234, \"(10094.5, 12683.5)\": 5.416, \"(12683.5, 27723.0)\": 5.705}\n\n\nYour task is to provide the mean value of the graph at 6083.89. What is the mean value of the graph at 6083.89?",
+    "1.439"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: symmetry_se\nFeature Type: continuous\nMeans: {\"(0.007882, 0.010595)\": 0.771, \"(0.010595, 0.011365)\": 0.697, \"(0.011365, 0.012135)\": 0.635, \"(0.012135, 0.01279)\": 0.576, \"(0.01279, 0.01352)\": 0.513, \"(0.01352, 0.014105)\": 0.455, \"(0.014105, 0.014499999999999999)\": 0.393, \"(0.014499999999999999, 0.014525)\": 0.332, \"(0.014525, 0.01489)\": 0.227, \"(0.01489, 0.01532)\": 0.169, \"(0.01532, 0.015805)\": 0.109, \"(0.015805, 0.017215)\": 0.05, \"(0.017215, 0.017855)\": -0.008, \"(0.017855, 0.018165)\": -0.073, \"(0.018165, 0.018685)\": -0.131, \"(0.018685, 0.019545)\": -0.193, \"(0.019545, 0.02068)\": -0.252, \"(0.02068, 0.024730000000000002)\": -0.31, \"(0.024730000000000002, 0.026770000000000002)\": -0.376, \"(0.026770000000000002, 0.027435)\": -0.316, \"(0.027435, 0.028380000000000002)\": -0.252, \"(0.028380000000000002, 0.02966)\": -0.19, \"(0.02966, 0.031865)\": -0.092, \"(0.031865, 0.03651)\": -0.034, \"(0.03651, 0.041944999999999996)\": 0.024, \"(0.041944999999999996, 0.04665)\": 0.086, \"(0.04665, 0.054805)\": 0.152, \"(0.054805, 0.05963)\": 0.232}\nLower Bounds (95%-Confidence Interval): {\"(0.007882, 0.010595)\": 0.336, \"(0.010595, 0.011365)\": 0.284, \"(0.011365, 0.012135)\": 0.24, \"(0.012135, 0.01279)\": 0.211, \"(0.01279, 0.01352)\": 0.226, \"(0.01352, 0.014105)\": 0.178, \"(0.014105, 0.014499999999999999)\": 0.123, \"(0.014499999999999999, 0.014525)\": 0.09, \"(0.014525, 0.01489)\": -0.155, \"(0.01489, 0.01532)\": -0.203, \"(0.01532, 0.015805)\": -0.266, \"(0.015805, 0.017215)\": -0.317, \"(0.017215, 0.017855)\": -0.138, \"(0.017855, 0.018165)\": -0.193, \"(0.018165, 0.018685)\": -0.264, \"(0.018685, 0.019545)\": -0.327, \"(0.019545, 0.02068)\": -0.388, \"(0.02068, 0.024730000000000002)\": -0.457, \"(0.024730000000000002, 0.026770000000000002)\": -0.569, \"(0.026770000000000002, 0.027435)\": -0.507, \"(0.027435, 0.028380000000000002)\": -0.46, \"(0.028380000000000002, 0.02966)\": -0.393, \"(0.02966, 0.031865)\": -0.281, \"(0.031865, 0.03651)\": -0.265, \"(0.03651, 0.041944999999999996)\": -0.233, \"(0.041944999999999996, 0.04665)\": -0.174, \"(0.04665, 0.054805)\": -0.12, \"(0.054805, 0.05963)\": -0.058}\nUpper Bounds (95%-Confidence Interval): {\"(0.007882, 0.010595)\": 1.206, \"(0.010595, 0.011365)\": 1.11, \"(0.011365, 0.012135)\": 1.031, \"(0.012135, 0.01279)\": 0.941, \"(0.01279, 0.01352)\": 0.8, \"(0.01352, 0.014105)\": 0.731, \"(0.014105, 0.014499999999999999)\": 0.662, \"(0.014499999999999999, 0.014525)\": 0.574, \"(0.014525, 0.01489)\": 0.609, \"(0.01489, 0.01532)\": 0.541, \"(0.01532, 0.015805)\": 0.484, \"(0.015805, 0.017215)\": 0.418, \"(0.017215, 0.017855)\": 0.123, \"(0.017855, 0.018165)\": 0.047, \"(0.018165, 0.018685)\": 0.002, \"(0.018685, 0.019545)\": -0.059, \"(0.019545, 0.02068)\": -0.116, \"(0.02068, 0.024730000000000002)\": -0.164, \"(0.024730000000000002, 0.026770000000000002)\": -0.182, \"(0.026770000000000002, 0.027435)\": -0.125, \"(0.027435, 0.028380000000000002)\": -0.043, \"(0.028380000000000002, 0.02966)\": 0.013, \"(0.02966, 0.031865)\": 0.097, \"(0.031865, 0.03651)\": 0.197, \"(0.03651, 0.041944999999999996)\": 0.281, \"(0.041944999999999996, 0.04665)\": 0.345, \"(0.04665, 0.054805)\": 0.424, \"(0.054805, 0.05963)\": 0.521}\n\n\nYour task is to provide the mean value of the graph at 0.02. What is the mean value of the graph at 0.02?",
+    "-0.252"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: AgriculturalPractices\nFeature Type: continuous\nMeans: {\"(0.0, 1.5)\": -0.02463, \"(1.5, 2.5)\": -0.01694, \"(2.5, 3.5)\": -0.01147, \"(3.5, 4.5)\": -0.00533, \"(4.5, 5.5)\": 0.00036, \"(5.5, 6.5)\": 0.00641, \"(6.5, 7.5)\": 0.01086, \"(7.5, 8.5)\": 0.01753, \"(8.5, 9.5)\": 0.02391, \"(9.5, 11.5)\": 0.03162, \"(11.5, 14.0)\": 0.0391, \"(14.0, 15.0)\": 0.05506}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02721, \"(1.5, 2.5)\": -0.01778, \"(2.5, 3.5)\": -0.01182, \"(3.5, 4.5)\": -0.00574, \"(4.5, 5.5)\": -9e-05, \"(5.5, 6.5)\": 0.00587, \"(6.5, 7.5)\": 0.01028, \"(7.5, 8.5)\": 0.01669, \"(8.5, 9.5)\": 0.02306, \"(9.5, 11.5)\": 0.02986, \"(11.5, 14.0)\": 0.03465, \"(14.0, 15.0)\": 0.03109}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02205, \"(1.5, 2.5)\": -0.0161, \"(2.5, 3.5)\": -0.01113, \"(3.5, 4.5)\": -0.00492, \"(4.5, 5.5)\": 0.0008, \"(5.5, 6.5)\": 0.00696, \"(6.5, 7.5)\": 0.01144, \"(7.5, 8.5)\": 0.01837, \"(8.5, 9.5)\": 0.02477, \"(9.5, 11.5)\": 0.03339, \"(11.5, 14.0)\": 0.04355, \"(14.0, 15.0)\": 0.07902}\n\n\nYour task is to provide the mean value of the graph at 14.78. What is the mean value of the graph at 14.78?",
+    "0.05506"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: texture_mean\nFeature Type: continuous\nMeans: {\"(9.71, 13.24)\": -1.121, \"(13.24, 14.075)\": -1.023, \"(14.075, 14.665)\": -0.921, \"(14.665, 15.010000000000002)\": -0.82, \"(15.010000000000002, 15.485)\": -0.718, \"(15.485, 15.774999999999999)\": -0.623, \"(15.774999999999999, 16.445)\": -0.523, \"(16.445, 17.045)\": -0.422, \"(17.045, 17.665)\": -0.324, \"(17.665, 18.335)\": -0.225, \"(18.335, 18.725)\": -0.129, \"(18.725, 19.075)\": -0.032, \"(19.075, 19.549999999999997)\": 0.063, \"(19.549999999999997, 19.915)\": 0.161, \"(19.915, 20.235)\": 0.26, \"(20.235, 20.8)\": 0.445, \"(20.8, 21.285)\": 0.549, \"(21.285, 33.81)\": 0.68}\nLower Bounds (95%-Confidence Interval): {\"(9.71, 13.24)\": -1.583, \"(13.24, 14.075)\": -1.428, \"(14.075, 14.665)\": -1.292, \"(14.665, 15.010000000000002)\": -1.127, \"(15.010000000000002, 15.485)\": -1.018, \"(15.485, 15.774999999999999)\": -0.932, \"(15.774999999999999, 16.445)\": -0.765, \"(16.445, 17.045)\": -0.657, \"(17.045, 17.665)\": -0.537, \"(17.665, 18.335)\": -0.404, \"(18.335, 18.725)\": -0.289, \"(18.725, 19.075)\": -0.203, \"(19.075, 19.549999999999997)\": -0.094, \"(19.549999999999997, 19.915)\": 0.017, \"(19.915, 20.235)\": 0.108, \"(20.235, 20.8)\": -0.11, \"(20.8, 21.285)\": -0.011, \"(21.285, 33.81)\": -0.0}\nUpper Bounds (95%-Confidence Interval): {\"(9.71, 13.24)\": -0.658, \"(13.24, 14.075)\": -0.619, \"(14.075, 14.665)\": -0.55, \"(14.665, 15.010000000000002)\": -0.512, \"(15.010000000000002, 15.485)\": -0.417, \"(15.485, 15.774999999999999)\": -0.314, \"(15.774999999999999, 16.445)\": -0.282, \"(16.445, 17.045)\": -0.187, \"(17.045, 17.665)\": -0.112, \"(17.665, 18.335)\": -0.045, \"(18.335, 18.725)\": 0.031, \"(18.725, 19.075)\": 0.139, \"(19.075, 19.549999999999997)\": 0.22, \"(19.549999999999997, 19.915)\": 0.306, \"(19.915, 20.235)\": 0.412, \"(20.235, 20.8)\": 0.999, \"(20.8, 21.285)\": 1.109, \"(21.285, 33.81)\": 1.36}\n\n\nYour task is to provide the mean value of the graph at 15.03. What is the mean value of the graph at 15.03?",
+    "-0.718"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: radius_worst\nFeature Type: continuous\nMeans: {\"(7.93, 10.585)\": -1.149, \"(10.585, 11.305)\": -1.016, \"(11.305, 11.965)\": -0.883, \"(11.965, 12.54)\": -0.747, \"(12.54, 13.315000000000001)\": -0.616, \"(13.315000000000001, 14.184999999999999)\": -0.485, \"(14.184999999999999, 14.875)\": -0.349, \"(14.875, 15.485)\": -0.212, \"(15.485, 15.955)\": -0.078, \"(15.955, 16.54)\": 0.055, \"(16.54, 17.22)\": 0.19, \"(17.22, 17.78)\": 0.335, \"(17.78, 18.655)\": 0.469, \"(18.655, 19.785)\": 0.601, \"(19.785, 20.445)\": 0.734, \"(20.445, 21.935000000000002)\": 0.866, \"(21.935000000000002, 23.625)\": 0.997, \"(23.625, 25.335)\": 1.132, \"(25.335, 30.71)\": 1.274, \"(30.71, 36.04)\": 1.406}\nLower Bounds (95%-Confidence Interval): {\"(7.93, 10.585)\": -1.554, \"(10.585, 11.305)\": -1.397, \"(11.305, 11.965)\": -1.223, \"(11.965, 12.54)\": -1.048, \"(12.54, 13.315000000000001)\": -0.881, \"(13.315000000000001, 14.184999999999999)\": -0.698, \"(14.184999999999999, 14.875)\": -0.522, \"(14.875, 15.485)\": -0.332, \"(15.485, 15.955)\": -0.179, \"(15.955, 16.54)\": -0.216, \"(16.54, 17.22)\": -0.079, \"(17.22, 17.78)\": 0.047, \"(17.78, 18.655)\": 0.152, \"(18.655, 19.785)\": 0.262, \"(19.785, 20.445)\": 0.384, \"(20.445, 21.935000000000002)\": 0.494, \"(21.935000000000002, 23.625)\": 0.572, \"(23.625, 25.335)\": 0.664, \"(25.335, 30.71)\": 0.756, \"(30.71, 36.04)\": 0.872}\nUpper Bounds (95%-Confidence Interval): {\"(7.93, 10.585)\": -0.745, \"(10.585, 11.305)\": -0.635, \"(11.305, 11.965)\": -0.542, \"(11.965, 12.54)\": -0.446, \"(12.54, 13.315000000000001)\": -0.351, \"(13.315000000000001, 14.184999999999999)\": -0.271, \"(14.184999999999999, 14.875)\": -0.176, \"(14.875, 15.485)\": -0.091, \"(15.485, 15.955)\": 0.022, \"(15.955, 16.54)\": 0.326, \"(16.54, 17.22)\": 0.459, \"(17.22, 17.78)\": 0.624, \"(17.78, 18.655)\": 0.785, \"(18.655, 19.785)\": 0.94, \"(19.785, 20.445)\": 1.085, \"(20.445, 21.935000000000002)\": 1.239, \"(21.935000000000002, 23.625)\": 1.422, \"(23.625, 25.335)\": 1.6, \"(25.335, 30.71)\": 1.792, \"(30.71, 36.04)\": 1.941}\n\n\nYour task is to provide the mean value of the graph at 31.93. What is the mean value of the graph at 31.93?",
+    "1.406"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Gender\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.4751, \"(0.5, 1.0)\": 0.2339}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.5571, \"(0.5, 1.0)\": 0.1936}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.3931, \"(0.5, 1.0)\": 0.2743}\n\n\nYour task is to provide the mean value of the graph at 0.23. What is the mean value of the graph at 0.23?",
+    "-0.4751"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: NativeCountry\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.195, \"(0.5, 1.5)\": 1.333, \"(1.5, 2.5)\": -0.02, \"(2.5, 3.5)\": -0.402, \"(3.5, 4.5)\": -1.423, \"(4.5, 5.5)\": 0.086, \"(5.5, 7.5)\": -0.843, \"(7.5, 8.5)\": -0.246, \"(8.5, 11.5)\": 0.062, \"(11.5, 20.5)\": -0.315, \"(20.5, 21.5)\": 0.109, \"(21.5, 22.5)\": 0.476, \"(22.5, 24.5)\": 0.133, \"(24.5, 26.5)\": -0.35, \"(26.5, 29.5)\": -0.489, \"(29.5, 32.5)\": -0.108, \"(32.5, 33.5)\": -0.483, \"(33.5, 35.5)\": -0.664, \"(35.5, 38.5)\": -0.396, \"(38.5, 39.5)\": 0.028, \"(39.5, 40.5)\": -0.596, \"(40.5, 41.0)\": 1.112}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.344, \"(0.5, 1.5)\": 0.452, \"(1.5, 2.5)\": -0.269, \"(2.5, 3.5)\": -0.76, \"(3.5, 4.5)\": -2.688, \"(4.5, 5.5)\": -0.257, \"(5.5, 7.5)\": -1.727, \"(7.5, 8.5)\": -0.488, \"(8.5, 11.5)\": -0.121, \"(11.5, 20.5)\": -0.631, \"(20.5, 21.5)\": -0.319, \"(21.5, 22.5)\": 0.048, \"(22.5, 24.5)\": -0.066, \"(24.5, 26.5)\": -0.66, \"(26.5, 29.5)\": -1.067, \"(29.5, 32.5)\": -0.254, \"(32.5, 33.5)\": -0.844, \"(33.5, 35.5)\": -1.156, \"(35.5, 38.5)\": -0.997, \"(38.5, 39.5)\": 0.02, \"(39.5, 40.5)\": -1.452, \"(40.5, 41.0)\": 0.408}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.045, \"(0.5, 1.5)\": 2.213, \"(1.5, 2.5)\": 0.228, \"(2.5, 3.5)\": -0.043, \"(3.5, 4.5)\": -0.158, \"(4.5, 5.5)\": 0.429, \"(5.5, 7.5)\": 0.04, \"(7.5, 8.5)\": -0.004, \"(8.5, 11.5)\": 0.245, \"(11.5, 20.5)\": 0.001, \"(20.5, 21.5)\": 0.537, \"(21.5, 22.5)\": 0.904, \"(22.5, 24.5)\": 0.331, \"(24.5, 26.5)\": -0.04, \"(26.5, 29.5)\": 0.089, \"(29.5, 32.5)\": 0.038, \"(32.5, 33.5)\": -0.121, \"(33.5, 35.5)\": -0.172, \"(35.5, 38.5)\": 0.204, \"(38.5, 39.5)\": 0.036, \"(39.5, 40.5)\": 0.26, \"(40.5, 41.0)\": 1.816}\n\n\nYour task is to provide the mean value of the graph at 26.86. What is the mean value of the graph at 26.86?",
+    "-0.489"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: fractal_dimension_mean\nFeature Type: continuous\nMeans: {\"(0.04996, 0.05075)\": 0.5962, \"(0.05075, 0.052285)\": 0.5519, \"(0.052285, 0.05393)\": 0.5087, \"(0.05393, 0.05455)\": 0.4681, \"(0.05455, 0.05505)\": 0.4248, \"(0.05505, 0.055349999999999996)\": 0.3799, \"(0.055349999999999996, 0.055665)\": 0.337, \"(0.055665, 0.055895)\": 0.2922, \"(0.055895, 0.055935)\": 0.2475, \"(0.055935, 0.056365)\": 0.2007, \"(0.056365, 0.05655)\": 0.1163, \"(0.05655, 0.056720000000000007)\": 0.0704, \"(0.056720000000000007, 0.056995000000000004)\": 0.0288, \"(0.056995000000000004, 0.058145)\": -0.0168, \"(0.058145, 0.059715)\": -0.0575, \"(0.059715, 0.06078)\": -0.0163, \"(0.06078, 0.061385)\": -0.0618, \"(0.061385, 0.0622)\": -0.102, \"(0.0622, 0.063145)\": -0.1453, \"(0.063145, 0.065105)\": -0.1865, \"(0.065105, 0.06564)\": -0.1448, \"(0.06564, 0.067575)\": -0.1025, \"(0.067575, 0.09744)\": -0.0621}\nLower Bounds (95%-Confidence Interval): {\"(0.04996, 0.05075)\": 0.3734, \"(0.05075, 0.052285)\": 0.3548, \"(0.052285, 0.05393)\": 0.2212, \"(0.05393, 0.05455)\": 0.2141, \"(0.05455, 0.05505)\": 0.1669, \"(0.05505, 0.055349999999999996)\": 0.1178, \"(0.055349999999999996, 0.055665)\": 0.0712, \"(0.055665, 0.055895)\": 0.0309, \"(0.055895, 0.055935)\": -0.0117, \"(0.055935, 0.056365)\": -0.072, \"(0.056365, 0.05655)\": -0.0136, \"(0.05655, 0.056720000000000007)\": -0.0905, \"(0.056720000000000007, 0.056995000000000004)\": -0.1461, \"(0.056995000000000004, 0.058145)\": -0.1995, \"(0.058145, 0.059715)\": -0.2455, \"(0.059715, 0.06078)\": -0.1126, \"(0.06078, 0.061385)\": -0.1738, \"(0.061385, 0.0622)\": -0.1944, \"(0.0622, 0.063145)\": -0.2238, \"(0.063145, 0.065105)\": -0.2687, \"(0.065105, 0.06564)\": -0.2312, \"(0.06564, 0.067575)\": -0.191, \"(0.067575, 0.09744)\": -0.1891}\nUpper Bounds (95%-Confidence Interval): {\"(0.04996, 0.05075)\": 0.819, \"(0.05075, 0.052285)\": 0.749, \"(0.052285, 0.05393)\": 0.7962, \"(0.05393, 0.05455)\": 0.722, \"(0.05455, 0.05505)\": 0.6828, \"(0.05505, 0.055349999999999996)\": 0.642, \"(0.055349999999999996, 0.055665)\": 0.6028, \"(0.055665, 0.055895)\": 0.5535, \"(0.055895, 0.055935)\": 0.5067, \"(0.055935, 0.056365)\": 0.4734, \"(0.056365, 0.05655)\": 0.2462, \"(0.05655, 0.056720000000000007)\": 0.2312, \"(0.056720000000000007, 0.056995000000000004)\": 0.2038, \"(0.056995000000000004, 0.058145)\": 0.1658, \"(0.058145, 0.059715)\": 0.1306, \"(0.059715, 0.06078)\": 0.0801, \"(0.06078, 0.061385)\": 0.0502, \"(0.061385, 0.0622)\": -0.0097, \"(0.0622, 0.063145)\": -0.0668, \"(0.063145, 0.065105)\": -0.1044, \"(0.065105, 0.06564)\": -0.0583, \"(0.06564, 0.067575)\": -0.0139, \"(0.067575, 0.09744)\": 0.0649}\n\n\nYour task is to provide the mean value of the graph at 0.06. What is the mean value of the graph at 0.06?",
+    "-0.0163"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: concavity_mean\nFeature Type: continuous\nMeans: {\"(0.0, 0.005855)\": -0.897, \"(0.005855, 0.011885)\": -0.811, \"(0.011885, 0.016545)\": -0.719, \"(0.016545, 0.02046)\": -0.631, \"(0.02046, 0.02373)\": -0.543, \"(0.02373, 0.02711)\": -0.458, \"(0.02711, 0.038885)\": -0.374, \"(0.038885, 0.044705)\": -0.29, \"(0.044705, 0.059585)\": -0.205, \"(0.059585, 0.06851)\": -0.121, \"(0.06851, 0.072265)\": -0.032, \"(0.072265, 0.092725)\": 0.14, \"(0.092725, 0.1015)\": 0.224, \"(0.1015, 0.11415)\": 0.309, \"(0.11415, 0.13)\": 0.397, \"(0.13, 0.14534999999999998)\": 0.486, \"(0.14534999999999998, 0.1525)\": 0.581, \"(0.1525, 0.1686)\": 0.665, \"(0.1686, 0.24280000000000002)\": 0.749, \"(0.24280000000000002, 0.29359999999999997)\": 0.657, \"(0.29359999999999997, 0.32699999999999996)\": 0.566, \"(0.32699999999999996, 0.4268)\": 0.48}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.005855)\": -1.183, \"(0.005855, 0.011885)\": -1.062, \"(0.011885, 0.016545)\": -0.961, \"(0.016545, 0.02046)\": -0.861, \"(0.02046, 0.02373)\": -0.749, \"(0.02373, 0.02711)\": -0.665, \"(0.02711, 0.038885)\": -0.545, \"(0.038885, 0.044705)\": -0.442, \"(0.044705, 0.059585)\": -0.43, \"(0.059585, 0.06851)\": -0.344, \"(0.06851, 0.072265)\": -0.246, \"(0.072265, 0.092725)\": -0.128, \"(0.092725, 0.1015)\": 0.093, \"(0.1015, 0.11415)\": 0.166, \"(0.11415, 0.13)\": 0.205, \"(0.13, 0.14534999999999998)\": 0.246, \"(0.14534999999999998, 0.1525)\": 0.264, \"(0.1525, 0.1686)\": 0.346, \"(0.1686, 0.24280000000000002)\": 0.435, \"(0.24280000000000002, 0.29359999999999997)\": 0.402, \"(0.29359999999999997, 0.32699999999999996)\": 0.316, \"(0.32699999999999996, 0.4268)\": 0.208}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.005855)\": -0.612, \"(0.005855, 0.011885)\": -0.559, \"(0.011885, 0.016545)\": -0.477, \"(0.016545, 0.02046)\": -0.4, \"(0.02046, 0.02373)\": -0.338, \"(0.02373, 0.02711)\": -0.252, \"(0.02711, 0.038885)\": -0.203, \"(0.038885, 0.044705)\": -0.138, \"(0.044705, 0.059585)\": 0.021, \"(0.059585, 0.06851)\": 0.103, \"(0.06851, 0.072265)\": 0.183, \"(0.072265, 0.092725)\": 0.409, \"(0.092725, 0.1015)\": 0.355, \"(0.1015, 0.11415)\": 0.452, \"(0.11415, 0.13)\": 0.589, \"(0.13, 0.14534999999999998)\": 0.726, \"(0.14534999999999998, 0.1525)\": 0.898, \"(0.1525, 0.1686)\": 0.984, \"(0.1686, 0.24280000000000002)\": 1.063, \"(0.24280000000000002, 0.29359999999999997)\": 0.912, \"(0.29359999999999997, 0.32699999999999996)\": 0.815, \"(0.32699999999999996, 0.4268)\": 0.752}\n\n\nYour task is to provide the mean value of the graph at 0.05. What is the mean value of the graph at 0.05?",
+    "-0.205"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: compactness_mean\nFeature Type: continuous\nMeans: {\"(0.01938, 0.03164)\": 0.0135, \"(0.03164, 0.035445000000000004)\": 0.0558, \"(0.035445000000000004, 0.03732)\": 0.0934, \"(0.03732, 0.038529999999999995)\": 0.1327, \"(0.038529999999999995, 0.040694999999999995)\": 0.1725, \"(0.040694999999999995, 0.042550000000000004)\": 0.2126, \"(0.042550000000000004, 0.044355000000000006)\": 0.2504, \"(0.044355000000000006, 0.045645000000000005)\": 0.299, \"(0.045645000000000005, 0.0498)\": 0.3373, \"(0.0498, 0.059495)\": 0.2969, \"(0.059495, 0.06042)\": 0.2605, \"(0.06042, 0.0618)\": 0.2247, \"(0.0618, 0.06289)\": 0.1851, \"(0.06289, 0.062985)\": 0.1459, \"(0.062985, 0.06375)\": 0.0823, \"(0.06375, 0.06615499999999999)\": 0.0446, \"(0.06615499999999999, 0.066575)\": 0.0084, \"(0.066575, 0.067345)\": -0.1354, \"(0.067345, 0.06788)\": -0.1923, \"(0.06788, 0.068945)\": -0.232, \"(0.068945, 0.07211999999999999)\": -0.2724, \"(0.07211999999999999, 0.07482)\": -0.309, \"(0.07482, 0.0785)\": -0.3463, \"(0.0785, 0.085875)\": -0.2755, \"(0.085875, 0.095275)\": -0.2297, \"(0.095275, 0.10439999999999999)\": -0.1927, \"(0.10439999999999999, 0.11305000000000001)\": -0.1576, \"(0.11305000000000001, 0.11465)\": -0.121, \"(0.11465, 0.1153)\": -0.0859, \"(0.1153, 0.119)\": -0.0125, \"(0.119, 0.12375)\": 0.024, \"(0.12375, 0.16655)\": 0.0599, \"(0.16655, 0.1923)\": 0.0956, \"(0.1923, 0.23235)\": 0.1316, \"(0.23235, 0.27165)\": 0.1705, \"(0.27165, 0.28075)\": 0.2103, \"(0.28075, 0.3114)\": 0.2453}\nLower Bounds (95%-Confidence Interval): {\"(0.01938, 0.03164)\": -0.4016, \"(0.03164, 0.035445000000000004)\": -0.3995, \"(0.035445000000000004, 0.03732)\": -0.3599, \"(0.03732, 0.038529999999999995)\": -0.3178, \"(0.038529999999999995, 0.040694999999999995)\": -0.2802, \"(0.040694999999999995, 0.042550000000000004)\": -0.2633, \"(0.042550000000000004, 0.044355000000000006)\": -0.2559, \"(0.044355000000000006, 0.045645000000000005)\": -0.2259, \"(0.045645000000000005, 0.0498)\": -0.1947, \"(0.0498, 0.059495)\": -0.2119, \"(0.059495, 0.06042)\": -0.1651, \"(0.06042, 0.0618)\": -0.1904, \"(0.0618, 0.06289)\": -0.2009, \"(0.06289, 0.062985)\": -0.2409, \"(0.062985, 0.06375)\": -0.1808, \"(0.06375, 0.06615499999999999)\": -0.2262, \"(0.06615499999999999, 0.066575)\": -0.2509, \"(0.066575, 0.067345)\": -0.7938, \"(0.067345, 0.06788)\": -0.7983, \"(0.06788, 0.068945)\": -0.838, \"(0.068945, 0.07211999999999999)\": -0.9135, \"(0.07211999999999999, 0.07482)\": -0.9538, \"(0.07482, 0.0785)\": -1.0103, \"(0.0785, 0.085875)\": -0.5241, \"(0.085875, 0.095275)\": -0.4606, \"(0.095275, 0.10439999999999999)\": -0.4301, \"(0.10439999999999999, 0.11305000000000001)\": -0.3863, \"(0.11305000000000001, 0.11465)\": -0.331, \"(0.11465, 0.1153)\": -0.2716, \"(0.1153, 0.119)\": -0.1247, \"(0.119, 0.12375)\": -0.0694, \"(0.12375, 0.16655)\": -0.0509, \"(0.16655, 0.1923)\": -0.025, \"(0.1923, 0.23235)\": 0.0179, \"(0.23235, 0.27165)\": 0.0681, \"(0.27165, 0.28075)\": 0.121, \"(0.28075, 0.3114)\": 0.148}\nUpper Bounds (95%-Confidence Interval): {\"(0.01938, 0.03164)\": 0.4286, \"(0.03164, 0.035445000000000004)\": 0.5111, \"(0.035445000000000004, 0.03732)\": 0.5468, \"(0.03732, 0.038529999999999995)\": 0.5831, \"(0.038529999999999995, 0.040694999999999995)\": 0.6252, \"(0.040694999999999995, 0.042550000000000004)\": 0.6885, \"(0.042550000000000004, 0.044355000000000006)\": 0.7567, \"(0.044355000000000006, 0.045645000000000005)\": 0.8238, \"(0.045645000000000005, 0.0498)\": 0.8693, \"(0.0498, 0.059495)\": 0.8057, \"(0.059495, 0.06042)\": 0.6861, \"(0.06042, 0.0618)\": 0.6397, \"(0.0618, 0.06289)\": 0.5712, \"(0.06289, 0.062985)\": 0.5328, \"(0.062985, 0.06375)\": 0.3454, \"(0.06375, 0.06615499999999999)\": 0.3154, \"(0.06615499999999999, 0.066575)\": 0.2677, \"(0.066575, 0.067345)\": 0.5229, \"(0.067345, 0.06788)\": 0.4136, \"(0.06788, 0.068945)\": 0.3741, \"(0.068945, 0.07211999999999999)\": 0.3686, \"(0.07211999999999999, 0.07482)\": 0.3358, \"(0.07482, 0.0785)\": 0.3178, \"(0.0785, 0.085875)\": -0.0269, \"(0.085875, 0.095275)\": 0.0012, \"(0.095275, 0.10439999999999999)\": 0.0448, \"(0.10439999999999999, 0.11305000000000001)\": 0.0711, \"(0.11305000000000001, 0.11465)\": 0.0889, \"(0.11465, 0.1153)\": 0.0998, \"(0.1153, 0.119)\": 0.0997, \"(0.119, 0.12375)\": 0.1174, \"(0.12375, 0.16655)\": 0.1706, \"(0.16655, 0.1923)\": 0.2162, \"(0.1923, 0.23235)\": 0.2452, \"(0.23235, 0.27165)\": 0.273, \"(0.27165, 0.28075)\": 0.2996, \"(0.28075, 0.3114)\": 0.3427}\n\n\nYour task is to provide the mean value of the graph at 0.06. What is the mean value of the graph at 0.06?",
+    "0.2605"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: sex\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": 0.01719, \"(0.5, 1.0)\": -0.00954}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.08236, \"(0.5, 1.0)\": -0.06482}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.11675, \"(0.5, 1.0)\": 0.04573}\n\n\nYour task is to provide the mean value of the graph at 0.07. What is the mean value of the graph at 0.07?",
+    "0.01719"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: petal_length\nFeature Type: continuous\nMeans: {\"(1.1, 1.65)\": 8.05, \"(1.65, 2.45)\": 7.28, \"(2.45, 3.15)\": -1.17, \"(3.15, 3.8)\": -2.4, \"(3.8, 4.45)\": -3.03, \"(4.45, 5.65)\": -3.73, \"(5.65, 6.9)\": -4.38}\nLower Bounds (95%-Confidence Interval): {\"(1.1, 1.65)\": 7.87, \"(1.65, 2.45)\": 7.08, \"(2.45, 3.15)\": -4.92, \"(3.15, 3.8)\": -2.6, \"(3.8, 4.45)\": -3.19, \"(4.45, 5.65)\": -3.87, \"(5.65, 6.9)\": -4.55}\nUpper Bounds (95%-Confidence Interval): {\"(1.1, 1.65)\": 8.24, \"(1.65, 2.45)\": 7.48, \"(2.45, 3.15)\": 2.57, \"(3.15, 3.8)\": -2.2, \"(3.8, 4.45)\": -2.86, \"(4.45, 5.65)\": -3.58, \"(5.65, 6.9)\": -4.2}\n\n\nYour task is to provide the mean value of the graph at 1.95. What is the mean value of the graph at 1.95?",
+    "7.28"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: concavity_se\nFeature Type: continuous\nMeans: {\"(0.0, 0.001156)\": -0.6445, \"(0.001156, 0.002325)\": -0.6016, \"(0.002325, 0.0037635)\": -0.5599, \"(0.0037635, 0.0053165)\": -0.5149, \"(0.0053165, 0.0058905)\": -0.4651, \"(0.0058905, 0.006987999999999999)\": -0.4227, \"(0.006987999999999999, 0.0077405)\": -0.3808, \"(0.0077405, 0.008344500000000001)\": -0.3373, \"(0.008344500000000001, 0.009263500000000001)\": -0.2906, \"(0.009263500000000001, 0.010215)\": -0.246, \"(0.010215, 0.010705)\": -0.2028, \"(0.010705, 0.01122)\": -0.1484, \"(0.01122, 0.011625)\": -0.1022, \"(0.011625, 0.01191)\": -0.0592, \"(0.01191, 0.012455)\": -0.0118, \"(0.012455, 0.0203)\": 0.0471, \"(0.0203, 0.022565)\": 0.0914, \"(0.022565, 0.02983)\": 0.1347, \"(0.02983, 0.032535)\": 0.0347, \"(0.032535, 0.0338)\": -0.0071, \"(0.0338, 0.038565)\": 0.0604, \"(0.038565, 0.04418)\": 0.1065, \"(0.04418, 0.059305)\": 0.1494, \"(0.059305, 0.065775)\": 0.1044, \"(0.065775, 0.07794000000000001)\": 0.0533, \"(0.07794000000000001, 0.08089)\": 0.0097, \"(0.08089, 0.096205)\": -0.0573, \"(0.096205, 0.22865000000000002)\": -0.1001, \"(0.22865000000000002, 0.396)\": -0.1471}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.001156)\": -0.9396, \"(0.001156, 0.002325)\": -0.8658, \"(0.002325, 0.0037635)\": -0.8192, \"(0.0037635, 0.0053165)\": -0.7681, \"(0.0053165, 0.0058905)\": -0.716, \"(0.0058905, 0.006987999999999999)\": -0.6675, \"(0.006987999999999999, 0.0077405)\": -0.6156, \"(0.0077405, 0.008344500000000001)\": -0.5789, \"(0.008344500000000001, 0.009263500000000001)\": -0.5182, \"(0.009263500000000001, 0.010215)\": -0.4535, \"(0.010215, 0.010705)\": -0.4164, \"(0.010705, 0.01122)\": -0.3446, \"(0.01122, 0.011625)\": -0.2792, \"(0.011625, 0.01191)\": -0.2184, \"(0.01191, 0.012455)\": -0.172, \"(0.012455, 0.0203)\": -0.1411, \"(0.0203, 0.022565)\": -0.0146, \"(0.022565, 0.02983)\": 0.021, \"(0.02983, 0.032535)\": -0.4149, \"(0.032535, 0.0338)\": -0.4515, \"(0.0338, 0.038565)\": -0.0686, \"(0.038565, 0.04418)\": -0.0006, \"(0.04418, 0.059305)\": -0.0422, \"(0.059305, 0.065775)\": -0.0685, \"(0.065775, 0.07794000000000001)\": -0.1246, \"(0.07794000000000001, 0.08089)\": -0.1713, \"(0.08089, 0.096205)\": -0.2715, \"(0.096205, 0.22865000000000002)\": -0.3432, \"(0.22865000000000002, 0.396)\": -0.3958}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.001156)\": -0.3494, \"(0.001156, 0.002325)\": -0.3374, \"(0.002325, 0.0037635)\": -0.3006, \"(0.0037635, 0.0053165)\": -0.2618, \"(0.0053165, 0.0058905)\": -0.2142, \"(0.0058905, 0.006987999999999999)\": -0.1779, \"(0.006987999999999999, 0.0077405)\": -0.1459, \"(0.0077405, 0.008344500000000001)\": -0.0957, \"(0.008344500000000001, 0.009263500000000001)\": -0.063, \"(0.009263500000000001, 0.010215)\": -0.0385, \"(0.010215, 0.010705)\": 0.0109, \"(0.010705, 0.01122)\": 0.0478, \"(0.01122, 0.011625)\": 0.0749, \"(0.011625, 0.01191)\": 0.0999, \"(0.01191, 0.012455)\": 0.1484, \"(0.012455, 0.0203)\": 0.2352, \"(0.0203, 0.022565)\": 0.1973, \"(0.022565, 0.02983)\": 0.2485, \"(0.02983, 0.032535)\": 0.4843, \"(0.032535, 0.0338)\": 0.4374, \"(0.0338, 0.038565)\": 0.1895, \"(0.038565, 0.04418)\": 0.2135, \"(0.04418, 0.059305)\": 0.3411, \"(0.059305, 0.065775)\": 0.2772, \"(0.065775, 0.07794000000000001)\": 0.2312, \"(0.07794000000000001, 0.08089)\": 0.1907, \"(0.08089, 0.096205)\": 0.1569, \"(0.096205, 0.22865000000000002)\": 0.1429, \"(0.22865000000000002, 0.396)\": 0.1015}\n\n\nYour task is to provide the mean value of the graph at 0.0. What is the mean value of the graph at 0.0?",
+    "-0.6445"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: housing_median_age\nFeature Type: continuous\nMeans: {\"(1.0, 4.5)\": -19998.0, \"(4.5, 7.5)\": -7788.2, \"(7.5, 16.5)\": -10680.2, \"(16.5, 18.5)\": -6304.4, \"(18.5, 27.5)\": -1760.6, \"(27.5, 34.5)\": 2164.8, \"(34.5, 38.5)\": -912.5, \"(38.5, 41.5)\": 4199.6, \"(41.5, 45.5)\": -497.4, \"(45.5, 47.5)\": -5189.8, \"(47.5, 48.5)\": 5201.0, \"(48.5, 49.5)\": 2159.0, \"(49.5, 50.5)\": 6135.7, \"(50.5, 51.5)\": 11513.8, \"(51.5, 52.0)\": 27549.7}\nLower Bounds (95%-Confidence Interval): {\"(1.0, 4.5)\": -26905.5, \"(4.5, 7.5)\": -11566.0, \"(7.5, 16.5)\": -12538.5, \"(16.5, 18.5)\": -7756.2, \"(18.5, 27.5)\": -3361.1, \"(27.5, 34.5)\": 124.5, \"(34.5, 38.5)\": -1933.4, \"(38.5, 41.5)\": 2260.6, \"(41.5, 45.5)\": -4429.7, \"(45.5, 47.5)\": -8697.7, \"(47.5, 48.5)\": 2180.3, \"(48.5, 49.5)\": -1981.1, \"(49.5, 50.5)\": 1581.5, \"(50.5, 51.5)\": 5647.5, \"(51.5, 52.0)\": 25827.1}\nUpper Bounds (95%-Confidence Interval): {\"(1.0, 4.5)\": -13090.4, \"(4.5, 7.5)\": -4010.4, \"(7.5, 16.5)\": -8821.8, \"(16.5, 18.5)\": -4852.5, \"(18.5, 27.5)\": -160.0, \"(27.5, 34.5)\": 4205.0, \"(34.5, 38.5)\": 108.5, \"(38.5, 41.5)\": 6138.7, \"(41.5, 45.5)\": 3434.9, \"(45.5, 47.5)\": -1682.0, \"(47.5, 48.5)\": 8221.7, \"(48.5, 49.5)\": 6299.1, \"(49.5, 50.5)\": 10689.9, \"(50.5, 51.5)\": 17380.1, \"(51.5, 52.0)\": 29272.3}\n\n\nYour task is to provide the mean value of the graph at 6.44. What is the mean value of the graph at 6.44?",
+    "-7788.2"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Pclass\nFeature Type: continuous\nMeans: {\"(1.0, 1.5)\": -0.009, \"(1.5, 2.5)\": 0.534, \"(2.5, 3.0)\": -0.532}\nLower Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -0.053, \"(1.5, 2.5)\": 0.174, \"(2.5, 3.0)\": -1.011}\nUpper Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": 0.035, \"(1.5, 2.5)\": 0.893, \"(2.5, 3.0)\": -0.052}\n\n\nYour task is to provide the mean value of the graph at 1.59. What is the mean value of the graph at 1.59?",
+    "0.534"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: smoothness_mean\nFeature Type: continuous\nMeans: {\"(0.05263, 0.0706)\": -0.835, \"(0.0706, 0.07455500000000001)\": -0.769, \"(0.07455500000000001, 0.07589499999999999)\": -0.697, \"(0.07589499999999999, 0.07727500000000001)\": -0.632, \"(0.07727500000000001, 0.078275)\": -0.569, \"(0.078275, 0.07952000000000001)\": -0.506, \"(0.07952000000000001, 0.080315)\": -0.437, \"(0.080315, 0.081035)\": -0.368, \"(0.081035, 0.08308499999999999)\": -0.304, \"(0.08308499999999999, 0.085165)\": -0.242, \"(0.085165, 0.086795)\": -0.177, \"(0.086795, 0.087785)\": -0.111, \"(0.087785, 0.088615)\": -0.047, \"(0.088615, 0.08918999999999999)\": 0.065, \"(0.08918999999999999, 0.090335)\": 0.142, \"(0.090335, 0.09454)\": 0.211, \"(0.09454, 0.11525)\": 0.107, \"(0.11525, 0.11765)\": 0.171, \"(0.11765, 0.12455)\": 0.267, \"(0.12455, 0.13845000000000002)\": 0.334, \"(0.13845000000000002, 0.1634)\": 0.396}\nLower Bounds (95%-Confidence Interval): {\"(0.05263, 0.0706)\": -1.454, \"(0.0706, 0.07455500000000001)\": -1.359, \"(0.07455500000000001, 0.07589499999999999)\": -1.244, \"(0.07589499999999999, 0.07727500000000001)\": -1.162, \"(0.07727500000000001, 0.078275)\": -1.087, \"(0.078275, 0.07952000000000001)\": -1.006, \"(0.07952000000000001, 0.080315)\": -0.882, \"(0.080315, 0.081035)\": -0.622, \"(0.081035, 0.08308499999999999)\": -0.547, \"(0.08308499999999999, 0.085165)\": -0.444, \"(0.085165, 0.086795)\": -0.357, \"(0.086795, 0.087785)\": -0.296, \"(0.087785, 0.088615)\": -0.23, \"(0.088615, 0.08918999999999999)\": -0.16, \"(0.08918999999999999, 0.090335)\": -0.309, \"(0.090335, 0.09454)\": -0.264, \"(0.09454, 0.11525)\": -0.005, \"(0.11525, 0.11765)\": 0.07, \"(0.11765, 0.12455)\": 0.022, \"(0.12455, 0.13845000000000002)\": 0.077, \"(0.13845000000000002, 0.1634)\": 0.127}\nUpper Bounds (95%-Confidence Interval): {\"(0.05263, 0.0706)\": -0.216, \"(0.0706, 0.07455500000000001)\": -0.178, \"(0.07455500000000001, 0.07589499999999999)\": -0.151, \"(0.07589499999999999, 0.07727500000000001)\": -0.102, \"(0.07727500000000001, 0.078275)\": -0.052, \"(0.078275, 0.07952000000000001)\": -0.006, \"(0.07952000000000001, 0.080315)\": 0.008, \"(0.080315, 0.081035)\": -0.113, \"(0.081035, 0.08308499999999999)\": -0.062, \"(0.08308499999999999, 0.085165)\": -0.04, \"(0.085165, 0.086795)\": 0.004, \"(0.086795, 0.087785)\": 0.075, \"(0.087785, 0.088615)\": 0.136, \"(0.088615, 0.08918999999999999)\": 0.291, \"(0.08918999999999999, 0.090335)\": 0.594, \"(0.090335, 0.09454)\": 0.685, \"(0.09454, 0.11525)\": 0.22, \"(0.11525, 0.11765)\": 0.273, \"(0.11765, 0.12455)\": 0.512, \"(0.12455, 0.13845000000000002)\": 0.591, \"(0.13845000000000002, 0.1634)\": 0.664}\n\n\nYour task is to provide the mean value of the graph at 0.14. What is the mean value of the graph at 0.14?",
+    "0.396"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Age\nFeature Type: continuous\nMeans: {\"(17.0, 18.5)\": -3.326, \"(18.5, 19.5)\": -2.358, \"(19.5, 20.5)\": -2.799, \"(20.5, 21.5)\": -2.354, \"(21.5, 22.5)\": -1.405, \"(22.5, 23.5)\": -1.633, \"(23.5, 24.5)\": -1.214, \"(24.5, 26.5)\": -0.789, \"(26.5, 27.5)\": -0.473, \"(27.5, 29.5)\": -0.216, \"(29.5, 33.5)\": 0.042, \"(33.5, 36.5)\": 0.351, \"(36.5, 44.5)\": 0.658, \"(44.5, 61.5)\": 0.897, \"(61.5, 66.5)\": 0.574, \"(66.5, 73.5)\": 0.099, \"(73.5, 74.5)\": 0.763, \"(74.5, 77.5)\": 0.502, \"(77.5, 79.5)\": 0.875, \"(79.5, 84.5)\": 0.065, \"(84.5, 90.0)\": -1.08}\nLower Bounds (95%-Confidence Interval): {\"(17.0, 18.5)\": -4.677, \"(18.5, 19.5)\": -3.672, \"(19.5, 20.5)\": -3.928, \"(20.5, 21.5)\": -2.706, \"(21.5, 22.5)\": -1.741, \"(22.5, 23.5)\": -1.856, \"(23.5, 24.5)\": -1.407, \"(24.5, 26.5)\": -0.941, \"(26.5, 27.5)\": -0.561, \"(27.5, 29.5)\": -0.322, \"(29.5, 33.5)\": -0.079, \"(33.5, 36.5)\": 0.229, \"(36.5, 44.5)\": 0.5, \"(44.5, 61.5)\": 0.753, \"(61.5, 66.5)\": 0.434, \"(66.5, 73.5)\": -0.37, \"(73.5, 74.5)\": 0.229, \"(74.5, 77.5)\": -0.136, \"(77.5, 79.5)\": 0.35, \"(79.5, 84.5)\": -0.573, \"(84.5, 90.0)\": -2.041}\nUpper Bounds (95%-Confidence Interval): {\"(17.0, 18.5)\": -1.975, \"(18.5, 19.5)\": -1.044, \"(19.5, 20.5)\": -1.669, \"(20.5, 21.5)\": -2.002, \"(21.5, 22.5)\": -1.069, \"(22.5, 23.5)\": -1.41, \"(23.5, 24.5)\": -1.021, \"(24.5, 26.5)\": -0.637, \"(26.5, 27.5)\": -0.385, \"(27.5, 29.5)\": -0.11, \"(29.5, 33.5)\": 0.164, \"(33.5, 36.5)\": 0.473, \"(36.5, 44.5)\": 0.816, \"(44.5, 61.5)\": 1.04, \"(61.5, 66.5)\": 0.714, \"(66.5, 73.5)\": 0.567, \"(73.5, 74.5)\": 1.297, \"(74.5, 77.5)\": 1.141, \"(77.5, 79.5)\": 1.401, \"(79.5, 84.5)\": 0.702, \"(84.5, 90.0)\": -0.119}\n\n\nYour task is to provide the mean value of the graph at 34.1. What is the mean value of the graph at 34.1?",
+    "0.351"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: SkinThickness\nFeature Type: continuous\nMeans: {\"(0.0, 3.5)\": 0.0121, \"(3.5, 7.5)\": -0.0407, \"(7.5, 9.0)\": -0.0873, \"(9.0, 11.5)\": -0.1192, \"(11.5, 13.5)\": -0.1587, \"(13.5, 20.5)\": -0.1856, \"(20.5, 22.5)\": -0.1532, \"(22.5, 24.5)\": -0.1123, \"(24.5, 26.5)\": -0.0708, \"(26.5, 28.5)\": -0.036, \"(28.5, 30.5)\": -0.0039, \"(30.5, 32.5)\": 0.0343, \"(32.5, 34.5)\": 0.0703, \"(34.5, 39.5)\": 0.1069, \"(39.5, 40.5)\": 0.143, \"(40.5, 41.5)\": 0.1769, \"(41.5, 43.5)\": 0.2279, \"(43.5, 47.5)\": 0.2859, \"(47.5, 49.5)\": 0.2453, \"(49.5, 51.0)\": -0.0169, \"(51.0, 55.0)\": -0.0754, \"(55.0, 77.5)\": 0.2174, \"(77.5, 99.0)\": 0.3109}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 3.5)\": -0.071, \"(3.5, 7.5)\": -0.1199, \"(7.5, 9.0)\": -0.1639, \"(9.0, 11.5)\": -0.1953, \"(11.5, 13.5)\": -0.2382, \"(13.5, 20.5)\": -0.2707, \"(20.5, 22.5)\": -0.2184, \"(22.5, 24.5)\": -0.1699, \"(24.5, 26.5)\": -0.1255, \"(26.5, 28.5)\": -0.0953, \"(28.5, 30.5)\": -0.0714, \"(30.5, 32.5)\": -0.0304, \"(32.5, 34.5)\": 0.0205, \"(34.5, 39.5)\": 0.0292, \"(39.5, 40.5)\": 0.0607, \"(40.5, 41.5)\": 0.0987, \"(41.5, 43.5)\": 0.0904, \"(43.5, 47.5)\": 0.0985, \"(47.5, 49.5)\": 0.0202, \"(49.5, 51.0)\": -0.3346, \"(51.0, 55.0)\": -0.5656, \"(55.0, 77.5)\": -0.4718, \"(77.5, 99.0)\": -0.4467}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 3.5)\": 0.0953, \"(3.5, 7.5)\": 0.0385, \"(7.5, 9.0)\": -0.0106, \"(9.0, 11.5)\": -0.0431, \"(11.5, 13.5)\": -0.0792, \"(13.5, 20.5)\": -0.1005, \"(20.5, 22.5)\": -0.088, \"(22.5, 24.5)\": -0.0547, \"(24.5, 26.5)\": -0.0161, \"(26.5, 28.5)\": 0.0233, \"(28.5, 30.5)\": 0.0636, \"(30.5, 32.5)\": 0.099, \"(32.5, 34.5)\": 0.12, \"(34.5, 39.5)\": 0.1847, \"(39.5, 40.5)\": 0.2253, \"(40.5, 41.5)\": 0.255, \"(41.5, 43.5)\": 0.3653, \"(43.5, 47.5)\": 0.4732, \"(47.5, 49.5)\": 0.4704, \"(49.5, 51.0)\": 0.3009, \"(51.0, 55.0)\": 0.4148, \"(55.0, 77.5)\": 0.9065, \"(77.5, 99.0)\": 1.0684}\n\n\nYour task is to provide the mean value of the graph at 27.13. What is the mean value of the graph at 27.13?",
+    "-0.036"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: diabetes\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": 0.3225, \"(0.5, 1.0)\": -0.415}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.1807, \"(0.5, 1.0)\": -0.5976}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.4643, \"(0.5, 1.0)\": -0.2325}\n\n\nYour task is to provide the mean value of the graph at 0.04. What is the mean value of the graph at 0.04?",
+    "0.3225"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: NumOfProducts\nFeature Type: continuous\nMeans: {\"(1.0, 1.5)\": -0.918, \"(1.5, 2.5)\": 0.96, \"(2.5, 3.5)\": -3.104, \"(3.5, 4.0)\": -2.768}\nLower Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -0.985, \"(1.5, 2.5)\": 0.893, \"(2.5, 3.5)\": -3.482, \"(3.5, 4.0)\": -3.159}\nUpper Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -0.852, \"(1.5, 2.5)\": 1.028, \"(2.5, 3.5)\": -2.727, \"(3.5, 4.0)\": -2.376}\n\n\nYour task is to provide the mean value of the graph at 1.27. What is the mean value of the graph at 1.27?",
+    "-0.918"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: households\nFeature Type: continuous\nMeans: {\"(2.0, 4.5)\": -5401.6, \"(4.5, 6.5)\": -23687.9, \"(6.5, 8.5)\": -53732.5, \"(8.5, 9.5)\": -14617.2, \"(9.5, 12.5)\": 16225.5, \"(12.5, 13.5)\": 21846.0, \"(13.5, 14.5)\": 29456.0, \"(14.5, 15.5)\": 14293.2, \"(15.5, 20.5)\": -21670.3, \"(20.5, 21.5)\": 3195.8, \"(21.5, 55.5)\": -12458.9, \"(55.5, 155.5)\": -20063.6, \"(155.5, 156.5)\": -15642.0, \"(156.5, 157.5)\": -6390.8, \"(157.5, 186.5)\": -19320.2, \"(186.5, 196.5)\": -23743.0, \"(196.5, 198.5)\": -18377.6, \"(198.5, 223.5)\": -12744.1, \"(223.5, 230.5)\": -6336.7, \"(230.5, 295.5)\": -10855.3, \"(295.5, 394.5)\": -6355.5, \"(394.5, 535.5)\": -443.1, \"(535.5, 561.5)\": 3934.9, \"(561.5, 599.5)\": 9004.1, \"(599.5, 600.5)\": 13667.2, \"(600.5, 634.5)\": 8706.3, \"(634.5, 635.5)\": 25959.4, \"(635.5, 824.5)\": 13815.1, \"(824.5, 864.5)\": 18503.2, \"(864.5, 962.5)\": 26367.0, \"(962.5, 964.5)\": 14554.6, \"(964.5, 976.5)\": 23227.2, \"(976.5, 978.5)\": 18664.6, \"(978.5, 990.5)\": 26114.1, \"(990.5, 1000.5)\": 30854.6, \"(1000.5, 1088.5)\": 25473.5, \"(1088.5, 1092.5)\": 21095.0, \"(1092.5, 1130.5)\": 26497.2, \"(1130.5, 1272.5)\": 33562.7, \"(1272.5, 3516.0)\": 28522.2, \"(3516.0, 6082.0)\": 21556.0}\nLower Bounds (95%-Confidence Interval): {\"(2.0, 4.5)\": -30426.4, \"(4.5, 6.5)\": -41560.8, \"(6.5, 8.5)\": -83483.7, \"(8.5, 9.5)\": -68637.5, \"(9.5, 12.5)\": -15018.5, \"(12.5, 13.5)\": -5488.2, \"(13.5, 14.5)\": 1721.7, \"(14.5, 15.5)\": -25117.7, \"(15.5, 20.5)\": -41734.0, \"(20.5, 21.5)\": -26800.7, \"(21.5, 55.5)\": -26732.7, \"(55.5, 155.5)\": -27250.3, \"(155.5, 156.5)\": -25256.4, \"(156.5, 157.5)\": -28521.9, \"(157.5, 186.5)\": -26383.4, \"(186.5, 196.5)\": -29250.8, \"(196.5, 198.5)\": -25752.9, \"(198.5, 223.5)\": -20683.5, \"(223.5, 230.5)\": -15595.3, \"(230.5, 295.5)\": -18207.8, \"(295.5, 394.5)\": -15406.0, \"(394.5, 535.5)\": -9211.1, \"(535.5, 561.5)\": -5668.7, \"(561.5, 599.5)\": 904.9, \"(599.5, 600.5)\": -3740.6, \"(600.5, 634.5)\": 3782.4, \"(634.5, 635.5)\": 139.1, \"(635.5, 824.5)\": 6137.4, \"(824.5, 864.5)\": 11294.8, \"(864.5, 962.5)\": 17755.5, \"(962.5, 964.5)\": -5105.1, \"(964.5, 976.5)\": 14837.4, \"(976.5, 978.5)\": 5892.7, \"(978.5, 990.5)\": 18169.8, \"(990.5, 1000.5)\": 15738.6, \"(1000.5, 1088.5)\": 19888.5, \"(1088.5, 1092.5)\": 9478.6, \"(1092.5, 1130.5)\": 20925.9, \"(1130.5, 1272.5)\": 24768.1, \"(1272.5, 3516.0)\": 19419.3, \"(3516.0, 6082.0)\": 8532.3}\nUpper Bounds (95%-Confidence Interval): {\"(2.0, 4.5)\": 19623.3, \"(4.5, 6.5)\": -5814.9, \"(6.5, 8.5)\": -23981.3, \"(8.5, 9.5)\": 39403.2, \"(9.5, 12.5)\": 47469.5, \"(12.5, 13.5)\": 49180.3, \"(13.5, 14.5)\": 57190.3, \"(14.5, 15.5)\": 53704.2, \"(15.5, 20.5)\": -1606.7, \"(20.5, 21.5)\": 33192.3, \"(21.5, 55.5)\": 1814.9, \"(55.5, 155.5)\": -12877.0, \"(155.5, 156.5)\": -6027.7, \"(156.5, 157.5)\": 15740.2, \"(157.5, 186.5)\": -12257.0, \"(186.5, 196.5)\": -18235.2, \"(196.5, 198.5)\": -11002.4, \"(198.5, 223.5)\": -4804.8, \"(223.5, 230.5)\": 2921.9, \"(230.5, 295.5)\": -3502.7, \"(295.5, 394.5)\": 2695.1, \"(394.5, 535.5)\": 8324.9, \"(535.5, 561.5)\": 13538.5, \"(561.5, 599.5)\": 17103.2, \"(599.5, 600.5)\": 31074.9, \"(600.5, 634.5)\": 13630.1, \"(634.5, 635.5)\": 51779.7, \"(635.5, 824.5)\": 21492.8, \"(824.5, 864.5)\": 25711.7, \"(864.5, 962.5)\": 34978.6, \"(962.5, 964.5)\": 34214.4, \"(964.5, 976.5)\": 31616.9, \"(976.5, 978.5)\": 31436.4, \"(978.5, 990.5)\": 34058.4, \"(990.5, 1000.5)\": 45970.6, \"(1000.5, 1088.5)\": 31058.5, \"(1088.5, 1092.5)\": 32711.5, \"(1092.5, 1130.5)\": 32068.4, \"(1130.5, 1272.5)\": 42357.3, \"(1272.5, 3516.0)\": 37625.1, \"(3516.0, 6082.0)\": 34579.6}\n\n\nYour task is to provide the mean value of the graph at 966.62. What is the mean value of the graph at 966.62?",
+    "23227.2"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: CapitalGain\nFeature Type: continuous\nMeans: {\"(0.0, 57.0)\": -0.25, \"(57.0, 3048.0)\": -4.83, \"(3048.0, 3120.0)\": 2.57, \"(3120.0, 4243.5)\": -4.43, \"(4243.5, 4401.0)\": 1.45, \"(4401.0, 4668.5)\": -1.82, \"(4668.5, 4826.0)\": 3.79, \"(4826.0, 4898.0)\": 0.57, \"(4898.0, 4973.5)\": 2.25, \"(4973.5, 5119.0)\": -3.52, \"(5119.0, 5316.5)\": 4.26, \"(5316.5, 5505.5)\": 0.43, \"(5505.5, 6457.5)\": 2.15, \"(6457.5, 6505.5)\": -0.16, \"(6505.5, 6745.0)\": 0.81, \"(6745.0, 7073.5)\": -1.33, \"(7073.5, 7436.5)\": 5.76, \"(7436.5, 7565.5)\": 2.02, \"(7565.5, 7792.0)\": 6.56, \"(7792.0, 7937.0)\": 4.88, \"(7937.0, 8296.0)\": 3.84, \"(8296.0, 10543.0)\": 7.18, \"(10543.0, 10585.5)\": -1.48, \"(10585.5, 30961.5)\": 8.61, \"(30961.5, 70654.5)\": -0.66, \"(70654.5, 99999.0)\": 9.72}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 57.0)\": -0.27, \"(57.0, 3048.0)\": -6.42, \"(3048.0, 3120.0)\": 2.14, \"(3120.0, 4243.5)\": -5.31, \"(4243.5, 4401.0)\": 1.09, \"(4401.0, 4668.5)\": -2.65, \"(4668.5, 4826.0)\": 2.87, \"(4826.0, 4898.0)\": -0.25, \"(4898.0, 4973.5)\": 1.55, \"(4973.5, 5119.0)\": -6.13, \"(5119.0, 5316.5)\": 3.51, \"(5316.5, 5505.5)\": -0.29, \"(5505.5, 6457.5)\": 1.3, \"(6457.5, 6505.5)\": -0.94, \"(6505.5, 6745.0)\": 0.19, \"(6745.0, 7073.5)\": -2.33, \"(7073.5, 7436.5)\": 4.95, \"(7436.5, 7565.5)\": 0.42, \"(7565.5, 7792.0)\": 5.41, \"(7792.0, 7937.0)\": 2.59, \"(7937.0, 8296.0)\": 1.32, \"(8296.0, 10543.0)\": 6.05, \"(10543.0, 10585.5)\": -2.73, \"(10585.5, 30961.5)\": 7.51, \"(30961.5, 70654.5)\": -3.56, \"(70654.5, 99999.0)\": 8.19}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 57.0)\": -0.23, \"(57.0, 3048.0)\": -3.24, \"(3048.0, 3120.0)\": 3.0, \"(3120.0, 4243.5)\": -3.54, \"(4243.5, 4401.0)\": 1.81, \"(4401.0, 4668.5)\": -1.0, \"(4668.5, 4826.0)\": 4.71, \"(4826.0, 4898.0)\": 1.38, \"(4898.0, 4973.5)\": 2.95, \"(4973.5, 5119.0)\": -0.92, \"(5119.0, 5316.5)\": 5.0, \"(5316.5, 5505.5)\": 1.16, \"(5505.5, 6457.5)\": 3.0, \"(6457.5, 6505.5)\": 0.62, \"(6505.5, 6745.0)\": 1.44, \"(6745.0, 7073.5)\": -0.34, \"(7073.5, 7436.5)\": 6.58, \"(7436.5, 7565.5)\": 3.62, \"(7565.5, 7792.0)\": 7.72, \"(7792.0, 7937.0)\": 7.16, \"(7937.0, 8296.0)\": 6.36, \"(8296.0, 10543.0)\": 8.31, \"(10543.0, 10585.5)\": -0.22, \"(10585.5, 30961.5)\": 9.71, \"(30961.5, 70654.5)\": 2.23, \"(70654.5, 99999.0)\": 11.26}\n\n\nYour task is to provide the mean value of the graph at 4568.36. What is the mean value of the graph at 4568.36?",
+    "-1.82"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Education\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.4028, \"(0.5, 1.5)\": -0.5397, \"(1.5, 3.5)\": -0.4851, \"(3.5, 4.5)\": -0.4021, \"(4.5, 5.5)\": -0.457, \"(5.5, 6.5)\": -0.2537, \"(6.5, 7.5)\": -0.0494, \"(7.5, 8.5)\": 0.0457, \"(8.5, 9.5)\": 0.1831, \"(9.5, 10.5)\": 0.1392, \"(10.5, 11.5)\": -0.0652, \"(11.5, 14.5)\": 0.1954, \"(14.5, 15.0)\": 0.1393}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.5596, \"(0.5, 1.5)\": -0.6499, \"(1.5, 3.5)\": -0.618, \"(3.5, 4.5)\": -0.5693, \"(4.5, 5.5)\": -0.5278, \"(5.5, 6.5)\": -0.3342, \"(6.5, 7.5)\": -0.0948, \"(7.5, 8.5)\": -0.0062, \"(8.5, 9.5)\": 0.1525, \"(9.5, 10.5)\": 0.1072, \"(10.5, 11.5)\": -0.0869, \"(11.5, 14.5)\": 0.1476, \"(14.5, 15.0)\": 0.1012}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.2459, \"(0.5, 1.5)\": -0.4295, \"(1.5, 3.5)\": -0.3523, \"(3.5, 4.5)\": -0.235, \"(4.5, 5.5)\": -0.3862, \"(5.5, 6.5)\": -0.1733, \"(6.5, 7.5)\": -0.0039, \"(7.5, 8.5)\": 0.0977, \"(8.5, 9.5)\": 0.2137, \"(9.5, 10.5)\": 0.1711, \"(10.5, 11.5)\": -0.0435, \"(11.5, 14.5)\": 0.2431, \"(14.5, 15.0)\": 0.1775}\n\n\nYour task is to provide the mean value of the graph at 14.65. What is the mean value of the graph at 14.65?",
+    "0.1393"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Glucose\nFeature Type: continuous\nMeans: {\"(0.0, 22.0)\": -0.728, \"(22.0, 86.5)\": -1.069, \"(86.5, 94.5)\": -0.907, \"(94.5, 99.5)\": -0.729, \"(99.5, 105.5)\": -0.491, \"(105.5, 114.5)\": -0.326, \"(114.5, 123.5)\": -0.157, \"(123.5, 130.5)\": 0.045, \"(130.5, 139.5)\": 0.208, \"(139.5, 147.5)\": 0.37, \"(147.5, 154.5)\": 0.535, \"(154.5, 159.5)\": 0.724, \"(159.5, 165.5)\": 0.984, \"(165.5, 169.5)\": 1.342, \"(169.5, 178.5)\": 1.502, \"(178.5, 187.5)\": 1.691, \"(187.5, 198.5)\": 1.853, \"(198.5, 199.0)\": 2.022}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 22.0)\": -1.316, \"(22.0, 86.5)\": -1.535, \"(86.5, 94.5)\": -1.3, \"(94.5, 99.5)\": -1.042, \"(99.5, 105.5)\": -0.722, \"(105.5, 114.5)\": -0.428, \"(114.5, 123.5)\": -0.249, \"(123.5, 130.5)\": -0.151, \"(130.5, 139.5)\": 0.044, \"(139.5, 147.5)\": 0.215, \"(147.5, 154.5)\": 0.135, \"(154.5, 159.5)\": 0.451, \"(159.5, 165.5)\": 0.509, \"(165.5, 169.5)\": 0.633, \"(169.5, 178.5)\": 0.768, \"(178.5, 187.5)\": 0.987, \"(187.5, 198.5)\": 1.135, \"(198.5, 199.0)\": 1.3}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 22.0)\": -0.14, \"(22.0, 86.5)\": -0.602, \"(86.5, 94.5)\": -0.514, \"(94.5, 99.5)\": -0.417, \"(99.5, 105.5)\": -0.26, \"(105.5, 114.5)\": -0.223, \"(114.5, 123.5)\": -0.064, \"(123.5, 130.5)\": 0.242, \"(130.5, 139.5)\": 0.373, \"(139.5, 147.5)\": 0.525, \"(147.5, 154.5)\": 0.936, \"(154.5, 159.5)\": 0.997, \"(159.5, 165.5)\": 1.458, \"(165.5, 169.5)\": 2.051, \"(169.5, 178.5)\": 2.237, \"(178.5, 187.5)\": 2.394, \"(187.5, 198.5)\": 2.571, \"(198.5, 199.0)\": 2.744}\n\n\nYour task is to provide the mean value of the graph at 198.65. What is the mean value of the graph at 198.65?",
+    "2.022"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: platelets\nFeature Type: continuous\nMeans: {\"(25100.0, 27700.0)\": -1.004, \"(27700.0, 34450.0)\": -0.687, \"(34450.0, 42200.0)\": 0.328, \"(42200.0, 56500.0)\": 1.717, \"(56500.0, 66050.0)\": 2.769, \"(66050.0, 74000.0)\": 2.195, \"(74000.0, 95500.0)\": 2.956, \"(95500.0, 104500.0)\": -0.265, \"(104500.0, 144000.0)\": -0.585, \"(144000.0, 150500.0)\": -0.895, \"(150500.0, 154000.0)\": 2.322, \"(154000.0, 169000.0)\": 0.469, \"(169000.0, 184500.0)\": -1.612, \"(184500.0, 195000.0)\": 1.111, \"(195000.0, 199000.0)\": 3.01, \"(199000.0, 200500.0)\": 1.837, \"(200500.0, 214000.0)\": 0.403, \"(214000.0, 217500.0)\": -0.825, \"(217500.0, 218500.0)\": -1.399, \"(218500.0, 220500.0)\": 0.341, \"(220500.0, 222500.0)\": 0.978, \"(222500.0, 226500.0)\": 1.584, \"(226500.0, 241500.0)\": 0.175, \"(241500.0, 242500.0)\": 0.642, \"(242500.0, 243500.0)\": 1.107, \"(243500.0, 244500.0)\": 1.516, \"(244500.0, 252500.0)\": -2.19, \"(252500.0, 261000.0)\": -0.878, \"(261000.0, 274500.0)\": -0.145, \"(274500.0, 283500.0)\": -0.968, \"(283500.0, 287500.0)\": 0.203, \"(287500.0, 289500.0)\": 1.032, \"(289500.0, 302500.0)\": -1.296, \"(302500.0, 305500.0)\": -2.984, \"(305500.0, 307000.0)\": 0.876, \"(307000.0, 332000.0)\": 0.368, \"(332000.0, 335000.0)\": 1.21, \"(335000.0, 343000.0)\": 0.8, \"(343000.0, 350500.0)\": -0.573, \"(350500.0, 354500.0)\": 3.0, \"(354500.0, 383500.0)\": -0.119, \"(383500.0, 449500.0)\": 0.655, \"(449500.0, 471000.0)\": 1.527, \"(471000.0, 500500.0)\": -2.247, \"(500500.0, 582000.0)\": -0.442, \"(582000.0, 675500.0)\": 2.645, \"(675500.0, 796000.0)\": 2.314, \"(796000.0, 850000.0)\": -0.709}\nLower Bounds (95%-Confidence Interval): {\"(25100.0, 27700.0)\": -1.75, \"(27700.0, 34450.0)\": -1.54, \"(34450.0, 42200.0)\": -0.532, \"(42200.0, 56500.0)\": 0.992, \"(56500.0, 66050.0)\": 1.538, \"(66050.0, 74000.0)\": 1.537, \"(74000.0, 95500.0)\": 1.91, \"(95500.0, 104500.0)\": -1.642, \"(104500.0, 144000.0)\": -1.428, \"(144000.0, 150500.0)\": -1.74, \"(150500.0, 154000.0)\": 1.125, \"(154000.0, 169000.0)\": 0.027, \"(169000.0, 184500.0)\": -2.523, \"(184500.0, 195000.0)\": 0.214, \"(195000.0, 199000.0)\": 0.239, \"(199000.0, 200500.0)\": 0.581, \"(200500.0, 214000.0)\": -0.252, \"(214000.0, 217500.0)\": -2.007, \"(217500.0, 218500.0)\": -3.583, \"(218500.0, 220500.0)\": 0.076, \"(220500.0, 222500.0)\": 0.244, \"(222500.0, 226500.0)\": -0.038, \"(226500.0, 241500.0)\": -0.123, \"(241500.0, 242500.0)\": 0.22, \"(242500.0, 243500.0)\": 0.116, \"(243500.0, 244500.0)\": 0.265, \"(244500.0, 252500.0)\": -4.008, \"(252500.0, 261000.0)\": -1.287, \"(261000.0, 274500.0)\": -0.465, \"(274500.0, 283500.0)\": -1.829, \"(283500.0, 287500.0)\": -1.587, \"(287500.0, 289500.0)\": -0.951, \"(289500.0, 302500.0)\": -1.857, \"(302500.0, 305500.0)\": -4.201, \"(305500.0, 307000.0)\": 0.125, \"(307000.0, 332000.0)\": -0.181, \"(332000.0, 335000.0)\": -0.179, \"(335000.0, 343000.0)\": 0.105, \"(343000.0, 350500.0)\": -1.469, \"(350500.0, 354500.0)\": 1.748, \"(354500.0, 383500.0)\": -0.848, \"(383500.0, 449500.0)\": 0.242, \"(449500.0, 471000.0)\": -2.033, \"(471000.0, 500500.0)\": -5.177, \"(500500.0, 582000.0)\": -1.795, \"(582000.0, 675500.0)\": 1.501, \"(675500.0, 796000.0)\": 0.104, \"(796000.0, 850000.0)\": -1.557}\nUpper Bounds (95%-Confidence Interval): {\"(25100.0, 27700.0)\": -0.258, \"(27700.0, 34450.0)\": 0.165, \"(34450.0, 42200.0)\": 1.188, \"(42200.0, 56500.0)\": 2.441, \"(56500.0, 66050.0)\": 4.0, \"(66050.0, 74000.0)\": 2.853, \"(74000.0, 95500.0)\": 4.001, \"(95500.0, 104500.0)\": 1.113, \"(104500.0, 144000.0)\": 0.258, \"(144000.0, 150500.0)\": -0.049, \"(150500.0, 154000.0)\": 3.518, \"(154000.0, 169000.0)\": 0.911, \"(169000.0, 184500.0)\": -0.702, \"(184500.0, 195000.0)\": 2.008, \"(195000.0, 199000.0)\": 5.781, \"(199000.0, 200500.0)\": 3.093, \"(200500.0, 214000.0)\": 1.058, \"(214000.0, 217500.0)\": 0.356, \"(217500.0, 218500.0)\": 0.785, \"(218500.0, 220500.0)\": 0.606, \"(220500.0, 222500.0)\": 1.711, \"(222500.0, 226500.0)\": 3.206, \"(226500.0, 241500.0)\": 0.472, \"(241500.0, 242500.0)\": 1.064, \"(242500.0, 243500.0)\": 2.099, \"(243500.0, 244500.0)\": 2.766, \"(244500.0, 252500.0)\": -0.372, \"(252500.0, 261000.0)\": -0.468, \"(261000.0, 274500.0)\": 0.176, \"(274500.0, 283500.0)\": -0.106, \"(283500.0, 287500.0)\": 1.993, \"(287500.0, 289500.0)\": 3.014, \"(289500.0, 302500.0)\": -0.734, \"(302500.0, 305500.0)\": -1.767, \"(305500.0, 307000.0)\": 1.626, \"(307000.0, 332000.0)\": 0.917, \"(332000.0, 335000.0)\": 2.599, \"(335000.0, 343000.0)\": 1.496, \"(343000.0, 350500.0)\": 0.324, \"(350500.0, 354500.0)\": 4.251, \"(354500.0, 383500.0)\": 0.609, \"(383500.0, 449500.0)\": 1.068, \"(449500.0, 471000.0)\": 5.088, \"(471000.0, 500500.0)\": 0.684, \"(500500.0, 582000.0)\": 0.912, \"(582000.0, 675500.0)\": 3.789, \"(675500.0, 796000.0)\": 4.525, \"(796000.0, 850000.0)\": 0.138}\n\n\nYour task is to provide the mean value of the graph at 243849.53. What is the mean value of the graph at 243849.53?",
+    "1.516"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Balance\nFeature Type: continuous\nMeans: {\"(0.0, 50418.515)\": -0.132, \"(50418.515, 53570.93)\": -0.285, \"(53570.93, 54249.445)\": -0.826, \"(54249.445, 57428.56)\": -0.404, \"(57428.56, 60041.265)\": -0.005, \"(60041.265, 64897.8)\": 0.215, \"(64897.8, 72985.875)\": 0.086, \"(72985.875, 74989.08499999999)\": -0.012, \"(74989.08499999999, 76596.815)\": 0.247, \"(76596.815, 79953.185)\": 0.829, \"(79953.185, 83348.07)\": 0.564, \"(83348.07, 101890.23999999999)\": 0.414, \"(101890.23999999999, 114327.485)\": 0.248, \"(114327.485, 123946.3)\": 0.164, \"(123946.3, 141661.24)\": 0.075, \"(141661.24, 174920.08000000002)\": 0.173, \"(174920.08000000002, 181813.135)\": 0.059, \"(181813.135, 191993.675)\": -0.349, \"(191993.675, 200829.925)\": -0.459, \"(200829.925, 206951.87)\": -0.616, \"(206951.87, 216109.88)\": -0.256}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 50418.515)\": -0.192, \"(50418.515, 53570.93)\": -0.628, \"(53570.93, 54249.445)\": -1.999, \"(54249.445, 57428.56)\": -0.798, \"(57428.56, 60041.265)\": -0.322, \"(60041.265, 64897.8)\": -0.105, \"(64897.8, 72985.875)\": -0.195, \"(72985.875, 74989.08499999999)\": -0.418, \"(74989.08499999999, 76596.815)\": -0.231, \"(76596.815, 79953.185)\": 0.338, \"(79953.185, 83348.07)\": 0.321, \"(83348.07, 101890.23999999999)\": 0.247, \"(101890.23999999999, 114327.485)\": 0.097, \"(114327.485, 123946.3)\": 0.069, \"(123946.3, 141661.24)\": -0.23, \"(141661.24, 174920.08000000002)\": -0.272, \"(174920.08000000002, 181813.135)\": -0.147, \"(181813.135, 191993.675)\": -0.864, \"(191993.675, 200829.925)\": -0.991, \"(200829.925, 206951.87)\": -1.401, \"(206951.87, 216109.88)\": -0.862}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 50418.515)\": -0.072, \"(50418.515, 53570.93)\": 0.057, \"(53570.93, 54249.445)\": 0.347, \"(54249.445, 57428.56)\": -0.011, \"(57428.56, 60041.265)\": 0.312, \"(60041.265, 64897.8)\": 0.534, \"(64897.8, 72985.875)\": 0.367, \"(72985.875, 74989.08499999999)\": 0.395, \"(74989.08499999999, 76596.815)\": 0.725, \"(76596.815, 79953.185)\": 1.32, \"(79953.185, 83348.07)\": 0.806, \"(83348.07, 101890.23999999999)\": 0.582, \"(101890.23999999999, 114327.485)\": 0.398, \"(114327.485, 123946.3)\": 0.259, \"(123946.3, 141661.24)\": 0.379, \"(141661.24, 174920.08000000002)\": 0.618, \"(174920.08000000002, 181813.135)\": 0.264, \"(181813.135, 191993.675)\": 0.166, \"(191993.675, 200829.925)\": 0.073, \"(200829.925, 206951.87)\": 0.169, \"(206951.87, 216109.88)\": 0.35}\n\n\nYour task is to provide the mean value of the graph at 18765.31. What is the mean value of the graph at 18765.31?",
+    "-0.132"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: age\nFeature Type: continuous\nMeans: {\"(40.0, 41.5)\": -1.489, \"(41.5, 43.5)\": -0.895, \"(43.5, 44.5)\": -0.02, \"(44.5, 47.5)\": 0.701, \"(47.5, 48.5)\": 1.245, \"(48.5, 58.5)\": -0.923, \"(58.5, 59.5)\": 0.647, \"(59.5, 60.8335)\": -0.288, \"(60.8335, 64.5)\": -1.035, \"(64.5, 65.5)\": 0.0, \"(65.5, 67.5)\": -0.73, \"(67.5, 68.5)\": 0.19, \"(68.5, 70.5)\": 0.784, \"(70.5, 80.5)\": 1.169, \"(80.5, 81.5)\": 0.839, \"(81.5, 85.5)\": 2.112, \"(85.5, 86.5)\": 3.884, \"(86.5, 95.0)\": 4.517}\nLower Bounds (95%-Confidence Interval): {\"(40.0, 41.5)\": -2.719, \"(41.5, 43.5)\": -2.486, \"(43.5, 44.5)\": -0.761, \"(44.5, 47.5)\": 0.297, \"(47.5, 48.5)\": 0.199, \"(48.5, 58.5)\": -1.235, \"(58.5, 59.5)\": 0.291, \"(59.5, 60.8335)\": -0.805, \"(60.8335, 64.5)\": -1.655, \"(64.5, 65.5)\": -0.281, \"(65.5, 67.5)\": -2.122, \"(67.5, 68.5)\": -0.059, \"(68.5, 70.5)\": 0.513, \"(70.5, 80.5)\": 0.404, \"(80.5, 81.5)\": 0.173, \"(81.5, 85.5)\": 1.308, \"(85.5, 86.5)\": 2.758, \"(86.5, 95.0)\": 3.244}\nUpper Bounds (95%-Confidence Interval): {\"(40.0, 41.5)\": -0.259, \"(41.5, 43.5)\": 0.696, \"(43.5, 44.5)\": 0.722, \"(44.5, 47.5)\": 1.105, \"(47.5, 48.5)\": 2.291, \"(48.5, 58.5)\": -0.612, \"(58.5, 59.5)\": 1.004, \"(59.5, 60.8335)\": 0.228, \"(60.8335, 64.5)\": -0.414, \"(64.5, 65.5)\": 0.281, \"(65.5, 67.5)\": 0.662, \"(67.5, 68.5)\": 0.44, \"(68.5, 70.5)\": 1.056, \"(70.5, 80.5)\": 1.934, \"(80.5, 81.5)\": 1.505, \"(81.5, 85.5)\": 2.916, \"(85.5, 86.5)\": 5.009, \"(86.5, 95.0)\": 5.79}\n\n\nYour task is to provide the mean value of the graph at 65.26. What is the mean value of the graph at 65.26?",
+    "0.0"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: texture_worst\nFeature Type: continuous\nMeans: {\"(12.02, 16.935000000000002)\": -1.885, \"(16.935000000000002, 18.335)\": -1.717, \"(18.335, 19.505)\": -1.55, \"(19.505, 20.225)\": -0.851, \"(20.225, 21.955)\": -0.612, \"(21.955, 23.59)\": -0.44, \"(23.59, 24.795)\": -0.272, \"(24.795, 25.18)\": -0.1, \"(25.18, 25.83)\": 0.078, \"(25.83, 26.855)\": 0.279, \"(26.855, 27.994999999999997)\": 0.451, \"(27.994999999999997, 29.225)\": 0.619, \"(29.225, 31.515)\": 0.878, \"(31.515, 32.485)\": 1.044, \"(32.485, 35.05)\": 1.256, \"(35.05, 49.54)\": 1.423}\nLower Bounds (95%-Confidence Interval): {\"(12.02, 16.935000000000002)\": -4.342, \"(16.935000000000002, 18.335)\": -4.128, \"(18.335, 19.505)\": -3.934, \"(19.505, 20.225)\": -1.264, \"(20.225, 21.955)\": -0.945, \"(21.955, 23.59)\": -0.663, \"(23.59, 24.795)\": -0.468, \"(24.795, 25.18)\": -0.274, \"(25.18, 25.83)\": -0.503, \"(25.83, 26.855)\": -0.327, \"(26.855, 27.994999999999997)\": -0.163, \"(27.994999999999997, 29.225)\": -0.01, \"(29.225, 31.515)\": -0.206, \"(31.515, 32.485)\": -0.081, \"(32.485, 35.05)\": -0.18, \"(35.05, 49.54)\": -0.014}\nUpper Bounds (95%-Confidence Interval): {\"(12.02, 16.935000000000002)\": 0.572, \"(16.935000000000002, 18.335)\": 0.695, \"(18.335, 19.505)\": 0.835, \"(19.505, 20.225)\": -0.437, \"(20.225, 21.955)\": -0.279, \"(21.955, 23.59)\": -0.218, \"(23.59, 24.795)\": -0.076, \"(24.795, 25.18)\": 0.073, \"(25.18, 25.83)\": 0.66, \"(25.83, 26.855)\": 0.884, \"(26.855, 27.994999999999997)\": 1.065, \"(27.994999999999997, 29.225)\": 1.248, \"(29.225, 31.515)\": 1.961, \"(31.515, 32.485)\": 2.17, \"(32.485, 35.05)\": 2.691, \"(35.05, 49.54)\": 2.861}\n\n\nYour task is to provide the mean value of the graph at 24.6. What is the mean value of the graph at 24.6?",
+    "-0.272"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: MaritalStatus\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.368, \"(0.5, 1.5)\": 0.724, \"(1.5, 2.5)\": 0.587, \"(2.5, 3.5)\": -0.221, \"(3.5, 4.5)\": -0.631, \"(4.5, 5.5)\": -0.545, \"(5.5, 6.0)\": 0.179}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.418, \"(0.5, 1.5)\": 0.02, \"(1.5, 2.5)\": 0.545, \"(2.5, 3.5)\": -0.336, \"(3.5, 4.5)\": -0.676, \"(4.5, 5.5)\": -0.688, \"(5.5, 6.0)\": 0.067}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.318, \"(0.5, 1.5)\": 1.428, \"(1.5, 2.5)\": 0.629, \"(2.5, 3.5)\": -0.106, \"(3.5, 4.5)\": -0.585, \"(4.5, 5.5)\": -0.403, \"(5.5, 6.0)\": 0.291}\n\n\nYour task is to provide the mean value of the graph at 0.49. What is the mean value of the graph at 0.49?",
+    "-0.368"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: median_income\nFeature Type: continuous\nMeans: {\"(0.4999, 0.5427500000000001)\": -16067.6, \"(0.5427500000000001, 1.4808)\": -55539.5, \"(1.4808, 2.1658999999999997)\": -71376.5, \"(2.1658999999999997, 2.6096)\": -56399.7, \"(2.6096, 3.2433)\": -40762.6, \"(3.2433, 3.66575)\": -25586.1, \"(3.66575, 4.3197)\": -8084.4, \"(4.3197, 4.691000000000001)\": 7391.3, \"(4.691000000000001, 5.1358)\": 22375.3, \"(5.1358, 5.59195)\": 40032.8, \"(5.59195, 5.8294)\": 56900.2, \"(5.8294, 6.29665)\": 75092.3, \"(6.29665, 6.3704)\": 96400.5, \"(6.3704, 6.874750000000001)\": 111491.7, \"(6.874750000000001, 7.6996)\": 135841.6, \"(7.6996, 7.8141)\": 151586.9, \"(7.8141, 8.3976)\": 170219.6, \"(8.3976, 9.046949999999999)\": 192482.3, \"(9.046949999999999, 15.00005)\": 214375.9, \"(15.00005, 15.0001)\": 193753.6}\nLower Bounds (95%-Confidence Interval): {\"(0.4999, 0.5427500000000001)\": -48216.1, \"(0.5427500000000001, 1.4808)\": -68098.8, \"(1.4808, 2.1658999999999997)\": -81907.3, \"(2.1658999999999997, 2.6096)\": -60824.9, \"(2.6096, 3.2433)\": -49299.1, \"(3.2433, 3.66575)\": -32546.2, \"(3.66575, 4.3197)\": -17048.9, \"(4.3197, 4.691000000000001)\": 1621.1, \"(4.691000000000001, 5.1358)\": 13670.4, \"(5.1358, 5.59195)\": 33628.4, \"(5.59195, 5.8294)\": 48173.8, \"(5.8294, 6.29665)\": 69358.1, \"(6.29665, 6.3704)\": 88897.2, \"(6.3704, 6.874750000000001)\": 105607.5, \"(6.874750000000001, 7.6996)\": 129446.9, \"(7.6996, 7.8141)\": 139775.0, \"(7.8141, 8.3976)\": 162646.8, \"(8.3976, 9.046949999999999)\": 184114.0, \"(9.046949999999999, 15.00005)\": 203670.8, \"(15.00005, 15.0001)\": 178950.1}\nUpper Bounds (95%-Confidence Interval): {\"(0.4999, 0.5427500000000001)\": 16080.9, \"(0.5427500000000001, 1.4808)\": -42980.1, \"(1.4808, 2.1658999999999997)\": -60845.8, \"(2.1658999999999997, 2.6096)\": -51974.6, \"(2.6096, 3.2433)\": -32226.2, \"(3.2433, 3.66575)\": -18626.1, \"(3.66575, 4.3197)\": 880.1, \"(4.3197, 4.691000000000001)\": 13161.4, \"(4.691000000000001, 5.1358)\": 31080.1, \"(5.1358, 5.59195)\": 46437.2, \"(5.59195, 5.8294)\": 65626.7, \"(5.8294, 6.29665)\": 80826.5, \"(6.29665, 6.3704)\": 103903.7, \"(6.3704, 6.874750000000001)\": 117376.0, \"(6.874750000000001, 7.6996)\": 142236.4, \"(7.6996, 7.8141)\": 163398.8, \"(7.8141, 8.3976)\": 177792.4, \"(8.3976, 9.046949999999999)\": 200850.6, \"(9.046949999999999, 15.00005)\": 225081.0, \"(15.00005, 15.0001)\": 208557.1}\n\n\nYour task is to provide the mean value of the graph at 5.82. What is the mean value of the graph at 5.82?",
+    "56900.2"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: RiverManagement\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.0273, \"(0.5, 1.5)\": -0.02345, \"(1.5, 2.5)\": -0.01571, \"(2.5, 3.5)\": -0.01174, \"(3.5, 4.5)\": -0.00519, \"(4.5, 5.5)\": 0.00111, \"(5.5, 6.5)\": 0.00506, \"(6.5, 7.5)\": 0.01056, \"(7.5, 8.5)\": 0.01706, \"(8.5, 9.5)\": 0.02398, \"(9.5, 11.5)\": 0.02821, \"(11.5, 12.5)\": 0.03673, \"(12.5, 13.5)\": 0.01311, \"(13.5, 16.0)\": 0.03206}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02945, \"(0.5, 1.5)\": -0.02501, \"(1.5, 2.5)\": -0.01619, \"(2.5, 3.5)\": -0.0121, \"(3.5, 4.5)\": -0.00549, \"(4.5, 5.5)\": 0.00069, \"(5.5, 6.5)\": 0.00469, \"(6.5, 7.5)\": 0.00991, \"(7.5, 8.5)\": 0.01638, \"(8.5, 9.5)\": 0.02306, \"(9.5, 11.5)\": 0.0266, \"(11.5, 12.5)\": 0.02982, \"(12.5, 13.5)\": -0.01689, \"(13.5, 16.0)\": 0.01715}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02515, \"(0.5, 1.5)\": -0.0219, \"(1.5, 2.5)\": -0.01524, \"(2.5, 3.5)\": -0.01139, \"(3.5, 4.5)\": -0.0049, \"(4.5, 5.5)\": 0.00152, \"(5.5, 6.5)\": 0.00544, \"(6.5, 7.5)\": 0.01121, \"(7.5, 8.5)\": 0.01774, \"(8.5, 9.5)\": 0.0249, \"(9.5, 11.5)\": 0.02981, \"(11.5, 12.5)\": 0.04363, \"(12.5, 13.5)\": 0.04312, \"(13.5, 16.0)\": 0.04696}\n\n\nYour task is to provide the mean value of the graph at 14.4. What is the mean value of the graph at 14.4?",
+    "0.03206"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: concave points_se\nFeature Type: continuous\nMeans: {\"(0.0, 0.002395)\": -0.0871, \"(0.002395, 0.0032875)\": -0.0609, \"(0.0032875, 0.0034045)\": -0.0373, \"(0.0034045, 0.0036125000000000003)\": -0.0134, \"(0.0036125000000000003, 0.004007999999999999)\": 0.015, \"(0.004007999999999999, 0.0044174999999999996)\": 0.0395, \"(0.0044174999999999996, 0.0048265)\": 0.0651, \"(0.0048265, 0.0049695)\": 0.092, \"(0.0049695, 0.005064)\": 0.1172, \"(0.005064, 0.0051675)\": 0.1498, \"(0.0051675, 0.0052465)\": 0.1811, \"(0.0052465, 0.0054895)\": 0.1545, \"(0.0054895, 0.00583)\": 0.1294, \"(0.00583, 0.006595999999999999)\": 0.1053, \"(0.006595999999999999, 0.006815)\": 0.1283, \"(0.006815, 0.00749)\": 0.151, \"(0.00749, 0.008282000000000001)\": 0.1286, \"(0.008282000000000001, 0.0088595)\": 0.1057, \"(0.0088595, 0.009246)\": 0.2195, \"(0.009246, 0.00954)\": 0.1918, \"(0.00954, 0.009698)\": 0.169, \"(0.009698, 0.00976)\": 0.1467, \"(0.00976, 0.009788999999999999)\": 0.1246, \"(0.009788999999999999, 0.009878999999999999)\": 0.0984, \"(0.009878999999999999, 0.0099215)\": -0.0268, \"(0.0099215, 0.010165)\": -0.0546, \"(0.010165, 0.010385)\": -0.0796, \"(0.010385, 0.010515)\": -0.1027, \"(0.010515, 0.010825)\": -0.1321, \"(0.010825, 0.011115)\": -0.1569, \"(0.011115, 0.011525)\": -0.181, \"(0.011525, 0.012580000000000001)\": -0.204, \"(0.012580000000000001, 0.012715)\": -0.1804, \"(0.012715, 0.012750000000000001)\": -0.1557, \"(0.012750000000000001, 0.01302)\": -0.1333, \"(0.01302, 0.013405)\": -0.1067, \"(0.013405, 0.01386)\": -0.0844, \"(0.01386, 0.014315)\": -0.061, \"(0.014315, 0.015605)\": -0.0317, \"(0.015605, 0.016655000000000003)\": -0.0097, \"(0.016655000000000003, 0.017509999999999998)\": 0.0142, \"(0.017509999999999998, 0.019655)\": 0.037, \"(0.019655, 0.021525)\": 0.0117, \"(0.021525, 0.02246)\": -0.018, \"(0.02246, 0.02611)\": -0.0625, \"(0.02611, 0.05279)\": -0.0866}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.002395)\": -0.2428, \"(0.002395, 0.0032875)\": -0.216, \"(0.0032875, 0.0034045)\": -0.1973, \"(0.0034045, 0.0036125000000000003)\": -0.1778, \"(0.0036125000000000003, 0.004007999999999999)\": -0.1423, \"(0.004007999999999999, 0.0044174999999999996)\": -0.1145, \"(0.0044174999999999996, 0.0048265)\": -0.0804, \"(0.0048265, 0.0049695)\": -0.0433, \"(0.0049695, 0.005064)\": -0.0289, \"(0.005064, 0.0051675)\": -0.0175, \"(0.0051675, 0.0052465)\": 0.0068, \"(0.0052465, 0.0054895)\": -0.0087, \"(0.0054895, 0.00583)\": -0.0088, \"(0.00583, 0.006595999999999999)\": -0.0253, \"(0.006595999999999999, 0.006815)\": 0.0061, \"(0.006815, 0.00749)\": 0.012, \"(0.00749, 0.008282000000000001)\": -0.0218, \"(0.008282000000000001, 0.0088595)\": -0.0694, \"(0.0088595, 0.009246)\": -0.313, \"(0.009246, 0.00954)\": -0.3369, \"(0.00954, 0.009698)\": -0.3531, \"(0.009698, 0.00976)\": -0.3635, \"(0.00976, 0.009788999999999999)\": -0.3663, \"(0.009788999999999999, 0.009878999999999999)\": -0.4105, \"(0.009878999999999999, 0.0099215)\": -0.1256, \"(0.0099215, 0.010165)\": -0.1624, \"(0.010165, 0.010385)\": -0.1986, \"(0.010385, 0.010515)\": -0.2385, \"(0.010515, 0.010825)\": -0.2937, \"(0.010825, 0.011115)\": -0.3279, \"(0.011115, 0.011525)\": -0.3717, \"(0.011525, 0.012580000000000001)\": -0.4177, \"(0.012580000000000001, 0.012715)\": -0.3676, \"(0.012715, 0.012750000000000001)\": -0.3439, \"(0.012750000000000001, 0.01302)\": -0.2873, \"(0.01302, 0.013405)\": -0.246, \"(0.013405, 0.01386)\": -0.2135, \"(0.01386, 0.014315)\": -0.1572, \"(0.014315, 0.015605)\": -0.1106, \"(0.015605, 0.016655000000000003)\": -0.0972, \"(0.016655000000000003, 0.017509999999999998)\": -0.0786, \"(0.017509999999999998, 0.019655)\": -0.0739, \"(0.019655, 0.021525)\": -0.099, \"(0.021525, 0.02246)\": -0.1362, \"(0.02246, 0.02611)\": -0.2114, \"(0.02611, 0.05279)\": -0.2994}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.002395)\": 0.0686, \"(0.002395, 0.0032875)\": 0.0943, \"(0.0032875, 0.0034045)\": 0.1227, \"(0.0034045, 0.0036125000000000003)\": 0.151, \"(0.0036125000000000003, 0.004007999999999999)\": 0.1722, \"(0.004007999999999999, 0.0044174999999999996)\": 0.1934, \"(0.0044174999999999996, 0.0048265)\": 0.2105, \"(0.0048265, 0.0049695)\": 0.2273, \"(0.0049695, 0.005064)\": 0.2632, \"(0.005064, 0.0051675)\": 0.3171, \"(0.0051675, 0.0052465)\": 0.3554, \"(0.0052465, 0.0054895)\": 0.3177, \"(0.0054895, 0.00583)\": 0.2677, \"(0.00583, 0.006595999999999999)\": 0.2359, \"(0.006595999999999999, 0.006815)\": 0.2505, \"(0.006815, 0.00749)\": 0.2901, \"(0.00749, 0.008282000000000001)\": 0.279, \"(0.008282000000000001, 0.0088595)\": 0.2808, \"(0.0088595, 0.009246)\": 0.752, \"(0.009246, 0.00954)\": 0.7204, \"(0.00954, 0.009698)\": 0.6912, \"(0.009698, 0.00976)\": 0.657, \"(0.00976, 0.009788999999999999)\": 0.6156, \"(0.009788999999999999, 0.009878999999999999)\": 0.6073, \"(0.009878999999999999, 0.0099215)\": 0.072, \"(0.0099215, 0.010165)\": 0.0531, \"(0.010165, 0.010385)\": 0.0395, \"(0.010385, 0.010515)\": 0.0331, \"(0.010515, 0.010825)\": 0.0294, \"(0.010825, 0.011115)\": 0.0142, \"(0.011115, 0.011525)\": 0.0097, \"(0.011525, 0.012580000000000001)\": 0.0096, \"(0.012580000000000001, 0.012715)\": 0.0069, \"(0.012715, 0.012750000000000001)\": 0.0325, \"(0.012750000000000001, 0.01302)\": 0.0207, \"(0.01302, 0.013405)\": 0.0325, \"(0.013405, 0.01386)\": 0.0446, \"(0.01386, 0.014315)\": 0.0352, \"(0.014315, 0.015605)\": 0.0471, \"(0.015605, 0.016655000000000003)\": 0.0777, \"(0.016655000000000003, 0.017509999999999998)\": 0.107, \"(0.017509999999999998, 0.019655)\": 0.1479, \"(0.019655, 0.021525)\": 0.1225, \"(0.021525, 0.02246)\": 0.1002, \"(0.02246, 0.02611)\": 0.0864, \"(0.02611, 0.05279)\": 0.1261}\n\n\nYour task is to provide the mean value of the graph at 0.01. What is the mean value of the graph at 0.01?",
+    "-0.0546"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: CoastalVulnerability\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.03259, \"(0.5, 1.5)\": -0.02272, \"(1.5, 2.5)\": -0.0157, \"(2.5, 3.5)\": -0.00983, \"(3.5, 4.5)\": -0.00444, \"(4.5, 5.5)\": -0.00035, \"(5.5, 6.5)\": 0.00575, \"(6.5, 7.5)\": 0.01126, \"(7.5, 8.5)\": 0.01651, \"(8.5, 9.5)\": 0.02143, \"(9.5, 12.5)\": 0.02903, \"(12.5, 13.5)\": 0.03437, \"(13.5, 15.0)\": 0.04826}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0359, \"(0.5, 1.5)\": -0.02356, \"(1.5, 2.5)\": -0.01657, \"(2.5, 3.5)\": -0.01034, \"(3.5, 4.5)\": -0.0048, \"(4.5, 5.5)\": -0.00077, \"(5.5, 6.5)\": 0.00528, \"(6.5, 7.5)\": 0.01081, \"(7.5, 8.5)\": 0.01566, \"(8.5, 9.5)\": 0.02049, \"(9.5, 12.5)\": 0.02706, \"(12.5, 13.5)\": 0.0298, \"(13.5, 15.0)\": 0.0329}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02927, \"(0.5, 1.5)\": -0.02189, \"(1.5, 2.5)\": -0.01482, \"(2.5, 3.5)\": -0.00931, \"(3.5, 4.5)\": -0.00409, \"(4.5, 5.5)\": 7e-05, \"(5.5, 6.5)\": 0.00622, \"(6.5, 7.5)\": 0.0117, \"(7.5, 8.5)\": 0.01736, \"(8.5, 9.5)\": 0.02236, \"(9.5, 12.5)\": 0.031, \"(12.5, 13.5)\": 0.03893, \"(13.5, 15.0)\": 0.06363}\n\n\nYour task is to provide the mean value of the graph at 1.47. What is the mean value of the graph at 1.47?",
+    "-0.02272"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: smoothness_se\nFeature Type: continuous\nMeans: {\"(0.001713, 0.0031539999999999997)\": 0.2958, \"(0.0031539999999999997, 0.003299)\": 0.2615, \"(0.003299, 0.003384)\": 0.185, \"(0.003384, 0.0034675)\": -0.1523, \"(0.0034675, 0.0036699999999999997)\": -0.1838, \"(0.0036699999999999997, 0.0041069999999999995)\": -0.2174, \"(0.0041069999999999995, 0.004215)\": -0.2532, \"(0.004215, 0.004436)\": -0.2879, \"(0.004436, 0.0045775)\": -0.3223, \"(0.0045775, 0.004612)\": -0.2905, \"(0.004612, 0.0048915)\": -0.2425, \"(0.0048915, 0.0053335)\": -0.2106, \"(0.0053335, 0.005443)\": -0.1771, \"(0.005443, 0.00554)\": -0.1453, \"(0.00554, 0.005729)\": -0.1136, \"(0.005729, 0.0058625)\": -0.0812, \"(0.0058625, 0.0058955)\": -0.0495, \"(0.0058955, 0.0067525)\": 0.0229, \"(0.0067525, 0.00682)\": 0.0562, \"(0.00682, 0.007338)\": 0.1146, \"(0.007338, 0.0074805)\": 0.1474, \"(0.0074805, 0.007967)\": 0.1839, \"(0.007967, 0.009857000000000001)\": 0.219, \"(0.009857000000000001, 0.010665000000000001)\": 0.1863, \"(0.010665000000000001, 0.011054999999999999)\": 0.1538, \"(0.011054999999999999, 0.011915)\": 0.1219, \"(0.011915, 0.012885)\": 0.0873, \"(0.012885, 0.03113)\": 0.0542}\nLower Bounds (95%-Confidence Interval): {\"(0.001713, 0.0031539999999999997)\": -0.864, \"(0.0031539999999999997, 0.003299)\": -0.919, \"(0.003299, 0.003384)\": -1.0196, \"(0.003384, 0.0034675)\": -0.6905, \"(0.0034675, 0.0036699999999999997)\": -0.7233, \"(0.0036699999999999997, 0.0041069999999999995)\": -0.7618, \"(0.0041069999999999995, 0.004215)\": -0.7976, \"(0.004215, 0.004436)\": -0.8492, \"(0.004436, 0.0045775)\": -0.8863, \"(0.0045775, 0.004612)\": -0.8426, \"(0.004612, 0.0048915)\": -0.7021, \"(0.0048915, 0.0053335)\": -0.6905, \"(0.0053335, 0.005443)\": -0.6659, \"(0.005443, 0.00554)\": -0.624, \"(0.00554, 0.005729)\": -0.5761, \"(0.005729, 0.0058625)\": -0.538, \"(0.0058625, 0.0058955)\": -0.5073, \"(0.0058955, 0.0067525)\": -0.1186, \"(0.0067525, 0.00682)\": -0.0928, \"(0.00682, 0.007338)\": -0.288, \"(0.007338, 0.0074805)\": -0.2553, \"(0.0074805, 0.007967)\": -0.2176, \"(0.007967, 0.009857000000000001)\": -0.1787, \"(0.009857000000000001, 0.010665000000000001)\": -0.2012, \"(0.010665000000000001, 0.011054999999999999)\": -0.2344, \"(0.011054999999999999, 0.011915)\": -0.2614, \"(0.011915, 0.012885)\": -0.2838, \"(0.012885, 0.03113)\": -0.4136}\nUpper Bounds (95%-Confidence Interval): {\"(0.001713, 0.0031539999999999997)\": 1.4555, \"(0.0031539999999999997, 0.003299)\": 1.442, \"(0.003299, 0.003384)\": 1.3896, \"(0.003384, 0.0034675)\": 0.386, \"(0.0034675, 0.0036699999999999997)\": 0.3557, \"(0.0036699999999999997, 0.0041069999999999995)\": 0.327, \"(0.0041069999999999995, 0.004215)\": 0.2913, \"(0.004215, 0.004436)\": 0.2734, \"(0.004436, 0.0045775)\": 0.2417, \"(0.0045775, 0.004612)\": 0.2615, \"(0.004612, 0.0048915)\": 0.2171, \"(0.0048915, 0.0053335)\": 0.2692, \"(0.0053335, 0.005443)\": 0.3117, \"(0.005443, 0.00554)\": 0.3335, \"(0.00554, 0.005729)\": 0.349, \"(0.005729, 0.0058625)\": 0.3757, \"(0.0058625, 0.0058955)\": 0.4082, \"(0.0058955, 0.0067525)\": 0.1644, \"(0.0067525, 0.00682)\": 0.2053, \"(0.00682, 0.007338)\": 0.5173, \"(0.007338, 0.0074805)\": 0.5502, \"(0.0074805, 0.007967)\": 0.5854, \"(0.007967, 0.009857000000000001)\": 0.6167, \"(0.009857000000000001, 0.010665000000000001)\": 0.5738, \"(0.010665000000000001, 0.011054999999999999)\": 0.5419, \"(0.011054999999999999, 0.011915)\": 0.5053, \"(0.011915, 0.012885)\": 0.4585, \"(0.012885, 0.03113)\": 0.522}\n\n\nYour task is to provide the mean value of the graph at 0.01. What is the mean value of the graph at 0.01?",
+    "0.1863"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: BMI\nFeature Type: continuous\nMeans: {\"(0.0, 9.1)\": -0.7, \"(9.1, 22.55)\": -0.961, \"(22.55, 23.65)\": -0.856, \"(23.65, 25.55)\": -0.762, \"(25.55, 26.35)\": -0.661, \"(26.35, 27.65)\": -0.24, \"(27.65, 28.45)\": -0.144, \"(28.45, 29.65)\": -0.051, \"(29.65, 30.45)\": 0.049, \"(30.45, 32.150000000000006)\": 0.153, \"(32.150000000000006, 37.650000000000006)\": 0.246, \"(37.650000000000006, 41.75)\": 0.34, \"(41.75, 42.849999999999994)\": 0.434, \"(42.849999999999994, 45.650000000000006)\": 0.529, \"(45.650000000000006, 48.349999999999994)\": 0.626, \"(48.349999999999994, 67.1)\": 0.784}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 9.1)\": -1.139, \"(9.1, 22.55)\": -1.349, \"(22.55, 23.65)\": -1.219, \"(23.65, 25.55)\": -1.281, \"(25.55, 26.35)\": -1.231, \"(26.35, 27.65)\": -0.568, \"(27.65, 28.45)\": -0.258, \"(28.45, 29.65)\": -0.157, \"(29.65, 30.45)\": -0.11, \"(30.45, 32.150000000000006)\": -0.086, \"(32.150000000000006, 37.650000000000006)\": 0.084, \"(37.650000000000006, 41.75)\": 0.189, \"(41.75, 42.849999999999994)\": 0.28, \"(42.849999999999994, 45.650000000000006)\": 0.348, \"(45.650000000000006, 48.349999999999994)\": 0.256, \"(48.349999999999994, 67.1)\": 0.265}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 9.1)\": -0.262, \"(9.1, 22.55)\": -0.573, \"(22.55, 23.65)\": -0.493, \"(23.65, 25.55)\": -0.243, \"(25.55, 26.35)\": -0.09, \"(26.35, 27.65)\": 0.088, \"(27.65, 28.45)\": -0.03, \"(28.45, 29.65)\": 0.054, \"(29.65, 30.45)\": 0.208, \"(30.45, 32.150000000000006)\": 0.392, \"(32.150000000000006, 37.650000000000006)\": 0.409, \"(37.650000000000006, 41.75)\": 0.491, \"(41.75, 42.849999999999994)\": 0.588, \"(42.849999999999994, 45.650000000000006)\": 0.709, \"(45.650000000000006, 48.349999999999994)\": 0.996, \"(48.349999999999994, 67.1)\": 1.303}\n\n\nYour task is to provide the mean value of the graph at 46.58. What is the mean value of the graph at 46.58?",
+    "0.626"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: DiabetesPedigreeFunction\nFeature Type: continuous\nMeans: {\"(0.078, 0.1265)\": -0.528, \"(0.1265, 0.128)\": -0.218, \"(0.128, 0.2185)\": -0.342, \"(0.2185, 0.3375)\": -0.168, \"(0.3375, 0.4215)\": -0.077, \"(0.4215, 0.4955)\": 0.015, \"(0.4955, 0.5874999999999999)\": 0.131, \"(0.5874999999999999, 0.7215)\": 0.223, \"(0.7215, 0.889)\": 0.316, \"(0.889, 1.0865)\": 0.407, \"(1.0865, 1.178)\": 0.498, \"(1.178, 1.275)\": 1.018, \"(1.275, 1.3925)\": 1.283, \"(1.3925, 1.4175)\": 1.168, \"(1.4175, 1.451)\": 0.065, \"(1.451, 1.837)\": -0.193, \"(1.837, 2.137)\": -0.092}\nLower Bounds (95%-Confidence Interval): {\"(0.078, 0.1265)\": -0.817, \"(0.1265, 0.128)\": -0.817, \"(0.128, 0.2185)\": -0.618, \"(0.2185, 0.3375)\": -0.533, \"(0.3375, 0.4215)\": -0.266, \"(0.4215, 0.4955)\": -0.104, \"(0.4955, 0.5874999999999999)\": -0.054, \"(0.5874999999999999, 0.7215)\": 0.138, \"(0.7215, 0.889)\": 0.186, \"(0.889, 1.0865)\": 0.263, \"(1.0865, 1.178)\": 0.35, \"(1.178, 1.275)\": 0.124, \"(1.275, 1.3925)\": 0.133, \"(1.3925, 1.4175)\": -0.063, \"(1.4175, 1.451)\": -1.163, \"(1.451, 1.837)\": -1.466, \"(1.837, 2.137)\": -1.112}\nUpper Bounds (95%-Confidence Interval): {\"(0.078, 0.1265)\": -0.238, \"(0.1265, 0.128)\": 0.381, \"(0.128, 0.2185)\": -0.067, \"(0.2185, 0.3375)\": 0.197, \"(0.3375, 0.4215)\": 0.113, \"(0.4215, 0.4955)\": 0.135, \"(0.4955, 0.5874999999999999)\": 0.316, \"(0.5874999999999999, 0.7215)\": 0.308, \"(0.7215, 0.889)\": 0.445, \"(0.889, 1.0865)\": 0.552, \"(1.0865, 1.178)\": 0.646, \"(1.178, 1.275)\": 1.912, \"(1.275, 1.3925)\": 2.433, \"(1.3925, 1.4175)\": 2.398, \"(1.4175, 1.451)\": 1.293, \"(1.451, 1.837)\": 1.08, \"(1.837, 2.137)\": 0.928}\n\n\nYour task is to provide the mean value of the graph at 1.27. What is the mean value of the graph at 1.27?",
+    "1.018"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: WetlandLoss\nFeature Type: continuous\nMeans: {\"(0.0, 1.5)\": -0.02419, \"(1.5, 2.5)\": -0.01693, \"(2.5, 3.5)\": -0.01069, \"(3.5, 4.5)\": -0.00585, \"(4.5, 5.5)\": 0.00051, \"(5.5, 6.5)\": 0.00676, \"(6.5, 8.5)\": 0.01245, \"(8.5, 10.5)\": 0.02257, \"(10.5, 11.5)\": 0.03265, \"(11.5, 13.5)\": 0.03889, \"(13.5, 14.5)\": 0.04912, \"(14.5, 16.0)\": 0.0585}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02604, \"(1.5, 2.5)\": -0.01758, \"(2.5, 3.5)\": -0.01104, \"(3.5, 4.5)\": -0.00622, \"(4.5, 5.5)\": 0.00022, \"(5.5, 6.5)\": 0.0063, \"(6.5, 8.5)\": 0.01194, \"(8.5, 10.5)\": 0.0215, \"(10.5, 11.5)\": 0.03022, \"(11.5, 13.5)\": 0.03581, \"(13.5, 14.5)\": 0.04439, \"(14.5, 16.0)\": 0.04645}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02235, \"(1.5, 2.5)\": -0.01628, \"(2.5, 3.5)\": -0.01034, \"(3.5, 4.5)\": -0.00547, \"(4.5, 5.5)\": 0.0008, \"(5.5, 6.5)\": 0.00723, \"(6.5, 8.5)\": 0.01295, \"(8.5, 10.5)\": 0.02363, \"(10.5, 11.5)\": 0.03508, \"(11.5, 13.5)\": 0.04198, \"(13.5, 14.5)\": 0.05386, \"(14.5, 16.0)\": 0.07055}\n\n\nYour task is to provide the mean value of the graph at 4.75. What is the mean value of the graph at 4.75?",
+    "0.00051"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: id\nFeature Type: continuous\nMeans: {\"(8.0, 349.5)\": -0.1954, \"(349.5, 1899.5)\": -0.1448, \"(1899.5, 4908.5)\": -0.18, \"(4908.5, 5578.5)\": -0.2082, \"(5578.5, 5813.5)\": -0.25, \"(5813.5, 6004.5)\": -0.345, \"(6004.5, 7170.5)\": -0.1246, \"(7170.5, 7335.5)\": 0.0378, \"(7335.5, 8083.0)\": 0.1773, \"(8083.0, 8604.0)\": 0.1221, \"(8604.0, 8759.0)\": -0.0027, \"(8759.0, 45049.5)\": -0.0395, \"(45049.5, 45346.5)\": -0.3688, \"(45346.5, 46184.5)\": -0.0125, \"(46184.5, 54575.0)\": 0.0215, \"(54575.0, 55661.5)\": -0.0521, \"(55661.5, 66954.0)\": 0.0101, \"(66954.0, 67057.0)\": -0.0227, \"(67057.0, 68275.0)\": 0.0595, \"(68275.0, 97577.5)\": 0.0244, \"(97577.5, 110643.5)\": 0.0529, \"(110643.5, 146554.5)\": 0.0211, \"(146554.5, 146921.5)\": -0.0139, \"(146921.5, 147131.5)\": -0.0861, \"(147131.5, 161901.5)\": -0.0139, \"(161901.5, 162437.5)\": -0.0745, \"(162437.5, 164212.5)\": -0.0061, \"(164212.5, 164569.5)\": -0.057, \"(164569.5, 164786.5)\": 0.0766, \"(164786.5, 165030.0)\": 0.1394}\nLower Bounds (95%-Confidence Interval): {\"(8.0, 349.5)\": -0.513, \"(349.5, 1899.5)\": -0.2797, \"(1899.5, 4908.5)\": -0.4617, \"(4908.5, 5578.5)\": -0.3743, \"(5578.5, 5813.5)\": -0.5275, \"(5813.5, 6004.5)\": -0.9752, \"(6004.5, 7170.5)\": -0.3878, \"(7170.5, 7335.5)\": -0.2528, \"(7335.5, 8083.0)\": -0.2278, \"(8083.0, 8604.0)\": -0.157, \"(8604.0, 8759.0)\": -0.2655, \"(8759.0, 45049.5)\": -0.1283, \"(45049.5, 45346.5)\": -1.0587, \"(45346.5, 46184.5)\": -0.1339, \"(46184.5, 54575.0)\": -0.1154, \"(54575.0, 55661.5)\": -0.241, \"(55661.5, 66954.0)\": -0.0113, \"(66954.0, 67057.0)\": -0.3245, \"(67057.0, 68275.0)\": -0.2172, \"(68275.0, 97577.5)\": -0.0853, \"(97577.5, 110643.5)\": -0.0258, \"(110643.5, 146554.5)\": -0.1398, \"(146554.5, 146921.5)\": -0.0713, \"(146921.5, 147131.5)\": -0.6452, \"(147131.5, 161901.5)\": -0.1222, \"(161901.5, 162437.5)\": -0.3435, \"(162437.5, 164212.5)\": -0.1164, \"(164212.5, 164569.5)\": -0.2376, \"(164569.5, 164786.5)\": -0.3913, \"(164786.5, 165030.0)\": -0.3304}\nUpper Bounds (95%-Confidence Interval): {\"(8.0, 349.5)\": 0.1221, \"(349.5, 1899.5)\": -0.0099, \"(1899.5, 4908.5)\": 0.1016, \"(4908.5, 5578.5)\": -0.0421, \"(5578.5, 5813.5)\": 0.0274, \"(5813.5, 6004.5)\": 0.2852, \"(6004.5, 7170.5)\": 0.1385, \"(7170.5, 7335.5)\": 0.3284, \"(7335.5, 8083.0)\": 0.5824, \"(8083.0, 8604.0)\": 0.4011, \"(8604.0, 8759.0)\": 0.2602, \"(8759.0, 45049.5)\": 0.0493, \"(45049.5, 45346.5)\": 0.321, \"(45346.5, 46184.5)\": 0.1088, \"(46184.5, 54575.0)\": 0.1583, \"(54575.0, 55661.5)\": 0.1369, \"(55661.5, 66954.0)\": 0.0316, \"(66954.0, 67057.0)\": 0.2791, \"(67057.0, 68275.0)\": 0.3361, \"(68275.0, 97577.5)\": 0.1341, \"(97577.5, 110643.5)\": 0.1316, \"(110643.5, 146554.5)\": 0.182, \"(146554.5, 146921.5)\": 0.0435, \"(146921.5, 147131.5)\": 0.4731, \"(147131.5, 161901.5)\": 0.0945, \"(161901.5, 162437.5)\": 0.1945, \"(162437.5, 164212.5)\": 0.1042, \"(164212.5, 164569.5)\": 0.1235, \"(164569.5, 164786.5)\": 0.5445, \"(164786.5, 165030.0)\": 0.6091}\n\n\nYour task is to provide the mean value of the graph at 139172.54. What is the mean value of the graph at 139172.54?",
+    "0.0211"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Tenure\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.3765, \"(0.5, 1.5)\": -0.0692, \"(1.5, 4.5)\": -0.016, \"(4.5, 5.5)\": 0.0109, \"(5.5, 6.5)\": 0.0432, \"(6.5, 7.5)\": 0.0871, \"(7.5, 9.5)\": 0.0554, \"(9.5, 10.0)\": -0.0599}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.4596, \"(0.5, 1.5)\": -0.1046, \"(1.5, 4.5)\": -0.0506, \"(4.5, 5.5)\": -0.017, \"(5.5, 6.5)\": 0.014, \"(6.5, 7.5)\": 0.0581, \"(7.5, 9.5)\": 0.004, \"(9.5, 10.0)\": -0.1542}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.2933, \"(0.5, 1.5)\": -0.0338, \"(1.5, 4.5)\": 0.0185, \"(4.5, 5.5)\": 0.0387, \"(5.5, 6.5)\": 0.0724, \"(6.5, 7.5)\": 0.1161, \"(7.5, 9.5)\": 0.1067, \"(9.5, 10.0)\": 0.0343}\n\n\nYour task is to provide the mean value of the graph at 0.28. What is the mean value of the graph at 0.28?",
+    "-0.3765"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: serum_creatinine\nFeature Type: continuous\nMeans: {\"(0.5, 0.6499999999999999)\": -0.26, \"(0.6499999999999999, 0.725)\": -1.08, \"(0.725, 0.875)\": -3.77, \"(0.875, 0.95)\": -0.9, \"(0.95, 1.1400000000000001)\": -0.15, \"(1.1400000000000001, 1.35)\": -0.88, \"(1.35, 1.45)\": 0.2, \"(1.45, 1.55)\": 1.18, \"(1.55, 1.815)\": 2.18, \"(1.815, 2.05)\": 4.74, \"(2.05, 2.45)\": 1.14, \"(2.45, 2.6)\": 3.63, \"(2.6, 2.95)\": -0.36, \"(2.95, 3.1)\": 2.57, \"(3.1, 3.45)\": 0.36, \"(3.45, 3.6)\": 3.06, \"(3.6, 3.75)\": 6.76, \"(3.75, 3.9)\": 2.31, \"(3.9, 4.7)\": 2.92, \"(4.7, 5.949999999999999)\": 0.76, \"(5.949999999999999, 6.199999999999999)\": -0.43, \"(6.199999999999999, 6.55)\": 0.23, \"(6.55, 9.4)\": 6.97}\nLower Bounds (95%-Confidence Interval): {\"(0.5, 0.6499999999999999)\": -1.13, \"(0.6499999999999999, 0.725)\": -1.45, \"(0.725, 0.875)\": -5.7, \"(0.875, 0.95)\": -1.31, \"(0.95, 1.1400000000000001)\": -0.41, \"(1.1400000000000001, 1.35)\": -1.92, \"(1.35, 1.45)\": -0.14, \"(1.45, 1.55)\": 0.46, \"(1.55, 1.815)\": 1.68, \"(1.815, 2.05)\": 2.75, \"(2.05, 2.45)\": 0.72, \"(2.45, 2.6)\": 1.94, \"(2.6, 2.95)\": -2.5, \"(2.95, 3.1)\": 0.3, \"(3.1, 3.45)\": -0.49, \"(3.45, 3.6)\": 1.58, \"(3.6, 3.75)\": 4.55, \"(3.75, 3.9)\": 0.4, \"(3.9, 4.7)\": 0.8, \"(4.7, 5.949999999999999)\": -0.63, \"(5.949999999999999, 6.199999999999999)\": -1.75, \"(6.199999999999999, 6.55)\": -2.74, \"(6.55, 9.4)\": 5.07}\nUpper Bounds (95%-Confidence Interval): {\"(0.5, 0.6499999999999999)\": 0.62, \"(0.6499999999999999, 0.725)\": -0.72, \"(0.725, 0.875)\": -1.84, \"(0.875, 0.95)\": -0.48, \"(0.95, 1.1400000000000001)\": 0.12, \"(1.1400000000000001, 1.35)\": 0.16, \"(1.35, 1.45)\": 0.53, \"(1.45, 1.55)\": 1.89, \"(1.55, 1.815)\": 2.68, \"(1.815, 2.05)\": 6.73, \"(2.05, 2.45)\": 1.56, \"(2.45, 2.6)\": 5.32, \"(2.6, 2.95)\": 1.77, \"(2.95, 3.1)\": 4.84, \"(3.1, 3.45)\": 1.2, \"(3.45, 3.6)\": 4.53, \"(3.6, 3.75)\": 8.97, \"(3.75, 3.9)\": 4.21, \"(3.9, 4.7)\": 5.04, \"(4.7, 5.949999999999999)\": 2.14, \"(5.949999999999999, 6.199999999999999)\": 0.9, \"(6.199999999999999, 6.55)\": 3.21, \"(6.55, 9.4)\": 8.88}\n\n\nYour task is to provide the mean value of the graph at 0.91. What is the mean value of the graph at 0.91?",
+    "-0.9"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: TopographyDrainage\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.0274, \"(0.5, 1.5)\": -0.02381, \"(1.5, 2.5)\": -0.01602, \"(2.5, 3.5)\": -0.01049, \"(3.5, 4.5)\": -0.00528, \"(4.5, 5.5)\": -0.00022, \"(5.5, 6.5)\": 0.00517, \"(6.5, 7.5)\": 0.01282, \"(7.5, 8.5)\": 0.01628, \"(8.5, 9.5)\": 0.02454, \"(9.5, 10.5)\": 0.02883, \"(10.5, 11.5)\": 0.03213, \"(11.5, 17.0)\": 0.03564}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.03013, \"(0.5, 1.5)\": -0.02484, \"(1.5, 2.5)\": -0.01655, \"(2.5, 3.5)\": -0.01088, \"(3.5, 4.5)\": -0.00574, \"(4.5, 5.5)\": -0.00046, \"(5.5, 6.5)\": 0.00473, \"(6.5, 7.5)\": 0.01242, \"(7.5, 8.5)\": 0.01574, \"(8.5, 9.5)\": 0.02354, \"(9.5, 10.5)\": 0.0277, \"(10.5, 11.5)\": 0.03039, \"(11.5, 17.0)\": 0.02281}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02466, \"(0.5, 1.5)\": -0.02278, \"(1.5, 2.5)\": -0.0155, \"(2.5, 3.5)\": -0.0101, \"(3.5, 4.5)\": -0.00482, \"(4.5, 5.5)\": 2e-05, \"(5.5, 6.5)\": 0.00561, \"(6.5, 7.5)\": 0.01323, \"(7.5, 8.5)\": 0.01683, \"(8.5, 9.5)\": 0.02554, \"(9.5, 10.5)\": 0.02996, \"(10.5, 11.5)\": 0.03386, \"(11.5, 17.0)\": 0.04848}\n\n\nYour task is to provide the mean value of the graph at 3.38. What is the mean value of the graph at 3.38?",
+    "-0.01049"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: serum_sodium\nFeature Type: continuous\nMeans: {\"(113.0, 114.5)\": -1.269, \"(114.5, 118.5)\": 0.283, \"(118.5, 124.5)\": 3.539, \"(124.5, 126.5)\": 2.46, \"(126.5, 127.5)\": 4.042, \"(127.5, 129.5)\": 3.553, \"(129.5, 130.5)\": 0.953, \"(130.5, 132.5)\": 1.22, \"(132.5, 133.5)\": -1.094, \"(133.5, 135.5)\": 0.587, \"(135.5, 138.5)\": -0.629, \"(138.5, 144.5)\": -0.233, \"(144.5, 148.0)\": 0.113}\nLower Bounds (95%-Confidence Interval): {\"(113.0, 114.5)\": -3.483, \"(114.5, 118.5)\": -4.768, \"(118.5, 124.5)\": 2.536, \"(124.5, 126.5)\": 1.699, \"(126.5, 127.5)\": 3.034, \"(127.5, 129.5)\": 2.614, \"(129.5, 130.5)\": 0.389, \"(130.5, 132.5)\": 0.304, \"(132.5, 133.5)\": -2.269, \"(133.5, 135.5)\": 0.366, \"(135.5, 138.5)\": -0.879, \"(138.5, 144.5)\": -0.845, \"(144.5, 148.0)\": -0.129}\nUpper Bounds (95%-Confidence Interval): {\"(113.0, 114.5)\": 0.944, \"(114.5, 118.5)\": 5.334, \"(118.5, 124.5)\": 4.542, \"(124.5, 126.5)\": 3.222, \"(126.5, 127.5)\": 5.05, \"(127.5, 129.5)\": 4.492, \"(129.5, 130.5)\": 1.517, \"(130.5, 132.5)\": 2.136, \"(132.5, 133.5)\": 0.08, \"(133.5, 135.5)\": 0.808, \"(135.5, 138.5)\": -0.38, \"(138.5, 144.5)\": 0.38, \"(144.5, 148.0)\": 0.354}\n\n\nYour task is to provide the mean value of the graph at 130.05. What is the mean value of the graph at 130.05?",
+    "0.953"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: total_bedrooms\nFeature Type: continuous\nMeans: {\"(2.0, 4.5)\": -10633.3, \"(4.5, 9.5)\": -19829.1, \"(9.5, 12.5)\": -33356.0, \"(12.5, 14.5)\": -27510.0, \"(14.5, 17.5)\": -34141.4, \"(17.5, 20.5)\": -50740.7, \"(20.5, 22.5)\": -59049.5, \"(22.5, 25.5)\": -37177.7, \"(25.5, 29.5)\": -30710.5, \"(29.5, 111.5)\": -36287.1, \"(111.5, 112.5)\": -22540.1, \"(112.5, 176.5)\": -33870.1, \"(176.5, 245.5)\": -27701.3, \"(245.5, 265.5)\": -20526.0, \"(265.5, 268.5)\": -26170.7, \"(268.5, 317.5)\": -17267.5, \"(317.5, 424.5)\": -8013.2, \"(424.5, 463.5)\": -1894.5, \"(463.5, 512.5)\": 5095.6, \"(512.5, 513.5)\": 17024.1, \"(513.5, 655.5)\": 9371.5, \"(655.5, 697.5)\": 15515.9, \"(697.5, 776.5)\": 22859.4, \"(776.5, 779.5)\": 13774.7, \"(779.5, 1008.5)\": 22608.4, \"(1008.5, 1012.5)\": 37458.5, \"(1012.5, 1081.5)\": 30023.9, \"(1081.5, 1449.5)\": 37066.8, \"(1449.5, 1490.5)\": 51601.0, \"(1490.5, 1616.0)\": 42837.8, \"(1616.0, 2714.5)\": 49023.6, \"(2714.5, 2865.5)\": 40592.1, \"(2865.5, 6445.0)\": 51586.1}\nLower Bounds (95%-Confidence Interval): {\"(2.0, 4.5)\": -43650.4, \"(4.5, 9.5)\": -54645.6, \"(9.5, 12.5)\": -52929.5, \"(12.5, 14.5)\": -57181.8, \"(14.5, 17.5)\": -49207.2, \"(17.5, 20.5)\": -72519.5, \"(20.5, 22.5)\": -82934.2, \"(22.5, 25.5)\": -50942.7, \"(25.5, 29.5)\": -45748.1, \"(29.5, 111.5)\": -47452.5, \"(111.5, 112.5)\": -42457.2, \"(112.5, 176.5)\": -41599.3, \"(176.5, 245.5)\": -35478.0, \"(245.5, 265.5)\": -27520.5, \"(265.5, 268.5)\": -32234.3, \"(268.5, 317.5)\": -23732.7, \"(317.5, 424.5)\": -13237.9, \"(424.5, 463.5)\": -7023.7, \"(463.5, 512.5)\": -1510.7, \"(512.5, 513.5)\": 6820.8, \"(513.5, 655.5)\": 341.5, \"(655.5, 697.5)\": 12634.4, \"(697.5, 776.5)\": 15982.1, \"(776.5, 779.5)\": 221.5, \"(779.5, 1008.5)\": 18345.9, \"(1008.5, 1012.5)\": 20622.3, \"(1012.5, 1081.5)\": 21931.2, \"(1081.5, 1449.5)\": 22140.8, \"(1449.5, 1490.5)\": 39761.7, \"(1490.5, 1616.0)\": 35441.7, \"(1616.0, 2714.5)\": 37135.8, \"(2714.5, 2865.5)\": 32716.4, \"(2865.5, 6445.0)\": 42203.8}\nUpper Bounds (95%-Confidence Interval): {\"(2.0, 4.5)\": 22383.8, \"(4.5, 9.5)\": 14987.3, \"(9.5, 12.5)\": -13782.5, \"(12.5, 14.5)\": 2161.9, \"(14.5, 17.5)\": -19075.5, \"(17.5, 20.5)\": -28961.9, \"(20.5, 22.5)\": -35164.8, \"(22.5, 25.5)\": -23412.7, \"(25.5, 29.5)\": -15672.9, \"(29.5, 111.5)\": -25121.6, \"(111.5, 112.5)\": -2622.9, \"(112.5, 176.5)\": -26141.0, \"(176.5, 245.5)\": -19924.6, \"(245.5, 265.5)\": -13531.5, \"(265.5, 268.5)\": -20107.0, \"(268.5, 317.5)\": -10802.3, \"(317.5, 424.5)\": -2788.6, \"(424.5, 463.5)\": 3234.7, \"(463.5, 512.5)\": 11701.8, \"(512.5, 513.5)\": 27227.4, \"(513.5, 655.5)\": 18401.4, \"(655.5, 697.5)\": 18397.4, \"(697.5, 776.5)\": 29736.8, \"(776.5, 779.5)\": 27327.8, \"(779.5, 1008.5)\": 26870.8, \"(1008.5, 1012.5)\": 54294.7, \"(1012.5, 1081.5)\": 38116.5, \"(1081.5, 1449.5)\": 51992.8, \"(1449.5, 1490.5)\": 63440.2, \"(1490.5, 1616.0)\": 50233.9, \"(1616.0, 2714.5)\": 60911.5, \"(2714.5, 2865.5)\": 48467.9, \"(2865.5, 6445.0)\": 60968.4}\n\n\nYour task is to provide the mean value of the graph at 20.22. What is the mean value of the graph at 20.22?",
+    "-50740.7"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: InadequatePlanning\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02553, \"(0.5, 2.5)\": -0.02038, \"(2.5, 4.5)\": -0.0099, \"(4.5, 6.5)\": 0.00082, \"(6.5, 7.5)\": 0.01088, \"(7.5, 9.5)\": 0.0178, \"(9.5, 10.5)\": 0.02657, \"(10.5, 12.5)\": 0.0329, \"(12.5, 13.5)\": 0.03982, \"(13.5, 15.0)\": 0.05043, \"(15.0, 16.0)\": 0.06084}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02806, \"(0.5, 2.5)\": -0.02117, \"(2.5, 4.5)\": -0.01033, \"(4.5, 6.5)\": 0.00032, \"(6.5, 7.5)\": 0.01025, \"(7.5, 9.5)\": 0.01687, \"(9.5, 10.5)\": 0.02522, \"(10.5, 12.5)\": 0.02998, \"(12.5, 13.5)\": 0.03567, \"(13.5, 15.0)\": 0.03659, \"(15.0, 16.0)\": 0.04096}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.023, \"(0.5, 2.5)\": -0.01959, \"(2.5, 4.5)\": -0.00946, \"(4.5, 6.5)\": 0.00132, \"(6.5, 7.5)\": 0.0115, \"(7.5, 9.5)\": 0.01874, \"(9.5, 10.5)\": 0.02792, \"(10.5, 12.5)\": 0.03583, \"(12.5, 13.5)\": 0.04397, \"(13.5, 15.0)\": 0.06426, \"(15.0, 16.0)\": 0.08071}\n\n\nYour task is to provide the mean value of the graph at 2.8. What is the mean value of the graph at 2.8?",
+    "-0.0099"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Spa\nFeature Type: continuous\nMeans: {\"(0.0, 130.5)\": 0.521, \"(130.5, 278.5)\": 0.118, \"(278.5, 452.5)\": -0.285, \"(452.5, 754.5)\": -0.907, \"(754.5, 1209.5)\": -1.309, \"(1209.5, 1808.0)\": -1.712, \"(1808.0, 2204.5)\": -3.029, \"(2204.5, 2207.5)\": -2.456, \"(2207.5, 2428.0)\": -2.956, \"(2428.0, 2462.5)\": -2.512, \"(2462.5, 2714.5)\": -3.402, \"(2714.5, 2745.0)\": -2.902, \"(2745.0, 2993.5)\": -4.077, \"(2993.5, 3132.0)\": -4.481, \"(3132.0, 3705.5)\": -5.377, \"(3705.5, 3747.0)\": -4.36, \"(3747.0, 22408.0)\": -7.183}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 130.5)\": 0.36, \"(130.5, 278.5)\": -1.599, \"(278.5, 452.5)\": -1.362, \"(452.5, 754.5)\": -1.291, \"(754.5, 1209.5)\": -2.117, \"(1209.5, 1808.0)\": -2.592, \"(1808.0, 2204.5)\": -3.856, \"(2204.5, 2207.5)\": -3.562, \"(2207.5, 2428.0)\": -3.549, \"(2428.0, 2462.5)\": -3.455, \"(2462.5, 2714.5)\": -4.525, \"(2714.5, 2745.0)\": -4.721, \"(2745.0, 2993.5)\": -5.493, \"(2993.5, 3132.0)\": -6.214, \"(3132.0, 3705.5)\": -6.767, \"(3705.5, 3747.0)\": -6.498, \"(3747.0, 22408.0)\": -9.024}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 130.5)\": 0.682, \"(130.5, 278.5)\": 1.834, \"(278.5, 452.5)\": 0.791, \"(452.5, 754.5)\": -0.524, \"(754.5, 1209.5)\": -0.502, \"(1209.5, 1808.0)\": -0.831, \"(1808.0, 2204.5)\": -2.202, \"(2204.5, 2207.5)\": -1.35, \"(2207.5, 2428.0)\": -2.364, \"(2428.0, 2462.5)\": -1.569, \"(2462.5, 2714.5)\": -2.28, \"(2714.5, 2745.0)\": -1.083, \"(2745.0, 2993.5)\": -2.661, \"(2993.5, 3132.0)\": -2.749, \"(3132.0, 3705.5)\": -3.986, \"(3705.5, 3747.0)\": -2.222, \"(3747.0, 22408.0)\": -5.342}\n\n\nYour task is to provide the mean value of the graph at 1723.82. What is the mean value of the graph at 1723.82?",
+    "-1.712"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: RoomService\nFeature Type: continuous\nMeans: {\"(0.0, 105.5)\": 0.328, \"(105.5, 296.5)\": 0.028, \"(296.5, 335.5)\": -0.208, \"(335.5, 340.0)\": 0.165, \"(340.0, 343.0)\": -0.1, \"(343.0, 596.5)\": -0.741, \"(596.5, 712.5)\": -0.978, \"(712.5, 734.0)\": -1.212, \"(734.0, 800.0)\": -1.446, \"(800.0, 816.0)\": -1.136, \"(816.0, 997.5)\": -1.454, \"(997.5, 1031.0)\": -1.106, \"(1031.0, 1041.0)\": -1.368, \"(1041.0, 2172.5)\": -1.866, \"(2172.5, 2283.5)\": -1.455, \"(2283.5, 2313.5)\": -1.171, \"(2313.5, 2336.5)\": -0.66, \"(2336.5, 2420.0)\": -2.559, \"(2420.0, 2992.5)\": -3.229, \"(2992.5, 3006.0)\": -2.708, \"(3006.0, 3196.5)\": -2.984, \"(3196.5, 3249.5)\": -2.709, \"(3249.5, 14327.0)\": -4.146}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 105.5)\": -0.06, \"(105.5, 296.5)\": -0.369, \"(296.5, 335.5)\": -1.022, \"(335.5, 340.0)\": -0.184, \"(340.0, 343.0)\": -1.038, \"(343.0, 596.5)\": -1.323, \"(596.5, 712.5)\": -1.547, \"(712.5, 734.0)\": -1.555, \"(734.0, 800.0)\": -1.8, \"(800.0, 816.0)\": -2.191, \"(816.0, 997.5)\": -1.824, \"(997.5, 1031.0)\": -1.706, \"(1031.0, 1041.0)\": -2.147, \"(1041.0, 2172.5)\": -2.244, \"(2172.5, 2283.5)\": -2.248, \"(2283.5, 2313.5)\": -1.568, \"(2313.5, 2336.5)\": -2.21, \"(2336.5, 2420.0)\": -3.537, \"(2420.0, 2992.5)\": -3.89, \"(2992.5, 3006.0)\": -3.955, \"(3006.0, 3196.5)\": -4.24, \"(3196.5, 3249.5)\": -3.98, \"(3249.5, 14327.0)\": -5.248}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 105.5)\": 0.716, \"(105.5, 296.5)\": 0.425, \"(296.5, 335.5)\": 0.607, \"(335.5, 340.0)\": 0.513, \"(340.0, 343.0)\": 0.837, \"(343.0, 596.5)\": -0.16, \"(596.5, 712.5)\": -0.409, \"(712.5, 734.0)\": -0.869, \"(734.0, 800.0)\": -1.092, \"(800.0, 816.0)\": -0.082, \"(816.0, 997.5)\": -1.083, \"(997.5, 1031.0)\": -0.506, \"(1031.0, 1041.0)\": -0.589, \"(1041.0, 2172.5)\": -1.488, \"(2172.5, 2283.5)\": -0.661, \"(2283.5, 2313.5)\": -0.774, \"(2313.5, 2336.5)\": 0.89, \"(2336.5, 2420.0)\": -1.582, \"(2420.0, 2992.5)\": -2.569, \"(2992.5, 3006.0)\": -1.461, \"(3006.0, 3196.5)\": -1.727, \"(3196.5, 3249.5)\": -1.438, \"(3249.5, 14327.0)\": -3.043}\n\n\nYour task is to provide the mean value of the graph at 6176.67. What is the mean value of the graph at 6176.67?",
+    "-4.146"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Relationship\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": 0.511, \"(0.5, 1.5)\": -0.233, \"(1.5, 2.5)\": -0.666, \"(2.5, 3.5)\": -1.006, \"(3.5, 4.5)\": -0.529, \"(4.5, 5.0)\": 1.753}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.453, \"(0.5, 1.5)\": -0.278, \"(1.5, 2.5)\": -0.789, \"(2.5, 3.5)\": -1.092, \"(3.5, 4.5)\": -0.6, \"(4.5, 5.0)\": 1.664}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.568, \"(0.5, 1.5)\": -0.188, \"(1.5, 2.5)\": -0.543, \"(2.5, 3.5)\": -0.921, \"(3.5, 4.5)\": -0.458, \"(4.5, 5.0)\": 1.842}\n\n\nYour task is to provide the mean value of the graph at 4.16. What is the mean value of the graph at 4.16?",
+    "-0.529"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: id\nFeature Type: continuous\nMeans: {\"(91.0, 2307.0)\": 0.00838, \"(2307.0, 4713.5)\": 0.00964, \"(4713.5, 6928.5)\": 0.0038, \"(6928.5, 9761.5)\": 0.00118, \"(9761.5, 13120.0)\": -0.00051, \"(13120.0, 14826.0)\": -0.00127, \"(14826.0, 20043.5)\": 5e-05, \"(20043.5, 22448.0)\": 0.00075, \"(22448.0, 23794.0)\": -0.00133, \"(23794.0, 28014.5)\": -0.00281, \"(28014.5, 28671.0)\": -0.00155, \"(28671.0, 37439.5)\": -0.00049, \"(37439.5, 40007.0)\": 0.00015, \"(40007.0, 41128.5)\": 0.00473, \"(41128.5, 50305.5)\": -0.0009, \"(50305.5, 51818.5)\": -0.00193, \"(51818.5, 66668.0)\": -0.00104, \"(66668.0, 67776.0)\": 0.0019, \"(67776.0, 75664.5)\": 1e-05, \"(75664.5, 76606.0)\": 0.0007, \"(76606.0, 89235.5)\": 0.00161, \"(89235.5, 227800.5)\": -0.00038, \"(227800.5, 231707.5)\": 0.00024, \"(231707.5, 257871.0)\": -0.00045, \"(257871.0, 503283.0)\": 0.00017, \"(503283.0, 507804.5)\": -0.00061, \"(507804.5, 517795.0)\": -0.00125, \"(517795.0, 616121.0)\": -0.00038, \"(616121.0, 622616.5)\": 0.00042, \"(622616.5, 647046.0)\": -0.00022, \"(647046.0, 662956.5)\": 0.00117, \"(662956.5, 667208.5)\": -0.00102, \"(667208.5, 689123.0)\": 0.00021, \"(689123.0, 872554.5)\": -0.00065, \"(872554.5, 942666.5)\": 0.00032, \"(942666.5, 983736.5)\": -0.00052, \"(983736.5, 1025442.0)\": 0.00017, \"(1025442.0, 1029281.5)\": -0.00099, \"(1029281.5, 1040563.0)\": -0.00029, \"(1040563.0, 1103097.0)\": 0.00069, \"(1103097.0, 1103695.0)\": 0.00289, \"(1103695.0, 1104610.5)\": -0.00013, \"(1104610.5, 1109548.0)\": 0.00181, \"(1109548.0, 1113474.5)\": 1e-05, \"(1113474.5, 1114673.5)\": -0.00091, \"(1114673.5, 1116159.5)\": 0.00326, \"(1116159.5, 1117955.0)\": 0.00422}\nLower Bounds (95%-Confidence Interval): {\"(91.0, 2307.0)\": 0.00427, \"(2307.0, 4713.5)\": 0.00579, \"(4713.5, 6928.5)\": 0.00041, \"(6928.5, 9761.5)\": -0.0007, \"(9761.5, 13120.0)\": -0.00221, \"(13120.0, 14826.0)\": -0.00399, \"(14826.0, 20043.5)\": -0.00257, \"(20043.5, 22448.0)\": -0.00208, \"(22448.0, 23794.0)\": -0.00391, \"(23794.0, 28014.5)\": -0.0066, \"(28014.5, 28671.0)\": -0.00305, \"(28671.0, 37439.5)\": -0.00164, \"(37439.5, 40007.0)\": -0.00259, \"(40007.0, 41128.5)\": -0.00544, \"(41128.5, 50305.5)\": -0.00224, \"(50305.5, 51818.5)\": -0.00485, \"(51818.5, 66668.0)\": -0.00248, \"(66668.0, 67776.0)\": -0.00461, \"(67776.0, 75664.5)\": -0.0014, \"(75664.5, 76606.0)\": -0.0013, \"(76606.0, 89235.5)\": -0.00066, \"(89235.5, 227800.5)\": -0.00254, \"(227800.5, 231707.5)\": -0.00057, \"(231707.5, 257871.0)\": -0.00369, \"(257871.0, 503283.0)\": -0.0015, \"(503283.0, 507804.5)\": -0.00235, \"(507804.5, 517795.0)\": -0.00383, \"(517795.0, 616121.0)\": -0.00166, \"(616121.0, 622616.5)\": -0.00158, \"(622616.5, 647046.0)\": -0.00171, \"(647046.0, 662956.5)\": -0.00058, \"(662956.5, 667208.5)\": -0.00336, \"(667208.5, 689123.0)\": -0.00275, \"(689123.0, 872554.5)\": -0.00294, \"(872554.5, 942666.5)\": -0.00097, \"(942666.5, 983736.5)\": -0.00265, \"(983736.5, 1025442.0)\": -0.00252, \"(1025442.0, 1029281.5)\": -0.00403, \"(1029281.5, 1040563.0)\": -0.00272, \"(1040563.0, 1103097.0)\": -0.00112, \"(1103097.0, 1103695.0)\": -0.00196, \"(1103695.0, 1104610.5)\": -0.00237, \"(1104610.5, 1109548.0)\": -0.00097, \"(1109548.0, 1113474.5)\": -0.00207, \"(1113474.5, 1114673.5)\": -0.00422, \"(1114673.5, 1116159.5)\": -0.00105, \"(1116159.5, 1117955.0)\": 0.00066}\nUpper Bounds (95%-Confidence Interval): {\"(91.0, 2307.0)\": 0.01249, \"(2307.0, 4713.5)\": 0.01349, \"(4713.5, 6928.5)\": 0.00719, \"(6928.5, 9761.5)\": 0.00306, \"(9761.5, 13120.0)\": 0.0012, \"(13120.0, 14826.0)\": 0.00144, \"(14826.0, 20043.5)\": 0.00267, \"(20043.5, 22448.0)\": 0.00358, \"(22448.0, 23794.0)\": 0.00124, \"(23794.0, 28014.5)\": 0.00098, \"(28014.5, 28671.0)\": -5e-05, \"(28671.0, 37439.5)\": 0.00065, \"(37439.5, 40007.0)\": 0.00288, \"(40007.0, 41128.5)\": 0.0149, \"(41128.5, 50305.5)\": 0.00044, \"(50305.5, 51818.5)\": 0.00099, \"(51818.5, 66668.0)\": 0.0004, \"(66668.0, 67776.0)\": 0.00841, \"(67776.0, 75664.5)\": 0.00142, \"(75664.5, 76606.0)\": 0.00269, \"(76606.0, 89235.5)\": 0.00388, \"(89235.5, 227800.5)\": 0.00178, \"(227800.5, 231707.5)\": 0.00106, \"(231707.5, 257871.0)\": 0.00278, \"(257871.0, 503283.0)\": 0.00185, \"(503283.0, 507804.5)\": 0.00113, \"(507804.5, 517795.0)\": 0.00134, \"(517795.0, 616121.0)\": 0.0009, \"(616121.0, 622616.5)\": 0.00241, \"(622616.5, 647046.0)\": 0.00128, \"(647046.0, 662956.5)\": 0.00293, \"(662956.5, 667208.5)\": 0.00132, \"(667208.5, 689123.0)\": 0.00317, \"(689123.0, 872554.5)\": 0.00165, \"(872554.5, 942666.5)\": 0.0016, \"(942666.5, 983736.5)\": 0.00161, \"(983736.5, 1025442.0)\": 0.00285, \"(1025442.0, 1029281.5)\": 0.00205, \"(1029281.5, 1040563.0)\": 0.00215, \"(1040563.0, 1103097.0)\": 0.0025, \"(1103097.0, 1103695.0)\": 0.00774, \"(1103695.0, 1104610.5)\": 0.00212, \"(1104610.5, 1109548.0)\": 0.00458, \"(1109548.0, 1113474.5)\": 0.00208, \"(1113474.5, 1114673.5)\": 0.00241, \"(1114673.5, 1116159.5)\": 0.00758, \"(1116159.5, 1117955.0)\": 0.00778}\n\n\nYour task is to provide the mean value of the graph at 68251.52. What is the mean value of the graph at 68251.52?",
+    "1e-05"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: CustomerId\nFeature Type: continuous\nMeans: {\"(15565796.0, 15566519.0)\": -0.8769, \"(15566519.0, 15567333.5)\": -0.8241, \"(15567333.5, 15567844.5)\": -0.1763, \"(15567844.5, 15568343.5)\": 0.0021, \"(15568343.5, 15571612.0)\": -0.2283, \"(15571612.0, 15571858.5)\": -0.0522, \"(15571858.5, 15591260.5)\": -0.1299, \"(15591260.5, 15598058.0)\": -0.0821, \"(15598058.0, 15602525.5)\": -0.1509, \"(15602525.5, 15607288.0)\": -0.0818, \"(15607288.0, 15664896.0)\": -0.0316, \"(15664896.0, 15772587.0)\": 0.0162, \"(15772587.0, 15797097.0)\": 0.0757, \"(15797097.0, 15799214.0)\": 0.0081, \"(15799214.0, 15807559.5)\": 0.0581, \"(15807559.5, 15812616.5)\": -0.0049, \"(15812616.5, 15814479.0)\": -0.0569, \"(15814479.0, 15815247.5)\": -0.111, \"(15815247.5, 15815626.0)\": -0.0335}\nLower Bounds (95%-Confidence Interval): {\"(15565796.0, 15566519.0)\": -1.3796, \"(15566519.0, 15567333.5)\": -1.4199, \"(15567333.5, 15567844.5)\": -0.741, \"(15567844.5, 15568343.5)\": -0.4552, \"(15568343.5, 15571612.0)\": -0.4861, \"(15571612.0, 15571858.5)\": -0.3268, \"(15571858.5, 15591260.5)\": -0.2064, \"(15591260.5, 15598058.0)\": -0.1582, \"(15598058.0, 15602525.5)\": -0.5056, \"(15602525.5, 15607288.0)\": -0.1812, \"(15607288.0, 15664896.0)\": -0.056, \"(15664896.0, 15772587.0)\": -0.142, \"(15772587.0, 15797097.0)\": -0.0689, \"(15797097.0, 15799214.0)\": -0.206, \"(15799214.0, 15807559.5)\": -0.0544, \"(15807559.5, 15812616.5)\": -0.1396, \"(15812616.5, 15814479.0)\": -0.2475, \"(15814479.0, 15815247.5)\": -0.4076, \"(15815247.5, 15815626.0)\": -0.3716}\nUpper Bounds (95%-Confidence Interval): {\"(15565796.0, 15566519.0)\": -0.3742, \"(15566519.0, 15567333.5)\": -0.2283, \"(15567333.5, 15567844.5)\": 0.3884, \"(15567844.5, 15568343.5)\": 0.4594, \"(15568343.5, 15571612.0)\": 0.0295, \"(15571612.0, 15571858.5)\": 0.2223, \"(15571858.5, 15591260.5)\": -0.0535, \"(15591260.5, 15598058.0)\": -0.0061, \"(15598058.0, 15602525.5)\": 0.2038, \"(15602525.5, 15607288.0)\": 0.0176, \"(15607288.0, 15664896.0)\": -0.0071, \"(15664896.0, 15772587.0)\": 0.1744, \"(15772587.0, 15797097.0)\": 0.2202, \"(15797097.0, 15799214.0)\": 0.2223, \"(15799214.0, 15807559.5)\": 0.1706, \"(15807559.5, 15812616.5)\": 0.1298, \"(15812616.5, 15814479.0)\": 0.1336, \"(15814479.0, 15815247.5)\": 0.1855, \"(15815247.5, 15815626.0)\": 0.3046}\n\n\nYour task is to provide the mean value of the graph at 15804780.51. What is the mean value of the graph at 15804780.51?",
+    "0.0581"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: VRDeck\nFeature Type: continuous\nMeans: {\"(0.0, 135.5)\": 0.445, \"(135.5, 215.5)\": 0.073, \"(215.5, 500.5)\": -0.294, \"(500.5, 727.5)\": -0.661, \"(727.5, 799.5)\": -1.026, \"(799.5, 831.5)\": -0.601, \"(831.5, 872.5)\": -1.156, \"(872.5, 993.5)\": -1.633, \"(993.5, 1430.5)\": -2.012, \"(1430.5, 1514.5)\": -1.512, \"(1514.5, 1796.0)\": -2.212, \"(1796.0, 1909.5)\": -1.699, \"(1909.5, 1970.0)\": -2.568, \"(1970.0, 2571.5)\": -3.006, \"(2571.5, 2582.0)\": -2.375, \"(2582.0, 2657.0)\": -2.964, \"(2657.0, 3710.5)\": -3.98, \"(3710.5, 4089.0)\": -4.347, \"(4089.0, 5089.5)\": -5.923, \"(5089.5, 24133.0)\": -6.634}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 135.5)\": -0.055, \"(135.5, 215.5)\": -0.275, \"(215.5, 500.5)\": -1.359, \"(500.5, 727.5)\": -0.968, \"(727.5, 799.5)\": -1.273, \"(799.5, 831.5)\": -1.285, \"(831.5, 872.5)\": -1.782, \"(872.5, 993.5)\": -2.358, \"(993.5, 1430.5)\": -2.589, \"(1430.5, 1514.5)\": -2.382, \"(1514.5, 1796.0)\": -2.87, \"(1796.0, 1909.5)\": -3.449, \"(1909.5, 1970.0)\": -3.46, \"(1970.0, 2571.5)\": -4.009, \"(2571.5, 2582.0)\": -4.195, \"(2582.0, 2657.0)\": -4.898, \"(2657.0, 3710.5)\": -5.152, \"(3710.5, 4089.0)\": -5.79, \"(4089.0, 5089.5)\": -7.804, \"(5089.5, 24133.0)\": -8.247}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 135.5)\": 0.945, \"(135.5, 215.5)\": 0.422, \"(215.5, 500.5)\": 0.772, \"(500.5, 727.5)\": -0.354, \"(727.5, 799.5)\": -0.779, \"(799.5, 831.5)\": 0.083, \"(831.5, 872.5)\": -0.529, \"(872.5, 993.5)\": -0.908, \"(993.5, 1430.5)\": -1.435, \"(1430.5, 1514.5)\": -0.643, \"(1514.5, 1796.0)\": -1.555, \"(1796.0, 1909.5)\": 0.051, \"(1909.5, 1970.0)\": -1.677, \"(1970.0, 2571.5)\": -2.002, \"(2571.5, 2582.0)\": -0.555, \"(2582.0, 2657.0)\": -1.03, \"(2657.0, 3710.5)\": -2.808, \"(3710.5, 4089.0)\": -2.905, \"(4089.0, 5089.5)\": -4.042, \"(5089.5, 24133.0)\": -5.02}\n\n\nYour task is to provide the mean value of the graph at 1555.23. What is the mean value of the graph at 1555.23?",
+    "-2.212"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Age\nFeature Type: continuous\nMeans: {\"(18.0, 32.5)\": 0.83, \"(32.5, 34.5)\": 0.681, \"(34.5, 37.5)\": 0.423, \"(37.5, 38.5)\": 0.281, \"(38.5, 39.5)\": 0.054, \"(39.5, 40.5)\": -0.193, \"(40.5, 41.5)\": -0.354, \"(41.5, 42.5)\": -0.494, \"(42.5, 44.5)\": -0.781, \"(44.5, 46.5)\": -1.075, \"(46.5, 48.5)\": -1.546, \"(48.5, 54.5)\": -1.717, \"(54.5, 56.5)\": -1.858, \"(56.5, 64.5)\": -1.707, \"(64.5, 66.5)\": -1.27, \"(66.5, 69.5)\": -1.118, \"(69.5, 70.5)\": -0.888, \"(70.5, 72.5)\": -0.587, \"(72.5, 74.5)\": -0.31, \"(74.5, 81.0)\": -0.157}\nLower Bounds (95%-Confidence Interval): {\"(18.0, 32.5)\": 0.581, \"(32.5, 34.5)\": 0.529, \"(34.5, 37.5)\": 0.367, \"(37.5, 38.5)\": 0.229, \"(38.5, 39.5)\": -0.051, \"(39.5, 40.5)\": -0.305, \"(40.5, 41.5)\": -0.462, \"(41.5, 42.5)\": -0.607, \"(42.5, 44.5)\": -0.855, \"(44.5, 46.5)\": -1.16, \"(46.5, 48.5)\": -1.704, \"(48.5, 54.5)\": -1.885, \"(54.5, 56.5)\": -2.031, \"(56.5, 64.5)\": -1.913, \"(64.5, 66.5)\": -1.66, \"(66.5, 69.5)\": -1.33, \"(69.5, 70.5)\": -1.222, \"(70.5, 72.5)\": -1.257, \"(72.5, 74.5)\": -1.055, \"(74.5, 81.0)\": -0.939}\nUpper Bounds (95%-Confidence Interval): {\"(18.0, 32.5)\": 1.079, \"(32.5, 34.5)\": 0.833, \"(34.5, 37.5)\": 0.48, \"(37.5, 38.5)\": 0.332, \"(38.5, 39.5)\": 0.159, \"(39.5, 40.5)\": -0.08, \"(40.5, 41.5)\": -0.246, \"(41.5, 42.5)\": -0.382, \"(42.5, 44.5)\": -0.706, \"(44.5, 46.5)\": -0.991, \"(46.5, 48.5)\": -1.387, \"(48.5, 54.5)\": -1.548, \"(54.5, 56.5)\": -1.684, \"(56.5, 64.5)\": -1.501, \"(64.5, 66.5)\": -0.88, \"(66.5, 69.5)\": -0.906, \"(69.5, 70.5)\": -0.554, \"(70.5, 72.5)\": 0.082, \"(72.5, 74.5)\": 0.436, \"(74.5, 81.0)\": 0.625}\n\n\nYour task is to provide the mean value of the graph at 41.25. What is the mean value of the graph at 41.25?",
+    "-0.354"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: concave points_mean\nFeature Type: continuous\nMeans: {\"(0.0, 0.0074145)\": -1.054, \"(0.0074145, 0.011665)\": -0.937, \"(0.011665, 0.01503)\": -0.821, \"(0.01503, 0.017865)\": -0.705, \"(0.017865, 0.019315)\": -0.582, \"(0.019315, 0.023185)\": -0.466, \"(0.023185, 0.026115)\": -0.352, \"(0.026115, 0.042455)\": -0.235, \"(0.042455, 0.048235)\": -0.115, \"(0.048235, 0.048865)\": 0.04, \"(0.048865, 0.059615)\": 0.233, \"(0.059615, 0.070395)\": 0.35, \"(0.070395, 0.08221500000000001)\": 0.474, \"(0.08221500000000001, 0.087175)\": 0.592, \"(0.087175, 0.091445)\": 0.711, \"(0.091445, 0.1006)\": 0.832, \"(0.1006, 0.122)\": 0.949, \"(0.122, 0.16544999999999999)\": 1.068, \"(0.16544999999999999, 0.2012)\": 1.187}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.0074145)\": -1.411, \"(0.0074145, 0.011665)\": -1.253, \"(0.011665, 0.01503)\": -1.095, \"(0.01503, 0.017865)\": -0.965, \"(0.017865, 0.019315)\": -0.823, \"(0.019315, 0.023185)\": -0.72, \"(0.023185, 0.026115)\": -0.517, \"(0.026115, 0.042455)\": -0.743, \"(0.042455, 0.048235)\": -0.628, \"(0.048235, 0.048865)\": -0.409, \"(0.048865, 0.059615)\": -0.151, \"(0.059615, 0.070395)\": 0.09, \"(0.070395, 0.08221500000000001)\": 0.219, \"(0.08221500000000001, 0.087175)\": 0.306, \"(0.087175, 0.091445)\": 0.39, \"(0.091445, 0.1006)\": 0.481, \"(0.1006, 0.122)\": 0.562, \"(0.122, 0.16544999999999999)\": 0.634, \"(0.16544999999999999, 0.2012)\": 0.74}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.0074145)\": -0.697, \"(0.0074145, 0.011665)\": -0.62, \"(0.011665, 0.01503)\": -0.546, \"(0.01503, 0.017865)\": -0.445, \"(0.017865, 0.019315)\": -0.34, \"(0.019315, 0.023185)\": -0.212, \"(0.023185, 0.026115)\": -0.188, \"(0.026115, 0.042455)\": 0.274, \"(0.042455, 0.048235)\": 0.398, \"(0.048235, 0.048865)\": 0.489, \"(0.048865, 0.059615)\": 0.617, \"(0.059615, 0.070395)\": 0.611, \"(0.070395, 0.08221500000000001)\": 0.728, \"(0.08221500000000001, 0.087175)\": 0.878, \"(0.087175, 0.091445)\": 1.032, \"(0.091445, 0.1006)\": 1.182, \"(0.1006, 0.122)\": 1.336, \"(0.122, 0.16544999999999999)\": 1.503, \"(0.16544999999999999, 0.2012)\": 1.634}\n\n\nYour task is to provide the mean value of the graph at 0.02. What is the mean value of the graph at 0.02?",
+    "-0.466"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Deforestation\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02956, \"(0.5, 2.5)\": -0.02081, \"(2.5, 3.5)\": -0.00998, \"(3.5, 4.5)\": -0.00524, \"(4.5, 5.5)\": 0.00043, \"(5.5, 6.5)\": 0.00515, \"(6.5, 8.5)\": 0.01107, \"(8.5, 10.5)\": 0.02102, \"(10.5, 11.5)\": 0.02728, \"(11.5, 13.5)\": 0.0456, \"(13.5, 14.5)\": 0.05244, \"(14.5, 17.0)\": 0.06161}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.03241, \"(0.5, 2.5)\": -0.02172, \"(2.5, 3.5)\": -0.01056, \"(3.5, 4.5)\": -0.0057, \"(4.5, 5.5)\": 1e-05, \"(5.5, 6.5)\": 0.00474, \"(6.5, 8.5)\": 0.01043, \"(8.5, 10.5)\": 0.01957, \"(10.5, 11.5)\": 0.02542, \"(11.5, 13.5)\": 0.04264, \"(13.5, 14.5)\": 0.04883, \"(14.5, 17.0)\": 0.05758}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02672, \"(0.5, 2.5)\": -0.0199, \"(2.5, 3.5)\": -0.0094, \"(3.5, 4.5)\": -0.00479, \"(4.5, 5.5)\": 0.00085, \"(5.5, 6.5)\": 0.00557, \"(6.5, 8.5)\": 0.01172, \"(8.5, 10.5)\": 0.02247, \"(10.5, 11.5)\": 0.02915, \"(11.5, 13.5)\": 0.04855, \"(13.5, 14.5)\": 0.05605, \"(14.5, 17.0)\": 0.06565}\n\n\nYour task is to provide the mean value of the graph at 6.38. What is the mean value of the graph at 6.38?",
+    "0.00515"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: HoursPerWeek\nFeature Type: continuous\nMeans: {\"(1.0, 1.5)\": -0.765, \"(1.5, 2.5)\": -0.375, \"(2.5, 4.5)\": -1.909, \"(4.5, 6.5)\": -1.117, \"(6.5, 7.5)\": -0.618, \"(7.5, 14.5)\": -0.822, \"(14.5, 19.5)\": -1.132, \"(19.5, 29.5)\": -0.765, \"(29.5, 33.5)\": -0.6, \"(33.5, 34.5)\": -0.921, \"(34.5, 39.5)\": -0.155, \"(39.5, 41.5)\": 0.03, \"(41.5, 50.5)\": 0.392, \"(50.5, 51.5)\": 0.131, \"(51.5, 55.5)\": 0.457, \"(55.5, 59.5)\": 0.676, \"(59.5, 63.5)\": 0.416, \"(63.5, 64.5)\": 0.952, \"(64.5, 65.5)\": 0.516, \"(65.5, 71.0)\": 0.071, \"(71.0, 75.5)\": 0.43, \"(75.5, 77.5)\": 0.235, \"(77.5, 79.0)\": 0.742, \"(79.0, 83.0)\": 0.977, \"(83.0, 85.5)\": 1.287, \"(85.5, 90.5)\": 0.192, \"(90.5, 97.5)\": -0.071, \"(97.5, 98.5)\": 0.119, \"(98.5, 99.0)\": -0.139}\nLower Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -2.672, \"(1.5, 2.5)\": -0.773, \"(2.5, 4.5)\": -2.709, \"(4.5, 6.5)\": -1.566, \"(6.5, 7.5)\": -1.241, \"(7.5, 14.5)\": -1.098, \"(14.5, 19.5)\": -1.535, \"(19.5, 29.5)\": -1.357, \"(29.5, 33.5)\": -1.248, \"(33.5, 34.5)\": -1.815, \"(34.5, 39.5)\": -0.223, \"(39.5, 41.5)\": -0.129, \"(41.5, 50.5)\": 0.212, \"(50.5, 51.5)\": -0.867, \"(51.5, 55.5)\": 0.357, \"(55.5, 59.5)\": 0.304, \"(59.5, 63.5)\": 0.014, \"(63.5, 64.5)\": 0.009, \"(64.5, 65.5)\": 0.379, \"(65.5, 71.0)\": -0.113, \"(71.0, 75.5)\": 0.054, \"(75.5, 77.5)\": -0.57, \"(77.5, 79.0)\": 0.234, \"(79.0, 83.0)\": 0.788, \"(83.0, 85.5)\": 0.721, \"(85.5, 90.5)\": -0.289, \"(90.5, 97.5)\": -0.504, \"(97.5, 98.5)\": -0.527, \"(98.5, 99.0)\": -0.548}\nUpper Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": 1.142, \"(1.5, 2.5)\": 0.023, \"(2.5, 4.5)\": -1.109, \"(4.5, 6.5)\": -0.668, \"(6.5, 7.5)\": 0.005, \"(7.5, 14.5)\": -0.546, \"(14.5, 19.5)\": -0.729, \"(19.5, 29.5)\": -0.172, \"(29.5, 33.5)\": 0.047, \"(33.5, 34.5)\": -0.027, \"(34.5, 39.5)\": -0.087, \"(39.5, 41.5)\": 0.19, \"(41.5, 50.5)\": 0.571, \"(50.5, 51.5)\": 1.13, \"(51.5, 55.5)\": 0.557, \"(55.5, 59.5)\": 1.048, \"(59.5, 63.5)\": 0.818, \"(63.5, 64.5)\": 1.896, \"(64.5, 65.5)\": 0.653, \"(65.5, 71.0)\": 0.254, \"(71.0, 75.5)\": 0.806, \"(75.5, 77.5)\": 1.04, \"(77.5, 79.0)\": 1.25, \"(79.0, 83.0)\": 1.166, \"(83.0, 85.5)\": 1.852, \"(85.5, 90.5)\": 0.673, \"(90.5, 97.5)\": 0.361, \"(97.5, 98.5)\": 0.765, \"(98.5, 99.0)\": 0.271}\n\n\nYour task is to provide the mean value of the graph at 3.14. What is the mean value of the graph at 3.14?",
+    "-1.909"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: population\nFeature Type: continuous\nMeans: {\"(3.0, 14.5)\": 125210.2, \"(14.5, 25.5)\": 92452.9, \"(25.5, 65.5)\": 80407.9, \"(65.5, 138.5)\": 91917.4, \"(138.5, 151.5)\": 103409.9, \"(151.5, 301.5)\": 85121.7, \"(301.5, 490.5)\": 73106.0, \"(490.5, 657.5)\": 57994.5, \"(657.5, 761.5)\": 44760.8, \"(761.5, 837.5)\": 32058.9, \"(837.5, 1019.5)\": 20715.6, \"(1019.5, 1220.5)\": 6507.2, \"(1220.5, 1267.5)\": -6199.6, \"(1267.5, 1269.5)\": 9858.1, \"(1269.5, 1497.5)\": -9812.8, \"(1497.5, 1886.5)\": -25776.4, \"(1886.5, 2129.5)\": -36953.6, \"(2129.5, 2425.5)\": -48605.9, \"(2425.5, 2686.0)\": -59914.9, \"(2686.0, 2718.5)\": -46231.6, \"(2718.5, 3175.5)\": -61061.6, \"(3175.5, 3965.0)\": -76216.0, \"(3965.0, 35682.0)\": -91117.9}\nLower Bounds (95%-Confidence Interval): {\"(3.0, 14.5)\": 103123.1, \"(14.5, 25.5)\": 58681.0, \"(25.5, 65.5)\": 62309.7, \"(65.5, 138.5)\": 75243.8, \"(138.5, 151.5)\": 78950.4, \"(151.5, 301.5)\": 69535.1, \"(301.5, 490.5)\": 60924.6, \"(490.5, 657.5)\": 45395.6, \"(657.5, 761.5)\": 35273.5, \"(761.5, 837.5)\": 26626.5, \"(837.5, 1019.5)\": 8057.5, \"(1019.5, 1220.5)\": -10609.9, \"(1220.5, 1267.5)\": -14462.5, \"(1267.5, 1269.5)\": -5022.3, \"(1269.5, 1497.5)\": -22884.3, \"(1497.5, 1886.5)\": -37619.7, \"(1886.5, 2129.5)\": -51088.1, \"(2129.5, 2425.5)\": -56504.4, \"(2425.5, 2686.0)\": -64158.2, \"(2686.0, 2718.5)\": -69408.6, \"(2718.5, 3175.5)\": -68643.2, \"(3175.5, 3965.0)\": -84318.8, \"(3965.0, 35682.0)\": -101928.5}\nUpper Bounds (95%-Confidence Interval): {\"(3.0, 14.5)\": 147297.2, \"(14.5, 25.5)\": 126224.8, \"(25.5, 65.5)\": 98506.2, \"(65.5, 138.5)\": 108591.1, \"(138.5, 151.5)\": 127869.3, \"(151.5, 301.5)\": 100708.2, \"(301.5, 490.5)\": 85287.5, \"(490.5, 657.5)\": 70593.3, \"(657.5, 761.5)\": 54248.0, \"(761.5, 837.5)\": 37491.3, \"(837.5, 1019.5)\": 33373.7, \"(1019.5, 1220.5)\": 23624.4, \"(1220.5, 1267.5)\": 2063.4, \"(1267.5, 1269.5)\": 24738.4, \"(1269.5, 1497.5)\": 3258.7, \"(1497.5, 1886.5)\": -13933.2, \"(1886.5, 2129.5)\": -22819.1, \"(2129.5, 2425.5)\": -40707.4, \"(2425.5, 2686.0)\": -55671.5, \"(2686.0, 2718.5)\": -23054.7, \"(2718.5, 3175.5)\": -53480.1, \"(3175.5, 3965.0)\": -68113.2, \"(3965.0, 35682.0)\": -80307.2}\n\n\nYour task is to provide the mean value of the graph at 12.16. What is the mean value of the graph at 12.16?",
+    "125210.2"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Urbanization\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02565, \"(0.5, 1.5)\": -0.02133, \"(1.5, 2.5)\": -0.01683, \"(2.5, 3.5)\": -0.00993, \"(3.5, 4.5)\": -0.00473, \"(4.5, 5.5)\": -1e-05, \"(5.5, 6.5)\": 0.00511, \"(6.5, 7.5)\": 0.01148, \"(7.5, 8.5)\": 0.01621, \"(8.5, 9.5)\": 0.02476, \"(9.5, 11.5)\": 0.02962, \"(11.5, 12.5)\": 0.03469, \"(12.5, 13.5)\": 0.04866, \"(13.5, 16.0)\": 0.05902}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02758, \"(0.5, 1.5)\": -0.02272, \"(1.5, 2.5)\": -0.01769, \"(2.5, 3.5)\": -0.01036, \"(3.5, 4.5)\": -0.00514, \"(4.5, 5.5)\": -0.0004, \"(5.5, 6.5)\": 0.00453, \"(6.5, 7.5)\": 0.01098, \"(7.5, 8.5)\": 0.01535, \"(8.5, 9.5)\": 0.0239, \"(9.5, 11.5)\": 0.02772, \"(11.5, 12.5)\": 0.03206, \"(12.5, 13.5)\": 0.04307, \"(13.5, 16.0)\": 0.0546}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02372, \"(0.5, 1.5)\": -0.01994, \"(1.5, 2.5)\": -0.01596, \"(2.5, 3.5)\": -0.00951, \"(3.5, 4.5)\": -0.00432, \"(4.5, 5.5)\": 0.00037, \"(5.5, 6.5)\": 0.00568, \"(6.5, 7.5)\": 0.01199, \"(7.5, 8.5)\": 0.01706, \"(8.5, 9.5)\": 0.02562, \"(9.5, 11.5)\": 0.03152, \"(11.5, 12.5)\": 0.03732, \"(12.5, 13.5)\": 0.05424, \"(13.5, 16.0)\": 0.06343}\n\n\nYour task is to provide the mean value of the graph at 14.97. What is the mean value of the graph at 14.97?",
+    "0.05902"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: concave points_worst\nFeature Type: continuous\nMeans: {\"(0.0, 0.02814)\": -0.771, \"(0.02814, 0.08293)\": -0.653, \"(0.08293, 0.08555)\": -0.533, \"(0.08555, 0.093225)\": -0.403, \"(0.093225, 0.1055)\": -0.234, \"(0.1055, 0.11510000000000001)\": -0.117, \"(0.11510000000000001, 0.1346)\": 0.002, \"(0.1346, 0.14545000000000002)\": 0.121, \"(0.14545000000000002, 0.15175)\": 0.241, \"(0.15175, 0.1603)\": 0.365, \"(0.1603, 0.1722)\": 0.539, \"(0.1722, 0.17695)\": 0.661, \"(0.17695, 0.18359999999999999)\": 0.781, \"(0.18359999999999999, 0.194)\": 0.9, \"(0.194, 0.2019)\": 1.022, \"(0.2019, 0.21275)\": 1.14, \"(0.21275, 0.2383)\": 1.259, \"(0.2383, 0.26865)\": 1.378, \"(0.26865, 0.291)\": 1.494}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.02814)\": -1.316, \"(0.02814, 0.08293)\": -1.204, \"(0.08293, 0.08555)\": -1.003, \"(0.08555, 0.093225)\": -0.715, \"(0.093225, 0.1055)\": -0.419, \"(0.1055, 0.11510000000000001)\": -0.299, \"(0.11510000000000001, 0.1346)\": -0.172, \"(0.1346, 0.14545000000000002)\": -0.125, \"(0.14545000000000002, 0.15175)\": 0.077, \"(0.15175, 0.1603)\": 0.185, \"(0.1603, 0.1722)\": 0.112, \"(0.1722, 0.17695)\": 0.052, \"(0.17695, 0.18359999999999999)\": 0.152, \"(0.18359999999999999, 0.194)\": 0.267, \"(0.194, 0.2019)\": 0.392, \"(0.2019, 0.21275)\": 0.477, \"(0.21275, 0.2383)\": 0.593, \"(0.2383, 0.26865)\": 0.693, \"(0.26865, 0.291)\": 0.803}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.02814)\": -0.226, \"(0.02814, 0.08293)\": -0.101, \"(0.08293, 0.08555)\": -0.064, \"(0.08555, 0.093225)\": -0.091, \"(0.093225, 0.1055)\": -0.049, \"(0.1055, 0.11510000000000001)\": 0.065, \"(0.11510000000000001, 0.1346)\": 0.176, \"(0.1346, 0.14545000000000002)\": 0.367, \"(0.14545000000000002, 0.15175)\": 0.406, \"(0.15175, 0.1603)\": 0.545, \"(0.1603, 0.1722)\": 0.966, \"(0.1722, 0.17695)\": 1.27, \"(0.17695, 0.18359999999999999)\": 1.41, \"(0.18359999999999999, 0.194)\": 1.533, \"(0.194, 0.2019)\": 1.653, \"(0.2019, 0.21275)\": 1.803, \"(0.21275, 0.2383)\": 1.926, \"(0.2383, 0.26865)\": 2.063, \"(0.26865, 0.291)\": 2.186}\n\n\nYour task is to provide the mean value of the graph at 0.17. What is the mean value of the graph at 0.17?",
+    "0.539"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: longitude\nFeature Type: continuous\nMeans: {\"(-124.35, -124.10499999999999)\": -50430.1, \"(-124.10499999999999, -124.08500000000001)\": -38925.6, \"(-124.08500000000001, -124.07499999999999)\": -23742.3, \"(-124.07499999999999, -123.3)\": -12526.0, \"(-123.3, -122.955)\": -1690.2, \"(-122.955, -122.66499999999999)\": 19040.8, \"(-122.66499999999999, -122.60499999999999)\": 29856.3, \"(-122.60499999999999, -122.58500000000001)\": 44315.6, \"(-122.58500000000001, -122.555)\": 75515.2, \"(-122.555, -122.455)\": 86444.1, \"(-122.455, -122.445)\": 99533.8, \"(-122.445, -122.42500000000001)\": 112351.5, \"(-122.42500000000001, -122.405)\": 89733.4, \"(-122.405, -122.39500000000001)\": 78586.0, \"(-122.39500000000001, -122.375)\": 46429.6, \"(-122.375, -122.36500000000001)\": 35622.6, \"(-122.36500000000001, -122.305)\": 20538.8, \"(-122.305, -122.155)\": 6386.6, \"(-122.155, -120.92500000000001)\": 24722.9, \"(-120.92500000000001, -120.91499999999999)\": 54457.6, \"(-120.91499999999999, -120.89500000000001)\": 34017.2, \"(-120.89500000000001, -120.86500000000001)\": 18216.5, \"(-120.86500000000001, -120.725)\": 6143.7, \"(-120.725, -120.63499999999999)\": 17429.8, \"(-120.63499999999999, -120.485)\": 407.5, \"(-120.485, -120.405)\": -15764.1, \"(-120.405, -120.10499999999999)\": 1041.6, \"(-120.10499999999999, -120.095)\": 24030.2, \"(-120.095, -119.91499999999999)\": -2161.3, \"(-119.91499999999999, -119.85499999999999)\": -20610.8, \"(-119.85499999999999, -119.795)\": -31705.4, \"(-119.795, -119.755)\": -20112.3, \"(-119.755, -119.525)\": -3774.8, \"(-119.525, -119.505)\": 10442.2, \"(-119.505, -119.295)\": -10555.7, \"(-119.295, -119.215)\": 3582.5, \"(-119.215, -118.905)\": -15819.3, \"(-118.905, -118.695)\": -2790.4, \"(-118.695, -118.57499999999999)\": 13581.3, \"(-118.57499999999999, -118.525)\": 26358.2, \"(-118.525, -118.495)\": 44919.9, \"(-118.495, -118.375)\": 60453.4, \"(-118.375, -118.35499999999999)\": 38572.6, \"(-118.35499999999999, -118.305)\": 21183.4, \"(-118.305, -118.265)\": -6755.4, \"(-118.265, -118.14500000000001)\": -17830.0, \"(-118.14500000000001, -117.985)\": -7071.0, \"(-117.985, -117.755)\": -26435.2, \"(-117.755, -117.725)\": -50667.3, \"(-117.725, -117.64500000000001)\": -63305.2, \"(-117.64500000000001, -117.57499999999999)\": -50999.4, \"(-117.57499999999999, -117.35499999999999)\": -38880.5, \"(-117.35499999999999, -117.285)\": -64800.9, \"(-117.285, -117.155)\": -47182.2, \"(-117.155, -117.13499999999999)\": -65749.6, \"(-117.13499999999999, -116.995)\": -77340.8, \"(-116.995, -116.795)\": -64524.7, \"(-116.795, -116.205)\": -53643.1, \"(-116.205, -116.1)\": -64388.1, \"(-116.1, -115.525)\": -75181.7, \"(-115.525, -115.1)\": -57014.4, \"(-115.1, -114.595)\": -74654.1, \"(-114.595, -114.31)\": -100620.1}\nLower Bounds (95%-Confidence Interval): {\"(-124.35, -124.10499999999999)\": -57749.3, \"(-124.10499999999999, -124.08500000000001)\": -46703.5, \"(-124.08500000000001, -124.07499999999999)\": -34644.5, \"(-124.07499999999999, -123.3)\": -20263.6, \"(-123.3, -122.955)\": -10198.1, \"(-122.955, -122.66499999999999)\": 10561.3, \"(-122.66499999999999, -122.60499999999999)\": 24254.2, \"(-122.60499999999999, -122.58500000000001)\": 34301.3, \"(-122.58500000000001, -122.555)\": 63992.2, \"(-122.555, -122.455)\": 76785.0, \"(-122.455, -122.445)\": 91169.4, \"(-122.445, -122.42500000000001)\": 103719.6, \"(-122.42500000000001, -122.405)\": 81277.9, \"(-122.405, -122.39500000000001)\": 66955.5, \"(-122.39500000000001, -122.375)\": 35631.1, \"(-122.375, -122.36500000000001)\": 24396.3, \"(-122.36500000000001, -122.305)\": 14520.6, \"(-122.305, -122.155)\": -1221.8, \"(-122.155, -120.92500000000001)\": 11630.1, \"(-120.92500000000001, -120.91499999999999)\": 11031.4, \"(-120.91499999999999, -120.89500000000001)\": 19105.3, \"(-120.89500000000001, -120.86500000000001)\": 4469.7, \"(-120.86500000000001, -120.725)\": -1198.1, \"(-120.725, -120.63499999999999)\": 7919.4, \"(-120.63499999999999, -120.485)\": -11276.9, \"(-120.485, -120.405)\": -23035.0, \"(-120.405, -120.10499999999999)\": -4074.0, \"(-120.10499999999999, -120.095)\": -10802.7, \"(-120.095, -119.91499999999999)\": -13216.2, \"(-119.91499999999999, -119.85499999999999)\": -33171.4, \"(-119.85499999999999, -119.795)\": -38315.5, \"(-119.795, -119.755)\": -24784.5, \"(-119.755, -119.525)\": -13160.2, \"(-119.525, -119.505)\": -4048.0, \"(-119.505, -119.295)\": -18789.9, \"(-119.295, -119.215)\": -8493.9, \"(-119.215, -118.905)\": -20485.4, \"(-118.905, -118.695)\": -7647.9, \"(-118.695, -118.57499999999999)\": 4745.1, \"(-118.57499999999999, -118.525)\": 17156.2, \"(-118.525, -118.495)\": 33913.3, \"(-118.495, -118.375)\": 52480.8, \"(-118.375, -118.35499999999999)\": 34068.9, \"(-118.35499999999999, -118.305)\": 14693.0, \"(-118.305, -118.265)\": -11878.1, \"(-118.265, -118.14500000000001)\": -21370.7, \"(-118.14500000000001, -117.985)\": -11803.1, \"(-117.985, -117.755)\": -35281.9, \"(-117.755, -117.725)\": -58041.5, \"(-117.725, -117.64500000000001)\": -72526.8, \"(-117.64500000000001, -117.57499999999999)\": -61627.3, \"(-117.57499999999999, -117.35499999999999)\": -45444.2, \"(-117.35499999999999, -117.285)\": -74287.3, \"(-117.285, -117.155)\": -55258.2, \"(-117.155, -117.13499999999999)\": -74456.9, \"(-117.13499999999999, -116.995)\": -86582.5, \"(-116.995, -116.795)\": -73433.2, \"(-116.795, -116.205)\": -69635.5, \"(-116.205, -116.1)\": -75131.9, \"(-116.1, -115.525)\": -97151.1, \"(-115.525, -115.1)\": -73988.5, \"(-115.1, -114.595)\": -91086.2, \"(-114.595, -114.31)\": -120109.7}\nUpper Bounds (95%-Confidence Interval): {\"(-124.35, -124.10499999999999)\": -43110.8, \"(-124.10499999999999, -124.08500000000001)\": -31147.7, \"(-124.08500000000001, -124.07499999999999)\": -12840.0, \"(-124.07499999999999, -123.3)\": -4788.4, \"(-123.3, -122.955)\": 6817.8, \"(-122.955, -122.66499999999999)\": 27520.3, \"(-122.66499999999999, -122.60499999999999)\": 35458.4, \"(-122.60499999999999, -122.58500000000001)\": 54329.8, \"(-122.58500000000001, -122.555)\": 87038.2, \"(-122.555, -122.455)\": 96103.2, \"(-122.455, -122.445)\": 107898.1, \"(-122.445, -122.42500000000001)\": 120983.4, \"(-122.42500000000001, -122.405)\": 98188.9, \"(-122.405, -122.39500000000001)\": 90216.5, \"(-122.39500000000001, -122.375)\": 57228.1, \"(-122.375, -122.36500000000001)\": 46849.0, \"(-122.36500000000001, -122.305)\": 26556.9, \"(-122.305, -122.155)\": 13995.0, \"(-122.155, -120.92500000000001)\": 37815.7, \"(-120.92500000000001, -120.91499999999999)\": 97883.7, \"(-120.91499999999999, -120.89500000000001)\": 48929.0, \"(-120.89500000000001, -120.86500000000001)\": 31963.3, \"(-120.86500000000001, -120.725)\": 13485.5, \"(-120.725, -120.63499999999999)\": 26940.3, \"(-120.63499999999999, -120.485)\": 12092.0, \"(-120.485, -120.405)\": -8493.1, \"(-120.405, -120.10499999999999)\": 6157.2, \"(-120.10499999999999, -120.095)\": 58863.0, \"(-120.095, -119.91499999999999)\": 8893.5, \"(-119.91499999999999, -119.85499999999999)\": -8050.3, \"(-119.85499999999999, -119.795)\": -25095.3, \"(-119.795, -119.755)\": -15440.1, \"(-119.755, -119.525)\": 5610.6, \"(-119.525, -119.505)\": 24932.3, \"(-119.505, -119.295)\": -2321.4, \"(-119.295, -119.215)\": 15659.0, \"(-119.215, -118.905)\": -11153.1, \"(-118.905, -118.695)\": 2067.1, \"(-118.695, -118.57499999999999)\": 22417.4, \"(-118.57499999999999, -118.525)\": 35560.1, \"(-118.525, -118.495)\": 55926.4, \"(-118.495, -118.375)\": 68426.1, \"(-118.375, -118.35499999999999)\": 43076.4, \"(-118.35499999999999, -118.305)\": 27673.7, \"(-118.305, -118.265)\": -1632.7, \"(-118.265, -118.14500000000001)\": -14289.3, \"(-118.14500000000001, -117.985)\": -2338.9, \"(-117.985, -117.755)\": -17588.5, \"(-117.755, -117.725)\": -43293.2, \"(-117.725, -117.64500000000001)\": -54083.7, \"(-117.64500000000001, -117.57499999999999)\": -40371.5, \"(-117.57499999999999, -117.35499999999999)\": -32316.9, \"(-117.35499999999999, -117.285)\": -55314.6, \"(-117.285, -117.155)\": -39106.3, \"(-117.155, -117.13499999999999)\": -57042.4, \"(-117.13499999999999, -116.995)\": -68099.0, \"(-116.995, -116.795)\": -55616.1, \"(-116.795, -116.205)\": -37650.7, \"(-116.205, -116.1)\": -53644.2, \"(-116.1, -115.525)\": -53212.4, \"(-115.525, -115.1)\": -40040.3, \"(-115.1, -114.595)\": -58221.9, \"(-114.595, -114.31)\": -81130.6}\n\n\nYour task is to provide the mean value of the graph at -114.37. What is the mean value of the graph at -114.37?",
+    "-100620.1"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: creatinine_phosphokinase\nFeature Type: continuous\nMeans: {\"(23.0, 32.0)\": -0.48, \"(32.0, 49.5)\": 0.68, \"(49.5, 56.5)\": -4.31, \"(56.5, 59.5)\": -2.44, \"(59.5, 64.5)\": -1.82, \"(64.5, 85.0)\": -1.1, \"(85.0, 87.0)\": 0.42, \"(87.0, 93.5)\": -0.75, \"(93.5, 94.5)\": 0.47, \"(94.5, 103.5)\": -0.53, \"(103.5, 107.5)\": 0.12, \"(107.5, 120.0)\": -0.5, \"(120.0, 121.5)\": 0.24, \"(121.5, 126.0)\": 1.25, \"(126.0, 127.5)\": -3.14, \"(127.5, 145.5)\": 1.51, \"(145.5, 147.0)\": 0.91, \"(147.0, 150.0)\": -0.15, \"(150.0, 160.5)\": -1.08, \"(160.5, 189.5)\": -0.45, \"(189.5, 232.5)\": -1.26, \"(232.5, 254.5)\": -0.16, \"(254.5, 258.5)\": 2.88, \"(258.5, 280.5)\": 1.68, \"(280.5, 331.5)\": 1.11, \"(331.5, 370.0)\": 0.44, \"(370.0, 462.0)\": 1.1, \"(462.0, 597.5)\": 0.53, \"(597.5, 751.0)\": -1.87, \"(751.0, 766.5)\": 0.06, \"(766.5, 806.0)\": 2.64, \"(806.0, 873.5)\": 2.05, \"(873.5, 1036.0)\": 0.28, \"(1036.0, 1415.0)\": 0.85, \"(1415.0, 1649.0)\": 0.18, \"(1649.0, 1726.0)\": 2.26, \"(1726.0, 1886.0)\": 0.04, \"(1886.0, 2038.5)\": 7.0, \"(2038.5, 2307.5)\": 2.26, \"(2307.5, 2444.0)\": 5.81, \"(2444.0, 3440.5)\": -2.71, \"(3440.5, 4253.0)\": -1.47, \"(4253.0, 5548.5)\": 1.68, \"(5548.5, 7861.0)\": 3.47}\nLower Bounds (95%-Confidence Interval): {\"(23.0, 32.0)\": -1.51, \"(32.0, 49.5)\": -0.87, \"(49.5, 56.5)\": -5.69, \"(56.5, 59.5)\": -3.58, \"(59.5, 64.5)\": -2.64, \"(64.5, 85.0)\": -2.07, \"(85.0, 87.0)\": -2.37, \"(87.0, 93.5)\": -1.85, \"(93.5, 94.5)\": -0.56, \"(94.5, 103.5)\": -0.85, \"(103.5, 107.5)\": -0.45, \"(107.5, 120.0)\": -1.09, \"(120.0, 121.5)\": -0.48, \"(121.5, 126.0)\": 0.88, \"(126.0, 127.5)\": -5.59, \"(127.5, 145.5)\": 0.93, \"(145.5, 147.0)\": 0.57, \"(147.0, 150.0)\": -0.64, \"(150.0, 160.5)\": -2.38, \"(160.5, 189.5)\": -1.47, \"(189.5, 232.5)\": -2.02, \"(232.5, 254.5)\": -1.04, \"(254.5, 258.5)\": 1.73, \"(258.5, 280.5)\": 0.55, \"(280.5, 331.5)\": 0.09, \"(331.5, 370.0)\": -0.26, \"(370.0, 462.0)\": 0.18, \"(462.0, 597.5)\": 0.4, \"(597.5, 751.0)\": -3.59, \"(751.0, 766.5)\": -2.06, \"(766.5, 806.0)\": 1.02, \"(806.0, 873.5)\": 0.45, \"(873.5, 1036.0)\": -0.52, \"(1036.0, 1415.0)\": 0.33, \"(1415.0, 1649.0)\": -0.68, \"(1649.0, 1726.0)\": -0.23, \"(1726.0, 1886.0)\": -1.16, \"(1886.0, 2038.5)\": 5.88, \"(2038.5, 2307.5)\": 1.8, \"(2307.5, 2444.0)\": 4.43, \"(2444.0, 3440.5)\": -5.48, \"(3440.5, 4253.0)\": -2.15, \"(4253.0, 5548.5)\": 0.41, \"(5548.5, 7861.0)\": 2.17}\nUpper Bounds (95%-Confidence Interval): {\"(23.0, 32.0)\": 0.54, \"(32.0, 49.5)\": 2.24, \"(49.5, 56.5)\": -2.93, \"(56.5, 59.5)\": -1.31, \"(59.5, 64.5)\": -1.0, \"(64.5, 85.0)\": -0.13, \"(85.0, 87.0)\": 3.22, \"(87.0, 93.5)\": 0.35, \"(93.5, 94.5)\": 1.51, \"(94.5, 103.5)\": -0.2, \"(103.5, 107.5)\": 0.69, \"(107.5, 120.0)\": 0.09, \"(120.0, 121.5)\": 0.97, \"(121.5, 126.0)\": 1.61, \"(126.0, 127.5)\": -0.68, \"(127.5, 145.5)\": 2.09, \"(145.5, 147.0)\": 1.25, \"(147.0, 150.0)\": 0.33, \"(150.0, 160.5)\": 0.22, \"(160.5, 189.5)\": 0.57, \"(189.5, 232.5)\": -0.49, \"(232.5, 254.5)\": 0.72, \"(254.5, 258.5)\": 4.03, \"(258.5, 280.5)\": 2.81, \"(280.5, 331.5)\": 2.12, \"(331.5, 370.0)\": 1.15, \"(370.0, 462.0)\": 2.02, \"(462.0, 597.5)\": 0.67, \"(597.5, 751.0)\": -0.15, \"(751.0, 766.5)\": 2.18, \"(766.5, 806.0)\": 4.25, \"(806.0, 873.5)\": 3.65, \"(873.5, 1036.0)\": 1.09, \"(1036.0, 1415.0)\": 1.38, \"(1415.0, 1649.0)\": 1.04, \"(1649.0, 1726.0)\": 4.75, \"(1726.0, 1886.0)\": 1.24, \"(1886.0, 2038.5)\": 8.11, \"(2038.5, 2307.5)\": 2.72, \"(2307.5, 2444.0)\": 7.19, \"(2444.0, 3440.5)\": 0.06, \"(3440.5, 4253.0)\": -0.79, \"(4253.0, 5548.5)\": 2.95, \"(5548.5, 7861.0)\": 4.78}\n\n\nYour task is to provide the mean value of the graph at 239.62. What is the mean value of the graph at 239.62?",
+    "-0.16"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: area_mean\nFeature Type: continuous\nMeans: {\"(143.5, 259.35)\": -0.759, \"(259.35, 289.4)\": -0.662, \"(289.4, 319.15)\": -0.567, \"(319.15, 348.3)\": -0.464, \"(348.3, 496.5)\": -0.368, \"(496.5, 548.75)\": -0.271, \"(548.75, 606.0)\": -0.173, \"(606.0, 696.25)\": -0.076, \"(696.25, 806.1500000000001)\": 0.309, \"(806.1500000000001, 901.8)\": 0.405, \"(901.8, 959.4000000000001)\": 0.51, \"(959.4000000000001, 1054.0)\": 0.607, \"(1054.0, 1150.0)\": 0.707, \"(1150.0, 1248.5)\": 0.806, \"(1248.5, 1341.0)\": 0.911, \"(1341.0, 1801.0)\": 1.01, \"(1801.0, 2501.0)\": 1.109}\nLower Bounds (95%-Confidence Interval): {\"(143.5, 259.35)\": -1.038, \"(259.35, 289.4)\": -0.892, \"(289.4, 319.15)\": -0.754, \"(319.15, 348.3)\": -0.634, \"(348.3, 496.5)\": -0.559, \"(496.5, 548.75)\": -0.436, \"(548.75, 606.0)\": -0.338, \"(606.0, 696.25)\": -0.727, \"(696.25, 806.1500000000001)\": -0.252, \"(806.1500000000001, 901.8)\": -0.022, \"(901.8, 959.4000000000001)\": 0.058, \"(959.4000000000001, 1054.0)\": 0.141, \"(1054.0, 1150.0)\": 0.243, \"(1150.0, 1248.5)\": 0.328, \"(1248.5, 1341.0)\": 0.393, \"(1341.0, 1801.0)\": 0.475, \"(1801.0, 2501.0)\": 0.574}\nUpper Bounds (95%-Confidence Interval): {\"(143.5, 259.35)\": -0.48, \"(259.35, 289.4)\": -0.432, \"(289.4, 319.15)\": -0.38, \"(319.15, 348.3)\": -0.294, \"(348.3, 496.5)\": -0.177, \"(496.5, 548.75)\": -0.106, \"(548.75, 606.0)\": -0.007, \"(606.0, 696.25)\": 0.575, \"(696.25, 806.1500000000001)\": 0.871, \"(806.1500000000001, 901.8)\": 0.831, \"(901.8, 959.4000000000001)\": 0.962, \"(959.4000000000001, 1054.0)\": 1.074, \"(1054.0, 1150.0)\": 1.171, \"(1150.0, 1248.5)\": 1.285, \"(1248.5, 1341.0)\": 1.428, \"(1341.0, 1801.0)\": 1.544, \"(1801.0, 2501.0)\": 1.644}\n\n\nYour task is to provide the mean value of the graph at 1305.91. What is the mean value of the graph at 1305.91?",
+    "0.911"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: perimeter_se\nFeature Type: continuous\nMeans: {\"(0.7714, 1.0579999999999998)\": -0.698, \"(1.0579999999999998, 1.1345)\": -0.618, \"(1.1345, 1.197)\": -0.539, \"(1.197, 1.2365)\": -0.461, \"(1.2365, 1.326)\": -0.384, \"(1.326, 1.4435)\": -0.256, \"(1.4435, 1.5314999999999999)\": -0.176, \"(1.5314999999999999, 1.807)\": -0.099, \"(1.807, 2.107)\": -0.023, \"(2.107, 2.593)\": -0.098, \"(2.593, 2.878)\": -0.018, \"(2.878, 3.292)\": 0.065, \"(3.292, 4.095000000000001)\": 0.14, \"(4.095000000000001, 4.714)\": 0.219, \"(4.714, 4.885999999999999)\": 0.296, \"(4.885999999999999, 5.2844999999999995)\": 0.372, \"(5.2844999999999995, 5.8425)\": 0.451, \"(5.8425, 7.104)\": 0.536, \"(7.104, 7.7765)\": 0.611, \"(7.7765, 10.594999999999999)\": 0.701, \"(10.594999999999999, 21.98)\": 0.786}\nLower Bounds (95%-Confidence Interval): {\"(0.7714, 1.0579999999999998)\": -1.131, \"(1.0579999999999998, 1.1345)\": -1.029, \"(1.1345, 1.197)\": -0.923, \"(1.197, 1.2365)\": -0.835, \"(1.2365, 1.326)\": -0.754, \"(1.326, 1.4435)\": -0.43, \"(1.4435, 1.5314999999999999)\": -0.378, \"(1.5314999999999999, 1.807)\": -0.215, \"(1.807, 2.107)\": -0.131, \"(2.107, 2.593)\": -0.215, \"(2.593, 2.878)\": -0.124, \"(2.878, 3.292)\": -0.022, \"(3.292, 4.095000000000001)\": 0.04, \"(4.095000000000001, 4.714)\": 0.049, \"(4.714, 4.885999999999999)\": -0.063, \"(4.885999999999999, 5.2844999999999995)\": 0.007, \"(5.2844999999999995, 5.8425)\": 0.088, \"(5.8425, 7.104)\": 0.151, \"(7.104, 7.7765)\": 0.21, \"(7.7765, 10.594999999999999)\": 0.257, \"(10.594999999999999, 21.98)\": 0.341}\nUpper Bounds (95%-Confidence Interval): {\"(0.7714, 1.0579999999999998)\": -0.265, \"(1.0579999999999998, 1.1345)\": -0.208, \"(1.1345, 1.197)\": -0.155, \"(1.197, 1.2365)\": -0.087, \"(1.2365, 1.326)\": -0.015, \"(1.326, 1.4435)\": -0.081, \"(1.4435, 1.5314999999999999)\": 0.026, \"(1.5314999999999999, 1.807)\": 0.016, \"(1.807, 2.107)\": 0.085, \"(2.107, 2.593)\": 0.019, \"(2.593, 2.878)\": 0.088, \"(2.878, 3.292)\": 0.152, \"(3.292, 4.095000000000001)\": 0.24, \"(4.095000000000001, 4.714)\": 0.388, \"(4.714, 4.885999999999999)\": 0.655, \"(4.885999999999999, 5.2844999999999995)\": 0.737, \"(5.2844999999999995, 5.8425)\": 0.814, \"(5.8425, 7.104)\": 0.921, \"(7.104, 7.7765)\": 1.012, \"(7.7765, 10.594999999999999)\": 1.146, \"(10.594999999999999, 21.98)\": 1.231}\n\n\nYour task is to provide the mean value of the graph at 1.26. What is the mean value of the graph at 1.26?",
+    "-0.384"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: fractal_dimension_se\nFeature Type: continuous\nMeans: {\"(0.0008948, 0.001092)\": 0.2818, \"(0.001092, 0.0014135)\": 0.3286, \"(0.0014135, 0.0015165)\": 0.2713, \"(0.0015165, 0.0017545)\": 0.2283, \"(0.0017545, 0.0017905)\": 0.144, \"(0.0017905, 0.0019039999999999999)\": 0.0956, \"(0.0019039999999999999, 0.0021525)\": 0.0526, \"(0.0021525, 0.002572)\": 0.0073, \"(0.002572, 0.002761)\": 0.1543, \"(0.002761, 0.003308)\": 0.1971, \"(0.003308, 0.0033604999999999998)\": 0.1525, \"(0.0033604999999999998, 0.0035329999999999997)\": 0.1049, \"(0.0035329999999999997, 0.003736)\": 0.0586, \"(0.003736, 0.003907)\": 0.0157, \"(0.003907, 0.004092500000000001)\": -0.029, \"(0.004092500000000001, 0.0045775)\": -0.0717, \"(0.0045775, 0.0045935)\": -0.1177, \"(0.0045935, 0.004644499999999999)\": -0.1739, \"(0.004644499999999999, 0.004809)\": -0.2208, \"(0.004809, 0.005856500000000001)\": -0.2666, \"(0.005856500000000001, 0.007497500000000001)\": -0.31, \"(0.007497500000000001, 0.009717)\": -0.356, \"(0.009717, 0.0127)\": -0.4, \"(0.0127, 0.02984)\": -0.4439}\nLower Bounds (95%-Confidence Interval): {\"(0.0008948, 0.001092)\": 0.0382, \"(0.001092, 0.0014135)\": 0.0989, \"(0.0014135, 0.0015165)\": 0.1004, \"(0.0015165, 0.0017545)\": 0.0661, \"(0.0017545, 0.0017905)\": -0.1939, \"(0.0017905, 0.0019039999999999999)\": -0.2485, \"(0.0019039999999999999, 0.0021525)\": -0.2947, \"(0.0021525, 0.002572)\": -0.3301, \"(0.002572, 0.002761)\": -0.1655, \"(0.002761, 0.003308)\": -0.1517, \"(0.003308, 0.0033604999999999998)\": -0.1674, \"(0.0033604999999999998, 0.0035329999999999997)\": -0.1413, \"(0.0035329999999999997, 0.003736)\": -0.1763, \"(0.003736, 0.003907)\": -0.2066, \"(0.003907, 0.004092500000000001)\": -0.2479, \"(0.004092500000000001, 0.0045775)\": -0.2863, \"(0.0045775, 0.0045935)\": -0.3224, \"(0.0045935, 0.004644499999999999)\": -0.372, \"(0.004644499999999999, 0.004809)\": -0.418, \"(0.004809, 0.005856500000000001)\": -0.4726, \"(0.005856500000000001, 0.007497500000000001)\": -0.5133, \"(0.007497500000000001, 0.009717)\": -0.5704, \"(0.009717, 0.0127)\": -0.6199, \"(0.0127, 0.02984)\": -0.6593}\nUpper Bounds (95%-Confidence Interval): {\"(0.0008948, 0.001092)\": 0.5254, \"(0.001092, 0.0014135)\": 0.5584, \"(0.0014135, 0.0015165)\": 0.4422, \"(0.0015165, 0.0017545)\": 0.3904, \"(0.0017545, 0.0017905)\": 0.482, \"(0.0017905, 0.0019039999999999999)\": 0.4397, \"(0.0019039999999999999, 0.0021525)\": 0.3999, \"(0.0021525, 0.002572)\": 0.3448, \"(0.002572, 0.002761)\": 0.4742, \"(0.002761, 0.003308)\": 0.5459, \"(0.003308, 0.0033604999999999998)\": 0.4723, \"(0.0033604999999999998, 0.0035329999999999997)\": 0.3511, \"(0.0035329999999999997, 0.003736)\": 0.2936, \"(0.003736, 0.003907)\": 0.238, \"(0.003907, 0.004092500000000001)\": 0.1899, \"(0.004092500000000001, 0.0045775)\": 0.1429, \"(0.0045775, 0.0045935)\": 0.087, \"(0.0045935, 0.004644499999999999)\": 0.0243, \"(0.004644499999999999, 0.004809)\": -0.0237, \"(0.004809, 0.005856500000000001)\": -0.0605, \"(0.005856500000000001, 0.007497500000000001)\": -0.1068, \"(0.007497500000000001, 0.009717)\": -0.1417, \"(0.009717, 0.0127)\": -0.1801, \"(0.0127, 0.02984)\": -0.2284}\n\n\nYour task is to provide the mean value of the graph at 0.0. What is the mean value of the graph at 0.0?",
+    "0.2818"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: DeterioratingInfrastructure\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02508, \"(0.5, 1.5)\": -0.01897, \"(1.5, 2.5)\": -0.01452, \"(2.5, 3.5)\": -0.01085, \"(3.5, 4.5)\": -0.00475, \"(4.5, 5.5)\": 0.00054, \"(5.5, 6.5)\": 0.00555, \"(6.5, 7.5)\": 0.01137, \"(7.5, 8.5)\": 0.01653, \"(8.5, 9.5)\": 0.0237, \"(9.5, 10.5)\": 0.02782, \"(10.5, 11.5)\": 0.03175, \"(11.5, 12.5)\": 0.03686, \"(12.5, 15.0)\": 0.04451}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02677, \"(0.5, 1.5)\": -0.01971, \"(1.5, 2.5)\": -0.01507, \"(2.5, 3.5)\": -0.0113, \"(3.5, 4.5)\": -0.00523, \"(4.5, 5.5)\": 0.00016, \"(5.5, 6.5)\": 0.00517, \"(6.5, 7.5)\": 0.01094, \"(7.5, 8.5)\": 0.01606, \"(8.5, 9.5)\": 0.02309, \"(9.5, 10.5)\": 0.02666, \"(10.5, 11.5)\": 0.03007, \"(11.5, 12.5)\": 0.03455, \"(12.5, 15.0)\": 0.03295}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02339, \"(0.5, 1.5)\": -0.01822, \"(1.5, 2.5)\": -0.01398, \"(2.5, 3.5)\": -0.0104, \"(3.5, 4.5)\": -0.00428, \"(4.5, 5.5)\": 0.00091, \"(5.5, 6.5)\": 0.00594, \"(6.5, 7.5)\": 0.0118, \"(7.5, 8.5)\": 0.017, \"(8.5, 9.5)\": 0.0243, \"(9.5, 10.5)\": 0.02899, \"(10.5, 11.5)\": 0.03343, \"(11.5, 12.5)\": 0.03916, \"(12.5, 15.0)\": 0.05607}\n\n\nYour task is to provide the mean value of the graph at 9.36. What is the mean value of the graph at 9.36?",
+    "0.0237"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: petal_width\nFeature Type: continuous\nMeans: {\"(0.1, 0.35)\": 8.07, \"(0.35, 0.45)\": 7.27, \"(0.45, 0.75)\": 6.18, \"(0.75, 1.25)\": -2.64, \"(1.25, 1.75)\": -3.46, \"(1.75, 2.5)\": -4.19}\nLower Bounds (95%-Confidence Interval): {\"(0.1, 0.35)\": 7.9, \"(0.35, 0.45)\": 7.05, \"(0.45, 0.75)\": 3.08, \"(0.75, 1.25)\": -2.81, \"(1.25, 1.75)\": -3.62, \"(1.75, 2.5)\": -4.29}\nUpper Bounds (95%-Confidence Interval): {\"(0.1, 0.35)\": 8.23, \"(0.35, 0.45)\": 7.49, \"(0.45, 0.75)\": 9.28, \"(0.75, 1.25)\": -2.47, \"(1.25, 1.75)\": -3.3, \"(1.75, 2.5)\": -4.08}\n\n\nYour task is to provide the mean value of the graph at 0.37. What is the mean value of the graph at 0.37?",
+    "7.27"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: PopulationScore\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02443, \"(0.5, 1.5)\": -0.02088, \"(1.5, 2.5)\": -0.01613, \"(2.5, 3.5)\": -0.01086, \"(3.5, 4.5)\": -0.00583, \"(4.5, 5.5)\": 0.00139, \"(5.5, 6.5)\": 0.00556, \"(6.5, 7.5)\": 0.01145, \"(7.5, 8.5)\": 0.01748, \"(8.5, 10.5)\": 0.0242, \"(10.5, 11.5)\": 0.03351, \"(11.5, 13.5)\": 0.03691, \"(13.5, 15.0)\": 0.03345, \"(15.0, 16.0)\": 0.02926}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02582, \"(0.5, 1.5)\": -0.02181, \"(1.5, 2.5)\": -0.01706, \"(2.5, 3.5)\": -0.01143, \"(3.5, 4.5)\": -0.00626, \"(4.5, 5.5)\": 0.00099, \"(5.5, 6.5)\": 0.00524, \"(6.5, 7.5)\": 0.01084, \"(7.5, 8.5)\": 0.0167, \"(8.5, 10.5)\": 0.02302, \"(10.5, 11.5)\": 0.03159, \"(11.5, 13.5)\": 0.03427, \"(13.5, 15.0)\": 0.02849, \"(15.0, 16.0)\": 0.02539}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02304, \"(0.5, 1.5)\": -0.01995, \"(1.5, 2.5)\": -0.01521, \"(2.5, 3.5)\": -0.01028, \"(3.5, 4.5)\": -0.00541, \"(4.5, 5.5)\": 0.00178, \"(5.5, 6.5)\": 0.00588, \"(6.5, 7.5)\": 0.01205, \"(7.5, 8.5)\": 0.01826, \"(8.5, 10.5)\": 0.02538, \"(10.5, 11.5)\": 0.03543, \"(11.5, 13.5)\": 0.03955, \"(13.5, 15.0)\": 0.03841, \"(15.0, 16.0)\": 0.03313}\n\n\nYour task is to provide the mean value of the graph at 1.58. What is the mean value of the graph at 1.58?",
+    "-0.01613"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: ejection_fraction\nFeature Type: continuous\nMeans: {\"(14.0, 16.0)\": 4.55, \"(16.0, 22.5)\": 3.26, \"(22.5, 27.5)\": 1.89, \"(27.5, 32.5)\": -0.42, \"(32.5, 36.5)\": -1.76, \"(36.5, 39.0)\": 0.48, \"(39.0, 61.0)\": -0.83, \"(61.0, 67.5)\": 0.08, \"(67.5, 75.0)\": 0.8, \"(75.0, 80.0)\": -5.67}\nLower Bounds (95%-Confidence Interval): {\"(14.0, 16.0)\": 2.65, \"(16.0, 22.5)\": 2.42, \"(22.5, 27.5)\": 1.26, \"(27.5, 32.5)\": -0.83, \"(32.5, 36.5)\": -2.57, \"(36.5, 39.0)\": 0.17, \"(39.0, 61.0)\": -1.16, \"(61.0, 67.5)\": -0.39, \"(67.5, 75.0)\": 0.32, \"(75.0, 80.0)\": -8.05}\nUpper Bounds (95%-Confidence Interval): {\"(14.0, 16.0)\": 6.45, \"(16.0, 22.5)\": 4.1, \"(22.5, 27.5)\": 2.51, \"(27.5, 32.5)\": -0.01, \"(32.5, 36.5)\": -0.95, \"(36.5, 39.0)\": 0.79, \"(39.0, 61.0)\": -0.49, \"(61.0, 67.5)\": 0.55, \"(67.5, 75.0)\": 1.28, \"(75.0, 80.0)\": -3.29}\n\n\nYour task is to provide the mean value of the graph at 37.49. What is the mean value of the graph at 37.49?",
+    "0.48"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: time\nFeature Type: continuous\nMeans: {\"(4.0, 11.5)\": 10.73, \"(11.5, 12.5)\": 1.29, \"(12.5, 15.5)\": 3.88, \"(15.5, 18.0)\": 2.22, \"(18.0, 28.5)\": 6.17, \"(28.5, 30.5)\": 4.47, \"(30.5, 52.0)\": 5.56, \"(52.0, 54.5)\": 3.38, \"(54.5, 67.5)\": 4.79, \"(67.5, 73.5)\": 2.76, \"(73.5, 76.5)\": -3.15, \"(76.5, 78.5)\": 2.29, \"(78.5, 82.5)\": -0.16, \"(82.5, 87.5)\": -2.8, \"(87.5, 90.5)\": 0.19, \"(90.5, 92.5)\": -1.08, \"(92.5, 95.5)\": -2.7, \"(95.5, 108.5)\": -0.98, \"(108.5, 117.5)\": 0.02, \"(117.5, 124.5)\": -3.44, \"(124.5, 137.5)\": 0.64, \"(137.5, 149.0)\": -0.8, \"(149.0, 171.5)\": 5.06, \"(171.5, 173.0)\": 2.66, \"(173.0, 182.5)\": -0.84, \"(182.5, 192.5)\": -3.42, \"(192.5, 193.5)\": -1.01, \"(193.5, 253.0)\": -2.58, \"(253.0, 285.0)\": -8.42}\nLower Bounds (95%-Confidence Interval): {\"(4.0, 11.5)\": 8.45, \"(11.5, 12.5)\": 0.25, \"(12.5, 15.5)\": 2.94, \"(15.5, 18.0)\": -0.25, \"(18.0, 28.5)\": 4.04, \"(28.5, 30.5)\": 3.69, \"(30.5, 52.0)\": 4.21, \"(52.0, 54.5)\": 1.74, \"(54.5, 67.5)\": 3.17, \"(67.5, 73.5)\": 1.96, \"(73.5, 76.5)\": -4.69, \"(76.5, 78.5)\": 1.19, \"(78.5, 82.5)\": -1.25, \"(82.5, 87.5)\": -3.84, \"(87.5, 90.5)\": -0.35, \"(90.5, 92.5)\": -2.75, \"(92.5, 95.5)\": -4.6, \"(95.5, 108.5)\": -1.62, \"(108.5, 117.5)\": -0.66, \"(117.5, 124.5)\": -4.94, \"(124.5, 137.5)\": -0.24, \"(137.5, 149.0)\": -1.83, \"(149.0, 171.5)\": 3.59, \"(171.5, 173.0)\": 1.61, \"(173.0, 182.5)\": -1.86, \"(182.5, 192.5)\": -4.51, \"(192.5, 193.5)\": -1.89, \"(193.5, 253.0)\": -4.11, \"(253.0, 285.0)\": -10.7}\nUpper Bounds (95%-Confidence Interval): {\"(4.0, 11.5)\": 13.0, \"(11.5, 12.5)\": 2.32, \"(12.5, 15.5)\": 4.82, \"(15.5, 18.0)\": 4.68, \"(18.0, 28.5)\": 8.31, \"(28.5, 30.5)\": 5.26, \"(30.5, 52.0)\": 6.91, \"(52.0, 54.5)\": 5.03, \"(54.5, 67.5)\": 6.41, \"(67.5, 73.5)\": 3.57, \"(73.5, 76.5)\": -1.61, \"(76.5, 78.5)\": 3.39, \"(78.5, 82.5)\": 0.92, \"(82.5, 87.5)\": -1.75, \"(87.5, 90.5)\": 0.72, \"(90.5, 92.5)\": 0.6, \"(92.5, 95.5)\": -0.81, \"(95.5, 108.5)\": -0.34, \"(108.5, 117.5)\": 0.7, \"(117.5, 124.5)\": -1.93, \"(124.5, 137.5)\": 1.53, \"(137.5, 149.0)\": 0.22, \"(149.0, 171.5)\": 6.52, \"(171.5, 173.0)\": 3.72, \"(173.0, 182.5)\": 0.18, \"(182.5, 192.5)\": -2.33, \"(192.5, 193.5)\": -0.13, \"(193.5, 253.0)\": -1.06, \"(253.0, 285.0)\": -6.14}\n\n\nYour task is to provide the mean value of the graph at 52.67. What is the mean value of the graph at 52.67?",
+    "3.38"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: area_se\nFeature Type: continuous\nMeans: {\"(6.802, 11.184999999999999)\": -0.919, \"(11.184999999999999, 12.765)\": -0.814, \"(12.765, 13.350000000000001)\": -0.704, \"(13.350000000000001, 15.3)\": -0.596, \"(15.3, 16.955)\": -0.49, \"(16.955, 18.515)\": -0.367, \"(18.515, 20.905)\": -0.256, \"(20.905, 32.985)\": -0.151, \"(32.985, 34.730000000000004)\": 0.081, \"(34.730000000000004, 41.21)\": 0.188, \"(41.21, 50.405)\": 0.292, \"(50.405, 56.915)\": 0.417, \"(56.915, 67.5)\": 0.53, \"(67.5, 81.56)\": 0.638, \"(81.56, 94.00999999999999)\": 0.751, \"(94.00999999999999, 106.2)\": 0.862, \"(106.2, 153.25)\": 0.974, \"(153.25, 542.2)\": 1.082}\nLower Bounds (95%-Confidence Interval): {\"(6.802, 11.184999999999999)\": -1.305, \"(11.184999999999999, 12.765)\": -1.176, \"(12.765, 13.350000000000001)\": -1.036, \"(13.350000000000001, 15.3)\": -0.901, \"(15.3, 16.955)\": -0.696, \"(16.955, 18.515)\": -0.504, \"(18.515, 20.905)\": -0.392, \"(20.905, 32.985)\": -0.922, \"(32.985, 34.730000000000004)\": -0.261, \"(34.730000000000004, 41.21)\": -0.102, \"(41.21, 50.405)\": 0.02, \"(50.405, 56.915)\": 0.072, \"(56.915, 67.5)\": 0.147, \"(67.5, 81.56)\": 0.223, \"(81.56, 94.00999999999999)\": 0.326, \"(94.00999999999999, 106.2)\": 0.402, \"(106.2, 153.25)\": 0.501, \"(153.25, 542.2)\": 0.571}\nUpper Bounds (95%-Confidence Interval): {\"(6.802, 11.184999999999999)\": -0.532, \"(11.184999999999999, 12.765)\": -0.452, \"(12.765, 13.350000000000001)\": -0.371, \"(13.350000000000001, 15.3)\": -0.291, \"(15.3, 16.955)\": -0.284, \"(16.955, 18.515)\": -0.23, \"(18.515, 20.905)\": -0.121, \"(20.905, 32.985)\": 0.62, \"(32.985, 34.730000000000004)\": 0.424, \"(34.730000000000004, 41.21)\": 0.479, \"(41.21, 50.405)\": 0.563, \"(50.405, 56.915)\": 0.762, \"(56.915, 67.5)\": 0.913, \"(67.5, 81.56)\": 1.052, \"(81.56, 94.00999999999999)\": 1.176, \"(94.00999999999999, 106.2)\": 1.323, \"(106.2, 153.25)\": 1.448, \"(153.25, 542.2)\": 1.593}\n\n\nYour task is to provide the mean value of the graph at 34.12. What is the mean value of the graph at 34.12?",
+    "0.081"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: CapitalLoss\nFeature Type: continuous\nMeans: {\"(0.0, 845.0)\": -0.044, \"(845.0, 1448.0)\": -1.147, \"(1448.0, 1551.5)\": 0.416, \"(1551.5, 1568.5)\": 3.928, \"(1568.5, 1748.0)\": -3.752, \"(1748.0, 1846.0)\": 1.139, \"(1846.0, 1862.0)\": 3.823, \"(1862.0, 1881.5)\": -1.36, \"(1881.5, 1894.5)\": 4.781, \"(1894.5, 1938.0)\": 3.172, \"(1938.0, 1975.5)\": 0.294, \"(1975.5, 1978.5)\": 4.013, \"(1978.5, 2139.0)\": -2.74, \"(2139.0, 2176.5)\": 0.361, \"(2176.5, 2190.0)\": -1.098, \"(2190.0, 2205.5)\": 1.259, \"(2205.5, 2262.5)\": 2.644, \"(2262.5, 2310.5)\": -0.616, \"(2310.5, 2364.5)\": -1.139, \"(2364.5, 2384.5)\": 1.07, \"(2384.5, 2450.5)\": 4.377, \"(2450.5, 2480.5)\": 1.517, \"(2480.5, 2553.0)\": 3.296, \"(2553.0, 2581.0)\": 5.5, \"(2581.0, 2678.5)\": -0.191, \"(2678.5, 2789.0)\": 0.326, \"(2789.0, 3343.5)\": 5.958, \"(3343.5, 3835.0)\": 2.152, \"(3835.0, 4356.0)\": -0.334}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 845.0)\": -0.934, \"(845.0, 1448.0)\": -2.192, \"(1448.0, 1551.5)\": 0.083, \"(1551.5, 1568.5)\": 2.921, \"(1568.5, 1748.0)\": -4.443, \"(1748.0, 1846.0)\": 0.39, \"(1846.0, 1862.0)\": 2.886, \"(1862.0, 1881.5)\": -2.359, \"(1881.5, 1894.5)\": 3.819, \"(1894.5, 1938.0)\": 2.82, \"(1938.0, 1975.5)\": -0.436, \"(1975.5, 1978.5)\": 3.487, \"(1978.5, 2139.0)\": -3.34, \"(2139.0, 2176.5)\": -0.308, \"(2176.5, 2190.0)\": -2.441, \"(2190.0, 2205.5)\": 0.899, \"(2205.5, 2262.5)\": 1.633, \"(2262.5, 2310.5)\": -2.272, \"(2310.5, 2364.5)\": -2.819, \"(2364.5, 2384.5)\": 0.659, \"(2384.5, 2450.5)\": 3.333, \"(2450.5, 2480.5)\": 0.001, \"(2480.5, 2553.0)\": 1.926, \"(2553.0, 2581.0)\": 4.074, \"(2581.0, 2678.5)\": -1.869, \"(2678.5, 2789.0)\": -1.325, \"(2789.0, 3343.5)\": 4.42, \"(3343.5, 3835.0)\": 0.138, \"(3835.0, 4356.0)\": -1.587}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 845.0)\": 0.845, \"(845.0, 1448.0)\": -0.101, \"(1448.0, 1551.5)\": 0.748, \"(1551.5, 1568.5)\": 4.935, \"(1568.5, 1748.0)\": -3.061, \"(1748.0, 1846.0)\": 1.889, \"(1846.0, 1862.0)\": 4.761, \"(1862.0, 1881.5)\": -0.361, \"(1881.5, 1894.5)\": 5.742, \"(1894.5, 1938.0)\": 3.524, \"(1938.0, 1975.5)\": 1.024, \"(1975.5, 1978.5)\": 4.539, \"(1978.5, 2139.0)\": -2.139, \"(2139.0, 2176.5)\": 1.029, \"(2176.5, 2190.0)\": 0.245, \"(2190.0, 2205.5)\": 1.619, \"(2205.5, 2262.5)\": 3.655, \"(2262.5, 2310.5)\": 1.041, \"(2310.5, 2364.5)\": 0.541, \"(2364.5, 2384.5)\": 1.481, \"(2384.5, 2450.5)\": 5.42, \"(2450.5, 2480.5)\": 3.034, \"(2480.5, 2553.0)\": 4.666, \"(2553.0, 2581.0)\": 6.926, \"(2581.0, 2678.5)\": 1.487, \"(2678.5, 2789.0)\": 1.978, \"(2789.0, 3343.5)\": 7.496, \"(3343.5, 3835.0)\": 4.167, \"(3835.0, 4356.0)\": 0.92}\n\n\nYour task is to provide the mean value of the graph at 1886.32. What is the mean value of the graph at 1886.32?",
+    "4.781"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: radius_se\nFeature Type: continuous\nMeans: {\"(0.1115, 0.15015)\": -0.773, \"(0.15015, 0.16904999999999998)\": -0.686, \"(0.16904999999999998, 0.1795)\": -0.589, \"(0.1795, 0.18535000000000001)\": -0.499, \"(0.18535000000000001, 0.19345)\": -0.412, \"(0.19345, 0.2103)\": -0.275, \"(0.2103, 0.2329)\": -0.187, \"(0.2329, 0.2939)\": -0.102, \"(0.2939, 0.368)\": -0.186, \"(0.368, 0.38585)\": -0.066, \"(0.38585, 0.42025)\": 0.064, \"(0.42025, 0.46775)\": 0.15, \"(0.46775, 0.54785)\": 0.239, \"(0.54785, 0.5881000000000001)\": 0.334, \"(0.5881000000000001, 0.66425)\": 0.422, \"(0.66425, 0.7562)\": 0.51, \"(0.7562, 0.9131)\": 0.594, \"(0.9131, 1.065)\": 0.683, \"(1.065, 1.2915)\": 0.774, \"(1.2915, 2.873)\": 0.866}\nLower Bounds (95%-Confidence Interval): {\"(0.1115, 0.15015)\": -1.244, \"(0.15015, 0.16904999999999998)\": -1.125, \"(0.16904999999999998, 0.1795)\": -1.008, \"(0.1795, 0.18535000000000001)\": -0.904, \"(0.18535000000000001, 0.19345)\": -0.8, \"(0.19345, 0.2103)\": -0.449, \"(0.2103, 0.2329)\": -0.273, \"(0.2329, 0.2939)\": -0.492, \"(0.2939, 0.368)\": -0.769, \"(0.368, 0.38585)\": -0.437, \"(0.38585, 0.42025)\": -0.188, \"(0.42025, 0.46775)\": -0.119, \"(0.46775, 0.54785)\": -0.037, \"(0.54785, 0.5881000000000001)\": -0.09, \"(0.5881000000000001, 0.66425)\": -0.016, \"(0.66425, 0.7562)\": 0.051, \"(0.7562, 0.9131)\": 0.051, \"(0.9131, 1.065)\": 0.113, \"(1.065, 1.2915)\": 0.123, \"(1.2915, 2.873)\": 0.198}\nUpper Bounds (95%-Confidence Interval): {\"(0.1115, 0.15015)\": -0.302, \"(0.15015, 0.16904999999999998)\": -0.247, \"(0.16904999999999998, 0.1795)\": -0.169, \"(0.1795, 0.18535000000000001)\": -0.094, \"(0.18535000000000001, 0.19345)\": -0.024, \"(0.19345, 0.2103)\": -0.1, \"(0.2103, 0.2329)\": -0.101, \"(0.2329, 0.2939)\": 0.289, \"(0.2939, 0.368)\": 0.396, \"(0.368, 0.38585)\": 0.304, \"(0.38585, 0.42025)\": 0.315, \"(0.42025, 0.46775)\": 0.42, \"(0.46775, 0.54785)\": 0.514, \"(0.54785, 0.5881000000000001)\": 0.758, \"(0.5881000000000001, 0.66425)\": 0.86, \"(0.66425, 0.7562)\": 0.968, \"(0.7562, 0.9131)\": 1.137, \"(0.9131, 1.065)\": 1.253, \"(1.065, 1.2915)\": 1.425, \"(1.2915, 2.873)\": 1.533}\n\n\nYour task is to provide the mean value of the graph at 0.92. What is the mean value of the graph at 0.92?",
+    "0.683"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n    \nThe graph is provided in the following format:\n    - The name of the feature depicted in the graph\n    - The type of the feature (continuous, categorical, or boolean)\n    - Mean values\n    - Lower bounds of confidence interval (optional)\n    - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: compactness_worst\nFeature Type: continuous\nMeans: {\"(0.02729, 0.049945)\": -0.0578, \"(0.049945, 0.06971)\": -0.0099, \"(0.06971, 0.099305)\": -0.0565, \"(0.099305, 0.10635)\": -0.1408, \"(0.10635, 0.1243)\": -0.1882, \"(0.1243, 0.14795)\": -0.2357, \"(0.14795, 0.1507)\": -0.1883, \"(0.1507, 0.1861)\": -0.1381, \"(0.1861, 0.20124999999999998)\": -0.0918, \"(0.20124999999999998, 0.3358)\": -0.0443, \"(0.3358, 0.3456)\": 0.0027, \"(0.3456, 0.35755000000000003)\": 0.0649, \"(0.35755000000000003, 0.3703)\": 0.1151, \"(0.3703, 0.39235)\": 0.1642, \"(0.39235, 0.4087)\": 0.2124, \"(0.4087, 0.4229)\": 0.2605, \"(0.4229, 0.4486)\": 0.3109, \"(0.4486, 0.48865000000000003)\": 0.3586, \"(0.48865000000000003, 0.54825)\": 0.4132, \"(0.54825, 0.5892999999999999)\": 0.4651, \"(0.5892999999999999, 0.65835)\": 0.5154, \"(0.65835, 0.7680499999999999)\": 0.572, \"(0.7680499999999999, 0.99795)\": 0.6264, \"(0.99795, 1.058)\": 0.6748}\nLower Bounds (95%-Confidence Interval): {\"(0.02729, 0.049945)\": -0.8125, \"(0.049945, 0.06971)\": -0.7624, \"(0.06971, 0.099305)\": -0.6001, \"(0.099305, 0.10635)\": -0.4033, \"(0.10635, 0.1243)\": -0.4448, \"(0.1243, 0.14795)\": -0.4969, \"(0.14795, 0.1507)\": -0.4446, \"(0.1507, 0.1861)\": -0.2722, \"(0.1861, 0.20124999999999998)\": -0.1924, \"(0.20124999999999998, 0.3358)\": -0.2305, \"(0.3358, 0.3456)\": -0.1741, \"(0.3456, 0.35755000000000003)\": -0.068, \"(0.35755000000000003, 0.3703)\": 0.0047, \"(0.3703, 0.39235)\": 0.0473, \"(0.39235, 0.4087)\": 0.1107, \"(0.4087, 0.4229)\": 0.1686, \"(0.4229, 0.4486)\": 0.2243, \"(0.4486, 0.48865000000000003)\": 0.2736, \"(0.48865000000000003, 0.54825)\": 0.2405, \"(0.54825, 0.5892999999999999)\": 0.2819, \"(0.5892999999999999, 0.65835)\": 0.3155, \"(0.65835, 0.7680499999999999)\": 0.3513, \"(0.7680499999999999, 0.99795)\": 0.3892, \"(0.99795, 1.058)\": 0.4487}\nUpper Bounds (95%-Confidence Interval): {\"(0.02729, 0.049945)\": 0.6969, \"(0.049945, 0.06971)\": 0.7425, \"(0.06971, 0.099305)\": 0.487, \"(0.099305, 0.10635)\": 0.1218, \"(0.10635, 0.1243)\": 0.0684, \"(0.1243, 0.14795)\": 0.0254, \"(0.14795, 0.1507)\": 0.068, \"(0.1507, 0.1861)\": -0.0039, \"(0.1861, 0.20124999999999998)\": 0.0087, \"(0.20124999999999998, 0.3358)\": 0.1418, \"(0.3358, 0.3456)\": 0.1794, \"(0.3456, 0.35755000000000003)\": 0.1979, \"(0.35755000000000003, 0.3703)\": 0.2255, \"(0.3703, 0.39235)\": 0.2811, \"(0.39235, 0.4087)\": 0.314, \"(0.4087, 0.4229)\": 0.3524, \"(0.4229, 0.4486)\": 0.3975, \"(0.4486, 0.48865000000000003)\": 0.4436, \"(0.48865000000000003, 0.54825)\": 0.5859, \"(0.54825, 0.5892999999999999)\": 0.6484, \"(0.5892999999999999, 0.65835)\": 0.7153, \"(0.65835, 0.7680499999999999)\": 0.7927, \"(0.7680499999999999, 0.99795)\": 0.8637, \"(0.99795, 1.058)\": 0.9008}\n\n\nYour task is to provide the mean value of the graph at 0.57. What is the mean value of the graph at 0.57?",
+    "0.4651"
+  ]
+]
\ No newline at end of file
diff --git a/benchmarks/benchmark/wide-confidence.json b/benchmarks/benchmark/wide-confidence.json
new file mode 100644
index 0000000..96ede71
--- /dev/null
+++ b/benchmarks/benchmark/wide-confidence.json
@@ -0,0 +1,402 @@
+[
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: smoothness_mean\nFeature Type: continuous\nMeans: {\"(0.05263, 0.0706)\": -0.835, \"(0.0706, 0.07455500000000001)\": -0.769, \"(0.07455500000000001, 0.07589499999999999)\": -0.697, \"(0.07589499999999999, 0.07727500000000001)\": -0.632, \"(0.07727500000000001, 0.078275)\": -0.569, \"(0.078275, 0.07952000000000001)\": -0.506, \"(0.07952000000000001, 0.080315)\": -0.437, \"(0.080315, 0.081035)\": -0.368, \"(0.081035, 0.08308499999999999)\": -0.304, \"(0.08308499999999999, 0.085165)\": -0.242, \"(0.085165, 0.086795)\": -0.177, \"(0.086795, 0.087785)\": -0.111, \"(0.087785, 0.088615)\": -0.047, \"(0.088615, 0.08918999999999999)\": 0.065, \"(0.08918999999999999, 0.090335)\": 0.142, \"(0.090335, 0.09454)\": 0.211, \"(0.09454, 0.11525)\": 0.107, \"(0.11525, 0.11765)\": 0.171, \"(0.11765, 0.12455)\": 0.267, \"(0.12455, 0.13845000000000002)\": 0.334, \"(0.13845000000000002, 0.1634)\": 0.396}\nLower Bounds (95%-Confidence Interval): {\"(0.05263, 0.0706)\": -1.454, \"(0.0706, 0.07455500000000001)\": -1.359, \"(0.07455500000000001, 0.07589499999999999)\": -1.244, \"(0.07589499999999999, 0.07727500000000001)\": -1.162, \"(0.07727500000000001, 0.078275)\": -1.087, \"(0.078275, 0.07952000000000001)\": -1.006, \"(0.07952000000000001, 0.080315)\": -0.882, \"(0.080315, 0.081035)\": -0.622, \"(0.081035, 0.08308499999999999)\": -0.547, \"(0.08308499999999999, 0.085165)\": -0.444, \"(0.085165, 0.086795)\": -0.357, \"(0.086795, 0.087785)\": -0.296, \"(0.087785, 0.088615)\": -0.23, \"(0.088615, 0.08918999999999999)\": -0.16, \"(0.08918999999999999, 0.090335)\": -0.309, \"(0.090335, 0.09454)\": -0.264, \"(0.09454, 0.11525)\": -0.005, \"(0.11525, 0.11765)\": 0.07, \"(0.11765, 0.12455)\": 0.022, \"(0.12455, 0.13845000000000002)\": 0.077, \"(0.13845000000000002, 0.1634)\": 0.127}\nUpper Bounds (95%-Confidence Interval): {\"(0.05263, 0.0706)\": -0.216, \"(0.0706, 0.07455500000000001)\": -0.178, \"(0.07455500000000001, 0.07589499999999999)\": -0.151, \"(0.07589499999999999, 0.07727500000000001)\": -0.102, \"(0.07727500000000001, 0.078275)\": -0.052, \"(0.078275, 0.07952000000000001)\": -0.006, \"(0.07952000000000001, 0.080315)\": 0.008, \"(0.080315, 0.081035)\": -0.113, \"(0.081035, 0.08308499999999999)\": -0.062, \"(0.08308499999999999, 0.085165)\": -0.04, \"(0.085165, 0.086795)\": 0.004, \"(0.086795, 0.087785)\": 0.075, \"(0.087785, 0.088615)\": 0.136, \"(0.088615, 0.08918999999999999)\": 0.291, \"(0.08918999999999999, 0.090335)\": 0.594, \"(0.090335, 0.09454)\": 0.685, \"(0.09454, 0.11525)\": 0.22, \"(0.11525, 0.11765)\": 0.273, \"(0.11765, 0.12455)\": 0.512, \"(0.12455, 0.13845000000000002)\": 0.591, \"(0.13845000000000002, 0.1634)\": 0.664}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.05263, 0.0706)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Spa\nFeature Type: continuous\nMeans: {\"(0.0, 130.5)\": 0.521, \"(130.5, 278.5)\": 0.118, \"(278.5, 452.5)\": -0.285, \"(452.5, 754.5)\": -0.907, \"(754.5, 1209.5)\": -1.309, \"(1209.5, 1808.0)\": -1.712, \"(1808.0, 2204.5)\": -3.029, \"(2204.5, 2207.5)\": -2.456, \"(2207.5, 2428.0)\": -2.956, \"(2428.0, 2462.5)\": -2.512, \"(2462.5, 2714.5)\": -3.402, \"(2714.5, 2745.0)\": -2.902, \"(2745.0, 2993.5)\": -4.077, \"(2993.5, 3132.0)\": -4.481, \"(3132.0, 3705.5)\": -5.377, \"(3705.5, 3747.0)\": -4.36, \"(3747.0, 22408.0)\": -7.183}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 130.5)\": 0.36, \"(130.5, 278.5)\": -1.599, \"(278.5, 452.5)\": -1.362, \"(452.5, 754.5)\": -1.291, \"(754.5, 1209.5)\": -2.117, \"(1209.5, 1808.0)\": -2.592, \"(1808.0, 2204.5)\": -3.856, \"(2204.5, 2207.5)\": -3.562, \"(2207.5, 2428.0)\": -3.549, \"(2428.0, 2462.5)\": -3.455, \"(2462.5, 2714.5)\": -4.525, \"(2714.5, 2745.0)\": -4.721, \"(2745.0, 2993.5)\": -5.493, \"(2993.5, 3132.0)\": -6.214, \"(3132.0, 3705.5)\": -6.767, \"(3705.5, 3747.0)\": -6.498, \"(3747.0, 22408.0)\": -9.024}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 130.5)\": 0.682, \"(130.5, 278.5)\": 1.834, \"(278.5, 452.5)\": 0.791, \"(452.5, 754.5)\": -0.524, \"(754.5, 1209.5)\": -0.502, \"(1209.5, 1808.0)\": -0.831, \"(1808.0, 2204.5)\": -2.202, \"(2204.5, 2207.5)\": -1.35, \"(2207.5, 2428.0)\": -2.364, \"(2428.0, 2462.5)\": -1.569, \"(2462.5, 2714.5)\": -2.28, \"(2714.5, 2745.0)\": -1.083, \"(2745.0, 2993.5)\": -2.661, \"(2993.5, 3132.0)\": -2.749, \"(3132.0, 3705.5)\": -3.986, \"(3705.5, 3747.0)\": -2.222, \"(3747.0, 22408.0)\": -5.342}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(3705.5, 3747.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: MaritalStatus\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.368, \"(0.5, 1.5)\": 0.724, \"(1.5, 2.5)\": 0.587, \"(2.5, 3.5)\": -0.221, \"(3.5, 4.5)\": -0.631, \"(4.5, 5.5)\": -0.545, \"(5.5, 6.0)\": 0.179}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.418, \"(0.5, 1.5)\": 0.02, \"(1.5, 2.5)\": 0.545, \"(2.5, 3.5)\": -0.336, \"(3.5, 4.5)\": -0.676, \"(4.5, 5.5)\": -0.688, \"(5.5, 6.0)\": 0.067}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.318, \"(0.5, 1.5)\": 1.428, \"(1.5, 2.5)\": 0.629, \"(2.5, 3.5)\": -0.106, \"(3.5, 4.5)\": -0.585, \"(4.5, 5.5)\": -0.403, \"(5.5, 6.0)\": 0.291}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.5, 1.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: EstimatedSalary\nFeature Type: continuous\nMeans: {\"(106.67, 780.2149999999999)\": 0.3865, \"(780.2149999999999, 4627.98)\": 0.3462, \"(4627.98, 6842.475)\": 0.0858, \"(6842.475, 7401.88)\": 0.157, \"(7401.88, 27330.43)\": 0.2048, \"(27330.43, 38816.375)\": 0.1737, \"(38816.375, 40348.645000000004)\": 0.1063, \"(40348.645000000004, 42807.509999999995)\": 0.0512, \"(42807.509999999995, 48226.81)\": 0.1098, \"(48226.81, 48498.15)\": -0.0771, \"(48498.15, 58535.68)\": 0.0187, \"(58535.68, 94498.98999999999)\": 0.0512, \"(94498.98999999999, 120892.955)\": 0.0186, \"(120892.955, 121151.28)\": -0.0263, \"(121151.28, 121482.61499999999)\": -0.0801, \"(121482.61499999999, 148569.97)\": -0.0388, \"(148569.97, 184522.325)\": -0.0796, \"(184522.325, 187947.635)\": -0.1332, \"(187947.635, 187985.865)\": -0.2342, \"(187985.865, 188452.565)\": -0.0632, \"(188452.565, 189006.61)\": -0.0053, \"(189006.61, 196418.97999999998)\": 0.0291, \"(196418.97999999998, 199505.41)\": -0.0098, \"(199505.41, 199992.48)\": 0.214}\nLower Bounds (95%-Confidence Interval): {\"(106.67, 780.2149999999999)\": 0.0871, \"(780.2149999999999, 4627.98)\": 0.1468, \"(4627.98, 6842.475)\": -0.2734, \"(6842.475, 7401.88)\": -0.01, \"(7401.88, 27330.43)\": 0.0941, \"(27330.43, 38816.375)\": 0.065, \"(38816.375, 40348.645000000004)\": -0.0568, \"(40348.645000000004, 42807.509999999995)\": -0.1427, \"(42807.509999999995, 48226.81)\": 0.0015, \"(48226.81, 48498.15)\": -0.404, \"(48498.15, 58535.68)\": -0.1286, \"(58535.68, 94498.98999999999)\": -0.003, \"(94498.98999999999, 120892.955)\": -0.0541, \"(120892.955, 121151.28)\": -0.186, \"(121151.28, 121482.61499999999)\": -0.2842, \"(121482.61499999999, 148569.97)\": -0.1593, \"(148569.97, 184522.325)\": -0.1401, \"(184522.325, 187947.635)\": -0.216, \"(187947.635, 187985.865)\": -0.7523, \"(187985.865, 188452.565)\": -0.2404, \"(188452.565, 189006.61)\": -0.1779, \"(189006.61, 196418.97999999998)\": -0.1285, \"(196418.97999999998, 199505.41)\": -0.2064, \"(199505.41, 199992.48)\": -0.3318}\nUpper Bounds (95%-Confidence Interval): {\"(106.67, 780.2149999999999)\": 0.6859, \"(780.2149999999999, 4627.98)\": 0.5457, \"(4627.98, 6842.475)\": 0.445, \"(6842.475, 7401.88)\": 0.3239, \"(7401.88, 27330.43)\": 0.3154, \"(27330.43, 38816.375)\": 0.2823, \"(38816.375, 40348.645000000004)\": 0.2695, \"(40348.645000000004, 42807.509999999995)\": 0.2451, \"(42807.509999999995, 48226.81)\": 0.2181, \"(48226.81, 48498.15)\": 0.2497, \"(48498.15, 58535.68)\": 0.166, \"(58535.68, 94498.98999999999)\": 0.1054, \"(94498.98999999999, 120892.955)\": 0.0913, \"(120892.955, 121151.28)\": 0.1335, \"(121151.28, 121482.61499999999)\": 0.1239, \"(121482.61499999999, 148569.97)\": 0.0817, \"(148569.97, 184522.325)\": -0.019, \"(184522.325, 187947.635)\": -0.0504, \"(187947.635, 187985.865)\": 0.2839, \"(187985.865, 188452.565)\": 0.1139, \"(188452.565, 189006.61)\": 0.1673, \"(189006.61, 196418.97999999998)\": 0.1867, \"(196418.97999999998, 199505.41)\": 0.1868, \"(199505.41, 199992.48)\": 0.7597}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(199505.41, 199992.48)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: PopulationScore\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02443, \"(0.5, 1.5)\": -0.02088, \"(1.5, 2.5)\": -0.01613, \"(2.5, 3.5)\": -0.01086, \"(3.5, 4.5)\": -0.00583, \"(4.5, 5.5)\": 0.00139, \"(5.5, 6.5)\": 0.00556, \"(6.5, 7.5)\": 0.01145, \"(7.5, 8.5)\": 0.01748, \"(8.5, 10.5)\": 0.0242, \"(10.5, 11.5)\": 0.03351, \"(11.5, 13.5)\": 0.03691, \"(13.5, 15.0)\": 0.03345, \"(15.0, 16.0)\": 0.02926}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02582, \"(0.5, 1.5)\": -0.02181, \"(1.5, 2.5)\": -0.01706, \"(2.5, 3.5)\": -0.01143, \"(3.5, 4.5)\": -0.00626, \"(4.5, 5.5)\": 0.00099, \"(5.5, 6.5)\": 0.00524, \"(6.5, 7.5)\": 0.01084, \"(7.5, 8.5)\": 0.0167, \"(8.5, 10.5)\": 0.02302, \"(10.5, 11.5)\": 0.03159, \"(11.5, 13.5)\": 0.03427, \"(13.5, 15.0)\": 0.02849, \"(15.0, 16.0)\": 0.02539}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02304, \"(0.5, 1.5)\": -0.01995, \"(1.5, 2.5)\": -0.01521, \"(2.5, 3.5)\": -0.01028, \"(3.5, 4.5)\": -0.00541, \"(4.5, 5.5)\": 0.00178, \"(5.5, 6.5)\": 0.00588, \"(6.5, 7.5)\": 0.01205, \"(7.5, 8.5)\": 0.01826, \"(8.5, 10.5)\": 0.02538, \"(10.5, 11.5)\": 0.03543, \"(11.5, 13.5)\": 0.03955, \"(13.5, 15.0)\": 0.03841, \"(15.0, 16.0)\": 0.03313}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(13.5, 15.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Race\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.8604, \"(0.5, 1.5)\": -0.0173, \"(1.5, 2.5)\": -0.2499, \"(2.5, 3.5)\": -0.3026, \"(3.5, 4.0)\": 0.0414}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -1.0291, \"(0.5, 1.5)\": -0.1456, \"(1.5, 2.5)\": -0.3118, \"(2.5, 3.5)\": -0.4557, \"(3.5, 4.0)\": 0.0349}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.6918, \"(0.5, 1.5)\": 0.1111, \"(1.5, 2.5)\": -0.1879, \"(2.5, 3.5)\": -0.1496, \"(3.5, 4.0)\": 0.048}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.0, 0.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: SkinThickness\nFeature Type: continuous\nMeans: {\"(0.0, 3.5)\": 0.0121, \"(3.5, 7.5)\": -0.0407, \"(7.5, 9.0)\": -0.0873, \"(9.0, 11.5)\": -0.1192, \"(11.5, 13.5)\": -0.1587, \"(13.5, 20.5)\": -0.1856, \"(20.5, 22.5)\": -0.1532, \"(22.5, 24.5)\": -0.1123, \"(24.5, 26.5)\": -0.0708, \"(26.5, 28.5)\": -0.036, \"(28.5, 30.5)\": -0.0039, \"(30.5, 32.5)\": 0.0343, \"(32.5, 34.5)\": 0.0703, \"(34.5, 39.5)\": 0.1069, \"(39.5, 40.5)\": 0.143, \"(40.5, 41.5)\": 0.1769, \"(41.5, 43.5)\": 0.2279, \"(43.5, 47.5)\": 0.2859, \"(47.5, 49.5)\": 0.2453, \"(49.5, 51.0)\": -0.0169, \"(51.0, 55.0)\": -0.0754, \"(55.0, 77.5)\": 0.2174, \"(77.5, 99.0)\": 0.3109}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 3.5)\": -0.071, \"(3.5, 7.5)\": -0.1199, \"(7.5, 9.0)\": -0.1639, \"(9.0, 11.5)\": -0.1953, \"(11.5, 13.5)\": -0.2382, \"(13.5, 20.5)\": -0.2707, \"(20.5, 22.5)\": -0.2184, \"(22.5, 24.5)\": -0.1699, \"(24.5, 26.5)\": -0.1255, \"(26.5, 28.5)\": -0.0953, \"(28.5, 30.5)\": -0.0714, \"(30.5, 32.5)\": -0.0304, \"(32.5, 34.5)\": 0.0205, \"(34.5, 39.5)\": 0.0292, \"(39.5, 40.5)\": 0.0607, \"(40.5, 41.5)\": 0.0987, \"(41.5, 43.5)\": 0.0904, \"(43.5, 47.5)\": 0.0985, \"(47.5, 49.5)\": 0.0202, \"(49.5, 51.0)\": -0.3346, \"(51.0, 55.0)\": -0.5656, \"(55.0, 77.5)\": -0.4718, \"(77.5, 99.0)\": -0.4467}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 3.5)\": 0.0953, \"(3.5, 7.5)\": 0.0385, \"(7.5, 9.0)\": -0.0106, \"(9.0, 11.5)\": -0.0431, \"(11.5, 13.5)\": -0.0792, \"(13.5, 20.5)\": -0.1005, \"(20.5, 22.5)\": -0.088, \"(22.5, 24.5)\": -0.0547, \"(24.5, 26.5)\": -0.0161, \"(26.5, 28.5)\": 0.0233, \"(28.5, 30.5)\": 0.0636, \"(30.5, 32.5)\": 0.099, \"(32.5, 34.5)\": 0.12, \"(34.5, 39.5)\": 0.1847, \"(39.5, 40.5)\": 0.2253, \"(40.5, 41.5)\": 0.255, \"(41.5, 43.5)\": 0.3653, \"(43.5, 47.5)\": 0.4732, \"(47.5, 49.5)\": 0.4704, \"(49.5, 51.0)\": 0.3009, \"(51.0, 55.0)\": 0.4148, \"(55.0, 77.5)\": 0.9065, \"(77.5, 99.0)\": 1.0684}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(77.5, 99.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: WetlandLoss\nFeature Type: continuous\nMeans: {\"(0.0, 1.5)\": -0.02419, \"(1.5, 2.5)\": -0.01693, \"(2.5, 3.5)\": -0.01069, \"(3.5, 4.5)\": -0.00585, \"(4.5, 5.5)\": 0.00051, \"(5.5, 6.5)\": 0.00676, \"(6.5, 8.5)\": 0.01245, \"(8.5, 10.5)\": 0.02257, \"(10.5, 11.5)\": 0.03265, \"(11.5, 13.5)\": 0.03889, \"(13.5, 14.5)\": 0.04912, \"(14.5, 16.0)\": 0.0585}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02604, \"(1.5, 2.5)\": -0.01758, \"(2.5, 3.5)\": -0.01104, \"(3.5, 4.5)\": -0.00622, \"(4.5, 5.5)\": 0.00022, \"(5.5, 6.5)\": 0.0063, \"(6.5, 8.5)\": 0.01194, \"(8.5, 10.5)\": 0.0215, \"(10.5, 11.5)\": 0.03022, \"(11.5, 13.5)\": 0.03581, \"(13.5, 14.5)\": 0.04439, \"(14.5, 16.0)\": 0.04645}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02235, \"(1.5, 2.5)\": -0.01628, \"(2.5, 3.5)\": -0.01034, \"(3.5, 4.5)\": -0.00547, \"(4.5, 5.5)\": 0.0008, \"(5.5, 6.5)\": 0.00723, \"(6.5, 8.5)\": 0.01295, \"(8.5, 10.5)\": 0.02363, \"(10.5, 11.5)\": 0.03508, \"(11.5, 13.5)\": 0.04198, \"(13.5, 14.5)\": 0.05386, \"(14.5, 16.0)\": 0.07055}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(14.5, 16.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: DeterioratingInfrastructure\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02508, \"(0.5, 1.5)\": -0.01897, \"(1.5, 2.5)\": -0.01452, \"(2.5, 3.5)\": -0.01085, \"(3.5, 4.5)\": -0.00475, \"(4.5, 5.5)\": 0.00054, \"(5.5, 6.5)\": 0.00555, \"(6.5, 7.5)\": 0.01137, \"(7.5, 8.5)\": 0.01653, \"(8.5, 9.5)\": 0.0237, \"(9.5, 10.5)\": 0.02782, \"(10.5, 11.5)\": 0.03175, \"(11.5, 12.5)\": 0.03686, \"(12.5, 15.0)\": 0.04451}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02677, \"(0.5, 1.5)\": -0.01971, \"(1.5, 2.5)\": -0.01507, \"(2.5, 3.5)\": -0.0113, \"(3.5, 4.5)\": -0.00523, \"(4.5, 5.5)\": 0.00016, \"(5.5, 6.5)\": 0.00517, \"(6.5, 7.5)\": 0.01094, \"(7.5, 8.5)\": 0.01606, \"(8.5, 9.5)\": 0.02309, \"(9.5, 10.5)\": 0.02666, \"(10.5, 11.5)\": 0.03007, \"(11.5, 12.5)\": 0.03455, \"(12.5, 15.0)\": 0.03295}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02339, \"(0.5, 1.5)\": -0.01822, \"(1.5, 2.5)\": -0.01398, \"(2.5, 3.5)\": -0.0104, \"(3.5, 4.5)\": -0.00428, \"(4.5, 5.5)\": 0.00091, \"(5.5, 6.5)\": 0.00594, \"(6.5, 7.5)\": 0.0118, \"(7.5, 8.5)\": 0.017, \"(8.5, 9.5)\": 0.0243, \"(9.5, 10.5)\": 0.02899, \"(10.5, 11.5)\": 0.03343, \"(11.5, 12.5)\": 0.03916, \"(12.5, 15.0)\": 0.05607}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(12.5, 15.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Relationship\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": 0.511, \"(0.5, 1.5)\": -0.233, \"(1.5, 2.5)\": -0.666, \"(2.5, 3.5)\": -1.006, \"(3.5, 4.5)\": -0.529, \"(4.5, 5.0)\": 1.753}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.453, \"(0.5, 1.5)\": -0.278, \"(1.5, 2.5)\": -0.789, \"(2.5, 3.5)\": -1.092, \"(3.5, 4.5)\": -0.6, \"(4.5, 5.0)\": 1.664}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.568, \"(0.5, 1.5)\": -0.188, \"(1.5, 2.5)\": -0.543, \"(2.5, 3.5)\": -0.921, \"(3.5, 4.5)\": -0.458, \"(4.5, 5.0)\": 1.842}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(1.5, 2.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Age\nFeature Type: continuous\nMeans: {\"(17.0, 18.5)\": -3.326, \"(18.5, 19.5)\": -2.358, \"(19.5, 20.5)\": -2.799, \"(20.5, 21.5)\": -2.354, \"(21.5, 22.5)\": -1.405, \"(22.5, 23.5)\": -1.633, \"(23.5, 24.5)\": -1.214, \"(24.5, 26.5)\": -0.789, \"(26.5, 27.5)\": -0.473, \"(27.5, 29.5)\": -0.216, \"(29.5, 33.5)\": 0.042, \"(33.5, 36.5)\": 0.351, \"(36.5, 44.5)\": 0.658, \"(44.5, 61.5)\": 0.897, \"(61.5, 66.5)\": 0.574, \"(66.5, 73.5)\": 0.099, \"(73.5, 74.5)\": 0.763, \"(74.5, 77.5)\": 0.502, \"(77.5, 79.5)\": 0.875, \"(79.5, 84.5)\": 0.065, \"(84.5, 90.0)\": -1.08}\nLower Bounds (95%-Confidence Interval): {\"(17.0, 18.5)\": -4.677, \"(18.5, 19.5)\": -3.672, \"(19.5, 20.5)\": -3.928, \"(20.5, 21.5)\": -2.706, \"(21.5, 22.5)\": -1.741, \"(22.5, 23.5)\": -1.856, \"(23.5, 24.5)\": -1.407, \"(24.5, 26.5)\": -0.941, \"(26.5, 27.5)\": -0.561, \"(27.5, 29.5)\": -0.322, \"(29.5, 33.5)\": -0.079, \"(33.5, 36.5)\": 0.229, \"(36.5, 44.5)\": 0.5, \"(44.5, 61.5)\": 0.753, \"(61.5, 66.5)\": 0.434, \"(66.5, 73.5)\": -0.37, \"(73.5, 74.5)\": 0.229, \"(74.5, 77.5)\": -0.136, \"(77.5, 79.5)\": 0.35, \"(79.5, 84.5)\": -0.573, \"(84.5, 90.0)\": -2.041}\nUpper Bounds (95%-Confidence Interval): {\"(17.0, 18.5)\": -1.975, \"(18.5, 19.5)\": -1.044, \"(19.5, 20.5)\": -1.669, \"(20.5, 21.5)\": -2.002, \"(21.5, 22.5)\": -1.069, \"(22.5, 23.5)\": -1.41, \"(23.5, 24.5)\": -1.021, \"(24.5, 26.5)\": -0.637, \"(26.5, 27.5)\": -0.385, \"(27.5, 29.5)\": -0.11, \"(29.5, 33.5)\": 0.164, \"(33.5, 36.5)\": 0.473, \"(36.5, 44.5)\": 0.816, \"(44.5, 61.5)\": 1.04, \"(61.5, 66.5)\": 0.714, \"(66.5, 73.5)\": 0.567, \"(73.5, 74.5)\": 1.297, \"(74.5, 77.5)\": 1.141, \"(77.5, 79.5)\": 1.401, \"(79.5, 84.5)\": 0.702, \"(84.5, 90.0)\": -0.119}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(17.0, 18.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: concave points_mean\nFeature Type: continuous\nMeans: {\"(0.0, 0.0074145)\": -1.054, \"(0.0074145, 0.011665)\": -0.937, \"(0.011665, 0.01503)\": -0.821, \"(0.01503, 0.017865)\": -0.705, \"(0.017865, 0.019315)\": -0.582, \"(0.019315, 0.023185)\": -0.466, \"(0.023185, 0.026115)\": -0.352, \"(0.026115, 0.042455)\": -0.235, \"(0.042455, 0.048235)\": -0.115, \"(0.048235, 0.048865)\": 0.04, \"(0.048865, 0.059615)\": 0.233, \"(0.059615, 0.070395)\": 0.35, \"(0.070395, 0.08221500000000001)\": 0.474, \"(0.08221500000000001, 0.087175)\": 0.592, \"(0.087175, 0.091445)\": 0.711, \"(0.091445, 0.1006)\": 0.832, \"(0.1006, 0.122)\": 0.949, \"(0.122, 0.16544999999999999)\": 1.068, \"(0.16544999999999999, 0.2012)\": 1.187}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.0074145)\": -1.411, \"(0.0074145, 0.011665)\": -1.253, \"(0.011665, 0.01503)\": -1.095, \"(0.01503, 0.017865)\": -0.965, \"(0.017865, 0.019315)\": -0.823, \"(0.019315, 0.023185)\": -0.72, \"(0.023185, 0.026115)\": -0.517, \"(0.026115, 0.042455)\": -0.743, \"(0.042455, 0.048235)\": -0.628, \"(0.048235, 0.048865)\": -0.409, \"(0.048865, 0.059615)\": -0.151, \"(0.059615, 0.070395)\": 0.09, \"(0.070395, 0.08221500000000001)\": 0.219, \"(0.08221500000000001, 0.087175)\": 0.306, \"(0.087175, 0.091445)\": 0.39, \"(0.091445, 0.1006)\": 0.481, \"(0.1006, 0.122)\": 0.562, \"(0.122, 0.16544999999999999)\": 0.634, \"(0.16544999999999999, 0.2012)\": 0.74}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.0074145)\": -0.697, \"(0.0074145, 0.011665)\": -0.62, \"(0.011665, 0.01503)\": -0.546, \"(0.01503, 0.017865)\": -0.445, \"(0.017865, 0.019315)\": -0.34, \"(0.019315, 0.023185)\": -0.212, \"(0.023185, 0.026115)\": -0.188, \"(0.026115, 0.042455)\": 0.274, \"(0.042455, 0.048235)\": 0.398, \"(0.048235, 0.048865)\": 0.489, \"(0.048865, 0.059615)\": 0.617, \"(0.059615, 0.070395)\": 0.611, \"(0.070395, 0.08221500000000001)\": 0.728, \"(0.08221500000000001, 0.087175)\": 0.878, \"(0.087175, 0.091445)\": 1.032, \"(0.091445, 0.1006)\": 1.182, \"(0.1006, 0.122)\": 1.336, \"(0.122, 0.16544999999999999)\": 1.503, \"(0.16544999999999999, 0.2012)\": 1.634}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.042455, 0.048235)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: population\nFeature Type: continuous\nMeans: {\"(3.0, 14.5)\": 125210.2, \"(14.5, 25.5)\": 92452.9, \"(25.5, 65.5)\": 80407.9, \"(65.5, 138.5)\": 91917.4, \"(138.5, 151.5)\": 103409.9, \"(151.5, 301.5)\": 85121.7, \"(301.5, 490.5)\": 73106.0, \"(490.5, 657.5)\": 57994.5, \"(657.5, 761.5)\": 44760.8, \"(761.5, 837.5)\": 32058.9, \"(837.5, 1019.5)\": 20715.6, \"(1019.5, 1220.5)\": 6507.2, \"(1220.5, 1267.5)\": -6199.6, \"(1267.5, 1269.5)\": 9858.1, \"(1269.5, 1497.5)\": -9812.8, \"(1497.5, 1886.5)\": -25776.4, \"(1886.5, 2129.5)\": -36953.6, \"(2129.5, 2425.5)\": -48605.9, \"(2425.5, 2686.0)\": -59914.9, \"(2686.0, 2718.5)\": -46231.6, \"(2718.5, 3175.5)\": -61061.6, \"(3175.5, 3965.0)\": -76216.0, \"(3965.0, 35682.0)\": -91117.9}\nLower Bounds (95%-Confidence Interval): {\"(3.0, 14.5)\": 103123.1, \"(14.5, 25.5)\": 58681.0, \"(25.5, 65.5)\": 62309.7, \"(65.5, 138.5)\": 75243.8, \"(138.5, 151.5)\": 78950.4, \"(151.5, 301.5)\": 69535.1, \"(301.5, 490.5)\": 60924.6, \"(490.5, 657.5)\": 45395.6, \"(657.5, 761.5)\": 35273.5, \"(761.5, 837.5)\": 26626.5, \"(837.5, 1019.5)\": 8057.5, \"(1019.5, 1220.5)\": -10609.9, \"(1220.5, 1267.5)\": -14462.5, \"(1267.5, 1269.5)\": -5022.3, \"(1269.5, 1497.5)\": -22884.3, \"(1497.5, 1886.5)\": -37619.7, \"(1886.5, 2129.5)\": -51088.1, \"(2129.5, 2425.5)\": -56504.4, \"(2425.5, 2686.0)\": -64158.2, \"(2686.0, 2718.5)\": -69408.6, \"(2718.5, 3175.5)\": -68643.2, \"(3175.5, 3965.0)\": -84318.8, \"(3965.0, 35682.0)\": -101928.5}\nUpper Bounds (95%-Confidence Interval): {\"(3.0, 14.5)\": 147297.2, \"(14.5, 25.5)\": 126224.8, \"(25.5, 65.5)\": 98506.2, \"(65.5, 138.5)\": 108591.1, \"(138.5, 151.5)\": 127869.3, \"(151.5, 301.5)\": 100708.2, \"(301.5, 490.5)\": 85287.5, \"(490.5, 657.5)\": 70593.3, \"(657.5, 761.5)\": 54248.0, \"(761.5, 837.5)\": 37491.3, \"(837.5, 1019.5)\": 33373.7, \"(1019.5, 1220.5)\": 23624.4, \"(1220.5, 1267.5)\": 2063.4, \"(1267.5, 1269.5)\": 24738.4, \"(1269.5, 1497.5)\": 3258.7, \"(1497.5, 1886.5)\": -13933.2, \"(1886.5, 2129.5)\": -22819.1, \"(2129.5, 2425.5)\": -40707.4, \"(2425.5, 2686.0)\": -55671.5, \"(2686.0, 2718.5)\": -23054.7, \"(2718.5, 3175.5)\": -53480.1, \"(3175.5, 3965.0)\": -68113.2, \"(3965.0, 35682.0)\": -80307.2}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(14.5, 25.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: sex\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": 0.01719, \"(0.5, 1.0)\": -0.00954}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.08236, \"(0.5, 1.0)\": -0.06482}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.11675, \"(0.5, 1.0)\": 0.04573}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.0, 0.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: WorkClass\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.013, \"(0.5, 1.5)\": 0.434, \"(1.5, 4.5)\": -0.066, \"(4.5, 5.5)\": 0.167, \"(5.5, 7.5)\": -0.464, \"(7.5, 8.0)\": -2.54}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.099, \"(0.5, 1.5)\": 0.319, \"(1.5, 4.5)\": -0.192, \"(4.5, 5.5)\": 0.106, \"(5.5, 7.5)\": -0.567, \"(7.5, 8.0)\": -4.038}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.074, \"(0.5, 1.5)\": 0.549, \"(1.5, 4.5)\": 0.059, \"(4.5, 5.5)\": 0.228, \"(5.5, 7.5)\": -0.362, \"(7.5, 8.0)\": -1.042}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(7.5, 8.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: TopographyDrainage\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.0274, \"(0.5, 1.5)\": -0.02381, \"(1.5, 2.5)\": -0.01602, \"(2.5, 3.5)\": -0.01049, \"(3.5, 4.5)\": -0.00528, \"(4.5, 5.5)\": -0.00022, \"(5.5, 6.5)\": 0.00517, \"(6.5, 7.5)\": 0.01282, \"(7.5, 8.5)\": 0.01628, \"(8.5, 9.5)\": 0.02454, \"(9.5, 10.5)\": 0.02883, \"(10.5, 11.5)\": 0.03213, \"(11.5, 17.0)\": 0.03564}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.03013, \"(0.5, 1.5)\": -0.02484, \"(1.5, 2.5)\": -0.01655, \"(2.5, 3.5)\": -0.01088, \"(3.5, 4.5)\": -0.00574, \"(4.5, 5.5)\": -0.00046, \"(5.5, 6.5)\": 0.00473, \"(6.5, 7.5)\": 0.01242, \"(7.5, 8.5)\": 0.01574, \"(8.5, 9.5)\": 0.02354, \"(9.5, 10.5)\": 0.0277, \"(10.5, 11.5)\": 0.03039, \"(11.5, 17.0)\": 0.02281}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02466, \"(0.5, 1.5)\": -0.02278, \"(1.5, 2.5)\": -0.0155, \"(2.5, 3.5)\": -0.0101, \"(3.5, 4.5)\": -0.00482, \"(4.5, 5.5)\": 2e-05, \"(5.5, 6.5)\": 0.00561, \"(6.5, 7.5)\": 0.01323, \"(7.5, 8.5)\": 0.01683, \"(8.5, 9.5)\": 0.02554, \"(9.5, 10.5)\": 0.02996, \"(10.5, 11.5)\": 0.03386, \"(11.5, 17.0)\": 0.04848}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(11.5, 17.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: SibSp\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.0751, \"(0.5, 2.5)\": 0.1633, \"(2.5, 3.0)\": -0.7301}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.1303, \"(0.5, 2.5)\": -0.2711, \"(2.5, 3.0)\": -2.435}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0198, \"(0.5, 2.5)\": 0.5976, \"(2.5, 3.0)\": 0.9748}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(2.5, 3.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Occupation\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.297, \"(0.5, 3.5)\": -0.074, \"(3.5, 4.5)\": 0.644, \"(4.5, 6.5)\": -0.723, \"(6.5, 7.5)\": -0.542, \"(7.5, 8.5)\": -0.665, \"(8.5, 9.5)\": -0.926, \"(9.5, 10.5)\": 0.423, \"(10.5, 11.5)\": 0.59, \"(11.5, 12.5)\": 0.27, \"(12.5, 13.5)\": 0.534, \"(13.5, 14.0)\": -0.133}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.409, \"(0.5, 3.5)\": -0.139, \"(3.5, 4.5)\": 0.592, \"(4.5, 6.5)\": -0.847, \"(6.5, 7.5)\": -0.624, \"(7.5, 8.5)\": -0.749, \"(8.5, 9.5)\": -1.549, \"(9.5, 10.5)\": 0.366, \"(10.5, 11.5)\": 0.452, \"(11.5, 12.5)\": 0.225, \"(12.5, 13.5)\": 0.445, \"(13.5, 14.0)\": -0.202}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.185, \"(0.5, 3.5)\": -0.01, \"(3.5, 4.5)\": 0.695, \"(4.5, 6.5)\": -0.598, \"(6.5, 7.5)\": -0.461, \"(7.5, 8.5)\": -0.581, \"(8.5, 9.5)\": -0.302, \"(9.5, 10.5)\": 0.48, \"(10.5, 11.5)\": 0.727, \"(11.5, 12.5)\": 0.315, \"(12.5, 13.5)\": 0.622, \"(13.5, 14.0)\": -0.064}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(8.5, 9.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: CustomerId\nFeature Type: continuous\nMeans: {\"(15565796.0, 15566519.0)\": -0.8769, \"(15566519.0, 15567333.5)\": -0.8241, \"(15567333.5, 15567844.5)\": -0.1763, \"(15567844.5, 15568343.5)\": 0.0021, \"(15568343.5, 15571612.0)\": -0.2283, \"(15571612.0, 15571858.5)\": -0.0522, \"(15571858.5, 15591260.5)\": -0.1299, \"(15591260.5, 15598058.0)\": -0.0821, \"(15598058.0, 15602525.5)\": -0.1509, \"(15602525.5, 15607288.0)\": -0.0818, \"(15607288.0, 15664896.0)\": -0.0316, \"(15664896.0, 15772587.0)\": 0.0162, \"(15772587.0, 15797097.0)\": 0.0757, \"(15797097.0, 15799214.0)\": 0.0081, \"(15799214.0, 15807559.5)\": 0.0581, \"(15807559.5, 15812616.5)\": -0.0049, \"(15812616.5, 15814479.0)\": -0.0569, \"(15814479.0, 15815247.5)\": -0.111, \"(15815247.5, 15815626.0)\": -0.0335}\nLower Bounds (95%-Confidence Interval): {\"(15565796.0, 15566519.0)\": -1.3796, \"(15566519.0, 15567333.5)\": -1.4199, \"(15567333.5, 15567844.5)\": -0.741, \"(15567844.5, 15568343.5)\": -0.4552, \"(15568343.5, 15571612.0)\": -0.4861, \"(15571612.0, 15571858.5)\": -0.3268, \"(15571858.5, 15591260.5)\": -0.2064, \"(15591260.5, 15598058.0)\": -0.1582, \"(15598058.0, 15602525.5)\": -0.5056, \"(15602525.5, 15607288.0)\": -0.1812, \"(15607288.0, 15664896.0)\": -0.056, \"(15664896.0, 15772587.0)\": -0.142, \"(15772587.0, 15797097.0)\": -0.0689, \"(15797097.0, 15799214.0)\": -0.206, \"(15799214.0, 15807559.5)\": -0.0544, \"(15807559.5, 15812616.5)\": -0.1396, \"(15812616.5, 15814479.0)\": -0.2475, \"(15814479.0, 15815247.5)\": -0.4076, \"(15815247.5, 15815626.0)\": -0.3716}\nUpper Bounds (95%-Confidence Interval): {\"(15565796.0, 15566519.0)\": -0.3742, \"(15566519.0, 15567333.5)\": -0.2283, \"(15567333.5, 15567844.5)\": 0.3884, \"(15567844.5, 15568343.5)\": 0.4594, \"(15568343.5, 15571612.0)\": 0.0295, \"(15571612.0, 15571858.5)\": 0.2223, \"(15571858.5, 15591260.5)\": -0.0535, \"(15591260.5, 15598058.0)\": -0.0061, \"(15598058.0, 15602525.5)\": 0.2038, \"(15602525.5, 15607288.0)\": 0.0176, \"(15607288.0, 15664896.0)\": -0.0071, \"(15664896.0, 15772587.0)\": 0.1744, \"(15772587.0, 15797097.0)\": 0.2202, \"(15797097.0, 15799214.0)\": 0.2223, \"(15799214.0, 15807559.5)\": 0.1706, \"(15807559.5, 15812616.5)\": 0.1298, \"(15812616.5, 15814479.0)\": 0.1336, \"(15814479.0, 15815247.5)\": 0.1855, \"(15815247.5, 15815626.0)\": 0.3046}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(15566519.0, 15567333.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Siltation\nFeature Type: continuous\nMeans: {\"(0.0, 1.5)\": -0.02643, \"(1.5, 2.5)\": -0.01529, \"(2.5, 3.5)\": -0.01037, \"(3.5, 4.5)\": -0.00562, \"(4.5, 5.5)\": 0.00068, \"(5.5, 6.5)\": 0.00591, \"(6.5, 7.5)\": 0.01127, \"(7.5, 8.5)\": 0.01553, \"(8.5, 10.5)\": 0.02363, \"(10.5, 11.5)\": 0.03038, \"(11.5, 12.5)\": 0.03607, \"(12.5, 13.5)\": 0.04087, \"(13.5, 15.0)\": 0.04477}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02798, \"(1.5, 2.5)\": -0.01578, \"(2.5, 3.5)\": -0.01088, \"(3.5, 4.5)\": -0.00595, \"(4.5, 5.5)\": 0.0002, \"(5.5, 6.5)\": 0.0054, \"(6.5, 7.5)\": 0.0105, \"(7.5, 8.5)\": 0.01459, \"(8.5, 10.5)\": 0.02243, \"(10.5, 11.5)\": 0.0283, \"(11.5, 12.5)\": 0.03438, \"(12.5, 13.5)\": 0.03775, \"(13.5, 15.0)\": 0.03258}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02487, \"(1.5, 2.5)\": -0.0148, \"(2.5, 3.5)\": -0.00987, \"(3.5, 4.5)\": -0.00529, \"(4.5, 5.5)\": 0.00116, \"(5.5, 6.5)\": 0.00643, \"(6.5, 7.5)\": 0.01205, \"(7.5, 8.5)\": 0.01648, \"(8.5, 10.5)\": 0.02483, \"(10.5, 11.5)\": 0.03246, \"(11.5, 12.5)\": 0.03776, \"(12.5, 13.5)\": 0.044, \"(13.5, 15.0)\": 0.05697}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(13.5, 15.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: CoastalVulnerability\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.03259, \"(0.5, 1.5)\": -0.02272, \"(1.5, 2.5)\": -0.0157, \"(2.5, 3.5)\": -0.00983, \"(3.5, 4.5)\": -0.00444, \"(4.5, 5.5)\": -0.00035, \"(5.5, 6.5)\": 0.00575, \"(6.5, 7.5)\": 0.01126, \"(7.5, 8.5)\": 0.01651, \"(8.5, 9.5)\": 0.02143, \"(9.5, 12.5)\": 0.02903, \"(12.5, 13.5)\": 0.03437, \"(13.5, 15.0)\": 0.04826}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0359, \"(0.5, 1.5)\": -0.02356, \"(1.5, 2.5)\": -0.01657, \"(2.5, 3.5)\": -0.01034, \"(3.5, 4.5)\": -0.0048, \"(4.5, 5.5)\": -0.00077, \"(5.5, 6.5)\": 0.00528, \"(6.5, 7.5)\": 0.01081, \"(7.5, 8.5)\": 0.01566, \"(8.5, 9.5)\": 0.02049, \"(9.5, 12.5)\": 0.02706, \"(12.5, 13.5)\": 0.0298, \"(13.5, 15.0)\": 0.0329}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02927, \"(0.5, 1.5)\": -0.02189, \"(1.5, 2.5)\": -0.01482, \"(2.5, 3.5)\": -0.00931, \"(3.5, 4.5)\": -0.00409, \"(4.5, 5.5)\": 7e-05, \"(5.5, 6.5)\": 0.00622, \"(6.5, 7.5)\": 0.0117, \"(7.5, 8.5)\": 0.01736, \"(8.5, 9.5)\": 0.02236, \"(9.5, 12.5)\": 0.031, \"(12.5, 13.5)\": 0.03893, \"(13.5, 15.0)\": 0.06363}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(13.5, 15.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Age\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": 0.68, \"(0.5, 3.5)\": 0.36, \"(3.5, 4.5)\": 0.254, \"(4.5, 14.5)\": 0.09, \"(14.5, 23.5)\": 0.028, \"(23.5, 24.5)\": -0.027, \"(24.5, 25.5)\": -0.135, \"(25.5, 39.5)\": -0.05, \"(39.5, 44.5)\": 0.042, \"(44.5, 48.5)\": -0.025, \"(48.5, 54.5)\": -0.102, \"(54.5, 56.5)\": -0.012, \"(56.5, 63.5)\": 0.078, \"(63.5, 64.5)\": -0.028, \"(64.5, 65.5)\": -0.141, \"(65.5, 68.5)\": 0.058, \"(68.5, 69.5)\": -0.021, \"(69.5, 71.5)\": 0.037, \"(71.5, 73.5)\": -0.022, \"(73.5, 74.5)\": 0.413, \"(74.5, 77.5)\": 0.211, \"(77.5, 79.0)\": -0.412}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.461, \"(0.5, 3.5)\": 0.228, \"(3.5, 4.5)\": 0.097, \"(4.5, 14.5)\": -0.111, \"(14.5, 23.5)\": -0.031, \"(23.5, 24.5)\": -0.079, \"(24.5, 25.5)\": -0.32, \"(25.5, 39.5)\": -0.113, \"(39.5, 44.5)\": -0.088, \"(44.5, 48.5)\": -0.081, \"(48.5, 54.5)\": -0.336, \"(54.5, 56.5)\": -0.102, \"(56.5, 63.5)\": -0.123, \"(63.5, 64.5)\": -0.219, \"(64.5, 65.5)\": -0.706, \"(65.5, 68.5)\": -0.265, \"(68.5, 69.5)\": -0.416, \"(69.5, 71.5)\": -0.213, \"(71.5, 73.5)\": -0.172, \"(73.5, 74.5)\": -0.439, \"(74.5, 77.5)\": -0.317, \"(77.5, 79.0)\": -1.348}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.9, \"(0.5, 3.5)\": 0.491, \"(3.5, 4.5)\": 0.411, \"(4.5, 14.5)\": 0.29, \"(14.5, 23.5)\": 0.087, \"(23.5, 24.5)\": 0.024, \"(24.5, 25.5)\": 0.05, \"(25.5, 39.5)\": 0.012, \"(39.5, 44.5)\": 0.172, \"(44.5, 48.5)\": 0.031, \"(48.5, 54.5)\": 0.132, \"(54.5, 56.5)\": 0.077, \"(56.5, 63.5)\": 0.278, \"(63.5, 64.5)\": 0.163, \"(64.5, 65.5)\": 0.424, \"(65.5, 68.5)\": 0.382, \"(68.5, 69.5)\": 0.373, \"(69.5, 71.5)\": 0.287, \"(71.5, 73.5)\": 0.129, \"(73.5, 74.5)\": 1.265, \"(74.5, 77.5)\": 0.739, \"(77.5, 79.0)\": 0.524}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(77.5, 79.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Parch\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": 0.085, \"(0.5, 1.5)\": -0.055, \"(1.5, 3.0)\": -0.299, \"(3.0, 4.0)\": -1.704}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02, \"(0.5, 1.5)\": -0.269, \"(1.5, 3.0)\": -0.62, \"(3.0, 4.0)\": -3.014}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.19, \"(0.5, 1.5)\": 0.158, \"(1.5, 3.0)\": 0.022, \"(3.0, 4.0)\": -0.395}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(3.0, 4.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: total_bedrooms\nFeature Type: continuous\nMeans: {\"(2.0, 4.5)\": -10633.3, \"(4.5, 9.5)\": -19829.1, \"(9.5, 12.5)\": -33356.0, \"(12.5, 14.5)\": -27510.0, \"(14.5, 17.5)\": -34141.4, \"(17.5, 20.5)\": -50740.7, \"(20.5, 22.5)\": -59049.5, \"(22.5, 25.5)\": -37177.7, \"(25.5, 29.5)\": -30710.5, \"(29.5, 111.5)\": -36287.1, \"(111.5, 112.5)\": -22540.1, \"(112.5, 176.5)\": -33870.1, \"(176.5, 245.5)\": -27701.3, \"(245.5, 265.5)\": -20526.0, \"(265.5, 268.5)\": -26170.7, \"(268.5, 317.5)\": -17267.5, \"(317.5, 424.5)\": -8013.2, \"(424.5, 463.5)\": -1894.5, \"(463.5, 512.5)\": 5095.6, \"(512.5, 513.5)\": 17024.1, \"(513.5, 655.5)\": 9371.5, \"(655.5, 697.5)\": 15515.9, \"(697.5, 776.5)\": 22859.4, \"(776.5, 779.5)\": 13774.7, \"(779.5, 1008.5)\": 22608.4, \"(1008.5, 1012.5)\": 37458.5, \"(1012.5, 1081.5)\": 30023.9, \"(1081.5, 1449.5)\": 37066.8, \"(1449.5, 1490.5)\": 51601.0, \"(1490.5, 1616.0)\": 42837.8, \"(1616.0, 2714.5)\": 49023.6, \"(2714.5, 2865.5)\": 40592.1, \"(2865.5, 6445.0)\": 51586.1}\nLower Bounds (95%-Confidence Interval): {\"(2.0, 4.5)\": -43650.4, \"(4.5, 9.5)\": -54645.6, \"(9.5, 12.5)\": -52929.5, \"(12.5, 14.5)\": -57181.8, \"(14.5, 17.5)\": -49207.2, \"(17.5, 20.5)\": -72519.5, \"(20.5, 22.5)\": -82934.2, \"(22.5, 25.5)\": -50942.7, \"(25.5, 29.5)\": -45748.1, \"(29.5, 111.5)\": -47452.5, \"(111.5, 112.5)\": -42457.2, \"(112.5, 176.5)\": -41599.3, \"(176.5, 245.5)\": -35478.0, \"(245.5, 265.5)\": -27520.5, \"(265.5, 268.5)\": -32234.3, \"(268.5, 317.5)\": -23732.7, \"(317.5, 424.5)\": -13237.9, \"(424.5, 463.5)\": -7023.7, \"(463.5, 512.5)\": -1510.7, \"(512.5, 513.5)\": 6820.8, \"(513.5, 655.5)\": 341.5, \"(655.5, 697.5)\": 12634.4, \"(697.5, 776.5)\": 15982.1, \"(776.5, 779.5)\": 221.5, \"(779.5, 1008.5)\": 18345.9, \"(1008.5, 1012.5)\": 20622.3, \"(1012.5, 1081.5)\": 21931.2, \"(1081.5, 1449.5)\": 22140.8, \"(1449.5, 1490.5)\": 39761.7, \"(1490.5, 1616.0)\": 35441.7, \"(1616.0, 2714.5)\": 37135.8, \"(2714.5, 2865.5)\": 32716.4, \"(2865.5, 6445.0)\": 42203.8}\nUpper Bounds (95%-Confidence Interval): {\"(2.0, 4.5)\": 22383.8, \"(4.5, 9.5)\": 14987.3, \"(9.5, 12.5)\": -13782.5, \"(12.5, 14.5)\": 2161.9, \"(14.5, 17.5)\": -19075.5, \"(17.5, 20.5)\": -28961.9, \"(20.5, 22.5)\": -35164.8, \"(22.5, 25.5)\": -23412.7, \"(25.5, 29.5)\": -15672.9, \"(29.5, 111.5)\": -25121.6, \"(111.5, 112.5)\": -2622.9, \"(112.5, 176.5)\": -26141.0, \"(176.5, 245.5)\": -19924.6, \"(245.5, 265.5)\": -13531.5, \"(265.5, 268.5)\": -20107.0, \"(268.5, 317.5)\": -10802.3, \"(317.5, 424.5)\": -2788.6, \"(424.5, 463.5)\": 3234.7, \"(463.5, 512.5)\": 11701.8, \"(512.5, 513.5)\": 27227.4, \"(513.5, 655.5)\": 18401.4, \"(655.5, 697.5)\": 18397.4, \"(697.5, 776.5)\": 29736.8, \"(776.5, 779.5)\": 27327.8, \"(779.5, 1008.5)\": 26870.8, \"(1008.5, 1012.5)\": 54294.7, \"(1012.5, 1081.5)\": 38116.5, \"(1081.5, 1449.5)\": 51992.8, \"(1449.5, 1490.5)\": 63440.2, \"(1490.5, 1616.0)\": 50233.9, \"(1616.0, 2714.5)\": 60911.5, \"(2714.5, 2865.5)\": 48467.9, \"(2865.5, 6445.0)\": 60968.4}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(4.5, 9.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: concavity_mean\nFeature Type: continuous\nMeans: {\"(0.0, 0.005855)\": -0.897, \"(0.005855, 0.011885)\": -0.811, \"(0.011885, 0.016545)\": -0.719, \"(0.016545, 0.02046)\": -0.631, \"(0.02046, 0.02373)\": -0.543, \"(0.02373, 0.02711)\": -0.458, \"(0.02711, 0.038885)\": -0.374, \"(0.038885, 0.044705)\": -0.29, \"(0.044705, 0.059585)\": -0.205, \"(0.059585, 0.06851)\": -0.121, \"(0.06851, 0.072265)\": -0.032, \"(0.072265, 0.092725)\": 0.14, \"(0.092725, 0.1015)\": 0.224, \"(0.1015, 0.11415)\": 0.309, \"(0.11415, 0.13)\": 0.397, \"(0.13, 0.14534999999999998)\": 0.486, \"(0.14534999999999998, 0.1525)\": 0.581, \"(0.1525, 0.1686)\": 0.665, \"(0.1686, 0.24280000000000002)\": 0.749, \"(0.24280000000000002, 0.29359999999999997)\": 0.657, \"(0.29359999999999997, 0.32699999999999996)\": 0.566, \"(0.32699999999999996, 0.4268)\": 0.48}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.005855)\": -1.183, \"(0.005855, 0.011885)\": -1.062, \"(0.011885, 0.016545)\": -0.961, \"(0.016545, 0.02046)\": -0.861, \"(0.02046, 0.02373)\": -0.749, \"(0.02373, 0.02711)\": -0.665, \"(0.02711, 0.038885)\": -0.545, \"(0.038885, 0.044705)\": -0.442, \"(0.044705, 0.059585)\": -0.43, \"(0.059585, 0.06851)\": -0.344, \"(0.06851, 0.072265)\": -0.246, \"(0.072265, 0.092725)\": -0.128, \"(0.092725, 0.1015)\": 0.093, \"(0.1015, 0.11415)\": 0.166, \"(0.11415, 0.13)\": 0.205, \"(0.13, 0.14534999999999998)\": 0.246, \"(0.14534999999999998, 0.1525)\": 0.264, \"(0.1525, 0.1686)\": 0.346, \"(0.1686, 0.24280000000000002)\": 0.435, \"(0.24280000000000002, 0.29359999999999997)\": 0.402, \"(0.29359999999999997, 0.32699999999999996)\": 0.316, \"(0.32699999999999996, 0.4268)\": 0.208}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.005855)\": -0.612, \"(0.005855, 0.011885)\": -0.559, \"(0.011885, 0.016545)\": -0.477, \"(0.016545, 0.02046)\": -0.4, \"(0.02046, 0.02373)\": -0.338, \"(0.02373, 0.02711)\": -0.252, \"(0.02711, 0.038885)\": -0.203, \"(0.038885, 0.044705)\": -0.138, \"(0.044705, 0.059585)\": 0.021, \"(0.059585, 0.06851)\": 0.103, \"(0.06851, 0.072265)\": 0.183, \"(0.072265, 0.092725)\": 0.409, \"(0.092725, 0.1015)\": 0.355, \"(0.1015, 0.11415)\": 0.452, \"(0.11415, 0.13)\": 0.589, \"(0.13, 0.14534999999999998)\": 0.726, \"(0.14534999999999998, 0.1525)\": 0.898, \"(0.1525, 0.1686)\": 0.984, \"(0.1686, 0.24280000000000002)\": 1.063, \"(0.24280000000000002, 0.29359999999999997)\": 0.912, \"(0.29359999999999997, 0.32699999999999996)\": 0.815, \"(0.32699999999999996, 0.4268)\": 0.752}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.1525, 0.1686)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: MonsoonIntensity\nFeature Type: continuous\nMeans: {\"(0.0, 1.5)\": -0.02446, \"(1.5, 2.5)\": -0.01712, \"(2.5, 3.5)\": -0.00908, \"(3.5, 4.5)\": -0.00529, \"(4.5, 5.5)\": 0.0003, \"(5.5, 6.5)\": 0.00497, \"(6.5, 7.5)\": 0.01093, \"(7.5, 8.5)\": 0.01787, \"(8.5, 9.5)\": 0.02262, \"(9.5, 11.5)\": 0.02707, \"(11.5, 12.5)\": 0.03735, \"(12.5, 13.5)\": 0.043, \"(13.5, 15.0)\": 0.01734}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02705, \"(1.5, 2.5)\": -0.01788, \"(2.5, 3.5)\": -0.00955, \"(3.5, 4.5)\": -0.00566, \"(4.5, 5.5)\": 4e-05, \"(5.5, 6.5)\": 0.00451, \"(6.5, 7.5)\": 0.01051, \"(7.5, 8.5)\": 0.01741, \"(8.5, 9.5)\": 0.02167, \"(9.5, 11.5)\": 0.02561, \"(11.5, 12.5)\": 0.03439, \"(12.5, 13.5)\": 0.03822, \"(13.5, 15.0)\": -0.00028}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02187, \"(1.5, 2.5)\": -0.01637, \"(2.5, 3.5)\": -0.00861, \"(3.5, 4.5)\": -0.00492, \"(4.5, 5.5)\": 0.00056, \"(5.5, 6.5)\": 0.00544, \"(6.5, 7.5)\": 0.01134, \"(7.5, 8.5)\": 0.01833, \"(8.5, 9.5)\": 0.02358, \"(9.5, 11.5)\": 0.02853, \"(11.5, 12.5)\": 0.04032, \"(12.5, 13.5)\": 0.04778, \"(13.5, 15.0)\": 0.03495}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(13.5, 15.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: concave points_worst\nFeature Type: continuous\nMeans: {\"(0.0, 0.02814)\": -0.771, \"(0.02814, 0.08293)\": -0.653, \"(0.08293, 0.08555)\": -0.533, \"(0.08555, 0.093225)\": -0.403, \"(0.093225, 0.1055)\": -0.234, \"(0.1055, 0.11510000000000001)\": -0.117, \"(0.11510000000000001, 0.1346)\": 0.002, \"(0.1346, 0.14545000000000002)\": 0.121, \"(0.14545000000000002, 0.15175)\": 0.241, \"(0.15175, 0.1603)\": 0.365, \"(0.1603, 0.1722)\": 0.539, \"(0.1722, 0.17695)\": 0.661, \"(0.17695, 0.18359999999999999)\": 0.781, \"(0.18359999999999999, 0.194)\": 0.9, \"(0.194, 0.2019)\": 1.022, \"(0.2019, 0.21275)\": 1.14, \"(0.21275, 0.2383)\": 1.259, \"(0.2383, 0.26865)\": 1.378, \"(0.26865, 0.291)\": 1.494}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.02814)\": -1.316, \"(0.02814, 0.08293)\": -1.204, \"(0.08293, 0.08555)\": -1.003, \"(0.08555, 0.093225)\": -0.715, \"(0.093225, 0.1055)\": -0.419, \"(0.1055, 0.11510000000000001)\": -0.299, \"(0.11510000000000001, 0.1346)\": -0.172, \"(0.1346, 0.14545000000000002)\": -0.125, \"(0.14545000000000002, 0.15175)\": 0.077, \"(0.15175, 0.1603)\": 0.185, \"(0.1603, 0.1722)\": 0.112, \"(0.1722, 0.17695)\": 0.052, \"(0.17695, 0.18359999999999999)\": 0.152, \"(0.18359999999999999, 0.194)\": 0.267, \"(0.194, 0.2019)\": 0.392, \"(0.2019, 0.21275)\": 0.477, \"(0.21275, 0.2383)\": 0.593, \"(0.2383, 0.26865)\": 0.693, \"(0.26865, 0.291)\": 0.803}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.02814)\": -0.226, \"(0.02814, 0.08293)\": -0.101, \"(0.08293, 0.08555)\": -0.064, \"(0.08555, 0.093225)\": -0.091, \"(0.093225, 0.1055)\": -0.049, \"(0.1055, 0.11510000000000001)\": 0.065, \"(0.11510000000000001, 0.1346)\": 0.176, \"(0.1346, 0.14545000000000002)\": 0.367, \"(0.14545000000000002, 0.15175)\": 0.406, \"(0.15175, 0.1603)\": 0.545, \"(0.1603, 0.1722)\": 0.966, \"(0.1722, 0.17695)\": 1.27, \"(0.17695, 0.18359999999999999)\": 1.41, \"(0.18359999999999999, 0.194)\": 1.533, \"(0.194, 0.2019)\": 1.653, \"(0.2019, 0.21275)\": 1.803, \"(0.21275, 0.2383)\": 1.926, \"(0.2383, 0.26865)\": 2.063, \"(0.26865, 0.291)\": 2.186}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.26865, 0.291)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Pclass\nFeature Type: continuous\nMeans: {\"(1.0, 1.5)\": -0.009, \"(1.5, 2.5)\": 0.534, \"(2.5, 3.0)\": -0.532}\nLower Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -0.053, \"(1.5, 2.5)\": 0.174, \"(2.5, 3.0)\": -1.011}\nUpper Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": 0.035, \"(1.5, 2.5)\": 0.893, \"(2.5, 3.0)\": -0.052}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(2.5, 3.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: diabetes\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": 0.3225, \"(0.5, 1.0)\": -0.415}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.1807, \"(0.5, 1.0)\": -0.5976}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.4643, \"(0.5, 1.0)\": -0.2325}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.5, 1.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Pregnancies\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.1506, \"(0.5, 1.5)\": -0.2484, \"(1.5, 2.5)\": -0.1873, \"(2.5, 3.5)\": -0.0302, \"(3.5, 4.5)\": 0.0211, \"(4.5, 5.5)\": 0.1013, \"(5.5, 6.5)\": 0.1489, \"(6.5, 7.5)\": 0.264, \"(7.5, 8.5)\": 0.3553, \"(8.5, 9.5)\": 0.4117, \"(9.5, 13.5)\": 0.2996, \"(13.5, 14.0)\": 0.6729}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.2406, \"(0.5, 1.5)\": -0.3636, \"(1.5, 2.5)\": -0.242, \"(2.5, 3.5)\": -0.093, \"(3.5, 4.5)\": -0.038, \"(4.5, 5.5)\": 0.0314, \"(5.5, 6.5)\": 0.0909, \"(6.5, 7.5)\": 0.1609, \"(7.5, 8.5)\": 0.2075, \"(8.5, 9.5)\": 0.248, \"(9.5, 13.5)\": 0.0671, \"(13.5, 14.0)\": 0.084}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0606, \"(0.5, 1.5)\": -0.1333, \"(1.5, 2.5)\": -0.1326, \"(2.5, 3.5)\": 0.0326, \"(3.5, 4.5)\": 0.0802, \"(4.5, 5.5)\": 0.1712, \"(5.5, 6.5)\": 0.207, \"(6.5, 7.5)\": 0.3671, \"(7.5, 8.5)\": 0.5032, \"(8.5, 9.5)\": 0.5755, \"(9.5, 13.5)\": 0.5321, \"(13.5, 14.0)\": 1.2617}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(13.5, 14.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: PoliticalFactors\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.0263, \"(0.5, 1.5)\": -0.02126, \"(1.5, 2.5)\": -0.01709, \"(2.5, 3.5)\": -0.01038, \"(3.5, 4.5)\": -0.00633, \"(4.5, 5.5)\": 0.00068, \"(5.5, 6.5)\": 0.00618, \"(6.5, 7.5)\": 0.01223, \"(7.5, 8.5)\": 0.01761, \"(8.5, 9.5)\": 0.02318, \"(9.5, 10.5)\": 0.02782, \"(10.5, 11.5)\": 0.03238, \"(11.5, 13.5)\": 0.03978, \"(13.5, 15.0)\": 0.04468, \"(15.0, 16.0)\": 0.0529}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02939, \"(0.5, 1.5)\": -0.02258, \"(1.5, 2.5)\": -0.01777, \"(2.5, 3.5)\": -0.01075, \"(3.5, 4.5)\": -0.00677, \"(4.5, 5.5)\": 0.00038, \"(5.5, 6.5)\": 0.00571, \"(6.5, 7.5)\": 0.01182, \"(7.5, 8.5)\": 0.01718, \"(8.5, 9.5)\": 0.02223, \"(9.5, 10.5)\": 0.02645, \"(10.5, 11.5)\": 0.02946, \"(11.5, 13.5)\": 0.03697, \"(13.5, 15.0)\": 0.03459, \"(15.0, 16.0)\": 0.03844}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02321, \"(0.5, 1.5)\": -0.01993, \"(1.5, 2.5)\": -0.01641, \"(2.5, 3.5)\": -0.01001, \"(3.5, 4.5)\": -0.00589, \"(4.5, 5.5)\": 0.00098, \"(5.5, 6.5)\": 0.00665, \"(6.5, 7.5)\": 0.01264, \"(7.5, 8.5)\": 0.01804, \"(8.5, 9.5)\": 0.02414, \"(9.5, 10.5)\": 0.02919, \"(10.5, 11.5)\": 0.0353, \"(11.5, 13.5)\": 0.04259, \"(13.5, 15.0)\": 0.05476, \"(15.0, 16.0)\": 0.06736}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(15.0, 16.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: age\nFeature Type: continuous\nMeans: {\"(40.0, 41.5)\": -1.489, \"(41.5, 43.5)\": -0.895, \"(43.5, 44.5)\": -0.02, \"(44.5, 47.5)\": 0.701, \"(47.5, 48.5)\": 1.245, \"(48.5, 58.5)\": -0.923, \"(58.5, 59.5)\": 0.647, \"(59.5, 60.8335)\": -0.288, \"(60.8335, 64.5)\": -1.035, \"(64.5, 65.5)\": 0.0, \"(65.5, 67.5)\": -0.73, \"(67.5, 68.5)\": 0.19, \"(68.5, 70.5)\": 0.784, \"(70.5, 80.5)\": 1.169, \"(80.5, 81.5)\": 0.839, \"(81.5, 85.5)\": 2.112, \"(85.5, 86.5)\": 3.884, \"(86.5, 95.0)\": 4.517}\nLower Bounds (95%-Confidence Interval): {\"(40.0, 41.5)\": -2.719, \"(41.5, 43.5)\": -2.486, \"(43.5, 44.5)\": -0.761, \"(44.5, 47.5)\": 0.297, \"(47.5, 48.5)\": 0.199, \"(48.5, 58.5)\": -1.235, \"(58.5, 59.5)\": 0.291, \"(59.5, 60.8335)\": -0.805, \"(60.8335, 64.5)\": -1.655, \"(64.5, 65.5)\": -0.281, \"(65.5, 67.5)\": -2.122, \"(67.5, 68.5)\": -0.059, \"(68.5, 70.5)\": 0.513, \"(70.5, 80.5)\": 0.404, \"(80.5, 81.5)\": 0.173, \"(81.5, 85.5)\": 1.308, \"(85.5, 86.5)\": 2.758, \"(86.5, 95.0)\": 3.244}\nUpper Bounds (95%-Confidence Interval): {\"(40.0, 41.5)\": -0.259, \"(41.5, 43.5)\": 0.696, \"(43.5, 44.5)\": 0.722, \"(44.5, 47.5)\": 1.105, \"(47.5, 48.5)\": 2.291, \"(48.5, 58.5)\": -0.612, \"(58.5, 59.5)\": 1.004, \"(59.5, 60.8335)\": 0.228, \"(60.8335, 64.5)\": -0.414, \"(64.5, 65.5)\": 0.281, \"(65.5, 67.5)\": 0.662, \"(67.5, 68.5)\": 0.44, \"(68.5, 70.5)\": 1.056, \"(70.5, 80.5)\": 1.934, \"(80.5, 81.5)\": 1.505, \"(81.5, 85.5)\": 2.916, \"(85.5, 86.5)\": 5.009, \"(86.5, 95.0)\": 5.79}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(41.5, 43.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: smoothness_se\nFeature Type: continuous\nMeans: {\"(0.001713, 0.0031539999999999997)\": 0.2958, \"(0.0031539999999999997, 0.003299)\": 0.2615, \"(0.003299, 0.003384)\": 0.185, \"(0.003384, 0.0034675)\": -0.1523, \"(0.0034675, 0.0036699999999999997)\": -0.1838, \"(0.0036699999999999997, 0.0041069999999999995)\": -0.2174, \"(0.0041069999999999995, 0.004215)\": -0.2532, \"(0.004215, 0.004436)\": -0.2879, \"(0.004436, 0.0045775)\": -0.3223, \"(0.0045775, 0.004612)\": -0.2905, \"(0.004612, 0.0048915)\": -0.2425, \"(0.0048915, 0.0053335)\": -0.2106, \"(0.0053335, 0.005443)\": -0.1771, \"(0.005443, 0.00554)\": -0.1453, \"(0.00554, 0.005729)\": -0.1136, \"(0.005729, 0.0058625)\": -0.0812, \"(0.0058625, 0.0058955)\": -0.0495, \"(0.0058955, 0.0067525)\": 0.0229, \"(0.0067525, 0.00682)\": 0.0562, \"(0.00682, 0.007338)\": 0.1146, \"(0.007338, 0.0074805)\": 0.1474, \"(0.0074805, 0.007967)\": 0.1839, \"(0.007967, 0.009857000000000001)\": 0.219, \"(0.009857000000000001, 0.010665000000000001)\": 0.1863, \"(0.010665000000000001, 0.011054999999999999)\": 0.1538, \"(0.011054999999999999, 0.011915)\": 0.1219, \"(0.011915, 0.012885)\": 0.0873, \"(0.012885, 0.03113)\": 0.0542}\nLower Bounds (95%-Confidence Interval): {\"(0.001713, 0.0031539999999999997)\": -0.864, \"(0.0031539999999999997, 0.003299)\": -0.919, \"(0.003299, 0.003384)\": -1.0196, \"(0.003384, 0.0034675)\": -0.6905, \"(0.0034675, 0.0036699999999999997)\": -0.7233, \"(0.0036699999999999997, 0.0041069999999999995)\": -0.7618, \"(0.0041069999999999995, 0.004215)\": -0.7976, \"(0.004215, 0.004436)\": -0.8492, \"(0.004436, 0.0045775)\": -0.8863, \"(0.0045775, 0.004612)\": -0.8426, \"(0.004612, 0.0048915)\": -0.7021, \"(0.0048915, 0.0053335)\": -0.6905, \"(0.0053335, 0.005443)\": -0.6659, \"(0.005443, 0.00554)\": -0.624, \"(0.00554, 0.005729)\": -0.5761, \"(0.005729, 0.0058625)\": -0.538, \"(0.0058625, 0.0058955)\": -0.5073, \"(0.0058955, 0.0067525)\": -0.1186, \"(0.0067525, 0.00682)\": -0.0928, \"(0.00682, 0.007338)\": -0.288, \"(0.007338, 0.0074805)\": -0.2553, \"(0.0074805, 0.007967)\": -0.2176, \"(0.007967, 0.009857000000000001)\": -0.1787, \"(0.009857000000000001, 0.010665000000000001)\": -0.2012, \"(0.010665000000000001, 0.011054999999999999)\": -0.2344, \"(0.011054999999999999, 0.011915)\": -0.2614, \"(0.011915, 0.012885)\": -0.2838, \"(0.012885, 0.03113)\": -0.4136}\nUpper Bounds (95%-Confidence Interval): {\"(0.001713, 0.0031539999999999997)\": 1.4555, \"(0.0031539999999999997, 0.003299)\": 1.442, \"(0.003299, 0.003384)\": 1.3896, \"(0.003384, 0.0034675)\": 0.386, \"(0.0034675, 0.0036699999999999997)\": 0.3557, \"(0.0036699999999999997, 0.0041069999999999995)\": 0.327, \"(0.0041069999999999995, 0.004215)\": 0.2913, \"(0.004215, 0.004436)\": 0.2734, \"(0.004436, 0.0045775)\": 0.2417, \"(0.0045775, 0.004612)\": 0.2615, \"(0.004612, 0.0048915)\": 0.2171, \"(0.0048915, 0.0053335)\": 0.2692, \"(0.0053335, 0.005443)\": 0.3117, \"(0.005443, 0.00554)\": 0.3335, \"(0.00554, 0.005729)\": 0.349, \"(0.005729, 0.0058625)\": 0.3757, \"(0.0058625, 0.0058955)\": 0.4082, \"(0.0058955, 0.0067525)\": 0.1644, \"(0.0067525, 0.00682)\": 0.2053, \"(0.00682, 0.007338)\": 0.5173, \"(0.007338, 0.0074805)\": 0.5502, \"(0.0074805, 0.007967)\": 0.5854, \"(0.007967, 0.009857000000000001)\": 0.6167, \"(0.009857000000000001, 0.010665000000000001)\": 0.5738, \"(0.010665000000000001, 0.011054999999999999)\": 0.5419, \"(0.011054999999999999, 0.011915)\": 0.5053, \"(0.011915, 0.012885)\": 0.4585, \"(0.012885, 0.03113)\": 0.522}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.003299, 0.003384)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: serum_creatinine\nFeature Type: continuous\nMeans: {\"(0.5, 0.6499999999999999)\": -0.26, \"(0.6499999999999999, 0.725)\": -1.08, \"(0.725, 0.875)\": -3.77, \"(0.875, 0.95)\": -0.9, \"(0.95, 1.1400000000000001)\": -0.15, \"(1.1400000000000001, 1.35)\": -0.88, \"(1.35, 1.45)\": 0.2, \"(1.45, 1.55)\": 1.18, \"(1.55, 1.815)\": 2.18, \"(1.815, 2.05)\": 4.74, \"(2.05, 2.45)\": 1.14, \"(2.45, 2.6)\": 3.63, \"(2.6, 2.95)\": -0.36, \"(2.95, 3.1)\": 2.57, \"(3.1, 3.45)\": 0.36, \"(3.45, 3.6)\": 3.06, \"(3.6, 3.75)\": 6.76, \"(3.75, 3.9)\": 2.31, \"(3.9, 4.7)\": 2.92, \"(4.7, 5.949999999999999)\": 0.76, \"(5.949999999999999, 6.199999999999999)\": -0.43, \"(6.199999999999999, 6.55)\": 0.23, \"(6.55, 9.4)\": 6.97}\nLower Bounds (95%-Confidence Interval): {\"(0.5, 0.6499999999999999)\": -1.13, \"(0.6499999999999999, 0.725)\": -1.45, \"(0.725, 0.875)\": -5.7, \"(0.875, 0.95)\": -1.31, \"(0.95, 1.1400000000000001)\": -0.41, \"(1.1400000000000001, 1.35)\": -1.92, \"(1.35, 1.45)\": -0.14, \"(1.45, 1.55)\": 0.46, \"(1.55, 1.815)\": 1.68, \"(1.815, 2.05)\": 2.75, \"(2.05, 2.45)\": 0.72, \"(2.45, 2.6)\": 1.94, \"(2.6, 2.95)\": -2.5, \"(2.95, 3.1)\": 0.3, \"(3.1, 3.45)\": -0.49, \"(3.45, 3.6)\": 1.58, \"(3.6, 3.75)\": 4.55, \"(3.75, 3.9)\": 0.4, \"(3.9, 4.7)\": 0.8, \"(4.7, 5.949999999999999)\": -0.63, \"(5.949999999999999, 6.199999999999999)\": -1.75, \"(6.199999999999999, 6.55)\": -2.74, \"(6.55, 9.4)\": 5.07}\nUpper Bounds (95%-Confidence Interval): {\"(0.5, 0.6499999999999999)\": 0.62, \"(0.6499999999999999, 0.725)\": -0.72, \"(0.725, 0.875)\": -1.84, \"(0.875, 0.95)\": -0.48, \"(0.95, 1.1400000000000001)\": 0.12, \"(1.1400000000000001, 1.35)\": 0.16, \"(1.35, 1.45)\": 0.53, \"(1.45, 1.55)\": 1.89, \"(1.55, 1.815)\": 2.68, \"(1.815, 2.05)\": 6.73, \"(2.05, 2.45)\": 1.56, \"(2.45, 2.6)\": 5.32, \"(2.6, 2.95)\": 1.77, \"(2.95, 3.1)\": 4.84, \"(3.1, 3.45)\": 1.2, \"(3.45, 3.6)\": 4.53, \"(3.6, 3.75)\": 8.97, \"(3.75, 3.9)\": 4.21, \"(3.9, 4.7)\": 5.04, \"(4.7, 5.949999999999999)\": 2.14, \"(5.949999999999999, 6.199999999999999)\": 0.9, \"(6.199999999999999, 6.55)\": 3.21, \"(6.55, 9.4)\": 8.88}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(6.199999999999999, 6.55)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: DamsQuality\nFeature Type: continuous\nMeans: {\"(0.0, 1.5)\": -0.02325, \"(1.5, 2.5)\": -0.01532, \"(2.5, 3.5)\": -0.01073, \"(3.5, 4.5)\": -0.00482, \"(4.5, 5.5)\": -0.00032, \"(5.5, 6.5)\": 0.0063, \"(6.5, 7.5)\": 0.01228, \"(7.5, 8.5)\": 0.01637, \"(8.5, 10.5)\": 0.02537, \"(10.5, 12.5)\": 0.03189, \"(12.5, 13.5)\": 0.03961, \"(13.5, 14.0)\": 0.01644}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02598, \"(1.5, 2.5)\": -0.01586, \"(2.5, 3.5)\": -0.01113, \"(3.5, 4.5)\": -0.00525, \"(4.5, 5.5)\": -0.00072, \"(5.5, 6.5)\": 0.00574, \"(6.5, 7.5)\": 0.01173, \"(7.5, 8.5)\": 0.01585, \"(8.5, 10.5)\": 0.02412, \"(10.5, 12.5)\": 0.02908, \"(12.5, 13.5)\": 0.03687, \"(13.5, 14.0)\": 0.00331}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02052, \"(1.5, 2.5)\": -0.01477, \"(2.5, 3.5)\": -0.01034, \"(3.5, 4.5)\": -0.00438, \"(4.5, 5.5)\": 9e-05, \"(5.5, 6.5)\": 0.00686, \"(6.5, 7.5)\": 0.01282, \"(7.5, 8.5)\": 0.01689, \"(8.5, 10.5)\": 0.02662, \"(10.5, 12.5)\": 0.0347, \"(12.5, 13.5)\": 0.04234, \"(13.5, 14.0)\": 0.02957}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(13.5, 14.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: perimeter_se\nFeature Type: continuous\nMeans: {\"(0.7714, 1.0579999999999998)\": -0.698, \"(1.0579999999999998, 1.1345)\": -0.618, \"(1.1345, 1.197)\": -0.539, \"(1.197, 1.2365)\": -0.461, \"(1.2365, 1.326)\": -0.384, \"(1.326, 1.4435)\": -0.256, \"(1.4435, 1.5314999999999999)\": -0.176, \"(1.5314999999999999, 1.807)\": -0.099, \"(1.807, 2.107)\": -0.023, \"(2.107, 2.593)\": -0.098, \"(2.593, 2.878)\": -0.018, \"(2.878, 3.292)\": 0.065, \"(3.292, 4.095000000000001)\": 0.14, \"(4.095000000000001, 4.714)\": 0.219, \"(4.714, 4.885999999999999)\": 0.296, \"(4.885999999999999, 5.2844999999999995)\": 0.372, \"(5.2844999999999995, 5.8425)\": 0.451, \"(5.8425, 7.104)\": 0.536, \"(7.104, 7.7765)\": 0.611, \"(7.7765, 10.594999999999999)\": 0.701, \"(10.594999999999999, 21.98)\": 0.786}\nLower Bounds (95%-Confidence Interval): {\"(0.7714, 1.0579999999999998)\": -1.131, \"(1.0579999999999998, 1.1345)\": -1.029, \"(1.1345, 1.197)\": -0.923, \"(1.197, 1.2365)\": -0.835, \"(1.2365, 1.326)\": -0.754, \"(1.326, 1.4435)\": -0.43, \"(1.4435, 1.5314999999999999)\": -0.378, \"(1.5314999999999999, 1.807)\": -0.215, \"(1.807, 2.107)\": -0.131, \"(2.107, 2.593)\": -0.215, \"(2.593, 2.878)\": -0.124, \"(2.878, 3.292)\": -0.022, \"(3.292, 4.095000000000001)\": 0.04, \"(4.095000000000001, 4.714)\": 0.049, \"(4.714, 4.885999999999999)\": -0.063, \"(4.885999999999999, 5.2844999999999995)\": 0.007, \"(5.2844999999999995, 5.8425)\": 0.088, \"(5.8425, 7.104)\": 0.151, \"(7.104, 7.7765)\": 0.21, \"(7.7765, 10.594999999999999)\": 0.257, \"(10.594999999999999, 21.98)\": 0.341}\nUpper Bounds (95%-Confidence Interval): {\"(0.7714, 1.0579999999999998)\": -0.265, \"(1.0579999999999998, 1.1345)\": -0.208, \"(1.1345, 1.197)\": -0.155, \"(1.197, 1.2365)\": -0.087, \"(1.2365, 1.326)\": -0.015, \"(1.326, 1.4435)\": -0.081, \"(1.4435, 1.5314999999999999)\": 0.026, \"(1.5314999999999999, 1.807)\": 0.016, \"(1.807, 2.107)\": 0.085, \"(2.107, 2.593)\": 0.019, \"(2.593, 2.878)\": 0.088, \"(2.878, 3.292)\": 0.152, \"(3.292, 4.095000000000001)\": 0.24, \"(4.095000000000001, 4.714)\": 0.388, \"(4.714, 4.885999999999999)\": 0.655, \"(4.885999999999999, 5.2844999999999995)\": 0.737, \"(5.2844999999999995, 5.8425)\": 0.814, \"(5.8425, 7.104)\": 0.921, \"(7.104, 7.7765)\": 1.012, \"(7.7765, 10.594999999999999)\": 1.146, \"(10.594999999999999, 21.98)\": 1.231}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(10.594999999999999, 21.98)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Age\nFeature Type: continuous\nMeans: {\"(21.0, 21.5)\": -0.481, \"(21.5, 23.5)\": -0.377, \"(23.5, 24.5)\": -0.294, \"(24.5, 26.5)\": -0.205, \"(26.5, 28.5)\": -0.106, \"(28.5, 30.5)\": 0.056, \"(30.5, 34.5)\": 0.184, \"(34.5, 39.5)\": 0.286, \"(39.5, 44.5)\": 0.389, \"(44.5, 54.5)\": 0.476, \"(54.5, 56.5)\": 0.374, \"(56.5, 58.5)\": 0.224, \"(58.5, 60.5)\": 0.121, \"(60.5, 61.5)\": -0.053, \"(61.5, 62.5)\": -0.314, \"(62.5, 64.5)\": -0.437, \"(64.5, 66.5)\": -0.598, \"(66.5, 67.5)\": -0.714, \"(67.5, 68.5)\": -0.823, \"(68.5, 76.5)\": -0.922, \"(76.5, 81.0)\": -1.102}\nLower Bounds (95%-Confidence Interval): {\"(21.0, 21.5)\": -0.733, \"(21.5, 23.5)\": -0.545, \"(23.5, 24.5)\": -0.449, \"(24.5, 26.5)\": -0.316, \"(26.5, 28.5)\": -0.204, \"(28.5, 30.5)\": -0.094, \"(30.5, 34.5)\": 0.033, \"(34.5, 39.5)\": 0.131, \"(39.5, 44.5)\": 0.234, \"(44.5, 54.5)\": 0.292, \"(54.5, 56.5)\": 0.179, \"(56.5, 58.5)\": 0.067, \"(58.5, 60.5)\": -0.026, \"(60.5, 61.5)\": -0.314, \"(61.5, 62.5)\": -0.776, \"(62.5, 64.5)\": -0.923, \"(64.5, 66.5)\": -1.089, \"(66.5, 67.5)\": -1.205, \"(67.5, 68.5)\": -1.322, \"(68.5, 76.5)\": -1.445, \"(76.5, 81.0)\": -1.674}\nUpper Bounds (95%-Confidence Interval): {\"(21.0, 21.5)\": -0.228, \"(21.5, 23.5)\": -0.21, \"(23.5, 24.5)\": -0.139, \"(24.5, 26.5)\": -0.094, \"(26.5, 28.5)\": -0.008, \"(28.5, 30.5)\": 0.206, \"(30.5, 34.5)\": 0.335, \"(34.5, 39.5)\": 0.441, \"(39.5, 44.5)\": 0.544, \"(44.5, 54.5)\": 0.66, \"(54.5, 56.5)\": 0.569, \"(56.5, 58.5)\": 0.382, \"(58.5, 60.5)\": 0.267, \"(60.5, 61.5)\": 0.208, \"(61.5, 62.5)\": 0.149, \"(62.5, 64.5)\": 0.05, \"(64.5, 66.5)\": -0.107, \"(66.5, 67.5)\": -0.222, \"(67.5, 68.5)\": -0.325, \"(68.5, 76.5)\": -0.399, \"(76.5, 81.0)\": -0.529}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(76.5, 81.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Watersheds\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02526, \"(0.5, 1.5)\": -0.02147, \"(1.5, 2.5)\": -0.01542, \"(2.5, 3.5)\": -0.01026, \"(3.5, 4.5)\": -0.00466, \"(4.5, 5.5)\": 0.00049, \"(5.5, 6.5)\": 0.00555, \"(6.5, 8.5)\": 0.01133, \"(8.5, 10.5)\": 0.02234, \"(10.5, 11.5)\": 0.03241, \"(11.5, 12.5)\": 0.03775, \"(12.5, 13.5)\": 0.04216, \"(13.5, 14.0)\": 0.04656}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0274, \"(0.5, 1.5)\": -0.02237, \"(1.5, 2.5)\": -0.01633, \"(2.5, 3.5)\": -0.01068, \"(3.5, 4.5)\": -0.005, \"(4.5, 5.5)\": 0.00014, \"(5.5, 6.5)\": 0.00514, \"(6.5, 8.5)\": 0.01068, \"(8.5, 10.5)\": 0.02129, \"(10.5, 11.5)\": 0.03073, \"(11.5, 12.5)\": 0.03466, \"(12.5, 13.5)\": 0.038, \"(13.5, 14.0)\": 0.043}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02312, \"(0.5, 1.5)\": -0.02056, \"(1.5, 2.5)\": -0.01451, \"(2.5, 3.5)\": -0.00985, \"(3.5, 4.5)\": -0.00431, \"(4.5, 5.5)\": 0.00084, \"(5.5, 6.5)\": 0.00596, \"(6.5, 8.5)\": 0.01197, \"(8.5, 10.5)\": 0.0234, \"(10.5, 11.5)\": 0.03409, \"(11.5, 12.5)\": 0.04085, \"(12.5, 13.5)\": 0.04633, \"(13.5, 14.0)\": 0.05012}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(12.5, 13.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Landslides\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02593, \"(0.5, 1.5)\": -0.02172, \"(1.5, 2.5)\": -0.01544, \"(2.5, 3.5)\": -0.0098, \"(3.5, 4.5)\": -0.00541, \"(4.5, 5.5)\": 0.00066, \"(5.5, 6.5)\": 0.00575, \"(6.5, 7.5)\": 0.01201, \"(7.5, 8.5)\": 0.01649, \"(8.5, 9.5)\": 0.0215, \"(9.5, 10.5)\": 0.0267, \"(10.5, 11.5)\": 0.03057, \"(11.5, 13.5)\": 0.0366, \"(13.5, 14.0)\": 0.03003}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02743, \"(0.5, 1.5)\": -0.02261, \"(1.5, 2.5)\": -0.01616, \"(2.5, 3.5)\": -0.0102, \"(3.5, 4.5)\": -0.00579, \"(4.5, 5.5)\": 0.00027, \"(5.5, 6.5)\": 0.00544, \"(6.5, 7.5)\": 0.01146, \"(7.5, 8.5)\": 0.01601, \"(8.5, 9.5)\": 0.02065, \"(9.5, 10.5)\": 0.02512, \"(10.5, 11.5)\": 0.0285, \"(11.5, 13.5)\": 0.02931, \"(13.5, 14.0)\": 0.02233}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02443, \"(0.5, 1.5)\": -0.02083, \"(1.5, 2.5)\": -0.01472, \"(2.5, 3.5)\": -0.0094, \"(3.5, 4.5)\": -0.00504, \"(4.5, 5.5)\": 0.00105, \"(5.5, 6.5)\": 0.00606, \"(6.5, 7.5)\": 0.01257, \"(7.5, 8.5)\": 0.01698, \"(8.5, 9.5)\": 0.02234, \"(9.5, 10.5)\": 0.02828, \"(10.5, 11.5)\": 0.03265, \"(11.5, 13.5)\": 0.04389, \"(13.5, 14.0)\": 0.03772}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(13.5, 14.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: texture_se\nFeature Type: continuous\nMeans: {\"(0.3602, 0.47535000000000005)\": -0.1353, \"(0.47535000000000005, 0.49585)\": -0.1099, \"(0.49585, 0.5344)\": -0.0872, \"(0.5344, 0.55835)\": -0.0633, \"(0.55835, 0.5779000000000001)\": -0.0358, \"(0.5779000000000001, 0.6065)\": -0.0122, \"(0.6065, 0.6938500000000001)\": 0.0114, \"(0.6938500000000001, 0.7878499999999999)\": -0.0151, \"(0.7878499999999999, 0.8181499999999999)\": 0.0089, \"(0.8181499999999999, 0.9497)\": 0.0332, \"(0.9497, 0.99)\": 0.0579, \"(0.99, 1.0579999999999998)\": 0.0811, \"(1.0579999999999998, 1.2845)\": 0.0581, \"(1.2845, 1.461)\": 0.0338, \"(1.461, 1.4785)\": 0.0097, \"(1.4785, 1.892)\": -0.0156, \"(1.892, 1.9255)\": -0.0438, \"(1.9255, 1.9945)\": -0.0684, \"(1.9945, 2.0999999999999996)\": -0.0943, \"(2.0999999999999996, 2.2295)\": -0.0299, \"(2.2295, 2.263)\": -0.2223, \"(2.263, 2.3085)\": -0.2461, \"(2.3085, 2.481)\": -0.2782, \"(2.481, 2.6235)\": -0.3069, \"(2.6235, 3.6075)\": -0.3341, \"(3.6075, 4.885)\": -0.3576}\nLower Bounds (95%-Confidence Interval): {\"(0.3602, 0.47535000000000005)\": -0.4063, \"(0.47535000000000005, 0.49585)\": -0.2824, \"(0.49585, 0.5344)\": -0.2408, \"(0.5344, 0.55835)\": -0.2114, \"(0.55835, 0.5779000000000001)\": -0.1606, \"(0.5779000000000001, 0.6065)\": -0.1405, \"(0.6065, 0.6938500000000001)\": -0.1139, \"(0.6938500000000001, 0.7878499999999999)\": -0.1541, \"(0.7878499999999999, 0.8181499999999999)\": -0.1144, \"(0.8181499999999999, 0.9497)\": -0.0897, \"(0.9497, 0.99)\": -0.0157, \"(0.99, 1.0579999999999998)\": 0.029, \"(1.0579999999999998, 1.2845)\": -0.012, \"(1.2845, 1.461)\": -0.0661, \"(1.461, 1.4785)\": -0.0888, \"(1.4785, 1.892)\": -0.2196, \"(1.892, 1.9255)\": -0.2461, \"(1.9255, 1.9945)\": -0.2824, \"(1.9945, 2.0999999999999996)\": -0.3214, \"(2.0999999999999996, 2.2295)\": -0.5019, \"(2.2295, 2.263)\": -0.6324, \"(2.263, 2.3085)\": -0.6746, \"(2.3085, 2.481)\": -0.7135, \"(2.481, 2.6235)\": -0.7339, \"(2.6235, 3.6075)\": -0.7611, \"(3.6075, 4.885)\": -0.7768}\nUpper Bounds (95%-Confidence Interval): {\"(0.3602, 0.47535000000000005)\": 0.1358, \"(0.47535000000000005, 0.49585)\": 0.0627, \"(0.49585, 0.5344)\": 0.0665, \"(0.5344, 0.55835)\": 0.0849, \"(0.55835, 0.5779000000000001)\": 0.089, \"(0.5779000000000001, 0.6065)\": 0.1161, \"(0.6065, 0.6938500000000001)\": 0.1366, \"(0.6938500000000001, 0.7878499999999999)\": 0.124, \"(0.7878499999999999, 0.8181499999999999)\": 0.1322, \"(0.8181499999999999, 0.9497)\": 0.1561, \"(0.9497, 0.99)\": 0.1314, \"(0.99, 1.0579999999999998)\": 0.1333, \"(1.0579999999999998, 1.2845)\": 0.1282, \"(1.2845, 1.461)\": 0.1336, \"(1.461, 1.4785)\": 0.1082, \"(1.4785, 1.892)\": 0.1884, \"(1.892, 1.9255)\": 0.1585, \"(1.9255, 1.9945)\": 0.1456, \"(1.9945, 2.0999999999999996)\": 0.1329, \"(2.0999999999999996, 2.2295)\": 0.4421, \"(2.2295, 2.263)\": 0.1879, \"(2.263, 2.3085)\": 0.1825, \"(2.3085, 2.481)\": 0.1571, \"(2.481, 2.6235)\": 0.1201, \"(2.6235, 3.6075)\": 0.0929, \"(3.6075, 4.885)\": 0.0616}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(2.0999999999999996, 2.2295)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: concavity_worst\nFeature Type: continuous\nMeans: {\"(0.0, 0.022775)\": -0.769, \"(0.022775, 0.024655)\": -0.671, \"(0.024655, 0.052095)\": -0.846, \"(0.052095, 0.10575)\": -0.943, \"(0.10575, 0.1313)\": -0.843, \"(0.1313, 0.14545000000000002)\": -0.745, \"(0.14545000000000002, 0.1694)\": -0.646, \"(0.1694, 0.1843)\": -0.54, \"(0.1843, 0.19235000000000002)\": -0.438, \"(0.19235000000000002, 0.1996)\": -0.332, \"(0.1996, 0.20695)\": -0.234, \"(0.20695, 0.20795)\": -0.081, \"(0.20795, 0.2539)\": 0.187, \"(0.2539, 0.273)\": 0.284, \"(0.273, 0.33975)\": 0.385, \"(0.33975, 0.3663)\": 0.486, \"(0.3663, 0.37695)\": 0.586, \"(0.37695, 0.39765)\": 0.698, \"(0.39765, 0.41025)\": 0.797, \"(0.41025, 1.252)\": 0.897}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.022775)\": -1.429, \"(0.022775, 0.024655)\": -1.337, \"(0.024655, 0.052095)\": -1.568, \"(0.052095, 0.10575)\": -1.701, \"(0.10575, 0.1313)\": -1.62, \"(0.1313, 0.14545000000000002)\": -1.521, \"(0.14545000000000002, 0.1694)\": -1.427, \"(0.1694, 0.1843)\": -1.324, \"(0.1843, 0.19235000000000002)\": -1.207, \"(0.19235000000000002, 0.1996)\": -1.093, \"(0.1996, 0.20695)\": -0.982, \"(0.20695, 0.20795)\": -0.814, \"(0.20795, 0.2539)\": -0.518, \"(0.2539, 0.273)\": -0.08, \"(0.273, 0.33975)\": 0.033, \"(0.33975, 0.3663)\": 0.265, \"(0.3663, 0.37695)\": 0.365, \"(0.37695, 0.39765)\": -0.026, \"(0.39765, 0.41025)\": -0.308, \"(0.41025, 1.252)\": -0.23}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.022775)\": -0.109, \"(0.022775, 0.024655)\": -0.005, \"(0.024655, 0.052095)\": -0.123, \"(0.052095, 0.10575)\": -0.186, \"(0.10575, 0.1313)\": -0.065, \"(0.1313, 0.14545000000000002)\": 0.031, \"(0.14545000000000002, 0.1694)\": 0.135, \"(0.1694, 0.1843)\": 0.244, \"(0.1843, 0.19235000000000002)\": 0.332, \"(0.19235000000000002, 0.1996)\": 0.428, \"(0.1996, 0.20695)\": 0.514, \"(0.20695, 0.20795)\": 0.653, \"(0.20795, 0.2539)\": 0.891, \"(0.2539, 0.273)\": 0.648, \"(0.273, 0.33975)\": 0.737, \"(0.33975, 0.3663)\": 0.708, \"(0.3663, 0.37695)\": 0.807, \"(0.37695, 0.39765)\": 1.423, \"(0.39765, 0.41025)\": 1.902, \"(0.41025, 1.252)\": 2.024}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.41025, 1.252)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Deforestation\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02956, \"(0.5, 2.5)\": -0.02081, \"(2.5, 3.5)\": -0.00998, \"(3.5, 4.5)\": -0.00524, \"(4.5, 5.5)\": 0.00043, \"(5.5, 6.5)\": 0.00515, \"(6.5, 8.5)\": 0.01107, \"(8.5, 10.5)\": 0.02102, \"(10.5, 11.5)\": 0.02728, \"(11.5, 13.5)\": 0.0456, \"(13.5, 14.5)\": 0.05244, \"(14.5, 17.0)\": 0.06161}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.03241, \"(0.5, 2.5)\": -0.02172, \"(2.5, 3.5)\": -0.01056, \"(3.5, 4.5)\": -0.0057, \"(4.5, 5.5)\": 1e-05, \"(5.5, 6.5)\": 0.00474, \"(6.5, 8.5)\": 0.01043, \"(8.5, 10.5)\": 0.01957, \"(10.5, 11.5)\": 0.02542, \"(11.5, 13.5)\": 0.04264, \"(13.5, 14.5)\": 0.04883, \"(14.5, 17.0)\": 0.05758}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02672, \"(0.5, 2.5)\": -0.0199, \"(2.5, 3.5)\": -0.0094, \"(3.5, 4.5)\": -0.00479, \"(4.5, 5.5)\": 0.00085, \"(5.5, 6.5)\": 0.00557, \"(6.5, 8.5)\": 0.01172, \"(8.5, 10.5)\": 0.02247, \"(10.5, 11.5)\": 0.02915, \"(11.5, 13.5)\": 0.04855, \"(13.5, 14.5)\": 0.05605, \"(14.5, 17.0)\": 0.06565}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(14.5, 17.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: ClimateChange\nFeature Type: continuous\nMeans: {\"(0.0, 1.5)\": -0.02549, \"(1.5, 2.5)\": -0.01575, \"(2.5, 3.5)\": -0.01061, \"(3.5, 4.5)\": -0.0046, \"(4.5, 5.5)\": 0.00059, \"(5.5, 6.5)\": 0.00567, \"(6.5, 7.5)\": 0.01201, \"(7.5, 9.5)\": 0.01601, \"(9.5, 10.5)\": 0.02531, \"(10.5, 11.5)\": 0.02956, \"(11.5, 12.5)\": 0.04031, \"(12.5, 14.0)\": 0.04423}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02735, \"(1.5, 2.5)\": -0.01647, \"(2.5, 3.5)\": -0.01101, \"(3.5, 4.5)\": -0.00502, \"(4.5, 5.5)\": 0.00018, \"(5.5, 6.5)\": 0.00528, \"(6.5, 7.5)\": 0.01139, \"(7.5, 9.5)\": 0.01505, \"(9.5, 10.5)\": 0.0236, \"(10.5, 11.5)\": 0.02677, \"(11.5, 12.5)\": 0.03846, \"(12.5, 14.0)\": 0.03359}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02363, \"(1.5, 2.5)\": -0.01503, \"(2.5, 3.5)\": -0.0102, \"(3.5, 4.5)\": -0.00418, \"(4.5, 5.5)\": 0.00101, \"(5.5, 6.5)\": 0.00607, \"(6.5, 7.5)\": 0.01263, \"(7.5, 9.5)\": 0.01697, \"(9.5, 10.5)\": 0.02702, \"(10.5, 11.5)\": 0.03236, \"(11.5, 12.5)\": 0.04216, \"(12.5, 14.0)\": 0.05488}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(12.5, 14.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Age\nFeature Type: continuous\nMeans: {\"(18.0, 32.5)\": 0.83, \"(32.5, 34.5)\": 0.681, \"(34.5, 37.5)\": 0.423, \"(37.5, 38.5)\": 0.281, \"(38.5, 39.5)\": 0.054, \"(39.5, 40.5)\": -0.193, \"(40.5, 41.5)\": -0.354, \"(41.5, 42.5)\": -0.494, \"(42.5, 44.5)\": -0.781, \"(44.5, 46.5)\": -1.075, \"(46.5, 48.5)\": -1.546, \"(48.5, 54.5)\": -1.717, \"(54.5, 56.5)\": -1.858, \"(56.5, 64.5)\": -1.707, \"(64.5, 66.5)\": -1.27, \"(66.5, 69.5)\": -1.118, \"(69.5, 70.5)\": -0.888, \"(70.5, 72.5)\": -0.587, \"(72.5, 74.5)\": -0.31, \"(74.5, 81.0)\": -0.157}\nLower Bounds (95%-Confidence Interval): {\"(18.0, 32.5)\": 0.581, \"(32.5, 34.5)\": 0.529, \"(34.5, 37.5)\": 0.367, \"(37.5, 38.5)\": 0.229, \"(38.5, 39.5)\": -0.051, \"(39.5, 40.5)\": -0.305, \"(40.5, 41.5)\": -0.462, \"(41.5, 42.5)\": -0.607, \"(42.5, 44.5)\": -0.855, \"(44.5, 46.5)\": -1.16, \"(46.5, 48.5)\": -1.704, \"(48.5, 54.5)\": -1.885, \"(54.5, 56.5)\": -2.031, \"(56.5, 64.5)\": -1.913, \"(64.5, 66.5)\": -1.66, \"(66.5, 69.5)\": -1.33, \"(69.5, 70.5)\": -1.222, \"(70.5, 72.5)\": -1.257, \"(72.5, 74.5)\": -1.055, \"(74.5, 81.0)\": -0.939}\nUpper Bounds (95%-Confidence Interval): {\"(18.0, 32.5)\": 1.079, \"(32.5, 34.5)\": 0.833, \"(34.5, 37.5)\": 0.48, \"(37.5, 38.5)\": 0.332, \"(38.5, 39.5)\": 0.159, \"(39.5, 40.5)\": -0.08, \"(40.5, 41.5)\": -0.246, \"(41.5, 42.5)\": -0.382, \"(42.5, 44.5)\": -0.706, \"(44.5, 46.5)\": -0.991, \"(46.5, 48.5)\": -1.387, \"(48.5, 54.5)\": -1.548, \"(54.5, 56.5)\": -1.684, \"(56.5, 64.5)\": -1.501, \"(64.5, 66.5)\": -0.88, \"(66.5, 69.5)\": -0.906, \"(69.5, 70.5)\": -0.554, \"(70.5, 72.5)\": 0.082, \"(72.5, 74.5)\": 0.436, \"(74.5, 81.0)\": 0.625}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(74.5, 81.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: AgriculturalPractices\nFeature Type: continuous\nMeans: {\"(0.0, 1.5)\": -0.02463, \"(1.5, 2.5)\": -0.01694, \"(2.5, 3.5)\": -0.01147, \"(3.5, 4.5)\": -0.00533, \"(4.5, 5.5)\": 0.00036, \"(5.5, 6.5)\": 0.00641, \"(6.5, 7.5)\": 0.01086, \"(7.5, 8.5)\": 0.01753, \"(8.5, 9.5)\": 0.02391, \"(9.5, 11.5)\": 0.03162, \"(11.5, 14.0)\": 0.0391, \"(14.0, 15.0)\": 0.05506}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02721, \"(1.5, 2.5)\": -0.01778, \"(2.5, 3.5)\": -0.01182, \"(3.5, 4.5)\": -0.00574, \"(4.5, 5.5)\": -9e-05, \"(5.5, 6.5)\": 0.00587, \"(6.5, 7.5)\": 0.01028, \"(7.5, 8.5)\": 0.01669, \"(8.5, 9.5)\": 0.02306, \"(9.5, 11.5)\": 0.02986, \"(11.5, 14.0)\": 0.03465, \"(14.0, 15.0)\": 0.03109}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02205, \"(1.5, 2.5)\": -0.0161, \"(2.5, 3.5)\": -0.01113, \"(3.5, 4.5)\": -0.00492, \"(4.5, 5.5)\": 0.0008, \"(5.5, 6.5)\": 0.00696, \"(6.5, 7.5)\": 0.01144, \"(7.5, 8.5)\": 0.01837, \"(8.5, 9.5)\": 0.02477, \"(9.5, 11.5)\": 0.03339, \"(11.5, 14.0)\": 0.04355, \"(14.0, 15.0)\": 0.07902}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(14.0, 15.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: IsActiveMember\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.555, \"(0.5, 1.0)\": 0.568}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.593, \"(0.5, 1.0)\": 0.529}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.518, \"(0.5, 1.0)\": 0.606}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.5, 1.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: compactness_worst\nFeature Type: continuous\nMeans: {\"(0.02729, 0.049945)\": -0.0578, \"(0.049945, 0.06971)\": -0.0099, \"(0.06971, 0.099305)\": -0.0565, \"(0.099305, 0.10635)\": -0.1408, \"(0.10635, 0.1243)\": -0.1882, \"(0.1243, 0.14795)\": -0.2357, \"(0.14795, 0.1507)\": -0.1883, \"(0.1507, 0.1861)\": -0.1381, \"(0.1861, 0.20124999999999998)\": -0.0918, \"(0.20124999999999998, 0.3358)\": -0.0443, \"(0.3358, 0.3456)\": 0.0027, \"(0.3456, 0.35755000000000003)\": 0.0649, \"(0.35755000000000003, 0.3703)\": 0.1151, \"(0.3703, 0.39235)\": 0.1642, \"(0.39235, 0.4087)\": 0.2124, \"(0.4087, 0.4229)\": 0.2605, \"(0.4229, 0.4486)\": 0.3109, \"(0.4486, 0.48865000000000003)\": 0.3586, \"(0.48865000000000003, 0.54825)\": 0.4132, \"(0.54825, 0.5892999999999999)\": 0.4651, \"(0.5892999999999999, 0.65835)\": 0.5154, \"(0.65835, 0.7680499999999999)\": 0.572, \"(0.7680499999999999, 0.99795)\": 0.6264, \"(0.99795, 1.058)\": 0.6748}\nLower Bounds (95%-Confidence Interval): {\"(0.02729, 0.049945)\": -0.8125, \"(0.049945, 0.06971)\": -0.7624, \"(0.06971, 0.099305)\": -0.6001, \"(0.099305, 0.10635)\": -0.4033, \"(0.10635, 0.1243)\": -0.4448, \"(0.1243, 0.14795)\": -0.4969, \"(0.14795, 0.1507)\": -0.4446, \"(0.1507, 0.1861)\": -0.2722, \"(0.1861, 0.20124999999999998)\": -0.1924, \"(0.20124999999999998, 0.3358)\": -0.2305, \"(0.3358, 0.3456)\": -0.1741, \"(0.3456, 0.35755000000000003)\": -0.068, \"(0.35755000000000003, 0.3703)\": 0.0047, \"(0.3703, 0.39235)\": 0.0473, \"(0.39235, 0.4087)\": 0.1107, \"(0.4087, 0.4229)\": 0.1686, \"(0.4229, 0.4486)\": 0.2243, \"(0.4486, 0.48865000000000003)\": 0.2736, \"(0.48865000000000003, 0.54825)\": 0.2405, \"(0.54825, 0.5892999999999999)\": 0.2819, \"(0.5892999999999999, 0.65835)\": 0.3155, \"(0.65835, 0.7680499999999999)\": 0.3513, \"(0.7680499999999999, 0.99795)\": 0.3892, \"(0.99795, 1.058)\": 0.4487}\nUpper Bounds (95%-Confidence Interval): {\"(0.02729, 0.049945)\": 0.6969, \"(0.049945, 0.06971)\": 0.7425, \"(0.06971, 0.099305)\": 0.487, \"(0.099305, 0.10635)\": 0.1218, \"(0.10635, 0.1243)\": 0.0684, \"(0.1243, 0.14795)\": 0.0254, \"(0.14795, 0.1507)\": 0.068, \"(0.1507, 0.1861)\": -0.0039, \"(0.1861, 0.20124999999999998)\": 0.0087, \"(0.20124999999999998, 0.3358)\": 0.1418, \"(0.3358, 0.3456)\": 0.1794, \"(0.3456, 0.35755000000000003)\": 0.1979, \"(0.35755000000000003, 0.3703)\": 0.2255, \"(0.3703, 0.39235)\": 0.2811, \"(0.39235, 0.4087)\": 0.314, \"(0.4087, 0.4229)\": 0.3524, \"(0.4229, 0.4486)\": 0.3975, \"(0.4486, 0.48865000000000003)\": 0.4436, \"(0.48865000000000003, 0.54825)\": 0.5859, \"(0.54825, 0.5892999999999999)\": 0.6484, \"(0.5892999999999999, 0.65835)\": 0.7153, \"(0.65835, 0.7680499999999999)\": 0.7927, \"(0.7680499999999999, 0.99795)\": 0.8637, \"(0.99795, 1.058)\": 0.9008}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.02729, 0.049945)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: FoodCourt\nFeature Type: continuous\nMeans: {\"(0.0, 593.5)\": -0.177, \"(593.5, 779.5)\": 0.043, \"(779.5, 1341.5)\": 0.27, \"(1341.5, 2175.5)\": 0.543, \"(2175.5, 3125.0)\": 0.863, \"(3125.0, 3637.0)\": 1.13, \"(3637.0, 4078.5)\": 1.479, \"(4078.5, 5218.5)\": 2.076, \"(5218.5, 6031.5)\": 1.81, \"(6031.5, 6171.5)\": 1.439, \"(6171.5, 8753.0)\": 2.236, \"(8753.0, 8824.0)\": 2.746, \"(8824.0, 10094.5)\": 3.43, \"(10094.5, 12683.5)\": 3.888, \"(12683.5, 27723.0)\": 4.131}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 593.5)\": -0.307, \"(593.5, 779.5)\": -0.11, \"(779.5, 1341.5)\": -0.04, \"(1341.5, 2175.5)\": -0.06, \"(2175.5, 3125.0)\": 0.404, \"(3125.0, 3637.0)\": 0.707, \"(3637.0, 4078.5)\": 0.742, \"(4078.5, 5218.5)\": 1.52, \"(5218.5, 6031.5)\": 1.485, \"(6031.5, 6171.5)\": 0.477, \"(6171.5, 8753.0)\": 1.548, \"(8753.0, 8824.0)\": 1.95, \"(8824.0, 10094.5)\": 2.626, \"(10094.5, 12683.5)\": 2.361, \"(12683.5, 27723.0)\": 2.558}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 593.5)\": -0.047, \"(593.5, 779.5)\": 0.196, \"(779.5, 1341.5)\": 0.58, \"(1341.5, 2175.5)\": 1.145, \"(2175.5, 3125.0)\": 1.322, \"(3125.0, 3637.0)\": 1.554, \"(3637.0, 4078.5)\": 2.216, \"(4078.5, 5218.5)\": 2.631, \"(5218.5, 6031.5)\": 2.135, \"(6031.5, 6171.5)\": 2.4, \"(6171.5, 8753.0)\": 2.925, \"(8753.0, 8824.0)\": 3.543, \"(8824.0, 10094.5)\": 4.234, \"(10094.5, 12683.5)\": 5.416, \"(12683.5, 27723.0)\": 5.705}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(12683.5, 27723.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: symmetry_mean\nFeature Type: continuous\nMeans: {\"(0.1167, 0.1384)\": -0.604, \"(0.1384, 0.14229999999999998)\": -0.55, \"(0.14229999999999998, 0.14565)\": -0.489, \"(0.14565, 0.1488)\": -0.428, \"(0.1488, 0.1507)\": -0.372, \"(0.1507, 0.15245)\": -0.316, \"(0.15245, 0.15375)\": -0.258, \"(0.15375, 0.15410000000000001)\": -0.087, \"(0.15410000000000001, 0.1545)\": -0.03, \"(0.1545, 0.15765)\": 0.279, \"(0.15765, 0.16625)\": 0.335, \"(0.16625, 0.16635)\": 0.258, \"(0.16635, 0.1684)\": 0.048, \"(0.1684, 0.17915)\": -0.007, \"(0.17915, 0.20355)\": -0.062, \"(0.20355, 0.20855)\": -0.005, \"(0.20855, 0.21105000000000002)\": 0.052, \"(0.21105000000000002, 0.21315)\": 0.107, \"(0.21315, 0.21705)\": 0.17, \"(0.21705, 0.22110000000000002)\": 0.234, \"(0.22110000000000002, 0.23020000000000002)\": 0.289, \"(0.23020000000000002, 0.2544)\": 0.347, \"(0.2544, 0.2626)\": 0.408, \"(0.2626, 0.304)\": 0.466}\nLower Bounds (95%-Confidence Interval): {\"(0.1167, 0.1384)\": -1.532, \"(0.1384, 0.14229999999999998)\": -1.437, \"(0.14229999999999998, 0.14565)\": -1.372, \"(0.14565, 0.1488)\": -1.313, \"(0.1488, 0.1507)\": -1.251, \"(0.1507, 0.15245)\": -1.195, \"(0.15245, 0.15375)\": -1.119, \"(0.15375, 0.15410000000000001)\": -0.701, \"(0.15410000000000001, 0.1545)\": -0.663, \"(0.1545, 0.15765)\": -0.664, \"(0.15765, 0.16625)\": -0.597, \"(0.16625, 0.16635)\": -0.613, \"(0.16635, 0.1684)\": -0.077, \"(0.1684, 0.17915)\": -0.11, \"(0.17915, 0.20355)\": -0.175, \"(0.20355, 0.20855)\": -0.112, \"(0.20855, 0.21105000000000002)\": -0.047, \"(0.21105000000000002, 0.21315)\": 0.003, \"(0.21315, 0.21705)\": 0.061, \"(0.21705, 0.22110000000000002)\": 0.118, \"(0.22110000000000002, 0.23020000000000002)\": 0.169, \"(0.23020000000000002, 0.2544)\": 0.191, \"(0.2544, 0.2626)\": 0.21, \"(0.2626, 0.304)\": 0.25}\nUpper Bounds (95%-Confidence Interval): {\"(0.1167, 0.1384)\": 0.323, \"(0.1384, 0.14229999999999998)\": 0.338, \"(0.14229999999999998, 0.14565)\": 0.394, \"(0.14565, 0.1488)\": 0.457, \"(0.1488, 0.1507)\": 0.507, \"(0.1507, 0.15245)\": 0.564, \"(0.15245, 0.15375)\": 0.603, \"(0.15375, 0.15410000000000001)\": 0.527, \"(0.15410000000000001, 0.1545)\": 0.602, \"(0.1545, 0.15765)\": 1.221, \"(0.15765, 0.16625)\": 1.266, \"(0.16625, 0.16635)\": 1.129, \"(0.16635, 0.1684)\": 0.174, \"(0.1684, 0.17915)\": 0.095, \"(0.17915, 0.20355)\": 0.05, \"(0.20355, 0.20855)\": 0.102, \"(0.20855, 0.21105000000000002)\": 0.151, \"(0.21105000000000002, 0.21315)\": 0.211, \"(0.21315, 0.21705)\": 0.279, \"(0.21705, 0.22110000000000002)\": 0.351, \"(0.22110000000000002, 0.23020000000000002)\": 0.408, \"(0.23020000000000002, 0.2544)\": 0.503, \"(0.2544, 0.2626)\": 0.606, \"(0.2626, 0.304)\": 0.681}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.1545, 0.15765)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: high_blood_pressure\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.1077, \"(0.5, 1.0)\": 0.1864}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.1574, \"(0.5, 1.0)\": 0.1003}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.058, \"(0.5, 1.0)\": 0.2724}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.5, 1.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: concavity_se\nFeature Type: continuous\nMeans: {\"(0.0, 0.001156)\": -0.6445, \"(0.001156, 0.002325)\": -0.6016, \"(0.002325, 0.0037635)\": -0.5599, \"(0.0037635, 0.0053165)\": -0.5149, \"(0.0053165, 0.0058905)\": -0.4651, \"(0.0058905, 0.006987999999999999)\": -0.4227, \"(0.006987999999999999, 0.0077405)\": -0.3808, \"(0.0077405, 0.008344500000000001)\": -0.3373, \"(0.008344500000000001, 0.009263500000000001)\": -0.2906, \"(0.009263500000000001, 0.010215)\": -0.246, \"(0.010215, 0.010705)\": -0.2028, \"(0.010705, 0.01122)\": -0.1484, \"(0.01122, 0.011625)\": -0.1022, \"(0.011625, 0.01191)\": -0.0592, \"(0.01191, 0.012455)\": -0.0118, \"(0.012455, 0.0203)\": 0.0471, \"(0.0203, 0.022565)\": 0.0914, \"(0.022565, 0.02983)\": 0.1347, \"(0.02983, 0.032535)\": 0.0347, \"(0.032535, 0.0338)\": -0.0071, \"(0.0338, 0.038565)\": 0.0604, \"(0.038565, 0.04418)\": 0.1065, \"(0.04418, 0.059305)\": 0.1494, \"(0.059305, 0.065775)\": 0.1044, \"(0.065775, 0.07794000000000001)\": 0.0533, \"(0.07794000000000001, 0.08089)\": 0.0097, \"(0.08089, 0.096205)\": -0.0573, \"(0.096205, 0.22865000000000002)\": -0.1001, \"(0.22865000000000002, 0.396)\": -0.1471}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.001156)\": -0.9396, \"(0.001156, 0.002325)\": -0.8658, \"(0.002325, 0.0037635)\": -0.8192, \"(0.0037635, 0.0053165)\": -0.7681, \"(0.0053165, 0.0058905)\": -0.716, \"(0.0058905, 0.006987999999999999)\": -0.6675, \"(0.006987999999999999, 0.0077405)\": -0.6156, \"(0.0077405, 0.008344500000000001)\": -0.5789, \"(0.008344500000000001, 0.009263500000000001)\": -0.5182, \"(0.009263500000000001, 0.010215)\": -0.4535, \"(0.010215, 0.010705)\": -0.4164, \"(0.010705, 0.01122)\": -0.3446, \"(0.01122, 0.011625)\": -0.2792, \"(0.011625, 0.01191)\": -0.2184, \"(0.01191, 0.012455)\": -0.172, \"(0.012455, 0.0203)\": -0.1411, \"(0.0203, 0.022565)\": -0.0146, \"(0.022565, 0.02983)\": 0.021, \"(0.02983, 0.032535)\": -0.4149, \"(0.032535, 0.0338)\": -0.4515, \"(0.0338, 0.038565)\": -0.0686, \"(0.038565, 0.04418)\": -0.0006, \"(0.04418, 0.059305)\": -0.0422, \"(0.059305, 0.065775)\": -0.0685, \"(0.065775, 0.07794000000000001)\": -0.1246, \"(0.07794000000000001, 0.08089)\": -0.1713, \"(0.08089, 0.096205)\": -0.2715, \"(0.096205, 0.22865000000000002)\": -0.3432, \"(0.22865000000000002, 0.396)\": -0.3958}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.001156)\": -0.3494, \"(0.001156, 0.002325)\": -0.3374, \"(0.002325, 0.0037635)\": -0.3006, \"(0.0037635, 0.0053165)\": -0.2618, \"(0.0053165, 0.0058905)\": -0.2142, \"(0.0058905, 0.006987999999999999)\": -0.1779, \"(0.006987999999999999, 0.0077405)\": -0.1459, \"(0.0077405, 0.008344500000000001)\": -0.0957, \"(0.008344500000000001, 0.009263500000000001)\": -0.063, \"(0.009263500000000001, 0.010215)\": -0.0385, \"(0.010215, 0.010705)\": 0.0109, \"(0.010705, 0.01122)\": 0.0478, \"(0.01122, 0.011625)\": 0.0749, \"(0.011625, 0.01191)\": 0.0999, \"(0.01191, 0.012455)\": 0.1484, \"(0.012455, 0.0203)\": 0.2352, \"(0.0203, 0.022565)\": 0.1973, \"(0.022565, 0.02983)\": 0.2485, \"(0.02983, 0.032535)\": 0.4843, \"(0.032535, 0.0338)\": 0.4374, \"(0.0338, 0.038565)\": 0.1895, \"(0.038565, 0.04418)\": 0.2135, \"(0.04418, 0.059305)\": 0.3411, \"(0.059305, 0.065775)\": 0.2772, \"(0.065775, 0.07794000000000001)\": 0.2312, \"(0.07794000000000001, 0.08089)\": 0.1907, \"(0.08089, 0.096205)\": 0.1569, \"(0.096205, 0.22865000000000002)\": 0.1429, \"(0.22865000000000002, 0.396)\": 0.1015}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.02983, 0.032535)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: DrainageSystems\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02593, \"(0.5, 1.5)\": -0.02211, \"(1.5, 2.5)\": -0.01611, \"(2.5, 3.5)\": -0.01125, \"(3.5, 4.5)\": -0.0047, \"(4.5, 5.5)\": 9e-05, \"(5.5, 6.5)\": 0.00652, \"(6.5, 8.5)\": 0.01219, \"(8.5, 10.5)\": 0.02253, \"(10.5, 11.5)\": 0.03412, \"(11.5, 12.5)\": 0.04015, \"(12.5, 14.0)\": 0.04564}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02795, \"(0.5, 1.5)\": -0.02324, \"(1.5, 2.5)\": -0.01672, \"(2.5, 3.5)\": -0.01177, \"(3.5, 4.5)\": -0.00504, \"(4.5, 5.5)\": -0.00021, \"(5.5, 6.5)\": 0.00613, \"(6.5, 8.5)\": 0.01137, \"(8.5, 10.5)\": 0.02139, \"(10.5, 11.5)\": 0.03184, \"(11.5, 12.5)\": 0.03703, \"(12.5, 14.0)\": 0.04222}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02391, \"(0.5, 1.5)\": -0.02097, \"(1.5, 2.5)\": -0.0155, \"(2.5, 3.5)\": -0.01073, \"(3.5, 4.5)\": -0.00435, \"(4.5, 5.5)\": 0.00039, \"(5.5, 6.5)\": 0.00691, \"(6.5, 8.5)\": 0.01301, \"(8.5, 10.5)\": 0.02367, \"(10.5, 11.5)\": 0.0364, \"(11.5, 12.5)\": 0.04328, \"(12.5, 14.0)\": 0.04907}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(12.5, 14.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: RiverManagement\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.0273, \"(0.5, 1.5)\": -0.02345, \"(1.5, 2.5)\": -0.01571, \"(2.5, 3.5)\": -0.01174, \"(3.5, 4.5)\": -0.00519, \"(4.5, 5.5)\": 0.00111, \"(5.5, 6.5)\": 0.00506, \"(6.5, 7.5)\": 0.01056, \"(7.5, 8.5)\": 0.01706, \"(8.5, 9.5)\": 0.02398, \"(9.5, 11.5)\": 0.02821, \"(11.5, 12.5)\": 0.03673, \"(12.5, 13.5)\": 0.01311, \"(13.5, 16.0)\": 0.03206}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02945, \"(0.5, 1.5)\": -0.02501, \"(1.5, 2.5)\": -0.01619, \"(2.5, 3.5)\": -0.0121, \"(3.5, 4.5)\": -0.00549, \"(4.5, 5.5)\": 0.00069, \"(5.5, 6.5)\": 0.00469, \"(6.5, 7.5)\": 0.00991, \"(7.5, 8.5)\": 0.01638, \"(8.5, 9.5)\": 0.02306, \"(9.5, 11.5)\": 0.0266, \"(11.5, 12.5)\": 0.02982, \"(12.5, 13.5)\": -0.01689, \"(13.5, 16.0)\": 0.01715}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02515, \"(0.5, 1.5)\": -0.0219, \"(1.5, 2.5)\": -0.01524, \"(2.5, 3.5)\": -0.01139, \"(3.5, 4.5)\": -0.0049, \"(4.5, 5.5)\": 0.00152, \"(5.5, 6.5)\": 0.00544, \"(6.5, 7.5)\": 0.01121, \"(7.5, 8.5)\": 0.01774, \"(8.5, 9.5)\": 0.0249, \"(9.5, 11.5)\": 0.02981, \"(11.5, 12.5)\": 0.04363, \"(12.5, 13.5)\": 0.04312, \"(13.5, 16.0)\": 0.04696}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(12.5, 13.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: platelets\nFeature Type: continuous\nMeans: {\"(25100.0, 27700.0)\": -1.004, \"(27700.0, 34450.0)\": -0.687, \"(34450.0, 42200.0)\": 0.328, \"(42200.0, 56500.0)\": 1.717, \"(56500.0, 66050.0)\": 2.769, \"(66050.0, 74000.0)\": 2.195, \"(74000.0, 95500.0)\": 2.956, \"(95500.0, 104500.0)\": -0.265, \"(104500.0, 144000.0)\": -0.585, \"(144000.0, 150500.0)\": -0.895, \"(150500.0, 154000.0)\": 2.322, \"(154000.0, 169000.0)\": 0.469, \"(169000.0, 184500.0)\": -1.612, \"(184500.0, 195000.0)\": 1.111, \"(195000.0, 199000.0)\": 3.01, \"(199000.0, 200500.0)\": 1.837, \"(200500.0, 214000.0)\": 0.403, \"(214000.0, 217500.0)\": -0.825, \"(217500.0, 218500.0)\": -1.399, \"(218500.0, 220500.0)\": 0.341, \"(220500.0, 222500.0)\": 0.978, \"(222500.0, 226500.0)\": 1.584, \"(226500.0, 241500.0)\": 0.175, \"(241500.0, 242500.0)\": 0.642, \"(242500.0, 243500.0)\": 1.107, \"(243500.0, 244500.0)\": 1.516, \"(244500.0, 252500.0)\": -2.19, \"(252500.0, 261000.0)\": -0.878, \"(261000.0, 274500.0)\": -0.145, \"(274500.0, 283500.0)\": -0.968, \"(283500.0, 287500.0)\": 0.203, \"(287500.0, 289500.0)\": 1.032, \"(289500.0, 302500.0)\": -1.296, \"(302500.0, 305500.0)\": -2.984, \"(305500.0, 307000.0)\": 0.876, \"(307000.0, 332000.0)\": 0.368, \"(332000.0, 335000.0)\": 1.21, \"(335000.0, 343000.0)\": 0.8, \"(343000.0, 350500.0)\": -0.573, \"(350500.0, 354500.0)\": 3.0, \"(354500.0, 383500.0)\": -0.119, \"(383500.0, 449500.0)\": 0.655, \"(449500.0, 471000.0)\": 1.527, \"(471000.0, 500500.0)\": -2.247, \"(500500.0, 582000.0)\": -0.442, \"(582000.0, 675500.0)\": 2.645, \"(675500.0, 796000.0)\": 2.314, \"(796000.0, 850000.0)\": -0.709}\nLower Bounds (95%-Confidence Interval): {\"(25100.0, 27700.0)\": -1.75, \"(27700.0, 34450.0)\": -1.54, \"(34450.0, 42200.0)\": -0.532, \"(42200.0, 56500.0)\": 0.992, \"(56500.0, 66050.0)\": 1.538, \"(66050.0, 74000.0)\": 1.537, \"(74000.0, 95500.0)\": 1.91, \"(95500.0, 104500.0)\": -1.642, \"(104500.0, 144000.0)\": -1.428, \"(144000.0, 150500.0)\": -1.74, \"(150500.0, 154000.0)\": 1.125, \"(154000.0, 169000.0)\": 0.027, \"(169000.0, 184500.0)\": -2.523, \"(184500.0, 195000.0)\": 0.214, \"(195000.0, 199000.0)\": 0.239, \"(199000.0, 200500.0)\": 0.581, \"(200500.0, 214000.0)\": -0.252, \"(214000.0, 217500.0)\": -2.007, \"(217500.0, 218500.0)\": -3.583, \"(218500.0, 220500.0)\": 0.076, \"(220500.0, 222500.0)\": 0.244, \"(222500.0, 226500.0)\": -0.038, \"(226500.0, 241500.0)\": -0.123, \"(241500.0, 242500.0)\": 0.22, \"(242500.0, 243500.0)\": 0.116, \"(243500.0, 244500.0)\": 0.265, \"(244500.0, 252500.0)\": -4.008, \"(252500.0, 261000.0)\": -1.287, \"(261000.0, 274500.0)\": -0.465, \"(274500.0, 283500.0)\": -1.829, \"(283500.0, 287500.0)\": -1.587, \"(287500.0, 289500.0)\": -0.951, \"(289500.0, 302500.0)\": -1.857, \"(302500.0, 305500.0)\": -4.201, \"(305500.0, 307000.0)\": 0.125, \"(307000.0, 332000.0)\": -0.181, \"(332000.0, 335000.0)\": -0.179, \"(335000.0, 343000.0)\": 0.105, \"(343000.0, 350500.0)\": -1.469, \"(350500.0, 354500.0)\": 1.748, \"(354500.0, 383500.0)\": -0.848, \"(383500.0, 449500.0)\": 0.242, \"(449500.0, 471000.0)\": -2.033, \"(471000.0, 500500.0)\": -5.177, \"(500500.0, 582000.0)\": -1.795, \"(582000.0, 675500.0)\": 1.501, \"(675500.0, 796000.0)\": 0.104, \"(796000.0, 850000.0)\": -1.557}\nUpper Bounds (95%-Confidence Interval): {\"(25100.0, 27700.0)\": -0.258, \"(27700.0, 34450.0)\": 0.165, \"(34450.0, 42200.0)\": 1.188, \"(42200.0, 56500.0)\": 2.441, \"(56500.0, 66050.0)\": 4.0, \"(66050.0, 74000.0)\": 2.853, \"(74000.0, 95500.0)\": 4.001, \"(95500.0, 104500.0)\": 1.113, \"(104500.0, 144000.0)\": 0.258, \"(144000.0, 150500.0)\": -0.049, \"(150500.0, 154000.0)\": 3.518, \"(154000.0, 169000.0)\": 0.911, \"(169000.0, 184500.0)\": -0.702, \"(184500.0, 195000.0)\": 2.008, \"(195000.0, 199000.0)\": 5.781, \"(199000.0, 200500.0)\": 3.093, \"(200500.0, 214000.0)\": 1.058, \"(214000.0, 217500.0)\": 0.356, \"(217500.0, 218500.0)\": 0.785, \"(218500.0, 220500.0)\": 0.606, \"(220500.0, 222500.0)\": 1.711, \"(222500.0, 226500.0)\": 3.206, \"(226500.0, 241500.0)\": 0.472, \"(241500.0, 242500.0)\": 1.064, \"(242500.0, 243500.0)\": 2.099, \"(243500.0, 244500.0)\": 2.766, \"(244500.0, 252500.0)\": -0.372, \"(252500.0, 261000.0)\": -0.468, \"(261000.0, 274500.0)\": 0.176, \"(274500.0, 283500.0)\": -0.106, \"(283500.0, 287500.0)\": 1.993, \"(287500.0, 289500.0)\": 3.014, \"(289500.0, 302500.0)\": -0.734, \"(302500.0, 305500.0)\": -1.767, \"(305500.0, 307000.0)\": 1.626, \"(307000.0, 332000.0)\": 0.917, \"(332000.0, 335000.0)\": 2.599, \"(335000.0, 343000.0)\": 1.496, \"(343000.0, 350500.0)\": 0.324, \"(350500.0, 354500.0)\": 4.251, \"(354500.0, 383500.0)\": 0.609, \"(383500.0, 449500.0)\": 1.068, \"(449500.0, 471000.0)\": 5.088, \"(471000.0, 500500.0)\": 0.684, \"(500500.0, 582000.0)\": 0.912, \"(582000.0, 675500.0)\": 3.789, \"(675500.0, 796000.0)\": 4.525, \"(796000.0, 850000.0)\": 0.138}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(449500.0, 471000.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: smoothness_worst\nFeature Type: continuous\nMeans: {\"(0.07117, 0.09376000000000001)\": -1.298, \"(0.09376000000000001, 0.099705)\": -1.161, \"(0.099705, 0.10519999999999999)\": -1.024, \"(0.10519999999999999, 0.10825)\": -0.889, \"(0.10825, 0.11549999999999999)\": -0.527, \"(0.11549999999999999, 0.12345)\": -0.394, \"(0.12345, 0.13074999999999998)\": -0.26, \"(0.13074999999999998, 0.13585)\": -0.124, \"(0.13585, 0.13640000000000002)\": 0.011, \"(0.13640000000000002, 0.13845000000000002)\": 0.154, \"(0.13845000000000002, 0.14065)\": 0.288, \"(0.14065, 0.14635)\": 0.439, \"(0.14635, 0.15585)\": 0.574, \"(0.15585, 0.16885)\": 0.708, \"(0.16885, 0.17825)\": 0.846, \"(0.17825, 0.19574999999999998)\": 1.17, \"(0.19574999999999998, 0.2226)\": 1.304}\nLower Bounds (95%-Confidence Interval): {\"(0.07117, 0.09376000000000001)\": -2.26, \"(0.09376000000000001, 0.099705)\": -2.132, \"(0.099705, 0.10519999999999999)\": -2.009, \"(0.10519999999999999, 0.10825)\": -1.875, \"(0.10825, 0.11549999999999999)\": -0.765, \"(0.11549999999999999, 0.12345)\": -0.589, \"(0.12345, 0.13074999999999998)\": -0.435, \"(0.13074999999999998, 0.13585)\": -0.384, \"(0.13585, 0.13640000000000002)\": -0.325, \"(0.13640000000000002, 0.13845000000000002)\": -0.247, \"(0.13845000000000002, 0.14065)\": -0.076, \"(0.14065, 0.14635)\": -0.12, \"(0.14635, 0.15585)\": 0.165, \"(0.15585, 0.16885)\": 0.27, \"(0.16885, 0.17825)\": 0.402, \"(0.17825, 0.19574999999999998)\": -0.484, \"(0.19574999999999998, 0.2226)\": -0.349}\nUpper Bounds (95%-Confidence Interval): {\"(0.07117, 0.09376000000000001)\": -0.336, \"(0.09376000000000001, 0.099705)\": -0.19, \"(0.099705, 0.10519999999999999)\": -0.039, \"(0.10519999999999999, 0.10825)\": 0.096, \"(0.10825, 0.11549999999999999)\": -0.289, \"(0.11549999999999999, 0.12345)\": -0.2, \"(0.12345, 0.13074999999999998)\": -0.086, \"(0.13074999999999998, 0.13585)\": 0.136, \"(0.13585, 0.13640000000000002)\": 0.348, \"(0.13640000000000002, 0.13845000000000002)\": 0.556, \"(0.13845000000000002, 0.14065)\": 0.652, \"(0.14065, 0.14635)\": 0.997, \"(0.14635, 0.15585)\": 0.983, \"(0.15585, 0.16885)\": 1.146, \"(0.16885, 0.17825)\": 1.289, \"(0.17825, 0.19574999999999998)\": 2.823, \"(0.19574999999999998, 0.2226)\": 2.957}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.17825, 0.19574999999999998)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: sepal_length\nFeature Type: continuous\nMeans: {\"(4.3, 4.55)\": 3.328, \"(4.55, 4.75)\": 2.995, \"(4.75, 4.85)\": 2.698, \"(4.85, 5.05)\": 1.665, \"(5.05, 5.25)\": 1.371, \"(5.25, 5.45)\": 1.085, \"(5.45, 5.55)\": 0.339, \"(5.55, 5.75)\": -0.057, \"(5.75, 5.85)\": -0.39, \"(5.85, 6.15)\": -0.757, \"(6.15, 6.45)\": -1.149, \"(6.45, 6.85)\": -1.436, \"(6.85, 7.7)\": -1.718}\nLower Bounds (95%-Confidence Interval): {\"(4.3, 4.55)\": 3.22, \"(4.55, 4.75)\": 2.846, \"(4.75, 4.85)\": 2.54, \"(4.85, 5.05)\": 1.185, \"(5.05, 5.25)\": 1.214, \"(5.25, 5.45)\": 0.892, \"(5.45, 5.55)\": -0.164, \"(5.55, 5.75)\": -0.32, \"(5.75, 5.85)\": -0.665, \"(5.85, 6.15)\": -0.888, \"(6.15, 6.45)\": -1.29, \"(6.45, 6.85)\": -1.575, \"(6.85, 7.7)\": -1.814}\nUpper Bounds (95%-Confidence Interval): {\"(4.3, 4.55)\": 3.437, \"(4.55, 4.75)\": 3.144, \"(4.75, 4.85)\": 2.857, \"(4.85, 5.05)\": 2.145, \"(5.05, 5.25)\": 1.528, \"(5.25, 5.45)\": 1.277, \"(5.45, 5.55)\": 0.843, \"(5.55, 5.75)\": 0.206, \"(5.75, 5.85)\": -0.116, \"(5.85, 6.15)\": -0.627, \"(6.15, 6.45)\": -1.009, \"(6.45, 6.85)\": -1.298, \"(6.85, 7.7)\": -1.623}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(5.45, 5.55)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: EducationNum\nFeature Type: continuous\nMeans: {\"(1.0, 1.5)\": -4.746, \"(1.5, 4.5)\": -1.252, \"(4.5, 6.5)\": -0.882, \"(6.5, 9.5)\": -0.483, \"(9.5, 11.5)\": -0.093, \"(11.5, 13.5)\": 0.276, \"(13.5, 14.5)\": 0.863, \"(14.5, 16.0)\": 1.487}\nLower Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -6.411, \"(1.5, 4.5)\": -1.52, \"(4.5, 6.5)\": -0.99, \"(6.5, 9.5)\": -0.541, \"(9.5, 11.5)\": -0.138, \"(11.5, 13.5)\": 0.205, \"(13.5, 14.5)\": 0.788, \"(14.5, 16.0)\": 1.332}\nUpper Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -3.082, \"(1.5, 4.5)\": -0.984, \"(4.5, 6.5)\": -0.775, \"(6.5, 9.5)\": -0.425, \"(9.5, 11.5)\": -0.049, \"(11.5, 13.5)\": 0.347, \"(13.5, 14.5)\": 0.938, \"(14.5, 16.0)\": 1.641}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(1.0, 1.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: total_rooms\nFeature Type: continuous\nMeans: {\"(2.0, 23.0)\": -70808.9, \"(23.0, 38.5)\": -78966.6, \"(38.5, 48.5)\": -28602.1, \"(48.5, 119.0)\": -47079.6, \"(119.0, 163.0)\": -52692.3, \"(163.0, 186.5)\": -60093.0, \"(186.5, 223.5)\": -51150.5, \"(223.5, 239.5)\": -39728.1, \"(239.5, 248.5)\": -7038.8, \"(248.5, 265.5)\": -691.1, \"(265.5, 280.5)\": -14052.2, \"(280.5, 342.5)\": -35705.6, \"(342.5, 364.5)\": -24578.4, \"(364.5, 385.5)\": -34007.7, \"(385.5, 406.5)\": -46655.0, \"(406.5, 413.5)\": -17805.2, \"(413.5, 443.5)\": -12192.7, \"(443.5, 452.5)\": -22779.7, \"(452.5, 502.5)\": -30652.6, \"(502.5, 508.5)\": -25165.4, \"(508.5, 515.5)\": -12943.4, \"(515.5, 1152.5)\": -21645.3, \"(1152.5, 1239.5)\": -16264.4, \"(1239.5, 1245.5)\": -7023.2, \"(1245.5, 1619.5)\": -12855.2, \"(1619.5, 1944.5)\": -7415.6, \"(1944.5, 2330.5)\": -1233.9, \"(2330.5, 2710.5)\": 4370.8, \"(2710.5, 2834.5)\": 9739.0, \"(2834.5, 2838.5)\": 16667.1, \"(2838.5, 3577.5)\": 10096.4, \"(3577.5, 5401.0)\": 15549.4, \"(5401.0, 5535.5)\": 24928.2, \"(5535.5, 9961.0)\": 19069.3, \"(9961.0, 18662.0)\": 26262.6, \"(18662.0, 39320.0)\": 20736.3}\nLower Bounds (95%-Confidence Interval): {\"(2.0, 23.0)\": -91545.9, \"(23.0, 38.5)\": -102966.4, \"(38.5, 48.5)\": -57179.9, \"(48.5, 119.0)\": -64507.9, \"(119.0, 163.0)\": -67051.1, \"(163.0, 186.5)\": -74986.7, \"(186.5, 223.5)\": -62447.2, \"(223.5, 239.5)\": -55573.0, \"(239.5, 248.5)\": -34485.5, \"(248.5, 265.5)\": -18815.6, \"(265.5, 280.5)\": -35576.3, \"(280.5, 342.5)\": -44957.9, \"(342.5, 364.5)\": -36592.4, \"(364.5, 385.5)\": -39620.4, \"(385.5, 406.5)\": -54434.9, \"(406.5, 413.5)\": -28898.3, \"(413.5, 443.5)\": -21926.2, \"(443.5, 452.5)\": -34828.5, \"(452.5, 502.5)\": -40304.3, \"(502.5, 508.5)\": -35649.5, \"(508.5, 515.5)\": -27403.5, \"(515.5, 1152.5)\": -28456.5, \"(1152.5, 1239.5)\": -20918.2, \"(1239.5, 1245.5)\": -15907.4, \"(1245.5, 1619.5)\": -19943.7, \"(1619.5, 1944.5)\": -13063.6, \"(1944.5, 2330.5)\": -8595.8, \"(2330.5, 2710.5)\": 2936.6, \"(2710.5, 2834.5)\": 7069.8, \"(2834.5, 2838.5)\": 1263.0, \"(2838.5, 3577.5)\": 7025.1, \"(3577.5, 5401.0)\": 10287.4, \"(5401.0, 5535.5)\": 10519.1, \"(5535.5, 9961.0)\": 12536.6, \"(9961.0, 18662.0)\": 16596.5, \"(18662.0, 39320.0)\": 17189.5}\nUpper Bounds (95%-Confidence Interval): {\"(2.0, 23.0)\": -50072.0, \"(23.0, 38.5)\": -54966.9, \"(38.5, 48.5)\": -24.3, \"(48.5, 119.0)\": -29651.4, \"(119.0, 163.0)\": -38333.5, \"(163.0, 186.5)\": -45199.3, \"(186.5, 223.5)\": -39853.9, \"(223.5, 239.5)\": -23883.2, \"(239.5, 248.5)\": 20408.0, \"(248.5, 265.5)\": 17433.4, \"(265.5, 280.5)\": 7471.9, \"(280.5, 342.5)\": -26453.2, \"(342.5, 364.5)\": -12564.3, \"(364.5, 385.5)\": -28395.1, \"(385.5, 406.5)\": -38875.1, \"(406.5, 413.5)\": -6712.1, \"(413.5, 443.5)\": -2459.1, \"(443.5, 452.5)\": -10730.8, \"(452.5, 502.5)\": -21000.9, \"(502.5, 508.5)\": -14681.3, \"(508.5, 515.5)\": 1516.8, \"(515.5, 1152.5)\": -14834.1, \"(1152.5, 1239.5)\": -11610.6, \"(1239.5, 1245.5)\": 1860.9, \"(1245.5, 1619.5)\": -5766.8, \"(1619.5, 1944.5)\": -1767.7, \"(1944.5, 2330.5)\": 6128.1, \"(2330.5, 2710.5)\": 5805.0, \"(2710.5, 2834.5)\": 12408.3, \"(2834.5, 2838.5)\": 32071.2, \"(2838.5, 3577.5)\": 13167.8, \"(3577.5, 5401.0)\": 20811.4, \"(5401.0, 5535.5)\": 39337.3, \"(5535.5, 9961.0)\": 25602.1, \"(9961.0, 18662.0)\": 35928.6, \"(18662.0, 39320.0)\": 24283.0}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(38.5, 48.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Fare\nFeature Type: continuous\nMeans: {\"(0.0, 6.325)\": -1.425, \"(6.325, 7.8500000000000005)\": -1.303, \"(7.8500000000000005, 9.256250000000001)\": -0.472, \"(9.256250000000001, 10.48125)\": -0.602, \"(10.48125, 12.9375)\": -0.14, \"(12.9375, 25.79375)\": 0.225, \"(25.79375, 26.46875)\": 0.355, \"(26.46875, 27.7354)\": 0.207, \"(27.7354, 29.85)\": -0.238, \"(29.85, 31.6604)\": 0.051, \"(31.6604, 55.22085)\": -0.075, \"(55.22085, 89.5521)\": 0.041, \"(89.5521, 149.0354)\": 0.152, \"(149.0354, 387.6646)\": -0.029, \"(387.6646, 512.3292)\": 0.808}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 6.325)\": -3.39, \"(6.325, 7.8500000000000005)\": -3.252, \"(7.8500000000000005, 9.256250000000001)\": -1.321, \"(9.256250000000001, 10.48125)\": -1.756, \"(10.48125, 12.9375)\": -0.444, \"(12.9375, 25.79375)\": -0.464, \"(25.79375, 26.46875)\": -0.48, \"(26.46875, 27.7354)\": -0.42, \"(27.7354, 29.85)\": -1.008, \"(29.85, 31.6604)\": -0.616, \"(31.6604, 55.22085)\": -0.278, \"(55.22085, 89.5521)\": -0.095, \"(89.5521, 149.0354)\": -0.062, \"(149.0354, 387.6646)\": -0.493, \"(387.6646, 512.3292)\": -0.839}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 6.325)\": 0.54, \"(6.325, 7.8500000000000005)\": 0.645, \"(7.8500000000000005, 9.256250000000001)\": 0.377, \"(9.256250000000001, 10.48125)\": 0.553, \"(10.48125, 12.9375)\": 0.163, \"(12.9375, 25.79375)\": 0.913, \"(25.79375, 26.46875)\": 1.191, \"(26.46875, 27.7354)\": 0.833, \"(27.7354, 29.85)\": 0.533, \"(29.85, 31.6604)\": 0.718, \"(31.6604, 55.22085)\": 0.127, \"(55.22085, 89.5521)\": 0.176, \"(89.5521, 149.0354)\": 0.367, \"(149.0354, 387.6646)\": 0.436, \"(387.6646, 512.3292)\": 2.455}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.0, 6.325)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: compactness_se\nFeature Type: continuous\nMeans: {\"(0.002252, 0.0046765)\": -0.0693, \"(0.0046765, 0.005634)\": -0.0214, \"(0.005634, 0.006059500000000001)\": 0.0214, \"(0.006059500000000001, 0.006774499999999999)\": 0.0648, \"(0.006774499999999999, 0.0072375)\": 0.1132, \"(0.0072375, 0.008034)\": 0.1583, \"(0.008034, 0.0082145)\": 0.2045, \"(0.0082145, 0.0085705)\": 0.2482, \"(0.0085705, 0.0089915)\": 0.2969, \"(0.0089915, 0.01089)\": 0.3467, \"(0.01089, 0.011715)\": 0.3948, \"(0.011715, 0.012025000000000001)\": 0.3506, \"(0.012025000000000001, 0.012535000000000001)\": 0.2891, \"(0.012535000000000001, 0.013225)\": 0.244, \"(0.013225, 0.014275)\": 0.2001, \"(0.014275, 0.015615)\": 0.1571, \"(0.015615, 0.017669999999999998)\": 0.1142, \"(0.017669999999999998, 0.020155)\": 0.0681, \"(0.020155, 0.022855)\": 0.0256, \"(0.022855, 0.02586)\": -0.0272, \"(0.02586, 0.027540000000000002)\": -0.098, \"(0.027540000000000002, 0.038220000000000004)\": -0.1414, \"(0.038220000000000004, 0.039245)\": -0.1853, \"(0.039245, 0.040514999999999995)\": -0.2301, \"(0.040514999999999995, 0.04309)\": -0.2754, \"(0.04309, 0.04922)\": -0.3233, \"(0.04922, 0.068925)\": -0.3675, \"(0.068925, 0.1354)\": -0.4112}\nLower Bounds (95%-Confidence Interval): {\"(0.002252, 0.0046765)\": -0.2881, \"(0.0046765, 0.005634)\": -0.2345, \"(0.005634, 0.006059500000000001)\": -0.1933, \"(0.006059500000000001, 0.006774499999999999)\": -0.1451, \"(0.006774499999999999, 0.0072375)\": -0.0877, \"(0.0072375, 0.008034)\": -0.0418, \"(0.008034, 0.0082145)\": -0.0092, \"(0.0082145, 0.0085705)\": 0.0302, \"(0.0085705, 0.0089915)\": 0.0617, \"(0.0089915, 0.01089)\": 0.105, \"(0.01089, 0.011715)\": 0.1231, \"(0.011715, 0.012025000000000001)\": 0.0874, \"(0.012025000000000001, 0.012535000000000001)\": 0.097, \"(0.012535000000000001, 0.013225)\": 0.063, \"(0.013225, 0.014275)\": 0.031, \"(0.014275, 0.015615)\": 0.0018, \"(0.015615, 0.017669999999999998)\": -0.0333, \"(0.017669999999999998, 0.020155)\": -0.0326, \"(0.020155, 0.022855)\": -0.0543, \"(0.022855, 0.02586)\": -0.1745, \"(0.02586, 0.027540000000000002)\": -0.258, \"(0.027540000000000002, 0.038220000000000004)\": -0.3097, \"(0.038220000000000004, 0.039245)\": -0.326, \"(0.039245, 0.040514999999999995)\": -0.3788, \"(0.040514999999999995, 0.04309)\": -0.4514, \"(0.04309, 0.04922)\": -0.5306, \"(0.04922, 0.068925)\": -0.5903, \"(0.068925, 0.1354)\": -0.6732}\nUpper Bounds (95%-Confidence Interval): {\"(0.002252, 0.0046765)\": 0.1496, \"(0.0046765, 0.005634)\": 0.1917, \"(0.005634, 0.006059500000000001)\": 0.2361, \"(0.006059500000000001, 0.006774499999999999)\": 0.2747, \"(0.006774499999999999, 0.0072375)\": 0.3141, \"(0.0072375, 0.008034)\": 0.3584, \"(0.008034, 0.0082145)\": 0.4182, \"(0.0082145, 0.0085705)\": 0.4662, \"(0.0085705, 0.0089915)\": 0.5321, \"(0.0089915, 0.01089)\": 0.5884, \"(0.01089, 0.011715)\": 0.6664, \"(0.011715, 0.012025000000000001)\": 0.6138, \"(0.012025000000000001, 0.012535000000000001)\": 0.4812, \"(0.012535000000000001, 0.013225)\": 0.4251, \"(0.013225, 0.014275)\": 0.3692, \"(0.014275, 0.015615)\": 0.3124, \"(0.015615, 0.017669999999999998)\": 0.2617, \"(0.017669999999999998, 0.020155)\": 0.1689, \"(0.020155, 0.022855)\": 0.1055, \"(0.022855, 0.02586)\": 0.1202, \"(0.02586, 0.027540000000000002)\": 0.062, \"(0.027540000000000002, 0.038220000000000004)\": 0.027, \"(0.038220000000000004, 0.039245)\": -0.0446, \"(0.039245, 0.040514999999999995)\": -0.0815, \"(0.040514999999999995, 0.04309)\": -0.0993, \"(0.04309, 0.04922)\": -0.1161, \"(0.04922, 0.068925)\": -0.1448, \"(0.068925, 0.1354)\": -0.1492}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.01089, 0.011715)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: radius_se\nFeature Type: continuous\nMeans: {\"(0.1115, 0.15015)\": -0.773, \"(0.15015, 0.16904999999999998)\": -0.686, \"(0.16904999999999998, 0.1795)\": -0.589, \"(0.1795, 0.18535000000000001)\": -0.499, \"(0.18535000000000001, 0.19345)\": -0.412, \"(0.19345, 0.2103)\": -0.275, \"(0.2103, 0.2329)\": -0.187, \"(0.2329, 0.2939)\": -0.102, \"(0.2939, 0.368)\": -0.186, \"(0.368, 0.38585)\": -0.066, \"(0.38585, 0.42025)\": 0.064, \"(0.42025, 0.46775)\": 0.15, \"(0.46775, 0.54785)\": 0.239, \"(0.54785, 0.5881000000000001)\": 0.334, \"(0.5881000000000001, 0.66425)\": 0.422, \"(0.66425, 0.7562)\": 0.51, \"(0.7562, 0.9131)\": 0.594, \"(0.9131, 1.065)\": 0.683, \"(1.065, 1.2915)\": 0.774, \"(1.2915, 2.873)\": 0.866}\nLower Bounds (95%-Confidence Interval): {\"(0.1115, 0.15015)\": -1.244, \"(0.15015, 0.16904999999999998)\": -1.125, \"(0.16904999999999998, 0.1795)\": -1.008, \"(0.1795, 0.18535000000000001)\": -0.904, \"(0.18535000000000001, 0.19345)\": -0.8, \"(0.19345, 0.2103)\": -0.449, \"(0.2103, 0.2329)\": -0.273, \"(0.2329, 0.2939)\": -0.492, \"(0.2939, 0.368)\": -0.769, \"(0.368, 0.38585)\": -0.437, \"(0.38585, 0.42025)\": -0.188, \"(0.42025, 0.46775)\": -0.119, \"(0.46775, 0.54785)\": -0.037, \"(0.54785, 0.5881000000000001)\": -0.09, \"(0.5881000000000001, 0.66425)\": -0.016, \"(0.66425, 0.7562)\": 0.051, \"(0.7562, 0.9131)\": 0.051, \"(0.9131, 1.065)\": 0.113, \"(1.065, 1.2915)\": 0.123, \"(1.2915, 2.873)\": 0.198}\nUpper Bounds (95%-Confidence Interval): {\"(0.1115, 0.15015)\": -0.302, \"(0.15015, 0.16904999999999998)\": -0.247, \"(0.16904999999999998, 0.1795)\": -0.169, \"(0.1795, 0.18535000000000001)\": -0.094, \"(0.18535000000000001, 0.19345)\": -0.024, \"(0.19345, 0.2103)\": -0.1, \"(0.2103, 0.2329)\": -0.101, \"(0.2329, 0.2939)\": 0.289, \"(0.2939, 0.368)\": 0.396, \"(0.368, 0.38585)\": 0.304, \"(0.38585, 0.42025)\": 0.315, \"(0.42025, 0.46775)\": 0.42, \"(0.46775, 0.54785)\": 0.514, \"(0.54785, 0.5881000000000001)\": 0.758, \"(0.5881000000000001, 0.66425)\": 0.86, \"(0.66425, 0.7562)\": 0.968, \"(0.7562, 0.9131)\": 1.137, \"(0.9131, 1.065)\": 1.253, \"(1.065, 1.2915)\": 1.425, \"(1.2915, 2.873)\": 1.533}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(1.2915, 2.873)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: area_mean\nFeature Type: continuous\nMeans: {\"(143.5, 259.35)\": -0.759, \"(259.35, 289.4)\": -0.662, \"(289.4, 319.15)\": -0.567, \"(319.15, 348.3)\": -0.464, \"(348.3, 496.5)\": -0.368, \"(496.5, 548.75)\": -0.271, \"(548.75, 606.0)\": -0.173, \"(606.0, 696.25)\": -0.076, \"(696.25, 806.1500000000001)\": 0.309, \"(806.1500000000001, 901.8)\": 0.405, \"(901.8, 959.4000000000001)\": 0.51, \"(959.4000000000001, 1054.0)\": 0.607, \"(1054.0, 1150.0)\": 0.707, \"(1150.0, 1248.5)\": 0.806, \"(1248.5, 1341.0)\": 0.911, \"(1341.0, 1801.0)\": 1.01, \"(1801.0, 2501.0)\": 1.109}\nLower Bounds (95%-Confidence Interval): {\"(143.5, 259.35)\": -1.038, \"(259.35, 289.4)\": -0.892, \"(289.4, 319.15)\": -0.754, \"(319.15, 348.3)\": -0.634, \"(348.3, 496.5)\": -0.559, \"(496.5, 548.75)\": -0.436, \"(548.75, 606.0)\": -0.338, \"(606.0, 696.25)\": -0.727, \"(696.25, 806.1500000000001)\": -0.252, \"(806.1500000000001, 901.8)\": -0.022, \"(901.8, 959.4000000000001)\": 0.058, \"(959.4000000000001, 1054.0)\": 0.141, \"(1054.0, 1150.0)\": 0.243, \"(1150.0, 1248.5)\": 0.328, \"(1248.5, 1341.0)\": 0.393, \"(1341.0, 1801.0)\": 0.475, \"(1801.0, 2501.0)\": 0.574}\nUpper Bounds (95%-Confidence Interval): {\"(143.5, 259.35)\": -0.48, \"(259.35, 289.4)\": -0.432, \"(289.4, 319.15)\": -0.38, \"(319.15, 348.3)\": -0.294, \"(348.3, 496.5)\": -0.177, \"(496.5, 548.75)\": -0.106, \"(548.75, 606.0)\": -0.007, \"(606.0, 696.25)\": 0.575, \"(696.25, 806.1500000000001)\": 0.871, \"(806.1500000000001, 901.8)\": 0.831, \"(901.8, 959.4000000000001)\": 0.962, \"(959.4000000000001, 1054.0)\": 1.074, \"(1054.0, 1150.0)\": 1.171, \"(1150.0, 1248.5)\": 1.285, \"(1248.5, 1341.0)\": 1.428, \"(1341.0, 1801.0)\": 1.544, \"(1801.0, 2501.0)\": 1.644}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(606.0, 696.25)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Insulin\nFeature Type: continuous\nMeans: {\"(0.0, 20.0)\": 0.0422, \"(20.0, 36.5)\": -0.0027, \"(36.5, 40.5)\": -0.0554, \"(40.5, 45.5)\": -0.0967, \"(45.5, 48.5)\": -0.0409, \"(48.5, 55.5)\": -0.2263, \"(55.5, 80.5)\": -0.2661, \"(80.5, 87.5)\": -0.227, \"(87.5, 97.5)\": -0.1794, \"(97.5, 111.0)\": -0.1356, \"(111.0, 123.5)\": -0.0968, \"(123.5, 137.5)\": -0.0561, \"(137.5, 144.5)\": -0.0187, \"(144.5, 157.0)\": 0.0208, \"(157.0, 170.5)\": 0.0623, \"(170.5, 186.5)\": 0.0999, \"(186.5, 190.5)\": 0.0538, \"(190.5, 192.5)\": 0.1059, \"(192.5, 271.0)\": -0.0027, \"(271.0, 277.5)\": 0.035, \"(277.5, 292.0)\": 0.0732, \"(292.0, 311.0)\": 0.1129, \"(311.0, 365.0)\": 0.1551, \"(365.0, 397.0)\": 0.196, \"(397.0, 452.5)\": 0.2331, \"(452.5, 476.0)\": 0.2839, \"(476.0, 487.5)\": 0.346, \"(487.5, 526.5)\": 0.3915, \"(526.5, 680.0)\": 0.4346}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 20.0)\": -0.0556, \"(20.0, 36.5)\": -0.2244, \"(36.5, 40.5)\": -0.2184, \"(40.5, 45.5)\": -0.2543, \"(45.5, 48.5)\": -0.7961, \"(48.5, 55.5)\": -0.5056, \"(55.5, 80.5)\": -0.551, \"(80.5, 87.5)\": -0.3117, \"(87.5, 97.5)\": -0.251, \"(97.5, 111.0)\": -0.2086, \"(111.0, 123.5)\": -0.1731, \"(123.5, 137.5)\": -0.137, \"(137.5, 144.5)\": -0.1027, \"(144.5, 157.0)\": -0.0751, \"(157.0, 170.5)\": -0.0506, \"(170.5, 186.5)\": -0.0163, \"(186.5, 190.5)\": -0.2256, \"(190.5, 192.5)\": -0.2869, \"(192.5, 271.0)\": -0.3659, \"(271.0, 277.5)\": -0.245, \"(277.5, 292.0)\": -0.1491, \"(292.0, 311.0)\": -0.0995, \"(311.0, 365.0)\": -0.0355, \"(365.0, 397.0)\": -0.0134, \"(397.0, 452.5)\": 0.0212, \"(452.5, 476.0)\": 0.0711, \"(476.0, 487.5)\": 0.1139, \"(487.5, 526.5)\": 0.1534, \"(526.5, 680.0)\": 0.0241}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 20.0)\": 0.14, \"(20.0, 36.5)\": 0.2189, \"(36.5, 40.5)\": 0.1076, \"(40.5, 45.5)\": 0.0609, \"(45.5, 48.5)\": 0.7143, \"(48.5, 55.5)\": 0.053, \"(55.5, 80.5)\": 0.0187, \"(80.5, 87.5)\": -0.1422, \"(87.5, 97.5)\": -0.1078, \"(97.5, 111.0)\": -0.0625, \"(111.0, 123.5)\": -0.0206, \"(123.5, 137.5)\": 0.0247, \"(137.5, 144.5)\": 0.0654, \"(144.5, 157.0)\": 0.1166, \"(157.0, 170.5)\": 0.1751, \"(170.5, 186.5)\": 0.2162, \"(186.5, 190.5)\": 0.3332, \"(190.5, 192.5)\": 0.4987, \"(192.5, 271.0)\": 0.3605, \"(271.0, 277.5)\": 0.315, \"(277.5, 292.0)\": 0.2956, \"(292.0, 311.0)\": 0.3253, \"(311.0, 365.0)\": 0.3457, \"(365.0, 397.0)\": 0.4055, \"(397.0, 452.5)\": 0.445, \"(452.5, 476.0)\": 0.4967, \"(476.0, 487.5)\": 0.5782, \"(487.5, 526.5)\": 0.6295, \"(526.5, 680.0)\": 0.8452}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(45.5, 48.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: NumOfProducts\nFeature Type: continuous\nMeans: {\"(1.0, 1.5)\": -0.918, \"(1.5, 2.5)\": 0.96, \"(2.5, 3.5)\": -3.104, \"(3.5, 4.0)\": -2.768}\nLower Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -0.985, \"(1.5, 2.5)\": 0.893, \"(2.5, 3.5)\": -3.482, \"(3.5, 4.0)\": -3.159}\nUpper Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -0.852, \"(1.5, 2.5)\": 1.028, \"(2.5, 3.5)\": -2.727, \"(3.5, 4.0)\": -2.376}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(3.5, 4.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: compactness_mean\nFeature Type: continuous\nMeans: {\"(0.01938, 0.03164)\": 0.0135, \"(0.03164, 0.035445000000000004)\": 0.0558, \"(0.035445000000000004, 0.03732)\": 0.0934, \"(0.03732, 0.038529999999999995)\": 0.1327, \"(0.038529999999999995, 0.040694999999999995)\": 0.1725, \"(0.040694999999999995, 0.042550000000000004)\": 0.2126, \"(0.042550000000000004, 0.044355000000000006)\": 0.2504, \"(0.044355000000000006, 0.045645000000000005)\": 0.299, \"(0.045645000000000005, 0.0498)\": 0.3373, \"(0.0498, 0.059495)\": 0.2969, \"(0.059495, 0.06042)\": 0.2605, \"(0.06042, 0.0618)\": 0.2247, \"(0.0618, 0.06289)\": 0.1851, \"(0.06289, 0.062985)\": 0.1459, \"(0.062985, 0.06375)\": 0.0823, \"(0.06375, 0.06615499999999999)\": 0.0446, \"(0.06615499999999999, 0.066575)\": 0.0084, \"(0.066575, 0.067345)\": -0.1354, \"(0.067345, 0.06788)\": -0.1923, \"(0.06788, 0.068945)\": -0.232, \"(0.068945, 0.07211999999999999)\": -0.2724, \"(0.07211999999999999, 0.07482)\": -0.309, \"(0.07482, 0.0785)\": -0.3463, \"(0.0785, 0.085875)\": -0.2755, \"(0.085875, 0.095275)\": -0.2297, \"(0.095275, 0.10439999999999999)\": -0.1927, \"(0.10439999999999999, 0.11305000000000001)\": -0.1576, \"(0.11305000000000001, 0.11465)\": -0.121, \"(0.11465, 0.1153)\": -0.0859, \"(0.1153, 0.119)\": -0.0125, \"(0.119, 0.12375)\": 0.024, \"(0.12375, 0.16655)\": 0.0599, \"(0.16655, 0.1923)\": 0.0956, \"(0.1923, 0.23235)\": 0.1316, \"(0.23235, 0.27165)\": 0.1705, \"(0.27165, 0.28075)\": 0.2103, \"(0.28075, 0.3114)\": 0.2453}\nLower Bounds (95%-Confidence Interval): {\"(0.01938, 0.03164)\": -0.4016, \"(0.03164, 0.035445000000000004)\": -0.3995, \"(0.035445000000000004, 0.03732)\": -0.3599, \"(0.03732, 0.038529999999999995)\": -0.3178, \"(0.038529999999999995, 0.040694999999999995)\": -0.2802, \"(0.040694999999999995, 0.042550000000000004)\": -0.2633, \"(0.042550000000000004, 0.044355000000000006)\": -0.2559, \"(0.044355000000000006, 0.045645000000000005)\": -0.2259, \"(0.045645000000000005, 0.0498)\": -0.1947, \"(0.0498, 0.059495)\": -0.2119, \"(0.059495, 0.06042)\": -0.1651, \"(0.06042, 0.0618)\": -0.1904, \"(0.0618, 0.06289)\": -0.2009, \"(0.06289, 0.062985)\": -0.2409, \"(0.062985, 0.06375)\": -0.1808, \"(0.06375, 0.06615499999999999)\": -0.2262, \"(0.06615499999999999, 0.066575)\": -0.2509, \"(0.066575, 0.067345)\": -0.7938, \"(0.067345, 0.06788)\": -0.7983, \"(0.06788, 0.068945)\": -0.838, \"(0.068945, 0.07211999999999999)\": -0.9135, \"(0.07211999999999999, 0.07482)\": -0.9538, \"(0.07482, 0.0785)\": -1.0103, \"(0.0785, 0.085875)\": -0.5241, \"(0.085875, 0.095275)\": -0.4606, \"(0.095275, 0.10439999999999999)\": -0.4301, \"(0.10439999999999999, 0.11305000000000001)\": -0.3863, \"(0.11305000000000001, 0.11465)\": -0.331, \"(0.11465, 0.1153)\": -0.2716, \"(0.1153, 0.119)\": -0.1247, \"(0.119, 0.12375)\": -0.0694, \"(0.12375, 0.16655)\": -0.0509, \"(0.16655, 0.1923)\": -0.025, \"(0.1923, 0.23235)\": 0.0179, \"(0.23235, 0.27165)\": 0.0681, \"(0.27165, 0.28075)\": 0.121, \"(0.28075, 0.3114)\": 0.148}\nUpper Bounds (95%-Confidence Interval): {\"(0.01938, 0.03164)\": 0.4286, \"(0.03164, 0.035445000000000004)\": 0.5111, \"(0.035445000000000004, 0.03732)\": 0.5468, \"(0.03732, 0.038529999999999995)\": 0.5831, \"(0.038529999999999995, 0.040694999999999995)\": 0.6252, \"(0.040694999999999995, 0.042550000000000004)\": 0.6885, \"(0.042550000000000004, 0.044355000000000006)\": 0.7567, \"(0.044355000000000006, 0.045645000000000005)\": 0.8238, \"(0.045645000000000005, 0.0498)\": 0.8693, \"(0.0498, 0.059495)\": 0.8057, \"(0.059495, 0.06042)\": 0.6861, \"(0.06042, 0.0618)\": 0.6397, \"(0.0618, 0.06289)\": 0.5712, \"(0.06289, 0.062985)\": 0.5328, \"(0.062985, 0.06375)\": 0.3454, \"(0.06375, 0.06615499999999999)\": 0.3154, \"(0.06615499999999999, 0.066575)\": 0.2677, \"(0.066575, 0.067345)\": 0.5229, \"(0.067345, 0.06788)\": 0.4136, \"(0.06788, 0.068945)\": 0.3741, \"(0.068945, 0.07211999999999999)\": 0.3686, \"(0.07211999999999999, 0.07482)\": 0.3358, \"(0.07482, 0.0785)\": 0.3178, \"(0.0785, 0.085875)\": -0.0269, \"(0.085875, 0.095275)\": 0.0012, \"(0.095275, 0.10439999999999999)\": 0.0448, \"(0.10439999999999999, 0.11305000000000001)\": 0.0711, \"(0.11305000000000001, 0.11465)\": 0.0889, \"(0.11465, 0.1153)\": 0.0998, \"(0.1153, 0.119)\": 0.0997, \"(0.119, 0.12375)\": 0.1174, \"(0.12375, 0.16655)\": 0.1706, \"(0.16655, 0.1923)\": 0.2162, \"(0.1923, 0.23235)\": 0.2452, \"(0.23235, 0.27165)\": 0.273, \"(0.27165, 0.28075)\": 0.2996, \"(0.28075, 0.3114)\": 0.3427}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.07482, 0.0785)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: texture_mean\nFeature Type: continuous\nMeans: {\"(9.71, 13.24)\": -1.121, \"(13.24, 14.075)\": -1.023, \"(14.075, 14.665)\": -0.921, \"(14.665, 15.010000000000002)\": -0.82, \"(15.010000000000002, 15.485)\": -0.718, \"(15.485, 15.774999999999999)\": -0.623, \"(15.774999999999999, 16.445)\": -0.523, \"(16.445, 17.045)\": -0.422, \"(17.045, 17.665)\": -0.324, \"(17.665, 18.335)\": -0.225, \"(18.335, 18.725)\": -0.129, \"(18.725, 19.075)\": -0.032, \"(19.075, 19.549999999999997)\": 0.063, \"(19.549999999999997, 19.915)\": 0.161, \"(19.915, 20.235)\": 0.26, \"(20.235, 20.8)\": 0.445, \"(20.8, 21.285)\": 0.549, \"(21.285, 33.81)\": 0.68}\nLower Bounds (95%-Confidence Interval): {\"(9.71, 13.24)\": -1.583, \"(13.24, 14.075)\": -1.428, \"(14.075, 14.665)\": -1.292, \"(14.665, 15.010000000000002)\": -1.127, \"(15.010000000000002, 15.485)\": -1.018, \"(15.485, 15.774999999999999)\": -0.932, \"(15.774999999999999, 16.445)\": -0.765, \"(16.445, 17.045)\": -0.657, \"(17.045, 17.665)\": -0.537, \"(17.665, 18.335)\": -0.404, \"(18.335, 18.725)\": -0.289, \"(18.725, 19.075)\": -0.203, \"(19.075, 19.549999999999997)\": -0.094, \"(19.549999999999997, 19.915)\": 0.017, \"(19.915, 20.235)\": 0.108, \"(20.235, 20.8)\": -0.11, \"(20.8, 21.285)\": -0.011, \"(21.285, 33.81)\": -0.0}\nUpper Bounds (95%-Confidence Interval): {\"(9.71, 13.24)\": -0.658, \"(13.24, 14.075)\": -0.619, \"(14.075, 14.665)\": -0.55, \"(14.665, 15.010000000000002)\": -0.512, \"(15.010000000000002, 15.485)\": -0.417, \"(15.485, 15.774999999999999)\": -0.314, \"(15.774999999999999, 16.445)\": -0.282, \"(16.445, 17.045)\": -0.187, \"(17.045, 17.665)\": -0.112, \"(17.665, 18.335)\": -0.045, \"(18.335, 18.725)\": 0.031, \"(18.725, 19.075)\": 0.139, \"(19.075, 19.549999999999997)\": 0.22, \"(19.549999999999997, 19.915)\": 0.306, \"(19.915, 20.235)\": 0.412, \"(20.235, 20.8)\": 0.999, \"(20.8, 21.285)\": 1.109, \"(21.285, 33.81)\": 1.36}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(21.285, 33.81)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: symmetry_se\nFeature Type: continuous\nMeans: {\"(0.007882, 0.010595)\": 0.771, \"(0.010595, 0.011365)\": 0.697, \"(0.011365, 0.012135)\": 0.635, \"(0.012135, 0.01279)\": 0.576, \"(0.01279, 0.01352)\": 0.513, \"(0.01352, 0.014105)\": 0.455, \"(0.014105, 0.014499999999999999)\": 0.393, \"(0.014499999999999999, 0.014525)\": 0.332, \"(0.014525, 0.01489)\": 0.227, \"(0.01489, 0.01532)\": 0.169, \"(0.01532, 0.015805)\": 0.109, \"(0.015805, 0.017215)\": 0.05, \"(0.017215, 0.017855)\": -0.008, \"(0.017855, 0.018165)\": -0.073, \"(0.018165, 0.018685)\": -0.131, \"(0.018685, 0.019545)\": -0.193, \"(0.019545, 0.02068)\": -0.252, \"(0.02068, 0.024730000000000002)\": -0.31, \"(0.024730000000000002, 0.026770000000000002)\": -0.376, \"(0.026770000000000002, 0.027435)\": -0.316, \"(0.027435, 0.028380000000000002)\": -0.252, \"(0.028380000000000002, 0.02966)\": -0.19, \"(0.02966, 0.031865)\": -0.092, \"(0.031865, 0.03651)\": -0.034, \"(0.03651, 0.041944999999999996)\": 0.024, \"(0.041944999999999996, 0.04665)\": 0.086, \"(0.04665, 0.054805)\": 0.152, \"(0.054805, 0.05963)\": 0.232}\nLower Bounds (95%-Confidence Interval): {\"(0.007882, 0.010595)\": 0.336, \"(0.010595, 0.011365)\": 0.284, \"(0.011365, 0.012135)\": 0.24, \"(0.012135, 0.01279)\": 0.211, \"(0.01279, 0.01352)\": 0.226, \"(0.01352, 0.014105)\": 0.178, \"(0.014105, 0.014499999999999999)\": 0.123, \"(0.014499999999999999, 0.014525)\": 0.09, \"(0.014525, 0.01489)\": -0.155, \"(0.01489, 0.01532)\": -0.203, \"(0.01532, 0.015805)\": -0.266, \"(0.015805, 0.017215)\": -0.317, \"(0.017215, 0.017855)\": -0.138, \"(0.017855, 0.018165)\": -0.193, \"(0.018165, 0.018685)\": -0.264, \"(0.018685, 0.019545)\": -0.327, \"(0.019545, 0.02068)\": -0.388, \"(0.02068, 0.024730000000000002)\": -0.457, \"(0.024730000000000002, 0.026770000000000002)\": -0.569, \"(0.026770000000000002, 0.027435)\": -0.507, \"(0.027435, 0.028380000000000002)\": -0.46, \"(0.028380000000000002, 0.02966)\": -0.393, \"(0.02966, 0.031865)\": -0.281, \"(0.031865, 0.03651)\": -0.265, \"(0.03651, 0.041944999999999996)\": -0.233, \"(0.041944999999999996, 0.04665)\": -0.174, \"(0.04665, 0.054805)\": -0.12, \"(0.054805, 0.05963)\": -0.058}\nUpper Bounds (95%-Confidence Interval): {\"(0.007882, 0.010595)\": 1.206, \"(0.010595, 0.011365)\": 1.11, \"(0.011365, 0.012135)\": 1.031, \"(0.012135, 0.01279)\": 0.941, \"(0.01279, 0.01352)\": 0.8, \"(0.01352, 0.014105)\": 0.731, \"(0.014105, 0.014499999999999999)\": 0.662, \"(0.014499999999999999, 0.014525)\": 0.574, \"(0.014525, 0.01489)\": 0.609, \"(0.01489, 0.01532)\": 0.541, \"(0.01532, 0.015805)\": 0.484, \"(0.015805, 0.017215)\": 0.418, \"(0.017215, 0.017855)\": 0.123, \"(0.017855, 0.018165)\": 0.047, \"(0.018165, 0.018685)\": 0.002, \"(0.018685, 0.019545)\": -0.059, \"(0.019545, 0.02068)\": -0.116, \"(0.02068, 0.024730000000000002)\": -0.164, \"(0.024730000000000002, 0.026770000000000002)\": -0.182, \"(0.026770000000000002, 0.027435)\": -0.125, \"(0.027435, 0.028380000000000002)\": -0.043, \"(0.028380000000000002, 0.02966)\": 0.013, \"(0.02966, 0.031865)\": 0.097, \"(0.031865, 0.03651)\": 0.197, \"(0.03651, 0.041944999999999996)\": 0.281, \"(0.041944999999999996, 0.04665)\": 0.345, \"(0.04665, 0.054805)\": 0.424, \"(0.054805, 0.05963)\": 0.521}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.007882, 0.010595)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: radius_mean\nFeature Type: continuous\nMeans: {\"(6.981, 9.281500000000001)\": -0.762, \"(9.281500000000001, 9.7015)\": -0.659, \"(9.7015, 10.165)\": -0.56, \"(10.165, 10.655000000000001)\": -0.461, \"(10.655000000000001, 12.465)\": -0.36, \"(12.465, 13.39)\": -0.262, \"(13.39, 14.43)\": -0.163, \"(14.43, 14.934999999999999)\": -0.065, \"(14.934999999999999, 15.08)\": 0.037, \"(15.08, 15.815)\": 0.137, \"(15.815, 16.925)\": 0.235, \"(16.925, 17.385)\": 0.394, \"(17.385, 18.0)\": 0.494, \"(18.0, 18.735)\": 0.599, \"(18.735, 19.240000000000002)\": 0.695, \"(19.240000000000002, 19.990000000000002)\": 0.793, \"(19.990000000000002, 20.595)\": 0.891, \"(20.595, 23.240000000000002)\": 0.99, \"(23.240000000000002, 28.11)\": 1.093}\nLower Bounds (95%-Confidence Interval): {\"(6.981, 9.281500000000001)\": -1.01, \"(9.281500000000001, 9.7015)\": -0.884, \"(9.7015, 10.165)\": -0.748, \"(10.165, 10.655000000000001)\": -0.611, \"(10.655000000000001, 12.465)\": -0.536, \"(12.465, 13.39)\": -0.396, \"(13.39, 14.43)\": -0.269, \"(14.43, 14.934999999999999)\": -0.226, \"(14.934999999999999, 15.08)\": -0.156, \"(15.08, 15.815)\": -0.059, \"(15.815, 16.925)\": -0.127, \"(16.925, 17.385)\": 0.041, \"(17.385, 18.0)\": 0.136, \"(18.0, 18.735)\": 0.205, \"(18.735, 19.240000000000002)\": 0.283, \"(19.240000000000002, 19.990000000000002)\": 0.385, \"(19.990000000000002, 20.595)\": 0.462, \"(20.595, 23.240000000000002)\": 0.519, \"(23.240000000000002, 28.11)\": 0.611}\nUpper Bounds (95%-Confidence Interval): {\"(6.981, 9.281500000000001)\": -0.515, \"(9.281500000000001, 9.7015)\": -0.435, \"(9.7015, 10.165)\": -0.373, \"(10.165, 10.655000000000001)\": -0.311, \"(10.655000000000001, 12.465)\": -0.184, \"(12.465, 13.39)\": -0.128, \"(13.39, 14.43)\": -0.057, \"(14.43, 14.934999999999999)\": 0.097, \"(14.934999999999999, 15.08)\": 0.231, \"(15.08, 15.815)\": 0.333, \"(15.815, 16.925)\": 0.597, \"(16.925, 17.385)\": 0.748, \"(17.385, 18.0)\": 0.853, \"(18.0, 18.735)\": 0.993, \"(18.735, 19.240000000000002)\": 1.107, \"(19.240000000000002, 19.990000000000002)\": 1.202, \"(19.990000000000002, 20.595)\": 1.32, \"(20.595, 23.240000000000002)\": 1.461, \"(23.240000000000002, 28.11)\": 1.575}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(23.240000000000002, 28.11)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: perimeter_worst\nFeature Type: continuous\nMeans: {\"(50.41, 71.06)\": -1.379, \"(71.06, 76.52000000000001)\": -1.223, \"(76.52000000000001, 80.9)\": -1.069, \"(80.9, 84.035)\": -0.914, \"(84.035, 86.48500000000001)\": -0.755, \"(86.48500000000001, 87.3)\": -0.599, \"(87.3, 91.49000000000001)\": -0.447, \"(91.49000000000001, 95.66)\": -0.292, \"(95.66, 101.15)\": -0.446, \"(101.15, 102.05000000000001)\": -0.294, \"(102.05000000000001, 109.6)\": 0.197, \"(109.6, 116.25)\": 0.351, \"(116.25, 120.35)\": 0.507, \"(120.35, 127.0)\": 0.748, \"(127.0, 133.10000000000002)\": 0.902, \"(133.10000000000002, 145.10000000000002)\": 1.059, \"(145.10000000000002, 160.0)\": 1.215, \"(160.0, 178.85)\": 1.368, \"(178.85, 251.2)\": 1.523}\nLower Bounds (95%-Confidence Interval): {\"(50.41, 71.06)\": -2.45, \"(71.06, 76.52000000000001)\": -2.257, \"(76.52000000000001, 80.9)\": -2.023, \"(80.9, 84.035)\": -1.85, \"(84.035, 86.48500000000001)\": -1.682, \"(86.48500000000001, 87.3)\": -1.531, \"(87.3, 91.49000000000001)\": -1.053, \"(91.49000000000001, 95.66)\": -0.915, \"(95.66, 101.15)\": -1.829, \"(101.15, 102.05000000000001)\": -1.642, \"(102.05000000000001, 109.6)\": -0.387, \"(109.6, 116.25)\": -0.238, \"(116.25, 120.35)\": -0.074, \"(120.35, 127.0)\": -0.761, \"(127.0, 133.10000000000002)\": -0.623, \"(133.10000000000002, 145.10000000000002)\": -0.494, \"(145.10000000000002, 160.0)\": -0.379, \"(160.0, 178.85)\": -0.29, \"(178.85, 251.2)\": -0.162}\nUpper Bounds (95%-Confidence Interval): {\"(50.41, 71.06)\": -0.307, \"(71.06, 76.52000000000001)\": -0.189, \"(76.52000000000001, 80.9)\": -0.114, \"(80.9, 84.035)\": 0.021, \"(84.035, 86.48500000000001)\": 0.172, \"(86.48500000000001, 87.3)\": 0.332, \"(87.3, 91.49000000000001)\": 0.159, \"(91.49000000000001, 95.66)\": 0.331, \"(95.66, 101.15)\": 0.936, \"(101.15, 102.05000000000001)\": 1.054, \"(102.05000000000001, 109.6)\": 0.782, \"(109.6, 116.25)\": 0.94, \"(116.25, 120.35)\": 1.088, \"(120.35, 127.0)\": 2.256, \"(127.0, 133.10000000000002)\": 2.428, \"(133.10000000000002, 145.10000000000002)\": 2.611, \"(145.10000000000002, 160.0)\": 2.809, \"(160.0, 178.85)\": 3.027, \"(178.85, 251.2)\": 3.208}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(178.85, 251.2)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: median_income\nFeature Type: continuous\nMeans: {\"(0.4999, 0.5427500000000001)\": -16067.6, \"(0.5427500000000001, 1.4808)\": -55539.5, \"(1.4808, 2.1658999999999997)\": -71376.5, \"(2.1658999999999997, 2.6096)\": -56399.7, \"(2.6096, 3.2433)\": -40762.6, \"(3.2433, 3.66575)\": -25586.1, \"(3.66575, 4.3197)\": -8084.4, \"(4.3197, 4.691000000000001)\": 7391.3, \"(4.691000000000001, 5.1358)\": 22375.3, \"(5.1358, 5.59195)\": 40032.8, \"(5.59195, 5.8294)\": 56900.2, \"(5.8294, 6.29665)\": 75092.3, \"(6.29665, 6.3704)\": 96400.5, \"(6.3704, 6.874750000000001)\": 111491.7, \"(6.874750000000001, 7.6996)\": 135841.6, \"(7.6996, 7.8141)\": 151586.9, \"(7.8141, 8.3976)\": 170219.6, \"(8.3976, 9.046949999999999)\": 192482.3, \"(9.046949999999999, 15.00005)\": 214375.9, \"(15.00005, 15.0001)\": 193753.6}\nLower Bounds (95%-Confidence Interval): {\"(0.4999, 0.5427500000000001)\": -48216.1, \"(0.5427500000000001, 1.4808)\": -68098.8, \"(1.4808, 2.1658999999999997)\": -81907.3, \"(2.1658999999999997, 2.6096)\": -60824.9, \"(2.6096, 3.2433)\": -49299.1, \"(3.2433, 3.66575)\": -32546.2, \"(3.66575, 4.3197)\": -17048.9, \"(4.3197, 4.691000000000001)\": 1621.1, \"(4.691000000000001, 5.1358)\": 13670.4, \"(5.1358, 5.59195)\": 33628.4, \"(5.59195, 5.8294)\": 48173.8, \"(5.8294, 6.29665)\": 69358.1, \"(6.29665, 6.3704)\": 88897.2, \"(6.3704, 6.874750000000001)\": 105607.5, \"(6.874750000000001, 7.6996)\": 129446.9, \"(7.6996, 7.8141)\": 139775.0, \"(7.8141, 8.3976)\": 162646.8, \"(8.3976, 9.046949999999999)\": 184114.0, \"(9.046949999999999, 15.00005)\": 203670.8, \"(15.00005, 15.0001)\": 178950.1}\nUpper Bounds (95%-Confidence Interval): {\"(0.4999, 0.5427500000000001)\": 16080.9, \"(0.5427500000000001, 1.4808)\": -42980.1, \"(1.4808, 2.1658999999999997)\": -60845.8, \"(2.1658999999999997, 2.6096)\": -51974.6, \"(2.6096, 3.2433)\": -32226.2, \"(3.2433, 3.66575)\": -18626.1, \"(3.66575, 4.3197)\": 880.1, \"(4.3197, 4.691000000000001)\": 13161.4, \"(4.691000000000001, 5.1358)\": 31080.1, \"(5.1358, 5.59195)\": 46437.2, \"(5.59195, 5.8294)\": 65626.7, \"(5.8294, 6.29665)\": 80826.5, \"(6.29665, 6.3704)\": 103903.7, \"(6.3704, 6.874750000000001)\": 117376.0, \"(6.874750000000001, 7.6996)\": 142236.4, \"(7.6996, 7.8141)\": 163398.8, \"(7.8141, 8.3976)\": 177792.4, \"(8.3976, 9.046949999999999)\": 200850.6, \"(9.046949999999999, 15.00005)\": 225081.0, \"(15.00005, 15.0001)\": 208557.1}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.4999, 0.5427500000000001)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: serum_sodium\nFeature Type: continuous\nMeans: {\"(113.0, 114.5)\": -1.269, \"(114.5, 118.5)\": 0.283, \"(118.5, 124.5)\": 3.539, \"(124.5, 126.5)\": 2.46, \"(126.5, 127.5)\": 4.042, \"(127.5, 129.5)\": 3.553, \"(129.5, 130.5)\": 0.953, \"(130.5, 132.5)\": 1.22, \"(132.5, 133.5)\": -1.094, \"(133.5, 135.5)\": 0.587, \"(135.5, 138.5)\": -0.629, \"(138.5, 144.5)\": -0.233, \"(144.5, 148.0)\": 0.113}\nLower Bounds (95%-Confidence Interval): {\"(113.0, 114.5)\": -3.483, \"(114.5, 118.5)\": -4.768, \"(118.5, 124.5)\": 2.536, \"(124.5, 126.5)\": 1.699, \"(126.5, 127.5)\": 3.034, \"(127.5, 129.5)\": 2.614, \"(129.5, 130.5)\": 0.389, \"(130.5, 132.5)\": 0.304, \"(132.5, 133.5)\": -2.269, \"(133.5, 135.5)\": 0.366, \"(135.5, 138.5)\": -0.879, \"(138.5, 144.5)\": -0.845, \"(144.5, 148.0)\": -0.129}\nUpper Bounds (95%-Confidence Interval): {\"(113.0, 114.5)\": 0.944, \"(114.5, 118.5)\": 5.334, \"(118.5, 124.5)\": 4.542, \"(124.5, 126.5)\": 3.222, \"(126.5, 127.5)\": 5.05, \"(127.5, 129.5)\": 4.492, \"(129.5, 130.5)\": 1.517, \"(130.5, 132.5)\": 2.136, \"(132.5, 133.5)\": 0.08, \"(133.5, 135.5)\": 0.808, \"(135.5, 138.5)\": -0.38, \"(138.5, 144.5)\": 0.38, \"(144.5, 148.0)\": 0.354}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(114.5, 118.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: HasCrCard\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.004421, \"(0.5, 1.0)\": 0.001379}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.037941, \"(0.5, 1.0)\": -0.009076}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.0291, \"(0.5, 1.0)\": 0.011834}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.0, 0.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: id\nFeature Type: continuous\nMeans: {\"(8670.0, 90271.0)\": 0.342, \"(90271.0, 467526.5)\": 0.574, \"(467526.5, 853506.5)\": 0.657, \"(853506.5, 859643.0)\": 0.719, \"(859643.0, 864727.5)\": 0.655, \"(864727.5, 871421.0)\": 0.593, \"(871421.0, 874848.5)\": 0.528, \"(874848.5, 880845.5)\": 0.464, \"(880845.5, 882230.0)\": 0.399, \"(882230.0, 883266.5)\": 0.319, \"(883266.5, 889561.0)\": 0.171, \"(889561.0, 892521.0)\": 0.103, \"(892521.0, 894330.5)\": 0.039, \"(894330.5, 896851.5)\": -0.023, \"(896851.5, 899167.0)\": -0.107, \"(899167.0, 902138.0)\": -0.176, \"(902138.0, 905080.5)\": -0.241, \"(905080.5, 906551.5)\": -0.305, \"(906551.5, 911540.5)\": -0.368, \"(911540.5, 917896.5)\": -0.431, \"(917896.5, 8810615.5)\": -0.493, \"(8810615.5, 9112480.5)\": -0.386, \"(9112480.5, 89803401.5)\": -0.323, \"(89803401.5, 91544001.5)\": -0.259, \"(91544001.5, 91903901.5)\": -0.191, \"(91903901.5, 911320502.0)\": -0.121}\nLower Bounds (95%-Confidence Interval): {\"(8670.0, 90271.0)\": -0.06, \"(90271.0, 467526.5)\": 0.079, \"(467526.5, 853506.5)\": 0.101, \"(853506.5, 859643.0)\": 0.139, \"(859643.0, 864727.5)\": 0.076, \"(864727.5, 871421.0)\": 0.038, \"(871421.0, 874848.5)\": 0.005, \"(874848.5, 880845.5)\": -0.028, \"(880845.5, 882230.0)\": -0.061, \"(882230.0, 883266.5)\": -0.137, \"(883266.5, 889561.0)\": -0.14, \"(889561.0, 892521.0)\": -0.196, \"(892521.0, 894330.5)\": -0.267, \"(894330.5, 896851.5)\": -0.331, \"(896851.5, 899167.0)\": -0.426, \"(899167.0, 902138.0)\": -0.541, \"(902138.0, 905080.5)\": -0.608, \"(905080.5, 906551.5)\": -0.738, \"(906551.5, 911540.5)\": -0.807, \"(911540.5, 917896.5)\": -0.885, \"(917896.5, 8810615.5)\": -0.946, \"(8810615.5, 9112480.5)\": -0.664, \"(9112480.5, 89803401.5)\": -0.626, \"(89803401.5, 91544001.5)\": -0.554, \"(91544001.5, 91903901.5)\": -0.463, \"(91903901.5, 911320502.0)\": -0.414}\nUpper Bounds (95%-Confidence Interval): {\"(8670.0, 90271.0)\": 0.744, \"(90271.0, 467526.5)\": 1.07, \"(467526.5, 853506.5)\": 1.212, \"(853506.5, 859643.0)\": 1.299, \"(859643.0, 864727.5)\": 1.234, \"(864727.5, 871421.0)\": 1.148, \"(871421.0, 874848.5)\": 1.051, \"(874848.5, 880845.5)\": 0.956, \"(880845.5, 882230.0)\": 0.86, \"(882230.0, 883266.5)\": 0.774, \"(883266.5, 889561.0)\": 0.482, \"(889561.0, 892521.0)\": 0.402, \"(892521.0, 894330.5)\": 0.345, \"(894330.5, 896851.5)\": 0.286, \"(896851.5, 899167.0)\": 0.212, \"(899167.0, 902138.0)\": 0.188, \"(902138.0, 905080.5)\": 0.127, \"(905080.5, 906551.5)\": 0.128, \"(906551.5, 911540.5)\": 0.07, \"(911540.5, 917896.5)\": 0.023, \"(917896.5, 8810615.5)\": -0.04, \"(8810615.5, 9112480.5)\": -0.107, \"(9112480.5, 89803401.5)\": -0.021, \"(89803401.5, 91544001.5)\": 0.036, \"(91544001.5, 91903901.5)\": 0.081, \"(91903901.5, 911320502.0)\": 0.171}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(853506.5, 859643.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: concave points_se\nFeature Type: continuous\nMeans: {\"(0.0, 0.002395)\": -0.0871, \"(0.002395, 0.0032875)\": -0.0609, \"(0.0032875, 0.0034045)\": -0.0373, \"(0.0034045, 0.0036125000000000003)\": -0.0134, \"(0.0036125000000000003, 0.004007999999999999)\": 0.015, \"(0.004007999999999999, 0.0044174999999999996)\": 0.0395, \"(0.0044174999999999996, 0.0048265)\": 0.0651, \"(0.0048265, 0.0049695)\": 0.092, \"(0.0049695, 0.005064)\": 0.1172, \"(0.005064, 0.0051675)\": 0.1498, \"(0.0051675, 0.0052465)\": 0.1811, \"(0.0052465, 0.0054895)\": 0.1545, \"(0.0054895, 0.00583)\": 0.1294, \"(0.00583, 0.006595999999999999)\": 0.1053, \"(0.006595999999999999, 0.006815)\": 0.1283, \"(0.006815, 0.00749)\": 0.151, \"(0.00749, 0.008282000000000001)\": 0.1286, \"(0.008282000000000001, 0.0088595)\": 0.1057, \"(0.0088595, 0.009246)\": 0.2195, \"(0.009246, 0.00954)\": 0.1918, \"(0.00954, 0.009698)\": 0.169, \"(0.009698, 0.00976)\": 0.1467, \"(0.00976, 0.009788999999999999)\": 0.1246, \"(0.009788999999999999, 0.009878999999999999)\": 0.0984, \"(0.009878999999999999, 0.0099215)\": -0.0268, \"(0.0099215, 0.010165)\": -0.0546, \"(0.010165, 0.010385)\": -0.0796, \"(0.010385, 0.010515)\": -0.1027, \"(0.010515, 0.010825)\": -0.1321, \"(0.010825, 0.011115)\": -0.1569, \"(0.011115, 0.011525)\": -0.181, \"(0.011525, 0.012580000000000001)\": -0.204, \"(0.012580000000000001, 0.012715)\": -0.1804, \"(0.012715, 0.012750000000000001)\": -0.1557, \"(0.012750000000000001, 0.01302)\": -0.1333, \"(0.01302, 0.013405)\": -0.1067, \"(0.013405, 0.01386)\": -0.0844, \"(0.01386, 0.014315)\": -0.061, \"(0.014315, 0.015605)\": -0.0317, \"(0.015605, 0.016655000000000003)\": -0.0097, \"(0.016655000000000003, 0.017509999999999998)\": 0.0142, \"(0.017509999999999998, 0.019655)\": 0.037, \"(0.019655, 0.021525)\": 0.0117, \"(0.021525, 0.02246)\": -0.018, \"(0.02246, 0.02611)\": -0.0625, \"(0.02611, 0.05279)\": -0.0866}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.002395)\": -0.2428, \"(0.002395, 0.0032875)\": -0.216, \"(0.0032875, 0.0034045)\": -0.1973, \"(0.0034045, 0.0036125000000000003)\": -0.1778, \"(0.0036125000000000003, 0.004007999999999999)\": -0.1423, \"(0.004007999999999999, 0.0044174999999999996)\": -0.1145, \"(0.0044174999999999996, 0.0048265)\": -0.0804, \"(0.0048265, 0.0049695)\": -0.0433, \"(0.0049695, 0.005064)\": -0.0289, \"(0.005064, 0.0051675)\": -0.0175, \"(0.0051675, 0.0052465)\": 0.0068, \"(0.0052465, 0.0054895)\": -0.0087, \"(0.0054895, 0.00583)\": -0.0088, \"(0.00583, 0.006595999999999999)\": -0.0253, \"(0.006595999999999999, 0.006815)\": 0.0061, \"(0.006815, 0.00749)\": 0.012, \"(0.00749, 0.008282000000000001)\": -0.0218, \"(0.008282000000000001, 0.0088595)\": -0.0694, \"(0.0088595, 0.009246)\": -0.313, \"(0.009246, 0.00954)\": -0.3369, \"(0.00954, 0.009698)\": -0.3531, \"(0.009698, 0.00976)\": -0.3635, \"(0.00976, 0.009788999999999999)\": -0.3663, \"(0.009788999999999999, 0.009878999999999999)\": -0.4105, \"(0.009878999999999999, 0.0099215)\": -0.1256, \"(0.0099215, 0.010165)\": -0.1624, \"(0.010165, 0.010385)\": -0.1986, \"(0.010385, 0.010515)\": -0.2385, \"(0.010515, 0.010825)\": -0.2937, \"(0.010825, 0.011115)\": -0.3279, \"(0.011115, 0.011525)\": -0.3717, \"(0.011525, 0.012580000000000001)\": -0.4177, \"(0.012580000000000001, 0.012715)\": -0.3676, \"(0.012715, 0.012750000000000001)\": -0.3439, \"(0.012750000000000001, 0.01302)\": -0.2873, \"(0.01302, 0.013405)\": -0.246, \"(0.013405, 0.01386)\": -0.2135, \"(0.01386, 0.014315)\": -0.1572, \"(0.014315, 0.015605)\": -0.1106, \"(0.015605, 0.016655000000000003)\": -0.0972, \"(0.016655000000000003, 0.017509999999999998)\": -0.0786, \"(0.017509999999999998, 0.019655)\": -0.0739, \"(0.019655, 0.021525)\": -0.099, \"(0.021525, 0.02246)\": -0.1362, \"(0.02246, 0.02611)\": -0.2114, \"(0.02611, 0.05279)\": -0.2994}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.002395)\": 0.0686, \"(0.002395, 0.0032875)\": 0.0943, \"(0.0032875, 0.0034045)\": 0.1227, \"(0.0034045, 0.0036125000000000003)\": 0.151, \"(0.0036125000000000003, 0.004007999999999999)\": 0.1722, \"(0.004007999999999999, 0.0044174999999999996)\": 0.1934, \"(0.0044174999999999996, 0.0048265)\": 0.2105, \"(0.0048265, 0.0049695)\": 0.2273, \"(0.0049695, 0.005064)\": 0.2632, \"(0.005064, 0.0051675)\": 0.3171, \"(0.0051675, 0.0052465)\": 0.3554, \"(0.0052465, 0.0054895)\": 0.3177, \"(0.0054895, 0.00583)\": 0.2677, \"(0.00583, 0.006595999999999999)\": 0.2359, \"(0.006595999999999999, 0.006815)\": 0.2505, \"(0.006815, 0.00749)\": 0.2901, \"(0.00749, 0.008282000000000001)\": 0.279, \"(0.008282000000000001, 0.0088595)\": 0.2808, \"(0.0088595, 0.009246)\": 0.752, \"(0.009246, 0.00954)\": 0.7204, \"(0.00954, 0.009698)\": 0.6912, \"(0.009698, 0.00976)\": 0.657, \"(0.00976, 0.009788999999999999)\": 0.6156, \"(0.009788999999999999, 0.009878999999999999)\": 0.6073, \"(0.009878999999999999, 0.0099215)\": 0.072, \"(0.0099215, 0.010165)\": 0.0531, \"(0.010165, 0.010385)\": 0.0395, \"(0.010385, 0.010515)\": 0.0331, \"(0.010515, 0.010825)\": 0.0294, \"(0.010825, 0.011115)\": 0.0142, \"(0.011115, 0.011525)\": 0.0097, \"(0.011525, 0.012580000000000001)\": 0.0096, \"(0.012580000000000001, 0.012715)\": 0.0069, \"(0.012715, 0.012750000000000001)\": 0.0325, \"(0.012750000000000001, 0.01302)\": 0.0207, \"(0.01302, 0.013405)\": 0.0325, \"(0.013405, 0.01386)\": 0.0446, \"(0.01386, 0.014315)\": 0.0352, \"(0.014315, 0.015605)\": 0.0471, \"(0.015605, 0.016655000000000003)\": 0.0777, \"(0.016655000000000003, 0.017509999999999998)\": 0.107, \"(0.017509999999999998, 0.019655)\": 0.1479, \"(0.019655, 0.021525)\": 0.1225, \"(0.021525, 0.02246)\": 0.1002, \"(0.02246, 0.02611)\": 0.0864, \"(0.02611, 0.05279)\": 0.1261}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.0088595, 0.009246)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: symmetry_worst\nFeature Type: continuous\nMeans: {\"(0.1565, 0.165)\": -0.295, \"(0.165, 0.19055)\": -0.472, \"(0.19055, 0.24485)\": -0.549, \"(0.24485, 0.25225)\": -0.469, \"(0.25225, 0.2583)\": -0.392, \"(0.2583, 0.26635)\": -0.31, \"(0.26635, 0.26959999999999995)\": -0.23, \"(0.26959999999999995, 0.27495)\": -0.112, \"(0.27495, 0.28035)\": -0.034, \"(0.28035, 0.28815)\": 0.046, \"(0.28815, 0.2986)\": 0.125, \"(0.2986, 0.31745)\": 0.202, \"(0.31745, 0.32125000000000004)\": 0.281, \"(0.32125000000000004, 0.33065)\": 0.363, \"(0.33065, 0.35335)\": 0.444, \"(0.35335, 0.36085)\": 0.526, \"(0.36085, 0.3702)\": 0.624, \"(0.3702, 0.4223)\": 0.705, \"(0.4223, 0.4697)\": 0.785, \"(0.4697, 0.6638)\": 0.867}\nLower Bounds (95%-Confidence Interval): {\"(0.1565, 0.165)\": -0.839, \"(0.165, 0.19055)\": -0.743, \"(0.19055, 0.24485)\": -0.802, \"(0.24485, 0.25225)\": -0.663, \"(0.25225, 0.2583)\": -0.595, \"(0.2583, 0.26635)\": -0.479, \"(0.26635, 0.26959999999999995)\": -0.388, \"(0.26959999999999995, 0.27495)\": -0.253, \"(0.27495, 0.28035)\": -0.172, \"(0.28035, 0.28815)\": -0.1, \"(0.28815, 0.2986)\": -0.015, \"(0.2986, 0.31745)\": 0.043, \"(0.31745, 0.32125000000000004)\": 0.141, \"(0.32125000000000004, 0.33065)\": 0.21, \"(0.33065, 0.35335)\": 0.276, \"(0.35335, 0.36085)\": 0.357, \"(0.36085, 0.3702)\": 0.348, \"(0.3702, 0.4223)\": 0.395, \"(0.4223, 0.4697)\": 0.478, \"(0.4697, 0.6638)\": 0.538}\nUpper Bounds (95%-Confidence Interval): {\"(0.1565, 0.165)\": 0.249, \"(0.165, 0.19055)\": -0.201, \"(0.19055, 0.24485)\": -0.296, \"(0.24485, 0.25225)\": -0.276, \"(0.25225, 0.2583)\": -0.188, \"(0.2583, 0.26635)\": -0.14, \"(0.26635, 0.26959999999999995)\": -0.072, \"(0.26959999999999995, 0.27495)\": 0.03, \"(0.27495, 0.28035)\": 0.104, \"(0.28035, 0.28815)\": 0.191, \"(0.28815, 0.2986)\": 0.264, \"(0.2986, 0.31745)\": 0.361, \"(0.31745, 0.32125000000000004)\": 0.42, \"(0.32125000000000004, 0.33065)\": 0.516, \"(0.33065, 0.35335)\": 0.612, \"(0.35335, 0.36085)\": 0.694, \"(0.36085, 0.3702)\": 0.901, \"(0.3702, 0.4223)\": 1.015, \"(0.4223, 0.4697)\": 1.092, \"(0.4697, 0.6638)\": 1.197}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.1565, 0.165)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: BMI\nFeature Type: continuous\nMeans: {\"(0.0, 9.1)\": -0.7, \"(9.1, 22.55)\": -0.961, \"(22.55, 23.65)\": -0.856, \"(23.65, 25.55)\": -0.762, \"(25.55, 26.35)\": -0.661, \"(26.35, 27.65)\": -0.24, \"(27.65, 28.45)\": -0.144, \"(28.45, 29.65)\": -0.051, \"(29.65, 30.45)\": 0.049, \"(30.45, 32.150000000000006)\": 0.153, \"(32.150000000000006, 37.650000000000006)\": 0.246, \"(37.650000000000006, 41.75)\": 0.34, \"(41.75, 42.849999999999994)\": 0.434, \"(42.849999999999994, 45.650000000000006)\": 0.529, \"(45.650000000000006, 48.349999999999994)\": 0.626, \"(48.349999999999994, 67.1)\": 0.784}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 9.1)\": -1.139, \"(9.1, 22.55)\": -1.349, \"(22.55, 23.65)\": -1.219, \"(23.65, 25.55)\": -1.281, \"(25.55, 26.35)\": -1.231, \"(26.35, 27.65)\": -0.568, \"(27.65, 28.45)\": -0.258, \"(28.45, 29.65)\": -0.157, \"(29.65, 30.45)\": -0.11, \"(30.45, 32.150000000000006)\": -0.086, \"(32.150000000000006, 37.650000000000006)\": 0.084, \"(37.650000000000006, 41.75)\": 0.189, \"(41.75, 42.849999999999994)\": 0.28, \"(42.849999999999994, 45.650000000000006)\": 0.348, \"(45.650000000000006, 48.349999999999994)\": 0.256, \"(48.349999999999994, 67.1)\": 0.265}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 9.1)\": -0.262, \"(9.1, 22.55)\": -0.573, \"(22.55, 23.65)\": -0.493, \"(23.65, 25.55)\": -0.243, \"(25.55, 26.35)\": -0.09, \"(26.35, 27.65)\": 0.088, \"(27.65, 28.45)\": -0.03, \"(28.45, 29.65)\": 0.054, \"(29.65, 30.45)\": 0.208, \"(30.45, 32.150000000000006)\": 0.392, \"(32.150000000000006, 37.650000000000006)\": 0.409, \"(37.650000000000006, 41.75)\": 0.491, \"(41.75, 42.849999999999994)\": 0.588, \"(42.849999999999994, 45.650000000000006)\": 0.709, \"(45.650000000000006, 48.349999999999994)\": 0.996, \"(48.349999999999994, 67.1)\": 1.303}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(25.55, 26.35)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: time\nFeature Type: continuous\nMeans: {\"(4.0, 11.5)\": 10.73, \"(11.5, 12.5)\": 1.29, \"(12.5, 15.5)\": 3.88, \"(15.5, 18.0)\": 2.22, \"(18.0, 28.5)\": 6.17, \"(28.5, 30.5)\": 4.47, \"(30.5, 52.0)\": 5.56, \"(52.0, 54.5)\": 3.38, \"(54.5, 67.5)\": 4.79, \"(67.5, 73.5)\": 2.76, \"(73.5, 76.5)\": -3.15, \"(76.5, 78.5)\": 2.29, \"(78.5, 82.5)\": -0.16, \"(82.5, 87.5)\": -2.8, \"(87.5, 90.5)\": 0.19, \"(90.5, 92.5)\": -1.08, \"(92.5, 95.5)\": -2.7, \"(95.5, 108.5)\": -0.98, \"(108.5, 117.5)\": 0.02, \"(117.5, 124.5)\": -3.44, \"(124.5, 137.5)\": 0.64, \"(137.5, 149.0)\": -0.8, \"(149.0, 171.5)\": 5.06, \"(171.5, 173.0)\": 2.66, \"(173.0, 182.5)\": -0.84, \"(182.5, 192.5)\": -3.42, \"(192.5, 193.5)\": -1.01, \"(193.5, 253.0)\": -2.58, \"(253.0, 285.0)\": -8.42}\nLower Bounds (95%-Confidence Interval): {\"(4.0, 11.5)\": 8.45, \"(11.5, 12.5)\": 0.25, \"(12.5, 15.5)\": 2.94, \"(15.5, 18.0)\": -0.25, \"(18.0, 28.5)\": 4.04, \"(28.5, 30.5)\": 3.69, \"(30.5, 52.0)\": 4.21, \"(52.0, 54.5)\": 1.74, \"(54.5, 67.5)\": 3.17, \"(67.5, 73.5)\": 1.96, \"(73.5, 76.5)\": -4.69, \"(76.5, 78.5)\": 1.19, \"(78.5, 82.5)\": -1.25, \"(82.5, 87.5)\": -3.84, \"(87.5, 90.5)\": -0.35, \"(90.5, 92.5)\": -2.75, \"(92.5, 95.5)\": -4.6, \"(95.5, 108.5)\": -1.62, \"(108.5, 117.5)\": -0.66, \"(117.5, 124.5)\": -4.94, \"(124.5, 137.5)\": -0.24, \"(137.5, 149.0)\": -1.83, \"(149.0, 171.5)\": 3.59, \"(171.5, 173.0)\": 1.61, \"(173.0, 182.5)\": -1.86, \"(182.5, 192.5)\": -4.51, \"(192.5, 193.5)\": -1.89, \"(193.5, 253.0)\": -4.11, \"(253.0, 285.0)\": -10.7}\nUpper Bounds (95%-Confidence Interval): {\"(4.0, 11.5)\": 13.0, \"(11.5, 12.5)\": 2.32, \"(12.5, 15.5)\": 4.82, \"(15.5, 18.0)\": 4.68, \"(18.0, 28.5)\": 8.31, \"(28.5, 30.5)\": 5.26, \"(30.5, 52.0)\": 6.91, \"(52.0, 54.5)\": 5.03, \"(54.5, 67.5)\": 6.41, \"(67.5, 73.5)\": 3.57, \"(73.5, 76.5)\": -1.61, \"(76.5, 78.5)\": 3.39, \"(78.5, 82.5)\": 0.92, \"(82.5, 87.5)\": -1.75, \"(87.5, 90.5)\": 0.72, \"(90.5, 92.5)\": 0.6, \"(92.5, 95.5)\": -0.81, \"(95.5, 108.5)\": -0.34, \"(108.5, 117.5)\": 0.7, \"(117.5, 124.5)\": -1.93, \"(124.5, 137.5)\": 1.53, \"(137.5, 149.0)\": 0.22, \"(149.0, 171.5)\": 6.52, \"(171.5, 173.0)\": 3.72, \"(173.0, 182.5)\": 0.18, \"(182.5, 192.5)\": -2.33, \"(192.5, 193.5)\": -0.13, \"(193.5, 253.0)\": -1.06, \"(253.0, 285.0)\": -6.14}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(15.5, 18.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: ejection_fraction\nFeature Type: continuous\nMeans: {\"(14.0, 16.0)\": 4.55, \"(16.0, 22.5)\": 3.26, \"(22.5, 27.5)\": 1.89, \"(27.5, 32.5)\": -0.42, \"(32.5, 36.5)\": -1.76, \"(36.5, 39.0)\": 0.48, \"(39.0, 61.0)\": -0.83, \"(61.0, 67.5)\": 0.08, \"(67.5, 75.0)\": 0.8, \"(75.0, 80.0)\": -5.67}\nLower Bounds (95%-Confidence Interval): {\"(14.0, 16.0)\": 2.65, \"(16.0, 22.5)\": 2.42, \"(22.5, 27.5)\": 1.26, \"(27.5, 32.5)\": -0.83, \"(32.5, 36.5)\": -2.57, \"(36.5, 39.0)\": 0.17, \"(39.0, 61.0)\": -1.16, \"(61.0, 67.5)\": -0.39, \"(67.5, 75.0)\": 0.32, \"(75.0, 80.0)\": -8.05}\nUpper Bounds (95%-Confidence Interval): {\"(14.0, 16.0)\": 6.45, \"(16.0, 22.5)\": 4.1, \"(22.5, 27.5)\": 2.51, \"(27.5, 32.5)\": -0.01, \"(32.5, 36.5)\": -0.95, \"(36.5, 39.0)\": 0.79, \"(39.0, 61.0)\": -0.49, \"(61.0, 67.5)\": 0.55, \"(67.5, 75.0)\": 1.28, \"(75.0, 80.0)\": -3.29}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(75.0, 80.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: petal_width\nFeature Type: continuous\nMeans: {\"(0.1, 0.35)\": 8.07, \"(0.35, 0.45)\": 7.27, \"(0.45, 0.75)\": 6.18, \"(0.75, 1.25)\": -2.64, \"(1.25, 1.75)\": -3.46, \"(1.75, 2.5)\": -4.19}\nLower Bounds (95%-Confidence Interval): {\"(0.1, 0.35)\": 7.9, \"(0.35, 0.45)\": 7.05, \"(0.45, 0.75)\": 3.08, \"(0.75, 1.25)\": -2.81, \"(1.25, 1.75)\": -3.62, \"(1.75, 2.5)\": -4.29}\nUpper Bounds (95%-Confidence Interval): {\"(0.1, 0.35)\": 8.23, \"(0.35, 0.45)\": 7.49, \"(0.45, 0.75)\": 9.28, \"(0.75, 1.25)\": -2.47, \"(1.25, 1.75)\": -3.3, \"(1.75, 2.5)\": -4.08}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.45, 0.75)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: IneffectiveDisasterPreparedness\nFeature Type: continuous\nMeans: {\"(0.0, 1.5)\": -0.02526, \"(1.5, 2.5)\": -0.01738, \"(2.5, 3.5)\": -0.01172, \"(3.5, 4.5)\": -0.00537, \"(4.5, 5.5)\": -0.00021, \"(5.5, 6.5)\": 0.0066, \"(6.5, 7.5)\": 0.01026, \"(7.5, 8.5)\": 0.01717, \"(8.5, 9.5)\": 0.02426, \"(9.5, 10.5)\": 0.02823, \"(10.5, 11.5)\": 0.03325, \"(11.5, 13.5)\": 0.03915, \"(13.5, 15.0)\": 0.03572}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02806, \"(1.5, 2.5)\": -0.01811, \"(2.5, 3.5)\": -0.01241, \"(3.5, 4.5)\": -0.0056, \"(4.5, 5.5)\": -0.00057, \"(5.5, 6.5)\": 0.00621, \"(6.5, 7.5)\": 0.00967, \"(7.5, 8.5)\": 0.01672, \"(8.5, 9.5)\": 0.02334, \"(9.5, 10.5)\": 0.02687, \"(10.5, 11.5)\": 0.03182, \"(11.5, 13.5)\": 0.03364, \"(13.5, 15.0)\": 0.0307}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02245, \"(1.5, 2.5)\": -0.01664, \"(2.5, 3.5)\": -0.01102, \"(3.5, 4.5)\": -0.00514, \"(4.5, 5.5)\": 0.00016, \"(5.5, 6.5)\": 0.00699, \"(6.5, 7.5)\": 0.01085, \"(7.5, 8.5)\": 0.01761, \"(8.5, 9.5)\": 0.02519, \"(9.5, 10.5)\": 0.02958, \"(10.5, 11.5)\": 0.03468, \"(11.5, 13.5)\": 0.04466, \"(13.5, 15.0)\": 0.04073}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(11.5, 13.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: housing_median_age\nFeature Type: continuous\nMeans: {\"(1.0, 4.5)\": -19998.0, \"(4.5, 7.5)\": -7788.2, \"(7.5, 16.5)\": -10680.2, \"(16.5, 18.5)\": -6304.4, \"(18.5, 27.5)\": -1760.6, \"(27.5, 34.5)\": 2164.8, \"(34.5, 38.5)\": -912.5, \"(38.5, 41.5)\": 4199.6, \"(41.5, 45.5)\": -497.4, \"(45.5, 47.5)\": -5189.8, \"(47.5, 48.5)\": 5201.0, \"(48.5, 49.5)\": 2159.0, \"(49.5, 50.5)\": 6135.7, \"(50.5, 51.5)\": 11513.8, \"(51.5, 52.0)\": 27549.7}\nLower Bounds (95%-Confidence Interval): {\"(1.0, 4.5)\": -26905.5, \"(4.5, 7.5)\": -11566.0, \"(7.5, 16.5)\": -12538.5, \"(16.5, 18.5)\": -7756.2, \"(18.5, 27.5)\": -3361.1, \"(27.5, 34.5)\": 124.5, \"(34.5, 38.5)\": -1933.4, \"(38.5, 41.5)\": 2260.6, \"(41.5, 45.5)\": -4429.7, \"(45.5, 47.5)\": -8697.7, \"(47.5, 48.5)\": 2180.3, \"(48.5, 49.5)\": -1981.1, \"(49.5, 50.5)\": 1581.5, \"(50.5, 51.5)\": 5647.5, \"(51.5, 52.0)\": 25827.1}\nUpper Bounds (95%-Confidence Interval): {\"(1.0, 4.5)\": -13090.4, \"(4.5, 7.5)\": -4010.4, \"(7.5, 16.5)\": -8821.8, \"(16.5, 18.5)\": -4852.5, \"(18.5, 27.5)\": -160.0, \"(27.5, 34.5)\": 4205.0, \"(34.5, 38.5)\": 108.5, \"(38.5, 41.5)\": 6138.7, \"(41.5, 45.5)\": 3434.9, \"(45.5, 47.5)\": -1682.0, \"(47.5, 48.5)\": 8221.7, \"(48.5, 49.5)\": 6299.1, \"(49.5, 50.5)\": 10689.9, \"(50.5, 51.5)\": 17380.1, \"(51.5, 52.0)\": 29272.3}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(1.0, 4.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: anaemia\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.0818, \"(0.5, 1.0)\": 0.0917}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.1464, \"(0.5, 1.0)\": 0.0194}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0173, \"(0.5, 1.0)\": 0.1641}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.5, 1.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: texture_worst\nFeature Type: continuous\nMeans: {\"(12.02, 16.935000000000002)\": -1.885, \"(16.935000000000002, 18.335)\": -1.717, \"(18.335, 19.505)\": -1.55, \"(19.505, 20.225)\": -0.851, \"(20.225, 21.955)\": -0.612, \"(21.955, 23.59)\": -0.44, \"(23.59, 24.795)\": -0.272, \"(24.795, 25.18)\": -0.1, \"(25.18, 25.83)\": 0.078, \"(25.83, 26.855)\": 0.279, \"(26.855, 27.994999999999997)\": 0.451, \"(27.994999999999997, 29.225)\": 0.619, \"(29.225, 31.515)\": 0.878, \"(31.515, 32.485)\": 1.044, \"(32.485, 35.05)\": 1.256, \"(35.05, 49.54)\": 1.423}\nLower Bounds (95%-Confidence Interval): {\"(12.02, 16.935000000000002)\": -4.342, \"(16.935000000000002, 18.335)\": -4.128, \"(18.335, 19.505)\": -3.934, \"(19.505, 20.225)\": -1.264, \"(20.225, 21.955)\": -0.945, \"(21.955, 23.59)\": -0.663, \"(23.59, 24.795)\": -0.468, \"(24.795, 25.18)\": -0.274, \"(25.18, 25.83)\": -0.503, \"(25.83, 26.855)\": -0.327, \"(26.855, 27.994999999999997)\": -0.163, \"(27.994999999999997, 29.225)\": -0.01, \"(29.225, 31.515)\": -0.206, \"(31.515, 32.485)\": -0.081, \"(32.485, 35.05)\": -0.18, \"(35.05, 49.54)\": -0.014}\nUpper Bounds (95%-Confidence Interval): {\"(12.02, 16.935000000000002)\": 0.572, \"(16.935000000000002, 18.335)\": 0.695, \"(18.335, 19.505)\": 0.835, \"(19.505, 20.225)\": -0.437, \"(20.225, 21.955)\": -0.279, \"(21.955, 23.59)\": -0.218, \"(23.59, 24.795)\": -0.076, \"(24.795, 25.18)\": 0.073, \"(25.18, 25.83)\": 0.66, \"(25.83, 26.855)\": 0.884, \"(26.855, 27.994999999999997)\": 1.065, \"(27.994999999999997, 29.225)\": 1.248, \"(29.225, 31.515)\": 1.961, \"(31.515, 32.485)\": 2.17, \"(32.485, 35.05)\": 2.691, \"(35.05, 49.54)\": 2.861}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(12.02, 16.935000000000002)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: creatinine_phosphokinase\nFeature Type: continuous\nMeans: {\"(23.0, 32.0)\": -0.48, \"(32.0, 49.5)\": 0.68, \"(49.5, 56.5)\": -4.31, \"(56.5, 59.5)\": -2.44, \"(59.5, 64.5)\": -1.82, \"(64.5, 85.0)\": -1.1, \"(85.0, 87.0)\": 0.42, \"(87.0, 93.5)\": -0.75, \"(93.5, 94.5)\": 0.47, \"(94.5, 103.5)\": -0.53, \"(103.5, 107.5)\": 0.12, \"(107.5, 120.0)\": -0.5, \"(120.0, 121.5)\": 0.24, \"(121.5, 126.0)\": 1.25, \"(126.0, 127.5)\": -3.14, \"(127.5, 145.5)\": 1.51, \"(145.5, 147.0)\": 0.91, \"(147.0, 150.0)\": -0.15, \"(150.0, 160.5)\": -1.08, \"(160.5, 189.5)\": -0.45, \"(189.5, 232.5)\": -1.26, \"(232.5, 254.5)\": -0.16, \"(254.5, 258.5)\": 2.88, \"(258.5, 280.5)\": 1.68, \"(280.5, 331.5)\": 1.11, \"(331.5, 370.0)\": 0.44, \"(370.0, 462.0)\": 1.1, \"(462.0, 597.5)\": 0.53, \"(597.5, 751.0)\": -1.87, \"(751.0, 766.5)\": 0.06, \"(766.5, 806.0)\": 2.64, \"(806.0, 873.5)\": 2.05, \"(873.5, 1036.0)\": 0.28, \"(1036.0, 1415.0)\": 0.85, \"(1415.0, 1649.0)\": 0.18, \"(1649.0, 1726.0)\": 2.26, \"(1726.0, 1886.0)\": 0.04, \"(1886.0, 2038.5)\": 7.0, \"(2038.5, 2307.5)\": 2.26, \"(2307.5, 2444.0)\": 5.81, \"(2444.0, 3440.5)\": -2.71, \"(3440.5, 4253.0)\": -1.47, \"(4253.0, 5548.5)\": 1.68, \"(5548.5, 7861.0)\": 3.47}\nLower Bounds (95%-Confidence Interval): {\"(23.0, 32.0)\": -1.51, \"(32.0, 49.5)\": -0.87, \"(49.5, 56.5)\": -5.69, \"(56.5, 59.5)\": -3.58, \"(59.5, 64.5)\": -2.64, \"(64.5, 85.0)\": -2.07, \"(85.0, 87.0)\": -2.37, \"(87.0, 93.5)\": -1.85, \"(93.5, 94.5)\": -0.56, \"(94.5, 103.5)\": -0.85, \"(103.5, 107.5)\": -0.45, \"(107.5, 120.0)\": -1.09, \"(120.0, 121.5)\": -0.48, \"(121.5, 126.0)\": 0.88, \"(126.0, 127.5)\": -5.59, \"(127.5, 145.5)\": 0.93, \"(145.5, 147.0)\": 0.57, \"(147.0, 150.0)\": -0.64, \"(150.0, 160.5)\": -2.38, \"(160.5, 189.5)\": -1.47, \"(189.5, 232.5)\": -2.02, \"(232.5, 254.5)\": -1.04, \"(254.5, 258.5)\": 1.73, \"(258.5, 280.5)\": 0.55, \"(280.5, 331.5)\": 0.09, \"(331.5, 370.0)\": -0.26, \"(370.0, 462.0)\": 0.18, \"(462.0, 597.5)\": 0.4, \"(597.5, 751.0)\": -3.59, \"(751.0, 766.5)\": -2.06, \"(766.5, 806.0)\": 1.02, \"(806.0, 873.5)\": 0.45, \"(873.5, 1036.0)\": -0.52, \"(1036.0, 1415.0)\": 0.33, \"(1415.0, 1649.0)\": -0.68, \"(1649.0, 1726.0)\": -0.23, \"(1726.0, 1886.0)\": -1.16, \"(1886.0, 2038.5)\": 5.88, \"(2038.5, 2307.5)\": 1.8, \"(2307.5, 2444.0)\": 4.43, \"(2444.0, 3440.5)\": -5.48, \"(3440.5, 4253.0)\": -2.15, \"(4253.0, 5548.5)\": 0.41, \"(5548.5, 7861.0)\": 2.17}\nUpper Bounds (95%-Confidence Interval): {\"(23.0, 32.0)\": 0.54, \"(32.0, 49.5)\": 2.24, \"(49.5, 56.5)\": -2.93, \"(56.5, 59.5)\": -1.31, \"(59.5, 64.5)\": -1.0, \"(64.5, 85.0)\": -0.13, \"(85.0, 87.0)\": 3.22, \"(87.0, 93.5)\": 0.35, \"(93.5, 94.5)\": 1.51, \"(94.5, 103.5)\": -0.2, \"(103.5, 107.5)\": 0.69, \"(107.5, 120.0)\": 0.09, \"(120.0, 121.5)\": 0.97, \"(121.5, 126.0)\": 1.61, \"(126.0, 127.5)\": -0.68, \"(127.5, 145.5)\": 2.09, \"(145.5, 147.0)\": 1.25, \"(147.0, 150.0)\": 0.33, \"(150.0, 160.5)\": 0.22, \"(160.5, 189.5)\": 0.57, \"(189.5, 232.5)\": -0.49, \"(232.5, 254.5)\": 0.72, \"(254.5, 258.5)\": 4.03, \"(258.5, 280.5)\": 2.81, \"(280.5, 331.5)\": 2.12, \"(331.5, 370.0)\": 1.15, \"(370.0, 462.0)\": 2.02, \"(462.0, 597.5)\": 0.67, \"(597.5, 751.0)\": -0.15, \"(751.0, 766.5)\": 2.18, \"(766.5, 806.0)\": 4.25, \"(806.0, 873.5)\": 3.65, \"(873.5, 1036.0)\": 1.09, \"(1036.0, 1415.0)\": 1.38, \"(1415.0, 1649.0)\": 1.04, \"(1649.0, 1726.0)\": 4.75, \"(1726.0, 1886.0)\": 1.24, \"(1886.0, 2038.5)\": 8.11, \"(2038.5, 2307.5)\": 2.72, \"(2307.5, 2444.0)\": 7.19, \"(2444.0, 3440.5)\": 0.06, \"(3440.5, 4253.0)\": -0.79, \"(4253.0, 5548.5)\": 2.95, \"(5548.5, 7861.0)\": 4.78}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(85.0, 87.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: NativeCountry\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.195, \"(0.5, 1.5)\": 1.333, \"(1.5, 2.5)\": -0.02, \"(2.5, 3.5)\": -0.402, \"(3.5, 4.5)\": -1.423, \"(4.5, 5.5)\": 0.086, \"(5.5, 7.5)\": -0.843, \"(7.5, 8.5)\": -0.246, \"(8.5, 11.5)\": 0.062, \"(11.5, 20.5)\": -0.315, \"(20.5, 21.5)\": 0.109, \"(21.5, 22.5)\": 0.476, \"(22.5, 24.5)\": 0.133, \"(24.5, 26.5)\": -0.35, \"(26.5, 29.5)\": -0.489, \"(29.5, 32.5)\": -0.108, \"(32.5, 33.5)\": -0.483, \"(33.5, 35.5)\": -0.664, \"(35.5, 38.5)\": -0.396, \"(38.5, 39.5)\": 0.028, \"(39.5, 40.5)\": -0.596, \"(40.5, 41.0)\": 1.112}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.344, \"(0.5, 1.5)\": 0.452, \"(1.5, 2.5)\": -0.269, \"(2.5, 3.5)\": -0.76, \"(3.5, 4.5)\": -2.688, \"(4.5, 5.5)\": -0.257, \"(5.5, 7.5)\": -1.727, \"(7.5, 8.5)\": -0.488, \"(8.5, 11.5)\": -0.121, \"(11.5, 20.5)\": -0.631, \"(20.5, 21.5)\": -0.319, \"(21.5, 22.5)\": 0.048, \"(22.5, 24.5)\": -0.066, \"(24.5, 26.5)\": -0.66, \"(26.5, 29.5)\": -1.067, \"(29.5, 32.5)\": -0.254, \"(32.5, 33.5)\": -0.844, \"(33.5, 35.5)\": -1.156, \"(35.5, 38.5)\": -0.997, \"(38.5, 39.5)\": 0.02, \"(39.5, 40.5)\": -1.452, \"(40.5, 41.0)\": 0.408}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.045, \"(0.5, 1.5)\": 2.213, \"(1.5, 2.5)\": 0.228, \"(2.5, 3.5)\": -0.043, \"(3.5, 4.5)\": -0.158, \"(4.5, 5.5)\": 0.429, \"(5.5, 7.5)\": 0.04, \"(7.5, 8.5)\": -0.004, \"(8.5, 11.5)\": 0.245, \"(11.5, 20.5)\": 0.001, \"(20.5, 21.5)\": 0.537, \"(21.5, 22.5)\": 0.904, \"(22.5, 24.5)\": 0.331, \"(24.5, 26.5)\": -0.04, \"(26.5, 29.5)\": 0.089, \"(29.5, 32.5)\": 0.038, \"(32.5, 33.5)\": -0.121, \"(33.5, 35.5)\": -0.172, \"(35.5, 38.5)\": 0.204, \"(38.5, 39.5)\": 0.036, \"(39.5, 40.5)\": 0.26, \"(40.5, 41.0)\": 1.816}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(3.5, 4.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: fractal_dimension_worst\nFeature Type: continuous\nMeans: {\"(0.05504, 0.058984999999999996)\": 0.1558, \"(0.058984999999999996, 0.065905)\": 0.0938, \"(0.065905, 0.070015)\": 0.0421, \"(0.070015, 0.071645)\": -0.0137, \"(0.071645, 0.07281)\": -0.0856, \"(0.07281, 0.075845)\": -0.1383, \"(0.075845, 0.083565)\": -0.1924, \"(0.083565, 0.08926)\": -0.3371, \"(0.08926, 0.09129999999999999)\": -0.285, \"(0.09129999999999999, 0.09222)\": -0.2283, \"(0.09222, 0.094545)\": -0.1756, \"(0.094545, 0.095845)\": -0.1188, \"(0.095845, 0.09595500000000001)\": -0.0641, \"(0.09595500000000001, 0.09849)\": 0.188, \"(0.09849, 0.1008)\": 0.244, \"(0.1008, 0.1018)\": 0.3042, \"(0.1018, 0.10569999999999999)\": 0.5739, \"(0.10569999999999999, 0.1074)\": 0.6302, \"(0.1074, 0.11810000000000001)\": 0.4862, \"(0.11810000000000001, 0.12475)\": 0.5412, \"(0.12475, 0.14024999999999999)\": 0.5946, \"(0.14024999999999999, 0.2075)\": 0.6515}\nLower Bounds (95%-Confidence Interval): {\"(0.05504, 0.058984999999999996)\": -0.1488, \"(0.058984999999999996, 0.065905)\": -0.1634, \"(0.065905, 0.070015)\": -0.1018, \"(0.070015, 0.071645)\": -0.1659, \"(0.071645, 0.07281)\": -0.2246, \"(0.07281, 0.075845)\": -0.2831, \"(0.075845, 0.083565)\": -0.3593, \"(0.083565, 0.08926)\": -0.8641, \"(0.08926, 0.09129999999999999)\": -0.8008, \"(0.09129999999999999, 0.09222)\": -0.734, \"(0.09222, 0.094545)\": -0.6937, \"(0.094545, 0.095845)\": -0.6426, \"(0.095845, 0.09595500000000001)\": -0.5681, \"(0.09595500000000001, 0.09849)\": -0.3082, \"(0.09849, 0.1008)\": -0.2557, \"(0.1008, 0.1018)\": -0.2006, \"(0.1018, 0.10569999999999999)\": -0.5367, \"(0.10569999999999999, 0.1074)\": -0.4808, \"(0.1074, 0.11810000000000001)\": -0.0451, \"(0.11810000000000001, 0.12475)\": 0.0507, \"(0.12475, 0.14024999999999999)\": 0.0965, \"(0.14024999999999999, 0.2075)\": 0.119}\nUpper Bounds (95%-Confidence Interval): {\"(0.05504, 0.058984999999999996)\": 0.4604, \"(0.058984999999999996, 0.065905)\": 0.3511, \"(0.065905, 0.070015)\": 0.186, \"(0.070015, 0.071645)\": 0.1385, \"(0.071645, 0.07281)\": 0.0533, \"(0.07281, 0.075845)\": 0.0064, \"(0.075845, 0.083565)\": -0.0255, \"(0.083565, 0.08926)\": 0.1899, \"(0.08926, 0.09129999999999999)\": 0.2307, \"(0.09129999999999999, 0.09222)\": 0.2773, \"(0.09222, 0.094545)\": 0.3426, \"(0.094545, 0.095845)\": 0.405, \"(0.095845, 0.09595500000000001)\": 0.4399, \"(0.09595500000000001, 0.09849)\": 0.6842, \"(0.09849, 0.1008)\": 0.7436, \"(0.1008, 0.1018)\": 0.8091, \"(0.1018, 0.10569999999999999)\": 1.6844, \"(0.10569999999999999, 0.1074)\": 1.7412, \"(0.1074, 0.11810000000000001)\": 1.0176, \"(0.11810000000000001, 0.12475)\": 1.0318, \"(0.12475, 0.14024999999999999)\": 1.0928, \"(0.14024999999999999, 0.2075)\": 1.184}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.10569999999999999, 0.1074)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Gender\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.4751, \"(0.5, 1.0)\": 0.2339}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.5571, \"(0.5, 1.0)\": 0.1936}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.3931, \"(0.5, 1.0)\": 0.2743}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(0.0, 0.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: area_se\nFeature Type: continuous\nMeans: {\"(6.802, 11.184999999999999)\": -0.919, \"(11.184999999999999, 12.765)\": -0.814, \"(12.765, 13.350000000000001)\": -0.704, \"(13.350000000000001, 15.3)\": -0.596, \"(15.3, 16.955)\": -0.49, \"(16.955, 18.515)\": -0.367, \"(18.515, 20.905)\": -0.256, \"(20.905, 32.985)\": -0.151, \"(32.985, 34.730000000000004)\": 0.081, \"(34.730000000000004, 41.21)\": 0.188, \"(41.21, 50.405)\": 0.292, \"(50.405, 56.915)\": 0.417, \"(56.915, 67.5)\": 0.53, \"(67.5, 81.56)\": 0.638, \"(81.56, 94.00999999999999)\": 0.751, \"(94.00999999999999, 106.2)\": 0.862, \"(106.2, 153.25)\": 0.974, \"(153.25, 542.2)\": 1.082}\nLower Bounds (95%-Confidence Interval): {\"(6.802, 11.184999999999999)\": -1.305, \"(11.184999999999999, 12.765)\": -1.176, \"(12.765, 13.350000000000001)\": -1.036, \"(13.350000000000001, 15.3)\": -0.901, \"(15.3, 16.955)\": -0.696, \"(16.955, 18.515)\": -0.504, \"(18.515, 20.905)\": -0.392, \"(20.905, 32.985)\": -0.922, \"(32.985, 34.730000000000004)\": -0.261, \"(34.730000000000004, 41.21)\": -0.102, \"(41.21, 50.405)\": 0.02, \"(50.405, 56.915)\": 0.072, \"(56.915, 67.5)\": 0.147, \"(67.5, 81.56)\": 0.223, \"(81.56, 94.00999999999999)\": 0.326, \"(94.00999999999999, 106.2)\": 0.402, \"(106.2, 153.25)\": 0.501, \"(153.25, 542.2)\": 0.571}\nUpper Bounds (95%-Confidence Interval): {\"(6.802, 11.184999999999999)\": -0.532, \"(11.184999999999999, 12.765)\": -0.452, \"(12.765, 13.350000000000001)\": -0.371, \"(13.350000000000001, 15.3)\": -0.291, \"(15.3, 16.955)\": -0.284, \"(16.955, 18.515)\": -0.23, \"(18.515, 20.905)\": -0.121, \"(20.905, 32.985)\": 0.62, \"(32.985, 34.730000000000004)\": 0.424, \"(34.730000000000004, 41.21)\": 0.479, \"(41.21, 50.405)\": 0.563, \"(50.405, 56.915)\": 0.762, \"(56.915, 67.5)\": 0.913, \"(67.5, 81.56)\": 1.052, \"(81.56, 94.00999999999999)\": 1.176, \"(94.00999999999999, 106.2)\": 1.323, \"(106.2, 153.25)\": 1.448, \"(153.25, 542.2)\": 1.593}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(20.905, 32.985)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Encroachments\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02484, \"(0.5, 1.5)\": -0.02089, \"(1.5, 2.5)\": -0.01739, \"(2.5, 3.5)\": -0.01124, \"(3.5, 4.5)\": -0.00474, \"(4.5, 5.5)\": 0.00077, \"(5.5, 6.5)\": 0.00574, \"(6.5, 7.5)\": 0.01068, \"(7.5, 8.5)\": 0.01599, \"(8.5, 9.5)\": 0.02231, \"(9.5, 10.5)\": 0.02667, \"(10.5, 13.5)\": 0.03305, \"(13.5, 16.0)\": 0.02016}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02637, \"(0.5, 1.5)\": -0.02217, \"(1.5, 2.5)\": -0.0179, \"(2.5, 3.5)\": -0.01163, \"(3.5, 4.5)\": -0.00519, \"(4.5, 5.5)\": 0.00046, \"(5.5, 6.5)\": 0.00525, \"(6.5, 7.5)\": 0.00992, \"(7.5, 8.5)\": 0.01538, \"(8.5, 9.5)\": 0.02115, \"(9.5, 10.5)\": 0.02528, \"(10.5, 13.5)\": 0.02547, \"(13.5, 16.0)\": 0.01297}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0233, \"(0.5, 1.5)\": -0.01962, \"(1.5, 2.5)\": -0.01689, \"(2.5, 3.5)\": -0.01085, \"(3.5, 4.5)\": -0.0043, \"(4.5, 5.5)\": 0.00109, \"(5.5, 6.5)\": 0.00623, \"(6.5, 7.5)\": 0.01144, \"(7.5, 8.5)\": 0.0166, \"(8.5, 9.5)\": 0.02348, \"(9.5, 10.5)\": 0.02807, \"(10.5, 13.5)\": 0.04062, \"(13.5, 16.0)\": 0.02734}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(10.5, 13.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Education\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.4028, \"(0.5, 1.5)\": -0.5397, \"(1.5, 3.5)\": -0.4851, \"(3.5, 4.5)\": -0.4021, \"(4.5, 5.5)\": -0.457, \"(5.5, 6.5)\": -0.2537, \"(6.5, 7.5)\": -0.0494, \"(7.5, 8.5)\": 0.0457, \"(8.5, 9.5)\": 0.1831, \"(9.5, 10.5)\": 0.1392, \"(10.5, 11.5)\": -0.0652, \"(11.5, 14.5)\": 0.1954, \"(14.5, 15.0)\": 0.1393}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.5596, \"(0.5, 1.5)\": -0.6499, \"(1.5, 3.5)\": -0.618, \"(3.5, 4.5)\": -0.5693, \"(4.5, 5.5)\": -0.5278, \"(5.5, 6.5)\": -0.3342, \"(6.5, 7.5)\": -0.0948, \"(7.5, 8.5)\": -0.0062, \"(8.5, 9.5)\": 0.1525, \"(9.5, 10.5)\": 0.1072, \"(10.5, 11.5)\": -0.0869, \"(11.5, 14.5)\": 0.1476, \"(14.5, 15.0)\": 0.1012}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.2459, \"(0.5, 1.5)\": -0.4295, \"(1.5, 3.5)\": -0.3523, \"(3.5, 4.5)\": -0.235, \"(4.5, 5.5)\": -0.3862, \"(5.5, 6.5)\": -0.1733, \"(6.5, 7.5)\": -0.0039, \"(7.5, 8.5)\": 0.0977, \"(8.5, 9.5)\": 0.2137, \"(9.5, 10.5)\": 0.1711, \"(10.5, 11.5)\": -0.0435, \"(11.5, 14.5)\": 0.2431, \"(14.5, 15.0)\": 0.1775}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(3.5, 4.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Balance\nFeature Type: continuous\nMeans: {\"(0.0, 50418.515)\": -0.132, \"(50418.515, 53570.93)\": -0.285, \"(53570.93, 54249.445)\": -0.826, \"(54249.445, 57428.56)\": -0.404, \"(57428.56, 60041.265)\": -0.005, \"(60041.265, 64897.8)\": 0.215, \"(64897.8, 72985.875)\": 0.086, \"(72985.875, 74989.08499999999)\": -0.012, \"(74989.08499999999, 76596.815)\": 0.247, \"(76596.815, 79953.185)\": 0.829, \"(79953.185, 83348.07)\": 0.564, \"(83348.07, 101890.23999999999)\": 0.414, \"(101890.23999999999, 114327.485)\": 0.248, \"(114327.485, 123946.3)\": 0.164, \"(123946.3, 141661.24)\": 0.075, \"(141661.24, 174920.08000000002)\": 0.173, \"(174920.08000000002, 181813.135)\": 0.059, \"(181813.135, 191993.675)\": -0.349, \"(191993.675, 200829.925)\": -0.459, \"(200829.925, 206951.87)\": -0.616, \"(206951.87, 216109.88)\": -0.256}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 50418.515)\": -0.192, \"(50418.515, 53570.93)\": -0.628, \"(53570.93, 54249.445)\": -1.999, \"(54249.445, 57428.56)\": -0.798, \"(57428.56, 60041.265)\": -0.322, \"(60041.265, 64897.8)\": -0.105, \"(64897.8, 72985.875)\": -0.195, \"(72985.875, 74989.08499999999)\": -0.418, \"(74989.08499999999, 76596.815)\": -0.231, \"(76596.815, 79953.185)\": 0.338, \"(79953.185, 83348.07)\": 0.321, \"(83348.07, 101890.23999999999)\": 0.247, \"(101890.23999999999, 114327.485)\": 0.097, \"(114327.485, 123946.3)\": 0.069, \"(123946.3, 141661.24)\": -0.23, \"(141661.24, 174920.08000000002)\": -0.272, \"(174920.08000000002, 181813.135)\": -0.147, \"(181813.135, 191993.675)\": -0.864, \"(191993.675, 200829.925)\": -0.991, \"(200829.925, 206951.87)\": -1.401, \"(206951.87, 216109.88)\": -0.862}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 50418.515)\": -0.072, \"(50418.515, 53570.93)\": 0.057, \"(53570.93, 54249.445)\": 0.347, \"(54249.445, 57428.56)\": -0.011, \"(57428.56, 60041.265)\": 0.312, \"(60041.265, 64897.8)\": 0.534, \"(64897.8, 72985.875)\": 0.367, \"(72985.875, 74989.08499999999)\": 0.395, \"(74989.08499999999, 76596.815)\": 0.725, \"(76596.815, 79953.185)\": 1.32, \"(79953.185, 83348.07)\": 0.806, \"(83348.07, 101890.23999999999)\": 0.582, \"(101890.23999999999, 114327.485)\": 0.398, \"(114327.485, 123946.3)\": 0.259, \"(123946.3, 141661.24)\": 0.379, \"(141661.24, 174920.08000000002)\": 0.618, \"(174920.08000000002, 181813.135)\": 0.264, \"(181813.135, 191993.675)\": 0.166, \"(191993.675, 200829.925)\": 0.073, \"(200829.925, 206951.87)\": 0.169, \"(206951.87, 216109.88)\": 0.35}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(53570.93, 54249.445)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: RoomService\nFeature Type: continuous\nMeans: {\"(0.0, 105.5)\": 0.328, \"(105.5, 296.5)\": 0.028, \"(296.5, 335.5)\": -0.208, \"(335.5, 340.0)\": 0.165, \"(340.0, 343.0)\": -0.1, \"(343.0, 596.5)\": -0.741, \"(596.5, 712.5)\": -0.978, \"(712.5, 734.0)\": -1.212, \"(734.0, 800.0)\": -1.446, \"(800.0, 816.0)\": -1.136, \"(816.0, 997.5)\": -1.454, \"(997.5, 1031.0)\": -1.106, \"(1031.0, 1041.0)\": -1.368, \"(1041.0, 2172.5)\": -1.866, \"(2172.5, 2283.5)\": -1.455, \"(2283.5, 2313.5)\": -1.171, \"(2313.5, 2336.5)\": -0.66, \"(2336.5, 2420.0)\": -2.559, \"(2420.0, 2992.5)\": -3.229, \"(2992.5, 3006.0)\": -2.708, \"(3006.0, 3196.5)\": -2.984, \"(3196.5, 3249.5)\": -2.709, \"(3249.5, 14327.0)\": -4.146}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 105.5)\": -0.06, \"(105.5, 296.5)\": -0.369, \"(296.5, 335.5)\": -1.022, \"(335.5, 340.0)\": -0.184, \"(340.0, 343.0)\": -1.038, \"(343.0, 596.5)\": -1.323, \"(596.5, 712.5)\": -1.547, \"(712.5, 734.0)\": -1.555, \"(734.0, 800.0)\": -1.8, \"(800.0, 816.0)\": -2.191, \"(816.0, 997.5)\": -1.824, \"(997.5, 1031.0)\": -1.706, \"(1031.0, 1041.0)\": -2.147, \"(1041.0, 2172.5)\": -2.244, \"(2172.5, 2283.5)\": -2.248, \"(2283.5, 2313.5)\": -1.568, \"(2313.5, 2336.5)\": -2.21, \"(2336.5, 2420.0)\": -3.537, \"(2420.0, 2992.5)\": -3.89, \"(2992.5, 3006.0)\": -3.955, \"(3006.0, 3196.5)\": -4.24, \"(3196.5, 3249.5)\": -3.98, \"(3249.5, 14327.0)\": -5.248}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 105.5)\": 0.716, \"(105.5, 296.5)\": 0.425, \"(296.5, 335.5)\": 0.607, \"(335.5, 340.0)\": 0.513, \"(340.0, 343.0)\": 0.837, \"(343.0, 596.5)\": -0.16, \"(596.5, 712.5)\": -0.409, \"(712.5, 734.0)\": -0.869, \"(734.0, 800.0)\": -1.092, \"(800.0, 816.0)\": -0.082, \"(816.0, 997.5)\": -1.083, \"(997.5, 1031.0)\": -0.506, \"(1031.0, 1041.0)\": -0.589, \"(1041.0, 2172.5)\": -1.488, \"(2172.5, 2283.5)\": -0.661, \"(2283.5, 2313.5)\": -0.774, \"(2313.5, 2336.5)\": 0.89, \"(2336.5, 2420.0)\": -1.582, \"(2420.0, 2992.5)\": -2.569, \"(2992.5, 3006.0)\": -1.461, \"(3006.0, 3196.5)\": -1.727, \"(3196.5, 3249.5)\": -1.438, \"(3249.5, 14327.0)\": -3.043}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(2313.5, 2336.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: area_worst\nFeature Type: continuous\nMeans: {\"(185.2, 357.5)\": -1.345, \"(357.5, 413.15)\": -1.192, \"(413.15, 471.9)\": -1.038, \"(471.9, 508.5)\": -0.878, \"(508.5, 633.9)\": -0.723, \"(633.9, 653.45)\": -0.565, \"(653.45, 710.2)\": -0.348, \"(710.2, 727.0999999999999)\": -0.165, \"(727.0999999999999, 805.95)\": 0.096, \"(805.95, 874.85)\": 0.253, \"(874.85, 928.5)\": 0.48, \"(928.5, 1033.5)\": 0.761, \"(1033.5, 1222.5)\": 0.932, \"(1222.5, 1346.5)\": 1.092, \"(1346.5, 1645.5)\": 1.245, \"(1645.5, 1979.0)\": 1.404, \"(1979.0, 4254.0)\": 1.557}\nLower Bounds (95%-Confidence Interval): {\"(185.2, 357.5)\": -2.413, \"(357.5, 413.15)\": -2.22, \"(413.15, 471.9)\": -2.004, \"(471.9, 508.5)\": -1.818, \"(508.5, 633.9)\": -1.868, \"(633.9, 653.45)\": -1.645, \"(653.45, 710.2)\": -0.767, \"(710.2, 727.0999999999999)\": -0.501, \"(727.0999999999999, 805.95)\": -0.573, \"(805.95, 874.85)\": -0.187, \"(874.85, 928.5)\": -0.49, \"(928.5, 1033.5)\": -0.484, \"(1033.5, 1222.5)\": -0.455, \"(1222.5, 1346.5)\": -0.298, \"(1346.5, 1645.5)\": -0.182, \"(1645.5, 1979.0)\": -0.049, \"(1979.0, 4254.0)\": 0.071}\nUpper Bounds (95%-Confidence Interval): {\"(185.2, 357.5)\": -0.278, \"(357.5, 413.15)\": -0.164, \"(413.15, 471.9)\": -0.073, \"(471.9, 508.5)\": 0.062, \"(508.5, 633.9)\": 0.423, \"(633.9, 653.45)\": 0.516, \"(653.45, 710.2)\": 0.071, \"(710.2, 727.0999999999999)\": 0.17, \"(727.0999999999999, 805.95)\": 0.764, \"(805.95, 874.85)\": 0.693, \"(874.85, 928.5)\": 1.449, \"(928.5, 1033.5)\": 2.006, \"(1033.5, 1222.5)\": 2.319, \"(1222.5, 1346.5)\": 2.482, \"(1346.5, 1645.5)\": 2.672, \"(1645.5, 1979.0)\": 2.857, \"(1979.0, 4254.0)\": 3.043}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(1979.0, 4254.0)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: CreditScore\nFeature Type: continuous\nMeans: {\"(350.0, 416.5)\": 0.62, \"(416.5, 421.5)\": 0.5698, \"(421.5, 427.5)\": 0.3799, \"(427.5, 437.5)\": 0.2757, \"(437.5, 464.5)\": 0.3274, \"(464.5, 470.5)\": 0.2778, \"(470.5, 477.5)\": 0.4561, \"(477.5, 478.5)\": 0.0595, \"(478.5, 494.5)\": 0.1431, \"(494.5, 515.5)\": 0.0909, \"(515.5, 523.5)\": -0.3342, \"(523.5, 539.5)\": -0.2192, \"(539.5, 566.5)\": -0.1337, \"(566.5, 598.5)\": -0.0838, \"(598.5, 661.5)\": -0.0327, \"(661.5, 684.5)\": 0.0186, \"(684.5, 741.5)\": 0.0696, \"(741.5, 769.5)\": 0.0206, \"(769.5, 792.5)\": 0.0691, \"(792.5, 805.5)\": 0.2231, \"(805.5, 806.5)\": 0.1131, \"(806.5, 850.0)\": -0.1138}\nLower Bounds (95%-Confidence Interval): {\"(350.0, 416.5)\": -0.0945, \"(416.5, 421.5)\": -0.1033, \"(421.5, 427.5)\": -0.0186, \"(427.5, 437.5)\": -0.1491, \"(437.5, 464.5)\": 0.0705, \"(464.5, 470.5)\": -0.0008, \"(470.5, 477.5)\": -0.0519, \"(477.5, 478.5)\": -0.3016, \"(478.5, 494.5)\": 0.0126, \"(494.5, 515.5)\": -0.1354, \"(515.5, 523.5)\": -0.5637, \"(523.5, 539.5)\": -0.3225, \"(539.5, 566.5)\": -0.2064, \"(566.5, 598.5)\": -0.1252, \"(598.5, 661.5)\": -0.1126, \"(661.5, 684.5)\": -0.0289, \"(684.5, 741.5)\": -0.0156, \"(741.5, 769.5)\": -0.0756, \"(769.5, 792.5)\": -0.016, \"(792.5, 805.5)\": -0.0471, \"(805.5, 806.5)\": -0.2533, \"(806.5, 850.0)\": -0.3888}\nUpper Bounds (95%-Confidence Interval): {\"(350.0, 416.5)\": 1.3346, \"(416.5, 421.5)\": 1.243, \"(421.5, 427.5)\": 0.7784, \"(427.5, 437.5)\": 0.7005, \"(437.5, 464.5)\": 0.5843, \"(464.5, 470.5)\": 0.5564, \"(470.5, 477.5)\": 0.9641, \"(477.5, 478.5)\": 0.4206, \"(478.5, 494.5)\": 0.2736, \"(494.5, 515.5)\": 0.3173, \"(515.5, 523.5)\": -0.1047, \"(523.5, 539.5)\": -0.1159, \"(539.5, 566.5)\": -0.061, \"(566.5, 598.5)\": -0.0424, \"(598.5, 661.5)\": 0.0472, \"(661.5, 684.5)\": 0.066, \"(684.5, 741.5)\": 0.1548, \"(741.5, 769.5)\": 0.1168, \"(769.5, 792.5)\": 0.1542, \"(792.5, 805.5)\": 0.4932, \"(805.5, 806.5)\": 0.4795, \"(806.5, 850.0)\": 0.1611}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(350.0, 416.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: households\nFeature Type: continuous\nMeans: {\"(2.0, 4.5)\": -5401.6, \"(4.5, 6.5)\": -23687.9, \"(6.5, 8.5)\": -53732.5, \"(8.5, 9.5)\": -14617.2, \"(9.5, 12.5)\": 16225.5, \"(12.5, 13.5)\": 21846.0, \"(13.5, 14.5)\": 29456.0, \"(14.5, 15.5)\": 14293.2, \"(15.5, 20.5)\": -21670.3, \"(20.5, 21.5)\": 3195.8, \"(21.5, 55.5)\": -12458.9, \"(55.5, 155.5)\": -20063.6, \"(155.5, 156.5)\": -15642.0, \"(156.5, 157.5)\": -6390.8, \"(157.5, 186.5)\": -19320.2, \"(186.5, 196.5)\": -23743.0, \"(196.5, 198.5)\": -18377.6, \"(198.5, 223.5)\": -12744.1, \"(223.5, 230.5)\": -6336.7, \"(230.5, 295.5)\": -10855.3, \"(295.5, 394.5)\": -6355.5, \"(394.5, 535.5)\": -443.1, \"(535.5, 561.5)\": 3934.9, \"(561.5, 599.5)\": 9004.1, \"(599.5, 600.5)\": 13667.2, \"(600.5, 634.5)\": 8706.3, \"(634.5, 635.5)\": 25959.4, \"(635.5, 824.5)\": 13815.1, \"(824.5, 864.5)\": 18503.2, \"(864.5, 962.5)\": 26367.0, \"(962.5, 964.5)\": 14554.6, \"(964.5, 976.5)\": 23227.2, \"(976.5, 978.5)\": 18664.6, \"(978.5, 990.5)\": 26114.1, \"(990.5, 1000.5)\": 30854.6, \"(1000.5, 1088.5)\": 25473.5, \"(1088.5, 1092.5)\": 21095.0, \"(1092.5, 1130.5)\": 26497.2, \"(1130.5, 1272.5)\": 33562.7, \"(1272.5, 3516.0)\": 28522.2, \"(3516.0, 6082.0)\": 21556.0}\nLower Bounds (95%-Confidence Interval): {\"(2.0, 4.5)\": -30426.4, \"(4.5, 6.5)\": -41560.8, \"(6.5, 8.5)\": -83483.7, \"(8.5, 9.5)\": -68637.5, \"(9.5, 12.5)\": -15018.5, \"(12.5, 13.5)\": -5488.2, \"(13.5, 14.5)\": 1721.7, \"(14.5, 15.5)\": -25117.7, \"(15.5, 20.5)\": -41734.0, \"(20.5, 21.5)\": -26800.7, \"(21.5, 55.5)\": -26732.7, \"(55.5, 155.5)\": -27250.3, \"(155.5, 156.5)\": -25256.4, \"(156.5, 157.5)\": -28521.9, \"(157.5, 186.5)\": -26383.4, \"(186.5, 196.5)\": -29250.8, \"(196.5, 198.5)\": -25752.9, \"(198.5, 223.5)\": -20683.5, \"(223.5, 230.5)\": -15595.3, \"(230.5, 295.5)\": -18207.8, \"(295.5, 394.5)\": -15406.0, \"(394.5, 535.5)\": -9211.1, \"(535.5, 561.5)\": -5668.7, \"(561.5, 599.5)\": 904.9, \"(599.5, 600.5)\": -3740.6, \"(600.5, 634.5)\": 3782.4, \"(634.5, 635.5)\": 139.1, \"(635.5, 824.5)\": 6137.4, \"(824.5, 864.5)\": 11294.8, \"(864.5, 962.5)\": 17755.5, \"(962.5, 964.5)\": -5105.1, \"(964.5, 976.5)\": 14837.4, \"(976.5, 978.5)\": 5892.7, \"(978.5, 990.5)\": 18169.8, \"(990.5, 1000.5)\": 15738.6, \"(1000.5, 1088.5)\": 19888.5, \"(1088.5, 1092.5)\": 9478.6, \"(1092.5, 1130.5)\": 20925.9, \"(1130.5, 1272.5)\": 24768.1, \"(1272.5, 3516.0)\": 19419.3, \"(3516.0, 6082.0)\": 8532.3}\nUpper Bounds (95%-Confidence Interval): {\"(2.0, 4.5)\": 19623.3, \"(4.5, 6.5)\": -5814.9, \"(6.5, 8.5)\": -23981.3, \"(8.5, 9.5)\": 39403.2, \"(9.5, 12.5)\": 47469.5, \"(12.5, 13.5)\": 49180.3, \"(13.5, 14.5)\": 57190.3, \"(14.5, 15.5)\": 53704.2, \"(15.5, 20.5)\": -1606.7, \"(20.5, 21.5)\": 33192.3, \"(21.5, 55.5)\": 1814.9, \"(55.5, 155.5)\": -12877.0, \"(155.5, 156.5)\": -6027.7, \"(156.5, 157.5)\": 15740.2, \"(157.5, 186.5)\": -12257.0, \"(186.5, 196.5)\": -18235.2, \"(196.5, 198.5)\": -11002.4, \"(198.5, 223.5)\": -4804.8, \"(223.5, 230.5)\": 2921.9, \"(230.5, 295.5)\": -3502.7, \"(295.5, 394.5)\": 2695.1, \"(394.5, 535.5)\": 8324.9, \"(535.5, 561.5)\": 13538.5, \"(561.5, 599.5)\": 17103.2, \"(599.5, 600.5)\": 31074.9, \"(600.5, 634.5)\": 13630.1, \"(634.5, 635.5)\": 51779.7, \"(635.5, 824.5)\": 21492.8, \"(824.5, 864.5)\": 25711.7, \"(864.5, 962.5)\": 34978.6, \"(962.5, 964.5)\": 34214.4, \"(964.5, 976.5)\": 31616.9, \"(976.5, 978.5)\": 31436.4, \"(978.5, 990.5)\": 34058.4, \"(990.5, 1000.5)\": 45970.6, \"(1000.5, 1088.5)\": 31058.5, \"(1088.5, 1092.5)\": 32711.5, \"(1092.5, 1130.5)\": 32068.4, \"(1130.5, 1272.5)\": 42357.3, \"(1272.5, 3516.0)\": 37625.1, \"(3516.0, 6082.0)\": 34579.6}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(8.5, 9.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: DiabetesPedigreeFunction\nFeature Type: continuous\nMeans: {\"(0.078, 0.1265)\": -0.528, \"(0.1265, 0.128)\": -0.218, \"(0.128, 0.2185)\": -0.342, \"(0.2185, 0.3375)\": -0.168, \"(0.3375, 0.4215)\": -0.077, \"(0.4215, 0.4955)\": 0.015, \"(0.4955, 0.5874999999999999)\": 0.131, \"(0.5874999999999999, 0.7215)\": 0.223, \"(0.7215, 0.889)\": 0.316, \"(0.889, 1.0865)\": 0.407, \"(1.0865, 1.178)\": 0.498, \"(1.178, 1.275)\": 1.018, \"(1.275, 1.3925)\": 1.283, \"(1.3925, 1.4175)\": 1.168, \"(1.4175, 1.451)\": 0.065, \"(1.451, 1.837)\": -0.193, \"(1.837, 2.137)\": -0.092}\nLower Bounds (95%-Confidence Interval): {\"(0.078, 0.1265)\": -0.817, \"(0.1265, 0.128)\": -0.817, \"(0.128, 0.2185)\": -0.618, \"(0.2185, 0.3375)\": -0.533, \"(0.3375, 0.4215)\": -0.266, \"(0.4215, 0.4955)\": -0.104, \"(0.4955, 0.5874999999999999)\": -0.054, \"(0.5874999999999999, 0.7215)\": 0.138, \"(0.7215, 0.889)\": 0.186, \"(0.889, 1.0865)\": 0.263, \"(1.0865, 1.178)\": 0.35, \"(1.178, 1.275)\": 0.124, \"(1.275, 1.3925)\": 0.133, \"(1.3925, 1.4175)\": -0.063, \"(1.4175, 1.451)\": -1.163, \"(1.451, 1.837)\": -1.466, \"(1.837, 2.137)\": -1.112}\nUpper Bounds (95%-Confidence Interval): {\"(0.078, 0.1265)\": -0.238, \"(0.1265, 0.128)\": 0.381, \"(0.128, 0.2185)\": -0.067, \"(0.2185, 0.3375)\": 0.197, \"(0.3375, 0.4215)\": 0.113, \"(0.4215, 0.4955)\": 0.135, \"(0.4955, 0.5874999999999999)\": 0.316, \"(0.5874999999999999, 0.7215)\": 0.308, \"(0.7215, 0.889)\": 0.445, \"(0.889, 1.0865)\": 0.552, \"(1.0865, 1.178)\": 0.646, \"(1.178, 1.275)\": 1.912, \"(1.275, 1.3925)\": 2.433, \"(1.3925, 1.4175)\": 2.398, \"(1.4175, 1.451)\": 1.293, \"(1.451, 1.837)\": 1.08, \"(1.837, 2.137)\": 0.928}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(1.451, 1.837)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: latitude\nFeature Type: continuous\nMeans: {\"(32.54, 32.565)\": 23234.8, \"(32.565, 32.685)\": -3182.4, \"(32.685, 32.715)\": 7727.3, \"(32.715, 32.915)\": 17670.3, \"(32.915, 33.275000000000006)\": 34030.3, \"(33.275000000000006, 33.355000000000004)\": 55000.2, \"(33.355000000000004, 33.465)\": 64326.4, \"(33.465, 33.504999999999995)\": 81519.1, \"(33.504999999999995, 33.555)\": 94496.7, \"(33.555, 33.565)\": 63293.1, \"(33.565, 33.575)\": 51665.3, \"(33.575, 33.635000000000005)\": 66563.2, \"(33.635000000000005, 33.655)\": 47304.3, \"(33.655, 33.765)\": 29789.1, \"(33.765, 33.894999999999996)\": 15892.8, \"(33.894999999999996, 33.985)\": 2769.6, \"(33.985, 33.995000000000005)\": 17775.7, \"(33.995000000000005, 34.045)\": 28884.5, \"(34.045, 34.085)\": 55702.3, \"(34.085, 34.165)\": 46322.8, \"(34.165, 34.175)\": 33820.1, \"(34.175, 34.195)\": 7500.1, \"(34.195, 34.215)\": -4126.2, \"(34.215, 34.254999999999995)\": -16649.8, \"(34.254999999999995, 34.325)\": -27636.8, \"(34.325, 34.345)\": 17113.4, \"(34.345, 34.375)\": 28769.5, \"(34.375, 34.455)\": 43828.3, \"(34.455, 34.474999999999994)\": 57774.8, \"(34.474999999999994, 34.504999999999995)\": 33279.2, \"(34.504999999999995, 34.545)\": 19368.1, \"(34.545, 34.625)\": 5698.9, \"(34.625, 34.635000000000005)\": -19637.8, \"(34.635000000000005, 34.644999999999996)\": -39271.0, \"(34.644999999999996, 34.715)\": -26993.1, \"(34.715, 35.325)\": -17344.4, \"(35.325, 36.375)\": -37699.6, \"(36.375, 36.535)\": -27730.8, \"(36.535, 36.635000000000005)\": -14690.6, \"(36.635000000000005, 36.845)\": -25070.6, \"(36.845, 37.275000000000006)\": -15387.7, \"(37.275000000000006, 37.335)\": -3329.1, \"(37.335, 37.425)\": 7953.5, \"(37.425, 37.445)\": 34546.2, \"(37.445, 37.465)\": 45097.3, \"(37.465, 37.495000000000005)\": 30019.5, \"(37.495000000000005, 37.585)\": 16643.2, \"(37.585, 37.595)\": -3057.8, \"(37.595, 37.605000000000004)\": -32379.8, \"(37.605000000000004, 37.754999999999995)\": -42729.0, \"(37.754999999999995, 37.775000000000006)\": -17898.2, \"(37.775000000000006, 37.795)\": -3229.6, \"(37.795, 37.805)\": 8902.6, \"(37.805, 37.855000000000004)\": -13456.8, \"(37.855000000000004, 37.915)\": -1362.3, \"(37.915, 37.925)\": -19143.7, \"(37.925, 37.945)\": -38768.9, \"(37.945, 38.355000000000004)\": -48247.9, \"(38.355000000000004, 39.085)\": -38467.7, \"(39.085, 39.474999999999994)\": -47690.5, \"(39.474999999999994, 40.135000000000005)\": -56986.6, \"(40.135000000000005, 40.665)\": -66271.5, \"(40.665, 41.775000000000006)\": -75627.3, \"(41.775000000000006, 41.95)\": -85116.1}\nLower Bounds (95%-Confidence Interval): {\"(32.54, 32.565)\": 964.8, \"(32.565, 32.685)\": -13385.7, \"(32.685, 32.715)\": -6553.6, \"(32.715, 32.915)\": 6526.7, \"(32.915, 33.275000000000006)\": 15999.9, \"(33.275000000000006, 33.355000000000004)\": 42157.4, \"(33.355000000000004, 33.465)\": 51350.1, \"(33.465, 33.504999999999995)\": 60415.4, \"(33.504999999999995, 33.555)\": 76698.7, \"(33.555, 33.565)\": 39537.3, \"(33.565, 33.575)\": 41623.9, \"(33.575, 33.635000000000005)\": 54208.1, \"(33.635000000000005, 33.655)\": 37976.2, \"(33.655, 33.765)\": 22371.1, \"(33.765, 33.894999999999996)\": 10058.4, \"(33.894999999999996, 33.985)\": -876.4, \"(33.985, 33.995000000000005)\": 13047.1, \"(33.995000000000005, 34.045)\": 23199.9, \"(34.045, 34.085)\": 50112.0, \"(34.085, 34.165)\": 40162.5, \"(34.165, 34.175)\": 28146.4, \"(34.175, 34.195)\": 2466.5, \"(34.195, 34.215)\": -9561.1, \"(34.215, 34.254999999999995)\": -23153.3, \"(34.254999999999995, 34.325)\": -37515.4, \"(34.325, 34.345)\": -3758.1, \"(34.345, 34.375)\": 15207.7, \"(34.375, 34.455)\": 33875.3, \"(34.455, 34.474999999999994)\": 41591.3, \"(34.474999999999994, 34.504999999999995)\": 20304.7, \"(34.504999999999995, 34.545)\": 13245.7, \"(34.545, 34.625)\": -12771.5, \"(34.625, 34.635000000000005)\": -37375.2, \"(34.635000000000005, 34.644999999999996)\": -49797.4, \"(34.644999999999996, 34.715)\": -34913.5, \"(34.715, 35.325)\": -47411.7, \"(35.325, 36.375)\": -46798.2, \"(36.375, 36.535)\": -34852.7, \"(36.535, 36.635000000000005)\": -23680.1, \"(36.635000000000005, 36.845)\": -34287.5, \"(36.845, 37.275000000000006)\": -23625.3, \"(37.275000000000006, 37.335)\": -9268.7, \"(37.335, 37.425)\": -4329.5, \"(37.425, 37.445)\": 29053.6, \"(37.445, 37.465)\": 29188.3, \"(37.465, 37.495000000000005)\": 21566.7, \"(37.495000000000005, 37.585)\": 8469.3, \"(37.585, 37.595)\": -16791.2, \"(37.595, 37.605000000000004)\": -38739.3, \"(37.605000000000004, 37.754999999999995)\": -51675.5, \"(37.754999999999995, 37.775000000000006)\": -25033.4, \"(37.775000000000006, 37.795)\": -8688.7, \"(37.795, 37.805)\": 36.5, \"(37.805, 37.855000000000004)\": -20482.2, \"(37.855000000000004, 37.915)\": -9472.3, \"(37.915, 37.925)\": -25360.4, \"(37.925, 37.945)\": -46246.0, \"(37.945, 38.355000000000004)\": -55734.3, \"(38.355000000000004, 39.085)\": -48831.7, \"(39.085, 39.474999999999994)\": -57243.4, \"(39.474999999999994, 40.135000000000005)\": -65954.6, \"(40.135000000000005, 40.665)\": -75283.4, \"(40.665, 41.775000000000006)\": -84501.7, \"(41.775000000000006, 41.95)\": -93657.9}\nUpper Bounds (95%-Confidence Interval): {\"(32.54, 32.565)\": 45504.8, \"(32.565, 32.685)\": 7020.9, \"(32.685, 32.715)\": 22008.3, \"(32.715, 32.915)\": 28813.8, \"(32.915, 33.275000000000006)\": 52060.7, \"(33.275000000000006, 33.355000000000004)\": 67843.0, \"(33.355000000000004, 33.465)\": 77302.7, \"(33.465, 33.504999999999995)\": 102622.7, \"(33.504999999999995, 33.555)\": 112294.7, \"(33.555, 33.565)\": 87049.0, \"(33.565, 33.575)\": 61706.7, \"(33.575, 33.635000000000005)\": 78918.4, \"(33.635000000000005, 33.655)\": 56632.4, \"(33.655, 33.765)\": 37207.2, \"(33.765, 33.894999999999996)\": 21727.2, \"(33.894999999999996, 33.985)\": 6415.7, \"(33.985, 33.995000000000005)\": 22504.3, \"(33.995000000000005, 34.045)\": 34569.1, \"(34.045, 34.085)\": 61292.7, \"(34.085, 34.165)\": 52483.1, \"(34.165, 34.175)\": 39493.7, \"(34.175, 34.195)\": 12533.7, \"(34.195, 34.215)\": 1308.7, \"(34.215, 34.254999999999995)\": -10146.2, \"(34.254999999999995, 34.325)\": -17758.2, \"(34.325, 34.345)\": 37984.8, \"(34.345, 34.375)\": 42331.3, \"(34.375, 34.455)\": 53781.2, \"(34.455, 34.474999999999994)\": 73958.2, \"(34.474999999999994, 34.504999999999995)\": 46253.8, \"(34.504999999999995, 34.545)\": 25490.4, \"(34.545, 34.625)\": 24169.2, \"(34.625, 34.635000000000005)\": -1900.3, \"(34.635000000000005, 34.644999999999996)\": -28744.6, \"(34.644999999999996, 34.715)\": -19072.7, \"(34.715, 35.325)\": 12722.9, \"(35.325, 36.375)\": -28601.0, \"(36.375, 36.535)\": -20608.8, \"(36.535, 36.635000000000005)\": -5701.1, \"(36.635000000000005, 36.845)\": -15853.6, \"(36.845, 37.275000000000006)\": -7150.2, \"(37.275000000000006, 37.335)\": 2610.4, \"(37.335, 37.425)\": 20236.5, \"(37.425, 37.445)\": 40038.7, \"(37.445, 37.465)\": 61006.3, \"(37.465, 37.495000000000005)\": 38472.2, \"(37.495000000000005, 37.585)\": 24817.1, \"(37.585, 37.595)\": 10675.7, \"(37.595, 37.605000000000004)\": -26020.2, \"(37.605000000000004, 37.754999999999995)\": -33782.5, \"(37.754999999999995, 37.775000000000006)\": -10763.1, \"(37.775000000000006, 37.795)\": 2229.6, \"(37.795, 37.805)\": 17768.7, \"(37.805, 37.855000000000004)\": -6431.3, \"(37.855000000000004, 37.915)\": 6747.6, \"(37.915, 37.925)\": -12927.0, \"(37.925, 37.945)\": -31291.8, \"(37.945, 38.355000000000004)\": -40761.5, \"(38.355000000000004, 39.085)\": -28103.6, \"(39.085, 39.474999999999994)\": -38137.5, \"(39.474999999999994, 40.135000000000005)\": -48018.6, \"(40.135000000000005, 40.665)\": -57259.6, \"(40.665, 41.775000000000006)\": -66753.0, \"(41.775000000000006, 41.95)\": -76574.4}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(34.715, 35.325)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: Urbanization\nFeature Type: continuous\nMeans: {\"(0.0, 0.5)\": -0.02565, \"(0.5, 1.5)\": -0.02133, \"(1.5, 2.5)\": -0.01683, \"(2.5, 3.5)\": -0.00993, \"(3.5, 4.5)\": -0.00473, \"(4.5, 5.5)\": -1e-05, \"(5.5, 6.5)\": 0.00511, \"(6.5, 7.5)\": 0.01148, \"(7.5, 8.5)\": 0.01621, \"(8.5, 9.5)\": 0.02476, \"(9.5, 11.5)\": 0.02962, \"(11.5, 12.5)\": 0.03469, \"(12.5, 13.5)\": 0.04866, \"(13.5, 16.0)\": 0.05902}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02758, \"(0.5, 1.5)\": -0.02272, \"(1.5, 2.5)\": -0.01769, \"(2.5, 3.5)\": -0.01036, \"(3.5, 4.5)\": -0.00514, \"(4.5, 5.5)\": -0.0004, \"(5.5, 6.5)\": 0.00453, \"(6.5, 7.5)\": 0.01098, \"(7.5, 8.5)\": 0.01535, \"(8.5, 9.5)\": 0.0239, \"(9.5, 11.5)\": 0.02772, \"(11.5, 12.5)\": 0.03206, \"(12.5, 13.5)\": 0.04307, \"(13.5, 16.0)\": 0.0546}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02372, \"(0.5, 1.5)\": -0.01994, \"(1.5, 2.5)\": -0.01596, \"(2.5, 3.5)\": -0.00951, \"(3.5, 4.5)\": -0.00432, \"(4.5, 5.5)\": 0.00037, \"(5.5, 6.5)\": 0.00568, \"(6.5, 7.5)\": 0.01199, \"(7.5, 8.5)\": 0.01706, \"(8.5, 9.5)\": 0.02562, \"(9.5, 11.5)\": 0.03152, \"(11.5, 12.5)\": 0.03732, \"(12.5, 13.5)\": 0.05424, \"(13.5, 16.0)\": 0.06343}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(12.5, 13.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: HoursPerWeek\nFeature Type: continuous\nMeans: {\"(1.0, 1.5)\": -0.765, \"(1.5, 2.5)\": -0.375, \"(2.5, 4.5)\": -1.909, \"(4.5, 6.5)\": -1.117, \"(6.5, 7.5)\": -0.618, \"(7.5, 14.5)\": -0.822, \"(14.5, 19.5)\": -1.132, \"(19.5, 29.5)\": -0.765, \"(29.5, 33.5)\": -0.6, \"(33.5, 34.5)\": -0.921, \"(34.5, 39.5)\": -0.155, \"(39.5, 41.5)\": 0.03, \"(41.5, 50.5)\": 0.392, \"(50.5, 51.5)\": 0.131, \"(51.5, 55.5)\": 0.457, \"(55.5, 59.5)\": 0.676, \"(59.5, 63.5)\": 0.416, \"(63.5, 64.5)\": 0.952, \"(64.5, 65.5)\": 0.516, \"(65.5, 71.0)\": 0.071, \"(71.0, 75.5)\": 0.43, \"(75.5, 77.5)\": 0.235, \"(77.5, 79.0)\": 0.742, \"(79.0, 83.0)\": 0.977, \"(83.0, 85.5)\": 1.287, \"(85.5, 90.5)\": 0.192, \"(90.5, 97.5)\": -0.071, \"(97.5, 98.5)\": 0.119, \"(98.5, 99.0)\": -0.139}\nLower Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -2.672, \"(1.5, 2.5)\": -0.773, \"(2.5, 4.5)\": -2.709, \"(4.5, 6.5)\": -1.566, \"(6.5, 7.5)\": -1.241, \"(7.5, 14.5)\": -1.098, \"(14.5, 19.5)\": -1.535, \"(19.5, 29.5)\": -1.357, \"(29.5, 33.5)\": -1.248, \"(33.5, 34.5)\": -1.815, \"(34.5, 39.5)\": -0.223, \"(39.5, 41.5)\": -0.129, \"(41.5, 50.5)\": 0.212, \"(50.5, 51.5)\": -0.867, \"(51.5, 55.5)\": 0.357, \"(55.5, 59.5)\": 0.304, \"(59.5, 63.5)\": 0.014, \"(63.5, 64.5)\": 0.009, \"(64.5, 65.5)\": 0.379, \"(65.5, 71.0)\": -0.113, \"(71.0, 75.5)\": 0.054, \"(75.5, 77.5)\": -0.57, \"(77.5, 79.0)\": 0.234, \"(79.0, 83.0)\": 0.788, \"(83.0, 85.5)\": 0.721, \"(85.5, 90.5)\": -0.289, \"(90.5, 97.5)\": -0.504, \"(97.5, 98.5)\": -0.527, \"(98.5, 99.0)\": -0.548}\nUpper Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": 1.142, \"(1.5, 2.5)\": 0.023, \"(2.5, 4.5)\": -1.109, \"(4.5, 6.5)\": -0.668, \"(6.5, 7.5)\": 0.005, \"(7.5, 14.5)\": -0.546, \"(14.5, 19.5)\": -0.729, \"(19.5, 29.5)\": -0.172, \"(29.5, 33.5)\": 0.047, \"(33.5, 34.5)\": -0.027, \"(34.5, 39.5)\": -0.087, \"(39.5, 41.5)\": 0.19, \"(41.5, 50.5)\": 0.571, \"(50.5, 51.5)\": 1.13, \"(51.5, 55.5)\": 0.557, \"(55.5, 59.5)\": 1.048, \"(59.5, 63.5)\": 0.818, \"(63.5, 64.5)\": 1.896, \"(64.5, 65.5)\": 0.653, \"(65.5, 71.0)\": 0.254, \"(71.0, 75.5)\": 0.806, \"(75.5, 77.5)\": 1.04, \"(77.5, 79.0)\": 1.25, \"(79.0, 83.0)\": 1.166, \"(83.0, 85.5)\": 1.852, \"(85.5, 90.5)\": 0.673, \"(90.5, 97.5)\": 0.361, \"(97.5, 98.5)\": 0.765, \"(98.5, 99.0)\": 0.271}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(1.0, 1.5)"
+  ],
+  [
+    "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n        \n    The graph is provided in the following format:\n        - The name of the feature depicted in the graph\n        - The type of the feature (continuous, categorical, or boolean)\n        - Mean values\n        - Lower bounds of confidence interval (optional)\n        - Upper bounds of confidence interval (optional)\n\nHere is the graph:\n\nThis graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n\nFeature Name: CapitalGain\nFeature Type: continuous\nMeans: {\"(0.0, 57.0)\": -0.25, \"(57.0, 3048.0)\": -4.83, \"(3048.0, 3120.0)\": 2.57, \"(3120.0, 4243.5)\": -4.43, \"(4243.5, 4401.0)\": 1.45, \"(4401.0, 4668.5)\": -1.82, \"(4668.5, 4826.0)\": 3.79, \"(4826.0, 4898.0)\": 0.57, \"(4898.0, 4973.5)\": 2.25, \"(4973.5, 5119.0)\": -3.52, \"(5119.0, 5316.5)\": 4.26, \"(5316.5, 5505.5)\": 0.43, \"(5505.5, 6457.5)\": 2.15, \"(6457.5, 6505.5)\": -0.16, \"(6505.5, 6745.0)\": 0.81, \"(6745.0, 7073.5)\": -1.33, \"(7073.5, 7436.5)\": 5.76, \"(7436.5, 7565.5)\": 2.02, \"(7565.5, 7792.0)\": 6.56, \"(7792.0, 7937.0)\": 4.88, \"(7937.0, 8296.0)\": 3.84, \"(8296.0, 10543.0)\": 7.18, \"(10543.0, 10585.5)\": -1.48, \"(10585.5, 30961.5)\": 8.61, \"(30961.5, 70654.5)\": -0.66, \"(70654.5, 99999.0)\": 9.72}\nLower Bounds (95%-Confidence Interval): {\"(0.0, 57.0)\": -0.27, \"(57.0, 3048.0)\": -6.42, \"(3048.0, 3120.0)\": 2.14, \"(3120.0, 4243.5)\": -5.31, \"(4243.5, 4401.0)\": 1.09, \"(4401.0, 4668.5)\": -2.65, \"(4668.5, 4826.0)\": 2.87, \"(4826.0, 4898.0)\": -0.25, \"(4898.0, 4973.5)\": 1.55, \"(4973.5, 5119.0)\": -6.13, \"(5119.0, 5316.5)\": 3.51, \"(5316.5, 5505.5)\": -0.29, \"(5505.5, 6457.5)\": 1.3, \"(6457.5, 6505.5)\": -0.94, \"(6505.5, 6745.0)\": 0.19, \"(6745.0, 7073.5)\": -2.33, \"(7073.5, 7436.5)\": 4.95, \"(7436.5, 7565.5)\": 0.42, \"(7565.5, 7792.0)\": 5.41, \"(7792.0, 7937.0)\": 2.59, \"(7937.0, 8296.0)\": 1.32, \"(8296.0, 10543.0)\": 6.05, \"(10543.0, 10585.5)\": -2.73, \"(10585.5, 30961.5)\": 7.51, \"(30961.5, 70654.5)\": -3.56, \"(70654.5, 99999.0)\": 8.19}\nUpper Bounds (95%-Confidence Interval): {\"(0.0, 57.0)\": -0.23, \"(57.0, 3048.0)\": -3.24, \"(3048.0, 3120.0)\": 3.0, \"(3120.0, 4243.5)\": -3.54, \"(4243.5, 4401.0)\": 1.81, \"(4401.0, 4668.5)\": -1.0, \"(4668.5, 4826.0)\": 4.71, \"(4826.0, 4898.0)\": 1.38, \"(4898.0, 4973.5)\": 2.95, \"(4973.5, 5119.0)\": -0.92, \"(5119.0, 5316.5)\": 5.0, \"(5316.5, 5505.5)\": 1.16, \"(5505.5, 6457.5)\": 3.0, \"(6457.5, 6505.5)\": 0.62, \"(6505.5, 6745.0)\": 1.44, \"(6745.0, 7073.5)\": -0.34, \"(7073.5, 7436.5)\": 6.58, \"(7436.5, 7565.5)\": 3.62, \"(7565.5, 7792.0)\": 7.72, \"(7792.0, 7937.0)\": 7.16, \"(7937.0, 8296.0)\": 6.36, \"(8296.0, 10543.0)\": 8.31, \"(10543.0, 10585.5)\": -0.22, \"(10585.5, 30961.5)\": 9.71, \"(30961.5, 70654.5)\": 2.23, \"(70654.5, 99999.0)\": 11.26}\n\nEvery interval in the graph comes with a lower and and an upper confidence bound.\n\nWe are now looking for the interval in the graph that has the widest confidence bound. \n\nWhat is the x-axis interval that has the widest confidence bound? Think step by step.",
+    "(30961.5, 70654.5)"
+  ]
+]
\ No newline at end of file
diff --git a/benchmarks/utils.py b/benchmarks/benchmark_utils.py
similarity index 57%
rename from benchmarks/utils.py
rename to benchmarks/benchmark_utils.py
index a27be95..508f4af 100644
--- a/benchmarks/utils.py
+++ b/benchmarks/benchmark_utils.py
@@ -3,9 +3,8 @@
 
 import pandas as pd
 import os
-import openai
-import guidance
 import pickle
+import numpy as np
 
 from sklearn.model_selection import train_test_split
 
@@ -16,44 +15,35 @@
 
 RANDOM_STATE = 1498672
 
-IRIS_SETOSA = (
-    "Iris-setosa"  # binary classification of Iris-setosa vs. all other species
-)
+IRIS = "Iris"
 TITANIC = "Titanic"
 SPACESHIP_TITANIC = "Spaceship-Titanic"
 CALIFORNIA_HOUSING = "California-Housing"
 OPENML_DIABETES = "OpenML-Diabetes"
+ADULT = "Adult-Income"
+KAGGLE_FLOOD = "Kaggle-Flood"
+KAGGLE_HEART_FAILURE = "Kaggle-Heart-Failure"
+BANK_CHURN = "Kaggle-Bank-Churn"
+CANCER = "Wisconsin-Cancer"
 
 DATASETS = [
     CALIFORNIA_HOUSING,
     OPENML_DIABETES,
-    IRIS_SETOSA,
+    IRIS,
     TITANIC,
     SPACESHIP_TITANIC,
+    ADULT,
+    KAGGLE_FLOOD,
+    KAGGLE_HEART_FAILURE,
+    BANK_CHURN,
+    CANCER,
 ]
 
 
-def openai_setup_gpt3_5():
-    openai.organization = os.environ["OPENAI_API_ORG"]
-    openai.api_key = os.environ["OPENAI_API_KEY"]
-    return guidance.llms.OpenAI("gpt-3.5-turbo-0125")
-
-
-def openai_setup_gpt4():
-    openai.organization = os.environ["OPENAI_API_ORG"]
-    openai.api_key = os.environ["OPENAI_API_KEY"]
-    return guidance.llms.OpenAI("gpt-4o-2024-05-13")
-
-
 def get_avaialble_datasets():
     return DATASETS
 
 
-def get_dataset_description(dataset_name):
-    """Returns: dataset description (str), dataset y axis description (str)"""
-    pass
-
-
 def get_dataset(dataset_name):
     """
     Loads the dataset with the given name and return the train and test splits.
@@ -76,11 +66,11 @@ def get_dataset(dataset_name):
         # drop rows with missing values
         df = df.dropna()
         y_data = df["Survived"].values
-        df = df.drop(columns=["PassengerId", "Name", "Survived"])
+        df = df.drop(columns=["PassengerId", "Name", "Survived", "Cabin", "Ticket"])
         X_data = df.values
         feature_names = df.columns.tolist()
-    elif dataset_name == IRIS_SETOSA:
-        df = pd.read_csv("../data/IRIS.csv")
+    elif dataset_name == IRIS:
+        df = pd.read_csv("../data/iris.csv")
         df = df.dropna()
         y_data = df["species"].values == "Iris-setosa"  # binary classification
         df = df.drop(columns=["species"])
@@ -101,6 +91,57 @@ def get_dataset(dataset_name):
         df = df.drop(columns=["Outcome"])
         X_data = df.values
         feature_names = df.columns.tolist()
+    elif dataset_name == ADULT:
+        df = pd.read_csv("../data/adult-train.csv")
+        # drop fnlwgt
+        df = df.drop(columns=["fnlwgt"])
+        y_data = df["Income"].values == " >50K"
+        df = df.drop(columns=["Income"])
+        # convert categorical columns to numbers
+        for col in df.columns:
+            if df[col].dtype == "object":
+                df[col] = df[col].astype("category").cat.codes
+        # drop na and inf values
+        df = df.replace([np.inf, -np.inf], np.nan).dropna()
+        X_data = df.values
+        feature_names = df.columns.tolist()
+    elif dataset_name == KAGGLE_FLOOD:
+        df = pd.read_csv("../data/kaggle-flood-train.csv")
+        df = df.dropna()
+        # subset 10 000 observations
+        df = df.sample(n=10000, random_state=RANDOM_STATE)
+        y_data = df["FloodProbability"].values
+        df = df.drop(columns=["FloodProbability"])
+        X_data = df.values
+        feature_names = df.columns.tolist()
+    elif dataset_name == KAGGLE_HEART_FAILURE:
+        df = pd.read_csv("../data/kaggle_heart_failure_clinical_records.csv")
+        df = df.dropna()
+        y_data = df["DEATH_EVENT"].values
+        df = df.drop(columns=["DEATH_EVENT"])
+        X_data = df.values
+        feature_names = df.columns.tolist()
+    elif dataset_name == BANK_CHURN:
+        df = pd.read_csv("../data/kaggle-bank-churn.csv")
+        df = df.dropna()
+        # drop surname feature
+        df = df.drop(columns=["Surname"])
+        # subset 10 000 observations
+        df = df.sample(n=10000, random_state=RANDOM_STATE)
+        y_data = df["Exited"].values == 0  # binary classification
+        df = df.drop(columns=["Exited"])
+        # drop na and inf values
+        df = df.replace([np.inf, -np.inf], np.nan).dropna()
+        X_data = df.values
+        feature_names = df.columns.tolist()
+    elif dataset_name == CANCER:
+        df = pd.read_csv("../data/Wisconsin-cancer.csv")
+        # ddrop 'Unnamed: 32'
+        df = df.drop(columns=["Unnamed: 32"])
+        y_data = df["diagnosis"].values == "M"
+        df = df.drop(columns=["diagnosis"])
+        X_data = df.values
+        feature_names = df.columns.tolist()
     else:
         raise ValueError("Unknown dataset: ", dataset_name)
 
@@ -112,7 +153,7 @@ def get_dataset(dataset_name):
 
 def get_ebm(dataset_name):
     """
-    Returns: the ebm
+    Returns: An EBM trained on the dataset.
     """
     X_train, X_test, y_train, y_test, feature_names = get_dataset(dataset_name)
     model_file = f"../models/{dataset_name}"
@@ -121,7 +162,16 @@ def get_ebm(dataset_name):
         with open(model_file, "rb") as file:
             ebm = pickle.load(file)
     else:  # otherwise train and save
-        if dataset_name in [SPACESHIP_TITANIC, IRIS_SETOSA, TITANIC, OPENML_DIABETES]:
+        if dataset_name in [
+            SPACESHIP_TITANIC,
+            IRIS,
+            TITANIC,
+            OPENML_DIABETES,
+            ADULT,
+            KAGGLE_HEART_FAILURE,
+            BANK_CHURN,
+            CANCER,
+        ]:
             # classification
             ebm = ExplainableBoostingClassifier(
                 interactions=0,
@@ -132,7 +182,7 @@ def get_ebm(dataset_name):
             # store for later use
             with open(model_file, "wb") as file:
                 pickle.dump(ebm, file)
-        elif dataset_name in [CALIFORNIA_HOUSING]:
+        elif dataset_name in [CALIFORNIA_HOUSING, KAGGLE_FLOOD]:
             # regression
             ebm = ExplainableBoostingRegressor(
                 interactions=0,
diff --git a/benchmarks/notebooks/confidence.ipynb b/benchmarks/notebooks/confidence.ipynb
new file mode 100644
index 0000000..00cbb0f
--- /dev/null
+++ b/benchmarks/notebooks/confidence.ipynb
@@ -0,0 +1,2029 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Wide Confidence Interval Benchmark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add parent directory to path\n",
+    "import sys\n",
+    "sys.path.append('..')\n",
+    "\n",
+    "import copy\n",
+    "import random\n",
+    "import numpy as np\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import t2ebm\n",
+    "from t2ebm import graphs\n",
+    "from t2ebm import prompts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the benchmark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/sebastian/Documents/GitHub/TalkToEBM/t2ebm/graphs.py:318: SyntaxWarning: invalid escape sequence '\\%'\n",
+      "  \" a simplification level of 10\\%. This graph is too complex to\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load graphs (pickle)\n",
+    "import pickle\n",
+    "\n",
+    "with open(\"all_graphs.pkl\", \"rb\") as f:\n",
+    "    all_graphs = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def widest_confidence_interval(graph):\n",
+    "    \"\"\"returns the position and the size of the largest jump in the graph\"\"\"\n",
+    "    idx = np.argmax(graph.stds)\n",
+    "    return graph.x_vals[idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "questions = []\n",
+    "for graph, graph_as_text in all_graphs:\n",
+    "    # only continuous graphs\n",
+    "    if graph.feature_type != \"continuous\":\n",
+    "        continue\n",
+    "    question = \"\"\"Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n",
+    "        \n",
+    "    The graph is provided in the following format:\n",
+    "        - The name of the feature depicted in the graph\n",
+    "        - The type of the feature (continuous, categorical, or boolean)\n",
+    "        - Mean values\n",
+    "        - Lower bounds of confidence interval (optional)\n",
+    "        - Upper bounds of confidence interval (optional)\\n\\n\"\"\"\n",
+    "    question += f\"Here is the graph:\\n\\n{graph_as_text}\\n\"\n",
+    "    question += \"\"\"Every interval in the graph comes with a lower and and an upper confidence bound.\n",
+    "\n",
+    "We are now looking for the interval in the graph that has the widest confidence bound. \n",
+    "\n",
+    "What is the x-axis interval that has the widest confidence bound? Think step by step.\"\"\"\n",
+    "    graph_ = graphs.text_to_graph(graph_as_text)\n",
+    "    pos = widest_confidence_interval(graph_)\n",
+    "    questions.append((question, str(pos)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# subset 100 random questions\n",
+    "random.shuffle(questions)\n",
+    "questions = questions[:100]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n",
+      "        \n",
+      "    The graph is provided in the following format:\n",
+      "        - The name of the feature depicted in the graph\n",
+      "        - The type of the feature (continuous, categorical, or boolean)\n",
+      "        - Mean values\n",
+      "        - Lower bounds of confidence interval (optional)\n",
+      "        - Upper bounds of confidence interval (optional)\n",
+      "\n",
+      "Here is the graph:\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: smoothness_mean\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.05263, 0.0706)\": -0.835, \"(0.0706, 0.07455500000000001)\": -0.769, \"(0.07455500000000001, 0.07589499999999999)\": -0.697, \"(0.07589499999999999, 0.07727500000000001)\": -0.632, \"(0.07727500000000001, 0.078275)\": -0.569, \"(0.078275, 0.07952000000000001)\": -0.506, \"(0.07952000000000001, 0.080315)\": -0.437, \"(0.080315, 0.081035)\": -0.368, \"(0.081035, 0.08308499999999999)\": -0.304, \"(0.08308499999999999, 0.085165)\": -0.242, \"(0.085165, 0.086795)\": -0.177, \"(0.086795, 0.087785)\": -0.111, \"(0.087785, 0.088615)\": -0.047, \"(0.088615, 0.08918999999999999)\": 0.065, \"(0.08918999999999999, 0.090335)\": 0.142, \"(0.090335, 0.09454)\": 0.211, \"(0.09454, 0.11525)\": 0.107, \"(0.11525, 0.11765)\": 0.171, \"(0.11765, 0.12455)\": 0.267, \"(0.12455, 0.13845000000000002)\": 0.334, \"(0.13845000000000002, 0.1634)\": 0.396}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.05263, 0.0706)\": -1.454, \"(0.0706, 0.07455500000000001)\": -1.359, \"(0.07455500000000001, 0.07589499999999999)\": -1.244, \"(0.07589499999999999, 0.07727500000000001)\": -1.162, \"(0.07727500000000001, 0.078275)\": -1.087, \"(0.078275, 0.07952000000000001)\": -1.006, \"(0.07952000000000001, 0.080315)\": -0.882, \"(0.080315, 0.081035)\": -0.622, \"(0.081035, 0.08308499999999999)\": -0.547, \"(0.08308499999999999, 0.085165)\": -0.444, \"(0.085165, 0.086795)\": -0.357, \"(0.086795, 0.087785)\": -0.296, \"(0.087785, 0.088615)\": -0.23, \"(0.088615, 0.08918999999999999)\": -0.16, \"(0.08918999999999999, 0.090335)\": -0.309, \"(0.090335, 0.09454)\": -0.264, \"(0.09454, 0.11525)\": -0.005, \"(0.11525, 0.11765)\": 0.07, \"(0.11765, 0.12455)\": 0.022, \"(0.12455, 0.13845000000000002)\": 0.077, \"(0.13845000000000002, 0.1634)\": 0.127}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.05263, 0.0706)\": -0.216, \"(0.0706, 0.07455500000000001)\": -0.178, \"(0.07455500000000001, 0.07589499999999999)\": -0.151, \"(0.07589499999999999, 0.07727500000000001)\": -0.102, \"(0.07727500000000001, 0.078275)\": -0.052, \"(0.078275, 0.07952000000000001)\": -0.006, \"(0.07952000000000001, 0.080315)\": 0.008, \"(0.080315, 0.081035)\": -0.113, \"(0.081035, 0.08308499999999999)\": -0.062, \"(0.08308499999999999, 0.085165)\": -0.04, \"(0.085165, 0.086795)\": 0.004, \"(0.086795, 0.087785)\": 0.075, \"(0.087785, 0.088615)\": 0.136, \"(0.088615, 0.08918999999999999)\": 0.291, \"(0.08918999999999999, 0.090335)\": 0.594, \"(0.090335, 0.09454)\": 0.685, \"(0.09454, 0.11525)\": 0.22, \"(0.11525, 0.11765)\": 0.273, \"(0.11765, 0.12455)\": 0.512, \"(0.12455, 0.13845000000000002)\": 0.591, \"(0.13845000000000002, 0.1634)\": 0.664}\n",
+      "\n",
+      "Every interval in the graph comes with a lower and and an upper confidence bound.\n",
+      "\n",
+      "We are now looking for the interval in the graph that has the widest confidence bound. \n",
+      "\n",
+      "What is the x-axis interval that has the widest confidence bound? Think step by step.\n",
+      "SOLUTION:  (0.05263, 0.0706)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print a random question\n",
+    "import random\n",
+    "random.shuffle(questions)\n",
+    "print(questions[0][0])\n",
+    "print('SOLUTION: ', questions[0][1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save the questions to json\n",
+    "import json\n",
+    "with open(\"../benchmark/wide-confidence.json\", \"w\") as f:\n",
+    "    json.dump(questions, f, indent=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Run the benchmark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# load the json file\n",
+    "with open(\"../benchmark/wide-confidence.json\", \"r\") as f:\n",
+    "    questions = json.load(f)\n",
+    "len(questions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.\n",
+      "\n",
+      "2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Compare the Widths**: Identify the interval with the maximum width.\n",
+      "\n",
+      "Let's perform these calculations:\n",
+      "\n",
+      "- For interval `(0.07117, 0.09376000000000001)`: Width = -0.336 - (-2.26) = 1.924\n",
+      "- For interval `(0.09376000000000001, 0.099705)`: Width = -0.19 - (-2.132) = 1.942\n",
+      "- For interval `(0.099705, 0.10519999999999999)`: Width = -0.039 - (-2.009) = 1.97\n",
+      "- For interval `(0.10519999999999999, 0.10825)`: Width = 0.096 - (-1.875) = 1.971\n",
+      "- For interval `(0.10825, 0.11549999999999999)`: Width = -0.289 - (-0.765) = 0.476\n",
+      "- For interval `(0.11549999999999999, 0.12345)`: Width = -0.2 - (-0.589) = 0.389\n",
+      "- For interval `(0.12345, 0.13074999999999998)`: Width = -0.086 - (-0.435) = 0.349\n",
+      "- For interval `(0.13074999999999998, 0.13585)`: Width = 0.136 - (-0.384) = 0.52\n",
+      "- For interval `(0.13585, 0.13640000000000002)`: Width = 0.348 - (-0.325) = 0.673\n",
+      "- For interval `(0.13640000000000002, 0.13845000000000002)`: Width = 0.556 - (-0.247) = 0.803\n",
+      "- For interval `(0.13845000000000002, 0.14065)`: Width = 0.652 - (-0.076) = 0.728\n",
+      "- For interval `(0.14065, 0.14635)`: Width = 0.997 - (-0.12) = 1.117\n",
+      "- For interval `(0.14635, 0.15585)`: Width = 0.983 - 0.165 = 0.818\n",
+      "- For interval `(0.15585, 0.16885)`: Width = 1.146 - 0.27 = 0.876\n",
+      "- For interval `(0.16885, 0.17825)`: Width = 1.289 - 0.402 = 0.887\n",
+      "- For interval `(0.17825, 0.19574999999999998)`: Width = 2.823 - (-0.484) = 3.307\n",
+      "- For interval `(0.19574999999999998, 0.2226)`: Width = 2.957 - (-0.349) = 3.306\n",
+      "\n",
+      "From the calculations, the interval `(0.17825, 0.19574999999999998)` has the widest confidence bound with a width of 3.307.\n",
+      "SOLUTION:  (0.17825, 0.19574999999999998)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds of the confidence interval for each interval and identify the one with the largest difference.\n",
+      "\n",
+      "Here are the calculations for each interval:\n",
+      "\n",
+      "1. Interval (4.3, 4.55): Upper - Lower = 3.437 - 3.22 = 0.217\n",
+      "2. Interval (4.55, 4.75): Upper - Lower = 3.144 - 2.846 = 0.298\n",
+      "3. Interval (4.75, 4.85): Upper - Lower = 2.857 - 2.54 = 0.317\n",
+      "4. Interval (4.85, 5.05): Upper - Lower = 2.145 - 1.185 = 0.96\n",
+      "5. Interval (5.05, 5.25): Upper - Lower = 1.528 - 1.214 = 0.314\n",
+      "6. Interval (5.25, 5.45): Upper - Lower = 1.277 - 0.892 = 0.385\n",
+      "7. Interval (5.45, 5.55): Upper - Lower = 0.843 - (-0.164) = 1.007\n",
+      "8. Interval (5.55, 5.75): Upper - Lower = 0.206 - (-0.32) = 0.526\n",
+      "9. Interval (5.75, 5.85): Upper - Lower = -0.116 - (-0.665) = 0.549\n",
+      "10. Interval (5.85, 6.15): Upper - Lower = -0.627 - (-0.888) = 0.261\n",
+      "11. Interval (6.15, 6.45): Upper - Lower = -1.009 - (-1.29) = 0.281\n",
+      "12. Interval (6.45, 6.85): Upper - Lower = -1.298 - (-1.575) = 0.277\n",
+      "13. Interval (6.85, 7.7): Upper - Lower = -1.623 - (-1.814) = 0.191\n",
+      "\n",
+      "From these calculations, the interval (5.45, 5.55) has the widest confidence bound with a difference of 1.007. This interval therefore has the widest confidence interval among all the intervals listed.\n",
+      "SOLUTION:  (5.45, 5.55)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval with the largest difference.\n",
+      "\n",
+      "Here are the calculations for each interval:\n",
+      "\n",
+      "1. Interval (1.0, 1.5):\n",
+      "   - Lower Bound: -6.411\n",
+      "   - Upper Bound: -3.082\n",
+      "   - Difference: -3.082 - (-6.411) = 3.329\n",
+      "\n",
+      "2. Interval (1.5, 4.5):\n",
+      "   - Lower Bound: -1.52\n",
+      "   - Upper Bound: -0.984\n",
+      "   - Difference: -0.984 - (-1.52) = 0.536\n",
+      "\n",
+      "3. Interval (4.5, 6.5):\n",
+      "   - Lower Bound: -0.99\n",
+      "   - Upper Bound: -0.775\n",
+      "   - Difference: -0.775 - (-0.99) = 0.215\n",
+      "\n",
+      "4. Interval (6.5, 9.5):\n",
+      "   - Lower Bound: -0.541\n",
+      "   - Upper Bound: -0.425\n",
+      "   - Difference: -0.425 - (-0.541) = 0.116\n",
+      "\n",
+      "5. Interval (9.5, 11.5):\n",
+      "   - Lower Bound: -0.138\n",
+      "   - Upper Bound: -0.049\n",
+      "   - Difference: -0.049 - (-0.138) = 0.089\n",
+      "\n",
+      "6. Interval (11.5, 13.5):\n",
+      "   - Lower Bound: 0.205\n",
+      "   - Upper Bound: 0.347\n",
+      "   - Difference: 0.347 - 0.205 = 0.142\n",
+      "\n",
+      "7. Interval (13.5, 14.5):\n",
+      "   - Lower Bound: 0.788\n",
+      "   - Upper Bound: 0.938\n",
+      "   - Difference: 0.938 - 0.788 = 0.150\n",
+      "\n",
+      "8. Interval (14.5, 16.0):\n",
+      "   - Lower Bound: 1.332\n",
+      "   - Upper Bound: 1.641\n",
+      "   - Difference: 1.641 - 1.332 = 0.309\n",
+      "\n",
+      "From these calculations, the interval (1.0, 1.5) has the widest confidence bound with a difference of 3.329. This is the interval with the largest difference between the upper and lower bounds of the confidence interval.\n",
+      "SOLUTION:  (1.0, 1.5)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds of the confidence interval for each interval and identify the one with the maximum difference.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.\n",
+      "\n",
+      "2. **Calculate the Difference**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Identify the Maximum Difference**: Compare the differences calculated in step 2 to find the maximum difference, which indicates the widest confidence interval.\n",
+      "\n",
+      "Let's perform these calculations:\n",
+      "\n",
+      "- For interval `(2.0, 23.0)`: Difference = `-50072.0 - (-91545.9) = 41473.9`\n",
+      "- For interval `(23.0, 38.5)`: Difference = `-54966.9 - (-102966.4) = 47999.5`\n",
+      "- For interval `(38.5, 48.5)`: Difference = `-24.3 - (-57179.9) = 57155.6`\n",
+      "- For interval `(48.5, 119.0)`: Difference = `-29651.4 - (-64507.9) = 34856.5`\n",
+      "- For interval `(119.0, 163.0)`: Difference = `-38333.5 - (-67051.1) = 28717.6`\n",
+      "- For interval `(163.0, 186.5)`: Difference = `-45199.3 - (-74986.7) = 29787.4`\n",
+      "- For interval `(186.5, 223.5)`: Difference = `-39853.9 - (-62447.2) = 22593.3`\n",
+      "- For interval `(223.5, 239.5)`: Difference = `-23883.2 - (-55573.0) = 31689.8`\n",
+      "- For interval `(239.5, 248.5)`: Difference = `20408.0 - (-34485.5) = 54893.5`\n",
+      "- For interval `(248.5, 265.5)`: Difference = `17433.4 - (-18815.6) = 36249.0`\n",
+      "- For interval `(265.5, 280.5)`: Difference = `7471.9 - (-35576.3) = 43048.2`\n",
+      "- For interval `(280.5, 342.5)`: Difference = `-26453.2 - (-44957.9) = 18504.7`\n",
+      "- For interval `(342.5, 364.5)`: Difference = `-12564.3 - (-36592.4) = 24028.1`\n",
+      "- For interval `(364.5, 385.5)`: Difference = `-28395.1 - (-39620.4) = 11225.3`\n",
+      "- For interval `(385.5, 406.5)`: Difference = `-38875.1 - (-54434.9) = 15559.8`\n",
+      "- For interval `(406.5, 413.5)`: Difference = `-6712.1 - (-28898.3) = 22186.2`\n",
+      "- For interval `(413.5, 443.5)`: Difference = `-2459.1 - (-21926.2) = 19467.1`\n",
+      "- For interval `(443.5, 452.5)`: Difference = `-10730.8 - (-34828.5) = 24097.7`\n",
+      "- For interval `(452.5, 502.5)`: Difference = `-21000.9 - (-40304.3) = 19303.4`\n",
+      "- For interval `(502.5, 508.5)`: Difference = `-14681.3 - (-35649.5) = 20968.2`\n",
+      "- For interval `(508.5, 515.5)`: Difference = `1516.8 - (-27403.5) = 28920.3`\n",
+      "- For interval `(515.5, 1152.5)`: Difference = `-14834.1 - (-28456.5) = 13622.4`\n",
+      "- For interval `(1152.5, 1239.5)`: Difference = `-11610.6 - (-20918.2) = 9307.6`\n",
+      "- For interval `(1239.5, 1245.5)`: Difference = `1860.9 - (-15907.4) = 17768.3`\n",
+      "- For interval `(1245.5, 1619.5)`: Difference = `-5766.8 - (-19943.7) = 14176.9`\n",
+      "- For interval `(1619.5, 1944.5)`: Difference = `-1767.7 - (-13063.6) = 11295.9`\n",
+      "- For interval `(1944.5, 2330.5)`: Difference = `6128.1 - (-8595.8) = 14723.9`\n",
+      "- For interval `(2330.5, 2710.5)`: Difference = `5805.0 - 2936.6 = 2868.4`\n",
+      "- For interval `(2710.5, 2834.5)`: Difference = `12408.3 - 7069.8 = 5338.5`\n",
+      "- For interval `(2834.5, 2838.5)`: Difference = `32071.2 - 1263.0 = 30808.2`\n",
+      "- For interval `(2838.5, 3577.5)`: Difference = `13167.8 - 7025.1 = 6142.7`\n",
+      "- For interval `(3577.5, 5401.0)`: Difference = `20811.4 - 10287.4 = 10524.0`\n",
+      "- For interval `(5401.0, 5535.5)`: Difference = `39337.3 - 10519.1 = 28818.2`\n",
+      "- For interval `(5535.5, 9961.0)`: Difference = `25602.1 - 12536.6 = 13065.5`\n",
+      "- For interval `(9961.0, 18662.0)`: Difference = `35928.6 - 16596.5 = 19332.1`\n",
+      "- For interval `(18662.0, 39320.0)`: Difference = `24283.0 - 17189.5 = 7093.5`\n",
+      "\n",
+      "From the calculations, the interval `(38.5, 48.5)` has the widest confidence bound with a difference of `57155.6`.\n",
+      "SOLUTION:  (38.5, 48.5)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: From the JSON object, extract the lower and upper bounds for each interval.\n",
+      "\n",
+      "2. **Calculate the Width of the Confidence Interval for Each Interval**: Subtract the lower bound from the upper bound for each interval.\n",
+      "\n",
+      "3. **Identify the Interval with the Maximum Width**: Compare the widths calculated in step 2 and identify the interval with the largest width.\n",
+      "\n",
+      "Let's perform these calculations:\n",
+      "\n",
+      "- For interval \"(0.0, 6.325)\": Width = 0.54 - (-3.39) = 3.93\n",
+      "- For interval \"(6.325, 7.8500000000000005)\": Width = 0.645 - (-3.252) = 3.897\n",
+      "- For interval \"(7.8500000000000005, 9.256250000000001)\": Width = 0.377 - (-1.321) = 1.698\n",
+      "- For interval \"(9.256250000000001, 10.48125)\": Width = 0.553 - (-1.756) = 2.309\n",
+      "- For interval \"(10.48125, 12.9375)\": Width = 0.163 - (-0.444) = 0.607\n",
+      "- For interval \"(12.9375, 25.79375)\": Width = 0.913 - (-0.464) = 1.377\n",
+      "- For interval \"(25.79375, 26.46875)\": Width = 1.191 - (-0.48) = 1.671\n",
+      "- For interval \"(26.46875, 27.7354)\": Width = 0.833 - (-0.42) = 1.253\n",
+      "- For interval \"(27.7354, 29.85)\": Width = 0.533 - (-1.008) = 1.541\n",
+      "- For interval \"(29.85, 31.6604)\": Width = 0.718 - (-0.616) = 1.334\n",
+      "- For interval \"(31.6604, 55.22085)\": Width = 0.127 - (-0.278) = 0.405\n",
+      "- For interval \"(55.22085, 89.5521)\": Width = 0.176 - (-0.095) = 0.271\n",
+      "- For interval \"(89.5521, 149.0354)\": Width = 0.367 - (-0.062) = 0.429\n",
+      "- For interval \"(149.0354, 387.6646)\": Width = 0.436 - (-0.493) = 0.929\n",
+      "- For interval \"(387.6646, 512.3292)\": Width = 2.455 - (-0.839) = 3.294\n",
+      "\n",
+      "From these calculations, the interval with the widest confidence bound is **\"(0.0, 6.325)\"** with a width of 3.93.\n",
+      "SOLUTION:  (0.0, 6.325)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the x-axis interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval with the maximum difference.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.\n",
+      "\n",
+      "2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Identify the Maximum Width**: Compare the widths of all intervals and identify the interval with the maximum width.\n",
+      "\n",
+      "Let's perform these calculations using the provided data:\n",
+      "\n",
+      "- For interval `(0.002252, 0.0046765)`, the lower bound is `-0.2881` and the upper bound is `0.1496`. The width is `0.1496 - (-0.2881) = 0.4377`.\n",
+      "- For interval `(0.0046765, 0.005634)`, the width is `0.1917 - (-0.2345) = 0.4262`.\n",
+      "- Continue this for all intervals.\n",
+      "\n",
+      "After calculating the widths for all intervals, we find:\n",
+      "\n",
+      "- Interval `(0.068925, 0.1354)` has a lower bound of `-0.6732` and an upper bound of `-0.1492`. The width is `-0.1492 - (-0.6732) = 0.524`.\n",
+      "\n",
+      "By comparing all calculated widths, we find that the interval `(0.068925, 0.1354)` has the widest confidence interval with a width of `0.524`. This is the interval with the widest confidence bound.\n",
+      "SOLUTION:  (0.01089, 0.011715)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.\n",
+      "\n",
+      "2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Compare the Widths**: Identify the interval where this width (difference between upper and lower bounds) is the largest.\n",
+      "\n",
+      "Let's perform these calculations using the provided data:\n",
+      "\n",
+      "- For interval (0.1115, 0.15015): Upper - Lower = -0.302 - (-1.244) = 0.942\n",
+      "- For interval (0.15015, 0.16904999999999998): Upper - Lower = -0.247 - (-1.125) = 0.878\n",
+      "- For interval (0.16904999999999998, 0.1795): Upper - Lower = -0.169 - (-1.008) = 0.839\n",
+      "- For interval (0.1795, 0.18535000000000001): Upper - Lower = -0.094 - (-0.904) = 0.810\n",
+      "- For interval (0.18535000000000001, 0.19345): Upper - Lower = -0.024 - (-0.8) = 0.776\n",
+      "- For interval (0.19345, 0.2103): Upper - Lower = -0.1 - (-0.449) = 0.349\n",
+      "- For interval (0.2103, 0.2329): Upper - Lower = -0.101 - (-0.273) = 0.172\n",
+      "- For interval (0.2329, 0.2939): Upper - Lower = 0.289 - (-0.492) = 0.781\n",
+      "- For interval (0.2939, 0.368): Upper - Lower = 0.396 - (-0.769) = 1.165\n",
+      "- For interval (0.368, 0.38585): Upper - Lower = 0.304 - (-0.437) = 0.741\n",
+      "- For interval (0.38585, 0.42025): Upper - Lower = 0.315 - (-0.188) = 0.503\n",
+      "- For interval (0.42025, 0.46775): Upper - Lower = 0.42 - (-0.119) = 0.539\n",
+      "- For interval (0.46775, 0.54785): Upper - Lower = 0.514 - (-0.037) = 0.551\n",
+      "- For interval (0.54785, 0.5881000000000001): Upper - Lower = 0.758 - (-0.09) = 0.848\n",
+      "- For interval (0.5881000000000001, 0.66425): Upper - Lower = 0.86 - (-0.016) = 0.876\n",
+      "- For interval (0.66425, 0.7562): Upper - Lower = 0.968 - 0.051 = 0.917\n",
+      "- For interval (0.7562, 0.9131): Upper - Lower = 1.137 - 0.051 = 1.086\n",
+      "- For interval (0.9131, 1.065): Upper - Lower = 1.253 - 0.113 = 1.140\n",
+      "- For interval (1.065, 1.2915): Upper - Lower = 1.425 - 0.123 = 1.302\n",
+      "- For interval (1.2915, 2.873): Upper - Lower = 1.533 - 0.198 = 1.335\n",
+      "\n",
+      "From these calculations, the interval (1.2915, 2.873) has the widest confidence bound with a width of 1.335.\n",
+      "SOLUTION:  (1.2915, 2.873)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.\n",
+      "\n",
+      "2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Compare the Widths**: Identify the interval where this width (difference between upper and lower bounds) is the largest.\n",
+      "\n",
+      "Let's calculate the width of the confidence interval for each interval:\n",
+      "\n",
+      "- **(143.5, 259.35)**: Upper - Lower = -0.48 - (-1.038) = 0.558\n",
+      "- **(259.35, 289.4)**: Upper - Lower = -0.432 - (-0.892) = 0.46\n",
+      "- **(289.4, 319.15)**: Upper - Lower = -0.38 - (-0.754) = 0.374\n",
+      "- **(319.15, 348.3)**: Upper - Lower = -0.294 - (-0.634) = 0.34\n",
+      "- **(348.3, 496.5)**: Upper - Lower = -0.177 - (-0.559) = 0.382\n",
+      "- **(496.5, 548.75)**: Upper - Lower = -0.106 - (-0.436) = 0.33\n",
+      "- **(548.75, 606.0)**: Upper - Lower = -0.007 - (-0.338) = 0.331\n",
+      "- **(606.0, 696.25)**: Upper - Lower = 0.575 - (-0.727) = 1.302\n",
+      "- **(696.25, 806.1500000000001)**: Upper - Lower = 0.871 - (-0.252) = 1.123\n",
+      "- **(806.1500000000001, 901.8)**: Upper - Lower = 0.831 - (-0.022) = 0.853\n",
+      "- **(901.8, 959.4000000000001)**: Upper - Lower = 0.962 - 0.058 = 0.904\n",
+      "- **(959.4000000000001, 1054.0)**: Upper - Lower = 1.074 - 0.141 = 0.933\n",
+      "- **(1054.0, 1150.0)**: Upper - Lower = 1.171 - 0.243 = 0.928\n",
+      "- **(1150.0, 1248.5)**: Upper - Lower = 1.285 - 0.328 = 0.957\n",
+      "- **(1248.5, 1341.0)**: Upper - Lower = 1.428 - 0.393 = 1.035\n",
+      "- **(1341.0, 1801.0)**: Upper - Lower = 1.544 - 0.475 = 1.069\n",
+      "- **(1801.0, 2501.0)**: Upper - Lower = 1.644 - 0.574 = 1.07\n",
+      "\n",
+      "From the calculations, the interval **(606.0, 696.25)** has the widest confidence interval with a width of 1.302. This is the interval with the widest confidence bound.\n",
+      "SOLUTION:  (606.0, 696.25)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the x-axis interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval with the maximum difference.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.\n",
+      "\n",
+      "2. **Calculate the Difference**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Identify the Maximum Difference**: Compare the differences calculated in step 2 to find the maximum difference, which indicates the widest confidence interval.\n",
+      "\n",
+      "Let's perform these calculations:\n",
+      "\n",
+      "- For interval (0.0, 20.0): Upper - Lower = 0.14 - (-0.0556) = 0.1956\n",
+      "- For interval (20.0, 36.5): Upper - Lower = 0.2189 - (-0.2244) = 0.4433\n",
+      "- For interval (36.5, 40.5): Upper - Lower = 0.1076 - (-0.2184) = 0.326\n",
+      "- For interval (40.5, 45.5): Upper - Lower = 0.0609 - (-0.2543) = 0.3152\n",
+      "- For interval (45.5, 48.5): Upper - Lower = 0.7143 - (-0.7961) = 1.5104\n",
+      "- For interval (48.5, 55.5): Upper - Lower = 0.053 - (-0.5056) = 0.5586\n",
+      "- For interval (55.5, 80.5): Upper - Lower = 0.0187 - (-0.551) = 0.5697\n",
+      "- For interval (80.5, 87.5): Upper - Lower = -0.1422 - (-0.3117) = 0.1695\n",
+      "- For interval (87.5, 97.5): Upper - Lower = -0.1078 - (-0.251) = 0.1432\n",
+      "- For interval (97.5, 111.0): Upper - Lower = -0.0625 - (-0.2086) = 0.1461\n",
+      "- For interval (111.0, 123.5): Upper - Lower = -0.0206 - (-0.1731) = 0.1525\n",
+      "- For interval (123.5, 137.5): Upper - Lower = 0.0247 - (-0.137) = 0.1617\n",
+      "- For interval (137.5, 144.5): Upper - Lower = 0.0654 - (-0.1027) = 0.1681\n",
+      "- For interval (144.5, 157.0): Upper - Lower = 0.1166 - (-0.0751) = 0.1917\n",
+      "- For interval (157.0, 170.5): Upper - Lower = 0.1751 - (-0.0506) = 0.2257\n",
+      "- For interval (170.5, 186.5): Upper - Lower = 0.2162 - (-0.0163) = 0.2325\n",
+      "- For interval (186.5, 190.5): Upper - Lower = 0.3332 - (-0.2256) = 0.5588\n",
+      "- For interval (190.5, 192.5): Upper - Lower = 0.4987 - (-0.2869) = 0.7856\n",
+      "- For interval (192.5, 271.0): Upper - Lower = 0.3605 - (-0.3659) = 0.7264\n",
+      "- For interval (271.0, 277.5): Upper - Lower = 0.315 - (-0.245) = 0.56\n",
+      "- For interval (277.5, 292.0): Upper - Lower = 0.2956 - (-0.1491) = 0.4447\n",
+      "- For interval (292.0, 311.0): Upper - Lower = 0.3253 - (-0.0995) = 0.4248\n",
+      "- For interval (311.0, 365.0): Upper - Lower = 0.3457 - (-0.0355) = 0.3812\n",
+      "- For interval (365.0, 397.0): Upper - Lower = 0.4055 - (-0.0134) = 0.4189\n",
+      "- For interval (397.0, 452.5): Upper - Lower = 0.445 - 0.0212 = 0.4238\n",
+      "- For interval (452.5, 476.0): Upper - Lower = 0.4967 - 0.0711 = 0.4256\n",
+      "- For interval (476.0, 487.5): Upper - Lower = 0.5782 - 0.1139 = 0.4643\n",
+      "- For interval (487.5, 526.5): Upper - Lower = 0.6295 - 0.1534 = 0.4761\n",
+      "- For interval (526.5, 680.0): Upper - Lower = 0.8452 - 0.0241 = 0.8211\n",
+      "\n",
+      "From the calculations, the interval (526.5, 680.0) has the widest confidence bound with a difference of 0.8211.\n",
+      "SOLUTION:  (45.5, 48.5)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds of the confidence interval for each interval and identify the one with the largest difference.\n",
+      "\n",
+      "Here are the intervals and their corresponding confidence bounds:\n",
+      "\n",
+      "1. Interval (1.0, 1.5):\n",
+      "   - Lower Bound: -0.985\n",
+      "   - Upper Bound: -0.852\n",
+      "   - Difference: -0.852 - (-0.985) = 0.133\n",
+      "\n",
+      "2. Interval (1.5, 2.5):\n",
+      "   - Lower Bound: 0.893\n",
+      "   - Upper Bound: 1.028\n",
+      "   - Difference: 1.028 - 0.893 = 0.135\n",
+      "\n",
+      "3. Interval (2.5, 3.5):\n",
+      "   - Lower Bound: -3.482\n",
+      "   - Upper Bound: -2.727\n",
+      "   - Difference: -2.727 - (-3.482) = 0.755\n",
+      "\n",
+      "4. Interval (3.5, 4.0):\n",
+      "   - Lower Bound: -3.159\n",
+      "   - Upper Bound: -2.376\n",
+      "   - Difference: -2.376 - (-3.159) = 0.783\n",
+      "\n",
+      "Comparing the differences:\n",
+      "- (1.0, 1.5): 0.133\n",
+      "- (1.5, 2.5): 0.135\n",
+      "- (2.5, 3.5): 0.755\n",
+      "- (3.5, 4.0): 0.783\n",
+      "\n",
+      "The interval (3.5, 4.0) has the widest confidence bound with a difference of 0.783.\n",
+      "SOLUTION:  (3.5, 4.0)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.\n",
+      "\n",
+      "2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Compare the Widths**: Identify the interval with the maximum width.\n",
+      "\n",
+      "Let's perform these calculations:\n",
+      "\n",
+      "- For interval `(0.01938, 0.03164)`: \n",
+      "  - Lower bound = -0.4016\n",
+      "  - Upper bound = 0.4286\n",
+      "  - Width = 0.4286 - (-0.4016) = 0.8302\n",
+      "\n",
+      "- For interval `(0.066575, 0.067345)`: \n",
+      "  - Lower bound = -0.7938\n",
+      "  - Upper bound = 0.5229\n",
+      "  - Width = 0.5229 - (-0.7938) = 1.3167\n",
+      "\n",
+      "- For interval `(0.07211999999999999, 0.07482)`: \n",
+      "  - Lower bound = -0.9538\n",
+      "  - Upper bound = 0.3358\n",
+      "  - Width = 0.3358 - (-0.9538) = 1.2896\n",
+      "\n",
+      "- For interval `(0.067345, 0.06788)`: \n",
+      "  - Lower bound = -0.7983\n",
+      "  - Upper bound = 0.4136\n",
+      "  - Width = 0.4136 - (-0.7983) = 1.2119\n",
+      "\n",
+      "- For interval `(0.068945, 0.07211999999999999)`: \n",
+      "  - Lower bound = -0.9135\n",
+      "  - Upper bound = 0.3686\n",
+      "  - Width = 0.3686 - (-0.9135) = 1.2821\n",
+      "\n",
+      "From these calculations, the interval `(0.066575, 0.067345)` has the widest confidence interval with a width of 1.3167. This is the interval with the widest confidence bound.\n",
+      "SOLUTION:  (0.07482, 0.0785)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.\n",
+      "\n",
+      "2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Compare the Widths**: Identify the interval with the largest width, which indicates the widest confidence interval.\n",
+      "\n",
+      "Let's perform these calculations:\n",
+      "\n",
+      "- For interval (9.71, 13.24): Width = -0.658 - (-1.583) = 0.925\n",
+      "- For interval (13.24, 14.075): Width = -0.619 - (-1.428) = 0.809\n",
+      "- For interval (14.075, 14.665): Width = -0.55 - (-1.292) = 0.742\n",
+      "- For interval (14.665, 15.010000000000002): Width = -0.512 - (-1.127) = 0.615\n",
+      "- For interval (15.010000000000002, 15.485): Width = -0.417 - (-1.018) = 0.601\n",
+      "- For interval (15.485, 15.774999999999999): Width = -0.314 - (-0.932) = 0.618\n",
+      "- For interval (15.774999999999999, 16.445): Width = -0.282 - (-0.765) = 0.483\n",
+      "- For interval (16.445, 17.045): Width = -0.187 - (-0.657) = 0.47\n",
+      "- For interval (17.045, 17.665): Width = -0.112 - (-0.537) = 0.425\n",
+      "- For interval (17.665, 18.335): Width = -0.045 - (-0.404) = 0.359\n",
+      "- For interval (18.335, 18.725): Width = 0.031 - (-0.289) = 0.32\n",
+      "- For interval (18.725, 19.075): Width = 0.139 - (-0.203) = 0.342\n",
+      "- For interval (19.075, 19.549999999999997): Width = 0.22 - (-0.094) = 0.314\n",
+      "- For interval (19.549999999999997, 19.915): Width = 0.306 - 0.017 = 0.289\n",
+      "- For interval (19.915, 20.235): Width = 0.412 - 0.108 = 0.304\n",
+      "- For interval (20.235, 20.8): Width = 0.999 - (-0.11) = 1.109\n",
+      "- For interval (20.8, 21.285): Width = 1.109 - (-0.011) = 1.12\n",
+      "- For interval (21.285, 33.81): Width = 1.36 - (-0.0) = 1.36\n",
+      "\n",
+      "From these calculations, the interval (21.285, 33.81) has the widest confidence interval with a width of 1.36.\n",
+      "SOLUTION:  (21.285, 33.81)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the x-axis interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: From the JSON object, extract the lower and upper bounds for each interval.\n",
+      "\n",
+      "2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Compare the Widths**: Identify the interval with the maximum width.\n",
+      "\n",
+      "Let's perform these calculations:\n",
+      "\n",
+      "- For interval `(0.007882, 0.010595)`, the width is `1.206 - 0.336 = 0.87`.\n",
+      "- For interval `(0.010595, 0.011365)`, the width is `1.11 - 0.284 = 0.826`.\n",
+      "- For interval `(0.011365, 0.012135)`, the width is `1.031 - 0.24 = 0.791`.\n",
+      "- For interval `(0.012135, 0.01279)`, the width is `0.941 - 0.211 = 0.73`.\n",
+      "- For interval `(0.01279, 0.01352)`, the width is `0.8 - 0.226 = 0.574`.\n",
+      "- For interval `(0.01352, 0.014105)`, the width is `0.731 - 0.178 = 0.553`.\n",
+      "- For interval `(0.014105, 0.014499999999999999)`, the width is `0.662 - 0.123 = 0.539`.\n",
+      "- For interval `(0.014499999999999999, 0.014525)`, the width is `0.574 - 0.09 = 0.484`.\n",
+      "- For interval `(0.014525, 0.01489)`, the width is `0.609 - (-0.155) = 0.764`.\n",
+      "- For interval `(0.01489, 0.01532)`, the width is `0.541 - (-0.203) = 0.744`.\n",
+      "- For interval `(0.01532, 0.015805)`, the width is `0.484 - (-0.266) = 0.75`.\n",
+      "- For interval `(0.015805, 0.017215)`, the width is `0.418 - (-0.317) = 0.735`.\n",
+      "- For interval `(0.017215, 0.017855)`, the width is `0.123 - (-0.138) = 0.261`.\n",
+      "- For interval `(0.017855, 0.018165)`, the width is `0.047 - (-0.193) = 0.24`.\n",
+      "- For interval `(0.018165, 0.018685)`, the width is `0.002 - (-0.264) = 0.266`.\n",
+      "- For interval `(0.018685, 0.019545)`, the width is `-0.059 - (-0.327) = 0.268`.\n",
+      "- For interval `(0.019545, 0.02068)`, the width is `-0.116 - (-0.388) = 0.272`.\n",
+      "- For interval `(0.02068, 0.024730000000000002)`, the width is `-0.164 - (-0.457) = 0.293`.\n",
+      "- For interval `(0.024730000000000002, 0.026770000000000002)`, the width is `-0.182 - (-0.569) = 0.387`.\n",
+      "- For interval `(0.026770000000000002, 0.027435)`, the width is `-0.125 - (-0.507) = 0.382`.\n",
+      "- For interval `(0.027435, 0.028380000000000002)`, the width is `-0.043 - (-0.46) = 0.417`.\n",
+      "- For interval `(0.028380000000000002, 0.02966)`, the width is `0.013 - (-0.393) = 0.406`.\n",
+      "- For interval `(0.02966, 0.031865)`, the width is `0.097 - (-0.281) = 0.378`.\n",
+      "- For interval `(0.031865, 0.03651)`, the width is `0.197 - (-0.265) = 0.462`.\n",
+      "- For interval `(0.03651, 0.041944999999999996)`, the width is `0.281 - (-0.233) = 0.514`.\n",
+      "- For interval `(0.041944999999999996, 0.04665)`, the width is `0.345 - (-0.174) = 0.519`.\n",
+      "- For interval `(0.04665, 0.054805)`, the width is `0.424 - (-0.12) = 0.544`.\n",
+      "- For interval `(0.054805, 0.05963)`, the width is `0.521 - (-0.058) = 0.579`.\n",
+      "\n",
+      "The interval with the widest confidence bound is `(0.054805, 0.05963)` with a width of `0.579`.\n",
+      "SOLUTION:  (0.007882, 0.010595)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: For each interval, we have a lower bound and an upper bound.\n",
+      "\n",
+      "2. **Calculate the Difference**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Compare the Differences**: Identify the interval where this difference (width of the confidence interval) is the largest.\n",
+      "\n",
+      "Let's calculate the differences for each interval:\n",
+      "\n",
+      "- **(6.981, 9.281500000000001)**: Upper - Lower = -0.515 - (-1.01) = 0.495\n",
+      "- **(9.281500000000001, 9.7015)**: Upper - Lower = -0.435 - (-0.884) = 0.449\n",
+      "- **(9.7015, 10.165)**: Upper - Lower = -0.373 - (-0.748) = 0.375\n",
+      "- **(10.165, 10.655000000000001)**: Upper - Lower = -0.311 - (-0.611) = 0.300\n",
+      "- **(10.655000000000001, 12.465)**: Upper - Lower = -0.184 - (-0.536) = 0.352\n",
+      "- **(12.465, 13.39)**: Upper - Lower = -0.128 - (-0.396) = 0.268\n",
+      "- **(13.39, 14.43)**: Upper - Lower = -0.057 - (-0.269) = 0.212\n",
+      "- **(14.43, 14.934999999999999)**: Upper - Lower = 0.097 - (-0.226) = 0.323\n",
+      "- **(14.934999999999999, 15.08)**: Upper - Lower = 0.231 - (-0.156) = 0.387\n",
+      "- **(15.08, 15.815)**: Upper - Lower = 0.333 - (-0.059) = 0.392\n",
+      "- **(15.815, 16.925)**: Upper - Lower = 0.597 - (-0.127) = 0.724\n",
+      "- **(16.925, 17.385)**: Upper - Lower = 0.748 - 0.041 = 0.707\n",
+      "- **(17.385, 18.0)**: Upper - Lower = 0.853 - 0.136 = 0.717\n",
+      "- **(18.0, 18.735)**: Upper - Lower = 0.993 - 0.205 = 0.788\n",
+      "- **(18.735, 19.240000000000002)**: Upper - Lower = 1.107 - 0.283 = 0.824\n",
+      "- **(19.240000000000002, 19.990000000000002)**: Upper - Lower = 1.202 - 0.385 = 0.817\n",
+      "- **(19.990000000000002, 20.595)**: Upper - Lower = 1.32 - 0.462 = 0.858\n",
+      "- **(20.595, 23.240000000000002)**: Upper - Lower = 1.461 - 0.519 = 0.942\n",
+      "- **(23.240000000000002, 28.11)**: Upper - Lower = 1.575 - 0.611 = 0.964\n",
+      "\n",
+      "From these calculations, the interval **(23.240000000000002, 28.11)** has the widest confidence bound with a difference of 0.964.\n",
+      "SOLUTION:  (23.240000000000002, 28.11)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the x-axis interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds of the confidence interval for each interval and identify the interval with the maximum difference.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds of the confidence interval from the provided data.\n",
+      "\n",
+      "2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Identify the Maximum Width**: Compare the widths of all intervals and identify the interval with the maximum width.\n",
+      "\n",
+      "Let's perform these calculations:\n",
+      "\n",
+      "- For interval (50.41, 71.06): Width = -0.307 - (-2.45) = 2.143\n",
+      "- For interval (71.06, 76.52000000000001): Width = -0.189 - (-2.257) = 2.068\n",
+      "- For interval (76.52000000000001, 80.9): Width = -0.114 - (-2.023) = 1.909\n",
+      "- For interval (80.9, 84.035): Width = 0.021 - (-1.85) = 1.871\n",
+      "- For interval (84.035, 86.48500000000001): Width = 0.172 - (-1.682) = 1.854\n",
+      "- For interval (86.48500000000001, 87.3): Width = 0.332 - (-1.531) = 1.863\n",
+      "- For interval (87.3, 91.49000000000001): Width = 0.159 - (-1.053) = 1.212\n",
+      "- For interval (91.49000000000001, 95.66): Width = 0.331 - (-0.915) = 1.246\n",
+      "- For interval (95.66, 101.15): Width = 0.936 - (-1.829) = 2.765\n",
+      "- For interval (101.15, 102.05000000000001): Width = 1.054 - (-1.642) = 2.696\n",
+      "- For interval (102.05000000000001, 109.6): Width = 0.782 - (-0.387) = 1.169\n",
+      "- For interval (109.6, 116.25): Width = 0.94 - (-0.238) = 1.178\n",
+      "- For interval (116.25, 120.35): Width = 1.088 - (-0.074) = 1.162\n",
+      "- For interval (120.35, 127.0): Width = 2.256 - (-0.761) = 3.017\n",
+      "- For interval (127.0, 133.10000000000002): Width = 2.428 - (-0.623) = 3.051\n",
+      "- For interval (133.10000000000002, 145.10000000000002): Width = 2.611 - (-0.494) = 3.105\n",
+      "- For interval (145.10000000000002, 160.0): Width = 2.809 - (-0.379) = 3.188\n",
+      "- For interval (160.0, 178.85): Width = 3.027 - (-0.29) = 3.317\n",
+      "- For interval (178.85, 251.2): Width = 3.208 - (-0.162) = 3.37\n",
+      "\n",
+      "From these calculations, the interval with the widest confidence bound is (178.85, 251.2) with a width of 3.37.\n",
+      "SOLUTION:  (178.85, 251.2)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds of the confidence interval for each interval and identify the one with the maximum difference.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: From the JSON object, extract the lower and upper bounds for each interval.\n",
+      "\n",
+      "2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Identify the Maximum Width**: Compare the widths of all intervals and identify the interval with the maximum width.\n",
+      "\n",
+      "Let's perform these calculations:\n",
+      "\n",
+      "- For interval `(0.4999, 0.5427500000000001)`: \n",
+      "  - Lower Bound = -48216.1\n",
+      "  - Upper Bound = 16080.9\n",
+      "  - Width = 16080.9 - (-48216.1) = 64297.0\n",
+      "\n",
+      "- For interval `(0.5427500000000001, 1.4808)`: \n",
+      "  - Lower Bound = -68098.8\n",
+      "  - Upper Bound = -42980.1\n",
+      "  - Width = -42980.1 - (-68098.8) = 25118.7\n",
+      "\n",
+      "- Continue this for all intervals...\n",
+      "\n",
+      "- For interval `(9.046949999999999, 15.00005)`: \n",
+      "  - Lower Bound = 203670.8\n",
+      "  - Upper Bound = 225081.0\n",
+      "  - Width = 225081.0 - 203670.8 = 21410.2\n",
+      "\n",
+      "- For interval `(15.00005, 15.0001)`: \n",
+      "  - Lower Bound = 178950.1\n",
+      "  - Upper Bound = 208557.1\n",
+      "  - Width = 208557.1 - 178950.1 = 29607.0\n",
+      "\n",
+      "After calculating the widths for all intervals, we find that the interval with the widest confidence bound is `(0.4999, 0.5427500000000001)` with a width of 64297.0.\n",
+      "SOLUTION:  (0.4999, 0.5427500000000001)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here are the calculations for each interval:\n",
+      "\n",
+      "1. Interval (113.0, 114.5):\n",
+      "   - Lower Bound: -3.483\n",
+      "   - Upper Bound: 0.944\n",
+      "   - Difference: 0.944 - (-3.483) = 4.427\n",
+      "\n",
+      "2. Interval (114.5, 118.5):\n",
+      "   - Lower Bound: -4.768\n",
+      "   - Upper Bound: 5.334\n",
+      "   - Difference: 5.334 - (-4.768) = 10.102\n",
+      "\n",
+      "3. Interval (118.5, 124.5):\n",
+      "   - Lower Bound: 2.536\n",
+      "   - Upper Bound: 4.542\n",
+      "   - Difference: 4.542 - 2.536 = 2.006\n",
+      "\n",
+      "4. Interval (124.5, 126.5):\n",
+      "   - Lower Bound: 1.699\n",
+      "   - Upper Bound: 3.222\n",
+      "   - Difference: 3.222 - 1.699 = 1.523\n",
+      "\n",
+      "5. Interval (126.5, 127.5):\n",
+      "   - Lower Bound: 3.034\n",
+      "   - Upper Bound: 5.05\n",
+      "   - Difference: 5.05 - 3.034 = 2.016\n",
+      "\n",
+      "6. Interval (127.5, 129.5):\n",
+      "   - Lower Bound: 2.614\n",
+      "   - Upper Bound: 4.492\n",
+      "   - Difference: 4.492 - 2.614 = 1.878\n",
+      "\n",
+      "7. Interval (129.5, 130.5):\n",
+      "   - Lower Bound: 0.389\n",
+      "   - Upper Bound: 1.517\n",
+      "   - Difference: 1.517 - 0.389 = 1.128\n",
+      "\n",
+      "8. Interval (130.5, 132.5):\n",
+      "   - Lower Bound: 0.304\n",
+      "   - Upper Bound: 2.136\n",
+      "   - Difference: 2.136 - 0.304 = 1.832\n",
+      "\n",
+      "9. Interval (132.5, 133.5):\n",
+      "   - Lower Bound: -2.269\n",
+      "   - Upper Bound: 0.08\n",
+      "   - Difference: 0.08 - (-2.269) = 2.349\n",
+      "\n",
+      "10. Interval (133.5, 135.5):\n",
+      "    - Lower Bound: 0.366\n",
+      "    - Upper Bound: 0.808\n",
+      "    - Difference: 0.808 - 0.366 = 0.442\n",
+      "\n",
+      "11. Interval (135.5, 138.5):\n",
+      "    - Lower Bound: -0.879\n",
+      "    - Upper Bound: -0.38\n",
+      "    - Difference: -0.38 - (-0.879) = 0.499\n",
+      "\n",
+      "12. Interval (138.5, 144.5):\n",
+      "    - Lower Bound: -0.845\n",
+      "    - Upper Bound: 0.38\n",
+      "    - Difference: 0.38 - (-0.845) = 1.225\n",
+      "\n",
+      "13. Interval (144.5, 148.0):\n",
+      "    - Lower Bound: -0.129\n",
+      "    - Upper Bound: 0.354\n",
+      "    - Difference: 0.354 - (-0.129) = 0.483\n",
+      "\n",
+      "From these calculations, the interval (114.5, 118.5) has the widest confidence bound with a difference of 10.102. This is the interval with the widest confidence bound.\n",
+      "SOLUTION:  (114.5, 118.5)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the width of the confidence interval for each interval and compare them. The confidence interval width is calculated as the difference between the upper bound and the lower bound of the confidence interval for each interval.\n",
+      "\n",
+      "Let's calculate the width of the confidence intervals for each interval:\n",
+      "\n",
+      "1. For the interval (0.0, 0.5):\n",
+      "   - Lower Bound = -0.037941\n",
+      "   - Upper Bound = 0.0291\n",
+      "   - Width = Upper Bound - Lower Bound = 0.0291 - (-0.037941) = 0.0291 + 0.037941 = 0.067041\n",
+      "\n",
+      "2. For the interval (0.5, 1.0):\n",
+      "   - Lower Bound = -0.009076\n",
+      "   - Upper Bound = 0.011834\n",
+      "   - Width = Upper Bound - Lower Bound = 0.011834 - (-0.009076) = 0.011834 + 0.009076 = 0.02091\n",
+      "\n",
+      "Comparing the widths:\n",
+      "- Interval (0.0, 0.5) has a width of 0.067041\n",
+      "- Interval (0.5, 1.0) has a width of 0.02091\n",
+      "\n",
+      "The interval (0.0, 0.5) has the widest confidence bound with a width of 0.067041.\n",
+      "SOLUTION:  (0.0, 0.5)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval with the maximum difference.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.\n",
+      "\n",
+      "2. **Calculate the Difference**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Identify the Maximum Difference**: Compare the differences calculated in step 2 to find the maximum difference, which indicates the widest confidence interval.\n",
+      "\n",
+      "Let's perform these calculations:\n",
+      "\n",
+      "- For interval (8670.0, 90271.0): \n",
+      "  - Lower Bound = -0.06\n",
+      "  - Upper Bound = 0.744\n",
+      "  - Difference = 0.744 - (-0.06) = 0.804\n",
+      "\n",
+      "- For interval (90271.0, 467526.5): \n",
+      "  - Lower Bound = 0.079\n",
+      "  - Upper Bound = 1.07\n",
+      "  - Difference = 1.07 - 0.079 = 0.991\n",
+      "\n",
+      "- For interval (467526.5, 853506.5): \n",
+      "  - Lower Bound = 0.101\n",
+      "  - Upper Bound = 1.212\n",
+      "  - Difference = 1.212 - 0.101 = 1.111\n",
+      "\n",
+      "- For interval (853506.5, 859643.0): \n",
+      "  - Lower Bound = 0.139\n",
+      "  - Upper Bound = 1.299\n",
+      "  - Difference = 1.299 - 0.139 = 1.16\n",
+      "\n",
+      "- For interval (859643.0, 864727.5): \n",
+      "  - Lower Bound = 0.076\n",
+      "  - Upper Bound = 1.234\n",
+      "  - Difference = 1.234 - 0.076 = 1.158\n",
+      "\n",
+      "- For interval (864727.5, 871421.0): \n",
+      "  - Lower Bound = 0.038\n",
+      "  - Upper Bound = 1.148\n",
+      "  - Difference = 1.148 - 0.038 = 1.11\n",
+      "\n",
+      "- For interval (871421.0, 874848.5): \n",
+      "  - Lower Bound = 0.005\n",
+      "  - Upper Bound = 1.051\n",
+      "  - Difference = 1.051 - 0.005 = 1.046\n",
+      "\n",
+      "- For interval (874848.5, 880845.5): \n",
+      "  - Lower Bound = -0.028\n",
+      "  - Upper Bound = 0.956\n",
+      "  - Difference = 0.956 - (-0.028) = 0.984\n",
+      "\n",
+      "- For interval (880845.5, 882230.0): \n",
+      "  - Lower Bound = -0.061\n",
+      "  - Upper Bound = 0.86\n",
+      "  - Difference = 0.86 - (-0.061) = 0.921\n",
+      "\n",
+      "- For interval (882230.0, 883266.5): \n",
+      "  - Lower Bound = -0.137\n",
+      "  - Upper Bound = 0.774\n",
+      "  - Difference = 0.774 - (-0.137) = 0.911\n",
+      "\n",
+      "Continue this calculation for all intervals. From the calculations above, the interval (853506.5, 859643.0) has the widest confidence bound with a difference of 1.16. This is the interval with the widest confidence bound based on the data provided.\n",
+      "SOLUTION:  (853506.5, 859643.0)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here's how we can do this step by step:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: For each interval, we have a lower bound and an upper bound.\n",
+      "\n",
+      "2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Compare the Widths**: Identify the interval where this width (difference between upper and lower bounds) is the largest.\n",
+      "\n",
+      "Let's perform these calculations using the provided data:\n",
+      "\n",
+      "- For interval `(0.0088595, 0.009246)`, the lower bound is `-0.313` and the upper bound is `0.752`. The width of the confidence interval is `0.752 - (-0.313) = 1.065`.\n",
+      "\n",
+      "We can compare this width with the widths of other intervals, but given the values provided, it's clear that this interval has a significantly larger width than the others, which generally have smaller differences.\n",
+      "\n",
+      "Thus, the interval `(0.0088595, 0.009246)` has the widest confidence bound in the graph.\n",
+      "SOLUTION:  (0.0088595, 0.009246)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: From the JSON object, extract the lower and upper bounds for each interval.\n",
+      "\n",
+      "2. **Calculate the Width of the Confidence Interval for Each Interval**: Subtract the lower bound from the upper bound for each interval.\n",
+      "\n",
+      "3. **Identify the Interval with the Maximum Width**: Compare the widths calculated in step 2 and identify the interval with the largest width.\n",
+      "\n",
+      "Let's perform these calculations:\n",
+      "\n",
+      "- For interval `(0.1565, 0.165)`: Width = 0.249 - (-0.839) = 1.088\n",
+      "- For interval `(0.165, 0.19055)`: Width = -0.201 - (-0.743) = 0.542\n",
+      "- For interval `(0.19055, 0.24485)`: Width = -0.296 - (-0.802) = 0.506\n",
+      "- For interval `(0.24485, 0.25225)`: Width = -0.276 - (-0.663) = 0.387\n",
+      "- For interval `(0.25225, 0.2583)`: Width = -0.188 - (-0.595) = 0.407\n",
+      "- For interval `(0.2583, 0.26635)`: Width = -0.140 - (-0.479) = 0.339\n",
+      "- For interval `(0.26635, 0.26959999999999995)`: Width = -0.072 - (-0.388) = 0.316\n",
+      "- For interval `(0.26959999999999995, 0.27495)`: Width = 0.030 - (-0.253) = 0.283\n",
+      "- For interval `(0.27495, 0.28035)`: Width = 0.104 - (-0.172) = 0.276\n",
+      "- For interval `(0.28035, 0.28815)`: Width = 0.191 - (-0.100) = 0.291\n",
+      "- For interval `(0.28815, 0.2986)`: Width = 0.264 - (-0.015) = 0.279\n",
+      "- For interval `(0.2986, 0.31745)`: Width = 0.361 - 0.043 = 0.318\n",
+      "- For interval `(0.31745, 0.32125000000000004)`: Width = 0.420 - 0.141 = 0.279\n",
+      "- For interval `(0.32125000000000004, 0.33065)`: Width = 0.516 - 0.210 = 0.306\n",
+      "- For interval `(0.33065, 0.35335)`: Width = 0.612 - 0.276 = 0.336\n",
+      "- For interval `(0.35335, 0.36085)`: Width = 0.694 - 0.357 = 0.337\n",
+      "- For interval `(0.36085, 0.3702)`: Width = 0.901 - 0.348 = 0.553\n",
+      "- For interval `(0.3702, 0.4223)`: Width = 1.015 - 0.395 = 0.620\n",
+      "- For interval `(0.4223, 0.4697)`: Width = 1.092 - 0.478 = 0.614\n",
+      "- For interval `(0.4697, 0.6638)`: Width = 1.197 - 0.538 = 0.659\n",
+      "\n",
+      "From these calculations, the interval `(0.4697, 0.6638)` has the widest confidence bound with a width of 0.659.\n",
+      "SOLUTION:  (0.1565, 0.165)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.\n",
+      "\n",
+      "2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Compare the Widths**: Identify the interval with the maximum width.\n",
+      "\n",
+      "Let's perform these calculations:\n",
+      "\n",
+      "- For interval (0.0, 9.1): Width = -0.262 - (-1.139) = 0.877\n",
+      "- For interval (9.1, 22.55): Width = -0.573 - (-1.349) = 0.776\n",
+      "- For interval (22.55, 23.65): Width = -0.493 - (-1.219) = 0.726\n",
+      "- For interval (23.65, 25.55): Width = -0.243 - (-1.281) = 1.038\n",
+      "- For interval (25.55, 26.35): Width = -0.09 - (-1.231) = 1.141\n",
+      "- For interval (26.35, 27.65): Width = 0.088 - (-0.568) = 0.656\n",
+      "- For interval (27.65, 28.45): Width = -0.03 - (-0.258) = 0.228\n",
+      "- For interval (28.45, 29.65): Width = 0.054 - (-0.157) = 0.211\n",
+      "- For interval (29.65, 30.45): Width = 0.208 - (-0.11) = 0.318\n",
+      "- For interval (30.45, 32.150000000000006): Width = 0.392 - (-0.086) = 0.478\n",
+      "- For interval (32.150000000000006, 37.650000000000006): Width = 0.409 - 0.084 = 0.325\n",
+      "- For interval (37.650000000000006, 41.75): Width = 0.491 - 0.189 = 0.302\n",
+      "- For interval (41.75, 42.849999999999994): Width = 0.588 - 0.28 = 0.308\n",
+      "- For interval (42.849999999999994, 45.650000000000006): Width = 0.709 - 0.348 = 0.361\n",
+      "- For interval (45.650000000000006, 48.349999999999994): Width = 0.996 - 0.256 = 0.74\n",
+      "- For interval (48.349999999999994, 67.1): Width = 1.303 - 0.265 = 1.038\n",
+      "\n",
+      "From the calculations, the interval with the widest confidence bound is (25.55, 26.35) with a width of 1.141. This interval has the largest difference between the upper and lower confidence bounds.\n",
+      "SOLUTION:  (25.55, 26.35)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.\n",
+      "\n",
+      "2. **Calculate the Difference**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Compare the Differences**: Identify the interval where this difference (width of the confidence interval) is the largest.\n",
+      "\n",
+      "Let's perform these calculations using the provided data:\n",
+      "\n",
+      "- For interval `(4.0, 11.5)`, the difference is `13.0 - 8.45 = 4.55`.\n",
+      "- For interval `(11.5, 12.5)`, the difference is `2.32 - 0.25 = 2.07`.\n",
+      "- For interval `(12.5, 15.5)`, the difference is `4.82 - 2.94 = 1.88`.\n",
+      "- For interval `(15.5, 18.0)`, the difference is `4.68 - (-0.25) = 4.93`.\n",
+      "- For interval `(18.0, 28.5)`, the difference is `8.31 - 4.04 = 4.27`.\n",
+      "- For interval `(28.5, 30.5)`, the difference is `5.26 - 3.69 = 1.57`.\n",
+      "- For interval `(30.5, 52.0)`, the difference is `6.91 - 4.21 = 2.7`.\n",
+      "- For interval `(52.0, 54.5)`, the difference is `5.03 - 1.74 = 3.29`.\n",
+      "- For interval `(54.5, 67.5)`, the difference is `6.41 - 3.17 = 3.24`.\n",
+      "- For interval `(67.5, 73.5)`, the difference is `3.57 - 1.96 = 1.61`.\n",
+      "- For interval `(73.5, 76.5)`, the difference is `(-1.61) - (-4.69) = 3.08`.\n",
+      "- For interval `(76.5, 78.5)`, the difference is `3.39 - 1.19 = 2.2`.\n",
+      "- For interval `(78.5, 82.5)`, the difference is `0.92 - (-1.25) = 2.17`.\n",
+      "- For interval `(82.5, 87.5)`, the difference is `(-1.75) - (-3.84) = 2.09`.\n",
+      "- For interval `(87.5, 90.5)`, the difference is `0.72 - (-0.35) = 1.07`.\n",
+      "- For interval `(90.5, 92.5)`, the difference is `0.6 - (-2.75) = 3.35`.\n",
+      "- For interval `(92.5, 95.5)`, the difference is `(-0.81) - (-4.6) = 3.79`.\n",
+      "- For interval `(95.5, 108.5)`, the difference is `(-0.34) - (-1.62) = 1.28`.\n",
+      "- For interval `(108.5, 117.5)`, the difference is `0.7 - (-0.66) = 1.36`.\n",
+      "- For interval `(117.5, 124.5)`, the difference is `(-1.93) - (-4.94) = 3.01`.\n",
+      "- For interval `(124.5, 137.5)`, the difference is `1.53 - (-0.24) = 1.77`.\n",
+      "- For interval `(137.5, 149.0)`, the difference is `0.22 - (-1.83) = 2.05`.\n",
+      "- For interval `(149.0, 171.5)`, the difference is `6.52 - 3.59 = 2.93`.\n",
+      "- For interval `(171.5, 173.0)`, the difference is `3.72 - 1.61 = 2.11`.\n",
+      "- For interval `(173.0, 182.5)`, the difference is `0.18 - (-1.86) = 2.04`.\n",
+      "- For interval `(182.5, 192.5)`, the difference is `(-2.33) - (-4.51) = 2.18`.\n",
+      "- For interval `(192.5, 193.5)`, the difference is `(-0.13) - (-1.89) = 1.76`.\n",
+      "- For interval `(193.5, 253.0)`, the difference is `(-1.06) - (-4.11) = 3.05`.\n",
+      "- For interval `(253.0, 285.0)`, the difference is `(-6.14) - (-10.7) = 4.56`.\n",
+      "\n",
+      "From the calculations, the interval `(253.0, 285.0)` has the widest confidence bound with a difference of `4.56`.\n",
+      "SOLUTION:  (15.5, 18.0)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here are the calculations for each interval:\n",
+      "\n",
+      "1. Interval (14.0, 16.0):\n",
+      "   - Lower Bound: 2.65\n",
+      "   - Upper Bound: 6.45\n",
+      "   - Difference: 6.45 - 2.65 = 3.8\n",
+      "\n",
+      "2. Interval (16.0, 22.5):\n",
+      "   - Lower Bound: 2.42\n",
+      "   - Upper Bound: 4.1\n",
+      "   - Difference: 4.1 - 2.42 = 1.68\n",
+      "\n",
+      "3. Interval (22.5, 27.5):\n",
+      "   - Lower Bound: 1.26\n",
+      "   - Upper Bound: 2.51\n",
+      "   - Difference: 2.51 - 1.26 = 1.25\n",
+      "\n",
+      "4. Interval (27.5, 32.5):\n",
+      "   - Lower Bound: -0.83\n",
+      "   - Upper Bound: -0.01\n",
+      "   - Difference: -0.01 - (-0.83) = 0.82\n",
+      "\n",
+      "5. Interval (32.5, 36.5):\n",
+      "   - Lower Bound: -2.57\n",
+      "   - Upper Bound: -0.95\n",
+      "   - Difference: -0.95 - (-2.57) = 1.62\n",
+      "\n",
+      "6. Interval (36.5, 39.0):\n",
+      "   - Lower Bound: 0.17\n",
+      "   - Upper Bound: 0.79\n",
+      "   - Difference: 0.79 - 0.17 = 0.62\n",
+      "\n",
+      "7. Interval (39.0, 61.0):\n",
+      "   - Lower Bound: -1.16\n",
+      "   - Upper Bound: -0.49\n",
+      "   - Difference: -0.49 - (-1.16) = 0.67\n",
+      "\n",
+      "8. Interval (61.0, 67.5):\n",
+      "   - Lower Bound: -0.39\n",
+      "   - Upper Bound: 0.55\n",
+      "   - Difference: 0.55 - (-0.39) = 0.94\n",
+      "\n",
+      "9. Interval (67.5, 75.0):\n",
+      "   - Lower Bound: 0.32\n",
+      "   - Upper Bound: 1.28\n",
+      "   - Difference: 1.28 - 0.32 = 0.96\n",
+      "\n",
+      "10. Interval (75.0, 80.0):\n",
+      "    - Lower Bound: -8.05\n",
+      "    - Upper Bound: -3.29\n",
+      "    - Difference: -3.29 - (-8.05) = 4.76\n",
+      "\n",
+      "From these calculations, the interval (75.0, 80.0) has the widest confidence bound with a difference of 4.76.\n",
+      "SOLUTION:  (75.0, 80.0)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds of the confidence interval for each interval and identify the one with the largest difference.\n",
+      "\n",
+      "Here are the intervals and their corresponding confidence bounds:\n",
+      "\n",
+      "1. Interval (0.1, 0.35):\n",
+      "   - Lower Bound: 7.9\n",
+      "   - Upper Bound: 8.23\n",
+      "   - Difference: 8.23 - 7.9 = 0.33\n",
+      "\n",
+      "2. Interval (0.35, 0.45):\n",
+      "   - Lower Bound: 7.05\n",
+      "   - Upper Bound: 7.49\n",
+      "   - Difference: 7.49 - 7.05 = 0.44\n",
+      "\n",
+      "3. Interval (0.45, 0.75):\n",
+      "   - Lower Bound: 3.08\n",
+      "   - Upper Bound: 9.28\n",
+      "   - Difference: 9.28 - 3.08 = 6.2\n",
+      "\n",
+      "4. Interval (0.75, 1.25):\n",
+      "   - Lower Bound: -2.81\n",
+      "   - Upper Bound: -2.47\n",
+      "   - Difference: -2.47 - (-2.81) = 0.34\n",
+      "\n",
+      "5. Interval (1.25, 1.75):\n",
+      "   - Lower Bound: -3.62\n",
+      "   - Upper Bound: -3.3\n",
+      "   - Difference: -3.3 - (-3.62) = 0.32\n",
+      "\n",
+      "6. Interval (1.75, 2.5):\n",
+      "   - Lower Bound: -4.29\n",
+      "   - Upper Bound: -4.08\n",
+      "   - Difference: -4.08 - (-4.29) = 0.21\n",
+      "\n",
+      "From the calculated differences, the interval (0.45, 0.75) has the widest confidence bound with a difference of 6.2. This is the interval with the widest confidence bound.\n",
+      "SOLUTION:  (0.45, 0.75)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here are the calculations for each interval:\n",
+      "\n",
+      "1. Interval (0.0, 1.5): Upper - Lower = -0.02245 - (-0.02806) = 0.00561\n",
+      "2. Interval (1.5, 2.5): Upper - Lower = -0.01664 - (-0.01811) = 0.00147\n",
+      "3. Interval (2.5, 3.5): Upper - Lower = -0.01102 - (-0.01241) = 0.00139\n",
+      "4. Interval (3.5, 4.5): Upper - Lower = -0.00514 - (-0.0056) = 0.00046\n",
+      "5. Interval (4.5, 5.5): Upper - Lower = 0.00016 - (-0.00057) = 0.00073\n",
+      "6. Interval (5.5, 6.5): Upper - Lower = 0.00699 - 0.00621 = 0.00078\n",
+      "7. Interval (6.5, 7.5): Upper - Lower = 0.01085 - 0.00967 = 0.00118\n",
+      "8. Interval (7.5, 8.5): Upper - Lower = 0.01761 - 0.01672 = 0.00089\n",
+      "9. Interval (8.5, 9.5): Upper - Lower = 0.02519 - 0.02334 = 0.00185\n",
+      "10. Interval (9.5, 10.5): Upper - Lower = 0.02958 - 0.02687 = 0.00271\n",
+      "11. Interval (10.5, 11.5): Upper - Lower = 0.03468 - 0.03182 = 0.00286\n",
+      "12. Interval (11.5, 13.5): Upper - Lower = 0.04466 - 0.03364 = 0.01102\n",
+      "13. Interval (13.5, 15.0): Upper - Lower = 0.04073 - 0.0307 = 0.01003\n",
+      "\n",
+      "From these calculations, the interval (11.5, 13.5) has the widest confidence bound with a difference of 0.01102. Therefore, the x-axis interval with the widest confidence bound is (11.5, 13.5).\n",
+      "SOLUTION:  (11.5, 13.5)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval with the maximum difference.\n",
+      "\n",
+      "Here are the calculations for each interval:\n",
+      "\n",
+      "1. Interval (1.0, 4.5):\n",
+      "   - Lower Bound: -26905.5\n",
+      "   - Upper Bound: -13090.4\n",
+      "   - Difference: -13090.4 - (-26905.5) = 13815.1\n",
+      "\n",
+      "2. Interval (4.5, 7.5):\n",
+      "   - Lower Bound: -11566.0\n",
+      "   - Upper Bound: -4010.4\n",
+      "   - Difference: -4010.4 - (-11566.0) = 7555.6\n",
+      "\n",
+      "3. Interval (7.5, 16.5):\n",
+      "   - Lower Bound: -12538.5\n",
+      "   - Upper Bound: -8821.8\n",
+      "   - Difference: -8821.8 - (-12538.5) = 3716.7\n",
+      "\n",
+      "4. Interval (16.5, 18.5):\n",
+      "   - Lower Bound: -7756.2\n",
+      "   - Upper Bound: -4852.5\n",
+      "   - Difference: -4852.5 - (-7756.2) = 2903.7\n",
+      "\n",
+      "5. Interval (18.5, 27.5):\n",
+      "   - Lower Bound: -3361.1\n",
+      "   - Upper Bound: -160.0\n",
+      "   - Difference: -160.0 - (-3361.1) = 3201.1\n",
+      "\n",
+      "6. Interval (27.5, 34.5):\n",
+      "   - Lower Bound: 124.5\n",
+      "   - Upper Bound: 4205.0\n",
+      "   - Difference: 4205.0 - 124.5 = 4080.5\n",
+      "\n",
+      "7. Interval (34.5, 38.5):\n",
+      "   - Lower Bound: -1933.4\n",
+      "   - Upper Bound: 108.5\n",
+      "   - Difference: 108.5 - (-1933.4) = 2041.9\n",
+      "\n",
+      "8. Interval (38.5, 41.5):\n",
+      "   - Lower Bound: 2260.6\n",
+      "   - Upper Bound: 6138.7\n",
+      "   - Difference: 6138.7 - 2260.6 = 3878.1\n",
+      "\n",
+      "9. Interval (41.5, 45.5):\n",
+      "   - Lower Bound: -4429.7\n",
+      "   - Upper Bound: 3434.9\n",
+      "   - Difference: 3434.9 - (-4429.7) = 7864.6\n",
+      "\n",
+      "10. Interval (45.5, 47.5):\n",
+      "    - Lower Bound: -8697.7\n",
+      "    - Upper Bound: -1682.0\n",
+      "    - Difference: -1682.0 - (-8697.7) = 7015.7\n",
+      "\n",
+      "11. Interval (47.5, 48.5):\n",
+      "    - Lower Bound: 2180.3\n",
+      "    - Upper Bound: 8221.7\n",
+      "    - Difference: 8221.7 - 2180.3 = 6041.4\n",
+      "\n",
+      "12. Interval (48.5, 49.5):\n",
+      "    - Lower Bound: -1981.1\n",
+      "    - Upper Bound: 6299.1\n",
+      "    - Difference: 6299.1 - (-1981.1) = 8280.2\n",
+      "\n",
+      "13. Interval (49.5, 50.5):\n",
+      "    - Lower Bound: 1581.5\n",
+      "    - Upper Bound: 10689.9\n",
+      "    - Difference: 10689.9 - 1581.5 = 9108.4\n",
+      "\n",
+      "14. Interval (50.5, 51.5):\n",
+      "    - Lower Bound: 5647.5\n",
+      "    - Upper Bound: 17380.1\n",
+      "    - Difference: 17380.1 - 5647.5 = 11732.6\n",
+      "\n",
+      "15. Interval (51.5, 52.0):\n",
+      "    - Lower Bound: 25827.1\n",
+      "    - Upper Bound: 29272.3\n",
+      "    - Difference: 29272.3 - 25827.1 = 3445.2\n",
+      "\n",
+      "From the above calculations, the interval (1.0, 4.5) has the widest confidence bound with a difference of 13815.1.\n",
+      "SOLUTION:  (1.0, 4.5)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the width of the confidence interval for each interval and compare them. The confidence interval width is calculated as the difference between the upper bound and the lower bound of the confidence interval.\n",
+      "\n",
+      "Let's calculate the width of the confidence intervals for each interval:\n",
+      "\n",
+      "1. For the interval (0.0, 0.5):\n",
+      "   - Lower Bound = -0.1464\n",
+      "   - Upper Bound = -0.0173\n",
+      "   - Width = Upper Bound - Lower Bound = -0.0173 - (-0.1464) = 0.1291\n",
+      "\n",
+      "2. For the interval (0.5, 1.0):\n",
+      "   - Lower Bound = 0.0194\n",
+      "   - Upper Bound = 0.1641\n",
+      "   - Width = Upper Bound - Lower Bound = 0.1641 - 0.0194 = 0.1447\n",
+      "\n",
+      "Comparing the widths:\n",
+      "- Interval (0.0, 0.5) has a width of 0.1291\n",
+      "- Interval (0.5, 1.0) has a width of 0.1447\n",
+      "\n",
+      "The interval (0.5, 1.0) has the widest confidence bound with a width of 0.1447.\n",
+      "SOLUTION:  (0.5, 1.0)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.\n",
+      "\n",
+      "2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Compare the Widths**: Identify the interval where this width (difference between upper and lower bounds) is the largest.\n",
+      "\n",
+      "Let's calculate the width of the confidence interval for each interval:\n",
+      "\n",
+      "- **(12.02, 16.935000000000002)**: \\(0.572 - (-4.342) = 4.914\\)\n",
+      "- **(16.935000000000002, 18.335)**: \\(0.695 - (-4.128) = 4.823\\)\n",
+      "- **(18.335, 19.505)**: \\(0.835 - (-3.934) = 4.769\\)\n",
+      "- **(19.505, 20.225)**: \\(-0.437 - (-1.264) = 0.827\\)\n",
+      "- **(20.225, 21.955)**: \\(-0.279 - (-0.945) = 0.666\\)\n",
+      "- **(21.955, 23.59)**: \\(-0.218 - (-0.663) = 0.445\\)\n",
+      "- **(23.59, 24.795)**: \\(-0.076 - (-0.468) = 0.392\\)\n",
+      "- **(24.795, 25.18)**: \\(0.073 - (-0.274) = 0.347\\)\n",
+      "- **(25.18, 25.83)**: \\(0.66 - (-0.503) = 1.163\\)\n",
+      "- **(25.83, 26.855)**: \\(0.884 - (-0.327) = 1.211\\)\n",
+      "- **(26.855, 27.994999999999997)**: \\(1.065 - (-0.163) = 1.228\\)\n",
+      "- **(27.994999999999997, 29.225)**: \\(1.248 - (-0.01) = 1.258\\)\n",
+      "- **(29.225, 31.515)**: \\(1.961 - (-0.206) = 2.167\\)\n",
+      "- **(31.515, 32.485)**: \\(2.17 - (-0.081) = 2.251\\)\n",
+      "- **(32.485, 35.05)**: \\(2.691 - (-0.18) = 2.871\\)\n",
+      "- **(35.05, 49.54)**: \\(2.861 - (-0.014) = 2.875\\)\n",
+      "\n",
+      "From the calculations above, the interval **(35.05, 49.54)** has the widest confidence bound with a width of \\(2.875\\). This is the interval with the widest confidence bound.\n",
+      "SOLUTION:  (12.02, 16.935000000000002)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.\n",
+      "\n",
+      "2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Compare the Widths**: Identify the interval with the maximum width of the confidence interval.\n",
+      "\n",
+      "Let's perform these calculations:\n",
+      "\n",
+      "- For interval (23.0, 32.0), the width is \\(0.54 - (-1.51) = 2.05\\).\n",
+      "- For interval (32.0, 49.5), the width is \\(2.24 - (-0.87) = 3.11\\).\n",
+      "- Continue this for all intervals...\n",
+      "\n",
+      "After calculating the widths for all intervals, we find:\n",
+      "\n",
+      "- For interval (1886.0, 2038.5), the width is \\(8.11 - 5.88 = 2.23\\).\n",
+      "- For interval (2038.5, 2307.5), the width is \\(2.72 - 1.8 = 0.92\\).\n",
+      "- For interval (2307.5, 2444.0), the width is \\(7.19 - 4.43 = 2.76\\).\n",
+      "- For interval (2444.0, 3440.5), the width is \\(0.06 - (-5.48) = 5.54\\).\n",
+      "- For interval (3440.5, 4253.0), the width is \\(-0.79 - (-2.15) = 1.36\\).\n",
+      "- For interval (4253.0, 5548.5), the width is \\(2.95 - 0.41 = 2.54\\).\n",
+      "- For interval (5548.5, 7861.0), the width is \\(4.78 - 2.17 = 2.61\\).\n",
+      "\n",
+      "From these calculations, the interval with the widest confidence bound is (2444.0, 3440.5) with a width of 5.54. This interval has the largest difference between the upper and lower confidence bounds among all the intervals provided.\n",
+      "SOLUTION:  (85.0, 87.0)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.\n",
+      "\n",
+      "2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Compare the Widths**: Identify the interval with the largest width.\n",
+      "\n",
+      "Let's perform these calculations:\n",
+      "\n",
+      "- For interval (0.0, 0.5): Upper - Lower = -0.045 - (-0.344) = 0.299\n",
+      "- For interval (0.5, 1.5): Upper - Lower = 2.213 - 0.452 = 1.761\n",
+      "- For interval (1.5, 2.5): Upper - Lower = 0.228 - (-0.269) = 0.497\n",
+      "- For interval (2.5, 3.5): Upper - Lower = -0.043 - (-0.76) = 0.717\n",
+      "- For interval (3.5, 4.5): Upper - Lower = -0.158 - (-2.688) = 2.53\n",
+      "- For interval (4.5, 5.5): Upper - Lower = 0.429 - (-0.257) = 0.686\n",
+      "- For interval (5.5, 7.5): Upper - Lower = 0.04 - (-1.727) = 1.767\n",
+      "- For interval (7.5, 8.5): Upper - Lower = -0.004 - (-0.488) = 0.484\n",
+      "- For interval (8.5, 11.5): Upper - Lower = 0.245 - (-0.121) = 0.366\n",
+      "- For interval (11.5, 20.5): Upper - Lower = 0.001 - (-0.631) = 0.632\n",
+      "- For interval (20.5, 21.5): Upper - Lower = 0.537 - (-0.319) = 0.856\n",
+      "- For interval (21.5, 22.5): Upper - Lower = 0.904 - 0.048 = 0.856\n",
+      "- For interval (22.5, 24.5): Upper - Lower = 0.331 - (-0.066) = 0.397\n",
+      "- For interval (24.5, 26.5): Upper - Lower = -0.04 - (-0.66) = 0.62\n",
+      "- For interval (26.5, 29.5): Upper - Lower = 0.089 - (-1.067) = 1.156\n",
+      "- For interval (29.5, 32.5): Upper - Lower = 0.038 - (-0.254) = 0.292\n",
+      "- For interval (32.5, 33.5): Upper - Lower = -0.121 - (-0.844) = 0.723\n",
+      "- For interval (33.5, 35.5): Upper - Lower = -0.172 - (-1.156) = 0.984\n",
+      "- For interval (35.5, 38.5): Upper - Lower = 0.204 - (-0.997) = 1.201\n",
+      "- For interval (38.5, 39.5): Upper - Lower = 0.036 - 0.02 = 0.016\n",
+      "- For interval (39.5, 40.5): Upper - Lower = 0.26 - (-1.452) = 1.712\n",
+      "- For interval (40.5, 41.0): Upper - Lower = 1.816 - 0.408 = 1.408\n",
+      "\n",
+      "From these calculations, the interval (0.5, 1.5) has the widest confidence bound with a width of 1.761.\n",
+      "SOLUTION:  (3.5, 4.5)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.\n",
+      "\n",
+      "2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Compare the Widths**: Identify the interval where this width (difference between upper and lower bounds) is the largest.\n",
+      "\n",
+      "Let's perform these calculations using the provided data:\n",
+      "\n",
+      "- For interval `(0.05504, 0.058984999999999996)`, the width is `0.4604 - (-0.1488) = 0.6092`.\n",
+      "- For interval `(0.058984999999999996, 0.065905)`, the width is `0.3511 - (-0.1634) = 0.5145`.\n",
+      "- For interval `(0.065905, 0.070015)`, the width is `0.186 - (-0.1018) = 0.2878`.\n",
+      "- For interval `(0.070015, 0.071645)`, the width is `0.1385 - (-0.1659) = 0.3044`.\n",
+      "- For interval `(0.071645, 0.07281)`, the width is `0.0533 - (-0.2246) = 0.2779`.\n",
+      "- For interval `(0.07281, 0.075845)`, the width is `0.0064 - (-0.2831) = 0.2895`.\n",
+      "- For interval `(0.075845, 0.083565)`, the width is `-0.0255 - (-0.3593) = 0.3338`.\n",
+      "- For interval `(0.083565, 0.08926)`, the width is `0.1899 - (-0.8641) = 1.054`.\n",
+      "- For interval `(0.08926, 0.09129999999999999)`, the width is `0.2307 - (-0.8008) = 1.0315`.\n",
+      "- For interval `(0.09129999999999999, 0.09222)`, the width is `0.2773 - (-0.734) = 1.0113`.\n",
+      "- For interval `(0.09222, 0.094545)`, the width is `0.3426 - (-0.6937) = 1.0363`.\n",
+      "- For interval `(0.094545, 0.095845)`, the width is `0.405 - (-0.6426) = 1.0476`.\n",
+      "- For interval `(0.095845, 0.09595500000000001)`, the width is `0.4399 - (-0.5681) = 1.008`.\n",
+      "- For interval `(0.09595500000000001, 0.09849)`, the width is `0.6842 - (-0.3082) = 0.9924`.\n",
+      "- For interval `(0.09849, 0.1008)`, the width is `0.7436 - (-0.2557) = 0.9993`.\n",
+      "- For interval `(0.1008, 0.1018)`, the width is `0.8091 - (-0.2006) = 1.0097`.\n",
+      "- For interval `(0.1018, 0.10569999999999999)`, the width is `1.6844 - (-0.5367) = 2.2211`.\n",
+      "- For interval `(0.10569999999999999, 0.1074)`, the width is `1.7412 - (-0.4808) = 2.222`.\n",
+      "- For interval `(0.1074, 0.11810000000000001)`, the width is `1.0176 - (-0.0451) = 1.0627`.\n",
+      "- For interval `(0.11810000000000001, 0.12475)`, the width is `1.0318 - 0.0507 = 0.9811`.\n",
+      "- For interval `(0.12475, 0.14024999999999999)`, the width is `1.0928 - 0.0965 = 0.9963`.\n",
+      "- For interval `(0.14024999999999999, 0.2075)`, the width is `1.184 - 0.119 = 1.065`.\n",
+      "\n",
+      "From these calculations, the interval `(0.10569999999999999, 0.1074)` has the widest confidence bound with a width of `2.222`.\n",
+      "SOLUTION:  (0.10569999999999999, 0.1074)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the width of the confidence interval for each interval provided in the graph. The width of the confidence interval can be calculated by subtracting the lower bound from the upper bound for each interval.\n",
+      "\n",
+      "From the data provided:\n",
+      "1. For the interval (0.0, 0.5):\n",
+      "   - Lower Bound = -0.5571\n",
+      "   - Upper Bound = -0.3931\n",
+      "   - Width = -0.3931 - (-0.5571) = 0.164\n",
+      "\n",
+      "2. For the interval (0.5, 1.0):\n",
+      "   - Lower Bound = 0.1936\n",
+      "   - Upper Bound = 0.2743\n",
+      "   - Width = 0.2743 - 0.1936 = 0.0807\n",
+      "\n",
+      "Comparing the widths:\n",
+      "- Interval (0.0, 0.5) has a width of 0.164\n",
+      "- Interval (0.5, 1.0) has a width of 0.0807\n",
+      "\n",
+      "The interval (0.0, 0.5) has the widest confidence bound with a width of 0.164.\n",
+      "SOLUTION:  (0.0, 0.5)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.\n",
+      "\n",
+      "2. **Calculate the Difference**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Compare the Differences**: Identify the interval where this difference (width of the confidence interval) is the largest.\n",
+      "\n",
+      "Let's perform these calculations using the provided data:\n",
+      "\n",
+      "- **Interval (6.802, 11.184999999999999)**:\n",
+      "  - Lower Bound: -1.305\n",
+      "  - Upper Bound: -0.532\n",
+      "  - Difference: -0.532 - (-1.305) = 0.773\n",
+      "\n",
+      "- **Interval (11.184999999999999, 12.765)**:\n",
+      "  - Lower Bound: -1.176\n",
+      "  - Upper Bound: -0.452\n",
+      "  - Difference: -0.452 - (-1.176) = 0.724\n",
+      "\n",
+      "- **Interval (12.765, 13.350000000000001)**:\n",
+      "  - Lower Bound: -1.036\n",
+      "  - Upper Bound: -0.371\n",
+      "  - Difference: -0.371 - (-1.036) = 0.665\n",
+      "\n",
+      "- **Interval (13.350000000000001, 15.3)**:\n",
+      "  - Lower Bound: -0.901\n",
+      "  - Upper Bound: -0.291\n",
+      "  - Difference: -0.291 - (-0.901) = 0.610\n",
+      "\n",
+      "- **Interval (15.3, 16.955)**:\n",
+      "  - Lower Bound: -0.696\n",
+      "  - Upper Bound: -0.284\n",
+      "  - Difference: -0.284 - (-0.696) = 0.412\n",
+      "\n",
+      "- **Interval (16.955, 18.515)**:\n",
+      "  - Lower Bound: -0.504\n",
+      "  - Upper Bound: -0.23\n",
+      "  - Difference: -0.23 - (-0.504) = 0.274\n",
+      "\n",
+      "- **Interval (18.515, 20.905)**:\n",
+      "  - Lower Bound: -0.392\n",
+      "  - Upper Bound: -0.121\n",
+      "  - Difference: -0.121 - (-0.392) = 0.271\n",
+      "\n",
+      "- **Interval (20.905, 32.985)**:\n",
+      "  - Lower Bound: -0.922\n",
+      "  - Upper Bound: 0.62\n",
+      "  - Difference: 0.62 - (-0.922) = 1.542\n",
+      "\n",
+      "- **Interval (32.985, 34.730000000000004)**:\n",
+      "  - Lower Bound: -0.261\n",
+      "  - Upper Bound: 0.424\n",
+      "  - Difference: 0.424 - (-0.261) = 0.685\n",
+      "\n",
+      "- **Interval (34.730000000000004, 41.21)**:\n",
+      "  - Lower Bound: -0.102\n",
+      "  - Upper Bound: 0.479\n",
+      "  - Difference: 0.479 - (-0.102) = 0.581\n",
+      "\n",
+      "- **Interval (41.21, 50.405)**:\n",
+      "  - Lower Bound: 0.02\n",
+      "  - Upper Bound: 0.563\n",
+      "  - Difference: 0.563 - 0.02 = 0.543\n",
+      "\n",
+      "- **Interval (50.405, 56.915)**:\n",
+      "  - Lower Bound: 0.072\n",
+      "  - Upper Bound: 0.762\n",
+      "  - Difference: 0.762 - 0.072 = 0.690\n",
+      "\n",
+      "- **Interval (56.915, 67.5)**:\n",
+      "  - Lower Bound: 0.147\n",
+      "  - Upper Bound: 0.913\n",
+      "  - Difference: 0.913 - 0.147 = 0.766\n",
+      "\n",
+      "- **Interval (67.5, 81.56)**:\n",
+      "  - Lower Bound: 0.223\n",
+      "  - Upper Bound: 1.052\n",
+      "  - Difference: 1.052 - 0.223 = 0.829\n",
+      "\n",
+      "- **Interval (81.56, 94.00999999999999)**:\n",
+      "  - Lower Bound: 0.326\n",
+      "  - Upper Bound: 1.176\n",
+      "  - Difference: 1.176 - 0.326 = 0.850\n",
+      "\n",
+      "- **Interval (94.00999999999999, 106.2)**:\n",
+      "  - Lower Bound: 0.402\n",
+      "  - Upper Bound: 1.323\n",
+      "  - Difference: 1.323 - 0.402 = 0.921\n",
+      "\n",
+      "- **Interval (106.2, 153.25)**:\n",
+      "  - Lower Bound: 0.501\n",
+      "  - Upper Bound: 1.448\n",
+      "  - Difference: 1.448 - 0.501 = 0.947\n",
+      "\n",
+      "- **Interval (153.25, 542.2)**:\n",
+      "  - Lower Bound: 0.571\n",
+      "  - Upper Bound: 1.593\n",
+      "  - Difference: 1.593 - 0.571 = 1.022\n",
+      "\n",
+      "From the calculations, the interval **(153.25, 542.2)** has the widest confidence bound with a difference of 1.022.\n",
+      "SOLUTION:  (20.905, 32.985)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here are the calculations for each interval:\n",
+      "\n",
+      "1. Interval (0.0, 0.5):\n",
+      "   - Lower Bound: -0.02637\n",
+      "   - Upper Bound: -0.0233\n",
+      "   - Difference: -0.0233 - (-0.02637) = 0.00307\n",
+      "\n",
+      "2. Interval (0.5, 1.5):\n",
+      "   - Lower Bound: -0.02217\n",
+      "   - Upper Bound: -0.01962\n",
+      "   - Difference: -0.01962 - (-0.02217) = 0.00255\n",
+      "\n",
+      "3. Interval (1.5, 2.5):\n",
+      "   - Lower Bound: -0.0179\n",
+      "   - Upper Bound: -0.01689\n",
+      "   - Difference: -0.01689 - (-0.0179) = 0.00101\n",
+      "\n",
+      "4. Interval (2.5, 3.5):\n",
+      "   - Lower Bound: -0.01163\n",
+      "   - Upper Bound: -0.01085\n",
+      "   - Difference: -0.01085 - (-0.01163) = 0.00078\n",
+      "\n",
+      "5. Interval (3.5, 4.5):\n",
+      "   - Lower Bound: -0.00519\n",
+      "   - Upper Bound: -0.0043\n",
+      "   - Difference: -0.0043 - (-0.00519) = 0.00089\n",
+      "\n",
+      "6. Interval (4.5, 5.5):\n",
+      "   - Lower Bound: 0.00046\n",
+      "   - Upper Bound: 0.00109\n",
+      "   - Difference: 0.00109 - 0.00046 = 0.00063\n",
+      "\n",
+      "7. Interval (5.5, 6.5):\n",
+      "   - Lower Bound: 0.00525\n",
+      "   - Upper Bound: 0.00623\n",
+      "   - Difference: 0.00623 - 0.00525 = 0.00098\n",
+      "\n",
+      "8. Interval (6.5, 7.5):\n",
+      "   - Lower Bound: 0.00992\n",
+      "   - Upper Bound: 0.01144\n",
+      "   - Difference: 0.01144 - 0.00992 = 0.00152\n",
+      "\n",
+      "9. Interval (7.5, 8.5):\n",
+      "   - Lower Bound: 0.01538\n",
+      "   - Upper Bound: 0.0166\n",
+      "   - Difference: 0.0166 - 0.01538 = 0.00122\n",
+      "\n",
+      "10. Interval (8.5, 9.5):\n",
+      "    - Lower Bound: 0.02115\n",
+      "    - Upper Bound: 0.02348\n",
+      "    - Difference: 0.02348 - 0.02115 = 0.00233\n",
+      "\n",
+      "11. Interval (9.5, 10.5):\n",
+      "    - Lower Bound: 0.02528\n",
+      "    - Upper Bound: 0.02807\n",
+      "    - Difference: 0.02807 - 0.02528 = 0.00279\n",
+      "\n",
+      "12. Interval (10.5, 13.5):\n",
+      "    - Lower Bound: 0.02547\n",
+      "    - Upper Bound: 0.04062\n",
+      "    - Difference: 0.04062 - 0.02547 = 0.01515\n",
+      "\n",
+      "13. Interval (13.5, 16.0):\n",
+      "    - Lower Bound: 0.01297\n",
+      "    - Upper Bound: 0.02734\n",
+      "    - Difference: 0.02734 - 0.01297 = 0.01437\n",
+      "\n",
+      "From these calculations, the interval (10.5, 13.5) has the widest confidence bound with a difference of 0.01515.\n",
+      "SOLUTION:  (10.5, 13.5)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here are the calculations for each interval:\n",
+      "\n",
+      "1. Interval (0.0, 0.5): Upper - Lower = -0.2459 - (-0.5596) = 0.3137\n",
+      "2. Interval (0.5, 1.5): Upper - Lower = -0.4295 - (-0.6499) = 0.2204\n",
+      "3. Interval (1.5, 3.5): Upper - Lower = -0.3523 - (-0.618) = 0.2657\n",
+      "4. Interval (3.5, 4.5): Upper - Lower = -0.235 - (-0.5693) = 0.3343\n",
+      "5. Interval (4.5, 5.5): Upper - Lower = -0.3862 - (-0.5278) = 0.1416\n",
+      "6. Interval (5.5, 6.5): Upper - Lower = -0.1733 - (-0.3342) = 0.1609\n",
+      "7. Interval (6.5, 7.5): Upper - Lower = -0.0039 - (-0.0948) = 0.0909\n",
+      "8. Interval (7.5, 8.5): Upper - Lower = 0.0977 - (-0.0062) = 0.1039\n",
+      "9. Interval (8.5, 9.5): Upper - Lower = 0.2137 - 0.1525 = 0.0612\n",
+      "10. Interval (9.5, 10.5): Upper - Lower = 0.1711 - 0.1072 = 0.0639\n",
+      "11. Interval (10.5, 11.5): Upper - Lower = -0.0435 - (-0.0869) = 0.0434\n",
+      "12. Interval (11.5, 14.5): Upper - Lower = 0.2431 - 0.1476 = 0.0955\n",
+      "13. Interval (14.5, 15.0): Upper - Lower = 0.1775 - 0.1012 = 0.0763\n",
+      "\n",
+      "From these calculations, the interval (3.5, 4.5) has the widest confidence bound with a difference of 0.3343. Therefore, the x-axis interval with the widest confidence bound is (3.5, 4.5).\n",
+      "SOLUTION:  (3.5, 4.5)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.\n",
+      "\n",
+      "2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Compare the Widths**: Identify the interval with the maximum width.\n",
+      "\n",
+      "Let's perform these calculations:\n",
+      "\n",
+      "- For interval (0.0, 50418.515): Width = -0.072 - (-0.192) = 0.12\n",
+      "- For interval (50418.515, 53570.93): Width = 0.057 - (-0.628) = 0.685\n",
+      "- For interval (53570.93, 54249.445): Width = 0.347 - (-1.999) = 2.346\n",
+      "- For interval (54249.445, 57428.56): Width = -0.011 - (-0.798) = 0.787\n",
+      "- For interval (57428.56, 60041.265): Width = 0.312 - (-0.322) = 0.634\n",
+      "- For interval (60041.265, 64897.8): Width = 0.534 - (-0.105) = 0.639\n",
+      "- For interval (64897.8, 72985.875): Width = 0.367 - (-0.195) = 0.562\n",
+      "- For interval (72985.875, 74989.08499999999): Width = 0.395 - (-0.418) = 0.813\n",
+      "- For interval (74989.08499999999, 76596.815): Width = 0.725 - (-0.231) = 0.956\n",
+      "- For interval (76596.815, 79953.185): Width = 1.32 - 0.338 = 0.982\n",
+      "- For interval (79953.185, 83348.07): Width = 0.806 - 0.321 = 0.485\n",
+      "- For interval (83348.07, 101890.23999999999): Width = 0.582 - 0.247 = 0.335\n",
+      "- For interval (101890.23999999999, 114327.485): Width = 0.398 - 0.097 = 0.301\n",
+      "- For interval (114327.485, 123946.3): Width = 0.259 - 0.069 = 0.19\n",
+      "- For interval (123946.3, 141661.24): Width = 0.379 - (-0.23) = 0.609\n",
+      "- For interval (141661.24, 174920.08000000002): Width = 0.618 - (-0.272) = 0.89\n",
+      "- For interval (174920.08000000002, 181813.135): Width = 0.264 - (-0.147) = 0.411\n",
+      "- For interval (181813.135, 191993.675): Width = 0.166 - (-0.864) = 1.03\n",
+      "- For interval (191993.675, 200829.925): Width = 0.073 - (-0.991) = 1.064\n",
+      "- For interval (200829.925, 206951.87): Width = 0.169 - (-1.401) = 1.57\n",
+      "- For interval (206951.87, 216109.88): Width = 0.35 - (-0.862) = 1.212\n",
+      "\n",
+      "From the calculations, the interval with the widest confidence bound is (200829.925, 206951.87) with a width of 1.57.\n",
+      "SOLUTION:  (53570.93, 54249.445)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the x-axis interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds of the confidence interval for each interval and identify the one with the maximum difference.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.\n",
+      "\n",
+      "2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Identify the Maximum Width**: Compare the widths of all intervals and identify the interval with the maximum width.\n",
+      "\n",
+      "Let's perform these calculations:\n",
+      "\n",
+      "- For interval (0.0, 105.5): Upper - Lower = 0.716 - (-0.06) = 0.776\n",
+      "- For interval (105.5, 296.5): Upper - Lower = 0.425 - (-0.369) = 0.794\n",
+      "- For interval (296.5, 335.5): Upper - Lower = 0.607 - (-1.022) = 1.629\n",
+      "- For interval (335.5, 340.0): Upper - Lower = 0.513 - (-0.184) = 0.697\n",
+      "- For interval (340.0, 343.0): Upper - Lower = 0.837 - (-1.038) = 1.875\n",
+      "- For interval (343.0, 596.5): Upper - Lower = -0.16 - (-1.323) = 1.163\n",
+      "- For interval (596.5, 712.5): Upper - Lower = -0.409 - (-1.547) = 1.138\n",
+      "- For interval (712.5, 734.0): Upper - Lower = -0.869 - (-1.555) = 0.686\n",
+      "- For interval (734.0, 800.0): Upper - Lower = -1.092 - (-1.8) = 0.708\n",
+      "- For interval (800.0, 816.0): Upper - Lower = -0.082 - (-2.191) = 2.109\n",
+      "- For interval (816.0, 997.5): Upper - Lower = -1.083 - (-1.824) = 0.741\n",
+      "- For interval (997.5, 1031.0): Upper - Lower = -0.506 - (-1.706) = 1.2\n",
+      "- For interval (1031.0, 1041.0): Upper - Lower = -0.589 - (-2.147) = 1.558\n",
+      "- For interval (1041.0, 2172.5): Upper - Lower = -1.488 - (-2.244) = 0.756\n",
+      "- For interval (2172.5, 2283.5): Upper - Lower = -0.661 - (-2.248) = 1.587\n",
+      "- For interval (2283.5, 2313.5): Upper - Lower = -0.774 - (-1.568) = 0.794\n",
+      "- For interval (2313.5, 2336.5): Upper - Lower = 0.89 - (-2.21) = 3.1\n",
+      "- For interval (2336.5, 2420.0): Upper - Lower = -1.582 - (-3.537) = 1.955\n",
+      "- For interval (2420.0, 2992.5): Upper - Lower = -2.569 - (-3.89) = 1.321\n",
+      "- For interval (2992.5, 3006.0): Upper - Lower = -1.461 - (-3.955) = 2.494\n",
+      "- For interval (3006.0, 3196.5): Upper - Lower = -1.727 - (-4.24) = 2.513\n",
+      "- For interval (3196.5, 3249.5): Upper - Lower = -1.438 - (-3.98) = 2.542\n",
+      "- For interval (3249.5, 14327.0): Upper - Lower = -3.043 - (-5.248) = 2.205\n",
+      "\n",
+      "From the calculations, the interval with the widest confidence bound is (2313.5, 2336.5) with a width of 3.1.\n",
+      "SOLUTION:  (2313.5, 2336.5)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.\n",
+      "\n",
+      "2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Compare the Widths**: Identify the interval where this width (difference between upper and lower bounds) is the largest.\n",
+      "\n",
+      "Let's perform these calculations:\n",
+      "\n",
+      "- For interval (185.2, 357.5): Width = -0.278 - (-2.413) = 2.135\n",
+      "- For interval (357.5, 413.15): Width = -0.164 - (-2.22) = 2.056\n",
+      "- For interval (413.15, 471.9): Width = -0.073 - (-2.004) = 1.931\n",
+      "- For interval (471.9, 508.5): Width = 0.062 - (-1.818) = 1.88\n",
+      "- For interval (508.5, 633.9): Width = 0.423 - (-1.868) = 2.291\n",
+      "- For interval (633.9, 653.45): Width = 0.516 - (-1.645) = 2.161\n",
+      "- For interval (653.45, 710.2): Width = 0.071 - (-0.767) = 0.838\n",
+      "- For interval (710.2, 727.0999999999999): Width = 0.17 - (-0.501) = 0.671\n",
+      "- For interval (727.0999999999999, 805.95): Width = 0.764 - (-0.573) = 1.337\n",
+      "- For interval (805.95, 874.85): Width = 0.693 - (-0.187) = 0.88\n",
+      "- For interval (874.85, 928.5): Width = 1.449 - (-0.49) = 1.939\n",
+      "- For interval (928.5, 1033.5): Width = 2.006 - (-0.484) = 2.49\n",
+      "- For interval (1033.5, 1222.5): Width = 2.319 - (-0.455) = 2.774\n",
+      "- For interval (1222.5, 1346.5): Width = 2.482 - (-0.298) = 2.78\n",
+      "- For interval (1346.5, 1645.5): Width = 2.672 - (-0.182) = 2.854\n",
+      "- For interval (1645.5, 1979.0): Width = 2.857 - (-0.049) = 2.906\n",
+      "- For interval (1979.0, 4254.0): Width = 3.043 - 0.071 = 2.972\n",
+      "\n",
+      "From these calculations, the interval (1979.0, 4254.0) has the widest confidence bound with a width of 2.972. This is the interval with the widest confidence interval in the graph.\n",
+      "SOLUTION:  (1979.0, 4254.0)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the x-axis interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval with the maximum difference.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.\n",
+      "\n",
+      "2. **Calculate the Difference**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Identify the Maximum Difference**: Compare the differences calculated in step 2 to find the maximum difference, which indicates the widest confidence interval.\n",
+      "\n",
+      "Let's perform these calculations:\n",
+      "\n",
+      "- For interval (350.0, 416.5): Upper - Lower = 1.3346 - (-0.0945) = 1.4291\n",
+      "- For interval (416.5, 421.5): Upper - Lower = 1.243 - (-0.1033) = 1.3463\n",
+      "- For interval (421.5, 427.5): Upper - Lower = 0.7784 - (-0.0186) = 0.7970\n",
+      "- For interval (427.5, 437.5): Upper - Lower = 0.7005 - (-0.1491) = 0.8496\n",
+      "- For interval (437.5, 464.5): Upper - Lower = 0.5843 - 0.0705 = 0.5138\n",
+      "- For interval (464.5, 470.5): Upper - Lower = 0.5564 - (-0.0008) = 0.5572\n",
+      "- For interval (470.5, 477.5): Upper - Lower = 0.9641 - (-0.0519) = 1.0160\n",
+      "- For interval (477.5, 478.5): Upper - Lower = 0.4206 - (-0.3016) = 0.7222\n",
+      "- For interval (478.5, 494.5): Upper - Lower = 0.2736 - 0.0126 = 0.2610\n",
+      "- For interval (494.5, 515.5): Upper - Lower = 0.3173 - (-0.1354) = 0.4527\n",
+      "- For interval (515.5, 523.5): Upper - Lower = -0.1047 - (-0.5637) = 0.4590\n",
+      "- For interval (523.5, 539.5): Upper - Lower = -0.1159 - (-0.3225) = 0.2066\n",
+      "- For interval (539.5, 566.5): Upper - Lower = -0.061 - (-0.2064) = 0.1454\n",
+      "- For interval (566.5, 598.5): Upper - Lower = -0.0424 - (-0.1252) = 0.0828\n",
+      "- For interval (598.5, 661.5): Upper - Lower = 0.0472 - (-0.1126) = 0.1598\n",
+      "- For interval (661.5, 684.5): Upper - Lower = 0.066 - (-0.0289) = 0.0949\n",
+      "- For interval (684.5, 741.5): Upper - Lower = 0.1548 - (-0.0156) = 0.1704\n",
+      "- For interval (741.5, 769.5): Upper - Lower = 0.1168 - (-0.0756) = 0.1924\n",
+      "- For interval (769.5, 792.5): Upper - Lower = 0.1542 - (-0.016) = 0.1702\n",
+      "- For interval (792.5, 805.5): Upper - Lower = 0.4932 - (-0.0471) = 0.5403\n",
+      "- For interval (805.5, 806.5): Upper - Lower = 0.4795 - (-0.2533) = 0.7328\n",
+      "- For interval (806.5, 850.0): Upper - Lower = 0.1611 - (-0.3888) = 0.5499\n",
+      "\n",
+      "From the calculations above, the interval with the widest confidence bound is (350.0, 416.5) with a difference of 1.4291.\n",
+      "SOLUTION:  (350.0, 416.5)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval with the maximum difference.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.\n",
+      "\n",
+      "2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Identify the Maximum Width**: Compare the widths of all intervals and identify the interval with the maximum width.\n",
+      "\n",
+      "Let's perform these calculations:\n",
+      "\n",
+      "For example, for the interval `(2.0, 4.5)`, the lower bound is `-30426.4` and the upper bound is `19623.3`. The width of the confidence interval is:\n",
+      "\\[ \\text{Width} = 19623.3 - (-30426.4) = 19623.3 + 30426.4 = 50049.7 \\]\n",
+      "\n",
+      "We need to perform similar calculations for all intervals and find the maximum width. Here are a few more calculations:\n",
+      "\n",
+      "- For `(4.5, 6.5)`: Width = `-5814.9 - (-41560.8) = 35745.9`\n",
+      "- For `(6.5, 8.5)`: Width = `-23981.3 - (-83483.7) = 59502.4`\n",
+      "- For `(8.5, 9.5)`: Width = `39403.2 - (-68637.5) = 108040.7`\n",
+      "- ...\n",
+      "\n",
+      "From the few calculations above, we can see that the interval `(8.5, 9.5)` has a very wide confidence interval with a width of `108040.7`. We would continue this process for all intervals, but based on the calculations shown, it appears that the interval `(8.5, 9.5)` might have the widest confidence interval.\n",
+      "\n",
+      "Thus, the x-axis interval with the widest confidence bound is `(8.5, 9.5)`.\n",
+      "SOLUTION:  (8.5, 9.5)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.\n",
+      "\n",
+      "2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Compare the Widths**: Identify the interval with the maximum width.\n",
+      "\n",
+      "Let's perform these calculations:\n",
+      "\n",
+      "- For interval (0.078, 0.1265): Upper - Lower = -0.238 - (-0.817) = 0.579\n",
+      "- For interval (0.1265, 0.128): Upper - Lower = 0.381 - (-0.817) = 1.198\n",
+      "- For interval (0.128, 0.2185): Upper - Lower = -0.067 - (-0.618) = 0.551\n",
+      "- For interval (0.2185, 0.3375): Upper - Lower = 0.197 - (-0.533) = 0.730\n",
+      "- For interval (0.3375, 0.4215): Upper - Lower = 0.113 - (-0.266) = 0.379\n",
+      "- For interval (0.4215, 0.4955): Upper - Lower = 0.135 - (-0.104) = 0.239\n",
+      "- For interval (0.4955, 0.5874999999999999): Upper - Lower = 0.316 - (-0.054) = 0.370\n",
+      "- For interval (0.5874999999999999, 0.7215): Upper - Lower = 0.308 - 0.138 = 0.170\n",
+      "- For interval (0.7215, 0.889): Upper - Lower = 0.445 - 0.186 = 0.259\n",
+      "- For interval (0.889, 1.0865): Upper - Lower = 0.552 - 0.263 = 0.289\n",
+      "- For interval (1.0865, 1.178): Upper - Lower = 0.646 - 0.350 = 0.296\n",
+      "- For interval (1.178, 1.275): Upper - Lower = 1.912 - 0.124 = 1.788\n",
+      "- For interval (1.275, 1.3925): Upper - Lower = 2.433 - 0.133 = 2.300\n",
+      "- For interval (1.3925, 1.4175): Upper - Lower = 2.398 - (-0.063) = 2.461\n",
+      "- For interval (1.4175, 1.451): Upper - Lower = 1.293 - (-1.163) = 2.456\n",
+      "- For interval (1.451, 1.837): Upper - Lower = 1.080 - (-1.466) = 2.546\n",
+      "- For interval (1.837, 2.137): Upper - Lower = 0.928 - (-1.112) = 2.040\n",
+      "\n",
+      "From these calculations, the interval (1.451, 1.837) has the widest confidence bound with a width of 2.546. This is the interval with the widest confidence bound in the graph.\n",
+      "SOLUTION:  (1.451, 1.837)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "The confidence bound width for each interval can be calculated as:\n",
+      "\\[ \\text{Width} = \\text{Upper Bound} - \\text{Lower Bound} \\]\n",
+      "\n",
+      "Let's calculate the width for each interval using the provided data:\n",
+      "\n",
+      "1. For the interval (32.54, 32.565):\n",
+      "   - Lower Bound = 964.8\n",
+      "   - Upper Bound = 45504.8\n",
+      "   - Width = 45504.8 - 964.8 = 44540.0\n",
+      "\n",
+      "2. For the interval (32.565, 32.685):\n",
+      "   - Lower Bound = -13385.7\n",
+      "   - Upper Bound = 7020.9\n",
+      "   - Width = 7020.9 - (-13385.7) = 20406.6\n",
+      "\n",
+      "... (similar calculations for other intervals) ...\n",
+      "\n",
+      "To find the interval with the widest confidence bound, we need to compare the calculated widths for all intervals. The interval with the highest width value will have the widest confidence bound.\n",
+      "\n",
+      "After calculating the widths for all intervals, we find that the interval with the widest confidence bound is (32.54, 32.565) with a width of 44540.0.\n",
+      "SOLUTION:  (34.715, 35.325)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here are the calculations for each interval:\n",
+      "\n",
+      "1. Interval (0.0, 0.5): Upper - Lower = -0.02372 - (-0.02758) = 0.00386\n",
+      "2. Interval (0.5, 1.5): Upper - Lower = -0.01994 - (-0.02272) = 0.00278\n",
+      "3. Interval (1.5, 2.5): Upper - Lower = -0.01596 - (-0.01769) = 0.00173\n",
+      "4. Interval (2.5, 3.5): Upper - Lower = -0.00951 - (-0.01036) = 0.00085\n",
+      "5. Interval (3.5, 4.5): Upper - Lower = -0.00432 - (-0.00514) = 0.00082\n",
+      "6. Interval (4.5, 5.5): Upper - Lower = 0.00037 - (-0.0004) = 0.00077\n",
+      "7. Interval (5.5, 6.5): Upper - Lower = 0.00568 - 0.00453 = 0.00115\n",
+      "8. Interval (6.5, 7.5): Upper - Lower = 0.01199 - 0.01098 = 0.00101\n",
+      "9. Interval (7.5, 8.5): Upper - Lower = 0.01706 - 0.01535 = 0.00171\n",
+      "10. Interval (8.5, 9.5): Upper - Lower = 0.02562 - 0.0239 = 0.00172\n",
+      "11. Interval (9.5, 11.5): Upper - Lower = 0.03152 - 0.02772 = 0.0038\n",
+      "12. Interval (11.5, 12.5): Upper - Lower = 0.03732 - 0.03206 = 0.00526\n",
+      "13. Interval (12.5, 13.5): Upper - Lower = 0.05424 - 0.04307 = 0.01117\n",
+      "14. Interval (13.5, 16.0): Upper - Lower = 0.06343 - 0.0546 = 0.00883\n",
+      "\n",
+      "From these calculations, the interval (12.5, 13.5) has the widest confidence bound with a difference of 0.01117. Therefore, the x-axis interval with the widest confidence bound is (12.5, 13.5).\n",
+      "SOLUTION:  (12.5, 13.5)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.\n",
+      "\n",
+      "2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Compare the Widths**: Identify the interval with the maximum width.\n",
+      "\n",
+      "Let's perform these calculations:\n",
+      "\n",
+      "- For interval (1.0, 1.5): Upper - Lower = 1.142 - (-2.672) = 3.814\n",
+      "- For interval (1.5, 2.5): Upper - Lower = 0.023 - (-0.773) = 0.796\n",
+      "- For interval (2.5, 4.5): Upper - Lower = -1.109 - (-2.709) = 1.600\n",
+      "- For interval (4.5, 6.5): Upper - Lower = -0.668 - (-1.566) = 0.898\n",
+      "- For interval (6.5, 7.5): Upper - Lower = 0.005 - (-1.241) = 1.246\n",
+      "- For interval (7.5, 14.5): Upper - Lower = -0.546 - (-1.098) = 0.552\n",
+      "- For interval (14.5, 19.5): Upper - Lower = -0.729 - (-1.535) = 0.806\n",
+      "- For interval (19.5, 29.5): Upper - Lower = -0.172 - (-1.357) = 1.185\n",
+      "- For interval (29.5, 33.5): Upper - Lower = 0.047 - (-1.248) = 1.295\n",
+      "- For interval (33.5, 34.5): Upper - Lower = -0.027 - (-1.815) = 1.788\n",
+      "- For interval (34.5, 39.5): Upper - Lower = -0.087 - (-0.223) = 0.136\n",
+      "- For interval (39.5, 41.5): Upper - Lower = 0.19 - (-0.129) = 0.319\n",
+      "- For interval (41.5, 50.5): Upper - Lower = 0.571 - 0.212 = 0.359\n",
+      "- For interval (50.5, 51.5): Upper - Lower = 1.13 - (-0.867) = 1.997\n",
+      "- For interval (51.5, 55.5): Upper - Lower = 0.557 - 0.357 = 0.200\n",
+      "- For interval (55.5, 59.5): Upper - Lower = 1.048 - 0.304 = 0.744\n",
+      "- For interval (59.5, 63.5): Upper - Lower = 0.818 - 0.014 = 0.804\n",
+      "- For interval (63.5, 64.5): Upper - Lower = 1.896 - 0.009 = 1.887\n",
+      "- For interval (64.5, 65.5): Upper - Lower = 0.653 - 0.379 = 0.274\n",
+      "- For interval (65.5, 71.0): Upper - Lower = 0.254 - (-0.113) = 0.367\n",
+      "- For interval (71.0, 75.5): Upper - Lower = 0.806 - 0.054 = 0.752\n",
+      "- For interval (75.5, 77.5): Upper - Lower = 1.04 - (-0.57) = 1.61\n",
+      "- For interval (77.5, 79.0): Upper - Lower = 1.25 - 0.234 = 1.016\n",
+      "- For interval (79.0, 83.0): Upper - Lower = 1.166 - 0.788 = 0.378\n",
+      "- For interval (83.0, 85.5): Upper - Lower = 1.852 - 0.721 = 1.131\n",
+      "- For interval (85.5, 90.5): Upper - Lower = 0.673 - (-0.289) = 0.962\n",
+      "- For interval (90.5, 97.5): Upper - Lower = 0.361 - (-0.504) = 0.865\n",
+      "- For interval (97.5, 98.5): Upper - Lower = 0.765 - (-0.527) = 1.292\n",
+      "- For interval (98.5, 99.0): Upper - Lower = 0.271 - (-0.548) = 0.819\n",
+      "\n",
+      "From these calculations, the interval (1.0, 1.5) has the widest confidence bound with a width of 3.814.\n",
+      "SOLUTION:  (1.0, 1.5)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.\n",
+      "\n",
+      "Here are the steps to find the interval with the widest confidence bound:\n",
+      "\n",
+      "1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.\n",
+      "\n",
+      "2. **Calculate the Difference**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.\n",
+      "\n",
+      "3. **Compare the Differences**: Identify the interval with the largest difference, which indicates the widest confidence bound.\n",
+      "\n",
+      "Let's perform these calculations:\n",
+      "\n",
+      "- For interval \"(0.0, 57.0)\": Difference = -0.23 - (-0.27) = 0.04\n",
+      "- For interval \"(57.0, 3048.0)\": Difference = -3.24 - (-6.42) = 3.18\n",
+      "- For interval \"(3048.0, 3120.0)\": Difference = 3.0 - 2.14 = 0.86\n",
+      "- For interval \"(3120.0, 4243.5)\": Difference = -3.54 - (-5.31) = 1.77\n",
+      "- For interval \"(4243.5, 4401.0)\": Difference = 1.81 - 1.09 = 0.72\n",
+      "- For interval \"(4401.0, 4668.5)\": Difference = -1.0 - (-2.65) = 1.65\n",
+      "- For interval \"(4668.5, 4826.0)\": Difference = 4.71 - 2.87 = 1.84\n",
+      "- For interval \"(4826.0, 4898.0)\": Difference = 1.38 - (-0.25) = 1.63\n",
+      "- For interval \"(4898.0, 4973.5)\": Difference = 2.95 - 1.55 = 1.40\n",
+      "- For interval \"(4973.5, 5119.0)\": Difference = -0.92 - (-6.13) = 5.21\n",
+      "- For interval \"(5119.0, 5316.5)\": Difference = 5.0 - 3.51 = 1.49\n",
+      "- For interval \"(5316.5, 5505.5)\": Difference = 1.16 - (-0.29) = 1.45\n",
+      "- For interval \"(5505.5, 6457.5)\": Difference = 3.0 - 1.3 = 1.70\n",
+      "- For interval \"(6457.5, 6505.5)\": Difference = 0.62 - (-0.94) = 1.56\n",
+      "- For interval \"(6505.5, 6745.0)\": Difference = 1.44 - 0.19 = 1.25\n",
+      "- For interval \"(6745.0, 7073.5)\": Difference = -0.34 - (-2.33) = 1.99\n",
+      "- For interval \"(7073.5, 7436.5)\": Difference = 6.58 - 4.95 = 1.63\n",
+      "- For interval \"(7436.5, 7565.5)\": Difference = 3.62 - 0.42 = 3.20\n",
+      "- For interval \"(7565.5, 7792.0)\": Difference = 7.72 - 5.41 = 2.31\n",
+      "- For interval \"(7792.0, 7937.0)\": Difference = 7.16 - 2.59 = 4.57\n",
+      "- For interval \"(7937.0, 8296.0)\": Difference = 6.36 - 1.32 = 5.04\n",
+      "- For interval \"(8296.0, 10543.0)\": Difference = 8.31 - 6.05 = 2.26\n",
+      "- For interval \"(10543.0, 10585.5)\": Difference = -0.22 - (-2.73) = 2.51\n",
+      "- For interval \"(10585.5, 30961.5)\": Difference = 9.71 - 7.51 = 2.20\n",
+      "- For interval \"(30961.5, 70654.5)\": Difference = 2.23 - (-3.56) = 5.79\n",
+      "- For interval \"(70654.5, 99999.0)\": Difference = 11.26 - 8.19 = 3.07\n",
+      "\n",
+      "From the calculations, the interval \"(30961.5, 70654.5)\" has the widest confidence bound with a difference of 5.79.\n",
+      "SOLUTION:  (30961.5, 70654.5)\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "system_msg = \"You are an expert statistician and data scientist. You interpret global explanations produced by a generalized additive model (GAM). You answer all questions to the best of your ability, combining the data contained in the graph, any data set description you are given, and your knowledge about the real world.\"\n",
+    "for question in questions[54:]:\n",
+    "    messages = [{\"role\": \"system\", \"content\": system_msg}, {\"role\": \"user\", \"content\": question[0]}]\n",
+    "    response = t2ebm.utils.openai_completion_query('gpt-3.5-turbo-0125', messages, temperature=0.0)\n",
+    "    messages.append({\"role\": \"assistant\", \"content\": response})\n",
+    "    messages.append({\"role\": \"user\", \"content\": \"Thanks. Now summarize your response by answering again with the interval.\"})\n",
+    "    response = t2ebm.utils.openai_completion_query('gpt-3.5-turbo-0125', messages, temperature=0.0)\n",
+    "    print('LLM RESPONSE: ', response)\n",
+    "    print('SOLUTION: ', question[1])\n",
+    "    print('-'*80)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tmcd",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/benchmarks/notebooks/recognizing-functions.ipynb b/benchmarks/notebooks/function-recognition.ipynb
similarity index 57%
rename from benchmarks/notebooks/recognizing-functions.ipynb
rename to benchmarks/notebooks/function-recognition.ipynb
index cd50afd..21b0bca 100644
--- a/benchmarks/notebooks/recognizing-functions.ipynb
+++ b/benchmarks/notebooks/function-recognition.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,12 +24,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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ACAe8QgIAAFYTSpPYSQQYAABCXqhNYicRYAAACHmhNomdRIABACDk2eyeTWL3rxaZxE4iwAAAEPLKj3rWr+XsxLZ+bonv+CXAfPvtt/r1r3+tzp07q23btho0aJA+/fRT135jjObMmaO0tDS1bdtW2dnZ2rNnj9s5ysvLlZubq/j4eCUmJmry5Mk6evSoP5oLAEBIO/DDMY/qktrH+LklvuPzAPPDDz9oxIgRatOmjd5//33t2LFD//Vf/6VOnTq5ahYsWKBFixZpyZIlKigoUPv27ZWTk6Pjx396xJWbm6vi4mKtXr1aK1eu1Pr16zVlyhRfNxcAgJDmcBq99dlBj2pTE6zzBCbCGOPTEd8zZ87UJ598on/84x8N7jfGKD09XTNmzNC///u/S5IqKyuVkpKiZcuWaeLEidq5c6f69++vTZs2aejQoZKkVatW6aqrrtKBAweUnp7ebDvsdrsSEhJUWVmp+Ph4310gAAAWkv/V97rhLxubrevcPkaF/5nd6n1gPP397fMnMG+//baGDh2q6667TsnJyRoyZIj+8pe/uPaXlJTIZrMpOzvbtS0hIUGZmZnKz8+XJOXn5ysxMdEVXiQpOztbkZGRKigo8HWTAQAIWaG2CnU9nweYr7/+Ws8884x69+6tDz74QLfffrvuvPNOLV++XJJks9kkSSkp7qtdpqSkuPbZbDYlJye77Y+OjlZSUpKr5lTV1dWy2+1uHwAAwl2orUJdL9rXJ3Q6nRo6dKj++Mc/SpKGDBmi7du3a8mSJZo0aZKvv85l/vz5mjdvnt/ODwCAJYXYEgL1fP4EJi0tTf3793fb1q9fP+3bt0+SlJqaKkkqLXWf0ri0tNS1LzU1VWVlZW776+rqVF5e7qo51axZs1RZWen67N+/3yfXAwCAlYXaEgL1fB5gRowYod27d7tt++KLL9SjRw9JUkZGhlJTU7VmzRrXfrvdroKCAmVlZUmSsrKyVFFRoaKiIlfN2rVr5XQ6lZmZ2eD3xsbGKj4+3u0DAEA4C8UlBOr5/BXS3XffreHDh+uPf/yjfvnLX6qwsFDPPfecnnvuOUlSRESEpk2bpoceeki9e/dWRkaGZs+erfT0dI0fP17SiSc2o0eP1i233KIlS5aotrZWU6dO1cSJEz0agQQAAEJzCYF6Pg8wF198sd544w3NmjVLDzzwgDIyMvTEE08oNzfXVXPvvfeqqqpKU6ZMUUVFhS699FKtWrVKcXE/pb8VK1Zo6tSpGjlypCIjIzVhwgQtWrTI180FACBkheISAvV8Pg9MsGAeGABAuHv+H1/rwXd3Nls3e2w/Tb7snAC0qHmtNg8MAAAIDqG4hEA9AgwAACEoVJcQqEeAAQAgBIVyB16JAAMAQEgK5Q68EgEGAICQVH7Us4npzk603usjiQADAEBICuUOvBIBBgCAkBPqHXglAgwAACEn1DvwSgQYAABCTtkRzzrwjrNoB16JAAMAQMjp0iHWo7qR/VL83BL/IcAAABBqPF0kyMKLCRFgAAAIMWUeDqH2tC4YEWAAAAgxn+z5zqM6T+eKCUYEGAAAQojDabR6R6lHtVadA0YiwAAAEFIKS8pVebzOo1qrzgEjEWAAAAgpng6hTmzXxrJzwEgEGAAAQoqnQ6hvyupp2TlgJAIMAAAhpbDke4/qLu5p3acvEgEGAICQ4XAaLd+w16Paw1XWHYEkEWAAAAgZhSXlqvix+TWQJCm5Y5yfW+NfBBgAAEJEuHTglQgwAACEjHDpwCsRYAAACBnh0oFXIsAAABASwqkDr0SAAQAgJIRTB16JAAMAQEiw2T3swNvW+h14JQIMAAAhwdMVqLP7JVu+A69EgAEAwPK8WYF6xLld/NyawCDAAABgceGyAvXJCDAAAFhcOE1gV48AAwCAxYXTBHb1CDAAAFhcOE1gV48AAwCAhYXbBHb1CDAAAFhYuE1gV48AAwCAhYXbBHb1CDAAAFhY+VHPXguFygR29QgwAABYWGK7GI/qsnqFxgR29QgwAABYWP5Xhz2qqzhW4+eWBBYBBgAAi/JmCYGk9p49qbEKAgwAABYVjksI1CPAAABgUeE6AkkiwAAAYFmf7PnOo7pQG4EkEWAAALAkb/q/jDg3tEYgSQQYAAAsKZz7v0gEGAAALCmc+79IBBgAACwpnPu/SAQYAAAsJ9z7v0gEGAAALCfc+79IBBgAACwn3Pu/SAQYAAAsJ9z7v0gEGAAALIX+LycQYAAAsBD6v5xAgAEAwELo/3ICAQYAAAspP1rtUV0o93+RCDAAAFhKYrsYj+qyeoVu/xcpAAHmkUceUUREhKZNm+badvz4ceXl5alz587q0KGDJkyYoNJS9w5J+/bt09ixY9WuXTslJyfrnnvuUV2dZ+/8AAAIVflfHfaoruJYjZ9b0rr8GmA2bdqkZ599Vueff77b9rvvvlvvvPOOXn/9da1bt04HDx7Utdde69rvcDg0duxY1dTUaMOGDVq+fLmWLVumOXPm+LO5AAAENW9GICW19+xJjVX5LcAcPXpUubm5+stf/qJOnTq5tldWVur555/XY489piuvvFIXXXSRli5dqg0bNmjjxo2SpL/97W/asWOHXnzxRQ0ePFhjxozRgw8+qKefflo1NaGdKAEAaAwjkH7itwCTl5ensWPHKjs72217UVGRamtr3bb37dtX3bt3V35+viQpPz9fgwYNUkpKiqsmJydHdrtdxcXFDX5fdXW17Ha72wcAgFDCCKSfRPvjpK+88oo2b96sTZs2nbbPZrMpJiZGiYmJbttTUlJks9lcNSeHl/r99fsaMn/+fM2bN88HrQcAIDgxA+9PfP4EZv/+/brrrru0YsUKxcXF+fr0jZo1a5YqKytdn/379wfsuwEA8DeH0+jdbYc8qg3lGXjr+TzAFBUVqaysTBdeeKGio6MVHR2tdevWadGiRYqOjlZKSopqampUUVHhdlxpaalSU1MlSampqaeNSqr/c33NqWJjYxUfH+/2AQAgVGz8+nv9WOv0qDbU+79IfggwI0eO1LZt27R161bXZ+jQocrNzXX97zZt2mjNmjWuY3bv3q19+/YpKytLkpSVlaVt27aprKzMVbN69WrFx8erf//+vm4yAABB78WNez2q6xAbHfL9XyQ/9IHp2LGjBg4c6Latffv26ty5s2v75MmTNX36dCUlJSk+Pl533HGHsrKydMkll0iSRo0apf79++vGG2/UggULZLPZdP/99ysvL0+xsbG+bjIAAEHN4TRa/4Vn/V8u69055Pu/SH7qxNucxx9/XJGRkZowYYKqq6uVk5OjP//5z679UVFRWrlypW6//XZlZWWpffv2mjRpkh544IHWaC4AAK2qsKRcVTUOj2p/ndnTv40JEhHGGNPajfAHu92uhIQEVVZW0h8GAGBp897erqUbmn+F1C4mStv+kGPpJzCe/v5mLSQAAIKYw2n0v5sPeFR71cBUS4cXbxBgAAAIYoUl5Tpy3LPXR+EwfLoeAQYAgCDm6ey7UngMn65HgAEAIIh5OvtufFx4DJ+uR4ABACBIeTP77oQLzwqb/i8SAQYAgKDlzey7owak+bk1wYUAAwBAkMr/6nuP6sJl9t2TEWAAAAhSRp5N1XZpmMy+ezICDAAAQcpW8aNHdRd17+TnlgQfAgwAAEHI4TR6b7vNo9ouHcJvnUACDAAAQcibDrzhNP9LPQIMAABB6MWNza99JIVnB16JAAMAQNBxOI3W7irzqPayMOzAKxFgAAAIOhu//l7VdZ69Pvp1Zk//NiZIEWAAAAgynr4+iouO1CW9Ovu5NcGJAAMAQBDx5vXRz/p2DcvXRxIBBgCAoMLrI88QYAAACCK8PvIMAQYAgCDB6yPPEWAAAAgSvD7yHAEGAIAgwesjzxFgAAAIArw+8g4BBgCAIMDrI+8QYAAACAIbvjrsUR2vj04gwAAAEAQ2lZR7VHf+2Qlh//pIIsAAANDqHE6jzXt/8Kh2aM9Ofm6NNRBgAABoZYvXfKE641ntiF5d/dsYiyDAAADQihxOo798XOJRLf1ffkKAAQCgFRWWlKuq2uFRLcOnf0KAAQCgFf2t+JDHtQyf/gkBBgCAVuJwGr28ab9HtW3b8ProZAQYAABaycavv9fxWs8mr5t4cTdeH52EAAMAQCvxdO0jSRo1IM2PLbEeAgwAAK3Am7WPOsRGaVhGkp9bZC0EGAAAWoE3ax/97tJzeH10CgIMAACt4H/yv/Gork1khO4Y2du/jbEgAgwAAAHmcBqt2VnqUe2Q7ok8fWkAAQYAgADb+PX38nDwEWsfNYIAAwBAgHkz+oi1jxpGgAEAIIAcTqMPd9g8qmXto8YRYAAACKDFa77w+PXRbVf0ov9LIwgwAAAEiMNp9PTfv/KoltFHTSPAAAAQICeevhiParP7J/P0pQkEGAAAAsCbpy8SK083hwADAEAAePP0hc67zSPAAADgZw6n0TPrPH/6Qufd5hFgAADwsxPrHnn29IXOu54hwAAA4GeernskSXk/O5enLx4gwAAA4EcOp9HqHZ6texQdKZ6+eIgAAwCAHy1e84Ucnr090sh+KTx98RABBgAAP/F26PRvLunpv8aEGAIMAAB+wtBp/yHAAADgB94+fWHotHcIMAAA+IE3T18YOu09AgwAAD7m7dMXhk57z+cBZv78+br44ovVsWNHJScna/z48dq9e7dbzfHjx5WXl6fOnTurQ4cOmjBhgkpL3YeY7du3T2PHjlW7du2UnJyse+65R3V1db5uLgAAPsfTF//zeYBZt26d8vLytHHjRq1evVq1tbUaNWqUqqqqXDV333233nnnHb3++utat26dDh48qGuvvda13+FwaOzYsaqpqdGGDRu0fPlyLVu2THPmzPF1cwEA8Clvlw3g6UvLRBhjPByd3jLfffedkpOTtW7dOl1++eWqrKxU165d9dJLL+kXv/iFJGnXrl3q16+f8vPzdckll+j999/X1VdfrYMHDyolJUWStGTJEt1333367rvvFBMT0+z32u12JSQkqLKyUvHx8f68RAAAXD758rBy/7vAo9o2kRHa9dAYAsxJPP397fc+MJWVlZKkpKQkSVJRUZFqa2uVnZ3tqunbt6+6d++u/Px8SVJ+fr4GDRrkCi+SlJOTI7vdruLi4ga/p7q6Wna73e0DAECgsWxAYPg1wDidTk2bNk0jRozQwIEDJUk2m00xMTFKTEx0q01JSZHNZnPVnBxe6vfX72vI/PnzlZCQ4Pp069bNx1cDAEDTWDYgcPwaYPLy8rR9+3a98sor/vwaSdKsWbNUWVnp+uzfv9/v3wkAwMnufLmIZQMCJNpfJ546dapWrlyp9evX6+yzz3ZtT01NVU1NjSoqKtyewpSWlio1NdVVU1hY6Ha++lFK9TWnio2NVWxsrI+vAgAAz7z3+UG9u82zpy8SywacKZ8/gTHGaOrUqXrjjTe0du1aZWRkuO2/6KKL1KZNG61Zs8a1bffu3dq3b5+ysrIkSVlZWdq2bZvKyspcNatXr1Z8fLz69+/v6yYDAHBGHE6ju1/d6nE9ywacOZ8/gcnLy9NLL72kt956Sx07dnT1WUlISFDbtm2VkJCgyZMna/r06UpKSlJ8fLzuuOMOZWVl6ZJLLpEkjRo1Sv3799eNN96oBQsWyGaz6f7771deXh5PWQAAQWfxmi9U7em7I7FsgC/4fBh1RETDN2Tp0qW66aabJJ2YyG7GjBl6+eWXVV1drZycHP35z392ez20d+9e3X777froo4/Uvn17TZo0SY888oiioz3LXAyjBgAEgsNp1Pf+972auI6h043z9Pe33+eBaS0EGABAIDyxereeWPOlx/XTRvbWtJ+f58cWWVvQzAMDAECocjiNnlrreXiJi45k6LSPEGAAAGihO18uUp0X7zEe++VgXh35CAEGAIAW8HbY9OBuCbrq/DQ/tii8EGAAAPCSt8OmJemeUX3905gwRYABAMBLd75c5NWw6Q6x0cz74mMEGAAAvODtqyNJWjDhfPq++BgBBgAAD7Xk1dHVg9Lo++IHBBgAADzk7Yy7sVERevKGIX5sUfgiwAAA4AGH02iRF3O+SNLj1w/h1ZGfEGAAAPDAdc98Ig9XC5DEqyN/I8AAANCMB1du1+b9lR7XR0eIV0d+RoABAKAJ731+UM9/vNerY6Ze2ZtXR35GgAEAoBEOp9Gdr2zx6hjWOwoMAgwAAI3Ie6lIdU7vjmG9o8AgwAAA0IAHVxZr1XbvJqybfGkGHXcDhAADAMApHlxZrOc//sarYy7slqDZV/f3T4NwGgIMAAAnefhd78NLVIT0+u0j/NMgNIgAAwDAP733+UH95R/feH3cExOZsC7QCDAAAOjEiKOpL3k34kiSLuqeqGsuSPdDi9AUAgwAAJJGLlwrLwccKTpCeu224X5pD5pGgAEAhL2xT67TN+XHvT5u0Q0X8uqolRBgAABhbeyT61R86KjXx91yGUOmWxMBBgAQtq5uYXiZfGlP/edYhky3JgIMACAs3fxCgba3ILzcPKKnZl89wA8tgjcIMACAsHPzCwX6+xeHvT7uyj5dNfcawkswiG7tBgAAEEhjF61X8cEjXh83ML2jXrh5mB9ahJYgwAAAwsalj3yoAxXVXh83IK2DVt55uR9ahJYiwAAAQp7DaZT50N90+Fid18cOSOugd++6wg+twpkgwAAAQto7nx3UHS97P8OuJPXoFEd4CVIEGABAyPrtskKt3fVdi46NkLT2nit92yD4DAEGABByHE6jSx5ere+qalt8jqd/xSy7wYwAAwAIKWfyyqjerZczy26wI8AAAEKCw2l03TMbtHl/RYvPERkhPXXDEF11PqtLBzsCDADA8t757KDufHmLzBmco0v7Nir4z5/z2sgiCDAAAMuqqXNq7KL12lNWdUbnOTsxVh/PzPZRqxAIBBgAgCU98E6xXvjkmzM+z4DU9np32r+c8XkQWAQYAIBlOJxGG/Yc1q0rPtWxGucZn29k3y56/qZMH7QMgUaAAQAEPYfT6MnVX+jpdV/Kcea5RZL01MTBunrwWb45GQKOAAMACFr1weWpj76U80x66J6ka/s22khnXcsjwAAAgk5NnVMz/99nemPLwTMaWXSqK/t01gs3X+LDM6K1EGAAAEGjps6pX/93vgq/qfDpeSMkLeaVUUghwAAAWlVNnVNLP/laz/+jRGVHa3x+/p6d47RmxpW8MgoxBBgAQMDVjyb6w8rt+uq7Y377nsmX9tDsqwf67fxoPQQYAEBAOJxGH+/+Tg+/v0NfnOHEc83J7NlJ//O7SxQTHenX70HrIcAAAPyi/inL60X7VPhNuWx2378eOlVCXJQ23T+K4BIGCDAAAJ+p78/ySuF+lXzvv1dDDbl5RHfNvWZQQL8TrYcAAwBokZOfsOw4ZNfByuM+mR3XW9cOTtcjv7iApy5hhgADAGjWqWHl8NFqVfzoaNU2XTUwRYt/dRGji8IUAQYA4ObHGoceWLldG748rKPHa+VwRqjieF1rN0vSiflc/o0nLhABBgDCzslPU4oPVuqHYzVyOKWoCOlojVM1Dl/OfesbqR1j9Oh1gzX83C48cYEkAgwAhJT6TrQfbLfpUOUxVdc65TAnwklsdJR+rHWo4njrvvrxRr+UDvq/vEvVNiaqtZuCIEOAAYAg1NRTktjoKElG1XU/hZOYqEhVHK/T8bqmnp5YI7jwmgieIMAAwBmof+Kxatsh7S2vajZk1Djcn4g0VHO0xqGjTY7maSiIWCOcNOXiHom6c+R5vCaCRwgwXji1F35Vde1pj2db8g9WoI8LxjZxLfwdBGubmjqu6f4inoSM0Awi3hjaPVF3ZRNa4L2gDjBPP/20Hn30UdlsNl1wwQVavHixhg0b1iptWbX9kKa/9pmO1TT1j4sv/8EK5HHB2KaWHheMbQr0ccHYppYeF4xtamwbPJHYto2uOK+rfnHR2YQWnJGgDTCvvvqqpk+friVLligzM1NPPPGEcnJytHv3biUnJwe0Lau2H9JtL24O6HcCQCiIi4rQgLMSlDMgVTeNyKBPC3wmwhgTfOPlJGVmZuriiy/WU089JUlyOp3q1q2b7rjjDs2cObPZ4+12uxISElRZWan4+PgWt8PhNBo+/0OVHvH/Gh4AYHUdY6PUpUOshvfqrPuvHsDoIXjN09/fQfkEpqamRkVFRZo1a5ZrW2RkpLKzs5Wfn9/gMdXV1aqurnb92W63+6QthSXlhBcAaEBsVIQ6tY9Rr67tNeXyXrq0d1deCSFggjLAHD58WA6HQykpKW7bU1JStGvXrgaPmT9/vubNm+fztpQdOe7zcwKAlbSNljq1i1FkZKRS4uN4HYSgEJQBpiVmzZql6dOnu/5st9vVrVu3Mz5vcse4Mz4HAAS7ttFS2zZRio2OUlQUQQXBLygDTJcuXRQVFaXS0lK37aWlpUpNTW3wmNjYWMXGxvq8LcMykpTSMYbXSAAsKSZSahsT5TYkvMZh1CY6ilc/sLSgDDAxMTG66KKLtGbNGo0fP17SiU68a9as0dSpUwPalqjICM0bN5BRSABa3clPSRqbm8ZERKprhxhde+HZ+u2l5/D0BCErKAOMJE2fPl2TJk3S0KFDNWzYMD3xxBOqqqrSzTffHPC2jB6YpiW/vtCDeWAAhLs2EVLHuKZDhieT5DkVoQ6x0bqweyddN7Qbc6YApwjaAHP99dfru+++05w5c2Sz2TR48GCtWrXqtI69gTJ6YJp+3j+VmXiD7LhgbBN/B+F1LW2iItU9qb1GD6S/CBBIQTsPzJny1TwwAAAgcDz9/c1/KgAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMsJ2qUEzlT9BMN2u72VWwIAADxV/3u7uYUCQjbAHDlyRJLUrVu3Vm4JAADw1pEjR5SQkNDo/pBdC8npdOrgwYPq2LGjIiJ8t4Kr3W5Xt27dtH///pBdYynUr5Hrs75Qv8ZQvz4p9K+R62s5Y4yOHDmi9PR0RUY23tMlZJ/AREZG6uyzz/bb+ePj40Py/5QnC/Vr5PqsL9SvMdSvTwr9a+T6WqapJy/16MQLAAAshwADAAAshwDjpdjYWM2dO1exsbGt3RS/CfVr5PqsL9SvMdSvTwr9a+T6/C9kO/ECAIDQxRMYAABgOQQYAABgOQQYAABgOQQYAABgOQSYBjz88MMaPny42rVrp8TExAZr9u3bp7Fjx6pdu3ZKTk7WPffco7q6uibPW15ertzcXMXHxysxMVGTJ0/W0aNH/XAFnvvoo48UERHR4GfTpk2NHvcv//Ivp9XfdtttAWy5d3r27Hlaex955JEmjzl+/Ljy8vLUuXNndejQQRMmTFBpaWmAWuy5b775RpMnT1ZGRobatm2rXr16ae7cuaqpqWnyuGC/h08//bR69uypuLg4ZWZmqrCwsMn6119/XX379lVcXJwGDRqk9957L0At9c78+fN18cUXq2PHjkpOTtb48eO1e/fuJo9ZtmzZafcqLi4uQC323h/+8IfT2tu3b98mj7HK/ZMa/vckIiJCeXl5DdYH+/1bv369rrnmGqWnpysiIkJvvvmm235jjObMmaO0tDS1bdtW2dnZ2rNnT7Pn9fZn2FsEmAbU1NTouuuu0+23397gfofDobFjx6qmpkYbNmzQ8uXLtWzZMs2ZM6fJ8+bm5qq4uFirV6/WypUrtX79ek2ZMsUfl+Cx4cOH69ChQ26f3/3ud8rIyNDQoUObPPaWW25xO27BggUBanXLPPDAA27tveOOO5qsv/vuu/XOO+/o9ddf17p163Tw4EFde+21AWqt53bt2iWn06lnn31WxcXFevzxx7VkyRL9x3/8R7PHBus9fPXVVzV9+nTNnTtXmzdv1gUXXKCcnByVlZU1WL9hwwbdcMMNmjx5srZs2aLx48dr/Pjx2r59e4Bb3rx169YpLy9PGzdu1OrVq1VbW6tRo0apqqqqyePi4+Pd7tXevXsD1OKWGTBggFt7P/7440ZrrXT/JGnTpk1u17Z69WpJ0nXXXdfoMcF8/6qqqnTBBRfo6aefbnD/ggULtGjRIi1ZskQFBQVq3769cnJydPz48UbP6e3PcIsYNGrp0qUmISHhtO3vvfeeiYyMNDabzbXtmWeeMfHx8aa6urrBc+3YscNIMps2bXJte//9901ERIT59ttvfd72lqqpqTFdu3Y1DzzwQJN1V1xxhbnrrrsC0ygf6NGjh3n88cc9rq+oqDBt2rQxr7/+umvbzp07jSSTn5/vhxb61oIFC0xGRkaTNcF8D4cNG2by8vJcf3Y4HCY9Pd3Mnz+/wfpf/vKXZuzYsW7bMjMzza233urXdvpCWVmZkWTWrVvXaE1j/xYFq7lz55oLLrjA43or3z9jjLnrrrtMr169jNPpbHC/le6fJPPGG2+4/ux0Ok1qaqp59NFHXdsqKipMbGysefnllxs9j7c/wy3BE5gWyM/P16BBg5SSkuLalpOTI7vdruLi4kaPSUxMdHuqkZ2drcjISBUUFPi9zZ56++239f333+vmm29utnbFihXq0qWLBg4cqFmzZunYsWMBaGHLPfLII+rcubOGDBmiRx99tMlXfkVFRaqtrVV2drZrW9++fdW9e3fl5+cHorlnpLKyUklJSc3WBeM9rKmpUVFRkdvffWRkpLKzsxv9u8/Pz3erl078TFrlXklq9n4dPXpUPXr0ULdu3TRu3LhG/60JFnv27FF6errOOecc5ebmat++fY3WWvn+1dTU6MUXX9Rvf/vbJhcOttr9q1dSUiKbzeZ2fxISEpSZmdno/WnJz3BLhOxijv5ks9ncwosk159tNlujxyQnJ7tti46OVlJSUqPHtIbnn39eOTk5zS6E+atf/Uo9evRQenq6Pv/8c913333avXu3/u///i9ALfXOnXfeqQsvvFBJSUnasGGDZs2apUOHDumxxx5rsN5msykmJua0PlApKSlBdb8a8uWXX2rx4sVauHBhk3XBeg8PHz4sh8PR4M/Yrl27GjymsZ/JYL9XTqdT06ZN04gRIzRw4MBG6/r06aMXXnhB559/viorK7Vw4UINHz5cxcXFfl20tqUyMzO1bNky9enTR4cOHdK8efN02WWXafv27erYseNp9Va9f5L05ptvqqKiQjfddFOjNVa7fyervwfe3J+W/Ay3RNgEmJkzZ+pPf/pTkzU7d+5stqOZVbTkeg8cOKAPPvhAr732WrPnP7nvzqBBg5SWlqaRI0fqq6++Uq9evVrecC94c43Tp093bTv//PMVExOjW2+9VfPnzw/aqb5bcg+//fZbjR49Wtddd51uueWWJo8NhnsY7vLy8rR9+/Ym+4dIUlZWlrKyslx/Hj58uPr166dnn31WDz74oL+b6bUxY8a4/vf555+vzMxM9ejRQ6+99pomT57cii3zveeff15jxoxRenp6ozVWu39WETYBZsaMGU0mZEk655xzPDpXamrqab2p60enpKamNnrMqZ2X6urqVF5e3ugxZ6Il17t06VJ17txZ//qv/+r192VmZko68V//gfrldyb3NDMzU3V1dfrmm2/Up0+f0/anpqaqpqZGFRUVbk9hSktL/XK/GuLt9R08eFA/+9nPNHz4cD333HNef19r3MOGdOnSRVFRUaeN+Grq7z41NdWr+mAwdepUV2d+b/8rvE2bNhoyZIi+/PJLP7XOtxITE3Xeeec12l4r3j9J2rt3rz788EOvn1pa6f7V34PS0lKlpaW5tpeWlmrw4MENHtOSn+EW8VlvmhDUXCfe0tJS17Znn33WxMfHm+PHjzd4rvpOvJ9++qlr2wcffBA0nXidTqfJyMgwM2bMaNHxH3/8sZFkPvvsMx+3zD9efPFFExkZacrLyxvcX9+J93//939d23bt2hW0nXgPHDhgevfubSZOnGjq6upadI5guofDhg0zU6dOdf3Z4XCYs846q8lOvFdffbXbtqysrKDsBOp0Ok1eXp5JT083X3zxRYvOUVdXZ/r06WPuvvtuH7fOP44cOWI6depknnzyyQb3W+n+nWzu3LkmNTXV1NbWenVcMN8/NdKJd+HCha5tlZWVHnXi9eZnuEVt9dmZQsjevXvNli1bzLx580yHDh3Mli1bzJYtW8yRI0eMMSf+zzdw4EAzatQos3XrVrNq1SrTtWtXM2vWLNc5CgoKTJ8+fcyBAwdc20aPHm2GDBliCgoKzMcff2x69+5tbrjhhoBfX0M+/PBDI8ns3LnztH0HDhwwffr0MQUFBcYYY7788kvzwAMPmE8//dSUlJSYt956y5xzzjnm8ssvD3SzPbJhwwbz+OOPm61bt5qvvvrKvPjii6Zr167mN7/5javm1Gs0xpjbbrvNdO/e3axdu9Z8+umnJisry2RlZbXGJTTpwIED5txzzzUjR440Bw4cMIcOHXJ9Tq6x0j185ZVXTGxsrFm2bJnZsWOHmTJliklMTHSN/LvxxhvNzJkzXfWffPKJiY6ONgsXLjQ7d+40c+fONW3atDHbtm1rrUto1O23324SEhLMRx995Havjh075qo59frmzZtnPvjgA/PVV1+ZoqIiM3HiRBMXF2eKi4tb4xKaNWPGDPPRRx+ZkpIS88knn5js7GzTpUsXU1ZWZoyx9v2r53A4TPfu3c1999132j6r3b8jR464fs9JMo899pjZsmWL2bt3rzHGmEceecQkJiaat956y3z++edm3LhxJiMjw/z444+uc1x55ZVm8eLFrj839zPsCwSYBkyaNMlIOu3z97//3VXzzTffmDFjxpi2bduaLl26mBkzZril8L///e9GkikpKXFt+/77780NN9xgOnToYOLj483NN9/sCkWt7YYbbjDDhw9vcF9JSYnb9e/bt89cfvnlJikpycTGxppzzz3X3HPPPaaysjKALfZcUVGRyczMNAkJCSYuLs7069fP/PGPf3R7WnbqNRpjzI8//mh+//vfm06dOpl27dqZf/u3f3MLBcFi6dKlDf7/9eQHrFa8h4sXLzbdu3c3MTExZtiwYWbjxo2ufVdccYWZNGmSW/1rr71mzjvvPBMTE2MGDBhg3n333QC32DON3aulS5e6ak69vmnTprn+LlJSUsxVV11lNm/eHPjGe+j66683aWlpJiYmxpx11lnm+uuvN19++aVrv5XvX70PPvjASDK7d+8+bZ/V7l/976tTP/XX4HQ6zezZs01KSoqJjY01I0eOPO26e/ToYebOneu2ramfYV+IMMYY372QAgAA8D/mgQEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJbz/wFdMjw09OJStwAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -42,7 +42,7 @@
     "# for each, function draw 1000 samples from a uniform distribution and plot the function\n",
     "function_points = []\n",
     "x = np.linspace(-10, 10, 1000)\n",
-    "y = -1/10 * x ** 3 + 20 * np.tanh(2*x) \n",
+    "y = 2 ** x\n",
     "plt.scatter(x, y)\n",
     "plt.show()"
    ]
@@ -51,19 +51,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# F-Bench (Well-known Functions)"
+    "# 50 well-known Functions"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "30\n"
+      "50\n"
      ]
     },
     {
@@ -118,7 +118,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -128,7 +128,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -136,703 +136,325 @@
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[38], line 57\u001b[0m\n\u001b[1;32m     55\u001b[0m plt\u001b[38;5;241m.\u001b[39mscatter(x, y)\n\u001b[1;32m     56\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(n)\n\u001b[0;32m---> 57\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/matplotlib/pyplot.py:527\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    483\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    484\u001b[0m \u001b[38;5;124;03mDisplay all open figures.\u001b[39;00m\n\u001b[1;32m    485\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    524\u001b[0m \u001b[38;5;124;03mexplicitly there.\u001b[39;00m\n\u001b[1;32m    525\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    526\u001b[0m _warn_if_gui_out_of_main_thread()\n\u001b[0;32m--> 527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_get_backend_mod\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/matplotlib_inline/backend_inline.py:90\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m     88\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     89\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m figure_manager \u001b[38;5;129;01min\u001b[39;00m Gcf\u001b[38;5;241m.\u001b[39mget_all_fig_managers():\n\u001b[0;32m---> 90\u001b[0m         \u001b[43mdisplay\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     91\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfigure_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     92\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_fetch_figure_metadata\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfigure_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     93\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     94\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     95\u001b[0m     show\u001b[38;5;241m.\u001b[39m_to_draw \u001b[38;5;241m=\u001b[39m []\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/IPython/core/display_functions.py:298\u001b[0m, in \u001b[0;36mdisplay\u001b[0;34m(include, exclude, metadata, transient, display_id, raw, clear, *objs, **kwargs)\u001b[0m\n\u001b[1;32m    296\u001b[0m     publish_display_data(data\u001b[38;5;241m=\u001b[39mobj, metadata\u001b[38;5;241m=\u001b[39mmetadata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    297\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 298\u001b[0m     format_dict, md_dict \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minclude\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexclude\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    299\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m format_dict:\n\u001b[1;32m    300\u001b[0m         \u001b[38;5;66;03m# nothing to display (e.g. _ipython_display_ took over)\u001b[39;00m\n\u001b[1;32m    301\u001b[0m         \u001b[38;5;28;01mcontinue\u001b[39;00m\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/IPython/core/formatters.py:182\u001b[0m, in \u001b[0;36mDisplayFormatter.format\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m    180\u001b[0m md \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    181\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 182\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[43mformatter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    183\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m    184\u001b[0m     \u001b[38;5;66;03m# FIXME: log the exception\u001b[39;00m\n\u001b[1;32m    185\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/decorator.py:232\u001b[0m, in \u001b[0;36mdecorate.<locals>.fun\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    230\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kwsyntax:\n\u001b[1;32m    231\u001b[0m     args, kw \u001b[38;5;241m=\u001b[39m fix(args, kw, sig)\n\u001b[0;32m--> 232\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcaller\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mextras\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/IPython/core/formatters.py:226\u001b[0m, in \u001b[0;36mcatch_format_error\u001b[0;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    224\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"show traceback on failed format call\"\"\"\u001b[39;00m\n\u001b[1;32m    225\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 226\u001b[0m     r \u001b[38;5;241m=\u001b[39m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    227\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m:\n\u001b[1;32m    228\u001b[0m     \u001b[38;5;66;03m# don't warn on NotImplementedErrors\u001b[39;00m\n\u001b[1;32m    229\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_return(\u001b[38;5;28;01mNone\u001b[39;00m, args[\u001b[38;5;241m0\u001b[39m])\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/IPython/core/formatters.py:343\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    341\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m    342\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 343\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprinter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    344\u001b[0m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[1;32m    345\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/IPython/core/pylabtools.py:152\u001b[0m, in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[1;32m    150\u001b[0m     FigureCanvasBase(fig)\n\u001b[0;32m--> 152\u001b[0m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbytes_io\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    153\u001b[0m data \u001b[38;5;241m=\u001b[39m bytes_io\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[1;32m    154\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msvg\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/matplotlib/backend_bases.py:2164\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2161\u001b[0m     \u001b[38;5;66;03m# we do this instead of `self.figure.draw_without_rendering`\u001b[39;00m\n\u001b[1;32m   2162\u001b[0m     \u001b[38;5;66;03m# so that we can inject the orientation\u001b[39;00m\n\u001b[1;32m   2163\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_draw_disabled\u001b[39m\u001b[38;5;124m\"\u001b[39m, nullcontext)():\n\u001b[0;32m-> 2164\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2165\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[1;32m   2166\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtight\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/matplotlib/artist.py:95\u001b[0m, in \u001b[0;36m_finalize_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[1;32m     94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 95\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     96\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m     97\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/matplotlib/figure.py:3154\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3151\u001b[0m         \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m   3153\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 3154\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3155\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3157\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubfigs:\n\u001b[1;32m   3158\u001b[0m     sfig\u001b[38;5;241m.\u001b[39mdraw(renderer)\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/matplotlib/image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    131\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 132\u001b[0m         \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    134\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    135\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/matplotlib/axes/_base.py:3034\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3031\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m spine \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspines\u001b[38;5;241m.\u001b[39mvalues():\n\u001b[1;32m   3032\u001b[0m         artists\u001b[38;5;241m.\u001b[39mremove(spine)\n\u001b[0;32m-> 3034\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_title_position\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3036\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxison:\n\u001b[1;32m   3037\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m _axis \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_axis_map\u001b[38;5;241m.\u001b[39mvalues():\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/matplotlib/axes/_base.py:2978\u001b[0m, in \u001b[0;36m_AxesBase._update_title_position\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   2976\u001b[0m top \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(top, bb\u001b[38;5;241m.\u001b[39mymax)\n\u001b[1;32m   2977\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m title\u001b[38;5;241m.\u001b[39mget_text():\n\u001b[0;32m-> 2978\u001b[0m     \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43myaxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_tightbbox\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# update offsetText\u001b[39;00m\n\u001b[1;32m   2979\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ax\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39moffsetText\u001b[38;5;241m.\u001b[39mget_text():\n\u001b[1;32m   2980\u001b[0m         bb \u001b[38;5;241m=\u001b[39m ax\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39moffsetText\u001b[38;5;241m.\u001b[39mget_tightbbox(renderer)\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/matplotlib/axis.py:1334\u001b[0m, in \u001b[0;36mAxis.get_tightbbox\u001b[0;34m(self, renderer, for_layout_only)\u001b[0m\n\u001b[1;32m   1332\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m renderer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1333\u001b[0m     renderer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39m_get_renderer()\n\u001b[0;32m-> 1334\u001b[0m ticks_to_draw \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_ticks\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1336\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_label_position(renderer)\n\u001b[1;32m   1338\u001b[0m \u001b[38;5;66;03m# go back to just this axis's tick labels\u001b[39;00m\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/matplotlib/axis.py:1277\u001b[0m, in \u001b[0;36mAxis._update_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1275\u001b[0m major_locs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_majorticklocs()\n\u001b[1;32m   1276\u001b[0m major_labels \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmajor\u001b[38;5;241m.\u001b[39mformatter\u001b[38;5;241m.\u001b[39mformat_ticks(major_locs)\n\u001b[0;32m-> 1277\u001b[0m major_ticks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_major_ticks\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmajor_locs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1278\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m tick, loc, label \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(major_ticks, major_locs, major_labels):\n\u001b[1;32m   1279\u001b[0m     tick\u001b[38;5;241m.\u001b[39mupdate_position(loc)\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/matplotlib/axis.py:1626\u001b[0m, in \u001b[0;36mAxis.get_major_ticks\u001b[0;34m(self, numticks)\u001b[0m\n\u001b[1;32m   1622\u001b[0m     numticks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_majorticklocs())\n\u001b[1;32m   1624\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmajorTicks) \u001b[38;5;241m<\u001b[39m numticks:\n\u001b[1;32m   1625\u001b[0m     \u001b[38;5;66;03m# Update the new tick label properties from the old.\u001b[39;00m\n\u001b[0;32m-> 1626\u001b[0m     tick \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_tick\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmajor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m   1627\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmajorTicks\u001b[38;5;241m.\u001b[39mappend(tick)\n\u001b[1;32m   1628\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_copy_tick_props(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmajorTicks[\u001b[38;5;241m0\u001b[39m], tick)\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/matplotlib/axis.py:1562\u001b[0m, in \u001b[0;36mAxis._get_tick\u001b[0;34m(self, major)\u001b[0m\n\u001b[1;32m   1558\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m   1559\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe Axis subclass \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must define \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1560\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_tick_class or reimplement _get_tick()\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1561\u001b[0m tick_kw \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_major_tick_kw \u001b[38;5;28;01mif\u001b[39;00m major \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_minor_tick_kw\n\u001b[0;32m-> 1562\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_tick_class\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmajor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmajor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mtick_kw\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/matplotlib/axis.py:486\u001b[0m, in \u001b[0;36mYTick.__init__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    481\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlabel1\u001b[38;5;241m.\u001b[39mset(\n\u001b[1;32m    482\u001b[0m     x\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m    483\u001b[0m     verticalalignment\u001b[38;5;241m=\u001b[39mva, horizontalalignment\u001b[38;5;241m=\u001b[39mha, transform\u001b[38;5;241m=\u001b[39mtrans,\n\u001b[1;32m    484\u001b[0m )\n\u001b[1;32m    485\u001b[0m trans, va, ha \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_text2_transform()\n\u001b[0;32m--> 486\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlabel2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    487\u001b[0m \u001b[43m    \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    488\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverticalalignment\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mva\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhorizontalalignment\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mha\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtransform\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrans\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    489\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/matplotlib/artist.py:147\u001b[0m, in \u001b[0;36mArtist.__init_subclass__.<locals>.<lambda>\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    139\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mset, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_autogenerated_signature\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m    140\u001b[0m     \u001b[38;5;66;03m# Don't overwrite cls.set if the subclass or one of its parents\u001b[39;00m\n\u001b[1;32m    141\u001b[0m     \u001b[38;5;66;03m# has defined a set method set itself.\u001b[39;00m\n\u001b[1;32m    142\u001b[0m     \u001b[38;5;66;03m# If there was no explicit definition, cls.set is inherited from\u001b[39;00m\n\u001b[1;32m    143\u001b[0m     \u001b[38;5;66;03m# the hierarchy of auto-generated set methods, which hold the\u001b[39;00m\n\u001b[1;32m    144\u001b[0m     \u001b[38;5;66;03m# flag _autogenerated_signature.\u001b[39;00m\n\u001b[1;32m    145\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m--> 147\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mset \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m \u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: \u001b[43mArtist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    148\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mset\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mset\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mset\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.set\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/matplotlib/artist.py:1227\u001b[0m, in \u001b[0;36mArtist.set\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m   1223\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mset\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m   1224\u001b[0m     \u001b[38;5;66;03m# docstring and signature are auto-generated via\u001b[39;00m\n\u001b[1;32m   1225\u001b[0m     \u001b[38;5;66;03m# Artist._update_set_signature_and_docstring() at the end of the\u001b[39;00m\n\u001b[1;32m   1226\u001b[0m     \u001b[38;5;66;03m# module.\u001b[39;00m\n\u001b[0;32m-> 1227\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_internal_update\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcbook\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormalize_kwargs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/matplotlib/artist.py:1219\u001b[0m, in \u001b[0;36mArtist._internal_update\u001b[0;34m(self, kwargs)\u001b[0m\n\u001b[1;32m   1212\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_internal_update\u001b[39m(\u001b[38;5;28mself\u001b[39m, kwargs):\n\u001b[1;32m   1213\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1214\u001b[0m \u001b[38;5;124;03m    Update artist properties without prenormalizing them, but generating\u001b[39;00m\n\u001b[1;32m   1215\u001b[0m \u001b[38;5;124;03m    errors as if calling `set`.\u001b[39;00m\n\u001b[1;32m   1216\u001b[0m \n\u001b[1;32m   1217\u001b[0m \u001b[38;5;124;03m    The lack of prenormalization is to maintain backcompatibility.\u001b[39;00m\n\u001b[1;32m   1218\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1219\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_props\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1220\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{cls.__name__}\u001b[39;49;00m\u001b[38;5;124;43m.set() got an unexpected keyword argument \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m   1221\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{prop_name!r}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/matplotlib/artist.py:1184\u001b[0m, in \u001b[0;36mArtist._update_props\u001b[0;34m(self, props, errfmt)\u001b[0m\n\u001b[1;32m   1177\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1178\u001b[0m \u001b[38;5;124;03mHelper for `.Artist.set` and `.Artist.update`.\u001b[39;00m\n\u001b[1;32m   1179\u001b[0m \n\u001b[1;32m   1180\u001b[0m \u001b[38;5;124;03m*errfmt* is used to generate error messages for invalid property\u001b[39;00m\n\u001b[1;32m   1181\u001b[0m \u001b[38;5;124;03mnames; it gets formatted with ``type(self)`` and the property name.\u001b[39;00m\n\u001b[1;32m   1182\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1183\u001b[0m ret \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m-> 1184\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mcbook\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_setattr_cm\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meventson\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m:\n\u001b[1;32m   1185\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m props\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m   1186\u001b[0m         \u001b[38;5;66;03m# Allow attributes we want to be able to update through\u001b[39;00m\n\u001b[1;32m   1187\u001b[0m         \u001b[38;5;66;03m# art.update, art.set, setp.\u001b[39;00m\n\u001b[1;32m   1188\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maxes\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/contextlib.py:301\u001b[0m, in \u001b[0;36mcontextmanager.<locals>.helper\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m    299\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[1;32m    300\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhelper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds):\n\u001b[0;32m--> 301\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_GeneratorContextManager\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/contextlib.py:108\u001b[0m, in \u001b[0;36m_GeneratorContextManagerBase.__init__\u001b[0;34m(self, func, args, kwds)\u001b[0m\n\u001b[1;32m    106\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwds \u001b[38;5;241m=\u001b[39m func, args, kwds\n\u001b[1;32m    107\u001b[0m \u001b[38;5;66;03m# Issue 19330: ensure context manager instances have good docstrings\u001b[39;00m\n\u001b[0;32m--> 108\u001b[0m doc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m__doc__\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    109\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m doc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    110\u001b[0m     doc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__doc__\u001b[39m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "fbench = [\n",
-    "    # polynomials\n",
-    "    (lambda x: x, 'x'),\n",
-    "    (lambda x: -2*x+5, '-2*x+5'),\n",
-    "    (lambda x: x**2, 'x^2'),\n",
-    "    (lambda x: -2*x**2, '-2*x^2'),\n",
-    "    (lambda x: (x-2)**2, '(x-2)^2'),\n",
-    "    (lambda x: 2 ** (x-5), '2^(x-5)'),\n",
-    "    (lambda x: (x-1)*(x+1), '(x-1)*(x+1)'),\n",
-    "    (lambda x: x**3, 'x^3'),\n",
-    "    (lambda x: -3*x**3, '-3*x^3'),\n",
-    "    (lambda x: x ** 4, 'x^4'),\n",
-    "    (lambda x: (x + 4) ** 4, '(x + 4)^4'),\n",
-    "\n",
-    "    # sign\n",
-    "    (lambda x: np.sign(x), 'sign(x)'),\n",
-    "    (lambda x: np.sign(x+3), 'sign(x+3)'),\n",
-    "    (lambda x: np.sign(x-1), 'sign(x-1)'),\n",
-    "\n",
-    "    # abs\n",
-    "    (lambda x: np.abs(x), '|x|'),\n",
-    "    (lambda x: np.abs(2*x+5), '|2*x+4|'),\n",
-    "\n",
-    "    # root\n",
-    "    (lambda x: np.sqrt(x+10), 'sqrt(x+10)'),\n",
-    "    (lambda x: np.sqrt(x ** 2 + 3*x + 5), 'sqrt(x ** 2 + 3*x +5)'),\n",
-    "\n",
-    "    # exponential\n",
-    "    (lambda x: np.exp(-x**2), 'exp(-x^2)'),\n",
-    "    (lambda x: np.exp(x), 'exp(x)'),\n",
-    "    (lambda x: np.exp(-x), 'exp(-x)'),\n",
-    "\n",
-    "    # logarithm\n",
-    "    (lambda x: np.log(x+10), 'log(x+10)'),\n",
-    "\n",
-    "    # trigonometric\n",
-    "    (lambda x: np.sin(x), 'sin(x)'),\n",
-    "    (lambda x: np.cos(x), 'cos(x)'),\n",
-    "    (lambda x: np.sinh(x), 'sinh(x)'),\n",
-    "    (lambda x: np.cosh(x), 'cosh(x)'),\n",
-    "    (lambda x: np.tanh(x), 'tanh(x)'),\n",
-    "    (lambda x: np.arcsinh(x), 'arcsinh(x)'),\n",
-    "    (lambda x: np.arctan(x), 'arctan(x)'),\n",
-    "\n",
-    "    # logistic function\n",
-    "    (lambda x: 1/(1+np.exp(-x)), '1/(1+exp(-x))'),\n",
-    "]\n",
-    "\n",
-    "print(len(fbench))\n",
-    "\n",
-    "# for each, function draw 1000 samples from a uniform distribution and plot the function\n",
-    "x = np.linspace(-10, 10, 100)\n",
-    "for f, n in fbench:\n",
-    "    y = f(x)\n",
-    "    plt.scatter(x, y)\n",
-    "    plt.title(n)\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Generate mulitple choice questions. graphs are combined so that the correct option has a fairly unique shape among all options"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  0\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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r1KxZM2VlZemuu+7SPffc41S3BwAAOKuquVPb7ByTpKQQrbnjiskwjNB7ApiX2e12mc1m2Ww2xcTEBLo7AAD4VFXdncLtVr3+9b5aa+4E+zJ0T35/B8UtLQAA4B8ri49o9kfbnSYsm0zShcMfFnO0Zt2UHtRhx1MEHgAAmoja6u5U/t+GsYM6KzvdEvI1d1wJijk8AADAt+qquyOdv431SbE1LMOOROABAKBJaGp1d6oj8AAA0AQ0tbo71RF4AABoAppa3Z3qCDwAADQBTa3uTnUEHgAAwlhFpaGivce0Yuth3dG/oyTVCD1V72fdlB6WE5YllqUDABC2XNXciW3VQpJ04pezjm3hWHenOgIPAABhqLaaO7ZfzsqQNDm7uzrHt1ZC2+iwXYp+IQIPAABhpq6aO4bO38J6e8NBfTX9+rAPOlWYwwMAQJhp6jV3XCHwAAAQZpp6zR1XCDwAAISZpl5zxxUCDwAAYaJqCbrVdlrtW0c22Zo7rjBpGQCAMOBqCborTaHmjisEHgAAQlxtS9BdaQo1d1wh8AAAEMLqWoJepX3rFnr0xktliWkaNXdcIfAAABDC6luCLknHT52VJSZaWV3j/NSr4MOkZQAAQhhL0N1D4AEAIISxBN09BB4AAEJURaWhykpDsS1b1NqmKS5Bd4U5PAAAhCB3lqE31SXorhB4AAAIMe4uQ2+qS9BdIfAAABBC3FmGHtuyhV4adZmu7BLX5Ed2qjCHBwCAEOLOMvQTp88qwmQi7FyAwAMAQAhhGXrDEHgAAAghLENvGAIPAAAhgCehNw6TlgEACHI8Cb3xCDwAAAQxnoTuHQG/pfX444/LZDI5vdLS0hz7z5w5o7y8PMXFxalNmza65ZZbVFpa6nSMAwcOKDc3V61atVJCQoKmTZumc+fO+ftUAADwKnefhP7s7X31t3FX6qvp1xN2ahEUIzyXXnqpVq1a5XjfvPl/ujV58mR9/PHHWrZsmcxmsyZMmKDf/va3+vrrryVJFRUVys3NlcVi0dq1a3XkyBHdc889atGihZ544gm/nwsAAN7Ck9C9JygCT/PmzWWxWGpst9lseu2117R06VJdf/31kqTFixerZ8+eWrduna688kr94x//0Pbt27Vq1SolJiaqb9++mjt3rqZPn67HH39ckZGRNY5bVlamsrIyx3u73e67kwMAoIFYgu49Ab+lJUm7d+9WcnKyunTpolGjRunAgQOSpI0bN+rs2bPKzs52tE1LS1PHjh1VVFQkSSoqKlJGRoYSExMdbXJycmS327Vt2zaX31dQUCCz2ex4paSk+PDsAADwXEWloR9PltXfUCxBd0fAA09mZqaWLFmilStX6uWXX1ZJSYmuvvpqnTx5UlarVZGRkYqNjXX6TGJioqxWqyTJarU6hZ2q/VX7XMnPz5fNZnO8Dh486P0TAwCggVYWH9FVT36muR/vqLMdS9DdF/BbWsOHD3f8uXfv3srMzFSnTp307rvvqmXLlj75zqioKEVFRfnk2AAANIa7q7JYgu6ZgI/wVBcbG6tLLrlEe/bskcViUXl5uU6cOOHUprS01DHnx2Kx1Fi1VfXe1bwgAACClTursqpYzNF6+a7LWJXlpqALPD///LP27t2rpKQkXX755WrRooVWr17t2L9r1y4dOHBAWVlZkqSsrCx99913Onr0qKNNYWGhYmJilJ6e7vf+AwDQUO6sypKkR3N7sgTdQwG/pfXf//3fuummm9SpUycdPnxYs2bNUrNmzTRy5EiZzWaNHTtWU6ZMUfv27RUTE6OJEycqKytLV155pSRp6NChSk9P191336358+fLarVq5syZysvL47YVACCkuLvaKr5tFLexPBTwwHPo0CGNHDlSx44dU4cOHXTVVVdp3bp16tChgyTp2WefVUREhG655RaVlZUpJydHf/7znx2fb9asmVasWKHx48crKytLrVu31ujRozVnzpxAnRIAAB6pqDT0Tclx7S496VZ7VmV5zmQYhju3CsOa3W6X2WyWzWZTTExMoLsDAGhC3H1OlnR+orLFHK2vpl/PCI88+/0d8BEeAACaKk+ek8WqrMYh8AAAEACerMiSeDBoYxF4AAAIAHdXZE24rpsGdYvXgNT2jOw0AoEHAIAAcHdFVvfENjwY1AuCrg4PAADhjudk+R8jPAAA+JG7q7KqVmTxnCzvIPAAAOAnPCcrcAg8AAD4gafPyWJFlncReAAA8ANPnpM1ZlAqIztexqRlAAD8gOdkBRYjPAAA+BDPyQoOBB4AAHykIc/JYlWWbxB4AADwAZ6TFVwIPAAAeBnPyQo+BB4AALyM52QFHwIPAABeVFFp6Os9P7rVludk+Q+BBwAAL/FkkrLEiix/IvAAAOAFnk5SZkWWf1F4EACARvJkkjIrsgKDER4AABqoqqjg13t+cPs2FiuyAoPAAwBAA3g6X0eSJlzXVZN/1YORnQAg8AAA4CFP5utcaFC3DoSdACHwAADgAU+LCkpMUg4GTFoGAMAD7hYVrMIk5eDACA8AAG6omqD89+IjHn2OScrBgcADAEA9GjZBmcdGBBMCDwAAdfB0gnLVfJ3Jv7qEoBNEmMMDAEAtPJ2gzHyd4MUIDwAALlRUGlrydYlHt7GYrxO8CDwAAFTj6Zyde7I6aXivJObrBDECDwAAF2hIUcHhvZKU1TXOZ31C4wV8Dk9BQYH69++vtm3bKiEhQSNGjNCuXbuc2lx77bUymUxOr4ceesipzYEDB5Sbm6tWrVopISFB06ZN07lz5/x5KgCAENeQOTtJFBQMCQEf4fniiy+Ul5en/v3769y5c3rkkUc0dOhQbd++Xa1bt3a0GzdunObMmeN436pVK8efKyoqlJubK4vForVr1+rIkSO655571KJFCz3xxBN+PR8AQOhpyENAmaAcWkyGYXj6KBCf+uGHH5SQkKAvvvhCgwcPlnR+hKdv375auHChy8/8/e9/14033qjDhw8rMTFRkrRo0SJNnz5dP/zwgyIjI53al5WVqayszPHebrcrJSVFNptNMTExvjkxAEBQakiNHen8yA4TlAPLbrfLbDa79fs74Le0qrPZbJKk9u2dhwffeustxcfHq1evXsrPz9cvv/zi2FdUVKSMjAxH2JGknJwc2e12bdu2rcZ3FBQUyGw2O14pKSk+OhsAQDCrmq/jadh5NLenvpp+PWEnhAT8ltaFKisrNWnSJA0aNEi9evVybL/zzjvVqVMnJScna+vWrZo+fbp27dql9957T5JktVqdwo4kx3ur1Vrje/Lz8zVlyhTH+6oRHgBA09GYh4COGZTKbawQE1SBJy8vT8XFxfrqq6+ctj/wwAOOP2dkZCgpKUlDhgzR3r171bVrV4+/JyoqSlFRUY3uLwAgNDWkxg5zdkJb0ASeCRMmaMWKFfryyy918cUX19k2MzNTkrRnzx517dpVFotF33zzjVOb0tJSSZLFYvFNhwEAIamhc3YoKhjaAh54DMPQxIkT9f7772vNmjVKTU2t9zNbtmyRJCUlnf9Hl5WVpT/96U86evSoEhISJEmFhYWKiYlRenq6z/oOAAgtDamxw0NAw0PAA09eXp6WLl2qDz74QG3btnXMuTGbzWrZsqX27t2rpUuX6oYbblBcXJy2bt2qyZMna/Dgwerdu7ckaejQoUpPT9fdd9+t+fPny2q1aubMmcrLy+PWFQA0cVVLzq2205r78Q4eAtpEBXxZusnk+h/R4sWLNWbMGB08eFB33XWXiouLderUKaWkpOg3v/mNZs6c6bQEbf/+/Ro/frzWrFmj1q1ba/To0Zo3b56aN68/03myrA0AEDoaevuq6jfTy3ddxi2sIObJ7++AB55gQOABgPDTkNtXVaixExo8+f0d8FtaAAB4W0OWnFd5NLcny87DEIEHABBWGrLkXKLGTrgj8AAAwkZj5+xQYyd8EXgAAGGhMXN2qLET/gg8AICQ1dAl55LUvnULPXrjpbLERFNjpwkg8AAAQlJjb1898ZsMRnSaEAIPACDkcPsKniLwAABCRkWloXV7j2nG/37HknN4hMADAAgJDb2FJbHkHAQeAECQqpqQfPTkGe378RctXPWvBo3qsOQcEoEHABCEGjOaUx1zdiAReAAAQaYxE5KrsOQc1RF4AABBobETkiWWnKN2BB4AQMB56xYWt69QGwIPAMDvvDUhuUpsyxZ6adRlurJLHLev4BKBBwDgV96ckFwVbebdkqFB3eIbfTyELwIPAMCnvD2acyFuYcFdBB4AgM94ezTHkDQ5u7s6x7dWQltWYMF9BB4AgFdVjegUbrfq9a/3ee24jOagMQg8AIAGu/B2VULbaP10qlxzP/bOiE4VJiTDGwg8AIAG8ebtKleYkAxvIvAAANzmq9tVrnALC95E4AEAuOSP21VVmJAMXyPwAAAk1Vw+/rdvDshq983tquoYzYGvEXgAoAny5+hNdYzmIBAIPADQBARy9KY6RnMQCAQeAAgzgRy9qcvYQZ2VnW5hNAcBQeABgBBSPcxc3qmdNu7/KejCzYWSGNFBECDwAECQaEiYiTBJld56MJUXJJmj9WhuT7VrHeXoNyM6CAYEHgAhpXooGJDaXpLqDAq+bOOtYzc0zARL2OF2FYIdgQdAo9U3MuGtMOFqsm1sqxaSpBO/nHVsqx4UfNnGW8d2JVjCTF24XYVQEVaB56WXXtKCBQtktVrVp08fvfDCCxowYECguwWENG/cZvFmmKjO1b7qQcGXbbx17FDA7SqEsrAJPO+8846mTJmiRYsWKTMzUwsXLlROTo527dqlhISEQHcPCEq+mjPiyzAB/7HERGnkgI7UykFYMBmGEYL/nVFTZmam+vfvrxdffFGSVFlZqZSUFE2cOFEzZsxwaltWVqaysjLHe7vdrpSUFNlsNsXExPi134C/uLNUOdgmwMJ/GL1BKLLb7TKbzW79/g6LEZ7y8nJt3LhR+fn5jm0RERHKzs5WUVFRjfYFBQWaPXu2P7sI+FVD67AQdpoORm/Q1IRF4Pnxxx9VUVGhxMREp+2JiYnauXNnjfb5+fmaMmWK433VCA8QqoKpii6CD6M3QJgEHk9FRUUpKioq0N0AGiRYq+giMKrfhiTcAK6FReCJj49Xs2bNVFpa6rS9tLRUFoslQL0CvIPRm6bLnTDjank/4QaoKSwCT2RkpC6//HKtXr1aI0aMkHR+0vLq1as1YcKEwHYO8ACjN54Llzo8jQkzWV3jamwD4CwsAo8kTZkyRaNHj9YVV1yhAQMGaOHChTp16pTuvffeQHcNcMvK4iOa/VF4hBtfhglXk22l0K+0TJgBfCtslqVL0osvvugoPNi3b189//zzyszMrPdznixrA7ypakSncLtVr3+9L9DdcUtDRyYk74QJbtkAqOLJ7++wCjwNReCBP4TC7SrmjAAIJU2uDg8Q7ILxdhVzRgA0JQQewEeC6XaVJ0uVCTMAwhGBB/CBQI/oUEUXAJwReAAvqF4rZ+Gqf8lfk+MoNAcA9SPwAI3k79EcRm8AwHMEHsBD/hzNYfQGALyDwAN4wF+jOWMHdVZ2uoVwAwBeQuAB3LSy+IjGv7nJp3NzkszRmnVTuob1SvLhtwBA00PgAepRUWlo3d5jmvG/33k17HC7CgD8h8AD1MEXt7C4XQUA/kfgAS7gywnJ3K4CgMAh8AD/x5ujOSZJhqTJ2d1ZPg4AQYDAA8j7E5ItjOYAQFAh8KBJ88aEZEZzACD4EXjQZHnrFhajOQAQ/Ag8aJK8cQsrtmULvTTqMl3ZJY7RHAAIcgQeNBlVK7CsttOa+/GORt3CkqR5t2RoULd4b3UPAOBDBB40Cd5cgcUtLAAIPQQehL3G3L5iQjIAhAcCD8JaRaWh2R9tb/DtK0ZzACA8EHgQtioqDS35uqRBt7GYkAwA4YXAg7DU0Dk7TEgGgPBE4EHYacycHW5hAUB4IvAgLDRmyXn71i306I2XyhLDhGQACFcEHoS8xt6+euI3GYzoAECYI/AgpHH7CgDgDgIPQlZjlpw/mttTYwalcvsKAJoIAg9CUkOXnJt0fmSHsAMATQuBByGnsXN2Zt2UTtgBgCaGwIOQwpwdAEBDEHgQMhoyZ4cl5wAASYoI1Bfv27dPY8eOVWpqqlq2bKmuXbtq1qxZKi8vd2pjMplqvNatW+d0rGXLliktLU3R0dHKyMjQJ5984u/TgQ9VVBoq2ntMzxbucvs2lun/Xk/8JkO/6XeRsrryiAgAaMoCNsKzc+dOVVZW6pVXXlG3bt1UXFyscePG6dSpU3rqqaec2q5atUqXXnqp431cXJzjz2vXrtXIkSNVUFCgG2+8UUuXLtWIESO0adMm9erVy2/nA99o6Hwdbl8BAC5kMgyjoQ+S9roFCxbo5Zdf1r///W9J50d4UlNTtXnzZvXt29flZ26//XadOnVKK1ascGy78sor1bdvXy1atMjlZ8rKylRWVuZ4b7fblZKSIpvNppiYGO+dEBqlofN1WHIOAE2D3W6X2Wx26/d3wG5puWKz2dS+ffsa22+++WYlJCToqquu0ocffui0r6ioSNnZ2U7bcnJyVFRUVOv3FBQUyGw2O14pKSneOQF4TUPm65gkJbHkHADgQtAEnj179uiFF17Qgw8+6NjWpk0bPf3001q2bJk+/vhjXXXVVRoxYoRT6LFarUpMTHQ6VmJioqxWa63flZ+fL5vN5ngdPHjQ+yeEBmnIfB2JJecAgLp5fQ7PjBkz9OSTT9bZZseOHUpLS3O8//777zVs2DDddtttGjdunGN7fHy8pkyZ4njfv39/HT58WAsWLNDNN9/c4D5GRUUpKiqqwZ+HbzR0vo7EnB0AQN28HnimTp2qMWPG1NmmS5cujj8fPnxY1113nQYOHKhXX3213uNnZmaqsLDQ8d5isai0tNSpTWlpqSwWi2cdR0A1dL7OhOu6aVC3eJacAwDq5PXA06FDB3Xo0MGttt9//72uu+46XX755Vq8eLEiIuq/w7ZlyxYlJf3nv+KzsrK0evVqTZo0ybGtsLBQWVlZHvcdgdHQ+ToWc7Qm/+oSgg4AoF4BW5b+/fff69prr1WnTp301FNP6YcffnDsqxqdeeONNxQZGal+/fpJkt577z29/vrr+utf/+po+/DDD+uaa67R008/rdzcXL399tv69ttv3RotQnD4puQ483UAAD4VsMBTWFioPXv2aM+ePbr44oud9l24Un7u3Lnav3+/mjdvrrS0NL3zzju69dZbHfsHDhyopUuXaubMmXrkkUfUvXt3LV++nBo8IaKi0tDXe3706DPM1wEAeCqo6vAEiifr+OE9nk5SZr4OAOBCnvz+5llaCAhPJikzXwcA0FhBU4cHTYcnk5SZrwMA8AZGeOA3FZWGvik5rq/3/OD2bSzm6wAAvIHAA79oSFHBCdd11eRf9WBkBwDQaAQe+FxDiwoO6taBsAMA8AoCD3yqMUUFB6TWfJAsAAANwaRl+BRFBQEAwYARHvgMRQUBAMGCwAOfoKggACCYEHjgdRQVBAAEG+bwwKsoKggACEaM8MCrPJmkzHwdAIC/EHjgVUdPujtnh6KCAAD/IfDAK6oeG7G79KRb7SkqCADwJwIPGs2TFVkUFQQABAKBB43i6YosiUnKAAD/I/CgwTx9bASTlAEAgULgQYO5uyKLooIAgEAj8KDB3F2R1T2xjbK6xvm4NwAA1I7Cg2iQikpDP54sc6ttQttoH/cGAIC6McIDj7m7KosVWQCAYEHggUfcXZXFiiwAQDAh8MBtnqzKYkUWACCYEHjgNndXZT2a21NjBqUysgMACBpMWobb3F2VFd82irADAAgqjPCgXp4+J4tVWQCAYEPgQZ14ThYAIBwQeFArnpMFAAgXBB64xHOyAADhhMADl3hOFgAgnBB44BLPyQIAhJOALkvv3LmzTCaT02vevHlObbZu3aqrr75a0dHRSklJ0fz582scZ9myZUpLS1N0dLQyMjL0ySef+OsUwhLPyQIAhJuAj/DMmTNH48aNc7xv27at4892u11Dhw5Vdna2Fi1apO+++0733XefYmNj9cADD0iS1q5dq5EjR6qgoEA33nijli5dqhEjRmjTpk3q1auX388n1PGcLABAOAp44Gnbtq0sFovLfW+99ZbKy8v1+uuvKzIyUpdeeqm2bNmiZ555xhF4nnvuOQ0bNkzTpk2TJM2dO1eFhYV68cUXtWjRIpfHLSsrU1nZf0Yw7Ha7l88qNPGcLABAuAp4peV58+YpLi5O/fr104IFC3Tu3DnHvqKiIg0ePFiRkZGObTk5Odq1a5d++uknR5vs7GynY+bk5KioqKjW7ywoKJDZbHa8UlJSvHxWocfT52S9fNdlrMgCAISMgI7w/P73v9dll12m9u3ba+3atcrPz9eRI0f0zDPPSJKsVqtSU1OdPpOYmOjY165dO1mtVse2C9tYrdZavzc/P19TpkxxvLfb7U0+9PCcLABAOPN64JkxY4aefPLJOtvs2LFDaWlpTqGjd+/eioyM1IMPPqiCggJFRUV5u2sOUVFRPj1+KOI5WQCAcOb1wDN16lSNGTOmzjZdunRxuT0zM1Pnzp3Tvn371KNHD1ksFpWWljq1qXpfNe+ntja1zQuCM56TBQBoCrweeDp06KAOHTo06LNbtmxRRESEEhISJElZWVn6wx/+oLNnz6pFixaSpMLCQvXo0UPt2rVztFm9erUmTZrkOE5hYaGysrIadyJNAM/JAgA0FQGbtFxUVKSFCxfqn//8p/7973/rrbfe0uTJk3XXXXc5wsydd96pyMhIjR07Vtu2bdM777yj5557zulW2MMPP6yVK1fq6aef1s6dO/X444/r22+/1YQJEwJ1aiGhakWWu2FHYlUWACB0mQzDcPdxSV61adMm/e53v9POnTtVVlam1NRU3X333ZoyZYrT/JqtW7cqLy9PGzZsUHx8vCZOnKjp06c7HWvZsmWaOXOm9u3bp+7du2v+/Pm64YYb3O6L3W6X2WyWzWZTTEyM184xWFVUGrrqyc/cCjuSlMRzsgAAQciT398BCzzBpKkFnqK9xzTyL+vqbcdzsgAAwcyT398BLzwI/+M5WQCApibghQfhf+6utGJFFgAgXBB4mpCKSkNFe4/Jajut9q0jVdtNKpPOz9thRRYAIFxwS6uJ8OShoBIrsgAA4YXA0wS4+1BQ6XytHVZkAQDCDYEnzLnzUND2rVvo0RsvlSUmmhVZAICwROAJc+48FPT4qbOyxESzIgsAELaYtBzm3F2C7m47AABCEYEnzLEEHQAAAk/YG5DaXknmaJagAwCaNAJPGKuoNPRNyXEN72WRIdUIPSxBBwA0FUxaDlOu6u6YTNKFT05jCToAoKkg8ISh2uruVP7fhrGDOis73cISdABAk8EtrTBTX90dk6RPiq2EHQBAk0LgCTP11d0xJB2xndE3Jcf91ykAAAKMwBNmqLsDAEBNBJ4wQ90dAABqIvCEiYpKQ0V7j8lqO632rSOpuwMAwAVYpRUGXC1Bd4W6OwCAporAE+JqW4LuCnV3AABNFYEnhNW3BF2S2rduoUdvvFSWmGiWogMAmiwCTwirbwm6JB0/dVaWmGhldY3zU68AAAg+TFoOYSxBBwDAPQSeEMYSdAAA3EPgCWEDUtsryRzNEnQAAOpB4AlhzSJMmnVTuiTVCD0sQQcA4D8IPCGqqtBg2blKTcq+RIkxzretLOZovXzXZSxBBwBArNIKSa4KDVpiojQ5u7s6x7dWQluWoAMAcCFGeEJMVaHB6svRS+1lWrhqt6KaRyiraxxhBwCACxB4QkhdhQarts3+aLsqKt2puwwAQNNB4Akh9RUaNCQdsZ3RNyXH/dcpAABCQMACz5o1a2QymVy+NmzYIEnat2+fy/3r1q1zOtayZcuUlpam6OhoZWRk6JNPPgnEKfkchQYBAGiYgAWegQMH6siRI06v+++/X6mpqbriiiuc2q5atcqp3eWXX+7Yt3btWo0cOVJjx47V5s2bNWLECI0YMULFxcX+PiWfo9AgAAANE7BVWpGRkbJYLI73Z8+e1QcffKCJEyfKZHKecBsXF+fU9kLPPfechg0bpmnTpkmS5s6dq8LCQr344otatGiRy8+UlZWprKzM8d5utzf2dPyiqtCg1XbG5Twek84vR6fQIAAAzoJmDs+HH36oY8eO6d57762x7+abb1ZCQoKuuuoqffjhh077ioqKlJ2d7bQtJydHRUVFtX5XQUGBzGaz45WSkuKdk/CRqpo7K7Ye1h39O0qi0CAAAJ4Imjo8r732mnJycnTxxRc7trVp00ZPP/20Bg0apIiICP3v//6vRowYoeXLl+vmm2+WJFmtViUmJjodKzExUVartdbvys/P15QpUxzv7XZ70IYeVzV3Ylu1kCSd+OWsY5vFHK1ZN6VTaBAAABe8HnhmzJihJ598ss42O3bsUFpamuP9oUOH9Omnn+rdd991ahcfH+8UTPr376/Dhw9rwYIFjsDTEFFRUYqKimrw5/2lquZO9dtXtl/OypAoNAgAgJu8HnimTp2qMWPG1NmmS5cuTu8XL16suLg4t0JMZmamCgsLHe8tFotKS0ud2pSWltY65ydU1FdzxyTp7Q0H9dX06wk6AADUw+uBp0OHDurQoYPb7Q3D0OLFi3XPPfeoRYsW9bbfsmWLkpL+c9smKytLq1ev1qRJkxzbCgsLlZWV5VG/g40nNXeyusb5r2MAAISggM/h+eyzz1RSUqL777+/xr433nhDkZGR6tevnyTpvffe0+uvv66//vWvjjYPP/ywrrnmGj399NPKzc3V22+/rW+//Vavvvqq387BF6i5AwCA9wQ88Lz22msaOHCg05yeC82dO1f79+9X8+bNlZaWpnfeeUe33nqrY//AgQO1dOlSzZw5U4888oi6d++u5cuXq1evXv46BZ+g5g4AAN5jMgyjyT94yW63y2w2y2azKSYmJtDdkXR+Ds9VT35Wb80d5vAAAJoqT35/B00dHjhrFmHSrJvSJVFzBwCAxiLwBKGqQoNl5yo1KfsSJcY437aymKP18l2XUXMHAAA3BXwOD5y5KjRoiYmi5g4AAI3ACE8QqSo0WH05eqm9TAtX7VZU8whldY0j7AAA4CECT5Cor9CgJM3+aLsqKpv8HHMAADxG4AkSnhQaBAAAniHwBAkKDQIA4DsEniBBoUEAAHyHwBMkBqS2V5I5ukbNnSomSUnm8yu0AACAZwg8QYJCgwAA+A6BJ8Cqigx+sOV7mVtG6qU7L5PFTKFBAAC8icKDAeSqyGCSOVqP5vZUu9ZROnryDIUGAQDwAkZ4AqS2IoNW2xnlLd0s2+ly/brvRRQaBADACwg8AUCRQQAA/IvAEwAUGQQAwL8IPAFAkUEAAPyLwBMAFBkEAMC/CDwBQJFBAAD8i8ATABQZBADAvwg8ATKsV5JevosigwAA+AOFB/2sotLQNyXHHUUFv5h2nTbu/4kigwAA+BCBx49qq6w866Z0/brvRQHsGQAA4Y1bWn5SV2Xl8W9u0sriIwHqGQAA4Y/A4wdUVgYAILAIPH5AZWUAAAKLwOMHVFYGACCwCDx+QGVlAAACi8DjB1RWBgAgsAg8fkBlZQAAAovA40MVlYaK9h7TB1u+l7llpF66k8rKAAAEAoUHfaS2IoOP5vZUu9ZRVFYGAMCPfDbC86c//UkDBw5Uq1atFBsb67LNgQMHlJubq1atWikhIUHTpk3TuXPnnNqsWbNGl112maKiotStWzctWbKkxnFeeuklde7cWdHR0crMzNQ333zjgzNyX11FBvOWbpbtdLl+3fciZXWNI+wAAOAHPgs85eXluu222zR+/HiX+ysqKpSbm6vy8nKtXbtWb7zxhpYsWaLHHnvM0aakpES5ubm67rrrtGXLFk2aNEn333+/Pv30U0ebd955R1OmTNGsWbO0adMm9enTRzk5OTp69KivTq1OFBkEACD4mAzD8Olv3iVLlmjSpEk6ceKE0/a///3vuvHGG3X48GElJiZKkhYtWqTp06frhx9+UGRkpKZPn66PP/5YxcXFjs/dcccdOnHihFauXClJyszMVP/+/fXiiy9KkiorK5WSkqKJEydqxowZLvtUVlamsrIyx3u73a6UlBTZbDbFxMQ06nyL9h7TyL+sq7fd38ZdqayucY36LgAAmjK73S6z2ezW7++ATVouKipSRkaGI+xIUk5Ojux2u7Zt2+Zok52d7fS5nJwcFRUVSTo/irRx40anNhEREcrOzna0caWgoEBms9nxSklJ8dp5UWQQAIDgE7DAY7VancKOJMd7q9VaZxu73a7Tp0/rxx9/VEVFhcs2VcdwJT8/XzabzfE6ePCgN05JEkUGAQAIRh4FnhkzZshkMtX52rlzp6/66jVRUVGKiYlxenkLRQYBAAg+Hi1Lnzp1qsaMGVNnmy5durh1LIvFUmM1VWlpqWNf1f9WbbuwTUxMjFq2bKlmzZqpWbNmLttUHcPfqooMjn9zk0yS0+RligwCABAYHo3wdOjQQWlpaXW+IiMj3TpWVlaWvvvuO6fVVIWFhYqJiVF6erqjzerVq50+V1hYqKysLElSZGSkLr/8cqc2lZWVWr16taNNIAzrlaSX76LIIAAAwcJnhQcPHDig48eP68CBA6qoqNCWLVskSd26dVObNm00dOhQpaen6+6779b8+fNltVo1c+ZM5eXlKSoqSpL00EMP6cUXX9T/+3//T/fdd58+++wzvfvuu/r4448d3zNlyhSNHj1aV1xxhQYMGKCFCxfq1KlTuvfee311am4Z1itJv0q36JuS4xQZBAAg0AwfGT16tKHzd3ScXp9//rmjzb59+4zhw4cbLVu2NOLj442pU6caZ8+edTrO559/bvTt29eIjIw0unTpYixevLjGd73wwgtGx44djcjISGPAgAHGunXrPOqrzWYzJBk2m60hpwoAAALAk9/fPq/DEwo8WccPAACCQ0jU4QEAAPAXAg8AAAh7BB4AABD2CDwAACDsEXgAAEDYI/AAAICwR+ABAABhj8ADAADCns8eLRFKqmov2u32APcEAAC4q+r3tjs1lAk8kk6ePClJSklJCXBPAACAp06ePCmz2VxnGx4tofNPWD98+LDatm0rk8m7D/e02+1KSUnRwYMHw/KxFeF+flL4nyPnF/rC/RzD/fyk8D9HX52fYRg6efKkkpOTFRFR9ywdRngkRURE6OKLL/bpd8TExITlP+Iq4X5+UvifI+cX+sL9HMP9/KTwP0dfnF99IztVmLQMAADCHoEHAACEPQKPj0VFRWnWrFmKiooKdFd8ItzPTwr/c+T8Ql+4n2O4n58U/ucYDOfHpGUAABD2GOEBAABhj8ADAADCHoEHAACEPQIPAAAIewQeAAAQ9gg8jfSnP/1JAwcOVKtWrRQbG+uyzYEDB5Sbm6tWrVopISFB06ZN07lz5+o87vHjxzVq1CjFxMQoNjZWY8eO1c8//+yDM/DMmjVrZDKZXL42bNhQ6+euvfbaGu0feughP/bcfZ07d67R13nz5tX5mTNnzigvL09xcXFq06aNbrnlFpWWlvqpx57Zt2+fxo4dq9TUVLVs2VJdu3bVrFmzVF5eXufngvkavvTSS+rcubOio6OVmZmpb775ps72y5YtU1pamqKjo5WRkaFPPvnETz31XEFBgfr376+2bdsqISFBI0aM0K5du+r8zJIlS2pcq+joaD/12DOPP/54jb6mpaXV+ZlQun6S6/9PMZlMysvLc9k+2K/fl19+qZtuuknJyckymUxavny5037DMPTYY48pKSlJLVu2VHZ2tnbv3l3vcT39OfYUgaeRysvLddttt2n8+PEu91dUVCg3N1fl5eVau3at3njjDS1ZskSPPfZYnccdNWqUtm3bpsLCQq1YsUJffvmlHnjgAV+cgkcGDhyoI0eOOL3uv/9+paam6oorrqjzs+PGjXP63Pz58/3Ua8/NmTPHqa8TJ06ss/3kyZP10UcfadmyZfriiy90+PBh/fa3v/VTbz2zc+dOVVZW6pVXXtG2bdv07LPPatGiRXrkkUfq/WwwXsN33nlHU6ZM0axZs7Rp0yb16dNHOTk5Onr0qMv2a9eu1ciRIzV27Fht3rxZI0aM0IgRI1RcXOznnrvniy++UF5entatW6fCwkKdPXtWQ4cO1alTp+r8XExMjNO12r9/v5967LlLL73Uqa9fffVVrW1D7fpJ0oYNG5zOr7CwUJJ022231fqZYL5+p06dUp8+ffTSSy+53D9//nw9//zzWrRokdavX6/WrVsrJydHZ86cqfWYnv4cN4gBr1i8eLFhNptrbP/kk0+MiIgIw2q1Ora9/PLLRkxMjFFWVubyWNu3bzckGRs2bHBs+/vf/26YTCbj+++/93rfG6O8vNzo0KGDMWfOnDrbXXPNNcbDDz/sn041UqdOnYxnn33W7fYnTpwwWrRoYSxbtsyxbceOHYYko6ioyAc99L758+cbqampdbYJ1ms4YMAAIy8vz/G+oqLCSE5ONgoKCly2/6//+i8jNzfXaVtmZqbx4IMP+rSf3nL06FFDkvHFF1/U2qa2/z8KRrNmzTL69OnjdvtQv36GYRgPP/yw0bVrV6OystLl/lC6fpKM999/3/G+srLSsFgsxoIFCxzbTpw4YURFRRl/+9vfaj2Opz/HDcEIj48VFRUpIyNDiYmJjm05OTmy2+3atm1brZ+JjY11GjHJzs5WRESE1q9f7/M+e+LDDz/UsWPHdO+999bb9q233lJ8fLx69eql/Px8/fLLL37oYcPMmzdPcXFx6tevnxYsWFDnLciNGzfq7Nmzys7OdmxLS0tTx44dVVRU5I/uNprNZlP79u3rbRds17C8vFwbN250+ruPiIhQdnZ2rX/3RUVFTu2l8z+ToXStJNV7vX7++Wd16tRJKSkp+vWvf13r/98Eg927dys5OVldunTRqFGjdODAgVrbhvr1Ky8v15tvvqn77rtPJpOp1nahdP0uVFJSIqvV6nSNzGazMjMza71GDfk5bgielu5jVqvVKexIcry3Wq21fiYhIcFpW/PmzdW+fftaPxMor732mnJycup92vydd96pTp06KTk5WVu3btX06dO1a9cuvffee37qqft+//vf67LLLlP79u21du1a5efn68iRI3rmmWdctrdarYqMjKwxhysxMTHorpcre/bs0QsvvKCnnnqqznbBeA1//PFHVVRUuPwZ27lzp8vP1PYzGQrXqrKyUpMmTdKgQYPUq1evWtv16NFDr7/+unr37i2bzaannnpKAwcO1LZt2+r9WfW3zMxMLVmyRD169NCRI0c0e/ZsXX311SouLlbbtm1rtA/l6ydJy5cv14kTJzRmzJha24TS9auu6jp4co0a8nPcEAQeF2bMmKEnn3yyzjY7duyod2JdKGnIOR86dEiffvqp3n333XqPf+H8o4yMDCUlJWnIkCHau3evunbt2vCOu8mT85syZYpjW+/evRUZGakHH3xQBQUFQf2cm4Zcw++//17Dhg3TbbfdpnHjxtX52UBfQ0h5eXkqLi6uc46LJGVlZSkrK8vxfuDAgerZs6deeeUVzZ0719fd9Mjw4cMdf+7du7cyMzPVqVMnvfvuuxo7dmwAe+Ybr732moYPH67k5ORa24TS9QslBB4Xpk6dWmf6lqQuXbq4dSyLxVJjpnnV6h2LxVLrZ6pP1Dp37pyOHz9e62caqyHnvHjxYsXFxenmm2/2+PsyMzMlnR9d8Mcvy8Zc08zMTJ07d0779u1Tjx49auy3WCwqLy/XiRMnnEZ5SktLfXa9XPH0HA8fPqzrrrtOAwcO1Kuvvurx9/n7GroSHx+vZs2a1VgRV9ffvcVi8ah9sJgwYYJjAYOn/5XfokUL9evXT3v27PFR77wnNjZWl1xySa19DdXrJ0n79+/XqlWrPB4VDaXrV3UdSktLlZSU5NheWlqqvn37uvxMQ36OG8Rrs4GauPomLZeWljq2vfLKK0ZMTIxx5swZl8eqmrT87bffOrZ9+umnQTVpubKy0khNTTWmTp3aoM9/9dVXhiTjn//8p5d75n1vvvmmERERYRw/ftzl/qpJy//zP//j2LZz586gnrR86NAho3v37sYdd9xhnDt3rkHHCJZrOGDAAGPChAmO9xUVFcZFF11U56TlG2+80WlbVlZW0E56raysNPLy8ozk5GTjX//6V4OOce7cOaNHjx7G5MmTvdw77zt58qTRrl0747nnnnO5P9Su34VmzZplWCwW4+zZsx59Lpivn2qZtPzUU085ttlsNrcmLXvyc9ygvnrtSE3U/v37jc2bNxuzZ8822rRpY2zevNnYvHmzcfLkScMwzv9D7dWrlzF06FBjy5YtxsqVK40OHToY+fn5jmOsX7/e6NGjh3Ho0CHHtmHDhhn9+vUz1q9fb3z11VdG9+7djZEjR/r9/GqzatUqQ5KxY8eOGvsOHTpk9OjRw1i/fr1hGIaxZ88eY86cOca3335rlJSUGB988IHRpUsXY/Dgwf7udr3Wrl1rPPvss8aWLVuMvXv3Gm+++abRoUMH45577nG0qX5+hmEYDz30kNGxY0fjs88+M7799lsjKyvLyMrKCsQp1OvQoUNGt27djCFDhhiHDh0yjhw54nhd2CZUruHbb79tREVFGUuWLDG2b99uPPDAA0ZsbKxjZeTdd99tzJgxw9H+66+/Npo3b2489dRTxo4dO4xZs2YZLVq0ML777rtAnUKdxo8fb5jNZmPNmjVO1+qXX35xtKl+jrNnzzY+/fRTY+/evcbGjRuNO+64w4iOjja2bdsWiFOo09SpU401a9YYJSUlxtdff21kZ2cb8fHxxtGjRw3DCP3rV6WiosLo2LGjMX369Br7Qu36nTx50vG7TpLxzDPPGJs3bzb2799vGIZhzJs3z4iNjTU++OADY+vWrcavf/1rIzU11Th9+rTjGNdff73xwgsvON7X93PsDQSeRho9erQhqcbr888/d7TZt2+fMXz4cKNly5ZGfHy8MXXqVKeE//nnnxuSjJKSEse2Y8eOGSNHjjTatGljxMTEGPfee68jRAWDkSNHGgMHDnS5r6SkxOnv4MCBA8bgwYON9u3bG1FRUUa3bt2MadOmGTabzY89ds/GjRuNzMxMw2w2G9HR0UbPnj2NJ554wmk0rvr5GYZhnD592vjd735ntGvXzmjVqpXxm9/8xilABJPFixe7/Dd74YBvqF3DF154wejYsaMRGRlpDBgwwFi3bp1j3zXXXGOMHj3aqf27775rXHLJJUZkZKRx6aWXGh9//LGfe+y+2q7V4sWLHW2qn+OkSZMcfx+JiYnGDTfcYGzatMn/nXfD7bffbiQlJRmRkZHGRRddZNx+++3Gnj17HPtD/fpV+fTTTw1Jxq5du2rsC7XrV/U7q/qr6hwqKyuNRx991EhMTDSioqKMIUOG1DjvTp06GbNmzXLaVtfPsTeYDMMwvHeDDAAAIPhQhwcAAIQ9Ag8AAAh7BB4AABD2CDwAACDsEXgAAEDYI/AAAICwR+ABAABhj8ADAADCHoEHAACEPQIPAAAIewQeAAAQ9v4/OOxfcWjWApkAAAAASUVORK5CYII=",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  1\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  2\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  3\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  4\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  5\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  6\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_29557/2008396885.py:34: RuntimeWarning: divide by zero encountered in log\n",
-      "  (lambda x: np.log(x+10), 'log(x+10)'),\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  7\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  8\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  9\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  10\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  11\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  12\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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eew733XcfXnjhBbz22mvYvHkz/ubmuHHfvn0AgFtuuUXyPhORuoSEhGD06NHYsmWL21hkt9sxYsQI7N27F4WFhc7HKysr8fbbbyMuLs45hVbjbLiwsDAkJCTAbrejtrYWAJDy6/mnu/d64403sGvXLrz99ttYuHAhbrnlFmRmZrqtW7Jv3z7069cPer3e95UnIvKjPXv24LXXXsP06dPxzDPP4LnnnsOKFSvw5Zdfyt01CkJNq3kR+erCBeCaa4D77wduvBHo0AEoKHDUDmluGqvRox21T555xpFd0qePYxCjrMzxvK/zo959N/DnPwM2GxAV5XjMbgcee8yRhbJqleOxJ55wDGo8/bQjcyQ21rf3A4CjRx2v8eCDjvUICXEM3OTlAXFxjvdo6OxZYP/+loveExH56KWXXkJxcTF27NiBjh07YsCAAZg7dy7mzJmD+++/HyNGjJC7i0SkYRcuXMA111yD+++/HzfeeCM6dOiAgoICfP3113hdzPSnfnT33Xfjz3/+M2w2G6J+PW602+147LHH0K5dO6z69bjxiSeewCeffIKnn34aaWlpiG1w3Lh9+3Zce+21GDRoUED7TkTqsGjRInz22Wf43e9+h8cffxx9+/aFxWLBxx9/jK+++gqzZs3C+++/jzvvvBNPPfUUoqOjsX79epSUlOCTTz5BSIjjftg77rgDBoMBqamp6NatGw4fPowVK1Zg5MiR6NixIwCgZ8+eSExMREFBgctc/4cPH8aLL76IiRMnYtSoUQCAdevWYeDAgZg6dSo++ugjZ9va2lp8+eWXmDp1agA/JSKi5lVVVWHChAmIj493Zi3Pnz8fW7ZswaOPPooDBw4gMjJS5l5SMGGmCflP+/aOabmKi4GcHCA7GzhyBHjrLWDGDPfLtGkDbN3qGGhYv94x0BEbeyXTJCLCt7488ogj82Xz5iuPLV8OfPGFY3quhtNhvfuuI7tl8mTf3ktwzTWODJbPPwdmz3as886djqLyX399pdaK4O9/B8LDHXVgiIj8qKioCIsWLcK0adPw+9//3vn4rFmzcPPNN2Py5MkoLy+Xr4NEpHnt27fH1KlTUVxcjJycHGRnZ+PIkSN46623MKO540aJPPLII6irq8PmBseNy5cvxxdffIHVq1e7FJN/9913UV9fj8kNjhvr6+vxySefYPz48SyITEQ+ufrqq7Fnzx7cf//92LBhA5566in87W9/w2233Yb27dujW7du2LVrF4YPH47ly5dj9uzZCAsLw5YtW3DPPfc4X+eJJ57AxYsX8cYbbyArKwubNm3CU089hTyhRuevHnvsMWzZsgWXLl0C4Mj+mzBhAjp37oxly5Y528XHxyM3Nxcff/yxy6DJjh07UFZWhgkTJkj7wRARifTCCy/g2LFjWL9+PSJ+vZYYFhaG9evX49SpU3juuedk7iEFG529ccVEIiXYtAm45x7gq6+u1BPx1qRJwPffA//5j1+75jeDBgG33QYsXSp3T4iIiIg0bdKkSfj+++/xHx+OGzdt2oSHH34Yx48fh9FolKB3RET+VVFRgZ49e+KVV17BpEmTvF5+9OjR0Ol02LhxowS9IyISJy4uDhMnTsS8efO8Wm7dunV49NFHwUvi1BJmmpD8fr27xamuzpEVEhUFJCX5/ro5OY4Mj507W9c/KZhMjum8Zs+WuydEREREmpeTk4Ovv/4aO304blyyZAmmTZvGARMiChp6vR7PP/88Xn31VdTX13u17OHDh/Hpp59i4cKFEvWOiIhIfqxpQvJ78knHwElKiqNg/N//DuzaBSxa5Kg/4qtrrwWqqvzXT3/KyAAuXpS7F0REREQE4Nprr0WVj8eNDQszExEFi5kzZ2LmzJleL9e3b19cvnxZgh4REREpBwdNSH7DhjkKxX/6qWOQ4/rrHZkm06bJ3TMiIiIiIiIiIiIi0hDWNCEiIiIiIiIiIiIiIgJrmhAREREREREREREREQHgoAkREREREREREREREREAFdY0qa+vx+nTp9GxY0fodDq5u0NECma323HhwgXExsYiJEQdY8iMgUQkhhrjH8AYSETiqDEGMv4RkViMgUSkVd7EP9UNmpw+fRrdu3eXuxtEFEROnTqFa665Ru5u+AVjIBF5Q03xD2AMJCLvqCkGMv4RkbcYA4lIq8TEP9UNmnTs2BGAY+WjoqJk7g0RKZnNZkP37t2dcUMNGAOJSAw1xj+AMZCIxFFjDGT8IyKxGAOJSKu8iX+qGzQR0vCioqIYKIlIFDWl7zIGEpE31BT/AMZAIvKOmmIg4x8ReYsxkIi0Skz8U8fkhURERERERERERERERK3EQRMiIiIiIiIiIiIiIiJw0ISIiIiIiIiIiIiIiAiAxDVN/v3vf+PVV1/Fvn37YLFYsHHjRowePbrFZb744gvMmDEDBw8eRPfu3TFnzhxMnDhRym6Sn9XV27G3pAxnL1Shc2Q4oAPO2qpQVlmD6A7h6NrBt8c6tQ9D+S+tew2+l/rfyxAVgSE9otEmRHnzs3qKiXa7HTk5OVizZg3Ky8uRmpqKVatWIT4+Xr5OE5HiNfzd7dpRuTGQiEgKSoyBPA/2P+F7tlZccp4TKPm4X278vLzDz0taSjsPVuLvBhF5T+p9WdJBk8rKStx444147LHHcO+993psX1JSgpEjR2LKlCnYsGEDduzYgT/96U8wGo1IT0+XsqvkJyazBfO3HIKlokrurpCGGfURyBmVgIxEo9xdceEpJr7yyiv4y1/+gvXr16NHjx548cUXkZ6ejkOHDiEiIkKGHhOR0rn73VVqDCQi8jelxkCeB/tXS+eYSvi+lYafl3f4eUlPSefBSv3dICLvBGJf1tntdrtfXsnTG+l0Hu+wmTlzJrZu3Qqz2ex87KGHHkJ5eTlMJpOo97HZbNDr9aioqEBUVFRru01eMJktyMwrQkA2KCIPdABWjUtqMVjKGS8ax0S73Y7Y2Fg888wzePbZZwEAFRUV6NatG9atW4eHHnpI1OsyBhJpx7b9FkzNL2ryuHBvTUsxUK2xQq3rRURNNXfuobQYyPPg1hFzjinmuF8r+Hl5R6ufl1bPg1vzu0FEyhGoY0BF1TQpLCxEWlqay2Pp6ekoLCyUqUckVl29HfO3HOKACSnK/C2HUFcfHFtlSUkJrFarSwzU6/VITk5mDCSiJrbtP41p7zcdMAHg/C0OphhIROSNls49gjEG+nIeXF1dDZvN5vKnNt6cYwbT9y0Vfl7e4eelDIE6D1bb7waRVgVyX1bUoInVakW3bt1cHuvWrRtsNhsuXbrkdhktHCwGg70lZZySixTFDsBSUYW9JWVyd0UUq9UKAG5joPCcO4yBRNpSV2/HmwVHMTX/W7R0HBhsMZCIyBuezj2CLQb6ch6cm5sLvV7v/OvevXsguhpQu0+cF3WOGWzft1S8/bx2Hz8vfacUTOw1DH5e0grUebDY3w1+z0TKFshjQEUNmvhCCweLweDsBQ6YkDKpfdtkDCTSDpPZgtTFO7C04HvRy6g9BhKRNomNbWqOgbNnz0ZFRYXz79SpU3J3ya9MZguyNrjPqGyOtcL9AJMW+PJ5ZeUXwWS2SNQj5bPavIsPWv+8lMbb82Cxvwf8nomUreBQ84OpDfnjGFBRgyYGgwFnzpxxeezMmTOIiopCu3bt3C6j9oPFYNG1I4tUkzIFy7ZpMBgAwG0MFJ5zhzGQSBu27bdgSl4RrLZqr5YLlhhIROQNsbEtWGKgL+fB4eHhiIqKcvlTC2Gu8vJLtV4tt3DrYU1e7PT18yq/VIvMPG1eIDaZLVj46UGvltHy5yWlQJ0Hi/094PdMpFwmswXv7iwV1dYfx4CKGjRJSUnBjh07XB7bvn07UlJSml1GzQeLwWRIj2gY9RHOojtEctMBMOojMKRHtNxdEaVHjx4wGAwuMdBms2HPnj2MgUQa11L9kpYEUwwkIvKGp3OPYDsO9OU8WK1aUyvz58oazV3s9EdtUa3VcRAGmcoqvRtkEmjt85JaoM6Dvb1mxe+ZSFmE3ztP/HkMKOmgycWLF1FcXIzi4mIAjgJPxcXFOHnyJADHyPD48eOd7adMmYITJ07g+eefx3fffYe33noLH330EbKzs6XsJvlBmxAdckYlAAAHTkgxckYloE2IcrbIlmKiTqfD9OnT8dJLL2Hz5s04cOAAxo8fj9jYWIwePVrWfhORPMTWL2mO0mIgEZG/tHTuIfxbzhjI82DftaZWphaLObe2tqjW6sG0dpBJa5+XvyjhPLjh74Yn/J6JlMebul3+OgaUdNDkm2++waBBgzBo0CAAwIwZMzBo0CDMnTsXAGCxWJwHjoBjhHnr1q3Yvn07brzxRrz++ut45513kJ6eLmU3yU8yEo1YNS4JBn1wpMGTehn1EVg1LgkZiUa5u+LCU0x8/vnn8eSTT+Lxxx/HzTffjIsXL8JkMiEigvsUkdb4Ur9EEKID3npYeTGQiMifmjv3MCjgOJDnwb5r7RzkWrvY6a+6PWqu/9NQaweZBFr5vPxFKefBwu9Gp3ahotpvF1k7gYik5U3drsdS4/x2DKiz2+2qugXDZrNBr9ejoqKC09TIpK7ejr0lZTh7oQqdI8MBHXDWVoWyyhpEdwhH1w6+PdapfRjKf2nda/C91P9ehihHGp6YUWU1xgs1rhOR1mzbb8HUfO+n4xK89fAgjBgQ22KbhrGiuLgYr776Kvbt2weLxYKNGze63Nlnt9uRk5ODNWvWoLy8HKmpqVi1ahXi4+OdbcrKyvDkk09iy5YtCAkJwX333Yc333wTHTp0cLbZv38/srKy8PXXX6NLly548skn8fzzz7v06+OPP8aLL76I0tJSxMfHY8mSJRgxYoTodWcMJNKehuceXTuKOw5UY6xQyzoVHj+PMWt2t/p13nxoIO4eeLUfeqRs/vq83p88FCm9YvzQI2X7R/GPePqD4la/TrB/XmqJFw15s047j53D2Hf2iHrd1Qq8GZNIS4QpFcUOXniKz97EirZe9JNIlDYhuqA+gCAiIpKLo37Jtz4ta9RHIGdUgtcndpWVlbjxxhvx2GOP4d57723y/CuvvIK//OUvWL9+PXr06IEXX3wR6enpOHTokPMOwLFjx8JisWD79u2ora3Fo48+iscffxz5+fkAHAend9xxB9LS0rB69WocOHAAjz32GDp16oTHH38cALBr1y6MGTMGubm5uOuuu5Cfn4/Ro0ejqKgIiYmJPn0mRKR+PPdQF6HugLWiqlV1OvxRADYYiPm8QnSA3Q63z+vgyM4Klvo/rdXa7UJrn5daDe0ZA6M+wmPWkQ6O6f6GJxg45S2RDLyZUlGK+KyoQvBEREREWmUyW3yuX5KdFo+vZg7z6U64O++8Ey+99BLuueeeJs/Z7XYsW7YMc+bMwd13340BAwbgb3/7G06fPo1NmzYBAA4fPgyTyYR33nkHycnJuPXWW7F8+XJ88MEHOH36NABgw4YNqKmpwdq1a9GvXz889NBDeOqpp/DGG2843+vNN99ERkYGnnvuOfTt2xcLFy5EUlISVqxY4f0HQkREQalNiA4vjkxo9gI/AHRqH9psHU1/FoANBp7q++gATP5Nj2afB7RVA81TMXAdrmxf7trYAbw4sq9mPi+1ElvfRGvT/REpjbdTKvr794yDJkREREQyq7lcjxc2mr1eTqhf8nRab0lO4EtKSmC1WpGWluZ8TK/XIzk5GYWFhQCAwsJCdOrUCTfddJOzTVpaGkJCQrBnzx5nm9/+9rcICwtztklPT8eRI0fw888/O9s0fB+hjfA+7lRXV8Nms7n8ERFR8DKZLVi49ZDb5wz6CKwel4TF9/YHwEEAwfAEA6an9Ya+UZ0Gob7P7BEJbuv/6NuFYnpaPIYnGALZXVl5GmQCgMX39m+xVuvCrYdhMluk6yQFREaiEZNS40S1tVZckrYzROSW2PpRndqHSlLPjoMmRERERDIymS0YmluAssoar5ddMWYQRgyQbp5lq9VRALNbt24uj3fr1s35nNVqRdeuXV2eb9u2LaKjo13auHuNhu/RXBvheXdyc3Oh1+udf927d/d2FYmISCGEecubu6v0xZF9kZFodBZz5iCA4zO7dcnnWFrwPcov1QIAOrULbZKBmpFoxFczhyE7rbezCHb5pVosLTiKW5d8rqlBAE+DTMI29uJI95kI1ooqZOYVaeozU6s0kbGCA2VE8ig9Vymq3cox0tQe4qAJERERkUy27bdgSl4RyiprvVrO+Ovdtp4Kvqvd7NmzUVFR4fw7deqU3F0iIiIfeJq3XAfHhcu6X+ew5CBA84NMFZdqsazgKLYfcr3pYPshK5Y1GFwRaGkQQOwgU129vdmMJ2Ebnb/lkHN7pODkabo2wc+VNZrZR4iUwmS2YGnB0RbbCFNyDpWoth0HTYiIiIhk4Cj6XuT1cq2pX+Itg8FxB96ZM2dcHj9z5ozzOYPBgLNnz7o8f/nyZZSVlbm0cfcaDd+juTbC8+6Eh4cjKirK5Y+IiIKPp3nL3dUW0PIgQEuDTO4u6nvbXo28GWTyZXuk4ONNbRNA/fsIkVIIv1liSDklJwdNiIiIiAKort6ONwuOel30Xer6Je706NEDBoMBO3bscD5ms9mwZ88epKSkAABSUlJQXl6Offv2Odt8/vnnqK+vR3JysrPNv//9b9TWXrmwtX37dtxwww246qqrnG0avo/QRngfIiJSL7HzlgvttD4I4O1Ffa0PAni7vXi7PVLwEqb7i44MbbGd2vcRIiXZfeK8qALw09N6S3ojIQdNiIiIiALEZLYgdfEOLC343utlpapfcvHiRRQXF6O4uBiAo/h7cXExTp48CZ1Oh+nTp+Oll17C5s2bceDAAYwfPx6xsbEYPXo0AKBv377IyMjA5MmTsXfvXuzcuRPTpk3DQw89hNhYx/RhDz/8MMLCwjBp0iQcPHgQH374Id58803MmDHD2Y+nn34aJpMJr7/+Or777jvMmzcP33zzDaZNm+b3dSYiImXp2tF90e3m2ml9EMDbi/paHwTwdnvxdnuk4JaRaMSLd/UT1bbxtHdE5F8mswVZG8TNxhDXub2kfWkr6asTEREREQBH/ZKp+d5PxxUTGYaX70mU7C6ab775Br///e+d/xYGMiZMmIB169bh+eefR2VlJR5//HGUl5fj1ltvhclkQkTElQsFGzZswLRp03D77bcjJCQE9913H/7yl784n9fr9fjss8+QlZWFwYMHo3Pnzpg7dy4ef/xxZ5tbbrkF+fn5mDNnDl544QXEx8dj06ZNSExMlGS9iSh41dXbsbekDGcvVKFrxwgM6REdsAw8koZQW8BaUeU2G0AHR6HuIT2iAXAQwNuL+lofBPB2e/F2e6TgZ4gSt+2v3VmKIT2iAzJNLpHWCNMois0Rlfo3i4MmRERERBJz1C/51uvloiNDUTj7doS1lS45+LbbboPd3vyhqU6nw4IFC7BgwYJm20RHRyM/P7/F9xkwYAD+85//tNjmgQcewAMPPNByh4lI00xmC+ZvOeRy17hRH4GcUQm8iBXEhNoCmXlF0AEuF0yE4bCG85ZrfRDA24v6Wh8E8HZ78XZ7pOAn7COepgTSwTGV2/AEA79/Ij9qaRrFxgL1m8XpuYiIiIgk5Mgw8a5+iWDRPf0lHTAhIgomzRVy1kLhby0QagsY9K4XuA36CKwal+QyKCZc4GzukqUOjsE0tQ4CNCxg3fgzcHdR39v2auPL9uLN9kjBz5ui8JaKKuw+fl76ThFpiKdpFBsLxG8WM02IiIiIJOJrhkmIDlgxhifkREQCT4WcefevOmQkGjE8weBx+jVmAly5qN8488rQTOaVt+3VxNftRez2SOqQkWjEpNQ4vLuz1GPbrPwiLL6vv6r3G6JAKhBZL6hT+1Asvjcw+x4HTYiIiIj8rK7ejhWfH/Op4DsgXdF3IqJg5U0h55ReMYHrGPmFL3VqtDwIIPD2or6WBwF83V7ahOicMYX1lNQvLcEgatCk/FItMvOKmHVE5Acms0XUfgcAK8ckITW+s7Qd+hUHTYiIiIj8yGS2YN7mg7Daqr1elvPyExG5p/XC32rWmjo1Wh4EEDS8qC9FezVpzfbCekra4Kn+T2PMcCRqHSGT2BOhjsnQAP5+cdCEiIiIyE8c9UuKfFo2Oy0e04bF86SLiMgNrRf+ViuhTk3ji5NCnRoxd3FrcRDAXxkPWsyc8GV78cd2SsGh4VRunjDDkaj1dp84L6qWiR2Bn3aTgyZEREREftDa+iWcjouIqHme7v4V7kBUa+FvNZKiTo0WBgH8lfGgtcwJX7cN1lPSHmEqt1mfHED5pVqP7bcfsnLQhMgHJrMFsz45IKrtY6lxAf9t4qAJERERUSuwfgkRkfRY+Ft9/F2nRguDAP7KeNBa5kRrtg3WU9KmjEQjOkaEYuw7ezy2XbuzFEN6RKtqnyGSWnO/Q80ZnmCQtD/uhAT8HYmIiIhUwmS2IHXxDp8GTEJ0wFsPJ2HEgFgJekZEpD7C3b8GvesUXAZ9hOou8mqBP+vUCBdfGl/cFgYBTGaLT31UEk8ZD4Aj46GuvuVLUP56nWDR2m2D9ZS0a2jPGBj1nqd8FLKN1LLPEEmtpd+hxnRwDHLLkUnMTBMiIiIiH7SmfgnADBMiIl+w8Ld6+KtOjVamT/JXxoOWMif8sW2wnpJ2CRmOUzzUN1HTPkMUCJ5+hxqTK5OYmSZEREREXnLUL/FtwMSoj8DqccwwISLylVDI+e6BVyOlV0xQXwjXMqFOTXPfnti7S70ZBAhm/sp40FLmhD+2DX9tpxScMhKNmJQaJ6qtGvYZokAQu690ah8qayYxB02IiIiIvGAyWzA1/1v4koGfnRaPr2YO4xQyRESkecJd3ACaXJD2pk6NVgYB/JXxoKXMCX9sG/7aTil4pYmspVB67heJe0KkDqXnKkW1WzlG3qlXOWhCREREJFLN5Xq8sNHs9XJC/ZKn03rzpJqIiOhX/qhTo5VBAH9lPGgpc8Jf2wbrKWmbp31GsKzge1XUTyKSkslswdKCoy22EX6Hhso83R1rmhARERGJYDJb8MLGAyirrPV6WdYvISIicq+1dWqEC5rWiiq3tSt0cFzcDvZBACHjITOvCDrAZV29yXjw1+sEA39uG6ynpF1ia5sA6qifRCQVoc6UGEr4HWKmCREREZEH2/ZbMCWvyOsBE9YvISIi8qw1dWq0NH2SvzIetJI54e9tg/WUtCsj0YjstPgW2wg1cnYfPx+YThEFGbEF4Ken9VbE7xAzTYiIiIha4Cj6/q3Xy2WnxWPasHieUBMR+UldvZ13eZNbwiDA/C2HXC7IGPQRyBmVoIiLL/7ir4wHrWROaGnbIGnFdY4U1S4rvwiL7+vPbYuokYJDVlHt4jq3l7gn4nDQhIiIiMiNuno7Vnx+DEsLvvdquRAdsGJMEqfjIiLyI5PZ0uSip5EXPakBrQwCAFcyHpTyOkqnpW2DpCO2Rk75pVpk5hWpKmuLqLVMZgve3Vkqqq1SapBx0ISIiIioEZPZgnmbD8Jqq/Z6WdYvISLyL5PZgsy8oiY1CawVVbwwRS60MghA3uO2Qa3lqUZOY6xvQuQgtpaJ0mqQsaYJERERUQNC/RJvB0xiIsNYv4SIyM+EE213F6iEx+ZvOYS6ejGXsEhp6urtKDx+Hv8o/hGFx8/ze6SgwO1WmxrWyPFEqG+yt6RM2k4RBYHdJ86LqmVih7JqkDHThIiIiOhXvtYviY4MReHs2xHWlvejEBH5k6eioQ0vTPEu8uDCKdcoGHG71TahRs6sTw6g/FKtx/bbD1n520SaZjJbMOuTA6LaPpYap6g4yjN7IiIiIjgyTKbmfwtfbhZcdE9/DpgQEUng7AXPdyZ6046UQZhyrfGAmDDlmslskalnyiZVhgMzJ8ThdkuAY+Bk5dgkUW3X7izldkGaJcRMMQOMADA8wSBxj7zDTBMiIiLSPF8zTISi70q6I4aISE3EFgNVStFQ8szTlGs6sBaAO1JlODBzQhxut9TQ0J4xMOojPE45xO2CtKqlmNmY0mqZCHhLJBEREWlWXb0dbxYc9TnDhEXfiYikJRTebe5Skw6OC7xKO9Gm5nkz5Zqv1JY5IVWGg9ozJ/y5HQRiu6XgIba+CbcL0ipPMbMxJdUyETDThIiIiDTJZLZg3uaDXhd8B3gHJhFRoAgXpjLziqADXO5YFE6tlXiiTc2Teso1tWVOSJXhoPbMCX9vB5wqUFp1dXWYN28e8vLyYLVaERsbi4kTJ2LOnDnQ6ZS5/WUkGjEpNQ7v7iz12NZacUn6DhEpiNhY2Kl9KBbf21+Rv8/MNCEiIiLN2bbfgil5RT4NmGSnxeOrmcMUeWBHRKRGQuFdg951Ci6DPgKrxnGKxGAj5ZRrasyckCrDQc2ZE1JsB5wqUFpLlizBqlWrsGLFChw+fBhLlizBK6+8guXLl8vdtRaliazBsHDr4aCMP0S+Kj1XKardSgVPdc1MEyIiItKU1tYv4XRcRESBl5FoxPAEA/aWlOHshSp07eiYkisY74DXOmHKNWtFldssB1/nNldr5oRUGQ5qzZyQajuQarslh127duHuu+/GyJEjAQBxcXF4//33sXfvXpl71jJP24Xg58oaZOYVcaCfNMFktmBpwdEW2wgxc2ivmMB0ygfMNCEiIiJNYP0SIqLg1iZEh5ReMbh74NVI6RUTVBe+6YqGtQAaf4OtmXJNrZkTUmU4qDVzQqrtQKrtlhxuueUW7NixA99//z0A4L///S+++uor3HnnnW7bV1dXw2azufzJwZvaJoBjwC7YaywRtUQYuBZD6TGTgyZERESkeiazBamLd2BpwfdeLxuiA956OAkjBsRK0DMiIiLtkWLKNbVmTgh3sjd3WUkHR60ObzMcpHpduUm5HXCqQOnMmjULDz30EPr06YPQ0FAMGjQI06dPx9ixY922z83NhV6vd/517949wD2+QtguoiNDW2wXrAO3RN7YfeK8qALw09N6Kz5mcnouIiIiUjVhXmtf7+lihgkRkXzq6u2ckkul/D3lmlozJ4Q72TPziqADXI5nWpPhINXryk3q7YBTBUrjo48+woYNG5Cfn49+/fqhuLgY06dPR2xsLCZMmNCk/ezZszFjxgznv202m+wDJ5dq65H9YbHHttsPWZGi4CmJiHxlMlsw65MDotrGdW4vcW9aj4MmREREpFp19XbM2+x+XmtPjPoI5IxKUPwdMEREamUyWzB/yyGXOxYZm9VFmHLNH9Rcc0K4k73x/mBo5f4g1evKKRDbgT+3W3J47rnnnNkmANC/f3/88MMPyM3NdTtoEh4ejvDw8EB3s0WGKHEDcWt3lmJIj+ig3L+ImuPtjYrBcAMDB02IiIhItVZ8fhRWm/fTL2SnxWPasHjeNUhEJJPmTr6tFVUspktuqTVzQiBVhoPaMifUvh2o1S+//IKQENcKAm3atEF9fb1MPfKeMGDnaWoiHRy1TYYnGLgdkioIdUzEDJgE0w0MrGlCREREqiMUfV9acNSr5YT6JU+n9eZJDBGRTFo6+WYxXWqJ2mtOCBkOdw+8Gim9Yvx2rCLV68pF7duBGo0aNQovv/wytm7ditLSUmzcuBFvvPEG7rnnHrm7Jpo3ReEtFVXYffy89J0iCoC9JWWi6pgIgmXgmpkmREREpComswXzNh+E1Vbt9bKsX0JEJD9PJ98Ni+lyihxqTG2ZE+QbbgfBZfny5XjxxRcxdepUnD17FrGxsXjiiScwd+5cubvmlYxEIyalxuHdnaUe22blF2Hxff05iEdBr+CQVVS7Tu1Dsfje4NnmA5JpsnLlSsTFxSEiIgLJycnYu3dvs23XrVsHnU7n8hcRofx5zoiI/GHevHlNYmCfPn3k7hZR0Ni234IpeUVeD5gY9RFYPS4JIwbEStQzIiIS6+wFcXcrim0nF54Hy0dtmRPkG24HwaNjx45YtmwZfvjhB1y6dAnHjx/HSy+9hLCwMLm75rW0BIOoduWXapGZVwST2SJxj4ikYzJbRA0SAsDKMcGV6Sd5psmHH36IGTNmYPXq1UhOTsayZcuQnp6OI0eOoGvXrm6XiYqKwpEjR5z/1un4w0ZE2tGvXz8UFBQ4/922LZMCicTYtv80pr3/rdfLsX4JEZGyiC0OquQiojwPJiLSJqG2ibWiSlSNB9Y3oWAlTKfqiVDHZGiQZQdLnmnyxhtvYPLkyXj00UeRkJCA1atXo3379li7dm2zy+h0OhgMBudft27dpO4mEZFitG3b1iUGdu7cWe4uESmaUL9kav638HZ6++y03qxfQkSkMMIFp+Yisw6ODEElFxHleTARkTaJrW0CuE43SRRsdp84L6qWiR3BU8ekIUkHTWpqarBv3z6kpaVdecOQEKSlpaGwsLDZ5S5evIjrrrsO3bt3x913342DBw8227a6uho2m83lj4gomB09ehSxsbHo2bMnxo4di5MnTzbbljGQtM5ktiB18Q4sLfje62UNUeGYNux6CXpFRESt0fCCU+PTa+HfSj755nlwy+rq7Sg8fh7/KP4RhcfPo87bOx6IFITbM7mTkWjEqnFJ6NQuVFT77SJrQhAphclsQdaGIlFtH0uNC6ppuQSSzvly7tw51NXVNblDplu3bvjuu+/cLnPDDTdg7dq1GDBgACoqKvDaa6/hlltuwcGDB3HNNdc0aZ+bm4v58+dL0n8iokBLTk7GunXrcMMNN8BisWD+/Pn4zW9+A7PZjI4dOzZpzxhIWrZtvwVT88UdqDWmAzDvD/0Ue8GNiEjrhAtO87cccrmL0aCPQM6oBEWffPM8uHkms6XJd2oMgu+UyB1uz9SSjEQjOkaEYuw7ezy2XbuzFEN6RHO7oaBgMluQmVckavo5ABguss6P0gSkELw3UlJSMH78eAwcOBC/+93v8Pe//x1dunTBX//6V7ftZ8+ejYqKCuffqVOnAtxjIiL/ufPOO/HAAw9gwIABSE9Px7Zt21BeXo6PPvrIbXvGQNIqR/0S3wZMjPoIrBoXXEXo5DRv3rwmxYn79OnjfL6qqgpZWVmIiYlBhw4dcN999+HMmTMur3Hy5EmMHDkS7du3R9euXfHcc8/h8uXLLm2++OILJCUlITw8HNdffz3WrVsXiNUjIoWqq7dD3y4Mz6ffgBdH9sXSBwfi/clD8dXMYaqM31o4DxYusjSeysNaUcViyB4EMpuBmRPicHsmMYb2jIFR77n+lg6O2ibc30jphDomYrbUYJhOtSWSZpp07twZbdq0aXLifObMGRgM4kaZQkNDMWjQIBw7dszt8+Hh4QgPD291X4mIlKhTp07o3bs3YyDRr+rq7Vjx+TGfpuMCWPTdV/369UNBQYHz323bXjmEzM7OxtatW/Hxxx9Dr9dj2rRpuPfee7Fz504AQF1dHUaOHAmDwYBdu3bBYrFg/PjxCA0NxaJFiwAAJSUlGDlyJKZMmYINGzZgx44d+NOf/gSj0Yj09PTAriwRya6lu7eDIX7zPLipli6y2HHlgiGLITcVyGwGZk6Iw+2ZxBKmm5yS1/LNXg1rm6QEWbFs0pa9JWWi6pgIguXYzR1JM03CwsIwePBg7Nixw/lYfX09duzYgZSUFFGvUVdXhwMHDsBo5A80EWnPxYsXcfz4ccZAIrSufkmIDnjr4SQWffdR27ZtXYoTd+7cGQBQUVGBd999F2+88QaGDRuGwYMH47333sOuXbuwe/duAMBnn32GQ4cOIS8vDwMHDsSdd96JhQsXYuXKlaipqQEArF69Gj169MDrr7+Ovn37Ytq0abj//vuxdOlS2daZiOShhru3eR7clKeLLFIVQw72rIlA7g9q2PcEUn/vcm3PFJwyEo2YlBonqq214pK0nSFqpbMXxA2YdGofGvSzO0g+PdeMGTOwZs0arF+/HocPH0ZmZiYqKyvx6KOPAgDGjx+P2bNnO9svWLAAn332GU6cOIGioiKMGzcOP/zwA/70pz9J3VUiItk9++yz+PLLL1FaWopdu3bhnnvuQZs2bTBmzBi5u0Ykq237LZiSVwSrrdqn5VeMGYQRA4L3gE1uR48eRWxsLHr27ImxY8fi5MmTAIB9+/ahtrbWpdhxnz59cO211zqLHRcWFqJ///4uc/unp6fDZrM5ixwXFha6vIbQpqWCyUBwF0ImoqY83b0NBM/0JTwPdiX2IovYdmKYzBbcuuRzjFmzG09/UIwxa3bj1iWfB83F/0DuD2ra9wLxvcuxPVNwSxNZ02Hh1sNBE6NIm0rPVYpqt3JMcA+YAAEYNHnwwQfx2muvYe7cuRg4cCCKi4thMpmcJ84nT56ExXIlIPz888+YPHky+vbtixEjRsBms2HXrl1ISEiQuqtERLL73//+hzFjxuCGG27AH//4R8TExGD37t3o0qWL3F0jkk1r65esHpeEEQNi/dwr7UhOTsa6detgMpmwatUqlJSU4De/+Q0uXLgAq9WKsLAwdOrUyWWZbt26wWq1AgCsVqvbYsjCcy21sdlsuHSp+TvucnNzodfrnX/du3dv7eoSkYzUdPc2z4Ndde3oeU5/b9p5ooasiUDuD2rZ9wL1vQd6e6bgN6RHNIz6CHjKd/+5siZoYhRpj8lswdKCoy22EeqYDFXBNHOS1jQRTJs2DdOmTXP73BdffOHy76VLl3IqBiLSrA8++EDuLhApyrb9FkzN/9anZVm/xD/uvPNO5/8PGDAAycnJuO666/DRRx+hXbt2MvbMUQh5xowZzn/bbDYOnBAFMbXdvc3z4CuEC4bWiiq32Qw6AAY/FYtVS72JQO4Patj3Avm9B3J7JnUQaptkiqhtEiwxirRFiLFiBHMdk4YkzzQhIiIi8oWvGSasXyKtTp06oXfv3jh27BgMBgNqampQXl7u0qZhsWODweC2GLLwXEttoqKiWhyYCQ8PR1RUlMsfEQUv3r2tXsIFQwBN7rQW/u2viyxqyZoI5P6ghn0vkN97ILdnUo+MRCNWjUtCdGRoi+2CJUaRtuw+cV5UAfjpab2DflouAQdNiIiISHFMZkeGiS9TZ7N+ibQuXryI48ePw2g0YvDgwQgNDXUpdnzkyBGcPHnSWew4JSUFBw4cwNmzZ51ttm/fjqioKOe0MykpKS6vIbQRWzCZiNTB0/QlwpQPvHs7OAkXDA161wvvBn2EX4vFqiFrAgjs/qCGfS/Q33ugtmdSl4xEI168q5+ottsPWSXuDZE4JrMFWRvE3cwY17m9xL0JnIBMz0VEREQkVs3leryw0ez1ckZ9BHJGJfAk1c+effZZjBo1Ctdddx1Onz6NnJwctGnTBmPGjIFer8ekSZMwY8YMREdHIyoqCk8++SRSUlIwdOhQAMAdd9yBhIQEPPLII3jllVdgtVoxZ84cZGVlITw8HAAwZcoUrFixAs8//zwee+wxfP755/joo4+wdetWOVediAKs4fQlOsBl2hveva0OGYlGDE8wYG9JGc5eqELXjo4L8f78TtWQNQEEdn9Qw74nx/ceiO2Z1McQJW4bXLuzFEN6RPPchmQl1IoSey+j0n9bvcFMEyIiIlIMk9mCobkFKKus8Wq57LR4fDVzGE8qJPC///0PY8aMwQ033IA//vGPiImJwe7du9GlSxcAjnn477rrLtx333347W9/C4PBgL///e/O5du0aYNPP/0Ubdq0QUpKCsaNG4fx48djwYIFzjY9evTA1q1bsX37dtx44414/fXX8c477yA9PT3g60tE8hqeYMD0tN7Qt3OdvoR3b6tHmxAdUnrF4O6BVyOlV4zfLzCrIWtCEMhshmDPnJDre5d6eyb1EbZVT4TaJnW+pN4T+UFLtaIaC6bfVrF0drtdVXufzWaDXq9HRUUF57UmohapMV6ocZ1IOxxF372rYRKiA1aMSeJ0XF5Sa6xQ63oRaYXJbMH8LYdc5szu1C4Uj6bGYdqweL9djFRjrFDjOrWGcGcs4D5rIhgGARqqq7cHLJshkO/lb2r73qWixngRbOtkMlswxUNReMGGSclIje8scY+Imio8fh5j1uwW1VaH4Iix3sQKZpoQERGR7Hwt+s76JURE6iBc7GxcZLTiUi2WFRzl3O7klWDPmmgskNkMwZw5obbvndQrI9GISalxotpm5RfBZLZI2yEiNwpEHnt1ah+qyhjLmiZEREQkm7p6O1Z8fgxLC773ajlmmBARqUdL0z/YcWWKkuEJhqC6gEvyYr0JbeL3TsEiLcGAd3eWemxXfqkWmXlFqrwoTcplMltEbZ8AsHJMkiqzoThoQkRERLIwmS2Yt/kgrLZqr5dlhgkRkXrsLSlrkmHSkB2ApaIKe0vKkNIrJnAdo6AnZE2QtvB7p2Ag1DaxVlSJqhnBmwcoUISbWTzRwZHJN1Sl8ZbTcxEREVHAbdvvmMfX2wGTmMgwrB6XhBEDYiXqGRERBdrZC80PmPjSjoiISOnahOiQMypBVNuGNw8QSc3TzSwCO4CcUQmqHcjjoAkREREFlK/1S6IjQ1E4+3ampRMRqUzXjhGeG3nRjoiIKBgIdXg6tQsV1Z43D1AgWG3itrPHUuNUfW7OQRMiIiIKiLp6O94sOIqp+d+iXkwOeiOL7umPsLY8dCEiUpvB112F6MiwZp/XATDqHXUJiIiI1CQj0YiVY5NEtS0994vEvSGtM5ktWPjpQVFthycYJO6NvFjThIiIiCTXmvolQtF3Nd/FQkSkVSazBfO3HEJZZY3b54UJH9Q8/QMREWnb0J4xouqbLCv4HjcYOvC8iCRhMluQmVfkscaOUMtE7Tez8HZNIiIikpSv9UsELPpORKROwsl5S/NmG/QRWDWOA+dqUFdvR+Hx8/hH8Y8oPH4edb6knRIpFLdvag2hvonYgvDcvsjfhOLvYgZMAG3czMJMEyIiIpKMo37Jtz4ta9RHIGdUAi+UERGpkJiT8+jIUHz53O85NaMKCBlFDQfI+DtPasHtm/whI9GI7LR4LC042mybhgXhU3rFBK5zpHpii79HR4bh5XsSNRHbePRJREREftfa+iXZafH4auYwTRyMERFpkZiT87LKWuz74ecA9Yik0lxGkbWiCpl5RTCZLTL1TNnkzlyQ+/2DBbdv8qe4zpGi2m0/ZJW4J6Q1BSK3qTkj+2rmHJ2ZJkRERORX/qhfwum4iIjU7ewFz3czetOOlKmljCI7HNN8zN9yCMMTDKqf5sMbcmcuyP3+wYLbN/lb144Rotqt3VmKIT2iuT+SX5jMFry7s1RUW4O+nbSdURBmmhAREZHfsH4JERGJUXquUlQ7sReQSJk8ZRQ1nGpGasGSOSF35oLc7+8rOb5fJW3fpA5DekTDqPf8uycMyCk1jlHwEAZ/PdHBMXiu9uLvDTHThIiIiPyC9UuIiEgMk9nS4pztgOPk3KCxk3M1UkpGUbBkTsiduSD3+/tKru9XKds3qYdQEH5KXlGL7YQBud3HzyM1vnNgOkeqJLaWiR3aKP7eEDNNiIiIqNVMZgvrlxARkUdi72gEtHdyrkZiM4WkzCgKpswJuTMX5H5/X8j5/Sph+yb1yUg0YlJqnKi2WfnKimEUfMTWMnksNU5z5+scNCEiIqJWqblcjxc2mr1eLkQHvPVwEp5O682LYkREGiH2jsbpab01d3KuRsJUM839yks93YenzAlAWVPcyJ25IPf7e0vu71fu7ZvUKy3BIKpd+aVaxQ3+UvDwppbJcJHbpJpw0ISIiIh8ZjJbMDS3AGWVNV4vy/olRETaY7WJu9ga17m9xD2hQBCmmgHQ5MKy8G8pM4qCLXNC7swFud/fW3J/v3Jv36RengbkGlPS4C8FB9Yy8YyDJkREROQToeh7WWWtV8sZ9RFYPS4JIwbEStQzIiJSIpPZgoWfHhTVVikXZan1MhKNWDUuCYZGxY0N+gisGpfEmhMNyJ25IPf7e0sJ36+c27fa/Pjjjxg3bhxiYmLQrl079O/fH998843c3ZJFwwE5T5Q2+EvBYfeJ86xl4gELwRMREZHXfC36np0Wj2nD4jV50EVEpGVC3QFP98GyALw6ZSQaMTzBgL0lZTh7oQpdOzq+Y6mPB4Itc0K4UJqZVwQd4LK/BCJzQe7395ZSvl+5tm81+fnnn5Gamorf//73+L//+z906dIFR48exVVXXSV312QjDMjN+uQAyi95vklt+yErUnrFBKBnFOxMZgtmfXJAVFst1jIRcNCEiIiIvLJtv6PouzdCdMCKMUmcjouISINaqjvQkBIvypL/tAnRBfyCnpA5Ya2ocrv9KXGQTrhQOn/LIZe7gA36COSMSpD84pXc7+8NJX2/cmzfarJkyRJ0794d7733nvOxHj16yNgjZchINKJjRCjGvrPHY9u1O0sxpEe0ovZRUh6xN7EItFjLRMBBEyIiIhLN1wwT1i8hItIuscXfoyPD8PI9ibzgQ34TbJkTArkzF+R+f7GC9fulpjZv3oz09HQ88MAD+PLLL3H11Vdj6tSpmDx5stxdk93QnjEw6iM8/o7q4KhtMjzBwG2e3BJ7EwugzJsKAo01TYiIiMijuno73iw4iqn538KbGoMxkWGsX0JEpHEFh6yi2s0Z2ZcDJuR3wVpzQshcuHvg1UjpFRPwi6Byv79Ywfr9kqsTJ05g1apViI+Pxz//+U9kZmbiqaeewvr16922r66uhs1mc/lTK7H1TVjbhDwRexOLQOuDzsw0ISIiohaZzBbM23wQVlu1V8tFR4aicPbtCGvLezSIiLTKZLbg3Z2lotoa9O2k7QxpVrBkTpBv+P0Gv/r6etx0001YtGgRAGDQoEEwm81YvXo1JkyY0KR9bm4u5s+fH+huyiYj0YhJqXGifk+tFZek7xAFpbMXxA2YdGofisX39tf8oDOvYhAREVGztu23YEpekdcDJgCw6J7+HDAhItKwmsv1eGGj2WM7HQCjxqeAIOkFS+YE+Ybfb3AzGo1ISHDNpujbty9Onjzptv3s2bNRUVHh/Dt16lQguimrNJG1JRZuPQyT2SJxbygYlZ6rFNVu5Rhm6QEcNCEiIqJmOOqXFHm9XIgOeOthHmgREWmZyWzB0NwClFXWeGxrB6eAICLSstTUVBw5csTlse+//x7XXXed2/bh4eGIiopy+VO7IT2iYdRHwNMv5c+VNcjMK+LACbkwmS1YWnC0xTbCTSxDe8UEplMKx0ETIiIicuFr/RIBi74TEWmbkKVYVlkrqv1jqXEcaCci0rDs7Gzs3r0bixYtwrFjx5Cfn4+3334bWVlZcndNMbypbQI4isLX+XIyR6ojFIAXgzexXMFBEyIiInIymS1IXbwDSwu+93pZIcOERd+JiLTLlyzF4SKnHCEiInW6+eabsXHjRrz//vtITEzEwoULsWzZMowdO1burilKRqIRq8YlIToytMV2LApPDe0+cV5UAfjpab15E0sDLARPREREABx3Bk/N9346LgEzTIiItKuu3o4Vnx/zatBdB8DAWiZERATgrrvuwl133SV3NxQvI9GIS7X1yP6w2GPb7YesSOFUS5pmMlsw65MDotrGdW4vcW+CCwdNiIiI6Nc7g7/1aVmjPgI5oxJ4VwoRkUaZzBbM23wQVlu118tyGggiIiLvGKIiRLVbu7MUQ3pE8zxNo0xmCzLziiB2krauHcVtV1rBQRMiIiIN8+XO4Iay0+IxbVg8L3gREWmUr1mKMZFhePmeRF7I0YC6ejv2lpTh7IUqdO3oyCzicQOpGbd5kppQFN7TlEs6OGqbDE8wcBvUGKGOiZgBE2b+usdBEyIiIo1qzZ3BITpgxZgkTsdFRKRhvmYpRkeGonD27QhryxKbamcyWzB/yyGXC3vMUCU14zZPgSAUhZ+S1/JNC0Jtk93HzyM1vnNgOkeKsLekTFQdEwEzf5viUSoREZEGbdtvwZS8Ip8GTADWLyEi0rK6ejveLDiKqfnfol7snA8NLLqnPwdMNECYFqTxRRtrRRUy84pgMltk6pmy1dXbUXj8PP5R/CMKj59HnS87mYr7o2Tc5imQMhKNmJQaJ6ptVj63P60pOGQV1a5T+1CsGpfEQV03mGlCRESkMaxfQkREvhCmdFz71QlUVF32enkhS5G/IerX0rQgdnDKmOYoLUtBaf1RMm7zJIe0BAPe3VnqsV35pVpk5hXx4rhGmMwWUdsFAKwck8QspGZw0ISIiEhDTGYLpub7NmDC+iVERNrRcE7+zpHh+Lq0DG//5wR+qanz+TWZpagdnqYFEaaM2VtShpReMYHrGJRbb6K5gr1ClkKgL3YqrT/NUcr3qeRtntRLqG1iragSVbuCA3fqJwzgeiLUMRnKeNQsDpoQERFpRM3leryw0ez1cqxfQkSkHUI2yXs7S1B+qdYvr8k707Xn7AVx86iLbecvSs2cUFqWgtL60xwlfZ9K3eZJ3YTaJpkeapsAHLjTCrG1TOxgHRNPOGhCRESkASazBS9sPICySu8vgPHOYCIi9WicQQIdcNZWhbLKGvyv/BI+/uZ/uFjt/dRbzWGWojZ17Rjh13b+oOTMCaVlKSitP+4o7ftU4jZP2pCRaMSqcUmY9ckBUTc7cOBO3aw2cd/vY6lxvJnFAw6aEBERqdy2/RZMzfd891FjSrjzkoiIrgx0WCsuoayyBtEdwtG1g+uAR6f2YSj/xf1zwmNfl5Zh3a5Sv2WQtIRZitrmacoYYVqQIT2iA9IfpWdOKC1LQWn9aUyJ36fStnnSloxEIzpGhGLsO3s8ti0990sAekRyMJktWPjpQVFthycYJO5N8AvIoMnKlSvx6quvwmq14sYbb8Ty5csxZMiQZtt//PHHePHFF1FaWor4+HgsWbIEI0aMCERXiYhk523MJGqJr0XfeWcwyYUxUDotZRj4ehHeX4/xvZp/7H/ll/CP4tMoq6yRexPyCrMUtX0e3HDKGB3gchFZOLII5LQgSs+cUFqWgtL605gSv0+lbfOkPUN7xoiqb7Ks4HvcYOjAG+NUprnsu8Y4gCue5IMmH374IWbMmIHVq1cjOTkZy5YtQ3p6Oo4cOYKuXbs2ab9r1y6MGTMGubm5uOuuu5Cfn4/Ro0ejqKgIiYmJUneXiEhW3sZMouYIc9IvLfjeq+V4ZzDJiTHQN2IGQwKZYUDaxixFB54HX5kypnHNCYMM24jSMyeUlqWgtP40ptTvU0nbPGmPMHA3RUR9EyXUJCL/aSn7riEO4HpHZ7fbPX2mrZKcnIybb74ZK1asAADU19eje/fuePLJJzFr1qwm7R988EFUVlbi008/dT42dOhQDBw4EKtXr/b4fjabDXq9HhUVFYiKivLfihCR6igxXngbMxtT4jpR4JnMFszbfBBWW7XXy7718CCMGBArQa9ISZQaKxgDxWk4VdPOY+ew/fBZVHAwhBQgWLIUAxEreB58RcOB3a4dHRfaA72NFB4/jzFrdnts9/7kobLX6ADcZykEukaH0vrTkNK/TyVs8y1RcrzwlRrXyVdvFnyPpQVHPbbbMCkZqfGdA9AjkprYmBgTGYaX70nU9ACuN7EiRMqO1NTUYN++fUhLS7vyhiEhSEtLQ2FhodtlCgsLXdoDQHp6erPtiYjUwpeYSdTYtv0WTMkr8nrAJCYyDKvHJXHAhGTDGOheXb0dhcfPY2PR//Duf05g/paDuPnlAoxZsxvZH/0X/1/RjxwwIdld1T4Uq8cl4em03oq6MCgXnge7ahOiQ0qvGNw98Gqk9IqRZRsRMieae2cdHFlSck5XImQpGPSuU14Z9BGyDFAorT8NKf37VMI2T9oV1zlSVLus/CKYzBaJe0OBUHDIKqrdnJF9NT1g4i1Jp+c6d+4c6urq0K1bN5fHu3Xrhu+++87tMlar1W17q9X9BlBdXY3q6isXhmw2Wyt7TUQkD19iJmMgNeRr/ZLoyFAUzr4dYW0lvZeCqEWMgVcId6huP2TFpiCsZUHaERnWBo//tmdQZJcEEs+DlSdY6k1kJBoxPMGgmCwFpfVHECzfJ5EcxNYaKr9Ui8y8ItkHQal1TGYL3t1ZKqqtQd9O2s6oTEAKwUspNzcX8+fPl7sbRESyYAwkwbb9FkzN937ABAAW3dOfAyYUlNQWA4VaRO/tLGHtEVK0Tu1C8WhqHAdLZKS2+BcIwVJvQshSUAql9UcQLN8nUaB5qknUGOubBC+hloknctehClaSDpp07twZbdq0wZkzZ1weP3PmDAwGg9tlDAaDV+1nz56NGTNmOP9ts9nQvXv3VvaciCjwfImZjIEE+J5hIhR950klKYFWY2DDrJKPvvkfLlZflrtLRC46tQvFhFuuw5AeMTh3sVoxd5orGc+DlUupmRPkG36fRE01zMTyxA7AUlGFvSVlihwcpZbtPnHeZdC4OXYw+84Xkt5WGhYWhsGDB2PHjh3Ox+rr67Fjxw6kpKS4XSYlJcWlPQBs37692fbh4eGIiopy+SMiCka+xEzGQG2rq7fjzYKjmJr/LerF3EbUyIoxgzBiAAdMSBm0FgOF/Xfwwu0Ys2Y31u4s5YAJKUZ0ZCgmpcbh/clDse/F4cgefgNSr+/M+flF4nmwsrHehLrw+yRqSsjE6tQuVFT77SJrYpBymMwWZG3wPDAGAI+lxvFGSR9IPj3XjBkzMGHCBNx0000YMmQIli1bhsrKSjz66KMAgPHjx+Pqq69Gbm4uAODpp5/G7373O7z++usYOXIkPvjgA3zzzTd4++23pe4qEZHsPMVMIoHJbMG8zQe9LvgOOApjctoCUiKtxECT2YJZfz+A8l84BRfJp2EGyVlbFcoqaxDdIRyGKN6p7Q88DyYiIjllJBrRMSIUY9/Z47Ht2p2lGNIjmueHQcJktiAzr0jU9GsAMDzBfdYqtUzyQZMHH3wQP/30E+bOnQur1YqBAwfCZDI5i9ydPHkSISFXEl5uueUW5OfnY86cOXjhhRcQHx+PTZs2ITExUequEhHJzlPMJAKE+iXi7ippLDstnnPQk2KpPQYKNUuWFnwvd1coCEVHhuLuG2NxzVXtEd0hHF07hAM6OAc8OrUPQ/kvNW6fa/gYp9gKDJ4HExGR3Ib2jIFRH+FxCicdWNskWAh1TMQMmLCWSevo7Ha7DxN6KJfNZoNer0dFRQVTlImoRWqMF2pcJ3Il1C/xdjouoX4Jp+MiQL2xQsnr1ZrsMH9rLsPA14vw/niM79XyY8z+8C8lxwpfqXGdiEgaaowXalwnfzGZLZgior4JAGyYlIzU+M4S94hao/D4eYxZs1tUWx2AVeNYw7Qhb2KF5JkmRERE1HqtvUOd9UuI5NOa7DBveBoMYYYBERERkbZkJBoxKTUO7+4s9dg2K78Ii+/rz4vsClYgsv5Mp/ahWHwvv8vW4KAJERGRwrXmDnVmmBDJS8gO87fGUzUxE4GIiIiI3ElLMIgaNCm/VIvMvCJmJyiUyWwR9T0CwMoxScwaaiUOmhARESmYt0XeGmOGCZF8TGYLpua3fsCkQ3gbPDD4Gg6QEFHQqKu3Y29JGc5eqGKGG2kW9wNSiiE9omHUR8BaUSXqvJL1TZRHqGXiiVDHZGivGOk7pXIcNCEiIlKouno75m0WV+StMaM+AjmjEniHEJFMai7X44WN5la9Rqd2oXg0NQ7ThsXzpJWIgobJbMH8LYdcCg/zuIS0hvsBKUmbEB1yRiUgU0RtEzsAS0UV9paUIYUX3hVjb0mZSzxpjh1AzqgEnjv4AQdNiIiIFGrF50dhtXk+MGosOy2eF1mJZGQyW/DCxgMoq6z1etkO4W3w4E3dkZZg4B2pRBR0msuQtVZUccqXFgRLRkKw9FNu3A9IiTISjVg1LgmzPjmA8kuej1HPXvD+PJSkI/a6wGOpcYwvfsJBEyIiIoW5UvT9qFfLsX4Jkfx8nVKPWSVEFOyEqUPcxT87HFOGcMqXpoIlIyFY+ik37gekZBmJRnSMCMXYd/Z4bFt67pcA9IjEMJktWPjpQVFthycYJO6NdoTI3QEiIiK6wmS2IHXxDiwt+N7rZVm/hEhevk6pl50Wj30vDsfTab15AYWIgpanqUMaTvmiFHX1dhQeP49/FP+IwuPnUVfvaxU53wgD7Y0/NyEjwWS2BLQ/zVFaP+X+3loSjPsBacvQnjEw6iPg6YhzWcH3iolBWibEX08Z7Do4BrKH9IgOTMc0gJkmRERECrFtvwVT8z3PM9sY7/IjUgZvp9RjdhgRqYnYqVyUMuWL3JkTwZKRoLR+yv29eRJs+wFpj1DfZIqI+iZKiEFa1lL8bUj4dljLxL+YaUJERKQA2/afxrT3vR8wyU6Lx1czhyniJJFIy0xmi9dT6jE7jIjUpGvHCL+2k5ISMieCJSNBSf1UwvfmSTDtB6RdGYlGZKfFt9hGKTFIy8QWf4+ODGOtJAlw0ISIiEhGdfV2vFlwFFPzv4W3Mwtkp/XmdD5ECiDcBSZWTGQYVo9LwogBsRL2iogosIb0iG5xyhelTB3iKXMCcNxdLfWUT8GSkaCUfirle/MkWPYDorjOkaLabT9klbgn1JwCkZ/9nJF9OWAiAQ6aEBERyaQ19UsMUeGYNux6CXpFRN4SexcYAERHhqJw9u08sSEi1RGmfAHQ5IKxkqYOUUrmRLBkJCiln0r53jwJlv2ASOw+u3ZnqSKyuLTGZLbg3Z2lotoa9O2k7YxGcdCEiIhIBtv2WzAlrwhWW7XXy+oAzPtDP55sESmE2LvAAGDRPf0R1paH4ESkThmJRqwalwSD3vVinEEfoZipQ5SSOREsGQlK6adSvjcxgmE/IBL2bU+EukVyZ3FpidgsdqX8TqgVC8ETEREFmKN+ybc+LaukQpdE5N1dYNlpvbnvEpHqZSQaMTzBgL0lZTh7oQpdOzou6CjlZg+lZE4IGQmZeUXQAS7TTikpI0Ep/VTK9yaW0vcDJVu8eDFmz56Np59+GsuWLZO7O6oltiC8kMW1+/h5pMZ3DkznNE5sFrsdyvidUCve5kZERBQgralfArDoO5HSeFPLhFPqEZGWtAnRIaVXDO4eeDVSesUo6oKOUjIngODJSFBCP5X0vYml5P1Aqb7++mv89a9/xYABA+TuiiZkJBoxKTVOVNus/CJO0xUgYrPYH0uNU8zvhBox04SIiCgATGYL5m0+6NN0XCE6YMWYJIwYwAMiIiXZfeK86FomnFKPiEgZlJI5IQiWjAS5+6m074387+LFixg7dizWrFmDl156Se7uaEZagkFU1nT5pVpk5hUpakBXjbzJYh+eYJC2MxrHTBMiIiKJtaZ+CQCsGDOIAyZECmMyW5C1oeXpDAS8C4yISFmUkDnRULBkJMjdT6V9b+RfWVlZGDlyJNLS0jy2ra6uhs1mc/kj33jK4mqM9U2kw1omysJMEyIiIgmxfgmR+pjMFmTmFUHs6SLvAiMiUh65MyfIN/ze1OmDDz5AUVERvv76a1Htc3NzMX/+fIl7pQ0Ns7g8Eeqb7C0pQ0qvGOk7pzGsZaIsHDQhIiKSyLb9FkzN923AJDstHtOGxfNAiEhhhDvAxAyY6OC4+5V3gRERKZOQOUHBhd+bupw6dQpPP/00tm/fjoiICM8LAJg9ezZmzJjh/LfNZkP37t2l6qLqCVlcsz45gPJLtR7bn70gbnpa8o7VJu5zZRZ7YHDQhIiISAK+ZpiwfgmRsom9A0zAu8CIiIiImrdv3z6cPXsWSUlJzsfq6urw73//GytWrEB1dTXatGnjskx4eDjCw8MD3VVVy0g0omNEKMa+s8dj29JzvwSgR9piMluw8NODotoyiz0wOGhCRETkZyaz7xkmrF9CpGxi76zr1D4Ui+/tz7vAiIiIiFpw++2348CBAy6PPfroo+jTpw9mzpzZZMCEpDO0ZwyM+ghYK6pazKpeVvA9bjB04HGun4id+pdZ7IHFQRMiIiI/qrlcjxc2mr1ejvVLiIJD147ipo1YOSYJqfGdJe4NERERUXDr2LEjEhMTXR6LjIxETExMk8dJWkJ9kyki6pvM33IIwxMMzKhuJbFT/wqfMrPYAydE7g4QERGphclswdDcApRV1ni1XHZaPL6aOYwDJkRB4OfKarR0nqKDYxB0KOdaJyIiIqIgk5FoRHZafItthILwu4+fD0ynVEzs1L/RkWFYNS6J1wwCiJkmREREfuAo+u75jpyGWL+EKLiYzBZk5X/r8U4w3gFGRFpVV2/H3pIynL1Qha4dHVOIMB6S1nG/8N4XX3whdxc0La5zpKh2WflFWHwfp6NtjYJDVlHt5ozsy885wDhoQkRE1Eq+Fn1n/RKi4CEmdV4YCOUJDRFpkclswfwth1zumOX0o6R13C8oGImdjrb8Ui0y84qYAeEjk9mCd3eWimpr0LeTtjPUBKfnIiIi8lFdvR1vFhzF1PxvUe/p1vMGQnTAWw8nYcSAWOk6R0R+JSZ1vt4OXBUZFqAeEREph1DEtnGctFZUITOvCCazRaaeEcmH+wUFqyE9omHUR0BsPtT8LYdQ580JMTlvyPJEmPqXxd8Dj4MmREREPjCZLUhdvANLC773ellmmBAFn7MXPM817E07IiK1aCkTT3iMF9Tcq6u3o/D4efyj+EcUHj8fNJ9RsPY7kLhfUDATCsKLIdQ32VtSJm2nVGb3ifOiapnYwal/5cLpuYiIiLzkS/0SAIiJDMPL9yQydZkoCImdpkBsOyIitfCUidfwglpKr5jAdUzhgnXapmDtd6Bxv6Bgl5FoxKpxSZj1yQGUX6r12H77ISu3ZZFMZgtmfXJAVNvHUuMYW2XCTBMiIiIvOOqXeD9gEh0ZisLZt/OAhyhIDb7uKkS3MPUWU+eJSKvUkIkX6MyJYJ22Se5+B1OGixr2C6KMRCNWjk0S1XbtzlLFxi4lEeKomIEoABieYJC4R9QcZpoQERGJUFdvx4rPj/k0HRcALLqnP8La8l4FomAk3FVbVlnj9nkhWZ6p80SkRcGeiRfozAlP0zbp4Ji2aXiCQVG/KXL3O9gyXIJ9vyASDO0ZA6M+wuNUUkqNXUrSUhxtTAfAwBuyZMWrN0RERB60pn6JUPRdiSdzRORZc3fVNmTQR2DVOO7nRKRNngoGKzkTT47MCW+mbVISOfstd4aLL4J5vyBqSGx9EyEG7D5+XvpOBSlPcbQx3pAlLw6aEBERtWDbfgum5BXBaqv2aXkWfScKXmLuBouODMWXz/2eAyZEpFkNL6g1vrSj5Ew8uQp1B+u0TXL1O1gLqgfrfkHkTkaiEZNS40S1zcpX5kCmEhQcsopq16l9KG/IUgAOmhARETXD1/olgOPOsdXjkjBiQKyfe0VEgSLmbrCyylrs++HnAPWIiEiZhILBBr3rVENKzsSTK3MiWKdtkqvfwZqZAwTnfkHUnDSRtTXKL9UqNgNMTiazBe/uLBXVduUYxgclYE0TIiKiRlpbvyQ7LR7ThsXzzjGiIBesdwMTEckhI9GI4QkG7C0pw9kLVeja0TH1kFKPh+SK8cK0TdaKKrfZE0qdx16ufgf7b3Gw7RdEzfEUAxpjfZMrhIw5T4Q4OrRXjPSdIo+YaUJERNSAP+qXPJ3WmweHRCoQrHcDExHJpU2IDim9YnD3wKuR0itG0cdDcsX4YJ22Sa5+q+G3OJj2C6LmiK1tAig7A0wOYmuZ2KHM+K9VHDQhIiL6FeuXEFFDLOJKRKRecsb4YJ22SY5+87eYSDmEGNCpXaio9krNAAs0q03c5/BYapxi478WcXouIiIiCPVLvvVpWaM+AjmjEniAQ6Qywh11mXlF0AEuUxEo+W5gIiLyTO4YH6zTNgW633J/T0TkKiPRiI4RoRj7zh6PbUvP/RKAHimbyWzBwk8Pimo7XGTdGAoMZpoQEZHmmcwWTM3/FvViJmdtJDstHl/NHMYBEyKVGp5gwPS03tA3uqNO6XcDExGRZ3JnfATrtE2B7rfc3xMRuRraM6bFDDDBsoLvNV0Q3mS2IDOvCGWVtS22Y8acMnHQhIiINK3mcj1e2Gj2ejnWLyGtiIuLg06nc/lbvHixS5v9+/fjN7/5DSIiItC9e3e88sorTV7n448/Rp8+fRAREYH+/ftj27ZtLs/b7XbMnTsXRqMR7dq1Q1paGo4ePSrpunliMltw65LPsbTge5RfcpzsdGoXysFSIiIVyUg04quZw/D+5KF486GBeH/yUMZ4BeL3RKQcQgaY2ILwdb7cnRjkhOLvntacGXPKxUETIiLSLJPZgqG5BSirrPF6WdYvIS1ZsGABLBaL8+/JJ590Pmez2XDHHXfguuuuw759+/Dqq69i3rx5ePvtt51tdu3ahTFjxmDSpEn49ttvMXr0aIwePRpm85UBy1deeQV/+ctfsHr1auzZsweRkZFIT09HVZU8cyELd4Y1LtpYcakWywqOYvshqyz9IiIi/wvWjA+t4fdEpBwZiUZkp8W32EYoCL/7+PnAdEpBxBZ/j44MY8acQnHQhIiINEko+u4pVbYxoz4Cq8clYcSAWIl6RqQ8HTt2hMFgcP5FRkY6n9uwYQNqamqwdu1a9OvXDw899BCeeuopvPHGG842b775JjIyMvDcc8+hb9++WLhwIZKSkrBixQoAjiyTZcuWYc6cObj77rsxYMAA/O1vf8Pp06exadOmQK9ui3eGCY9p9a45IiIiIiIAiOsc6bkRgKz8Is1N01Ug8garOSP7csBEoThoQkREmuMo+l7k9XKckoe0avHixYiJicGgQYPw6quv4vLly87nCgsL8dvf/hZhYWHOx9LT03HkyBH8/PPPzjZpaWkur5meno7CwkIAQElJCaxWq0sbvV6P5ORkZxt3qqurYbPZXP78wdOdYcJdc3tLyvzyfkREREREwaZrxwjPjQCUX6pFZp52Bk5MZgve3Vkqqq1B307azpDPOGhCRESasm2/90XfWb+EtOypp57CBx98gH/961944oknsGjRIjz//PPO561WK7p16+ayjPBvq9XaYpuGzzdczl0bd3Jzc6HX651/3bt393EtXZ29IG5KMLHtiIiIiIjUZkiPaFEF4QVayNQWMtY9YfF35ZN00KSsrAxjx45FVFQUOnXqhEmTJuHixYstLnPbbbc1KTY6ZcoUKbtJRKQIYootU+v4mmHC+iWkNjk5OQAc2RyN447w99133wEAZsyYgdtuuw0DBgzAlClT8Prrr2P58uWorq6WcxUAALNnz0ZFRYXz79SpU355XbF3zYltR0TaorXz4Lp6OwqPn8c/in9E4fHzqr8gRtQa3F9ITYSC8GJoJVN794nzomqZ2MHi70rXVsoXHzt2LCwWC7Zv347a2lo8+uijePzxx5Gfn9/icpMnT8aCBQuc/27fvr2U3SQiUowFCxZg8uTJzn937NhRxt6oR129HSs+P4alBd97tVxMZBhevieR03GR6jz55JNYtmwZvv76a3To0MFtm549e7p9PDk5GZcvX0ZpaSluuOEGGAwGnDlzxqWN8G+DweD8r7s2DZ8XHjMajS5tBg4c2Ox6hIeHIzw8vIU19Y1w15y1osptXRMdAAPvDCOiZmjpPNhktmD+lkMuF4iM+gjkjErg8RNRI9xfSI0yEo1YNS4Jsz45gPJLnuuFbj9kRUqvmAD0LPBMZgtmfXJAVNvHUuO43yucZIMmhw8fhslkwtdff42bbroJALB8+XKMGDECr732GmJjmy+g2759e+fJMxGRlgjFlsl/TGYL5m0+CKvNu7vioyNDUTj7doS15UyWpD6dO3cGAPTu3RtRUVFeLVtcXIyQkBB07doVAJCSkoI///nPqK2tRWhoKABg+/btuOGGG3DVVVc52+zYsQPTp093vs727duRkpICAOjRowcMBgN27NjhHCSx2WzYs2cPMjMzW7OqPhHumsvMK4IOcBk4Ee4F451hROSOls6DTWYLMvOKmgwuWyuqkJlXhFXjknhByI26ejv2lpTh7IUqdO3oGIAP5t8Tta2PVLi/kJplJBrRMSIUY9/Z47Ht2p2lGNIjWnXbe3P7eHOGJwTP771WSXYlqLCwEJ06dXIeKAJAWloaQkJCsGdPyzvRhg0b0LlzZyQmJmL27Nn45Zdfmm0rVQFQIiI5tFRs2R3GwJZt22/BlLwirwdMAGDRPf05YEKaV1hYiGXLluG///0vTpw4gQ0bNiA7Oxvjxo1zDog8/PDDCAsLw6RJk3Dw4EF8+OGHePPNNzFjxgzn6zz99NMwmUx4/fXX8d1332HevHn45ptvMG3aNACATqfD9OnT8dJLL2Hz5s04cOAAxo8fj9jYWIwePVqOVXfeNWfQu07BZdBH8MIGETVLK+fBwpzt7i4OCY8F69z1Uk6fZDJbcOuSzzFmzW48/UExxqzZjVuXfB60xZEDsT5qmM5KzfsLkWBozxgY9Z6nrtVBfdt7S/t4Y6xlEjwkyzSxWq3OOxCdb9a2LaKjo1ss6Pnwww/juuuuQ2xsLPbv34+ZM2fiyJEj+Pvf/+62fW5uLubPn+/XvhMRyeGpp55CUlISoqOjsWvXLsyePRsWiwVvvPFGs8swBjbPUb/kW6+XC9EBK8bwgigR4Jj+6oMPPsC8efNQXV2NHj16IDs722VARK/X47PPPkNWVhYGDx6Mzp07Y+7cuXj88cedbW655Rbk5+djzpw5eOGFFxAfH49NmzYhMTHR2eb5559HZWUlHn/8cZSXl+PWW2+FyWRCRETg64YId81WX67Ha/ffCOiAcxereQctEXmklfPgvSVlLc7Z3nDu+mCahkXK6ZPUlmkQiPVRy3RWat1fiBoSMrWn5LVcQ1SN27unfbwxZqwHB68HTWbNmoUlS5a02Obw4cM+d6jhCXb//v1hNBpx++234/jx4+jVq1eT9rNnz3Y5cbfZbOjevbvP709E5E9iY2afPn1cYtmAAQMQFhaGJ554Arm5uc3O2c8Y2JSv9UsELPpOdEVSUhJ2797tsd2AAQPwn//8p8U2DzzwAB544IFmn9fpdFiwYIHLfP5yaOkCjVpO7IjIezwPdnX2griLQ2LbKYGUgwCeMg2EO6+HJxiC4kJaINZHTYNMatxfiNzJSDRiUmoc3t1Z6rGtteKS9B0KELH7bqf2oVh8b/+giV1a5/WgyTPPPIOJEye22KZnz54wGAw4e/asy+OXL19GWVmZV/O0JicnAwCOHTvm9mBRqgKgRET+IDZmutO42LI7jIGufK1fAlzJMOGACZF2qekCDRH5F8+DXXXtKC4LUGw7uUk9CKC2TAOp10dtg0xq21+IWpKWYBA1aLJw62G0C2ujimPr0nOVotqtHJOE1PjOEveG/MXrQZMuXbqgS5cuHtulpKSgvLwc+/btw+DBgwEAn3/+Oerr650HgGIUFxcDAIzG4N+JiEh7xMZMdxoXW6aWbdtvwdT8llOBW8IMEyJtU9sFGiLyL54HuxrSIxpGfQSsFVVu46YOjhpQwTJnu9SDAGrLNJB6fdQ2yKS2/YWoJZ62d8HPlTWquCnJZLZgacHRFtsI+/jQIIhXdIVkFW779u2LjIwMTJ48GXv37sXOnTsxbdo0PPTQQ4iNjQUA/Pjjj+jTpw/27t0LADh+/DgWLlyIffv2obS0FJs3b8b48ePx29/+FgMGDJCqq0REshNTbJma56hf4tuAiVEfgdXjkjBiQKyfe0VEwcSbCzRERM3RynmwMHc94LgY1JDw72Cas13qQQC1ZRpIvT5qG2RS2/5C1JKG23tLhAGVYC4KL9x0JQb38eAj2aAJAGzYsAF9+vTB7bffjhEjRuDWW2/F22+/7Xy+trYWR44cwS+//AIACAsLQ0FBAe644w706dMHzzzzDO677z5s2bJFym4SEclOKLb8u9/9Dv369cPLL7+M7Oxsl5hJTdXV2/FmwVFMzf8WvhxnZafF46uZw4L6zhYi8g+1XaAhIvlo5Tw4I9GIVeOSYNC7Xhg36COC7s5hqQcBhDuvm7tcpoPjRp5gyTSQen3UNsgEqGt/IfJE2N6jI0NbbBfsNyXtPnFeVAH46Wm9uY8HIa+n5/JGdHQ08vPzm30+Li4OdvuVq1zdu3fHl19+KWWXiIgUSWyxZbqC9UuIyJ/UeIGGiOShpfPgjEQjhicYsLekDGcvVKFrR8eF8mC7m1bq6ZOEO68z84qgA1zeIxgzDaReH7VOZ6WW/YVIjIxEIy7V1iP7w2KPbbcfsgbFVHsNmcwWzPrkgKi2cZ3bS9wbkoKkmSZERERS2Lbfgil5RT4NmACsX0JETantLmAiokBpE6JDSq8Y3D3waqT0ignKC8CBmD5JbZkGUq6PmqezUsP+QiSWIUrczUZrd5bCZLZI3Bv/MZktyMwrQvmlWlHtedNVcJI004SIiMjfHPVLvvVpWaM+AjmjEoLupJSIpKe2u4CJiMg7wiDA/C2HXKZbMfjx+FFtmQZSrk8gvg9SjtzcXPz973/Hd999h3bt2uGWW27BkiVLcMMNN8jdNWoF4aYkT1NY6eCobTI8waD4eCjUMREzO3iwZsWRAwdNiIgoaJjMFkzN923AJDstHtOGxSv+IIyI5MMLNERE2haIQQ0h00AtpFwftQ0yUfO+/PJLZGVl4eabb8bly5fxwgsv4I477sChQ4cQGRkpd/fIR8JNSVPyilps17C2idLj496SMlF1TAS86Sp4cdCEiIiCQs3leryw0ez1cqxfQkTe4AUaIiJtU9ugRrDj96ENJpPJ5d/r1q1D165dsW/fPvz2t7+VqVfkDxmJRkxKjcO7O0s9tg2G2iYFh6yi2nVqH4rF9/bnTVdBjIMmRESkeCazBS9sPICySnFzhjbE+iVE5C1eoCEiIiKST0VFBQAgOtr9tEbV1dWorr5S39JmswWkX+SbtASDqEGTtTtLMaRHtGIHGkxmi6j1AICVY5KQGt9Z2g6RpFgInoiIFE0o+u7tgIlRH4HV45IwYkCsRD0jIiIiIiIif6qvr8f06dORmpqKxMREt21yc3Oh1+udf927dw9wL8kbQm0TT4TaJnX1YiqGBJZQy8QTHRzXIobyBqygx0ETIiJSLEfR95bnP3UnOy0eX80cptg7VIhImerq7Sg8fh7/KP4RhcfPK/KEjYhIKRgziXzH/ad5WVlZMJvN+OCDD5ptM3v2bFRUVDj/Tp06FcAekreE2iaeCLVNdh8/L32nvCS2lokdrGOiFpyei4iIFKeu3o4Vnx/D0oLvvVqO9UuIyFcms6VJAXgjC8ATEbnFmOm9unq7qutlqX39/In7T/OmTZuGTz/9FP/+979xzTXXNNsuPDwc4eHhAewZtZY3tU2y8ouw+D5l1QMRW8vksdQ4RfWbfMdBEyIiUhST2YJ5mw/Caqv23LgR1i8hIl+YzBZk5hWh8T2e1ooqZOYVYdW4JJ78EBH9ijHTe2q/SK729fMn7j/u2e12PPnkk9i4cSO++OIL9OjRQ+4ukQTE1jYpv1SrqP3Bm1omwxMM0naGAobTcxERkWII9Uu8HTCJiQxj/RIi8okwP7G7STGEx5Q6tzIRUaBpKWb6a/ok4SJ542ldhIvkJrPFH92VjVTrp8bpq7S0/3grKysLeXl5yM/PR8eOHWG1WmG1WnHp0iW5u0Z+JNQ2EZuDpoT9wdtaJkN6REvfKQoIZpoQEZEiOOqXfOv1ctGRoSicfTvC2vI+ACLynqf5iYW5lfeWlCGFBR2JSOO0EjP9lTnh6SK5UPR4eIIhKKeykmr91Jq5opX9xxerVq0CANx2220uj7/33nuYOHFi4DtEkhBqm2Tmea5bqpT9gbVMtItXmIiISHbb9lswNf9b+HITyaJ7+nPAhIh8dvaC55Mgb9oREamZFmKmPzMnvLlIHoykWD81Z+ZoYf/xld1ud/vHARP1yUg0YtW4JHRqFyqqvdz7g9Um7v1Zy0R9eJWJiIhk5cgw8XynSWMhOuCth5UxxykRBa+uHSP82o6ISM3UHjP9PX2S2i+S+3v91D59ldr3HyKxMhKNWDk2SVTb0nO/SNyb5pnMFiz89KCotqxloj4cNCEiIlnU1dvxZsFRnzNMWPSdiPzB09zKnJ+YiOgKtcdMf2dOqP0iub/XT+2ZOWrff4i8MbRnjKj6JssKvpclw0zIeiurrG2xHfdb9eKgCRERBZzJbEHq4h1YWvC918sa9REs+k5EfiPMrQygyUmb8G/OT0xE5KD2mOnvzAm1XyT39/qpPTNH7fsPkTeE/UHM/ZOBzjBrKeutIe636sZBEyIiCqht+y2YklcEq63a62Wz0+Lx1cxhnJKLiPxKmFvZoHe9E9agj8CqcZwGkIioITXHTH9nTqj9Irm/10/tmTmAuvcfIm9lJBqRnRbfYhshw2z38fOB6RTEF3+PjgzjfqtibeXuABERaYejfsm3Xi8XogNWjEnidFxEJJmMRCOGJxiwt6QMZy9UoWtHx52xwXohi4hISmqNmULmhLWiyu0dxjo4Lm57kxkiXCSfv+WQy0U4gz4COaMSgv5imz/XT4rPX4nUuv8Q+SKuc6Sodln5RVh8X/+AxMyCQ1ZR7eaM7Bv0MZyax0ETIiKSXF29HSs+P+bTdFwA65cQUWC0CdEhpVeM3N0gIgoKaoyZQuZEZl4RdIDLhfvWZIao/SK5v9ZPqs9fidS4/xD5QmzmWPmlWmTmFUme2WEyW/DuzlJRbQ36dpL1g+THQRMiIpKUyWzBvM0HfZqOixkmRERERBRIUmWGqP0iub/WT+2ZOUTkylOGWWPztxzC8ASDJIOnQi0TT9SS9UYt46AJERFJxmS2IDOvSNTBjzvMMCEiIiKiQFN7ZojS8fMn0o6GGWaeCPVN9paUSTIIvfvEeVG1TOxQT9YbNY+DJkREJIm6ejvmbT7k04CJkXeSERERESlOXb1dMxey1Z4ZonRa+vy1tF8RuSNkmM365ADKL9V6bL/9kNXv8cFktmDWJwdEtX0sNY7XKjSAgyZERCSJFZ8fhdXm+S6NxrLT4jFtWDxPFIiIiIgUxGS2NJkyiTe6tEyrF8O1ut6+4H5F5JCRaETHiFCMfWePx7Zrd5ZiSI9ov+0j3s6QMTzB4Jf3JWXjoAkREfnVlaLvR71ajvVLiEgOvLBDRORZcxeUrBVVASnMG4y0ejFcq+vtC+5XRK6G9oyBUR/hcYosHfxX20SoYyJmwIS1TLQlRO4OEBGRepjMFqQu3oGlBd97vSzrlxBRoJnMFty65HOMWbMbT39QjDFrduPWJZ/DZLbI3TUiIsVo6YKS8Nj8LYdQV+9rFTvlq6u3o/D4efyj+EcUHj/vcV2Fi+GNL/wJF8PV+jvT2vX29nMOZtyviJoS6pt40rC2SWvtLSkTVcdEwFom2sFMEyIi8ott+y2Ymu+5eFtjvPOMiOTAuzuJSMu8ybLzdEFJ6sK8cvM2c8LTxXB/3iGtJK1db61lqPhrv2LGLKlNRqIRk1Lj8O7OUo9trRWXWv1+Zy+IGzDp1D4Ui+/tr8p4RO5x0ISIiFpt2/7TmPb+t14vx/olRCQHrV7QIiICvL84LfaCkth2wcSXAXatDjK1Zr21eCODP/YrrQ00kXakJRhEDZos3HoY7cLatGp7Lz1XKardyjFJSI3v7PP7UPDh9FxEROSzuno73iw4iqn538LbzPHstN54Oq03L0gSUcB5c2GHiEhNfJk+qWvHCFGvLbZdsPB1+iStDjL5ut5anaaqtfuVVqeAI20Y0iMaRn0EPF0p+LmyplXbu8ls8ViLVQfHYORQFQ1ykzgcNCEiIp+0pn6JISoc04ZdL0GviIg80+oFLSLSNl8vTnu6eCVcUFJbYVxfB9i1Osjk63pr9UaG1uxXWh1oIu3wprYJ4Nv2LuxHYrCOiTZx0ISIiLy2bb8FU/KKYLVVe72sDsC8P/TjQQcRyUarF7SISNt8vTjd8OJV46M34d9qvKDk6wC7VgeZfF1vrd7I0Jr9SqsDTaQtGYlGrBqXhOjI0Bbb+bq97z5xXlQB+OlpvTndnUZx0ISIiLziqF/ifcF3wHGipMY5iYkouGj1ghYRaVtrLk4LF68MetfBZIOKj+18HWDX6iCTr+ut5RsZfN2vtDrQRNqTkWjEi3f1E9V2+yGr6Nc1mS3I2iDumkZc5/aiX5fUhYXgiYhIlLp6O1Z8fsyn6bgAFn0nIuUQLuxk5hVBB7hMb6HmC1pEpG2tvTidkWjE8AQD9paU4eyFKnTt6BhcVmusFAbYrRVVbqdB0sFxcdvdALtwMbxxkW6Dyot0+7Lerfmc1cCX/UrLA02kPYYocdvx2p2lGNIj2mN8FeoBiZ3Mi/uRdnHQhIiIPDKZLZi3+aBP03GF6IAVY5IwYoA6Tw6JKDhp9YIWEWlXay5O19XbXS7q3jUgVrWDJYLWDrBrbZBJ4O1680YGx2eQ0ivGuZ99uv90i5+b1geaSFuE7d3TVFo6OGqbDE8wNBsvWqoH5O71uB9pG6fnIiKiFrWmfgkArBgziAMmRAr16quv4pZbbkH79u3RqVMnt21OnjyJkSNHon379ujatSuee+45XL582aXNF198gaSkJISHh+P666/HunXrmrzOypUrERcXh4iICCQnJ2Pv3r0uz1dVVSErKwsxMTHo0KED7rvvPpw5c8Zfq+pWRqIRX80chvcnD8WbDw3E+5OH4quZwzhgQkSq5Ov0SSazBbcu+Rxj1uzG0x8UY8ya3bh1yecwmS3Sd1pmvk6fVFdvR+Hx8/h0/2kAwF0DYpHSK0bVF/4bEgYB7hoQCwD4dP9pFB4/32yhZi1O/9aYN/uZVqeAI23ypii8paIKu4+fb7aNp3pAjXE/0jZmmhARUbMc9Uu+9WlZI+/WJlK82tpaPPDAA0hJScG7777b5Pm6ujqMHDkSBoMBu3btgsViwfjx4xEaGopFixYBAEpKSjBy5EhMmTIFGzZswI4dO/CnP/0JRqMR6enpAIAPP/wQM2bMwOrVq5GcnIxly5YhPT0dR44cQdeuXQEA2dnZ2Lp1Kz7++GPo9XpMmzYN9957L3bu3CnpZyBc2CEi0gJvs+yam8bEWlGFzLwiTVzQbpg5Ya24hLLKGkR3CIe+XRjq6u1uB5kaf75aPC725nOoq7dD3y4Mz6ff4Px8DVHayMwBfNvPmDFLWpKRaMSk1Di8u7PUY9us/CIsvq+/232gQGTdk07tQ7H4XvevQdqhs9vtYqdxCwo2mw16vR4VFRWIioqSuztEpGBqjBf+XKdt+y2Ymu9bwXfWLyFStsaxYt26dZg+fTrKy8td2v3f//0f7rrrLpw+fRrdunUDAKxevRozZ87ETz/9hLCwMMycORNbt26F2Wx2LvfQQw+hvLwcJpMJAJCcnIybb74ZK1asAADU19eje/fuePLJJzFr1ixUVFSgS5cuyM/Px/333w8A+O6779C3b18UFhZi6NChPq0XEZE7aowV3q6TMA1Qw0GAxhep6+rtuHXJ583elStMXfLVzGGaOOYTMwjQ3MVv4dPRwiAT4N3noPVBJl/2s4bT5XWODAd0wLmL1aKngGMMpGBUePw8xqzZLaqtDk3jrcnsmEFDjA2TkpEa39mXbpLCeRMrOD0XERE14cgw8X7AJEQHvPVwEp5O662Jk2citSssLET//v2dAyYAkJ6eDpvNhoMHDzrbpKWluSyXnp6OwsJCAEBNTQ327dvn0iYkJARpaWnONvv27UNtba1Lmz59+uDaa691tnGnuroaNpvN5Y+IiDxrE6JDxaUavPLPI1i49TCyP2w6HZCnaUyEqVD2lpQFqNfyEQYBGn8eQiaAyWxpca584bH5Ww41O0WVWnjzOYj5XNXO2/2s8TReY9/dg2c//i/C24Zoago40h6htonYLbxhvBXikic6OAZthzILncBBEyIiasRktmBq/rfw5XyO9UuI1MVqtboMmABw/ttqtbbYxmaz4dKlSzh37hzq6urctmn4GmFhYU3qqjRs405ubi70er3zr3v37j6tJxGR1oi5WH32grh538W2C1ZiBwF2nzjPQSaIHwTYffw8B5kgfv85e6GKg0ykaWJrmwBN462n+NxwOdYxIQEHTYiIyKnmcj1e2Gj23LARoz4Cq8clYcSvxR6JSD6zZs2CTqdr8e/777+Xu5t+MXv2bFRUVDj/Tp06JXeXiIgUT+wgQOcO4aJer2vHCM+NgpjYQYDCFooPN6T2QSax61d44hwHmSB+/+kcGc5BJtI8oZZPp3ahotpvP2SFyWxB1gZxs2g8lhqniWkBSRwWgiciIgCOOw5f2HgAZZW1Xi3H+iVEyvLMM89g4sSJLbbp3FncHL0GgwF79+51eezMmTPO54T/Co81bBMVFYV27dqhTZs2aNOmjds2DV+jpqYG5eXlLtkmDdu4Ex4ejvBwcRf1iIjIQewgQH2dHdGRYSirrHHbTqi1MKRHtDQdVQixgwBiy8WqfZBJ7PqJra6r9kEmYcoha0WV2wERAIiODEW93S56kCmFUwuRimUkGtExIhRj39njse3anaVYK6J4vGB4QvPnHaQ9zDQhIiJs2+8oiubNgAnrlxApU5cuXdCnT58W/8LCwkS9VkpKCg4cOICzZ886H9u+fTuioqKQkJDgbLNjxw6X5bZv346UlBQAQFhYGAYPHuzSpr6+Hjt27HC2GTx4MEJDQ13aHDlyBCdPnnS2ISIi/xB7EfrJD75tccAE0MY0JmIHAfL3nmzxeWGufLUPMomtO+Dp8xKofZCp4ZRDzX1mZZW1ePL9b0W9ntoHmYgAYGjPGBj1/osNWonP5B0OmhARaZyvRd9Zv4Qo+J06dQrFxcU4efIk6urqUFxcjOLiYly8eBEAcMcddyAhIQGPPPII/vvf/+Kf//wn5syZg6ysLGeGx5QpU3DixAk8//zz+O677/DWW2/ho48+QnZ2tvN9ZsyYgTVr1mD9+vU4fPgwMjMzUVlZiUcffRQAoNfrMWnSJMyYMQP/+te/sG/fPjz66KNISUnB0KFDA//BEBGpmNiL0OWXmr+ZxqCPwKpxSZqYxkTsIMDPvzT/eWlpkEnMIADQ8uclLKuVi5jClEOGFi4Ct7Q/NqT2QSYiwLv6JmJpIT6TdySbnuvll1/G1q1bUVxcjLCwMJSXl3tcxm63IycnB2vWrEF5eTlSU1OxatUqxMfHS9LHuno79paU4eyFKnSODAd0wFlbFcoqaxDdIRxdO/jvsU7tw1D+i/9fV4nvde5iNbp2dBzcMOAQXSEmLp48eRKZmZn417/+hQ4dOmDChAnIzc1F27b+D9d19Xas+PwYlhZ4V9sgRAesGJPEARMiFVi0aBHy8/Od/x40aBAA4F//+hduu+02tGnTBp9++ikyMzORkpKCyMhITJgwAQsWLHAu06NHD2zduhXZ2dl48803cc011+Cdd95Benq6s82DDz6In376CXPnzoXVasXAgQNhMplcisMvXboUISEhuO+++1BdXY309HS89dZbkq17w+NAHrcQkT8p/VxYzHRALYmODMWXz/0eYW21cQ+mcHEuM68IOsCnz8ygj0DOqARNDDIBVwYB5m0+CKut2ufX0dJFzIxEI4b16YahuTuazfBqiVamyyMSZCQaMSk1Du96Mf2WO53ah2Lxvf01E59JPJ1d7MSbXsrJyUGnTp3wv//9D++++66oA8UlS5YgNzcX69evR48ePfDiiy/iwIEDOHToECIixI2W22w26PV6VFRUICoqqtl2JrMF87ccanFOSGodo8YODCn4iI0X/uIpLtbV1WHgwIEwGAx49dVXYbFYMH78eEyePBmLFi0S9R7exEBfT2LeengQC74TBblAx79Aac1xII9biLRD6hgox7mwt+tkMjumZvXV+5OHaq5uQmuOnzdMSkZqvLh6Ymqy89g5UXUHGtPqTVqFx89jzJrdPi2rA0Rnf6nxOFCN60SetWafEWg1PmuVN7FCsltD5s+fj+zsbPTv319Ue7vdjmXLlmHOnDm4++67MWDAAPztb3/D6dOnsWnTJr/2zWS2IDOviAMmErNWVCEzrwgms0XurhApgqe4+Nlnn+HQoUPIy8vDwIEDceedd2LhwoVYuXIlamq8v9uoOUIM9PaELyYyDKvHJXHAhIiCWnPHgTxuISJ/UfK5sCAj0YjsNN+zWLRYNyEj0YjX/zjQp2XPVfqebRHMzl30bb3r7cBVkeLqr6lJa/ar6Wm9VXHjx8qVKxEXF4eIiAgkJydj7969cneJFEzs9InuCFMADtXYDQAknmLyaUtKSmC1WpGWluZ8TK/XIzk5GYWFhX57n7p6O+ZvOeRTSi15R/iM5285hLp6fuJEnhQWFqJ///4u09Wkp6fDZrPh4MGDfnkPX2NgdGQoCmffrooDcSLSrpZiII9biEgugToXbiyuc6TPy2q1boKvgwBa/bxas95aHJhrzecV17m9H3sijw8//BAzZsxATk4OioqKcOONNyI9PR1nz56Vu2ukUML0ib4euWtpCkDynmIGTaxWKwC4XCwU/i085051dTVsNpvLX0v2lpQxwySA7AAsFVXYW1Imd1eIFM9qtbqNgcJz7gQqBi66p79m5q0mIvXyFAN53EJEcvDlXNjbY0B3fL1Aq5Xi3O748plp+fMS7gL3hRYHmrT+eb3xxhuYPHkyHn30USQkJGD16tVo37491q5dK3fXSMF8zZxUS3YWScerK2CzZs2CTqdr8e+7776Tqq9u5ebmQq/XO/+6d+/eYnst3q2gBPzcSa3kjotSx8AQHfDWw+LmxiUiUjqxMZDHLUTUmNzHfI15ewzojq/Tmmj5zlxfLmpr+fMS7gL3hjBljhYHmrT8edXU1GDfvn0uGXchISFIS0uTNOOO1MGXzEk1ZGeRtNp60/iZZ57BxIkTW2zTs2dPnzpiMBgAAGfOnIHReOXi3JkzZzBw4MBml5s9ezZmzJjh/LfNZmvxgFENo+/BiJ87qZU/46LBYGgyZ+uZM2ecz7kjdQxcMWaQ5gowEpF6iY2BPG4hosaUdi7s7TGgO8IF2kwvCsJna/zOXOEzmyLyM9P65wVcuQt8acFR0ctoeaBJq5/XuXPnUFdX5zbjzt2AdHV1Naqrr0yX50u2HamHL8fuPN4nT7waNOnSpQu6dOkiSUd69OgBg8GAHTt2OA8MbTYb9uzZg8zMzGaXCw8PR3h4uOj3Ee4MsVZUsa5JAOgAGFRw1wNRc/wZF1NSUvDyyy/j7Nmz6Nq1KwBg+/btiIqKQkKC+zuOpIqBRn0EckYlaP4kj4jUxVMM5HELETVHaefC3h4DNicj0YhV45Iwb/NBWG0t1+swRIVj2rDrW/2ewS4j0Yi3Hh6Eae9/i5ZKYPHzumLasHi8v/cUrLaWMzl5DuLAz8uz3NxczJ8/X+5ukEJ4c62Xx/sklmQT1J88eRLFxcU4efIk6urqUFxcjOLiYly8eNHZpk+fPti4cSMAQKfTYfr06XjppZewefNmHDhwAOPHj0dsbCxGjx7tt341THcM7nF45RM+XzXc9UDkD57i4h133IGEhAQ88sgj+O9//4t//vOfmDNnDrKysvxyUgyIi4HZafH4auYwTR58E5G6tRQDedxCRP6i1HPh5mQkGrFz1u3ITuvt9nndr3/z/tCP8fFXIwbEYsWYJLfP8fNqqk2IDvP+kOD8bNzhOcgVWvy8OnfujDZt2jhnWhCcOXPG7awLs2fPRkVFhfPv1KlTgeoqKZDYa7083idvSDZoMnfuXAwaNAg5OTm4ePEiBg0ahEGDBuGbb75xtjly5AgqKiqc/37++efx5JNP4vHHH8fNN9+MixcvwmQyISLCvylTwt00Bh8LbJE4Bn0EVo1jLQQigae42KZNG3z66ado06YNUlJSMG7cOIwfPx4LFizwaz+ai4FGfQRWj0vC02m9eQBBRKrVXAzkcQsR+YuSz4Wb0yZEh6fT4rF6XFKTmh2Mj+6NGGDk5+UFnoN4R2ufV1hYGAYPHowdO3Y4H6uvr8eOHTuQkpLSpH14eDiioqJc/kjbxFzrZXwmb+jsdruqZqmy2WzQ6/WoqKjwGDTr6u3YW1KGsxeq0DkyHNABZ21VKKusQXSHcHTt4L/HOrUPQ/kv/n9dJb7XuYvV6NrRkeqmph9xUh9v4kWw8DUGcp8l0hY1xj+AMZCIxFFjDPTnOjE+eoefl3f4eXlHis9LqTHwww8/xIQJE/DXv/4VQ4YMwbJly/DRRx/hu+++a1LrpDGlrhMFnrtrvbxOSQJvYoVXNU3Upk2IDim9YuTuBhGRLBgDiUjLGAOJiNxjfPQOPy/v8PPyjpY+rwcffBA//fQT5s6dC6vVioEDB8JkMnkcMCFqSEv7DElL04MmREREREREREREJL9p06Zh2rRpcneDiEi6miZERERERERERERERETBRHWZJkKJFpvNJnNPiEjphDihptJOjIFEJIYa4x/AGEhE4qgxBjL+EZFYjIFEpFXexD/VDZpcuHABANC9e3eZe0JEweLChQvQ6/Vyd8MvGAOJyBtqin8AYyAReUdNMZDxj4i8xRhIRFolJv7p7GoaWgZQX1+P06dPo2PHjtDpdB7b22w2dO/eHadOnUJUVFQAeig/rjPXWY18WV+73Y4LFy4gNjYWISHqmK2QMdAzrrP611lr6wt4v85qjH8AY6AYWltnra0vwHXWagz0Nv4B2ttWtLa+ANeZ6+weYyC3E66zOmltfQFp45/qMk1CQkJwzTXXeL1cVFSUZjYoAddZG7S2zt6ur1rurBEwBorHdVY/ra0v4N06qy3+AYyB3tDaOmttfQGusydqi4G+xj9Ae9uK1tYX4DprBWMgjwHF4Dqrn9bWF5Am/qljSJmIiIiIiIiIiIiIiKiVOGhCREREREREREREREQEDpogPDwcOTk5CA8Pl7srAcN11gatrbPW1tdftPi5cZ3VT2vrC2hznf1Bi5+b1tZZa+sLcJ1JPK19blpbX4DrrBVaXOfW0uJnxnVWP62tLyDtOquuEDwREREREREREREREZEvNJ9pQkREREREREREREREBHDQhIiIiIiIiIiIiIiICAAHTYiIiIiIiIiIiIiIiABw0ISIiIiIiIiIiIiIiAiAxgdNXn75Zdxyyy1o3749OnXq5LbNyZMnMXLkSLRv3x5du3bFc889h8uXLwe2oxKLi4uDTqdz+Vu8eLHc3fKblStXIi4uDhEREUhOTsbevXvl7pJk5s2b1+S77NOnj9zd8qt///vfGDVqFGJjY6HT6bBp0yaX5+12O+bOnQuj0Yh27dohLS0NR48elaezCscYqP74BzAGMgYyBjaHMZAxUG0YAxkDxWL8c2AMVA/GP8Y/bzAGMv6pDWOgNDFQ04MmNTU1eOCBB5CZmen2+bq6OowcORI1NTXYtWsX1q9fj3Xr1mHu3LkB7qn0FixYAIvF4vx78skn5e6SX3z44YeYMWMGcnJyUFRUhBtvvBHp6ek4e/as3F2TTL9+/Vy+y6+++kruLvlVZWUlbrzxRqxcudLt86+88gr+8pe/YPXq1dizZw8iIyORnp6OqqqqAPdU+RgDHdQa/wDGQMZAxsCWMAY6MAaqC2MgY6AYjH9XMAaqB+Mf459YjIEOjH/qwhgoQQy0k/29996z6/X6Jo9v27bNHhISYrdarc7HVq1aZY+KirJXV1cHsIfSuu666+xLly6VuxuSGDJkiD0rK8v577q6OntsbKw9NzdXxl5JJycnx37jjTfK3Y2AAWDfuHGj89/19fV2g8Fgf/XVV52PlZeX28PDw+3vv/++DD0MDlqOgWqOf3Y7Y6DaMQb6B2PgUrm7IRnGQHVjDGw9Lcc/u50xUE0Y/xj/fKHlGMj4py6MgdLEQE1nmnhSWFiI/v37o1u3bs7H0tPTYbPZcPDgQRl75n+LFy9GTEwMBg0ahFdffVUVaYc1NTXYt28f0tLSnI+FhIQgLS0NhYWFMvZMWkePHkVsbCx69uyJsWPH4uTJk3J3KWBKSkpgtVpdvnO9Xo/k5GRVf+dS0UoMVGP8AxgDGQMdGAN9xxgY3BgDGQMBxkBfaSX+AYyBasL4x/jnL1qJgYx/6sIY6P8Y2NYfnVMrq9XqEiQBOP9ttVrl6JIknnrqKSQlJSE6Ohq7du3C7NmzYbFY8MYbb8jdtVY5d+4c6urq3H6H3333nUy9klZycjLWrVuHG264ARaLBfPnz8dvfvMbmM1mdOzYUe7uSU7YL91952raZwNFCzFQrfEPYAxkDLyCMdA3jIHBjTGQMVDAGOg9LcQ/gDFQTRj/GP/8SQsxkPFPXRgDpYmBqss0mTVrVpPiN43/1LqTNOTN5zBjxgzcdtttGDBgAKZMmYLXX38dy5cvR3V1tcxrQd6688478cADD2DAgAFIT0/Htm3bUF5ejo8++kjurlGAMAYy/mkZYyAxBjIGahljoLYx/jkwBmoT4x8xBjL+aRljoDRUl2nyzDPPYOLEiS226dmzp6jXMhgM2Lt3r8tjZ86ccT6nZK35HJKTk3H58mWUlpbihhtukKB3gdG5c2e0adPG+Z0Jzpw5o/jvz186deqE3r1749ixY3J3JSCE7/XMmTMwGo3Ox8+cOYOBAwfK1KvAYgxk/BMwBjIGChgDXTEGMgYq+fvzJ8ZAOP+thRjI+OfAGOig9RjI+Afnv7UQ/wDGQIDxT6D1+AcwBgpaGwNVN2jSpUsXdOnSxS+vlZKSgpdffhlnz55F165dAQDbt29HVFQUEhIS/PIeUmnN51BcXIyQkBDnOgersLAwDB48GDt27MDo0aMBAPX19dixYwemTZsmb+cC5OLFizh+/DgeeeQRubsSED169IDBYMCOHTucgdFms2HPnj3IzMyUt3MBwhjI+CdgDGQMBBgDW4MxMLgxBjIGAtqKgYx/DoyBDlqPgYx/2op/AGMgwPgn0Hr8AxgDAf/EQNUNmnjj5MmTKCsrw8mTJ1FXV4fi4mIAwPXXX48OHTrgjjvuQEJCAh555BG88sorsFqtmDNnDrKyshAeHi5v5/2ksLAQe/bswe9//3t07NgRhYWFyM7Oxrhx43DVVVfJ3b1WmzFjBiZMmICbbroJQ4YMwbJly1BZWYlHH31U7q5J4tlnn8WoUaNw3XXX4fTp08jJyUGbNm0wZswYubvmNxcvXnQZLS8pKUFxcTGio6Nx7bXXYvr06XjppZcQHx+PHj164MUXX0RsbKzzx5Ku0HoMVHv8AxgDGQMZA1vCGMgYqDaMgYyBYmk9/gGMgWrD+Mf45w2tx0DGP/VhDJQoBto1bMKECXYATf7+9a9/OduUlpba77zzTnu7du3snTt3tj/zzDP22tpa+TrtZ/v27bMnJyfb9Xq9PSIiwt63b1/7okWL7FVVVXJ3zW+WL19uv/baa+1hYWH2IUOG2Hfv3i13lyTz4IMP2o1Goz0sLMx+9dVX2x988EH7sWPH5O6WX/3rX/9yu99OmDDBbrfb7fX19fYXX3zR3q1bN3t4eLj99ttvtx85ckTeTiuU1mOgFuKf3c4YyBjIGNgcxkDGQLVhDGQMFEvr8c9uZwxUG8Y/xj9vaD0GMv6pD2OgNDFQZ7fb7b4PuRAREREREREREREREalDiNwdICIiIiIiIiIiIiIiUgIOmhAREREREREREREREYGDJkRERERERERERERERAA4aEJERERERERERERERASAgyZEREREREREREREREQAOGhCREREREREREREREQEgIMmREREREREREREREREADhoQkREREREREREREREBICDJkRERERERERERERERAA4aEJERERERERERERERASAgyZEREREREREREREREQAOGhCREREREREREREREQEAPj/AaRc3Sm0vZQQAAAAAElFTkSuQmCC",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  13\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  14\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  15\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  16\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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iBgIIViaTSS+//LJ27dqljz/+uNH7JSUlCgsLU6dOnRps79q1q0pKSqxtzkyOWN63vNdcm4qKCv3444/6/vvvVVtba7PN559/3mT/c3NzlZOT02g7MRCAPVq6BwyMBVgBAAAAAAAANPLvf/9bU6ZM0UsvvaSIiAhvd8dhWVlZKi8vt76++eYbb3cJQAAhQQIAAAAAgJPee+89DR8+XAkJCTKZTHrjjTda3Gf79u1KTk5WeHi4evbsqTVr1ri9nwCC1+7du3XkyBElJyerTZs2atOmjd599109++yzatOmjbp27aqamhodP368wX6lpaWKi4uTJMXFxam0tLTR+5b3mmsTFRWldu3aqUuXLgoNDbXZxnIMW8LDw62zRZg1AsDVSJAAAAAAAOCkyspK9e/fX8uXL7erfXFxsYYNG6af//zn2r17t6ZOnar77rtPb731lpt7CiBYXXvttdq7d692795tfQ0aNEh33XWX9f/btm2r/Px86z4HDhzQ4cOHlZqaKklKTU3V3r17deTIEWubLVu2KCoqSklJSdY2Zx7D0sZyjLCwMA0cOLBBm7q6OuXn51vbAICnubUGyXvvvadFixZp586dMpvNev311zVixAjr+4ZhKDs7Wy+88IKOHz+uIUOGaOXKlerVq5c7uwXAz9XWGSosLtORE1WKjYxQSmKMQkMCZ01pAGiOL8bAlu75bNm+fbsyMzO1b98+devWTTNnztS4ceM80l/A4szfpy4dwiWTdKSiSmWVNYrpGK7Yjq7b1ql9mI7/4PrjBuu54qJ8I/5J0s0336ybb77Z7varVq1SYmKinn76aUnSpZdeqvfff1+LFy9Wenq6u7qJAGUrjh07We0z9wjwDZGRkTrvvPMabOvQoYM6d+6svn37SpLuvfdeZWZmKiYmRlFRUXrwwQeVmpqqK6+8UpJ04403KikpSXfffbcWLlyokpISzZw5U5MnT1Z4eLgkacKECVq2bJkeffRR3XPPPdq2bZteffVVbdq0yXrezMxMjR07VoMGDVJKSoqWLFmiyspKjR8/3kN/GgD8kTu/B7s1QWIZSXPPPffol7/8ZaP3Fy5cqGeffVZr165VYmKiZs2apfT0dO3fv98v10QE4H55RWblbNwvc3mVdVt8dISyhyfppr7xXuwZALiP5WZwy/4SvbH7W5VV1ljf84UY2NI939kso6cnTJigl156Sfn5+brvvvsUHx/Pw0F4jK17CvgXX4h/zigoKFBaWlqDbenp6Zo6dap3OgS/1VIc89ffEXjH4sWLFRISottvv13V1dVKT0/XihUrrO+HhobqzTff1MSJE5WamqoOHTpo7Nixmjt3rrVNYmKiNm3apGnTpmnp0qU6//zz9cc//rHB/d3IkSN19OhRzZ49WyUlJRowYIDy8vIaFW4HAAt3Pws0GYZhtPoo9pzIZGowmtAwDCUkJOihhx7Sww8/LEkqLy9X165dtWbNGt1xxx12HbeiokLR0dEqLy9nDUIgwG3eY9ak9bsabbfki1eOTm4yMAZqrAjU6wJQr7bO0LJth/TiB8U6/uMpm218LQaefc9ny/Tp07Vp0yYVFRVZt91xxx06fvy48vLy7DoP8Q+tkVdk1sR1u+SRL0JwK5Oaj3+S78XAiy++WOPHj1dWVpZ12+bNmzVs2DD98MMPateuXaN9qqurVV1dbf25oqJC3bp1IwYGMXvimD33CAhsgXq/FKjXBaCxpj7vXPk92Gs1SIqLi1VSUtJg5Ex0dLQGDx6sgoICb3ULgI/avOdbZfylcXJEkjVI5mzcr9o6HnUA8G+1dYYKvvxOczfuU/+ct7V46xdNJkck/4yBTY2e5h4QnlBbZyhn436SIwHEn+Kfs3JzcxUdHW19devWzdtdghfZG8f88R4BAACL5j7vXPkZ57UESUlJiSQ1mkLXtWtX63u2VFdXq6KiosELQOCqrTO0dOtBTVr/qZqLd4Ykc3mVCovLPNY3AHAlS7wb+PgWjXrhQ63+4GudrD5t177+FgNLSkps3gNWVFToxx9/tLkP94BwlcLiMpbVCiD+Fv8kKS4uTqWlpQ22lZaWKioqyubsEUnKyspSeXm59fXNN994oqvwUY7EMX/8HQEAQGr5885Vn3FurUHiDrm5ucrJyfF2NwB4QF6RWXM27FNJRXXLjf/ryAkeeADwH2fWFnn1k3/bnRBpSiDHQO4B4SqB/HsSzPzp7zU1NVWbN29usG3Lli1KTU1tcp/w8HBrEWTAmX/v/vQ7AgCAZP9nV2s/47w2gyQuLk6SbI6csbxnCyNngOCweY9ZE9btcig5IkmxkRFu6hEAuE5rZos0x19iIKOn4U3+8nsCx3jz7/XkyZPavXu3du/eLal+Oendu3fr8OHDkurj15gxY6ztJ0yYoK+++kqPPvqoPv/8c61YsUKvvvqqpk2b5o3uww858++d2AcA8Df2fna19jPOazNIEhMTFRcXp/z8fA0YMEBSfeGUjz76SBMnTmxyP0bOAIGvvt7Ipw7vFx8doZTEGDf0CABcw1J0/Q/vfakfampddlyTpDg/ioGMnoY3pSTGKD46QiXlVdQhCQC+EP8++eQT/fznP7f+nJmZKUkaO3as1qxZI7PZbE2WSPXfhTdt2qRp06Zp6dKlOv/88/XHP/5R6enpHu87/JMjccwXfkcAAHBGS593rvqMc2uC5OTJkzp06JD1Z8tImpiYGHXv3l1Tp07VE088oV69eikxMVGzZs1SQkKCRowY4c5uAfBRlgeHi7d+4dT+2cOTFBpicnGvAKB1XL2M1tksUc+bMbCle76srCz95z//0Z///GdJ9aOnly1bpkcffVT33HOPtm3bpldffVWbNm3ySv8RXEJDTMoenqSJ63bJJJEkCQDevge87rrrZBhN/0tas2aNzX0+/dTxAUGAZH8c84V7BAAAnNXc550rP+PcmiBpaSTNo48+qsrKSj3wwAM6fvy4rr76auXl5SkigqmfQLBxpt6IRYhJWjYqWTf1jXdDzwDAeXlFZuVs3O/WgtBx0RHKHp7k1RjI6Gn4m5v6xmvl6GS3/37CveJ9IP4B3mJPHPOFewQAAFqjqc87V37GmYzmhrr4gYqKCkVHR6u8vFxRUVHe7g4AJ2zeY9ak9buc3n/FnZfrln4JzbYJ1FgRqNcFBILWxrbmdAwP1chB3ZSWFKeUxJgWR8wEYqwIxGuC51lmeB05UaUuHcIlk3SkokpllTWK6Riu2I6u29apfZiO/+D64wbrueKiIuyKf1JgxotAvCY4x1YcO3ayWrGR9v+OIHAFaqwI1OsC0LQzP+/s/YyzN1Z4rQYJAEjO1xuRGDUIwPdYbtre2mfW2oJ/ufz4ndq11fghPZQxtBcPPAAXCA0xKfWizt7uBgA4jTgGAAgG7vy8I0ECwGvyisyatN655Mi0tF48IATgMyw1lF78oFjHfzzl0mM7OlsEAAAAAAB/58ysEWeQIAHgFTWn6/TY60UO72epN3JLP2aNAPAudxdfZ7YIAAAAACAY2arn6a6VZEiQAPC4vCKzHnt9r8oqHR9lvWzU5SRHAHgVs0UAAAAAAHCPvCKzJq7bpbMLp5eUV2niul1aOTrZpUkSEiQAPMrZosXUGwHgTe6eLdIhLFQP/OxCZosAAAAAAIJWbZ2hnI37GyVHJMmQZJKUs3G/bkiKc9l3ZxIkADzG2YLs1BsB4C3unC0isYwWAABwTnPrsntqzXYAAFytsLiswbJaZzMkmcurVFhc5rKi7SRIALid5QHj4q1fOLQf9UYAeIO7Z4uYJI27qodu7MMyWgAAwHHNrcsuyWNrtgMA4GpHTjSdHHGmnT1IkABwq7wis+Zs2KeSimqH96XeCABPs/XAwdWW33m5bumX4LbjAwCAwNXcuuwT1tleythda7YDAOBqsZERLm1njxCXHQkAzrJ5j1kT1u1yODnSuUOYVo1O5gEiAI+yxCx3JUfioyOIbQAAwGktrcveFMt7ORv3q7auuZYAAHhXSmKM4qMj1NQ6CybVf7dOSYxx2TmZQQLALZytNxLToa0Ksq5XWBvytwA8o7bO0LP5B/Vs/kGXH7tjeKhGDuqmtCSW0wIAAK3T0rrszXHHmu0AALhaaIhJ2cOTNHHdLpnUcACA5dt09vAkl363JkECwOU27zFr0nrHkyOSNP+2y0iOAHA7d9cZofg6AABwNVest+7KNdsBAHCHm/rGa+Xo5EbLX8e5qaYWCRIALuXszBFLQXbWxAXgTrV1hpZtO6QXPyjW8R9PufTYzBYBAADu5Ir11l25ZjsAAO5yU9943ZAUp8LiMh05UaXYyAi3fc8mQQLAJSwPHRdv/cKp/SnILi1YsEBZWVmaMmWKlixZ4u3uAAGD2SIAACAQWNZlLymvarbmiC0m1Y+8deWa7QAAuIPlO7wlMfI//RLc+l2bBAmAVssrMmvOhn0OF2OX6gsruWN6nL/5+OOP9Yc//EH9+vXzdleAgJJXZG40LdcVmC0CAAA8raV12Q0b/2/5WXL9mu0AALiare/w7n52yEL/AFpl8x6zJqzb5VRyZFpaL70/fWjQJ0dOnjypu+66Sy+88ILOOeccb3cHCBiW+OTK5Eindm01La2X/i87XbOG91HqRZ150AAAADzGsi57XHTDpbLioiO0anSyVjXx3srRLGcMAPBteUVmTbTxHb6kvEoT1+1SXpHZLedlBgkAp7W23kiwL6llMXnyZA0bNkxpaWl64oknmm1bXV2t6uqfklEVFRXu7h7gVyxTcd/aZ9bagn+55JgmSeOu6qEb+zBbBAAAeF9L67J7as12AABcpbbOUM7G/TaXkDRU/708Z+N+3ZAU5/LPNBIkABxGvRHXefnll7Vr1y59/PHHdrXPzc1VTk6Om3sF+B93Fl9ffufluqVfgkuPCQAA0BqhISalXtTZ4fcAAPBFhcVlza7+YEgyl1epsLjM5Z9xJEgAOKQ19UaYOdLQN998oylTpmjLli2KiIhoeQdJWVlZyszMtP5cUVGhbt26uauLgE9zd/H1c9q3Ve4vL2M5CgAAAAAA3OjICfuWxra3nSNIkACw2+Y9Zk1av8vp/Zk50tDOnTt15MgRJScnW7fV1tbqvffe07Jly1RdXa3Q0NAG+4SHhys8PNzTXQV8ijtni0j1dUbGD+mhjKG9WI4CAAAAAAA3i420b+Cwve0cQYIEgF2crTciSfHREcoensQo7LNcf/312rt3b4Nt48ePV+/evTV9+vRGyREA9bPYZvxtr47/4NrESMfwUI0c1E1pSdQZAQAAAADAk1ISYxQfHaGS8iqbdUhMkuKi6+tquRoJEgAtyisya9J655Ij09J6MQq7CZGRkerbt2+DbR06dFDnzp0bbQeCXWtrHzWF2SIAAAAAAHhXaIhJ2cOTNHHdLpmkBkkSyzf17OFJbvneToIEQLNqTtfpsdeLHN6PeiMAXMGSGFn9/lcqr3JNjRGTpHFX9dCNfZgtAgAAAACAL7ipb7xWjk5Wzsb9DQq2x7l5ZRoSJACalFdk1mOv71VZpeNL2VBvxDnbt2/3dhcAn2BJjPzhvS/1Q02tS4+9/M7LdUu/BJceEwAAAAAAtM5NfeN1Q1KcCovLdORElWIjI9w+sJEECQCbnC3ITr0RAM6qrTNUWFymLftL9Oon/9bJatfMGLEgPgEAAAAA4HsszwM8lRQ5EwkSAI04W5CdeiMAnJVXZG40jdYVKL4OAAAAAIDvsvU8wJMDHEmQALBytggy9UYAtIazM9aaQ/F1AAAAAAB8W16RWRPX7WpQlF2SSsqrNHHdLq0cnez2JAkJEgCS6gPSnA37VFJR7fC+1BsB4CjL9Nm39pm1tuBfLjkms0UAAAAAAPAPtXWGcjbub5QckSRDkklSzsb9uiEpjhokANzL2dHbnTuEad5tfVnPH4DdLDPVXvygWMd/POWSYzJbBAAAAAAA/1JYXNbsMtuGJHN5lQqLy5R6UWe39YMECRDknK03EtOhrQqyrldYmxA39ApAIMorMmvG3/bq+A+uSYxI1D4CAAAAAMAfHTlhXw1Se9s5iwQJEKScrTdiMf+2y0iOALBLa+ONLee0b6vcX17GDDYAAAAAAPxQbGSES9s5i6ebQBDKKzJryIJ8px5WhpikFXe6v0ASAP9XW2do6daDSp77tsuSIx3CQjUtrZc+mXkDcQgA4DOWL1+uHj16KCIiQoMHD1ZhYWGz7ZcsWaJLLrlE7dq1U7du3TRt2jRVVbl3dCQAAIAvSUmMUXx0hJpaD8IkKT46QimJMW7tBzNIgCDjbL0RCwqyA2iOpfj6lv0levWTf+tk9WmXHJc6IwAAX/XKK68oMzNTq1at0uDBg7VkyRKlp6frwIEDio2NbdR+/fr1mjFjhlavXq2rrrpKX3zxhcaNGyeTyaRnnnnGC1cAAADgeaEhJmUPT9LEdbtkkhoUa7d8688enuT2ZwAkSIAg4my9Eak+Y5s9PIkR2wCalFdkVs7G/c0WWXOESdK4q3roxj5xSkmMITECAPBJzzzzjO6//36NHz9ekrRq1Spt2rRJq1ev1owZMxq137Fjh4YMGaI777xTktSjRw+NGjVKH330kUf7DQAA4G039Y3XytHJjZ4lxHnwOSQJEiBI1M8ccS45QhFkAC1p7ew0W5bfeblu6Zfg0mMCAOBKNTU12rlzp7KysqzbQkJClJaWpoKCApv7XHXVVVq3bp0KCwuVkpKir776Sps3b9bdd9/d5Hmqq6tVXV1t/bmiosJ1FwEAAOAltXWGotuF6dH0S1RWWaOYjuGKi4rw6CBJEiRAEHB25kiISVo2KpkltQA0qbbO0LP5B/Vs/kGXHZMZawAAf3Hs2DHV1taqa9euDbZ37dpVn3/+uc197rzzTh07dkxXX321DMPQ6dOnNWHCBD322GNNnic3N1c5OTku7Tv8k2U50yMnqhQb2fIDJEfbAwDgKbZWobA8D/DkZxVF2oEAZimQPGn9p6ozWm5/NuqNALClts5QwZffae7Gfeqf87aW5h+UEyGmgY7hobp3SA/95f4r9f70oSRHAAABa/v27Zo/f75WrFihXbt26W9/+5s2bdqkxx9/vMl9srKyVF5ebn198803HuwxfEVekVlXP7lNo174UFNe3q1RL3yoq5/cprwis0vaI3D98Y9/VL9+/RQVFaWoqCilpqbqH//4h/X9qqoqTZ48WZ07d1bHjh11++23q7S0tMExDh8+rGHDhql9+/aKjY3VI488otOnG9Yb3L59u5KTkxUeHq6ePXtqzZo1jfqyfPly9ejRQxERERo8eLAKCwvdcs0AfFtekVkT1+1qtER3SXmVJq7b5dHPKmaQAAEqr8isORv2qaSiuuXGZ2H0NoCmuLrOCMXXAQD+rEuXLgoNDW30ILG0tFRxcXE295k1a5buvvtu3XfffZKkyy67TJWVlXrggQf0+9//XiEhjccxhoeHKzw83PUXAL9heZB09qAUy4OklaOTG3x/c7Q9Att5552nBQsWqFevXjIMQ2vXrtWtt96qTz/9VH369NG0adO0adMmvfbaa4qOjlZGRoZ++ctf6oMPPpAk1dbWatiwYYqLi9OOHTtkNps1ZswYtW3bVvPnz5ckFRcXa9iwYZowYYJeeukl5efn67777lN8fLzS09MlSa+88ooyMzO1atUqDR48WEuWLFF6eroOHDig2NhYr/35APCs2jpDORv32xxoaai+HmnOxv26ISnOI88JmEECBCDLzbAzyZFpab0YvQ3Aps17zJpgY4SHs6al9dLOWTdoStrFJEcAAH4pLCxMAwcOVH5+vnVbXV2d8vPzlZqaanOfH374oVESJDQ0VJJkGK2dk4lA1NKDJKn+QVLtf5cNcLQ9At/NN9+sW265Rb169dLFF1+sefPmqWPHjvrwww9VXl6uP/3pT3rmmWc0dOhQDRw4UC+++KJ27NihDz/8UJL09ttva//+/Vq3bp0GDBigm2++WY8//riWL1+umpoaSdKqVauUmJiop59+WpdeeqkyMjL0q1/9SosXL7b245lnntH999+v8ePHKykpSatWrVL79u21evVqr/y5APCOwuKyZp8rGJLM5VUqLC7zSH9IkAABprbO0JwNtm+GmxNiklbcmcyDSgANWJbTmrOhSJP/4poi7Oe0b6tVo4k3AIDAkJmZqRdeeEFr167VZ599pokTJ6qyslLjx4+XJI0ZM6ZBEffhw4dr5cqVevnll1VcXKwtW7Zo1qxZGj58uDVRApzJ0QdJvvbgCb6ltrZWL7/8siorK5WamqqdO3fq1KlTSktLs7bp3bu3unfvroKCAklSQUGBLrvssgb1ltLT01VRUaF9+/ZZ25x5DEsbyzFqamq0c+fOBm1CQkKUlpZmbQMgOBw5Yd+gS3vbtRZLbAEBZtm2gyqpcDyAUG8EwJlq6wwt23ZIL35QrOM/nnLJMVlOCwAQiEaOHKmjR49q9uzZKikp0YABA5SXl2d9kHj48OEGM0Zmzpwpk8mkmTNn6j//+Y/OPfdcDR8+XPPmzfPWJcDHOfogydcePME37N27V6mpqaqqqlLHjh31+uuvKykpSbt371ZYWJg6derUoH3Xrl1VUlIiSSopKWmQHLG8b3mvuTYVFRX68ccf9f3336u2ttZmm88//7zZvldXV6u6+qcVMioqKuy/cAA+JzYywqXtWosECRAgLA8zF2896NB+ISZp2ahkkiMArPKKzJrxt706/kPrEyMmSeOu6qEb+8QpJTGGxAgAICBlZGQoIyPD5nvbt29v8HObNm2UnZ2t7OxsD/QMgcDRB0m+9uAJvuGSSy7R7t27VV5err/+9a8aO3as3n33XW93yy65ubnKycnxdjcAuEhKYozioyNUUl5lcwUck6S46AilJMZ4pD8ssQUEgLwis4YsyNfirV84vC8zRwBY1NYZWrr1oCas2+WS5IgkLb/zcmX/oo9SL+pMcgQAAMAJlgdJTd1JmSTFn/EgydH2CA5hYWHq2bOnBg4cqNzcXPXv319Lly5VXFycampqdPz48QbtS0tLFRcXJ0mKi4tTaWlpo/ct7zXXJioqSu3atVOXLl0UGhpqs43lGE3JyspSeXm59fXNN984fP0AfEdoiEnZw5MkqdFnleXn7OFJHnuG4PUEyZw5c2QymRq8evfu7e1uAX7DUjTZ0YLs8dERWjU6Wbf0S3BTzwD4C0tiJHnu204lWm2x1BkhxgSX5cuXq0ePHoqIiNDgwYNVWFjYZNs1a9Y0ugeMiGAkKwAAZ3P0QZKvPXiCb6qrq1N1dbUGDhyotm3bKj8/3/regQMHdPjwYaWmpkqSUlNTtXfvXh05csTaZsuWLYqKilJSUpK1zZnHsLSxHCMsLEwDBw5s0Kaurk75+fnWNk0JDw9XVFRUgxcA/3ZT33itHJ2suOiG3wHjoiO0cnSyburrucHcPrHEVp8+fbR161brz23a+ES3AJ+3ec+3yvjLpw7vNy2tFzUAAEhy7XJaEnVGgtkrr7yizMxMrVq1SoMHD9aSJUuUnp6uAwcOKDY21uY+UVFROnDggPVnk4l/MwAA2GJ5kJSzcX+DAuxx0RHKHp7U6EGSo+0R2ObMmaMRI0aoe/fuOnHihNavX6/t27frrbfeUnR0tO69915lZmYqJiZGUVFRevDBB5Wamqorr7xSknTjjTcqKSlJd999txYuXKiSkhLNnDlTkydPVnh4uCRpwoQJWrZsmR599FHdc8892rZtm1599VVt2rTJ2o/MzEyNHTtWgwYNUkpKipYsWaLKykqNHz/eK38uALynts5QdLswPZp+icoqaxTTMVxxURFeWZrbJzIRbdq0aXE6HYCf/FRvxPGR3tPSLtaUtF5u6BUAf9KaOHI26oxAkp555hndf//91i+4q1at0qZNm7R69WrNmDHD5j4mk4l7QAAA7HRT33jdkBSnwuIyHTlRpdjI5h8kOdoegevo0aMaM2aMzGazoqOj1a9fP7311lu64YYbJEmLFy9WSEiIbr/9dlVXVys9PV0rVqyw7h8aGqo333xTEydOVGpqqjp06KCxY8dq7ty51jaJiYnatGmTpk2bpqVLl+r888/XH//4R6Wnp1vbjBw5UkePHtXs2bNVUlKiAQMGKC8vr1HhdgCBLa/I3CiBH//fBL43PqN8IkFy8OBBJSQkKCIiQqmpqcrNzVX37t1ttq2urlZ19U9LCVVUVHiqm4BPyCsya86GfQ4vqSVJcVHhyhja0w29AuAvLImR1e9/pfKq0y455vI7L2cprSBXU1OjnTt3Kisry7otJCREaWlpKigoaHK/kydP6oILLlBdXZ2Sk5M1f/589enTx2Zb7gEBAKhfPiv1os5ua4/AtHz58maXpYqIiNDy5cu1fPnyJttccMEF2rx5c7Pnue666/Tpp82vcpGRkaGMjIzmOwwgYOUVmTVx3a5GxdlLyqs0cd0ujy+vJflADZLBgwdrzZo1ysvL08qVK1VcXKxrrrlGJ06csNk+NzdX0dHR1le3bt083GPAe5ytNyLVj/Ce84s+jBYCglhekVkDn9iixVu/cElyhFpGsDh27Jhqa2sbjf7r2rWrSkpKbO5zySWXaPXq1fr73/+udevWqa6uTldddZX+/e9/22zPPSAAAAAA+K/aOkM5G/c3So5Ism7L2bhftXW2WriP12eQ3Hzzzdb/79evnwYPHqwLLrhAr776qu69995G7bOyspSZmWn9uaKigi/ICArO1huRfpqmxjqzQHBy5XJaHcNDNXJQN6UlsZwWWic1NbVBQc6rrrpKl156qf7whz/o8ccfb9See0AAAAAA8F+FxWUNltU6myHJXF6lwuIyj85+9HqC5GydOnXSxRdfrEOHDtl8Pzw83FoACggGrX2wSUF2IHi5cjktiq+jOV26dFFoaKhKS0sbbC8tLbW7xkjbtm11+eWXcw8IAAAAAAHoyImmkyPOtHMVry+xdbaTJ0/qyy+/VHw8I92BvCKzhizIdyo5EmKSVtyZrClpF/MwEwhCrlxOa1paL+2cdQPxBE0KCwvTwIEDlZ+fb91WV1en/Pz8BrNEmlNbW6u9e/dyDwgAAAAAASg2MsKl7VzF6zNIHn74YQ0fPlwXXHCBvv32W2VnZys0NFSjRo3ydtcAr9q8x6xJ63c5vf+yUZfrln48ZAKCUWvjh8U57dsq95eXsTwf7JKZmamxY8dq0KBBSklJ0ZIlS1RZWanx48dLksaMGaPzzjtPubm5kqS5c+fqyiuvVM+ePXX8+HEtWrRI//rXv3Tfffd58zIAAAAAAG6Qkhij+OgIlZRX2axDYpIUFx2hlMQYj/bL6wmSf//73xo1apS+++47nXvuubr66qv14Ycf6txzz/V21wCvod4IAEfV1hkqLC7TW/vMWlvwr1Ydi+W04IyRI0fq6NGjmj17tkpKSjRgwADl5eVZC7cfPnxYISE/TV7+/vvvdf/996ukpETnnHOOBg4cqB07digpKclblwAAAAAAcJPQEJOyhydp4rpdMkkNkiSWJw/Zw5M8/hzCZBiGZ8vCu1hFRYWio6NVXl6uqKgob3cHaBXqjbhPoMaKQL0uOCavyKycjfubLXZmL+JIYArEWBGI1wTAPQIxXgTiNQFwvUCNFYF6XUCwsPUMwx0Dvu2NFV6fQQKgXl6RWXM27FNJRbXD+4aYpGWjkllSCwhCLKcFAAAAAAB8nWXli+rTdXrqV/0lk3TsZLViI+uX1fLWQE0SJIAPyCsya+K6XTbX37MH9UaA4MJyWgAAAAAAwF80N2sk9aLOXuwZCRLA62rrDM3ZsN+p5Aj1RoDgw3JaAAAAAADAXzQ1MLykvEoT1+3SytHJXn22SYIE8LJl2w6qpMLxB5082ASCD8tpAQAAAAAAf1FbZyhno+2B4Ybqi7PnbNyvG5LiWGILCEab95i1eOtBh/ah3ggQXFy5nJZJ0pTre+nB60muAgAAAAAA9yosLmt2BQxDkrm8SoXFZV5baivEK2cFoM17vlXGXxwfCU69kcCxcuVK9evXT1FRUYqKilJqaqr+8Y9/eLtb8CF5RWZd/eQ2jXrhQ63Z8S8ZzhYq+q/ld16uqTdcTHIEAAAAAAC43ZET9q2aY287d2AGCeBhtXWGlm07pMVbv3BoP+qNBJ7zzz9fCxYsUK9evWQYhtauXatbb71Vn376qfr06ePt7sHLXLWclkT8AAAAAAAAnhcbGeHSdu5AggTwoLwis+Zs2KeSimqH9qPeSGAaPnx4g5/nzZunlStX6sMPPyRBEqRcvZzWuKt66MY+cUpJjCF+AAAAAAAAj0pJjFF8dIRKyqts1iExSYqLjlBKYoynu2ZFggTwEGdHg09Lu1hT0nq5oUfwJbW1tXrttddUWVmp1NRUb3cHXpBXZFbOxv3Nrs3piOV3Xq5b+iW45FgAAAAAAACOCg0xKXt4kiau2yWT1CBJYhnGmT08yauDOkmQAB5QX2/kU4f3i4sKV8bQnm7oEXzF3r17lZqaqqqqKnXs2FGvv/66kpKSmmxfXV2t6uqfZiBVVFR4optwM5bTAgAAAAAAgeiGpDhNTbtYL35QrOM/nrJuj/OR5xckSAA3crbeiMWcX/RhWZwAd8kll2j37t0qLy/XX//6V40dO1bvvvtuk0mS3Nxc5eTkeLiXcCdnE6hnYjktAAAAAADga2ytltGpXVuNH9LDZ8oJkCAB3MTZeiOSFGKSlo1K9noGFe4XFhamnj3rZwkNHDhQH3/8sZYuXao//OEPNttnZWUpMzPT+nNFRYW6devmkb7CtVqbQD0Ty2kBAAAAAABfkldk1sR1uxrVHin/8ZSWbD2oS+IifeLZJwkSwA1au1zOslGX65Z+3g8Q8Ly6uroGS2idLTw8XOHh4R7sEVzNkhhZ/f5XKq863apjsZwWAAAAAADwNbV1hnI27rdZmN1Q/UoYORv364akOK/PIiFBArhYa5bL4WFncMnKytLNN9+s7t2768SJE1q/fr22b9+ut956y9tdg5vkFZk14297dfyHUy03bgLLaQEAAAAAAF9WWFzWYFmtsxmSzOVVKiwuU+pFnT3XMRtIkAAu0trlcqal9fKZtffgGUeOHNGYMWNkNpsVHR2tfv366a233tINN9zg7a7BDVxViJ3ltAAAAAAAgC87cqLp5Igz7dyJBAngAq6oN8KSWsHnT3/6k7e7AA9xRSF2ZpgBAAAAAAB/EBsZ4dJ27kSCBGgl6o0AaEprZ5axnBYAAAAAAPA3KYkxio+OUEl5lc06JCZJcdERSkmM8XTXGiFBArQC9UYA2OKqQuwspwUAAIDaOkOFxWU6cqJKsZERTg2cccUxAABwxB1XdLc5YNTy6ZM9PMknPotIkABOyisya9J655Ij1BsBApcrCrGTQAUAAIBUf2+Zs3F/g0K3jt4ruuIYAADYy9bnzpnifOwzKMTbHQD8Uc3pOj32epHD+4WYpBV3JmtK2sUkR4AAU1tnaOnWg5qwblerkiPT0nrp/elDfeZGAQAAtGz58uXq0aOHIiIiNHjwYBUWFjbb/vjx45o8ebLi4+MVHh6uiy++WJs3b/ZQb+Ev8orMmrhuV6MHTCXlVZq4bpfyisweOQYAAPZq6nPHwhefeZAgARyUV2TWlblbVVZZ4/C+1BsBAlNekVlDFuQ7XWtEks5p31arRpNABQDA37zyyivKzMxUdna2du3apf79+ys9PV1Hjhyx2b6mpkY33HCDvv76a/31r3/VgQMH9MILL+i8887zcM/hy2rrDOVs3G9z3XbLtpyN+1VbZ6uF644BAIC9mvvckeqX1nr542882SW7sMQW4ABnC7IzfRkIXM7GBQuTpCnX99KD17PsHgAA/uiZZ57R/fffr/Hjx0uSVq1apU2bNmn16tWaMWNGo/arV69WWVmZduzYobZt20qSevTo4ckuww8UFpc1OfpWqk9wmMurVFhcptSLOrvtGAAA2MtfP3eYQQLYqb4gu+MPQX1x6hgA13A2Lpxp+Z2Xa+oNzBoBAMAf1dTUaOfOnUpLS7NuCwkJUVpamgoKCmzus2HDBqWmpmry5Mnq2rWr+vbtq/nz56u2trbJ81RXV6uioqLBC4HtyImmHzDZ284VxwAAwF7++rnDDBKgBbV1hpZtO+Tw0jkhJmnZqGSW1AICkLNx4UzMLAMAwP8dO3ZMtbW16tq1a4PtXbt21eeff25zn6+++krbtm3TXXfdpc2bN+vQoUOaNGmSTp06pezsbJv75ObmKicnx+X9h++KjYxodTtXHAMAAHv56+cOCRKgGXlFZs3ZsE8lFdUO70u9ESAwtSYuWExL66WMoSypBQBAMKqrq1NsbKyef/55hYaGauDAgfrPf/6jRYsWNZkgycrKUmZmpvXniooKdevWzVNdhhekJMYoPjpCJeVVNtdyN0mKi45QSmKMW48BAIC9/PVzhyW2gCZs3mPWhHW7HH4I2rlDmFaNTtYt/RLc1DMA3uJsXLCgEDsAAIGlS5cuCg0NVWlpaYPtpaWliouLs7lPfHy8Lr74YoWGhlq3XXrppSopKVFNTY3NfcLDwxUVFdXghcAWGmJS9vAkSfUPlM5k+Tl7eFKz95SuOAYAAPYKDTFp1rCkJpMjkm9+7pAgAWxwtq5ATIe2Ksi6niVzgABSW2eo4MvvNGdDkSY7WW+kU7u2mpbWS5/MvIH4AABAAAkLC9PAgQOVn59v3VZXV6f8/Hylpqba3GfIkCE6dOiQ6urqrNu++OILxcfHKywszO19hv+4qW+8Vo5OVlx0w6VI4qIjtHJ0sl33la44BgAA9sgrMuvxTfttvufLnzsssQWcZfMesyat/9SpfeffdpnC2pB3BAJFXpFZORv3y1zufAExltMCACCwZWZmauzYsRo0aJBSUlK0ZMkSVVZWavz48ZKkMWPG6LzzzlNubq4kaeLEiVq2bJmmTJmiBx98UAcPHtT8+fP1u9/9zpuXAR91U9943ZAUp8LiMh05UaXYyPqlSRy5t3TFMQAAaE5ekVkT1+2yOXtEkmYNu9QnkyMSCRKggfqZI44nRywF2X31Fx2A4+qTpc7NGJF+igvUIgIAILCNHDlSR48e1ezZs1VSUqIBAwYoLy/PWrj98OHDCgn5aRBVt27d9NZbb2natGnq16+fzjvvPE2ZMkXTp0/31iXAx4WGmJR6UWevHwMAAFtq6wzlbNzfZHLEJOnxTZ8pvW+8TybnSZAAqv9FXrbtkBZv/cKp/SnIDgQWZ5OlZyIuAAAQPDIyMpSRkWHzve3btzfalpqaqg8//NDNvQIAAHC/wuKyZlfeMCSZy6tUWFzmk8l6EiQIenlFZs3ZsM+posvx0RHKHp7EzBEgQLQ2WSoRFwAAAAAAQPA4csK+ZcntbedpJEgQ1FqzhA51BYDA0ppkqQVxAQAAAAAABJPYyAiXtvM0EiQIWq2tN8LSOUDgoN4IAAAAAACA41ISYxQfHaGS8iqbdUhMkuKiI5SSGOPprtklpOUmQGCprTO0dOtBTVr/qeqaqh7UDOoKAIGlPlnqfHJEIi4AAAAAAIDgdccV3ZtMjkhS9vAkn11tgxkkCCqtWUKHEeJA4KmfOeJ8MXbqjQAAAAAAgGCVV2RWzsb9TRZpj/OD5yYkSBA0WruEDiPEgcDi7DJ7JknjruqhG/vEKSUxxmdHQAAAAAAAALhLXpFZE9ftsjlzRPKfOq0kSBAUnH0QKjFCHAg0tXWGlm07pMVbv3Bq/+V3Xq5b+iW4uFcAAAAAAAD+obbOUM7G/U0mR0ySXv74G2UM7eXJbjmFGiQIeHlFZqfrjUxL66X3pw8lOQIEiLwis4YsyHcqORIfHaFVo5NJjgAAAADwK08//bSuuOIKRUZGKjY2ViNGjNCBAwcatKmqqtLkyZPVuXNndezYUbfffrtKS0sbtDl8+LCGDRum9u3bKzY2Vo888ohOnz7doM327duVnJys8PBw9ezZU2vWrGnUn+XLl6tHjx6KiIjQ4MGDVVhY6PJrBuBehcVlTS6rJUmGJHN5lQqLyzzXKSeRIEFAqzldp8deL3J4vxCTtOLOZE1Ju9jnp4EBsM/mPWZNWLfLqRpEJEsBAAAA+KsPPvhAkydP1ocffqgtW7bo1KlTuvHGG1VZWWltM23aNG3cuFGvvfaa3n33XX377bf65S9/aX2/trZWw4YNU01NjXbs2KG1a9dqzZo1mj17trVNcXGxhg0bpp///OfavXu3pk6dqvvuu09vvfWWtc0rr7yizMxMZWdna9euXerfv7/S09N15MgRz/xhAHCJIyeaTo44086bTIZhODGu3ndUVFQoOjpa5eXlioqK8nZ34EPyisx67PW9Kqs85fC+K1hCJ+AEaqwI1OtyNcsye47OJAsxSctGJVN/CH4vEGNFIF4TAPcIxHgRiNcEwPWaihVHjx5VbGys3n33Xf3sZz9TeXm5zj33XK1fv16/+tWvJEmff/65Lr30UhUUFOjKK6/UP/7xD/3P//yPvv32W3Xt2lWStGrVKk2fPl1Hjx5VWFiYpk+frk2bNqmo6KeBqnfccYeOHz+uvLw8SdLgwYN1xRVXaNmyZZKkuro6devWTQ8++KBmzJjRqusC4DkFX36nUS982GK7v9x/pVIv6uyBHjVmb6xgBgkCkmWkuKPJEZbQAQJLbZ2hpVsPOr3M3rJRl5McAQAAABBQysvLJUkxMTGSpJ07d+rUqVNKS0uztundu7e6d++ugoICSVJBQYEuu+wya3JEktLT01VRUaF9+/ZZ25x5DEsbyzFqamq0c+fOBm1CQkKUlpZmbWNLdXW1KioqGrwAeFdKYozioyPU1Lo7JtU/Z01JjPFkt5ziEwkS1h6EK9WPFN/l8H4soQMEltbUG7Ess0eyFHCMo/d0r732mnr37q2IiAhddtll2rx5s4d6CgAAEJzq6uo0depUDRkyRH379pUklZSUKCwsTJ06dWrQtmvXriopKbG2OTM5Ynnf8l5zbSoqKvTjjz/q2LFjqq2ttdnGcgxbcnNzFR0dbX1169bN8QsH4FKhISbNGpZks0i7JWmSPTzJL0oXeD1BwtqDcBVnR4pTbwQIPK2pNyIxcwRwhqP3dDt27NCoUaN077336tNPP9WIESM0YsSIBksyAAAAwLUmT56soqIivfzyy97uit2ysrJUXl5ufX3zzTfe7hIQ9PKKzHp8036b78VFR2jl6GS/GYTu9QTJM888o/vvv1/jx49XUlKSVq1apfbt22v16tXe7hr8SGtGivMgFAgszs4ik1hmD2gNR+/pli5dqptuukmPPPKILr30Uj3++ONKTk62rkcNAAAA18rIyNCbb76pd955R+eff751e1xcnGpqanT8+PEG7UtLSxUXF2dtU1pa2uh9y3vNtYmKilK7du3UpUsXhYaG2mxjOYYt4eHhioqKavAC4D15RWZNXLdL5nLbBdhnDbvUb5IjkpcTJM6uPQicydmR4p07hPEgFAgweUVmp+uNsMwe4Dxn7ulaWqMaAAAArmEYhjIyMvT6669r27ZtSkxMbPD+wIED1bZtW+Xn51u3HThwQIcPH1ZqaqokKTU1VXv37m0wO3jLli2KiopSUlKStc2Zx7C0sRwjLCxMAwcObNCmrq5O+fn51jYAfFttnaGcjfttLq0l1S+v9fimz1TrzIMZL2njzZM3t/bg559/bnOf6upqVVf/9CCcwkzBrX6k+KcO7xfToa0Ksq5XWBuvT6IC4CI1p+v02OuOL80TYpKWjUpmJhnQCs7c0zW1RnVT609zDwgAAOCchx56SH/961/197//XZGRkdb7rejoaLVr107R0dG69957lZmZqZiYGEVFRenBBx9UamqqrrzySknSjTfeqKSkJN19991auHChSkpKNHPmTE2ePFnh4eGSpAkTJmjZsmV69NFHdc8992jbtm169dVXtWnTJmtfMjMzNXbsWA0aNEgpKSlasmSJKisrNX78eM//wQBwWGFxWZMzRyTJkGQur1JhcZlSL+rsuY61glcTJM7Izc1VTk6Ot7sBL6utM7Rs2yGnltSSpPm3XUZyBAggeUVmPfb6XpVVnnJ4X5bZA/wD94AAAADO+dOf/iRJuu666xpsf/HFFzVu3DhJ0uLFixUSEqLbb79d1dXVSk9P14oVK6xtQ0ND9eabb2rixIlKTU1Vhw4dNHbsWM2dO9faJjExUZs2bdK0adO0dOlSnX/++frjH/+o9PR0a5uRI0fq6NGjmj17tkpKSjRgwADl5eU1GjgDwDcdOdF0csSZdr7AqwkSZ9YezMrKUmZmpvXniooKdevWza39hG/JKzJrzoZ9ThVftowUZwkdIHBs3mPWpPWO1xyJj45Q9vAk4gHgAs7c0zW1RjX3gAAAAK5VXl7eYt2OiIgILV++XMuXL2+yzQUXXKDNmzc3e5zrrrtOn37a/EofGRkZysjIaLYNAN8UGxnh0na+wKtD6J1Ze5DCTMHN2XojFowUBwKLswXZqTcCuJYz93QtrVF9Nu4BAQAAAMC7Bl5wjmI6hDX5vkn1A1JTEmM816lW8voSW6w9CHs5W29EYqQ4EGicXWaPeiOA+7R0TzdmzBidd955ys3NlSRNmTJF1157rZ5++mkNGzZML7/8sj755BM9//zz3rwMAAAAAIANeUVm5Wzcr7LKGpvvm/773+zhSQoNMdls44u8niBh7UHYo34JHeeSI9PSeiljaC+/+sUE0LTWLLPHLDLAfVq6pzt8+LBCQn6avHzVVVdp/fr1mjlzph577DH16tVLb7zxhvr27eutSwAAAAAA2JBXZNbEdbtkNNMmzk8HqJsMw2juunxeRUWFoqOj7VpPEf7JMnOkzsF/qYwUx5kCNVYE6nU1xdl6I507hGnebX397kMacJVAjBWBeE0A3CMQ40UgXhMA1wvUWBGo1wX4qto6Q1c/uU3m8qYLr8d0aKsPs9IU1sarFT0asDdW+E6PgbPU1hlauvWgJq13PDkiMVIcvi83N1dXXHGFIiMjFRsbqxEjRujAgQPe7pbPcrbeSEyHtirIup7kCAAAAAAAgIMKi8uaTY5IUlnlKe381/ce6pFreX2JLcCW1iyhQ70R+It3331XkydP1hVXXKHTp0/rscce04033qj9+/erQ4cO3u6eT8krcn6Zvfm3XeZTIxgAAAAAe9TWGSosLtORE1WKjawveOuKpaPddVwAQGA6cqL55Iij7XwNCRL4HHvWtGsK9UbgT/Ly8hr8vGbNGsXGxmrnzp362c9+5qVe+Z6a03V67PUih/ezLLNHshQAAAD+xlII98wRu64YDOiu4wIAAldsZIRL2/kahtTCp9TWGZqzYb/DyZEQk7TizmRNSbuY5Aj8Vnl5uSQpJibGyz3xHXlFZl2Zu1VllTUO78syewAAAPBHlkGDZy9nUlJepYnrdimvyOxTxwUABLbvK6vV3ONWk+qT7SmJ/vk8iwQJfMqybQdVUuH4dCwehMLf1dXVaerUqRoyZIj69u3bZLvq6mpVVFQ0eAWqzXvMmrBul8oqTzm0X3x0hFaNTtYt/RLc1DMAAADAPWrrDOVstD1o0LItZ+N+1TpYqNNdxwUABLa8IrMm21EfOnt4kt8OWidBAp9gKci+eOtBh/azzBzhQSj83eTJk1VUVKSXX3652Xa5ubmKjo62vrp16+ahHnqWswXZp6X10vvTh7I8AAAAAPxSS4VwDUnm8ioVFpf5xHEBAIGrueS6RYhJWn6nfy9vTg0SeF1rCrIzcwSBICMjQ2+++abee+89nX/++c22zcrKUmZmpvXnioqKgEuSOFOQ3VJvhHgAAAAAf+auQriBXmAXAOB6LSXXJanOkM7pEOahHrkHCRJ41eY9Zk1a7/gocYrIIRAYhqEHH3xQr7/+urZv367ExMQW9wkPD1d4eLgHeucdljpEjiJZCgAAgEDgrkK4gV5gFwDgesGSXCdBAq+pX0LHsVHiUv0SOhlDe/ntunaAxeTJk7V+/Xr9/e9/V2RkpEpKSiRJ0dHRateunZd75x2O1iHq3CFM827rS7IUAAAAASElMUbx0REqKa+yuaSJSVKcE4Vw3XVcAEDgCpbkOjVI4HGWeiOT7Cjwc7ZpaRdrStrFJEcQEFauXKny8nJdd911io+Pt75eeeUVb3fN45ypQxTToa0Ksq4nOQIAALxu+fLl6tGjhyIiIjR48GAVFhbatd/LL78sk8mkESNGuLeD8BuhISZlD0+SVJ+0OJPlZ2cK4brruACAwDXwgnMU08zyWSbVr/Lj78l1EiTwqLwis4YsyNfirV84vG9cVLgyhvZ0Q68A7zAMw+Zr3Lhx3u6aRzkbF+bfdpnC2vAxBgAAvOuVV15RZmamsrOztWvXLvXv31/p6ek6cuRIs/t9/fXXevjhh3XNNdd4qKfwFzf1jdfK0cmKi244IjcuOkIrRztfCNddxwUABJ68IrOuXfSOyiprbL4fSMl1ltiCxzhbb0Sq/6Wb84s+fv8LB6ChvCKzJq7bZXOaf1MsBdn5AgcAAHzBM888o/vvv1/jx4+XJK1atUqbNm3S6tWrNWPGDJv71NbW6q677lJOTo7++c9/6vjx4x7sMfzBTX3jdUNSnAqLy3TkRJViI+tH6Lb2O7G7jgsACBz2PKuJC6D60CRI4BHO1huRKMgOBCpLQXYHV9qjIDsAAPAZNTU12rlzp7KysqzbQkJClJaWpoKCgib3mzt3rmJjY3Xvvffqn//8pye6Cj8UGmJS6kWd/ea4AAD/V1tnKGdj889qYjq01buP/DxgVvUgQQK3qq0ztGzbIaeW1JIoyA4EMkcLskv1dYhu6Zfgph4BAAA45tixY6qtrVXXrl0bbO/atas+//xzm/u8//77+tOf/qTdu3fbfZ7q6mpVV1dbf66oqHCqvwAAAM0pLC6Tubz5ZzVllae081/fB0yynQQJ3CavyKw5G/appKK65cZnsSyhwyhxIDDlFZkdKsguUYcIAAD4vxMnTujuu+/WCy+8oC5duti9X25urnJyctzYMwAAAOnICfsGstrbzh+QIIFbtKbeiMQSOkAgqzldp8deL3JoH+oQAQAAX9SlSxeFhoaqtLS0wfbS0lLFxcU1av/ll1/q66+/1vDhw63b6urqJElt2rTRgQMHdNFFFzXaLysrS5mZmdafKyoq1K1bN1ddBgAAgCQpNjLCpe38AQkSuBz1RgA0Ja/IrMde36uyylN270NcAAAAviosLEwDBw5Ufn6+RowYIak+4ZGfn6+MjIxG7Xv37q29e/c22DZz5kydOHFCS5cubTLpER4ervDwcJf3HwAA4EzfV1YrxCTVNVGExKT6Au0piTEe7Zc7kSCBy1BvBEBz8orMmrhul0NF2YkLAADA12VmZmrs2LEaNGiQUlJStGTJElVWVmr8+PGSpDFjxui8885Tbm6uIiIi1Ldv3wb7d+rUSZIabQcAAPCkvCKzJq//tMXnNtnDkwLqOQ0JErgE9UYANKe2ztCcDfsdTI5crClpvdzWJwAAAFcYOXKkjh49qtmzZ6ukpEQDBgxQXl6etXD74cOHFRIS4uVeAgAANK22zlDOxuaf21ie4QbaCh8kSNBqzowKPxP1RoDAt2zbQZVU2F/Ai4LsAADAn2RkZNhcUkuStm/f3uy+a9ascX2HAAAAHFBYXCZzefPPbeoM6ZwOYR7qkeeQIEGrODMq3IK6AkBwyCsya/HWg3a3pyA7AAAAAACA5xw5Yd+gVnvb+RMSJGgVR0eFW1BXAAgONafr9NjrRXa379whTPNu60viFAAAAAAAwEO+PlZpV7vYyAg398TzSJDAaZv3ODYqXKLeCBBM8orMeuz1vSqrPGVX+5gObVWQdb3C2rBGNwAAAAAAgCfYs/KHSVJcdIRSEmM80ykPIkECp2ze860y/vKpw/tRbwQIDs7UJpp/22UkRwAAAAAAADzEUpzdHtnDkwJyNSASJHBIbZ2hZdsOafHWLxzaj3ojQPBwpjbRtLSLiQ8AAAAAAAAeZE9xdkmaGsDPbUiQwG55RWbN2bBPJRXVDu1HvREguDhamyguKlwZQ3u6sUcAAAAAAAA4m71F13t0ae/mnngPCRLYZfMesyat3+XwftPSLtaUtF5u6BEAX2TPupVnMkma84s+JFABAAAAAAA8rEvHcLvaBWJxdgsSJGiRs/VGGBUOBJea03V67PUiu9t37hCmebf1DdgpmgAAAAAAAL7KslpQcwK5OLsFCRI0ydl6IxaMCgeCR16RWY+9vldllafsah/Toa0Ksq6nKDsAAAAAAICH5RWZNXHdrmbrx1qe6gZqcXYLEiSwydl6I5IUYpKWjUpmVDgQJOz5UD3b/NsuIzkCAAAAAADgYbV1hnI27m/xOU5cdISyhycF/DNeEiRoxNl6IxbLRl2uW/oF9i8OgHq1dYbmbGj5Q/VM09IuDvgPVwAAAAAAAF9UWFwmc3nLxdmf+lV/DenVxQM98i4SJGjA2XojkhQfJFlFAD9Ztu2gSipa/lC1oDYRAAAAAACA9xw5Yd9znGOVjq8s5I9IkEBS6+uNTEvrpYyhvQJ6PToADeUVmbV460G725tEbSIAAACgObV1hgqLy3TkRJViI+uL4rrr/tmT5wIA+I6vj1Xa1S42MsLNPfENJEjgknojLKkFBBfLepX26twhTPNu68sMMwAAAKAJeUVm5Wzc32DZE3et1ODJcwEAfIc9g11Nqq8/kpIY45lOeRkVcoPc5j1mTVi3y6nkiES9ESBYffjVd3atVylJMR3aqiDrer5oAQAAAE3IKzJr4rpdje6xS8qrNHHdLuUVmf3yXAAA3+HIYNfs4UlBM6uQBEkQq6834lwx9vjoCK0anaxb+iW4uFcAfF1ekVmTX7I/dsy/7TKFteHjBgAAALDF8sDKsPGeZVvOxv2qrbPVwnfPBQDwLfYOdp2adnFQDXLliVWQyisya9L6T+XMPc+0tF56f/rQoPpFAVDPMtrs+I+n7Go/Lcg+VAEAAABHFRaXNfvAypBkLq9SYXGZX50LAOA7HBns2qNLezf3xrdQgyQI1Zyu02OvFzm8H/VGgODW3GgzW+KiwpUxtKdb+wQAAAD4uyMn7Fu61t52vnIuAIBvsAx2tfd5TrAUZ7dgBkmQySsy68rcrSqrrHF4X+qNAMGtpdFmZzJJmvOLPkGzXiUAAADgLHsfRLnigZUnzwUA8D5HBruaVF9WIViKs1uQIAkiloLsZZX2LY1jQb0RAJK0dX+JXe06tW+rlaOTWVoLAAAAsENKYozioyPU1NAiVz6w8uS5AADe58hgVym4irNbkCAJEs4WZKfeCACpfvbZnz742q62y0eRHAEAAADsFRpiUvbwJElqlLiw/OyqB1aePBd8y3vvvafhw4crISFBJpNJb7zxRoP3DcPQ7NmzFR8fr3bt2iktLU0HDx5s0KasrEx33XWXoqKi1KlTJ9177706efJkgzZ79uzRNddco4iICHXr1k0LFy5s1JfXXntNvXv3VkREhC677DJt3rzZ5dcLoJ69SyYG82BXEiQBrrbO0NKtBx0uyB5iklbcmawpaRdzYwQEOct0zJZYRptdeVFn93cKAAAACCA39Y3XytHJiotuuLRVXHSEyx9YefJc8B2VlZXq37+/li9fbvP9hQsX6tlnn9WqVav00UcfqUOHDkpPT1dV1U8PV++66y7t27dPW7Zs0Ztvvqn33ntPDzzwgPX9iooK3Xjjjbrgggu0c+dOLVq0SHPmzNHzzz9vbbNjxw6NGjVK9957rz799FONGDFCI0aMUFGR47VyAbTs62OVdrUL5sGuJsMwHHhs7lo9evTQv/71rwbbcnNzNWPGDLuPUVFRoejoaJWXlysqKsrVXfRreUVmzdmwTyUV1Q7vu+LOy1lSCwElUGOFJ66r4MvvNOqFD+1qu4ovVIBPcnesKCsr04MPPqiNGzcqJCREt99+u5YuXaqOHTs2uc91112nd999t8G23/72t1q1apVd5wzUuA7A9QIxXgTiNaFebZ2hwuIyHTlRpdjI+qWu3DVo0ZPngnc0FStMJpNef/11jRgxQlL97JGEhAQ99NBDevjhhyVJ5eXl6tq1q9asWaM77rhDn332mZKSkvTxxx9r0KBBkqS8vDzdcsst+ve//62EhAStXLlSv//971VSUqKwsDBJ0owZM/TGG2/o888/lySNHDlSlZWVevPNN639ufLKKzVgwADuAwEXyyuqL7fQHJPqE+TvTx8acJ8B9sYKr88gmTt3rsxms/X14IMPertLAcFSb8TR5EjnDmHUGwHQgL21R+4Z0oPkCBCkWhpN2JT777+/wX2grSUYAAAIJqEhJqVe1Fm3DjhPqRd1duvDKk+eC76tuLhYJSUlSktLs26Ljo7W4MGDVVBQIEkqKChQp06drMkRSUpLS1NISIg++ugja5uf/exn1uSIJKWnp+vAgQP6/vvvrW3OPI+ljeU8tlRXV6uioqLBC0Dz7F0NRGJpxTbe7kBkZKTi4uK83Y2AUl9v5FOH94vp0FYFWdcrrI3X82YAfIQjtUduSCKWA8Hos88+U15eXoPRhM8995xuueUWPfXUU0pIaHrQRfv27bkPBAAA8LKSkvpBcV27dm2wvWvXrtb3SkpKFBsb2+D9Nm3aKCYmpkGbxMTERsewvHfOOeeopKSk2fPYkpubq5ycHCeuDAheH371nV3F2aemXRz0g129/iR8wYIF6ty5sy6//HItWrRIp0+fbrY9WePmbd5jdrjeiMX82y4jOQLAytHaIymJMe7vFACfY89owqa89NJL6tKli/r27ausrCz98MMPTbblHhAAACA4ZWVlqby83Pr65ptvvN0lwKflFZk1+aXml9ay6NGlvZt74/u8OoPkd7/7nZKTkxUTE6MdO3YoKytLZrNZzzzzTJP7kDVumrMzR0JM0rIgLsQDwLbC4jK7RhsYYjomEMzsGU1oy5133qkLLrhACQkJ2rNnj6ZPn64DBw7ob3/7m8323AMCAAC4h2VGb2lpqeLjf3o2VFpaqgEDBljbHDlypMF+p0+fVllZmXX/uLg4lZaWNmhj+bmlNs3NKg4PD1d4eLgTVwYEn7wisyau2yV7x87HRka4tT/+wOXTBWbMmCGTydTsy1KYKTMzU9ddd5369eunCRMm6Omnn9Zzzz2n6uqm62aQNW6sts7Q0q0HnZ45smzU5bqlH8kRwBvee+89DR8+XAkJCTKZTHrjjTe83SWrkoqWkyMStUeAQOXIPZ0zHnjgAaWnp+uyyy7TXXfdpT//+c96/fXX9eWXX9pszz0gAACAeyQmJiouLk75+fnWbRUVFfroo4+UmpoqSUpNTdXx48e1c+dOa5tt27aprq5OgwcPtrZ57733dOrUKWubLVu26JJLLtE555xjbXPmeSxtLOcB4DzLSiD2PB5mNZCfuHwGyUMPPaRx48Y12+bCCy+0uX3w4ME6ffq0vv76a11yySU225A1biivyKw5G/Y5XIxdqv8lyB6exINNwIsqKyvVv39/3XPPPfrlL3/p7e5Y5RWZ9fib++xqS+0RIDDZe09nz2hCe1i+WB86dEgXXXRRo/e5BwQAAHDeyZMndejQIevPxcXF2r17t2JiYtS9e3dNnTpVTzzxhHr16qXExETNmjVLCQkJGjFihCTp0ksv1U033aT7779fq1at0qlTp5SRkaE77rjDWnPuzjvvVE5Oju69915Nnz5dRUVFWrp0qRYvXmw975QpU3Tttdfq6aef1rBhw/Tyyy/rk08+0fPPP+/RPw8gENm7EogFq4HUc3mC5Nxzz9W5557r1L67d+9WSEhIo2UaYFt9vRH71pM727S0XsoY2otfAsDLbr75Zt18883e7kYD9k7HNEmKY7QBELDsvac7czThwIEDJTUeTWiP3bt3S1KDZR0AAADgGp988ol+/vOfW3/OzMyUJI0dO1Zr1qzRo48+qsrKSj3wwAM6fvy4rr76auXl5Ski4qfld1566SVlZGTo+uuvV0hIiG6//XY9++yz1vejo6P19ttva/LkyRo4cKC6dOmi2bNn64EHHrC2ueqqq7R+/XrNnDlTjz32mHr16qU33nhDffv29cCfAhDYtu5veonjM3Vq31YLfnkZg+b/y2s1SAoKCvTRRx/p5z//uSIjI1VQUKBp06Zp9OjR1ml3aFpr642wpBYAW+ydjmlJrTLaAIA9own/85//6Prrr9ef//xnpaSk6Msvv9T69et1yy23qHPnztqzZ4+mTZumn/3sZ+rXr5+XrwgAACDwXHfddTKMpr/pmUwmzZ07V3Pnzm2yTUxMjNavX9/sefr166d//vOfzbb59a9/rV//+tfNdxiAQ/KKzPrTB1/b1Xb5qGQN6dXFvR3yI15LkISHh+vll1/WnDlzVF1drcTERE2bNs2awYZttXWGlm07pMVbv3Bqf+qNAP6turq6QZ2miooKlx7f3umYMR3CNO+2vow2ACCp5dGEp06d0oEDB/TDDz9IksLCwrR161YtWbJElZWV6tatm26//XbNnDnTW5cAAAAAAH7JMti1JZaVQK68qLP7O+VHvJYgSU5O1ocffuit0/ul1tQbYeYIEBhyc3OVk5PjtuMfOWHfWpUzh11KcgSAVUujCXv06NFgxGK3bt307rvveqJrAOARy5cv16JFi1RSUqL+/fvrueeeU0pKis22L7zwgv785z+rqKhIkjRw4EDNnz+/yfYAAADN+fCr7+wa7GqIlUBsCfF2B2CfzXvMmrBul1PJEYmZI0CgyMrKUnl5ufX1zTffuPT4Xx+rtKtdXHQ7l54XAADAX73yyivKzMxUdna2du3apf79+ys9PV1Hjhyx2X779u0aNWqU3nnnHRUUFKhbt2668cYb9Z///MfDPQcAAP4ur8isyS/ZV6P6niE9GOxqAwkSP1Bfb8S5Yuzx0RFaNTpZt/RLcHGvAHhDeHi4oqKiGrxcJa/IrMVbDzbbxqT6uEJhdgAAgHrPPPOM7r//fo0fP15JSUlatWqV2rdvr9WrV9ts/9JLL2nSpEkaMGCAevfurT/+8Y+qq6tTfn6+h3sOAAD8WV6RWRPX7dLxH0/Z1f6GpDg398g/eW2JLdgnr8isSesdL8YuSdPSeiljaC+mTQE+7OTJkzp06JD15+LiYu3evVsxMTHq3r27x/ph73qVEtMxAQAALGpqarRz505lZWVZt4WEhCgtLU0FBQV2HeOHH37QqVOnFBPT9AAUd9ehAwAA/qW2ztCcDftltNzUWnuEwa62kSDxYTWn6/TY60UO70e9EcB/fPLJJ/r5z39u/TkzM1OSNHbsWK1Zs8Zj/bC3OPvUtIuZjgkAAPBfx44dU21trbp27dpge9euXfX555/bdYzp06crISFBaWlpTbZxdx06eF9tnaHC4jIdOVGl2Mj6h1ieHJTk7fMDAByzbNtBlVTYV0dWYrBrc0iQ+Ki8IrMee32vyirtmyJ1JuqNAP7juuuua1C42Fvs/VDt0aW9m3sCAAAQPBYsWKCXX35Z27dvV0RERJPtsrKyrANppPoZJN26dfNEF+EBeUVm5Wzc32DAUnx0hLKHJ3lkcJK3zw8AcIw9S6RbdGrfVgt+eRnxvBnUIPFBloLsjiZHqDcCwBl5RWY9/uY+u9rGRjb9xR0AACDYdOnSRaGhoSotLW2wvbS0VHFxza/z/dRTT2nBggV6++231a9fv2bburMOHbzLsn782bO5S8qrNHHdLuUVmQP6/AAAxziyRLokLR+VTHKkBSRIfIyzBdmnpfXS+9OH8g8egEMsX4haSshSnB0AAKCxsLAwDRw4sEGBdUvB9dTU1Cb3W7hwoR5//HHl5eVp0KBBnugqfJDlIZet+eSWbTkb96u2zj0zzr19fgCA4z786ju7lkiX6p/jXHlRZzf3yP+RIPERtXWGlm49qEnrP5Uj9x4hJmnFncmaknYx68gBcEhzX4jOZIksrFcJAADQWGZmpl544QWtXbtWn332mSZOnKjKykqNHz9ekjRmzJgGRdyffPJJzZo1S6tXr1aPHj1UUlKikpISnTx50luXAC9pqQ6gIclcXqXC4rKAPD8AwDF5RWZNfsn+gfU8x7EPNUh8QF6RWXM27FNJRbXD+1JvBICz7C3MHtMhTPNu68sMNQAAABtGjhypo0ePavbs2SopKdGAAQOUl5dnLdx++PBhhYT8NDZx5cqVqqmp0a9+9asGx8nOztacOXM82XV42ZET9o0Atredv50fAGA/ywog9o6rn5Z2Mc9x7ESCxMs27zFr0nrHl9TqzANLAK1k7xedmcMuJdYAAAA0IyMjQxkZGTbf2759e4Ofv/76a/d3CH7B3vp+7qoD6O3zAwDsU1tnaM6GllcAsYiLClfG0J5u7VMgIUHiRfX1Rj51eL+YDm1VkHW9wtqwQhoA59n7RScuup2bewIAAAAEn5TEGMVHR6ikvMrmQy+TpDg31gH09vkBAPZZtu2gSirsG+RqkjTnF31YWssBPGH3AmfrjVjMv+0ykiMAWi0lMUZxUU0nSSjMDgAAALhPaIhJ2cOTJP1U98/CE3UAvX1+AEDL8orMWrz1oF1tO7Vvq5Wjk1kFxEE8ZfewvCKzhizI1+KtXzi8r6UgO//IAbjClv0lqjpda/M9vhABAAAA7ndT33itHJ2suOiGA5fioiM88pDL2+cHADTNsrSWvZaPIm47gyW2PMjZeiMWFGQH4CotFffq1L6tcn95GR+sAAAAgJvd1DdeNyTFqbC4TEdOVCk2sn4Wt6cGKnn7/AAA2xxZWis+OkJXXtTZzT0KTCRIPMTZeiNS/T/w7OFJPKgE4BK1dYZyNjZf3Cu8TYhuSIrzWJ8AAACAYBYaYlKqFx9sefv8AICf1NYZWrbtkN1La0msANIaJEg8oH7miHPJkWlpvZQxtBf/wAG4TGFxmczlzY9AKKmoVmFxGV+SAAAAAAAAPCSvyKw5G/appKLa7n2mpV3MwPpWIEHiZs7OHAkxSctGJbOkFgCXO3LCvumZ9rYDAAAAAABA67S0HLotcVHhyhja0219CgYkSNzkp6lQjhdjl6g3AsB9YiMjWm7kQDsAAAAAAAA4z1KQ3ZHkiCTN+UUfVh5qJRIkbuDMVCgL6o0AcLeUxBjFR0eopLzK5gevSVJcdH1hRgAAAAAAALiXIwXZLVhayzVIkLiYM1OhLKg3AsATQkNMyh6epInrdskkNYhXluhDcS8AAADAvWrrDBUWl+nIiSrFRtYPUPL2Pbgv9gkAApkzBdklltZyJRIkLuTsVCjqjQDwpNo6Q9HtwjR+SA+9sftblVXWWN+LYxYbAAAA4HZ5RWblbNwvc/lPo4W9vaKEL/YJAAKZs6sQmcTSWq5EgsSFnJkKJVFvBIDn2PrSE9OhrW4bcJ7SkuIYIQYAAAC4WVMrT5SUV2niul1aOTrZ4wkJX+wTAASyzXvMmrR+l8P7kbh2vRBvdyAQ1NYZWrr1oMNToUJM0oo7k3VLvwQ39QwAfmL50nNmckSSvq88pdUffK3yH2tIjgAAAABuVFtnKGej7ZUnLNtyNu5XbZ0zC3cHTp8AIJBt3vOtMv7ieHJkWlovvT99KMkRFyNB0kp5RWYNWZCvxVu/cHhfZo4A8BS+9AAAAADeV1hc1mjA0pkMSebyKhUWlwV1nwAgEFkG2U9a/6kcffwyLe1iTUm7mIGtbsASW63AVCgA/sKRLz2pF3X2XMcAAACAIHLkhH3LctvbzhV8sU8AEGicrTciUZDd3UiQOKl+KtSnDu83La2XMob2ItsHwKP40gMAAAB4X2xkhEvbuYIv9gkAAomzg+wlCrJ7AktsOYipUAD8EV96AAAAAO9LSYxRfHSEmnoqYFL9qhMpiTFB3ScACBTO1huR6mPvytHJrELkZiRIHNCaeiNMhQLgTXzpAQAAALwvNMSk7OFJktTo3tzyc/bwJI8OrPTFPgGAv2vNIHuJguyeRILETpv3mDVh3S6n1oljKhQAb+NLDwAAAOAbbuobr5WjkxUX3XD2dpwXRwr7Yp8AwB9ZEiPJc992apB9iElacWcyqxB5EDVI7OBsvRGJguwAfIflS0/Oxv0NCrbHEacAAAAAj7qpb7xuSIpTYXGZjpyoUmxk/Wxubz4M88U+AYA/ySsya8bf9ur4D6ecPsayUZfrln48n/EkEiTNqK0ztGzbIaeyfRIF2QH4Hr70AAAAAN5VW2c0uB//n34JPnM/HhpiUupFnSU17iffGwDAttY+Q5YYZO9NJEiakFdk1pwN+5xaUivEJC0blUy2D4BPOvNLDwAAAADPySsyN5rR7YsPxfylnwDgTZbEyOr3v1J51Wmnj8Mge++iBokNrak3IjEVCgAAAAAANJRXZNbEdbsaJB0kqaS8ShPX7VJekdlLPWvIX/oJAN5iqTNy2Zy3tHjrF04nR6g34huYQXIW6o0AAAAAAABXqq0zlLNxvwwb7xmSTJJyNu7XDUlxXn1I5i/9BABPsyw7uGV/iV795N86We38jBELBtn7BhIk/0W9EQAAAAAA4A6FxWWNZmScyZBkLq9SYXGZV5fD9Zd+Availg5ay/LM+MUPinX8R+eLr5+JQfa+hQSJqDcCAAAAAADcp6Si6aTDmY6csK+du9h7fm/3E7CHrVo6ndq11dirLlBKYmcdO1lN0gQ2uWO2iAWD7H1P0CdILGtr2po+ag+mQgEAAAAAgKbkFZn1+Jv77GobGxnh5t645vxfH/vBzT0BWqep533HfzylpfmHJB2ybuvUrq3GD+nBQ+sgdeYsoy4dwvXx12Vas+Nrl80WsTinfVvl/vIyZo34oKBOkNTWGZqzwfbami1hKhQAAAAAAGiOvYMyTZLioutHsntTSmKM4qMjVFJe1Wyfl2z9QpfEdeSZCHxSc7V0bDn+4ykt3npQz//zK/1m4Pk6/5z2iukYrrgoZpcEMncsnWULCTjfF9QJkmXbDto9zfVMTIUCAAAAAADNsXdQpuXJQvbwJK8/ZwgNMSl7eJImrNvVbDtD0pwN+yjWDp/UUi2dplRW1+rFHf9qsC2mQ1vd2j+BpImfs8wSKSn/UWWVNfr38R/1mouXzrKFZ8j+IWgTJJv3mLV460GH9qHeCAAAAAAAsIe9gzJjOoRp3m19fWY2xk194zUtrVeLz0xKKqq1bNshTUnr5aGeAfZxZY2csspTzSZNOrUP0/Efakie+ICzkyCWv5t/H/9Rf9/9rcoqazzWF5bT8i9BmSDZvOdbZfzlU4f3o94IAAAAAACw5cyHcx8cOqa/7vqPXfvNHHapzz1E69Glg13tFm/9Qt+UVWpIr3N5OAyf4e5aPraSJhZnzziJ7RgumaQjFVUqq6xpclsw//6cXQPEnj+vs7d5IwliS4ewUD3wswuZNeJn3JYgmTdvnjZt2qTdu3crLCxMx48fb9Tm8OHDmjhxot555x117NhRY8eOVW5urtq0cU+3LGvLLd76hUP7UW8EgDstX75cixYtUklJifr376/nnntOKSkpLj/PmTcdsZHBe/MFwLXsuec7m2EYys7O1gsvvKDjx49ryJAhWrlypXr1cs8IVFd86bJ325mjCF15XM7lnnMF+wMJuI6j93OvvfaaZs2apa+//lq9evXSk08+qVtuucUtfXM0Bgbq77u7z9Waor5x0e3c8DffOo48YP7rrv9Yk0HOPhwO5H8b7jhXIHx2ufN7sKWWjjPLbLVWc8mTlrT0++Ouf6ve/B1wV0F0T6POiH9zW4KkpqZGv/71r5Wamqo//elPjd6vra3VsGHDFBcXpx07dshsNmvMmDFq27at5s+f7/L+5BWZNWfDPpVUVDu0H2vFAXCnV155RZmZmVq1apUGDx6sJUuWKD09XQcOHFBsbKzLzpNXZFbOxv0NbhBJ/gJwhZbu+WxZuHChnn32Wa1du1aJiYmaNWuW0tPTtX//fkVEuHbEn634B5yNz0S0hqP3czt27NCoUaOUm5ur//mf/9H69es1YsQI7dq1S3379nVp34iBvi/eBwqz2+LsA+bWPByGY/z5s8vd34PtraXja/j98S8dw0M1clA3pSXF+X3CMtiZDMNoqV5Yq6xZs0ZTp05tNJrwH//4h/7nf/5H3377rbp27SpJWrVqlaZPn66jR48qLCzMruNXVFQoOjpa5eXlioqKstkmr8isiet2tVgY7WzT0i5mLU0gQNgTK7xh8ODBuuKKK7Rs2TJJUl1dnbp166YHH3xQM2bMaHH/1sRAy0f3ytHJfnlTDcB+noiBTd3znc0wDCUkJOihhx7Sww8/LEkqLy9X165dtWbNGt1xxx12nc+d94AITibxmRio3B0DHb2fGzlypCorK/Xmm29at1155ZUaMGCAVq1aZdc5iYGBY5UPx528IrPfPWAONi19dgXz92Cp/t/wjL/t1fEf/Ht2AnwLs0X8h72xIsSDfWqgoKBAl112mTU5Iknp6emqqKjQvn37XHae2jpDORv3O3xTGBcVroyhPV3WDwA4W01NjXbu3Km0tDTrtpCQEKWlpamgoMAl52guBlq25Wzcr9o6vjoD8Izi4mKVlJQ0iH3R0dEaPHiwy2Kf5Pw9IIIbn4lwlDP3cwUFBQ3aS/XfhYmBwWda2sU+mxyRfirWDt/mb59dnvgebHFT33jtnHmDpqVdrE7t2rr02AguHcNDde+QHvrL/Vdq56wbNCXtYpIjAcRrRdpLSkoaJEckWX8uKSlpcr/q6mpVV/+0TFZFRUWz5yksLnNqOvGcX/ThHzoAtzp27Jhqa2ttxsLPP//c5j6ujoGGJHN5lQqLy5R6UWf7Ow8ATrLc59mKfb5wD4jgxWcinOHM/VxT34WJgcHFXwZlZgztpb8UfqOSCv49+SJ//OzyxPfgM4WGmDQlrZcyhvZsUI8pUGpfwL2YLRIcHJpBMmPGDJlMpmZfTQUzV8nNzVV0dLT11a1bt2bbHznh2Id4iElacafvTnEFENzcFQMdjZUAApsv3POdzd33gIAF/3bgi4iBgcUk/xmUGRpi0pxfJMn3exrcAv133tEYaEtoiEmpF3XWrQPO05BeXTT1hou1c9YN+sv9V+qeIT0U08G+pf4RuDq1a6sp1/fUS/cN1tI7BjBbJIg4NIPkoYce0rhx45ptc+GFF9p1rLi4OBUWFjbYVlpaan2vKVlZWcrMzLT+XFFR0WxgjI10rNDnslGX65Z+JEcAuF+XLl0UGhpqjX0WpaWlTcZBd8VAR2MlgMDmynu+s1niW2lpqeLjf7rnKi0t1YABA5rcz933gIAF/3bgCGfu5+Li4hxqLxEDA4k/Fta+qW+8Vo5O1pwN+1RSUd3yDvA4f/qd98T3YHtZkiapF3XW74clqbC4TCXlP6qsskb/Pv6j/r77W5VV1rT6PPBdMR3a6rYB51FoPcg5lCA599xzde6557rkxKmpqZo3b56OHDmi2NhYSdKWLVsUFRWlpKSkJvcLDw9XeHi43edJSYxRfHSESsqrml1/1R9vUgD4t7CwMA0cOFD5+fkaMWKEpPridPn5+crIyLC5j6tjoElSXHSEUhJjHL8AAAHLlfd8Z0tMTFRcXJzy8/OtCZGKigp99NFHmjhxYpP7ueseELDgMxHOcOZ+LjU1Vfn5+Zo6dap125YtW5SamtrkeYiBgWFaWi+/Xablpr7xuiEpTsu2HdLirV94uzv4L3/87PLE92BnWJIlZ5pJ0iSgxHRoq1v7J+j8c9orpmO44qIiSIpAkhtrkBw+fFhlZWU6fPiwamtrtXv3bklSz5491bFjR914441KSkrS3XffrYULF6qkpEQzZ87U5MmTXRr0QkNMyh6epInrdskk2bw59OebFAD+LTMzU2PHjtWgQYOUkpKiJUuWqLKyUuPHj3fJ8ZuLgZaIlz08ifgHwGkt3fNJUu/evZWbm6vbbrtNJpNJU6dO1RNPPKFevXopMTFRs2bNUkJCgvVLsivYcw8InI3PRDijpfu5MWPG6LzzzlNubq4kacqUKbr22mv19NNPa9iwYXr55Zf1ySef6Pnnn3dZn4iBviVQBmRaajlcEtdRORv3U+fGR/jjZ5e7vwe7ij1Jk07tw3T8B5InvuLMJIjl74ZkCFritgTJ7NmztXbtWuvPl19+uSTpnXfe0XXXXafQ0FC9+eabmjhxolJTU9WhQweNHTtWc+fOdXlfLNNBz/4AD5SbFAD+a+TIkTp69Khmz56tkpISDRgwQHl5eY0K1rVGUzEwjhgIwAVauueTpAMHDqi8vNza5tFHH1VlZaUeeOABHT9+XFdffbXy8vIUEeHa5SGain/A2fhegNZo6X7u8OHDCgn5qfznVVddpfXr12vmzJl67LHH1KtXL73xxhvq27evS/tFDPSe6Ig2uiGpq4b0OjcgH8pZZpMwst67/PmzyxPfg93FVtLE4uzkSUzHcMV2DJdM0pGKqia38ftTr1O7thp71QVKSezc7J9XU9sCMd7CM0yGYfj1YJKKigpFR0ervLxcUVFRzbatrTNUWFymIyeqFBvJLw0QTByJFf6EGAjAHoEYA52Nf1062PcFy9ltZ45Uc+VxOZd7zsWX6eBADHQsBgbq77snznXsZHVQ32db/q05+nA4GP5tuPpc9n52BWL8kwLzuuz5/XHXv1Vv/w4Ee+yE+9gbK9w2g8QXNZflBYBARwwEEKyIfwCCGTEQnsK/NcB5/P4A3hPSchMAAAAAAAAAAIDAQoIEAAAAAAAAAAAEHb9fYstSQqWiosLLPQHgyywxws/LLjVCDARgj0CMgcQ/APYiBgIIVoEY/yRiIAD72BsD/T5BcuLECUlSt27dvNwTAP7gxIkTio6O9nY3XIYYCMARgRQDiX8AHEUMBBCsAin+ScRAAI5pKQaaDD9PI9fV1enbb79VZGSkTCZTi+0rKirUrVs3ffPNN81Wrw8kXHPgX3OwXa/k+DUbhqETJ04oISFBISGBs7ogMbBlwXbNwXa9EtccrDGQ+NcyrplrDkTOXC8xMPj+nUhcczBcc7Bdr8Q9oAUxsGXBds3Bdr0S1+zKGOj3M0hCQkJ0/vnnO7xfVFRU0Pz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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  17\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  18\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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AgbVuV73uXHUPBzlqa1cdrwepLgyQkFH4+fkhOTlZY5lqoLl16xYGDBiAsrIypKamQiaTNfh7kpKS8Mknn2D27NnYsWMHxo8fj5MnT2rMMnnyySdrvGkIAN7e3ggJCdHr++7evYubN2/WiFQTETWGM2fOYMmSJZg4cSKysrLwxhtv4OzZs5BIJA3eZ15eHioqKtCrV68a67744gt88cUX2LNnD4YOHfoIPSciqqlt27Zo0aIFKisr673m0nX8q6qqwq+//qqeNQIAly5dAgD1izhPPPEEmjZtitzcXK3fFR8fj+TkZLz//vuIi4vDv/71L3z77bc12v3+++8oLy9H586d9TlsIrImbdsCLVooC6vXd+945gywZIlyZklWFvDGG8DZs8DD13FVVcCvv/49awQA/hrH8Nc4hieeUAYWHh7Hxo0Dqr/893DwITYWOH9eGUiZMweYOxfQkmWhwTZuVKYSmzlTuf+65OYCHD+JbJZMJsObb76JN998E4WFhQgICMD7779fZ4CkMfF6kOrCFFtkFK1atUJISIjGx8nJCSUlJQgPD8fvv/+OQ4cOoWPHjg3+jjt37uCNN95Ajx49sHz5cnzyySfIzMzE8uXLH7n/paWluHv3bo3lS5cuhSAICAsLe+TvICKqrqKiAhMmTICHhwfWr1+P7du3o6CgANHR0Y+035EjR2LPnj01PgAQHh6OPXv2ICgoqDEOgYhIg52dHYYPH47//Oc/yM7OrrH+j79Sw+g7/m3YsEH934IgYMOGDWjSpAleeOEFAECTJk3QvXt3/PLLLzW2zc3NxaxZszB8+HC8++67+PDDD7Fv3z588cUXNdpmZGQAAJ577jn9D56IrIOdHTB8uLIOiZZxTJ3iqqICmDAB8PAA1q9X1gIpKABqu46rNo5BEJQ/N2kC/DWOoUkToHt34OFx7MknlYEa1af6CzAnTyoDIzNnAm+/DcyapdzvDz808OAfsnu3sqbKmDHAmjX1t8/MBDh+EtmcyspKFBcXayxzc3ODh4cHysrKjNYPXg9SXTiDhExqzJgxSE9Px6RJk3D+/HmcP39eva558+Z6vcE8Y8YM3Lp1CykpKbCzs0NYWBjeeOMNLFu2DEOGDIFfbblQdSCXy+Hv749Ro0bBy8sLgDLtw6FDhxAWFlajEDwR0aNatmwZsrKykJqaihYtWsDX1xcLFy7E/PnzMWLECISHhzdov15eXupx7GGenp6cOUJEBvXBBx/g+++/R1BQEKZMmQJvb28UFRUhMzMTKSkpKCoq0mv8c3JyQlJSEsaPH4+goCD8v//3/3Dw4EG8++67GjN8hwwZgvfeew8KhQIuLi4AlMGUSZMmoWnTpti8eTMA4F//+hf+85//YMaMGQgJCdEoJJqcnIwnnnhCpzp1RGTFPvgA+P57ICgImDIF8PYGioqUAYCUFOV/L1umnDWSmqqcceLrCyxcCMyfD4wYAVS/jnNyUtbvGD9euc//9/+UBdrffVc5Y0VlyBDgvfcAhQL4axyrVWmpcn8dOwLvv69ctngxsH+/ckbL2bOAs3PD/w3S05WzV1q3VgZxduzQXP/cc5p1VTIylP8uvG8msjl3797F448/jhEjRsDPzw/NmzdHSkoKTp06hdWrVxu1L7wepFoJRCbUvn17AYDWT/v27XXez7fffisAEFavXq2xXKFQCO3btxf8/PyE8vLyWrcHIHz22We1rr99+7YwduxY4emnnxaaNWsmODo6Cl26dBGWL19e536JiBoiIyNDsLe3F6ZPn66x/MGDB8Kzzz4reHh4CLdv327U7wQgREZGNuo+iYi0KSgoECIjI4V27doJTZo0EaRSqfDCCy8IW7Zs0Wv8Gz9+vODs7CxcvXpVGDBggNCsWTPB3d1diI2NFSorK2t8p729vfDvf/9bvWz9+vUCAOE///mPRtu8vDzBxcVFCA8PVy+rrKwUZDKZMH/+/Eb+1yAii1RQIAiRkYLQrp0gNGkiCFKpILzwgiBs2SIIGRmCYG8vCA+NY8KDB4Lw7LOC4OEhCKrruPHjBcHZWRCuXhWEAQMEoVkzQXB3F4TYWEF4aBwTCgqU+602jtUqOloQ7OwE4eRJzeW//KLcR0REAw/8L599JgjKuS7aPw/fW8+ZIwhPPCEIVVWP9r1EZHHKysqEWbNmCX5+fkKLFi0EZ2dnwc/PT9i0aZO6zfjx4zWeAebm5goAhFWrVtXYHwAhNjZWY1tnZ+ca7WJjY4WHH3vzepBqIxKEhyrbEBERERERmbkJEybg66+/xr1793RqP3nyZFy6dAk//fST3t+1d+9ejB49GlevXn2kenlERBomTAC+/hrQcRzD5MnK+iQNGMdMpqxMWUtl7lxgxgxT94aIbByvB0kb1iAhIiIiIiKrFxsbi1OnTuHYsWN6b7tixQpERUXxZpiITCs2Fjh1CmjAOGYyn32mrKEybZqpe0JExOtB0oozSIiIiIiIyOLoO4OEiMjs6DuDhIiIiBodZ5AQEREREREREREREZHN4QwSIiIiIiIiIiIiIiKyOZxBQkRERERERERERERENocBEiIiIiIiIiIiIiIisjn2pu7Ao6qqqsKNGzfQokULiEQiU3eHiMyUIAi4e/cuPDw8IBZbT2yYYyAR6cIax0COf0SkK46BRGSrrHH8AzgGEpFudB0DLT5AcuPGDbRr187U3SAiC3H9+nU8/vjjpu5Go+EYSET6sKYxkOMfEemLYyAR2SprGv8AjoFEpJ/6xkCLD5C0aNECgPJAXVxcTNwbIjJXCoUC7dq1U48Z1oJjIBHpwhrHQI5/RKQrjoFEZKuscfwDOAYSkW50HQMtPkCimkrn4uLCQZGI6mVt0285BhKRPqxpDOT4R0T64hhIRLbKmsY/gGMgEemnvjHQehIQEhERERERERERERER6YgBEiIiIiIiIqIG+vHHHzF48GB4eHhAJBJh79699W5z9OhRBAQEwNHREU8//TS2b99u8H4SERERUU0MkBARERERERE1UElJCfz8/LBx40ad2ufm5mLQoEH45z//iaysLMycORNvvPEGvvvuOwP3lIiIiIgeZlMBksoqAWlXb+HbrN+RdvUWKqsEU3eJiEygvrf8BEHAwoULIZPJ0LRpU4SEhODy5csabYqKijBmzBi4uLigZcuWmDx5Mu7du6fR5syZM+jTpw+cnJzQrl07rFy5skZfvvrqK3h5ecHJyQldu3bFoUOHGv14VTgGElkHnstEZKvMdfwbOHAgli1bhmHDhunUPj4+Hp6enli9ejU6d+6MqKgojBgxAmvXrjVwT8kaVT8vjl2+iWNXbprdOUJERPSoDHkdaPFF2nWVlJ2PxftzkF9cql4mkzghdrA3wnxkJuwZERmb6i2/SZMm4eWXX66xfuXKlfjoo4/w+eefw9PTEwsWLEBoaChycnLg5OQEABgzZgzy8/ORnJyMiooKTJw4EVOnTkViYiIAQKFQYMCAAQgJCUF8fDzOnj2LSZMmoWXLlpg6dSoA4Pjx4xg1ahTi4uLw4osvIjExEUOHDkVmZiZ8fHwa9Zg5BhJZB57LRGSrrGn8S0tLQ0hIiMay0NBQzJw50zQdIoul7byozlLPESIiouoMfR1oEzNIkrLzEZGQWeOiIb+4FNMSMrE+5RLfrCCyIXW95ScIAtatW4f58+djyJAh8PX1xRdffIEbN26oZ5qcP38eSUlJ+OSTTxAUFITevXvj448/xq5du3Djxg0AwI4dO1BeXo5t27ahS5cuGDlyJN566y2sWbNG/V3r169HWFgYZs2ahc6dO2Pp0qUICAjAhg0bGvV4axsD5cWliEjIRFJ2fqN+HxEZBs9lIrJV1jb+yeVyuLu7ayxzd3eHQqHA/fv3tW5TVlYGhUKh8SHbVtt5UZ2lniNEREQqxrgOtPoASWWVgMX7c1BX+GNtymX0+uAILxqICLm5uZDL5Rpv9UkkEgQFBSEtLQ2A8q2/li1bonv37uo2ISEhEIvFOHnypLpN37594eDgoG4TGhqKixcv4vbt2+o22t4eVH1PY6hrDFQtW7w/h0FiIjPHc5mIbBXHP6W4uDhIJBL1p127dqbuEpmQLs85ANs6R4iIyPoY6zrQ6gMk6blFdb5RoSJXKGeTHDpzwwi9IiJzJZfLAUDrW32qdXK5HG5ubhrr7e3t4erqqtFG2z6qf0dtbVTrtdH37cH6xkABytl06blFde6HiEyL5zIR2SprHP+kUikKCgo0lhUUFMDFxQVNmzbVus28efNQXFys/ly/ft0YXSUzpetzDsAyzxEiIiLAeNeBVh8gKbyr20WDStTO0zh0hjNJiMg86fv2oK5joL5jJREZF89lIrJV1jj+BQcHIzU1VWNZcnIygoODa93G0dERLi4uGh+yXQ35fbekc4SIiAgw3nWg1QdI3Fo46dW+SgDeTORMEiJbJZVKAUDrW32qdVKpFIWFhRrrHzx4gKKiIo022vZR/Ttqa6Nar42+bw/qOgbqO1YSkXHxXCYiW2UJ49+9e/eQlZWFrKwsAMqUrVlZWcjLywOgvH4bN26cuv20adPw66+/Yvbs2bhw4QI2bdqEL7/8EtHR0aboPlmghvy+8xqBiIgsjbGuA60+QNLD0xUyiRNEem7HmSREtsnT0xNSqVTjrT6FQoGTJ0+q3+oLDg7GnTt3kJGRoW5z5MgRVFVVISgoSN3mxx9/REVFhbpNcnIynnnmGbRq1UrdxtBvD9Y3BooAyCRO6OHpWud+iMi0eC4Tka2yhPHvl19+gb+/P/z9/QEAMTEx8Pf3x8KFCwEA+fn56mAJoLzePHjwIJKTk+Hn54fVq1fjk08+QWhoqEn6T5ZHn+cc5nCOEBERNYSxrgOtPkBiJxYhdrC33tupZpKsT7nEYmZEVqaut/xEIhFmzpyJZcuWYd++fTh79izGjRsHDw8PDB06FADQuXNnhIWFYcqUKUhPT8exY8cQFRWFkSNHwsPDAwAwevRoODg4YPLkyTh37hx2796N9evXIyYmRt2PGTNmICkpCatXr8aFCxewaNEi/PLLL4iKimq0Y60+Bj78B0X1c+xgb9iJ9Q0jE5Ex8VwmIltlCePf888/D0EQany2b98OANi+fTuOHj1aY5vTp0+jrKwMV69exYQJE4zeb7JcdZ0X1ZnLOUJERNQQxroOtPoACQCE+ciweWwApC6Oem+7NuUyen1wBEnZnE1CZC3qe8tv9uzZmD59OqZOnYpnn30W9+7dQ1JSEpyc/p6yt2PHDnh5eeGFF15AeHg4evfujS1btqjXSyQSHD58GLm5uQgMDMTbb7+NhQsXYurUqeo2zz33HBITE7Flyxb4+fnh66+/xt69e+Hj49Oox6seAyWaUw6lEidsHhuAMB9Zo34fERkGz2UislUc/4hqqu28qI7nCBERWTpjXAeKBEGw6OkRCoUCEokExcXF9aaaqawSsOHIFaxNudSg79o02h/hvh4N2paITEufscKS6DsGpucWofBuKdxaKKcg8k0yIsvTkHPZGsdAazwmIqpbQ69lrHG8sMZjooapfl60cXYERMDNe2W83icAmmNFVlYWVq1ahYyMDOTn52PPnj3qLAkAIAgCYmNjsXXrVty5cwe9evXC5s2b0bFjR3WboqIiTJ8+Hfv374dYLMbw4cOxfv16NG/eXN3mzJkziIyMxKlTp9C2bVtMnz4ds2fP1ujXV199hQULFuDatWvo2LEjVqxYgfDw8AYdF8dAIttgyPtg+8burDmzE4swI6QjOro5I2rnaeibOStq52lsgAjhvnz7gogsj51YhOCnWpu6G0T0iHguE5Gt4vhHVBPPC9JVSUkJ/Pz8MGnSJLz88ss11q9cuRIfffQRPv/8c3h6emLBggUIDQ1FTk6OOpvCmDFjkJ+fj+TkZFRUVGDixImYOnUqEhMTASgfRg4YMAAhISGIj4/H2bNnMWnSJLRs2VKdTeH48eMYNWoU4uLi8OKLLyIxMRFDhw5FZmZmo2dTICLrYci/dzY1g6S6Q2fy8WZiZoO+M55TVIksjrW+YWKtx0VEjcsaxwprPCYiMgxrHC+s8ZiIqPHVNlaIRCKNGSSCIMDDwwNvv/023nnnHQBAcXEx3N3dsX37dowcORLnz5+Ht7c3Tp06he7duwMAkpKSEB4ejt9++w0eHh7YvHkz3nvvPcjlcjg4OAAA5s6di7179+LChQsAgNdeew0lJSU4cOCAuj89e/ZEt27dEB8f/0jHRURUna5jhU3UINEm3FeGTaP90ZDZpu/uOYvyB1WN3ykiIiIiIiIiIiIjys3NhVwuR0hIiHqZRCJBUFAQ0tLSAABpaWlo2bKlOjgCACEhIRCLxTh58qS6Td++fdXBEQAIDQ3FxYsXcfv2bXWb6t+jaqP6Hm3KysqgUCg0PkREjcVmAyQAEO7rgQ2jAvTerqikAj3jUlm4nYiIiIiIiIiILJpcLgcAuLu7ayx3d3dXr5PL5XBzc9NYb29vD1dXV4022vZR/Ttqa6Nar01cXBwkEon6065dO30PkYioVjYdIAGUM0nixwZA6uKo13ZFJeWYlpCJQ2duGKhnREREREREREREtm3evHkoLi5Wf65fv27qLhGRFbGpIu21CfORob+3FBuOXMHalEt6bcvC7URkaSqrBKTnFqHwbincWjihh6cr7BqSb5CIjIbnLREREVVX17UBrxtIX1KpFABQUFAAmezv51sFBQXo1q2buk1hYaHGdg8ePEBRUZF6e6lUioKCAo02qp/ra6Nar42joyMcHfV7sZmISFcMkPzFTizCjJCO6OjmjKidp1GlY+n6KgF4MzET0YUdEdWvIy86iMisJWXnY/H+HOQXl6qXySROiB3sjTAfBnqJzBHPWyIiIqqurmsDALxuIL15enpCKpUiNTVVHRBRKBQ4efIkIiIiAADBwcG4c+cOMjIyEBgYCAA4cuQIqqqqEBQUpG7z3nvvoaKiAk2aNAEAJCcn45lnnkGrVq3UbVJTUzFz5kz19ycnJyM4ONhIR0tEpMnmU2w9rKF1SdamXEavD46wLgkRma2k7HxEJGRq3CwBgLy4FBEJmRy/iMwQz1siIiKqrq5rg2kJmZjG6waqxb1795CVlYWsrCwAysLsWVlZyMvLg0gkwsyZM7Fs2TLs27cPZ8+exbhx4+Dh4YGhQ4cCADp37oywsDBMmTIF6enpOHbsGKKiojBy5Eh4eHgAAEaPHg0HBwdMnjwZ586dw+7du7F+/XrExMSo+zFjxgwkJSVh9erVuHDhAhYtWoRffvkFUVFRxv4nISICwACJVqq6JK7OTfTaTq4oZV0SIjJLlVUCFu/PgbbJcapli/fnoFLX6XNEZHA8b4mIiKg6Xa4NtOF1AwHAL7/8An9/f/j7+wMAYmJi4O/vj4ULFwIAZs+ejenTp2Pq1Kl49tlnce/ePSQlJcHJyUm9jx07dsDLywsvvPACwsPD0bt3b2zZskW9XiKR4PDhw8jNzUVgYCDefvttLFy4EFOnTlW3ee6555CYmIgtW7bAz88PX3/9Nfbu3QsfHx8j/UsQkaWorBKQdvUWvs36HWlXbxnsbxgDJLUI85HhxLwQuDo76L1t1M7TOHSGb2YQkflIzy2q8SZZdQKA/OJSpOcWGa9TRFQnQ5y3q1evxrPPPosWLVrAzc0NQ4cOxcWLFzXalJaWIjIyEq1bt0bz5s0xfPjwGnmi8/LyMGjQIDRr1gxubm6YNWsWHjx4oNHm6NGjCAgIgKOjI55++mls3769Rn82btyIDh06wMnJCUFBQUhPT9f5WIiIiGxNfdcGdeH1Pj3//PMQBKHGR3WNJhKJsGTJEsjlcpSWliIlJQWdOnXS2IerqysSExNx9+5dFBcXY9u2bWjevLlGG19fX/z0008oLS3Fb7/9hjlz5tToyyuvvIKLFy+irKwM2dnZCA8PN9hxE5FlSsrOR+8VRzBq6wnM2JWFUVtPoPcKw2RvYoCkDg72Yiwfpn8EW1WXZH3KJb6dQURmofCubjdSurYjIsMzxHl77NgxREZG4sSJE0hOTkZFRQUGDBiAkpISdZvo6Gjs378fX331FX744QfcuHEDL7/8snp9ZWUlBg0ahPLychw/fhyff/45tm/frn77EFCmbBg0aBD++c9/IisrCzNnzsQbb7yB7777Tt1m9+7diImJQWxsLDIzM+Hn54fQ0NAaxT+JiIhIqTGu1Xm9T0RE5s7YqaYZIKlHmI8Mm0b7oyG111mXhIjMhVsLp/ob6dGOiAzPEOftN998gwkTJqBLly7w8/PD9u3bkZeXh4yMDABAcXExPv30U6xZswb9+vVDYGAgPvvsMxw/fhwnTpwAABw+fBg5OTlISEhAt27dMHDgQCxduhQbN25EeXk5ACA+Ph6enp5YvXo1OnfujKioKIwYMQJr165V92XNmjWYMmUKJk6cCG9vb8THx6NZs2bYtm2bzsdDRERkSxrjWp3X+0REZM5MkWqaARIdNLRwO8C6JERkHnp4ukImcUJtsV4RAJnECT08XY3ZLSKqgzHO2+LiYgDKdAkAkJGRgYqKCoSEhKjbeHl54YknnkBaWhoAIC0tDV27doW7u7u6TWhoKBQKBc6dO6duU30fqjaqfZSXlyMjI0OjjVgsRkhIiLrNw8rKyqBQKDQ+REREtqS+a4O68HqfiIgsgSlSxDNAoiNV4Xapi2ODtmddEiIyJTuxCLGDvQGgxg2V6ufYwd6wa8h0OSIyCEOft1VVVZg5cyZ69eqlLoopl8vh4OCAli1barR1d3eHXC5Xt6keHFGtV62rq41CocD9+/dx8+ZNVFZWam2j2sfD4uLiIJFI1J927do16LiJiIgslS7XBnWt4/U+ERGZO1OkiGeARA9hPjIcm/sCokM61d/4IaxLQkSmFuYjw+axAZBKNKfVSyVO2Dw2AGE+MhP1jIhqY8jzNjIyEtnZ2di1a9ejdtMo5s2bh+LiYvXn+vXrpu4SERGR0dV1bRA/NkD5Yiev94mIyEKZIkW8faPtyUbYiUWYEdIRHd2cEbXzNPSNdaxNuYyd6dex6CVvXpwQkdGF+cjQ31uK9NwiFN4thVsL5TR7vklGZL4Mcd5GRUXhwIED+PHHH/H444+rl0ulUpSXl+POnTsas0gKCgoglUrVbdLT0zX2V1BQoF6n+l/VsuptXFxc0LRpU9jZ2cHOzk5rG9U+Hubo6AhHx4bN5CUiIrIm9V0b8HqfiIgslSqdpLy4VGsdEhGUgf/GTBlp0BkkcXFxePbZZ9GiRQu4ublh6NChuHjxokab0tJSREZGonXr1mjevDmGDx9e42bZHLEuCRFZKjuxCMFPtcaQbo8h+KnWvFkisgCNdd4KgoCoqCjs2bMHR44cgaenp8b6wMBANGnSBKmpqeplFy9eRF5eHoKDgwEAwcHBOHv2LAoLC9VtkpOT4eLiAm9vb3Wb6vtQtVHtw8HBAYGBgRptqqqqkJqaqm5DREREtavr2oDX+0REZKlMkSLeoAGSH374AZGRkThx4gSSk5NRUVGBAQMGoKSkRN0mOjoa+/fvx1dffYUffvgBN27cwMsvv2zIbjUa1iUhIiIiS/L2228jISEBiYmJaNGiBeRyOeRyOe7fvw8AkEgkmDx5MmJiYvD9998jIyMDEydORHBwMHr27AkAGDBgALy9vfH666/jv//9L7777jvMnz8fkZGR6hke06ZNw6+//orZs2fjwoUL2LRpE7788ktER0er+xITE4OtW7fi888/x/nz5xEREYGSkhJMnDjR+P8wREREREREZBaMnSJeJAiC0Qpi/PHHH3Bzc8MPP/yAvn37ori4GG3btkViYiJGjBgBALhw4QI6d+6MtLQ09Y14XRQKBSQSCYqLi+Hi4mLoQ9CqskrAhiNXsDblUoO23zTaH+G+Ho3cKyKqzhzGCkOw1uMiosalGitq89lnn2HChAkAlLN73377bezcuRNlZWUIDQ3Fpk2bNFJf/e9//0NERASOHj0KZ2dnjB8/Hh988AHs7f/O3nr06FFER0cjJycHjz/+OBYsWKD+DpUNGzZg1apVkMvl6NatGz766CMEBQXpdUwc/4ioPtY4XljjMRFR47PWscJaj4uINFVWCY+UMlLXscKoNUiKi4sBAK6uyhxhGRkZqKioQEhIiLqNl5cXnnjiiVoDJGVlZSgrK1P/rFAoDNzr+j1qXZKonaexASKE+7ImCRERERmOLjeRTk5O2LhxIzZu3Fhrm/bt2+PQoUN17uf555/H6dOn62wTFRWFqKioOtsQERERERGR7VGljDQ0g6bYqq6qqgozZ85Er1694OPjAwCQy+VwcHDQKAIKAO7u7pDL5Vr3ExcXB4lEov60a9fO0F3XWUPrklQJwJuJmUjKZrotIiIiIiIiIiIiIiJjMFqAJDIyEtnZ2di1a9cj7WfevHkoLi5Wf65fv95IPWwcj1KX5N09Z1H+oMoAvSIi0q6ySkDa1Vv4Nut3pF29hUp9p8ARkUHxHCUiIiIiIiIyHKOk2IqKisKBAwfw448/4vHHH1cvl0qlKC8vx507dzRmkRQUFGjkua7O0dFRXQDUXIX5yNDfW6p3XZKikgr0jEvF8mE+jV5shojoYUnZ+Vi8Pwf5xaXqZTKJE2IHe3MMIjIDPEeJiIiIiIiIDMugM0gEQUBUVBT27NmDI0eOwNPTU2N9YGAgmjRpgtTUVPWyixcvIi8vD8HBwYbsmsGp6pJsGu0PPWrHoKikHNMSMnHozA3DdY6IbF5Sdj4iEjI1HrwCgLy4FBEJTPlHZGo8R4mIiIiIiIgMz6ABksjISCQkJCAxMREtWrSAXC6HXC7H/fv3AQASiQSTJ09GTEwMvv/+e2RkZGDixIkIDg7WWqDdEjW0LknUztM4dIYPP4io8VVWCVi8PwfaEvWoli3en8NUPkQmwnOUiIiIiIiIbJWxU00bNMXW5s2bAQDPP/+8xvLPPvsMEyZMAACsXbsWYrEYw4cPR1lZGUJDQ7Fp0yZDdsvown1l2AR/RO08DV3//1QVbo8u7Iiofh1hp880FCKiOqTnFtV4K706AUB+cSnSc4sQ/FRr43WMiADwHCUiIiIiIiLbZIpU0wZPsaXtowqOAICTkxM2btyIoqIilJSU4Jtvvqm1/ogla+hMkrUpl9HrgyNMpUFEjabwbu0PXhvSjogaF89RIiIiIiIisjWmSjVt0AAJaQr3lSF+bABcnZvotZ1cUcq6JETUaNxaODVqOyJqXDxHiYg0GTvNAhEREREZlylTTRs0xRbVFOYjQz8vd/SMS0VRSble20btPI0NECHc1zDTiYjINvTwdIVM4gR5canWPzwiAFKJE3p4uhq7a0QEnqNERNWZIs0CERERERmXKVNNcwaJCTjYi7F8mI/e26nqkqxPucS3poiowezEIsQO9gagfNBanern2MHerH1EZCI8R4mIlEyVZoGIiIiIjMuUqaYZIDGRMB8ZNo32R0OebbAuCRE9qjAfGTaPDYBUopmiRypxwuaxAXwjk8jEeI4Ska0zZZqFhti4cSM6dOgAJycnBAUFIT09vc7269atwzPPPIOmTZuiXbt2iI6ORmkpa0sRERGRbTJlqmkGSEyooYXbAdYlITKkyspKLFiwAJ6enmjatCmeeuopLF26FILw9w24IAhYuHAhZDIZmjZtipCQEFy+fFljP0VFRRgzZgxcXFzQsmVLTJ48Gffu3dNoc+bMGfTp0wdOTk5o164dVq5caZRjBJQPYH+e0w87p/TE+pHdsHNKT/w8px8fvBKZCZ6jRGTL9EmzYGq7d+9GTEwMYmNjkZmZCT8/P4SGhqKwsFBr+8TERMydOxexsbE4f/48Pv30U+zevRvvvvuukXtOREREZB5UqaZrm0sggjLNqiFSTTNAYmKqwu1SF8cGbR+18zQOneFMEqLGtGLFCmzevBkbNmzA+fPnsWLFCqxcuRIff/yxus3KlSvx0UcfIT4+HidPnoSzszNCQ0M13vwbM2YMzp07h+TkZBw4cAA//vgjpk6dql6vUCgwYMAAtG/fHhkZGVi1ahUWLVqELVu2GO1Y7cQiBD/VGkO6PYbgp1ozZQ+RmeE5SkS2ypRpFvS1Zs0aTJkyBRMnToS3tzfi4+PRrFkzbNu2TWv748ePo1evXhg9ejQ6dOiAAQMGYNSoUfXOOiEiIiKyVqZMNc0AiRkI85Hh2NwXEB3SSe9tWZeEqPEdP34cQ4YMwaBBg9ChQweMGDECAwYMUN+0CoKAdevWYf78+RgyZAh8fX3xxRdf4MaNG9i7dy8A4Pz580hKSsInn3yCoKAg9O7dGx9//DF27dqFGzeUM7927NiB8vJybNu2DV26dMHIkSPx1ltvYc2aNaY6dCIiIiKzYMo0C/ooLy9HRkYGQkJC1MvEYjFCQkKQlpamdZvnnnsOGRkZ6mvLX3/9FYcOHUJ4eHit31NWVgaFQqHxISIiIrImpko1zQCJmbATizAjpCPrkhCZgeeeew6pqam4dOkSAOC///0vfv75ZwwcOBAAkJubC7lcrnEjLJFIEBQUpL4RTktLQ8uWLdG9e3d1m5CQEIjFYpw8eVLdpm/fvnBwcFC3CQ0NxcWLF3H79m2DHycRERGRuTJlmgV93Lx5E5WVlXB3d9dY7u7uDrlcrnWb0aNHY8mSJejduzeaNGmCp556Cs8//3ydKbbi4uIgkUjUn3bt2jXqcZDlqKwSkHb1Fr7N+h1pV2/V+6Kkvu2JiIhMyRSppu0NtmdqkHBfD2yACG8mZuq9raouyabR/gj39TBA74hsw9y5c6FQKODl5QU7OztUVlbi/fffx5gxYwBAfbNb142wXC6Hm5ubxnp7e3u4urpqtPH09KyxD9W6Vq1a1ehbWVkZysrK1D831tuDlVUC0nOLUHi3FG4tlA8bmMqHyDR4PhIR/Z1mISIhEyJAo1i7odMsGNrRo0exfPlybNq0CUFBQbhy5QpmzJiBpUuXYsGCBVq3mTdvHmJiYtQ/KxQKBklsUFJ2Phbvz9GozyOTOCF2sLfWB0f6ticiIjIHqlTTxsIAiRkK95UhXhyARfvOQa4oq3+Dh0TtPI0NECHclxc8RA3x5ZdfYseOHUhMTESXLl2QlZWFmTNnwsPDA+PHjzdp3+Li4rB48eJG3SdvnIjMB89HIqK/qdIsPDwuSs1oXGzTpg3s7OxQUFCgsbygoABSqVTrNgsWLMDrr7+ON954AwDQtWtXlJSUYOrUqXjvvfcgFtdM9ODo6AhHx4bVrSTrkJSdj4iETDw8/0NeXIqIhMwaqUf0bU9ERGSrmGLLTDVGXRKm2yJqmFmzZmHu3LkYOXIkunbtitdffx3R0dGIi4sDAPXNbl03wlKpFIWFhRrrHzx4gKKiIo022vZR/TseNm/ePBQXF6s/169ff6RjVd04VX/oAPx948RxhMh4eD4SEdVkijQL+nBwcEBgYCBSU1PVy6qqqpCamorg4GCt2/z55581giB2dnYAlLXuiB5WWSVg8f6cGsEO4O/ZVYv356jTZ+nbnoiIyNRMmRKSARIz9qh1Sd7dcxblD6oav2NEVq62m9aqKuX55OnpCalUqnEjrFAocPLkSfWNcHBwMO7cuYOMjAx1myNHjqCqqgpBQUHqNj/++CMqKirUbZKTk/HMM89oTa8FKN8edHFx0fg0FG+ciMwHz0ciotqp0iwM6fYYgp9qbXZptWJiYrB161Z8/vnnOH/+PCIiIlBSUoKJEycCAMaNG4d58+ap2w8ePBibN2/Grl27kJubi+TkZCxYsACDBw9WB0qIqkvPLarxAkV1AoD84lKk5xY1qD0REZEpJWXno/eKIxi19QRm7MrCqK0n0HuF8WptM0BiAcJ9PbBhVIDe2xWVVKBnXCrfOCXS0+DBg/H+++/j4MGDuHbtGvbs2YM1a9Zg2LBhAACRSISZM2di2bJl2LdvH86ePYtx48bBw8MDQ4cOBQB07twZYWFhmDJlCtLT03Hs2DFERUVh5MiR8PBQ1ggaPXo0HBwcMHnyZJw7dw67d+/G+vXrNfJLGxJvnIjMB89HIiLL9dprr+HDDz/EwoUL0a1bN2RlZSEpKUldWy4vLw/5+X/fk82fPx9vv/025s+fD29vb0yePBmhoaH4v//7P1MdApm5wru1XyNoa6dveyIiIlMxh0wKrEFiIRpal6SopJyF24n09PHHH2PBggV48803UVhYCA8PD/zrX//CwoUL1W1mz56tzhV9584d9O7dG0lJSXByclK32bFjB6KiovDCCy9ALBZj+PDh+Oijj9TrJRIJDh8+jMjISAQGBqJNmzZYuHAhpk6dapTj5I0Tkfng+UhEZNmioqIQFRWldd3Ro0c1fra3t0dsbCxiY2ON0DOyBm4tnOpvVK2dvu2JiIhMob5MCiIoMyn095YadAYxAyQWJMxHhv7eUmw4cgVrUy7ptS0LtxPprkWLFli3bh3WrVtXaxuRSIQlS5ZgyZIltbZxdXVFYmJind/l6+uLn376qaFdfSS8cSIyHzwfiYiIqDY9PF0hkzhBXlyq9SGSCIBU4oQenq4Nak9ERGQK+mRSCH6qtcH6wRRbFqahdUlUhdsPnblhuM4RkUVR3TjVNpSIAMh440RkFDwfiYiIqDZ2YhFiB3sDQI1rBdXPsYO91W/X6tueiIjIFMwlkwIDJBaqoXVJonaexqEzrElCRLxxIjInPB+JiIioLmE+MmweGwCpRHM2qVTihM1jAxDmI3uk9kRERMZmLpkUmGLLgqnqkry75yyKSip02kY1kyS6sCOi+nXkgxYiG6e6cVq8P0djWqNU4oTYwd68cSIyIp6PREREVBdV2u303CIU3i2FWwvl7NLa7uv1bU9ERGRM5pISkgESCxfmI0M/L3f0jEtFUUm5ztutTbmMnenXseglPnAhsnW8cSIyHzwfiYiIqC52YpFeedj1bU9ERGQsqkwKEQmZEAEaQRJjZlJgii0r4GAvxvJhPnpvJ1eUYloC65IQ0d83TkO6PYbgp1rzYSyRCfF8JCIiIiIiIltgDikhGSCxEmE+Mr0Lt6uwLgkRERERERERke1atGgRRCKRxsfLy0u9vrS0FJGRkWjdujWaN2+O4cOHo6CgQGMfeXl5GDRoEJo1awY3NzfMmjULDx480Ghz9OhRBAQEwNHREU8//TS2b99ujMMjIjNVWSVA0tQBs0OfwYJBnbH2tW7YOaUnfp7Tz2hZj5hiy4qE+3pgA0R4MzFTr+1Yl4SIiIiIiIiIyLZ16dIFKSkp6p/t7f9+bBgdHY2DBw/iq6++gkQiQVRUFF5++WUcO3YMAFBZWYlBgwZBKpXi+PHjyM/Px7hx49CkSRMsX74cAJCbm4tBgwZh2rRp2LFjB1JTU/HGG29AJpMhNDTUuAdLRCaXlJ1fowan7K8anMZ8Ps0ZJFYm3LfhM0nWplxGrw+OICmbs0mIbFlllYC0q7fwbdbvSLt6C5VV2kplEVFj47lHRERERKZkb28PqVSq/rRp0wYAUFxcjE8//RRr1qxBv379EBgYiM8++wzHjx/HiRMnAACHDx9GTk4OEhIS0K1bNwwcOBBLly7Fxo0bUV6urJkbHx8PT09PrF69Gp07d0ZUVBRGjBiBtWvXmuyYicg0krLzEZGQqREcAQB5cSkiEjKN+nyaARIrFO7rgQ2jAhq0LeuSENm2pOx89F5xBKO2nsCMXVkYtfUEeq9g4JTI0HjuEREREZGpXb58GR4eHnjyyScxZswY5OXlAQAyMjJQUVGBkJAQdVsvLy888cQTSEtLAwCkpaWha9eucHd3V7cJDQ2FQqHAuXPn1G2q70PVRrWP2pSVlUGhUGh8iMhyVVYJWLw/B9peCVQtW7w/x2gvDTJAYqXCfWWIHxsAqYtjg7ZnXRIi22NO0XsiW8Jzj4iIiIhMLSgoCNu3b0dSUhI2b96M3Nxc9OnTB3fv3oVcLoeDgwNatmypsY27uzvkcjkAQC6XawRHVOtV6+pqo1AocP/+/Vr7FhcXB4lEov60a9fuUQ+XiEwoPbeoxv1vdQKA/OJSpOcWGaU/DJBYsTAfGY7NfQHRIZ303lZVl2R9yiWm+CCyAeYWvSeyFTz3iIiIiMgcDBw4EK+88gp8fX0RGhqKQ4cO4c6dO/jyyy9N3TXMmzcPxcXF6s/169dN3SUiegSFd2sPjjSk3aNigMTK2YlFmBHSkXVJiKhO5ha9J7IVPPeIiIiIyBy1bNkSnTp1wpUrVyCVSlFeXo47d+5otCkoKIBUKgUASKVSFBQU1FivWldXGxcXFzRt2rTWvjg6OsLFxUXjQ0SWy62FU6O2e1QMkNiIR61LwhQfRNbN3KL3RLaC5x4RERERmaN79+7h6tWrkMlkCAwMRJMmTZCamqpef/HiReTl5SE4OBgAEBwcjLNnz6KwsFDdJjk5GS4uLvD29la3qb4PVRvVPojINvTwdIVM4oTa3uUXAZBJnNDD09Uo/WGAxIY8Sl0SAcCifeeY4oPISplb9J7IVvDcIyIiIiJz8M477+CHH37AtWvXcPz4cQwbNgx2dnYYNWoUJBIJJk+ejJiYGHz//ffIyMjAxIkTERwcjJ49ewIABgwYAG9vb7z++uv473//i++++w7z589HZGQkHB2Vz6GmTZuGX3/9FbNnz8aFCxewadMmfPnll4iOjjbloRORkdmJRYgdrAycPhwkUf0cO9gbdg1Jh9QADJDYmEepSyJXlGHDkSsG6BURmZq5Re+JbIWxz71jx45h8ODB8PDwgEgkwt69ezXWT5gwASKRSOMTFham0aaoqAhjxoyBi4sLWrZsicmTJ+PevXsabc6cOYM+ffrAyckJ7dq1w8qVK2v05auvvoKXlxecnJzQtWtXHDp0qFGOkYiIiIj099tvv2HUqFF45pln8Oqrr6J169Y4ceIE2rZtCwBYu3YtXnzxRQwfPhx9+/aFVCrFN998o97ezs4OBw4cgJ2dHYKDgzF27FiMGzcOS5YsUbfx9PTEwYMHkZycDD8/P6xevRqffPIJQkNDjX68RGRa/b2lmBnSCZKmTTSWSyVO2Dw2AGE+MqP1xd5o30RmQ1WXpKObM6J2noY+k0LWplwCICCqX0ejRfGIyPBU0fuIhEyIAI2C0aaI3hPZCmOfe3/++Sf8/PwwadIkvPzyy1rbhIWF4bPPPlP/rHrjT2XMmDHIz89HcnIyKioqMHHiREydOhWJiYkAAIVCgQEDBiAkJATx8fE4e/YsJk2ahJYtW2Lq1KkAgOPHj2PUqFGIi4vDiy++iMTERAwdOhSZmZnw8fFplGMlIiIiIt3t2rWrzvVOTk7YuHEjNm7cWGub9u3b1/vSy/PPP4/Tp083qI9EZB2SsvOxeH+ORj3Olk2bYGKvDiZ55swZJDasoXVJWLidyDqF+ciweWwApBLNVD6miN4T2RJjnnv9+/fHsmXLMGzYsFrbODo6QiqVqj+tWrVSrzt//jySkpLwySefICgoCL1798bHH3+MXbt24caNGwCAHTt2oLy8HNu2bUOXLl0wcuRIvPXWW1izZo16P+vXr0dYWBhmzZqFzp07Y+nSpQgICMCGDRsa7ViJiIiIiIjIvCRl5yMiIVMjOAIAxfcrsC7lMpJz5EbvE2eQ2LhwXxnixQFYtO8c5IoynbeTK0oxLSETm0b7I9zXw4A9JCJjCvORob+3FOm5RSi8Wwq3FsrUPpw5QmRY5nTuHT16FG5ubmjVqhX69euHZcuWoXXr1gCAtLQ0tGzZEt27d1e3DwkJgVgsxsmTJzFs2DCkpaWhb9++cHBwULcJDQ3FihUrcPv2bbRq1QppaWmIiYnR+N7Q0NAaKb+IyLZVVglmMS4SERER0aOrrBKweH8OtCUzEqDMorB4fw76e0uNes3HAAmpH8psOHLlrxRauovaeRobIEK4L98sJ7IWdmIRgp9qbepuENkcczj3wsLC8PLLL8PT0xNXr17Fu+++i4EDByItLQ12dnaQy+Vwc3PT2Mbe3h6urq6Qy5Vv+sjlcnh6emq0cXd3V69r1aoV5HK5eln1Nqp9aFNWVoaysr9f5lAoFI90rERk3rSlXpBJnBA72JuzWomIiIgsUHpuUY2ZI9UJAPKLS5GeW2TUe2Om2CIAf9cliQ7pqNd2VQLwZmIm1qdcQqU+xUyIiIjI7IwcORIvvfQSunbtiqFDh+LAgQM4deoUjh49auquIS4uDhKJRP1p166dqbtERAZSW+oFeXEpIhIymeqXiIiIyAIV3q09ONKQdo2FARLSENWvI6QuTvU3fAjrkhBZn8oqAWlXb+HbrN+RdvUWg6BEBmDu59mTTz6JNm3a4MqVKwAAqVSKwsJCjTYPHjxAUVERpFKpuk1BQYFGG9XP9bVRrddm3rx5KC4uVn+uX7/+aAdHRGapvtQLgDL1grmNl0RERERUN7cWuj1z1rVdY2GKLdJgJxZh0UveiEjI1HpTUhfWJSGyHkxrQWR4lnCe/fbbb7h16xZkMmV/goODcefOHWRkZCAwMBAAcOTIEVRVVSEoKEjd5r333kNFRQWaNGkCAEhOTsYzzzyjLvgeHByM1NRUzJw5U/1dycnJCA4OrrUvjo6OcHR0NMRhEpEZMdfUC0RERET0aALbt4KrswOKSsq1rhcBkEqUdeeMiTNIqIYwHxk2jw2A1KVhDyGidp7GoTOcSUJkqZjWgsjwTHWe3bt3D1lZWcjKygIA5ObmIisrC3l5ebh37x5mzZqFEydO4Nq1a0hNTcWQIUPw9NNPIzQ0FADQuXNnhIWFYcqUKUhPT8exY8cQFRWFkSNHwsND+XLE6NGj4eDggMmTJ+PcuXPYvXs31q9fr1GUfcaMGUhKSsLq1atx4cIFLFq0CL/88guioqIMctxEZDnMNfUCERERETVcUnY+/rHq+zqDIwAQO9jbqAXaAQZIqBZhPjIcm/sCokM66b2tqi7JoTM3DNAzIjIkprUgMjxTnmenT5+Gv78//P39AQAxMTHw9/fHwoULYWdnhzNnzuCll15Cp06dMHnyZAQGBuKnn37SmLmxY8cOeHl54YUXXkB4eDh69+6NLVu2qNdLJBIcPnwYubm5CAwMxNtvv42FCxdi6tSp6jbPPfccEhMTsWXLFvj5+eHrr7/G3r174ePj0+jHTESWxVxTLxARERFRw9T2gmB1UokTNo8NMEk2BabYolqpCrd3dHNG1M7T0Pc5TdTO09gAEcJ9zSNNCBHVj2ktiAzPlOdZnz59IAi1/0H/7rvv6t2Hq6srEhMT62zj6+uLn376qc42r7zyCl555ZV6v4+IbEsPT1fIJE6QF5dqDSSbKvUCEREREemvrhcEVVydm+CHWf+Eg71p5nJwBgnVK9zXAxtGBei9nWomyfqUS3zbnMhCMK0FkeHxPCMiqp2dWITYwd4A/k61oGLK1AtEREREpL/6XhAEgKKSCmT877aRelSTQQMkP/74IwYPHgwPDw+IRCLs3btXY70gCFi4cCFkMhmaNm2KkJAQXL582ZBdogYK95UhvoF1SdamXEavD46wbgGRBWBaCyLD43lGRFQ3dU1EieY4aMrUC0RERESkP0t4QdCgAZKSkhL4+flh48aNWtevXLkSH330EeLj43Hy5Ek4OzsjNDQUpaV8Y9IcPUpdErmiFNMSWJeEyNyp0lrU9k6mCICMaS2IHgnPMyKi+oX5yPDznH7YOaUn1o/shp1TeuLnOf0YHCEiIiKyINdulujUzpQvCBq0BsnAgQMxcOBAresEQcC6deswf/58DBkyBADwxRdfwN3dHXv37sXIkSMN2TVqINYlIbJuqrQWEQmZEAEaOSKZ1oKocfA8IyLSjZ1YxJpnRFDmb0/PLULh3VK4tVC+RKHvdUJj7IOIiEgfSdn5WJtSd7Yoc6gvZ7Ii7bm5uZDL5QgJCVEvk0gkCAoKQlpaGgMkZi7c1wMbIMKbiZl6baeqSxIv5tR4InOlSmuxeH+ORp5IqcQJsYO9ee4SNQKeZ0RERKSLpOz8GtcLMj2vFxpjH0RERPpQFWfXhalfEDRZgEQulwMA3N3dNZa7u7ur12lTVlaGsrIy9c8KhcIwHaR6hfvKsAn+DZpJ8u6es+jn5Q4He4NmeSOiBgrzkaG/t5RvmREZEM8zIiLrsXHjRqxatQpyuRx+fn74+OOP0aNHj1rb37lzB++99x6++eYbFBUVoX379li3bh3Cw8ON2Gsyd0nZ+YhIyMTDt9vy4lJEJGTqVJOnMfZBRESkL12KswPAzJBOJv87ZHFPp+Pi4iCRSNSfdu3ambpLNi3c1wMbRgXovV1RSQV6xqWycDuZrd9//x1jx45F69at0bRpU3Tt2hW//PKLer0gCFi4cCFkMhmaNm2KkJAQXL6sOW2wqKgIY8aMgYuLC1q2bInJkyfj3r17Gm3OnDmDPn36wMnJCe3atcPKlSuNcny6UKW1GNLtMQQ/1ZoPbYkMgOcZEZHl2717N2JiYhAbG4vMzEz4+fkhNDQUhYWFWtuXl5ejf//+uHbtGr7++mtcvHgRW7duxWOPPWbknpM5U715q+1dRNWyxftzUFnH24qNsQ8iIqKGkCt0qzHeoU0zA/ekfiYLkEilUgBAQUGBxvKCggL1Om3mzZuH4uJi9ef69esG7SfVL9xXhvixAZC6OOq1XVFJOQu3k1m6ffs2evXqhSZNmuD//b//h5ycHKxevRqtWrVSt1m5ciU++ugjxMfH4+TJk3B2dkZoaChKS//+AzBmzBicO3cOycnJOHDgAH788UdMnTpVvV6hUGDAgAFo3749MjIysGrVKixatAhbtmwx6vHqorJKQNrVW/g263ekXb3FmyiiR8RziojIeqxZswZTpkzBxIkT4e3tjfj4eDRr1gzbtm3T2n7btm0oKirC3r170atXL3To0AH/+Mc/4OfnZ+Sekzmr781bAUB+cSnSc4sMug8iIiJ9JWXnY+mBczq1NWVxdhWTpdjy9PSEVCpFamoqunXrBkD5sPDkyZOIiIiodTtHR0c4Our3IJ4MT5UmZMORK1ibckmvbVm4nczNihUr0K5dO3z22WfqZZ6enur/FgQB69atw/z58zFkyBAAwBdffAF3d3fs3bsXI0eOxPnz55GUlIRTp06he/fuAICPP/4Y4eHh+PDDD+Hh4YEdO3agvLwc27Ztg4ODA7p06YKsrCysWbNGI5BiasxZTNS4eE4REVmP8vJyZGRkYN68eeplYrEYISEhSEtL07rNvn37EBwcjMjISHz77bdo27YtRo8ejTlz5sDOzk7rNkw1bXsK7+r25m1d7RpjH0RERPqoLbXjw8yhOLuKQWeQ3Lt3D1lZWcjKygKgLMyelZWFvLw8iEQizJw5E8uWLcO+fftw9uxZjBs3Dh4eHhg6dKghu0UGYicWYUZIR2wa7Q99MoSoCrevT7nEN2jJLOzbtw/du3fHK6+8Ajc3N/j7+2Pr1q3q9bm5uZDL5QgJCVEvk0gkCAoKUt8Ip6WloWXLlurgCACEhIRALBbj5MmT6jZ9+/aFg4ODuk1oaCguXryI27dvG/owdaL6w/bwm2eqnMVMk0ekH55TRETW5ebNm6isrNSrtuavv/6Kr7/+GpWVlTh06BAWLFiA1atXY9myZbV+D1NN2x5d36itq11j7IOIiEhXdaV2rE712NjUxdlVDBog+eWXX+Dv7w9/f38AQExMDPz9/bFw4UIAwOzZszF9+nRMnToVzz77LO7du4ekpCQ4OfGPsyVraF2StSmX0euDI3w4RCb366+/YvPmzejYsSO+++47RERE4K233sLnn38OAOqb3bpuhOVyOdzc3DTW29vbw9XVVaONtn1U/46HlZWVQaFQaHwMhTmLiRoXzykiIgKAqqoquLm5YcuWLQgMDMRrr72G9957D/Hx8bVuw1TTtqeHpytkEifU9thIBOUM1LrevG2MfRAREelK18Lsrs4O2Dw2wGwyKBg0QPL8889DEIQan+3btwMARCIRlixZArlcjtLSUqSkpKBTp06G7BIZiaouiatzE722kytKWZeETK6qqgoBAQFYvnw5/P39MXXqVEyZMqXOm1ZjMebbg8xZTNS4eE4REVmfNm3awM7OTq/amjKZDJ06ddJIp9W5c2fI5XKUl5dr3cbR0REuLi4aH7JudmIRYgd7A0CNAIeub942xj6IiIh0lZKj/WXfh80f1NlsgiOACYu0k/UL85HhxLwQuDo71N/4IVE7T+PQGc4kIdOQyWTw9vbWWNa5c2fk5eUBgPpmt64bYalUisLCQo31Dx48QFFRkUYbbfuo/h0PM+bbg8xZTNS4eE4REVkfBwcHBAYGIjU1Vb2sqqoKqampCA4O1rpNr169cOXKFVRVVamXXbp0CTKZTCP1KlGYjwybxwZAKtHMsiGVOOn85m1j7IOIiKg+Sdn5+PTYNZ3aSiVNDdsZPZmsSDvZBgd7MZYP88G0hEy9tlPVJYku7Iiofh35RgsZVa9evXDx4kWNZZcuXUL79u0BKAu2S6VSpKamolu3bgCUhTJPnjyJiIgIAEBwcDDu3LmDjIwMBAYGAgCOHDmCqqoqBAUFqdu89957qKioQJMmytlWycnJeOaZZ9CqVSutfXN0dISjo2OjH7M2zFlM1Lh4ThERWaeYmBiMHz8e3bt3R48ePbBu3TqUlJRg4sSJAIBx48bhscceQ1xcHAAgIiICGzZswIwZMzB9+nRcvnwZy5cvx1tvvWXKwyAzFeYjQ39vKdJzi1B4txRuLZQpsfS5R26MfRAREdWm/EEV3t2TXW87cyrMXh0DJGRwYT4ybBrtj6idp6FvWvW1KZexM/06Fr3kzTdbyGiio6Px3HPPYfny5Xj11VeRnp6OLVu2YMuWLQCU6QFnzpyJZcuWoWPHjvD09MSCBQvg4eGBoUOHAlDOOAkLC1On5qqoqEBUVBRGjhwJDw8PAMDo0aOxePFiTJ48GXPmzEF2djbWr1+PtWvXmurQNahyFsuLS7XWTDDXP2xE5ornFBGRdXrttdfwxx9/YOHChZDL5ejWrRuSkpLUteXy8vIgFv+dvKFdu3b47rvvEB0dDV9fXzz22GOYMWMG5syZY6pDIDNnJxYh+KnWJt8HERHRw5Ky8/HunrMoKqmot60A80ztKBIEwaIrgSoUCkgkEhQXFzMPq5k7dCYfbybqN5Okuk2j/RHu69GIPSJbou9YceDAAcybNw+XL1+Gp6cnYmJiMGXKFPV6QRAQGxuLLVu24M6dO+jduzc2bdqkUUepqKgIUVFR2L9/P8RiMYYPH46PPvoIzZs3V7c5c+YMIiMjcerUKbRp0wbTp0/X6+bY0GNgUnY+Iv6aAVb9j4XqTxmn5RPpx1TnlDVeL1njMRGRYVjjeGGNx0REjc9axwprPS4iS6Pvs95JvTpg4eAuBuyRJl3HCgZIyKiSsvOxaN85yBVlem8rFgEbRgUg3JcPY0l/1jpWGOO4krLzsXh/jkZxaZnECbGDObOLqCFMcU5Z4xhojcdERIZhjeOFNR4TETU+ax0rrPW4iCzJoTM39M4WtHNKT6POZtR1rGCKLTIqVe7TDUeuYG3KJb22ZV0SItNgzmKixsVzioiodpVVAsdHIiIiIjNVWSXo/VzX3NNJM0BCRmcnFmFGSEd0dHNmXRIiC6HKWax6aHHgzA0+tCBqgIcf/L3o68FziIjoL5y1SkRERGS+HiUzkDnWHlFhgIRMJtzXAxsgalBdErmiFNMSMlmXhMiI+NCC6NHwHCIiqp2qRtPD707Ji0sRkZDJumdEREREJtTQ2tKtnR3w/jAfs76OE5u6A2Tbwn1liB8bAKmLY4O2j9p5GofO5Ddyr4joYaqHFtUf7AJ/P7RIyuZ5SFQXnkNERLWrrBKweH9OjeAIAPWyxftzUKnv1HMiIiIiemTKeiP6B0dcnZsgbd4LZh0cARggITMQ5iPDsbkvIDqkk97bquqSHDpzwwA9IyKADy2IHhXPISKiuqXnFtUIIFcnAMgvLkV6bpHxOkVERERk4yqrBKxPuYw3E/UvkQAAy4d1hYO9+YcfzL+HZBNUdUk2jfZHQ9LRcSYJkeHwoQXRo+E5RERUt8K7tY+RDWlHRERERA2nCowELDmsVzF2FbEI2DTactKjsgYJmZWG1iVRzSSJF1vOyUdkKfjQgujR8BwiIqqbWwunRm1HRERERLqprBKQnluEwrulaOPsiFPXirDlp1/xZ3llg/e5YZQ/wn0t5/ksAyRkdsJ9ZYgXB2DRvnOQK8r02vbdPWfRz8vdIqZvEVkKPrQgejQ8h4iI6tbD0xUyiRPkxaVa0xGKAEglTujh6WrsrhERERFZpcoqARuOXMFnx3Jx535Fo+xTJnFC7GBvi3t5nQESMkthPjL095Ziw5Erek3lKiqpQM+4VCwf5mNxJyORueJDC6JHw3OIiKhudmIRYgd7IyIhEyJAY6xUZd+NHewNu4bk4iUiIiKyQQ/PDIEIKFSUoqikHL/duY+vfvkN98oeNNr3RYd0RFS/jhZ5vcYACZktVV2Sjm7OiNqpezGgopJyTEvIxKbR/gj39TBsJ4lsAB9aED0ankNERPUL85Fh89gALN6fo1G3SWqhbyISERER6UsV1JAX30dRSTlcmzvCrblmcKNlMwfc+VP7OtWyU9eKsP34tUabGVIXsQjYMCrAolJqPYwBEjJ7Da1LErXzNDZAZNEnKJG54EMLokfDc4iIqH6qWeSqtx3dWihn1zGATERkfTZu3IhVq1ZBLpfDz88PH3/8MXr06GHqblmFumYONPSBe2Mt43fVvuy3O/fxbdYNFJWUm/pXSC+WVm9EGwZIyCKE+8qwCf56zSRRFW6PLrTcKV5E5qT6Q4vqbzNImjqgskrgOUZUC9UNStmDKnw4wg8QATfvlfHBHxGRFnZiEYKfam3qbhARkQHt3r0bMTExiI+PR1BQENatW4fQ0FBcvHgRbm5upu6e2dIl8GHMmQNk2yy13og2DJCQxWjoTJK1KZexM/06Fr1kHSctkSnZiUUovl+Old9d1HgL3pr+MBI1pqTs/BqzRlTnCx8AEhEREZEtWrNmDaZMmYKJEycCAOLj43Hw4EFs27YNc+fONXHvzEf1dEvHrtxE8vlCFDPwQWbAkuuNaMMACVmUcF8Z4sUBeHfPWRSV6P5HQa4oZV0SokaQlJ2PiITMGoWm5cWliEjIxOaxAQySEP2F5wsRERERkaby8nJkZGRg3rx56mVisRghISFIS0szYc9M6+HaE5aabomsW6tmTRD3cleru49lgIQsTpiPDP283NEzLlXvPxSsS0LUcJVVAhbvz6nxsBdQFp0WAVi8Pwf9vaVW8xYBUUPxfCEiIiIiqunmzZuorKyEu7u7xnJ3d3dcuHBB6zZlZWUoKytT/6xQKAzaR2NRBUWSc+TYy2AImTFnBztM7fukVc0aqU5s6g4QNYSDvRjLh/novZ2qLsn6lEuo1LWYCREBANJzizTSBD1MAJBfXIr03CLjdYrITPF8ISIiIiJqHHFxcZBIJOpPu3btTN2lR1JZJWB9ymUELk3GqK0nsO3YNQZHyCy1bNoE0SEdcWZRKGaEdLLK4AjAGSRkwcJ8ZNg0Wr/C7SqsS0Kkv8K7tT/sbUg7ImvG84WIiIiIqKY2bdrAzs4OBQUFGssLCgoglUq1bjNv3jzExMSof1YoFBYXJKk+W+TLX37DvbIHpu4SkYaWTZtg/HPt0cOzNW7eK4NbCyf08HS12qBIdQyQkEVraOF2gHVJiPTl1sKpUdsRWTOeL0RERERENTk4OCAwMBCpqakYOnQoAKCqqgqpqamIiorSuo2joyMcHR2N2MvGU1klYMORK/jsWC7usMA6mRlX5yYY1u0xhHhLbSYYog0DJGTxVIXbF+07B7mirP4NHsK6JES66eHpCpnECfLiUq11FUQApBLlGwZEto7nCxERERGRdjExMRg/fjy6d++OHj16YN26dSgpKcHEiRNN3bVGlZSdj7nfnMWdPxkYIdOpPjOkUFGKopJyuDZ3hNTFdmaI1IcBErIKYT4y9PeWYsORK1ibckmvbVV1SaILO1ptsSGixmAnFiF2sDciEjIhAjQe+qrOmtjB3jyHiMDzhYiIiIioNq+99hr++OMPLFy4EHK5HN26dUNSUlKNwu2WSjVrRN/nU0SAclbHED8PPN6qGVybO8KtuSMggjq40bKZA+78Wa51XfVltpYm61EwQEJWw04swoyQjujo5sy6JEQGEuYjw+axAVi8P0ejALVU4oTYwTx3iKozx/Pl2LFj2LRpEzIyMpCfn489e/aoUxsAgCAIiI2NxdatW3Hnzh306tULmzdvRseOHdVtioqKMH36dOzfvx9isRjDhw/H+vXr0bx5c3WbM2fOIDIyEqdOnULbtm0xffp0zJ49W6MvX331FRYsWIBr166hY8eOWLFiBcLDww3+b0BEREREphcVFVVrSi1LlpSd3+AMJ42ttpkDDX3g3hjL+F11L+OsDtNggISsDuuSEBmWasZWem4RCu+Woo3z328npF29xT/mRH+prBIgaeqA2aHPmM0F759//gk/Pz9MmjQJL7/8co31K1euxEcffYTPP/8cnp6eWLBgAUJDQ5GTkwMnJ2W9lDFjxiA/Px/JycmoqKjAxIkTMXXqVCQmJgJQFs0cMGAAQkJCEB8fj7Nnz2LSpElo2bIlpk6dCgA4fvw4Ro0ahbi4OLz44otITEzE0KFDkZmZCR8fH+P9gxCR2VAVry28W8q3HYmIyCIdOpPfoGdR+qov8MGZA0T6EQmCoOd79uZFoVBAIpGguLgYLi4upu4OmZFHidqLRcCGUQGsS2JFrHWsMPVxJWXn13g7XsbZJERmd25oGytEIpHGDBJBEODh4YG3334b77zzDgCguLgY7u7u2L59O0aOHInz58/D29sbp06dQvfu3QEASUlJCA8Px2+//QYPDw9s3rwZ7733HuRyORwcHAAAc+fOxd69e3HhwgUAyrQKJSUlOHDggLqPPXv2RLdu3RAfH9/gYyIiy2ToMdMaxwtrPCaqyVCBQwYkbYe1jhXmeFyHztxoUDaT+jycbsnUL1wRWRJdxwrOICGr1Rh1SeLFAXzIS1SLpOx8RCRk1ihALS8uRURCJjaP5flDtslSz43c3FzI5XKEhISol0kkEgQFBSEtLQ0jR45EWloaWrZsqQ6OAEBISAjEYjFOnjyJYcOGIS0tDX379lUHRwAgNDQUK1aswO3bt9GqVSukpaUhJiZG4/tDQ0Oxd+/eWvtXVlaGsrK/X3pQKBSNcNREZGqWOmYSGZqhAofm9hIHkTVIys7Hm4mnH3k/zR3t8Erg4wyGEBmZ2NQdIDIkVV2STaP90ZC/J+/uOYvyB1WN3zEiC1dZJWDx/pwaDzOAv4tRL96fg8rGfn2GyMxZ8rkhl8sBoEZxTHd3d/U6uVwONzc3jfX29vZwdXXVaKNtH9W/o7Y2qvXaxMXFQSKRqD/t2rXT9xCJyMxY8phJZEiqwGH1IAbwd+AwKTvfrPZLZMvKH1Th3T3Zj7SPlk2bIDqkI/4bG4rYl3wwuc+TGOb/GIKfas3gCJERMEBCNiHc1wMbRgXovV1RSQV6xqXyQpHoIem5RTVurKoTAOQXlyI9t8h4nSIyAzw3DGfevHkoLi5Wf65fv27qLhHRI+KYSVSToQKHDEgSNb6k7Hz0jEtBUUm53ts2d7TD5F4dsHNKT2Qs6I8ZIZ0YDCEyEQZIyGaE+8oQPzYAUhdHvbYrKinHtIRMHDpzw0A9I7I8hXdrf5jRkHZE1sKSzw2pVAoAKCgo0FheUFCgXieVSlFYWKix/sGDBygqKtJoo20f1b+jtjaq9do4OjrCxcVF40NEls2Sx0wiQzFU4JABSaLGpZqRVVRSodd21WeLLBjchbNEiMwAAyRkU8J8ZDg29wVEh3TSe9uonadx6AxnkhABgFsLp0ZtR2QtLPnc8PT0hFQqRWpqqnqZQqHAyZMnERwcDAAIDg7GnTt3kJGRoW5z5MgRVFVVISgoSN3mxx9/REXF3zeLycnJeOaZZ9CqVSt1m+rfo2qj+h4isg2WPGYSGYqhAocMSBI1nsoqAYv2aZ+RVZfokI6cLUJkhhggIZvT0LokqsLtnElCBPTwdIVM4oTaTiERlMUee3i6GrNbRCZn7ufGvXv3kJWVhaysLADKwuxZWVnIy8uDSCTCzJkzsWzZMuzbtw9nz57FuHHj4OHhgaFDhwIAOnfujLCwMEyZMgXp6ek4duwYoqKiMHLkSHh4eAAARo8eDQcHB0yePBnnzp3D7t27sX79eo2i7DNmzEBSUhJWr16NCxcuYNGiRfjll18QFRVl7H8SIjIhcx8ziUzBUIFDBiSJGs+GI5chV+geTBSLgE2jAxgYITJTDJCQzWpoXRLOJCFSBhpjB3sDQI2HGqqfYwd78+KPbI65nxunT5+Gv78//P39AQAxMTHw9/fHwoULAQCzZ8/G9OnTMXXqVDz77LO4d+8ekpKS4OT098OSHTt2wMvLCy+88ALCw8PRu3dvbNmyRb1eIpHg8OHDyM3NRWBgIN5++20sXLgQU6dOVbd57rnnkJiYiC1btsDPzw9ff/019u7dCx8fHyP9SxCROTD3MZPIFAwVOGRAkqhxJGXnY23KZb222TDKH+G+MgP1iIgeFQMkZNNUdUlcnZvovI1qJsn6lEssYEc2LcxHhs1jAyCVaL5lJmnaBDNDOqK/d+21BIisVWWVAElTB0zs1QGtnB001kklTtg8NgBhPqa7OerTpw8EQajx2b59OwBAJBJhyZIlkMvlKC0tRUpKCjp10kxL6erqisTERNy9exfFxcXYtm0bmjdvrtHG19cXP/30E0pLS/Hbb79hzpw5Nfryyiuv4OLFiygrK0N2djbCw8MNdtxEZL5qu54whzFTHxs3bkSHDh3g5OSEoKAgpKen67Tdrl27IBKJ1DP1iAwVOGRAkujRVVYJWLw/R+f2rZ0dED82AOG+HgbsFRE9KgZIyOaF+chwYl4IXB96kFWftSmX0euDI0jK5mwSa/fBBx+oU8+olJaWIjIyEq1bt0bz5s0xfPjwGgWH8/LyMGjQIDRr1gxubm6YNWsWHjx4oNHm6NGjCAgIgKOjI55++mn1Q0pLEeYjw89z+iE6pBNaNlUGGu/cr8DalMvovYLnB9mWpOx89F5xBKO2nsC2Y9dQVFIOV+cmmNyrA3ZO6Ymf5/SzmAd9RETGpLqe2DmlJ9aP7GZxY+bu3bsRExOD2NhYZGZmws/PD6GhoSgsLKxzu2vXruGdd95Bnz59jNRTshSGChxaS0CSyFTSc4uQX6xbai1X5yZIm/cCzysiC2Bv6g4QmQMHezGWD/PBtIRMvbaTK0oxLSETm0b7840AK3Xq1Cn83//9H3x9fTWWR0dH4+DBg/jqq68gkUgQFRWFl19+GceOHQMAVFZWYtCgQZBKpTh+/Djy8/Mxbtw4NGnSBMuXLwegzP0/aNAgTJs2DTt27EBqaireeOMNyGQyhIaGGv1YGyo5R451KZdqFKiTF5ciIiGTN1tkE5Ky8xGRkFnjPLhdUoFtx67hWU9XvpFJRFQHO7EIwU+1NnU3GmTNmjWYMmUKJk6cCACIj4/HwYMHsW3bNsydO1frNpWVlRgzZgwWL16Mn376CXfu3DFij8kShPnI0N9bivTcIhTeLYVbC2X6q0e9njDUfolsQUqOXOe2y4d1hYM930snsgQ8U4n+EuYj07twuwrrkline/fuYcyYMdi6dStatWqlXl5cXIxPP/0Ua9asQb9+/RAYGIjPPvsMx48fx4kTJwAAhw8fRk5ODhISEtCtWzcMHDgQS5cuxcaNG1FeXg5AefPs6emJ1atXo3PnzoiKisKIESOwdu1akxxvQ6imGGtLNqdatnh/DtPRkVXjeUBEZLvKy8uRkZGBkJAQ9TKxWIyQkBCkpaXVut2SJUvg5uaGyZMnG6ObZKFUgcMh3R5D8FOtGy2IYaj9ElmzpOx8fHrsmk5to0M68SVBIgvCAAlRNQ0t3M66JNYpMjISgwYN0rjhBYCMjAxUVFRoLPfy8sITTzyhvhFOS0tD165d4e7urm4TGhoKhUKBc+fOqds8vO/Q0NA6b6bNTX1TjAUA+cWlSM8tMl6niIyM5wERke26efMmKisrNa75AMDd3R1yufY3jX/++Wd8+umn2Lp1q87fU1ZWBoVCofEhIiLj0Kf2iNTFEVH9njZwj4ioMTFAQvSQcN+GzyRhXRLrsWvXLmRmZiIuLq7GOrlcDgcHB7Rs2VJjefUbYblcrvVGWbWurjYKhQL379/X2i9zuzkuvKtb/lVd2xFZIp4HRESkq7t37+L111/H1q1b0aZNG523i4uLg0QiUX/atWtnwF4SEVF1J369pXPtkUUvdeGsLCILwwAJkRYNnUkC/F2X5NCZG43cKzKW69evY8aMGdixYwecnJzq38CIzO3m2K2Fbv8+urYjskQ8D4iIbFebNm1gZ2eHgoICjeUFBQWQSqU12l+9ehXXrl3D4MGDYW9vD3t7e3zxxRfYt28f7O3tcfXqVa3fM2/ePBQXF6s/169fN8jxEBGRpqTsfETu0K1e7aReHZhai8gCmUWAZOPGjejQoQOcnJwQFBSE9PR0U3eJCOG+MsSPDYDUxbFB27MuieXKyMhAYWEhAgIC1DeuP/zwAz766CPY29vD3d0d5eXlNYppVr8RlkqlWm+UVevqauPi4oKmTZtq7Zu53Rz38HSFTOKE2t6PEQGQSZSFH4msFc8DIiLb5eDggMDAQKSmpqqXVVVVITU1FcHBwTXae3l54ezZs8jKylJ/XnrpJfzzn/9EVlZWrS+/ODo6wsXFReNDRESGlZSdj4iETNy5X6FT+/7eNQPjRGT+TB4g2b17N2JiYhAbG4vMzEz4+fkhNDQUhYWFpu4aEcJ8ZDg29wVEh3TSe1vWJbFcL7zwQo0b1+7du2PMmDHq/27SpInGjfDFixeRl5envhEODg7G2bNnNcay5ORkuLi4wNvbW92m+j5UbbTdTKuY282xnViE2MHK49H2cFgAMPJZpoAg62YnFmHBIG+tRdpV50XsYG9OtSciekhllYC0q7fwbdbvSLt6y2KvmWNiYrB161Z8/vnnOH/+PCIiIlBSUoKJEycCAMaNG4d58+YBAJycnODj46PxadmyJVq0aAEfHx84ODiY8lCIiOgvqrojuvxl4gtRRJbN3tQdWLNmDaZMmaK+eIyPj8fBgwexbds2zJ0718S9I1I++JoR0hEd3ZwRtfM09L1vW5tyGTvTr2PRS96camkhVDeo1Tk7O6N169bq5ZMnT0ZMTAxcXV3h4uKC6dOnIzg4GD179gQADBgwAN7e3nj99dexcuVKyOVyzJ8/H5GRkXB0VM5KmjZtGjZs2IDZs2dj0qRJOHLkCL788kscPHjQuAf8iMJ8ZNg8NgCL9+dozcu6NuUydp26jtjBPAfIOiVl52PpQe1FG6USJ/7uExFpkZSdX+PaQWahY+Zrr72GP/74AwsXLoRcLke3bt2QlJSkrjWXl5cHsdjk7yYSEZEe0nOLdK47AvCFKCJLZtKrtPLycmRkZCAkJES9TCwWIyQkBGlpaVq3MbcCxWQ7HrUuSURCJou3W5G1a9fixRdfxPDhw9G3b19IpVJ888036vV2dnY4cOAA7OzsEBwcjLFjx2LcuHFYsmSJuo2npycOHjyI5ORk+Pn5YfXq1fjkk08QGhpqikN6JGE+Mvw8p1+ts63kxTwHyDqppt3XdvO0YFBni3vQR0RkaLWNnZZ8vRAVFYX//e9/KCsrw8mTJxEUFKRed/ToUWzfvr3Wbbdv3469e/cavpNERKSzwru6BUdaNmuCzWMDeM1PZMFMOoPk5s2bqKysVL9Zo+Lu7o4LFy5o3SYuLg6LFy82RveIagj3lSFeHIBF+85BrijTa1sBwKJ959DfW8q3CizQ0aNHNX52cnLCxo0bsXHjxlq3ad++PQ4dOlTnfp9//nmcPn26MbpoFnadytO6XIBy2vHi/Tk8B8hq1DftXgRg6cHzCPWR8XeeiOgvdY2dvF4gIiJz4dbCSad2G0cFoFfHNgbuDREZksXN8zW3AsVkex6lLolcUYYNR64YoFdEplffFGQBQH5xKdJzi4zXKSID4u88EZH+OHYSEZEluF1Shrri9Kq6Iz2fam20PhGRYZh0BkmbNm1gZ2eHgoICjeUFBQWQSqVat3F0dFTn7ycylUepS7I25RIAAVH9OvKtOLIquk5B1rUdkbnj7zwRkf44dhIRkblLys5HZOLpegu0s+4IkXUw6QwSBwcHBAYGIjU1Vb2sqqoKqampCA4ONmHPiHTT0Loka1Muo9cHRywyvzJRbXSdgqxrOyJzx995IiL9cewkIiJzVl8aXQAQi4CNo1l3hMhamDzFVkxMDLZu3YrPP/8c58+fR0REBEpKSjBx4kRTd41IJ+G+MsSPDYDURb+ZTXJFKaYlZOLQmRsG6hmRcfXwdIVM4oTa3p9RTUHu4elqzG4RGQx/54mI9Mexk4iIzFl9qSABoEoAWjk7GKlHRGRoJg+QvPbaa/jwww+xcOFCdOvWDVlZWUhKSqpRuJ3InD1KXZKonadx6AxnkpDlsxOLEDvYGwC0PvQQACwY1JlTkMmqjHz2Ca1vl6l+yzntnohIU13XCxw7iYjI1JgKksj2mDxAAgBRUVH43//+h7KyMpw8eRJBQUGm7hKR3lR1SaJDOuq1XZUAvJmYifUpl1CpTzETIjMU5iPD5rEBkEq0p8VYevA8U8uRVUjKzkfvFUf+qitVk1TihM1jOe2eiEib2q4XOHYSEZGpMRUkke0xaZF2ImsU1a8jdqZfh1yh39sEa1MuY2f6dSx6yZs3hWTRwnxkqKpSBv4eJi8uRURCJh9+kEVLys5HREJmrXmJo0M6IqpfR779TERUhzAfGfp7S5GeW4TCu6Vwa6FMq8Wxk4iITCmwfSu4OjugqKRc63oRlAF9poIksh5mMYOEyJrYiUVY9JJ3rXmV68K6JGQNKqsELD2Yo3Wd6oHy4v05nDFFFqm+oo0iALtOXTdml4iILJadWITgp1pjSLfHEPxUawZHiIjIpJKy8/GPVd/XGRwBmAqSyNowQEJkAOq0AXoWbldhXRKyZPUVtRMA5BeXIj23yHidImok/P0mIiIiIrI+qlnidV3rMxUkkXVigITIQB6lcLuqLglnkpAlYlE7smb8/SYiIiIisi71zRIHAFfnJvhh1j8ZHCGyQgyQEBmQqnD7ptH+aMjsS84kIUvEonZkzfj7TUT06CqrBKRdvYVvs35H2tVbTLtJREQmVd8scQAoKqlAxv9uG6lHRGRMLNJOZAThvh7YAJHWotV1Uc0kiS5kwV+yHD08XSGTOEFeXFrrGziuzk0Q2L6VUftF1Bh6eLpC6uIEuUL7DRSLNhIR1S0pOx+L9+doPIiSSZwQO9ibb+USQRlATM8tQuHdUri1UF5TGOo+0JjfRWTOOEucyLYxQEJkJOG+MsSLA7Bo3znIFWV6bbs25TJ2pl/Hopd440jmz04sQuxgb0QkZEIEaA2SFJVU4B+rvufDELI4yTlylD6o1LqORRuJiOqmyu/+8LWBvLgUEQmZzOtONs+YAUQGK4n+xlniRLaNKbaIjOhR6pLIFaWYlsC6JGQZwnxk2Dw2AFJJ7ReQqochSdlMI0eWQfVg786fFVrXt2zWhA/3iIhqUVd+d9WyxftzmG6LbFZtBaINcc1szO8isgSqLAi1veIkgjKAyFniRNaJARIiI2NdErIVYT4y/DDrn3B1dtC6ng9DyJLoUrjR0V6M/t5So/WJiMiS1JffXQCQX1yK9Nwi43WKyEwYM4DIYCVRTaosCABqBEk4S5zI+jFAQmQi4b4e2DAqQO/tVHVJ+FYPWYKM/91GUUl5rev5MIQshS6FG+WKMv4uExHVgvndiWpnzAAig5VE2vX3lmJmSCdImjbRWC6VOHGWOJGVY4CEyITCfWUNnkny7p6zKH9Q1fidImpEfBhC1oK/y0REj4b53YlqZ8zrDF7TUG06dOgAkUik8fnggw802pw5cwZ9+vSBk5MT2rVrh5UrV9bYz1dffQUvLy84OTmha9euOHTokMZ6QRCwcOFCyGQyNG3aFCEhIbh8+bJBj60+Sdn56L3iCNamXMKd+8p0ui2bNkF0SEf8PKcfgyNEVo4BEiITa+hMkqKSCvSMS+VMEjJrfBhC1qJNc0ed2vF3mYhIO+Z3J6qdMa+ZeX1OdVmyZAny8/PVn+nTp6vXKRQKDBgwAO3bt0dGRgZWrVqFRYsWYcuWLeo2x48fx6hRozB58mScPn0aQ4cOxdChQ5Gdna1us3LlSnz00UeIj4/HyZMn4ezsjNDQUJSWmiYoV1tNnuL7FViXchnJOXKT9IuIjIcBEiIzEO4rQ/zYAEhddHsAp1JUUs7C7WTW6nsYAgBiEXC7jjRcRKaWlJ2Pt7/MqrMNH+wREdWN+d2JamfMACKDlVSXFi1aQCqVqj/Ozs7qdTt27EB5eTm2bduGLl26YOTIkXjrrbewZs0adZv169cjLCwMs2bNQufOnbF06VIEBARgw4YNAJSzR9atW4f58+djyJAh8PX1xRdffIEbN25g7969xj5c1uQhIgAMkBCZjTAfGY7NfQHRIZ303paF28lcVX8YUpsqAYhkXR0yU6o3yuSKslrb8MEeEZFuwnxk2Dw2AFKJ5pvpzO9Ots6YAUQGK6kuH3zwAVq3bg1/f3+sWrUKDx48UK9LS0tD37594eDgoF4WGhqKixcv4vbt2+o2ISEhGvsMDQ1FWloaACA3NxdyuVyjjUQiQVBQkLqNNmVlZVAoFBqfxsCaPEQEMEBCZFbsxCLMCOmod10SVeH29SmX+GYDmZ0wHxk26vA7zTdzyNzU9UZZdXywR0SkuzAfGX6e0w87p/TE+pHdsHNKT+Z3J4JxA4gMVpI2b731Fnbt2oXvv/8e//rXv7B8+XLMnj1bvV4ul8Pd3V1jG9XPcrm8zjbV11ffTlsbbeLi4iCRSNSfdu3aNfAoNbEmDxEBgL2pO0BENYX7emADRHgzMVOv7damXMbO9OtY9JI3L2rJrLRydkRdsY/qb+YEP9XaaP0iqkt9b5SpfDjCD706tjFCj4iIrIOdWMS/90RahPnI0N9bivTcIhTeLYVbC2WqK0PM5jDmd5HpxMbGAlDO0qjN+fPn4eXlhZiYGPUyX19fODg44F//+hfi4uLg6KhfOvDGNm/ePI3+KRSKRgmSsCYPEQEMkBCZrXBfGeLFAXh3z1kUlVTovJ1cUYppCZnYNNof4b4eBuwhke74Zg5ZIl1/H2+W1J5+i4iIlCqrBD6IJdKBMQOIDFZav+nTp2PdunU4deoUmjdvrrXNk08+qXV5UFAQHjx4gGvXruGZZ56BVCpFQUGBRhvVz1KpVP2/2tpUX69aJpPJNNp069at1uNwdHQ0SJBGVZNHXlyqdda4CMqZVazJQ2TdGCAhMmNhPjL083JHz7hUFOlZxDpq52lsgAjhvpxJQqan6xs3bZxN+2YSUXV8o4yIqHEkZedj8f4cjVl5MokTYgdz1jMRkSG1aaOc5dypUye4uLjotW1WVhbEYjHc3NwAAMHBwXjvvfdQUVGBJk2aAACSk5PxzDPPoFWrVuo2qampmDlzpno/ycnJCA4OBgB4enpCKpUiNTVVHRBRKBQ4efIkIiIiHuVQG0RVkyciIRMiQCNIwpo8RLaDNUiIzJyDvRjLh/novR3rkpA5Ub2ZU99l5dtf/ZfF2sls3C4pq7N2jgjKB3x8o4yIqHZJ2fmISMiskbJQXlyKiIRM/t0nIjIDaWlpWLduHf773//i119/xY4dOxAdHY2xY8eqgx+jR4+Gg4MDJk+ejHPnzmH37t1Yv369RuqrGTNmICkpCatXr8aFCxewaNEi/PLLL4iKigIAiEQizJw5E8uWLcO+fftw9uxZjBs3Dh4eHhg6dKgpDp01eYiIM0iILEGYjwybRvsjaufpOus4aMO6JGQO6nozp7oChfJhCS9EydSSsvMRmXi63gLtfKOMiKh2lVUCFu/P0TqWClAGmhfvz0F/bynHUiIiE3J0dMSuXbuwaNEilJWVwdPTE9HR0RrBD4lEgsOHDyMyMhKBgYFo06YNFi5ciKlTp6rbPPfcc0hMTMT8+fPx7rvvomPHjti7dy98fP5+6XP27NkoKSnB1KlTcefOHfTu3RtJSUlwcjL+rGxV+seyB1X4cIQfIAJu3itjKkgiG8MZJEQWItzXAxtGBTRoW1VdkkNnbjRyr4h0p3ozx92l9jRaqgcoi/fncOYTmUxdD/RUxCJg42jrDOQtWrQIIpFI4+Pl5aVeX1paisjISLRu3RrNmzfH8OHDa+SazsvLw6BBg9CsWTO4ublh1qxZePDggUabo0ePIiAgAI6Ojnj66aexfft2YxweERlRem5RjZkj1QkA8otLkZ5bZLxOERFRDQEBAThx4gTu3LmD+/fvIycnB/PmzatR98PX1xc//fQTSktL8dtvv2HOnDk19vXKK6/g4sWLKCsrQ3Z2NsLDwzXWi0QiLFmyBHK5HKWlpUhJSUGnTp0MenzaJGXno/eKIxi19QRm7MrCmE9P4p2v/gtHezGCn2rN4AiRDWGAhMiChPvKED82ANI6HjDXJWrnaRw6wzQGZDphPjKsfrVbnW34sIRMrb4HeoAyjWErZwcj9cj4unTpgvz8fPXn559/Vq+Ljo7G/v378dVXX+GHH37AjRs38PLLL6vXV1ZWYtCgQSgvL8fx48fx+eefY/v27Vi4cKG6TW5uLgYNGoR//vOfyMrKwsyZM/HGG2/gu+++M+pxEpFhFd6teyzVtx0REVFjYPpHIqqOARIiCxPmI8OxuS8gOkT/NyxYl4TMwc17ZTq148MSMhU+0APs7e0hlUrVH1WBz+LiYnz66adYs2YN+vXrh8DAQHz22Wc4fvw4Tpw4AQA4fPgwcnJykJCQgG7dumHgwIFYunQpNm7ciPLycgBAfHw8PD09sXr1anTu3BlRUVEYMWIE1q5da7JjJqLG59ZCt3QpurYjIiJ6VPWlfwSY0YDI1jBAQmSB7MQizAjpiE2j/essIFybtSmX0euDI3wrgkyCD0vI3LVprtssPWv+Hb18+TI8PDzw5JNPYsyYMcjLywMAZGRkoKKiAiEhIeq2Xl5eeOKJJ5CWlgZAWeSza9eucHd3V7cJDQ2FQqHAuXPn1G2q70PVRrUPbcrKyqBQKDQ+RGTeeni6QiZxQm2XqyIAMokyzzsREZExMP0jET2MARIiC8a6JGSJ6ntYAijrO9wuKTdan4hUkrLz8faXWXW2sfYHekFBQdi+fTuSkpKwefNm5Obmok+fPrh79y7kcjkcHBzQsmVLjW3c3d0hl8sBAHK5XCM4olqvWldXG4VCgfv372vtV1xcHCQSifrTrl27xjhcIjIgO7EIsYO9AaDG333Vz7GDvZnnnYiIjIazxYnoYQyQEFk41iVpfHFxcXj22WfRokULuLm5YejQobh48aJGGxYpbrjqD0tqUyUAkYnM/UrGpcpFLFfUngbOFh7oDRw4EK+88gp8fX0RGhqKQ4cO4c6dO/jyyy9N2q958+ahuLhY/bl+/bpJ+0NEuunvLcXMkE6QNG2isVwqccLmsQEI85GZqGeNa+PGjejQoQOcnJwQFBSE9PT0Wttu3boVffr0QatWrdCqVSuEhITU2Z6IiBoPMxoQ0cMYICGyAo1Rl4QzSf72ww8/IDIyEidOnEBycjIqKiowYMAAlJSUqNuwSPGjCfORYaMOKeKY+5WMpa5cxNVZ2wM9XbRs2RKdOnXClStXIJVKUV5ejjt37mi0KSgogFQqBQBIpdIaAWPVz/W1cXFxQdOmTbX2w9HRES4uLhofIjJvSdn56L3iCNamXMKd+xUAgJZNmyA6pCN+ntPPasbS3bt3IyYmBrGxscjMzISfnx9CQ0NRWFiotf3Ro0cxatQofP/990hLS0O7du0wYMAA/P7770buORGR7WH6RyJ6GAMkRFbiUeuScCbJ35KSkjBhwgR06dIFfn5+2L59O/Ly8pCRkQGARYobSytnR9QV+2DuVzKm+nIRq3w4ws9qHujp6t69e7h69SpkMhkCAwPRpEkTpKamqtdfvHgReXl5CA4OBgAEBwfj7NmzGg8Gk5OT4eLiAm9vb3Wb6vtQtVHtg4gsn2pW3sNja/H9CqxLuYzkHLmJetb41qxZgylTpmDixInw9vZGfHw8mjVrhm3btmltv2PHDrz55pvo1q0bvLy88Mknn6CqqqrGuEhERI2P6R+J6GEMkBBZmYbWJVHNJGFKo5qKi4sBAK6uyjdITFmk2JromtNVXqy9HgFRY9L19/FmSe3pt6zFO++8gx9++AHXrl3D8ePHMWzYMNjZ2WHUqFGQSCSYPHkyYmJi8P333yMjIwMTJ05EcHAwevbsCQAYMGAAvL298frrr+O///0vvvvuO8yfPx+RkZFwdFSmg5w2bRp+/fVXzJ49GxcuXMCmTZvw5ZdfIjo62pSHTkSNpK5Zeapl1jJLtLy8HBkZGRrXdGKxGCEhITpf0/3555+oqKhQX2tqU1ZWBoVCofEhIqKGCfORYfPYAEglmmm0bHG2OBEB9qbuABE1vnBfGeLFAVi071ydufS1eXfPWfTzcoeDPeOnAFBVVYWZM2eiV69e8PHxAQCjFSnWlmamrKwMZWV//39qyTfHuuZ0XXrwPJo62PEilQzq2s2S+hvBNnIR//bbbxg1ahRu3bqFtm3bonfv3jhx4gTatm0LAFi7di3EYjGGDx+OsrIyhIaGYtOmTert7ezscODAAURERCA4OBjOzs4YP348lixZom7j6emJgwcPIjo6GuvXr8fjjz+OTz75BKGhoUY/XiJqfPXNyqs+SzT4qdbG65gB3Lx5E5WVlVqv6S5cuKDTPubMmQMPD48aL85UFxcXh8WLFz9SX8m8VVYJSM8tQuHdUri1UKb3MeYb7Kb+fiJjC/ORob+3lL/3RMQACZG1Uv2x33DkCtamXNJ5u6KSCvSMS8XyYT58IA0gMjIS2dnZ+Pnnn03dFQDWdXOsyv0qLy6ts+7D7ZJyRCRk8k0eMpik7HysTblcZxsRlG+U2UIu4l27dtW53snJCRs3bsTGjRtrbdO+fXscOnSozv08//zzOH36dIP6SETmTddZebq2s2YffPABdu3ahaNHj8LJqfYg/Lx58xATE6P+WaFQoF27dsboIhlBUnY+Fu/P0QgsyiROiB3sbZTrX1N/P5Gp2IlFFh+oJ6JHx1fEiaxYQ+uSFJWUY1oCC7dHRUXhwIED+P777/H444+rl5uySPG8efNQXFys/ly/fv2RjtGUqud+rYu1peIg86JKA6ML5iImItKNrrPtrGFWXps2bWBnZ6f1mk51zVebDz/8EB988AEOHz4MX1/fOts6OjrCxcVF40PWobZ6PfLiUkQkGD4Fsqm/n4iIyNQYICGyAQ2tS2KrhdsFQUBUVBT27NmDI0eOwNPTU2O9KYsUW9vNsSr3q6tzkzrbsWA7GYquxdlnhnTiG5RERDpSzRKtLaQsgvLtdGuYlefg4IDAwECNazpVwfW6rulWrlyJpUuXIikpCd27dzdGV8kMmbpej6m/n8hUKqsEpF29hW+zfkfa1Vv8HSeycQyQENmIcF+Z3jNJVIXb16dcsqkLhsjISCQkJCAxMREtWrSAXC6HXC7H/fvKYuEsUty4wnxkWPBiF53aJufIDdwbsjUpOv5OdWjTzMA9ISKyLiOffULrQ1fVpag1zcqLiYnB1q1b8fnnn+P8+fOIiIhASUkJJk6cCAAYN24c5s2bp26/YsUKLFiwANu2bUOHDh3U15r37t0z1SGQiehTr8cav5/IFJKy89F7xRGM2noCM3ZlYdTWE+i94ghnSxHZMAZIiGxIQ2eSrE25jF4f2M4Fw+bNm1FcXIznn38eMplM/dm9e7e6zdq1a/Hiiy9i+PDh6Nu3L6RSKb755hv1elWRYjs7OwQHB2Ps2LEYN26c1iLFycnJ8PPzw+rVq222SLHURbcUG9uOXbOZ30MyvKTsfHx67JpOba0hDQwRkTGoHjzVVgNPKnGyurpir732Gj788EMsXLgQ3bp1Q1ZWFpKSktSF2/Py8pCf//f1y+bNm1FeXo4RI0ZoXGt++OGHpjoEMhFT1+sx9fcTGRtTyhGRNizSTmRjwn1liBcH4N09Z1FUUqHzdnJFKaYlZGLTaH+E+3oYsIemJwj1z5ZhkeLGpUrFUV+qIxGU0/z7e0ut5q1TMg1da4/YUnF2IqJHpXrwVNuVVHRIR0T162iVf8OjoqIQFRWldd3Ro0c1fr527ZrhO0QWwdT1ekz9/UTGVF9KOd5rEtkuziAhskFhPjKcmBcCV2cHvbe11bokZFj6FGznNH9qDLrWHhFgXWlgiIgMpa4HT4DywdOuU9eN2SUis2fqej2m/n4iY2JKOSKqDQMkRDbKwV6M5cN89N7OVuuSkOGF+cgwuVcHndpymj89KrlCt9+hSb06WFUaGCIiQ+GDJyL9VX9J6OEghTHq9Zj6+4mMiSnliKg2DJAQ2bAwH/0Lt6vYWl0SMo4Qb6lO7a7d/NPAPSFrlpSdj6UHzunUtr+Ov5NERLaOD56IGibMR4bNYwMglWimsTJWvR5Tfz+RsTClHBHVhjVIiGxcuK8HNkCENxMz9d7WluqSkHGopvnLi0trTdEBAOtSLuEZaXPesJHe6suPr8LaI0RE+uGDJ6KGC/ORob+3FOm5RSi8Wwq3FsprEGPN3DD19xMZQ333mrz+J7JdnEFCRMrC7WMDIHVxbND2rEtCjUU1zb++h9cCgHf3nEX5gypjdIusRGWVgEX7as+Pr8KUEkRE+rtdUlbnrGTWMiCqm51YhOCnWmNIt8cQ/FRro1+DmPr7iQyNKeWIqDYGC5C8//77eO6559CsWTO0bNlSa5u8vDwMGjQIzZo1g5ubG2bNmoUHDx4YqktEVIcwHxmOzX0B0SGd9N6WdUmoMYX5yBAd0rHedkUlFegZl8o0b6SzDUcu61R7xNXZgSkliIj0kJSdj8jE06jvMpAPnoiIyJSYUo6ItDFYiq3y8nK88sorCA4OxqefflpjfWVlJQYNGgSpVIrjx48jPz8f48aNQ5MmTbB8+XJDdYuI6mAnFmFGSEd0dHNG1M76b3IftjblMnamX8eil7x5YUGPpEMbZ53aFZWUIyIhkxezVK+k7HysTbmsU9v5gzrz94mISEeVVQIW7697dp5YBGwYxb/VRERkekwpR0QPM9gMksWLFyM6Ohpdu3bVuv7w4cPIyclBQkICunXrhoEDB2Lp0qXYuHEjysvLDdUtItJBuK8HNowKaNC2qrokh87caORekS3RNz/54v05nL1EtVI9vNOVVNLUgL0hIrIu6blFyC+ue3ZelQC0cnYwUo+IiIjqxpRyRFSdyWqQpKWloWvXrnB3d1cvCw0NhUKhwLlz50zVLSL6C+uSkCmpCujpcpkqAMgvLkV6bpGhu0UW6sSvt+p9eKfC/PhERPrRJXUhABTe1a0dEREREZExmSxAIpfLNYIjANQ/y+XyWrcrKyuDQqHQ+BCRYTRGXRLWh6CGqF5AT1fJObX/7SDblZSdj8gdmTq3Z358IiLdJWXnY+kB3V5u03d2KJEtqKwSkHb1Fr7N+h1pV2+ZxYxoc+wTERGRIelVg2Tu3LlYsWJFnW3Onz8PLy+vR+pUXeLi4rB48WKD7Z+IND1qXZJ395xFPy93ONibLB5LFkpVQO/dPWdRVFJRb/ttx66hh6cr85uTWlJ2PiISMuvMi19ddEgn/v4QEelI1zFWBGXxW87OI9KUlJ2PxftzNGa5yiROiB1sunqO5tgnIiIiQ9PrieXbb7+N8+fP1/l58sknddqXVCpFQUGBxjLVz1KptNbt5s2bh+LiYvXn+vXr+hwCETVQQ+uSFJVUoGdcKmeSUIOE+chwYl4IXHXMW/7unrMof1Bl4F6RJaisErBoX91Fg6uTujgiqt/TBu0TEZG10KUwOwB1qkzOziPSpAowPpwCVF5ciogE08zCN8c+ERERGYNeAZK2bdvCy8urzo+Dg24PsYKDg3H27FkUFhaqlyUnJ8PFxQXe3rWnVXF0dISLi4vGh4iMo6F1SYpKylm4nRrMwV6M5cN8dGrLgBypbDhyWee8+CIAi17qwod3REQ60qUwOwC4Ojtg89gAvnlOVE1dAUbVssX7c4ya2soc+0TU2Jg+johqY7CcN3l5ecjKykJeXh4qKyuRlZWFrKws3Lt3DwAwYMAAeHt74/XXX8d///tffPfdd5g/fz4iIyPh6NiwotBEZHiPUpeEhdupocJ8ZJjcq4NObYtKyvmWm41Lys7H2pTLOrVt2awJH94REekpRce6X/MHdeb4SvSQ+gKMwv9v7+6Do6rvvo9/diPZ8JRggLCJIqDEh4hAABOCfVCMBolYrfWiWBTpwwyUUCHWAr2RAHcRkVqwAjK1HeOMA1WuuWsFabwg2HaUaBRkegcvFLzhCi3ZBJomC9EkkJz7j3TXBEL2Ift4zvs1wzA5OWf399s9+8nZ8z3n95NU09isyuP1lm4TEEplVTX62rp9mvXS+3r894c066X39bV1+/jOCEBSGAskK1asUHZ2tkpKSnTu3DllZ2crOztbH330kSQpISFBu3btUkJCgvLy8jR79mw9+uijWr16dbiaBCBEPPOSbHk4W4FccO2ZuJ07SRCM/KzLD794MUPSyjcPc1WQBbVeaNfP/1Dl9/qbZ1EcAYBAlFXV6HfvnfBrXWdK3/A2BohDdWf9u8PV3/VCIRbbBIQKw8cB8CVsBZLS0lIZhnHJv9tvv927zogRI7R792598cUXOn36tH75y1/qiisCmjceQBQFOy8Jd5IgGDmjUpWekiR/a3Iud4s27TsW1jYhtpRV1Wjy2r2qb2r1a/30lCRNvm5wmFsFAObhGYbHF5s6MpaJ2YFLpQ1MCul6oRCLbQJCgeHjAPgjbAUSANbgmZcktX8fv7fx3Eny/N7POBCB3xLsNpXMuPwcVd3ZsPcz9jOL2P23Gs179aDqm877vQ2TBgNAYPyde8QQGQtcjq+LfqJRYIzFNgGhwPBxAPxBgQRAr00bk673l+UrtX9iQNtt2HtUtz3DuJ/w37Qx6XoxwIIc+5n57f7bKRVtPxjQNovzr2doLQAIkL9zj3z/tpFkLHAZnS/6ubgg4fk50gXGWGwTEAoMHwfAHxRIAIRE4hV2Pf3AmIC3c7mbNe9V5iWB/4IpyLGfmVNbu6Hn9x7Vj7d9rEBuEnImO1Q0dXT4GgYAJuPJW3/nHrkrgHnDACvyXPTjTOk6ZJUzJUkvzo7O/Gix2Cagtxg+DoA/mPADQMhMG5OuLQ9nq2h7YCcrpY55STbJpuljOfCGb56C3LxXA7trgP3MPMqqarTyzcNyuVsC2s4maeV9N3MFJAD4KZC8tanjZCrD8AC+TRuTrruynKo8Xq+6s81KG9jx2YnmMUostgnoDc/wca7G5m7nIeHvFgCJO0gAhFiwE7czLwkCNW1MuhbnZwa0DfuZOXjmGwm0ODK4fyJXQAJAAMqqajQ/gLxl7hHAP23thio+/6d2/fvu5nvHZijvusEx8dlJsNuUd91gfWv8VcoZlarK4/X646F/qOLzf3L8jLjD8HEA/MEdJABCbvrYdG1RcHeSbNh7VNsrT2rlfVmcxIRPRVMztb3ypFzuwMaMZT+LXx3zjXwc8Hap/fuoYtmdSryCa0MAwB9t7YZWvvlJt1fcXg5zjwC+lVXVaNXOT7pMHJ2ekqSSGbF1XBov7QR88Qwfd/H+7GR/BvBvnCUAEBbB3kkiMV8E/Jdgt2nlfVmXXA3kD/az+BLsfCMeTz9wC8URAPCD58r2om0HAr4AgblHgJ557srqfJJWklyNzZr/6kGVVdVEqWVdxUs74b/169drypQp6tevnwYNGtTtOtXV1SosLFS/fv2UlpamJ598UhcuXOiyzp///GdNmDBBDodDo0ePVmlp6SWPs3nzZo0cOVJJSUnKzc1VZWVll983NzdrwYIFGjx4sAYMGKAHH3xQtbW1oepqt6aNSde7S6Zq+48m6/nvjtf2H03Wu0umUhwBIIkCCYAwmj42XVtnT5Az2RHU9kXbP9buv3HwjZ55J5QMcj9bsO1jrXrzMMMGxChPYWTC6v/Shr2fBby93SZteZhhtQDAH2VVNfraun2a9dL7+lOV/yerbOq4spwx3IHLa2s3tGpn93dleZat2vlJ1I9H46WdCMz58+f10EMPaf78+d3+vq2tTYWFhWptbdX+/fv1yiuvqLS0VCtWrPCuc/z4cRUWFuqOO+7QoUOHtGjRIv3whz/U22+/7V3ntddeU3FxsUpKSnTw4EGNGzdOBQUFqqur866zePFi7dy5Uzt27NBf/vIXnTp1St/+9rfD1/l/6zx8XKwMaQcgNtgMw4jrv2put1spKSlqbGxUcnJytJsDoBtt7YY27TsW1MlNSVqcn6miqZm9OoAxa1aYtV/B6O1+JjFsQKwpq6rR0v/zf9XwxfmgH2PLw9maPjYjhK2KT2bMCjP2CYim3X+r0Y+3HQxqW5sU03M8mTEvzNgns6v4/J+a9dL7Ptfb/qPJyrtucARa1L14aSf8c3FWlJaWatGiRWpoaOiy3p/+9Cfde++9OnXqlIYNGyZJ2rp1q5YsWaLTp08rMTFRS5Ys0VtvvaWqqirvdt/97nfV0NCgsrIySVJubq5uvfVWbdq0SZLU3t6u4cOHa+HChVq6dKkaGxs1dOhQbdu2Td/5znckSUeOHNFNN92kiooKTZ48Oah+AUB3/M0K7iABEHYJdpsez8/UloezFUyNY8Peo7rtmX3cyo0e9XY/k6SaRobdihWeidiDLY6kpyRp6+wJFEcAoAee4bRWvlmlBduDK46kpyTFdHEEiBX+DllXdzawoe1Czd/nj3Y7EVoVFRW65ZZbvMURSSooKJDb7dbhw4e96+Tn53fZrqC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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  19\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  20\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  21\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  22\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  23\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  24\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  25\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Question  26\n"
+      "/tmp/ipykernel_101807/3910453938.py:46: RuntimeWarning: divide by zero encountered in log\n",
+      "  (lambda x: np.log(x+10), 'log(x+10)'),\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Question  27\n"
+      "/tmp/ipykernel_101807/3910453938.py:47: RuntimeWarning: divide by zero encountered in log\n",
+      "  (lambda x: -np.log(x+10), '-log(x+10)'),\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  28\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question  29\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# first, randomly select the other multiple choice options\n",
-    "np.random.seed(1)\n",
-    "fbench_questions = []\n",
-    "for idx, _ in enumerate(fbench):\n",
-    "    mc_options = [idx]\n",
-    "    # select 4 more random functions\n",
-    "    for _ in range(4):\n",
-    "        random_idx = np.random.randint(0, len(fbench))\n",
-    "        while random_idx in mc_options:\n",
-    "            random_idx = np.random.randint(0, len(fbench))\n",
-    "        mc_options.append(random_idx)\n",
-    "    # shuffle options\n",
-    "    np.random.shuffle(mc_options)\n",
-    "    # store the options and the correct answer\n",
-    "    fbench_questions.append((mc_options, idx))\n",
-    "\n",
-    "# assure that the shape of the correct answer is unique among the mc options\n",
-    "fbench_questions[4] = ([16, 13, 4, 7, 22], 4)\n",
-    "fbench_questions[7] = ([12, 11, 17, 7, 15], 7)\n",
-    "fbench_questions[9] = ([9, 8, 26, 1, 7], 9)\n",
-    "fbench_questions[10] = ([1, 10, 4, 27, 29], 10)\n",
-    "fbench_questions[13] = ([7, 13, 24, 0, 6], 13)\n",
-    "fbench_questions[19] = ([0, 19, 22, 8, 2], 19)\n",
-    "fbench_questions[21] = ([21, 5, 13, 22, 7], 21)\n",
-    "fbench_questions[22] = ([8, 10, 0, 20, 22], 22)\n",
-    "fbench_questions[27] = ([27, 2, 20, 5, 14], 27)\n",
-    "fbench_questions[29] = ([18, 23, 10, 3, 29], 29)\n",
-    "\n",
-    "# plot the 5 functions for each question\n",
-    "# make a 1x5 grid of plots\n",
-    "for idx, (options, correct) in enumerate(fbench_questions):\n",
-    "    fig, axes = plt.subplots(1, 5, figsize=(20, 3))\n",
-    "    print('Question ', idx)\n",
-    "    for i, ax in enumerate(axes):\n",
-    "        f, n = fbench[options[i]]\n",
-    "        y = f(x)\n",
-    "        ax.scatter(x, y)\n",
-    "        ax.set_title(n)\n",
-    "        # if it is the correct one, set the title color to red\n",
-    "        if options[i] == correct:\n",
-    "            ax.title.set_color('red')\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# F-Bench (Hard subset with less well-known functions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "30\n"
-     ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -842,7 +464,7 @@
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -852,7 +474,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -862,7 +484,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -872,7 +494,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -882,7 +504,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -892,7 +514,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -902,7 +524,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -912,7 +534,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -922,7 +544,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -932,7 +554,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -942,7 +564,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -952,262 +574,291 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "fbench = [\n",
+    "    # polynomials\n",
+    "    (lambda x: x, 'x'),\n",
+    "    (lambda x: -2*x+5, '-2*x+5'),\n",
+    "    (lambda x: x**2, 'x^2'),\n",
+    "    (lambda x: -2*x**2, '-2*x^2'),\n",
+    "    (lambda x: (x-2)**2, '(x-2)^2'),\n",
+    "    (lambda x: (x-1)*(x+1), '(x-1)*(x+1)'),\n",
+    "    (lambda x: x**2+3*x-1, 'x^2+3*x-1'),\n",
+    "    (lambda x: x**3, 'x^3'),\n",
+    "    (lambda x: -3*x**3, '-3*x^3'),\n",
+    "    (lambda x: x ** 4, 'x^4'),\n",
+    "    (lambda x: (x + 4) ** 4, '-(x + 4)^4'),\n",
+    "    (lambda x: x ** 5, 'x^5'),\n",
+    "    (lambda x: -x ** 5, '-x^5'),\n",
+    "\n",
+    "    # sign\n",
+    "    (lambda x: np.sign(x), 'sign(x)'),\n",
+    "    (lambda x: -np.sign(x), '-sign(x)'),\n",
+    "    (lambda x: -np.sign(-x), '-sign(-x)'),\n",
+    "    (lambda x: np.sign(x+3), 'sign(x+3)'),\n",
+    "    (lambda x: np.sign(x-1), 'sign(x-1)'),\n",
+    "\n",
+    "    # abs\n",
+    "    (lambda x: np.abs(x), '|x|'),\n",
+    "    (lambda x: -np.abs(x), '-|x|'),\n",
+    "    (lambda x: -np.abs(-x), '-|-x|'),\n",
+    "    (lambda x: np.abs(2*x+5), '|2*x+4|'),\n",
+    "    (lambda x: np.abs(x ** 3), '|x^3|'),\n",
+    "\n",
+    "    # root\n",
+    "    (lambda x: np.sqrt(x+10), 'sqrt(x+10)'),\n",
+    "    (lambda x: -np.sqrt(x+10), '-sqrt(x+10)'),\n",
+    "    (lambda x: np.sqrt(x ** 2 + 3*x + 5), 'sqrt(x ** 2 + 3*x +5)'),\n",
+    "\n",
+    "    # exponential\n",
+    "    (lambda x: np.exp(x), 'exp(x)'),\n",
+    "    (lambda x: -np.exp(x), '-exp(x)'),\n",
+    "    (lambda x: np.exp(-x), 'exp(-x)'),\n",
+    "    (lambda x: np.exp(-x**2), 'exp(-x^2)'),\n",
+    "    (lambda x: 2 ** x, '2^x'),\n",
+    "    (lambda x: 3 ** x, '3^x+1'),\n",
+    "    (lambda x: 2 ** (x-5), '2^(x-5)'),\n",
+    "\n",
+    "    # logarithm\n",
+    "    (lambda x: np.log(x+10), 'log(x+10)'),\n",
+    "    (lambda x: -np.log(x+10), '-log(x+10)'),\n",
+    "    (lambda x: np.log(np.exp(x)), 'log(exp(x))'),\n",
+    "\n",
+    "    # trigonometric\n",
+    "    (lambda x: np.sin(x), 'sin(x)'),\n",
+    "    (lambda x: -np.sin(x), '-sin(x)'),\n",
+    "    (lambda x: -np.sin(-x), '-sin(-x)'),\n",
+    "    (lambda x: np.sin(x+2)+2, 'sin(x+2)+2'),\n",
+    "    (lambda x: np.cos(x), 'cos(x)'),\n",
+    "    (lambda x: 1/2*np.cos(x-2), '1/2*cos(x-2)'),\n",
+    "    (lambda x: np.sinh(x), 'sinh(x)'),\n",
+    "    (lambda x: -np.sinh(x), '-sinh(x)'),\n",
+    "    (lambda x: np.cosh(x), 'cosh(x)'),\n",
+    "    (lambda x: -np.cosh(x), '-cosh(x)'),\n",
+    "    (lambda x: np.tanh(x), 'tanh(x)'),\n",
+    "    (lambda x: np.arcsinh(x), 'arcsinh(x)'),\n",
+    "    (lambda x: np.arctan(x), 'arctan(x)'),\n",
+    "\n",
+    "\n",
+    "    # sigmoid\n",
+    "    (lambda x: 1/(1+np.exp(-x)), '1/(1+exp(-x))'),\n",
+    "]\n",
+    "\n",
+    "print(len(fbench))\n",
+    "\n",
+    "# for each, function draw 1000 samples from a uniform distribution and plot the function\n",
+    "x = np.linspace(-10, 10, 100)\n",
+    "for f, n in fbench:\n",
+    "    y = f(x)\n",
+    "    plt.scatter(x, y)\n",
+    "    plt.title(n)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate mulitple choice questions. graphs are combined so that the correct option has a fairly unique shape among all options"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "([9, 40, 26, 25, 7], 9)"
       ]
      },
+     "execution_count": 44,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fbench_questions[9]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  0\n"
+     ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 2000x300 with 5 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  1\n"
+     ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 2000x300 with 5 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  2\n"
+     ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 2000x300 with 5 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "data": {
-      "image/png": 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ae36ULTydemcLO4yMiIj0rrjUgiVbjmPtX9k4cfEKCoosKDZX9sewuRb3Ve5KsRnjl+3BotGd7RpAXLrJ2I3MFoFWL69BiaXmWi83I1Jn3MXZDyIissm1YjNmrDqA1fuzkV8oPSyoJcTPC8kv9K3V7zU5v7858/FfZQtPpaz9KFt4yrUfREQkVdm6i+e+24ez+cWOHk452XmF2JFxUVbzzdpg+LiBnIWn246fZ/ggIqIamS0C89anYcGmdJglzK47Su7lQrs9F8PHDWKa+sPDBBRLmAE7mlug/oCIiMiprdmfjSnf7EWJE/SIalTPfhvAunyfjxuZjAYM6RgqqXZz2jk2HCMioirNXHUQT321xymCR4ifF7pHNLTb8zF83CS2eSNJdddK2HCMiIgqMlsE7vkwGYuTMxw9FMmmD2lj14soGD5uEuwrfdqJO90SEdGN1uzPRvOX1mDvqTxHD0USd5PB7pfZAlzzUUH3iIao62nClaKaF36w4RgREZV5c/VBfPqH88x2DGwXhMQHu7DDqRaYjAY8ERvBhmNERCTZzFWpWJycqdrjexgB/7oesKXDqaebCQYDYDQaEeTrhf5tg/Forwh4uDnu5AfDRyUmxbXAgo3pkhqOLdp0DJPimnP2g4jIRb3+cyqWbMlU7PHcDYBfHXc0blgXd7VzfFBQA8NHJdhwjIiIpHhsyQ78duRcrR+nUT0PPB7bVJdBozIMH1XgTrdERFSdIYmb8dfpy7V6jH90CsXb93V0icBxI4aPKsjZ6Xbj4XNceEpE5EJmrjpQq+DROcwX306IddnfG64VtWQo2+lWirJTL0REpH/Xt7s/YdOxBgAfjuiE/0u4zWWDB8DwUa1JcS3gJvHfxhcpmaqOhYiItOG22Uk2HdekoRfS3xqIwZ1uUXhEzofhoxomowGdwxtIqmW7dSIi/Rs8b5NNO9L2bRmA3/8Z59KzHTdi+KhBN4m97q+VWLAj46LKoyEiIkcZu3Q7DmTL31R0TK9wfDYmWoUROS+Gjxr0jJR+FcuvqdkqjoSIiBxl1b7TSDp8XvZxT9zWBNOHtFNhRM6N4aMGMU394eUu7W1avv0kT70QEemM2SIwZcU+2cd9OCIK/xrUVvkB6QDDRw1MRgNGdguTVFtsvt5unYiI9GPSV7thlvl35bwHOmFwp1B1BqQDDB8S9Gsrfbe/fycf5+wHEZFOrNl/BmsOnJV1TFyrRhgaxStaqsPwIUHZTrdSFBSZufCUiEgHzBaByd/slXVMu9B6WPxoN5VGpB8MHxKU7XQrFReeEhE5v+ELt6BUwgajZYJ8PLBq8u3qDUhHGD4kmhTXAu4Sr8/mwlMiIuc2c9UB7MnKk3XMHy/GqTQa/WH4kMhkNGB0TGNJtVx4SkTkvNbsPyO7ffrg9iEutzlcbfCdkoELT4mI9M1sEXj+P3/KOsbdaMC8kVEqjUifGD5k4MJTIiJ9Szl+AVeKZSz0ADBvRBTbpsvE8CGD3IWnOXnXVBwNEREp7d11h2TVj42NwMAO0mfF6TqGD5nk7HSbnC6/FS8RETnGmv1nsC8rX3J95zA/vDK4jYoj0i+GD5lMRgPi2wRJql29P5vrPoiInIDZIvC0jBbqRgDfTuil2nj0juHDBs0a1ZNUV1hqQcqxCyqPhoiIaisxKQ1FMnqoT45rznUetcDwYYMekf6Sa5dtz1RvIEREVGtmi8CCjcck17sbDZgU11zFEekfw4cNYpr6w1Piwo8NB3N56oWISMMmf70bJTL+P53QpxlnPWqJ4cMGJqMBE3pHSqotsbDhGBGRVq3Zfwar/5K+cZyXm5GzHgpg+LDR9Xbr0moXbEzn7AcRkcbY0lDs/fs7cdZDAQwfNrp+1UuwpFrOfhARaY/chmKD24ewp4dCGD5qYXRMuOTaRZuOcfaDiEhDvtyWKbnWzQC2UFcQw0ctyFl4ystuiYi0w2wRWH9Q+lqPiX15aa2SGD5qQc7CU4CX3RIRaUViUhqktvXgpbXKY/iopesLT6Wl4Y2Hz/HUCxGRg8nt68FLa5XH8FFLJqMBCX2kzX7w1AsRkeMlJqVJ7uvBWQ91MHwoQM5mc1+kZKo6FiIiqhpnPbSB4UMBJqMBncMbSKpNOnSWp16IiByEsx7awPChkG4RDSXVlVrAnh9ERA5gtggs3MRZDy1g+FBIz8gAybXs+UFEZH8pxy+gqJSzHlqgePgwm8145ZVXEBERAW9vb0RGRmLmzJkQQt+/bNnzg4hI2+Q0FeOsh7oUDx/vvPMOFi5ciA8//BCHDh3CO++8g9mzZyMxMVHpp9IU9vwgItIuOU3F3IzgrIfKFA8fW7duxdChQzFo0CA0adIE9913H/r164cdO3Yo/VSaI6fnx4aDuTz1QkRkJ3KaisW1DuKsh8oUDx89e/ZEUlIS0tLSAAB//vknkpOTMWDAgErri4qKkJ+fX+7mrOT0/OBmc0RE9iH38tqHY5qoNxgCoEL4ePHFFzFixAi0atUK7u7uiIqKwtSpUzFq1KhK62fNmgU/Pz/rLSwsTOkh2dX12Q9ptVx4SkSkPjmX13q5GRET6a/yiEjx8LFy5UosX74cX331Ffbs2YPPP/8cc+bMweeff15p/bRp05CXl2e9ZWVlKT0kuzIZDYhvEyyplgtPiYjUJXfWY3zvSJ5ysQM3pR/w+eeft85+AED79u1x4sQJzJo1C4888kiFek9PT3h6eio9DIcaHROOXw7kSKr9IiUTvZpLv0yXiIikY1MxbVJ85uPq1aswGss/rMlkgsViUfqpNCumqb/kUy/seEpEpA62UtcuxcPHkCFD8Oabb2L16tXIzMzE999/j/fffx/33HOP0k+lWSajAXGtgyTVsuMpEZE6OOuhXYqHj8TERNx333146qmn0Lp1azz33HMYN24cZs6cqfRTadpDPZpIrl2wMZ2zH0RECmIrdW1TfM1HvXr1MHfuXMydO1fph3YqZR1PpbTyLbvsduqdLewwMiIi/WMrdW3j3i4qkdvxlLMfRETK2SbjSkLOetgfw4eK5HQ8ZdMxIiLlpOdellTHWQ/HYPhQkZyOpwBnP4iIlGC2CGyQuI/L3Z1COevhAAwfKuPsBxGRfSUmpUHicg/ENmOfJUdg+FCZ3NkPtlwnIrKd3N4ewX7eKo6GqsLwYQdyZj/Ycp2IyHZyenv4eJrQPaKhyiOiyjB82IHc2Y8vUjLVGwwRkU7J7e3xeGxTrvdwEIYPO5kU1wImif/G2XKdiEg+9vZwHgwfdmIyGnBnG7ZcJyJSy7KUE5Jr2dvDsRg+7EhOy/UPfzvK2Q8iIomuX14rbTdxzno4HsOHHZW1XJeiVABTvt6r8oiIiPTh+kJTabWc9XA8hg87kttyfdVf2VizP1vFEREROT85C00566ENDB92JueyWwB4ZuU+nn4hIqqGnIWm8W0acdZDAxg+7EzuZbeFpRYuPiUiqsaX2zIl146ObqLaOEg6hg8HkDv7wa6nRESVM1sEfk87J6nWw2RATKS/yiMiKRg+HMCW2Q92PSUiqmhHxkUUSlxp2qcVT7loBcOHg0yKawFPqV3HALz762EVR0NE5Jx+TZW+KP/hmCbqDYRkYfhwEJPRgA8e6CS5fl9WHq98ISK6gdkisCzlpKRab3cjT7loCMOHAw3sEIpOYb6S659esZdrP4iI/kvOJnIjuoXxlIuGMHw42PP9W0uuLTILNh4jIoL8TeT6tQ1RcTQkF8OHg8npegqw8RgRESCvt4ePpwndIxqqPCKSg+HDweR2PQWAf363n6dfiMilydlE7vHYpjzlojEMHxog98qXgqJSXnpLRC7LbBH47XCupFq2U9cmhg8NkHvlCwC89P1+dQZDRKRx10+5SOvtwU3ktInhQyMGdgjFoPZBkutPXLyGmasOqjgiIiJtktpOnbMe2sXwoSHzR3aBjLWnWJycwcWnRORS5LRTj2pcn7MeGsXwoSEmowET+zaTdcykr/Zw8SkRuQw57dS7Nmmg8mjIVgwfGiN30zkzgDvf/1218RARaYmcduq9IgNVHAnVBsOHxpiMBnxwf0dZxxw/f5XrP4hI99hOXT8YPjRocKdb0Lmxn6xjFidnoFji6m8iImfEdur6wfChUd+O7wU3mZ/ObW8nqTMYIiIHM1sEPk3OkFzPduraxvChUSajAfNHRMk65mxBMQbN26TSiIiIHGdHxkVcKTJLqmU7de1j+NCwgR1CMTY2XNYxqdkFDCBEpDtyFpqynbr2MXxo3CuD2yEqzFfWMQwgRKQnZovA1zuzJNV6mNhYzBkwfDiB/0yIlf1BMYAQkV6kHL8gubfHqOjGnPVwAgwfTuD6+o9Oso9LzS7AHbOT2ISMiJyanB1sudDUOTB8OInBnW5BXKsA2cdlXixE85fWYM3+MyqMiohIXXJ2sOVCU+fB8OFEFj8ajXYhPrKPswB46qu9mLnqgPKDIiJSkZwdbLnQ1HkwfDiZVVN6o60NAQQAFiefQPx7G9mMjIicBnew1SeGDye0ekpvNGnoZdOx6eeuosXLv2D4oi0MIUSkadzBVr8YPpxU0nN9YarF8TszL6HFy78gbs7v+CPtHBelEpHmcAdb/XJz9ADINiajAQtGd8b4ZXtq9TjHzl/BQ5/tAACE+Hqie4Q/7utyK3o2C+BfEUTkUNzBVr8MQghN/cmbn58PPz8/5OXlwddXXnMtV7T2QDaeWrYHapxA8fMyoY67EUWlFpgFYDIAHiYjis3/+9rTzQRAlKvxdDPBaDSgrqcbWof4McwQkWxmi0Crl3+RtJGct7sRB16/i/+PcTA5v7858+Hk7moXgqNvDUTfd3/Dib8LFX3svEIz8gpv3kuhpq9vvK8IablX8OOf1y/zretugIe7GwJ9PPCPzrfisdim8JC7ex4RuQTuYKtvnPnQkcHzN+PAmcuOHoYs9b3d0btFIGdHiMjKbBHo8Po6yRvJff1EDHpE+qs8KqqJnN/f/LNTR1ZNvh1xrRo5ehiyXLpWgh//PIOHPtuB5i+twfvrDnPxK5GL4w62+sfwoTOLH+2GxJFRcMYJBAuA+RuPoRlDCJFL4w62+sfwoUNDOobi6JsDMbBdsKOHYhOB/4WQn/accvRwiMiOuIOta2D40CmT0YCPRndB2hsD0LxRXUcPxyYCwOSVf+KOd7k5HpGr4A62roHhQ+c83IxY/8wdODTjLrQKtq0tu6NlXihE5EtrsGrfaUcPhYhUxh1sXQPDh4vw9jBh7dTeSHtjAKIjnLMT4MRv9mHs0u2OHgYRqYQ72LoOhg8X4+FmxIpxPZH2xgBMG9ASncP80KCO87R7STp8HoPnbXL0MIhIBdzB1nWo0ufj9OnTeOGFF/DLL7/g6tWraNasGZYsWYKuXbvWeCz7fDiG2SKQfOQcFm1Ox7FzBSg1WyrtXiqlw2lhqcBViedsbdU2xAerp/RW9TmIyL7Gf7kLa1PP1ljnbjTg8BsDGD40xqEdTv/++2/06tULffr0wS+//ILAwEAcPXoUDRo451S/qzAZDejduhF6t1amT0hxqQVLthzHugM5yMm/BmER+PtaKQpLlcm6qdkFGDRvEwMIkU5wB1vXonj4eOeddxAWFoYlS5ZY74uIiFD6aUjjPNyMGNe7Gcb1blbu/rJQsviPDOQWFNfqORhAiPSDO9i6FsXXfPz000/o2rUrhg8fjkaNGiEqKgqffvpplfVFRUXIz88vdyP9KgslO16+E2lvDMA/okJRm79fygIIETk37mDrWhQPH8ePH8fChQvRvHlzrFu3DhMmTMDkyZPx+eefV1o/a9Ys+Pn5WW9hYWFKD4k0ysPNiPcfiEL6WwMxuU+zmg+oQmp2AQbP36zgyIjInswWgWUpJyXVersbEcN9XJye4gtOPTw80LVrV2zdutV63+TJk7Fz505s27atQn1RURGKioqsX+fn5yMsLIwLTl2Q2SJw38It2JuVZ9PxfVsG4rMx3RUeFRGpbe76I5iblC6pdkzPcEy/u53KIyJbOHRjuZCQELRp06bcfa1bt8bJk5WnWk9PT/j6+pa7kWsyGQ34PiEW8x7oZNPxvx05h5mrDio7KCJSldki8GlyhuR6NhbTB8XDR69evXDkyJFy96WlpSE8PFzppyKdGhp1Cz56sLNNxy5OzsCa/dLPHRORY3EHW9ekePh4+umnkZKSgrfeegvp6en46quv8MknnyAhIUHppyIdG9ghBItGd7bpH+ikr/ZwLxgiJ8EdbF2T4uGjW7du+P777/H111+jXbt2mDlzJubOnYtRo0Yp/VSkc3e1C8HRtwYivIGXrOPMAOLf26jOoIhIMdzB1nWp0l598ODB+Ouvv1BYWIhDhw7hiSeeUONpyAWYjAZseiEOTRrKCyAZF65h7NKdKo2KiJTAHWxdF/d2IaeQ9Fxf2f9Ykw7n4uc/z6gyHiKqva3Hzkuu5UJTfWH4IKdgMhrw4YNRso+b+s1erv8g0qidGRcl1Xm7G7nQVGcYPshpDOwQiiduayLrGLO4vgCViLTFbBHYc+JvSbW3twjkKRedYfggp/KvQW0xppe8y7bXHMjh5bdEGpOYlAap+0w+HNNE1bGQ/TF8kNOZPqQd+rYMkHXM0yt4+oVIK8wWgYWbjkmq9XJjO3U9Yvggp/TZmGgE+3pIri8yC0z5eq+KIyIiqVKOX0CRxGmPPq14ykWPGD7IaW3+Z5ys+lV/ZfP0C5EGLEs5Ibl2dHQT9QZCDsPwQU7Lw82IQe2DZB3zz+/28/QLkQOZLQIbDuZIquUpF/1i+CCnNn9kF7jJ+FdcUFSKlGMX1BsQEVUrMSkNEvuKYXzvSJ5y0SmGD3JqJqMB80fI6//x7q+HVRoNEVVHzkJTdyPbqesZwwc5vYEdQmWdftmXlce1H0QOIGehaXybRpz10DGGD9KF+SO7wE3G/6eeWbmPaz+I7IwLTakMwwfpgslowMS+zSTXF5ZakJh0VMUREdGNzBaB3w7nSqrlQlP9Y/gg3ZgU1wLuMqZpF2xM5+wHkZ1cP+UibaUpF5rqH8MH6YbJaEBCn0jJ9SUWwdkPIjv5clumpDouNHUNDB+kK5PiWsDTJP0vpsTfjnL2g0hlZovA+oNnJdVGNa7PWQ8XwPBBumIyGvDBA50k15sF2HadSGWJSWkwS8z4XZs0UHcwpAkMH6Q7ci+9XfVXNoolnosmInnMFoFPkzMk1/eKDFRxNKQVDB+kS/NHdpG1+HTa/+1XcTRErmtHxkVcKTJLquVVLq6D4YN0Se7i0+/3nubaDyIV/JoqvaEfr3JxHQwfpFuT4lpA6tpTiwCvfCFSmNkisCzlpKRaDxOvcnElDB+kWyajAQl3SJ/9YN8PImVd30RO2s/UqOjGnPVwIQwfpGtT7mwpefaDfT+IlCNnEzkA6Nc2RMXRkNYwfJCumYwGTJLRdp2zH0TKkLOJnI+nCd0jGqo8ItIShg/SPTlt1zn7QaSMrcfOS659PLYpT7m4GIYP0j25V74s2nSMsx9EtbQz46KkOjcjuNDUBTF8kEuQM/tRWGpByrELKo+ISL/MFoE9J/6WVBvXOoizHi6I4YNcgtzZjy9SMtUbDJHOJSalQeJyDzwc00TVsZA2MXyQy5DT9yPp0FmeeiGygdkisGCjtKtc2NHUdTF8kMswGQ24s420PV9KLWw6RmQLOb09+rQK5CkXF8XwQS7loR5NJNfyslsieeTMegDA6Ogm6g2GNI3hg1xKTFN/eLrxslsiNciZ9eApF9fG8EEuxWQ0YEJvtlwnUprcWQ9uIufaGD7I5bDpGJHy5Mx6uBu5iZyrY/gglyP3stt/Jx/n7AdRNeTu45LQpxlnPVwcwwe5JDmzHwVFZuyQ2K2RyBXJ2ceFsx4EMHyQi5I7+/FraraKoyFybl9uy5Rcy1kPAhg+yIXJmf34ZmcWT70QVcJsEVh/8KykWu7jQmUYPshlmYwGjI5pLKn2Wgn3eyGqTGJSGswSczn3caEyDB/k0vq1DZFcy/1eiMqTe3kt93GhMgwf5NK6RzSEl7u0HwPu90JUHpuKka0YPsilmYwG3NEiUFIt93sh+h82FaPaYPgglydnv5dFm45x9oMIbCpGtcPwQS5Pzn4vhaVceEokd9aDl9fSzRg+yOXJ3e9l2fZM9QZD5AQ460G1xfBBBHk9PzYczOWpF3JZbKVOSmD4IIK8jqfcbI5cGVupkxIYPoj+6/rsh7TaBRvTOftBLomt1EkJDB9E/2UyGhDfJlhSLWc/yBWxlTopheGD6AajY8Il1/KyW3I1k7/ezVbqpAiGD6Ib8LJbosqt2X8Gq/+SNusBsJU6VY/hg+gGci+73Xb8vIqjIdIGs0Xg6RX7JNezlTrVhOGD6CaT4lpA4uQHjuYWqDsYIg1ITEpDkdTzLWArdaqZ6uHj7bffhsFgwNSpU9V+KiJFmIwGDI0KlVS78TB7fpC+ye3rwctrSQpVw8fOnTvx8ccfo0OHDmo+DZHiYps3klRXbOZVL6Rvcvp6ALy8lqRRLXwUFBRg1KhR+PTTT9GgQQO1noZIFcG+XpJredUL6dm/vt8vudbLzchZD5JEtfCRkJCAQYMGIT4+vtq6oqIi5Ofnl7sROVr3iIao62mSVMurXkivVu07jcwL1yTXv39/J856kCSqhI9vvvkGe/bswaxZs2qsnTVrFvz8/Ky3sLAwNYZEJIvJaMATsRGS67nZHOmN2SIwRcYVLp3C/DCwQ4h6AyJdUTx8ZGVlYcqUKVi+fDm8vGqeup42bRry8vKst6ysLKWHRGQTbjZHrmz4wi2SG4oBwPP9Wqk3GNIdxcPH7t27kZubi86dO8PNzQ1ubm7YtGkT5s+fDzc3N5jN5nL1np6e8PX1LXcj0gJuNkeuauaqA9iTlSe53tudfT1IHsXDR1xcHP766y/s27fPeuvatStGjRqFffv2wWSSdh6dSAvkbDbHhaekB2v2n8Hi5BOyjpl9X0eu9SBZ3JR+wHr16qFdu3bl7qtbty78/f0r3E+kdWWbzf1yIKfG2rKFp72aB9hhZETKk9vJFAC6NK6PIR2l9cUhKsMOp0Q1kLPZHNutkzOT28nUZABWju+p4ohIrxSf+ajM77//bo+nIVJFTFN/eJiAYnPNtWy3Ts7KbBGY/1u6rGPmjoji6RayCWc+iGpgMhokTyuz3To5q+ELt0DOP12ebqHaYPggkoDt1knPXv9Z3tUtRvB0C9UOwweRBHLarf87+ThnP8hpvLk6FUu2yLu6ZXJcc55uoVph+CCSQE679YIiM3ZkXFR5RES1t2b/GXz6R6asY7h/CymB4YNIArnt1n9NzVZxNES1Z7YITPxqr+zjuH8LKYHhg0giOe3Wl28/yVMvpGlxc36DReYxY2MjuH8LKYLhg0gik9GA0TGNJdVy4Slp2aB5m5B5sVDWMZ3D/PDK4DYqjYhcDcMHkQz92kr/q48LT0mLBs3bhNRsef1o3AzAtxN6qTQickUMH0QycOEpOSuzRaD3O0mygwcAzB/Zmes8SFEMH0QycOEpOaM1+7PR7KU1OPG3vFMtANd5kDoYPohkkrPw9JudWTz1Qg414+dUPPXVHtjyr7Bvy0Cu8yBVMHwQySRn4em1kus73RLZW3GpBTFvrsdnWzJtOr5daD18Nqa7soMi+i+GDyIbyFl4yp1uyZ6KSy144OOtaPHyL8i5XGzTY7QN8cGqybcrPDKi/7HLrrZEetM9oiHqeBhxtbjmTgnc6ZbUZrYIbD16Hq/9fADHzl+t1WOFN/DC6im9FRoZUeUYPohsYDIaMKBdML7bc6bG2rKdbnm1ACmpuNSCxcnH8PnWTOTk2zbDcTMjgN+e76vIYxFVh+GDyEaxzRtJCh9lDcem3tnCDqMiLSsLDN/tPoVzl4tgMgCebiYAAkWlFpgFYDIAHiYjis3/+/rmmqJSCwpLlV/I/NFoXlJL9sHwQWQjuTvdTuJOoC6huNSCJVuOY92BHOTkX4OwXA8NBcUWFJsrCwxmCfdVVqMckwFYMKoz7mrHS2rJPhg+iGxU1nDsSlHNvxjKGo71iPS3w8jI3soCx+I/MpBboMwpEHuJCvPFfybEMhiTXTF8ENmorOHY3KR0SfW/pmYzfOiIdZHnqgM4dq52izwdZWxsOF4Z3M7RwyAXxEttiWqBO926HrNF4P11R9Di5TV4aMkOpwweBgAfPRjF4EEOw/BBVAvc6da1/PznGbR4eQ3mb0yHWe5+9BpxV7tGSH9rIAZ2CHX0UMiFMXwQ1RJ3unUNjy3dgUlf73Xa0BHdpAHS3hiARaO7cX0HORzXfBDVEhee6pvZIhDz5nqcu1Li6KHYpHlgHaye0hsebvxbk7SD/xqJaok73erXz3+eQeRLa5wyeNwW6Y9DM+7C+mf7MHiQ5nDmg0gBk+JaYMHGYyiRcErlm51ZeHlwW059a9zYpTuRdDjX0cOQrK6HEXGtgjC8axh6Ngvgvy/SNIYPIgWULTxdsvVEjbVlO932ah5gh5GRLQbN34zUM5dVfx4PI+Bf1wNyO5yaDIC3hzuC/bzQv20wHu0VwdkNcioMH0QK6dc2RFL4AK7vdMvwoU2xb2/AqUtFij2eO4B63iaYBeBuMqJxw7q4qx0DA7k2hg8ihXCnW+enVPDw9XLDkA4heHlwW3h7mBQYGZG+MHwQKYQ73Tq3gXN/r3XwiG7SAF8+HsMZDaIa8CeESEGxzRtJqmPDMW0ZNG8TDuZcsfn4f3QKRdobA7BifE8GDyIJOPNBpCDudOt8Bs/bhNRs206DBdZ1R8q/7uRnSCQTIzqRgsoajklR1nCMHGfs0u04YGPw6NvSHztf6cfgQWQDhg8iBbHhmPNYte80kg6fl32cAcCHIzrhszExyg+KyEUwfBApjDvdap/ZIjD5m32yjwuo6470twZicKdblB8UkQth+CBSGHe61b77FiZD7v5wt9b3xC6eZiFSBMMHkQq40612zVx1AHuz8mUd0ya4LpJfjFdpRESuh+GDSAVceKpNa/afweJkaV1oy7QN8cGaqXeoMyAiF8XwQaQCuQtPc/KuqTgaAsrWeeyVdUyQjwdWT+mt0oiIXBfDB5FKJsW1gJvE5QHJ6fKvuiB5Jn+9G6UyF3r88WKcOoMhcnEMH0QqMRkNiG8TJKl29f5srvtQUXGpBav/OivrmLGx3PiNSC38ySJSUbNG9STVFZZakHLsgsqjcV2D5m+WVd80oA5eGdxGpdEQEcMHkYp6RPpLrl22PVO9gbiwVftO42iu9H1bTADWP3OHauMhIoYPIlXFNPWHp8SFHxsO5vLUi8LMFoEpK/bJOibxwc7s5UGkMoYPIhWZjAZM6B0pqbbEwoZjShu+aAvMMvLcwHbBGNhBeo8WIrINwweRyq63W5dWu2jTMc5+KGTVvtPYczJPcr3JcH3Wg4jUx/BBpLLrV70ES6rlwlNlmC0CT6/8U9Yxc0dE8XQLkZ0wfBDZweiYcMm1XHhae4lJaSiRMYPUpXF9DOkYquKIiOhGDB9EdsCFp/Zjtggk/pYuud5kAFaO76niiIjoZgwfRHbAhaf2M2/9EVmLTHm6hcj+GD6I7IQLT9Vntggs+P2Y5HqebiFyDIYPIjvhwlP1JSalyZr14OkWIsdg+CCyIzkLT79IyVRvIDpktggs2Ch91uPezrfwdAuRgzB8ENlRTFN/yadeNqed46kXGeRe4TLrHx1UHA0RVUfx8DFr1ix069YN9erVQ6NGjTBs2DAcOXJE6achckomowFxraXtdHutxIIdGRdVHpE+yL3CZXD7EO5YS+RAiv/0bdq0CQkJCUhJScH69etRUlKCfv364coV6Rs7EenZQz2aSK79NTVbvYHoiJw26iYDMG9klLoDIqJquSn9gGvXri339dKlS9GoUSPs3r0bt99+u9JPR+R0Ypr6w8vdiMISS421y7efxMuD23JtQjXktlGf1Lc5308iB1N93jEv7/r/FBo2bFjp94uKipCfn1/uRqRnJqMBI7uFSaotNrPnR3XMFoHnvtsvud7daMCkuOYqjoiIpFA1fFgsFkydOhW9evVCu3btKq2ZNWsW/Pz8rLewMGn/UyZyZv3aSt85dcHGdC48rULK8QuSZpDKJPRpxlkPIg1QNXwkJCTgwIED+Oabb6qsmTZtGvLy8qy3rKwsNYdEpAndIxqirqdJUi07nlbty22Zkms9TJz1INIK1cLHxIkTsWrVKmzcuBG33nprlXWenp7w9fUtdyPSO5PRgCdiIyTX/zv5OGc/bmK2CKw/eFZy/Xv3d+KsB5FGKB4+hBCYOHEivv/+e/z222+IiJD+P1giV3K93bq0X4YFRWZednsTOd1Mm/jXYRt1Ig1RPHwkJCRg2bJl+Oqrr1CvXj3k5OQgJycH165dU/qpiJyayWhAQh9pm80BvOz2RnK7mb45rL2KoyEiuRQPHwsXLkReXh7uuOMOhISEWG8rVqxQ+qmInJ6c2Y9vdmbx1Mt/yelm6uVmREykv8ojIiI5FO/zIQT/50gklclowOiYxliy9USNtddKrm8216t5gB1Gpl1yZz3G947kWg8ijWF/YSIHk3PZLTebkzfrwb4eRNrE8EHkYN0jGsJL4m5zSYfOuvSpF7NFYOEm6bMe7OtBpE0MH0QOZjIacEeLQEm1pRa4dM+PlOMXUFTKWQ8iZ8fwQaQBcjabc+WOp3KainHWg0i7GD6INCCmqT883aT9onTVjqdmi8Dvaeck1boZwVkPIg1j+CDSAJPRgAm9pff8cMXZjx0ZFyXv4xLXOoizHkQaxvBBpBFyen644uyHnCZrD8c0UW8gRFRrDB9EGiG34+miTcdcZvbDbBFYlnJSUq23O5uKEWkdwweRhsiZ/Sgsvd50zBXI6e0xolsYT7kQaRzDB5GGyJ392Hb8vIqj0Qa5HU3lNG0jIsdg+CDSmElxLSDxwhcczS1QdzAaIGfWw8fThO4RDVUeERHVFsMHkcaYjAYMjZK2/ftvOu94KnfW4/HYpjzlQuQEGD6INCi2eSNJdSUWYMrXe1UejeNwHxcifWL4INKgYF8vybWr/srGmv3SL0N1FtzHhUi/GD6INKh7REPU9TRJrn9m5T7dnX7hPi5E+sXwQaRBJqMBT8RGSK4vLLXorunYNhmXEXPWg8i5MHwQaZScnh+A/lqup+dellTHWQ8i58PwQaRRcnt+6KnlutkisOHgWUm1d3cK5awHkZNh+CDSsElxLeBpkv6LVS8t1xOT0iBxuQdimwWoOxgiUhzDB5GGmYwGfPBAJ8n1emi5Lre3R7Cft4qjISI1MHwQadzADqEY1D5Icv0XKZnqDcYO2NGUSP8YPoicwPyRXSD17MuGg87b9dRsEfg0OUNyPTuaEjknhg8iJ2AyGnBnG2mzH2bhvF1Pd2RcxJUis6RaXuVC5LwYPoicxEM9mkiuddaup7+mSh8ze3sQOS+GDyInEdPUH55St7uF83U9NVsEvt6ZJanWw8RZDyJnxvBB5CRMRgMm9Jbe98PZup6mHL+AwhKLpNpR0Y0560HkxBg+iJyInruefrktU3Jtv7Yh6g2EiFTH8EHkRPTa9dRsEfg97ZykWm93Iy+vJXJyDB9ETkZu19MPfzuq+dmPHRkXJZ9yub1FIE+5EDk5hg8iJyO362mpE1x6K+cql4djmqg3ECKyC4YPIic0sEMoOoX5Sq7X8qW3ZovAspSTkmq93Y2IifRXeUREpDaGDyIn9Xz/1rLqn16xV5OnX+S0Ux/RLYynXIh0gOGDyEnFNPVHXQ/pP8JFZqG50y9y26nzKhcifWD4IHJSJqMB797XUdYxWjv9IqedOjeRI9IPhg8iJyZ3x1tAW51P5Sw05SZyRPrB8EHk5OaP7CLr0lutdD6Vs9CU7dSJ9IXhg8jJyb30FgDmJTm+94echaZsp06kLwwfRDog9/SLABA35zf1BlQDLjQlcm0MH0Q6MX9kF8jY9BaZFwsxaN4m9QZUDS40JXJtDB9EOmEyGjCxbzNZx6RmF2BI4h8qjahqOfmFkmu50JRIfxg+iHRE7r4vAPDX6Xy8/nOqSiOqXPLRXEl1XGhKpE8MH0Q6YsviUwBYsiUTP+49rfyAKmG2CPz85xlJtX1aNeKsB5EOMXwQ6czADqEYGxsu+7gpK/bhzdXqz4CkHL+AYmnLPdC8kY+6gyEih2D4INKhVwa3Q9+WAbKP+/SPTLy5+qAKI/qfZSknJNf2aCr/NRCR9jF8EOnUZ2Oi0S5E/szBp39kYNU+aadF5DJbBDYczJFU6+XGHWyJ9Irhg0jHVk3pjSYNvWQfN/Gbvfhpj/JrQK43FpNWO753JNd7EOkUwweRziU919emH/TJK/fhHwv+UKwTqtkisHDTMUm17kZe5UKkZwwfRDpnMhowf0Qnm47dk5WPZi+twap9tZ8FSTl+AUWl0oJMfBte5UKkZwwfRC5gcKdbEN860KZjBYCJ3+zDne9tRHGpxHMmlZCz0HR0dBObn4eItI/hg8hF/PuR7ohrZVsAAYCj566ixcu/oOdbG7DpSK6s0zFcaEpEN3Jz9ACIyH4WP9od0386gM+3Sp+FuNmZ/CI8smQnAKBpQB2M6NYYj/aKgIdb1X/LcKEpEd3IIIRw7L7aN8nPz4efnx/y8vLg6+vr6OEQ6dJjS7bjtyPnFX1MLxNQx8MEswBMBsDTzQRAoLDEjL8LpSUPd6MBh98YwPBB5ITk/P7maRciF/TZmGi0D62n6GMWmoGL18zIKzTj4jUzsi8XI/tyieTgAXChKZGrUC18LFiwAE2aNIGXlxeio6OxY8cOtZ6KiGzw8+TbEdeqkaOHUQ4XmhK5BlXCx4oVK/DMM89g+vTp2LNnDzp27Ij+/fsjN1faTpZEZB+LH+2GxJFRjh4GAMDbnQtNiVyFKuHj/fffxxNPPIExY8agTZs2WLRoEerUqYPPPvusQm1RURHy8/PL3YjIfoZ0DMWxtwYiwr+OQ8dxe4tAnnIhchGKh4/i4mLs3r0b8fHx/3sSoxHx8fHYtm1bhfpZs2bBz8/PegsLC1N6SERUA5PRgI3P98HY2AiHjYE72BK5DsXDx/nz52E2mxEUFFTu/qCgIOTkVLzOf9q0acjLy7PesrKylB4SEUn0yuA2SHtjAKIjGtj9ubmDLZHrcHifD09PT3h6ejp6GET0Xx5uRqwY1xPFpRY8tDgF2zP+Vv0569dx53oPIhei+MxHQEAATCYTzp49W+7+s2fPIjg4WOmnIyKVlIWQtDcGYFinEKi5GuPtf7Tneg8iF6J4+PDw8ECXLl2QlJRkvc9isSApKQk9evRQ+umISGUebkbMHdEZ6W8NxJdjuqNreH3FgkgdDyMWje6Mu9qFKPSIROQMVDnt8swzz+CRRx5B165d0b17d8ydOxdXrlzBmDFj1Hg6IrIDk9GA21oG4raWgTBbBLYePY9vd5/Ewex8XC0uhbAIFJVaKnQ4vfE+L3c3+Hi5oXWIH+7rcit6NgvgjAeRC1IlfDzwwAM4d+4cXn31VeTk5KBTp05Yu3ZthUWoROScbgwiRERycW8XIiIiqjXu7UJERESaxfBBREREdsXwQURERHbF8EFERER2xfBBREREdsXwQURERHbF8EFERER2xfBBREREduXwXW1vVtbzLD8/38EjISIiIqnKfm9L6V2qufBx+fJlAEBYWJiDR0JERERyXb58GX5+ftXWaK69usViwZkzZ1CvXj0YDMpuOJWfn4+wsDBkZWXpsnW73l8foP/XyNfn/PT+Gvn6nJ9ar1EIgcuXLyM0NBRGY/WrOjQ382E0GnHrrbeq+hy+vr66/UcF6P/1Afp/jXx9zk/vr5Gvz/mp8RprmvEowwWnREREZFcMH0RERGRXLhU+PD09MX36dHh6ejp6KKrQ++sD9P8a+fqcn95fI1+f89PCa9TcglMiIiLSN5ea+SAiIiLHY/ggIiIiu2L4ICIiIrti+CAiIiK7YvggIiIiu9JV+HjzzTfRs2dP1KlTB/Xr16+05uTJkxg0aBDq1KmDRo0a4fnnn0dpaWm1j3vx4kWMGjUKvr6+qF+/PsaOHYuCggIVXoE8v//+OwwGQ6W3nTt3VnncHXfcUaF+/Pjxdhy5dE2aNKkw1rfffrvaYwoLC5GQkAB/f3/4+Pjg3nvvxdmzZ+00YnkyMzMxduxYREREwNvbG5GRkZg+fTqKi4urPU7Ln+GCBQvQpEkTeHl5ITo6Gjt27Ki2/ttvv0WrVq3g5eWF9u3bY82aNXYaqXyzZs1Ct27dUK9ePTRq1AjDhg3DkSNHqj1m6dKlFT4rLy8vO41Yntdee63CWFu1alXtMc70+QGV/z/FYDAgISGh0nqtf36bN2/GkCFDEBoaCoPBgB9++KHc94UQePXVVxESEgJvb2/Ex8fj6NGjNT6u3J9juXQVPoqLizF8+HBMmDCh0u+bzWYMGjQIxcXF2Lp1Kz7//HMsXboUr776arWPO2rUKKSmpmL9+vVYtWoVNm/ejCeffFKNlyBLz549kZ2dXe72+OOPIyIiAl27dq322CeeeKLccbNnz7bTqOWbMWNGubFOmjSp2vqnn34aP//8M7799lts2rQJZ86cwT/+8Q87jVaew4cPw2Kx4OOPP0Zqaio++OADLFq0CC+99FKNx2rxM1yxYgWeeeYZTJ8+HXv27EHHjh3Rv39/5ObmVlq/detWjBw5EmPHjsXevXsxbNgwDBs2DAcOHLDzyKXZtGkTEhISkJKSgvXr16OkpAT9+vXDlStXqj3O19e33Gd14sQJO41YvrZt25Yba3JycpW1zvb5AcDOnTvLvb7169cDAIYPH17lMVr+/K5cuYKOHTtiwYIFlX5/9uzZmD9/PhYtWoTt27ejbt266N+/PwoLC6t8TLk/xzYROrRkyRLh5+dX4f41a9YIo9EocnJyrPctXLhQ+Pr6iqKiokof6+DBgwKA2Llzp/W+X375RRgMBnH69GnFx14bxcXFIjAwUMyYMaPaut69e4spU6bYZ1C1FB4eLj744APJ9ZcuXRLu7u7i22+/td536NAhAUBs27ZNhREqb/bs2SIiIqLaGq1+ht27dxcJCQnWr81mswgNDRWzZs2qtP7+++8XgwYNKndfdHS0GDdunKrjVEpubq4AIDZt2lRlTVX/P9Ki6dOni44dO0qud/bPTwghpkyZIiIjI4XFYqn0+870+QEQ33//vfVri8UigoODxbvvvmu979KlS8LT01N8/fXXVT6O3J9jW+hq5qMm27ZtQ/v27REUFGS9r3///sjPz0dqamqVx9SvX7/cTEJ8fDyMRiO2b9+u+pjl+Omnn3DhwgWMGTOmxtrly5cjICAA7dq1w7Rp03D16lU7jNA2b7/9Nvz9/REVFYV333232tNku3fvRklJCeLj4633tWrVCo0bN8a2bdvsMdxay8vLQ8OGDWus09pnWFxcjN27d5d7741GI+Lj46t877dt21auHrj+M+lMnxWAGj+vgoIChIeHIywsDEOHDq3y/zdacPToUYSGhqJp06YYNWoUTp48WWWts39+xcXFWLZsGR577LFqd1F3ps/vRhkZGcjJySn3Gfn5+SE6OrrKz8iWn2NbaG5XWzXl5OSUCx4ArF/n5ORUeUyjRo3K3efm5oaGDRtWeYyjLF68GP37969xV+AHH3wQ4eHhCA0Nxf79+/HCCy/gyJEj+L//+z87jVS6yZMno3PnzmjYsCG2bt2KadOmITs7G++//36l9Tk5OfDw8Kiw5icoKEhzn1dl0tPTkZiYiDlz5lRbp8XP8Pz58zCbzZX+jB0+fLjSY6r6mXSGz8pisWDq1Kno1asX2rVrV2Vdy5Yt8dlnn6FDhw7Iy8vDnDlz0LNnT6Smpqq+g7dc0dHRWLp0KVq2bIns7Gy8/vrruO2223DgwAHUq1evQr0zf34A8MMPP+DSpUt49NFHq6xxps/vZmWfg5zPyJafY1toPny8+OKLeOedd6qtOXToUI2LopyJLa/51KlTWLduHVauXFnj49+4XqV9+/YICQlBXFwcjh07hsjISNsHLpGc1/fMM89Y7+vQoQM8PDwwbtw4zJo1S9N7L9jyGZ4+fRp33XUXhg8fjieeeKLaYx39GRKQkJCAAwcOVLsmAgB69OiBHj16WL/u2bMnWrdujY8//hgzZ85Ue5iyDBgwwPrfHTp0QHR0NMLDw7Fy5UqMHTvWgSNTx+LFizFgwACEhoZWWeNMn58z0Xz4ePbZZ6tNpQDQtGlTSY8VHBxcYcVu2VUQwcHBVR5z8yKb0tJSXLx4scpjasuW17xkyRL4+/vj7rvvlv180dHRAK7/1W2PX1y1+Uyjo6NRWlqKzMxMtGzZssL3g4ODUVxcjEuXLpWb/Th79qxqn1dl5L7GM2fOoE+fPujZsyc++eQT2c9n78+wMgEBATCZTBWuLKruvQ8ODpZVrxUTJ060Lj6X+9evu7s7oqKikJ6ertLolFO/fn20aNGiyrE66+cHACdOnMCGDRtkzxY60+dX9jmcPXsWISEh1vvPnj2LTp06VXqMLT/HNlFs9YiG1LTg9OzZs9b7Pv74Y+Hr6ysKCwsrfayyBae7du2y3rdu3TpNLTi1WCwiIiJCPPvsszYdn5ycLACIP//8U+GRKW/ZsmXCaDSKixcvVvr9sgWn//nPf6z3HT58WNMLTk+dOiWaN28uRowYIUpLS216DK18ht27dxcTJ060fm02m8Utt9xS7YLTwYMHl7uvR48eml2waLFYREJCgggNDRVpaWk2PUZpaalo2bKlePrppxUenfIuX74sGjRoIObNm1fp953t87vR9OnTRXBwsCgpKZF1nJY/P1Sx4HTOnDnW+/Ly8iQtOJXzc2zTWBV7JA04ceKE2Lt3r3j99deFj4+P2Lt3r9i7d6+4fPmyEOL6P5p27dqJfv36iX379om1a9eKwMBAMW3aNOtjbN++XbRs2VKcOnXKet9dd90loqKixPbt20VycrJo3ry5GDlypN1fX1U2bNggAIhDhw5V+N6pU6dEy5Ytxfbt24UQQqSnp4sZM2aIXbt2iYyMDPHjjz+Kpk2bittvv93ew67R1q1bxQcffCD27dsnjh07JpYtWyYCAwPFww8/bK25+fUJIcT48eNF48aNxW+//SZ27dolevToIXr06OGIl1CjU6dOiWbNmom4uDhx6tQpkZ2dbb3dWOMsn+E333wjPD09xdKlS8XBgwfFk08+KerXr2+9wuyhhx4SL774orV+y5Ytws3NTcyZM0ccOnRITJ8+Xbi7u4u//vrLUS+hWhMmTBB+fn7i999/L/dZXb161Vpz82t8/fXXxbp168SxY8fE7t27xYgRI4SXl5dITU11xEuo1rPPPit+//13kZGRIbZs2SLi4+NFQECAyM3NFUI4/+dXxmw2i8aNG4sXXnihwvec7fO7fPmy9XcdAPH++++LvXv3ihMnTgghhHj77bdF/fr1xY8//ij2798vhg4dKiIiIsS1a9esj9G3b1+RmJho/bqmn2Ml6Cp8PPLIIwJAhdvGjRutNZmZmWLAgAHC29tbBAQEiGeffbZc8t24caMAIDIyMqz3XbhwQYwcOVL4+PgIX19fMWbMGGug0YKRI0eKnj17Vvq9jIyMcu/ByZMnxe233y4aNmwoPD09RbNmzcTzzz8v8vLy7DhiaXbv3i2io6OFn5+f8PLyEq1btxZvvfVWuVmqm1+fEEJcu3ZNPPXUU6JBgwaiTp064p577in3y1xLlixZUum/2RsnJZ3tM0xMTBSNGzcWHh4eonv37iIlJcX6vd69e4tHHnmkXP3KlStFixYthIeHh2jbtq1YvXq1nUcsXVWf1ZIlS6w1N7/GqVOnWt+PoKAgMXDgQLFnzx77D16CBx54QISEhAgPDw9xyy23iAceeECkp6dbv+/sn1+ZdevWCQDiyJEjFb7nbJ9f2e+sm29lr8FisYhXXnlFBAUFCU9PTxEXF1fhdYeHh4vp06eXu6+6n2MlGIQQQrmTOERERETVc6k+H0REROR4DB9ERERkVwwfREREZFcMH0RERGRXDB9ERERkVwwfREREZFcMH0RERGRXDB9ERERkVwwfREREZFcMH0RERGRXDB9ERERkV/8PI9vgxueNIMMAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  3\n"
+     ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 2000x300 with 5 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  4\n"
+     ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 2000x300 with 5 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_29557/1061528540.py:26: RuntimeWarning: divide by zero encountered in log\n",
-      "  (lambda x: np.log(x+10) + 1/3 * x , 'log(x+10) + 1/3 * x '),\n"
+      "Question  5\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 2000x300 with 5 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  6\n"
+     ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 2000x300 with 5 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  7\n"
+     ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 2000x300 with 5 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  8\n"
+     ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 2000x300 with 5 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fbench_hard = [\n",
-    "    # composite functions\n",
-    "    (lambda x: -np.tanh(x) + 1/4 * x, '-tanh(x) + 1/4 * x'),\n",
-    "    (lambda x: np.arctan(x) + np.sin(x), 'arctan(x) + sin(x)'),\n",
-    "    (lambda x: np.exp(-x**2+1) + 1/3*np.abs(x), 'exp(-x^2+1)+ 1/3 * |x|'),\n",
-    "    (lambda x: np.exp(-x+1) + 2000* np.abs(x+1), 'exp(-x+1)+ 2000 * abs(x+1)'),\n",
-    "    (lambda x: np.exp(x) + 2000* np.abs(x), 'exp(x)+ 2000 * abs(x)'),\n",
-    "    (lambda x: np.exp(x) + 4000* np.sign(x), 'exp(x)+ 4000 * sign(x)'),\n",
-    "    (lambda x: np.sin(x) + np.cos(x), 'sin(x)+cos(x)'),\n",
-    "    (lambda x: np.abs(np.sin(x/2)), '|sin(x/2)|'),\n",
-    "    (lambda x: np.exp(x) + 4000* np.sin(x), 'exp(x) + 4000* sin(x)'),\n",
-    "    (lambda x: np.sign(np.sin(x)), 'sign(sin(x))'),\n",
-    "    (lambda x: np.sign(np.cos(x)), 'sign(cos(x))'),\n",
-    "    (lambda x: np.sin(x) + np.sin(2*x), 'sin(x)+sin(2*x)'),\n",
-    "    (lambda x: 1/2 * (x+1) ** 4 - 5 * (x+2) **3 + 3 * x ** 2 + 2 * x + 1, '1/2 * (x+1) ** 4 - 5 * (x+2) **3 + 3 * x ** 2 + 2 * x + 1'),\n",
-    "    (lambda x: -1/2 * (x+1) ** 4 + 5 * (x+2) **3 + 3 * x ** 2 + 2 * x + 1, '-1/2 * (x+1) ** 4 + 5 * (x+2) **3 + 3 * x ** 2 + 2 * x + 1'),\n",
-    "    (lambda x: np.sign(x ** 2 - 15), 'sign(x ** 2 - 15)'),\n",
-    "    (lambda x: np.abs(x ** 2 - 20), 'abs(x ** 2 - 20)'),\n",
-    "    (lambda x: np.abs(x) ** (1/10), 'abs(x) ** (1/10)'),\n",
-    "    (lambda x: np.sin(x) + np.sin(3*x), 'sin(x) + sin(3*x)'),\n",
-    "    (lambda x: np.sin(x) + np.sin(0.5 * x), 'sin(x) + sin(0.5 * x)'),\n",
-    "    (lambda x: np.abs(x) + np.sin(x), 'abs(x) + sin(x)'),\n",
-    "    (lambda x: np.sign(x) + np.cos(x), 'sign(x) + cos(x)'),\n",
-    "    (lambda x: x ** 3 + 250 * np.sin(x), 'x ** 3 + 250 * sin(x)'),\n",
-    "    (lambda x: np.sqrt(x+10) + 1/3 * x , 'sqrt(x+10) + 1/3 * x '),\n",
-    "    (lambda x: np.log(x+10) + 1/3 * x , 'log(x+10) + 1/3 * x '),\n",
-    "    (lambda x: np.tanh(x+10) - 1/3 * x , 'tanh(x+10) - 1/3 * x '),\n",
-    "    (lambda x: np.tanh(x+10) - 1/3 * x + 1/8 * np.sin(5*x), 'tanh(x+10) - 1/3 * x + 1/8 * sin(5*x)'),\n",
-    "    (lambda x: x + 1/3 * np.sin(5*x) + 3, 'x + 1/3 * sin(5*x) + 3'),\n",
-    "    (lambda x: -x ** 2 + 2 * np.cos(5*x), '-x ** 2 + 2 * cos(5*x)'),\n",
-    "    (lambda x: -x ** 2 + 20 * np.tanh(5*x), '-x ** 2 + 20 * tanh(5*x)'),\n",
-    "    (lambda x: -1/10 * x ** 3 + 20 * np.tanh(2*x), '-1/10 * x ** 3 + 20 * tanh(2*x)'),\n",
-    "]\n",
-    "\n",
-    "print(len(fbench_hard))\n",
-    "\n",
-    "# for each, function draw 1000 samples from a uniform distribution and plot the function\n",
-    "function_points = []\n",
-    "x = np.linspace(-10, 10, 1000)\n",
-    "for f, n in fbench_hard:\n",
-    "    y = f(x)\n",
-    "    plt.scatter(x, y)\n",
-    "    plt.title(n)\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Generate multiple choice questions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  0\n"
+      "Question  9\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1219,20 +870,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  1\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_29557/1061528540.py:26: RuntimeWarning: divide by zero encountered in log\n",
-      "  (lambda x: np.log(x+10) + 1/3 * x , 'log(x+10) + 1/3 * x '),\n"
+      "Question  10\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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LL2D48OH1msbXysjIQPfu3Zs8HsZY89HQea/1Pw1AnZ2dMXToULPH2Lx5Mz7//HO89NJL+OabbzBu3Djs3bsXTk71b2+ys7NRVlZWb7fJqVOnAAChoaEAas6HQM35rCHz5s3D8ePHsXDhQrz88suYNWtWgztbah/P50TGmFxMXTc2ZurUqbhy5QoWLFiA2bNn48MPP8TMmTPlHCZjzJFFRNQkKK5Vex7q1q0mwVFSAlw334ayMmDCBCA8HOjXr2ZXyD33AA3Mt93gllvq/3ftYhnCNWM9WVnAmTM1JbKu98wzNf9bVAT4+Pz7/YwMQIpru6ws4Ny5mqTJ9f7zn5rXq7i46c/DJMNJE6Y4BoMBV69erZfQaNmyJVq3bl236rkp4uLiIAgCUlJSMHjw4Ho/e/nll5GVlYWkpCR07doV27dvx7hx43Dw4EG4uLjUi01JSUFcXFyTx8MYa958fX1vmCAsLy9HZWUlXF1d677XsWNHtGjRQpLzIFBzLvz888+Rnp6ODtddNE6aNAlGoxFffPEFNBoNbrrpJkycOBHx8fE3lJ05cOAAHnnkEUnGxBhrHho67wHAoEGDsGrVKjz77LMIDg6u97PLly/XLVQpLi7Gk08+iZiYGLzzzju47bbbcMcdd+Cdd97B3Llz6z2uuroaq1atqpsg1Ov1WLVqFQIDAxEdHQ0AaNOmDUJCQupWYl9r7969WLhwIaZPn47nn38e+fn5eO+99zB27Fjceuut9WJTUlKgUqn4+pAxJpvGzp+N+fHHH/H9999j6dKlePbZZ3Ho0CHMmTMHd955Z12pQsYYE8XXt/FkRVxcTTmrlBTguvk2vPxyTeIgKQno2hXYvh0YN65m98l1822yeeutf3d31EpLA157rWYnSFwccF1ZVxw4AEhxv/vppzf2RdmxA1i2rKZs2T+LeJhycNKEKc6VK1fQtm1b3HvvvYiIiICnpye2bduGffv2YdGiRU0+fv/+/eHv749t27bVS5rs2LEDK1aswLx58xAVFQUAWL16NQYNGoTXXnsN719TG/HSpUs4fPgwpk6d2uTxMMbY9U6dOoUhQ4bg/vvvR3h4OJycnPDzzz8jLy8PDz74oCTPMWrUKDg5OWHbtm146qmn6r6/evVqbNy4EWvWrEHbtm0BAMuWLcOjjz6KTz75BM/UrsBBzQRhYWFhg82XGWNMrOXLl6N///7o2bMnJk2ahA4dOiAvLw+JiYm4cOECDh06BAB47rnnUFBQgG3btkGj0WDEiBF48skn8dZbb+Huu+9GRERE3TFbt26N9957D5mZmejSpQu+//57pKam4tNPP4Wzs3Nd3N13342ff/4ZgiDUJYcrKysxbtw4dO7cGW+//TYA4I033sCGDRswYcIEHDlypN4Olvj4eNxyyy115b4YY8yWLl26hClTpuC2227DtGnTAAAff/wx/vzzT4wfPx67d++GmuvqM8ak1L9/TYmubdvqJ0127ABWrADmzQP+mW/D6tU1ZbJee818LxIpx3e92l0lffoAo0fX/1lKClBYCEhxvzts2I3fq91ZcuutwM03N/05mKT4E5Ipjru7O5555hmkpqZi3rx5mDFjBk6ePIkVK1ZIso1Yq9XikUcewbp16+q+d+XKFTzxxBPo3bs3Xn311brvDxgwAM899xwWLVqEpKSkuu+vX78eLi4uuP/++5s8HsYYu15ISAgeeugh7Ny5E7Nnz8bs2bNRWlqKH374AWPHjpXkOVq1aoWRI0fihx9+qPvehQsXMGPGDNx1110YN25c3fcfeeQR3HPPPXjppZfqla9Zt24d2rVrd8OuPcYYs0R4eDj279+PUaNGYc2aNZg6dSpWrlwJtVpdt4Pkt99+w//+9z+8/fbbdWW1AGDx4sVo3bo1xo0bh6qqqrrv+/r6YtOmTdi/fz9efPFFnD9/Hh9//DEmTZpU77mfeOIJXLx4EXv27Kn73iuvvIIzZ85g7dq1dTv/tFot1q5di/Pnz+PF2nraAEpKSrB161aMHz9ejpeGMcZEmzJlCnQ6HVavXl2XDPb398enn36KxMRELFy40MYjZIw5HK22ZlfGNfNtuHIFeOKJmmbu18y3YcAA4LnngEWLanafKNG6dUC7djfummHNgkq4tissY81Eeno6unXrhj/++ANDhgwR/fjevXtj0KBBWLJkiQyjY4wx6/j7778xaNAgnDhxAp07dxb1WJ1Oh9DQUMyaNQvPPfecTCNkjDHLDRo0CPn5+UhLSyPFDxkyBK1bt8ZXX30l+rk+/PBDvP/++zh79izcmtoolDHGLKBSqbB69WqLkrevv/461qxZg8zMTMnHxRhrZtLTa0pN/fEHYMF8m2LodEBoKDBrVk1yhzU7vNOENUsdOnTAxIkT8e6774p+7ObNm3H69GnMnj1bhpExxpj1DBgwAMOGDatXfpBq9erVcHZ2xuTJk2UYGWOMWd8777yD77//HufOnRP1uKqqKixevBhz5szhhAljjDHGmrcOHYCJEwEL5tsUZfVqwNkZ4PvdZot3mjDGGGOMMcYcjtidJowxZs94pwljjDEmHd5pwhhjjDHGGGOMMcYYY4wxBt5pwhhjjDHGGGOMMcYYY4wxBoB3mjDGGGOMMcYYY4wx1my9++67UKlUmD59et33KisrMXXqVPj7+8PT0xNjx45FXl5evcdlZWVh1KhRcHd3R8uWLfHiiy+iurq6XszOnTsRFRUFFxcXdOrUCWvWrLnh+ZcvX47Q0FC4urqib9++SE5OluPXZIwxMk6aMMYYY4wxxhhjjDHWDO3btw+rVq1Cr1696n1/xowZ2LBhA9atW4e//voL2dnZGDNmTN3PDQYDRo0aBb1ej4SEBKxduxZr1qzB3Llz62IyMjIwatQo3HbbbUhNTcX06dPx5JNPYsuWLXUx33//PWbOnIl58+bhwIEDiIiIwPDhw3Hp0iX5f3nGGGuEw5XnMhqNyM7ORosWLaBSqWw9HMaYggmCgCtXrqB169ZQqx0jh8znQMYYhSOe/wA+BzLGaBzxHMjnP8YY1bXnwPLyckRFRWHFihV46623EBkZiQ8//BAlJSUIDAzEt99+i3vvvRcAcOLECXTv3h2JiYmIjY3FH3/8gTvvvBPZ2dlo1aoVAGDlypV4+eWXcfnyZWi1Wrz88svYuHEj0tLS6p7/wQcfRHFxMTZv3gwA6Nu3L/r06YOPP/4YQM35LCQkBM8++yxmzZpF+p34HMgYoxBzDehkpTFZTXZ2NkJCQmw9DMaYHTl//jzatm1r62FIgs+BjDExHOn8B/A5kDEmjiOdA/n8xxgT6/z583j11VcxatQoDB06FG+99Vbdz1JSUlBVVYWhQ4fWfa9bt25o165dXdIkMTERPXv2rEuYAMDw4cMxZcoUHD16FL1790ZiYmK9Y9TG1JYB0+v1SElJwezZs+t+rlarMXToUCQmJpJ/Fz4HMsbEoFwDOlzSpEWLFgBqfnkvLy8bj4YxpmSlpaUICQmpO284Aj4HMsYoHPH8B/A5kDFG44jnQD7/Mcaoas+BW7duxYEDB7Bv374bYnJzc6HVauHj41Pv+61atUJubm5dzLUJk9qf1/7MVExpaSkqKipQVFQEg8HQYMyJEyca/R10Oh10Ol3df9cW0eFzIGPMFDHXgA6XNKndhufl5cUnSsYYiSNt3+VzIGNMDEc6/wF8DmSMieNI50A+/zHGxJo1axa2bdsGV1dXWw9FtAULFuCNN9644ft8DmSMUVCuAR2jgCtjjDHGGGOMMcYYY4zk8uXLiIqKgpOTE5ycnPDXX39h6dKlcHJyQqtWraDX61FcXFzvMXl5eQgKCgIABAUFIS8v74af1/7MVIyXlxfc3NwQEBAAjUbTYEztMRoye/ZslJSU1H2dP3/eoteAMcYaw0kTxhhjjDHGGGOMMcaakcTERKSmptZ93XzzzXjkkUfq/r+zszO2b99eF3/y5ElkZWUhLi4OABAXF4cjR47g0qVLdTHx8fHw8vJCeHh4Xcy1x6iNqT2GVqtFdHR0vRij0Yjt27fXxTTExcWlblcJ7y5hjMnB4cpzMcYYY4wxxhhjjDHGGhceHl4v2eDh4QF/f3/06NEDADBx4kTMnDkTfn5+8PLywrPPPou4uDjExsYCAIYNG4bw8HA89thjeP/995Gbm4s5c+Zg6tSpcHFxAQBMnjwZH3/8MV566SU88cQT2LFjB3744Qds3Lix7nlnzpyJcePG4eabb0ZMTAw+/PBDlJWVYcKECVZ8NRhjrD5Zkya7du3CBx98gJSUFOTk5ODnn3/G6NGjTT5m586dmDlzJo4ePYqQkBDMmTMH48ePl3OYzZK+2oi1CRlISs/H2cvl8HRxQmSIN14ddRPctBpbD0+RKvQGvP7bEfx+KBvlVQLUKqBHmxb43xNx8HZ3tvXwFOdyqQ6jV+zGpZJKVNX0ZIOLkwrP3NIBU27vAq2T4290a+7nwAq9Aa/+dBC/HMqD8Z/v3dLBB6se7wtPV87ZN0RfbcQnO05jxc4z0BkBFYAwfzf8OKU//Dy1th6eIlXoDZj762H8kZaHqmojgrxdMfeOcAy6qRU0asepVd+cLF++HB988AFyc3MRERGBZcuWISYmRvLnuVhYgREf7cQVnbHe91UAnNSAwQgYG34oAMBZDTipVXBzVsPd1RmXSyuhM0gzNq0a0Jt4chUAAYDTP/+n2sSx1AB83J3Q2scN2cUVKK2ohkEAXDRAYIuaCY1Lpbq659NqADcnNSqqjNA1MIbaT+/aMahVgCDUvFZCA8/fQquCr4czzhfpb/i5RgVonVTQalS4Umk0+XoDgOs/r0tDcRpVzVdDr5ubBlCrgbKqho/rrKq5RtFVCbg+xMNZBS9XZ0AloLisChXX/RtrALg4Abpq4Pp/fjcnAFBBXy1AgOn3k7dWBb0RqBYEGA3/vA81Na9plaHmsU4qQOukRo/Wnrg5NBC3dA5AbAd/Ptc5GGudA5njq9Ab8M6mY8gsKEeovzteGRnO9/pMlCVLlkCtVmPs2LHQ6XQYPnw4VqxYUfdzjUaD33//HVOmTEFcXBw8PDwwbtw4zJ8/vy4mLCwMGzduxIwZM/DRRx+hbdu2+PzzzzF8+PC6mAceeACXL1/G3LlzkZubi8jISGzevPmG5vCMMXYtuT/nVIIgNHR/I4k//vgDe/bsQXR0NMaMGWN2wjAjIwM9evTA5MmT8eSTT2L79u2YPn06Nm7cWO+EakppaSm8vb1RUlLC2/Ma8ebvR/HF7sxGfz64WyC+HM8X5teauGYftp+41OjP2/u74a8XB1txRMrWY+4fuGpqtgfA0wPDMHtkuJVG1DC5zxfN+Rw46X/7EH+s8b+ZUD83bH/hNp7ouYa5c3Ogpxb75txuvQHZAVPvMxWA5Q/3xshera07KDuhlHPF9b7//ns8/vjjWLlyJfr27YsPP/wQ69atw8mTJ9GyZUuzj6f+Xl1e3QS9QbZLYMasRgXA30ODkgoDjAJgFP5NXrk5qdAtqAXu6NEa4/uHNYsFK1SOeA5U6u/EbGPimmRsP3H5hu/fHt4Snz3exwYjYkriiOcLR/ydGGONa2wuwNznnJhzhaxJk3pPpFKZnTB8+eWXsXHjRqSlpdV978EHH0RxcTE2b95Meh4+UZr2n4//xuELpWbjAjydsX/OMCuMSPlu/WAHzhVUmI3jxEmNTq9sRLW5paL/sHXixJrni+Z0DjSXMKmlAvDJo1EY0SNY/kEp3J1L/0Ja9lWzcf7uTkiZS0ugObon1yZj2/EbJwOuZ+vzjFIp4VzRkL59+6JPnz74+OOPAdTUtA4JCcGzzz6LWbNmmX085ffihAljQAsXNSYN6IjJgzo1y2SKI54Dlfo7Mesb8O52nC+ubPTnnDhhjni+cMTfiTHWMHNzTqY+58ScKxR1hZyYmIihQ4fW+97w4cORmJhooxE5lrc2HCUlTAAg/2oV7vzoL5lHpHxvbDhCSpgAwLmCCpSUN1L3oZl4/Iu95IQJAKzalQG9mAc4OEc4B1boDaSECVCzEnby1wewOS1H3kEp3F3L/iYlTACgoLwa477YK/OIlO/X1IukhAlQc57ZdLh5v8fshV6vR0pKSr3zoFqtxtChQyU7D14srOCECWMAruiMWLztNLrM+QMdZ23EhC/34mqlqUJvTG7WOAcyxxc1f4vJhAkAxB+7hAq9RPUkGWOMMSuizDlJ9TmnqKRJbm7uDTULW7VqhdLSUlRUNDxxrdPpUFpaWu+L3UhfbcTnezJFPSYt5yp+Tb0oz4DsgL7aiNV7skQ9pvf8rTKNRvkq9AbsOp0v+nED398hw2jskyOcA8cs3y36Mf/9v4MwGJvnJOabvx/DkYvi/s3+Op2PSf/bJ9OIlG9zWg6e+y5V1GOe+fZAs32P2ZP8/HwYDIYGz4O5ubkNPkbsOfCOpbwghLHrGQD8eSofPV7fgtBZGzHqo53NfiGQLYg9ByrtGpDZ3sD3t6OwnJb8HLNij8yjYYwxxqT39sajksaZoqikiSUWLFgAb2/vuq+QkBBbD0mRYt6Ot+hxz/9wqNlONFmymtsI4K6lu6QfjB2wZLIcAHJLdfjtwAWJR9N8KOkcuOlwNo7n0XZMXEtvEPDRtlMyjEjZ9NVGfLE7w6LHNtcVggajgGnfHrTose9vOibxaJgSiD0HlknVqZ0xB3Y0pwwR87cidNZGjFn+NydQFEpJ14DM9n5OuYCsQtM7TK51PPcK7/hnjDFmdw6eL5Y0zhRFJU2CgoKQl5dX73t5eXnw8vKCm5tbg4+ZPXs2SkpK6r7Onz9vjaHalZ8PXERxhWXb7auNApZtPy3xiJRPX21EYkahRY89kn2l2ZU30FcbLZosrzXzx+abnLuWPZ8DDUYBM35Itfjxy3acaXbvgVk/HWrS44cu2inNQOzI/Sv3oNrC98mq3ZnN7j1mbwICAqDRaBo8DwYFBTX4GLHnQA8XjWTjZaw5OHC+FBHztyLurea7m9paxJ4DlXINyGzPYBQwY53468rwuX/IMBrGGGNMPucLaS0UpFgsp6ikSVxcHLZv317ve/Hx8YiLi2v0MS4uLvDy8qr3xf5lMAp48cemTcx9tP10s5toemX9kSY9/uHPmlfd4c93nW3S46uNQNLZAolGY7/s+Ry4dPtp6KotP08IAP46SetR4QgMRgG/pmY36RgXSyqb1W6TCr0BKVklTTrGR/HNb0eTPdFqtYiOjq53HjQajdi+fXuj50Gx58A//nurpGNmrLnIuVqF0Fkbm93CIGsSew5UyjUgs71dJ2j9BK9XbQTGf5ks8WgYY4wxeeirjSglXot2DPRo8vPJmjS5evUqUlNTkZqaCgDIyMhAamoqsrJq+kTMnj0bjz/+eF385MmTkZ6ejpdeegknTpzAihUr8MMPP2DGjBlyDtOhJaUXWLwqt5YAYPep5jOZuTktBz82sVxUWnZps0o0WVpi6FpfJWU2fSAK01zOgQajgE/+bPqOtMXxJyUYjX1IOlsAKXpRv9OMSk49/dX+Jh9jxV9nm9W52R7NnDkTn332GdauXYvjx49jypQpKCsrw4QJEyQ5fhs/N2g1KkmOxVhz1OP1Leg2ZxOX7JKJ3OdA5pgm/M/ya6Sdpy43q0U4jDHG7NfahExybGwH/yY/n6xJk/3796N3797o3bs3gJqLwN69e2Pu3LkAgJycnLrJQwAICwvDxo0bER8fj4iICCxatAiff/45hg8fLucwHVqiRKv3P/07XZLjKJ3BKOD5H5q2MwcAjEJNwqo5MBgFFEhw47ztWJ7DTWY2l3Ng0tkC6CUoiZxZUNb0g9iJr/dmSnKc9MvN4zUzGAXsOZPf5ONUGwUkSHAcJp8HHngACxcuxNy5cxEZGYnU1FRs3rz5hsbITXHq7ZGcOGGsCSqrBUTM34qB7203H8xEscY5kDmWcV8kNfkYfbj8HmOMMTuwL5M+zzquX1iTn8+pyUcwYdCgQRCExidB16xZ0+BjDh60rMkra4g0k9CnL1ner8KeJJzJR5lEK20SzuTjlk4BkhxLyZZtl6bcTfU/iSZHes2ayznwf4mZkhznqs4AfbURWidFVY6UnMEoYNdpaSbuc0po9TztnVQ7cwBg/YELGNAlUJqDMVlMmzYN06ZNk/U5Tr09EhcLKzDio524oquf9VUBcFIDBiNgKh/srAac1Cq4Oavh7uqMy6WVkKrPvFYNk8loFWqu8Jz++T+mNqmrAfi4O6G1jxuyiytQWlENgwC4aIDAFi4AgEulurrn02oANyc1KqqM0DUwhtozdO0Y1CpAEGpeq4b+TFtoVfD1cMb5Iv0NP9eoAK2TClqNClcqjSZfbwBw/ed1aShOo6r5auh1c9MAajVQ1sgaD2cV4OKkgq5KwPUhHs4qeLk6AyoBxWVVqLju31gDwMUJ0FUD1//zuzkBgAr6agECTL+fvLUq6I1AtSDAaPjnfaipeU2rDKYfaytZRZXoMfcPpM2/w9ZDcSjWOAcyx/B76kX8dbrpC/Wu6o34+cBF3BPVRoJRMcYYY/Jwc6alMToGeEgyryRr0oTZXkZ+uSTHKbiqg8EoQKN27JWZSyVKAADA1qO5eHFEN8mOp0QGo4ClO85IdrzEs46VNGkODEYBW4/lmQ8keunHVHz4YJRkx1Oi5IxCSZqSAUBWQXmzODcnnJVud8gVHdfjZzXa+LnhyBs82cvsS4XegPm/pWHHyUso1xvQsoULDEYDLhbrYBRqdjtbc9/uVb0R3eZsxNH5Ix3+s4gxJTEYBcyQoEJCrRk/pOI/ka3575gxxphiaYh5kPv7hEjyfJw0cWD6aiP+SMuR5FgGoWal7y2dHXdC22AUcCCrWLLjnblc5vCr5nefvAwpK2ptPHwRLwzvKt0BmewSTudLOjmz4VAOFt3v2EmAS1cqJTtWdTM4NwPAxWLpdtToq7luN2PMfrlpNVhwb4TZuAq9AW9sSMPmtDxcqaySbLdeQyqrgY6vbMLKR6MwokewfE/EGKuTcDofVRKXNr598U7seOE2SY/JGGOMScFgFLD5KG3BbksvV0mek5MmDuyrxExJJ7QT0/MdemIuOaNQ0htKATX/BhMHdJDuoArz6W5pe91kFFSgQm+Am1Yj6XGZfH46eEHS4xmEmr/FuI5Nb9qlVBmXpS136OjnZgCSLpvefaagWezOYYw1b25aDd4dG4F3x9b/vsEoYNeJS/hg6wmczLsq6bXv5K8PYMXDvTGyV2vpDsoYa9DE/+2T/Jjp+eW4WlkNT1eeJmKMMaYsSWcLUE5spxAkUdLEcZfAM5wrpJXmaufnRoo76+ANh7NFrGSm/uGkSzw5qjTni6Qp/3attzcek/yYTD5lMpQ6ulDouOcag1HAWmIPGPoHtONP/ldWS/c+MwrAfSsTJDseY4zZE41ahdvCW2HT9FtxdsEonH1nJD6+P1Ky4z/z7UFsOizNTnfGWMPuXPoX9DJtHxuxZKcsx2WMMcaaYs/Zy6Q4d2c1YsL8JHlOTpo4sPZ+7qS4gZ1pDXH/Pp0Pg8RbgJVkSfxJcuxNrVuQ4tKySywdjuIZjAJyS3WkWDEVyg5dKLZsQMwmKqtorWk1Iub1t0jYI0VpkjMKUVROSwAMu6kVKc7JwXdMGIwC/iY2OXV1pp1sDmQVo4K4SoUxxhyZRq3CnVFtkPnuKByaOwwtPZ2bfMxnvj2AzRKVCGaM1Xe1shpp2fItzLtQooO+mnZ9zxhjjFlLdjGtzPlNbbwlqyrBSRMHll1ifueEWgW8OiocHi7myyFd1VXj4x2npRia4lToDbhA/ANs7++GXiG+pNhTeVcdNtGUlF5AvqBeOCYCzsRZcy/Xpt+sM+swGAXszaBNZg8Nb4UWrrSyawfPFzdhVMompp/JY7Gh8HE3//fw4fbTDj059fGO0ygjJDg8XTQY1JW2CADgXW2MMXY9b3dnJM8ZhlNv3YE7wls26VjTvj3gsNfAjNnSHR/tFBX/1RMxiG7nI+oxt77/p6h4xhhjTCna+NCqKVFw0sRBLdh0DF/szjQbN2lAGNy0Gjx4cwjpuJ/uSnfIG6D5G46SY3uH+KJDgAcptqLKiOSMQkuHpWiJZ2mT5S1baDH65rZ4eiCtt8tTDtwDxtEknS0glwbo3LIFerXxIcUWXNU77Ao3aj8TPw8tYjv6490xPUnxz32X6pDnZoNRwOo9maTYB24OwWN9Q8nHTnXg5BxjjDWF1kmNTx7vg7PvjERkWy+LjlFtBGLfiZd4ZIw1b/pqI84X0Xb6A4CzRoV+nQLww+R+op4np7QSvx24KHZ4jDHGmGyoyRBOmjCT9NVGfPZ3htm4J28Jw+yR4QCAoeFBpGOX6Q1ISqdNltuTP09eIseOjWqLx+JCyV0ExKwstyeCQJugvS+6JiE34/aupDJClBXlTBkS0vPJsXEd/fFUf3pCbG1CpgUjUjaDUcCnu9JJsY/2bQeNWoURPYIx7baOZuN11UbsPkWr8WlPkjMKUVxRRYodGh6E2I7+5HMz9RzGGGPNlUatwi/TBuD4/BGiymzWuny1ChPX7JV+YIw1U71e3ywqfsl9EdCoVdCoVVhyf4Sox05f55gLchhjjNknZw0thdGvY4Bkz8lJEwf0VWImKNc3wT6udf8/JsyPVKILAPacoU+U2ourIppZ9+sUAK2TGqN60voN/C/BfALLHuWU0JrAe7nVlBfSqFVY+mCk2fhXfznCF+h2IrvIfAlAoGaVW2wHf/TvGkie0E7OdLzkbNLZApQTe8Bc+ydwIKuY9JhVf9MSMvaEmnT2cXdGTJgfNGoVuQRFhV665vKMMebI3LQanF0wCp5a8beO20/kcw8pxiSwfl8WKqvp90idAt1xZ2Sbuv++J6ot2vm6mnhEfUYBeO7/UkSNkTHGGJODwSjgM8J8h7ebE2I7+kv2vJw0cUDnCmmT2dfGadQq3BRM236fXUybKLUn1KaXgR5OdQ2FHiKWgUnJKnG4m0WDUcCWo7TdOaWV/64S93bXmo0vKq9yyN1MjiijoIwUN7hby7pVbl1a0krbZeXTjm1P9pwVsxPk35vi9EtXSI+gxtmTTOL7YEK/sLpzc1/iRVJ6QYXDloFjjDE5pM2/AwM7i78RvfmtrTKMhrHmw2AUMPOnI6Ies+m5W2/43q6Xh8BJxAzQ70fy+FqJMcaYzU3/7gCpKs0Tt4RJ1gQe4KSJQwrxdbco7ub2fqTHOVoCAACqjLQ/qh5tfer+f/5Vej3ZdzY5VsPh5IxCchmta19Zah+Ur5POWTAqZk36aiMOnS8hxT4eG1r3/7sSk7OZBeUOt+Mou5heqi+uw79bSquJrwM1zl4YjAKW/3nGbJyPuzOmDe5U999ituM6Yhk4xhiT0/8mxmJQV3FlD8r0RkxckyzTiBhzfEu3nxYV387XFdpGsiNfPN5H1LH6vM1JT8YYY7ajrzZiw+FcUmwosf80FSdNHFCXlp4Wxfl6mN8FAAA7Tl5yqMnMmoZ6tN0zrX3+TTS1bEHf3pzhYKvmc0stm/y9dvW8KbtOXXao95gjWpuQSfrXdHVS19seSU3q6gwCkjMKLRydMrXxpTUkc9Go6r1mvoQdWmLi7MXuU5ehN5h/l9X2f6kV28EfzsTVJckZvKuNMcbEWjOhL0L96NfBALD9xGWHXHjFmNwMRgEfiUyaNLTLpFb/LoGidpuUVBjw5m9HRT0/Y4wxJhUxCx3FzNNScNLEAX23P4sUt+9c/QnJAE/ahFuVQUCCA/U1EfMH2OGarGVMmF+jK3iuV1xOa2RsLwqJu2zcnOtPmNdPoDSuTG9wuAlzR0OdbO4S5FlvQrtfJ/rqVGo/C3tBLTU1ZVDHeq9Z99a03TnUOHuxapf5XSYAcOC6zzKNWoWOgbQVJueJ5SwZY4zVt/2FweQ+ZbVumieuiTVjDOgh8u/Gx00DT1enRn9e02eyt6hjfpGQyWW6GGOM2cTedFqZcxcnNWLCaBWUqDhp4mAMRgHbjtN6TeC6W50gb9oqaAD46cAFEaNStr0iVho/Fhda9/81ahV6EicpT+SWOtTOiQtFtInGB/uE1F8B3tEf7loN6bGONmHuaKirRb1c6vcLiu3gD08X2nvg7KWroselVPpqIzYeMb+l1EOrwbNDutT73n1RIaTnqDI41s1sej7tPNNQ3KBuLUmPzXDAMnCMMWYNGrUKHz4YKeoxRgEY8O52eQbEmAO6XKpDRZW467vE2bebjRnZqzVG9ggSddw7PtolKp4xxhiTQk4JbdF2S0+tpP1MAE6aOJyEM/moIpQzAYC465rlxoT5wUVDe4OV6apFj02pcktok/MBHs437Czx93QhPbbaCCQR+3koncEo4NdDOaTYYTcF1/tvjVqFpwd2JD02kzhhymyjZ4i3RXEatQrDb6LdpK1JyHSYCe21CRmkuBE9gm74oO/XOQBuzuY/rjenOVazTi+XxldJmosb0DmQ9FhdtdFhzs2MMWZtd0e2QVALcaUhzxdXosTBdmAzJpfbFu4QFR/VzgduxAVqyx6OErVb7OzlMi6xxxhjzOoyLtMW07o40z7/xOCkiYOh7gBx1qgQ26F+0kSjVqEvcSvTReJOA3tATURGhvjc8L0+ofStXwnpjlHSLDmjEIVlerNxfh7ODW6Nmza4E7zdzE+Gfrcvy2EmzB1R/460SemG4jyIk+GlldUOU6ZtX2YRKe5K5Y0JaY1ahVE9W5t9rADglfVHxA5NsaJDfS2Oi+3gDy1xEcAeByo3yRhj1rbr5SGiHzN6+W4ZRsKYY1mw6Riu6umLYdQqYN3kfuR4jVqF0ZHmry+vNXTxTlHxjDHGWFNU6A0or6bNC0q8yaTmmNIfktnSkQslpLh2vu4Nblu6u3db0uOP5151iBXNBqOAjAJaAqhvA/04xvULJT9XNrHZvNJRy2bdE9mmwfeYRq3CE7eEmX18Tkmlw0yYO6KSCvOJMx9353o9bWq196M1gwccp0ybB3HVX2Nx7sSSZpvSchwm2UjtGXVz+xuTsxq1Ch0CPUmPP3yxWMywGGOMXUPrpMbE/qGiHsOlERkzTV9txKpdtF3KtT56sLfosiTv3RshKv5icSV+T80W9RjGGGPMUu9sOkaO7RZMq4YiBidNHEhNAoDWaLhHI704WvvQ+poIAL5KzCSOTLmSMwobXNndkIYSJFonNSJDaH1NKqocYztzgAetJNngbq0a/VloAK1Js6NMmDsag1HAK7+kmY17Z3TPBm/eHosLJZcDaNnCVeTolCnEl5YoGhPVcOKammgq1xscItloMArYcJhWBrB1I69tW1/a55mrDNt4GWOsOXntzpvQ3o92fVhr6fbTMo2GMfs3aqm4/iEDOvvjrghxu0aAmnvZPsSdvbVe+OkQJz0ZY4xZRSZxkTsA3BdN2wQgBidNHEjCmXxQr1/C2zQ80R8T5gc34gSSmDevUuWW0iblb+sa2Oiq5xeHdScd4+/Tlx3jApM6220ijjoR7igT5o4mKb0AxYR65N7uzg1+X+ukxhP925t9vApAdHtxN3JKZDAK+CHlvNk4DxcN+nW6cUcbIC7RRD2vKVlNGUDz7zF/D22DZQABevlEMWUWGWOMNWzHC+LKdH2847RjXBczJrEKvQGnL9EWQgKAsxr4amKsxc/3zZPiHltZZcTHO85Y/HyMMcYYVag/bfGok1rV6FxKU3DSxIGsJ/YzAYCWXg2vwNWoVSJWm9j/jU7hVR0prr+JP77Yjv6k0jtlesdoOLzjeB4pLt/Ea9tQf5imxDHrSiS+j03FDerS+E6kWgKAven2/zeTnFGI3FLz55qnBnRstKyC1klN7jmV7wA7tLKJfbPu7BXU6GsWHkzbBVimo+02ZIwx1jiNWoUVD/cmxxuEmgVfjLH6Bi/8kxyrBnD6nVFNej6tkxojewSJesyKnWc46ckYY0x2fdrR5qcX3R8hukQlBSdNHAi1zBQABHk1voL/buLW3si2PuTnU6os4sScj7u20Z9p1CoM6Hxj34aG7Dl7mRSnVAajgJ9TL5JiTe0S+XbvOdIxqHHM2qg3SY3HUZO8yxxgJVtuCa2fUTs/0+WkerfzIR2nsMx8vxml+/UQrV62qfv1wnLa67BqVzrf+DPGmARG9mqNFsQeXACwbn+WjKNhzP5U6A3IISy0qfXyHd0ked5lD0eRdzQDgK7aiGVcYo8xxpiMDEYBc38339OkVxsv3B3ZRpYxcNLEkahokz5aNRotZwIAQd60OvDUOKUyGAWsP0BLABSbmXxzVtNuEM8X2HczeClK5gDAuUJaskrM7ilmPXEdaNseTcWV6Wk9fg5dKLb7Ce38q7TJe3Nx2UW08wc1TqkMRgFJ5B1Gjd/iU8v76aqNvNqZMcYk0rcDveThxiO5Mo6EMfvTe/4WUfETbgmT5Hk1ahWWPUjfKQYAy7jEHmOMMRklnaWVhX95BK1lgiU4aeJAWhEniPp29De9bYm4zOT/ku17dZiYJvB+Ho3vNAGA9IKrpONQ45SK2pj97sjWJt9j1KbWR3OuQF9tJMUy64nt6N9oj59aPu7OiO3Y+A4sahlAXbXR7hubXyb+3ZiLyyfuIKHGKVVSegH0BtpNuKkapzFhfnDR0D7Q1qVwgpYxxqTw4QNR5FiDAMzfcFTG0TBmP/q8FY/KanoSIsDdyez1uBh3RrbG7eEtyfEGAXju/w5K9vyMMcbYtRLO0hY2UuMswUkTB9Ih0JMUN6iL6YshU70orvX7kRy7ntCmJgAA87tqqDXx7b12foCHCyluSDfT/SoeiwslP+fahExyLLOO9zcfN/u3/+6YniYTZ+P60VfG2Xtj8z/SaCtpE8z0inEn9E4CgMMXiklxSkXtmaOC6XOJRq2CP/Gctd/OE3OMMaYUnq5O8HN3Jsd/uSfTru8nGJNC4VU9LhN3Jtfa/sJgycfx2eN9RPU3sff5AMYYY8q1L5N2j55dLF+lDU6aOJDH4kKhMrOoVq0yP2FNLWkCAGsTMsixSkP9Pf08nE2WmgKANsRSZdQ4paKetMztVtI6qdHeTP+GWskZ9t8I3JHoq4349G/Tf/cqAIPNJM60TmrEERubFxITuUpkMAq4KNGHeEwYrXfSFZ0BV0X0uFIag5F2892rrZfZFZbBPrSkSU5pJZeYYIwxieyZNURU/Jo99ns/wZgUYt/ZJio+0NMZ3iKSk2IsezgKxHU6AIDINzbLMg7GGGPNl8Eo4NCFElJsa1/55lk5aeJAdpzIg2BmzmfSgDCzk0wxYX7QmirfdY29drw6NybMDz6Ei8237u5hupwZgKcGdiQ9JzVOiQxGAWuIuz4ou5VCfGklus4T+58w61ibkGn2PCOAtkPo3ui2pOfcffoSKU6JEs7km2xWfq3IEG+TPx/XL5T8vDO+t99yCSUV5uuWAsBNrU2/XgAwPDyYdCwB4L4mjDEmETetBm196Iuwlu88I+NoGFO23w5cgF7kwo2kV26XaTQ1O3WfGdSJHF9eJeCNX9NkGw9jjLHmJym9ADriTsZ+xJ67luCkiYMwGAXM/OGQyRgntQovERrkaNQqdGhJK/VVWUVr5qxE8cdyzTYVenpgGEb2am32WP27BMLFTDJKo66Js1fJGYUoJk5mUnbx9DQzQVwro6CMV4ArCHW3ESWugNh7489TBXa79X/9AXqvjFdH3WTy51onNXzdnEjHyiwoIz+v0lwm7iyixI3vTy8D9yP3NWGMMcnEzxxEji2pqEaF3n7vKRizlMEo4Dkz9/DXW/lolNkFfU317JAuILaFAwCsTjxnt9fqjDHGlIdastvVSW2yl25TcdLEQSSczke5mZuNaqOAhNO0lbSDu9KawPVq40OKUxqDUcCs9UdMxnhoNaQkE1CTaProwUgzz1nTC8JeUXvA+LiZL2cGAP070hJIumoBScQTJpNfcTkt0UHpv3Esp5T8vPZauqOMOAkU6KmFG+E1C2hBKzd1xY7Lc3loaYkhSpzWSY2WLbSk450v4l1tjDEmFTetBp0CabuKAeCt37khPGt+/vt/ByBmaVh4K3eM6EHbRdsUGrUKUweJq5Dw0jr73eXMGGNMWXafoVUbGdQ1QNaFBJw0cRA/HaStkKXG3dKJtr1pb4Z9ljNJSi8wu8ukTG9AUjp9st5cDwcA+HRXht2uwvFzp008Ph7XjnTSiu3ob3Z3Tq3EdPt8nzkag1HAqbwrpNixvc2X3jKX6L3WlqM55FgliQrxIcVNvIW2I6IdsV5n/hWd3e7QGhtFK9tGjWtJTDTpq3mVM2OMSWnTc7eSY+OP228pTsYsoa82YuORXFGP+WHKAJlGc6Pnbu9qrk1lPb8cyrXba0/GGGPKoa824tB52gLb6Pby7TIBOGniMI4QG+RcIK6kje3oD29X86t4U7JK7HI7PXWrFzUOANYmmF8JLxDjlOg4cVeApwutKaFGrcLgbrQdTWY7yzOrqCnRZn4Hg6uTGv06m0+89gn1JT/3FZ197pw4SUwyUd/iLdxoyctqAaKSvkrSr1OA2Z1KHloN+hGT+9SdJtQ4xhhjNGLKSl6y42Q/Y5a4Y8lOUfGhfq7wJNyfS6Wmt4m43Sa939gi02gYY4w1F18lZpJ3YVIrcViKkyYOwGAUcLGkghTb1oe2TV6jViGiLa3nxNsb7XE7PfVPkH7zti+ziBT3+2H7XDG/7xyxlwUxDgAejW1PiouTsUYho8stpZVoeyiGtttoXD96v4luQbTzkZIYjAJ+Tc0mxR7IKibFUXbw1LLXxuYatQqL748wGbPo/gj6NlyVxHGMMcbInrqVPum6+9RlGUfCmHJU6A04W0C7f6+1/YXBMo2mcTOHdRUVX6oz4CfuEccYY6wJ0vPp/VmDvMz3U24KTpo4gOSMQlRW0Uo+UcuZAEChmfJVtVLP03a5KEnfUNokPDUOqFn5THEq74pdrqQ7X0DbpVSuo+88iu3gT1pRHtuBkyZKUEhs0N2WWEJK66TGyB7my9oBwH3R9HOXUuw+dRnUYnyUHjAA0K9zALkx58VicTfjSnJ7eBBmDO0Cr+tWVAZ5uWDlo1Gi6nlrVLRLHWocY4wxuon9O5BjP/07XcaRKFtmZiYmTpyIsLAwuLm5oWPHjpg3bx70elovOWZfJq1NFhW/4uHesjd/b4hGrcKS+yNFPebFHw/Z5b0uY4wxZfibuIjGRaMi9VNuCp4hcAC5xF0mWo2KVDKn1lUdLWlCjVMSo0C7kKPGAcAYYkKqosqI5Az6bgwlMBgFZBFLu/Vs6yXq2FozfU3M/ZxZD7VRtp8HvczRsoej4WTmJtDFSU0uxaQkYiZ/qDtINGoVbg+nJZpa+9CSV0qzOS0H/d/bgSXbTqH0n4b2Pm7OmDG0M/bMGiK6AaqHC62URUmF/X2WMcaY0mmd1PBypS0MyCbe0ziiEydOwGg0YtWqVTh69CiWLFmClStX4pVXXrH10JjEDEYBu8/S7wXb+bpiZK/WMo7ItHui2sDHjVZ+GQCMgv3udmaMMWZb+mojsopo14NdgjxlX1DAs5EOoLCMtgLpPxGtRb2hzE1kio1Tkp9TL0oaB9TU4ac2Nr90hVbmSCmSMwpRrqetmR/Qidqn5J8eGWZ2NBWVV9ldkskRGYwCfjtEKy0X5E2frNeoVfj44d4mY3TVRsQfE9coUwmyS+h/52IS2o/FhZLibrHDRNPmtBxM+foAcq577YorqvDhttMWvQ+oOyyPZpfwykjGGJNBzza0Epta6lZKBzRixAisXr0aw4YNQ4cOHfCf//wHL7zwAtavX2/roTGJ/ff/DoiK3/TcrTKNhC751aGi4qd9K+53ZIwxxoCafiZUHQNbyDeQf3DSxAF4E1d+iC1xRD2umN0YSlGupzWVpsYBNZO/U4h1mwM85G1WJDVqksddq0GsiP4j1OPaW5LJESVnFJIStH4ezqK3SN4eHgQf98bPNyoAb2w4ZncT2sFetB03XVp6iEpo9wn1I7XfoCbUlcJgFPDGhmONdpISYNn7gJrQLtMbkXS2QNSxGWOMmffUANr18cm8MmxOs8/ef3IoKSmBn5+8ZSeYdemrjdh4hL4ApJWXi1WbvzdG66TG+Nh25PiSymq8scEe+54yxhizpTOXrpBjxfR7tRQnTRzAb4douyFSzxeLOu6wm2glUC5f0dvdZGYfYq8SalxdPHWy2M4W0lGTPJMGhIma/G3Zgta0iRrH5ENNXN0T2Ub0FklzO44EADkllXa346hlC9qOmzEiP+xTzhWBkquevf6IXZ2bkzMKb9hhcj1L3gcatQqDuwWSYhPTuZwEY4xJrX+XQHK5VXv77JLLmTNnsGzZMjz99NONxuh0OpSWltb7YsrW9+14UfF/v2T95u+NeX10TxBb8AEAVu/JxKbDnARVus8//xy9evWCl5cXvLy8EBcXhz/++KPu55WVlZg6dSr8/f3h6emJsWPHIi8vr94xsrKyMGrUKLi7u6Nly5Z48cUXUV1df/Hpzp07ERUVBRcXF3Tq1Alr1qy5YSzLly9HaGgoXF1d0bdvXyQni+v9wxizf6fyrpLiVBBXrcNSnDSxcwajgKR02srYvFJxq/Un3BJGiruiM9jdZGawl/lJeJUKGNcvVNRx84mNsqlxikGcA48JE5dkignzQ7C3a6OHVwEI9naVvbkTMy/Ak5Y4G9yd1m/jWo6448hgFPDnqUuk2FYie49QX4erumry54MSUPtzUeOuRd+6a2cZbcYYswMatQpLH4wkxRaVV9nVZ5c5s2bNgkqlMvl14sSJeo+5ePEiRowYgfvuuw+TJk1q9NgLFiyAt7d33VdISIjcvw5rgp8PXERRBb2KwaieQYrr7Zj2xh2i4qd+e4CToArXpk0bvPvuu0hJScH+/fsxePBg3H333Th6tGan0IwZM7BhwwasW7cOf/31F7KzszFmzJi6xxsMBowaNQp6vR4JCQlYu3Yt1qxZg7lz59bFZGRkYNSoUbjtttuQmpqK6dOn48knn8SWLVvqYr7//nvMnDkT8+bNw4EDBxAREYHhw4fj0iXa/RRjzDFc1RtIcV1byd/PBOCkid1LzigE8T1FboZbS+ukxhDi6lx7m8x8+4/jZuOe7B8m+kKVOrFMjVMKuZJBGrUK8+4Kb/TnAoD/RARb5WTIzKDe71hwX+SIO46SMwpRQrwxDiIkca8l5nXYffqyqGPbUv5VWjkxaty1+hITr9Q4pdFXG/HF3+mY+2savvg7HfpqWg8qqtDQ0Bsm+d599916MYcPH8aAAQPg6uqKkJAQvP/++zccZ926dejWrRtcXV3Rs2dPbNq0qd7PBUHA3LlzERwcDDc3NwwdOhSnT5+W9HdhjNnGiB7BGHETbWFFwlnH2fX3/PPP4/jx4ya/OnToUBefnZ2N2267Df369cOnn35q8tizZ89GSUlJ3df58+fl/nWYhQxGAc+vSyXHO6lVWPpQlHwDspDWSY023vTrUAHAvSv2yDcg1mR33HEHRo4cic6dO6NLly54++234enpiaSkJJSUlOCLL77A4sWLMXjwYERHR2P16tVISEhAUlISAGDr1q04duwYvv76a0RGRuKOO+7Am2++ieXLl0Ovr7lmX7lyJcLCwrBo0SJ0794d06ZNw7333oslS5bUjWPx4sWYNGkSJkyYgPDwcKxcuRLu7u748ssvbfK6MMasz2AUcCqXVp6LXOWniThpYufEJCssqff2JLEGsb1NZporAQMAg7uJXzFPnTC2t505ck5qj+gRjKcGNr6radWuDK5vrQDbT+SZDwKQXyZ+F1V0e1+zPTpUqpo4e0E9N/u4i+8BExPmB2diIvHwhRJRx7al4gpaMoQady01pQmMiDglWbDpGLq99gfe3Hgc/0s8hzc3Hke31/7Agk3HJH2e+fPnIycnp+7r2WefrftZaWkphg0bhvbt2yMlJQUffPABXn/99XoTfgkJCXjooYcwceJEHDx4EKNHj8bo0aORlpZWF/P+++9j6dKlWLlyJfbu3QsPDw8MHz4clZX2szCDMdY4V2dabZ+LReJ3FCpVYGAgunXrZvJLq63pgXbx4kUMGjSobmJSrTZ9q+7i4lJXUqf2iynTs98egJgNF0seiFTsorF37ukpKv7ghRJUUFd5MpsyGAz47rvvUFZWhri4OKSkpKCqqgpDhw6ti+nWrRvatWuHxMREAEBiYiJ69uyJVq3+nTsZPnw4SktL63arJCYm1jtGbUztMfR6PVJSUurFqNVqDB06tC6mIVyikDHHsuvkJfIa3FdGNr74WkqcNLFz1ElqVye1RfXeotv7wtz1mtrOJjNziWXKqHHXok4Yr03MtKutykWE38vSMloGo4DfDplOisz84ZBdvV6OxmAU8MP+C6RYSxJn+zIKzfboEISaOHtBfR0m9BPXBwio2aEV4kcr6aWrsp+bVOqrYMkUAvXcbEnSz5YWbDqGVbsybpiIMQo1CWcpEyctWrRAUFBQ3ZeHh0fdz7755hvo9Xp8+eWXuOmmm/Dggw/iv//9LxYvXlwX89FHH2HEiBF48cUX0b17d7z55puIiorCxx9/DKBml8mHH36IOXPm4O6770avXr3wv//9D9nZ2fjll18k+z0YY7bT2of22UiNcyS1CZN27dph4cKFuHz5MnJzc5GbS28azpRp0+FsbEqj/zsGempxV0RrGUfUNP27BEKrEXc1NnTxTnkGwyRx5MgReHp6wsXFBZMnT8bPP/+M8PBw5ObmQqvVwsfHp158q1at6s5Nubm59RImtT+v/ZmpmNLSUlRUVCA/Px8Gg6HBGFPnQC5RyJhjWRh/khTnqVXDTUyTrSbgpImdq+0JYc7i+y1brZJyrsjsqhijUBNnLwqJJaSocdeiTpQWl1fZzW4Tg1HAmxvNlzN7bVS4Re8xys6fcr0Bz313UPSxmTSSzhbgqs58qSlfdyeLEmfU5ttf780UfWxbMdevB6jZZTJtcCeLjt+rjQ8prq2vu0XHt4W4DrTEPjXuWtRzc2Z+uehj24q+2ohP/84wGfPp3xmSlep699134e/vj969e+ODDz6o1+AzMTERAwcOrFstDdSsIDx58iSKiorqYkytMszIyEBubm69GG9vb/Tt29fkKkPGmP24pSOt7K9WY50bYSWJj4/HmTNnsH37drRt2xbBwcF1X8x+GYwCpn0r7h7mzxduk2k00tCoVVj6UG9Rj7lYXMm7TRSsa9euSE1Nxd69ezFlyhSMGzcOx45Ju2NZDlyikDHHcoG401ijsV4qg5Mmdk6jViGwhdZkTHt/N4zsZdkFN7Xh7taj9lM+afcZ2gStn4fp17UhMWF+8HFzJsXaSx8YajkzXwteL4D+Omw8kiN5nX5Gk0BMavQN87ewlADtMX+fLrCbHUfX9utp7Ld7d0xPi0svjI2ilVts52c/SZOdp8w3evRxd0ZsR3/Rx44J80OQl/leUt/ty7Kb99jahEzSDq21CZlNfq7//ve/+O677/Dnn3/i6aefxjvvvIOXXnqp7udNWWV47c+vfVxDMQ3h0gyM2Y/Yjv7wdjPfY9HedmRLYfz48RAEocEvZr8WbT0BMXcvQV5aeLqK60NqCyN6BOOeSHHzC0+u3SfTaFhTabVadOrUCdHR0ViwYAEiIiLw0UcfISgoCHq9HsXFxfXi8/LyEBQUBAAICgpCXl7eDT+v/ZmpGC8vL7i5uSEgIAAajabBmNpjNIRLFDLmWKhLZjy11vuctErSZPny5QgNDYWrqyv69u2L5OTkRmPXrFlzQ7NRV9fmt0WbqkJvwOELpicIzhVUWLyyg9pw96cDF+3i5kZfbcTOk7TGyEHetPI319KoVRjXrz0pNsDDPprBU5MaliaBqCvABQH4KjHTouewJUc4/2UTM/5uxFrl14sjToJf1VXbzQ4toOaGcvnDUTckFIO9XbHy0SiM6GH56tF+nQPg5mz+I3zdgQt2c27+fLfpXRMA8NZ/brIo0aRRq/BQTDuzcTkllXbzHtuXSRtnY3Hz5s0DULOb4/rzTu3XiRMnAAAzZ87EoEGD0KtXL0yePBmLFi3CsmXLoNPZvpwZl2ZgzH5o1Co8cUvjfexqFZVX4eMdp60wIsbkYzAKWLEzXdRjdr00RKbRSO+9eyNFxe85W8B9Ku2E0WiETqdDdHQ0nJ2dsX379rqfnTx5EllZWYiLiwMAxMXF4ciRI7h06d/FT/Hx8fDy8kJ4eHhdzLXHqI2pPYZWq0V0dHS9GKPRiO3bt9fFMMYcm8EooLDCfHUTAHikr/n7eqnInjT5/vvvMXPmTMybNw8HDhxAREQEhg8fXu+kej0vL696zUbPnTsn9zDt1jvEeuXUuOtRG+6WVtrHZObahExSY6EWrhqLygwBQEwYcRW0Mnv73UDOJvBAzQpwd+Jk+7lC+ymdAzjO+a+1Dy2BSI27XmwHf/J7IKfYfprDbk7LwZsbj6Gw7N/zqJ+HM14b1b1JCROgZuJp8q0dzcbZSxKAsmsCAHJKLZ+kb+fvYT4IlvWzsgUXZ9qHSGNxtY3c9+3bh+PHjzf41aFDhwYf27dvX1RXVyMzMxNA01YZXvvzax/XUExDuDQDY/YlNIB2Ll61K90ukv6MNWby/8TtrBjVMwhaJ/spBKJ1UmPSAPNJ0Gu9/NNh/rtWmNdffx27du1CZmYmjhw5gtmzZ2Pnzp145JFH4O3tjYkTJ2LmzJn4888/kZKSggkTJiAuLg6xsbEAgGHDhiE8PByPPfYYDh06hC1btmDOnDmYOnUqXFxqFolOnjwZ6enpeOmll3DixAmsWLECP/zwA2bMmFE3jpkzZ+Kzzz7D2rVrcfz4cUyZMgVlZWWYMGGCTV4Xxph1DVn0Jzn2yYHm50GkIvun8uLFizFp0iRMmDAB4eHhWLlyJdzd3fHll182+hiVSlWv2ej1pRqkYjAK+PNoHu5c+jcGvLcDT67dh6uVtMyWUmQW0CaRqXHXEzOvbw/lppIzCkhxYQGeFpfNuXSFNqm37Xie+SAFKCD8PmoVEN3e16Lja9Qq3NGz8Qmxa4XYUX8GQNnnPzFiiYlAatz1NGoVbg6lvX8OnreP/kmb03Iw5esDN5S2KyyrwtRvD0qy0s6RkgDJmbRzMzWuIdQ+VT/ss32iksKDuC25sbiAgJreMF26dEG3bt0a/Lq2R8m1UlNToVar0bJlSwA1Kwh37dqFqqqqupj4+Hh07doVvr6+dTGmVhmGhYUhKCioXkxpaSn27t1rcpUhl2ZgzL5QF9mU6w1IOmv5OZ8xW9JXGxF/glbdoNbSh6JkGo18Xh0VDj83+k7zkopq/rtWmMuXL+Pxxx9H165dMWTIEOzbtw9btmzB7bffDgBYsmQJ7rzzTowdOxYDBw5EUFAQ1q9fX/d4jUaD33//HRqNBnFxcXj00Ufx+OOPY/78+XUxYWFh2LhxI+Lj4xEREYFFixbh888/x/Dhw+tiHnjgASxcuBBz585FZGQkUlNTsXnzZkXcCzPG5HW1shqZBbTFsZ0DPay6wEDWZ9Lr9UhJSanX1FOtVmPo0KEmm3pevXoV7du3R0hICO6++24cPXq00VhLa1lvTstB1zl/YMJX+5GWXYrzRRXYdvwSery+Bf/5+G/6L2lj1Hn9UH/LJpvFNNy1h3JTZXpaUszTxfIGlNSJuZ9SlF82x2AU8PyPh8zGGQUg5Zzlk9mjI9qQ4rq1amHxc1ibNc5/gHXq+as1tBMNNa4hbf1ou1QMdlDb22AU8MaGY43uahMAvLHhWJP//qnnGmqcLZXraCUkqXENofapSkwvsov+SQVltJ2g1LjGJCYm4sMPP8ShQ4eQnp6Ob775BjNmzMCjjz5alxB5+OGHodVqMXHiRBw9ehTff/89PvroI8ycObPuOM899xw2b96MRYsW4cSJE3j99dexf/9+TJs2DUBNwnj69Ol466238Ntvv+HIkSN4/PHH0bp1a4wePbpJvwNjTDliwvzgQbzO3nNW3KQzY0oxe/1hUfGjI1pbvGDP1na9PNR80DWmfJMi00iYJZYvX47MzEzodDpcunQJ27Ztq0uYAICrqyuWL1+OwsJClJWVYf369TfsAG7fvj02bdqE8vJyXL58GQsXLoSTU/1FO4MGDcLBgweh0+lw9uxZjB8//oaxTJs2DefOnYNOp8PevXvRt29fWX5nxpiyzPj+IDk2LNBTxpHcSNakSX5+PgwGg6imnl27dsWXX36JX3/9FV9//TWMRiP69euHCxcuNBhvSS3rzWk5mPz1AVQ3MmF1+EKpXSRODEYBh86XkGJfGRlu0XPEdvSHh5b4NrGD6zzBSJsIo8Y1hDoxZw8lzRJO50NHnDxsyk4jufum2II1zn+Ader55xMn3alxDaF+GNlD0YLkjMIbdphcT4qyWdRzDTXOlnq2pe0OoMY1REyfKnvon5RxuYwUR92R0hgXFxd89913uPXWW3HTTTfh7bffxowZM/Dpp5/WxXh7e2Pr1q3IyMhAdHQ0nn/+ecydOxdPPfVUXUy/fv3w7bff4tNPP0VERAR+/PFH/PLLL+jRo0ddzEsvvYRnn30WTz31FPr06YOrV69i8+bNiujtxBiThkatQngQ7Vy+L8M+dpcydq3NaTn46cBFUY95/74ImUYjP09XJ/h5OJPjSyurMXENN4VnjDFWI0tEGf4+oZa1UbCU9VrOE8XFxdUrw9CvXz90794dq1atwptvvnlD/OzZs+utZCwtLTU5aWgwCnjtZ/MrPw5fKMXVymp4uiruJaqTlF6A4ooqs3FR7bzhprVs54RGrcKDfdrhiz2ZZmObMmFqLen5tC1fVU1YZCxmYk7pSYCfDjQ+WX89S3uaAEDqhWJy3NibHbfJr9jzHyD+HGgJufvaAICXK21inxpnS7kltPMMNa4x1HONmHOSrdzSIRAr/zLfCP6WDoEWP0dMmB/ctRqU683vVlF6/yR9tRFniUmTsVFtm/RcUVFRSEpKMhvXq1cv/P236QUn9913H+67775Gf65SqTB//vx6JR0YY46HWlYh9UIxDEbBblfgs+bHYBTw3Hepoh7zWGw7u+pl0pA9Lw9B97mbyfHbT1xChd5g8RwFY4wxx1FcTq+MMK5fqHwDaYCsn84BAQHQaDSim3pey9nZGb1798aZM2ca/LnYWtbJGYW4XEYr0TRdxBYhW0g4k0+KE1NiqyFDw2n/Vk2ZMLUGfbURecR+I219LO+dERPmB1932mqbAE9llzS7UEybOHTWqBAT1pSML+1mOCXLflYcWuP8B1innn9MmB98TLynVQCCvV2b9B5QEz+NqHG2VEgsh0SNa0x0e1+zJRqb0m/ImqxRAk6jVuGOHo7RP2ltQmaj5d+u5awG+nVq2jUAY4xJjTpRWmUQFL8rm7Fr/ff/DpB36QOARgW8ObqnjCOyDjetBlHtfEQ95p7lu+UZDGOMMbtRM09Lmxfp1aaF1RcZyPpsWq0W0dHR9Zp6Go1GbN++3WRTz2sZDAYcOXIEwcHBkoxJzMr+E7lXJHlOuewjNsS9UNS0FbMxYX4I9nY1Oa3t6+7cxElz+YkptzI22vKVuRq1Co/HtacFK7w9Q1vixGHvEJ8mrQKk9tw5nn3FLnoNAMo8/1kq/lguissb39UmAJh3V3iT3gPU5G5GPm11vS35EZOh1LjGpJwrgrm2KE3tN2Qt1igBBwD/6dmaFNfFyrVSxaJ+/oe39uIV2owxxekr4p4hu4n3MYxZi77aiI1HGi7B25hTb4+UaTTWt25yP1GTSyfyrmLT4RzZxsMYY0z5Vu8xX22i1sAuLWUcScNkT9HMnDkTn332GdauXYvjx49jypQpKCsrw4QJEwAAjz/+OGbPnl0XP3/+fGzduhXp6ek4cOAAHn30UZw7dw5PPvmkJOMRsxuiCQtaZWcwCtiXWUwLbuLEvEatwry7wk0epqi8CvHHxF0kWltmAW2yVaNq+srcDsQJt/wyZZc0u4fYoH3qrZ2a9DyPxYWS9poIsI9eA7WUdv6zBKXMgJNahduJO9IaE9vRH96Ecoh/pOUqPnGWRTzXBHk1bXeeI/UCyiQmw5q6ozH5HG3FMjXOVtyJfUrCApSd/GGMNU/j+oWRY6klXBmztb5vx4uKnzG0i0MtbNCoVVj6YKSoxzz73UEYzK0AYowx5rC2iphHbmoVJUvInjR54IEHsHDhQsydOxeRkZFITU3F5s2b65ojZ2VlISfn3xUGRUVFmDRpErp3746RI0eitLQUCQkJCA+3rJH59WLC/OBMvDa5qbX0ZW6kknA6n5wLUUlwMXZ7eJDJ8jwA8MaGYw5x0XNLp4AmX8D6uNHKc1HjbMUaJXOAmtrW3YNbkGIzC+xnxaHSzn+W2H3qstkyA9VGAbtPXW7S82iIiRejoOzEmcEo4P+Ss8zGNbWcGWCdXjPWYM3XLLuY1keGGmcr1D4lTe1nwhhjctA6qRFOvO6jlnBlzJZ+2X8eRRW0EuAA4K5VY9rgpi06U6I7I9sg2Ivef9BgFPDstykyjogxxpiSpV+iLZ5UoWahrbVZpRjYtGnTcO7cOeh0Ouzduxd9+/at+9nOnTuxZs2auv9esmRJXWxubi42btyI3r17SzYWjVqFiBBvcqxS/XSQ3qC7jU/TmwAnZxSaLM8DADkllYquOxwZQqvrf3ckbXeFKdRsqZisqi0kpdNKwFDjTIlqR/v3EQT7Sswp6fxniZW7zkoaZ4q7C63GuZKbdCdnFCK31PwOsgf7tGvyZ0xt6URzdpxQ9nnGmq9Za1/a52FFlflm8bbUtwPtgpEaxxhj1kZN6lbo6RPRjNmCwShg+o+HRT1m4b0Rip5raIodLwwWFb8pLU/xu8gZY4xJT19tRFGF6XnmWje397bJ56YdtNSVnoszraxFYRntH88WrlbSx9avY9O3MOWW0sq7UONsoTUxeUSNMyWrkLZKmRpnK9Zcle3pSpswp8YxaZzMo/V2osaZQm2+XaZT7gQKtRRWaEDTG41r1Cq8MrK72bjPd2cq+mY0t4R2/mjn1/Rzcz/ilt69GQWK3jlJ7VNjD/1sGGPNE7U0656zyj4fM3bfJ+Iamo/s0Qoje9F6rNmjmqbwtEWqtSLnb5FpNIwxxpRqbUImOfa/g7vKNxATmmXSxE1L+7WpcTahomXYnNQqSbYwFRKb71LjbCEmzM9siTEpyr8A9MbmSl9gRF2VTY0zxUlN3GVQoOxEk6Op1NNW3GslaALVLYhWqmPHiUuKnUCxdsms3BLzSRpBEHdBYm2FZXpJ40yJ7egPTxfzCyeKyqsVvXOS2kPMHvrZMMaaJ62TGnf2Ml+WU+k72VnzVqE34MD5UlGPWfZwtEyjUY51k28RVVivXG/ET/vplTQYY4zZv32ZtOs7tQro19n6/UyAZpo0CfKiTfBS42yhVQsXUlxcR39JtjB5ERo0i4mzhfhjuWZLjM27K1yS1+uVkbQeFCdzryp28hegr8qmxpkSR0zubTuep+jXzJHoq40or6LtULita8smP19hOW1SvKi8SrETKDFhfnDXmk4Aemg1kiRnAfqFBjXOFvw8aZ9n1DhTNGoVxkTRVndmFymzDJzBKOCX1GxSrNL72TDGmrehhF5mACeAmXLd/Ka4HRI9gj0dtizXtTRqFZY9KK7E8PM/HuJ7PMYYa0aKifM/fdr72uyzs1kmTXqH+JDi1Ap+dUIDPElxAzsHSvJ8qReKJY2zNoNRwKz1R0zGeGg1pEbUFG5aDaLb+5iNyy1V9uq52I7+ZieAfdydJdnNFNvBH+7O5v/oqgwCdp9uWtNxRiOm4frcu3o0+fnETPDmKLRRt8EomO2HUV5lkOym0Nzfp9g4W8gqoDV/C/KSJgFAbYuk1M+z5IxC0q4bPw9nyZJzjDEmB2vvzmRMSiXlVSirEnc9993Tt8g0GuW5M7I1ooi9ZGvd9sEOmUbDGGNMSQxGAWeITeCn3dZZ5tE0TsFpAfm0JtbN33g4V7GrHbq1opWxocaZc6mUlgGkxllb0tkCs7tMyvQGJJ1tekPzWo/HhZLilLx6Lv5YLsrNlGd6d0xPSbK+GrUK7fw9SLGf/Z3e5Odj5mUSJ7Pb+rjCTYJJ+ZgwP7gREmcAcPC8Mns1fJWYaXZSXhDEJaRMGdub1kiXGmdtBqOA/0vOMhsnVenEGtTzlTJXglJ7wNwd0bpZrGZljNmvmDA/BHubT4gUlSm3/C9rvmLf2SYqvmebFvBUcFUGOaybIi5JlFVUiauVyu1dyBhjTBrJGYWkSiMuTmqbleYCmmnSJCbMD34epntbAEBBmV6xuwAKK4g14Ilx5ni6EJt0E+OsbfdZ2s4EahxFALGUDDXO2gxGAW9sOGYyxsfdWbLdOUBNDx4KcwkwJg0jcUn+rV2l2dGmUavQJ5Q2Ma7MdDZwrpBW0okaZ06/zgGkXSRXdMq8AU3OKERuqfnJsAf7tJMsAUDtOUWNs7Z8Yu+w1j7KLTHKGGNAzef+a6PMl7R95Zc0xS5kY83Tm7+noaKaVsIWAFycVNjw7EAZR6RMGrUK/TqIW/TS63VuCs8YY45uG7FH56N9pZsHsESzTJpo1CrcHUGraZ5bqsxdANbezj4mirZKOcRPmZNMR86XSBpHQr23U+g9YHJGIXLMNJkulri3RKsWWknjWNO0cDGfXBYTR3FrF1oCJoy4K8na2hPPgdQ4czRqFRbe28ts3Ku/HFHkhBP1M7adhAmMh/u2J8UFEHuHWVtRGS1pTI1jjDFb8nYzfw1RXF4l6W5wxppCX23EF7vPiXpM6tzhMo1G+b4YHyMq3ghg3f7z8gyGMcaYzRmMAr4jnuep/e/k0iyTJgDQlliiq5C4otPaigj1zKUsZ9KvUwA8CKuZf9h/XpETc9TSQVKUGKqVTywlQI2zNmoJGGocRedWXpLGsabZd46WELtQLF3D7MfiQmFuIYFaVROnRK0IE+1Sj9/b3XwSsai8CknpyptwukRMmlDjKFLPF5Pi5m84psjPsxziOZcaxxhjtrSHuMubGseY3Gb9mCoqvmOAu6T3mPbGTatBr9a0fqy1XvrxsCKvwRhjjDVdUnoBynSm2wAAgJ+H1uY9Optt0sTPg7ZSnRpnTQajgDc3mi6bBACvjeou2TYmjVqFpwZ2NBuXW6pTZEmzPu1pf2jUOArqLp/4Y3mSPaeU9pyhTbBSGhJTaTS09ys1jlnOYBRw4FwxKTbtonQ7tLROagzp3tJkzJDuLaF1Ut7Hl8EoYM5vR83GTewfJun4E4mrb6lx1pSWXSxpHAW1j5RSS3RSy25xeS7GmD3ILqadk6lxjMnJYBSwPjVH1GP+mH6rTKOxHz9PE1eaTACQcCZfnsEwxhizqYSztPN7TJivzXt0Km/WyUqCvGmTCdQ4a6KUTQIAXw9pS4uEBtB25yixsXnXoBaSxlHEhPmRykhtPJIDvYiauNZgMArYlEa7IfCTsCeLjxstSUmNY5ZLOJNPrhxXbZBuJZjBKCDtYqnJmLSLpYpcfZZ0toDUb2dQF9NJIfHstxZgDnESjBpHIaZspRI/z/p1pDXCo8YxxpgttSbea1Xoza9IZExu/RaIa/4+skcrRS70sTaNWoV7Imnl0Wu9scH8QiTGGGP250IhrSKCi8b2uzSb7Sd4TJgfgr3NT5xQymBZG3USR+rJHmv3UZEStSyNlOVrNGoV+nc2359BEIC1CZmSPa8UktILUE68OQ3yku7fO8CTlgyhxjHLrT9wgRzbLVi6cmmUpHBOSaUidwDYqsSILXbSSaWNDy0ZT42jiAnzg58HrQ+PEj/PYjv6w8fd9Ph93J0R29HfSiNijDHL+RJ39e86na/IBROs+Sgpr0LeFfrcgArAsoej5RuQnXnv3ghR8WculyluYSFjjLGm25tO22lyqdT25aabbdJEo1bhtVHdzca9uVF5Nc39CPXrxcRR2XOi6Ug2sRE8MY7qSmU1KW5fprImgPcQt0N7umgkrTFozzvAHE2ZiBWdHz7QW7LnpSZ744/lSvacUrFViZGTuVckjbOm8Na0hBs1jkKjVuGtu3uYjZOyL5iUNGoVHri5rcmYd8f0tPlWZsYYo6AuhKmoMiiyNxdrPmIXxIuKX/ZgJH8WX0PrpMbTA8NEPSZy/haZRsMYY8wW9NVG5BIXIOgNtk+cN9ukCUArX6XEFc3HcmgT+9Q4KntONF2pNF8yBwDcnJ0kfV53YtM/apy1ZBfTMrrhwV6S3gxQE3M7TiizD4wjiW7vS4pr1UILT1fp/m6oK/t/2H9BcecZ6nCkHnbyOdpnFDXOmlq2oJX3o8ZRjezVGreHmy6T9p+IYEVOdmxOy8GnuzIa/fnTA8MwokewFUfEGGOWE7MQRom9uVjz8OTaZFRU0S/gotv54M7INjKOyD7NHhmOif3bk+PL9Ub8tP+8jCNijDFmTV8lZpJjQ/w85BsIUbNOmuSW0lb7UuOsJYXYnJkaJ4Y9JpoMRgFnL5eRYmPCaBPFVGN7m14NLDbOWtoQGwj3CZV2FbZGrcKrd5hPzH2xO4O3a8usS0tPUty7Y3pJ+rwxYX7wdTefhLmqq0aSwiZPjhJ3qkn9wZtL6HEFKHOnia12l1F65/x2KEdxiTmDUcAbG4412p1GBWWOmzHGGhMT5gd3Z9ono0EBKw5Z81OhN2DbcXGlVX+Y3E+m0di/1+7sgb4i7rmf//EwX9cwxpiD2HWaVtUGAMZG2X6etFknTQqv6iSNsxYP4q4EapwYtuqn0hTJGYW4qqOVGhrXT9yWYXP6dQ4w2/zPSa1Cv87KatjbrxOx0TAxTow8wnvHKIjLUDPxfjuULWkclUatQlwHWi+GRGItTGvQVxvJydnWvtImAIK8aLsw8koqFXfTSdldJkeZLHvtnWNu3AKUOW7GGGuMRq0i724tIe4cZ0xKb/4uriH5lAFhitypqiRfTYwVFX/vJ3tkGgljjDFrMRgFHMgqIsVqNSpZ5hvFatZJEz9i40FqnLWMIWbbqHFi2GMzeGoCZ0i3QLMJDkuYK71VbRSwJU1Z/RliO5hvNOzr7oxY4uS2GGcu0VbDU+OYZa7qaP14qHFihAXSdrkoafp/bULj5ZKud4vEH/59iX+HeqOguMl0jVqFeXeFN/pzFYB5d4VLPvlgjwsAAPsdN2OMmeLqTFvolZZteocgY3L4fh+9PJQKwAuEXfPNndZJjVYt6HMsB8+X4O2N4pJXjDHGlCU5o5Dc93nR/croC9askyb22nR61+lLZmM8XDSyZOUiQ3wkjbMGPzfaBdn4OGl3mQA1J4XicvOr4l77NU1RK8A1ahXeHdPTZMwCmRoNn8y7Kmkcs0wr4u4FapwYPsS/WWqcNSRn0FZMOKshebJRzA45JU6mG40CPFxunDDzcXfGJ49GydKfwx4XAADAtmO0BLvSxs0YY6Z4uNB6o53Ku6Ko62Xm+H7Zfx4GEW+55Q9HKWKSxx5snTFIVPxnf2dyeWbGGLNj1LmIHm28cFdEa5lHQ9OskyaUsiC+7s6SlwVpCn21EV/szjQb9+6YXrJcsH2ddE7SOGs4kUfbkUCNE4N6Uigo0ytuBfjBrCKoGngLeWg1WCnTRCYA6KpppdSoccwyUe1o5z1qnBgBnrRkSOJZcfWl5VSup62Y6NiyheTnZq2TGnf2ov09Km0yfcGmY3jm24Moa6CEYgkh4Wwpyuc/ABSV6WUbg1j6aiN+P2w+aSJHOTPGGJMTtbdfZZVRcdfLzHEZjAKm/3iYHB/T3hcjiddjDPB2d4aPGy1hWuvxL5JkGg1jjDG5ZRDLmc9W0I7NZp00MVcWBACKyqsQT1zZaQ1fJWaCssDqkkzN65MzaD0EqHHWsPs0bWI1q7Bc8ucWM0GppBXgCzYdw6pdGRAaeK+V6Q04SKxDaAkVaBPK1DhmmR0nze9oA4BgH+l34lF39+04ma+YFWd+nqbL2dXq3MpDludffH+k2RgVQK4bbw2bDudg1a7Gy5oJAN7YcEyWVcUatQqvjTJ/MfbmRnme3xJfJWaSStLd0tGfV7kyxuxKv84BIPaCR3aR9NfrjDWkx7zNouK/niSuTwcDkl+9XVR8UkaRYq79GWOM0RmMAj77O50WrIzbbwDNPGkCALeHB5ntOTFr/RHFTJqcI07sU+PEamg1cFPi5GYwCkhILyBGS/9vHBPmBz8P2mSqUlaA66uN+Oxv0/0ZPvs7Q7YL1v7EsnLUOCaevtqITUdyzMYFebnIsqI9JswPLYilOtbspvcSkVOILy0ZQo0Tax9h5a1AjLMGg1HAnF/TzMbJ2dTc18N8aTklNVXPLKCtzHEzc03DGGNKo1GryKUrqQ1EGWuKy6U6VFTR73Xa+7rL0hvT0Wmd1IgNE7eg55X1R2QaDWOMMbkknS1AmZ42T5xfppN5NHTN/pM96WwBys38wxWXVyHpLHXiXV7t/dwljRMrkDixT42TW9LZAlQRC9FGtvWR/Pk1ahXm33WT2Ti1SjkrwCm7mYxCTZwcBnQJlDSOiUfd0da/U6AsK9o1ahUCW9CSjVuOmU/uWEMccbKHGidWYjptd983ezNleX6xkjMKUUgsfSXXLjx7a6pubGjrXxPiGGNMScqJC65O5EhfTldpdDodIiMjoVKpkJqaauvhNEt9F2wTFb/xuQEyjcTx/W+iuB06Px64gM1pyrj+Z4wxRkOdrwCUs6Ac4KQJEs7S/uGocXILaEFruvxw3/ayPH9bP1rZHKX0m/j7DK3EEAC09pUn0eRP+IM3CkDKOWWsnLP1bqbYDv7wcTc9Ye6h1UjeTJv9i76iXb6PECcNbbX8FYXsarM9WvLqr9P5itg5KSYRIddFk587rXcONU5uni60RCI1jjHGlKRUR0ukU+Ps2UsvvYTWrZXRALU5+mX/edLioVphAe7wdBXXm4P9S+ukxqQBYaIe88zXBxRxPcsYY4zmLLGfiYeLRlH9OZt90uQ8sS4uNU5OBqOANzYcI8Wmni+WZQz9OtJKIu3NKFTEhcyRC6WkOK1GJdsfpr2tZg4hJo+ocWJp1Cq8O6anyZgyvUFRvYaY9Lq2aiFpnNz2ZtB2I1LjxIrrSEsilukMiig3RU2E+Hk4y3ZuPnqxRNI4ueURe5VR4xhjTEkCPWkLwxy9p90ff/yBrVu3YuHChbYeSrMktvk7AGybOUiewTQjr44Kx5Bu9CoCRgCL40/INyDGGGOSMRgF7DiRR4qd1D9MUf05m33SpOAqbbUSNU5OSihnEtvBH56EXgNF5VWKmJhzd6GtVu8W3EK2P0zq5GBmvu0TcwCQco42qdspQJ7eDEBNryEXM3WBn193SBGJOUcUGUIrFUeNs0TPNt6SxsmPev6Q5zwT28HfbH+uWkpI0MaE+SHY2/y58a27e8h2bo4/Trtw+yHlvCzPL1aQF+2zhBrHGGNKEhlCS5BfLK5w2Ou/vLw8TJo0CV999RXc3eVZnMRMu3fFHlHxTw5or6jJHXv2xfgY+JqpNnCt5X+mO+y5gDHGHMmy7aehqzZ/vnZ1VuPZIV2sMCK6Zp80oU4yUePkpIRyJhq1Cvff3JYUq4SJueh2tEndkT2CZRtDTJgfgrzMr5779O+zNr/w01cbsfkoraTZr4ezZRtHwpl86Mw0mi/TGZBwRhll8xxNxmVavfAAD/nKFvkRj02Nkxt1pwc1TiyNWoWJ/UJJsT4KKN+kUasw765wkzFPDwzDyF7ylSe5oqsixWXkl9v83AwAx3JoOyev6qplHgljjEnvls603exleqMiFmZJTRAEjB8/HpMnT8bNN99MeoxOp0NpaWm9L2a531Mv4uAFcbtLZ99hvnclo7uzl7h78iGL/pRpJIwxxqRgMAr45K+zpNhBXeXpmdsUzT5pcnMobVVTC1fbTzIpoZwJULMLgEIJzXtUxL83apwlahJNIWbjlJAEWLM7gxxbrpdvYm79gQuSxjE6g1HAql3ppNj44/KVSDt0oVjSOLmVlJvfBejr7ixrL55CwhgAYKuM/25S6k1Meluqe5AXKU4AbH5uNhgFJJwllnZT1nUmY4yR2NuOSapZs2ZBpVKZ/Dpx4gSWLVuGK1euYPbs2eRjL1iwAN7e3nVfISHm7zdYwwxGAf/9PlXUYz68t5fiJnfs3SsjTS+ouV5mQQWuVvJiEcYYU6qk9AKzC6JruTnZfrPC9Zp90iQ8mDZpsvPUZZuvNI1u7wvKddn8u+QrZwLQyqoEe7sqonnPxWLaTRU1zlLniU3TbZ0EEDOZ2idUvsnfMj2tuXd6/lXZxtBcJZ0tQBXtMw3nCivkHYydMBgFvECof/326J6ynpsPEVdHUuPkZDAKmLX+SKM/VwF4Y8MxWT93742mTy79ZONzc8KZfFQTX4swf/lKJzLGmFw0ahWeHtiRFKuEhVlUzz//PI4fP27yq0OHDtixYwcSExPh4uICJycndOrUCQBw8803Y9y4cQ0ee/bs2SgpKan7On9eGeUk7dFH8adENX/31GowmrAojonjptVgSFd6bxMAGLV0l0yjYYwx1lRfJ50jx7b2cZNxJJYx35zCwVFX5haW6ZGcUShbaRWKlHNFpIs5/xa0RoqW0qhV+E9EMFbtanxXwn8ighWx8qa9H60eMDXOUleI5VKocXIRMz05jlgKyBJ9Qn2x9Zj5fgMncq/AYBQU8V5zFInp9BX1of7y/d2EEid+qXFySjiTj3JCoq+Fa7P/yK3z8Y7TKC5vvDyWACCnpFLWz91+nQKgUQEGwolPzp11FGKSNo/Fhco3EMYYk9FTAztgybZTZuMiQ3zkH4xEAgMDERhofhJ46dKleOutt+r+Ozs7G8OHD8f333+Pvn37NvgYFxcXuLjIe9/XHBiMApYTS4fU2jfndplGw76YEIOuczaR6t8DNYu49NVGaM30w2SMMWZdBqOALUfpC7P7daSVarWmZv/JImalkq23glOfX+5xGowCfjuUYzLmt0M5Nt+ZAwAtCQ1x1Sr5J5n8PWnl3ahxcunaqgUprlsrT1kvTMf1CyPF6aoFJFFL1jAiegJK7BZ6MR6LCyXtrGulgNWm1AltuXcr9OtESy5Q4+RiMApYvSeTFCvn55lGrcJdEbSeKVEh8pYKM+dCEW23YisvLU8aMMbsFnU14pxfGt+paK/atWuHHj161H116VLTCLVjx45o25bWT5JZZvp3B0Tdt3YOdIebAvqdOrLUucNFxd+5jHebMMaY0uw+dZm8i9PFSYVYG25SaEyzv7OOCfODrzttojrA07YreagJHrm3rCdnFCKnxPREVu0KYVsyGAW89mua2biJ/UNln2Qq19HqHVHj5NKbuHJvYn9aUsNSWic1IkO8SbEJInZGMPP6EsvqRYV4y3rDqHVSk95nr/2WZvMEbRmxoTg1zlK3dmlJinN3tu2Ol+SMQhRX0F4LuT/PuhNLdKpsvJutDXGrcmyY8i40GWOMKjmDthDmt0PZNv/sZ45BX23EhsPier1tfO5WmUbDarlpNRjchb7i+FReGX5PzZZxRIwxxsRa8MdxcuzDMe0UWUGm2SdNNGoVHottT4o1KqCnibmG5WpVTZycqCt/44/ZttlwUnqByfIvtQZ1bSX7WLKLab0fqHFyKa6glaChxjUFtS5/dhH31ZAU8TQ3Y2hXeccBYFBX80mAovIqJKXbdrcRtXxAKy95a3TGdvCHt5v5hMhnu9NtOtmUW0L7m/Vxc5a9N1YWsd8UNU4u1P4rYvq0MMaY0lQQe9pVGQSbf/bLLTQ0FIIgIDIy0tZDcWiR87eIih/VoxXv6LSSL5/oC60TfQLt2e8OcjKVMcYUwmAUcCKP3oN42E3BMo7GcvyJD5C3C+218c6JT3aegWBmrEahpveJnKgrf39Nte0qsERi2SZqXFO4OtNW5FPj5FJcQevxQ41riiBv2vuMGsdo9mbS/h6ocU2hpL/hxhiMAvZl0j4botrJm9DWqFUYFxtqNq5MZ0DCGdvt0Coso50/hnZvKftqk7xSWgKHGieXfp0C4G5mZ5eHVoN+nZRXB5Yxxqh6EncZA7Dp5xhzDPN+O4JyPX2Xv0YFLH04WsYRseulvT6CHCsAuG9lgnyDYYwxRrb71GVyrKuzSvbFkpbipAkAevtr2yUAlFIDHqgpaebnYb6kWUGZ3sYlupTz79qjLa0EjIfWtn+SP+w/T4qzxqY5XzetpHGMRjl/NWKexXbn5qSzBaioot1wBxNLLDVFFrH3hdz9VUzxI5a6vKWz+ca5TSYQz2bUOJlo1Cosvj/CZMyi+yMsSjJ98MEH6NevH9zd3eHj49NgTFZWFkaNGgV3d3e0bNkSL774Iqqr6+843LlzJ6KiouDi4oJOnTphzZo1Nxxn+fLlCA0NhaurK/r27Yvk5OR6P6+srMTUqVPh7+8PT09PjB07Fnl5eaJ/J8aYferfkX7ev2jj3dnMvumrjVibkCXqMcsfiVJk6RBHpnVSo1NLWvUBADiQVUzescYYY0w+C/44QY59/55eiv185aQJgD7taBktapwclFQDXqNW4W5i89zcUnkTOKbEdaCtuKXGNUWAB+3fZNfpfJvtzrlaWY3LV2grwK3xmpUQ+z9Q4xiNDzEJRY1rCiX9DTcmkdhTx8NFY5XVExeI5eqocXII8iLuIiPGNYWnK62/CzVObj4N9GDzcXPCykejMKKHZVuaq6qqcN9992HKlCkN/txgMGDUqFHQ6/VISEjA2rVrsWbNGsydO7cuJiMjA6NGjcJtt92G1NRUTJ8+HU8++SS2bPm37Mn333+PmTNnYt68eThw4AAiIiIwfPhwXLp0qS5mxowZ2LBhA9atW4e//voL2dnZGDNmjEW/F2PM/sR29IdWQ7tpbm2FhQjMcd36/nZR8R8/2Nviz1nWNPPuvElU/NP/2yfTSBhjjFEYjAJOX7pCinV1UuE/UW1lHpHlOGkC4CTxH5MaJwfq7hEfd/lrwANAW193UlzhVZ3MI2lcbEf/BieYruXj7ozYjvI3zg1oQVtZXVltuxrNM74/SIpzUqus8ppR88zKzEfbrwBPWjKEGtcUlL9hACghJpTlQXsHDuwcaJXVE218aIkGapwcYsL84GKmHri1PsvGEi/QqHFy2ZyWgylfH2iwT1dTe0y98sormDFjBnr27Nngz7du3Ypjx47h66+/RmRkJO644w68+eabWL58OfT6mkT7ypUrERYWhkWLFqF79+6YNm0a7r33XixZsqTuOIsXL8akSZMwYcIEhIeHY+XKlXB3d8eXX34JACgpKcEXX3yBxYsXY/DgwYiOjsbq1auRkJCApKSkJv2OjDH7oFGr8MygTqTYW7gcIbPQr6kXkVNKLzXs6aLBnZG0BYNMev06BcDVmT5t9feZAu5twhhjNpScUQgD8TQcHkyrymMrnDQBsP8crYQUNU4O1N0jE/qFWWVizseduBqdGCcHjVqFd8c0PAlU690xPa3yeolZMb3r9CXzQTKgNjpu2UJrldesbygtMaPmtImktp+gvf+CvOVf4alRq/DO6B5m41786ZDNbo76Eif2H45pJ/NIaoyJpE3uU+Pk8O4fx6CrNl3SzFp/1ZReIQBwpbJpiYmmMBgFvLHhmMkidG9sOCbb30BiYiJ69uyJVq1a1X1v+PDhKC0txdGjR+tihg4dWu9xw4cPR2JiIgBAr9cjJSWlXoxarcbQoUPrYlJSUlBVVVUvplu3bmjXrl1dDGPM8T07pDM8zJyXXZzUiO0g/wIe5ngMRgEv/nhY1GOm3tpRptEwCo1ahcX3RZLjBQDTvzsg23gYY4yZJqZlRFdOmiifu5ZWdqOhFZ7WEt3eF+bmqVUApgyyzkUdtZEvNU5OcpQzESsmzM/sv1+t3ads09jSw4XWhN5ajdfVxPIMXyZk8moiieirjdh0JMdsXJCXi9UadbVwNb/TxJaNzauJ/UyocU1F/buhxklNX23EZ39nmo0rKq+ySk8sjVqFhff2Mhv35kb5khLmJGcUIqfE9IVnTkmlbK9Xbm5uvYQJgLr/zs3NNRlTWlqKiooK5Ofnw2AwNBhz7TG0Wu0NfVWujWmITqdDaWlpvS/GmP3SqFV4NNb0QgNdtRHvbz5upRExR5J0tgB6Mws3rjdxICdNbG1kr2DccVMr84H/2HA4V/S/M2OMMWlsIcwp1eoU6CnjSJqOkyagl904c7nMZpMmKeeKYO6phX/irKGonFZ2ixonBznLmYilUavI5YzKbdS87uZQ2iQ4Na6p8oml3a7qqm1W0szRfJWYafY8AwD9O1mn1BQArCc2LKfGSe3zhAxJ45pqbwbtb4EaJ7W1Il4HMStUmsLXw3z5RDmTEubkltD6z1wbN2vWLKhUKpNfp06dkmvIVrVgwQJ4e3vXfYWEhNh6SIyxJjAYBfyaav5m+9NdGTwpykT7am+mqPhJA8KgNVNSlFnHx49EQyPinyLm7Xj5BuNAFi1ahD59+qBFixZo2bIlRo8ejZMnT9aLqaysxNSpU+Hv7w9PT0+MHTsWeXl59WKysrIwatQouLu7o2XLlnjxxRdRXV1/zmXnzp2IioqCi4sLOnXqhDVr1twwnuXLlyM0NBSurq7o27cvkpOTJf+dGWPy0VcbselonvnAfzwWFyrfYCTAVwCoKc9hrr46ULNrwlaTJtnFtEkTalxTqVW0t05GPq3kk9RsXc6kIZ1btiDF2aqxpTPxKpQa11TUknQAkHiWkyZSyCgoI8W5aq330VFGTCJS46R25GIJKS6bOPHddMruBrRXRIJTzDmgKXJLackZapzULNnZ+fzzz+P48eMmv0JDQ0nHDQoKuuHGuPa/g4KCTMZ4eXnBzc0NAQEB0Gg0DcZcewy9Xo/i4uJGYxoye/ZslJSU1H2dP3+e9HsxxpQpOaOQdL4VULPYgzEqg1HA1jT6RM7gboF4dVS4jCNiYmjUKix7MIocX1xRjfm/HZVxRI5hz549mDp1KpKSkhAfH4+qqioMGzYMZWX/3hfOmDEDGzZswLp16/DXX38hOzsbY8aMqfu5wWDAqFGjoNfrkZCQgLVr12LNmjWYO3duXUxGRgZGjRqF2267DampqZg+fTqefPJJbNmypS7m+++/x8yZMzFv3jwcOHAAERERGD58OC5dsk35csaYeKv3pJNj2/i4Kn5hgrJHZyUatQqP9qXVm7fWytfrpZ6n7SChxjVVHLER+N4M2zRis3U5k4Y8NaCDpHFS83EzXwZJTFxTxYT5ma1p/S8uzyUFJU63R7f3lTROSvpqI0qJvS5aW6EHDEA/N1PjpJZbSttB5qxWWa0EXD7xc50aJzU/T/M7Ya6PCwwMRLdu3Ux+abW03Y9xcXE4cuRIvRvW+Ph4eHl5ITw8vC5m+/bt9R4XHx+PuLg4AIBWq0V0dHS9GKPRiO3bt9fFREdHw9nZuV7MyZMnkZWVVRfTEBcXF3h5edX7YozZLzH3WueI/fgYA4Dbl+wEdW+Sv7sTvhwfI+t4mHgjewWjU6AHOf7LhEzekWbG+vXrMX78eNx0002IiIjAmjVrkJWVhZSUFABASUkJvvjiCyxevBiDBw9GdHQ0Vq9ejYSEBCQlJQEAtm7dimPHjuHrr79GZGQk7rjjDrz55ptYvnw59PqaRT0rV65EWFgYFi1ahO7du2PatGm49957sWTJkrqxLF68GJMmTcKECRMQHh6OlStXwt3dHV9++aX1XxjGmEW+25dFjh3craWMI5EGJ03+MTS88VWM17LWytfrWavsFlVsB394upjvBVNYZp269NezpJyJ3GKIDSupcVILIE7MUeOaSqNW4ckBYaTYuA4BMo+meegdQks8UOOkEE5sDEaNk5KYFa5P9bdOMjS2g3+DfZyu5evubLMGus7EjFtbH1erlYCj9iuzVV+zli1o51xq3PXOnz+P1NRUZGVlwWAwIDU1Fampqbh69SoAYNiwYQgPD8djjz2GQ4cOYcuWLZgzZw6mTp0KF5ea55w8eTLS09Px0ksv4cSJE1ixYgV++OEHzJgxo+55Zs6cic8++wxr167F8ePHMWXKFJSVlWHChAkAAG9vb0ycOBEzZ87En3/+iZSUFEyYMAFxcXGIjY216HdjjNkfMfdaIb7uMo6EOZK3NhxF+mV6km3pQ9EyjoY1xby7bhIVz2W6xCkpqdlF7+dXs3gpJSUFVVVVGDp0aF1Mt27d0K5dOyQmJgIAEhMT0bNnz3q964YPH47S0lIcPXq0LubaY9TG1B5Dr9cjJSWlXoxarcbQoUPrYq7Hfe0YUxaDUUBmPn2O9ZWRyt/NaZWkidi6hOvWrUO3bt3g6uqKnj17YtOmTbKPkdRoXWW71czHc66QYkP96SsvmkKjVuH+m2m9YGyxOyf/Kq2cCTVOCt/uPUeKe2L1PplH0rAg4kp4apwU/juki9nSeR5aDWJttGqewh7Of7WCiaXhqHFSoPa2ocZJibrCVaUC+ncNlHk0NTRqFd4d09NkzIIxPa2WkLiezkBb7edG3mXWdCoV7bVIPJsv80gaQd1IZ+GGu3feeQe9e/fGvHnzcPXqVfTu3Ru9e/fG/v37AQAajQa///47NBoN4uLi8Oijj+Lxxx/H/Pnz644RFhaGjRs3Ij4+HhEREVi0aBE+//xzDB8+vC7mgQcewMKFCzF37lxERkYiNTUVmzdvrneDvWTJEtx5550YO3YsBg4ciKCgIKxfv96yX4wxZpdiwvzgayb5X6uLwpuHMmXQVxvx+Z5Mcryni7LvLZq7fp0CIKaaS3FFNe5atku+ATkQo9GI6dOn45ZbbkGPHj0AALm5udBqtfDx8akX26pVK+Tm5tbFXHs9V/vz2p+ZiiktLUVFRQXy8/NhMBgajKk9xvW4rx1jypKUXkC+JXVzVlv1nt9SsidNxNYlTEhIwEMPPYSJEyfi4MGDGD16NEaPHo20tDRZx0lqtC4An+w8K+s4GvJVYib5jWfNJjpDurUyHwQggNBkV2rF5bRkCDVOCtQJ1sSMAptsI44J80Owt+nVfcHerlYrmVPL3IlUyYW57OX8VysmzM/sLgVrvwcs6edgLe39aCtcx/ZuY9UkxYgewVj5aBSCvOr/Pft5OGPFw70xokew1cZyvSpiuUZqnBSopcoOnC+xybk5v4yYOCTGXe+TTz6BIAg3fA0aNKgupn379ti0aRPKy8tx+fJlLFy4EE5O9XebDho0CAcPHoROp8PZs2cxfvz4G55r2rRpOHfuHHQ6Hfbu3Yu+ffvW+7mrqyuWL1+OwsJClJWVYf369Sb7mTDGHI9GrULfDrTrjOQs2/SaZPalx7zNouLfH9vLZotLmHkatQqL7osU9ZgjF6/gKrGkbnM2depUpKWl4bvvvrP1UEi4rx1jyiKm1/A9vVvLOBLpyJ40EVuX8KOPPsKIESPw4osvonv37njzzTcRFRWFjz/+WNZxUndDrE7IsHqPjkxic+bw4BbWbaKjxAYI/xCIU+nUOClQJ1gBYG1CpnwDaYRGrcJ/IhqfTFUBmHdXuFVvIpIzCs2WxCnXG/DxjjNWGpE49nL+qxV/LNfs623t94Al/Rys5bG4UNLp7Z0xvWQfy/VG9AjG3DvD4efxb9+KwrIqzPn1KDYdzrH6eGp5EXsiUeOkENvBH27OtM9OWzQdzsynJdxtVT6UMcak5uZEW3mYXWS9MrvMPr3+yxHoDfT7vbgwf4zsZR8TOc3Z3b3bIMxfXHm+kR/xbhNTpk2bht9//x1//vkn2rb9t6JIUFAQ9Ho9iouL68Xn5eXVLWwJCgpCXl7eDT+v/ZmpGC8vL7i5uSEgIAAajabBmMYW0HBfO8aU5dNd9E0Gr93ZQ8aRSEfWGXZL6hKaq3UoF+pkQ3G5bXp0UFi7dNilK7RVrdQ4Kfm60yZQqXFSELMLaF+m9d9jm9NysGpXRqM/f2pgmNVXqCs5mWmOPZ3/gJr6k29sOGYyxsfdGbcT+z9J5frdEo3JKrB+M1iNWmU2Ue3ipLbJasXNaTmY+u2BG3bgFJbp8cy3B7Bgk+l/a7k4EV+LYVZ8n2nUKsSE0XabWLvpsMEo4P+SzTfTC/JysfouQMYYk0uQmZ3Ptcr1vHKcNU5fbcSaJHpDWgBYO5Gbv9uLbc8PEhWfVVTBTeEbIAgCpk2bhp9//hk7duxAWFj9nqLR0dFwdnbG9u3b67538uRJZGVlIS4uDgAQFxeHI0eO1KumEB8fDy8vL4SHh9fFXHuM2pjaY2i1WkRHR9eLMRqN2L59e10MY0y5LpfqyIsUotp520VpLkDmpIkldQkbq3XYWLxUzZ9iwvzgQ1zZau0eHZHEpsvUOKkUEnsIUOOkFEBsiEuNk4LWSY3OgbSeM+5WPoEYjAJmrT9iMub7/Resnpiw52SmNc5/gHTnwOSMQuSUmD632eJ1jgnzQ5CX+b/T7/ZlWf39mXS2ADozN1+6aiOSRGxTlUJtAszUq7FqVwY2Hc622piAmsmL5IwiUuyEW8LMB0loYOcAUpyYHYNSSM4oRG6p+WuOh2LacSkRxpjD8COW9k04W6C4RTNMOcZ9YbqP4PVG9QiybtUG1iQatQoDOonrPTPrx0MyjcZ+Pf/88/j666/x7bffokWLFsjNzUVubi4qKmp28nl7e2PixImYOXMm/vzzT6SkpGDChAmIi4tDbGwsAGDYsGEIDw/HY489hkOHDmHLli2YM2cOpk6dCheXmvP55MmTkZ6ejpdeegknTpzAihUr8MMPP2DGjBl1Y5k5cyY+++wzrF27FsePH8eUKVNQVlaGCRMmWP+FYYyJMvzDv8ix6ybfIuNIpGX3VwVSNX/SqFWYcEsoKdbaJTBaE5suU+Okcm3ZFynipERdnU6Nk8qcUeGkuHsi28g8kvqSzhaYLctUXF5l9clfJSczlUKqcyD19bP266xRq/BQTDuzcTkllVZP6CSm0xqDU+OkQkmAAcCLPx226mTT2gRaf67YMF+rT1o8FhcKczkHtcq6fcMAkBImANDOn5aQZ4wxe0Bd1HRFZ1DcohmmDPpqIxIzxN23LH04SqbRMLl8+ngfUfHrU7M50XqdL774AiUlJRg0aBCCg4Prvr7//vu6mCVLluDOO+/E2LFjMXDgQAQFBWH9+vV1P9doNPj999+h0WgQFxeHRx99FI8//jjmz59fFxMWFoaNGzciPj4eERERWLRoET7//HMMHz68LuaBBx7AwoULMXfuXERGRiI1NRWbN2++YVEhY0xZDEYBhWbmE2tpNSq7Wuwn66yEJXUJG6t12Fi8lM2fpg3urLgmyEBN2S0VYTLH2uW5grxpSZosK5czAZTb1Fxt7h9SZJxUlDr5q1GrML5fe1JsgA16WphijfMfIN05kPr62eJ1butLW92fXWzt2ubKbOxETWyV6QxWTYQmZ9Keq4UV+5nU0jqpMWmA6d0tkwaEWT2Zo+QdnYwxJhcxi5qa66IZZtrjX+4VFT/51jC7msRhNdy0Ggzp1lLUY3rM2yzTaOxTSUkJBEG44Wv8+PF1Ma6urli+fDkKCwtRVlaG9evX33B/2r59e2zatAnl5eW4fPkyFi5cCCcnp3oxgwYNwsGDB6HT6XD27Nl6z1Fr2rRpOHfuHHQ6Hfbu3Yu+ffvK8WszxiS068Ql80H/aGnFaj9SkPXu35K6hOZqHV5PyuZPGrUKD9zc1mTMfyKCrX5B9cnOMxDMLIgwCkDKOVrZE6nUlM0xf1Pz5R7r95vQqFWYd1c4VLhxurL2e9ZuaA0Au8/Skg7UOOkoc/IXAPqEErddK2zRkDXOf4CE50Dq62eD1zn1PO3cRo2TCnX+PK6juNIBTSVmN6Q1E6HlOlrteWqc1GaPDMfTA8MaPMu5O6vRu511FyYAyt7RyRhjcokJ8yOf1wKIpbxY87Fg0zEkpYvbgfTi8O4yjYbJ7YvxfeDtSi9tXVFlxLxf02QcEWOMNS9zN9DPqT8/01/GkUhP9iWT5uoSPv7445g9e3Zd/HPPPYfNmzdj0aJFOHHiBF5//XXs378f06ZNk3uoMBgF/HYox2TMb4dyrJoAMBgFrN6TSYq1RdmcB/uYLwVUUlFt9bJOADCiRzCWPtQbnq71VzgEebvik0ejrN7UHAD2nKFNUH6795zMI6mvL3HHDTVOSvlltBXU1Dhrsqfzn5JfZ+o519rn5u/2md/V4+vujNgO1k2axIT5wcOFdvNoLiEvJerEli0nwHq3820wL1heZcTkrw9gc5rpawSpZRXSdk9Rd34yxpg90KhVeLSv+dKcAGC05gcZUzx9tRGrdmWIeszKR6N4l4md2zdnmKj4tYnnuCk8Y4xJwGAUcL6IPhcdSOhXqySyJ03M1SXMyspCTs6/kxD9+vXDt99+i08//RQRERH48ccf8csvv6BHjx5yD5VUB97adfOTMwpRXEGrDWftXisAUG2kXWxYu6wTULPK6LnvDuJK5b+rllUA7uwVZJOEiRhXdAZU6A1We759mcqtB20PE62Nsafzn5Jf58tXaIkaapwUahp0m3++8f2sX+5Bo1ZhxE2Nl3S7lo+79XYotCU2UafGSc1gFDBr/RGTMbPWH7Facs5gFPB/yVlm44K8XKxeapIxxuRGTYbsFdm3gjm22esPi4r/+MHeir8vZOZpndQY1VNc34uX1nFTeMYYa6pdJ+mluYZ2D5RxJPKwSnFuU3UJd+7ciTVr1tSLv++++3Dy5EnodDqkpaVh5MiR1himIhshU5/Lx93ZJpMmxJwJOU4qCzYdw6pdGbh+bksA8NnfmViw6Zh1B/SPyBBvcuxbvx+VcST/MhgFfLGHtiJrry2abSq3chiJvZz/lPw6B3gSS3QQ46RAPTeHBtgmAdC/UwApzpqvGXXHjbV35tRKOluAYjMN7IrLq6y2c7ImMWf+ffZQTDteIcsYczjUDSS80YTVMhgF/HTgIjl+0oAw3BnZWsYRMWta+lC0qNuUXw5xU3jGGGuqhVtPkGM/fCBKxpHIw7odTRXOj7jilhonBepzPR7b3iaTJoXEUj3UOCnoq4347G/TSYDP/s6wyZbcV0fdRI7dY6W+JskZhSjTUXe1WP/C8hJx9wA1jjUsn9hImhonJScN7aOKGicFJX5eXItarsmaZZ2+32d+1wQAqG2UAKDuiLTWzsncElpprnY22pnDGGNyou6EtOaOSaZsYz/ZTY5t4+2KV0eFyzgaZm0atQoL740Q9Zh7V9DfM4wxxm50+lIZKU4N3NA6wR5w0uQaJ3JLJY2TwrEc2nN5uNjmzUdtPm/NJvVfJWbesMPkekahJs7a3LQauDrRJgSrrbTyRczOqbgOtNXrUiokTtJT41jDMvNpH3a2KAMYGUJrwE2Nk8KJ3CuSxkktJswPwd6m/62CvV2ttkNRX23ExiO5pNg8M2Uy5UI95VprUWL+Vb2kcYwxZk+UuMuUKVeF3oDU8/R79P/05h0mjmjszW3hJGKG6+CFUry90TYVKBhjzN5tOpwNvYF2c9zax/rzSFLgpMk1zhfRVnVS46SgxKTEtaqJe+JLKml9WaRwrrBc0jip3R5Oq7faO8RH3oH8gzoJ7umiQWxH65fN8fMgrugnxrEbGYwCVv511mycNSfZr9Xah7Yb4kKR9f6mzxOfixonNY1ahXl3hZssUzDvrnCr7VD8KjGTvE/t4HnbfJ55umgkjWuq4nJaMoQaxxhj9oS6EzLLRtfzTFmGLvpTVHz/TvZXV53RpM4dLireVhUoGGPMnhmMAqb930Fy/CN928k4Gvlw0uQa7YklLsrJpYyazkNLm5yhxkktjlh7/qrOYLWaoeW6avNBoP97S+3+m2knC2pcU1FWpAPA+2N72aQEnBLLDDmahDP5qKgyf7NwX3Rbm7wHYsL8EORl/j36f8lZVjvPhPjS3m/UODmM6BGMTx6Ngo+78w0/83az7u7EzALaTibAFkUAa+w+Q+tVQo1rMgX3GWKMMbkp8bOfKdOGQ9m4WELfce6ksl3/NCY/T1cn+LqLu84d8eFfMo2GMcYc064Tl0RVYJg4oKN8g5ERJ02u8VhcKCjzgduO51rt4nxMVFtJ46T22p20Hh3legOSrdBE3GAU8NuhbLNxalXNv7ct9OsUYDbJ5eGiQT9iI+em0qhV6NHGy2RMr7ZeGNnLNtvYlVZmyBH9dOACKc5Wqzk1ahUeijGfRMwt1VnlPAMAVyppydluQab/tqyhoebmJRXVmPz1AWxOy7HKGAQRnXrD/D1kHEnjcohlwahxTeVFrPlKjWOMMXuixM9+pjwGo4D/iljpCgAf3Bdhk0VAzHoSZg0VFZ+eX44KvfUWxjLGmL2bu+EoOTYqxBtaMbUTFcQ+Ry0TrZMad/QIMhtXXFGNpLPWWWm66/QlszHWnGC/nptWgx6taZOCuaXyTzTtPnmZVFNvRHgrm/3RatQqPBpr+ibw0b7trHYxr682Iv6Y6ffZkQulNtu2bK7MkArWLTPkiMr1tAQANU4OoQG0nWFievRYymAU8L/Ec6TYQhuWTjIYBcxaf8RkzKz1R6yyCMDD5cbdLo2xVUK7tbcLKc5aGztLK2h/b9Q4xhizN0r67GfKtGTrCVE7VL1dnXCPjRYbMutx02rQRmT9/DHLuSk8Y4xRGIwCLohoW/H8sG4yjkZenDS5TlgAbYVrYnq+zCOpmcz+Ynem2bh3x9imbFKte3q3IcVZo1H3p7vTSXElxFXicqjZDWN6dfcP+y9YbTfTY18kmY0RAKxNyJB/MI2oLTN0/Y4TF40KY6JaY3A3Wp8Y1rAoYgN1apwcAjxoE9rUuKZIzihEcQWtTxO1Z5Acks4WNLjL5FrF5VVWWQRwiZg07xToYbOE9lP9aVuGLxRVWuX8fPYysaQZ54sZYw5KSZ/9THkMRgEf76Td+9XaN+d2mUbDlGbbzEGi4o/nXcWmw9bZgc0YY/Zs2fbT5AULTmqVTXojS4WTJtehVhARUWnEYl8lZpJqxFEno+Ti40ZbQUyNa4pSYsN5apwckjMKzZZ3KSqvwsc7Tss+Fn21EXszaE2X92XapjlzrRE9gvHaqHC4XjOhqjMI+OlANrq+9gcWbDpmw9HZNxUx6UqNk4WC+jtQV7T6uDnbtGwcNblvjUUArQh16QFgaHfbJUD7dw0EJV9TpjfInmgyGAXsPGV+pykA+LhpZR0LY4zZjII++5ny3PvJHlHxMaG+dlsehInnptXg1s7iJur++90B7pHEGGMmGIwCPvnrLDn+g3ttu8i/qfiq4To+7rTJB2uUmjpH7B9AjZNLQRmt/Aw1ril8if9+EW195B2ICdQJ11W70mW/aPsqMZMca64Pi9w2p+XgmW8PoLKBMmGCAKzalcGJEwslECeALxbTt2BKLZ+4U23H8TyZRwIEeNJWtI7vF2rjCwTlzDaVVNDO/9Q4OWjUKgwLpyVt5E40JaUXoLKKVhIxwJOTJowxx0T97KfGMcfxe+pFHDxfIuoxXz8ZK9NomFKtnSju37zaCHy07ZRMo2GMMfuXlF4AHbF0v5cDlMTkpMl1qJMP247nyT6h3d6PVseXGieXYzmlksZZymAUcIL4HK+OCpd1LKZQy/WUW2E1s5iE2xgbnuwMRgHzfjXfaOqzvzNs1nvFXhmMApIzaO8zW55rqH83P6delH+FGPHwfUJtt8sEAOKI22CpcU1x+tJVSePkEhboSYqTew1ioohzf5C3m4wjYYwx26EuUqDGMcdgMAqY9l2qqMeM6GG7fpbMtk69dYeo+KU7zvBuE8YYa8Se0/TFg588Ei3jSKyDrxyuQ518KKmoRnJGoaxjeSwuFCozC4BVKts1za1VpqP1B6HGWSo5oxCXrppfpRzd3gduNtw1ERPmR961IfdqZuokuFajQr9OAbKOxZTkjELkXTG/itAoiNs9w2pWClQQV7Tb8lwTE+YHPw/zSe3CsirZz835ZcSVr8Q4ucR28IePu+myiC5OasR2kD9pcr6ImqC17dbdFlonSeMsR7tZ99BqbFoCjjHGZEWdt3Sg+c2NGzeib9++cHNzg6+vL0aPHm3rISnOkviToh+z/GH7n7hhltE6qTGqZ5Cox4gt/cYYY83Fr6kXSXFOath1L5NanDS5TkyYH7zdaJMhF8mTQJbRqFVw1pj+J3LWqG1eH66lF211FzXOUtnE0kEPx7SXdRzmaNQqDOwSSIyW99/2gT7tSHHvjbVtHUJqSTMAyCggNk9mAOgr2qNCvG26Qk+jVmF0ZGtSrJj3iyWou15s2QQeqHnN3h3T02SMrtqI9zcfl3Uc+moj8kppZbduD28p61jMOZZNK/VBjbNUXAdakvrJAWE2vwZgjDG52MsiBan89NNPeOyxxzBhwgQcOnQIe/bswcMPP2zrYSmKwSjg4z/ptdQB4KMHIvmzsplb+lCUqLvqg+dL8DtxYpAxxpqLTYdzcNFMf+Za4cFeDvHZy0mT62jUKrT3o+022ZyWI+tYks4WmC01pK82yl7CyZyodrRVrtQ4S6Wco70O1Dg5PRpLS9zIXTbn26RzpLjLpba9GRUz+Wz/p2Vroy3PtOVOo1q3h9NWicmdrCgi9GcK9nZVxA6Awd1amf2bkLus3dqEDHLshFs6yDYOihOXrkgaZ6mIEB9S3KQBHWUdB2OM2RL18zwz37b9HaVQXV2N5557Dh988AEmT56MLl26IDw8HPfff7+th6Yo075JERXv4gTc3buNTKNh9kKjVuGeSHHvg/9+l8pluhhj7B8Go4A5v6aR4++KoC14VTpOmjTAKNCmXXNlnkj+em8mKU7uEk7mtPahJZmocZY6mUurhU+NkxOlbI6vu7PsZXO2Hs+VNE4uMWF+aOFCK2nWs42PvINxMNQV7dQ4OcWE+Zn9u/Fxd5Y1WWEwCnhz4zGzca+N6q6IlRVfJWaaTYvJXdZuX2YRKa69n7vN6417EMtuUeMs9R5x9w81jjHG7FFMmB+CCDvVv9uXZfeTmwcOHMDFixehVqvRu3dvBAcH44477kBaGn2CwtHpq43442ieqMckvzJMptEwe/Puvb1ExRsBDFuyU5axMMaYvUnOKEQhYfForXH9wmQcjfVw0qQB5iblxMZZwmAUsOPEZWK0bSfmYsL8EOxtfiXYjhPiLnLFMtf/RWycnChlcxaM6Sn7pKvBQFtdTo2Ti0atQm/iyuvDF2gTtKwGdUU7NU5uVWZ2RFTJ/F5NzihEDmFLqq+HMprSUsvVyVnWjtrDKaqdj2xjoFIRd15R4yyVWUBbNU2NY4wxe6RRq/BQjPlSsjkllbL3M5Nbeno6AOD111/HnDlz8Pvvv8PX1xeDBg1CYWHDv5tOp0NpaWm9L0f22OcJouJDfF3hLeP9OrMvWic1JvYTV6b77OVyXK2Uty8rY4zZg61H6Qup7+oVZPPFkFJxjN9CYk8Qy4NQ4yyRlF4AHbFcitwlnMzRqFV49Y5uZuO+2C1vCZih3VpJGie3ET2CsfLRqAYnFFUADmbJP/l/RWeQNE5O1JNuLrHGIqux4A/zuybExMkpKb0AZXrT78UynQFJ6fKV4KP2S5G7rwqVINAm96lxlhgT1VbSODm5EXeQlOvlTc6F+rtLGscYY/YqNMCDFKeUz93rzZo1CyqVyuTXiRMnYDTWfK68+uqrGDt2LKKjo7F69WqoVCqsW7euwWMvWLAA3t7edV8hISHW/NWsatPhbOzNpPcTU6uAv18eIuOImD167T890M5XXBnfW9/fIdNoGGPMPhiMAr4mlvZ31qjw4YNRMo/Iejhp0gBn4up+apwl9pymldxydVLLXsKJIu+K+VJlcpeAuam1t6Rx1nAwq6jBSWABwKpdGViwSd6J6nIdbeWMAqoMoZKYcKPGsRp/n6Kdaw5fkLfxNQW1aT01zhIBxB0k1Di5ebrQVlhS4yzRt4O/2f2Qqn/ibK1DoCcpLqOgXNZSMK+MDJc0jjHG7FWAJ/Fzlxhnbc8//zyOHz9u8qtDhw4IDg4GAISH/3ted3FxQYcOHZCVldXgsWfPno2SkpK6r/Pnz1vld7I2g1HAtP87KOoxR98YIdNomL3b9fIQ+IvYgVRQXoUKM4u2GGPMkS3dfgpVxHvfR/q2U0SZcqlw0qQBSZm0CTdqnCUOXSgmxYX6uyviDXmukFYihBpnie0naeW/8sts29S8lr7aiM/+Nt0gWc4GzQajgILyKlJse3/aKj859SAmu65U0H4nVvMeyCmlrcz0dlNCeQPaB3X6ZfmadBuJOzKocXJLu1gsaZwlUs4Vmf2XE/6JszVqEkJXbZR1R5ObVoP2/qb7gN0e3hJuxNJnjDFmt6gfp8r42L1BYGAgunXrZvJLq9UiOjoaLi4uOHnyZN1jq6qqkJmZifbtGy4p5OLiAi8vr3pfjui+lQkQs06hc6AHfz4ykz5+JFpU/C3vbpNpJIwxpmwGo4AVf54hxw+/KVjG0VgfJ00akF1UIWmcJSr0tB0A7gq5IGzvRysRQo0Ty2AU8NOBi6RYMc2L5PRVYqbZGwA5d+ckZxSSS8AteaC3LGMQw99TS4o7dLFU1jJwjiQ5oxBVBtpd6KQB8pUjpKI2o//r1GXZdgEkZRCT6sQ4uVVU0f4WqHGWsKeSZm5aDcICaJ9Tcu5oWrDpGM4VNH6N0d7fDZ893ke252eMMaWgLnZSyqIoS3l5ef1/e3ce1tSZ/QH8mwTCKjsI1AVwR0VEZXOpVRTr0jpap3ax1nG0dWo3nWm1o2Jrq/3p1Fatra0drd33RaultWrrAoobKorWBcQFUERA1pDk/v5gQFFIzoV7yU1yPs+Tx0dybvImhDf3vss5ePLJJ5GUlIRff/0Vp06dwowZMwAAEyZMsHDrLKdCZ8ChnCJRx2x+dpA8jWE2IzrUR1RV2MJyPdc2YYzZpb1nr4E6VOCgrulfbQlPmjQg2Mv06k6xcU3h7EibDKHGye3hGFpRtcvX5dlpkpZViBvEExkfhWzft/TuHOoAZVsfF7g70/L8y0lM2gU508DZEupnwFGjwoBO/jK3xrzYDr5wdzL/WSzTybcL4BJxspwaJ7e2XrS8zRoZNyxmF9CKzAe0EpdjWi6jelJXx8gzMafTG/H+TtO7EHOuVfDkMGPMLlC/G5TyHdIcy5Ytw8SJEzFp0iT069cP58+fx/bt2+Ht7W3ppllMwvLfRcWP7hlkM8VnmXw0ahXujxS3GnrwMq5twhizP3vOXiXHBnm6KCITkpT4jKIB8R1oq5kdNPK9fRFtvCSNk1v6hSJS3LqU87IM9IhZoRzooYyLKkvvzqFeXC4d30uW5xcr0JM+SSlnGjhbQv0MzLynkyK+/DRqFQZ2ovXPcu0CMBJ3sFDj5Nb9Li9S3Kn8Ull25xiMAtbtMT0BAABBns6KWZVC3dFEjRNrQ0oWKZ3ZhhTz7ytjjFm76FAfBHk6N7oqXAVlfYc0h6OjI/7zn/8gPz8fJSUl2Lp1K7p3727pZllMhc6AS0X0azw1gBUPWX53PLMOSx+IFBVfUFaNn9JpmS0YY8xWfEfM6AMAd3eW5/rYknjSpAGxHXzh5WJ+NfNHqdmypYC5WkrbYu6rkF0T1EmLmoGebMmfn7oLwcdNq5iLqklxIWYLrKtVNXFyqL0INUVJF6HRoT5oRdzxItdEk62JDvWBl5lCiK5aDWYO6dhCLTKvgz+1vo48fbOKOHdEjZObP3GSuKRSj7SsQsmff++5ayiuML8L8MG+bRUxMQf87xzAzN+Fl6sjYjvIU7h+H3GXFDWOMcasmUatQtKYmnpTt39L1P4/aUy4Yr5DmHQS3tghKv4vUXfx54CRaR3UmDYwRNQxM79Il238hzHGlOa1zSeQV0JPf/rvUba30IMnTRqgUavweHyI2bjr5dWypIAxGAX8nJFHivVrpYxJEzFb4tPkyPVPPHd5LLa9Yk6ma07UQk3GTBsYKtsWc41ahft6md6WfF+vIMW8Xxq1Cq/d14MUS00Xx8xzUliKA0vvAqDWAWrjrYyJOzE76+SoKULd8aNX0AWoRq3C6+N6mox5sG8b2fpG6ompmBNYxhizZiN6BOHdR6MQeNtin0BPZ7z7aBRG9LCtoqMM2HTkMi4Vi/ueWzIuQqbWMFv171HdMbiLuGuG2Ne2ytQaxhhTDp3eiLW76JkNotp5wkUhNbelpKzRMAWpJg7gpJwtkPy59567hnKdgRSrlFRT0aE+cCQmxae+NjGuEHfmtPejrlJvGXNHhuOJQaF37DhRq2omTOaODJftuQ1GARuP5JqM2XgkV1GrafzN7IypRU0XZ+/SsgpRVF5tMuZ6ebUsOxCaKraDLxzMDFY7qFWy7AIwGAWkZV8nxcZ3VMbW1OhQH3ib2TVRS0zdIDpq/6GcfgaoGaB7YlDjk9rv78xCcobp/rOp/FtpJY1jjDFbMKJHEHa/OASfT4vFiomR+HxaLHa/OIQnTGyQwSjg6c8PizpGzoVmzLZ9OCVG1KDY1bJqFJu5fmKMMWv36Ad7ybEaFfD1k/1lbI3l8JlFIy4W0or4UuPEoK7MdXPSKCZ1kkatwmBirYHuwR6SP38BcYU0Na4lzR0ZjpUPRsLd6eafo1GoWWEl16AcUDNgnlts+v3ILa5U1IA5dSW8HCvmbdHWE7QdbUp6P3V6o9ldCXqjIEvtpLSsQhSW6czGuTtpEBsmT+omsTRqFR6jpviTYd7C0juDmsrcpLIA4OVNJ2SZVNYQc7tR4xhjzFZo1CpEtvXCgexCvPP7GSzcmIEKGRZjMcv6xycHRcVHtfXEv0fJt9CM2b57uvqLik9YLi51HGOMWROd3kheLAoAzwztrJgMNVLjSZNG0QZCLhfJUXCa9tx3d/JT1AezNbFQtxw7TQqIO02ocS0pOSMXM79IR2lV/UHevJIqPPnJIdkmTqxxAoKaBk5Mujh7ZTAK+CH9MilWSe/n4i0nJI0Tg/q3oKT6HAAQRqwDU1Amff9YWG5+kkkFoJ9CFgDUsuSkcmviDlJqHGOM2YppH+1HtwXJ+HhvDnadLsDHe3PQbUEypn2039JNYxLR6Y345US+qGO+nmGbq1tZy1kxMUpU/NXSamw5Kt/iRsYYs6SPU7PJsSpAUTVwpcaTJo2g5qM/fKFY8pWm1BW3j8SESPq8zWUkTvZQ48TIuFQiaVxLMRgFzPnumMmYOd8dk2U1szVOQNQWr29sOFoFZRWvVzLqrgkfN0dFvZ/Z12gT1dQ4Mah/CwnhgZI/d3N4OdPSc1HjqAxGAXPN9G9AzTKBg+fpK1laQl4JbYKMGidGlZ7W34f5u0v+3IwxplTTPtqPrSeuNHjf1hNXeOLERkSLrBcxLpKLv7Pmc3d2QPcgcedVT39xWFFprBljTCrr99BrmXQOcLXp72GeNGkENR+93igg5bS0dU36hfo0OihcS4krc9VmWy0uTow8MyuCa1XplbWFf+/Za2ZrShSVV2MvMWWbGNcJA+ZKm4DQqFVIGtP49nsByiper2TUXRN/UdjFaIgvbUJbjhZHh/rAy0x9EG9XZU0yAcCvxDRs1DiqvWevobRKT4pV0o42ACi4Qdy9SIyjMhgF7Dp91WycWgVMoqZdY4wxK1ehMzQ6YVJr64krnKrLyi3aeBxFFbTzhlqvP8DF35k0Nj97t6jrB4NRwIQ1KbK1hzHGLKFCZ8DFIvq1+Usju8vYGsvjSZNGxIb5glpL7utDFyV97oPnr5vdi6HElbnuTg6SxlEZjAJyCmmrytt40QZcW0rKWdqEGzWOymAUsGiz+fRF80d1U9SAOVBTCHS6iQLN78lYoNmWWOuuiZdG0nJW/5l/wyKrv5S43uzopWJJ46hSz9H7LSXtaAOAa2W0E0VqHFVaViHyCRMxI3sGSVbwdtmyZYiPj4erqyu8vLwajFGpVHfcvvjii3oxv//+O6KiouDk5ISOHTviww8/vONxVq9ejZCQEDg7OyMmJgZpaWn17q+srMRTTz0FX19fuLu7Y/z48cjPF5emhTFmeyyZmpO1DJ3eiP+mZIs6JrFbABd/Z5I69eq9ouIP5RTxZC1jzKa8tvk4OVajAgZ0FlcTytrwWUYjNGoV/N2dSLGZudIONFljrQnAculM9p69hmri4Oj4qDaSPndzXbxOm+yhxlFR8vUDgLcb7W+gJRmMAr48YHqiUq6UZrakT3tvmJsPU6tq4pTERatB3/ZeZuPySqokrzeRllVI2hkmR52L5vAgpt3S643mg0ShTbi6OWkUtzsnr5i2g4QaR0X9Xh8W3lqy56yursaECRMwY8YMk3Hr169Hbm5u3W3s2LF192VlZWHUqFG45557kJ6ejueeew5///vf8csvv9TFfPnll5g1axaSkpJw6NAh9OrVC4mJibhy5ebq8eeffx6bNm3C119/jT/++AOXL1/GuHHjJHutjDHrZMnUnKxlTPogVfQx70zqK0NLmD3TOqgRGyru2ucvq3fL1BrGGGt5n+y7QI615QLwtXjSxIRqA20AiZLmSAxrrDUBAMFetPZQ46ioq5kd1CrEd6KlXWspBaW0zw41jsqS+fqby5IpzWzJwfPXYW5eySgob0cbQE9LJPXEsrVOaE8fEEaKO1tQKulkYwxxImRqfIjiTraCPV0kjaPyI05UU+MoXnrpJTz//PPo2bOnyTgvLy8EBgbW3Zydb36Xr1mzBqGhoXjjjTfQrVs3zJw5Ew888ADefPPNupjly5dj2rRpmDJlCsLDw7FmzRq4urpi3bp1AIDi4mL897//xfLlyzFkyBD06dMH69evR0pKCvbu3SvZ62WMWZ92PrS+lhrHlEWnN2JfdpGoY955OEpx5w7MNnw0NVZU/Mn8UugkX3jEGGMtr0fSz+RYjdq2C8DX4kkTE1y1tLeHGkcVHeoDN62m0fuVWuy6fwfatiytpvHX1jS0E+Zh4a0Vd3LtauL3fKsbVaYnCcQqLKWtjqbGtSRLpTSzNXnFFZLGtSRLTSxnF9BWsCptQntAF39QehqdAZLW6FKraP1tTJiyJrMBwNtNK2kc1af7sklxRqHld9I99dRT8PPzQ3R0NNatWwfhljakpqYiISGhXnxiYiJSU2tWDut0Ohw8eLBejFqtRkJCQl3MwYMHUV1dXS+ma9euaNeuXV0MY8w+DSemCqXGMWV54Zt0UfH3dm+NkRFB8jSG2T2tgxrTBjaeCrohj/2XF3cwxqzb1ZIqlFbRJ4BnDOqguPFVOfCkiQkdAzxIcYLEJYeXJmeizERuTAFA0phwxX1AYzv4wtPFfL2SD1OzJV3NHNfBlxT3aGx7yZ5TKtGhtLZnFZRL+p5dIKb78pF4QFAKl4tog/jUOHtVSNwhR41rSZZILWYwCvg8LcdsXKCHk+ImtDVqFfqE0N6Lbw9LV6OroIxYTJ0Y15L83Gl9HzWOQqc3YksGrX7HvhZOAffKK6/gq6++wtatWzF+/Hj84x//wKpVq+ruz8vLQ+vW9VOGtW7dGiUlJaioqEBBQQEMBkODMXl5eXWPodVq76ircmtMQ6qqqlBSUlLvxhizLdeIO66pcUw5DEYBP6SLq0X49iN9ZGoNYzX+PSocQR70c7y9Wdex5ehlGVvEGGPyil78GzlWpQKeH95FxtYoB0+amNC5dStS3IXrlZJtydTpjXh/Z5bZuCFdpctnLhWNWoXH40PMxkmdOik2zNfkzhwA8HZ1RGwYbYKiJU2ODyFNud2o1EtWJ8FgFPDNwUuk2ECJU89IIYjYJmqcvaLWyfEh1nZqSZZILZaWVUhKV/dQdDvFTWgDgKcLra5JWZVesue01p05AL3vk7KPXL/nnIho038Ac+bMabB4+623P//8k/xs8+fPR//+/dG7d2+8+OKLeOGFF7Bs2TIR7ZXPkiVL4OnpWXdr27atpZvEGJPY4Qu073NqHFOObvPpqUAA4O2JkYo8z2K2Z/s/h4iKn/n5Ya6pyRizSsXl1WauLusb37uN3XwX86SJCQ4a+ofg49RsSZ5zQ0oW6cO6IcX8xIolUE8UqHVIKLaeyDO5MwcAlozrqcg/aq2DGkO60tKaSVUnYe+5ayglDIz6uDkqbsU8AHi70QZ/qXH2yGAU8OMR2mqoQA/lDWhborZILnHnUltvV8meU0qtib9Hapw51rwzB6hJkxnkafq9kDpN5tYTV8wH/U+cmZRms2fPRmZmpslbSEhIk9saExODixcvoqqqZpdQYGAg8vPr75LJz8+Hh4cHXFxc4OfnB41G02BMYGBg3WPodDoUFRU1GtOQuXPnori4uO524QK9eCFjzDpQL+TPcyF4q/Jl2gXoDPRhmrs8nTE68i4ZW8TYTS5aDaLaepLjjQLw5tZTMraIMcbkMWW9uBSDi8eZroVpS3jSxARzgxK3Ol8ozUn6/mzaCilqXMujTkxIM4FhMAqY9dURkzFaBzWGKTjH8d8HdiDFSbUam1rrIzrEV5ETTX7EnQ/UOHuUllWIwjLzdXJ83bSKHNCm/i1sPUFLdURxKIfW51LjWlrvtl6Sxplj7TtzNGoVksaEm/ymuq9XkMRtpw0cadQ16TBN8ff3R9euXU3etNqmpxZLT0+Ht7c3nJxq+tm4uDhs27atXszWrVsRFxcHANBqtejTp0+9GKPRiG3bttXF9OnTB46OjvViTp06hZycnLqYhjg5OcHDw6PejTFmW0J93Uhx+7Ov80pvK2EwCnjxu6Oijvlt9mB5GsNYI76e0V9U/OodZ7kPYoxZnUMX6OmNO/u7QutgP1MJ9vNKmyC2gy+ciR8GqVYXUwuDU+NaGrW+CDXOnJTTBSg3s8tEpzdKWtxYan3ae8NcvWSVhPUZLl2nrZh3dlRm90BNh5Mj0USmLaIWdx8j+aCwNKJDfdC6lfkB3y3HciVLnZhPmAAQE9fSgonfUdQ4c6i7fEL8aANhljCiRxCmD2q8EOh7O7OQnCEuD7spCd1oaTfHRARL+nd54cIFpKenIycnBwaDAenp6UhPT0dpaSkAYNOmTfjggw+QkZGBM2fO4N1338XixYvx9NNP1z3Gk08+iXPnzuGFF17AyZMn8c477+Crr77C888/Xxcza9YsrF27Fhs2bEBmZiZmzJiBsrIyTJkyBQDg6emJqVOnYtasWdixYwcOHjyIKVOmIC4uDrGxsZK9XsaY9ZkUR0tnW1FtwN5z0qUAZvLZ/edVUfEJ3fzhotDrX2a7NGoVnh3akRwvAFi17bR8DWKMMYmNWbVLVPyPTw+SqSXKpMxRUYXQqFV4LL4dKTbES5q85tRfSHiQMldSxob5wsvVfFqk4nJpCjVSixZLWdxYavuzCyGYWZAiCDVxUgj2oq3Sp8a1tOhQH1LKqM/TcnilTyMKiIVS7/JSZqopjVqFgZ3Mp7UzCtKlTiyuML8zBwDcnBwkeT6pUdJNAcB1iYqy+7jSdjFQ4yzBYBTw5QHT3x2zvzoiWT/TPYiWAmJ87zaSPF+txYsXo3fv3khKSkJpaSl69+6N3r1748CBAwAAR0dHrF69GnFxcYiMjMR7772H5cuXIykpqe4xQkNDsXnzZmzduhW9evXCG2+8gQ8++ACJiYl1MQ8++CD+85//YMGCBYiMjER6ejqSk5PrFYd/8803MXr0aIwfPx6DBg1CYGAgvvvuO0lfL2PM+mgd1Igk7oRMlbBuIpPPjE8OkGPVKuCDydEytoaxxj0ztDPErFVZtf00X4MyxqzCxkOXcOwSfZdJmJ+L3S1g4EkTM1LO0E68V/ze/BUFBqOA5ON5pFj/VspMPaRRq7B4rPn8dos2Z0pyMmFul4nYOEugXtxJdRHYvwOthgo1rqVp1Co8FG1+MjOvpAppWdJMNNma62W0SRNqnCVQt4SevVra7OcyGAWcuVJGipV6QFsqGrUK80eFm42Tqm8+kUs7+aLGWcLes9dQVG56sqxMZ8DKbfSC6qYUEBcTUOOo3n33XQiCcMdt8ODBAIARI0bg8OHDuHHjBkpLS5Geno4nnngCanX9v8HBgwfj8OHDqKqqwtmzZ/H444/f8VwzZ87E+fPnUVVVhX379iEmJqbe/c7Ozli9ejUKCwtRVlaG7777zmQ9E8aY/YjvSNulLogqZcos4eVNx1Gup/+e3nk4SsbWMGaaRq3CPwbT0mkDgEEAhi3/Xb4GMcaYBAxGAc98lS7qmOTnBsvSFiXjSRMzLhBTGVHjTNl79hrKq2mpZKgpiizB2838yuHc4kpJBrT7hdDqLVDjLIN60SDNRWAhYcBNBaCfAmtZ1Arxo+2AkLIQuC355TgtpdClIuWmODtJHGynxpmSllVI+rtxclAjvhO9FlZLa8m++eB5Wm0XapwlpJ6jpXX8YFeWJBNNX6XlkOIKS6XZDcQYY9bEy8X8TnYxccwythzNxfo92aKOGdadJ8+ZZT0/rAs0IkbOzhWUY9FPJ+RrEGOMNdO4d8Sl5YoL9bGrWia17O8Vi9SKmGqFGmcKdYDGzUmjyOLMtagD1VIMaD8a217SOEuIC6MNslLjTDEYBcz97pjZOAHKHszkYvBNp9MbcbZAuZMhVJXVtN1jUtQYofZVj8Yos6h5LWotG2qcKdZen6sG7XdZpjM0e6JJpzdiL/ExfAiTX4wxZmu8iOkcqXGs5RmMAp754pCoY2be00HR51bMPmjUKqyaKG7H0393Z0lWW1FOe/bswZgxYxAcHAyVSoUffvih3v2CIGDBggUICgqCi4sLEhIScPp0/SwrhYWFeOSRR+Dh4QEvLy9MnTq1rjZeraNHj2LgwIFwdnZG27ZtsXTp0jva8vXXX6Nr165wdnZGz549sWXLFslfL2MMqNAZcOTiDVHHbJgaYz7IBvGkiRkPE9IAiYkzjXZCOKiTv6JPHgNa0WphUONMSb9QJGmcJcR2INaBIdZUMGXvuWsordKTYpW8S0NPPAGlxtmTDSlZ5Ng2EhUFl8PtqYEac7m4qtm7AKh9VUK4sldCUmvZUONM+UuvuySNs4S4DrRUMACQ18zJuY9Ts8l7CZW805QxxuRy9GKRpHGs5a347RTEnJqrVTUr/BlTgpERQRjVU9y5/pxv0+VpjITKy8vRq1cvrF69usH7ly5dipUrV2LNmjXYt28f3NzckJiYiMrKm+e+jzzyCI4fP46tW7fip59+ws6dOzF9+vS6+0tKSjB8+HC0b98eBw8exLJly7Bw4UK8//77dTEpKSl46KGHMHXqVBw+fBhjx47F2LFjkZGRId+LZ8xOTduQJip+Sv92drnLBJB50oQy43y7wYMHQ6VS1bs9+eSTcjbTpL8PouWvpMaZQh2gUfKuCQDo094bKjNzOipVTVxzteSuFrnU1IHpYTZu0eYTzR78FVMXRYpJLbl8n35J0jg5KLX/SzlL29EGAPEdlZtqagAxDZYAIOUM/TU3hFJEPcjTWdE7AAGgiFgLgxpnioMj7fSCGmcJsWG+cCG2r7kps7Kv0XZ/uTiqFf85Y4wxxm5nMApYuf2sqGPefDBS0QsFmf1Z+VAUxJy6fnc4F8kZtLTIljJs2DC8+uqr+Mtf/nLHfYIg4K233sK8efNw//33IyIiAh999BEuX75ctyMlMzMTycnJ+OCDDxATE4MBAwZg1apV+OKLL3D58mUAwKeffgqdTod169ahe/fumDhxIp555hksX7687rlWrFiBESNG4F//+he6deuGRYsWISoqCm+//XaLvA+M2QuDUcCes+KyJCSNMV+32lbJOlphbsa5MdOmTUNubm7draGtey1F66DGE4NCTcYMCw+QZNYtqh1tEoEaZyn7swshmBnbF4SauObKJqYZUvIEAAB4u5lPIyVNrQHapIvSU8CV62i7ZahxclBq/5eZS9uGqULNoLFSDezkT4799tDFZj2XRq3Cfb2CTMbc1ytI8Rf21EnXP6+I26rbkHW7z5HiChRcn0OjVmFCnzakWA/n5qXoNAq0pbd923sr/nPGGGNyCPF1kzSOtaynPj0gKj7Iwwn3Ryp3NyqzTxq1Cssf7C3qmKc/OyRJ7TtLyMrKQl5eHhISEup+5unpiZiYGKSmpgIAUlNT4eXlhb59+9bFJCQkQK1WY9++fXUxgwYNglZ7M31iYmIiTp06hevXr9fF3Po8tTG1z9OQqqoqlJSU1Lsxxkx7e/sZUdWS7+6s3DGhliDbpAllxrkxrq6uCAwMrLt5eHjI1UySuSPDMSw8oNH7fztxRZIVBPN/MF9rAgA+23e+2c8lJ+puBjG7HhpiMAr4nFA4N9DDSdETAAA9tUtzU8D0a0d7H6bEhSh6YK5fCK3jLpEgpVlTKLn/cyD+Xn1cHRX9GYgN84UjsX3NnTwzGAV8ecD0xMvGI7mKvyA6Q5wM+ePklWa9Fp3eiO2nrpJilT6hTX0bjlwsbtbzeDjRChf3CPZs1vMwxpi1mhQXAnNf+2pVTRxTFp3eiOTjV0Qd88cLQ2RqDWPNM6ZXMDr501MYVxuB3cTzYqXJy8sDALRu3brez1u3bl13X15eHgIC6o+VOTg4wMfHp15MQ49x63M0FlN7f0OWLFkCT0/Pulvbtm3FvkTG7IrBKODN3/4Udczqh/uaD7Jhsk2aUGacG/Ppp5/Cz88PPXr0wNy5c1Fe3vhugpaYXTYYBWRcMv24L29qXuokg1HAxiOmB1NrnS9UehFn6vvQvAHGtKxC0iTCQ9HKLs4MAAU3aKutqXGN+fOq6fRQtTwVXkRzcnwIqQLQ3qzrFinA11L9HyC+D+wa1Ir0Gnq39yLFWYpGrcJ9kcGkWOokW2Pe3n4aReWmJ+Ck2Qkmr0ri34LOiGa9lo9Ts83uNgRqdmcofkK7uELSuMaoNbTvKGocY4zZGq2DGtMGmt79P21gqN3m3FayyJd/ERXfLdCdf49M0TY/e7eo+Ckf7ZepJfZt7ty5KC4urrtduHDB0k1iTNES3tghKr5HsDvcm5lRwdrJdjZCmXFuyMMPP4xPPvkEO3bswNy5c/Hxxx/j0UcfbTS+JWaX07IKkVvc+OC8gOYPmO09dw06A20Sob2PcoszA0BcGK3WADWuMdRBqnYKf78A4Ho5bTJk89Hm1ejYdYa2yuV8YVmznkduWgc1YkJpaerEFD6XSkv1f4D4PvCtB6NIr4EaZ0kv32e+FhBQM3HaVAajgHd2nCHFKrl2EgCE+tFTlly83vTJ+exrtP6jd1svxU9oV1TTJpqocY3JM3GOcSsvF2VPaDPGmJx6m0lRbO5+1vK+PXAB5SK/I7/7xwCZWsOYNLQOaoQTF6IBNTuXv95vfQP6gYE1he/z8/Pr/Tw/P7/uvsDAQFy5Un8nmV6vR2FhYb2Yhh7j1udoLKb2/oY4OTnBw8Oj3o0x1rAfD19C1jX6Qr9WThr89Iy4CWJbJHrSZM6cOXcUKr79dvLkySY3aPr06UhMTETPnj3xyCOP4KOPPsL333+Ps2cbLhzXErPLLVFsnJqqSgXlbzuP7eALL1fTqUa8XB0RSyx835jCMlqxYmqcJalVtD/FIxdLmrxzwmAUcCD7OilW2cOYNTyIg4f7ia+ZQmn9HyC+D3R3dkBEG9MnlBFtPKxiRQElPZ+YuIaknClAFXFCW+mppl4aGU6O/fV445N7Umnnq/wJ7V5tvCSNa4jBKGDLMVqKTz93njRhjNkng1HAy5tONHq/Cs3f+c+kZTAKmP3NUVHHDO7iBxetRqYWMSadb2f0FxX/r2+PWl3/FBoaisDAQGzbtq3uZyUlJdi3bx/i4uIAAHFxcSgqKsLBgwfrYrZv3w6j0YiYmJi6mJ07d6K6+ubO/a1bt6JLly7w9vaui7n1eWpjap+HMdZ0BqOA2V+nizrm/cf6ydMYKyN60mT27NnIzMw0eQsLCyPNOFPUdrRnzjS80rclZpf93M0X6QaArKtNX51PLQLbq42H4rcra9QqvD6up8mY18f1bPYKYx/i74UaZ0lxxAkkATWpb5oiLasQ5ToDKbZ3W+Wv1HMm/h1Q4yiU1v8BTesDN84c2OjESUQbD2ycOZDcRkvan03b3UeNa8h3xCLyLo5qxaeactFq4GNmQrtWRTWtr2gItWuPtIJ+pn8n2o5IrUPTv8/2nrtG3qkS6OnS5OdhjDFr1hI7/5m0VojMm+7ioMaHU2Jkag1j0nLRanBPV39Rx/x+It98UAsrLS1Feno60tPTAdQUf09PT0dOTg5UKhWee+45vPrqq9i4cSOOHTuGxx57DMHBwRg7diwAoFu3bhgxYgSmTZuGtLQ07NmzBzNnzsTEiRMRHFyTSvnhhx+GVqvF1KlTcfz4cXz55ZdYsWIFZs2aVdeOZ599FsnJyXjjjTdw8uRJLFy4EAcOHMDMmTNb+i1hzOas3HYaYtZeu2o1ih/baCmilxL7+/vD39/8l8OtM859+vQBcOeMM0Vt5x0UFCS2qdIhLgj4YHcWnh7aqUmTAd6utIH9URF3iX5sSxjRIwhrHo3Cwo3HkVdyM/WUp7MD/jYgFMPC6QPHjQn0oK3qpsZZUmyYL7QaFSlFW1Nr2ojZCRXkpfyBuTJicW9qHIUt9X8bZw5EaaUez395GDnXK9DO2wVvPtjbKnaY1HIlrkSkxjWktIo2edApoJXiU00BwMieQfhkn/mdNyG+9FRetzIYBWw6Sts1EWwF/UxsWM3OSXM1bb7YfwFPD+3cpM9AytkCUpwbn7wyxuxYS+z8Z9IxGAWs2k5Lb1or45URMrWGMXmsfzwaYXM2gzoWOfWTg8h+fZSsbRLr8OHDGD16dN3/aycyJk+ejA8//BAvvPACysrKMH36dBQVFWHAgAFITk6Gs/PNMZZPP/0UM2fOxNChQ6FWqzF+/HisXLmy7n5PT0/8+uuveOqpp9CnTx/4+flhwYIFmD59el1MfHw8PvvsM8ybNw8vvfQSOnXqhB9++AE9etDSMTPGGmYwClix7bSoY5ZIsMjdVsi2ZYEy43zp0iV07doVaWlpAICzZ89i0aJFOHjwILKzs7Fx40Y89thjGDRoECIiIuRqqlkFZbR6E6VVeuw9R0uzdTtfN1rKDWqcEozoEYQ9c4bi+YTO8HKpWd1cXKnHm7+dxoD/247kDNrAWmP+u7vxlEW1gjydrWKQSaNW4b5etKLWTa1pQ00d5OPmaBXvGbWoNTVOStbS/7k7O2Dt5H745blBWDu5n1VNmADA+N5tJI1rSIAHrc/taSblmVIM69Za0rjbpWUVorDM9AQDUPNdZg39jEatwpR404WHASCvpKrJq5svXafllu0e7MEnr4wxu0U9j1V6qkx7MeHdPdR1hwCA+yNa83ccs0oH5g0TFf/l/qanDZbDwIEDIQjCHbcPP/wQAKBSqfDKK68gLy8PlZWV+O2339C5c+d6j+Hj44PPPvsMN27cQHFxMdatWwd3d/d6MREREdi1axcqKytx8eJFvPjii3e0ZcKECTh16hSqqqqQkZGBkSNHyva6GbMXPRb8LCo+0EOL+yOtY7F+S5A1z9Onn36Krl27YujQoRg5ciQGDBiA999/v+7+6upqnDp1CuXlNSvntVotfvvtNwwfPhxdu3bF7NmzMX78eGzatEnOZpol5uSbWpvkdteIdTeocUqx9UQe3vrtTxRV1B9EyyuuxIxPDjV54qRCZ8BvmeaLms+5t6vVnIAvHhdhtpaIStX0mjbRoT5ma80AwKv397CK9yyMWNSaGic1W+n/lCy+kx9pF8mNqqbvNooippCixllaUaX5CQ0A+OO0+f61IdQVvvdHBltFPwMAIX60ieqmrm4O9qKdY/QNsY7PGGOMySE61AdBns6NniurYD2LpWzdpiOXcehCsahjlv01SqbWMCYvH3ctNCJG1V789lizF48yxhhF0g/HUKEXV0tp5wtDZWqNdZJ10sTcjHNISAgEQcDgwYMBAG3btsUff/yBa9euobKyEqdPn8bSpUtlqVMiRnSoD9yI6V0EUWtqbrpeTpsMocYpQW3BxobekdqfNbVg42ubGy8Eeav9VpTXWKNWwdFM/Q1HjbpZA43misi7ajVI7GHBVHgiUItaiyl+LSVb6f+UTKNW4T8PmN+Fs2hz0wvDBnvTBsypcZZGXQTwY/rlJr1n1BpgQ5u4k8US5F7d3L8DLR82NY4xxmyRRq1C0piac7rbz4Rr/580JtxqJuQb8ueff+L++++Hn58fPDw8MGDAAOzYscPSzRLFYBTw9OeHRR0zqkeg4mt2MmbKoXnDRcU/+ckhqysKzxizLjq9ERv2itvZNrpna/4+vg2/GwQatQojutNqcNSmoRKLen5vTdcBchZsPHKxSNI4Jdh79prZSQ2d3oi9TdzN9Pb2M2YLwZfrDFZTQNNFq8Gw8ACTMcPCA+DSjHoWTPm83cwP0jenMOx1wu4+a1rZGh3qAx83899T18p0TXvPqNd/VnSdWLu62RzKZ6UhsR18ze4C9HJ1RGwH3yY9PmOM2YoRPYLw7qNRCLytTw70dMa7j0ZhhJUs/GnM6NGjodfrsX37dhw8eBC9evXC6NGjkZeXZ+mmkf1fMm1hWy0VgJUP8y4TZt08XR1Fj9MM+c92eRrDGGMABv3fNlHxahWw4qE+MrXGevGkCdGATn6kOOoq29s5qGm/irgwWjuUQM6CjR7E2gvUOCVIPUcrBvzpvmzRj20wCli/J4sUa00FNNc+1q/RiZNh4QFY+1i/Fm4Ra2l5xbR6ENS4WxmMAhYRdrXNH9XNala2atQq/IWYo7QpfYEtFurVqFWYP6qb2bim7mjSqFV4sK/pujuvczE+xhgDUDNxsvvFIfh8WixWTIzE59NisfvFIVY/YVJQUIDTp09jzpw5iIiIQKdOnfD666+jvLwcGRkZlm4eicEo4P2d2aKOWTWxN3+/MZuwb26CqPjzhZUorWx6CmHGGGvMxkOXkHdD3IK+lQ9G8vdxA3jShEjO9BwGo4Av9l8wG+fp4mBVK039CCvAxcTdqn8H2uQRNU4ZaB3UH6cLRA/MpWUV3lFXpjHWVkBz7WP9kPnKCEyKbYeBnfwwKbYdMl8ZwRMmdkLOQXpzu+VqUXa7KElCOG3nZFP6gkLibgtqnFLIuaMpOSMX7+9sfFL7iUGhVj8YyBhjUtKoVYgO9UFAK2dcuVHT91p7qhtfX1906dIFH330EcrKyqDX6/Hee+8hICAAffpYx8rPFb/9KSq+k78bRkcGy9QaxlqWv4cTXLXihtfilvwmU2sYY/bKYBTwzFfpoo4J8tBiNBd/b5D1LMO3NOqEWxMm5tKyCpFXYn5g7m/9Q61r5k/G96ys2nSaKbFxShDXwRdv7zhjNq6sqiaFVpyICTTK5wuomZizljRDt3LRarBobE9LN4NZQGbuDVLctswreOLujqIe2xZ3TQA16aa0DmqT6QCdHNRN6gt8iLstqXFKQe1DqXG1TNX+Amq+HjceycULI6xnNxNjjMktOSMXL286UW9hQ5CnM5LGhFvtJLNKpcJvv/2GsWPHolWrVlCr1QgICEBycjK8vb0bPKaqqgpVVVV1/y8pKWmp5t7BYBSwcrv565hbbX52kEytYcwyji0cgQ4vbSHH36gyYOOhi7gvyvSOY8YYo+r9yq+ij9n+zyEytMQ28E4TooLSKvNBALZn5ot+bOqAW4ifm+jHtqQrN2jv2ZItmaIfO+tqKSnOmoaYYsN84UqsvyF2kLaQ+Pkd1q01D8wxq2KuTk+tIxeLRa9C9XKm1aiiximFTm80Wz+pihDTkJxrZaS4QA/r2tFG7UOpcbXkrP3FGGO2KDkjFzM+OXRH35lbXIkZnxxCckauhVrWsDlz5kClUpm8nTx5EoIg4KmnnkJAQAB27dqFtLQ0jB07FmPGjEFubsOvacmSJfD09Ky7tW3btoVf3U0Jb/wuKr5vO08uNstsjkatwvLx4hbyPfvVEavfKccYU4aXf8xAici0f73benIdYBP4TIWImqbk+/RLor/05Ez9ZUkFxEmTjMslqCAOfAI1K5l+//MqKdaaasBo1Co8MagDKVbsZ8HHTUuK69/Ret4vxgCgXwhtN0SV3ih64Pn15JOkuF9OWE+BVgBYvIVWpJUaV8tgFPB5Wo7ZuCBPZ6vb0UbtQ6lxtWx1NxNjjMnB3O48AcDLm5pWX0ous2fPRmZmpslbWFgYtm/fjp9++glffPEF+vfvj6ioKLzzzjtwcXHBhg0bGnzsuXPnori4uO524YL5dM9yeHXTcWRdKxd1zGfT42VqDWOWNa5fO7Ryog9ACgCe+fyQfA1ijNkFnd6I9annRR/3zYz+MrTGdvCkCVF0qA9pMKSwrFr0wFx0qA+cTKy0UcE6B5mul9Nz1osZnNt77hrKdeZXQLs7WVcNGACYOaQjvFwbX7Xe1M+CjytxwI8Yx5hSTI4PIceKSZ2k0xuRmUdL/XXsUjH5cZUgmziwQY2rVZNq0vxk+cR+7axuR1sAcWfMtswr4h7XRhdNMMaYHCi1xpS2O8/f3x9du3Y1edNqtSgvr/nOVavrXxOq1WoYjQ1f9zg5OcHDw6PeraXp9EZ8sCdb1DFTB4TyLhNm09KTEkXFbz6W16Qd3owxVisi6WfRx7zzcG+ruy5vaXy2QqRRqzCWWKhO7IrQJz85gCoTX5ICgKQx4Vb3YRbT3HNXaSldACD17DVS3MBOflb3nmnUKrw+ruEtvbWvpCmfhdeTaSnQtjYhvRxjlqR1UCMurOFc37cTkzrpwz2NF+a+nYeVpecK8XUlxYntPfOKK0hx7XxcRD6y5RkNtFXLmzNyRV30Rof6wM3EdmhrXTTBGGNyoH7PUOOUJC4uDt7e3pg8eTKOHDmCP//8E//617+QlZWFUaNGWbp5jVq365yo+CAPZ8wfHS5TaxhTBjFjR7VGrdgpU2sYY7YudM5mVIos5zw1vj1GRojrp+wRT5qIMLRra1Kcnxu9wG2FzoCtJ8yvTL27cwD5MZVCTGqsimp63j29kdYbtPe1voE5ABjRIwhrHo1CkGf9lcXuzhqsmhgpusBlzYp5Wg2Y84XiVpYzpgTjomg5vHf+Sd8F8KuIlFvTB4aRY5XgpZG0wYr0C9dFpTi5SkzJSI1Tkn3ZtMl6QQA+Ts0mP+7S5EyUmUhPaa2LJhhjTA6FZbRd7HvO0PpsJfHz80NycjJKS0sxZMgQ9O3bF7t378aPP/6IXr16Wbp5jVr6yylR8X+8cI9MLWFMWZY+IO7v9vTVMlEpyxljDAB6LPyl0bSljQlwd8T8+3rI0h5bw5MmYlDHLESMbSz66bikcUoS28GX/AHzdqGv1D5+iZYyp6RCXAEkJRnRIwijI4KguuWzdKPSgGe+TMcSkXUGNqTQV8xTV6AzpiRHLxaR4v44fU3ELgB6Rz6gsz85VglctBpEtfMyG1dSacBe4s4+ADiRWyJpnLLQPw/UyWed3oi1u0z3zyoAQ4gLNhhjzNb5uNMWpv2Wma+ouiZUffv2xS+//IJr166hpKQEqampuPfeey3drEaNXrUTYhIKxYR4c1ouZje0DmrEitwpPO6dPTK1hjFmi66WVKFUZOF3ANg9J0GG1tgmPmsRoYCY2uX1n2mpkAAg5WyBpHFKolGr0CeEljanlQutlobBKJDfi6siUvEozZItJ7B2VxaE2673jALw3s4sURMn+7Ovk2OpK9AZs1bUXQDDwmm7+8ZFBlnlLoB4Yr2n1HP0755y4uo4apySxImoj9Xehzb5/HFqNsyN6QkQt3OFMcZsWSCxvlRRhfgak0ycVzcdRwZxIVutj/8eK1NrGFOmj6bGiIrPzLuBLUcvy9Qaxpit6bf4N9HHTBvIdcXE4HdKBGoh1mOXSshbK0srqiWNU5rBXWgrsDu1difFpZwpADG1PNy0DrRAhdHpjXjfzOrj93dlkVfMOxE7xNattHAxkVufMaUK8XUjx1J3ATwSE0KKe2VsBPm5lUX6rZP9iJPk1DgliQ3zRSsnWl/6cEx7Ulz2NdpnkRrHGGO2LjrUB17E3elia0wyuqYUf49p78WDNMzuaB3U+Hv/UFHHzPrqiFXulGOMtayeC38RfcyQLv749yheKC0Gn7mIEB3qAxdH2lu2mLgTwMGBNkjtLaJOipKcJtbS+P3UVVLcNwdyyM89PqoNOVZJNqRk37HD5HaCUBNHUaGjbdfreZcnKY4xpZkUF0Ie2qfuApj2URop7sv99D5JSag7J8TssJgcb/6iUEWMUxqNWoW/D+xAik2/UER8VOoFMV84M8YYUNMXPx4fQor1I6byYuJ9uJue+rfWx9PiZGgJY8o3b0w4QkWkwK7UG7Fq22kZW8QYs3aTP0jBDZFpuboHtcK6KdEytch28aSJCBq1iryimbIy1GAUcK2UVtAwPMg6B7TLq2k7bg6dpxUczsynTcKoAMR3pBeiV5I0YsHhTcStu5l5tPoBFeRaD4wpi9ZBjan9Q0ixk+LMx+n0RqSeo6W1o+5cUZrYMF94uZpfrVtcTt/l+MAa83mYpw+y3u3A7Yjf/3kltNXN1KmQHjyhzRhjdfqFEGsE8HyzbP67R9ykydT49lb73c+YFH6bPVhU/Iptp3m3CWOsQfN/OIY/ztBT8AOAu6MKm58dJFOLbBufvYjUl5hWhFJQe+/Za6gmfhk+0Nc6d01QL2wMQk3qLXPcnWgpt0L9XK2yzgAAlFfRJpoyc0vMnkzp9EZcKqLVdgnzo6c4Ykxp5o3pjog2Hmbj3vj1pNkY6i4ugL5zRWk0ahUWj+1hNm7R5hOki7bSSj2OXjQ/Qfv0kM6k9ilRIbFO1p7T5ndOGowCvjt0ifR4GZeKSXGMMWYPCspofTE1jomj0xuRf4P+3rbzdsb8+8yfbzBmyzRqFd55uDc5XgCQsPx32drDGLNOg5Zux8d7xWe6OPLyvTK0xj7wpIlI1ELZlDhqgV1Hjcpqd01MJm6hB4CvD14wG+PiQJsIIa9CU6CehIFfAKg2CGaLXK7bdY78vFwEnlm7b57sbzZmLaEe0P5sevFYys4VpaKkfcwtriQV0314bSrpOZ//8jApTol83LSkuK0n8s1ONKVlFaKcWPuMMcbYTdQak9Q4Jk704q3kWA8nNXa+OFTG1jBmPUZGBGNUz0ByfFZBOX48TFtgwxizfYP+bxtyCitEH7fqod5Wu6BcCXjSRCQXrQbDwgNMxrT3dSEV1KbuuBzSNcBqP+RaBzUpBQwAHMgyvcXMYBRwIJu2Da2CmBZMiQZ2NP35upW5IpffpV8kPY6bVsNF4JnV+zg122yMUTAf50r8W+jo72bV6SaoaaTMxRmMAo5dpqUBzLku/kRPKQI9XUhxxZV6sxNN1PceADktKGOM2YPoUB8EeTo3WstMBSDI0xnRoda7gEqpXt6UgaJyeg71A/MTZWwNY9Zn5UNREDOs869vuCg8Ywx4+cdjyLlOv36sNbiLH8b0CpahRfbDekd7LGjtY/3Q3rfxwZPz1yqwhFAI3suFNpnQpx0tJZhSdfCjpa+5Ulpp8qQgLasQVcSyG5XV1lufI7aDL1wdaWdTPq6mVz5T544CPHg1HrN+2dfKJIkb35uWDjFpdHdSnFLR002Z3hWZcqYAAvF6rp03beJBiaJDfcjf2+YmtAtEpDax5t1MjDEmNY1ahaQx4Y2WLBEAJI0Jt9oFZ0ql0xuxfs95cnxcqI9VLyxhTA4atQp/iaQPYOoMAvaepdU7ZYzZps/3ncf6VPEpuXxdHfDhlBgZWmRf+EymCXR6Iy6Y2RZFSQFzvZxWBJ4ap1SJ3YNIcXojTK7OFbMy15rTc2nUKtzbk/aency7YfL+WOIqO2ocY0pGXYllLu53Qk0KAIjp4EuKUypyuqnMPJPv2XeHaDvaAODNB+n5nJVGo1aRU076mUl9Rv1ej2zjyYNOjDHGLG7ud0dExW+YygM1jDVkyfheouL/80umTC1hjCldxMJfMPf7DNHHtXJ2wMEFvNtTCnwl3gQfp2abTa1FSQGTW0ybBKDGKdXj/UPJsaZW54pZmSumlooSuWppq5lzCstN3u/jTnucEeH0/KqMKVVpJS1lhKk4nd6IdXuySI9z8DwtXaBSkdNNVZhON1VGrM3RylkDd2cHUqxSkdO9mFngnFVQSnqYAZ2ss54ZY4zJxWAU8PKmxnf0qwC8vOkEp7SRkMEo4NtDl8nx3YJa8YQ/Y43QOqhxb/fW5PjDF0tIWUwYY7al+/yfUUIc37jV3R29cWwhT5hIhc9mmuC8mYFqahw1hdRdXtabzgSoOTEYHUHbOWGqaOP1ctqkSWRbW1iZS73QazzOYBSwdlc26VGKqqqJz8eYcl0uptXLOJ5b3Oh9H6dmk1NNmUvBpHTRoT7wJE5i5Jl4b/u0p6WQnHlPJ1KckhUQU5qZijMYBewzU8OrVlwYT5owxtit0rIKTS4oE1Cz4MxcbSlG1/uVX0XFfzejv0wtYcw2vP1IH1Hx7+00n8WEMWY7+i/eirImlBwY3i0AG/4eL0OL7Je1jyxbRHsfWo0OU3E1gya0/JTxHa1/0GT5XyPNxqhgevDt3FVavYL4Dtb/fkW28SLFmUrXnHK6gHxyZWqyijFr4UScLD1/raLRFajZ12iT4oD1/91o1CoMC6etdCssazydVFkVbQVMeJAHKU7JqL/z7ILGv6/SsgpNvp+13Jw0iLXyFHCMMSY16oIFa1/YoBR3L90uaqVrVFsvuGg1MraIMeunUavw3NCOoo7pvuBnmVrDGFOS0St24lJJ00o0vDupr8StYTxp0gST4kJMDlbXMlVcOy2rENfLza/ud3dyQGyY9Q+a7M82v9pLAPDOjjMN3mcwCth9xnQx4lreZoqjW4Ngb9rE3LeHLjc6+PvNoQukx3ByUNFTzjCmYD3beJLi9EYBe881NmlN22bi4qi2ib8b6veLZyMF0A1GAe/tPEd6jEIrr88F1OzOCTTx3V5r/Z6sRvtm6kDexL5tuZAxY4zdhjp5be0LG5SguLwa583U8bzd1zN4hStjFE8P7Uxe8AUA1UZg9MqdMraIMWZpj7yfgoxc03WLG/P2xN587SgDnjRpAq2DGlMHhJiNW7wls9FBk9wi2gnoA1F32cQHP/UsbVfNB7vPNfiepWUVorSKljffr5XpArzWIDrUB96u5tPmlFbpsbeR9/YS8TPWxsvVJj5jjA3sGECO3dPIJOwhYp2Sv/ZtYxN/N0cuFpHiNh5pOJd5ypkCVNnRjjaNWoWHotuZjSuq0OPt7acbvI/6PiS0UK2pp556CqGhoXBxcUGHDh2QlJQEna7+BNfRo0cxcOBAODs7o23btli6dOkdj/P111+ja9eucHZ2Rs+ePbFly5Z69wuCgAULFiAoKAguLi5ISEjA6dP136PCwkI88sgj8PDwgJeXF6ZOnYrSUlr9F8aYfYgO9UGQp3OjpaNUAII8nW1iYYOl9RKZlmtQJz+bODdirCVo1Cr8Y7C43SYZl2+QazgyxqxHaaUeoXM2Y8+5ptVMTegWgNGRwRK3igE8adJkQ7qaH8wwlU/3h/RLpOcxUJPrKx7tdZRWGRp8z8RssaesAlY6jVqF6BDaxd5L3x9p8OeUnUwAfXU+Y0oX28EX1AVbl6/fOalYoTPgOHFlx4getnFSQv2G2XOmoMEJ7W8OXiQdb0s72kL8aDsB1+/JbvA9u15mvi5KSw74CYKA9957D8ePH8ebb76JNWvW4KWXXqq7v6SkBMOHD0f79u1x8OBBLFu2DAsXLsT7779fF5OSkoKHHnoIU6dOxeHDhzF27FiMHTsWGRkZdTFLly7FypUrsWbNGuzbtw9ubm5ITExEZeXN7/dHHnkEx48fx9atW/HTTz9h586dmD59eou8D4wx66BRq5A0JrzR+wUA9/UK4sH7Zmrs+sKU9zgtCGOizBzSEY4iR+Se/eKQPI1hjFnEfat2ocfCX8jX5bdL6OaPDyb3k7RN7CaeNGmii9dpee8bijMYBVK6KoA+oKV0YorZNjRBkl1Ae7993bQ2MzBXRtxZc76wEhW6+rE6vRFniTVgxke1Ed02xpRIo1ahd1svUmxDeyMWbzlBOtbVRlJzAUCorxspziDU1Em6XWZeMel4X1cnmxnAou4UKaqovmMRgMEo4KUfMho54qb5o8Jb7P165513MHz4cISFheG+++7DP//5T3z33Xd193/66afQ6XRYt24dunfvjokTJ+KZZ57B8uXL62JWrFiBESNG4F//+he6deuGRYsWISoqCm+//TaAmomZt956C/PmzcP999+PiIgIfPTRR7h8+TJ++OEHAEBmZiaSk5PxwQcfICYmBgMGDMCqVavwxRdf4PLlhnc6Mcbs04geQZg+KLTR+9/fmYXkjNwWbJFt0emN+GwfbVFErYRu/lzLhDGRNGoV3nggUtQx205elacxjLEWpdMbEbnwFxy9VNLkx1jxYCQ+mBwtYavY7XjSpIl+PZ7X5Li0rEJUVNPSmVAHtJQutoMv3J1oJ9K3D0jV5Mw/Szp24X3dbWZgzoX4fgF3DvZuSMkiHafVqBHfkT6hxZjS3eVF2wXQ0JdfFnFytr2vm830M5PiQsixXzdQJ6m6mja529rD+tMm1ooO9Wm0xsvt8orr72jae/Yaigi7AKmPL4fi4mL4+NycFExNTcWgQYOg1d6sF5aYmIhTp07h+vXrdTEJCQn1HicxMRGpqakAgKysLOTl5dWL8fT0RExMTF1MamoqvLy80LfvzZXKCQkJUKvV2Ldvn/QvlDFmtQxGARuPND4pIgB4edOJRtMkM9PmfHtUVLyLBjxow1gT3Rd1F0J8XUQdc7XE/K5lxphyzfn2KDrP+xlFzUi3t/Kvkbi/910Stoo1hCdNmqiCOFDUUNxlYq0JQNyAlpJp1Cq8/peepNhrt+00STldgHIdsZ6Juw0NzIXQCjQDQPa1+oO9jaWFu13XQHebGfxlDACCvWm7ABqKKyYWKr+L+BzWQOughhdxgH7Xn/V3mhiMAs4X0lInertbbhJAahq1CsO60ernFJbV/0ylnmu4ls7tqHFSO3PmDFatWoUnnnii7md5eXlo3bp1vbja/+fl5ZmMufX+W49rLCYgoP776uDgAB8fn7qYhlRVVaGkpKTejTFm29KyCpFbbPr7x1SaZNY4g1HAd4dpaaRr7Z+fKFNrGLMP22bfIyq+3+LfZGoJY0xOOr0RIXM244v9dy5GFCOhmz/ui+IJk5bAkyZNFOpH2wGiVt05IP3fXedIx3YLagUtNUG/FfAlpjT557dH660M+/ogvUMRU/tE6SbHh5Bjb5/3uD1dV2M8nG1nIJMxAOjfwZ8Ud/62iUaDUcDJPNpgawc/d9HtUrIOAbTvs+sV1dDdUvR979lrDaY5a0iQh7gVdEoX14G2Q8/LVVvv/9RFz81dHD1nzhyoVCqTtz///LPeMZcuXcKIESMwYcIETJs2rXkNaEFLliyBp6dn3a1t27aWbhJjTGbUBWhiFqqxGuNX7xYV7+Wkhruzg0ytYcw+aNQqPD1EXFH4Pot+lak1jDE5LNyYgc7zfm724yR0C+DdnS3IdkbkW9hLIxsvQHirtKxr9SYAdHojTuTRCg33be/dpLYpFXVCo7LaiJQzN1fZHsy5Tn4Oaq55a6B1UONv/UNIsafySut9zlyJOYV73MVF4Jltie3gC0/CxfvPGXl3TAAQsybC0cG2cnYPDw8kx67bfXPSX8xuiDB/25poKiQUc28ozvu2SZTGUOMaM3v2bGRmZpq8hYSE1MVfvnwZ99xzD+Lj4+sVeAeAwMBA5Ofn1/tZ7f8DAwNNxtx6/63HNRZz5cqVevfr9XoUFhbWxTRk7ty5KC4urrtduNC8lVuMMeVLv0C7NqDGsRoVOgPSReZWT/33cJlaw5h9eS6hs6j4a2XVeHmj+Tp5jDHL0umNCJ//Mz5MOd+sx1EBWPVQby763sJ40qSJXLQaRLXzMhtXqRewctvpuv9vSMkmP0eIjdQzqSVmQuPbW3aXUHdNALCZ4sy1FozpjtYe5gfP8kpupiAwGAVsO3XFzBE1fG0onRljwP9SJxEmAYwC8HFqdt3/xUwAxHWgp86zBlP6N15M93bfHMi55X+01H4q2E6qyVpFFebrkgDA5mP1c+77taL1udS4xvj7+6Nr164mb7U1Si5fvozBgwejT58+WL9+PdTq+qeGcXFx2LlzJ6qrb77mrVu3okuXLvD29q6L2bZtW73jtm7diri4OABAaGgoAgMD68WUlJRg3759dTFxcXEoKirCwYMH62K2b98Oo9GImJiYRl+rk5MTPDw86t0YY4yJN+4dcbtMOga4cfF3xiSiUavwf+N6iDpmfcr5eovAGGPKUaEzYMSbv6PzvJ9RTl2d2QhHNXBm8UiM6RUsUesYFU+aNEM8ceDsnR1n6nYB/HT0MvnxbW2QKTrUB44a2iDbxaKbu1L83GgrbgM9tDZZn2POiG6kuNqCw7tPX4WB2Cc3d2COMSVydaJdwJ8vvJmii5oOyclBhdgw25o00Tqo4Ug8G7i1b6aeQNzbo7VNpZoEABVxwujIheJ6F7M518pIxwV6tNyuyVGjRqFdu3b4z3/+g6tXryIvL69eDZGHH34YWq0WU6dOxfHjx/Hll19ixYoVmDVrVl3Ms88+i+TkZLzxxhs4efIkFi5ciAMHDmDmzJkAAJVKheeeew6vvvoqNm7ciGPHjuGxxx5DcHAwxo4dCwDo1q0bRowYgWnTpiEtLQ179uzBzJkzMXHiRAQH8wUCY+wm6sIyW1uAJqctRy8jM69U3DHPDJKpNYzZpwej29+RdtuckSt2ytMYxliTlFbq0XfRr+i2IBkn82nXfqa4atU4vXiUTY51WgPbGsVocbQPbbVRQMqZAhiMAk5cJubM93ezuUEmjVqF3m29SLG39gfj+rQhHfN4PH21tDW5vZCwubj3d54lP3ZLDswx1lLa+7iKjqOmQxodEWyTJyytiPWNKvUCDMaa2wd7aPW5HokJaUbLlIm620jAzR1NBqOA1TvOmD0myNO5RXdNnjt3Dtu2bUObNm0QFBRUd6vl6emJX3/9FVlZWejTpw9mz56NBQsWYPr06XUx8fHx+Oyzz/D++++jV69e+Oabb/DDDz+gR4+bKyZfeOEFPP3005g+fTr69euH0tJSJCcnw9n55vfQp59+iq5du2Lo0KEYOXIkBgwYcEe6MMYYmxQXYnZgUa2yvQVocjEYBcz8/LCoYybHtrO5a1XGlOD4yyNExZ+5Woaf0i/J1BrGGFWFzoDIhcnosfAXFJTRshKYEx7kjhOv3CvJY7Gm4TOdZhCTouXlTcex99w1VBOXM0eH2lY9k1pPD+lEijuRe6Nud85d3rQB0L8NCGtyu5TMh5hC6+L1mlXzf+bTVolpVLaXzowxgDaYAgC5t+5oI+66GtCJVmje2nQJbEWO3XvuGvaeu4ZyHW1LWwGx/oc1iQ3zhZa4c3Ln6asAgL++lwKdwfw5wMR+7Vp0Yq64uBiCINxxu1VERAR27dqFyspKXLx4ES+++OIdjzNhwgScOnUKVVVVyMjIwMiRI+vdr1Kp8MorryAvLw+VlZX47bff0Llz/fzdPj4++Oyzz3Djxg0UFxdj3bp1cHe3rXo4jLHm0zqoMW2g6cVS0waG8qA+0YQ1e8g7boGaNCEvj+0pX4MYs2MuWg0i24hLNfrMF+n16psyxlqGwShg16mriHr5Z3RbkIyiSnppAXOGdvXHlmfvluzxWNPwmWQzxIb5ktNNnblahl1/XiU/toPaNn818R394ER4z0qr9Nh79hqSM3LxNGHl0xODbPfCiLob5Mcjl6HTG3G9nDarHezlYpMr5hmjDKYAwAd7srBkywkAykyb1JKeHNSBHJtypgC7z9C/z8TUs7IWGrUK9/W6ixS7+0wBSiv1OHi+iBQf4kdbKMAYY/Zs7shwDAsPaPC+YeEBmDsyvIVbZJ0qdAYcyikWdcxxXvXKmKy+/ccAUfFG1CzOYXSFpToMWbYdYXM2I3TOZtyzbBsKS2kZPhir0Bkw6YN96PDSFkxan4bCCulqCzlqVHh7Ym/89/FoyR6TNZ2DpRtgzWoGTYLx7SHadsjNx+j1TGw1B69GrcLQbgHYkpFvNvbjvVnYm3XdbNy0gSE2fWEUHeoDHzet2TRdhWXV+Dg1G3riKpNRPYPMBzFmpeaODIfeKOC/u7NNxq3dlYXnErpg3Z4ss4/Z0mmTWtKAzv5Qo+aiy5zk43nkySOtRmWz79nicT3x7aGLMNfjGozA058fNBN1ky1OMjHGmNSSM3Kx9cSVBu/beuIKkjNyMaIHn+uaE7dkq6j4UT0DbXahGmNKoVHXDJrO/IKeNu/g+SJU6Axw0dJqO9qzfq9uxdXbJkiyrlUi6tWa/rCTvxu+fCIePu609M3MPlwtqcJ9q/5A7g1pUm815LmhnfD00E68uFlB+IynmZaMiyDHXrheaT7of2w5B2+YPy0NzLbMqygi7JoY3KV1c5ukaBq1CqMjAkmx20+Zn4yqNdBG0wwxVivY08VsjFEAXtt8AsUVerOxD/Zta7MnMBq1Cn3ae5Fiz14tQ04hbWdO16BWNvueaR3UCPaiTXDs+rOAFOegtt1JJsYYk4rBKGDOd8dMxsz+6ginqzGjtFKPogp6KhFHtQorH4qSsUWMsVqjI4MR1dZT1DFDlm2TqTW2o6EJk9udvlqGqFe3ImTOZgxb/jvW7DgDnV66nQTMOhiMAvacKcDSn08ibM5m9Fv8m6wTJmcXj8Rzwzrb7LWzteKdJs2kdVDDx9URhcSUSBTBns42vYInroMv3iYUw6XWf9lzugD9O/o1t1mKJhCv+facKSTFOTuqESuiJg9j1uh8YTkp7kdi8UTqLi5rFR3mi/3EFFLURQBjImgprKwVNUWnnvjR6ejvxifKjDFmxt5z18wurCrTGbBq22k8N6yzyTh7NnLFH6LiV0yM5O8oxlrQ1zP6o8u8n8nXILk3qnm3iQmFpTqzEya3O32lDK//cgqv/3Kq7mcB7o7Y/Mzd8Peg1cRk1qG0Uo8ZH+3H7nOFZjMJSKm1qwb7FoxowWdkYvCkiQR83bWSTpp0v0tc4S9rExvmCycHNaokmq0/crFIksdRNmkvUB7qZ7sr5hmr1d6HVhviRhV1laVtT5rEh/lh9Y6zkj2eCsDk+BDJHk+J3LTSnkaN7W3bk0yMMSaF1LPXSHEf7M7iNBeN2HL0MnJEZEHo2toNIyOCZWwRY+x2GrUKbz/cG09+coh8zLh3duPn57h4dEMmvi9N3ZcrpdXot/g3AICPqyN83bUYF9UGUweE2fTiZ1tSXF6Nx9ftRWZuCaqNgMECl/kOKuDg/OHwdHVs+SdnZDxpIgEPZ2k/5NEhtr0DQKNWYUhXf/xMqGtC4aq1/S+mEF9pCwMP7845npntmxQXgte2ZEKqDSJxYba9oy22gy80KulOGiPbetn8hcPATv44nntDssdrTawVwxhj9o32RVVapUdaViHieHd1PQajgKc+o9dKAIDvnxooU2sYY6aM6BGENl7OuFhEm+TMzCvFlqO5GBnB1/u3u3JD+kLvheXVKCyvxv8ln8L/Jd/cjaLCzW8qBxUQfpcHPv5bLA+QtxCd3ogNKVlIPXcNZ6+UoqxKj4Iy+VJrifXmXyPxlyheLGcNeNJEAsO7B+JgTpFkj2frK3MB4NGYEMkmTaJDbXsgE6gZ/H11c6Yk69ydHNScM5/ZBa2DGn1DvJGWdb3Zj+WgVtl8SjuNWoWEbq3xywlp+ub+HW37/QKAgZ39sWbnOckeL5BQh4cxxuxdXJgf3ibujLxyg76bwl5MWJMi6pqig78rp/thzIIW3dcDUz46QI5/+vNDSOwxknfZ3SaglRZFFS0zcH5rH6sXgKMXS9DrlV8bjFUB0Kjqp/NV/+8xfF3VSOgejKQxPeyuHy4ur8bkdak4fvkGDALg5qhGmc6IhvLVqACoVTX1SpWcG+KpQR0wa0QX/tu0IrJNmrz22mvYvHkz0tPTodVqUVRUZPYYQRCQlJSEtWvXoqioCP3798e7776LTp06ydVMSUzpH4olP5+U5LFG9Qyy+ZW5QM2K5tpOrbnsYZJJ66DGqJ5B+OlYbrMfq1cbT+6kW4A99YFKZpRom0lUOy+7+Lt5LD5EskkTW9+ZA9Skm3R1VKO8uvnpJp0duQg8Y4xRxHbwhZuTBmWE9JoBrZSxg49yXpiTk4MZM2Zgx44dcHd3x+TJk7FkyRI4OEh3yV6hM+CQyMV+Pz/LqX4Ys6RBXQNEjZ0YBODZzw/h7Uf6yNswmaxevRrLli1DXl4eevXqhVWrViE6OrrZj/vF9HhEvbpVghZKS8Cd9Q9rrywKyo34Yv9FfLH/Yr37a6dPhFtiG+PtokH3YA+cu1qOq6VV0BsBRzXg4axBKxctCkqrUFZlhADAQQ1oNSpUVQswoOEJCAcADhpAZwCgqqm/66ACqhsIVgPwdXdAaaUeFfr692nVNbV2VQAqqo3QmXkhN0wECLBMii0KZwcV1j7WD/Ed/exiPMHWyDY6r9PpMGHCBMyYMYN8zNKlS7Fy5UqsWbMG+/btg5ubGxITE1FZqewVQloHNWJCvSR5rJUP9ZbkcZROo1YhsFXzC2c5alR2MckEACse6i1JZZOnh/AAfEuwpz5Q2aQ5MbGXv5vYMF+4SrCKydEOduYANd9lT9zdUZLHWvqXCD6RZowxAo1ahWXjI8zGuTlpFDMZbe680GAwYNSoUdDpdEhJScGGDRvw4YcfYsGCBZK2Y963R0TFj+4ZaDfXWowplUatwtsix4l+OpYHnUQ1ZFvSl19+iVmzZiEpKQmHDh1Cr169kJiYiCtXrjT7sX3ctfB310rQSssz/O9G+Q1frzBg99nruFxShWpjzQSDzggUlBuQda0CN6pqdm8IAKqNQFm1AD0a37GhB1BpqHnu2p0dDU2Y4H/tu1p654QJ/teGkiojiqvMT5hYIxWAIwuG4+SrIzGwsz9f51kp2c6AXn75ZTz//PPo2bMnKV4QBLz11luYN28e7r//fkREROCjjz7C5cuX8cMPP8jVTMl8PDWu2Y/ROcDNrv6Q+oV4N/sxBnZQxoVQS9CoVejbzPdMBSC+o+2v/lYCe+sDlWpYeOtmP4ZaZT9/Nxq1Ck8MCmv249wfGWw332czh3Rs9tSco1qF+6LaSNIexhizBwnhgWZjyqoMMEhV2KyZzJ0X/vrrrzhx4gQ++eQTREZG4t5778WiRYuwevVq6HTS5eH/UcSudY0aWPFQlGTPzRhrupERwYgVOQn84jfiJkmVYPny5Zg2bRqmTJmC8PBwrFmzBq6urli3bp0kj79/3jCbmThhyuSoBvbOGYqs10dxDRsboJhlI1lZWcjLy0NCQkLdzzw9PRETE4PU1FQLtoxG66AW/SV2u3+PDJeoNdbhgb7tmv0YKx/uK0FLrMczzVzt3r+Dr90MZFoba+8DlWpK/9BmP8Y/Bnewq7+bmUM6wbmZq0oXjzO/AthWaNQqzBjcvImmnm08JGoNY4zZhw0pWZLGWVpqaip69uyJ1q1vLvZITExESUkJjh8/LtnzGESs5n36nk52df7DmNJ9NDVGVPymo7mKmTim0Ol0OHjwYL3rYbVajYSEBEmvh/fPG4ZD84bB2YH7NyYNtQp4OLotMl8ZgdOLRyHQSxmpQVnzKWbSJC8vDwDqnSjW/r/2voZUVVWhpKSk3s1SxH6J3W5AZ3+JWmIdmrtyO8THGe7OspXlUaT4jn7NGsxcO7mfhK1hUrKFPlCJtA5qTOnfvsnHqwA8P6yLdA2yAhq1Ck/e3aHJx48ID7C7VB6zh3dt1vGJ3YMkagljjNmH/dnXJY2ztLy8vAbPAWvva0hTzgF9RKx6fXqofaQmZcxaaB3UGN2Tfs6oNwpIyyqUsUXSKigogMFgIF8PN+c62Mddi5OvjkTmKyPwSHRbiRI6M3vT6y53HFkwHOeWjMLicRFwkSDNNVMWUaMac+bMgUqlMnk7eVKaguhUS5YsgaenZ92tbdu2Lfr8t9I6qNEtqFWTjp15j32tZAZqBube/GuvJh2rArDtn0OkbZAV0KhVWP7XyCYdO7iTD3fizcR9oHVKGtMDbk387L81MdLu+magZqBE08R5j9WP2tcOQKCmb37jgabvrpFiRxRjjNkT6vd6U7//KSx9XtiUc8DNzwwiPfbrY8Pt8vyHMaVbIbK2yZUbtlsbU4rrYBetBq+Ni0DW66NwZMFw9G7jAe76WEPUAEL9XPHI/3aUZL8+Cj8+fTen4LJxopbpz549G48//rjJmLCwpqWoCAysyUubn5+PoKCbs+f5+fmIjIxs9Li5c+di1qxZdf8vKSmx6KDhdzP6o9uCZFHHqGF/K5lr/SWqDd7cego518V9mb/7aJTdnsiPjAhC+I5WOJF7g3yMgxr4UIK6O/aO+0DrdWDeMNF9c49gd9wfeZdMLVI2jVqFNx/ohWe+EpcLefn4nnbbN4/v2xavbT6BwoYqHZowbWCo3e3MYYyx5hoX1Qbfp18mxclFyvPCwMBApKWl1ftZfn5+3X0Naco5YKCXM1wc1aiobjxPl1ajwsRYnsxnTIk0ahXGRgbhh3RafaKAVtaTJsjPzw8ajaau76uVn5/fYD8o9XWwp6sjvp85EACg0xvxzu+n8d7vZxosYs7sgxpARFsPbJgSy5MjdkrUpIm/vz/8/eVJIRUaGorAwEBs27atboCwpKQE+/btw4wZMxo9zsnJCU5OTrK0qSlctBoM6uCNnWfpW8HfseMJAADY+eJQRL2cjMIKAyl+2QMRGNHDvlOZfCtycu7EK/fK2Br7wX2g9XLRajAsPABbT1whxbf1dsZPz9wtc6uU7b6oNli7+xyOXaZN0Pq4OmBcv+bXqrJmh5ISETpnM6jZowd39sO/R9lXPTPGGJNCfEc/uGk1KNM1fv3g5qRpdjpgU6Q8L4yLi8Nrr72GK1euICAgAACwdetWeHh4IDy84e+Jpp4DZi66F93m/9zgxImLoxqZi/i6gTElW/pAJGnSJMjTGdHNrLvbkrRaLfr06YNt27Zh7NixAACj0Yht27Zh5syZd8TLeR2sdVDjuYQueC7h5uLm0ko9nv3iEI5cKEJltR7l1QKsqGQMM0MNwMNZg4BWzhgX1QZ/GxjGC9uYfDVNcnJykJ6ejpycHBgMBqSnpyM9PR2lpaV1MV27dsX3338PAFCpVHjuuefw6quvYuPGjTh27Bgee+wxBAcH13WY1uKjafGg/m2teTTK7icAAOBQ0gj4EmZu/dwcMaEvr6KvHQCmeGIQr2K2BHvuA5Vq7WP9SH83Pe7ywK4Xh7ZAi5Rv0zOD0N7HxWycgxo4tCCxBVqkfFmvjwKlrqSfmyM+/FvzaqExxpi90qhVeMNMmt83JvRSzMI0c+eFw4cPR3h4OCZNmoQjR47gl19+wbx58/DUU0/JMiiYuehe7J0zFH5ujtBqVPBzc8TeOUN5woQxK6B1UOOJQeZ3gyWNsb40e7NmzcLatWuxYcMGZGZmYsaMGSgrK8OUKVMs3TS4Ozvgv49H48D84ch4ZSTOLRmFjIWJGNLFHx7OGq6LYmUcVIC/mwMm9muDzFdG4Nzro5C+cAR+nT0YT97TkcfQGACRO03EWLBgATZs2FD3/969a3Iv7tixA4MHDwYAnDp1CsXFxXUxL7zwAsrKyjB9+nQUFRVhwIABSE5OhrOz9WwprHVm8Sj0SEpGaVXDq59CfFyw7Z/3WN2XmJwOLhiOMat24dilhgt4+btrsX/esBZulXKtfawfpn203+TK+ScGhWLuSF7FbAn23gcq1drH+qFCZ8C4d/YgM+/OHRRTB4Rg/ujuFmiZcv3xwhAs2piB/6acb/D+Nl5a7J7DffOtziwZhX9/fxSf7rvQ4P3tfJyx8wWemGOMseYY0SMIax6NwsKNJ5BXcjPVb5CnM5LGhCtqYZq580KNRoOffvoJM2bMQFxcHNzc3DB58mS88sorsrUp0MsZB+YPl+3xGWPyqb3Gf29n1h33uTlp8MaEXorqA6kefPBBXL16FQsWLEBeXh4iIyORnJx8R3F4pXB3dsC6KdEN3ldaqccznx/CgfOFqNAZ0FhWRBUAjQrQ866VJlMBUKsAo4B6O/4dVEB8Rx+880g/uDvLNvzNbJhKEASb+tMsKSmBp6cniouL4eHhYenm4GpJFe57eyfybuigVgHxHXzwLv/BmlRaqcfMTw9iz7lrMAoC2ns545t/DISPu9bSTVOkCp0BL2/KwI6TV1CuMyDQwxnj+7TB3wbwdkJzlNZfSMEWX5NcdHojPk7NxvnCcrT3ccWkuBD+mzFBpzdi7R9nsD7lPKr0BnQKcMN6zu9qkk5vxNrfz2B9au175o71U2IU8Z7Zal9hq6+LMdY4g1FAWlYhrtyoRECrmnQ05ham2WJfYYuviTFmmk5vxIaULOzPvg43rQbjotogvqMf94E28JpKK/WY8fF+7D5beEfqX83//hUANF6hqoa3iwbdgz1w7mo5rpZWQW8EHNU1qahauWhRUFqFsiojBNRkD9BqVKiqFmAAGkw57ADAQQPoDABUgCDUTE5UNxCsBuDr7oDSSv0dtWG0asDZUQ0VgIpqI3QNvBBHFdChtTv6tvfBv0eFw0WruTOIMZHE9BU8acIYs1u22F/Y4mtijEnPVvsKW31djDFp2WJfYYuviTEmD1vsL2zxNTHGpCemr+AltYwxxhhjjDHGGGOMMcYYY+BJE8YYY4wxxhhjjDHGGGOMMQAyFoK3lNpsYyUlDRcTZ4yxWrX9hC1lKeQ+kDFGYYv9H8B9IGOMxhb7QO7/GGNU3AcyxuyVmP7P5iZNbty4AQBo27athVvCGLMWN27cgKenp6WbIQnuAxljYthS/wdwH8gYE8eW+kDu/xhjYnEfyBizV5T+z+YKwRuNRly+acoisQAACb5JREFUfBmtWrWCSqUyG19SUoK2bdviwoULdlMsil8zv2ZbJfY1C4KAGzduIDg4GGq1bWQrFNMH8meEX7Ot4tdsn/0fwH2gOfya+TXbKu4D+TqYgl8zv2ZbxX0gnwOaw6+ZX7OtkrP/s7mdJmq1Gm3atBF9nIeHh918oGrxa7YP/JpNs5WVNbWa0gfyZ8Q+8Gu2D/bc/wHcB1Lxa7YP/JpNs7U+kK+D6fg12wd+zaZxH8ifEXvBr9k+yNH/2caUMmOMMcYYY4wxxhhjjDHGWDPxpAljjDHGGGOMMcYYY4wxxhh40gROTk5ISkqCk5OTpZvSYvg12wd+zcwce3y/+DXbB37NjMIe3zN+zfaBXzOjsMf3jF+zfeDXzMyxx/eLX7N94NcsLZsrBM8YY4wxxhhjjDHGGGOMMdYUdr/ThDHGGGOMMcYYY4wxxhhjDOBJE8YYY4wxxhhjjDHGGGOMMQA8acIYY4wxxhhjjDHGGGOMMQaAJ00YY4wxxhhjjDHGGGOMMcYA2PGkyWuvvYb4+Hi4urrCy8urwZicnByMGjUKrq6uCAgIwL/+9S/o9fqWbajMQkJCoFKp6t1ef/11SzdLUqtXr0ZISAicnZ0RExODtLQ0SzdJNgsXLrzj99m1a1dLN0tSO3fuxJgxYxAcHAyVSoUffvih3v2CIGDBggUICgqCi4sLEhIScPr0acs0VsG4D6zBfaBt4T6Q+0Aq7gO5/7NF3AdyH0jB/V8N7gNtC/d/3P9RcR9Yg/tA28J9oDx9oN1Omuh0OkyYMAEzZsxo8H6DwYBRo0ZBp9MhJSUFGzZswIcffogFCxa0cEvl98orryA3N7fu9vTTT1u6SZL58ssvMWvWLCQlJeHQoUPo1asXEhMTceXKFUs3TTbdu3ev9/vcvXu3pZskqbKyMvTq1QurV69u8P6lS5di5cqVWLNmDfbt2wc3NzckJiaisrKyhVuqbNwH3sR9oG3hPpD7QAruA2tw/2d7uA/kPtAc7v9u4j7QtnD/x/0fBfeBN3EfaFu4D5ShDxTs3Pr16wVPT887fr5lyxZBrVYLeXl5dT979913BQ8PD6GqqqoFWyiv9u3bC2+++aalmyGb6Oho4amnnqr7v8FgEIKDg4UlS5ZYsFXySUpKEnr16mXpZrQYAML3339f93+j0SgEBgYKy5Ytq/tZUVGR4OTkJHz++ecWaKHycR/IfaAt4T6Q+0Cx7LkP5P7P9nAfyH2gGPbc/wkC94G2hvs/7v/E4j6Q+0Bbwn2gPH2g3e40MSc1NRU9e/ZE69at636WmJiIkpISHD9+3IItk97rr78OX19f9O7dG8uWLbOZbYc6nQ4HDx5EQkJC3c/UajUSEhKQmppqwZbJ6/Tp0wgODkZYWBgeeeQR5OTkWLpJLSYrKwt5eXn1fueenp6IiYmx6d+5HLgPtH7cB3IfCHAf2FT20gdy/2d7uA/kPrC57KX/A7gPtDXc/3H/JwXuA60f94HcBwLS9IEOUjTOFuXl5dXrJAHU/T8vL88STZLFM888g6ioKPj4+CAlJQVz585Fbm4uli9fbummNVtBQQEMBkODv8eTJ09aqFXyiomJwYcffoguXbogNzcXL7/8MgYOHIiMjAy0atXK0s2TXe3fZkO/c1v6u20J3AdyH2iNuA/kPlAq9tAHcv9ne7gP5D5QCvbQ/wHcB9oa7v+4/5MK94HcB1oj7gPl6QNtaqfJnDlz7ih8c/vNVv9AbiXmfZg1axYGDx6MiIgIPPnkk3jjjTewatUqVFVVWfhVsKa49957MWHCBERERCAxMRFbtmxBUVERvvrqK0s3jbUA7gNrcB9ov7gPtG/cB3L/Z++4D7Rf3P/V4D7QfnH/Z9+4D6zBfaD94j5QHja102T27Nl4/PHHTcaEhYWRHiswMBBpaWn1fpafn193n5I1532IiYmBXq9HdnY2unTpIkPrWo6fnx80Gk3d761Wfn6+4n+HUvHy8kLnzp1x5swZSzelRdT+XvPz8xEUFFT38/z8fERGRlqoVS2H+8Aa3AfW4D6Q+8Ba3AfeZOt9IPd/Nbj/q8F9IOr+b+t9IPd/NbgPrMF9IPd/teyh/wO4D6zFfWAN7gO5D6zV3D7QpiZN/P394e/vL8ljxcXF4bXXXsOVK1cQEBAAANi6dSs8PDwQHh4uyXPIpTnvQ3p6OtRqdd1rtmZarRZ9+vTBtm3bMHbsWACA0WjEtm3bMHPmTMs2roWUlpbi7NmzmDRpkqWb0iJCQ0MRGBiIbdu21XWMJSUl2LdvH2bMmGHZxrUA7gNrcB9Yg/tA7gMB7gObylr7QO7/anD/V4P7QPvpA7n/q8F9YA3uA7n/A+yn/wO4D6zFfWAN7gO5DwSk6QNtatJEjJycHBQWFiInJwcGgwHp6ekAgI4dO8Ld3R3Dhw9HeHg4Jk2ahKVLlyIvLw/z5s3DU089BScnJ8s2XiKpqanYt28f7rnnHrRq1Qqpqal4/vnn8eijj8Lb29vSzZPErFmzMHnyZPTt2xfR0dF46623UFZWhilTpli6abL45z//iTFjxqB9+/a4fPkykpKSoNFo8NBDD1m6aZIpLS2tN1uelZWF9PR0+Pj4oF27dnjuuefw6quvolOnTggNDcX8+fMRHBxc92XJanAfyH2gLeI+kPtAKnvvA7n/s03cB3IfSGHv/R/AfaAt4v6P+z8q7gO5D7RF3AfK1AcKdmry5MkCgDtuO3bsqIvJzs4W7r33XsHFxUXw8/MTZs+eLVRXV1uu0RI7ePCgEBMTI3h6egrOzs5Ct27dhMWLFwuVlZWWbpqkVq1aJbRr107QarVCdHS0sHfvXks3STYPPvigEBQUJGi1WuGuu+4SHnzwQeHMmTOWbpakduzY0eDf7uTJkwVBEASj0SjMnz9faN26teDk5CQMHTpUOHXqlGUbrUDcB3IfaIu4D+Q+kMre+0Du/2wT94HcB1LYe/8nCNwH2iLu/7j/o+I+kPtAW8R9oDx9oEoQBKHpUy6MMcYYY4wxxhhjjDHGGGO2QW3pBjDGGGOMMcYYY4wxxhhjjCkBT5owxhhjjDHGGGOMMcYYY4yBJ00YY4wxxhhjjDHGGGOMMcYA8KQJY4wxxhhjjDHGGGOMMcYYAJ40YYwxxhhjjDHGGGOMMcYYA8CTJowxxhhjjDHGGGOMMcYYYwB40oQxxhhjjDHGGGOMMcYYYwwAT5owxhhjjDHGGGOMMcYYY4wB4EkTxhhjjDHGGGOMMcYYY4wxADxpwhhjjDHGGGOMMcYYY4wxBoAnTRhjjDHGGGOMMcYYY4wxxgDwpAljjDHGGGOMMcYYY4wxxhgA4P8B3F5hD/wTTf8AAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1244,12 +887,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  2\n"
+      "Question  11\n"
      ]
     },
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",
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       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1261,12 +904,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  3\n"
+      "Question  12\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1278,12 +921,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  4\n"
+      "Question  13\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1295,12 +938,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  5\n"
+      "Question  14\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1312,12 +955,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  6\n"
+      "Question  15\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1329,12 +972,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  7\n"
+      "Question  16\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1346,12 +989,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  8\n"
+      "Question  17\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1363,12 +1006,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  9\n"
+      "Question  18\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1380,12 +1023,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  10\n"
+      "Question  19\n"
      ]
     },
     {
      "data": {
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Ahw7Af/+bPakybRpw/372/2uiUABhYUDZssDMmUDTpsCsWcBvv72JycwEjh8HNH1exMYCTZoAFy8CI0Zkt23eHIiJeROze3f2MR48ACZNyq5tEheXXbsld85dMa81Li67rz4+b7bJZMCyZdm1UnJ/Fk2cCFy4AERHZ0/q5FanTnbtlrfVrZs9WfO/cYmIpBs8eDAWLVqELl26YOHChfjyyy/h4OBQ4ASBUqlEhw4d8N///hcRERGYNm0a7t+/j4h8xi6FQoGwsDCULVsWM2fORNOmTTFr1iz8lmvsyu9ct1q1apg6dSpWrlyJLVu2AADS0tIQGRkJf39/TJkypcDXJ5fL1SZBNJ1f5T4Hyy9GjBcvXuDFixdq553G8M8//6BEiRIoVaoUPDw88O233yIzM1NjrDHOz4lMwaeffopWrVoBgFoqmBzz5s1DUFAQpkyZgu+//x7W1tbo1q2bxgtohw4dwmeffYbu3btjxowZeP36Nbp06ZInjTQAfPTRR3j+/DmmT5+Ojz76CMuXL8+TmiwuLi7PWOjm5oZFixbhwIEDqhz/SqUSkZGRKFWqFBYuXKgWby7fhbZv356n3srz588xZswYjBs3Dh4eHpL2l/t7tNjv1PPmzYOrqysiIiJUF6V//fVX7Nq1C/Pnz4eXl5ekPhDpk9TrjW/LufaU++e3/060uTaV49WrV3j06BFu3LiBFStWIDo6GiEhIZImXfUlKSkp33O0SpUqwdnZGaVKlULv3r2RnJys9rwxrvk9ePAArVu3xo0bN/D1119j/vz56NWrl8YUY5rw88YAjLpuxoz1799f8PT0FB49eqS2vXv37oKzs7NqCawgCMLYsWMFuVwuHDx4ULWEf+7cuWrtIiIiBADCsGHDVNuUSqUQHh4u2NraqpamXb16VQAgzJ8/X3Rf09LShMqVKws1atQQXr9+LYSHhwtOTk7CzZs3C2ynzTLgwtKCCYIgDBo0SHBwcND5sQsSGhoqODk5CU+fPlVti4qKEqysrDTGu7q6Ct27dxcE4U3qqSlTpuSJW7BggQBAuHz5sqR9anL27FnB399fGDZsmGqJ4B9//CH4+vrm+X15W+7ftxyffvqp4OjoKLx+/Vq1rWnTpgIAYdasWapt6enpQmBgoODm5iZkZGQIgiAIHTt2LDSVxPjx4wUAGpf8nTt3TrC1tRUGDBggPH36VHjnnXeEevXqCZmZmQXuU2xasBxi0oIJgiC8++67Qtu2bUXvl6jInJ0FISoq/+cjIgQh9+/6xo3ZKa9y/60rFILQokX29uho9baAILw9JgUFCULdum9+vno1O+7tz4usLEHw9c0+fq4xURAEQcid+iYwUBDc3AThfyloBEEQhDNnBEEuF4Q+fcS/VkEQhPfeU+9bbr/+mt3PVasEIT5eEKysBGHkSM2xgwZlpzl727172fv48ceC+0FE+XJ2dhaiCvlbjoiIUPuc3rhxY57zWoVCIbRo0UIAIETnGrtyznXfPp8KCgoS6uYaHwo611UoFMJ7770nuLu7C48ePRKioqIEa2tr4fjx4wX2e9++fYKvr68wZcoUVVqwhQsXqqUFa9WqldCqVSvh+vXramnBevbsWWBaME2mTp2q9zRbhaUF69evnzBp0iRh48aNwn/+8x/hgw8+EAAIH330kcZ4MefnRJaqoLRVb3/PysjIEGrWrCm0aNFCbTsAwdbWVi2lypkzZ/KMZzmpZ/r166fWvnPnzkLZsmVVP2dmZgoymUz44osvNParR48egqOjo/DPP/8IP/30kwBA2Lx5s8ZYbb8L6eo7eWFpwa5fv65xPPvyyy8FX19f1fdZsWnB5s6dK1SsWFH4448/VGnBhg0bJiot2M6dOwUAwnfffSdcv35dKFmypNCpU6fCXySRkYi53vi2O3fuCPXq1RN69OihlhbM399fGD16tCAIgjB69Og8acF69OghOi3Y9OnTBQCqR8uWLYVbt25p9Rq1TQumycGDBwWZTCZ8++23atvnzp0rDB06VFi9erWwYcMGYcSIEYK1tbVQpUoVISUlRRVnjGt+f/75pwCg0PPdt6+NmdPnjbmz1vfkjSUSBAEbN27ERx99BEEQ8OjRI9VzYWFhWLt2LU6dOoXGjRsDyF6KFRMTg4iICLx48QJNmzbF8OHDNe576NChqv/PWVq8bds27N69G927d1fd9VJQ4fi3OTo6Yvny5WjSpAmaNGmCY8eOYenSpahQoYJaXEpKitrdbCkpKQCyVzrkXkpnb28vKiVZfsqUKYNXr17h5cuXcHR01Puxv//+e+zevRsLFy5E6dKlVdtfvXoFW1tbjW3s7e1VSytz/mtnZ6cxLneM2H1qUqFCBURHR6Nhw4bYv38/AKBr164IDQ0ttHhs7tn/58+fIz09He+//z5+/fVXXL58WW21iLW1NT799FPVz7a2tvj0008xZMgQnDx5Eg0bNkTp0qVx584dHD9+XC0VWm6PHz+GtbW1xn+PmjVrYvLkyRg7dizOnj2LR48eYdeuXbC2Vh9ycv/tAMDLly+hVCrzbC9VqpTG91+sMmXK5NknkV6VLp2dduvePUDMXW47dgA2NsDAgW+2yeVAVFR2ai5N3l55+P77QK47LZFzl+TbnxenTwOJicCcOdn9zC3nrpn794GEBOCrr7JTceWoXTs7Bdf27W+2iXmtjx8D+S0fHzQI2LQJGDYMKFcuOx3Y999rji1TBnj1Cnj5Evjf54faa+TfOZHWSpcujaNHj+LevXui787dsWMHbGxsMDDX2CWXyxEVFYW9+Yxdb6+afv/999XuEi/oXFcul2P58uUICAhA27ZtceLECYwfP17j6uLcfH19sW3bNlSrVg3Lly+HTCbDkCFD0LFjRzx+/Fi1+rdNmzaqNgEBAThy5Aj27t0r6U7NgwcPYvLkyfjoo4/QokUL0e10benSpWo/f/LJJxg0aBCWLFmCzz//HA0bNlR7XtP5ORGpf896+vQpFAoF3n//ffz3v//NExsaGqqWUqV27dpwcnLC9evX88RqGgv//PNPpKamwsnJCU+ePIEgCPl+7//ll1+wf/9+dO3aFf/88w8++eQTdOzYUWOs2O9Chroe8LZt27bB2dkZ7733nmrbP//8g3nz5uG///2v5O+BDRs2xKlTp1CmTBnExMSgZMmS+Pnnn3H06FF4e3sX2LZ169b49NNPMWXKFGzYsAH29vb49ddftXpdRIYg5XpjDnd3d0yfPh2hoaG48b+MBM2aNcPp06dV2UrCw8MxefJk1Rjo6uqKNWvWYM+ePXB3dy+0Xz169EC9evXw8OFDxMTEIDk5ucDrYTkyMzNVY0/ubenp6XnGMRcXF9ElCoDsFSA9e/aEr68vvvrqK7XnRowYofZzly5d0KBBA/Tq1QsLFy5UZYUxxjW/nOuYMTExCAgIgI2NTWEvVY0pft5YGqYF08LDhw/x7Nkz/Pbbb3B1dVV79O3bF0D2H20OW1tbLFu2DImJiXj+/Dmio6PzTUVQqVIltW3vvvsuAKgGvBzCW/n/Xrx4gaSkJNXj4cOHas83btwYQ4YMwbFjxxAWFpYnNx8AdOzYUe21dOrUCUB2moDc23NPAGkjp++53wN9HXvdunUYP348+vfvjyFDhqg95+DgkG++x9evX6s+RHL+m56erjEud4zYfWri7Oyc54sukD2QBgcH59sOAC5cuIDOnTvD2dkZTk5OcHV1Re/evQEgzweTl5cXSryVauft37MxY8agZMmSaNCgAapUqYKoqCjJS/tGjx6NgIAAHDt2DBMnTkT16tXzxLz99/PTTz/h9u3bebZr+vIihSAIWqf2INLKjBnA+fOAtzfQoEF2Wi0NX6xVbt4EPD3VJwwAoHJlzfH29tm1SnIrUwZ4+jRv7Nv5Yq9dy/5vzZoF9wcAqlbN+1y1atmTGDn1UMS+1nzy1gIAli7NnjD599/s+jL5jZU5+3j77zm/7UQk2owZM3D+/Hl4e3ujQYMGmDRpksYLgrndvHkTnp6eeS7GV85n7LK3t4frW2NXmTJl8FTD2PX2uW4OPz8/TJo0CcePH0eNGjXw7bffFthHAPDx8UG1atXybPfy8lLl3889sZLDyspKlS5IjMuXL6Nz586oWbMmfv/990LjCzt/17UvvvgCALB79+48z2k6PyeyJBkZGWp/b0lJSfnWI8gtJiYGDRs2hL29PVxcXODq6opFixbl+Y4FIM/Ni0D+Y9zbsTkXtd6OzW8sdHFxwc8//4yzZ8/C2dkZP//8c76vQex3IUNdD3jbtm3b0Lp1a7Ub8UaMGIFGjRqhS5cukvcXHBys8SJhcHCw2s2W+Zk5cyZcXFyQkJCAn3/+GW5ubpL7QGQoYq835mZtbZ2ndi6QfZ7WtGlTAEDTpk01Xr9q2bJlnptmNfHx8UFoaCh69OiB1atXo1KlSggNDS10guXw4cN5rgfFxcVh7dq1ebbfunWr0H7kSEtLQ/v27fH8+XP89ddfoiaIe/bsCQ8PD7XzJmNc82vatCm6dOmCyZMno1y5cujYsSOio6M1XqPUxBQ/bywNV65oQalUAgB69+6db07p2rVrq/28c+dOANl/bP/++y98fX21OnbZsmUB5P0jmDlzplrOPB8fH7UJmfT0dNXM6LVr1zTelTZr1iy1/Z45cwZffvklVq1apTYzXdRco0+fPoWjo6PaoKOPY8fGxqJPnz4IDw/XWLDd09MTCoUCDx48UDthysjIwOPHj1XHcnFxgZ2dHe7fv59nHznbcmLF7rMwzZo1Q7NmzUTFPnv2DE2bNoWTkxOmTJkCPz8/2Nvb49SpUxgzZozq91WKatWq4cqVK4iJicGOHTuwceNGLFy4EBMmTFD9npUtWxZZWVl4/vw5SpUqlWcf169fx7///gsAOHfunMbjvF3o6j//+Q927dqFVatWqW2vUaOG5NeQ29OnT1GlSpUi7YNIko8+yl5J8uefwK5dwE8/AT/+mL1Co23bou+/gPy5Kv/7vNA44aJLYl5r2bIF92P/fiDn5PDcOSAkRHPc06fZE1Bvn7Tm7NvI9Q2IzNlHH32kupNt165d+Omnn/Djjz9i06ZNaKuLcQsoMPd3jvzOdXPbtWsXAODevXt4/PixpFz8kZGRhca8fVOTGLdv30br1q3h7OyM7du3azw3elth5++6lnPH9pMnT/I8p+n8nMiSxMXFoXnz5mrbEhMT8xRQz+3vv//GBx98gCZNmmDhwoXw9PSEjY0NoqOjsWbNmjzx+Y1xmi5YFRbr4uICmUxW4FiYc43h6dOnuHPnTr4TB2K/CxnqekBuL1++xP79+7Fo0SLVtr1792LHjh3YtGmT2piYlZWFV69e4caNG3BxcYGTk1Oh+1++fLnkPp0+fVp1s+y5c+fQo0cPyfsgMqSiXG+sWLGi6lphfgp7XoyuXbtiyZIlOHjwIMLCwvKNCwgIyHOd6IsvvoCHhwdGjx6ttl3s+V9GRgY+/PBDnD17Fjt37kTNgm4yfIu3t7faeZMxrvnJZDJs2LAB8fHx2Lp1K3bu3Il+/fph1qxZiI+PL3SiyBQ/bywNJ1e04OrqilKlSkGhUGic7X3b2bNnMWXKFPTt2xcJCQkYMGAAzp07B2dnZ7U4pVKJ69evq1YRANnLYQGoTvoqVKgABwcHJCYmqrXt06eP2jLat78YTZw4EZcuXcLMmTMxZswYfP3113lmG+vWrav2c85sdOPGjQs86ZQqMTExz92Duj720aNH0blzZ9SrVw/r16/XOLMeGBgIADhx4oRa8bwTJ05AqVSqnpfL5ahVqxZOnDih8TiVKlVSfYEWu09d2r9/Px4/foxNmzahSZMmqu1v/47kuHfvHtLS0tRWr7z9ewYAJUqUwMcff4yPP/5Y9WE0bdo0jB07Fvb29vD391cd5+3JxJwiV05OThg5ciS+//57dO3aFR9++KFa3Nt/P4cOHYK9vb2ovyuxsrKycPv2bXzwwQc62yeRKJ6ewGefZT8ePMguxj5tmubJFR8fYN++vOmurl7V/vgVKmRPQrw9FuSkqzh/Hsjvby2n8Lym5cmXL2dPYuReAVfYa/X3BzZu1Hys+/ezU4K1bg3Y2gJffgmEhb3pQ26JidkrZzRtBzQ/R0SieXp64rPPPsNnn32GBw8eoE6dOpg2bVq+kys+Pj7Yt29fnpt2rhZh7MrvXDfH4sWLERsbi2nTpmH69On49NNP8ddff2l9PF14/PgxWrdujfT0dOzZsweenp6i2hV2/q5rOSuR3l49BGg+PyeyJJou2OVcmMvvLtuNGzfC3t4eO3fuVEtNFR0drb+O/o+1tTX8/PzyHQt37NiB33//HV999RVWr16NiIgIHD16NM/3XinfhQx1PSC3vXv3Ij09Xe1zJudu9Le/OwLA3bt34evrizlz5mDkyJE6709aWhr69u2L6tWro1GjRpgxYwY6d+6cb6psImMTe73R2HJWrGha9ZdbmTJl8lwPKlOmDDw9PbW6TqRUKtGnTx/s2bMH69evV63MEUMQBNy4cQNBQUGqbca45pejYcOGaNiwIaZNm4Y1a9agV69eWLt2LQYMGFCk/Rrj88bSMC2YFqysrNClSxds3LgR58+fz/N87iX9mZmZiIyMhJeXF+bNm4fly5cjOTkZn3/+ucZ9//LLL6r/FwQBv/zyC2xsbNCyZUsAgI2NDerVq5fnQn/OErucR069FyB7AmDmzJkYOXIkvvjiC4wePRq//PILDhw4UKT3QVunTp1Co0aN9Lb/S5cuITw8HBUrVkRMTEy+X1RbtGgBFxcXtbtkAGDRokVwdHREeHi4alvXrl1x/Phxtff9ypUr2Lt3L7p166bVPnUlZxY69x1RGRkZWLhwocb4rKwstbyxGRkZ+PXXX+Hq6qo6oc7Jd57D1tYW1atXhyAIqjy8If+7s1zTpNPs2bMRFxeH3377DVOnTkWjRo0wZMgQo+RevHjxIl6/fq3X3zkiNQoF8PZJo5tbdj2S/JbuhoUBmZnAkiVvtimVwIIF2vfDxgaoVw94+2+0Th3A1xeYOxd49kz9uZxxxNMTCAwEVqxQjzl/Pnt1Ss6JpNjXGhKSvbpEU4qhgQOzX+vSpcBvvwHW1kD//prTiJ06BWj6Wz55MjslWH4rXoioQAqFIs+XXTc3N3h5eRWYciAsLAyZmZlYkmvsUiqVWFCEsSu/c10gewJg9OjR6NKlC8aNG4eZM2diy5Yt+M9//qP18YoqLS0N7dq1w927d7F9+3ZJd+sVdP5eFKmpqXn+3QRBwHfffQcAGu8Y1ff5OZGx5Vywy/3IqZ+Zc9PZs7fOi6ysrCCTydTSh924cQObN282SJ9DQkI0joXPnj3DgAED0KBBA3z//ff4/fffcerUKXyvoWadqX8X2r59O+rVq6e2MqZFixb4888/8zxcXV1Rr149/Pnnn+jQoYNe+jNmzBjcunULK1aswOzZs1GxYkVERESITr9DZEhSrzcaQn4pTpcuXQqZTIY6deoYtD/Dhg3DunXrsHDhQo0Ttjk09XvRokV4+PChWupYY1zze/r0aZ4VkDmTOLoam4rD540+ceWKln744Qfs27cPwcHBGDhwIKpXr44nT57g1KlT2L17t2rZ2HfffYeEhATs2bMHpUqVQu3atTFhwgSMHz8eXbt2VZvptLe3x44dOxAREYHg4GD83//9H7Zt24Zx48ap3WHWsWNHfPPNN6riQwV5/fo1IiIiUKVKFUybNg0AMHnyZGzduhV9+/bFuXPn8tTfkOrgwYM4ePAggOwBKS0tTfXlrUmTJmqrKU6ePIknT57kW/yoqJ4/f46wsDA8ffoUo0ePxrZt29Se9/PzU00KODg4YOrUqYiKikK3bt0QFhaGv//+G6tWrcK0adPgkquI82effYYlS5YgPDwcX375JWxsbDB79my4u7ur8ldL3aeuNGrUCGXKlEFERASGDx8OmUyGlStX5psv0cvLCz/++CNu3LiBd999F+vWrUNCQgJ+++03VWGs1q1bw8PDA40bN4a7uzsuXbqEX375BeHh4apVOpUqVULNmjWxe/dutRo+ly5dwrfffovIyEjVSe/y5csRGBiIzz77DOvXry/ya165ciVu3ryJly9fAsj+Hcz5nfvkk0/gk+uO99jYWDg6OkrKmU5UJM+fA+XLA127AgEBQMmSwO7dwPHjwKxZmtt06pRdr+SLL7JXq/j7A1u2ADlLkLXNW9qxI/DNN0BqKpDzeSGXA4sWAR06ZE+g9O2bPZly+TJw4QLwvyW/+Omn7JUnISHZkx2vXgHz5wPOztl1VaS81vDw7EmT3buzC9jniI4Gtm3LrrNSvnz2tvnzgd69s/v42WdvYk+ezH4/NH1+xMYCjRu/SYVGRJI8f/4c5cuXR9euXREQEICSJUti9+7dOH78OGblN24B6NSpExo0aIAvvvgCV69ehb+/P7Zs2aI6D9Y257Kmc11BENCvXz84ODiovtB++umn2LhxI0aMGIHQ0FCdpqoRq1evXjh27Bj69euHS5cu4dKlS6rnSpYsqapZoCs55zsXLlwAkH1OdOjQIQDA+PHjAWRPlPTo0QM9evRA5cqV8erVK/z55584fPgwBg0alOfChr7Pz4lMXc4NZsOHD0dYWBisrKzQvXt3hIeHY/bs2WjTpg169uyJBw8eYMGCBahcuTLOnj2r93517NgRK1euxD///KOW3WLEiBF4/Pgxdu/eDSsrK7Rp0wYDBgzAd999h44dOyIgIEAVa6zvQjdv3sTKlSsBvLkZL2f88vHxwSeffAIge3Ilp25tjgoVKmisXzNy5Ei4u7vrfFzNsXfvXixcuBATJ05UjZPR0dFo1qwZvv32W8yYMUMvxyXSlpTrjYYybdo0HD58GG3atEGFChXw5MkTbNy4EcePH8ewYcPyrcunD3PnzsXChQsREhICR0fHPOnnO3furLoe6uPjg48//hi1atWCvb09Dh06hLVr1yIwMBCffvqpqo0xrvmtWLECCxcuROfOneHn54fnz59jyZIlcHJy0tm/sTl/3pgEgbSWnJwsREVFCd7e3oKNjY3g4eEhtGzZUvjtt98EQRCEkydPCtbW1sKwYcPU2mVlZQn169cXvLy8hKdPnwqCIAgRERFCiRIlhGvXrgmtW7cWHB0dBXd3d2HixImCQqHIc1xra2th5cqVhfbx888/F6ysrISjR4+qbT9x4oRgbW0tDBkyJN+2+/btEwAIiYmJBR5j4sSJAgCNj4kTJ6rFjhkzRqhQoYKgVCoL3KfYY78tMTEx374AECIiIvK0+e2334SqVasKtra2gp+fnzBnzhyN/bt9+7bQtWtXwcnJSShZsqTQvn174d9//9XYD7H71JXDhw8LDRs2FBwcHAQvLy/hq6++Enbu3CkAEPbt26eKa9q0qVCjRg3hxIkTQkhIiGBvby/4+PgIv/zyi9r+fv31V6FJkyZC2bJlBTs7O8HPz08YPXq0kJKSohY3e/ZsoWTJksLLly8FQXjzu12+fHnh2bNnarHz5s0TAAjr1q3L93VMnDhR8PHxKfT1Nm3aNN9/49yvVxAEITg4WOjdu3eh+yTSmfR0QRg9WhACAgShVClBKFEi+/8XLnwTExEhCG//rj98KAg9e2a3cXYWhMhIQTh8WBAAQVi7Vr1tiRJ5jztxYnZsbsnJgmBtLQiaPi8OHRKEVq3e9LF2bUGYP189ZvduQWjcWBAcHATByUkQOnQQhIsXpb3WHB98IAgtW775+fbt7NfZoUPe2M6ds/d1/fqbbWPGCEKFCoLw9lj67Jkg2NoKwu+/590PEYmSnp4ujB49WggICBBKlSollChRQggICBAWvvW3HBERkedz+uHDh0LPnj2FUqVKCc7OzkJkZKRw+PBhAYCwNtfYlXOu+7ac88jcNJ3r5pxHbNy4US321q1bgpOTk9CuXTttX36R+Pj45HtOIuacRqqCznNzXL9+XejWrZtQsWJFwd7eXnB0dBTq1q0rLF68WOP5qNjzcyJLlZWVJQwbNkxwdXUVZDKZ2t/T0qVLhSpVqgh2dnaCv7+/EB0drXHcAiBERUXl2bePj4/ad9Cctg8fPlSLi46OzvP9Nz09XShXrpwwdepU1ba//vpLACDMmjVLrX1qaqrg4+MjBAQECBkZGartRfkupO138txtNT2aNm0qCIIgnD9/XgAgHDt2TNQ+fXx8hPDwcMl9ESPn/atTp46QmZmp9tznn38uyOVy4ciRI3o5NpE2pFxvNKRdu3YJ7du3F7y8vAQbGxuhVKlSQuPGjYXo6GitzzOaNm2q8VpeYSIiIgo8b8o9tg0YMECoXr26UKpUKcHGxkaoXLmyMGbMGCE1NVXjvg15ze/UqVNCjx49hAoVKgh2dnaCm5ub0L59e+HEiRNqcW9fgzWnzxtzJxOEfG5vJ4OKjIzEhg0b8OLFC1Hx/fv3xz///IO///5bzz3TnfT0dFSsWBFff/01RowYYezukA6kpKSgUqVKmDFjBvr372/s7uSRkJCAOnXq4NSpU3rNfUmkN5s3A507A4cOZa/M0Eb//sA//wDG/rz4+2+gWbPsFTJSi9ylpwMVKwJffw28/fkxdy4wYwZw7VreQvdEZBSbN29G586dcejQIa1TXZnjua454vk5kWmbOnUqoqOj8e+//+ZblDg/pv5daMaMGZg9ezbu37+v9UpHIiLSDUv+vNE31lwxUxMnTsTx48dx+PBhY3dFtOjoaNjY2GDw4MHG7grpiLOzM7766iv89NNPUCqVxu5OHj/88AO6du1aLAd3MkP/K/KnolBkp8hycsquk6KtiROz03QZ+/Pi/fezi9Zrk1IhOjq7hszbnx+ZmcDs2cD48ZxYITKSV2+NXQqFAvPnz4eTk1OR8mqb47muOeL5OZFp+/zzz/HixQusXbtWcltT/y5UsWJFzJkzhxMrREQmwJI/b/SNK1dMhNSVK0REZGEGDMieYAkJyV6psWkTEBcHfP89MHassXtHRKTRgAED8OrVK4SEhCA9PR2bNm1CXFwcvv/+e4zl2EVERERERBaMBe2JiIhMQYsW2UXgY2KA16+BypWzV64MHWrsnhER5atFixaYNWsWYmJi8Pr1a1SuXBnz58/HUI5dRERERERk4bhyhYiIiIiIiIiIiIiISALWXCEiIiIiIiIiIiIiIpKAkytEREREREREREREREQSFOuaK0qlEvfu3UOpUqUgk8mM3R0iMlGCIOD58+fw8vKCXG4Zc9Ic/4hILI6BRFRcWeL4B3AMJCJxLHEM5PhHRGKJHQOL9eTKvXv34O3tbexuEJGZuH37NsqXL2/sbugExz8ikopjIBEVV5Y0/gEcA4lIGksaAzn+EZFUhY2BkiZXFAoFJk2ahFWrViEpKQleXl6IjIzE+PHjVTO+giBg4sSJWLJkCZ49e4bGjRtj0aJFqFKlimo/T548wbBhw7B161bI5XJ06dIF8+bNQ8mSJVUxZ8+eRVRUFI4fPw5XV1cMGzYMX331lVp//vjjD3z77be4ceMGqlSpgh9//BHt2rUT/XpKlSoFIPtNcnJykvJWEFExkpqaCm9vb9WYYQk4/hGRWBwDiai4ssTxD+AYSETiWOIYyPGPiMQSOwZKmlz58ccfsWjRIqxYsQI1atTAiRMn0LdvXzg7O2P48OEAgBkzZuDnn3/GihUr4Ovri2+//RZhYWG4ePEi7O3tAQC9evXC/fv3ERsbi8zMTPTt2xeDBg3CmjVrVJ1v3bo1QkNDsXjxYpw7dw79+vVD6dKlMWjQIABAXFwcevTogenTp6N9+/ZYs2YNOnXqhFOnTqFmzZqiXk/OhJCTkxMHVSIqlCUtG+b4R0RScQwkouLKksY/gGMgEUljSWMgxz8ikqqwMVAmCIIgdmft27eHu7s7li5dqtrWpUsXODg4YNWqVRAEAV5eXvjiiy/w5ZdfAgBSUlLg7u6O5cuXo3v37rh06RKqV6+O48ePo169egCAHTt2oF27drhz5w68vLywaNEifPPNN0hKSoKtrS0A4Ouvv8bmzZtx+fJlAMDHH3+MtLQ0xMTEqPrSsGFDBAYGYvHixaJeT2pqKpydnZGSksJBlYjyZYljhSW+JiLSD0scLyzxNRGR7lnqWGGpr4uIdMsSxwpLfE1EpB9ixwtJFakaNWqEPXv24J9//gEAnDlzBocOHULbtm0BAImJiUhKSkJoaKiqjbOzM4KDg3HkyBEAwJEjR1C6dGnVxAoAhIaGQi6X4+jRo6qYJk2aqCZWACAsLAxXrlzB06dPVTG5j5MTk3McTdLT05Gamqr2ICIiIiIiIiIiIiIikkJSWrCvv/4aqamp8Pf3h5WVFRQKBaZNm4ZevXoBAJKSkgAA7u7uau3c3d1VzyUlJcHNzU29E9bWcHFxUYvx9fXNs4+c58qUKYOkpKQCj6PJ9OnTMXnyZCkvmYiIiIiIiIiIiIiISI2kyZX169dj9erVWLNmDWrUqIGEhASMHDkSXl5eiIiI0FcfdWbs2LEYNWqU6uecwjRiKJQCjiU+wYPnr+FWyh4NfF1gJbecvJNEZDwHDx7ETz/9hJMnT+L+/fv4888/0alTpwLb7N+/H6NGjcKFCxfg7e2N8ePHIzIy0iD91acXr7MwbPVxxF19gnTRSSv1TwbAzlqOSq4l8GVrfzSt6moSnwEZWUos+fsq/nP4OpJfKIzdHTU2chncnOzQK9gHA96vBFtrSYtl9SLn/VoZl4jk51kwoV8xWMkAJwcbhNXwwMQONeBga2XsLhmMqY+Bpvx7Y2pMday0dKb62WmKTHGslToG7t+/H82bN8+z/f79+/Dw8NBjT4nIXCmUAuL+fYSNp+/gZYYC9Su6IKJRRZM4Pyci0rcXr7Pw+brTuPX0FSqUccCcj4NQ0l7SlEiBJO1p9OjR+Prrr9G9e3cAQK1atXDz5k1Mnz4dERERqpO55ORkeHp6qtolJycjMDAQAODh4YEHDx6o7TcrKwtPnjxRtffw8EBycrJaTM7PhcUUdEJpZ2cHOzs7KS8ZALDj/H1M3noR91Neq7Z5OttjYofqaFPTs4CWRESFS0tLQ0BAAPr164cPP/yw0PjExESEh4dj8ODBWL16Nfbs2YMBAwbA09MTYWFhBuixfnzwy984e8c00zUKAF5nKXHx/nP0W3EcNlYyzO8RZNTPgOnbL+LXg4lGO35hMpUC7j57jRk7r2DGziv4tIkvxrarbrT+mPr7pRCApy8zsfb4baw9fhutqrthSZ/6xu6WQZjyGGjqvzemxhTHSktnyp+dpsgUx1qpY2COK1euqOX/fjs7BBERkH09a9T6M3iZ8eZGrF0Xk/H9/13CoPeNe35ORKRPCqWAljP348aTl6ptV5Keo+aknahd3glbhr6vk+NImqZ++fIl5HL1JlZWVlAqlQAAX19feHh4YM+eParnU1NTcfToUYSEhAAAQkJC8OzZM5w8eVIVs3fvXiiVSgQHB6tiDh48iMzMTFVMbGwsqlatijJlyqhich8nJybnOLqy4/x9DFl1Sm1iBQCSUl5jyKpT2HH+vk6PR0TFT9u2bfHdd9+hc+fOouIXL14MX19fzJo1C9WqVcPQoUPRtWtXzJkzR8891R9zuziUqRAw2IifAeZ4wffXg4mYvv2iUY5tju9X7MUHGPif48buhkGY6hhojr83psbYY6WlM7fPTlNkCmOt1DEwh5ubGzw8PFSPt7+nExHtOH8fg1edUptYySEIxj0/JyLSl1cZCvRYEge/cdvVJlZyO3snFR/88rdOjifpDKxDhw6YNm0atm3bhhs3buDPP//E7NmzVSeCMpkMI0eOxHfffYctW7bg3Llz6NOnD7y8vFRLm6tVq4Y2bdpg4MCBOHbsGA4fPoyhQ4eie/fu8PLyAgD07NkTtra26N+/Py5cuIB169Zh3rx5aim9RowYgR07dmDWrFm4fPkyJk2ahBMnTmDo0KE6eWOA7BmuyVsvakz/kLNt8taLUCi5/p6IDOfIkSMIDQ1V2xYWFoYjR44YqUdF8+J1ltleHJq0xfCfARlZSrO94Lvk70RkZCkNekxzfr9iLz7AKw1fhos7Q4yB5vx7Y4qMMVZaOnP+7DQ15jrWBgYGwtPTE61atcLhw4eN3R0iMjEKpYCoVacKjTPG+TkRkS68eJ2FvkuPoOrYbaj49ZtHtQk7cOTa00Lbn72Tihevs4rcD0mTK/Pnz0fXrl3x2WefoVq1avjyyy/x6aefYurUqaqYr776CsOGDcOgQYNQv359vHjxAjt27IC9vb0qZvXq1fD390fLli3Rrl07vPfee/jtt99Uzzs7O2PXrl1ITExE3bp18cUXX2DChAkYNGiQKqZRo0ZYs2YNfvvtNwQEBGDDhg3YvHkzatasWZT3Q82xxCd5VqzkJgC4n/IaxxKf6OyYRESFSUpKgru7u9o2d3d3pKam4tWrVxrbpKenIzU1Ve1hKj5fd9rYXdBaUqrhPwNWHrlh0OPpklIwfP/N+f0CgO95N2EehhgDzf33xtQYY6y0dOb82WmKzGms9fT0xOLFi7Fx40Zs3LgR3t7eaNasGU6dyv8iqimfBxKRfnRbdAhipo2NcX5ORCRVRpYSC/b9g4bTdsL3f5MoNSftxL5/i1ZzUBfn1JJqrpQqVQpz587F3Llz842RyWSYMmUKpkyZkm+Mi4sL1qxZU+Cxateujb//Lnh5Trdu3dCtW7cCY4riwfP8J1a0iSMiMpbp06dj8uTJxu6GRreear4Yai4M/RlwM59lrebC0P039/frxmPz7r+pkDoGmvvvjSni+bJumftnp6kxp7G2atWqqFq1qurnRo0a4dq1a5gzZw5WrlypsY0pnwcSke7FJNzFqdviJ1F53kNEpupVhgIdF/yNf5LT9LJ/XZxTMzFrAcqVsNNpHBGRLnh4eCA5OVltW3JyMpycnODg4KCxzdixY5GSkqJ63L592xBdFaVCGc19NhdupewLD9IhHxdHgx5P1wzdf3N/vyqWNe/+64MhxkBz/70xRYYeKy2duX92mhpzH2sbNGiAq1ev5vu8KZ8HEpFuKZQChq5NkNSG5z1EZGoUSgGdfjmEahN26G1iBdDNOTUnVwoi03EcEZEOhISEYM+ePWrbYmNjERISkm8bOzs7ODk5qT1MxZyPg4zdBa15ONmjga+LQY/5SUhFgx5Pl+Qyw/ffnN8vABjXrrqxu2ByDDEGmvvvjakxxlhp6cz5s9MUmftYm5CQAE9Pz3yfN+XzQCLSreBpuyS34XkPEZmS7Wfvw2/cdiTcSdH7sXRxTs3JlQI8epGu0zgiIk1evHiBhIQEJCQkAAASExORkJCAW7duAci+27BPnz6q+MGDB+P69ev46quvcPnyZSxcuBDr16/H559/bozuF1lJe2vULm+eX/InfVAdVnLDzrDbWsvxaRNfgx5TVwa+7wtba8Oeepjz+9WquhscbK2M3Q29M8Ux0Jx/b0yRMcZKS2fOn52mxthjrdQxcO7cufjrr79w9epVnD9/HiNHjsTevXsRFRVljO4TkQnpFx2PR2nSijO3q+lh8PNzIqL8TNt2EZ+tyb+OnC7V8HRCSXtJFVM04ghaAKYFIyJDOHHiBIKCghAUlD1jPmrUKAQFBWHChAkAgPv376u+YAOAr68vtm3bhtjYWAQEBGDWrFn4/fffERYWZpT+68KWoe+b1UUiGysZFveugzY1879LVJ/Gtqtudhd+P23ii7FGujPYHN+vVtXdsKRPfWN3wyBMdQw0x98bU2PssdLSmdtnpykyhbFW6hiYkZGBL774ArVq1ULTpk1x5swZ7N69Gy1btjRK/4nINMQk3MXeK48ltZHLgPk96+ipR0RE0kzbdgFL/k40yLFsrGTYNuJ9nexLJgiCoJM9maHU1FQ4OzsjJSVF49Low1cfodfvRwvdz+oBwWhcuZw+ukhEJqCwscIcmeprevE6C8NWH0fc1SdIN6FPJxkAO2s5KrmWwJet/dG0qqtJ3IWdkaXEkr+v4j+HryP5hcLY3VFjI5fBzckOvYJ9MOD9SiZxR1zO+7UyLhHJz7NgQr9isJIBTg42CKvhgYkdapjUihVTHS+KQsprMuXfG1NjqmOlpTPVz05TJHWstcTxD7Dc10VUXCmUAiqP2y75HGV+jyB0CPDK93lLHCss8TURWYLtZ+/hszWnDXIsGxnw7/TwQuPEjhdFX/tiwZgWjIjIsEraWyO6f/51E0idrbUcUc3fRVTzd43dFbPA94u0wd8bMnX87CQiKt66LTokeWKlhb9rgRMrRESGolAKBptYaValDJb3b6TTfXJypQBupex1GkdERERERERERKQLMQl3cep2qqQ2riVssCyygZ56REQkTY1vt+v9GI0rueD3yAZ6yRDByZUC1PUpA7kMUBZwC4Bclh1HRERERERERERkCAqlgKFrEyS3i/+mle47Q0SkhXZz9+G1jjOcGzpVsfEToJuwkzefFjixAmRPvJy8+dQwHSIiIiIiIiIiomKv5cy9ktvM7xFktHpoFStWhEwmy/OIiooCADRr1izPc4MHD1bbx61btxAeHg5HR0e4ublh9OjRyMrKMsbLIaIi2nLqDi4mvSzSPuyt5ajuWQrLIurj2vftcOOHcCT+EI7L37XF9hFN0KKam97HPK5cKcCD569FxcVeTEKIX1k994aIiIiIiIiIiIq7Lafu4MYTcdesctTxLm3UOivHjx+HQvHmFvXz58+jVatW6Natm2rbwIEDMWXKFNXPjo6Oqv9XKBQIDw+Hh4cH4uLicP/+ffTp0wc2Njb4/vvvDfMiiEgnFEoBw9ef0aptp9qemPFRIGytTWPNCCdXCiC2lspfCffwTXh1o83+ExERERERERGR5dPmoqQMwB9DdFvEWSpXV1e1n3/44Qf4+fmhadOmqm2Ojo7w8PDQ2H7Xrl24ePEidu/eDXd3dwQGBmLq1KkYM2YMJk2aBFtbW732n4h0p+uiQ5Lb2MiBy9+1M7nr76YxxWOiGvi6wKWETaFxj9MycCzxiQF6RERERERERERExZU2FyV/NmI6ME0yMjKwatUq9OvXDzLZm36tXr0a5cqVQ82aNTF27Fi8fPkmZdCRI0dQq1YtuLu7q7aFhYUhNTUVFy5cMGj/iUh7MQl3cfp2qqQ2znZy/Pt9uEmNYzm4cqUAVnIZOge+g6WHbxQaKzaFGBERERERERERkVTaXJRs4e9q1HRgmmzevBnPnj1DZGSkalvPnj3h4+MDLy8vnD17FmPGjMGVK1ewadMmAEBSUpLaxAoA1c9JSUkaj5Oeno709HTVz6mp0t47ItIthVLA8LUJktrYyoEzk9vqp0M6wMmVQrTwdxc1uVKuhJ3+O0NERERERERERMWOQilgqMSLks72VlgW2UA/HSqCpUuXom3btvDyejPpM2jQINX/16pVC56enmjZsiWuXbsGPz8/rY4zffp0TJ48ucj9JSLdmBd7BUqJbS59104vfdEVpgUrjNjVRqa3KomIiIiIiIiIiCxA8LRdktscH99aDz0pmps3b2L37t0YMGBAgXHBwcEAgKtXrwIAPDw8kJycrBaT83N+dVrGjh2LlJQU1eP27dtF7T4RaUmhFDB/3zVJbeZ1DzTJVGC5cXKlEI9epBceJCGOiIiIiIiIiIhIrH7R8XiUliWtTeOKsLU2vct+0dHRcHNzQ3h4eIFxCQkJAABPT08AQEhICM6dO4cHDx6oYmJjY+Hk5ITq1atr3IednR2cnJzUHkRkHPNir0CQEO/pZIeOge/orT+6wrRghRCb7otpwYiIiIiIiIiISJdiEu5i75XHktqUdrDGhA419NQj7SmVSkRHRyMiIgLW1m8uSV67dg1r1qxBu3btULZsWZw9exaff/45mjRpgtq1awMAWrdujerVq+OTTz7BjBkzkJSUhPHjxyMqKgp2drwmR2TKFEoBC/ZLW7Vy4KsWeuqNbnFypTBMC0ZERERERERERAamTZ0VADj2TSvdd0YHdu/ejVu3bqFfv35q221tbbF7927MnTsXaWlp8Pb2RpcuXTB+/HhVjJWVFWJiYjBkyBCEhISgRIkSiIiIwJQpUwz9MohIorirj6CQsGylXU0Pk1x5pwknVwrBtGBERERERERERGRoXRcdktzGVNOBAdmrTwQh7xVWb29vHDhwoND2Pj4+2L59uz66RkR6NH/vP6JjZQDm96yjv87omGmOtiaEacGIiIiIiIiIiMiQYhLu4vTtVElt3ErammQ6MCIqvhRKAcduPBMdP6x5ZZMvYp8bJ1cKw7RgRERERERERERkINqmAzsyLlT3nSEiKoJ5sVdEx8oAjGj1rv46owecXCmE2HRfey4l67knRERERERERERk6VrO3Cu5zfweQWZ1tzcRWT6FUsCiA+IL2beq7mZ24xgnVwrhVspeVNxfCfegUEqozENERERERERERJTLllN3cOPJa0ltWvi7okOAl556RESknfjrj5GpFB8fEeKrv87oCSdXCtHA1wUuJWwKjXucloFjiU8M0CMiIiIiIiIiIrI0CqWA4evPSGrjbG+FZZEN9NQjIiLt/ScuUXSsrZUMDf3K6rE3+sHJlUJYyWXoKHL2PynllZ57Q0RERERERERElqjrokOS2xwf31oPPSEiKhqFUkDsxQei4wc38TO7lGAAJ1dEKV/GUVTck7QMPfeEiIiIiIiIiIgsTUzCXZy+nSqpTb/GFWFrzUt7RGR64q4+gtiMYOZYyD4HR2ARXEra6TSOiIiIiIiIiIgIyL7De+jaBEltSjtYY0KHGvrpEBFREU3eekF07Id13jHLVSsAJ1dEcRM5aSI2joiIiIiIiIiICACCp+2S3ObYN6300BMioqLLyFLi6sM00fHTP6ytx97oFydXxBA7cWaeE2xERERERERERGQE/aLj8SgtS1obpgMjIhM2dtMZ0bF+ro5mPZ6Zb88N6NGLdJ3GERERERERER08eBAdOnSAl5cXZDIZNm/eXGib/fv3o06dOrCzs0PlypWxfPlyvfeTiPQjJuEu9l55LKkN04ERkSlTKAX8dfqe6PhJ7WvqsTf6x8kVEcqVEJfuS2wcERERERERUVpaGgICArBgwQJR8YmJiQgPD0fz5s2RkJCAkSNHYsCAAdi5c6eee0pEuqZNnRWA6cCIyLTFX3+MLEFcrFwGNKpSTr8d0jNrY3fALDAtGBEREREREelY27Zt0bZtW9Hxixcvhq+vL2bNmgUAqFatGg4dOoQ5c+YgLCxMX90kIj3ouuiQ5DZMB0ZEpm7lkRuiY0OruZltIfscHJFFEJvua8+lZD33hIiIiIiIiIqrI0eOIDQ0VG1bWFgYjhw5YqQeEZE2YhLu4vTtVElt3EraMh0YEZk0hVKQdH08IsRXj70xDE6uiOBWyl5U3F8J96BQilz3RERERERERCRBUlIS3N3d1ba5u7sjNTUVr1690tgmPT0dqampag8iMh5t04EdGRdaeBARkRHFX3+MTKW4WFsrGRr6ldVvhwyAkysiNPB1gUsJm0LjHqdl4FjiEwP0iIiIiIiIiKhw06dPh7Ozs+rh7e1t7C4RFWstZ+6V3GZ+jyCzT51DRJZPSkqw5v7mnxIM4OSKKFZyGToGeImKTUrRfLcQERERERERUVF4eHggOVk93UZycjKcnJzg4OCgsc3YsWORkpKiety+fdsQXSUiDbacuoMbT15LatPC3xUdRF6TIiIyFqkpwfo0rKi/zhgQC9qLVL6Mo6i4J2kZeu4JERERERERFUchISHYvn272rbY2FiEhITk28bOzg52dnb67hoRFUKhFDB8/RlJbZztrbAssoGeekREpDvFMSUYwJUrormUFHcyKjaOiIiIiIiIircXL14gISEBCQkJAIDExEQkJCTg1q1bALJXnfTp00cVP3jwYFy/fh1fffUVLl++jIULF2L9+vX4/PPPjdF9IpJAm3Rgx8e31kNPiIh0L+7aI9GxlpISDODkimhuIidNxMYRERERERFR8XbixAkEBQUhKCgIADBq1CgEBQVhwoQJAID79++rJloAwNfXF9u2bUNsbCwCAgIwa9Ys/P777wgLCzNK/4lIHG3SgfVrXBG21rxsR0TmYcf5+6JjLSUlGMDJFfHETqZZxqQbERERERER6VmzZs0gCEKex/LlywEAy5cvx/79+/O0OX36NNLT03Ht2jVERkYavN9EJJ426cBKO1hjQocaeuqR4U2aNAkymUzt4e/vr3r+9evXiIqKQtmyZVGyZEl06dIlT32pW7duITw8HI6OjnBzc8Po0aORlZVl6JdCRBpkZClx7eFLUbGWlBIMYM0V0R6kirvDQGwcERERERERERFZtuBpuyS3OfZNKz30xLhq1KiB3bt3q362tn5zSfLzzz/Htm3b8Mcff8DZ2RlDhw7Fhx9+iMOHDwMAFAoFwsPD4eHhgbi4ONy/fx99+vSBjY0Nvv/+e4O/FiJStyLuhuhYS0oJBnByRTSxhepZ0J6IiIiIiIiIiPpFx+NRmrTVFZaaDsza2hoeHh55tqekpGDp0qVYs2YNWrRoAQCIjo5GtWrVEB8fj4YNG2LXrl24ePEidu/eDXd3dwQGBmLq1KkYM2YMJk2aBFtbW0O/HCLKZevZu6JjLSklGMC0YKKJLVR/59krPfeEiIiIiIiIiIhMWUzCXey98lhSG7eSthaVDiy3f//9F15eXqhUqRJ69eqlqid18uRJZGZmIjQ0VBXr7++PChUq4MiRIwCAI0eOoFatWnB3d1fFhIWFITU1FRcuXMj3mOnp6UhNTVV7EJFuKZQCLtwV97dlLbeslGCAFpMrd+/eRe/evVG2bFk4ODigVq1aOHHihOp5QRAwYcIEeHp6wsHBAaGhofj333/V9vHkyRP06tULTk5OKF26NPr3748XL16oxZw9exbvv/8+7O3t4e3tjRkzZuTpyx9//AF/f3/Y29ujVq1a2L59u9SXI5qHk72ouC0J96BQCnrrBxERERERERERmS6FUsDQtQmS2x0ZF1p4kBkKDg7G8uXLsWPHDixatAiJiYl4//338fz5cyQlJcHW1halS5dWa+Pu7o6kpCQAQFJSktrESs7zOc/lZ/r06XB2dlY9vL29dfvCiAjx1x9DIfJSeAt/V4tKCQZInFx5+vQpGjduDBsbG/zf//0fLl68iFmzZqFMmTKqmBkzZuDnn3/G4sWLcfToUZQoUQJhYWF4/fpNLZJevXrhwoULiI2NRUxMDA4ePIhBgwapnk9NTUXr1q3h4+ODkydP4qeffsKkSZPw22+/qWLi4uLQo0cP9O/fH6dPn0anTp3QqVMnnD9/vijvR74a+LrApYRNoXGP0zJwLPGJXvpARERERERERESmreXMvZLbzOseaHEXHXO0bdsW3bp1Q+3atREWFobt27fj2bNnWL9+vV6PO3bsWKSkpKget2/f1uvxiIqjw/8+Eh0bEeKrx54Yh6TJlR9//BHe3t6Ijo5GgwYN4Ovri9atW8PPzw9A9qqVuXPnYvz48ejYsSNq166N//znP7h37x42b94MALh06RJ27NiB33//HcHBwXjvvfcwf/58rF27Fvfu3QMArF69GhkZGVi2bBlq1KiB7t27Y/jw4Zg9e7aqL/PmzUObNm0wevRoVKtWDVOnTkWdOnXwyy+/6OitUWcll6FjgJeo2KQUpgYjIiIiIiIiIiputpy6gxtPXhcemItvWUd0DHxHTz0yPaVLl8a7776Lq1evwsPDAxkZGXj27JlaTHJysqpGi4eHB5KTk/M8n/Ncfuzs7ODk5KT2ICLd2ns5/9VjuVliSjBA4uTKli1bUK9ePXTr1g1ubm4ICgrCkiVLVM8nJiYiKSlJLU+is7MzgoOD1fIkli5dGvXq1VPFhIaGQi6X4+jRo6qYJk2aqBWkCgsLw5UrV/D06VNVTO7j5MTkHEeTouZaLF/GUVQci9oTERERERERERUvCqWA4evPSG63+4tmuu+MCXvx4gWuXbsGT09P1K1bFzY2NtizZ4/q+StXruDWrVsICQkBAISEhODcuXN48OCBKiY2NhZOTk6oXr26wftPRNkUSgFXktNExVZwcbDI1XmSJleuX7+ORYsWoUqVKti5cyeGDBmC4cOHY8WKFQDe5DnUlAcxd55ENzc3teetra3h4uIiKZdifjH6zLUotqi92DgiotwWLFiAihUrwt7eHsHBwTh27Fi+scuXL4dMJlN72NuLqw1FRGRqOP4RERGRJdAmHdj8HkEWecExty+//BIHDhzAjRs3EBcXh86dO8PKygo9evSAs7Mz+vfvj1GjRmHfvn04efIk+vbti5CQEDRs2BAA0Lp1a1SvXh2ffPIJzpw5g507d2L8+PGIioqCnR2vwREZS/z1xxBbebzWO8567YuxWEsJViqVqFevHr7//nsAQFBQEM6fP4/FixcjIiJCLx3UpbFjx2LUqFGqn1NTUyVNsLiJnDQRG0dElGPdunUYNWoUFi9ejODgYMydO1e1Yu/tCekcTk5OuHLliupnmcyyT8iJyDJx/CMiIiJLoE06sBb+ruggMgW9Obtz5w569OiBx48fw9XVFe+99x7i4+Ph6uoKAJgzZw7kcjm6dOmC9PR0hIWFYeHChar2VlZWiImJwZAhQxASEoISJUogIiICU6ZMMdZLIiIAcdfE11vpWkfaIgdzIWlyxdPTM89yu2rVqmHjxo0A3uQ5TE5OhqenpyomOTkZgYGBqpjcy/gAICsrC0+ePJGUSzG/mMJyLRZpRlvs93Z+vyciiWbPno2BAweib9++AIDFixdj27ZtWLZsGb7++muNbWQyWYFjHhGROeD4R0REROZOm3RgriVssCyygZ56ZFrWrl1b4PP29vZYsGABFixYkG+Mj48Ptm/fruuuEVER7Dh/X1SctRxoVKWcnntjHJLSgjVu3FjtLkEA+Oeff+Dj4wMA8PX1hYeHh1qexNTUVBw9elQtT+KzZ89w8uRJVczevXuhVCoRHBysijl48CAyMzNVMbGxsahatSrKlCmjisl9nJyYnOPow4NUcXcgiI0jIgKAjIwMnDx5Uq2OlFwuR2hoaIF1pF68eAEfHx94e3ujY8eOuHDhQr6xRa05RUSkD4YY/wCOgURERKRfwdN2SW4T/00rPfSEiMgwMrKUuPbwpajYIO/SFpv+UNLkyueff474+Hh8//33uHr1KtasWYPffvsNUVFRALLvIhw5ciS+++47bNmyBefOnUOfPn3g5eWFTp06Ache6dKmTRsMHDgQx44dw+HDhzF06FB0794dXl7ZSyF79uwJW1tb9O/fHxcuXMC6deswb948tZReI0aMwI4dOzBr1ixcvnwZkyZNwokTJzB06FAdvTV5iS1Uf/iq+CVRRESPHj2CQqGQVEeqatWqWLZsGf766y+sWrUKSqUSjRo1wp07dzTGF7XmFBGRPhhi/AM4BhIREZH+TN56Do/SsiS1mdc90GIvNBJR8bAi7obo2Pq+LvrriJFJmlypX78+/vzzT/z3v/9FzZo1MXXqVMydOxe9evVSxXz11VcYNmwYBg0ahPr16+PFixfYsWOHWqHR1atXw9/fHy1btkS7du3w3nvv4bffflM97+zsjF27diExMRF169bFF198gQkTJmDQoEGqmEaNGqkmdwICArBhwwZs3rwZNWvWLMr7USCxhep3X3oAhVJsOR8iIulCQkLQp08fBAYGomnTpti0aRNcXV3x66+/aowfO3YsUlJSVI/bt28buMdERLohdfwDOAYSERGRfmRkKRF9+JakNh5OtugY+I6eekREZBhbz94VHdvYz1WPPTEuSTVXAKB9+/Zo3759vs/LZDJMmTKlwKJSLi4uWLNmTYHHqV27Nv7+++8CY7p164Zu3boV3GEd8nCyLzwIwLNXmTiW+AQhfmX13CMisgTlypWDlZWV5DpSudnY2CAoKAhXr17V+HyRa04REemBIcY/gGMgERER6Uf976SnAzv4VUs99ISIyHAUSgEX7opLtWwtl6GhBV8jl7Rypbhr4OsCZ3tx81FJKa/03BsishS2traoW7euWh0ppVKJPXv2iK4jpVAocO7cOXh6euqrm0REOsfxj4iIiMxVv+h4pLxWSGvTuCJsrXkpjojMW/z1x1CITNrUwt/VotMgckSXwEouQ6vq7oUHQnx9FiIiABg1ahSWLFmCFStW4NKlSxgyZAjS0tLQt29fAECfPn0wduxYVfyUKVOwa9cuXL9+HadOnULv3r1x8+ZNDBgwwFgvgYhIKxz/iIiIyNzEJNzF3iuPJbVxK2mLCR1q6KlHRESGE3dNfL3xiBBfPfbE+CSnBSvuGldxxYZTheeUE1ufhYgIAD7++GM8fPgQEyZMQFJSEgIDA7Fjxw5Vkedbt25BLn8zH/706VMMHDgQSUlJKFOmDOrWrYu4uDhUr17dWC+BiEgrHP+IiIjInCiUAoauTZDc7si4UN13hojICI4lPhEVZ2tl2SnBAEAmCEKxrbyempoKZ2dnpKSkwMnJSVSbw/8+Qq+lRwuNW90/GI2rlCtqF4nIBGgzVpg6S3xNRKQfljheWOJrIiLds9SxwlJfF5Gh1Ju6E4/SsiS1md8jCB0CvPTUI/2wxLHCEl8TkaEplAKqjNsOpYjY+j6l8ceQxnrvkz6IHS+YFkwqsSniLDeVHBERERERERFRsTN56znJEytB3s5mN7FCRJSfuKuPRE2sAEB9Xxe99sUUcHJFogepr3UaR0REREREREREpi0jS4now7ckt9tgpndtExFpMn/vP6JjG/u56rEnpoGTKxKJLVR/+Kr4wj5ERERERERERGS63p+xW3Kb+T2CYCVnahMisgwKpYATN5+JirWSw+LrrQAsaC+Z2EL1uy89gEIp8EOUiIiIiIiIiHTuxessDFt9HHFXnyBdAGzkMrg52aFXsA8GvF8Jtta8n1ZXtpy6g+TUTEltWvi7Mh0YEVmU+OuPoRRZvb2Od+licV2ckysSeTjZi4p79ioTxxKfIKQYzNARERERERERkf4plAIOXnqAIf89iddZ6le4MpUC7j57jRk7r2DGzisIq+GKhb3qF4uLW/qkUAoYvv6MpDauJWywLLKBnnpERGQccdfEZ2oa3uJdPfbEdPA2Boka+LrA2V7cnFRSyis994aIiIiIiIiIioOtZ+6hyrjt6LvyRJ6JFU12XngIv3HbseP8fQP0znIFT9sluU38N6300BMiIuM6lvhEVJy1HGhUpZyee2MaOLkikZVchlbV3UXFiq3PQkRERERERMXTggULULFiRdjb2yM4OBjHjh3LN3b58uWQyWRqD3t7cdkVyLz1X34cw/57Gkot2g5edYoTLFrqFx2PR2lZktqwzgoRWSKFUsDpm09FxQYVk5RgACdXtBLiJ27mrbSjrZ57QkREREREROZq3bp1GDVqFCZOnIhTp04hICAAYWFhePDgQb5tnJyccP/+fdXj5s2bBuwxGUP4zwex53L+vxNiDF51CgqxifIJABCTcBd7rzyW1KayqyPrrBCRRYq//hgiFk0CAOr7uui3MyaEkytaePZS3IoUsXFERERERERU/MyePRsDBw5E3759Ub16dSxevBiOjo5YtmxZvm1kMhk8PDxUD3d3cZkVyDw1/mE3Ltx7rpN9BU+L1cl+igOFUsCwtQmS220f0VT3nSEiMgErj9wQHdvYz1V/HTExnFzRgtgVKVy5QkRERERERJpkZGTg5MmTCA0NVW2Ty+UIDQ3FkSNH8m334sUL+Pj4wNvbGx07dsSFCxcM0V0ygqApO3H3WbrO9vcoLRP9luefdo7e6LboEKSu8+nXuCJsrXmZjYgsj0IpYM+lZFGxtlYyNPQrq+cemQ5xldlJjdgVKUeuPUKXuuX13BsiIiIiIiIyN48ePYJCociz8sTd3R2XL1/W2KZq1apYtmwZateujZSUFMycORONGjXChQsXUL685u+e6enpSE9/c4E+NTVVdy+C9CZo8g48faXQ+X73Xn6IrWfuMXVVAWIS7uLUbWl/J24lbTGhQw099YiITFlGlhJL/r6KlXGJSH6epZqYtZEDXqUd8G37Gmju72bWNUjirz9GpsiiXwHlnc36tUrFKXUtuJS0ExW3+9ID5jQlIiIiIiIinQgJCUGfPn0QGBiIpk2bYtOmTXB1dcWvv/6ab5vp06fD2dlZ9fD29jZgj0kbjafH6mViJcfIdad5rSIfCqWAoVqkAzsyLrTwoGJs+vTpqF+/PkqVKgU3Nzd06tQJV65cUYtp1qwZZDKZ2mPw4MFqMbdu3UJ4eDgcHR3h5uaG0aNHIysry5AvhUhFoRQw+D8n8O74/8NPO/9FUq6JFQDIVAI3n7zCgP+cgN+47dh84rbR+lpUcdceiY4tTvVWAE6uaMXDyV5U3LNXmTiW+ETPvSEiIiIiIiJzU65cOVhZWSE5WT3NRnJyMjw8PETtw8bGBkFBQbh69Wq+MWPHjkVKSorqcfu2+V7cKQ7C5+7H3RT91m9VKIF5sf/o9RjmKnjaLslt5vcIKlZ3aWvjwIEDiIqKQnx8PGJjY5GZmYnWrVsjLS1NLW7gwIG4f/++6jFjxgzVcwqFAuHh4cjIyEBcXBxWrFiB5cuXY8KECYZ+OUTYfvY+/MZtx46L4lJlAcDIDWfReLr0McYUSLm+XZzqrQCcXNFKA18XONuLy6iWlPJKz70hIiIiIiIic2Nra4u6detiz549qm1KpRJ79uxBSEiIqH0oFAqcO3cOnp6e+cbY2dnByclJ7UGmqf/yo7iQlFZ4oA4sPniNq1feMnnrOTxKk7YKoo53aaZYE2HHjh2IjIxEjRo1EBAQgOXLl+PWrVs4efKkWpyjoyM8PDxUj9zj1a5du3Dx4kWsWrUKgYGBaNu2LaZOnYoFCxYgI0O/E5JEuU3bdhGfrTmlVdu7KZmoMnabjnukXwqlgNM3n4qKLW71VgBOrmjFSi5Dq+ruhQcCeJLGAZ6IiIiIiIjyGjVqFJYsWYIVK1bg0qVLGDJkCNLS0tC3b18AQJ8+fTB27FhV/JQpU7Br1y5cv34dp06dQu/evXHz5k0MGDDAWC+BdCQm4S72XBafdiU3KxlwXGJaqgyFgPhrj7U6niXKyFIi+vAtSW1kAP4Y0kg/HbJwKSkpAAAXF/X0QatXr0a5cuVQs2ZNjB07Fi9fvlQ9d+TIEdSqVUutTlVYWBhSU1Nx4cIFjcdJT09Hamqq2oOoKKZtu4AlfycWaR+ZAlD5a/OZYIm//hhZIufizb22jDY4uaKlEL9youJKO9rquSdERERERERkjj7++GPMnDkTEyZMQGBgIBISErBjxw7VxcNbt27h/v37qvinT59i4MCBqFatGtq1a4fU1FTExcWhevXqxnoJpAPa1vkAgPLOtrg2PRyuTnb4pXugpLYzdl7S6piWKHzeAcltfmY6MK0olUqMHDkSjRs3Rs2aNVXbe/bsiVWrVmHfvn0YO3YsVq5cid69e6ueT0pKUptYAaD6OSkpSeOxWHOKdGn72XtY8vcNnewrCzCbFSxS6q30aVhRfx0xUeJyW1Eez16KW5EiNo6IiIiIiIiKn6FDh2Lo0KEan9u/f7/az3PmzMGcOXMM0CsypBrfbteqXfN3yyK6X0PVz+0D38Gyw9dx6ra4u/PP3ElFRpYSttbF+77bmIS7+Pfhy8IDc2nh78p0YFqKiorC+fPncejQIbXtgwYNUv1/rVq14OnpiZYtW+LatWvw8/PT6lhjx47FqFGjVD+npqZygoW0olAK+GzNaZ3uM1MAakzYgQtT2uh0v7omtt5KcUwJBnDlitbErkjhyhUiIiIiIiIi0qTd3H14rZDerm9jH7WJlRx/DHlP0n7Gbjor/eAWRJtVQ64lbLAssoF+OmThhg4dipiYGOzbtw/ly5cvMDY4OBgAcPXqVQCAh4cHkpPVi4fn/Ozh4aFxH6w5RbrSddGhwoO0kJahQPuf/9bLvnVBoRRw8oa4eisB5Z2L5Wo+Tq5oSeyKlCMSlk4RERERERERUfGw5dQdXEyStmICAPo2roiJHWpqfM5KLkOXOuJXVPyVcK9YF7YPnrZLcpv4b1rpoSeWTRAEDB06FH/++Sf27t0LX1/fQtskJCQAADw9PQEAISEhOHfuHB48eKCKiY2NhZOTE1Mjkl7FJNzFaZErArVx/l4q/kq4q7f9F0Xc1UdQioyt7+tSeJAF4uSKllxK2omK234+qVifqBARERERERGROoVSwPD1ZyS3a1HVFRM71CgwZvqHAaL3l6UsvoXt+0XH41FalqQ2w5tXLpZ3ZhdVVFQUVq1ahTVr1qBUqVJISkpCUlISXr16BQC4du0apk6dipMnT+LGjRvYsmUL+vTpgyZNmqB27doAgNatW6N69er45JNPcObMGezcuRPjx49HVFQU7OzEXaMjkqooNbGkGLE2wSSvH288dUd0bGM/Vz32xHRxckVLHk72ouJeZiiK7YkKEREREREREeWlzYqJGl6lsKxv4emobK3lCCwvPv3Rf+JvSO6LuYtJuIu9V6Rdq7GSAyNavaunHlm2RYsWISUlBc2aNYOnp6fqsW7dOgCAra0tdu/ejdatW8Pf3x9ffPEFunTpgq1bt6r2YWVlhZiYGFhZWSEkJAS9e/dGnz59MGXKFGO9LCoGotacMNixQmftN9ixxDp755moOCs5imW9FYAF7bXWwNcFJWytkJZReHLUI9cfoXGVcgboFRERERERERGZsr7LpK+YeMfZDtuGNxEdP7pNNfT6/aio2H2XH0ChFIrNigyFUsAwLe5En/txULF5j3RNEAq+I9/b2xsHDhwodD8+Pj7Yvn27rrpFVKCMLCV2nH9QeGAuNjLgwtS2UCgFVJuwQ1LbxMcv8VfCXXQMfEdSO31RKAXceCQudWVl15LFdnzk5IqWrOQyvF+lHHZcSC401gRXdRERERERERGRgU2NOY99/0hbMWEtBw6PDZXUpmGlsrCRA5kikuVnKLJTgxWXm0K7LToEqZdpWvi7okOA+Fo2RJbiVYYCE7acxY6z9/E8Q4AcgIOtFRr4umB+jzooaW+5l5bfn7FbcpvL09qpJhlu/BAOv6+3ofDb8t/4fF0C2tf2MomJivjrj0XXW2nuXzxTggFMC1YkdX3EFeop42ir554QERERERERkSnbfvYelh66KbndxSltJbexksvQspq76PjD1x5KPoY5ikm4i1MSC1O7lrDBssjC07ERWZKMLCVaztqHahN24I8T9/A8I3tKUgkgLUOBfVceouaknQiatAOvRGT1MTdbTt1BcmqmpDbze+Rd3fbP9+0k7UMpAPNi/5HURl9WHrkhOvb9ym7664iJ4+RKEbiUEDdpIjaOiIiIiIiIiCyPQingszWnJbfr17gibK21u3TzSUhF0bE7LyRpdQxzom1h6vhvWum+M0QmbGrMRbw7/v9w7WHhKaGevlag2oQd6LvsiAF6ZhgKpYAR689IapPf6jYruQw/fxQgaV+/7L9q9OL2CqWAPZcKz9YEALZWsmJbbwXg5EqRPHuZodM4IiIiIiIiIrI8XRcdktzGraQtJnSoofUxc1KDiXHt4UtkZIlNAGOegqftktxG053oRJbsg/l/Y+mhRMnt9v3zBDW+tYx6OPNir0hKHehsb1Xg6rYP6pRHRRd70fszhdUr8dcfi0orCQAB5Z2L9TjJyZUiKC0y3detJ+KK/xARERERERGRZYlJuIvTElNRAcCRcdLqrLxNamqwFXHSL6iai8lbz+FRWpakNnW8S7POChUr/Zcfxdm70seqHGmZAqpP+D8d9sjwFEoB8/ddk9Tm+PjWhcbs+bKFpH0ae/VK3LVHomPr+4orm2GpOLlSBGJXpPx5+q7Rl3MRERERERERkWFpm4pKVysmpKQG23rmXpGPZ4oyspSIPnxLUhsZgD+GNNJPh4hMUEzCXey5LP6Cen5eZijx3g97dNAj45C6aqVtDXdRqRut5DJ8GCh+stbYq1eOJT4RHdvYr/gWswc4uVIkLiXtRMWlvs6S9EtJREREREREROZPm1RU+eXu10bDSmVhJfLKz8X7zy3yxtB28w5IbvMz04FRMaLtJHB+7jx7jX7Lj+lsf4aiUApYsF/aqpVfetUVHftDV/OovaJQCjh986mo2OJebwXg5EqReDiJz5eXlPJKjz0hIiIiIiIiIlOiTSqq8mXsC8zdL5WVXIZQfzdRsVlKAfHXHuvs2KYgJuEuroooyp2bLie3iMxBy5l7db7PvZcfmt1quLirj6CQMJcxvHllSZOwttZytKspPlWjsVavxF9/jCyR70Nzf7diPxHNyZUiaODrglL2VqJin6SxqD0RERERERFRcaBNKip7a+DQmJY670ufRr6iYw9fe6jz4xuLNnfju5aw0enkFpGp23LqDm48ea2XfQ/772mzWg03eesF0bFyGTCi1buSjzG/p/iVLoBxVq/8R0L9rT4NK+qvI2aCkytFYCWX4cOgd0TFlna01XNviIiIiIiIiMgU1Ptup+Q2Zye11UNPslODWYu8sfjuU8vJuqFNSrb4b1rpoSdEpkmhFDB8/Rm9HiN01n697l9XMrKUuPowTXT80GbSVq3ksJLLMLy5n+h4Q69eUSgF7L78QFSstZwpwQBOrhRZ+TKOouKepKXruSdEREREREREZGz9ouOR+loprU3jiqKKImvDSi5DYIXSomLvPbOMyZV+0fGSU7LN6x5Y7NPbUPEybM1JvR8j8fFL/JVwV+/HKapPfo8XHavtqpUcI1pVhZSRxpCrV+KvP4ZC5MdXdc9SHDPByZUie/YqU1TcyVviCgERERERERERkXmKSbiLvVek1S1xK2mLCR1q6KlH2cTeGHrmTopZpfHRRJt/A08nO3QMFJeZhMgSZGQpsf18sqQ2Lo5W+KRhBcnHGrE2waTHlYwsJY7eEH/dVttVKzms5DIMM9HVKyuP3BAdy9pU2Ti5UkQykXON+y8/NOmBhIiIiIiIiIi0p02NDwA4Mi5U9515yztlHETFZSjMu6i9tv8GB75qofvOEJkwKSs1AKCkrRynJrTB1E610Lexj+TjDV2t/1Uy2lp+WHyNERmKtmolh9TVKwsP6H/1ikIpYM8l8RNuERLqeVkyTq4UUYjI3HKvs5RmfYJCRERERERERPnTpsaHoVJRNfIrJzrWnIvad110SHIbfaZkIzJFUldqAMCZSW1U/z+xQ000f1darY3/u5CMjCxp6RINZekh8ZMrnYO8dDJmS129kqUE4v59VOTjFiT++mNkivwn8nN15Lj5P3wXiqhhpbKwE/nLdOS6fv8IiIiIiIiIiMjwJm89J7nGh29ZR4OlopJS1P544hP9dkZPYhLu4vTtVEltDJGSjcjUfL1BWhH74c3zpsGK7tcQ5UpYS9pP+M8HJcUbQkaWEsnPxdfJ/qFLgM6OLXX1yqSY8zo7tiZSUoKF1fDQX0fMDCdXishKLkPzqq6iYpkVjIiIiIiIiMiyZGQpEX34luR2u79opvvO5MNKLkOQTxlRseZYd8WUU7IRmRKFUsCmhHui463k+afBOvpNa0nH/vdBGraeEX9sQ5Ay0aTr1RpSV69ce/hSb6t/pKYEa+wn7lp4ccDJFR0IqiDuBMXZwUbPPSEiIiIiIiIiQ6r/nfR0YPN7BBkkHVhuDXxdRMWZY90VbVKyGePfgMjY5sVekRQ/9+P8/06s5DL8/JG0lRwj1502mclbqRNNk9rX1HkfpK5e+WSptFo5YklJCWZrJUNDkWUyioMiTa788MMPkMlkGDlypGrb69evERUVhbJly6JkyZLo0qULkpPVZ75u3bqF8PBwODo6ws3NDaNHj0ZWlvry2f3796NOnTqws7ND5cqVsXz58jzHX7BgASpWrAh7e3sEBwfj2LFjRXk5Wkt9nSkq7vRtafkMiYiIiIiIiMh09YuOR8prhaQ2Lfxd0SHAS089yp+l1l3RJiWbsf4NiIxJoRSw6MA10fFV3EoU+nfyQZ3ycHcSfzO5QgnMi/1HdLw+SZloksuARlXEj6FiSV29cjTxqV5Wr0hJCdbc340T07loPbly/Phx/Prrr6hdu7ba9s8//xxbt27FH3/8gQMHDuDevXv48MMPVc8rFAqEh4cjIyMDcXFxWLFiBZYvX44JEyaoYhITExEeHo7mzZsjISEBI0eOxIABA7Bz505VzLp16zBq1ChMnDgRp06dQkBAAMLCwvDgwQNtX5LWZCLnGPdffmgys7NEREREREREpL2YhLvYe0XaCg/XEjZYFtlATz0qmCXWXdEmJZuzvZXR/g2IjEnK6gQA2Da8iai4v7+Sll5v4YGrRr8+KnWiqVOgbgrZazKiVVVJ8bpevSI1JVifhhV1enxzp9XkyosXL9CrVy8sWbIEZcq8SYmVkpKCpUuXYvbs2WjRogXq1q2L6OhoxMXFIT4++x9+165duHjxIlatWoXAwEC0bdsWU6dOxYIFC5CRkQEAWLx4MXx9fTFr1ixUq1YNQ4cORdeuXTFnzhzVsWbPno2BAweib9++qF69OhYvXgxHR0csW7asKO+HVkJELoV6naU0u6W1RGQ4Ulfj/fHHH/D394e9vT1q1aqF7du3G6inRES6xfGPiIozjoHmSdsaH/HftNJ9Z0SyxLor9b7bWXjQW46Pl1YngkyPqWSyMTc/7bgkOlZKfRFbazmCK4obWwAgSwnE/ftIdLw+SJ1o0mUh+7dZyWX4MFD8Sjpdr15hSrCi0WpyJSoqCuHh4QgNVZ+ZPHnyJDIzM9W2+/v7o0KFCjhy5AgA4MiRI6hVqxbc3d1VMWFhYUhNTcWFCxdUMW/vOywsTLWPjIwMnDx5Ui1GLpcjNDRUFaNJeno6UlNT1R660LBSWdhaiZu9NKeltURkOFJX48XFxaFHjx7o378/Tp8+jU6dOqFTp044f/68gXtORFQ0HP+IqDjjGGi+ui46JLmNKdT4sKS6K5O3nkPqa2kXGPs1rqjTgtRkeKaUycacZGQpkXBH/HVQqfVFVg5oKCl+3l7jpgbT10STtn7oKm3yJvrwdZ0d+z9xiaJjmRIsL8m/GWvXrsWpU6cwffr0PM8lJSXB1tYWpUuXVtvu7u6OpKQkVUzuiZWc53OeKygmNTUVr169wqNHj6BQKDTG5OxDk+nTp8PZ2Vn18Pb2FveiC2EllyHQu7So2HvPXuvkmERkWaSuxps3bx7atGmD0aNHo1q1apg6dSrq1KmDX375xcA9JyIqGo5/RFSccQw0TzEJd3H6trSbNYO8nU2ixoel1F3RJh2YW0lbTOhQQ089IkMxpUw25mTspjOiY7WpL2JrLUe7mu6FB/7PiZvPjLY6Tt8TTdqQuvpn2SHxEyIFUSgFxF4UPzHJlGB5SZpcuX37NkaMGIHVq1fD3t5eX33Sm7FjxyIlJUX1uH37ts72XVfkH8CrTGmF7ojI8mmzGq+wFX5EROaA4x8RFWccA82TtunANgxprPvOaMFS6q7U/26X5DZHxkmrC0GmR9tMNsWdQingr9P3RMd3DnpHq9UJ83vWlRRvrML2+p5o0paU1T/JzzN0khos7uojiN2LtZwpwTSRNLly8uRJPHjwAHXq1IG1tTWsra1x4MAB/Pzzz7C2toa7uzsyMjLw7NkztXbJycnw8PAAAHh4eCA5OTnP8znPFRTj5OQEBwcHlCtXDlZWVhpjcvahiZ2dHZycnNQeuuLiaCcq7uA/LGpPROq0WY2X3wq//OL1lRaRiKgoDDH+ARwDicg0cQw0T+aaDiyHJdRd6Rcdj5TX0m5cndc90GT+DUh7UsdNjn/Z4q8/RpaEP+XpH9bW6jhSa4f8st/whe0NNdGkDVtrOfzKlRAdr4vC9pO3XhAd2zHQi+OoBpImV1q2bIlz584hISFB9ahXrx569eql+n8bGxvs2bNH1ebKlSu4desWQkJCAAAhISE4d+6cWi7E2NhYODk5oXr16qqY3PvIicnZh62tLerWrasWo1QqsWfPHlWMoZUrJW5y5VUmi9oTkeHpKy0iEZE54BhIRMUZx0DdMed0YLmZc92VmIS72HtFWp98yzqiY+A7euoRmTKOf9kOSygeH1DeqUj1RaTUDlEKhl+9YqiJJm1N+kB86sKiFrbPyFLi6sM00fGGfi/MhaS/llKlSqFmzZpqjxIlSqBs2bKoWbMmnJ2d0b9/f4waNQr79u3DyZMn0bdvX4SEhKBhw+ylTa1bt0b16tXxySef4MyZM9i5cyfGjx+PqKgo2NllT1AMHjwY169fx1dffYXLly9j4cKFWL9+PT7//HNVX0aNGoUlS5ZgxYoVuHTpEoYMGYK0tDT07dtXh2+PeB5O4tOkHbkuflAjIsunzWq8/Fb45Revz7SIRETaMsT4B3AMJCLTxDHQvJh7OrDczLXuirb/Bru/aKbzvpBxSB03Of5l23s5/9WNb/sqrFqRjmVrLUdlV/GrLwy9ekVKIfuiTjRpo1HlcpCyNqQoq1dWxN0QHevn6mjw98Jc6PxdmTNnDtq3b48uXbqgSZMm8PDwwKZNm1TPW1lZISYmBlZWVggJCUHv3r3Rp08fTJkyRRXj6+uLbdu2ITY2FgEBAZg1axZ+//13hIWFqWI+/vhjzJw5ExMmTEBgYCASEhKwY8eOPEsDDaWBrwscbcW9nSa4qpaIjEib1XiFrfB7mz7TIhIRacsQ4x/AMZCITBPHQPMSPE16jQ9TSgeWm5S6K3efvtJvZySwpH8D0o7UcZPjX/ak5JVkcasTrOTQSU2NiR3Er74w5OoVqYXsizrRpA0ruQydJaRWK8rqlVXxN0THTmpfU6tjFAfWRd3B/v371X62t7fHggULsGDBgnzb+Pj4YPv27QXut1mzZjh9+nSBMUOHDsXQoUNF91WfrOQytKnhjk2n7xcam5Ty2gA9IiJzMmrUKERERKBevXpo0KAB5s6dq7Yar0+fPnjnnXcwffp0AMCIESPQtGlTzJo1C+Hh4Vi7di1OnDiB3377zZgvg4hIMo5/RFSccQw0D/2i4/EoLUtSmxb+riaXDixHTt2V4zeeFhp730SuX2jzb2CKKdmo6AobN0ld3NVHEHuPdx3v0jqZjGxUuRysZIBC5IEXHriKEa3e1ftEqJRC9sYs3v5D1wBsShBfF6bpjL04Mi5U0jEyspS4+UTc5LkcQKMq4lc8FjdFnlyhNzxLO4qK237uPn7qFsC7J4hI5eOPP8bDhw8xYcIEJCUlITAwUG013q1btyCXv1kd16hRI6xZswbjx4/HuHHjUKVKFWzevBk1a/JuAiIyLxz/iKg44xho+rSp8eFsb4VlkQ301CPdaODrImpy5ez/itob8/qFNv8GgGmmZKOiK2zcJHXz94pfFTK8xbs6OaaVXIaoZn74ed81UfFZSiDu30d4v6qrTo6viUIpYNMp8RMWxizebmstR3DFMjgqYowGgPup6fgr4a6k2lJSJppqvuPEa9gFkAmCUGyTVKWmpsLZ2RkpKSk6WRo4c+cV/LLvqqjY1f2D0ZizfkRmQddjhSmwxNdERPphieOFJb4mItI9Sx0rLPV16YNCKcBvXMFZNzT557u2Jp+b/vDVR+j1+1FRsca8fqFQCqg8brvoO+9zzO8RxFUrRWSJY4UlvqaCKJQCqozbDjFJo+Qy4N9p7XR2EV3q366fqyP2fNFcJ8fWZPbOy6InewDjj+MZWUq8O/7/RMdL+feT+m/zTTt/DGziJ7ovlkLseGHan/ZmJkTCcjEWtSciIiIiIiIyXS1n7pXcpl/jiiY/sQJk112xtRJ3EdWYRe2HrTkpeWLFlFOyERlS3NVHoiZWAKBuBd2kBMthJZdhWHPxF+SvPXypde2QwiiUAhYdED+xYoxC9m/LWb0ilpTaNfNir0gaVyMa+UqILn5M/xPfjGSfnIiLZVF7IiIiIiIiItO05dQd3Hgird6IW0lbTJBQyNmYrOQyBJR3FhV7PPGJnnujWUaWEtvPJ0tq41rCxuRTshEZijFSguU2olVVSJmu+WRpvM77AADx1x8jU8K8jTEK2WuyckBDSfE/77sKRSEXnBVKQdIKHlOYaDJ1fHd0yEouQ/vanqJiWdSeiIiIiIiIyPQolAKGrxefjz6H1ILCxvZOGXF1Y8/8r+6KodX7bqfkNvHftNJDT4jMj0Ip4MTNZ6Ji5TL9FCyXunrlaOJTvaxe+WnHJdGxtlbGK2T/NltrOdrUdJPUpuuiwwU+P2zNSUn7M5WJJlPGyRUdk1LU3hgnJ0RERERERESUv66LDkluM79HkNkV/H2njIOouAyFgPhr0gvKF0W/6HikvpZ2kdUc/w2I9CX++mPRWXN0nRIstxGtqkqK1/XqlYwsJRLupIqOH9zEz6TGkQU960mKP307BVvP3NP4nNTVgKY00WTKOLmiYzKRC95eZykNfnJCRERERERERPmLSbiL07fFX4gDzLfGRyM/8XeqG7LuSkzCXey9Iu16SR3v0mb5b0CkL/+JSxQdq4+UYDms5DJ8GCj+b1PXq1eWHxb/PsgAjGilv/dCG1ZyGYZLWP0DAMP+e1rjDf31v9slaT+mNtFkqji5omNSitobsygcEREREREREb2hUAoYujZBUhtzrvHRsFJZWIu8brbzQpJ+O/M/2vwbAMAfQxrpvjNEZkqhFLD78gNRsfpKCZbbD10DJMXrcvXK0kPiJ1c+rPOOSU4mSK1dAwDBb02k9F0Wj5TXCtHt5TLTm2gyVZxc0TEpJyfGKgpHREREREREROqCp0m7qxcw7xofVnIZgnzKiIq99vClXmohvE2bf4N53QNN8oIokbHEX38Mhcg/15peTnr/+7G1liO4orixBtDd6pWMLCWSn6eLjp/+Ye0iH1MfrOQyzPtI2gTVo5dZaDt3PwBg4pbz2PePtNWAQ5tV5rgqEidXdEzKyYmxisIRERERERER0RuTt57Do7QsSW2GNTP/lCkNfF1Ex66QkGZIG/2i4yX/G/iWdUTHwHf01CMi8xR37ZHoWEOl01s5oKGkeF2sXvnkd/H78HN1hK216V4m/6BOebg72UhqcykpDRW/3oYVcTcltbOSc9WKFKb7W2PGxJ6cGKMoHBERERERERG9kZGlRPThW5LayGXAyNbSCjWbIil1V/IrkqwL2tRZAYDdXzTTfWeIzNyO8/dFx0Y08tVjT94w9OqVjCwljt54Kjp+UvuaWh/LUP7+KtQgx5n7cZDZ3zhgSJxc0QNTLQpHREREREREROqkFvkFgHndLePiU8NKZWEl8srQxfvP9ZJ9Q9s6K/N7WMa/AZEuZWQpce3hS1Gxhl6tIXX1ytcbz2h9LCmrVgxRd0YXbK3l6Nu4gl6PEeTtbLDVTJaCkyt6wLorRERERERERKavX7S0Ir8A0MLf1WIuPlnJZQj1dxMVm6XUT/YNbeqsWNK/AZEurYi7ITo2rIaH/jqigdTVK5tO39NqQlfqqpVOgV5mM1E7sUMtONnr73L+hiGN9bZvS8XJFT1g3RUiIiIiIiIi06ZNKirXEjZYFtlATz0yjj4S0gLpOvuGNnVWLPHfgEhXtp69Kzq2sZ+rHnuimdTVK8PWnJJ8jPB5ByTF/9BFWrF4YzsxPkwv++VqQO1wckVPWHeFiIiIiIiIyDRpm4oq/ptWuu+MkUnJvrHzQpLOjqttnRVL/Dcg0gWFUsCFu6miYq3lMjT0K6vnHuVlay2HX7kSouO3n0+SVHslJuEu/hWZFg0Agn3LmHQhe01sreXo/56PTvfJ1YDaM6/fHjPCuitEREREREREpkmbVFSWelevlOwb1x6+LFKR6Ryss0Kke/HXH0MhMjlOC39Xo/0tTfqghqT48J8PiopTKAUMkziurOwvbSWNqfi2fU3U9Cqpk32VL2PP1YBFwMkVPWHdFSIiIiIiIiLTM3nrOcmpqCy9yK/Y7BsAsCIuscjH67rokOQ2vLOaqGArj9wQHRsRIj4doK41qlwOVhLmdf59kIatZ+4VGjdszUlIKbxgjqtWcosZ3hQ1vUoVaR9lHK1xaExLHfWoeDLf3yATx7orRERERERERKYlI0uJ6MO3JLez9CK/UrJviLnIWZCYhLs4fVtc6qIcrLNCVDCFUsCeS8miYm2tjJMSLIeVXIaoZn6S2gz77+kCr51mZCmx/by415/DXFet5BYzvAla+rtp1dbFwQqnJ+infktxwskVPWLdFSIiIiIiIiLTIbXQMVA8UlE1rFQWYl/ixfupWt8gylo3RPoRf/0xMkVm7Aso72z0MW1Eq6qSL0o3nBab73P1vpOW6tHcV63ktjSyPub3CJL0fraoWhanJrbRW5+KE8v4LTJRrLtCREREREREZBqkFjoGik8qKiu5DHUrlBYVm6WE1jeIVv92u+Q2xWFyi6io4q49Eh1bX0IaQH2xksvwc/dASW0epmUicll8nu3hc/cj9bVC0r4sYdVKbh0CvPDv9+0Q/Uk9+Ls5wvqt561kQBlHG3Sv741LU9pgWV/Lev3G9PZ7TTqUU3clS8QNHXefvtJ/h4iIiIiIiIiKIW1WTBS3VFQNKpXF8ZvPRMX+J/4GGlcRf0MpADT6fhfSpV3/tPhaN0S6ckxCPefGfq567Il47QPfwbw9/0ia9N7/z2NELo3H8v9NjvRddhQXktIkHbddTQ+LWbWSm5VchuY13NG8hruxu1KsWN5vkgmxkssQKPLOj/P3UvTbGSIiIiIiIqJiKniatJQxQPFLRSUl+8aeS8mSUoOFz92Pe6mZkvtk6bVuiHRBoRRw+uZTUbHGrrfytm0jmkpus//fx6g6bhuCv9uJff+IX7EDAHIZML9nHcnHJMoPJ1f0rHwZR1Fx1x6+REaWyOSIRERERERERCTK5K3n8CgtS1Kb4c0rF7tUVA0rlYWNyKtEWUog7l9xFzX7RUu/sxxgOjBLcePGDfTv3x++vr5wcHCAn58fJk6ciIyMDLUYmUyW5xEfr54C6o8//oC/vz/s7e1Rq1YtbN8uPc2cJYq//lhU1hwAaO7vZlJ/V7bWcvRtXEFyu3QlkPxC2rgOAPO6c1wh3eLkip69U8ZBdOyKuEQ99oSIiIiIiIioeMnIUiL68C1JbazkwIhW7+qpR6bLSi5Dy2ri08lMijlfaMzkreex94q0O8uB4lPrpji4fPkylEolfv31V1y4cAFz5szB4sWLMW7cuDyxu3fvxv3791WPunXrqp6Li4tDjx490L9/f5w+fRqdOnVCp06dcP584b+Hlk5KvZU+DSvqryNamtihFsqV0H/lCqYZJH3g5IqeSVlWu/XMPT32hIiIiIiIiKh4qf+d9HRgcz8uvnc2fxJSUXRsYRk4pm27gOjDNyX3oXxp+2JV68bStWnTBtHR0WjdujUqVaqEDz74AF9++SU2bdqUJ7Zs2bLw8PBQPWxsbFTPzZs3D23atMHo0aNRrVo1TJ06FXXq1MEvv/xiyJdjknacvy8qztRSguV29JvWej8G0wySPnByRc8aVioLK5Hv8vl7qZJylhIRERERERGRZv2i45HyWloF9eK+YkJKajAAaDpjr8btMQn3sOTvG5KPb2cFHPq6peR2ZF5SUlLg4uKSZ/sHH3wANzc3vPfee9iyZYvac0eOHEFoaKjatrCwMBw5ciTf46SnpyM1NVXtYWkyspS4JrIgfEB5Z5OdOLaSy/BL90C97Z9pBklfOLmiZ1ZyGUL93UTFKgXxOUuJiIiIiIjIfD158gS9evWCk5MTSpcujf79++PFixcFtmnWrFmemgSDBw82UI/NS0zCXey98lhSG9cSNsV+xYSVXIYhTf1Ex99PTcdfCXfVtv11+i6Grj2t1fEvTm2nVTsyH1evXsX8+fPx6aefqraVLFkSs2bNwh9//IFt27bhvffeQ6dOndQmWJKSkuDurp62zt3dHUlJSfkea/r06XB2dlY9vL29df+CjGxF3A3RsfV9805omZL2ge+gRVXdr6wp7pPmpF+cXDGAPo18Rcf+vPcfPfaEiIiIiIiITEGvXr1w4cIFxMbGIiYmBgcPHsSgQYMKbTdw4EC1mgQzZswwQG/Ni0IpYOjaBMnt4r9ppfvOmKERrapKi1+bgIwsJRRKAZ1+OYQR6xK0Oi7vLDcvX3/9tcYi9Lkfly9fVmtz9+5dtGnTBt26dcPAgQNV28uVK4dRo0YhODgY9evXxw8//IDevXvjp59+KlIfx44di5SUFNXj9u3bRdqfKTp2Q/wkcmM/Vz32RDeW9W0I79J2Ottf+TJMM0j6pf9qQYSGlcpCLstemVKYk7eeQaEUeEJBRERERERkoS5duoQdO3bg+PHjqFevHgBg/vz5aNeuHWbOnAkvr/zvsHV0dISHh4ehumqWgqdJr7PCC/tvWMll+DDQC5sSxNeFfXf8/xXpmC393XhnuZn54osvEBkZWWBMpUqVVP9/7949NG/eHI0aNcJvv/1W6P6Dg4MRGxur+tnDwwPJyclqMcnJyQWOh3Z2drCz092FelN067G4lGDWctOtt/K2v78OxXs/7MadZ+lF2o+LozUOjWGaQdIvrlwxACu5DPV8SouKZWowIiIiIiIiy3bkyBGULl1aNbECAKGhoZDL5Th69GiBbVevXo1y5cqhZs2aGDt2LF6+FHdhrbiYvPUcHqVlSWoT5O3MC/tv+aFrgMGOVcOrFJZG1jfY8Ug3XF1d4e/vX+DD1tYWQPaKlWbNmqFu3bqIjo6GXF745ciEhAR4enqqfg4JCcGePXvUYmJjYxESEqLbF2ZGFEoBV5MLTieZI9DbdOutaHLo61DUfMdJ6/blS9vi1IQwHfaISDOuXDGQYS3exSfLjomKnRRzHnuqNtdzj4iIiIiIiMgYkpKS4OamXpvT2toaLi4uBdYP6NmzJ3x8fODl5YWzZ89izJgxuHLlCjZt2pRvm/T0dKSnv7n71xILOufIyFIi+vAtye02DGmsh96YN1trOdrUdMOO8w/0epx3Stth2/Amej0GGVfOxIqPjw9mzpyJhw8fqp7LWXWyYsUK2NraIigoCACwadMmLFu2DL///rsqdsSIEWjatClmzZqF8PBwrF27FidOnBC1CsZSxV9/DIXI2AYmXm9Fk5hh72NqzEUsPZQoqV3fxhUwsUMtPfWKSB0nVwykUeVykANQioi99vAlMrKUsLXmwiIiIiIiIiJz8fXXX+PHH38sMObSpUta7z93TZZatWrB09MTLVu2xLVr1+Dnp7kI+fTp0zF58mStj2lOwucdkNyG6cDyt6BnPfiN2663/ZdxsMbhr0P1tn8yDbGxsbh69SquXr2K8uXLqz0nCG/y50+dOhU3b96EtbU1/P39sW7dOnTt2lX1fKNGjbBmzRqMHz8e48aNQ5UqVbB582bUrFnTYK/F1MRdE5/5xhzqrWjybfvqGNPGH6M3JOCvhPsFxnaq7YkZHwXyeioZFCdXDMRKLkO9iqVx7MYzUfFjN53FrI8C9donIiIiIiIi0h2xNQg8PDzw4IH6ioCsrCw8efJEUj2V4OBgAMDVq1fznVwZO3YsRo0apfo5NTUV3t7eoo9hLmIS7uLfh9JSpLXwd2U6sAJYyWX4+aMADF9/Ruf7drCW4fREpuwpDiIjIwsdFyMiIhAREVHovrp164Zu3brpqGfm71jiE1FxtlbmU29FE1trOeZ1r4PZHwk4eOkBftx5EYmPXkIml6OSawl82dofTau6cqKcjIKTKwYkJTXYXwn3MKNrAAcGIiIiIiIiM+Hq6gpX18LvDg4JCcGzZ89w8uRJ1K1bFwCwd+9eKJVK1YSJGAkJCQCgVpfgbcWhoLNCKWDo2gRJbVxL2GBZZAP9dMiCfFCnPBYf/BcXk3RX28dGDlz6rp3O9kdUHCmUAk7eeCoqNqC8edVbyY+VXIbmNdzRvIa7sbtCpMJ1UgaUkxpMjCylgPhrj/XaHyIiIiIiIjK8atWqoU2bNhg4cCCOHTuGw4cPY+jQoejevTu8vLJXUty9exf+/v44diz7Br1r165h6tSpOHnyJG7cuIEtW7agT58+aNKkCWrXrm3Ml2N0wdN2SW4T/00rPfTEMm0f2RyOOro118Ea+Pf7cN3sjKgYi7v6SFTpAQCob4b1VojMBSdXDMhKLkOr6m6FB/7PjJ3a5+IlIiIiIiIi07V69Wr4+/ujZcuWaNeuHd577z21wsyZmZm4cuUKXr7MXjFga2uL3bt3o3Xr1vD398cXX3yBLl26YOvWrcZ6CSahX3Q8HqVlSWozvHlli7iL25AufhcOmyJeQXrH2QaXvuPECpEuzN/7j+hYc623QmQOmBbMwPo08sXOiw8KDwRw5k4qC9sTERERERFZIBcXF6xZsybf5ytWrKhW7Nnb2xsHDkgv2G7JYhLuYu8VaRkfrOTAiFbv6qlHlu3f78NRc+JOvEiXNpkFAH0bV8DEDrX00Cui4kehFHDi5jNRsVZymHW9FSJTx8kVA2tYqSysZUCWUHgswML2RLqS8jITfX6Pw8WkFwCAym6lMDqMRc+IiIiIiMyRQilgmMQ6KwAw9+Mgnv8XwfnJYegbfQz7rjwUFe9XzgH/N7IZbxol0qH464+hFHldsY53aY55RHrEyRUDs5LL0DHICxtP3RMVv+nUXRa2J9KSQing4KUHGLT6BDLfSkZ6Kek5+q04DhsrGeb3CEKbmvkXASUiIiIiItPSbdEhiLy2qNLC3xUdArz00p/iJLpvA7zKUODbv84gJuE+XivePGclA5wcbBBWwwMTO9SAg62V8TpKZKHirj0SHTu8BVfqEekTJ1eMYPqHAaInVwQA82L/waiwqvrtFJEFychSYvSGBPyVcL/Q2EyFgMGrTmFx7zqcYCEiIiIiMgMxCXdx6naqpDauJWywLLKBnnpU/DjYWmFmtzqY2c3YPSEqfo4lPhEVZy0HGlUpp+feEBVvXJdpBLbWcgSWdxId/8v+q1CIXe9HVIy9eJ2F93/Yg3fH/5+oiZXcJm25yL8zIiIiIiITp1AKGKpFOrD4b1rpvjNERAamUAo4ffOpqNggpgQj0jtOrhjJ6DbVRMcqhezVK0SkWcrLTFQb/3+oOWknbj97rdU+klJfi777g4iIiIiIjCN42i7Jbeb3YJ0VIrIM8dcfi67jXN/XRb+dISJpkyvTp09H/fr1UapUKbi5uaFTp064cuWKWszr168RFRWFsmXLomTJkujSpQuSk5PVYm7duoXw8HA4OjrCzc0No0ePRlZWllrM/v37UadOHdjZ2aFy5cpYvnx5nv4sWLAAFStWhL29PYKDg3Hs2DEpL8eoGlYqCxsJ7/7CA1y9QpSbQilg34VkVBm3DQFTduFVlrLwRoV48Fy7iRkiIiIiItK/yVvP4VFaVuGBudTxLs06K0RkMf4Tlyg6trGfqx57QkSAxMmVAwcOICoqCvHx8YiNjUVmZiZat26NtLQ0Vcznn3+OrVu34o8//sCBAwdw7949fPjhh6rnFQoFwsPDkZGRgbi4OKxYsQLLly/HhAkTVDGJiYkIDw9H8+bNkZCQgJEjR2LAgAHYuXOnKmbdunUYNWoUJk6ciFOnTiEgIABhYWF48OBBUd4Pg7GSyzCkqZ/o+CwlEPev+IJVRJYqI0uJEWtPwW/cdvRdmbdQfVG4lbLX3c6IiIiIiEhnMrKUiD58S1IbGYA/hjTST4eIiAxMoRSw+7K4657Wchka+pXVc4+ISNLkyo4dOxAZGYkaNWogICAAy5cvx61bt3Dy5EkAQEpKCpYuXYrZs2ejRYsWqFu3LqKjoxEXF4f4+HgAwK5du3Dx4kWsWrUKgYGBaNu2LaZOnYoFCxYgIyMDALB48WL4+vpi1qxZqFatGoYOHYquXbtizpw5qr7Mnj0bAwcORN++fVG9enUsXrwYjo6OWLZsma7eG70b0aoqpCxMnhRzXm99ITJ1RamnIoaHkz0aGGnJ7JMnT9CrVy84OTmhdOnS6N+/P168eFFgm2bNmkEmk6k9Bg8ebKAeExHpBsc/IiISq928A5Lb/Mx0YERkQeKvP4ZC5A2m1T1LcfwjMoAi1VxJSUkBALi4ZF+QPHnyJDIzMxEaGqqK8ff3R4UKFXDkyBEAwJEjR1CrVi24u7urYsLCwpCamooLFy6oYnLvIycmZx8ZGRk4efKkWoxcLkdoaKgqxhxYyWUY1lz86pVrD18iQwepj4jMiS7qqYgx6YPqRjvx6NWrFy5cuIDY2FjExMTg4MGDGDRoUKHtBg4ciPv376seM2bMMEBviYh0h+MfERGJEZNwF1cfvpTUpoW/K9OBEZFFibsmPqMNxz8iw7DWtqFSqcTIkSPRuHFj1KxZEwCQlJQEW1tblC5dWi3W3d0dSUlJqpjcEys5z+c8V1BMamoqXr16hadPn0KhUGiMuXz5cr59Tk9PR3p6uurn1NRUCa9YP0a0qoqf910THd90xl4cGRdaeCCRGVMoBRy89ACDVus27ZcmNlYyzO8RhDY1PfV7oHxcunQJO3bswPHjx1GvXj0AwPz589GuXTvMnDkTXl75nxA5OjrCw8PDUF0lItIpjn9ERCSGQilg6NoESW1cS9hgWWQD/XSIiMhIjiU+ER0b0chXjz0hohxar1yJiorC+fPnsXbt2v9v787joqr3/4G/ZkAGUEFB1iQFU3ADXBJxSUtyXCq9v7K0xa3ym9cdriamuIvpdUstb4uK/TSzvrkrirh1E9HQSS0lNYlUBrdwBIVxZs7vD39Mkqhz4JxZX8/HYx73zvA5c96fofPCOZ9zPh8p65FVamoqfH19zY+wsDBblwQ3pQL/J9by0eQCXRk2ay7JWBGR7ci5nsrfqdyAlYOexpmZPW02sALcu1OvTp065hOLAJCQkAClUons7OxHbrt27VrUq1cPLVq0QHJyMm7ffvjVfGVlZdDpdBUeRES2ZK38A5iBRESOLG72btHbHP7geRkqISKyHaNJwPHf/7SobaMAb3i4V2uyIiKyUJXuXBk5cqR56ob69eubXw8ODoZer0dRUVGFu1cKCwvNVxcGBwfjyJEjFd6vsLDQ/LPy/y1/7f42Pj4+8PLygpubG9zc3Cpt86irGJOTk5GYmGh+rtPp7GKAZe4rMfhOc9ni9mPWa/BCdCjnTiSnUVxqQM/FB2Sd9qucv7c7DkzohlqeVb5xT1JarRaBgYEVXnN3d4efn5/5br7KvP7662jQoAFCQ0Nx4sQJvP/++8jNzcV3331XafvU1FRMnz5d0tqJiKrDWvkHMAOJiBzV0FWHca3EIGqbJf1j+V2ZiJzO4d+uwyBY1lbdnHd4E1mLqGFMQRAwcuRIbNy4EXv37kV4eMVbzNq0aYMaNWogMzPT/Fpubi7y8/MRHx8PAIiPj8fJkydx5coVc5uMjAz4+PigWbNm5jb3v0d5m/L38PDwQJs2bSq0MZlMyMzMNLepjEqlgo+PT4WHPfBwVyKuYV1R27zyyQ8yVUNkPdZaTwUAIoNq4vSMHshJUVtlYGXixIkPLLj898ejpjF8nGHDhkGtVqNly5Z44403sGbNGmzcuBHnz1c+zWBycjJu3rxpfvzxxx9V3jcR0aPYW/4BzEAiIke0TXMJe3Ovi9omxEeFPrFPyFQREZHtrDl0weK2HRsFyFgJEd1P1BnGESNGYN26ddi8eTNq165tvqrQ19cXXl5e8PX1xdtvv43ExET4+fnBx8cHo0aNQnx8PNq3bw8A6N69O5o1a4a33noL8+bNg1arxeTJkzFixAioVCoAwHvvvYdly5ZhwoQJGDp0KPbu3YsNGzZg+/bt5loSExMxaNAgtG3bFu3atcPixYtRUlKCIUOGSPXZWNWX77RHk8k7LW5//I+b2PrTZS5QRQ7HmuupuCuAxO6ReKdzhNVviU1KSsLgwYMf2SYiIgLBwcEVBpsBwGAw4MaNG6LWE4iLiwMAnDt3Do0aNXrg5yqVypyxRERysrf8A5iBRESOpirrrADAgQnPSV8MEZGNGU0C9py58viGANyVCrRv5C9zRURUTtTgyieffAIA6Nq1a4XXV61aZf4SvWjRIiiVSrz88ssoKyuDWq3Gxx9/bG7r5uaGbdu2Yfjw4YiPj0fNmjUxaNAgzJgxw9wmPDwc27dvx7hx47BkyRLUr18fn3/+OdRqtbnNa6+9hqtXryIlJQVarRaxsbFIT09/YJF7R+HhrkSPFoFIP2VZWALAqK+Oo1fLEN7yTA5BbzBh/LcabNYUyL6vmjUUyP6gu02n/goICEBAwOOvFomPj0dRURFycnLQpk0bAMDevXthMpnMJwwtodFoAAAhIbZbP4aICGD+ERFR9b3yyX9FbzO0Y0OuMUBETunwb9dhtPDi1GYhtXmekMiKRE8LVtnj/qsTPT09sXz5cty4cQMlJSX47rvvHrj6sEGDBtixYwdu376Nq1ev4t///jfc3SueBO3atSuOHz+OsrIynD9/vtIrIEeOHInff/8dZWVlyM7OFvVF3B4tf73t4xv9TfvZGTJUQiSd4lIDOs/NRJPJO2UfWPH3dsepaWr8PLOX3ayp8jhNmzZFjx498O677+LIkSP44YcfMHLkSPTv3x+hoffuTLt06RKioqLM61WdP38eM2fORE5ODvLy8rBlyxYMHDgQzzzzDKKjo23ZHSIiizH/iIioMts0l3D8D52obQJreSDlxeYyVURE1dWwYcMHpomdO3duhTYnTpxA586d4enpibCwMMybN++B9/nmm28QFRUFT09PtGzZEjt27LBWF2zq0PlrFrflDDdE1sXLOuyIm1KBj16NEbXN1ZK7GLr6iEwVEVWdM6+nIrW1a9ciKioK3bp1Q69evdCpUyd8+umn5p/fvXsXubm5uH37NoB7607t2bMH3bt3R1RUFJKSkvDyyy9j69attuoCEVGVMP+IiOh+VZ0OLGtSgvTFEJGkZsyYgYKCAvNj1KhR5p/pdDp0794dDRo0QE5ODubPn49p06ZV+HfhoUOHMGDAALz99ts4fvw4+vbti759++LUqVO26I5VpZ+y/ELVQR3CH9+IiCTjeGchndxLretjdvovKNTdtXibvWeucv0Vsguusp6K1Pz8/LBu3bqH/rxhw4YQBMH8PCwsDAcOHLBGaUREsmL+ERHR/eJm7xa9zdIBrTgFDpEDqF279kPX1Vu7di30ej1WrlwJDw8PNG/eHBqNBgsXLsSwYcMAAEuWLEGPHj0wfvx4AMDMmTORkZGBZcuWYcWKFVbrh7XpDSacv3rboraNArwd/vwIkaPhEWeHvp8g/qqbUV8dh9EkPL4hkQz0BhPGrD+GRpN2YMiX8g6s1KyhwKlpapxL7Y1/PvsU/+FAREREROQEpm89iWslBlHbPBcVwIsMiRzE3Llz4e/vj1atWmH+/PkwGP463rOysvDMM8/Aw8PD/JparUZubi7+/PNPc5uEhIrny9RqNbKysqzTARtJO5RncVt188oHr4hIPrxzxQ55uCsxpOOTWPVDvqjtmk3ZgdzZvWWqiuhBxaUG9Fx8QPZpv4B766kcmNDNIaf9IiIiIiKih9MbTKK///p6umHl4HYyVUREUho9ejRat24NPz8/HDp0CMnJySgoKMDChQsBAFqtFuHhFaezCgoKMv+sbt260Gq15tfub6PVah+637KyMpSVlZmf63Ti1nOyB1tPXLK4bcdGATJWQkSV4SXfdmrqiy1Rr6a4k8hlRqBj6h6ZKiL6C9dTISIiIiIiqXT+UPz32KOTu8tQCRFZauLEiQ8sUv/3x5kzZwAAiYmJ6Nq1K6Kjo/Hee+9hwYIFWLp0aYWBDzmkpqbC19fX/AgLC5N1f1IzmgT8fMmyASF3pQLtG/nLXBER/R3PVNqx7A+6o9GkHaK2uXSzDD0X78fOsV3lKYpcFtdTISIiIiIiqW05dhGFtyxfcxQAhnZsyO8JRDaWlJSEwYMHP7JNREREpa/HxcXBYDAgLy8PkZGRCA4ORmFhYYU25c/L12l5WJuHreMCAMnJyUhMTDQ/1+l0DjXAcvi36zBauALAc1EBXH+KyAY4uGLH3JQKLOsfi5HrNaK2O60tQcfUDPyQ/Lw8hZFL0RtMGP+tBps1BbLvq2YNBbI/6M47VIiIiIiIXIDRJGD0hp9EbRNYywMpLzaXqSIislRAQAACAqo2DZVGo4FSqURgYCAAID4+Hh988AHu3r2LGjVqAAAyMjIQGRmJunXrmttkZmZi7Nix5vfJyMhAfHz8Q/ejUqmgUqmqVKM9+OHsNYvbDooPf3wjIpIcz2DauRdin8B3x//A3tzrora7dFOP2Gk7oZnWU6bKyNlxPRUiIiIiIpJT3OzdorfJmpTw+EZEZDeysrKQnZ2NZ599FrVr10ZWVhbGjRuHN9980zxw8vrrr2P69Ol4++238f777+PUqVNYsmQJFi1aZH6fMWPGoEuXLliwYAF69+6N9evX48cff8Snn35qq67Jbu+Zh68ncz9OCUZkO7yP1gGsHNIe9bzdRG9XVGpC1OTtMlREzozrqRARERERyW/27Nno0KEDvL29UadOHYu2EQQBKSkpCAkJgZeXFxISEnD27Fl5C5XJ0FWHca3EIGqbJf1jOe0NkYNRqVRYv349unTpgubNm2P27NkYN25chUERX19f7N69GxcuXECbNm2QlJSElJQUDBs2zNymQ4cOWLduHT799FPExMTg22+/xaZNm9CiRQtbdEt2RpOA3MISi9o+6efFbCSyEZ7NdBDZk9Wi118BgFIDEDFxO87O6cWgpYfieipERERERNal1+vRr18/xMfH44svvrBom3nz5uGjjz5CWloawsPDMWXKFKjVavzyyy/w9PSUuWLpbNNcEj07Q7i/N/rEPiFTRUQkl9atW+Pw4cOPbRcdHY3vv//+kW369euHfv36SVWaXTt07hosXG4FLZ/wlbUWIno4Dq44CDelAh+/3gr/XHdc9LYmAI0m7cCy/rF4gf8YpftwPRUiIiIiItuYPn06AGD16tUWtRcEAYsXL8bkyZPRp08fAMCaNWsQFBSETZs2oX///nKVKimjSRC9rigA7EnqKnktRET2auneXy1u+0rrMBkrIaJH4WXjDqRXdCje7tSgytuPXK/B0FWPv1qAnF9xqQGd52aiyeSdsg+s+Hu749Q0NX6e2YsDK0REREREVXThwgVotVokJPy15oivry/i4uKQlZVlw8rE6fbvvaK34XRgRORKjCYBP+YVWdRWqQA6NK4nb0FE9FA80+lgprzQAheulmBv7rUqbb839zrazkhH9mQ1/3Hqgm7evov2c/bgjkHmub9wbz2VTSM6w8tD/HpBRERERERUkVZ7b2HjoKCgCq8HBQWZf1aZsrIylJWVmZ/rdDp5CrTAlmMXkXdD3LqOnA6MiFzNoXPXYOlZmzZP1uH5PSIb4p0rDmjlkDi0DK1d5e2v3Tai0aQd2HLsooRVkb0ymgTs+7kQjSdtR8yM3bIOrLgrgAnqSPw6qyd2jevKgRUiIiIicikTJ06EQqF45OPMmTNWrSk1NRW+vr7mR1iYbaaPMZoEjN7wk+jtOB0YEbkaMVOCjX6uiYyVENHj8M4VB7V19DMYuuoI9uZerfJ7jN7wExZknMHe8d04yu2E7uiNGJqWjazzf8q+L66nQkREREQEJCUlYfDgwY9sExERUaX3Dg4OBgAUFhYiJCTE/HphYSFiY2Mful1ycjISExPNz3U6nU0GWOJm7xa9zdIBrfhdlYhcCqcEI3IsPBPqwFYOaYeZ237BF/+9UOX3+P3PMjSatAOLX4lG37ZcAMsZFJca0GXeXly/fVf2ffl7u+PAhG4cVCEiIiIiAhAQEICAgABZ3js8PBzBwcHIzMw0D6bodDpkZ2dj+PDhD91OpVJBpVLJUpOlpm89iWslBlHbPBcVgBdjQmWqiIjIPnFKMCLHwmnBHNyUF5rh49dbV/t9xn57Am1m7ITRJEhQFdnCzdt30XTyTrSYtkv2gZXIoJo4PaMHclLUHFghIiIiIqqC/Px8aDQa5Ofnw2g0QqPRQKPRoLi42NwmKioKGzduBAAoFAqMHTsWs2bNwpYtW3Dy5EkMHDgQoaGh6Nu3r4168Xh6gwmrfsgXtU1AzRpYObidTBUREdmv6Vt/trgtpwQjsj2eFXUCvaJDcL5FLzSetMPi0e3KXL9t4l0sDsZoEnDw9BUMW/sj7sq8Rr27AkjsHol3OkfAw53jskRERERE1ZGSkoK0tDTz81atWgEA9u3bh65duwIAcnNzcfPmTXObCRMmoKSkBMOGDUNRURE6deqE9PR0eHp6WrV2MTp/uEf0Noc/eF6GSoiI7JveYMK5qyUWteWUYET2gYMrTsJNqcBvc3ujyaTt0FfzJPvYb0/g/Y0nkDOFdyXYK66nQkRERETk2FavXo3Vq1c/so0gVJxZQKFQYMaMGZgxY4aMlUlny7GLKLwl7q76Jf1jOc0NEbmk5O9+srgtpwQjsg88W+pkfp3TG61n7MaNak4LVWYEWkzbhbqebjg06Xl4ebhJVCFVB9dTISIiIiIiR2A0CRi9wfIThQAQ7u+NPrFPyFQREZH9MpoEbDx22eL2nBKMyD7wrKkTOpbSHUNWHcG+3KvVfq8/S41ompKOUB8PZP7rOQ6y2MjN23fRfs4e3DHIPPcX7q2nsmlEZ/6uiYiIiIioyuJm7xa9zZ6krtIXQkTkAMQsZM8pwYjsBwdXnNSqIe2w9afLGPXVcUne77JOj6Yp6WhUzxM7xz7LNTesgOupEBERERGRIxq66jCulRhEbcPpwIjIlYlZyD6haSDzkshOcHDFib0YE4peLUPQbcF+5F2/Lcl7nr9WiiaTd+Lphr5Y+04HnoiXAddTISIiIiIiR7VNcwl7c6+L2obTgRGRKxOzkD0ADIoPl7EaIhKDZ1SdnJtSgf3jn8VmzSWMWa+R7H2P5t3kIIvEuJ4KERERERE5MqNJwMgqfO/kdGBE5Momfmv5+lQebgq0b+QvYzVEJAbPrLqIPrFP4IXoULz88Q/QXLwp2ftykKX6rLmeSscIP3w+uB3XUyEiIiIiIsl1+/de0dtwOjAicmVGk4DvNJYvZP/eM42YmUR2hIMrLsRNqcCmkZ1wR29E86npMAnSvXf5IAsXQ7eMNddTUbkp8MmbbdElMoB/gImIiIiISBZbjl1E3o1SUdtwOjAicnVLMnItbqsAMOb5JvIVQ0SicXDFBXl5uOG31N4YvPII9v96VdL3zi0s+f8L33th59iuvJPlb7ieChERERERORujScDoDZZPa1OO04ERkSszmgQs3Xfe4vb/aBXKi2aJ7AzPurqw1UPb4Y7eiPi5e1B02yDpe5+/dod3styH66kQEREREZGzipu9W/Q2Swe04klCInJpSzJyIWZSmbkvx8hWCxFVDc++ujgvDzdoUtQoLjWg7awMlEq87kf5nSyuesKf66kQEREREZEzm771JK6ViLtY77moALwYEypTRURE9s9oErB8v+V3rTQK8ObsMER2iEclAQBqebrjzKyeWPRqrCzvf/22AS2m7ULkBzuw9/QVGKVc8MXOGE0C9v1ciMaTtiNmxm5ZB1ZUbgqsHPQ0zs/phbXD4jmwQkREREREVqM3mLDqh3xR2wTUrIGVg9vJVBER2Zv9+/dDoVBU+jh69CgAIC8vr9KfHz58uMJ7ffPNN4iKioKnpydatmyJHTt22KJLkjh07hqMIk6NTXuhhXzFEFGVudZtBPRY/2j9BF6KDcXCXblYfsDyEXRLlRkFDE2798dzxDMRSOwR5TS3gnM9FSIiIiIiciVtZ+0Svc3hD56XoRIislcdOnRAQUFBhdemTJmCzMxMtG3btsLre/bsQfPmzc3P/f39zf//0KFDGDBgAFJTU/HCCy9g3bp16Nu3L44dO4YWLRxv4GHEVzkWt3VXAh0a15OxGiKqKp6ZpQe4KRUY3zMKiepI/PPLHOw6XSjLfpYf/A3LD/7m8IMsXE+FiIiIiIhczfStJ6ErFXeX/pL+sQ77vY+IqsbDwwPBwcHm53fv3sXmzZsxatQoKBQV88Df379C2/stWbIEPXr0wPjx4wEAM2fOREZGBpYtW4YVK1bI1wEZbDl2Ebo7Rovb/7PLU8xOIjvFacHoodyUCvxnUFv8Oqsnnm5YR7b9LD/4GxpN2oGx645Bb4W1SaRy8/ZdNJ28Ey2m7ZJ9YKVjhB9Oz+iBnBQ1B1aIiIiIiMimqjIdWLi/N/rEPiFTRUTkKLZs2YLr169jyJAhD/zspZdeQmBgIDp16oQtW7ZU+FlWVhYSEhIqvKZWq5GVlSVrvVIzmgSM3vCTxe0VAMY830S+goioWniWlh7Lw12Jb97rCL3BhDc+z8LRvCJZ9rPpRAE2nShAWF1P7BzTxS4HEYwmAQdPX8GwtT/irszjQCo3BT55sy26RAbwCgUiIiIiIrIbT8/aLXqbPUldpS+EiBzOF198AbVajfr165tfq1WrFhYsWICOHTtCqVTif//3f9G3b19s2rQJL730EgBAq9UiKCiownsFBQVBq9U+dF9lZWUoKyszP9fpdBL3Rry42eLy8x+tQnlOiMiO2d/Za7Jb1hpk+ePPUrSYtsuu1hXheipERERERETA0FWHcbPU8ulsAE4HRuSMJk6ciA8//PCRbU6fPo2oqCjz84sXL2LXrl3YsGFDhXb16tVDYmKi+fnTTz+Ny5cvY/78+ebBlapITU3F9OnTq7y91IasPIxrJQZR28x9OUamaohICjx7S6JZa5Cl5K6AFtN22fQODq6nQkREREREdM82zSXszb0uahtOB0bknJKSkjB48OBHtomIiKjwfNWqVfD397dowCQuLg4ZGRnm58HBwSgsrLgmcGFh4UPXaAGA5OTkCoM2Op0OYWFhj923HKZvPYV9v4rLz57Ng+DhzhUdiOwZz+JSlVlrkKXMKGBo2lEAQN/oEMx7NVb2Py7FpQa0n5OBYr38a8B0jPDD54PbwcvDTfZ9ERERERERVYXRJGDkeo3o7TgdGJFzCggIQEBAgMXtBUHAqlWrMHDgQNSoUeOx7TUaDUJCQszP4+PjkZmZibFjx5pfy8jIQHx8/EPfQ6VSQaVSWVyjXKZtOYXVh34Xvd2yN9rIUA0RSYmDK1Rt9w+yjP9Wg82aAtn2Vb4uS2RQTWwa0VnSAQm9wYTPvj+HRRlnYeB6KkRERERERGZi1wkAgKUDWvH7DhEBAPbu3YsLFy7gnXfeeeBnaWlp8PDwQKtWrQAA3333HVauXInPP//c3GbMmDHo0qULFixYgN69e2P9+vX48ccf8emnn1qtD1XRa/FB/KK9JXo7TqdI5Bg4uEKS8XBXYkn/1lj46r1F34d/lYNSgyDLvnILS9A0JR2hPjWQ+a9u1RpkuaM3os/y7/FrYYmEFVaO66kQEREREZGjmb71pOh1AlqF+eLFmFCZKiIiR/PFF1+gQ4cOFdZgud/MmTPx+++/w93dHVFRUfj666/xyiuvmH/eoUMHrFu3DpMnT8akSZPQuHFjbNq0CS1atLBWFyxWXGrAqLVHse/sjSptz+kUiRyHQhAEec5+OwCdTgdfX1/cvHkTPj4+ti7HKd2bXmsPivXiFjwUqyrrlXA9FbKUM2aFM/aJiOThjHnhjH0iIuk5a1aI7ZfeYEKTyTtF7+f8nF686prIgTljBkrZpzt6I1K2nMDOny6jWOLTSsxPItuzNC94ppdkVcvTHadm9JB9IOP6bQNaTNsFdwWQ2D0S73SOeOi6LFxPhYiIiIiIyDJPz+J0YERE5VPJf3noArS3xN3JJwbzk8ixcHCFrKKWpztyUrrjjt6I5xbsQ8HNMln2YxCAebtyMW9XboV1WbieChERERERkTjTt57EzVJxsxA8FxXA6cCIyGnoDSa88XkWjuYVyb4v5ieR4+HgClmVl4cbspITrLLOSfm6LEoA8t+jwvVUiIiIiIjIeegNJqz6IV/UNgE1a2Dl4HYyVUREZF0ztv6MlT/kWWVf9et6Mj+JHJDDnwVevnw55s+fD61Wi5iYGCxduhTt2jGM7J2Xhxt2j+tqvqNkwe6zMMm0+o/cAytcT4WqY/bs2di+fTs0Gg08PDxQVFT02G0EQcDUqVPx2WefoaioCB07dsQnn3yCxo0by1Kj0STg4Okr+HDXLzh35TbkuwEaUABQuSvQPsIfy15vYzfHVfl8uuknCnBLb19LldVQKhDoo8IbcQ0eOSWiNdnz5+WmAIJ8PPFme/v5vKx5jClx729wu3A/LB3Q2ubHmCNkIFnujt6IKZt/wjZNAURe6E7VYG/HNVmuKhk4ePBgpKWlVXhNrVYjPT1d8vraztolepvDHzwveR1ERLbQZf5e/H79jlX2VdfbHf99v5tV9kVE0rL9GYVq+Prrr5GYmIipU6fi2LFjiImJgVqtxpUrV2xdGlnIw12JEc82wW+pvfFTSnd42cFJLkt1jPDD6Rk9kJOi5pdYqjK9Xo9+/fph+PDhFm8zb948fPTRR1ixYgWys7NRs2ZNqNVqlJaWSl5f+qkCRE7eiSFf/ogzMp/0BQABQKlBwP5fr6HFtF14adn3Mu/x8d5dcxRNU9LxzY+X7W6gAADumgRcKirFvF25aDJ5J1J3/GLTeuz98zIKwOWb9vN5WfsYMwEo0RuxL/eqXRxj9p6BZLnyY//bHA6sWJu9HddkuapkIAD06NEDBQUF5sdXX30leW3Tt56ErlTcZWpcJ4CInMXbq7OtNrDi5+WG4ylqq+yLiKTnOGeyK7Fw4UK8++67GDJkCJo1a4YVK1bA29sbK1eutHVpVAW+3jVwelZPnJqmhr93DVuXUyl3BbBy0NM4P6cX1g6L50L1VG3Tp0/HuHHj0LJlS4vaC4KAxYsXY/LkyejTpw+io6OxZs0aXL58GZs2bZK0tvRTBXjv/x6DQa7byixw4qLOpieJ3l1zFBm/ONaA/X8OXrDZgAE/L3F4jNl3BpLlHPHYd2a2Pq7JcmIzsJxKpUJwcLD5UbduXUnrqsp0YK3CfLlOABE5hTt6IzLPXLPKvpqH1MSxqT2ssi8ikofDDq7o9Xrk5OQgISHB/JpSqURCQgKysrJsWBlVVy1Pd+SkdMfpGT3QJKimrcsBAHi6KfBTSnecS+2N55oG8oosspkLFy5Aq9VWyD5fX1/ExcVJmn1Gk4ApG09I9n7VceKiDsWlcl/P/6A7eqPDniz87PsL0BussdrUX/h5icNjrGqslYFkOUc+9p2ZIx3XJN7+/fsRGBiIyMhIDB8+HNevX5f0/dMO5Yne5tvhHSWtgYjIVmZt+9kq+/no1RhsH9PVKvsiIvk47ODKtWvXYDQaERQUVOH1oKAgaLXaSrcpKyuDTqer8CD7Vb4uy6+zeqJPbIhNagir64lT09Q4M7sXfO30bhpyLeX5Jib7APH5d+TCDVwtsZ+TMuO+Pm71fc6x8XRR1WESgC+z8qy6T35e4vAYqxprZSBZzpGPfWfnKMc1idOjRw+sWbMGmZmZ+PDDD3HgwAH07NkTRuPD5+MTm4FH826IqmlJ/1hefEZETuPQb9IOWP/dk3U9cH5OL7zUur6s+yEi63DYwZWqSE1Nha+vr/kRFhZm65LIAh7uSizp3xrn5/TCPztHWGWffaND8Ousnvj+fS5UT+JNnDgRCoXikY8zZ85YtSax+Xflln2tXZD/p3Xmu71f3vXbVt+nlH6/Yd36+XmJ48zHmDNkIFnO0Y99Z2aLv50kfwb2798fL730Elq2bIm+ffti27ZtOHr0KPbv3//QbcRmoLeIqY/D/b3RJ/YJi9sTEdm7Gm7SnypVAHg2MgCnpqlx8P3nOSBN5EQc9qxxvXr14ObmhsLCwgqvFxYWIjg4uNJtkpOTkZiYaH6u0+n45dqBuCkVmNC7KZJ6RuHg6SsY/lUOSg3SzVPvrgA+Hfg0ukQG8A8dVUtSUhIGDx78yDYREVUbKCzPt8LCQoSE/HVHV2FhIWJjYx+6ndj8C6ztWaX65PJkXS+r77Ohvze+P2v13UqmgZ+3VffHz0scZz7GnCEDyXKOfuw7M1v87SR5M/Bh71WvXj2cO3cO3bp1q7SN2Ax8uVV9bNJctmj/e5K6iqqXiMje/aNVKD5M/7Va76HEvdlY2oX7YemA1rxol8iJOezR7eHhgTZt2iAzMxN9+/YFAJhMJmRmZmLkyJGVbqNSqaBSqaxYJcnBTanAs82DcGZWLxSXGtBz8QH8UVT1K4A93RTI/uB5TvtFkgkICEBAQIAs7x0eHo7g4GBkZmaaTyTqdDpkZ2dj+PDhD91ObP61C/dDQE13u5m2aNFrray+z0m9muHLw+IWc7UXSgXwVnxDq+6Tn5c4znyMOUMGkuUc+dh3drb420nyZmBlLl68iOvXr1cYcP47sRnYoXE9eHu44bb+4VONAcDHr7fmRWlE5HTe7tTI4sEVpQKICq6Nf3WP4oW6RC7KoacFS0xMxGeffYa0tDScPn0aw4cPR0lJCYYMGWLr0shKanm64/uJ3aq0LgvXUyF7kJ+fD41Gg/z8fBiNRmg0Gmg0GhQXF5vbREVFYePGjQAAhUKBsWPHYtasWdiyZQtOnjyJgQMHIjQ01DzQLAU3pQIz/xEt2ftVR3R9H5tc6ePl4YbnmwVafb9SeLdzODzcrfsnnp+XODzG7rHXDCTLOfKx78xseVyT5cRmYHFxMcaPH4/Dhw8jLy8PmZmZ6NOnD5566imo1WrJ6nJTKrDw1ZhHtvmfZ8LRK9o262ISEcnJw12J/3km/JFtgmrVwK+zeuK31N7YMeYZPNc0kAMrRC7Kof/F/dprr+Hq1atISUmBVqtFbGws0tPTH1jklJxf+bosC18VsGDnGXz8/W8Pbds3OgTzXo21+ok0osqkpKQgLS3N/LxVq3tXme7btw9du3YFAOTm5uLmzZvmNhMmTEBJSQmGDRuGoqIidOrUCenp6fD0lHaaoR4tQrDizdYYue44DCbppuATI7q+D7aM7GyTfQPAZwOfxrtrjiLjlys2q0Gs/3kmHMm9mtlk3/y8xOExZt8ZSJZzxGPfmdn6uCbLic1ANzc3nDhxAmlpaSgqKkJoaCi6d++OmTNnSn53XvnfqKmbT6Hwlt78el0vd8z+R0v0ig6VdH9ERPak/PvBfw5eeOBnb3dqgCkvtLB2SURkpxSCINjm27wd0Ol08PX1xc2bN+Hj42PrckhCRpOAg6ev4N97cnHlVhmCfDyR+Hwkb9OkKnHGrBDTp/Lj6cNdv+DclduQcxIjBQCVuwLtI/yx7PU2dnPV7R29ESlbTiD9RAFu6e3rz2YNpQKBPiq8EdcA73SOsIuBY3v+vNwUQJCPJ95sbz+flzWPsarM/+zqGUiWu6M3Ysrmn7BNU4DSR88mRBLivO7ycdasEPvvwCMXbuDKrVIE1vZEu3A/fp8ichHOmIFi+6Q3mPBlVh5+v3EbDfy88VZ8Q7v4/kBE8rM0Lzi44mR/KIhIes6YFc7YJyKShzPmhTP2iYik56xZ4az9IiJpOWNWOGOfiEgeluYFh1uJiIiIiIiIiIiIiIhE4OAKERERERERERERERGRCC49IW/5jGg6nc7GlRCRPSvPCGeaRZH5R0SWYgYSkatyxvwDmIFEZBlnzEDmHxFZytIMdOnBlVu3bgEAwsLCbFwJETmCW7duwdfX19ZlSIL5R0RiMQOJyFU5U/4BzEAiEseZMpD5R0RiPS4DXXpBe5PJhMuXL6N27dpQKBSPba/T6RAWFoY//vjDJRa+crX+Auwz+1w5QRBw69YthIaGQql0jtkUmX+Pxz6zz86oKv1lBrrefycA++wKfXa1/gL8N2A5ZuDjuVqfXa2/APvsqhkoNv8A1/tvxdX6C7DPrtBnOb8Hu/SdK0qlEvXr1xe9nY+Pj0v8h1fO1foLsM+uQkyfneVKnXLMP8uxz67B1fostr/MwHtc7b8TgH12Ba7WX8C1/w0IMAPFcLU+u1p/Afb5cZwtA6uaf4Dr/bfiav0F2GdXIMf3YOcYeiYiIiIiIiIiIiIiIrISDq4QERERERERERERERGJwMEVEVQqFaZOnQqVSmXrUqzC1foLsM+uwhX7XF2u+Jmxz67B1frsav2Viit+buyz83O1/gKu2WcpuOLn5mp9drX+AuwzWc7VPjdX6y/APrsCOfvr0gvaExERERERERERERERicU7V4iIiIiIiIiIiIiIiETg4AoREREREREREREREZEIHFwhIiIiIiIiIiIiIiISgYMrREREREREREREREREInBwxUKzZ89Ghw4d4O3tjTp16lTaJj8/H71794a3tzcCAwMxfvx4GAwG6xYqo4YNG0KhUFR4zJ0719ZlSWr58uVo2LAhPD09ERcXhyNHjti6JNlMmzbtgd9nVFSUrcuSzMGDB/Hiiy8iNDQUCoUCmzZtqvBzQRCQkpKCkJAQeHl5ISEhAWfPnrVNsXaO+cf8czbOnn8AM1BKzEBmoLNhBjIDxWAGMgOdDTOQGWgp5t89zp6BzD/mX3Xzj4MrFtLr9ejXrx+GDx9e6c+NRiN69+4NvV6PQ4cOIS0tDatXr0ZKSoqVK5XXjBkzUFBQYH6MGjXK1iVJ5uuvv0ZiYiKmTp2KY8eOISYmBmq1GleuXLF1abJp3rx5hd/nf//7X1uXJJmSkhLExMRg+fLllf583rx5+Oijj7BixQpkZ2ejZs2aUKvVKC0ttXKl9o/5dw/zz7k4c/4BzEApMQPvYQY6F2YgM9BSzMB7mIHOhRnIDLQE8+8vzpqBzD/mnyT5J5Aoq1atEnx9fR94fceOHYJSqRS0Wq35tU8++UTw8fERysrKrFihfBo0aCAsWrTI1mXIpl27dsKIESPMz41GoxAaGiqkpqbasCr5TJ06VYiJibF1GVYBQNi4caP5uclkEoKDg4X58+ebXysqKhJUKpXw1Vdf2aBCx8D8W2TrMmTD/HNuzEBpMAMX2boM2TADnRszUBrMwEW2LkM2zEDnxgysPlfOP0Fw7gxk/jk3a+Uf71yRSFZWFlq2bImgoCDza2q1GjqdDj///LMNK5PW3Llz4e/vj1atWmH+/PlOc7ujXq9HTk4OEhISzK8plUokJCQgKyvLhpXJ6+zZswgNDUVERATeeOMN5Ofn27okq7hw4QK0Wm2F37evry/i4uKc+vctF+afY2P+uVb+AcxAqTEDHRszkBkIMAOrgxno2JiBzECAGVhVrpJ/gHNmIPOP+QdIk3/uUhRHgFarrRCoAMzPtVqtLUqS3OjRo9G6dWv4+fnh0KFDSE5ORkFBARYuXGjr0qrt2rVrMBqNlf4Oz5w5Y6Oq5BUXF4fVq1cjMjISBQUFmD59Ojp37oxTp06hdu3ati5PVuXHZGW/b2c5Xq2J+efYmH+ulX8AM1BqzEDHxgxkBpZjBlYNM9CxMQOZgeWYgeK5Qv4BzpuBzD/mX7nq5p9L37kyceLEBxby+fvDWQ+ocmI+g8TERHTt2hXR0dF47733sGDBAixduhRlZWU27gVVRc+ePdGvXz9ER0dDrVZjx44dKCoqwoYNG2xdGlkB84/558qYf8QMZAa6MmYgMQOZga6MGejamH/3MANdE/NPHi5950pSUhIGDx78yDYREREWvVdwcDCOHDlS4bXCwkLzz+xVdT6DuLg4GAwG5OXlITIyUobqrKdevXpwc3Mz/87KFRYW2vXvT0p16tRBkyZNcO7cOVuXIrvy32lhYSFCQkLMrxcWFiI2NtZGVVkX84/5V47551r5BzADAWYgwAwsxwxkBpZjBlbEDGQG2vPvT0rMQJifu0IGMv/uYQYy/wDmX7nq5p9LD64EBAQgICBAkveKj4/H7NmzceXKFQQGBgIAMjIy4OPjg2bNmkmyDzlU5zPQaDRQKpXm/joyDw8PtGnTBpmZmejbty8AwGQyITMzEyNHjrRtcVZSXFyM8+fP46233rJ1KbILDw9HcHAwMjMzzQGq0+mQnZ2N4cOH27Y4K2H+Mf/KMf9cK/8AZiDADASYgeWYgcxAgBlYHcxAx8YMZAYCrpWBzL97mIHMP4D5B0iTfy49uCJGfn4+bty4gfz8fBiNRmg0GgDAU089hVq1aqF79+5o1qwZ3nrrLcybNw9arRaTJ0/GiBEjoFKpbFu8BLKyspCdnY1nn30WtWvXRlZWFsaNG4c333wTdevWtXV5kkhMTMSgQYPQtm1btGvXDosXL0ZJSQmGDBli69Jk8a9//QsvvvgiGjRogMuXL2Pq1Klwc3PDgAEDbF2aJIqLiyuMvl+4cAEajQZ+fn548sknMXbsWMyaNQuNGzdGeHg4pkyZgtDQUPMfVfoL84/552ycPf8AZqCUmIHMQGfDDGQGisEMZAY6G2YgM9BSrp5/gPNnIPOP+SdJ/glkkUGDBgkAHnjs27fP3CYvL0/o2bOn4OXlJdSrV09ISkoS7t69a7uiJZSTkyPExcUJvr6+gqenp9C0aVNhzpw5Qmlpqa1Lk9TSpUuFJ598UvDw8BDatWsnHD582NYlyea1114TQkJCBA8PD+GJJ54QXnvtNeHcuXO2Lksy+/btq/SYHTRokCAIgmAymYQpU6YIQUFBgkqlErp16ybk5ubatmg7xfxj/jkbZ88/QWAGSokZyAx0NsxAZqAYzEBmoLNhBjIDLeXq+ScIrpGBzD/mX3XzTyEIglD1oRkiIiIiIiIiIiIiIiLXorR1AURERERERERERERERI6EgytEREREREREREREREQicHCFiIiIiIiIiIiIiIhIBA6uEBERERERERERERERicDBFSIiIiIiIiIiIiIiIhE4uEJERERERERERERERCQCB1eIiIiIiIiIiIiIiIhE4OAKERERERERERERERGRCBxcISIiIiIiIiIiIiIiEoGDK0RERERERERERERERCJwcIWIiIiIiIiIiIiIiEgEDq4QERERERERERERERGJ8P8AIjCbNQbeQt8AAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1397,12 +1040,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  11\n"
+      "Question  20\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1414,12 +1057,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  12\n"
+      "Question  21\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1431,12 +1074,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  13\n"
+      "Question  22\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1448,12 +1091,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  14\n"
+      "Question  23\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1465,12 +1108,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  15\n"
+      "Question  24\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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OH374oUDnjouLQ8eOHaHVarFx40aULl1aoayJiJSh5jXw5s2bKPPYpsAxMTEIDw9HgwYNCnRuIrITJUo8WSR48CCzFZSLy38/CwgAihcH0tIy/7t9e8DJCZg/P3P1xcMCx5w58j6/aNHM/01IePK9kJDMfUQel5AAvP020KhR5n4mbdpkbsI+bRowfry8zy+Itm0zV7Z88032wsg33wBFimRuTP+okyczf12bNrVcjkRktdS6B+zZsyfqPF6YBfDCCy8gNDQU/fv3z3HDYiJyPA9bLv/zzz/ZCiOzZ8/G3r17sWbNGnTt2hXbt2/He++9h5YtW2atfFCqeHDy5Emkpqaiqcz7o5CQEMyYMQNpaWlwdnbO+rmaz9f5ERkZCXd393wXjkh5LIyQVZkyZQqioqIQHh6O4sWLIzg4GOPHj8e4cePQo0cPhD76oClT586dcfHiRYwaNQq7d+/G7t27s94rW7bsE/0JiYgsTc1rYK1atdCuXTvUqVMHJUqUwLlz57Bo0SKkp6djxowZCn4LIrIrZ88C7doBL7+cubpCrwf+/BO4cQPo2TMzpnRp4P33M9trdeuWWRg4fBhYvx6Q0yqgfv3M//3oo8xzOzlltvAqWjRzM/Uff8zMp2rV/44ZOjSzVdWWLZkbp3funFkomTIl85jatQv2/S9dyvxcIHP1B5B5biBzpUzv3pn/39UVmDwZGDgwc6+TTp2AXbsyV9RMnZpZNHnU5s2ZBRPefxIR1LsHDAwMzJoF/jg/Pz+uFCGiLP7+/ggKCsKWLVvw1ltvAQBOnTqFjz/+GH369MEzzzwDAFiyZAnq1KmD//3vf/j111/zPO+PP/6IS5cu4cGDBwCAnTt3Ysq/91K9e/dGxYcrj5G58qRIkSKyx+eee+45TJ48GTt27EDHR1qkqvl8DQCJiYmYP38+AGDPnj0AgC+//BIeHh7w8PDAoEGDssVv3rwZzzzzDPcYsSYSkZWIjIyU9Hq9NHjw4Gw/z8jIkBo2bCj5+PhId+/ezff5AeT6atWqVcGSJyIqILWvgZ988onUoEEDqUSJEpJer5d8fHyknj17SkePHi1g5kRk127flqSBAyUpMFCSihaVJHd3SWrcWJJ+/TV7nMkkSRMnSpK3tyS5ukpS69aSdPy4JFWsKElvvvlf3OLFkgRI0sGDOX/e5MmSVK6cJGm1mXHR0Zk/T0uTpFKlMt9/6K+/MmM+/zz7OZKSMj+3dm1JMhpz/25AZj5Ps21bZlxOr5zuHxculKRq1STJYJCkgABJ+uILSTKbn4xr3FiSXn/96Z9NRA5B7XvAnACQBg4cqOg5icj2zZ49WypWrJj04MGDrGtQ+fLlpYSEhGxxc+fOlQBIv/zyS57nbNWqVa5jcdu2bcsW27hxY+n1fN4fBQcHS/369cv674JeWytWrCh98sknT/3M6OjoXL9bxYoVs8WeOnVKAiBt2bJF7lcjFWkkSZIsWYghIiIiIiIHUakS0Lo1sGRJwc81eTKweHHmXiY6XcHPV1iiooB69YBDh57ce4SIiIiokCQmJsLf3x8zZ85Ev379LPrZUVFRqFevHg4dOpRjC8C8/Pjjjxg4cCAuX74MDw8PxfMrqGHDhmHnzp2IjIzkihEroi3sBIiIiIiIiPI0fDiQnAysXFnYmRTMjBlAjx4sihAREZFVcXd3x6hRozBr1iyYzWaLfvaMGTPQo0ePfBVFAKBXr16oUKECvvrqK2UTU8CdO3fw/fffY8qUKSyKWBmuGCEiIiIiInUouWKEiIiIiIhIIVwxQkREREREREREREREDoMrRoiIiIiIiIiIiIiIyGFwxQgRERERERERERERETkMFkaIiIiIiIiIiIiIiMhh6As7gfwwm82IjY1F8eLFodFoCjsdIrJSkiTh3r178PHxgVZrP3VgXgOJSIQ9XgN5/SMiUbwGEpGjssfrH8BrIBGJkXMNtMnCSGxsLHx9fQs7DSKyEVeuXEH58uULOw3F8BpIRHLY0zWQ1z8ikovXQCJyVPZ0/QN4DSQieUSugTZZGClevDiAzC/o5uZWyNkQkbVKSkqCr69v1jXDXvAaSEQi7PEayOsfEYniNZCIHJU9Xv8AXgOJSIyca6BNFkYeLplzc3PjxZCI8mRvy2x5DSQiOezpGsjrHxHJxWsgETkqe7r+AbwGEpE8ItdA+2k2SERERERERERERERElAcWRoiIiIiIiIiIiOgJO3fuxDPPPAMfHx9oNBqsXr062/uSJGH8+PHw9vaGq6sr2rdvj3PnzmWLiY+PR69eveDm5gYPDw/069cPycnJFvwWRERPYmGEiIiIiIiIiIiInnD//n3Url0bX331VY7vz5w5E/PmzcOCBQuwf/9+FC1aFJ06dUJqampWTK9evXDixAls3rwZa9euxc6dOzFgwABLfQUiohzJLoywUkxEhSHFaMLHq4+h96L9+Hj1MaQYTYp/hqWub0ePHkWLFi3g4uICX19fzJw5U/HvQkT2xRLXQHuU+CAd3b/eg5Dp4ej+9R4kPkgv7JSIyML27NljNfd3v/32GwIDA+Hi4oJatWohLCxM8e9LRPbFGu4Bu3TpgilTpuCFF1544j1JkjBnzhyMGzcOzz33HIKDg7Fs2TLExsZmXW9PnTqFDRs24Pvvv0fjxo3RvHlzzJ8/HytXrkRsbKyFvw0R2RK1r4GyCyOsFBORpfVfdhDVx2/Aj/suY9e52/hx32VUH78B/ZcdVPRzLHF9S0pKQseOHVGxYkVERkZi1qxZmDBhAhYuXKjodyEi+9FvyQGLXAPtTatZW1F70iZEXk7A9cRURF5OQO1Jm9Bq1tbCTo2ILOjBgwdWcX+3d+9evPrqq+jXrx8OHz6M559/Hs8//zyOHz+u3pcnIptmqefggoiOjkZcXBzat2+f9TN3d3c0btwYERERAICIiAh4eHigQYMGWTHt27eHVqvF/v37cz13WloakpKSsr2IyHG8vVT952CNJElSvg/WaPDnn3/i+eefB5BZKfbx8cHIkSPx/vvvAwASExNRtmxZLFmyBD179sSpU6dQo0YNHDx4MOuiuGHDBoSGhuLq1avw8fHJ83OTkpLg7u6OxMREuLm55Td9IrIB/ZcdxOaTN3N9v0ONMvjujYY5vleQa4Va17dvvvkGH330EeLi4mAwGAAAo0ePxurVq3H69Gmh3HgNJHIcrWZtxaU7Kbm+r9Y10FqJfqe8ft0qerpixwdt1UiRiKxETteLwry/e+WVV3D//n2sXbs2K8cmTZqgTp06WLBgQb6/ExHZp8J6Ds7L49fRvXv3olmzZoiNjYW3t3dW3MsvvwyNRoNffvkF06ZNw9KlS3HmzJls5ypTpgwmTpyI9957L8fPmjBhAiZOnPjEz3kNJLJ/z365C0ev5l4MVeoaqOgeI2pWivOLLRSIbFeK0fTUm0EA2HzypkWWEyt1fYuIiEDLli2zHpoBoFOnTjhz5gzu3r2r+vcgItsx8e8TTx3cByx3DbQliQ/S8/x1u3QnhfeERGTR+7uIiIhsn/Mw5uHn5ISzpYkckzU9BxemMWPGIDExMet15coV4WM5Fkhku/46fO2pRRFAuWugooWRuLg4AEDZsmWz/bxs2bJZ78XFxaFMmTLZ3tfr9ShZsmRWzOPye0OYWwuFlp+Gy/1qRFQIJv0t1lpANK4glLq+xcXF5XiORz/jcXwoJnI8xgwzFu+JEYqduu6EusnYmL5LxCbaiMYRkf2y5P1dbjG53f8BwPTp0+Hu7p718vX1lfsVicgGTQs7qWicmry8vAAAN27cyPbzGzduZL3n5eWFmzezF3oyMjIQHx+fFZMTZ2dnuLm5ZXuJYDtVIttlMkv4cNVRodjJawv+HKxoYUQt+bkhfFoLhct3UxE8YaPSaRKRwtZEiW3Etu3MLZUzKVx8KCZyPIv3RAvHHrmaqGImtifm9gNF44iICktBZksTke26ePu+onFq8vPzg5eXF8LD/5uAnJSUhP379yMkJAQAEBISgoSEBERGRmbFbN26FWazGY0bN1Y0n6eNBV66k8LiCJGVOxAdj9R0s1BsxMX4An+eooURtSrFcm8IRVooJKVmYMJqbnRHZK2MGWbcF7wYWoJS1zcvL68cz/HoZzyOD8VEjueXg5eFY91dnFTMxPbodRpF44jIflny/i63GDVmSwNsI0Nky1z0YkN1onEFlZycjKioKERFRQHIbEMYFRWFy5cvQ6PRYNiwYZgyZQrWrFmDY8eO4Y033oCPj0/WPiTVq1dH586d0b9/fxw4cAB79uzBoEGD0LNnT6F9hkWxnSqR7YtNePrf4Uc56Qr+eYpeRdWqFMu9IRRtjbBk3yUYM6xn4JWI/rN0b4xwbJtqpdVL5F9KXd9CQkKwc+dOpKf/dzO2efNmVKtWDSVKlMjxswvyUExEtsdklnBRxmqG/i38VczG9tSt4KFoHBHZL0ve34WEhGT7nIcxDz9HSbm2lJ7JmdJEtqCoQa9oXEH9888/qFu3LurWrQsAGDFiBOrWrYvx48cDAEaNGoXBgwdjwIABaNiwIZKTk7Fhwwa4uLhknWP58uUIDAxEu3btEBoaiubNm2PhwoWK5tnnh31Ccc0/3aLo5xKRcv6KuiYc+2Ld8gX+PNlX0eTkZJw/fz7rvx9WikuWLIkKFSpkVYqrVKkCPz8/fPzxx7lWihcsWID09HTFK8Uxd8QHE37YdRHvtqmsyOcSkXIORN8Rjh3/TJAin2mJ69trr72GiRMnol+/fvjwww9x/PhxzJ07F1988YUi34GIbN+cLWeEY7UaoHlV9YvDtuReaoZQXMytwm8/QUTqS05OxsWLF7P+u7Du74YOHYpWrVrh888/R9euXbFy5Ur8888/ig8MPrWldHwKgidsxNEJnRT9TCJS1pFrCUJxqRaa6Nu6dWtIkpTr+xqNBpMmTcKkSZNyjSlZsiRWrFihRnpZTt9IFoq7l2ZGitEEV4MC082JSDEms4T9MsYC32pe8AmCsleM2EKl2E1GS4mlETGKfS4RKedErNgG4yWKOCl2Q2OJ65u7uzs2bdqE6Oho1K9fHyNHjsT48eMxYMAARb4DEdk2k1nCwh0X8w7816A2laHTsiXUo9IE2zBeuH0fJnPuD/lEZB8OHz5sFfd3TZs2xYoVK7Bw4ULUrl0bv//+O1avXo2gIGUm+ADiLaXHrz6m2GcSkbKMGeY8/x4/1LBSzh0HHJVexj3x1HWFv3E9EWV3IDoeaRliz2eVSxeFQYF2ghrpaWVfK5WUlAR3d3ckJibm2FIm/Hgc+v0UmcORObswLZSDCkRWxJhhRtVx64Vi32vtjw87V8/xvbyuFbbKXr8XEQERF+7g1e/E2gBoAJx/yj2MPV4rRL7TsJ8PYfWR60Ln+7l/E4QEeCqZIhFZCUe8Bnb/eg8iLycInevslC6KDCgQkbK+23kRU8NOCcXm9vfYHq9/QN7fa9TvUfj1H7E2PL4lXbFrVFulUySiAvjkr2NYGiG21+aPbzVCi1w6J8i5BtrlnVDrGmVlxe+7KL5Mh4jUt3hPtHBs88psIUNE9uO7nefzDvpXw0oenNiRg+71fYVj4xLFN/cjIrJ2sYmpwrFL94rfbxOR5RyMERufqljSlcXNx0x8tpZwbGxCClcOE1kRk1nCLwevCMVqNUDTyqUU+Vy7vIrqtBoMahMgHB9xgYURImuy+eQNoTitBmjiz5m+RGQfjBlmbD1zWzh+cNuqKmZju5pWLgWDTqxgdDvZqHI2RESW4+PuknfQvw7G3FUxEyLKryKCG6rXrcA2Wo9zNehQsaSrUKzJnNm2h4isw4HoeKQKttGq5eOm2ARBuyyMAMDwDtWEY8/fFNvLgIgs4+6DNKE4P8+inC1NRHaj9yKxFloA4KzXKDZLxt7otBq0rV5GKDbyMh+Iich+/NCnkXCsixM3HSayRi/UKadonKOZ9mKwcOzGE2KtV4lIfTfvia969S9dTLHPtdvCiE6rQfd6PkKxe87f4RI6IithMkuIEdxs7pUG4u1SiIismTHDjP3R4rN3/9eam64/TWXBm+XtZ27xHpCI7IZ7EScUdRJ7xC9qYGGEyBrpBdtjicY5mib+nnARvA6uOnSN94FEVqJUUWfh2BfrlVfsc+36Stq8ithswXtpJi6hI7ISL3+7V/jmpE9zP5WzISKyjO93XhCO1WqAQW2rqJiN7QvxF1tNk5pu5l5zRGRXQgLErn+37omt0CYiywo/JdZW+nYy/w7nRKfV4NWGFYRik1IzOBZIZCU+33xGKM7FSato5wS7Lox4uYn3WJ267qSKmRCRiBSjCZGXEoRi2wWW5mZzRGQ3fj5wWTi2foUSXC2ShyYBnnDWi/0aca85IrInqRkmReOIyHJMZgmro2KFYssUFx/vcjQda3oJx8YlibfvISJ1pBhNOHQ5QSi2V6MKij4L2/WoYiO/kijuIrZx1fHYJKQYeXNIVJgmrT0uHPt2iwAVMyEisqw4GTN3h7TjapG86LQatAsUWzl88dY9lbMhIrKcWj7uQnFspUVkfQ5ExyP+vjHPuJJFndDIr6QFMrJNjfxKopjgNW7nGbEVOkSknhe+2iUc276GeOFThF0XRnRaDXrUE9+QiqtGiArX7/9cFYrTacAbQSKyG2ujYpFuEmshqNWAm64L6tW4klDc+uM32F+aiOxG8yqlheIOxtzltY/IysQliu21+VxtH64efgqdVoNyJcRW1PwVdZ3XQqJCZMww4/SN+0KxOq1G8bFAuy6MAEDHmt7CsdtZKSYqNMmpGUg3i8UWcdbzRpCI7ILJLGH4r1HC8Z+/VJvXP0FNAjxRRGDzTQnA7I1iPW2JiKxdkwBPFHPOe6Z0/IN09tYnsjI3BVcQe7m7qpyJ7TPoxVaMmAHsPXdb3WSIKFdy9tosW9xZ8Wdhuy+MNPIrCdFfs2sJaawUExWS4b8cFo6tUrqYipkQEVnO3vO3kS5471HMWYcX6pVXOSP7odNq4FnMIBS7YOcF3gMSkV3QaTV4qb7YvxXsrU9kXU7FJika58hql/cQjv3jkFjnCiJSnpy9Nt8Iqaj459t9YUSn1aCmj5tQrARg30VuwElUGC7fFVs2DACdgpTtKUhEVFgG/3xIOHZ4+6oqZmKvxGbHmCRgHzdhJyI7Ub5EEaG4+GTx/a2ISH0P0jMUjXNkH3WtIRy77exNFTMhoqdJNQm2jgHwVnN/xT/f7gsjAPBsbfF9RnbygkhUKJx14svh+jbzUzETIiLL+OvwNSSkiD/Y9g6ppF4ydqqaV3Hh2D3n2UaBiOxDyaJiq+VE44jIMhpW8lQ0zpG5GnQoUcRJKDYxJQMpRpPKGRFRTryKOwvFVSjhAoNe+TKGQxRG3mxaSTg27FiceokQUY5MZgnRt5KFYvs0rajKxZCIyJLk7i3SxK8Er335MOeVusKx4ae51xwR2YcyxcU2HRaNIyLLEBm70mjkjXE5slcairegnRZ2UsVMiCgnJrOEszfuCcWGDW2lSg4O8YRt0GvhKTgbJjYhhT2miSxs34U7uGfMe/mcm4seE54NskBGRETq2n3uFuTcbizr10S9ZOxYMRc9nARXJJ69kcx7QCKyD4ILsQ/GcPN1ImuyVWCSxoAWfpwsI6hF5TLCsQejeT0ksrR54eeQJrBYy69UERRz0auSg8NcTbsI7klgkrjPCJGl/bgvRiiudxPlN1oiIioMC3acF44t567OsmFHEVxOxl5z3GeEiOzAbcG9Q5bsjWFBmMhKmMwSJv799FULRZ11GNW5uoUysn1NAjyhE7yFPneLE2SILMlklrBgxwWh2K61vFXLw2GesuVsvLSXPaaJLMZklhB+Wmxvn9gE8Q3aiYisWcTFu8Kx056vpWIm9q9TTfEbadFCPRGRNRNtkZWQko4DnCVNZBUORMfjemLqU2Pup5n4d1YGnVaD1xtVEIo1mTlBhsiS9l24g7QM0Y3XxfcklsthCiOuBh283MQ2dOGSYiLL2XfxDtJNYjMzfEq4qpwNEZH63lq8X1Z882qlVcrEMfRp5iccu/nkDc4WJCKb18ivJDxcxTYdvnnv6QOxRGQZ3+8SmznNv7PydK7lIxz70/4Y9RIhomx+lPH3LSTAU7U8HKYwAgAv1hPbeOnQ5bt8KCaykFkbTwvHNvUvpWImRETqSzGasPWM+MrU5gElodOqN0PGERj0WtTxdReKNUnA0JWHVc6IiEhdOq0GfQWLwtyAnajwGTPMCD99SyiWf2flaeRXEsWcdUKxW07d5FggkQWYzBK2nMx7TyUAMOg0aOLPwogimlUWG1TNMLOdFpElGDPMiLqSKBTrpNWgiYpVYiIiS+j+zR5Z8d+92UilTBzLBx0DhWPXHr0Oo/CybiIi6/Re6wChxhOi+5EQkXqW7o0RinNz0aORX0l1k7EzOq0Gbzf3F4pNN0kcCySygL3nb0P0cattYBlVJwo6VGGkib8nnHRiv5h5bXpFRAUnegMIAO2ql+WsaSKyacYMM05evyccX6KIE1wNYjPc6OmaBHjCxUn8tvfHiBj1kiEisoDIS3chMu95xK9RnCFNVMgORIsNxlfyLMJn4nwY3K6K8Cbs87eeUzcZIsI8GX/PejeppF4icLDCiE6rQQ3v4kKx528lc7Ygkcp+krHJbe+QiuolQkRkAR/8Jq9F05yX66iTiAPSaTV4taGvcPy2M2JLu4mIrJXoPgTpJgm7z4m18CEiddxPMwnFFXPWq5yJfdJpNQjydhOKPXwlgcViIhWZzBIOxtwVitUCqneOcajCCAB0CxbfeKnrvJ0qZkLk2IwZZlyKTxGK1Wqgak9BIiK1mcwS/joSJxyv1wLNq3LTdSV1rOktHLv7fDwfionIpsnZh+C7XRdVzISI8lKyqEHROHpSUHmx/ebSTRL2XbyjcjZEjmvvOfF2dVXLFlV9lZzDFUbebCq2CR0AnLt5HylGsco9Eckjp41WTZ/iXDJMRDbts02nZcXP61mP1z2FNfIrCYNgS1UAGPjTPypmQ0SkrkZ+JaEXfNpPTElXNxkieqr4+0ZF4+hJ9SqI782y88xNFTMhcmy/H7oiHNsmsKyKmWRyuMKIQa+Fj7v47Jkp606omA2R49p/UbxK/Gzt8ipmQkSkLpNZwjfbxWfjliyiR2iw+OoGEqPTamStPtxw8ibbqhKRzdJpNWgiuElzUDmxFjNEpI7jsYlCcWm8L8k3Hw9X4dilEZdUzITIsV2JfyAc27yK+h0UHK4wAgDP1RVvpxV1OUG9RIgcWNSVBOHYN5tWUi0PIiK17T4jr3f75z3qqJMI4dveDWTFy1ndSERkbfo19xeK61jdS+VMiCg3KUYTklLFOpX4liyicjb2q5FgoRgAUjPM7B5DpJLLgoURS7XUd8jCSPPK4hWnawlieyAQkThjhhm374st2a9Y0hUG0T4ARERWaPqGU7LiWwaWUSkTcjXoUMxZJxw/LUze7x0RkTWJvCy2uen8redUzoSIcjMt7KRwbPd67KSQXzqtBr4yVo288yNbqhIpTc5YYFA5N4u0lnbI0cYm/p7CXzwhJYObbxIp7AcZGzy+3qSSeokQEanMZJZw9maycPycHsF2vbfI1KlT0bRpUxQpUgQeHh6FksP8nnWFYyUAK/dFq5cMEZGqxP49OXQlka0DiQpJzB2x2dM6DdC0cimVs7FvE58LEo7dde42xwKJFNbruwjh2GeCy6mYyX8csjCi02rQrLL4cpw5m8+omA2R41kSESMcyzZaRGTLOszeDtFnqmIGHZ5v4KtuQoXMaDTipZdewnvvvVdoObSsJm9FzujVJ/lgTEQ2KSRA/Jn3Rxn350SknEqeYu2xmlcpZdeTZyyhVbXS0Aj+EkoAdp+T1w6XiHJnzDDj4KUE4XhLjQU6ZGEEABa+0VA4dv62C3wgJlKIySwhLilNKNZZp2EbLSKyWZPXHsfF2+Kbyx0c10HFbKzDxIkTMXz4cNSqVavQctBpNWhRRV6/2p2nbqqUDRGRepr4ewrfS1+8Lb66kYiUMza0hlDcgtfl7ZNGT9JpNZjzSh3h+KnrTqiXDJGDGbPqqHCsm4veYmOBDjvi6GrQwVknXm3fdpoPxERKGPLzIeHYitxcjohslDHDjEW7LwnH16/oAVeD+N4XjiItLQ1JSUnZXkpY2Ft8ggwAfLRG/EaeiMha6LQaBJYtJhR7IylV5WyIKCc7zuY91tShRhmrv080mUz4+OOP4efnB1dXVwQEBGDy5MmQpP8mGUuShPHjx8Pb2xuurq5o3749zp2z7B5Hz9UpJ9hkEDh78wEnSRMpwGSW8Ofha8Lx3YK9VcwmO4ctjABABcEli0DmrE8iKhhjhhnrjsUJx7/IzeWIyEYt2i2+l5JWA/z6TlMVs7Fd06dPh7u7e9bL11eZVmOuBh0qlXAWjo9NNGLD8euKfDYRkSWVcXMRirspuKKbiJRjMksYverYU2P0Wo1NrBb59NNP8c033+DLL7/EqVOn8Omnn2LmzJmYP39+VszMmTMxb948LFiwAPv370fRokXRqVMnpKZatjDrJGOS9N5zt1XMhMgx7D53S7i9NAB83K2mesk8RvHCiK1UiQGge13xQddL8ZxBQ1RQi/eIDxQCwFst/FXKhIhIXQt3XhCObVCxhE33jB49ejQ0Gs1TX6dPn87XuceMGYPExMSs15UrVxTLe9PItrLih/x8mLMGieyQUs+v8fHx6NWrF9zc3ODh4YF+/fohOTl7e6qjR4+iRYsWcHFxga+vL2bOnKn69yvmrBeKO3czmdc4Igvbd+EOEh6kPzUmwyxh34U7Fsoo//bu3YvnnnsOXbt2RaVKldCjRw907NgRBw4cAJB5HZ0zZw7GjRuH5557DsHBwVi2bBliY2OxevVqi+bq4iQ+FDqBk6SJCmyBjGfjer6W7aSgeGHElqrEcgddE/P4B4uInu6H3dHCsXV83bm/CBHZpLCjsbj7IEM4fnCbKipmo76RI0fi1KlTT335++ev0O3s7Aw3N7dsL6UY9Fr4ylg1YjRJmLvlrGKfT0TWQann1169euHEiRPYvHkz1q5di507d2LAgAFZ7yclJaFjx46oWLEiIiMjMWvWLEyYMAELFy5U9ft1F1yBnZJuxoHoeFVzIaLsIi6KrUYQjStMTZs2RXh4OM6ezbxXOnLkCHbv3o0uXboAAKKjoxEXF4f27dtnHePu7o7GjRsjIiIix3Oq1VK1QcUSwrEXbj2AMcOsyOcSOarImLvCsb+9Z9lOCmLTR2R4tEoMAJUqVcLPP/+ca5UYAJYtW4ayZcti9erV6Nmzp9Ip5cqg16J0USfcui9W8Oi7eD9WDWyuclZE9smYYcaNe0bh+A86BqqYDRGROkxmCR/8Ib4fhU4DNK1SSsWM1Fe6dGmULl26sNPIl/VDWyNowkbh+G+2X8DQ9lVteoUPEWWnxPPrqVOnsGHDBhw8eBANGmS2vJk/fz5CQ0Px2WefwcfHB8uXL4fRaMQPP/wAg8GAmjVrIioqCrNnz85WQFFa08qlYNBpYDTlvRrk5j12SSCyJElwkZZoXGEaPXo0kpKSEBgYCJ1OB5PJhKlTp6JXr14AgLi4zJbaZcuWzXZc2bJls9573PTp0zFx4kTFc533an1Z939L9kRjQKsAxfMgcgQpRpPQPQgAlCiit/hzluLTsdWoEqvp7ZbiF7dTccpUp4kc0dK9McKxOg3QJMBTvWSIiFSy78Id3E8zCcd/8VJthxpkv3z5MqKionD58mWYTCZERUUhKirqiXYzllLMRQ//0uJ7zqWbJey7aP3tLIhInBLPrxEREfDw8MgqigBA+/btodVqsX///qyYli1bwmAwZMV06tQJZ86cwd27Oc+kVGK2tE6rwf9aVxaKLVVMfBUdERWcRxFD3kEy4grTr7/+iuXLl2PFihU4dOgQli5dis8++wxLly7N9znVaqlazEUPOc0pfjmoXCtXIkczdd1J4djmlS0/2U7xFSNqVInT0tKQlvbfZnBKLZ8DgL7N/DB9vVjf69R0CSaz5FADGERKWXPkqnBs++ql+feMiGzSuNXiq0X8SxXBs4ItTuzF+PHjsz0g161bFwCwbds2tG7dulBy2jy8NSqPDYPoZMyP/jyG7R+0UTMlIrIgJZ5f4+LiUKZMmWzv6/V6lCxZMluMn5/fE+d4+F6JEk+2dlFqtnT9CmJtY8yCMzqJSBnXEh4IxZUqZv2FkQ8++ACjR4/O6gJTq1YtXLp0CdOnT8ebb74JLy8vAMCNGzfg7e2dddyNGzdQp06dHM/p7OwMZ2d1CrYvN/DFigNiBY/L8fc5FkiUT9vP3BSOfbm+r4qZ5EzxFSNqVImnT58Od3f3rJevr3K/UAa9FvUruAvFSgD7rhLlg8ks4fi1e8Lxb4Rw03Uisj39lx1E9J0UoVitBtg8orW6CVmhJUuWQJKkJ16FVRQBMmdTD24rNpsaAGLuPECKUXxVEBFZNzWeX5Wi1Gzp/TFiK91E44io4ExmCb9HXhOK9XJ3VTmbgnvw4AG02uxDjDqdDmZz5v4cfn5+8PLyQnh4eNb7SUlJ2L9/P0JCQiyaKwB83K2mcGy6mWOBRPlhMku4liDeprMwWkwrXhh5tEpcq1Yt9O7dG8OHD8f06dMBIFuV+FE3btzIeu9xai2fe+jnAeIbu1y7K1bRJ6L/7D1/W3gmLttoEZEtSjGasPmk+GyYjjXLctaZFRnavirk/Ha8+M0e9ZIhIotS4vnVy8sLN29m/zcgIyMD8fHx2WJyOsejn/E4Z2dnuLm5ZXvlj+gFjv8uEVnKvot3kJyWkWdcyaJOaORX0gIZFcwzzzyDqVOnYt26dYiJicGff/6J2bNn44UXXgAAaDQaDBs2DFOmTMGaNWtw7NgxvPHGG/Dx8cHzzz9v8XxdDTq0CxRv28OxQCL55IwF1ipXvFCejxUvjKhRJVbuhjBnBr0WFUuKVeCXRcQo+tlEjmDVIfE2Ws/X9eFgIRHZnPazt8uK792kkip5UP7otBoMkbFq5NT1ezBmmFXMiIgsRYnn15CQECQkJCAyMjIrZuvWrTCbzWjcuHFWzM6dO5Genp4Vs3nzZlSrVi3HNlpKChGcdKTnPTiRxURcEFuh1djP0yaej+fPn48ePXrgf//7H6pXr473338f77zzDiZPnpwVM2rUKAwePBgDBgxAw4YNkZycjA0bNsDFxaVQcl7UpxFcncSGRTkWSCSfnLHAZ2sXTotpxQsjtlYlfqh8SbGNN49dS4LJzN6rRHJcvH1fOHb6i7VVzISISHkpRpOsJcJFDTo08efKOGszuF1V6GTcGTeaulm9ZIjIYpR4fq1evTo6d+6M/v3748CBA9izZw8GDRqEnj17wsfHBwDw2muvwWAwoF+/fjhx4gR++eUXzJ07FyNGjFD9Ozbx94S7a97bi/7yzxU+6xJZjNjftYDSRVXOQxnFixfHnDlzcOnSJaSkpODChQuYMmUKDIb/9kfRaDSYNGkS4uLikJqaii1btqBq1aqFmDVQr6JYYfpELMcCieSSMxb4ZtNK6iXyFIoXRmyxSgwA/qXE/rGRAMwLP6duMkR2xGSWhC+GTfxKwKBX/LJERKSqOhM3yoof0DLAJmb+ORqdVoMvetQRjk9IyUDfH/aplxARWYRSz6/Lly9HYGAg2rVrh9DQUDRv3hwLFy7Met/d3R2bNm1CdHQ06tevj5EjR2L8+PEYMGCA6t9Rp9XgrWZ+ecZdT0xlH30iC9EKtq5rXImTadQkOhZokoB9gqt8iMh2xgI1kiTZXMkzKSkJ7u7uSExMVKytVorRhOrjNwjFOuu1ODmpMwc1iAREXLiDV78TGzg6O6WLohdDNa4V1sBevxeRLZrw13EsibgkHF/ESYtjEy1zD2GP1wpLfKeQ6eG4nii+Aqh/Cz981LWGKrkQUf7xGpjdqsirGPHbkTzjZr9UGy/WL5x2FkSOwmSWUHviJqE9Rpa/3RjNKsvbkNger39A4Y8Fdq5ZFgt6N1Dkc4nsna2MBXJq9r9cDTr4eYq100rLMLNSTCTo+10XhOLaBZa2+tUiEyZMgEajyfYKDAzMej81NRUDBw6Ep6cnihUrhu7duz+xyebly5fRtWtXFClSBGXKlMEHH3yAjIy8b4iJyPoYM8yyiiIA8NlLtTmxwsrt+KCNrPjvdkVzvxEisnqHr9xVNI6I8k9043UAuJ2cpnI2js3VoIO3m7NQ7I6zt9hOi0iQrYwFWvcopIVNeaGWcOy41cdUzITIPhgzzAg/fUso9u0WASpno4yaNWvi+vXrWa/du3dnvTd8+HD8/fff+O2337Bjxw7ExsbixRdfzHrfZDKha9euMBqN2Lt3L5YuXYolS5Zg/PjxhfFViKiA6kyS10KrcaUSCA32USkbUopBr0WFkq6yjuF+I0Rk7USH8i7dEe8HTkT5s/fCbeHYMsULr+W8oxBdJZeSbma7QSIBtjQWyMLII5r4e8JZsEoVfecBUowmlTMism1L98YIxbm56NHIr6S6yShEr9fDy8sr61WqVOay5sTERCxatAizZ89G27ZtUb9+fSxevBh79+7Fvn2Zywc3bdqEkydP4qeffkKdOnXQpUsXTJ48GV999RWMRmNhfi0ikumtxfvxwChvlcCPbzdRKRtSWtiQlrLiE1Iy8GfkVZWyISIqOD9PsT76+y7Gc0Y0kcquxqcIxbnotTbznGzLmvqLtyq7eveBipkQ2Yc3Fom10LKGsUAWRh6h02rwXivxStW0sJMqZkNk+9ZEXROKq+hZxGZay5w7dw4+Pj7w9/dHr169cPnyZQBAZGQk0tPT0b59+6zYwMBAVKhQAREREQCAiIgI1KpVC2XLls2K6dSpE5KSknDixAnLfhEiyrcUowlbz4jP9AOAviEVrb5dIP2nmIseweXk9a4e/tsRDiYSkdXqHVJJKM5okrD3nLx/44hInmsJYoPrQeXcbOY52ZY1CfCETvA2/ceIGFVzIbJ1xgwz9kWLteWsZAVjgXxCf8zgdlUg+nuy7fRNdZMhsmEms4TjsUlCsfdSbWOPjcaNG2PJkiXYsGEDvvnmG0RHR6NFixa4d+8e4uLiYDAY4OHhke2YsmXLIi4uDgAQFxeXrSjy8P2H7+UmLS0NSUlJ2V5EVHgmr5VXyHRz0eOT54JUyobUsmZwC3i46mUdM2j5PyplQ0RUMAa9FlVKi60a+eMwV8ARqcVklnDsaqJQrI+HvNaelD86rQZVyxQXij16LQkbjl9XOSMi2yXaOQYAirs4qZeIIBZGHqPTatC8stgyuqsJqdxskygX+y7eEe5lXNRgG5eiLl264KWXXkJwcDA6deqEsLAwJCQk4Ndff1X1c6dPnw53d/esl6+vr6qfR0RPt+LAFVnxh8d3VCkTUtuBjzrIil9/4ibvDYnIalUqJVYY2X1erC84Ecl3IDoeaSaxJ2UtuFrEUhpUKiEcO/Hvk1wlTJSLA9F3hGNrlXdXMRMxtjEaaWHf9m4gHMtldEQ5+2nfJeHYuhVss2+qh4cHqlativPnz8PLywtGoxEJCQnZYm7cuAEvLy8AgJeXF27cuPHE+w/fy82YMWOQmJiY9bpyRd6gLBEp56M/j8qKD/IuXujLgyn/DHotqpUuIuuYquPWq5QNEVHBlHUT28T5dnI699MkUsnNe6nCsT4luGLEUsaG1hCOvZ6Yyk3YiXJxJV58H54WVUqrmIkYFkZy4GrQobyH2E3jTvZfJXqCySxh1znxmWYfdRW/CbEmycnJuHDhAry9vVG/fn04OTkhPDw86/0zZ87g8uXLCAkJAQCEhITg2LFjuHnzvzZ8mzdvhpubG2rUyP3XwNnZGW5ubtleRGR5YUdjsXy/vMLkyneaqpQNWcrqwfI2YgeAgDHrVMiEiKhg6lYQnxE9ZR33vyNSg4ezeOuYZoLdTKjgXA06BMnYX27zydxbYRM5KpNZQvQdscKIs06DJv6eKmeUNxZGctG3mZ9Q3J7zt7mEjugxB6LjkZwmNsusXgUPuBp0KmekjPfffx87duxATEwM9u7dixdeeAE6nQ6vvvoq3N3d0a9fP4wYMQLbtm1DZGQk+vbti5CQEDRp0gQA0LFjR9SoUQO9e/fGkSNHsHHjRowbNw4DBw6Es7NzIX87Inoak1nC/1YclnVMrXJuKOYib48Ksj6uBh3aVZM3m8kkAW8silApIyKi/JGzX8FRwT0QiEiezadv5B0EwGAlg4aO5CMZq0Z+jLjEsUCix3y59RzSBNsKv9u6slV0VmBhJBe9QyoJxWWYJew+yx6sRI/adEJs9oRWA/z2ru3Mpr569SpeffVVVKtWDS+//DI8PT2xb98+lC6dOWD2xRdfoFu3bujevTtatmwJLy8vrFq1Kut4nU6HtWvXQqfTISQkBK+//jreeOMNTJo0qbC+EhEJqjtxo6x4DYC/B7dQJxmyuEV9G8FNZpFr57l4tqIhIqvSyK8knHRigxByr3lEJOaSYJuZgNLFrGLQ0JE08isJLzexCYvpHAskysZklvDtzotCsQadBkPaVVE5IzEsjOTCoNfCx12sndbCXWK/8USOwGSW8PPBy0KxL9YtZ1M3eytXrkRsbCzS0tJw9epVrFy5EgEBAVnvu7i44KuvvkJ8fDzu37+PVatWPbF3SMWKFREWFoYHDx7g1q1b+Oyzz6DX88GTyJpNWHMcSYKr4B46MLa9StlQYTk8vqPsY+pMkldQIyJSk06rQZeg3Pe1e1SIP1v4EKlB9OlXzmbgpAydVoMJz9YUjudYINF/9l24gweCk8IGtrGO1SIACyNP5VnMIBR3Ji5J5UyIbMe+C3eQmi62dI49U4nI2hkzzFiy95KsY/RaoLTgbDOyHTqtBvNeriPrmLQMCS1mbFEnISKifBAdhjh3856qeRA5IpNZwj8xYpt2y9kMnJTTOcgbFUqKtR3kWCDRf/ZcEFtB5aLXYlBb61gtArAw8lS1y3sIxd2+nw6jYA81InsXcfG2cKyXu3ifYyKiwtDrO/n7RByb0FmFTMgaPFuvHHzdxSbOPHQlIQ0T/+YmxkRkHVLSxWZzxty5r3ImRI5n7/nbeCAwibCur7vN7MNpj1pWEdtbjmOBRP+JTUgVigsu7241q0UAFkae6qOu4hX6NxbtUzETItux5/wdobiizjo08iupcjZERPkXdjQWBy8lyDqmbbVSfJC1c9s/lN8mbfGeGD44E5FVaFhJbDPnY9eSuLEwkcJ+j7wqFFehZFGVM6GnkTMWOGbVURUzIbIde86LTZJuaGXjgCyMPIWrQYfSgu209kXf5QMvObywo9dx+EqCUGz/5n5WVSUmInqUySxh0IrDso4pWcQJP/RtrFJGZC10Wg3mv1pX9nE1x69XIRsiInnebFpJqJ2WWQJ2n+PGwkRKOnU9UdE4UoecscC1R2JZRCaH9/bSg7iVbBSKbRpgXS31WRjJg5w9EH6MiFEvESIrZzJLGPXHEaFYFyctBrerqnJGRET51+ObPZA73eFQPjbnJtv0TG0ftK0m76Y+3cziCBEVPoNeCx8PF6HY6etPqZwNkWO5b8xQNI7UU61scaG4NJOEA9Fi+8YQ2aMUowlbTt0Uii3mrEcTf7GVq5bCwkgeutcrLxz7e+QVFTMhsm77Lt5BcppYz+LgctbVU5CI6FFT/j6Bw1fkzdQ7wqKIw/mhb2Ph2YQP3Tea0Xw6N2MnosJVoojYtetMXDJnQhMpyKATa7fqVVyseEnqqeXrLhy7YMd5FTMhsm5T14nvpfhyg/JWNxbIwkgemlYuJfyLdCoume20yGH9tO+ScKxHEScVMyEiyr+wo7H4fk+MrGPKFHOCO69rDmnfWPn7jVxNTMNbiw+okA0RkRgvN2ehOAnA7rNsp0WkBJNZQmxiilBsp5reKmdDeWkeILYBOwDsOHubY4HksDaciBOO7VDDS8VM8oeFkTzotBpU8SomHB86d6eK2RBZJ5NZwkYZF0PRTR+JiCzJZJbwP5n7igBAxNgOKmRDtkCn1eDzHsGyj9t65hbWRsWqkBERUd46BYkPui7YeUHFTIgcx74Ld5CWIbYCq09zP5Wzobw0CfCUNbN9yZ5oFbMhsk7GDDNuJ6cLxRp0GjSyso3XARZGhDSqJP4bd/7WfaQYxdoJEdmL3WdvQc4q+zebVlItFyKi/Ko7aaPsY77sWdfqlgOTZXVv4AvPonrZxw1aeZgtaoioUJQvUUQ49vT1JBUzIXIcw34Rm3xTx9cdBj2H6gqbTqtBswDxCZ0/H7ysYjZE1mnR7ovCsQNa+FvlczOvtgLGhtaQFT913UmVMiGyTmP+PCYc27ZaKd7oEZHVeWPRPiSlypvY0C6wNLrV8VEpI7IlkR93gj4f9/nVxoUpnwwRUR7kzNhMN7E9DFFBJadm4FayUSjWz7OoytmQqG97NxCOjb79gBNeyOEs3CG+qnR4x2oqZpJ/HJ0U4GrQoUpp8Vk1O8+xDys5DmOGGbGJqcLx/VtWVjEbIiL5npm/EzvP3ZF1TCVPVyzq00iljMgWnZkaKvuYDDPQfPpmFbIhIsqdTqtBCVexTaDdXbmHFlFBDRdcLQIAPiVcVcyE5HA16OCiE5/5ciA6XsVsiKyLMcOMuykZQrFlijtb5WoRgIURYeuGthKOvXY3hZVichiLZfTSdHGyzp6CROS4Jv59HMeu3ZN1jF4DhI9so1JGZKt0Wg3mvVxb9nFXE4347eAVFTIiIspdoLebUJzoLHciyt3l+AfCsU39S6mYCcn1Yv3ywrEbj19XMRMi69L7+33CsR2ql1Exk4JhYUSQQa9Fo0oeQrEmCdh3Ud7MUyJbtVJGL82ZLwRbbZWYiBzP30disXjPJdnHzXuV+4pQzp6tVx7B5cQGGx/1wR9HMT2MrViJyHLq+JYQijOaJCQ+ENtYlYhyVtRZbC8ynSZz02+yHh93qykc++s/VzhJmhyCMcOM/TF3hePHyfh7ZGksjMjwaqOKwrGfbTyjYiZE1sFklhBzR2z2i5NWg2fric+2ICJS04bj1zH4Z/G2Bg+92aQCQoO5rwjlbs3gFgjykV8c+XZnNMKOxqqQERHRk5pXKS0c23fxfhUzIbJ/9St6CMW1rVaKk2+sjKtBh/IeLkKxD9LNbKdFDmHp3hjhWHdXPVwNYu07CwMLIzJ4uYv3ejx8JQHGDG5UR/btQHQ8JMEJEbXKyx8kIiJSg8ks4d2fDsk+zs1Zh4nP11IhI7I3a4e0QC1v8f3pHvrfisOcaUhEFtHEX3xW+rlbySpmQmT/Lt+5LxQXULa4yplQfvRt5iccu+EE22mR/Vu4U3zT9YGtrXufYRZGZGjkVxIliogtgQTkVdCIbNGC7eeEYzvV9FYxEyIicQFjw/J13OFPOimcCdmz1YNb5+u4oI/z9+eTiEgOnVYDN2exGZzOOg4bEOWXySxh25nbQrHJqSaVs6H86B1SSTj2x4hLnORCdi3FaJK1/1gfGYXFwsA7HBl0Wg2mypgp+vmm0ypmQ1S4jBlm7DgnvpeOnFkWRERq8Ru9Ll/HzXu5NlsbkCw6rQZze9aRfVyKCag6Nn9/TomI5BjQ0l8orin3PCDKt30X7sBoEhso12h4r2mNDHotaniLreYxS8DczWdVzoio8Lz41W7h2NAgLxj01l16sO7srFBosA88izoJxaZmcKM6sl/f7jwvHFvOw8XqL4ZEZP+qj1uH/MzfCi7nxj2SKF+eq1MOtcrJb4thNANVP2JxhIjUNaCVWHuLAzF3OQOaKJ/2XhRbLQIAlTzlt+Eky+gu41ngqx0XeM0ku2TMMOPUDfH2mvNfq6diNsrgSGU+1KtQQji2z+J9KmZCVHi+2ireU7BtYBkVMyEiypvf6HVIyZB/XE3v4lgzuIXyCZHD+HtwSwR5F5N9nNEENJ+xRYWMiIgyGfRadAvOu91tXFIaNxQmyqeDMv7uyGnZZI2uXbuG119/HZ6ennB1dUWtWrXwzz//ZL0vSRLGjx8Pb29vuLq6on379jh3Trw9d2GS83tjMkvYe168IEZkK36MiBGOLeKktYmOCyyM5EMjP/GlxCdj76mYCVHhMGaYkZphFo4fG1pDxWyIiJ6u0uj8rRQp7+6MdUNbKp4POZ61Q1uhhpf84sjVhDSEzt2hQkZERJnaVS8rFLfpRJzKmRDZH5NZQtSVBKHY2uXdbLrLwt27d9GsWTM4OTlh/fr1OHnyJD7//HOUKPHfxOKZM2di3rx5WLBgAfbv34+iRYuiU6dOSE1NLcTMxRj0Wrg6iQ/y/hZ5VcVsiArHxdv3hWPLFDeomIlyVLnq2nOVGADebFpJODbNJHEJHdkdOVViNxc9XA1iGzsSESktv3uKlHDVY/eY9gpnQ44sbFgrFDXIv/U+eT0Z3VgcIVKVEs+v8fHx6NWrF9zc3ODh4YF+/fohOTl7u4mjR4+iRYsWcHFxga+vL2bOnGmR7/c0t++JDUj+HnmFz7VEMu27KL6/SLdgH5WzUdenn34KX19fLF68GI0aNYKfnx86duyIgIAAAJnX0Tlz5mDcuHF47rnnEBwcjGXLliE2NharV68u3OQFhcjYb+lgtPh+rES24maSeBGzWeXSKmaiHMULI/ZeJQYyK8Wli+qF4/dd5AWR7MuM9aeFY3eNaqtiJkREuQuZtjlfK0U0AA5/0knpdIhwYlIX6POxovz49WR0m8fiCJEalHp+7dWrF06cOIHNmzdj7dq12LlzJwYMGJD1flJSEjp27IiKFSsiMjISs2bNwoQJE7Bw4UKLft/HJQjuiXkvzcR2WkQyRVwQHwsqVdxFxUzUt2bNGjRo0AAvvfQSypQpg7p16+K7777Lej86OhpxcXFo3/6/iUfu7u5o3LgxIiIiCiNl2eb1rC8cez0pjcVksjvbz9wUjh3XraaKmShH8cKII1SJAaB/S7GN6gBgmYzZ9UTW7lZSGtIF/4H3KKKHexEnlTMiIsrOZJZQecw6XE8y5uv46BldFc6I6D9npoYiP912j8cm481F+xXPh8jRKfH8eurUKWzYsAHff/89GjdujObNm2P+/PlYuXIlYmNjAQDLly+H0WjEDz/8gJo1a6Jnz54YMmQIZs+eXVhfHQCg0YhfkeJkzBQlIgAypuh4udl2YeTixYv45ptvUKVKFWzcuBHvvfcehgwZgqVLlwIA4uIy2/GVLZu9fV/ZsmWz3ntcWloakpKSsr0KUzEXPdxdxLthzN1yVsVsiCwrcyxQLLZq2WI20zlG8cKII1SJAaBPMz/h2E0nb7BSTHaj0xzxGasHxnZQMRMioietOXQNAWPDkJGPf3Y1AGJYFFFNTEwM+vXrBz8/P7i6uiIgIACffPIJjMb8FbBslU6rwVev1cvXsTvO3UbLmVsVzojIsSnx/BoREQEPDw80aNAgK6Z9+/bQarXYv39/VkzLli1hMPzXc7tTp044c+YM7t69m2NulhgUlNMaJj45TfHPJ7JnIf6lhOJKFjWgkV9JlbNRl9lsRr169TBt2jTUrVsXAwYMQP/+/bFgwYJ8n3P69Olwd3fPevn6+iqYcf58+ar4qpEFOy5wLJDshpyxwLWDW6iYibIUL4w4QpUYyGynVdHTVShWkoAhPx9SOSMi9ZnMEuIFl9vrNLDpzeOIyPaEzt2BIb9G5etYVz1Xiqjt9OnTMJvN+Pbbb3HixAl88cUXWLBgAcaOHVvYqVlcaLA3+jUXn2TzqMvxKQj9YpvCGRE5LiWeX+Pi4lCmTJls7+v1epQsWTJbTE7nePQzHmeJQcEm/p4oIjir08OVK8GJ5EhMEXt2nvJcEHTa/KwntR7e3t6oUaNGtp9Vr14dly9fBgB4eXkBAG7cuJEt5saNG1nvPW7MmDFITEzMel25ckWFzOVpWqUUdIK/VUaTxNb6ZBfseSxQ8UwdpUoMAK83riQcu+5YHIwZgmuOiKzUztPi/QS93W17KTAR2Rb/0etw8npy3oG5ODWFRRG1de7cGYsXL0bHjh3h7++PZ599Fu+//z5WrVpV2KkVio+71UC7wPxtSnjyxgPUm7hR4YyIHJMaz69KscSgoE6rQWhQzoOSj4u4yD1GiESZzBImrzuZZ1z/FpUQGuxtgYzU1axZM5w5cybbz86ePYuKFSsCAPz8/ODl5YXw8PCs95OSkrB//36EhITkeE5nZ2e4ubllexU2nVaD5+uWE46ftUF8f1Yia2XPY4GKF0YcpUoMAG82rSQrfvTvR9VJhMhCPvjjiHDsn/9rrmImRESZjBlmVBq9DgWZenBhWqhi+ZA8iYmJKFky99YR1rhqWEmL+jRCm6ribWweFZ+SgcBxYQpnROR4lHh+9fLyws2b2QcNMjIyEB8fny0mp3M8+hmPs9SgYLMqYkXaLafYIppI1IHoeFxPzHtfnraBYoVJazd8+HDs27cP06ZNw/nz57FixQosXLgQAwcOBJC5n9GwYcMwZcoUrFmzBseOHcMbb7wBHx8fPP/884WbvEzTXwwWjo26mshJ0mTz3lsh3gXJ1sYCFS+MOEqVGMhcGtS1lvg/YquPXOONJNksY4YZt++LLZ0DgNJuzipmQ0QETPzrOKqOW1+gcyx4vZ7Nty6wVefPn8f8+fPxzjvv5BpjrauGlbT4rSYI8snfvW1qhoTAsesUzojIsSjx/BoSEoKEhARERkZmxWzduhVmsxmNGzfOitm5cyfS0/+7n968eTOqVauGEiVKqPb9RIhu+pyQko4D0Vw1QiRiy8mcW+Q97ua9vIsntqBhw4b4888/8fPPPyMoKAiTJ0/GnDlz0KtXr6yYUaNGYfDgwRgwYAAaNmyI5ORkbNiwAS4utjXD3KDXokJJ8Zx7f287+ykTPW7K3yeQKqO4Z2tjgYoXRhypSgwA814V3zzTLAF7z99WMRsi9Xy/64JwbPvq+WsNQkQkwmSWEDxhAxZHXMr3OTTILIp0DrL91gWFbfTo0dBoNE99nT6dvY3AtWvX0LlzZ7z00kvo379/rue21lXDSls7pAVqlctnccQM1J+8WeGMiByHEs+v1atXR+fOndG/f38cOHAAe/bswaBBg9CzZ0/4+PgAAF577TUYDAb069cPJ06cwC+//IK5c+dixIgRhfXVszTyKwl3F71QbFxiisrZENk+k1nCyn/E7lnKFLetosDTdOvWDceOHUNqaipOnTr1xD2eRqPBpEmTEBcXh9TUVGzZsgVVq1YtpGwLpncT8b3i9sckcNUI2SRjhhnf74kRjrfFsUDFCyOOVCUGMvsLNqjoIRw/L/yseskQqWjF/svCsXNeES8YEhHJ8cuBKwgYG4akVFO+z6HXAOenhbIoopCRI0fi1KlTT335+/tnxcfGxqJNmzZo2rQpFi5c+NRzW+uqYTX8PbgFeofkb0XMnftGjF99TOGMiByDUs+vy5cvR2BgINq1a4fQ0FA0b9482zXO3d0dmzZtQnR0NOrXr4+RI0di/PjxGDBggEW/b050Wg061BDrhPDNdvHJUkSOat/FO7iflve9asmiBjTyy72lKFkvua31F++5qE4iRCpaujdGVrwtjgWKTQuRqVu3bujWrVuu7z+sEk+aNEmNj7e4oW2rovfiA0KxBy8lwGSW2LaDbM7VBLElvloNUExwxhkRkShjhhk1xq9HQSdbGbTA2WncaF1JpUuXRunSYrODrl27hjZt2qB+/fpYvHgxtFrF5+jYtMnPBSP2birCT9+SfeyyfZeh1QATnqulQmZE9k2J59eSJUtixYoVT/2c4OBg7Nq1K995qqlZlVL4/dDVPOPO3kxGitEEV4POAlkR2aa9F8Q6hTTyK8GxIRtl0GvRqFIJHIi5KxT/w+5ovNOqsspZESlr7dFrwrFFnLQ2ORbIp1EFNK1SSlb83nNsp0W2pdu8HcKxNbyLq5gJETmih3uJFLQoUrKInkWRQnTt2jW0bt0aFSpUwGeffYZbt24hLi4OcXFiPbgdxaI+jdC2Wv6WoS+JuIxn5u1UOCMicgSi+4wAwLSwkypmQmT7rsWLtZxz0bHAaMt+eruJcOyNe0a20yKbYjJLOHI1STh+WDvbbIvHwogCdFoNXqzrIxw/N/x03kFEViI5NQPHY5OF40e2r6ZiNkTkSIwZZtQcv75Ae4k8VN7dGYfGd1IgK8qvzZs34/z58wgPD0f58uXh7e2d9aLsfujbCK2qypt489Cx2HvoyuIIEcnUyK8kDDqxmesxdx6onA2RbTNLYgPgonFknQx6LQJKFxWO/2EX22mR7dhxRt4K9j7NxffdsSYsjChkRvfawrH/XE6CySypmA2RcoatPCQcqwHQMrCMeskQkUNITs1AixnhqDpuPe4bC/7AGORTDLvHtFcgMyqIPn36QJKkHF/0pKVvNUbFkq75OvZE7D08M9862/UQkXXSaTVo4u8pFFvJs4jK2RDZtn8E2yvduW9UORNS24RuNYVjZ2/hnsNkOz7fJD6pv0210jDobbPEYJtZWyGDXgs3V/FeajtP3VQxGyLl7D5/Rzi2e71y7JFKRPlmzDAjZFo4giZsxBXBfY3yMrdnHawd0kqRcxFZ2o5RbdE2vytHriVh7KojCmdERPbs294NhOL0gitLiByRMcOM2KQ0odgiBtvrx0/ZyWmtbzRJSE7NUDEbIuWcvSneOWZAywAVM1EXCyMK6lZLvBXEkF8Pq5gJkTLWRsUiVUYfzGkvBquYDRHZI2OGGV9vPYfAj9ah6rj1uJ6kTEHERa/BhWmheK5OOUXOR1RYfnirMd4IqZCvY1ccuIqg8RsUzoiI7JWrQYd2Aqu/l+69xF75RLn4MSJGOLaRn9gqLbJeOq0GVcuKt9Nq89k2FbMhUsbaqFikm8RW9eu1GjTyK6lyRuphYURBH8tYQncvzYQUo0nFbIgKxmSW8P4f4jNNO9csa7NL54jI8lKMJnSZswNVx63HzE1nkargP4ltqpbG6SmhXMFGdmPSc7VQw7tYvo5NNppQeew6hTMiInvVNCDvgVqzJG/wl8iR7Dx3Wzj2zaaV1EuELOajzjWEY28lGzkWSFZN7ljgOy39bfq5m6OYCnI16FC6mEE4fsCygypmQ1Qw+y7eQWq6+Eywr3rVVzEbIrJ1JrOE8GNxaPlpOPxGr0P18RtwKk58ea4IPYBTkzpj8VuNFD0vkTUIG9oKRfLZcSPDDDSdulHZhIjILl2KF9tYXTSOyJGYzBIOXRbbXyTEryQnFtqJ5tVKQ86wMMcCyZrJGQvUABjRsZq6CamMV2GFbXu/jXDsrvN3uAk7Wa29F8RnuvSoV96mK8REpI5r8SmoNX49Ko1eh4CxYei3PBKX76ZCjX/5gnyK4fyMrnA16FQ4O5F1ODmlK1yc8nf7HnsvA13n7lQ4IyKyNxVLim2sLhpH5EgORMfjnuAeEkv7NVY5G7IUnVaDL3qItxXnWCBZMzkrQrvbwVggCyMKK+aih7OMzejmbjmrYjZE+XcgOl44dtqLtVTMhIhsRYrRhA9+PYwqo9eh0uh1aDZzK+4Z1e1BrgU3WCfHcnpyF7SpWjpfx564fg+tZm7lwzgR5ap3SCWIjHGUcXNRPxkiG3Pzntheee0CS3O1iJ15voEv5PyW7j0vPhGVyFJMZgnhp24Ix9vDWCCvxCoY3r6qcOyCHRf4cEpWx2SWcFhwCXBA6SK8qSNyQMmpGei7eD+CP1mPgH8LIdXHb8Bvh2KRbqEcutXyxjlusE4OaPFbjXBqUmcUd5a/QupSfAoCxobhz8irKmRGRLbOoNeiX/NKeca9/9sRPscSPaZMcbGC4dstAlTOhArD+x3EWwr9EXlFxUyI8mffhTsQ7ahfx9fNLsYC89mpmJ7mrRb+mLHxjFCs0SThy63nMbR9FZWzIhL35dbzyBC8GHaq6aVuMkRkMSlGE8atPoI1Udez3RBpAei1mfsUqLv+I286DfB+h6ro1zLALm7EiPLL1aBD1CedEDg2LF/FyOG/HcGc8LPYMaqt4rkRkW1rHlAa3+2KeWpMWoYZu8/eQqvAMpZJisgG3LmXlmeMVgPUr1jCAtmQpckZC9x0UnxWPpGl/LQ/Rjj2g47V1UvEglgYUYFBr0UTv5LYJ9iK6Nsd5zGobWWb78tG9sFklvD19vPC8c0C8tfOw9F99dVXmDVrFuLi4lC7dm3Mnz8fjRpxw2h7lmI0YeLfx7Ht9E3E3zfmOhPD01WDxFQJGTlMwtRpMl85dacq7gQAGtxLz3n2ppMGcNZrkJYuPTGIatACEpBrTmbk/JmW1i6wNBb14d8Tood0Wg3mv14P7/50KF/HX4pPQctPw7Hzw3YKZ0ZEtmzhrovCcSyMEGUymSWM/vNYnnFmCYi8dBchAZ4WyIosyaDXonLpojh/636esQ/SzVhz6BqerceV72QdTGYJW07dFIp10WvRxE6uYSyMqGRZv8aoOm69UOyDdDP2XbyDZpVLqZwVUd7mh59DmuBykaLOOru5GFrSL7/8ghEjRmDBggVo3Lgx5syZg06dOuHMmTMoU4YPl7bqVlIanpm/A3H3spcddBpAksRXWtxJyb0thUnKfOUk82NzPzZdAtJzKZpYQ9HjaZoFlMD3bzbmxupEOegc5I0FBSiOXL6bio/+OIKp3WsrnBkR2aqkVLF1aFfuPlA5EyLbse/CHSSniW28LroXCdmeT56pid4/HBCKHfnHEXSt48NJ0mQV5oefQ3pugw2PebdVgN38uWUPCpUY9FrU8C4uHD/sl/w9zBIpyWSW8NU28dUis7oH283F0JJmz56N/v37o2/fvqhRowYWLFiAIkWK4Icffijs1EhQfLIR7T7bCv9/99aoNHodGk7b8kRRBMgsZFh53cFqubnocWFaKJb3b8qiCNFTdA7yxoVpoSjhkr9/k5cfvIraEzbAKNpHk4jsWu3yHkJx1xNSuc8I0b8iLopvpi26FwnZnqaVS0F0iCTdJGHfxTvqJkQkwGSWMCf8nFCsk06Dwe3sZzsIFkZU1L1eeeHYW/fSkZwqNruASC3zws8iXfDhpq6vO0KDfVTOyP4YjUZERkaiffv2WT/TarVo3749IiIiCjEzeprk1Az0+WE/qn8chkqj16HelM24cDuFBQ+VuLvqcWhcBxyd0InFVyJBOq0GhyeEoqaMiTmPSkw1oeq49Zi85oTCmRGRrfmoaw2huHSzhH0XOKhHlEnsnrWosw6N/EqqnAsVFp1Wg+YyusGM+eOIitkQiekwe7twbPvqZezqGZ2FERX1DqkkK37wz5HqJEIkwGSW8LWM1SLvdwxUMRv7dfv2bZhMJpQtWzbbz8uWLYu4uLgcj0lLS0NSUlK2F6nLZJaw4/RNvPrtXviNXoegCRux/extpOTSiooKrrizDh+FVsfZKV1w5JNOKFnMUNgpEdmkdUNbooZX/oojALBobwyaTtusYEZEZGtcDTpU8iwiFLtsX4y6yRDZCNE9Q/o397OrQUV60re9GwjHXr6bihSjScVsiJ4uOTUDF2+Lt8Z8vXEl9ZIpBCyMqMig16KOr7tw/H7BzdqJ1LDvwp1cN15+nFYD7i1iQdOnT4e7u3vWy9fXt7BTsluJD9LRZtZWBIwNw5tLDiIi+u5Tdu2ggipm0KBtVU8cn9AJxyZ2Rv+W/jDoeWtCVFBhw1rCs2j+txKMTTLCb/Q6BTMiIltT19dDKG7r6Ztsp0UEIPGBMc+Yos46DG5X1QLZUGFyNegg55Hm3Z/+US8Zojy89p145xJnO9p0/SGOPqjsg07is+ofGM28qaRCs+fCLeHYch6unOWST6VKlYJOp8ONGzey/fzGjRvw8vLK8ZgxY8YgMTEx63XlyhVLpOowEh+k49l5O1Bp9DrUnrQJ0XdSCjslu9aoogfOTumCmBldcXxSKH54qwmKueR/AJeIchb5cSf4uOX/75YEwJ/FESKH5VPCVSiOPfKJMlebD1kZlWfcrO61+RztIILLiU+S3n3uNscCqVCYzBJOxIp3JHmvlb/dXcNYGFFZE39PGHTiv8xDfuYm7FQ4Np24kXfQv1pVFe+ZSdkZDAbUr18f4eHhWT8zm80IDw9HSEhIjsc4OzvDzc0t24sKxmSWsO3UDVQem1kMORqbXNgp2TUPZy3e71AFZ6d0wa/vNeOqECIL2Tu2E4J88t9Wywygxkfr+LBO5ICa+ovf70dwnxFycF9sPoMMgX8rixp0FsiGrMHivo2FY00SMF9w42siJR2IjodJ8DZfC9jlijdO0VSZTqvBrJeCMVRg9gAArDsWhy8yzBw0IosKO3od52/dF47/qGtNFbOxfyNGjMCbb76JBg0aoFGjRpgzZw7u37+Pvn37FnZqds9kljAv/Bzm8sZTFVoAzk4aNPIria9ea8CVIERWYO2Qlui35CDCT9/M1/EPTEDA2DDM7VkHz9Upp3B2RGStmgR4wqDTwCgwYnLuBve/I8dlMkv4dudFodiFuy6iVWAZlTMia+BexAm+JVxw5W6qUPxX289jcLsqdjcbn6zbphM573Obk/da299qEYCFEYt4rk45TFl3Erfu5d1zEgCW7InGgFYBKmdFlMlkljBIxkqlehXc4cqZLgXyyiuv4NatWxg/fjzi4uJQp04dbNiw4YkN2UlZvx+4hPdXHS/sNGySFoBeC2SYM2eQP1TUSYPG/p6Y92p9FkGIrNiiPg2xNioWg1Yezvc5hq6MwqJdF7FmcAsFMyMia6XTatDE3xM7z93OM3bLqcx9RuxxwIQoLwei45EuOOU6KTVd5WzImuz6sB38x6yDyMLbdJOEvedvo0XV0uonRoTMscDFe2OEYjUARnQU3yrClnAUw0LmvFwXvRbtF4r9cus5FkbIYmZvOiP0DzUA6DTAb+82UzchBzFo0CAMGjSosNNwCMmpGag9caPwEtHC5umqQWKqhIwc8tVpMl9G85PvFXcCAA3upef8RZ00gLNeg7R0CY8/khm0gGdRAwx6LYq5OqGubwl81LUGi6BEdqRbHR90CfZG4LgwpOdwDRFx9FoSQufuQNjQVsomR0RWqaJnEUBgka1JAvaeu40W1TigR45n80nxGde1y3uolwhZpXdbBeDr7ReEYiesOYHw91urmxDRvwYujxSOfbNpRbud/MDCiIU0CfCEBpkbWeYlKc2EtVGx6FbHR+20yMGZzBIW7BD7RxoAhrSrarcXQ7I/JrOE9rO3I/r2g0LNQ6cBDHot3F30eGA0waDXItjXHfN6cpUFEVmWTqvBuWld0Wz6ZlxLFFvJ/LiT15NR65MwRH7cma1fiexcJc+iwrG/HbrCwgg5HJNZwor9l4XjP+paQ8VsyBo1q1xKuDBy4fZ9GNlanyzAmGHGBhn7DHeq6a1iNoWLIzIWotNqUMnTFdF3UoTih/8WhS7B3hyEJlXtu3hH1iz6QW0rq5cMkUJMZgmzN53GV9vFev0qxbOIE0oUM6CGtxtequ+LppVL8RpORFZpz5gO6PvDfmw7m3eLnJzcS5NQddx6vNWsEsY/w33HiOxV75BKmLzulFBs+Kn87WNEZMv2nruN1AyxZZj1KnhwNbYDauLvCS2ytyN+mg//OIIvXqmrZkpEWCrYQgvIbKvdyK+karkUNhZGLOjVRhUxbf1poVj2FyRL+GzjGeHY8h4uHOQlq/f7gct4f9Uxi31ekE8xLH+7KdyLOFnsM4mIlLD4rcaYuu4kvtsVne9z/LAnBptPxmHXh+0UzIyIrIVBr0VZNwNuJOW9wuy+0YQUo4kDv+RQ/jh8VTj2zaaV1EuErJZOq8HzdX2w6nCsUPzaqFh89lIdjr2QqpbvjxGOrelT3K7/PHJ9lgX1aeYnK37+VoGGrkT5ZMww4/CVBOH4yc8FqZcMUQEZM8yoNi5M1aKIXgO80rA8Tk3qjJgZXREzoyvWDmnFoggR2ayPutbA2SldYCjAOa7cTUWNcetgEt2wjIhsylvN/IVjp647qWImRNbn6l3xlr1liruomAlZsxndawvHpkvAgeh4FbMhR2fMMCNGsJsRAPw8oKmK2RQ+FkYsyKDXIjTISzj+QMxdPmSSan7YJd5mSAugZbUy6iVDVABT/j6JquPWIy2n3coLyFmnwWuNfHFqUmecn94Vn3avzZmQRGRXDHotzs7oCmdd/meCPcgAAsaGIeyo2GxIIrIdfWVM7jtyNUG9RIis0JV4scKIs9a+W9HQ0xn0WgSULiIcP2iF+KbYRHLJGQus5Olq9/uisjBiYfNfqyfrF73tZ+Gq5UKO7bPN4m20vnytrl0vnSPbZMwwI2TqZny/J/9tYHLTvZ43Tk3qjDNTQzHtxWAWQ4jI7p2ZGgoft4KtgPvfisOcMU5kZwx6LXzcxWa6GwX3WiCyBylGE27cy7vNHAB0rc39Yx3dhG7iHTju3E/HxL8t1x6aHIucscDwkW1UzMQ6sDBiYTqtBnNfFd9I6VJ8GpJTM1TMiBxRvyX7Ifrc4uaiQ2iwj7oJEcn0cJXIdcGHERGeRZxwfEInxMzois9frsdiCBE5nL1jO6Kmt1uBzvHdrmg0n7GF969EdqR3SEWhuOjbyex4QA5jWpj4RIAWVcuqmAnZgqZVSsGgEx+CXbznMovNpDg5Y4FlihscoqDLwkgheKa2D4q7iA+4Df2Zy+hIOSlGE8JP3xaO78aiCFmZrnN2KLpKpIZXMZya1BmR4zva/TJRIqK8rBvaAsHlClYcuZqQhqAJG9F1znZlkiKiQuXlJrhixATsu3hH5WyIrMPFW8nCsaJ/h+zFjBkzoNFoMGzYsKyfpaamYuDAgfD09ESxYsXQvXt33Lhxo/CStDCdVoOBbQJkHbN4j3jLI6K8yB0LbF/dMQq6LIwUkudqlxOO3X2BN5eknPF/yVuS+XG3miplQiRfzY/DcCJO/CHkaRr4uuPslC4IG9aKq0OIiB6xZnALzHtZfKPQ3JyIu48qY8MUyIiICpOXu6tw7E/7LqmYCZH1SEkXm3btpNU41P4iBw8exLfffovg4OBsPx8+fDj+/vtv/Pbbb9ixYwdiY2Px4osvFlKWhWNQ2yqQM/9+0S7lW0aT43r+y12y4h1lLFD1wggrxTn7qGsN4di0DIlL6Egxv0VeE46tWrYoB4zJKhgzzKg0eh3upxe8PcPzwV44O6ULfh/YHAY95wcQEeXk2XrlcWFaKJwKeJlMN0uoPHYd72XJZuT3+fXy5cvo2rUrihQpgjJlyuCDDz5ARkb2lnLbt29HvXr14OzsjMqVK2PJkiUW+EYF18ivJIoKPhNsPX2T7bTIIaSbTEJxgV7FHKIdDQAkJyejV69e+O6771CiRImsnycmJmLRokWYPXs22rZti/r162Px4sXYu3cv9u3bV4gZW5ZOq8Hztb2F428mG3n/RIowZphx5uZ94XhHGgtUdUSIleLcuRp0cJPRsqXVrK0qZkOOIvFBuqz4tYNbqpQJkbjpYZn7iRSUm4seF6aFYs5r9VkQISISoNNqcG5aVxT0uSjDDFQdtx6T1x5XJjEileT3+dVkMqFr164wGo3Yu3cvli5diiVLlmD8+PFZMdHR0ejatSvatGmDqKgoDBs2DG+//TY2btxose+XXzqtBm+38BOKTcswYx87HpCdM5klHL92Tyg2oExxlbOxHgMHDkTXrl3Rvn37bD+PjIxEenp6tp8HBgaiQoUKiIiIsHSaherTl+rIih+7ipuwU8G9/+thWfGONBao2sgQK8V5G9S6snDs9URuwk4F13ex+N8xF72Wg8dU6CavPYlvdxZ8CXGfphVxdEInh5mtRUSkpLNTu6J1lYK3AVm0+xKembdTgYyIlFeQ59dNmzbh5MmT+Omnn1CnTh106dIFkydPxldffQWj0QgAWLBgAfz8/PD555+jevXqGDRoEHr06IEvvviiUL6vXEPaVYVO8DZq9/lb6iZDVMgGr4iE6Lqo7nXLq5qLtVi5ciUOHTqE6dOnP/FeXFwcDAYDPDw8sv28bNmyiIuLy/WcaWlpSEpKyvaydQa9Fo0rlcg78F9/HLrKVXhUICazhDVHc/979rjSxQwONRao2jdlpThvfZqLzbp5qPWsbSplQo4i6or4jcTgtvI2BiNS2idrjmLR7vwXRTQARravjLNTumDCs0HKJUZE5ICW9AvBqUmdC3yeY7H3EDhuvexVrERqK8jza0REBGrVqoWyZf/bqLRTp05ISkrCiRMnsmIeP3enTp1s5hlYp9WgculiQrGL9rAvPtkvY4YZYcfFWsFrNUDTKqVUzqjwXblyBUOHDsXy5cvh4qLcRvPTp0+Hu7t71svX11excxemH99uIhwrAXhpwV71kiG79+XWc7Lit4xorU4iVkqVwojSlWJ7rBIDmZXit5tVEo6/fd+IFKNYH0uix01eexJyulP2bym+oolIac/O34Wle6/k+3gfd2dEz+iKwe2rOdRsByIiNbkadIiZ0RUtq3gW6DypGWbUnrQJLT8NVygzooIp6PNrXFxctqLIw/cfvve0mKSkJKSkpOSYl7U9BxdzFWsFbcyQWPwku/XdrvPCsbXKuTnEivXIyEjcvHkT9erVg16vh16vx44dOzBv3jzo9XqULVsWRqMRCQkJ2Y67ceMGvLy8cj3vmDFjkJiYmPW6ciX/z4fWxKDXopy7s3D8ocsJHAukfDGZJXy1/YJwvFYDuBdxUjEj66P4aJEalWJ7rRIDwLhnasJFxqDdgGUHVcyG7JUxwyxr5n3DCu4cTKZC03fJARy9lv8Hfxe9FnvHtM87kIiI8mVZvyaY/2pdFHSs5/LdVASMWceHfSpUas10VoK1PQf7ligiHNt3yX4VMyEqPIt2xQjHdgsup14iVqRdu3Y4duwYoqKisl4NGjRAr169sv6/k5MTwsP/mxBx5swZXL58GSEhIbme19nZGW5ubtle9qJt9bJ5Bz1iytoTKmVC9mzvudswZohPkf7u9foqZmOdFB/5VKNSbK9V4oe+6VVPOHbX+TvsL0iydZXZz3v5gKYqZUL0dBP/Po5tp/Pfl9rXwxmnp3RRMCMiIsrJM7V9cG5qKFydCvY4YZKA6uM3oB8HUamQKPH86uXlhRs3bjzx/sP3nhbj5uYGV1fXHHOztufg7vXE90o4Eye2MTWRLTGZJcTLWA31ZtNK6iVjRYoXL46goKBsr6JFi8LT0xNBQUFwd3dHv379MGLECGzbtg2RkZHo27cvQkJC0KSJeFspezI2tIas+L+PXlcpE7Jn7/8eJRyrBdBaZsHOHiheGFGjUmzPVWIAaFmtjKz4uVvOqpQJ2aMUownnbt4Xjg/xK8nVIlQoJq45jsV7LuX7+CPjO2LXaK4UISKyFJ1Wg1OTu8CziFh7nacJP30bDSZv5AQgsjglnl9DQkJw7Ngx3Lx5Mytm8+bNcHNzQ40aNbJiHj3Hwxhbmi3dtLL4Xgn3jWb+fSa7s+/iHeFYz6JOfK5+xBdffIFu3bqhe/fuaNmyJby8vLBq1arCTqvQuBp0aF+9tHB8UmqGrJn/RClGE27cMwrHD21f1SFa/z2u4E8xj3lYKX7Uo5ViAFmV4pIlS8LNzQ2DBw926EqxTqvBC3W88WeUWAV43tbzDvsHluRrNmOLrPil/RqrlAlR7p6dvyvf7bM0AKJndFU2ISIiEhY5vhMm/nUciyPyX9wGgNv3MxAwNgzzX62LZ2r7KJQd0dMp8fzasWNH1KhRA71798bMmTMRFxeHcePGYeDAgXB2zuwj/+677+LLL7/EqFGj8NZbb2Hr1q349ddfsW7dOst+4QLQaTUo5+6Ma4lpQvH7Lt5BMxnFFCJrF3FBvDDy2Yu1VczE+m3fvj3bf7u4uOCrr77CV199VTgJWaHv32yE4PHrkWQUK3i0mrkNEWPbqZwV2Yuan2wQjnXSAoPaOuY+w4VSvmal+Emf9qgjK37wikh1EiG7kpyagfgHGcLxoUFlOKuFLK7v4v0sihAR2bhPngvC2Sld4OFS8HlXg38+jLeXHlAgKyJl5PX8qtPpsHbtWuh0OoSEhOD111/HG2+8gUmTJmXF+Pn5Yd26ddi8eTNq166Nzz//HN9//z06depUGF8p39oEinc7+GzjGRUzIbK8ZRExQnEaAC2ry+sMQo7pq9cbCMdeT0rF5LXHVcyG7EVcQirkLNp8r3WAw06+10iSZHPrW5OSkuDu7o7ExMRCX06spJ7fRmBfdLxw/NkpXTiITU/19tID2HJKfL+GC9NC7epiaK/XCnv6Xm8t3oetZ8RnXj1KqwEuTmdRhCg39nSteMgev5M9mvh3wVojPlSphDM2jWzL+13KF3u8XljDd0oxmlB9vNgsVA2AM3xmJTuR+CAdtSdtEood2q4KhneoqnJGubOGa4Ua7PF7mcwSqn4UBpOMkVmOBVJeGkzehNv3xfdDcuSxQP5NsiLLZLYw+lFwtgI5rv0XxQttxZ11dnUhJOvXf9nBfBdFmvp7sChCZIOeffZZVKhQAS4uLvD29kbv3r0RGxtb2GmRwj55JnP1iKu+YPcVMXfTUHXcekxYw9mRRNbC1aBD/QoeQrES+MxK9qPP4v3CsUPaVVExE7InOq0GA9vIa2HE6yrlRU5R5Pk6Pg49FsjCiBUx6LVo4ldCOH77mZt5B5HDCjt6HffSTMLxDWX82SMqqBSjCZtP5u8a1qZaaawY0EzhjIjIEtq0aYNff/0VZ86cwR9//IELFy6gR48ehZ0WqcCg1+LUlFD4lnAp8LmW7L0E/9HrkJwq3h6UiNTz67tNhWN3nuUzK9mHo1cTheJcnLQOPchI8g1tXxVy/shwLJCeZuq6k7LiZ/Zw7P2QWBixMsv6iW9Av+v8HZjkNI0jh2EySxj+y2FZx8zrWV+lbIieFCTYguFxNbyKYnHfRgpnQ0SWMnz4cDRp0gQVK1ZE06ZNMXr0aOzbtw/p6eKzmsi27PqwHfqGVCjwecwAgiZsRJc5OwqeFBEViE6rQcsqYpuq773AZ1ayfSlGk3Cro2LOBd9rixyLTqvBvFfqCMdzLJByY8ww47td0cLx7QJLOXxbNsf+9lbIoNfCx8NZOL76x+tVzIZs1d7zt5Emo0llrXLFUUyBzVKJRNT8OAzia5n+41lEj7BhrZVOh4gKSXx8PJYvX46mTZvCyckpx5i0tDQkJSVle5Ht+eS5Wvj6tbqKnOtUXDIqjV7HAQGiQhbk4y4Ul24G9l3IX+tUImsxZe0J4di+TSqplwjZrW51yqFEkZzvh3NST3C/G3Iso/84Iit+4RucdMrCiBVqF1hWONZoktDnB/Fel+QYBv18SDjWzVWHvwe3VDEbov80nx6O++nyB7M8izghcnwnFTIiIkv78MMPUbRoUXh6euLy5cv466+/co2dPn063N3ds16+vr4WzJSUFBrsgwvTQlG6mPhD/9MEjA3DX1HXFDkXEcmnlTGSsPvcLfUSIbKAzafEWxf1bx2gYiZkz7oFewvHJqZm4I9/rqqYDdkak1nCqsPiezcOb1+Vbf/AwohVGhtaQ1b89rO3kWLMz/xrskd/Hb6GxBTxHtzf9GqgYjZE/3lm/i5cTUyVfVxgGVdEju+oQkZEpITRo0dDo9E89XX69Oms+A8++ACHDx/Gpk2boNPp8MYbb0CSci6YjhkzBomJiVmvK1euWOprkQp0Wg0OjuuIIwpd04eujELdiRt5H0xUCEL8xVppAcDKfy6rmAmRukxmCTfvpQnFlnDVO3xbGso/uWOBo1cd4QpaytLj6z3CsVoNMKhtZRWzsR3snWOFXA061KvggUOXE4SPmRZ2EpOfr6VeUmQTTGYJH/wuvnTOoNOgib+nihkRZfor6hqOXZPfAqekqw4bRrRVISMiUsrIkSPRp0+fp8b4+/tn/f9SpUqhVKlSqFq1KqpXrw5fX1/s27cPISEhTxzn7OwMZ2fxFqNkG9yLOCFmRldU+2gd0gpY07ibkoHq4zegddVSWPJWY2USJKI8NQnwhE4DoX0X7j7IQIrRBFeDTv3EiBS2U8ZG1wNa+ecdRJQLuWOB6WbgQHQ8QgI4puPoUowmHL6aKBz/fB1vrhb5F0vZVuq3d5vKil91iK0EKLN/r1HG3iLvtgzgxZBUZzJLGLoySvZxLnotDn3SWfmEiEhRpUuXRmBg4FNfBoMhx2PNZjOAzL1EyPGcmdoVn74YpMi5tp+9Df8x67h6hMhCdFoN6lfwEI4fsPSAeskQqeizTafzDvpXv+Zso0UFI3cs8Nvt51XKhGzJgGUHZcXP6F5HnURsEAsjVkqn1eC5WuL9Be8bTXh7qby/CGR/hv1yWFb80A5VVcqE6D9VPwqTfYxnET1OT+miQjZEVFj279+PL7/8ElFRUbh06RK2bt2KV199FQEBATmuFiHH8EqjirgwLRSuTgV/LDFLQPXxG9BvCQdgiSxhcFvxZ4k9F+LZ8oVs0rmb94XiDDqwjRYVmE6rQU3vYsLx28/dRtjR6ypmRNbOZJaw6/wd4fjqXsV5rXoEfyWs2KxX6siK33LqJmfJObApf5/ArWSjcPwLXDpHFhA6d6dQi4XHHRjHPUWI7E2RIkWwatUqtGvXDtWqVUO/fv0QHByMHTt2sF2Wg9NpNTg1uQsOjm2vyPnCT99CtXFhSE4V33ONiORrWkV8nxEzMle3E9mSsKOxwh0ZyhR3UTkbchS/vNNMVvyoP7jXiCNr//k2WfGr/ifvz5e9Y2HEihn0WtTwLi7rmBe/3qVSNmTNjBlmfL8nRtYxn/aoo0ouRA+tOXQNJ6/fk33c/FfrsmhHZIdq1aqFrVu34s6dO0hNTUV0dDS++eYblCtXrrBTIytR2s0ZMTO6KvKAkpYhIWjCRrT4dAsHC4hUotNq8EId8S4Hey/cVjEbImWZzBKGrBTvyNCrcQUVsyFHUsxFD88iTsLxyWkm7LvIwrMjWnPoKqLvpAjH1/V1535fj2FhxMr98Z68St6puPswZphVyoasVa/v9smK71arLJfOkapMZglDfo2SfVyHGmXwTG0f5RMiIiKbcXFGV7zSSJl/C67cTUPA2DD8cuCKIucjouzkTLbaf5GFEbIdu8/egpyhlX4tuL8IKWf36Hay4necvalSJmStMsdcjsg65neZY8yOgCOjVs7VoEM9GZvaAcCbi/arkwxZJWOGGQcv3RWO12qAua/WVzEjIqD713tkH9M3pAK+e6OhCtkQEZGt+fTFujg7pQvKFhOfMfk0H646Cv/R3JydSGkGvRaeRcX+nv5zOZEruMhmLNx5QTi2nq87Jx6SolwNOpTzEG/PtnBntIrZkDXaeVpeMax7vXLszJEDXrltwG/vNpUVHxEdz1UjDmTU71Gy4ge1CeDFkFS15tBVRF1NlHVMLe9i+OS5WiplREREtsig12L/uI7o17yiIuczg5uzE6nB291VOHbnKc5qJttwLDZJOHZkx0AVMyFHtWVEa1nxDSZvUicRskojf5e3WmT6i8EqZWLbWBixATqtBt3ryWsn0GXuDpWyIWtiMktYHXVd1jFD21dTKRsiYMPx67KXcwLA6sEtVciGiIjswcfdgnB2ShcoNa0j/PQt1JmwkROJiBRSx9dDOPb9VfLvE4kszZhhRlJqhlCsTqtBkwBPlTMiR+Rq0KGU4Io8ALh9Px0T/zquYkZkLcKOXkf8g3Th+MCyRbmqLRf8VbER01+sLSv+wq0HbBXgAAb99I+s+Odr+3C1CKnGZJbw3k+HZB+34PV6/HNJRERPZdBrET2jK2p4F1fkfAmpGag6bj06zN7Oe2aiAvqoaw3h2Dv301mUJKs3dtUx4dj3WvnzWYZUs/2DtrLiF0dc4jXWzpnMEob/cljWMX8ObKFSNraPhREbYdBr8U5LP1nHTFl7QqVsyBoYM8xYf1LeUvSZL8krsBHJMfCng5DbNXpuzzroHOStSj5ERGR/woa2xPEJnVDSVa/I+c7dvI/q4zegD/foI8o3V4MOpYsZhOPfWLRPxWyICsZklrDmyDWhWA2A4R3YkYHUU8xFD293Z1nHLNnD/Ubs2d7zt5FmEh958S5ugKtBp2JGto2FERsyJrQGPIuIPwSuORKrYjZU2LrKbJf2VrNKXDpHqgk7GosNJ2/JOibA0xXP1SmnUkZERGSvirnoceiTTpj/al3Fzrn93G1UHruOsyyJ8mnb+22EY/dF3+XfNbJa+y7egVFw0LFFlVJcLUKq2yFz1cii3SyM2LNBP8vr0rFV5p8fR8NRUhvzWfc6wrH30kyY8vdJ9ZKhQrM26hrO3XogHO+i12L8MzVVzIgcmcks4X8r5C3lBID1w1srngsRETmOZ2r74MK0ULi5KDMLLsMMVB23HlP+5qprIrmKuejhIWMl19K9MeolQ1QAP0ZcEo7lJC+yBINei4aVPITjb9xLY/HZTk35+wQSU8T2PwKA8h4uXC2SBxZGbEzL6mVkxX+/JxrTw1gcsScms4QRv0bJOuafcR3USYYIQPevd8k+pm9IRa5gIiKiAtNpNTg6oTM+76Fcu9Dv98QgaPx6JAtuvEtEmea/Wk849m/BVkVElmQySwg/fUM43sfDVcVsiP6z/O0QWfFvLjqgUiZUWIwZZny/J0bWMZtHtFYlF3vCUSkbo9Nq8GXPOrKO+W5XNKvFdmTfhTswyvjt9HZzRjEXZfpwEz0uxWhC1NV7so4pbtDik+eCVMqIiIgcUfcG5XFhWiiCfYopcr5koxlBEzai5YwtMJnl7qBF5JiaVi4F0aZCR68l8e8WWZ0vt55HumAbrWLOOjTyK6lyRkSZDHot3m5WSTg+IvoOxwHtzJhVR2XFVyldlKtFBLAwYoO61SmHqmWLCsebJeDHiBj1EiKLGvaLvH6CO0axnyCpp91n22QfEzWhswqZEBGRo9NpNVgzpBX6t/BT7JyXE9IQMDYMqw5eUeycRPZKp9UgyKe4cPwQmX3SidRkMktYuPOCcPzbzf25vwhZ1LhnasraiF3uvrRkvUxmCX8ckrfSct3QliplY19YGLFRawe3FJ6NAwAz1p9WLReynMlrj+NWcrpwvG8JF7YrItVM+fskYpPSZB0zt2cdPkAQEZGqPupaA2endMGLdX0UO+eIP44icFwYZ18S5eFZGXsurDsWx79TZDX2XbyD+0aTUKyzXovB7aqonBHRk+RsxH7u1gM0nLJZxWzIUl76Zo+s+Gpli3IsUBB/lWyUQa/FgJbis+HSzRJafBquYkakNmOGGYt2i28EBwDrh7ZSKRvHVKlSJWg0mmyvGTNmZIs5evQoWrRoARcXF/j6+mLmzJlPnOe3335DYGAgXFxcUKtWLYSFhVnqKygm7Oh1fL8nWtYxQd7FuUEhERFZhEGvxexX6iq6OXtqhoSq49ajxafh3H+EKBdvNpW3YqsbZzSTlZCz6foXL9fmZC8qFAa9Fk38xVu43Uo24q3F3G/ElqUYTTh0JVHWMasHtlApG/vDwogNGxNaA/2aVxKOv3I3Fav/uapeQqSqoAkbZcX7lyrCvUVUMGnSJFy/fj3rNXjw4Kz3kpKS0LFjR1SsWBGRkZGYNWsWJkyYgIULF2bF7N27F6+++ir69euHw4cP4/nnn8fzzz+P48ePF8bXyReTWcL/VshrfeCs12Atl3ISEZGFPdycvW21Uoqd88rdVARN2IhWM8O5RwLRYwx6LbrWKiscf/bWA6QIztInUovJLGHTyTih2MqliyI0WLkViURyLXursawOMlvP3OJ11obVkjkW2L56ae4tIgMLIzbu42414VnESTh++O9H+ABng24lpcleZr55RGt1knFwxYsXh5eXV9araNH/9vtZvnw5jEYjfvjhB9SsWRM9e/bEkCFDMHv27KyYuXPnonPnzvjggw9QvXp1TJ48GfXq1cOXX35ZGF8nX2ZtOCX7mJOTuqiQCRERkZgf+jbGqUmd4e1mUOycl+JTETA2DGsOceIR0aPmvVpf1qDdhNW2M0GI7NPcLWcgOkzSsaaXuskQ5UFuBxkA6P7NbpWyITXdSkpDhowxXG83Z3z/ZiMVM7I/LIzYgerebsKxEoC5W86qlwyposn0LbLih7StzKW9KpkxYwY8PT1Rt25dzJo1CxkZ/7XSiIiIQMuWLWEw/Dfo0qlTJ5w5cwZ3797Nimnfvn22c3bq1AkRERG5fmZaWhqSkpKyvQqLySxhwU55LbT455GIiKyBq0GHiLEdcGpSZzSV0YYiL0N+PYIm0zZzrwSif+m0GrxQV7x96l9HY1XMhujpTGYJ32wX33S9WWXlViAS5deY0Boo4So+Sfrk9WTep9gguWOBO0aJ70FDmVgYsQP9W/jLiv9m+3muGrEhby89CJOM3y4nnQZD21dVLyEHNmTIEKxcuRLbtm3DO++8g2nTpmHUqFFZ78fFxaFs2eytAx7+d1xc3FNjHr6fk+nTp8Pd3T3r5evrq9RXkm1++DlZ8RqAfx6JiMiquBp0WDEgBBemhaJiSRdFzhmXZETVcevx0te7OfBABGBG92Dh2NQMM59PqdDsu3AH6YKXbZ0GaOLvqW5CRILeaSVvLHDJbnkTHKlwPTt/l6yxwC41y3LD9Xzgr5gdaF61NJxk/E6mm4Evt55XLyFSTIrRhC2nbso6Zu4rdTk7X4bRo0c/saH646/Tp08DAEaMGIHWrVsjODgY7777Lj7//HPMnz8faWlpquY4ZswYJCYmZr2uXLmi6uflxmSWMFdmYeSLHsH880hERFZJp9Vgx6h2qFVOfPV1Xg5eTkTVcevx3o//cKCXHJpBr0XpouKzmb/cKu8ek0gpszaKtwmuV9GDzzZkNd5qLq8w8v0e8ZVRVLj+OnwNR6/J6xTyZa/6KmVj31gYsQM6rQbzX6sn65j5W8/xYc0GtP98q6z4DoGlERrsrVI29mnkyJE4derUU1/+/jnfcDRu3BgZGRmIiYkBAHh5eeHGjRvZYh7+t5eX11NjHr6fE2dnZ7i5uWV7FYYOs7dDzlWjVDE9nm9QeKtbiIiIRPw9uAXebiavV3de1p+4gYCxYfh1/yVFz0tkS7aMbCMcOzecz6dkeWFHryPqqvjg45A2XAmfm+nTp6Nhw4YoXrw4ypQpg+effx5nzpzJFpOamoqBAwfC09MTxYoVQ/fu3Z94NiZxBr0W/VuI37/cvJeOvksOqJgRKcFkljDslyhZx7zTvBKLtvnEwoid6BzkjSFtKwvHZ5glNJXZq44sK8VowrVEo6xjFrzRUKVs7Ffp0qURGBj41Neje4Y8KioqClqtFmXKlAEAhISEYOfOnUhPT8+K2bx5M6pVq4YSJUpkxYSHh2c7z+bNmxESEqLSN1TGX1HXcPH2A1nH7B/bUaVsiIiIlDXumRo4O6UL6vu6K3reUX8eR6XR65D4ID3vYCI7417ECSWK6IVizRLwxeYzeQcSKcRkljB45WHheL1Wg6ZVuL9Ibnbs2IGBAwdi37592Lx5M9LT09GxY0fcv38/K2b48OH4+++/8dtvv2HHjh2IjY3Fiy++WIhZ276PutZAldJFheO3nb6FZ+fvUjEjKqjdZ2/JmpAKAKNCa6iSiyNQvDDCKnHhGdq+KooYxH9Lb9wzot+S/SpmRAUxYJm8Sv7Xr9VjhVhFERERmDNnDo4cOYKLFy9i+fLlGD58OF5//fWsosdrr70Gg8GAfv364cSJE/jll18wd+5cjBgxIus8Q4cOxYYNG/D555/j9OnTmDBhAv755x8MGjSosL5ankxmCcNWRsk6Zli7KvzzSERENsWg1+KPgc1xdkoXBJQqoui5a0/ahOBPNnD/ESuk1PPr5cuX0bVrVxQpUgRlypTBBx98gIyMjGwx27dvR7169eDs7IzKlStjyZIlan+9Qje+W03h2C+3XeCqEbKYHWduyfrzNqhNAJ9vnmLDhg3o06cPatasidq1a2PJkiW4fPkyIiMjAQCJiYlYtGgRZs+ejbZt26J+/fpYvHgx9u7di3379hVy9rZt3dCWsuKPXkvC5LXHVcqGCmpa2ElZ8RwLLBjFCyOsEhcenVaD2S/XkXVM+OnbSDGa1EmI8q3/soPYdT5eOL5RpRJsoaUyZ2dnrFy5Eq1atULNmjUxdepUDB8+HAsXLsyKcXd3x6ZNmxAdHY369etj5MiRGD9+PAYMGJAV07RpU6xYsQILFy5E7dq18fvvv2P16tUICgoqjK8lZOCKSFkzFvRaDQa3q6JaPkRERGoy6LUIf78NTk3qjBKuYrPdRSSlmVB13HpMXntCsXNSwSnx/GoymdC1a1cYjUbs3bsXS5cuxZIlSzB+/PismOjoaHTt2hVt2rRBVFQUhg0bhrfffhsbN2606Pe1NC93V1nxc7ecVSkTouzG/3VMONbFSYvB7dhGS47ExEQAQMmSJQEAkZGRSE9PR/v27bNiAgMDUaFCBURERBRKjvbCoNfimeDcW3PnZNHuS5ysYYXeXnoAZ27ezzvwX6E1y3IssIA0kiSpOiXj1q1bKFOmDHbs2IGWLVsiMTERpUuXxooVK9CjRw8AwOnTp1G9enVERESgSZMmeZ4zKSkJ7u7uSExMLLRe+9bsi81nZW2Q7KQFzk3rqmJGJMfUdSfx3a5oWcecndIFBj074z3OXq8Vlvxexgwzqo5bL+uYYe2qYFgHPjgQFTZ7vAba43ci67fm0FUM+fWIoud0d9Fh39gOcDXoFD0v/Se/14v8PL+uX78e3bp1Q2xsLMqWLQsAWLBgAT788EPcunULBoMBH374IdatW4fjx/+bpduzZ08kJCRgw4YNqn6nwmQySwgcF4Z0wfE3LYBz00I5+5VUZTJLCBgbJhz/9Wv1bGrwsbCvFWazGc8++ywSEhKwe/duAMCKFSvQt29fpKWlZYtt1KgR2rRpg08//fSJ86SlpWWLT0pKgq+vr01dAy3FZJZQ85MNSBW92ALwcNUj6pNOKmZFcry99CC2nLopHK8BcJ7/XuZIzjVQ9ZFUVoktb0i7KnCRMUiebga6zdupYkYkyphhll0UeatZJRZFSDWL91yUFa/VgKtFiIjIrjxbrzwuTAtFMYNyD56JqSZUH78BzWaEc/W2lcnP82tERARq1aqVVRQBgE6dOiEpKQknTpzIinn0HA9j7P0ZWKfV4J1WAcLxZnDVCKlv6M/ie4sUNWhtqihiDQYOHIjjx49j5cqVBTrP9OnT4e7unvXy9fVVKEP7o9NqMPulOrKOSUjJwMS/uYrVGqyNipVVFAGAOT3rsCiiAFVHU81mM4YNG4ZmzZpltYmJi4uDwWCAh4dHttiyZcsiLi4ux/OkpaUhKSkp24typ9Nq8FmPYFnHHI+9h+TUjLwDSVWvfy+vt2ZFT1eMf0a8by+RXHO3iK8+A4A5L/EfZyIisj86rQbHJ4Xi0LgOcHVS7hHqWkIqqo/fgH5L5O0tR+rI7/NrXFxctqLIw/cfvve0mKSkJKSkpOSYj708Bw/vUA1y7g6/3Hqee42QaowZZqw9dl04flCbyipmY38GDRqEtWvXYtu2bShfvnzWz728vGA0GpGQkJAt/saNG/DyyrkN1JgxY5CYmJj1unLlipqp27zQYG+EBslrqbV4TwxbahUyk1nC0F/Fi7UAEFzeDc/VKadSRo5F1cIIq8SFp1udcqjr6y7rmNaztqqUDYkwZphxIOaucHzFki7Y8UFbFTMiR7fm0DU8kLEUN8inOJ6tx3+ciYjIfpUsZsCpyV3wZc+6ip43/PQtBIxZh20nbnBAuBAp9fyqFHt5DtZpNRjcVnxw2QxgDleNkEqW7JHXoaFfC/EVT45MkiQMGjQIf/75J7Zu3Qo/P79s79evXx9OTk4IDw/P+tmZM2dw+fJlhISE5HhOZ2dnuLm5ZXvR081/rR7kNhR544f96iRDQuZuOQOTjNpUJU9XrBnUQr2EHIxqhRFWiQvf7+81kxV/+346pvx9UqVsKC+9FspbRj/txdoqZUKUOWth+O9RwvEGnQZrh7RULyEiIiIr0q2ODy5MC0WnGmUUO6dJAvr++A8CxoZh1vpTLJBYWEGeX728vHDjxo0n3n/43tNi3Nzc4Oqa8wbl9vQcPLR9VehkLBuZz1UjpJJp608Lx3at5c221YIGDhyIn376CStWrEDx4sURFxeHuLi4rBVx7u7u6NevH0aMGIFt27YhMjISffv2RUhIiNBewyRGp9VgnszJG/suxiPsqPgqKlKOySxh/tYLso6Z+oK8DkH0dIpf4Vklth46rQaD2sib3fD9nmiEHY1VKSPKzTPzd+Lg5QTheBe9Fk38PdVLiBzesJWHZM1aWPRGQ/WSISIiskI6rQbfvtEQZ6d0QeVSRRQ991c7LiJgbBhmbzzDwWGVKfH8GhISgmPHjuHmzf/6g2/evBlubm6oUaNGVsyj53gYk9szMGBfz8E6rQZzZA7WbT91I+8gIhlqjl8vK37eq8quDrRn33zzDRITE9G6dWt4e3tnvX755ZesmC+++ALdunVD9+7d0bJlS3h5eWHVqlWFmLV9Cg32QbWyRWUdM/SXw7zfKATtPt8GOb/qRQ06jgUqTPHCCKvE1mV4h2qyf5OHrOQF0ZLeXnoAx67dk3XMrJdqcx8HUo0xw4y/j+a851NOdBqgaZVSKmZERERkvQx6Lba83wZf9qyj+LnnbTuPymPD8GfkVcXPTZmUeH7t2LEjatSogd69e+PIkSPYuHEjxo0bh4EDB8LZ2RkA8O677+LixYsYNWoUTp8+ja+//hq//vorhg8fXmjf3dKeqe2Dsm4G4fj+P0aqmA05mltJabhvFJ/59WJdHz5zyyBJUo6vPn36ZMW4uLjgq6++Qnx8PO7fv49Vq1bl2jmGCmb1QHmtltJNEgav4DXXkp6ZvxMxd3LeYyw3s3pwLFBpihdGWCW2LjqtBl+/Xk/WMRlm8IJoISlGE7acuiXrmCqli+KZ2j4qZUQEdJu3S1b8F9xwnYiICN3qlMOFaaHwdRcf+BUhARj+2xG0msn9ANWgxPOrTqfD2rVrodPpEBISgtdffx1vvPEGJk2alBXj5+eHdevWYfPmzahduzY+//xzfP/99+jUqZNFv29h+6x7HeFYM4B+Sw6olgs5lobTtsiKn9GdravJdrkadKhf0UPWMWHHb7ClloWsOXRN9gTp0CAvhAZ7q5SR49JIkmRzSwOSkpLg7u6OxMREm15ObElro2IxaOVhWcd8/VpdhAZzAF5NgR+tQ6pJ3jFnp3Rhn1NB9nqtUPN7pRhNqD5+g3B8cDk3rBnMjb+IrJE9XgPt8TuRfUpOzcDLC/bgZFyyoufVaYBXGpbHx92C4GrQKXpue2OP1wt7+E4ms4SAsWGyjjk1qTP/vFOBxCcbUW/KZuH4Gl7FETbMdvdPtIdrRU7s9XupxWSWUP3j9TCa5A37XpgWyomPKsrPv4N6LXBmCn9fRMm5VnB01UF0q+OD0KCyso753wq21FLTpDUnZBdF+rfwY1GEVPXOjweFY73dnFkUISIiykExFz3ChrXChWmhKFNMuRUkJglYceAqqo/fgLoTNyA5NUOxcxNZgk6rwbPB8lrnvPj1bpWyIUfRaJp4UQQA/vhfM5UyIbIcnVaTr31yXvhKXgcJkmfQcvkdeub1rMeiiEo4wupA5r9WX/YxQZ+IzxwnccYMM37YGyPrmPbVy+CjrjXUSYgImTMXdp27Ixy/Y1RbFbMhIiKyfTqtBgfGdcCR8R3h5qzso9fdFBOCJmxEq5nhnMxENuWzl+UN1J2KS0bY0ViVsiF712/JQWSIby0Cf09XrlAiu9E5yBtD2gTIOubotXvoyzaGqjBmmLH+xA1Zx/Rv4ccWWipiYcSB6LQatKjsKeuYlHQzWn4arlJGjqvzbHk9on09XPD9mw1VyoYo07zwcxAdVvFxd+HqJSIiIkHuRZxwdGIXfNlT/szNvFyKT0XA2DAMXhEJo5zRP6JCYtBr8XazSrKOGbaS3QxIvhSjCeGnb8o6ZsPw1qrkQlRYhnaoBrmP7ttO30L/ZeLdJEiM3LHAamWLcYK0yjiq5WAWviF/cP3y3VQkPkhXIRvH1HDKZlyMT5N1zPphrVTKhiiTySxh/tZzwvHTX6ilYjZERET2qVsdH1yYFop6vh6Kn/vvo3GoOm49xv91XPFzEylt3DM14VXcSTjeaAbmh4vfqxIBQA0ZeycCQMfqpTn5i+yOTqvB5y/VkX3c5pM3kWKU2f+dctVgyibZY4GrBzZXKRt6iFd8B+Nq0KFdYGnZx9WfvEmFbBzPW4sP4FayUdYxQeXcUMxFr1JGRJnmbjkL0Ul4Wg3QvKr86wgRERFlDlCsGtgMpyZ1ho+7s+LnXxZxCZVGr0NcQqri5yZS0s4P28uKnxt+jqtGSFjo3B3Cq+Ef+qY3uzSQfXqubjkE+RSXfVyIzP15KGfd5u3E7WR5E87/396dx0VV7/8Df83isMjihiyKoigqouASuGSlkghodStvq+FSv5u5lJpdNUQlFq+2aGp5f5nLr273Wt97u30VcAlNTUlLo8INNQ1TwS1BFBhm5vz+4EJxJZwzc84s57yej8f8wXA+Z94fGF7MOZ9zPp8RPQM4rZ8DcGBEhd6fEIvQ1p6i2pgE4O4lnFLLHlVGM3aeuCyqjV4LbOHi1iQzs0XA2ztPWb39qMhALvxFRERkJy+DDvvnxeNY+mgM7dpG8v0PWpKPsLk52Ft8mSeTySUZ9Fp0EnFcKgAY8bq4aUhInSqrTTh6sVJUm5VP9OMxDinalhn3wLuFuNPA16vNmLie643Y49PD51F04YaoNh56DdZNiJWpIvotDoyo1N4/j4R3C3H/9H++Xo1hXG/EZr1tWMj+h0WjZaiEqLEhSz4Xtf34QWHyFEJERKRCXgYd/vZ/BuN0VhKSo4Ik3//4dQcR8WouPj18XvJ9E9kr90VxUwb/dK0akzcckKkaUoqkFbtFbT+iR1uMjQ6RqRoi11G4MEF0m10nuN6IrXK/v4CZHxeKbleYJv73RLbhwIiKHVog/g/t3C/VmLieH0TFGvDadqunKap3f2R73jZHsiu/VYuyCuund/PUazAovK2MFREREamTTqvB6qcHoDgjEe19pJ1G1SwAMz8uRPSibVygnVyKj6ceXdp5i2qTf/wKthRekKkicne5319EyS/iphJcN3GQTNUQuRaDXouUwZ1Et+N6I+JtLbqIFz76VnQ7ngt0LA6MqJiXQYf4Xu1Ft9t14goyc47KUJEylV6vxtWbIucS7BGA957h/KYkv4EZ4tYPev3RaN5iTkR2q6mpQUxMDDQaDQoLC51dDpFLMei1OJiagPhe0q/nVV5tQkRqHoZk7kBltUny/RPZ4vNZ90Hsp8tp//iWU8TRbcwWAS98dFhUm3sipJ/KkMiVLX6wD/xsWMc2yoZZUNTKbBEw/W/isgjguUBn4MCIyq1NuQtRIX6i27239wyvNrPCgk+LMEjk2ixd23pi3UTOJUjyK79Vi1oRf8YdW3liTEwH+QoiItV45ZVXEBLCKSuImrM2JRbH0kdjmAx3al64YUTUom2IWbyNV4CS0+m0Gqx8PEZ0u0ff2Sd9MeTW+i3eJrrNO0/yJCSpz/eLEuAr8q4EswD0Txf/N6ZGcZk7UCty7D46xIfnAp2AAyOELTOGoa23+LdCn7Q8GapRjvB5OfjgwE+i222dOVyGaohu98CqPaK2nzi0i0yVEJGa5OXlYfv27Xj99dedXQqRy/My6PDBc4NwOisJiZHtJN//9SoTeqVtRfd5ObhcUSP5/omsNSamA/qHthLV5tufyzmwRw1S1h1ARY2490OfDr7wseHKeSIlKFwkfnr9a7dMGJolbtYJtQmbm4MrImeNAYB/TbtHhmroTjgwQgCAg6mjRd++XGMBYhZxcKQpYXNzYLbhzu7Jd4fBoOefJcnPbBHw0zVxc++OHxwmTzFEpBplZWV47rnn8MEHH8Db+85zytfU1KCioqLRg0iNdFoN3n0mDsUZiZiT0F3y/dcKwF1ZnyPi1VzeFU5O88mUIaKnbI1exKldCJi84SB2F18R1SbQ14DN03kiktRLp9Vg5RP9RLc7X1GLodk7ZKjI/YXNzbGp3arHYzhluZPwDCwB+M/B1tP9Rbe7Xm1B+Dzb/vCVqveCXJvaRQb6YMGY3hJXQ9S0/afEHTgkRgVy0I6I7CIIAiZMmIDnn38eAwcOtKpNdnY2/P39Gx6hoaEyV0nk2gx6LaYOj8DZJcl4apD0fw9Gs4CI1DyMenMXr8Qnh6ubUkvcSTqjBYjiTAaqlplzFPnHL4tqo9dqsH9evEwVEbmPsdEhNq1pdr7ciJ6pzN7fsnVQZHj3tpyy3Il4losajI4KxjtPih8tNgu2B4DS3L3kc9wUO5EgAL0WyJ15rwwVETXtTx98I2r7VU8OkKkSInJ3c+fOhUajafZx/PhxrFy5Ejdu3MC8efOs3ve8efNQXl7e8Dh37pyMPSFyL5kP9UVxRiLat2wh+b6LL91Cr7StGJC+jYu0k0Ml9Q1GaCtPUW0qjRb0SuXxqBoZTRa8t/eM6HZv/DGaV2cT/cfalFgMjxC/nlm1iRdK1+tq4zlRP0891k8eJHE1JAYHRqiRpL4heOPRaJva2hoESjHm7d34+bptczMfTU+UuBqi31d+qxa3RKy6/mBMMA8ciOh3zZ49G8eOHWv20bVrV+zcuRMFBQXw8PCAXq9Ht27dAAADBw5ESkpKk/v28PCAn59fowcR/cqg1+LgglEoWpSAlgbpD+2u3jIhatE2DF+2C2aLDfPEEtkg7yXxF4xVmYAeqbbduU/uq4cNV6yHtfXCg7w6m6iR9ZMGITLIR3Q7swDEpas7e4dkboctk5CGtvLE9zas80LS4sAI3eaRgR3RqbW4q3QAwAL1Do6kbz6CoguVNrV9bhjXFSHHenDVXlHbL3s0Rp5CiEgRAgIC0LNnz2YfBoMBb7/9Nr777jsUFhaisLAQubl1B1GbNm1CZmamk3tB5N58PPU4kp6IokUJ8NRJfzHDmau3ED4/F+PXfsUptkh2Pp569AkRPxBeYxIwbEm+DBWRK+o6Nwe2DNfmzx4ueS1ESpD70r3w1Iv/DFF2S0D/xepc7yl5xW5cuCF+ofUnYjti79yRMlREYvFsLDVpz59Hoq23+NvyLQAiXlXX4Mhnheexbt9Zm9rG9wrAq8lcV4Qcx2wRcPZaldXbJ3FtESKSSKdOnRAVFdXwiIiIAACEh4ejY8eOTq6OSBl8PPU4npmE79JGIcBH+im29p66+p8ptrZzii2S1eYZw+DrqRPd7tz1avzPwRIZKiJX0nVujk1XaL/zZD/eCU/UjCM2zmZyrcqM/q9tl7ga15b89m4cuSj+AmmvFhpkP2zbTD0kPZ7tot91KG0UWnuKf4sYzUDS8t0yVOR6XttyBC/+o9CmtimDO2NtSqy0BRHdwZfF4hYmXMm1RYiIiNyOv3cLfJ06CsUZ8kzXevVWLaIWbUPEqzkovyX+Skkia/ywaDRaGcS3e/lfPyB98xHpCyKX0OPVXJsGRSbf3RlJfUMkr4dISXRaDdY83d+mttdu1iJ5xR6JK3JNySv24IiNs8YcXsDps1wJB0aoWd8uSkRLvfh2R0srEffaVkXfaj9x3QG8/+VZm9pGBvlg8YNR0hZEZIXsvGNWb/tANNcWISL5hIWFQRAExMTEOLsUIsUy6LU4uyQZe16WZ+oYoxmITt+OAelbuQYJyeLQoiSb2q3bdxZJK9RxsZ5aGE0WRKXlocYsPmvu6d4OC8bw+JvIGqOjgrHq8Rib2h65eAPJy5W9LtnQ7M9x5OINm9rG9wqAl0H83ZAkHw6M0B19n27bh9Gym2b0StuKCeu+krgi5xuavQO7iq/Y1NZDp0GuDQsKEtnLbBFQXGb9VQ2vj4uRrxgiIiJymE7tvHF2STKKZFrk8+otM8Ln5+LeJTt4BwlJyp6rl49erET3+Tkwmmy5v4BcSXbuUUSk5qHSKP532UIL/L/JcTJURaRcY2I6YNLQMJvaHimtW5dsS+EFaYtyMrNFQFRaHs6X19jUPirYj7PGuCAOjNAd2fNhFAC+KL6Knqm5ElbkPEaTBV3n5uB8udGm9loNcCLTtoEmInutzD9p9W3nkcG+XFuEiIhIYXw89Ti7JBn7XhkBOe4J/em6EdHp29F1bg7e2XmSJ6RJEvZcvVxrASJS85CZw6m13NWiz4rw1z1nbG5/PIPH30S2SBvbG306+Nrcfto/vsWk9QckrMh5Nn93AeHzc20anAWA0FYe2PLiMImrIinwrBdZxZ4PowBQbRLQbV6OW99Ol5lTd5WKrYd3rTx0+DE7WdKaiKxltghYkX/S6u3/OWWojNUQERGRM3Vo44UzS5JxLH00DDrph0gsAJZuL0ZEah4WflYk+f5JfcbEdMDku8Nsbv/e3rOYvEEZJ+jUJGnFHmwo+Mnm9mue7s+pgYnssHn6PYgM9La5/c4TV3D3knwJK3K8iesPYvrfv7W5fafWntg7N17CikhKHBghq42J6YDnhnWxub1JAMLn5yL3e/e7nW7i+oN4b6/tV6n0CvRG4eLRElZEJM7Ujw7B2mHJLm29Oe8lERGRCngZdCjOTMLh1Pvh3UKeQ8ONBT8hfF4O9hZfduuLpMj5FozpjZE9A2xun3/8Ch5c+SXfh27AbBHQa0Eejto4jz9QNygyOipYwqqI1Cl35nC08zHY3P7n69XomZrrdtlrNFnQa0Eudp24bPM+JgzqhD1/HilhVSQ1DoyQKK8mR9o1OAIAL3z0LRZ/9oNEFcnLbBEwMH2bXUHYwd+AvJnyLHhJZA2jyYKtRWVWb5/xUB8ZqyEiIiJX08bHgKOvJWLV4/0gx8XVZgEYv+4gus3PxUv/OMwptshm70+ItWtw5Lvz5Qifn4ulW4+53Uk6NagymjH+va8QPj8XVbW250RxRiIHRYgk9E3q/YgKsX1arWqTgPD5udj8nXtcKJ2x+QgiUvNQVWv7/4mUIaFYxHMrLo8DIyTaq8mRWPV4P7v2sb6gBN3n57j04oz//OZnhM/PxZVbJpv30da7BfbNu1/CqojEG/aXz63eVgtgUHhb+YohIiIilzUmJgQnM5MwY0Q3WdYgEQD8u/AiIlLzkLh8N6qMZhlehZTu/Qmxdl+s984XPyJ8fi7e2H6cAyQuwGwR8PA7+9ArbSv2nr5q177WPN2fayUSyWDLjHvw1rhou/Yx/e/fYsTSnS59gUTSit1Yu++sXfvoE+KLxQ/0laYgkhX/W5BNxsSE1M3Xacc+ai1AdPp29F6Q51IHRdcqjQibm4PZ//OdXftp663HobRRElVFZJvKahPKblg/ADm0W1vOw0tERKRiOq0Gs0b1wKmsugESuT4WHCutRK+0rYhckIdrlUZ5XoQU69XkSBxLt3+q4pU7TyN8fi6W5B7lAIkTGE0WTP/wa4TPz8Xhkut274/TZxHJ6w8DOtp9LvDHa1WISM3DCx9841K5W3q9GmFzc3D0YqVd++kT4ovNM+6RqCqSm0YQBNd5F1qpoqIC/v7+KC8vh5+fn7PLUTWzRUDvBbmolmBcY1i3Nvjg2cH278hGRpMFMYu34ZYdt+zWGx4RgPWTYiWoiuyh1KwQ068HVu7F9+crrN73sfTRXF+ESCGUmIFK7BORqzNbBHx58jImb/wacl7gqdcAPyyW7nOIEvNCiX2y19aii3j+w8OS7a9TKwM2z7gP/t4tJNsn3c5sETDlw6+x/ajtU1b/lhbAyawkXuD1H0rNCqX2yx2ZLQK6zc+1eh3T5ix7pC/G3RUqwZ5sU2U0o/fCrZBijGbS0DCkje1t/47ILmKyggMjJInur+ai1izNW+mRfiHI+ENfh52cNVsETP/oMHKLSiXZ38on+mFsdIgk+yL7KDUrrO2X2A8r3QNaYsfs+ySpkYicT4kZqMQ+EbmTa5VG9M/YIetrtPXWY/crI+HjqbdrP0rMCyX2SQpSD44AgAZAv1B/rJ8Yx0ESiRhNFvzf3aexZvcpVBqlG2XVaYDT2cmS7U8JlJoVSu2XOwubmyPZvl4a3g0vjOzusKnwjCYLxry9B8WXbkqyv3ee7I+kvrxjzRVwYIScImn5bhwtte+Ws98a2bM93p9wl2T7+63yW7WYsP4rFP1cATvWUmpEB6CYV6m4FKVmhbX92nfyCp56/4DV+y3OSOR8vEQKosQMVGKfiNxR6fVqDFqSL+trBPgYsPzxfhjU1bZpPpWYF0rsk1TMFgEPr96D785Ldzz6W/06+iGxTwgmDO3Cz8tWMlsE7D91BZ8cOocvjpehokb6W87aerfg9NVNUGpWKLVf7q73glzclOrEGoBnh3ZB6thIyfb3W9cqjRi3Zh9+vHJLkrtdAMCgAY5l8lygK+HACDnN/x4+jxkfF0q6T4MWCG7lhbSkSNwXGWhz2BhNFqzb+yOWbT8BiW5uaaDXAKd4lYrLUWpWWNuvKR9+g7yiMqv2OToyEGueGShViUTkApSYgUrsE5E7u1ZpRFzWDkgwE22zlj8ajYcGdhTVRol5ocQ+Se2zb8/jxU2FDnktrQaICvbBB88OUf1dJeW3ajFh3VcoOi/dhYd3kjIklIsb/w5XzorVq1dj2bJlKC0tRXR0NFauXInYWOumIXflfqndJ1+XYM4/f5B0nx46IDLYDxsmDbIrY6uMZizeXIR/fP2zhNXV4eCsa+LACDmV2SJgUNYOXK60fsFnMTQA/D116BXsi+fv6467uwfcNlhSWW3CjI8OoeDMVVTJ/MnMx0OHosX2L/xH0lNqVljTL7NFQGRaHmpM1r3/T/NuJyLFUWIGKrFPREpQWW3C6Ld24edy+RZR1wGYk9ADk4Z1teqKfSXmhRL7JAc5ptYSSwOgpR7Q67TQ63Xo1KYlRvcOwoS73eeOE6PJgrV7T+PDr87iaqURRjNuu8JarwGsPNyQTEBLHfbNG+U2P0dncNWs2LRpE5555hmsWbMGcXFxWL58OT755BOcOHEC7du3v2N7V+0X1TFbBMQszsONGnlCwUMH+Hvq0S3QF3+6t9vvnguc+sE32Hv6KmS+ZgMjewTg/YlcW9gVuc3AiK0jxQxD97B48xGs33fWYa9n0AK+nnpcvWVy2Gsue6QPxt3VyWGvR+IoNSus6de+U1fw1FrrptF6pH8I3vhjPylLJCIXoMQMVGKfiJTEaLJgzsff4rPvpVm77/dMGhKGtAeaX9zUlfOCx8HyM1sE9E7LQ7Wjz9rbQQfA3Mz360///XePrDkO9m6hgUGnQXm1pVH7FhqgdUsDOrf2QMkvVbhUaZJsehk5jOgZgHUTeCLyTlw1K+Li4nDXXXdh1apVAACLxYLQ0FBMnz4dc+fOvWN7V+0XNTbm7T0ounDDYa/XQgN46DWodNDtav6eOnw1/36HrYtM4onJCqcNsW/atAmzZs3CwoULcfjwYURHRyMhIQGXLl1yVkkksYVje6M4IxF6B12EbrTAYYMindt44nRWEgdFyGXN/9f3Vm97d7cAGSshIiIitTDotVjx5ACczkpCUlSgbK+zbv9Z3Ltsp2z7lxOPgx1Dp9XgeEYShvdwn8+5zQ2KAHUDIk2d9rPmOPhWrYDr/zUoAgC1AnCp0oivz91AmYsPijw3rAsHRdyY0WjEoUOHEB8f3/CcVqtFfHw8CgoKnFgZSW3LjHvw7NAwh71erQCHDYpMHNoZ3y0azUERBXHawMibb76J5557DhMnTkRkZCTWrFkDb29vrFu3zlklkQwMei1OZScjKtjH2aVIZvLdYdj9ykhOO0Quq8poxk/XqqzePsjfS8ZqiIiISG10Wg3eeXogijMS8cqoCFkulPrpahUmbzgo/Y5lxuNgx1o/MRbH0kdjaHgbZ5dCNtAA+EO/EBRnJOLVZHkWYybHuHLlCsxmMwIDGw+aBwYGorS06bsMa2pqUFFR0ehB7iG1/kJpBc14986T/bBwbJSzyyCJOeUtypFi9dny4r0oWpTgsLtH5ODVQovijEQsGNP8bfvknjIzMzFkyBB4e3ujVatWTW5TUlKC5ORkeHt7o3379pgzZw5MpsZXZ33xxRfo378/PDw80K1bN2zYsOG2/axevRphYWHw9PREXFwcDh6U9qB+4f9av+iZXgPEduGBIhEREUnPoNfihRHdcSo7GfG9pL9yP//4ZVQZ73SdvevgcbBzeBl0+Ntzg3E6Kwn9Qv2dXQ5ZQacF/jY5DqeykvDWY/24nohKZWdnw9/fv+ERGhrq7JJIBINei1NZyfjLI32cXYpd+nbwq7sTtm+Is0shGTjlv4vYkWKOEiuDj6cep7KT0aeD+80F+eYjfXHstUR+IFMwo9GIcePGYcqUKU1+32w2Izk5GUajEfv378fGjRuxYcMGpKWlNWxz5swZJCcnY/jw4SgsLMRLL72EZ599Ftu2bWvYxhHTJ+T9YP283kO6tePdT0RERCS7tSn1V+63lnS/WblHJd2fnHgc7Fw6rQafTr0bx9JHIyLA29nl0O9Y+ofeOJ2VjKHdeZyiJO3atYNOp0NZWVmj58vKyhAUFNRkm3nz5qG8vLzhce7cOUeUShJ77K5OOJ2VhAAfg7NLEaV7Oy8cSx+N/50+jFmkYG5xlpejxMqyefowFC1KQM9A1/4wGt7OCx9MjMXprCQ8fBffc0q3ePFizJw5E336NH01w/bt23H06FF8+OGHiImJQWJiIl577TWsXr0aRqMRALBmzRp06dIFb7zxBnr16oVp06bh0UcfxVtvvdWwH0dMn2CyWD+/5l/HD5TsdYmIiIiaU3fl/hCczkrCXx/vJ8k+z169Jcl+XBGPg+XhZdBh++zhKM5IxNg+7Z1dDgHw89Th/afr1if6Y1yYs8shGRgMBgwYMAD5+fkNz1ksFuTn52Pw4MFNtvHw8ICfn1+jB7knnVaDr1Pvx3dpo9DOW+/scpr1UEwwijMSsePlEVxLRAWcMjAidqSYo8TK4+Opx9aZw3E6KwnxPdo6u5xGWnnqUZyRiPyXR2BYjwCODBMAoKCgAH369Gl0hV9CQgIqKipw5MiRhm1+OzVC/Tb1UyPYOn2C2KsFewZat6aPr4eW/+iJiIjI4XRaDRJiQnB2STK+SxsFX4Ptn7fD2rr2xVa/xeNg12LQa7HyqbtwOisJ61MGonewLzx0PPZzpDnxESjOSMT3i0ZjZFQQj70VbtasWXjvvfewceNGHDt2DFOmTMHNmzcxceJEZ5dGDuLv3QLfpCWgOCMR/Tv6OrucRvqH+uN0VhKWP96fs8WoiFOG6X47UvzQQw8B+HWkeNq0abdt7+HhAQ8PDwdXSY6g02qwduIgGE0WrN93Btl5x51Wi6dOgwOv3g9/7xZOq4FcV2lpaZPTHtR/r7ltKioqUFVVhV9++eV3p084fvz33/vZ2dlYvHix1bVumDQI0enb77jdl3+Ov+M2RERERHLy926BH9KTUGU0o++ibagVcecrAMxPcp8FmXkc7Jp0Wg2G9wrE8F51n9GNJgve230Ky/NPotbi5OIURgOgW4A3Nv1pKNq42bQ6ZL/HHnsMly9fRlpaGkpLSxETE4OtW7fednxMymfQa/GvaffAaLJg/NqvcODsL06rJdi3BXbOGcmLRlXKafcvzZo1CykpKRg4cCBiY2OxfPlyjhSrmEGvxZ/uDcef7g3H+WtVGL3iC9yokf9TqF4DDA1vjdVPx8LH07Vv5yPxFi5cCADw9//9RRaPHTuGnj17Oqokm8ybNw+zZs1q+LqioqLZqRT8vVugc1sv/HS16ne36dzWi4OARERE5DK8DDqczErC5YoaDF3yOYxWHArcH9ne7U5k8DjY9Rn0WkwdGYGpIyNQZTQjfUsRvjxxCefLjeA4iW0eiAnEXx7u53Z/ryS9adOmNTkQTOpk0Gux6fkhMJoseHfnSazZcxpVJnEXSNjCQweMiQ5GxkPRzCWVc9qZYI4U0+/p0MYLPyxOBABcrqjBg6v24kJFjd371QAI8GmBkb0CkTY2iuGnAtOnT8fy5cvx9ddfw8en6emlunbtatW+goKCcPDgwUbP1U+DUD/1QVBQUJNTI/j5+cHLyws6nU70gnOAbVcL7p4zAvcu29nk4Ejntl7YPWeEqP0REREROUKAnweKs5JRZTRj5Os7caHC2OR290e2x3vP3OXg6uzH42D34mXQIfvh6EbPmS0C9p+8go8PleDbkuuoqTWhvMrUaDBPA6ClHqg0ObZeR9NrAEEAtFogwNeA8bFhmHxvOKehISJRDHotXhzVAy+O6gEAKL1ejeS39+DqrVq7991CB3Rq7Y1HB4Zi8t1dmU/UiFMvkedIMd1JgJ8H9s9vPN1PZbUJ0/72Dfafvtrow6dBC/h66nH11q+fPkP8DPhs2j0I8OMt6GrUrl07AEBERITdC7UNHjwYmZmZuHTpEtq3r1ukcceOHfDz80NkZGTDNrm5uY3a7dixo2ExObHTJ9hr95wRKL9Vi0kbDuJCeTVC/D2xbkIs7xQhIiIil+dl0GH//PvrrtjfXIRdJy4DAIb3CHD7i5x4HOzedFoNhvUIwLAeAVZtf63SiHFrvsSZK1XN3nGiA2Bu5vv1q2/897XUTR0H/zfvFhoYdBqUV1satW+hAVq3NKBzaw+U/FKFS5WmRt/30gODurbDyicHcIYFInKYoFaeOJQ2qtFz1yqNGPful/jxatVtOeah16Cytu5ZDYCoYB98+NwQnvugO+J/NnI7Pp56bJg8yNllkMKUlJTg2rVrKCkpgdlsRmFhIQCgW7du8PHxwahRoxAZGYnx48dj6dKlKC0tRWpqKqZOndpwN8fzzz+PVatW4ZVXXsGkSZOwc+dOfPzxx8jJyWl4HUdPn+Dv3QL/fGGoLPsmIiIikpuXQYfsR6LvvCGRi2rjY0D+y7xbm4jIHm18DMjnzBckMQ6MEBEBSEtLw8aNGxu+7tevHwBg165duO+++6DT6bBlyxZMmTIFgwcPRsuWLZGSkoL09PSGNl26dEFOTg5mzpyJFStWoGPHjli7di0SEhIatuH0CURERERERERERM6lEQRB/lVtJFZRUQF/f3+Ul5fbPT0OESmXUrNCqf0iImkpMSuU2CcikocS80KJfSIi6Sk1K5TaLyKSlpiscMs7RurHcioqKpxcCRG5svqMcMPx32YxA4nIGkrMQOYfEVmLGUhEaqXE/AOYgURkHTEZ6JYDIzdu3AAAhIaGOrkSInIHN27cgL+/v7PLkAwzkIjEUFIGMv+ISCxmIBGplZLyD2AGEpE41mSgW06lZbFYcOHCBfj6+kKj0TS7bUVFBUJDQ3Hu3DnV3GrHPrPPSiW2z4Ig4MaNGwgJCYFWq3VAhY7BDGwe+8w+KxUzUFz+AXyfsM/KxT4zA/kZ8HbsM/usVMy/OszA5rHP7LNSyZmBbnnHiFarRceOHUW18fPzU80bph77rA7sc/OUdIVMPWagddhndWCfm6e0DLQl/wC+T9SCfVYHZiA/A94J+6wO7HPzlJZ/ADPQWuyzOrDPzbM2A5UzdExERERERERERERERHQHHBghIiIiIiIiIiIiIiLVUPzAiIeHBxYuXAgPDw9nl+Iw7LM6sM9kDTX+zNhndWCfyRpq/Jmxz+rAPtOdqPHnxT6rA/tM1lDjz4x9Vgf2WVpuufg6ERERERERERERERGRLRR/xwgREREREREREREREVE9DowQEREREREREREREZFqcGCEiIiIiIiIiIiIiIhUgwMjRERERERERERERESkGooeGMnMzMSQIUPg7e2NVq1aNblNSUkJkpOT4e3tjfbt22POnDkwmUyOLVRmYWFh0Gg0jR5LlixxdlmSWr16NcLCwuDp6Ym4uDgcPHjQ2SXJZtGiRbf9Pnv27OnssiS1Z88ejB07FiEhIdBoNPj3v//d6PuCICAtLQ3BwcHw8vJCfHw8Tp486ZxiXRgzkPmnRMxAZqC1mIHMQKVh/jH/rMX8q8MMVBZmIDPQWsxAdeQfwAxkBtqfgYoeGDEajRg3bhymTJnS5PfNZjOSk5NhNBqxf/9+bNy4ERs2bEBaWpqDK5Vfeno6Ll682PCYPn26s0uSzKZNmzBr1iwsXLgQhw8fRnR0NBISEnDp0iVnlyab3r17N/p9fvnll84uSVI3b95EdHQ0Vq9e3eT3ly5dirfffhtr1qzBgQMH0LJlSyQkJKC6utrBlbo2ZmAd5p/yMAOZgdZgBtZhBioL84/5Zw3m36+YgcrCDGQGWoMZWEfJ+QcwA5mBEmWgoALr168X/P39b3s+NzdX0Gq1QmlpacNz7777ruDn5yfU1NQ4sEJ5de7cWXjrrbecXYZsYmNjhalTpzZ8bTabhZCQECE7O9uJVcln4cKFQnR0tLPLcBgAwqefftrwtcViEYKCgoRly5Y1PHf9+nXBw8ND+Pvf/+6ECl2fmjOQ+ac8zEBmoFjMwLecXYZs1JaBzD/mn1hqzj9BYAYqDTOQGSiWmjNQ6fknCMxApXNUBir6jpE7KSgoQJ8+fRAYGNjwXEJCAioqKnDkyBEnVia9JUuWoG3btujXrx+WLVummFsEjUYjDh06hPj4+IbntFot4uPjUVBQ4MTK5HXy5EmEhISga9eueOqpp1BSUuLskhzmzJkzKC0tbfQ79/f3R1xcnKJ/53JQSwYy/5SHGcgMlAIz0L2pNQOZf8w/Kagl/wBmoNIwA5mBUlBLBio1/wBmIDOwjhQZqJeiOHdVWlraKAgBNHxdWlrqjJJkMWPGDPTv3x9t2rTB/v37MW/ePFy8eBFvvvmms0uz25UrV2A2m5v8PR4/ftxJVckrLi4OGzZsQI8ePXDx4kUsXrwYw4YNQ1FREXx9fZ1dnuzq/zab+p0r6e/WEdSQgcw/5WEGMgOlwgx0b2rMQOYf808qasg/gBmoNMxAZqBU1JCBSs4/gBnIDPyVvRnodneMzJ0797bFZv77odQ/gt8S83OYNWsW7rvvPvTt2xfPP/883njjDaxcuRI1NTVO7gXZIjExEePGjUPfvn2RkJCA3NxcXL9+HR9//LGzSyMHYAYy/9SOGahuzEBmoJox/9SN+VeHGahezEB1YwYy/9SOGSgPt7tjZPbs2ZgwYUKz23Tt2tWqfQUFBeHgwYONnisrK2v4niuz5+cQFxcHk8mEs2fPokePHjJU5zjt2rWDTqdr+L3VKysrc/nfoVRatWqFiIgInDp1ytmlOET977WsrAzBwcENz5eVlSEmJsZJVTkOM5D5V4/5V4cZiIavmYF1mIHMQFf+/UmJ+YeGr5l/dZSefwAzsB4zkBlYjxn4K6VnIPPvV8xAZmA9ezPQ7QZGAgICEBAQIMm+Bg8ejMzMTFy6dAnt27cHAOzYsQN+fn6IjIyU5DXkYs/PobCwEFqttqHP7sxgMGDAgAHIz8/HQw89BACwWCzIz8/HtGnTnFucg1RWVuL06dMYP368s0txiC5duiAoKAj5+fkN4VdRUYEDBw5gypQpzi3OAZiBzL96zL86zEBmoK2Yge6NGcj8A5h/tnLX/AOYgfWYgcxAgBloK3fNQObfr5iBzEBAmgx0u4ERMUpKSnDt2jWUlJTAbDajsLAQANCtWzf4+Phg1KhRiIyMxPjx47F06VKUlpYiNTUVU6dOhYeHh3OLl0hBQQEOHDiA4cOHw9fXFwUFBZg5cyaefvpptG7d2tnlSWLWrFlISUnBwIEDERsbi+XLl+PmzZuYOHGis0uTxcsvv4yxY8eic+fOuHDhAhYuXAidTocnnnjC2aVJprKystGo95kzZ1BYWIg2bdqgU6dOeOmll5CRkYHu3bujS5cuWLBgAUJCQhr+IVIdtWcg80+ZmIHMQGsxA5mBSsP8Y/5ZS+35BzADlYgZyAy0ltozUA35BzADmYESZaCgYCkpKQKA2x67du1q2Obs2bNCYmKi4OXlJbRr106YPXu2UFtb67yiJXbo0CEhLi5O8Pf3Fzw9PYVevXoJWVlZQnV1tbNLk9TKlSuFTp06CQaDQYiNjRW++uorZ5ckm8cee0wIDg4WDAaD0KFDB+Gxxx4TTp065eyyJLVr164m/3ZTUlIEQRAEi8UiLFiwQAgMDBQ8PDyEkSNHCidOnHBu0S5I7RnI/FMmZiAz0FrMQGag0jD/mH/WUnv+CQIzUImYgcxAa6k9A9WSf4LADGQG2p+BGkEQBNuHVYiIiIiIiIiIiIiIiNyH1tkFEBEREREREREREREROQoHRoiIiIiIiIiIiIiISDU4MEJERERERERERERERKrBgREiIiIiIiIiIiIiIlINDowQEREREREREREREZFqcGCEiIiIiIiIiIiIiIhUgwMjRERERERERERERESkGhwYISIiIiIiIiIiIiIi1eDACBERERERERERERERqQYHRoiIiIiIiIiIiIiISDU4MEJERERERERERERERKrBgREiIiIiIiIiIiIiIlKN/w9IxhmJ+xEccAAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1482,12 +1125,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  16\n"
+      "Question  25\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1499,12 +1142,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  17\n"
+      "Question  26\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1516,12 +1159,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  18\n"
+      "Question  27\n"
      ]
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1533,12 +1176,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  19\n"
+      "Question  28\n"
      ]
     },
     {
      "data": {
-      "image/png": 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jXbt2Bs9ZVh8rxOjRo9GvXz9MnTpVMyhw/vx5zJ49G2PGjEGfPn2MOi8hUjtx4gRiYmIwZMgQLF26FO+++y7279+Prl276izrEhYWhkuXLiEiIgIjRozATz/9hIEDB2oGHe/du4eePXsiOTkZH3/8MZYtW4Zhw4bh2LFjmnPo63+lvK4x9F2XyWRa9et5+gKhf0fKsnv3boSEhMDBwUHQcXfu3MG9e/fQpk2bUj9r164dzpw5U+p5sa6lCZGSpV0fWtN912+//YaYmBgsXLhQb8yZM2fQsGFDrYkQAJprJ/Vkz/nz51FUVFSqj3F0dETLli21+hjecwLafbJMJuPqcyMiIjB79mx069YNy5cvxyeffILatWtrrsvLcuTIEUyYMAFDhgzBwoUL8eTJE7zyyivIyMjQxKj7xWf/7WfMmIGWLVtizJgxmtKGe/bswbfffouZM2eWKqHYunVrJCQkICcnx2C7CLEYJ04AMTHAkCHA0qXFJaH27we6di2eIHlWWFjxAH5ERPEA/k8/FU8CqCeh790DevYsnjj4+GNg2TJg2DCgxPUgCguLX/fZ66gmTYC5c4EffgD+62/x6FHxhEPjxsX7fJT0X39r1GbpxrpwAejXD8jPL27P4sXASy/xtUGpLJ5MqloV+PJLoEuX4uNLjCda9WdjD8o30YWUF3d3dzZx4kS9Px85cqRWSRF1GayqVauyzMxMzfPbt29nANiff/6pee7NN99kVatW1XnePXv2MABs3rx57Pr168zV1ZUNHDhQZ+xnn33GALC0tDS97eQpz/XVV18xAOz+/fsGz1OyHMLIkSMZADZnzhyt2MDAQNa6dWvNf1+7do0BYMuWLdN7/pLGjBnDFAoFu3r1KmOsuJyEOk25ZDmJkJAQrfJcSqWSderUiXl7e7P09HQ2ceJE5uDgoLd8hqOjIxs/fjxXmwgxhzFjxjBfX1+Wnp6u9fyQIUOYu7s7e/z4sea58PBwJpfL2eHDhzXlpb7++mut49Tf0ffee0/znEqlYn379mWOjo6a77zQ7yhjjD169Ig1aNCANWvWjD158oT17duXubm5aZVEEVNGRgbz8vKSpEQWT3muu3fvMgDsiy++EPW1deEpzzV8+HDWpUuXUs/HxsYyAJpHo0aNDJZnfPjwoaY/vX79ulZ5rjfeeMOo8lwpKSnM09OThYSEsPz8fBYYGMhq166tVc6CMSrPRSxLyT5WTf2d+v777zXPqcvFtG7dmhUUFGieX7hwIQPAtm/fzhhjbOvWrQbLeJXV/4p9XcNzPfXLL7+w2rVrs5UrV2rKc82ZM8dgeS7evyOGynM9evSIOTs76y2/VVZ5LvXPSv5bqU2bNo0BYE+ePNF6nudampDyZonXh+a67zKlPNfjx49Z7dq1WXh4OGOsuCw0dJTnatasGXvxxRdLHX/hwgUGgK1evZox9vR68fDhw6ViX331Vebj4yP4nNOmTStVnmvo0KEGy3MFBASwvn37lvn+Z82aVeqzA8AcHR3ZtWvXNM+dPXu21L/zjBkzGACdZdnOnz/PHB0d2dixY9mDBw9YjRo1WJs2bVhhYWGp2E2bNjEA7Pjx42W2lRCLouN6kMXGFpeuKnmNoS5n1bo1YyWuB9nChcXP/3c9yLZuNVzG69q14hhd11FKJWOdOjHm7c1YejpjEycy5uCg/3yOjowZM85lbHmur74qPq6M8USWlFQcU/L6beTI4ueeGU9kgYHFn6maJXw2RC/KNLFTHh4eOH78uFZJLB6vv/46qlSpovlvdRpwyXIyGRkZWjEl9ezZE++88w7mzJmDQYMGwdnZGd98843OWPU50tPTNc/l5uYiPT1d81CXicjOztZ6Pjs7W+u9AsD27dsNbhasy7Orxzt37lzq/ZZsb1k2bdqE7777Dh988AGee+45AMUrbkaNGoVjx45pVq/4+/tj7969mDp1qiZdXC6XY8OGDcjNzUXv3r2xcuVKhIeH61xxqG5Pyc+OkPLEGMPvv/+O/v37gzGm9X0NDQ1Fdna21uqxiIgINGvWDCNHjsSECRPQpUsXvRurh4WFaf6/Oi2/oKBAs8m3kO+oWsWKFbFhwwZcunQJL7zwAnbt2oWvvvpKqySKWFQqFYYNG4asrCwsW7ZM62eFhYVan1V6ejoKCwuRn59f6nlj+jc1Xf2tuV77WSqVClFRUTpLczVt2hTR0dHYtm0bPvzwQ1SqVAm5ubllns/V1RVTp07F3r174e/vDwAICAhAbGwsRo0aZVTGiY+PD1asWIHo6Gh07twZ8fHxWLduXakVl4RYEhcXF83/LywsREZGBho0aAAPDw+dq3fHjRuHChUqaP57/PjxcHBw0JSgU19f7dy5UyvjuKSy+l+xr2t4rqeaNWuGmJgYjB8/HjKZDBUqVMCnn36K3bt3a/oHoe9DiAMHDiA/Px+9e/cWfKy6JJGTk1Opnzk7O2vFqOnq2wmxJJZ6fSjVfdeDBw+03qP6GubZ66qyNnVX+/zzz1FYWKhVclqXvLw8rn7DUB9Tsn/hPWffvn1x+vRpTcnq6tWrY9OmTfj888/h7e2tt80eHh64cOEC/v333zLfmy49evRA/fr1Nf/dokULuLm5lbp/d3Bw0Fm2t3nz5pg9ezbWrl2L0NBQpKenY+PGjTqzA6mPJVapxPUgCguBjAygQYPi0lO6srnGjQNKXA9i/HjAwQFQlyT+73oQO3cWn08XdaaXrusouby4rFVuLtC7d/Fm5uHhgJ7+FlWqADzfuQcPiuPUD/U9Y8nn0tN1Z9eUpH5/27cDxtzzPru5e+fOQMly3OXx2RBuNGlipxYuXIiEhATUqlUL7dq1Q0REBFcd/WcHDNUXCs/WuGbP1Ist6csvv4Snpyfi4+OxdOlSeHl56YxTn6PkgFZYWBiqV6+ueahTagcOHKj1/IABAzTHvP766+jYsSPGjh0Lb29vDBkyBL/++ivXIJ+zszOqV69e6j0/+34NvWcA+OeffzBmzBiEhoZi/vz5Wj8LCQnRSilX69Wrl9Z/169fHxEREThx4gSaNWuGTz/9VO/rMcaMLj9DiNju37+PrKwsrFmzRuu7Wr16dYwePRpAcakXNUdHR6xbtw5JSUl4+PChZh+JZ8nlctSrV0/ruYYNGwJAqTr9z35Hc3NzkZqaqnncv39f6+cdO3bE+PHjERcXh9DQ0FJ1+sXy3nvvISoqCmvXri2V9n/06NFSn1dMTAx++eWXUs/fvHnT6Dbo6m/N9drPOnHiBO7fv69z0sTNzQ09evTAgAED8MUXX+CDDz7AgAEDtGpM6/JsXwoACoUCISEhRrdzyJAh6Nu3L+Li4vD222+je/fuRp+LEHPIy8vDzJkzUatWLTg5OaFatWqoXr06srKytBabqKkXd6i5urrC19dX07d26dIFr7zyCmbPno1q1aphwIABWL9+PfLz80udS981ktjXNYaup5o3b66zjn7jxo259l0ydK1nyK5du9CmTZsyBwv1UU966fp8nzx5ohWjpqtvJ8SSWOL1oZoU912BgYFa7/G9994DgFLvvaxyW+r3sGjRIsyfP9/gfn0uLi5c/YahPqZk/8J7zi5dupTqlwCge/fuZZYonDNnDrKystCwYUM8//zzmDZtGs6dO6c3viRdC5z03b/rM23aNAQEBCAuLg6zZs1C06ZNdcZRH0usUl4eMHMmUKsW4ORUvA9J9epAVhag43oQz1wPwtUV8PV9ugdKly7AK68As2cXn2vAAGD9+uJyVs/Sdx1Vv35x+a8TJ4BmzYAy+lswVrx/iCGBgcXvS/34r7/Veq56dcBAf4vXXwc6dgTGjgW8vYvLmv36K98EirNz8WuUVKVK6T1h1O9LFyk+G8JNWDFdYjNee+01dO7cGVu3bsXevXuxaNEifPHFF/jjjz/KXP2m60YU0L7YrFq1apkXJWfOnNFc/J4/fx5Dhw7VGac+R8n6/h9++CHefPNNzX+npaXhzTffxJdffqk12FhyxZCLiwsOHz6MgwcPYteuXYiKisLmzZvx4osvYu/evXrfU1nvt6SqVatqtVeXs2fP4qWXXkLz5s2xZcuWMi8S9W3IqrZ3714AwN27d5GRkaF389CsrKxy2RuBEF3Uk5RvvvkmRo4cqTOmRYsWWv+9Z88eAMU3YP/++2+Zq4DLou87+uWXX2L27Nma/65Tp47W9y8/Px+HDh0CACQmJuLx48eoWLGiUW3QZ/bs2Vi5ciU+//xzDB8+vNTPAwICtDa9BIAPPvgAPj4+mDZtmtbzvBsJ66KrvzXXaz9r9+7dqFu3rt4b1JIGDRqE4cOH45dffik14aSPoT6WV0ZGBk6ePAkAuHjxIlQqldY+CYRYmvfeew/r16/H5MmTERQUBHd3d8hkMgwZMsSobDGZTIYtW7bg2LFj+PPPP7Fnzx689dZbWLx4MY4dOwZXV1euaySprmsMfdcjIiK4z8XzPnjs3r1bMxAslK+vL4DiPbaelZKSAk9Pz1Irv3X17YRYEku8PixJ7P7pp59+0srYUN+HP3u99eyEz7NmzpyJGjVqoGvXrpq+Tr033v3795GcnIzatWtDLpfD19cXd+7cKXUOdV+i3lfUUB9Tcv9R3nOWpL6m5vHCCy8gMTER27dvx969e7F27Vp89dVXWL16NcaOHVvmsbzjFUVFRXj48CEqV65cKvb69euaLJfz58/rfS3qY4lVeu+94kmNyZOBoCDA3b14oH3IEOMyKWQyYMuW4j1M/vwT2LOneBP4xYuLn3N1Ld7TA9A9WaD2X3+Lu3eLsy/03V9mZRVPzhjy00/FE0Qlz79oUfGm7iUZ6G/h4gIcPgwcPAjs2gVERQGbNwMvvlh8zrLGDDnGE8vlsyH8zFwOjFiotLQ0VqNGDdaxY0fGmP49TRYtWlTqWDxTm37evHlMJpOxrKysUrG5ubmsfv36rHnz5mzcuHFMoVCwuLg4nW0aO3Ysq1atWpnt5tnTRJf58+czACw6OlrrPM/uaVKpUqVSxz5bP7WgoIC5uLiwKVOm6Hyta9euMR8fH9awYUN27949Qe181qpVqxgANn/+fObq6speeuklnXG3b98WvIcDIVIqKipilStXZkOHDuWKP3v2LHN0dGSjR49mgYGBrFatWqX6FHXN6itXrmg9/9dffzEA7Oeff2aM6f+OJiYmsujoaM3jyJEjWj//6KOPmFwuZ19++SVTKBRatbHFsHz5cgaATZ48WdBxUuxpcuTIkVL7U4nx2roY2tOkdevWbMKECVznysrKYgAk3b/p2b8Naq+//jqrWLEiW7BgAQPAFi9eXCqG9jQhlsTd3Z2NHj1a67m8vDymUCi0vtfqGvvffPONVuzDhw+Zg4MDe+edd/S+xk8//cQAsG+//ZYxZvgayVquawy9D7Wy9jQ5f/48A6D3upexsvc0YYyx6tWrs1dffbXU8w0bNtS5vwDPtTQh5ckSrw/VzNE/GbunSZcuXbT2eNP1ePDgAWOMsf/9739MoVCU2ndNfT988+ZNxljxNZWDgwObNm2aVlx+fj5zdXVlb731luY53nOK5eHDhywwMJDVqFFD85y+PU107dtap04drb9zP/74IwPAzp49WypWqVSy4OBg5uPjw6ZPn84AsN9//11nu+bNm8fkcrnOcQ9CLJa7O2PPXA+yvDzGFIrifTjU1HuAPHM9yB4+LN5Xo4zrQfbTT8XH/nc9yAoKGHNxYUzfddSqVcXx8+cz5urKmJ7+lt2+rX//D0OM3dNEl/nzi8/133ii3j1NdIwnslmztNthCZ8N0YuWRNohpVJZqgyDl5cX/Pz8dKbZChUUFATGGE6dOlXqZx999BFu3ryJjRs3YsmSJahbty5Gjhyp83VPnTqFoKAgk9uTmZlZ6rmWLVsC0J1+LFSFChXQpk0bzYrjklJTU9GzZ0/I5XLs2bOnVKkvIZKSkjBt2jS88sormD59Or788kvs2LED33//falY9WcfHBxs9OsRIiaFQoFXXnkFv//+OxISEkr9vGRprMLCQowaNQp+fn6IjIzEhg0bkJaWhilTpug89/LlyzX/nzGG5cuXo0KFCppySfq+o/Xq1UOPHj00j44dO2p+dvz4cXz55ZeYPHkyPvjgA0ybNg3Lly/H33//bdLnoLZ582a8//77GDZsGJYsWSLKOU1x6tQpyGQyUfpcU6SlpeH06dOlSnNlZWXp3DNh7dq1AKC3xrhUtmzZgs2bN+Pzzz/Hxx9/jCFDhmDGjBm4evWqWdtBiBAKhaJUGZply5ZBqVTqjF+zZo3W927VqlUoKirSZCQ/ePCg1Pmevb4q6xrJmq5rynofvHbv3g1vb2+T+qtXXnkFO3fuxK1btzTP7d+/H1evXsWrr75aKl6sa2lCpGKJ14eA5fdP8+bNw9atW7Uec+fOBVBcmWHr1q2oVKkSAGDw4MFQKpVYs2aN5vj8/HysX78e7du3R61atQAA7u7u6NGjB3788Uc8fPhQE/vDDz8gNzdXq4/hPaex1PvNqLm6uqJBgwai3LsD0PSLuv7tlyxZgpiYGKxZswZz585FcHAwxo8fr3PfklOnTqFZs2Zwd3cXpV2EmIVCUboU1LJlgJ7rQaxZo71XyapVQFFR8R4bQHGGxLPn++96UFOiq0KF4n04dF1HJSUB06YVl/iaPh348ktgxw5AR38L9RijOa8HdYwnlnp/prDmz8YOUHkuO/Tw4UPUrFkTgwcPRkBAAFxdXbFv3z6cOHECixcvNvn8nTp1QtWqVbFv3z68+OKLmucPHDiAlStXYtasWZq9SNavX4+uXbvi008/1ardeu/ePZw7dw4TJ040uT1z5szB4cOH0bdvX9SpUwf37t3DypUrUbNmTXTq1Mnk8wPAgAED8MknnyAnJ0drI+BevXrh+vXr+PDDD3HkyBEcOXJE8zNvb2/uevqMMbz11ltwcXHBqlWrAADvvPMOfv/9d0yaNAk9evTQSoOOjo5G7dq1ERgYKMr7I0QMn3/+OQ4ePIj27dvj7bffRtOmTZGZmYnTp09j3759mgnOefPmIT4+Hvv370flypXRokULzJw5EzNmzMDgwYPRp08fzTmdnZ0RFRWFkSNHon379vjrr7+wa9cuTJ8+XWuSUt93VJcnT55g5MiReO655zT7D82ePRt//vknRo8ejfPnz2tuRI0RFxeHESNGoGrVqujevTt++uknrZ8HBwcbLMvAY968eQCACxcuACi+6VX3QTNmzNCKjY6ORseOHTWlKqTwww8/4MaNG5rNTQ8fPqxp4/Dhw1GnTh3s3r0bzs7O6Natm9axhw4dwvvvv4/BgwfjueeeQ0FBAf755x/88ccfaNOmjVbZRqndu3cP48ePR7du3TSbzC5fvhwHDx7EqFGjcOTIESrTRSxSv3798MMPP8Dd3R1NmzZFbGws9u3bp/d7X1BQgO7du+O1117DlStXsHLlSnTq1AkvvfQSAGDjxo1YuXIlXn75ZdSvXx8PHz7Et99+Czc3N61+Wlf/a43XNfr+jmRnZ2PZsmUAiveCAor7BA8PD3h4eGj6iV27dqF37946a98vX74cWVlZuHv3LgDgzz//xO3btwEUl1VTD8hNnz4dv/32G7p164ZJkyYhNzcXixYtwvPPP1+q7JeY19KESMnSrg+toX/SdQ/r8d9mxW3btsXAgQM1z7dv3x6vvvoqwsPDce/ePTRo0AAbN25EcnIyvvvuO61zzJ8/H8HBwejSpQvGjRuH27dvY/HixejZs6fW/nBCzmmMpk2bomvXrmjdujU8PT1x8uRJbNmyRdOfmqpevXpo3rw59u3bp7Vn4aVLl/Dpp59i1KhR6N+/PwBgw4YNaNmyJSZMmIBff/1VE1tYWIi///4bEyZMEKVNhJhNv37ADz8Ul+Vq2hSIjQX27XtaJupZBQVA9+7Aa68BV64Ub0beqRPw3/UgNm4sfu7ll4v333j4EPj2W8DNDSjRL2PAAOCTT4CcnOKfAcWTLW+9VVwC67/+Fu+8A/z+OzBpEtCjB1Cy3F90NFC7dvF+JeYyZ05xea6+fYE6dYB794rfb82axZ+DGKz1s7EH5ZfkQspLfn4+mzZtGgsICGCVK1dmlSpVYgEBAWzlypWaGFPKczHG2Pvvv88aNGig+e+cnBxWp04d1qpVK1ZYWKgVO2XKFCaXy1lsbKzmuVWrVrGKFSuynJycMt8LT3mu/fv3swEDBjA/Pz/m6OjI/Pz82NChQ9nVq1dLnceY8lyMFZc3c3BwYD/88IPW8ygjZbpLly5lvreSIiMjdaYG37x5k7m5ubE+ffponlMqlczX15fNmDGD+/yEmEtaWhqbOHEiq1WrFqtQoQLz8fFh3bt3Z2vWrGGMMXbq1Cnm4OBQqhRWUVERa9u2LfPz89OUG1B/RxMTE1nPnj1ZxYoVmbe3N5s1axZTKpWlXlfXd1SXKVOmMIVCwY4fP671/MmTJ5mDg4PJpaDUpRj0PfSVZVHjLZFV1muUlJWVxRwdHdnatWsNntOU8lxllZJQ9+GDBw/W6s/Url27xkaMGMHq1avHXFxcmLOzM2vWrBmbNWsWy83NNao9vJ79Nxk0aBCrXLkyS05O1orbvn07A8C++OILzXNUnotYkgcPHrDRo0ezatWqMVdXVxYaGsouX75cqmyJuo/6+++/2bhx41iVKlWYq6srGzZsGMvIyNDEnT59mg0dOpTVrl2bOTk5MS8vL9avXz928uRJrdfV1f9a43WNvr8j6mtIXQ/1919d9ubXX3/Vee46deroPcezpb4SEhI0f/M8PDzYsGHDWGpqaqlz8l5LE2IJLOn60Jz9k7HluXQ5ePAgA8B+++23Uj/Ly8tj//vf/5iPjw9zcnJibdu2ZVFRUTrP888//7Dg4GDm7OzMqlevziZOnKizHxFyTqHmzZvH2rVrxzw8PJiLiwtr3Lgxmz9/PisoKNDEmFKeizHGlixZwlxdXdnjx48ZY09/l2rWrFmq3Jb6d2Lz5s2a59Tl3v79919T3y4h5vXgQXF5rmrViss9hYYydvkyY3Xq6C7P9fffjI0bx1iVKsXxw4YxVuJ6kJ0+zdjQoYzVrs2YkxNjXl6M9evH2DPXgywtrbisV8nrqMjI4td4tgTezZuMubkxVvK+UKlkzNeXMWOvB40tz7V/P2MDBjDm58eYo2Px/w4dyliJ8USTynMxVv6fDdFLxtizeVSEmO769eto3Lgx/vrrL00KtBCBgYHo2rUrvvrqKwlaJ40xY8bg6tWr+Oeff8q1Hdu2bcMbb7yBxMREzYZ+hNiiUaNGYcuWLcjNzeWKt5TvqKX5+uuvsXDhQiQmJsLFxaXc2lFUVISqVatiwYIFFrVqTyaTYf369Rg1apTgYyMiIrBhwwbRNp8nxFqZ0v9a0nWNse/j119/xbBhw5Cenm62Mi7WeC1NiBjMeX1oSf0TESY7Oxv16tXDwoULMWbMGMHHDxw4EDKZDFu3bpWgdYTYqDFjgKtXAWPux7dtA954A0hMBGyxv6XPxiJR/QgiiXr16mHMmDH4/PPPBR8bFRWFf//9F+Hh4RK0TDqzZs3CiRMnNKUZyssXX3yBsLAwunAn5BmW8h21JIWFhViyZAlmzJhRrhMmQPH+U1OmTMHLL79cru0ghIjPlP7Xkq5rjH0fHh4eWLp0qdkmTKz1WpqQ8mAr/RMRxt3dHR9++CEWLVoElUol6NhLly5h586dmn1kCCGcZs0CTpwAjLkf/+ILICzMdicF6LOxSJRpQgghxCoJXUlIiDEo04QQQgixHnR9SAghhBAxUKYJIYQQQgghhBBCCCGEEEIIKNOEEEIIIYQQQgghhBBCCCEEAGWaEEIIIYQQQgghhBBCCCGEAKBJE0IIIYQQQgghhBBCCCGEEACAQ3k3QGwqlQp3795F5cqVIZPJyrs5hBALxhjDw4cP4efnB7ncNuaQqQ8khPCwxf4PoD6QEMLHFvtA6v8IIbyoDySE2Csh/Z/NTZrcvXsXtWrVKu9mEEKsyK1bt1CzZs3yboYoqA8khAhhS/0fQH0gIUQYW+oDqf8jhAhFfSAhxF7x9H82N2lSuXJlAMVv3s3NrZxbQwixZDk5OahVq5am37AF1AcSQnjYYv8HUB9ICOFji30g9X+EEF7UBxJC7JWQ/s/mJk3UaXhubm7UURJCuNhS+i71gYQQIWyp/wOoDySECGNLfSD1f4QQoagPJITYK57+zzaKFxJCCCGEEEIIIYQQQgghhJiIJk0IIYQQQgghhBBCCCGEEEJAkyaEEEIIIYQQQuzQ4sWL0bZtW1SuXBleXl4YOHAgrly5ohXz5MkTTJw4EVWrVoWrqyteeeUVpKWlacXcvHkTffv2RcWKFeHl5YVp06ahqKhIK+bQoUNo1aoVnJyc0KBBA2zYsKFUe1asWIG6devC2dkZ7du3R1xcnOjvmRBCCCGEGGZze5qQpwqKVPj2n2v46dgNZOcVoUpFR7zRvg7Gdq4HRweaL9Ml90kR3t90EqduZkMhl6FnMx/M6t8MLo6K8m6aRcorUGLmjnPYd/E+GGNoVbsKlg5tBVdn6lqsQUGRCj/EJuNG5mPU8ayI4UF1qW8gxArRd9k46r9hexPSkF+kQlVXJwyj6yRCrIYY3+GjR49i4sSJaNu2LYqKijB9+nT07NkTFy9eRKVKlQAAU6ZMwa5du/Dbb7/B3d0dYWFhGDRoEI4ePQoAUCqV6Nu3L3x8fBATE4OUlBSMGDECFSpUwGeffQYASEpKQt++ffHuu+/ip59+wv79+zF27Fj4+voiNDQUALB582ZMnToVq1evRvv27fH1118jNDQUV65cgZeXlwSfICHEFihVDHFJmbj38Am8Kjujnb8nFHLb2atECvSZEUJ4yBhjrLwbIaacnBy4u7sjOzvbrjd/WrD7Ir45nKT35++84I/wPk3N2CLL13/ZPzh/J0fnz0KaeuHbEW3N3CLLNnbjCey7dE/nz5r7uWLn+13M3CLhbLG/4H1PC3ZfxLf/JEH1zF+AMZ3q4NN+zSVuJSFELPN3XcTaI0koeTUnlwFvdy7777wt9n8A//t6+/sTiL6o+28YAFR2UuCr1wPRrbEX3UQTYkHUEyV/nLoLZRl3sYbudfT1Fffv34eXlxf+/vtvvPDCC8jOzkb16tWxadMmDB48GABw+fJlNGnSBLGxsejQoQP++usv9OvXD3fv3oW3tzcAYPXq1fjoo49w//59ODo64qOPPsKuXbuQkJCgea0hQ4YgKysLUVFRAID27dujbdu2WL58OQBApVKhVq1aeO+99/Dxxx8b/GxstV8nhOgXlZCC2X9eREr2E81zvu7OmNW/KXo199V7nC32F7zvKSohBRE7LiA1J1/zXJWKFTB/YHP0aeFnjqYSQsqRkP6PltHZIEMTJgDwzeEkLNh90Uwtsnxt50XrnTABgOiL9/D29yfM2CLL9tLyf/ROmABAwt1ctJ0XbcYWESHUfcSzEyYA8N2RG3hp+T/mbxQhRLC3vz+Bb//RnjABABWjv/NlMTRhAgAP85UY+/1J1J++G/0iDyP3SVGZ8YQQ6ShVDPvPp6LxjN1oMjMKv50se8IEML4PzM7OBgB4enoCAE6dOoXCwkL06NFDE9O4cWPUrl0bsbGxAIDY2Fg8//zzmgkTAAgNDUVOTg4uXLigiSl5DnWM+hwFBQU4deqUVoxcLkePHj00Mc/Kz89HTk6O1oMQYj+iElIw/sfTWhMmAJCa/QTjfzyNqISUcmqZ5YpKSMG7P57WmjABgAePCzFh0xm6diaEaKFJExtTUKQyOGGi9s3hJBQUqSRukeWbteMc7ucWGIyLvngPeQVKM7TIsu04fRvnbhu+KbufW4DZOy6YoUVEiOKyfWX3Eedu52DuTrpgJMSS7Yy/Y3Dgf80/9Hf+WXkFSoOf27MSUh6iecQe1Pt4Fz7+/RxdCxBiJrlPitAv8m/Un74bY346hSdFwgokfCuwD1SpVJg8eTI6duyI5s2Ls25TU1Ph6OgIDw8PrVhvb2+kpqZqYkpOmKh/rv5ZWTE5OTnIy8tDeno6lEqlzhj1OZ61YMECuLu7ax61atXifq+EEOumVDHM/vMidPWK6udm/3kRSl2r5OyUUsXw8R/ny4z55nASdp+jySZCSDGaNLExH285Kyz+d2HxtqagSIWNMbe4419eeUTC1lg+pYrhAwG/Y+tjkmnAzsL8EJusM8PkWd8docFWQiyVUsUw5dd4g3GMARtjkiVvjzX5zIQVhCoAv5y4hSYzo9Dok91YefAa9ZOEiCyvQIlpW86g3se70DxiDxJSco0+l4oVX/fwmjhxIhISEvDLL78Y/ZrmFB4ejuzsbM3j1i3+expCiHWLS8oslWFSEgOQkv0EcUmZ5muUhTuWmIGsx4UG4z78/RxNNhFCANCkiU1Rqhi2n70r6JjtZ+/a9R8EoYNJl1Nz7XqA5Nj1DBQKfPv2PjFnaW5kPuaOpX87QixTzLV07r74RDLdLJeUnMHfB5YlX8mwcM8VNJzxF0K/OkTZJ4SYQKliOHghDc1n/qUpvyXW1TbvdU9YWBh27tyJgwcPombNmprnfXx8UFBQgKysLK34tLQ0+Pj4aGLS0tJK/Vz9s7Ji3Nzc4OLigmrVqkGhUOiMUZ/jWU5OTnBzc9N6EELsw76LujPQnnXvof6JFXsTez2dKy43vwjHEjMkbg0hxBrQpIkNOXY9w2B932cpVbDrPwh/nrsj+JiNMXzlz2zR90a8d3ufmLM0dTwrcsduPUP/doRYotl/8pc+rOiokLAl1qduVf4+kNeVtEeUfUKIEfIKlBj6bQzqT9+N0T+cRG6B+N8dQ9c9jDGEhYVh69atOHDgAPz9/bV+3rp1a1SoUAH79+/XPHflyhXcvHkTQUFBAICgoCCcP38e9+49Lf0XHR0NNzc3NG3aVBNT8hzqGPU5HB0d0bp1a60YlUqF/fv3a2IIIQQonmjeGs83juFV2Vni1lgTGXfkxlj7HfMhhDxFkyY2JCaRb+b8WUcT74vcEuugVDFcKGPzd33+FJjNYyuUKoZ9l4XVgS8+zr4n5izN8KC63LEMQGT0VcnaQggRrqBIhWv3H3HHvxJY03CQHZnep6lk5y6ZfdL5i/20eTwhOjybVRKb+ECy15LLDF/3fPDBB/jxxx+xadMmVK5cGampqUhNTUVeXh4AwN3dHWPGjMHUqVNx8OBBnDp1CqNHj0ZQUBA6dOgAAOjZsyeaNm2K4cOH4+zZs9izZw9mzJiBiRMnwsnJCQDw7rvv4vr16/jwww9x+fJlrFy5Er/++iumTJmiacvUqVPx7bffYuPGjbh06RLGjx+PR48eYfTo0dJ8QIQQqxSXlInMR4bLTFWt5Ih2/p5maJF1CKpflTv2wOX7tHiQEAKH8m4AEY+x9SpP2GmdS2MycwDgYspDKFUMCjn/SgVbcOx6BpRGLgA8mngfHZ+rJm6DiFEcHeRoX7cKjifzDVIsP3QNk0Ia2t3vOyGWavjaY9yxDnIgmPpeLS6OCoQ09RK8GbxQtx48QfOIPaggA1YNb4Nujb2oHyV2La9Aibc2Hpd0kuRZb3f2h6ND2WsEv/vuOwBA165dtZ5fv349Ro0aBQD46quvIJfL8corryA/Px+hoaFYuXKlJlahUGDnzp0YP348goKCUKlSJYwcORJz5szRxPj7+2PXrl2YMmUKIiMjUbNmTaxduxahoaGamNdffx3379/HzJkzkZqaipYtWyIqKqrU5vCEEPvGW3JrQEs/uvYooUO9qnCQAUUcY0BFKoZjiRk0hkGInaNJExuhVDGcuWHcTcjZ29l2OQlgbGaOvf4BjTUhW8ReJ+Ys1Q9jO6DhjL+4YlWsONtkamgjiVtFCDGkoEjFPeEJABO6NLC7v+08vh3RFm9/f0LyiRMAKGTA2O9PAgA61vPE2lHt4EIl04gdyX1ShC4L9yPjsXkzr955wR/hHJll2dnZBvcCcXZ2xooVK7BixQq9MXXq1MHu3bvLPE/Xrl1x5syZMmPCwsIQFhZWZgwhxL5Vc3XiiuvehCZcS1LIZRgQ6IffT/NVDvn+WLLdjfkQQrRReS4bcex6BteMuS4FSmaX5ZOMzcwB7LOkGYPx6akJd3MovdWCODrI0aB6Je745Yeu0b8fIRZASJaJDMCkkIbSNcbKfTuiLS7N6YWG3vx9oamOXs9Ek5lRaD1nD5XuIjZNXYKr8YzdaB6xx6wTJgNb+OLqvN5cEyaEEGKVeG/L6PatlAWDArhjD16+R/fAhNg5mjSxEcZmTajZ2ySAUsVw7naW0cffeZAnXmOsRErWY6OPzStUmTRJRcQ3q38z7lh1tgkhpPwIzTJ5OZBKMhji4qjA3ildcXVeb0wLfQ7mSgDJeFyE5hF70OiT3ThwiW7Iie0oKFJh0i+nNRu7PzF2RZdANT2csW5kWyR+1gdfv9HKYEkuQgixZumP8kWNM4fDhw+jf//+8PPzg0wmw7Zt27R+zhjDzJkz4evrCxcXF/To0QP//vuv6O1wdJCjfvWKXLH2uriYEPIUXVHaCFMH8e1tEiAuKRP5JtzIpWTz1RG1FUoVQ9SFNJPOkZptX79jli64QTUoBIynrvybsk0IKU8fbzkrKP7zV/hX0tk7Rwc5JnZriKvz++LSnF54tY2fWS6Q85UMb208gfrTd2Ph7ovUxxKrlVegRM+vDqHhjL+wPT7FLK/p6ijTTJQc+bg7XmxC+wYRQuwDb3ku3jhzePToEQICAvSWOVy4cCGWLl2K1atX4/jx46hUqRJCQ0Px5In44y69mvtyx35/LFn01yeEWA/a08RG+Ho4m3S8vU0CpOaY9n7P2dk+MHFJmXhcYOQu8P9Jz7WclS6kuKbrxK71sfRgIld8kQqI+TcdnRtVl7hlhJBnKVUM28/y1V8GgPb+VWiltZFcHBVYNDgQiwYHIvdJEYZ8cxQJKbmSv+7Kw0lYeTgJA1v4YuFrLenfj1iFvAIlXlx8ACnZBWZ5PTmA19rWwqz+zWhvIEKI/bLC8ly9e/dG7969df6MMYavv/4aM2bMwIABAwAA33//Pby9vbFt2zYMGTJE1LYE16+GFZz3wOoSXfYy7kMI0UZ3ZDbCw8XRpOPVkwD2Iv2haQP4T4pUdpWqaeokEwBkPjbPDTXhNymkEYRc/kXsTJCsLYQQ/Y5dz4BSwJ/oH8Z0kK4xdsTV2QE7J3VB4md9sH54G7g6Sn/ZvO1cChrO+AuhXx1CXoFS8tcjxBi5T4rQes5eNJkZZZYJk+a+lZEQEYrrn/fF56+0oAkTQohds8byXGVJSkpCamoqevTooXnO3d0d7du3R2xsrOiv16FeVVTgvKSjEl2E2DeaNLERZ27y1znXxd4mAR6IMIAfe920fWSsSaYIWSKnBNTiJ+ahkMvwXrf63PGJ9x+joMi0jCNCiHCLoi5xx9avXpGyFESmkMvQrZk3Eub0xqU5vRBUv4rkr3kl7RFtGk8sSunN3QslfT0nxdPyWzsnvQBXZyqQQAghgHWW5ypLamoqAMDb21vreW9vb83PdMnPz0dOTo7Wg4dCLkP3Jt6GA/9jb/v/EkKekvSu2tBmT886dOgQZDJZqUdZHSUpvon551/TB/DtaRJAJkJ2pR0l5sCjommZTID9ZTNZC6HZJsO/OyZZWwghpRUUqRB/m+8mEAAi+jWXsDXExVGBn98O1mSfeLpIO5Cr3jS++ad/0eQJKRdKFcOivy6bbXN3dVbJlfl9aJ8SQgjRxQrLc0lhwYIFcHd31zxq1arFfezwoLrcsXsu0HgkIfZK0kkTQ5s96XPlyhWkpKRoHl5eXhK10DbEJWXikQglHOxpPDs1y/RNyd1dKojQEuuQ+cj0zBx7y2ayFkKzTY4nPaBsE0LMaPha/olKuQwIfq6ahK0haursk9OzQnF1Xm9MC30OUib45Baq0DxiDwIjoqhsFzGb30/dRv3pu7Hib77a78ZydZRTVgkhhHA6cDmNK85aynP5+PgAANLStN9XWlqa5me6hIeHIzs7W/O4desW92sKKdFF1RYIsV+SXpGWtdlTWby8vODh4SF+g2yUGPtNAEAVEbIJrIFSxbA7wfTVAmdu2U+5KTHKmQHF2UwdaUDP4kwKacS9ITwAfPz7WSx5PVDCFhFCgOIsk+MCShu+HFiDVmWXA0cHOSZ2a4iJ3Roi90kRuizcj4zH0mSFPHiiRJOZUfBzc8T+/71IezsQSeQ+KULgnL0olHhFVcd6nlg7qh39HhNCCCelimFr/B2uWK/KzhK3Rhz+/v7w8fHB/v370bJlSwBATk4Ojh8/jvHjx+s9zsnJCU5OxpUgU5foirrANwG1MSYJb7/Av9CQEGIbLLLodcuWLeHr64uQkBAcPXq0zFhj6xjaEjH2mwCA2w8ei3IeS3fsegbyCk1fKXDk3wy7KTclRjkzwL6ymayJQi7DoJZ+3PFbz9y1m999QsqTkCwTAFgwqIVELSG8XJ0dcGpmqOR7n9zNKUCTmVEI/iyaMk+IaHKfFKH5zCg0j9gj2YRJBRmwdkQbJH7WBz+NC6IJE0IIESAuKROZjwzvKVW1kiPa+XuaoUV8cnNzER8fj/j4eADFm7/Hx8fj5s2bkMlkmDx5MubNm4cdO3bg/PnzGDFiBPz8/DBw4EDJ2iSkRNefZ+9K1g5CiOWyqEkTX19frF69Gr///jt+//131KpVC127dsXp06f1HmNKHUNbIcZ+EwCw9cwduxgIjRWpRFRufhHikjJFOZelE6OcGWA/2UzW6PPBAdyxDEBk9FXpGkMIEZxl0t6/isVtAC90b7s//vgDISEhqF69Otzc3BAUFIQ9e/aYp7Eie3bvE2cHaTKA1JMn3b88QKUjiNHyCpRoOWcPmkfsQa5Ek3BVKzogISIU/y7oix5NvSkrjhBCjHDvIV+VkQEt/Syqnz158iQCAwMRGFhcrWDq1KkIDAzEzJkzAQAffvgh3nvvPYwbNw5t27ZFbm4uoqKi4OwsXbZMh3pVoeC8dE64m2MXY2WEEG0WdXfdqFEjvPPOO2jdujWCg4Oxbt06BAcH46uvvtJ7jCl1DG1Flkilk3Ke2MckABNxR7TUbHEmEyyZUsWw79I9Uc7lWYkmTSyVo4Mc9atV4o5ffTiRLhwJkdDHW84Kiv9hTAeJWmI8oXvbHT58GCEhIdi9ezdOnTqFbt26oX///jhz5ozELZWOeu+Ty/P6ICEiFFUrSrMfWmJ6HhrO+Atvf3+c+mbCLa9AiaAF+9BkZhSyJCop18i7Ei7N6YVTM0NprxJCCDFRNVe+clTdm3hL3BJhunbtCsZYqceGDRsAADKZDHPmzEFqaiqePHmCffv2oWHDhpK2SSGXoUdjvv2TVQyI+Tdd0vYQQoRTqhhiEzOwPf4OYhPFrwZk8Veu7dq1w5EjR/T+3JQ6hrZCrEwTwD4mATxE3MA9XaTSaJYsLikT2U/EuZGOTUzHK61rinIuIr6Il5ph+Lo4rtgCJcOxxAzao4YQCShVDH/E85cBsMQsE0D43nZff/211n9/9tln2L59O/7880/NykRrVly6qyfyCpR4cfEBpGSLs+ilpOiL6ag/fTe+HtwCA9vYX/Y14aNUMbyy4iji72RL9hoDW/hi4WstLbJvIoQQq8U7HkjrJ7iMCPbHnot8C0SXHriKzo2qS9wiQuxXQZEK3/5zDd8fvY60XOMyn33cnBHxUlP0au4rSpssftIkPj4evr7ivFlbFZso3ox35iPxb+AtjWcl8SbZsvIM1xO1dqk5fCnAPPZdugelillUqjB5KrhBNShkgJLzInvhnkvY/lxnaRtFiB2KjL4iKN4Ss0zEoFKp8PDhQ3h66q/JnZ+fj/z8pwsYrGFvOxdHBWLDQ5BXoMSAFf/gatoj0V9j8pZzmL79PE59Gkp7RhAtW0/fwZRf4yU7/4QX/PFBryZ0rUcIIRJIf8S3aJM3zt51qFcVchnf3qunbmbRWAYhIlNPlKw4cA2PC02f7U3NeYJ3fzyN1W+2EmXiRNJJk9zcXFy7dk3z3+rNnjw9PVG7dm2Eh4fjzp07+P777wEUrzD09/dHs2bN8OTJE6xduxYHDhzA3r17pWymVVOqGKIvpol2PjGzViyVWOXMAIDZwQqOTBGzabLyChGXlImg+lVFOycRj0Iuw8Su9bH0YCJX/NnbOSgoUtEqUkJEpFQxLOP8DgKWm2Uihi+//BK5ubl47bXX9MYsWLAAs2fPNmOrxOPiqMDeKV1RUKTCtC3x2B6fIur5HxcyNJkZhfrVXPDX5K42+3tC+OQVKNF63l48LhB/7xsnhQyr3myDLo2q02ASIYRIiLc8F2+cvVPIZWhTxwNxyVkGY9UluijbhBDTFRSpMGxtLE5wfPeM8cFvZxHS1Mfk61JJ754MbfaUkpKCmzdvauILCgrwwQcf4Pnnn0eXLl1w9uxZ7Nu3D927d5eymVZNzNJJgLgTCpbq9oPHop0rTcQsDEvFO5HmzDkYYw8l4KzZpJBGEPJnZfh3xyRrCyH2KDL6iqCKCraaZbJp0ybMnj0bv/76K7y89NebtoW97Rwd5Igc0kqzaXwFka/O1fudvPNDHO13YocKilTovvggmsyMEn3CRL25+5X5ffBiEy+aMCGEEKlReS7Rvfci/94pSw9clbAlhNiH2TsuoOGMvySbMAGAR/lKxFwzvSqTpJkm6s2e9FFv+qT24Ycf4sMPP5SySTaHt3SSs4MMT4oM/+W09UwTpYph+1n+OvGG7LeDclO8E2nPeVXC+bsPDcbZwz4w1kwhl2FQKz/8fprve3I86QFlmxAiEqFZJvWrV7TJ794vv/yCsWPH4rfffkOPHj3KjLWlve3Um8b/+1lfSUoo7blwn/Y7sSNKFcPEH08hSsSMdDU/N0fs/9+LVPqNEELMjMpziS+4QTXIAfAsKzh9i0p0EWKK1vOikZFrnsX6f5y+jc4NTcsMs707bTvDWzqpoXdlrjgx90exRHFJmch8ZHgfEjdnvvlEdbkpW8Y7kebl5swVZw/7wKitWrUKLVq0gJubG9zc3BAUFIS//vqrzGN+++03NG7cGM7Oznj++eexe/duM7X2qQWDAgTFU7YJIeIQmmUS0a+5ZG0pLz///DNGjx6Nn3/+GX379i3v5pSbl1vVQOJnfTCxS33Rzz15yzm0mRtFWSc2bHv8HdSfvlv0CRMPZzkuzemFmOkhNGFCCCHlgMpziU8hl6FNXQ+uWKUKOJaYIW2DCLFRTT/9y2wTJgDwqMC4zeRLokkTK8c7oN28hjtXnHqjblvFm5nzcqsacOecOLH1clO8mSZPCvk6JHvYB0atZs2a+Pzzz3Hq1CmcPHkSL774IgYMGIALFy7ojI+JicHQoUMxZswYnDlzBgMHDsTAgQORkJBg1nY7OsjRvm4V7nh1tgkhxHhKFcOKQ/xZJg5yIPi5ahK2yHS5ubmIj49HfHw8gKd726lLs4aHh2PEiBGa+E2bNmHEiBFYvHgx2rdvj9TUVKSmpiI7O7s8ml/uFHIZpvVuLMnkSfojJepP341tJ62vnBnRT6li6LroICb9Ei/qeV0ryJEQEYr4iN40WUIIIeUoLolzwN6O7rnFIKRE18bYJAlbQohtajxjFx4XmnfMqK2AMS19aNLEyvEOaPOmD9p65gRvZk7tKhUR0tSb75yPbHsfGN49YKpW4lvNUsXGS8CV1L9/f/Tp0wfPPfccGjZsiPnz58PV1RXHjunOzIiMjESvXr0wbdo0NGnSBHPnzkWrVq2wfPlyM7cc+GGssH0SPv79rEQtIcQ+xFxLh1LADe6ELg0svjSA0L3t1qxZg6KiIkycOBG+vr6ax6RJk8ql/ZZCysmTyVvOoUXEXzTxbQPU2SXJGeLt3eekABIiQpEwtzdcORcTEUIIkYZSxbAx5gZXLJXnEkZdoovHgcv3bXqhMSFia/Lpboi4FTcXGYCRwf4mn4cmTawcb6ZJi5oelDkB/s/Lo6IjgurzreC15X1ghOwBU8uzIldctcr2mSqsVCrxyy+/4NGjRwgKCtIZExsbW6p+f2hoKGJjY/WeNz8/Hzk5OVoPMQjNNtl65i5dPBJigtl/6s5A00UuAyaF8K+IKy/qve2efaj3tNuwYQMOHTqkiT906FCZ8fau5ORJL86FHTxynqjQcMZfmLXjnGjnJOYjVXbJ14Nb4Mr8vjRZQgghFiIuKZO71LVXZb7S2aSYQi5D85puXLFFKkYlugjh1DLiL+QVmn+caNwL/qLs/UmTJlaON9MkJ6+QMifA/3llPS4QFGurePeAqVrJER3qVeU6p5ed1Vc9f/48XF1d4eTkhHfffRdbt25F06ZNdcampqbC21v7e+rt7Y3U1FS951+wYAHc3d01j1q1xNvcV0i2CQMQGX1VtNcmxJ4UFKlw7f4j7viwrpafZUKko5DLsHpEG1yd1xv1q/MtWOCxMeYWGn+yi7JOrIgU2SUTX6iHxM/6YGAb8a4nCCGEmO7eQ75S4x4VK6Cdv6fErbE9/VvU4I49mnhfwpYQYhs6LYhG1hPz31e884I/wvvoHnMTiiZNrBxv6STPSpQ5ARR/DrxxQrJSbBXvHjAvtfSDnHMA70Sy7ZZ/06VRo0aIj4/H8ePHMX78eIwcORIXL14U7fzh4eHIzs7WPG7dEq8+vdBsk+WHrlG2CSFG+HgLf3k7a8kyIdJzdJBj/wfdcGlOL1R0FOeS/okSlHViBaTILgmo4YrEz/pgWp8mNClLCCEWiHdz91FBdakfN8LI4LrcsbZc0p4QMby1/hhuZ5tngblCBvi5O+PD0Ea4Oq+3aBMmAED51lZMSOkkH3cXXLzLt5GqLWdOeLnxpal6uTnjcgpfmaPYxHS80rqmKc2yWLx7wNT0cEE6Z+yG2GS81/05u7mQc3R0RIMGDQAArVu3xokTJxAZGYlvvvmmVKyPjw/S0tK0nktLS4OPj4/e8zs5OcHJSbrsnR/GdkDDGX9xxapYcbbJ1NBGkrWHEFujVDH8Ec/3txygLBNSmoujAhfn9MbW03cw5dd4Uc65MeYWdpy+gzMRvUU5HxHPn2fv4r2fz4h2vooVZDj1aSht8E4IIZaOc21a27qUZWIMRwc5/NydcDfb8LhG/K1sKFWMrskJ0WFn/B0cuGJ8CbtKTgpM7NoAYzvXE6XEliko08SKCSmd1M7fkzInAMQlcX5xGeDJuZJj36V7Nru6XkhmDm/d1KzHhXa9MkOlUiE/X/eFWFBQEPbv36/1XHR0tN49UMzB0UGOBtUrccdTtgkhwkRGXxEUT1kmRJ+XW9UQdb+TB09U8P+YynVZkrc2xIk6YfL14Ba4OLcPTZgQQogVuMe5SJE3jpTWrIY7Vxzta0KIbkoVw3tGZkLXr+aCq/N648LsXpjQrUG5T5gANGli1YSUTlLIZXa/R4dSxbAx5gZXbPqjfPhwZqVk5dnuJICQzJx2/p5w59wsNDU7z5RmWY3w8HAcPnwYycnJOH/+PMLDw3Ho0CEMGzYMADBixAiEh4dr4idNmoSoqCgsXrwYly9fRkREBE6ePImwsLDyegsAgFn9m3HHqrNNCCGGKVUMyw4mcse3qeNBK9pImUrud+LtZvoiGIbicl2z/zxveuOISTp9vh8HLotTQz20WXXat4QQQqwMbxUI3jhSWru6fPu0AsD3x5KlawghVuq9Tad4k+K0jOlUB/v/96JFTJSUZFmtIYIIKZ0E8GeQ2GqmSVxSJrLyDGfmAIBX5eJJAA+XClzxvJuyWR3e3o4VD9SEcK5uzXxkmxNzz7p37x5GjBiBRo0aoXv37jhx4gT27NmDkJAQAMDNmzeRkpKiiQ8ODsamTZuwZs0aBAQEYMuWLdi2bRuaN29eXm+huF0NqkEhYJx25d+UbUIIj8joK4IuKie9SFkmhI+jgxzHp4cgckhLUc63/uhNdFywV5RzEWGUKoZGn+zC7SzTrzXdnOW4Oq83vhnejiZgCSHEygipAkGMI2Rfk4OXbbfiCCHGKChSYXdCmuHAZywfEohP+5XvmJc+NGlixYT+0bT3TBPeiQ2PihXQzt8TCrkMI4PrcB1TrZJ0e0qUJ6EpwEH1q3HF2+rE3LO+++47JCcnIz8/H/fu3cO+ffs0EyYAcOjQIWzYsEHrmFdffRVXrlxBfn4+EhIS0KdPHzO3ujSFXIaJXetzxxepgJh/0yVsESHWT6liWPU3f5aJgxwIfo6vjyVEbUDL4pJdLWvylZsoy53sQjQIp3Jd5rQ9/g7qT9+NfKXp5xrdsTbORfS2uBV8hBBC+AipAkGM4+ggR/3qFbliC5RUoouQkjov3Cf4mOVDAtGvpZ8ErREHXTVbMaF/NHn36LidZZulk6pxvv9RQXU1q+/a+XOmZ9roYj2hKcD2PjFnyyaFNBL0a770AJXoIqQsx65noFDA2POELrQBPDGOQi7DtrBOuDSnFxwdTPsdKmJUrstc+i39B5OMrAldkrdrBVyd1xuz+j9veqMIIYSUGyH7sxLj9Wruyx1LJboIKbbj9G2k5fBV9lEb08nfoidMAJo0sW4CSicB4N6jY0f8XdtMM+R8S23remr+/z3OfWN446zN7QePueLU2Uz2XgLOlinkMrzXjT/b5OSNLNvsRwgRyaKoS9yxchltAE9M5+KowNV5fTBaQOkJfahcl3SUKobGM3Yj4W6Oyeda+loAjs/oSdklhBBi5YTuz0qMF8xZPQOgEl2EAMX905Tfzgo6JrCmOz7t11SiFomHrqCtGO8fQ3VcO39PeFYyvEdHxqMCm9zYXGipKYB/7w1b3KNDqWLYfvYuV6yPe/G+OZRpYtsmhTTijmWgDeEJ0aegSIX42/wDomFdKcuEiGfWS81wdV5vuFQw7XfqTnYhmn66W6RWEQDYfS4F9afvxpMi0wZg6lRxQuJnffBSq5oitYwQQkh5Ero/KzFeh3pVUYFzpJRKdBECxFxLh1LApasMwJYJHSVrj5ho0sSK8ZabUscp5DIMCOBLfUrNtr0SXUJLTQH2nTkRl5SJzEeGL8yqVnJEO//i7Bx7LwFn6xRyGUKbenHHLz9EG8ITosvwtce4Y2WgLBMiPkcHOS7N7YNujaqbdJ7HhQyNZ+wSqVX2bf6ui5iw6bTJ51n6WgD+/qgHTbQSQogNSeWsbOHhUkFzb06Mo5DL0L2JN3f80cT7EraGEMsXseOCoPilQwOt5jqVJk2smcDyXABQswrfpla2mDmhLiElJI43IyI20fY2vb73kO/CbEBLP02HZ/cl4OzAiGB/7lgVo2wTQp5VUKTC8eQH3PGDWtWwmotKYn3Wj26HZUMDTTrHkyLguem76O+6CWb/eQHf/pNk0jlcK8gou4QQQmwU7wLQHk286LpRBMOD6nLH7rmQKl1DCLFwBUUqJKY/4o4PrOWO/pyL+S0BTZpYsQOX07jiSpbxsufMCS/OAf2ScbyZE/su2V4tS95MppKrMOy9BJw9EJKuDFC2CSHPEpJlAgALBrWQqCWEFOsf4IfEz/qgspPC6HMUqoD603dj9zm+sp7kqbfWx2H90WSTztHMtxIS5vahgTIjHD16FP3794efnx9kMhm2bdum9fNRo0ZBJpNpPXr16qUVk5mZiWHDhsHNzQ0eHh4YM2YMcnNztWLOnTuHzp07w9nZGbVq1cLChQtLteW3335D48aN4ezsjOeffx67d1P5O0JIMd7xmSAB+3EQ/YTc8ybef4yCIpW0DSLEQgm9t90y3jrKcqnRpImVUqoYtsbf4YotWdPSrvecMCIzhzdzIiuv0PYmAYz4vOy9BJw9UMhlGN+Ff0N4yjYh5CmhWSbt/avQBs7ELBRyGc7P7mVyua4Jm85g7s4EkVpl+/ouPYwDV0wr67H0tQDsmtRVnAbZocePHyMgIAArVqzQG9OrVy+kpKRoHj///LPWz4cNG4YLFy4gOjoaO3fuxOHDhzFu3DjNz3NyctCzZ0/UqVMHp06dwqJFixAREYE1a9ZoYmJiYjB06FCMGTMGZ86cwcCBAzFw4EAkJND3iRBi5+M45UBoia6NMaZlixJijQRXUAj0s7oFPnQnbqWM2W8CsO9ME2Myc9r5e8LDxXDmBMBfzspa3ONMAX42zp5LwNmLSSGNIORP3erDiZRtQgiAj7ecFRT/w5gOErWEEN3EKNf13ZEbeGv9cZFaZLs6fr4PF+4+NPp4JzmoHJcIQkJCMG/ePLz88st6Y5ycnODj46N5VKlSRfOzS5cuISoqCmvXrkX79u3RqVMnLFu2DL/88gvu3i3OvPrpp59QUFCAdevWoVmzZhgyZAjef/99LFmyRHOeyMhI9OrVC9OmTUOTJk0wd+5ctGrVCsuXL5fuzRNCrIYxpcaJaYSU6Prx2A3pGkKIhRKaZfL5KwEStUQ6kk6aHD58uMx0Z10OHTqEVq1awcnJCQ0aNMCGDRukbKLV4t0I7KWW2jN59rpCwdjMHIVchpHBdbiOq1aJr5yVteCtm/psnD1PzNkLhVyG97rxZ5sUKBmOJWZI2CJCLJ9SxfBHPH/pIsoyIeVFjHJdB66ko1/k3yK2yrYEztmDO1l811m6NPWpiCuf9bW61XrW6tChQ/Dy8kKjRo0wfvx4ZGQ8vaaJjY2Fh4cH2rRpo3muR48ekMvlOH78uCbmhRdegKPj02vf0NBQXLlyBQ8ePNDE9OjRQ+t1Q0NDERsbq7dd+fn5yMnJ0XoQQmyTMaXGiWk61KsKBeel+I3MPCrRReyKvVRQkLTFjx49MpjuXFJSUhL69u2Lbt26IT4+HpMnT8bYsWOxZ88eKZtplXgHtGt6uGj9N+8eHbezbKt0krGZOQDQzr8q34vY2H2rsatZ7HVizt4IzTZZuOeSZG0hxBpERl8RFE9ZJqQ8qct1dW1ofG30hJRc9KWJk1Jazo7Cg8dFRh+/9LUA7J7cTcQWkbL06tUL33//Pfbv348vvvgCf//9N3r37g2lUgkASE1NhZeXl9YxDg4O8PT0RGpqqibG21u7zIv6vw3FqH+uy4IFC+Du7q551KpVy7Q3SwixWHFJnAvQKLlfNAq5DD0aexkO/E/4H+ckbA0hliX8D/uooCDppEnv3r0NpjuXtHr1avj7+2Px4sVo0qQJwsLCMHjwYHz11VdSNtMqGTugzbtHx474uzZVToe3dNaAlqVr7N3jzOrhjbMWxq5m4Z2Y440jlklotsnZ2zm0+qYc5D4pwpj1x9F6zh60iNiDl1ccxZq/E+nfwsyUKoZlBxO54611JQ6xPRveao/RHesaffyFlFz0W3pYvAZZuZazo5CVpzTq2ApUjqtcDBkyBC+99BKef/55DBw4EDt37sSJEydw6NCh8m4awsPDkZ2drXncunWrvJtECJGAUsWwMYav/FPJUuPEdCOC/bljt9vYGBoh+ihVDFtP20cFBYtqNaUl8zN2QLudvyc8KxneoyPjUYFNbWxejXOAXtdmX7x7b9jcHh1GbAQPAF6cnzVvHLFcQrNNhn8nrOYlMU3/Zf+gecQe7L+SjozHRch5UoQzt7Lw2V+X0XDGX1iw+2J5N9FuREZfEbTwz1pX4hDbNKt/M7zdmX/Q4FkJdx/irfVxIrbIOpkyYeLsAPxL5bgsQr169VCtWjVcu3YNAODj44N79+5pxRQVFSEzMxM+Pj6amLQ07b0V1f9tKEb9c12cnJzg5uam9SCE2J64pExk5RmumgFolxonphNSoqtIRSWpiX2IuZYOIUswrfne1qImTfSlJefk5CAvT3e5KLtNSzZyQFshl2FAgB/XoanZNlSiy8jPC7DfzIkDl9MMB0HHahbO+/kTybYzKWevFHIZBrXi608A4HjSA8pwMJO286Jx/k7Ziwi+OZxEEydmoFQxrDjEn2VSv3pFq12JQ2zXJ32bYuUbrYw+/sCV+5j95wURW2RdAk2YMKlYQYbL8/qK3CJirNu3byMjIwO+vr4AgKCgIGRlZeHUqVOamAMHDkClUqF9+/aamMOHD6Ow8OmgZ3R0NBo1aqTZVD4oKAj79+/Xeq3o6GgEBQVJ/ZYIIRaOdz9bD5cKpUqNE9MILdH1/bFk6RpDiIUQck0fUNPNqu9trbfl/7HXtGTetEtdcTWrVOQ61pYyJ0z5vOwxc0KpYtgaf4cr9tnVLOmc++1siE2m9FUbsGBQgKB4yjaR3qwd53A/l6///uZwEk1kSSzmWjqUArq6iH7NpWsMISbo08IXiZ/1gaORdw/rjyZj/i77m6jttCAaD4ycMPFzq4CLc/uI3CJSUm5uLuLj4xEfHw+geI/N+Ph43Lx5E7m5uZg2bRqOHTuG5ORk7N+/HwMGDECDBg0QGhoKAGjSpAl69eqFt99+G3FxcTh69CjCwsIwZMgQ+PkVLyx544034OjoiDFjxuDChQvYvHkzIiMjMXXqVE07Jk2ahKioKCxevBiXL19GREQETp48ibCwMLN/JoQQy8K7n22PJl5WnZGoVCrx6aefwt/fHy4uLqhfvz7mzp0Lxsp3zEBIia6Dl+/RGAexaQVFKly7/4g7/sPQJhK2RnoWNWmiLy3Zzc0NLi4uOo+x17Rk3nJTuuLsMXPClM/LHjMn4pIykfnIcApw1UqOpVaz8KYEZz0utKkScPbK0UGO9nWrcMdTtom0CopU2BgjbPHA+qPXJWoNAYCIHfwrcRzkQPBzxm+8TYjUFHIZrn7WF1VcHIw6/tt/krD7XIrIrbJco9cdw+1s4xYhNfOphJjpPUVuEXnWmTNnEBgYiMDAQADA1KlTERgYiJkzZ0KhUODcuXN46aWX0LBhQ4wZMwatW7fGP//8Ayenp/cMP/30Exo3bozu3bujT58+6NSpE9asWaP5ubu7O/bu3YukpCS0bt0aH3zwAWbOnIlx48ZpYoKDg7Fp0yasWbMGAQEB2LJlC7Zt24bmzWkinRB751GRbz/boPrWfQ35xRdfYNWqVVi+fDkuXbqEL774AgsXLsSyZcvKtV0d6lVFBc6R0wIllegitu3jLfwbwDsqZOhQv6qErZGeRU2aUFqyACaUm7LHzAlTPi97zJy495AvBXhAS79Sq1na+XvC3ZlvMMWmSsDZsR/GCqtR+fHv/H9oiTAbY5IFH7P5hH1kaJaHgiIVEtP5V+JM6NLAqlcIlnT48GH0798ffn5+kMlk2LZtW5nxKSkpeOONN9CwYUPI5XJMnjzZLO0kxjkzKxQ1PIy7Tpyw6bTNXC+VZe7OBBy8atzAyYuNqmHX5K7iNojo1LlzZzDGSj02bNgAFxcX7NmzB/fu3UNBQQGSk5OxZs2aUuWkPT09sWnTJjx8+BDZ2dlYt24dXF1dtWJatGiBf/75B0+ePMHt27fx0UcflWrLq6++iitXriA/Px8JCQno04eyjAghQNZjvsl33jhLFRMTgwEDBqBv376oW7cuBg8ejJ49eyIurnz3RVPIZTr3vtVnY2yShK0hpPwUV6Th3wD+3RfqW/29raSTJmWlOwPFpbVGjBihiX/33Xdx/fp1fPjhh7h8+TJWrlyJX3/9FVOmTJGymVbJ6P0mAO7MCUE7PFs4k8pz2WHmBG9mjq6LB4VchpCmfBcVtlQCzp4JzTbZeuauXQyYlYc/z/GV1SspOeMx/XtIJPwP/glCGYBJIQ2la4yZPXr0CAEBAVixYgVXfH5+PqpXr44ZM2YgIEBY2T9SPo5+3ANNfV0NB+rQZMZukVtjWXafu4vvjtww6tjRHetg3ej2IreIEEKIteLNNOGNs1TBwcHYv38/rl69CgA4e/Ysjhw5gt69e+uMz8/PR05OjtZDKsOD6nLH7rtEJbqIbYq5ls69Ht1W7m0lnTQ5efKk3nRnoHhVoXoCBQD8/f2xa9cuREdHIyAgAIsXL8batWs1NWNJMVP2mwCAe5wbifHGWQNTynPZZeaECZk5ANDxuepch9tSCTh7JyTbhAGIjL4qXWPslFLFcMHA5u+6qBgojVwCShXDH6f5V+K8HFg6c8+a9e7dG/PmzcPLL7/MFV+3bl1ERkZixIgRcHd3l7h1RCy7J3VBU59Kgo8rUAGBs/dI0KLyp1QxTNh0xqhjx3Sqi1n9qRwTIYSQp2IT07nirD3T5OOPP8aQIUPQuHFjVKhQAYGBgZg8eTKGDRumM37BggVwd3fXPGrVqiVZ2zrUqwoHzst0FQNi/uX7NyPEmgjZAN5W7m0lnTTp2rWr3nRnANiwYQMOHTpU6pgzZ84gPz8fiYmJGDVqlJRNtEqm7DcB8K/ut6ksABMmAewxc8KUzBzATkvA2Tmh2SYr/75GK3BEdux6hqANx0s6mnhf3MYQREZf4f7TAwCfv0LZFcQ67Z7cFTXcha9ufZBXhE5f7DccaGWafmpcFs2YTnXxab9mIreGEEKINVOqGHad59sLzLOSdWea/Prrr/jpp5+wadMmnD59Ghs3bsSXX36JjRs36owPDw9Hdna25nHrlnQlhxVyGQYE+nHHR+xMkKwthJQHoRvA28q9rUXtaUL4mLLfBMC/uv92lo1kTcDEcmawv8wJUzJzANhlCTgiLNukSEUrcMQWw7kKTZcTNlJa0FIoVQzLDiZyxwfUdIOjA12SGcOcpRmIfkfDQ1DFRSH4uNsPnmD0+uMStKh8BH+2F/lK4ceN7kgTJoQQQko7dj0DeYUqrlgfdxeJWyOtadOmabJNnn/+eQwfPhxTpkzBggULdMY7OTnBzc1N6yGlBYP4B4ET7z9GQRHfvxsh1kBI2en61SvazL2tbbwLO2PKfhMA4OPGt0fHjnjb2HfA1HJmgB1mTphYnsseS8CR4myTBtX5y7TQChxx3Xlg/ER3wt0cm+jvLYXQLJMPQ5tI1hZbZ87SDKRsZ2b1gocREycHr6Rj7s6LErTIvPp+fQh3cwxngj/rxUbVMas/TZgQQggpLZazhK6rk4POKiPW5PHjx5DLtYcoFQoFVCrLmHwQeq+7MYY2hCe2Qali2H6Gv+x0RD/bKTVLkybWyMQB7Xb+nvCsVMHg4RmPCmxiY3NTy5kBsLvMCVPLc9llCTgCAIIGfmgFjrjuPHhs9LF5hSqb6O8tgdAsE0eFDB3qV5WwRbbNnKUZiGHxs3rB3Vn47cV3R5Kw+xxf+RFLNGbDcVxI5S9ZoNbMrzLWjW4nQYsIIYTYAsY5+NPpuapWv39A//79MX/+fOzatQvJycnYunUrlixZwr0/njkIudf98yz/IDMhluzY9QwUcY5Dy2VA8HPVpG2QGdGkiRUydUBbIZdhQABfPUZb2Njc1HJmgP1lTujLuOGNs8cScKRYcINqguYOh393TLK22BOliuHCXdPKEtlCf28JhGaZvPtCfau/yS1P5i7NQAw7G9EbjsITTjBx02mrzHjbGX8H+y8LL49Yw8MJu95/QYIWEUIIsRUeLoYXuwJA69r8e0taqmXLlmHw4MGYMGECmjRpgv/973945513MHfu3PJumoaQe92EO5TJT2zD9wKypno08bKpe1uaNLFCpg5oA0DNKhW5zmELmQCmljMD7C9zonWdKjDUz8llxXG62FsJOPKUQi7Dyy35N8k7nvSAsk1EEJeUicec9Y71sZX+qzwpVQyr/ubPMpHLgEkhDSVsUfnJzc1FfHw84uPjAQBJSUmIj4/HzZs3ARRniIwYMULrGHV8bm4u7t+/j/j4eFy8aP1lm+zRpbl9BB/DAAxedVT8xkhIqWII+yVe8HFVXBxw9OMe4jeIEEKITfGsxDeWwRtnySpXroyvv/4aN27cQF5eHhITEzFv3jw4OlrOBvcKuQzP1+BboKMC7eFJrJ9SxbDv8j3u+JFB/hK2xvxo0sQKmTqgDQAeFfn+8PDGWTQTy5kB9pc5cerGAxiay1Cx4jhd7K0EHNH2+WD+TfIAIPyPcxK1xH6kipDlZhP9fTk7dj0DQuauwro2sKmVOCWdPHkSgYGBCAwMBABMnToVgYGBmDlzJgAgJSVFM4Gipo4/deoUNm3ahMDAQPTpI3zwnZQ/hVyGlW8ECj7uzK1sqypn0f3LA4KPcZQDZ2aFStAaQgghtiY2kW/QPesxLX4yl/4BNbhjaQ9PYu2OXc+AkvP+1kFue2WnadLECpk6oA3w/1G1hT++ppYzA+wvc2LfxVSuOH2lz+ytBBzR5uggR/u6/Cnif5y+YxPfm/KUmcvXz5XFFvr78rYo6hJ3rC1nmQBA165dwRgr9diwYQMAYMOGDTh06JDWMbrik5OTzd52Io4+LfwwplMdwce99/MZq/ibsOP0bSRnCp+wvjSPJgIJIYQYplQx7DrPt9+XZyVa/GQuI4PrcsfSHp7E2h0VkC1V1pYH1oomTawQ7x4dZcXxZk7wxlky3vJcZcXZU+aEUsWwNf4OVyyVgCP6/DC2A3csAxAZfVW6xtgBMbJEKNPENAVFKsTf5t9XxpazTAhR+7Rfc3RrKHzFmaWX6VKqGN7/9azg45YNDaTvPSGEEC7HrmcgjzOF2cfdReLWEDVHBzkaVK/EHU97eBJrto1zbBAAFgxqIWFLygdNmlghMSYBvDjPwRtn0UQoz2VPmRNxSZnIfFRoMK5qJUe08/fU+3N7mpgjpTk6yNGyJv+GzMsPXbOKlcWWSowsEd70f6Lb8LX8N0Qy2HaWCSElrX+rA6pVchB0jKWX6Xp11RHBx3Rv7IX+nNeShBBCSGxiBlecq5NDmfflRHyz+jfjjqU9PIm1KihS4W4236J978qOcHSwvSkG23tH9kCESQBwLnI7kWzdWROAOOW5APvJnODNZDKUemdXE3NEp2m9mnDHqhhlm5hCjCyRfZfu0cSVkQqKVDierL8k5rMGtapBq82JXTn+SU/Bx1hqma6d8Xdw+hZ/VhkANPOrjO9GtZWoRYQQQmwR4xz46fRcVbquNLPgBtV4h9QA0B6exDptjEnmju1Qz7b2MlGjSRMrdOByGldcWZMA6Zz17zfEJlvkDasQZZWQEhJnL5kTvJlM3Zt4lx3AexVho9d3CxYsQNu2bVG5cmV4eXlh4MCBuHLlSpnHbNiwATKZTOvh7Mz3+2uJOtSrigoC/spQtonxxMgSycortPryguVFSJYJYJupy4SURSGXYfmQloKPs7QyXUoVw3u/xAs6xs1Jjl3vvyBNgwghhNgsN2fD5cEBILAW/16SRBwKuQwvt+TPHqU9PIk1+vFYMnfs4Fa1pGtIOaJJEysj1n4TvBMJWY+tfxCtdZ0qMLTwQi4rjiuL3WROiJHJBOBeDufeO5xx1ubvv//GxIkTcezYMURHR6OwsBA9e/bEo0ePyjzOzc0NKSkpmseNGzfM1GLxKeQyjO9Snzuesk2Mo1QxRF/km0w3xNrLC5YHoVkm7f2r2GTqMiGG9GtZAy82ErYKzdLKdL266gj3ZZLayU9DJWkLIYQQ23bmJt/1ZXae4dLaRHyfDw7gjqU9PIm1KShS4UYm39iAXAYEP1dN4haVD7prtzJi7TfRzt8THi58Kxd4yzVZqlM3HsDQpL6KFceVyU4yJ8QqZ8Zbpszay5npExUVhVGjRqFZs2YICAjAhg0bcPPmTZw6darM42QyGXx8fDQPb28DGT0WblJII0FfidWHE2kVjkBxSZnIflIkyrls9fsopY+3CNsM+ocxHSRqCSGWb93oDnB3Vgg65n0LKdNlTFmuPs19aJKUEEKIYEoVw8Er97liqTJX+aA9PIktE1Kaq0cTL5stEUhX8VZGrP0mFHIZRgbX4TpXtUrWnTnB+5kZirOXzAne8lyG4njLlN3Oso+V7dnZ2QAAT8+yN+nLzc1FnTp1UKtWLQwYMAAXLlwoMz4/Px85OTlaD0uikMvwXjf+bJMCJcMxzk0PSbFUEfscMfZGsSfF2Z/8q+Apy4QQ4MQMYfubMABhP5W94EBqShXD+wLLcsllwLI3WknTIEIIITbt2PUM5HNuHh5UzzZXeFsD2sOT2CohpblGBvlL15ByRnfuVoa3rFZIUx+DMe38OUskWPmEoViTAHaTOSFSeS4fN77f1R3xd21+xYVKpcLkyZPRsWNHNG/eXG9co0aNsG7dOmzfvh0//vgjVCoVgoODcfv2bb3HLFiwAO7u7ppHrVqWV0tSaLbJwj2XJGuLLcrk3KOKR9ZjK++/zCzmWrqgUj2UZUJI8crM0R1rCzrmrwtpKOAcPJJCZPQVCH31yCGBNrvqjhBCiLRiOReROTvI0aG+bW7AbA1oD09ii4SU5nKQy2y6D6JJEysj1v4cgP1kTog1CWAvG8GLVZ6rnb8nPCsZLgGX8ajA6vfNMWTixIlISEjAL7/8UmZcUFAQRowYgZYtW6JLly74448/UL16dXzzzTd6jwkPD0d2drbmcevWLbGbbzKh2SZnb+eU6+CYtREzO4QyTYSJ2FF2JlhJlGVCyFOz+j8vuExXl4UHJGpN2ZQqhqUHEwUd06qWB/oH8G8QSwghhJTEOAcxujauThP05Yj28CS2SEhprhdtvA+iu3crI9r+HLCfzIkDl/k2SDY0CWAvG8HzZjMZilPIZRjAOWBgy5tPh4WFYefOnTh48CBq1qwp6NgKFSogMDAQ165d0xvj5OQENzc3rYclEpptMvy7Y5K1xdaImR1CmSb8CopUSEx/xB1PWSaEaBNapislJx/b4+9I1Br9Jm46KSheBuC38cHSNIYQQohdSMl6zBUXWMvwYlkiLaH3ucsOUrYJsWxUmuspmjSxMmLtzwHYR+ZEcb15vhtsg5MFnH8JTyRbd9aEmNlMNatU5HpNa5+Y04UxhrCwMGzduhUHDhyAv7/wPyZKpRLnz5+Hr6+vBC00L4VchkGt+FfdHk96QNkmnDwriZcdYi97DIlh+Fr+iT3vyo6UZULIM4wp0/XBr2fNOtBQUKRCVMI9QccsHUpluQghhBhPqWKIusC38DM7r1Di1hBDhFZVYADe23RaugYRYgIqzaWN7uCtjFj7cwD2kTkRl5SJzEeGLySqVnJEO/+yN+hO59w3YENsslWvHBAzm4m31I8tlgSaOHEifvzxR2zatAmVK1dGamoqUlNTkZf39A/QiBEjEB4ervnvOXPmYO/evbh+/TpOnz6NN998Ezdu3MDYsWPL4y2IbsGgAEHxlG3Cx4tz/yAe9rDHkBgKilQ4nmy4D1R7q5Ntr8AhxFhCy3QVqRhi/k2XsEXahEyOAlSWixBCiOnikjLxuIBv8RjN0VsGodkmuxNSaYEgsUhUmkubWSZNVqxYgbp168LZ2Rnt27dHXFyc3tgNGzZAJpNpPZydxRsQsnoi7c8BwC4yJ3gzcwa09DP4ZectW5X1uNCq9+jYdzGVK47ns+Ut9WOLJYFWrVqF7OxsdO3aFb6+vprH5s2bNTE3b95ESkqK5r8fPHiAt99+G02aNEGfPn2Qk5ODmJgYNG3atDzegugcHeRoX5c/hZyyTfjEJfFtFFnZ2cFgjD3sMSQGoQOpozvWk6glhFg/oWW6InYmSNQSbUInRwEqy0UIIcR0qQL2lA2qV03ClhBeQrNNgPLbq42Qsvx5jr8Urq2X5gLMMGmyefNmTJ06FbNmzcLp06cREBCA0NBQ3LunP9Xdzc0NKSkpmseNGzekbqbVEGuTbsA+Mid4JzpCmvoYjGnn7wkPF8MbmwP8kzWWRtRyZrCPEnD6MMZ0PkaNGqWJOXToEDZs2KD576+++go3btxAfn4+UlNTsWvXLgQGBpq/8RL6YaywfR3WH70uUUtsg1LFsDGG729kYC13rjhb3mNIDEIHUmkDeELK5uggR6/mXtzxifcfm2VCXejkaOSQlja/2o4QQoj00h/yjdO4VJDbfGkcayI026S89mojRB+liiHhdg5XrD2U5gLMMGmyZMkSvP322xg9ejSaNm2K1atXo2LFili3bp3eY2QyGXx8fDQPb29vqZtpNUQtz2UHmRNi7s+hkMswMrgO1+tWq2SdkwBiljMD7KMEHBFGaLbJuiNJErbG+sUlZSKLs5ZxnaqVuOJscY8hMYX/cVZQPG0AT4hhK95oIyhe6vKNQidHfd2cMKBlDQlbRAghxF5kPuabNHmhoe2XxrEmxmSbTPol3moXKBPbE3MtHbzLkuyhNBcg8aRJQUEBTp06hR49ejx9QbkcPXr0QGxsrN7jcnNzUadOHdSqVQsDBgzAhQsX9Mbm5+cjJydH62HTRCzPZQ+ZE2LuzwEA7fw5Z1KttO8Qs5wZALsoAUeEE5JtkvawgEp0lYH3O+tRsQICa/FNVtli5pdYlCqGP07f5Y6nLBNC+CjkMgxqyb8XiNTlGz/eImxy9O8PX5SoJYQQQuzNSc5Je5cK/HuCEfOYFNJI8CBr+3l7JWkLIUItO3CVO9YeSnMBEk+apKenQ6lUlsoU8fb2Rmqq7n0TGjVqhHXr1mH79u348ccfoVKpEBwcjNu3b+uMX7BgAdzd3TWPWrVqif4+LImY5bnsIXOCd0CRO46zvihvnKURs5wZYB8l4Ihwjg5y1K/Gl/UA0IbwZeHNPhwVVBfenBvGU+aXfpHRV7jXLgCUZUKIEJ8PDhAUL9XfBqWK4Y94mhwlhBBifkoVw7nbWVyxjNH9s6VRyGVYOqSloGPSHxehT+Tf0jSIEE5KFcPJG1lcsQo57KI0F2CmjeCFCAoKwogRI9CyZUt06dIFf/zxB6pXr45vvvlGZ3x4eDiys7M1j1u3bpm5xeaVnP6IK4538NvWMyfELGcG8JetsdbyNmKWMwPsowQcMU7ES824Y2lD+DJw3iu1revJ349baX8vNaWKYdnBRO74gJpuNJBKiABCyzdK9bchMvqKoHiaHCWEECKWuKRM5BfxXeDXqOIicWuIMfq1rIEG1SsKOuZiSi76fH1ImgYRwuHY9QyDVXrUWtXysIvSXIDEkybVqlWDQqFAWlqa1vNpaWnw8eFbqV6hQgUEBgbi2rVrOn/u5OQENzc3rYetUqoYfo67aTDO192Za78JwPYzJ8QsZwYAHhUdRY2zNOKXM7P9EnDEOMENqgkam6dsE92EZB/yZn7xxtkboVkmH4Y2kawthNgqIeUbAfH/NihVDCsO8U+O1q9ekSZHCSGEiCZVwLhLx/rVJWwJMcXuSV0EH3Mx9RFaz4miChykXHwfw7+X7PsvNpSwJZZF0qt8R0dHtG7dGvv379c8p1KpsH//fgQFBXGdQ6lU4vz58/D19ZWqmYIoVQwHL6Sh15KDaPzJbjSbGYXR6+OQ+6RI8teOS8pEao7hwawhbWtzz/qZI3Mi90kRRn8Xi6YzdqPxjL/QJ/IwDly6Z5Y/BmKWMwOArMd8nwNvnC4FRSqsOHgVQZ/tReMZu9Fqzl58/Ps55BUojT4nL7HLmZmjBJz6O9lv6T/o/MUBjN14wizfR2IahVyGly2ofr21EpJNx/sds9ZyjFISmmXiqJDZTcoyIWIq72yTmGvpUAq4PI3o11y01yaEEELSH/KNS7hUkNO1pgVzdJBjdMfago/LeKxE/em7sXD3RZo8IWajVDHsu3yPK1YuA4KfqyZxiyyHg9QvMHXqVIwcORJt2rRBu3bt8PXXX+PRo0cYPXo0AGDEiBGoUaMGFixYAACYM2cOOnTogAYNGiArKwuLFi3CjRs3MHbsWKmbalBUQgrCNp1BUcnOS6nEwSv30TxiD5r7uWLn+8JnlHnxDlTXrcafCih15kT/Zf/g/J2cEs8wXEx5iLc2noBCBqwY1gq9mks3IcZbHoo3jneD5NtZeVxxz5q/6yK+/Ud7hvdJUSF+OXELv5y4hR5NqmPtyHZGnZuH2OXMAHUJON2ZYlqMyO4r/k6eRsnxklsP8tA8Yg9a1HTDjrDOwk9KzObzwQGC6sYP/+4YNr8TLGGLrJCQbDrO79iJ5Ex0tKMLIR5Cs0zefaG+3aQsEyK2H8Z2QMMZf3HHh/9xDotfaynKa8/+8wJ3rIPcvm4aCSGESO9kcgZX3AsNq9O1poWb1f95/Bl/F+mPhC/oXHk4CSsPJ0EOwL1iBYQ288Gs/s3g4qgQv6HE7h27ngEl5xqk5n5udtX3SD5p8vrrr+P+/fuYOXMmUlNT0bJlS0RFRWk2h7958ybk8qcJLw8ePMDbb7+N1NRUVKlSBa1bt0ZMTAyaNm0qdVPLFJWQgnd/PF1mTMLdXLSdF40TM0IkaYMUA9pSZk60nReN+7n6j1My4N0fT2P1m9JNnKj36Chrkl7IHh0+nBsp74i/ixl9mwrqTN7+/gSiL5Y9u7vv0n28tPwf6SYDRC5nBkhXAs7Qd/Lc7RxpPytiMvWK4uPJfOXe1CuKqRTKU/c4S2ndy803uF+R2obYZLzX/Tm7uhgqi1LFsOpv/iwTuQyYFGI/KcuEiE3o34atZ+5g4eAAk/usgiIVrt3n2zsQACZ0aUD9JCGEENEoVQwHOFd7u1SgwXNrcPyTnqg/fbfRx6sAPHj8dBGtIQoZ4OZCkyxEmB9ik7lj+wfwVwuxBWYZeQoLC8ONGzeQn5+P48ePo3379pqfHTp0CBs2bND891dffaWJTU1Nxa5duxAYGGiOZuqlVDFMNDBhonY/twCzd/CvUhNEggFtqTInZu04V+aESUnv/xIvWeqhFHt0eFYyvEdHxqMCQRub74y/Y3DCRO3c7Rxsj7/DfW4hxC5nBkhTAk6pYgYnMYHiz4pKdVk2ofXrw/84J1FLrFMm56RJZm4+d0Zd1uNCQf2XrTt2PQOFAqr/hHWlgVRCTCXkb4OKATH/ppv8mh9vOcsdS5OjhBBCxCbkmtPPg++6npQvhVyG5UNamu31lOzpJEuTmVFoPSeKxkNImZQqhv2X0gwH/mdksL+ErbE8tFyXQ2T0FQjZTWJ9TLIktfelGNAWkjnBO7FRUKTCxhjDs+Al4yOjr3LHC7HvYipXnJA9OgZwzqymZvNNNClVDJM3x3PFqn3w61lJJprELmcGSFMCbuKmk9yxUzaf4Y4l5ie0fv0fp+9QfdcSbj94zBXnWckR7fw94eFieNIX4O8T7cGiqEvcsTSQqu3w4cPo378//Pz8IJPJsG3bNoPHHDp0CK1atYKTkxMaNGigtbCG2A9HBzla1nTjjo/YmWDS6ylVDFsFlIukyVHbcfTo0TL7KcYYZs6cCV9fX7i4uKBHjx74999/tWIyMzMxbNgwuLm5wcPDA2PGjEFubq5WzLlz59C5c2c4OzujVq1aWLhwYam2/Pbbb2jcuDGcnZ3x/PPPY/du41cnE0Ksj5DV3rQJvPXo17IGujcun3KeGY+VaB6xB+3n7aX9QYlOQiZr61evaHdVP+zr3RpBqWJYcYi/LIfax7/zr1bjJcWAthSZE+F/CH/vqw8nij4QWnwDzJeRIeQzq1mFb88Y3syJmGvpKBL41otUTJRVlc9SlzMri5ByZoD4JeAKilSISuDLygGAmw+M21+GmI+QFcUMkGyS1dooVQzbz/IN8vm4u0Ahl2FkcB2ueNoMvlhBkQrxt3MMB/6HBlK1PXr0CAEBAVixYgVXfFJSEvr27Ytu3bohPj4ekydPxtixY7Fnzx6JW0os0bReTbhjE+8/NmkwIOZaOneitgw0OWpLHj9+XGY/tXDhQixduhSrV6/G8ePHUalSJYSGhuLJk6eLC4YNG4YLFy4gOjoaO3fuxOHDhzFu3DjNz3NyctCzZ0/UqVMHp06dwqJFixAREYE1a9ZoYmJiYjB06FCMGTMGZ86cwcCBAzFw4EAkJJg2IUgIsQ5CVns7KmS0CbyV+W5UezT3dS2310/LLUTDGX9h1g6q2kC0xSTyjyuGNvORsCWWiSZNDIi5lg6lEWP52wRkZvCSYkBb7MwJpYph+xn+lXpqBUqGY4l8m57xikvKROajQoNxVf9bgc1L7MwJIZuOlrT0gPgDx2KXMwPELwG3MSaZ+7UBoHYVF0HxxPyErihefugaZZvAuD6unT/nDRaN+wMAhq89xh1LA6ml9e7dG/PmzcPLL7/MFb969Wr4+/tj8eLFaNKkCcLCwjB48GB89dVXEreUWKIO9aqigoA7FVPKNwq5Fns50I8mR21ISEiI3n6KMYavv/4aM2bMwIABA9CiRQt8//33uHv3riYj5dKlS4iKisLatWvRvn17dOrUCcuWLcMvv/yCu3eL74l++uknFBQUYN26dWjWrBmGDBmC999/H0uWLNG8VmRkJHr16oVp06ahSZMmmDt3Llq1aoXly5eb5XMghJQvIau9A2q6098hK7RzUhc096tcrm3YGHMLgRF/lWsbiGWJSkjhjrXHDDeaNDHA2AFtseorlyTFgDYgbubEsesZgrMm1DbGJhl3oB685WUGtBR28ytm5oTQTUdLOn0rS/SBY7HLmQHil4D786yw/Vy+er1890QifISsKFYxyjYBjOvj0jn3QOGNs2UFRSrujagBYFCrGnQDa6LY2Fj06NFD67nQ0FDExsbqPSY/Px85OTlaD2IbFHIZxnepzx2/3cgFS0KvxT5/JUDwaxDrlJSUhNTUVK1+yd3dHe3bt9f0S7GxsfDw8ECbNm00MT169IBcLsfx48c1MS+88AIcHZ8uqAoNDcWVK1fw4MEDTYzQ/o8QYjuErPZuK2DBJ7EsO99/Ad0be5VrGx48UaFB+K5ybQOxDAVFKiTe5yv3ba8ZbjRpUgZTBrQB8TMBeAfIhNaiFzNz4vsY4yc+Dly+L+okAG/JrZCmwlLMxPy8hGZNlKRUQdTsHKnKmYlZAk6pYki4yz8g1ryGG1ydHbjjSfkRuqKYsk2AapxZXN2beD89hrPsFpXnErYpNAAsGNRCopbYj9TUVHh7e2s95+3tjZycHOTl6c5GXLBgAdzd3TWPWrVqmaOpxEwmhTTiji1SGZe1LKSsrD3WcrZnqanFi4l09Uvqn6WmpsLLS3sAzMHBAZ6enloxus5R8jX0xah/rgtNGhNiO3hLoQO2vdr7zp07ePPNN1G1alW4uLjg+eefx8mT/PuZWoPvRrXFsqHlu7CziAENPqaJE3snZDyyW2Mvu1wgSFf9ZTBlQBsQPxOAd4CMN05NrMwJpYph32X+vSaeZezNrj5SlDMDxM00+fOcsKyJZx1NvG/S8SVJVc5MzBJwx65nGMy2UnOQAzvf68wXTMqd0BXFlG0CcBfgLxnHe51jf9dDWpQqhj8EbArd3r8KDaSWk/DwcGRnZ2set27dKu8mEREp5DKENuVfkSk0a1loWdmIfs0FnZ8QKdGkMSG2QaliOMWZ3WzLq70fPHiAjh07okKFCvjrr79w8eJFLF68GFWqCBuvsQb9A/yQ+FkftKzpXm5tKALwHGWc2DUh45EjOtSVriEWjO7wy2DqgLbYmQBGDZBxECtz4tj1DCiN34MTgLiTAFKVMxNrjw6liuHCHdNWhN0RcZNzqcqZAeKVgBOStvxRr8bcscQyTAppJGisfuXf9p1tkv6Is9RWiTgqz8UnMvqKoPgfxnSQqCX2xcfHB2lp2pugpqWlwc3NDS4uuvencnJygpubm9aD2JYRwf7csUKzloWUlZXLgODnqnGfm1g/H5/ibHRd/ZL6Zz4+Prh3T3vRWFFRETIzM7VidJ2j5Gvoi1H/XBeaNCbENsRcSwfvMIotr/b+4osvUKtWLaxfvx7t2rWDv78/evbsifr1+RfWWROFXIZtYZ1waU4v+LrzjcmJrZABTT+lPU7skZDxSAe57U7WGkKTJnqIMaANiDsJYMwAGQ/ezIlYAwPWQga09bmbJay0WFmkKmcm1h4dx65nQGnieO9dzs3TeUhVzgwQb2JOSNrySAGDLMQyKOQyvNeN/6K4SCX+3lHWxJjsQyrPZZhSxbDq70TueCrXI56goCDs379f67no6GgEBQWVU4uIJehQryocOMeHhGYtC7l27dHEdgeqiG7+/v7w8fHR6pdycnJw/PhxTb8UFBSErKwsnDp1ShNz4MABqFQqtG/fXhNz+PBhFBY+zeiOjo5Go0aNNCuojen/aNKYENsgZB9dW17tvWPHDrRp0wavvvoqvLy8EBgYiG+//VZvvK2UKHRxVCA2PASX5vTCq238UNnRvNcajwtV6PzFAbO+Jil/QsYjW9Zyt9trYLrL10OMAW1A3EmA5HS+/VWE7DcB8GdO7Lt0r8xJACED2vrkFSpNPoeaVOXMxNqjQ4xJprO3s0VbaS9VOTNAnJJmShXDGc6sIBrEtF5Cs03E3jvKqlB5Lkkcu56BQgFZk1SuR7/c3FzEx8cjPj4eQPGmyvHx8bh58yaA4lXSI0aM0MS/++67uH79Oj788ENcvnwZK1euxK+//oopU6aUR/OJhVDIZRgQyFfmExC2YCkqIYU7dmQQLcawRWX1UzKZDJMnT8a8efOwY8cOnD9/HiNGjICfnx8GDhwIAGjSpAl69eqFt99+G3FxcTh69CjCwsIwZMgQ+PkV/96+8cYbcHR0xJgxY3DhwgVs3rwZkZGRmDp1qqYdkyZNQlRUFBYvXozLly8jIiICJ0+eRFhYmLk/EkKIGQnZR1chh02v9r5+/TpWrVqF5557Dnv27MH48ePx/vvvY+PGjTrjba1EoYujAosGB+L8nD5I/rwvEj/rg/XD26CxV0VIvUvrrQd5mCNg8o5YPyHjkULK89sa2iFZDzEGtAHA10PYBIY+ShXDz3E3Db+eu7PgX2jezImsvELEJWUiSMcfaiED2mU5fLW4rIIos5gSlTNT79GxPuaGwdiy9ugQY5KpQFm8orKjCOUihJQz0/U7UBYxMk2ElNAIbSY8G4ZYBnW2ydKDfCv9T97IEq/PsDJSlufafykNHRvYZxmaRVGXuGMd5FSupywnT55Et27dNP+tHiAcOXIkNmzYgJSUFM0EClC8qnvXrl2YMmUKIiMjUbNmTaxduxahoaFmbzuxLAsGBeD303x7j/BeXxUUqZB4/zFXrC3XkLd3Z86cQb9+/TT//Ww/9eGHH+LRo0cYN24csrKy0KlTJ0RFRcHZ+en9008//YSwsDB0794dcrkcr7zyCpYuXar5ubu7O/bu3YuJEyeidevWqFatGmbOnIlx48ZpYoKDg7Fp0ybMmDED06dPx3PPPYdt27aheXOamCfEloX/cZY7tlUtD5u+51GpVGjTpg0+++wzAEBgYCASEhKwevVqjBw5slR8eHi41uRzTk6O1U+clKSQy9CtmTe6NfPmileqGA5dSMPbP53iLvdW0rqjyfi4dxNafGonhCwc6li/uoQtsWw0aaKHGAPaAJCWLU5d+LikTKTmGD7XkLa1Bf8hbefvCQ+XCsjKM7wJuL5SVkIGtMuSV6gSbRJAqnJmgOl7dIg1yQQUr6gU4/OSqpwZIKwE3Cuta+r8mZCJTHvu1G3BpJBG3JMmDMUbwk8NbSRtoyyQMdl0vJmI2+Pv4pO+TW36xkyXgiIV4m/zp/ZP6NLA7j4jIbp27QrG9F8cbNiwQecxZ86ckbBVxBo5Osjh5+6EuxzX1fG3srkm0zfGJHO/vi3XkLd3nTt3LrOfkslkmDNnDubMmaM3xtPTE5s2bSrzdVq0aIF//vmnzJhXX30Vr776atkNJoTYDKWKYfsZvgUBAPD+iw0lbE358/X1RdOmTbWea9KkCX7//Xed8U5OTnByst+Sws9SyGXo/rwPrn/eF7N3XMB6Adc5an0i/8a+D7oZDiRWjRYO8aMpRB3EHNDeezFVlPJJvAPVdavxDeaXpJDLMDK4Dlesvjr3YmXmAOLtA8M7OCi0nBlgeuaEWJNMgHgl4KQqZwaIUwKOdyLT3jt1W6CQyxDa1Is7fvkhO90Q3ohsOrHKC9qq4WuPccfKAEwKse2bV0IsSbMa7lxxvPua/HnuDvdr23INeUIIIeVDyJiAXGb72c0dO3bElStXtJ67evUq6tThG6siT816qRmuzust+Lhr9x/jz7P8E3nEOtHCIX40aaKDmAPauflKUQaepBzQBoB2/pyDzHq+K2IOrok1CWDJe3SIOckk2j4wEpUzA4SXgHuWkInMgJr2u0mVLRkRzF87XsWKs03szYHLaVxxJbPpFHIZXm5Zg+s4Y7LKrFlBkQrHk/kXTAxqVYP6GkLMqF1d/gURhhbgKFUMF+7wZZU5yGkxBiGEEPEJKQnbo4ntD1xOmTIFx44dw2effYZr165h06ZNWLNmDSZOnFjeTbNKjg5yJH/eFwqBx0365Yx9Lki0I7RwiB9Nmugg5oA2UPa+FtwkHNAGgHs5nKWZdMSJmZkDiDcJIGSPDqFMzTQRc5IpNjFDlD9qUpYza+fvCXdnvmqAur4vQiYy29rxJlW2pEO9qqgg4C+UvWWbKFUMW+P5LnaezaZ7sTFfXVx9mYW2SkiWCQAsGNRCopYQQnQZGVyXO/aEgeusY9czoOT8k/Fi4+o2P1BFCCHEvISWhB0ZxL+gzFq1bdsWW7duxc8//4zmzZtj7ty5+PrrrzFs2LDybppVu/pZH0HxKga8t+m0RK0h5U2pYkjg7Hto4RBNmuh054EIkxwl8G68W+Y5JBzQBvTvvcETJ2ZmDiDeJMC+i6lccVLv0fEssSeZcp4UiTIJI2U5M4VchpCmfAO1un7HaD8T+6OQyzC+S33ueHvLNolLykTmI8P7UFWt5Ih2z04k8o792dEYodAsk/b+VWiTRELMzNFBjvrV+crQnrmVVea15A+xydyvaw8DVYQQQsxLyGIdexq47NevH86fP48nT57g0qVLePvtt8u7SVZPIZdh6WsBgo7ZnZCKgiJjtpMnli7mWjp4/2Vp4RBNmujk68E7eMyXbcCzwbrh15JuQBswLXMilqNutBBiTAKYsgqbhyl7dIg9yQSIk80kZTkzAAiqz1eDVdfvGO1nYp8mhTQSNG6/+nCi3WSb8E72DmjpV+pCh3ciX4wJf2vx8ZazguJ/GNNBopYQQsrSq7kvV1yRCoj5V/eCC6WKYf8lvvKGdF1BCCFEbEIX6+i6nidEiJda1YS3m+F9LUsa/p2wLHxiHZYd4F9oSguHaNJEpzTOAej61StxxV27l2tKcwBIP6BtSuYE46wJVkHAH3pTJwFMWoXNwZQ9OoRMMjk78H1mYgxuSlnODDB+HxiliuHc7SyuY2k/E9uikMvwXjf+bJMCJd/mv7aAd/+q7k1KZ3jxlt2yl/JcShXDH/H8Gx5Slgkh5SeYcwEGACzVc1N47HoGCjmX2NF1BSGEELFRSVhSHv75sIeg+ONJDyjbxMYoVQwnk7O4YhVy0MIh0KRJKUoVw85zKXzBMr6bKDHKTUk9oM2bObE7IbXUe0nJesx1bL8WvqjszLcNFW+5MH1MWYXNw5Q9OngnmZwd5OjS0IsrVoxsJinLmQHGZzPFJWUinzM1h/YzsT1Cs00W7uHfUNGqmbLPFZXn0hIZfUVQPGWZEFJ+OtSranARkdppPSW6hJT8pOsKQgghYqKSsKS8ODrI0ac5X8l0Nco2sS1CSnO1quVBC4dgpkmTFStWoG7dunB2dkb79u0RFxdXZvxvv/2Gxo0bw9nZGc8//zx2795tjmYCKF59VsC5D7mrI9+guRjlpngHqo0d0ObNnHhcoNRayS1kksnXwwWDAmtwxfIOsOvDW3IrpKmPUec3ZY8ON2e+tMg+z/uivpcrV6yp2UxSlzMDjM80Sc3h/52m/Uxsj9Bsk7O3c+xiRYwp+1xRea6nlCqGZQcTuePpxpWQ8qWQy9CmjgdXrFIFndmHQvYupOsKQgghYqKSsKQ8LXujtaB1cZRtYluElOZ6/8WGErbEekh+579582ZMnToVs2bNwunTpxEQEIDQ0FDcu3dPZ3xMTAyGDh2KMWPG4MyZMxg4cCAGDhyIhIQEqZta/PoCVp+18+dPVTK13JTUe5q08/dEJUe+LJDY608/IyGTTHIZULMK3waemUZuaK8mdTkzwPg9Ok7f4JtA83Z3QhXOySNTs5mkLmcGGJ9pkv6Q73fBpYKc0gdtlNBsE3tYEcNbnktXHJXneioy+gp30g5AN66EWIL3BNzElbxmVWOM71tP+5kQQggRE5WEJeVNIZdh2ZCWgo6xh3treyCkNJdcBgQ/x18S15ZJ3gMvWbIEb7/9NkaPHo2mTZti9erVqFixItatW6czPjIyEr169cK0adPQpEkTzJ07F61atcLy5culbioA/tVnjgoZRgbXNVu5KaknARRyGTpzfilKjs0LmWQKqleNu4zUqZvGlRnTHC9xOTPAuMwJpYrhwGXdE4bPSsl6gmqV+QYtTc1mkrqcGWD8vjknk/n2qHihYXWLSh9csGAB2rZti8qVK8PLywsDBw7ElSuGywCVZ6adpVLIZRjUyo873i5WxFB5LpMJzTKpX70i3bgSYgGCG1QD55ZvOq8FnxTyrfbp1tjLoq4rCCGEWLeJm04KiqfFOkQK/VrWwHPV+RYzA3Zyb20HhJTmal2bSnOpSXr3X1BQgFOnTqFHj6cbDsnlcvTo0QOxsbE6j4mNjdWKB4DQ0FC98WK784Bvf46Amu5wdJCbrdyUOSYBWtfhyyAomf0gZJKpQ/2qkHGOwh3517TMCanLmQH8/6Y3M5/+TgnZfNTPw5m7bBpgWjaT1OXMAP59c/Zduqf5txcyyeRSgW8C01z+/vtvTJw4EceOHUN0dDQKCwvRs2dPPHr0SO8x5Z1pZ8kWDAoQFG/rK2KoPJfphGaZRPRrLllbCCH8FHIZXmrpyxWbmq19nadUMRy6ep/r2DYmZCMTQgghJRUUqRCVwHdfC9BiHSKtXZO6CIr/+HdhZeWI5aHSXMaRtBdOT0+HUqmEt7f23g/e3t5ITdW96XRqaqqg+Pz8fOTk5Gg9jKVUMZy9nc0Vq94YsrZnJa543lX2+ki9STcAeFbimwQoGSdkkkkhlyGIs8xBbr5pmRNSlzMD+P9Nt565o5kEEJKZ07F+dbTz9zRLNpM5ypnxTgBl5RVq/u2FTjJZkqioKIwaNQrNmjVDQEAANmzYgJs3b+LUqVN6jynvTDtL5uggR/u6/L9/tr4ixpQ+jvfY5HS+/t0aKVUMKw7xZ5k4yClFmRBL4uvBt0Jy9/kUrUU4x65nIL+Ib7qUtwwiIYQQYkjnhfsExdNiHSIloffWW8/cNWlRMylfVJrLeFY/db1gwQK4u7trHrVq1TL6XMX7c/B1BOqNIY3JNhDKHJt0A8LLJxkzydShXlW4VOD7tTMlc8IckwC8mRMlS2cJzcxRyGVmyWYyRyZTO39PuDs7cMWq/+2FTjJZsuzs4u+Kp6f+jC5jMu3EnDi2dD+MFZaibssrYkzp49r5e8LHzXD/9cuJmzZ7cRxzLR2cf+4BABO6NKAUZUIsCG/m8pMildZm8EKuK3zcXQS3ixBCCHnWjtO3kZbDV6YcABzkMhq0JJITcm/NAERG82cqEMtCpbmMJ+mkSbVq1aBQKJCWlqb1fFpaGnx8dJf58fHxERQfHh6O7OxszePWrVtGt5f3RsrZ4emG08ZkGwhljk26Af5JgN0JqVCqmFGTTAq5DH2f5yupYErmhDkmAYwpnZVXUMQVX7KOtjmymcxRzkwhl6FHEy+uWHVZIKGTTJZKpVJh8uTJ6NixI5o3179qSGimHSDuxLGloxUxT5nSxynkMgxtV9vga6RkPzEp48+SCUlPlsuASSGUokyIJeHNXAaAo4lPy3Hx9mkuFeQmXVMTQgghQPFC0/d/FbaQa/FrATRoSSQn9N569eFEm723tnVUmst4kk6aODo6onXr1ti/f7/mOZVKhf379yMoKEjnMUFBQVrxABAdHa033snJCW5ubloPY/HeSLX4r9QUYFy2gVDm2KQb4J8EeFygxLHEDKMmmQAgqD7fqglTMifMMQkgtHSWsXW0eT8HUz4vc5QzAwAfD75Vm1l5xZOExkwyWaKJEyciISEBv/zyi+jnFnPi2BrQiphipvZxdavxTcaa0kdaKqWKIY4zPRkAwrpSlgkhlqZDvarcm8GrF2AoVQznbmdxHdPcz42+94QQQkw2eNURQfG+bk4Y0JKv0gQhphJyb12gZFrZu8Q6UGku00henmvq1Kn49ttvsXHjRly6dAnjx4/Ho0ePMHr0aADAiBEjEB4eromfNGkSoqKisHjxYly+fBkRERE4efIkwsLCJG2nkBspX/enA8fm2KjbHJt0A8WTAJUc+SYBYq+nGzXJBPBnRJiSOcFbh9qUetVCS2cZW0dbaNk0Y5ijnBkAMM6FCdfu5drMZq1hYWHYuXMnDh48iJo1a5YZKzTTDhB34tgaCF0Rs/Lvaza5IsbUPq5aJc7jOeOsSWT0Fe5YGSjLhBBLpJDLEMj5tz/lv83g45Iyua/D2lKWCSGEEBPtjL+DM7eElU7++8MXJWoNIaU5OsjRoDrfYjoAWLjnkoStIVKg0lymkXzS5PXXX8eXX36JmTNnomXLloiPj0dUVJSmBM3NmzeRkpKiiQ8ODsamTZuwZs0aBAQEYMuWLdi2bVuZJW3EIORGqkaVp6vlzbFRt7kGtBVyGTpzzioWqRjOcJa2KjnJBJgncwK8Y6QmjqXWrMK3EWnmo3yj62jzZjPtu3TP6MFhc5QzA4AqnP+msYkZiLmWbtWbtTLGEBYWhq1bt+LAgQPw9/c3eIzQTDt7JWRFTJEKiPnX+AlFi2VqH8d7LWRj10xKFcOyg/wbwIc0tewsNkLsGW/5rHO3s6FUMaTm8GfOWfo+aYQQQiybUsXw/i/xgo7p3cwbjg5Wv+0wsTKz+jfjjj17OwcFRbxD8MQSzP7zAncsleYqzSw9clhYGG7cuIH8/HwcP34c7du31/zs0KFD2LBhg1b8q6++iitXriA/Px8JCQno06eP5G009kbKHBt1m2tAGwBa1+G7Ac3JKwTneLbWJBNgnsyJ9Ef5osbpoy4jZcipmw+MrqPNm82UlVcoeQk4U0v1VKvMX87u99O3uc9riZu1Tpw4ET/++CM2bdqEypUrIzU1FampqcjLe5pxZimZdtZG6IqYiJ0JEramfBy4nGY4CPr7OPW+QQaP54yzFpHRVwTNlY8MMjzZSQgpH8Gc5V7Vm8GnP+Trz1wqyC16nzRCCCGWLzL6CvfqbrXlw1pL0hZCyhLcoJqggeGNMUmStYWIq6BIhWv3H3HFUmku3Wga+z+m3EhJvVG3uQa0AcCzEt/Ezj3OzwsovVrPHJkTyel8HYOpe3TIOJdhH7x0D+duZXHFPltHu52/J9ydHbiONbYEnDnKmQHCytnxlsuz1M1aV61ahezsbHTt2hW+vr6ax+bNmzUxlpJpZ42ErIhJvP/YplbEKFUMW+PvcMXq6+PssTyX0CwTR4WMBk4JsWAd6lWFo4LvOuxo4n1kPua7dn2hYXXKMCOEEGI0pYphqYBrTgCIHNKS/vaQcqGQy/ByKz/u+D/P3pWwNURM4X+c5Y6l0ly60aTJf04m821opOtGSupyU+Ya0AaKy0jxuJXJNynx7CbwgPSZE0oVw89xNw3G+bo7mzzYHsQ5oJavZMhXGldHWyGXIaSpN9exxpaAM1c5s3b+nnB14ut2ktIfc8VZ6matjDGdj1GjRmliLCXTzhoFN6gmqHLU8O+OSdYWc4tLykTmI8NZblUrOerv4+ywPJfQLJN3X6hvkX0LIaSYQi5Dy1oeXLF3s57gVDJfRrZLBb6yu4QQQoguEzedFBRPm7+T8rZgUAB3bMKdHJvcM9TWKFUM28/wT3BRaS7daNIExb9MBy7f44rVdSMlebkpMw1oA/zlpv5N45s06fO8b6lBJ6kzJ+KSMpGaY3jyZ0jb2iYPiHWoVxVOItcd1VVHO4izBIWxE3PmKmemkMvQqQFfnXDeX2farNU+KeQyvNySf0XM8aQHNpNtwptVOKCln94+zt7KcylVDKv+5l/xJ5fRBvCEWIPWdfn283tcUIR4zoxfPw/TspAJIYTYr4IiFaIS+MaW1Gjzd1LeHB3kqOPJV/JcBRvdM9TGHLuewb2lApXm0o8mTVD8y1TIOZam60ZK6nJT5hrQBvjLTfEOPfq4l/68pM6c4B1QrFuNbxP3sijkMnRrJN5mofrqaPNOzBlbAs5c5cwAoF51V5PPURJt1mq/Ph/MvyIGAML/OCdRS8yLN6uwexP9/ay9lecS8nceAMK6NqAsE0KsgGdFvj7qn3/TUcCZ8UvXFYQQQozVN/JvQfF9mvvQ5u/EIrzZoS53rC3uGWprjgqY2OrRxIvuffWg3hlAjIAMEF03UlKXm+IdqBZjQJu33BQvfd87KTMnzFnODAACa/OtcuShr462lCXgzFnODACqGJkNowvtOWDfHB3kaM+5yhgA/jh9xzZSicXIPrSz8lyLoi5xx1KWibhWrFiBunXrwtnZGe3bt0dcXJze2MLCQsyZMwf169eHs7MzAgICEBUVZcbWEmtTrTLftVwe56yprrKyhBBCCI+8AiX+vc9XYhoovsxe9kYr6RpEiAAjg+tyx9ranqG2aBvnHqgAMDLIX8KWWDeaNAG4JzL0DdC28/eEh0sFrnMYs1l76zpV9E4+qMllxXGmKi43Jd4oWVA93ZMjkmZOmLGcGQDkPOEracZDXx1tKUvAmbOcGcA/wMEjoKY7zYjbuR/GduCOZQAio69K1xgzESP7kLfs1v5LaVxxlqygSIX42znc8ZRlIp7Nmzdj6tSpmDVrFk6fPo2AgACEhobi3j3dZStmzJiBb775BsuWLcPFixfx7rvv4uWXX8aZM2fM3HJiLXgXLvHq2pg2gSeEEGKcQSuOCIr/mjZ/JxbE0UGOBtUrccfbShUHW1RQpMLdbL6xZ4UctGCoDHY/aaJUMZy5wbcxpL4BWoVchpHBdbjOYUypk1M3HsDQ4mgVK44zlUIuQ9eG4pQlKCsLQMrMCXOWMwP4S5rx0FdHW8oScOYsZwaIO8BB+5kQRwc5WtZ0445ffuia1WebiJFNx5uZuD3+rtV/XsPXHuOOlYGyTMS0ZMkSvP322xg9ejSaNm2K1atXo2LFili3bp3O+B9++AHTp09Hnz59UK9ePYwfPx59+vTB4sWLzdxyYi3a+XuisrN4G7e3FjF7mBBCiP0oKFLhUloudzxt/k4s0az+zbhjbeE+0VaF/3GWO7ZVLQ+avC2D3U+aCNkcp6wB2nb+nDNzRvwu8g5qG5PFoouLI98m7YaUlQUgZeaEOcuZAeKWNNNXR1vKEnDmLmfWzt9TtGwmqjtOAGBarybcsSpmA9kmImTTtfP3hGclwxmSGY8KjCoraSkKilQ4nsy/oGBQqxp00SiSgoICnDp1Cj169NA8J5fL0aNHD8TGxuo8Jj8/H87O2n/vXFxccOSIsJWbxH4o5DIMChRv0MnTRvZxIoQQYl5CFukAtPk7sUzBDapxDxIXqRiOJWZI2h4inFLFsP3MXe7491+kBYNlsftJk+9jkrhjyxqgvZfDObHBGVeSuQe1a1RxEeU8fh76zyNl5oQ5y5kBxSXNxJgDKCstrp2/J9yd+SazUrPzhL2wmcuZKeQytKjpYfJ5aD8T8v/27j0u6ir/H/hrZpCbCoggFyUBLREvgJYIdtHEMM3u/rp5ycw2V83bZrKpaFvhWllWlt/cTLtttbt2d20JNStRSqPS1BUFMQVMSUhUkJn5/cEyiVzmnJnPZ+Yz83k9H4959GjmfGbODM6Zz+ec836/Gw2O74x2Er9mnh5tokQ0nclowC2Cu9uUWpB3B9kL2Jxb+6vUE/05ceIEzGYzIiIimtwfERGB8vLyFo/JzMzE8uXLceDAAVgsFuTm5mL9+vUoKytr9XVqa2tRXV3d5Eb60q2TMpGwgINpYYmISNdkN+kMig1h8XfSJJPRgBGJXYTbr8sXn08l15AJDDAagPRLxepN65WuR2qzxYrP97WcV/tiPsa2J2gra8QuskTbNeHiSe10wSLt9rS1+KJm5IQr05kBDT8sKQoswLQVFtfw4xXR4mMXk/035up0ZgAUKSjPeibUyGQ0YOo1PYTbe3q0iVLRdBmJkYq+ntbIXsCmxnXiBaybrVixApdeeikSEhLg6+uL6dOnY9KkSTAaW/+75OTkIDg42HaLiYlxYY9JC06dVa62XGh7+bSwRESkb7KbdN68P02lnhA5b0K6eFHwTft+8ejNiN7oqY17hdtm9O7COTU7dD07sP3QSZgtYm0Tozq2+Y/Jm2p0KBU50VZkjpqRE65OZwYoswhgLyxuyKViqahEo3gauTqdGaDMwhzrmdCFZo7oJZX9cNXWgx57gqdUNJ2ro/Jcbf4/xXO5AsAbkwer1BN9CgsLg8lkQkVFRZP7KyoqEBnZ8oJdeHg4PvjgA9TU1ODw4cPYt28fOnTogPj4+FZfJysrC1VVVbbbkSNHFH0fpH1K1paLDFYm2pqIiPSBm3TI28jMBzJFl7bU1VtQ+LN41P3ENPEFMr3S9Wj95/U/CLcdkxTd5uNq1ugoOVEj1E6pSW0lIifspU5SM3LC1enMAOcXAUTC4roI9le0XSN3TJwqsTDHeiZ0IZPRgBnDxKNN6syee4KnVDSdq6PyXMlssWJ9oXguV17AKs/X1xcDBw5EXl6e7T6LxYK8vDykpbW9w9Lf3x9du3ZFfX09/vWvf+Gmm25qta2fnx+CgoKa3EhflKotF+Tvo8gmGCIi0g+ZgssAN+mQ9pmMBtyU0vb854WYoks7ZMYje9mUqIFuZwj+8sluHK4Uj2CYaCdETa0aHWaLFX8vKLXbLirYX9ELPWefa1iC/TAvtSInXJ3ODGhYBDA5sQjQNzrIflic6PNL9sMdE6fOLsy1Vf+F9Es22mTZZ+Khq1ry+U8t14O4mL1oOndE5bnKitz9Uu15AauOOXPmYPXq1Vi3bh327t2LqVOnoqamBpMmTQIATJgwAVlZWbb2O3bswPr163Ho0CF8+eWXGDlyJCwWC+bNm+eut0AeYHB8ZwTIFLZqxS0pXZmigIiIhJktVqzfxU065H1ybk0SbssUXdogOx7dlBzN814BuhyxN/xwDK9+dVi4fffQALs/bmrV6CgorkR5tf20W3decYmi/+CdjZyYMDjWbhu1IifcUaPDZDSgT1fHd7fai2QCgOPVghOcgu1s7d00cerMwlzP8A4c4KkZ2WiT73+uRl29YI5GjTBbrHi/8KhQW3vRh2HtBaPyBNtphdlixcotB4Xb8wJWPXfccQeefvppLFq0CMnJySgsLMTGjRttxeFLS0ubFHk/d+4cFixYgMTERNxyyy3o2rUrvvrqK4SEhLjpHZAnMBkNGN0vyunnuSRUuYLyRETk/Vbk7pfah8lNOuQpfH2M6B4qlrKUKbq0QXY8yrm1v2p98Sa6myUwW6xSabkAYNzg7nbbDIoLRUhAO6Hnk5l8Fm0bG6bshZ4zkRPCYV4qRU64Op1ZozH9uzp8rL1IJkA8TdnXRXIp4NyRzgxwbmFuWAJTc1HLZKNNxr8qV7jR3QqKK1FZY7/ocef2vvYXJlUag91tW9EJmCXOGHkBq67p06fj8OHDqK2txY4dO5Cammp7bMuWLVi7dq3t/6+55hr89NNPOHfuHE6cOIHXX38d0dHi6QFIv9IUqJXmSN1BIiLSJ7PFipe/EN+kk9QtiJt0yKOME9gI3YgputzLbLHihc0cj9Sgu0+poLgSp86ZpY4RmdA2GQ2YmG5/cQWQ27Xrrgltk9GAmwSiH1oiGualRuSEu9KZAcDE9FiHjusRHig0YKmVAs4d6cyAhoU5R7NpXNWzi7KdIa9hMhpw6wDxsWtH8a8eFW0iupAuMg6fOC0YlSfYTisWf7RHuK3o+EtE2iZaW1Dt5yDvtHjxYhgMhia3hIQE2+Pnzp3DtGnT0LlzZ3To0AG33XYbKioqmjxHaWkpRo8ejcDAQHTp0gUPP/ww6uvrm7TZsmULBgwYAD8/P/Ts2bPJojIRacv2QydxXuISYl5mb/U644WWLl0Kg8GAWbNmubsruiUzv8UUXe4lG2XC8Uic7mYKZFMMyaTtGBQnWGNBZteumya0AWDp7eJ5DC8kGuYlGjkhUwjeXenMgIYQxt4RHaSPW3xDX6F2aqWAc0c6M6BhcnvqNeKplBr5tzOyngm1SSYHK+BZ0SaiC+TDe0fYfy4vTM9VV2/BQcFoQ0B8/CUibZOuf9fSc7RnpAm1rk+fPigrK7PdvvrqK9tjs2fPxscff4x//OMf+OKLL3Ds2DHceuuttsfNZjNGjx6Nuro6bNu2DevWrcPatWuxaNEiW5vi4mKMHj0aw4YNQ2FhIWbNmoX7778fn332mUvfJxGJeWqjeG1EXxMLLsv45ptv8H//93/o35/pg9yJKbo8g2yUCccjObpbNJFNySSTtkONyAl3TWgDDYPkyL5yO/qv7xMhvMgkmgZBJl2Cu9KZNVo/7Uqp9v4+RqRfKpZSQq0UcKLfCaXTmQENqZRkB6Gnbk9iPRNqk6+PEamxnYTbe1S0iZIL6V6Ynmv838QXwIwGCI+/RKRtsvXvWhIZLDYxQPrk4+ODyMhI2y0srOH3o6qqCq+++iqWL1+Oa6+9FgMHDsRrr72Gbdu2Yfv2ht+k//znP/jpp5/w5ptvIjk5Gddffz3+8pe/YOXKlaira9gctmrVKsTFxeGZZ55B7969MX36dNx+++149tln3faeiahldfUWFP5cLdz+wat78PpV0OnTp3HPPfdg9erV6NRJ/HqO1MEUXdonG2XC8UiO7hZNBsWFoksHsYnn+4bESqXtUCNywp0T2gCw8u7LhdsaDcCL9wwUbi+aBkEmXYK70pk1CvA1Ydhl4mm/lv+/ZOEBS60UcAO7d4K9LhgNDe2UZjIa8PydycLtr00IxxgH08aRvrxxv1ydite+PqRST5Sl5EK6aNqtvL0V9htpQF29BTtKfhVuf7NgKkki8gBOfpWF6kCRrh04cADR0dGIj4/HPffcg9LShnTAO3fuxPnz55GRkWFrm5CQgEsuuQT5+fkAgPz8fPTr1w8REb9HgWZmZqK6uhp79uyxtbnwORrbND4HEWlH1vrvhdsaAMwccZl6nfEy06ZNw+jRo5uNhy2pra1FdXV1kxspiym6tE02ysRo4HgkS9VFk8rKStxzzz0ICgpCSEgIJk+ejNOnT7d5zNChQ5vljH3wwQcV65PJaMBjN/ez26575wAsGtNH6rnViJxw54Q20PB5rRo3QKjtS/cMkJqAEk2l8POps8LP6c50Zo1euy8NQf4+dtv94eo4jOofJfXcaqSA23n4V9j7bbNYG9qp4YbkrhiRaD+i6ZJQf6y5d5AqfSDvIxttsuYrz9gZo+TCsOhi+4eFxzziBFjmAhYAlt7mWApKItIeZ2sv3chFVGpDamoq1q5di40bN+Lll19GcXExrrrqKvz2228oLy+Hr68vQkJCmhwTERGB8vJyAEB5eXmTBZPGxxsfa6tNdXU1zp5t+VqIE4ZErme2WPHhd8eE2986oCt/XwS988472LVrF3JycoTa5+TkIDg42HaLiYlRuYf6wxRd2iYbZTJ9aE+OR5JUXTS55557sGfPHuTm5uKTTz7B1q1b8cADD9g9bsqUKU1yxi5btkzRfo3sG4VV4wa0GkUyPCEMXzx8rfTzqhE54e4JbeD3z6u1oBsjgFXjBmBkX7kFANEaHR9JTNi5M53ZhX5YnIm+XYNafMwA4KW7U5A1KlH6edVIAff5T+VizylZD0jG6glXYMpVca0+PjwhDFvnDVft9ck7yUSbVPxW5xkpuhRcGB4UF4rQ9vYjL0/W1EnVSXIHs8WK9bvEL2Bl6pURkfY5G3HdLYSpuah1119/PcaOHYv+/fsjMzMTGzZswKlTp/Dee++5tV+cMCRyve2HTqJeYpZStN6r3h05cgQzZ87EW2+9BX9/sd/0rKwsVFVV2W5HjhxRuZf6xBRd2mS2WPE8o0xUZ387vIP27t2LjRs34ptvvsHllzekeHrhhRcwatQoPP3004iObj3FTmBgICIjI9XqGoCGhYC9j0Xiq/2/4JWvDqH63HkkdQvBo6MTEeBrcug51YicEJ2oVnNCG2j4vPY/Pgpb9x7H05/vx/HfahER5I85I3rhml7hDq1WNk7YVdacb7Nd44RdmkCxInenM7vQJzOuwulz9Zj1zi7srziN4IB2Tn1egPIp4MwWK94vPCrUVu3P7NHRiXg4MwGvfnUQ7393DOfNFqTHd8aCG/o4/J0kffP1MaJHWHvhwuDjX92Od/+QrnKvnKPkwrDJaMAtyV3x6tcldtuq/RvjLNldNjL1yohI+xrrvp062/Y5ZWtYBJ5khISE4LLLLkNRURFGjBiBuro6nDp1qkm0SUVFhe16NjIyEgUFBU2eo6KiwvZY438b77uwTVBQEAICWl7Uy8rKwpw5c2z/X11dzYUTIpW9vk18UjipWxA36QjauXMnjh8/jgEDfs9yYjabsXXrVrz44ouora2FydR0TsDPzw9+fuqkXaffTUyPxRMb9gq1bUzRxWgG9c14e6dUe0aZOEa1RZP8/HyEhITYFkwAICMjA0ajETt27MAtt9zS6rFvvfUW3nzzTURGRmLMmDFYuHAhAgOVL9xtMhpwTe8uuKa3XLHz1shETiwYnSj0D9bdNTouZDIaMKxPBIb1ibDfWPD5bkqKxmvbDtttW14lttDUmM6srcAUNdOZXayDvw/+pmBKKaVTwBUUV9pdtAJcl+vb18eIqUMvxdShl6r+WqQPi2/sg/FrCuw3xO8F4bV8caP0b8K1CRFCiyYydZJcTTaXKy9gibxPY923FXlFDh3fRfAcnghoKFR88OBBjB8/HgMHDkS7du2Ql5eH2267DQCwf/9+lJaWIi0tDQCQlpaGJ554AsePH0eXLg3Xnbm5uQgKCkJiYqKtzYYNG5q8Tm5uru05WsIJQyLXMlusyP3puHD7eZm9VeyNdxk+fDh+/PHHJvdNmjQJCQkJeOSRR5otmJDrNKboOlxpf06uMUXXkEvDXNAz/aqrt2DDbvG6o4wycZxqiybl5eW2k0Lbi/n4IDQ01Ja7tSV33303unfvjujoaPzwww945JFHsH//fqxfv77F9rW1tait/X1HrTtzuaoROaGFGh1q6tZJbDFMNHJCJp2Z0OevMUqngBPdPX4Tc32Th0rvGQYDxIdIzUebKP2bIPq11vDXXzbKhBewRN6poe6bY4smnnoeTa7xpz/9CWPGjEH37t1x7NgxZGdnw2Qy4a677kJwcDAmT56MOXPmIDQ0FEFBQZgxYwbS0tIweHBDVON1112HxMREjB8/HsuWLUN5eTkWLFiAadOm2RY9HnzwQbz44ouYN28e7rvvPmzatAnvvfcePv30U3e+da91ts6MhR9+j3//WIF6sxWRwf5YeEMfDEvowmueFpytM2PRRz9g4w9lOHPeCv92JgyKC8ULdw1AB4Fant5iW9EJiCbz9TEaMNgD5xvcpWPHjujbt2+T+9q3b4/OnTs3u59cb9xg8WiTdfnFXDRR2egVX0i1Z5SJ46R/4ebPn4+//vWvbbbZu1fsy9SSC2ue9OvXD1FRURg+fDgOHjyIHj16NGufk5ODJUuWOPx6SlIjckIrNTrUonTkhFbSmalF6RRwoim3RiSqmy6PSC0NKaiisb5QrN6F1qNNNu0T21Ei/NshWDzZ2SLLapGNMvE18QKWyFs5M0556nk0ucbPP/+Mu+66CydPnkR4eDiuvPJKbN++HeHh4QCAZ599FkajEbfddhtqa2uRmZmJl156yXa8yWTCJ598gqlTpyItLQ3t27fHxIkT8dhjj9naxMXF4dNPP8Xs2bOxYsUKdOvWDX/729+QmZnp8vfrzc7WmXHtM5tRVtX0O3+48izuf/1bAMC0q+MxZ2QCJ5jQ+udVU2fG5v2/oO/iz9ChnRHbHx2hi8WTJR/vEW7LTYfkTZiiSzs+KTyKA7+cEW7PKBPnSP+yzZ07F/fee2+bbeLj4xEZGYnjx5uGLtbX16OyslKqXklqaioAoKioqMVFE63lclU6cqJEMBe/K2p0qEHpyAktpTNTg9Ip4LSWzoxIDUtvTxJeNAG0G22iRg0i0bRbWk3PJRtl8uDVPXgCT+SlnBmnPPW8kFzjnXfeafNxf39/rFy5EitXrmy1Tffu3Zul37rY0KFD8d133znUR7Jv0msF2Lz/F7vtVm49hJVbD+HFO5NxQ3JXF/RMm+5bW4BN++x/XqfPW9B38WdIjAzEhlnDXNAz96irt6DoF7G5GYAF4JWwZcsWd3eB/ocpurTBbLFi+juFUscwysQ50ltpw8PDkZCQ0ObN19cXaWlpOHXqFHbu/L04zaZNm2CxWGwLISIKCwsBAFFRUS0+7ufnh6CgoCY3dxKNBBBpZ7ZY8feCUrvtooL9XVJvQg1Kfl4AvD6dWWMKOHsaU8DZI5POjMhT+foYkRorvvDXGG2iNarUIPLg9FxmixUvfyEeZcJdNkRezplxykPPC4lITN/sz4QWTC40/Z1CTF67Q6Ueadvlj+cKLZhc6KfyM7g0y3vTya3bViLctkd4oGaj1okcNW5wrHDbrw/KjR8kpv/ijVLtef3rPNVG8t69e2PkyJGYMmUKCgoK8PXXX2P69Om48847ER0dDQA4evQoEhISUFDQUKT34MGD+Mtf/oKdO3eipKQEH330ESZMmICrr74a/ft7xkp9F8HJfZF2BcWVKK+2ny7gzisu8diVQyU/L8D705k1poATIZICztvTmRE1euP+wVLts9b/oFJPHKdGDSJPTs+1/dBJnJdY2+IuGyLvxvRcRNSSS//8KU7X1jt0bN6+Exjz/FaFe6RtV+bk4sRpsSwPFztvBS77s3cunHz8vVi0NwAsvoE1OMj7TEyPFW4rsoGX5CQv2YiaOrmNnbz+dZ6qy99vvfUWEhISMHz4cIwaNQpXXnklXnnlFdvj58+fx/79+3HmTEM+Nl9fX3z++ee47rrrkJCQgLlz5+K2227Dxx9/rGY3laXgrl3RCbLYMLGUYJok+Hl9UyI26Hp7OjNA2RRw3p7OjKiRbLTJ+l1HYbYXhuViot/D4b0jxJ/Tg9NzPbVRvH4ad9kQeT9nxilPPi8kotb1XvCp1AaLlvx47DdMXvuNMh3SuPte246fqxxbMGlUZwEGPPYfhXqkDWaLFbuPVQu1NQJIZ1oi8kK+PkZEB4udaxUeqdLctbSnMlus6LNwA06dNUsdZzLy+lcJqi6ahIaG4u2338Zvv/2GqqoqrFmzBh06dLA9HhsbC6vViqFDhwIAYmJi8MUXX+DkyZM4d+4cDhw4gGXLlrk95ZaM49WCO/cF2ulhQlt0V+Da/BK7g64e0pkBCqc08/J0ZkQXkok2sQJYkftf9TrjCDW+rx6anquu3oLCn8UuXgHusiHSBQe/4lIpDYnIYwzJ+Q/OOhZg0kzevuP4+Hvx+nie6JPCo9i0/6Qiz1V55jwmvVagyHNpwfZDJ+2mtG7Ut2sQzznJa/XpGizUrrGuCTnn/V1H0ePPG1BzXn5C7rk7UjgWKYCJFhUmWuD966IT9hvpYEJbdGffqTPn7Yb46SGdGaBsSjNvT2dGdCFfHyOSu4kvwr+4pUhTO2TU+L56anqu8X/bLtzWAO6yIdIDR8cpmZSGROQZlnz8I45W2a8DJ2PG37/T1HmhkhwpLmzP5v2/eM1C0+vbioXbjhFMpU3kiQbFdhZu+/r2EvU64sXMFis276nApX/+FLPfK3ToOa5NCOdYpBAfd3fA24hGAXy+9zjMFmubF2l6mNAeFBeKkIB2OHXW/kmtvXRlukhnBii6M1wP0UzutHXrVjz11FPYuXMnysrK8P777+Pmm29utf2WLVswbNiwZveXlZUhMjJSxZ7qx8Mje+Oev4kV9bRYG6JN5mT2UrlXYtT4vnpieq66egt2lPwq3P7WAV05IUqkA46m2JJJaUhE2ldXb8FrX9vPPuCIjGe2YPPDzc/VPd3wpzep8rwP/f07jOoX5dHnYWaLFZ/vOy7cfmJ6nIq9IXKviemxeGKDWIrkzfvsz3nqwelz9Zjx1jfYVlSJWhesu4e3b4c19w5S/4V0gpEmCosMEoycOGs/ckIPE9omowET07sLtbU3aaeHzwtQNgWcHqKZ3KmmpgZJSUlYuXKl1HH79+9HWVmZ7dalSxeVeqg/g+M7o53EL5+mok2YnguAXJQJAOTc2l+lnhCRlgyKC0Vo+3byB2pkiCciZVy17HPVnrv45Bl8WCheENwTfLTrZ5RUil1fyrICmP7WTlWe21W2HzoJs2BdnO6hAfD14RQbeS9fHyN6hIttQq4z6zNFV129BS9s2o/kxRsQO/9T9F38GTYfcM2CCQBsf3SEa15IJziiK6wxckKE3cgInUxoD4oTDPGzN2mnk89LNAWcSDs9RDO50/XXX4/HH38ct9xyi9RxXbp0QWRkpO1mNHKoVorJaMDUa3oIt2+MNtECd6bnyttbIfycapKNMkmN68SLVyKdMBkNuCW5q/RxPMch8h4f7foZFdXKpuW62Ox3C7WzocZJZosVM9/7XtXX+PeeCtTVC646aJBMaq5xg8U2gxJ5spF9o4Tbfn3wFxV7oi119RaMXfU1LlvwbzzznyKcOuf634mX7h6g+8gepXEmQWFKRk7oZUJbqcgJvXxeoingfj511m6bkhM1Qs/laMoLckxycjKioqIwYsQIfP311222ra2tRXV1dZMbtW3miF5SgROrth7UxMWxGtF0ot/tDwuPaeIzWPu1+IUrALwxebBKPSEiLcpIlE9lyXMcIu9gtlgx+x/yCwAmyfZa2lDjrBW5+6X3EyaGy4+Z1yxTJ/2X2piai6i59B5hwm0/21OuYk+0Y8lHe3DZgn/jm5JTbuvDlKviMKq/+IIWieGiiQqUipwQvYjz9Is9pSIn9JKeSzQF3Ed2JjnNFiv+XmA/329UsD8GxYUK948cFxUVhVWrVuFf//oX/vWvfyEmJgZDhw7Frl27Wj0mJycHwcHBtltMTIwLe+yZTEYDZgwTjzbRTGixCtF0oulsTtbU2U0p6QqvfiW+aMIoEyL9Gdi9E2Q22BkNDccQkefbVnQCZskVgP8+fj0OLh2N3pHtpY57YbOG0rc6yGyx4oXNB6WO6RPdERvmDseLdyZLHVdWXeuRac2YmouoOZl01wd/OePRkWb2mC1W9F/8GV7bVuLWfky+MhaPjk50ax+8FUd1FSgVOSFy4ecNF3uikRN22+kkPZdSk5wFxZUor7YfdXPnFZcwxM9FevXqhT/84Q8YOHAg0tPTsWbNGqSnp+PZZ59t9ZisrCxUVVXZbkeOHHFhjz2XbLTJss/ECt6padM+sRRZMtF0Muls7KaUVFldvQUVv4m/N0aZEOnPzsO/QmYe02JtOIaIPN/ij/ZItX/hrhTbJPe/Zw1FWHsf4WOtAJ77z36p19Ma2SiTsEAffPrQ1QCAG5K74tpeghtF/2fue9973EITU3MRNWcyGjC8d4Rw+3US3yNPsuGHMvT48wZUn6t3az+mXBWLhTf0cWsfvBkXTVSgVOSEyIWfN1zsdRFcNLHXTi/puUxGA25KihZqW17Veoou0QnQ2DCxQl+kjkGDBqGoqKjVx/38/BAUFNTkRvbJRpt8/3O1W3fJmC1WvC+4Q082+vDaBLGTXnspJdUmUwC+R3ggd/sR6ZAji7vuXhAmIufV1VtwUDDtMACkxARjzEXXUzsevU7qNV/WSPpWRzgSZbJjQdPPZ82kwQj2F09uVm+xYtuBE1Kv6U5MzUXUuvFpscJtP/7+mHodcZMnPv0Jf3y79WwgrvLS3Sl4dDQXTNTEGQUVKBU58flPYvn/PP5iT3C79zclbaeG0Ut6LgDo1klsIaOthTk9fV6erLCwEFFRzE2pBtlok/n/UrdQZlsKiitRWWO/sGnn9r7y6fREPwQ3BpzJFoBffENfFXtDMlauXInY2Fj4+/sjNTUVBQUFbbZ/7rnn0KtXLwQEBCAmJgazZ8/GuXMefp5DLuNIylpPT3NLRHIbKwDgn1OHNLvPZDTgIYkNNfUWaCN9qwNko0weGtazxcwD3yyQW2ha/MluqfbuxNRcRK0bHN8ZJsF/8ruPVXvsAnNLnvh0D1Z/6d7ome6d/HDwyVEY1V9sMzU5jiO7CpSInFBzV7HWnDgtFvmxNr+k7cFWJ+m5AIUW5nT0ebnL6dOnUVhYiMLCQgBAcXExCgsLUVraUEsmKysLEyZMsLV/7rnn8OGHH6KoqAi7d+/GrFmzsGnTJkybNs0d3fd6JqMBtw4QP9F4/zv3FUMXXRy/KTlaOp2e6Bgs2k4NMpMhRgOQfql4gUJSz7vvvos5c+YgOzsbu3btQlJSEjIzM3H8eMs7N99++23Mnz8f2dnZ2Lt3L1599VW8++67+POf/+zinpOnGhQXipBA+ylMG3UKbMe6baRbZosV+QdP4sPCo8g/eNJjJ7VkN1bcmtL6udLMEb2kXlsL6VtlyUaZGA3AzBGXtfiYr48RI/t2EX4uT6pvwNRcRK0zGQ3ISBD77lus8Kgos7Zs+OEYVn9Z4tY+PP//kvDFIxlMoe8iXDRRgwKRE6ruKtYY0UWfU2fOt1mjQy/puQBlFub09Hm5y7fffouUlBSkpKQAAObMmYOUlBQsWrQIAFBWVmZbQAGAuro6zJ07F/369cM111yD77//Hp9//jmGDx/ulv7rQc6tScJtrQBW5P5Xvc60QTTiSya/rO25BdNuuSs9l+xkyM0OLByROpYvX44pU6Zg0qRJSExMxKpVqxAYGIg1a9a02H7btm0YMmQI7r77bsTGxuK6667DXXfdZTc6hchRnjlFTOS8jbvLMGRpHu5avR0z3ynEXau3Y8jSPGzcXeburkmTjTJZelvr534mowEzhnpO+lZHyEaZTB/acpRJo5V3Xy71+uNflft7uQNTcxHZN0Hi3/3zm9xzDa0ks8WKP779nVteu4OvEWsmXoGDT47CjQO6uaUPesVFExUoETmh5q5irRkUF4qQALFdgW19LiWCeWw9PTIHgCLpdJieS31Dhw6F1Wptdlu7di0AYO3atdiyZYut/bx581BUVISzZ8/i5MmT2Lx5M4YNG+aezuuEr48RPcLaC7d/6Ysi9+zEVDMyTOPpuZScDCHXqaurw86dO5GRkWG7z2g0IiMjA/n5+S0ek56ejp07d9oWSQ4dOoQNGzZg1KhRrb5ObW0tqqurm9xIvwqKK3HqjP1NR43sbcgh8kYbd5fhwTd3oby66TVreXUtHnxzl0ctnMhurEiN62Q3jdKs6+SiTTxhEaCR2WLFy18oE2XSSDat2Y7iXzW/0MTUXET2DY7vDNGpyJ2lpzw2mrHR8Kc3uey1TIaGaOg7r4jB3sdGYvdj1+Pa3l08fu7XE/m4uwPeSDZyIq1H52aPqbmrWGtMRgMmpnfHirzWi103am2ns9lixd8LSlt87EJRwf4eH5kDAMerxRbV2mzH9FxEAIDFN/bB+DViO9nrLQ3hxVf1Cle5V02pGRmm5fRcakyGkGucOHECZrMZERFNz1MiIiKwb9++Fo+5++67ceLECVx55ZWwWq2or6/Hgw8+2GZ6rpycHCxZskTRvpPnYiF4oraZLVbMX/9jm23mr/8RIxIjPWJyJmu9XL25NyYPttvGZDTgtgHR+NcuseLFjYsAnnD+sf3QSZyXWK+wF2XSaOaIXnheIuXX/H99j+V3pIh3xMXeyC8RbsvUXKRXJqMBl3cPQUHJKbttG1N0ufoaWikf7foZJZWOny/6GIGrLg3HC3cNQAd/TsN7Eu3/snsgRSIndDahPSiu+cJRi1o5Zysormy2W6old15xiUdcANjTVoH3C31d1HruSKbnImqQ3jMMJolhwR1FLNWMDNNyei41JkNIu7Zs2YInn3wSL730Enbt2oX169fj008/xV/+8pdWj8nKykJVVZXtduTIERf2mLSGheCJ2rb94Em70Vinzpz3iALnZosV7wsubAByGytk0rcCQNb6H6Tau8tTG8VrsIhEmTQyGQ24NVm8TuAHhe6rE2iP2WJF3t4K4fZMzUV6NuNasTEC8NwUXWaLFQ+9J3dN2uiK2GD89/HrUfTkaLw2aRAXTDwQF01U0Bg5IaK1SSi9TWg7GzkhukswNixQuE9aJloI/vO9x1s9IWV6LqIGJqMB0yTyV7uliKUG0nO1VYdLDWaLFetVmgwh9YWFhcFkMqGiounEQ0VFBSIjI1s8ZuHChRg/fjzuv/9+9OvXD7fccguefPJJ5OTkwGJp+Tvn5+eHoKCgJjfSL9lC8N4SgUwk6ssisToNnlDgfFvRCcicjclsrPD1MSI1tpNw+w81vAjQqK7egsKfxVNYikaZNFp6u/hCk5YLQ8tE4/QID+S5J+laes8w4UnlXUc8M0XX7S9/JX2MyQD89/Hr8Y8Hr+QY4eH411OJs5ETepvQFo2caK2d3j6vyCDBFHBn28jVrbNoJqK2zBzRS6pkh6vzVx8XTI0l2u5CStThUoNsoVJGmWiLr68vBg4ciLy8PNt9FosFeXl5SEtLa/GYM2fOwGhsempqMpkAAFYrf4xIeQtHJ3pFBDKRqB8FJ809ocD5ko/3CLdN6hYkPXH1xv3i5xX1Fqvmo3PWbSsRbmuAeJRJI9mFJndEbovYdlB8MSezT8ubQIj0wmQ04PLYEKG2Zgs0P05e7JPCo/juiFy9xGB/Iw7mjOZiiZfgX1ElTtec0NmEtmjkRKvtdPZ5KZECbtM+sbBjb4lmImqLyWjADA0XsawUXNgQbXch2TpcrmC2WPGCRG5sRyZDSH1z5szB6tWrsW7dOuzduxdTp05FTU0NJk2aBACYMGECsrKybO3HjBmDl19+Ge+88w6Ki4uRm5uLhQsXYsyYMbbFE6K2yBaC79TeV8XeEGlPoJ/4WKrllFN19RYU/VIj3H5eZm/p1/D1MSK5m3j0otajc97cXiLc9tYBXR1aUJZZaHJL5LaAjbvLhNsO6eGZ9RmIlCSTomtdfrGKPVGW2WLFQ+8USh3jZwK+X3y9Oh0it+AMg0qcjZzQW3quLoKLJq2109vn5WwKOLPFivcLjwodz1zfpBczR/SSau/KaJOffz0j1C7UgQlARepwKUw2ysSRyRBS3x133IGnn34aixYtQnJyMgoLC7Fx40ZbcfjS0lKUlf0+ObFgwQLMnTsXCxYsQGJiIiZPnozMzEz83//9n7veAnkY2TGKReBJbwbFCmZDgLZTTsnUPPM1GTC4h/j7vtDDI8XPL7QcnVNXb8HhyrPC7XNu7e/Q6/j6GNEzvL1w+9e+PuTQ66ilrt6Cg7+InXM78++KyJvIpOjatO8Xzf6uXGxF7n6pFJAA8OMSLph4Gy6aqMTZyAm9pZtyNqe+7j4vOJcCrqC4EpU19ndidm7vy1zfpBuyRSxdFW1itljx4fditT0igwOkn1+JOlxKko0y4UWrtk2fPh2HDx9GbW0tduzYgdTUVNtjW7Zswdq1a23/7+Pjg+zsbBQVFeHs2bMoLS3FypUrERIS4vqOk0eS3ejBjSGkNxPTY4XbajXllNlixYffidc8e/DqHg6n4Rsc3xk+EodqNTpHZpGpe2iAU9G72WP6CLdd85W2dp3LpDAbltCF6R2J0HAt2VcwKk+rvysXM1useF7iehQA7hsSy8wHXki1v+gTTzyB9PR0BAYGCl/sWq1WLFq0CFFRUQgICEBGRgYOHDigVhdV5WzkhN7STTmdU19nnxfgXAo40Z2VNyVH82SQdEWmiCXgmmgTVyxyOluHS0myUSbOTIYQkXcZFBeKqGCxhRAWgSc9kk05JVo43pW2HzqJesETBUdqc1zIZDTgphTxDTVajM6RXWQaN1hsI01r0nuGCbet+K1OU9E5H/8glokBACYMjlWvI0QeZkz/rsJtX5dIFegu097+Vqp9SIAPFkksGJPnUG3RpK6uDmPHjsXUqVOFj1m2bBmef/55rFq1Cjt27ED79u2RmZmJc+c8MHTeycgJvdWbcDanvt7ScwHOpYATjbgZ3jtCqk9Enk62iKUrok1cscgpunAt2s5RZosVL38hvqvHaHBuMoSIvIvJaMCNSVFCbW9MiuKCK+mSTMqpjwrF6zu4yuvbxKMTRiQ6Hw2Qc6v4hhot7qKWWWQCgInpcU69nslowBXdQ4TbayU6x2yxYvfPYgWffYyMcia6kEwU4+Z9xzW3uHyhunoLNu6W2zBQ8OgIlXpD7qbaosmSJUswe/Zs9OvXT6i91WrFc889hwULFuCmm25C//798frrr+PYsWP44IMP1OqmapyJnNBjvYlBcaEI9vcRalte1Twfqx7TczmVAk6HkTlEomSKWALqX+y5YpFTNO2W2um5th86ifMSa1DTh/bkpCcR2ZgtVnz0vdgk70ffl2n6op1ILYPjOwtPAhyrOqepSACzxYrcn8QnsyamObcAAMhH52it0LHMIlNStyBF0ss8NFx8Q4tWonO2FZ0Qrl9wbUI4zz+JLuDrY0SP8EChtnVm7S0uX2j83+QySYzqG8m0XF5MM3/Z4uJilJeXIyMjw3ZfcHAwUlNTkZ+f3+pxtbW1qK6ubnLTAmciJ/RYb8JkNGBEotiEX4sRFjpcBHAmBZweI3OIRMlGm6zfdVTdiz1XjG+i130qXx8+tXGvcFtGmRDRxQqKK1FWJRadV1Z1rsXoZSJvZzIacHlsiHB7rUQCAHIT20pGA8hE53y+Vzu7qM0WKz7fJ77INC9T/H22RaYwtFaic17Y9F/htkosxhF5m5F9xSJ9Ae2m6Kqrt2BHya/C7Q0AXrh7gHodIrfTzKJJeXk5ACAiounEeUREhO2xluTk5CA4ONh2i4mJUbWfopyJnNBrvYkhl4YLtWspckJv6cwAODXJqcfIHCIZMtEmVgArcsUvtGQdF4xcFG3X4rFO1EhSSl29BYWCaREARpkQUXOi59COtifyFjOu9bxIAABY8vEe4bZKXivLFIS3WIFtB04o8rrO2n7oJMyCq0xKLjI1bIjsItze3dE5ZosV3x4+JdTWZARTcxG1IL2HeD0jrabomv/P76XaP3dnMq9HvZzUosn8+fNhMBjavO3bt0+tvrYoKysLVVVVttuRI0dc+vqtcSZyQq/1JhyNnNBjOjPAyUlOHUbmEMmQTcXw4pYi1U78KgUXQ0TbtXisEzWSlCJzkupsYVci8k6y53nedF5IJMMTIwHq6i0o+qVGuH3Orf0Ve23ZgvDPS0QtqEkmNZfSGzInSNRGcXd0zvZDJyH68gNiQjhJStSCwfGd0U7wh0WLKbrMFivWFx4Tbh8V5Iebkruq2CPSAqlFk7lz52Lv3r1t3uLj4x3qSGRkJACgoqJpxEBFRYXtsZb4+fkhKCioyU0rHI6c0OuEtoORE3pMZwaIT15+XdR8pxPTcxHZJ5OKwWJVL9oktL2vou1aPFZw0frnU81rSilB9iT11gFdecFKRM0MigtFVLD9hRADgKhgf686LySS4WmRAACQtV58c0WP8EDFc8zLFITfWXrK7buoZeu/KLnIBHhWdM62g+Kv/ZBElBbJycnJwRVXXIGOHTuiS5cuuPnmm7F//353d4sEmYwGqY3dXx/8RcXeyFuRK/dv7Yt516rUE9ISqTOJ8PBwJCQktHnz9XVs0iYuLg6RkZHIy8uz3VddXY0dO3YgLS3Noed0N0cjJ/Q6oe1o5ES54HE3elk6M9FJzpZ27jA9F5F9MrtlAOClL9SJNukSJLYTWrRdSyIFj/1IpRQdsiepSl/YE5F3MBkNyB6TKLQPJ3tMoledFxLJkokE2LTvF7cuApgtVnz4nfjmisU39FW8D74+RvQMby/U1t2LAIBc/ZfuoQGKLzJ5UnROwSGxHe8+RiD9UvEURCTniy++wLRp07B9+3bk5ubi/PnzuO6661BTIx5hRu41Pi1WuK2W6sqZLVas3HJQuH1qXCcWf9cJ1f7KpaWlKCwsRGlpKcxmMwoLC1FYWIjTp0/b2iQkJOD9998HABgMBsyaNQuPP/44PvroI/z444+YMGECoqOjcfPNN6vVTXU5GDmh1wltRyMnRFPSdAsJkO6TlolOcp46e775D5Jeo5mIJJiMBky9podw+3qLOhfIBcWCoctOfF8HxYUitH07u+1O1tQpfoIre5Kqxu5RIvIeI/tG4eVxA1qNOIkK9sfL4wZIFSwl8kYykQDuTtG1/dBJ1Aue5xgN6k1sZ4/pI9x28Se7VemDKJn6L+MGd1elD54QnWO2WLGz9JRQ257hHbjYrqKNGzfi3nvvRZ8+fZCUlIS1a9eitLQUO3fudHfXSNDg+M4wCX5FdmkgIq/RtqITMEt05Y3J4vVPybOpNuuwaNEipKSkIDs7G6dPn0ZKSgpSUlLw7bff2trs378fVVVVtv+fN28eZsyYgQceeABXXHEFTp8+jY0bN8Lf3zPzDTtcc0KnE9qORk64InWNFg2KC0VIgP1JTqB5odNN+ypaadmUt0UzEcmaOaKX8Po3AKxQeJec2WLFum2Hhdo68301GQ24KUlsN2B5lbIpumRPUtXYPUpE3mVk3ygsHJ2IDn4+zR47U1fvhh4RaY9sJIA7U3R9LbEpJaN3F9UmtmVqwRz85Qzq6kVjPZQlW/9lokTUkQxPiM6RqWcSExqobmeoica5wtDQllNp1tbWorq6usmN3MtkNKBPV7GSCVqIyGsks8jMDXz6otpfeu3atbBarc1uQ4cOtbWxWq249957bf9vMBjw2GOPoby8HOfOncPnn3+Oyy7z3JyRjhbW1Wt6LkcjJ1yRukaLTEYDJqaL7QoKa//7gpTZYsX7hUeFjmOBVNI7k9GAGcPEo02+PazsjpmC4kqcOmu/ZhPg/Pe1WyexC0Gli8HLnKQyLQIRidi4uwx/fHsXTtc2XyCpOluPB9/chY27y9zQMyL7Vq5cidjYWPj7+yM1NRUFBQWqvZZMJIA7i3Vv2lcu3HZimjoLAEDDeeHlsSHC7ddJFGJXkrvrv1xI69E5b+SXCLdlHSzXsVgsmDVrFoYMGYK+fVveMJWTk4Pg4GDbLSYmxsW9pJaM6S9eHN2dafkayS4ycwOfvnB5TEUhgWKRDRe302t6LocjJ3QamQMAg+I6izW8YLNVQXElKmvsT8J2bu/LE0MiNESbyFCyILxozaaQgHZOf18d/c1yhuxJ6h+v6cm0CETUJrPFisUf2V+MXfLxT5pJC0HU6N1338WcOXOQnZ2NXbt2ISkpCZmZmTh+XLygtwxfHyO6h4qlMHbXrmCzxYr9FWLnCiYjMLiH4PWRg2ZIFAL/+HvxOixK0UL9lwtpOTrHbLEib69YBgZAvYgcam7atGnYvXs33nnnnVbbZGVloaqqynY7cuSIC3tIrZmYHivc1l1p+S609mvxxW1u4NMfLpqo6NQZsd24F7dzRf56LXI0ckKvkTmAYyngLk7V1ZqbkqM5OUmEhrHp1mTx9BUvblGuILxozSYlUlE4+pvljPF/2y7c1mgAZo7w3OhTInKNguJKlFfbHzvLqs5pqggpEQAsX74cU6ZMwaRJk5CYmIhVq1YhMDAQa9asUe01xw2OFW7rjkiAbUUnhC97B8SEqH79kt4zTDh1655j1S6fENRK/ZdGstE5Wet/UK8zF9l+6CTOC67RMCWP60yfPh2ffPIJNm/ejG7durXazs/PD0FBQU1u5H6ekJbvQq9+Jb5owg18+sNRX0WiNTp+PvV7fnhX5a/XKkciJ/QamQM4lgJO9HMY3jvCoT4ReaOlt4unr7BYlYs2Ea3FNKSn8xe8or9Zou3sqau3YEfJr8Ltpw/lSSoR2Se6OUS2LZHa6urqsHPnTmRkZNjuMxqNyMjIQH5+vmqvK7Mr2B11OmTSeD4kEQXiKJPRgH6COfvNVmD7QcENkQqRSTelZv2XC8lE53xYeMxlC00yn1Vmn0j1OkIAGtL3T58+He+//z42bdqEuDhG9ngqrafla1RXb0HFb+LzqtzApz9cNFGRaI2Ojy44MXBl/notciRyQs/puRxKp6Pjz4vIUTI7ZgDlok1cWbOpi+BiiGg7e2SiTAzgSSoRiZE5P/bGc2nyXCdOnIDZbEZERNONSxERESgvb17TQ6kiyLLnOK6s0yGTxtMVURONxiSJ5+xfl++6z0s63ZSK9V8uJJOiq95idclCk+xnNaRHuIq9IaAhJdebb76Jt99+Gx07dkR5eTnKy8tx9uxZ+weTpmg5Ld+FZOo/Xd5d/UhG0h4umqhoUFwoQtvbr9FxsqbOlh5AdMdbSKDz+eu1yJHIiU37xE52vDEyRzRNTv7B30Mejwum+xFtR6QXMjtmFIs2ceUip+A54DclzqezkY0yuSWF6QKJSMyguFBEBtlf3I0K9vfKc2nSDyWLIMuc47y5XSwrghLWbSsRbjvwEtdNaMlE53y+97jLIidk0k35mgyq139pZDIaMCKxi3D717eXqNeZ/9HqZ6VnL7/8MqqqqjB06FBERUXZbu+++667u0aStJyWr5Fs/aeZLohkJO3hoomKTEYDbkoSy4NfXtWwei6aOunetFivnECSjZwwW6x4v/Co0DHeuJtQNE3OhSfrojUSRNsR6UV6zzCYJIbdl75wPtrElYucJwSfY21+idPvSybKBACW3iaeHo2I9M1kNGDxjfYngLPHJHrluTR5rrCwMJhMJlRUNN0QVlFRgcjI5qmBlCyCLLMr+HDlWZftCn5TYvLcFam5Gvn6GNE9NECorStz9r8uEQU0LME1qbkaTZAoor55n/oLTTKpuVz9WemV1Wpt8Xbvvfe6u2vkAK2m5WuktfpPpE1cNFFZt06BQu1skROCX9orYr1zZ5xs5ERBcSUqa+ynM+vc3tcrdxOKpoA7dfa8LZpJtEaCaDsivTAZDZg2tIdw+3qL8xfJXx/4RaidEoucogvLp86cd6p4smyUSWpcJxbeJCIpI/tGYdW4AQgJbB7x3SmwHVaNG4CRfaPc0DOi1vn6+mLgwIHIy8uz3WexWJCXl4e0tLRm7ZUsgiwbCeCKXcF19RYcrhRLy+OOCa1xg2OF27oiZ7/ZYsXn+44Lt58g0X8lDI7vjHaCp3N1ZnVTdMmm5nL1Z0XkDbSYlu9CMovMrqr/RNrDWQiVyUZO6D11kmzkRLlgDZQbk70ztcuguFCEBNhPAQf8nvrNlTUSiLzNzBG9RLNYAXDuItlssSL3J7ELOiUWOQfFhSLY30eobWN0pCNkcscCwBuTBzv8WkSkXyP7RmHnghF4a3Iqpg/rgenDeuKt+1Px7YIRXDAhzZozZw5Wr16NdevWYe/evZg6dSpqamowadIk1V9bJhLAFbuCZc4X3DGhJZOiyxU5+7cfOgmz4Ev4GF2fbspkNGB47wj7Df9HzVowTM1FpD7ZxXhX13/K/Ul8kdlV9Z9Ie7hoojLRyInGdnpPnSQbOSH6OXQLEQuf9jQmowET07sLtQ1r37AgVVAsuILPQvBEzZiMBswYJh5t4sxFckFxJarO1Qu1jQx2foxrOLEVu5gVrT91MbPFivW7xHPHMsqEiJxhMhow5NIw/CkzAX/K7IUhPcO8chMNeY877rgDTz/9NBYtWoTk5GQUFhZi48aNzYrDq2FwfGeYBH9y1d4VLJtr3h0TWr4+RvQMby/cfp3ErmZHyOyavslNGwrHp8UKt1WzFgxTcxG5hsxivCvrP20rOgHRK3R3LDKTdnAmQmWykSZ6T50ku9NZ9vP1RoPiBAdwQ8MFyLptYsUbT9R458IckbNko03GvypXv6ORaCRdSEA7xdIPpvUQS23h6Ji6Ine/1Hoso0yIyBlmixVfHziBpz/bh6c/24+vi064PGc2kazp06fj8OHDqK2txY4dO5CamuqS1zUZDchI0MauYJlc8+6c0MoeY79+UqM3t4tdgzlCdtd0zq39VetLWwbHd4aP4Em0WrVgmJqLyHW08J1vyZKP9wi3ddciM2kDF01UJlujQ++pk2R3OstG8nij44ITq8erz6GguBKnztqvAQOI1zcg0hvZaJMdxb86FG0iGkmnZEoKNcdUs8WKlVsOCrdP6hbEKBMictjG3WUY+Hgu7nl1B17cfBAvbi7CPX/bgYGP52Lj7jJ3d49Ik7SyK1gmauLahHC3TWjJ5Ow/XHlWtRRdMrumu4cGuO38ymQ04KaUaOH2atSCYWouItfRwnf+YnX1FhT9UiPc3l2LzKQNnI1QmWyNDqZOktvprPfIHEA8TU5lTZ1bdq7r2datWzFmzBhER0fDYDDggw8+sHvMli1bMGDAAPj5+aFnz55Yu3at6v0keTNH9JJq70i0iWg0h+iYqeRrOhJpsq3oBMwSv13zMntLvwZpz8qVKxEbGwt/f3+kpqaioKCg1bZDhw6FwWBodhs9erQLe0zeYOPuMjz45i6cOtN8o8ipM+fx4Ju7uHBC1AIt7Ar2pFzzsjn7s9b/oEo/Xtj0X+G24waLpXZWS86tScJt1agFw9RcRK7l7u/8xWTqZfUID+QmPp3jX19lMjU6th88ydRJkNvprPfIHEBuktMdO9f1rKamBklJSVi5cqVQ++LiYowePRrDhg1DYWEhZs2ahfvvvx+fffaZyj0lWSajAbcmi++acSTaxB2RdLLRkTIWfyQeBs2dfd7h3XffxZw5c5CdnY1du3YhKSkJmZmZOH685cmw9evXo6yszHbbvXs3TCYTxo4d6+KekyczW6xC482Sj39iqi6ii2hhV7Cn5ZqXic75sPCY4uOO2WLFtyWnhNtPlOivGtxZC4apuYhcT/Y7P/9f4osasmTrZS2+oa9qfSHPwEUTlcnU6Nh28ARTJ0Fyp7PoOacXXxPLTHKKRtwM6ancznU9u/766/H444/jlltuEWq/atUqxMXF4ZlnnkHv3r0xffp03H777Xj22WdV7ik5Yunt4rtmAPlok9JKsbBhJWs2iUZHbthdLnXRX1dvwcET4mHQD17dgwu3XmD58uWYMmUKJk2ahMTERKxatQqBgYFYs2ZNi+1DQ0MRGRlpu+Xm5iIwMJCLJiSloLgS5dX2N4mUVTWkLSWipty9K9jTcs0Pju8Mk+CsSr3Fiu0HBTNLCNp+6KTwIlN0sL8mdk3L1IL5+HvxCU57mJqLyD1kvvPvf6f84nIjmXpZRgOQfinnxfTO/b+YXk6mRsfRU2eF2nl76iSZRYDjgpETou08kUwKuDDBtt4cmaNl+fn5yMjIaHJfZmYm8vPzWz2mtrYW1dXVTW7kGr4+RqTGdhJuLxNtYrZYsf67o0JtlYw0EY2OPFNnlrron/9P8R1DBgAzR1wm3J60qa6uDjt37mwyphmNRmRkZLQ5pl3o1VdfxZ133on27cV3pxEd/00sFalsWyK9cGckgCfmmjcZDchIEE/RtS5fuc8LkKv/cmNylKKv7aj0nmEQXerafbRasQlUmc+KqbmIlCPznbcCWJErnnJQhkx6PmZfIYCLJi4hmm9e9FzA27+8MosAXx34RaitaFoqTySTAm5fueCEuhdH5mhZeXk5IiKaLrJGRESguroaZ8+2vKiak5OD4OBg2y0mJsYVXaX/eeP+wVLtRXNZFxRX4rdzZqG2StZsGhQXiva+JqG2+YfEUnSZLVasLxTfJXhLivt3jZLzTpw4AbPZ3OKYVl5ebvf4goIC7N69G/fff3+b7bhwTBeTicb25shtIme4KxLAU3PNy6Toyv3puGKLALL1X67qKb64oyaT0YB+XYOE2lqgTO0cs8WKz/eJf1ZMzUWkHJPRgFskUlu/uKVIlVSGMun53Fkvi7RDG2cZXk50F7BRcKba21MnySwC/FuwiKc3F4KXSQFXIpgex5sjc7xNVlYWqqqqbLcjR464u0u6Ihtt8v53R4VOAMurxXc/RwYHCLe1x2Q04CrBMGTR89gVuful+rD0Nrm0Z+SdXn31VfTr1w+DBg1qsx0Xjulig+JCERlkfwNOVLC/V0duEznDHZEAnpxrfnB8Z/gIfmBK7qL2tPovFxqT1FW4rRK1c7YfOgmz4Ieltc+KyBvIpLa2WJWPNmF6PnIEF01cQDTfvOjEl7enTpJZBDhTJzbqKTmpqDUyKeB+PFol1M6bI3O0LDIyEhUVTXc/VFRUICgoCAEBLf8b9vPzQ1BQUJMbuZZMtInFKrZbTvQ7GOTvo/ik38DuYs/XSeC3zWyx4oXNB4VfOzWuk2Z2jZJzwsLCYDKZWhzTIiMj2zy2pqYG77zzDiZPnmz3dbhwTBczGQ1YfKP9XfLZYxIZ1UbUCndEAnhyrnmT0YCbUsR3Ua/aelCRhSZPq/9yoYnpscJtlaid89TGvcJttfZZEXkD2dSPSo2TjWRSczE9HzXizIQLiEaa7DkmNqHt7amTZBYBRHh7DRhAPAXcfytOC7Xz5sgcLUtLS0NeXl6T+3Jzc5GWluamHpEIXx8jkruJL1aJ7JYTXWy/JaWr4id0ot//n389Y7fNitz9Uj9Zb0yWS3dG2uXr64uBAwc2GdMsFgvy8vLsjmn/+Mc/UFtbi3Hjxtl9HS4cU0tG9o3CqnEDEBLYrtljnQLbYdW4ARjZVxu5/Ym0ytWRADKT2lpMV51zq/gu6jqz8wXhPbH+y4VkJ1Dn/0s8ddvF6uotKPxZPH2n1j4rIm8hk/pRiXGykWxqLqbno0aqLZo88cQTSE9PR2BgIEJCQoSOuffee2EwGJrcRo4cqVYXXUa0Rsc2wQFBD6mTRBcBRGjxpFpplTVi/ybOCe7Q8ebIHFc6ffo0CgsLUVhYCAAoLi5GYWEhSktLATTskJ4wYYKt/YMPPohDhw5h3rx52LdvH1566SW89957mD17tju6TxIeHtlbuK3IbrnKGrHF9m6dAoVfV5ToQr+9VGOyUSZayk1OypgzZw5Wr16NdevWYe/evZg6dSpqamowadIkAMCECROQlZXV7LhXX30VN998Mzp3Zlg8OW5k3yjsXDACb01OxfRhPTB9WE+8dX8qvl0wggsmRAJcGQkgO6mtxVzzsosAzhaEn/9Pz6z/ciGZCdT3vzvm8K5zmVo53UMDNPlZEXmD9J5hMElMzTk7TjZiai5ylGq/BnV1dRg7diymTp0qddzIkSNRVlZmu/39739XqYeuI1qjwyx4DqCH1Emik3YilFyA0apTZ88r9lx6iMxxlW+//RYpKSlISUkB0DCBmJKSgkWLFgEAysrKbAsoABAXF4dPP/0Uubm5SEpKwjPPPIO//e1vyMzMdEv/Sdzg+M5SJ4DjX93e5uM7D1cKPc+vCo6VjUQX+qvP1aOguPV+ykaZaCk3OSnjjjvuwNNPP41FixYhOTkZhYWF2Lhxo604fGlpKcrKmtYm279/P7766iuh1FxE9piMBgy5NAx/ykzAnzJ7YUjPMK/fSEOkFFdGAqzbViLcVsv1JmQWAZwpCG+2WLG+0DPrv1xIpnaOo7VgZGvljBvcXfo1iEiMyWjAtKE9hNv/x4lx8kJMzUWOEisc4YAlS5YAANauXSt1nJ+fn91c155mUFwo2vuaUFNnVuT59JA6STQ1jQglF2C0yiB8ummfHiJzXGXo0KGwWlv/kW9pfBw6dCi+++47FXtFajAZDbgpKVr4AnZH8a+oq7e0uJPNbLFi8/5fhJ5Hja+q6EI/AJRXnW3xftkoEx+jtnKTk3KmT5+O6dOnt/jYli1bmt3Xq1evNsdNIiJynewxfTB+TYFQ2/e/O4anxiY7dB3x5vYS4bZarjeR3jMMRkCoOHvjIsCczF7Sr7Mid79wW63Vf7mQyWjALcni58+rth7EzBGXSf39ZWrlAMDEdO1FMRF5k5kjeuF5ievE6W/txMvjL3f49Ziai5yhubjDLVu2oEuXLujVqxemTp2KkyfbTllVW1uL6urqJjetMRkNuErBExU9pE5ScqFDyQUYrUpTcLeVHiJziNSw9HbxXNZA69Em2w+dRK1giou0eOW/r4PiQtHBT+z04EQrkY+yUSZ/vKanZidAiIiI9MoVkQB19RYcrmx5E0ZLtFxvwmQ04JYB4gXhX/qiSHoXtdlixcot4hOON2t4kQmQO392pMbB69vE0/skdQtiai4ilZmMBgyKDRFu/+89FU6lf2RqLnKGpn4RRo4ciddffx15eXn461//ii+++ALXX389zObWIzRycnIQHBxsu8XExLiwx+IGdlcm3VGQv48uUieJpocRoYdIk8HxnRHQTpmvsx4+LyI1+PoYkRrbSbh9Y7TJxbYdPCF0vL+PUZWTOpPRgCGCi6eVLYwXslEmRgMwc8Rlwu2JiIjINRojAUS9uEV+EcDb6k3IFISvtwDbDoid9zXaVnRCOK03ACy9TW5Tj6vJpoFb9tle4bZmixWf/XRcuP28TPEahUTkuBnXyl37jX5+q8OvJXptDTA1FzUndcYxf/78ZoXaL77t27fP4c7ceeeduPHGG9GvXz/cfPPN+OSTT/DNN9+0mL6hUVZWFqqqqmy3I0eOOPz6alIqpdYtKV118SWWSQ9jjx7SmZmMBozqq0xaOz1E5hCp5Y37B0u1byna5OivYrst+3cLVu33IMBXLHvnzpJfm90nG2UyfSijTIiIiLRKJhLAYpWLNjFbrFi/y7vqTcguAqzYJBeds+TjPcJttVoA/mIytWC+/7laeNe5TBozkxHcYU7kIrIF4Q8cr8HH34v/Vlxo4+4y+43+h6m56GJSv6Bz587F3r1727zFx8cr1rn4+HiEhYWhqKio1TZ+fn4ICgpqctMipXbvXxIaqMjzaJ1Mehh79JDODAAiQ5R5n4w0IXKcEtEmUSFii8YDJV5HVtdOYuPJDz9XNdlRyigTIiIi7yK7CCATbSK70cJT6k3ILAJ8e/iU8OdVV29B0S81ws+t1QLwF2usBSOqtRS3F5I9Jx0QE8JNPEQuYjIa8OxYuSi4h/7+nXQkY129BQd/OSPUlqm5qCVSs9Lh4eFISEho8+brq9wu9Z9//hknT55EVFSUYs/pLkqlm9JLFIDJaMCVPcOdfp4OfvpIZwYAStXN1UNkDpGanI02qWiluPrFQgLU+66mC6bnOldvaZJbmlEmRERE3kdmEUA02kR2UtuT6k3I1IIBxKNzRq/4Qvg5fYzaLQB/MdlaMK2luL2Q7DnpQ5LpgojIOTcO6IaIoHbC7a0Axr68Teo11m0rEW7L1FzUEtXOOkpLS1FYWIjS0lKYzWYUFhaisLAQp0+ftrVJSEjA+++/DwA4ffo0Hn74YWzfvh0lJSXIy8vDTTfdhJ49eyIzM1OtbrqMUumm9BQFEB/ewennuPLSzroZ+DoptKCml8gcIrU4E21itljxyQ9iIcRVZ8871D8Rg+M7w1cwZvrrg78AYJQJERGRt5JNpSISbSI7qe1J9SZka8E8v9n+5/VJ4VEcENwxDQB/vMazNqbI1IIBgGuWbWr1MdlzUk9aYCLyJl/Oy5Bqv+vIKak0XR//cFS4LVNzUUtUWzRZtGgRUlJSkJ2djdOnTyMlJQUpKSn49ttvbW3279+PqqoqAIDJZMIPP/yAG2+8EZdddhkmT56MgQMH4ssvv4Sfn3JFwd1FqXRTeooCCA4QX3VuTUqMeulrtCaso/PfkyB//UTmEKnJ0WiT7YdOos4sdoya18EmowFJ3YKF2n5TXAmAUSZERETeymQ0YNrQHsLt7UWbmC1WPC8xqe2JaVNkasEAwPS3drb6mNlixUPvFAo/lwGetzFFdtNRWXUtPixseUJU9pzU0xaYiLyF7PceAGYIpukyW6zYc7Ra6Dl9jJ73G0Ouodqiydq1a2G1Wpvdhg4damtjtVpx7733AgACAgLw2Wef4fjx46irq0NJSQleeeUVREREqNVFlzIZDRgimO6kLXqKAqg+5/wuajV3YmuNEtFMaT30E5lDpCZHo022HTwhfExavLo74rp2Equh9f3PVairtzDKhIiIyIvNHNFLKuVUW9ETt7/8ldRrP3h1D4+7RpGtBfPvPRWtppxakbsfYqXPG9ySEu1xnxcgv+lo9ruFzf6NyS7I8ZyUyL1kv/cA0HvBBrttth86CbPg6mlyTLBHjpmkPs9ICuolAnx9nDpeb1EABqnT8pbpadwbFBeKQF/nvtIB7UwK9YaIZE8Ar1m2CQX/i9qwxxU7LkWLwdeZrZj3z+8ZZUJEROTFTEYDZgwTjzYBWs4//0nhUXx3RGz3L+DZk9oytWAA4IrH/9PsPtlFAABYeptclItWyG46aimiafhTraftagnPSYncy9fHiFF95TbL11mA5MUb22wjsxlRT/OsJIeLJi4kOgHVGr1FAaQpMCGo9k5sLTEZDRjZx7nIrOgQZWrvEJFjaQa+LflVqG1SN/V3w4gWgweADwrFc8t68uQHERGRnslGm1ycf95ssWK6RJopwLMntWVrwVSdM+O+tQVN7pONykmN6wRfH8+d5pHddPT85iJbhM6kNTtQ8us54WN5TkqkDS/cPVB6y/Spc2b0Wbih1YjGjbvF6oQCwJAe4ZKvTnrhub+mHkhmAqoleosCGBzfGX4+jp8g+/sYdZeXMCpELJ1Oa/hjQaQs2Qs/0WiNK1ywG2ZwfGc4MQS3ypMnP4iIiPTMkWiTC/PPpz6RK3Wsp09qm4wGPDtWLupj075f8OF3DbU6PtolF5UDAG9Mlk91oyW+PkaM7NtF6pjLFvwbI5dvxub/iu8sB3hOSqQVJqMBL9yZLH1czXkrevx5A57asLfJ4kldvQUHfzkj9ByeWDOLXIeLJi40OL4zfJ1Y99BbFIDJaMDQyxyfxO/vgp3YWuNMSjM9LjIRqc2R4nYiXLHAaTIakNJd2b57YmFSIiIi+p1stAkA9PrzBgxYshEnauTqTT57R7LHX8/dOKAbIoLaSR0z891CzP9nIR56r1DquFF9Iz06yqTRyrsvlz5m33GxCdJGnr4gR+Rtbkjuimt7OTYftXLrIfT48wb0enQDJr1WgFe2iqc0HJbQxeN/Z0g9nv+L6kFMRgOSYhyfgNJjFIAzdWCigvW1yAQ4l9JMj4tMRK7gSHG7tpiMcNkCp9L5XT21MCkReaa6egte/fIQFn24G69+eajVIstEJM6RaJN6AJVnzVLHRAX54abkrlLHaNWX8zKkj3nn26PSx7xw9wDpY7TIZDTg1uRoVV/DGxbkvNXKlSsRGxsLf39/pKamoqCgwP5B5BXWTBqMrpKLzBeqNVuxef8vePo//7Xf+H8mDI51+PXI+3HRxMU6Bfo6dJxeQ8acqQPjbA0ZTzQ4vrNU3twL6XGRicgVHEkz0JYBMSEuu8hzNq3kxTy1MCkReZ6cDT8hYeG/8ZdP9+L1/MP4y6d7kbDw38jZ8JO7u0bk8WaO6KX6RMIX865V+RVcR+lzwZbMGNrDqxYBlt6u3jmjNy3IeZt3330Xc+bMQXZ2Nnbt2oWkpCRkZmbi+PHj7u4aucjXf74O7Vw0U+1j1Oc8K4njoomLXRHr2K7d+LD2XnUSJMqZCTs9RuaYjAb06Rrk0LF6XGQichVH0gy05qFrXZdKYHB8Z8VOFLwlZQQRaV/Ohp/wf1uLcXFtUIsV+L+txVw4IXKSyWjA8w7knxfljecMSp4LXsxoAGZd10u153cHXx8jJg25RJXn9qYFOW+zfPlyTJkyBZMmTUJiYiJWrVqFwMBArFmzxt1dIxfa89j1LnmdaxPCdTnPSuK860zEA0xMj3XouJhQ5wp8e6rB8Z1hcuBfqSvT12jNmP6O7ZrR4yITkauYjAY8//+c3zFnNADplyob/dEWk9GAy2NDnH4eo8F7UkYQkbbV1Vuw+sviNtus/rKYqbqInHRDclekxDi2WastJi89ZzAZDXhRpYWmFXemeOXEX/aYfuga7HiqnpZMvjLO6xbkvEVdXR127tyJjIzf09kZjUZkZGQgPz/fjT0jV/P1MWLKVbGqv87EtDjVX4M8G38tXMzXx4ie4e2lj1M6r7ynMBkNyEiQD2V2ZfoarXFkYc5k0O8iE5Gr3DigGyI6Onfhd3Oy62uCzFAgssVbL+aJSHveyC9pFmFyMYu1oR0ROeefU69U/DlfuGuA154zOFPouDXXJoRjTJK69T/c6eus6xDgeJnTJvp2DcLCGxKVeTJS3IkTJ2A2mxEREdHk/oiICJSXlzdrX1tbi+rq6iY38h6Pju6DjN7qbezV80ZrEsdFEzfIHtNH+piJ6fpdAZ3gwHt3ZfoarfH1MaJ3ZAepY9J7dvbaixMiLfnyEflCoBdyR02Q9J5h8HFiePD2i3ki0pbDlWcUbUdErTMZDXjp7hTFnm/ylXEY1T9KsefTImcLHV+oW4g/1tw7SJHn0rK9j492usZBtxA/fDLjKmU6RJqQk5OD4OBg2y0mJsbdXSKF/W3iIEy+Up25UD1vtCZxXDRxg/SeYVLFulPjOuk6hHRwfGf4SXxg7UwGl6av0aL1f5Tb9fXK+CtU6gkRXciZ/Mz3DYl1y2+ByWjAc3ckO3SsXi7miUg7ugumtBVtR0RtG9U/WpE0Ktf2CtdNFMDXf74Oge2cm6wL9DXiq/nDFeqR9h14cjTa+5ocOjYmxA9fzXdu4xKpLywsDCaTCRUVFU3ur6ioQGRkZLP2WVlZqKqqst2OHDniqq6SCy28IREvqZCyUc8brUmcfmfi3chkNODZseK7hd+YPFjF3mifyWjAMxKf1/L/l6z7FeMAXxOGXSaW0m1Yr3AEOHgCSkTyssf0wyWh/lLHxIQGYJEDUYpKuSG5K4YnyC1Gt9fZxTwRacP4tFjYOw00GhraEZEyHh3dB1Oucnw3cL+uHbFmkr42Wfz0l1Fo7+fYNVj7dgb85KJCyVqy57GR6BstV0fn2l6d8SUXTDyCr68vBg4ciLy8PNt9FosFeXl5SEtLa9bez88PQUFBTW7knUb1j8LBJ0chuVuwIs8X6GvS/UZrEsNFEze5cUA39O9mf1D/w9UsVAY0TNiNSLRf22REYhemgfmf1+5LQ5B/2wlgg/x98JrOLlCItGDrvOHo11XsxL5zoA++nHetyj2y79V7U9FfsM+hASbs0eHFPBG5X0Px0LYnb6dcxfNrIqU9Otqx3cCTr+yOj2dcrUKPtG/PEvlFgGGXhWLPX0ap1CPt++Shq7DizmSIbJF88c5krJmk7w2onmbOnDlYvXo11q1bh71792Lq1KmoqanBpEmT3N01cjOT0YAPpl+JvY+NRFSwn1PPtfz/Jel+ozWJ4dWCG300/Spk9G59IeAPV8cha5Q+QpRFrJ5wRZsXwVOuisXqCUwzdaEfFmeibyuTnH2i2uOHxZku7hERNfp4hv2Lvmt7dcbORdr5nn404yq7eWUnpsdgV/ZIF/WIiKi5rFGJ+MPVcc0iTowGnl+TvNjYWBgMhia3pUuXNmnzww8/4KqrroK/vz9iYmKwbNmyZs/zj3/8AwkJCfD390e/fv2wYcOGJo9brVYsWrQIUVFRCAgIQEZGBg4cOKDqe1Na427gkYkRdtv2CAvAfx+/Hgtv6OuCnmmX6CJAJ38T9j42Eq/d13zHvd7clNwVRU+OwmvjL0dCl0D4ADAA8PcxIjGqI9ZMvAIHnxyFG5K7ururJOmOO+7A008/jUWLFiE5ORmFhYXYuHFjs+LwpF8BvibkZ2Vg72MjMfbyaNjZJ9yEyQCsGjcAI/t6d+0sUo7BarVa3d0JJVVXVyM4OBhVVVUeE553ts6Mxz7Zje2HKuFrMuKWlK6478p47oBrRV29Ba99fQi5Px0HYMV1iZG4dwh3DLbl9Ll6zH73O5T+ehaXdArAs3ekoIPMr4uX8sTxwh5vfE/ezmyxYuve41j2n70orTwLv3YmXNcnEtlj+mg2dV5dvQWrvyzCW9sPo+psPToF+uLu1O64/yr+dnkKbx0rvPV9kWPq6i14I78EhyvPoHtoIManuac2FGmPzFgRGxuLyZMnY8qUKbb7OnbsiPbt29ue67LLLkNGRgaysrLw448/4r777sNzzz2HBx54AACwbds2XH311cjJycENN9yAt99+G3/961+xa9cu9O3bsGjw17/+FTk5OVi3bh3i4uKwcOFC/Pjjj/jpp5/g728/rafWxr/Gc4U3thXj+G/1MBiAoIB2yNT4OY67NJ4P/vWzn1B0/AwsaJgcHBQXihfuGsBrN1KU1sYLJXjjeyIxF4+f9f+73wDAz8eI+PD2+NN1CbimVzgjTEhqrOCiCRHpljeOF974nohIea4aK1auXImnnnoK5eXlSEpKwgsvvIBBg1pPC3nq1Ck8+uijWL9+PSorK9G9e3c899xzGDVKLBUJx0AiEiG7aDJr1izMmjWrxcdffvllPProoygvL4evry8AYP78+fjggw+wb98+AA27p2tqavDJJ5/Yjhs8eDCSk5OxatUqWK1WREdHY+7cufjTn/4EAKiqqkJERATWrl2LO++8U9H3RET65o3jhTe+JyJSnsxYwa1WRERERKS4d999F3PmzEF2djZ27dqFpKQkZGZm4vjx4y22r6urw4gRI1BSUoJ//vOf2L9/P1avXo2uXZleg4jca+nSpejcuTNSUlLw1FNPob6+3vZYfn4+rr76atuCCQBkZmZi//79+PXXX21tMjKaFqPOzMxEfn4+AKC4uBjl5eVN2gQHByM1NdXWhoiIiIhchzGeRERERKS45cuXY8qUKbbinatWrcKnn36KNWvWYP78+c3ar1mzBpWVldi2bRvatWsHoGGHNxGROz300EMYMGAAQkNDsW3bNmRlZaGsrAzLly8HAJSXlyMurmm9r8b8++Xl5ejUqRPKy8ub5eSPiIhAeXm5rd2Fx7XU5mK1tbWora21/X91dbUT75KIiIiILuR1iyaN2cZ40khE9jSOE96UpZBjIBGJUHv8q6urw86dO5GVlWW7z2g0IiMjo9Vd0x999BHS0tIwbdo0fPjhhwgPD8fdd9+NRx55BCZTy7nvL540rKqqAsAxkIja1jg2BQcHt9pm7969SEhIwJw5c2z39e/fH76+vvjDH/6AnJwc+Pn5qd7X1uTk5GDJkiXN7uf4R0T28DqYiPRKZvzzukWT3377DQAQExPj5p4Qkaf47bff2rxo9iQcA4lIhlrj34kTJ2A2m1vcNd2Y4/9ihw4dwqZNm3DPPfdgw4YNKCoqwh//+EecP38e2dnZLR7T2qQhx0AiErF582ZERka2+Fh8fHyL96empqK+vh4lJSXo1asXIiMjUVFR0aRN4/83PndrbS58vPG+qKioJm2Sk5Nb7EdWVlaTBZ2jR48iMTGR4x8RCeN1MBHplcj453WLJtHR0Thy5Ag6duwIg8Fgt311dTViYmJw5MgR3RSL4nvme/ZGjrxfq9WK3377DdHR0Sr3znU4BtrH9+z971lv7xeQf89aHP8sFgu6dOmCV155BSaTCQMHDsTRo0fx1FNPtbpocvGkocViQWVlJTp37swxsBV6e896e78A37PsGGg0ypX5LCwshNFoRJcuXQAAaWlpePTRR3H+/HlbasHc3Fz06tULnTp1srXJy8trUkw+NzcXaWlpAIC4uDhERkYiLy/PtkhSXV2NHTt2YOrUqS32w8/Pr0mkS4cOHaTOARtfQ0//VvT2fgG+Z77nlmnxPNBZvA62j+/Z+9+z3t4voO7453WLJkajEd26dZM+LigoSDf/oBrxPeuD3t6z7Pv1lp01jTgGiuN79n56e7+A3HtWc/wLCwuDyWRqc2f1xaKiotCuXbsmqbh69+6N8vJy1NXVNSmy3OjiSUMACAkJke4v/614P729X4Dv2R6RMTA/Px87duzAsGHD0LFjR+Tn52P27NkYN26cbUHk7rvvxpIlSzB58mQ88sgj2L17N1asWIFnn33W9jwzZ87ENddcg2eeeQajR4/GO++8g2+//RavvPIKAMBgMGDWrFl4/PHHcemllyIuLg4LFy5EdHQ0br75ZqH34+g5IKC/fyt6e78A37NeaOU80B14HSyO79n76e39AuqMf3LbaoiIiIiI7PD19cXAgQORl5dnu89isSAvL8+2s/piQ4YMQVFRESwWi+2+//73v4iKimpxwYSISG1+fn545513cM0116BPnz544oknMHv2bNtiB9Bw4f2f//wHxcXFGDhwIObOnYtFixbhgQcesLVJT0/H22+/jVdeeQVJSUn45z//iQ8++AB9+/a1tZk3bx5mzJiBBx54AFdccQVOnz6NjRs3wt/f36XvmYiIiIi8MNKEiIiIiNxvzpw5mDhxIi6//HIMGjQIzz33HGpqajBp0iQAwIQJE9C1a1fk5OQAAKZOnYoXX3wRM2fOxIwZM3DgwAE8+eSTeOihh9z5NohIxwYMGIDt27fbbde/f398+eWXbbYZO3Ysxo4d2+rjBoMBjz32GB577DHpfhIRERGRsnS/aOLn54fs7OxmqR28Gd+zPujtPevt/SpFj58b37P309v7BbT5nu+44w788ssvWLRoEcrLy5GcnIyNGzfaisOXlpY2qSUQExODzz77DLNnz0b//v3RtWtXzJw5E4888ohqfdTi56Y2vb1nvb1fgO+ZxOntc9Pb+wX4nvVCj+/ZWXr8zPievZ/e3i+g7ns2WK1Wq+LPSkRERERERERERERE5GFY04SIiIiIiIiIiIiIiAhcNCEiIiIiIiIiIiIiIgLARRMiIiIiIiIiIiIiIiIAXDQhIiIiIiIiIiIiIiICoPNFkyeeeALp6ekIDAxESEhIi21KS0sxevRoBAYGokuXLnj44YdRX1/v2o6qLDY2FgaDoclt6dKl7u6WYlauXInY2Fj4+/sjNTUVBQUF7u6SahYvXtzsb5mQkODubilq69atGDNmDKKjo2EwGPDBBx80edxqtWLRokWIiopCQEAAMjIycODAAfd0VuM4Bnr/+AdwDOQYyDGwNRwDOQZ6G46BHANFZMJXLgAAB3xJREFUcfxrwDHQe3D84/gng2Mgxz9vwzFQnTFQ14smdXV1GDt2LKZOndri42azGaNHj0ZdXR22bduGdevWYe3atVi0aJGLe6q+xx57DGVlZbbbjBkz3N0lRbz77ruYM2cOsrOzsWvXLiQlJSEzMxPHjx93d9dU06dPnyZ/y6+++srdXVJUTU0NkpKSsHLlyhYfX7ZsGZ5//nmsWrUKO3bsQPv27ZGZmYlz5865uKfaxzGwgbeOfwDHQI6BHAPbwjGwAcdA78IxkGOgCI5/v+MY6D04/nH8E8UxsAHHP+/CMVCFMdBK1tdee80aHBzc7P4NGzZYjUajtby83Hbfyy+/bA0KCrLW1ta6sIfq6t69u/XZZ591dzdUMWjQIOu0adNs/282m63R0dHWnJwcN/ZKPdnZ2dakpCR3d8NlAFjff/992/9bLBZrZGSk9amnnrLdd+rUKaufn5/173//uxt66Bn0PAZ68/hntXIM9HYcA5XBMfBZd3dDNRwDvRvHQOfpefyzWjkGehOOfxz/HKHnMZDjn3fhGKjOGKjrSBN78vPz0a9fP0RERNjuy8zMRHV1Nfbs2ePGnilv6dKl6Ny5M1JSUvDUU095RdhhXV0ddu7ciYyMDNt9RqMRGRkZyM/Pd2PP1HXgwAFER0cjPj4e99xzD0pLS93dJZcpLi5GeXl5k795cHAwUlNTvfpvrha9jIHeOP4BHAM5BjbgGOg4joGejWMgx0CAY6Cj9DL+ARwDvQnHP45/StHLGMjxz7twDFR+DPRRonPeqry8vMkgCcD2/+Xl5e7okioeeughDBgwAKGhodi2bRuysrJQVlaG5cuXu7trTjlx4gTMZnOLf8N9+/a5qVfqSk1Nxdq1a9GrVy+UlZVhyZIluOqqq7B792507NjR3d1TXeP3sqW/uTd9Z11FD2Ogt45/AMdAjoG/4xjoGI6Bno1jIMfARhwD5elh/AM4BnoTjn8c/5SkhzGQ45934RiozhjodZEm8+fPb1b85uKbt35JLiTzOcyZMwdDhw5F//798eCDD+KZZ57BCy+8gNraWje/C5J1/fXXY+zYsejfvz8yMzOxYcMGnDp1Cu+99567u0YuwjGQ45+ecQwkjoEcA/WMY6C+cfxrwDFQnzj+EcdAjn96xjFQHV4XaTJ37lzce++9bbaJj48Xeq7IyEgUFBQ0ua+iosL2mJY58zmkpqaivr4eJSUl6NWrlwq9c42wsDCYTCbb36xRRUWF5v9+SgkJCcFll12GoqIid3fFJRr/rhUVFYiKirLdX1FRgeTkZDf1yrU4BnL8a8QxkGNgI46BTXEM5Bio5b+fkjgGwvb/ehgDOf414BjYQO9jIMc/2P5fD+MfwDEQ4PjXSO/jH8AxsJGzY6DXLZqEh4cjPDxckedKS0vDE088gePHj6NLly4AgNzcXAQFBSExMVGR11CLM59DYWEhjEaj7T17Kl9fXwwcOBB5eXm4+eabAQAWiwV5eXmYPn26ezvnIqdPn8bBgwcxfvx4d3fFJeLi4hAZGYm8vDzbwFhdXY0dO3Zg6tSp7u2ci3AM5PjXiGMgx0CAY6AzOAZ6No6BHAMBfY2BHP8acAxsoPcxkOOfvsY/gGMgwPGvkd7HP4BjIKDMGOh1iyYySktLUVlZidLSUpjNZhQWFgIAevbsiQ4dOuC6665DYmIixo8fj2XLlqG8vBwLFizAtGnT4Ofn597OKyQ/Px87duzAsGHD0LFjR+Tn52P27NkYN24cOnXq5O7uOW3OnDmYOHEiLr/8cgwaNAjPPfccampqMGnSJHd3TRV/+tOfMGbMGHTv3h3Hjh1DdnY2TCYT7rrrLnd3TTGnT59uslpeXFyMwsJChIaG4pJLLsGsWbPw+OOP49JLL0VcXBwWLlyI6Oho248l/U7vY6C3j38Ax0COgRwD28IxkGOgt+EYyDFQlN7HP4BjoLfh+MfxT4bex0COf96HY6BKY6BVxyZOnGgF0Oy2efNmW5uSkhLr9ddfbw0ICLCGhYVZ586daz1//rz7Oq2wnTt3WlNTU63BwcFWf39/a+/eva1PPvmk9dy5c+7ummJeeOEF6yWXXGL19fW1Dho0yLp9+3Z3d0k1d9xxhzUqKsrq6+tr7dq1q/WOO+6wFhUVubtbitq8eXOL39uJEydarVar1WKxWBcuXGiNiIiw+vn5WYcPH27dv3+/ezutUXofA/Uw/lmtHAM5BnIMbA3HQI6B3oZjIMdAUXof/6xWjoHehuMfxz8Zeh8DOf55H46B6oyBBqvVanV8yYWIiIiIiIiIiIiIiMg7GN3dASIiIiIiIiIiIiIiIi3gogkRERERERERERERERG4aEJERERERERERERERASAiyZEREREREREREREREQAuGhCREREREREREREREQEgIsmREREREREREREREREALhoQkR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",
       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1550,12 +1193,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  20\n"
+      "Question  29\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1567,12 +1210,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  21\n"
+      "Question  30\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1584,12 +1227,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  22\n"
+      "Question  31\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1601,12 +1244,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  23\n"
+      "Question  32\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1618,12 +1261,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  24\n"
+      "Question  33\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1635,12 +1278,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  25\n"
+      "Question  34\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1652,12 +1295,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  26\n"
+      "Question  35\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1669,12 +1312,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  27\n"
+      "Question  36\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1686,12 +1329,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  28\n"
+      "Question  37\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1703,12 +1346,199 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Question  29\n"
+      "Question  38\n"
      ]
     },
     {
      "data": {
-      "image/png": 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bavaAqytw8aL0WUAZqd/HPH7v9crTU1W6KiNzc9Wsmv/+054JsW2bqvTbBx+82VapkqrM244dqs9FTvLzXh058uaz4uenes9lMlUZLQD4+2+gf3+gWjVVn11cVLN/Mt/lPXSo6t/zwQOgTx/V/1eqBHz1lWomUnbWrFF9PszNVX04c0b7+X/+AZ4+BTJ9b5b8fnbsqJqh8uoVMHkysHgxcOECcPYsoL5e9fUFLCyyznDx8VGVHMs4s9HREWjQQPXvQMVWcRpvvHbtGiwtLTFkyBCt2H/++QfGxsaYNGmS1vYuXbrg7t27iDLwLLL4+HgEBgZi1qxZsLOzyzbGEGN5UtvMTn6uxQDVbJGZM2fi7bffhoWFBRwcHPDOO+9ojXNlt+ZKSkoKxowZg4oVK6J8+fJ477338ODBgyzl9dX73rp1C0OHDoWdnR1sbW3h5+eHV69eabW5fft2VK9eHe7u7pptcXFxqFSpEtq3b6/1mbp16xasra2zfLcubeN0TK7k0+PHj9GyZUscOHAAAQEBWLJkCWrWrInhw4dj8eLFAABLS0ts3LgRt27dwrfffqvZ19/fHwkJCdiwYYPWFCqFQoFu3bqhcuXKmD9/Ppo0aYLp06dj+vTpmpi0tDScOXMGjRs3ltTPjh074osvvkBwcLBm0PHRo0cYPXo0OnfuXCSlxRo3bpyvhbEKQl3vsWKGi5fz588DAJo2baoV26RJExgZGWmeVyqVuHjxYpY4AGjevDmio6Px8v8vfKS2mROZTAYjIyPNCVE9mJuXDRs2oFy5cpgwYQKWLFmCJk2aYNq0afjmm2+yxD5//hw9evRAkyZNMH/+fFStWhWjRo3S+kO9du1ajBkzBnXr1sXixYsxc+ZMNGzYUKs82YkTJwAgy2cxMDAQDRs2xPDhwzXvy759+7B27VpMmzYNXl5eeb4efapbty4sLS0L/TNHhvX5559j5cqV6Nu3L3788Ud89dVXsLS01Eqgrlu3Dp999hmcnJwwf/58tG7dGu+9916WATa1oKAg7N69G1999RXmzp2Lrl27YsyYMQCAKVOm4JdffsEvv/yiKV114sQJ1KtXD6bqMhfQ/bxvSDmdj5ydnVG1atUs5yNbW1u4u7sX+9+VPXv2oEuXLloJIwBITk7G06dPER0djR9++AF79+5Fp06d8mzPyMhIa6BTyjnXz88PvXr1woQJEzSfp0uXLmHmzJkYPnw4evTooeOrItKvGTNmwN/fH87Ozli4cCH69u2L1atXo2vXrkjLUKN/5cqVCAgIQNWqVTF//ny0adMGffr0wX///afV3oMHD3Dv3r0sf/OL03Xm+fPnUatWLdjY2Ghtb968OQBk+YLfpEkTCCE01zPFUWxsLM6fP5/tOeXZs2eIi4vD2bNn4efnBwCSz3m6XGeqE8mvX7/W+vecPn06rly5gpCQkBxLcRBhzx6gSRPVILsulErVQHo237/QvDkQHZ13SSMjI9XgOvBmoD03Mhmwfr2qnFjGc9f06aqyXCEhqnVDpB5T3WbmY+jSp4zkctUA/f37wJ9/qsqDubqqSkoVtdhYwMpK9VA7fx5o3Fj1nmTUvLkqSXDjRu5t6vpe1akDzJql+v+RI4FfflE91DdB/f676rijRqnWAfLxUf0302AxAFUSxccHcHBQvc/t2qkSdtmVmw0NBb7/XpUAmj1btb7PBx9or4dz4oSq/3mVwlPL7v00MtJ+LzO/H0uWqJJAvr5vkkCrVwP796tep7OzdnyTJqp+UbFU3MYb69Spg6CgIPzyyy+aNTqTk5MxdOhQeHh4YJb6d+//NWnSBAAM/r1y6tSpcHJywmeffZbt84YYy9OlzZzkZ8xvxowZmDlzJjp06IDly5fj22+/RbVq1bLcXJrZ0KFDsWzZMvTo0QPz5s2DpaVlrjftDBgwAC9fvkRwcDAGDBiADRs2YObMmVoxJ06cyPIZcXR0xMqVK3H06FHNOjVKpRJDhw5F+fLls5SJK3XjdEU5baYkGz58uKhSpYp4+vSp1vaBAwcKW1tb8erVK822yZMnCyMjI3Hs2DFNiZnFixdr7efr6ysAiNGjR2u2KZVK0bNnT2FmZqaZ2nbr1i0BQCxbtkxyX5OTk0XNmjWFp6eneP36tejZs6ewsbERd+/ezXW//JTmklLeYOTIkcLS0lLvx85N586dhY2NjXj+/Llmm7+/vzA2Ns42vlKlSmLgwIFCiDelp2bNmpUlbsWKFQKA+Pfff3VqMzsXL14UHh4eYvTo0ZqyYL///rtwc3PL8nnJLOPnTe2zzz4TVlZW4vXr15pt7dq1EwDEwoULNdtSU1NFw4YNhaOjo5DL5UIIIXr37p1n+bDAwEABINtpfJcuXRJmZmbi008/Fc+fPxdvvfWWaNq0qVZpouzoWmZDSlkwIYSoVauW6N69u+R2qfiztbUV/v7+OT4vl8uFo6OjaNiwoUhNTdVsX7NmjQCgdZ5Sn29q1KiR5Xcpt7JgVatWFX379s32+FLO+9mRUhYso9zKaKmfu3fvXpbnmjVrJlq2bJlle9euXUWdOnUkH1+tIGXBMsqrLFhycrKwsLAQIdmUe/jss88EAAFAGBkZiX79+uVailEI1b+9m5ubmDVrlqYs2I8//iipLNijR4+Evb296NKli0hNTRWNGjUS1apVEwkJCVJfLpFeZC4LFhcXJ8zMzETXrl015aeEEGL58uUCgFi/fr0QQvX338HBQTRr1kzr7/OGDRuynCcPHDggAIidO3dmOX5xuc709PQUHTt2zLL9ypUrAoBYtWqV1vaHDx8KAGLevHmSj69WkLJgGeV13bxu3TphaWmZ7XWeubm55pzn4OAgli5dmufxJk6cmKUUxUcffSSpFMXq1asFALFp0yZx6tQpYWxsLMaNG5fnMakUk1IWrFq1nMt+5VYWTP1cNt+/xIoVquf+//tXtiZOzFoW7KOPpJUFW71a1f6mTUKcOqUqn5TXZ/3lS1VJsC5dhLh9W7ss2Mcfq8qCLV6sKnX1++9vSl2NHi29LNivv6r6pX40bZq/cmJC5L8sWHZu3lSVfvvkE+3t1tZCDBuWNX73blX/w8JybjO/71VuZcGyOY+K4GAhZDIhMv698vXN/rPXqJEQTZq8+VldFszBQYiM15s7dqi2Z/x7OXiwKk6K7N7Pw4eFcHNT9UldFuzHH7OWBdu3T3Xs2bNVn8Ny5YTo0yf748ydq4p9/Fhav6hQFcfxRoVCId555x1RuXJl8fTpU+Hv7y9MTEzEmTNnsn0NZmZmYtSoUbm+zoKUBbtw4YIwNjYW+/btE0Kors2QqSyYIcbydGkzO/m9FvPy8spSsiwz9XugFhkZKQBkuV4bOnRolnE09b7DMp2333//feGQ4fyVlpYmZDKZ+PLLL7Ptw0cffSSsrKzEjRs3NN/tt2/fnm1saRqn48yVfBBC4H//+x/effddCCHw9OlTzcPHxwcJCQla2cMZM2bA09MTvr6++OKLL9CuXTvN3dCZBQQEaP5fJpMhICAAcrkcB/5/ITz14khSFsBUs7KywoYNG3Dt2jW0bdsWu3fvxg8//IBq1appxSUkJGi9loSEBACqmQ4ZtyclJUk+dnYqVKiAlJQUrallhjz23LlzceDAAXz33XdaUwVTUlJgZmaW7T4WFhaaqZXq/5qbm2cblzFGapvZqVatGkJCQrB06VLNFMx+/frh3LlzaNmyZa6v0dLSUvP/L1++xNOnT9GmTRu8evUK//77r1asiYmJVmbfzMwMn332GeLi4hAZGQkAsLOzw3///Yczmac0Z/Ds2TOYmJhkW56uXr16mDlzJn766Sf4+Pjg6dOn2LhxY5Y7zTP+2z59+hSvXr2CUqnMsj01t2njElSoUAFPnz4tUBtUvNjZ2eH06dM5Lt579uxZxMXF4fPPP9f6nRw6dGiOi8/7+vpq/S7l5dmzZzmei6Wc91NTU7N81pVKJV69epVle37kde7K7nwk9XclKSlJq3/P/3/R0ZzO5fpy6NAhpKamonvG8iL/b9y4cQgPD8fGjRvRvXt3KBQKyOXyXNtzc3PD7t27MXXqVJiamkImk2HUqFE4ceIE6tatm+u+Tk5OWLFiBcLDw9GmTRtERUVh/fr1We6aJypsBw4cgFwux7hx47RmZY0YMQI2NjaasgZnz57Fs2fPMGLECK2/z4MGDcpybsvt+rO4XGempKRIulZTU78WKee8wrpeyWzPnj3o0KFDtn+b9u7diz179mDhwoWoVq2apNJcPXv2xLlz5zQl3ypVqoTQ0FB89913qJzHzIKRI0fCx8cHo0ePxieffAJ3d3fMnTs3X6+LyojLl1ULcedR1i5b6t/XbH6n8f+/01nKOWXUsydw7pxq8XRAdUd/aCjw3Xd5z6IZOVI1a2H0aOCTT1TlnvL6rJcrpypltX+/atFyQLXo/MmTqjJTMhnQsqWqT/36vdln6VJV2SoXl9zbB1Qly8LDVTMwPv8cMDXNuUxZRkqlasZLxkdqqmpmRebtGWdbSPHqlarUlqWl6r3NKCUl//9+BX2vspPxPJqcrHq9rVqpUlXZVZfIPPOyTRvg9u2scR9+qCq7lTEO0I599kw7Jic5vZ9ubsDu3aoF701NVZ+nUaNUM08yXq927aqaQTNrlmr2jIWFavZKdtT94XfkYqe4jjcaGRlhw4YNSEpKQvfu3fHjjz9i8uTJ2c7gULeR8Rorp+umtLS0LNvTJJyLxowZg+7du6Nr1645xhhiLE+XNrOT32sxOzs7XLlyBTdv3swxJrOwsDAAyFLqbPTo0Tnuk3nWeZs2bfDs2TMkJiYCUJViE0LkOA6yfPly2Nraol+/fpg6dSo++eQT9O7dO9vY0jROZ5J3CGX25MkTvHjxAmvWrMGa7KaGAlo1ic3MzLB+/Xo0a9YMFhYWmlrtmRkZGaFGjRpa22rVqgUAuHPnjtZ2kakuYlJSktaXUWNjY1SqVEnzc+vWrTFq1CisWLECPj4+GDZsWJbj9+7dG0ePHs2yPfN0L19fX2xQ1y7NB3XfM74Hhjr21q1bERgYiOHDh2PUqFFaz1laWuY48Pb69WvNF1n1f7P7wvz69WutGKltZsfW1jbbJIqdnR1atGiR434AcOXKFQQGBuLQoUOak55a5sFNZ2fnLOUbMn7OWrZsiUmTJuHAgQNo3rw5atasia5du+Ljjz9G69atc+1HRhMnTsSWLVsQERGBuXPnZjtQmfEzmtv2kJAQDB06VPKxMxNCSJpqSSXH/Pnz4evrCxcXFzRp0gQ9evTAkCFDNOfQu3fvAgDefvttrf1MTU2znGfV3NRfhnWQ+VysJuW8/+uvv2pKuWT0/fff4/vvv5d0nNzkde7K7nwk9XclICAAGzduzLK9T58+Wj+3a9dOs0aNPuzevRtNmzbN9sJTXXMYAIYMGYKuXbvi3XffxenTp3N8Ta6urtlud3Z2hnPm8gnZGDhwIDZt2oTdu3dj5MiRkkryEBma+vxXu3Ztre1mZmaoUaOG5nn1f2tmKiljYmKS49pmOZ2LisN1pqWlpaRrNbXsrkdzUljXKxmlpaUhPDwcwcHB2T7foUMHAED37t3Ru3dv1KtXD+XKldMaOMmsXbt22W6Xeu5at24d3N3dcfPmTZw4cUKnGxKoBJHLgcxrEVWqBOha1nT3blUiI4eBt1ypP1vZJSz//3cauX3+cvisQ+rf6XXrVEmVmzdVA9hSPuvdumXdZmysWusFAHL6PpfH9zyNypXfJIb69VMlfLp0UfXRySnn/e7de5PwySzzue3w4TcJqbwoFMDAgcDVq8DevVnLTlla5v/fr6DvVXbu3QOmTQP++gv4/5uCNDLfDGRhkfW9qVAh636Aag2XzHFA1ti8ruVzez9zuF6Fs3PW933BAtVaKlFRqoSio2P2+6r7w+/IxU5xHG9Uc3d3x4wZMzBx4kTUq1cPU6dOzfF1ZP5eee/evRy/b2e+njp8+HCWtf8y2rp1K06cOIHLly/nGAMYZixPlzazk99rsVmzZqF3796oVasW6tWrh27duuGTTz5BgwYNctzn7t27MDIyyvK+Z772zyjzzVHqJMrz58+1biLM6TNib2+vWbu2cuXKWLp0aY7HKk3jdEyu5INSqQQADB48GL6+vtnGZP6A79u3D4Dql+3mzZv5GsQDAAcHBwDQ3CWstmDBAq06eK6urlonyNTUVM0AV3R0NF69egWrjDU8oVpYKWO7Fy5cwFdffYVNmzZpDWRJGXDKzfPnz2FlZaV1wjHEscPDwzFkyBD07Nkz2wXbq1SpAoVCgbi4ODhmuOiQy+V49uyZ5lj29vYwNzfHo0ePsrSh3qaOldpmXrJbSDYnL168QLt27WBjY4NZs2bB3d0dFhYWOHfuHCZNmqT5vOqiTp06uH79Onbt2oWwsDD873//w48//ohp06ZpPmcODg5IT0/Hy5cvs12M+vbt25qs+qVLl7I9TubFq37++Wfs378fmzZt0tru6emp82vI6Pnz51kG2alkGzBgANq0aYM///wT+/fvx/fff4958+bhjz/+yHZWgxS6DhI5ODhkORdnlNd538fHJ8vvwODBg9G1a9csiwXmR5UqVQCozlMume7ye/TokWYdgoyeP3+utTZVTr7++msMHjxY8/Pjx48xePBgLFiwQGtdJV1mWUqxZ8+ebBNS2enXrx8+++wz3LhxI8sgc3byMyD67NkznD17FgBw9epVKJVKnRaqJiopcrr+VCsO15lVqlTBgwcPsmzPfK2mpu6LlHNeYV2vZPTPP/8gMTFR0hpO7u7uaNSoETZv3pxrciWj/CS+jxw5ohlMuHTpEry9vXVug0qAEydUsyQyiomRvhC92p49qoRDfgZO7O1Vsx6y+f6l2Sb1XJGfmzyOHHmTGLh0CdD1s55poDKLAtyoqNGvH/Dtt6qB9BzWGwCgSrxkXjD4++9V63osXKi9XZe1MUeMAHbtAjZvVi24nlmVKvr599PHe6VQqBJR8fHApEmAh4dq/ZwHD1QzizJ/X9YlkZhTbMaBRweH7BMzGeX1fqrldb16/jygHni/dAn46KPs49T9kfA3kApXcRxvzGj//v0AgIcPH+LZs2dwyiG5++LFC61rLCcnpyzXU99//z1iY2OxMNO5KK91eidOnIj+/fvDzMxMM+754sULAMD9+/chl8vh7OxskLE8XdrMiy7XYm3btkV0dDR27NiB/fv346effsIPP/yAVatW4dNPP5XcTl5yWh9WnUyxt7eHTCaTNA7y/Plz/Pfff1oVhDIqTeN0TK7kQ6VKlVC+fHkoFAp07tw5z/iLFy9i1qxZ8PPzQ1RUFD799FNcunQpS2kapVKJ27dva7LHAHDj/xd6U99BWK1aNVhaWiImJkZr3yFDhuCdd97R/Jx5kHD69Om4du0aFixYgEmTJuGbb77JkkFULzqlpi4R0bp16xzvYMyPmJgYzYLQhjr26dOn8f7776Np06b47bffspSjAoCGDRsCUJXFyPjF9ezZs1AqlZrnjYyMUL9+fc0AWubj1KhRQ5NckNqmPh05cgTPnj3DH3/8gbbqBfuALJ8RtYcPHyI5OVlr9krmzxkAWFtb48MPP8SHH34IuVyODz74AHPmzMHkyZNhYWGhuUM8JiYmyx939cJVNjY2GDduHObOnYt+/frhgw8+0IrL/Pvzzz//wMLCQtLvlVTp6em4f/8+3nvvPb21ScVDlSpV8MUXX+CLL75AXFwcGjdujDlz5qB79+6aGQk3b95ExwxfUNLS0hATE5PnBZtabndSeHh45Ph7JuW8X6VKFU0CRM3CwgI1atTQy+9AxvNRxkTKw4cP8d9//2HkyJFZ9pH63tStW1drNpr6orZJkyaSE8O6unz5Mu7du5frAnwZqadj67s0WUb+/v6aBf8mT56MxYsXY8KECQY7HpEU6vPf9evXte5QlMvliImJ0Zxf1HG3bt3SzIIAVH8379y5o/W3PePf/OwUh+vMhg0b4vDhw0hMTNS6s+706dOa5zNSv5bM16TZKYzrlcx2796NunXrSn5vUlJS9F6WLKNHjx5h9OjR6Nq1K8zMzPDVV1/Bx8cnxxmAVIJ5eWUdjM9tZkR2XrxQJWkkJvuyMDIC6tcHsvn+hdOngRo1gGxu7tKLR49UJcG6dgXMzICvvlKVCStun3V12Zm8rnMsLIDM56pNm1TJo/yewyZOBEJCgMWLcx68b9gQ+PtvVeIi440np0+rFmrPMOahNzldt1+6BNy4AWzcqL2AfebPuaF4eKiSJgkJQHbliaW8n1IkJwN+fqpSYa1aAfPnA++/DzRrljU2JkaVWMlhZiYVneI43qi2atUqhIeHY86cOQgODsZnn32GHTt2ZIl78OAB5HK51jVWdtdNmzZtQmpqqs7XU/fv30doaChCQ0OzPNe4cWN4eXkhKirKIGN5urSpb/b29vDz84Ofnx+SkpLQtm1bzJgxI8fkiqurK5RKJWJiYrSSGLdu3cp3H0xMTODu7p7jZyQsLAw//fQTvv76a2zevBm+vr44ffp0ljHZ0jZOx9sr88HY2Bh9+/bF//73v2ynoT158kTz/2lpaRg6dCicnZ2xZMkSbNiwAY8fP8b48eOzbXv58uWa/xdCYPny5TA1NdVMETM1NUXTpk2z/CKrB+PUj4zlm06fPo0FCxZg3Lhx+PLLLzFx4kQsX74829IMheHcuXNo1aqVwdq/du0aevbsierVq2PXrl053o3esWNH2NvbY+XKlVrbV65cCSsrK60BvH79+uHMmTNa7/v169dx6NAh9O/fP19t6os6s5xxWp5cLsePP/6YbXx6ejpWZ6i9KpfLsXr1alSqVEkz8KGutalmZmaGunXrQgihqX+pvlsxuz8qixYtwokTJ7BmzRoEBQWhVatWGDVqVJHUU7x69Spev35t0M8cFS6FQpFlwNzR0RHOzs6agaWmTZuiUqVKWLVqldb03g0bNmjuapFCnYTMbh9vb29cvnw5y2CWrud9Q/H09ISHhwfWrFkDhUKh2b5y5UrIZDL0U9ex/n8JCQmIjo4utr8re/bsQeXKlbPU9c04LV4tLS0NP//8MywtLfNcOyW/tm3bhq1bt+K7777DN998g4EDByIwMFDzJYWoqHTu3BlmZmZYunSp1rXBunXrkJCQoLkWadq0KRwcHLB27Vqkp6dr4jZv3pzlbrS33noLLi4uOX6RLA7Xmf369YNCodAqoZGamoqQkBC0aNEiywy+yMhIyGSyYjv7Ys+ePVmuG9PT07O9UzAiIgKXLl3Kse65PowYMQJKpRLr1q3DmjVrYGJiguHDh+erbCUVcxUqqAbdMz7U62RI9f93NiOXWvh56tcPOHNGO8Fy/Tpw6JBqXQpDGTFClRBYtw5YswYwMQGGD8+7rJOhPH2a/bF/+kn1XwP+3mfr++9VpaemTAHGjs05rl8/4PFj4I8/3mx7+lS1Zsy772a/HktBqW8ezHzdrr4TO+P7KASwZIn++5Adb2/V8f5/fVMtUt9PKSZNUpU/27gRWLRINdvM1zf78myRkbrPyKJCURzHGwHVTSkTJ05E3759MWXKFCxYsAB//fUXfv755yyx6rV8DfW98s8//8zy+PDDDwGoZhf/8MMPmlhDjOVJbVOfMo/RlStXDjVr1sz1xhofHx8AyDI2uGzZsgL1xdvbO9vPyIsXL/Dpp5+iefPmmDt3Ln766SecO3cu2zX6St04ncSF7ymT2NhY4erqKqysrMTYsWPF6tWrRXBwsOjfv7+oUKGCJm7atGlCJpOJQ4cOabbNnj1bABC7d+/WbPP19RUWFhbi7bffFkOGDBErVqwQvXr1EgDElClTtI69YMECYW5uLhISEvLsZ0pKiqhdu7bw8PAQKSkpQgghUlNThaenp3BzcxNJSUk57nv48GEBQMTExOR6jKNHj4qgoCARFBQkHB0dRfXq1TU/Hz16VCv27NmzAoA4cOBArm1KPXZmiYmJwsXFRRgZGYnvvvtO/PLLL1qPEydOaMWvWLFCABD9+vUTa9euFUOGDBEAxJw5c7K06+7uLhwdHcX8+fPFDz/8IFxcXISzs7OIi4vLV5v68vTpU1GhQgXh6uoqFi5cKBYtWiQaNWokvLy8BABx+PBhTWy7du2Es7OzcHR0FKNHjxbLli0T77zzjgAg1qxZo4lr3Lix6NGjh5gzZ4746aefxJdffinMzc3Fu+++q3XsevXqiY8++khr29WrV4WFhYUYOnSoZtuNGzeElZWV6N+/f66vZfr06cLV1TXP1/zzzz+LoKAgMXnyZAFAdOjQQfOZu3PnjlbsggULhJWVlUhMTMyzXSoZnj9/LqytrYWvr69YtGiRWLNmjRgwYIAAIBYuXKiJW716tQAgWrduLZYuXSrGjx8v7OzsRI0aNUS7du00cerzze+//57lWI8ePRLGxsaiZcuWYsOGDeLXX38Vjx8/FkK8OZ/t27dPax+p5/3suLq6iunTp+ca8+LFC83nvVu3bgKA+PLLL0VQUJBYtmyZVuzOnTuFTCYTHTt2FGvWrBFjxowRRkZGYsSIEVna3bZtmwAgbt26levxsxMTE5PlfCOV1NfTtm1brfOKWp8+fUTHjh3FjBkzxNq1a0VQUJDw8PDI8nnQp8ePH4uKFSuKDh06CKVSKYRQnYsrV64svL29hUKhMMhxibITEhKS5Zpp+vTpAoDo2rWrWL58uRg9erQwNjYWzZo1E3K5XBO3bNkyAUC0adNGLFu2THz55ZfCwcFBuLu7i/bt22sdJyAgQLz11luaz7wQxe86s3///sLExERMnDhRrF69WrRq1UqYmJhkiRNCiF69eol33nkn12PnROr1SnakvJ7bt28LAOLIkSNa+6r//g0bNkwsXLhQrFq1Svj7+wsrKythb28vbty4ka8+5WX9+vUCgNiwYYNm26ZNmwQAsWLFCoMck4qxoCDVY+BAIQAhhg17s01tyBAhMp1DNH7+WRU7ebJq/w4d3uyf8To+MVEId3chHB2FmD9fiB9+EMLFRQhnZyEyff/Sm/XrVX3K8FkXmzapthXVZ/2HH4SoXVuISZOEWL1aiAULhOjSRdWnTN/NJPP1FSLDtbBkf/yhOu7bbwvxyy9ZH7Gxb2LT04Vo2VKIcuWEmDlT9f55egpRvrwQ//6bv37nRS4Xws5O9X799JMQv/4qxO3bqu3u7kJUrCjEnDlCLFum+nx6ealeT0jImzZ8fYWwts7a9vTpqli1mBjVz99/nzUWUMWrpaYK4eCg+sxnpMv7mZeDB4WQyYSYMePNtmPHhDAyEmLiRO3Yx4+FMDZWvUdULBW38UalUinat28vKlWqpDX+1aVLF2FnZycePHig1UZAQICoVq2a1jVjdnx9fbW+lxeE+tr3yZMnWtsNMZanS5v64ujoKAYMGCDmzZsn1q5dKz777DMhk8nE6NGjs7wHGfXt21cAEJ988olYsWKFGDBggGjYsKEAIGZkOF/k9P5l9z1DPW5w/fp1rdghQ4YICwsLce3aNc22Tz/9VJiamoqoqCit2NI2TsfkSgE8fvxY+Pv7CxcXF2FqaiqcnJxEp06dNIPUkZGRwsTEROvDLoQQ6enpolmzZsLZ2Vk8f/5cCKE6qVhbW4vo6GjRtWtXYWVlJSpXriymT5+eZZDm8ePHwsTERPzyyy959nH8+PHC2NhYnD59Wmv72bNnhYmJiRg1alSO+0r90qv+JczukXmQcNKkSZJOsvlNrqgH+HJ6+Pr6ZtlnzZo1onbt2sLMzEy4u7uLH374Idv+3b9/X/Tr10/Y2NiIcuXKiV69eombN29m2w+pberL8ePHRcuWLYWlpaVwdnYWX3/9tdi3b1+2yRVPT09x9uxZ4e3tLSwsLISrq6tYvny5VnurV68Wbdu2FQ4ODsLc3Fy4u7uLiRMnZknoLVq0SJQrV068evVKCPHms121alXx4sULrdglS5YIAGLr1q05vg6pgxXt2rXL8d848+BuixYtxODBg/Nsk0qO1NRUMXHiROHl5SXKly8vrK2thZeXl/jxxx+zxP7444/Czc1NmJubi6ZNm4pjx46Jdu3aSU6uCCHE2rVrRY0aNYSxsXGWz1iDBg3E8OHDNT/rct7PjpTkSm7nuex+f/7880/RsGFDYW5uLqpWrSoCAwO1BlfVPvzww3wPNBYkuSLl9bx48UKYmJiI3377Lcv+v/76q+jcubOoXLmyMDExERUqVBCdO3cWO3bsyNdrkeKDDz4Q5cuXz5LM3bFjhwAg5s2bZ7BjE2WW3ZceIYRYvny58PDwEKampqJy5cpi1KhR2Z5/li5dKlxdXYW5ublo3ry5OH78uGjSpIno1q2bVty5c+cEAPH3339rthW368yUlBTx1VdfCScnJ2Fubi6aNWsmwsLCsrT54sULYWZmJn7K58BSQZIrUl7P8uXLha2trUhLS9PaNzU1VYwdO1Y0aNBA2NjYCFNTU+Hq6iqGDx+u8zWzVPfv3xe2trZZbrARQoj3339fWFtbi9u3bxvk2FRMqe7Bz/4hhBBK5ZuESHbatct5/8zXEffvC9GvnxA2NqpB+l69hMjh+1eB3b8vhK1t9gmL999XDbgXxWf9zBkh+vcXolo1IczNVf1o3FiIRYuEyHSOkCy/yRV1gkHqv198vBDDh6sSC1ZWqmOeOZO/Pku1Y4cQdesKYWKinTi5elWIzp1Vn6OKFYUYMUKICxcKJ7kihBBjxghRs2b2bUp9P3OSmCiEq6vqc5H5MzF+vCrBcvLkm20rV6r+PUrJoGZpVZzGG9VjOf/73/+0Yu/duydsbGxEjx49NNsUCoWoUqWKCAwMzPM1FkZyRQjDjOXp0qY+zJ49WzRv3lzY2dkJS0tL4eHhIebMmaP1vT675EpycrLw9/cX9vb2oly5cqJPnz7i+vXrAoD47rvvsuwrJbmSmpoqKlasKIIy3FSh/h6c+ebGxMRE4erqKry8vLT6WtrG6WRCcC53cTB06FBs27YNSUlJkuKHDx+OGzdu4O+//zZwz/QnNTUV1atXxzfffIOxBZ3ySsVCQkICatSogfnz52P48OFF3Z0soqKi0LhxY5w7d84g691QyaReEyQ/i/lm9ssvv8Df3x/37t3LcaG2kiA2NhZubm7YsmULevfuXdTdyeK3337DoEGD8PTp0yz1g4lIv5RKJSpVqoQPPvgAa9eu1XquU6dOcHZ2xi+//FJEvdOPxYsXY/78+YiOjs6xfGxR6tGjB8qVK4fffvutqLtCpLuICKBFC+DKFdXaD0QE3L6tWntl717g/0swFZlGjYD27YEMpZOodCvM8cbt27fj448/RnR0dJb1Ral4iIqKQqNGjbBp0yYMGjQoX20EBQUhJCQEN2/e1CxVoMvxS9s4HddcKaGmT5+OM2fO4Pjx40XdFclCQkJgamqKzz//vKi7Qnpia2uLr7/+Gt9//z2USmVRdyeL7777Dv369Ss1J2wqfgYNGoRq1aphxYoVRd2VAlm8eDHq169fLBMrAGBnZ4elS5cysUKkZ69fv86yZsbPP/+M+Ph4TSI6o7lz52Lr1q24e/duIfVQ/9LS0rBo0SIEBgYWy8QKoLoJoLDX6SLSq7lzmVghyqhGDdXaPd99V7T9CAsDbt4EJk8u2n5QsVaQ8cZ58+YhICCAiZViIiUlJcu2xYsXw8jICG3bts13u+PHj0dSUhK2bNmi876lcZyOM1eKCV0zyURElD/6nLlCRFSSHTlyBOPHj0f//v3h4OCAc+fOYd26dahTpw4iIyNhZmZW1F0kIiIiogLgeGPZNXPmTERGRqJDhw4wMTHB3r17sXfvXowcORKrV68u6u6VGiZF3QEiIiIiIip81atXh4uLC5YuXYr4+HjY29tjyJAh+O6775hYISIiIiIqwVq1aoXw8HAEBQUhKSkJ1apVw4wZM/Dtt98WdddKFc5cISIiIiIiIiIiIiIi0gHXXCEiIiIiIiIiIiIiItIBkytEREREREREREREREQ6KNNrriiVSjx8+BDly5eHTCYr6u4QUTElhMDLly/h7OwMI6PSkZPm+Y+IpOI5kIjKqtJ4/gN4DiQiaUrjOZDnPyKSSuo5sEwnVx4+fAgXF5ei7gYRlRD3799H1apVi7obesHzHxHpiudAIiqrStP5D+A5kIh0U5rOgTz/EZGu8joH6pRcUSgUmDFjBjZt2oTY2Fg4Oztj6NChCAwM1GR8hRCYPn061q5dixcvXqB169ZYuXIl3n77bU078fHxGD16NHbu3AkjIyP07dsXS5YsQbly5TQxFy9ehL+/P86cOYNKlSph9OjR+Prrr7X68/vvv2Pq1Km4c+cO3n77bcybNw89evSQ/HrKly8PQPUm2djY6PJWEFEZkpiYCBcXF805ozTg+Y+IpOI5kIjKqtJ4/gN4DiQiaUrjOZDnPyKSSuo5UKfkyrx587By5Ups3LgRnp6eOHv2LPz8/GBra4sxY8YAAObPn4+lS5di48aNcHNzw9SpU+Hj44OrV6/CwsICADBo0CA8evQI4eHhSEtLg5+fH0aOHInQ0FBN57t27YrOnTtj1apVuHTpEoYNGwY7OzuMHDkSAHDixAl89NFHCA4ORq9evRAaGoo+ffrg3LlzqFevnqTXo04I2djY8KRKRHkqTdOGef4jIl3xHEhEZVVpOv8BPAcSkW5K0zmQ5z8i0lVe50CdiiaeOHECvXv3Rs+ePVG9enX069cPXbt2RUREBADVrJXFixcjMDAQvXv3RoMGDfDzzz/j4cOH2L59OwDg2rVrCAsLw08//YQWLVrgnXfewbJly7BlyxY8fPgQALB582bI5XKsX78enp6eGDhwIMaMGYNFixZp+rJkyRJ069YNEydORJ06dRAUFITGjRtj+fLlurwkIqISSaFQYOrUqXBzc4OlpSXc3d0RFBQEIURRd42IiIiIiIiIiKjU0ym50qpVKxw8eBA3btwAAFy4cAH//PMPunfvDgCIiYlBbGwsOnfurNnH1tYWLVq0wMmTJwEAJ0+ehJ2dHZo2baqJ6dy5M4yMjHD69GlNTNu2bWFmZqaJ8fHxwfXr1/H8+XNNTMbjqGPUx8lOamoqEhMTtR5ERCWReibh8uXLce3aNcybNw/z58/HsmXLirprREREREREREREpZ5OZcG++eYbJCYmwsPDA8bGxlAoFJgzZw4GDRoEAIiNjQUAVK5cWWu/ypUra56LjY2Fo6OjdidMTGBvb68V4+bmlqUN9XMVKlRAbGxsrsfJTnBwMGbOnKnLSyYiKpYyziQEgOrVq+PXX3/VzCQkIiIiIiIiIiIiw9EpufLbb79h8+bNCA0NhaenJ6KiojBu3Dg4OzvD19fXUH3Um8mTJ2PChAman9UL00ihUApExMQj7uVrOJa3QHM3exgblZ66k0RUsrRq1Qpr1qzBjRs3UKtWLc1MwozlEzNKTU1Famqq5mfO3CMqWxRKgWPX4rDw4A0kpKShtlN5LP6wEcpZ6HQpSERUIiW9TseY0LOIvJcAYyMZuno6Yfq7nrA0My7qrhV7/B5MREREJZmhr2V0+kY9ceJEfPPNNxg4cCAAoH79+rh79y6Cg4Ph6+sLJycnAMDjx49RpUoVzX6PHz9Gw4YNAQBOTk6Ii4vTajc9PR3x8fGa/Z2cnPD48WOtGPXPecWon8+Oubk5zM3NdXnJAICwy48wc+dVPEp4rdlWxdYC09+ti271quSyJxGRYeQ1kzAzztwjKrt2RD3AuC1RyLgi0/3nKag3Yx8aVLXBXwFtiqxvRESGlPQ6HS3nHkCSXKG1fcuZ+9hy5j661HXE2iHNiqh3xV/Y5UeYvuMyHr+Ua7ZVLm+Gmb3r8XswERERFXuFMaav05orr169gpGR9i7GxsZQKpUAADc3Nzg5OeHgwYOa5xMTE3H69Gl4e3sDALy9vfHixQtERkZqYg4dOgSlUokWLVpoYo4dO4a0tDRNTHh4OGrXro0KFSpoYjIeRx2jPo6+hF1+hFGbzmn9IwBAbMJrjNp0DmGXH+n1eEREUmScSXju3Dls3LgRCxYswMaNG7ONnzx5MhISEjSP+/fvF3KPiaiwKZQC7b8/jLGZEisZXfwvEe8t/7tQ+0VEZGhJr9NRb1oY6s3YlyWxklH41TiM+PlMIfas5Ai7/AifbzqnlVgBgMcv5fic34OJiIiomCusMX2dkivvvvsu5syZg927d+POnTv4888/sWjRIrz//vsAAJlMhnHjxmH27Nn466+/cOnSJQwZMgTOzs7o06cPAKBOnTro1q0bRowYgYiICBw/fhwBAQEYOHAgnJ2dAQAff/wxzMzMMHz4cFy5cgVbt27FkiVLtEp6jR07FmFhYVi4cCH+/fdfzJgxA2fPnkVAQIBe3hhANSgxc+fVbAck1Ntm7rwKhTKnIQsiIsPIOJOwfv36+OSTTzB+/HgEBwdnG29ubg4bGxutBxGVXjsvPIT7lD248+xVnrEX/0tE0uv0QugVEZFhJbxKQ53AvXkmVTIKvxqHFImxZYVCKTDhtwu5xozZEsXvwURERFQsFeaYvk7JlWXLlqFfv3744osvUKdOHXz11Vf47LPPEBQUpIn5+uuvMXr0aIwcORLNmjVDUlISwsLCYGFhoYnZvHkzPDw80KlTJ/To0QPvvPMO1qxZo3ne1tYW+/fvR0xMDJo0aYIvv/wS06ZNw8iRIzUxrVq1QmhoKNasWQMvLy9s27YN27dvR7169QryfmiJiInPkt3KSAB4lPAaETHxejsmEZEUec0kJKKyyy8kAqN/Pa/TPuO36hZPRFScJL1Oh0fgXnjN2o+UdN2vhebuuWqAXpVcJ24+xas8Ek7ydCWWhN8opB4RERERSVeYY/o6rblSvnx5LF68GIsXL84xRiaTYdasWZg1a1aOMfb29ggNDc31WA0aNMDff+depqJ///7o379/rjEFEfcy53+E/MQREemLeiZhtWrV4OnpifPnz2PRokUYNmxYUXeNiIqIQinQcOY+vEzV/Q7se89TDNAjw3nw4AEmTZqEvXv34tWrV6hZsyZCQkLQtGnTou4aERUShVLg2LU4jPo1Eq/TC3bXoZRZfmXJ/87/Jylu1bFojO1SiwvcExERUbFSmGP6OiVXypqK1uZ6jSMi0pdly5Zh6tSp+OKLLxAXFwdnZ2d89tlnmDZtWlF3jYiKwI6oBxi7JSrf+1erYKm/zhjY8+fP0bp1a3To0AF79+5FpUqVcPPmTc26fERUuimUAov2XceKo9F6a7O6g5Xe2ioN8pq1oiZXCJyKfobWb1c0cI+IiIiIpKtYTuKYvsS43DC5khupN+DwRh0iKmRSZhISUemnUAp0WnikwHdd//BhIz31yPDmzZsHFxcXhISEaLa5ubkVYY+IqDAYIqmiNqVHXb23mV/Vq1fH3bt3s2z/4osvsGLFCrRv3x5Hjx7Veu6zzz7DqlWr9NaHZtXtsf/qY0mxG0/GMLlCRERExYvUSc16WD5OpzVXypqnSal6jSMiIiLSlx1RDyQvWp+bem/ZoJxFybnf5q+//kLTpk3Rv39/ODo6olGjRli7dm1Rd4uIDEShFPh+779wn7LHIImVLnUdYWlmrPd28+vMmTN49OiR5hEeHg4AWuWwR4wYoRUzf/58vfbBt1V1ybGH/n3Che2JSrmFCxeiWbNmKF++PBwdHdGnTx9cv35dK6Z9+/aQyWRaj88//1wr5t69e+jZsyesrKzg6OiIiRMnIj09XSvmyJEjaNy4MczNzVGzZk1s2LAhS39WrFiB6tWrw8LCAi1atEBERITeXzMRlWxPkyWO6UuMyw2TK7lgWTAiIiIqjnou+btAZcDUbCxMsGt0m4J3qBDdvn0bK1euxNtvv419+/Zh1KhRGDNmDDZu3JjjPqmpqUhMTNR6EFHxZuikCqBKrKwd0swgbedXpUqV4OTkpHns2rUL7u7uaNeunSbGyspKK8bGxkavfTAzMYKrvbRykelKVWkwIiq9jh8/Dn9/f5w6dQrh4eFIS0tD165dkZycrBWXW+JXoVCgZ8+ekMvlOHHiBDZu3IgNGzZolbWOiYlBz5490aFDB0RFRWHcuHH49NNPsW/fPk3M1q1bMWHCBEyfPh3nzp2Dl5cXfHx8EBcXZ/g3gohKjMIsC8bkSm5YFoyIiIiKEXm6Eu6Td+PKo4InB+o6WeHiDB899KpwKZVKNG7cGHPnzkWjRo0wcuRIjBgxIteSOMHBwbC1tdU8XFxcCrHHRKSLwkiqVLAwxrVZ3YpdYiUzuVyOTZs2YdiwYZDJ3nzp3Lx5MypWrIh69eph8uTJePUq9xmM+UkwD25ZXXI/j0c/kRxLRCXPH3/8gaFDh8LT0xNeXl7YsGED7t27h8jISK243BK/+/fvx9WrV7Fp0yY0bNgQ3bt3R1BQEFasWAG5XA4AWLVqFdzc3LBw4ULUqVMHAQEB6NevH3744QdNO4sWLcKIESPg5+eHunXrYtWqVbCyssL69esL580gopKBZcGKB5YFIyIiouJi5l9XUCtwLxR6uABcOsALe8Z1KHhDRaBKlSqoW1d7fYQ6derg3r17Oe4zefJkJCQkaB737983dDeJSEfydCXGbjln0KRKOVMjXJ7hg/MzuhWrUmA52b59O168eIGhQ4dqtn388cfYtGkTDh8+jMmTJ+OXX37B4MGDc20nPwlmXUqDRcTES44lopIvISEBAGBvb6+1PbfE78mTJ1G/fn1UrlxZs83HxweJiYm4cuWKJqZz585abfr4+ODkyZMAVAnnyMhIrRgjIyN07txZE5MZZy8TlU2FWRas5BTYLgKO5S30GkdERESkK3m6Eg1n7cMrubLAbZUzleHCzO4wNiq5025bt26dpc73jRs34OrqmuM+5ubmMDdnGVei4kihFPjil0jsuyZtAfX8KGdqhFPfdilR60sBwLp169C9e3c4Oztrto0cOVLz//Xr10eVKlXQqVMnREdHw93dPdt2Jk+ejAkTJmh+TkxMzDPBYmZiBGdbczxMyHvQIep+AhRKUaL/thCRNEqlEuPGjUPr1q1Rr149zfaPP/4Yrq6ucHZ2xsWLFzFp0iRcv34df/zxBwAgNjZWK7ECQPNzbGxsrjGJiYlISUnB8+fPoVAoso35999/s+1vcHAwZs6cWbAXTUQlTmGWBStZV5eFrIlrBRjJgNzW5zOSqeKIiIiI9G3mX1cQcuKOXtrybeWCme810EtbRWn8+PFo1aoV5s6diwEDBiAiIgJr1qzBmjVrirprRKQDhVJg0b7rBpulAgDWpjKc/rZriUuqAMDdu3dx4MABzcBkTlq0aAEAuHXrVo7JlfwmmD3fssXDhLzXMVCvu9L67Yo6H4OIShZ/f39cvnwZ//zzj9b2/CR+C0N+kstEVAoUYlmwkneVWYgi7z7PNbECqBIvkXefw9vdoXA6RURERKWePmermMqAK0HdYWZSOqrBNmvWDH/++ScmT56MWbNmwc3NDYsXL8agQYOKumtEJEFhJFVK6kyVjEJCQuDo6IiePXvmGhcVFQVAVTJR35pXd0D4VWmLRG88GcPkClEpFxAQgF27duHYsWOoWrVqrrGZE79OTk6IiIjQinn8WDVj0cnJSfNf9baMMTY2NrC0tISxsTGMjY2zjVG3kRlnLxOVTYf+lTYjWh9lwUrHt2wDiXv5WlJc+NVYA/eEiIiIygr12ir6SKzUdbLCzeCepSaxotarVy9cunQJr1+/xrVr1zBixIii7hIR5aEw1lRxsDLB5Rk+uBzUvUQnVpRKJUJCQuDr6wsTkzevIzo6GkFBQYiMjMSdO3fw119/YciQIWjbti0aNND/zERd1l05cC0OirzuTCSiEkkIgYCAAPz55584dOgQ3Nzc8twnc+LX29sbly5dQlzcm4RteHg4bGxsNGvpeXt74+DBg1rthIeHw9vbGwBgZmaGJk2aaMUolUocPHhQE0NEpFAK/Bn1QFKsPpb6KLlXnIVA6hu8I+ohvu1ZlzVmiYiIqECaBIXjWbJcL20tHeCF9xrnflchEZGhydOVGPTTSZy588Jgx3C2McPBrzqWiEXqpThw4ADu3buHYcOGaW03MzPDgQMHsHjxYiQnJ8PFxQV9+/ZFYGCgQfphZmIEV3tL3I1PyTNWKYATN5+iTe1KBukLERWdL7/8Etu2bcOOHTtQvnx5zRoptra2sLS0RHR0NEJDQ9GjRw84ODjg4sWLGD9+vFbit2vXrqhbty4++eQTzJ8/H7GxsQgMDIS/v79mZsnnn3+O5cuX4+uvv8awYcNw6NAh/Pbbb9i9e7emLxMmTICvry+aNm2K5s2ba86Hfn5+hf/GEFGxFBETj/jktDzjHKzN0NzNvsDHY3IlF83d7GFvbZrnP8izZDkiYuJZGoyIiIjyRZ6uhEfgXhR8rgrgWsEchyZ24k0fRFSkFEoB/02RCLtquIXqK1gY48SULqUmqaLWtWtXCJF1FoiLiwuOHj1aqH0Z3LI65uy5Jil26aEbTK4QlULr1q0DALRv315re0hICIYOHSop8WtsbIxdu3Zh1KhR8Pb2hrW1NXx9fTFr1ixNjJubG3bv3o3x48djyZIlqFq1Kn766Sf4+PhoYj788EM8efIE06ZNQ2xsLBo2bIiwsLAsi9wTUdkVmyitEtV7DZ318p2ZyZVcGBvJ8H7Dt7Du+J08Y6WWECMiIiLKSJ+L1nO2ChEVtcJYU8XBygRHv+5Uokt/lRS+raQnVyLvvYBCKZjcJyplEhISYGNjk+PzUhO/rq6u2LNnT64x7du3x/nz53ONCQgIQEBAQJ7HI6KyKT5J2joqVe0s9XI8Xo3moaNHZUnJlYrWXCCLiIiIpNPnovXWpsDFmT04oEVERUaersTEbVHYEfXIYMcobeW/SgIzEyPUrGSNW0+S84xlaTAiIiIqavbWZnqNywuTK3mROkbBsQwiIiKSSJ+zVXxbuWDme/pfyJiISAquqVL6TX/XE5+sj5AUy9JgREREVJQcbaStoS41Li9MruThqcSpRFLjiIiIqOzS52wVC2Pg4szuMDMx0kPPiIh0UxhJldqVrbHdvw2TKkWsVc2KMAIkrQvG0mBERERUpLIuW1ewuDwwuZIHqeW+WBaMiIiIcjNr5xWsl1BqVArOViGiolIYC9W7V7TA3nEdmDwuJoyNZGha3Q4REhJpLA1GRERERelpssSJEhLj8sLkSl5YFoyIiIgKqM28Q7j/PKXA7XC2ChEVlcJYqJ7lv4qv0R1rSS4NNmPXZRys3cHAPSIiIiLKqmI5iRMlJMblhcmVPLAsGBEREeWXPF2JutP2Ir3gVcDQoZY9QoZ5F7whIiIdMKlCgG6lwaKfvII8XckbAYiIiKjwsSxY8cKyYERERJQf+ly0fvnAhujV8C29tEVEJIU8XYmJ26KwI+qRwY7BNVVKDl1KgwHA5D8uYuGAhgbtExEREVFmLAtW3LAsGBEREelAn4vWlzOV4cLM7lwYmIgKjUIp8MUvkdh3zZBrqlhi77j2nNlQwuhSGmxH1EPM7+fFv19ERERUqFgWrJiRWu7r4LXHaF2zooF7Q0RERMWZPmercNF6IipMLP9FedGlNFi6UuBU9DO0fpvfkYmIiKjwRMQ8kxbIsmCFw7G8haS4HVEP8W3Purwzh4iIqIxqMiscz17JC9wOF60nosLEpApJZWwkQ5e6jth3NU5S/MaTMUyuEBERUaFRKAU2nrgrKZZlwQpJczd72FubIj45Lde4Z8lyRMTEw9vdoZB6RkRERMWBPF2JWoF79dIWZ6sQUWEpjKQK11QpfYa0cpOcXDn07xMolII3IBIREVGhiIiJx4uU3Mfw1aROqMgLkyt5MDaSobeXM0IkZL1iE1IKoUdERERUXOirDJipDLgSxNkqRGR4hZFUaVbdFps/bcVzWinUsoYDTGRAuoRSGiwNRkRERIUp7uVrSXF2VqZo7mavl2MyuSJB1QpWkuLikwteCoSIiIiKP30uWl/XyQp7xnXQQ6+IiHL357kHGP9blMHa50L1pZ+xkQy9Gznjf+ceSopnaTAiIiIqLFIXqR/qXV1vM2uZXJHAXuI/jNQ4IiIiKrn0uWj90gFeeK9xVb20RUSUk6TX6Wg6Oxyv0wueEM4O11QpW4I/8JKcXAm/GsfSYERERFQ4JC5S36y6fmatAEyuSOIoMWkiNY6IiIhKHoVSoNGs/Uh8nV7gtsqZynBhZncONhGRQSW9TkfLuQeQJFcYpH0mVcomMxMjuNpb4m583mWxBYAl4Tcwwae24TtGREREZVpckrRF6qXGScH52lJIHffg+AgRFaIHDx5g8ODBcHBwgKWlJerXr4+zZ88WdbeISqUdUQ/gPmWPXhIrvq1ccDmoBxMrRGQwSa/TUW9aGOrN2GeQxIqzjRmuzeqGE1O6MLFSRg1uWV1y7Kpj0VAoJd5KSkRERJRP8RKTJlLjpODMFQmeSnzDpcYRERXU8+fP0bp1a3To0AF79+5FpUqVcPPmTVSoUKGou0ZU6vRa+jcuP0wscDsWxsDFmVy0nogMx9AzVWpXtsZ2/zZMqBB8W1XHnD3XJMXKFVzYnoiIiAzP3tpMr3FSMLkiQUVraeW+pMYRERXUvHnz4OLigpCQEM02Nze3IuwRUekjT1fCc/pepOlhjNK3lQtmvteg4A1RFt999x0mT56MsWPHYvHixUXdHaIikSJXwPu7A3jxquCz67LjXtECe8d1YHKYNMxMjFCzkjVuPUmWFP/3rTgmV4iIiMigHG0s9BonBa+OpWBZMCIqZv766y80bdoU/fv3h6OjIxo1aoS1a9cWdbeISo2Zf11BrUD9JFZuzO7OxIqBnDlzBqtXr0aDBnx/qWxKkSvgHXwAdaaFGSSxoi7/dfCrTkysUBbT3/WUHPtX1CMD9oSIiIgIiIh5Ji1Qj9VKOXNFAqnlvg5ee4zWNXk3DhEZ3u3bt7Fy5UpMmDABU6ZMwZkzZzBmzBiYmZnB19c3S3xqaipSU9+cyxITC17iiKg00uei9XbmMkTN7KGHXlF2kpKSMGjQIKxduxazZ88u6u4QFaoUuQIdFx7GowTDlCXmQvUkRauaFSGDtPGJhwmvIU9XMklHREREBqFQCmw8cVdS7NNkLmhfqBzLS5sqtCPqIRfqI6JCoVQq0bhxY8ydOxeNGjXCyJEjMWLECKxatSrb+ODgYNja2moeLi4uhdxjouJP34vWM7FiWP7+/ujZsyc6d+5c1F0hKjQZZ6oYIrHChepJF8ZGMjSrbic5fvIfFw3XGSIiIirTImLi8SIlTVKs1LF+KZhckaC5mz3srU3zjHuWLEdETHwh9IiIyroqVaqgbt26Wtvq1KmDe/fuZRs/efJkJCQkaB73798vjG4SlRg9l/yNsVuiCtyOhTHLgBWGLVu24Ny5cwgODpYUn5qaisTERK0HUUli6KRKBQtjJlWKiRkzZkAmk2k9PDw8NM+/fv0a/v7+cHBwQLly5dC3b188fvy4yPo7umMtybG8GZGIiIgMJTbxtaQ4O0tTNHez19txWRZMAmMjGXp7OSNEwtSi2ISUQugREZV1rVu3xvXr17W23bhxA66urtnGm5ubw9zcvDC6RlSiyNOVqDN1LxR6GOvhovWF4/79+xg7dizCw8NhYSHtjqPg4GDMnDnTwD0j0j95uhLdlxxF9JNXBmm/nKkRTn3bBeUs+LWwOPH09MSBAwc0P5uYvPn3GT9+PHbv3o3ff/8dtra2CAgIwAcffIDjx48XRVfRqmZFGAFQSohNVwqcin7Ghe2JiIhI7+IlLuvRuY4jjI30t3A6Z65IVLWClaS4+GS5gXtCRKT6Yn3q1CnMnTsXt27dQmhoKNasWQN/f/+i7hpRiTFrp2rR+oImVkxknK1SmCIjIxEXF4fGjRvDxMQEJiYmOHr0KJYuXQoTExMoFIos+3D2HpU08nQl+q86jlqBew2SWClnaoTLM3xwOag7EyvFkImJCZycnDSPihVVyYiEhASsW7cOixYtQseOHdGkSROEhITgxIkTOHXqVJH01dhIhi51HSXHz993zYC9ISJDWLhwIZo1a4by5cvD0dERffr0yXKjn5RZdffu3UPPnj1hZWUFR0dHTJw4Eenp2uV4jxw5gsaNG8Pc3Bw1a9bEhg0bsvRnxYoVqF69OiwsLNCiRQtERETo/TUTUcljb20mKU7f66UzuSKRfTlpd3xLjSMiKohmzZrhzz//xK+//op69eohKCgIixcvxqBBg4q6a0TFnkIp0Gx2ONYfv1Pgtt6yNcWt4J5coLcQderUCZcuXUJUVJTm0bRpUwwaNAhRUVEwNs5a0sjc3Bw2NjZaD6LiKGNS5cydF3pvn0mVkuHmzZtwdnZGjRo1MGjQIE3Z18jISKSlpWmtNeXh4YFq1arh5MmTObZn6NKIQ1q5SY698F8i5OlS5rkQUXFx/Phx+Pv749SpUwgPD0daWhq6du2K5ORkTcz48eOxc+dO/P777zh69CgePnyIDz74QPO8QqFAz549IZfLceLECWzcuBEbNmzAtGnTNDExMTHo2bMnOnTogKioKIwbNw6ffvop9u3bp4nZunUrJkyYgOnTp+PcuXPw8vKCj48P4uLiCufNIKJiy9FGWlUDqXFS8YpaIkeJSROpcUREBdWrVy/06tWrqLtBVKLsiHqgl7VVAMCvdTVMf7e+Xtoi6cqXL4969eppbbO2toaDg0OW7UQlhUIp4L8pEmFXDbN2hrkxEDnVhwmVEqBFixbYsGEDateujUePHmHmzJlo06YNLl++jNjYWJiZmcHOzk5rn8qVKyM2NjbHNg1dGrFlDQeYyIB0iTNBJ/9xEQsHNDRYf4hIv/744w+tG1M2bNgAR0dHREZGom3btppZdaGhoejYsSMAICQkBHXq1MGpU6fQsmVL7N+/H1evXsWBAwdQuXJlNGzYEEFBQZg0aRJmzJgBMzMzrFq1Cm5ubli4cCEA1Zqi//zzD3744Qf4+PgAABYtWoQRI0bAz88PALBq1Srs3r0b69evxzfffFPI7wwRFStSK1Loefk33mYpldRSbPor2UZERER61GupfhetZ2KFiPThf5H/wX3KHoMlVhb3a4Drc3oysVJCdO/eHf3790eDBg3g4+ODPXv24MWLF/jtt9/y3aahSyMaG8nQu5Gz5Pg/zz/gwvZEJVhCQgIAwN5etSC0lFl1J0+eRP369VG5cmVNjI+PDxITE3HlyhVNTMY21DHqNuRyOSIjI7VijIyM0Llz51xn7xFR2RAncc0VqXFS8QpborjE13qNIyIiosIhT1fCc/pepGVdikNnXLS+eDpy5EhRd4FIZ0mv09Fo1n6kGWiQ2b9tDUzo5qHXBTup8NnZ2aFWrVq4desWunTpArlcjhcvXmjNXnn8+DGcnJxybMPc3Bzm5oatsBD8gRf+d+6hpFilAE7cfIo2tSsZtE9EpH9KpRLjxo1D69atNTOGpcyqi42N1UqsqJ9XP5dbTGJiIlJSUvD8+XMoFIpsY/79999s+5uamorU1DcDqfoui0hExcfxm08kxUld+F4qzlyRSOpC9VzQnoiIqPhQL1pf0MSKKRetJyI9SXqdjnrTwlBvxj6DJFb829ZA9NwemNijDhMrpUBSUhKio6NRpUoVNGnSBKampjh48KDm+evXr+PevXvw9vYuwl4CZiZGaFhV+npWM3ZdNmBviMhQ/P39cfnyZWzZsqWouyJJcHAwbG1tNQ8XF5ei7hIRGYBCKRAucRa41IXvpeLMFYmkLlT/34sUA/eEiIiI8qJQCrScewBPkgp+00NdJyvsGddBD70iorIs6XU6Ws49gCS5HqbRZYMzVUqHr776Cu+++y5cXV3x8OFDTJ8+HcbGxvjoo49ga2uL4cOHY8KECbC3t4eNjQ1Gjx4Nb29vtGzZsqi7jond6mDQT6clxUY/eQV5uhJmJrzfk6ikCAgIwK5du3Ds2DFUrVpVs93JySnPWXVOTk6IiIjQau/x48ea59T/VW/LGGNjYwNLS0sYGxvD2Ng425icZu9NnjwZEyZM0PycmJjIBAtRKRQRE4+E1+mSYp1sLfV6bJ2vZB48eIDBgwfDwcEBlpaWqF+/Ps6ePat5XgiBadOmoUqVKrC0tETnzp1x8+ZNrTbi4+MxaNAg2NjYwM7ODsOHD0dSUpJWzMWLF9GmTRtYWFjAxcUF8+fPz9KX33//HR4eHrCwsED9+vWxZ88eXV+OZE42FpLi/op6yPqxRERERWjnhYdwn7JHL4mVpQO8mFghogJJkSvQcNY+1JuxzyCJFc5UKV3+++8/fPTRR6hduzYGDBgABwcHnDp1CpUqqUpo/fDDD+jVqxf69u2Ltm3bwsnJCX/88UcR91qlZQ0HmOowwvDN/y4YrjNEpDdCCAQEBODPP//EoUOH4ObmpvW8lFl13t7euHTpEuLi4jQx4eHhsLGxQd26dTUxGdtQx6jbMDMzQ5MmTbRilEolDh48mOPsPXNzc9jY2Gg9iKj0iZW4TIedpSmau9nr9dg6zVx5/vw5WrdujQ4dOmDv3r2oVKkSbt68iQoVKmhi5s+fj6VLl2Ljxo1wc3PD1KlT4ePjg6tXr8LCQpWgGDRoEB49eoTw8HCkpaXBz88PI0eORGhoKABVJrlr167o3LkzVq1ahUuXLmHYsGGws7PDyJEjAQAnTpzARx99hODgYPTq1QuhoaHo06cPzp07p6n7qE/N3exhb22K+OS0XOOeJcsRERMPb3cHvfeBiIiIcucXEoHD16XVWs2NtSlwcWYPDlQSUb7J05XovuQoop+8Mkj7Pp6V8OOgZjxPlTJ5ldqxsLDAihUrsGLFikLqkXTGRjKMaueOpYejJcX/ef4hvu/fkJ9homLuyy+/xLZt27Bjxw6UL19es0aKra0tLC0tJc2q69q1K+rWrYtPPvkE8+fPR2xsLAIDA+Hv769ZE+rzzz/H8uXL8fXXX2PYsGE4dOgQfvvtN+zevVvTlwkTJsDX1xdNmzZF8+bNsXjxYiQnJ8PPz6/w3xgiKjakrqPSuY6j3q87dEquzJs3Dy4uLggJCdFsy5ixFkJg8eLFCAwMRO/evQEAP//8MypXrozt27dj4MCBuHbtGsLCwnDmzBk0bdoUALBs2TL06NEDCxYsgLOzMzZv3gy5XI7169fDzMwMnp6eiIqKwqJFizTJlSVLlqBbt26YOHEiACAoKAjh4eFYvnw5Vq1aVbB3JRvGRjL09nJGyIm7ecbGJrA0GBERUWGSpyvhNTMMKWkFnz3aoZY9QoYVbe16Iiq55OlKDPrpJM7ceWGQ9ptVt8XmT1uxnBIVS2O71JacXBEAloTfwASf2obtFBEVyLp16wAA7du319oeEhKCoUOHAlDNqjMyMkLfvn2RmpoKHx8f/Pjjj5pYY2Nj7Nq1C6NGjYK3tzesra3h6+uLWbNmaWLc3Nywe/dujB8/HkuWLEHVqlXx008/wcfHRxPz4Ycf4smTJ5g2bRpiY2PRsGFDhIWFZVnknojKFqnrqLSuWVHvx9YpufLXX3/Bx8cH/fv3x9GjR/HWW2/hiy++wIgRIwAAMTExiI2NRefOnTX72NraokWLFjh58iQGDhyIkydPws7OTpNYAYDOnTvDyMgIp0+fxvvvv4+TJ0+ibdu2MDN788b4+Phg3rx5eP78OSpUqICTJ09q1U1Ux2zfvj3H/qempiI19U0mKzExUZeXj6oVrCTFcVF7IiKiwjNr5xWsP35HL20tH9gQvRq+pZe2iKhsUSgF/DdFIkziYpq6YlKFSgJjIxk+aOiMP6IeSor/8egtjO1Si7NXiIqxhISEPMtpSZlV5+rqmmc5//bt2+P8+fO5xgQEBCAgICDXGCIqWxwlLuchNU4XOl2Z3759GytXrsTbb7+Nffv2YdSoURgzZgw2btwIAJqpgZkzxpUrV9Y8FxsbC0dHR63nTUxMYG9vrxWTXRsZj5FTjPr57AQHB8PW1lbz0HURK6mL2kuNIyIiooJp890hvSRWXCuYI3puDyZWiEhnCqXA93v/hfuUPQZJrDSrbosbs7vj98/fYWKFSoTv+nlJjk1XAiduPjVgb4iIiKjUk1rAwgDLpOs0c0WpVKJp06aYO3cuAKBRo0a4fPkyVq1aBV9fX/33Ts8mT56sNdslMTFRpwSLo8SkidQ4IiIiyh95uhIegXuh1ENbSwd44b3GVfXQEhGVJQqlwKJ917HiqLQSSLpyr2iJvePaM6FCJY6ZiRFqVrLGrSfJkuJn7LqMg7U7GLhXREREVFrFSVxzRWqcLnRKrlSpUgV169bV2lanTh3873//AwA4OTkBAB4/fowqVapoYh4/foyGDRtqYuLi4rTaSE9PR3x8vGZ/JycnPH6sfdeX+ue8YtTPZ8fc3FyzUFa+SJ2pzBnNREREBqOvMmDmRsDV2Vy0noh09+e5Bxj/W5RB2razMMLJKV1haWZskPaJCsP0dz3xyfoISbHRT15Bnq5kIpGIiIjy5fjNJ5LipC58rwudrl5at26N69eva227ceMGXF1dAagWn3JycsLBgwc1zycmJuL06dPw9lYtDOvt7Y0XL14gMjJSE3Po0CEolUq0aNFCE3Ps2DGkpaVpYsLDw1G7dm1UqFBBE5PxOOoY9XEMIS7xtV7jiIiISDqFUqDZ7HC9JFbqOlnh+tyeTKwQkU5S5ArUnbbXIImVcqZGuDzDB1EzujOxQiVeq5oVYazDn9hP1p0yXGeIiIio1FIoBcIlluaVuvC9LnRKrowfPx6nTp3C3LlzcevWLYSGhmLNmjXw9/cHAMhkMowbNw6zZ8/GX3/9hUuXLmHIkCFwdnZGnz59AKhmunTr1g0jRoxAREQEjh8/joCAAAwcOBDOzs4AgI8//hhmZmYYPnw4rly5gq1bt2LJkiVaJb3Gjh2LsLAwLFy4EP/++y9mzJiBs2fPGnRRK6kL1R+/xZqxRERE+rTzwkO4T9mDJ0nS/hbnZukAL+wZx/IjRCSdPF2JTgsPo860MLyS66Mg4RvqpMrloO4oZ6FTYQGiYsvYSAb/9u6S40/HPIc8Xb+/W0RERFT6RcTEI+F1uqRYJ1tLvR9fp6v3Zs2a4c8//8TkyZMxa9YsuLm5YfHixRg0aJAm5uuvv0ZycjJGjhyJFy9e4J133kFYWBgsLCw0MZs3b0ZAQAA6deoEIyMj9O3bF0uXLtU8b2tri/3798Pf3x9NmjRBxYoVMW3aNIwcOVIT06pVK4SGhiIwMBBTpkzB22+/je3bt6NevXoFeT9yJXWh+gPX4qBQCt4NS0REpAd+IRE4fF3aNN/cVLQ2xulvffj3mYgkk6crMeinkzhz54Xe2zY3BiKn+jChQqXW2C61sfSw9DWJQo7fxmftahqwR0RERFTaxEqsIGVnaYrmbvZ6P77OV/K9evVCr169cnxeJpNh1qxZmDVrVo4x9vb2CA0NzfU4DRo0wN9//51rTP/+/dG/f//cO6xHTjYWeQcBeJGShoiYeHi7Oxi4R0RERKWXPF0Jr5lhSEkTBW7Lr3U1TH+3vh56RURlgSGTKgCwuF8D9GnqYpC2iYoLYyMZPmjojD+iHkqKX/9PDJMrREREpBOp66h0ruNokBsteZuUDpq72cPWwkTSVKPYhJRC6BEREVHpFLTrKtb9E1PgdiyMgYszu3ORXCKSbPqOy9h48q5B2u5StyJWDW7OGXRUZnzXz0tycuXxSzkXticiIiKd2FlJW0fF272iQY7P5IoOjI1k6FK3Mrade5BnrNT1WYiIiEhbr6V/4/LDxAK306GWPUKGeeuhR0RUFqTIFag/IwyGWPahWXVbbP60FQeNqcwxMzGCe0VrRD9NlhT/ybpT2PpZKwP3ioiIiEqLF6+kjcFLjdMVkys6av12JUnJFanrsxAREZGKPF2JBjP2QuJadLlaPrAhejV8q+ANEVGplyJXoOPCw3iUIK2kgC7cK1pi77j2TKpQmTbjPU98sj5CUqx6YXv+zhAREZEU9+Kl3cAhdYaLrphc0ZGjxKSJ1DgiIiICZu28gvXH7xS4HWtT4OLMHiy5Q0R5kqcr0X3JUUQ/eaX3tp1tzHDwq46wNDPWe9tEJU2rmhUhAyB1BTXOXiEiIiIpFEqBP87nPQkC4MyV4kPqWA3HdIiIiPKkUAq0nHsAT5IKfqHDMmBEJIUhF6uvYGGME1O6MKlClIGxkQzv67CwPWevEBERkRQRMfF4+VohKdbe2jAzV3i1oqO4xNd6jSMiIiqrdl54CPcpe/SSWFk+sCETK0SUp+k7LqNW4F6DJFaWDvDC+RndmFghysZ3/bx0iv9k3SkD9YSIiIhKi1gdxt+dbC0N0gfOXNGR1IXqj996ivcbVzVwb4iIiEomv5AIHL7+pMDtVLQ2xulvfVgGjIhyZcjF6v3b1sCEbh48DxHlwszECC2qV8DpO88lxXP2ChEREeUlPknamok2FiZo7mZvkD7wSkVHUheqP3AtDgql1KqyREREZUfjWfv1kljxa10NZ6d244BmGRQcHIxmzZqhfPnycHR0RJ8+fXD9+vWi7hYVQylyBbyDD6DONP0nVnw8KyF6bg9M7FGH5yEiCX75tKVO8Zy9QkRERLmRukj9+43eMtj1Omeu6MjJxkJS3IuUNETExMPb3cHAPSIiIioZ5OlKeATuRUHHN01kwNWg7rybtQw7evQo/P390axZM6Snp2PKlCno2rUrrl69Cmtr66LuHhUDhlysvll1W2z+tBXPQUQ64uwVIiIi0iepi9RXs7cyWB+YXNFRczd72FqYIOF1ep6xsQkphdAjIiKi4m/mX1cQcuJOgdt5y9YUxyd3LXiHqEQLCwvT+nnDhg1wdHREZGQk2rZtW0S9ouJAoRTw3xSJsKuP9d42kypEBffLpy1RK3Cv5PhP1p3C1s9aGbBHREREVFJJnbkiNS4/mFzRkbGRDF3qVsa2cw/yjJW6PgsREVFppVAKNJq1H4kSbkrIi1/rapj+bn099IpKm4SEBACAvb1h6uhSyfC/yP/w5e8X9N6ujbkMZ6d2Y1KFSA84e4WIiIj05WT0U0lxUme45AeTK/ng7V5RUnLFkFkxIiKi4m5H1AOM3RJV4HYsjIGLM1kGjLKnVCoxbtw4tG7dGvXq1cs2JjU1FampbxY7TExMLKzuUSFIkSvgNWsf5On6X+9wcb8G6NPURe/tEpVlnL1CREREBaVQCuy+9EhSrL214cboOUqRD1KzXYbMihERZfTdd99BJpNh3LhxRd0VIgBAr6V/6yWx0qGWPf6d05OJFcqRv78/Ll++jC1btuQYExwcDFtbW83DxYWD5aWBPF2JTgsPo860ML0nVvzb1kD03B5MrBAZgHr2ilTq2StEREREaqduP0NKmrTrAydbS4P1gyMV+VAc6rkREamdOXMGq1evRoMGDYq6K0RQKAU8Avfg8sOCzwxYPrAhQoZ566FXVFoFBARg165dOHz4MKpWrZpj3OTJk5GQkKB53L9/vxB7SfqmUAp8/vNZ1Arcq/cF6308KyF6bg9M7FEHxkYyvbZNlJfg4GA0a9YM5cuXh6OjI/r06YPr169rxbRv3x4ymUzr8fnnnxdRj/Pvl09b6hT/ybpTBuoJERERlUQno59JiitnboLmboYrH83kSj5InZEite4bEVF+JSUlYdCgQVi7di0qVJB+ByCRIey88BDuU/bgdQHvIK9oZYzouT3Qq+FbeuoZlTZCCAQEBODPP//EoUOH4Obmlmu8ubk5bGxstB5UMv157gHcp+zR+4L1zarb4sbs7lj9SXMmVajIHD16FP7+/jh16hTCw8ORlpaGrl27Ijk5WStuxIgRePTokeYxf/78Iupx/nH2ChERERWEgLRxh3fedjDo9T3XXMkH+3LmkuIOXIuDQin4BY2IDMbf3x89e/ZE586dMXv27KLuDpVhfiEROHz9SYHb6VjbAev9dLublcoef39/hIaGYseOHShfvjxiY2MBALa2trC0NNyUbyo6KXIFmszej1dy/Q6ucrF6Kk7CwsK0ft6wYQMcHR0RGRmJtm3barZbWVnBycmpsLund7quvdJz6TGET2hvuA4RERFRiWFnaSoprkk1w96IzORKPjjZWEiKe5GShoiYeHi7Oxi4R0RUFm3ZsgXnzp3DmTNn8ozlYs5kKAqlQMOZ+/AyVVHgtpYPbMjZKiTJypUrAajK42QUEhKCoUOHFn6HyGAUSoG+K44j6kGC3tvmYvVU3CUkqD739vbapSw2b96MTZs2wcnJCe+++y6mTp0KKyurHNsprteB6tkrp+88lxR/My4ZKXIFLM2MDdwzIiIiKu7+ey6tPLC9tbRJEvnFW7TyobmbPWwtpOWlYhNSDNwbIiqL7t+/j7Fjx2Lz5s2wsMg74cvFnMkQdkSpyvMUNLFSzlTGMmCkEyFEtg8mVkoXdQkwfSdWuFg9lQRKpRLjxo1D69atUa9ePc32jz/+GJs2bcLhw4cxefJk/PLLLxg8eHCubRXn60Bd1155/8d/DNQTIsrJ8ePH8e6778LZ2RkymQzbt2/Xen7o0KFZ1oLq1q2bVkx8fDwGDRoEGxsb2NnZYfjw4UhKStKKuXjxItq0aQMLCwu4uLhkW/Lw999/h4eHBywsLFC/fn3s2bNH76+XiIo/hVLgj/MPJMVKXd4jvzhzJR+MjWToUrcytp3L+x8xPtmw/4BEVDZFRkYiLi4OjRs31mxTKBQ4duwYli9fjtTUVBgbv7mrb/LkyZgwYYLm58TExGL1xZpKnl5L/9bLovW+rVww870GeugREZUWhioB5uNZCT8OasaSvVQi+Pv74/Lly/jnH+1kwsiRIzX/X79+fVSpUgWdOnVCdHQ03N3ds22rOF8H6jp75d/YJMjTlSzlR1SIXr16BS8vLwwbNgwffPBBtjHdunVDSEiI5mdzc+07xQcNGoRHjx5p1pPy8/PDyJEjERoaCkB1XuratSs6d+6MVatW4dKlSxg2bBjs7Ow0570TJ07go48+QnBwMHr16oXQ0FD06dMH586d00pCE1HpFxETj5evpd3kaW9tZtC+MLmST97uFSUlV+ysDPsPSERlU6dOnXDp0iWtbX5+fvDw8MCkSZO0EiuA6uI28wUuUX7I05XwnL4XaQWsAmYqA64EdefgCBFpyNOV6L7kKKKfSJviL5V7RUvsHdee5xsqMQICArBr1y4cO3YMVatWzTW2RYsWAIBbt27lmFwp7teBuq690m7+IZyc0tmAPaK8yNOVWPv3Lfx8/DYeJ+V9UWhqJIOjjTkGtXDFp21q8HxcwnTp0gV9+/bNNcbc3DzHtaCuXbuGsLAwnDlzBk2bNgUALFu2DD169MCCBQvg7OyMzZs3Qy6XY/369TAzM4OnpyeioqKwaNEiTXJlyZIl6NatGyZOnAgACAoKQnh4OJYvX45Vq1bp8RUTUXEXm/hacqyTrWHX5GRyJZ+kTiky9NQjIiqbypcvn+XuHGtrazg4OPCuHTKYoF1Xse6fmAK3U9fJCnvGddBDj4ioNFAoBfw3RSLs6mO9tmtlCkRO7cb1GajEEEJg9OjR+PPPP3HkyBG4ubnluU9UVBQAoEqVKgbuneGYmRihWXU7nLnzQlL8o8RU7Ih6gN4sJ1qoFEqBY9fi8OW2KMSnpOu0b5pS4MGL15i/7zrm77uu2W4sA2wsTeHj6YTp73ryfF2CHTlyBI6OjqhQoQI6duyI2bNnw8FBtf7wyZMnYWdnp0msAEDnzp1hZGSE06dP4/3338fJkyfRtm1bmJm9uUHZx8cH8+bNw/Pnz1GhQgWcPHlSaxaeOiZzmTIiKv2evkzNOwiAjYUJmrvZ5x1YAEyu5JPUGSmcuUJERKVBr2V/4/KDgpcBWzrAC+81zv0uXCIqO/489wDjf4vSe7tcrJ5KIn9/f4SGhmLHjh0oX748YmNjAQC2trawtLREdHQ0QkND0aNHDzg4OODixYsYP3482rZtiwYNSnaJzc2feus0e2Xclij0auDMMn+FZEfUA4zbEgWh53YVAnj+Kg1bztzHljP3AQAWJjK0rOGA5R83QTmJa91S0erWrRs++OADuLm5ITo6GlOmTEH37t1x8uRJGBsbIzY2Fo6Ojlr7mJiYwN7eXnOei42NzZJQrly5sua5ChUqIDY2VrMtY4y6jeykpqYiNfXNIGxiYsG/zxBR0XsucTKDt7uDwa8V+Jcqn6TOSDkZ/RR9m3AQiYgM78iRI0XdBSqFFEqB5rP349kr3e5QzMwIwM25PTgIQkQAVCVlms0JR4KOdz/nheuqUEm2cuVKAED79u21toeEhGDo0KEwMzPDgQMHsHjxYiQnJ8PFxQV9+/ZFYGBgEfRWv3Rde0UAGB16Dj8ObmLYjpHebrCR6nW6wJEbT1Fvxj4AQAUrzmwp7gYOHKj5//r166NBgwZwd3fHkSNH0KlTpyLsGRAcHIyZM2cWaR+ISP9uP0mSFFfTsZyBe8LkSr7Zl5NWs3bP5VjM6yf4BY+IiEqcnRceYvSv5wvcjrONCU5M8dFDj4iopDNUCTCuq0KlgRC5zwtwcXHB0aNHC6k3hU/XtVf2XI7l4vYG1iRoP54lpxVpHzLObDGRAf2aujDRUszVqFEDFStWxK1bt9CpUyc4OTkhLi5OKyY9PR3x8fGadVqcnJzw+LH2tYH657xiclrrBQAmT56sVUosMTERLi6c2UpUkimUAv/ceiIp1s7S1MC9Ud1ISvngZGMhKe6VXIFT0c8M3BsiIiL98guJ0Etixa91NSZWiAiAqqyM+5Q9ek2sWJnKcG1WNxz8qiMHWIlKODMTI3Sr55h3YAY9lx4zUG/one/Cizyxklm6ALacuY8608LQZFYYkl7rd/Yj6cd///2HZ8+eadaC8vb2xosXLxAZGamJOXToEJRKJVq0aKGJOXbsGNLS3nzmwsPDUbt2bVSoUEETc/DgQa1jhYeHw9vbO8e+mJubw8bGRutBRCVbREw8klKVkmIrSpwcURD8BpJPzd3sYS3xTomTt58auDdERET6oVAK1J8ehsPXpd0JkhNTGXBjdndMf7e+nnpGRCWVPF2JFnPDMXZLlF7bXdyvAa4G9eDdy0SlyIqPm+YdlMHNuGTsvPDQQL0pu2buvIT/XkgrhV5Unr1SoN6Mfag5eTd+PHwL8nRpA22ku6SkJERFRSEqKgoAEBMTg6ioKNy7dw9JSUmYOHEiTp06hTt37uDgwYPo3bs3atasCR8f1Q1WderUQbdu3TBixAhERETg+PHjCAgIwMCBA+Hs7AwA+Pjjj2FmZobhw4fjypUr2Lp1K5YsWaI162Ts2LEICwvDwoUL8e+//2LGjBk4e/YsAgICCv09IaKiE5v4WnKsk62lAXuiwuRKPhkbydDm7YqSYpX6XvWNiIjIANR3lb9MVRSonbpOVrgZ3JN3kROVcQqlwOc/n0WtwL14nKi/QTqvt8ohem4PLlhPVAoZG8kwpoO7TvuM+fU8FPzSrTfydCVCjt8r6m5Ili6A+fuuo1bgXvRf9Q+TLAZw/vx5NGrUCI0aNQIATJgwAY0aNcK0adNgbGyMixcv4r333kOtWrUwfPhwNGnSBH///TfMzd/cMb5582Z4eHigU6dO6NGjB9555x2sWbNG87ytrS3279+PmJgYNGnSBF9++SWmTZuGkSNHamJatWqF0NBQrFmzBl5eXti2bRu2b9+OevXqFd6bQURF7unLVElxNhYmaO5mb+DecM2VAmniao+wK3mXNahgZVYIvSEiIso/fS1WunSAF95rXFUPPSKikkxfazZlZGUqQ+RUH85UISrlxnapjeWHoyF1iFwA6L/yBP7wb23IbpUZPZeU3HV9ztxJQK3AvWhW3RabP23FG330pE2bNrmuCbVv374827C3t0doaGiuMQ0aNMDff/+da0z//v3Rv3//PI9HRKXX81fSbtrydncolDXQ+ZemAOytpSVNpMYREREVhcaz9hc4sWJmBETP7cHEClEZp1AK9Fn+j94TKywBRlR2GBvJsHRgQ532OXf/BcuD6UGKXIGbT14VdTcKTJ1kGfHzac5qIiIqZW4/SZIUV9OxnIF7osLkSgG8kJgpkxpHRERUmOTpSrhP3o34VwVbrPQtG1PcmNuzUO4KIaLiS11aMOq/BL216eNZiSXAiMqgXg3fwtuVrHTaZ+wWlgcrqJEbzxR1F/Qq/OpTuE/Zg7/O/VfUXSEiIj1QKAUOX4+TFGtnaWrg3qiwLFgB2Eks93UvvuTf+UFERKXLrJ1XsP74nQK307G2A9b7tSx4h4ioxJKnK9Fm/kG9rqtSuZwp/v6mM0u6EJVhu8e2Q63AvZLjlQIYHXoOPw5uYsBelV4KpcDf0c902sdEBkzoWhuftqmR4/k6Ra7AtL8uIuziI7yUF03ya8xvFzBnz1X+XSEiKuFO3X6G1HRpf0sqljPPO0gPmFwpAKkzUv48/wDT3vXkHb1ERFQstJl3CPefpxS4neUDG6JXw7f00CMiKqlm/nUFISfu6LVNrt1ERABgZmKEHvUqY8/lvNc5VdtzORbydCUH0PPhxK2nOsV/4l0NQb3r5xlnaWaM7/s1wvf9GmltVygFjl2Lw7x9V3Ez7hUUOh1dd4+T0lArcC98W7lg5nsNDHw0IiIyhJM63ATgZGtpwJ68weRKAdhLzIAlvk5HREw8vN0dDNwjIiKinMnTlag7bS/Spa4Qm4OK1sY4/a0PbxogKsNS5Ap4zdoHucQ7x6Tw8ayEHwc147mFiDSWfdwEe6fsgS5nmh5LjuLAlx0M1qfSaubOK5Jj3StaSkqs5MbYSIYOnpXRwbOyZlvS63SM3nwGf9+MR3qBWs/ZxhP3sfX0fVyc2Z1JOCKiEkZIvCIoZ26M5m72Bu6NCpMrBeBkYyE5Njah4HcIExER5Ze+yoD5ta6G6e8W7Ms0EZVsfiEROHz9id7aYwkwIsqJsZEMywY2RMCWKMn73HryCjsvPMS7Xs6G61gpI09X4taTZMnxe8e1N0g/ylmYIGS4N4A3M1sCd1zEAz2WnQSA1wqgVuBeXtcSEZUwNhbS1lHpWtep0G7YYnKlAJq72aO8hTFevs57Amt8Mhe1JyKiotFm/iHcjy9Ykt9EBlwN4h1+RGWZPF2JejPC9DpbhSXAiCgvvRq+hfXHb+Pc/UTJ+4z+9Tx61K/CmXASbdShvKN7JatCuR5Uz2w57tkFCqXAkSuP8fmvkUgr4AzsjEKO38O+Sw9xYoqP/holIiKDOXc3XlJcZdvCWW8FADhCUgDGRjJ80EharXk7KzMD94aIiEibQilQb9reAidW3rIxxa3gnkysEJVRCqXA5z+fRa3AvXpLrPh4VkL03B5MrBCRJL+PekfnfVrOCTdAT0qnTafuSI6d0aue4TqSA2MjGTrVd8LNuT1xeYYPXOykVxHJy8PEdNT4ZjcUSv3dOEBERPqnUAocvSltfbBHL14buDdvcJSkgKpWsJIUF5+cauCeEBERvbHzwkO4T9mDJHnBbu/rWNsBx6d01VOviKik2RH1AO5T9iDsqvQFpXNjYQzcmN0dqz9pzjvKiUgyYyMZlg7w0mmfJ8lpGLYhwkA9Kj3k6UrclXgjjpEMaPV2RQP3KHflLEzw9zedcGN2dzSrbqeXNpUA3KfswZ6LD/XSHhER6V9ETDxeS5y+6KzHJHxemFwpoBcpaZLiIu89N3BPiIiIVIZtiMDoX88XuJ3lAxtivV9LPfSIiEoahVKg/feHMVaHdQ7y4tvKBf/O4Sw4Isqf9xpXRWUbabXW1Q79+wQ7L3DAPDe6lATrXMex2CTGzUyM8PvnrfWaZPki9DyCdl3WS1tERKRfsYnSZ6O0dq9kwJ5o4zebApJB2oXFkX+fcJopEREZlEIp0DRoPw79W7CFpsuZyhA9twd6NZRW+pKoKKxYsQLVq1eHhYUFWrRogYgI3p2sL+rZKneevdJLe5XLmeLG7O6Y+V4DvbRHRGXX31931nmf0b+e53fxXOy8+EByrK+3mwF7kj8ZkyyVbQpejn3dP3fht/6UHnpGRET69PSltKpQlqZGaOnuYODevMHkSgF5S/zHep2uxKnoZwbuDRERlVXqMmBPk6XNqMxJh1r2uBzUo9jclUiUna1bt2LChAmYPn06zp07By8vL/j4+CAuLq6ou1bi9Vzyt15nqywd4IXTgV05W4WI9MLMxAh+ravpvB/XX8meQilw5UGipFgTI1mhDlbpyszECKendMGSgQ0L3NbhG8/wTjA/M0RExcnZO9LG1dvWqlSo4xn8llNALWs4wFzil8WTt6UtukNERKQLfZYBCxnmrYceERnWokWLMGLECPj5+aFu3bpYtWoVrKyssH79+qLuWoklT1fCffJuXHkkbZAtL1ywnogMZfq79VHR2kSnfZ4kp8Ev5LSBelRynbr9DAqJk3o6ehTuYFV+9W74FqLn9kC3upUL1M5/CXI0mhmmp14REVFBKJQCh/6VdiOdpamxgXujjcmVAjI2kqFDbWl13DgTmYiI9O2deQcLXAbMmmXAqASRy+WIjIxE585vSsMYGRmhc+fOOHnyZBH2rOSa+dcV1ArcK3mALTdcsJ6ICsPpb7vqvM/h608RtOuqAXpTcp2Iln4DaHEsCZYTYyMZVg1pihuzu8PSNP9/i56nKNCQCRYioiJ36vYzSFzLvlAXsweYXNGLRtUqSIqztdRt8T0iIqKcKJQC9abtxX/PpS/qlp26la1whWXAqAR5+vQpFAoFKlfWviO1cuXKiI2NzXaf1NRUJCYmaj1INVul7rS9CNFhMePccMF6IiosxkYyLM9H+ad1/8Rgz8VH+u9QCRUREy8pzsy4eJcEy4mZiRGuBfWQfENsdl4wwUJEVOR0uRmgMBezBwqYXPnuu+8gk8kwbtw4zbbXr1/D398fDg4OKFeuHPr27YvHjx9r7Xfv3j307NkTVlZWcHR0xMSJE5Genq4Vc+TIETRu3Bjm5uaoWbMmNmzYkOX4xWUh08TX0urbn7//3MA9ISKiskC9vkqSXOKtGzno5FERe8Z30FOviIqv4OBg2Nraah4uLi5F3aUip56t8qqA5xHgzWwVLlhPRIWpV8O30LG27gP+X4Se4wL3UN2oc/6utDEKr6q2JfpGnBC/5lj2UaN87/8iRYFGs/bpsUdERKSLB89TJMUVxc0A+U6unDlzBqtXr0aDBtpfosaPH4+dO3fi999/x9GjR/Hw4UN88MEHmucVCgV69uwJuVyOEydOYOPGjdiwYQOmTZumiYmJiUHPnj3RoUMHREVFYdy4cfj000+xb9+bP2bFaSFTGaRdZBz59wkv4oiIqED0ub7KuqEt9NAjosJVsWJFGBsbZ7l55/Hjx3Bycsp2n8mTJyMhIUHzuH//fmF0tVhSKAUazNjH2SpEVCqs92up8/orANBi9n4D9KZkOXX7GdIlDk80c7M3bGcKwbtezoie2wNm+fxz9fxVOlp/d0C/nSIiIklS5Ol5BwHo4OFY6DcD5OvPSlJSEgYNGoS1a9eiQoU3JbESEhKwbt06LFq0CB07dkSTJk0QEhKCEydO4NSpUwCA/fv34+rVq9i0aRMaNmyI7t27IygoCCtWrIBcLgcArFq1Cm5ubli4cCHq1KmDgIAA9OvXDz/88IPmWMVpIVNviRmx1+lKnIp+ZuDeEBFRafXOdwVfX6WilTHXV6ESzczMDE2aNMHBgwc125RKJQ4ePAhvb+9s9zE3N4eNjY3WoyzaEfUA7lP2IPG1tC8nualczpSzVYiKUHGp4lAc5Gf9laev0tFjyVED9KbkKM4lVgzF2EiGG3N7ooKl7gk5AHjwIrXMf26IiAqbQilw5Ia0cZCmrtKW7tCnfCVX/P390bNnT62FRAEgMjISaWlpWts9PDxQrVo1zQKjJ0+eRP369bXqZPv4+CAxMRFXrlzRxGRu28fHR9NGfhcyNVS97ZY1HGBmLC0rdjy6YINiRERU9sjTlag5ZTf+e1Gw9VU61nbA2WndSnRZByIAmDBhAtauXYuNGzfi2rVrGDVqFJKTk+Hn51fUXSu2ei39G2O3ROmlraUDvHA6sCtnqxAVkeJUxaE4yO/6K1cfJaHX0mP671AJUdrXW8nN+ek+qFohfwsel/XPDRFRYTt1+xlSJU61rFjO3MC9yUrnb0RbtmzBuXPnEBwcnOW52NhYmJmZwc7OTmt7xgVGY2Njs12AVP1cbjGJiYlISUnJ10KmgOHqbRsbydDQxU5S7MMCDowREVHZMmunal2E9AIui7B8YEOs92upn04RFbEPP/wQCxYswLRp09CwYUNERUUhLCwsy7Uhqe708gjcg8sPC35TkWsFc0TP7YH3GlfVQ8+IKL+KUxWH4iK/669cfvgSw0LK3qyfsrTeSk7+mdQJHWpXzNe+ZfVzQ0RUFHSZaelka2nAnmRPp+TK/fv3MXbsWGzevBkWFvnL8hclQ9bbblJd2rSjlDSF3o5JRESlW5v5h7D++J0CteHAMmBUSgUEBODu3btITU3F6dOn0aIF1xDKTF0G7LXUovq5WDrAC0cndS6VA2xEJUl+qziUBfldf+XQ9SeYufOKAXpUfJW19VZyEuLXAn6tq+dr30PXnyBo11X9doiIiLKQOtPS0tQIzYvgb5ZOyZXIyEjExcWhcePGMDExgYmJCY4ePYqlS5fCxMQElStXhlwux4sXL7T2y7jAqJOTU7YLkKqfyy3GxsYGlpaW+VrIFDBsvW17K2nTjo7d4KL2RESUt0Yz9+F+fEqB2vB0skYky4ARlUn6KgNmbQrOViEqRvJTxcFQ5bGLo/ysvwIAIcfvYObOy3ruTfFVFtdbycn0dz0x/J3q+dp33T8x2HPxkX47REREGgqlQOQdaTMt6znbFMnYh07JlU6dOuHSpUuIiorSPJo2bYpBgwZp/t/U1FRrgdHr16/j3r17mgVGvb29cenSJa16sOHh4bCxsUHdunU1MRnbUMeo28jPQqaGVrG8tORKShoXtSeiggsODkazZs1Qvnx5ODo6ok+fPrh+/XpRd4v0QKEUqDVlN56nFGzB6U4eFbF7XHv9dIqISgx9lgHzbeWCK0E9maAlKuEMVR67ODI2kuHHjxvla9+Q43cxLOS0nntUPJXl9VayM7WXJ/xau+Zr3y9Cz/EGWiIiAzlx6ymkVkgvqpmWOiVXypcvj3r16mk9rK2t4eDggHr16sHW1hbDhw/HhAkTcPjwYURGRsLPzw/e3t5o2VJV571r166oW7cuPvnkE1y4cAH79u1DYGAg/P39YW6uSlB8/vnnuH37Nr7++mv8+++/+PHHH/Hbb79h/Pjxmr4Ut4VMnWykl0k7eVv6XSJERNk5evQo/P39cerUKYSHhyMtLQ1du3ZFcnJyUXeNCmDnhYdwn7IHcj2sr7JuKEskEZU1+ioDZmEM3JjdHTPfa6CnnhGRvuSnioMhy2MXRz0aOGP4O/kbKD90/SneLeWLlXO9lexNf7ceOuZzDZa6U/fouTdERAQAyw7dkBxbVDMtdV7QPi8//PADevXqhb59+6Jt27ZwcnLCH3/8oXne2NgYu3btgrGxMby9vTF48GAMGTIEs2bN0sS4ublh9+7dCA8Ph5eXFxYuXIiffvoJPj4+mpjitpBpczd7WJlJezt5UwMRFVRYWBiGDh0KT09PeHl5YcOGDbh37x4iIyOLumuUT8M2RGD0r+cL1EZFrq9CVGbpqwyYbysX/DunJ8xM9P41gYj0ID9VHAxZHru4mtor/wPllx6+hN/60juDheut5Gy9XwvUq1JO5/1SFUCbeYcM0KPi4fjx43j33Xfh7OwMmUyG7du3az0vhMC0adNQpUoVWFpaonPnzrh586ZWTHx8PAYNGgQbGxvY2dlh+PDhSEpK0oq5ePEi2rRpAwsLC7i4uGD+/PlZ+vL777/Dw8MDFhYWqF+/PvbsYWKLqLRSKAXO3n0hKdbYCEU207LA35qOHDmCxYsXa362sLDAihUrEB8fj+TkZPzxxx9Z7qBxdXXFnj178OrVKzx58gQLFiyAiYn2wnPt27fH+fPnkZqaiujoaAwdOjTLsYvTQqbGRjJ085SW2IlNeG3g3hBRWZOQkAAAsLfP/gtQWaq1XdIolAJNg/bj0L9PCtROx9oOOMv1VYjKJM9pYQUuA2Yi42wVopKiuFVxKK7yO1AOAIdvPMXQdaf03KPigeut5G7X2HbwzMfn5v7zFMzaecUAPSp6r169gpeXF1asWJHt8/Pnz8fSpUuxatUqnD59GtbW1vDx8cHr12/GvgYNGoQrV64gPDwcu3btwrFjxzBy5EjN84mJiejatStcXV0RGRmJ77//HjNmzMCaNWs0MSdOnMBHH32E4cOH4/z58+jTpw/69OmDy5fLznpJRGXJqdvPJE9QaOxiV2RjIbwlTY+q2FlJittz6RFrchKR3iiVSowbNw6tW7dGvXr1so0pS7W2SxJ1GbCnyWkFamf5wIZY79dST70iopJCoRRw/2Y3kuWKArXzlq0pbgVztgpRSVHcqjgUZ7vGtkNdJ+t87Xvk5jO0nrtfzz0qelxvJW+78/m5WX/8DuTpBazvWwx16dIFs2fPxvvvv5/lOSEEFi9ejMDAQPTu3RsNGjTAzz//jIcPH2pmuFy7dg1hYWH46aef0KJFC7zzzjtYtmwZtmzZgocPHwIANm/eDLlcjvXr18PT0xMDBw7EmDFjsGjRIs2xlixZgm7dumHixImoU6cOgoKC0LhxYyxfvrxQ3gciKly63AwwpmMtA/Ykd/wGpUcySMuQvU7novZEpD/+/v64fPkytmzZkmNMWau1XRIM33CmwGXArE1lLANGVEapk7MFS6sAfq2r4fjkrnrpExEVnuJUxaG42zOuPd6yNcvXvg8S01Bryu5Sc3Mk11uRLr+fm2azS19CLjcxMTGIjY1F586dNdtsbW3RokULnDx5EgBw8uRJ2NnZoWnTppqYzp07w8jICKdPn9bEtG3bFmZmb95zHx8fXL9+Hc+fP9fEZDyOOkZ9HCIqXaTeDGBiBLR6O3+lQPWByRU98tbhrg4uak9E+hAQEIBdu3bh8OHDqFq1ao5xZbHWdnHWc+kxHPw3rkBt1K1shStBPcr0l16iskofazSZ/n8ZsOnv1tdTr4iIiq/jk7uggqVxvvaVKwH3KXuwK+qBnntV+Ljeim6OT+4COwvdhs0SXiswbEOEgXpU/MTGxgJAlplzlStX1jwXGxsLR0dHredNTExgb2+vFZNdGxmPkVOM+vnssDw2UcmkUApE3pF2M0CjIiwJBjC5olctazjATOL1Wim58YWIiogQAgEBAfjzzz9x6NAhuLm5FXWXSKLWwQdw5eHLArXRyaMi9ozvoKceEVFJ8s53Bwu8RlNdJyvcZBkwIipjzk/vhgpWJnkH5iBgSxT81pfsO+S53oruomZ0lzzOo3bo3yfYeeGhYTpEOmF5bKKS6cStp5BaZLGobwbgNyo9MjaSoVeDKpJiuag9ERWEv78/Nm3ahNDQUJQvXx6xsbGIjY1FSkpKUXeNcqBQCtT+djceJKQWqJ3lAxti3VCW/iAqi+pO3Yv/XhTsGnLpAC/sGcfkLBGVTeen+cA5nyXCAODwjXh4Ti25ZcK43kr+XAvqofM+o389X2I/J7pwcnICADx+/Fhr++PHjzXPOTk5IS5Oe9Z+eno64uPjtWKyayPjMXKKUT+fHZbHJiqZlh26ITm2qG8GYHJFz7ioPREVhpUrVyIhIQHt27dHlSpVNI+tW7cWddcoG+q1EVILsDiCmRG4vgpRGaVeuP5VWv4XyTX//3PIe41zLiFJRFQWnJjcpUAJluQ0VZmwv879p8deGR7XW8k/YyMZlg7w0nm/lnPCDdCb4sXNzQ1OTk44ePCgZltiYiJOnz4Nb29vAIC3tzdevHiByMhITcyhQ4egVCo160V5e3vj2LFjSEtL08SEh4ejdu3aqFChgiYm43HUMerjZIflsYlKHoVS4OydF5JijY1Q5DcDMLmiZ1zUnogKgxAi28fQoUOLumuUiT7WRnjLxhQ35vbkl1yiMmjPxUcFXri+bmUrXOc5hIhI48TkLrAvQIkwABjz2wW0mL0f8vT8J74LE9dbKZj3GldFdXsLnfZ5kpxWKtZfSUpKQlRUFKKiogCoFrGPiorCvXv3IJPJMG7cOMyePRt//fUXLl26hCFDhsDZ2Rl9+vQBANSpUwfdunXDiBEjEBERgePHjyMgIAADBw6Es7MzAODjjz+GmZkZhg8fjitXrmDr1q1YsmQJJkyYoOnH2LFjERYWhoULF+Lff//FjBkzcPbsWQQEBBT2W0JEBqRLSbDGRbzeCsDkit7psqj98eiC1csmIqLiTR9rI3Ss7YDjU7rqqUdEVJLM2X0VX4SeK1Abfq2rcY0mIqJsnJvmg6oVdBssz+xxUhpqBe7FZ79EFPvKFFxvpeAOftVR531Kw/or58+fR6NGjdCoUSMAwIQJE9CoUSNMmzYNAPD1119j9OjRGDlyJJo1a4akpCSEhYXBwuLN79fmzZvh4eGBTp06oUePHnjnnXewZs0azfO2trbYv38/YmJi0KRJE3z55ZeYNm0aRo4cqYlp1aoVQkNDsWbNGnh5eWHbtm3Yvn076tWrV0jvBBEVBl1Kgo3pWMuAPZFGJoQo3lcABpSYmAhbW1skJCTobWqgqqb+Hkl3hDRztcPvo1rr5bhEZDiGOFcUtdL4moqbuoF78aqAdzIuH9iQZcCoyJXG80VJeE0zd15ByPE7BWrjx48boUcDZ/10iKgMKgnnivwora8rv4ZtiCjwzTBqi/s1QJ+mxXPB7P4rj+PM3Rd5xpkZy3AtqHuR3wlcXO2KeoCALVE67xc9t0eJe09L47miNL4motJEoRR4e8oeSTNXjGTAzTmGO7dKPV9w5oqeGRvJ0Mi1gqTYC/8lFPu7W4iISDf/196dhzV1pv0D/yaBBFCJyk5FBRcUraKoiEsVq4Kg1qndq3WrrY5aFasVF0BrxXGrS612c3nntVPbmba2BRfEra24FEVfFFBcRsvmVokbBJL8/vBHKhXlJORk/X6u61wzCc85uQ/UO8m5z/PcGq0OgbNT6lRYqecsYX8VIgc2dtPROhVWqno0sbBCRFS7jaO7Ye2rnUxyrGn/PoXguSm4U1ZpkuOZikarw28CCisAEORZz+aKAOY0OPQp9As2fH3/F9b/KkI0RET2xZAlwcKaWn5JMIDFFVF0E7g+qVqjY98VIiI7UtW4vi5l8yZKOU6/b3t3thGRaQxecxB784y/g5o9moiIDDekoz/OL46BQlb3Y5VrgPZJu9B+/g6rKbIcyr8u+PNpQGM3UWOxBxvHdIdnPcN69py4Umrzy4MREYltwY+nBY+1hiXBABZXRNGjhafgsey7QkRkH0zRuL5fsAd+iR9gooiIyNbErj6A7MLbRu/PHk1ERMaTSSXI+yAWAY1cTXK8OxVatE/ahZbxKfh4X75FG98bcrFK6M2iju7IXMPfb9/51wmuXkJE9BjqSi3yr90VNFYqAXq0En79XUwsroige5AHnATeLHjs4k1xgyEiItGZonH9R6+EYuOY7iaKiIhsTczqAzhddMfo/ZlDiIhM4+f3+mFsz+YmO16lDli6Kw+t5+1A5/d3Y2/OVbNeYDfkYhUAjOoRKGI09kMmleCjV0IN2kcHYPLWTFHiISKydZt/vSh4rLUsCQYAhs1jJEGq+q4cu/RHrWOr+q5Yy38QREQknEarQ8j8VJRrjD+GXArkLOIyYESOrNeSPfj9VrlR+0oBnLPBJrlERNYsYUg7zB7UFm3n74DGhHWQm3crMHbLMQBAk4YuWPjc0+gT7CVqDu+6aLfgsS283CB34j24Qg0OfQrfnriCvXnCl3vfcboE6kotf89ERH/xxS/CiyvWsiQYwJkromHfFSIi+1bVX6UuhRX2RiCiuhRWnCTAhSXMIUREYpA7SXE+ORbt/NxFOf7vt8owdssxtJiTilZzUhCz+qDJZ7WM/uIwSsuEf1hNGtzeZK/tKDaO6Q6li2HNemJWHxApGiIi26Su1KLktrDvRBJYz5JgAIsromHfFSIi+/XmlmMm6a/C3ghEhrl06RLGjRuHwMBAuLq6okWLFkhMTIRarbZ0aEaJWbXf6MKKixOQnxxr4oiIiOivUqb2xmoDl38yVIUWOFN0W19saT47BcFzdxhdcNFodRi65iD2nxN+I6c1rV9va47NM+wzff61e2xuT0T0kJGfHxY8NqyZ9SwJBnBZMNFU9V2pFPAZiH1XiIhsx9hNR7E3r+79VQaHPmWiiIgcR25uLrRaLT755BO0bNkS2dnZGD9+PO7evYvly5dbOjyDDF59AGeKha+B/7CGLlJkJQ0ycURERPQ4z4U+hcEd/DHpfzOx80yJWV6zXKPVF1zMYViov1VdrLIlcicpxvRsik2/Xha8z7RtJxDztB9/50Tk8NSVWhwR0FqjylQrWhIM4MwV0VT1XRGiqu8KERFZt9EbD9e5sHJ+cQwLK0RGio6OxqZNmzBw4EAEBQVh6NChePfdd/Htt99aOjSDjN10BNlGNq9/SilnYYWIyAJkUgk2vNEFZxcNQgsvN0uHY3JLhne0dAg2LXHI06jnLPwSm0YLrE47K2JERES2wZBZK9Y4y5LFFRGx7woRkf3otSQd+88an6tdZMAl9kYgMrnS0lI0bizsM5c1eP+nbOzNu27UviG+9fBr/AATR0RERIaQO0mRPiMSOQuj4Sa3j0sqg9r5sMG6Cfw237Dlwdbsy+eNtkTk0AydtWKNsyz57iki9l0hIrJ9Gq0OwXNT8PutMqOP8ZS7M3I/YG8EIlPLz8/H2rVr8fbbbz9xXHl5OVQqVbXNElJPFeKLX/5r1L7t/OojdVpf0wZERERGc5XLcGbhIHz4UqilQ6kTCYCPXg+zdBh2wVUuQysDZzVN3popUjRERNbPkFkrgHXOsmRxRURVfVeEKPjjvrjBEBGRwX48WYgWc1JRrjH+GGxcT1S72bNnQyKRPHHLzc2ttk9BQQGio6Px4osvYvz48U88fnJyMpRKpX4LCAgQ83RqpNHq8PcvTxi1b3u/+kiZ2sfEERERkSn8rfNTOL84BptGdoGL0AsAVmTNq52s7i5gW2bo+/WO0yVQV2pFioaIyHoZOmslPLCRVc6yZEN7EcmkEoQ2bYjf/nur1rHZhaXiB0RERIKN23wM6blX63QMNq4nEmbGjBkYPXr0E8cEBQXp/39hYSEiIyPRo0cPfPrpp7UePz4+HnFxcfrHKpXK7AWWkPmpRu0X2doTm8aGmzgaIiIyJZlUgsh2PshdFIM7ZZUYtOoArtRh1rO59GvjhSEd/S0dhl2RO0kR094Hqdklgvfps3QvMub0FzEqIiLr03vpHoPG/3Ncd5EiqRsWV0TWpJGboOLK+Wv3oK7UWmUFjojI0cSuOYjThbeN3t9ZCuQuiuFdgEQCeXl5wcvLS9DYgoICREZGIiwsDJs2bYJUWvtnJ4VCAYVCUdcwjdZz8W6jZsCxsEJEZHvquzjh59nPQl2pxWc/5+PDtHOwxokJAY1csHF0N0uHYZfWvhaG1DnCb6ooUpVje1YBnuNNWUTkIH44/jtKVBWCx1vrrBWAy4KJ7qlGroLHbjl0UcRIiIhIiJ5L9tSpsKJ0keLcYjauJxJDQUEB+vbti6ZNm2L58uW4du0aiouLUVxcbOnQHmvspsMoMOCLQ5V2/g1YWCEismFyJykmRbZG/uJYZCdFIbJVY1jLp8OARgr8/N6zlg7DbsmkEqx5ybC+ANO3ZbG5PRE5BI1Wh3e+PmnQPtY6awVgcUV0hjS1//FkoYiREBFRbTot2IWCW+VG79/IVYaTSYNMGBERPSwtLQ35+flIT09HkyZN4Ofnp9+s0U9ZBdibd8Pg/Z5qqEDKO8+IEBERWatLly5h3LhxCAwMhKurK1q0aIHExESo1epqY2rqSXX4sGHNYMn86rs4YdO4CFxcEouchdF4sYs/GsgtU2oZ07Mpfn6PS1CJbWjnJvBxdxY8XqsDVqedFTEiIiLrEP7BbsPGW/GsFYDLgomue5AHZFJAI2AacHahChqtjnc7ExFZQNt5qbhfafzdYk2UcvwSP8CEERHRX40ePbrW3izWQqPVYfJXWQbv18jNCb/O5kUvIkeTm5sLrVaLTz75BC1btkR2djbGjx+Pu3fvYvny5dXG7tmzB+3atdM/9vDwMHe4VAeuchmWvdAJy17oBAC4r9Yg4YdT2HGyEHcMn+goWAtPV+yY1teqL1DZm59n9UfreTsEj1+zLx9TB7TmNSEisltjNx3G9buVBu1jzbNWABZXRCeTStC/jTd2nam9KbJWBxw6dx29g4WtOU5ERHWn0erQek4qjGiHoNcv2AMbx1j3Gz4RmZehd2QBQEMXGU4kRIkQDRFZu+joaERHR+sfBwUFIS8vD+vXr3+kuOLh4QFfX19zh0gi+WuxBfiz4LLzVBFuq427+UcKoJ5ChkFP+2HB0PZwlctMFDEJJXeSIrq9N3Zm1349qMrkrZlYP7KLiFEREVmGMbP6B7XzsfqbAlhcMYM3egQKKq4AwJq9Z1lcISIykx9PFmLKv07U6RgfvRKKwWw+SUQPMeaOLLkUyEqKrn0gETmM0tJSNG7c+JHnhw4dirKyMrRu3RqzZs3C0KFDLRAdiammggvZpnWvdUELA5rb7zhdAnWl1uovJhIRGcLYWf0fvR5m+mBMjNnaDLoHeUDorM7My7fYxIyIyAzGbT5Wp8KKXAqcXxzDwgoRVWNsn5WcRTEiRENEtio/Px9r167F22+/rX+ufv36WLFiBb755hukpKSgV69eGDZsGH744YcnHqu8vBwqlaraRkTmIZNK8E5kC4P2GfkF+ygRkX3pkLTT4H1WvxJqE8sksrhiBjKpBF2aNRQ0tmppMCIiEk/smoNIzxU+Pf+v/N2dcXZxrE280ROR+Wi0Okwx4o6sta92Yj4hslOzZ8+usQn9w1tubm61fQoKChAdHY0XX3wR48eP1z/v6emJuLg4hIeHo2vXrliyZAlGjBiBZcuWPTGG5ORkKJVK/RYQECDKuRJRzaYOCIYh7/JHLv4BdaWAxr1ERDYgdMFO3FUbltMCPdzwnI3cyMriiplM6dda8Nikn7JFjISIyLH1TN6D04W3jd4/xLceDs0ZaMKIiMherNqdB0PnH/dr44UhHf1FiYeILG/GjBnIycl54hYUFKQfX1hYiMjISPTo0QOffvpprccPDw9Hfn7+E8fEx8ejtLRUv125cqXO50VEwsmkEqx+qaNB+8z+z0mRoiEiMg+NVod281Nx677hHW73zOhr+oBEwp4rZtKjpSekAITU6c5fu8c1NomIRBCSsAP3DLxj4mHt/erjp6l9TBgREdkLjVaHtfvPG7SPVz1nbBzdTaSIiMgaeHl5wctLWE/NgoICREZGIiwsDJs2bYJUWvv3waysLPj5+T1xjEKhgEKhEBQDEYljaOcm+GDnGZSoKgSN/+5EIZa9aBtL4hAR/VVd+tva2qx+Xr03E5lUgi7NGwoeH//tKfGCISJyQG3npdapsPJsG08WVojosV5Y/4vB+xyeO0CESIjIFhUUFKBv375o2rQpli9fjmvXrqG4uBjFxcX6MVu2bMG//vUv5ObmIjc3F4sXL8bGjRsxZcoUC0ZOREL9PKu/4LE6AKvTzooXDBGRCDRaHYZ99IvRhZVOAUqbm9XP4ooZGbI02PasQja2J6JarVu3Ds2bN4eLiwvCw8Nx9OhRS4dkldrMTcH9SuNz6kevhOKL0eEmjIiI7MlPWQU4ccWwBtG2dkcWEYkrLS0N+fn5SE9PR5MmTeDn56ffHvb+++8jLCwM4eHh2L59O7Zt24YxY8ZYKGoiMoTcSYoWnvUEj/9of77NXBdKSkp6pJ9UmzZt9D8vKyvDpEmT4OHhgfr162P48OEoKSmpdozLly8jNjYWbm5u8Pb2xsyZM1FZWVltzP79+9G5c2coFAq0bNkSmzdvNsfpEZEA27MK0GJOKrJ+LzVqfwmAf0/sadqgzIDFFTOqWhpMiEqtDofP3xA1HiKybdu2bUNcXBwSExNx/PhxdOzYEVFRUbh61fhG7fZGo9WhxewUlBm+xCeAB2+S5xfHYLCNNFIjIvPTaHV4x8Am9rZ4RxYRiWv06NHQ6XQ1blVGjRqFM2fO4O7duygtLcWRI0fwwgsvWDBqIjJU0tB2gsdqdbY1e6Vdu3YoKirSb7/88ues3unTp+PHH3/EN998gwMHDqCwsBDPP/+8/ucajQaxsbFQq9U4dOgQtmzZgs2bNyMhIUE/5uLFi4iNjUVkZCSysrIwbdo0vPnmm9i1a5dZz5OIqrtTVon2CTsx1cDvRH+17rXONnnzGYsrZiSTSjAgxFvw+KW7ckSMhohs3cqVKzF+/HiMGTMGISEh2LBhA9zc3LBx40ZLh2YVUk8VocWcVBhZV4GrE3BhSaxNvrkTkfmsTssT1FPvYbZ4RxYRERHVXY+WnpAZ8PXClmavODk5wdfXV795enoCAEpLS/HFF19g5cqV6Nevn76v1KFDh3D48GEAwO7du3HmzBn87//+L0JDQzFo0CC8//77WLduHdRqNQBgw4YNCAwMxIoVK9C2bVtMnjwZL7zwAj788EOLnTORo1JXarFu31m0nJOC9km7cEdt7JWXB8b3DkRMhyf3kLNWLK6Y2Rs9AgWPPfm7CupK4/sDEJH9UqvVyMzMRP/+f67bK5VK0b9/f2RkZFgwMuvw/k9n8Pcvjxu9v7+7M3IWxZowIiKyRxqtDmv3GdbEfvUrbE5LRETkqGRSCSb1bSF4vC3NXjl37hz8/f0RFBSE119/HZcvXwYAZGZmoqKiotp31zZt2qBp06b6764ZGRl4+umn4ePjox8TFRUFlUqF06dP68c8fIyqMfz+S2Qe99UazPz3CbSak4LW83Zg2a5zMMVl63G9mmNubEjdD2QhTpYOwNF0D/KAkwQQuvR//LensOKlUFFjInIEpfcq8Mbnh3Cm+A4AoKV3A8yMaoM+wV42eZHr+vXr0Gg01T58AoCPjw9yc3MfGV9eXo7y8nL9Y5XKsN4AtmTspqPYm3fN6P3b+dZDyrS+pguIiOzW6rQ8GHIvqZ+7As9xmUEiIiKHNnVAMNbuOy/4M8RH+/MxdUBrq/7eGh4ejs2bNyM4OBhFRUVYsGABevfujezsbBQXF0Mul6Nhw4bV9vHx8UFxcTEAoLi4uMbvtlU/e9IYlUqF+/fvw9XV9ZG4HOl7MJGp3SmrxJStx3Ao/ybKRZpAN65Xc8wfLHy5RGvE4oqZyaQSPNfJH/85Xiho/LfHC7D0hY5W/SZKZK00Wh0O5lzFW1t/Q8Vfquk5xbcxdssxOMskWPtqJ0S3t83ph0IlJydjwYIFlg5DdLFrDuJ04W2j9+8X7ImNY9i4nohqZ8yslQOz+okUDREREdkKmVSCKZEtsEbg54iq2StxUcEiR2a8QYMG6f9/hw4dEB4ejmbNmuHrr7+usehhLo7yPZjIWPfVGiT8cAo7ThbiToV5X3t87+aYG2vbhRWAxRWLSH6+o+Diig7W/yZKZG3UlVrM/HcWtmcV1Tq2QqPDhP89jg0jOttUgcXT0xMymQwlJSXVni8pKYGvr+8j4+Pj4xEXF6d/rFKpEBAQIHqc5tRryR78fqu89oGPMaZnMyQOaW/CiIjInhk6ayWmvS/kTlyRl4iIiOxz9srDGjZsiNatWyM/Px8DBgyAWq3GrVu3qs1eefi7q6+vL44ePVrtGFXfdR8eU9P3X3d398cWcBzhezA5rvtqDeZvP4mfsopQVreWJ2b38WudENPB39JhmASLKxYgd5IitIk7sn4XNh3R1t5EiSzlTlklBq06gCu3ygzeN+mHMxgQ4msz/87kcjnCwsKQnp6OYcOGAQC0Wi3S09MxefLkR8YrFAooFAozR2k+dS2s2MsdE0RkHhqtDuv2C5+1IgGw9rXO4gVERERENsUeZ6887M6dOzh//jxGjhyJsLAwODs7Iz09HcOHDwcA5OXl4fLly4iIiAAARERE4IMPPsDVq1fh7e0NAEhLS4O7uztCQkL0Y1JTU6u9Tlpamv4YNbH378FUd+pKLT77OR//PHQRJbcrDbp5igznWU+GI3OjbObamxC8fc5CZka3FTzWlhqYEVlC6b0KtJ23A+2TdhlVWAGAYlUZjl68aeLIxBUXF4fPPvsMW7ZsQU5ODiZOnIi7d+9izJgxlg7NrAat2l+nwsrHr3ViYYWIDHIo/zo0BnzzmhLZ0q6+QBAREVHdTR0QDEM+HWw4eB4arXVe+n333Xdx4MABXLp0CYcOHcLf/vY3yGQyvPrqq1AqlRg3bhzi4uKwb98+ZGZmYsyYMYiIiED37t0BAAMHDkRISAhGjhyJkydPYteuXZg3bx4mTZqkL45MmDABFy5cwKxZs5Cbm4uPP/4YX3/9NaZPn27JUycblpx6Rt+YvZiFFdGN6dkUv82PtrvvRQYVV5KTk9G1a1c0aNAA3t7eGDZsGPLy8qqNKSsrw6RJk+Dh4YH69etj+PDhj0zbu3z5MmJjY+Hm5gZvb2/MnDkTlZWV1cbs378fnTt3hkKhQMuWLbF58+ZH4lm3bh2aN28OFxcXhIeHPzKF0Jp1D/KAswG//Y8P5FvtmyiRJWi0Ouw7XYJWc1LQceFu3K/U1r5TLa7eNq4wYykvv/wyli9fjoSEBISGhiIrKws7d+58pMmfPeu5ZA9yiu8ata8UwPnFMXYzFZWIzGfBj6cFj5VKgKkDWosYDREREdmiqtkrQqk1Ohw+f0PEiIz3+++/49VXX0VwcDBeeukleHh44PDhw/Dy8gIAfPjhhxg8eDCGDx+OZ555Br6+vvj222/1+8tkMvz000+QyWSIiIjAiBEj8MYbb2DhwoX6MYGBgUhJSUFaWho6duyIFStW4PPPP0dUVJTZz5dsX3LqGXxy8KKlw3AI7i5SnF00CIlDnrZ0KKIwaFmwAwcOYNKkSejatSsqKysxZ84cDBw4EGfOnEG9evUAANOnT0dKSgq++eYbKJVKTJ48Gc8//zx+/fVXAIBGo0FsbCx8fX1x6NAhFBUV4Y033oCzszMWL14MALh48SJiY2MxYcIEbN26Fenp6XjzzTfh5+enT5rbtm1DXFwcNmzYgPDwcKxatQpRUVHIy8vTTyG0ZjKpBBP7CJ8CWqkFDp27jt7BXiJHRmTdDOmnYijvBi4mP6bYJk+eXOMyYI6g08Jd+ONeZe0Da+DiBOQuijVxRETkCNSVWuRfE17UndyXs1aIiIioZob2Xlm6KwfbW/UWNSZjfPXVV0/8uYuLC9atW4d169Y9dkyzZs0eWfbrr/r27YsTJ04YFSNRFXWlloUVM1n1QgcM62LffY4kOp3O6OkQ165dg7e3Nw4cOIBnnnkGpaWl8PLywpdffokXXngBAJCbm4u2bdsiIyMD3bt3x44dOzB48GAUFhbq767esGED3nvvPVy7dg1yuRzvvfceUlJSkJ2drX+tV155Bbdu3cLOnTsBAOHh4ejatSs++ugjAA96DQQEBGDKlCmYPXu2oPhVKhWUSiVKS0vh7u5u7K/BaBqtDi3npAp+E23h5Yb0GZGixkRkrerST0UIX3cX/Dq7X40XwCydK8Rg6+cUmrQTt4zs2NbQRYqspEEmjojIftl6vqhJXc4p7qsT+DarUNBYqQQ490EMiytENsoe8x9gv+dFZKtW7soVfOMtAJxdNAhyJ/FX+bfHXGGP50SG++LnC3g/JcfSYdgthUyC9SO6oE+wl01/DxKaL+qUjUtLSwEAjRs3BgBkZmaioqIC/fv3149p06YNmjZtioyMDABARkYGnn766WrL1kRFRUGlUuH06dP6MQ8fo2pM1THUajUyMzOrjZFKpejfv79+jC0wdAro+Wv3oDbB0kdEtsQU/VSESBoaYtNJ35G0nZdqdGHlKaWchRUiMppGq8N3AgsrAGetEBERUe0M7b0y8ovDosVC5Aj+e/OepUOwSwGNXJCdFIW8D2LQr623w3wPMrq4otVqMW3aNPTs2RPt27cHABQXF0Mul6Nhw4bVxvr4+KC4uFg/5q/9AKoe1zZGpVLh/v37uH79OjQaTY1jqo5Rk/LycqhUqmqbpU0dEGzQ+D5L94oUCZH1EKOfyuM4yyTYMKIzotv7ifYaZDqt4lNwv9K4CZchvvXwa/wAE0dERJZSXl6O0NBQSCQSZGVlmeU1D+VfFzzjWAL2WiEiIqLayaQSPN9ZeB/IIxf/4I23RHXQrLGbpUOwG/UUMsyKCsbZRYPw83vPor6LQR1I7ILRxZVJkyYhOzu71nUVrUlycjKUSqV+Cwiw/JpvMqkEz4cKfxMtUpVje1aBiBERWY66UoupXx1HizmpGPPP31Ah4udFhQzYOKorct8fxMKKjWg5OwUVRi5k2c6vPlKn9TVpPERkWbNmzYK/v/DPUKawdu9ZwWP/1snfYe7WIiIiorpJfr6jQePjvz0lUiRE9m9kRHNLh2CznKUSPNXQRV9QOb0gGn+PbGmWpQqtlVHlpMmTJ+Onn37CwYMH0aRJE/3zvr6+UKvVuHXrVrXZKyUlJfD19dWPOXr0aLXjlZSU6H9W9b9Vzz08xt3dHa6urpDJZJDJZDWOqTpGTeLj4xEXF6d/rFKprKLAsuSFjoLX7gaAqV9lYXAHfmEn+yF2P5WHebg54cAsx6ym27KW8SkwrnX9g8JKytQ+Jo2HiCxrx44d2L17N/7zn/9gx44dZnlNjVaH3/57S/D4JcMNu0hCREREjkvuJEV480Y4cukPQeO/O1GApS905HUhIiPInaR4+5lANrWvhRSAq1yGboGNsfbVzryO9hgG/VZ0Oh2mTJmC7777Dvv370dgYGC1n4eFhcHZ2Rnp6ekYPnw4ACAvLw+XL19GREQEACAiIgIffPABrl69Cm9vbwBAWloa3N3dERISoh+Tmppa7dhpaWn6Y8jlcoSFhSE9PR3Dhg0D8GCZsvT0dEyePPmx8SsUCigUCkNO2SwMfRMFgBfW/4rvJvUSMSoi8ZXeq0D3xXtEXfarSrBPPXw/qTdc5TLRX4tMq83cFBi5Ehja+9XHTyysENmVkpISjB8/Ht9//z3c3Mw3pf/whRvQCsxFLbzcHPruLSIiIjLcP9/sjtbzhN00otUBh85dR+9gL5GjIrJP8TEPrkE7eoFFAkDhJEWQVz28O7CNzTehtwSDiiuTJk3Cl19+ie3bt6NBgwb6/iZKpRKurq5QKpUYN24c4uLi0LhxY7i7u2PKlCmIiIhA9+7dAQADBw5ESEgIRo4ciaVLl6K4uBjz5s3DpEmT9IWPCRMm4KOPPsKsWbMwduxY7N27F19//TVSUlL0scTFxWHUqFHo0qULunXrhlWrVuHu3bsYM2aMqX43ZmXImygAnLhSih9PFmJIR/Muh0FUVxqtDgdzruKtreIu+wUAThIgbmAw3uwdxItcNqrN3BQY2bse/YI9sXFMuGkDIiKL0ul0GD16NCZMmIAuXbrg0qVLgvYrLy9HeXm5/rExffd+PXdd8Nikwe0NPj4RERE5NrmTFKFN3JH1u7DPKav3nmVxhagO4mNCMGNgG3z2cz7+eegiSm5XCu6vaA1YGLEOBhVX1q9fDwDo27dvtec3bdqE0aNHAwA+/PBDSKVSDB8+HOXl5YiKisLHH3+sHyuTyfDTTz9h4sSJiIiIQL169TBq1CgsXLhQPyYwMBApKSmYPn06Vq9ejSZNmuDzzz9HVFSUfszLL7+Ma9euISEhAcXFxQgNDcXOnTsfaXJvK+ROUkS398bO7KuC95nyrxOIedqP/3DIJqgrtZj57yxszyoS/bXqOUtwZO5ATlm0ca3ije+xMqZnMyQO4cVNIlsxe/Zs/OMf/3jimJycHOzevRu3b99GfHy8QcdPTk7GggUL6hIi9uYWCxonlQA9WnnW6bWIiIjIMc2MbovXPz8iaOxv/70FjVbHa0JEdSB3kmJSZGtMimxt6VDIRkl0Op0tFeVMSqVSQalUorS0FO7u7pYOBxqtDi3mpNY+8CFe9ZxxbP5AkSIiqjt76KdibbnCFKz9nFrONr7HyvjezTE3tp1J4yFyZObIF9euXcONGzeeOCYoKAgvvfQSfvzxR0gkf15E0Gg0kMlkeP3117Fly5Ya961p5kpAQIDgc9JodWg5J1XQnWxBnm7Y+26kgJFEZO2s/fOSsez1vIjsgUarQ+u5qdAIvFL3TmRLxEUFixKLPeYKezwnIhKH0HzB27qtiEwqwZqXOuKdr08K3ufa3QqM3XwUG0d3EzEyIsOxnwoZqy6FlY9f64SYDlwukcjWeHl5wcur9mUt1qxZg0WLFukfFxYWIioqCtu2bUN4+OOXAaxr373DF24IXiLg6aeURr8OEREROTaZVILnOvrj26xCQeM3HDyPqQNac/YKEZGFsLhiZYZ2boIPdp5BiapC8D57c6+x/wpZBfZToboytrAiAZC/OIZfKojsXNOmTas9rl+/PgCgRYsWaNKkiWive+i88H4rL3QOEC0OIiIisn9LXugouLii1uhw+PwN9OSSpEREFsHiihX6eVZ/g5rbA+y/QpbFfipkCnWZscLCChGJqeCP+4LGOUnZb4WIiIjqRu4kRUuvesi/dlfQ+KW7crC9VW+RoyIioprw6qQVkjtJMaZnU2z69bJB+4XMT0XeB7EiRUX0KHvop0LWoWV8XZYC68zCCpGDat68OczRPrDgj3uCxnUKaMh8RERERHWWOKQdRm48Kmjsyd9VUFdquaIDEZEFMPNaqcQhT8OznmEXkcs1QM/kPSJFRPSn0nsVaDtvB9on7RK9sBLsUw85C6ORmRDFwoqdajsvBZVGXht9+5lAxHTwM21AREQP0Wh1OPl7qaCxXQMbixwNEdmz5s2bQyKRVNuWLFlSbcypU6fQu3dvuLi4ICAgAEuXLrVQtEQkph4tPQ26YBf/7SnRYiEiosdjccWKHZk70OB9CkrLMWjVftMHQw5Po9Vh3+kStJqTgo4Ld4vaqN5JAsyKCsbZRYOwa3pfNqq3Yz0W78Z9I6esfPxaJ8THhJg2ICKivzh84QbUGmEV4J4tvESOhojs3cKFC1FUVKTfpkyZov+ZSqXCwIED0axZM2RmZmLZsmVISkrCp59+asGIiUgMMqkEf+ssvK/udycKoNGKP5uXiIiq423gVkwmleCjV0Ix+assg/bLKb6Lnslp+DV+gDiBkUNhPxUSS8yq/ShUVRi8nxTAOfZYISIzEdrM3sVJiu4tPESOhojsXYMGDeDr61vjz7Zu3Qq1Wo2NGzdCLpejXbt2yMrKwsqVK/HWW2+ZOVIiElvy8x3xn+PCGttrdcChc9fRO5g3ehARmRNnrli5waFPoV+w4V/UC0rVCE3aIUJE5CjulFWi95J0tJ63Q/TCioebE7KTonD6/RgWVhxE7OoDOFMsrEHjw5wAXFgSy8IKEZmN0Gb2HZoomZuIqM6WLFkCDw8PdOrUCcuWLUNl5Z9TfDMyMvDMM89ALpfrn4uKikJeXh7++OOPxx6zvLwcKpWq2kZE1k/uJEVoE3fB45N+yhYxGiIiqgmLKzZg45ju8HQzfFmkW2VatJmXIkJEZM/YT4XENnj1AZwuumPwfk4A8pfEmj4gIqInuK8WtnZhWPNGIkdCRPbunXfewVdffYV9+/bh7bffxuLFizFr1iz9z4uLi+Hj41Ntn6rHxcXFjz1ucnIylEqlfgsICBDnBIjI5GZGtxU89vy1e1CLuHw3ERE9isUVG3FkXpRR+5VVAkGzU7j2Jj0R+6mQuYzddATZLKwQkY3QaHX4VeCyYI3d5LUPIiKHM3v27Eea1P91y83NBQDExcWhb9++6NChAyZMmIAVK1Zg7dq1KC8vr1MM8fHxKC0t1W9XrlwxxakRkRl0D/KAswFX7tjYnojIvHiruI2QSSX4+LVO+PuXJwzeVwugxZxUfPRKKAaHPmX64MhmsZ8KmdP7P2Vjb56wi5R/xcIKEVnC0Ys3cadc2A0HnvUVIkdDRLZoxowZGD169BPHBAUF1fh8eHg4KisrcenSJQQHB8PX1xclJSXVxlQ9flyfFgBQKBRQKJijiGyRTCrBxD4tsGbfeUHjvztRgKUvdORSpUREZsKrnDYkpoM/xl2+iS9++a9R+0/+KgvfnriCjWO6mzgysjV3yioxaNUB0Zf9Ah70Uzkw61kWVRxc6qlCo3PX2UWDTBwNEZEwxSrh75O+SlcRIyEiW+Xl5QUvL+MaTGdlZUEqlcLb2xsAEBERgblz56KiogLOzs4AgLS0NAQHB6NRIy5NSGSvpg4IFlxcYWN7IiLz4rJgNmb+4PboF+xp9P57826gy8KdXCbMQbGfClmCRqszatYdAIzrFQi5E9+qiMgybt4RthSPu4sTugU2FjkaIrJnGRkZWLVqFU6ePIkLFy5g69atmD59OkaMGKEvnLz22muQy+UYN24cTp8+jW3btmH16tWIi4uzcPREJCaZVIKoEG/B49nYnojIfHjF0wZtHBOOIWsO4v8Kbxu1//V7GrSYk4o1L3XE0M5NTBwdWRuNVoeDOVfx1tbfUCFybzsnCRA3MBhv9g7iBXHSazs/1aj9nn7KHfMHh5g4GiIi4RoK7KPyt05PcfkNIqoThUKBr776CklJSSgvL0dgYCCmT59erXCiVCqxe/duTJo0CWFhYfD09ERCQgLeeustC0ZORObwRo9A7DpzVdDYqsb2/E5ORCQ+Flds1I/vPIOxm45ib941o4/xztcnsSItF3tnPssLAnbovlqDsVuOIOP8H6K/Fvup0ON0StoBtcbw/dr51cePU3qbPiAiIgPcuqcWNK5pYzeRIyEie9e5c2ccPny41nEdOnTAzz//bIaIiMiaVDW2F3rDZPy3p7DipVBRYyIiIi4LZtM2jumGcb0C63SM//5RjhZzUvH9b1dMFBVZ2p2ySoQt3I22CTtFL6x4uDkhOykKp9+PYWGFHhG7aj/+KDN8ulQ7v/pImdpHhIiIiAwjdOaK0HFERERExqhqbC/U9qxCu14Oft26dWjevDlcXFwQHh6Oo0ePWjokInJQLK7YuPmDQ/Dxa53rfJxp/z6FsIU77PrN19493E/lxr0KUV+L/VSoNgt+/D+cLr5r8H4hLKwQkRUROnNF6DgiIiIiY00dECx4bKVWh8Pnb4gYjeVs27YNcXFxSExMxPHjx9GxY0dERUXh6lVhy6YREZkSiyt2IKaDH84vjqnzH/PGPS1nsdgYjVaHfadL0GpOCjou3I37leI1VXGSALOignF20SDsmt4XrnKZaK9Fti31VCE2/XrZ4P2eaqhAKgsrRGRFfv/jnqBxjetx5goRERGJy9DG9lsyLooYjeWsXLkS48ePx5gxYxASEoINGzbAzc0NGzdutHRoROSAWFyxEzKpBBeWxEJugr/otH+fQvDcFNwpq6z7wUgU99UavPrZIbSYk4ox/xS3UX09Zwmyk6KQnxyLv0e2ZFM8K3Dp0iWMGzcOgYGBcHV1RYsWLZCYmAi12vJ3Tmu0Ovz9yxMG79fI1Qm/zu4vQkRERMbRaHXYfrJQ0FhfpavI0RARERE9aGwv1J6cq3a3OolarUZmZib69//zu6NUKkX//v2RkZFhwciIyFFxPR87c3ZxLDov3I2bdVwWqlwDtE/ahUYuMhyaM4CzFKzEnbJK9Fm6V/Rlv4AH/VQOzHqWy35ZodzcXGi1WnzyySdo2bIlsrOzMX78eNy9exfLly+3aGzhH+w2eB+5FDiRGCVCNERExjt68SZu3q39/dajnhzdAhubISIiIiJydN2DPOAkASoF1Ey0OuDQuevoHewlfmBmcv36dWg0Gvj4+FR73sfHB7m5uY+MLy8vR3l5uf6xSqUSPUYiciy8amqHjicMxJhNR7Ev71qdj/VHmQZtE3bC312O9Hf7schiIaX3KtB98R5Rl/2qEuxTD99P6s2/tRWLjo5GdHS0/nFQUBDy8vKwfv16ixZXxm46jOt3DZ/xlrMoRoRoiIjqplhVJmjc0FB/yKQSkaMhIiIierBqyXOd/PGf48Jm167Ze9auiiuGSk5OxoIFCywdBhHZMa7vY6c2jemGta92MtnxClVqtE3YiWeXp0Nthgv8xH4qZJjS0lI0bmy5O6d/yirA3jzDGyaufbUTL0oSkVW6eae89kEAmjTkkmBERERkPsnPdxQ89viVW3a1NJinpydkMhlKSkqqPV9SUgJfX99HxsfHx6O0tFS/XbnCHsNEZFqcuWLHhnT0R8zTfnh2xX5cuiGsIWttzl8vQ+t5O9C1uRJb3+zB/hsiuK/WYOyWI8g4/4for1XPWYIjcwdy6S8bl5+fj7Vr1z5x1oqY06E1Wh0mf5Vl8H792nhhSEd/k8VBRGRKQpvUs5k9ERERmZPcSQp/pQsKS2ufZavRAofP30DPVp5miEx8crkcYWFhSE9Px7BhwwAAWq0W6enpmDx58iPjFQoFFAqFmaMkIkfCK+N2TiaVYP/MSKx+JdSkxz12qRSt5+3Aixt+4UwWE7lTVomwhbvRNmGn6IUVDzcnZCdF4fT7MSysWJHZs2dDIpE8cfvrOrIFBQWIjo7Giy++iPHjxz/22MnJyVAqlfotICDAZHEb02elSSMXbBzdzWQxEBGZmtAm9WxmT0REROY2LPQpwWP/5/Al8QKxgLi4OHz22WfYsmULcnJyMHHiRNy9exdjxoyxdGhE5IB4VdVBPBf6FAZ38Mfwj39F1u+lJjtuVZGFM1mMZ85+Kj2DGuPz0d247JeVmjFjBkaPHv3EMUFBQfr/X1hYiMjISPTo0QOffvrpE/eLj49HXFyc/rFKpTJJgcWYPiv15VL88t6zdX5tInJcKSkpWLhwIU6dOgUXFxf06dMH33//vUlfI6xZI0glD5rBPo5U8mAcERERkTn1bOWJjw+cFzR2X+5VaLQ6u1mO+eWXX8a1a9eQkJCA4uJihIaGYufOnY80uSciMgcWVxyITCrB95N74b5ag3aJO594scBQVUUWNkMXRqPV4WDOVby19TdUiFxTUcgkWD+iC/oEe9nNhyl75eXlBS8vYc0GCwoKEBkZibCwMGzatAlS6ZMLm2JMhza2z8rJpGiTxkFEjuU///kPxo8fj8WLF6Nfv36orKxEdna2yV8n879/1PpZSat7MC6ihYfJX5+IiIjocboHecBZCkHXE9QanV0tDQYAkydPrnEZMCIic2NxxQG5ymW4kByL0RuPYv/ZayY9dl7JXbRN2IkWnq7YMa0vZ7L8BfupkCkUFBSgb9++aNasGZYvX45r1/78d1xTEz8xGNtnhQ3siaguKisrMXXqVCxbtgzjxo3TPx8SEmLy17p6u/Z1zA0ZR0RERGQqMqkEz7b1wc7TJbUPBrAl46JdFVeIiKwFr7o6sM1ju+G+WoOIJXtw655hy/rU5vz1+5zJ8pA7ZZXos3QvbtyrEP21PNyccGDWsyyq2LG0tDTk5+cjPz8fTZo0qfYznc6EU9KeYNKXvxm8DxvYE1FdHT9+HAUFBZBKpejUqZN+KYhly5ahffv2Jn0t7wYuJh1HREREZEojI5oLLq7sybGvpcGIiKwFpxU4OFe5DFkJUchOioKLCLNMqmayhC3chTtlpi3g2ILSexVoO28H2iftEr2w0jOoMXIWRiMzIYqFFTs3evRo6HS6GjdzUFdqsTP7qkH7eNVzZgN7IqqzCxcuAACSkpIwb948/PTTT2jUqBH69u2LmzdvPna/8vJyqFSqalttugU2RkM35yeOaeTmjG6BjQ07CSIiIiIT6B7kASeBtRKtDjh07rq4AREROSAWVwgAUN/FCbmLBuHDl0JFOf6Ne5Von7QLwXNTsff/3zFhrzRaHfadLkGrOSnouHC3qI3qFTIJNo7qivOLY7D1rQiHnyFE5tH7H3sM3ufw3AEiREJE9mL27NmQSCRP3HJzc6HVPnhPnTt3LoYPH67vOyWRSPDNN9889vjJyclQKpX6LSAgwCRx2++nGSIiIrJ2MqkEz3USvjLAmr1nRYyGiMgx8fZ2quZvnZ/C0FB/rNyVh3UHzpv8+OUaHcZuOQYAmPRMEOKi29jNtFT2UyFH8MPx31Fy27BZWOyzQkS1mTFjBkaPHv3EMUFBQSgqKgJQvceKQqFAUFAQLl++/Nh94+PjERcXp3+sUqlqLbAcvXgTt2qZdXrrXgWOXrzJhvZERERkEcnPd8R/jhcKGpt5+RaXBiMiMjFemaVHyKQSzBzUBnFRwfj7PzOxK0fYGp6GWnfwAtYdvGDzRRb2UyFHodHqMPXrkwbtwz4rRCSEl5cXvLy8ah0XFhYGhUKBvLw89OrVCwBQUVGBS5cuoVmzZo/dT6FQQKFQGBQTG9oTERGRtZM7SdHSqx7yr92tdWzV0mC9g2v/zEVERMLwCi09lkwqwSejukBdqcXrn2fg2KVborxOVZFlWAc/LH0pFHIRer+IofReBbov3iPqsl9VegY1xueju3HZL7Ko1Wl5Bi2BU89Zyj4rRGRS7u7umDBhAhITExEQEIBmzZph2bJlAIAXX3zRpK/FhvZERERkCxKHtMPIjUcFjV2z9yyLK0REJsTiCtVK7iTFNxN6il5k+f5UEb4/VYSARi7YMbWPVc7O0Gh1OJhzFW9t/Q0VItdUFDIJ1o/ogj7BXjY7q4fsh0arw9p9hi0V+Nv8gSJFQ0SObNmyZXBycsLIkSNx//59hIeHY+/evWjUqJFJX6eqof3jlgaTAPBVurChPREREVlUj5aekAIQcomCS4MREZmW9V29JqtlriLLlT/K0D5pl1X1FWE/FXJ0hs5a6RSg5EwrIhKFs7Mzli9fjuXLl4v6Omlnip/Yc0UHIHFICC9OEBERkUXJpBJ0ad4QRwVco+HSYEREpsWrt2QwcxVZ7lbo0D5pl0VncLCfCpFxs1b+PbGnSNEQEYlPo9VhwY9nnjimoZszBoT4mikiIiIioseb0q81lwYjIrIAXsUlo5mryFKu0WHslmMAYLa+LHfKKtF9cRruqNlPhcjQWSvvRLbkndxEZNOOXryJotInN6q/da8CRy/eREQLDzNFRURERFQzLg1GRGQZttE5nKxaVZHl7KJBeC7UT9TX+v5UEVrP24GoD/fjvlpj0mOrK7VYt+8sWs5JQfukXaIWVhQyCTaO6orzi2Ow9a0IFlbIamm0OqzbL3zWilQCTB3QWsSIiIjEd/X2kwsrho4jIqrN/v37IZFIatyOHXtwo9mlS5dq/Pnhw4ctHD0RWVrV0mBCVC0NRkREdceZK2QycicpVr/SGStfetD0feK/MlFWacj97sLlldxF24Sd8Hd3Rvq7z9apOHFfrcFz637G2ZK7JoywZuynQrbmUP51aAz4Zzy5L2etEJHt827gYtJxRES16dGjB4qKiqo9N3/+fKSnp6NLly7Vnt+zZw/atWunf+zhwRl0RMSlwYiILIFXeMnkZFIJItv5IHdRzP9fXmsP7ph4lkmVQlUF2ibsNKpfCfupENVu7d6zgsdy1goR2YtugY3hp3RBcWlZjcsiSgD4Kl3QLbCxuUMjIjsll8vh6/tnH6eKigps374dU6ZMgURS/cYVDw+PamOJiAAuDUZEZAlcFoxEVd/FCdkLo5GdFAUPN2fRXufGvUq0T9qFlvEp+HhfPtSVj/84caesEu0TdqB90i7RCys9gxojZ2E0MhOiWFghm6PR6vDbf28JHs9ZK0RkL2RSCRKHhAB4UEh5WNXjxCEhzHlEJJoffvgBN27cwJgxYx752dChQ+Ht7Y1evXrhhx9+qPVY5eXlUKlU1TYisj9cGoyIyPxYXCGzqO/ihMyEgchZGA0/pUK016nUAUt35T3Sl4X9VIgMd/jCDWgFLgnGWStEZG+i2/th/YjO8FVWX/rLV+mC9SM6I7q9uH3miMixffHFF4iKikKTJk30z9WvXx8rVqzAN998g5SUFPTq1QvDhg2rtcCSnJwMpVKp3wICAsQOn4gsZEo/4d/J1hiwSgEREdWMt9KTWbnKZciI72+WPidVfVmEToutK/ZTIXtz6LzwO5k4a4WI7FF0ez8MCPHF0Ys3cfV2GbwbPFgKjPmOiISaPXs2/vGPfzxxTE5ODtq0aaN//Pvvv2PXrl34+uuvq43z9PREXFyc/nHXrl1RWFiIZcuWYejQoY89fnx8fLX9VCoVCyxEdopLgxERmZfNXwVet24dli1bhuLiYnTs2BFr165Ft27dLB0W1cJVLsPu6X2hrtTis5/zsWL3OcF3yBtK7MIK+6mQvSr4476gcZy1QkRERFSzGTNmYPTo0U8cExQUVO3xpk2b4OHh8cSCSZXw8HCkpaU9cYxCoYBCId7qAURkPaqWBjt66VatY6uWBmNj+8crvVeBNz4/hP8rvGOWm3bJMpylEni7K/B6eDO82TsIcicu9ETC2fTV4G3btiEuLg4bNmxAeHg4Vq1ahaioKOTl5cHb29vS4ZEAcicpJkW2xqTI1ii9V4Hui/fg/hP6pViTnkGN8fnoblz2i+yWTies4hnWtCHvdiIiu7QzuwgLfjyDotIy/XN+ShckDgnhsmBEJIiXlxe8vIRfuNTpdNi0aRPeeOMNODvX3rMyKysLfn7MR0T0pyn9WmPkxqOCxq7Ze5bFlcfos2wv/ntD2A2HZNsqtDoU3CrD0l15WLorD28/E4j4mBBLh0U2wqaLKytXrsT48eP1Tf42bNiAlJQUbNy4EbNnz7ZwdGQopZszchYNwp2ySvRZulf0ZvPGcJIAn77RFX2CvXgxmeyeulIjaFzXwMYiR0JEZH47s4sw8X+P469l5uLSMkz83+Psu0JEoti7dy8uXryIN99885GfbdmyBXK5HJ06dQIAfPvtt9i4cSM+//xzc4dJRFbMkKXBjl/h0mA1YWHFsX1y8CIAsMBCgtjsPCe1Wo3MzEz0799f/5xUKkX//v2RkZFhwcioruq7OCEzYSByFkajtU89S4cDAHCRSXAyYSDyk2PRr603P3iQ3dNodTh88aagsT1b8E4nIrIvGq0OC34880hhBYD+uQU/noFGrDVNichhffHFF+jRo0e1HiwPe//99xEWFobw8HBs374d27Zt099sSEQE/Lk0mBAaLXD4/A1xA7IxpfcqWFghfPbzRahtZGUdsiybnbly/fp1aDQa+Pj4VHvex8cHubm5Ne5TXl6O8vJy/WOVSiVqjFQ3D/dlmfnvLGzPKjJ7DAGNXLBjah/2UyGHc/TiTfxxr7LWcfUVTujewsMMERERmc/RizerLQX2VzoARaVlOHrxJiKYA4nIhL788svH/mzUqFEYNWqUGaMhIltlyNJgGReuo2crT5Ejsh1jNwv7vZF90+qAf2ZcwrjeQbUPJodmszNXjJGcnAylUqnfAgICLB0SCSB3kmL1K51xfnEM/m6mpDasgx/OLhqEn99jo3pyTFdvP/6i4sNe6tKEM7mIyO4IzYFCxxERERGZU4+WnnAW/D2N3+ceVviEG2zIsfz35j1Lh0A2wGaLK56enpDJZCgpKan2fElJCXx9fWvcJz4+HqWlpfrtypUr5giVTEQmlWBWbFucXxyDTSO7wMXJtB8AnCTAxlFdcX5xDFa91hlyJ5v950FUZ94NXASNGxBSc74lIrJlQnOg0HFERERE5iSTSjApsoWgsZyFW52/kp/v6IFmjd0sHQLZAJu9eiyXyxEWFob09HT9c1qtFunp6YiIiKhxH4VCAXd392ob2R6ZVILIdj7IXRSD7KQoBDSs2xsf+6kQPapbYGP4KV2eeA+Tn9IF3djMnojsUG05UALmQCIiIrJuU55tjXpy2RPHNHJzRveguhVXmjdvDolEUm1bsmRJtTGnTp1C79694eLigoCAACxduvSR43zzzTdo06YNXFxc8PTTTyM1NbXaz3U6HRISEuDn5wdXV1f0798f586dq1PsNdk4upvJj0m2RyoBRkY0t3QYZANstrgCAHFxcfjss8+wZcsW5OTkYOLEibh79y4b+jmQ+i5O+Hn2szi7aBCeC/UzaN+ARi7ITopC7gcxULo5ixQhkW2SSSVIHBIC4NFJ4pL/vyUOCWExkojsUm05EGAOJCIiIusmk0qw4qWOTxyT/PzTJvk8s3DhQhQVFem3KVOm6H+mUqkwcOBANGvWDJmZmVi2bBmSkpLw6aef6sccOnQIr776KsaNG4cTJ05g2LBhGDZsGLKzs/Vjli5dijVr1mDDhg04cuQI6tWrh6ioKJSVmXYZL6WbM5p5uJr0mGR7xvcO5Io2JIhN/1fy8ssvY/ny5UhISEBoaCiysrKwc+fOR5rck/0zpC8L+6kQCRPd3g/rR3SG71+mRfsqXbB+RGdEtzesoElEZEuYA4mIiMjWRbf3w4YRneHrXv3zjJ/SBRtM+HmmQYMG8PX11W/16tXT/2zr1q1Qq9XYuHEj2rVrh1deeQXvvPMOVq5cqR+zevVqREdHY+bMmWjbti3ef/99dO7cGR999BGAB7NWVq1ahXnz5uG5555Dhw4d8D//8z8oLCzE999/b5JzeNiBmf1YYHFgbz8TiPiYEEuHQTZCotPpdJYOwlJUKhWUSiVKS0u5RJid0Wh1OJhzFcv35OHq7XL4uLsgbkAw+gR78S5TMpg95gpDzkmj1eHoxZu4ersM3g0eLIPDf0dEjoM5kDmQyFHZY/4D7Pe8iOjxjPk8IzRXNG/eHGVlZaioqEDTpk3x2muvYfr06XByenAz6xtvvAGVSlWtCLJv3z7069cPN2/eRKNGjdC0aVPExcVh2rRp+jGJiYn4/vvvcfLkSVy4cAEtWrTAiRMnEBoaqh/Tp08fhIaGYvXq1YJ+D4bmv9J7FXjj80P4v8I70Ap6BbJFzlIJvN0VeD28Gd7sHcQZKwRAeL7gbftkl6r6skS24ywmorqSSSVsckhEDos5kIiIiGydmJ9n3nnnHXTu3BmNGzfGoUOHEB8fj6KiIv3MlOLiYgQGBlbbp2rFmeLiYjRq1AjFxcWPrELj4+OD4uJi/biH96tpTE3Ky8tRXl6uf6xSqQw6N6WbM7a/08egfYjIsbAUR0RERERERERERACA2bNnQ6lUAgCUSuUjDeslEglyc3MBPOiH3LdvX3To0AETJkzAihUrsHbt2mpFDUtJTk6GUqnUbwEBAZYOiYjsDIsrREREREREREREBACYMWMGjh07BgA4duwYcnJyHtmCgmrudxseHo7KykpcunQJAODr64uSkpJqY6oe+/r6PnHMwz9/eL+axtQkPj4epaWl+u3KlStCTp+ISDCHXhasqt2ModMCicixVOUIe2pRxfxHREIxBxKRo7LH/AcwBxJR7RQKhb5o0apVK/0sFiGysrIglUrh7e0NAIiIiMDcuXNRUVEBZ2dnAEBaWhqCg4PRqFEj/Zj09PRqPVfS0tIQEREBAAgMDISvry/S09P1PVdUKhWOHDmCiRMnPvE8FAqF/jHzHxEJJfRzoEMXV27fvg0AnBZIRILcvn3boA+V1oz5j4gMxRxIRI7KnvIfwBxIRIZ5Ug7MyMjAkSNHEBkZiQYNGiAjIwPTp0/HiBEj9IWT1157DQsWLMC4cePw3nvvITs7G6tXr8aHH36oP87UqVPRp08frFixArGxsfjqq6/w22+/4dNPPwUASCQSTJs2DYsWLUKrVq0QGBiI+fPnw9/fH8OGDTPoXADmPyISrrbPgRKdvd2GYwCtVovCwkI0aNAAEomk1vEqlQoBAQG4cuUK3N3dzRChZTna+QI8Z55zzXQ6HW7fvg1/f39IpfaxmiLzX+14zjxne2TM+TIHOt5/J4DjnbOjnS/Ac3bUz4AAc6AQjnbOjna+AM/ZVDnw+PHj+Pvf/47c3FyUl5cjMDAQI0eORFxcXLUZI6dOncKkSZNw7NgxeHp6YsqUKXjvvfeqHeubb77BvHnzcOnSJbRq1QpLly5FTExMtXgSExPx6aef4tatW+jVqxc+/vhjtG7dWvDvwND8BzjefyuOdr4Az9kRzlnM78EOXVwxlEqlglKpRGlpqcP8h+dI5wvwnHnO9DiO+DvjOfOc7ZGjna+pOOLvzdHO2dHOF+A5O8o5m4Ij/t4c7Zwd7XwBnrOjnLMpONrvzdHOF+A5O8I5i3m+9nP7DRERERERERERERERkRmwuEJERERERERERERERGQAFlcMoFAokJiYWG3dSHvmaOcL8JwdhSOec1054u+M5+wYHO2cHe18TcURf2+Ods6Odr4Az5mEc8Tfm6Ods6OdL8BzJuEc7ffmaOcL8JwdgZjny54rREREREREREREREREBuDMFSIiIiIiIiIiIiIiIgOwuEJERERERERERERERGQAFleIiIiIiIiIiIiIiIgMwOIKERERERERERERERGRAVhcEeiDDz5Ajx494ObmhoYNG9Y45vLly4iNjYWbmxu8vb0xc+ZMVFZWmjdQETVv3hwSiaTatmTJEkuHZVLr1q1D8+bN4eLigvDwcBw9etTSIYkmKSnpkb9nmzZtLB2WyRw8eBBDhgyBv78/JBIJvv/++2o/1+l0SEhIgJ+fH1xdXdG/f3+cO3fOMsFaOeY/5j97Y+/5D2AONCXmQOZAe8McyBxoCOZA5kB7wxzIHCgU898D9p4Dmf+Y/+qa/1hcEUitVuPFF1/ExIkTa/y5RqNBbGws1Go1Dh06hC1btmDz5s1ISEgwc6TiWrhwIYqKivTblClTLB2SyWzbtg1xcXFITEzE8ePH0bFjR0RFReHq1auWDk007dq1q/b3/OWXXywdksncvXsXHTt2xLp162r8+dKlS7FmzRps2LABR44cQb169RAVFYWysjIzR2r9mP8eYP6zL/ac/wDmQFNiDnyAOdC+MAcyBwrFHPgAc6B9YQ5kDhSC+e9P9poDmf+Y/0yS/3RkkE2bNumUSuUjz6empuqkUqmuuLhY/9z69et17u7uuvLycjNGKJ5mzZrpPvzwQ0uHIZpu3brpJk2apH+s0Wh0/v7+uuTkZAtGJZ7ExERdx44dLR2GWQDQfffdd/rHWq1W5+vrq1u2bJn+uVu3bukUCoXuX//6lwUitA3Mfx9aOgzRMP/ZN+ZA02AO/NDSYYiGOdC+MQeaBnPgh5YOQzTMgfaNObDuHDn/6XT2nQOZ/+ybufIfZ66YSEZGBp5++mn4+Pjon4uKioJKpcLp06ctGJlpLVmyBB4eHujUqROWLVtmN9Md1Wo1MjMz0b9/f/1zUqkU/fv3R0ZGhgUjE9e5c+fg7++PoKAgvP7667h8+bKlQzKLixcvori4uNrfW6lUIjw83K7/3mJh/rNtzH+Olf8A5kBTYw60bcyBzIEAc2BdMAfaNuZA5kCAOdBYjpL/APvMgcx/zH+AafKfkymCI6C4uLhaQgWgf1xcXGyJkEzunXfeQefOndG4cWMcOnQI8fHxKCoqwsqVKy0dWp1dv34dGo2mxr9hbm6uhaISV3h4ODZv3ozg4GAUFRVhwYIF6N27N7Kzs9GgQQNLhyeqqn+TNf297eXfqzkx/9k25j/Hyn8Ac6CpMQfaNuZA5sAqzIHGYQ60bcyBzIFVmAMN5wj5D7DfHMj8x/xXpa75z6FnrsyePfuRRj5/3ez1H1QVQ34HcXFx6Nu3Lzp06IAJEyZgxYoVWLt2LcrLyy18FmSMQYMG4cUXX0SHDh0QFRWF1NRU3Lp1C19//bWlQyMzYP5j/nNkzH/EHMgc6MiYA4k5kDnQkTEHOjbmvweYAx0T8584HHrmyowZMzB69OgnjgkKChJ0LF9fXxw9erTacyUlJfqfWau6/A7Cw8NRWVmJS5cuITg4WITozMfT0xMymUz/N6tSUlJi1X8/U2rYsCFat26N/Px8S4ciuqq/aUlJCfz8/PTPl5SUIDQ01EJRmRfzH/NfFeY/x8p/AHMgwBwIMAdWYQ5kDqzCHFgdcyBzoDX//UyJORD6x46QA5n/HmAOZP4DmP+q1DX/OXRxxcvLC15eXiY5VkREBD744ANcvXoV3t7eAIC0tDS4u7sjJCTEJK8hhrr8DrKysiCVSvXna8vkcjnCwsKQnp6OYcOGAQC0Wi3S09MxefJkywZnJnfu3MH58+cxcuRIS4ciusDAQPj6+iI9PV2fQFUqFY4cOYKJEydaNjgzYf5j/qvC/OdY+Q9gDgSYAwHmwCrMgcyBAHNgXTAH2jbmQOZAwLFyIPPfA8yBzH8A8x9gmvzn0MUVQ1y+fBk3b97E5cuXodFokJWVBQBo2bIl6tevj4EDByIkJAQjR47E0qVLUVxcjHnz5mHSpElQKBSWDd4EMjIycOTIEURGRqJBgwbIyMjA9OnTMWLECDRq1MjS4ZlEXFwcRo0ahS5duqBbt25YtWoV7t69izFjxlg6NFG8++67GDJkCJo1a4bCwkIkJiZCJpPh1VdftXRoJnHnzp1q1feLFy8iKysLjRs3RtOmTTFt2jQsWrQIrVq1QmBgIObPnw9/f3/9myr9ifmP+c/e2Hv+A5gDTYk5kDnQ3jAHMgcagjmQOdDeMAcyBwrl6PkPsP8cyPzH/GeS/KcjQUaNGqUD8Mi2b98+/ZhLly7pBg0apHN1ddV5enrqZsyYoauoqLBc0CaUmZmpCw8P1ymVSp2Li4uubdu2usWLF+vKysosHZpJrV27Vte0aVOdXC7XdevWTXf48GFLhySal19+Wefn56eTy+W6p556Svfyyy/r8vPzLR2Wyezbt6/Gf7OjRo3S6XQ6nVar1c2fP1/n4+OjUygUumeffVaXl5dn2aCtFPMf85+9sff8p9MxB5oScyBzoL1hDmQONARzIHOgvWEOZA4UytHzn07nGDmQ+Y/5r675T6LT6XTGl2aIiIiIiIiIiIiIiIgci9TSARAREREREREREREREdkSFleIiIiIiIiIiIiIiIgMwOIKERERERERERERERGRAVhcISIiIiIiIiIiIiIiMgCLK0RERERERERERERERAZgcYWIiIiIiIiIiIiIiMgALK4QEREREREREREREREZgMUVIiIiIiIiIiIiIiIiA7C4QkREREREREREREREZAAWV4iIiIiIiIiIiIiIiAzA4goREREREREREREREZEBWFwhIiIiIiIiIiIiIiIywP8D3eCputiRjFgAAAAASUVORK5CYII=",
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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  39\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  40\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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9+uHJJ59sETN79mx8/PHHLR6/+uqrm/17zJgxLZrDG43Gprr+V1xxBS655BKMHDkSsbGxuOKKK1odI+dGIgKADRs2YP78+cjKykJNTU2z58rLyxEWFtb0765du7bYvq358NzYxuTIubFtzZtGoxEffvghLrjgAvj7+zf1tmuNLMuc04hIFY7MRe2Rnp7e9P9Tp05Fn9PXGf/xj38odgxyP1xpQp6nTx9g3z5r0uPCC4Gvv7b+d/78MzFRUYDZfKZvyblyc4H9+63/v3Nn28dqPNmMjlZm7ETkcTIzMxEfH9/s68iRI7j//vvx559/YuHChfD398eTTz6JPn364I8//lDs2GfXmj7b2R+Mo6KiALT88Hzfffc1G/M111zT7Pni4mL8/vvvAICcnBxIktTqsRr3G815ksijXXfddcjNzcUbb7yBhIQEvPzyy+jbt2+zHkod0dZ8dra25rOz/fTTTwCA48ePN61MOdujjz6KVatWNX0tWbIEgPVD89mPv/LKK3bHM2LECMTHx+PTTz9t8RznRiJqdPDgQVxyySUoKirCq6++ihUrVmDVqlV44IEHAKDFOZbI+Z0jsVFRUTbnzR9//BEAUFdXh/2Nn5FbUVpayjmNiFQjOhd1VEREBMaOHdvq+Rt5FyZNSBtiYoDQUGtT9bYkJVmTIefau/fM842CgqzN5BctAg4fBiZNOtM4HgB697b+Ny+v5f4kCbj1Vut4Hn8c+O9/gW++aX1MeXmAXg+cd57dl0hE3mngwIHNLrStWrWqaTVJ9+7d8dBDD+Gnn37Crl27YDKZbF6IS0pKwsGDB1t8KD5w4EC7x9f79HyYd858eO6Fw3PHdffdd6OyshILFy7E+vXr8dprr7W6/8b9Nt6tQ0SeKz4+HnfddReWLl2KvLw8REVF4bnnnms1NikpCQUFBS3uqO7IfNa1a1cEBAS0mM8avfvuu1i1ahWee+45mEwm3H777S1iUlNTMW7cuKavkadXKg8dOrTZ40OHDhUaU11dXaulvPLy8hAdHd1i9QwRebbW7oxevnw56uvrsWzZMtx+++2YOHEixo0b16xkl5p69+7d5ry5Y8cOLFiwANOnT8fgwYNx2223tTqnAdZ5jed7RKQGR+YiJdTW1qq6f3IPTJqQNuj1wNVXA8uXA6fvXG5GloGJE63lsrKyzjxeXQ289x6QnHymhNa5dw0ajdbnZBloaLA+1rj0rrVjvfoqkJlp3e+zzwIjRgB33tl635ItW4C+fYGzlksTEZ0tIiKi2YW2cePGQZIk1DUmcU/r3r07QkJCUF9f3+a+xo8fj2PHjmHZsmVNj9XV1eH9999v9/g6d+6MxMTEplUjjc69cHj2BcL//e9/+OKLL/DCCy9gzpw5mDp1KubNm4c///yzxf63bNmCsLAw9O3bt91jJCJts1gsLT5YxsbGIiEhoc05bfz48WhoaGg2f0mShLfeeqvd4/D19cX555/fYj4DrBfzHnnkEUyZMgWPP/44/vGPf2DZsmX45JNP2n28RtXV1S2SPwDw9ddfo7S0tEXfFMA6N55dCoKIvENQUBAAoKysrOmxxtUgZ98UU15ejkWLFjllTOnp6di1a1eL+bqhoQG33norEhIS8Prrr+Ojjz7CiRMnmlbAnK28vBwHDx7EiBEjnDJmIvIejsxFjjp58mSLx/Lz87FmzZpWz9/Iu7CnCWnH888DP/0EjBljbe7epw9QUAB89RWwfj0wZ4511cfllwP33gtERlobtOflWUtw6U/nAC+7DIiLs/Yw6dQJ2LMHePNN62qTkBBrTLdu1v4nq1cDf/vbmTHs2QM8+aR1pcnkydbHPvoIGDQIuOsu4Msvz8Q2NAC//WZ9nIjIAX/++ScuueQSXHfddUhNTYWPjw++/fZbnDhxAlOnTm1zu9tvvx1vvvkmbrjhhqbyWZ9++in8/f0BtL82/lVXXYVvv/1WqBb1yZMnceedd+Liiy/G7NmzAQBvvvkmfvnlF9x6661Yv3499Poz92SsWrUKkydPZo1rIg9WWVmJLl264Nprr8XAgQMRHByM1atXY/PmzW2unrv66quRlpaGhx56CAcOHEDv3r2xbNmyph5QHZnPnnjiCVRUVCA0NBSA9ULk3/72NwQEBOCdd94BYJ1Pv/76a9x3330YN24cEhIS2nU8ANi/fz/GjRuH66+/Hr1794Zer8fvv/+OJUuWIDk5Gffdd1+z+JMnT2LHjh02m94TkWdqvAnliSeewNSpU+Hr64vRo0fDaDRi8uTJuP3221FVVYX3338fsbGxKCgoUH1MV111FZ599ln89ttvuOyyy5oe//vf/45t27ZhzZo1CAkJwYABA/DUU09h3rx5uPbaazFx4sSm2NWrV0OWZVx11VWqj5eIvIsjc5Gj+vfvj0suuQSDBg1CREQE9u/fj//85z9oaGjACy+8oOCrILckE2nJoUOyfMstshwTI8t+frLcrZss3323LNfXW58/eFCWr71WlsPDZdnfX5bT0mT5+++b7+Pf/5bl0aNlOSrKuo/u3WX5kUdkuby8edyrr8pycLAs19RY/202y/IFF8hyly6yXFbWPPb112UZkOUvvjjz2A8/WB/bv1/Z94CIPF5RUZF89913y71795aDgoLksLAwediwYfKXX37ZLG7MmDHymDFjmj2Wm5srT5o0SQ4ICJBjYmLkhx56SP76669lAPLGjRubbdu3b98Wx542bZqclJTU7LGtW7fKAOR169bZHfs111wjh4SEyPn5+c0e/+6772QA8osvvtj02J49e2QA8urVq+3ul4jcV319vfzII4/IAwcOlENCQuSgoCB54MCB8ttvv90U09rcc+rUKfnGG2+UQ0JC5LCwMPnWW2+VN2zYIAOQP//882bbBgUFtTju/Pnz5XM/zpw4cUL28fGRFy9e3PTY66+/LgOQv/7662axhw8flkNDQ+WJEye2+dry8vJkAPIvv/zSZsypU6fkWbNmNc3pRqNR7tmzp3z//ffLp06dahH/zjvvyIGBgXJFRUWb+yQiz/Xss8/KnTt3lvV6vQxAzsvLk5ctWyYPGDBA9vf3l5OTk+UXX3xR/vDDD5ueb5SUlCRPmjSpxT7PPWdctGiRDEDevHlzs7hffvml1TltwIAB8owZM5r+vWXLFtnHx0e+5557msWZzWb5ggsukBMSEuTS0tKmx6+//nr5wgsvdPzNICKywdG5yFHz58+Xzz//fDkiIkL28fGRExIS5KlTp8o7duzo4MjJE+hkuZVuYUTeoLzcuuLkpZeAGTMc3/7qqwGdDvj2W8WHRkTkiNdeew0PPPAAjh49is6dO7drH5dccgkSEhKwePFixcZ1//33Y+3atdiyZQtXmhCRkKVLl+Ivf/kL1q9f39RPxFEzZszAn3/+iXXr1ik8OmUMHjwYF110Ef75z3+6eihERACAxYsX4+6778bhw4cRHh7u0LaFhYVISUnB559/zpUmRETkMZg0Ie/24ovWZvE5OWfKe4nYswfo3x/Yts1a5ouIyElqa2ubNQatq6vD4MGDYbFYWu0pImrTpk0YNWoU9u/fj6SkpA6Ps7i4GElJSfjyyy87tGSaiDzXufOZxWLBZZddht9//x2FhYXtboJ8+PBhnHfeeVizZk27Ey9qycjIwLXXXovc3FzExsa6ejhERACsPaUGDBiAG264AU888YRD286ZMwc///wzsrOzVRodERGR8zFpQkRE5EYuv/xydO3aFYMGDUJ5eTmWLFmC3bt349NPP8WNN97o6uEREQm77bbbUFtbi/T0dNTX1+Obb75BZmYmnn/+ecydO9fVwyMiIiIiIi/FpAkREZEbee211/DBBx8gPz8fFosFqampePTRR3H99de7emhERA757LPP8Morr+DAgQOoq6tDjx49cOedd2L27NmuHhoREREREXkxJk2IiIiIiIiIiIiIiIgAONDEgYiIiIiIiIiIiIiIyHMxaUJERERERERERERERATAx9UDUJokSTh+/DhCQkKg0+lcPRwi0jBZllFZWYmEhATo9Z6RQ+YcSEQiPHH+AzgHEpEYT5wDOf8RkSjOgUTkrRyZ/zwuaXL8+HEkJia6ehhE5EaOHDmCLl26uHoYiuAcSESO8KT5D+AcSESO8aQ5kPMfETmKcyAReSuR+c/jkiYhISEArC8+NDTUxaMhIi2rqKhAYmJi07zhCTgHEpEIT5z/AM6BRCTGE+dAzn9EJIpzIBF5K0fmP49LmjQuwwsNDeVESURCPGn5LudAInKEJ81/AOdAInKMJ82BnP+IyFGcA4nIW4nMf55RvJCIiIiIiIiIiIiIiKiDmDQhIiIiIiIiIiIiIiICkyZEREREREREREREREQAVO5psnbtWrz88svYsmULCgoK8O233+Lqq6+2uc2vv/6KBx98ELt370ZiYiLmzZuHW2+9Vc1huo2qOjPu+ex3bMorQZ1Zhr+PHsNSwvHGjRcg2N/j2tN0mEWSkbm/CF9uPoSs/BLodDoMTAjDazcM4fvVBoskY+2+k3gxYw/+PFkNyECnUCOWzR6NmFA/Vw/P7XAOJFezSDLW7z+Fd9cexP7CChRVm1uNMwAI8NVDkiXUnBPiawDC/X1h0MuoqbegyiRDOut5PQCjHjBJaPY4AOgA+OgAi3zmMfn017lignxQbbLAZJZhka3b+vsAfj56VNVLaDi9UaCvDuH+BhRXWyDpZMQEGREZ6IO84jrUmSVI8pn9GwDIOkBq7YAAfHVAw+ljNY6tLQZYX5/u9Jf+9G0nIX4+0Otk1Jll1DZIzV6rLToAfnogJtQf9WYLymsaIMH6fpslwGRpfbvGu10a32vD6bE0SNbnUqID8NUdFyIy2Cg2EBVxDvQ+5TUNuOmDLOw8Xtn0WIhRh4ggXxwpNbX4HTPoAKOPDkaDDpV1Uos55Fz+bcw1jfsy6KzPnyvAYP09qW5ofb++OsDPR4f6BhnnhgT56hDq7wvoZJRVN6D2nN9NAwA/H6DeDJz7axvgAwA6mMwyZLQ+7kZhRh1MEmCWZUiW0/OnwTovNVis2/roAB+DDoE+OtSenmziw/whSRKOldVDktFsDmycr9o6buPcZwDQ+l+HM3x1gPms+dKgB3z0QHiAEfUNFtRbrHOgve+hDtbx6QCE+ekRGxaAY2W1qG+Q4GOw/ixU10st3sumcQDNvkf6s16jrwFI7x6Nt24c6vJzfc5/RM3VmiyY9812fL2toMVzOljnk8bzNYPO+m+DQQ+TWYLJYp03rHMg4KvXwSLr4KPXQQcZsiyj3mw9P204axIynHMOejY9rPOGHkCArw7VDW2fwJ19nqjH6blZto4x1M+AGjNQb7bAbLE9zzd7vTogLMAAH4MBpdWm03OY9Ui1DXKr56Q6nDnnPvtct3G+DA8w4IVrBuLSvnEw6JXrVWJvPpNlGfPnz8f777+PsrIyjBw5Eu+88w569uzZFFNSUoJ77rkHy5cvh16vx5QpU/D6668jODi4KWbHjh24++67sXnzZsTExOCee+7Bo48+qtjrICLPU2uy4PmVOcgvrkFyVCAen5iKAKNBsf2rejZZXV2NgQMH4m9/+xuuueYau/F5eXmYNGkS7rjjDnz66adYs2YNbrvtNsTHx2P8+PFqDlXzJr+xDjuPVTR7rKZBwi9/lqDf0z+if+cQLL9ntItGpz0rdxzHfZ//0eykCQBW7zvF96sNy7db37NzLy4WVJhwwfOrEWTUY/eCy10zODfFOZBcKWNXAe77fBvqzfY/vlkAVJ07YZ7WYAFOtXWlEdYPh3VtHEIGYOMzaDOnzknoyABqzEDNOeOvaZBR03Am9niFCccrTK3u09K4ozY0jk1kiI0fSBtjLaeHVVJr71Jj62RY37cjZXXNx2Tn23Xu05azxiIBOFhUiyF/X4WYYCM2z7u0XWNTCudA7zLm5Z9xqLi2xeOVJhmVpjZ+R2XrxaFawYmirbmmcV9tXRyrtaBlRuMsDTLQ0MYYqhtkVDe0Pn6c3u25yeam45oBsRkGKDe1jDOfM2azDJjN1iRto9yilu95o7aS1Gc/D9hPmAAt50tJss5XtZVtvze2jikDKKuXUHayuum5c19bq+M4599n/0iYLMBvfxah39M/YkCXUCybPcqhsSmJ8x95q1qTBQuW78Yve0+gsq4BdQ2yrekXwOnzxbN+mS3y6QT4OeeA1jkQqLM7u53ZT1uks/5rK2HSOL6zt2u8saVBAmrPnagFNJ4fF9U0/+PU0NZdPmdtV3/O38Gzj15Wa8Edn26FDsA7Nw3BhH7xDo+tNfbms5deegn/+te/8PHHHyMlJQVPPvkkxo8fj5ycHPj7+wMA/vrXv6KgoACrVq1CQ0MDpk+fjlmzZuGzzz4DAFRUVOCyyy7DuHHj8O6772Lnzp3429/+hvDwcMyaNUuR10FEnmXmJ5uxKudk07/X7QcWbzyMS1Nj8f4tFyhyDJ0sy4KXMzp4IJ3O7h02jz32GFasWIFdu3Y1PTZ16lSUlZUhIyND6DgVFRUICwtDeXk5QkNDOzpsTRj67I8obuPu4LNFB/vi93mXOWFE2rZwZQ7+vTbPblxUkC+2PMn3C2g52diS/8IklUfjPM6cLzgHkjNl7CrAHUu2unoY5GK2EifOnis4B3q2thImRK5kK3HCc0Ai5ZjMEt5fdxCvrfrT7s0f5Dzv2kictHe+OHc+k2UZCQkJeOihh/Dwww8DAMrLy9GpUyd89NFHmDp1Kvbs2YPU1FRs3rwZ559/PgAgIyMDEydOxNGjR5GQkIB33nkHTzzxBAoLC2E0WldLz5kzB0uXLsXevXuFxsY5kMh72LuGaStx4shcoameJllZWRg3blyzx8aPH4+srCwXjcj1rnh9rVDCBACKqhowfdEmlUekbSt3FAglTACguLoBg58W+xDiyZ5bsVs4YQIAvZ5YoeJovBvnQFKCRZLx9LLdrh4GacCpKhNKqhy7C9yVOAe6p/KaBiZMSJN2HK1AVV37VgM6G+c/ckcms4Sp/87EefN+wMs/MmGiNfOX7YbFzuqVjsrLy0NhYWGz+SssLAzDhg1rmr+ysrIQHh7elDABgHHjxkGv12PTpk1NMaNHj25KmADWOXDfvn0oLS1V9TUQkXupNVnsXsNclXMStW3Vu3aAppImhYWF6NSpU7PHOnXqhIqKCtTWtv5hrL6+HhUVFc2+PMWz3+dgV0Gl/cCz/LKvCMu3H1dpRNpmkWTc9Zljd1aX1lkw9NmfVBqR9lnvCsp3aJt6CzD9PxvVGZCX4xxISsjOK0FhRb2rh0EaMfW9TFcPQRjnQPf0t4+yXT0EojY98MUfrh6CEM5/5G4WLNuF8+b9gI15vKCtVScq6pGdV6LqMQoLCwGg1fmr8bnCwkLExsY2e97HxweRkZHNYlrbx9nHOBfnQCLv9Je31gnFLVi+y36QHZpKmrTHwoULERYW1vSVmJjo6iEpwmSW8J/1YismzvXgF3+ofkeBFv3jxz3t2q64ugHPLOv4L5M7euCL9pXv+WV/sSJZW+o4T50Dqf1OVtbZDyKvcdLBfgPuhnOg6x0v55xD2nW41HNXQXH+I1e58IU1+DDzkKuHQQI8+XMB50Ai72MyS9h7otp+IIBf9p3q8PE0lTSJi4vDiRMnmj124sQJhIaGIiAgoNVt5s6di/Ly8qavI0eOOGOoqnv0f9vbvW2DBGQeKFJwNNpnkWS881v7kkwAsCjzEEwCzZI9iUWSsWLnCfuBbXj2e+9MNKmJcyApITbE39VDIA2JDTHaD9IIzoHuKSGMcw5pV9eI1ucOreH8R+6i1xMrcbTMcy/Eexq1PxfExcUBQKvzV+NzcXFxOHmyeSkds9mMkpKSZjGt7ePsY5yLcyCR9/k4M9+px9NU0iQ9PR1r1qxp9tiqVauQnp7e5jZ+fn4IDQ1t9uXuLJKM7zpYYqsjSRd3tDG3uMP7uPkD76oZ3NH37Ns/vLMMnJo4B5IS0lIiERfq5+phkEZ8PmuEq4cgjHOge/rw1jRXD4GoTf+8frCrhyCE8x+5gx5zVqDe4n0VLdxVp1A/pKVEqnqMlJQUxMXFNZu/KioqsGnTpqb5Kz09HWVlZdiyZUtTzM8//wxJkjBs2LCmmLVr16KhoaEpZtWqVejVqxciIiJaPTbnQCLvs2RjvnDsxb1iOnw8VZMmVVVV2LZtG7Zt2wbA2iRq27ZtOHz4MABrZviWW25pir/jjjuQm5uLRx99FHv37sXbb7+NL7/8Eg888ICaw9ScjbnFkDt4LlJQUe9V5ZMWrc/t8D425Zd51WqTJRs7tqS6tkHy2v45ojgHkisY9Do8fWVfVw+DNCAm2IjIYNetNOEc6B3CAn2RFOUed/OTdxnQJRTB/j4uOTbnP/I03eesgNnVgyCHPHNlXxj0ug7vx9Z8ptPpcP/99+Pvf/87li1bhp07d+KWW25BQkICrr76agBAnz59MGHCBMycORPZ2dnYsGEDZs+ejalTpyIhIQEAcOONN8JoNGLGjBnYvXs3vvjiC7z++ut48MEHOzx+IvIMJrOEQyXiZVefmtyvw8dUNWny+++/Y/DgwRg82HqHz4MPPojBgwfjqaeeAgAUFBQ0nTgC1iz1ihUrsGrVKgwcOBCvvPIKPvjgA4wfP17NYWpO5kFlSms9t2K3IvvROoskY/XejteqA4BFG9pf4sudWCQZq/e0vzRXo0f/t8Mr++eI4hxIrjKhXzzevWkI/Hw0taCUnCgm2IjN8y516Rg4B3qP3x4Zy8QJacqALqFYNnuUy47P+Y88ScqcFfCe2zHdnw7AuzcNwYR+8Yrsz9589uijj+Kee+7BrFmzcMEFF6CqqgoZGRnw9z9TGuzTTz9F7969cckll2DixIm48MIL8d577zU9HxYWhp9++gl5eXkYOnQoHnroITz11FOYNWuWIq+BiNyfI9drkyIDEGA0dPiYOlnu6JoGbamoqEBYWBjKy8vddnnepa/8iv2nxBrb2NI1MgBrHx2rwIi0bcP+Ivz1P5sU2deQrmH45q4LFdmXlr226k+8tma/Ivv69LZhGNkjWpF9OZsnzBfn8sTXRO1nkWSs338K7649iP2FFSiqbv0eQQOAAF89JFlCzTkhvgYg3N8XBr2MmnoLqkwyzl6Tpwdg1AMmCTh3rZ4OgI8OOLuSg3z661wxQT6oNllgMsuwyNZt/X0APx89quolNJzeKNBXh3B/A4qrLZB0MmKCjIgM9EFecR3qzBIk+cz+DQBkHdBWbtdXBzScPlbj2NpigPX16U5/6U/no0L8fKDXyagzy6htkCBatUIHwE8PxIT6o95sQXlNAyRY32+zBLS1WLQxDdb4XhtOj6VBsj6XEh2Ar+640O4KE0+dKzz1dbmL8poG3PRBFnYer2x6LMSoQ0SQL46Umlr8jhl0gNFHB6NBh8o6qcUcci7/Nuaaxn0ZdNbnzxVgsP6eVDe0fA6wzgV+PjrUN8g4NyTIV4dQf19AJ6OsugG15/xuGgD4+QD1ZrS4qBjgAwA6mMwyZLQ+7kZhRh1MEmCWZUiW0/OnwTovNVis2/roAB+DDoE+OtSenmziw/whSRKOldVDktFsDmycr9o6buPcZwDs3kHuqwPMZ82XBj3gowfCA4yob7Cg3mKdA+19D3Wwjk8HIMxPj9iwABwrq0V9gwQfg/VnobpeavMCrS/Q7HukP+s1+hqA9O7ReOvGoXZXmHjiXOGJr4lcb8gzP6KkVt01JjpY55PG8zWDzvpvg0EPk1mCyWKdN6xzIOCr18Ei6+Cj10EHGbIso95sPT9tOGsSMpxzDno2Pazzhh5AgK8O1Q1tn8CdfZ6ox+m5WbaOMdTPgBozUG+2wGyxPc83e706ICzAAB+DAaXVptNzmPVItQ1yq+ekOpw55z77XLdxvgwPMOCFawbi0r5xdleYeOJ84YmviYjOGPj0jyivE/t7ZOs6pSNzhWvWK1ObTGZJkYQJABwtrYVFkhVZkqllr/y0V7F9FXpBUzuLJOOtXw8otr+sg8VumzQh8nQGvQ5jesViTK9YVw+FiLxAWKAvlt872tXDICIiBXz9+1FFEiZ6WBOt8aF+uPC8WDx5RV9F7gAmIiLv8NyK3cIJE4MOGN4tSpHjMmmiMYs2dLw3RyNJBjL3F2GUAs1vtMpklrD1SLli+9N5QSWbzANFaFCwgd+GA6fw8Pheiu2PiIiIiIiIXMciyXj4f9vbvb1BB2ycOw4xoX4KjoqIiLyNySzh/XX5wvFXDkpQbPEAkyYa8+F6ZXtqPL18F9b0uljRfWrJx5n5iu7Pz8fz73j5ZutRRff3x5FymMwSjOydQERERERE5PZeX7XPZtnStiRF+GPFfWPslsgjIiIS4Wjv6RenDFTs2PxLpiEms4QTlSahWFv1Oc92sKjGoy9ob84vVnR/uUU1Hl/SbOvhUqE40Z8xwJq8mjm6WwdGRURERERERK5mLed80KFtdAD2/f1yj73uQERErvHmmj+FYycPiFP07xD/omnI4qx84diIQF/hWKVXY2hJgK/YypChieKNwK59Z0N7h6N5JrOEQyW1QrEje0QhKTJAKDZb4eQVEREREREROd8ba/4UvnkOsDYjz3thEhMmRESkqFqTBZUmSTj+talDFD0+/6ppyK/7TgrHDk+JFI7dlFfUnuG4BYNe7Ed4fL8E9IgJEor940g5ak2WjgxLsxxJoF01qAu6hIslTY4U17RzRERERERERKQFFknGO7851mf14AuTVBoNERF5s+dX5gjHhgX4KF41iEkTjbBIMjJzxe7W7xoZgOsvSBLe98bckvYOS9MskoyM3YVCsTGh/ugmmDQBgOdWiP9iupPsPPEVIQnhAZAFK9nmFlXDIinXXJ6IiIiIiIica+PBYtSbxe/q/fPvl6s4GiIi8ma/7BVfXNAvXrzCkCgmTTRiY24xLILnJqN6xmBEz2iI5s+q6i0euXJi48Fi1Ai+rrhQf1yQHCW8762HPTPRdKREbEWIrx5IS4mEySyWCDFZZGTneeZ7RkRERERE5A2e+G6ncOwV/eNZkouIiFRhMks4WlYnHD9rTHfFx8C/cBqx4YB4Ca1u0UEw6HW4ZkiC8DaOLGlyF1m5Yu9ZkNGAtJRITBuRLLzvY6VifT/ciUWSkS+YNBmYGA6DXocuEYHC+z9ZKT6ZERERERERkXY8t2I38ovEPi8adMDrNwxWeUREROStbvnPJuFYPx89LuwZo/gYmDTRiGOl4j0hbk5PBgAsvGag8Db5HthzYsXOAqG40efFwKDXweijR1SQr9A2FXUWjys3lZ1XgroGseVMw1Ksq3KmDOkivP/oYL92jYuIiIiIiIhcx2SW8P66fOH4e8b2VLx2PBEREWD9m7TRgWo2r08dpMrfJCZNtEIW++Z2jwlqWgJr9NEjNT5EaLvkKPEVA+6g1mRBnuBdMDcNP9P/5fJ+8ULbyLCW//IkjqwEGdEj2vrfntHwMwhOPJ6VYyIiIiIiIvIKc77eLhzr56PHPZf0VHE0RETkzaY5sMokLTkCEwSv9TqKSRON0Al+J/p3Dmv27y9vHyG03ZCuEY4OSdNEG7X7+egxvNuZXiZPTEoVPoZo+S93IboSJNjP0PSeGfQ6/PWspJMta/aeaPfYiIiIiIiIyPkskozvth0Xjr/roh5cZUJERKowmSVkObDK5J6x6iXxmTTRiNoGsYbmCeH+zf6981i50HZzv9npUeWmth0pFYqLDTE2O6ELMBqQIrzqxrNOBEUbtc+4sFuz9+zS1Dih7b7bdtyjfsaIiIiIiIg83cbcYlgEP8YZdMDssT3UHRAREXmtmz/YKBxr0J+plKMGJk00wCLJ2JwvlgQY2b15YxvRkkt1ZgmZ+z1p5YRYQiMswNjisb//pb/QtvlFVQ6NSMsskoz31+UKxXaLCWr277SUSEQK9IIprjYJJ2aIiIiIiIjI9TYcEL9OcNWgBK4yISIiVZjMEjYJXh8HgJuHJan6N4lJEw3IzitBSbXJblyQnwHDu0c1eyw2xL+N6Jb+t/WIw2PTqpToIPtBbcQN7xaFsAAfu9uu2FkIk1mscbrWbcwtRo1JbDXTuT9TBr0OVw1MENq2sEK8bwoRERERERG51pKN+cKxL0wZqN5AiIjIq30oeLN3o/Eq9TJpxKSJBoiuFpl6fmKLDFpaSiR8Bb+Lx8o854L2kdJqobiurZTiMuh1GNenk91tZQAfZ+Y7ODJtyhJsah/kZ0BaSmSLx7tEiJU027D/lEPjIiIiIiIiIteoqjOjok7s5rqoIF8YfXgJiYiI1PHGLweEY/18dK1ev1QS/+JpgOhqkXGt9JYw6HUYlBgutL0OntFvwmSWsO1IhVDsiG6t17arrGsQ2n5zvliyQesaLGIrZkZ2i2p1aVtkUMsyZ6359o9j7GtCRERERETkBu77fKtwbHxYgIojISIib1ZrsqBasEIOANw+urvq5SKZNNGA/6w/aDcmPsy/zQyaaKPuncfKPeKC9seZeUJx/j66FuXMGgUa7ZfnciRO637YeVwoTmojsRYneIJskYH1+7jahIiIiIiISOu2HykTjh2UGKbeQIiIyKvd9nG2cKwOwH3jzlNvMKcxaeJitSYLVu+xf5F5zuW928ygxYT4CR2rzixjo2CZJi1bvkMsAXBep5A237O/DOostA/ROC0zmSUcLhUrzVbX0PqKlLSUSPgaxDK47613rAYhEREREREROV9pjVgFBgB4YlJfFUdCRETeyiLJ2HCwRDj+9esGqr7KBGDSxOWe/X63UNym3LaTHaKrAABgvZv3nLBIMvYWVArFhvi3vUpErxP75RKN0zLRlTkAkBId1OrjBr0O8WFiZeQqBEufERERERERkWvUmiywCBaiCPHTI8BoUHdARETklS555RfhWD2AK4d0UW8w5xyLXGjDAbEkhq24tJRIGAVXAew8Xi4Up1XZeSUwCZ7ZRQe3fZF/k2CvEtE4LbOVcDvX4xNT23zuwh6t94c5V//OXLZNRERERESkZc+vzBGOvWes+mVQiIjI+1TVmZFfXCscf1EvsWuTSmDSxMVOVZqE4spsLJs16HXoEx8qtJ8AX/fu0XGkpFo4tnOErRU4oitI3H+lSWGFWGmuED+DzbuHxgv2zokMFGsaT0RERERERK6Re6pKOPbWkSkqjoSIiLxV+sI1DsX/64ahKo2kJSZNXMgiyag3t95D4lwRdi5EXzEgXmg/aSkRQnFa9cXmI8KxI22sjEhvo0F8e+O07GRlvVBcz04hNp8vEyy7tWTTYVgkwXXeRERERERE5HQF5WI313UKMcLow0tHRESkrKo6MyrrzcLx8aF+CLbRikFp/MvnQtl5JcI1REd2t738aNqIFKE1EdNGuPcdIqK/TDoAw7u1nfAY3i0K4YG+dvdT7kBjPC0ymSWcFFzNlBgRaPP52BCxniZltQ3IzhNv4ERERERERETOY5FkHC0VS5qM7dNJ5dEQEZE3uujlnx2K/+3RsSqNpHVMmrhQYbl4zbZ5k/vafN7oo8es0fYTIq/8tFf4mFqUaLPk1hn9EkJg0LedRjLodXj+6n529/PE0p1uvWrio/XiTeCvHWq7kVJaSiTCA+wnmgDgZKXYCTgRERERERE518bcYpgsYlUvesQEqzwaIiLyNrUmC4qqxW9UT0sKd/qqRyZNXKikWmwFwHmdgmz2mmj00GW97a42eX9dHkyCJcG0KFDgfQCACan2y5VFBPnZjSmtacCbP+8XOqYW/ZhTIBSnAzDCTqN3g16HW0ckC+0vOtj+e0tERERERETOl3WwWChOB+Dm9GRVx0JERN7njiW/OxS/ZGa6SiNpG5MmLhQmeNf+rFHdheIWZ+XD3poISbbGuSOLJCNj1wmh2O3Hy+zGiK6GWLQh321Xm1TWW4Ti4kP9ba7MaXRBcqTYgd3z7SIiIiIiIvJ4B05VCsUNTAxjPxMiIlKURZKxbn+RcPyE1BiX/C3iXz8X+uNIqaJxuaeqFI3Tmo25xWgQTF4EGu03BvKGHh2940KF4s5PEUuGFFWLNZVfs1csuUVERERERETOY5FkrBe8WDWpv/0KDkRERI64//OtcOTe9LduukC9wdjApIkLnawQuwAtGndCcOXEjqPlQnFaI7qEGACmDLHdnwOw9ugQXe3jSP8ZLZkypLOicaKJpu+2HXfb1TlERERERESeKjuvBFWCFQlYdpmIiJRkMktYvqNQOP5f1w0UqoyjBiZNXCjYz/5qCEfiIIv9EO0pqHDLC9qyLDZmX73Obn8OwNqj49I+sUL7FO0/ozU+erFfcdG4tJRIRAbZTzQVV5vcdnUOERERERGRpxItUw0AcWEBKo6EiIi8zU0fbBSO7RYdiCsFbopXC5MmLnSN4DdeNC7EXyy5Ypatpa7cTXig2KqQKwclCGch07vbT65Yj20UitOaDzfkCsWJlt0y6HX4yyCxVSmOnIwTERERERGR+kSrB0QG+SJNsIwzERGRPSazhOx8sRYUOgCrHrxI1fHYw6SJC1XWNdiNCfIzCK2aAMSTK4Bjpa604niZ2EX4C3vGCO+zrEZsBYlonJaYzBLW7D0lFCt64gwA41LjFN8nERERERERqa9Y8Oa2BZP7uqwkChEReZ7Hv9khHBsW4OPyv0FMmriIRZLx7Io9duNeniJeu21Ej2j4CH5HJVkSC9QIiyRj6bajQrGxDtRdjRSMXX9ArFGelnycmScUF+JncOgOorSUSLurfiICeVcSERERERGRllgkGU8t3y0UG8Wb4IiISCHW67rHheNT40NUHI0YJk1cJDuvBAXl9u/wiAgSLwtl0OswuX+8UGyon1ipK63IzitBaY1ZLNiBRGRcqNiJ4K/7TsFkdq9E0ybBniIp0UGKZ2/dr2MOERERERGRZ8vOK0FJtf2KFwDLLRMRkXLeWLMfZgf6a98+poeKoxHDpImLFJTVKhrXSNaJXfzee6LSof26miMnbEVVYv05AOuqCZFeMDKAxVn5wvvVgroGi1BcaIBjCbTsvBKU1dg+0S6raWAjeCIiIiIiIg1x5HM1yy0TEZESLJKMd349IBzv56N3qPWCWpg0cZE/jog1vhGNa1RjEluNkVdU5dB+Xc2REzZHYg16HYZ0jRCKzS+uEd6vFgzoHK5oXCPRE+3VOYUO7ZeIiIiIiIjUEx0kVp46kuWWiYhIIW+s2Y96i/gqk9enDnJ5PxOASROXOSR4Ad7RMkcXJEcJxe0rrILFgWVRrpaWEom4UPsnePFh/g6f3HWJEEuyuFsfmMMlYomxkT2iHdqvaFLq223H3OpnjIiIiIiIyJOZLWKfaW8c1lUTF6yIiMi9ZewqwGtr9gvH33dJT0zoJ9Z6Qm1MmriARZLxx5EyodiUqCCH9j1tRLJQS486s4SNucUO7duVDHod+ncJsxs3f3Kqwyd3IX72y3M5EqcFJrOEFTtP2I0LD/TF8O5iibZGaSmRiBTotVNSzRJdREREREREWvHttmNCcUdLHSsTTkREdC6LJOPez7cJxxt9dLj3kp7qDchBTJq4QHZeCSrr7JfR0gG4OT3ZoX0bffQYlBguFJt10H2SJit3FGBVzkmbMeGBvrg0Nc7hfZ+oEOuBsmbPKYf37SqLs/KFVimN6x3rcJLJoNfhqoFiWd/Ccp5sExERERERaYFoOW/ROCIiorbc998/YDKLV+25a0x3Ta1yZNLEBUR7QoztHQOjj+PfopE9RFcOuEfpJIsk48Evt9mNa2/z8YSIAKG4/aeqHPpld6W84mqhOH+joV377xIRKBRXUm1q1/6JiIiIiIhIWSH+vkJxomW/iYiIWmMyS/h+Z4FwvEEH3HPJeSqOyHFMmrhAZKD90kYAcOuIlHbtP72bWI8K0ThXy9xfhDrBZIVoQupsIxx4HxZn5Tu8f1eQZbGEmGjcuSKDxRoIHi3jShMiIiIiIiJXs0gy1u8vshung7XsNxERUXuNeWmNQ/EX9ozW1CoTgEkTl9hbWKlo3LmGd4+yu0LFz0fvcC8LV/n6j6PCsaJNys82vHsUjAaxX4VDJTUO798VwvzFEnOiceeKCxV7n7/6/SibwRMREREREblYdl4JTlTaL009aUB8uypeEBERAUBVnRkFFY5Vnnn3pvNVGk37OeUv4VtvvYXk5GT4+/tj2LBhyM7ObjP2o48+gk6na/bl7+/4hXAtW39ArDfGkdL2XaC3SDIaLLZXZpgskttczD5SIlZqykdvbVLuKINehysGiPVC6RIuVsrL1fSC2VnRuHOlpUQiItDHblxVvRkb3ah3jho4/xGRN+McSETejHMgaUlhhVhVhkv6dFJ5JORJnn766RZzV+/evZuer6urw913342oqCgEBwdjypQpOHHiRLN9HD58GJMmTUJgYCBiY2PxyCOPwGxmXx0id3XDe1kOxQ/pGoaAdrYPUJPqSZMvvvgCDz74IObPn4+tW7di4MCBGD9+PE6ebLupd2hoKAoKCpq+Dh06pPYwncYiydgk2HcjKVKsb8S5Fmflw17VJVl2n1JTNQ0Wobi4UP92L+XqERMsFGe2k4zSCtG3YVg7kkyANdGU3k1spVJWrv0l4J6K8x8ReTPOgUTkzTgHktZ8mX1EKK6kyv5qFKKz9e3bt9nctX79+qbnHnjgASxfvhxfffUVfvvtNxw/fhzXXHNN0/MWiwWTJk2CyWRCZmYmPv74Y3z00Ud46qmnXPFSiKiDLJKMnccrHNrmqztGqjSajlE9afLqq69i5syZmD59OlJTU/Huu+8iMDAQH374YZvb6HQ6xMXFNX116uQ5dzpsPFiMugb7F951OuDm9OR2HUO0hJS7lJoyW8RWxHQkK7lm7wn7QQ7EuZJFkrF4o9gHrPauNAGAbjEhgpHaqknoTJz/iMibcQ4kIm/GOZC0xGSWsDFPrAJAZFD7SjiT9/Lx8Wk2d0VHW/vGlpeX4z//+Q9effVVjB07FkOHDsWiRYuQmZmJjRs3AgB++ukn5OTkYMmSJRg0aBAuv/xyPPvss3jrrbdgMjlW3oeIXK/n4ysdin9z6iDN9TJppGrSxGQyYcuWLRg3btyZA+r1GDduHLKy2l6qU1VVhaSkJCQmJuKqq67C7t2724ytr69HRUVFsy8tyxS8635gl7B21xEVXaHS3pUszhbq76toXGvKa8X+GIvGuVJ2XglKaxqEYos6cBdRumBPHNE4T+OM+Q9wvzmQiLwD50Ai8mb8HExa83FmPkSLc8eFuUdJatKO/fv3IyEhAd26dcNf//pXHD58GACwZcsWNDQ0NJsLe/fuja5duzbNhVlZWejfv3+zJPH48eNRUVFh9zyQiLTlyW92wJH6PD2iA3DFoM6qjaejVE2aFBUVwWKxtLhDplOnTigsLGx1m169euHDDz/Ed999hyVLlkCSJIwYMQJHj7beDHzhwoUICwtr+kpMTFT8dSjpeGmtUFxKVFC7j3FzerLd8kz6DqxkcbbL+or1GxGNa42fj/3+HABQZ9Z+HxjRWrUAEBvS/jrJw7tFwc9OYs/PR4/hgmW8PI0z5j/A/eZAIvIOnAOJyJvxczBpTbbgKhM/g65dfULJew0bNgwfffQRMjIy8M477yAvLw+jRo1CZWUlCgsLYTQaER4e3mybs+fCwsLCVufKxufawsQxkbaYzBIWC5aBbLTy/ovUGYxCnNII3hHp6em45ZZbMGjQIIwZMwbffPMNYmJi8O9//7vV+Llz56K8vLzp68gRx75BzpYg2EhcNK41Rh89Zo5KsRkzc1RKu1eyONv0kbZfi6NxrfH3FSvtdaKiHhZJ24mTU+ViSZNQf58OnRBbJBkmOz1eTBZJ8++Xljg6/wHuNwcSEbWFcyAReTNP/xxMrlVtEmuqnRwdqNkyKaRNl19+Of7v//4PAwYMwPjx47Fy5UqUlZXhyy+/VPW4TBwTaUvqUz84FN8nLkTz16VVHV10dDQMBgNOnGjeB+LEiROIixNbFeDr64vBgwfjwIEDrT7v5+eH0NDQZl9aJnoC0tG78+dOTMXto1NarDjR64DbR6dg7sTUDu3fmYw+etw+2nZC5PbRHUsCJUaIlSprsMjIzitp93GcYdfxMqG482KDOnRCvDgrH7KdfIgsW+O8kTPmP8D95kAi8g6cA4nIm/FzMGmNLIkVTIkMbH/JayIACA8Px3nnnYcDBw4gLi4OJpMJZWVlzWLOngvj4uJanSsbn2sLE8dE2vHF5sMwO1KXC8A3d2mz+fvZVE2aGI1GDB06FGvWrGl6TJIkrFmzBunp6UL7sFgs2LlzJ+Lj49UaptNYJBkfbsgTC1bg5vy5E1Ox99nL8eSkPrglPQlPTuqDvc9e7lYJk0aNSaBzL/HroEwSaMqQLsKxheViJdZc5Xi5YJ8SXcd+/Q+V1Cga52k4/xGRN+McSETejHMgaU1hpVhvznqLygMhj1dVVYWDBw8iPj4eQ4cOha+vb7O5cN++fTh8+HDTXJieno6dO3fi5MmTTTGrVq1CaGgoUlPbvs7DxDGRNlgkGY99vdOhbcb0iEKAUazijyuJNXLogAcffBDTpk3D+eefj7S0NLz22muorq7G9OnTAQC33HILOnfujIULFwIAFixYgOHDh6NHjx4oKyvDyy+/jEOHDuG2225Te6iq25hbjCrBs5BN+cUY1Sumw8c0+ugxY1S3Du9HC+ZOTMVDl/XG4qx8HCqpQVJkIG5OT1ZkOdeIntHw1evQIFBKqqhK283gO4cH4PdDpUJxHZEUKbY6RzTOE3H+IyJvxjmQiLwZ50DSCosk41ipWAnnAMGy1USNHn74YUyePBlJSUk4fvw45s+fD4PBgBtuuAFhYWGYMWMGHnzwQURGRiI0NBT33HMP0tPTMXz4cADAZZddhtTUVNx888146aWXUFhYiHnz5uHuu++Gn5+fi18dEdkz5e31DsUbdMDHtw1XaTTKUj1pcv311+PUqVN46qmnUFhYiEGDBiEjI6OpsdPhw4eh15+56F1aWoqZM2eisLAQERERGDp0KDIzM21mmN1F1kGx5mtWytYRNZklVZINzqZWEsig1+GSPp2QsbvtRmONymq0nTQxCP7o9I3v2J0YN6cn47mVe2Arz6TTWeO8Fec/IvJmnAOJyJtxDiStyM4rsduLstGAxDCVR0Oe5ujRo7jhhhtQXFyMmJgYXHjhhdi4cSNiYqw3Af/zn/+EXq/HlClTUF9fj/Hjx+Ptt99u2t5gMOD777/HnXfeifT0dAQFBWHatGlYsGCBq14SEQmqNVmw7WiFQ9scXDhJpdEoTyfL9roSuJeKigqEhYWhvLxcc8vz/vHjXrz5y0Gh2E9vG4aRPaIVOe7ClTl4f11es4vbep21Gby7lOqySNZeIicr6xAb4o+0lEjFG9SJfn9mX9wdD4/vreixlWKRZAxZ8BPK6+w3+vvndQPxFwfKkrVm4coc/Hut7ZJz7940BBP6abOsgJbni/byxNdERMrz1LnCU18XESnLE+cKT3xNpIxv/ziGB77YJhT76YxhGNlTmesQpF2eOF944msi0rpuc1bAkVYmmx8fh5hQ164gc2SucL+lBm5sWIpYc/cgo6HDjeAbNV7UPnc1gCQD/16bh4UrcxQ5jpoydhXgwhd/xg3vb8R9n2/DDe9vxIUv/oyMXQWKHmdYsth7LhrnCtl5JUIJEwCIC+tYeS4AeHRCH/jYSV499NV2WATKnhEREREREZGySqrEel76++oxvLt2P+sSEZF2DF3wo0MJE4MOLk+YOIpJEyfS68RWRswc1U2RVRQms4T319leBfD+ujyYzI78mDtXxq4C3LlkKwrKm9dgLSyvw51LtiqaONEL1rUSjXOFk5VitWrDA3yRlhLZ4eNlHiiC2U5CpLregswDRR0+FhERERERETkmMsgoFHfDBYmKV3MgIiLPM/+7XSiuEbthu9H2+eNVGo16mDRxoqJqsTs8UmKCFDne4qx8m/0mAOuKk8VZ+YocT2kWScYzy3PQ2kuQT389szxHsVUMRYJ34Py854Qix1NDbIi/UNz0kcmKnBB/s/WoonFERERERESkHNEKA5f11WZJZSIi0o6VO47j46xDDm0TbNQj2F/1tuqKY9LEicL9fBWNs+dQSY2icc6WnVfSYoXJuQrK65CdV6LI8UQTDl/8fkSz5abSUiIRHmj75yfIz4DZY3sqcryqeotQXG5RlSLHIyIiIiIiInFpKZEINBpsxoQHKlOJgIiIPJdFknHv5384vN3Gxy9VYTTqY9LEif6TabtUVqNVe5VZyZAUGahonLMVVoiVmhKNsyctJRIRdhIOgDVRsPFgsSLHdAVfg3K/9rGhYku9/yys1GyiiYiIiIiIyFP9uKsQNSbbN7uxKBcREdkz+7MtcLTDQ//OIW65ygRg0sRpLJKMDQfELrQrtfLj5vRk2KvApNdZ47RItGGdaJw9Br0Ow7qJ3V2TmavNHh3ZeSUoq2mwGVNW06DY6pwhiRFCcbVmWbFjEhERERERkX0WScaDX26zG1eq4GdEIiLyPM+tyMEPuxy7yT8q0BfL7xmt0ojUx6SJk2w8WAyLLHanfXKUMis/jD56zByVYjNm5qgUGH20+WMg2rBONE6Ev4/tZcuNjpXWKnZMJYk2gheNsychQvxntbBcm+8ZERERERGRJ8o8UIQ6wduClfqMSEREnmXljuN4f51Y9aRGfgZgy1OXqTQi59Dm1XIPlOXAyoTHJ6Yqdty5E1Nx++iUFitO9Drg9tEpmKvgsZQWHeynaJwQ0QpSGq00JdqXRTTOnrSUSAT7iU0jRQqtCCIiIiIiIiL7vt56VDhWqc+IRETkOax9TLY5vF3OsxOVH4yTuWdRMbckViU0JSoQAXaatDlq7sRUPHRZb3ycmY/N+SUINBowZXAXjOgZrehxlLa3sFI4btR5MYocMy5c7ERRNM7Zft5baDcmPsxfsSZ/Br0OF/aIQcZu+0v0ymptlw0jIiIiIiIi5dSYzEJxfj46NoInIqIW7vn0d5gd7FF879geMNjrF+EGuNLESYYJnoAsuKqfKsf/ee8JfLghDz/lnMDSbcdx86JsXPjiz8jYVaDK8ZRwpFSst4tonIioILFVK6JxzmQyS/jP+ny7cY9P7KPo5NUjNkQoTsf2gkRERERERE5zflex6xCT+sd7xAUuIiJSzrPLdmPl7pMObWM06HDfuPNUGpFzMWniJHqd2AmIaJwjMnYV4M4lW1FQ3rxGaUF5He5cslWziZOkSLF+GaJxIqJDBEuCCcY50+KsfIgkf09WKFurNr17lKJxRERERERE1HHdo4OE4ib1jVd5JERE5E5mfrIZ/8nMd3i7f90w2GOS8EyaOElRtVg/B9E4URZJxjPLc9pswSEDeGZ5DiwOLrVyhpvTk+3G6HVicaLiQgXLcwnGOdOhErEVN6JxooZ3i0J4oK/NmIhAXwzvxqQJERERERGRs3yYla9oHBEReb7vtx3HqhzHVpgAwNs3DsGEfp6ThGfSxEnyi8QuVCvdfC07r6TFCpNzFZTXITuvRNHjKuHuz7bYjZk5KgVGH+V+jNNSIhEfZvt7oGRPECUlRgQoGifKoNfhhWv624w5PznCYzLNRERERERE7kC0lHVFHftPEhGR9eb72Z//4fB2o3pEYeIAz0mYAEyaOIVFkrEoM89uXFyon+IX4wvLaxWNc5Zak0Uoq3n/uF6KHteg12H+5FTogBYdOBofmz85VZMJgN5xoYrGOWJCv3hcmhrb5vOrck5i4cocxY9LRERERERELVkkGacqxSpZDOwSru5giIjILQx8OqNd2713ywUKj8T1mDRxgo25xSirsX/nxtQLEhW/GF9SbRKK++L3I4oet6OeF7zALhrniAn94vHOTUMQd86Kk7gwf7xzk3aXmn0kkJgDgJIasZ8JR5jMElbvsZ3kem9dHkxmSfFjExERERERUXPZeSWobRD7/PXEpFSVR0NERFqXMmcFqkyOX7cb1ycGAUaDCiNyLR9XD8AbZB4sEoprUKGvSGSwWMPyTbklMJklRUtddUTuqWpF4xxlXTkRh+y8EhSW16Kk2oTIYD+EBRhhkWTNrTQxmSX8vPeUUKzSJeAA4OPMfMh2fnxl2Ro3c3Q3xY9PREREREREZ5ystF2mu1H/zqEeebGLiIjEJc9Z0a7tBnQOxQfT0hQejTYwaeIEx0rFSl+JxjlCtGG5DGBxVj5mjNLGBe1ak1nRuPYw6HUorzXhpR/3NesLEx/mj/mTUzW14mRxVj5EUm6h/j6q9GPJzisWjmPShIiIiIiISF2RAUahuEcv663ySIiISMuG//3Hdm13c1oinr1mgMKj0Q5tLCvwcJIktrRJNM4RaSmRCBS8aySvWJ1VG+1RUi1We1WW1Sv3lLGrAHcu2dosYQIAheV1uHPJVmTsKlDt2I7KLxZr8De4qzoN2avrxRoHisYRERERERFR++09UaloHBEReZ6/vp+FwirHb0iPDfb16IQJwKSJU+h0YhepReMcYdDrMDQpXChWtldfyUkskoxj5WJJE4NBncVSFknGM8tzWl29IZ/+emZ5DiwqlFRrH7FxdI0MUHkcRERERERE5GpHSsVurBONIyIiz9Jr3g/YcLCkXdtmPX6pwqPRHiZNnKBLRKCicY5KjQ8Vigv200a1tuy8EjRYxJIAl6Z2Um0M564wOVdBeR2y89o3uShtUJdwReMcFeTvKxRXZbKocnwiIiIiIiI648/CCqG4RJWuQxARkXZ1n7sC9eb2Ve9596Yhmuv1rAYmTZxA9AdpeLcoVY6/+7jYyZJonNoKK8Qa1gHA9JEpLh2DI2NVU4Lgia5onKPSksV+dvOKajS0OoeIiIiIiMjzmMwSNuaVCsX27hSi8miIiEhLRjz/IwTvVW/h3ZuGaKrHs5qYNFGZRZKxeOMhoVi9CuW5ACDAV6yniWic2ooqxUpzDU+JgNFHnR/hkiqxMYjGqa1/5zBF4xw1bUQyRH56K+vMmlmdQ0RERERE5IkWbcgVji2pNak4EiIi0pLL//kbjlc43sMEAPYsmOA1CROASRPVZeeVoLRGrPl1kWDzc0elpUQqGqc20Sbwg7tGqDaGyCCjonFqu+vTLUJxL2bsUeX4Rh89xvaOEYo9WamN1TlERERERESeaFXOSeHY2BB/FUdCRERaMWTBj9hzoqpd2942MgUBRm3cbO8sTJqozJHyTWqdrEwbkSK0CmDaCHVKXTmq0E4vEUfj2iMuTKxhumicmiySjHX7i4Ri84vVa/J326juQnE8KSciIiIiIlKTWN0VX4NOMzdPEhGROkxmCX3mrURJTftWmPTvHIp5k1MVHpX2MWmiMtHyTSH+BtVOVow+eswabT8h8vPeE6oc31HxgokI0bj2SEuJRHyY7Yv78WH+mjjBXL/vlOApMZAcpV6Tv0GJ4YrGERERERERkePG9u4kFDepX7xXNPMlIvJWz3y3C+fN+wG15vY1MemfEILl94xSeFTugUkTlYmWb7p2SBdVT1YendAHgTaWUekAPLM8RxNNuiOCfBWNaw+DXof5k1NtrtC5cqA2TjDfWy9er/bxieplhj/bJNa7RzSOiIiIiIiIHGcQvNLTJ4FN4ImIPNXQBT9iUVb7r8H9dXgilt87WsERuRcmTVQWHewnFCd6J0h7ZeeVoMZkafN5GUBBeZ0mmnSLvmeice01oV+8zRU6763NQ8auAlXHIKJcsHFfsJ9e1fqDB4vE6iKKxhEREREREZHjjpWJlbIWjSMiIvdhkWT0emIFittZjgsA+iWE4LmrByg4KvfDpInK9hZWKhrXXqLNt7XQpFsr/UQskoxl220nRbSwOkf0fRjeLVrVcZwU7N8jGkdERERERESOS4oUK8ssGkdERO5h+fbj6P74StS3fd+8XV0j/fG9F68wacSkicoOl1QrGtdeos23tdCkWyv9RLLzSlBgo9m8VlbnTOgbp2hce+lsFjNzPI6IiIiIiIgcFxtq/3O9XgfcnJ6s/mCIiMgp/rZoE+757x8d2se09CSsffQShUbk3pg0UdnhkhpXDwEAMDQpwu6lat3pOFez109EB2D+5FTV+4m4y+qczhFidweJxrVXkJ+PonFERERERETkGIsk48nvdtmNm3FhMow+vCREROTuTlXUo9ucFfh5X1GH9jNjRDKeuaqfQqNyf/wLqSKLJGPbkXKh2EGJ6iYrNueVwF4RKfl0nBZM6BePd24a0mLFSXyYP965aQgm9ItXfQzRQYK9VQTj1KKVlTlTBndRNI6IiIiIiIgcszG3GGU1DXbjLuqlbl9VIiJSl0WS0efJH3DB86shdXBf4/rE4Mkr+yoyLk/BW75VlJ1XgvJa+ycrAJAQrm5/jqxcsWzjkk35GNlT3d4Xoib0i8elqXHIzivByco6xIZYL/yrvcKkiehhXFxtqnFlzp1LtrZIjDUOzRkrc0b0jEag0YAaU9uFE/189BihkZ8vIiIiIiIiT5N1sFg4bmQPfjYjInJH3207hvs+36bIvmaOSsETk1IV2ZcnYdJERaJlm8IDfFVfBSB6Zf/nvadgkWTnJSbaUGuy4PmVOcgvrkFyVCAen5iKAKPBqWMoqqpXNE5tYYG+Le4oCg/0xcJr+jtlZY5Br8Or1w3EHUu2thlTb5awKqfQKeMhIiIiIiLyPvZqTDgaR0REWmEySxjz8s8oKO/4tUgdgH1/v5ylGtvAd0VFok3Vp49MVj1Jkd49Siiu3ixhY67YnSlqmfnJZvR5KgOLNx7Guv1FWLzxMPo8lYGZn2x26jhEv3+icWrJ2FWAO5dsbXUJdqnAsmwlXZoaZ3eyffDL7bBIPEEnIiIiIiJSWno3sdUjonFEROR6tSYLxr/6K86b94MiCROjAch7YRITJjbwnVFRWkokguysjvDz0WP22J6qj2V4tyj4C/4iiC7nVcPMTzZjVc7JVp9blXPSqYmTxl4httJZgUaDE1YJtc0iyXhmeU6b9wjpADyzPMdpSYrM/UUwmW1XUqwxWZC5v2PNqYiIiIiIiKil4d2jEB7oazMmPNAXwwVvrCQiItexSDKueXsD+jyVgX0nqxXZ55ieUfjzuUmK7MuTMWmiIosk2+zvAFiXVTnjgrZBr8PFvWMEo12zCqDWZGkzYdJoVc5J1Np5T5XS2CvE1rtRY7LgpYw9ThlPa7LzSlBQ3nYZOBlAQXkdsvNKnDKeL7YcVjSOiIiIiIiIxBn0OrxwTX+bMS9c09/lJbmJiKh1JrOEd389gPOfyUD3x1di6+Eyxfb95tTB+HjGcMX258mYNFHR4qx8u+kH+XScM9w0LFkozlXLdJ9bsVvROCWM7d0JOjvnku+vy7O7ukIton1zROM6alOuWHJGNI6IiIiIiIgc19pqk/AAH7x70xD2mCQi0qBakwWXv74W5837AS9k7ENRrXI3jYf6++Dg8xNxxaAExfbp6dgIXkWHSmoUjeuoxmW6rfW+aOTKZbrbjpQrGqeExVn5kO1kviTZGjdjVDfnDOos2uu7wqaDRERERERErtLY87K1T1xltWanj4eIiNpWUmXC9f/egAOnalS7UvbKtQMw5fxElfbuuZg0UVFNvVhGMCkyUOWRWDUu071jydY2Y7hMtzmtJb7ONTQpwm6MTjBOCQM6h2HNPvv9SgZ0DnPCaIiIiIiIiLyHvZ6XgLXn5aWpcfzcT0TkAoVldZj4+q8oUXAVSVuSI/2x5uGxnO/bieW5VGKRZKzbf8punF4H3JyerP6ATpvQLx7v3jQEcaF+zR6PCPTF2zcOduky3eRoseSRaJwSRBNazkp8neutX/bbjZEBbHZST5PXbxiqaBwRERERERGJsdfzEnBuz0siIm9nMkt4+5f9SH/uJyTPWYHhL6xxSsLk9amD8OujlzBh0gFcaaKS7LwSnKistxs3sX88jD7OzV1N6BcPSQLmfbcLJdUmAEBpTQOeXbEHer3OZYmTLhEBisYp4eb0ZPx9xR6bd+ro4NzEVyOLJOPd33KFYrNyizCyp/q9aoL9fTCgSyh2HK1oM2ZAl1AE+3PqISIiIiIiUlJhea2icUREJK6qzoz7Pt+K7UdKUVlnhmABIkUNSQzHV3eOYLJEAU65Wv/WW28hOTkZ/v7+GDZsGLKzs23Gf/XVV+jduzf8/f3Rv39/rFy50hnDVJRo4+1LUzupPJKWMnYV4O7PtjYlTBoVltfhziVbkbGrwOljAoCoILG+G6JxSjDodQg0GmzGBPoZXDIZbTxYjHrhBvTOG9+y2aMwoEtoq88N6ByKZbNHOW0sWuCN8x8RUSPOgUTkzTgHkrMVVZnsBzkQR0Tk7SySjIwdBRj49A9InrOi6avbnBXo/fgKnDdnBVJOP9bv6R+xZu8pFFU7N2GiA3DDBYnYs2ACvrl7JBMmClH9du8vvvgCDz74IN59910MGzYMr732GsaPH499+/YhNja2RXxmZiZuuOEGLFy4EFdccQU+++wzXH311di6dSv69eun9nAVEx3kZz/IgTil2KpxKsP6i+aqGqcRgb6KxikhO68E1SbbM111vQXZeSVI7x7lpFFZZeXa7x3SyNljWzZ7FJb+fhSPfrsDJsuZn7ajZbVYueM4Jg5IcOp4XMVb5z8iIoBzIBF5N86B5AplNWLJENE4Imd766238PLLL6OwsBADBw7EG2+8gbS0NFcPi1RUXtOAWxdtwr7CCtQ0tF7nJcgXqG0AWrttWAdr6wNJRotrnUY9EOLvg+Iac5vHD/TVQQegpkFutr2vDgjwM6CirvVrghKAOtH7mFXUNcIfax+7xNXD8Eg6WZZtVR7qsGHDhuGCCy7Am2++CQCQJAmJiYm45557MGfOnBbx119/Paqrq/H99983PTZ8+HAMGjQI7777rt3jVVRUICwsDOXl5QgNbf1ud2fYcKAIf/1gk924T28bhpE91C+b1CjrYDFueH+j3bj/zhzu9Avtt/xnE9but58IuGlYV/z9L/2dMCLgu23HcN/n2+zGzRiZjCcn91V/QGf5x4/78OYvB+zG+fnokbNgglOTYBm7CnDnkq1tljW7fXQK5k5Mddp42qL2fOHs+Q/QzhxIRNrmjLmCcyARaZUnzoGc/wgAXsrYg7d/tV/C+a6LuuHRCX2cMCLSIq3OF1988QVuueWWZsnmr776qs1k89m0+pq8XVWdGXd/+jsyDxaj4awEg1EP+PvqUVGvgayDm9IDePXaAbj6/ERXD8WtODJXqFqey2QyYcuWLRg3btyZA+r1GDduHLKyslrdJisrq1k8AIwfP77NeK0qqrLfz8SROKWIlg0TjVOKRZKxSbAZ3YkK59VfjQ0RKwX27bZjsEiq5h9bEE1q3Tmmu1MTJrZWMzX699o8rNzhmjJwzuLN8x8REedAIvJmnAPJVSICxSpZiMYROdOrr76KmTNnYvr06UhNTcW7776LwMBAfPjhh64eGgmoqjPjlv9kocfcMyWs+j39I37b3zxhAgAmCUyYtJOvQYdPZwzD/ucnMmGiMlXLcxUVFcFisaBTp+Z9Ozp16oS9e/e2uk1hYWGr8YWFha3G19fXo77+TOKhoqLtBtTOdOBklVBcZIBR5ZE0J5oEEI1TSnZeiXB/jiA/55XnSkuJRGSQsUX/l3OVVDc4vUTXBcmR0OkAW2vFdADuuriH08YEWL+XBeX2k25PfrcL4/s5vwycszhj/gO0OwcSkXfjHEhE3sybPweTa0WHCJYJF4wjcpbGZPPcuXObHrOXbCbXqjVZsOD7XVi/7wSOlDe4ejgez8+gw/rHLkFMKOdvZ3FKI3g1LVy4EGFhYU1fiYmuz7JZJBmLNuQJxe49UanyaJpLS4lEfJi/zbbgEYG+SEuJdNqYAMdWtkwZ0kXFkTRn0Otw9SCx/hvOXp2z5VCpzYQJYK3nuOVQqVPG00j0fSiuNiFbcHURtU2LcyARkbNwDiQib8X5j1oTFyp286NoHJGz2Eo2t5Y8rq+vR0VFRbMvUp/JLOGN1X+i29wV6PNUBv6bfZQJExX5GXS4eXhX7FkwAfuem8iEiZOpmjSJjo6GwWDAiRMnmj1+4sQJxMXFtbpNXFycQ/Fz585FeXl509eRI0eUGXwHZOeVoKredvPwRkdKa1QeTXMGvQ7zJ6faLJ1UWtOAVTlt39WphuhgsV98P4MOI5zYAwYALu5lu3Zmo8hA564a0mqpNUdWKTl7bM7kjPkP0OYcSETEOZCIvJm3fg4m12u8SdKW+DB/p98kSaQ0Jo6dp6rOjBkfbUKvJ1bivHk/4JXV++Hk6vRexQBgcJdQbH/qMux7biKevbo/AowGVw/LK6maNDEajRg6dCjWrFnT9JgkSVizZg3S09Nb3SY9Pb1ZPACsWrWqzXg/Pz+EhoY2+3K1wnLxnhtJkYEqjqR1l6bGITyw7RJXOgDPLM9xao8OSfBYt4/p5vRyTruOlysapxStllqzljQTK6Hm7LE5kzPmP0CbcyAREedAIvJm3vo5mFyv8SZJHdCiukTjY/Mnp3psiWRyX44mm5k4VletyYJHvvyjqS/Jmr1FqLcwU6KWruF+eGJib/z598tx8IVJ+Hb2KITZuG5LzqFqTxMAePDBBzFt2jScf/75SEtLw2uvvYbq6mpMnz4dAHDLLbegc+fOWLhwIQDgvvvuw5gxY/DKK69g0qRJ+Pzzz/H777/jvffeU3uoirHX/6KRDsDN6cmqjqU12XklKKtpe/mcDKCgvM6pPTo25hYLxVlc0CdqzZ6TwnF3XuS8/iE/7z1hN8YVdxEZ9Do8PSkV93653W7soMRw9QfkQt44/xERNeIcSETejHMgucqEfvH41w2D8fi3O1FZZ256PC7MH/Mnp2JCv3gXjo6odWcnm6+++moAZ5LNs2fPbhHv5+cHPz+WKlJaeU0DLnxxNSrZpF11egB3jknBfZf2htHH7btneCTVkybXX389Tp06haeeegqFhYUYNGgQMjIymuoUHj58GHr9mR+OESNG4LPPPsO8efPw+OOPo2fPnli6dCn69eun9lAVExYglg0c3TPaJb8YWizrdKxMbHWOaJyyxLLppYLJMiWYzBI+WG+/b84Tl/dxyV1EBZX19oMAfJKVj9vHdFd5NK7jjfMfEVEjzoFE5M04B5KrLFyZg/fX5TUrn6MDcMWAOCZMSNPsJZtJPSazhAv+/hPK68RaDZA4gw7w1evQJcIfU87vihkXdmOSxE3oZNleK2n3UlFRgbCwMJSXl7tsifLcr3fgv5vtLw284YJELJwywAkjai7rYDFueH+j3bj/zhzutJUmL2Xsxdu/HrQbd9dF3fHohN5OGNEZ7/12EM//sNdunL+PDrsXXO6UJMX7a3Px3Mo9duOemNgHM0d3U30855ry9gZsOVxmN25o13B8fddI9QfUBi3MF0rzxNdERMrz1LnCU18XESnLE+cKT3xN5LiFK3Pw77Vt31x3++gUzJ2Y6sQRkRZpeb5488038fLLLzclm//1r39h2LBhdrfT8mvSsqo6My5/7VccKRO78ZXO0AMw6gFJAhpw5nZrXx3Qv0soFk0fzhJbGuTIXKH6ShNvtOVQiaJxSmtsDldQ3vZKEmeXdTIaxBINI53cBB4Abh2ZIpQ0qTPL2HiwGCN7qj/Gzfli5cw25xe7JGly9jJwJeKIiIiIiIiobSazhPfX2a5G8P66PDx0GUvBkHbNnj271XJcpCyTWcKol9bgRIXzKqZ0RJAvUNsAtFY0TAdArwMkuWWdGKMeCPH3QXFN29eeAn110AGoaZCbbe+rAyKCjEiO9ENpnYSwAF9clhqHW0emcA71EkyaqMAk2HhDNE5pBr0OVw6Mt3kHypUD451W1skiyfhcYGVORKAvhndzzsqXsxl99BicGIY/jthv9J6VW+SUpEmgUexXVzROaefFBePPk1VCcURERERERNQxi7Pym5Xkao0kW+NmjHL+jXVEpA1/X56DDzbYL/euJqMe8PfVQweg3izDoNfhgpRIvHnjUAT781I1aQN/ElXQLToI+cX2e290iw5ywmhaskgyvvj9qM2YL38/ikcnOKcfRnZeCQor7C8FvHVEikv6cwDAsG5RQkkTs72zVIVMGdIFS7cdF4pzhVDBP3KicURERERERNS2QyU1isYRkWepqjNjxMLVqKh3Xt+SQB8gKSYYncMDMSwlCtNGJHOVBrkNXrFUgY9ebAI4r5Nr6ixuzC1GWU2DzZjSmgZszC12Sjks0YbzydGBKo+kbZW1tt8vR+M6akSPaAQaDagxtf3HLshowAgXlDMD0KyppS3bjpSpOxAiIiIiIiIvUFMvVvo4KdJ1n6uJyPmq6swY9twqVDc4p9pNp2AfTBvZHbeNYsNzcm9MmijMIsnYcFCs34SPYB8PpWUJji/roHOSJrEh/orGqeFEhVhiRzSuowx6HV69biDuWLK1zZhXrhvospU5KVFiq6j2FFTBZJb4h5SIiIiIiKidLJKMZdvtVyLQ64Cb05PVHxARacLkf63FzuOVqh7j8n6dcGNaEkb0iHbZNSgiNfBKpcKy80pQbePu/7Old3PNKoCWrZE6GtcxQ5MiYG9e1eusca5SK5iRF41TwoR+8Xj3piHoFOLX7PG4UD+8e9MQTOgX77SxnEv0RFyGtaYuERERERERtc/6fadgstj//D4htRNvWCPyAiazhNSnflAlYaIDMLZ3NHY9PR75L0zCOzedj1HnxTBhQh6HK00UJlpqKtBowPDuzm9qDliTNW/+clAozhm2HCoVali35VAp0l30ng3sEi60gmhgl3D1B3OWCf3icWlqHLLzSnCysg6xIf5IS4l0+R8ro48eqfEhyCmw/wc6v7jaCSMiIiIiIiLyTO+tzxWKK68TK+FFRO5r/tKd+HjjYcX3Gxvog4wHL0ZksFHxfRNpEZMmCosO8rMfBGDWqG4uu7A9vHsUwgN97fY1Ka81OWU8q3MKheJEE1JqSO8ehbd/E0g0uSCpY9DrXJZMsmVoUoRQ0oSIiIiIiIjar6JOrLemaBwRuad+T/2AKpOyFVBmX9wdD1zay+U35xI5G9dlKkySxUpaubLUlEGvw/NX97cb98TSXbDYWwLSQRZJxrfbjgnFurKniV4n9sdBNM4bDEoU+xkXjSMiIiIiIqKWRCseOLsyAhE5R63JguQ5KxRLmIQF+GDx9DQcfH4iHh7fmwkT8kpcaaKwTXklwnGjzotReTRtCwv0tRtTWtOAjbnqNoPPzitBSbX9u12igoxIS4lUbRz2FFXXKxqnFIska640V6OE8ABF44iIiIiIiKilOZf3wZJN9svxzLm8jxNGQ0TONOOjzViz96Qi+woy6rDp8csQ7M/LxUT8LVCctpqstyXzYJFwnJpJk8LyWqG4KwfGuzQZILrKxZmrYTJ2FeCZ5TkoKD9Ttiw+zB/zJ6e6tAl8o7SUSMSH+Tcb37kiAn1dmgwjIiIiIiJyd3O+2SEUt/NYuSZLOxNR+1y4cDWOlnf85t2oQB/89uglTJYQnYXluRQm2jzdWU3W23KsVCxZIRrXXiXVYn1TukQEqjoOexoTAPaUOmmlScauAty5ZGuLhERheR3uXLIVGbsKnDIOWwx6HeZPTrUZU1rTgFWCPW2IiIiIiIioOZNZwvc7xD7/ubJPKBEpq/fjKxRJmLw5dRC2PDWeCROiczBporDh3aMQaDTYjAkP9MVwF9/dkRAutiJCNK69IoP9FI1Ti0Gvw5OTbCcAAODZFXuc0gfmmeU5ra5VanzsmeU5qo9DxKWpcTD62J5mHvxyuybGSkRERERE5G4WbcgTjnVln1AiUoZFkpE8ZwXqOti+JDkyAAefn4grBnVWZmBEHoZJE4WtyilEjcliM+aFa/q7vO/EyO5i/VSMBtsJoI6KDjIqGqemCIExFJTXIVuwr017ZeeV2Cx5JTtpHCIy9xfBZLb9l7zGZEHmfrFycURERERERHSG6Mp9Xz1YGpnIzWXsKkD3x1d2aB+J4f7Y9fR4/ProWJdfmyTSMiZNFNS4AsCWiEBfXJoa56QRtW149yiEBdhfevfF70dUXQWQU1CuaJyaRPuviMa1l+iSai0svf76j6OKxhEREREREZHjukYG8QIpkRvL2FWAO5Zsbff2vnrgz79fjnVz2LuESASTJgqytwIAsPZw0MIKAINeh7+NTLEbp/aKhS2HyhSNU5No/5Uvfj+i6jjyi2qE4rSw9NreqitH44iIiIiIiOiMcb07CcX939AuKo+EiNRikeQOJUwAYP/zk+yWTyeiM/jboiB3WgEAAMnRQUJxao5XtHF6oK/rf1RF+6psyi2xW5KqvSySjP9mH7YbFxfqp4ml1xcki41BNI6IiIiIiIjO6BMXqmgcEWlP/6c6VpIr/4VJCo2EyHu4/kq0B4kOEruoLhqnNtGVCGqtWLBIMnYeqxCKTU0IU2UMjogLFXsfZACLs/JVGUN2XgkKK+wnsW5I66qJpdfTRiQrGkdERERERERnZB8SqwwhGkdE2tLriRWoMbdv285hfkyYELUTkyYKkmSx3h+icWobmhQBe9fV9TprnBo25hajTnBFRoxgwkJNaSmRCDQahGLzi6tVGYPoqh/RVUTOIJK6+WlXgerjICIiIiIi8jTHy8R6aorGEZF2dJ+7AvXtrGY+tlcMNswdp+yAiLwIkyYK2iTY+0M0Tm1bDpXCXo93SbbGqSHrYLFwrOgqDzUZ9DoMTQoXilUrMebq1UGOWpyVD5F3Yu7SnbDY+2EkIiIiIiKiZhIiAhSNIyJt6PX4CljaeZnk9amD8OH0NGUHRORlmDRRlOhspo2LwyJlngDgp5xCVY4vC74PwX4GTfTnAIDUeLEyYcF+vqocf1BiuKJxajtUIta0vrLOgmyNJBOJiIiIiIjchY9gWeYR3aJVHgkRKaXbnBWob2er3HdvGoKrBnVWdkBEXohJEwWlC56EiMaprUiw1NP/fj+iyiqA8ACxxML4vnGa6M8BACcEE02icY76bNMhRePUlhQZKBxbWM7l4kRERERERKIskowvNh+xGxce6Ivh3aOcMCIi6qjuc1egPfmSIF/g4PMTMaFfvOJjIvJGTJooqLy2wW6Mlk5WymrsjxcAKuvVWQUQHewnFHdhD20kmQCgs+CSZtE4R4n2SlGrp4qjbk5PFuppAgAl1SZVx0JERERERORJsvNKUFhRbzdu+ogUzdyISERt++sHG9tVkisq0Be7n53E33MiBTFpohCLJOPxpTvtxj1/dT/NTGI6nfg4REt5OSI6SCxpIhrnDCO6iyVwROM8ndFHj4t6xQjFRgom0YiIiIiIiEi8uXsX9jMh0rwnv9uODQfEe/82CvfTYctTl6kwIiLvxqSJQjYeLBZauREWYHTCaMSkO7DiRbSUlyP2nqhUNM4ZhneLQnig/bJi5YKreBw1KDFC0ThnmDW6u1BcXKg2mtcTERERERG5g62HxSpCiMYRkWtc+cY6LM466vB2fgZg2zMTVRgRETFpopD1B08pGucMw7tFwc9H7EdAtJSXI46UijUJF41zBoNeh+ev7mc37tkVOar0gTlaKnYnUUK4du4kGpoUYbdEl+50HBEREREREYkpFFxpIhpHRM4346PN2HGswuHtDDpg33OTVBgREQFMmihm55FyReOcwaDX4aLzxEonSbLyCQDRJuGONBN3hgiBcmEF5XWK94GxSDL+m33YblxcqB/SUiIVPXZHbM4rgb2fHvl0HBEREREREYnZf1Ksl+WJSvaPJNKi77cdx5q9Jx3ezgfAwYVMmBCpiUkThQQYDYrGOUtMiFgfico6s+LHvjk9Gfbaquh11jgtEe3vonQfGGuTP/v7vCGtq2b65gBA5sEiobhPNuarOxAiIiIiIiIPYZFkHC8XW0ESF8ZSyERaY5FkzP78j3Ztu+95luQiUhuTJgqprBMrX5WWIt5HxBlEe8E70DNe2M97T8DeApaZo1JgFCwh5iwlVfWKxok6KdhXJjk6SNHjdpRoc8K1f55SpaQZERERERGRp9l4sBhmSSx2mMauQxARMOy5n9q13ds3DtHUjbJEnkpbV6PdlMksYVNeqVDstBHJ6g7GQclRYhfYReNEWSQZc77ZaTPG6KPHoxP6KHpcJYQH2G8E70icqOhgsVVBonHOkhAh1l+ltkFSvKQZERERERGRJ8rKFVvRD2jvOgSRt7vijXUoqna8osv0kcmYOCBehRER0bmYNFHAx5n5dns2AMDwlAjNrZq4OT0Z9hLUOhVKZG08WGy3ubzJLGHjwWJFj6uEslqxVUWicaKEe35obLHGiG7RwrFKlzQjIiIiIiLyTGJ3mg9ODNfcdQgib/b35buxqx2N3/snhGD+5L4qjIiIWsO/nArIzhO7wyPE30flkTjO6KPHzFEpNmNk2VpKS0mid8U4cveMs0QKruQQjRNhkWS8ty5XKLaoWtmyYB01vHsU/H3FphqlS5oRERERERF5ovTuYiW3Hh7fS+WREJEok1nCBxvyHd4uNT4Yy+8drfyAiKhNTJoooMYkVkhUNM7ZHp3QB4E2GtTrADyzPEfhfhOi9Re1V6cxLlSsiZ5onIiNucWoMVmEYmNDtNXkz6DX4YYLEoViI4OMKo+GiIiIiIjI/Q3vFgU/OytIIgJ9Mbwb+5kQacWgZ350eJsu4f5Yed8YFUZDRLYwaaKAgV3CFY1ztuy8EpsX5GUABeV1ivabEL0rRjTOmdJSIhEfZjsxEWQ0IC0lUrFjZgmWKQvyU/a4Srmsr1jNzbgwsf4nRERERERE3uyljD2ot9MJfuE1/dkwmkgjZizKRk2DYzdT+/vosX7OJSqNiIhsYdJEAe6cAACAk5VifSRE40QM7xaFIBurWwDt3hVj0Oswf3KqzZhqkwUvZexR8Khiq3zG9IzW5Enx0KQI+71zTscRERERERFR20xmCe+ty7MbN7Z3JyeMhojsqTVZsGbfKYe3+33epSqMhohEMGmiAMkidkFbNM7ZooPEem+IxolYlVOIajvlprR8V8zY3p2gszO099flwWTnzh9Rw1LEkkc3pCUpcjylbTlUCnvV3WQA7/x60CnjISIiIiIiclcfZ+ZDFri88HFmvupjISL7Up/KcHibfgnBCNZgb2Qib8GkiQK+3X5M0Tinc3J7EYsk45nlOTZjwgN9cWlqnDIHVMHiLPsnqZJsjVOC3l6GxsE4ZxNdpfT2rwcU7p1DRERERETkWTbni5VvFo0jIvVMen2tYO2QM6KDffH9vexjQuRKTJooQLRBt2icsxVV1SsaZ092XgkKym1fRC+raVC0h4rSDpXUKBpnT1G14PdIMM7ZRJvT15slZB4oUnk0RERERERE7ivQKHb3uWgcEamjqs6M3QWVDm0zpmcMfp93mUojIiJRTJooIERwudwFydpr0A2IX9AWjbPHFT1UlJYUGahonD3O/h4pLS0lEv6+YtPNN1uPqjwaIiIiIiIi9zVlSBdF44hIHQOe/tGh+NggH3w8I02l0RCRI5g06SCLJGN1zgm7cToA00Ykqz6e9khLiUR8mL/N6lvxYf5IS1Em6RMdLNhDRTDOFa6/oKuicfY0fo/aooOy3yOlGfQ69OoUIhRrr9cNERERERGRNxvWLcpu9Wzd6Tgico1Jr/8GR7vcrp/Lxu9EWsGkSQdtPFiM8jqz3biJ/eNg9NHm223Q6zB/cqrNmCsHxivXlF20mKOGW1t8sfmwonH2GPQ6XDkw3mbM/Mmpyn2PVDCxv+3xNxqaFKHySIiIiIiIiNzXlkOldj8uy6fjiMj5lm09it0FVQ5tMy09SbPXDYm8EX8bOygrV6z/Qkp0kMoj6ZgJ/eIxa3RKm8+/tzYPGbsKFDmWu/fnAJzf0yRjVwHeW5vX5vOzRqdgQj+xpISrpMaHKhpHRERERETkjVblFArFabnkNZGnskgy7v9qu0Pb+OiAZ67qp9KIiKg9VE2alJSU4K9//StCQ0MRHh6OGTNmoKrKdqb1oosugk6na/Z1xx13qDnMDpEEV0OIxrmKRZKxbLvtpMgzy3NgUeCFuHt/DkC8V0lNvf1VSPZYJBnPLM9p804iHYBl2wsU+d6oqaTGpGic1nnD/EdE1BbOgUTkzTgHkposkoyl244LxWr5MzWRp/rXmv0OXwPMefZydQZDRO2matLkr3/9K3bv3o1Vq1bh+++/x9q1azFr1iy7282cORMFBQVNXy+99JKaw+yQExVid25EBBpVHknHZOeVoKC87dciAygor0N2XkmHj1UqsIJEy/05AODm9GS7NWQBYMPB4g4nM5z5vVGTJyTLHOEN8x8RUVs4BxKRN+McSGrKzitBSbX9G80ig3w1/ZmayBNZJBmvr9nv0DYTUjuxLBeRBqn2W7lnzx5kZGTggw8+wLBhw3DhhRfijTfewOeff47jx23fFREYGIi4uLimr9BQbZbrsUgyVu85KRQbHaLdpuaA+LLd1YLLgNtikWQ8u2KP3bgnJ2m7P4fRR48rBsTZjVMimSH6vdH60uvGZva2vqtaT5aJ8ob5j4ioLZwDicibcQ4ktRWW1wrFXTUwQdOfqckzJCcnt1gl98ILLzSL2bFjB0aNGgV/f38kJia2mhD+6quv0Lt3b/j7+6N///5YuXKls16CovrNz3B4m7duGqrCSIioo1RLmmRlZSE8PBznn39+02Pjxo2DXq/Hpk2bbG776aefIjo6Gv369cPcuXNRU9N2X4j6+npUVFQ0+3KW7LwSlNc2CMXGhWr77nnRu/u/3XasQysn7K2aaBQRpO2VOQAwLtV+0gToeDIjOlgs4SYa5yoGvQ7zJ6fajLlyYLxHnNg7a/4DXDsHEhG1hnMgEXkzb/gcTK4lssoEALpEiJWUJuqoBQsWNFsld8899zQ9V1FRgcsuuwxJSUnYsmULXn75ZTz99NN47733mmIyMzNxww03YMaMGfjjjz9w9dVX4+qrr8auXbtc8XLa7amlO1HbIDm0zRs3DPaIayBEnshHrR0XFhYiNja2+cF8fBAZGYnCwrZXK9x4441ISkpCQkICduzYgcceewz79u3DN99802r8woUL8cwzzyg6dlGFgqW5wgO0vyw2LSUSkUFGuydgJdUNyM4rQXr3qHYdp6BM7K4Y0ThXclq5KdEclbZbmgAAJvSLx6zRKfh3G03t31ubh8FdIzTf1N4eZ81/gGvnQCKi1nAOJCJv5g2fg8m1IgVvlhONI+qokJAQxMW1flPpp59+CpPJhA8//BBGoxF9+/bFtm3b8OqrrzaVLXz99dcxYcIEPPLIIwCAZ599FqtWrcKbb76Jd99912mvoyNMZgmfbDzs0DZDuoZh8sAElUZERB3l8EqTOXPmtFh6d+7X3r172z2gWbNmYfz48ejfvz/++te/4pNPPsG3336LgwcPtho/d+5clJeXN30dOXKk3cd2VEmV/d4cADCuTyfNZ44Neh2uGiQ2WYsmi1rzx5FSReNcaWhSBOx9W3U6a1xHFAn0gHEkzpUskoxl2wtsxjyzPEezTe21Nv8Brp0Dici7cA4kIm+mtTmQ85/3WrIxXyhO69UuyHO88MILiIqKwuDBg/Hyyy/DbDY3PZeVlYXRo0fDaDxTTWT8+PHYt28fSktLm2LGjRvXbJ/jx49HVlZWm8fU2mq7jza0fmNoW/QAvrpjpDqDISJFOLzS5KGHHsKtt95qM6Zbt26Ii4vDyZPN+32YzWaUlJS0mYFuzbBhwwAABw4cQPfu3Vs87+fnBz8/19xBESlYQmpkj/atynC2hDCxk6qiDpSbyi+uFoqzyNq8aH62LYdKYe/aviwD7/x6APeNO6/dx/GkBuqONLVv72omNWlt/gNcOwcSkXfhHEhE3kxrcyDnP+9Ua7Jgy6Eyu3FxoZ7RK5K0795778WQIUMQGRmJzMxMzJ07FwUFBXj11VcBWFffpaSkNNumU6dOTc9FRESgsLCw6bGzY2ytztPaaru3fj3gUPy/pg7S/M3VRN7O4aRJTEwMYmJi7Malp6ejrKwMW7ZswdCh1qZGP//8MyRJajoBFLFt2zYAQHy89sr1xIUFKBrnamU1Yv1ZROPOZZFkoRM8ADDotP/HQ7RXydu/HsTssT3b/QexsYF6YXldqxW4dADi3KSBurs3tef8R0TejHMgEXkzzoGkBc+tyBGKO69TMC/IUrvNmTMHL774os2YPXv2oHfv3njwwQebHhswYACMRiNuv/12LFy4UNXE7ty5c5sdu6KiAomJiaodz5bvtx1Dea3ZfuBp4X46XDGos4ojIiIlqNYIvk+fPpgwYQJmzpyJ7OxsbNiwAbNnz8bUqVORkGAtA3Xs2DH07t0b2dnZAICDBw/i2WefxZYtW5Cfn49ly5bhlltuwejRozFgwAC1htpuP+89YTcm3k0uZgOwXn1XMu4c2XklqDFZhGIHJ3aspJUziK7sqDdLyDxQ1O7jGPQ6RAf72mxZMn9yqlucFHtKU3t7vGH+IyJqC+dAIvJmnANJTduPlgnFldaINYsnas1DDz2EPXv22Pzq1q1bq9sOGzYMZrMZ+fn5AIC4uDicONH82lnjvxtX37UVY2t1np+fH0JDQ5t9uYJFkjH7820ObbN+7mXqDIaIFKVaI3jA2vBp9uzZuOSSS6DX6zFlyhT861//anq+oaEB+/btQ01NDQDAaDRi9erVeO2111BdXY3ExERMmTIF8+bNU3OY7WIyS/hgvf2ahU9c3sctLmYDQHiAWLmxQhvllWxxZPVAfLj2V+ekpUTC31ePugbJbuzXW49i1Hn270xrzZVvrsPOY5VtPt+/S6j7NE4XrLqWnVeMkT2i1R2Lyjx5/iMisodzIBF5M86BpJYQP7FLOKJxRK0RXVnXmm3btkGv1yM2NhaAdfXdE088gYaGBvj6+gIAVq1ahV69eiEiIqIpZs2aNbj//vub9rNq1Sqkp6d37IU4Qb/5GQ7Fp0QFItifv59E7kDV39TIyEh89tlnbT6fnJwM+azeFYmJifjtt9/UHJJiPs7Mh0jbjYIONE13tuhgsaTJDzsL8NK1Ax1OBomuzIgM8nWL1TkGvQ69OgVj+1H7DcdqTOJLNc/23bZj2GFn/zuOVqDWZEGA0dCuYziTaLP6jzMP4d5LznObhGNrPHn+IyKyh3MgEXkzzoGklq5RgcjMLRGKI1JbVlYWNm3ahIsvvhghISHIysrCAw88gJtuuqkpIXLjjTfimWeewYwZM/DYY49h165deP311/HPf/6zaT/33XcfxowZg1deeQWTJk3C559/jt9//x3vvfeeq16akFsXbUKtwE20Z1v90EXqDIaIFKdaeS5Ptzm/WNE4LRDtvVLTIGFjruOvKy0lEuGBvnbj/n5VP7e5WD6pX4JQ3PldHU8CWSQZD325XSj2+ZVitW1dTTRxVlbbgOw8+x8GiIiIiIiIvEVBmdhNmb4G7d9QR+7Pz88Pn3/+OcaMGYO+ffviueeewwMPPNAs2REWFoaffvoJeXl5GDp0KB566CE89dRTmDVrVlPMiBEj8Nlnn+G9997DwIED8b///Q9Lly5Fv379XPGyhNSaLPh1n2Nl2N9k83cit8I1Ye0U6Cv21onGaUFaSiQCjXrUmOxnyjccKHK4fNKqnEK7TeQvTY3FxAFiiQgt6BkbLBRXXud4TdnMA0UwS2L1rPKLaxzevyukpUQiPMAXZbW2fw4A7TaDJyIiIiIicjaLJGNTvtiNZclcaUJOMGTIEGzcuNFu3IABA7Bu3TqbMf/3f/+H//u//1NqaKq75u31DsVHB/my+TuRm+FKk3bqkyDWZEo0TgsMeh36J4QJxR4vq3Vo3xZJxtPLdtuN23WsAhbBRIEWfLfjuFDc++vyHH5d32w9KhzrLifFBr0O00emCMWKrkohIiIiIiLydBtzi4X6aep0wM3pyeoPiMhLmcwS9hRWObTNr4+MVWk0RKQWJk3aKTbET9E4rTg/WayMVGcHG7Vn55WgsMJ+P4uC8jq3KstUY7IIxZksMjYedKykWVW92L4B4PGJqQ7t25Vmj+1ht0xbeKB79LUhIiIiIiJyhg0HxEoBDewcBqMPL/UQqeWKf611KL5vXBCbvxO5If4lbSfR/h+icVoxvFuUonGNCsvFV6a4U1mmCwSTTACQletYvcvYUKNQXOdwP7doAt/IoNfhhWv624wpq2nAqpxCJ42IiIiIiIhI246VipVkTnKTKgRE7qjWZMGfJ6uF4w16YMX9F6k3ICJSDZMm7STS1Dw+zN/t7pbX68SaUn2++YhD+y2pFu/p4U5lmaaNSBaOdbTo2JDECKG4B8f1cnDPrndpapzN3x8dgGeW57hVqTYiIiIiIiK1bMwVq1zAj1BE6nluRY5D8XsWXK7SSIhIbUyatJNIU/P5k1Nh0IslIbSiqNp+CS0AWLmrACaz/XqqjSKDxcqUhQe4V1kmo48ew1PEkhvhAWIrRxodFewbkxDhfncSZeeV2Pz9keF+pdqIiIiIiIjUUGuy4ESl2I2IjtywSESOydhVIBw7sV8nlsojcmP87W0HiyRjzjc7bcYEGQ24NDXOSSNSjugqD1kGFmflC+83LlRsv9NHJrtdoun6C7oKxR0vE1tODVh/xv6bfdhunDuuZgLES7C5U6k2IiIiIiIiNfx9xW7h2EA3Kt1M5E5MZglF1bZvnj7bGzcOVXE0RKQ2Jk3aYWNusd1VJtUmi/DyWS1JS4lEgK/YSVZ+sXgSQKScWZCfAbPH9hTep1aI9q35astR4XJT2XklKKywv+pn6gVd3S7JBIgn59ypVBsREREREZEadhwtF45NS3Gs/ygRiRnz8i/CsbMv7u6W12qI6AwmTdoh66BYMkQ0TksMeh0uSBYrN+Vol44ak8Xm874G9/xxTEuJRISdhBAAVNVbsFHwZ+K4YJO/LuHumVQYmhQBe+1zdDprHBERERERkTcL9hNfPeJI300iErNs6zEUlItVwtABeOBS9+s9S0TNuedVapcTTRa4Zwe2qwYmCMUN6hIuvM/7Pv/Dbg+UspoGt+xhYdDrMEywr8mGg6eE4rYdLVM0Tms255VAtvPrIcvWOCIiIiIiIm9WUSdWEigxwp89FIgUZpFkPPC/bcLxU4Z05ioTIg/Av6btkN4tWtE4rRFtLC4aZzJL+H6HWLMsd+1h4e/rIxR3vEz09Yn+gXXPP8RZuUVCcUs25as7ECIiIiIiIg2zSDL2FlQJxYYF2K+AQESOufe/W2GxfQ9wM89fM0C9wRCR0zBp0g7Du0fZ7c8RHuiL4d3ds5bo0KQIiCTFV+85IbS/jzbkCR/bXXtYxIT4KRpXYxK7k6hrpFjiSnvEkj2/7Dsl3AeGiIiIiIjI02TnlcAi+JEoLMCo7mCIvMzKHcexYmehcHyf+BCu9iLyEPxNbgeDXoeukbabf79wTX+3XY635VApRK5TL9qQZ7fkFgD8uFtslYmP3tofxB1tyRcrIyUSZ5Fk/LBT7D3rHRciFKc16YIJxboGCRtz3a83EBERERERkRIKy2uFY2eN6qbiSIi8i0WS8cCX2x3a5ps7R6o0GiJyNiZN2mHmJ5ux42hFm88P6BKKCf3inTgiZYmWyJJkYHFWvt24g0ViTc1jg/3dNtFUZafJvSNxGw8Wo6ZB7FaikhqTUJzWDO8WBT/Buy8yD4iV8iIiIiIiIvI06/eLfR4y6IALz4tReTRE3iPzQBHqBW4UbjSkazgCjAYVR0REzsSkiYNqTRasyjlpM2bH0QrUCl5E1yJHSmQdLLJdW9VkllBWI1Zq6nzBZupalCRYJkskbt0B2z9fZ3PXcmYGvQ4DuoQJxR4rFb+zioiIiIiIyFNYJBkZu8VKA13at5Pb3oRIpEX/23JUOFYH4Ks7Rqg3GCJyOiZNHPT8yhxF47QoLSUSRoPYydapinqbz3+cmS983P8bmigcqzX/vH6wYnE7baxiOpuvQee25cwAoHOYWMJn5/EydQdCRERERESkQdl5JagWvCGzR4x7lm4m0qrvth8Xjr33kp5MWhJ5GCZNHPR7fqlQXH6xWEkqLTLodRjeTaznRKdQ2xe+Nwv2+jDogBE9ooVitSjY3wcDuoTajXvj5z/txgT6iS3n7BMf6tZ/lDsLrs7JPVUj1DuHiIiIiIjIk4iWzgbE+0YSkX03f7BBOFavsyZNiMizMGniAIskI7+4Wig2OUrsgrBWjRGshZqdb7tJt79g34ohXSPcOgEAAP+7w37Dr3+vzbObAEhLFjvZnTwgQShOq0Z0E0uSyRDrnUNERERERORJPtqQLxQX7OcjfOMjEdlWa7Jg3YEy4fjZF3d3++tZRNQSkyYOyM4rQW2D2B3vj09MVXk06ro5PRkiU/6fJ6pt9m8RXTXRPTZIcGTa9XFmnlDc3K+323x+2ohk6Oy8+TqdNc6dDe8eJVwG7lCJ+67cIiIiIiIiclStyYI/jpQJxY5nPxMixVz88hqH4u8b10ulkRCRKzFp4oDCCrGlsf0SQhFgFEsWaJXRR4/eccFCsc9+v7vN5zbsPyW0j1OC762WbRYs3fb9zkJYJLnN540+eswalWJzH7NGpcAouIpHqwx6Ha4cKLZaJkmwlBcREREREZEneHrZLuHYID8fFUdC5D1qTRYUVjYIx0cG+jJhSeSh3Puqq5Nt2F8kFPeXwZ1VHolz2FpBcrbMA62/LyazhCNlthvFNzpY5P4rCYIEE2X1ZgnZebZ7vQzsEg6/VpIiOh1w++gUzHXzlUyNnr9mgN0YHawrn4iIiIiIiLzFTzknhGN5kxmRMv7y1jqH4n+8f4xKIyEiV2PSRJBFkrEqp1AoNjLIqPJonKOkRiy7fqKy9cSII30oPOHOmGuGdBGOtdXQb+HKHNz12R+ob6X3iSwDg7tGtGt8WmTQ6+wmmwL9DLxzg4iIiIiIvIokt12d4Fy8yYyo40xmCXtPiPUxbhQT6qfSaIjI1Zg0EZSdV4LyOrNQbFxYgMqjcY6IALFERm2D1Gq5qbxi8T82gxPDhWO1akSPaPj7iv1KRQe1/od15Y4C/Hut7d4ozyzPsVney51k55Wg2s6Kpup6i92VOURERERERJ4kMUJs9UhiuL/bl24m0oK53+xwKP69m4aqNBIi0gL+ZRUk2s8kPMAXaSmRKo/GOUb2jBGObe2itiNrA56Y5P7lpgx6He4Y3V0suJU3xyLJePRr+3+kC8rrPCaJYGvFjSNxtSYLnly6Ezf/ZxOeXLpTuLQcERERERGR1mTsKsCu4xVCsT+wPBBRh1kkGd9uPebQNpekdlJpNESkBe5fE8lJSqrEenOM69PJY0oJPXlFX3yWfUQotrWL2gO7hGMxDtvdNjUhBAGC/UC0Lik6SCjuZCslzTbmFqOqXmw1k2iyQetiQ/w7HDfzk81YlXOy6d/r9gOLNx7GpamxeP+WCzo8RiIiIiIiImexSDLmfLNTKHZAl1AE+/OyDlFHZR4oQssC6W17feogj7n2R0St40oTQYdKxEpNpXfzjFUmABBgNOD8pHCh2NYuapfXivVEmTJYvBeI1okm1zbsL2rxWNbBYuHjiCYbtC4tJRLxYfZfy3/WH2z18XMTJmdblXMSMz/Z3KHxEREREREROdPG3GKUCfQXTYkOxLLZo5wwIiLP9/D/tgvH+uiBqwZ1VnE0RKQFTJoIsEgyvhFcpldSY1J5NM712cx0obiiVpIFkUFGoW1F49yB6GtZtaewlb4kYn1KgowGjykBZ9Dr8KRAabbVe07huRU5zR6rNVnaTJg0WpVzkqW6iIiIiIjIbbycsVco7vK+8SqPhMg71JosOFEhdgMsAFzUS7yUPRG5L69OmlgkGb/sPoEr/rUOo178Gbd9vBlVrTR7z84rQVW92IXXMsHVFe5ik+Dqh/nLdrdIAhwuqRHaNi4swOFxaZXoaymvNbfoSyLa2v3WkUketQw0QjDR9MG6PJjMZxbMPr8yx0b0GaJxRERERERErmQyS9h2tFwodvvRMnUHQ+Qlbl/8u0Pxr10/RKWREJGWeG3SJGNXAXrN+wHTF/+OXccrcKS0Fqv3nES/p3/ElW+uaxZbWF4rvF+dQ+3Pte/rP44KxZVUm5olATJ2FeCfq/fb3S4+zN9jVk0A1nJTYYI1ZX/aXdD0/xZJxn/W5wltFx7g166xaZVofxYZwMeZ+U3/PnCqSmg70TgiIiIiIiJXeviLbcKxgUavvZxDpBiLJGPDgZbl09uSHBXAPkJEXsIr/8pm7CrAHUu2wtyiPJLVjqMVzRInJdXiJbfSu0d1eHxaUuNAaaPG5JJo4zodgPmTUz1q1YRBr8O41Fih2K+3HmtanbMxtxh1DWJtx46Uiq3gcReO9GfZnH8mMbf3eIXQNsWV4stsiYiIiIiIXMEiyVi2s8B+4GlpKdEqjobIO2zMLYZFtOwHgDUPXazeYIhIU7wuaWKRZDz57Q67cTuOVjSV6ooMFruzP8howPBunpU0uSBZfBVIY3Jp40GxxnX3XtITE/p5Xh1W0RJdFXVnSnQ5cmdDUmRgu8alVWkpkfATnIkOnKwEYF22XlrbspRea6IFf3+JiIiIiIhcZf2+Uw7FTxuRrM5AiLzIE9/Yvz7Y6F/XDfKom36JyDavS5pk55XgVLXYxdb7P98KAHjj5z+F4meN7u5xE6gjJ2JHT6+AyMoVSwBYJLGVFe7GkZ+Awgpraarf80vsRJ5xc3qyYwPSOINeh7QUsWRjblENTGapWZkue7rHBrdzZERERERERM4x/ePNwrEhRj2MPl53OYdIUbUmC/JLxMrxh/v74MohnVUeERFpidf9lRXtnwAA246WoarOjNxT9sshhfjpMXtsj44MTZOMPnpMHhAnFPvVlqOwSDJkwaWNonHuJr2b+DLpE+W1sEgytgs2+xucGO6RJ8fJMeKJjY8z87E5v1g4/vGJqe0ZEhERERERkVP0efIHOHJL4T+nshE1UUdd8/YG4di/Dk9ScSREpEWed/XVDkf6J5jMFtzz3y1CsUlRQR63yqTRa1OHwN/X/o9KVb0FG3OLm1ZP2BMeaOzo0DRpePco4V+spX8cw8aDxag3i50iPzy+V/sHpmHJUeIlx5ZszEepQPk3AOgUakSA0dDeYREREREREanqia+3o1awvyVgrWxwcW+xPppE1DqTWcKewkrh+BE92EOIyNt4XdIkLUW8R4dep8OGA2J3tB/2sObcZzPodRjTM0Yodt2fp7B8+3Gh2Ohgz0yaGPQ6dIkQ62tysKgaGw6K1a7199F7XM+cRo6UHDtUUos/DpcKxf5tZLd2joiIiIiIiEhdzyzfiU83H3Vom9emsq8CUUc9+uU24Vi9Dh57LYaI2uZ1SRODXof4ULHG0GW1FpgsYjWkGsweWmvqtMo6sTv7v9x8WPg9E22Y7o5GnSd2F0KDRcbXW8VOki/qFeuxJ8dGHz0GdQkVjhdcmIPYUPGVZURERERERM4y46PNWLThsEPbDOgSiqsGsa8CUUdYJBlLdxQIx185MMFjr8UQUdu8LmkCAGnJ4qtNRCWEefbFWdHyUSW1ZqG4YD+DQ6t+3M28SX2FY09UmITihiZFtHc4buGRCX0U32cckyZERERERKQxf1u0CWv2nnR4u2WzR6kwGiLv8saa/Q7Fv3TtQJVGQkRa5pVJk2uHJiq+z/9TYZ9akhgp3nNCxKie0R6dqQ8wGnBepyBF9+mp5cwaKb3c1deg8+jEHBERERERuQ+LJOO3vScx6Jkf8fO+Ioe3v2ZwnAqjIvIuFknGB+tzheMTw/1g9PHKS6dEXs/H1QNwhRE9o+Gj18EsKVdS62+jPLt3wpQhXbB0m1ivEhE3DUtWbF9adeeYHnjgy+2K7c+Ty5kBp3vBhPvhaFm9IvsblBju0Yk5IiIiIiLStlqTBfO+2YGvFfgs/dxfBnV8QEReLjuvBFX1FuH4H+6/SL3BEJGmeWXSxKDX4e6Lu+P1NQcU2Z+PHh6feR7RIxr+vnrUNQg2k7BjeHfPb6KlZJLDoIdXrJq4YkBnvLtW/K4PW+69uKci+yEiIiIiIjqbRZKxds9JvLxqD/KKqlFnBtTscjq2VxQCjAYVj0DkHX7cJd7LJCU6EMH+XnnZlIigYtLkueeew4oVK7Bt2zYYjUaUlZXZ3UaWZcyfPx/vv/8+ysrKMHLkSLzzzjvo2VP5i5/3XnIe3vrloCKrTaYM6qLAiLTNoNfh1f8bhLs+29rhffn76L1iBUBaSiSMBh1Mlo7/jF3ZP94r3rNRPWMUSZoYdNYVZa6k9TkQAE5V1OPKN35DQWUDAEB3+uvs1KgObX8ANAIICjRAsshokACLJMFkORNva9tztRarPz2WtvbT+LFRsrFto4gAA/omhCL3VA1OVdXDLAG+eiDU34CQACNOVdWjql6CDo2JcB3qTTKkc/bTyFdnTWaaLNYBSnLLYzby0QGRQT6orDPj3LZPvjpr0l2ns/aOspeXDjXqUG2S0dq9UbrTr7u157pF+eF/d45GpIeX+SPtcIc58FhJLSa8/isq65v/4jXOAxap9d/pRr56wEevQ4CvHoH+vjhVUQcHbly0yagHTDYO3jgv+pz+H1sd5fQAwgN9kBAegP9v796jmjzzPIB/k0ASwiWIgoF6AY71LqCtZLQXtaVFpU6dHV2n01rU0+mW0XG8jK2eUVDrpYvH+7q13RmLc3p2xp6Z0e3WSy9WbT1S7OiyY2111NLBHQjYIgYQEpK8+0dMLBIgIW9u7/v9nJOjJM/75vck5MubPHmet6axFeZWG+wCoFEByfEaAEC92eK+P7UKiIlSorXdAYuHGlxfE3LVoFQAgnA3ixUA0pM0+NPPmTkkX5GQgVX1LZi68xQsHt6rRCmcr3VrFwdySjiPg6KUgEKhBCDAbhc8ZoY37j3W6+qYytVWeedfKABbDweb0UpgQB8tdJpoXK1rgtXu3DY5VgV1dDSaLO1obrW7j5+00QrERCnQZOn5uCxY+sVGY9/8H4S6DKKIZ3cI2F/+d6/bf7RscuCKIaKwF7DpEVarFbNnz0ZRUZHX25SWlmLXrl3Yu3cvKioqEBsbi/z8fLS1tYlen2u2iRjWzhwtyn7C3fSsVOii/f+VSYyRx0i9SqnAnPHiDKj96+wcUfYT7sSagTT6voSQDzKFewZmrX0f4zd95B4wAZxvVu99b9jd+1ArgJu37bhlceB2uwMWe8f2vgwXemrr6OY2wDk4YO9hW5ebrXacvnYTNWYL2h3ObawO4NvbdlR914rmO+/yBQDtDqDFKsDmYT8u7QLQZnfe7hp776qtTQDqmzsPmLj209LuQLPVuzfm5i4GTFy1d3Xb199ZMG7Dhxi/4cOe74RIBOGegUN/fQQPlX7cacAEuJsDPb0k2x1Aq01AQ6sd/3dTvAEToPsBE+Bu7tl6GDABnP1ouG3DFzVNaLhtg01wbt9mB643WnC90QLLnVwUAFjsQKPF84CJa38OOPPGcaeG72exAKCqgZlD8hbuGZi56jCmbDvpccAEcL6uuxowAZyvfWcGArfbHbjd3vsBE6DzsVx3u3Id79jQ84AJ4Kyz6rs2XKxpch+rOgDUtdhxvbENja122HA3A1vbBTS0hs+AyQhDLP6y5slQl0EkCds/vOz1e9RH7+8b8s8UiCi0AjZosm7dOixduhRjxozxqr0gCNixYwdWr16Np59+GllZWfjd736HmpoaHDp0KCA1Ln58qN/7GDtQL6tpsgP6+L/k1JTh/UWoJDJMH3Of3/sYlRov+eXfXFRKBTL66vzez1NZaSJU459wzsCste/D3NbTx2wkRTearfwQk4IinDNw6K+PiDILlHrGzCG5CucMzFx1GCKe2pMCqK8umudTIBKJ3SHg305c87r9pKEpAayGiCJB2HwSW1VVBZPJhLy8PPd1er0eRqMR5eXlAblPlVKBlHj/lg34Y9FDIlUTGf5pnP8zJ4pnjBKhksiQm5GEPrpov/YhxmMeSQ4tfNjvfRROzBChkuAKVgbeMFs4YCJzN5qtaGi2hroMog6ClYH/aGjlgEmQMXOIehasDKyqb+GASYTQRilwrpgzTCi0Nm7ciIkTJ0Kn0yExMdFjm+rqahQUFECn0yElJQUrVqyAzdbx/ebJkycxbtw4aDQaDBkyBGVlZZ32s2fPHqSnp0Or1cJoNOLs2bOi9uXkl3U+tZ87IV3U+yeiyBM2gyYmkwkA0L9/x1kI/fv3d9/micVigdls7nDxxaYfZfle7B3pSVrZTddb8HCmX9s/PjxZVjNzVEoFNvq5fJvc/ljrddGI8+N3JDk2OiJn5gQrA3/076f9L5Yi3k/ePBPqEog6CFYGTtt1yv9iyWfMHKLu9SYDe/M+eOpOZmAkGGWIw6UN00NdBlGPSw3a7XYUFBTAarXizJkz2L9/P8rKylBcXOxuU1VVhYKCAkyZMgWVlZVYsmQJXnjhBbz//vvuNgcOHMCyZctQUlKC8+fPIzs7G/n5+aivrxetL68e/crrtokxkfmZAhGJy6cUWLlyJRQKRbeXS5cuBapWjzZv3gy9Xu++DBw40Kftpwzv/ZS7/1r0aK+3jVTqKCWeGpPaq21j1Ur8dl6uyBWFv+lZadBrejcIUDDGIMs/1p+vfqLX2360fIqIlXQkhQxsaGnv9naSh/omfuubfCeFDGwR88Qj5DVmDklBuGVgb94Hd3UOEwofO3+Sg8NLJoW6DCIAPS81+MEHH+DLL7/E22+/jZycHEybNg2vvvoq9uzZA6vV+bd/7969yMjIwNatWzFixAgsWrQIs2bNwvbt29372bZtG372s59h/vz5GDlyJPbu3QudTod9+/aJ1hdzm/fvg1961L8vCxORNPj0aezy5cvx1VdfdXvJzOxduBgMBgBAXV3HKXN1dXXu2zxZtWoVbt265b5cv37dp/tVKRXYOivb53oH9tFC7+eyS5Fq5zNjEd2LCTZ/WS3f6cUnVjzWq+12PTNO5EoiQ4xahUlD+/m83eC+MQF9XUohA5Ni5Zlb1JG/S1OSPEkhA2N7+SUG8g8zh6Qg3DKwN++DNSp5rZIQSdL7anFt03Q8neP/OTGJgqW8vBxjxozpMEsuPz8fZrMZFy9edLf5/tKDrjaupQetVivOnTvXoY1SqUReXl63yxP6Otsu3Ydzpy54hIMmRARE+dI4OTkZycnJASkkIyMDBoMBx48fR05ODgDAbDajoqKiy6mAAKDRaKDRaPy67x8/OACbj36Jb738BrY2SolPX3ncr/uMZCqlArufHYeX3j7v9TaPDesnq2W57pUUp0ZfXRS+u+39uSS2zs6W3fJv37d/gRHDVx9Fm83hVfuBSTE41cvBKW9JIQMP/vxhjN/0kb/lUoT7w4sTQ10CRSApZODRxZPwUOnH/pZLPmLmkBSEWwb25n3wsV9OwpRtJ/2slsQ0OjUOf/iXhxCn9emjGaKwYDKZPC4r6LqtuzZmsxmtra24efMm7Ha7xzbdzd7bvHkz1q1b53Wtb80zInv9Bz22m/eDwbJc7YOIOgtYElRXV6OyshLV1dWw2+2orKxEZWUlmpub3W2GDx+OgwcPAgAUCgWWLFmCDRs24N1338WFCxfw/PPPIy0tDTNnzgxUmW5/WfMk4rU9f6g/IFGDSxumBbyecDd1dCr2PufdLIjkODX2zTcGuKLwd644H2ovv92VHKfGjx+Q1wngPbm0YRriND3H1OT7k/Dpy4EdMPFVuGZgcoIGCXxTJmvJcWokxfFb3xRY4ZqB9yXFeP23mMTBzCE5CtcMzEiJhYy/kxU2EjRKvPHMWFzbNB3v/XISB0woqFauXAm9Xg8A0Ov1IV9qsLd8nW2n10VjcN+YbtskaKOw1s9z0hKRdATsr3NxcTH279/v/nns2LEAgBMnTmDy5MkAgMuXL+PWrVvuNi+//DJaWlrw4osvorGxEQ8//DCOHTsGrVYbqDI7uLB2KtYeuoCyz6o93r5gYjqKfzgqKLVEgqmjU3Ft03T8+PXTqLzueSrkY8OSsW++/M5j0pW/bZyOwt9W4NSVb7tsMyhJi09elu9Mpnt9sW4aSg5dwP4uXpdPjEzBfzw/PshV9SycM/Cva/ORtfZ9mNu8n/lE0pAcp/brnEFE3grnDPzbxukY+usjsHJt/4Bj5pBchXMGfr25AJmrDsPBCAw4BQBDvBoPpifhnx8chIn395P1SgIUHpYvX45Zs2Zh/Pjx+PzzzxEXF9epjbdLDRoMBpw9e7bDda5lBl1LCxoMBo9LDyYkJCAmJgYqlQoqlcrnJVp7M9vu1IrHMGnLx/j7d62dbhvUR4tPZLyiDBF1phAEQVKHS2azGXq9Hrdu3UJCQkKv9mG1OVB2ugoffGUCoMATI/tj/kMZnKLXjVarHev/+yJOXK4HAEweloySGaNlvSRXd1qtdqz58//i0F9r4Vp9apQhBv/54iOyPVdOT6w2B/Z9+jX+/D//QLvDgQmZfbHmqVF+/Y6JkRfhxpc+3TBb8MPdp1Db5FyaUHHn8v0F0RQAuvojoQYQq1PBYRfQ7gDsDges9rvtu9v2Xp7aKu/U0tV+XM+8o5ttXfrEqDAqLQFf37iNG80W2BxAtBJI0KoQH6PGjWYLmi0OKABEKQF1lAIWqwDHPftxiVYAKiVgtTsLdAid79MlSgEkxUahqc2GVlvn/aijlFAoAIvNgfYeVqNLUCvQYhXg6VTWijv99nRbZl8N/lj0KL/tTW5SzD/At379o6EVU3eeRJOl4wvPlQN2h+fXtEu0EohSKhATrYROG40b5jaIdZ55tRKwdnPnrlyMuvOf7obAlQASdVFIS4xBTWMrzK022AVAowKS450fNtSbLe77U6uAmCglWtsdsHiowXU07KpBqQAE4W4WKwCkJ2nwp58zcyh8STEDfe1TVX0Lpu485fHk8FEK52vd2sWBnBLO46AoJaBQKAEIsNsFj5nhjXuP9bo6pnK1Vd75FwrA1sPBZrQSGNBHC50mGlfrmmC1O7dNjlVBHR2NJks7mlvt7uMnbbQCMVEKNFm6Py5TKZz9T4xVY0PBaDw2xsABEYoYvuZFWVkZlixZgsbGxg7XHz16FE899RRqa2uRkpICAHjzzTexYsUK1NfXQ6PR4JVXXsGRI0dw4cIF93Y//elP0dDQgGPHjgEAjEYjcnNzsXv3bgCAw+HAoEGDsGjRIqxcuVL0Pt263Y4FZWdRc6sNaXot9s3L5ecwRDLhS1Zw0ISIZEuKeSHFPhGR+KSaFVLtFxGJS4pZIcU+EVFgeJsX1dXVaGhowLvvvostW7bg008/BQAMGTIEcXFxsNvtyMnJQVpaGkpLS2EymTB37ly88MIL2LRpEwCgqqoKo0ePxsKFC7FgwQJ8/PHHWLx4MQ4fPoz8/HwAwIEDB1BYWIg33ngDubm52LFjB9555x1cunSp07lO/O0TEcmbL1nBxTOJiIiIiIiIiIjIraelBlUqFd577z0UFRVhwoQJiI2NRWFhIdavX+/eJiMjA4cPH8bSpUuxc+dODBgwAL/5zW/cAyYAMGfOHNy4cQPFxcUwmUzIycnBsWPHvB4wISIKBM40ISLZkmJeSLFPRCQ+qWaFVPtFROKSYlZIsU9EFBhSzAsp9omIxCfrmSauMSCz2fOJyYmIXFw5IaWxY2YgEXlDivkHMAOJyDtSzEDmHxF5ixlIRHLlS/5JbtCkqakJADBw4MAQV0JEkaKpqQl6vT7UZYiCGUhEvpBS/gHMQCLyjZQykPlHRL5iBhKRXHmTf5JbnsvhcKCmpgbx8fFQKBQ9tjebzRg4cCCuX78umyl87DP7LFW+9lkQBDQ1NSEtLQ1KpTIIFQaeLxnI3xH2WarYZ3nmH8AM7An7zD5LFTOQ74O9wT6zz1LFDOQxYE/YZ/ZZqgKZf5KbaaJUKjFgwACft0tISJDNL5QL+ywP7HP3pPLNGpfeZCB/R+SBfZYHOecfwAz0FvssD+xz96SWgXwf7D32WR7Y5+4xA/k7IhfsszwEIv+kMaRMRERERERERERERETkJw6aEBERERERERERERERgYMm0Gg0KCkpgUajCXUpQcM+ywP7TD2R4+PFPssD+0zekONjxj7LA/tM3pDjY8Y+ywP7TD2R4+PFPssD+ywuyZ0InoiIiIiIiIiIiIiIqDdkP9OEiIiIiIiIiIiIiIgI4KAJERERERERERERERERAA6aEBERERERERERERERAeCgCREREREREREREREREQAZD5ps3LgREydOhE6nQ2Jiosc21dXVKCgogE6nQ0pKClasWAGbzRbcQgMsPT0dCoWiw+W1114LdVmi2rNnD9LT06HVamE0GnH27NlQlxQwa9eu7fR8Dh8+PNRlieqTTz7BjBkzkJaWBoVCgUOHDnW4XRAEFBcXIzU1FTExMcjLy8OVK1dCU2wYYwY6MQOlhRnIDPQWM5D5J0XMQGagN5h/TsxAaWH+Mf+8xQx0YgZKCzMwMBko20ETq9WK2bNno6ioyOPtdrsdBQUFsFqtOHPmDPbv34+ysjIUFxcHudLAW79+PWpra92XX/ziF6EuSTQHDhzAsmXLUFJSgvPnzyM7Oxv5+fmor68PdWkBM2rUqA7P5+nTp0NdkqhaWlqQnZ2NPXv2eLy9tLQUu3btwt69e1FRUYHY2Fjk5+ejra0tyJWGN2bgXcxAaWEGMgO9wQx0Yv5JDzOQGdgT5t9dzEBpYf4x/7zBDLyLGSgtzMAAZKAgc2+99Zag1+s7XX/kyBFBqVQKJpPJfd3rr78uJCQkCBaLJYgVBtbgwYOF7du3h7qMgMnNzRUWLlzo/tlutwtpaWnC5s2bQ1hV4JSUlAjZ2dmhLiNoAAgHDx50/+xwOASDwSBs2bLFfV1jY6Og0WiE3//+9yGoMPwxA5mBUsIMZAb6Ss4ZyPyTHmYgM9AXcs4/QWAGSg3zj/nnK2YgM1BKmIGByUDZzjTpSXl5OcaMGYP+/fu7r8vPz4fZbMbFixdDWJn4XnvtNfTt2xdjx47Fli1bJDPt0Gq14ty5c8jLy3Nfp1QqkZeXh/Ly8hBWFlhXrlxBWloaMjMz8eyzz6K6ujrUJQVNVVUVTCZTh+dcr9fDaDRK+jkPBGZg5GMGMgMBZmBvySUDmX/SwwxkBvpLLvkHMAOlhvnH/BMDMzDyMQOZgYA4GRglRnFSZDKZOoQkAPfPJpMpFCUFxOLFizFu3DgkJSXhzJkzWLVqFWpra7Ft27ZQl+a3b7/9Fna73ePzeOnSpRBVFVhGoxFlZWUYNmwYamtrsW7dOjzyyCP44osvEB8fH+ryAs712vT0nEvpdRsMzEBmYCRiBjIDxSKHDGT+SQ8zkBkoBjnkH8AMlBrmH/NPLMxAZmAkYgYGJgMlNdNk5cqVnU58c+9Fqi+Q7/PlcVi2bBkmT56MrKwsvPTSS9i6dSt2794Ni8US4l5Qb0ybNg2zZ89GVlYW8vPzceTIETQ2NuKdd94JdWkUBMxAJ2agfDED5Y0ZyPyTO2agfDH/nJiB8sX8kzdmoBMzUL6YgYEhqZkmy5cvx7x587ptk5mZ6dW+DAYDzp492+G6uro6923hzJ/HwWg0wmaz4ZtvvsGwYcMCUF3w9OvXDyqVyv28udTV1YX9cyiWxMREDB06FFevXg11KUHhel7r6uqQmprqvr6urg45OTkhqip4mIFOzEAnZiAz0IUZeJfUM5D558T8c2IGwv2z1DOQ+efEDHRiBjL/XOSQfwAz0IUZ6MQMZAa6+JuBkho0SU5ORnJysij7mjBhAjZu3Ij6+nqkpKQAAD788EMkJCRg5MiRotxHoPjzOFRWVkKpVLr7HMnUajUeeOABHD9+HDNnzgQAOBwOHD9+HIsWLQptcUHS3NyMa9euYe7cuaEuJSgyMjJgMBhw/PhxdzCazWZUVFSgqKgotMUFATPQiRnoxAxkBgLMwN6K1Axk/jkx/5yYgfLJQOafEzPQiRnI/APkk38AM9CFGejEDGQGAuJkoKQGTXxRXV2NhoYGVFdXw263o7KyEgAwZMgQxMXF4cknn8TIkSMxd+5clJaWwmQyYfXq1Vi4cCE0Gk1oixdJeXk5KioqMGXKFMTHx6O8vBxLly7Fc889hz59+oS6PFEsW7YMhYWFePDBB5Gbm4sdO3agpaUF8+fPD3VpAfGrX/0KM2bMwODBg1FTU4OSkhKoVCo888wzoS5NNM3NzR1Gy6uqqlBZWYmkpCQMGjQIS5YswYYNG3D//fcjIyMDa9asQVpamvuPJTkxA5mBUsQMZAZ6S+4ZyPyTJmYgM9Abcs8/gBkoRcw/5p+3mIHMQCliBgYoAwWZKiwsFAB0upw4ccLd5ptvvhGmTZsmxMTECP369ROWL18utLe3h65okZ07d04wGo2CXq8XtFqtMGLECGHTpk1CW1tbqEsT1e7du4VBgwYJarVayM3NFT777LNQlxQwc+bMEVJTUwW1Wi3cd999wpw5c4SrV6+GuixRnThxwuNrt7CwUBAEQXA4HMKaNWuE/v37CxqNRnj88ceFy5cvh7boMMQMZAZKETOQGegtuWcg80+amIHMQG/IPf8EgRkoRcw/5p+3mIHMQCliBgYmAxWCIAi9H3IhIiIiIiIiIiIiIiKSBmWoCyAiIiIiIiIiIiIiIgoHHDQhIiIiIiIiIiIiIiICB02IiIiIiIiIiIiIiIgAcNCEiIiIiIiIiIiIiIgIAAdNiIiIiIiIiIiIiIiIAHDQhIiIiIiIiIiIiIiICAAHTYiIiIiIiIiIiIiIiABw0ISIiIiIiIiIiIiIiAgAB02IiIiIiIiIiIiIiIgAcNCEiIiIiIiIiIiIiIgIAAdNiIiIiIiIiIiIiIiIAHDQhIiIiIiIiIiIiIiICADw/ytMNuFoNYsDAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  41\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  42\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  43\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  44\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  45\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  46\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  47\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  48\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  49\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
       "text/plain": [
        "<Figure size 2000x300 with 5 Axes>"
       ]
@@ -1719,28 +1549,55 @@
    ],
    "source": [
     "# first, randomly select the other multiple choice options\n",
-    "np.random.seed(0)\n",
-    "fbench_hard_questions = []\n",
-    "for idx, _ in enumerate(fbench_hard):\n",
+    "np.random.seed(1)\n",
+    "fbench_questions = []\n",
+    "for idx, _ in enumerate(fbench):\n",
     "    mc_options = [idx]\n",
     "    # select 4 more random functions\n",
     "    for _ in range(4):\n",
-    "        random_idx = np.random.randint(0, len(fbench_hard))\n",
+    "        random_idx = np.random.randint(0, len(fbench))\n",
     "        while random_idx in mc_options:\n",
-    "            random_idx = np.random.randint(0, len(fbench_hard))\n",
+    "            random_idx = np.random.randint(0, len(fbench))\n",
     "        mc_options.append(random_idx)\n",
     "    # shuffle options\n",
     "    np.random.shuffle(mc_options)\n",
     "    # store the options and the correct answer\n",
-    "    fbench_hard_questions.append((mc_options, idx))\n",
+    "    fbench_questions.append((mc_options, idx))\n",
+    "\n",
+    "# assure that the shape of the correct answer is unique among the mc options\n",
+    "def replace(q, i, v):\n",
+    "    question = fbench_questions[q]\n",
+    "    options = list(question[0])\n",
+    "    options[i] = v\n",
+    "    fbench_questions[q] = (options, question[1])\n",
+    "    \n",
+    "replace(4, 4, 0)\n",
+    "replace(7, 2, 0)\n",
+    "replace(9, 3, 0)\n",
+    "replace(11, 4, 0)\n",
+    "replace(16, 2, 0)\n",
+    "replace(18, 4, 0)\n",
+    "replace(22, 4, 0)\n",
+    "replace(22, 1, 1)\n",
+    "replace(30, 2, 0)\n",
+    "replace(32, 1, 0)\n",
+    "replace(34, 1, 0)\n",
+    "replace(36, 1, 0)\n",
+    "replace(37, 4, 0)\n",
+    "replace(38, 0, 0)\n",
+    "replace(42, 4, 0)\n",
+    "replace(44, 2, 0)\n",
+    "replace(44, 3, 1)\n",
+    "replace(44, 4, 2)\n",
+    "replace(47, 3, 0)\n",
     "\n",
     "# plot the 5 functions for each question\n",
     "# make a 1x5 grid of plots\n",
-    "for idx, (options, correct) in enumerate(fbench_hard_questions):\n",
+    "for idx, (options, correct) in enumerate(fbench_questions):\n",
     "    fig, axes = plt.subplots(1, 5, figsize=(20, 3))\n",
     "    print('Question ', idx)\n",
     "    for i, ax in enumerate(axes):\n",
-    "        f, n = fbench_hard[options[i]]\n",
+    "        f, n = fbench[options[i]]\n",
     "        y = f(x)\n",
     "        ax.scatter(x, y)\n",
     "        ax.set_title(n)\n",
@@ -1754,28 +1611,1543 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Fit EBMs to the functions and convert the EBM graphs to text"
+    "# F-Bench (Hard subset with less well-known functions)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# run this for hard, otherwise standard\n",
-    "fbench = fbench_hard\n",
-    "fbench_questions = fbench_hard_questions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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11aMsJSVFsbGxkqSAgAD169fPo47L5VJqaqq7DqpuSI8IJd91lZoF+NX1UBqM5jY/Jd91VaO+N8WF901j/LCuDh9Jv2vk98FhX1N17Guqz6s3+jt9+rT2798vSerbt6+WLFmim266SSEhIWrXrp0SEhJ09OhRvfTSS5J+vEy8R48emjp1qn7729/q448/1gMPPKD33ntP8fHxkn68THzcuHFauXKlBgwYoKVLl+q1117T3r17S52bUx5u9Fe2+nZ30fp051LuLlo+7mTMnYyrqr7ta+rTPop9TcWq8vnt1YCzefNm3XTTTaXKx40bp7Vr12r8+PE6cOCANm/e7NHmoYce0u7du3X55Zdrzpw5Gj9+vEf7Z599VosWLZLD4VCfPn20bNkyxcTEVHpcBBwAABqeehNw6isCDgAADU+9+akGAACAukDAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAllMrAWfFihXq0KGDAgMDFRMTo61bt5Zb98Ybb5SPj0+pZfjw4e4648ePL7V+yJAhtTEVAADQAPh7+wleffVVzZgxQ8nJyYqJidHSpUsVHx+vffv2qW3btqXqv/HGGyoqKnI/PnnypHr37q3bbrvNo96QIUO0Zs0a92Obzea9SQAAgAbF6wFnyZIlmjRpkiZMmCBJSk5O1nvvvafVq1dr9uzZpeqHhIR4PN6wYYOaNWtWKuDYbDaFh4dXagyFhYUqLCx0P3Y6nVWdBgAAaEC8+hVVUVGRMjIyFBcX958n9PVVXFyc0tLSKtXHqlWrNHr0aDVv3tyjfPPmzWrbtq26dOmiKVOm6OTJk+X2kZSUJLvd7l6ioqKqNyEAANAgeDXgnDhxQiUlJQoLC/MoDwsLk8PhuGj7rVu3aufOnbrnnns8yocMGaKXXnpJqampWrBggT799FMNHTpUJSUlZfaTkJCg/Px893L48OHqTwoAANR7Xv+K6lKsWrVKPXv21IABAzzKR48e7f53z5491atXL3Xq1EmbN2/WoEGDSvVjs9k4RwcAgEbEq0dwQkND5efnp+zsbI/y7Ozsi54/U1BQoA0bNmjixIkXfZ6OHTsqNDRU+/fvv6TxAgAAa/BqwAkICFC/fv2UmprqLnO5XEpNTVVsbGyFbV9//XUVFhbqrrvuuujzHDlyRCdPnlRERMQljxkAADR8Xr8PzowZM/Tiiy9q3bp12rNnj6ZMmaKCggL3VVVjx45VQkJCqXarVq3SyJEj1bp1a4/y06dP65FHHtGXX36pAwcOKDU1VSNGjFDnzp0VHx/v7ekAAIAGwOvn4Nxxxx06fvy45s6dK4fDoT59+mjTpk3uE48PHTokX1/PnLVv3z59/vnn+vDDD0v15+fnp2+//Vbr1q1TXl6eIiMjNXjwYM2fP5/zbAAAgCTJxxhj6noQtc3pdMputys/P19BQUF1PRwAAFAJVfn85reoAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5dRKwFmxYoU6dOigwMBAxcTEaOvWreXWXbt2rXx8fDyWwMBAjzrGGM2dO1cRERFq2rSp4uLi9N1333l7GgAAoIHwesB59dVXNWPGDCUmJmr79u3q3bu34uPjlZOTU26boKAgZWVluZeDBw96rF+4cKGWLVum5ORkpaenq3nz5oqPj9e5c+e8PR0AANAAeD3gLFmyRJMmTdKECRPUvXt3JScnq1mzZlq9enW5bXx8fBQeHu5ewsLC3OuMMVq6dKkeffRRjRgxQr169dJLL72kY8eOaePGjWX2V1hYKKfT6bEAAADr8mrAKSoqUkZGhuLi4v7zhL6+iouLU1paWrntTp8+rfbt2ysqKkojRozQrl273OsyMzPlcDg8+rTb7YqJiSm3z6SkJNntdvcSFRVVA7MDAAD1lVcDzokTJ1RSUuJxBEaSwsLC5HA4ymzTpUsXrV69Wm+99ZbWr18vl8ulgQMH6siRI5LkbleVPhMSEpSfn+9eDh8+fKlTAwAA9Zh/XQ/gp2JjYxUbG+t+PHDgQHXr1k0rV67U/Pnzq9WnzWaTzWarqSECAIB6zqtHcEJDQ+Xn56fs7GyP8uzsbIWHh1eqjyZNmqhv377av3+/JLnbXUqfAADA2rwacAICAtSvXz+lpqa6y1wul1JTUz2O0lSkpKRE//znPxURESFJio6OVnh4uEefTqdT6enple4TAABYm9e/opoxY4bGjRun/v37a8CAAVq6dKkKCgo0YcIESdLYsWN12WWXKSkpSZI0b948XX311ercubPy8vK0aNEiHTx4UPfcc4+kH6+wmj59uv74xz/qiiuuUHR0tObMmaPIyEiNHDnS29MBAAANgNcDzh133KHjx49r7ty5cjgc6tOnjzZt2uQ+SfjQoUPy9f3PgaQffvhBkyZNksPhUKtWrdSvXz9t2bJF3bt3d9f5/e9/r4KCAk2ePFl5eXm69tprtWnTplI3BAQAAI2TjzHG1PUgapvT6ZTdbld+fr6CgoLqejgAAKASqvL5zW9RAQAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAy6mVgLNixQp16NBBgYGBiomJ0datW8ut++KLL+q6665Tq1at1KpVK8XFxZWqP378ePn4+HgsQ4YM8fY0AABAA+H1gPPqq69qxowZSkxM1Pbt29W7d2/Fx8crJyenzPqbN2/WnXfeqU8++URpaWmKiorS4MGDdfToUY96Q4YMUVZWlnv561//6u2pAACABsLHGGO8+QQxMTH6+c9/rmeffVaS5HK5FBUVpfvvv1+zZ8++aPuSkhK1atVKzz77rMaOHSvpxyM4eXl52rhxY6XGUFhYqMLCQvdjp9OpqKgo5efnKygoqOqTAgAAtc7pdMput1fq89urR3CKioqUkZGhuLi4/zyhr6/i4uKUlpZWqT7OnDmj4uJihYSEeJRv3rxZbdu2VZcuXTRlyhSdPHmy3D6SkpJkt9vdS1RUVPUmBAAAGgSvBpwTJ06opKREYWFhHuVhYWFyOByV6mPWrFmKjIz0CElDhgzRSy+9pNTUVC1YsECffvqphg4dqpKSkjL7SEhIUH5+vns5fPhw9ScFAADqPf+6HkBFnnrqKW3YsEGbN29WYGCgu3z06NHuf/fs2VO9evVSp06dtHnzZg0aNKhUPzabTTabrVbGDAAA6p5Xj+CEhobKz89P2dnZHuXZ2dkKDw+vsO3ixYv11FNP6cMPP1SvXr0qrNuxY0eFhoZq//79lzxmAADQ8Hk14AQEBKhfv35KTU11l7lcLqWmpio2NrbcdgsXLtT8+fO1adMm9e/f/6LPc+TIEZ08eVIRERE1Mm4AANCwef0y8RkzZujFF1/UunXrtGfPHk2ZMkUFBQWaMGGCJGns2LFKSEhw11+wYIHmzJmj1atXq0OHDnI4HHI4HDp9+rQk6fTp03rkkUf05Zdf6sCBA0pNTdWIESPUuXNnxcfHe3s6AACgAfD6OTh33HGHjh8/rrlz58rhcKhPnz7atGmT+8TjQ4cOydf3Pznr+eefV1FRkX7961979JOYmKjHHntMfn5++vbbb7Vu3Trl5eUpMjJSgwcP1vz58znPBgAASKqF++DUR1W5jh4AANQP9eY+OAAAAHWBgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACyHgAMAACynVgLOihUr1KFDBwUGBiomJkZbt26tsP7rr7+url27KjAwUD179tT777/vsd4Yo7lz5yoiIkJNmzZVXFycvvvuO29OAQAANCBeDzivvvqqZsyYocTERG3fvl29e/dWfHy8cnJyyqy/ZcsW3XnnnZo4caK+/vprjRw5UiNHjtTOnTvddRYuXKhly5YpOTlZ6enpat68ueLj43Xu3DlvTwcAADQAPsYY480niImJ0c9//nM9++yzkiSXy6WoqCjdf//9mj17dqn6d9xxhwoKCvTuu++6y66++mr16dNHycnJMsYoMjJSM2fO1MMPPyxJys/PV1hYmNauXavRo0eX6rOwsFCFhYXux06nU1FRUcrPz1dQUFBNTxkAAHiB0+mU3W6v1Oe3V4/gFBUVKSMjQ3Fxcf95Ql9fxcXFKS0trcw2aWlpHvUlKT4+3l0/MzNTDofDo47dbldMTEy5fSYlJclut7uXqKioS50aAACox7wacE6cOKGSkhKFhYV5lIeFhcnhcJTZxuFwVFj/wn+r0mdCQoLy8/Pdy+HDh6s1HwAA0DD41/UAaoPNZpPNZqvrYQAAgFri1SM4oaGh8vPzU3Z2tkd5dna2wsPDy2wTHh5eYf0L/61KnwAAoHHxasAJCAhQv379lJqa6i5zuVxKTU1VbGxsmW1iY2M96ktSSkqKu350dLTCw8M96jidTqWnp5fbJwAAaFy8/hXVjBkzNG7cOPXv318DBgzQ0qVLVVBQoAkTJkiSxo4dq8suu0xJSUmSpAcffFA33HCDnn76aQ0fPlwbNmzQV199pRdeeEGS5OPjo+nTp+uPf/yjrrjiCkVHR2vOnDmKjIzUyJEjvT0dAADQAHg94Nxxxx06fvy45s6dK4fDoT59+mjTpk3uk4QPHTokX9//HEgaOHCgXnnlFT366KP6wx/+oCuuuEIbN25Ujx493HV+//vfq6CgQJMnT1ZeXp6uvfZabdq0SYGBgd6eDgAAaAC8fh+c+qgq19EDAID6od7cBwcAAKAuEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDleDXg5ObmasyYMQoKClJwcLAmTpyo06dPV1j//vvvV5cuXdS0aVO1a9dODzzwgPLz8z3q+fj4lFo2bNjgzakAAIAGxN+bnY8ZM0ZZWVlKSUlRcXGxJkyYoMmTJ+uVV14ps/6xY8d07NgxLV68WN27d9fBgwd177336tixY/rb3/7mUXfNmjUaMmSI+3FwcLA3pwIAABoQH2OM8UbHe/bsUffu3bVt2zb1799fkrRp0yYNGzZMR44cUWRkZKX6ef3113XXXXepoKBA/v4/5jEfHx+9+eabGjlyZKX6KCwsVGFhofux0+lUVFSU8vPzFRQUVLWJAQCAOuF0OmW32yv1+e21r6jS0tIUHBzsDjeSFBcXJ19fX6Wnp1e6nwuTuBBuLpg6dapCQ0M1YMAArV69WhXltKSkJNntdvcSFRVV9QkBAIAGw2sBx+FwqG3bth5l/v7+CgkJkcPhqFQfJ06c0Pz58zV58mSP8nnz5um1115TSkqKRo0apfvuu0/Lly8vt5+EhATl5+e7l8OHD1d9QgAAoMGo8jk4s2fP1oIFCyqss2fPnmoP6AKn06nhw4ere/fueuyxxzzWzZkzx/3vvn37qqCgQIsWLdIDDzxQZl82m002m+2SxwQAABqGKgecmTNnavz48RXW6dixo8LDw5WTk+NRfv78eeXm5io8PLzC9qdOndKQIUPUsmVLvfnmm2rSpEmF9WNiYjR//nwVFhYSZAAAQNUDTps2bdSmTZuL1ouNjVVeXp4yMjLUr18/SdLHH38sl8ulmJiYcts5nU7Fx8fLZrPp7bffVmBg4EWfa8eOHWrVqhXhBgAASPLiZeLdunXTkCFDNGnSJCUnJ6u4uFjTpk3T6NGj3VdQHT16VIMGDdJLL72kAQMGyOl0avDgwTpz5ozWr18vp9Mpp9Mp6cdg5efnp3feeUfZ2dm6+uqrFRgYqJSUFD355JN6+OGHvTUVAADQwHj1Pjgvv/yypk2bpkGDBsnX11ejRo3SsmXL3OuLi4u1b98+nTlzRpK0fft29xVWnTt39ugrMzNTHTp0UJMmTbRixQo99NBDMsaoc+fOWrJkiSZNmuTNqQAAgAbEa/fBqc+qch09AACoH+rFfXAAAADqCgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYjlcDTm5ursaMGaOgoCAFBwdr4sSJOn36dIVtbrzxRvn4+Hgs9957r0edQ4cOafjw4WrWrJnatm2rRx55ROfPn/fmVAAAQAPi783Ox4wZo6ysLKWkpKi4uFgTJkzQ5MmT9corr1TYbtKkSZo3b577cbNmzdz/Likp0fDhwxUeHq4tW7YoKytLY8eOVZMmTfTkk096bS4AAKDh8DHGGG90vGfPHnXv3l3btm1T//79JUmbNm3SsGHDdOTIEUVGRpbZ7sYbb1SfPn20dOnSMtf//e9/1y9/+UsdO3ZMYWFhkqTk5GTNmjVLx48fV0BAQKk2hYWFKiwsdD92Op2KiopSfn6+goKCLnGmAACgNjidTtnt9kp9fnvtK6q0tDQFBwe7w40kxcXFydfXV+np6RW2ffnllxUaGqoePXooISFBZ86c8ei3Z8+e7nAjSfHx8XI6ndq1a1eZ/SUlJclut7uXqKioS5wdAACoz7z2FZXD4VDbtm09n8zfXyEhIXI4HOW2+81vfqP27dsrMjJS3377rWbNmqV9+/bpjTfecPf73+FGkvtxef0mJCRoxowZ7scXjuAAAABrqnLAmT17thYsWFBhnT179lR7QJMnT3b/u2fPnoqIiNCgQYP0/fffq1OnTtXq02azyWazVXtMAACgYalywJk5c6bGjx9fYZ2OHTsqPDxcOTk5HuXnz59Xbm6uwsPDK/18MTExkqT9+/erU6dOCg8P19atWz3qZGdnS1KV+gUAANZV5YDTpk0btWnT5qL1YmNjlZeXp4yMDPXr10+S9PHHH8vlcrlDS2Xs2LFDkhQREeHu94knnlBOTo77K7CUlBQFBQWpe/fuVZwNAACwIq+dZNytWzcNGTJEkyZN0tatW/XFF19o2rRpGj16tPsKqqNHj6pr167uIzLff/+95s+fr4yMDB04cEBvv/22xo4dq+uvv169evWSJA0ePFjdu3fX3XffrW+++UYffPCBHn30UU2dOpWvoQAAgCQv3+jv5ZdfVteuXTVo0CANGzZM1157rV544QX3+uLiYu3bt899lVRAQIA++ugjDR48WF27dtXMmTM1atQovfPOO+42fn5+evfdd+Xn56fY2FjdddddGjt2rMd9cwAAQOPmtfvg1GdVuY4eAADUD/XiPjgAAAB1hYADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsx6sBJzc3V2PGjFFQUJCCg4M1ceJEnT59utz6Bw4ckI+PT5nL66+/7q5X1voNGzZ4cyoAAKAB8fdm52PGjFFWVpZSUlJUXFysCRMmaPLkyXrllVfKrB8VFaWsrCyPshdeeEGLFi3S0KFDPcrXrFmjIUOGuB8HBwfX+PgBAEDD5LWAs2fPHm3atEnbtm1T//79JUnLly/XsGHDtHjxYkVGRpZq4+fnp/DwcI+yN998U7fffrtatGjhUR4cHFyqbnkKCwtVWFjofux0Oqs6HQAA0IB47SuqtLQ0BQcHu8ONJMXFxcnX11fp6emV6iMjI0M7duzQxIkTS62bOnWqQkNDNWDAAK1evVrGmHL7SUpKkt1udy9RUVFVnxAAAGgwvBZwHA6H2rZt61Hm7++vkJAQORyOSvWxatUqdevWTQMHDvQonzdvnl577TWlpKRo1KhRuu+++7R8+fJy+0lISFB+fr57OXz4cNUnBAAAGowqf0U1e/ZsLViwoMI6e/bsqfaALjh79qxeeeUVzZkzp9S6/y7r27evCgoKtGjRIj3wwANl9mWz2WSz2S55TAAAoGGocsCZOXOmxo8fX2Gdjh07Kjw8XDk5OR7l58+fV25ubqXOnfnb3/6mM2fOaOzYsRetGxMTo/nz56uwsJAgAwAAqh5w2rRpozZt2ly0XmxsrPLy8pSRkaF+/fpJkj7++GO5XC7FxMRctP2qVav0q1/9qlLPtWPHDrVq1YpwAwAAJHnxKqpu3bppyJAhmjRpkpKTk1VcXKxp06Zp9OjR7iuojh49qkGDBumll17SgAED3G3379+vzz77TO+//36pft955x1lZ2fr6quvVmBgoFJSUvTkk0/q4Ycf9tZUAABAA+PV++C8/PLLmjZtmgYNGiRfX1+NGjVKy5Ytc68vLi7Wvn37dObMGY92q1ev1uWXX67BgweX6rNJkyZasWKFHnroIRlj1LlzZy1ZskSTJk3y5lQAAEAD4mMqur7aopxOp+x2u/Lz8xUUFFTXwwEAAJVQlc9vfosKAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYjn9dD8BKis67tOrz7/W/GUd0/FSh/Hwkm7+fJKPC8y6VGMnPRwrw81VRyX8el1Wnuu0upe9il1ETfz91atNck6/vpGuvaCM/X5+6fEnrjRKX0ZbvTuj1jEPadSxfP5wpUomrfm/P2mrn7+ujkOY2dY+069f9LtfAzqG8b/7P2aISzXt3p7bsP6HT54rVxLf+b0/2NXXLCvsaX18fNbf5q1tE3e4TfIwxptaftY45nU7Z7Xbl5+crKCioRvpMen+3Vn6WWSN91Rc2f1/9aXQfDekRUddDqVObdmZpxmvf6ExRSV0PpUFoHuCnp2/v3ejfN5Ne2qaU3Tl1PYwGgX3Nj6y6r6nJfUJVPr+99hXVE088oYEDB6pZs2YKDg6uVBtjjObOnauIiAg1bdpUcXFx+u677zzq5ObmasyYMQoKClJwcLAmTpyo06dPe2EGlWfFcCNJheddunf9dm3amVXXQ6kzm3Zm6d712y23w/GmgqKSRv++IdxUDfsaa+9r6mqf4LWAU1RUpNtuu01TpkypdJuFCxdq2bJlSk5OVnp6upo3b674+HidO3fOXWfMmDHatWuXUlJS9O677+qzzz7T5MmTvTGFSik677JkuPlvj729WyWuRnegTyUuo8S3dtb1MBqsx99pnO+bs0UlhJtqYl9jbbW9T/BawHn88cf10EMPqWfPnpWqb4zR0qVL9eijj2rEiBHq1auXXnrpJR07dkwbN26UJO3Zs0ebNm3Sn//8Z8XExOjaa6/V8uXLtWHDBh07dqzcvgsLC+V0Oj2WmvKXtAM11ld95XCe09bM3LoeRq3bmpmr7FNFdT2MBisrv3G+b558f3ddD6HBYl9jbbW9T6g3V1FlZmbK4XAoLi7OXWa32xUTE6O0tDRJUlpamoKDg9W/f393nbi4OPn6+io9Pb3cvpOSkmS3291LVFRUjY37YO6ZGuurPss5de7ilSymMc65pjXG1/DAycaxT/CWxvieaUxzrs251puA43A4JElhYWEe5WFhYe51DodDbdu29Vjv7++vkJAQd52yJCQkKD8/370cPny4xsbdPqRZjfVVn7VtGVjXQ6h1jXHONa0xvoYdWjeOfYK3NMb3TGOac23OtUoBZ/bs2fLx8alw2bt3r7fGWm02m01BQUEeS025O7ZDjfVVX4UHBWpAdEhdD6PWDYgOUVjLgLoeRoMVYW+c75s/DOte10NosNjXWFtt7xOqFHBmzpypPXv2VLh07NixWgMJDw+XJGVnZ3uUZ2dnu9eFh4crJ8fz5L3z588rNzfXXae2Bfj76nfXR9fJc9eWx37VvVHeo8LP10ePj+hR18NosBJvaZzvm6YBfrq5e9uLV0Qp7Gusrbb3CVUKOG3atFHXrl0rXAICqpdCo6OjFR4ertTUVHeZ0+lUenq6YmNjJUmxsbHKy8tTRkaGu87HH38sl8ulmJiYaj1vTUgY1t2SIcfm76vku65q1PemGNIjQsl3XaVmAX51PZQGo7nNr9G/b14c+3NCThWwr7H2vqau9gleu9HfoUOHlJubq7fffluLFi3SP/7xD0lS586d1aJFC0lS165dlZSUpFtvvVWStGDBAj311FNat26doqOjNWfOHH377bfavXu3AgN//N5u6NChys7OVnJysoqLizVhwgT1799fr7zySqXH5o0b/UncydjKrHB3Ue5kXPu4kzH7mqqywr7Gm3cyrsrnt9cCzvjx47Vu3bpS5Z988oluvPHGH5/cx0dr1qzR+PHjJf14qXhiYqJeeOEF5eXl6dprr9Vzzz2nK6+80t0+NzdX06ZN0zvvvCNfX1+NGjVKy5Ytc4emyvBWwAEAAN5TLwJOfUbAAQCg4akXP9UAAABQVwg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcvzregB14cK9DZ1OZx2PBAAAVNaFz+3K3KO4UQacU6dOSZKioqLqeCQAAKCqTp06JbvdXmGdRvlTDS6XS8eOHVPLli3l41OzP/DmdDoVFRWlw4cPW/JnIJhfw2f1OTK/hs/qc7T6/CTvzdEYo1OnTikyMlK+vhWfZdMoj+D4+vrq8ssv9+pzBAUFWfaNKzE/K7D6HJlfw2f1OVp9fpJ35nixIzcXcJIxAACwHAIOAACwHAJODbPZbEpMTJTNZqvroXgF82v4rD5H5tfwWX2OVp+fVD/m2ChPMgYAANbGERwAAGA5BBwAAGA5BBwAAGA5BBwAAGA5BBwAAGA5BJwqeuKJJzRw4EA1a9ZMwcHBZdY5dOiQhg8frmbNmqlt27Z65JFHdP78+Qr7zc3N1ZgxYxQUFKTg4GBNnDhRp0+f9sIMqmbz5s3y8fEpc9m2bVu57W688cZS9e+9995aHHnldejQodRYn3rqqQrbnDt3TlOnTlXr1q3VokULjRo1StnZ2bU04so7cOCAJk6cqOjoaDVt2lSdOnVSYmKiioqKKmxX37ffihUr1KFDBwUGBiomJkZbt26tsP7rr7+url27KjAwUD179tT7779fSyOtuqSkJP385z9Xy5Yt1bZtW40cOVL79u2rsM3atWtLba/AwMBaGnHVPPbYY6XG2rVr1wrbNKTtV9b+xMfHR1OnTi2zfkPYdp999pluueUWRUZGysfHRxs3bvRYb4zR3LlzFRERoaZNmyouLk7ffffdRfut6t9xVRFwqqioqEi33XabpkyZUub6kpISDR8+XEVFRdqyZYvWrVuntWvXau7cuRX2O2bMGO3atUspKSl699139dlnn2ny5MnemEKVDBw4UFlZWR7LPffco+joaPXv37/CtpMmTfJot3DhwloaddXNmzfPY6z3339/hfUfeughvfPOO3r99df16aef6tixY/qf//mfWhpt5e3du1cul0srV67Url279Mwzzyg5OVl/+MMfLtq2vm6/V199VTNmzFBiYqK2b9+u3r17Kz4+Xjk5OWXW37Jli+68805NnDhRX3/9tUaOHKmRI0dq586dtTzyyvn00081depUffnll0pJSVFxcbEGDx6sgoKCCtsFBQV5bK+DBw/W0oir7mc/+5nHWD///PNy6za07bdt2zaPuaWkpEiSbrvttnLb1PdtV1BQoN69e2vFihVlrl+4cKGWLVum5ORkpaenq3nz5oqPj9e5c+fK7bOqf8fVYlAta9asMXa7vVT5+++/b3x9fY3D4XCXPf/88yYoKMgUFhaW2dfu3buNJLNt2zZ32d///nfj4+Njjh49WuNjvxRFRUWmTZs2Zt68eRXWu+GGG8yDDz5YO4O6RO3btzfPPPNMpevn5eWZJk2amNdff91dtmfPHiPJpKWleWGENWvhwoUmOjq6wjr1efsNGDDATJ061f24pKTEREZGmqSkpDLr33777Wb48OEeZTExMeZ3v/udV8dZU3Jycowk8+mnn5Zbp7z9UX2UmJhoevfuXen6DX37Pfjgg6ZTp07G5XKVub4hbTtjjJFk3nzzTfdjl8tlwsPDzaJFi9xleXl5xmazmb/+9a/l9lPVv+Pq4AhODUtLS1PPnj0VFhbmLouPj5fT6dSuXbvKbRMcHOxxRCQuLk6+vr5KT0/3+pir4u2339bJkyc1YcKEi9Z9+eWXFRoaqh49eighIUFnzpyphRFWz1NPPaXWrVurb9++WrRoUYVfKWZkZKi4uFhxcXHusq5du6pdu3ZKS0urjeFekvz8fIWEhFy0Xn3cfkVFRcrIyPB47X19fRUXF1fua5+WluZRX/rxb7IhbCvpx+0l6aLb7PTp02rfvr2ioqI0YsSIcvc39cF3332nyMhIdezYUWPGjNGhQ4fKrduQt19RUZHWr1+v3/72t/Lx8Sm3XkPadj+VmZkph8PhsY3sdrtiYmLK3UbV+Tuujkb5a+Le5HA4PMKNJPdjh8NRbpu2bdt6lPn7+yskJKTcNnVl1apVio+Pv+ivsf/mN79R+/btFRkZqW+//VazZs3Svn379MYbb9TSSCvvgQce0FVXXaWQkBBt2bJFCQkJysrK0pIlS8qs73A4FBAQUOocrLCwsHq3vX5q//79Wr58uRYvXlxhvfq6/U6cOKGSkpIy/8b27t1bZpvy/ibr+7aSJJfLpenTp+uaa65Rjx49yq3XpUsXrV69Wr169VJ+fr4WL16sgQMHateuXRf9W61tMTExWrt2rbp06aKsrCw9/vjjuu6667Rz5061bNmyVP2GvP02btyovLw8jR8/vtw6DWnbleXCdqjKNqrO33F1EHAkzZ49WwsWLKiwzp49ey56IlxDUp05HzlyRB988IFee+21i/b/3+cP9ezZUxERERo0aJC+//57derUqfoDr6SqzG/GjBnusl69eikgIEC/+93vlJSUVG9/K6Y62+/o0aMaMmSIbrvtNk2aNKnCtnW9/fCjqVOnaufOnRWeoyJJsbGxio2NdT8eOHCgunXrppUrV2r+/PneHmaVDB061P3vXr16KSYmRu3bt9drr72miRMn1uHIat6qVas0dOhQRUZGllunIW27hoaAI2nmzJkVJmxJ6tixY6X6Cg8PL3Um+IWra8LDw8tt89MTq86fP6/c3Nxy21yq6sx5zZo1at26tX71q19V+fliYmIk/XgEoTY+IC9lm8bExOj8+fM6cOCAunTpUmp9eHi4ioqKlJeX53EUJzs722vb66eqOr9jx47ppptu0sCBA/XCCy9U+flqe/uVJzQ0VH5+fqWuWKvotQ8PD69S/fpi2rRp7gsOqvp/8k2aNFHfvn21f/9+L42u5gQHB+vKK68sd6wNdfsdPHhQH330UZWPejakbSf953MtOztbERER7vLs7Gz16dOnzDbV+Tuulho7m6eRudhJxtnZ2e6ylStXmqCgIHPu3Lky+7pwkvFXX33lLvvggw/q1UnGLpfLREdHm5kzZ1ar/eeff24kmW+++aaGR1bz1q9fb3x9fU1ubm6Z6y+cZPy3v/3NXbZ37956e5LxkSNHzBVXXGFGjx5tzp8/X60+6tP2GzBggJk2bZr7cUlJibnssssqPMn4l7/8pUdZbGxsvT1J1eVymalTp5rIyEjzr3/9q1p9nD9/3nTp0sU89NBDNTy6mnfq1CnTqlUr86c//anM9Q1t+12QmJhowsPDTXFxcZXa1fdtp3JOMl68eLG7LD8/v1InGVfl77haY62xnhqJgwcPmq+//to8/vjjpkWLFubrr782X3/9tTl16pQx5sc3Z48ePczgwYPNjh07zKZNm0ybNm1MQkKCu4/09HTTpUsXc+TIEXfZkCFDTN++fU16err5/PPPzRVXXGHuvPPOWp9feT766CMjyezZs6fUuiNHjpguXbqY9PR0Y4wx+/fvN/PmzTNfffWVyczMNG+99Zbp2LGjuf7662t72Be1ZcsW88wzz5gdO3aY77//3qxfv960adPGjB071l3np/Mzxph7773XtGvXznz88cfmq6++MrGxsSY2NrYuplChI0eOmM6dO5tBgwaZI0eOmKysLPfy33Ua0vbbsGGDsdlsZu3atWb37t1m8uTJJjg42H3l4t13321mz57trv/FF18Yf39/s3jxYrNnzx6TmJhomjRpYv75z3/W1RQqNGXKFGO3283mzZs9tteZM2fcdX46x8cff9x88MEH5vvvvzcZGRlm9OjRJjAw0OzatasuplChmTNnms2bN5vMzEzzxRdfmLi4OBMaGmpycnKMMQ1/+xnz44d1u3btzKxZs0qta4jb7tSpU+7POklmyZIl5uuvvzYHDx40xhjz1FNPmeDgYPPWW2+Zb7/91owYMcJER0ebs2fPuvv4xS9+YZYvX+5+fLG/45pAwKmicePGGUmllk8++cRd58CBA2bo0KGmadOmJjQ01MycOdMjxX/yySdGksnMzHSXnTx50tx5552mRYsWJigoyEyYMMEdmuqDO++80wwcOLDMdZmZmR6vwaFDh8z1119vQkJCjM1mM507dzaPPPKIyc/Pr8URV05GRoaJiYkxdrvdBAYGmm7dupknn3zS42jbT+dnjDFnz5419913n2nVqpVp1qyZufXWWz1CQ32xZs2aMt+v/33wtiFuv+XLl5t27dqZgIAAM2DAAPPll1+6191www1m3LhxHvVfe+01c+WVV5qAgADzs5/9zLz33nu1POLKK297rVmzxl3np3OcPn26+/UICwszw4YNM9u3b6/9wVfCHXfcYSIiIkxAQIC57LLLzB133GH279/vXt/Qt58xPx6Bl2T27dtXal1D3HYXPrN+ulyYh8vlMnPmzDFhYWHGZrOZQYMGlZp7+/btTWJiokdZRX/HNcHHGGNq7gsvAACAusd9cAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOX8fw4eZ8tFzCS5AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_29557/1061528540.py:26: RuntimeWarning: divide by zero encountered in log\n",
+      "  (lambda x: np.log(x+10) + 1/3 * x , 'log(x+10) + 1/3 * x '),\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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MvL298cUXX0Cr1WL69OmKxFerVi1UrVoVy5cvR5UqVeDm5obQ0FDUr18ftWrVwnvvvYfr16/D3d0dv/zyS5nDMabSoUMHeHl54c8//zS4XnI+z7Ls3LkTX3zxBaZNm4aWLVsCePj/uVOnTpgyZQrmzZunb3vjxg2cOnUKUVFRJnh3ZFcstt6IyIRQTFGtwocQDwun+fr6innz5hV7fMeOHUs8/vGlzikpKeKll14S7u7uonLlyqJ3797iwoULiryvlJQU4eHhIfr06VNk3/PPPy/c3NzE5cuXFXnt0hw+fFj069dP1KhRQzg7Ows3NzfRsmVLsXDhQpGfn2/UOY1dnjxt2rRSr//j1+/27dti2LBhwsvLS1SqVEl07NhRHD582KiYpVq/fr1o2LChcHBwMFiqfObMGREeHi4qV64svL29xfDhw8XJkyeLLGcuLPj2uML3Xqi0yrQoZtn9mDFjRO3atYs9p9TPsyTZ2dkiKChItGzZssj/iXfffVeo1Wqxf/9+/bZly5aJSpUqGRSUIxJCCJUQFuo7JjKjQ4cOITQ0FAkJCWjYsKGlwyGyCpcvX0b9+vWxZcsWyavPlNKiRQt06tQJixYtsmgcZH04R4UqjDlz5jBJIXpESEgIhg0bVqTejLlt3boVFy5cwMSJEy0aB1kn9qgQERGR1WKPChEREVktJipERERktZioEBERkdViokJERERWy+YLvul0OqSmpqJKlSq8kRUREZGNEELgzp07CAgIMLilxeNsPlFJTU01+hbrREREZFkpKSn6qtvFsflEpfAmcCkpKXB3d7dwNERERCRFdnY2AgMDy7yZq80nKoXDPe7u7kxUiIiIbExZ0zY4mZaIiIisFhMVIiIislpMVIiIiMhqMVEhIiIiq8VEhYiIiKwWExUiIiKyWkxUiIiIyGoxUSEiIiKrZfMF35Si1QkcSrqNG3dy4FvFBW2Dq0Gj5r2EiIiIzImJSjG2xqdh+oYzSMvK0W+r7uGCaX0aokfj6haMjIiIqGJRdOhHq9ViypQpCA4OhqurK2rVqoWZM2dCCKFvI4TA1KlTUb16dbi6uiI8PBwXLlxQMqxSbY1Pw8g1xwySFABIy8rByDXHsDU+zUKRERERVTyKJipz587FsmXL8N///hdnz57F3LlzMW/ePCxdulTfZt68eViyZAmWL1+OgwcPws3NDREREcjJySnlzMrQ6gSmbzgDUcJ+AWD6hjPQ6kpqQURERKak6NBPXFwcnnvuOURGRgIAatasie+//x6HDh0C8LA35bPPPsPkyZPx3HPPAQC++eYb+Pn54ffff0f//v2LnDM3Nxe5ubn659nZ2SaL91DS7SI9KY9Ly8rBoaTbCKvlZbLXJSIiouIp2qPSvn17xMbG4vz58wCAkydPYu/evejZsycAICkpCenp6QgPD9cf4+HhgdDQUOzfv7/Yc0ZHR8PDw0P/CAwMNFm86VkPTNqOiIiIykfRRGXChAno378/6tevD0dHR7Ro0QJjx47FwIEDAQDp6ekAAD8/P4Pj/Pz89PseN3HiRGRlZekfKSkpJov31t3cshsB2Hvxlslek4iIiEqmaKLy008/4bvvvsPatWtx7NgxrF69GgsWLMDq1auNPqezszPc3d0NHqaS+SBfUrtNp9I4T4WIiMgMFE1U3n//fX2vSpMmTTBo0CC8++67iI6OBgD4+/sDADIyMgyOy8jI0O8zJxWk1UnJKdDhwKV/FI6GiIiIFE1U7t+/D7Xa8CU0Gg10Oh0AIDg4GP7+/oiNjdXvz87OxsGDBxEWFqZkaMWSM0F2zcErygVCREREABRe9dOnTx/Mnj0bNWrUQKNGjXD8+HEsXLgQb7zxBgBApVJh7NixmDVrFurUqYPg4GBMmTIFAQEB6Nu3r5KhFatdiBecHVTILSh7WOfPMzeg1QlWqyUiIlKQoonK0qVLMWXKFLz99tu4ceMGAgIC8Oabb2Lq1Kn6Nh988AHu3buHESNGIDMzEx06dMDWrVvh4uKiZGjF0qhVGNmxFj6LvVhm23ydwNLYCxjbra4ZIiMiIqqYVOLRMrE2KDs7Gx4eHsjKyjLJxFqtTqD+5M3I15Xd1sVBjYQZPdirQkREJJPU72/ePfkxGrUK4Q2lTeTlpFoiIiJlMVEpxmvtgiS35aRaIiIi5TBRKUbhpFopdiXeZE0VIiIihTBRKUbhpFopOPxDRESkHCYqJRjdtS4kdqrgmwNXFI2FiIioomKiUgKNWoWWQZ6S2u5KvMHhHyIiIgUwUSlFm+BqktrlaQWHf4iIiBTARKUU7Wt5S27L1T9ERESmx0SlFHJW/xSW1CciIiLTYaJSCjmrfwpL6hMREZHpMFEpw+iudeEo8VP6fNdF9qoQERGZEBOVMsgpqc9eFSIiItNioiKBnJL6/9t7mb0qREREJsJERQI5k2rv5mpxKOm2whERERFVDExUJJAzqRYAtiekKRgNERFRxcFERaKHk2ql9ar8cDiFwz9EREQmwERFIo1ahdfa1ZDU9kE+b1RIRERkCkxUZOjeqLrktrxRIRERUfkxUZGhbXA1uEgsqhJ7NoPDP0REROXEREUGjVqFTnV9JLUt0IE1VYiIiMqJiYpMg8JqSm7LmipERETlw0RFJtZUISIiMh8mKjKxpgoREZH5MFExgpyaKt8dTObwDxERkZGYqBhBTk2VPC1vVEhERGQsJipGklNT5fNdF9mrQkREZAQmKkZqG1wNbs4aSW3zdexVISIiMgYTFSNp1CoM7xAsuf3y3ZfYq0JERCQTE5VykDOpNqeA9/8hIiKSi4lKOWjUKkR1lr5Ued+lmwpGQ0REZH+YqJTT6K51oZHWqYIjV/5VNhgiIiI7o3iicv36dbz22mvw8vKCq6srmjRpgiNHjuj3CyEwdepUVK9eHa6urggPD8eFC7Yz8VSjVqFbQz9JbY8nZ3KeChERkQyKJir//vsvnnrqKTg6OmLLli04c+YMPv30U3h6eurbzJs3D0uWLMHy5ctx8OBBuLm5ISIiAjk5OUqGZlJS7//D1T9ERETyqIQQiv2JP2HCBOzbtw979uwpdr8QAgEBARg/fjzee+89AEBWVhb8/PywatUq9O/fv8gxubm5yM3N1T/Pzs5GYGAgsrKy4O7urswbKYNWJ9Bw6lbkFujKbOuoViFxVk9oJE7CJSIiskfZ2dnw8PAo8/tb0R6VP/74A61bt0a/fv3g6+uLFi1a4KuvvtLvT0pKQnp6OsLDw/XbPDw8EBoaiv379xd7zujoaHh4eOgfgYGBSr4FSTRqFbrU95XUlr0qRERE0imaqFy+fBnLli1DnTp1sG3bNowcORJjxozB6tWrAQDp6ekAAD8/wzkefn5++n2PmzhxIrKysvSPlJQUJd+CZK+1C5LclpVqiYiIpHFQ8uQ6nQ6tW7fGnDlzAAAtWrRAfHw8li9fjsGDBxt1TmdnZzg7O5syTJNoF+IFZwcVcgvKTkAKe1XGdqtrhsiIiIhsl6I9KtWrV0fDhg0NtjVo0ADJyckAAH9/fwBARkaGQZuMjAz9PluhUaswsqP0miqsVEtERFQ2RROVp556CufOnTPYdv78eQQFPRwmCQ4Ohr+/P2JjY/X7s7OzcfDgQYSFhSkZmiJYqZaIiMi0FE1U3n33XRw4cABz5szBxYsXsXbtWnz55ZeIiooCAKhUKowdOxazZs3CH3/8gdOnT+P1119HQEAA+vbtq2RoipBbqfabA1eUC4aIiMgOKJqotGnTBr/99hu+//57NG7cGDNnzsRnn32GgQMH6tt88MEHGD16NEaMGIE2bdrg7t272Lp1K1xcXJQMTTFyKtXGns3g8A8REVEpFK2jYg5S12Gb01vfHsHWhIyyGwIY27UOJ9USEVGFYxV1VCoqqZVqAS5VJiIiKg0TFQUULlWWggXgiIiISsZERQFylyqzV4WIiKh4TFQUImepMntViIiIisdERSFylyqzV4WIiKgoJioKYq8KERFR+TBRUZDcXhWW1SciIjLEREVhLKtPRERkPCYqCmNZfSIiIuMxUTEDltUnIiIyDhMVM9CoVejW0E9S2wIdOKmWiIjo/2OiYiYsq09ERCQfExUzYVl9IiIi+ZiomAnL6hMREcnHRMWMWACOiIhIHiYqZsSy+kRERPIwUTEz9qoQERFJx0TFzFhWn4iISDomKhbAsvpERETSMFGxAJbVJyIikoaJioWwrD4REVHZmKhYCMvqExERlY2JigWxrD4REVHpmKhYEMvqExERlY6JigWxrD4REVHpmKhYGAvAERERlYyJioWxABwREVHJmKhYARaAIyIiKh4TFSsgt1dl/vZEBaMhIiKyHkxUrIScAnAnUrKw+VSasgERERFZASYqVkJOATgAGPfTCc5VISIiu2e2ROWTTz6BSqXC2LFj9dtycnIQFRUFLy8vVK5cGS+++CIyMjLMFZLVkVMALqdAxxVARERk98ySqBw+fBgrVqxA06ZNDba/++672LBhA9atW4fdu3cjNTUVL7zwgjlCskpyCsABrKtCRET2T/FE5e7duxg4cCC++uoreHp66rdnZWVh5cqVWLhwIbp06YJWrVohJiYGcXFxOHDgQInny83NRXZ2tsHDXsgtAMe6KkREZO8UT1SioqIQGRmJ8PBwg+1Hjx5Ffn6+wfb69eujRo0a2L9/f4nni46OhoeHh/4RGBioWOyWMLprXThLnVUL9qoQEZF9UzRR+eGHH3Ds2DFER0cX2Zeeng4nJydUrVrVYLufnx/S09NLPOfEiRORlZWlf6SkpJg6bIvSqFVY9Epzye3Zq0JERPZMsUQlJSUF77zzDr777ju4uLiY7LzOzs5wd3c3eNibXk0DENlE+gog9qoQEZG9UixROXr0KG7cuIGWLVvCwcEBDg4O2L17N5YsWQIHBwf4+fkhLy8PmZmZBsdlZGTA399fqbBsxpIBrXgPICIiqvAUS1S6du2K06dP48SJE/pH69atMXDgQP2/HR0dERsbqz/m3LlzSE5ORlhYmFJh2Qy51WrZq0JERPbIQakTV6lSBY0bNzbY5ubmBi8vL/32YcOGYdy4cahWrRrc3d0xevRohIWFoV27dkqFZVNGd62LpTsvQish/yjsVRnbra7ygREREZmJRSvTLlq0CL1798aLL76IZ555Bv7+/vj1118tGZJVkVutlr0qRERkb1RCCJv+ZsvOzoaHhweysrLscmLtvou3MPB/ByW3H9u1DntViIjI6kn9/ua9fqxcuxAvuDlJv0xLd15grwoREdkNJipWTqNWYf5LzSS31wrgne+PKxgRERGR+TBRsQFy66psPJ2GvAKdghERERGZBxMVGyGnrgoATPz1lILREBERmQcTFRsht67Kr8euc64KERHZPCYqNmR017qQer9CAc5VISIi28dExYZo1CpEdZLeq7LxdBo2n0pTMCIiIiJlMVGxMe90qye5VwUAxv10gkNARERks5io2BiNWoXRXWpLbp9ToOMNC4mIyGYxUbFBo7vWlbUCiEXgiIjIVjFRsUFyVwCxCBwREdkqJio2anTXunCWMVmFReCIiMgWMVGxURq1CoteaS7rGBaBIyIiW8NExYbJLa3/C4vAERGRjWGiYuOWDGgla7nyy8vjlAuGiIjIxJio2Di5ReCOJmdiw8lUBSMiIiIyHSYqdkBuEbixPxznEBAREdkEJip2QG4ROK3gEBAREdkGJip2Qm4ROA4BERGRLWCiYic0ahUWvdxM1jHjeR8gIiKyckxU7Ejv5k+gZQ0Pye3ztIL3ASIiIqvGRMXOrHvrKVkTa//L+wAREZEVY6JiZzRqFRbLqFhbwPsAERGRFWOiYod6N38CNb1cJbffeDoNm0+lKRgRERGRcZio2KnZzzeV1X4cJ9YSEZEVYqJip9qFeMHNSfrlzSnQcWItERFZHSYqdkqjVmH+S/KWKy+J5cRaIiKyLkxU7JjcuyvrAIxee0y5gIiIiGRiomLnlgxoBWcZ65U3x6dzYi0REVkNJip2TqNWYZGM5coAMOb7YxwCIiIiq+Bg6QBIeb2aBqDd/is4kPSvpPYF//+mhb+8/ZTCkRFZj7wCHWL2XcbW02m4evsetDpAowKcHTQABHILdBAqNXwqO+GFlk/ijQ4hcHLg33pESlMJIRT70zk6Ohq//vorEhMT4erqivbt22Pu3LmoV6+evk1OTg7Gjx+PH374Abm5uYiIiMAXX3wBPz9pcyuys7Ph4eGBrKwsuLu7K/VWbF5egQ51J2+RdczSAS3Qp1mAQhERmZ9WJxB34RbWHU3GmbRs3MvNR26+DnfzdMjTyv9V6KgCqrhoDJIZrXiY4Lg4OqCyiwMaVPfAS62eRPva3tDIuHEokb2T+v2taKLSo0cP9O/fH23atEFBQQE++ugjxMfH48yZM3BzcwMAjBw5Eps2bcKqVavg4eGBUaNGQa1WY9++fZJeg4mKdFHfHcGm0xmS2zuogXOzevGXK9k8rU5g8Y7z+Hz3RWh1lovDw1kNjVrFnhkiWEmi8ribN2/C19cXu3fvxjPPPIOsrCz4+Phg7dq1eOmllwAAiYmJaNCgAfbv34927doVOUdubi5yc3P1z7OzsxEYGMhERQKtTqDe5M0okPGLuneT6vjvwJbKBUWkoMIE5b9/XYQ1T7tyUgOuTho4atSoUc0NPRr7Y8hTwUxgyK5JTVTMOkclKysLAFCtWjUAwNGjR5Gfn4/w8HB9m/r166NGjRolJirR0dGYPn26eQK2Mxq1Ckv6t8Dba6Xf22fj6TQsLNDxFybZnA0nUzH2x+MW7UGRKk8H5OVoAWhx614mjqVkYs6WRDhrgBrV3NAwgMNHVHGZrUdFp9Ph2WefRWZmJvbu3QsAWLt2LYYOHWrQQwIAbdu2RefOnTF37twi52GPSvnN3BiPlXuvSm5fx8cNO8Z3Ui4gIhMbtuowYhNvWDoMRVSr5IgOtb3Rr3UgExeyaVbXoxIVFYX4+Hh9kmIsZ2dnODs7myiqimlK78Y4fjUTx1KyJLW/cPMepm9IwLQ+jRSOjKj8ei/5G/GpdywdhmJu38/HH6fS8Mf/r3fkW8UJ3Rr4YXLvRnB10lg4OiLTM0t//qhRo7Bx40bs2rULTz75pH67v78/8vLykJmZadA+IyMD/v7+5gitwlo38inI+TssZt8VzN50RrF4iEwhcvFuu05SinPjTh6+O5SCBlO3otGULXjn++PYc/4mayGR3VA0URFCYNSoUfjtt9+wc+dOBAcHG+xv1aoVHB0dERsbq9927tw5JCcnIywsTMnQKjyNWoXRnWvJOuarPUmsWktWK3LxbiSk3bV0GBZ1L1+H9SdTMejrQ6j10Wb0W7aPSQvZPEXnqLz99ttYu3Yt1q9fb1A7xcPDA66urgAeLk/evHkzVq1aBXd3d4wePRoAEBcXJ+k1uDzZeFqdQN1JmyGnfISjWoXEWT05Lk5Wpffi3Yiv4ElKWZ6u5YUvB7fh8BBZDatYnqxSFf9lFhMTgyFDhgD4v4Jv33//vUHBN6lDP0xUymfjiesY9cMJWcdwyTJZk2GrDiI28ZbJz/t4Mbd/HxQgp8D2eyY8XDSI6lyHy5/J4qwiUTEHJirl98IXe3EsWdrE2kLnZ/XkLzmyOGMS7cfJqWEipcy+VgC5BTqbSGoC3J0R/WJTdKjjw15SMjsmKiSZMYXguGSZLE2rE6g/eQvyjZx/8ULzAHzyUjPFEu7CpGZbfDrSsx8A4uG8PWvtmVH68yB6HBMVkmXzqVRZheAAYOhTNblkmSzmsx3n8FnsRdnH9Wrsh6WvtrJoD8KjSUxa1v1y3W/I1NrUrIrv/hPGhIUUx0SFZJu9KQFf7bki65jhTwdjUmRDZQIiKoFWJ1Bv0mbI6ZhQA/jvqy3Qq6n13mgzr0CHlXsv4Zej15CamYP7+ZYrqxta0xPf/qcdExZSDBMVMspba45ga7z0GxcCwBevtkSvptUVioioKLm9KTWruSD2vS42Nw+j8G7PPx25ir2X/sG/9wvMHkMDv8r4NaoDVwuRyTFRIaMYs2TZWaPCmZlcskzmIXduilclBxydGqFwVOZRmLisO5qM3edvISvHfIlLHZ9K2PROR/awkMlI/f7m/zgyoFGrsPiV5rKOydUKvPO9vPktRMZaGnte1gTaJQNaKRiNeWnUKjxdzwdLXm2Fkx9H4PysnpjYsx58Kzsp/toXbt5H3clb8MryOOTJmXlPVE7sUaFiGbNkmUNApDS5vSmVnR1wclr3CtHbVzhB94dDKUj6577ir2cNk5LJtnHoh8rFmCXLGgDn5/TiLy5SjNy5KRU1eS4cIvp4Qzwu3VI2aRnTuRbe6VaPP/ckG4d+qFw0ahWW9G8h6xgtgK4LdioTEFV4Wp3Ast2XJLfv3aR6hUxSgP8bIop9r7Piw0NLdl1C7Y82449j1xQ5PxETFSpRr6YBiGziJ+uYK7dz0GfpHoUioorswOV/kCtxPbKDClg8QF6iba+cHNR4s2NtHJrcDedn9UTf5tVl3TldCgFgzE8nETpnO+evkMkxUaFSLRnQCs4aeb/WTl/PxsyNZxSKiCqq/Zf+kdx2VJc6HIoohpODGp/1b4mLc3rh26Ft4edu2l6WjOx81J28BW+vOcI7NpPJMFGhUmnUKiySuQoIAFbuTeJfVmRSF2/ckdTOUa3C6K51FI7GthUODR38qBvOzuiB+v6VTXr+zfEZqP3RZmw8cd2k56WKiYkKlalX0wAMf7qm7OOe/iTW9MFQhaTVCfx5RlohwmebB7A3RQZXJw22ju2I87N6IjTY02TnFQBG/XACL3y+h70rVC5MVEiSSZGNMPSpIFnHZNzNQ+Ti3QpFRBXJ0tjzksvld6jtrWwwdsrJQY0f32yP87N6om3NqiY777GUbPauULkwUSHJpvVpjC715H0JJKTdZbJC5aLVCXy1N0lye38PVwWjsX9ODmr89NZTOD+rJ+r4upnknIW9K2/EHDDJ+ahiYaJCsnw9NBQ1q7nIOiYh7S56L/lboYjI3h1Kuo17uVpJbSs7a9A2uJrCEVUMTg5q7BjXCUsHtIDGRN8UO8/9gzYzt3MoiGRhokKyxb7XRfZ/nPjUOxi26rAi8ZB9256QJrntfzqEcH6KifVpFoDzs3phTOfaMMVHe/NePmpxKIhkYKJCsj0sBtdc9nGxiTew4WSq6QMiu6XVCfwssZCYk4arfZSiUaswLqIeLsx+mLCYAifaklRMVMgovZs/ga715U9afOf74/zFRJIdSrqNOznShn0GhtZgb4rCChOWS3N6oWcjecUgi3MsJRt1PtqMzaf4BwyVjIkKGW3lkFA0ri6v/oIOLLNP0skZ9uneqGKWy7cEjVqFZYNa4/ysnqjtU6lc59IBeHvtcczcGG+a4MjuMFGhctn4Tkc0kpmsXLmdw8m1VCY5wz7uLg6cRGsBTg5q/Dm+M4Z1CC73uVbuvYrnORRExWCiQuW26Z2OslcCxafewRsxhxSKiOyBnGGfF1s+wWEfC5rSuyG+eLUlHMt5DY5zKIiKwUSFTMKYlUA7z93EtD/Y3UvF47CPbenVtDoSZ/Us92TbwqGg2ZsSTBMY2TwmKmQSGrUK/31V/t1qV8ddxRsxBxWIiGwZh31s06OTbX3cHMt1rq/2XMH0DfxDhpiokAn1ahqAYR3kldkHgJ3nbqE3q9fSIzjsY9s0ahUOT+mOLvV9ynWemH38Q4aYqJCJTektv8w+AMSzei09Ij07R3JbDvtYr6+HtMXSAfJ7Wh+189wtRH72l2kCIpvERIVMzpgy+wAn2NL/uX03V1I7DvtYvz7NAnBpTi/UrGb8PZgS0u+hwyd/mjAqsiVMVEgRxkyuBR5OsJ2+gZPoKrrk2/cktXu+BYd9bIFGrcJfH3RB1/q+Rp/jWmYuk5UKiokKKcLYybUAELOPk+gqMq1O4Nfj0u4DU6Na+YqNkXmtHNIGSwe0gLGp5bXMXHScF8taKxUMExVSTK+mARj+dE2jjo3ZdxXDVnEYqCKSM5G2mpuTwtGQqfVpFoCLc3qh+RPuRh1/9XYOa61UMExUSFGTIhth+NPGVa2MTbzJZKUCkjOR1t/D+HkPZDkatQq/j34aXeoZtyqosNZK9OYzpg2MrJJVJCqff/45atasCRcXF4SGhuLQIX452ZNJkQ+rVhojNpFF4SqafRduSmrHibS27+uhbRHewPh5Kyv+TsLGE+xZsXcWT1R+/PFHjBs3DtOmTcOxY8fQrFkzRERE4MaNG5YOjUyoV9PqOD+rp1Fj0ywKV3FodQKbTkurSMv6Kfbhf4PblGsJ86gfjjNZsXMWT1QWLlyI4cOHY+jQoWjYsCGWL1+OSpUq4euvvy62fW5uLrKzsw0eZBucHNT43MgJtqylUDEcuPwPHuTrJLVl/RT7UbiE2buSg1HHj/qBJfftmUUTlby8PBw9ehTh4eH6bWq1GuHh4di/f3+xx0RHR8PDw0P/CAwMNFe4ZALlmWDLWgr2b82Bq5LaVXbmsI+90ahVODI1Ak9WdTbq+K/2XMHMjUxW7JFFE5Vbt25Bq9XCz8/PYLufnx/S09OLPWbixInIysrSP1JSUswRKpnQpMhGGNahplHHXsvMReuZ27k80Q5pdQJ/n5c2P+XpOl4c9rFTeyeEo2H1ykYdu3LvFczexAm29sbiQz9yOTs7w93d3eBBtmdKb+OTlVv38rk80Q4dSrqNe3nSliW/FlpT2WDIoja/0xGNA6oYdexXezjB1t5YNFHx9vaGRqNBRkaGwfaMjAz4+/tbKCoyl/IkK4XLE2du5Ioge7E9Qdok2kpOGrSr5aVwNGRpG8c8g8515d83DOAEW3tj0UTFyckJrVq1QmxsrH6bTqdDbGwswsLCLBgZmUt5khUAWLmXK4LsgVYn8POxa5La9mrsz2GfCiLmjdByJSucYGsfLD70M27cOHz11VdYvXo1zp49i5EjR+LevXsYOnSopUMjM5nS2/iicMDDFUG9F+82YURkbnKq0T5V27gvLrJNMW+EoonRw0Ccs2IPLJ6ovPLKK1iwYAGmTp2K5s2b48SJE9i6dWuRCbZk38pTFA4A4tPuohPvAWKzWI2WSrNhzDNGV7H9ak8SNp+SNqxI1kklhLDp3+zZ2dnw8PBAVlYWJ9baAa1OIHTWdty6X2DU8WoA/321BXo1DTBtYKSo9346gZ+PlX0jQncXBxyf2p1DPxXU9A0JiNl3RfZxKgAX5/Ti/xsrI/X72+I9KkSPKm8tBU6ytT2sRktSTevTCEOfCpJ9nADQZf5O0wdEZsFEhazS3gnhaGTkuDTwcJLt85/v4VCQDWA1WpJjWp/G6FJP/jylq//mIJJz2WwSExWyWpvKMS4NAMdTslH7o83YeKLsIQWynP2X/pHUjtVoqdDXQ42bYJuQdhe9l/ytQESkJCYqZNW+HtoWQ5+qafTxAsCoH05g2CouYbZWF2/ckdSuA6vR0iOMnWAbn3oHw1YdViAiUgoTFbJ60/qUb/kyAMQm3uKqICuk1QnsvSitbH6rGp4KR0O25uuhbTG4vfw5K7GJN7DhJAvC2QomKmQTCpcvl+fv6Su3c1Dro81YuC2RCYuVOJR0G3dzpc1P8a5s3ARrsm/TnzVuzsro74/z94CNYKJCNqNX0+q4OKcXgjxdynWeJbsuoe4k3ivIGkgtmw+wfgqV7OuhoWhsxI0MuRLINjBRIZuiUauw+8OuRt+wrJBWPFzG/PaaI/yrykLklM13d+FEWirdxnc6opHMZIUrgWwDExWySRvHPIOu9X3LfZ7N8RmoM4krgyxBTtl81k8hKTYZkaxwJZD1Y6JCNmvlkDZYOqBFueatAIBOPFwZ9ALrrpiVnGEf1k8hqTa90xF+7k6yjuFKIOvGRIVsWp9mAbg4pxdaPOlR7nMdS8nmZFsz0eoE1hxIltSWwz4k154Puso+hiuBrBcTFbJ5GrUKv43qgGEdyreEudCSXZdQ+6PN+EPi/AmSb2nseeRLTAY57ENyOTmoMayD/GXLXAlknZiokN2Y0vvhEmZT/KcWAMb8dBKhc7Yjr0Da8lmSRqsT+GpvkuT2HPYhY0zp3RiNA+SvBOq6YJcC0VB5MFEhu9KraXVcmNMLLQOrmuR8Gdn5qDt5C15ZHseExUQOJd3GvVxpk2grO2s47ENG2zhG/uTaK7cf4I2YQwpFRMZgokJ2R6NW4deop0wy0bbQwSv/ou7kLei3fB8TlnJKz86R3PY/HUI47EPlYsxKoJ3nbmLmxjMKRURyMVEhu1U40dZUvSsAcPhKJupO3oKuC/7CnvM3OZ5thL0Xbkhq56RRYXTXOgpHQxWBMSuBVu5NwuZT0lemkXKYqJBde7R3xZR/mF+6dQ+Dvj6EOlwlJItWJ7D+uLSVFZ3r+7I3hUzGmJVAY74/xp9tK8BEhSqEPs0CcGF2L/Rq7G/S8+rwcJVQrY82Y9wPxzksVIalsedRIPH3fh1f+RMhiUpizEqgAgGM+u6oQhGRVExUqMLQqFX44rVW+OLVlnBU4C/1X0+kch5LKbQ6gc93XZLcPixE/o3miEpjzEqgLQkZmL2J81UsiYkKVTi9mlZH4qyeGNO5tkmHgwoVzmNpO/tPfLn7EpOW/2/M90cl105xcVCjXS0vhSOiimjjmI6o6SXvBpdf7eF8FUtSCSFsegAuOzsbHh4eyMrKgru7u6XDIRuj1Qks3nEeS3ZdVPR1QrwrYfqzjdG+tneFnHex+VQq3l57XHL7sV3rYGy3ugpGRBWZVidQZ9JmyJl+4qACzs3uVSF/fpUi9fubiQoRHv7iGvXdUWxJyFD8ter5VsZHkQ3QoY5Phfilp9UJ1Ju8GVI7lhzVKiTO6lkhPhuynI0nrmPUDydkHdOqRlX88vZTygRUAUn9/ubQDxEezl9ZNqg1zs/qidBgT0Vf69yNuxgccxi1PtqMLgt22f3w0Jjvj0pOUgAgqnNtJimkuN7Nn0DLGvLuEXY0OZP3A7IA9qgQFSOvQIfIJX/jwo17ZnvNqq4O6FjXFy+1etJuhojyCnSoO3mL5PbsTSFzktvbBwAqABfncAjIFDj0Q2QCD/K0eP6LvUhMv2v21/as5IgQbzdENPLHkKeC4eRgWx2gWp1A65nb8e+DAsnHcG4KmZvc+VMA0OJJD/w2qoNCEVUcTFSITCivQIdBKw/gYNK/FovBWQPUqOaGhgEeVt/rsuFkKkZ/L++Xv4uDGgkzeljteyL7NXtTAr7ac0XWMcM6BGNK74bKBFRBMFEhUkBegQ4TfjmJ30+kyloxoBQ3RxWqVnKGv4eLVfS8aHUC/ZbF4VhKpuxjv3i1JXo15Z2SyTKmb4hHzL6rso7h/9nyYaJCpCCtTiDuwi2898sJZGTnWTocA64OQFVXR+QW6CBUavhUdsILLZ/EGx1CFEtiHuRpMfybw9h78R+jju/V2B9fvNbKxFERydPt079w4ab0eWmcU1U+TFSIzMSS81jkclIDrk4aaFSAk0aNPK0OWgFoVICzgwYajRp+7sX3zhQmZz8duYqjyf/iXq4WGhVwN0+HPK3xv0Y0KuA861OQFZA7+RsAejepjv8ObKlQRPaNiQqRmeUV6BCz7zI+33UJ2TnSJ5Bas8LemTu5WtzNU2YJ9dIBLdCnWYAi5yaSa+bGeKzcK28I6PysnjY32d0asI4KkZk5OajxZsfaOPVxBM7O6IEOtb1g630EDwqAtDv5iiUpXev7MkkhqzKld2MEe8srsR+5+G+FoiFAwUTlypUrGDZsGIKDg+Hq6opatWph2rRpyMszHM8/deoUnn76abi4uCAwMBDz5s1TKiQis3F10mDNf9rh4pxe+HZoW7SWWViqImgcUAUrh7SxdBhERfw5rjPkdJBcuHkPMzfyxoVKcVDqxImJidDpdFixYgVq166N+Ph4DB8+HPfu3cOCBQsAPOz26d69O8LDw7F8+XKcPn0ab7zxBqpWrYoRI0YoFRqR2WjUKjxdzwdP1/PRz/FYdzQZfybewH2FeilsQePqlbFxzDOWDoOoWBq1Ckv6t5BVX2Xl3iS0quHJVUAKMOsclfnz52PZsmW4fPkyAGDZsmWYNGkS0tPT4eTkBACYMGECfv/9dyQmJhZ7jtzcXOTm5uqfZ2dnIzAwkHNUyOY8yNNixsZ4/HkmAzfv5ls6HLPpUs8bXw8NtXQYRGV6e80RbI6Xfv8vZ40KZ2ZyFZBUVjlHJSsrC9WqVdM/379/P5555hl9kgIAEREROHfuHP79t/jCWtHR0fDw8NA/AgMDFY+bSAmuThpEv9AMhyd3x6U5vbB6cBuEBXvCt7IjXBzs8xfdsA5BTFLIZix9tRU0Mn4Uc7UC78gsdEhlU2zo53EXL17E0qVL9cM+AJCeno7g4GCDdn5+fvp9np5Fbw43ceJEjBs3Tv+8sEeFyJZp1Cp0bOCLjg189dvyCnRYufcSfjl6DamZObifb7tDRRrVw9U9vZpy4izZDo1ahcWvNJd1l+WNp9PQ61Qah4BMSHaiMmHCBMydO7fUNmfPnkX9+vX1z69fv44ePXqgX79+GD58uPwoH+Hs7AxnZ+dynYPIFjg5qDGyUx2M7FQHQPF1TO7naWHN+YtGBUR1qoV3utVjdzjZpN7Nn8DXcUk4lpwl+ZhxP51ARGN//p83EdmJyvjx4zFkyJBS24SEhOj/nZqais6dO6N9+/b48ssvDdr5+/sjI8Nw/K/wub+/v9zQiOzaoxNzH1U41yXu4i3czcmHk0YDQODfBwXIKbBMmaQQ70qY/mxjq74fEZFU6956StZdlnMKdFgae4E32DQR2YmKj48PfHx8ym6Ihz0pnTt3RqtWrRATEwO12nBKTFhYGCZNmoT8/Hw4OjoCAHbs2IF69eoVO+xDREUVznUpTmERum3x6UjLuo/c/P+rRPt4ZVpjKsy6Oarg7uJYakVbIltnzCqg/+68gNFd6zBRNwHFVv1cv34dnTp1QlBQEFavXg2NRqPfV9hbkpWVhXr16qF79+748MMPER8fjzfeeAOLFi2SvDyZlWmJTKek3pncAh10UKGyswNa1vBEv9aB7C2hCifquyPYdFr6KiCW1y+dxUvor1q1CkOHDi1236MveerUKURFReHw4cPw9vbG6NGj8eGHH0p+HSYqRERkDlqdQL1JmyFnRJV3WC6ZxRMVc2GiQkRE5vLZjnP4LPai5PasrVIyq6yjQkREZMtGd60LZxnFVVhbpfyYqBAREUmkUauw6JXmso7ZeDoNm0+lKRNQBcBEhYiISIZeTQMQ2cRP1jEf/HIKWp1Nz7SwGCYqREREMi0Z0ErWENDd3AIcuPSPghHZLyYqREREMhkzBDTp99PKBGPnmKgQEREZQe4Q0JV/7mPDyVQFI7JPTFSIiIiMtGRAK8i52fnYH45zropMTFSIiIiMpFGrMKpLbcnttQJ4eXmcghHZHyYqRERE5TC6a104yijodjQ5k0NAMjBRISIiKgeNWoVFLxd/Y9CSfPDzSQ4BScREhYiIqJx6N38CLWt4SG7/IF/H5coSMVEhIiIygXVvPQUZpVW4XFkiJipEREQmoFGrsFhGbRUuV5aGiQoREZGJ9G7+BGp6uUpuP+5HLlcuCxMVIiIiE5r9fFPJbfN14N2Vy8BEhYiIyITahXjBxVH61yvvrlw6JipEREQmpFGrsOBF6b0qADDupxMcAioBExUiIiITk7tcOadAh6WxFxSMyHYxUSEiIlKA3OXKy3dfYq9KMZioEBERKUCjVmG0jPsA5RSwCFxxmKgQEREpZHTXunCW0a3CInBFMVEhIiJSiEatwiIWgSsXJipEREQK6tU0AM0D3SW3H88VQAaYqBARESns/YgGktvmaQVXAD2CiQoREZHC5BaBWxJ7gb0q/x8TFSIiIoXJLQKnA/Dy8jjlArIhTFSIiIjMQG4RuKPJmZxYCyYqREREZiO3CNwHP5+s8ENATFSIiIjMRG4RuAf5LALHRIWIiMiMRnetC0e19G6Vbw5cUS4YG8BEhYiIyIw0ahUWvdxMcvvYsxkVevjHLIlKbm4umjdvDpVKhRMnThjsO3XqFJ5++mm4uLggMDAQ8+bNM0dIREREFtO7+ROo6eUqqW2BDhW6ropZEpUPPvgAAQEBRbZnZ2eje/fuCAoKwtGjRzF//nx8/PHH+PLLL80RFhERkcXMfl76cuWKXFdF8URly5Yt2L59OxYsWFBk33fffYe8vDx8/fXXaNSoEfr3748xY8Zg4cKFJZ4vNzcX2dnZBg8iIiJb0y7EC84O0uaqVOS6KoomKhkZGRg+fDi+/fZbVKpUqcj+/fv345lnnoGTk5N+W0REBM6dO4d///232HNGR0fDw8ND/wgMDFQsfiIiIqVo1CqM7FhLcvuKWldFsURFCIEhQ4bgrbfeQuvWrYttk56eDj8/P4Nthc/T09OLPWbixInIysrSP1JSUkwbOBERkZnIXQFUEeuqyE5UJkyYAJVKVeojMTERS5cuxZ07dzBx4kSTBuzs7Ax3d3eDBxERkS3SqFWI6iy9V6Ui1lVxkHvA+PHjMWTIkFLbhISEYOfOndi/fz+cnZ0N9rVu3RoDBw7E6tWr4e/vj4yMDIP9hc/9/f3lhkZERGRzRneti893XUK+xJ6Sbw5cwVN1vBWOynrITlR8fHzg4+NTZrslS5Zg1qxZ+uepqamIiIjAjz/+iNDQUABAWFgYJk2ahPz8fDg6OgIAduzYgXr16sHT01NuaERERDansK7KqB9OSGpfWFdFI2PIyJYpNkelRo0aaNy4sf5Rt25dAECtWrXw5JNPAgBeffVVODk5YdiwYUhISMCPP/6IxYsXY9y4cUqFRUREZHVYV6VkFq1M6+Hhge3btyMpKQmtWrXC+PHjMXXqVIwYMcKSYREREZmdnLoqn++6WGEm1coe+jFWzZo1IUTRD7Vp06bYs2ePucIgIiKySoV1VXILyk5A8nUCS2MvYGy3umaIzLJ4rx8iIiIrILeuSkXpVWGiQkREZCXk1FUp7FWxd0xUiIiIrITcuioVoVeFiQoREZEVYa+KISYqREREVkRur8ry3ZfsuleFiQoREZGVkdOrklNg32X1magQERFZGbm9Kt8cuKJcMBbGRIWIiMgKje5aFxqJVfK3J2TY7fAPExUiIiIrpFGr0K2hn6S2AsDLy+OUDchCmKgQERFZqUFhNSW3PZqciQ0nU5ULxkKYqBAREVmpwrL6Uo3/6YTdDQExUSEiIrJScsvq52ntr64KExUiIiIrJmepMmB/1WqZqBAREVkxjVqFRS83k9ze3qrVMlEhIiKycr2bP4GWNTwkt7enarVMVIiIiGzAureeklxXxZ6q1TJRISIisgEatQqju9SW3H7fpZsKRmM+TFSIiIhshJxqtUeu/KtsMGbCRIWIiMhGyKlWezw50y7mqTBRISIisiFSq9Xay+ofJipEREQ25GG1Wmlf3/ZQU4WJChERkQ3RqFXoUt9XUlt76FVhokJERGRjXmsXJLmtrfeqMFEhIiKyMXJuVmjrvSpMVIiIiGyM3JsV2nKlWiYqRERENkjOzQptuVItExUiIiIbpFGrENVZeq/KNweuKBeMgpioEBER2Sg5lWpjz2bY5PAPExUiIiIbJadSbYEONjmplokKERGRDZNaqRawzaXKTFSIiIhsmL0vVVY0Udm0aRNCQ0Ph6uoKT09P9O3b12B/cnIyIiMjUalSJfj6+uL9999HQUGBkiERERHZFblLlW2tV0WxROWXX37BoEGDMHToUJw8eRL79u3Dq6++qt+v1WoRGRmJvLw8xMXFYfXq1Vi1ahWmTp2qVEhERER2Sc5SZVvrVVEJIUyeVhUUFKBmzZqYPn06hg0bVmybLVu2oHfv3khNTYWf38OJQMuXL8eHH36ImzdvwsnJqdjjcnNzkZubq3+enZ2NwMBAZGVlwd3d3dRvhYiIyCZ8tuMcPou9KKmti4MaCTN6QCMxuVFCdnY2PDw8yvz+VqRH5dixY7h+/TrUajVatGiB6tWro2fPnoiPj9e32b9/P5o0aaJPUgAgIiIC2dnZSEhIKPHc0dHR8PDw0D8CAwOVeAtEREQ2xV4LwCmSqFy+fBkA8PHHH2Py5MnYuHEjPD090alTJ9y+fRsAkJ6ebpCkANA/T09PL/HcEydORFZWlv6RkpKixFsgIiKyKXILwK05eEW5YExIVqIyYcIEqFSqUh+JiYnQ6XQAgEmTJuHFF19Eq1atEBMTA5VKhXXr1pUrYGdnZ7i7uxs8iIiISF6vyq7EmzYxqdZBTuPx48djyJAhpbYJCQlBWloaAKBhw4b67c7OzggJCUFycjIAwN/fH4cOHTI4NiMjQ7+PiIiI5CnsVZEyV6Vw+OepOt5miMx4shIVHx8f+Pj4lNmuVatWcHZ2xrlz59ChQwcAQH5+Pq5cuYKgoCAAQFhYGGbPno0bN27A19cXALBjxw64u7sbJDhEREQk3eiudfHfnRdRIKGz5JsDV6w+UVFkjoq7uzveeustTJs2Ddu3b8e5c+cwcuRIAEC/fv0AAN27d0fDhg0xaNAgnDx5Etu2bcPkyZMRFRUFZ2dnJcIiIiKyexq1Ci2DPCW1tYX7/yhWR2X+/Pno378/Bg0ahDZt2uDq1avYuXMnPD0ffngajQYbN26ERqNBWFgYXnvtNbz++uuYMWOGUiERERFVCG2Cq0lqZwv3/1Gkjoo5SV2HTUREVFHsu3gLA/93UFJbR7UKibN6mr2mikXrqBAREZHl2NP9f5ioEBER2Rm59/9ZvvuS1c5VYaJCRERkh+ylUi0TFSIiIjtkL5VqmagQERHZKXuoVMtEhYiIyE7J6VWx1uEfJipERER2bHTXupC4AAjfHLiiaCzGYKJCRERkx2y9Ui0TFSIiIjtny5VqmagQERHZufa1pN948H97L1tVrwoTFSIiIjsnp1Lt3VwtDiXdVjgi6ZioEBER2Tm5lWq3J6QpGI08TFSIiIgqADk1Vb47mGw1wz9MVIiIiCoAjVqF19rVkNQ2T2s9NypkokJERFRBdG9UXXJba7lRIRMVIiKiCqJtcDW4OWsktbWWSrVMVIiIiCoIjVqF4R2CJbe3hhsVMlEhIiKqQGztRoVMVIiIiCoQW7tRIRMVIiKiCsaWblTIRIWIiKiCkXOjwl2JNyw6/MNEhYiIqAKSeqPCPK2w6PAPExUiIqIKSM6NCi25+oeJChERUQUk50aFf56x3PAPExUiIqIKSM6NCvN1liupz0SFiIiognpYU0Va2893XbRIrwoTFSIiogpKo1YhvKG/pLaW6lVhokJERFSBvdYuSHLb/+29bPZeFSYqREREFZicSbV3c7U4lHRb4YgMMVEhIiKqwORMqgWA7QlpCkZTFBMVIiKiCk7OjQp/OJxi1uEfxRKV8+fP47nnnoO3tzfc3d3RoUMH7Nq1y6BNcnIyIiMjUalSJfj6+uL9999HQUGBUiERERFRMTRqFV5rV0NS2wf55r1RoWKJSu/evVFQUICdO3fi6NGjaNasGXr37o309HQAgFarRWRkJPLy8hAXF4fVq1dj1apVmDp1qlIhERERUQm6N6ouue2+SzcVjMSQIonKrVu3cOHCBUyYMAFNmzZFnTp18Mknn+D+/fuIj48HAGzfvh1nzpzBmjVr0Lx5c/Ts2RMzZ87E559/jry8vBLPnZubi+zsbIMHERERlU/b4GpwkVhUJTUzR+Fo/o8iiYqXlxfq1auHb775Bvfu3UNBQQFWrFgBX19ftGrVCgCwf/9+NGnSBH5+fvrjIiIikJ2djYSEhBLPHR0dDQ8PD/0jMDBQibdARERUoWjUKnSq6yOp7ROergpH838USVRUKhX+/PNPHD9+HFWqVIGLiwsWLlyIrVu3wtPz4W2l09PTDZIUAPrnhcNDxZk4cSKysrL0j5SUFCXeAhERUYUzKKympHZybmhYXrISlQkTJkClUpX6SExMhBACUVFR8PX1xZ49e3Do0CH07dsXffr0QVpa+ZY1OTs7w93d3eBBRERE5dcuxAtVKzmW2sazkiPahXiZKSLAQU7j8ePHY8iQIaW2CQkJwc6dO7Fx40b8+++/+kTiiy++wI4dO7B69WpMmDAB/v7+OHTokMGxGRkZAAB/f2nlfImIiMh0NGoVPnmhCd5ac6zENtEvNIFG4lJmU5CVqPj4+MDHp+zxq/v37wMA1GrDDhu1Wg2dTgcACAsLw+zZs3Hjxg34+voCAHbs2AF3d3c0bNhQTlhERERkIj0aV8fy11ri4z/OID37/ybNVvdwwbQ+DdGjsfTVQaYgK1GRKiwsDJ6enhg8eDCmTp0KV1dXfPXVV0hKSkJkZCQAoHv37mjYsCEGDRqEefPmIT09HZMnT0ZUVBScnZ2VCIuIiIgk6NG4Oro19MehpNu4cScHvlVc0Da4mll7Ugopkqh4e3tj69atmDRpErp06YL8/Hw0atQI69evR7NmzQAAGo0GGzduxMiRIxEWFgY3NzcMHjwYM2bMUCIkIiIikkGjViGslvnmopREJYQw720QTSw7OxseHh7IysrixFoiIiIbIfX7m/f6ISIiIqvFRIWIiIisFhMVIiIislpMVIiIiMhqMVEhIiIiq8VEhYiIiKwWExUiIiKyWkxUiIiIyGopUpnWnArr1WVnZ1s4EiIiIpKq8Hu7rLqzNp+o3LlzBwAQGBho4UiIiIhIrjt37sDDw6PE/TZfQl+n0yE1NRVVqlSBSmXamyVlZ2cjMDAQKSkpdlmen+/P9tn7e+T7s332/h75/ownhMCdO3cQEBAAtbrkmSg236OiVqvx5JNPKvoa7u7udvkfsBDfn+2z9/fI92f77P098v0Zp7SelEKcTEtERERWi4kKERERWS0mKqVwdnbGtGnT4OzsbOlQFMH3Z/vs/T3y/dk+e3+PfH/Ks/nJtERERGS/2KNCREREVouJChEREVktJipERERktZioEBERkdViokJERERWq0InKrNnz0b79u1RqVIlVK1atdg2ycnJiIyMRKVKleDr64v3338fBQUFpZ739u3bGDhwINzd3VG1alUMGzYMd+/eVeAdyPPXX39BpVIV+zh8+HCJx3Xq1KlI+7feesuMkUtXs2bNIrF+8sknpR6Tk5ODqKgoeHl5oXLlynjxxReRkZFhpoilu3LlCoYNG4bg4GC4urqiVq1amDZtGvLy8ko9ztqv3+eff46aNWvCxcUFoaGhOHToUKnt161bh/r168PFxQVNmjTB5s2bzRSpPNHR0WjTpg2qVKkCX19f9O3bF+fOnSv1mFWrVhW5Vi4uLmaKWL6PP/64SLz169cv9RhbuX5A8b9PVCoVoqKiim1vC9fv77//Rp8+fRAQEACVSoXff//dYL8QAlOnTkX16tXh6uqK8PBwXLhwoczzyv05lqNCJyp5eXno168fRo4cWex+rVaLyMhI5OXlIS4uDqtXr8aqVaswderUUs87cOBAJCQkYMeOHdi4cSP+/vtvjBgxQom3IEv79u2RlpZm8PjPf/6D4OBgtG7dutRjhw8fbnDcvHnzzBS1fDNmzDCIdfTo0aW2f/fdd7FhwwasW7cOu3fvRmpqKl544QUzRStdYmIidDodVqxYgYSEBCxatAjLly/HRx99VOax1nr9fvzxR4wbNw7Tpk3DsWPH0KxZM0RERODGjRvFto+Li8OAAQMwbNgwHD9+HH379kXfvn0RHx9v5sjLtnv3bkRFReHAgQPYsWMH8vPz0b17d9y7d6/U49zd3Q2u1dWrV80UsXEaNWpkEO/evXtLbGtL1w8ADh8+bPDeduzYAQDo169ficdY+/W7d+8emjVrhs8//7zY/fPmzcOSJUuwfPlyHDx4EG5uboiIiEBOTk6J55T7cyybIBETEyM8PDyKbN+8ebNQq9UiPT1dv23ZsmXC3d1d5ObmFnuuM2fOCADi8OHD+m1btmwRKpVKXL9+3eSxl0deXp7w8fERM2bMKLVdx44dxTvvvGOeoMopKChILFq0SHL7zMxM4ejoKNatW6ffdvbsWQFA7N+/X4EITWvevHkiODi41DbWfP3atm0roqKi9M+1Wq0ICAgQ0dHRxbZ/+eWXRWRkpMG20NBQ8eabbyoapyncuHFDABC7d+8usU1Jv4us1bRp00SzZs0kt7fl6yeEEO+8846oVauW0Ol0xe63tesHQPz222/65zqdTvj7+4v58+frt2VmZgpnZ2fx/fffl3geuT/HclXoHpWy7N+/H02aNIGfn59+W0REBLKzs5GQkFDiMVWrVjXooQgPD4darcbBgwcVj1mOP/74A//88w+GDh1aZtvvvvsO3t7eaNy4MSZOnIj79++bIULjfPLJJ/Dy8kKLFi0wf/78Uofqjh49ivz8fISHh+u31a9fHzVq1MD+/fvNEW65ZGVloVq1amW2s8brl5eXh6NHjxp89mq1GuHh4SV+9vv37zdoDzz8mbSVawWgzOt19+5dBAUFITAwEM8991yJv2usxYULFxAQEICQkBAMHDgQycnJJba15euXl5eHNWvW4I033oBKpSqxna1dv0clJSUhPT3d4Bp5eHggNDS0xGtkzM+xXDZ/92QlpaenGyQpAPTP09PTSzzG19fXYJuDgwOqVatW4jGWsnLlSkRERJR59+lXX30VQUFBCAgIwKlTp/Dhhx/i3Llz+PXXX80UqXRjxoxBy5YtUa1aNcTFxWHixIlIS0vDwoULi22fnp4OJyenInOU/Pz8rO56Pe7ixYtYunQpFixYUGo7a71+t27dglarLfZnLDExsdhjSvqZtPZrpdPpMHbsWDz11FNo3Lhxie3q1auHr7/+Gk2bNkVWVhYWLFiA9u3bIyEhQfG7xBsjNDQUq1atQr169ZCWlobp06fj6aefRnx8PKpUqVKkva1ePwD4/fffkZmZiSFDhpTYxtau3+MKr4Oca2TMz7FcdpeoTJgwAXPnzi21zdmzZ8uc8GVLjHnP165dw7Zt2/DTTz+Vef5H59c0adIE1atXR9euXXHp0iXUqlXL+MAlkvP+xo0bp9/WtGlTODk54c0330R0dLTV3ovDmOt3/fp19OjRA/369cPw4cNLPdbS14+AqKgoxMfHlzp/AwDCwsIQFhamf96+fXs0aNAAK1aswMyZM5UOU7aePXvq/920aVOEhoYiKCgIP/30E4YNG2bByExv5cqV6NmzJwICAkpsY2vXz1bYXaIyfvz4UjNeAAgJCZF0Ln9//yIzlwtXg/j7+5d4zOMTiAoKCnD79u0SjykvY95zTEwMvLy88Oyzz8p+vdDQUAAP/6I3xxddea5paGgoCgoKcOXKFdSrV6/Ifn9/f+Tl5SEzM9OgVyUjI0Ox6/U4ue8vNTUVnTt3Rvv27fHll1/Kfj1zX7+SeHt7Q6PRFFlhVdpn7+/vL6u9NRg1apR+Ur3cv6odHR3RokULXLx4UaHoTKtq1aqoW7duifHa4vUDgKtXr+LPP/+U3Qtpa9ev8DpkZGSgevXq+u0ZGRlo3rx5sccY83Msm0lmuti4sibTZmRk6LetWLFCuLu7i5ycnGLPVTiZ9siRI/pt27Zts6rJtDqdTgQHB4vx48cbdfzevXsFAHHy5EkTR2Z6a9asEWq1Wty+fbvY/YWTaX/++Wf9tsTERKudTHvt2jVRp04d0b9/f1FQUGDUOazp+rVt21aMGjVK/1yr1Yonnnii1Mm0vXv3NtgWFhZmlZMxdTqdiIqKEgEBAeL8+fNGnaOgoEDUq1dPvPvuuyaOThl37twRnp6eYvHixcXut6Xr96hp06YJf39/kZ+fL+s4a79+KGEy7YIFC/TbsrKyJE2mlfNzLDtOk5zFRl29elUcP35cTJ8+XVSuXFkcP35cHD9+XNy5c0cI8fA/WePGjUX37t3FiRMnxNatW4WPj4+YOHGi/hwHDx4U9erVE9euXdNv69Gjh2jRooU4ePCg2Lt3r6hTp44YMGCA2d9fSf78808BQJw9e7bIvmvXrol69eqJgwcPCiGEuHjxopgxY4Y4cuSISEpKEuvXrxchISHimWeeMXfYZYqLixOLFi0SJ06cEJcuXRJr1qwRPj4+4vXXX9e3efz9CSHEW2+9JWrUqCF27twpjhw5IsLCwkRYWJgl3kKprl27JmrXri26du0qrl27JtLS0vSPR9vY0vX74YcfhLOzs1i1apU4c+aMGDFihKhatap+pd2gQYPEhAkT9O337dsnHBwcxIIFC8TZs2fFtGnThKOjozh9+rSl3kKJRo4cKTw8PMRff/1lcK3u37+vb/P4+5s+fbrYtm2buHTpkjh69Kjo37+/cHFxEQkJCZZ4C2UaP368+Ouvv0RSUpLYt2+fCA8PF97e3uLGjRtCCNu+foW0Wq2oUaOG+PDDD4vss8Xrd+fOHf13HQCxcOFCcfz4cXH16lUhhBCffPKJqFq1qli/fr04deqUeO6550RwcLB48OCB/hxdunQRS5cu1T8v6+e4vCp0ojJ48GABoMhj165d+jZXrlwRPXv2FK6ursLb21uMHz/eIKvetWuXACCSkpL02/755x8xYMAAUblyZeHu7i6GDh2qT36swYABA0T79u2L3ZeUlGTwGSQnJ4tnnnlGVKtWTTg7O4vatWuL999/X2RlZZkxYmmOHj0qQkNDhYeHh3BxcRENGjQQc+bMMej9evz9CSHEgwcPxNtvvy08PT1FpUqVxPPPP2/w5W8tYmJiiv3/+mjHqC1ev6VLl4oaNWoIJycn0bZtW3HgwAH9vo4dO4rBgwcbtP/pp59E3bp1hZOTk2jUqJHYtGmTmSOWpqRrFRMTo2/z+PsbO3as/rPw8/MTvXr1EseOHTN/8BK98soronr16sLJyUk88cQT4pVXXhEXL17U77fl61do27ZtAoA4d+5ckX22eP0Kv7MefxS+D51OJ6ZMmSL8/PyEs7Oz6Nq1a5H3HhQUJKZNm2awrbSf4/JSCSGEaQaRiIiIiEyLdVSIiIjIajFRISIiIqvFRIWIiIisFhMVIiIislpMVIiIiMhqMVEhIiIiq8VEhYiIiKwWExUiIiKyWkxUiIiIyGoxUSEiIiKrxUSFiIiIrNb/A35OSRWqwdIEAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fbench_hard = [\n",
+    "    # composite functions\n",
+    "    (lambda x: -np.tanh(x) + 1/4 * x, '-tanh(x) + 1/4 * x'),\n",
+    "    (lambda x: np.arctan(x) + np.sin(x), 'arctan(x) + sin(x)'),\n",
+    "    (lambda x: np.exp(-x**2+1) + 1/3*np.abs(x), 'exp(-x^2+1)+ 1/3 * |x|'),\n",
+    "    (lambda x: np.exp(-x+1) + 2000* np.abs(x+1), 'exp(-x+1)+ 2000 * abs(x+1)'),\n",
+    "    (lambda x: np.exp(x) + 2000* np.abs(x), 'exp(x)+ 2000 * abs(x)'),\n",
+    "    (lambda x: np.exp(x) + 4000* np.sign(x), 'exp(x)+ 4000 * sign(x)'),\n",
+    "    (lambda x: np.sin(x) + np.cos(x), 'sin(x)+cos(x)'),\n",
+    "    (lambda x: np.abs(np.sin(x/2)), '|sin(x/2)|'),\n",
+    "    (lambda x: np.exp(x) + 4000* np.sin(x), 'exp(x) + 4000* sin(x)'),\n",
+    "    (lambda x: np.sign(np.sin(x)), 'sign(sin(x))'),\n",
+    "    (lambda x: np.sign(np.cos(x)), 'sign(cos(x))'),\n",
+    "    (lambda x: np.sin(x) + np.sin(2*x), 'sin(x)+sin(2*x)'),\n",
+    "    (lambda x: 1/2 * (x+1) ** 4 - 5 * (x+2) **3 + 3 * x ** 2 + 2 * x + 1, '1/2 * (x+1) ** 4 - 5 * (x+2) **3 + 3 * x ** 2 + 2 * x + 1'),\n",
+    "    (lambda x: -1/2 * (x+1) ** 4 + 5 * (x+2) **3 + 3 * x ** 2 + 2 * x + 1, '-1/2 * (x+1) ** 4 + 5 * (x+2) **3 + 3 * x ** 2 + 2 * x + 1'),\n",
+    "    (lambda x: np.sign(x ** 2 - 15), 'sign(x ** 2 - 15)'),\n",
+    "    (lambda x: np.abs(x ** 2 - 20), 'abs(x ** 2 - 20)'),\n",
+    "    (lambda x: np.abs(x) ** (1/10), 'abs(x) ** (1/10)'),\n",
+    "    (lambda x: np.sin(x) + np.sin(3*x), 'sin(x) + sin(3*x)'),\n",
+    "    (lambda x: np.sin(x) + np.sin(0.5 * x), 'sin(x) + sin(0.5 * x)'),\n",
+    "    (lambda x: np.abs(x) + np.sin(x), 'abs(x) + sin(x)'),\n",
+    "    (lambda x: np.sign(x) + np.cos(x), 'sign(x) + cos(x)'),\n",
+    "    (lambda x: x ** 3 + 250 * np.sin(x), 'x ** 3 + 250 * sin(x)'),\n",
+    "    (lambda x: np.sqrt(x+10) + 1/3 * x , 'sqrt(x+10) + 1/3 * x '),\n",
+    "    (lambda x: np.log(x+10) + 1/3 * x , 'log(x+10) + 1/3 * x '),\n",
+    "    (lambda x: np.tanh(x+10) - 1/3 * x , 'tanh(x+10) - 1/3 * x '),\n",
+    "    (lambda x: np.tanh(x+10) - 1/3 * x + 1/8 * np.sin(5*x), 'tanh(x+10) - 1/3 * x + 1/8 * sin(5*x)'),\n",
+    "    (lambda x: x + 1/3 * np.sin(5*x) + 3, 'x + 1/3 * sin(5*x) + 3'),\n",
+    "    (lambda x: -x ** 2 + 2 * np.cos(5*x), '-x ** 2 + 2 * cos(5*x)'),\n",
+    "    (lambda x: -x ** 2 + 20 * np.tanh(5*x), '-x ** 2 + 20 * tanh(5*x)'),\n",
+    "    (lambda x: -1/10 * x ** 3 + 20 * np.tanh(2*x), '-1/10 * x ** 3 + 20 * tanh(2*x)'),\n",
+    "]\n",
+    "\n",
+    "print(len(fbench_hard))\n",
+    "\n",
+    "# for each, function draw 1000 samples from a uniform distribution and plot the function\n",
+    "function_points = []\n",
+    "x = np.linspace(-10, 10, 1000)\n",
+    "for f, n in fbench_hard:\n",
+    "    y = f(x)\n",
+    "    plt.scatter(x, y)\n",
+    "    plt.title(n)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate multiple choice questions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_29557/1061528540.py:26: RuntimeWarning: divide by zero encountered in log\n",
+      "  (lambda x: np.log(x+10) + 1/3 * x , 'log(x+10) + 1/3 * x '),\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  2\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  3\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  4\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  6\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  7\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  8\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  9\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  10\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  11\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  12\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  13\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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q76KLF2XBvKoBqr/+YkwsZqzKOVUJlyCIOn/+Kdv2zz+rPxYczBiX30KFhYzNmiU7Bi4ujE2cWH3AeudOxoKCZH/zbt1kr0ubvDxZ32bO5PZaKhM6CMKnL3w/F1Kp7P1Qv74s+CRXViYLWjZrpv19pe34ZmYy9sorss+Zj48sMFL5XMXHtGmyYyEPvvKhaxBE375wOZ9Vpc9nUh1dz1WVGTgIUlhYyKZMmcJ8fX2Zra0ta9CgAQsLC2MpKSmKNqNHj2a+vr6Kf2dmZjIAbNmyZeybb75hzZo1Y7a2tqxTp05Kk0MYY+zw4cMMADt69KjS/aNGjWJ2dnbs4sWLSveHh4czNzc3dvv2baX79blW0zUIYqxjo84XX3zBALBLly4p9XHWrFmsXr16zMXFhU2cOFHpd3BNHmdCahqfa2FVJBIJ27BhA2vRogVzcHBgffv2Zf9W+a7Q5ZpNnZiYGCYSiZT6ypWuQRB1Xn75ZeZe5TpJfi2Ym5vLCgoK1L5OPtfMqpSVlbFly5YxHx8fVqdOHfbqq6+yGxyuvbiMqZrk9W0tQOmwzNypU6eQmJiIYcOG4auvvsLEiRORkJCA0NBQPH78uFr7mJgYXLp0CfPnz8eoUaOwdetWDB48WJHvLzc3F+Hh4bhx4wY+/PBDrF69Gq+//rrSMrAnT57g1KlT6NChg9K+/f39sWjRImzevBl//vknAKC4uBhjxoxB69atsXDhQqX2HTt2BABOxYzFYrFSAd2qxXRFIhHEYrHax1VR9zp0sXfvXvTt2xfW1ta8trt9+zZyc3PRqVOnao916dIFZ8+erXZ/x44dkZiYqHNfCRFaTk6OYplqTEwMVq1ahRYtWmD8+PFYuXIlAMDBwQGbNm3CtWvXlJbWRkdHo6CgALGxsUpLOSUSCfr16wdPT098/vnn6NixI+bNm4d58+Yp2hjjXKSPjz/+GF5eXnj77bdVPi6VSnH+/Hm154OMjAzFsn/5uaFq244dO0IsFise57NPdSqff+X5VrWZP38+FixYgN69e+Prr7/GRx99hCZNmuDMmTMatxszZgxWr16N/v3747PPPoODg4MizaAqr776Kh49eoSlS5fi1VdfRWxsLBYsWKDUJjExsdp7xMPDA+vWrcOxY8cUOWGlUinGjBkDZ2fnaunRAgIC4ODgwP89sncv0LGjLH+/JtnZsjzy2ohEgFgs+6/831yKyycmyoqi29g8u8/BQZb26No1oPJy9+hoWeqt2FhAh+XVeunYUZYC58IFze3kx6Hyv6seB12Ok9x778nqvvTvz30bQxk7FnBxkdVU6d2be/0YVcdImydPgFOngKq/i/z9gUWLZKm4np5TUVwsq+HQurUsFY4hyGs1qfpsdOwoe19rIxZrPw6V3yvq2gBAfj6Qmyv7G4wdK7uvTx/tfTAEXfvC93MhEgEbNsjSM1VOkTZvnuxzunGj9pRx2o5v5X7w/axWlZ0NODrKbsbGtS9czmdVGeszaWQTJ07EunXrMGTIEKxduxYffPABHBwcVKZZqmrbtm1YtmwZ3n77bSxevBg3btzAyy+/rFS3IjExESKRCMHBwUrbrlq1Cg0aNMDo0aMVKZq/+eYbHDx4EKtXr4aPj49Se2Ncqxnr2MiVl5cjLy8PN2/exI4dO/DFF1/A19cXLSqlbxSLxRqv183hOBOiK77XwlWpGu+q+hnS5ZpNrqSkBHl5ebhx4wY2bdqEjRs3IiQkBA4ODpz3YSjZ2dmor+Y6qVmzZnB1dYWzszNGjhyJnJwcpce5XjOrIz/ufI4rlzFVTUz++tbcGTsKQ/SjKjKblJTEALAff/xRcZ98JUjHjh1ZeaXZVZ9//jkDoFhZsWPHDq1ps65du8YAsNWrV1d7TCKRsOeee455enqyvLw8Fh0dzaytrdXuz9bWlk2aNEntc0mlUsUyvsrpsPr27atIh7V9+3bWpEkTtnbtWkU6rIULF2pNh6XpdVSmbSVIcXExs7e3V7vSQ9NKEPljlf9WctOnT2cAWGlpqdL9S5YsYQBYTuUZeYQY0fjx45m3tzfLy8tTun/YsGHM1dVV6Tw1a9YsJhaL2fHjx9mvv/7KALCVK1cqbTd69GgGgE2ePFlxn1QqZVFRUczW1lYxa6wmz0Xyfum6EuTcuXPMysqKHThwgDGmegnvvXv3GAC2cOHCatuvWbOGAWCXL19mjDEWHR3NrKysVD5XgwYN2LBhw3jvU5Xp06dXW1o9fPhwrUurAwMDqy07rqrqSpCUlBQGgL333ntK7caMGaN2psy4ceOU2r700kusXr16in8/efKEiUQi9v7776vsw/Dhw5mjoyO7cuWK4ly/c+dOlW1btmzJIiMjn93BZSVIkybaZ9YePy5bLfLxx5rbnT8vS68yefKzdFi//ipLcVTlM1RNo0aMDRmi+rFZs2Qzh48ff/aatO2PMcOsBElMlLX5+Wf1bW7dkqXvGT5cOX1M69aMTZ8uazN9evW0P8OHc0v7s2cPY9bWjF24IPu3sVaCnDgh+5v98ANju3bJUh3Vqyeb/a1tBe327bL33tq1z9JhLVyoPR3WtWuy46/qd5FEwthzzzHm6SlbeRAdLTtO2tKs6roSpKyMsYAAWZ9VrYJZskS2X02/hY4ckW2/cOGzdFhr1yqnw+rbV3a7fl05XdOIEdVXSNjZyZ4TkP0tvvqK32uq3C99V4Lo0hd9PhfffCN7ri1bGDt5UpaOrMq5uhoux3flSlk6rF9/fZYOa/Jk7emwVLl6VfXqCK6EXAnCtS9czmfq6PqZVMcMVoK4urqy6OhojW3UrXaoV6+eUvYD+Srg3bt3K+4bOXKk0m+Iyg4cOMAAsMWLF7Pr168zJycnNnjwYJVt9blW03UliDGPDWOM/fTTTwyA4tapUyd2vtJn+MiRI8zPz48tXLhQkQ5r7dq11dJh1dRxJsRYuFwLV3Xr1i3WqVMnNnz4cKV0WK1bt2bTn35X6HrNJrd06VKlz3CfPn1YVlaWTq9RyJUgx48fZyKRiH1c5Tpp5cqVLCYmhm3dupX99ttvbMqUKcza2pr973//YwUFBYp2XK+ZVTl//jxr3bo1mzx5siId1q+//sr8/Pw0/s24jKkypn4liFGvb2sBftPWicmpHJl98uQJCgsL0aJFC7i5ueHMmTN44403lNq/9dZbsKk0E3TSpEmYPXs29u7dixdffBFubm4AgD179iAwMFCprZy8kG7dunWrPSYWixEbG4vAwEBERkbi9OnTmDNnjspZyPJ95OXlqX19IpEIY8aMwQsvvKCIjPv5+eHgwYPYv38/nJyc0KZNGyQmJqJhw4aYP38+bGxs8PHHH+OVV16Bvb292n1reh18HD58GGVlZYiMjOS9rbwosJ2dXbXH5H0vKSlRelze37y8PHh4eOjSZUIEwxjD77//jldffRWMMaXPc0REBLZv344zZ86gR48eAGQrBPbs2YPRo0ejqKgIvXr1wrvvvqty3zExMYr/F4lEiImJQVxcHA4dOoRhw4YZ9FwklUpx//59pTZlZWV48uRJtXOWq6urynNlZe+++y4iIyMRHh6utg3X84H8v7a2tir3Y29vr9SO6z5ViYqKwoIFCxTfNQ0aNMC2bduQkJAATw2rG9zc3HDhwgVcvXoV//vf/9S2q2z//v0AgHfeeUfp/smTJyM2NlblNhOrFG9+/vnnsWPHDhQWFsLFxQX3798HY0ztef7rr7/G0aNHMXToUFy5cgVvvPEGBg0apLKttu+ratLSZIWZNaxkQW4uMGLEs0LHmjRpIpt13a0bcPSo7L6hQ4GwMFnBa03y8wF133Xz5wN79gCjR8uKgffqJSvGXVlZWfUi1FIp8PixrChuZVxWtKgj76Om4+zpCSxdKnvd8iLfoaGygtHyIoVRUcCCBbLVLgDQoAGwbZusALGmVTnl5cDUqbIZ7wEB/Psv5HHq3l12k3vxRdnfu317YNYs4OnnRaU2bWSrJBo2lP19bWxkxZpfeUW2okSdp+dUle8VsVi2OigwEIiMlK1AmDMHqHpOLSpSLr4tL3pdUKB8DGxsAFdX9X2JiQEuXgTi4gBVq2wrv1fU/Rby85Nt7+8v67tIBEyaBAwaJHutTk6yQuz9+j3bJjAQSEoCDh+uPht/3z7Za7t0CdiyRTbznouCAtkqm8r/BmTHxsnp2f329sr/1kSXvuj6uQCAt94C/vhDVsC+fn2geXNgyRLN23A5vt26AWfOyP6ee/bItvnqK9lnuXFj7a9J7vFj2fvbwQH49FPt7aVSoMp3PMrKZH+nqp9VV1flVXRC9oXL+Uwdrp9JVWrqnC4wNzc3/PPPP7hz5061VQHavPbaa0q/BZ5//nkAwPXr1xX35efnq/29EB4ejrfffhsLFy7Eb7/9Bnt7e3zzzTcq23K9VisrK6u2GlcqleLx48fVfm+omwUtZ8xjAwC9e/dGfHw8Hj58iISEBJw7dw7Flc5Lfn5+iIuLg7+/P2JjYyESiTBp0iQMGjRI8XseMMxxJsSU8LkWlvP09MTSpUsRFhaGG0+/K0JDQ3H27FlFgW5dr9nkhg8fjk6dOuHevXvYs2cPcnJyNF4jyj158gQF8t81le4rKyurdh5zd3dXWs2iTW5uLkaMGAE/Pz/MqHKdNGXKFKV/DxkyBF26dMHrr7+OtWvX4sMPPwTA/ZpZlSZNmmDjxo3o1q0bjj699ho6dCjCwsKQruHai8uYqiYmfX1rASgdlpkrKSnB3Llz0bhxY9jZ2aF+/fpo0KABHj58WO1kBKDaYJSTkxO8vb0VJ9NevXphyJAhWLBgAerXr49BgwZh48aNKCsrq7Yv9jSFVlXNmzfH/PnzcerUKbRp0wYff/yx2v4zxrQuJ+vbt6/KpYH9nl5UtW3bFg0bNqz2eOvWrdG0aVON+5b3QR9xcXHo1KkTpy+XquRfUqqOb+nTQYSqSxDl/eWzvJEQQ7l37x4ePnyIb7/9Fg0aNFC6jX2aoiM3N1fR3tbWFhs2bEBmZiYePXqEjRs3qnwvi8ViNGvWTOm+li1bAoDifCVniHNRVlZWtdezfft2JCYmVrtf2xLSn3/+GYmJiVi+fLnGdnzOBw4ODigvL1e5n9LSUqV2XPepSq9evVQ+3qdPH43p/xYuXIiHDx+iZcuWaNeuHaZPn47z58+rbQ8A//33H8RiMfz8/JTur5xKoaomTZoo/Vv+Y/CBfOD1KXXvEXd3d3z11Vc4f/48XF1d8dVXX8kGwrOzlW8SCafvKyVxcbIBLnUDUsXFwIABsoGoXbu0D366usoGDKtycwO6dtXeH3Xfdba2snQ3mZmyvmzcWH3w96efZAOmlW83bwLLllW/Xx/yPmo6ztbWsgHDquztZQEcQPZfVe/rPn1UD6jLrVghGwCssuScM0MfpxYtZAP4R44AT1OFqNS2rSwAUlXr1gCH30Vq3yvNm8uCKqdOyQItqs6pMTHKr1O+VH/wYOX71VyMAZAdr+++k6X7UZeSjMt7xddXFgCpyscHaNdO9v+VB+jlrKyAvn2r39+7t2ywedo04NdfZe+Tr79W//xygwYpv/bBg2X3d+igfH+lwL9WuvRF18+F3A8/yAbJr16VDb5zSZGh7fh27ao66Na1q+zcxoVEAgwbJgua/fab7O+rTVZW9c/k9u2y4GHV+/mkieDbFy7nM024fCZVqalzusA+//xzpKWloXHjxujSpQvmz5+vNFCvib6/FwDgiy++gLu7O1JTU/HVV1+pHXjneq32008/VftNefPmTSxbtqza/doY+9h4enoiLCwMQ4cOxbp16zBgwAD07dsX2U/TGvr6+sJfxfnYx8cH7eTn46eEPs6EmBKu18KVWVtbI0zFd4W9vT16Pf2u0PWaTc7X1xdhYWEYPnw4tm7dimbNmiEsLExrIOTEiRPVzleJiYnYvn17tfuzsrK09kOuuLgYAwYMwKNHj7Br1y44cZgkMmLECHh5eeHQoUOK+7heM6vi6uqKbiquvdzc3NBVw7UXnzFVVQxyfasG7+tbC0ArQczc5MmTsXHjRrz33nsICQmBq6srRCIRhg0bBqlUynt/IpEIv/32G06ePIndu3fjwIEDGDduHJYvX46TJ0/CyckJ9erVA1D9Q1jZwYMHAQB37txBfn4+vLy8VLZ7+PCh1pktlVUd/Kxq/vz5nPfF5XVwsXfvXsVgL1/e3t4AgLt371Z77O7du3B3d682g1veXz7HjRBDkZ9nRo4cidGjR6ts0759e6V/HzhwAIDsh8fVq1erDXpzZchzkZeXF+Lj45XaLFu2DNnZ2dWCGYGBgRr7OX36dLzyyiuwtbVVnMMePnwIALh58ybKy8vh4+Oj+LyrOx8AUMzu8/b2hkQiQW5urtLFYXl5OfLz8xXt+OxTG/kMGC569uyJjIwM7Nq1CwcPHsT333+PFStWYP369ZgwYQLn/WijLneu/Eehu7s7RCKRxveI/P344MED3Lp1C255ebIBxsoyM/HgwQPOq1oAyOqB9OunepC2vBx4+WXg/HngwAHZoDUfoaGyG1f16j2bka/K02OA0lLZAGfVz2REBFDl84CRI4HwcGDUKO790EbeR67fb02bPlsVow7X921BAbB4MfDOO0BhoewGyFY2MCabpe3oqH7VAVAzx6lxY9n7p7hYVitEGx6/i/D0nKrxvfL0nIo7d2SrKaqeU2fMkL1muZwc2b+/+EI2Y11O3Yzi2Fhg5kzZapw5c9T3g+97ZcwY7W20/MZU0rw5EBwMbN2qPXixfLnyMT13DvjgA9kKjsoTaHjO3tapL3I8zudK28gv4P/9FwgJ4be9tuOrZsWfVm++KVtFsnUr8MIL3Lbx8qr+WV22TBb0rjphQct3vN59keNyPlNF22dSlZo6pwvs1VdfVcyIPXjwIJYtW4bPPvsMf/zxh9YV+dp+LwCy35Wafi+cPXtWMbHn33//xfDhw1W243qtFhERUe235siRIxEeHo5RPP8Oxj42VQ0dOhQfffQRdu3aVa0W3hgt52OhjzMhpkafa+GmTZtqvSbjc82mztChQ/Hdd9/h+PHjiIiIUNsuMDCw2nns/fffh5eXF6ZPn650v7rr8KrKy8vx8ssv4/z58zhw4ADa8rhOaty4sVI2B67XzNqEhoYilOO1F5cxVU0Mcn2rZmIJ7+tbS1CTubeI8FxdXdnYsWOV7ispKWFWVlZKefjkNUG++eYbpbaPHj1i1tbW7O2331b7HFu3bmUA2HfffccYY6y8vJw5ODiwqVOnqmy/bt06BoB98sknzMnJib344osq2926dYtTTQ5D0fY65DTVBPn3338ZAJacnKx2e001QRiT5SJ85ZVXqt3fsmVL9sILL1S7f8KECax+/foa+0xITamoqGDOzs5s+PDhnNqfO3eO2drasrFjx7Lg4GDWuHFj9vDhQ6U28pog6enpSvfv27ePAWA//fQTY6zmz0W61gRBpfyqqm6BgYGKtp06dWKdO3euto++ffuyZs2aKf69Z88eBoDFxcUptTtx4gRDlTpDXPdpSI8ePWLBwcGsYcOGivuq1gT55JNPGAB25coVpW3ltUJU5UytXFOFsWffdZXP1y1atGAvvfSSyn7J31MzZsxgDRs2ZB06dGBPcnMZi49Xuj159IjZ29sr517VVBPkwQNZbvZffqn+mETC2GuvyXLq//67yn4JLiyMseBg1Y+dO8eYrS1jY8fK2jRuzFiVz6RKhqgJsmWLrM2//3Lfr1DktSs03QYN4r9fIfLsVzZkiKzWgEQi3D7lyssZc3BgTN3vonXrZMfhk08Yc3JiTM05VQmfmiA7d8o+F0OGaH99EyYwZuzfQkFBjPn7899OiJogQvWFqzt3GKtbl7HwcMYGDGDM2ZmxGzcM93xcffAB9zpG2uhbE0TIvnCly2dSHTOoCVJVTk4Oa9iwIevRo4fiPnV1L5YtW1Zt+6q/LRYvXsxEIlG136WMMVZUVMSaN2/O2rZty9566y1mZWWl9vpPn2s1XWuCVFWTx0aV1NRUBoB99tlnvPpdU8eZEGPhci1sCnbu3MkAsJ811elTQ5+aIBKJhL322mvMysqK/c7zOkkqlbIGDRqw8PBwxX18rpkNqeqYKmPqa4IY5PpWRX29J0+eVL++rQUoHZaZs7KyqrYMavXq1ZCoSZPw7bff4kmlvMTr1q1DRUWFYobIgwcPqu0vKCgIwLN0KjY2NujUqRNOnz5dbf+ZmZmYPn06hgwZgtmzZ+OLL77An3/+iR9//LFa25SUFABA98o5r2uQptfB1d69e+Hp6am2zgAXQ4YMwZ49e3Dz5k3FfQkJCbhy5QpeeeWVau1TUlIQwnf2HSEGYmVlhSFDhuD3339HWlpatcfv3bun+P8nT55gzJgx8PHxwapVqxAbG4ucnBxMnTpV5b6/rpTagzGGr7/+GjY2NujTpw8A8zkX7dixo9rttddeAwD8+OOPWLFihaLt0KFDcerUKaXXlJ6ejsOHDyudD1544QW4u7tj3bp1Ss+1bt06ODo6IqpSHQqu+xRS5fzOgCz1YosWLTQuA5bPMlq7dq3S/atXr9arLyEhISrfIw8fPsSECRPQpUsXLFmyBN9//z3OnDmDJevWydKTVLpdvH4dpaWl3N8j8pm5qmrATJ4M/PwzsHatbDVITQgJkdUoqXr8nzyRzZD38QFWrZLNxM7JkdXFMIaUFFnarzZtav65PTyAHTuq33r3lqWm2bFDVoujplQ6dyqcOwf8+afsfcUjpzJnNjay9G2qfhdlZgLTpwNDhgCzZ8tWdvz5J6DinKqT48dlaYR69pTNotf2+lJS+K9E0EVFheqVMcnJstUQevz+M6u+vPmmrGbEDz8A334rS+M0frz61Gk1Ydky2ftw9mygSm7wWtEXQ38mTYxEIqmW6tnDwwM+Pj6cU4xoExISAsaY4ndhZTNnzkRWVhY2bdqEL7/8Ek2bNsXo0aNVPndNX6sZ89jk5eWpTMny/fffAwDva2RTPs6E6IvvtXBNuKfq9yaAH374ASKRCB3kaU1ryOTJk/Hzzz9j7dq1eFnDdZKqfq9btw737t1TpM0H+F0zC4XLmKq+eF/fqqjjdvHiRX7XtxaC0mGZuQEDBmDz5s1wdXVFQEAAkpKScOjQIUWamKrKy8vRp08fvPrqq0hPT8fatWvx3HPP4cUXXwQAbNq0CWvXrsVLL72E5s2b49GjR/juu+/g4uKC/pXyMg8aNAgfffSRojgPIBukHDduHBwcHBQnmbfffhu///47pkyZgrCwMKXlZvHx8WjSpAmCg4MNdXi0UvU6AKCgoEAx8CbP9//111/Dzc0Nbm5uioLNcXFxiIyMVJlH7+uvv8bDhw9x584dAMDu3btx69YtALKTu+vTgqCzZ8/Gr7/+it69e2PKlCkoKirCsmXL0K5du2pptnJzc3H+/HlER0cLfCQI0d2nn36KI0eOoGvXrnjzzTcREBCA+/fv48yZMzh06JBiSerixYuRmpqKhIQEODs7o3379pg7dy7mzJmDoUOHKp1j7O3tsX//fowePRpdu3bFvn37EBcXh9mzZyvlRjaHc9Fgef73SlJTUwEAkZGRSsv433nnHXz33XeIiorCBx98ABsbG3z55Zfw9PTE+++/r2jn4OCARYsWITo6Gq+88goiIiLw119/YcuWLfjkk0/g7u7Oe59CCggIQGhoKDp27Ah3d3ecPn0av/32m1Kx+6o6duyIIUOGYOXKlcjPz0e3bt1w7NgxXLlyBYDuOZ8HDRqEzZs348qVK4q6MoCsoF5+fj4OHToEKysr9OvXDxMmTMDixYsxaNAgpTRn8fHxcHR0RN++fWUpkwDgwgXZfzdvBv7+W/b/8vQ9cXHAc89VL/y8cqUs+BESIkuttGWL8uMvvQTUqaPT69Ro0CBZfYVjx5QDM4sXA6mpssLIzs6yottz58pex9Ch6usxcFVQAMiDWPK8+l9/Lcv17+ZWPXVPfDwwcKDmOg+G4uj4rFZDZTt3ygaZVT1mSK+9Jqu50L27LEBz8aJs8NnRkVvhZ10NGgR89JEsHZj8dxFjwLhxsv7ILyLffhv4/XfZgG9YmO6pnADgv/9khd9FItn77tdflR9v3152k8vNlaWSq4nfQkVFshRkr70mC87VqSMLOGzcKPt8c63BYM592bhRdk6LjQUaNZLdt3q1LH3SunWyFHI1bccOWeq1//1PVvel6rm0b1/txd7NuS+G/kzy9fXXwMOHspRcALB7N/D0mgeTJ1f/LtTBo0eP0KhRIwwdOhSBgYFwcnLCoUOHcOrUKa0117h67rnnUK9ePRw6dAgvVEpndvjwYaxduxbz5s1TDAhu3LgRoaGh+Pjjj/H5558r2hrjWs2Yx2bLli1Yv349Bg8ejGbNmuHRo0c4cOAA4uPjMXDgQKW22pj6cSZEX3yuhWvKJ598ghMnTqBfv35o0qQJ7t+/j99//x2nTp3C5MmTNdZmFNrKlSuxdu1ahISEwNHREVuqfJ++9NJLqPP0OsnX1xevvfYa2rVrB3t7e/z999/Yvn07goKClFLw8blmFgrXMVV9CH59W5sYbQ0KEcSDBw/Y2LFjWf369ZmTkxOLiIhgly9fZr6+virTYR07doy99dZbrG7duszJyYm9/vrrLD8/X9HuzJkzbPjw4axJkybMzs6OeXh4sAEDBrDTp08rPW9OTg6ztrZmmzdvVty3atUqBqDasrWsrCzm4uLC+vfvr7hPIpEwb29vNmfOHIGPCD+qXgdjz5YHq7rJlxE/fPiQWVtbs19UpTthsuXM6vZRNbVWWloaCw8PZ46OjszNzY29/vrrLDs7u9o+161bxxwdHVlhYaEgr58QoeTk5LDo6GjWuHFjZmNjw7y8vFifPn3Yt99+yxiTpTSytrZmkydPVtquoqKCde7cmfn4+LAHDx4wxmTL9evUqcMyMjIUnwtPT082b948JqmSIqUmz0W6psNSRd1yV8YYu3nzJhs6dChzcXFhTk5ObMCAAezq1asq9/Ptt9+yVq1aMVtbW9a8eXO2YsUKJpVK9dqnEBYvXsy6dOnC3NzcmIODA2vdujX75JNPWHl5uaJN1XRYjDFWXFzMoqOjmbu7O3NycmKDBw9m6enpDAD79NNPq23LZblwWVkZq1+/Plu0aJHivl27djEAbPny5UrbFxYWMl9fXxYYGKjU165du7KRI0fK/qEpXRJjjEmljHl4MPb559UPzOjRmrcXMj1OVe3bMzZ+/LN/p6TIUnZV+UyyigrGOndmzMdHltZLHS6pUzSlmKqUkoMxxtilS7L7Dx3i/JJqxOjRjNWpo/v2uqaYWbWKsS5dGHN3l/2dvL0ZGzmSMQN+bhljjOXkyJ6v8u+iVatkf5uqaQmyshhzcWGs0jm1Gi7psOTpodTdqh6/desYc3RkrCZ+C5WVMTZliuzz4+LCmI2N7G86frzun1dd02EZoi/a3LzJmKsrYwMHVn/spZdkn43r1w3z3JrMm6f5PcMl/VpVuqbDMkRftNHnM6mOPumwfH0N/r1WVlbGpk+fzgIDA5mzszOrU6cOCwwMZGvXrlVqp0/KJ8YYe/fdd1mLFi0U/5b/LlCVTmTq1KlMLBazpKQkxX36Xqvpkg7LWMeGMVna51deeUUxdlCnTh3WoUMH9uWXX6pMv6JOTR9nQmoan2vhmnTw4EE2YMAA5uPjw2xsbJizszPr0aMH27hxo8prSi50TYclT4nNZQxtwoQJLCAggDk7OzMbGxvWokULNnPmTLXnBK7XzELgOqZa9Txr1OvbWkTEmDHXMRNzNn78eFy5cgV//fUX72137tyJESNGICMjQ1Ec3Fh0fR2//PILXn/9deTl5SlWdRhacHAwQkNDldLnEGJpxowZg99++w1FRUWc2lvKuYiolpqaiuDgYGzZsgWvv/66TvtYtGgRNm7ciKtXr6otNqfp+Tt06IAzZ84oljJrlJwMdO0qWykSEKBTfw1i82bZzPmsLNkqDFPz3nuylEgpKcZZCUKeGT8euHIF0OGcWiOCg4HQUIB+CxFCBHb9+nW0bt0a+/btU6Rf5cOSr9X0PTZCsuTjTAghXNTo9a0FoSAI0VlWVhZatmyJhIQE9OjRg9e2ISEheP7555WWtRqLrq/j4MGDyMjIwKRJkwzYu2f279+PoUOH4vr16/Dw8KiR5yTEGPgGQSzlXESAkpISODg4KN03ZswYbN68GTdu3EDjxo112m9RURGaNWuGFStW8A6kDBs2DFKpFL/88gu3DZKTZemlarJ+BBdSqSyl0PDhsnRHpiQ/H/D1BX75Rf8UXER/WVlAy5ay9zHPc6rB7d8vS5l1/bosTRghhAhs0qRJuHbtGuLj43ltVxuu1XQ9NkKqDceZEEK0qdHrWwtCQRBCCCEmhW8QhFiOBQsWICUlBb1794a1tTX27duHffv24a233sI333xj7O4RQgghhBBCCCHEDFFhdEIIIYSYhO7duyM+Ph6LFi1CUVERmjRpgvnz5+MjU1u5QAghhBBCCCGEELNBK0EIIYQQQgghhBBCCCGEEGKRxMbuACGEEEIIIYQQQgghhBBCiCFQEIQQQgghhBBCCCGEEEIIIRbJLGqCSKVS3LlzB87OzhCJRMbuDiHERDHG8OjRI/j4+EAstpwYL50DCSFcWOI5kM5/hBAuLPH8B9A5kBDCDZ0DCSG1GddzoFkEQe7cuYPGjRsbuxuEEDNx8+ZNNGrUyNjdEAydAwkhfFjSOZDOf4QQPizp/AfQOZAQwg+dAwkhtZm2c6BZBEGcnZ0ByF6Mi4uLkXtDCDFVhYWFaNy4seKcYSnoHEgI4cISz4F0/iOEcGGJ5z+AzoGEEG7oHEgIqc24ngPNIggiX/bm4uJCJz5CiFaWtlSWzoGEED4s6RxI5z9CCB+WdP4D6BxICOGHzoGEkNpM2znQcpIFEkIIIYQQQgghhBBCCCGEVEJBEEIIIYQQQgghhBBCCCGEWCQKghBCCCGE1DJLly5F586d4ezsDA8PDwwePBjp6elKbUpLSxEdHY169erByckJQ4YMQU5OjlKbrKwsREVFwdHRER4eHpg+fToqKiqU2hw9ehQdOnSAnZ0dWrRogdjYWEO/PEIIIYQQQgghRMEsaoLwUVIuwdw/z+NgWg7KKqSo52SH17v6YsLzzWBrTTEfQkwdfYYJIQSQSBmSM+8j91EpPJzt0cXPHVZi4fI8Hzt2DNHR0ejcuTMqKiowe/ZshIeH4+LFi6hTpw4AYOrUqYiLi8Ovv/4KV1dXxMTE4OWXX8aJEydkfZRIEBUVBS8vLyQmJuLu3bsYNWoUbGxssGTJEgBAZmYmoqKiMHHiRGzduhUJCQmYMGECvL29ERERIdjrIYTUnKLSCkzeegqJ1+6jjGlvLwbg4mCDfm29MG9gGzjYWhm8j7WRob83appEyvB3+j18+/d1FJY+QWAjN3wUFSDI+0ciZTh+KRfLE66goOQJWnk5Y+VrwXCyN9/hEUO/ppJyCRbuScPJ6/dhayXGS8ENMe45874+M+RrKq+QYuOJ64i/mAuAITzAC2N6+Jn18SKEEEMrKZdgcdwFnL9VAFcHG7z5fDM8978Ggv2eETHGOPx0Na7CwkK4urqioKBAYzGkN3889fRLRrU6dlaIDm1Bg6mEmJjyCim+++saVh26inKJ+nZv9/TDrP4Bah/neq4wN5b6ugghqu1Pu4sFuy/ibkGp4j5vV3vMGxiAfm291W6nz7ni3r178PDwwLFjx9CzZ08UFBSgQYMG2LZtG4YOHQoAuHz5Mvz9/ZGUlIRu3bph3759GDBgAO7cuQNPT08AwPr16zFz5kzcu3cPtra2mDlzJuLi4pCWlqZ4rmHDhuHhw4fYv3+/1n7R+Y8Q45P/TtucmInsRxXaN9BCDKBXqwZYPbyDYAO0lnqu4Pq69qfdxfw/LyC7sExxn5eLHea/2Ebj94ap2p92F+9uT0V5hbTaY2H+DfD96C567Ttm21lUSKsPg7Rr6Izdk3vqvG9jMfRrmrDpFA5dUj3Oou36zFRpGjvS9zUt3XsR3xzPNMi+1ant50BCiPkbsyEZR6/cq3a/nbUYq4YFCXIdbDGRAG0BEAAoLpPg8wPpaDlnHzosOojDl3IhUfFDgRBieBIpw5ELOeiw4ABaztmHZQc0B0AA4JvjmVi692LNdJAQQoxgf9pdTNpyRikAAgDZBaWYtOUM9qfdNcjzFhQUAADc3d0BACkpKXjy5AnCwsIUbVq3bo0mTZogKSkJAJCUlIR27dopAiAAEBERgcLCQly4cEHRpvI+5G3k+6iqrKwMhYWFSjdCSM1T9TtNiAAIAEgBHEm/h7bzD6Dtx/tQVCrMfmur/Wl3MXHLGaUACABkF5ZhogG/NwxF/npUBUAA4NCle3jx67/02reqYAEA/Hv7EXotO6zTvo3F0K/pxa//UhsAAczz+kzb2JE+r0lTAETffRNCiKUpr5BizZEraPphnMoACACUVUgF+z1jEUGQknKJ1gBIVfeLn2DcplNoPnsvXv82ESXaRl8JIYIoKq3AgFXH0Hz2XozdfBr3S/hd+H73V6baiyJCCDFnEinDgt0XoWoYQ37fgt0XBZ/AIZVK8d5776FHjx5o27YtACA7Oxu2trZwc3NTauvp6Yns7GxFm8oBEPnj8sc0tSksLERJSUm1vixduhSurq6KW+PGjQV5jYQQbsorpJiy/YzOv9P4KnoiNZtgyPHjxzFw4ED4+PhAJBJh586dGtsfPXoUIpGo2k1+fhSCRMrw4R//amwz7ZdzZjPxTyJleO+nM1rbnb9ViF2pt3nv+4Nfzmpt919+CXac4bdvYzH0a/rzzC2cv6V9MsK3x83n+ozr2JEur6m8QqoxACL3jRkdL0IIEUJJuQTTfzuLdnP3oumHcYqbfKINF/P/1P862CKCIEv0jKSfuP4A/nP3o9VHe7H2yDX6QiJEYPLobovZcWg7/wDS7hbpvC8pAzYn3RCuc4QQYiKSM+9XWwFSGQNwt6AUyZn3BX3e6OhopKWlYfv27YLuVxezZs1CQUGB4nbz5k1jd4mQWkEiZZj442m0nLMPu1JrfuWAPBjS67NDJjtgX1xcjMDAQKxZs4bXdunp6bh7967i5uHhIVifTmbk4+HjJxrbPC6XYHUCtwEGY0u8lodSjnMTp/92ntd75eT1fBSVc2s//TfzCBwZ8jVJpAzv/3aOU1sGYFOi9sF/U/D25lOc2unymj7keLwA4MPfubclhBBzIl9N3O/LI2jxNNjhP3c/fj19B484fmepkl2o/3Ww+Vb+quRG/mNB9lMmYfj8QDo+P5COxnXtsW9KL7MujkaIsRWVViBy5THcfKh+UE8X/90X5jNPCCGm5NBFbrODcx8Jd06NiYnBnj17cPz4cTRq1Ehxv5eXF8rLy/Hw4UOl1SA5OTnw8vJStElOTlbaX05OjuIx+X/l91Vu4+LiAgcHh2r9sbOzg52dnSCvjRCinUTK8OWBdKw5lmHsrgAA/ntQhuaz9+LrYUEYENTQ2N1REhkZicjISN7beXh4VFtVJ5Sk63mc2q0/loHJff5n8oXSfz9zi3Pb8gopTmbko8f/6nNqn5jB7VgBQIWU8dq3sRjyNZ28no8nPOaG7j53B2/2bM59AyOQSBn+vpbPuT2f1ySRMuw6d4f7vs/fxbJXgkz+M0kIIVyVV0gx/bdUg06m0fc62CJWgjSt5yj4Pm8+KEXb+QfQ7MM4fPj7eUqXRQhH8mVuzT6UrfoQOgACAL7uwn/mCSHEmCRShh0cU3t4ONvr/XyMMcTExGDHjh04fPgw/Pz8lB7v2LEjbGxskJCQoLgvPT0dWVlZCAkJAQCEhITg33//RW7us7QS8fHxcHFxQUBAgKJN5X3I28j3QQgxnh1nbqP57L0mEwCpLGZ7KsZtPGnsbggiKCgI3t7e6Nu3L06cOKGxLf+6SNwGUEufBgxM3b+3Cni15xoEAsB79uiJDNW5yU3J7QfV00pqwuc1JfF8v1zOLjL51TMnr+eDTxcv3n3E+TWdvJ4PCY99P5EwwVf2EkKIsSz480KNrCbW9zrYIoIgs/sHGGzfUgDbT92E/9z96LjwgMnnqiXEWAoeP0H7efsVy9wMlVROLALeCGlqoL0TQohxJGfex/1izSlNAKBeHVt08XPX+/mio6OxZcsWbNu2Dc7OzsjOzkZ2draiToerqyvGjx+PadOm4ciRI0hJScHYsWMREhKCbt26AQDCw8MREBCAN954A+fOncOBAwcwZ84cREdHK1ZzTJw4EdevX8eMGTNw+fJlrF27Fr/88gumTp2q92sghOimpFyCgLn7MPWXVGN3RaPD6fnoseSgsbuhM29vb6xfvx6///47fv/9dzRu3BihoaE4c0Z9zQu+dZFCmtfj3B9TH9SXSBn+yy/mtQ3XAW2JlOHsfw947fuOASZyCe32A36r40/xGHRnKiuUqVcukZr8oD7fwI589Ywh9g0A2QX8gliEEGKKOi6Mx8bEGwZ/HndH/a+DLSII4mBrhb4BwuVWVSf/cQXazj+AFrPiqHYIIXhW66PZrDgELjyIwjLDr5h683k/2FprPnUtX74cnTt3hrOzMzw8PDB48GCkp6crtQkNDa1WqHLixIlKbbKyshAVFQVHR0d4eHhg+vTpqKhQDoQePXoUHTp0gJ2dHVq0aIHY2Nhq/VmzZg2aNm0Ke3t7dO3atVr6GEII4bq0d1CQjyCpE9atW4eCggKEhobC29tbcfv5558VbVasWIEBAwZgyJAh6NmzJ7y8vPDHH38oHreyssKePXtgZWWFkJAQjBw5EqNGjcLChQsVbfz8/BAXF4f4+HgEBgZi+fLl+P777xEREaH3ayCE8FNeIUWf5UfgP3c/Hpebx3XM7cIn8J8TZ+xu6KRVq1Z4++230bFjR3Tv3h0bNmxA9+7dsWLFCrXb8K2L1K1ZPdhwvKI39UH9k9fzUcFzIYGrg43B9l3yxLQzQUikDBfuaC9aXlnanULOKxtc7Lkd28pMfVCfb2AH4L7aSJd95xWV8d6GEEJMSfMP45D/uLxGnmvx4LZ6XwdbTMGL70Z1xps/nkL8xVztjfVUwUC1Q0itZqhaH9q83dMPszis/Dpx4gSio6PRuXNnVFRUYPbs2QgPD8fFixdRp04dRbs333xTabDO0fFZmi2JRIKoqCh4eXkhMTERd+/exahRo2BjY4MlS5YAADIzMxEVFYWJEydi69atSEhIwIQJE+Dt7a0Y4Pv5558xbdo0rF+/Hl27dsXKlSsRERGB9PR0QQtjEkLMW30nbnUw+vh7CvJ8jGm/WLe3t8eaNWs0FgH29fXF3r17Ne4nNDQUZ8+e5d1HQohwFu6+gA0nbhi7GzopqQBaf7wPlxfxr8dharp06YK///5b7eN86yJZiUV4obUHDnC4BvZ20z+VoiHpMpP+7E1uqzt02XdSRj4kUmayNRuSM+/jMZ+iHQBKnshWa3BZQXQ2i9/KGQC4X1wzA2G60iWww3W1kRvHgFxlD0u0rwAmhBBT5fdhnA7hX9283dMP/dt7670fixq5/25UZ5SUSzBozV+4ksNvKa2u5LVD7K1E+OejvnB15P/lR4g5kEgZjl/KxaSfUlDKdyqVnjo3dcXWCd21rgCR++OPP+Di4qL4d2xsLDw8PJCSkoKePXsq7nd0dFQU763q4MGDuHjxIg4dOgRPT08EBQVh0aJFmDlzJubPnw9bW1usX78efn5+WL58OQDA398ff//9N1asWKEIgnz55Zd48803MXbsWADA+vXrERcXhw0bNuDDDz/U6XgQQiwQ19OqaafbJoSYGImUoduSQ7hXZNqDk9qUPpFiwFd/Yc+7zxu7K3pJTU2Ft7f+F/GVdWpaj1MQJLfQtGed6zKT/u+r3AIVuuy7sLSCc8DAGLILdZuMxmW1hkTK8NdV7vVW5NwcbXXpUo3RJbDDdbXRLZ6pyQCAw3wUQggxSa0+qrkAyNoRwejf3keQfVlUEASQpcY6ODUU5RVSfPfXNaw5fA2Pnxj+T1MqYQhceBDWIuDbUZ3Rq1UDk501QggfEinD8n2Xsfav6zX6vHXsrBAd2gITnm/GOfihTkGBrMiiu7ty/sCtW7diy5Yt8PLywsCBA/Hxxx8rVoMkJSWhXbt28PR8Nus6IiICkyZNwoULFxAcHIykpCSEhYUp7TMiIgLvvfceAKC8vBwpKSmYNWuW4nGxWIywsDAkJSWp7W9ZWRnKyp5dqGovikkIMXd5xdwGp7i2I4SQXam3MWV7qkH2LRYBrb2c8UF4a43XPfJJNJ8duIjLufwHCStLu1OIotIKo63ALyoqwrVr1xT/zszMRGpqKtzd3dGkSRPMmjULt2/fxo8//ggAWLlyJfz8/NCmTRuUlpbi+++/x+HDh3HwoLB1Tuo7c1s5knAp16RXNugyk76ojFugQpd9A6ad3um+jqmUuKzWSM68j+Jy/unAHtZQShRd6BrY4bLaSCJl+OPsbd77ztExkEUIIcYUNH8faiATPprUtcWR6WGC/m6xuCCInK21GNG9WyK6d0tFQGTVoavQ4buclwoGjNt0CgAwuL03Pn81SO8BXEKMobxCium/pWJX6t0ae047KxGmhLUUJPAhJ5VK8d5776FHjx5o27at4v4RI0bA19cXPj4+OH/+PGbOnIn09HRFvvvs7GylAAgAxb+zs7M1tiksLERJSQkePHgAiUSiss3ly5fV9nnp0qVYsGCB7i+aEGJ2uKbD4tqOEFJ7SaQMfZYfxY18/YIOVenyO81KLELvNp7o3Ub2W6iotAKdFsejVMfailN/PovvRnfWaVt9nT59Gr1791b8e9q0aQCA0aNHIzY2Fnfv3kVWVpbi8fLycrz//vu4ffs2HB0d0b59exw6dEhpH0LwcuGW5uphyROTXtngXke37zcugQpd923K6Z10XXXBZTtdV5mY8koQXQM7XFYbJWfex6NS/vs29cAkIYRU9dyn8XhYati6coYsO2GxQZDKKgdESsolGBd7EknXHxr8eXeev4ud5++ilWcd7Ix+Hg62VgZ/TkL0VdMp5UQAXuvcGPMGtjHIZyQ6OhppaWnV8i+/9dZbiv9v164dvL290adPH2RkZKB58+aC94OPWbNmKS6sAdlKkMaNGxuxR4QQg6N0WIQQARhi9YePiy0SPnhBkN9pTvbWuLw4EjvO3MbUX1J5b5/1wHgz80NDQzXWU4qNjVX694wZMzBjxgwD9wro4ucOV3trFJRWaG1ryisbkjL4z9IHuAUqdF2h4G7CEw90fU1cttN1lYkprwTRNbDDZbWRrvs29cAkIYRUtmD3v7j1UPjzvI1YBA8XO7ze1VfQCdGq1IogSGUOtlb46a0eiqXZ7/+Wivsl2n8w6iM9pxj+c/ejnqM1js3oQ0XUiUkqKq1Ar88PI/9xzRRoa+Bkg0PTehu0jk5MTAz27NmD48ePo1GjRhrbdu3aFQBw7do1NG/eHF5eXkhOTlZqk5OTAwCKOiJeXl6K+yq3cXFxgYODA6ysrGBlZaWyjbpaJAD/opiEEPNH6bAIIfoa8NVfSLsjXApNIYMfVb3UoSFeDPJB10/ikVfM/bdnk7oOgvfF3FmJRegb4InfzmhPx2OqKxskUob4iznaG6rAZfWBrisUPEw4CGLIlSCG3Lex6BrYAbQHDw25b2Np2rQp/vvvv2r3v/POO1izZg1CQ0Nx7NgxpcfefvttrF+/XvHvrKwsTJo0CUeOHIGTkxNGjx6NpUuXwtqaxsMIMTflFVJsPJGlvaEGNRnsUKfWnn3kS7PPtIlQpMtaEX8VOq7M5iT/cQXazj8AJxsxTn7Ul4IhxCQUPH6CbksOocSQb/6nrEXAtPBWBj/hMcYQExODHTt24OjRo/Dz89O6TWpqKgAoilWGhITgk08+QW5uLjw8PAAA8fHxcHFxQUBAgKLN3r17lfYTHx+PkJAQAICtrS06duyIhIQEDB48GIAsPVdCQgJiYmKEeKmEEAtB6bAIIbqSSBnazN2H0gphlooZMvhRmZVYhNMfh2NcbDIOX77HaZsVrwUbtE/mqsf/GnAKgpjqyobkzPucVrKowmX1ga6rTGDCWYp0XXWRlJGHIR01Tw4z5L6Nxb2O7gEabcFDQ+7bWE6dOgWJ5FmKr7S0NPTt2xevvPKK4r4333wTCxcuVPxbXlsTACQSCaKiouDl5YXExETcvXsXo0aNgo2NDZYsWVIzL4IQIpjnPz+k03amlhmJRuGhnC6rqLQCkSuP4eZDwxWpKnoipWAIMTp9czLz0dbbGdvf7l5j7/X3338fv/32G3bt2gVnZ2dFDQ9XV1c4ODggIyMD27ZtQ//+/VGvXj2cP38eU6dORc+ePdG+fXsAQHh4OAICAvDGG2/g888/R3Z2NubMmYPo6GjFKo2JEyfi66+/xowZMzBu3DgcPnwYv/zyC+Li4hR9mTZtGkaPHo1OnTqhS5cuWLlyJYqLizF27NgaORaEEPOQnJnPrSGlwyKEVLL73B1M/umsIPtytBEh5eOIGr9Q3TCmC6fX0b6RC103qcF1xYKprmzIfaT7tbe21Qf6rDLJNeHC1bquujjEoQ6FIfdtLB4ca+eooi14aMh9G0uDBg2U/v3pp5+iefPm6NWrl+I+R0dHtdkNDh48iIsXL+LQoUPw9PREUFAQFi1ahJkzZ2L+/PmwtTXdVUOEEGV/nrmFnEL+GWOuLI40uRrZptUbE+Bkb42/PuyDK4sjMT3ifzDk30seDGn78T4U6TjzhRC+ikor0HbufrSdf8CgARA7K2DD6M7IWNIfe6b0rNGL1h9++AEFBQUIDQ2Ft7e34vbzzz8DkK3QOHToEMLDw9G6dWu8//77GDJkCHbv3q3Yh5WVFfbs2QMrKyuEhIRg5MiRGDVqlNJsFz8/P8TFxSE+Ph6BgYFYvnw5vv/+e0RERCjavPbaa/jiiy8wd+5cBAUFITU1Ffv3769WLJ0QUntJpAybEqunHFCF0mERQuTGbkwWLACycmh7XFzU32gz9QYG+iBjSX80reeo8vH2jVzwZ8zzNdwrM8J1zNn0xqYB6LfKUdsqD31WmZjqLH1Aj5ogT+tQaKLryhku+zYaPSaRaA0eGnLfJqC8vBxbtmzBuHHjIBI9O4ls3boV9evXR9u2bTFr1iw8fvxY8VhSUhLatWundM0bERGBwsJCXLhwoUb7TwjRnUTK8O4v53hvl7Gkv8kFQABaCaJWTa4OoZUhpCYUlVag25JDKCqXaG+sh8Z17bFvSi+jvo8LCgrg4uKi9vHGjRtXy2Gqiq+vb7V0V1WFhobi7FnNAxAxMTGU/ooQolZy5n08LOE2u8bDWffZhoQQyyCRMgQtOIBHZfr/pvOta4fD0/uYxMxtK7EIR6f3RlFpBab+fBZZD0rQpK4DVrwWTNdHWnBdsWCyKxv0GETWtvpAn1Umtx6aZr0GALj14LH2RmpoOib6rJzRtm9j0msSiZbTY64eNUFMNTBZ2c6dO/Hw4UOMGTNGcd+IESPg6+sLHx8fnD9/HjNnzkR6ejr++OMPAEB2dna1SX/yf8uzNKhSVlaGsrJnx7OwULg6V4QQ/qK3nea9zdoRHUzid6Uq9GuSA/nqEEPXDpEHQ+raWyFxdl+TyZlGzFtNBD9qqtYHIYRYIq4DBm6ONuji527g3hBCTJmQ6a++ejUQL3Ywvfz9TvbW+G50Z2N3w6xwXbFgqisb9BlElq8+CGleT+Xj+qwy+TP1DuZEBZjcYI5EyrDr3B2dt69fR/0x0WfljLZ9G5M+7wNtwUN9CqPn6RNAqSE//PADIiMj4ePjo7jvrbfeUvx/u3bt4O3tjT59+iAjIwPNmzfX+bmWLl2KBQsW6NVfQogwyiuk2J+Wy2ub8c/5oX97bwP1SH+8RyuPHz+OgQMHwsfHByKRCDt37tTY/o8//kDfvn3RoEEDuLi4ICQkBAcOHNC1v0YlXx1ybUkU0uZHwMnWMIO9D0ol8J+7H32+OIzyGqjXQCxTeYUUXZfEo+38AwYLgNRztEba/AhcWxqFd3q3oAAIIYTogOuF+ZiQpiY3EEMIqTlCpb/yrWuHjCX9TTIAQnTDta6AqdYf0GcQGdAymUCPVSb5xeUmmd4pOfM+7hfzz8+uoOGnhN4rOUz0Zwrn2msqGLIwuqkGjeT+++8/HDp0CBMmTNDYrmvXrgCAa9euAQC8vLyQk6O8okj+b3V1RABg1qxZKCgoUNxu3rypT/cJIXp44/uTvNoHNXLBxwMCDNQbYfAesSwuLkZgYCDWrFnDqf3x48fRt29f7N27FykpKejduzcGDhyoNX2MqXOyt0bawkikzY9APUcbgzxHRl4JWs7Zh1fW/03BEMJZeYUUr6w/gZZz9iGn0DCzvXxcbHBpYT+kzI2g9ASEEKIvjgM0nZvSKhBCaiOJlKHdvP04kn5P73199Wogjs0Mo4CqhTH3wuj6DCIDmgeS9UpVBNNM76RvnzStbNBnxQRgmisb+NReU8WQhdFNNWgkt3HjRnh4eCAqKkpju9TUVACAt7dsBnhISAj+/fdf5OY+m0UeHx8PFxcXBASoHyS1s7ODi4uL0o0QUvPKK6T458YDXtv8/s5zBuqNcHiPXkZGRiIyMpJz+5UrVyr9e8mSJdi1axd2796N4OBgvk9vcpzsrZEyNxwl5RIMWvMXruQUC/4cp24UoOWcfYho0wBrX+9MFy1EJYmUIXpLCvbrkcNVm1aedbAz+nlK1UYIIQLimqeaiqITUvsIlf7KlGp/EAMw88Loeg0iAxpfl76rTExxpr6+gQqNKxv0WDkDmObx4lN7TRVtwUN9VpkkXMpBjxb1dd7ekKRSKTZu3IjRo0fD2vrZ0GFGRga2bduG/v37o169ejh//jymTp2Knj17on379gCA8PBwBAQE4I033sDnn3+O7OxszJkzB9HR0bCzM733CCFEGd9VIKuGBZnFb8waz10jlUrx6NEjuLurn81YVlaGwsJCpZupc7C1wsGpobiyOBKDggyT/+zAhXtoPnsvdp6mJYFE2Y4zt9F89l6DBUAGt/fGlcWRODA1lAIghBAiMK6DGfoOehBCzMu4WGHSX9HqD8vHdfZ9wiXDTZbSi54D75pWNui7ysQkA0d6Hi9NKxv0nnBhgsdL35Uzp26oT4mm7yqTXal3IJHq+Qc1kEOHDiErKwvjxo1Tut/W1haHDh1CeHg4Wrdujffffx9DhgzB7t27FW2srKywZ88eWFlZISQkBCNHjsSoUaOwcOHCmn4ZhBCe+K4C8Xaxw6CghgbskXBqPI/NF198gaKiIrz66qtq25hzMSRbazFWDeuAL19l+PJAOtYcyxD8Od777Tzm7knD6TkRVIOhlispl6Dj4oN4XC58ujQ7KxHWjeyEXq0a0EUzIYQYEtdrX9O8RiaEGMBznybg1kP9Bu7EAK4u6U+/42oBD2duKyl2pd7BRyZY6FvfgXdNKxv0XWViiumd9D1emlY2WGI6LK6fD3Vik25gcp//qfzc6LvKRF53JqR5PX26aBDh4eFgrPqPz8aNG+PYsWNat/f19cXevXsN0TVCiAFtSrzBq/2xGS8YpiMGUKMj6Nu2bcOCBQvwyy+/wMPDQ207SyiGZCUWYXpka2Qs6Y+Nb3SCvbWwPzQLS6VoOWcf3t6cbLIzB4jhSKQMg1f/Df+5+wUPgNSxESFtfgTSP+mPF/w9TO4iiRBCLA2lwyKEyJVXSNFidpzeARAfF2tc/zSKfsfVEl383OFeR3udSlMt9H0jT7+U0hprNlhgeie9V4ZqOi1Y4PHq6FsX+pwKHz5+ovZzI0TNGFOsO0MIqb22nLzBuW1Xv7pmNTm/xnq6fft2TJgwAb/88gvCwsI0trWkYkhWYhF6t/HE5cX9kTY/Ak4CpxKiFFm1jzz1VertAkH362QjRtr8CFxY1J+KnRNCSA2idFiEEABYuPsCWs7Zhwo957eM7dEEibMjhOkUMQtWYhFe4piKwtQGXCVShp+Ss/Tah6aVDYcv65kCzBTjiHoGKjSlRbPEdFgp/z2AvvNG1X1uhPhtZoqBI0JI7VReIcV/90s4t988vpsBeyO8Ghnp/OmnnzBu3Dhs374dUVFRNfGUJsnJ3hppC/uhqLQCnRbHo1TfK5xKKEWW5SuvkKLzJ/EoKKkQdL9ONmKc/KgvBT4IIcRIOBfUpIWfhFis5z87jJsPuF90qmItAi4uiqRrgVoqLMALP5y4obWdvqmBhJaceR/ZhYYZeJdIGXak3tZr16aY3knfQIWmtGiWmA5LiMCf2kCFEL/NTDBwRAipnT787Rzntr7uDmb3m5P3qGdRURGuXbum+HdmZiZSU1Ph7u6OJk2aYNasWbh9+zZ+/PFHALIUWKNHj8aqVavQtWtXZGdnAwAcHBzg6uoq0MswL0721ri8OBI7ztzG1F9SBduvPEVWRJsGWPt6Z1r+biEkUoboLSmCFz2n4AchhBgfn4KalA6LEMtTXiFFwFz9V380dLXBiVnhwnSKmCV5yh9NM97FIlk7UyLEALW6gffkzPu4X6x7vQbANGfp6xvI0liHQs9B/Rt5j/XbgQEIspJWzdCKEL/NTDFwRAipfWQTB+5wbj+ym68Be2MYvEM2p0+fRnBwMIKDgwEA06ZNQ3BwMObOnQsAuHv3LrKyni1n/fbbb1FRUYHo6Gh4e3srblOmTBHoJZivlzo0RMaS/oju1VzQ/cpTZO3Rc9YLMb5dqbLUV0IGQMQA0uZHIG1RJAVACCHEyPgU1DS12buEEP0s2nNRsPRXFAAhXFL+SJmsnSkxZDohQVJ/meC8Qn1rXADqj42+6cO2n8oyvZqlAnRHXaCC0mERQixF4rU8XqfL0d39DNYXQ+E9AhoaGgrG1B+W2NhYpX8fPXqU71PUKvIC6tMiWgk+2z9meyp+P5OFjeNCBNsnqRkSKUOf5UdxI1/YmTRvhDTCokGBgu6TEEKI7rgO0Lg52qCLn7uBe0MIqSkDvvoLaXcK9dqHCED6Ykp/RWS4fp+YWk0QQ6YTEmKA2hRn6QtR40LVwLsQ6cPuFpSqX2ViJEKs1qB0WIQQS7dg9wXObc2tILqc+fXYQlmJRVg/qhOuLI5E56Zugu33yJX7aP1RHMoFrD9CDGv3uTtoPnuvoAGQzk1dcWVxJAVACCHExHAdoBkT0pTSXBJiAcorpGg9J07vAEhdezEyP40yywtQYhhcZ5Ob2qxzIQao1Rb6FmCA2hTTOx26mK3/TlT8pBAifRhgeoE2QVbSUjosQogFK6+Q4tq9Ys7tza0guhz9ajYxttZi/DqxB64sjoSni60g+yyVAC3n7MO8P88Lsj9iOGM3JmPyT2cF21/z+g64sjgSv058ji6SCSHEFHEcoOnclFaBEGLuFu6+gJZz9qG0Qr/99G7pjrPzI4XpFLEcXOPkJhZPF2KAelfqHZUpmIQYoDa19E5CrNYAVA+8CxW8MLX0nUKkDzNkOixTO16EkNpn1h/cC6I3b+BotuOLVBDARNlai/HP7L7YlXobU7anCrLPTYk38fupWzi3IJJmk5oYiZQhaMEBPCqTCLI/RxsRUj6OgIOtlSD7I4QQYhi5HGf/cW1HCDE9EilDtyWHcK+oXO99fT0sCAOCGgrQK2JpuM4mN7VZ51wKumujrtC3EAPUppbeSajVGqpWBAlxvMQi2d/UlBgqfRgAvVcbmeLxIoTULhIpw66z3Auizx/Q1oC9MSzzDN3UIoOCZMXT+wV4CrK/oicMzWfvxZ9nbgmyP6I/efFzoQIgK4e2x8VF/SkAQgghZuA+x8Eoru0IIaZF/jtP3wCIk40IGUv6UwCEqMV1NrmpzToXYoAaULOKQaAFHKaU3kmwvqiaEynA8ZIy2d/UlBgqfRigfyF5UzxehJDa5eT1fFRwPP+LRUD3/9U3bIcMiFaCmAF5vZDyCikiVx1Dxj3985K++8s5rD9+FXvf6y1AD4muhCiKKRfY0Al/RPekVT6EEGJG3OtwS33JtR0hxHRErfoLF+7q/zuvd0t3bBwXIkCPiCXjsqLCFGedGzIFkxDpsNTt21iE6ouqFUFCHS9TChoZMn2YUPs2peNFCKl9fkzM5Nw2zN/DrMccKQhiRmytxUh4vzdKyiUIXHgA5VxDdWpczH6MgDlxuLg4SqAeEq4kUoY2c/ehVM+/IQDYiIHz8/vRyg9CCDFDHi4cZ+5ybEcIMb7yCin8P94HiQCzqin9FeGKy4oK+axzU0ntBAgzqK8uuGOJ6Z2ESB8GGC4dlrp9G4tQ6cNu5FWfiGrIfRNCSE2QSBkOXc7l3H50iJ8Be2N4lA7LDDnYWuHK4v4Y272p3vt6XAH4fRiH8gqp/h0jnMjTIggRABndvTGuLomiAAghhJip5Mx8bg1NpyYrIUSDBX/Kip/rGwCxFYPSX5mY48ePY+DAgfDx8YFIJMLOnTu1bnP06FF06NABdnZ2aNGiBWJjYw3WP66zyU1t1rkQRavVphSywPROQqUPM1Q6LLX7NhKh3u/bT2VBUuXAG3LfhBBSE05ez4eE43CwtViEbiY0iUIXFAQxY/NebIMriyPhaKvfn5EBaDlnHxbs/leYjhG1Bqz+S5BC955ONriyOBILXmyvf6cIIYQYhUTKsCnxP05thUpRQQgxjJJyCVrO2YuNiTf03ldDVxtcWRJl1ukGLFFxcTECAwOxZs0aTu0zMzMRFRWF3r17IzU1Fe+99x4mTJiAAwcOGKR/XFdUmNqsc0PWBLHE9E5C9SXhUvVaFkIdL1Wpo4xFqPRhdwtKkZx5v8b2TQghNYFPKqxBQT5m/9uU0mGZOVtrMS4ujMSCPy/ofdG18UQWDqbdxYlZ4cJ0jijpsPAg7j/Wf7nsV68G4sUOjQToESGEEGNKzryPhyXcvhdMKR85IeQZiZRhyNoTSL1VIMj+xvZognkD2wmyLyKsyMhIREZGcm6/fv16+Pn5Yfny5QAAf39//P3331ixYgUiIiIE718XP3d4udghu1DzAPT2U1mIeaGFyQxkCDWoryoF0428YkH2bUrfwUL1ZVfqHXwUFaD0PhAqHZYpHS8u6cNE4LYIpup71ZD7JoQQQ+ObCmvpy+Y/CZtWglgIoVaF3C54Av85cQL1isj5z9mrdwDEyUaEjCX9KQBCCCEWIruQ2wWvm4MNuvi5G7g3hBC+dpyRpTgVIgBibwVcWRxJARALkpSUhLCwMKX7IiIikJSUZJDnsxKLMLxLE63tTG3WuVCBiqopmCRShp+Ss/TeranWBNFXfnF59feBACtyTO14cVlpxPVlVw20CblvUwocEUJqBz6psHzdHWBrbf4hBPN/BURBvipE31ohJRVAsw/jKC+lACRShuYfxqFEz/ofo7s3Rtqi/iYzY4sQQoj+7nNMFxHm70Hnf0JMSHmFFIELDmDqL6mC7K93S3dc/iTKIi4uyTPZ2dnw9PRUus/T0xOFhYUoKSlRuU1ZWRkKCwuVbnw0rV+HUztTmXUuVKACqJ6CKTnzvtZVMVxYbE0QVH8fHL5cPUUWX6Z2vAR9r1f5KcZ139p+wZla4IgQUjvwSYU1spuvAXtSc+iXtgWSrwqx0aNWthRA89l7sSf1tmD9qm12n7uD5rP3QqLHPmxEoNofOli+fDk6d+4MZ2dneHh4YPDgwUhPT1dqU1paiujoaNSrVw9OTk4YMmQIcnKUf/hnZWUhKioKjo6O8PDwwPTp01FRUaHUhkvByzVr1qBp06awt7dH165dkZycLPhrJoSYH/c6tpza9WhR38A9IYRwIZEyTPzxNFrO2YeCkgrtG3Dw9bAgbBwXIsi+iPlbunQpXF1dFbfGjRvz2p7rbHJTmXUuVKACqD5LX8jBb1MJGgHC9qXy+0AiZdgh0LW/KR0vId/rVQNtXNOHaYtZmVrgiBBi+fimwhrd3c+Avak5FASxULbWYlz9JAptvJ312k/M9lRM2EQDtnyNjz2FyT+d1WsfAV6OuLqUZgXq4sSJE4iOjsbJkycRHx+PJ0+eIDw8HMXFz5bbT506Fbt378avv/6KY8eO4c6dO3j55ZcVj0skEkRFRaG8vByJiYnYtGkTYmNjMXfuXEUbLgUvf/75Z0ybNg3z5s3DmTNnEBgYiIiICOTmcv/CIYRYJg8XjoNVHNsRQgxDImVYtu8yms/ei/0X9Z8pDQC+de2QsaQ/BgQ1FGR/xPR4eXlVm2CTk5MDFxcXODg4qNxm1qxZKCgoUNxu3rzJ6zm5pEoypVnnhpylL1R9C8B0gkYA976IeL4PkjPv436x/vUrAdM6Xl383OHmaKOxTR2OKcWr1Z0RMHGGKQWOCCGWrzamwgIoCGLx4qb0xLgeTfXax6FL9zBu4z/CdKgWiPrqOBJ4RFRV+erVQOx9r7dAPap9/vjjD4wZMwZt2rRBYGAgYmNjkZWVhZSUFABAQUEBfvjhB3z55Zd44YUX0LFjR2zcuBGJiYk4efIkAODgwYO4ePEitmzZgqCgIERGRmLRokVYs2YNysvLASgXvPT390dMTAyGDh2KFStWKPry5Zdf4s0338TYsWMREBCA9evXw9HRERs2bKj5A0MIMS1cL54pOyUhRlE5+LHmWIZg+/3q1UAcmxlGae4sXEhICBISEpTui4+PR0iI+pU/dnZ2cHFxUbrxwSVVkinNOhdysDzhUpUAJcfvTr7BAmPjEugSAWA83wdcB+EdbbWnm3hQXM5pX2anynHPKxZmFRNgWoEjQojl25x0g3NbS0mFBVAQpFaYO7AN1o7ooNc+DqfnYewGCoRo0+PTQ7hw55HO29uKQcXPDaCgQFaw1N1dVlg4JSUFT548USpW2bp1azRp0kRRrDIpKQnt2rVTyuUcERGBwsJCXLhwQdFGU8HL8vJypKSkKLURi8UICwszWFFMQoj5yOVYE4RrO0KIcH5PuSV48KOODf3OM2dFRUVITU1FamoqANmK4NTUVGRlyWpazJo1C6NGjVK0nzhxIq5fv44ZM2bg8uXLWLt2LX755RdMnTrVYH3kOpBtKrPOuQ7oc7Er9Y5STUuuA9R8gwXGJmQx7srvA66D8G8+30xrm0VxF02mvmhy5n08fKx5hUtxObfp0LqmwzK3QBsAzJ8/HyKRSOnWunVrxeNCpZYmhNQ8iZRVnziggaWkwgIoCFJr9G/vjYwl/eFsp3uhkCNX8jBg1TEBe2VZOiw8gNsPdR+oauhigytLomhWoMCkUinee+899OjRA23btgUgK1Rpa2sLNzc3pbaenp7Izs5WtFFVzFL+mKY28oKXeXl5kEgkKtvI96GKvkUxCSHm4cTVe5zacS2gTgjRj0TKcORCDprPisP7v54TdN+juzfGhUX0O8+cnT59GsHBwQgODgYATJs2DcHBwYpUqXfv3lUERADAz88PcXFxiI+PR2BgIJYvX47vv/8eERERBuujudUE4Tqg72xvrXVf+cXlSM68r/i3kOmwTCVoBAjbl8rpnbimUuvEYbD+bkGp0t/CmAx1vABwjjaZW6BNrk2bNrh7967i9vfffyseEyK1NCHEOE5ez8cTjqmwmjdwtJhUWACg/dcEsRhWYhH+XdAPYzcm40g6t4GXqtLuFiFq1THETeklcO/MW4+l8bj/WPdZDS+0qocNY7sJ2CMiFx0djbS0NKUfbaZu6dKlWLBggbG7QQgxIImUIZ5jbQGuBdQJIbopKZdg3KZ/kJQh/CCUi70Yp+dEWNQFZG0VGhoKpmE0MzY2VuU2Z8/qVyeQD3n9A00z390cbdDFz73G+qQJ1wHqDk3ccOxKHr/9CbgQ4UbeY+F2pidBA1iVgh5cU6n9wzG4YSqBI0MdL0DYdFimcrwqs7a2hpeXV7X75amlt23bhhdeeAEAsHHjRvj7++PkyZPo1q2bIrX0oUOH4OnpiaCgICxatAgzZ87E/PnzYWtLv20JMZbEDO3fp3IRbaqfA8wZ/RqvhTaO7YLVw4N13v7C00AIkem/8ihuF+ie9/TrYUEUADGQmJgY7NmzB0eOHEGjRs9ST3h5eaG8vBwPHz5Uap+Tk6P4oaeumKX8MU1t5AUv69evDysrK5VtVP2glNO3KCYhxPQlZ95HQSm34LmXq+oCuoQQ3clXfbSduw/+c/cbJACycmh7nJ8fSQEQYlJMaS0S1wHqnv9rwHt/XAeoXTmsMtl+Kstk0js94PC63Bw0FwKXq5zeifsgPLfjYCqrjYRMuVY1HdaNvGLdOqWCqRyvyq5evQofHx80a9YMr7/+umKlm1CppVWhjAiEGB6flXo9mnP7/jUX9Iu8lhoY6IOMJf2ha3IsCoTIRK06hovZuv34EUOWF3pAUENhO0XAGENMTAx27NiBw4cPw89POYdhx44dYWNjo1SsMj09HVlZWYpilSEhIfj333+Rm/usyH18fDxcXFwQEBCgaKOp4KWtrS06duyo1EYqlSIhIcGgRTEJIaYvu5DbYIObg+nM2CXEEhSVViBq5VE0n70XYzefRhHHXPB8+Na1Q8aS/hjcqbHg+yZEEy71Dx48fmIyqYq4pmB6rXMTTu0q11XgOqjcN0D7LFdTSe8kkTIsiruktd2oEG5FbCund+KaPqyzrzvvv4UxCVlDpfKKIImU4afkLA2tZbxc7LQGpUxpdZZc165dERsbi/3792PdunXIzMzE888/j0ePHgmWWlqVpUuXwtXVVXFr3Ji+RwkRkkTKkHKD28QfWysRujWvZ+Ae1SwKgtRiVmIRMj6NgoO1bvOBansgJGrVMVy4W6TTtg7WwPVPKS+0obz//vvYsmULtm3bBmdnZ2RnZyM7OxslJSUAAFdXV4wfPx7Tpk3DkSNHkJKSgrFjxyIkJATduslW5YSHhyMgIABvvPEGzp07hwMHDmDOnDmIjo6GnZ3sIoFLwctp06bhu+++w6ZNm3Dp0iVMmjQJxcXFGDt2bM0fGEKIyeBa5yPM34O+KwjRg3zFR78vj6Dph3FoO/8ALug4gYWLr14NxLGZYfS5JUZhboXRuaZg+vlUFqd2lesqcA2wdOc4wGMKxyw58z7uFmjvh7M9t5UgSksgOEYCruQW8f5bGBPXvxvfFUHJmfeRXaj9t9xrnZtoXWpiit8WkZGReOWVV9C+fXtERERg7969ePjwIX755ReDPi9lRCDEsBKv5YHr9J/erS3vOpRqghBcWtwfQfP342GphPe2F+4WYcBXx7Hn3Z4G6JnpGqBHAMTHxQaJs8MF7hGp7IcffgAgy8Nc2caNGzFmzBgAwIoVKyAWizFkyBCUlZUhIiICa9euVbS1srLCnj17MGnSJISEhKBOnToYPXo0Fi5cqGgjL3g5depUrFq1Co0aNapW8PK1117DvXv3MHfuXGRnZyMoKAj79++vNjOGEFK7cK3z0aNFfQP3hBDLU1RagclbT+Gvq/ehe8U2fiLaNMDa1ztb3MUiMS/mVhid6wD1f/e51eSovD+uAZYHj7mlNTaFY8b1eN18wO14VU7vxDV9GNd9m0LQCOC3Iui3M7c0tpGvCAppXo/z65NIpZxXZ4WY8IxrNzc3tGzZEteuXUPfvn0VqaUrrwapmlo6OTlZaR9VU0urYmdnp5hwSAgR3u9aznOVjerW1HAdMRIKghAAQOr8fgheeAAPdCjunXbnEcbHnsIPYzoboGemZ9zGf5CmYwCkjVcdxL0XKmyHSDUFBQVaU0jZ29tjzZo1WLNmjdo2vr6+2Lt3r8b9cCl4GRMTg5iYGI1tCCG1i4cLx4Eqju0Iqa3KK6T47q9r2JyYiZxHFULWQuakeX0H7HsvlOp+EJMgX/2gafDflFIVcR2gblyXW22syumdDl1Un3KnMvc6tmZTTJ7r8fJ1d+TUrnJ6J6H3bQpBI4BbDRVvV3v0+F99rUEQ4Flwh2v6MK7fSaYSNFKnqKgIGRkZeOONN5RSSw8ZMgSA6tTSn3zyCXJzc+Hh4QGgemppQkjNO3/rIad2VmJYXCosQId0WMePH8fAgQPh4+MDkUiEnTt3amx/9+5djBgxAi1btoRYLMZ7772nY1eJoZ2dG4GGbrpF3RMu52L3uTsC98j0LNqThsPpeTpt27tlfQqAEEIIkeF6VWwadVgJMbqi0gqM+T4RLT6MQ9NKt5Zz9mHZgavIruEAiI+LLS4t7IeED16gAAgxGVxXP5hKqqIufu5wc9Scuqmuow1ae3Gsj/d0IZZEyrAj9TanTbgM1pvK+i6uKb5GdPWFl4v26/rK6Z347Fvb38xUgkZca6h8HBUAD45BDUWgjeMXjpsDt5W/lQNSpuCDDz7AsWPHcOPGDSQmJuKll16ClZUVhg8fLlhqaUJIzZJIGedzTYsGTha5upn3SpDi4mIEBgZi3LhxePnll7W2LysrQ4MGDTBnzhysWLFCp06SmnPiwzD0X3UMF3VY6TD5p7Po387bIj8oALD3/B388Pd/Om3bu2V9bBzXVeAeEUIIMVe5HGuCcG1HiD6MvZrCnPi42CLhgxfgYGtl7K4QUo251QThggHIK+aWskqe3ik58z7uF2tOQQQA9erYAiKYTboirkGu1JsPMbxLE6w4dFVj28rpnbju+wyHAJqpjAZwraFSt44tpIzjN9/TF8c1fVh9J1t4udhprR+y/VQWYl5oYTJjKbdu3cLw4cORn5+PBg0a4LnnnsPJkyfRoEEDAMKkliaE1KyT1/N51ANpYNC+GAvvIEhkZCQiIyM5t2/atClWrVoFANiwYQPfpyNGsHdKL52LfgfO34+0hdzfH+ZCImV4Z5vmlEfqtPVxpgAIIYQQJVwLo3NtZ+rWrFmDZcuWITs7G4GBgVi9ejW6dOli7G7VOpWDHdmPaqpahmVwsxcjaXY4BT+ISTO3miDJmfe1BiAePn7C+btQ/rq4BnkGBfko1cXQxBQCR3yCXE3r1+G1T67pw5Ku55lN0MgQQUH5+4VrOiwPF3veASlTsH37do2PC5VamhBSczYn3eDc9vkWHobriBFRTRCiUpyOgZCicime+ywBf8/sY6CeGUfQ/P06bdfG26nWFY0nhBCinZsjt/QIXNuZsp9//hnTpk3D+vXr0bVrV6xcuRIRERFIT09X5IkmhkFBD/3Vc7TGsRl94GRPl03E9HXxc4e3q73W2e8POK6sMDSug89uDja8ap1wHaDu4+8JsYjbzHtTCBzxCXJJtS3reKp+HTte6cO4rvMwhaCRIYKCirY80pryDUgRQojQJFKGhEs5nNraWokssh4IoENNkJpQVlaGwsJCpRupeXFTeqGNtxPv7W49KMXYjf8YoEfGMeaHJDwq57po7Jk23k6Im9LLAD0ihBBi7pIyuNWXevjYNAaq9PHll1/izTffxNixYxEQEID169fD0dGRVggbiETKkPBvNtrO3adUL4Pw06OZOy4t7IeUuREUACFmw0oswsdR/lrbLYq7qKgFYUxcB58fljzhV+uExwA1l7okplLjgmsNlS5+7txzUon4pQ/julLBFIJGXOucdPSty6stwD0dVl5xmdmt0CKEWJ6T1/PxhOOwZmAjV5NJzSc0kwyCLF26FK6uropb48aNjd2lWkvXQMiR9Dws2nPRAD2qWYv2pOHo1fu8t6MACCGEEHUkUob4i9xm4rjXMe+VIOXl5UhJSUFYWJjiPrFYjLCwMCQlJVVrTxNhdFdSLsHw7xLRfPZejN+agiIdJnDUdk62YmwY3RkZS/pj61shlPqKmKW6dbSvgpCn3jG2BxwGkr1d7eHOcWVH/NOUTnwGqLkwp6EgefyHa5qvvKIyXunDOjd15xUsMCaudU5S/nvAqy3Ab5WJOQXaCCGWKZHjBDwA6GzB5yKTDILMmjULBQUFitvNmzeN3aVaLW5KL/h7OvLe7oe/M7H3/F0D9Khm6FoIPYACIIQQQjRIzryPglJuM/O9XB0M3BvDysvLg0Qigaenp9L9np6eyM6unn+cJsLwV1IuQcjSQ/Cfux9JGdoL1hJlIgCvdmqESwv7IW1hJF7w97DY2W+kdjCX4ugSKcOiuEta230cFQAvF24DzrtS70AiZbwGqLnUJZHXuDA2rjVUkjPv8zoGfNKH8Q0WGBOfzwLfzw3flSPa0LcOIcSQ+HyH9WhumUXRARMNgtjZ2cHFxUXpRoxr39TecLLl/3Z5Z9sZk1hqzZeuhdAbutlhLwVACCGEaJBdyD0Hem2bFUgTYbirHPy4W8Btxi+REQPo3aoB0uZHIPPTKHw+NJBWfRCLYS6pd5Iz72utXQIAdevYooufO9zraJ5JDwD5xeVIzrzPa4DaXIJGfPqQ+6iU3yA9j/Rh5nS8eAWCOKygAqBoxycYZE6BNkKI5ZFIGc5yDExbcj0QQIfC6EVFRbh27Zri35mZmUhNTYW7uzuaNGmCWbNm4fbt2/jxxx8VbVJTUxXb3rt3D6mpqbC1tUVAQID+r4DUmHPz+6H57L28t+v2STxOfRxugB4ZTtdPDvLexslWjBMfhmlvSAghpFa7zzFFRZgFzEivX78+rKyskJOjnP4rJycHXl5e1drb2dnBzo7bQERtJZEyDFl7Aqm3CozdFbNS19EGEW28MG9gGwp4EIsmT72jadDVFFLv8BlMtxKL8FJQQ/xw4gan9nwGqM0laMSnDx7O9ryOgaXWt+DyWZDXUDl5PZ/bTp/+LDt0sfpqVlX4BINMIXBECLE8J6/no4JjsLt3a/O//tSE99T+06dPIzg4GMHBwQCAadOmITg4GHPnzgUA3L17F1lZWUrbyNunpKRg27ZtCA4ORv/+/QXoPqlJVmIR1o4I5r3dveInGBebbIAeGca4jSeRV8y/gOi5+f0M0BtCCCGWxs2RW52PkOb1DdwTw7O1tUXHjh2RkJCguE8qlSIhIQEhISFG7Jl52pV6G81n76UACAd2ViIENXbFubnhuPFpFM7ODcenQ9pTAIQQmEbqHb6D6WEB1QPn6trzCbCYU70GPoXR+RwDvvUtvF21t39QXM5pn8amSw0ViZRhR+ptTu09nO3NKnBECLE8fOqBjOrW1HAdMQG8V4KEhoaCMfUhpNjY2Gr3aWpPzEv/9j4Yn3Wfd62Mw5fvYfe5OxgY6GOgngljT+ptHE7nOAukktXDgy06WkoIIUQ49zkODHBtZ+qmTZuG0aNHo1OnTujSpQtWrlyJ4uJijB071thdMysDvvoLaXeoULwqNmIRPFzs8HpXX0x4vhlsrU0y4y8hNYJP6p0QI6a84DNLH3hWg0HT6gZ5eqdTN7ilFeJaC8OcrvLkh4dPeic+x9ZKLMLHUf5aU0cviruIiLZeRr1GNkQNlRt5j5GceR/3izXvFwDqPU3lBsAsVmcRQizT/jRutZotPRUWoEMQhJCPB7TF9dwiHLnCL1gwZftZ9G/nbbLBAomUYfL2VN7bvdC6gckHdwghhJiOlP+4Dc48eGwZQZDXXnsN9+7dw9y5c5GdnY2goCDs37+/WrF0oppEytBm7j6Ucl3HbqHEABxsrdDFzx2rh3eAkz1dxhCiijnVbNCm8lmPV0FuHjUuzCVoBPAb1OccuRHxO7YhzeuhLocAy92CUqMfMz6fhQHtfeDlYofsQs0rQrafykKTeo6c9jsoyAdWYhGnGqmmOUJCCDF35RVSZNx7zKltYCNXkx2vFQpdPRCdbBzXDc8tjcetAu4DNFIGTN52BmtHdjRgz3T3yrq/Of9elmtU1x4bxnQxSH8IIYRYHomU4a+r3JYkW9Jv0JiYGMTExBi7G2Zn97k7mPyT5tm2QqHVFIRYBnNJvcNnQD+keT2DBHe41sLgu19DMcQxSLiUg8DGbrz2ay6BNj6fBSuxCMO7NMGKQ1c1tr1bUMq5tlvfpynczCnQRgixLJsSb3Bu27kWrEajIAjR2d+z+qLt3H0oKpdy3mZvWjbKK6Qmd2G9J/U2ztzkl2LCyVaMv2f2MVCPCCGEWKLkzPsoLpdwahvSzPxrghDdjYtNxuHL9wTfrwiAj5s9BTsIsVCWWBgd4Jfe6TTHFZd8AkE38rjNpDUkQwS4dqXeQe9WHpzaytOHmUugjW/Ktab163Dar5uDDef0YYD5BI0IIZZn93lu9YsAoEfzBgbsiWmgIAjRy7n5/dB89l5e2/RfdQyH3u9toB7xp2saLCqETgghhK/sQm4XuI62Vhafk5Wo99xnCbj1QLjBEDsrYEpYKwp6EEIAmEbqHd4D6Rw7LWUMPyVnaW3n7WqvGPzmmgYp5oUWRk0V8oDDypXKr8u9jo3W2hX5xeW4nM1xMuDTQX9zCbRxUTmOwTXQdv/xE17pw8wlaEQIsSwSKcOF29zO79Ziy68HAsjS6xKiMyuxCF8PC+K1zbV7j7H73B3DdEgHk7el8E6DRYXQCSGE6IJrCoX+Ri4mSowneMEBwQIgPZq549LCfkj/JArv9G5BARBCagE+qXeMST6QrknlWfp5HL8//8nM1xrQAIBhnZvASixSpEHSRl7jwlgkUoZFcZe0tvs4KkDxul4Kashp339f45amk0/6MFP4BcOrhgrAudMPS7ilBJev7ODyXjeXoBEhxHycvJ4PCcfBzhdaN6gV1550JUT0NiCoITo0duG1zbs/neVUIMzQyiuk2JuWw2ubDo3dqBA6IYQQnbg52nJqF9KcUmHVRv5z9uJBSYXe+4nu2QwZS/pj61shcLC1EqBnhBBzYUmpd3SZpc84XmI2re9Y6f+5pUEy5jFLzryPuwXan79unWe/M8Ke1qTQ5kzWQ07t5CsVzCXQxvezwDXQxnUGpTx9GBeWP/RICKlpiRncAtwAMDrEz4A9MR0UBCGC+HXSc7zaMwAxW1MM0xkeXv8+iVd7EYBfJ3U3TGcIIYRYvIePuc0e5NqOWI7/zY5DSYV+E0TkwY/p/f1rxWwuQkh15pJ6x1Cz9F0dNM+4l6s8QG0Ox0yX4FZH37rQ9lUgAvCoVHvwvV4dW8VKBXMJtPH9u3INtLlwfI/JgyXmEjQihFgWrucUW6vakQoLoCAIEYiVWISvXg3ktc2+Czkor+BeVF1o5RVSnLrxkNc2X1EaLEIIIXrIul/MqR3XFSPEMgTN34cnevwkql/HioIfhNSANWvWoGnTprC3t0fXrl2RnJystm1sbCxEIpHSzd7e8IPo5pJ6x1Cz9M9kcRxIrhRz7uLnDm9XzX+byrU2jEGXQE3Kfw+01q7gGnofFOSj+H4xh6ARwL+GCtdAW2Gp5oCGnDx9mLkEjQghlkMiZTj73wNObQMbudaa6wcKghDBvNihETxdOM6KeKrX54cN1Bvtnv/8EK/2lAaLEEKIPiRShj/O3ubUllaC1B79VxzBw1LdIyArh7bH6Y/71ZqLF0KM5eeff8a0adMwb948nDlzBoGBgYiIiEBubq7abVxcXHD37l3F7b///qvBHqtnCmcLvgPpXNv/fS2fU7vK9S2sxCK8GOitsf2Lgd5GPc/yraECCDuo3rdSai1zCLTxraECGC4dlrkEjQghluPk9XxwXWDeuRbVI6IgCBHUXzPCeLW/W1iGXancBoSE9OeZW8gp5DaDQ47SYBFCCNFHcuZ9PCqVcGrrXodWgtQG4zb+g4s5j3Xa1lYMZCzpj8GdGgvcK0KIKl9++SXefPNNjB07FgEBAVi/fj0cHR2xYcMGtduIRCJ4eXkpbp6engbvp7mk3uE7qN/Fzx3udbRPuCsq4/Y9WzkdlkTK8Oe5uxrb/3nurknUtNSkau+4pncSaYntiEWy1Fp8GDvQpksNFUOlwzKHoBEhxLL8mJjJuW2P5g0M2BPTQkEQIihbazHG9mjCa5upP6fW6A9KiZRhyi/neG2zalgQzbAkhBCil+xC7jMyvVwdDNgTYgoW7UnD4XTuBQsra+higytLoui3CSE1pLy8HCkpKQgLezbhSywWIywsDElJ6msMFhUVwdfXF40bN8agQYNw4cIFg/fVklLvVL5CtBKL8FJQQ4PsnMuA+d2CUqMGjnjXUAE4RyK0FZOXMllqLT59MXagTafPAcfjdZZjIfk8Dum4eD41IURPEinDkQs56PflEbT4MA5Nn96az4pD8MKD+PD38ygp5xZMN1USKcOhy+pXqVZmLa499UAACoIQA5g3sB1c7a04t5cyYFX8FQP2SNmq+HTOuU8BwNvFDoOE/MFNCCGkVrrPMc2Ci701zQa0cHvP38EPf+uWFqd3y3o4MTtc4B4RQjTJy8uDRCKptpLD09MT2dnZKrdp1aoVNmzYgF27dmHLli2QSqXo3r07bt26pfZ5ysrKUFhYqHTjy1xS7+gyqB9WKSWTvioPUJtD4EiXPnJO78Rzv+ZwvHT5HHA9Xn9dvcdr3+YQNKpq6dKl6Ny5M5ydneHh4YHBgwcjPT1dqU1oaGi1ukcTJ05UapOVlYWoqCg4OjrCw8MD06dPR0VFRU2+FEIUdpy5jeaz92Ls5tO4nPsYld+JEib7HG4/dRP+c/dj8NfHTX71nzonr+dDwjHTboC3c62aVEVBEGIQp+bwuzj/+ui1GjnBSKQMq49k8Nrm2IwXDNQbQgghtQnXYucvBTesVT9GaxuJlOGdbWd12nb8c77YOK6bwD0ihBhCSEgIRo0ahaCgIPTq1Qt//PEHGjRogG+++UbtNkuXLoWrq6vi1rgx/3R3XIp8A8CDYuPWntJlIL2jb10I9fVYOR2WOQSOdOkj1/ROXJjb8dKlhgrX/hZzmCVer46tYt/mEDSq6tixY4iOjsbJkycRHx+PJ0+eIDw8HMXFxUrt3nzzTaW6R59//rniMYlEgqioKJSXlyMxMRGbNm1CbGws5s6dW9MvhxA8/9lhTP0llXP71FuP0Hz2Xuw9f8dwnTKQxAzuK81rW91jCoIQg7C1FqNfWw/O7WtqNQjfVSCRbTxha00fE3N0/PhxDBw4ED4+PhCJRNi5c6fS42PGjKk2c6Vfv35Kbe7fv4/XX38dLi4ucHNzw/jx41FUVKTU5vz583j++edhb2+Pxo0bK/3wk/v111/RunVr2Nvbo127dti7d6/gr5cQYvrucxxwalTX0cA9IcbU9ZODOm339bBgfDygrcC9IYRwUb9+fVhZWSEnJ0fp/pycHHh5cVudYGNjg+DgYFy7dk1tm1mzZqGgoEBxu3nzJu++WolF+DjKX2u7RXEXjTrLVZeB9JT/HkCwLlfajznUbNBlUF/QHEtVjpe2QJu3q73Jr2qt+lbq4ucOLxdhAkeDgnwUE1rMIWhU1f79+zFmzBi0adMGgYGBiI2NRVZWFlJSUpTaOTo6KtU9cnFxUTx28OBBXLx4EVu2bEFQUBAiIyOxaNEirFmzBuXlxg3Cktql1Ud7cfNBiU7bvrPtLD6JM3wqSyHxWVU2urufAXtiemh0lxjMmhGdeLU39GoQXVaBfP16RwP1hhhacXExAgMDsWbNGrVt+vXrpzRz5aefflJ6/PXXX8eFCxcQHx+PPXv24Pjx43jrrbcUjxcWFiI8PBy+vr5ISUnBsmXLMH/+fHz77beKNomJiRg+fDjGjx+Ps2fPYvDgwRg8eDDS0tKEf9GEEJOW8h+3H6QPHtOFoaUat/Ek8or5p4FY9VoQBgTVrplahJgSW1tbdOzYEQkJCYr7pFIpEhISEBISwmkfEokE//77L7y9vdW2sbOzg4uLi9JNF3U5rAAwdo0LXQb1hZwpz6deA2AeNRuqXkkLmQ6r8vGyEovwYqD69zEAvBjobdRVrbqkW7MSizC8C7/6pur0rZS6zVxWZ2lSUFAAAHB3Vw5sbd26FfXr10fbtm0xa9YsPH78WPFYUlIS2rVrp5RGMCIiAoWFhWrrIwmREpCQylrMikOZRL9xxu/+uoFFe8wjECKRMpytVMNJk+YNHGvdpO/a9WpJjbISi/Bu7+ac2xt6NQjfVSDv9m5B6UjMWGRkJBYvXoyXXnpJbRs7OzulmSt169ZVPHbp0iXs378f33//Pbp27YrnnnsOq1evxvbt23HnjmxJ5NatW1FeXo4NGzagTZs2GDZsGN599118+eWXiv2sWrUK/fr1w/Tp0+Hv749FixahQ4cO+Prrrw334gkhJkciZTiSzi2HNH31WKY9qbdxOD2f93Z9WntgUDDVJiPE2KZNm4bvvvsOmzZtwqVLlzBp0iQUFxdj7NixAIBRo0Zh1qxZivYLFy7EwYMHcf36dZw5cwYjR47Ef//9hwkTJhi8r+aYfkeVqtduQqZ3qjzr3hxqNugyqG+odFgSKcOf5+5qbP/nubtGXWmk62egaf06ej+3WCRL3SZnLquz1JFKpXjvvffQo0cPtG37bEXqiBEjsGXLFhw5cgSzZs3C5s2bMXLkSMXj2dnZKusoyR9TRYiUgITIBcyJQ4VAH6kf/r6BT+IuCrMzAzp5PZ/za45oI1ydLXNBQRBiUFP6tuI1a8ZQq0H4rgIRi4ApfVsK3g9iWo4ePQoPDw+0atUKkyZNQn7+s8GppKQkuLm5oVOnZyuawsLCIBaL8c8//yja9OzZE7a2z/L8R0REID09HQ8ePFC0CQsLU3reiIgIJCUlGfKlEUJMzMnr+Sir4FahLqRZfQP3htQ0iZQhZnsq7+3a+DjjhzGdhe8QIYS31157DV988QXmzp2LoKAgpKamYv/+/YpBvaysLNy9+2xg+MGDB3jzzTfh7++P/v37o7CwEImJiQgICDB4X80h/Y4ug/pCLceoXK8BMI+gkU59NFA6rOTM+7hboLk/xl5ppOtnQIjAkZTJUrdVZg6rs9SJjo5GWloatm/frnT/W2+9hYiICLRr1w6vv/46fvzxR+zYsQMZGfyyb1QmREpAQgCg/8ojeMx/8bVG3/2Vib3nNQeAjY1PPZAezRsYsCemiYIgxKCsxCJMNoHVIHxXgcSE0ioQS9evXz/8+OOPSEhIwGeffYZjx44hMjISEoms0F12djY8PJTr2lhbW8Pd3V0xc4XL7BZ1bdTNfgFoGTAhligpg9sKAHtrMbo1r2fg3pCa1ueLw7y3aehmh7h3exqgN4QQXcXExOC///5DWVkZ/vnnH3Tt2lXx2NGjRxEbG6v494oVKxRts7OzERcXh+Dg4BrppznUuNBlUF+o9E6V6zUA5hE00qWPhkqHZQ5BI53rlgg0BFD1tZvDMVMlJiYGe/bswZEjR9CoUSONbeXnQ3ndIy8vL5V1lOSPqSJUSkBSu/155hYuZj/W3lAH0dvOmOSKLbn9adyCNLZWolp5zUlBEGJwfFeDrD0m7GoQiZRhzVFaBUKUDRs2DC+++CLatWuHwYMHY8+ePTh16hSOHj1q7K7RMmBCLBDjGIoPbd2AgvAW5s8zt3DjPr9BDSdbMU58GKa9ISGE6MjY3zS6DOoLld6pj7/yBCVzCBrpUkNFyKBN5XRY5hA00rVuiVCBo8rHCzCPY1YZYwwxMTHYsWMHDh8+DD8/7cWTU1NTAUBR9ygkJAT//vsvcnNzFW3i4+Ph4uJSIyviSO0kkTK8+8s5g+2fARi67oTB9q+P8gopMu5xC/4ENnKtldecFAQhBsd3NUiFFEi8yn0JlzaJ1/LApw4SrQKpnZo1a4b69esrzVyp/IMNACoqKnD//n3FzBUus1vUtVE3+wWgZcCEWCIXe80DF3LBjetqb0TMhq4XYufm9zNAbwghtYU51Lh4wKEwebWZ+kJdoukw384crg6rvqwufu5wr8Pt9wefnZtDoW9d65YIVkelyh9D55UpRhIdHY0tW7Zg27ZtcHZ2RnZ2NrKzs1FSUgIAyMjIwKJFi5CSkoIbN27gzz//xKhRo9CzZ0+0b98eABAeHo6AgAC88cYbOHfuHA4cOIA5c+YgOjoadnbC1ashpDJdVl/zdfZmAXafu2Pw5+FrU+INzm07m8i5pqZREITUCL6rQb46LFxKrAW7L3BuS6tAaq9bt24hPz9faebKw4cPkZKSomhz+PBhSKVSxVLfkJAQHD9+HE+ePLvIjI+PR6tWrRRF1kNCQpCQkKD0XPHx8QgJCVHbF1oGTIjlOZv1QHsjAAUlmgetiHmZvC1Fe6MqVg8PpskYhBC9mHrqHYmUYVHcJa3tPo4KUDofCjVLP69KAMYcgka61FCxEovwUlBDQZ6/8jEzh0LfOtctESodVpX3qq4rU4xl3bp1KCgoQGhoKLy9vRW3n3/+GQBga2uLQ4cOITw8HK1bt8b777+PIUOGYPfu3Yp9WFlZYc+ePbCyskJISAhGjhyJUaNGYeHChcZ6WcTC6bL6Wlfv/nTW5NJiJd/gln4ZqJ31QADA2tgdILWDfDXIVxyLk6dkPYREyvT+EVBeIcW1e8Wc29MqEMtRVFSkWNUBAJmZmUhNTYW7uzvc3d2xYMECDBkyBF5eXsjIyMCMGTPQokULREREAAD8/f3Rr18/vPnmm1i/fj2ePHmCmJgYDBs2DD4+PgCAESNGYMGCBRg/fjxmzpyJtLQ0rFq1CitWrFA875QpU9CrVy8sX74cUVFR2L59O06fPo1vv/22Zg8IIcRoJFKGI+n3OLWlryDLUV4hxd60HO0NK3mhdQMMDPQxUI8IIbWFqafe4TJADQB169gq/Vuo/lZNVWTqQSM+z1213QutPfHDiRt6P3/VY8+n0HeIEfLO63q8hAq03a+yH64rU2b08zeJ8QjGNA/uNm7cGMeOHdO6H19fX+zdu1eobhGilq6rry8t7AcrsQjt5u1DmYT7dgxAzNYUrHujE+/nNJSsfG6psKzFtbMeCEArQUgNmtK3Fee2UiZMSqwPf+N+EqRVIJbl9OnTCA4OVhSgnDZtGoKDgzF37lxYWVnh/PnzePHFF9GyZUuMHz8eHTt2xF9//aW0NHfr1q1o3bo1+vTpg/79++O5555TCl64urri4MGDyMzMRMeOHfH+++9j7ty5eOuttxRtunfvjm3btuHbb79FYGAgfvvtN+zcuRNt27atuYNBCDGqk9fzUVYh5dQ2pFl9A/eG1JTnPz/Eq72rvRU2jOlioN4QQmoTU0+9o+sAtWDpnaqM75p60IjPc1drJ8B4er06ttXeK6YeONL1eAmVDsu9SgBP55UphPBQXiHFmiNX0O2TA/D7MA5+H8ah9Zx96L/qOA5fyjW5lQtC0mX19ZXFkXCwtYKttRjpn0TBzcGK1/b7LuSgnOM1nqFJpAzXcoo4tQ1qXDvrgQA6BEGOHz+OgQMHwsfHByKRCDt37tS6zdGjR9GhQwfY2dmhRYsWiI2N1aGrxNxZiUWICPDg3H7+njS9nk8iZdiRyj1PH60CsSyhoaFgjFW7xcbGwsHBAQcOHEBubi7Ky8tx48YNfPvtt/D0VC6S6O7ujm3btuHRo0coKCjAhg0b4OTkpNSmffv2+Ouvv1BaWopbt25h5syZ1fryyiuvID09HWVlZUhLS0P//v0N+toJIaYlKYPb0mR7a3GtnZVjaf48cws5hfxSm52aE26g3hBCahtTT72j6wC1lViEQQKslquaDsscalxoK4wugurAlhArGwYF+VR7r5h64EiXQvIABEuH5eXqoPRvUw8aEfMmkTJM/PE0Ws7Zh2UHriL7UQUYZPHe0gopLt59hHGbTqH57L3Yedry6o3qsvp6XI+msLVWHhJPndcP9jb8TgJRXx3n1d5QTl7PB9eFLKZSe8gYeAdBiouLERgYiDVr1nBqn5mZiaioKPTu3Rupqal47733MGHCBBw4cIB3Z4n5G9Xdj3PbjHuP9YqqJl7L41zzTgRaBUIIIcQwGMdvo9DWDSgYbwF0WY7fv61XtQsxQgjRla5FoWuKPitVGtV11Pv5q6bDMocaF/EXszXWBGEA5g0MqPY7QoiVDX38PavdxyXI4KYqyGBCVP0lhQgaqVo5Y+pBI2K+9p6/i+az92L/RW5BgPd+O4/uSyxrPPaN70/yau/hZIu5A9uofOz8vH689nU1txgl5TzyaBnIj4mZnNvW1noggA5BkMjISCxevBgvvfQSp/br16+Hn58fli9fDn9/f8TExGDo0KFKOfNJ7dGtWT3Y8HjXzfrjvM7PtZpHcfWXgqvPbiGEEEKEcPcht/yswY3rGrgnpCbwXY4vFgGrR3QwUG8IIbWRqafe0WeliruTAOmKVIx+86lxUdMkUoYFuy9qbOPmaIO+AV7VHxDiElfHuI8xr651KSQPCBOEULVyxhKCRsT0LN17Ee9sO8N7uzuFFWj2YZwBelTzyiuk+OfGA17bJM0OU/uYrbUYY3s04bW/l9b+zau90CRShkOXczm1rc31QIAaqAmSlJSEsDDlN1hERASSkpLUblNWVobCwkKlG7EMVmIRJvVqzrn9rtQ7Os22kUgZTt94yLn9p0MCeT8HIYQQoo1EyrD/AreZWQUl/NInEdOjy3L8VcOCaSIGIURQpp56R5+VKh4CBEGqpsMCTPuYcQlqqRrQB4DcQv37m6tidQSXIMMDNX2qCfrUnXFz0K/ujKqVM1zQLwHCx97zd/DNce6z/6uSAhYRCOG7CuTd3trT4M8b2A6u9tzrg1zOLjJqbZCT1/Mh4fj0Ad7Otfq6w+BBkOzs7Gp59j09PVFYWIiSkhKV2yxduhSurq6KW+PGjQ3dTVKD+BRIr5AynOSYS72yk9fzwfUU5ONqTykoCCGEGERy5n08Luf2jVSLf49aDL4XYsGNXTFQgPz2hBBSmamn3tFrpYoA35WqXrcpHzN9AjT3Bahjcl9FEMSUg0aAfnVnRnf31e/JVczhNPWgETEvEinDO9vO6r0fKYCAOfv075CR8F0FYiXmngafb62+N37gdw0gpMSMPM5ta/t1h0mO/M6aNQsFBQWK282blle4pzbjWyB9UxL/6DaffHgvBmleik0IIYToKpvHDMyQZvUN2BNiaLosx/9tUg8D9YYQUpuZeuodfQbQ9V3ZoKpeA6BfnRJD0ydAI0T6MPc6toL2qSboXBgdQBc//VLFqFo5Y+pBI2Jeun4iXE2PxxVSPPdpgmD7q0l8Jx+tfI376mtbazE6N3XjvO9/Mh8YbTUIn+DpaB51mi2RwYMgXl5eyMlRTguQk5MDFxcXODg4qNzGzs4OLi4uSjdiWfgUSD98+R6vlFh88uEBwPMtuAdkCCGEED7yHnErsOlgI67V+VktgSGW4xNCiKEY8+yjzwC6visbXlRRrwHQr06JoekzoO/lon8QwkPFPkw5aMSVuhEGfQNtqlbOmHrQiJiPBbv/RV6xsIW4bz0sxbjYZEH3aWh8Jx/9z6MO71UQWyeE8GpvjNUgEilDCsfj0LyBY63PgmPwVx8SEoKEBOWoYnx8PEJC+L2ZiGXp1qwerDn+fuSbEotPPrzaXhSIEEKIYZ2+we37q2fLBjQgbsYMuRyfEEL4MvXUO/oM6uu7sqGRm+qJmPrUKTEF6nrVxc8d7nX0q3GhauemHDQCdC+MDugfaFO1csbUV2cR81BeIcXGE1kG2ffhy/ew+9wdg+zbEGb9cY5X+7h3e/J+DltrMbo2rcu5vTFWgyRey+NcCiCijZdB+2IOeAdBioqKkJqaitTUVABAZmYmUlNTkZUl+yDOmjULo0aNUrSfOHEirl+/jhkzZuDy5ctYu3YtfvnlF0ydOlWYV0DMkpVYhEHB3KOwP568wbnt5iTubQepmQlECCGE6EsiZTh2lVuOVgcb7sX3iOl5/vNDvNrzWY5PCCF8WULqHXWD+vqubHBzrD5ADehZp8TA9BnQtxKLMEjPHPCqCsmbetBIn8+AvoE2VStnuKBfBUSbqFXHDLr/yT+dNdlAb2USKcMfZ7gHbLr61dV5BcTmCd14ta/p1SCrD1/h3LZH8wYG7Il54P0uOH36NIKDgxEcHAwAmDZtGoKDgzF37lwAwN27dxUBEQDw8/NDXFwc4uPjERgYiOXLl+P7779HRESEQC+BmKulLwdybnvkci6nk7FEypBwKUdru2d9aM+5LSGEEMJHcuZ9lD7hNjfHx43SH5irP8/cQk6h5sGpynRZjk8IIXyYeuodfQb19V3Z8PCx6ln+phw40rdvjeo66vX8qt4nphw0AvT7DHjoW0eFCqMTA9iTehtX7z02+PMMXXfC4M+hr1Xx6WoD5apsHs8vkFGZKa8GkUgZTv/3kFNbKzEoCw50CIKEhoaCMVbtFhsbCwCIjY3F0aNHq21z9uxZlJWVISMjA2PGjBGg68Tc2VqL0bwBtx9k5RJuKbFOXs8Hx/EmyodHCCHEoPgURaeZOeZJImV4/zfDL8cnhBA+uNRrAIAHeqb90ZU+g/r6rmxQtxLElANH+vZN3Wvmws1BdYomUw4aAdrTT4mgoWaJnksyVK2cMfXjRUybRMrw7vZUXtuIAZybG877uc7eLDDptFgSKcOaoxmc2wc2ctF73M9UV4OcvJ4Prgt3OjR2o1XoqIGaIIRo0q+t5jyilZ3IuKe1TWIGt7QjAOXDI4QQYlhUFN3y8Zl8Aei3HJ8QQriyEovwcZS/1naL4i4aJfWJvoP6+qxsULcSxJRrNuhbhFzda+YizN9D5cCZKQeNACD+YrbGlRcMwLyBASpfW56KwuZ8qFxdYuLHi5i2VfHpnOs+yF1d0h+ujjbIWNKf9/O997PppsVKvJYHCY+uzYjQ/l2ojamuBvkxMZNz23dfoFqEAAVBiJF1b16fc9tTHJaG8lk+SrNuCSGEGBIVRbd8fC4+AP2W4xNCCB9162hP6WOsdEX6FEYH9FvZoKpoNVfG+qbWtwi5PscrRM31uikHjSRShgW7L2ps4+Zog74BqidF6hOIULdyRh7I0vQeosLoRBW+Kx8AYPXwZ7XnrMQifD0siOdzAqviudeaqEl8amDYWokEm2jGdzXIh7/zWynOl0TKcOhyLqe2YhHQ/X/cx14tGQVBiFF1a1YPNhzfheduFWiMRkukDGf/e8BpX0KeDAkhhJCqJFKGwxx/mFJRdPMkkTIcuMjtbwwAkW08aRUIIaTGmHv6HU0TffVZ2eDl6qDyflOu2aBvEXJ9jpc+2xoraMSlXom6mjOALGDham+t03OrWzljJRZh3sAALe/rJ4i/mK3T8xLLxXflg6racwOCGuKFVvzGv1YfuWZyq0EkUoZTNx5ybj+xZ3PBJprxXQ2y4+wdgx6/k9fzIeG42KStjwtNuHuKrsSIUVmJRejj78mprba6ICev56OC4zmmd2vVP04IIYQQIfBJk0RF0c3Tqvh0Xu2/fr2jgXpCCCHVmXL6HX0KowO6r2xQN0sfMO2gkb5FyN31KPSt7libctBI37+llViEMH8PnZ5b3coZAOgb4KW1TsmC3cZJUUdM14LdF3i1V1d7bsPYbnC15z7xigGYvO0Mr+c2tMRreZwLoosATOkrbAooPqtBGAy7mobPavSqQbHajIIgxOjeCGnKue2mJPUfdD4ngVHduD8nIYQQwhefGlWUntH8SKQMq49wT03Q2ZeKERJCapa+dSQMSd9Bal1XJ6ibpQ+YdtBI3+Pl5aJ7n9Uda1MOGgnxt/RyU71iSJv7xerfm9oCRwzGS1FHTFN5hRTX7hVzbt+/rZfGVcen5vArlL43LbtGaltwxScg1DdA+InPfFeDfH3UMKtp+KTCAoDR3f0E74O5oiAIMbpuzerBiuO56fDleypPInxOAtZiSoVFCCHEsG4/KOHUjtIzmqdV8emcZ6IBVIyQEFLz9K0jYUj6DlLrurJB0yx9Uw4a6Xu89EnvpK6GiikHjfStOQMATMdxywcaAnSmHDgipunD37jXlRABWD2ig8Y2ttZi9G/LLROL3Bs/nOTV3lD4BoRGhxhm4J/PahApM8xqED6psHzdHSgdbyV0JIjRWYlFaNPQhVPbCqnqlFh8TgIB3s40G5MQQohBlZRXcGpH6RnNj0TKsO4Y91Ug1mIqRkgIqXn61pEwJG2D1CJoDjjourJB0yx9Uw4a6Tuor096J3U1VLgEjQDggYZjbkza3vV1dUy5puntYcqBI2J6JFKGP1LvcG4/uXcLTuen1SP4pWf9J/OBSawG4RMQMuQkM1trMVo0qMO5vSFWg/DJgjOym6+gz23uKAhCTMLA9g05tz2Rca/afXzSjlA+PEIIIYYkkTIcvVL9u0qVTr7cl1QT08Cn3gsAvNOL20UpIcT0rVmzBk2bNoW9vT26du2K5ORkje1//fVXtG7dGvb29mjXrh327t1bQz3Vv46EOkWlFZgQm4znPzuMAV/9hcOXcnkP8MRfzNaaFmjewAC1584ufu5wsuM/lKFplr6hgkYl5RLM+uMcen9xBBErjmH90WsGGVDU1isPDgGLqlzsrTUGVj6O8te6j0Vx/GpcSKQMRy7kYMBXf+H5zw5jwqZTKCrlNrFETt+aMwBQ31nH1UbNNK820hbMctOyQsUc8T1vEhk+tef41L+wEovwbu/mvPry4e/cAxCGwDcgJGRBdFXmDWzDua3Qq0EkUob4i5QKS1cUBCEmYXT3ppzb3nlY/cc817QjsueikwAhhBDDOXk9H2UV3C746+tRrJQYx7L9lzi3FYuEL8pICDGOn3/+GdOmTcO8efNw5swZBAYGIiIiArm5qgcjEhMTMXz4cIwfPx5nz57F4MGDMXjwYKSlpdVIfw2ReufF1X+h7fwDOHT5Hm4+KEHanUKM23QKLT/ai/1pmgMIchIpw4LdFzW2cXO0Qd8AL7WPW4lF6KEhtZU6msbEDBE0evPHU/Cfux8/Jd9CZt5jpOcU4dP96Wg5Zx+W7tV8DKr2Td9B/bsqrqG1CWleT+NAYt062n/D8Dlm+9PuotWcfRi7+TTS7hTi5oMSHLqUi7bzD2Dg6uOc+y3Ee1+X1Ub21mK9Z59b2pQJvudNIsN31fFLwT68Bv2n9G3FazB417k7Rlk1KGeogJCuureozzmlPwCsP54h2PFLvJYHrmF0SoVVHR0NYhJsrcUIbOTMqW3JE0n1+zimHQlq7EonAUIIIQbFZ3WiujQTxDSVV0iRequQc/uYUFoFQoil+PLLL/Hmm29i7NixCAgIwPr16+Ho6IgNGzaobL9q1Sr069cP06dPh7+/PxYtWoQOHTrg66+/rpH+Cp16p9eywzh/W/X5T8KAiVvOcAqEcAk2aBvQBwAHW/41LjTN0hc6aPTmj6c0ztb95ngm50CIEH1rWJf/7w0HGyudn49vu/1pdzFxyxlUqBko/Pf2I/RadpjT8wnx3u/i5w47a37f3+0buWr8zucSzHrA4b1vTvieN4kM31XHnw4J5LV/K7EIXw0L4txeIoXKtPQ1wdABIV1YiUWIDuW+mqZcojqtvy74FIenVFjV0WgwMRkD2jfi1O74FeXi6HzSjkS105znlRBCCNEX19WJDjZii0t5YOlm/cGvQCWtAiHEMpSXlyMlJQVhYWGK+8RiMcLCwpCUlKRym6SkJKX2ABAREaG2vdCETL2z8/RN/Jev/btt2i/ntM52FWrgnO+gvrVYc454IYNGJeUSTulKvjmeySk1lhB9667DyhkfN83PK9Qxk0gZpmw7o3U//+WXYMeZ21rbCVHk3kosQvtGblqfq+o+NalthdF1OW8SGT41H5o3cNRpou+AoIbwcuZe++bzA9xXQgvJ0AEhXU3p24rXyq1NSdz/purwLg5PWXCqoSAIMRlc826WPJEqRVEp7QghhBBTcv7WA07t2vq40CoBMyKRMuw4wz0f8csdGtLflxALkZeXB4lEAk9PT6X7PT09kZ2drXKb7OxsXu0BoKysDIWFhUo3Q+JyhpJIGd7/4zyn/T0ulyDxqubVkEINnPMd1G/i7qDxnCxk0Ojtzac494tLrn19C8kDQLdm9TSmA1OlR/P/t3fn8U1VaR/Af0lKN5CW7i1ri0ApSwsItYgKWCyCIDryghvLy6AwomhRhipSFrW8IIiiMwwqggsDo4OIytSpVdxadiqylGFpAaEpQmkrBVqa5P2DSWzaNDk3uUlukt/388lHSU5uzr1NT5PznPM8kTb75WiwAbie3qVWcJLz2Y9tB9rkKnIvdaGKrcCcrxVGt2fcdPUYqERSaz7Mv7un3a+1dGyKcNuffql2S4F0VwSE7KFRq/CEhNoqeYek189qTMpiLFdeC0/CK0KKISXvZsPi6Ew7QkRESlFXr8eJ82I7QeJC+TfJk0jJwQsAOff1dlpfiMg75eTkICQkxHRr37693ceSK/XO9hMXoJMw+L3+tfUCsCLBhjYCwYabE8Il5WTv1TZEvHEzRINGPxwTT3vy2X7bxdYdLSQPXJ+wu6ljqHC/NGrYrG8hV7Dhn3t/Ee5Xvd52Whm5itxLDbS5KmjkzeQcAz2VlM+bahUwsIv0XV5GA2+MkDQp/Mg72+1+LXvo9AZ86aKAkD1mDusm3NYAxwqk6/QGfLpPfDGWq6+Fp2AQhBRDSt7NXQ0+rIvmzGTaESIicrZ1BaXCbe3Jz+2o0tJSTJkyBfHx8QgKCkLnzp2RnZ2Nuro6s3b79+/HrbfeisDAQLRv3x5LlixpcqyPPvoIiYmJCAwMRK9evbB161azxw0GA+bNm4fY2FgEBQUhPT0dR48eder5OZOUHLzJ7Vpz9RWRF4mIiIBGo0F5ebnZ/eXl5YiJsVzAOyYmRlJ7AMjKykJVVZXpdvr0abv7LFfqHSkLzgCg6Jcqh1e7ijxbo1bhnuQ44WPe39f6ZKqcQSMpp39NZ7B6TDkKyRs9MVQ8ReM9ybbz6ssVbPj5lyrhfgHmCyItkavI/c0J4RAtC+IvUBRdrqCRp7Bn3JRzDPRUUoKC9/ZxbNexRq3CvX3Fx9EdJRdduhtESkF0RwNC9tCoVRjQKVS4/Rvbjtn993H7iQsQTIDjlmvhKfjtjBRDSt7NA2erodMboNMbsP+XSqHnMO2Ib/nuu+8watQoxMXFQaVSYfPmzWaPi0zOVVRU4KGHHkLr1q0RGhqKKVOm4NKlS2Zt5JgoJCLv8dl+27mqjWytGHSG4uJi6PV6/O1vf8PBgwfx6quvYtWqVXjuuedMbaqrq3HnnXeiY8eO2LNnD5YuXYr58+dj9erVpjYFBQV44IEHMGXKFOzbtw9jxozBmDFjcODAAVObJUuW4PXXX8eqVauwY8cOtGzZEhkZGbh61fNyXUvNwTs7o7sTe0NErubv749+/fohPz/fdJ9er0d+fj7S0tIsPictLc2sPQDk5eU12x4AAgIC0Lp1a7ObveRKvSO1SLOtSX2RYINIYXQAWHy/WO73YH+NzQkhdwWNAEBb1fwOUrkKyQPXV30HCc7qi+TVlyPYoNMbcPKC+N9XwHxBpCVy/Sw1ahWWjxV7jy29v7fLgkaewp5xU84x0FMVShhD5Nh1nHOftBoartoNIr0gunvS0EoJLusN9u8GkZIWLL17FOc+m8EgCCmK6E6NK9f02FlSgZ0lFcL1QPpzF4hPqampQXJyMt58802Lj4tMzj300EM4ePAg8vLy8Pnnn+O7777Do48+anpcrolCIvIOOr0BB8+I5S62VaDVWYYPH453330Xd955JxISEjB69Gg888wz2LRpk6nNhx9+iLq6OqxZswY9evTA+PHj8eSTT2L58uWmNq+99hqGDx+OZ599Ft27d8eiRYvQt29fvPHGGwCuB5pXrFiBuXPn4p577kHv3r3x3nvv4ezZs02C0p5gzsfiOXj9Ne752RKRc2VmZuKtt97CunXrcPjwYUyfPh01NTWYPHkyAGDChAnIysoytZ85cyZyc3OxbNkyFBcXY/78+di9ezdmzJjhkv7KkXpHyoKzhqxN6stZHNrfT43HbrNd+HX5/yTbnBCSK2h05qJYSsyGzl+qbfYxOa+XRq3Cq+P72Gz32G3xQrsZ5eiblNXNRsYFkc2Rs/bG6L7t0Lud9Yn43u1a456UtjaPJdcOFU9ia9wkc3X1epT/Vme7IYC4kEBZdh37+6mR2qmNcHtX7QaRWhDdXWloB94YISk14+vfSN8NotMb8NVh8bRgE9NYEL05DIKQokjJu6mtugJttfhqUnesuCX3ueuuu/Diiy/i3nvvbfKYyOTc4cOHkZubi7fffhupqakYNGgQVq5ciQ0bNuDs2eu5GOWYKCQi77H9xAXoBD/TDk2MVMwKnaqqKoSF/T4JVlhYiNtuuw3+/v6m+zIyMnDkyBFcvHjR1CY9Pd3sOBkZGSgsLAQAlJSUQKvVmrUJCQlBamqqqY0lSiyIqdMb8EmReA7eabd1VszPlojkM27cOLzyyiuYN28eUlJSUFRUhNzcXFPR31OnTqGs7PdV3gMHDsT69euxevVqJCcn4+OPP8bmzZvRs6dr8nTLkXpHyoKzhqxN6stdHDprRBIeuy3eYr2OoBYqrHq4L4b3tH4dgN+DRtZGb5HC6AaD9OtVeaX5nTFyX6/hPWOx6uG+CPbXWHz8sdvikTUiSdbXtNbOnp0zxgWRzZGr7ozRlhm3Ir17lMXH0rtHYsuMW4WOI2dAy1PYGjfJnJS0uqNTbI9rot7/482S2s/5p/jiIHstzT0s3NadRcA1ahUeHyxeIB0AZny4R1J7Kd8xReo5+TI/d3eAqKGbE8IR4KcS+rB9/cO12CRDUAvbOTrJd9ianBs/fjwKCwsRGhqKm266ydQmPT0darUaO3bswL333tvsROH//d//4eLFi2jTpg0KCwuRmZlp9voZGRkeuRKaiKyT8kVeKSt0jh07hpUrV+KVV14x3afVahEfb94/45dVrVaLNm3aQKvVNvkCGx0dDa1Wa2rX8HmW2liSk5ODBQsW2H9CTlBw7LxQfnrg+qeSmcPEt8UTkWeZMWNGszs5tm3b1uS+sWPHYuzYsU7ulWWiqXdmD+/ebCBEyoKzhqxN6huDDdZWxEstDp01Igmz7kzEuoIS7Cq9iJb+GtzXt931FbqCQWmNWoXsUUmY9sHeZttUXr6GvENaq0GVq9d0wv02OnbuUrOPGSf0raUQkzKhD1wPhAxLikHB0fP4575fcLlOh/6dwjBxYCdJE4kifbMVOLJn5wxgfbeRCKmhqrcn9seVOh1e3noIpRcuo1N4MJ4bkYSgZoJJlsgd0PIU1sZNMvfB9lLhtrfeaDkwZw/jbpAdpReF2n+y7yyWjk1x2qKfuno9in4RXwjl7iLgM4d1w8pvjguPK/86WI66er3weCslINS3fSgXY1nBnSCkKBq1CoO7iu3YqLhch4rLza8yaui2rspZcUvuJzI5p9VqERVl/sHCz88PYWFhZm0sHaPha9iaKLREiSuhici23APWJ5uMnJEuac6cOQgJCQFwPairUqma3IqLi82ec+bMGQwfPhxjx47F1KlTZe2PvZRYEFNKQfR7+9guJEtE5ApypN6psLKjwxprmyGcVRza30+Nqbd1xuoJN+HV8X1wqx3f/4YlxVjdQaACsOCzQ82mMtHpDfjRjp0NhccvOFQHwp5natQq3NotEivG98HqCTdh6m0JTllJbesnEBtq34R/RU3zKYPkrDvTUJC/BovG9ML7U1KxaEwvSQEQQGyHishuI/JOdfV6nKwQC+45I62ulN0gBthf20KElDS0SigCrlGr8MQQabtBRr7+nVA7qQGhJyXUKPFFDIKQ4gT5i21QKqu8ij2CkeqgFtI+oBC5U05ODkJCQky39u3bu7tLRGRDXb0ex3+9LNQ2uV2I7BPls2bNwq5duwAAu3btwuHDh5vcEhISTO3Pnj2LIUOGYODAgWZ1jAAgJiYG5eXlZvcZ/x0TE2O1TcPHGz7PUhtLlFYQU2pBdJFCskREriBH6p3QYP9mH7Om3MoOEiUXh7Y1eW6A9cDRzpIKXKqVniu/+mq91WM6Y0JfDiJ9u2ijb+dsBOqaY+296clpp7iMwndJSYXljLS6UmuDvLFNem0LETq9AZskpKF1V0H0xmYO6ybp9/fouRp89pPt8xz52rfCx1RCQEjpGAQhxRFdDXK5rh5FpyuF2sbZucKEvJPI5FxMTAzOnTMvPlVfX4+Kigqbk4ANX8PWRKElSlwJTUTWSfni0t8JK/wiIyPRtev1lT9du3ZFYmJik5sxdd+ZM2cwePBg9OvXD++++y7UavOPg2lpafjuu+9w7drvExt5eXno1q0b2rRpY2qTn59v9ry8vDykpaUBAOLj4xETE2PWprq6Gjt27DC18QRZm8RXorkzHzERUWNypN6pvCxWoLex/MPnmp0cU3JxaEcnz+1NHwY0n95JyRP6jvZNarHfhqy9N5WadkqOoBF5r8/2nxFu66y0ulJ2g+gNztkN8lreEUnt3VUQvTGNWoXX/kfaYqgn/77PaiDp86IzOCq4yA5QTkBIyfhNjRQnLDhAqN33R8+jTrA6EIuiU0Mik3NpaWmorKzEnj2/F636+uuvodfrkZqaamrj6EShJUpbCU1EtknJ4evOv0nGAEiHDh3wyiuv4Ndff4VWqzVL0ffggw/C398fU6ZMwcGDB7Fx40a89tprZvWNZs6cidzcXCxbtgzFxcWYP38+du/ebcr5rFKp8NRTT+HFF1/Eli1b8PPPP2PChAmIi4vDmDFjXH3adtHpDfh0n/hKNHfnIyYiashYe8OWi1bSCtm7E6TySvMTuUqe1Hd08tze9GFA8+mdlDqhL+U1m2u3s6QCVVfr7Xpta+9NW2mnVJBed0YOSn7vk3vp9AYcEEx55IxUWEb+fmrcGNlSuL3cu0F0egPe3HZcuH1qfBtFLUAa3bcdoltbT3nXkAHA2L8WWHxMpzfgiQ1Fkl5fKQEhJbPr3fLmm2+iU6dOCAwMRGpqKnbu3Nls22vXrmHhwoXo3LkzAgMDkZycjNzcXLs7TN4v4gaxIMiVa2JbjQP9WBTdF126dAlFRUUoKioCcL0YelFREU6dOiU0Ode9e3cMHz4cU6dOxc6dO/Hjjz9ixowZGD9+POLi4gDIM1FIRJ7P3Tl8pcjLy8OxY8eQn5+Pdu3aITY21nQzCgkJwb///W+UlJSgX79+mDVrFubNm4dHH33U1GbgwIFYv349Vq9ejeTkZHz88cfYvHkzevb8PRAwe/ZsPPHEE3j00UfRv39/XLp0Cbm5uQgM9IzdmdtPXEC94Pc6bj8nIqXRqFV4YWR3m+0WfdF8jYtCO+pbGDW3s0HJk/qO1mwIa2lf0Oj6cS0/V6RPUgujy0Uk0GYt2ODIzhlrO0HyDmltpjXLHpXk8hXTSn7vk3sVHDsP0UR6zkiF1VD2qB7CbeXeDVJw7DwE1zkDAN6fIr5zxVW+n50uqf3e05UW02KN/esPkuo9KS0gpFSSr9DGjRuRmZmJ7Oxs7N27F8nJycjIyGiSNsZo7ty5+Nvf/oaVK1fi0KFDmDZtGu69917s27fP4c6Td4ppLe8f/cFO/iNByrR792706dMHffr0AQBkZmaiT58+mDdvHgCxybkPP/wQiYmJuOOOOzBixAgMGjTILHe+XBOFROTZ3J3DV4pJkybBYDBYvDXUu3dvfP/997h69Sp++eUX/PnPf25yrLFjx+LIkSOora3FgQMHMGLECLPHVSoVFi5cCK1Wi6tXr+Krr74ypezyBAUSJv/Su0fxswYRKU6blrYXlzWXekqnNyDvULmFZ4hpbmeDkif1RVgb6aMc+B5rb+oxwL7C6HJwtMi9Iztnmgsa6fQGLPjskI3ntsCwpOZTEzuLMWhk7T3Ewui+aeXX4oEEZ6XCMhp4YwQ0Ej7SyrkbRMp1UGoaWqm1VQDgiUZpsT4vOoO9p8WLoQPKDAgpkeR3zPLlyzF16lRMnjwZSUlJWLVqFYKDg7FmzRqL7d9//30899xzGDFiBBISEjB9+nSMGDECy5Ytc7jz5J0GxIfhhkD5Cpn36yBtACLvMHjwYIuTfGvXrgUgNjkXFhaG9evX47fffkNVVRXWrFmDVq1ambWRY6KQiDyblFRYzv7iQvLZeeKCcFv+XIlIiRxJv+NIqiLA/lRagPsm9R2u2eBAx8NaWQ5YKbkwuqNF7h3ZOdPcLiWRmjPuul4atQrZo5Ksvk0qL19D3iGtlRbkbXR6A3afrBRqq1HD6TvKNWoVHh/cWbi9XLtBdHoDdpZWCrdXchpaKbVVjHrN2wrgeoaBGRLTYHEXiDhJV6murg579uxBevrv23vUajXS09NRWFho8Tm1tbVN0h4EBQXhhx9+sKO75As0ahXu69NWtuOFCayAIiIisocnpcIicVK+kPq54AspEZE9HEm/40iqIqD5nQ1KntR3tGbDOQd2NkQ1EwRRch0JR4vcO7Jz5qvD5ywGV5R8vQBgWFKMzXolCz5rPkUdeZ/tJy5A9Mfdt32oS3YezxzWzeqOpcb+8q3ju0GkFERXehpafz81RvSMlvScy/VApzlfoOvcf0l+Pe4CEScpCHL+/HnodDpER5v/MKOjo80KajaUkZGB5cuX4+jRo9Dr9cjLy8OmTZtQVtb8ioHa2lpUV1eb3ci3tGsTLNuxHNlaTEREZE3Wpp+E27o7FRaJKzh2XnhBb1Jsa/5ciUiRHKnZ4EiqIqD5nQ1KnqR2tGbDj0d/tfu1d5U2EyhQcB0Jh3+WDsyZVl6xHChT8vUCbAcBDbAeOCLv835hqXDbJ4e6Jq2sRq3CE0PEd4PU64GCo/bXkNLpDVj5jXhBdE9IQ7vywX6SAkn2GtEzhrtAJHD6lXrttdfQpUsXJCYmwt/fHzNmzMDkyZOhVjf/0jk5OQgJCTHd2rdv7+xuksJUXrG+OkgKR7bZEhERNUenN+CTvU0L2TWHKZM8h5ScxKOS45zYEyIi+zlSs8HR71DN7WxQ8iS1I4XRHa2hsraw1OJKakeLjzuToz9LR3bOAJaDK0qvOaPkICC5nk5vQP5hsXHDT+3a3Q9Sd4PM//yA3a/1Wt4RSTFRT/hOpVGrsHJ8ilNfQ60CVj7Y16mv4W0kBUEiIiKg0WhQXm7+S1peXo6YGMuFpSIjI7F582bU1NTg5MmTKC4uRqtWrZCQkNDs62RlZaGqqsp0O336tJRukhdQyRgzjQkJku1YRERERgXHzkMv2JapsDyHTm/Abgk5iScOVP4XMSLyTY7UbHAkVRGAZiuI25qkVsF9k/oimvuW6mgNleZSgDlafNyZRAI0AHCxxnJmBkd3G0XYmfbanYmmlBwEJNfbfuICrgl+mejjolRYRlJ3gxz/9TLq6kW/Gf1O6i4Qf43nfKe6O6Ut+rRv7bTjvza+j+J3xCiNpCCIv78/+vXrh/z8fNN9er0e+fn5SEtLs/rcwMBAtG3bFvX19fjnP/+Je+65p9m2AQEBaN26tdmNfEuaTINa60A/xX6AJiIiz7bgs4PCbZkKy3NICW51jgzmFnQiUiyHajY4OFN8rpmaInmHtDbTAWWPSnLL30xHCqPLsXLf0jEcLT7uTBq1Ci+M7G6z3aIvLNe4+OXiZcc6YOEtouSaM4Bju43I+xQcF08h1d8N74mZw7pJan/7kq8lv4bUXSDTbuvsUd+pPp4+yCnHHZoYyd3odpD8rS0zMxNvvfUW1q1bh8OHD2P69OmoqanB5MmTAQATJkxAVlaWqf2OHTuwadMmnDhxAt9//z2GDx8OvV6P2bNny3cW5HVuTghHUAvHJxXu7dPWowZIIiLyDHX1ehz7tUa4vSds26brpKTCmn93Tyf2hIjIMY6k3nE0VVGFhdX/Or0BCz47ZPV5ocEtMCzJcpYJZ3PkekU0k/5LCks7GxwtPu5sbQR2Y1jqn05vwKc/iacUtcRSoM0b0k1x9sJ3SPm9vaVzpBN7YplGrcJ9KeIT7WXVtfi06Ixwe53egNcl7AJRq4CZw1xTF0UuGrUKf3mwj6zHbBcaiDWTBsh6TF8heZZ53LhxeOWVVzBv3jykpKSgqKgIubm5pmLpp06dMit6fvXqVcydOxdJSUm499570bZtW/zwww8IDQ2V7STI+2jUKozsZX3br4gOYfIVWCciIjJ65O3twm09adu2r5OSCkutcm1uZiIiqRxJveNoqqJfKq80uU9kQt+dq/QdSlUkx0YMC7PfSp/Ut7d/O0sqUFHjWB1QS4E2paebcmS3EXkXnd6AfScvCrV153eJxfcnS2o/c0OR8M60+//6g6Rjzxh8o0cuch7ROw5Tb+0ky7GCW6jxw5w7ZDmWL7Jrqf2MGTNw8uRJ1NbWYseOHUhNTTU9tm3bNqxdu9b079tvvx2HDh3C1atXcf78ebz33nuIi+OWHbItrbPjEwuhwSyKTkRE8qqr12NHqdiXFsDztm37MimpsPp1cG1uZiIiqRxJveNoqqItRWebTIQpfULfkSLk52scCxoBlnc2KH1S397+aZtJlyZFmIXdNyyMTp5i+4kLqBcMng5JjHLbZ05/PzVSO7WR9Jyxfy2w2ebzojPYd7pa+JieuAukoedH9sDUWx3LDNBCBRxadJdMPfJNTGJMilV52XIBNVcfg4iIqKE5H/8k3FYFz/7A7mukpMJ6cih/rkTk+SxNq8mRquhCTV2T1exKn9B3pAi5HOmwLO1suCgQXHFnIXl7A0eO7jQCgCg7rzkLo5MSvFdQItx2ws2dnNcRAe//8WZJ7feersRnVv6G6PQGPLGhSNIxPXUXSEPPj0zCXx7sa9dzW6iAozkjZe6R72EQhBTL0soOycdoyZ0gREQkH53egE1F4hND9/aJ8/gP7L6CqbCIyNvYm3pHjlRFQNPV7Epfpe9QEXIZZtYbf//V6Q1Y9MVhm897YaR7CskD9geOZPmezsLo5KF0egO+Kj4n1NZP7f60uv5+agzvGSXpOU/8fV+zabHu/+sPkoZMT98F0tCI3rE4/vIIdAoXT92fFBPMAIhMGAQhxbJ3ZYfZMVpzBQUREcnntbwjktov/oO0PLrkPkyFRUText7UO3Kl4rFU6NsWd67Sd6QIuRzpsBp//xXpDwC0cePCP3sDR3J8T88/XN7kPm9IN8VPF95v+4kL0Al+6EyKvUERnznffPAmyc8ZsOjLJvct+vyApDRYAPDquBRFXAO5aNQqbHt2CA7Mz8CQLmEIaHRqagAt/TUY0i0SB+ZnYOtTQ9zST2/k5+4OEDVLjjHOnZ+iiYjIq+j0Brz+zXHh9p0jg+Hvx/UmnoKpsIjI29ibekeO1E4Amnyfk7JKP80NK58dmUCX5Zo1ul6eMKEvJXBk9jOV4Xv6p0Vn8XyjXTBKTzclZXeWO34HyDUKjp8XbjsqWRk1lTVqFZ4c0lnSd6ELV3RIe/nfKHzuTgDA50Vn8c4PJyW9bmzrANyT0lbSczxFq0A/vDslzd3d8Cn8Zk6KdV6GPKFyrMghIiICgCfW75HUfv7dPZ3UE5KbTm/A7pOVQm2ZCouIPIXdqXdkWkjW+Puc0if1HZpAl+GaNd7ZoPQJfcD+n6kc39Mt1Z1xpLi9Kyj9d4BcQ0o6tokDHSumLaeZw7pJnkQuq76GTnO+wPQPdmHGhn2SX/Pb2UMlP4eoOQyCkGLJ8WGOBcWIiEgOdfV6bD3QNO1Cc/zUnCj3JNtPXEAzaYubYCosIvImlkYzuRaSNU6HpfRJfZEJdAC4aKGA+dfF4p8RmvNp0VmztFFKr6ECuH+3UeNggSPF7V1B6b8DpaWlmDJlCuLj4xEUFITOnTsjOzsbdXV1Zm1UKlWT2/bt282O9dFHHyExMRGBgYHo1asXtm7d6urTUSSd3oB9Jy8KtVXarnKNWoXXx6fY9dx/HRCrgdLQiJ4xijp/8nx8N5FiDYgPQ2iQ9Q991rCgGBERyaX/i/+W1P5Pt9/IiXIP8n5hqXBbpsIiIk9hb2F0Z6XDUvqkvkatwgsju9tst+iLQ2bBCp3egE+Kzjj8+pZ2Ntji7uzP7t5t1DjQ5lBxexdQ+k6V4uJi6PV6/O1vf8PBgwfx6quvYtWqVXjuueeatP3qq69QVlZmuvXr18/0WEFBAR544AFMmTIF+/btw5gxYzBmzBgcOHDAlaejSNtPXEC94Nsvo0eMcztjh7tT2mJoN+enalOrgJUP9nX665BvYRCEFEujVmHiwI52P39SWidOQBERkcP+993tqLqqE26vVgEzh3Gi3FPo9AaLxVUt4Q4fIvIkdqfecVI6LBHuntRvI1DMvXFx9J0lFaiosR5sEtXwZyGlhoqSWfpGLsfOGUsHd6S4vSsofafK8OHD8e677+LOO+9EQkICRo8ejWeeeQabNm1q0jY8PBwxMTGmW4sWvwfDXnvtNQwfPhzPPvssunfvjkWLFqFv37544403XHk6iiSlHsgtnSOd2BP7rZl8MyJaOrfE9Gvj+3A+j2THIAgp2oB4+yPM/TtxFwgRETnm86Iz+PrIBUnPmTGYu0A8yfYTF3BNL9a2T3umwiLyRRUVFXjooYfQunVrhIaGYsqUKbh06ZLV5wwePLhJuphp06a5qMfX2Zt6x1npsDxhUt+ewJGc9RsaXjNPqB9hz24juXbOAJ5Xd0bpO1UsqaqqQlhY07mV0aNHIyoqCoMGDcKWLVvMHissLER6errZfRkZGSgsLLT6WrW1taiurja7eZudJ8S+V/hrVLi5s/N3XNhrx/N3Ou3YQxMjFVMQnrwLgyCkaI4UR2dRdCIicoROb8CMDUWSnqNRcxeIp5GyIq8/02wS+aSHHnoIBw8eRF5eHj7//HN89913ePTRR20+b+rUqWbpYpYsWeKC3v7O3hoXzkqHpfQJasC+wJFs1wswu2ZKrx8B2PczlXPnjKfVnVH6TpXGjh07hpUrV+Kxxx4z3deqVSssW7YMH330Eb744gsMGjQIY8aMMQuEaLVaREdHmx0rOjoaWq3W6uvl5OQgJCTEdGvfvr28J+RmOr0Be05VCrVNiGip6IU3GrUKb9hZH8SadqGBWDNpgOzHJQIYBCGFa/yhRtJz5fwwSkREPqf3/FzJz1kxjlu3PY2UiQalpiUgIuc5fPgwcnNz8fbbbyM1NRWDBg3CypUrsWHDBpw9e9bqc4ODg83SxbRu3dpFvb7O3hoXcuWkapxqUOkT1ICdNS5kXLTfcBGg0utHAPb9TGUNcjX6yHVRYCGkO6+ZuwKBc+bMsVjMvOGtuLjY7DlnzpzB8OHDMXbsWEydOtV0f0REBDIzM5Gamor+/ftj8eLFePjhh7F06VKH+5mVlYWqqirT7fTp0w4fU0m2n7gA0U0+7cOCndsZGdyd0hbp3eX7bBzcQo0f5twh2/GIGmMQhJTNkXkk5ewgJSIiD9NnQS5q6gRzJBmf0z6EW7c9jE5vwJ7Si0JtlZ6WgIico7CwEKGhobjppptM96Wnp0OtVmPHjh1Wn/vhhx8iIiICPXv2RFZWFi5fvuzs7jZhT40Lueo1fFp01iy4ovTC6KIaf0WVMwNBw2CB0utHAPYFjeRcrNgwaKTTG7Doi8M2n/PCyCS3XTN3BQJnzZqFw4cPW70lJCSY2p89exZDhgzBwIEDsXr1apvHT01NxbFjx0z/jomJQXm5+ThSXl6OmBjrhb4DAgLQunVrs5s3kbL7WOnjoNHbEwfgjkTHAyEt1MChRXfJ0COi5jm3kg2Rg5gOi4iIXO2WnDxcvCJeCN3o4+m3OKE35EwFx85DNNQ1JDGKu3yIfJBWq0VUVJTZfX5+fggLC7Oa2uXBBx9Ex44dERcXh/379+PPf/4zjhw5YrHAsFFtbS1qa3//DiNHPnypK8/lrNdwoaYOO0sqkCYhgOzudWxSalwYz0t0Ul+lAgxWTlCtAvp1bGP6t2j9iNnDuyv671OTnsn4Q26YOUIk1RQAtGnpL18HJDLu7rHVz8Yp6hwVGRmJyEixieozZ85gyJAh6NevH959912o1bbXThcVFSE29veAXVpaGvLz8/HUU0+Z7svLy0NaWprkvnsTKbuPJw6Md2JP5PXOpAFY9PkhvPNDiV3PD/YDDr04UuZeETXFnSCkaEyHRURErnTXq9twpkr6F8+VDzANlida+fV/hNtOuLmT8zpCRC5nT3oYKR599FFkZGSgV69eeOihh/Dee+/hk08+wfHjx5t9jjPy4UtdeS5aryHAT2wqoXEtCG8sjC46qW8tAAIAegOw5+TvuxM9oX6EPYXRZV2s2OCjlyfUnLE7RZ2LnDlzBoMHD0aHDh3wyiuv4Ndff4VWqzUL+K5btw5///vfUVxcjOLiYrz88stYs2YNnnjiCVObmTNnIjc3F8uWLUNxcTHmz5+P3bt3Y8aMGS4/J6XQ6Q3Yd1Js93HnyGD4C46xSvHC3Un4y4N9IfXr0JCuYQyAkMtwJwgpG9NhERGRC+j0BiTPz8UliSmwAGBoYiTTYHkgnd6A3Scrhdpq1GAqLCIvM2vWLEyaNMlqm4SEBMTExODcuXNm99fX16OiosJmapeGUlNTAVwvNNy5c2eLbbKyspCZmWn6d3V1tcOBEGO6ImsT1Q3TFYlOEA/pFoHcg+dstmu4qM0TJqntSVck56R+w3P3hOtlTx/lTPWUf7gct9wYIem47qw5A0hLUSdlF5Uc8vLycOzYMRw7dgzt2rUze8zQIIq3aNEinDx5En5+fkhMTMTGjRtx//33mx4fOHAg1q9fj7lz5+K5555Dly5dsHnzZvTs2dNl56I0209cQL3gHFVGD/G/LUoyoncsjvYcge8On8P0v+/BVSsn3C26JTY/fiuC/DUu7CH5OgZBSNGYDouIiJztk71n8PQ/iux6bmTLFlgzaYC8HSKXkFKcsm/7UO70IfIyoulh0tLSUFlZiT179qBfv34AgK+//hp6vd4U2BBRVFQEAGYpYxoLCAhAQIDrd7M3HN1EJ4j7dQwXCoI0PLgnTFJLDRoB8mYgaBg08oTrZU8f+3VsA7UKVv8GqyC2pvHTorN4/r81PkR+dkqoOaPk4NakSZNsBocnTpyIiRMn2jzW2LFjMXbsWJl65vmk1AO5pbN8xcZdTaNWYUiPaBS/OAKXrtbjiQ93YfuJCtQbVIhqHYCHUjvij7cmeNxOF/IOfNeRojnygc7dKzxI2ebPn98k5UFiYqLp8atXr+Lxxx9HeHg4WrVqhT/84Q9NirudOnUKI0eORHBwMKKiovDss8+ivr7erM22bdvQt29fBAQE4MYbb8TatWtdcXpEJOBKnQ5J8/5ldwAEALY/P0y+DpFLSfky+uTQrk7sCREpWffu3TF8+HBMnToVO3fuxI8//ogZM2Zg/PjxiIu7vgvwzJkzSExMxM6dOwEAx48fx6JFi7Bnzx6UlpZiy5YtmDBhAm677Tb07t3bpf2Xmq7IOEFtjVoFhAvWVWi4qO2iwCK12JBAt09S2+LMGhcND+4JheSNNS5saVjjYs/JizYXIRgA3BBoe82use6MKCUki/CE4BbJL/eA9fo+Rv4aldfsPm4V6Id3p6Th8EsjcfTlEfhxzh3405AbGQAht+E7jxRtQHwYwlpa/+BnSXhLf8V/eCb369GjB8rKyky3H374wfTY008/jc8++wwfffQRvv32W5w9exb33Xef6XGdToeRI0eirq4OBQUFWLduHdauXYt58+aZ2pSUlGDkyJEYMmQIioqK8NRTT+GPf/wjvvzyS5eeJxGZu1KnQ1pOHrrPy8VlO9JfGf3lwb7cHeDBRCdN/NTAwC4RTu4NESnZhx9+iMTERNxxxx0YMWIEBg0ahNWrV5sev3btGo4cOYLLly8DAPz9/fHVV1/hzjvvRGJiImbNmoU//OEP+Oyzz1zed6mrzkUmqPUG4OJlsfpZxolcnd6ARV8cttn+hf+u6ncXe2pcfF1cbqW1NPmHpR3L3ZP69tS4EH1P9u0QKtTOeDxPqDkDiAW3Gu82Is9WV6/H8V8vC7VNbhfC7xdETsJ0WKRoGrUK96a0xTs/lkp63j0pcfzDQTb5+flZzOVcVVWFd955B+vXr8fQoUMBAO+++y66d++O7du34+abb8a///1vHDp0CF999RWio6ORkpKCRYsW4c9//jPmz58Pf39/rFq1CvHx8Vi2bBmA6ysJf/jhB7z66qvIyMhw6bkS+Tqd3oDvDp/DExv32lX3o7Epg+IxonfzKU1I2aQUp+zDVFhEPi8sLAzr169v9vFOnTqZ5ctv3749vv32W1d0zSapq85FJ6hDg1rYTGmkVl3fWQKIFfkGgDaCO0ycRWrQSKc34JOiM7K9fsP0TlIm9V1dO6IhqTUuRNOHDboxEt/+x/auTanvXXfWUBHFTx3eZV1BqXDb/gx+ETkNd4KQ4qUnSS8KNcyO55DvOXr0KOLi4pCQkICHHnoIp06dAgDs2bMH165dQ3p6uqltYmIiOnTogMLCQgBAYWEhevXqhejoaFObjIwMVFdX4+DBg6Y2DY9hbGM8BhE5V129Hm9+8x+kzP8XOj+3FZPf3y1LAGRot0i8cHeSDD0kd5FSnJJfRonIk4mkK2qYgkp0grri8jWhHSN7/htw9pQJaqlBo50lFaiosR6oAICw4BZoY2P1P2Ce3slTrpnkfgr+/e0a2UooNZsx0OYpaabs2W1Enu2z/eKBUk+uB0KkdNwJQoonUjitoYYfhIiak5qairVr16Jbt24oKyvDggULcOutt+LAgQPQarXw9/dHaGio2XOio6Oh1WoBAFqt1iwAYnzc+Ji1NtXV1bhy5QqCgoIs9q22tha1tb/nTK6urnboXIl8xZU6HeZt2Y9//XQWl2zPR9ilZ9wNWDOZhdA93Y9HfaM4JRGRRq3C6ORY/O27kmbbjE6O/X3Hm+B3rkrBdFjGiW9PmaCWWhhdNAAwpk9bqAChDAeeds2k9lM0fdiuUxXCgba0zuEsjE6KpNMbcPCM2Pd5P7X31AMhUiIGQUjxRPLSNtTwgxBRc+666y7T//fu3Rupqano2LEj/vGPfzQbnHCVnJwcLFiwwK19IFIqY6Ajd38ZfqtzbSbsnnGt8PmTt7n0Nck5vi7WCrXjl1Ei8nQ6vQFbfrJekHfLT2WYPbw7NGoVzgsULwcAg2C0JOK/qZI8ZYJaRMPNCaIBgGFJMdDrDUJBEOM185RC8lICR9LSh4klhZISLHB3DRXAc4JbJI/tJy5AJ/jGG5oYyRSsRE7EIAgpnj0rILhqgqQKDQ1F165dcezYMQwbNgx1dXWorKw02w1SXl5uqiESExODnTt3mh2jvLzc9Jjxv8b7GrZp3bq11UBLVlYWMjMzTf+urq5G+/btHTo/IiVwZwDDUVMGdcQLd/d0dzdIBjq9AUfKa4TadggL4pdRIvJoIrU47KnXEBokWLtDwhCqhE8GUlIVpXUOF8paYMxUsKtUML2RynMKyYsy9lA0fVh4S3+kxofhjW9sH9sYNPKUGipSdxuRZys4Lr77eGJavBN7QkR2BUHefPNNLF26FFqtFsnJyVi5ciUGDGg+NcSKFSvw17/+FadOnUJERATuv/9+5OTkIDCQkW2yzZ4VEFw1QVJdunQJx48fxyOPPIJ+/fqhRYsWyM/Pxx/+8AcAwJEjR3Dq1CmkpaUBANLS0vDSSy/h3LlziIqKAgDk5eWhdevWSEpKMrXZunWr2evk5eWZjtGcgIAABASIfQEl36PTG7DtYDmyP/8Zv1SJpaIg+7VQAQcX3QV/P5ZR8xbbT1wQnmjr1TbEqX0hInI2Z9VrqLoilncy/3A5brkxwmMmqKVeL5GsBcZMBecvie2yOX+p1mMKyQPSAkei1/eelDioRYM7/23mTWmmlB/WIlGitV38Ndx9TORskr/Rb9y4EZmZmcjOzsbevXuRnJyMjIwMnDt3zmL79evXY86cOcjOzsbhw4fxzjvvYOPGjXjuuecc7jz5BuNKCVGeso2a3OuZZ57Bt99+i9LSUhQUFODee++FRqPBAw88gJCQEEyZMgWZmZn45ptvsGfPHkyePBlpaWm4+eabAQB33nknkpKS8Mgjj+Cnn37Cl19+iblz5+Lxxx83BTCmTZuGEydOYPbs2SguLsZf/vIX/OMf/8DTTz/tzlMnD5Z7oAxdnt+KKR/uYQDEBYZ0DcPRnJEMgHgZKSvy7u/LXXhE5Nmkpt4RTYelEpyl/bToLHR6g8dMUEu9XlLOS8qxPeV6SemDlGswLClGUtAI8Jw0UyyM7jt0egP2lF4UapvcLsQjdnUReTLJ3+qXL1+OqVOnYvLkyUhKSsKqVasQHByMNWvWWGxfUFCAW265BQ8++CA6deqEO++8Ew888ECTNDJEclHCNmpSvl9++QUPPPAAunXrhv/5n/9BeHg4tm/fjsjI6wVwX331Vdx99934wx/+gNtuuw0xMTHYtGmT6fkajQaff/45NBoN0tLS8PDDD2PChAlYuHChqU18fDy++OIL5OXlITk5GcuWLcPbb7+NjIwMl58veb7cA2WY9sFeSTWSyD7BLVQ4vHA43v1f67u2yDOJTir4qYGBXSKc3BsiIucypmuyxpiuCQBKz4ulC0xLiEBYS9sL1S7U1GFnSYXHTFAPiA9DbIjtPlysub4YRTR9WESrAEk/C0+5XlL6EHVDoKRrYExzZUvjujPWKGHBpCcFuMgxBcfOQy/Ytj8X8hI5naR0WHV1ddizZw+ysrJM96nVaqSnp6OwsNDicwYOHIgPPvgAO3fuxIABA3DixAls3boVjzzySLOvU1tbi9ra36P+1dXVUrpJXkZkpURDSthGTcq3YcMGq48HBgbizTffxJtvvtlsm44dOzZJd9XY4MGDsW/fPrv6SGSk0xswd9NP7u6GT1hxf2+MuYmr/72VTm/AvpNiK/L6tA/lijwi8nhS0jUNiA/D33eesnnM2JBA3Nw5HPemtBUq9H3ut6u4u3ecRxRG16hVeGFkd/xpvfXP74u+OISMnjHiK/AM0n8WnnC9AGk1LnaWVAhfA+GcUB5Wd8aTAlzkmJVf/0e47S2dI53YEyICJAZBzp8/D51Oh+joaLP7o6OjUVxcbPE5Dz74IM6fP49BgwbBYDCgvr4e06ZNs5oOKycnBwsWLJDSNfJiLIxORL5uZ0kFzl/WubsbXu1Pt8Vj1vDunPT2cttPXEC94AwIV+QRkTeQsup8Z0kFtNW2UxCN798BGrUK6UkxQkEQ0clcJUxQA0AbgR0IxmLyounDRNsB4j8zpVwvEcZPV87YBWFMh+UpdWdYGN036PQG7D5ZKdRWowbrgRC5gNOTXG/btg0vv/wy/vKXv2Dv3r3YtGkTvvjiCyxatKjZ52RlZaGqqsp0O336tLO7SQrGwuhE5OsY2HWOVv5qrJnYH8dfHoHZI5IYAPEBUuqBcEUeEXkDZ9Sh6BQRDEBaqi0pE9Tu5qw6H1LSO3nS9ZJS40JK+jDRa1t6/jIA70ozxU+knm/7iQvCaYz7cvcxkUtI2gkSEREBjUaD8vJys/vLy8sRExNj8TkvvPACHnnkEfzxj38EAPTq1Qs1NTV49NFH8fzzz0OtbhqHCQgIMBUWJhJZKdGQUrYFExHJhYFd+QT4qXBvn3bIHtUDQf4ad3eHXEx0sshfo+KKPCLyCsYaF2VV1id+L9bUSZqgBqSld/KkCWp7alxYuw7GQNCuUsGAhcqzJvSl9FU0EAQDMCAhDDGtA2zuTtqw6xRmDL3RY9JMSQkaMcW355Ky8ObJoV2d2BMiMpK0E8Tf3x/9+vVDfn6+6T69Xo/8/HykpVkuHnr58uUmgQ6N5vqkg8HgSRs4yVPwXUVE3mZAfBgigjlhbw81gLiQQMzO6Ib/vHgXjrw4Aov/0JsBEB8kpR5IcrsQrsgjIq9grHFhy6IvDkEvumz5v82ctWPC3UQKbBvTFUkJBBnTNtly/lKtR10vKX2Vkj5Mo1bhgQEdbLY1pia7KHDs2JBAty+Y9KQAF9lPdOGNnxoY2CXCyb0hIkDiThAAyMzMxMSJE3HTTTdhwIABWLFiBWpqajB58mQAwIQJE9C2bVvk5OQAAEaNGoXly5ejT58+SE1NxbFjx/DCCy9g1KhRpmAIkTUsjE5Evk6jVuHF+5Ix7YO97u6KoqkABPipkRDZEs/cmYjbu0VyIptMWA+EiHyVaI2LHYKTdsaJbCnpnTyp0LcI46eLrw5phdpLCQSVnr+Mu3rGCu8wcTcpNS62n7ggdEzjbqNOES2F2murrmDJl0dstnthpPvTn0pN80WeR6c3YE+p2MKbPkyFReQykoMg48aNw6+//op58+ZBq9UiJSUFubm5pmLpp06dMtv5MXfuXKhUKsydOxdnzpxBZGQkRo0ahZdeekm+syCvxsLoRETA8J6xWPVwX/zpw73C+WW9VQu1ClGtA/BQakf88dYE+Ps5vcQZeQHWAyEiXyX+3UjsA4ZpEld03k6wnVI+3oimK9p+/AI+KTojdMyoG67vQBBN73RTpzbCO0w8YfGf6S0g+kP+bzvRQNv5S3U2U74BQJuW/oIdcB4p74MZQ2/kBLkHKjh2HnrBtlx4Q+Q6koMgADBjxgzMmDHD4mPbtm0zfwE/P2RnZyM7O9uelyJiYXQiov8a3jMWR18agW0Hy5H9+c/4parO3V2SrIUaiAsNwgt398CQxCh+sSOXYT0QIvJVot+NUjuF4y+q48K7D6Skd5JS6Nvdk/qiQaPCE+dRUWM7Y0F4S38MiA8zpXd69aujVtuXVV1F4XGxHRNKWPwnpcaFlHRYAIQDaJVXxD4TK+F6SXkfKOH3gaRb+fV/hNty4Q2R69gVBCFyJWMxP23VVZsLR1QAYhSQ55OIyFk0ahXu6BWDO3rFuLsrRB6D9UCIyJeJpitSa1SSdh9IqQXhSXUQxBfUif2tuCclzvR3RTS9k+RdOW4k5Wdber5GqK3xvEQDbaJ/tZVwvQDx94ESfh9IGp3egN0nK4XaatTgwhsiF2L+CFI8jVqF7FFJQh8DDQCyR7k/zycREREpB+uBEBFZpwJw7jexCWfjxGy/jm1g62uXceeI1MlvdxI9r/6dxOpx3NE92vT/oumdUjuF2yzOrpQaKqI/sxO/1uDvO0/ZbNeweLmUXUyi70UlUHLh+06dOkGlUpndFi9ebNZm//79uPXWWxEYGIj27dtjyZIlTY7z0UcfITExEYGBgejVqxe2bt3qqlNwq+0nLginLu7LeiBELsUgCHmEYUkxCAmyvXEpNLgFhiVxdTQREZEttbW1SElJgUqlQlFRkdljcny5NRgMmDdvHmJjYxEUFIT09HQcPWo99YOzsB4IEfky0XRF5wWDIMai1XtOXhTaObKrpELy5Lc7iZ7Xf8p/Eztgw2PJWEdFKTVUjDUubHl/e6nNOhgAML5/B9PEsGhACjaKyAO/72JSAuPuLGtC3RjkWrhwIcrKyky3J554wvRYdXU17rzzTnTs2BF79uzB0qVLMX/+fKxevdrUpqCgAA888ACmTJmCffv2YcyYMRgzZgwOHDjgjtNxqfcLS4XbPjm0q/M6QkRNMAhCHmFnSQWqrtTbbGfMI0tERETWzZ49G3FxcU3ul+vL7ZIlS/D6669j1apV2LFjB1q2bImMjAxcver61A6sB0JEvkw0pc7Fy2JBEOPsu+hxP9ghffLbnUTP6/TFK0LtGtbBEE3vtKPkgnANFXcz1riwRaR+CgB0igg2/b9oQGqH4HXwpPRS7vxNuOGGGxATE2O6tWz5e/quDz/8EHV1dVizZg169OiB8ePH48knn8Ty5ctNbV577TUMHz4czz77LLp3745Fixahb9++eOONN9xxOi6j0xuQf7hcqK2fGhjYJcLJPSKihhgEIY8g5cOKJ32wISIicod//etf+Pe//41XXnmlyWNyfLk1GAxYsWIF5s6di3vuuQe9e/fGe++9h7Nnz2Lz5s2uOk0ArAdCRCSaUketEpseME7qix73+6Niu/EaTn67k+h5dQwT62/D48ldb0Qp333Fa53YZtxpBEg5P8+poQJIKybvDosXL0Z4eDj69OmDpUuXor7+9wWphYWFuO222+Dv72+6LyMjA0eOHMHFixdNbdLT082OmZGRgcLCQtecgJtsP3EB1/RibfswFRaRyzEIQh5ByocVpXywISIiUqLy8nJMnToV77//PoKDm07gyPHltqSkBFqt1qxNSEgIUlNTrX4Brq2tRXV1tdnNUawHQkS+Tu4aF8ZJ6gHxYQhraT2lDwBcqtUJHVcp3+NEUxU9mNpRch0K0Z+F6NyoUq6ZrP1o8DfbG2uoANKKybvak08+iQ0bNuCbb77BY489hpdffhmzZ882Pa7VahEdHW32HOO/tVqt1TbGx5vjjM+BriQl/So/cxK5HoMg5BFE84wqJY8sERGREhkMBkyaNAnTpk3DTTfdZLGNHF9ujf+V+gU4JycHISEhplv79u0lnJ1lrAdCRL7OWTUuNGoV7k1pK/SUYH+N1ceVNEEtQgVg7ymx69qwDoXoz+L97Sdt9kFJ331FAke23gNGDdOHeWMNFcD1hdHnzJnTpNh541txcTEAIDMzE4MHD0bv3r0xbdo0LFu2DCtXrkRtrWC6PAc443OgK50RTJEH8DMnkTswCEIeQaNWYf7oHjbbZY9K4pZCIiLyOXPmzEFISAiA6zsumvtyu3LlSvz222/Iyspyc48ty8rKQlVVlel2+vRph4/JeiBE5OucWeNiaGK0lZa/U6usf0dT0gS1aKqiwuMXhI7X8PqL12exXT9DKTVURNmzu0W0hsr6nSc9poYKIL4jqOEuIkfMmjULhw8ftnpLSEiw+NzU1FTU19ejtLQUABATE4PycvO6F8Z/x8TEWG1jfLw5zvgc6Er7fxFLv8rPnETu4efuDhCJGt4zFqse7os5/9yPykZF0tsEt0DOfb0wvGesm3pHRETkPrNmzcL999+P/v37Y9euXWjVqlWTNgkJCfj6669RWFiIgADz3ZU33XQTHnroIaxbt06WL7fG/5aXlyM2NtasTUpKSrPnERAQ0KRvjmA9ECIi8dXkl+1JWyU4bF6qrbf6uHGCOk0BE4OigQqDQSx007DGhWh6JxFKqaECiAWOLtXqoFIB1i5b44l/uevOKKWGiuiOoD0nL8ryOxEZGYnISPt2HhQVFUGtViMqKgoAkJaWhueffx7Xrl1DixbXd//k5eWhW7duaNOmjalNfn4+nnrqKdNx8vLykJaWZvW15P4c6Ep19XqcOC8WSOZnTiL34E4Q8kBN/1joBT+AEhEReaPIyEh07doVANC1a1ckJiY2ufn7++P111/HTz/9hKKiIhQVFWHr1q0AgI0bN+Kll14CcP2L63fffYdr136fzGjuy21DDb/cxsfHIyYmxqxNdXU1duzYYfMLsJxYD4SI5PDSSy9h4MCBCA4ORmhoqNBzDAYD5s2bh9jYWAQFBSE9PR1Hjx51bkebIbLqXAXg+6O/2jxW4xRMoiv1RShlglp04l1bJdjfhn+HZJz3VEo9EEBK4Mj6443Th3lr3Rml1gQpLCzEihUr8NNPP+HEiRP48MMP8fTTT+Phhx82fQZ88MEH4e/vjylTpuDgwYPYuHEjXnvtNWRmZpqOM3PmTOTm5mLZsmUoLi7G/PnzsXv3bsyYMcOl5+NK6wpKhdvyMyeRezAIQh4j90AZpn2wF5VXmq4wqbpSj2kf7EXugTI39IyIiMgzdOjQAT179jTdjIGTzp07o127dgDk+XKrUqnw1FNP4cUXX8SWLVvw888/Y8KECYiLi8OYMWNcdr6iqUoA5mYmoubV1dVh7NixmD59uvBzlixZgtdffx2rVq3Cjh070LJlS2RkZODqVddP9IusOjcAKP/NdkCjcQomOSeVlTJBLVqPMr/4nNDxGqYPEw0a2YqVyJkqSQ5y/uwaTvxLqTvjSdfM1TVBRAUEBGDDhg24/fbb0aNHD7z00kt4+umnsXr1alObkJAQ/Pvf/0ZJSQn69euHWbNmYd68eXj00UdNbQYOHIj169dj9erVSE5Oxscff4zNmzejZ8+eLj0fV/pge6lwW37mJHIPpsMij6DTGzB/y0Gb7RZ8dgjDkmK4tZCIiMhOxi+3jz/+OPr164eIiIhmv9zOnTsXzz33HLp06dLky+3s2bNRU1ODRx99FJWVlRg0aBByc3MRGOi6L/QGwSzzgX5q5mYmomYtWLAAALB27Vqh9gaDAStWrMDcuXNxzz33AADee+89REdHY/PmzRg/fryzumqRnKvJG6dgMu4ysRVksdVGSRPUGrUKDwzogFe/sr5zx9LiPEsaTmSLpsOy9ddLzlRJchB9H4homD4MANKTYvDOj6U2n+dJ10zkernjd6Jv377Yvn27zXa9e/fG999/b7XN2LFjMXbsWLm6pmh19XqcrBBLheWnZj0QIndhEIQ8ws6SCmirba+aKau6qphcskRERErXqVMniznN5fhyq1KpsHDhQixcuNDhftqrrPKyULsRvWK5gIKIZFNSUgKtVov09HTTfSEhIUhNTUVhYWGzQZDa2lrU1v7+nae6ulqW/si5mrzxBLXILhPA9uS4kiaoAaBTREtZjtM4fZic6bCUkj4MEH8fCGl0HJGAgarp0yxSyjVzdU0Qci4pqbCGJkbyMyeRmzAdFnkEKR9WlPLBhoiIiNxHpzfg8/1iaTJjQpSRgoWIvINWqwUAREdHm90fHR1tesySnJwchISEmG7t27eXpT8D4sMQK9c412jiVs7vXkr6HidX4Khx+jA5a6goJX0YIO/PrmH6MEA8nZsIpVwzpdYEIftISYU1MS3eeR0hIqsYBCGPIOXDilI+2BAREZH7bD9xAXVidVJtFgwmIu8zZ84cqFQqq7fi4mKX9ikrKwtVVVWm2+nTp2U5rkatwgsju8tyrMYT1KLpnUQo6XucSDF5EY3Th4meoyfVtwCcu9tIrkBAk105biR6vUrPi+1oJfdhKiwiz8F0WOQRjMXpbKXEUtIHGyIiInKfguPnhdumJUQ4sSdEpESzZs3CpEmTrLZJSEiw69gxMTEAgPLycsTGxpruLy8vR0pKSrPPCwgIQECAfEGFhtrIFKxoPEEtmt5JpQIsZF80UdqkvlzpnRpfL9HvtZ5U3wKQtyZI45OXK9DWeFeOO4m+DzbsOoUZQ29UTL+pKabCIvIc3AlCHkGjVmH+6B4222WPSuIfFSIiIsLOkgqhdv4arsoj8kWRkZFITEy0evP397fr2PHx8YiJiUF+fr7pvurqauzYsQNpaWlynYIksqXVaTRBLZreyVoABPh9Ul8pnHW9jEXX5aCkVEly1gRpvNtIrjoqjXfluJPo+8BY85SUi6mwiDwHgyDkMYb3jMWqh/siNLhFk8faBLfAqof7YnjPWAvPJHK/N998E506dUJgYCBSU1Oxc+dOd3eJiMhr6fQG7BOcTEtuF8IFFERk1alTp1BUVIRTp05Bp9OhqKgIRUVFuHTpkqlNYmIiPvnkEwCASqXCU089hRdffBFbtmzBzz//jAkTJiAuLg5jxoxxyznItZq+8QS1nGmQlDSpL9d5NZnQh3xF15WUPkzOn13j85KrjoqSrhcg/j5Q0u8FmWMqLCLPwnRY5FGG94zFsKQYbD9+AYUnzgNQIa1zOG5OCOcEBinWxo0bkZmZiVWrViE1NRUrVqxARkYGjhw5gqioKHd3j4jI62w/cQH1gitS+zONJhHZMG/ePKxbt8707z59+gAAvvnmGwwePBgAcOTIEVRVVZnazJ49GzU1NXj00UdRWVmJQYMGITc3F4GBbpqIlemrkqX0TmEtW6Ci5prDx1bSJLVc6Z2apA+DPAEppaUPk+tnF97Sv0l6a2+8XoD4NVPS7wWZy9r0k3BbpsIicj/uBCGPo1GrcEuXCDyTkYhnMrrhlhsj+MeEFG358uWYOnUqJk+ejKSkJKxatQrBwcFYs2aNu7tGROSVpNQDuaVzpBN7QkTeYO3atTAYDE1uxgAIABgMBrMaIyqVCgsXLoRWq8XVq1fx1VdfoWvXrq7v/H/JtZreUnqne1PaOnxYpdV2lC29k6VjyPDVVWnpw+QqJH9PSlzT7/ZeeL0AsWumxOANXafTG/DpvrPC7ZkKi8j9GAQhj6PTG/Dj0fN45ctivPLlEfx47Dx0ciUgJZJZXV0d9uzZg/T0dNN9arUa6enpKCwsdGPPiIi815mLYqkJWA+EiHyFM9M7DU2Mdvi4SipaDciXgsjS9ZIrIKWkNElyBY2GJcU0uc8brxcgds2UGLyh66TsOmYqLCJlYDos8ii5B8owZ9PPqLz8+3brN745htDgFlh8Xy/WBCHFOX/+PHQ6HaKjzb8cRkdHo7i42OJzamtrUVv7+4f96upqp/aRiMjbXKmrF2o3JDFKUZNuRETOIld6J4vBFBmGUSUVrQbkCxo5Kx0WoKw0SXIEGJrb9eDMn4U7iV4zpQVv6LqluYeF2zIVFpEy2LUTREqB38GDB0OlUjW5jRw50u5Ok2/KPVCGaR/sNQuAGFVevoZpH+xF7oEyN/SMSF45OTkICQkx3dq3b+/uLhEReQyd3oBt//lVqO1NTDFBRD5CjpX6luo1APKs1FfaBLVc6Z2clQ5LaWmS5AhUNLfrwVh3xmEKSx7BmiCeq65ej6JfxBcqMhUWkTJIDoIYC/xmZ2dj7969SE5ORkZGBs6dO2ex/aZNm1BWVma6HThwABqNBmPHjnW48+Q7dHoD5m85aLPdgs8OMTUWKUpERAQ0Gg3Ky8vN7i8vL0dMTNPt3gCQlZWFqqoq0+306dOu6CoRkVfYfuICagXzEyht0o2IyFnkWE1usV4DZNrZoLCvcHKld3JWOiylpUmSK1Bh6X0qV90ZSz8Ld2JNEM8lpSA6U2ERKYfkIIjUAr9hYWGIiYkx3fLy8hAcHMwgCEmys6QC2mrbH1rKqq5iZ0mFC3pEJMbf3x/9+vVDfn6+6T69Xo/8/HykpaVZfE5AQABat25tdiMiIjFSiqLHhAQ5sSdERMohx2ryO7o3U/tDhp0NSpuglisFkaXrLlc6LCWlSZIrUNHc4gQ56s4obUcFa4J4Jp3egE/2ihdEby54TESuJykIIkeB33feeQfjx49Hy5YtpfWUfJqUD3hK+jBIBACZmZl46623sG7dOhw+fBjTp09HTU0NJk+e7O6uERF5HdGi6EEt1BbTuhAReSNZ0js1M2Erx84GpU1Qy9Gf5tKHyRE0ApR3zdItFDWXrLmggIPXTIk7KlgTxDMVHDsPvYT2Off1dlpfiEgaSYXR7Snw29DOnTtx4MABvPPOO1bbsSgwNSblA57SPgwSjRs3Dr/++ivmzZsHrVaLlJQU5ObmNhlLiYjIcQaDWP6SnnGtuTKPiHyGHOmdmtut4ejOBiVOUBvTO1XUNK1HKaq5FeByBI2aDbC4kTHQ5sj77Fwz18bRa2bcUZGmoLREor83cu0cInks+Mx2mnaj5Hat4e9nVylmInICl/42vvPOO+jVqxcGDBhgtR2LAlNjA+LDENPa9h//2JBAxX0YJAKAGTNm4OTJk6itrcWOHTuQmprq7i4REXmls5ViO0HiQpkKi4h8hxyryZuto+RgPFmJKX/kSO/UXPowORbtKTHFjhyBtopmgh1yBAIUt6NC9MenrB+zT6ur1+PYrzXC7WdndHdib4hIKklBEHsK/BrV1NRgw4YNmDJlis3XYVFgakyjVmH+6B4222WPSlLch0EiIiJyDZ3egJ9+qRJq27YNgyBE5DucWbxcjp0Nipughgx1KJq5XnIUER8mR+opmcnxMwxr6W/5ARm+4istY4To703+4XLbjcglHnl7u3Bbfw0LohMpjaQgiD0Ffo0++ugj1NbW4uGHH7b5OiwKTJYM7xmLVQ/3RWhw0w+MbYJbYNXDfTG8Z6wbekZERERKsP3EBdTpxJah3tI50sm9ISJSECcWL5djcrnZXSbu5OA1a+56adQq3JMcZ/dxlZg+DJAn0BYTYnmBgqOBNiWmDxP9vfm06Cx0jm6xIYfV1euxo1R8x9q02zpzgS6RwkiqCQJcL/A7ceJE3HTTTRgwYABWrFhhVuB3woQJaNu2LXJycsye984772DMmDEID2cklOw3vGcshiXFYPvxCyg8cR6ACmmdw3FzQjj/wBAREfm4guPnhdoF+qm5Oo+IfIocuzWaC1TIUT+j2YLYbuToNbMW2GnXJtju4yqxvgUAh4NG1gIVjgZYlJg+TPT35kJNHXaWVCjv5+1j5nz8k3BbFYCZw7o6rzNEZBfJNUHGjRuHV155BfPmzUNKSgqKiorMCvyeOnUKZWVlZs85cuQIfvjhB6FUWES2aNQq3NIlAs9kJOKZjG645cYIxX2gISIiItfbWVIh1K53uxB+diAinyJLKqBmAhVy1M9obteEOzm8s8FKYCfMwZ0vSkwf5mjQyGqgwsE/2c3VZ3EnKb83rvp5b9u2DSqVyuJt165dAIDS0lKLj2/fbp4q6qOPPkJiYiICAwPRq1cvbN261SXn4Aw6vQGbis4Kt7+3j/KCbkRkx04Q4HqB3xkzZlh8bNu2bU3u69atGwwGBS7tICIiIiKvoNMbsP+XSqG2sSHKygtORORscuzWsBaoGJoYjXd+LLX72Eqr1wDA4Yn3c1aCAlEOBkGUmD7M0aCRtUCFwzuZFDodJfp7I0tNHwEDBw5ssqj5hRdeQH5+Pm666Saz+7/66iv06PF73daGWV8KCgrwwAMPICcnB3fffTfWr1+PMWPGYO/evejZs6dzT8IJnli/R1L7xX9IdlJPiMgRkneCEBEREREpzc6SCtTWi81ysCg6EfkaOXZrWA1UOBAwUGK9BsDxifcKa893dJG4Eif1nXhOjgbJlLjTCID4NXPRpgJ/f3/ExMSYbuHh4fj0008xefJkqFTmnQgPDzdr26LF77VbX3vtNQwfPhzPPvssunfvjkWLFqFv37544403XHMiMqqr12PrAfHi9J0jg+Hvx6lWIiXibyYREREReTxttXiqCBZFJyJfNDTR/pRAtgIV5ySMwY0psV4D4Pjq+7CW/s0+5sj1AqzvMnEXR4NG1s5pQHwYQoNaNPu4LUrcOQOIXzM5avrYY8uWLbhw4YKpBnBDo0ePRlRUFAYNGoQtW7aYPVZYWIj09HSz+zIyMlBYWOjU/jpD/xf/Lan9/Ls9b6cLka9gEISIiIiIPN7538QmCIJasCg6EfkoB+IMo20EKipq6uw+thLrNQBwePV9TEjzuw4duV6AjV0mbuLobg1r56RRqzBxYEf7D67EnTMQD7S5Kh1WY++88w4yMjLQrl07032tWrXCsmXL8NFHH+GLL77AoEGDMGbMGLNAiFarNdUNNoqOjoZWq7X6erW1taiurja7udP/vrsdVVd1wu391MDALhFO7BEROYJBECIiIiLyeLtLLwi1u61rpCJXHBMROZsjuw/ahVpPIxga3PyuB5sUOkHtyOp7WztnHC2Mbm2Xibs4ulvD1ntoQLz9CxiUuHMGgMvSYc2ZM6fZgufGW3FxsdlzfvnlF3z55ZeYMmWK2f0RERHIzMxEamoq+vfvj8WLF+Phhx/G0qVLHeskgJycHISEhJhu7du3d/iY9vq86Ay+PiL22dLoT7ffyM+YRApmV2F0IiIiIiKl0OkN+PboeaG2QS00Tu4NEZEyObL7wNYEdeVl+4+t1AlqR3Y22No5E9PasV0T1naZuItxt8Zr+cfser6t95AjQTwl7pwBxM/J0fRps2bNwqRJk6y2SUhIMPv3u+++i/DwcIwePdrm8VNTU5GXl2f6d0xMDMrLzetolJeXIyYmxupxsrKykJmZafp3dXW1WwIhOr0BMzYUSXqORg3MHNbVOR0iIlkwCEJEREREHm1nSQWuXtMLtY0LdWziiYjIUzmy+8DWBLUjx1bqBPWA+DCEtWyBipprkp9ra+eMI8dWaiF5wLhbw74giK3dLY4E8ZS4cwYQPydH06dFRkYiMlK8HprBYMC7776LCRMmmBU8b05RURFiY2NN/05LS0N+fj6eeuop0315eXlIS0uzepyAgAAEBLi3fktdvR5d5/5L8vNWjOvDXSBECscgCBERERF5NBZFJyKyLcqBQIWtnSCO7GxwKJWWE2nUKtyTHId3C05Kfq6tc3Lk2LZ2mbiTIzsWbO1uceR9osSdM4B48PCXyitO7om5r7/+GiUlJfjjH//Y5LF169bB398fffr0AQBs2rQJa9aswdtvv21qM3PmTNx+++1YtmwZRo4ciQ0bNmD37t1YvXq1y87BHgs/O4g1P5ZKfl6f9iEYlRwnf4eISFYMghARERGRR2NRdCIiAQ7Mm9vaCTIgPgwhgX6oulov+7HdqV2bYLueJ3JO9h7b1i4Td7J3x0KYwO4We98nSt45Ixo8/Gj3L5g7Msllwa933nkHAwcORGJiosXHFy1ahJMnT8LPzw+JiYnYuHEj7r//ftPjAwcOxPr16zF37lw899xz6NKlCzZv3oyePXu6pP9SXKnTYd6W/fh491m7yxN9PP0WWftERM7BIAgRERERebSKy2JBEBZFJyJf5kihb1vphDRqFYYlRePjvWdkP7Y72bv7QOR5zjy2u9jbt9T4MJt/n+1NuabknTMD4sPQJtgPFy9bDx5eqq3H9uMXcEuXCJf0a/369c0+NnHiREycONHmMcaOHYuxY8fK2S271dXr8db3x/DejydQfkkn67FXPsA0WESegkEQIiIiIvJoe0ovCrVjUXQi8mWOFPoWSSeU1jnCriCIUlMVAfbvPhB5XkWNfUEpJe+csbdvCZEtbbaxN+WaknfOaNQqpCWEY+uBcpttC0+cd1kQxNPp9AZ8d/gc/u/LQzhy7rLdOzxsGZoYyTRYRB5E7e4OEBERERHZS6c3oOh0pVBbFkUnInu89NJLGDhwIIKDgxEaGir0nEmTJkGlUpndhg8f7tyO2jAgPgwt/aUHg0ODWgilE7JnAlz02O5i784Gkd0tlVekF0UHlL0TxN7dGqFBtotvG1OuST62gq8XAHSKaCXUTu+smXwvotMbsPRfxej83FZMfn83ip0YAGkXGog1kwY46ehE5AwMghARERGRx9p+4gLqdGJfcVkUnYjsUVdXh7Fjx2L69OmSnjd8+HCUlZWZbn//+9+d1EMxGrUKt9qxkvyO7lFC6V7smWwWPba72LuzQWR3i8rOIi1K3gli726NsJa2gycatQrp3aMkH9veOiWu0kbw90a0na/67KezuPG5rXjz2+NOf63gFmr8MOcOp78OEcmL6bDI4+j0Bmw/fgGFJ84DUCGtczhuTghX9IdnIle5dLUeT3y4CwXHKlDL1UJeS6MCWge1QEaPGGSP6oEgO1Z1EnmLguPnhdoF+rEoOhHZZ8GCBQCAtWvXSnpeQEAAYmJinNAj+/Xp0Aa5B22n3mkoqrXY6n57Juej7Zw0dxV7dja0DvQT2t2S1jkcb3xzTHqfFFxDxbjbqKZOWt0F0fdOVIj098tFBQeNAPGf5y8XLzu5J55r6nu7kHfonEteq4UKOLToLpe8FhHJi0EQ8ii5B8owZ9PPqLz8+9bhN745htDgFlh8Xy8M7xnrxt4RudfoN77H/l+q3d0NcgGdAbh4+Ro27DqNDbtOY1hSFN6a0N/d3SJyizMXrwi1690uhAsmiMiltm3bhqioKLRp0wZDhw7Fiy++iPDw5oOxtbW1qK39vU5EdbX8n+uqr0pPwVRWeVWonT0BA5XCh2V7djakdRZboHdzQjj8NSrh3YymPim4hopGrcKgG8PxpcQJadFAgOh7sSGl/+kXDQD9fecpzBvVg59lGnnpi4MuC4D4qYCjOSNd8lpEJD+mwyKPkXugDNM+2GsWADGqvHwN0z7Yi9wDZW7oGZH7MQDi2/IOncPU93a5uxtEbnFGcGVkrB2rR4mI7DV8+HC89957yM/Px//93//h22+/xV133QWdrvkV8jk5OQgJCTHd2rdvL3u/7EnBJFpPyZ6AgUgtCHcaEB+GAD9p1yyohdgOXY1aheR2IRKPrVZ0DRUAiLxB+vtANLDTto30AFBagrKLiYsGD+t0BhQcFdv96ivq6vV46/tSl7xWsB9wjAEQIo/GIAh5BJ3egPlbDtpst+CzQ9CxYhj5mEtX6xkAIeQdOocrElMPEHk6nd6Ag2fFxj97Jk6IyHvNmTOnSeHyxrfi4mK7jz9+/HiMHj0avXr1wpgxY/D5559j165d2LZtW7PPycrKQlVVlel2+vRpu1+/OWl2pAUUrac0ID4MLTTSAgYRdhbSdhWNWoXe7UIlPcdgEP8+2rZNsKRj94xrrfidACqJ23v8NeKBnYGdpQU0/DUqxafClBI8/Oe+X5zYE8+z9scSl7xOUkwwDr3IAAiRp2M6LPIIO0sqoK2utdmurOoqdpZU2PXhnshTPb1xn7u7QArx8tZDWDSml7u7QeQyO0sqcPmaXqgti6ITUUOzZs3CpEmTrLZJSEiQ7fUSEhIQERGBY8eO4Y47LBfUDQgIQECAc4MCNyeEQ6O6nlpThEYN4UlkjVqF7rE3SFqco+TUTkYD4sOwq/SicHspQXepAfr+Ct8FAgCdwqUFdhJjWgkHdm5OCIcKgGiYKdkDUmEadxvV1ts+q8tc8GTm34e0Tn+N1/8nGaP7tnP66xCR8zEIQh7h3G/iuT+ltCXyBqcE8+GT9yu9wIKJ5FtE/+YH+2sUvxKUiFwrMjISkZGuC47+8ssvuHDhAmJj3VvDUKNWoUfb1sKBir7tQyVNIo/q3Vb42Bq1SvGpnYDruw/e/Oa4cHspQXdnHttdHknrhEVfHBZuPyo5TritRq1CQkQwjp8X+8zrCUEjjVqFu3vH4p97z9ps27+T8s/HtZwT4GoXGoiF9/TC7d0iFR9EIyJxTIdFHiFKQl5RKW2JvEEHpnih/5K68o7I04n+zX/sts78EktEdjt16hSKiopw6tQp6HQ6FBUVoaioCJcuXTK1SUxMxCeffAIAuHTpEp599lls374dpaWlyM/Pxz333IMbb7wRGRkZ7joNk1G92wq3fXJoV0nHnjiwk3DbtIQwjxibb04Ih5/gzIm/n1pS0P3mhHC0cNKx3cXfT43uMa2E208cGC/p+OP6dxBu6wlBIwDIuS/Z5nS+SiXt98sXDEuKcvgYKgCBfmokxd6ANRP74/jLI/DDnDswtHuUR4xPRCSOQRDyCAPiwxDT2vbW8NiQQI9YTUTu16lTpyZ5nxcvXmzWZv/+/bj11lsRGBiI9u3bY8mSJU2O89FHHyExMRGBgYHo1asXtm7dava4wWDAvHnzEBsbi6CgIKSnp+Po0aOynsur4/rIejzyXM+NSHJ3F4hcakB8GGJDAq1OHIQGt8CMoTe6rE9E5H3mzZuHPn36IDs7G5cuXUKfPn3Qp08f7N6929TmyJEjqKqqAgBoNBrs378fo0ePRteuXTFlyhT069cP33//vdPTXYkQnUgN9tdgYBeJNRj81Jh8i9gk9VsT+ks6trto1Cosvz9ZqO3S+3tLmjjVqFVY5qRju9OmPw0Save/t3SCv2iE6b8m3SIWNAkNbuERQSPg+u/No7dZP69Hb42XfK283eRbpKUsbBsaiNkZ3fCfF+9C6eKRKF08EiWLR6L4xbuwdeZtDHwQeTmOoOQRNGoV5o/uYbNd9qgk/tEiYQsXLkRZWZnp9sQTT5geq66uxp133omOHTtiz549WLp0KebPn4/Vq1eb2hQUFOCBBx7AlClTsG/fPowZMwZjxozBgQMHTG2WLFmC119/HatWrcKOHTvQsmVLZGRk4OpV+dK2tQr0Q+92rWU7HnmmYUlRCPLXuLsbRC6lUauQPep68K+5v/6L7+vFzwZE5JC1a9fCYDA0uQ0ePNjUxmAwmGqMBAUF4csvv8S5c+dQV1eH0tJSrF69GtHR0e45gUb8/dR4zMaEKwAs/59ku8bP7FG90DHc+k5lT/vcMrpvO5uft3u3a417UsR32bji2O4S5K+xuUq/Y3gQ5o2y/R2/MdH3r6f9/c8akYTHbotH4y6rVcBjt8Uji4udmhB5LwS3UOHwwuEoXTwSP865A38aciODSUQ+SmUwGERrSrlNdXU1QkJCUFVVhdatOdHny3IPlGHOpp9Refma2f1tglsg575eGN7TvTl2yb2kjBWdOnXCU089haeeesri43/961/x/PPPQ6vVwt/fHwAwZ84cbN68GcXFxQCAcePGoaamBp9//rnpeTfffDNSUlKwatUqGAwGxMXFYdasWXjmmWcAAFVVVYiOjsbatWsxfvx4Wc9r9BvfSypESd5jWFKUx6ymJOfxxs9LoueUe6AMCz47hLKq3wPMsSGByB6VxM8GRD7AG8c/wPnnlbP1EFZ/V9KkyHRQCxVeHdfH4fFzytpdyC8+1+R+T/7c8sd1u/DV4abnlN49Em9PHKDYY7vL1Pd2Ie9Q03O6IzEC70xKdejYzb1/g/3VWP4/KR7797+uXo/3C0txsuIyOoYF45E027tlfH0MzNl6CH/7rqTJ/ZNv6YDsUb2c2UUiUgDRsYJBEPI4Or0B249fQOGJ8wBUSOscjpsTwj1qlQc5h9QgyNWrV3Ht2jV06NABDz74IJ5++mn4+fkBACZMmIDq6mps3rzZ9JxvvvkGQ4cORUVFBdq0aYMOHTogMzPTLJCSnZ2NzZs346effsKJEyfQuXNn7Nu3DykpKaY2t99+O1JSUvDaa6/Jfl6XrtbjiQ93oeBYBWoVP7qTvTQqoHVQC2T0iEH2qB4etZKSnMcbPy9JOSed3oCdJRU499tVRN1wPT0mPxsQ+QZvHP8A15xXXb0e6wpKsKv0Ilr6a3Bf33YYeGOEbOPnlTodXt56CKUXLqNTeDCeG5Hk8Z9bnHlOvF7SOPv96yk4BtoXPCIi7yA6VvjZc/A333wTS5cuhVarRXJyMlauXIkBA5pfmVBZWYnnn38emzZtQkVFBTp27IgVK1ZgxIgR9rw8+TiNWoVbukTgFon5aYkaevLJJ9G3b1+EhYWhoKAAWVlZKCsrw/LlywEAWq0W8fHmW2uNKQy0Wi3atGkDrVbbNYta7AAADn9JREFUJK1BdHQ0tFqtqV3D51lqY0ltbS1qa2tN/66uFt/d0SrQD+9OSRNuT0TkLTTq6wsjiIhInL+fGlNv64yptznn+EH+Giwa410rsZ15Trxe0jj7/Uuew99PjSm3SqsRQkS+RXIQZOPGjcjMzMSqVauQmpqKFStWICMjA0eOHEFUVNOcj3V1dRg2bBiioqLw8ccfo23btjh58iRCQ0Pl6D8RkUl2djYAICQkpNk2hw8fRmJiIjIzM0339e7dG/7+/njssceQk5Pj9oKVOTk5WLBggVv7QERERERERERE5A0k7w1bvnw5pk6dismTJyMpKQmrVq1CcHAw1qxZY7H9mjVrUFFRgc2bN+OWW25Bp06dcPvttyM5OdnhzhMRNWQsbL5r1y4cPnzY4i0hwfLqkNTUVNTX16O0tBQAEBMTg/LycrM2xn/HxMRYbdPw8YbPs9TGkqysLFRVVZlup0+fFjl9IiIiIiIiIiIiakTSTpC6ujrs2bMHWVlZpvvUajXS09NRWFho8TlbtmxBWloaHn/8cXz66aeIjIzEgw8+iD//+c/QaCzngWycCqaqqgqAtJQwROR7jAXMu3TpYnU3iCVFRUVQq9WmHW1paWl4/vnnce3aNbRo0QIAkJeXh27duqFNmzamNvn5+WY1QfLy8pCWdj0dVXx8PGJiYpCfn2+qCVJdXY0dO3Zg+vTpzfYlICDAbDeKsXQTx0AissY4RnhAuTdhHP+ISIQ3jn8Ax0AiEsMxkIh8mfAYaJDgzJkzBgCGgoICs/ufffZZw4ABAyw+p1u3boaAgADD//7v/xp2795t2LBhgyEsLMwwf/78Zl8nOzvbAIA33njjza7b6dOnrY5lBQUFhldffdVQVFRkOH78uOGDDz4wREZGGiZMmGBqU1lZaYiOjjY88sgjhgMHDhg2bNhgCA4ONvztb38ztfnxxx8Nfn5+hldeecVw+PBhQ3Z2tqFFixaGn3/+2dRm8eLFhtDQUMOnn35q2L9/v+Gee+4xxMfHG65cuWK1jw2dPn3a7deUN95485ybrTHQk3D844033qTcvGn8Mxg4BvLGG2/SbhwDeeONN1++2RoDVQaDeKj47NmzaNu2LQoKCkwrnQFg9uzZ+Pbbb7Fjx44mz+natSuuXr2KkpIS086P5cuXY+nSpSgrK7P4Oo13guj1elRUVCA8PBwqlcpmP6urq9G+fXucPn3aalV4b+Jr5+xr5wvwnEXO2WAw4LfffkNcXBzU6uaz/e3duxd/+tOfUFxcjNraWsTHx+ORRx5BZmam2Q6M/fv34/HHH8euXbsQERGBJ554An/+85/NjvXRRx9h7ty5KC0tRZcuXbBkyRKMGDHCrE/Z2dlYvXo1KisrMWjQIPzlL39B165dha+DXq/H2bNnccMNN3AMtMDXzhfgOfOcLRMdAz0Jxz/beM48Z2/E8e86joG28Zy9/5x97XwBjoFGHAOt87XzBXjOPGfLRMdASemwIiIioNFoJOW3j42NRYsWLcxSX3Xv3h1arRZ1dXWm9DUNNU4FA8CuQuqtW7f2mTeIka+ds6+dL8BztkUkDVbfvn2xfft2m+169+6N77//3mqbsWPHYuzYsc0+rlKpsHDhQixcuNDm6zVHrVajXbt2kp/na+8VXztfgOfsK+QeAz0Jxz9xPGff4Gvn7MvjH8AxUAqes/fztfMFOAZyDBTja+cL8Jx9hdxjoKQQsb+/P/r164f8/HzTfXq9Hvn5+WY7Qxq65ZZbcOzYMej1etN9//nPfxAbG2sxAEJERERERERERERERCQHyfvkMjMz8dZbb2HdunU4fPgwpk+fjpqaGkyePBkAMGHCBLPC6dOnT0dFRQVmzpyJ//znP/jiiy/w8ssv4/HHH5fvLIiIiIiIiIiIiIiIiBqRlA4LAMaNG4dff/0V8+bNg1arRUpKCnJzcxEdHQ0AOHXqlFn+rfbt2+PLL7/E008/jd69e6Nt27aYOXNmk7z6cgoICEB2dnaTlFrezNfO2dfOF+A5kzhfu26+dr4Az9lX+OI5O8oXrxnP2Tf42jn72vnKxRevG8/Z+/na+QK+ec5y8LXr5mvnC/CcfYWzzllSYXQiIiIiIiIiIiIiIiJPITkdFhERERERERERERERkSdgEISIiIiIiIiIiIiIiLwSgyBEREREREREREREROSVGAQhIiIiIiIiIiIiIiKv5HVBkJdeegkDBw5EcHAwQkNDLbY5deoURo4cieDgYERFReHZZ59FfX29azvqRJ06dYJKpTK7LV682N3dktWbb76JTp06ITAwEKmpqdi5c6e7u+Q08+fPb/LzTExMdHe3ZPXdd99h1KhRiIuLg0qlwubNm80eNxgMmDdvHmJjYxEUFIT09HQcPXrUPZ1VOI6BHAO9jbePgRz/5MPxj+Oft/H28Q/gGCgnjoEcA70Nx0COgVJwDOQY6G28fQx0x/jndUGQuro6jB07FtOnT7f4uE6nw8iRI1FXV4eCggKsW7cOa9euxbx581zcU+dauHAhysrKTLcnnnjC3V2SzcaNG5GZmYns7Gzs3bsXycnJyMjIwLlz59zdNafp0aOH2c/zhx9+cHeXZFVTU4Pk5GS8+eabFh9fsmQJXn/9daxatQo7duxAy5YtkZGRgatXr7q4p8rHMfA6joHexZvHQI5/8uH4dx3HP+/izeMfwDFQThwDr+MY6F04BnIMFMUx8DqOgd7Fm8dAt4x/Bi/17rvvGkJCQprcv3XrVoNarTZotVrTfX/9618NrVu3NtTW1rqwh87TsWNHw6uvvurubjjNgAEDDI8//rjp3zqdzhAXF2fIyclxY6+cJzs725CcnOzubrgMAMMnn3xi+rderzfExMQYli5darqvsrLSEBAQYPj73//uhh56Bo6Br7q7G07DMdB7cfyTB8e/V93dDafh+OfdOAbKg2Pgq+7uhtNwDPRuHAPlwTHwVXd3w2k4BnovV41/XrcTxJbCwkL06tUL0dHRpvsyMjJQXV2NgwcPurFn8lq8eDHCw8PRp08fLF261Gu2+NXV1WHPnj1IT0833adWq5Geno7CwkI39sy5jh49iri4OCQkJOChhx7CqVOn3N0llykpKYFWqzX7mYeEhCA1NdWrf+bOwjHQs3EM9K0xkOOfvDj+eTaOf741/gEcA+XGMdCzcQzkGAhwDHQEx0DPxjHQt8ZAZ41/fnJ0zpNotVqzQQ+A6d9ardYdXZLdk08+ib59+yIsLAwFBQXIyspCWVkZli9f7u6uOez8+fPQ6XQWf4bFxcVu6pVzpaamYu3atejWrRvKysqwYMEC3HrrrThw4ABuuOEGd3fP6Yy/l5Z+5t7yO+tKHAM9G8dA3xoDOf7Ji+OfZ+P451vjH8AxUG4cAz0bx0COgUYcA+3DMdCzcQz0rTHQWeOfR+wEmTNnTpNiMI1v3vqmN5JyDTIzMzF48GD07t0b06ZNw7Jly7By5UrU1ta6+SzIHnfddRfGjh2L3r17IyMjA1u3bkVlZSX+8Y9/uLtr5CIcAzkG+jKOgb6N4x/HP1/G8Y84BnIM9GUcA4ljIMdAX8YxUH4esRNk1qxZmDRpktU2CQkJQseKiYnBzp07ze4rLy83PaZUjlyD1NRU1NfXo7S0FN26dXNC71wnIiICGo3G9DMzKi8vV/TPT06hoaHo2rUrjh075u6uuITx51peXo7Y2FjT/eXl5UhJSXFTr1yLYyDHQCOOgb41BnL84/gHcPwz4vjnW+MfwDEQ4BgIcAw04hjIMdCIY6A5joEcA5X885OTL42Bzhr/PCIIEhkZicjISFmOlZaWhpdeegnnzp1DVFQUACAvLw+tW7dGUlKSLK/hDI5cg6KiIqjVatP5ejJ/f3/069cP+fn5GDNmDABAr9cjPz8fM2bMcG/nXOTSpUs4fvw4HnnkEXd3xSXi4+MRExOD/Px802BXXV2NHTt2YPr06e7tnItwDOQYaMQx0LfGQI5/HP8Ajn9GHP98a/wDOAYCHAMBjoFGHAM5BgIcAx3BMdCzcQz0rTHQWeOfRwRBpDh16hQqKipw6tQp6HQ6FBUVAQBuvPFGtGrVCnfeeSeSkpLwyCOPYMmSJdBqtZg7dy4ef/xxBAQEuLfzMigsLMSOHTswZMgQ3HDDDSgsLMTTTz+Nhx9+GG3atHF392SRmZmJiRMn4qabbsKAAQOwYsUK1NTUYPLkye7umlM888wzGDVqFDp27IizZ88iOzsbGo0GDzzwgLu7JptLly6ZRbNLSkpQVFSEsLAwdOjQAU899RRefPFFdOnSBfHx8XjhhRcQFxdn+uNHv+MYyDHQ23j7GMjxTz4c/zj+eRtvH/8AjoFy4hjIMdDbcAzkGCgFx0COgd7G28dAt4x/Bi8zceJEA4Amt2+++cbUprS01HDXXXcZgoKCDBEREYZZs2YZrl275r5Oy2jPnj2G1NRUQ0hIiCEwMNDQvXt3w8svv2y4evWqu7smq5UrVxo6dOhg8Pf3NwwYMMCwfft2d3fJacaNG2eIjY01+Pv7G9q2bWsYN26c4dixY+7ulqy++eYbi7+3EydONBgMBoNerze88MILhujoaENAQIDhjjvuMBw5csS9nVYojoEcA72Nt4+BHP/kw/GP45+38fbxz2DgGCgnjoEcA70Nx0COgVJwDOQY6G28fQx0x/inMhgMBvtDKERERERERERERERERMqkdncHiIiIiIiIiIiIiIiInIFBECIiIiIiIiIiIiIi8koMghARERERERERERERkVdiEISIiIiIiIiIiIiIiLwSgyBEREREREREREREROSVGAQhIiIiIiIiIiIiIiKvxCAIERERERERERERERF5JQZBiIiIiIiIiIiIiIjIKzEIQkREREREREREREREXolBECIiIiIiIiIiIiIi8koMghARERERERERERERkVdiEISIiIiIiIiIiIiIiLzS/wPO95DYIcovcQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  14\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  15\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  16\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  17\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  18\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Npug6t/67Km+8AURFASNGAFevFh1bt64oRdiUKUBkZOny7dsXpdvKzZVrE5HK6tati4ULF+KPP/7A/P/fX8dkMmHYsGGoU6cOPvnkk0rPLx6DKr7nKd4b4lZq3R8JIZCRkWHz+yOlbZG5hyzPW2+9hcGDB6NWrVqYPn063nrrLYSEhGDTpk0lZaZNm4YxY8YgODgY77//Ph5++GF8+umn6Nu3b8k9m+x394SEhDL3z7fq1asXPD094eHhgfvvvx8nT54s97lW9ForvRdv3749ANSM+0sbB2CcUlxcnNBqtWLr1q0lM78++ugjs+qy1gqQN998s8xsQSGEuHDhggBQ8mjQoIH4/vvvK72WyWQSTz75ZJllWX369KlyWZaXl1eVaagqWgHi5+cnsm5ZolscyVy5cmXJsaefflr4+fmVW2/Xrl3FRx99JH799VexcOFC0bp1awFAfPLJJyVlvvvuO9GwYUPxySeflKTAmj59epkUWJ9++qkAIL7++muxY8cOodPpxEsvvVTudZ999lnh7u5e6XOuCKq5AiQ2NrbktdXr9eK5556rdPULkbOpqs+5vb/5+eefy/TpRqNR3H333eXOpAYgpk+fXqrO6Oho0b59+5J/JycnCwBi/vz5pcrt379fABA/VpFSoKIVIL179y41w+jll18WOp2u1AyUBg0aiIcffrjcepV8lvXt21e0aNGizPGEhAQBoMrPDSKSY859kjX7reK6u3XrJurVqycuXbokxowZI1xcXCpd0i+EEJs3bxZhYWFi+vTpJekLPvnkk1IpsIrvJVNTU0ulwHryyScVrzSbMWOGxVNKVbUC5JlnnhHTpk0TP//8s/jqq6/E/fffLwBUOBO0OveMRHbBy0uIKvqwClc5+PmVTsf0669Fx2/5rieefrqoXHnWrSsqP3OmEKmpQtxxhxAPPFB+2XfeKSp7y4p+aeauALHV72bMmKKUX+UJCBDi8ccrb9P27UI8/HBRyrBffxVi1qyi67i5CVHVjOLvvhOiYUMhPvnknxRY06fLpcD666+i1S4jRwpx5YoQ9esXrXwpbwXKt98W/T527qy8TiILe+KJJ4SHh4c4ceKEmDNnjgAgfvnll0rPOXjwoIiIiBAvvvhiSQqsH3/8UYSFhYmPPvqoWuNv5Vm+fLkAIL4wMw2gmitAZNtS1T1kRU6ePCm0Wq148MEHy6RKLb6vvHjxotDr9aJv376lynz88ccCgFiyZIkQQu67e0FBgdBoNGLixIllfvb999+LYcOGiS+//FL873//E2+88Ybw8PAQ/v7+4syZMyVtknmtld6L6/V68fzzz1fYbmfBAIgF5OfnizZt2oiwsDAREBBQZplbRYxGo8jMzCz1ePzxx0XXrl3LHL81XYmMqgIg7du3Fy+88EK5zyU+Pl6sXLlSTJ8+XURFRUl1hOvXry9ZsnXrl/Dff/+90vNCQ0NFhw4dxLnblyHfoqIAyO3tz8rKEgDE3LlzS471799fNGnSpMr2C/FP2ipvb++SNDB//fWX+Pvvv4UQQkydOrVk0ODo0aMiLS2t1PmxsbHCx8dHNG3aVDRr1qxUKplbvfrqqwKAuHbtWqXtuXbtWpm/g+LBh1uPZVWVp/UW+/fvF+vXrxdffPGFuOuuu8Tw4cNtukcLkbVV1efc3t+MGjVK1KpVq8z7tXiAsbyBxNvTTo0bN074+PiU/Hvnzp0lAdNbpaamCgBi5MiRlfYPFQVAfvjhh1LlivONHjhwoOSYu7u7GDlyZLn1Kvkse+yxx0RAQECZ40ePHhUAxIIFCypsPxHJM+c+yZr9VrHk5GRRu3Zt0bFjR6HRaMSbErnXT506JY78f3qapUuXlnx5PnfunDh48KAQovR9ZPFzLCwsFOvXr6+y/lv98ccfwsXFpcqUE9VVVQCkPKNGjRIARGJiYpmfyd4zEtmt0NCiQepK+rAKB/lv/66alVV0/JbveqJ/fyEq+6733HNFg+ZRUUX7YVQU4Fi4sKjuw4crfz43bwqRmVn6ERIixCuvlD1eFVv9bp55RoiKAqshIUIMGlR122938mRRnVXtLfnXX0L8/3drMXWqEMWfR0ePFj23qsyaVfQ8O3UqSnFW0ev1++/lp/kisrLLly+LoKAg0bZt23LTFJUnOzu75J5g8+bNJd/7rly5Inbs2CGEMH/87XZHjx4Vnp6e0un7b968WWaMKiQkRLzyyitljiulpC0y95DlKQ5C7d+/v8Iy3377rQAg1qxZU+p4fn6+8PT0LJlMKPPdPSMjQwAQM2fOrPT5FNu2bZvQaDTiueeeKzkm+1oruRevV6+eeOSRR6Ta5MiYAssC9Ho9lixZgrS0NFy9ehVLly6VWn515swZBAQElHp89913SEhIKHNczeVJ6enp2LdvH+69995yn0vv3r1x33334c0338SCBQswYsQIrFq1qtI6+/TpU266r379+lV63uzZs3Ho0CGEhISgU6dOmDZtGlJTU6WeR8OGDUv928fHBwBw5cqVUseFEFL16fV6jB07FtnZ2di7dy8AoHXr1qhfv36ZshEREWjUqFGpY1988QWuX7+OkydPYtmyZXB3dy/3OsXtqepvZPbs2WX+DoCiNDe3HqsovVd5oqKi0KdPHzzzzDOIj4/Hrl27MGzYMOnziRyd0j7n9OnTCAoKgoeHR6njTZo0Kbe8m5tbyXu1mI+PT5l+CSjbN4WFhWHChAlYvHgx/P39ERsbiwULFiAnJ0fquVW3T1TyWSaEKPdnsv0bEckx5z7Jmv1WscaNG2PatGnYvXs3WrVqhTfffLPSNgJAaGgoWrRoUeZ4cHAw2rRpA6D8+0idToc+ffpUWX+xY8eO4cEHH0Tr1q2xePHiKsvn5eUhPT295JGZmSl9LXNMnDgRALBhw4YyP2OfSg5v9uyiVEQhIUCnTsC0aYDkdz3cdl+D/7+vwe19U2Xf9d57D/D1BZKSgHnzgLp1yy9XXEdV77X//rcotdatj7Nni9JR3X68Krb63bi7AwZD+fXevFn0c6WaNAEGDQI2b/4n5Vh5WrcGyvlujYgI4Lbv1uV65ZWidFe7dgFTpwItW5ZfTvb1JKomg8FQ6p4hPT0dxlveA76+vpg3bx4OHjwILy8vzJs3r8o6vby80KVLlzLHvb290blzZwDmj7/dKj09Hffeey+8vLzw008/SaXv/+9//1tmjOrs2bOYM2dOuWNXlmqLzD1keVJSUqDVatGyor4DRffRANC8efNSx/V6PcLDw0t+ruS7u+yYZLdu3dC5c+dS94Syr7WSe/GKvss7GwZALGTdunUAgJs3b5abs608gYGBiI+PL/Xo27cv2rZtW+Z45O15Lavh999/h5ubG3r16lVl2a5duyIoKAjffPONdP2nTp2SLvvoo48iNTUV8+fPR3BwMObMmYNWrVrh999/r/LcijrFWzsXPz+/cr/AVyQkJAQAkJWVVeZn06ZNqzRYsGXLFuTn5wMA/vrrrwrLXblyBR4eHhUGSIoNGTKkzN8BALzyyiuljil5bW6l1+tx//33Y8WKFbhx44ZZdRA5mur0OTJkbhz9/PwAlA1MAMD777+PgwcP4rXXXsONGzcwbtw4tGrVCn///bfZ11bSJ8p+ll25cqXc3KzFddtDDlkiZ2DpPguofr9VbP369QCA8+fP4/Lly4raMGzYMEybNq3SMkruL4udPXsWffv2hZeXF9asWYM6depUec57772HoKCgkkfHjh0VX1eJyu49Ze8ZiezWo48WDeoX77sxZw7QqhUg04dV1DfdOpDk51d20P9W+/cDFy8W/X8l389K6qjq/iU2FoiPL/2oVw8YPLjs8arY6ncTFFQUpCj+vRQzGIDLl8vujyIrJKSojmvX5MpPmwYonYiXmgoU35+q8XoSVVNCQkKpe4agoCCcPXu2VJni71dXrlyR+k53q549e2LZsmWVljHn/ignJwf9+/dHdnY21q5di2DJ931sbGyZMap69eph8ODB5Y5dWbItxWTuIS2lqu/uvr6+0Gg0iscky7snBKp+rWXvxbOzs2vE93UXWzfAGR08eBDTp0/H8OHDkZSUhJEjR+Kvv/6Cl5dXpee5ubmhd+/epY59/fXXyM/PL3NcTatXr0avXr2kv0zdvHlTegayOYKCgvDCCy/ghRdewMWLF9GuXTu8/fbb6N+/f7XrjoiIwDfffIOcnJwqXw8AJbMqlUasL1y4gBdffBF9+/aFXq/Hv//9b8TGxiI0NLRM2bS0tHKj1bcLDw9HeHh4meMtW7ZU7e/jxo0bEELg6tWr/HJNNYaSPic0NBSbN2/G9evXS82mTk5ONvv6DRs2hLu7O9LS0sr9eZs2bdCmTRu88cYbSEhIwJ133olFixZh5syZZl+zWERERIXXVfJZlpaWVm5gvrhumT6OiOQovU+yRb+1aNEixMfH4+2338asWbPw3HPP4ddffzX7emq4fPky+vbti/z8fGzcuBFBQUFS5w0ZMgTdunUr+bel748qu/eUvWcksmtBQcALLxQ9Ll4E2rUD3n4bUOG7HiIigG++AXJygNvvV65dK9qou2VLoGvXohUXDz4IlBfUTEsrGiyv6jtgUFDR41ZubkB4OGDO9zNb/G6ioor+u2cPMGDAP8f37AFMpn9+rlRqatHv4o47zG1x5UymooCJpyfw0kvAO+8A//pX0ebvt0tLK9povVkzy7SF6P9FRkaWGewPDAws+f+1a9di8eLFmDRpEr755hsMHToUO3fuhIuL7YZmb968iYEDB+LEiRPYsGFDpashblcc5LmVm5sbwsPDzRqjqk5bzNG4cWOYTCYcOXIEURX0dcVjeMePHy81HmcwGJCWllbmeVb23d3FxQWNGzeu8P65PKmpqYrHIwH5e/Fz587BYDDUiPtLrgBRWUFBAYYNG4bg4GDMnTsXy5YtQ0ZGBl5++WVbN61cBQUFiI+PL5P+6tq1a7h+/XqZ8j///DOuXLmCDh06qN4Wo9FYJrBSt25dBAcHl6ykqK6YmBgIIUpSWhUrL53B1atX8dFHH8Hf3x/t27dXdJ1Ro0bBZDLhiy++wGeffQYXFxeMGDGi3KVu+/btQ9euXZU9kWq6ePsMHxRFfX/++WeEhISgbkXLwYmciDl9TmxsLAoKCvD555+XHDOZTFiwYIHZ7ahVqxY6dOiAPXv2lDqem5uLwsLCUsfatGkDrVarap946NChMvUp+SzLyclBSkpKuf3Y3r174eXlhVatWqnSXqKazNz7JGv2W0DRIP0rr7yChx9+GK+99hree+89/Pbbb/jqq6/Mvl51Xbt2DQMGDMC5c+ewZs0aNG3aVPrc4i/xxY8777xTlTbl5uaWed2EECXB7djY2DLn2OKekUg1RmPR4Put6tYtWmGg0n0NYmKKVj3c9l0PAPDqq8CZM8CXXwIffFCUYmno0PKvvXdvUV3WYsvfzd13F6UFW7iw9PGFCwEPD+DWcYJLl4Bjx4BbxwnKSwt44ADw229A375FgQdL+OADICEB+OwzYMaMoqDW888XtfF2e/cWraaRmABJVB0+Pj6l7hl69+4NNzc3AEXjLSNHjkSnTp3wzjvvYPHixdi3bx/eeecdm7XXaDTiscceQ2JiIn788UfEWLPfs4O2PPDAA9BqtZg+fTpMJlOpnxWP3fXu3Rt6vR7z5s0rNZ73xRdfICcnp2QsVfa7e0xMTLn3z+WNSa5ZswZ79+5VlMoMUHYvXjw2WhPuL7kCRGUzZ85EUlISNm7ciDp16qBt27aYMmUK3njjDfzrX//CgFtnVVjYypUrceDAAQBFg1kHDx4s+VJ1//33o23btvjzzz+Rm5tbJgBy8uRJ9O7dG4899hgiIiKg1WqxZ88efP3112jUqBHGjx+venuvXr2KBg0a4F//+hciIyNxxx13YMOGDdi9ezfef/99Va7RrVs3+Pn5YcOGDbj77rtLji9YsAC//PILBg4ciIYNG+LChQtYsmQJzpw5g+XLl0Ov10tfY+nSpVi9ejWWLVuGBg0aAADmz5+Pp59+GgsXLsQLL7xQUnbv3r3IysrCoEGDVHl+svr3748GDRqgc+fOqFu3Ls6cOYOlS5fi/Pnz+P77763aFiJbMafPeeCBB9CpUydMnDgRycnJiIiIwG+//VayLNXc3JmDBg3C66+/jtzcXHh6egIANm3ahLFjx+KRRx5Bs2bNUFhYiOXLl0On0+Hhhx8270mXc90ZM2bgjz/+QN++fUuOK/ks27BhA4QQ5fZj8fHxGDhwYI3IKUpkaebeJ1mz3xJC4JlnnoG7uzsW/v9g2nPPPYeff/4Z48ePR+/evRWnMlDDU089hV27duGZZ57B0aNHcfTo0ZKf3XHHHXjggQdUvV7x/fbhw4cBAMuXL8eff/4JAHjjjTcAFAUznnjiCTzxxBNo0qQJbty4gf/973/Yvn07nn32WbRr165Unba6ZyRSzdWrQIMGRbP0IyOLVgZs2ADs3g2o9F0P3boVpXrasKFoYL/Ypk3AJ58U7RNR/N5auhTo2RN4882i1SDFLl4EDh4ExoxRp00ybPm7cXcvCiCMGQM88khRWq9t24Cvvy5afeLr+0/Zjz8G3nqraG+Pnj2Ljj32WFEdXbsWBW2OHCkKSnh4AP/5jzptv93Ro0Wv27BhwMCBRceWLStarfLCC8APP/xTtqAA+OOPouNENjR+/HhcvnwZGzZsgE6nQ79+/TBy5EjMnDkTgwYNUjXNvayJEyfit99+w8CBA5GVlYWvv/661M+ffvppp25LkyZN8Prrr2PGjBno3r07HnroIbi6umL37t0IDg7GrFmzEBAQgLi4OLz11lvo168f7r//fhw/fhyffPIJOnbsWNIu2e/ugwYNwvLly3HixAk0u2VVWteuXREdHY0OHTrAy8sL+/btw5IlSxASEoLXXntN+jkpvRePj49Hw4YNFe0l7LCsuuW6k9u7d69wcXERL774YqnjhYWFomPHjiI4OFhcuXJFUZ1Dhw4VPXr0MKs9Q4cOFQDKfSxdulQIIcS///1v0bJlyzLnZmZmimeffVZERESI2rVrC71eL5o2bSpeeuklkZmZaVZ7qpKfny9eeeUVERkZKerUqSNq164tIiMjxSeffFLmeYWGhpb8Oy0tTQAQc+bMKVMnADF16tRSx8aNGyeaNGlS6tj69etFnz59RGBgoKhVq5bw9vYWffv2FRs3blT0HM6ePSu8vLzEwIEDy/zswQcfFLVr1xapqaklx1599VXRsGFDYTKZFF2n2K2vpRIff/yx6Natm/D39xcuLi4iICBADBw4UGzdutWsdhA5Ipk+5/b+Roii/vHJJ58UderUEV5eXmLYsGFi+/btAoD47rvvSp1bu3btMtedOnWquP3jNyMjQ7i4uIjly5eXHEtNTRXPPPOMaNy4sXBzcxO+vr6iV69eYsOGDaXODQ0NFUOHDi3599KlSwUAsXv37lLlNm/eLACIzZs3lzretm1bMWLEiJJ/K/0se+yxx0S3bt3KPM+jR48KAGXaS0TmMfc+SQjr9Vtz584VAMTPP/9cquyZM2eEp6enGDBggLlPv1pCQ0MrvCe+/XelhoqudevvMDU1VTzyyCOiUaNGws3NTXh4eIj27duLRYsWlXtfWN17RiKby88X4pVXhIiMFKJOHSFq1y76/9v6MDF0qBC3vi/T0oQAhCjnu54AhLjtu54YN06IW7/r5eYW1deunRAFBaXLvvyyEFqtEImJ/xxbuFAID4+i88wRGlq2TVWx1e/mVp99JkTz5kLo9UI0bizEhx8KcXt/M3VqUb233kvOnStEp05C+PoK4eIiRFCQEE8/LcTJk1U+bbMUFgrRsaMQDRoIkZ1d+mdz5xa17/vv/zn2++9FxyzVHiIJv/76qwAg3n///VLHc3NzRWhoqIiMjBQGg8Hq7erRo4fUPYsSoaGhZcbgbNUWWUuWLBHR0dHC1dVV+Pj4iB49eoj4+PhSZT7++GMREREhatWqJerVqyeef/75Ut+JZb+75+fnC39/fzFjxoxSx19//XURFRUlvLy8RK1atUTDhg3F888/L9LT0xU9FyX34kajUQQFBYk33nhD0TUclUYIye3nySm1bNkS9913H2bfOuvFyaWmpiIiIgK///477rnnHpu1Iz8/H40aNcLkyZMtsqKGiKznl19+wYMPPog///zT7PQoI0aMwIkTJ7Bt2zaVW1e55cuXY8yYMThz5gy8vb0VnZueno6wsDB89913ZWYlv/TSS9i6dSv27t3LFSBEdsiR+62ahveMRAqkphbtd/H774A53/Wio4tWN3z4oepNs7nq/m4c0QMPABoN8L//2bolREQAgBkzZmDp0qU4efIkdDqdzdrxyy+/4Mknn0RKSor03niOjAGQGsxgMGDWrFl49NFHa8SGN7d6/vnnkZycXGaDKmtatGgR3nnnHZw8eRKurq42awcRKXPjxo1Sm+AajUb07dsXe/bsQXp6utkb5J45cwbNmjXDxo0bVcsxL8NkMqFt27Z44okn8Prrrys6d/Lkydi0aRN27dpV6vjly5cRGhqKH374waqpH4mofM7Wb9U0vGckUuj554HkZEDpd721a4vSUKWmFqVzckbm/m4c0dGjQJs2QFIS0Lq1rVtDRAQAyMvLQ3h4OD788EM89dRTNmtHTEwMunfvXmMmxDMAQkREpMDIkSNx48YNxMTEID8/HytWrEBCQgLeeecdxMXF2bp5RERlsN8iIiIiIqKaigEQIiIiBb799lu8//77SE5Oxs2bN9GkSRM8//zzGDt2rK2bRkRULvZbRERERERUUzEAQkRERERERERERERETkdr6wYQERERERERERERERGpjQEQIiIiIiIiIiIiIiJyOi62bkBlTCYTzp8/jzp16kCj0di6OURkp4QQuHr1KoKDg6HVOk9cl30gEclwxj6Q/R8RyWIfSEQ1lTP2fwD7QCKSo6QPtOsAyPnz5xESEmLrZhCRgzh79iwaNGhg62aohn0gESnhTH0g+z8iUop9IBHVVM7U/wHsA4lIGZk+0K4DIHXq1AFQ9EQ8PT1t3Boisle5ubkICQkp6TOcBftAIpLhjH0g+z8iksU+kIhqKmfs/wD2gUQkR0kfaNcBkOKlbp6enuz0iKhKzrY8ln0gESnhTH0g+z8iUop9IBHVVM7U/wHsA4lIGZk+0HmSBBIREREREREREREREf0/BkCIiIiIiIiIiIiIiMjpMABCREREREREREREREROx673ACGimsdQaMLyxFM4nXUdob4eGBzTCHoX28dqt27dijlz5mDv3r24cOEC/ve//+GBBx6o9JwtW7ZgwoQJOHz4MEJCQvDGG29g2LBhFmnfDYMRU347iLUHL+CqQZT5eS2tBnU9XfFU51CM7B5uF79TQ6EJn29LxvKENGRcLUTZVtuOTgN4utdCbKtATB3YCu56na2bBADIu1mIsV/vwp/JV1Bo68bcQgPA1UWDLuF++PjJ9rjDzT5uL6p6X9iSPb4nK2Lv/R9Q9N548ZvdSEjOQr59vdR2x177N2dmz5939qbo80SL8IDa+HffCPRoHgCd1rly2xPZi+L7pN8PnEdeQdHs2NquOvRvE4S37m/Nzwcisrlb76EyrxZC60Dfoci+aIQQdnsPnpubCy8vL+Tk5HDjI6Ia4O3VR7D4zzTc2itpNcCo7mGIG9CywvOs0Vf8/vvv2L59O9q3b4+HHnqoygHAtLQ0tG7dGqNHj8bIkSOxceNGvPTSS1i9ejViY2Olrin7vEZ9tRvxRy4qej7P3VX579TSZq05gk+3ptns+kr1aVkXnw/paNM23P/xNhz8O9embZDVtoEnfhvb3aZtMOd9YUvVeU9aug+05/4PcKz3hj2yh/7NmTna5529qaXTYP4T0ejXOqjCMs74ndEZnxPZl2eW7cKmY5mVlrm7uR+WDO9ipRaROZy1r3DW50XKvL36CD7fVvk91KjujfD6va2s1CKyN0r6CgZAiMguVDVYWdngoLX7Co1GU+UA4KuvvorVq1fj0KFDJccef/xxZGdnY+3atVLXkXle1RnktVUQxFEHg2w5SOiIA7y2DII4WvCjmLnvSWv2gfbU/wGO+d6wRwyCWIajft7Zo0VPt6swCOKM3xmd8TmR/eg4Mx6ZeQapsn4eLtg7RW7yAlmfs/YVzvq8SJ6S73O8j625lPQVXCtERDa3KulclR9un21Lg6HQZKUWVV9iYiJ69+5d6lhsbCwSExNVu8YNg7Fag7yf2+B3aig0OexgUPyRi7hhMFr9unk3Cx1ygPfg37nIu2n9RF3VfV/Yki3ek5Zgjf4PcNz3hj2yVf/mzBz5884eTfvtCIwmu523R+Qw7p27RTr4AQCXrxei++xNFmwREdUENwxG/PvHfWj55ho0fW01OsxYj483nSz3u4/M+NCt4o9cxMoD58u95is/7Ufbqb+j6WurET19PSb/fJD3vDUUAyBEZBV5Nwsx/ItERLy2Go3j/vnwybtZiHHfJ1V5vhDAlwmnLN5OtaSnp6NevXqljtWrVw+5ubm4ceNGuefk5+cjNze31KMy76w5Uq02mgSwPPFUtepQytrXU1t1f+fmePn7/Va/plps0XZbvEZqscV70hKs0f8Bjv3esEeO/N6xR87wXrYn6bk3sSsty9bNIHJov+37G4cvXFN83tmsG5i+8rAFWkRENcEzy3ahxZS1+GnvBVwvECgwAZeuFeC99SfQ7I3fMWPVP6vGjSaBsd8lKb7GuP/uLzVRoviaP+45j9x8EwpMwJXrBfhu91m0mLIWzyzdocZTIwfCAAgRWdx987ah9bR12HwyCzdNgFH88+HTeto6yE7o233Kub/4zpo1C15eXiWPkJCQSsufuny92tc8nVX9Ouz5empT43eu1Jkr5Q8YOwJbtN0Wr5GaHP09Yi6l/R/g2O8Ne+To7x17U1Pfy5Z08epNWzeByGEZTQLjfjhg9vlLtp9yilWqZD0LFixAo0aN4Obmhs6dO2PXrl22bhLZQMeZ8VXuN/TFn6cxcP5WAMCYb/eYdR0BYOw3e6Wvuen4ZXSYsd6sa5FjYgCEiCwq4s3fcei8OilKPPQ6VeqxhsDAQGRkZJQ6lpGRAU9PT7i7u5d7TlxcHHJyckoeZ8+erfQajfw8qt3OUN/q12HP11ObGr9zpRr6lP/34ghs0XZbvEZqcvT3CGCd/g9w7PeGPXL09469cYb3sr2pW8fN1k0gcljmDireavAXnDFNcr7//ntMmDABU6dOxb59+xAZGYnY2FhcvOiYaWrJPEpS7v117iqGL9mJtYfM/xv5/XAGBnwkf81L1wpw3/xtZl+PHAsDIERkMdHTfsfNAvVmCj0c3UC1uiwtJiYGGzduLHUsPj4eMTExFZ7j6uoKT0/PUo/KvFbNDcy1GmBwTKNq1aGUta+ntur+zs3x4WPRVr+mWmzRdlu8RmqxxXvSEqzR/wGO/d6wR4783rFHzvBetieBnm7oFOZr62YQOSRDoalag4rFdqZd4SoQkvLBBx9g1KhRGD58OFq2bIlFixbBw8MDS5YssXXTyErMSbm3+cSlal/3SLqyax46l4tfk85V+7pk/xgAISKLmPrbQVy5qd4NsosW6NrUX7X6lMrLy0NSUhKSkpIAAGlpaUhKSsKZM2cAFM1eHjJkSEn50aNHIzU1FZMmTcKxY8fwySef4IcffsDLL7+sWpvc9Tr0aVnX7PNHdQ+D3sW6HwN6Fy2euyvMqtdUS5+WdeFug1VId7i5oG2DqgeD7U3bBp64w83F6tet7vvClmzxnpRhj/0f4LjvDXtkq/7NmTny5509mnZ/S+i0Gls3g8ghTf7J/NRXt+MqEKqKwWDA3r170bt375JjWq0WvXv3RmJiYrnnmLMXHNkvo0lgfDVS7lnby98nldo/hJyT/X3LJiKHZyg04cuEqtOXKPFCjyY2/eK7Z88eREdHIzq6aMbxhAkTEB0djSlTpgAALly4UDIYCABhYWFYvXo14uPjERkZiffffx+LFy9GbGysqu36fEhHswZ7n7srDHE2mu0bN6Clww0K9WlZF58P6Wiz6/82trtDDfS2beCJ38Z2t9n1zX1f2JIt35NVsdf+D3C894Y9snX/5swc8fPO3tTSabDo6Xbo1zrI1k0hckhGk8D/ks6rVh9XgVBVLl26BKPRiHr16pU6Xq9ePaSnp5d7jjl7wZH9mht/HI4UTjAJYG78CVs3gyxMI4Sw27/L3NxceHl5IScnRyoVAhHZh8cWJWDnqSuq1acBkPzOgAoDIM7aVyh5XjcMRkz57SDWHryAq4ay3XotrQZ1PV3xVOdQjOwebhezzA2FJny+LRnLE9KQcbXQrm6SdBrA070WYlsFYurAVnYzMzrvZiHGfr0LfyZfQaGtG3MLDQBXFw26hPvh4yfb22TlR3mqel/YkprvSWfsA5U+p7ybhXjxm91ISM5Cvn291HbHXvs3Z2bPn3f2pujzRIvwgNr4d98I9GgeUOUEGPaBRBXbdiITg5eou/n0Q9HB+IBpKO2CPfYV58+fR/369ZGQkFAq/emkSZPwxx9/YOfOnWXOyc/PR35+fsm/c3NzERISYlfPi+QYTQJNXlvjcPc6Wg1w8u2Kx5zIPinpA+1jhIKInIah0KRq8AMAxvZszA+iKrjrdZjzr2jM+ZfjfBnRu2gxplczjOnVzNZNcRh3uLlg2ciutm6Gw3DE9wWZ5w43FywdUfEeI0S2xM87IrKVt1YeVr3OX5LOY84jUfx+RuXy9/eHTqdDRkZGqeMZGRkIDAws9xxXV1e4urpao3lkYY62+qNY8SqQCbHNbd0UshDbTwEmIqcyeLG6eWFdtBq81JcfQkRERERERLIMhSYkZyrbEFiGSQAJJ6u/WTE5J71ej/bt22Pjxo0lx0wmEzZu3FhqRQg5H6NJYP7mFFs3w2wfb0nmXiBOjCtAiEg1llj98fGT0ZxdREREREREpMCXCaekyzYO8IB/bVfp73LTVh3Cxua9zGwZObsJEyZg6NCh6NChAzp16oSPPvoI165dw/Dhw23dNKqG21PO3p7S9+ONJxxy9Uex4lUgY+9pWip1qQZFmQU6hfli/hPt7CbdMynDV42IVBO34oCq9XHTSyIiIiIiIuW+3nFKuuy0+1qjc2M/NHvjd6nyKZnXYSg02cW+gmR/HnvsMWRmZmLKlClIT09HVFQU1q5dW2ZjdHIc983fhkPncksdKzAJnMu+idnrjmP2uuMWb8OLPRvjz5RM7D+bW3VhM83bnIx5m5NLHRMArhmM2Hw8E62nrUPr4DuwalwPi7WBLIOfVkSkCqNJYMW+86rUFerjipR3BjD4QURERERUCaNJIDHlMn5NOofElMtM30EAilbmn866IVVWqwG6NvWH3kWLJgG1pa/xZUKauc2jGmDs2LE4ffo08vPzsXPnTnTu3NnWTSIztZ22rkzww9p0GuClvs3x0/PdbNoOADh0Pg8dZ8bbuhmkEAMgRKQKpZtdHZ3eD0sHd0BEXQ+46TSordehV/MAHJoWiz9e7c20V0RERERkc1u3bsXAgQMRHBwMjUaDX375pdLyW7ZsgUajKfNIT09XvW1rD11At3c34YnPd2D8d0l44vMd6Ph2PNYcVGdSEjkuJemvereoW/Lda+rAVtLnrTzAvzMiZzfgo83IvVlo62Zg/hPtoNNqoNNq8MmT0bZuDjLzDHjrt8O2bgYpwBRYRCTNUGjC59uS8d9dZ5FfaEJkfS989Hg7uOt1ija7imzgCXe9Dr1a1UOvVlwGS0RERET26dq1a4iMjMQzzzyDhx56SPq848ePw9PTs+TfdevWVbVdaw9dwPNf7yszASnrWgFe+HY/nvs7G3EDWqp6TXIcKw+eky47NCas5P+7NvGHFoBJ4rxD53NhNAlOXCNyUr/t+xtH0q+rXu+4Xk2QbzTi061yq8ieuysMA9r+kx1kQNtgPPd3tqLzXXVazFN5g/alCacQN6AFUwE6CAZAiEjKW78dxtLbZhJtOFaUA9EFULT6Y1JsCzWbRkRERERkEf3790f//v0Vn1e3bl14e3ur3yAUpb16a+WRSu+/P92ahsgGPqUGjahmMJoEDkumq3HRatClsV/Jv3VaDfq0rIt1Ry5Wea5JAAknL6F78wCz20pE9sloEnj5R3X3eAWKUu6N79MMOq0GkQ18MPa/+1BR5kYNgAVPRmNA2+AyP4sb0FLR+UaTwPzNKapv0j74ix34/rmuKtdKlsAwFRFVqf3M+DLBj1spWRCp15W+ySYiIiIicjZRUVEICgpCnz59sH379krL5ufnIzc3t9SjMrvSsnAh52aVbZj080HuCVID7Ui9DKPky353RECZFRxDuoZVULqseZtOKGkaETmIhORL0v2IEmN7Ninpcwa0DcLJtweUpEZ31QJuLlq0DKqDJUM7IvmdAeUGP4opOV+n1eDFXo1Vfz47067AUCizZo5sjStAiKhSraf8jjyDeh366Lsac5k0ERERETmloKAgLFq0CB06dEB+fj4WL16Mnj17YufOnWjXrl2558yaNQtvvfWW9DUuXq06+AEAefmF2JFyGXc29ZeumxxfQsol6bK3pr8q1iXcD1oNKpxVfat9Z7OZBovICU2zwP4Wxas/bqXTaqqVGl3J+eP7NFc9DRbAVSCOgitAiKhCAz7arGrwo7wPPCIiIiIiZ9G8eXM899xzaN++Pbp27YolS5aga9eu+PDDDys8Jy4uDjk5OSWPs2fPVnqNunXcpNuzPSVTuiw5h3NXbkiVq2hlvk6rQYdQb6k6jCZgR8plJc0jIjtnKDQh5dI11ev98LEomwZLdVoNXuzJVSA1FQMgRFQuS2x4detyRyIiIiKimqBTp05ITk6u8Oeurq7w9PQs9ai0vjBfuLrI3VOvO5yuqK3k+P6+IvcdLrKBV4XfzV68W37SGoNsRM4lboX6e3+E+XlgUFR91etV6qW+zWGJEam4FQctUCupiQEQIirDaBIY/4O6H3pc/UFERERENVFSUhKCgtTbjFyn1aBtA2+psimZ1zkztQYxmgSSzmRLlQ32dq/wZ12b+EsPFsmuOCEi+2c0Cfy6/7zq9W6Y2FP1Os2h02ow//Eo1ev9Nek899yycwyAEFEZc+OPQ+2u29bLHYmIiIiIlMrLy0NSUhKSkpIAAGlpaUhKSsKZM2cAFKWvGjJkSEn5jz76CL/++iuSk5Nx6NAhvPTSS9i0aRPGjBmjars6hflKl/0yIU3Va5P92pF6GYWSX+Tq+1QcANFpNWgnmQbrfDYDIETOQkkfImvR0+3saizovqj66NOyrqp1FpoE0wHaOQZAiKgUo0lgwRZ1N4ZqHexpF8sdiYiIiIiU2LNnD6KjoxEdHQ0AmDBhAqKjozFlyhQAwIULF0qCIQBgMBgwceJEtGnTBj169MCBAwewYcMG3HPPPaq2q2tj+Y3NVx5QfzYv2SclG6Df2Tig0p838PGQqufA3zmc+UzkJL5SEDBvW78OGvlV3k8serod+rVWbwWkWj4f0hGjuodVWibUxxVtG1SekvJWXyZysoE9c7F1A4jIviQkX4JRxfvX2nodVo3rrl6FRERERERW0rNnTwhR8c3xsmXLSv170qRJmDRpkoVbBXQJ94NOW7QJdVWOXLgKo0nY1QxcsoxdaVlS5SraAP1Wla0QuZXBWDTz+c6m8kE5IrI/RpPAuiMXpcu/2q8l7mzqj7ybhRj79S4kpl6BCUXp9d68rxV6RdS168+d1+9tiVdiI/D5tmQsT0jD5WuF0Lvo0CnMF/OfaIc73FywPfkSnlq8U6q++CMX+VlrxxgAIaJS5m08oVpdei1weHo/1eojIiIiIqKiFEW9I+pKDVYVp+bgALVzM5oE9p++IlW2sg3Qi3Vt7I8Fm+UyA2xPyeTfF5GDmxt/XLqsi/afIOodbi5YNrKrpZplUXoXLcb0aoYxvcrfr7ZLuB9cNJBKCyYAzI0/gQmxzdVtJKmCKbCIqITRJLD7dLYqdbUM9MCJd+5VpS4iIiIiIiptSNfK03fcantKpgVbQvZASe7+jhJ7yBQP/MnYLbnyhIhsz1BowoLNJ9Dt3Y3o+HY8Ri7bhZzrBZgvGfAEgEFRwTVipYNOq8Gg6GDp8h9vSUbO9QKMWLoTHWfGo9t/NuGTzckwFEos1ySL4goQIiqhJOKvAZA0pS/Gf7sbfyZfgRGAq4sGXcL98PGT7XGHG7sXIiIiIiJLUTIz9dwVblTt7BIVbMBb1f4fQNHAX3SoD3afqnpVSfE+IDVhQJTIkU1feRhLtp8qdWzDsUxETl+vqJ5ZD7VVsVX2bdZDkfh5n9xeWiaBMr/L2euOY/a64xh+Z0NMHdjGEk0kCRyhJCIARas/lET8H4wOhpdHLYdd6khERERE5Mh0Wg2iGnpjj8QK7vPZDIA4OwG55R9uLtoq9/8o1inMVyoAwn1AiOzfXbM34UxW9T8LGgd4QO9ScxIK6V20aBJQG8mZ16pVz9LtZ7DhSAa2vdpbpZaREmb/xW7duhUDBw5EcHAwNBoNfvnll1I/F0JgypQpCAoKgru7O3r37o2TJ09Wt71EVA03DEa88tN+tJmyBo0mr0bY5NVo/voaPLDgT7y79qjkLXOR/zwcabF2EhERERFR1Rr4eEiVK56hT87rQvZ1qXID2gRJr9To2lg+oME0a0T2q/u7G1QJfgDAtPtaq1KPI5k6sJUq9Zy9ko+7Zm9SpS5SxuwAyLVr1xAZGYkFCxaU+/PZs2dj3rx5WLRoEXbu3InatWsjNjYWN2/eNLuxRGS+Ect2o8WUtfhxz3lcNRR9+REA8o0CSWdz8NnWNOm6alrEn4iIiIjIHtX3cZcqVzxDn5yT0SSw6uAFqbKBXm7S9XIfECLH99bKv3D2Sr4qdbloga41cKVX1yb+0KmU4e9M1g1MX3lYncpImtkjmP3798fMmTPx4IMPlvmZEAIfffQR3njjDQwaNAht27bFV199hfPnz5dZKUJEltdj9iZsPHZRtfpqYsSfiIiIiMjeKJmhn5h6yYItIVvakXoZBqNcWSXbdBTvAyKDq4yI7I+h0ISl28+oVt8LPZrUyL1+dFoNxvRsrFp9S7af4sboVmaRKdxpaWlIT09H797/5DXz8vJC586dkZiYaIlLElEFnlm6A6dVWuoI1NyIPxERERGRvekS7ge9Tq4sx6adV0KKfHArJlzZd7lOYb5S5bjKiMj+DF68Q7W6tBpgfJ9mqtXnaMb3aQ41Qz+Dv1DvtaGqWSQAkp6eDgCoV69eqeP16tUr+Vl58vPzkZubW+pBROZblXQOm46rexNaUyP+RERERET2RqfV4L62QVJl03OYjtpZnbsiN+FNr9NIb4BejPuAEDkmQ6EJO09dUa2+Dx+LqtFjQTqtBnMfVW8v3J1pV7gKxIrsKon/rFmz4OXlVfIICQmxdZOIHJbRJDDuuyRV66zpEX8iIiIiInsT5C23Efqavy4wRZGTumEolCrXK6Ku4gFM7gNC5JjiVhxQra7WwZ4YFFVftfoc1f3tGqBloNxnroy4FQdVq4sqZ5EASGBgIAAgIyOj1PGMjIySn5UnLi4OOTk5JY+zZ89aonlENcLc+ONQO5Zc0yP+RERERET2RiOZlONmoYkpipyQ0SSw5YTcyosOkvt53Ir7gBA5HqNJ4Nf951Wpq7Zeh1XjuqtSlzNY81IvuEumnqzKr0nn2WdaiUUCIGFhYQgMDMTGjRtLjuXm5mLnzp2IiYmp8DxXV1d4enqWehCRckaTwPzNKarWGebnwYg/EREREZGdiVGQ0ogpipzPjtTLyC+UG0Dzv8PVrGtwHxAix7Ij9TIku4VK6bXA4en9ql+Rkzn69r2opav+5OBCE/tMazE7AJKXl4ekpCQkJSUBKNr4PCkpCWfOnIFGo8FLL72EmTNn4rfffsNff/2FIUOGIDg4GA888IBKTSeiisyNPw61Y8gbJvZUuUYiIiIiIqoupiiq2RIVDJ4FermbdQ3uA0LkWL5KSKt2HS0DPXDinXtVaI1zOvn2AIT4mNen3mpb8kUVWkNVMTsAsmfPHkRHRyM6OhoAMGHCBERHR2PKlCkAgEmTJuHFF1/Es88+i44dOyIvLw9r166Fm5ubOi0nonIZTQILtqi7+mPR0+2Y+oqIiIiIyA4xRVHNJiSnvt3hqpNeyXG7LuF+qCU5enQ++6ZZ1yAidRhNAhuOyQ+qT+zbFBF1PeCm06C2XodezQNwaFos1rzUy4KtdA7bXr0bB6b0RWTwHdDrNHBz0aBnM38sHtJBuo7Nxxg0tgYXc0/s2bMnhKj4g1aj0WD69OmYPn26uZcgIjMkJF+CUaXvNFoAnzzdDv1aB6lTIRERERERqa5TmC92n7pSZbniFEV3NpWf0U/27UL2dalyfVsGmj2pTafV4O6Iulh3pOpB1RsFRrOuQUTq2JF6GUbJDWFdtBq80LMpXry7mWUb5cS8PGrh13E9Sh0zmgS0gNS+vCcv5sFoEpx0bGEW2QOEiGznrZWHFZXv2dQXrv/fz2oAuLlo0TKoDpYM7YiT7wxg8IOIiIiIyM4pSVGUmHrJgi0hazKaBNYezpAqG+hVvWwcHRrJ7TWz9UQmVxkR2dD2k/J9/KCoYA68W4BOq0GHRt5SZU0CSFDwmpF5zF4BQkT2x1BoQnLmNeny43o1wYTY5hZsERERERERWVqXcD/odYBBYvI9x6adx660LFw3yE31ru4Yp38duQ3UbxSYuMqIyIY2HUuXLjvrobYWbEnN9uLdzTB4yS6psvM2nUD35gEWblHNxgAIkYMxFJrw6daT+GJrCnJuCrhogSZ16+CV2Ags/CNZuh6tBhjfh8sciYiIiIgcnU6rwX1tg7Bi/4Uqy6bncI8GZ5GeK/9axoRXLyAR6Cm/gmR7SiYDIEQ2YDQJHM+QmxRbr44eehcmBrKUrk38pdNg7T2TzTRYFsa/dCIHMmPVETR743e8vz4Z2TeLtrsrMAFH06/imS93S+X9LTa2ZxN2rkRERERETiLI20Oq3Jq/LjBFkZO4dDVfqpx7LS26NJZLYVWRTmG+cHWR+/64Oy2rWtciIvMkJF+CbO/eJbx6fQJVjmmw7AsDIEQO4v752/DFn2mq1KUBV38QEREREVVl69atGDhwIIKDg6HRaPDLL79Uec6WLVvQrl07uLq6okmTJli2bJnF2wkAGsgNTt8sLEpRRI7vynWDVLm7mgVUe/KbTqtB2wbeUmUPnc9lkM3JnDp1CiNGjEBYWBjc3d3RuHFjTJ06FQZD6b/BgwcPonv37nBzc0NISAhmz55toxbXTEr2hP1XuxALtoQAKNpcftqqQxZsCTEAQuQARizbiYPnclWr78FobnRFRERERFSVa9euITIyEgsWLJAqn5aWhnvvvRe9evVCUlISXnrpJYwcORLr1q2zcEuBGAUz/LenZFqwJWQtqZl5UuWa1L1Dlet1CvOVKnejwIRdXAXiVI4dOwaTyYRPP/0Uhw8fxocffohFixbhtddeKymTm5uLvn37IjQ0FHv37sWcOXMwbdo0fPbZZzZsec2hZE9YrQboyjR1FlecBktGSuZ1GArl9nQi5bgHCJGdW5V0DhuPqbsU7j8PR6paHxERERGRM+rfvz/69+8vXX7RokUICwvD+++/DwBo0aIF/vzzT3z44YeIjY21VDMBFKUzcdEAhRIT789duWHRtpDlGU0CfybLBbK83Wupcs2ujf2xYHOKVNn0HP6NOZN+/fqhX79+Jf8ODw/H8ePHsXDhQrz33nsAgG+++QYGgwFLliyBXq9Hq1atkJSUhA8++ADPPvusrZpeY8StOCBdtn1Db06KtYLiNFi7TmVLlY9bcRDvPxpl0TbVVFwBQmTHjCaBcd8lqVpnROAd3OiKiIiIiMgCEhMT0bt371LHYmNjkZiYWOE5+fn5yM3NLfUwh06rQXSoj1TZC9wI3eHtSstCXr7cbGH/O1xVuWaXcD/pfUAu5cntT0KOKycnB76+/6wKSkxMxF133QW9Xl9yLDY2FsePH8eVK/L7lZJyRpPAin3npcuPU5CaiapHSRqsFfvOMX2ghXAUlMiOzY0/DrUXwP3vhW4q10hERERERACQnp6OevXqlTpWr1495Obm4saN8mfEz5o1C15eXiWPkBDz87LLpig6+HcOB1kcXHqufBAr0MtdlWvqtBr0bBYgVXbPaQ54O7Pk5GTMnz8fzz33XMmxivq/4p9VRK0gcE32wbrj0pufM/2VdSlJgyUAvL/umCWbU2MxAEJkp4wmgfmSy4tlRYd4wV2vU7VOIiIiIiIyX1xcHHJyckoeZ8+eNbuuro3lBrW4Ebrju3RVboWFp5uLdGBMhrteLpP65mMXGWRzAJMnT4ZGo6n0cexY6QHZc+fOoV+/fnjkkUcwatSoardBzSBwTZF3sxDDFiegyeTVaDR5NRb8IT921LtFXaa/siKdVoM+LetKl//kj1Q0mrwaLd9ci+FLdyHvZqEFW1dzcA8QIjs1N14+gi9DA+Cn5+9UsUYiIiIiIrpVYGAgMjIySh3LyMiAp6cn3N3Ln4Xv6uoKV1f1UhTpdRoYjFV/k9iekok7OQvYYV25bpAqF9PYT9XBzvo+cqtJDEaBHSmX+Tdm5yZOnIhhw4ZVWiY8PLzk/8+fP49evXqha9euZTY3r6j/K/5ZReLi4jBhwoSSf+fm5jIIUon75m/DoXPmr5IZGhOmYmtIxpCuYVh35KKic64XGLH5eCZaT1uHVkG1sXp8T8s0robgChAiO2SJ1R8LnmzHKH81LViwAI0aNYKbmxs6d+6MXbt2VVh22bJlZWbOuLm5WbG1RETqYh9IRFS1mJgYbNy4sdSx+Ph4xMTEWOX6Oq0GkQ28pMruTsuycGvIklIz86TKNal7h6rXlV1lBACJqZdUvTapLyAgABEREZU+ivf0OHfuHHr27In27dtj6dKl0GpLDynGxMRg69atKCgoKDkWHx+P5s2bw8en4v2JXF1d4enpWepB5WszbV21gh96nQZdGvup2CKS0SXcD7WqMQJ/+MI1tJ7yu3oNqoEYACGyQ2qv/njurjAMaBukYo01z/fff48JEyZg6tSp2LdvHyIjIxEbG4uLFyuO4nt6euLChQslj9OnT1uxxURE6mEfSEQ1VV5eHpKSkpCUlAQASEtLQ1JSEs6cOQOgaObykCFDSsqPHj0aqampmDRpEo4dO4ZPPvkEP/zwA15++WWrtbm+j4dUuUPnc5miyEEZTQJ/JmdKlfV2r6XqtYtWGcmV5Z+X8ygOfjRs2BDvvfceMjMzkZ6eXmpvjyeffBJ6vR4jRozA4cOH8f3332Pu3LmlVneQ+e6ctR5Xq5kOafRdjTkx1gZ0Wg2e79G4WnXkGUzo/u4mlVpU8zAAQmRnjCaBhQryN1ZGA+CTJ6MRN6ClKvXVZB988AFGjRqF4cOHo2XLlli0aBE8PDywZMmSCs/RaDQIDAwsedy+IRwRkaNgH0hENdWePXsQHR2N6OhoAMCECRMQHR2NKVOmAAAuXLhQEgwBgLCwMKxevRrx8fGIjIzE+++/j8WLFyM2NtZqbZZNUXSjwIRdXAXikHalZSEv3yRV1v8OddKrFdNpNbhPcnJdeo78Ru1k3+Lj45GcnIyNGzeiQYMGCAoKKnkU8/Lywvr165GWlob27dtj4sSJmDJlCp599lkbttw5vLXyL5zLKai6YCW0GmB8n2YqtYiUGt+nOaobejp75QamrzysSntqGgZAiOzMjtTLKJC7lwUAjOvVBIemxaJXU194uGjg5qJFy6A6WDK0I5LfGYABbYMt19gawmAwYO/evejdu3fJMa1Wi969eyMxMbHC8/Ly8hAaGoqQkBAMGjQIhw9X/kGVn5+P3NzcUg8iIluzRh/I/o+I7FXPnj0hhCjzWLZsGYCilH9btmwpc87+/fuRn5+PlJSUKvPrq01JiqL0nBsWbAlZSnqufGAh0EsuIKZEkLfcKqM1f13gKiMnMWzYsHL7QiFKv75t27bFtm3bcPPmTfz999949dVXbdRi52EoNGHp9jNVF6zCh49FcfWHDem0Gsx9NLLa9SzZfgqGQgWDhgSAARAimzAUmrBg8wnc+Z8NiHxrHe6btxWbjl6E0STw+oqD0vUUR/DvcHPB0hExODJzAI7N7I814+/C3S3q8sNNJZcuXYLRaCwze7levXqllvzeqnnz5liyZAl+/fVXfP311zCZTOjatSv+/vvvCq8za9YseHl5lTy48RsR2QNr9IHs/4iI1NMl3A+uLnLfAy7l5Vu4NWQJl67KvW6ebi7oFOar+vU1kvOYbxaasCPlsurXJ6pJJv90oNp1tA72xKCo+iq0hqrj/nYN0DJQLoBcmck/V/9voqZhAITIyqavPIxmb/yOOetO4lx2PnJuFOLQ+at45svdaPzaGpzKkp+FNbZnEwY57FRMTAyGDBmCqKgo9OjRAytWrEBAQAA+/fTTCs+Ji4tDTk5OyePs2bNWbDERkXqU9oHs/4iI1KPTatCzWYBU2T2nr1i4NWQJV64bpMrFNPazyPfFGAWbKHMjdCLzGU0C/0s6X6067nDVYdW47iq1iKprzUu9cIe+esPxvySd5+o6hVxs3QCimuSu2ZtwRkGAozIaMH+jtfj7+0On0yEjI6PU8YyMDAQGBkrVUatWLURHRyM5ObnCMq6urnB1VTdHLxFRdVmjD2T/R0SkLne93Ff9rScyYTQJTqpyMKmZeVLlmtS9wyLXL94I3WCsuizH6IjMl5B8CdV5C/m667Bvaj/V2kPqODS9P9rPjMflPLlg9u1MAkg4eQndm8tNdiCuACGymu7vblAt+AEAD7Wrzy8qVqLX69G+fXts3Lix5JjJZMLGjRsRExMjVYfRaMRff/1VapM4IiJHwD6QiMjxcCN052U0CfyZnClV1tu9lkXawI3Qiaxj/qYTZp87tGsIgx92bO8bfTC8ayOzz59Xjb+NmogBECIreGvlXzh7Rd38urMeaqtqfVS5CRMm4PPPP8eXX36Jo0eP4vnnn8e1a9cwfPhwAMCQIUMQFxdXUn769OlYv349UlNTsW/fPjz99NM4ffo0Ro4caaunQERkNvaBRESOhRuhO69daVnIy5fbANf/DsutruRG6ESWZTQJ7DmVLV1eqwHqe7thUmxznJjZH2/dzzEjezf1/lY4MbM/Xoltinp36BSdu/dMNvtWBZgCi8jCDIUmLN1+RtU6O4f5QO/C+KU1PfbYY8jMzMSUKVOQnp6OqKgorF27tmRT4DNnzkCr/ec1uXLlCkaNGoX09HT4+Pigffv2SEhIQMuWLW31FIiIzMY+kIjIsRRvhJ5fWPXgCDdCdyzpufIrKgK95FYCmUPpRuh3NpUPyhFRUforuVAnUM9Tj52v9bFoe8gy9C5ajOnVDGN6FaW47/LOBqTnVv25zDRYyjAAQmRhgxfvUL3O5SO6qF4nVW3s2LEYO3ZsuT/bsmVLqX9/+OGH+PDDD63QKiIi62AfSETkOIo3Ql935GKVZfecvoJRVmgTqSNLMmDl6eaCTmG+FmtHTGM/fLy54v0Nb7U9JZMBECKFlKS/eu/hKMs1hKxqzr8iMXjJLqmy8zadYABEEqeQE1mQodCEnaeuqFrngNaBXP1BRERERESVUroROjkGbw+9VLkHoy27Z2SXcD/Ukvxaej6b+4AQKWE0Cew5nS1VVqsBujLA6DS6NvGXHqzfd5ZpsGRxFJXIguJWHFC1Pq0GmP9kO1XrJCIiIiIi58ON0J1T1jWDVLkGPnJ7dJhLp9Xg7oi6UmWDvN0s2hYiZ7Mj9TJkx7XbN/S2aLCTrEun1aBDI2+pskYTsCPlsmUb5CQYACGyEKNJYMW+86rWOffxaH6wERERERFRlbgRunO6cl0uACJbrjrahcql2MrI4T4zREosTzwlXXbc3c0s1xCyiRcVvKZf7ThluYY4EQZAiCxkbvxxqLkQrU/LuhgYGaxijURERERE5KyKN0KXwY3QHUdqZp5UOY0V5s3l3iyQKrfmrwtM00IkyWgS2Hg0Q6qsi5bpr5yRkjRYm49dZP8qgQEQIgswmgTmb05Rrb5R3Rvh8yEdVauPiIiIiIicW/FG6DL2nFZ330KyDKNJ4M/kTKmy3u61LNwaQAO5KMvNQhPTtBBJ2pF6GQUmubLRIUx/5Yx0Wg3aN/KRKmswCvavEhgAIbIApas/XugZjkc6BKOOvugWspZWg/rebpgU2xwnZvbH6/e2slRTiYiIiIjIScluhM4ZpI5hV1oW8vLlRkb973C1cGuAmMZ+0mUTUy9ZsCVEziMhRf690jFMLg0dOZ5OCl7b7SlygfGaTO5uiIikGU0CC7bIr/5w0QIT+0ZAp9Vgzr+iLdgyIiIiIiKqSWQ3Qi+eQXonU6nYtfTcm9JlA73kXvvq6BLuB70OMBirLsv4GpGcXWlZ0mXvbCy3yo8cT9fG/lggmVlmt4K/mZqKARAiM9wwGDHlt4NYe/ACrhqK7uRqaTWo6+mKRn4eMCq4uXuhRxMuWSQiIiIiItUpGUBJTL3EAIidy5Lcq8XTzUXR7GFz6bQa3Nc2CCv2X6iybHqOfPCGqKYymgT2S6Yk1Os06KJgFRY5li7hfqilhVQ6tAN/58BoEhxbrARTYBEpNGLZbrSYshY/7jlfEvwAgAKTwLnsm9ieIh951WqA8X2aWaKZRERERERUwxXP0JfBGfr2z9tDL1Xuwej6VhsIC/L2kCrHjdCJqrYj9TIKJd8mvSLqcsDbiem0GtzTop5UWe4DUjUGQIgU6DF7EzYeu6hafWN7cvUHERERERFZRvEMfRleVtg0m6on+7pBqlxDX7mghBq4ETqRer5KSJMuO6RLI8s1hOzC4JhG0mW3Jas3VumMGAAhkvTM0h04nXVDtfo04OoPIiIiIiKyrHqSe0HsOyOXdoVs50zWNalysitF1KBkI3Ru1EtUMaNJIP6I3CC2i5bpr2qCLuF+0gP3vyVVnYqwJmMAhEjCqqRz2HRc3dkqD7Wz3rJkIiIiIiKqmS5ky+29sPVEJlMU2TGjSWDF/nNSZWVXiqihOE+9jPOSf4tENVFC8iVIbPcAAGgZVIfjSTWATqtBI3+5FX3nc27CUCj7F1TzMABCVAWjSWDcd0mq1zvrobaq10lEREREROpasGABGjVqBDc3N3Tu3Bm7du2qsOyyZcug0WhKPdzc3KzY2rLq+8itALlRYMKuNPn9DMm6dqVl4epNo1RZ39rWWwGi02pwd0RdqbI3CuTaT1QTzd90QrrswMhgC7aE7EnbBt7SZeNWHLRcQxwcAyBEVZgbf1w6Ci9rQOtA6F349iMiIiIismfff/89JkyYgKlTp2Lfvn2IjIxEbGwsLl6sOE2Jp6cnLly4UPI4ffq0FVtcVtfG/tJl03PUS/lL6krPlV89ESiZ9kwtHRrJpeLhKiOi8hlNArtOZUuXH9o1zHKNIbvycLsG0mV/3neOfWwFOAJLVAmjSWDBlhRV69RqgPlPtlO1TiIiIiIiUt8HH3yAUaNGYfjw4WjZsiUWLVoEDw8PLFmypMJzNBoNAgMDSx716tWzYovL6hLuB1cXuVQpl/LyLdwaMleW5Gvj6eaCTmG+Fm5Naf51XKXK3SjgRuhE5Xn+6z3SZYO93Dihtgbp2sRf0eD9C8t3W6wtjozvGKJKJCRfglHl4Oncx6OZq5GIiIiIyM4ZDAbs3bsXvXv3Ljmm1WrRu3dvJCYmVnheXl4eQkNDERISgkGDBuHw4cOVXic/Px+5ubmlHmrSaTXo2SxAquye09wI3V7Jbmz+YLT195oM9JRP88aN0ImKGE0Cmw9noPnrq7FecvNzALg/KsiCrSJ7o9Nq0KelXJpBAFh3NBPNX1+DTUcvcjXILRgAIarEWysr/7KiVJ+WdZmrkYiIiIjIAVy6dAlGo7HMCo569eohPT293HOaN2+OJUuW4Ndff8XXX38Nk8mErl274u+//67wOrNmzYKXl1fJIyQkRNXnAQDuehepcpuPccDEXmVdk9vYvIGP3Ia5auoU5iu9yujcFaZZI1pz8AKavr4Gw5fvQb7CrXG6N5EfDCfnMERhyrN8o8AzX+5Gk9fWYM3B8xZqlWNhAISoAoZCE5Izr6lW36jujfD5kI6q1UdERERERPYlJiYGQ4YMQVRUFHr06IEVK1YgICAAn376aYXnxMXFIScnp+Rx9uxZ1dsluxG6wSiYoshO7T0tt0H9letygRI16bQa6Y16L+TI72VC5IxmrTmCF77dB3NizXqdBl0ay+25Q86jS7gfapkxgi8AvPDtfsxac0T1NjkaBkCIKjD5pwOKyr/cpwm83f6Z9VJLq0F9bzdMim2OEzP74/V7W6ndRCIiIiIishB/f3/odDpkZGSUOp6RkYHAwECpOmrVqoXo6GgkJydXWMbV1RWenp6lHmpTshE6UxTZH6NJYNvJS1JlbZVtWXbfkYN/53CVEdVYaw6ex6db08w+f/RdjZlSvQbSaTV4vkdjs8//dGsa1hy8oGKLHI/cOliiGsZoEliRJL9MbFyvJhh/T3OMv6e5BVtFRERERETWotfr0b59e2zcuBEPPPAAAMBkMmHjxo0YO3asVB1GoxF//fUXBgwYYMGWVq149miBqeqy57Pta4a+odCEz7clY3lCGjKuFkKgaCanu16HTmG+mP9EO9zh5txDG7vSsnDNIJcnJyZcPtilpq6N/bFgc0qV5W4WFm2EfmdT27SzPDcMRkz57SDWHryAq4ai4EwtrQZ1PV3xVOdQjOwezk2nqdqMJoEx3+43+3ytBhjfp5mKLSJHMr5Pc8zfnAJzw8fjv9uP2NaBNTaAZtG7hGnTpuGtt94qdax58+Y4duyYJS9LJKX4Rvqr7am4mGeEVgP4eNTCsDvDcENhEkZ+CBEREREROZ8JEyZg6NCh6NChAzp16oSPPvoI165dw/DhwwEAQ4YMQf369TFr1iwAwPTp09GlSxc0adIE2dnZmDNnDk6fPo2RI0fa8mlAp9Xg7oi6WCex0e6NAoUJ6S1o+srDWLL9VJnjJgDXDEZsPp6J1tPWoWWgB9a81Mvq7bOW9Fy5oJSHXmez9Dhdwv2g12lgMFY9PLc9JdNuAiDDl+7C5uNlVz0VmATOZd/E7HXHMXvdcQy/syGmDmxjgxaSs3jx271mD14DwIePRdXYwWsq+hyf+2gkxv2gLFtNsQKTwNz4E5gQWzMnblt8mkSrVq2wYcOGfy7o4twzM8gxzFh1BF/8WXrZoVEAl64V4L31JxTV1SHUmx9CRERERERO6LHHHkNmZiamTJmC9PR0REVFYe3atSUbo585cwZa7T8zw69cuYJRo0YhPT0dPj4+aN++PRISEtCyZUtbPYUSHRr5SQVAtp7IhNEkbP4d567Zm3AmS27D7CPp19E0bjVOzrrXwq2yjay8fKlyA2w4u1en1SCygRd2n86usuzuNLn9TCyt9dR1yMsvlCq7dPsZbDiSgW2v9rZwq8gZGQpNWHMoo+qCFWhd3xODouqr2CJyRPe3a4BPtyXj8AXz9iteuDUF4/s0s/nnuy1YfA2fi4sLAgMDSx7+/vYR5aea6/7528oEP6pj/N1c/UFERERE5KzGjh2L06dPIz8/Hzt37kTnzp1LfrZlyxYsW7as5N8ffvhhSdn09HSsXr0a0dHRNmh1Wf51XKXK3Sgw2Xwj9O7/2SAd/ChWIICmcast1CLb8vbQS5WLUbDXiyXU9/GQKnfADvYBafraaungR7GzV/LR/d2NFmoRObMvE06Zfa5/7VpY9WJ39RpDDm31+J7wr13LrHMLjMLmn++2YvEAyMmTJxEcHIzw8HA89dRTOHPmjKUvSVShEct24uC5XNXqc9ECXe1k6S4REREREVFFAj3dpMvaciP0Z5buwNlsuRUPtysQQKspa1Vuke1lXTOoWs5S6vu4S5Uz2HgQLnra71L74ZTn7JWbeGbZLnUbRE5v5YFzZp3XKqg29rzZV+XWkKPb82ZftK7vada5XyaqNyHckVg0ANK5c2csW7YMa9euxcKFC5GWlobu3bvj6tWr5ZbPz89Hbm5uqQeRWlYlncPGY5dUrfOFHk1q5NIxIiIiIiJyLJ3CfOHqIvfdxVYpilYlncOm49UbGL9mMOK+edtUapF92Hta7vW4ct22AZCuClagJKaq+91c1vAlibhy08zox//bdCwTKw+cV6lFjic/Px9RUVHQaDRISkoq9bODBw+ie/fucHNzQ0hICGbPnm2bRtoRo0ngLzMm4s57NBKrx/dUv0HkFFa92B1zH4+C0hHJDUcv2nwFni1YNADSv39/PPLII2jbti1iY2OxZs0aZGdn44cffii3/KxZs+Dl5VXyCAkJsWTzqAYxmgTGfZekap1aDTc/JyIiIiIix6DTatC2gbdU2UPnc60+QGI0CYxV6TvbofO5+DXJvBnX9sZoEth2Ui5YYOu5eUUbocuVtcX426qkc9h8Qp3g3vjv9tfIQUQAmDRpEoKDg8scz83NRd++fREaGoq9e/dizpw5mDZtGj777DMbtNJ+JCRfUrT5+eIhHZDyzgDc366BxdpEzmFQVH0kvzMAnz/RTvockwASJD9TnInFU2DdytvbG82aNUNycnK5P4+Li0NOTk7J4+zZs9ZsHjmxufHHUb05HmWN7cnVH0RERERE5Dg6hflKlbtRYMIuK68C+dfCP1Wtb+IPB5xigHpXWhauGYxSZWPCbZueWafV4L62QVJl03NuWrg1pakZYAOKBhE/Wn9ctfocxe+//47169fjvffeK/Ozb775BgaDAUuWLEGrVq3w+OOPY9y4cfjggw9s0FL7MX/TCemyD7erj94t63GsiaTptBr0iQxClzAf6XPmKfibdBZWDYDk5eUhJSUFQUHlfyC6urrC09Oz1IOouowmgfmbU1StU6fl6g8iIiIiInIsSlIUpeco24S8OlYlncP+s+qmwC40CaeY5ZqeKxco8NDr0KWxn4VbU7Ugb7mN0Nf8dcGqAaox3+5Rvc6Pt6Q4RZBNVkZGBkaNGoXly5fDw6Ps65yYmIi77roLer2+5FhsbCyOHz+OK1euWLOpdsNoEthzOlu6/KyH2lquMeTUvhrRRbrsvrPZNarvAiwcAPn3v/+NP/74A6dOnUJCQgIefPBB6HQ6PPHEE5a8LFEpc+OPK1puKOOjx6IZkSciIiIiIofSJdxPeh+QS3nmbUSulCXSFRebtuqQReq1pizJ12FA60C7+I6qkcxIf7PQZLWN0A2FJqw9dFH1egWAufE1Yya1EALDhg3D6NGj0aFDh3LLpKeno169eqWOFf87PT29wrqdeT/gHamXpdO9NQ7wgN7FqvPUyYnoXbRoElBbqqzRBKv1v/bCou+sv//+G0888QSaN2+ORx99FH5+ftixYwcCAgIseVmiEkaTwMI/1F390btFXQyMLJvvkoiIiIiIyJ7ptBr0bCb3fXzPaevM2LZEuuJiKZnXYSi0VO3W4e2hr7oQgBgFq3ssKUbBKpTtKZkWbMk/Bi/eYbG6P/kj2aFnUk+ePBkajabSx7FjxzB//nxcvXoVcXFxqrfBmfcDXp54SrrstPtaW64hVCNMHdhKuuxXO05ZriF2yMWSlX/33XeWrJ6oSjtSL6NAxfvd3i0CsHhoR/UqJCIiIiIisiJ3vdwwwOZjF2E0CYuuKjAnXXGLwNo4mn5NuvzgL3bg++e6Km2a3ci6ZlC1nKV1CfeDiwYolIgJ7LbCPjOGQhN2nlIWzAvxdsPZbLnUY4Wmog2Fuzd3zIm+EydOxLBhwyotEx4ejk2bNiExMRGurq6lftahQwc89dRT+PLLLxEYGIiMjIxSPy/+d2BgYIX1x8XFYcKECSX/zs3NdYogiNEksPFoRtUFAbhoga5N7SOISY6raxN/aAGpSQXW+Iy3JxYNgBDZ2py1R1Wr6+PHo3BfVH3V6iMiIiIiIrK2+j7uUuUMRoEdKZdxpwUH5ZSmK/Z01eL3l3piwnf7sSLpvNQ5O9OuwFBoctjUMntPywUJrly3jwCITqtBdKgPdksEHQ78nWPxATilqz/ujgjA50M6ovFra6TPmbbqEDY276W0aXYhICBAKkvLvHnzMHPmzJJ/nz9/HrGxsfj+++/RuXNnAEBMTAxef/11FBQUoFatWgCA+Ph4NG/eHD4+FW/Q7OrqWiaw4gyUTMiNDvGuMQPRZDk6rQbtG8n1v9b4jLcnjnkHQCTBUGhC0t/yuSPH9WqCEzP745XYpqjv7Qovdxe0Dq6DJUM7IuWdAQx+EBERERGRw1OyEbolUxSZs/pjz5uxAID//CtS0XlxKw4qKm8vjCaBbZIbudvT2GmnMF+pcsUDcJaidPWHl5sOS4Z1gk6rwbhejaXPc4ZUa1Vp2LAhWrduXfJo1qwZAKBx48Zo0KABAODJJ5+EXq/HiBEjcPjwYXz//feYO3duqdUdNUlCitx7FwA6Sr5niKoi2/8C1ktDaA8YACGHZig0YcHmE4h5Zz0i3liDdtPXY/LPB3HDYFQ000MDYHyfZtC7aDGmVzNsn9wbB6bGYtW4u3B3i7qMxBMRERERkVMoTlEkw5IpipSu/hjQOrBkFYfeRYvOjSqeUX67X5POO+Q+DbvSsnDNYJQqGxNuP7N47SXIpnT1x+43+pb8//g+zSW3cy/iqEE2NXl5eWH9+vVIS0tD+/btMXHiREyZMgXPPvusrZtmE7sU9J93NnbMFGpkf5T0v9ZIQ2gvmAKLHNb0lYexZPupUsduFhbgu91n8d3us4rqeqhdfQY5iIiIiIjI6dlDiiKjSWDBFvnVHxoA859sV+rY8pFd0OyN36XOLzQ5ZqqP9Fy5fSg89Dp0UbD5uKXZwz4gSld/dA7zKZUmTafV4MVejTFPcpXSr0nnMftfkTVmXKFRo0YQouwL3LZtW2zbts0GLbIvRpPA/tNyf396ncau3r/k2LqE+6GWFlLp16yRhtBecAUIOaQeszeVCX5Ux6yH2qpWFxERERERkT2zdYqihORLMCpYkPFiryZlBmj0LlpENfCUruPLxDT5C9qJrLx8qXIDWgfa1QBWcZBNRvEAnNqUrv5YPqJLmWPj+zSXPr84yEYEFO3/IRMABIBeEcw6QurRaTW4p0U9qbKWTkNoTxgAIYdz37w/cDrrhmr13T7Tg4iIiIiIyJnZOkXRWysPS5fVaorSFZfnlX4tpOvZdCzT4dJgeXvopcrFKHg9rcWWQbbqrv4optNqENuyrnQ9jhhkI8tQsv/HkC6NLNcQqpEGxzSSLltT9gHhqC85lN/2/Y1D5/NUrbO8mR5ERERERETOypb7gBgKTUjOvCZdfmzPsqs/iil5Ho44Qz/7ukHVctZkyyDb5J8OKCpf2ZjAkK5h0vU4YpCNLEN2/w+mvyJLsJe9vuwJAyDkMIwmgfE/KLuRqQpXfxARERERUU1jyxRFSlITVbb6Ayh6HoOig6Xrc7QZ+mey5AJFsitFrMlWA3BGk8CKpPPS5asaE3D2IBupT8n+H5ENvJj+ilRnD2kI7Q1HfslhzI0/DrXfklz9QURERERENZEtUhQpTU1U2eqPYrMeipSuz5Fm6BtNAiv2n5Mqa48rQGw1ADc3/rii8lWNCSgNsn2145Si65PzUbL/R0fJfphIKVvv9WVvGAAhh2A0CczfnKJqnZ0aeXP1BxERERER1Ui2SFH0ZcIp6bIaVL76o5jeRYtQX3epOh1phv6utCxcvWmUKutb2/5WgADWH4AzmgQW/iE/biCbEUJJkG3zsYsOE2Qjy1Cy/8edjQMs2BKqyWy915e94egvOQRLrP74emSMyjWSs1uwYAEaNWoENzc3dO7cGbt27aq0/I8//oiIiAi4ubmhTZs2WLNmjZVaSkSkPvaBRFRTOWv/Z4sURV8rmB3/YHSwdGqYpxVsIuwoAz3puTelywZ6yQWArM3aA3A7Ui+jwCRfXjYjhN5Fi8YBHlJla8psaqoY9/8ge8B9QEpjAITsntEksGCLuqs/RnQL4+oPUuT777/HhAkTMHXqVOzbtw+RkZGIjY3FxYsXyy2fkJCAJ554AiNGjMD+/fvxwAMP4IEHHsChQ4es3HIioupjH0hENZUz93/WTlFkKDThdNYN6fL/eVh+1v3Qro2kyzrKQE9WXr5UOU83F+mVFtamZADu3BX5v42KfJUgv8dL4wAPRWMC/VoHSZd1lCAbqc9oEtgrmeaP+3+QJSn5jN9/NtvpV65xBJjsXkLyJRhVfB+2beCJN+9rqV6FVCN88MEHGDVqFIYPH46WLVti0aJF8PDwwJIlS8otP3fuXPTr1w+vvPIKWrRogRkzZqBdu3b4+OOPrdxyIqLqYx9IRDWVs/d/1kxRFLfigHTZUF93RYPTSmboO8pAj+zG5g9G17fbQVSdVoOoht5SZc9nVy8AYjQJbDhafmCyPNPua62ofiWrWRwlyEbqS0i+BNlFSNz/gyxN9jO+0AQknJRP3eaIGAAhu/fWysOq1TWiWyh+G9tdtfqoZjAYDNi7dy969+5dckyr1aJ3795ITEws95zExMRS5QEgNja2wvJERPaKfSAR1VQ1of+zVooio0ngf/vOS5d/ukuo4mvIztB3lIEe2Y3NG/rKBX5spYGPXPuqu8poR+pl6YmTWg3Qtan83z5QtJqlluQImpqbupNjmb/phHRZ7v9BlqbkM36egr9dR8QACNk1Q6EJyZnXpMu/0CMcr8Q2RWAdF+gA1NJqUN/bDZNim+PEzP54U+EsDyIAuHTpEoxGI+rVq1fqeL169ZCenl7uOenp6YrKA0B+fj5yc3NLPYiIbM0afSD7PyKyRzXhHtBaOcKVzIoGgKFdwxRfw9kGes5kyX0Pll0pYiv1feT2J6nuKqM5a49Kl23f0FvxqhmdVoN7WtSruiC4D0hNZTQJ7DmVLVVWpwX3/yCL6xLuB9mubu8Zx1gdaS4GQMiuDV68Q7qsVgNMjI3AmF7NsOP1WKT8516cfGcAtk++By/0asI9P8juzZo1C15eXiWPkJAQWzeJiMgq2P8RUU1myz5QSYqi6sxqV7KqP7KBp1nf3ZQM9Oyz8zRYRpPAiv3npMrKrhSxFSWBqa92nDLrGoZCE5L+lg8cjru7mVnXGRzTSLos9wGpeZQEetuFKA/CESml02rQIdRbqqxJOMbqSHNxRJhsylBowoLNJ9Dl7XUIm7wajSavRuO41Yievh7//jEJOyU3jwKAsT2b8AOELMLf3x86nQ4ZGRmljmdkZCAwMLDccwIDAxWVB4C4uDjk5OSUPM6ePVv9xhMRVZM1+kD2f0Rkj2rKPaBsiiJzZ7UrXdU/KbaF4msAygZ6jCbY9Qz9XWlZuHrTKFXWt7Z9rwBRsspo87GLZgWmlOwvY076q2JKnsu6wxWv+iLnpCTQa24QjkipFxX8rTnC6khzMQBCNvP26iNo9sbvmLPuJNKvFqL4NscogCvXC/DTXrkZLwCgATC+Dz9AyDL0ej3at2+PjRs3lhwzmUzYuHEjYmJiyj0nJiamVHkAiI+Pr7A8ALi6usLT07PUg4jI1qzRB7L/IyJ7VFPuAWVTFAHmzWpXsqpfr9NUKy2MkoEee56hn557U7psoJf862cLOq0G0aE+UmXNCbIZTQIrFOwvU51N45U8l5TM6zAUKkn8Ro5MSaC3OkE4IqW6NvGHbI+3+7R9r46sDgZAyCZGfbUbn29LU62+B6ODufqDLGrChAn4/PPP8eWXX+Lo0aN4/vnnce3aNQwfPhwAMGTIEMTFxZWUHz9+PNauXYv3338fx44dw7Rp07Bnzx6MHTvWVk+BiMhs7AOJqKaqCf2fkhRFSme1GwpNilb1j76rcbW+13Vt4i89yFGdPU0sLSsvX6qcp5sLOoX5Wrg11aekjUoDU3Pjj0PJcN2sh9oqqv92Sp7LlwnqjXmQfeswc710WXP2oCEyl06rQfN6taXL95q9sepCDogBELK6VUnnEH/koqp1/ufhSFXrI7rdY489hvfeew9TpkxBVFQUkpKSsHbt2pJNLs+cOYMLFy6UlO/atSu+/fZbfPbZZ4iMjMRPP/2EX375Ba1bt7bVUyAiMhv7QCKqqWpC/9cl3A+1JEcGlM5qV5KaSI1V/TqtBu0byc3Qr86eJpYmu7F5dVYzWJOlgmxGk8DCP1Kky5u7v8ytlDyXlQfkV6aQ4+r6TjxyJVPWAUx/RdZ3d0TFaThvdyY7H13fkQ/oOQoXWzeAahajSWDsd0mq1tk5zIcbnJNVjB07tsLZe1u2bClz7JFHHsEjjzxi4VYREVkH+0Aiqqmcvf/TaTW4p0U9rD2cUXVhFM1qH3VX4yrLKU9NpM6q/k5hvtgtseqkON3SnXaYikZ2Y/OGvnL7t9hacZCtQCJ2Vhxkk/mOvyP1slSdxczdX+ZWXcL9oNMW7SNTlSMXrsJoEg4RpCLztJzyO64b5P8Imf6KbOHOpv74REGw+HxuAVq+uQZHZgywYKusi6PGZFVjvt2jep3LR3RRvU4iIiIiIqKaYnBMI+myX+84LVVOaWoitVb1K5mhb6/7gMiuAJEtZ2vFQTZZsqmj5qw9Kl1ndfeXKabTatA7oq5U2UKT8j1NyHG0fHONouAHADwQxfTtZH1KVnoWu14g0HLK75ZpkA0wAEJWYyg0Ye0hdVNf9W9Vj6s/iIiIiIiIqqF4VruM01k3qkyDZTQJLNhi3dRExbqE+8FFcnzRXvcBSUy5JFVOdqWIPVA7yGYoNCHp71zpOqu7v8ythnQNky6bmCr3WpJjuXPWelwvUJ5Cj+nbyRZ0Wg2e71H1ys3bXTeY0P3dTRZokfVx5JisZvDiHarWpwHw8VPtVa2TiIiIiIioplEyqx0A4lYcrPTnCcmXYFQwNqhGaqJiOq0G0aGOuw+I0SSw+q8LVRcE4FvbMVaAAOoH2ZSML6ixv8ytuoT7Qa+TK2tnf16kgrdW/oVzOQWKz+vUyJsTeMlmxvdpblYQ4OyVG5i+8rDq7bE2vvPIKgyFJuyUyMOqxLwnorl0kIiIiIiISAVKZrX/mnS+0sDBKz/Jb36uVmqiW3UK85UqV7wPiD3ZkXoZNyQ3tgj0crdwa9SjZpBN6fjCQ+3U3Sxep9XgvrZBUmW93Gupdl2yPUOhCUu3nzHr3K9HxqjcGiJ5Oq0G8x6PMuvcJdtPVRmUtncMgJBVLNsul8NTVp+WdTEwMljVOomIiIiIiGoqJTP0K9vbYFXSOaTn5ktfV83URMUceR+QRMmAzB2uLtKBHnuhJMi2Yt+5CoNsSrNLzHqoraLyMupJBp/2nVF3IijZlrmZTUZ0C+PqD7K5+6Lq454I+c/HWw3+Qt2sPtbGdx9ZxRd/qhcAGdW9ET4f0lG1+oiIiIiIiGo6pTP0Z68ruwG10SQw7rsk6Tq0GnVTExVTsg/IuSs3VL9+dQjJreO7NfVzuIwISoJsAsDc+BNljitd/dE5zMciA88Xsm9Kldt87KLdpVkj85ib2aRtfU+8eV9LC7SISLkvhnVGm+A6is/bmXbFoVeBMABCFmcoNCHjqvwMoM+fbIfmAe4ovpXTaYBgLzdMim2OEzP74/V7W1mmoURERERERDWYkhn6B/7OLTMYMjf+OJQMj4zt2cQig/g6rQZRDb1Vr9cavCVTJrVvKLfPiT1RGmT7eEtymeDBvXP/UHTN5SO6KCovq76P3AoQe0yzRuaJWyGf2q/YPRH++O3F7hZoDZH5Vo67C/co6IuLVbX/lz1jAIQsTskSwcYBHujTNgjrJt6NtP/ci1P/uRcps+5FQtw9eKFXEy4ZJCIiIiIishAlKyeA0ikxjCaB+ZtTpM9Ve2Pq2zXw8ZAqdyFHbia/tfjWdlW1nL1REmQzidKrQFYlncPJzOvS51tq9Qfg2GnWSDmjSeDX/ecVnTPv0Sh8MayzhVpEVD1fDOuIuY9FKTqnqv2/7JmLrRtAjs9QaMLn25KxPCENGVcLoQHgrtehU5gv3n8kStESwWn3tbZcQ4mIiIiIiKhCOq0Gg6KD8fM+uYG+4pQYehct5sYfl0zeVOTB6GCLpnCSnaF/8O8cGE3CbtJJJaZckiqXfd1g4ZZYRpdwP9TSApL7vGPe5uSSQJmS9GqA5VZ/AP8ECwsl/uh3p2VZrB1kHTtSL0u91sXmPhaF+6PrW65BRCoYFF0fGgGM+yFJqnzx/l93NjVvHxFb4nR6qpa3Vx9Bszd+x5x1J5F+tRACgAnANYMRm49not3MeOm6tBqgqwO+iYiIiIiIiJzFrIciFZXvMGMdjCaBeQpWfwDAfx5Wdh2lZGfo3yw02U2KIqNJYPVfF6TK+tbWW7g1lqHTavB8j8aKznlkYQLGfLtXUXo1S67+AIqeR3SoXBqyA/8fZCPHlSAZmASAUD93DGLwgxzE/e3qo5Gv3IQBwHFXtDEAQmYb9dVufL5Nvc3NH4iy7AwgIiIiIiIiqpzeRYvOjeT3l8jNN6Hxa2sUXWNA60CLpzfuEu4HvU7u+6W9DOjsSL2MG5JLIwK95Aes7M34Ps2h5Jv/vrPZWHsoQ9E1LLn6o1inMF+pctwHxPGtPSQXmASAdx5oa8GWEKnv7Yfk/2YddUUbAyBkllVJ5xB/5KKqdVp6BhARERERERFVbflIyw4ez3+ynUXrB4pm6Ec28JIqay8DOomSg+R3uLpID77bI51Wgxd7KVsFooSlV38U4z4gNYOh0IQUyb1n9DoNujT2s3CLiNSlZP8vR13RxgAIKWY0CYxVmHuzKta6QSEiIiIiIqLKKV0FosRDFt7741b1JTdCt5cBHSG5i0q3pn4Onz1B6SoQJayx+gNQNmh47soNyzaGLObLhFPSZXtF1HX49ybVPEpS+jnqijaOOJNiY77do3qd1rpBISIiIiIioqpZahWINVf+y26Ebi8DOt7utaTKtW9omeCUNVlqFYg1J1cqGTS8kHPTwq0hS1l58Jx02SFdGlmuIUQWpGRVoSOuaGMAhBQxFJqw9pC6qa/6t6rH1R9ERERERER2RO+iRb/WdVWt85k7G1n1u5+jpSjyre2qajl7N75Pc9UHpaw9uVJ20PCgnawyqq7Vq1ejc+fOcHd3h4+PDx544IFSPz9z5gzuvfdeeHh4oG7dunjllVdQWFhom8aqwGgSOHwuV6qsi5bpr8hxKfm8tJe0kUpw1JkUGbx4h6r1aQB8/FR7VeskIiIiIiKi6lvwZAfV6qp7hx5TBrZSrT4ZXcL9UEty1ON8tu1n6GdfN6hazt7ptBrMezxKtfqsHWAD5AcNbxaa7GKVUXX8/PPPGDx4MIYPH44DBw5g+/btePLJJ0t+bjQace+998JgMCAhIQFffvklli1bhilTptiw1dWzI/UyjJJxq7sjApj+ihyWks9Le0kbqQQDICTNUGjCzlNXVK1z3hPR/IAgIiIiIiKyQzqtBh+rNECd+FpvVepRQqfV4O4IuVUsNwqMFm5N1c5kXZMq5+2ht3BLrOe+qPqIDvGsdj0hvu5WD7ABRYOGep3cmIY9rDIyV2FhIcaPH485c+Zg9OjRaNasGVq2bIlHH320pMz69etx5MgRfP3114iKikL//v0xY8YMLFiwAAaDYwbtElIuSZcdGhNmwZYQWZZOq8E9LepJlbWXtJFKMABC0tRe/dG7RV0MjAxWtU4iIiIiIiJSz31R9XF38+qldVn0dDubTXzr0Eiu7VtPZNp0RqvRJLBiv9xeA86yAqTYT893q9b5Ph4u2DbpbpVao4xOq0FkAy+pso6YNqbYvn37cO7cOWi1WkRHRyMoKAj9+/fHoUOHSsokJiaiTZs2qFfvn0HU2NhY5Obm4vDhw7ZodrXJbl6v1zH9FTm+wTGNpMs6WkCXARCSovbqj94tArB4aEfV6iMiIiIiIiLLWDK8Cxr6uJl17qKn26Ff6yCVWyTPv47cfhk3CmybomhXWhau3pRbheJb23lWgABFQYRFT7cz61xfdx32T4lVuUXK1PfxkCrniGljiqWmpgIApk2bhjfeeAOrVq2Cj48PevbsiaysosBOenp6qeAHgJJ/p6enV1h3fn4+cnNzSz3shRByr1dkAy9mNyGH1yXcDy6Sf8aywUF7wQAISYlbcUC1uj5+PAqLh3ZSrT4iIiIiIiK1ZWVl4amnnoKnpye8vb0xYsQI5OXlVXpOz549odFoSj1Gjx5tpRZb1tZX70Gb+vKpiu6opUHKOwNsGvwAgEBP+cCNLWe0pufK70ES6OVuwZbYRr/WQVj0dDsoGUJuFVQb+6b2s1ibZNX3kXs97DFtzOTJk8v0Wbc/jh07BpPJBAB4/fXX8fDDD6N9+/ZYunQpNBoNfvzxx2q1YdasWfDy8ip5hISEqPHUVHE+W26QN9jb+d6TVPPotBpEh/pIlb2QY/t9s5RgAISqZDQJrNh3Xrp85zAfpLwzAEsHd0CroDoIuEOP1sGeWDK0I1LeGYD7oupbsLVERERERETV99RTT+Hw4cOIj4/HqlWrsHXrVjz77LNVnjdq1ChcuHCh5DF79mwrtNY6Vr7YHXMfj6pykHpo1xAcmjHALmZEdwrzhavklFZbpijKysuXKufp5oJOYb4Wbo1t9GsdhOR3BqBfy6rz0M97NBKrx/e0fKMkyG6EDgCJqfJ7SljDxIkTcfTo0Uof4eHhCAoqCmS2bNmy5FxXV1eEh4fjzJkzAIDAwEBkZGSUqr/434GBgRW2IS4uDjk5OSWPs2fPqv00zWI0CRz4O0eqrGwQjMjeyX6+HHSwFW0u1rjIggULMGfOHKSnpyMyMhLz589Hp05cAWBPDIUmfL4tGcsT0nD5WiH0Ljp0CvPF/Cfa4bM/kqHkT3r5iC7QaTXo1aoeerWS20CHiIiIiIjIXhw9ehRr167F7t270aFDBwDA/PnzMWDAALz33nsIDq54L0MPD49KB/sc3aCo+rivbTC2Hr2Id9cdQWrmdQiNBnU9XfFU51CM7B4OvYv9zLXUaTVo28AbuyVSOh86nwujSdgkcCO7sfmD0fXtIrBkKTqtBouGdCg1RnEprxAuOi3CA2rj330j0KN5gF39Doo2QgcMEhnM7G28MCAgAAEBAVWWa9++PVxdXXH8+HF061a0Z0tBQQFOnTqF0NBQAEBMTAzefvttXLx4EXXr1gUAxMfHw9PTs1Tg5Haurq5wdZVLVWdNO1Ivw2CUe8HubFz175DIEXRt7I8Fm1OqLHezsCht5J1N5QPAtmTxAMj333+PCRMmYNGiRejcuTM++ugjxMbG4vjx4yUdItnW26uP4PNtaaWOFRiM2Hw8E62nrVNUV2QDT7u62SUiIiIiIlIqMTER3t7eJcEPAOjduze0Wi127tyJBx98sMJzv/nmG3z99dcIDAzEwIED8eabb8LDQ26PAEfhaBPeOoX5SgVAbhSYsCstCzE22MxYdmPzhr7O9bdUEb2LFmN6NcOYXs1s3ZQq6bQa3Nc2CCv2X6iybLqDpY0p5unpidGjR2Pq1KkICQlBaGgo5syZAwB45JFHAAB9+/ZFy5YtMXjwYMyePRvp6el44403MGbMGLsMcFQlIUVutY6bi5YboJPTKAroaqSCf9tTMh0mAGLxkeoPPvgAo0aNwvDhw9GyZUssWrQIHh4eWLJkiaUvTRJGfbW7TPCjOibFtlCtLiIiIiIiIltIT08vM2HPxcUFvr6+lW7m++STT+Lrr7/G5s2bERcXh+XLl+Ppp5+u9Fr2vAGws1CSoig9xzYbu57JuiZVTnalCFlXkLdcYGrNXxccKm3MrebMmYPHH38cgwcPRseOHXH69Gls2rQJPj5FewbodDqsWrUKOp0OMTExePrppzFkyBBMnz7dxi03zy7JlHhtuQE6ORGdVoPIBl5SZW2ZNlIpi64AMRgM2Lt3L+Li4kqOabVa9O7dG4mJiZa8NElYlXQO8UcuqlafXqdh1JuIiIiIiOzW5MmT8e6771Za5ujRo2bXf+seIW3atEFQUBDuuecepKSkoHHjxuWeM2vWLLz11ltmX5Oq1iXcD64uGuQXVj3wfElyLw41GU0CK/afkyoru1KErEsjuX27o6WNuVWtWrXw3nvv4b333quwTGhoKNasWWPFVlmG0SSw/3TVq8YAIMjLzcKtIbKu+j4e2H06u8pyB/5/HxBHCABadAXIpUuXYDQaUa9e6WWx9erVK3fWDGe+WI/RJDD2uyRV6xx9V2OH+KMnIiIiIqKaSXbD38DAQFy8WHqyWGFhIbKyshTt79G5c2cAQHJycoVl7HUDYGei02rQs5lcjv49koOeatqVloWrNyU2kADgW5srQOyRkrRp21MyLdgSUsOO1MuQiJcC4Abo5Hxk/6YNRoEdKZct3Bp1WGUTdFmc+WI9Y77do2p9GgDj+9h/bk4iIiIiIqq5ZDf8jYmJQXZ2Nvbu3Yv27dsDADZt2gSTyVQS1JCRlJQEAAgKCqqwjL1uAOxs3PVywx9bT2RafUZreq78vhCBXhxstUddwv1QSwsUmKouez7bMfcBqUlk9/8AuAE6OR/ZjdABx9kHxKIrQPz9/aHT6ZCRkVHqeEZGRrmzZjjzxToMhSasPaRe6isAGNuTqz+IiIiIiMg5tGjRAv369cOoUaOwa9cubN++HWPHjsXjjz+O4OBgAMC5c+cQERGBXbt2AQBSUlIwY8YM7N27F6dOncJvv/2GIUOG4K677kLbtm1t+XQI8jNaizdCt6YsybRbnm4u6BTma+HWkDl0Wg3ujqhbdUEANwrkVvuQ7cj2AUwFT86oS7gfXCSHeB1lHxCLBkD0ej3at2+PjRs3lhwzmUzYuHEjYmJiypR3dXWFp6dnqQepb/DiHarWpwHwUt/mqtZJRERERERkS9988w0iIiJwzz33YMCAAejWrRs+++yzkp8XFBTg+PHjuH79OoCi778bNmxA3759ERERgYkTJ+Lhhx/GypUrbfUU6Bb2vBG67MbmD0bX58RDO9ahkdxAePEqI7JPSvb/iOQG6OSEdFoNokN9pMoW7wNi7yyeAmvChAkYOnQoOnTogE6dOuGjjz7CtWvXMHz4cEtfmsphKDRh5yl1c5p+9HgUO3wiIiIiInIqvr6++Pbbbyv8eaNGjSDEP1/6Q0JC8Mcff1ijaWQGe94IPeua3MbmDXw8LNwSqg7/OnKp7G4UOO5G6DWBkv0/OnJFFjmpTmG+2C0xfly8D4i992cWXQECAI899hjee+89TJkyBVFRUUhKSsLatWvLbIxO1qH26o829T0xKKq+qnUSERERERERqcmeN0Lfe1ouhciV63KBErKNQE836bLcCN1+cf8PImWrJh2hP7PKJuhjx47F2LFjrXEpqoTaqz8a+rph5YvdVauPiIiIiIiIyFJkN0LffOyi1TZCN5oEtp2UG3Bl4gX71inMF261tLgpsRM6N0K3X9z/g+iffUBkVkM5wj4gFl8BQvZDzdUfw+9siK2T7lGtPiIiIiIiIiJLkt0IvTilhzXsSsvCNYPcptgx4fadYqSm02k16CGZBoYbodsno0lgr+TEYe7/Qc5MyT4g+89m2/0+IAyA1BBKV390auSNpYM7IKKuB/QaoJZWg/rebpgU2xwnZvbH1IFtLNhaIiIiIiIiInUpSemRmCqfBqc60nPlVgJ46HWcbe4AuBG6Y0tIvoSq1+8U4f4f5Ow6Sf6NF5qABMmVjLZilRRYZHtxKw4oKv/1yBjoXbTo1Yp7tRAREREREZHj6xLuB70OkFlwYa2x6UtX5TZc7986kLPNHQA3Qnds8zedkC7L/T/I2XVt7I8Fm1Okys7bdALdm9vve4IBECdhKDTh823JWJ6QhoyrhRAoWt7jrtehYyMfbDkhH4nrHOYDvQsXBxEREREREZHz0Gk1uK9tEFbsv1Bl2fQc6+zRILuxeT0FG2yT7SjdCJ0BEPthNAnsOpUtVVanBVdkkdPrEu4HrUZuQsCe09lW2zvLHBzldgJvrz6CZm/8jjnrTiL9/4MfAGACcM1gVBT8AIDlI7qo3kYiIiIiIiIiWwvy9pAqt+avC1ZJUaSRHCuSLUe21SnMF64uci/WuSs3LNwaUmJu/HHpsu1CvO12oJdILTqtBh1CvaXKCgBz4+VXUFkbAyAObtRXu/H5tjTV6ots4MnVH0REREREROSUNJAbtLxZaLLKRujp2XKD4N7utSzcElKDTqtB2wbeUmUvWGmVEVXNaBKYL5nqBwDG3d3Mgq0hsh8vKvhb/3hLst3ubcSRbge2Kukc4o9cVLXOSbEtVK2PiIiIiIiIyF7EKEhbY+mN0I0mgTWH0qXK+t8ht7cE2Z7sxsEH/86x28HCmmZu/HHIvhJaDdCVqcuohujaxF86eGAS9rsKhAEQB2U0CYz7LknVOvUuWuYwJCIiIiIiIqdVvBG6DEuPTe9IvYwbBSapsoFe7pZtDKmma2O5wXFrrTKiyhlNAvMUrP5o35Dpr6jm0Gk16NDIW7r8vM32uQqEARAHNTf+OORuk+TN+VdbduJERERERETktIo3Qpdh6Y3QEyUHv+9wdZFeVUC2VxRkkxtb2Z6SaeHWUFWipq1VVJ7pr6imUZIGCwDueW+zhVpiPgZAHJDRJPCxgui0jCBPVwyKqq9qnURERERERET2xl42QheSSXe6NfXjZEUHotNqENnAS6osN0K3rXvnbsFVg/z0Yhct019RzdO1iT8kY7oAgFNZNzBi2W7LNcgMDIA4oITkS6qv/vhj0t0q10jkPLKysvDUU0/B09MT3t7eGDFiBPLy8io9p2fPntBoNKUeo0ePtlKLiYjUwz6QiIicjb1shO7pJrexeXSIj8XaQJZR30cuyMaN0G3nt31/4/CFa4rOeaFHEwYjqcbRaTUY07OxonM2HruIlQfOW6hFyjEA4oDeWnlY1fpGdAuD3oV/CkQVeeqpp3D48GHEx8dj1apV2Lp1K5599tkqzxs1ahQuXLhQ8pg9e7YVWktEpC72gURE5GyUbIRuyRRF+89ckSqXc6PAYm0gy6jvI7dnCzdCtw2jSWD8DwcUnaPVAOP7MP0V1Uzj+zRXfM5L3++3m/6No94OxlBoQnKmsgh1Zdo28MSb97VUrT4iZ3P06FGsXbsWixcvRufOndGtWzfMnz8f3333Hc6frzya7eHhgcDAwJKHp6enlVpNRKQO9oFEROSMuoT7oZbkaMj5bMvM0DeaBDYflwuucMK54+FG6PZtbvxxyQR0//jwsSiu/qAaS6fV4EWFq0CMJmBu/AkLtUgZBkAczODFO1Sra0S3UPw2trtq9RE5o8TERHh7e6NDhw4lx3r37g2tVoudO3dWeu4333wDf39/tG7dGnFxcbh+/Xql5fPz85Gbm1vqQURkS9bqA9n/ERGRNem0GtwdUVeq7I0Co0XasCP1MvIL5ZJbx4RzzwFHw43Q7ZfRJDBf4b66YX4e3DeXaryX+jaXTCD5j4+3JNvFKhAGQByIodCEnafklsgCQN8If/Rq6gv9//9bpwH8a9fCv/s2w4mZ/fHmfa0t01AiJ5Keno66dUt/OXJxcYGvry/S09MrPO/JJ5/E119/jc2bNyMuLg7Lly/H008/Xem1Zs2aBS8vr5JHSEiIKs+BiMhc1uoD2f8REZG1dWgklwZr64lMiwzeJErO+ndz0aKLgpRdZB+4Ebr9Mmf1x4aJPS3RFCKHotNqMP/xKEXnmIR9rAJxsXUDSN7kn5TlJ1w4pBOX5xFVYPLkyXj33XcrLXP06FGz6781P36bNm0QFBSEe+65BykpKWjcuPxlg3FxcZgwYULJv3NzczkISEQWYW99IPs/IiKyNv86rlLlbhQUpSi6s6m6qzCE5BBsz4gAfq93UMHe7sDpbFs3g25hNAks/EPZ6o/5T0TzPUj0/+6Lqo//Jf2NjccuSZ+zaGsKxvdpZtP3EQMgdsRoEth69CLeXXcEyRevwwjA1UWL8IDamNC7OVYkVZ5r+1YPRQezgyaqxMSJEzFs2LBKy4SHhyMwMBAXL14sdbywsBBZWVkIDAyUvl7nzp0BAMnJyRUGQFxdXeHqKvdFjIioOuytD2T/R0RE1hbo6SZddntKpuoBEE+3WlLlokN8VL0uWY9GIzcmcyHHMvvMUFk7Ui+jQC7zHACgXYg3BkYGW65BRA7oi2Gd0XP2RpzKkuu7DEZhkYkESjAAYifWHLyAsf/dh9tX1t4sNOHIhasYuXyPovr+83Ckiq0jcj4BAQEICAioslxMTAyys7Oxd+9etG/fHgCwadMmmEymkgE9GUlJSQCAoKAgs9pLRKQm9oFERFV7++23sXr1aiQlJUGv1yM7O7vKc4QQmDp1Kj7//HNkZ2fjzjvvxMKFC9G0aVPLN5gU6RTmC7daWtyUGA21xEbo+8/IpbfOuVGg+rXJOur7uEuVO/h3DowmwUmsVvBVQpqi8j8+39VCLSFybBv/fTcav7ZGuvyXiWk2DYBwDxA7MGvNEbzwbdngh7k6h/lA78KXlkgNLVq0QL9+/TBq1Cjs2rUL27dvx9ixY/H4448jOLhoJsi5c+cQERGBXbt2AQBSUlIwY8YM7N27F6dOncJvv/2GIUOG4K677kLbtm1t+XSIiBRhH0hENZnBYMAjjzyC559/Xvqc2bNnY968eVi0aBF27tyJ2rVrIzY2Fjdvcoa3vdFpNeghORgT5C2/WkSG0SSw+bjcxtccE3dcXRvL/X3dLCxKs0aWZTQJbDh2seqC/29cryYMShFVQKfVYFyv8rOblGfTMcvspyWLo+Q2tubgeXy6VVkEuirLR3RRtT6imu6bb75BREQE7rnnHgwYMADdunXDZ599VvLzgoICHD9+HNevXwcA6PV6bNiwAX379kVERAQmTpyIhx9+GCtXrrTVUyAiMhv7QCKqqd566y28/PLLaNOmjVR5IQQ++ugjvPHGGxg0aBDatm2Lr776CufPn8cvv/xi2caSWdqF+kqVy8jJV/W6O1IvI79QLg9PTLjtZsxS9XQJ94NeJzeAvj1FLiBG5tuRehlGyfRXGgDj+zSzaHuIHN34Ps0hGyIsNAmbBnqZAsuGjCaBMd/uV7XOFkF3cPUHkcp8fX3x7bffVvjzRo0aQYh/ItkhISH4448/rNE0IiKLYx9IRCQnLS0N6enp6N27d8kxLy8vdO7cGYmJiXj88cfLPS8/Px/5+f8MsOfm5lq8rVQk96Zceqk1f13AnEciVZsNnig5COTmokWXxn6qXJOsT6fVILKBF3ZLbIS+Oy3L8g2q4ZYnnpIu+1C7+lz9QVQFnVaDh9oF4+d9cntWf7XjlM3SYHGk3IZe/HYv1F78s+L5birXSERERERERFVJT08HANSrV6/U8Xr16pX8rDyzZs2Cl5dXySMkJMSi7aR/aCTnrqqdokhIjgT0jAjgIKyDq+/jIVXu0Plcm6aHcXZGk8DGoxnS5Wc9xLStRDJmPSS/B/XmYxdt1s8xAGIjhkIT1hyS73xlNK1bG+56nap1EhEREREROYvJkydDo9FU+jh27JhV2xQXF4ecnJySx9mzZ616/ZosRsHqCjVTFHm61ZIqFx3io9o1yTZkN0K/UWDCLq4CsZgdqZdRIJn+qnGABzOrEEnSu2jROEAu0Gsw2i4NFlNg2cjgxTtUr3P1uLtUr5OIiIiIiMhZTJw4EcOGDau0THh4uFl1BwYGAgAyMjIQFBRUcjwjIwNRUVEVnufq6gpXV1ezrknV0yXcDy4aoFBiQuq5KzdUu+7+M1ekyuXckEvRRfara2N/LNicIlU2PUe9vzEqLSHlknTZ2FaBFmwJkfPp1zpIup/bnpJpkzRYDGnagKHQhJ2n5G54ZD1zZyNGqImIiIiIiCoREBCAiIiISh96vd6susPCwhAYGIiNGzeWHMvNzcXOnTsRExOj1lMgFem0GkSHyq2yuJBzU5VrGk0Cm4/LrSZh9ivH1yXcD64uci/kpbz8qgvZ0IkTJzBo0CD4+/vD09MT3bp1w+bNm0uVOXPmDO699154eHigbt26eOWVV1BYWGijFv9DSQDzzsYBFmwJkfPp2lg+oHE+W53PUqU4Ym4DcSsOqFpfqJ87pgxspWqdRERERERENdmZM2eQlJSEM2fOwGg0IikpCUlJScjLyyspExERgf/9738AAI1Gg5deegkzZ87Eb7/9hr/++gtDhgxBcHAwHnjgARs9C6pKpzBfqXIH/85RJXf5jtTLyC+Uy8UTE26bzWJJPTqtBj2byQ2o7zmt7kRZtd13330oLCzEpk2bsHfvXkRGRuK+++4r2ePIaDTi3nvvhcFgQEJCAr788kssW7YMU6ZMsXHLgRsGuSCMm4sWXRSkxiOiokCvXjLCcKPAaNnGVIABECszmgT+t++8avXdE+GPP165W7X6iIiIiIiICJgyZQqio6MxdepU5OXlITo6GtHR0dizZ09JmePHjyMnJ6fk35MmTcKLL76IZ599Fh07dkReXh7Wrl0LNzc3WzwFkiA7c1WtjdATJevgQKzzcNfLZZ+35QbBVbl06RJOnjyJyZMno23btmjatCn+85//4Pr16zh06BAA/F979x4XVZ3/D/w1Z3BGUC4Cg4CSMl4gTBAzEF0LL4nmurlbfqtfmrZbu/o1Wy+bC/szvEV4+VaW221rNzTbdvvtt1of5kJmmrbgJQ3Na15QMrmYhCMqjDNzfn+wM0YinBnOmcs5r+fjMY+HwGfO+RyY85rxfM7n88bHH3+Mw4cPY/369Rg0aBDGjx+PZcuW4eWXX4bVavVZ3+0OEdu+ljbrakJqHPScekXkFr2gw8RB8ZLabv/6vE9yjgMgXlZ64jtIrLsEAIgIbv2NMj7MgCNLx+HP0zPl6RgRERERERG5FBUVQRTFGx7Z2dmuNqIotqgpotPpsHTpUlRXV6OxsRGffPIJ+vfv7/3Ok2RDzVEw6KVd8JSjELoIaRd+spNNvBCrElILofuyQHB7oqKikJSUhHXr1uHy5cuw2Wx4/fXXERMTg9tvvx0AUFZWhoEDB6J79+6u5+Xk5MBiseDQoUO+6vp/Zl1JO+9+0pezrog88ZN+MZLaXb0mz80E7mIRdC9bvEF66PeKCsZnT43CVasdz246jNMXrqB3VAj+cE8Kgg16BXtJREREREREpH56QYe0nuHYc6a+3bZ7Kuo6vL+q+iuS2qUnSKtNQv7PnULovioQ3B6dTodPPvkEkyZNQmhoKARBQExMDIqLi9GtW/Nrtbq6usXgBwDX185lslrT1NSEpqbr9U8sFousfXenAHpsuLTBKiJqKTZM+kxXX+QcB0AU0NBowxPrd6Ps1PdwAIiPCMbTPx2A4X2jcfK7y5K38+ykVABAsEGPZZMGKtRbIiIiIiIiIu3q0S1E0gDI/v/UAfF0ZobdIaL4UI2kthevXvNoH+R/hpqj0EkArklYDsTbBYJzc3OxYsWKNtscOXIESUlJmDVrFmJiYrBjxw4EBwfjzTffxMSJE7Fnzx7ExcV53IfCwkIsWbLE4+e3Z7fEgcvgToLkmkBE1FJGYiSMQTpJs63kuJnAXRwAkZHdIWL0c9tw+kLLOzrO1F3FY+u+uMmzWqcXwPU+iYiIiIiIiBTm7hJFnt65uruiDles0hbF5upX6qEXdBiVHIOSw7XttvV2geD58+e3WMavNWazGZ9++ik2btyI77//HmFhYQCAV155BZs3b8batWuRm5uL2NhY7N69u8Vza2qaB/xiY2Nvuv28vDzMmzfP9bXFYkFCQoKHR9SS3SHiS4nF5W+LD+Oyc0Qe0gs6pPaMwJ7T7Z9vHb2ZwBMcAJFJ8cEqzFi/T7bt3ZsWz+AlIiIiIiIiUpi3liiqtki/uz/L7H/LIJHnhvSOkjQA4iwQ7K3rQSaTCSaTqd12V6403+grCC1LCQuCAIejeVAvKysLBQUFqK2tRUxMcz2AzZs3IywsDCkpKTfdttFohNFo9PQQ2rTz1AVILP+BOzj7g6hDMhIjJQ2AdPRmAk+wCLoM5B78AIDl96XJuj0iIiIiIiIiupFziSIpOrJE0XeXmtpvhOaleLgihLpEh0q7wO+rAsHtycrKQrdu3TBt2jTs378fX3/9NZ566ilUVFRgwoQJAICxY8ciJSUFU6dOxf79+1FSUoKFCxdi1qxZig1wtMed+h/D+7Q/EERENzesj/QBjX+fPK9gT27EAZAOsjtEzJR58CMzsRsMQfzTEBERERERESnNuUSRFB1ZouiL09IubN/Z38QVIVTG3QLB/iY6OhrFxcVoaGjAqFGjMGTIEHz++ef45z//ibS05ht49Xo9Nm7cCL1ej6ysLEyZMgWPPPIIli5d6rN+f/v9VUntDHodBx2JOshbNxN4gktgddDsv+6FxNl0kr39q6Eyb5GIiIiIiIiIbkbpJYrsDhGfHZd2N3pwJ71b2yb/5+8FgqUYMmQISkpK2mzTq1cvbNq0yUs9at9Vq01Su5HJMRx0JOogd+odxUVIHxSWA6cZdIDV5sCmgzWybpOzP4iIiIiIiIi8S+klinZX1KHxmrQC6PFevjBEynMWCJbCWSCYOsbuELHta2mzaYb06qZwb4i0YXAvabV0ai5KWxJSLrzS3gFT39wp+zY5+4OIiIiIiIjIu5ReosidAuisRaBOGRKLbDsLBFPH7Dx1QdKMGwCI7uqbGiVEamNpvCap3aavqrw60MsBEA9ZbQ7sklDZ3h2/HN6bsz+IiIiIiIiIvMy5RJEUnixRxALo5M8FgtXInQLoseHBCvaESDt0kPY+2mjzbDalp3i13UO5/9gv6/YSIoORP3GArNskIiIiIiIiovYpvURR3RVpAyAsgK5eQ81RkDjG5rd1QAKJ1ALowZ0EybNziKhtWW4M4HtzoJcDIB6wO0S8X35Otu1FhQRhx4JRsm2PiIiIiIiIiNyj5BJFX0hcQYIF0NVLL+iQLrHWBOuAdNy331+R1O62+DAOOhLJZKg5Cp0kjjacq5e+NGRHcQDEAy9uPibbtkYlRWFvfo5s2yMiIiIiIiIi9ym1RJHdIeLLM9IGQESRF73VjHVAvMPuELH/7EVJbe/g7A8i2egFHUYlx0hqe/WaXeHeXMcBEDfZHSLWbD0puX2/mC44snQcJg+JR5hRQCcB6BbSCQ/ekYAjS8fhL4+y6DkRERERERGRrym1RNHOUxcgsRYzenRjLQI1Yx0Q79h56gKsdmkn3fA+JoV7Q6QtQ3pLWwZr+9fnvTbTLUipDffu3Rtnzpxp8b3CwkLk5uYqtUtZXbXa8cxHh3Dg7EWEB3fC4yPM+Ek/E17cfAzu/Gk+evJOGIIErLo/HavuV6y7RERERERERNQBziWK9khYrsq5RJGUpXPK3LiTnxdj1c05yCZlQIx1QDwntQB65yABQ92oWUBE7YsONUpqd/VacyH04f2kDwx7SrEBEABYunQpHn/8cdfXoaGhSu5OFnaHiPte+TfKfzRV7vMT7k897GMKgSGIk2yIiIiIiIiIAkFGYqSkARDnEkVSLtyIEm+j5MVY9VNqkI1akloAPbVnOH+/RDKLDessue2/T573ygCIolfnQ0NDERsb63p06dJFyd11WPHBKvT5w6YbBj88tfint8myHSIiIiIiIiJSnhJLFFXVSyvGfM/AOF6M1QDWAVHeWYkF0OPCpV+oJSJpMhIjYZS4nqS3ZropOgCyfPlyREVFIT09HatWrYLNZlNydx1SfLAKM9bvk217QQIwzAsjWEREREREREQkD7nrgNgdIjYeqJK0vVhejNUE1gFRlt0horyy3tfdINIsvaBDas8ISW2dM92UptgSWE8++SQGDx6MyMhIlJaWIi8vD1VVVXj++edv+pympiY0NTW5vrZYLEp1rwW7Q5R18AMA/vuuvrxzg4iIiIiIiCiAuLNE0Zff1Le7RFFzMWZp++YlBG1gHRBl7Tx1QdLvFgB6dAtWtjNEGqXEcpId4dYMkNzcXOh0ujYfR48eBQDMmzcP2dnZSE1NxYwZM/Dcc89hzZo1LQY4fqywsBDh4eGuR0JCQseOTqJZf/1C1u0JOuC3d/eXdZtERERERETkPQUFBRg2bBhCQkIQEREh6TnTp0+/4f/I48aNU7ajJDupSxTZHEDp8baLLUstxgwAWWauIqEFzkE2Kbx1d7SauHPODe9jUrAnRNrlbzPd3BoAmT9/Po4cOdLmw2w2t/rczMxM2Gw2nD59+qbbz8vLw8WLF12Pb775xq2D8YTV5kDxwVpZt/lENmd/EBERERERBTKr1YrJkydj5syZbj1v3LhxqKqqcj3effddhXpISnHnws1Ln37d5s93S7yD36DXsQC6hrAOiHKKD0pbco7nHJFy3FlOsuRQtbKdgZtLYJlMJphMno2OlpeXQxAExMTE3LSN0WiE0Wj0aPuemvrmTlm3x9kfREREREREgW/JkiUAgKKiIreeZzQaERsbq0CPyFuGmqMg6AApN97va2MZLLtDxF4JS4AAQFrPcN5IqSHD+kTj5a0nJbXdcaJW8eVh1MJqc+DkeWkF0HnOESnHneUkT56/AqvNAUOQcqXKFdlyWVkZVq9ejf379+PUqVN45513MHfuXEyZMgXdukmb5ucNVpsDuyR+GJHqhQcGMUCJiIiIiIg0atu2bYiJiUFSUhJmzpyJCxfavnu7qakJFoulxYN8Sy/oMKRXhKS2dgdueod+6Ynv4JC4zzskzgggdRhqjoJe4qWjr84yE6RaW3pacluec0TKkjrTDQDWllYo2BOFBkCMRiP+9re/4a677sKAAQNQUFCAuXPn4k9/+pMSu/OY3LM/bosPw72Desi6TSIiIiIiIgoM48aNw7p167BlyxasWLECn332GcaPHw+7/eZVsH1VC5PaNnuU9JUd1u083er317SzPNYPsRaBtugFHQb0CJPUNsSoV7g36rH+Judia3jOESnLneUk1+88o2BP3FwCS6rBgwdj5055BxfkJvfsj1CjHhufHCHb9oiIiIiIiEheubm5WLFiRZttjhw5guTkZI+2/+CDD7r+PXDgQKSmpqJPnz7Ytm0bRo8e3epz8vLyMG/ePNfXFouFgyB+YFjfaAiApBkcW4/W3rAMlt0h4osz9ZL2pRfAWgQaNDG1Bw5ImN2R0ZuvDSmsNgfO1F2V1DZIYP0PIqUNNUdBLzTPlGzPmbqrii6DpdziWn4u7/39sm1rQFwXfLVknGzbIyIiIiIiIvnNnz8fR44cafNhNptl25/ZbEZ0dDROnDhx0zZGoxFhYWEtHuR7ekGH23tLW8K7tULVO09dkFRDBAAGJ0RwKW0NmjasN3Tt/Nl1uuZ21L7cf0i/zjcq2cRzjkhhekGHMck3rwX+Y7n/K9+1+h9TZAaIv7M7RLy/75zk9uaozrg9MRLFB6pwySpCABBs0CMjMRJrHhqMrp01+WskIiIiIiIKKCaTCSaT95Y9OXv2LC5cuIC4uDiv7ZPkk5EYKamAKwD8++T5FoWq3y47LXk/T7qx3BaphyFIwK9HJOL17Tdf+/7XIxIVLQysFnaHiPfLpV/nm5aVqGBviMjpkWGJKDlcK6nt+1+ew6rJytTW1uSV+xc3H4PEGzEAAMVzR8IQJGDV/emK9YmIiIiIiIj8R2VlJerq6lBZWQm73Y7y8nIAQN++fdG1a1cAQHJyMgoLC/Hzn/8cDQ0NWLJkCe677z7Exsbi5MmTWLBgAfr27YucnBwfHgl5alifaLy89aSktrsr6lz/tjtEbDlSI+l5QQIwrJ/0ddJJXfLuSQEAvLGjosWMIUEHPD4i0fVzatvsv+6V3JZLzhF5z1BzFPQ6wC7xQvzsv+7DK1Nul70fqhwAsTtEbD9SixUlh3Gi9gpsADoJOsSEGfFQxi14SeIHGABI6xnG0XYiIiIiIiKNyc/Px9q1a11fp6c33xC3detWZGdnAwCOHTuGixcvAgD0ej0OHDiAtWvXor6+HvHx8Rg7diyWLVsGo9Ho9f5Tx7lz4WbvmXpXHZCdpy7gmpTiIQDSufyV5uXdk4L5Y5PxdtlpnKm7gl6RIZia1ZvXoiSy2hzYdFDagCMA3JsWz3OOyEv0gg73psVLnqG16WC1IrVAVDcAUnywCk/8dR9sP/qwcc0h4tv6RvzPx1+7tb0FObfK2DsiIiIiIiIKBEVFRSgqKmqzjShevzIeHByMkpIShXtF3qQXdBjQI0xSoWoRwIubv8a8nCSsK735kkY/dkdiZAd6SGphCBLwqxHy1R/SkhErP3Gr/fL70hTqCRG1Zvn9aW4tUXfXyk9R9ocxsvZBVcPJxQerMGP9jYMfnjLodZwWR0RERERERKRRE1N7SG77x20nYLU5JK93DgDD+3ivJg2R2mzYdxY1lmuS2/cxhXBmDZGXGYIE9DV1kdy+ytKEf5Z/K2sfVHPW2x0iZqzfJ+s2Z9zZh9PiiIiIiIiIiDRq2rDekts6RODhN8oktw8SeNMlkafsDhG/fW+/W89Z/NPbFOoNEbVl0cQBbrWf/95+2B3uVPBum2oGQEb/z6eybk/QAb+9u7+s2yQiIiIiIiKiwOHunat7ztRLbnvvINYiIPLUi5uPwZ3Lo530OgzrF61Yf4jo5ob1jUaQG293NoeI0uPfybZ/VQyAbNh3FqfrGmXd5hPZfflBhIiIiIiIiEjj3L1zVarCX6Qqsl0itbM7RKzZetKt5zz/X4N4nY/IR/SCDqsfGOTWcxZvPCjb/gO+CLonU97aoxc4+4OIrisoKMBHH32E8vJyGAwG1NfXt/scURSxaNEivPHGG6ivr8fw4cPx6quvol+/fsp3+EeuWu3I33AAxQeqcMkq3xRCOXQSdIgJM+LhzF54bITZL9ZjtTtEbD9SixUlh3Gi9gpsvu7QDwgAgg16ZCRGYs1Dg9G1s3+8jTc02jD7nT0oPVGHJj96iekAGIMEmE1d8LuxybgryeQX/+mx2hx4Y8cJvF1agepLN77C/O28CPQMpOvsDhHbDtVg0cavcPai1dfd0RR/O6+JyD3D+kZDrwPsMn7OYS0CIs+5O/sjPSEcE9PiFesPEbXvp4N64M//PoUvv7FIan/y/BVYbQ5Z3isD/t3W3dCTYvUD6X5xgYSI/IPVasXkyZMxc+ZMyc9ZuXIlXnrpJbz22mvYtWsXunTpgpycHDQ2yjtbrT2Pr9uDW/OL8f++OOd3gx8AcM0h4tv6RqwsOYb+C/+Fwk2Hfdqf4oNVSFr4Lzz69hc46meDHwDgAHDZasfWY+dx2+IS/OyPO3zdJfzsjztw2+ISbD3uX4MfACACaLQ5cLjqEn65dg+Sn/4Xig9W+bRPhZsOo//Cf2FVyfFWBz8A/zsvAjkD6brig1Xo93834Vfv7OXghw/423lNRO7RCzrMyu4j6zZZi4DIM3aHiFc/c2/2xz9mDleoN0Tkjn/M/Ilb7XP/V55JDwE9AOJJ6LVnVLKJo8JE1MKSJUswd+5cDBw4UFJ7URSxevVqLFy4EPfeey9SU1Oxbt06nDt3Dh9++KGynf2Bx9ftwebDtV7bnxxe317hs4tCxQerMGP9PthkLLSltANnLT4dBPnZH3fgwFlpd2/4g2t2ETPW7/PZIEjhpsN4fXuF28/z5XkBBG4G0nXOfAugeFM9X5/XROS+396dBLlukwwSwFoERB7aeeoCrjmkt39yJJe4J/IXekGHXwySft39n/vPyVIMPaAHQNwNvfbcEtkZf5meId8GiUiTKioqUF1djTFjxri+Fx4ejszMTJSVlXmlD1et9oAb/HB6Y0cFrDYZw10Cu0PE0x8c8Oo+5XLgrAUNjd6fq9LQaAuowY8fWrzhsCwfotxhtTk8Gvxw8sV54Sl/yEC6zu4QsfB9eZeLJXkE0nlNRM0XbWaPlGcWyH/fxQuyRJ4qPSm9MLKg4xL3RP5m+f1pktvaHcDOkxc6vM+AHgApk+EX4DQ6ORrbF4yWbXtEpF3V1dUAgO7du7f4fvfu3V0/a01TUxMsFkuLh6eeDeC7Sh0i8HbZaa/uc3dFHc5f9rcFr6Sb+/cvNbFPuVRbGrG7os6r++zoa9oX54WnPMlAOfOPWtpdUYfvrth93Q1qRSCd10TU7Ld3J3X4IgprjpK/KigowLBhwxASEoKIiIhW21RWVmLChAkICQlBTEwMnnrqKdhsLf8ftW3bNgwePBhGoxF9+/ZFUVGRrP389vurkts+kc3BRiJ/YwgSkNm7m+T2ZaekD3reTEAPgMCN6h9ZvcLRvave9bUAICK4Ex68IwFHlo7Dn6dnKtA/IvJXubm50Ol0bT6OHj3q1T4VFhYiPDzc9UhISPB4W6cvXJGxZ953ps67/a+9FNh1CSrd+E9AIO9TTt7+m8vxmpbzvPC3DJQz/6ilQM83tfP2+x0RdYxe0OGlBwd1aBusOUr+qr26b3a7HRMmTIDVakVpaSnWrl2LoqIi5Ofnu9pUVFRgwoQJGDlyJMrLyzFnzhw89thjKCkpka2f8RGdJbXTc/YHkd96+7GhbrTu+HtmUIe34ENZ5mj8cWv7NUA6CTqs/81wfsggIpf58+dj+vTpbbYxm80ebTs2NhYAUFNTg7i4ONf3a2pqMGjQoJs+Ly8vD/PmzXN9bbFYPL4I2DsqBDuOe/RUv9ArMsSr+4sJlfYh2l/d0i3YJ/s8Vn3J6/uVi7f/5nK8puU8L/wtA+XMP2op0PNN7bz9fkdEHffTQT3w/pff4NNj7q9IMTKJNUfJfy1ZsgQAbjpj4+OPP8bhw4fxySefoHv37hg0aBCWLVuG3//+91i8eDEMBgNee+01JCYm4rnnngMA3Hrrrfj888/xwgsvICcnR5Z+Du9jwivbTrXbbvaofrwOSOSnDEECJgzsjo++qmm3bVafqA7vL6BngAztE4WIkE7ttnvxQd5hQUQtmUwmJCcnt/kwGAwebTsxMRGxsbHYsmWL63sWiwW7du1CVlbWTZ9nNBoRFhbW4uGpP9yT4vFzfU3QAVOzent1nxmJkTB1Cdx7Al54IF0T+5RLbFhnZCRGenWfHX1Ny31e+FsGypl/1FJGYiSiQ/TtNySv88X7HRHJ4y+PDkVChNGt50R26YS3HmXNUQpcZWVlGDhwYItlTnNycmCxWHDo0CFXmx/WgXO2kbMOnJRrgSEGPWaP7ifbPolIfi89dDuMQW0PTXQL6YShZo0PgOgFHZb/YmCbbX5zZyLuSY1rsw0RUVsqKytRXl6OyspK2O12lJeXo7y8HA0NDa42ycnJ+OCDDwAAOp0Oc+bMwTPPPIMNGzbgq6++wiOPPIL4+HhMmjTJK30ONuhxd0qMV/Ylt8dHJMLQzpug3PSCDst+nurVfcoltWcYunb2/uBN185BSO0ZmBepF/8sxes3RhiCBPzmzkSPn++L88IpEDOQrtMLOjzzC+mFBsl7fHleE1HH7cgdg1sipc3C7WoQsO/psQr3iEhZ1dXVrdZ4c/6srTYWiwVXr7a+hK67teCkXAt8/r/SeCM0kZ/TCzq82M6ykoW/GCjLuRzwn7jH3RaH16YMRmxYy7svuoV0wiv/Jx15AXwXNBH5h/z8fKSnp2PRokVoaGhAeno60tPT8cUXX7jaHDt2DBcvXnR9vWDBAsyePRu//vWvcccdd6ChoQHFxcXo3Nl7S5G88cgdATcI8ps7E32W2873k6AA+qCc2jMMG54Y4bP9b3hiREANgnTS6/DalMEYd5tvbozIuyfFo0EQX54XQOBmIF3nzLcAijfV8/V5TUTy2L5gFH45vHebbUb2j8TBpeO90yGiH/G3um+t8aQW3M2uBcaGGX36eZ+I3HP9XG75/8S48M6ynss6URSlVxL3MovFgvDwcFy8eLHdpRDsDhG7K+pQe6kRMaHNS1twtJdIG9zJikAi13FdtdqRv+EAig9U4ZLVvyK/k6BDTJgRD2f2wmMjzH5xJ6zdIWL7kVqsKDmME7VXYPN1h35AQPPsnozESKx5aLBPZn60pqHRhtnv7EHpiTo0+dFLTAfAGCTAbOqC341Nxl1JJr/4bGC1OfDGjhN4u7QC1ZdufIW5e16oMQPVeEz+wO4Qse1QDRZt/ApnL1p93R1N8cf3O7VQY16o8ZjUzvnevr7sNL6/YkOIIQhjB8Ri0cQBCDZwGUJShpSsOH/+PC5caLtejdlsbrH0aVFREebMmYP6+voW7fLz87FhwwaUl5e7vldRUQGz2Yx9+/YhPT0dd955JwYPHozVq1e72rz11luYM2dOi5tlfqipqQlNTU0tjishIYHXAok0xJNz2Z3PS/5x5UQGekEnS1EUIiK1CTboser+dKy6P3BrNniTXtBh5IDuGDmge/uNCUDzclhv/erm9W2oJUOQgFkj+2PWyP6+7gppjF7QYfTAWIweGOvrrhARqQrf28lfmUwmmEwmWbaVlZWFgoIC1NbWIiameaWBzZs3IywsDCkpKa42mzZtavG8zZs3t1sL02h0r6aOE68FEqmD0ucybz0iIiIiIiIiIiLSsPbqvo0dOxYpKSmYOnUq9u/fj5KSEixcuBCzZs1yDWDMmDEDp06dwoIFC3D06FG88soreO+99zB37lxfHhoRaZxqZoAQERERERERERGR+/Lz87F27VrX1+npzSsIbN26FdnZ2dDr9di4cSNmzpyJrKwsdOnSBdOmTcPSpUtdz0lMTMRHH32EuXPn4sUXX0TPnj3x5ptvIicnx+vHQ0Tk5NcDIM7yJBaLxcc9ISJ/5swIPy5p5BFmIBFJocYMZP4RkVTMQCLSKrnzr6ioCEVFRW226dWr1w1LXP1YdnY2vvzyS4/7wQwkIincyUC/HgC5dOkSACAhIcHHPSGiQHDp0iWEh4f7uhuyYQYSkTvUlIHMPyJyFzOQiLRKTfkHMAOJyD1SMlAn+vGtMg6HA+fOnUNoaCh0urYrvwPNIz8JCQn45ptv2q3+rhY8ZvUfs9aOF3D/mEVRxKVLlxAfHw9BUE9pI2Zg+7R2zFo7XoDHrNUMdDf/AO29VrR2vACPmcfcOmYgXyc8ZvXS2jEz/5oxA9untWPW2vECPGa5M9CvZ4AIgoCePXu6/bywsDDNvDiceMzqp7XjBdw7ZjXd8eLEDJROa8esteMFeMztUVsGepp/gPZeK1o7XoDHrBXMQH4GlILHrA1aO2Yt5x/ADHSH1o5Za8cL8JjbIzUD1TNETERERERERERERERE9B8cACEiIiIiIiIiIiIiItVR1QCI0WjEokWLYDQafd0Vr+Exq5/WjhfQ5jHLQYu/N60ds9aOF+Axk3Ra+71p7XgBHrNWaPGYO0qLvzMeszZo7Zi1drxy0eLvTWvHrLXjBXjMcvPrIuhERERERERERERERESeUNUMECIiIiIiIiIiIiIiIoADIEREREREREREREREpEIcACEiIiIiIiIiIiIiItXhAAgREREREREREREREamOagZACgoKMGzYMISEhCAiIqLVNpWVlZgwYQJCQkIQExODp556CjabzbsdVVDv3r2h0+laPJYvX+7rbsnq5ZdfRu/evdG5c2dkZmZi9+7dvu6SYhYvXnzD3zM5OdnX3ZLV9u3bMXHiRMTHx0On0+HDDz9s8XNRFJGfn4+4uDgEBwdjzJgxOH78uG866+eYgcxAtWEGMgOlYv41Ywaqi9ozkPknH2Yg809t1J5/ADNQTsxAZqDaMAOVyUDVDIBYrVZMnjwZM2fObPXndrsdEyZMgNVqRWlpKdauXYuioiLk5+d7uafKWrp0KaqqqlyP2bNn+7pLsvn73/+OefPmYdGiRdi3bx/S0tKQk5OD2tpaX3dNMQMGDGjx9/z888993SVZXb58GWlpaXj55Zdb/fnKlSvx0ksv4bXXXsOuXbvQpUsX5OTkoLGx0cs99X/MwGbMQHVhBjIDpWD+XccMVBc1ZyDzTz7MwGbMP3VRc/4BzEA5MQObMQPVhRmoQAaKKvPWW2+J4eHhN3x/06ZNoiAIYnV1tet7r776qhgWFiY2NTV5sYfK6dWrl/jCCy/4uhuKycjIEGfNmuX62m63i/Hx8WJhYaEPe6WcRYsWiWlpab7uhtcAED/44APX1w6HQ4yNjRVXrVrl+l59fb1oNBrFd9991wc9DAzMwBd83Q3FMAPVjRnYcVrOP1FkBqqNljKQ+ScPLWcg809dtJR/osgMlAsz8AVfd0MxzEB181YGqmYGSHvKysowcOBAdO/e3fW9nJwcWCwWHDp0yIc9k9fy5csRFRWF9PR0rFq1SjXT+qxWK/bu3YsxY8a4vicIAsaMGYOysjIf9kxZx48fR3x8PMxmMx5++GFUVlb6ukteU1FRgerq6hZ/8/DwcGRmZqr6b64UZmBgYwYyAwFmoKe0kn8AM1BttJqBzD95aSUDmX/qotX8A5iBcmMGBjZmIDMQkCcDg+ToXCCorq5uEXgAXF9XV1f7okuye/LJJzF48GBERkaitLQUeXl5qKqqwvPPP+/rrnXYd999B7vd3urf8OjRoz7qlbIyMzNRVFSEpKQkVFVVYcmSJRgxYgQOHjyI0NBQX3dPcc7zsrW/uVrOWW9iBgY2ZiAz0IkZ6D4t5B/ADFQbLWcg809eWshA5p+6aDn/AGag3JiBgY0ZyAx06mgG+vUMkNzc3BsKv/z4odYXvJM7v4N58+YhOzsbqampmDFjBp577jmsWbMGTU1NPj4K8sT48eMxefJkpKamIicnB5s2bUJ9fT3ee+89X3eNvIQZyAzUMmagtjH/mjEDtYsZqG3MQOafljH/iBnIDNQyZqAy/HoGyPz58zF9+vQ225jNZknbio2Nxe7du1t8r6amxvUzf9WR30FmZiZsNhtOnz6NpKQkBXrnPdHR0dDr9a6/mVNNTY1f//3kFBERgf79++PEiRO+7opXOP+uNTU1iIuLc32/pqYGgwYN8lGvvIsZyAx0YgYyA520koHMv2bMwGbMQG1loNbzD2AGAsw/J+aftvIPYAYCzECAGejEDGQGOnU0A/16AMRkMsFkMsmyraysLBQUFKC2thYxMTEAgM2bNyMsLAwpKSmy7EMJHfkdlJeXQxAE1/EGMoPBgNtvvx1btmzBpEmTAAAOhwNbtmzBE0884dvOeUlDQwNOnjyJqVOn+rorXpGYmIjY2Fhs2bLFFXIWiwW7du3CzJkzfds5L2EGMgOdmIHMQEBbGcj8a8YMbMYM1FYGaj3/AGYgwPxzYv5pK/8AZiDADASYgU7MQGYgIE8G+vUAiDsqKytRV1eHyspK2O12lJeXAwD69u2Lrl27YuzYsUhJScHUqVOxcuVKVFdXY+HChZg1axaMRqNvOy+DsrIy7Nq1CyNHjkRoaCjKysowd+5cTJkyBd26dfN192Qxb948TJs2DUOGDEFGRgZWr16Ny5cv49FHH/V11xTxu9/9DhMnTkSvXr1w7tw5LFq0CHq9Hg899JCvuyabhoaGFqPYFRUVKC8vR2RkJG655RbMmTMHzzzzDPr164fExEQ8/fTTiI+Pd73x0XXMQGag2jADmYFSaT3/AGagGqk9A5l/8tF6BjL/1Eft+QcwA+XEDGQGqg0zUKEMFFVi2rRpIoAbHlu3bnW1OX36tDh+/HgxODhYjI6OFufPny9eu3bNd52W0d69e8XMzEwxPDxc7Ny5s3jrrbeKzz77rNjY2OjrrslqzZo14i233CIaDAYxIyND3Llzp6+7pJgHHnhAjIuLEw0Gg9ijRw/xgQceEE+cOOHrbslq69atrZ6306ZNE0VRFB0Oh/j000+L3bt3F41Gozh69Gjx2LFjvu20n2IGMgPVhhnIDJRK6/knisxANVJ7BjL/5KP1DGT+qY/a808UmYFyYgYyA9WGGahMBupEURQ9Hz4hIiIiIiIiIiIiIiLyP4KvO0BERERERERERERERCQ3DoAQEREREREREREREZHqcACEiIiIiIiIiIiIiIhUhwMgRERERERERERERESkOhwAISIiIiIiIiIiIiIi1eEACBERERERERERERERqQ4HQIiIiIiIiIiIiIiISHU4AEJERERERERERERERKrDARAiIiIiIiIiIiIiIlIdDoAQEREREREREREREZHqcACEiIiIiIiIiIiIiIhUhwMgRERERERERERERESkOv8fdR2JykW/SWYAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  19\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  20\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  21\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  22\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  23\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  24\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  25\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  26\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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kgReJRD6msDDG/v+YzRiTv48yx22WkSEf0/z5jH3/PWMNG8r3bduWsReO2+zgQXnbF47bjLH/gijPnjH2/HnFY3zwgLFRoxhzcmLM1ZWxqVMZU1xfymSMdesmD9RkZf23T1ERYy1bysf24rVo7dqMffJJlT8atYQMojAmH2OHDsL1Z2QonZcJ8vDwwIoVK/D3339j2bJlAOQpBoYNG4aaNWtWmUpBJBJBLBZDJBIp/634f4WyzyvaaIMxhqysLLi5uWm1v5A0GYtYLIZY/N+fB+/7nzVrFj744APY2Nhg9uzZmDVrFnx8fFRSnMycORNRUVHw9vbGggULMGjQIHz//ffo2bMnSkpKAADFxcUIDw9HUlISxo4di7i4OHz00UdIT09XSfuQmJiIkJCQCsfTvXt3ODk5wcHBAf3791ebbqKy33Xz5s0xZ84crF+/XpnXuqCgAMOGDVPWFCirTZs2AKBVUVqi3sCBA5UphxYtWqRMzePu7g4AWLFiBXx9ffH5559jwYIF8PHxwccff4y4uLhyfV27dg2DBw9Gjx49sGDBAtSqVQvDhg3D+fPny7UdO3Yszpw5gxkzZmDMmDHYsWNHubzoiYmJaNmyJWxsbJTb7O3tsW7dOly7dg1Tp05Vbo+KikJeXh7Wrl2r1VJ9bZw+fRoA0LZtW5Xt3t7eqFevnvJ5BWdnZzRq1Mjgn9/du3ejR48esH5hyXRBQQEePnyI69evY9GiRdizZw9effXVKvvT5ngWGRmJ1157DRMnTsTt27cBAOfOncOsWbMwfPhw9OnTR8N3pT8v/vwU9T6GDx+O1NRU3L59G5s2bcKKFSvwySefwNHRsdL+FN+ZZf9dlYSEBLzzzjuoVasWvv76a8ybNw/dunXj+mw9fvwYvXr1QmBgIBYsWAB/f3989tln2LNnj7LN3bt3cevWrQqP/8+ePYOTkxOcnZ1Ru3ZtREVF4enTp1W+r7LvTdPPQJs2bZCbm6v2eEL4ZGVloWPHjti/fz+io6OxZMkSNG7cGMOHD8fixYsBaH6MlUql6NWrFzw9PfHNN9+gTZs2mDFjBmbMmKFsU1JSgpMnT5b7PJnKOUBmZiYcHBzg4OCg3Hb69GmEhISofMYBoH379nj27JmyRp4m3xO8fVak7N+YunNvdfLz87Fq1Sp069YNX3/9NWbOnIns7GyEh4cjNTW1XPuff/4ZS5cuRVRUFGJiYpCWloZXXnkFWVlZyjaDBg3Ctm3bEBkZieXLl+OTTz7BkydPcOvWLWUbIb7fjeU7lZDqGj16NFasWIFBgwZh+fLl+N///gd7e3u16VLL+vXXXzF//nyMGjUKc+fOxY0bNzBw4EDl9R4g/1sTiUQIDg5W2XfJkiVwd3dHRESEMvXy999/j3379mHZsmXw9vZWad+mTRskJiYK8n6N4XpW0/mOgIAA2Nvb0/GGmD57e2DdOuDaNXkKLIWoKCAvT14/Q0/X0ho7eVJe6+TF60TGgIcP5amejh4FPvlE/h54ajKKxfL0YAo883KjR8truQwaBCxfLk8zZW8PVHHMBgD8+iswfz4wahQwd648vdbAgUCZ4zYSE+XjeOG4rbR2LeDoKH/NgAB5ny8SieTvTd37EomA1auBwkL5e1GYMQM4f16eDu3F69eQEMPVIsnPl/9+L12S14hJSwM45ihMlqGjOER777zzDnNwcGBXrlxR3p37559/VrrP2bNnmb+/Pxs7dqwyndcff/zB/Pz82OLFi5lMJmPvvvtuuRRPPXr00CrF0/r169XeycpLyJUovGM5dOgQ8/PzY7Nnz1amv1m+fHmV6W+uXr3KxGIxe+ONN8rdpai4g+bBgwdMIpGwnj17qrT57rvvGAC2evVqxhhjp0+fLpcy4kUlJSVMJBKxTz/9tNxzmzZtYsOGDWPr1q1j27ZtY9OmTWMODg7Mzc2N3bp1Szkmnt+1VCplL7/8MvP09GQPHz5kUVFRzNrausJ0TxKJhI0ZM6bCcRPNVXb3fdm7QRXCw8NZw4YNVbb5+voyAOzIkSPKbQ8ePGC2trYqnyHFSpSwsDCVO78mTJjArKysVO4cq1evHhs0aJDaMcfExDCxWMyOHDnC/vjjDwZA5S78igi5EkXxnOIzX1a7du1Yx44dy23v2bMna968Offra6qqlRQFBQXMzs6OrVFzN8ioUaOUK5HEYjEbPHhwpamoGNP+eMaY/G7l2rVrsx49erCioiIWHBzM6tevX+nqhoroayVKRT+/OXPmMHt7e5XVXFOnTq3y9TZu3Mjq16/Pli9frkznNXv27CrTeY0bN445OTlVuuqqopUoANjPP/+s3FZUVMS8vLxU/tb279/PALAdO3aU63fKlCnss88+Y5s2bWK//fYbi4iIYADYSy+9pFydc+fOHda2bVv2zjvvqKTz8vf3Z5MmTVL2pclnIDExkQFgmzZtqvA9k8oNHz6c1alThz18+FBl+5AhQ5izs7PK8Z7nGKv43Y8dO1a5TSaTsb59+zKJRKJcnXXt2jUGgC1btqzcmHRxDiDkSpSrV6+qXS3j6OjIPvzww3Ltd+3axQCw+Ph4xphm3xO8faqzePFi1qBBA/bHH38o03mNHTu2ynRepaWl5dLTPn78mHl6eqqMRXGMtbe3Z3fu3FFuP378OAPAJkyYoNwXHGm/hPp+1/V3KiH64OzszKIqSc9S0UoUV1dXlfM0xWq9st/d77//PnN1dVXb7969exkANnfuXJaens5q1KjBXn/9dbVtv/rqKwaAZZW9a/kFPOdixnQ9y5hm8x1NmzZlvXv3rvC1CDEpMTGMicXylRh//CFfLcBxLa2WvlaifPGF6uoOhfv35eNXPOrVY6yq6wWZjLF33y2fzqtHj6rTeTk7V51Sq6KVKK6ujJW9vt6+Xb697DXX++/L26nTqZP897R9u3ylTcuW8v3LZgrZuJGx+vXl2xTpvGbPLp/O6/vv5ftu2MBYUhJjVlaMjR+v/nU/+ogxe/vK33NFqrsSJTz8v9+tRCJfYVPZKhwTR0EUE/bo0SNWp04d1rp1a7UXkOrk5uayY8eOMcbkEzgR/38QfPz4MUtKSmKMMbZv3z7lxE/ZE8I9e/ZoNL6LFy8yJyenKtP3KBQWFrLs7GyVh4+PD5s0aVK57ZrSZCw3btxQLp1es2aNckL37t27lV7oKk7sTp8+XWGbX3/9lQFgu3fvVtleVFTEnJyclBes6enpDAAbMWIEKygoUNtXVlaW8sSax9GjR5lIJGKjRo1SbuP9XV+7do05Ojqydu3aMZFIxL744osKX8fT05O9+eabXGMifHhrouTm5rLs7GzlhVTZgIevry8LCAgot0/r1q3ZG2+8ofy3Iojy+++/q7RT5Cs+c+aMcpu9vT0bMWKE2rEUFRWxVq1aMT8/P+bu7l5uOT5jwvzNV/azmT17doUXlJ07d2aBgYHltr/99tvM3d29wterrqp+lzt27GAikYhlZmaWe+7ixYssISGBrVu3jvXt25e98cYbatuVpe3xTOG3335jAFj79u2ZSCTizo//5MkTld+fIr/+n3/+qbK97GeUh7Y/v/Xr17Pw8HD2ww8/sC1btrAPP/yQiUQitZPGZZ07d045ITljxgxlcObixYuV/j3OmDGDWVlZVfq9WVEQpUaNGuX+Vvr378+Cg4OV/1bUZvjnn38qHb+CIgXXb/+fe7ikpIQlJCQwxuSTKooJ7efPn5dLKcL7Gbh48aLatEiEj0wmYy4uLuyjjz4qd/wrm2ZRgecYqwiiXL58WWX7nj17VD4Pion2DRs2qB1bdc4BpFJpufczZMgQ1qlTp3Lbq0pt9aKCggIWFBTEatWqVS61jVgsVhvMOXDgAAPAtm3bxhjT7HuCt091kpKSlJOpERERyr/7pKSkculvKiKVStmjR49YdnY269u3LwsKClI+p5gcVVebpUOHDsr6WYWFhUwikbC+fftWGoSv7ve7gq6/UwnRB19fX9a2bdtyxxmFioIoH3/8sUq7nJwcBoAtWbJEua13796scePGFb72qFGjlCmX3dzcKgySrFixggFg58+fV27T5lzM2K5nNZnv6NChA2vXrl2FzxNiUoqKGGvVSj6x7u4uD4LwpMyVShnLzlZ9DBkin9x/cbuG511VBlHatGHsheOe8r0kJMgDEbNny9Ny8dxkvW/ff6nLygY8qpqX9PWVB18qOGYzxioOorw4/pwc+fYyx23WuzdjlRy3VShScLm4/Jee7dw5xhQ3vMyY8V8A4+JF+TjKCg9nrFYtxpo0YaxpU9UUb2V99pl8nBUca5UKCsp/DgDGli1T3VbFjZoqTp+W/65++omxLl0Yi4w0bM0aHaN0XkasuLgYmZmZKg/Fcl4AqF27NpYuXYqzZ8/C2dkZS5curbJPZ2dndOzYsdx2FxcXdOjQAQDQo0cPtel2evXqxT32zMxM9O3bF87Ozti8eTNX+p7ffvsN7u7uKo/bt29j/vz55bZrQtOx+Pr6onnz5uW2e3t7o1WrVhXud/36dYjFYgQEBFTY5ubNmwCAZs2aqWyXSCRo2LCh8nk/Pz9MnDgRq1atgpubG8LDwxEXF4e8vLxyfTLGKn0/Ci+//DI6dOiA/fv3K7fx/q4bNWqEmTNn4uTJk2jRogW++OKLCl+HMaZ1+jeiuX///RdhYWFwdHSEi4sL3N3d8fnnnwNAuc9L/fr1y+1fq1YtPH78uNz2F9vWqlULAMq1rejzJ5FIsHr1amRkZODJkydYs2ZNuc+Frv7mFezt7QEARUVF5Z4rLCxUPv/i+6nq85uXl6dyXM7JydFqfOrs2rULbdu2haenZ7nn/P39ERYWhqFDh2Lnzp14+vQp+vXrV+kxQNvjmcKQIUPQt29fnDhxAiNHjuRKHwYA0dHRKr8/RZqG119/XWX7gAEDuPrjpe7nt3HjRnz00UdYtWoVRo4ciYEDB+Knn35CREQEPvvsMzx69KjC/lq2bIm6deuW2+7v748GDRpUuN/HH3+Mpk2bonfv3qhXrx4+/PBDxMfHc72HevXqlfsMVvR3ynv8nzBhAsRisfL4b21tjbCwsHLt7Ozs0LVrV5VtvJ8BxVjo+K+d7Oxs5Obm4ocffih3/IuMjAQAPHjwQNme5xgLyNP5NWzYUGVb06ZNAQA3btxQ2V7R56k65wC3bt0q9342btyIxMTEcts1ScMilUoxZMgQXLhwAZs3by6X2sbe3r7CY7/i+bL/5fme4O1TnQ4dOii/R1/c7uLiUuF+ALBu3Tq0bt0adnZ2cHV1hbu7O3bt2qX2nLBJkybltjVt2lT5u7a1tcXXX3+NPXv2wNPTE126dME333yDzMzMcvtV5/u9bB90TCCm7ptvvkFaWhp8fHzQvn17zJw5E+np6VXuV91zaQD49ttvUbt2baSmpmLp0qXw8PBQ207dd7A252LGdj2ryXwHHW+IWZFI5CmdMjKAJ0/kKZx4Pt+3bgHu7qqPjRvlKahe3C5k+qfMTCAlBejbV/17CQsDXnsN+OILIC4OGD4c2Lmz8j579FCfuqyqeclvvpGnlPLxAdq3B2bOBDiO2QCAF+dLFOduL16HcV6DQSIBoqOB3FwgOVm+rWVLQM31Jfz9gRevL3/6CXj2DLh6VZ4mrKJzTcV4qvqMfPNN+c8BAIwdq7qtolRl6gQFyX9XH34IJCQAJ04Aw4bx729irKtuQgwlMTER3bt3V9mWkZGhMnGzd+9eAPKTsTt37lR5IVZWt27dlLniK/LiBTaPvLw89O7dG7m5uTh69Gi5C9uKhIeHIyEhQWXb+++/j549e2Lo0KEaj6M6Y1EYZsA//gULFmDYsGHYvn079u3bh08++QSxsbFISkpCvXr1ULt2bYhEIrUTaxXx8fHB5cuX1T5X1e963759AIB79+7h0aNH8PLyUtsuNzfXKGrgWILr16/j1Vdfhb+/PxYuXAgfHx9IJBLs3r0bixYtgkwmU2lfUQBR3YULT1tXV9dKP3+K41NhYSGuXr0KPz8/led18TdfVp06dQAA9+/fh4+Pj8pz9+/fR/v27cvt8/jx4yo/v+PGjcO6deuU/+7atSsOHz5c7fEC8noeignTqgwePBijRo3ClStXyl3IqqPN8ezRo0c4deoUAODChQuQyWTl6gGoM3nyZLz//vvKf2dlZeH999/Ht99+i8DAQOV2dZOK1aHu57d8+XIEBwejXr16Ktv79++PtWvX4vTp02oDCi+aOXMm9zg8PDyQmpqKvXv3Ys+ePdizZw/WrFmDoUOHqnx21OH92wPKT8RUxN7eHq6urmoDfg0aNKj088v7GVCMhY7/2lEcr99//31ERESobdO6dWuVf1d1jOXF83nS9hzAy8ur3HF+/vz5yMzMxIIFC1S2lz02VGXkyJHYuXMnfvnlF7zyyivlnq9Tpw7u379fbrtim+J8UJPvCd4+q7J27VqudgCwYcMGDBs2DK+//jomTZoEDw8PWFlZITY2FtevX+fup6zx48ejX79++PPPP7F371588cUXiI2NxcGDB5V1Gar7/a7A851KiLF766230LlzZ2zbtg379u3D/Pnz8fXXX2Pr1q3o3bt3hfsJcS59+vRpZQD93LlzyjqJL1L3HWyoc7GyhLie5Z3vePz4sdpAMiEm6/8/+ygslE+i85zneXnJJ7LLmj9fHuR44bwLGpx3VWnPHsDODnhh/lKtTp2AOnWAX36RB1Z4aDIv+dZbQOfOwLZtwL598vf/9dfA1q1AJcdsABXXmyk7X+LqWj6oUhnF+aW6Gy+rur48fBhQ3MBz7hwQGqq+3ePHgINDxUEWhaFDgZdfVt3WowcwaRLQs+d/26rqpyISCdC/PzBvHvD8ufb9GDEKohixwMDAcheeZS9a4+PjsWrVKkyePBm//PILIiIicPz48XLFiPWpsLAQ/fr1w5UrV7B///5K72J5UZ06dZQXswp2dnZo2LAh1wSXkGPRRqNGjSCTyXDhwgUEBQWpbePr6wsAuHz5ssqdocXFxcjIyCj3Plu1aoVWrVph2rRpSExMxEsvvYSVK1di7ty5sLa2RqNGjZCRkcE9xvT0dK3u6l+5ciUSEhLw5ZdfIjY2FqNGjcL27dvLtbt79y6Ki4vV3vlOtFfRXVU7duxAUVER/vrrL5W73Q4dOqSXcfn7+1f4+Tt79ixmz56NyMhIpKamYsSIETh37hycnZ2VbYT+m3+R4u/w1KlTKhNh9+7dw507d/DRRx+V2ycjI6PKibwXL0qFuvhMS0vDrVu30FfdHTxqPH/+HED5FUdCioqKwpMnTxAbG4uYmBgsXrwYEydOrHK/gIAAlWOuIkjbpk2bKoP32qro55eVlaX2d6QofFpaWqqT8UgkEvTr1w/9+vWDTCbDxx9/jO+//x5ffPEFGjduXK2+/f39AYD7+P/kyRM8fPhQq+M/72dAMRY6/mvH3d0dNWvWhFQq5Tr+8RxjAXlwJj09Xbn6BICyALrippz69evD3t6+ws9Tdc4B7Ozsyr2fDRs2oKioSOvj/KRJk7BmzRosXry4wgnFoKAgHD16tFzQ7/jx43BwcFD+PDT5nuDtU0ibN29Gw4YNsXXrVpVzgRkzZqhtf/Xq1XLbrly5Um7lXKNGjfDpp5/i008/xdWrVxEUFIQFCxZgw4YNAKr//a7A851KiCmoU6cOPv74Y3z88cd48OABQkJC8OWXX1YaROHh7++PX375BXl5eeX+hgoKChAZGYmAgAB06tQJ33zzDd544w20a9euXD8ZGRlwc3NT+Z7X5lzM2K5neec7SktLcfv2bfTv319tP4SYnLNngdmzgchIIDUVGDFCPomu5rtWhZ2dfNVHWRs2yCfiBbi+rtCuXfIACu+keWEhoMNrWNSpA3z8sfzx4IG88PqXX1YdROHh7y8PAOXlVf37AP5bBaPpddj9+/IVIj17yoMT//sfEB4O/P8xWEVGBsBzDdawofzxooAA4T4fz5/Lg05PnphlEIXSeRmxWrVqISwsTOVhZ2cHQH6n34gRI9C+fXt89dVXWLVqFVJSUvDVV18ZbLxSqRRvv/02jh07hj/++AOhFUVJzXQsr7/+OsRiMWbPnl1uBYDijqOwsDBIJBIsXbpU5S6kn376CXl5ecrJv/z8/HITe61atYJYLFZJJREaGqq8Q7is7Ozsctt2796N5ORkjdKyAfKT8kmTJmHQoEH4/PPP8e233+Kvv/7Czz//XK5t8v8vUezUqZNGr0Eq5+joCED+d1+W4g63sp+lvLw8rFmzRi/jCg0NRVpaWrn0JiUlJRg2bBi8vb2xZMkSrF27FllZWZgwYYJexqXQokUL+Pv744cfflBJhbhixQqIRCIMHjxYpX1eXh6uX79e5ec3ICBA5bjcpk0bQca7e/dueHp6om3btirby6bwUSgpKcHPP/8Me3t7nQWIN2/ejE2bNmHevHmYMmUKhgwZgmnTpiknYI1NRT+/pk2b4vTp0+XG/dtvv0EsFpe7u18IL6YIK/s66tIBaapu3brw8fEpd/wvLCzEkydPyrWfM2cOGGMaH/81+QwkJyfD2dkZLVq00OzNEADy4/mgQYOwZcsWpKWllXu+7Pe6psfY7777Tvn/jDF89913sLGxUaZms7GxQdu2bdWeTxjbOcD8+fPx7bff4vPPP8e4ceMqbDd48GBkZWVh69atym0PHz7EH3/8gX79+sHW1haAZt8TvH0KSd33/PHjx3Hs2DG17f/880/cvXtX+e8TJ07g+PHjyoneZ8+eKdOPKTRq1Ag1a9Ysd35Z3e933u9UQoyZVCotd7OKh4cHvL29Bfk+Dw0NBWNMefws67PPPsOtW7ewbt06LFy4EA0aNEBERITa101OThbketeYrmc1me+4cOECCgsL6XhDzENJiTwdkrc3sGSJPI1TVhag52tpbiUl8tUvL94IWFAgT0X1oi1b5CsnXrhmE4RUWj444+Eh/1kKcMwGIF8Nwth/6bkU1MzB4ckTYPFiwM0N0HTOYORIQCaTp/T64QfA2lqeBk1dKrGUFPkKH31SM0eB3Fz579fHR/5zN0O0EsVEjRs3Do8ePcL+/fthZWWFXr16YcSIEZg7dy4GDBhgkLu+Pv30U/z111/o168fcnJylHezKZS9c9scx9K4cWNMnToVc+bMQefOnTFw4EDY2tri5MmT8Pb2RmxsLNzd3RETE4NZs2ahV69e6N+/Py5fvozly5ejXbt2ynEdPHgQ0dHRePPNN9G0aVOUlpZi/fr1ykkWhQEDBmD9+vW4cuWKyh2QnTp1QnBwMNq2bQtnZ2ekpKRg9erV8PHxUdbL4MEYw4cffgh7e3usWLECADBq1Chs2bIF48aNQ1hYmEr6ioSEBNSvX1+ZDoIIQzFJP3XqVAwZMgQ2Njbo168fevbsqbzbfdSoUXj69Cl+/PFHeHh4qE05IrQBAwZgzpw5+Pvvv9GzzPLPuXPnIjU1FQcOHEDNmjXRunVrTJ8+HdOmTcPgwYPRp0+far1uXl4eli1bBgDKHPrfffcdXFxc4OLigujoaGXb+fPno3///ujZsyeGDBmCtLQ0fPfddxgxYkS5O+b3798PxpjgdTp4x7tr1y707t273MqjUaNGIT8/H126dEHdunWRmZmJX375BZcuXcKCBQtQo0YNQccLyAM3Y8aMQffu3ZXj++6773Do0CEMGzYM//zzD1daLyFU9+c3adIk7NmzB507d0Z0dDRcXV2xc+dO7NmzByNGjNA4zSOPESNGICcnB6+88grq1auHmzdvYtmyZQgKChJspcaAAQOwbds2lRzgmZmZCA4OxjvvvKNcrbJ3717s3r0bvXr10uizrelnICEhAf369aN85NUwb948HDp0CB06dMDIkSMREBCAnJwcpKSkYP/+/cp0bJocY+3s7BAfH4+IiAh06NABe/bswa5du/D555+r3LE8YMAATJ06Ffn5+XBycgJgfOcA27Ztw+TJk9GkSRM0b9683Lldjx49lPWQBg8ejI4dOyIyMhIXLlyAm5sbli9fDqlUilmzZqnsx/s9oUmfQnnttdewdetWvPHGG+jbty8yMjKwcuVKBAQE4OnTp+XaN27cGC+//DLGjBmDoqIiLF68GK6urpg8eTIA+aqUV199FW+99RYCAgJgbW2Nbdu2ISsrC0OGDFH2I8T3u66+UwnRpydPnqBevXoYPHgwAgMDUaNGDezfvx8nT54sl5JQGy+//DJcXV2xf/9+ldSEBw8exPLlyzFjxgxlLZM1a9agW7du+OKLL/DNN98o2z548ABnz55FVFRUtcdjTNezmsx3JCQkwMHBAT169Kj2z4AQg5s7V7765MABoGZNoHVrYPp0YNo0YPBgoJrX0hrZsQM4c0b+/yUl8hUyc+fK/92/v3xs//wD5OeXD6JcvSpf3fD22/LVG2IxcOqUfGVMgwZAJTfDaO3JE6BePfnPKTAQqFED2L8fOHmyfDozbb38sjyl1/79QNmUsnFxwJ9/Av36yWur3L8vr2tz6xawfr18NQmvNWvkq3vWrpW/HwBYtgx4/31gxQr5ChuF5GR5qjB9n2/17i0fW4cO8oDJrVvycd+7B2zapN+x6JPOS9cTwW3fvp0BYAsWLFDZnp+fz3x9fVlgYCArLi7W+7i6du3KAFT40Iavry+bMWOGUYyF1+rVq1lwcDCztbVltWrVYl27dmUJCQkqbb777jvm7+/PbGxsmKenJxszZgx7/Pix8vn09HT24YcfskaNGjE7OztWu3Zt1r17d7Z//36VfoqKipibmxubM2eOyvapU6eyoKAg5uzszGxsbFj9+vXZmDFjWGZmpkbvZcmSJQwA27Jli8r2W7duMScnJ9anTx/lNqlUyurUqcOmTZum0WsQPnPmzGF169ZlYrGYAWAZGRmMMcb++usv1rp1a2ZnZ8caNGjAvv76a7Z69WqVNozJ/5b69u1brt+uXbuyrl27Kv+9Zs0aBoCdPHlSpd2hQ4cYAHbo0CGV7a1bt2bDhw9X/js5OZlZW1uzsWPHqrQrLS1l7dq1Y97e3iqf9Rfx/M1nZGRU+Lft6+tbrv22bdtYUFAQs7W1ZfXq1WPTpk1Te4x8++232csvv1zpa2uDZ7y5ubnM2tqa/f777+X2/+2331hYWBjz9PRk1tbWrFatWiwsLIxt375d8LEqDBw4kNWsWZPduHFDZbvi++frr7/WqD/Fz+DFz48m+2r782OMsePHj7PevXszLy8vZmNjw5o2bcq+/PJLVlJSovF4eGzevJn17NmTeXh4MIlEwurXr89GjRrF7t+/r2yj7m+qa9eurEWLFuX6i4iIKPfZTklJYQDY0aNHldseP37M3n//fda4cWPm4ODAbG1tWYsWLdhXX32l8XmBJp+BixcvMgDlvqOI5rKyslhUVBTz8fFhNjY2zMvLi7366qvshx9+YIxpdoyNiIhgjo6O7Pr166xnz57MwcGBeXp6shkzZjCpVFruda2trdn69euV23R1DhAREaHyvcNrxowZlZ7bvXh8ycnJYcOHD2eurq7MwcGBde3atdx3mwLv94QmfQpBJpOxr776ivn6+jJbW1sWHBzMdu7cWe6YoDhOzp8/ny1YsID5+PgwW1tb1rlzZ3bmzBllu4cPH7KoqCjm7+/PHB0dmbOzM+vQoYPaY2d1v9919Z1KiD4VFRWxSZMmscDAQFazZk3m6OjIAgMD2fLly5VtKvt7fBGAcue5n3zyCWvcuLHy34pr+pCQkHLnKRMmTGBisZgdO3ZMuW3FihXMwcGB5efnV/peNDkXM/T1rKbzHR06dGDvv/9+le+LEKOXnMyYtTVjL3zXstJSxtq1Y8zbm7FKrqXViohgTIvzLuW+8rUP5R9r1sjb/O9/jAUElN83O5uxjz5izN+fMUdHxiQSxpo0YWz8ePlzulBUxNikSYwFBjJWs6b8dQMDGStzzFa+r7LXVhkZ8vek5rjNAMZenJ/45BPGyhy3GWOM7dvHWI8ejHl5MWZjw5iLC2M9ezJ24IBm7+H2bcacnRnr16/8c2+8IX9P6en/bfvsM8bq12dMJtPsdRTK/i418d13jL38MmNubvLPrLu7fMxHjmg3DhMhYkzdWiBCiKmYM2cO1qxZg6tXr1ZYwFAf/vzzT7z77ru4fv16uToXxHytX78eUVFRuHXrVoWFHk1BZmYm/Pz8sHHjRoPcNfv777/jvffew8OHD9XmlSeVs9Sf36uvvgpvb2+sX7/eoOMYP348jhw5guTkZFqJYkSGDRuGzZs3q12xoM7w4cNx5coVHD16VOPXonMA81Od73dDf6cSYkrS09Ph7++PPXv2KNMsaiI4OBjdunXDokWLdDA6/ajO9WxqaipCQkKQkpJSYR0XQogOBQTIC8SXWSFn9tLT5atr9uwBtDhuC6aoSL6qZ8oU3azsIeVQTRRCTNyECRPw9OlTbNy40aDj+PrrrxEdHU2TJxbmvffeQ/369REXF2fooVTL4sWL0apVK4NN9ri4uGDp0qUWFQAQkqX+/L766its2rQJN2/eNNgYHj16hFWrVmHu3LkUQDFxM2bMwMmTJ5Vp8zRB5wDmpzrf74b+TiXElDRs2BDDhw/HvHnzNN43Pj4eV69eRUxMjA5Gpj/VuZ6dN28eBg8eTAEUQgyhuFierisy0tAj0a+GDeX1SbQ4bgtqzRrAxgYYPdqw47AgtBKFEEIIIYQQM6PpShRCCCGEEEIIIerRShRCCCGEEEIIIYQQQgghhBA1aCUKIYQQQgghhBBCCCGEEEKIGrQShRBCCCGEEEIIIYQQQgghRA0KohBCCCGEEEIIIYQQQgghhKhhbegB6INMJsO9e/dQs2ZNiEQiQw+HEGKkGGN48uQJvL29IRabR4yZjn+EEF50DCSEWCpzPP4BdAwkhPAxx2MgHf8IIbx4j4EWEUS5d+8efHx8DD0MQoiJuH37NurVq2foYQiCjn+EEE3RMZAQYql0efw7cuQI5s+fj+TkZNy/fx/btm3D66+/XmH7w4cPo3v37uW2379/H15eXlyvScdAQogm6ByQEGLJqjoGWkQQpWbNmgDkPwwnJycDj4YQYqzy8/Ph4+OjPGaYAzr+EUJ40TGQEGKp9HH8KygoQGBgID788EMMHDiQe7/Lly+rHL88PDy496VjICGEB50DEkIsGe8x0CKCKIqle05OTnTwJIRUyZiX+86bNw8xMTEYN24cFi9eXGV7Ov4RQjRlzMdATdExkBCiCV0e/3r37o3evXtrvJ+HhwdcXFy0ek06BhJCNEHngIQQS1bVMdA8kh0SQogFOHnyJL7//nu0bt3a0EMhhBBCCCF6EBQUhDp16qBHjx74999/K21bVFSE/Px8lQchhBBCCKk+CqIQQogJePr0Kd577z38+OOPqFWrlqGHQwghhBBCdKhOnTpYuXIltmzZgi1btsDHxwfdunVDSkpKhfvExsbC2dlZ+aB6AIQQQgghwqAgCiGEmICoqCj07dsXYWFhlbajOxAJIYQQQkxfs2bNMGrUKLRp0wadOnXC6tWr0alTJyxatKjCfWJiYpCXl6d83L59W48jJoQQQggxXxZRE4UQhefFUkz/6yziz95HQTGDxFqMhu6O+F9Pf3Rt5g4rsfnkACXmY+PGjUhJScHJkyerbBsbG4tZs2bpYVTqFZfK8OPRa1ifmIHsJ6UQi0XwcLLFex18MaJzQ0isKXZPCCGEEN2RyhhOZOTgwZNCeNS0Q3u/2mZzjt++fXv8888/FT5va2sLW1tbPY7IuEllDEcuPsDXey8gPfsZmIjOS6vytLAUY385icRrOShhgL3ECu39amPZOyGoYUfTRy+i+QVCCDEeUhlD0vVHOJb+EIAIoY1c0bGhq2DHYvoWJBZjxLqT2H/xgcq2wlIZLtx/gg/XnYQYwPL3Q9CrZR3DDJAQNW7fvo1x48YhISEBdnZ2VbaPiYnBxIkTlf/Oz8/XWyqHL3ddwI9HM1S2SWUMd3ML8c3ey/hm72WM7NwAU/u20Mt4CCGEEGJZ4tPuY9aOC7ifV6jcVsfZDjP6BZjFOX5qairq1DH996EPu8/eR/RvKZCxMhsZnZdW5rVlR5F2V3UVe0GxFIcuZ6PlzL1o6V0DOz/paqDRGZ/ha0/iwCWaXyCEEGMQn3YfU7acQ+7zEuW27w5dg4uDDeYNbCXIsZhuvSAWof93R8sFUF4kAzB6Qwri0+7rZ1CEcEhOTsaDBw8QEhICa2trWFtb4++//8bSpUthbW0NqVSq0t7W1hZOTk4qD30Y+fPJcgEUdX48egMjf656RQ0hhBBCiCbi0+5jzIYUlQAKAGTmFWKMEZzjP336FKmpqUhNTQUAZGRkIDU1Fbdu3QIgvxFm6NChyvaLFy/G9u3bce3aNaSlpWH8+PE4ePAgoqKiDDF8kxK7+wI+/vWFAIoadF76n9Yz95YLoLwo7d5TtJ2zT08jMm5d5x8sF0B5Ec0vEEKIfsSn3cfoDSkqARSF3Gclgh2LaSUKMUmKZcZJ6TkoZZUvy/4r5Q7O3uGvCzF6Qwquf9Wn3HIvxVLdvecy8bxEhhp2Nghv4YUZ/VrAXmIlyPsi5EWvvvoqzp07p7ItMjIS/v7++Oyzz2BlZfjP3s7Uu0i4UPlFRFkJFx5gx5l76BforcNREUIIIcRSSGUMs3ZcgLo5c8W2WTsuoEeAl8HS65w6dQrdu3dX/luxcjgiIgJr167F/fv3lQEVACguLsann36Ku3fvwsHBAa1bt8b+/ftV+iDl7T57D98fqfrGHgU6LwX6LD6E/MJSrrYPC0oQueYE1kS21/GojNesHedw89Fz7vYf/5KCq1+Wn18ghBBSfVIZwye/na6ynRDngRREISbntaVHkXavbFBEdVn28Jd98cVrLQH8/x/T72c0fo2wBYdxaNJ/Fygfrj2Bg5eyVdo8flaCjSdvY+PJ23ilmStWR3bU6v0QUpmaNWuiZcuWKtscHR3h6upabrshSGUM0RtTNd5v4u+p6NOqDl1MEEIIIaTaTmTklFuB8qL7eYU4kZGD0EauehqVqm7duoGxipdGrF27VuXfkydPxuTJk3U8KvMilTFE/1r1RMqLPvnttMWel/6VcgcXMp9ptM+hy9kWG3gqLpVhzb+3qm5YhowB0b8kY8UHbXU0KkIIMW9la+8+fFoKa6v/6k+dvpmDYmkVS08hzHmgyaTziouLQ4MGDWBnZ4cOHTrgxIkThh4SMQD/L/a8EEAp76d/bqLfsiMAgKhfT2n1OhmPnmF76l0AQNu5CeUCKC86ePkR2s1N0Oq1CDFlr357UKv9SqQMiVcfCjwaQgghhFiizPzKAyiatiOmaUnCZci02I9BPsltaaQyhnFa3HAIyG+IklaVL80MfbAqSav99pzPQnGpNp9OQgixbHN2XkDTaXswf+9VZD4pRSlTrT+17PB17r4ePKneeaBJBFE2bdqEiRMnYsaMGUhJSUFgYCDCw8Px4AF/+hhi+oJn7kFhCd+Jx7m7TxC5+jji07T/jIzfmIqXYhPw8GkxV/vsp8X4cC0F94juHT58GIsXL9bLaz0vlmLS5tNoNX03GsXsQovp8YhccwJPC0vxV8od3MjR/kto5s40AUdKCCGEEEuV87RI0HbE9EhlDHEaTKS8yBInuZckXFabAo+HJd4QVVwqw/Ebj7Xe/4OftAvAEEKIpeq/7Ch++oc/RWdVPGraVWt/kwiiLFy4ECNHjkRkZCQCAgKwcuVKODg4YPXq1YYeGtGTGX+dxeNCzU5qD12p3kkdA3A3jy+AonDwUjaeF0urbkiICfhw7Qk0nx6PP07dw5NiBikDCoqlOHQ5Gy1n7tUqVV5Z17OfWdzFKiGEEEKEV9tRImg7YnoSrz0ERzaPSsVsPSvMYEyAVMaw4m/tg06A5d0QNWVz9a59jmc8pmsfQgjhNHztcZy9y1/fuir2NmK096tdrT6MPohSXFyM5ORkhIWFKbeJxWKEhYXh2LFjavcpKipCfn6+yoOYruJSGdYl3jb0MLi9sfwfQw+BkGprx5HGTgh0RxYhhBBCqsvDie/OQt52xPQsO3il2n1sT71nMSmqktIfgTPJQ4Us6YYoqYxhW+q9avczZUv1AjGEEGIJdqbexYFLwq52HPlyw2rXPjP6IMrDhw8hlUrh6empst3T0xOZmZlq94mNjYWzs7Py4ePjo4+hEh3RNu+ooVzKfGoxJ5PEPPVdchjZnGnsqovuyCKEEEJIdZ3IeMTX0DLmxy2OVMZw6kZutfsplTEkXef8LJm4nxOFSY9iKat3Eq89FOTwsf2M5QTqCCFEG1IZwycbUwXtUywCxvVoWv1+BBiL0YmJiUFeXp7ycfu26axiIKqqm3fUUOjuemKq/kq5g/P3C/T6mpZy8UWIKZo3bx5EIhHGjx+v3FZYWIioqCi4urqiRo0aGDRoELKyslT2u3XrFvr27QsHBwd4eHhg0qRJKC0tVWlz+PBhhISEwNbWFo0bN8batWvLvX5cXBwaNGgAOzs7dOjQASdOUO0xQogqqYxhXeJNrrYPC6gmijlKSn+kVUF5ddYdEy73urGSyhj2XxKmvqylrN7ZknJHkH6kMlhMoI4QQrSxJOGyYN/pCtHdGld7FQpgAkEUNzc3WFlZlbs4z8rKgpeXl9p9bG1t4eTkpPIgpsnUVqEo0N31xBRJZQwT/tD/EvOtKXfxvFiKuENX0PHLvfCbsgt+U3bBf9oe9FlyBAcvPrCIizNCjM3Jkyfx/fffo3Xr1irbJ0yYgB07duCPP/7A33//jXv37mHgwIHK56VSKfr27Yvi4mIkJiZi3bp1WLt2LaZPn65sk5GRgb59+6J79+5ITU3F+PHjMWLECOzdu1fZZtOmTZg4cSJmzJiBlJQUBAYGIjw8HA8eCDPxQwgxDycycpD7vISrbXULihLjlHhduJQfBy9lm/15Z1L6I0gFulS1lNU7xwT8jB29RucxhBCijlTGsOxQ9ep1vchKLMwqFMAEgigSiQRt2rTBgQMHlNtkMhkOHDiA0NBQA46M6JqprkJRoNUoxNQIUZBTGwxA8+nxmL/3KjKflIL9/7bCUhku3H+CD9edRJPPdyM+7b7+B0eIhXr69Cnee+89/Pjjj6hVq5Zye15eHn766ScsXLgQr7zyCtq0aYM1a9YgMTERSUny7719+/bhwoUL2LBhA4KCgtC7d2/MmTMHcXFxKC6WpwpcuXIl/Pz8sGDBAjRv3hzR0dEYPHgwFi1apHythQsXYuTIkYiMjERAQABWrlwJBwcHrF69Wr8/DEKIUcvML+Rq52JvU+2CosQ4ncjIEawvSwgKrD92Q9D+/r2u+zqKhlRcKkPWE+FSHR/SQ91JQggxRUsSLgueeXXx28GCrEIBTCCIAgATJ07Ejz/+iHXr1uHixYsYM2YMCgoKEBkZaeihkWp4WliKyJ+OoemUXWgwZReafL4LXb85iP0XsiCVMUzZbNpF1xSrUZ4XSzFp82m0mr4bDabsQqOYXQievQ9TtpzF82KpoYdJiNLSA9UvyFmWjYDfMDIAozekUCCFED2JiopC3759ERYWprI9OTkZJSUlKtv9/f1Rv359HDt2DABw7NgxtGrVSqWeXXh4OPLz83H+/Hllmxf7Dg8PV/ZRXFyM5ORklTZisRhhYWHKNoQQAgA5T/lSdIU19xDsIpoYD6mM4fRNYW+8M+eggFTGcOBiVtUNNXBSwCCWMVqXeEPQ/q5lPzX71U6EEKIpqYxhxd/CrkIJa+6BfoHegvVnLVhPOvT2228jOzsb06dPR2ZmJoKCghAfH1+u2DwxHa8tO4q0u/kq20pkwM2c5xjx8ym9jGHJkCCs/Tcdp2/nV91YS+3m7kNeoWqgRMqAx89KsPHkbWw8eRuv+rvhp2EddDYGQnhIZQwnb+YK2ufqYe3xwWph6xd8/EsKrn7ZhyZBCNGhzZs3IyUlBSdPniz3XGZmJiQSCVxcXFS2e3p6IjMzU9nmxXM0xb+rapOfn4/nz5/j8ePHkEqlattcunSpwrEXFRWhqOi/CdX8fN19xxNCjIOLg4SrXWgjNx2PhBhCUvojlAo8H3338XNhOzQiSemPUCJw1ukzd/IglTGzPT/fcfauoP0p6qK81ISOSYQQoiD091NYc3esimgnXIcwkZUoABAdHY2bN2+iqKgIx48fR4cONOlsqlrP3FsugKJvLb2dMCCoLjaPeVmnr/NiAEWdA5ceouv8gzodByFVSbwmXJ5fAFj2TjA6NXYTdDUKAMgYEP1LsrCdEkJUTJkyBb/88gvs7EyvdkBsbCycnZ2VDx8fH0MPiRCiY7nP+NLs8LYjpkWTeih1atpytbuXa75BFE1+Xn5uDlztiqXmmwJNKmM4zzl3oUkIyZxXOxFCiDbmx18UrK/vhgRhVUR7wfpTMJkgCjEPfRYfQn5hqaB91rTV7GPsKLHCzk86AwCsxCIsfzdYo/3trDRqzuXmo+eYveO88B0TwmnZQeFSefUIkC+ZtBKLMKZrI8H6VdhzPgvFpQLfQkcIUcrOzkZISAisra1hbW2Nv//+G0uXLoW1tTU8PT1RXFyM3NxclX2ysrLg5eUFAPDy8kJWVla55xXPVdbGyckJ9vb2cHNzg5WVldo2ij7UiYmJQV5envJx+/ZtrX4GhBDTwbsShbcdMS289VAkViK0b+jK1VaxssIcaVI/5p229bnbmmtQICn9EXfNyLDm7twTbOaeAo0QQjRRXCpD6h3+m+0/6d4YaTPD0b1JbThYi2BnLUZAnZpYHdEO17/qg9eC6upknBREIXrzV8odXMh8Jni/y99ti9b1nLjaSsTA+dm9VLb1ae2NUV38uPe/9GVfdGhQq+rGGlr97w2aGCYGIZUxnBIoldfIzg3w49D/lkyO69FMo7uyeH3wU5IOeiWEAPJ6JampqcpH27Zt8d577yn/38bGBgcOHFC2v3z5Mm7duoXQ0FAAQGhoKM6dO4cHDx4o2yQkJMDJyQkBAQHKNmX7ULRR9CGRSNCmTRuVNjKZDAcOHFC2UcfW1hZOTk4qD0KIeaOVKJZLk3oogfWcUa+2PVdbc11ZIZUxnL2Ty9VWYiXCsJf9YM15Im+uQQFNVu4M69QQbTjnCcw5UEcIIRXJe1aCgXH/IGTOPnSff0irmtgiAON6NEUNO2usGR6KC3P74NLc3tg9rgte0XH9OwqiEL2QyhjG/S58oXhrMdCpiRv+iu6M4S9XHggJ8HLAla/6qn0upk8Alr8bUulkb9n914/oqO2QK0UTw8QQktIfgfcc3s/VDpPCm8CrpjWsANiIRajrYofJ4c1wZW5vTO3bQqW9lViEsd2FX41yPOMxBR0J0ZGAgAC0bNlS+XB0dISrqytatmwJZ2dnDB8+HBMnTsShQ4eQnJyMyMhIhIaGomNH+Xdjz549ERAQgA8++ABnzpzB3r17MW3aNERFRcHWVp5KZfTo0UhPT8fkyZNx6dIlLF++HL///jsmTJigHMfEiRPx448/Yt26dbh48SLGjBmDgoICREZGGuTnQggxTnce892kVduRVqKYG03qobTzq41OGtTFMceVFScyclDE+QPr7u8BibUYwb6WHRTQZKVTx0auaO9Xm6u9uQbqCCFEneJSGQJn7UXg7H1IuZ2HnIISZDx6hhE/n0Kjz3dja+o97r4GhtQ1WA0ukygsT0zfkoTL0MUp1cddGyv/eL54LQCf9fLHj0evYX1iBh4VlEJibYX2frWx7J0Q1LCr/OPep3UdXGvZB4fPZ2HGznPIfFIMa7EIHRu64rt326jsL7EWo0ODWjh+g+/OJ16KiWGJNcU3if5ocofV7P6t0bmZO6K6N+XeZ1yPZvju0HUIHfKI2XoWC94KErhXQkhVFi1aBLFYjEGDBqGoqAjh4eFYvny58nkrKyvs3LkTY8aMQWhoKBwdHREREYHZs2cr2/j5+WHXrl2YMGEClixZgnr16mHVqlUIDw9Xtnn77beRnZ2N6dOnIzMzE0FBQYiPjy9XbJ4QYrmkMobtZ/guvL2c+VYhENOhyTnsS43c0bGhK6xF4Aq8mOPKisz8Qu62Qzs2AAC096uNkxzXvIqggDkVS9dk5U5gPWdYiUXo1MgNcYeuc+3z7/Vss/p5EUKIOl/uuoAfj2YI1l/swNaC9aUpCqIQnZPKGOIO851IaEIski/hKktiLUZU96YaTfCWZSUW4dVWXni1VcX51hXWj+iIptP2aPU6lfngpyRsGtVJ8H4Jqcjdx3zFMxUrvzRlJRZh6ZAgRG9M1XjfymxPvYdvBgca7C4EQizF4cOHVf5tZ2eHuLg4xMXFVbiPr68vdu/eXWm/3bp1w+nTpyttEx0djejoaO6xEkIsy4mMHOQUlFTZztVRwn2HODEdmq4SsBKLEOxbiysooFhZYU7nmQ+fFHG1s7cRo2Mjef0YSw4KaLJyp93/H180CdTxXoMRQoipGvnzSSRceFB1Q06N3B0MetM53e5OdC7x2kPuYmyaWPR2kEFPaiXWYvRq6SF4v5SmiOgbY3x/oME+Llr/zb0WVBdhzd212rcipTJaBk8IIYRYMt476/sHeZvVZDjRvB6K4vdvyemWTt3gez9dmrorf16KoAAPcwsKaLJy56VG8uscRaCOx/08/v4JIcTU7Ey9K2gABQBmvtZS0P40RUEUonOzdpwXvE8/VwcMCKoreL+ainu3rU76jdl6Vif9EqJO3Vp86S3aVfMOzlUR7RHWXNjA47pjwi0LJYQQQohpyXnKd2d9PRdK5WVuNK2HomCpdVGkMoaDl/gms+xtrJT/b8lBAW1W7gD8gbqzZlpHhhBCpDKGTwTORKJtZhQhURCF6FRxqQzXsgsE73f/p90E71MbVmIRvhsSJHi/21Pv0QkV0RvFnVNCtavMqoh2WPZOcIVfPvVrSbD+w/bc/R289ID+VgghhBAL5eLAVyyetx0xHZrWQ1Ho2NAVNpyzIPdyzScokJT+CCWcyQ68XexU/m2pQQFtVu4A/IG6wlKZ2a12IoQQQF4XW+j8OmVrYhsKBVGITsVsPSN4nyvfDzH4H05ZrwXVRY8AYe+upzRFRJ86NnKFi4NNpW1cHGxU7rCqjn6B3rj6VR+s+aAtWno7waeWPcKaeyBtZjiOfNYDnRq7cX85lcqApOuPIJUxHDqfhb5LjqDd3AS8tvQoDl6kAAshhBBiznKfFQvajpgO3tRRinooClZiEV7x57t2e14i1WpsxuiYBteWL944ZYlBAamM4e+rfIG6sit3AHmgTmLFN19hTqudCCEEkB8/l3HW0uJlJS5fE9sQKIhCdEYqY9h++h53+/AAz0o/kGLIAyi9Wtap9tiE9uPQdhjZ2a/SNs097RFUz4m7z6PXhM0dSEhFrMQizBvYqtI28wa2EjR4aSUWoXsLT+z8pDOOfvYKVkW0Qw07a+VzbRu4cPcVs/UMmny+G5HrT+H8/SfIflqMtHv5+HDdSTT6fDd2pt4VbNyEEEIIMR60EsVyPS8u5WrX3d+j3Dls2wZ8NwYduZJtNjfkMPC9DztrcbkbpywxKHAiIweFnEt3Xly5YyUWIbCeM9e+5lZHhhBCliRc5vzG4bf47WCjuJmegihEZzTJUysCsPz9Nsq70/09HGArlp/EBdSpidUR7XD1qz5GGUBRmNo3AFfm9sak8CbwqmkNGzHgKLFC92buSJsZjj0TXsGkXs25+/sr9b4OR0uIql4t62Dl+yHwcrJV2e7lZGuQ4OXYV/jvMrj1uLDSpaLRG1MxYt2J6g+KEEIIIUaFVqJYJqmM4fAVvsn6tmrqebjVtFXTsrznJeazssLJrvJV5wp9WtUpN1FlJRYhyMeFa39zSYGmTVH5surWcuDa19zqyBBCLJtUxhB3WNhVKK/6e6BfoLegfWrL2tADIOZrfvxF7rY9Av67Q6h7C090b+Gpq2HplMRajKjuTRHVXf0EcMeGrhADXLkB7+UVorhUBok1xTqJfvRqWQc9ArxwIiMHD54UwqOmHdr71TZIxF+R0kuoPJr7L2ZjxLqTWBXRTqAeCSGEEGJotBLFMiWlP0IR5916bjXKB0y8nOzUtFTvWPpDvGTgQrZCyH3OF0j0dFYfYGrToBZO3Hhc5f7mkgJN26LyCnVr2XPtr6gjYwx3WBNCSHUlXnsIqYDLUFrVrYmfhhnPHA7NzpJqe14sxWdbUtHxq/0I/Wo/YracRd6zEqTeyefuIyK08lRY5kLTNEUf/JSEvGclGBj3D0Lm7EP3+Yew/0KW2SwrJ8bHSixCaCNXDAiqi9BGrgY7obcSi/BGiLB3G+y/+AA7zvCnGCSEEEKIcTvGWVycVqKYF03qe3g5l5/Mbu9XGw4SvqkQc7nsSuYIgADA/QpWktR24Fu9Yy4p0HKe8QVRXiwqr2CJdWQIIWTWjvOC9TX8ZV/sGNtFsP6EQEEUUi2Ra06g+fR4bDp5F5n5RbifX4TfTt5G4Ox93H1Yi0WCFaw2BZqkKTqe8RiBs/ch5XYecgpKkPHoGUb8fIrqPFiQFStWoHXr1nBycoKTkxNCQ0OxZ88eQw9LL2IHBgre5/hNp83iwo4QQgixdFIZQ8KFLK62tR1pJYo54a3vUcPWCu39apfbbiUWoRdn5oNMM0i3JJUxpN7O5Wr7Yn0PBUtLgcYbdHqxqLyCJdaRIYRYtuJSGa5lF3C3H93FD2+29UZNiQgiADZiEeq62GFyeDNcmdsbX7zWUneD1RKl8yJaazVzL54U8hX0q8yAIG+LWr7aqbEbREC1Cy1Fb0zFn2fuYlVEeyGGRYxUvXr1MG/ePDRp0gSMMaxbtw4DBgzA6dOn0aJFC0MPT6ck1mJ0aFALxzkvYnhIZcCShCuYGN5MsD4JIYQQon8nMnKQx3ktom41AjFdvPU9egZ4VXidWceFr2bFvguZJp9uKSn9EYo586uoq+8BWFYKNCGCToo6Mjwp0MyljgwhxLJ9sCqJu61YBEzq1RxWYhHmDw7W4aiERStRiFZCZsULEkABgNiBrQXpx1RYiUV4I0iYNEWKOg/EfPXr1w99+vRBkyZN0LRpU3z55ZeoUaMGkpL4v6BM2foRHQXvc+WR67QahRBCCDFxvIWfXext1K5G0LcjR46gX79+8Pb2hkgkwp9//lnlPocPH0ZISAhsbW3RuHFjrF27VufjNAWnb/HdYOPlXPHEv/y+16o9LZLiREYOV1tjlciZ9s7OWn19D8CyUqAJEXQC5HVkeJhLHRlNVXVMZIxh+vTpqFOnDuzt7REWFoarV68aZrCEEBSXyhB36Ao6frkXflN2ocGUXWjy+W68NO8Alh64otHNr9HdGpvkzQkURCEam/HXWeQ8F+aLvoNfLYssnD5vsHBpiqjOg+WQSqXYuHEjCgoKEBoaaujh6IViNYqQiqXMLNIMEEIIIZaMt/Dzq809jOJCvaCgAIGBgYiLi+Nqn5GRgb59+6J79+5ITU3F+PHjMWLECOzdu1fHIzVuUhnDoct86Y8q+7WHapBOOjPvOXdbY3T3Md/4W9dzrvBvRZMUaM72fCuFjBVvzZ3Kgk4Afx2ZY9cfWeQNXlUdE7/55hssXboUK1euxPHjx+Ho6Ijw8HAUFtLKHUL0LXb3BTSdtgfz915F5pNSZWadEhnD3dxCLEzgD3CKAIzrwV/mwJhQOi+ikeJSGdYl3hasv/XDhb/L3BQInaZo/KbT6NOqjlFcIBLhnTt3DqGhoSgsLESNGjWwbds2BAQEqG1bVFSEoqL/JhXy8/P1NUydWT+iI5pOE7YOzDd7L2J7k86C9kkIIYQQ/XnMWSzeU4M0RLrUu3dv9O7dm7v9ypUr4efnhwULFgAAmjdvjn/++QeLFi1CeHi4roZp9JLSH6GoVMbVNrRhxSmlOjZ0ha21CEWlVU9eP3zKF7AzVs+L+TJIVLVygjcF2unbwqXiNQTemjvd/NUXlVfgrSOTX1iKExk5GgX2zEFlx0TGGBYvXoxp06ZhwIABAICff/4Znp6e+PPPPzFkyBB9DpUQixa7+wK+P5IhWH9vBJtuSQfLWwJAqkWTHHdV8feqYZGrUBSETFOkqPNAzFOzZs2QmpqK48ePY8yYMYiIiMCFCxfUto2NjYWzs7Py4ePjo+fRCk9iLcbwl30F7fPMnXwUc16AE0IIIcT4iDivv3nbGZtjx44hLCxMZVt4eDiOHTtmoBEZB6FWCViJRejezIOrr9znJVztjJFUxvAvZzqv2g6SSp/nTYF2+FK2Sa+s4K25E+xTedBJkzoypr7aSWgZGRnIzMxUOQY6OzujQ4cOFn8MJESfiktlggZQAGDeIOEy8+ib5c5gE40Vl8oELfC87eOXBevLFEmsxejVku/EnQfVeTBfEokEjRs3Rps2bRAbG4vAwEAsWbJEbduYmBjk5eUpH7dvC7dyzJC+eK0lWtdzErTPmK1nBe2PEEIIIfqTmcs36ehioqmFMjMz4empmj7J09MT+fn5eP5c/XsvKipCfn6+ysPcCLVKAAAautfg6uvag6dc7YzRiYwcPC3iu3HIrUblKyd4V0oUlspMOnUub82dvCqCa+39aqOGLd+Um6mvdhJaZmYmAKg9Biqee5ElHP8I0bfOX+8XtD9TL+lguiMnehez9YxgfXVv5g57iZVg/ZmquHfbCtYX1XmwHDKZTCVlV1m2trZwcnJSeZiLv6I7Y/jLfoL1tz31HgUeCSGEEBMklTHsv/iAq21VE8PmxBxXJL9IqFUCAFCripUXCqZcsyIzn79+hJezfaXPy1Og8U0hHUvnW/1ibISquQPIVzu93LjiwvNlmfJqJ2NhCcc/QvTpr5Q7yHoi7LHJ1Es6UBCFcJHKGLafFqZ4eQ1ba6yJbC9IX6bOSizC8neDBetv3TFhl9kRw4uJicGRI0dw48YNnDt3DjExMTh8+DDee+89Qw/NIL54LQBX5vbGpPAm8KppDWuRPF1DQJ2aWB3RDhdn9+Luq1RGgUdCCCHEFJ3IyEFeIV+dh6omho2Vl5cXsrKyVLZlZWXByckJ9vbq35O5rkguS6hVAoDmNStM0cMnfCscnOys0d6vdqVt5CnQ+IICJhpzEqzmjoIlrHbSBS8vLwBQewxUPPciSzj+EaIvUhnDuN+Fu5EeANo3cDHpVSgAFZYnnJLSH4Gj5l6VJGIgbZblFkJUp09rb4y6kytInsH9Fx9AKmMmW6SJlPfgwQMMHToU9+/fh7OzM1q3bo29e/eiR48ehh6awUisxYjq3hRR3ZuqfT6onhNS7/At31YUmC8ulWHNv+lIuPAAAEPPAC8Me8nP5L/kCSGEEHPEe3e9i71NlRPDxio0NBS7d+9W2ZaQkIDQ0NAK97G1tYWtrfmuvBFylQBgGTUrHj8r5moX2siV6xoyuH4txJ/PqrKds4mm0ROq5o6Cpqud6Dpezs/PD15eXjhw4ACCgoIAAPn5+coaoeqY+/GPEH1aknCZM3kmvw0jKj5/MRUURCFcfk6s/gR/gJcDdo/vLsBozE9MnwAE1quF6N9SqnXXjowBiVcfojPnHULE+P3000+GHoLJmdSrOd5bdZyr7Zk7+Ri17iT2vpAS5NTNXHy15xIiX6qPGf1a6WKYhBBCCNFSDmf9gLDmHkYzKfn06VNcu3ZN+e+MjAykpqaidu3aqF+/PmJiYnD37l38/PPPAIDRo0fju+++w+TJk/Hhhx/i4MGD+P3337Fr1y5DvQWDE3qVQHu/2qhpZ4UnhdIq2+YU8AUjjI2I8+Pf2INvxUR+IV9qF952xkbImjuA5qudeOvOmIOqjonjx4/H3Llz0aRJE/j5+eGLL76At7c3Xn/9dcMNmhALIJUxLDt0XdA+P3ypgVncoGr674DonFTGsP8SX85hAPi0ZxP4ezjAzkoER4kVujdzR9rMcAqgVKFP6zq4+mUfrPmgbbmfX//W3tz9LD14RYejJMT4dWzoCmsN5kteDKCUtebfW+jyzQEBRkUIIYQQobhw3t0d2qjqiXR9OXXqFIKDgxEcLE/lO3HiRAQHB2P69OkAgPv37+PWrVvK9n5+fti1axcSEhIQGBiIBQsWYNWqVQgPt9xV/UKvErASizAwuC5Xn7yfOWOTmcu3gsaFc+UI47zhz1TTUwlZcwewjNVO2qrqmDh58mSMHTsWH330Edq1a4enT58iPj4ednb8P1NCiOaEXoXi62qP6f1aCNij4dBKFFKlpPRHkPLd8ANrsQgfd2uCsa+oT7NDKmclFqF7C090b+Gpsr24VIa/zvLVpEm+lUtLgYlFsxKLMCDYG1tShKnjdCunEP2WHcWOsZ0F6Y8QQggh1cO7KsCYVg9069YNrJIZ6LVr16rd5/Tp0zoclWkRepUAANSr5cDVLqeAb/WTMZHKGHanZXK1davBt2LC3NNTCVlzB7CM1U7aquqYKBKJMHv2bMyePVuPoyLEskllDCv+Fm4Vyqv+bvhpWAfB+jM0WolCqqRJKq8BQd4md6JkCiTWYjR2d+Rqq0jpRYglix0YKGh/5+7mY3vqXUH7JIQQQoh2km/yFfnmrQdBTIPQqwQAIJdzMjyZc3LdmCSlP8LzEr67Ib2c7bnaaZqeypRIZQxHOa+jeac8LGG1EyHEfCSlPwLn1wYAINTXGV41raE4JFqJgFoONhjSzgcXZ/cyqwAKQCtRSBU0TeUVO7C1Dkdj2Wb0a4EPVp/garv04BWqi0IsmsRajA4NauH4DeEueCdsSsVrrSlQTAghhBiSLiY6iWkQepUAAIjA9yE5fCnb5FZW8KY/q2FrjfZ+tbnamnN6qhMZOSgornrFCMBXc0fBnFc7EULMy/z4i9xtxSJgw6iXTOp7sbpoJQqplCapvHxr25tFoSBj1amxG/cfrCKlFyGWbP2IjoL2J2PAkgSqOUQIIYQYkq4mOolx01XwjLeQd2GpDEmcQQljwZv+7OUmrtyTYIr0VDxMLT1VZn4hVzsHiRVXzR0Fc17tRAgxLVIZw6HzWei18BCaTNmFRjG7EDx7H6ZsOYu8ZyVIvZPP3Vd0t8YWFUABKIhCqqBJKq/3O/rqcCTESixC2wYuXG0ppRch8tUojdz40uDx+u7wNQpQEkIIIQakq4lOYtx0FTzr2NAVtpw3Ah5LN63rK12kPzPn9FQPn/CtBOnd0kujiUPe1U7/XH1E1xmEEJ3ZffY+mkzdjcj1p3DpwTOUAJAy4PGzEmw8eRuBs/dx9yUCMK6H5dXCNlgQ5caNGxg+fDj8/Pxgb2+PRo0aYcaMGSguVr1b4ezZs+jcuTPs7Ozg4+ODb775xkAjtjxSGUPCBf5UXhGd/HQ4GgIAY1/hP0jN3Jmmw5EQYhpm9m8haH+0GoUQQggxLF1NdBLjpqvgmZVYhO6caZBNbX5bF+nPAPNNT8VbQ8lTg5RmAP9qp6dFpldHhhBiGmJ3X8DHv6YI9j02MKSuRZ5jGSyIcunSJchkMnz//fc4f/48Fi1ahJUrV+Lzzz9XtsnPz0fPnj3h6+uL5ORkzJ8/HzNnzsQPP/xgqGFblMRrD8FbT4hSeemHJim9rmc/Q3GpBhWhCDFDnRq7wUbgQ9PKI9fpLjFCCCHEQHQ10UmMmy6DZ218+eqB1DKhlRW6rB1krumpRJw/B952Ch0busKe84LE1OrIEEKM3+6z9/D9Ef4sQzwstR62wWa9e/XqhTVr1qBnz55o2LAh+vfvj//973/YunWrss0vv/yC4uJirF69Gi1atMCQIUPwySefYOHChYYatkWZteM8d1tK5aUfmqT0AoCYrWd1NxhCTICVWIQlQ4IF7bNYykwuJzYhhBBiLnQ10UmMmy6DZ7Ud+YIjvO2MgS5rB5lreqrMXL4Ahos9X5o0BSuxCH1aenG1ffjUtFbvEEKMm1TG8PGvpwXts4NfLYu9id6o3nVeXh5q1/7vLpBjx46hS5cukEj+O1kJDw/H5cuX8fixad3VYGqKS2W4ll3A3Z5SeemPJim9tp2+a1InroToQp/W3hjVRdhj1Dd7LwraHyGEEEL46Gqikxg3XQbPcjkDNMeum05NFF3WDjLH9FRSGcPutEyutm41bDXu38vFnqsd7yofQgjhEfXrKcH7XD+8o+B9mgqjCaJcu3YNy5Ytw6hRo5TbMjMz4enpqdJO8e/MzIq/4IqKipCfn6/yIJrpvfhv7raN3B0sNgppCJqk9JIx4J/L2TodDyGmIKZPAJa/G6JxuoKKnLmTT+nyCCGEED3T9UQnMV66DJ7V5vys7E7LNJkb1HI4VzT00SL9mTmmp0pKf4TnJXzn9l7OfAGRshjnx4a3HSGEVKW4VIb4NP461zx6t/C06Plfwd/5lClTIBKJKn1cunRJZZ+7d++iV69eePPNNzFy5MhqjyE2NhbOzs7Kh4+PT7X7NGdSGcOh81notfAQGk/ZhQZTduH6w2fc+898raUOR0deZCUWoUeAB3f7iHUn4TdlF1pMj0fkmhN4Wliqw9ERYrz6tK6Dq1/2wZoP2sLfwwESEWAjFqGuix0mhzfDxdm9OJMTyFG6PEIIIUS/dD3RSYyTVMaw/yLfRJBWqwQ4U4A9K5aaTEpXF876LaGNNEvlBcivR/u2qsPVNqeAb5WPoR3j/L3WsLVGez++Gjpl8dbTyeJcQUQIIVX5YFWSoP2JAHz3XhtB+zQ11kJ3+Omnn2LYsGGVtmnYsKHy/+/du4fu3bujU6dO5QrGe3l5ISsrS2Wb4t9eXhXnlIyJicHEiROV/87Pz6dASgXi0+4j+tcUaHtDtVgEdGqi+YkXqZ6hnfyw9wJ/RJkBKCiW4tDlbLScuRct6jhi17huOhsfIcbKSixC9xae6N7CU+3zA0O8sSXlHldf21Pv4ZvBgQCAxKsPseX0HTwrlqJdg9qI6NTAou/QIIQQQnRB1xOdxDidyMhBHueNYNoEz9r71YajxIqrhsix9Id4yQSuf3mDF9oGOUIbuWFzyt0q2/EGcwyNgW8JyMtNXDVeuQMAbjX5gnsHLj6AVMa0eg1CCFEoLpXh+A1hy2AsfSfY4o9NggdR3N3d4e7uztX27t276N69O9q0aYM1a9ZALFadcAoNDcXUqVNRUlICGxv5styEhAQ0a9YMtWrVqrBfW1tb2NrS8u2qxKfdx+gNKdXq4/Ugb4v/IzKEjg1dYSUCpFou9z1/vwAtp+9B2uzewg6MEBMXOzCQO4hSKmNYsOcSVv6TjrKZHfZdyMKXuy8iopMPZvVvraOREkIIIZZH1xOdxDjx1vdwsbfRKnhmJRahcxM3xJ/PqrKtiWTzQvJNvlokjznrwbwop4AvXRhvO0PjTQPXpn7F81CV4V3tlPu8BCcycrjrzhBCiDpTNp8RtL9X/T3QL9Bb0D5NkcFuk7179y66deuG+vXr49tvv0V2djYyMzNVap28++67kEgkGD58OM6fP49NmzZhyZIlKqtMiHakMlbtAAoAzBsUKMBoiKasxCIMqOYB7GmxDJ2/PijQiAgxDxJrMYLqOXG3X340vcKL6XWJtxEyK16gkRFCCCHEyY5vojPYR7uJTmKceOt7hDX30Dp41saXL/jCm5bJkKQyhqNXH3K11TbWyFsAPfmWsHdC60ptR76bcHnbvai9X2042/Hdw2wqdWQIIcZJKmPYlsp3YyiPVnVr4qdh7QTrz5QZLIiSkJCAa9eu4cCBA6hXrx7q1KmjfCg4Oztj3759yMjIQJs2bfDpp59i+vTp+Oijjww1bLPx6rfVnzzv4FeL0tUY0LzB1Q9g3X78HLN3nBdgNISYj0m9mgvWV85zKVrOoEAKIYQQIoTTnBOyeZwTvMQ06LK+h0JtR77X4G1nSCcycrhSkwFAaEPtfmYizkqC/1x9BKkJLN85dp0v6JSr5codK7EIYc356po+5AwaEkKIOonXHnKu263a8Jd9sWNsF4F6M30GmwEfNmwYGGNqH2W1bt0aR48eRWFhIe7cuYPPPvvMQCM2H3+l3MGNnOoXLFs/vKMAoyHakliL0aslf4H5iqz+9waKtS2KQ4gZ6tjQFdYCZgB5WiTFa0uPCtchIYQQYoGkMoZDl7O52lImL/Oi6/oeAP/kOO9kuyHxpj9zkFiho5Zpo3jTTT0tKsWJDL7UYoYilTEkXKg6lRtQvSCalwtfvR7eVT6EEKLOLA1ulG7k7oArc3vj056NUdvBChIrEdwcbfC/nk1xZW5vfPFaSx2O1PQIXhOFGDepjOFTAXLjRVLRZKMQ925bNPp8d7X7WfNvOkZ1bSzAiAgxfVZiEQYE8xeY55F2Lx/bU+9iQFBdwfokhBBCLElS+iMUcd74o+3d9cQ48dbt0La+BwDUrsGXpmm/CRT+5k1/1qell9bvo2NDV9jbiPG8pOq/SWNPT3UiIwd5haVcbb2c+QIh6jDOW8N52xFCLNfTwlJ88uspJN/Kg5VYhJ4tvDCjXwtYiUW4ll3A3c/M11pCYi3G2FeaYewrzXQ4YvNAs+AWJin9ETjOcyrlVkOCGf1bCDMgUi1WYhFWvh9S7X5W/5MhwGgIMR+xA4Wv9zRhU6pJpDMghBBCjNGx64+42tlZi7W+u54YJxHnPD9vO3U0LfxtzPSR/sxKLELfVnWqbojqrRDSB96VOy72Nmjvx1c7Rx3eejpZnOMhhFgeqYyh2/xDaDlzLw5eeYS8wlLkPCvBxpO30Xx6PJpN28Pdl7UY6NSEbjrRBAVRLMzPidWbLHd1sMapaT0EGg0RQq+WdaodSMl6UkwpvQgpQ2ItRocGwhallTFgScIVQfskhBBCLAXjzPDdzd/dqFcJEM1l5vKtZHCxt9H6Ncyp8DdvajJt63so8AZheIM6hvLwCd/KnVebe1Tr2OJWk2+104H/X+1ECCFlxafdR6PPd+PGo2cVttHkyPFx18Z0vqQhCqJYEKmMYf+lB1rvH9HJB8nTwwUcERFKr5Z1cP2rPgiq56x1H+uqGWAjxNysHyF83aeVR67TRRExeqtWrULr1q3h5OQEJycnhIaGYs+e/+5qKiwsRFRUFFxdXVGjRg0MGjQIWVmqucRv3bqFvn37wsHBAR4eHpg0aRJKS1VTZRw+fBghISGwtbVF48aNsXbt2nJjiYuLQ4MGDWBnZ4cOHTrgxIkTOnnPhBDjdz+34kmDsoJ9hL0JghiWVMaw/yLfNawbZ0oudcyp8Ddv0KK6wQ19BWt0jTcNnCfnaqWKmNNqJ0KIfsWn3cfoDSmC9ScCMK5HU8H6sxQURLEgSemPINVgsYGNWIS6LnaYHN4MV+b2xqz+rXU3OFJtVmIR/ox+GRdn98Kbbb1RU6JZRHlD0k0djYwQ0ySxFiPypfqC9lksZUjiTEdCiKHUrVsX8+bNQ3JyMk6dOoVXXnkFAwYMwPnz8iKFEyZMwI4dO/DHH3/g77//xr179zBw4EDl/lKpFH379kVxcTESExOxbt06rF27FtOnT1e2ycjIQN++fdG9e3ekpqZi/PjxGDFiBPbu3atss2nTJkycOBEzZsxASkoKAgMDER4ejgcPtL8hhBBimqQyhvjzfIWf86gos1nRV70KwHwKfx+7/pCrXXWDG/oK1uiaPtLFAea12okQoj9SGRM0gAIAbwR70yoULVAQxYL8e5XvZAoABoXUxdWv+uDfKa/i4+6NqYi8CbGXWGH+4GCcm90H17/qw73fzZznlNKLkBfM6NcKdZ21Tw2hzrpjtOqLGLfevXujT58+aNKkCZo2bYovv/wSNWrUQFJSEvLy8vDTTz9h4cKFeOWVV9CmTRusWbMGiYmJSEpKAgDs27cPFy5cwIYNGxAUFITevXtjzpw5iIuLQ3GxfMJm5cqV8PPzw4IFC9C8eXNER0dj8ODBWLRokXIcCxcuxMiRIxEZGYmAgACsXLkSDg4OWL16tUF+LoQQwzmRkYNnxXznqTQnYF70Va8CMI/C31IZQ8IFvoBjbUf9rEThDeoYCm8auOqkiwPkNz32CPDkamvsdWQIIfozeMU/gvc5b5DwNWAtAc2MW5CDlzK528YOpFUn5sBKLEJ4AN+ydIBSehmj2NhYtGvXDjVr1oSHhwdef/11XL582dDDsij/xvRETc67xngcvER5jonpkEql2LhxIwoKChAaGork5GSUlJQgLCxM2cbf3x/169fHsWPHAADHjh1Dq1at4On530RBeHg48vPzlatZjh07ptKHoo2ij+LiYiQnJ6u0EYvFCAsLU7YhhFgO3ol0AAhtSEVSzUkOZ+qssGrWqwDMo/C3Plfu1OZMn7bfyGt81Hbkex+87SpjLnVkCCH6sTP1Lk7fzhe0z94tPOlGeS3RT81CSGUMl7MKuNp61pTQH5QZGdrJj7vtjjP3dDgSoo2///4bUVFRSEpKQkJCAkpKStCzZ08UFPD9PRNhnJsZjpZ1nQTpq1QGSulFjN65c+dQo0YN2NraYvTo0di2bRsCAgKQmZkJiUQCFxcXlfaenp7IzJTfrJGZmakSQFE8r3iusjb5+fl4/vw5Hj58CKlUqraNog91ioqKkJ+fr/IghJg+3sLP9jZidGzkquPREH3inUzmnZyujDkU/tbnyh1zqfGhr/RnmvRh7HVkCCG6J5UxfPrHGcH7/e69NoL3aSloptxCJKU/Au9pXseGdOFhTjo2dIUV51/6hftPjPaCwFLFx8dj2LBhaNGiBQIDA7F27VrcunULycnJhh6axdk5tjPSZoajW+NasIH8C7SmrRXealsPF2f3QvsGLtx9UUovYuyaNWuG1NRUHD9+HGPGjEFERAQuXLhg6GFVKTY2Fs7OzsqHj4+PoYdECBFAzjO+IEqXpu6U49vM6HPS2RyCArwBx1cFWLljDjU+9Jn+DDCfOjKEEN1LSn+EIqmw83NLhgTReVI1CJefhBi19cducLcdHEITDubESixCmL8H9l6ouhBvqUxe9PqlJpQGwVjl5eUBAGrXVn/nWFFREYqK/rt4oruwhVXDzhprR3RS+9zYV5rig9UnuPpJuCC/g5FOYIixkkgkaNy4MQCgTZs2OHnyJJYsWYK3334bxcXFyM3NVVmNkpWVBS8vLwCAl5cXTpxQ/VvIyspSPqf4r2Jb2TZOTk6wt7eHlZUVrKys1LZR9KFOTEwMJk6cqPx3fn4+BVIIMQPJNx5ztbO3sdLxSIi+6XPSWREU4EmHZaxBgcecwSRPzoBRZazEIoQ198CW01VnM3jImZZN3/SZ/gyglSiEEH7z4y8K2l/Luk4YEFRX0D4tDa1EsQBSGcOBi3x3V1iLgU40gW52NEnpRXfIGy+ZTIbx48fjpZdeQsuWLdW2obuwDadTYzfuL1UG+UnRpM2n0Wr6bjSYsgt+U3bBf9oe9FlyBAeNOE0EsUwymQxFRUVo06YNbGxscODAAeVzly9fxq1btxAaGgoACA0Nxblz5/DgwX/B+4SEBDg5OSEgIEDZpmwfijaKPiQSCdq0aaPSRiaT4cCBA8o26tja2sLJyUnlQQgxbVIZQ+rtXK623i7VnxgmxkWfqZbMofC3iPP+HN52VfFy4Qss5D4vEeYFBabP9GcArUQhhPApLpUh9Y5wN8S6Odpg59jOgvVnqSiIYgGS0h+hRMbXNtjHhe6MNkMdG7rCivPXevBSNk3eGqmoqCikpaVh48aNFbaJiYlBXl6e8nH79m09jtCyWYlFeCPEm7v9yiMZ+OPUPTwplv+9MQCFpTJcuP8EH647iaZTdyM+7b6ORktIxWbOnIkjR47gxo0bOHfuHGJiYnD48GG89957cHZ2xvDhwzFx4kQcOnQIycnJiIyMRGhoKDp27AgA6NmzJwICAvDBBx/gzJkz2Lt3L6ZNm4aoqCjY2srzzY8ePRrp6emYPHkyLl26hOXLl+P333/HhAkTlOOYOHEifvzxR6xbtw4XL17EmDFjUFBQgMjISIP8XAghhpGU/gjFnOksXmrkruPRVE9cXBwaNGgAOzs7dOjQodyqvbLWrl0LkUik8rCzs6wgkb5TLQGmX/g7M5dvhYyLvY0gr8c4Lxt52+lbDucKmTAB0p8B/ME+3uAhIcQ8fbAqSbC+XmnmilNf9BSsP0tG6bwsQKIGX8DtBLi7ghgfK7EILeo64SxHJJtSehmn6Oho7Ny5E0eOHEG9evUqbGdra6ucpCT6FzswEFtSqk5pwEPKgNEbUrDy/RD0allHkD4J4ZGdnY2hQ4fi/v37cHZ2RuvWrbF371706NEDALBo0SKIxWIMGjQIRUVFCA8Px/Lly5X7W1lZYefOnRgzZgxCQ0Ph6OiIiIgIzJ49W9nGz88Pu3btwoQJE7BkyRLUq1cPq1atQnh4uLLN22+/jezsbEyfPh2ZmZkICgpCfHx8uWLzhBDzxnstY2dt3EXlN23ahIkTJ2LlypXo0KEDFi9ejPDwcFy+fBkeHh5q93FycsLly5eV/xYJtXzAROg71RJg2umWpDKG/RerTuEMAG41hLleqMUZTMriXPGhb7zBMN7gWlVqc/7c91+k1L+EWKriUhmOc6YxBYD2vi7o6u+O9YkZePCkFCIR4GRvg/AWXpjRrwXsJZTqVCgURLEAmhS9M/a7t4j2+rWuyxVEAYB/r2dTEMVIMMYwduxYbNu2DYcPH4afH39qNqJ/EmsxGrs74lp2gWB9Rv96GpfnetFFFNGbuLi4StNg2dnZIS4uDnFxcRW28fX1xe7duyt9nW7duuH06dOVtomOjkZ0dHTlAyaEmDXea5nW9ZyN+rty4cKFGDlypHI13cqVK7Fr1y6sXr0aU6ZMUbuPSCSqtA6UudN3qiXAtNMtGSLo5FaTLyhwwEiDAvoOmnlx1qLJfV6CExk5CDXiwDAhRDembD6jUfsNI0MhsRYjqntTHY2IKFA6LzMnlTGcvskXwZRYiYz67i1SPRGdGnC3PalB4I3oVlRUFDZs2IBff/0VNWvWRGZmJjIzM/H8uXEWsyTAjH4tBO2vVMYw9tcUQfskhBBCTIFUxnD2Ti5X2zrOxpvqqri4GMnJyQgLC1NuE4vFCAsLw7Fjxyrc7+nTp/D19YWPjw8GDBiA8+fP62O4RkPfqZYA016JYoigk6ZBAWOj76BZe7/acLbju5c5M4+u9wgxR1IZw4FzmXg5NgENpuxCgym70ChmF4Jn78PkzWewNZU/s0UHv1qQWNPUvr7QT9rMJaU/Qiln/tHu/sKdfBLjI7EWo5G7A1fb07dzqS6KkVixYgXy8vLQrVs31KlTR/nYtGmToYdGKtCpsRt3DSJeu9MyUVzKWdyKEEIIMRMnMnJQxHkxU7eWMHfW68LDhw8hlUrLpSP09PREZmam2n2aNWuG1atXY/v27diwYQNkMhk6deqEO3fuVPg6RUVFyM/PV3mYMn2nWtLkNY1xJYohgk6mHhTgrT0iVNDMSixCjwC+tKQ5BcYXqCOEVE982n00mbobw39Jxp28//7GpQx4/KwEv5+q+DtenfXDOwo9RFIJCqKYuZ8TM7jbDu3YQHcDIUaBt65CqQxIvErF7IwBY0ztY9iwYYYeGqmAlViEqG6NBO93yhbNlvUSQgghpo73znrA/NISh4aGYujQoQgKCkLXrl2xdetWuLu74/vvv69wn9jYWDg7OysfPj4+ehyx8AyxKsSUC38bIuhkykEBqYwh4UIWV9vajsIFzXh//sYYqCOEaC8+7T5Gb0iBUPcr0yoU/aOfthmTnxTwFZazFlMqL0vQSYMT5pk703Q4EkLM27gezSD0ur5tp+/RCjFCCCEW5eETvjvr7W2Mu6i8m5sbrKyskJWlOmGblZXFXfPExsYGwcHBuHbtWoVtYmJikJeXp3zcvn27WuM2NEOsCtG08LcxMVQqMlMNChiihgxg2injCCHakcoYRm8QNkU3rULRPwqimLHEaw/Bm/wloE5NSuVlATo2dAXvr/l69jNKH0SIlqzEIix5K1DQPhmAJQlXBO2TEEIIMWanbjziatelqbtRX8tIJBK0adMGBw4cUG6TyWQ4cOAAQkNDufqQSqU4d+4c6tSpeGW5ra0tnJycVB6mTN+plgDTrvFhqFRkphoUMEQNGcC0U8YRQrTz6rcHBe3P29mOVqEYAP3EzdisHfyFB/sFeutwJMRYWIlFaOvrwt0+ZutZ3Q2GEDPXP6QeWtRxFLTPlUeuG91dj4QQQoguSGUMBy/xraq3t7HS8Wiqb+LEifjxxx+xbt06XLx4EWPGjEFBQQEiIyMBAEOHDkVMTIyy/ezZs7Fv3z6kp6cjJSUF77//Pm7evIkRI0YY6i3olaFSLZlyjQ9DBJ0A0w0KGKKGDGDaKeMIIZr7K+UObuTwpyflEft6K0H7I3woiGKmiktluJZdwN0+opOfDkdDjMnYV5pyt92ScpcmbAmphl3jusG9hnAXjMVShqTrfHflEkIIIaYsKf0RSjgXRXu78K0eMKS3334b3377LaZPn46goCCkpqYiPj5eWWz+1q1buH//vrL948ePMXLkSDRv3hx9+vRBfn4+EhMTERAQYKi3oFeGSrVkqjU+DBV0Akw3KGCIGjKAaaeMI4RoRipjGPe7sLVNxSLg5WbmVQfOVFAQxUx1nrefu20jdwdaBmZBOjV20+gPP2rDKZ2NhRBLcHJaD7ziL9xJzrpjGYL1RQghhBirRA0mXE2lqHx0dDRu3ryJoqIiHD9+HB06dFA+d/jwYaxdu1b570WLFinbZmZmYteuXQgODjbAqA3DUKmWANOs8WGooBNgukEBQ6UhM+WUcYQQzSxJuAyhj3qL3g4y6hSm5oxmzs2IVMZw6HwWGkzZhaynJdz7zXytpQ5HRYyN/O4qD+728RceoM3svXjKeVJOCClv9bD2uDi7F95s642aEhHEAOysxQioUxOrI9phXWQ77r4SLhjXBSghhBCiC3cf86VKkliJjLqoPNGOoVItAaZZ48OQQSdTDQoYKg2ZKaeMI4Twk8oYVvx9XdA+W3o7YUBQXUH7JPz4jtzE6O0+ex/Rv6VA03k1sQjo1ETY5anE+A3t5Ie9F/hyTAPAo2elaDlzLwK8HLB7fHcdjowQ82UvscL8wcGYP7j8XaRSGYMYAE/WEkWB+YnhzYQeIiGEEGI07jx+xtUusJ4z3ZFphgyVakmT1zamlSiGDDopggI8K2GMKShgqBoyipRxm1PuVtnWmFLGEUI0o0laUh41ba2w85POwnVINEYrUcxA7O4L+PhXzQMoAPB6kDdddFigjg1dYaPFX/+FzGdoNnWX8AMixMJZiUV4I8Sbu33c4Ws4kJaJXgsPodGUXWgwZReafL4LXb85iP0XsmilCiGEEJMmlTGk3srlauvtImxqImIcDLkaxBRrfBgy6GSKdWQMWUMGMM2UcYQQzcyPvyhYXy3qOOLcrF6C9Ue0Q0EUE7f77D18f0T7/PjzBgUKOBpiKqzEIozp2kirfYukQMjsfQKPiBASO5D/eCxlwPANybj04Bmk/7+tRAbczHmOET+fQqPPdyM+7X6lfRBCCCHGKin9EUo57weoW4uCKObIkKtBTLHGh6FTkJlaUMCQNWQAw/++CCG6VVwqQ+qdfO72/Vt6wN/DQZkuSgzAUWKF7s3ckTYzHLvGddPFMImGKJ2XCZPKGKJ+Pa31/h38alFBeQs2rkczLDt0XasiVznPShC55gTWRLYXfFyEWCqJtRiN3R1xLbtAkP5Gb0jByvdD0KtlHUH6I4QQQvTFHIvKE80YKtUSoHmNj1AjqMlj6BRkphYUMGQNGcDwvy9CiG59sCqJu60IwKJ321KWIBNAM+gmbOyvyVpNgCusH95RsLEQ02MlFmHJW9qvRDp0ORs7ztwTcESEkBn9Wgja38e/pBjNHZKEEEIIL97i01RU3jwZOtWSKRb+NmTQCTC9oIAha8gAppkyTt9mzpwJkUik8vD39zf0sAgBIP+eOnQ+C70WHkLjKbvQcMoutJgej8g1J5DztBjHbzzm7mtgSF0KoJgICqKYqOJSGXan8Z1YqjP8ZT9ahULQP6QeArwctN5/0uYzNEFLiIA6NXaDkKdPMgZE/5IsYI+EEEKIbkllDKdv8k0+UFF582ToVEumVuPD0EEnwPRWohiyhgxgminjDKFFixa4f/++8vHPP/8YekiEYPfZ+2gydTci15/CpQfPUApABqCgWIpDl7MRMjdBo/5iB7bWyTiJ8GgW3UTFbD2j9b6t6znhi9cCBBwNMWW7x3dHDYl2h4LCEhmSrj8SeESEWC4rsQhvBPEXmOex53wWiktlgvZJCCGE6Iom9VDa6SDNDjE8Q6daAkyrxoehg04Af1DgTq5xrNwxdNBH05Rxlsra2hpeXl7Kh5ubboJahPCK3X0BH/+aAqFim1RmwbTQb8oESWUM209rl0Zp+Mu++Cu6s8AjIqYubXZvuNbQ7gJg3bEMgUdDiGWbN1j7NHsV+eAn/pyshBBCiCFRPRRi6FRLgOEn2TVhDEEn3qDAX6n3jGJlhaHTj5liyjhDuHr1Kry9vdGwYUO89957uHXrlqGHRCzY7rP38P0RYee/qMyCaTGKIEpRURGCgoIgEomQmpqq8tzZs2fRuXNn2NnZwcfHB998841hBmlENLk7S8HJTowrc3vji9da6mZQxOQlT+uByE4NNN7P0pcYEyI0ibUYHRrUErTP4xmPaTUKIYQQk0D1UIihUy1pMgZjWIliDEGn9n61UdvRpsp2jwqKjWJlhaFryJhayjhD6NChA9auXYv4+HisWLECGRkZ6Ny5M548eaK2fVFREfLz81UehAhFKmP4+NfTgvZJq1BMj1H8tiZPngxv7/LpS/Lz89GzZ0/4+voiOTkZ8+fPx8yZM/HDDz8YYJTG4+dEzSKfKdN64OzM3vTHSao0o38LXJnbGxN6NOLeR8aAxKuWW/COEF1YP0L4O1KmbNE+DSQhhBCiD1QPhQDGsQrElAp/89Y5eamx7oJOVmIRBgTypaQ19MoKY6ghA5hWyjhD6N27N9588020bt0a4eHh2L17N3Jzc/H777+rbR8bGwtnZ2flw8fHR88jJuYs6tdTgvdJq1BMj8Fn1ffs2YN9+/bh22+/LffcL7/8guLiYqxevRotWrTAkCFD8Mknn2DhwoUGGKlxkMoY9l96wN1+/KtNUFvLNE3EMkmsxRj3qj86+vHfCb/04BUdjogQyyOxFiPypfqC9rnttHGkTyCEEEIqQvVQCGAcq0BMqfC3B2cqLd522qpXy4GrnaFXVhhDDRnAOIKFpsTFxQVNmzbFtWvX1D4fExODvLw85eP27dt6HiExV8WlMsSn8c/D8qBVKKbJoL+xrKwsjBw5EuvXr4eDQ/kv3GPHjqFLly6QSP47OQoPD8fly5fx+DHfHUrmJin9EaScGVnEImDsq010OyBitn7WICqecjvX4BcPhJibGf1awddVuAs3BmBJAgU8CSGEGC+qh0IAw6daAkys8DfvZZiOL9eMIfjFwxhqyACm8/MyFk+fPsX169dRp04dtc/b2trCyclJ5UGIEKZsFj6jA61CMU0GC6IwxjBs2DCMHj0abdu2VdsmMzMTnp6qeSIV/87MzKywb3POhajJhcUbwXVpiTvRmsRajMbujlxtpTIg6fojHY+IEMvz96RX8Kq/h2D9rTxynQKehBBCjFZ82n2udlQPxXwZS6olUyr8/YCzJgpvO22ZysoKY6ghA5hWyjhD+N///oe///4bN27cQGJiIt544w1YWVnhnXfeMfTQiAWRyhi2pd4TtM8PX2pAq1BMlOC/tSlTpkAkElX6uHTpEpYtW4YnT54gJiZG6CGYdS5E3gsLAIgd2FqHIyGWYEa/Ftxtf066obuBEGLBfhrWDhdn98Kbbb1RUyKCCICNWIS6LnaYHN4MaTPDufsqljIKeBJCCDFKxaUyXM9+xtWW6qGYL2NJtWRKhb95gwK87bRlKisreF+ft2aJtkwpZZwh3LlzB++88w6aNWuGt956C66urkhKSoK7O61CJPqTeO2hoIv4fGrbY7oG82zEuPDdWqGBTz/9FMOGDau0TcOGDXHw4EEcO3YMtraqXxxt27bFe++9h3Xr1sHLywtZWap3oSj+7eXlVWH/MTExmDhxovLf+fn5ZhFI0eTCopG7A0U2SbV1auwGMQCeDHKHLslP7uiClhDh2UusMH9wMOYPDlb7fGN3R1zLLuDqa92xDLzURLcXhYQQQoim1iXe4G5L9VDMl7GkWgLkk+ibU+5WPRYTCQroepyarKwY1KaeTsdSGWNZMaNpyrhQC1t9t3HjRkMPgRDM2nFesL5cHaxxdPIrgvVH9E/wIIq7uztXZHjp0qWYO3eu8t/37t1DeHg4Nm3ahA4dOgAAQkNDMXXqVJSUlMDGxgYAkJCQgGbNmqFWrYqLXtva2pYLzpgDTS4swltUHGQihJeVWIQ2DWrh5I2qaxAp7nCnyVlC9G9Gvxb4YPUJrrYJFx4g71kJxv92ConXclDEACsR4GRvg/AWXpjRrwXsJVY6HjEhhBCiasfZqierFageivkyllRLgPFMtlfFGGrIAJqvrDDUzXfGEnRSpIzjWXll6JRxhFii4lIZ942KAGBvLcLzUvXrVl5p5orVkVQHxdQZbKlC/fr10bJlS+WjadOmAIBGjRqhXj35XQnvvvsuJBIJhg8fjvPnz2PTpk1YsmSJyioTS0IXFsQQNLnD69/r2TocCSGkIopVYzwYgMDZ+3DoqjyAAgBSBjx+VoKNJ2+j+fR4DF97XFdDJYQQQsqRyhjO3+WrY2ktpnoo5sxYUi0B/EEB3na6YCw1ZADNV1YYirEEx0wpZRwhluiDVUncba3FQNrs3kibGY5XmrrC2c4atR1sMKSdDy7O7kUBFDNh1PmenJ2dsW/fPmRkZKBNmzb49NNPMX36dHz00UeGHpre0YUFMZROGlyg3H1Md8gI7ciRI+jXrx+8vb0hEonw559/GnpIxAhZiUV4I8RbsP4OXHqIrvMPCtYfIYQQUpmk9EeQciYdf8XfndLHmjFjmeAGAA/O4AhvO10wlhoywH8rK3gYcmXFncd8KdJ1HXQC+IOBhk4ZR4i5elpYisifjiFg2m74T9uDPkuO4ODFB3heLMVxjowsCh93bQwrsQg17Kyx+sOOODMzHCnTe2LeoNaU5cGMCJ7OS1sNGjQAY+XPnFu3bo2jR48aYETGhS4siKF0bOgKaxFQwapEFfdyKYgitIKCAgQGBuLDDz/EwIEDDT0cYsRiBwZiS8o9wfq7+eg5Zu84T4XvCCGE6NzPiRncbSNC/XQ4EmJoxpJqCQDAeUl98kaOwVIaG1MNGcXKCp46MoZaWSGVMWw/w3e+rOugE2BcQUNCLIlUxvDqgsO48ahsUJXhwv0n+HDdSY36EgEY16OpoOMjxsmoV6KQ/yRy5jkF6MKCCMtKLEKwb8U1iMo6cycPUhlntI9w6d27N+bOnYs33njD0EMhRk5iLUZjd0dB+1z97w0Ul8oE7ZMQQggpSypj2H/pAVdbWnFv/oylvgcAPOSsz7L22A2DXQMZUw0ZwPhXVpzIyEFOQUmV7VwdJToPOgFGFjQkxELEp91Ho893vxBA0d4bwd50I7uFoCCKieDNGSqxogsLIjzeE0hFcXliOEVFRcjPz1d5EMsxQwerRj74iT8XLCGEEKKppPRHkHLG6wPq1KSJCjNmTPU9AMCjJmeNj2eGq/FhTDVkAONfWcG7cqd/kH4mRXl/DrzBRUJI5eLT7mP0hhRB+5w3KFDQ/ojxoiCKCZDKGE7f5MvFF1jPmS4siOA0qYtCxeUNKzY2Fs7OzsqHj4+PoYdE9KhTYzdYCfwVcDzjMa1GIYQQojOapPLqFyhc/S9ifIypvgdgGjU+jC1oYewrK3hX7tRz0f3nCwBqc9bT2X/xAWV8IKSapDImeAClg18tSKxpat1S0G/aBCSlP+KqRwEA7fSw5JRYHkVdFB4nDXQXFpGLiYlBXl6e8nH79m1DD4nokZVYhEVvCn8nzJp/0wXvkxBCCJGvPOBL5QUAEZ0obbE5M6b6HsB/NT54GKrGh7EFLYx9ZYWx/by8nDhXOz033GonQsxF1K+nBO9z/fCOgvdJjBcFUUyAJvVQXmrkrsOREEtFdVFMh62tLZycnFQexLL0D6mHBrX5Lsh4rf6H/y5hQgghhFfitYfgXevoW9ue7vY0c8ZW3wMw/hofxlRDBjD+lRXGtnLHFFY7EWIOiktliE/jv2mDB61CsTz02zYBdx/zfVlSPRSiS1QXhRDTceB/rwjaX9aTYkrpRQghRHCzdpznbvt+R18djoQYA2Or7wEY36R7WcZWQwYw/pUVxrYSxRRWOxFiDj5YJXydT1qFYnkoiGIC7jx+xtWO6qEQXaK6KIbx9OlTpKamIjU1FQCQkZGB1NRU3Lp1y7ADI0bNSizCyvdDBO1zypYzgvZHCCHEshWXynAtu4C7vTmk8oqLi0ODBg1gZ2eHDh064MSJE5W2/+OPP+Dv7w87Ozu0atUKu3fv1tNIDcMYAxbGNulelrHVkAGMf2WFsa3cAYCXmvBlE+Fd5UMIUVVcKsPxG3x1pnl9+FIDWoVigeg3buSkMoaUm7lcbb31VPyMWCZN6qLsPZ+p28FYkFOnTiE4OBjBwcEAgIkTJyI4OBjTp0838MiIsevVsg5Wvh8CoWLrf6beo1R9hBBCBBOzlT8438jdweQnKzZt2oSJEydixowZSElJQWBgIMLDw/Hggfr0IomJiXjnnXcwfPhwnD59Gq+//jpef/11pKWl6Xnk+sO7WkJfqyoA467xYWw1ZADjXllhjCt3AMCDMzjC244QokroVSg+te0xvV8LQfskpsG0z0QtgCZ5guvWoiAK0R1N6qJcz35GqX8E0q1bNzDGyj3Wrl1r6KERE9CrZR1c/bIPfnqvDeo5SyCC/Ivfxd4GQ9r5IG1mOHhjLDIGJF41TBFQQggh5kUqY9h++h53+5mvtdThaPRj4cKFGDlyJCIjIxEQEICVK1fCwcEBq1evVtt+yZIl6NWrFyZNmoTmzZtjzpw5CAkJwXfffafnkeuPB2cqKN52QjDmGh/GWEMGMN46Msa4cgcAuE/GKekIIRoTehWKq4M1jk4WNnU2MR0URDFyyw5e4W5LReWJrmlyB9O6RCpETYgxsBKL8GorL/wT0wMZ8/oifV5fpM7oiXmDWqOGnTXeCPLm7mvmTvO9+5UQQoj+JKU/QinnfLNYBHRqor8aGLpQXFyM5ORkhIWFKbeJxWKEhYXh2LFjavc5duyYSnsACA8Pr7C9OTiRwVlXUY+xCmOu8WGMNWQA40zLBhjnyh0AeMA5Lt52hJD/aLLqtSqvNHNF8vRwwfojpoeCKEZMKmM4dSOXq62VGFRUnuicJnVRNiTd1OFICCFCmTc4kLvt9exneFpYirhDV9Dxy71oOGUXGsXsQvDsfZiy5SyeF0t1OFJCCCHm4mcNbrbR9130uvDw4UNIpVJ4eqqmOfL09ERmpvo0uJmZmRq1B4CioiLk5+erPEyFVMawLpHv+uFhAd8KDCEYc40PYw1WGGsdGWNducOb1uzfa7QinBBNSGUM21L4V722b+CCNR+0hb+HA2wAWImAWg7yDA4XZ/fC6kgqJG/pKIhixDRJ5RXi42LyFxfE+HVs6AorzqPGzZznlNKLEBMgsRajsbsjd/uWM/di/t6ryHxSChkAKQMePyvBxpO30Xx6PIavPa67wRK9WLBgAdq1a4eaNWvCw8MDr7/+Oi5fvqzSprCwEFFRUXB1dUWNGjUwaNAgZGWp5hm/desW+vbtCwcHB3h4eGDSpEkoLVVNo3H48GGEhITA1tYWjRs3VpuqUNNCzNoqLpXh+7+vYfCKRAxe8S9++Ps6fY8RogNSGcP+i+rrgKgTEWr6BeX1JTY2Fs7OzsqHj4+PoYfE7URGDnKfl3C19aipv3Rexlzjw1iDFaYe3NH3yh1jThlHiCnTZE4VADaMCEX3Fp6In9gdV+f1xfXYvjg9XZ7BwV5ipbNxEtNBQRQjNmvHee62n7zSVIcjIUTOSixCmL8Hd/uYrWd1OBpCiFBmCFgY78Clh+jyzQHB+iP69++//yIqKgpJSUlISEhASUkJevbsiYKCAmWbCRMmYMeOHfjjjz/w999/4969exg4cKDyealUir59+6K4uBiJiYlYt24d1q5di+nTpyvbZGRkoG/fvujevTtSU1Mxfvx4jBgxAnv37lW20bQQs7Zid19A02l7ELvnMk7dfIxTN3Px1Z5LaDptD2btOCfoaxFi6ZLSH0HKOQ9oLqvt3dzcYGVlVS7YnJWVBS8vL7X7eHl5adQeAGJiYpCXl6d83L59u/qD1xNjTbUEGG+ND2MNVvAGBXjbCcVYf17GnDKOEGP3tLAUw1YlovGUXWgwZRf8puxCwBfxiFxzAp/+kcrdT2A9J0isaYqcVI4+IUaquFSGa9kFVTeEeeQJJqZjaCf+uwG3pNylu2UIMQGdGrsJWqvyVk4hPlyrm9UCRPe2bt2KYcOGoUWLFggMDMTatWtx69YtJCcnAwDy8vLw008/YeHChXjllVfQpk0brFmzBomJiUhKSgIA7Nu3DxcuXMCGDRsQFBSE3r17Y86cOYiLi0NxsXxyYuXKlfDz88OCBQvQvHlzREdHY/DgwVi0aJFyLJoWYtZG7O4L+P5IxamF1vx7Cy/F7hPs9QixdPPjL3K3NZfV9hKJBG3atMGBA//dZCCTyXDgwAGEhoaq3Sc0NFSlPQAkJCRU2B4AbG1t4eTkpPIwFcaaagkw3sn3O4+fcbWr7ajf4I4HZ3CEt51QjHXljjGnjCPEmL227ChaztyLw9ceQ7HWnQF4ViLFocvZePCE/5g8Oby5TsZIzAsFUYxU65nx3G3b1DePiwtiGjRJ6QUA3emOdEKMnpVYpFGBeR4HL2Vjxxn+HLTEeOXl5QEAateW3/mbnJyMkpISlYLH/v7+qF+/vrLg8bFjx9CqVSuVfP7h4eHIz8/H+fPnlW0qK5qsTSFmTRWXyioNoCjczStB08930Y0BhFRTcakMqXf463SY02r7iRMn4scff8S6detw8eJFjBkzBgUFBYiMjAQADB06FDExMcr248aNQ3x8PBYsWIBLly5h5syZOHXqFKKjow31FnTKWFMtAcY5+S6VMWznPM/ycrbX8WhewDk1cfKGfldVHLvOV1NE38EwY04ZR4ixajVzL9LuClP3y1xWvRLdoyCKkZHKGJp+vguFpfwX6eZ0cUGMn6YpvW7lFuGl2AQdjogQIgRNCszzmvh7Kk06mziZTIbx48fjpZdeQsuWLQHIix1LJBK4uLiotC1b8LiigsiK5yprk5+fj+fPn2tViFnTosrrj92o9PmyimVAo893Y2fqXe59CCGqPliVxN3W3Fbbv/322/j2228xffp0BAUFITU1FfHx8cpj3K1bt3D//n1l+06dOuHXX3/FDz/8gMDAQGzevBl//vmn8lhsbox1tYcmr8k7SS+EExk5yCmouoaMq6NE7+nPHnKuKlp77IbezhOlMoaEC1lVN4T+V+4AxpsyjhBj9FLsPjwpLK26IacBgd50YzrhQkEUIxKfdh+NPt+NYg0qH5nbxQUxDZqk9AKAu3nFCJm9t+qGhBCDkViL0aFBLUH7LJEyJF7V34QCEV5UVBTS0tKwceNGQw+Fi6ZFlW/m8KVCKSt6YyqGrz2u7RAJsVjFpTIcv/GYu/0bwXXNblIjOjoaN2/eRFFREY4fP44OHToonzt8+DDWrl2r0v7NN9/E5cuXUVRUhLS0NPTp00fPI9YfY1ztoWCMhb95a8j0D9L/5KBHTc4aH8/0V+PjREYO8jgnXfW+cgfGHUQkxJjM2nEOd/OqDiBrYt4g4W8mJOaJgihGIj7tPkZvSNF4v9cNcFJESMeGrrDR8OiR86wUL39Nqb0IMWbrR3QUvM+ZO9ME75PoR3R0NHbu3IlDhw6hXr16yu1eXl4oLi5Gbm6uSvuyBY8rKoiseK6yNk5OTrC3t9eqELOmRZV9aztU+nxFDlx6iL6LD2u1LyGWKmbrGY3axw5sraOREGNkrKmWAOMs/M1bQ6aei/4DAsZY44M36ORib6P3lTuAcQcRCTEWxaUyrPn3lqB9dvCrRQXlCTf6pBgBqYxpFUABKGJKDMNKLMKYro003u/OYyo2TYgxk1iL0aslf7o+Htezn6G4VIMllsTgGGOIjo7Gtm3bcPDgQfj5qa4+bNOmDWxsbFQKHl++fBm3bt1SFjwODQ3FuXPn8ODBA2WbhIQEODk5ISAgQNmmsqLJ2hRi1rSo8gehDar4aVTsfGYBOn1FBecJ4SGVMWxN4a+TRZMalsXYUy2196sNF3sbrrYPnvBN1leXMU+6G2OND96gU1hzD4PcpGqMKeMIMTaapATltX648DcREvNFZ6ZGYPCKf7Tar3cLT7q4IAYzrkczrQ4gVGyaEOMW925bwfucskWzu4+JYX366afYsGEDfv31V9SsWROZmZnIzMzE8+fyu0WdnZ0xfPhwTJw4EYcOHUJycjIiIyMRGhqKjh3lFyI9e/ZEQEAAPvjgA5w5cwZ79+7FtGnTEBUVBVtbeVqU0aNHIz09HZMnT8alS5ewfPly/P7775gwYYJyLFUVYq4uibUYw1/21Xr/e/klaD5tlyBjIcScLUm4DE2SHNGkhmUx9lRLVmIRIjrxfVe4OfKl/qouY0//ZGw1PniDby81NkyqdGNMGUeIMdE0JSiPD19qQHOqRCP0aTGw58VSnL5dedFTdcQAvnuvjfADIoSTlViEpUOCtNp3/KbTdPJHiJGyEouw8v0QQfv8M/Ue/c2bkJ9++gl5eXno1q0b6tSpo3xs2rRJ2WbRokV47bXXMGjQIHTp0gVeXl7YunWr8nkrKyvs3LkTVlZWCA0Nxfvvv4+hQ4di9uzZyjZ+fn7YtWsXEhISEBgYiAULFmDVqlUIDw9XtqmqELMQvnitJVrVran1/s9LgYZTdtFnnJAKSGUMSw9d524fWM+JJjUsjLGnWgKA9n6ufA31tIjBmFeiAMYX5PHgTMnG205oxpgyjhBjIvQqFJ9a9pjer4WgfRLzx5eokujMwDjtVqF8924I1UIhBvdaUF1sS72DA5c0W1YslQFLEq5gYngzHY2MEFIdvVrWwcr3QxD9awqEyMQlY0Di1Yfo3My9+p0RncvLy6syDZadnR3i4uIQFxdXYRtfX1/s3r270n66deuG06dPV9omOjoa0dHRlbaprh1ju2D42pM4cOlB1Y3VkAFo9PlufDckCK8F1RV2cISYuKhfT2nUfnJ4cx2NhBgrY0+1BAAPOAM9vO2qy5hryABGGOThvc/BQPdDKOrI8KzI0lcdGUKMhdCrUGpIxDj62SuC9UcsB93iY0DFpTJczHqq8X6j/q+9O49r6kr/B/7JDYZFZRUE1Cq4IS6AG+JWdxR0ahendrHVOnbq1KXVaX9gLe7L2NVa+3W6uHQbO0s70yrFUrWLitqqaF2gilgUBauoFBUiSX5/UKhUhHOTm+Te5PN+vfJ6tXByOJFwkpznPs8zMAJJ3cLssCIi+d6ZGI8uYU1k32/NN3m8apdIxUZ2CUPu4iSsm9ATUSE+8EDVxZVeHhKiw5pi7aO9cHdMuPB8bDBPavfOxF5Y9UCcTXNM25iNx9YpX6+ZSKuMlWZkHBYPThr0OvRpK3jFP7kM0YN00RJR9iDau8MRPT7U3kMGUF+Pj/OCgTrRcUpTYx8ZIrVI/Vi50tCdwxrj8MJRis1H7oWZKE5kzUbwxoNxSOomfmhF5AibZt6J0a99g8NnfxG+j9Fkwe68i+jX3nkfhoiofnpJh8Gdm2Nw57o/1PVv3wyfCPY4qm4wzxItpGZjYsKR1DUM0c+no8Jk3Rzbci+i39IvsHPOCGUXR6RBySu/ljX+iYFtmW3vhtRW+qkuasqsUHsPGUB+jw97/93vPP6z0DjRrCh7SGjbDP/eX9jgOGeVaCNyBpPZgo/3i/fUjQj0RJugxth1ogQVlqrMAW+DHr0jArHqge5o4sVjcLIenz1OYjJb8L8D4huBDsCJpUn8UEGqtWnGQExauwfbfxS/mmhDVj6DKEQaZvCQ0C64MU78fFVo/LqdJzG+V2s88vYu/HC2DBYA3o30iI/km1pSD72kQ+6SZPRb9iUKr1h3mFJYegMd5mzGscV870bua1N2IY7/fE14vKQDZg7vYMcVkVqpKUBxO3IyK+7t0dKua9FCDxm5PT4S7JiBpoXMHUAbwUQiR1uZmSuryt6WWUN40R7ZDZ9ZTrL75EVUytgJcheP4odwUr11j8UjtmX9dfRvti3nZ5b0ItK4eTIa8i37PBcxC7/AwbNlMKOq7PS1GyZsz/0ZXeZvQfLKr+y1TCLZdqYOQ+dw6xvOG81VfVI2ZTd8VSmRqzGZLZixMVvWfV65P5afd9yU2vt7APIzK+xJCz1kqnt8iLB3jw8tZO4A2ggmEinNZLZg+5FijHx5O9qlbEZEymZEzf0cSSu/wZdHivHa9jzhuWJa+jKAQnbFZ5eT7JJR+5MbAWnJf/7SX3hspbmqpBcRaVffdsplkx05dxVd52UoNh+RrTbPGIghHYNtmmPaxmxMXr9HoRURacPKzFyYZYwP8/XEXbEt7LYeUi+tZAnIzaywJy30kFFTjw8tZO4AzEQh95Nx+Bw6zk3HpPe+R875a6hE1UV25ZVmHD33C/703vey5ns2sZNd1klUjSfzTpJx+JzwWG4EpCV6SYd7u4v37Xl39yn7LYaI7E4v6dCrtb9i8/1SYcLo175VbD4iW62d1BuT+0fYNMfWnAtIfvUrZRZEpHIms0XWlaMA8PWzQ+y0GlI7rWQJqCmzQiuH7aJBHHtnVmghcwcQz3Y6c9m+zy8iR8g4fA5PvL8flXKuuKiHQa9DHzuWBSQCGERxCmOlGXmC9YG5EZAWLbsnRnjs9hz7p7wTkX3NGKpsDfvDZ0vxP5ZAIhV5fnQ03niwu01zHCm6ir5Lv1BoRUTq9eoXubLGx0cEMOteBUxmC7LyLuJ/2YXIyrvosPfnWskSUFNmhVbKPqkl2KOFzB1APNvp0+yz/PxMmmYyW/DE+/sVnfOJgW1ZEpTsju9WnWDDrlPCYwdHOfdqCCJrGDwktA32ERprNLGkF5HW9W3XDEq/Uj39UTY/IJKqJHULQ97SJHjqrZ/j7K8N5/ncJldlMluw6it5WSjvTe5jp9WQqIzD59D/b9vwwFu7MXNjNh54azf6/22brOoJ1tJKlgCgnswKLfSQAdQT7FFLMKchvSMCEdi4UYPjLl412r1kHJE93fd/OxSdT9IBM4cre1EfUV0YRHGCzw6JX137SJ829lsIkR2N7BImPHZn3s92XAkR2Zte0uHuWPEyfiLMFmBl5o+KzklkK72kQ+6SZLT0F7tatC5sOE+uLH6JvGyrpC6hzEJxsozD5zD1/f04d6V2Rsi5K+WY+v5+uwdSRPuc9FOwB5u11HAYr5UeMoD4v4NoUMhaagnmNEQv6XBXjNj7aXuXjCOyl+tGEw6cLlV0zmmD2jk9yE7uwenvWDdv3oz4+Hh4e3sjICAAY8eOrfX9goICJCcnw8fHByEhIXjmmWdQWSlWM1WNTGYLjhSKbRgeEkt5kXb1lZEO/R2vpGnQ6tWr0aZNG3h5eSE+Ph579+519pKIall+n3gZP1FvfH2CV+yTKu1IGYohUbY3nH9s3W6FVkTkfAs++wEXrsr7nLbKxjJ5ZBuT2YIFnx3F7V5pLQAWfHbUrq/FIYIljETH2ZMaDuO10kMGEO/x8eUx+5Z31krmDgC0DBCr5mDvknFE9nLPamWzUPQSs1DIcZwaRPnPf/6DCRMmYNKkSTh48CB27tyJBx98sOb7JpMJycnJMBqN2LVrFzZs2ID169cjLS3Niau2ze6TF2ESfH8wJCqY0VTSrD6RQWgkuMMcPHOFB6X1+OijjzBr1izMmzcP+/fvR0xMDBITE3H+/HlnL42ohsFDwqR+dyg6Z6UZ2HXcvlcnEllr7cTeWPVAnE1zbMu9iH7sk0IuwFhpxrqdBbLuM30Q65c72978klsyUH7v3JVy+5YOEv0IoIKPCmrIrNBKDxlAvMfH5es37PYc01LmDqCOQB2RvRgrzThWXKbonK/eH8f3EuQwTguiVFZWYubMmXjhhRfwxBNPoEOHDoiOjsYf//jHmjFffPEFjh49ivfffx+xsbEYNWoUFi1ahNWrV8No1GbkfZeMN1SPJkTYcSVE9qWXdBjaSaz5Ivui1O/ll1/GlClTMGnSJERHR2PNmjXw8fHB2rVrnb00olrmjemKFn4N13KWY/6mw4rOR6SkMTHhyFuaBIMN76gLS28g6rnNyi2KyAl6LZYXDNQBeGpER/sshoSJlgSyZ+mg84I9UUTH2ZMaMiu01EOmd0Qg/Lw8hMba6zmmpcwdQB0l44jsZcLbymZgD+4YjDGCJfCIlOC0IMr+/ftRWFgISZIQFxeHsLAwjBo1CocP/3ZYkpWVha5du6J5898OYhMTE1FaWoojR444Y9k2E73CwqBnKS/SvgkJbYTHsi9K3YxGI/bt24dhw4bVfE2SJAwbNgxZWVlOXBlR3XamjkBTwQ/MIvJ+vgZjpVmx+YiUppd0+HFpMgK8rX/el5uANilsOE/a9Ni63bhSbpJ1n1fHxzr9gJfESwLZs3SQaFBAdJw9qSGzQjQDIUFGaWV70Us6DI8Wu6jOXs8xLWXuAMxEIddlrDRjz6lLis0X2LgR1k3qrdh8RCKcFkQ5efIkAGD+/PmYO3cuNm3ahICAAAwaNAglJVVvOIqKimoFUADU/H9RUdFt566oqEBpaWmtmxqYzBYcOnNZaGxMSz9+sCDN6xMZBA/Bp3HhJTbHq8uFCxdgMpnq3Avr2gfVuv+Re/lhfiK6tPBVbL6U/xxUbC4iezkwLxEtA2yr2c+G86Q1m7ILsS1XXjZxRJAP7optYacVkRxqOLBVwxpEqSGzQmuZCqLBHHv9frWUuQOoo2QckT2k/Fu5z3NNDBL2Pz9CsfmIRCkeRElJSYFOp6v3lpOTA7O56qrS5557Dvfeey969OiBdevWQafT4V//+pdNa1i2bBn8/Pxqbq1atVLiodlsb34JKirFrjDspYKrIIhspZd0iGsdIDS2oXrMJEat+x+5n03TB+Dw/EQMbh8Iz18/kzaSdGjh74VnEzviyTvbCs/13+yzvEKfNGHH/2PDeXIfJrMF0zZmy77fl7MHKb4Wso4aDmy11PRbDZkVWgo6Ac4P+mgpcwdQR8k4IqWZzBZ8nH1WeHx9vXUHdwjE4YWjFFgVkXzK1dv41ezZszFx4sR6x0RGRuLcuXMAgOjo6Jqve3p6IjIyEgUFVU0JQ0NDsXfv3lr3LS4urvne7aSmpmLWrFk1/19aWqqKg0TRVFIA6NfWtg/gRGrROyIQ3wmkbR76tbm8Gq4AUpNmzZpBr9fX7H3ViouL69wH1br/kXtq4uWBdZMT6vyeyWzB6q/zhOYxW6oazA/oyNdGUr+1E3vjs4NnMf0fB6yeo7rh/M45vMqO1Kvz8+my77PqATaAVRO5B7ZK/+601vQbqDps//f+hjMG7RXE0FLQCXB+0MfZQRy55JaMS2AJeNKAlZm5ssYfWTgKJrMF8z/7AV/nXoAOwKCOIUgb0xneBr19FkkkQPEgSnBwMIKDGz7k6NGjBzw9PZGbm4v+/fsDAG7cuIFTp06hdevWAICEhAQsWbIE58+fR0hICAAgMzMTvr6+tYIvv+fp6QlPT7E3hI504RexVFLvRhL7oZDL6Nu2GVZvb/igtLzSjN15F9GvvTquAlILg8GAHj16YOvWrRg7diwAwGw2Y+vWrZg2bdot49W6/xH9nl7S4Z7YcOGrkuZvOoxNEQOR9ukhZBw6h6tGCwweEiKDG+OvI6JwZ8dgHsyRaoyJCUdS1zBEP5+OCnmtImoUlt5AhzmbcWxxEp/bpDpJr2yHzDYoiGvlxwawKuPsA1utNf0GnHsor8Wgk5xsp3t7tFT85zs7iCNXdck4kb8Le5WMU5vVq1fjhRdeQFFREWJiYrBq1Sr07s1eGGpTVl6J6R98h90nS1Bp0SHE1xMPxbfGpH4ReE3gPKhafEQADB5VqSh/uzfWTqslso7TeqL4+vriiSeewLx58/DFF18gNzcXU6dOBQCMGzcOADBixAhER0djwoQJOHjwILZs2YK5c+fiySef1OQh4fenxOoFD+zAgyByHX0ig2DQiz2f2Vy+brNmzcJbb72FDRs24NixY5g6dSquXr2KSZMmOXtpRDZZfl+M8Ni8n6+hU1oG/vX9WfxitMCMquDr0XO/4LEN36H9nHRkHD5nv8USyaSXdMhdkoyW/tb3STGa2SeF1GfBZz/gaPE12ff799R+dlgN2cLZPT601vQbcO6hvBaDTs4uT6W1TBQ1lIxTk48++gizZs3CvHnzsH//fsTExCAxMRHnz5939tLoJqNf+xZd5m/B9uMluG4CbpgtKLxcjhVbctEpLUPWXO9N7mOnVRLZzmlBFAB44YUXMH78eEyYMAG9evXCTz/9hG3btiEgoKqHgl6vx6ZNm6DX65GQkICHH34YjzzyCBYuXOjMZVvFZLZgW47YRu/diOlp5Dr0kg4xLf2Exn6XX2Ln1WjT/fffjxdffBFpaWmIjY1FdnY2MjIybmk2T6Q1Bg8J7YIbKzKXGcAT7+9nIIVUZ0cK+6SQ60g/dBbrdhbIvh/LeKmTsw9stdb0G3BuHxktBp3kZjspTWuZKIB4fxY1rdleXn75ZUyZMgWTJk1CdHQ01qxZAx8fH6xdu9bZS6NfRT3/OQ6fLVVkrrbBPjVZKERq5NRnZ6NGjfDiiy+iuLgYpaWlyMzMROfOnWuNad26NdLT03Ht2jX8/PPPePHFF+HhoXgVMrvbffIibpjFxobbcMUikRq1CPARGnfw174odKtp06bhp59+QkVFBfbs2YP4+HhnL4lIEfPGdG54kAx/+WA/9xFSnbUTe2PVA3E2zbEt9yL6L8tUaEVE8pnMFvzlQ/m9foZEBbOMl4o588BWa02/AedmVmgx6OTsbCet9ZABtJc9Yy9GoxH79u3DsGHDar4mSRKGDRuGrKwsJ66MqkXP3Yxy0YNOAfNHd1FsLiJ7YIjPQXbJuBKFTeXJ1bQIEEsnN5os2J0nVvaOiFxD33bNoOTHfLMFmPbBPgVnJFLGmJhw5C1Nsun5fuaKEbHzP1dsTURydLKikXxLfy+sncja9WrmzANbLR4WOzOzQotBJ2dmO2mxhwygzewZe7hw4QJMJtMt1ReaN2+OoqKiW8ZXVFSgtLS01o3sJ+nV7bgmVl1QiIcE9GV/XFI5BlEcRPQNlEGvY1N5cjl9ZbyRZ18UIveil3S4O1bZK5Q/P1IMY6VyV0URKUUv6ZC/PBk+NpQquFxuRvs5m5lxRQ4VM/9zGGU2kveQqsrZEVBSUoKHHnoIvr6+8Pf3x+TJk1FWVlbvfQYNGgSdTlfr9sQTTyi+Nmce2GrxsNiZmRVaDDoBzst20mIPGUC7v2dnW7ZsGfz8/GpurVq1cvaSXNan+8/gaJH83mj1+cud7VSTQUd0OwyiOIDJbMGBny4JjY1p6ceNg1xOn8ggeAg+rQsvKZ/GTUTqJqfBvKgJ77B/BKnX0cWjbGo4f4MN58mBYhdk4Eq5/MD00YWj7LAabXrooYdw5MgRZGZmYtOmTfjmm2/w+OOPN3i/KVOm4Ny5czW3FStWKL42Z/b40GKpJWdmVmgx6AQ4LyigxR4ygHZ/z0pr1qwZ9Ho9iotrZxMVFxcjNDT0lvGpqam4cuVKze306dOOWqpbMZktmPnPg4rOqZeAmcM7KDonkT0wiOIAu09eRKXgxYK9VPTiTaQUvaRD7B3+QmPPXmYQhcjdGDwkdGreRNE59+RfYjYKqRobzpMWxC7IwOXrMlNQADzWrw2bw/7q2LFjyMjIwNtvv434+Hj0798fq1atwsaNG3H27Nl67+vj44PQ0NCam6+vr+Lrc1aPD62WWgKcl1mhxaAT4LyggBZ7yADODWyqicFgQI8ePbB169aar5nNZmzduhUJCQm3jPf09ISvr2+tGylvZWYulM6FfvX+OFX9DRLdDt/ZOgD7oRABLdlcnojq8fGT/RWfM/XjQ4rPSaQkpRrO91v6hUIrIvqNtQGUVoHeSBvT2Q4r0qasrCz4+/ujZ8+eNV8bNmwYJEnCnj176r3vBx98gGbNmqFLly5ITU3FtWvKlk8BnNfjQ6ullgDnZFZoOejkrKCAFnvIAM4LbKrRrFmz8NZbb2HDhg04duwYpk6diqtXr2LSpEnOXppbMpktWP1VnqJzDu4YjDExypZ2JrIXBlEcgP1QiNhcnojq523QY3AHZbMxPzlQ6PIfLkn7qhvOe+qtn6Ow9AY6sE8KKSjOygBKgLcHvn12iB1WpF1FRUUICQmp9TUPDw8EBgbW2Ry52oMPPoj3338f27dvR2pqKt577z08/PDD9f4saxorO6vHh1ZLLQHOyazQctDJWUEBrfYWcVZgU43uv/9+vPjii0hLS0NsbCyys7ORkZFxS7N5coxdJy7ApOBbzcDGjbBuUm/lJiSyMwZR7Iz9UIiqsLk8ETVk3WMJ8BU8yBFhtgC7jrt2qQNyDXpJh9wlyWjhJ3bQVBfjr31S0g/VXx6IqCH9lmXikhUBFIMEHJiXaIcVqVNKSsotjd9/f8vJybF6/scffxyJiYno2rUrHnroIbz77rv45JNPkJd3+6uArWms7KweH1ottQQ453Bey0EnZwUFtNpbxFmBTbWaNm0afvrpJ1RUVGDPnj2Ij4939pLc1vxPjyg2VxODhP3Pj1BsPiJHYBDFztgPhaiKnObyW47c/qo8InJth+YnoksL5WoYz990WLG5iOxtZ+owdA5vatMcf/nwABbxeU9WSnr1KxRese7g99jiJIVXo26zZ8/GsWPH6r1FRkYiNDQU58+fr3XfyspKlJSU1Nkc+XaqDw5PnDhx2zHWNlZ2Ro8PrZZaApxzOK/loJOzggJa7SHjrMAmUX2MlWbkXbiqyFyDOwTi8MJRisxF5EgMotjZu7vyhceyHwq5Mr2kQ1zrAKGxeT9fY0NoIje2afoAHJ6fiMHtA+H56zmABKCxQY/BHYPx9iM9673/zbifkNZsnjEQQ6NCGh5Yj3d2/IRJa9lwnuRJXvk1jhZZd0DyxoPdVXdwa2/BwcGIioqq92YwGJCQkIDLly9j3759Nffdtm0bzGazrCuqs7OzAQBhYWG3HWNtY2VnZFZotdQS4JweH1oOOjkjKKDlHjKAcwKbRPVJ+fdB4bE6AIfnJ+K+HmHw9gD0OqCppx5/7NkSxxaOxLrHEuy3UCI7Uq5mBt3CZLbgy5zzDQ8E4CGxHwq5vt4RgfjulFh5uw278jFlYFs7r4iI1KqJlwfWTa77DbbJbEEjCbghGBtJ/fgQXvpjrHKLI7Kzdyb2wmcHz2L6Pw5YPcf2H6sazu+cw1IJ1LDklV/jyLkyq+47ZUAEkrrd/mDf3XXq1AkjR47ElClTsGbNGty4cQPTpk3D+PHjER5e1Uy3sLAQQ4cOxbvvvovevXsjLy8PH374IZKSkhAUFIRDhw7h6aefxsCBA9GtWzfF1+iMzIqCErGAnRoPieX2+FAiwKjloBNQFRT49/7CBscp9fvWcg8ZQPu/b3ItJrMFH2eLl4u9Oy4cTbw88OK47nhxnB0XRuRgDKLY0e6TF2ESPOCJDmvqdldvkfvp27YZVm+/fR3nm72/+ycGUYioTnpJh6l3tsVrgvvJx/sLsXhsV6zdmYf3duXj518qIUk6hPh64qH41vjTgEgYPJicS+oyJiYcSV3DEP18Oirkt6cA8FvD+WOLk/g+k24raeXXOGplAGVy/zZ4Ljla4RW5ng8++ADTpk3D0KFDIUkS7r33Xrz22ms1379x4wZyc3Nx7do1AIDBYMCXX36JV199FVevXkWrVq1w7733Yu7cuXZZn5zMint7tLT555nMFnx8oOEDdUCdh8Rye3wkKHCxpFb7e1RzdFBAyz1kAPHfY0HJNTuvhNxFWXklpn/wHXadKEHFry0JGklAuL83msnM1lp+b4wdVkjkfAyi2JGcUl5jYsLtuBIidegTGQS9BKHg4k8l12GsNPNgk4jqNHN4R+EgigVAp7SMWl8zmS0ovFyOFVtysWJLLqYMaIPnkjvbYaVE1qtuON9/+VacuSx2IPR71Q3nXx8fi9GxLRReIWldv+VfovCyWK+F35vcvw2eH819U0RgYCA+/PDD236/TZs2sFh+a6TZqlUrfP31145YGgDxzIr0w0X42322Z1bszS/BL+Vi0WE1llqq7vEhkumgVI8Prfb3qOboIJCWe8gA4r/HTw4UIm1MZ1U+BtKO0au+xeHC0lu+fsNcdS7zU4n4PhYfEcAzHHJZfGbbiclswZajYqW8AODRvhF2XA2ROuglHYbJqPM+4R3WcyeiuuklHRKjbesbcbO3vj2FKe9+p9h8RErakTIUQ6Js6503bWM2HlvH11X6TdyCLVYHUCb1YwDFlYhmVlwzmrA776LNP080SwBQZ6klR/f40Hp/D8DxfWS03EMGEA9slpZXYm9+iZ1XQ66s2/wtdQZQrPXe5D6KzUWkNgyi2MnKzFzhsc2bGhipJbfxiIyA4Z78S2wITUS3JWc/EZF59Dw+Oyhe75fIkdZO7I1VD8TZNMe23Ko+KUSd5qbj0nWxfgG/N6RjMOaNYQDFlfSOCERjg15obNZJ2w+5RbMEfL08VFlqCXBs42+t9/cA5PeRsZXWe4qIBjYB5bKdyP0kvbodpYJ7i4i2wT482ySXxme3HZjMFuESI0BViSMid9EnMgh6GdnGPRdtsd9iiEjT+kQGoZHC72Rm/OOAIh/eiexhTEw48pYmQeyos26FpTfQae5mxdZE2tM+dTOuV1q3z3UJb4q1k3orvCJyNr2kw4D2YkEBJV4iRQMLd8e1UG2ZIkce0mu9vwcgv4+MrbTeQ6Z3RCCaeom92iuR7UTu59P9Z3C0SNmeOvNHd1F0PiK1YRDFDmLmZzQ86Cb3dW9lp5UQqY9e0uEuGT2ASivMSH7tGzuuiIi0qrrBvJIsAKZ9sE/ROYmUpJd0yFueDB8bIojXK4HIlM0MGLqhdimbccPKX3vnsCbYNGOgsgsi1ejRWuzwPUCBQ2fRwMIdgT42/yx7ceQhvdb7ewC/9ZERoURmhdZ7yOglHe6JE+tjptZAEKmXyWzBjH8eVHTORnod+goG44m0ikEUhfVfnokyo3j5IQ8J3GjI7Sy/L0bW+CNnf8Hk9exVQES3mjm8I5Q+Lvj8SDFLCZLqHV00Ci39xct9/J4ZVQ3nN2UXKrcoUi2T2YLIlM2wtmhH57Am2DzzTkXXROoi2ktDiZ4bWs8SABzb40Pr/T0Ax/aRMZkt2PzDOaGxau0hAwB3BDYWGqfWQBCp15Mffq/4nC//MVa1QVwipTCIoqAFn/2AM5flvYD95c523GjI7Rg8JMS3CZB1n6057FVARLfSSzpMH6xsNgoApH58SPE5iZTGhvMkIv3QObSdkw5rQ8MMoLgHRwYFtJ4lAIj3+Eg/XGRz1p/W+3tUc1Qfmd0nL+L6DbEdT609ZADXCDaS+hgrzcg4fF7ROQd3DMYYGdVGiLSKQRSFGCvNWLezQNZ9JB0wc3gHO62ISN3e+1Mf2fd56iP2KiCiW80c3lHxNzSfHCjkfkOawIbzVJ9Fm47iLx/ut/r+XRhAcRuOavxtMluQebRYbE0qzhIQ7fFxzWjC7ryLNv2sgpKrQuPUfpjuqGBQluC/dxNPD9X2kAEcG9gk95H6sbJlvAJ9GmEde6WRm2AQRSET3pZ/Bd8r9zPdjdyXwUPCyC4hsu5jMgMrM3+004qISKv0kg6vjY9VdE6zBdh13D0/lO7cuRNjxoxBeHg4dDod/vvf/9b6vsViQVpaGsLCwuDt7Y1hw4bh+PHjtcaUlJTgoYcegq+vL/z9/TF58mSUlZXVGnPo0CEMGDAAXl5eaNWqFVasWHHLWv71r38hKioKXl5e6Nq1K9LT0xV/vK6guuG8pw0d5wtLb6DDHPZJcSWT1u3BOzvyrb7/kI7NsIkBFLfhqMbfe/NLcKVcrLCcmrMEekcEorFBbNPNOmn9+wmT2YKPD4iVXVR7JopokKegxLZm1xaIvY71bx+k6vMYRwU2yX2YzBZ8vF+56h6eemB/2gjF5iNSOwZRFGCsNGPPqUuy7hMR5IO7YsUahRG5qtUP9pR9nze+PsE3iUR0i9GxLTA8Wl5gtiGvbXPPoO21a9cQExOD1atX1/n9FStW4LXXXsOaNWuwZ88eNG7cGImJiSgvL68Z89BDD+HIkSPIzMzEpk2b8M033+Dxxx+v+X5paSlGjBiB1q1bY9++fXjhhRcwf/58vPnmmzVjdu3ahQceeACTJ0/GgQMHMHbsWIwdOxaHDx+234PXML2kQ+6SZJv6pBjN7JPiKvr/bSu251p/cDu5f2usnRSv4IpI7RzV+LuotLzhQQD8vRupOktAL+kwQLC3qS0fXfbml+CXcpPQWDVn7gDiQR5bs4H9vRsJjetxh7zy0o7mqMAmuY+VmbmCIcaGRYf6IHdJskKzEWkDgygKsCYL5cvZg5RfCJHG6CUdXpd59Xil2X2vDiei+r31SC9MGRCh2Hz7T192y6Dt8OHDsXjxYtx99923fM9iseDVV1/F3Llzcdddd6Fbt2549913cfbs2ZqMlWPHjiEjIwNvv/024uPj0b9/f6xatQobN27E2bNVV7998MEHMBqNWLt2LTp37ozx48djxowZePnll2t+1sqVKzFy5Eg888wz6NSpExYtWoTu3bvj9ddfd8i/g1btSBmKwR1tay48bWM2Jq/fo9CKyNHiFmzBmUtiB9V1eX18HJ4f3UXBFZEWOKrxd0lZhdC4YZ1CVJ0lAAA9WosFeQJsKLMlGnQC1J25A4hnVpSWV9oUFAhsLPZzRMc5i6MCm+QeTGYLVn+VJzw+0FuPcT3D0dRQtQ9LABob9BjcMRiH5yci/anBdlopkXoxiGIja7JQ3niwu+rfEBI5yujYFhgaJe+wZ6WbXh1ORA17LjkaPy4ehV5t/Ov8/ojoZtALvgSbzLC5jrmryc/PR1FREYYNG1bzNT8/P8THxyMrKwsAkJWVBX9/f/Ts+Vu24bBhwyBJEvbs2VMzZuDAgTAYfjtYSkxMRG5uLi5dulQz5uafUz2m+ufQ7a2bFI/J/W0LKG7NuYDkV79SZkHkMJ3mpuPSdbFSSXV548E4jI5lc1h35YjG36L3FV2LM4lmftiSISIadPL1Und/D0A8swKwLSgg2iNE7eXPHBXYJPew68QFmGRcG7bqwZ544b44/LAwCaeWJ+Pk8mQcWTgS6yb1RhPB4B6Rq+Ez30ZymzJN7h+BpG5hdloNkTa9MzEeg1ZsxakSsSut9v1UdXW4OwQjlyxZgs2bNyM7OxsGgwGXL1929pKIVM/gIeFfT/SDsdKM97JO4aeSa2gd6IMJCW1g8JDw53e/w5aj54XmWrHlGP7XfoCdV6wdRUVFAIDmzWt/qG/evHnN94qKihASUru0moeHBwIDA2uNiYiIuGWO6u8FBASgqKio3p9Tl4qKClRU/HbgVFpaKufhuZTnR0ejxx0BNjUVP1J0FX2XfoFdc1jvWu1MZgs6zEmHWNGfuq15uDtGduHnFHdWclXswF50XN33FTvs1cKhsJzG3/f2aGnVzxANOt0d10L1n416RwSiqZdeqDyZtb9/k9mCzKPFQmPVXv4MqAom/nt/wyU2bQlskusoK6/E9A++w64TJaiwAI0kHUJ8PfFQfGts2HVKeB6DXoc+bYPst1AijWImig1MZgv+d0C8KVO7YB88Pzrajisi0q6tfx0iPNYC9ynpZTQaMW7cOEydOtXZSyHSHIOHhMkDIrHwri6YPCASBo+qtz2P9BW/Qv/gmVJcN5qw/UgxRr68HR1SN6P9nHT0W74Vb2w/AWOl2V7LJyssW7YMfn5+NbdWrVo5e0lOldQtzOaG82fZcF710g+dQ1sbAigSgLylSQygEC5fvyE0bl+BvEoMte77k1iZpksqzxIAHNP4WzRQc0egj1XzO5Je0uGeOLG+sNYGBfbml+BKuVg2ntrLnwGOCWySaxi96lt0mb8F249XBVAA4IbZgsLL5VixJRfFv4g/R54Y2Fb1QVkiZ2AQxQa7T15EpYz3Qukz77TfYog0Ti/pcI+M8hHu0vB5wYIFePrpp9G1a1dnL4XIZfSJDIKHjM8FndIyMOm975Fz/hqMltofSDrM/RyLNrlPo/PQ0FAAQHFx7as8i4uLa74XGhqK8+drZ/pUVlaipKSk1pi65rj5Z9xuTPX365KamoorV67U3E6fPi33IbocNpx3bYs2HbUp28jLAzi5PJmHJQQA0EHsebDj+EWrggImswXfCl4IpYWnpCMaf4sGE7SSiXBHYGOhcdaW2hLtIePv3Uj15c8AxwQ2Sfu6zd+Cw4XKZF9LOmDm8A6KzEXkahhEscEuwVqbABDT0rfmClgiqtvy+2KEx7prw+eGVFRUoLS0tNaNiGrTSzrcFadczf93dvyEP7z+rWLzqVlERARCQ0OxdevWmq+VlpZiz549SEhIAAAkJCTg8uXL2LdvX82Ybdu2wWw2Iz4+vmbMN998gxs3fjscyMzMRMeOHREQEFAz5uafUz2m+ufUxdPTE76+vrVuVGVHylAMiQq2aY5pG7Px2LrdCq2IbDVp3R68syPf6vv7e0nIWZys4IpI6xIEy7eUVVjX+HtvfgmuGsVyphIi1d8TxRGNv0WDCWrv71HN3kGhC4JX2w/tFKKJ4LG9A5ukfUmvbkepYPaViGmD2mnib4PIGXiqb4OMw+eExz6b2MmOKyFyDQYPCe2Cxa5OYsPnurGUDZGYZfeIB21FHDpTikWbjio6p7OUlZUhOzsb2dnZAKqayWdnZ6OgoAA6nQ5PPfUUFi9ejE8//RQ//PADHnnkEYSHh2Ps2LEAgE6dOmHkyJGYMmUK9u7di507d2LatGkYP348wsOrglcPPvggDAYDJk+ejCNHjuCjjz7CypUrMWvWrJp1zJw5ExkZGXjppZeQk5OD+fPn4/vvv8e0adMc/U/iMtZO7I1VD8TZNMe23Ivot/QLhVZE1uq/fCu251pf2rSlnwHZ80cpuCJyBX0ig+DdSOyIwJqggGiWgI9Br4l6/HpJh2GdQhoeCOCCYIP43ysouSo0TiuZKHL6yFhDtAxccxlN7p3J3oFN0rZP95/B0aJris7JLBSi22MQxUrGSjPyfhbbrNiUiUjcvDGdhcduyLL+6ktnSklJgU6nq/eWk5Nj1dwsZUMkxuAhIbalslkK7+zId4keKQcOHEBcXBzi4qoO22fNmoW4uDikpaUBAJ599llMnz4djz/+OHr16oWysjJkZGTAy+u3A4kPPvgAUVFRGDp0KJKSktC/f3+8+eabNd/38/PDF198gfz8fPTo0QOzZ89GWloaHn/88Zoxffv2xYcffog333wTMTEx+Pe//43//ve/6NKli4P+JVzTmJhw5C1NgsGGTwGFpTcQ9dxm5RZFwkxmCzo+txlnLosdRtdlcIcg7EgdruCqyFXoJR2Su4r1xrGm8XeJYCAhqUuoZq6EDvUX66shWpbpZiazBR8fECujqJVMFNE+MumHi6zKrNAJPm1ExzmbvQObpF0mswUz/nlQ0TnHxoZpZu8lcgax3FO6xYZdp4THDo7SRqookRr0bdcMEgCRY8htOT/DZLZo7u9r9uzZmDhxYr1jIiMjrZrb09MTnp5iH06I3N0zIzvhobf3KDrnhHd246M/91V0TkcbMGAALJbbH1zodDosXLgQCxcuvO2YwMBAfPjhh/X+nG7duuHbb+svgzZu3DiMGzeu/gWTbHpJhx+XJiNuwRZcum5dCYhyE9AmZTPyliZp7nVYq9IPnbOp/wkATOrXGvPGMBBJt5fQthn+vb/hg3trMh9E75PQVv2lvKrV83Jp1bib7c0vwS/lYuXPAhtrIxNFtI/MNaMJu/Muol97ec+FostigQR/70ay5nUWvaRDUpdQ/OfA2QbHWpvtRNr05IffKz7nivtiFZ+TyJUwiGKlvafEywg90qeN/RZC5GL0kg5dWvri0JmGe3lUmi1Wvbl2tuDgYAQH21aXnohs1ycyCI0k4IaCySN78i/BWGlmHzTShAPzEtH/b1tx5pL1WQ1t56Tj9fGxGB3bQsGV0e8t2XwUb31rWwbulAFt8FyyeMYvuaeSq2IHsaLjat9HLFvCmiwXZwkQDAwVC5Yyu5lo+TMACPUTy4hxtt4RgWhs0Av1xsk6eUHW5zyT2YL0w0VCY5sJZsSogT2znUibjJVmZBw+r+icozo35+cXogY49S/kxx9/xF133YVmzZrB19cX/fv3x/bt22uNKSgoQHJyMnx8fBASEoJnnnkGlZXKNU2y1rUKsStCPCSW8iKSa0w38YOYrJPW1wPXgoKCgppeBCaTqaZPQVlZmbOXRqR5ekmHl+5TtjcKUJWNQqQVO/4fG86r3YLPjtgcQHnjwTgGUEiI6EHsvoJLsufe95NYzwbRvhZq0Kyp2GH851aUpxItf+br5YHeEYGy5nYWvaTDAMHAiNxqXrtPXsR1wStjtBJ0Auyb7UTalPJvZct4STrg9Yd6KDonkStyahBl9OjRqKysxLZt27Bv3z7ExMRg9OjRKCqqunrAZDIhOTkZRqMRu3btwoYNG7B+/fqamtzO1FiwkPTgqGCWOCCS6dG+bYTHWlEqV1PS0tIQFxeHefPmoaysrKZPwfffK5++S+SO/tC9JZr7KlvSoTobhUgr2HBevSat24N1O09Zff9GEpC3NAlJ3cKVWxS5NB3EPrvuOH5RVlDAZLbg2+NiFz9p6eOz3PJUcoiWP7s7roWmzhx6tBYL+Ihm+VTLEvz3beKpnaATYN9sJ9Iek9mCj7MbLu8mxxsPddfUHkLkLE4Loly4cAHHjx9HSkoKunXrhvbt22P58uW4du0aDh8+DAD44osvcPToUbz//vuIjY3FqFGjsGjRIqxevRpGo/OuTjGZLfha8A1g7zbaeXEmUguDh4T4Nv5CY4uuuPabxfXr18NisdxyGzRokLOXRuQyvn12mOJzpn58SPE5ieypuuG8p976OQpLb6DDnM1WNQOmW/VfvhXbc63PuPX3knB8aTIPRkiWBMEqCmUVldibL5ZZAlT19xAp4QQACZHaKdVbXZ5KhNwMetGyZi0DfGTN62yi/Vvk9nmxQOy1p3/7IE3ti/bMdiLtWZmZq+h8ax7ujpFdwhSdk8hVOS2IEhQUhI4dO+Ldd9/F1atXUVlZib///e8ICQlBjx5VaWRZWVno2rUrmjdvXnO/xMRElJaW4siRI85aOnafvIiKSrEXJy3V2iRSk54RYh/g0n84xzeLRGQTg4eEPw+MUHTOTw4Ucm8izdFLOuQuSUZLf7Erq+tiNFf1SdmU3XBjaqqbyWxBx+c248xl6y8UaelnQPb8UQquitxFn8ggeDcSOyYouiLWxBsQ7+/hY9Brqhy2PctTuWL5M8B+fXdEm8X3uCNA1rzOZs9sJ9IWk9mCVdvzhMc3qidWmNg5GHlLkxhAIZLBaY3ldTodvvzyS4wdOxZNmzaFJEkICQlBRkYGAgKqXtSKiopqBVAA1Px/dcmvulRUVKCi4rcX3NLShhtUy7ErT/wKEi3V2iRSE9FSAuWVZk02lycidUlNigYA/P0b2/oOVDNbgF3HL2BAR9t6TRA5w46UoXhs/V5sy/nZ6jmmbczGxwdOY+2kPgquzPV9dvAspv/jgE1zDOkYxH93sppe0iGpSyj+c6DhcjEXBHt2AMCFX8TGjuoSqqksAaCqPFXGkeIGx8kpT2UyW7A9V2wP1tg/l6y+O1NkzHvm0jWhcYGNtXWha3W2k0gmV9bJC/xc7MJWZuYK5ltVObJoFPSSDruOX8B/DpzBNaMJvdoE4tG+bdhEnsgKiv/VpKSkQKfT1XvLycmBxWLBk08+iZCQEHz77bfYu3cvxo4dizFjxuDcuXM2rWHZsmXw8/OrubVq1UqhR1dFNG3Zu5GkqVqbRGoiWkoAAHbmWX/IQ0RULTUpGj8uHoXmvnUfcgR46dG1ha/wfPM3HVZqaUQOp1SflP7LMhVaket7bP1emwMor4+PZQCFbBbqL3YhoOhhOCCeLdFc8Kp7NREtOyV6yA9UV78Q66+mpfJngPjFcl/l/Cyc1WsyW/DxAbEMyMsay9yxZ7YTqc91ownP/PsAus37HO3nbEbcwi+Q8p9DKCuvlJWF0jbYBwYPqer50zEYr46Pw5uP9MSUgZEMoBBZSfFMlNmzZ2PixIn1jomMjMS2bduwadMmXLp0Cb6+VQcSb7zxBjIzM7FhwwakpKQgNDQUe/furXXf4uKqKzxCQ0NvO39qaipmzZpV8/+lpaWKBVJMZgsO/HRJaGyXcF/NXUVDpBZ9IoPgoQNEKud9J6MeMxFRfQweEvbMGY6y8ko8/dEBFFy6jjsCvPHK/XFo4uWBnScu4KG39wjNlffzNRgrzfygQpo1JiYcSV3D0G5OuqwrH2925ooRsfM/Z2mpBvT/21acuWR9+S4JwPGlSfzsQYqwCP7BnzhfJjznyZ/Fxuo0+BQWPZT/5EAh0sZ0Fvo7FW2S7uUhaar8GVB1sdzr2080OE5OxYG9+SX4pVys547cXitqYI9sJ1KfSev23pKBdunaDWz87jQ2fnda1lzzR3dRcmlEBDsEUYKDgxEc3HDpimvXqq7CkKTaBwuSJMFsrrriIiEhAUuWLMH58+cREhICAMjMzISvry+io6NvO7enpyc8Pe2Torn75EWhQ10A6MUsFCKr6SUd4loH4LtTDQctD565ApPZwoMDIlJMEy8PvPVor1u+3icyCI0k4IbYxaFI/fgQFo/tirRPD2HLD0W4fsOMJl6NkNg5FPPGdIa3YDNaImfRSzrkL09G9NzPcU3wqujfu1xuRvs5m5GzmIf8dYlbsAWXrldafX8vDyBncbKCKyJ3J3oQm5V3Ueg9uMlswY4TYpnjon0t1CRQsA9qaXkl9uaXCGXcizZJHxQVrLl9tU9kEDw9JKFMG9HyVKI9dwBtlly3R7YTqUvX+VvwS7n17wVu5iEBfVnWjUhxTrs0MiEhAQEBAXj00Udx8OBB/Pjjj3jmmWeQn5+P5OSqDwEjRoxAdHQ0JkyYgIMHD2LLli2YO3cunnzySbsFSRoipx9Kv7asg05kC9FyeEaThU30iMgh9JIOU+9sKzz+P/sL0SktA//6/ixKK8y4Yf7tirJOaRmYvF4sq4XI2Y4uHmVTw/kbbDh/C5PZgg5zNtsUQPH3khhA0YAlS5agb9++8PHxgb+/v9B9LBYL0tLSEBYWBm9vbwwbNgzHjx+370J/1aypvKBAQ/bml6CsQiwI20wwIKEmoo2/AaDoynWhca7aJB2oei81WLBnnGh5KtGeO75eHposuS4n20m0BBqpR/cFGYoFUADgL3e201xwlUgLnBZEadasGTIyMlBWVoYhQ4agZ8+e2LFjB/73v/8hJiYGAKDX67Fp0ybo9XokJCTg4YcfxiOPPIKFCxc6a9kovCT2pseg12kurZZIbfq2Fb96IuukeICTiMgWM4d3VGyurTkXMHDFVsXmI7KnHSlDMSTKtouEpm3MxmPrdiu0Iu367OBZtJ2TDqN1yT0AgJZ+BpZJ0wij0Yhx48Zh6tSpwvdZsWIFXnvtNaxZswZ79uxB48aNkZiYiPJy68u+iVI6KODqWQK9IwLRxFPsaOVCmdhhv6s2Sa8WJxj88RMMJon23EloG6TJw2W52U6kHZPWZqHkulgpOhGSDpg5vINi8xHRbxQv5yVHz549sWXLlnrHtG7dGunp6Q5aUcOuG8Wiw4OjQjT54kykJn0ig2DQA0aB9xS84IaIHEUv6ZAYHYItR88rMl9BSTkeW78Xayf2VmQ+IntaO7E3Pjt41qYG6NtyL6Lf0i+wc84IBVemHY+t34ttOWKljW5nSMcgNpDXkAULFgAA1q9fLzTeYrHg1Vdfxdy5c3HXXXcBAN599100b94c//3vfzF+/Hh7LRXAb0EBkewRkaCAq2cJ6CUd+rcLFupZcfn6jQbHuHKT9Gql5Q3/OwDAgdNi/WhFe+60C2kiNE5t7JHtRM63KbsQ239UNug1bRCzUIjshZ1OZTCZLfjqR7EPPD1bay+tlkht9JIOo7uFCY0tumL/q/KIiKo90jdC0fm25fyMzw6eVXROInsZExOOvKVJ8LShpU9h6Q1EPbdZuUVpRP/lW20OoLw+PpYBFBeXn5+PoqIiDBs2rOZrfn5+iI+PR1ZW1m3vV1FRgdLS0lo3a1QHBUSIBAVcPUsAACKDxQ7nT5xv+LDf1ZukA4AOYr/nr3J+brA8lav33AHsk+1EzmUyW/DUR9mKzsksFCL7YhBFht0nL6JCsKu8Fmu5EqlRmL+P0Lj0H86x/isROUyfyCDoFT7neeqjA9zHSDP0kg65S5LRws/697zlJqBNyma3eN6bzBZ0fG4zzly2/qIPgwTkLU3C6NgWCq6M1KioqAgA0Lx581pfb968ec336rJs2TL4+fnV3Fq1amX1GkSDAhaBP19XzxIAgAAfsWBGVt7FBvc8Vy9/BlQFzESUV5ob7H3p6j13AOUDm+R8u05cgODxorBX7o/VbCCaSAsYRJFBTlN5rb6ZIVIb0auURN5gExEpRS/pcFdMuKJzmszAyswfFZ2TyN52pg5D5/CmNs3h6g3nMw6fQ9s56aiwoeR5uG8j/Lg0mYcjKpKSkgKdTlfvLScnx6FrSk1NxZUrV2pup0+ftnou0V4UDY1zhywBAGjWVLmeFa5e/gyouhjF00PsOKqh3pfuEHQClM12Iueb/+kRRefrEu6Lu3iRBZFdMYgig2hTee9GkmbfzBCpjehVSgCwM8+28hhERHIsvy9G8TnXfJPnFlflk2vZPGMghkaF2DSHqzacTz90Dk+8v9+mOTqHNsYuN+0fo2azZ8/GsWPH6r1FRkZaNXdoaCgAoLi4do+N4uLimu/VxdPTE76+vrVu1lKqZ4U7ZAkAyvasKLkmFkTRcvkzvaTD4I5imRUNvS1yh6ATIJ7t9M2PDZdAI+cyVpqRd+GqYvM1bqTHphkDFJuPiOrGIIoMhZeuCY3rEu6r2TczRGrTJzIIjQR3qrM2lMggIpLL4CFhZBfbDo5/z2iyMKuONOmdib2w6oE4m+aobjjvKv53oBB/+dC2AMrQqGbY/NQgZRZEigoODkZUVFS9N4PBun4VERERCA0NxdatW2u+Vlpaij179iAhIUGph1AvpXpWuEuWgJI9K/adEmum7t3IhsZUKhB3h1gf2Yayndyh5w4gnu10/QYrNKhd6scHFZtLD+DIopGKzUdEt8cgiiCT2YKDZ64Ije2l4asbiNRGL+kwRPDq1us3bKiTQURkhdUP9lR8zhVbjik+J5EjKNVwvsMc7fdJeWz9Xsy0sWHs6+Nj8c7EeGUWRE5VUFCA7OxsFBQUwGQyITs7G9nZ2Sgr+63sTlRUFD755BMAgE6nw1NPPYXFixfj008/xQ8//IBHHnkE4eHhGDt2rEPWrFTPCnfJEtBLOvRr20xobEk9h/4mswXZpy8LzRPuL579okZKZTu5Q88dQF62U0Ml0Mh5TGYLPt5/VpG5Wvg1Qt7yZEXmIqKGMYgiaPfJizCaxD7M9WsrlpZKRGJ6thH7EMfUZSJyNL2kw5qHuys658EzpTBWipU+IVKb6obzLW043DOatd0npf/yrdiWY32JUTaQdz1paWmIi4vDvHnzUFZWhri4OMTFxeH777+vGZObm4srV367aO/ZZ5/F9OnT8fjjj6NXr14oKytDRkYGvLwcc3DeJzIIBr3YVfv1ldR1h9JU1bwNHkLj6ss0cadzByWynUxmC7bnnheaR8s9d4CqbCcfg9gRHj8Sq9fKzFzI+fUcWzgS6yb0RMdgbzSSAE+9DrGt/HAwbQR2prLUJ5EjMYgiSLSpvJeHhD4yejgQUcOYukxEajayS5jigZTUjw8pOh+Ro+1IGYohUbYd8GmtT4rJbEHH5zbjjA3lRVuwgbxLWr9+PSwWyy23QYMG1YyxWCyYOHFizf/rdDosXLgQRUVFKC8vx5dffokOHTo4bM16SYfYVv5CY+srqXtO8O9B66WpAKBFgFg5skNnrtw2KOBO5w5KZDvtPnkRFZViR9Ja7rkDVP1NjuzcXGhs0RXtl7lu06YNdDpdrdvy5cudvSwhJrMFXx87j4fe2o0xq77F3E9+wHWjCSazBau25wnPE9PSF94GPQZ3bo4ts4fg+NJk5C5Jwn+f7A8/H20HBYm0iEEUQXvzS4TGdWvpxw89RAqTk7rM5vJE5Awju4Qhb2kS2gT53HZM22bitd7/l32WmXWkeWsn9lakT0r/ZZkKrch+Pjt4Fm3npKPChsqiQzoGYScbyJOK9Ggj1rOivpK6142VQnOEabw0FQD0FSznVV9QoPBS/U3nq7nCuYMS2U5ZMi6g03LPnWph/rd/n3mzL44WucT7yIULF+LcuXM1t+nTpzt7SQ36X3Yh2j+Xjkc3fIedeRfxQ2Ep3t9TgE5pGeg273NZWSjPJnay2zqJSD4GUQSYzBYc+EmsuVuYn/bf/BGpTe+IQHh6iL3B/k4w4ElEpDS9pMNXzwzG4fmJGNqxGYKbGNDS3xvPJnbEj4tH4XMZzaErzWwwT66huk+KLUd9Z64YETv/c8XWpLTH1u/F9H8csGmO18fHYu2kPgqtiEgZgT5iV+7frqSuyWzBVz+KXeAU6GOQtTY1UiIoIBp0Eg1wqZle0iGmpZ/Q2NsFlyyCR9JNPPWa7rlTTbQEWlmFSfhCYDVr2rQpQkNDa26NGzd29pLqNWbVt5i5Mfu25dSu3hAPoRj0Os1nmxG5GgZRBOw+eRGCGaLCKbxEJE4v6dCtpb/Q2IP1pMcTETlCEy8PvDMpHt/NHY4dKUPwl8HtYPCQYPCQENvSV3ieFVuOVdX6PlKMkS9vR9Rz6eicloFJ6/airFzskIVIDfSSDvnLk+HjYf1Hj8vlZrRXYcP5/n+zrf+JBPY/IfWytaSuO5VaAmwPCrhb0AkAWgSIZVacu015qnOXrwndf0R0qOYzdwDxEmgAUHRFLKtJzZYvX46goCDExcXhhRdeQGWlet//DvzbVvxQWKrYfE8MbOsSz1kiV8IgigDRuqSA9pu7EamV6JVDRhOv3iYi9XpmpHha/sEzpWg3Jx2T3vseOeevodxkwVWjCdtzf0aX+VuQ9Op2O66USHlHF4+yqeH8DZU1nI9bsAVnLllfd95DB5xczv4npF62ltSV8znaFUotAbYFBdwt6ATY1kfGZLZg06FzQvcPdZGKIX0ig4QrNFwoq7DzauxrxowZ2LhxI7Zv344///nPWLp0KZ599tnbjq+oqEBpaWmtm6M8tm43Cmx4P/B7kg6YOdxxPbCISAyDKAJE0yCZbkdkP6I1hgH2RSEi9eoTGQTBz74AUG+RiqNF19Dxuc02r4nIkVyh4bzJbEGHOZtx6br1V8R6ewAnliUruCoi5fWOCIRXI7Ejg7qay4v29/BuJLlEqSXAtqCAOwadbOkjs/vkRRgF+1C5SqxaL+kwqIPYa+j3giXpHSklJeWWZvG/v+Xk5AAAZs2ahUGDBqFbt2544okn8NJLL2HVqlWoqKg7OLRs2TL4+fnV3Fq1auWQx7QpuxDbcpW9iHPaoHa8wIJIhRhEaYDJbMG+U2IvPjEu0NyNSK3kHDxuOVJk38UQEVlJL+lwV1y4YvNVmIAei9TfdJvoZko1nO+39AuFViSuuoG80Wz9HC18G+HYYgZQSP30kg53thc75K6rubxof4+BHYJd5nO0LUEBdww62dJHRk7QKSFS/II8tfM2eAiN255zXnUlMGfPno1jx47Ve4uMjKzzvvHx8aisrMSpU6fq/H5qaiquXLlSczt9+rQdH0kVk9mCGRuzFZ2TWShE6sUgSgN2nbgA0c9IvVzkjQyRGuklHeJaizVQzPv5GoyVNpxuqMSpU6cwefJkREREwNvbG23btsW8efNgNBqdvTQissGye2IUne/iVSMWfHpE0TmJ7K264byn3vo5CktvoIMD+6Qo0UB+SMcg7JwzQqEVEdlfzzZilRZ+31xeTn+PnoLv8bXAlqBA4SWx/h5dwn1dJugkp4/Md7+rECIadHK1iiGi2U5qLHMdHByMqKioem8GQ939frKzsyFJEkJCQur8vqenJ3x9fWvd7G1lZq7weaGoV+6PdZm/byJXwyBKA1Zt+1F4LPuhENmXnCuuNuzKt+NKHCMnJwdmsxl///vfceTIEbzyyitYs2YN5syZ4+ylEZENDB4S4tsoe2C0btcplwgek3vRSzrkLklGCz/ra/sbf+2Tkn7orIIru1X/5bY1kAeA18fHYu2kPgqtiMgxrG0u7479PQDrgwImswUHz1wRup+rXbwp2kfm4O9KoFksYs8vV6sY4g5lrrOysvDqq6/i4MGDOHnyJD744AM8/fTTePjhhxEQoI6gq8lswarteYrOGRHkg7tiWyg6JxEph0GUepjMFuw9dVlorF6CS13dQKRGct4wfnbQvocpjjBy5EisW7cOI0aMQGRkJP7whz/gr3/9Kz7++GNnL42IbPTen5Q/SJ3wjvN6RBDZYmfqMHQOb2rTHH/58AAWbTqs0Ip+YzJb0PG5zThTR78HUQYJyFuahNE8GCENsra5vDv296hmTVCgqr+HWFDA1S7etDazoryOEnJ16eliQSc5Za5Fs3XUxtPTExs3bsSdd96Jzp07Y8mSJXj66afx5ptvOntpNVZm5tbbu9AaX84epPCMRKQkBlHqsTIzV3hs91b+LnV1A5Ea9YkMEm4KePhsqepqwCrhypUrCAx0rQ8CRO7IHtkoe/IvMRuFNGvzjIEYGlV3iQ5R7+z4CZPWKhdMTD90Dm3npKNCsHFxXcJ9G+HHpcn8nECa1TsiEJ6CJ7Y3Z1bs/V3ppdtxpf4e1awJCogGnbw8JJe7eNOazAo55eICfeouD6VVekmH2Dv8hcaevazNIEr37t2xe/duXL58GdevX8fRo0eRmpoKT091ZK2ZzBb839fKZqGsebg73ysQqRyDKLchd1OcMYSNn4jsTS/p0EPwDaPZAuw6Ln4FnBacOHECq1atwp///OfbjqmoqEBpaWmtGxGpkz2yUVL+c1DxOYkc5Z2JvWxuOL/9R2Uazi/adBR/+XC/TXN0Dm2MXex/Qhqnl3To1tJfaGx1ZoXJbMGBny4J3ceV+ntUsyYoIBp06uZipakAeZkV1YE6dy0XV62llSXQSBm7T17EDYWuW5JQFUAZ2SVMmQmJyG4YRLkNOZuipAP6thd/o0RE1usdKX7l1V//k22/hdggJSUFOp2u3ltOTk6t+xQWFmLkyJEYN24cpkyZctu5ly1bBj8/v5pbq1at7P1wiMhKBg8Jj/ZV9m/0fwfP8sMyaZoaGs5PWrcH7+ywrbfa0Khm2PzUIJvmIFIL0UyR6syK3ScvQvB82+X6ewDyyy2ZzBbsOyUWdArzEy+vphV6SYe41mLZudVBgXdl9L90tXJxgLaby7uCFzKOyRo/e0R7hDb1gA6ADlUZZdFhTbH20V44vjSJARQijWAQ5Tae+/iQ8Nged7CUF5GjyLmyq7jUqMreKLNnz8axY8fqvUVGRtaMP3v2LAYPHoy+ffs2WAc2NTUVV65cqbmdPn3a3g+HiGyw4A/dEOCl3Nsxkxn8sEyaV91wvqW/9YeF1Q3nN2UXyrpf/+VbsT3XtkzW18fH4p2J8TbNQaQmcjMr3ss6JTze1fp7APLLLe06cQGiF7WLHp5rjZxA3a7jF/Blznmh8Qa965WLA9yjubxaGSvNyD4jXu1hxuB2mD6kA3Y/l4j85cnIX56MnMWjkD5zIIZ0CuFZIpGGeDh7AWp03WjCqRLx2pEs5UXkOH0ig9BIgnCm2MyNB5DUNUxVb06Cg4MRHCz2gbGwsBCDBw9Gjx49sG7dOkhS/Yetnp6eqqkVS0RiDswfhajnP0e5QnUBNmTlox8zZMkF7EgZisfW78W2HOsPgKZtzMbHB05j7aT6y+eZzBZEP29b/xODBBxbnKSq9xxESqjOrBDJLsk4fA6nBT9LG/Q6l+vvUa1lgA++/+lyg+MOnL6M17b+KDyvKwadgKqgwOrtYuXU/7m/ACbBt0xRoU1cck+W8zf5nWCpOPrNdaMJz//vIDZln0O5qerqc1/vRhjZJRTXKiqF59EBmDmc54VEroKZKHXouzxTeCxLeRE5ll7SYeqdbYXHmy3AdBtrmjtLYWEhBg0ahDvuuAMvvvgifv75ZxQVFaGoqMjZSyMiheUsGoUu4b63/X5kkPgV+ZlHz7OkF7mMtRN729wnZVtu/X1SPjt41uYG8i3YQJ5cmJxySycvXBe+2CnGBft7VBPNGKk0A/sEgi0AoJfgskEnOSXQvhVsKA9UlYh0RXL+Jg+cvsz3hTJMXv8dOqVl4N/7qgIoAGAGcPn6DWz87jQ+PXROeK57urdw2T2OyB0xiPI7n+4/g0vXxD9B3R3HTZHI0WYO7yhrfPrhIhgrFer85kCZmZk4ceIEtm7dipYtWyIsLKzmRkSuZ9OMATg8PxFDOgTBz8sDgT6NML5XKxxbOBKZs4cIv2mzAFiZKX5VK5HaVfdJsaFNCgpLb6DT3M23fP2x9Xsx/R8HbJgZGNIxCDvZQJ5cnD1KIrliP5RqcsotiX5K6d7KdcuIywkKXL4ufl7zaN8Ia5ekeqJ/k5VmYNdx28pUuos7V2zDVsFScSKW3dNNsbmIyPkYRLmJyWzBzH8elHUfbopEjqeXdLgnVt5VRRPe2W2n1djPxIkTYbFY6rwRkWtq4uWBtY/1wcH5idifNgLL7+0Gb4MeekmHu7uL73trvsnjVYfkUvSSDnnLk+HTyPqPL9crgciU3xrO91++1aZSYUBV/5OGSoURuQI5QQFRrlqaCqjKrFA63uHqZcSVDtS1DfaBwcN1j7zk/E3O33TYjitxDY+t242fZJT1b4irP/+I3BH/om+yMjMXco4berfx56ZI5CTL74uRNX5P/iVNZqMQEVVbdo/4vmc0WdhgnlzS0UWjbGo4b0ZVw/kOczbjzOVyq+eRAOQtTcLo2BZWz0GkJX0ig6BX8KOvK/dDAaoCvz1b+ys2nzuUEVc6UJfYOVTR+dRGTqAu7+dr/Cxcj03ZhdiWq+z75vmjuyg6HxE5HyMAvzKZLVgl2Mis2vt/SrDTaoioIQYPCUldmsu6jxazUYiIqhk8JLQLbiw8fuK6PXj47d34Opc9Usi17EgZiiFRtl3BbrThLMnbAzi5nP1PyL3oJR2GRYUoNt/gqBCX/xuarmDmSJdwX5f/91I6UOfKmU6A/EDdI+/ssd9iNMxktmDGxmxF5/SQdC4f9CRyRwyi/EpuFsqozs2ZhULkZKse7AE5HyWYjUJEWjdvTGfhsTfMwI4TF/Houu8QnZaBjMPijTDVZvXq1WjTpg28vLwQHx+PvXv3OntJ5GRKNJy3RgvfRji2ONnhP5dIDR5RsL/EI33aKDaXWvVt10yxAxdXbZB+MyUDdXoJLp3pVE1OoG53fgk/C9dhZWaucF8iUS/9Mcblg55E7ohRAFRFnv/va3lZKK8/1MNOqyEiUXpJh1XjY2XdJ+U/8voeERGpibUHMhWVZjzx/n5NBlI++ugjzJo1C/PmzcP+/fsRExODxMREnD+vXONP0qbqhvOetnScl4EN5Mnd9YkMgocC54IekmuX8qqml3To2cZfkblcuUH6zZQK1N0VE+4Wh9hy3xe+l3XKXkvRJJPZgtVfyTsLbEhEkA/uYqlPIpfEIAqA3Scv4oaM0POMwe3c4gWZSAtGx7ZA+2Af4fH/O3iWZW2ISLPkNpj/vfmfHtXcHvjyyy9jypQpmDRpEqKjo7FmzRr4+Phg7dq1zl4aqYBe0iF3SbJNfVJEsIE8UdXf211xtmdE3BXrHgfcgDIlvZo3NbhNFYw+kUFopMBDXX6vvP6ZWqWXdBgeLZ6981PJNTuuRnt2nbgAk8Jvi7+cPUjZCYlINdzjlbgBu/IuCI+VdMDM4crVNiUi222eeafwWJMZbLZMRJomp8H87xWVlmNvfomCq7Evo9GIffv2YdiwYTVfkyQJw4YNQ1ZWlhNXRmqzI2UoBndUvv64QWIDeaKb2fIa9Nsc3RRYiTb0bdcMehvjRY/1d48sFKAqKDD1zrY2zdE22Mdtgk6AvOyd1oHiFx+6g//sP6PofGse7u42AWIid2S3V5YlS5agb9++8PHxgb+/f51jCgoKkJycDB8fH4SEhOCZZ55BZWVlrTFfffUVunfvDk9PT7Rr1w7r169XfK2Fl64Lj502iFkoRGpj8JAQ3yZAeHzWSfHAKRGR2sjd837v/C/lCq7Gvi5cuACTyYTmzZvX+nrz5s1RVFR0y/iKigqUlpbWupH7WDcpHpMVPGxs4dsIPy5lA3mim9n6GhQfEeBWB9x6SYcnB9kWFJjUL1Kh1WjDzOEdbbr//NFdFFqJNvSJDIKvV8N1LSUdMCGhjf0XpCFnZJwF1kdCVQBlZJcwReYjInWy27sXo9GIcePGYerUqXV+32QyITk5GUajEbt27cKGDRuwfv16pKWl1YzJz89HcnIyBg8ejOzsbDz11FP405/+hC1btii61nDB9H89s1CIVOu9P8kpscHDECLSNnl7Xm0hTe1b9siZli1bBj8/v5pbq1atnL0kcrDnR0fjjQe72zzP4A7sf0J0O7a8Br032f3K4s0c3tHqg5dRnZu7VdAJqAo83RNrXdm4Rnod+rZXPitRzfSSDivuazhDbMqACLd7LjWkhYxSoFEhPvD49b91ALw8JESHNcXaR3vh+NIkBlCI3IDddtAFCxbg6aefRteuXev8/hdffIGjR4/i/fffR2xsLEaNGoVFixZh9erVMBqNAIA1a9YgIiICL730Ejp16oRp06bhvvvuwyuvvKLoWvu1DRYaN31Ie16JRqRSBg8JyV2bNzwQQIIbNLIkItdm8JAwqd8dsu8X6GNA74hAO6zIPpo1awa9Xo/i4uJaXy8uLkZoaOgt41NTU3HlypWa2+nTpx21VFKRpG5hNjWcn9y/NdY95n4HvUSirH0NeqxfG7c8xNVLOrw2Plb2/SQArz/UQ/H1aMFygaBAXV7+Y6xbntmM7BKGNQ93h4/h1hc+nQ7488AIpCZFO2Fl6nZfd7GLbWYMboeMWYNxYnkyTi1PRv7yZOQsHoX0mQMxpFOIWz7niNyR097BZGVloWvXrrXKMyQmJqK0tBRHjhypGXNzDezqMUrXwO7TNgj+Po3qHeNj0GP60PaK/lwiUtZrD/SAZwMfzAJ8GqFPJIMoRKR988Z0RUt/g6z7LB7bRVMf9AwGA3r06IGtW7fWfM1sNmPr1q1ISEi4Zbynpyd8fX1r3cg9Wdtw/o0H4/C8m5WCIfsTKXX9exMnToROp6t1GzlypH0XKsO8MV3Rwq/+z9A3axXojbQxne24InUbHdsCQ6PkZUi8/qD79lcweEiYMqCNrPsMjQrBmBjrMlhcwcguYfhhfiLem9QbY2PDMSK6OZ5L6oTcRaMYQLmNvu2b1Rl4upnBQ2JFGiIC4MQgSlFRUZ31rau/V9+Y0tJSXL9++9qFcuth6yUdlt9Td8ZMtZf/GOO2b2CItEIv6bCygau8lt3TlX/LROQydqQMR7MmYoGUPw+MQFI37ZUamDVrFt566y1s2LABx44dw9SpU3H16lVMmjTJ2UsjDdiRMhRDohrOOvf8tYF8Ujf3PYAj+2mo1PXtjBw5EufOnau5/eMf/7DTCq2zM3UEmnp5NDguyMcD3z47xAErUrd3JsajWwux4L5WX7OV9FxyZwyPDhEa27VFU7wzsZedV6R+ekmHAR2D8er4OLz5SE9MGRjpltlfovSSDi//sf6sp9fGu2d2ExHdStZumpKScsvVML+/5eTk2Gutwqyph12d/hjq61nr66G+nmwQRaQhv/0t177yNMzPi3/LROSSvp87vN5DYh2qrq7X6lWI999/P1588UWkpaUhNjYW2dnZyMjIuOVCG6LbWTuxN1Y9EAf9bc5ABncIRC4byJMdNVTq+nY8PT0RGhpacwsIsL6hu738MD8RXeoJDAzpGIR9aYkOXJG6fTp9ACb3j7jt97X+mq20tx7phVUPxNV7cDW5f2t8Nn2gw9ZErqX6/KB509oXJTVvauD5ARHV0vBlIzeZPXs2Jk6cWO+YyMhIoblCQ0Oxd+/eWl+rrnddXeM6NDS0zhrYvr6+8Pb2vu3cqampmDVrVs3/l5aWCgdShkeHYm9+Cc7/Uo6Qpl7oHRHID1REGsO/ZSJyN2sn9sZ1owlpnx5C5pHzuGEyo3VQY/x1RBTu7Bis+f1v2rRpmDZtmrOXQRo2JiYcSV3DsCP3Z7y54yRKy28gpqU/nkuOhncDpTyInOWrr75CSEgIAgICMGTIECxevBhBQbcvS1tRUYGKioqa/2+oIoNSNk0fgLLySsz48HvsK7gCvaTDiM6hmDemM/++6vD86Gj8v5FReOvbE/hwTwHKKkxoFeCNWcNd4zVbadX79zfHzuOFzBwUXi5HU69GeDD+DvxpADMtyHY8PyAiEbKCKMHBwQgOFmvC3pCEhAQsWbIE58+fR0hIVYpmZmYmfH19ER0dXTMmPT291v0yMzPrrIF9M09PT3h6etY75nb0ko5Np4lcAP+WicjdeBv0eOG+OOA+Z6+ESJ30kg53dgrBnZ3EysMQOdPIkSNxzz33ICIiAnl5eZgzZw5GjRqFrKws6PV1ByaWLVuGBQsWOHilVZp4eWDtY32c8rO1yOAh4cnBHfDkYPZaEKGXdBjcuTkGd2YWKtkHzw+IqCF2C9kXFBQgOzsbBQUFMJlMyM7ORnZ2NsrKygAAI0aMQHR0NCZMmICDBw9iy5YtmDt3Lp588smaAMgTTzyBkydP4tlnn0VOTg7eeOMN/POf/8TTTz9tr2UTERERERER1cvepa7Hjx+PP/zhD+jatSvGjh2LTZs24bvvvsNXX3112/ukpqbiypUrNbfTp09b/fOJiIiI6DeyMlHkSEtLw4YNG2r+Py4uDgCwfft2DBo0CHq9Hps2bcLUqVORkJCAxo0b49FHH8XChQtr7hMREYHNmzfj6aefxsqVK9GyZUu8/fbbSExkTVUiIiIiIiJyDiVLXYuIjIxEs2bNcOLECQwdOrTOMbZUZCAiIiKi27NbEGX9+vVYv359vWNat259S7mu3xs0aBAOHDhg01osFgsAx9WEJSJtqt4jqvcMV8D9j4hEcQ8kIndlzf6nZKlrEWfOnMHFixcRFibe5Jh7IBGJ4HtAInJnonug3YIoavLLL78AgFBzeSKiX375BX5+fs5ehiK4/xGRXNwDichd2Wv/KygoQElJSa1S1wDQrl07NGnSBAAQFRWFZcuW4e6770ZZWRkWLFiAe++9F6GhocjLy8Ozzz6Ldu3ayarKwD2QiOTge0AicmcN7YE6iyuFmm/DbDbj7NmzaNq0KXQ6XYPjS0tL0apVK5w+fRq+vr4OWKHzudtjdrfHC/Axizxmi8WCX375BeHh4ZAku7WMcii5+x/gfs8Vd3u8AB8zH3PduAfyecLH7Jrc7fEC6tv/Jk6cWKvUdbXqUtcAoNPpsG7dOkycOBHXr1/H2LFjceDAAVy+fBnh4eEYMWIEFi1ahObNxRtrcw9smLs9Znd7vAAfsxr2QGfg5+CGudvjBfiY+ZjrJroHukUmiiRJaNmypez7+fr6us0TrJq7PWZ3e7wAH3NDXOXKm2rW7n+A+z1X3O3xAnzM7oJ7IN8DiuBjdn3u9ngB9ex/IqWub7620dvbG1u2bLH553IPFOduj9ndHi/Ax9wQvgf8jbs9V9zt8QJ8zO5C6T3QNULMRERERERERERERERECmMQhYiIiIiIiIiIiIiIqA4MotTB09MT8+bNg6enp7OX4jDu9pjd7fECfMwkzt3+3dzt8QJ8zO7CHR+zrdzx34yP2fW52+MF3PMxK8Ed/93c7TG72+MF+JhJnLv9u7nb4wX4mN2FvR6zWzSWJyIiIiIiIiIiIiIikouZKERERERERERERERERHVgEIWIiIiIiIiIiIiIiKgODKIQERERERERERERERHVgUEUIiIiIiIiIiIiIiKiOjCI8jtLlixB37594ePjA39//zrHFBQUIDk5GT4+PggJCcEzzzyDyspKxy7Ujtq0aQOdTlfrtnz5cmcvS1GrV69GmzZt4OXlhfj4eOzdu9fZS7Kb+fPn3/L7jIqKcvayFPXNN99gzJgxCA8Ph06nw3//+99a37dYLEhLS0NYWBi8vb0xbNgwHD9+3DmLVTHuf1W4B7oO7n/c/+TgHsj9z9VwD+QeKAf3QO6BroZ7IPdAUdz/qnAPdC2uvgc6Y/9jEOV3jEYjxo0bh6lTp9b5fZPJhOTkZBiNRuzatQsbNmzA+vXrkZaW5uCV2tfChQtx7ty5mtv06dOdvSTFfPTRR5g1axbmzZuH/fv3IyYmBomJiTh//ryzl2Y3nTt3rvX73LFjh7OXpKirV68iJiYGq1evrvP7K1aswGuvvYY1a9Zgz549aNy4MRITE1FeXu7glaob97/fcA90Hdz/uP+J4h5Yhfufa+EeyD1QFPfAKtwDXQv3QO6BIrj//YZ7oGtx5T3QKfufheq0bt06i5+f3y1fT09Pt0iSZCkqKqr52v/93/9ZfH19LRUVFQ5cof20bt3a8sorrzh7GXbTu3dvy5NPPlnz/yaTyRIeHm5ZtmyZE1dlP/PmzbPExMQ4exkOA8DyySef1Py/2Wy2hIaGWl544YWar12+fNni6elp+cc//uGEFaqfO+9/Fgv3QFfC/Y/7nzXceQ/k/udauAdyD7QG98BXnL0Mu+Ee6Nq4B9rOnfc/i4V7oKtxpz3QUfsfM1FkysrKQteuXdG8efOaryUmJqK0tBRHjhxx4sqUtXz5cgQFBSEuLg4vvPCCy6QpGo1G7Nu3D8OGDav5miRJGDZsGLKyspy4Mvs6fvw4wsPDERkZiYceeggFBQXOXpLD5Ofno6ioqNbv3M/PD/Hx8S79O7cHd9n/AO6BroT7H/c/pbjLHsj9z7VwD+QeqBTugdrGPZB7IMA90Frusv8B3ANdjbvugfba/zyUWJw7KSoqqrVxAqj5/6KiImcsSXEzZsxA9+7dERgYiF27diE1NRXnzp3Dyy+/7Oyl2ezChQswmUx1/g5zcnKctCr7io+Px/r169GxY0ecO3cOCxYswIABA3D48GE0bdrU2cuzu+q/y7p+567yN+so7rD/AdwDXQn3P+5/SnKHPZD7n2vhHsg9UEncA7WNeyD3wGrcA+Vzh/0P4B7oatx5D7TX/ucWmSgpKSm3NNP5/c1V/2iqyfk3mDVrFgYNGoRu3brhiSeewEsvvYRVq1ahoqLCyY+CrDFq1CiMGzcO3bp1Q2JiItLT03H58mX885//dPbSyAG4/1XhHuieuP8R90Duf+6MeyBxD+Qe6M64B7o37n9VuAe6L+6BynOLTJTZs2dj4sSJ9Y6JjIwUmis0NBR79+6t9bXi4uKa76mVLf8G8fHxqKysxKlTp9CxY0c7rM5xmjVrBr1eX/M7q1ZcXKzq35+S/P390aFDB5w4ccLZS3GI6t9rcXExwsLCar5eXFyM2NhYJ63Kcbj/VeEeWMXd90Duf6j5f3fY/wDugQD3v2ruvv8B3AOrcQ+sjXsg90A1//6UxD0QNf/vDnsg978q3AOrcA90rz3QXvufWwRRgoODERwcrMhcCQkJWLJkCc6fP4+QkBAAQGZmJnx9fREdHa3Iz7AHW/4NsrOzIUlSzePVMoPBgB49emDr1q0YO3YsAMBsNmPr1q2YNm2acxfnIGVlZcjLy8OECROcvRSHiIiIQGhoKLZu3VqzWZaWlmLPnj2YOnWqcxfnANz/qnAPrOLueyD3P/fa/wDugQD3v2ruvv8B3AMB7oG24B6obdwDuQcC7rUHcv+rwj2wCvdA99oD7bX/uUUQRY6CggKUlJSgoKAAJpMJ2dnZAIB27dqhSZMmGDFiBKKjozFhwgSsWLECRUVFmDt3Lp588kl4eno6d/EKyMrKwp49ezB48GA0bdoUWVlZePrpp/Hwww8jICDA2ctTxKxZs/Doo4+iZ8+e6N27N1599VVcvXoVkyZNcvbS7OKvf/0rxowZg9atW+Ps2bOYN28e9Ho9HnjgAWcvTTFlZWW1oun5+fnIzs5GYGAg7rjjDjz11FNYvHgx2rdvj4iICDz//PMIDw+vefGkKu6+/wHcA10N9z/uf3K4+x7I/c/1cA/kHigH90Duga6GeyD3QFHuvv8B3ANdkavvgU7Z/yxUy6OPPmoBcMtt+/btNWNOnTplGTVqlMXb29vSrFkzy+zZsy03btxw3qIVtG/fPkt8fLzFz8/P4uXlZenUqZNl6dKllvLycmcvTVGrVq2y3HHHHRaDwWDp3bu3Zffu3c5ekt3cf//9lrCwMIvBYLC0aNHCcv/991tOnDjh7GUpavv27XX+3T766KMWi8ViMZvNlueff97SvHlzi6enp2Xo0KGW3Nxc5y5ahdx9/7NYuAe6Gu5/3P/kcPc9kPuf6+EeyD1QDu6B3ANdDfdA7oGi3H3/s1i4B7oiV98DnbH/6SwWi8X6EAwREREREREREREREZFrkpy9ACIiIiIiIiIiIiIiIjViEIWIiIiIiIiIiIiIiKgODKIQERERERERERERERHVgUEUIiIiIiIiIiIiIiKiOjCIQkREREREREREREREVAcGUYiIiIiIiIiIiIiIiOrAIAoREREREREREREREVEdGEQhIiIiIiIiIiIiIiKqA4MoREREREREREREREREdWAQhYiIiIiIiIiIiIiIqA4MohAREREREREREREREdWBQRQiIiIiIiIiIiIiIqI6/H9/GORtQb1KbQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  27\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  28\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question  29\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# first, randomly select the other multiple choice options\n",
+    "np.random.seed(0)\n",
+    "fbench_hard_questions = []\n",
+    "for idx, _ in enumerate(fbench_hard):\n",
+    "    mc_options = [idx]\n",
+    "    # select 4 more random functions\n",
+    "    for _ in range(4):\n",
+    "        random_idx = np.random.randint(0, len(fbench_hard))\n",
+    "        while random_idx in mc_options:\n",
+    "            random_idx = np.random.randint(0, len(fbench_hard))\n",
+    "        mc_options.append(random_idx)\n",
+    "    # shuffle options\n",
+    "    np.random.shuffle(mc_options)\n",
+    "    # store the options and the correct answer\n",
+    "    fbench_hard_questions.append((mc_options, idx))\n",
+    "\n",
+    "# plot the 5 functions for each question\n",
+    "# make a 1x5 grid of plots\n",
+    "for idx, (options, correct) in enumerate(fbench_hard_questions):\n",
+    "    fig, axes = plt.subplots(1, 5, figsize=(20, 3))\n",
+    "    print('Question ', idx)\n",
+    "    for i, ax in enumerate(axes):\n",
+    "        f, n = fbench_hard[options[i]]\n",
+    "        y = f(x)\n",
+    "        ax.scatter(x, y)\n",
+    "        ax.set_title(n)\n",
+    "        # if it is the correct one, set the title color to red\n",
+    "        if options[i] == correct:\n",
+    "            ax.title.set_color('red')\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fit EBMs to the functions and convert the EBM graphs to text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this for hard, otherwise standard\n",
+    "fbench = fbench_hard\n",
+    "fbench_questions = fbench_hard_questions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: x\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(-9.99, -9.79)\": -9.99, \"(-9.79, -9.59)\": -9.78, \"(-9.59, -9.38)\": -9.58, \"(-9.38, -9.19)\": -9.38, \"(-9.19, -8.97)\": -9.16, \"(-8.97, -8.76)\": -8.96, \"(-8.76, -8.53)\": -8.74, \"(-8.53, -8.29)\": -8.51, \"(-8.29, -8.07)\": -8.27, \"(-8.07, -7.84)\": -8.04, \"(-7.84, -7.65)\": -7.84, \"(-7.65, -7.43)\": -7.62, \"(-7.43, -7.19)\": -7.41, \"(-7.19, -6.99)\": -7.19, \"(-6.99, -6.79)\": -6.98, \"(-6.79, -6.58)\": -6.78, \"(-6.58, -6.38)\": -6.58, \"(-6.38, -6.15)\": -6.36, \"(-6.15, -5.94)\": -6.14, \"(-5.94, -5.74)\": -5.94, \"(-5.74, -5.53)\": -5.73, \"(-5.53, -5.31)\": -5.5, \"(-5.31, -5.1)\": -5.3, \"(-5.1, -4.91)\": -5.09, \"(-4.91, -4.69)\": -4.88, \"(-4.69, -4.48)\": -4.66, \"(-4.48, -4.28)\": -4.46, \"(-4.28, -4.08)\": -4.25, \"(-4.08, -3.86)\": -4.05, \"(-3.86, -3.63)\": -3.85, \"(-3.63, -3.43)\": -3.63, \"(-3.43, -3.2)\": -3.42, \"(-3.2, -3.0)\": -3.2, \"(-3.0, -2.78)\": -2.99, \"(-2.78, -2.52)\": -2.71, \"(-2.52, -2.32)\": -2.5, \"(-2.32, -2.07)\": -2.28, \"(-2.07, -1.84)\": -2.05, \"(-1.84, -1.63)\": -1.83, \"(-1.63, -1.43)\": -1.62, \"(-1.43, -1.25)\": -1.42, \"(-1.25, -1.02)\": -1.21, \"(-1.02, -0.84)\": -1.01, \"(-0.84, -0.62)\": -0.81, \"(-0.62, -0.4)\": -0.61, \"(-0.4, -0.16)\": -0.36, \"(-0.16, 0.03)\": -0.14, \"(0.03, 0.26)\": 0.06, \"(0.26, 0.46)\": 0.27, \"(0.46, 0.68)\": 0.48, \"(0.68, 0.88)\": 0.69, \"(0.88, 1.11)\": 0.9, \"(1.11, 1.33)\": 1.13, \"(1.33, 1.55)\": 1.35, \"(1.55, 1.76)\": 1.56, \"(1.76, 1.97)\": 1.77, \"(1.97, 2.16)\": 1.98, \"(2.16, 2.37)\": 2.18, \"(2.37, 2.59)\": 2.39, \"(2.59, 2.83)\": 2.61, \"(2.83, 3.06)\": 2.86, \"(3.06, 3.31)\": 3.09, \"(3.31, 3.53)\": 3.33, \"(3.53, 3.77)\": 3.56, \"(3.77, 3.95)\": 3.77, \"(3.95, 4.16)\": 3.97, \"(4.16, 4.38)\": 4.18, \"(4.38, 4.62)\": 4.42, \"(4.62, 4.85)\": 4.63, \"(4.85, 5.06)\": 4.88, \"(5.06, 5.29)\": 5.09, \"(5.29, 5.49)\": 5.31, \"(5.49, 5.7)\": 5.51, \"(5.7, 5.91)\": 5.71, \"(5.91, 6.1)\": 5.92, \"(6.1, 6.32)\": 6.12, \"(6.32, 6.53)\": 6.33, \"(6.53, 6.7)\": 6.53, \"(6.7, 6.91)\": 6.73, \"(6.91, 7.14)\": 6.94, \"(7.14, 7.37)\": 7.14, \"(7.37, 7.58)\": 7.38, \"(7.58, 7.79)\": 7.59, \"(7.79, 7.97)\": 7.79, \"(7.97, 8.2)\": 8.0, \"(8.2, 8.42)\": 8.21, \"(8.42, 8.66)\": 8.45, \"(8.66, 8.87)\": 8.67, \"(8.87, 9.09)\": 8.88, \"(9.09, 9.31)\": 9.12, \"(9.31, 9.52)\": 9.33, \"(9.52, 9.72)\": 9.53, \"(9.72, 9.94)\": 9.73, \"(9.94, 9.95)\": 9.93}\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: x\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(-9.98, -9.78)\": 24.95, \"(-9.78, -9.55)\": 24.53, \"(-9.55, -9.29)\": 23.98, \"(-9.29, -9.07)\": 23.53, \"(-9.07, -8.84)\": 23.11, \"(-8.84, -8.63)\": 22.67, \"(-8.63, -8.4)\": 22.22, \"(-8.4, -8.21)\": 21.79, \"(-8.21, -7.98)\": 21.39, \"(-7.98, -7.78)\": 20.95, \"(-7.78, -7.57)\": 20.52, \"(-7.57, -7.35)\": 20.1, \"(-7.35, -7.17)\": 19.7, \"(-7.17, -6.95)\": 19.28, \"(-6.95, -6.73)\": 18.83, \"(-6.73, -6.5)\": 18.38, \"(-6.5, -6.3)\": 17.97, \"(-6.3, -6.08)\": 17.57, \"(-6.08, -5.84)\": 17.14, \"(-5.84, -5.6)\": 16.6, \"(-5.6, -5.38)\": 16.16, \"(-5.38, -5.19)\": 15.76, \"(-5.19, -4.97)\": 15.36, \"(-4.97, -4.79)\": 14.94, \"(-4.79, -4.56)\": 14.53, \"(-4.56, -4.35)\": 14.11, \"(-4.35, -4.17)\": 13.71, \"(-4.17, -3.93)\": 13.3, \"(-3.93, -3.73)\": 12.84, \"(-3.73, -3.52)\": 12.42, \"(-3.52, -3.31)\": 11.99, \"(-3.31, -3.08)\": 11.59, \"(-3.08, -2.86)\": 11.13, \"(-2.86, -2.62)\": 10.68, \"(-2.62, -2.4)\": 10.18, \"(-2.4, -2.18)\": 9.74, \"(-2.18, -1.96)\": 9.33, \"(-1.96, -1.7)\": 8.85, \"(-1.7, -1.5)\": 8.39, \"(-1.5, -1.25)\": 7.97, \"(-1.25, -1.05)\": 7.48, \"(-1.05, -0.81)\": 7.07, \"(-0.81, -0.63)\": 6.62, \"(-0.63, -0.41)\": 6.2, \"(-0.41, -0.19)\": 5.79, \"(-0.19, 0.01)\": 5.38, \"(0.01, 0.23)\": 4.96, \"(0.23, 0.43)\": 4.53, \"(0.43, 0.65)\": 4.09, \"(0.65, 0.87)\": 3.68, \"(0.87, 1.08)\": 3.24, \"(1.08, 1.27)\": 2.83, \"(1.27, 1.49)\": 2.41, \"(1.49, 1.72)\": 1.99, \"(1.72, 1.96)\": 1.51, \"(1.96, 2.14)\": 1.08, \"(2.14, 2.38)\": 0.67, \"(2.38, 2.56)\": 0.26, \"(2.56, 2.77)\": -0.14, \"(2.77, 2.98)\": -0.54, \"(2.98, 3.19)\": -0.96, \"(3.19, 3.4)\": -1.39, \"(3.4, 3.62)\": -1.81, \"(3.62, 3.81)\": -2.23, \"(3.81, 4.03)\": -2.65, \"(4.03, 4.24)\": -3.07, \"(4.24, 4.46)\": -3.53, \"(4.46, 4.66)\": -3.93, \"(4.66, 4.86)\": -4.33, \"(4.86, 5.08)\": -4.75, \"(5.08, 5.29)\": -5.18, \"(5.29, 5.49)\": -5.58, \"(5.49, 5.68)\": -6.03, \"(5.68, 5.95)\": -6.46, \"(5.95, 6.15)\": -6.91, \"(6.15, 6.37)\": -7.31, \"(6.37, 6.59)\": -7.77, \"(6.59, 6.82)\": -8.22, \"(6.82, 7.01)\": -8.66, \"(7.01, 7.23)\": -9.05, \"(7.23, 7.45)\": -9.48, \"(7.45, 7.66)\": -9.93, \"(7.66, 7.9)\": -10.38, \"(7.9, 8.09)\": -10.79, \"(8.09, 8.3)\": -11.2, \"(8.3, 8.53)\": -11.64, \"(8.53, 8.73)\": -12.08, \"(8.73, 8.96)\": -12.51, \"(8.96, 9.17)\": -12.95, \"(9.17, 9.37)\": -13.35, \"(9.37, 9.58)\": -13.77, \"(9.58, 9.8)\": -14.19, \"(9.8, 9.97)\": -14.61}\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: x\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(-9.98, -9.93)\": 99.4, \"(-9.93, -9.87)\": 98.31, \"(-9.87, -9.81)\": 97.19, \"(-9.81, -9.76)\": 96.11, \"(-9.76, -9.7)\": 95.04, \"(-9.7, -9.65)\": 94.01, \"(-9.65, -9.58)\": 92.73, \"(-9.58, -9.51)\": 91.67, \"(-9.51, -9.47)\": 90.48, \"(-9.47, -9.43)\": 89.37, \"(-9.43, -9.34)\": 88.16, \"(-9.34, -9.28)\": 87.09, \"(-9.28, -9.23)\": 86.04, \"(-9.23, -9.15)\": 84.82, \"(-9.15, -9.1)\": 83.54, \"(-9.1, -9.02)\": 82.31, \"(-9.02, -8.92)\": 80.93, \"(-8.92, -8.87)\": 79.41, \"(-8.87, -8.78)\": 78.31, \"(-8.78, -8.71)\": 76.47, \"(-8.71, -8.62)\": 75.46, \"(-8.62, -8.54)\": 74.01, \"(-8.54, -8.48)\": 72.62, \"(-8.48, -8.42)\": 71.53, \"(-8.42, -8.32)\": 70.24, \"(-8.32, -8.27)\": 69.21, \"(-8.27, -8.2)\": 68.18, \"(-8.2, -8.12)\": 66.92, \"(-8.12, -8.05)\": 65.63, \"(-8.05, -7.97)\": 64.56, \"(-7.97, -7.89)\": 63.45, \"(-7.89, -7.83)\": 62.36, \"(-7.83, -7.77)\": 61.32, \"(-7.77, -7.71)\": 60.21, \"(-7.71, -7.62)\": 59.18, \"(-7.62, -7.56)\": 58.13, \"(-7.56, -7.47)\": 57.06, \"(-7.47, -7.41)\": 55.65, \"(-7.41, -7.33)\": 54.58, \"(-7.33, -7.25)\": 53.55, \"(-7.25, -7.18)\": 52.43, \"(-7.18, -7.12)\": 51.43, \"(-7.12, -7.04)\": 50.41, \"(-7.04, -6.95)\": 49.31, \"(-6.95, -6.88)\": 48.23, \"(-6.88, -6.79)\": 47.12, \"(-6.79, -6.72)\": 46.02, \"(-6.72, -6.63)\": 44.95, \"(-6.63, -6.55)\": 43.75, \"(-6.55, -6.45)\": 42.74, \"(-6.45, -6.35)\": 41.39, \"(-6.35, -6.24)\": 40.11, \"(-6.24, -6.17)\": 38.97, \"(-6.17, -6.06)\": 37.89, \"(-6.06, -5.95)\": 36.45, \"(-5.95, -5.85)\": 35.25, \"(-5.85, -5.77)\": 34.13, \"(-5.77, -5.66)\": 33.06, \"(-5.66, -5.56)\": 31.88, \"(-5.56, -5.45)\": 30.75, \"(-5.45, -5.34)\": 29.55, \"(-5.34, -5.24)\": 28.49, \"(-5.24, -5.15)\": 27.39, \"(-5.15, -5.01)\": 26.33, \"(-5.01, -4.92)\": 25.2, \"(-4.92, -4.79)\": 23.89, \"(-4.79, -4.67)\": 22.84, \"(-4.67, -4.54)\": 21.69, \"(-4.54, -4.43)\": 20.6, \"(-4.43, -4.31)\": 19.6, \"(-4.31, -4.2)\": 18.5, \"(-4.2, -4.07)\": 17.46, \"(-4.07, -3.93)\": 16.43, \"(-3.93, -3.8)\": 15.33, \"(-3.8, -3.64)\": 14.3, \"(-3.64, -3.51)\": 13.27, \"(-3.51, -3.34)\": 12.22, \"(-3.34, -3.15)\": 10.98, \"(-3.15, -2.98)\": 9.9, \"(-2.98, -2.79)\": 8.85, \"(-2.79, -2.58)\": 7.72, \"(-2.58, -2.37)\": 6.53, \"(-2.37, -2.14)\": 5.51, \"(-2.14, -1.86)\": 4.5, \"(-1.86, -1.54)\": 3.39, \"(-1.54, -1.11)\": 2.25, \"(-1.11, -0.39)\": 1.18, \"(-0.39, 1.09)\": 0.16, \"(1.09, 1.47)\": 1.17, \"(1.47, 1.76)\": 2.17, \"(1.76, 2.03)\": 3.19, \"(2.03, 2.27)\": 4.2, \"(2.27, 2.49)\": 5.22, \"(2.49, 2.7)\": 6.26, \"(2.7, 2.92)\": 7.5, \"(2.92, 3.08)\": 8.6, \"(3.08, 3.27)\": 9.65, \"(3.27, 3.43)\": 10.71, \"(3.43, 3.62)\": 12.0, \"(3.62, 3.78)\": 13.22, \"(3.78, 3.91)\": 14.3, \"(3.91, 4.04)\": 15.43, \"(4.04, 4.17)\": 16.43, \"(4.17, 4.3)\": 17.43, \"(4.3, 4.45)\": 18.5, \"(4.45, 4.62)\": 20.3, \"(4.62, 4.74)\": 21.54, \"(4.74, 4.85)\": 22.57, \"(4.85, 4.95)\": 23.58, \"(4.95, 5.06)\": 24.58, \"(5.06, 5.14)\": 25.64, \"(5.14, 5.26)\": 26.66, \"(5.26, 5.39)\": 27.87, \"(5.39, 5.48)\": 29.18, \"(5.48, 5.62)\": 30.42, \"(5.62, 5.73)\": 31.7, \"(5.73, 5.8)\": 32.8, \"(5.8, 5.9)\": 33.81, \"(5.9, 6.02)\": 34.97, \"(6.02, 6.14)\": 36.63, \"(6.14, 6.2)\": 37.66, \"(6.2, 6.29)\": 38.67, \"(6.29, 6.41)\": 39.71, \"(6.41, 6.55)\": 41.78, \"(6.55, 6.65)\": 42.94, \"(6.65, 6.75)\": 44.48, \"(6.75, 6.83)\": 45.59, \"(6.83, 6.91)\": 46.74, \"(6.91, 6.99)\": 47.92, \"(6.99, 7.08)\": 49.12, \"(7.08, 7.16)\": 50.33, \"(7.16, 7.23)\": 51.38, \"(7.23, 7.31)\": 52.48, \"(7.31, 7.38)\": 53.62, \"(7.38, 7.47)\": 54.74, \"(7.47, 7.55)\": 55.87, \"(7.55, 7.63)\": 57.06, \"(7.63, 7.68)\": 58.15, \"(7.68, 7.75)\": 59.17, \"(7.75, 7.84)\": 60.33, \"(7.84, 7.93)\": 61.95, \"(7.93, 8.05)\": 63.71, \"(8.05, 8.11)\": 64.79, \"(8.11, 8.18)\": 65.93, \"(8.18, 8.27)\": 67.71, \"(8.27, 8.37)\": 68.96, \"(8.37, 8.45)\": 70.29, \"(8.45, 8.51)\": 71.47, \"(8.51, 8.59)\": 72.6, \"(8.59, 8.62)\": 73.67, \"(8.62, 8.72)\": 74.69, \"(8.72, 8.78)\": 76.09, \"(8.78, 8.85)\": 77.31, \"(8.85, 8.89)\": 78.33, \"(8.89, 8.99)\": 79.44, \"(8.99, 9.06)\": 81.18, \"(9.06, 9.16)\": 82.62, \"(9.16, 9.24)\": 84.01, \"(9.24, 9.3)\": 85.56, \"(9.3, 9.38)\": 86.58, \"(9.38, 9.45)\": 88.34, \"(9.45, 9.53)\": 89.65, \"(9.53, 9.59)\": 91.14, \"(9.59, 9.64)\": 92.15, \"(9.64, 9.72)\": 93.3, \"(9.72, 9.78)\": 94.6, \"(9.78, 9.82)\": 95.66, \"(9.82, 9.89)\": 97.11, \"(9.89, 10.0)\": 98.32}\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: x\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(-10.0, -9.95)\": -200.0, \"(-9.95, -9.88)\": -197.6, \"(-9.88, -9.84)\": -195.0, \"(-9.84, -9.76)\": -192.9, \"(-9.76, -9.7)\": -189.9, \"(-9.7, -9.64)\": -187.2, \"(-9.64, -9.54)\": -184.0, \"(-9.54, -9.45)\": -181.6, \"(-9.45, -9.35)\": -176.4, \"(-9.35, -9.26)\": -173.7, \"(-9.26, -9.2)\": -171.5, \"(-9.2, -9.14)\": -169.0, \"(-9.14, -9.07)\": -166.8, \"(-9.07, -9.01)\": -164.7, \"(-9.01, -8.96)\": -162.0, \"(-8.96, -8.88)\": -159.7, \"(-8.88, -8.81)\": -157.6, \"(-8.81, -8.74)\": -154.6, \"(-8.74, -8.67)\": -152.6, \"(-8.67, -8.61)\": -149.9, \"(-8.61, -8.55)\": -147.7, \"(-8.55, -8.47)\": -145.6, \"(-8.47, -8.41)\": -143.1, \"(-8.41, -8.33)\": -140.6, \"(-8.33, -8.26)\": -138.6, \"(-8.26, -8.19)\": -136.4, \"(-8.19, -8.12)\": -133.7, \"(-8.12, -8.04)\": -131.5, \"(-8.04, -7.97)\": -129.3, \"(-7.97, -7.9)\": -126.9, \"(-7.9, -7.82)\": -124.1, \"(-7.82, -7.73)\": -122.0, \"(-7.73, -7.65)\": -118.8, \"(-7.65, -7.55)\": -116.1, \"(-7.55, -7.48)\": -113.6, \"(-7.48, -7.36)\": -110.7, \"(-7.36, -7.26)\": -107.9, \"(-7.26, -7.15)\": -104.8, \"(-7.15, -7.07)\": -102.1, \"(-7.07, -6.99)\": -99.7, \"(-6.99, -6.93)\": -97.5, \"(-6.93, -6.81)\": -95.2, \"(-6.81, -6.75)\": -92.7, \"(-6.75, -6.64)\": -90.1, \"(-6.64, -6.55)\": -87.6, \"(-6.55, -6.43)\": -85.4, \"(-6.43, -6.34)\": -82.2, \"(-6.34, -6.25)\": -80.0, \"(-6.25, -6.17)\": -77.9, \"(-6.17, -6.07)\": -75.7, \"(-6.07, -5.99)\": -73.6, \"(-5.99, -5.89)\": -71.5, \"(-5.89, -5.81)\": -69.4, \"(-5.81, -5.72)\": -67.2, \"(-5.72, -5.63)\": -65.1, \"(-5.63, -5.51)\": -63.1, \"(-5.51, -5.43)\": -60.8, \"(-5.43, -5.31)\": -58.5, \"(-5.31, -5.21)\": -56.2, \"(-5.21, -5.09)\": -53.9, \"(-5.09, -4.99)\": -51.9, \"(-4.99, -4.85)\": -49.4, \"(-4.85, -4.74)\": -46.8, \"(-4.74, -4.63)\": -44.7, \"(-4.63, -4.52)\": -42.7, \"(-4.52, -4.41)\": -40.6, \"(-4.41, -4.27)\": -38.3, \"(-4.27, -4.13)\": -36.3, \"(-4.13, -4.03)\": -34.2, \"(-4.03, -3.87)\": -32.2, \"(-3.87, -3.73)\": -29.9, \"(-3.73, -3.58)\": -27.7, \"(-3.58, -3.41)\": -25.6, \"(-3.41, -3.25)\": -23.3, \"(-3.25, -3.06)\": -20.7, \"(-3.06, -2.88)\": -18.4, \"(-2.88, -2.65)\": -16.2, \"(-2.65, -2.48)\": -14.1, \"(-2.48, -2.28)\": -12.0, \"(-2.28, -1.99)\": -10.0, \"(-1.99, -1.71)\": -7.9, \"(-1.71, -1.38)\": -5.8, \"(-1.38, -0.92)\": -3.8, \"(-0.92, 1.35)\": -1.7, \"(1.35, 1.68)\": -3.7, \"(1.68, 1.98)\": -5.8, \"(1.98, 2.2)\": -7.9, \"(2.2, 2.44)\": -10.0, \"(2.44, 2.65)\": -12.0, \"(2.65, 2.83)\": -14.1, \"(2.83, 3.03)\": -16.4, \"(3.03, 3.22)\": -18.5, \"(3.22, 3.38)\": -20.7, \"(3.38, 3.51)\": -23.0, \"(3.51, 3.68)\": -25.1, \"(3.68, 3.83)\": -27.2, \"(3.83, 3.99)\": -29.5, \"(3.99, 4.16)\": -32.3, \"(4.16, 4.27)\": -34.5, \"(4.27, 4.39)\": -36.6, \"(4.39, 4.53)\": -38.6, \"(4.53, 4.65)\": -41.2, \"(4.65, 4.79)\": -43.3, \"(4.79, 4.91)\": -46.4, \"(4.91, 5.02)\": -48.5, \"(5.02, 5.12)\": -50.7, \"(5.12, 5.25)\": -52.7, \"(5.25, 5.34)\": -54.9, \"(5.34, 5.44)\": -57.3, \"(5.44, 5.54)\": -59.5, \"(5.54, 5.64)\": -61.6, \"(5.64, 5.72)\": -63.8, \"(5.72, 5.83)\": -66.1, \"(5.83, 5.92)\": -68.2, \"(5.92, 6.03)\": -70.4, \"(6.03, 6.15)\": -73.7, \"(6.15, 6.24)\": -75.7, \"(6.24, 6.32)\": -77.9, \"(6.32, 6.41)\": -80.1, \"(6.41, 6.49)\": -82.6, \"(6.49, 6.6)\": -84.9, \"(6.6, 6.7)\": -87.7, \"(6.7, 6.78)\": -89.7, \"(6.78, 6.88)\": -92.4, \"(6.88, 6.95)\": -94.9, \"(6.95, 7.03)\": -97.0, \"(7.03, 7.1)\": -99.1, \"(7.1, 7.2)\": -101.6, \"(7.2, 7.28)\": -104.0, \"(7.28, 7.35)\": -106.2, \"(7.35, 7.43)\": -108.4, \"(7.43, 7.51)\": -110.9, \"(7.51, 7.58)\": -113.1, \"(7.58, 7.65)\": -115.2, \"(7.65, 7.71)\": -117.3, \"(7.71, 7.79)\": -119.4, \"(7.79, 7.87)\": -121.7, \"(7.87, 7.95)\": -124.2, \"(7.95, 8.04)\": -127.3, \"(8.04, 8.1)\": -129.5, \"(8.1, 8.17)\": -131.6, \"(8.17, 8.26)\": -134.0, \"(8.26, 8.33)\": -136.8, \"(8.33, 8.37)\": -139.0, \"(8.37, 8.46)\": -141.4, \"(8.46, 8.52)\": -143.8, \"(8.52, 8.6)\": -146.5, \"(8.6, 8.68)\": -148.5, \"(8.68, 8.75)\": -150.8, \"(8.75, 8.8)\": -154.1, \"(8.8, 8.92)\": -156.8, \"(8.92, 9.06)\": -161.9, \"(9.06, 9.15)\": -165.8, \"(9.15, 9.23)\": -168.3, \"(9.23, 9.29)\": -170.8, \"(9.29, 9.36)\": -173.4, \"(9.36, 9.42)\": -175.5, \"(9.42, 9.49)\": -178.0, \"(9.49, 9.54)\": -180.4, \"(9.54, 9.61)\": -182.9, \"(9.61, 9.66)\": -184.9, \"(9.66, 9.74)\": -187.4, \"(9.74, 9.83)\": -191.4, \"(9.83, 9.89)\": -193.6, \"(9.89, 9.97)\": -196.6}\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: x\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(-10.0, -9.94)\": 144.0, \"(-9.94, -9.85)\": 142.0, \"(-9.85, -9.77)\": 140.4, \"(-9.77, -9.66)\": 137.5, \"(-9.66, -9.58)\": 135.8, \"(-9.58, -9.51)\": 134.0, \"(-9.51, -9.41)\": 131.7, \"(-9.41, -9.35)\": 130.1, \"(-9.35, -9.27)\": 128.6, \"(-9.27, -9.18)\": 126.7, \"(-9.18, -9.09)\": 124.5, \"(-9.09, -9.01)\": 122.7, \"(-9.01, -8.93)\": 121.1, \"(-8.93, -8.84)\": 119.2, \"(-8.84, -8.76)\": 117.3, \"(-8.76, -8.69)\": 115.5, \"(-8.69, -8.59)\": 114.0, \"(-8.59, -8.52)\": 112.2, \"(-8.52, -8.42)\": 110.3, \"(-8.42, -8.35)\": 108.6, \"(-8.35, -8.29)\": 106.9, \"(-8.29, -8.22)\": 105.4, \"(-8.22, -8.11)\": 103.7, \"(-8.11, -8.03)\": 102.1, \"(-8.03, -7.92)\": 99.8, \"(-7.92, -7.86)\": 98.4, \"(-7.86, -7.76)\": 96.8, \"(-7.76, -7.67)\": 95.1, \"(-7.67, -7.56)\": 92.8, \"(-7.56, -7.48)\": 91.2, \"(-7.48, -7.4)\": 89.8, \"(-7.4, -7.32)\": 88.2, \"(-7.32, -7.23)\": 86.7, \"(-7.23, -7.13)\": 84.9, \"(-7.13, -7.06)\": 83.1, \"(-7.06, -6.96)\": 81.6, \"(-6.96, -6.88)\": 80.1, \"(-6.88, -6.78)\": 78.6, \"(-6.78, -6.71)\": 76.9, \"(-6.71, -6.6)\": 75.4, \"(-6.6, -6.53)\": 73.8, \"(-6.53, -6.42)\": 72.3, \"(-6.42, -6.32)\": 70.8, \"(-6.32, -6.25)\": 69.2, \"(-6.25, -6.12)\": 67.6, \"(-6.12, -6.03)\": 66.0, \"(-6.03, -5.94)\": 64.4, \"(-5.94, -5.82)\": 62.8, \"(-5.82, -5.71)\": 60.8, \"(-5.71, -5.61)\": 59.1, \"(-5.61, -5.48)\": 57.6, \"(-5.48, -5.41)\": 56.1, \"(-5.41, -5.29)\": 54.6, \"(-5.29, -5.18)\": 53.1, \"(-5.18, -5.08)\": 51.5, \"(-5.08, -4.96)\": 49.8, \"(-4.96, -4.87)\": 48.4, \"(-4.87, -4.76)\": 46.9, \"(-4.76, -4.62)\": 45.4, \"(-4.62, -4.47)\": 43.4, \"(-4.47, -4.36)\": 41.9, \"(-4.36, -4.23)\": 40.4, \"(-4.23, -4.14)\": 38.8, \"(-4.14, -3.99)\": 37.3, \"(-3.99, -3.85)\": 35.7, \"(-3.85, -3.68)\": 33.8, \"(-3.68, -3.54)\": 32.1, \"(-3.54, -3.42)\": 30.6, \"(-3.42, -3.26)\": 29.2, \"(-3.26, -3.12)\": 27.6, \"(-3.12, -2.97)\": 26.0, \"(-2.97, -2.79)\": 24.5, \"(-2.79, -2.62)\": 22.9, \"(-2.62, -2.45)\": 21.3, \"(-2.45, -2.29)\": 19.9, \"(-2.29, -2.12)\": 18.3, \"(-2.12, -1.93)\": 16.8, \"(-1.93, -1.72)\": 15.3, \"(-1.72, -1.52)\": 13.8, \"(-1.52, -1.29)\": 12.3, \"(-1.29, -1.05)\": 10.7, \"(-1.05, -0.8)\": 9.3, \"(-0.8, -0.51)\": 7.8, \"(-0.51, -0.22)\": 6.3, \"(-0.22, 0.2)\": 4.8, \"(0.2, 0.65)\": 3.3, \"(0.65, 1.31)\": 1.9, \"(1.31, 3.39)\": 0.4, \"(3.39, 3.82)\": 1.9, \"(3.82, 4.19)\": 3.4, \"(4.19, 4.51)\": 4.8, \"(4.51, 4.79)\": 6.3, \"(4.79, 5.04)\": 7.8, \"(5.04, 5.28)\": 9.3, \"(5.28, 5.5)\": 10.8, \"(5.5, 5.71)\": 12.4, \"(5.71, 5.92)\": 13.9, \"(5.92, 6.12)\": 15.4, \"(6.12, 6.29)\": 17.1, \"(6.29, 6.47)\": 18.5, \"(6.47, 6.63)\": 20.1, \"(6.63, 6.8)\": 21.5, \"(6.8, 6.96)\": 23.0, \"(6.96, 7.14)\": 24.8, \"(7.14, 7.3)\": 26.7, \"(7.3, 7.46)\": 28.3, \"(7.46, 7.6)\": 29.8, \"(7.6, 7.74)\": 31.5, \"(7.74, 7.87)\": 33.1, \"(7.87, 8.0)\": 34.5, \"(8.0, 8.13)\": 36.1, \"(8.13, 8.23)\": 37.7, \"(8.23, 8.37)\": 39.1, \"(8.37, 8.48)\": 40.7, \"(8.48, 8.6)\": 42.2, \"(8.6, 8.73)\": 43.7, \"(8.73, 8.83)\": 45.2, \"(8.83, 8.94)\": 46.9, \"(8.94, 9.06)\": 48.4, \"(9.06, 9.2)\": 50.2, \"(9.2, 9.28)\": 51.9, \"(9.28, 9.37)\": 53.3, \"(9.37, 9.52)\": 54.9, \"(9.52, 9.62)\": 56.6, \"(9.62, 9.72)\": 58.1, \"(9.72, 9.82)\": 59.7, \"(9.82, 9.92)\": 61.2, \"(9.92, 9.97)\": 62.9}\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: x\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(-9.97, -9.92)\": 98.39, \"(-9.92, -9.85)\": 97.15, \"(-9.85, -9.81)\": 95.88, \"(-9.81, -9.74)\": 94.86, \"(-9.74, -9.68)\": 93.59, \"(-9.68, -9.62)\": 92.55, \"(-9.62, -9.55)\": 91.55, \"(-9.55, -9.46)\": 89.64, \"(-9.46, -9.41)\": 88.35, \"(-9.41, -9.33)\": 87.32, \"(-9.33, -9.25)\": 85.49, \"(-9.25, -9.17)\": 84.3, \"(-9.17, -9.13)\": 83.01, \"(-9.13, -9.05)\": 81.89, \"(-9.05, -8.97)\": 80.48, \"(-8.97, -8.93)\": 79.36, \"(-8.93, -8.86)\": 78.35, \"(-8.86, -8.79)\": 77.27, \"(-8.79, -8.73)\": 76.13, \"(-8.73, -8.65)\": 74.9, \"(-8.65, -8.59)\": 73.6, \"(-8.59, -8.51)\": 72.49, \"(-8.51, -8.43)\": 71.2, \"(-8.43, -8.36)\": 69.95, \"(-8.36, -8.3)\": 68.88, \"(-8.3, -8.24)\": 67.85, \"(-8.24, -8.2)\": 66.82, \"(-8.2, -8.09)\": 65.74, \"(-8.09, -7.99)\": 64.03, \"(-7.99, -7.92)\": 62.6, \"(-7.92, -7.84)\": 61.47, \"(-7.84, -7.77)\": 60.46, \"(-7.77, -7.7)\": 59.21, \"(-7.7, -7.64)\": 58.1, \"(-7.64, -7.56)\": 57.06, \"(-7.56, -7.47)\": 55.86, \"(-7.47, -7.38)\": 54.54, \"(-7.38, -7.3)\": 53.23, \"(-7.3, -7.2)\": 52.18, \"(-7.2, -7.12)\": 50.56, \"(-7.12, -7.05)\": 49.46, \"(-7.05, -6.96)\": 48.46, \"(-6.96, -6.86)\": 47.23, \"(-6.86, -6.78)\": 45.85, \"(-6.78, -6.69)\": 44.83, \"(-6.69, -6.61)\": 43.72, \"(-6.61, -6.48)\": 42.57, \"(-6.48, -6.39)\": 40.79, \"(-6.39, -6.31)\": 39.61, \"(-6.31, -6.19)\": 38.35, \"(-6.19, -6.11)\": 37.14, \"(-6.11, -6.01)\": 36.08, \"(-6.01, -5.91)\": 35.08, \"(-5.91, -5.8)\": 33.79, \"(-5.8, -5.71)\": 32.62, \"(-5.71, -5.62)\": 31.54, \"(-5.62, -5.54)\": 30.51, \"(-5.54, -5.42)\": 29.34, \"(-5.42, -5.32)\": 28.19, \"(-5.32, -5.21)\": 27.1, \"(-5.21, -5.13)\": 26.08, \"(-5.13, -4.99)\": 24.98, \"(-4.99, -4.88)\": 23.88, \"(-4.88, -4.78)\": 22.76, \"(-4.78, -4.66)\": 21.64, \"(-4.66, -4.55)\": 20.58, \"(-4.55, -4.4)\": 19.56, \"(-4.4, -4.27)\": 18.08, \"(-4.27, -4.1)\": 17.07, \"(-4.1, -3.94)\": 15.39, \"(-3.94, -3.78)\": 14.35, \"(-3.78, -3.67)\": 13.2, \"(-3.67, -3.51)\": 12.18, \"(-3.51, -3.34)\": 11.09, \"(-3.34, -3.19)\": 10.02, \"(-3.19, -3.01)\": 8.96, \"(-3.01, -2.81)\": 7.91, \"(-2.81, -2.64)\": 6.89, \"(-2.64, -2.43)\": 5.88, \"(-2.43, -2.18)\": 4.83, \"(-2.18, -1.92)\": 3.8, \"(-1.92, -1.68)\": 2.8, \"(-1.68, -1.35)\": 1.78, \"(-1.35, -0.86)\": 0.78, \"(-0.86, 1.35)\": -0.23, \"(1.35, 1.69)\": 0.83, \"(1.69, 1.95)\": 1.89, \"(1.95, 2.22)\": 2.9, \"(2.22, 2.45)\": 3.93, \"(2.45, 2.64)\": 4.99, \"(2.64, 2.82)\": 6.0, \"(2.82, 3.01)\": 7.09, \"(3.01, 3.17)\": 8.1, \"(3.17, 3.33)\": 9.18, \"(3.33, 3.48)\": 10.2, \"(3.48, 3.63)\": 11.21, \"(3.63, 3.76)\": 12.21, \"(3.76, 3.9)\": 13.23, \"(3.9, 4.02)\": 14.25, \"(4.02, 4.14)\": 15.25, \"(4.14, 4.33)\": 16.51, \"(4.33, 4.43)\": 18.04, \"(4.43, 4.59)\": 19.11, \"(4.59, 4.72)\": 20.23, \"(4.72, 4.83)\": 21.31, \"(4.83, 4.94)\": 22.41, \"(4.94, 5.06)\": 23.55, \"(5.06, 5.21)\": 24.74, \"(5.21, 5.33)\": 26.5, \"(5.33, 5.44)\": 27.56, \"(5.44, 5.56)\": 28.81, \"(5.56, 5.7)\": 30.24, \"(5.7, 5.79)\": 31.47, \"(5.79, 5.9)\": 32.75, \"(5.9, 5.97)\": 33.84, \"(5.97, 6.06)\": 34.84, \"(6.06, 6.17)\": 35.97, \"(6.17, 6.26)\": 37.13, \"(6.26, 6.36)\": 38.33, \"(6.36, 6.44)\": 39.56, \"(6.44, 6.53)\": 40.65, \"(6.53, 6.64)\": 42.19, \"(6.64, 6.72)\": 43.31, \"(6.72, 6.79)\": 44.31, \"(6.79, 6.87)\": 45.37, \"(6.87, 6.95)\": 46.4, \"(6.95, 7.04)\": 47.51, \"(7.04, 7.12)\": 48.74, \"(7.12, 7.21)\": 49.81, \"(7.21, 7.3)\": 51.11, \"(7.3, 7.37)\": 52.36, \"(7.37, 7.46)\": 53.51, \"(7.46, 7.55)\": 54.83, \"(7.55, 7.64)\": 56.16, \"(7.64, 7.75)\": 57.7, \"(7.75, 7.82)\": 59.25, \"(7.82, 7.88)\": 60.47, \"(7.88, 7.98)\": 61.58, \"(7.98, 8.07)\": 62.96, \"(8.07, 8.13)\": 64.28, \"(8.13, 8.22)\": 65.68, \"(8.22, 8.3)\": 66.71, \"(8.3, 8.37)\": 68.12, \"(8.37, 8.43)\": 69.16, \"(8.43, 8.52)\": 70.63, \"(8.52, 8.59)\": 71.64, \"(8.59, 8.63)\": 72.97, \"(8.63, 8.72)\": 74.06, \"(8.72, 8.78)\": 75.16, \"(8.78, 8.82)\": 76.16, \"(8.82, 8.89)\": 77.2, \"(8.89, 8.96)\": 78.23, \"(8.96, 9.02)\": 79.31, \"(9.02, 9.07)\": 80.46, \"(9.07, 9.14)\": 81.5, \"(9.14, 9.2)\": 82.54, \"(9.2, 9.26)\": 84.28, \"(9.26, 9.34)\": 85.34, \"(9.34, 9.43)\": 86.69, \"(9.43, 9.51)\": 88.17, \"(9.51, 9.59)\": 89.86, \"(9.59, 9.66)\": 91.37, \"(9.66, 9.73)\": 92.47, \"(9.73, 9.81)\": 94.14, \"(9.81, 9.86)\": 95.26, \"(9.86, 9.92)\": 96.46, \"(9.92, 9.96)\": 97.73}\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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ppOQdd3d3rF+/Xu63JiIiogpodQLvbkmSXO/t4qhgN4YzvXM6REREZLDDKZnIzNNIqq3pYIOOAe4Kd2QYBhQiIiILsuO09FuLuzapa1JrnzyIAYWIiMhCaHUC/z10WXL9S538FeymchhQiIiILMSSnedRpJV274ubky06NTKdpe0fxoBCRERkAbQ6ga/2XpRc/9GgIJO9vAMwoBAREVmEQ3/flnz2JLi+K/oE+ijcUeUwoBAREVmA/ck3Jdf2CzLtcAIwoBAREVmEk9eyJdeO6Fz6oqqmhAGFiIjIAjjb20iq83d3Msml7R9m+h0SERFRhToGSLsj56VODZVtxEgYUIiIiCyAlDVNVCpgROeGyjdjBAwoREREZi4qMQ1dPt5VYd2YrgFmcXkHMGCzQCIiIjIdEVGnsXJfSrk1ahUwumsAZoa3rKKuKo8BhYiIyExFJaZWGE5cHG0R904YnCROojUV5nGeh4iIiErQ6gTe3ZJUYV1OQTESrmYp35CRMaAQERGZocMpmcjMK5JUe+NOgcLdGB8DChERkRn6et8FybWetRwV7EQZDChERERmRlOsw+5ztyTVujjaomOAu8IdGR8DChERkZlZG1P+xNgHDWpXz6R3LS4LAwoREZGZ+Xrf35Jrn2pl+hsDloYBhYiIyIws+OM0buZqJNU62anN8vIOwIBCRERkNjTFOnx7QPrlndFdG5nl5R2AAYWIiMhs/Df2EoSQVmurVmFyWFNlG1IQAwoREZGZ2J98U3LtxJ5NzPbsCcCAQkREZBa0OoH9F6TdWuxop8aEno8p3JGyGFCIiIjMwJKd56HVSatdNCjIrM+eAAwoREREJk+rE/hit7SVY71dHNCvTT2FO1IeAwoREZGJi7lwC1qJk2OD6rsq20wVYUAhIiIycct2JUuu7dDQQ8FOqg4DChERkQnT6gTir2RJrh/RuaFivVQlBhQiIiITdujv2yjWSbu+0ynAHfa2lvHRbhmvgoiIyEKtO3RZcu33o0IU7KRqMaAQERGZKK1OYO+5G5Jq2/m5WszZE4ABhYiIyGQNXRmDu0XSFj+Z9lRzhbupWgwoREREJmj090dw9HKWpFpnext0amwZd+/cx4BCRERkYvI1Wuw4Le3SDgCM7dbY7FeOfRgDChERkYlZ8McpybU1HWzNft+d0jCgEBERmZh9ydI2BQSA59vXt7izJwADChERkUnR6gSuZeZLrn+ypbeC3VQfBhQiIiITMnnDcUjctBi1nW3RMcBd0X6qi+yAsm/fPvTv3x++vr5QqVT47bffSjwvhMCcOXPg4+MDJycnhIWFITm55B4CmZmZGD58OFxcXODm5oZRo0YhNze3Ui+EiIjI3GmKdYhMTJNcv2BAa4u8vAMYEFDy8vIQHByML774otTnFy5ciKVLl2LFihWIi4tDjRo10Lt3bxQUFOhrhg8fjlOnTmHHjh2IjIzEvn37MGbMGMNfBRERkQV4Z1Oi5Np+QT4ID/JVsJvqpRJCSNzAuZTBKhU2b96MAQMGALh39sTX1xfTpk3Df/7zHwBAdnY2vLy8sGbNGgwbNgxnzpxBy5YtceTIEbRv3x4AsG3bNoSHh+PatWvw9a34h52TkwNXV1dkZ2fDxcXF0PaJiIhMhlYn0GLONmiKK77AY69W4cwHT5vd2RM5n99GnYOSkpKC9PR0hIWF6R9zdXVFSEgIYmNjAQCxsbFwc3PThxMACAsLg1qtRlxcXKnHLSwsRE5OTokvIiIiS3Lo4m1J4QQAnmnja3bhRC6jBpT09HQAgJeXV4nHvby89M+lp6fD09OzxPO2trZwd3fX1zwsIiICrq6u+i8/Pz9jtk1ERFTt1sVdklz74cAg5RoxEWZxF8/MmTORnZ2t/7p69Wp1t0RERGQ0Wp2QvPaJpW0KWBajvkJv73v3YmdkZJR4PCMjQ/+ct7c3btwouXxvcXExMjMz9TUPc3BwgIuLS4kvIiIiS3E4JRN5hVpJtdN6W9amgGUxakAJCAiAt7c3oqOj9Y/l5OQgLi4OoaGhAIDQ0FBkZWXh2LFj+ppdu3ZBp9MhJCTEmO0QERGZhW/3X5RUV8PeBp0aWdamgGWxlTsgNzcXFy5c0P85JSUFCQkJcHd3R4MGDTBlyhR88MEHaNKkCQICAjB79mz4+vrq7/Rp0aIF+vTpg9GjR2PFihUoKirChAkTMGzYMEl38BAREVmSqMQ0RJ+9Kal2jAVuClgW2QHl6NGjeOKJJ/R/fvPNNwEAI0aMwJo1azB9+nTk5eVhzJgxyMrKwuOPP45t27bB0dFRP+aHH37AhAkT0KtXL6jVagwaNAhLly41wsshIiIyH1qdwPRfpa19YqmbApalUuugVBeug0JERJZgyc5kLN55XlLtq10aYk7/Vgp3pKxqWweFiIiIpNHqBFYfTJFcb6mbApaFAYWIiKgaHE7JRFZ+kaRa9xp2FrspYFkYUIiIiKrBztOlL05amg+eDbSaybH3MaAQERFVMa1OYHPCdUm1/YO8LXpTwLIwoBAREVWxwymZyMyr+PJOTQdbfD6sXRV0ZHoYUIiIiKrYh1GnJdU9376+1V3auY8BhYiIqAot+OM0Tl7PkVRrbXfuPIgBhYiIqIpoinX4Zr+0W4ut8c6dBzGgEBERVZHv9v8tufa5NvWs9vIOwIBCRERUZTYfl3bnDgCEWfHlHYABhYiIqMpc+eeupDq1ClZ9eQdgQCEiIqoSo78/gvwinaTaLo09rPryDsCAQkREpLh8jRY7Tt+QXP/1yx0U7MY8MKAQEREpbMz3RyTXPtnSE072Ngp2Yx4YUIiIiBSk1QkcvHhbUq2TnRrf8OwJAAYUIiIiRR1OyYROSKv1q+2sbDNmhAGFiIhIQTtk7Fo8sG09BTsxLwwoRERECtmWlIbvDl6SXP9q10bKNWNmGFCIiIgUoNUJTN6QILl+dNcA2NvyY/k+/iSIiIgUMPnH4ygslrbuyZMtPTGrb0uFOzIvDChERERGpinWIfJkmqTa3i29eOdOKRhQiIiIjGxtzCXJtU28airXiBljQCEiIjKyyMRUybWhjeoo2In5YkAhIiIyIq1O4FRqtqRaO7UKnRp7KNyReWJAISIiMqLJG45D4txYvN6jsdVvClgWBhQiIiIjiUpMQ2SitMmxtmoVpoQ1Vbgj88WAQkREZARancDUjQmS6yf2bMKzJ+VgQCEiIjKCmAu3JK974mCrxoSejynckXljQCEiIjKCX+OvSa7t2dyTZ08qwIBCRERkBMev/iO59qVO/gp2YhkYUIiIiCopKjENl2/nS6p1tFOjUyPeWlwRBhQiIqJK0OoE3vw5QXL9okHBvLwjAQMKERFRJSyLPo+CImmTY1vXc0G/Nr4Kd2QZGFCIiIgMpNUJLN99UXL9O+HcsVgqBhQiIiIDxSTfQrFOSKqtYW+DjgHuCndkORhQiIiIDDQv8pTk2te6BnDuiQwMKERERAaIiDqNCzfzJNXaqIFJvbisvRwMKERERDJpinVYuS9Fcv2ANvV49kQmBhQiIiKZwpfslVUfMTBIoU4sFwMKERGRDPkaLS7cvCu5fmy3ANjb8uNWLqP/xLRaLWbPno2AgAA4OTmhcePGeP/99yHE/89yFkJgzpw58PHxgZOTE8LCwpCcnGzsVoiIiIzu9XVHJde6ONpiJm8tNojRA8rHH3+Mr776CsuXL8eZM2fw8ccfY+HChVi2bJm+ZuHChVi6dClWrFiBuLg41KhRA71790ZBQYGx2yEiIjIarU5gf/ItyfX9grwV7May2Rr7gDExMXj22WfRt29fAEDDhg3x448/4vDhwwDunT35/PPP8e677+LZZ58FAHz//ffw8vLCb7/9hmHDhhm7JSIiIqOIuXALEpc9AQDM7heoXDMWzuhnUDp37ozo6GicP38eAHDixAkcOHAATz/9NAAgJSUF6enpCAsL049xdXVFSEgIYmNjSz1mYWEhcnJySnwRERFVtflbpa970rNZHTjZ2yjYjWUz+hmUGTNmICcnB82bN4eNjQ20Wi0WLFiA4cOHAwDS09MBAF5eXiXGeXl56Z97WEREBObNm2fsVomIiCTTFOuQLHHdEzs18N3IEIU7smxGP4OyceNG/PDDD1i/fj3i4+Oxdu1aLFq0CGvXrjX4mDNnzkR2drb+6+rVq0bsmIiIqGL/jb0kuXZ6n+bKNWIljH4G5a233sKMGTP0c0lat26Ny5cvIyIiAiNGjIC3970JQxkZGfDx8dGPy8jIQJs2bUo9poODAxwcHIzdKhERkWS/Hr8muXZE5wAFO7EORj+DcvfuXajVJQ9rY2MDne7eVtQBAQHw9vZGdHS0/vmcnBzExcUhNDTU2O0QERFVWkTUaZxOvSOptlMjd657YgRGP4PSv39/LFiwAA0aNECrVq1w/PhxfPbZZ3j11VcBACqVClOmTMEHH3yAJk2aICAgALNnz4avry8GDBhg7HaIiIgqRc6y9ioA37/KuSfGYPSAsmzZMsyePRtvvPEGbty4AV9fX4wdOxZz5szR10yfPh15eXkYM2YMsrKy8Pjjj2Pbtm1wdHQ0djtERESVMmLVYcm1Y7hqrNGoxINLvJqJnJwcuLq6Ijs7Gy4uLtXdDhERWShNsQ5N3/1TUm0rn1r4Y3I3hTsyb3I+vxnziIiIyvDOppOSawe2q69gJ9aHAYWIiKgUWp3A5oTrkuv/HdpQuWasEAMKERFRKWKSb0ErcV173rljfPxpEhERlWLprvOSa3nnjvExoBARET1k9PdHcORylqRafw9nnj1RAH+iRERED1jwx2nsOH1Dcv1LIf4KdmO9GFCIiIj+R1Oswzf7pS3KBgAqFTCic0PlGrJiDChERET/02/pfln1Y7pyYTal8KdKREQEIF+jxfkbuZLr+wX5YGZ4SwU7sm4MKERERADG/veo5Fp7NbBkWFsFuyEGFCIisnpancD+5FuS68f2aAwbtUrBjogBhYiIrN6SnechdWM6NYApYc2UbIfAgEJERFZOqxP4YvcFyfWfPx/MsydVgAGFiIis2uQNx6GVePrExdEWz3BTwCrBgEJERFZLU6zDH4lpkusn9myiYDf0IAYUIiKyWv+NvSR57okKXJStKjGgEBGR1dqffFNy7WtclK1K8SdNRERWSasTOHo5U1JtU68amNWXi7JVJdvqbsCUaHUCh1MyceNOATxrOaJjgDtnahMRWajluy4gt1BXYZ0KQOTEbso3RCUwoPzPtqQ0vPf7KaTnFOof83ZxwHvPtEKfQJ9q7IyIiIxtW1IaFu88L6n2ieZ1eWmnGvAnjnv/o76+Lr5EOAGA9JxCvL4uHtuSpM/wJiIi06bVCUzbeEJy/eiujRXshspi9QFFqxOYselkuTXTNp6AVid1njcREZmyZdHnkafRSqr1cb13uZ+qntUHlEMXbyPrblG5NXkaLZZGSzsVSEREpkurE1i+S/qqsXP7t+RcxGpi9QEl9m9pm0Mtjb7AsyhERGZu8o/HUSzxr/LwQC/OQaxGVh9Q7s3PrpgAMGm99K24iYjItEQlpiHypPQ5hcNDGirXDFXI6gNKaGMPybV/JN2AprjiW9KIiMi0aHUCk386Lrne0VaNTjI+H8j4rD6gdGrkATsb6dcXZ/6aqGA3RESkhAPJN1EkdUdAAK93b8y5J9XM6gOKjVqFZ4J9JddvTrjOuShERGbmldVHJNfa2agwsRc3BaxuVh9QACBiYJDkWp24tzU3ERGZh75L90neEBAAxvfg2RNTwIACwN5WjfBAb8n1kYlpnItCRGQGcguKcSr1juR6FYCJvZoq1xBJxoDyP8tebAcbGT+Nmb9KX4WQiIiqR9+l+2TVj3+CZ09MBQPK/9ioVVg2rJ3k+q2JaZyLQkRkwjTFOlzOzJc1ZuqTzRTqhuRiQHlAeJAPHqtbU1KtRitvNUIiIqpaw7+NlVX/+eBgnj0xIQwoD5nbv6Xk2sU7z3MjQSIiE6Qp1uHIpSzJ9TXt1RjQvr5yDZFsDCgP6fxYHTjaSf+xTPzxOC/1EBGZmL5L5M09WfnvDgp1QoZiQHmIjVqFz4a0kVxfpBWYuP6Ycg0REZEs+Rotkm/mSa63U4GrxpogBpRShAf5YGy3AMn1UUkZvO2YiMhEDPrqoKz6T4e04dwTE8SAUoaZ4S0RVM9Fcv3bvyQo1wwREUkSlZiK02nS1z0JqOOMZ9rVU7AjMhQDSjn6y1oCn7cdExFVJ61OYMpPCZLrVQB2vtlDqXaokhhQyjGis/TLPAAwcX28Qp0QEVFFJm84Do2MDQE/H8ZLO6ZMkYBy/fp1vPTSS/Dw8ICTkxNat26No0eP6p8XQmDOnDnw8fGBk5MTwsLCkJycrEQrlWJvq0YLL2nrogBAVFI656IQEVUDTbEOkYnSl31wcbDBs214aceUGT2g/PPPP+jSpQvs7Ozw559/4vTp0/j0009Ru3Ztfc3ChQuxdOlSrFixAnFxcahRowZ69+6NgoICY7dTaZvGPy6rvt8yebe2ERFR5cndfoS7FZs+lRDCqBMnZsyYgYMHD2L//v2lPi+EgK+vL6ZNm4b//Oc/AIDs7Gx4eXlhzZo1GDZs2CNjCgsLUVhYqP9zTk4O/Pz8kJ2dDRcX6RNZDfXkZ3uQfEP6LWtPtvTENy/znnoioqqg1Qk0mRUFqdMAVQDOffA07G05y6Gq5eTkwNXVVdLnt9Hfnd9//x3t27fHkCFD4OnpibZt2+Kbb77RP5+SkoL09HSEhYXpH3N1dUVISAhiY0tfljgiIgKurq76Lz8/P2O3Xa4/JnWTVb/j9A3ka7QKdUNERA9aFp0sOZwAwJhuAQwnZsDo79Dff/+Nr776Ck2aNMH27dsxbtw4TJo0CWvXrgUApKenAwC8vLxKjPPy8tI/97CZM2ciOztb/3X16lVjt10ue1s1Rj3eUNaYsf89WnERERFVilYn8NXei5LrwwO9MDNc+pYmVH1sjX1AnU6H9u3b48MPPwQAtG3bFklJSVixYgVGjBhh0DEdHBzg4OBgzDZlm92vFY5cykTitRxJ9Qcu3IJWJzhDnIhIQVM2xKNQ4s0JdjYqLHvxXwp3RMZi9DMoPj4+aNmyZDpt0aIFrly5AgDw9vYGAGRkZJSoycjI0D9nqn6f0BW+ro6SanUCmPQjbzsmIlJKVGIatiaWfua9NON7PMZ/NJoRoweULl264Ny5cyUeO3/+PPz9/QEAAQEB8Pb2RnR0tP75nJwcxMXFITQ01NjtGF3EgNaSa/84mY6oxFQFuyEisk5ancD0XxMl1zvYqnnnjpkxekCZOnUqDh06hA8//BAXLlzA+vXr8fXXX2P8+PEAAJVKhSlTpuCDDz7A77//jpMnT+Lll1+Gr68vBgwYYOx2jO7xZnUhJ4BP4G7HRERGd+jibeQWFkuuX/x8MM+emBmjB5QOHTpg8+bN+PHHHxEYGIj3338fn3/+OYYPH66vmT59OiZOnIgxY8agQ4cOyM3NxbZt2+DoKO3ySXWyUaswsedjkut1Api0nhNmiYiM6d0tJyXX9gvyQXiQ9K1LyDQYfR2UqiDnPmolaHUCLedskzwxCwDO8557IiKjWPDHaXyzP0VSrYOtGqfn9+HZExNRreugWAMbtQqLnw+WNWb1QWm/TEREVDZNsU5yOAGAcd0bMZyYKQYUA4UH+cKzlvRbn787+LeC3RARWYeZm+ROjG2qYDekJAaUSniti/TdjjNyNIiIOq1gN0RElk2rE/g9QfqdkW/wtmKzxoBSCa88Lj2gAMDKfSnc7ZiIyEDLopNRJPGuSDsbFSbIuKGBTA8DSiXY26oRHihvcbmOC3Yo1A0RkeXalpSGz6OTJddz7on5Y0CppGUvtoOtjF+CrPxivP/7KQU7IiKyLFqdwLSNJyTXq1XA5LBmCnZEVYEBpZJs1Cosf7GtrDGrYi7xUg8RkUTPr4xBnowd4if25NwTS8CAYgR9An0wSea1zn+vOqRQN0REliMyIRXHLmdJrq9hb4NJvHPHIjCgGMnksKawkfHTjEv5h2dRiIjKodUJ/OdX6Zd2AOBTLmlvMRhQjMRGrcLiwW1kjWkzf7syzRARWYDluy6goEj6P+S+fLEt+gT6KNgRVSUGFCN6pl09BHg4Sa6/q9Hh1dWHFeyIiMg8aXUCX+65ILl+ULt63G/HwjCgGNnOaU9AzsnFXeduIl/G5C8iImsQc+GWrP3OIgYGKdgNVQcGFCOTu9sxAAz66qBC3RARmacJ649Jrh3dNYCbsVogvqMKmBzWFHLmaJ1Ou8MJs0RE/9Nv6V5kF0g7s1ynph1m9W2pcEdUHRhQFGCjVuHzIW1kjZGzARYRkaUateYIklJzJdeP7dZYwW6oOjGgKOSZdvVQ3036bseRiWnQStxjgojIEkUmpCL67A1ZY0Z0lrcnGpkPBhQFfTJE+gqzhcU6LN8lfZ8JIiJLotUJTP05QdaY0Ea1OffEgvGdVVDHAHfUdrKVXL94ZzK2JaUp2BERkWmKuXALRVp5Z5HXvtpJoW7IFDCgKMhGrcKC51rLGjN5QwIv9RCR1ZmwPl5W/ajHeeeOpeO7q7DwIF+M7tpQcn1hsQ5DVsQo1xARkYnpv2w/sguKJde39q2F2f14546lY0CpArP6tsLortIncsVfyUJkQqqCHRERmYYPtp7Cyes5kuu9atlj66RuCnZEpoIBpYrM6tsSvVt5Sa6fuOE4L/UQkUXTFOvw7cFLssbsf7uXMs2QyWFAqUIvd2oouVYA6PzRTsV6ISKqbv2W7ZNVz7t2rAvf6SrUqbEH7GykLzGbkaPBb0evKtgREVH1yNdocT4jT9YY3rVjXRhQqpCNWoVx3RrJGjPll0Re6iEiizPwC3l7kI16vCHPnlgZvttVbPKTzeAg85dsvIxNs4iITJ2mWIczGXck19+7a6eVgh2RKWJAqWI2ahWWDGsja8y2pAxEJXIBNyKyDHL2HrO3UfGuHSvFgFIN+gT6YHx3eRtcTfwxnpd6iMjsaXUCkYnSl1GY+mRTBbshU8aAUk3e7N1MVr1WAIt3nFOoGyKiqnHo79soLJb+j61Rj8ubt0eWgwGlmtioVVj6fLCsMct3X+RZFCIyW9uS0jD+B+lL2ocHenFirBXjO1+NnmlXH0H1XGSNGfLVAYW6ISJSzrakNLy+Lh5Z+UWS6m1UwLIX/6VwV2TKGFCq2e8Tu6K2s/Qdj+Ov5mDBH6cV7IiIyLi0OoE3N56QNWbZC21ho5a+bhRZHgYUExAzI0xW/Tf7U6Ap1inUDRGRcU1cH4+7Gq3k+rHdAhAe5KtgR2QOGFBMgJO9DZp61pQ15t/fHlKoGyIi4/lg62lEJaVLqq3pYIsvX2yHmeHcqZgYUExG5KSusurjLv3DsyhEZNIiok7j24MpkutXvvQvhAf5KNgRmRMGFBNhb6vG6K4Bssb0XbJXoW6IiCpHU6zD1/ulhxNnOxt0auyhYEdkbhhQTMisvi3RpK6z5Prkm3cxf+spBTsiIjLMO5sSIWSsihDe2puTYqkEBhQT88fk7rLqvzt4CRFRvKuHiEyHView+fh1yfUqFfDhwCAFOyJzxIBiYuxt1Rj1eENZY1bu4109RGQ6Jv94HFoZZ0/GdA3ggmz0CP4fYYJm92sFd2c7WWP6Lt2nUDdERNJFJaYi8qT0zU17NK3Du3aoVIoHlI8++ggqlQpTpkzRP1ZQUIDx48fDw8MDNWvWxKBBg5CRkaF0K2bl4IxesuqTb+QhMkH6BlxERMam1QnM+i1J1pix3R9TqBsyd4oGlCNHjmDlypUICip5bXHq1KnYunUrfv75Z+zduxepqakYOHCgkq2YHSd7GzzZ0lPWmAkbjnOvHiKqNst3JeOfu9KWsgeA2s526BjgrmBHZM4UCyi5ubkYPnw4vvnmG9SuXVv/eHZ2NlatWoXPPvsMPXv2xL/+9S+sXr0aMTExOHSo9MXHCgsLkZOTU+LLGnzzcge09ZO7V0+MQt0QEZVtW1IaFu9MljVmwYBA3rlDZVIsoIwfPx59+/ZFWFjJZdyPHTuGoqKiEo83b94cDRo0QGxsbKnHioiIgKurq/7Lz89PqbZNzi/jHpdVH381C/kylpQmIqosrU7g9XXSdykGgNFdG3I5eyqXIgFlw4YNiI+PR0RExCPPpaenw97eHm5ubiUe9/LyQnp66cshz5w5E9nZ2fqvq1evKtG2SbJRq9C6Xi1ZYzpH7FSoGyKiRwW/t01W/WtdAjCrbyuFuiFLYfSAcvXqVUyePBk//PADHB0djXJMBwcHuLi4lPiyJj+O7iyr/p/8YnRbuEuhboiI/t/I1YeRq5G+zMHorgF4tz/v2qGKGT2gHDt2DDdu3EC7du1ga2sLW1tb7N27F0uXLoWtrS28vLyg0WiQlZVVYlxGRga8vb2N3Y5FqOloi9b15IWyK5n5GLUmTqGOiIiArSdSsfvcTcn1QfVdMKsvwwlJY/SA0qtXL5w8eRIJCQn6r/bt22P48OH6/7azs0N0dLR+zLlz53DlyhWEhoYaux2LsXViV7g728gaE332FuejEJEitDqB6b+ckDWmf1A9hbohS2Rr7APWqlULgYGBJR6rUaMGPDw89I+PGjUKb775Jtzd3eHi4oKJEyciNDQUnTp1MnY7FiV+Th8Ev7cd2QXFksd0+Sga8XOeUrArIrJGh/6+jfwieStYj+jcUJlmyCJVy0qyixcvRr9+/TBo0CB069YN3t7e2LRpU3W0YnaOvPukrPrMu0V4be0RhbohImv1ziZ5Z09GdvbncvYki0oIOftNmoacnBy4uroiOzvb6ibMAsD7kUlYdeCyrDFn5veBk728S0RERKXp/skuXL6dL7neo4Ytjs3urWBHZC7kfH4zzpqh2f0CEegr79bjXp/uVqgbIrIm87eekhVOajmoGU7IIAwoZipyUjd41rSXXJ+aXYhRaw4r2BERWTpNsQ7fHbwkud7BVo2EuX2Ua4gsGgOKGYt9J6ziogdEn72J9yPlbeRFRHTf9I0JsuqXDGvDpezJYAwoZsxGrcKANj6yxqw6cBlRidz1mIjk6b9sP35LTJNc371pHfQJlPf3E9GDGFDM3MLBbWSPmcRdj4lIhu6f7MLJ6/I2aV3xUnuFuiFrwYBi5uxt1RjdNUDWmGIdMPirAwp1RESW5Lej12RNigWAXs3r8K5BqjQGFAswq29L9GpeV9aY41dz8MHW0wp1RESWQKsTmCJztVgvF3useiVEoY7ImjCgWIhVr3RE2/qussZ8ezAFUTKuKRORdQmN2CGr3slOjZgZ8ibvE5WFAcWC/PJGF8idMD9xQzznoxDRI+b9fgo37hTJGrN4KO/aIeNhQLEgNmoVJvZsImuMVgeEfbZHmYaIyCxFJaZhdcwlWWMGBHvxrh0yKgYUCzOpVxPInZuWcusuthy/rkxDRGRWtDqBCT/Gyx63cEg7Bboha8aAYmFs1Cp8PrSt7HGTf0rgpR4iQq9Pd0PuXwVjuwVwI0AyOv4fZYHCg3wxtpu8W48BIPTDnQp0Q0Tm4v3fk3BJ5i3FfVp5YmZ4S4U6ImvGgGKhZoa3xKSej8kacyNXg/7L9inUERGZsqjEVKyKkbdLugrAF8O5IBspgwHFgk0OawoHme/wyet38Hs856MQWROtTmDij8dlj1v6QlvetUOKYUCxYDZqFT59vo3scZM3cj4KkTUJWfAXtDJ/5Xs1r4P+wb7KNEQEBhSL169NPYS18JQ1RuDeX1hEZPm6fhyNW3nFssY09HDmarGkOAYUK/DtiA5o4ydvldlbecVo/z5DCpEle3XNYVz9p0DWGLUKiJ7WQ5mGiB7AgGIlfh3XRfaYW3lFeHXNYQW6IaLqFpmQil1nb8oet/yFdpx3QlWCAcVK2KhVWD5M/voou87eRL5Gq0BHRFRdtDqBCRvkT4od3TUA4UFcLZaqBgOKFenXxhdhLeTtegwAA788oEA3RFRd2s7fLnvMq539Masv1zuhqsOAYmW+HdERPZvVkTXmTHouIqJOK9QREVWlX49eQ06BvLOiPZrVwZxnAhXqiKh0DChW6LuRIWhT30XWmJX7UqAp1inUERFVBa1OYPqvJ2SNqe/mgDUjeccOVT0GFCv16xuPyx4TsmAH10chMmOTfzwma70TVydbHJgRplxDROVgQLFSNmoVvnxR3u6j/+QXo+msKGxLSlOoKyJSygdbTyPyZIasMUdmPalQN0QVY0CxYuFBPrI3FdQK4PV18QwpRGZkwR+n8e3BFFljerf05A7FVK34f5+VmxneEksNWA7/9XXxvNxDZAaiElPxzX554QQAvnyJmwBS9WJAITzTrh46BdSWPS74vW0KdENExpKv0WKyAeudrHiJi7FR9WNAIQDA96M6yR6Tq9GhyTtRCnRDRJUVEXUaLeZsQ5HMm+++fLEd+gRyMTaqfgwoBACwt1Vj1OMNZY8r0gm0M2DRJyJSTkTUaazcJ/+yzvJhbbhSLJkMBhTSm92vFVr71pI9LvNuMTYduapAR0Qkl6ZYZ1A4Gd21Ifq1qadAR0SGYUChErZO6oZW3vJDypu/JnLSLJEJaDn7T9ljXusSgFl9WynQDZHhGFDoEX9M6QY/N0fZ48I+3a1AN0QkVeCcbSiW+e+Efq198G5/7rFDpocBhUq1f0YveDjbyhqTcjsf834/pVBHRFSe8CV7kStz53FnezWWvCB/l3OiqsCAQmU6/O5TssesjrmED7ZyY0GiqvT+70k4nZYre9yiwcG8nZhMFgMKlclGrcKKl+Qthw8A3x5MwbytSQp0REQPi0y4jlUxl2WPe7VzQ4QH+SrQEZFxMKBQufoE+uDLF+WfAl598DJGrjmsQEdEdF9UYhombEiQPS7QxwVznuGkWDJtDChUofAgX3w+OFj2uN1nb+KZZfsV6IiItiWl4Y318bLH1bRXIXJyVwU6IjIuBhSSZED7+mhdT/7tx4nXc/B+JCfOEhmTVicwYb38JewB4MR7Txu5GyJlGD2gREREoEOHDqhVqxY8PT0xYMAAnDt3rkRNQUEBxo8fDw8PD9SsWRODBg1CRoa8bcCp6m2d2M2ghdxWHbgETbHM9baJqEzj/nsUxQasO8Q9dsicGD2g7N27F+PHj8ehQ4ewY8cOFBUV4amnnkJeXp6+ZurUqdi6dSt+/vln7N27F6mpqRg4cKCxWyEFbJ3UDSM7N5Q9LnzJXuM3Q2SFRn53GH+duSF73OIhwdxjh8yKSgih6PKfN2/ehKenJ/bu3Ytu3bohOzsbdevWxfr16zF48GAAwNmzZ9GiRQvExsaiU6eKN63LycmBq6srsrOz4eLiomT7VIY31h1DVFK6rDE9m9XBdyNDFOqIyPJ1/TgaV/8pkD0u0LcWIid1U6AjInnkfH4rPgclOzsbAODu7g4AOHbsGIqKihAWFqavad68ORo0aIDY2NhSj1FYWIicnJwSX1S9lr3YDg428k4V7zp3CyPXxCnUEZFle/yjnQaFE48adgwnZJYUDSg6nQ5TpkxBly5dEBgYCABIT0+Hvb093NzcStR6eXkhPb30f5FHRETA1dVV/+Xn56dk2ySBjVqFxUPl3368++wtvLb2iAIdEVmufkv34lpWoexx9VztcWy2/AUXiUyBogFl/PjxSEpKwoYNGyp1nJkzZyI7O1v/dfUqd841BeFBPhjV2V/2uJ1nbiAyIVWBjogsz7ubTyApVf4qsX5ujjg480kFOiKqGooFlAkTJiAyMhK7d+9G/fr19Y97e3tDo9EgKyurRH1GRga8vb1LPZaDgwNcXFxKfJFpmP1MIAJ9a8oe9+bPCciXuW8IkbXpv3Qf1sVdkz0u0LcW9s/opUBHRFXH6AFFCIEJEyZg8+bN2LVrFwICAko8/69//Qt2dnaIjo7WP3bu3DlcuXIFoaGhxm6HqkDkpO4I9JUXGjVagRZztnGNFKIy9F+2HydT78ge93KnBpxzQhbB6HfxvPHGG1i/fj22bNmCZs2a6R93dXWFk5MTAGDcuHGIiorCmjVr4OLigokTJwIAYmJiJH0P3sVjmkZ+dxi7z9+UPa51PRdsnciVLYnue//3U1gVc0n2uDb1XfHbhMeN3xCRkcj5/DZ6QFGpSr+zY/Xq1XjllVcA3Fuobdq0afjxxx9RWFiI3r1748svvyzzEs/DGFBM16ur47Dr3C3Z4wJ9aiJycncFOiIyL5EJqZiwQf4qsWoVkLwgnAuxkUmr1oBSFRhQTNvINXHYfdaAkMK1GsjKRSWm4g0Dl7D/8sV2CA/iQmxk2kxqHRSyPqtfCUFYC0/Z45JS72Dkd9wBmaxTZILh4WRstwCGE7I4DCikiG9HdMCkno/JHrf7/E2M/O6QAh0Rma73I08ZdFkHAJYPa4OZ4S2N3BFR9WNAIcVMDmsKr1oOssftPn8b4Yt3K9ARkekZ/f0RrDpwyaCxy4e1Rb829YzbEJGJYEAhxdioVZj3bCuDxp7OuIug97ZDa8COrUTmYuuJVOw4LX/jP+DeZZ1+bXyN3BGR6WBAIUX1CfTBipfawdaAOwtyCorR+J0obEtKU6AzouqVr9FiioGXdZa90JaXdcjiMaCQ4voE+uDcB0+jobujQeNfXxePzcfkr6ZJZKrmbT2FFnO2QWvACcIvX2yL/sE8c0KWjwGFqoSNWoU903sh0LeWQeOn/nwC/ZftN3JXRFWv60c7sfrgJYPGLh/WFuFBDCdkHRhQqEpFTuqGJ5rWNWjsyes56P7JLiN3RFQ1tDqBoPe246oBuxIDwOiunHNC1oUBharc6lc74ommHgaNvXw7H78e5W7WZF62JaWh+btRyCkoNmj82G4BmNWXc07IujCgULVY/WontPRyNmjstF8SsSXhupE7IlLGtqQ0vL4uHkU6+WPVAM7M78MJsWSVGFCo2kRNfQIeNewMGjt5QwJGrTli5I6IjCtfo8Xr6+INHr/8xbZwsrcxYkdE5oMBharVsdlPoZ6rvUFjo8/eQI+F0VwrhUzSgj9Oo8WcbQaPv7d8PeeckPViQKFqd3Dmk/BwtjVo7KXMAjSdxbVSyLSM/v4IvtmfYtBYR1s1vnyxHS/rkNVjQCGTcGxOb/i7Oxk0VivurZUSmZBq5K6I5Pv16FWDV4etW8MWp+b34cZ/RABUQgizOz8uZ7tmMi/Zd4vQ7oO/oDVgQiEAjAz1x9xnA43bFJEEWp3A4C8P4Pi1HIPG16lph6PvPmXkrohMi5zPb55BIZPi6myH8x+Ew9nesP81V8deRtePo43cFVH5IhOuo/E7UQaHkyea1WU4IXoIAwqZHBu1CosGtzF4/NV/CtB6zp+cPEtVYtSaw5iwIcHg8R8PDMTqkR2N1xCRhWBAIZMUHuSD0V0DDB5/R6ND43ei8Hs89/AhZWh1Ak98shvRZ28afAx/dycM7ehvxK6ILAcDCpmsWX1bYnTXhpU6xqSN3MOHjG9bUhpazP4TKbfvGnwMf3cn7J3e04hdEVkWBhQyabP6tsKXL7aFqhLHOHk9Bx0++IuXfMgo7q8MqzFkK+L/+XRwEMMJUQUYUMjkhQf54sKH4WjoYdhtyABwM7cITWZFISqRtyKTYbQ6gV1nblRqZVgA+PLFthjU3s9IXRFZLgYUMgs2ahX2vNUTfm6OBh9DJ4A31h9HRNRpI3ZG1iAqMQ3NZ/+JV9cavr2CnQ2w4qV2XB2WSCIGFDIr+2f0Qn0Dl8a/b+W+FGw9wTMpJM28LUl4Y308iipxScezlj3Ovh+OPoFcgI1IKgYUMjsHZj6J1r6VW6Bv4o/H8eZPx6EpNnBFOLIKj0fswOrYy5U6Rq9mdXF41pOwUVdmJhWR9WFAIbO0dVJXvNbF8NuQAWDT8VQ0ffdPvB/JSz5UklYn0PzdKFzL1lTqOEuGtsEqrnFCZBAudU9mTVOsQ9ePdyLjTlGljtOojjN2vNmD/8olRCWm4Y31lZsIC9ybDMv5JkQlcal7shr2tmrEzXoKS4a2qdRx/r51F43ficKWhOvGaYzM0oI/TlU6nKhVnAxLZAw8g0IWQ6sTGPjFfpy4fqdSx3GvYYeDb/eCk72NkTojU6bVCcQk38J7kadw8WZepY4V4OGEndOe4Jk4ojLI+fxmQCGL03/Zfpy8btimbQ9q5+eGn8d15oeNhdLqBBb9eRZf7f+70sfycLbF3um9UNPR1gidEVkuXuIhq7Z1Ylf0al6n0seJv5qFJu9wcTdLFJmQisbvRBklnPh7OOHYnN4MJ0RGxoBCFmnVKyGVnpcCADrcW9xtwg9HuVS+hRj53SFM2HDcOMfq0hB73+KS9URKYOQni/Vs23pwsFPjzY0ncFejrdSxIk9mYMeZP7H4+Tac/GimNMU6tJ2/HXmayq99Y2cDnJr3NOxt+W88IqXwt4ssWp9AH5x8rzcm93qsUhsOAkBhsfjf2ZR4nk0xM/O3nkLTd/80Sjjxq+2I5AV9GU6IFMZJsmQ1tDqBIV8dQPzVyk+gBYDWvrXw45jOnHtgojTFOqw68Dc+/es8io0UKEd1bojZz7QyyrGIrBHv4iEqR2TCdUzakABjLXLvVcsO+98O47+oTUS+RouBXxzAmYxcox2zU0BtfD+qE99jokpiQCGqgFYnMGTFQcRfyTbaMTs2dMO610L5IVaNRq05jOizN412PEdbNT57vg3Cg7jJH5ExMKAQSZSv0aLDBzuQW8lJtA8a9bg/ZvcLNNrxqHz5Gi0+jDqNjUevorDYeH+d9QvywZJhbbkODpERMaAQyTRvaxJWH6zcrrUPUgMIrl8LTwfWwyuPB/CsipFpdQKHLt7Gu7+dRMrtu0Y9tq0aOD2fd+gQKYEBhcgAmmId/r3qEOJS/jH6sUMCauO/nMNQaVqdwLLoZHy55wI0WuP/1dWjaR2seTXE6MclonsYUIgqYcEfp/HN/hRFjt3Iwxnzng1E58fq8NKBDFqdwPJdyVi+KxlFxprd/AB3ZzscnMH9l4iUZjYB5YsvvsAnn3yC9PR0BAcHY9myZejYsWOF4xhQSGlRiamYuvEECosV+DQEYKcGJvRsigk9H2NQKYNWJ7ArKR3/2XQC2QXGmyP0IEc7NY7OepK3ihNVEbMIKD/99BNefvllrFixAiEhIfj888/x888/49y5c/D09Cx3LAMKVQWtTmBp9Hl8sfui0dbRKI2Hsx1e7RqA0V0b8xIQgNyCYgz7OgZJqZXblboivZrXwapXeDmHqCqZRUAJCQlBhw4dsHz5cgCATqeDn58fJk6ciBkzZpQ7lgGFqtL9eQ+fRycr/r1s1cCAtr54/9kgq7rcoCnWYW3MJSzZeQ65RljttTz13Ryx480eVvXzJTIVJh9QNBoNnJ2d8csvv2DAgAH6x0eMGIGsrCxs2bKlRH1hYSEKCwv1f87JyYGfnx8DClUprU6g16e7cel2fpV8Pzu1CoPa1cPcZwIt8sM0X6PFgj9OISoxDZn5xYp/v+beNbH5jcct8mdJZC7kBJRqufB669YtaLVaeHl5lXjcy8sLZ8+efaQ+IiIC8+bNq6r2iEplo1Zhz1s9kVtQjB6f7MKtvCJFv1+RTmDD0WvYcPQa7G0ATxdHvNDRD6O7PmZ2l4K0OoFDf9/GzlMZ2HD0CvKVmOlaho7+blg3mgvoEZkbs5gZNnPmTLz55pv6P98/g0JUHWo62uLo7KcUWVK9LBotcO2fAnyyPRmfbE+GnRqY3b8Fmni6omOAu8lNtM2+W4RXVh/ChRt5yC/SQqG5xmXyc3PEK10C8O/QhgwmRGaqWgJKnTp1YGNjg4yMjBKPZ2RkwNvb+5F6BwcHODg4VFV7RJI42dvgz6ndoSnWofvCXUjLKax4kJEU6YA5W87o/+xiDxQJNdRQoU5NOzwZ6I0nmnmhUyMPRcOLVidwOCUTqVn5SLj6D3Q6gd8Srhtl12BD1HNzxE7OLyGyCNU6SbZjx45YtmwZgHuTZBs0aIAJEyZwkiyZpdyCYoQv2Ycr/1TNHBWpbFSAky1Qw8EOmXeLUKQDVACcbFVwdrDBXY0OxVodajjYIaxlXfRu5Ys/ElPxz91CnE+/gzuFxdDqgFoOatz936UZJzsbqFTAzdwiKHiDkyyjuwZgVt+W1d0GEZXD5CfJAvduMx4xYgRWrlyJjh074vPPP8fGjRtx9uzZR+amPIwBhUyZpliHGb+ewG/HU422YzKVzsFWhX5BvogYGMRLOURmwOQnyQLA0KFDcfPmTcyZMwfp6elo06YNtm3bVmE4ITJ19rZqfDa0LT4Z0gYxF25hwvp4ZBcof5eKtXC2V2PBc0HwdnE0yfk3RGQcXOqeqArkFhRj6k/HcSo1B6nZBdXdjlmyt1Hh0MwwuNe0r+5WiMhAZnEGhcia1HS0xTcjOgC4N7H007/O4ss9f1dzV+bBw9kWe6f34nL0RFaGZ1CIqolWJ7Dv3A1M3RCPrELOVnmQWgW82LEBZvVtyTtyiCyIWUySrQwGFLI095d6j71wAzEXb0OhvfFMmo0KaFSnBn4a25mXcYgsFAMKkZnTFOvw39hLOHcjB3+dTEeWhSaW1j410D/YD688HsC7cIisAAMKkYXRFOuwbPdZfL3nEgq1ZvcrCxvcuyXY2cEWwX6uWDLsX5xTQmSFGFCILFz23SKMXHMYKbfyoNNp4WijQma+DkXVvGqanY0KnQLc4V7DHvlFWnRo6IERnbncPBHdw7t4iCycq7MdNr3R5ZHH7y89n55TgOu38vD94Uv4524xHNTC6CvJ1nK0hVqtRt2aDgj2q43HH6uDTo2VXVqfiKwHz6AQERFRlZDz+c3zrkRERGRyGFCIiIjI5DCgEBERkclhQCEiIiKTw4BCREREJocBhYiIiEwOAwoRERGZHAYUIiIiMjkMKERERGRyzHKp+/uL3+bk5FRzJ0RERCTV/c9tKYvYm2VAuXPnDgDAz8+vmjshIiIiue7cuQNXV9dya8xyLx6dTofU1FTUqlULKpVxNibLycmBn58frl69arH7+1j6a7T01wfwNVoCS399gOW/Rkt/fYByr1EIgTt37sDX1xdqdfmzTMzyDIparUb9+vUVObaLi4vF/g93n6W/Rkt/fQBfoyWw9NcHWP5rtPTXByjzGis6c3IfJ8kSERGRyWFAISIiIpPDgPI/Dg4OmDt3LhwcHKq7FcVY+mu09NcH8DVaAkt/fYDlv0ZLf32AabxGs5wkS0RERJaNZ1CIiIjI5DCgEBERkclhQCEiIiKTw4BCREREJocBhYiIiEyOVQWUBQsWoHPnznB2doabm1upNVeuXEHfvn3h7OwMT09PvPXWWyguLi73uJmZmRg+fDhcXFzg5uaGUaNGITc3V4FXIM+ePXugUqlK/Tpy5EiZ43r06PFI/euvv16FnUvXsGHDR3r96KOPyh1TUFCA8ePHw8PDAzVr1sSgQYOQkZFRRR3Lc+nSJYwaNQoBAQFwcnJC48aNMXfuXGg0mnLHmfp7+MUXX6Bhw4ZwdHRESEgIDh8+XG79zz//jObNm8PR0RGtW7dGVFRUFXUqT0REBDp06IBatWrB09MTAwYMwLlz58ods2bNmkfeK0dHxyrqWL733nvvkX6bN29e7hhzef+A0v9OUalUGD9+fKn15vD+7du3D/3794evry9UKhV+++23Es8LITBnzhz4+PjAyckJYWFhSE5OrvC4cn+P5bKqgKLRaDBkyBCMGzeu1Oe1Wi369u0LjUaDmJgYrF27FmvWrMGcOXPKPe7w4cNx6tQp7NixA5GRkdi3bx/GjBmjxEuQpXPnzkhLSyvx9dprryEgIADt27cvd+zo0aNLjFu4cGEVdS3f/PnzS/Q6ceLEcuunTp2KrVu34ueff8bevXuRmpqKgQMHVlG38pw9exY6nQ4rV67EqVOnsHjxYqxYsQLvvPNOhWNN9T386aef8Oabb2Lu3LmIj49HcHAwevfujRs3bpRaHxMTgxdeeAGjRo3C8ePHMWDAAAwYMABJSUlV3HnF9u7di/Hjx+PQoUPYsWMHioqK8NRTTyEvL6/ccS4uLiXeq8uXL1dRx4Zp1apViX4PHDhQZq05vX8AcOTIkRKvbceOHQCAIUOGlDnG1N+/vLw8BAcH44svvij1+YULF2Lp0qVYsWIF4uLiUKNGDfTu3RsFBQVlHlPu77FBhBVavXq1cHV1feTxqKgooVarRXp6uv6xr776Sri4uIjCwsJSj3X69GkBQBw5ckT/2J9//ilUKpW4fv260XuvDI1GI+rWrSvmz59fbl337t3F5MmTq6apSvL39xeLFy+WXJ+VlSXs7OzEzz//rH/szJkzAoCIjY1VoEPjW7hwoQgICCi3xpTfw44dO4rx48fr/6zVaoWvr6+IiIgotf75558Xffv2LfFYSEiIGDt2rKJ9GsONGzcEALF3794ya8r6+8hUzZ07VwQHB0uuN+f3TwghJk+eLBo3bix0Ol2pz5vb+wdAbN68Wf9nnU4nvL29xSeffKJ/LCsrSzg4OIgff/yxzOPI/T02hFWdQalIbGwsWrduDS8vL/1jvXv3Rk5ODk6dOlXmGDc3txJnJMLCwqBWqxEXF6d4z3L8/vvvuH37NkaOHFlh7Q8//IA6deogMDAQM2fOxN27d6ugQ8N89NFH8PDwQNu2bfHJJ5+Ue0nu2LFjKCoqQlhYmP6x5s2bo0GDBoiNja2KdistOzsb7u7uFdaZ4nuo0Whw7NixEj9/tVqNsLCwMn/+sbGxJeqBe7+X5vB+ZWdnA0CF71dubi78/f3h5+eHZ599tsy/b0xFcnIyfH190ahRIwwfPhxXrlwps9ac3z+NRoN169bh1VdfhUqlKrPO3N6/B6WkpCA9Pb3Ee+Tq6oqQkJAy3yNDfo8NYZa7GSslPT29RDgBoP9zenp6mWM8PT1LPGZrawt3d/cyx1SXVatWoXfv3hXuBP3iiy/C398fvr6+SExMxNtvv41z585h06ZNVdSpdJMmTUK7du3g7u6OmJgYzJw5E2lpafjss89KrU9PT4e9vf0jc5C8vLxM7v0qzYULF7Bs2TIsWrSo3DpTfQ9v3boFrVZb6u/Z2bNnSx1T1u+lqb9fOp0OU6ZMQZcuXRAYGFhmXbNmzfDdd98hKCgI2dnZWLRoETp37oxTp04ptmt7ZYSEhGDNmjVo1qwZ0tLSMG/ePHTt2hVJSUmoVavWI/Xm+v4BwG+//YasrCy88sorZdaY2/v3sPvvg5z3yJDfY0OYfUCZMWMGPv7443Jrzpw5U+EkLnNiyGu+du0atm/fjo0bN1Z4/Afnz7Ru3Ro+Pj7o1asXLl68iMaNGxveuERyXt+bb76pfywoKAj29vYYO3YsIiIiTHqfDEPew+vXr6NPnz4YMmQIRo8eXe7Y6n4PCRg/fjySkpLKnZ8BAKGhoQgNDdX/uXPnzmjRogVWrlyJ999/X+k2ZXv66af1/x0UFISQkBD4+/tj48aNGDVqVDV2ZnyrVq3C008/DV9f3zJrzO39MydmH1CmTZtWbroFgEaNGkk6lre39yOzkO/f3eHt7V3mmIcnBRUXFyMzM7PMMZVlyGtevXo1PDw88Mwzz8j+fiEhIQDu/eu9Kj7cKvOehoSEoLi4GJcuXUKzZs0eed7b2xsajQZZWVklzqJkZGQo9n6VRu5rTE1NxRNPPIHOnTvj66+/lv39qvo9LEudOnVgY2PzyF1T5f38vb29ZdWbggkTJugnzMv9V7SdnR3atm2LCxcuKNSdcbm5uaFp06Zl9muO7x8AXL58GTt37pR91tHc3r/770NGRgZ8fHz0j2dkZKBNmzaljjHk99ggRpvNYkYqmiSbkZGhf2zlypXCxcVFFBQUlHqs+5Nkjx49qn9s+/btJjVJVqfTiYCAADFt2jSDxh84cEAAECdOnDByZ8a3bt06oVarRWZmZqnP358k+8svv+gfO3v2rElPkr127Zpo0qSJGDZsmCguLjboGKb0Hnbs2FFMmDBB/2etVivq1atX7iTZfv36lXgsNDTUJCdZ6nQ6MX78eOHr6yvOnz9v0DGKi4tFs2bNxNSpU43cnTLu3LkjateuLZYsWVLq8+b0/j1o7ty5wtvbWxQVFckaZ+rvH8qYJLto0SL9Y9nZ2ZImycr5PTaoV6MdyQxcvnxZHD9+XMybN0/UrFlTHD9+XBw/flzcuXNHCHHvf6zAwEDx1FNPiYSEBLFt2zZRt25dMXPmTP0x4uLiRLNmzcS1a9f0j/Xp00e0bdtWxMXFiQMHDogmTZqIF154ocpfX1l27twpAIgzZ8488ty1a9dEs2bNRFxcnBBCiAsXLoj58+eLo0ePipSUFLFlyxbRqFEj0a1bt6puu0IxMTFi8eLFIiEhQVy8eFGsW7dO1K1bV7z88sv6modfnxBCvP7666JBgwZi165d4ujRoyI0NFSEhoZWx0uo0LVr18Rjjz0mevXqJa5duybS0tL0Xw/WmNN7uGHDBuHg4CDWrFkjTp8+LcaMGSPc3Nz0d8/9+9//FjNmzNDXHzx4UNja2opFixaJM2fOiLlz5wo7Oztx8uTJ6noJZRo3bpxwdXUVe/bsKfFe3b17V1/z8OubN2+e2L59u7h48aI4duyYGDZsmHB0dBSnTp2qjpdQoWnTpok9e/aIlJQUcfDgQREWFibq1Kkjbty4IYQw7/fvPq1WKxo0aCDefvvtR54zx/fvzp07+s87AOKzzz4Tx48fF5cvXxZCCPHRRx8JNzc3sWXLFpGYmCieffZZERAQIPLz8/XH6Nmzp1i2bJn+zxX9HhuDVQWUESNGCACPfO3evVtfc+nSJfH0008LJycnUadOHTFt2rQSCXr37t0CgEhJSdE/dvv2bfHCCy+ImjVrChcXFzFy5Eh96DEFL7zwgujcuXOpz6WkpJT4GVy5ckV069ZNuLu7CwcHB/HYY4+Jt956S2RnZ1dhx9IcO3ZMhISECFdXV+Ho6ChatGghPvzwwxJnux5+fUIIkZ+fL9544w1Ru3Zt4ezsLJ577rkSH/imZPXq1aX+P/vgyU9zfA+XLVsmGjRoIOzt7UXHjh3FoUOH9M91795djBgxokT9xo0bRdOmTYW9vb1o1aqV+OOPP6q4Y2nKeq9Wr16tr3n49U2ZMkX/s/Dy8hLh4eEiPj6+6puXaOjQocLHx0fY29uLevXqiaFDh4oLFy7onzfn9+++7du3CwDi3Llzjzxnju/f/c+th7/uvw6dTidmz54tvLy8hIODg+jVq9cjr93f31/MnTu3xGPl/R4bg0oIIYx3wYiIiIio8rgOChEREZkcBhQiIiIyOQwoREREZHIYUIiIiMjkMKAQERGRyWFAISIiIpPDgEJEREQmhwGFiIiITA4DChEREZkcBhQiIiIyOQwoREREZHL+DyDQRzD1xiCFAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: x\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(-9.94, -9.85)\": 67.7, \"(-9.85, -9.78)\": 66.3, \"(-9.78, -9.67)\": 64.9, \"(-9.67, -9.56)\": 63.0, \"(-9.56, -9.45)\": 61.7, \"(-9.45, -9.35)\": 59.6, \"(-9.35, -9.26)\": 58.2, \"(-9.26, -9.17)\": 56.9, \"(-9.17, -9.07)\": 55.2, \"(-9.07, -8.96)\": 53.9, \"(-8.96, -8.86)\": 52.3, \"(-8.86, -8.79)\": 50.8, \"(-8.79, -8.66)\": 49.3, \"(-8.66, -8.56)\": 47.8, \"(-8.56, -8.46)\": 46.5, \"(-8.46, -8.35)\": 45.0, \"(-8.35, -8.26)\": 43.6, \"(-8.26, -8.14)\": 42.2, \"(-8.14, -8.02)\": 40.7, \"(-8.02, -7.91)\": 39.2, \"(-7.91, -7.81)\": 37.8, \"(-7.81, -7.7)\": 36.4, \"(-7.7, -7.59)\": 35.1, \"(-7.59, -7.47)\": 33.7, \"(-7.47, -7.36)\": 32.4, \"(-7.36, -7.24)\": 31.0, \"(-7.24, -7.1)\": 29.6, \"(-7.1, -6.95)\": 27.8, \"(-6.95, -6.81)\": 26.4, \"(-6.81, -6.68)\": 24.9, \"(-6.68, -6.55)\": 23.5, \"(-6.55, -6.4)\": 22.1, \"(-6.4, -6.23)\": 20.7, \"(-6.23, -6.05)\": 18.9, \"(-6.05, -5.9)\": 17.5, \"(-5.9, -5.74)\": 16.1, \"(-5.74, -5.59)\": 14.7, \"(-5.59, -5.41)\": 13.4, \"(-5.41, -5.22)\": 12.0, \"(-5.22, -5.03)\": 10.5, \"(-5.03, -4.81)\": 9.1, \"(-4.81, -4.6)\": 7.7, \"(-4.6, -4.36)\": 6.3, \"(-4.36, -4.11)\": 4.9, \"(-4.11, -3.81)\": 3.4, \"(-3.81, -3.51)\": 2.1, \"(-3.51, -3.11)\": 0.7, \"(-3.11, -2.62)\": -0.6, \"(-2.62, 0.11)\": -2.0, \"(0.11, 0.48)\": -0.6, \"(0.48, 0.78)\": 0.7, \"(0.78, 1.08)\": 2.1, \"(1.08, 1.33)\": 3.5, \"(1.33, 1.59)\": 4.9, \"(1.59, 1.8)\": 6.4, \"(1.8, 2.0)\": 7.7, \"(2.0, 2.18)\": 9.1, \"(2.18, 2.37)\": 10.4, \"(2.37, 2.55)\": 11.8, \"(2.55, 2.72)\": 13.2, \"(2.72, 2.89)\": 14.7, \"(2.89, 3.05)\": 16.2, \"(3.05, 3.2)\": 17.5, \"(3.2, 3.33)\": 18.9, \"(3.33, 3.49)\": 20.2, \"(3.49, 3.63)\": 21.7, \"(3.63, 3.74)\": 23.1, \"(3.74, 3.89)\": 24.4, \"(3.89, 4.06)\": 26.2, \"(4.06, 4.16)\": 27.5, \"(4.16, 4.29)\": 28.9, \"(4.29, 4.41)\": 30.3, \"(4.41, 4.53)\": 31.8, \"(4.53, 4.63)\": 33.1, \"(4.63, 4.77)\": 34.6, \"(4.77, 4.89)\": 36.0, \"(4.89, 4.97)\": 37.6, \"(4.97, 5.07)\": 38.9, \"(5.07, 5.19)\": 40.3, \"(5.19, 5.28)\": 41.7, \"(5.28, 5.42)\": 43.2, \"(5.42, 5.57)\": 45.3, \"(5.57, 5.65)\": 46.7, \"(5.65, 5.74)\": 48.0, \"(5.74, 5.83)\": 49.4, \"(5.83, 5.93)\": 50.8, \"(5.93, 6.04)\": 52.1, \"(6.04, 6.15)\": 53.8, \"(6.15, 6.26)\": 55.4, \"(6.26, 6.36)\": 57.4, \"(6.36, 6.46)\": 58.9, \"(6.46, 6.56)\": 60.3, \"(6.56, 6.65)\": 62.0, \"(6.65, 6.74)\": 63.3, \"(6.74, 6.82)\": 64.8, \"(6.82, 6.9)\": 66.1, \"(6.9, 7.01)\": 67.6, \"(7.01, 7.13)\": 69.7, \"(7.13, 7.24)\": 71.5, \"(7.24, 7.3)\": 73.1, \"(7.3, 7.39)\": 74.5, \"(7.39, 7.47)\": 76.0, \"(7.47, 7.57)\": 77.6, \"(7.57, 7.65)\": 79.2, \"(7.65, 7.72)\": 80.7, \"(7.72, 7.82)\": 82.2, \"(7.82, 7.9)\": 83.8, \"(7.9, 7.99)\": 85.6, \"(7.99, 8.07)\": 87.1, \"(8.07, 8.15)\": 88.5, \"(8.15, 8.28)\": 90.7, \"(8.28, 8.35)\": 92.5, \"(8.35, 8.44)\": 94.1, \"(8.44, 8.53)\": 96.2, \"(8.53, 8.61)\": 97.5, \"(8.61, 8.67)\": 99.3, \"(8.67, 8.75)\": 100.7, \"(8.75, 8.82)\": 102.0, \"(8.82, 8.92)\": 103.8, \"(8.92, 8.99)\": 105.6, \"(8.99, 9.06)\": 107.1, \"(9.06, 9.14)\": 108.6, \"(9.14, 9.19)\": 110.3, \"(9.19, 9.27)\": 111.7, \"(9.27, 9.37)\": 113.7, \"(9.37, 9.43)\": 115.2, \"(9.43, 9.52)\": 116.7, \"(9.52, 9.6)\": 118.6, \"(9.6, 9.66)\": 120.1, \"(9.66, 9.71)\": 121.4, \"(9.71, 9.77)\": 122.8, \"(9.77, 9.86)\": 124.2, \"(9.86, 9.96)\": 126.8, \"(9.96, 9.99)\": 128.5}\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: x\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(-9.96, -9.87)\": -981.6, \"(-9.87, -9.79)\": -955.1, \"(-9.79, -9.69)\": -932.7, \"(-9.69, -9.57)\": -904.2, \"(-9.57, -9.46)\": -866.0, \"(-9.46, -9.38)\": -845.6, \"(-9.38, -9.3)\": -819.6, \"(-9.3, -9.2)\": -796.3, \"(-9.2, -9.11)\": -774.0, \"(-9.11, -9.01)\": -753.7, \"(-9.01, -8.91)\": -727.4, \"(-8.91, -8.82)\": -705.4, \"(-8.82, -8.73)\": -684.2, \"(-8.73, -8.63)\": -662.3, \"(-8.63, -8.53)\": -642.0, \"(-8.53, -8.41)\": -618.4, \"(-8.41, -8.29)\": -590.8, \"(-8.29, -8.19)\": -567.9, \"(-8.19, -8.08)\": -548.0, \"(-8.08, -7.96)\": -523.1, \"(-7.96, -7.86)\": -503.0, \"(-7.86, -7.75)\": -482.3, \"(-7.75, -7.63)\": -462.2, \"(-7.63, -7.5)\": -441.6, \"(-7.5, -7.36)\": -420.2, \"(-7.36, -7.23)\": -393.7, \"(-7.23, -7.05)\": -371.0, \"(-7.05, -6.93)\": -348.8, \"(-6.93, -6.77)\": -328.8, \"(-6.77, -6.62)\": -309.0, \"(-6.62, -6.47)\": -288.8, \"(-6.47, -6.28)\": -268.8, \"(-6.28, -6.11)\": -247.8, \"(-6.11, -5.89)\": -226.5, \"(-5.89, -5.68)\": -203.7, \"(-5.68, -5.44)\": -183.1, \"(-5.44, -5.23)\": -162.3, \"(-5.23, -4.98)\": -141.7, \"(-4.98, -4.66)\": -121.6, \"(-4.66, -4.39)\": -100.7, \"(-4.39, -3.95)\": -80.5, \"(-3.95, -3.46)\": -60.5, \"(-3.46, -2.73)\": -40.7, \"(-2.73, -0.89)\": -20.4, \"(-0.89, 2.65)\": -0.7, \"(2.65, 3.4)\": 19.2, \"(3.4, 3.88)\": 39.1, \"(3.88, 4.26)\": 59.0, \"(4.26, 4.62)\": 79.1, \"(4.62, 4.95)\": 101.5, \"(4.95, 5.24)\": 124.1, \"(5.24, 5.48)\": 144.9, \"(5.48, 5.72)\": 166.2, \"(5.72, 5.91)\": 188.2, \"(5.91, 6.11)\": 208.0, \"(6.11, 6.29)\": 228.8, \"(6.29, 6.46)\": 249.7, \"(6.46, 6.62)\": 273.2, \"(6.62, 6.8)\": 293.0, \"(6.8, 6.95)\": 316.3, \"(6.95, 7.06)\": 336.2, \"(7.06, 7.22)\": 356.6, \"(7.22, 7.37)\": 378.8, \"(7.37, 7.46)\": 398.8, \"(7.46, 7.6)\": 418.8, \"(7.6, 7.72)\": 440.3, \"(7.72, 7.83)\": 461.9, \"(7.83, 7.95)\": 482.2, \"(7.95, 8.09)\": 510.1, \"(8.09, 8.2)\": 531.8, \"(8.2, 8.3)\": 553.7, \"(8.3, 8.41)\": 574.4, \"(8.41, 8.5)\": 594.9, \"(8.5, 8.62)\": 617.7, \"(8.62, 8.72)\": 642.2, \"(8.72, 8.81)\": 665.0, \"(8.81, 8.9)\": 689.1, \"(8.9, 9.0)\": 708.9, \"(9.0, 9.08)\": 728.8, \"(9.08, 9.17)\": 753.1, \"(9.17, 9.26)\": 778.1, \"(9.26, 9.36)\": 797.9, \"(9.36, 9.45)\": 824.2, \"(9.45, 9.55)\": 848.0, \"(9.55, 9.65)\": 878.4, \"(9.65, 9.7)\": 900.2, \"(9.7, 9.8)\": 921.8, \"(9.8, 9.89)\": 943.0, \"(9.89, 9.97)\": 972.2}\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: x\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(-9.97, -9.89)\": 2958.3, \"(-9.89, -9.81)\": 2897.4, \"(-9.81, -9.68)\": 2812.0, \"(-9.68, -9.56)\": 2682.0, \"(-9.56, -9.47)\": 2614.1, \"(-9.47, -9.38)\": 2537.9, \"(-9.38, -9.29)\": 2468.1, \"(-9.29, -9.2)\": 2393.1, \"(-9.2, -9.1)\": 2330.5, \"(-9.1, -9.0)\": 2246.2, \"(-9.0, -8.91)\": 2181.9, \"(-8.91, -8.82)\": 2120.3, \"(-8.82, -8.73)\": 2055.4, \"(-8.73, -8.62)\": 1989.1, \"(-8.62, -8.51)\": 1908.4, \"(-8.51, -8.4)\": 1848.1, \"(-8.4, -8.3)\": 1768.3, \"(-8.3, -8.2)\": 1703.2, \"(-8.2, -8.08)\": 1642.7, \"(-8.08, -7.97)\": 1575.7, \"(-7.97, -7.86)\": 1511.5, \"(-7.86, -7.73)\": 1449.0, \"(-7.73, -7.62)\": 1385.1, \"(-7.62, -7.49)\": 1322.4, \"(-7.49, -7.38)\": 1260.6, \"(-7.38, -7.23)\": 1200.2, \"(-7.23, -7.12)\": 1133.8, \"(-7.12, -6.97)\": 1073.0, \"(-6.97, -6.82)\": 1012.6, \"(-6.82, -6.67)\": 950.1, \"(-6.67, -6.51)\": 884.4, \"(-6.51, -6.35)\": 824.5, \"(-6.35, -6.17)\": 764.7, \"(-6.17, -5.98)\": 702.6, \"(-5.98, -5.77)\": 639.3, \"(-5.77, -5.54)\": 574.1, \"(-5.54, -5.3)\": 507.0, \"(-5.3, -5.02)\": 442.9, \"(-5.02, -4.74)\": 377.2, \"(-4.74, -4.39)\": 316.1, \"(-4.39, -4.05)\": 255.3, \"(-4.05, -3.55)\": 194.5, \"(-3.55, -2.86)\": 130.4, \"(-2.86, -1.5)\": 70.2, \"(-1.5, 2.49)\": 9.8, \"(2.49, 3.34)\": -51.9, \"(3.34, 3.83)\": -113.0, \"(3.83, 4.27)\": -173.2, \"(4.27, 4.63)\": -242.4, \"(4.63, 4.96)\": -303.0, \"(4.96, 5.23)\": -370.0, \"(5.23, 5.5)\": -431.9, \"(5.5, 5.75)\": -511.6, \"(5.75, 5.96)\": -574.8, \"(5.96, 6.17)\": -641.3, \"(6.17, 6.3)\": -702.0, \"(6.3, 6.49)\": -763.0, \"(6.49, 6.64)\": -827.7, \"(6.64, 6.84)\": -888.6, \"(6.84, 6.96)\": -956.7, \"(6.96, 7.11)\": -1018.6, \"(7.11, 7.26)\": -1082.9, \"(7.26, 7.4)\": -1158.2, \"(7.4, 7.53)\": -1219.9, \"(7.53, 7.65)\": -1286.5, \"(7.65, 7.78)\": -1349.6, \"(7.78, 7.91)\": -1429.7, \"(7.91, 8.01)\": -1491.5, \"(8.01, 8.16)\": -1579.4, \"(8.16, 8.28)\": -1640.6, \"(8.28, 8.38)\": -1703.9, \"(8.38, 8.48)\": -1766.3, \"(8.48, 8.57)\": -1832.3, \"(8.57, 8.65)\": -1894.9, \"(8.65, 8.75)\": -1958.6, \"(8.75, 8.87)\": -2021.5, \"(8.87, 8.97)\": -2108.5, \"(8.97, 9.05)\": -2172.1, \"(9.05, 9.13)\": -2237.2, \"(9.13, 9.25)\": -2314.5, \"(9.25, 9.32)\": -2383.5, \"(9.32, 9.41)\": -2450.4, \"(9.41, 9.49)\": -2510.6, \"(9.49, 9.58)\": -2579.4, \"(9.58, 9.64)\": -2642.5, \"(9.64, 9.74)\": -2709.6, \"(9.74, 9.82)\": -2777.4, \"(9.82, 9.92)\": -2841.5, \"(9.92, 10.0)\": -2946.7}\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: x\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(-9.99, -9.96)\": 9925.4, \"(-9.96, -9.93)\": 9796.5, \"(-9.93, -9.9)\": 9661.4, \"(-9.9, -9.87)\": 9554.9, \"(-9.87, -9.84)\": 9418.4, \"(-9.84, -9.76)\": 9288.8, \"(-9.76, -9.7)\": 8939.3, \"(-9.7, -9.64)\": 8838.8, \"(-9.64, -9.58)\": 8517.3, \"(-9.58, -9.52)\": 8394.2, \"(-9.52, -9.47)\": 8158.8, \"(-9.47, -9.4)\": 7950.1, \"(-9.4, -9.35)\": 7749.0, \"(-9.35, -9.31)\": 7601.3, \"(-9.31, -9.27)\": 7473.3, \"(-9.27, -9.24)\": 7343.3, \"(-9.24, -9.16)\": 7209.1, \"(-9.16, -9.09)\": 6937.9, \"(-9.09, -9.05)\": 6809.9, \"(-9.05, -9.01)\": 6682.3, \"(-9.01, -8.96)\": 6567.6, \"(-8.96, -8.91)\": 6439.4, \"(-8.91, -8.87)\": 6272.3, \"(-8.87, -8.82)\": 6151.3, \"(-8.82, -8.78)\": 6018.8, \"(-8.78, -8.72)\": 5918.4, \"(-8.72, -8.65)\": 5727.7, \"(-8.65, -8.6)\": 5568.2, \"(-8.6, -8.54)\": 5452.7, \"(-8.54, -8.49)\": 5307.5, \"(-8.49, -8.45)\": 5201.6, \"(-8.45, -8.4)\": 5061.1, \"(-8.4, -8.36)\": 4937.8, \"(-8.36, -8.29)\": 4834.7, \"(-8.29, -8.23)\": 4689.8, \"(-8.23, -8.17)\": 4539.8, \"(-8.17, -8.13)\": 4433.5, \"(-8.13, -8.05)\": 4290.6, \"(-8.05, -8.0)\": 4179.2, \"(-8.0, -7.94)\": 4070.6, \"(-7.94, -7.87)\": 3950.9, \"(-7.87, -7.82)\": 3832.5, \"(-7.82, -7.76)\": 3714.3, \"(-7.76, -7.69)\": 3608.4, \"(-7.69, -7.64)\": 3476.8, \"(-7.64, -7.55)\": 3372.5, \"(-7.55, -7.49)\": 3240.1, \"(-7.49, -7.4)\": 3114.9, \"(-7.4, -7.32)\": 2986.2, \"(-7.32, -7.26)\": 2873.7, \"(-7.26, -7.19)\": 2745.2, \"(-7.19, -7.12)\": 2636.4, \"(-7.12, -7.03)\": 2518.3, \"(-7.03, -6.92)\": 2412.6, \"(-6.92, -6.83)\": 2281.7, \"(-6.83, -6.73)\": 2140.2, \"(-6.73, -6.64)\": 2038.3, \"(-6.64, -6.51)\": 1923.8, \"(-6.51, -6.4)\": 1777.9, \"(-6.4, -6.27)\": 1661.1, \"(-6.27, -6.1)\": 1499.1, \"(-6.1, -6.0)\": 1381.6, \"(-6.0, -5.84)\": 1271.8, \"(-5.84, -5.7)\": 1158.4, \"(-5.7, -5.55)\": 1055.8, \"(-5.55, -5.38)\": 937.1, \"(-5.38, -5.2)\": 828.2, \"(-5.2, -5.0)\": 723.0, \"(-5.0, -4.78)\": 622.8, \"(-4.78, -4.51)\": 517.8, \"(-4.51, -4.23)\": 416.4, \"(-4.23, -3.86)\": 312.5, \"(-3.86, -3.27)\": 211.5, \"(-3.27, -1.87)\": 111.0, \"(-1.87, 3.23)\": 11.3, \"(3.23, 3.81)\": 111.0, \"(3.81, 4.19)\": 210.8, \"(4.19, 4.47)\": 313.0, \"(4.47, 4.76)\": 412.7, \"(4.76, 5.0)\": 526.3, \"(5.0, 5.21)\": 628.6, \"(5.21, 5.39)\": 741.5, \"(5.39, 5.56)\": 855.7, \"(5.56, 5.71)\": 966.2, \"(5.71, 5.87)\": 1080.9, \"(5.87, 5.98)\": 1194.9, \"(5.98, 6.11)\": 1296.9, \"(6.11, 6.22)\": 1396.9, \"(6.22, 6.35)\": 1500.1, \"(6.35, 6.47)\": 1646.1, \"(6.47, 6.56)\": 1750.7, \"(6.56, 6.69)\": 1915.2, \"(6.69, 6.76)\": 2024.1, \"(6.76, 6.86)\": 2128.4, \"(6.86, 6.93)\": 2230.8, \"(6.93, 7.04)\": 2336.2, \"(7.04, 7.12)\": 2474.0, \"(7.12, 7.19)\": 2586.1, \"(7.19, 7.27)\": 2691.0, \"(7.27, 7.33)\": 2792.1, \"(7.33, 7.4)\": 2899.9, \"(7.4, 7.47)\": 3006.4, \"(7.47, 7.52)\": 3106.3, \"(7.52, 7.58)\": 3209.3, \"(7.58, 7.66)\": 3325.2, \"(7.66, 7.71)\": 3443.2, \"(7.71, 7.79)\": 3554.1, \"(7.79, 7.86)\": 3717.2, \"(7.86, 7.91)\": 3824.6, \"(7.91, 7.97)\": 3924.9, \"(7.97, 8.05)\": 4041.8, \"(8.05, 8.14)\": 4277.8, \"(8.14, 8.2)\": 4406.8, \"(8.2, 8.25)\": 4519.3, \"(8.25, 8.3)\": 4648.7, \"(8.3, 8.36)\": 4783.7, \"(8.36, 8.41)\": 4889.3, \"(8.41, 8.47)\": 5066.2, \"(8.47, 8.53)\": 5208.2, \"(8.53, 8.6)\": 5321.3, \"(8.6, 8.67)\": 5533.3, \"(8.67, 8.72)\": 5675.8, \"(8.72, 8.77)\": 5787.3, \"(8.77, 8.81)\": 5946.8, \"(8.81, 8.85)\": 6055.1, \"(8.85, 8.88)\": 6163.1, \"(8.88, 8.92)\": 6268.4, \"(8.92, 9.01)\": 6460.5, \"(9.01, 9.1)\": 6730.9, \"(9.1, 9.13)\": 6901.6, \"(9.13, 9.19)\": 7004.2, \"(9.19, 9.25)\": 7189.6, \"(9.25, 9.31)\": 7384.8, \"(9.31, 9.36)\": 7605.4, \"(9.36, 9.44)\": 7732.9, \"(9.44, 9.53)\": 8100.3, \"(9.53, 9.57)\": 8299.2, \"(9.57, 9.6)\": 8408.9, \"(9.6, 9.64)\": 8511.4, \"(9.64, 9.71)\": 8735.4, \"(9.71, 9.75)\": 8946.3, \"(9.75, 9.8)\": 9104.9, \"(9.8, 9.84)\": 9250.9, \"(9.84, 9.88)\": 9427.2, \"(9.88, 9.91)\": 9555.6, \"(9.91, 9.93)\": 9676.0}\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: x\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(-10.0, -9.46)\": 1274.2, \"(-9.46, -8.75)\": 886.7, \"(-8.75, -7.28)\": 497.8, \"(-7.28, 0.69)\": 111.5, \"(0.69, 1.45)\": 492.6, \"(1.45, 1.97)\": 885.5, \"(1.97, 2.35)\": 1270.5, \"(2.35, 2.71)\": 1652.0, \"(2.71, 3.05)\": 2053.5, \"(3.05, 3.29)\": 2485.8, \"(3.29, 3.56)\": 2876.7, \"(3.56, 3.77)\": 3263.2, \"(3.77, 3.98)\": 3674.7, \"(3.98, 4.16)\": 4070.3, \"(4.16, 4.34)\": 4463.6, \"(4.34, 4.54)\": 4887.1, \"(4.54, 4.67)\": 5300.3, \"(4.67, 4.82)\": 5691.3, \"(4.82, 4.98)\": 6079.9, \"(4.98, 5.11)\": 6499.4, \"(5.11, 5.21)\": 6898.9, \"(5.21, 5.38)\": 7293.8, \"(5.38, 5.56)\": 7946.6, \"(5.56, 5.65)\": 8361.8, \"(5.65, 5.78)\": 8749.0, \"(5.78, 5.89)\": 9184.1, \"(5.89, 5.98)\": 9633.8, \"(5.98, 6.1)\": 10024.0, \"(6.1, 6.2)\": 10461.3, \"(6.2, 6.32)\": 10919.6, \"(6.32, 6.44)\": 11527.8, \"(6.44, 6.54)\": 11952.1, \"(6.54, 6.64)\": 12413.7, \"(6.64, 6.72)\": 12900.0, \"(6.72, 6.79)\": 13284.0, \"(6.79, 6.89)\": 13742.0, \"(6.89, 6.98)\": 14148.6, \"(6.98, 7.08)\": 14617.6, \"(7.08, 7.17)\": 15182.3, \"(7.17, 7.24)\": 15610.5, \"(7.24, 7.31)\": 16008.4, \"(7.31, 7.39)\": 16442.2, \"(7.39, 7.46)\": 16860.6, \"(7.46, 7.53)\": 17308.5, \"(7.53, 7.59)\": 17854.2, \"(7.59, 7.69)\": 18245.9, \"(7.69, 7.76)\": 18739.1, \"(7.76, 7.85)\": 19394.4, \"(7.85, 7.92)\": 19808.5, \"(7.92, 8.02)\": 20237.5, \"(8.02, 8.12)\": 21073.7, \"(8.12, 8.18)\": 21752.6, \"(8.18, 8.26)\": 22200.1, \"(8.26, 8.33)\": 22682.2, \"(8.33, 8.37)\": 23133.9, \"(8.37, 8.41)\": 23516.2, \"(8.41, 8.49)\": 23945.3, \"(8.49, 8.54)\": 24356.6, \"(8.54, 8.6)\": 24814.4, \"(8.6, 8.65)\": 25251.2, \"(8.65, 8.72)\": 25658.7, \"(8.72, 8.77)\": 26235.9, \"(8.77, 8.85)\": 26774.0, \"(8.85, 8.92)\": 27450.7, \"(8.92, 8.98)\": 27930.6, \"(8.98, 9.06)\": 28553.9, \"(9.06, 9.12)\": 29333.3, \"(9.12, 9.17)\": 29758.9, \"(9.17, 9.21)\": 30151.3, \"(9.21, 9.25)\": 30544.4, \"(9.25, 9.32)\": 31134.0, \"(9.32, 9.39)\": 31752.0, \"(9.39, 9.45)\": 32191.1, \"(9.45, 9.51)\": 32826.5, \"(9.51, 9.58)\": 33580.9, \"(9.58, 9.64)\": 34169.5, \"(9.64, 9.69)\": 34833.3, \"(9.69, 9.76)\": 35471.4, \"(9.76, 9.82)\": 36008.6, \"(9.82, 9.86)\": 36565.4, \"(9.86, 9.92)\": 37131.8, \"(9.92, 9.98)\": 37787.8}\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: x\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(-10.0, -9.94)\": -99557.1, \"(-9.94, -9.87)\": -96593.9, \"(-9.87, -9.8)\": -92323.8, \"(-9.8, -9.75)\": -90239.8, \"(-9.75, -9.69)\": -87419.5, \"(-9.69, -9.66)\": -85244.4, \"(-9.66, -9.59)\": -82995.3, \"(-9.59, -9.53)\": -79941.3, \"(-9.53, -9.47)\": -77943.9, \"(-9.47, -9.42)\": -75872.4, \"(-9.42, -9.34)\": -73673.1, \"(-9.34, -9.27)\": -70761.4, \"(-9.27, -9.2)\": -67965.4, \"(-9.2, -9.13)\": -65606.9, \"(-9.13, -9.08)\": -63123.1, \"(-9.08, -8.99)\": -61011.9, \"(-8.99, -8.92)\": -58173.1, \"(-8.92, -8.85)\": -56091.4, \"(-8.85, -8.79)\": -54078.6, \"(-8.79, -8.69)\": -51785.0, \"(-8.69, -8.64)\": -49625.6, \"(-8.64, -8.54)\": -47329.4, \"(-8.54, -8.47)\": -45334.5, \"(-8.47, -8.37)\": -43209.7, \"(-8.37, -8.27)\": -40830.7, \"(-8.27, -8.21)\": -38709.7, \"(-8.21, -8.11)\": -36509.5, \"(-8.11, -7.98)\": -34502.1, \"(-7.98, -7.86)\": -31966.0, \"(-7.86, -7.74)\": -29902.9, \"(-7.74, -7.62)\": -27722.2, \"(-7.62, -7.49)\": -25582.8, \"(-7.49, -7.35)\": -23520.0, \"(-7.35, -7.22)\": -21271.2, \"(-7.22, -7.06)\": -19262.1, \"(-7.06, -6.85)\": -17069.6, \"(-6.85, -6.62)\": -14927.7, \"(-6.62, -6.36)\": -12436.0, \"(-6.36, -6.1)\": -10319.6, \"(-6.1, -5.75)\": -8248.6, \"(-5.75, -5.29)\": -6131.1, \"(-5.29, -4.62)\": -4096.1, \"(-4.62, -2.29)\": -2062.3, \"(-2.29, 4.53)\": -62.6, \"(4.53, 5.25)\": 1949.1, \"(5.25, 5.69)\": 3975.1, \"(5.69, 6.05)\": 6004.5, \"(6.05, 6.32)\": 8199.9, \"(6.32, 6.57)\": 10215.0, \"(6.57, 6.78)\": 12239.7, \"(6.78, 6.94)\": 14276.3, \"(6.94, 7.1)\": 16300.8, \"(7.1, 7.29)\": 18314.3, \"(7.29, 7.41)\": 20523.8, \"(7.41, 7.56)\": 22578.8, \"(7.56, 7.71)\": 25162.1, \"(7.71, 7.82)\": 27395.1, \"(7.82, 7.94)\": 29501.6, \"(7.94, 8.05)\": 31955.0, \"(8.05, 8.18)\": 33949.1, \"(8.18, 8.32)\": 37824.2, \"(8.32, 8.42)\": 40198.4, \"(8.42, 8.48)\": 42348.1, \"(8.48, 8.57)\": 44454.7, \"(8.57, 8.64)\": 46718.3, \"(8.64, 8.74)\": 48729.4, \"(8.74, 8.81)\": 51162.2, \"(8.81, 8.88)\": 53381.5, \"(8.88, 8.95)\": 55474.6, \"(8.95, 9.02)\": 57580.5, \"(9.02, 9.09)\": 59897.9, \"(9.09, 9.16)\": 62683.8, \"(9.16, 9.23)\": 64958.2, \"(9.23, 9.29)\": 67087.1, \"(9.29, 9.35)\": 69474.3, \"(9.35, 9.41)\": 71754.7, \"(9.41, 9.45)\": 74257.3, \"(9.45, 9.53)\": 76720.0, \"(9.53, 9.57)\": 79235.1, \"(9.57, 9.68)\": 81360.8, \"(9.68, 9.76)\": 86671.1, \"(9.76, 9.8)\": 88741.6, \"(9.8, 9.88)\": 91245.8, \"(9.88, 9.94)\": 95023.7, \"(9.94, 9.99)\": 97534.0}\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: x\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(-9.98, -9.92)\": 97596.5, \"(-9.92, -9.87)\": 95386.4, \"(-9.87, -9.82)\": 92906.9, \"(-9.82, -9.77)\": 90789.5, \"(-9.77, -9.73)\": 88739.1, \"(-9.73, -9.67)\": 86372.3, \"(-9.67, -9.61)\": 83417.2, \"(-9.61, -9.55)\": 81399.7, \"(-9.55, -9.47)\": 78651.7, \"(-9.47, -9.43)\": 76343.1, \"(-9.43, -9.37)\": 74281.1, \"(-9.37, -9.31)\": 71960.0, \"(-9.31, -9.25)\": 69716.9, \"(-9.25, -9.18)\": 67306.3, \"(-9.18, -9.11)\": 64721.0, \"(-9.11, -9.05)\": 62448.9, \"(-9.05, -8.98)\": 60066.7, \"(-8.98, -8.84)\": 56569.9, \"(-8.84, -8.77)\": 53375.2, \"(-8.77, -8.67)\": 51396.9, \"(-8.67, -8.6)\": 48788.7, \"(-8.6, -8.52)\": 46641.7, \"(-8.52, -8.44)\": 44610.9, \"(-8.44, -8.4)\": 42620.4, \"(-8.4, -8.27)\": 40658.6, \"(-8.27, -8.18)\": 38372.2, \"(-8.18, -8.07)\": 36396.0, \"(-8.07, -7.97)\": 33978.4, \"(-7.97, -7.86)\": 31832.1, \"(-7.86, -7.74)\": 29861.4, \"(-7.74, -7.61)\": 27695.1, \"(-7.61, -7.44)\": 24874.1, \"(-7.44, -7.31)\": 22722.9, \"(-7.31, -7.14)\": 20565.6, \"(-7.14, -6.95)\": 18333.0, \"(-6.95, -6.74)\": 15934.8, \"(-6.74, -6.54)\": 13902.4, \"(-6.54, -6.31)\": 11943.2, \"(-6.31, -6.02)\": 9987.1, \"(-6.02, -5.7)\": 7940.7, \"(-5.7, -5.23)\": 5925.0, \"(-5.23, -4.53)\": 3864.7, \"(-4.53, 2.3)\": 1883.6, \"(2.3, 4.57)\": -76.3, \"(4.57, 5.27)\": -2037.1, \"(5.27, 5.71)\": -4056.9, \"(5.71, 6.04)\": -6043.2, \"(6.04, 6.34)\": -8145.1, \"(6.34, 6.56)\": -10131.9, \"(6.56, 6.76)\": -12232.2, \"(6.76, 6.97)\": -14359.1, \"(6.97, 7.14)\": -16496.5, \"(7.14, 7.29)\": -18634.4, \"(7.29, 7.44)\": -20792.0, \"(7.44, 7.56)\": -22972.9, \"(7.56, 7.7)\": -24955.7, \"(7.7, 7.81)\": -27051.9, \"(7.81, 7.92)\": -29257.6, \"(7.92, 8.03)\": -31248.9, \"(8.03, 8.11)\": -33285.0, \"(8.11, 8.22)\": -35417.7, \"(8.22, 8.32)\": -37847.2, \"(8.32, 8.41)\": -39947.8, \"(8.41, 8.51)\": -42702.9, \"(8.51, 8.59)\": -44771.4, \"(8.59, 8.66)\": -46756.9, \"(8.66, 8.73)\": -48792.3, \"(8.73, 8.8)\": -51013.6, \"(8.8, 8.88)\": -53144.3, \"(8.88, 8.96)\": -55753.0, \"(8.96, 9.03)\": -57851.1, \"(9.03, 9.09)\": -60222.0, \"(9.09, 9.16)\": -62299.4, \"(9.16, 9.23)\": -64844.5, \"(9.23, 9.29)\": -67171.3, \"(9.29, 9.37)\": -69567.5, \"(9.37, 9.42)\": -73029.5, \"(9.42, 9.5)\": -75308.4, \"(9.5, 9.58)\": -79080.1, \"(9.58, 9.64)\": -81046.9, \"(9.64, 9.7)\": -83741.1, \"(9.7, 9.74)\": -85870.7, \"(9.74, 9.81)\": -88266.1, \"(9.81, 9.86)\": -91495.1, \"(9.86, 9.93)\": -94432.6, \"(9.93, 9.97)\": -97692.9}\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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wHAIOAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwnCYJOMuXL1eXLl0UGRmp1NRUbd68uda611xzjWw22xnLiBEjAnUmTJhwxvbMzMymmAoAAGgBwkK9gxdeeEEzZ87UihUrlJqaqkcffVQZGRnauXOnOnTocEb9l19+WZWVlYH1Y8eOqX///rrxxhuD6mVmZmr16tWBdafTGbpJAACAFiXkAeeRRx7R5MmTNXHiREnSihUr9MYbb2jVqlWaM2fOGfXbtm0btP78888rOjr6jIDjdDqVmJhYrzF4vV55vd7Ausfjaeg0AABACxLSj6gqKytVUFCg9PT0f+/Qbld6erry8vLq1cfKlSs1ZswYtWrVKqh848aN6tChg3r27KmpU6fq2LFjtfaRnZ0tl8sVWFJSUho3IQAA0CKENOAcPXpUPp9PCQkJQeUJCQlyu91nbb9582Z9/vnnuuWWW4LKMzMztW7dOuXm5uqhhx7Se++9p2uvvVY+n6/GfrKyslRSUhJY9u3b1/hJAQCAZi/kH1F9FytXrlTfvn01ZMiQoPIxY8YEfu7bt6/69eun7t27a+PGjRo2bNgZ/TidTq7RAQDgAhLSMzjx8fFyOBwqKioKKi8qKjrr9TPl5eV6/vnnNWnSpLPup1u3boqPj9euXbu+03gBAIA1hDTgREREaNCgQcrNzQ2U+f1+5ebmKi0trc62L730krxer371q1+ddT/79+/XsWPHlJSU9J3HDAAAWr6QPwdn5syZevrpp7V27Vrt2LFDU6dOVXl5eeCuqnHjxikrK+uMditXrtTIkSPVrl27oPKysjLde++9+uijj7R7927l5ubqhhtuUI8ePZSRkRHq6QAAgBYg5NfgjB49WkeOHNG8efPkdrs1YMAAbdiwIXDh8d69e2W3B+esnTt36oMPPtDf//73M/pzOBz67LPPtHbtWhUXFys5OVnDhw/XwoULuc4GAABIkmzGGHO+B9HUPB6PXC6XSkpKFBMTc76HAwAA6qEh7998FxUAALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALCcJgk4y5cvV5cuXRQZGanU1FRt3ry51rpr1qyRzWYLWiIjI4PqGGM0b948JSUlKSoqSunp6frqq69CPQ0AANBChDzgvPDCC5o5c6bmz5+vrVu3qn///srIyNDhw4drbRMTE6NDhw4Flj179gRtX7RokZYuXaoVK1YoPz9frVq1UkZGhioqKkI9HQAA0AKEPOA88sgjmjx5siZOnKg+ffpoxYoVio6O1qpVq2ptY7PZlJiYGFgSEhIC24wxevTRR/W73/1ON9xwg/r166d169bp4MGDWr9+fY39eb1eeTyeoAUAAFhXSANOZWWlCgoKlJ6e/u8d2u1KT09XXl5ere3KysrUuXNnpaSk6IYbbtD27dsD2woLC+V2u4P6dLlcSk1NrbXP7OxsuVyuwJKSknIOZgcAAJqrkAaco0ePyufzBZ2BkaSEhAS53e4a2/Ts2VOrVq3SX//6Vz3zzDPy+/0aOnSo9u/fL0mBdg3pMysrSyUlJYFl375933VqAACgGQs73wP4b2lpaUpLSwusDx06VL1799aTTz6phQsXNqpPp9Mpp9N5roYIAACauZCewYmPj5fD4VBRUVFQeVFRkRITE+vVR3h4uAYOHKhdu3ZJUqDdd+kTAABYW0gDTkREhAYNGqTc3NxAmd/vV25ubtBZmrr4fD7985//VFJSkiSpa9euSkxMDOrT4/EoPz+/3n0CAABrC/lHVDNnztT48eM1ePBgDRkyRI8++qjKy8s1ceJESdK4cePUsWNHZWdnS5IWLFigK664Qj169FBxcbEWL16sPXv26JZbbpF06g6rGTNm6A9/+IMuvvhide3aVXPnzlVycrJGjhwZ6ukAAIAWIOQBZ/To0Tpy5IjmzZsnt9utAQMGaMOGDYGLhPfu3Su7/d8nkr799ltNnjxZbrdbcXFxGjRokDZt2qQ+ffoE6vzmN79ReXm5pkyZouLiYl155ZXasGHDGQ8EBAAAFyabMcac70E0NY/HI5fLpZKSEsXExJzv4QAAgHpoyPs330UFAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsp0kCzvLly9WlSxdFRkYqNTVVmzdvrrXu008/rauuukpxcXGKi4tTenr6GfUnTJggm80WtGRmZoZ6GgAAoIUIecB54YUXNHPmTM2fP19bt25V//79lZGRocOHD9dYf+PGjbrpppv07rvvKi8vTykpKRo+fLgOHDgQVC8zM1OHDh0KLH/6059CPRUAANBC2IwxJpQ7SE1N1eWXX67HHntMkuT3+5WSkqI77rhDc+bMOWt7n8+nuLg4PfbYYxo3bpykU2dwiouLtX79+nqNwev1yuv1BtY9Ho9SUlJUUlKimJiYhk8KAAA0OY/HI5fLVa/375CewamsrFRBQYHS09P/vUO7Xenp6crLy6tXHydOnFBVVZXatm0bVL5x40Z16NBBPXv21NSpU3Xs2LFa+8jOzpbL5QosKSkpjZsQAABoEUIacI4ePSqfz6eEhISg8oSEBLnd7nr1MXv2bCUnJweFpMzMTK1bt065ubl66KGH9N577+naa6+Vz+ersY+srCyVlJQEln379jV+UgAAoNkLO98DqMuDDz6o559/Xhs3blRkZGSgfMyYMYGf+/btq379+ql79+7auHGjhg0bdkY/TqdTTqezScYMAADOv5CewYmPj5fD4VBRUVFQeVFRkRITE+tsu2TJEj344IP6+9//rn79+tVZt1u3boqPj9euXbu+85gBAEDLF9KAExERoUGDBik3NzdQ5vf7lZubq7S0tFrbLVq0SAsXLtSGDRs0ePDgs+5n//79OnbsmJKSks7JuAEAQMsW8tvEZ86cqaefflpr167Vjh07NHXqVJWXl2vixImSpHHjxikrKytQ/6GHHtLcuXO1atUqdenSRW63W263W2VlZZKksrIy3Xvvvfroo4+0e/du5ebm6oYbblCPHj2UkZER6ukAAIAWIOTX4IwePVpHjhzRvHnz5Ha7NWDAAG3YsCFw4fHevXtlt/87Zz3xxBOqrKzUz3/+86B+5s+fr/vuu08Oh0OfffaZ1q5dq+LiYiUnJ2v48OFauHAh19kAAABJTfAcnOaoIffRAwCA5qHZPAcHAADgfCDgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAy2mSgLN8+XJ16dJFkZGRSk1N1ebNm+us/9JLL6lXr16KjIxU37599eabbwZtN8Zo3rx5SkpKUlRUlNLT0/XVV1+FcgoAAKAFCXnAeeGFFzRz5kzNnz9fW7duVf/+/ZWRkaHDhw/XWH/Tpk266aabNGnSJH3yyScaOXKkRo4cqc8//zxQZ9GiRVq6dKlWrFih/Px8tWrVShkZGaqoqAj1dAAAQAtgM8aYUO4gNTVVl19+uR577DFJkt/vV0pKiu644w7NmTPnjPqjR49WeXm5Xn/99UDZFVdcoQEDBmjFihUyxig5OVmzZs3SPffcI0kqKSlRQkKC1qxZozFjxpzRp9frldfrDax7PB6lpKSopKREMTEx53rKAAAgBDwej1wuV73ev0N6BqeyslIFBQVKT0//9w7tdqWnpysvL6/GNnl5eUH1JSkjIyNQv7CwUG63O6iOy+VSampqrX1mZ2fL5XIFlpSUlO86NQAA0IyFNOAcPXpUPp9PCQkJQeUJCQlyu901tnG73XXWP/1nQ/rMyspSSUlJYNm3b1+j5gMAAFqGsPM9gKbgdDrldDrP9zAAAEATCekZnPj4eDkcDhUVFQWVFxUVKTExscY2iYmJddY//WdD+gQAABeWkAaciIgIDRo0SLm5uYEyv9+v3NxcpaWl1dgmLS0tqL4k5eTkBOp37dpViYmJQXU8Ho/y8/Nr7RMAAFxYQv4R1cyZMzV+/HgNHjxYQ4YM0aOPPqry8nJNnDhRkjRu3Dh17NhR2dnZkqS77rpLV199tR5++GGNGDFCzz//vD7++GM99dRTkiSbzaYZM2boD3/4gy6++GJ17dpVc+fOVXJyskaOHBnq6QAAgBYg5AFn9OjROnLkiObNmye3260BAwZow4YNgYuE9+7dK7v93yeShg4dqueee06/+93v9Nvf/lYXX3yx1q9fr8suuyxQ5ze/+Y3Ky8s1ZcoUFRcX68orr9SGDRsUGRkZ6ukAAIAWIOTPwWmOGnIfPQAAaB6azXNwAAAAzgcCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsJyQBpzjx49r7NixiomJUWxsrCZNmqSysrI6699xxx3q2bOnoqKi1KlTJ915550qKSkJqmez2c5Ynn/++VBOBQAAtCBhoex87NixOnTokHJyclRVVaWJEydqypQpeu6552qsf/DgQR08eFBLlixRnz59tGfPHt122206ePCg/vznPwfVXb16tTIzMwPrsbGxoZwKAABoQWzGGBOKjnfs2KE+ffpoy5YtGjx4sCRpw4YNuu6667R//34lJyfXq5+XXnpJv/rVr1ReXq6wsFN5zGaz6ZVXXtHIkSPr1YfX65XX6w2sezwepaSkqKSkRDExMQ2bGAAAOC88Ho9cLle93r9D9hFVXl6eYmNjA+FGktLT02W325Wfn1/vfk5P4nS4OW3atGmKj4/XkCFDtGrVKtWV07Kzs+VyuQJLSkpKwycEAABajJAFHLfbrQ4dOgSVhYWFqW3btnK73fXq4+jRo1q4cKGmTJkSVL5gwQK9+OKLysnJ0ahRo3T77bdr2bJltfaTlZWlkpKSwLJv376GTwgAALQYDb4GZ86cOXrooYfqrLNjx45GD+g0j8ejESNGqE+fPrrvvvuCts2dOzfw88CBA1VeXq7FixfrzjvvrLEvp9Mpp9P5nccEAABahgYHnFmzZmnChAl11unWrZsSExN1+PDhoPLq6modP35ciYmJdbYvLS1VZmam2rRpo1deeUXh4eF11k9NTdXChQvl9XoJMgAAoOEBp3379mrfvv1Z66Wlpam4uFgFBQUaNGiQJOmdd96R3+9Xampqre08Ho8yMjLkdDr16quvKjIy8qz72rZtm+Li4gg3AABAUghvE+/du7cyMzM1efJkrVixQlVVVZo+fbrGjBkTuIPqwIEDGjZsmNatW6chQ4bI4/Fo+PDhOnHihJ555hl5PB55PB5Jp4KVw+HQa6+9pqKiIl1xxRWKjIxUTk6OHnjgAd1zzz2hmgoAAGhhQvocnGeffVbTp0/XsGHDZLfbNWrUKC1dujSwvaqqSjt37tSJEyckSVu3bg3cYdWjR4+gvgoLC9WlSxeFh4dr+fLluvvuu2WMUY8ePfTII49o8uTJoZwKAABoQUL2HJzmrCH30QMAgOahWTwHBwAA4Hwh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsJacA5fvy4xo4dq5iYGMXGxmrSpEkqKyurs80111wjm80WtNx2221Bdfbu3asRI0YoOjpaHTp00L333qvq6upQTgUAALQgYaHsfOzYsTp06JBycnJUVVWliRMnasqUKXruuefqbDd58mQtWLAgsB4dHR342efzacSIEUpMTNSmTZt06NAhjRs3TuHh4XrggQdCNhcAANBy2IwxJhQd79ixQ3369NGWLVs0ePBgSdKGDRt03XXXaf/+/UpOTq6x3TXXXKMBAwbo0UcfrXH73/72N/3kJz/RwYMHlZCQIElasWKFZs+erSNHjigiIuKMNl6vV16vN7Du8XiUkpKikpISxcTEfMeZAgCApuDxeORyuer1/h2yj6jy8vIUGxsbCDeSlJ6eLrvdrvz8/DrbPvvss4qPj9dll12mrKwsnThxIqjfvn37BsKNJGVkZMjj8Wj79u019pednS2XyxVYUlJSvuPsAABAcxayj6jcbrc6dOgQvLOwMLVt21Zut7vWdr/85S/VuXNnJScn67PPPtPs2bO1c+dOvfzyy4F+/zPcSAqs19ZvVlaWZs6cGVg/fQYHAABYU4MDzpw5c/TQQw/VWWfHjh2NHtCUKVMCP/ft21dJSUkaNmyYvv76a3Xv3r1RfTqdTjmdzkaPCQAAtCwNDjizZs3ShAkT6qzTrVs3JSYm6vDhw0Hl1dXVOn78uBITE+u9v9TUVEnSrl271L17dyUmJmrz5s1BdYqKiiSpQf0CAADranDAad++vdq3b3/WemlpaSouLlZBQYEGDRokSXrnnXfk9/sDoaU+tm3bJklKSkoK9Hv//ffr8OHDgY/AcnJyFBMToz59+jRwNgAAwIpCdpFx7969lZmZqcmTJ2vz5s368MMPNX36dI0ZMyZwB9WBAwfUq1evwBmZr7/+WgsXLlRBQYF2796tV199VePGjdMPfvAD9evXT5I0fPhw9enTRzfffLM+/fRTvfXWW/rd736nadOm8TEUAACQFOIH/T377LPq1auXhg0bpuuuu05XXnmlnnrqqcD2qqoq7dy5M3CXVEREhN5++20NHz5cvXr10qxZszRq1Ci99tprgTYOh0Ovv/66HA6H0tLS9Ktf/Urjxo0Lem4OAAC4sIXsOTjNWUPuowcAAM1Ds3gODgAAwPlCwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJYT0oBz/PhxjR07VjExMYqNjdWkSZNUVlZWa/3du3fLZrPVuLz00kuBejVtf/7550M5FQAA0IKEhbLzsWPH6tChQ8rJyVFVVZUmTpyoKVOm6LnnnquxfkpKig4dOhRU9tRTT2nx4sW69tprg8pXr16tzMzMwHpsbOw5Hz8AAGiZQhZwduzYoQ0bNmjLli0aPHiwJGnZsmW67rrrtGTJEiUnJ5/RxuFwKDExMajslVde0S9+8Qu1bt06qDw2NvaMurXxer3yer2BdY/H09DpAACAFiRkH1Hl5eUpNjY2EG4kKT09XXa7Xfn5+fXqo6CgQNu2bdOkSZPO2DZt2jTFx8dryJAhWrVqlYwxtfaTnZ0tl8sVWFJSUho+IQAA0GKELOC43W516NAhqCwsLExt27aV2+2uVx8rV65U7969NXTo0KDyBQsW6MUXX1ROTo5GjRql22+/XcuWLau1n6ysLJWUlASWffv2NXxCAACgxWjwR1Rz5szRQw89VGedHTt2NHpAp508eVLPPfec5s6de8a2/ywbOHCgysvLtXjxYt1555019uV0OuV0Or/zmAAAQMvQ4IAza9YsTZgwoc463bp1U2Jiog4fPhxUXl1drePHj9fr2pk///nPOnHihMaNG3fWuqmpqVq4cKG8Xi9BBgAANDzgtG/fXu3btz9rvbS0NBUXF6ugoECDBg2SJL3zzjvy+/1KTU09a/uVK1fqpz/9ab32tW3bNsXFxRFuAACApBDeRdW7d29lZmZq8uTJWrFihaqqqjR9+nSNGTMmcAfVgQMHNGzYMK1bt05DhgwJtN21a5fef/99vfnmm2f0+9prr6moqEhXXHGFIiMjlZOTowceeED33HNPqKYCAABamJA+B+fZZ5/V9OnTNWzYMNntdo0aNUpLly4NbK+qqtLOnTt14sSJoHarVq3SRRddpOHDh5/RZ3h4uJYvX667775bxhj16NFDjzzyiCZPnhzKqQAAgBbEZuq6v9qiPB6PXC6XSkpKFBMTc76HAwAA6qEh7998FxUAALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALCcsPM9ACspq6jWXc9v1af7SuT3+2S323Sy0i+bTeoYG6m4VpGq9PkVYTcqqfDpWHmlyiqq5Td+tYoIV3zrMFX5pKPllZLNppTYSN31o57adaRUr3yyX8dOVCnSYVdVdbW8fqlNZLi6t4+Wp6JaR0u98vuNKqp88hmpqtovY5N8PiOH3aZwh01RYTZ5vH5VVhvJJrWNDlPfi+I0/NJEfX6gRPuPefTpwXJVVlXLZrMp2hmmNs4wlVdUyeszinDY1CW+lXx+KckVqYhwmzZ+eUQlFdUKs9vUOsKuaGeYYiIj1DHOqZ3uclX7jSLD7YoKt+lQSZWiIxwqPenViSq/7Ha7urSLVrcOrWSXTcfKK3WyslrHy6vUKsKuCIdDh8u8Kvf61DEuUtddliyPt0rbD3gUGe6QzSa1bxMhSSo9Wa0v3aU6XFoh4zeKCnfIYbepourUcYiJClN5RbXKK/3y+Y0cNqNWkeGKCnfIFRUmd2mlKqp8CrNLrZ3hstlsMsaorKJK1Ubq6IrUtX2SlLfnuL45UibPySpJRg6bTRFhdoXZ7arw+XTS61eVOfX3wemQuraLVpcOrfXlwVKVVlSpotqnqPAwtY9x6tKkNvrmyEkVeU6q5GSV/H4jh8Om+NYRKvf6VHyiStV+KSrCrsgwm6r9krfaL7tNssvI4XCostqvap+RzSbFRofr0o4uJbSJ1DeHPdp+qFQVPiMZyWakuOgwdYyLVlx0mAqPndDJKqNqn08nvdWq9EtGkv9UdUlSu+gwtXE6tL/YK5+RHJJaRznUuW0rSUZFpZWS3y9vtU+lXr/8RrLbpOgIm9q3csoe5lC4XarySa0jT/1T463y6USVX53jnNr/bYWKSitUVnlqj60j7Err2lY7i0r17clq2WxSq3C7fD6fyqqkymq/wuw2tW8ToXZtnIoMs8tTUa0TXp+qfadeT9V+yemwa3CXOH3/4g4aP7SLIsL4fxxwIbIZY8zZq1mLx+ORy+VSSUmJYmJizkmfP33sH/psv+ec9AXg3LDZpClXdVXWdX3O91AAnAMNef8O2X9t7r//fg0dOlTR0dGKjY2tVxtjjObNm6ekpCRFRUUpPT1dX331VVCd48ePa+zYsYqJiVFsbKwmTZqksrKyEMyg/gg3QPNkjPTk+4XKfvOL8z0UAE0sZAGnsrJSN954o6ZOnVrvNosWLdLSpUu1YsUK5efnq1WrVsrIyFBFRUWgztixY7V9+3bl5OTo9ddf1/vvv68pU6aEYgr1UlZRTbgBmrmn/1Goymr/+R4GgCYU8o+o1qxZoxkzZqi4uLjOesYYJScna9asWbrnnnskSSUlJUpISNCaNWs0ZswY7dixQ3369NGWLVs0ePBgSdKGDRt03XXXaf/+/UpOTq6xb6/XK6/XG1j3eDxKSUk5Jx9RTV67RTk7Dn+nPgCE3twRvTXpqm7nexgAvoNm8RFVQxUWFsrtdis9PT1Q5nK5lJqaqry8PElSXl6eYmNjA+FGktLT02W325Wfn19r39nZ2XK5XIElJSXlnI1777cnz1lfAEJnz/ET53sIAJpQswk4brdbkpSQkBBUnpCQENjmdrvVoUOHoO1hYWFq27ZtoE5NsrKyVFJSElj27dt3zsbdKS7qnPUFIHQ6t40+30MA0IQaFHDmzJkjm81W5/Lll1+GaqyN5nQ6FRMTE7ScK38cPfCc9QUgNOw26ea0Lud7GACaUIOegzNr1ixNmDChzjrdujXuM+7ExERJUlFRkZKSkgLlRUVFGjBgQKDO4cPB17tUV1fr+PHjgfZNrXVkmPpdFMOFxkAzNvmqrjwPB7jANCjgtG/fXu3btw/JQLp27arExETl5uYGAo3H41F+fn7gTqy0tDQVFxeroKBAgwYNkiS988478vv9Sk1NDcm46uPV6VdxqzjQDPEcHODCFbInGe/du1fHjx/X3r175fP5tG3bNklSjx491Lp1a0lSr169lJ2drZ/97Gey2WyaMWOG/vCHP+jiiy9W165dNXfuXCUnJ2vkyJGSpN69eyszM1OTJ0/WihUrVFVVpenTp2vMmDG13kHVVF6dfhVPMuZJxjzJmCcZA2gmQnab+IQJE7R27dozyt99911dc801p3Zus2n16tWBj72MMZo/f76eeuopFRcX68orr9Tjjz+uSy65JND++PHjmj59ul577TXZ7XaNGjVKS5cuDYSm+gjFk4wBAEBoNeT9m69qIOAAANAitMjn4AAAAJwrBBwAAGA5BBwAAGA5BBwAAGA5BBwAAGA5BBwAAGA5BBwAAGA5BBwAAGA5Ifuqhubs9LMNPR6+OwoAgJbi9Pt2fZ5RfEEGnNLSUklSSkrKeR4JAABoqNLSUrlcrjrrXJBf1eD3+3Xw4EG1adNGNpvtnPXr8XiUkpKiffv2WfYrIKw+R6vPT7L+HK0+P8n6c7T6/CTrzzFU8zPGqLS0VMnJybLb677K5oI8g2O323XRRReFrP+YmBhL/oX9T1afo9XnJ1l/jlafn2T9OVp9fpL15xiK+Z3tzM1pXGQMAAAsh4ADAAAsh4BzDjmdTs2fP19Op/N8DyVkrD5Hq89Psv4crT4/yfpztPr8JOvPsTnM74K8yBgAAFgbZ3AAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAa6P7779fQoUMVHR2t2NjYGuvs3btXI0aMUHR0tDp06KB7771X1dXVdfZ7/PhxjR07VjExMYqNjdWkSZNUVlYWghnU38aNG2Wz2WpctmzZUmu7a6655oz6t912WxOOvGG6dOlyxngffPDBOttUVFRo2rRpateunVq3bq1Ro0apqKioiUZcf7t379akSZPUtWtXRUVFqXv37po/f74qKyvrbNfcj+Hy5cvVpUsXRUZGKjU1VZs3b66z/ksvvaRevXopMjJSffv21ZtvvtlEI2247OxsXX755WrTpo06dOigkSNHaufOnXW2WbNmzRnHKzIysolG3DD33XffGWPt1atXnW1a0vGTav43xWazadq0aTXWb+7H7/3339f111+v5ORk2Ww2rV+/Pmi7MUbz5s1TUlKSoqKilJ6erq+++uqs/Tb0ddxQBJwGqqys1I033qipU6fWuN3n82nEiBGqrKzUpk2btHbtWq1Zs0bz5s2rs9+xY8dq+/btysnJ0euvv673339fU6ZMCcUU6m3o0KE6dOhQ0HLLLbeoa9euGjx4cJ1tJ0+eHNRu0aJFTTTqxlmwYEHQeO+44446699999167bXX9NJLL+m9997TwYMH9T//8z9NNNr6+/LLL+X3+/Xkk09q+/bt+uMf/6gVK1bot7/97VnbNtdj+MILL2jmzJmaP3++tm7dqv79+ysjI0OHDx+usf6mTZt00003adKkSfrkk080cuRIjRw5Up9//nkTj7x+3nvvPU2bNk0fffSRcnJyVFVVpeHDh6u8vLzOdjExMUHHa8+ePU004oa79NJLg8b6wQcf1Fq3pR0/SdqyZUvQ/HJyciRJN954Y61tmvPxKy8vV//+/bV8+fIaty9atEhLly7VihUrlJ+fr1atWikjI0MVFRW19tnQ13GjGDTK6tWrjcvlOqP8zTffNHa73bjd7kDZE088YWJiYozX662xry+++MJIMlu2bAmU/e1vfzM2m80cOHDgnI+9sSorK0379u3NggUL6qx39dVXm7vuuqtpBnUOdO7c2fzxj3+sd/3i4mITHh5uXnrppUDZjh07jCSTl5cXghGeW4sWLTJdu3ats05zPoZDhgwx06ZNC6z7fD6TnJxssrOza6z/i1/8wowYMSKoLDU11dx6660hHee5cvjwYSPJvPfee7XWqe3fo+Zo/vz5pn///vWu39KPnzHG3HXXXaZ79+7G7/fXuL0lHT9J5pVXXgms+/1+k5iYaBYvXhwoKy4uNk6n0/zpT3+qtZ+Gvo4bgzM451heXp769u2rhISEQFlGRoY8Ho+2b99ea5vY2NigsyLp6emy2+3Kz88P+Zjr69VXX9WxY8c0ceLEs9Z99tlnFR8fr8suu0xZWVk6ceJEE4yw8R588EG1a9dOAwcO1OLFi+v8SLGgoEBVVVVKT08PlPXq1UudOnVSXl5eUwz3OykpKVHbtm3PWq85HsPKykoVFBQE/e7tdrvS09Nr/d3n5eUF1ZdOvSZbwrGSTh0vSWc9ZmVlZercubNSUlJ0ww031PrvTXPw1VdfKTk5Wd26ddPYsWO1d+/eWuu29ONXWVmpZ555Rr/+9a9ls9lqrdeSjt9/KiwslNvtDjpGLpdLqamptR6jxryOG+OC/DbxUHK73UHhRlJg3e1219qmQ4cOQWVhYWFq27ZtrW3Oh5UrVyojI+Os38T+y1/+Up07d1ZycrI+++wzzZ49Wzt37tTLL7/cRCNtmDvvvFPf+9731LZtW23atElZWVk6dOiQHnnkkRrru91uRUREnHENVkJCQrM6XjXZtWuXli1bpiVLltRZr7kew6NHj8rn89X4Gvvyyy9rbFPba7K5HytJ8vv9mjFjhr7//e/rsssuq7Vez549tWrVKvXr108lJSVasmSJhg4dqu3bt5/19drUUlNTtWbNGvXs2VOHDh3S73//e1111VX6/PPP1aZNmzPqt+TjJ0nr169XcXGxJkyYUGudlnT8/tvp49CQY9SY13FjEHAkzZkzRw899FCddXbs2HHWC+FaisbMd//+/Xrrrbf04osvnrX//7x2qG/fvkpKStKwYcP09ddfq3v37o0feAM0ZI4zZ84MlPXr108RERG69dZblZ2d3Wy/J6Yxx/DAgQPKzMzUjTfeqMmTJ9fZtjkcQ0jTpk3T559/Xuc1KpKUlpamtLS0wPrQoUPVu3dvPfnkk1q4cGGoh9kg1157beDnfv36KTU1VZ07d9aLL76oSZMmnceRhcbKlSt17bXXKjk5udY6Len4tSQEHEmzZs2qM11LUrdu3erVV2Ji4hlXgp++uyYxMbHWNv99YVV1dbWOHz9ea5vvojHzXb16tdq1a6ef/vSnDd5famqqpFNnD5rqzfG7HNPU1FRVV1dr9+7d6tmz5xnbExMTVVlZqeLi4qCzOEVFRSE5XjVp6PwOHjyoH/7whxo6dKieeuqpBu/vfBzDmsTHx8vhcJxxx1pdv/vExMQG1W8upk+fHrjhoKH/iw8PD9fAgQO1a9euEI3u3ImNjdUll1xS61hb6vGTpD179ujtt99u8JnPlnT8Th+HoqIiJSUlBcqLioo0YMCAGts05nXcKOfsap4LzNkuMi4qKgqUPfnkkyYmJsZUVFTU2Nfpi4w//vjjQNlbb73VbC4y9vv9pmvXrmbWrFmNav/BBx8YSebTTz89xyMLjWeeecbY7XZz/PjxGrefvsj4z3/+c6Dsyy+/bLYXGe/fv99cfPHFZsyYMaa6urpRfTSnYzhkyBAzffr0wLrP5zMdO3as8yLjn/zkJ0FlaWlpzfYiVb/fb6ZNm2aSk5PNv/71r0b1UV1dbXr27Gnuvvvuczy6c6+0tNTExcWZ//u//6txe0s7fv9p/vz5JjEx0VRVVTWoXXM+fqrlIuMlS5YEykpKSup1kXFDXseNGus56+kCsWfPHvPJJ5+Y3//+96Z169bmk08+MZ988okpLS01xpz6i3nZZZeZ4cOHm23btpkNGzaY9u3bm6ysrEAf+fn5pmfPnmb//v2BsszMTDNw4ECTn59vPvjgA3PxxRebm266qcnnV5O3337bSDI7duw4Y9v+/ftNz549TX5+vjHGmF27dpkFCxaYjz/+2BQWFpq//vWvplu3buYHP/hBUw+7XjZt2mT++Mc/mm3btpmvv/7aPPPMM6Z9+/Zm3LhxgTr/PUdjjLnttttMp06dzDvvvGM+/vhjk5aWZtLS0s7HFOq0f/9+06NHDzNs2DCzf/9+c+jQocDyn3Va0jF8/vnnjdPpNGvWrDFffPGFmTJliomNjQ3cuXjzzTebOXPmBOp/+OGHJiwszCxZssTs2LHDzJ8/34SHh5t//vOf52sKdZo6dapxuVxm48aNQcfrxIkTgTr/Pcff//735q233jJff/21KSgoMGPGjDGRkZFm+/bt52MKdZo1a5bZuHGjKSwsNB9++KFJT0838fHx5vDhw8aYln/8TvP5fKZTp05m9uzZZ2xracevtLQ08F4nyTzyyCPmk08+MXv27DHGGPPggw+a2NhY89e//tV89tln5oYbbjBdu3Y1J0+eDPTxox/9yCxbtiywfrbX8blAwGmg8ePHG0lnLO+++26gzu7du821115roqKiTHx8vJk1a1ZQgn/33XeNJFNYWBgoO3bsmLnppptM69atTUxMjJk4cWIgNJ1vN910kxk6dGiN2woLC4Pmv3fvXvODH/zAtG3b1jidTtOjRw9z7733mpKSkiYccf0VFBSY1NRU43K5TGRkpOndu7d54IEHgs62/fccjTHm5MmT5vbbbzdxcXEmOjra/OxnPwsKDc3F6tWra/z7+p8nb1viMVy2bJnp1KmTiYiIMEOGDDEfffRRYNvVV19txo8fH1T/xRdfNJdccomJiIgwl156qXnjjTeaeMT1V9vxWr16daDOf89xxowZgd9HQkKCue6668zWrVubfvD1MHr0aJOUlGQiIiJMx44dzejRo82uXbsC21v68TvtrbfeMpLMzp07z9jW0o7f6fes/15Oz8Hv95u5c+eahIQE43Q6zbBhw86Yd+fOnc38+fODyup6HZ8LNmOMOXcfeAEAAJx/PAcHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYzv8H67Ptww85HOAAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: x\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(-9.98, -0.1)\": -1.003, \"(-0.1, -0.01)\": -0.981, \"(-0.01, -0.0)\": -0.903, \"(-0.0, 0.01)\": 0.828, \"(0.01, 0.02)\": 0.938, \"(0.02, 0.55)\": 0.979, \"(0.55, 9.97)\": 0.999}\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: x\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(-9.99, -0.24)\": 1.004, \"(-0.24, -0.02)\": 0.984, \"(-0.02, -0.0)\": 0.924, \"(-0.0, 0.02)\": -0.664, \"(0.02, 0.3)\": -0.974, \"(0.3, 9.97)\": -0.996}\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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AYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDnnJeAsX75cXbp0UXR0tFJTU7Vly5Yaa6+66irZbLYq28iRI4M1kyZNqrI/MzPzfCwFAAA0AxHhfoL169dr9uzZWrFihVJTU/X4448rIyNDu3btUocOHarU//Wvf1V5eXnw8YkTJzRgwADdeOONIXWZmZlatWpV8LHT6QzfIgAAQLMS9oDz2GOPaerUqZo8ebIkacWKFXrjjTe0cuVKzZ07t0p927ZtQx6vW7dOLVq0qBJwnE6nXC5Xnebg8/nk8/mCj71eb32XAQAAmpGwvkVVXl6u/Px8paen/+sJ7Xalp6crLy+vTmM888wzGjdunFq2bBnSvnHjRnXo0EE9e/bU9OnTdeLEiRrHyM7OVlxcXHBLSUk5twUBAIBmIawB5/jx4/L7/UpMTAxpT0xMlNvtPmv/LVu2aPv27brllltC2jMzM/Xcc89pw4YNWrRokd5//31de+218vv91Y6TlZUlj8cT3A4cOHDuiwIAAE1e2N+i+j6eeeYZ9evXT0OHDg1pHzduXPDP/fr1U//+/dW9e3dt3LhRw4cPrzKO0+nkGh0AAC4gYT2Dk5CQIIfDoYKCgpD2goKCs14/U1JSonXr1mnKlClnfZ5u3bopISFBu3fv/l7zBQAA1hDWgBMVFaXBgwdrw4YNwbZAIKANGzYoLS2t1r4vvviifD6fbrrpprM+z8GDB3XixAklJSV97zkDAIDmL+z3wZk9e7aefvppPfvss9q5c6emT5+ukpKS4KeqJk6cqKysrCr9nnnmGY0ePVrt2rULaS8uLtbdd9+tjz/+WHv37tWGDRs0atQo9ejRQxkZGeFeDgAAaAbCfg3O2LFjdezYMc2fP19ut1sDBw5UTk5O8MLj/fv3y24PzVm7du3Shx9+qL///e9VxnM4HPr888/17LPPqrCwUMnJyRoxYoQWLlzIdTYAAECSZDPGmMaexPnm9XoVFxcnj8ej2NjYxp4OAACog/r8/ua7qAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOWcl4CzfPlydenSRdHR0UpNTdWWLVtqrF29erVsNlvIFh0dHVJjjNH8+fOVlJSkmJgYpaen65tvvgn3MgAAQDMR9oCzfv16zZ49WwsWLNDWrVs1YMAAZWRk6OjRozX2iY2N1ZEjR4Lbvn37QvYvXrxYS5cu1YoVK7R582a1bNlSGRkZKisrC/dyAABAMxD2gPPYY49p6tSpmjx5svr06aMVK1aoRYsWWrlyZY19bDabXC5XcEtMTAzuM8bo8ccf13333adRo0apf//+eu6553T48GG98sor1Y7n8/nk9XpDNgAAYF1hDTjl5eXKz89Xenr6v57Qbld6erry8vJq7FdcXKzOnTsrJSVFo0aN0o4dO4L79uzZI7fbHTJmXFycUlNTaxwzOztbcXFxwS0lJaUBVgcAAJqqsAac48ePy+/3h5yBkaTExES53e5q+/Ts2VMrV67U3/72N61Zs0aBQEDDhg3TwYMHJSnYrz5jZmVlyePxBLcDBw5836UBAIAmLKKxJ/Cf0tLSlJaWFnw8bNgw9e7dW08++aQWLlx4TmM6nU45nc6GmiIAAGjiwnoGJyEhQQ6HQwUFBSHtBQUFcrlcdRojMjJSgwYN0u7duyUp2O/7jAkAAKwtrAEnKipKgwcP1oYNG4JtgUBAGzZsCDlLUxu/368vvvhCSUlJkqSuXbvK5XKFjOn1erV58+Y6jwkAAKwt7G9RzZ49WzfffLOGDBmioUOH6vHHH1dJSYkmT54sSZo4caI6duyo7OxsSdIDDzygyy67TD169FBhYaGWLFmiffv26ZZbbpH03SesZs2apQcffFAXX3yxunbtqnnz5ik5OVmjR48O93IAAEAzEPaAM3bsWB07dkzz58+X2+3WwIEDlZOTE7xIeP/+/bLb/3Ui6dSpU5o6darcbrfatGmjwYMHa9OmTerTp0+w5p577lFJSYmmTZumwsJCXX755crJyalyQ0AAAHBhshljTGNP4nzzer2Ki4uTx+NRbGxsY08HAADUQX1+f/NdVAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHLOS8BZvny5unTpoujoaKWmpmrLli011j799NO64oor1KZNG7Vp00bp6elV6idNmiSbzRayZWZmhnsZAACgmQh7wFm/fr1mz56tBQsWaOvWrRowYIAyMjJ09OjRaus3btyo8ePH67333lNeXp5SUlI0YsQIHTp0KKQuMzNTR44cCW5/+ctfwr0UAADQTNiMMSacT5CamqpLL71Uy5YtkyQFAgGlpKTotttu09y5c8/a3+/3q02bNlq2bJkmTpwo6bszOIWFhXrllVfqNAefzyefzxd87PV6lZKSIo/Ho9jY2PovCgAAnHder1dxcXF1+v0d1jM45eXlys/PV3p6+r+e0G5Xenq68vLy6jRGaWmpKioq1LZt25D2jRs3qkOHDurZs6emT5+uEydO1DhGdna24uLigltKSsq5LQgAADQLYQ04x48fl9/vV2JiYkh7YmKi3G53nca49957lZycHBKSMjMz9dxzz2nDhg1atGiR3n//fV177bXy+/3VjpGVlSWPxxPcDhw4cO6LAgAATV5EY0+gNg8//LDWrVunjRs3Kjo6Otg+bty44J/79eun/v37q3v37tq4caOGDx9eZRyn0ymn03le5gwAABpfWM/gJCQkyOFwqKCgIKS9oKBALper1r6PPPKIHn74Yf39739X//79a63t1q2bEhIStHv37u89ZwAA0PyFNeBERUVp8ODB2rBhQ7AtEAhow4YNSktLq7Hf4sWLtXDhQuXk5GjIkCFnfZ6DBw/qxIkTSkpKapB5AwCA5i3sHxOfPXu2nn76aT377LPauXOnpk+frpKSEk2ePFmSNHHiRGVlZQXrFy1apHnz5mnlypXq0qWL3G633G63iouLJUnFxcW6++679fHHH2vv3r3asGGDRo0apR49eigjIyPcywEAAM1A2K/BGTt2rI4dO6b58+fL7XZr4MCBysnJCV54vH//ftnt/8pZf/7zn1VeXq7/+q//ChlnwYIF+v3vfy+Hw6HPP/9czz77rAoLC5WcnKwRI0Zo4cKFXGcDAAAknYf74DRF9fkcPQAAaBqazH1wAAAAGgMBBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWM55CTjLly9Xly5dFB0drdTUVG3ZsqXW+hdffFG9evVSdHS0+vXrpzfffDNkvzFG8+fPV1JSkmJiYpSenq5vvvkmnEsAAADNSNgDzvr16zV79mwtWLBAW7du1YABA5SRkaGjR49WW79p0yaNHz9eU6ZM0WeffabRo0dr9OjR2r59e7Bm8eLFWrp0qVasWKHNmzerZcuWysjIUFlZWbiXAwAAmgGbMcaE8wlSU1N16aWXatmyZZKkQCCglJQU3XbbbZo7d26V+rFjx6qkpESvv/56sO2yyy7TwIEDtWLFChljlJycrDlz5uiuu+6SJHk8HiUmJmr16tUaN25clTF9Pp98Pl/wsdfrVUpKijwej2JjYxt6yQAAIAy8Xq/i4uLq9Ps7rGdwysvLlZ+fr/T09H89od2u9PR05eXlVdsnLy8vpF6SMjIygvV79uyR2+0OqYmLi1NqamqNY2ZnZysuLi64paSkfN+lAQCAJiysAef48ePy+/1KTEwMaU9MTJTb7a62j9vtrrX+zH/rM2ZWVpY8Hk9wO3DgwDmtBwAANA8RjT2B88HpdMrpdDb2NAAAwHkS1jM4CQkJcjgcKigoCGkvKCiQy+Wqto/L5aq1/sx/6zMmAAC4sIQ14ERFRWnw4MHasGFDsC0QCGjDhg1KS0urtk9aWlpIvSTl5uYG67t27SqXyxVS4/V6tXnz5hrHBAAAF5awv0U1e/Zs3XzzzRoyZIiGDh2qxx9/XCUlJZo8ebIkaeLEierYsaOys7MlSXfccYeuvPJKPfrooxo5cqTWrVunTz/9VE899ZQkyWazadasWXrwwQd18cUXq2vXrpo3b56Sk5M1evTocC8HAAA0A2EPOGPHjtWxY8c0f/58ud1uDRw4UDk5OcGLhPfv3y+7/V8nkoYNG6bnn39e9913n37729/q4osv1iuvvKK+ffsGa+655x6VlJRo2rRpKiws1OWXX66cnBxFR0eHezkAAKAZCPt9cJqi+nyOHgAANA1N5j44AAAAjYGAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALCesAefkyZOaMGGCYmNjFR8frylTpqi4uLjW+ttuu009e/ZUTEyMOnXqpNtvv10ejyekzmazVdnWrVsXzqUAAIBmJCKcg0+YMEFHjhxRbm6uKioqNHnyZE2bNk3PP/98tfWHDx/W4cOH9cgjj6hPnz7at2+fbr31Vh0+fFgvvfRSSO2qVauUmZkZfBwfHx/OpQAAgGbEZowx4Rh4586d6tOnjz755BMNGTJEkpSTk6PrrrtOBw8eVHJycp3GefHFF3XTTTeppKREERHf5TGbzaaXX35Zo0ePrtMYPp9PPp8v+Njr9SolJUUej0exsbH1WxgAAGgUXq9XcXFxdfr9Hba3qPLy8hQfHx8MN5KUnp4uu92uzZs313mcM4s4E27OmDFjhhISEjR06FCtXLlSteW07OxsxcXFBbeUlJT6LwgAADQbYQs4brdbHTp0CGmLiIhQ27Zt5Xa76zTG8ePHtXDhQk2bNi2k/YEHHtALL7yg3NxcjRkzRr/5zW/0xBNP1DhOVlaWPB5PcDtw4ED9FwQAAJqNel+DM3fuXC1atKjWmp07d57zhM7wer0aOXKk+vTpo9///vch++bNmxf886BBg1RSUqIlS5bo9ttvr3Ysp9Mpp9P5vecEAACah3oHnDlz5mjSpEm11nTr1k0ul0tHjx4Naa+srNTJkyflcrlq7V9UVKTMzEy1bt1aL7/8siIjI2utT01N1cKFC+Xz+QgyAACg/gGnffv2at++/Vnr0tLSVFhYqPz8fA0ePFiS9O677yoQCCg1NbXGfl6vVxkZGXI6nXr11VcVHR191ufatm2b2rRpQ7gBAACSwvgx8d69eyszM1NTp07VihUrVFFRoZkzZ2rcuHHBT1AdOnRIw4cP13PPPaehQ4fK6/VqxIgRKi0t1Zo1a+T1euX1eiV9F6wcDodee+01FRQU6LLLLlN0dLRyc3P10EMP6a677grXUgAAQDMT1vvgrF27VjNnztTw4cNlt9s1ZswYLV26NLi/oqJCu3btUmlpqSRp69atwU9Y9ejRI2SsPXv2qEuXLoqMjNTy5ct15513yhijHj166LHHHtPUqVPDuRQAANCMhO0+OE1ZfT5HDwAAmoYmcR8cAACAxkLAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlhPWgHPy5ElNmDBBsbGxio+P15QpU1RcXFxrn6uuuko2my1ku/XWW0Nq9u/fr5EjR6pFixbq0KGD7r77blVWVoZzKQAAoBmJCOfgEyZM0JEjR5Sbm6uKigpNnjxZ06ZN0/PPP19rv6lTp+qBBx4IPm7RokXwz36/XyNHjpTL5dKmTZt05MgRTZw4UZGRkXrooYfCthYAANB82IwxJhwD79y5U3369NEnn3yiIUOGSJJycnJ03XXX6eDBg0pOTq6231VXXaWBAwfq8ccfr3b/W2+9peuvv16HDx9WYmKiJGnFihW69957dezYMUVFRVXp4/P55PP5go+9Xq9SUlLk8XgUGxv7PVcKAADOB6/Xq7i4uDr9/g7bW1R5eXmKj48PhhtJSk9Pl91u1+bNm2vtu3btWiUkJKhv377KyspSaWlpyLj9+vULhhtJysjIkNfr1Y4dO6odLzs7W3FxccEtJSXle64OAAA0ZWF7i8rtdqtDhw6hTxYRobZt28rtdtfY7xe/+IU6d+6s5ORkff7557r33nu1a9cu/fWvfw2O++/hRlLwcU3jZmVlafbs2cHHZ87gAAAAa6p3wJk7d64WLVpUa83OnTvPeULTpk0L/rlfv35KSkrS8OHD9e2336p79+7nNKbT6ZTT6TznOQEAgOal3gFnzpw5mjRpUq013bp1k8vl0tGjR0PaKysrdfLkSblcrjo/X2pqqiRp9+7d6t69u1wul7Zs2RJSU1BQIEn1GhcAAFhXvQNO+/bt1b59+7PWpaWlqbCwUPn5+Ro8eLAk6d1331UgEAiGlrrYtm2bJCkpKSk47h/+8AcdPXo0+BZYbm6uYmNj1adPn3quBgAAWFHYLjLu3bu3MjMzNXXqVG3ZskUfffSRZs6cqXHjxgU/QXXo0CH16tUreEbm22+/1cKFC5Wfn6+9e/fq1Vdf1cSJE/WjH/1I/fv3lySNGDFCffr00S9/+Uv94x//0Ntvv6377rtPM2bM4G0oAAAgKcw3+lu7dq169eql4cOH67rrrtPll1+up556Kri/oqJCu3btCn5KKioqSu+8845GjBihXr16ac6cORozZoxee+21YB+Hw6HXX39dDodDaWlpuummmzRx4sSQ++YAAIALW9jug9OU1edz9AAAoGloEvfBAQAAaCwEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDlhDTgnT57UhAkTFBsbq/j4eE2ZMkXFxcU11u/du1c2m63a7cUXXwzWVbd/3bp14VwKAABoRiLCOfiECRN05MgR5ebmqqKiQpMnT9a0adP0/PPPV1ufkpKiI0eOhLQ99dRTWrJkia699tqQ9lWrVikzMzP4OD4+vsHnDwAAmqewBZydO3cqJydHn3zyiYYMGSJJeuKJJ3TdddfpkUceUXJycpU+DodDLpcrpO3ll1/Wz3/+c7Vq1SqkPT4+vkptTXw+n3w+X/Cx1+ut73IAAEAzEra3qPLy8hQfHx8MN5KUnp4uu92uzZs312mM/Px8bdu2TVOmTKmyb8aMGUpISNDQoUO1cuVKGWNqHCc7O1txcXHBLSUlpf4LAgAAzUbYAo7b7VaHDh1C2iIiItS2bVu53e46jfHMM8+od+/eGjZsWEj7Aw88oBdeeEG5ubkaM2aMfvOb3+iJJ56ocZysrCx5PJ7gduDAgfovCAAANBv1fotq7ty5WrRoUa01O3fuPOcJnXH69Gk9//zzmjdvXpV9/942aNAglZSUaMmSJbr99turHcvpdMrpdH7vOQEAgOah3gFnzpw5mjRpUq013bp1k8vl0tGjR0PaKysrdfLkyTpdO/PSSy+ptLRUEydOPGttamqqFi5cKJ/PR5ABAAD1Dzjt27dX+/btz1qXlpamwsJC5efna/DgwZKkd999V4FAQKmpqWft/8wzz+iGG26o03Nt27ZNbdq0IdwAAABJYfwUVe/evZWZmampU6dqxYoVqqio0MyZMzVu3LjgJ6gOHTqk4cOH67nnntPQoUODfXfv3q0PPvhAb775ZpVxX3vtNRUUFOiyyy5TdHS0cnNz9dBDD+muu+4K11IAAEAzE9b74Kxdu1YzZ87U8OHDZbfbNWbMGC1dujS4v6KiQrt27VJpaWlIv5UrV+qiiy7SiBEjqowZGRmp5cuX684775QxRj169NBjjz2mqVOnhnMpAACgGbGZ2j5fbVFer1dxcXHyeDyKjY1t7OkAAIA6qM/vb76LCgAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWE5EY0/ASorLKjVr/VZ9daRYEQ6pXbRDO4+WKmCMkmOdCpiAjnjK5TdShN0mm4yiIx1yxccoKsKuUyU+7T/lC44XYfvuv5F2ydhsKq80sv3zcVSUQ/ExkerlitXnBwtVVFapgAmolTNS/kBAlQGjioCRTTa1jLKppTNSbVpEyRnpkKfUJ3dRhSLtRhEOh0wgIG9Zpfx+o/KA5LBLDpsU6bDLbpci7HbFx0QoKsKho0U+FZ2ulLFJEQ6b2rdyqnNbp/ad9Ol4SbkibDbFRNrVO6m1ApI+23dKReVGkhTtkIZ2aSe7w2jv8dOSbGrptKt1dKTKKwNyRtjlOV2ho97TOlnql5EUHSEN6dxGp05XylcZUGtnpC5ObKmSskrtOlokGZsiI+xyOmwqq/Crwi8Vni6XzWbUIjJCEXabDntPyxib4mIidUnnODnsDp0o9mnv8RIdKypXpfluvR1aRWn8ZZ0UabfrnZ1H5T1dLoddOlZcodPlfsVE2dWjXUsVlftVUl6pmEiHSssDKjxdLsmoa9uWSoiNVmJstIwx2neiRIcLyySb1LVdC3lOV8rnD0hGCvj9Ou03csU6FRXh0BcHvTpd4VdMpE2tIu3ylvvl90t2u03OSLvKKwIKmIAq/EYOu12RETYlxToVF+PUiZJynSotl6+iQr5KyW6zqXV0hNq1ipTD4VCE3aaisgodLSpXi0i7WkdHqKzCrwLvd38Xoxw2dWwTI2eETb4Kv3yVRi2cEUpo5VSbFhHae7xEx4vLVezzy2GXWkRFqLertSIcdgUk+Sr8Chijikqj9rFOHT5Vqv87XqKyioAkqTLw3c+3tdOhDnHROlJYKq/PBP+ex0TYFB8TqRZRdhWVVaq43MhuM0ppE6MrLu6ghNZOHSspU97uk5Kk9i0jdboyoL0nSlXqq5RNRq1iojQoJV79LopTh9gYuWKjNbRrWznstrC/7gE0TTZjjDl7mbV4vV7FxcXJ4/EoNja2Qca8Ydn/p88PehtkLADfX1JctBb8pI8y+yY19lQANJD6/P4O21tUf/jDHzRs2DC1aNFC8fHxdepjjNH8+fOVlJSkmJgYpaen65tvvgmpOXnypCZMmKDY2FjFx8drypQpKi4uDsMK6o5wAzQ9Rzxlmr5mq3K2H2nsqQBoBGELOOXl5brxxhs1ffr0OvdZvHixli5dqhUrVmjz5s1q2bKlMjIyVFZWFqyZMGGCduzYodzcXL3++uv64IMPNG3atHAsoU6KyyoJN0ATdv9rX8ofuOBOVAMXvLC/RbV69WrNmjVLhYWFtdYZY5ScnKw5c+borrvukiR5PB4lJiZq9erVGjdunHbu3Kk+ffrok08+0ZAhQyRJOTk5uu6663Tw4EElJydXO7bP55PP969rW7xer1JSUhrkLaqpz36i3J1Hv9cYAMLrL1MvU1r3do09DQDfU5N4i6q+9uzZI7fbrfT09GBbXFycUlNTlZeXJ0nKy8tTfHx8MNxIUnp6uux2uzZv3lzj2NnZ2YqLiwtuKSkpDTbv/adON9hYAMLjaFHZ2YsAWEqTCThut1uSlJiYGNKemJgY3Od2u9WhQ4eQ/REREWrbtm2wpjpZWVnyeDzB7cCBAw02705tYhpsLADh0aF1dGNPAcB5Vq+AM3fuXNlstlq3r776KlxzPWdOp1OxsbEhW0P549hBDTYWgIZl03efphratW1jTwXAeVav++DMmTNHkyZNqrWmW7du5zQRl8slSSooKFBS0r8+1llQUKCBAwcGa44eDb3epbKyUidPngz2P99aRUeo/0WxXGgMNFELftKH++EAF6B6BZz27durffv2YZlI165d5XK5tGHDhmCg8Xq92rx5c/CTWGlpaSosLFR+fr4GDx4sSXr33XcVCASUmpoalnnVxaszr+Cj4kATw31wgAtb2O5kvH//fp08eVL79++X3+/Xtm3bJEk9evRQq1atJEm9evVSdna2fvrTn8pms2nWrFl68MEHdfHFF6tr166aN2+ekpOTNXr0aElS7969lZmZqalTp2rFihWqqKjQzJkzNW7cuBo/QXW+vDrzCu5kzJ2MuZMxdzIG0ESELeDMnz9fzz77bPDxoEHfXavy3nvv6aqrrpIk7dq1Sx6PJ1hzzz33qKSkRNOmTVNhYaEuv/xy5eTkKDr6XxcIrl27VjNnztTw4cNlt9s1ZswYLV26NFzLqJdW0RH6n5uHNvY00ACmX31xY08BAPA98FUNDXjBMQAACJ9meR8cAACAhkLAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlkPAAQAAlhO2Oxk3ZWfubej18t1RAAA0F2d+b9flHsUXZMApKiqSJKWkpDTyTAAAQH0VFRUpLi6u1poL8qsaAoGADh8+rNatW8tma7gv4/N6vUpJSdGBAwcs+RUQVl+fZP01Wn19kvXXaPX1SdZfo9XXJ4VvjcYYFRUVKTk5WXZ77VfZXJBncOx2uy666KKwjR8bG2vZv7SS9dcnWX+NVl+fZP01Wn19kvXXaPX1SeFZ49nO3JzBRcYAAMByCDgAAMByCDgNyOl0asGCBXI6nY09lbCw+vok66/R6uuTrL9Gq69Psv4arb4+qWms8YK8yBgAAFgbZ3AAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHDq4Q9/+IOGDRumFi1aKD4+vtqa/fv3a+TIkWrRooU6dOigu+++W5WVlbWOe/LkSU2YMEGxsbGKj4/XlClTVFxcHIYV1M/GjRtls9mq3T755JMa+1111VVV6m+99dbzOPO669KlS5W5Pvzww7X2KSsr04wZM9SuXTu1atVKY8aMUUFBwXmacf3s3btXU6ZMUdeuXRUTE6Pu3btrwYIFKi8vr7VfUz+Gy5cvV5cuXRQdHa3U1FRt2bKl1voXX3xRvXr1UnR0tPr166c333zzPM20frKzs3XppZeqdevW6tChg0aPHq1du3bV2mf16tVVjlV0dPR5mnH9/f73v68y3169etXap7kcP6n6f1NsNptmzJhRbX1zOH4ffPCBfvKTnyg5OVk2m02vvPJKyH5jjObPn6+kpCTFxMQoPT1d33zzzVnHre/ruL4IOPVQXl6uG2+8UdOnT692v9/v18iRI1VeXq5Nmzbp2Wef1erVqzV//vxax50wYYJ27Nih3Nxcvf766/rggw80bdq0cCyhXoYNG6YjR46EbLfccou6du2qIUOG1Np36tSpIf0WL158nmZdfw888EDIXG+77bZa6++880699tprevHFF/X+++/r8OHD+tnPfnaeZls/X331lQKBgJ588knt2LFDf/zjH7VixQr99re/PWvfpnoM169fr9mzZ2vBggXaunWrBgwYoIyMDB09erTa+k2bNmn8+PGaMmWKPvvsM40ePVqjR4/W9u3bz/PMz+7999/XjBkz9PHHHys3N1cVFRUaMWKESkpKau0XGxsbcqz27dt3nmZ8bn74wx+GzPfDDz+ssbY5HT9J+uSTT0LWlpubK0m68cYba+zT1I9fSUmJBgwYoOXLl1e7f/HixVq6dKlWrFihzZs3q2XLlsrIyFBZWVmNY9b3dXxODOpt1apVJi4urkr7m2++aex2u3G73cG2P//5zyY2Ntb4fL5qx/ryyy+NJPPJJ58E29566y1js9nMoUOHGnzu30d5eblp3769eeCBB2qtu/LKK80dd9xxfib1PXXu3Nn88Y9/rHN9YWGhiYyMNC+++GKwbefOnUaSycvLC8MMG97ixYtN165da61pysdw6NChZsaMGcHHfr/fJCcnm+zs7Grrf/7zn5uRI0eGtKWmpppf//rXYZ1nQzh69KiRZN5///0aa2r696ipWrBggRkwYECd65vz8TPGmDvuuMN0797dBAKBavc3t+Mnybz88svBx4FAwLhcLrNkyZJgW2FhoXE6neYvf/lLjePU93V8LjiD04Dy8vLUr18/JSYmBtsyMjLk9Xq1Y8eOGvvEx8eHnBFJT0+X3W7X5s2bwz7n+nj11Vd14sQJTZ48+ay1a9euVUJCgvr27ausrCyVlpaehxmem4cffljt2rXToEGDtGTJklrfUszPz1dFRYXS09ODbb169VKnTp2Ul5d3Pqb7vXk8HrVt2/asdU3xGJaXlys/Pz/k52+325Wenl7jzz8vLy+kXvruddkcjpfH45Gksx6v4uJide7cWSkpKRo1alSN/940Fd98842Sk5PVrVs3TZgwQfv376+xtjkfv/Lycq1Zs0a/+tWvZLPZaqxrbsfv3+3Zs0dutzvkGMXFxSk1NbXGY3Qur+NzcUF+m3i4uN3ukHAjKfjY7XbX2KdDhw4hbREREWrbtm2NfRrLM888o4yMjLN+E/svfvELde7cWcnJyfr888917733ateuXfrrX/96nmZad7fffrsuueQStW3bVps2bVJWVpaOHDmixx57rNp6t9utqKioKtdgJSYmNrnjVZ3du3friSee0COPPFJrXVM9hsePH5ff76/2dfbVV19V26em12VTP16BQECzZs3S//t//099+/atsa5nz55auXKl+vfvL4/Ho0ceeUTDhg3Tjh07zvpabQypqalavXq1evbsqSNHjuj+++/XFVdcoe3bt6t169ZV6pvr8ZOkV155RYWFhZo0aVKNNc3t+P2nM8ehPsfoXF7H5+KCDzhz587VokWLaq3ZuXPnWS+Ca07OZc0HDx7U22+/rRdeeOGs4//79UP9+vVTUlKShg8frm+//Vbdu3c/94nXUX3WN3v27GBb//79FRUVpV//+tfKzs5u0t8Tcy7H8NChQ8rMzNSNN96oqVOn1tq3sY8hpBkzZmj79u21Xp8iSWlpaUpLSws+HjZsmHr37q0nn3xSCxcuDPc06+3aa68N/rl///5KTU1V586d9cILL2jKlCmNOLOG98wzz+jaa69VcnJyjTXN7fg1Jxd8wJkzZ06t6VqSunXrVqexXC5XlavAz3y6xuVy1djnPy+qqqys1MmTJ2vs832dy5pXrVqldu3a6YYbbqj386Wmpkr67uzB+fjl+H2OaWpqqiorK7V371717Nmzyn6Xy6Xy8nIVFhaGnMUpKCgI2/GqTn3XePjwYV199dUaNmyYnnrqqXo/3/k+hjVJSEiQw+Go8qm12n7+LperXvVNwcyZM4MfOKjv/8VHRkZq0KBB2r17d5hm17Di4+P1gx/8oMb5NsfjJ0n79u3TO++8U++zns3t+J05DgUFBUpKSgq2FxQUaODAgdX2OZfX8TlpsKt5LiBnu8i4oKAg2Pbkk0+a2NhYU1ZWVu1YZy4y/vTTT4Ntb7/9dpO6yDgQCJiuXbuaOXPmnFP/Dz/80Egy//jHPxp4Zg1vzZo1xm63m5MnT1a7/8xFxi+99FKw7auvvmrSFxkfPHjQXHzxxWbcuHGmsrLynMZoSsdw6NChZubMmcHHfr/fdOzYsdaLjK+//vqQtrS0tCZ5kWogEDAzZswwycnJ5uuvvz6nMSorK03Pnj3NnXfe2cCzC4+ioiLTpk0b86c//ana/c3p+P27BQsWGJfLZSoqKurVr6kfP9VwkfEjjzwSbPN4PHW6yLg+r+NzmmuDjXQB2Ldvn/nss8/M/fffb1q1amU+++wz89lnn5mioiJjzHd/Mfv27WtGjBhhtm3bZnJyckz79u1NVlZWcIzNmzebnj17moMHDwbbMjMzzaBBg8zmzZvNhx9+aC6++GIzfvz4876+mrzzzjtGktm5c2eVfQcPHjQ9e/Y0mzdvNsYYs3v3bvPAAw+YTz/91OzZs8f87W9/M926dTM/+tGPzve0z2rTpk3mj3/8o9m2bZv59ttvzZo1a0z79u3NxIkTgzX/uT5jjLn11ltNp06dzLvvvms+/fRTk5aWZtLS0hpjCWd18OBB06NHDzN8+HBz8OBBc+TIkeD27zXN6RiuW7fOOJ1Os3r1avPll1+aadOmmfj4+OCnF3/5y1+auXPnBus/+ugjExERYR555BGzc+dOs2DBAhMZGWm++OKLxlpCjaZPn27i4uLMxo0bQ45VaWlpsOY/13f//febt99+23z77bcmPz/fjBs3zkRHR5sdO3Y0xhLOas6cOWbjxo1mz5495qOPPjLp6ekmISHBHD161BjTvI/fGX6/33Tq1Mnce++9VfY1x+NXVFQU/H0nyTz22GPms88+M/v27TPGGPPwww+b+Ph487e//c18/vnnZtSoUaZr167m9OnTwTGuueYa88QTTwQfn+113BAIOPVw8803G0lVtvfeey9Ys3fvXnPttdeamJgYk5CQYObMmROS4N977z0jyezZsyfYduLECTN+/HjTqlUrExsbayZPnhwMTU3B+PHjzbBhw6rdt2fPnpCfwf79+82PfvQj07ZtW+N0Ok2PHj3M3XffbTwez3mccd3k5+eb1NRUExcXZ6Kjo03v3r3NQw89FHK27T/XZ4wxp0+fNr/5zW9MmzZtTIsWLcxPf/rTkMDQlKxatarav7P/fvK2OR7DJ554wnTq1MlERUWZoUOHmo8//ji478orrzQ333xzSP0LL7xgfvCDH5ioqCjzwx/+0LzxxhvnecZ1U9OxWrVqVbDmP9c3a9as4M8iMTHRXHfddWbr1q3nf/J1NHbsWJOUlGSioqJMx44dzdixY83u3buD+5vz8Tvj7bffNpLMrl27quxrjsfvzO+t/9zOrCMQCJh58+aZxMRE43Q6zfDhw6usvXPnzmbBggUhbbW9jhuCzRhjGu4NLwAAgMbHfXAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDl/P+uT9gQ1+3hqwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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pyJEjeuCBB3w9VQAAYAGfx8uwYcP07bffasaMGXK73YqLi9OaNWs8F/FmZ2crKOgfJ4C+//57jR8/Xm63W40bN1aPHj2Unp6uzp07+3qqAADAAg5jjPH3JKpTXl6ewsPDlZubq7CwMH9PB0A1+aHwsjrPWCtJ2j07SaHBPv+3F4AaVJmf3wH3aSMAAIDyEC8AAMAqxAsAALAK8QIAAKxCvAAAAKsQLwAAwCrECwAAsArxAgAArEK8AAAAqxAvAADAKsQLAACwCvECAACsQrwAAACrEC8AAMAqxAsAALAK8QIAAKxCvAAAAKsQLwAAwCrECwAAsArxAgAArEK8AAAAqxAvAADAKsQLAACwCvECAACsQrwAAACrEC8AAMAqxAsAALAK8QIAAKxCvAAAAKsQLwAAwCrECwAAsArxAgAArEK8AAAAqxAvAADAKsQLAACwCvECAACsQrwAAACrEC8AAMAqxAsAALAK8QIAAKxCvAAAAKsQLwAAwCrECwAAsArxAgAArEK8AAAAqxAvAADAKsQLAACwCvECAACsQrwAAACrEC8AAMAqxAsAALAK8QIAAKxCvAAAAKsQLwAAwCrECwAAsArxAgAArEK8AAAAqxAvAADAKsQLAACwCvECAACsUiPxsmDBArVp00YhISGKj4/Xli1byh3/zjvvKDY2ViEhIeratatWr15dE9MEAAAW8Hm8LF++XMnJyZo5c6a+/PJLdevWTUlJSTp16lSp49PT0zVixAiNGzdO27dv15AhQzRkyBDt3LnT11MFAAAWcBhjjC+fID4+Xr/4xS/0yiuvSJKKi4sVHR2tRx55RNOnTy8xftiwYTp//rw++ugjz7JbbrlFcXFxWrhw4VWfLy8vT+Hh4crNzVVYWFj17QgAv/qh8LI6z1grSdo9O0mhwXX9PCMA1akyP799eualsLBQmZmZSkxM/McTBgUpMTFRGRkZpa6TkZHhNV6SkpKSyhxfUFCgvLw8rxsAAKi9fBov3333nYqKihQREeG1PCIiQm63u9R13G53pcanpqYqPDzcc4uOjq6eyQMAgIBk/aeNUlJSlJub67kdPXrU31MCAAA+5NM3jZs1a6Y6deooJyfHa3lOTo5cLlep67hcrkqNdzqdcjqd1TNhAAAQ8Hx65iU4OFg9evTQ+vXrPcuKi4u1fv16JSQklLpOQkKC13hJSktLK3M8AAD45+Lzy/WTk5M1evRo9ezZU7169dL8+fN1/vx5jR07VpI0atQotWzZUqmpqZKkxx57TH369NG8efM0aNAgvf3229q2bZtee+01X08VAABYwOfxMmzYMH377beaMWOG3G634uLitGbNGs9FudnZ2QoK+scJoN69e+svf/mLnnzySf32t7/V9ddfr1WrVqlLly6+nioAALCAz7/npabxPS9A7cT3vAC1W8B8zwsAAEB1I14AAIBViBcAAGAV4gUAAFiFeAEAAFYhXgAAgFWIFwAAYBXiBQAAWIV4AQAAViFeAACAVYgXAABgFeIFAABYhXgBAABWIV4AAIBViBcAAGAV4gUAAFiFeAEAAFYhXgAAgFWIFwAAYBXiBQAAWIV4AQAAViFeAACAVYgXAABgFeIFAABYhXgBAABWIV4AAIBViBcAAGAV4gUAAFiFeAEAAFYhXgAAgFWIFwAAYBXiBQAAWIV4AQAAViFeAACAVYgXAABgFeIFAABYhXgBAABWIV4AAIBViBcAAGAV4gUAAFiFeAEAAFYhXgAAgFWIFwAAYBXiBQAAWIV4AQAAViFeAACAVYgXAABgFeIFAABYhXgBAABWIV4AAIBViBcAAGAV4gUAAFiFeAEAAFYhXgAAgFWIFwAAYBXiBQAAWIV4AQAAViFeAACAVYgXAABgFeIFAABYxafxcubMGY0cOVJhYWFq1KiRxo0bp3PnzpW7Tt++feVwOLxuDz30kC+nCQAALFLXlxsfOXKkTp48qbS0NF26dEljx47VhAkT9Je//KXc9caPH6/Zs2d77oeGhvpymgAAwCI+i5c9e/ZozZo12rp1q3r27ClJevnll3XnnXdq7ty5ioqKKnPd0NBQuVwuX00NAABYzGdvG2VkZKhRo0aecJGkxMREBQUFafPmzeWuu2zZMjVr1kxdunRRSkqKfvjhhzLHFhQUKC8vz+sGAABqL5+deXG73WrRooX3k9WtqyZNmsjtdpe53n333aeYmBhFRUXp66+/1rRp07Rv3z699957pY5PTU3VrFmzqnXuAAAgcFU6XqZPn65nn3223DF79uyp8oQmTJjg+e+uXbsqMjJSAwYM0MGDB9W+ffsS41NSUpScnOy5n5eXp+jo6Co/PwAACGyVjpepU6dqzJgx5Y5p166dXC6XTp065bX88uXLOnPmTKWuZ4mPj5ckHThwoNR4cTqdcjqdFd4eAACwW6XjpXnz5mrevPlVxyUkJOjs2bPKzMxUjx49JEmffvqpiouLPUFSEVlZWZKkyMjIyk4VAADUQj67YLdTp0664447NH78eG3ZskVffPGFHn74YQ0fPtzzSaPjx48rNjZWW7ZskSQdPHhQv//975WZmanDhw/rgw8+0KhRo3Tbbbfppptu8tVUAQCARXz6JXXLli1TbGysBgwYoDvvvFO//OUv9dprr3kev3Tpkvbt2+f5NFFwcLDWrVun22+/XbGxsZo6daqGDh2qDz/80JfTBAAAFvHpl9Q1adKk3C+ka9OmjYwxnvvR0dH67LPPfDklAABgOX63EQAAsArxAgAArEK8AAAAqxAvAADAKsQLAACwCvECAACsQrwAAACrEC8AAMAqxAsAALAK8QIAAKxCvAAAAKsQLwAAwCrECwAAsArxAgAArEK8AAAAqxAvAADAKsQLAACwCvECAACsQrwAAACrEC8AAMAqxAsAALAK8QIAAKxCvAAAAKsQLwAAwCrECwAAsArxAgAArEK8AAAAqxAvAADAKnX9PQEAuJodx3J18VKRv6cBIEBw5gUAAFiFeAEAAFYhXgAAgFWIFwAAYBXiBQAAWIV4AQAAViFeAACAVYgXAABgFeIFAABYhXgBAABWIV4AAIBViBcAAGAV4gUAAFiFeAEAAFYhXgAAgFWIFwAAYBXiBQAAWIV4AWAFY/w9AwCBgngBEPCMMZr+3tf+ngaAAEG8AAh4BZeL9bfvzkuSOkeGqX69On6eEQB/Il4AWOWdhxLkcDj8PQ0AfkS8ALAK3QKAeAEAAFYhXgAENGOMLl4q8vc0AASQuv6eAACUxRij/7cwQ5lHvvf3VAAEEM68AAhYFy4VeYVLJz5pBECceQFgibfu76Xw+vX4pBEAzrwAsENIvTqECwBJxAuAALbreJ6/pwAgABEvAADAKsQLAACwis/i5Y9//KN69+6t0NBQNWrUqELrGGM0Y8YMRUZGqn79+kpMTNT+/ft9NUUAAYzvdwFQFp992qiwsFD33nuvEhIS9Prrr1donTlz5uill17SG2+8obZt2+qpp55SUlKSdu/erZCQEF9NtVJ2HMv19xSAWs8Yo2n/+7X2uPP9PRUAAchn8TJr1ixJ0pIlSyo03hij+fPn68knn9TgwYMlSW+++aYiIiK0atUqDR8+3FdTBRBgCi4Xe4VLp8gwOevyLjeAHwXM97wcOnRIbrdbiYmJnmXh4eGKj49XRkZGmfFSUFCggoICz/28PN98OsEYowuXijiNDdSAn77O+H4XAD8XMPHidrslSREREV7LIyIiPI+VJjU11XOWx5cuXCpS5xlrff48ALzx/S4Afq5S52GnT58uh8NR7m3v3r2+mmupUlJSlJub67kdPXq0Rp8fgO/wdhGA0lTqzMvUqVM1ZsyYcse0a9euShNxuVySpJycHEVGRnqW5+TkKC4ursz1nE6nnE5nlZ6zMurXq6Pds5P40iygBjnrBnHWBUAJlYqX5s2bq3nz5j6ZSNu2beVyubR+/XpPrOTl5Wnz5s2aOHGiT56zMhwOh0KD6yqEXwoH1JiurcIl8Sk/AN58dj42OztbWVlZys7OVlFRkbKyspSVlaVz5855xsTGxmrlypWSfoyDyZMn6w9/+IM++OAD7dixQ6NGjVJUVJSGDBniq2kCAADL+OyC3RkzZuiNN97w3O/evbskacOGDerbt68kad++fcrN/ce/qJ544gmdP39eEyZM0NmzZ/XLX/5Sa9asCZjveJH+8S9BAADgHw5jjPH3JKpTXl6ewsPDlZubq7CwMH9PB0A1+OnbRvwDAqidKvPzm8v4AQCAVYgXAABgFeIFAABYhXgBAABWIV4AAIBViBcAAGAV4gUAAFiFeAEAAFYhXgAAgFWIFwABr2urcL5ZF4AH8QIAAKxCvAAAAKsQLwAAwCrECwAAsArxAgAArEK8AAAAqxAvAADAKsQLAACwCvECAACsQrwAAACrEC8AAMAqxAsAALAK8QIAAKxCvAAAAKsQLwCs0bVVuL+nACAAEC8AAMAqxAsAALAK8QIAAKxCvAAAAKsQLwAAwCrECwAAsArxAgAArEK8AAAAqxAvAADAKsQLAACwCvECAACsQrwAAACrEC8AAMAqxAsAALAK8QIAAKxS198TqG7GGElSXl6en2cCAAAq6srP7Ss/x8tT6+IlPz9fkhQdHe3nmQAAgMrKz89XeHh4uWMcpiKJY5Hi4mKdOHFCDRs2lMPhqLbt5uXlKTo6WkePHlVYWFi1bTeQ1PZ9rO37J9X+fWT/7Ffb97G275/ku300xig/P19RUVEKCir/qpZad+YlKChIrVq18tn2w8LCau3/kFfU9n2s7fsn1f59ZP/sV9v3sbbvn+SbfbzaGZcruGAXAABYhXgBAABWIV4qyOl0aubMmXI6nf6eis/U9n2s7fsn1f59ZP/sV9v3sbbvnxQY+1jrLtgFAAC1G2deAACAVYgXAABgFeIFAABYhXgBAABWIV7+7o9//KN69+6t0NBQNWrUqNQx2dnZGjRokEJDQ9WiRQs9/vjjunz5crnbPXPmjEaOHKmwsDA1atRI48aN07lz53ywB5WzceNGORyOUm9bt24tc72+ffuWGP/QQw/V4Mwrp02bNiXm+8wzz5S7zsWLFzVp0iQ1bdpUDRo00NChQ5WTk1NDM664w4cPa9y4cWrbtq3q16+v9u3ba+bMmSosLCx3vUA/hgsWLFCbNm0UEhKi+Ph4bdmypdzx77zzjmJjYxUSEqKuXbtq9erVNTTTyktNTdUvfvELNWzYUC1atNCQIUO0b9++ctdZsmRJieMVEhJSQzOunP/8z/8sMdfY2Nhy17Hp+Eml/53icDg0adKkUscH+vH7/PPPdffddysqKkoOh0OrVq3yetwYoxkzZigyMlL169dXYmKi9u/ff9XtVvZ1XFnEy98VFhbq3nvv1cSJE0t9vKioSIMGDVJhYaHS09P1xhtvaMmSJZoxY0a52x05cqR27dqltLQ0ffTRR/r88881YcIEX+xCpfTu3VsnT570uj3wwANq27atevbsWe6648eP91pvzpw5NTTrqpk9e7bXfB955JFyx0+ZMkUffvih3nnnHX322Wc6ceKEfv3rX9fQbCtu7969Ki4u1quvvqpdu3bphRde0MKFC/Xb3/72qusG6jFcvny5kpOTNXPmTH355Zfq1q2bkpKSdOrUqVLHp6ena8SIERo3bpy2b9+uIUOGaMiQIdq5c2cNz7xiPvvsM02aNEl//etflZaWpkuXLun222/X+fPny10vLCzM63gdOXKkhmZceTfeeKPXXDdt2lTmWNuOnyRt3brVa//S0tIkSffee2+Z6wTy8Tt//ry6deumBQsWlPr4nDlz9NJLL2nhwoXavHmzrrvuOiUlJenixYtlbrOyr+MqMfCyePFiEx4eXmL56tWrTVBQkHG73Z5lf/rTn0xYWJgpKCgodVu7d+82kszWrVs9yz755BPjcDjM8ePHq33u16KwsNA0b97czJ49u9xxffr0MY899ljNTKoaxMTEmBdeeKHC48+ePWvq1atn3nnnHc+yPXv2GEkmIyPDBzOsXnPmzDFt27Ytd0wgH8NevXqZSZMmee4XFRWZqKgok5qaWur43/zmN2bQoEFey+Lj482DDz7o03lWl1OnThlJ5rPPPitzTFl/JwWimTNnmm7dulV4vO3HzxhjHnvsMdO+fXtTXFxc6uM2HT9JZuXKlZ77xcXFxuVymeeee86z7OzZs8bpdJr/+Z//KXM7lX0dVwVnXiooIyNDXbt2VUREhGdZUlKS8vLytGvXrjLXadSokdeZjMTERAUFBWnz5s0+n3NlfPDBBzp9+rTGjh171bHLli1Ts2bN1KVLF6WkpOiHH36ogRlW3TPPPKOmTZuqe/fueu6558p9qy8zM1OXLl1SYmKiZ1lsbKxat26tjIyMmpjuNcnNzVWTJk2uOi4Qj2FhYaEyMzO9/uyDgoKUmJhY5p99RkaG13jpx9elDcdK+vF4SbrqMTt37pxiYmIUHR2twYMHl/l3TiDYv3+/oqKi1K5dO40cOVLZ2dlljrX9+BUWFmrp0qW6//77y/1FwDYdv586dOiQ3G631zEKDw9XfHx8mceoKq/jqqh1v5jRV9xut1e4SPLcd7vdZa7TokULr2V169ZVkyZNylzHX15//XUlJSVd9Zda3nfffYqJiVFUVJS+/vprTZs2Tfv27dN7771XQzOtnEcffVQ333yzmjRpovT0dKWkpOjkyZN6/vnnSx3vdrsVHBxc4rqniIiIgDtmP3fgwAG9/PLLmjt3brnjAvUYfvfddyoqKir1dbZ3795S1ynrdRnox0qSiouLNXnyZP3Lv/yLunTpUua4jh07atGiRbrpppuUm5uruXPnqnfv3tq1a5dPfwltVcTHx2vJkiXq2LGjTp48qVmzZunWW2/Vzp071bBhwxLjbT5+krRq1SqdPXtWY8aMKXOMTcfv564ch8oco6q8jquiVsfL9OnT9eyzz5Y7Zs+ePVe9oMwmVdnnY8eOae3atVqxYsVVt//T63W6du2qyMhIDRgwQAcPHlT79u2rPvFKqMw+Jicne5bddNNNCg4O1oMPPqjU1NSA/fruqhzD48eP64477tC9996r8ePHl7tuIBxDSJMmTdLOnTvLvSZEkhISEpSQkOC537t3b3Xq1Emvvvqqfv/73/t6mpUycOBAz3/fdNNNio+PV0xMjFasWKFx48b5cWa+8frrr2vgwIGKiooqc4xNx88mtTpepk6dWm4RS1K7du0qtC2Xy1Xiaukrn0BxuVxlrvPzC5QuX76sM2fOlLnOtarKPi9evFhNmzbVPffcU+nni4+Pl/Tjv/pr6gfftRzX+Ph4Xb58WYcPH1bHjh1LPO5yuVRYWKizZ896nX3Jycnx2TH7ucru34kTJ9SvXz/17t1br732WqWfzx/HsDTNmjVTnTp1Snyyq7w/e5fLVanxgeLhhx/2XMBf2X9916tXT927d9eBAwd8NLvq06hRI91www1lztXW4ydJR44c0bp16yp9xtKm43flOOTk5CgyMtKzPCcnR3FxcaWuU5XXcZVU29UztcTVLtjNycnxLHv11VdNWFiYuXjxYqnbunLB7rZt2zzL1q5dG1AX7BYXF5u2bduaqVOnVmn9TZs2GUnmq6++quaZ+cbSpUtNUFCQOXPmTKmPX7lg99133/Us27t3b8BesHvs2DFz/fXXm+HDh5vLly9XaRuBdAx79eplHn74Yc/9oqIi07Jly3Iv2L3rrru8liUkJATsBZ/FxcVm0qRJJioqynzzzTdV2sbly5dNx44dzZQpU6p5dtUvPz/fNG7c2Lz44oulPm7b8fupmTNnGpfLZS5dulSp9QL5+KmMC3bnzp3rWZabm1uhC3Yr8zqu0lyrbUuWO3LkiNm+fbuZNWuWadCggdm+fbvZvn27yc/PN8b8+D9cly5dzO23326ysrLMmjVrTPPmzU1KSopnG5s3bzYdO3Y0x44d8yy74447TPfu3c3mzZvNpk2bzPXXX29GjBhR4/tXlnXr1hlJZs+ePSUeO3bsmOnYsaPZvHmzMcaYAwcOmNmzZ5tt27aZQ4cOmffff9+0a9fO3HbbbTU97QpJT083L7zwgsnKyjIHDx40S5cuNc2bNzejRo3yjPn5PhpjzEMPPWRat25tPv30U7Nt2zaTkJBgEhIS/LEL5Tp27Jjp0KGDGTBggDl27Jg5efKk5/bTMTYdw7fffts4nU6zZMkSs3v3bjNhwgTTqFEjz6f8/v3f/91Mnz7dM/6LL74wdevWNXPnzjV79uwxM2fONPXq1TM7duzw1y6Ua+LEiSY8PNxs3LjR63j98MMPnjE/38dZs2aZtWvXmoMHD5rMzEwzfPhwExISYnbt2uWPXSjX1KlTzcaNG82hQ4fMF198YRITE02zZs3MqVOnjDH2H78rioqKTOvWrc20adNKPGbb8cvPz/f8vJNknn/+ebN9+3Zz5MgRY4wxzzzzjGnUqJF5//33zddff20GDx5s2rZtay5cuODZRv/+/c3LL7/suX+113F1IF7+bvTo0UZSiduGDRs8Yw4fPmwGDhxo6tevb5o1a2amTp3qVd0bNmwwksyhQ4c8y06fPm1GjBhhGjRoYMLCwszYsWM9QRQIRowYYXr37l3qY4cOHfL6M8jOzja33XabadKkiXE6naZDhw7m8ccfN7m5uTU444rLzMw08fHxJjw83ISEhJhOnTqZp59+2utM2c/30RhjLly4YP7jP/7DNG7c2ISGhpp//dd/9QqCQLF48eJS/5/96QlVG4/hyy+/bFq3bm2Cg4NNr169zF//+lfPY3369DGjR4/2Gr9ixQpzww03mODgYHPjjTeajz/+uIZnXHFlHa/Fixd7xvx8HydPnuz584iIiDB33nmn+fLLL2t+8hUwbNgwExkZaYKDg03Lli3NsGHDzIEDBzyP2378rli7dq2RZPbt21fiMduO35WfWz+/XdmH4uJi89RTT5mIiAjjdDrNgAEDSux3TEyMmTlzptey8l7H1cFhjDHV9yYUAACAb/E9LwAAwCrECwAAsArxAgAArEK8AAAAqxAvAADAKsQLAACwCvECAACsQrwAAACrEC8AAMAqxAsAALAK8QIg4H377bdyuVx6+umnPcvS09MVHBys9evX+3FmAPyB320EwAqrV6/WkCFDlJ6ero4dOyouLk6DBw/W888/7++pAahhxAsAa0yaNEnr1q1Tz549tWPHDm3dulVOp9Pf0wJQw4gXANa4cOGCunTpoqNHjyozM1Ndu3b195QA+AHXvACwxsGDB3XixAkVFxfr8OHD/p4OAD/hzAsAKxQWFqpXr16Ki4tTx44dNX/+fO3YsUMtWrTw99QA1DDiBYAVHn/8cb377rv66quv1KBBA/Xp00fh4eH66KOP/D01ADWMt40ABLyNGzdq/vz5euuttxQWFqagoCC99dZb+r//+z/96U9/8vf0ANQwzrwAAACrcOYFAABYhXgBAABWIV4AAIBViBcAAGAV4gUAAFiFeAEAAFYhXgAAgFWIFwAAYBXiBQAAWIV4AQAAViFeAACAVYgXAABglf8P8B3h5zFZzVcAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: x\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(-9.96, -0.21)\": -1.003, \"(-0.21, -0.13)\": -0.982, \"(-0.13, -0.06)\": -0.929, \"(-0.06, -0.0)\": -0.883, \"(-0.0, 0.03)\": 0.704, \"(0.03, 0.34)\": 0.978, \"(0.34, 9.99)\": 0.998}\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: x\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(-9.96, -3.18)\": -1.007, \"(-3.18, -3.09)\": -0.986, \"(-3.09, -3.05)\": -0.941, \"(-3.05, -2.99)\": -0.839, \"(-2.99, -2.56)\": 0.969, \"(-2.56, 9.97)\": 0.99}\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: x\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(-10.0, 0.81)\": -1.002, \"(0.81, 0.97)\": -0.981, \"(0.97, 0.98)\": -0.928, \"(0.98, 1.0)\": -0.816, \"(1.0, 1.01)\": 0.877, \"(1.01, 1.01)\": 0.914, \"(1.01, 1.04)\": 0.944, \"(1.04, 1.4)\": 0.98, \"(1.4, 9.88)\": 1.001}\n",
+      "\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1785,7 +3157,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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BZbXBwcwH0sLyWwBuY1ltcDDzgbSw/BaAl7Cs1t8oPpC2ZMtvJdqvA7BPop4eLKv1N4oPtAv5DwB2IucRTGQ+kDHyHwCcQk+PYGLmAxkj/wHADfT0CA6KD7QJ7dcBOC3a0wP+x2UXWIb26wCscq6ZWH2zkCmCgxIS7UL7dQBWI2QafMx8oF0StV8HgPYgZBp8thQfn332mf7pn/5JPXv2VOfOnTV8+HBVVlbacVfwgPj26wDQVvF7t1TOK9OuReNYyh8wls+N//Wvf9WYMWN07bXX6ne/+50uuugiffzxx7rwwgtb/2YEAo3HALRFossthEyDyfIz+sQTT6ioqEirVq2K3VZcXGz13cDDaDwGoC3YuyU8LL/s8pvf/EYlJSWaPHmyevfurauuuko/+clPkh5fW1urSCTS7AP+Q+MxAFaqnFfGm5cAs7z4+N///V+tWLFCAwcO1Nq1azV9+nTdd999WrNmTcLjKyoqlJ+fH/soKiqyekhwQDR4umvROMKnANJ2fkltPXu3hEiWsbgpQ05OjkpKSrRp06bYbffdd5+2bdumzZs3tzi+trZWtbW1sc8jkYiKiopUXV2tvLw8K4cGh5yqq9eQ+Wsl0ZEQQHKpltTuWjSOrIfPRCIR5efnp/X6bfmZ7du3r4YMGdLstssvv1z/+Z//mfD43Nxc5ebmWj0MeAT5DwDJJFpSK5H1CAPLi48xY8Zoz549zW776KOP1L9/f6vvCh4V33hMovkYgNSis6QSK+XCwPJXggceeECjR4/WY489pu9973vaunWrnn32WT377LNW3xU8KtnGc+z9AiAqvp8HS2rDxfLA6dVXX61XXnlFL774ooYNG6ZHHnlEy5cv15QpU6y+K3hYdOO5ps3H2PsFgHQ+68GO2OFlS5l5ww036IYbbrDjR8Nn2PsFQDz6eYBXANgqegnmeE0d73IAJGyf3rNrDpdjQ4biA7Zj7xcAUvL26RQe4UPxAcex9wsQTlxuQRTFBxxH7w8AXG4JN8tXuwCJsPcLEF7nW6jTPh3nMPMBRyTr/QEg2FK1UEd4UXzAMdHeH02R/wCCLVELdbIeoPiAq8h/AOHBRpOIIvMBx5H/AMIhWQt1Cg8w8wHHsfcLEHxkPZAKMx9wBXu/AMFGTw+kwswHXMXeL0Dw0dMD8Zj5gKuil2Aq55W5PRQAFqCnB9LB20u4Ln7vF5bfAv5EzgPpoviA57D8FvAnenogXRQf8IT47IdE/gPwM3p6IBX+qsMTaL8O+Fuynh5AIvxmwDOStV/nnRPgbWQ9kClWu8DT6P0BeB89PZApZj7gOfT+APzBGBO7VBpFTw+kg7/m8Jxo/uN4TV2z1usSy28Br0h2qYWeHkgHxQc8Kb73B8tvAW9hWS3ag+IDnsXyW8AfWFaLTPEXHJ7F8lvAexLlPFhWi0zx2wJPY/kt4B0sqYVVWGoL32H5LeAOch6wCjMf8AWW3wLeQs4D7cHMB3whmv+onFfm9lAA6HzOg8IDbcHbRvhG/PJben8AzkgUMgXag+IDvkXvD8B+hExhBy67wFei2Y+movkPANYjZAo7MPMBX6H3B+AeQqawCsUHfIfeH4A7aCYGq3DZBYFA7w/AWsYYnaqrJ2QKW1DCwrfo/QHYg5Ap7MbMB3wrUe+PU3UNOlVXzwwI0A6ETGE33iLC1+J7f7D8FrAWIVPYgZkP+B7LbwHrnMt6tNyxlsIDVmLmA77H8lvAGmQ94BSKDwRCouW3ADITn/Ug5wG78NcagcXeL0B6Eu3dUjmvTD275vDcgS0oPhBYhE+B1iW71NIlh6Id9iFwikAhfApkhmW1cAMzHwgUwqdA27GsFk6h+EDgsPcLkJ5ky2oBu/FbhlAoWbyO7AfQBMtq4SYyHwis+PwH2Q/gPJbVwk3MfCCwovmP4zV1ZD+A/8OyWngBxQcCLX7vF3p/IMxYVguvoPhAqND7A2HGslp4BcUHAi+a/ahs8kc3mv8g2Y+wYlkt3MRfXgQevT8AltXCW/jNQygk6/0hkf9A8LGsFl5D8YHQIv+BsGBZLbyG4gOhQv4DYceyWngBf20RKuQ/ECaJenqwrBZeYHuH08cff1xZWVmaMWOG3XcFpCWa/4jv/2GMcXFUgLWiOY8h89dSZMNzbC0+tm3bpmeeeUZXXHGFnXcDtFvJ4nWavHIzBQgCg54e8DLbLrt8+eWXmjJlin7yk59o8eLFdt0N0Gbx+Q+yHwiK+GW19PSA19g281FeXq4JEyaorKws5XG1tbWKRCLNPgAnRPMflfNS/44CfhK93NL0Uku0pweFB7zClrd4v/jFL/TOO+9o27ZtrR5bUVGhhQsX2jEMoFXs/YKgYVkt/MDy4uPgwYO6//779eabb6pTp06tHj937lzNnDkz9nkkElFRUZHVwwLSQu8PBAnLauFVlhcf27dv17Fjx/T1r389dltDQ4M2bNigf//3f1dtba06dDhfhefm5io3N9fqYQBpo/cHgoBltfATy/+yXnfddXr//feb3TZt2jQNHjxYDz74YLPCA/ACen/A72ifDr+xvPjo1q2bhg0b1uy2rl27qmfPni1uB7yCvV/gZyyrhd8wpwwkQf4DfsSyWviBI8XH+vXrnbgboN3If8DvostqAS/jNxRogvwH/Ca+oRjgBxQfQBzyH/ALgqbwK4oPIA3kP+BFNBSDX1F8AEmQ/4BXJerpQUMx+Al/QYEkyH/Ai5JdaqGhGPyE4gNIIVn+g+wH3EJPDwQBxQeQoZLF68h+wBPo6QG/ynZ7AIAfRPMfUdHsB+Ck+GW10Z4eFB7wG2Y+gDRE8x/Ha+pi2Q+W38JJLKtFkFB8AGk6l/84f12d5bdwEstqESQUH0AGWH4Lp7GsFkHEX0sgAyy/hZNYVougovgAMpRo+a0xLg0GgcayWgQVxQdggckrN+u3932Td6OwDctqESQstQXaqHPHDhrSN0+StOtIhKW3sBXLahEkFB9AG0XzH1Gn6hp0qq5ehmswaKdz/Tzqm4VMgSDhsgvQDk3fhLL0FlagnwfCgJkPoB3iO59KdD9F+xAyRRgw8wG0A0tvYSdCpggqig+gnZLtfCvReh3pS9RMLBoyBYKG32rABuQ/kAlyHggbMh+ARch/oK3IeSBsmPkALEL+A1Yg54EwoPgALET+A5kg54Gw4jccsBn5DyRCzgNhRuYDsAH5D7SGnAfCjJkPwAbkP5AJch4IG4oPwCbJ8h+8wODc3i3kPBBe/LYDDipZvI7sR8iR9QDIfAC2i89/kP0It/isBzkPhBEzH4DNovmP4zV1ZD/QTOW8MvXsmsMsGEKHmQ/AAefyH+ff3Z6qa5AxxsURwQ2Jsh4UHggjZj4AF5D9CB+yHsB5zHwADiH7EW5kPYDzmPkAHJIo+0Hr9eBL1EKdrAfCjuIDcFB89oPW68GW7FILWQ+EHZddAIfRej08aKEOJMbMB+AwWq+HEy3UgfMoPgAXJGu9LpH/CApaqAPJ8UwAPIL8R3CwrBZIjcwH4CLyH8HEslogNWY+ABeR/wg+ltUCLVF8AC5Llv8g++E/iXp6sKwWaIniA/Ag2q/7DzkPIH1kPgCPoP26v9HTA0gfMx+ARyRqvw5/oqcHkBozH4CHxLdfP1XXIGOMiyNCOpL19KDwABJj5gPwMLIf3kfWA8gcMx+Ax5D98Bd6egCZY+YD8JhE2Q9ar/sDPT2A9FB8AB4Un/2g9br30NMDaDuKD8CjopdfKptM6UcvwbBBmbvIeQDtw18wwKNove5d9PQA2ofiA/CwZK3XJfIfXkFPDyBzFB+Az5D/cE+ynAeXwYDM8IwBfID8h/vIeQDWsbzPR0VFha6++mp169ZNvXv31qRJk7Rnzx6r7wYIlWj+Y9eicaqcV+b2cEKJnAdgHcvfMr399tsqLy/X1Vdfrfr6ej300EO6/vrrtWvXLnXt2tXquwNCI1H+A+4g5wG0j+V/yd54441mn69evVq9e/fW9u3bNXbsWKvvDgBsRc4DsJ7tz57q6mpJUo8ePey+KyB0mr4g8i7ceuQ8AHvYWnw0NjZqxowZGjNmjIYNG5bwmNraWtXW1sY+j0Qidg4JCJSmvT9Y/WI9ch6APWwtPsrLy/XBBx9o48aNSY+pqKjQwoUL7RwGECiJVr5IrH6xGzkPwDq2/ZW655579Nprr2nDhg26+OKLkx43d+5czZw5M/Z5JBJRUVGRXcMCfK9p51NJdD+1iTGGnAdgE8ufScYY3XvvvXrllVe0fv16FRcXpzw+NzdXubm5Vg8DCLRkK19O1TXwztwCZD0Ae1ne56O8vFzPP/+8XnjhBXXr1k1VVVWqqqrS6dOnrb4rAHFKFq/T5JWbZYxxeyi+Fp/1IOcBWMvymY8VK1ZIkr71rW81u33VqlX6wQ9+YPXdAaEXnwEh+2Gtynll6tk1h9kkwEK2XHYB4JxoBuR4TR3Zj3ZK1tODwgOwFm+NgAA4lwHhskB7kPMAnEPxAQQQzccyR08PwDkUH0AA0XysfejpAdjL8tUuANwRDZ7GiwZQkdi5fh71CXt6UHgA9mDmAwgImo9ljpwH4A6KDyBAaD6WGXIegDsoPoAQKFm8juxHK8h5AM4h8wEEVHwGhOxHc8n2bqHwAOzHzAcQUImaj0VfbMP+7p6sB+Auig8gwOKbj0WLkLBfgmHvFsBdFB9AwMXv/SKx/0tT7N0COI+/PEDANV2CG/blt+zdAngDxQcQAomW4IZt+S05D8A7WO0ChFTJ4nWavHJzaHaipqcH4B3MfAAhEp//CGv2g54egLuY+QBCJJr/qJxX5vZQHMPeLYD3hOvtDoAWy2+D3PuDnAfgTRQfQMgFufcHOQ/Amyg+gBAKY+8Pch6AdwTzrwyAlILe+yNZP4+gFlaA3/BMBEIqUe+PICDnAXhf8P7yAGizIIRPyXkA3kfxASAmaOFTch6AN9HnAwi5aPi0qWj41G/O9fSgnwfgdcx8ACEXlPApWQ/APyg+AARi47n4rAc5D8C7KD4AJFSyeJ1vsh/xl1sq55WpZ9ccz48bCCuKDwAxftx4LtHlli45/pmxAcKIwCmAGD9uPMflFsB/vPt2BoAr/LzxHJdbAH+g+ACQkld7fyRroe6V8QFIjuIDQAte33iOZbWAv7n/VwSA53i99wct1AF/o/gAkFCy3h+Su/mPRMtqaaEO+AvFB4C0uZ3/SLas1guXggCkj6W2AFLy0t4vLKsFgoG3CwBS8mr+g2W1gH9RfABoldv5D5bVAsFC8QGgTZzKf7CsFggeMh8A0uZG/oNltUDwMPMBIG1u5z9YVgsEA8UHgIwky3/YURDE9/RgWS0QDDyLAbRbyeJ1lmc/yHoAwUXmA0CbxOc/rM5+0NMDCC5mPgC0STT/cbymzvbsBz09gGCh+ADQZufyH+dnI6zo/UFPDyD4KD4AWKa9vT/IeQDhQOYDQLtY2fuDnh5AODDzAaBd7Or9QU8PILgoPgC0mx17v9DTAwguntkAbJFJ/iNRyBRAcFF8ALBMNP9R2SS3Ec1/JJvFIGQKhA/FBwDLtCX/QcgUCB+KDwCWSpT/SBchUyAcKD4A2C5Z+JSN44Bw4lkOwHaJwqdkPYDwoskYAFu01nyMjeOA8LKt+Hj66ac1YMAAderUSaWlpdq6datddwXAg6Lh012LxqlyXlns9lN1DTpVV9/sckvlvLI2tWMH4E+2XHb55S9/qZkzZ2rlypUqLS3V8uXLNW7cOO3Zs0e9e/e24y4BeFCi8GmiFTBsHAeEiy0zH8uWLdPtt9+uadOmaciQIVq5cqW6dOmin/70p3bcHQCPS3QJJorLLUD4WD7zUVdXp+3bt2vu3Lmx27Kzs1VWVqbNmze3OL62tla1tbWxzyORiNVDAuCypv0/4rGsFggfy2c+Pv/8czU0NKhPnz7Nbu/Tp4+qqqpaHF9RUaH8/PzYR1FRkdVDAuAB0Usw8R8UHkD4uL7aZe7cuaquro59HDx40O0hAQAAG1l+2aVXr17q0KGDjh492uz2o0ePqqCgoMXxubm5ys3NtXoYAADAoyyf+cjJydE3vvENvfXWW7HbGhsb9dZbb2nUqFFW3x0AAPAZW5bazpw5U1OnTlVJSYlGjhyp5cuXq6amRtOmTbPj7gAAgI/YUnzcdNNN+stf/qL58+erqqpKI0aM0BtvvNEihAoAAMInyxhj3B5EU5FIRPn5+aqurlZeXp7bwwEAAGnI5PXb9dUuAAAgXCg+AACAoyg+AACAoyg+AACAoyg+AACAoyg+AACAoyg+AACAoyg+AACAo2zpcNoe0Z5nkUjE5ZEAAIB0RV+30+ld6rni4+TJk5KkoqIil0cCAAAydfLkSeXn56c8xnPt1RsbG3X48GF169ZNWVlZlv3cSCSioqIiHTx4MLBt24P+GIP++KTgP8agPz4p+I+Rx+d/dj1GY4xOnjypwsJCZWenTnV4buYjOztbF198sW0/Py8vL7C/UFFBf4xBf3xS8B9j0B+fFPzHyOPzPzseY2szHlEETgEAgKMoPgAAgKNCU3zk5uZqwYIFys3NdXsotgn6Ywz645OC/xiD/vik4D9GHp//eeExei5wCgAAgi00Mx8AAMAbKD4AAICjKD4AAICjKD4AAICjAlV8PProoxo9erS6dOmi7t27Jzzm008/1YQJE9SlSxf17t1bs2fPVn19fcqf+8UXX2jKlCnKy8tT9+7dddttt+nLL7+04RFkZv369crKykr4sW3btqTf961vfavF8XfddZeDI0/fgAEDWoz18ccfT/k9Z86cUXl5uXr27KkLLrhA3/3ud3X06FGHRpy+Tz75RLfddpuKi4vVuXNnXXrppVqwYIHq6upSfp/Xz9/TTz+tAQMGqFOnTiotLdXWrVtTHv/yyy9r8ODB6tSpk4YPH67XX3/doZFmrqKiQldffbW6deum3r17a9KkSdqzZ0/K71m9enWL89WpUyeHRpyZH/7why3GOnjw4JTf46fzJyX+m5KVlaXy8vKEx3v9/G3YsEE33nijCgsLlZWVpVdffbXZ140xmj9/vvr27avOnTurrKxMH3/8cas/N9PncaYCVXzU1dVp8uTJmj59esKvNzQ0aMKECaqrq9OmTZu0Zs0arV69WvPnz0/5c6dMmaKdO3fqzTff1GuvvaYNGzbojjvusOMhZGT06NE6cuRIs49/+Zd/UXFxsUpKSlJ+7+23397s+5YsWeLQqDO3aNGiZmO99957Ux7/wAMP6L//+7/18ssv6+2339bhw4f1D//wDw6NNn0ffvihGhsb9cwzz2jnzp168skntXLlSj300EOtfq9Xz98vf/lLzZw5UwsWLNA777yjK6+8UuPGjdOxY8cSHr9p0ybdcsstuu2227Rjxw5NmjRJkyZN0gcffODwyNPz9ttvq7y8XH/605/05ptv6uzZs7r++utVU1OT8vvy8vKana8DBw44NOLMDR06tNlYN27cmPRYv50/Sdq2bVuzx/fmm29KkiZPnpz0e7x8/mpqanTllVfq6aefTvj1JUuW6Mc//rFWrlypLVu2qGvXrho3bpzOnDmT9Gdm+jxuExNAq1atMvn5+S1uf/311012drapqqqK3bZixQqTl5dnamtrE/6sXbt2GUlm27Ztsdt+97vfmaysLPPZZ59ZPvb2qKurMxdddJFZtGhRyuOuueYac//99zszqHbq37+/efLJJ9M+/sSJE6Zjx47m5Zdfjt22e/duI8ls3rzZhhFaa8mSJaa4uDjlMV4+fyNHjjTl5eWxzxsaGkxhYaGpqKhIePz3vvc9M2HChGa3lZaWmjvvvNPWcVrl2LFjRpJ5++23kx6T7O+RFy1YsMBceeWVaR/v9/NnjDH333+/ufTSS01jY2PCr/vp/Ekyr7zySuzzxsZGU1BQYH70ox/Fbjtx4oTJzc01L774YtKfk+nzuC0CNfPRms2bN2v48OHq06dP7LZx48YpEolo586dSb+ne/fuzWYSysrKlJ2drS1bttg+5kz85je/0fHjxzVt2rRWj/35z3+uXr16adiwYZo7d65OnTrlwAjb5vHHH1fPnj111VVX6Uc/+lHKy2Tbt2/X2bNnVVZWFrtt8ODB6tevnzZv3uzEcNulurpaPXr0aPU4L56/uro6bd++vdm/fXZ2tsrKypL+22/evLnZ8dK556QfzpV07nxJavWcffnll+rfv7+Kioo0ceLEpH9vvODjjz9WYWGhLrnkEk2ZMkWffvpp0mP9fv7q6ur0/PPP69Zbb025kamfzl9T+/fvV1VVVbNzlJ+fr9LS0qTnqC3P47bw3MZydqqqqmpWeEiKfV5VVZX0e3r37t3stq985Svq0aNH0u9xy3PPPadx48a1ujHfP/7jP6p///4qLCzUn//8Zz344IPas2eP/uu//suhkabvvvvu09e//nX16NFDmzZt0ty5c3XkyBEtW7Ys4fFVVVXKyclpkfnp06eP585XvL179+qpp57S0qVLUx7n1fP3+eefq6GhIeFz7MMPP0z4Pcmek14/V9K5HbhnzJihMWPGaNiwYUmPGzRokH7605/qiiuuUHV1tZYuXarRo0dr586dtm6i2RalpaVavXq1Bg0apCNHjmjhwoX6m7/5G33wwQfq1q1bi+P9fP4k6dVXX9WJEyf0gx/8IOkxfjp/8aLnIZNz1JbncVt4vviYM2eOnnjiiZTH7N69u9VQlJ+05TEfOnRIa9eu1UsvvdTqz2+aVxk+fLj69u2r6667Tvv27dOll17a9oGnKZPHN3PmzNhtV1xxhXJycnTnnXeqoqLCs+2P23L+PvvsM33nO9/R5MmTdfvtt6f8XrfPH84pLy/XBx98kDITIUmjRo3SqFGjYp+PHj1al19+uZ555hk98sgjdg8zI+PHj4/9/xVXXKHS0lL1799fL730km677TYXR2aP5557TuPHj1dhYWHSY/x0/vzE88XHrFmzUlalknTJJZek9bMKCgpaJHajqyAKCgqSfk98yKa+vl5ffPFF0u9pr7Y85lWrVqlnz576u7/7u4zvr7S0VNK5d95OvHi155yWlpaqvr5en3zyiQYNGtTi6wUFBaqrq9OJEyeazX4cPXrUtvMVL9PHd/jwYV177bUaPXq0nn322Yzvz+nzl0yvXr3UoUOHFiuLUv3bFxQUZHS8V9xzzz2x8Hmm7347duyoq666Snv37rVpdNbp3r27LrvssqRj9ev5k6QDBw5o3bp1Gc8Y+un8Rc/D0aNH1bdv39jtR48e1YgRIxJ+T1uex21iWXrEQ1oLnB49ejR22zPPPGPy8vLMmTNnEv6saOC0srIydtvatWs9FThtbGw0xcXFZtasWW36/o0bNxpJ5r333rN4ZNZ7/vnnTXZ2tvniiy8Sfj0aOP3Vr34Vu+3DDz/0bOD00KFDZuDAgebmm2829fX1bfoZXjp/I0eONPfcc0/s84aGBvPVr341ZeD0hhtuaHbbqFGjPBtYbGxsNOXl5aawsNB89NFHbfoZ9fX1ZtCgQeaBBx6weHTWO3nypLnwwgvNv/3bvyX8ut/OX1MLFiwwBQUF5uzZsxl9n5fPn5IETpcuXRq7rbq6Oq3AaSbP4zaN1bKf5AEHDhwwO3bsMAsXLjQXXHCB2bFjh9mxY4c5efKkMebcL82wYcPM9ddfb959913zxhtvmIsuusjMnTs39jO2bNliBg0aZA4dOhS77Tvf+Y656qqrzJYtW8zGjRvNwIEDzS233OL440tm3bp1RpLZvXt3i68dOnTIDBo0yGzZssUYY8zevXvNokWLTGVlpdm/f7/59a9/bS655BIzduxYp4fdqk2bNpknn3zSvPvuu2bfvn3m+eefNxdddJH5/ve/Hzsm/vEZY8xdd91l+vXrZ37/+9+byspKM2rUKDNq1Cg3HkJKhw4dMl/72tfMddddZw4dOmSOHDkS+2h6jJ/O3y9+8QuTm5trVq9ebXbt2mXuuOMO071799gKs3/+5382c+bMiR3/xz/+0XzlK18xS5cuNbt37zYLFiwwHTt2NO+//75bDyGl6dOnm/z8fLN+/fpm5+vUqVOxY+If48KFC83atWvNvn37zPbt283NN99sOnXqZHbu3OnGQ0hp1qxZZv369Wb//v3mj3/8oykrKzO9evUyx44dM8b4//xFNTQ0mH79+pkHH3ywxdf8dv5OnjwZe62TZJYtW2Z27NhhDhw4YIwx5vHHHzfdu3c3v/71r82f//xnM3HiRFNcXGxOnz4d+xnf/va3zVNPPRX7vLXnsRUCVXxMnTrVSGrx8Yc//CF2zCeffGLGjx9vOnfubHr16mVmzZrVrPL9wx/+YCSZ/fv3x247fvy4ueWWW8wFF1xg8vLyzLRp02IFjRfccsstZvTo0Qm/tn///mb/Bp9++qkZO3as6dGjh8nNzTVf+9rXzOzZs011dbWDI07P9u3bTWlpqcnPzzedOnUyl19+uXnssceazVLFPz5jjDl9+rS5++67zYUXXmi6dOli/v7v/77ZC7pXrFq1KuHva9MJST+ev6eeesr069fP5OTkmJEjR5o//elPsa9dc801ZurUqc2Of+mll8xll11mcnJyzNChQ81vf/tbh0ecvmTna9WqVbFj4h/jjBkzYv8effr0MX/7t39r3nnnHecHn4abbrrJ9O3b1+Tk5JivfvWr5qabbjJ79+6Nfd3v5y9q7dq1RpLZs2dPi6/57fxFX7PiP6KPobGx0Tz88MOmT58+Jjc311x33XUtHnf//v3NggULmt2W6nlshSxjjLHuIg4AAEBqoerzAQAA3EfxAQAAHEXxAQAAHEXxAQAAHEXxAQAAHEXxAQAAHEXxAQAAHEXxAQAAHEXxAQAAHEXxAQAAHEXxAcB2f/nLX1RQUKDHHnssdtumTZuUk5Ojt956y8WRAXADe7sAcMTrr7+uSZMmadOmTRo0aJBGjBihiRMnatmyZW4PDYDDKD4AOKa8vFzr1q1TSUmJ3n//fW3btk25ubluDwuAwyg+ADjm9OnTGjZsmA4ePKjt27dr+PDhbg8JgAvIfABwzL59+3T48GE1Njbqk08+cXs4AFzCzAcAR9TV1WnkyJEaMWKEBg0apOXLl+v9999X79693R4aAIdRfABwxOzZs/WrX/1K7733ni644AJdc801ys/P12uvveb20AA4jMsuAGy3fv16LV++XD/72c+Ul5en7Oxs/exnP9P//M//aMWKFW4PD4DDmPkAAACOYuYDAAA4iuIDAAA4iuIDAAA4iuIDAAA4iuIDAAA4iuIDAAA4iuIDAAA4iuIDAAA4iuIDAAA4iuIDAAA4iuIDAAA4iuIDAAA46v8D6Mce6AohTdcAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1797,18 +3169,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.1%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-9.99, -9.97)\": -9.98, \"(-9.97, -9.95)\": -9.96, \"(-9.95, -9.91)\": -9.93, \"(-9.91, -9.88)\": -9.9, \"(-9.88, -9.86)\": -9.87, \"(-9.86, -9.82)\": -9.84, \"(-9.82, -9.77)\": -9.8, \"(-9.77, -9.75)\": -9.76, \"(-9.75, -9.72)\": -9.73, \"(-9.72, -9.68)\": -9.7, \"(-9.68, -9.64)\": -9.66, \"(-9.64, -9.61)\": -9.63, \"(-9.61, -9.56)\": -9.6, \"(-9.56, -9.51)\": -9.53, \"(-9.51, -9.48)\": -9.5, \"(-9.48, -9.45)\": -9.47, \"(-9.45, -9.43)\": -9.44, \"(-9.43, -9.4)\": -9.42, \"(-9.4, -9.37)\": -9.39, \"(-9.37, -9.35)\": -9.36, \"(-9.35, -9.32)\": -9.33, \"(-9.32, -9.28)\": -9.29, \"(-9.28, -9.22)\": -9.26, \"(-9.22, -9.16)\": -9.19, \"(-9.16, -9.14)\": -9.16, \"(-9.14, -9.14)\": -9.14, \"(-9.14, -9.09)\": -9.11, \"(-9.09, -9.03)\": -9.05, \"(-9.03, -9.01)\": -9.03, \"(-9.01, -8.98)\": -9.0, \"(-8.98, -8.97)\": -8.98, \"(-8.97, -8.95)\": -8.95, \"(-8.95, -8.91)\": -8.93, \"(-8.91, -8.88)\": -8.9, \"(-8.88, -8.86)\": -8.88, \"(-8.86, -8.85)\": -8.86, \"(-8.85, -8.81)\": -8.83, \"(-8.81, -8.79)\": -8.8, \"(-8.79, -8.77)\": -8.78, \"(-8.77, -8.73)\": -8.74, \"(-8.73, -8.69)\": -8.72, \"(-8.69, -8.65)\": -8.67, \"(-8.65, -8.63)\": -8.65, \"(-8.63, -8.61)\": -8.62, \"(-8.61, -8.59)\": -8.6, \"(-8.59, -8.56)\": -8.57, \"(-8.56, -8.52)\": -8.54, \"(-8.52, -8.47)\": -8.51, \"(-8.47, -8.42)\": -8.44, \"(-8.42, -8.39)\": -8.42, \"(-8.39, -8.37)\": -8.38, \"(-8.37, -8.33)\": -8.36, \"(-8.33, -8.3)\": -8.32, \"(-8.3, -8.28)\": -8.29, \"(-8.28, -8.24)\": -8.27, \"(-8.24, -8.22)\": -8.23, \"(-8.22, -8.19)\": -8.21, \"(-8.19, -8.14)\": -8.18, \"(-8.14, -8.12)\": -8.14, \"(-8.12, -8.1)\": -8.12, \"(-8.1, -8.08)\": -8.1, \"(-8.08, -8.04)\": -8.06, \"(-8.04, -8.02)\": -8.04, \"(-8.02, -7.99)\": -8.01, \"(-7.99, -7.94)\": -7.96, \"(-7.94, -7.89)\": -7.91, \"(-7.89, -7.87)\": -7.89, \"(-7.87, -7.84)\": -7.87, \"(-7.84, -7.81)\": -7.84, \"(-7.81, -7.79)\": -7.81, \"(-7.79, -7.76)\": -7.78, \"(-7.76, -7.74)\": -7.76, \"(-7.74, -7.72)\": -7.73, \"(-7.72, -7.67)\": -7.7, \"(-7.67, -7.65)\": -7.66, \"(-7.65, -7.63)\": -7.64, \"(-7.63, -7.6)\": -7.62, \"(-7.6, -7.58)\": -7.58, \"(-7.58, -7.55)\": -7.56, \"(-7.55, -7.52)\": -7.53, \"(-7.52, -7.49)\": -7.5, \"(-7.49, -7.45)\": -7.47, \"(-7.45, -7.42)\": -7.44, \"(-7.42, -7.4)\": -7.42, \"(-7.4, -7.35)\": -7.39, \"(-7.35, -7.29)\": -7.32, \"(-7.29, -7.22)\": -7.26, \"(-7.22, -7.16)\": -7.18, \"(-7.16, -7.13)\": -7.14, \"(-7.13, -7.11)\": -7.12, \"(-7.11, -7.08)\": -7.09, \"(-7.08, -7.02)\": -7.05, \"(-7.02, -6.97)\": -7.01, \"(-6.97, -6.95)\": -6.98, \"(-6.95, -6.94)\": -6.95, \"(-6.94, -6.9)\": -6.92, \"(-6.9, -6.86)\": -6.87, \"(-6.86, -6.81)\": -6.84, \"(-6.81, -6.79)\": -6.81, \"(-6.79, -6.75)\": -6.77, \"(-6.75, -6.71)\": -6.73, \"(-6.71, -6.69)\": -6.71, \"(-6.69, -6.67)\": -6.69, \"(-6.67, -6.65)\": -6.67, \"(-6.65, -6.62)\": -6.63, \"(-6.62, -6.59)\": -6.6, \"(-6.59, -6.54)\": -6.56, \"(-6.54, -6.5)\": -6.53, \"(-6.5, -6.47)\": -6.5, \"(-6.47, -6.45)\": -6.47, \"(-6.45, -6.42)\": -6.45, \"(-6.42, -6.41)\": -6.43, \"(-6.41, -6.38)\": -6.4, \"(-6.38, -6.35)\": -6.38, \"(-6.35, -6.33)\": -6.35, \"(-6.33, -6.3)\": -6.32, \"(-6.3, -6.29)\": -6.3, \"(-6.29, -6.24)\": -6.26, \"(-6.24, -6.2)\": -6.23, \"(-6.2, -6.15)\": -6.19, \"(-6.15, -6.14)\": -6.16, \"(-6.14, -6.11)\": -6.14, \"(-6.11, -6.09)\": -6.1, \"(-6.09, -6.06)\": -6.08, \"(-6.06, -6.02)\": -6.05, \"(-6.02, -5.98)\": -6.01, \"(-5.98, -5.97)\": -5.98, \"(-5.97, -5.95)\": -5.96, \"(-5.95, -5.91)\": -5.93, \"(-5.91, -5.88)\": -5.9, \"(-5.88, -5.85)\": -5.88, \"(-5.85, -5.83)\": -5.85, \"(-5.83, -5.82)\": -5.81, \"(-5.82, -5.78)\": -5.79, \"(-5.78, -5.74)\": -5.76, \"(-5.74, -5.72)\": -5.74, \"(-5.72, -5.68)\": -5.71, \"(-5.68, -5.66)\": -5.67, \"(-5.66, -5.61)\": -5.65, \"(-5.61, -5.58)\": -5.61, \"(-5.58, -5.55)\": -5.58, \"(-5.55, -5.53)\": -5.54, \"(-5.53, -5.48)\": -5.52, \"(-5.48, -5.47)\": -5.49, \"(-5.47, -5.44)\": -5.47, \"(-5.44, -5.42)\": -5.43, \"(-5.42, -5.41)\": -5.4, \"(-5.41, -5.37)\": -5.38, \"(-5.37, -5.32)\": 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\"(7.88, 7.9)\": 7.89, \"(7.9, 7.94)\": 7.92, \"(7.94, 7.96)\": 7.95, \"(7.96, 7.99)\": 7.98, \"(7.99, 8.03)\": 8.0, \"(8.03, 8.08)\": 8.07, \"(8.08, 8.13)\": 8.09, \"(8.13, 8.19)\": 8.16, \"(8.19, 8.25)\": 8.2, \"(8.25, 8.32)\": 8.3, \"(8.32, 8.34)\": 8.32, \"(8.34, 8.36)\": 8.34, \"(8.36, 8.38)\": 8.36, \"(8.38, 8.41)\": 8.4, \"(8.41, 8.44)\": 8.42, \"(8.44, 8.49)\": 8.46, \"(8.49, 8.52)\": 8.5, \"(8.52, 8.55)\": 8.53, \"(8.55, 8.58)\": 8.55, \"(8.58, 8.6)\": 8.58, \"(8.6, 8.63)\": 8.61, \"(8.63, 8.65)\": 8.63, \"(8.65, 8.67)\": 8.66, \"(8.67, 8.7)\": 8.68, \"(8.7, 8.73)\": 8.71, \"(8.73, 8.76)\": 8.74, \"(8.76, 8.82)\": 8.78, \"(8.82, 8.86)\": 8.83, \"(8.86, 8.9)\": 8.87, \"(8.9, 8.91)\": 8.9, \"(8.91, 8.94)\": 8.93, \"(8.94, 8.97)\": 8.96, \"(8.97, 8.99)\": 8.98, \"(8.99, 9.02)\": 9.01, \"(9.02, 9.06)\": 9.04, \"(9.06, 9.11)\": 9.1, \"(9.11, 9.15)\": 9.12, \"(9.15, 9.18)\": 9.16, \"(9.18, 9.2)\": 9.18, \"(9.2, 9.24)\": 9.21, \"(9.24, 9.28)\": 9.26, \"(9.28, 9.32)\": 9.3, \"(9.32, 9.36)\": 9.34, \"(9.36, 9.39)\": 9.38, \"(9.39, 9.43)\": 9.4, \"(9.43, 9.49)\": 9.44, \"(9.49, 9.55)\": 9.52, \"(9.55, 9.6)\": 9.55, \"(9.6, 9.65)\": 9.63, \"(9.65, 9.66)\": 9.65, \"(9.66, 9.68)\": 9.67, \"(9.68, 9.71)\": 9.7, \"(9.71, 9.74)\": 9.72, \"(9.74, 9.79)\": 9.77, \"(9.79, 9.87)\": 9.81, \"(9.87, 9.94)\": 9.91, \"(9.94, 9.98)\": 9.96}\n",
+      "Means: {\"(-9.98, -9.87)\": 9.962, \"(-9.87, -9.75)\": 9.851, \"(-9.75, -9.63)\": 9.743, \"(-9.63, -9.5)\": 9.604, \"(-9.5, -9.38)\": 9.485, \"(-9.38, -9.26)\": 9.362, \"(-9.26, -9.16)\": 9.262, \"(-9.16, -9.02)\": 9.122, \"(-9.02, -8.91)\": 9.01, \"(-8.91, -8.79)\": 8.905, \"(-8.79, -8.66)\": 8.773, \"(-8.66, -8.55)\": 8.655, \"(-8.55, -8.45)\": 8.54, \"(-8.45, -8.34)\": 8.439, \"(-8.34, -8.2)\": 8.319, \"(-8.2, -8.11)\": 8.204, \"(-8.11, -8.0)\": 8.1, \"(-8.0, -7.89)\": 7.986, \"(-7.89, -7.77)\": 7.881, \"(-7.77, -7.66)\": 7.768, \"(-7.66, -7.54)\": 7.645, \"(-7.54, -7.43)\": 7.538, \"(-7.43, -7.34)\": 7.424, \"(-7.34, -7.22)\": 7.322, \"(-7.22, -7.11)\": 7.207, \"(-7.11, -7.01)\": 7.103, \"(-7.01, -6.89)\": 6.987, \"(-6.89, -6.75)\": 6.856, \"(-6.75, -6.66)\": 6.727, \"(-6.66, -6.52)\": 6.618, \"(-6.52, -6.42)\": 6.511, \"(-6.42, -6.3)\": 6.406, \"(-6.3, -6.18)\": 6.284, \"(-6.18, -6.06)\": 6.168, \"(-6.06, -5.94)\": 6.037, \"(-5.94, -5.81)\": 5.919, \"(-5.81, -5.7)\": 5.806, \"(-5.7, -5.58)\": 5.661, \"(-5.58, -5.47)\": 5.561, \"(-5.47, -5.35)\": 5.456, \"(-5.35, -5.25)\": 5.345, \"(-5.25, -5.14)\": 5.226, \"(-5.14, -5.01)\": 5.122, \"(-5.01, -4.92)\": 5.013, \"(-4.92, -4.81)\": 4.897, \"(-4.81, -4.7)\": 4.792, \"(-4.7, -4.58)\": 4.684, \"(-4.58, -4.46)\": 4.555, \"(-4.46, -4.33)\": 4.434, \"(-4.33, -4.2)\": 4.318, \"(-4.2, -4.07)\": 4.17, \"(-4.07, -3.97)\": 4.058, \"(-3.97, -3.85)\": 3.956, \"(-3.85, -3.72)\": 3.826, \"(-3.72, -3.6)\": 3.702, \"(-3.6, -3.5)\": 3.6, \"(-3.5, -3.39)\": 3.492, \"(-3.39, -3.32)\": 3.393, \"(-3.32, -3.2)\": 3.289, \"(-3.2, -3.07)\": 3.167, \"(-3.07, -2.95)\": 3.056, \"(-2.95, -2.85)\": 2.945, \"(-2.85, -2.74)\": 2.824, \"(-2.74, -2.64)\": 2.717, \"(-2.64, -2.5)\": 2.602, \"(-2.5, -2.4)\": 2.493, \"(-2.4, -2.29)\": 2.388, \"(-2.29, -2.19)\": 2.288, \"(-2.19, -2.02)\": 2.161, \"(-2.02, -1.89)\": 1.976, \"(-1.89, -1.77)\": 1.858, \"(-1.77, -1.61)\": 1.719, \"(-1.61, -1.52)\": 1.611, \"(-1.52, -1.39)\": 1.503, \"(-1.39, -1.28)\": 1.385, \"(-1.28, -1.15)\": 1.272, \"(-1.15, -1.02)\": 1.126, \"(-1.02, -0.92)\": 1.008, \"(-0.92, -0.79)\": 0.89, \"(-0.79, -0.66)\": 0.767, \"(-0.66, -0.54)\": 0.643, \"(-0.54, -0.43)\": 0.533, \"(-0.43, -0.3)\": 0.412, \"(-0.3, -0.17)\": 0.287, \"(-0.17, -0.06)\": 0.151, \"(-0.06, 0.15)\": 0.048, \"(0.15, 0.26)\": 0.161, \"(0.26, 0.39)\": 0.266, \"(0.39, 0.51)\": 0.403, \"(0.51, 0.63)\": 0.524, \"(0.63, 0.75)\": 0.654, \"(0.75, 0.86)\": 0.757, \"(0.86, 0.96)\": 0.857, \"(0.96, 1.07)\": 0.974, \"(1.07, 1.16)\": 1.079, \"(1.16, 1.29)\": 1.193, \"(1.29, 1.4)\": 1.316, \"(1.4, 1.53)\": 1.416, \"(1.53, 1.65)\": 1.541, \"(1.65, 1.78)\": 1.672, \"(1.78, 1.9)\": 1.793, \"(1.9, 2.0)\": 1.899, \"(2.0, 2.12)\": 2.006, \"(2.12, 2.25)\": 2.149, \"(2.25, 2.37)\": 2.26, \"(2.37, 2.47)\": 2.366, \"(2.47, 2.6)\": 2.492, \"(2.6, 2.71)\": 2.609, \"(2.71, 2.82)\": 2.717, \"(2.82, 2.91)\": 2.823, \"(2.91, 3.03)\": 2.933, \"(3.03, 3.14)\": 3.055, \"(3.14, 3.29)\": 3.166, \"(3.29, 3.42)\": 3.304, \"(3.42, 3.53)\": 3.431, \"(3.53, 3.64)\": 3.541, \"(3.64, 3.74)\": 3.643, \"(3.74, 3.86)\": 3.753, \"(3.86, 3.99)\": 3.888, \"(3.99, 4.09)\": 3.993, \"(4.09, 4.21)\": 4.097, \"(4.21, 4.3)\": 4.215, \"(4.3, 4.41)\": 4.319, \"(4.41, 4.53)\": 4.427, \"(4.53, 4.63)\": 4.537, \"(4.63, 4.74)\": 4.644, \"(4.74, 4.86)\": 4.756, \"(4.86, 5.01)\": 4.895, \"(5.01, 5.14)\": 5.032, \"(5.14, 5.26)\": 5.152, \"(5.26, 5.36)\": 5.264, \"(5.36, 5.48)\": 5.371, \"(5.48, 5.59)\": 5.488, \"(5.59, 5.72)\": 5.6, \"(5.72, 5.81)\": 5.713, \"(5.81, 5.9)\": 5.817, \"(5.9, 6.04)\": 5.943, \"(6.04, 6.14)\": 6.044, \"(6.14, 6.27)\": 6.147, \"(6.27, 6.4)\": 6.286, \"(6.4, 6.5)\": 6.413, \"(6.5, 6.62)\": 6.516, \"(6.62, 6.72)\": 6.622, \"(6.72, 6.86)\": 6.75, \"(6.86, 6.97)\": 6.867, \"(6.97, 7.08)\": 6.977, \"(7.08, 7.19)\": 7.099, \"(7.19, 7.3)\": 7.203, \"(7.3, 7.42)\": 7.304, \"(7.42, 7.52)\": 7.42, \"(7.52, 7.62)\": 7.53, \"(7.62, 7.75)\": 7.633, \"(7.75, 7.84)\": 7.749, \"(7.84, 7.98)\": 7.858, \"(7.98, 8.11)\": 8.019, \"(8.11, 8.21)\": 8.124, \"(8.21, 8.32)\": 8.224, \"(8.32, 8.44)\": 8.348, \"(8.44, 8.56)\": 8.457, \"(8.56, 8.7)\": 8.588, \"(8.7, 8.81)\": 8.705, \"(8.81, 8.92)\": 8.821, \"(8.92, 9.02)\": 8.928, \"(9.02, 9.15)\": 9.039, \"(9.15, 9.3)\": 9.182, \"(9.3, 9.43)\": 9.322, \"(9.43, 9.53)\": 9.446, \"(9.53, 9.66)\": 9.551, \"(9.66, 9.75)\": 9.664, \"(9.75, 9.87)\": 9.774, \"(9.87, 9.99)\": 9.882, \"(9.99, 10.0)\": 9.993}\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1818,7 +3189,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1830,18 +3201,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.1%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-9.99, -9.96)\": 24.97, \"(-9.96, -9.94)\": 24.92, \"(-9.94, -9.9)\": 24.86, \"(-9.9, -9.87)\": 24.79, \"(-9.87, -9.83)\": 24.7, \"(-9.83, -9.8)\": 24.63, \"(-9.8, -9.77)\": 24.58, \"(-9.77, -9.73)\": 24.49, \"(-9.73, -9.7)\": 24.45, \"(-9.7, -9.69)\": 24.4, \"(-9.69, -9.66)\": 24.36, \"(-9.66, -9.64)\": 24.32, \"(-9.64, -9.61)\": 24.25, \"(-9.61, -9.58)\": 24.19, \"(-9.58, -9.54)\": 24.13, \"(-9.54, -9.5)\": 24.04, \"(-9.5, -9.47)\": 23.99, \"(-9.47, -9.45)\": 23.93, \"(-9.45, -9.41)\": 23.86, \"(-9.41, -9.34)\": 23.73, \"(-9.34, -9.3)\": 23.65, \"(-9.3, -9.27)\": 23.56, \"(-9.27, -9.22)\": 23.52, \"(-9.22, -9.17)\": 23.39, \"(-9.17, -9.13)\": 23.33, \"(-9.13, -9.1)\": 23.26, \"(-9.1, -9.06)\": 23.16, \"(-9.06, -9.05)\": 23.12, \"(-9.05, -9.02)\": 23.08, \"(-9.02, -8.99)\": 23.0, \"(-8.99, -8.94)\": 22.95, \"(-8.94, -8.92)\": 22.86, \"(-8.92, -8.89)\": 22.8, \"(-8.89, -8.87)\": 22.76, \"(-8.87, -8.82)\": 22.71, \"(-8.82, -8.74)\": 22.56, \"(-8.74, -8.73)\": 22.47, \"(-8.73, -8.69)\": 22.4, \"(-8.69, -8.64)\": 22.33, \"(-8.64, -8.62)\": 22.28, \"(-8.62, -8.59)\": 22.24, \"(-8.59, -8.58)\": 22.18, \"(-8.58, -8.55)\": 22.14, \"(-8.55, -8.52)\": 22.05, \"(-8.52, -8.48)\": 22.0, \"(-8.48, -8.43)\": 21.92, \"(-8.43, -8.41)\": 21.85, \"(-8.41, -8.38)\": 21.79, \"(-8.38, -8.35)\": 21.71, \"(-8.35, -8.29)\": 21.63, \"(-8.29, -8.24)\": 21.53, \"(-8.24, -8.2)\": 21.46, \"(-8.2, -8.18)\": 21.4, \"(-8.18, -8.16)\": 21.36, \"(-8.16, -8.11)\": 21.27, \"(-8.11, -8.09)\": 21.2, \"(-8.09, -8.07)\": 21.15, \"(-8.07, -8.04)\": 21.11, \"(-8.04, -8.02)\": 21.04, \"(-8.02, -7.98)\": 20.97, \"(-7.98, -7.95)\": 20.92, \"(-7.95, -7.9)\": 20.87, \"(-7.9, -7.89)\": 20.81, \"(-7.89, -7.87)\": 20.76, \"(-7.87, -7.84)\": 20.71, \"(-7.84, -7.8)\": 20.66, \"(-7.8, -7.78)\": 20.58, \"(-7.78, -7.74)\": 20.53, \"(-7.74, -7.7)\": 20.46, \"(-7.7, -7.68)\": 20.39, \"(-7.68, -7.64)\": 20.34, \"(-7.64, -7.63)\": 20.29, \"(-7.63, -7.62)\": 20.25, \"(-7.62, -7.59)\": 20.2, \"(-7.59, -7.54)\": 20.13, \"(-7.54, -7.5)\": 20.05, \"(-7.5, -7.47)\": 19.98, \"(-7.47, -7.42)\": 19.88, \"(-7.42, -7.39)\": 19.83, \"(-7.39, -7.35)\": 19.76, \"(-7.35, -7.33)\": 19.69, \"(-7.33, -7.31)\": 19.62, \"(-7.31, -7.26)\": 19.57, \"(-7.26, -7.21)\": 19.49, \"(-7.21, -7.19)\": 19.41, \"(-7.19, -7.15)\": 19.37, \"(-7.15, -7.1)\": 19.25, \"(-7.1, -7.07)\": 19.21, \"(-7.07, -7.04)\": 19.15, \"(-7.04, -7.02)\": 19.1, \"(-7.02, -7.0)\": 19.02, \"(-7.0, -6.98)\": 18.98, \"(-6.98, -6.96)\": 18.94, \"(-6.96, -6.94)\": 18.87, \"(-6.94, -6.9)\": 18.82, \"(-6.9, -6.86)\": 18.77, \"(-6.86, -6.82)\": 18.72, \"(-6.82, -6.81)\": 18.65, \"(-6.81, -6.78)\": 18.6, \"(-6.78, -6.75)\": 18.54, \"(-6.75, -6.72)\": 18.49, \"(-6.72, -6.71)\": 18.45, \"(-6.71, -6.69)\": 18.39, \"(-6.69, -6.67)\": 18.34, \"(-6.67, -6.63)\": 18.28, \"(-6.63, -6.6)\": 18.23, \"(-6.6, -6.58)\": 18.18, \"(-6.58, -6.55)\": 18.14, \"(-6.55, -6.53)\": 18.06, \"(-6.53, -6.5)\": 18.02, \"(-6.5, -6.47)\": 17.96, \"(-6.47, -6.44)\": 17.92, \"(-6.44, -6.41)\": 17.86, \"(-6.41, -6.39)\": 17.81, \"(-6.39, -6.35)\": 17.75, \"(-6.35, -6.31)\": 17.68, \"(-6.31, -6.28)\": 17.6, \"(-6.28, -6.26)\": 17.56, \"(-6.26, -6.21)\": 17.49, \"(-6.21, -6.2)\": 17.42, \"(-6.2, -6.18)\": 17.36, \"(-6.18, -6.14)\": 17.32, \"(-6.14, -6.12)\": 17.27, \"(-6.12, -6.1)\": 17.23, \"(-6.1, -6.06)\": 17.18, \"(-6.06, -6.01)\": 17.11, \"(-6.01, -6.01)\": 17.05, \"(-6.01, -5.99)\": 17.0, \"(-5.99, -5.95)\": 16.96, \"(-5.95, -5.94)\": 16.92, \"(-5.94, -5.94)\": 16.88, \"(-5.94, -5.91)\": 16.84, \"(-5.91, -5.87)\": 16.78, \"(-5.87, -5.84)\": 16.71, \"(-5.84, -5.79)\": 16.64, \"(-5.79, -5.73)\": 16.52, \"(-5.73, -5.69)\": 16.47, \"(-5.69, -5.69)\": 16.39, \"(-5.69, -5.64)\": 16.34, \"(-5.64, -5.6)\": 16.27, \"(-5.6, -5.58)\": 16.21, \"(-5.58, -5.57)\": 16.16, \"(-5.57, -5.55)\": 16.12, \"(-5.55, -5.49)\": 16.06, \"(-5.49, -5.47)\": 15.97, \"(-5.47, -5.44)\": 15.9, \"(-5.44, -5.42)\": 15.86, \"(-5.42, -5.38)\": 15.81, \"(-5.38, -5.33)\": 15.73, 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\"(7.75, 7.77)\": -10.5, \"(7.77, 7.8)\": -10.55, \"(7.8, 7.81)\": -10.6, \"(7.81, 7.83)\": -10.65, \"(7.83, 7.85)\": -10.7, \"(7.85, 7.91)\": -10.74, \"(7.91, 8.01)\": -10.87, \"(8.01, 8.13)\": -11.14, \"(8.13, 8.18)\": -11.31, \"(8.18, 8.2)\": -11.37, \"(8.2, 8.26)\": -11.45, \"(8.26, 8.27)\": -11.5, \"(8.27, 8.29)\": -11.55, \"(8.29, 8.31)\": -11.6, \"(8.31, 8.36)\": -11.65, \"(8.36, 8.37)\": -11.72, \"(8.37, 8.4)\": -11.77, \"(8.4, 8.45)\": -11.83, \"(8.45, 8.49)\": -11.93, \"(8.49, 8.53)\": -11.98, \"(8.53, 8.56)\": -12.06, \"(8.56, 8.57)\": -12.11, \"(8.57, 8.6)\": -12.16, \"(8.6, 8.63)\": -12.23, \"(8.63, 8.65)\": -12.28, \"(8.65, 8.68)\": -12.33, \"(8.68, 8.72)\": -12.38, \"(8.72, 8.74)\": -12.46, \"(8.74, 8.78)\": -12.5, \"(8.78, 8.83)\": -12.58, \"(8.83, 8.87)\": -12.68, \"(8.87, 8.9)\": -12.75, \"(8.9, 8.92)\": -12.82, \"(8.92, 8.94)\": -12.87, \"(8.94, 8.97)\": -12.92, \"(8.97, 9.01)\": -12.97, \"(9.01, 9.05)\": -13.08, \"(9.05, 9.11)\": -13.15, \"(9.11, 9.17)\": -13.28, \"(9.17, 9.19)\": -13.34, \"(9.19, 9.22)\": -13.38, \"(9.22, 9.24)\": -13.45, \"(9.24, 9.27)\": -13.5, \"(9.27, 9.28)\": -13.55, \"(9.28, 9.31)\": -13.6, \"(9.31, 9.33)\": -13.64, \"(9.33, 9.38)\": -13.69, \"(9.38, 9.45)\": -13.85, \"(9.45, 9.48)\": -13.92, \"(9.48, 9.53)\": -13.99, \"(9.53, 9.56)\": -14.09, \"(9.56, 9.58)\": -14.14, \"(9.58, 9.61)\": -14.18, \"(9.61, 9.63)\": -14.23, \"(9.63, 9.65)\": -14.28, \"(9.65, 9.69)\": -14.34, \"(9.69, 9.72)\": -14.4, \"(9.72, 9.74)\": -14.44, \"(9.74, 9.77)\": -14.5, \"(9.77, 9.8)\": -14.55, \"(9.8, 9.85)\": -14.65, \"(9.85, 9.89)\": -14.73, \"(9.89, 9.91)\": -14.8, \"(9.91, 9.94)\": -14.85, \"(9.94, 9.97)\": -14.91}\n",
+      "Means: {\"(-9.99, -9.9)\": -9.994, \"(-9.9, -9.81)\": -9.891, \"(-9.81, -9.69)\": -9.775, \"(-9.69, -9.57)\": -9.656, \"(-9.57, -9.47)\": -9.557, \"(-9.47, -9.33)\": -9.429, \"(-9.33, -9.23)\": -9.326, \"(-9.23, -9.12)\": -9.221, \"(-9.12, -9.02)\": -9.112, \"(-9.02, -8.92)\": -9.012, \"(-8.92, -8.8)\": -8.894, \"(-8.8, -8.64)\": -8.769, \"(-8.64, -8.55)\": -8.638, \"(-8.55, -8.41)\": -8.53, \"(-8.41, -8.3)\": -8.383, \"(-8.3, -8.16)\": -8.27, \"(-8.16, -8.05)\": -8.15, \"(-8.05, -7.93)\": -8.045, \"(-7.93, -7.82)\": -7.91, \"(-7.82, -7.71)\": -7.807, \"(-7.71, -7.6)\": -7.703, \"(-7.6, -7.47)\": -7.569, \"(-7.47, -7.36)\": -7.455, \"(-7.36, -7.27)\": -7.355, \"(-7.27, -7.16)\": -7.247, \"(-7.16, -7.05)\": -7.144, \"(-7.05, -6.92)\": -7.032, \"(-6.92, -6.84)\": -6.922, \"(-6.84, -6.72)\": -6.819, \"(-6.72, -6.62)\": -6.715, \"(-6.62, -6.52)\": -6.612, \"(-6.52, -6.43)\": -6.504, \"(-6.43, -6.31)\": -6.397, \"(-6.31, -6.2)\": -6.294, \"(-6.2, -6.09)\": -6.188, \"(-6.09, -5.99)\": -6.085, \"(-5.99, -5.9)\": -5.985, \"(-5.9, -5.76)\": -5.878, \"(-5.76, -5.6)\": -5.697, \"(-5.6, -5.48)\": -5.578, \"(-5.48, -5.36)\": -5.478, \"(-5.36, -5.26)\": -5.367, \"(-5.26, -5.15)\": -5.245, \"(-5.15, -5.06)\": -5.131, \"(-5.06, -4.91)\": -5.019, \"(-4.91, -4.81)\": -4.916, \"(-4.81, -4.7)\": -4.804, \"(-4.7, -4.6)\": -4.701, \"(-4.6, -4.49)\": -4.589, \"(-4.49, -4.39)\": -4.473, \"(-4.39, -4.26)\": -4.367, \"(-4.26, -4.13)\": -4.239, \"(-4.13, -4.04)\": -4.13, \"(-4.04, -3.91)\": -4.014, \"(-3.91, -3.81)\": -3.905, \"(-3.81, -3.71)\": -3.803, \"(-3.71, -3.59)\": -3.691, \"(-3.59, -3.49)\": -3.58, \"(-3.49, -3.37)\": -3.466, \"(-3.37, -3.26)\": -3.356, \"(-3.26, -3.15)\": -3.233, \"(-3.15, -3.04)\": -3.12, \"(-3.04, -2.93)\": -3.015, \"(-2.93, -2.82)\": -2.912, \"(-2.82, -2.71)\": -2.802, \"(-2.71, -2.59)\": -2.687, \"(-2.59, -2.47)\": -2.576, \"(-2.47, -2.37)\": -2.454, \"(-2.37, -2.21)\": -2.347, \"(-2.21, -2.04)\": -2.139, \"(-2.04, -1.93)\": -2.038, \"(-1.93, -1.82)\": -1.91, \"(-1.82, -1.71)\": -1.806, \"(-1.71, -1.62)\": -1.697, \"(-1.62, -1.5)\": -1.596, \"(-1.5, -1.4)\": -1.496, \"(-1.4, -1.28)\": -1.378, \"(-1.28, -1.18)\": -1.273, \"(-1.18, -1.06)\": -1.159, \"(-1.06, -0.95)\": -1.047, \"(-0.95, -0.84)\": -0.937, \"(-0.84, -0.73)\": -0.833, \"(-0.73, -0.6)\": -0.713, \"(-0.6, -0.45)\": -0.561, \"(-0.45, -0.33)\": -0.426, \"(-0.33, -0.22)\": -0.327, \"(-0.22, -0.1)\": -0.201, \"(-0.1, 0.17)\": -0.067, \"(0.17, 0.28)\": -0.179, \"(0.28, 0.41)\": -0.307, \"(0.41, 0.52)\": -0.415, \"(0.52, 0.63)\": -0.545, \"(0.63, 0.74)\": -0.645, \"(0.74, 0.86)\": -0.751, \"(0.86, 0.99)\": -0.891, \"(0.99, 1.11)\": -1.006, \"(1.11, 1.21)\": -1.125, \"(1.21, 1.32)\": -1.226, \"(1.32, 1.44)\": -1.35, \"(1.44, 1.56)\": -1.459, \"(1.56, 1.68)\": -1.566, \"(1.68, 1.8)\": -1.703, \"(1.8, 1.95)\": -1.827, \"(1.95, 2.08)\": -1.985, \"(2.08, 2.17)\": -2.088, \"(2.17, 2.29)\": -2.19, \"(2.29, 2.43)\": -2.307, \"(2.43, 2.56)\": -2.464, \"(2.56, 2.69)\": -2.592, \"(2.69, 2.79)\": -2.7, \"(2.79, 2.9)\": -2.803, \"(2.9, 3.0)\": -2.905, \"(3.0, 3.12)\": -3.01, \"(3.12, 3.23)\": -3.118, \"(3.23, 3.34)\": -3.237, \"(3.34, 3.44)\": -3.344, \"(3.44, 3.56)\": -3.457, \"(3.56, 3.68)\": -3.582, \"(3.68, 3.8)\": -3.686, \"(3.8, 3.89)\": -3.8, \"(3.89, 4.01)\": -3.903, \"(4.01, 4.11)\": -4.011, \"(4.11, 4.23)\": -4.124, \"(4.23, 4.31)\": -4.228, \"(4.31, 4.43)\": -4.328, \"(4.43, 4.53)\": -4.43, \"(4.53, 4.62)\": -4.535, \"(4.62, 4.75)\": -4.644, \"(4.75, 4.85)\": -4.76, \"(4.85, 4.98)\": -4.865, \"(4.98, 5.1)\": -4.993, \"(5.1, 5.2)\": -5.11, \"(5.2, 5.3)\": -5.216, \"(5.3, 5.43)\": -5.315, \"(5.43, 5.52)\": -5.441, \"(5.52, 5.63)\": -5.543, \"(5.63, 5.77)\": -5.671, \"(5.77, 5.86)\": -5.785, \"(5.86, 5.97)\": -5.885, \"(5.97, 6.09)\": -5.984, \"(6.09, 6.19)\": -6.086, \"(6.19, 6.3)\": -6.21, \"(6.3, 6.43)\": -6.33, \"(6.43, 6.55)\": -6.443, \"(6.55, 6.64)\": -6.566, \"(6.64, 6.76)\": -6.668, \"(6.76, 6.88)\": -6.79, \"(6.88, 6.99)\": -6.893, \"(6.99, 7.1)\": -7.004, \"(7.1, 7.22)\": -7.124, \"(7.22, 7.37)\": -7.255, \"(7.37, 7.48)\": -7.375, \"(7.48, 7.62)\": -7.5, \"(7.62, 7.74)\": -7.64, \"(7.74, 7.85)\": -7.747, \"(7.85, 7.96)\": -7.858, \"(7.96, 8.08)\": -7.99, \"(8.08, 8.19)\": -8.092, \"(8.19, 8.3)\": -8.193, \"(8.3, 8.43)\": -8.318, \"(8.43, 8.56)\": -8.468, \"(8.56, 8.66)\": -8.568, \"(8.66, 8.8)\": -8.687, \"(8.8, 8.91)\": -8.807, \"(8.91, 8.99)\": -8.91, \"(8.99, 9.13)\": -9.02, \"(9.13, 9.24)\": -9.147, \"(9.24, 9.34)\": -9.255, \"(9.34, 9.44)\": -9.357, \"(9.44, 9.58)\": -9.459, \"(9.58, 9.7)\": -9.609, \"(9.7, 9.83)\": -9.719, \"(9.83, 9.94)\": -9.848, \"(9.94, 10.0)\": -9.95}\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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op07VSoh2wbEpJVkyuD06NKimOn7L8St4eQUH1hJZAxsVIhdQng/NLZO7Wb8QB/b1iEeF4rccv4yNKZmSqiFyHWxUiJzcjE3p2PvrNaGciJY14Wk0SKrIMRnd9BjZRWzK8vjVydwLiKiC2KgQObG4+HQs2Z0hlNO6jje+GNJeUkWOLTYyUGjKsgKg72eJ8goicgF2a1TOnDmD4cOHIyAgAJ6enmjcuDGmTZsGk8lkr5KInIqp0ILPdwk2KX4PYcP4zpIqcg7zBrSByPDipPPXsTHlorR6iJyd3RqVEydOwGKxYPHixTh27Bjmzp2LRYsW4fXXX7dXSUROJfa7IxC96fDf8V2l1OJMDHodHm9SXShn0tojvAVEVE46RVE089vz4Ycf4rPPPsNvv/2mOicvLw8+Pj7Izc2Ft7e3xOqIHMfmtCyM+jpJKGdCeFNM6tFMUkXO5bbJjJZvbxbKmRjeBBN7NJdUEZHjUfv5rakxKrm5uahevey/VAoKCpCXl1fsQUT/Y7YoiFmdLJRj0AEx4U0lVeR8PI0GhLfwFcr5dPtpXlUhKgfNNCqnT5/GggULMHLkyDLj4uLi4OPjU/SoV6+ejSokcgwxK5NgKhT7QJzblwu7iVo6tAPqVfNQHW+yAP0WcWAtkSirNypTp06FTqcr83HixIliORcvXkSvXr3Qt29fjBgxoszXj42NRW5ubtHj/Pnz1j4EIocVn5qJTWliS7g3quGFp9rWlVSRc9v9jwg0fNhTdfzhc9e5ai2RIKuPUbly5QquXSt7zYZGjRrBaDQCADIzM9GtWzc8+uijWL58OfR6sd6JY1SI7jFbFDR+PV4ox92gw4n3evNqSgWYLQqav/kDCgVu6ywcEMpVf8nlqf38drP2D/b19YWvr7p7txcvXsQTTzyBdu3aYdmyZcJNChH9z/Of7RXOWTCwDZuUCjLodZg/oA3GrFQ/eHn86hT0Dvbn/3siFezWGVy8eBHdunVD/fr1MXv2bFy5cgXZ2dnIzubOo0SiZmxKR/L5XKGchQPaoFeQ+sXLqHSRwX7CC8GN/ffP8goiciJ2a1QSEhJw+vRpbN26FXXr1oWfn1/Rg4jUMxVahFefjQyqhehQf0kVuaZ5A9rAXeAddfOxyxjxFTcuJHoQuzUqQ4cOhaIoJT6ISL2p644Ixet1wIJB7SRV47oMeh1Gd2silJOQfhkbjnDjQqKycFAIkQMzWxR8lyz2QTe3fyjHRkgyIaKZ8JvqRG5cSFQmNipEDuzR9xOE4oPreONpzjaRxqDX4eN+oUI5ZgWYl/CLnIKInAAbFSIH9d2hc7hy467q+FpV3LGeGw5K91TbOmjk6yWUM3/7r7yqQlQKNipEDmhzWhYmrzsqlLP7HxGSqqG/SpjUDQbBu2vjVh6WUguRo2OjQuRgzBYFk79JEcqJbu0Hoxt/3W3FoNdhwcA2Qjk/pF2CqdAiqSIix8V3LiIHE7MqCbfuin2gzRP80KSKiwz2R++gWkI5/9p3Rk4xRA6MjQqRA4lPzcSmo2KLIo7p3IizfOxk4aB2Qm+y65IuSKuFyFGxUSFyEGaLgnGrkoXzpvRuIaEaUkN0FlB6Vj5eXsFF4Ij+jI0KkYPot2gvRCeGfDqIe/nY21Nt66C1fxXV8VuOX8Y/N6RLrIjIsbBRIXIAt01mHD4ntpfPiM4NERnMZfK1YENMFzSo7qk6/ou9GYhP5Yq1RAAbFSKH8NjMrULxHQOq4Y2oVpKqofLY+Vp3tKhdWXX82JVcsZYIYKNCpHkvrziInFvqF3YDgH8Nf1RSNVQRfdvVUx2rAHjukz3yiiFyEGxUiDTstsmMLcevCOWM6NyQa6Zo1IudGkJkxFDKxTys+5kzgci18d2MSMNe+UpsBkj35r685aNhRjc9Xu7cUChnyrdHMELw3wGRM2GjQqRR8amZ2H36mur4gIe98OWwDhIrImt4I6oV6lStJJSTkH4ZMzYdk1QRkbaxUSHSoM1pWRizUv2aKToAW6Z0k1YPWdeWyd2Ec5bsPsMl9sklsVEh0hizRcFUwQ0Hxz7RmOulOBBPowHhzX2F85bvyZBQDZG2sVEh0piF207j+m31s3wMemBSj+YSKyIZlg7rAN/KRqGcT3ecllQNkXaxUSHSELNFweJdvwrlLBjA1Wcd1aE3e6Dxw+oXgrt+pxAbU7gQHLkWNipEGjJ+ZRJumcyq46OD/bj6rIP7YVI3ofhJa1K4EBy5FDYqRBox4qtDiE9TvzOyTyU3zBvQRmJFZAtGNz2GPx6gOv6uRUHMqiSJFRFpCxsVIg3YmHIRCemXhXI+eD6Yt3ycxFvRgfDz8VAdv+loNvcCIpfBRoXIzswWBZPXpAjlTAhvgl5BfnIKIrvY+Wp3ofjX1qXyFhC5BDYqRHY2fmUSTALLY1T1ckdMeDN5BZFdGN30iG5dW3X8jQIzFm47JbEiIm1go0JkR6LjUgBg5rOtecvHSc0b2BbuBvXndtneM7yqQk6PjQqRnZRnXMr47rzl48wMeh3m9Vc/QPr67btIPHVVYkVE9sdGhcgOzBYF41enCOUY3XSYGMFbPs4uMtgPIzqrnwU0eNlBxMWnS6yIyL7YqBDZwcdbTkL0gv3H/UJ5y8dFvBEViCiV41UUAIt3ZbBZIafFRoXIxswWBZ/vFFt9tqVfFS7s5mLmD2wLH0831fGLd2Vw00JySmxUiGxswupkFJjFrqd8N/oxSdWQVhn0OnzwXLBQTvT8XZKqIbIfNipENhQXn46NqVlCOeEtasDTaJBUEWlZryA/LPq/tvByV3f+T16+iacW7pZcFZFtsVEhshFToQWf78oQyvGt7I6lQztKqogcQa8gP0zqoX4QdeqFPKxPuiixIiLbYqNCZCOx36UKDaB10wP7X+8hrR5yHEPCGkJkHHUMNy4kJ8JGhcgGNqdlYZ3gX7kLB7XlLB8CcG/VWpEpywDQ6f0tkqohsi02KkSSmS0KYgTXTJkU0ZQLu1ExsZGBqONTSXX85Rsm9FnA8Srk+NioEEkWszJJaNpoNS93jOveVGJF5Kjef6a1UPzRi3n4PpnjVcixsVEhkmjDkUxsEtzLZ8Yz3MuHSvZ4c1+4Cb5rT1nL8Srk2NioEEmyOS0L41clC+WM7BKAyGDe8qGSGfQ6zB+gfi8gACi0AAu2npRUEZF8bFSIJDBbFIz7d5JQTkz3xoiNDJRUETmLyGB/DH+8oVDOvK2neVWFHBYbFSIJ+n62B4UCnws6ABMimkurh5zLW9Gt4O/toTpeATD23z/LK4hIIjYqRFa24Ugmks7nCeU83rg6x6WQkK1/f0IofvOxy5ixiRsXkuNho0JkRWaLgtf/c1Q47/MhHSRUQ87M02hARMuaQjlLdmcgXnALByJ7Y6NCZEUHM3KQf6dQKId7+VB5fTGkPap7uQvlTOYsIHIwbFSIrOinY2J/rdasbORePlQhe6eGC8XfuWtB4umrkqohsj42KkRWEp+aheX7zqqO1wPY93qEvILIJdy7BeQrlDPyXxxYS46DjQqRFWxOy8KYlUlQBK6ocy8fspYvhnRAoxpequNv3bUgmsvrk4Ngo0JUQWaLgqnfiQ2g5cJuZG0Jk7vBIND3pl3Mw/QNx+QVRGQlbFSIKmjhtlO4fuuu6viJ4U24sBtZnUGvw7z+YqvWLtt7BvGpmZIqIrIONipEFWC2KPhk+2nV8X4+lTA+vJnEisiVRYf6o1mth4RyJn6TzFlApGlsVIgqoP/iRJjM6t/kp/UJ5LgUkmrj+C5C8SYzMEFwTyoiW2KjQlROG45k4uez11XF6nTAp4PaoFcQx6WQXEY3PV4KayCUs/FoFheCI81io0JUDmaLgle/PaI6fkL3pogM9pdYEdH/vP1UEGpWNgrlvPrtEd4CIk1io0JUDgu3ncaduxZVsUY3PcaHN5VcEVFx+16PEHqDv2kyY8Jq3gIi7WGjQiTIbFHw5d4M1fFPBftxXArZnEGvQ4xgg7wxlbeASHvYqBAJ6rtoL3Jvq5+O/P6zwRKrISrd+PCm8HATa5LHr+YsINIWNipEAp5auBtJ53JVx4/o3BBGN/6akX0Y9DrM7RcqlGO2KBi/8rCcgojKQRPvoAUFBQgNDYVOp0NKSoq9yyEq0fcpF5F6IU91fLsGVfFGVCuJFRE9WGSwP0Z2CRDKiU+7BFOhujFYRLJpolF57bXX4O/PGRGkXWaLgqnrUlXHV/Vyx5qRYRIrIlIvNjIQMd2bCOVMXad+VhuRTHZvVH744Qf89NNPmD17tr1LISrVvC0ncVvlLB8AmPlsaw6gJU2ZENFMaC+g75IzMWNTuryCiFRys+cPv3TpEkaMGIH//ve/8PJSt/NnQUEBCgoKir7Oy1N/KZ6oPEZ8dQgJ6ZdVx3dtVoMLu5HmGPQ6jH2iMeZv+1V1zpLdGdDrwL2pyK7sdkVFURQMHToUo0aNwiOPPKI6Ly4uDj4+PkWPevXqSaySXN2MTceEmhQAWPR/6v89E9nShIjmMIpcVgGweFcGx6uQXVm9UZk6dSp0Ol2ZjxMnTmDBggXIz89HbGys0OvHxsYiNze36HH+/HlrHwIRAMBUaMGS3WeEciJa+sLTaJBTEFEFGfQ6zB8otsMyAETN2yWhGiJ1dIqiWHXC/JUrV3Dt2rUyYxo1aoR+/fphw4YN0On+192bzWYYDAa88MILWLFihaqfl5eXBx8fH+Tm5sLb27tCtRP9Wb/P9uKgyr18ACC4rjfWj+ssryAiK4lPzcSYlWKr0I7oHIA3ongLiKxH7ee31RsVtc6dO1dsfElmZiZ69uyJb7/9Fh07dkTdunVVvQ4bFZJhxqZ0LNmtfvXZcU80xt97tpBYEZF1fZxwEh9vPSWUc/KfvbkuEFmN2s9vuw2mrV+/frGvK1euDABo3Lix6iaFSIb41CyhJsXdoMOkHs0lVkRkfePDm+KzHadRYFb/t+rgLw9g9SudJFZFdD+2xkR/YrYoGLcqSSinT2t/TkUmh2PQ6zBbcNXa/b/lcGAt2ZxmGpWGDRtCURSEhobauxRyYREf7YDoNiczn+dePuSY+oT4o1nNykI5g5cekFQNUck006gQ2dv6pAvIuHZLKGdE5wDesyeHtjFGbAD4/owcjPjqkKRqiO7Hd1gi3LvlM0VgiXzg3lRkzoIgR2d00wvvBZSQfhkbUzIlVURUHBsVIgD7f72GuwKDCjs0qIovhnSQWBGR7cRGBmL44w2FcsatToZZ9D4pUTmwUSECMPunE0LxX4/gzAdyLm9Ft8LLjzUUymnz7k9yiiH6EzYq5PK+T76I5PO5quNfCmvAcSnklN7s0wqdAqqrjs+7U4h1P3N1cJKL77bk0mZsOoYJ36Sojq9VxYi3nwqSVxCRna0Y3lEofsq3qbwFRFKxUSGXFRefLrSXjx5AYmyEtHqItMDopsejAdWEcngLiGRio0IuyVRoweJd6lefBYAJEU25sBu5hK+GPyoUn3enEDk3TJKqIVfHRoVc0otf7BOKr+zhhnHdm0qqhkhbjG56DO5U/8GBfxI5b6ekasjVsVEhlxOfmokDZ64L5cx6LphXU8ilvPt0axjd1P+bz843YXNalsSKyFWxUSGXYrYomLBKbHv7RxtVR2Swn6SKiLQr7Z1eQvGx3x3lwFqyOjYq5FJiVibhruD76Fcvic2CIHIWRjc9erSsoTr+91t3MX/rSYkVkStio0IuIy4+HZvSsoVyhj/ekGumkEtb9KLYCszztp5GfCpvAZH18B2YXEJ5ZvnU9vbAW9GtJFVE5BgMeh0W/V9boZwxK5M4XoWsho0KuYTY78Q2HASAXa91l1AJkePpFeSHTwe1hchw8slrUjhehayCjQo5PbNFwX+SLwrlvPwYb/kQ/VlksB9iwtVP0b9lsiBGcOA6UUn4TkxOr++iRIj8YRdc1xtv9uEtH6K/iglviqpe7qrjNx3NQnxqpsSKyBWwUSGn9s8N6Ug6d111fK9WNbF+XGd5BRE5MINeh5nPthbKGb8qmbeAqELYqJDTik/NxBd71Q+gNRp0+OSFRyRWROT4egX5YWJ4E9XxZgV4/rO9EisiZ8dGhZyS2aLgze/ThHI+7h/K1WeJVBgf3gwPeRhUxyefz8V7G49JrIicGRsVckoHM3KQc/Ou6vhOAQ8jMthfYkVEzsOg1+HD54KFcpbuOcPxKlQubFTIKb22NkUofsVwsUWtiFxdZLA/Hg2oLpQzee0RjlchYWxUyOl0+WArzl+/ozp+RGdORSYqj6+Gi20vceeuBTGrkiRVQ86K787kVIYtP4hzv6tvUiJa1sQbUZyKTFQeRjc9ooJqCeVsOprNW0AkhI0KOY0NRzKx/cQV1fHdmvviiyHtJVZE5PzmD2oH0QuSr61L5S0gUo2NCjkFs0URXiZ/ZJfGkqohch0GvQ4LB4ntBXSjwIz9v16TVBE5GzYq5BQWbjuFGwVm1fFe7np0EBwISEQl+2MvIBETvuHy+qQOGxVyeJvTsjB3yymhnJl/C+aaKURWFBnsh4UDQlXHX71hQvt/JsgriJwGGxVyaGaLgn+sE7vlE1DDC0+1rSOpIiLXFR1aB1Gta6uOv3LDhJeWHZBYETkDNirk0BZuO43c24Wq4w06YMvkbvIKInJx8we2hYfA6Nptv1zFbZP627bketiokMMyWxQs2HZSKOeTF9rylg+RRAa9DmO6qd8LCABeXnFQUjXkDNiokMMat/IwCi3q4ydFNEOvID95BRERAGBc9yZwN6j/g2DvrzmIi0+XWBE5MjYq5JBmbDqGH9IuqY6v7e2Bcd3F/sojovIx6HX4SHAvoMW7MrgQHJWIjQo5nPjUTCzZfUYo552nWvGWD5ENPdW2Lqp5uQnlTFqTwoXg6D5sVMihmC0KJqxOEcp5rm0d3vIhsoPEqRFC8QWFChJPXZVUDTkqNirkUMavPIy7gn9xxT0rdgmaiKzD02hAj8CaQjl//zZFTjHksNiokMOIT81EvMC4FIA7IxPZ25LB7dG2vo/q+Ev5Jmw4wrEq9D98ByeHYLYoGLNSbMnttvWrcmdkIg1YO+oxuAuMEXvjP0c5VoWKsFEhh9Dm3Z+E4t30wNpRYZKqISIRBr0Oo7s2Uh2fd6cQC7aKbYtBzouNCmned4fOIe+O+tVnAeDjfqGc5UOkIRN6NIebwO/kx1tP4b2NaRIrIkfBRoU0zWxRMHndUaGcpjUfQnQo9/Ih0hKDXoe5/UOFcpbuOYuXVxySUxA5DDYqpGnj/31YOGdTTBcJlRBRRfUJ8UdES1+hnC3HL2PGJq5a68rYqJBmzdiUjvhjYrN8Xn4sgLN8iDTsiyEdhJuVJbszYBLZL4OcCt/RSZPurT6bIZRTv6oH3uwTKKkiIrKWL4Z0QIzglhaDlx6QVA1pHRsV0hyzRcHkNUeE87a/Fi6hGiKSYUJEM3gZ1X8E7c/I4VUVF8VGhTRn/tZTuCP4hrRwAGf5EDkSg16HVzo3Fsrp/fEOOcWQprFRIU0xWxTh9ROacZYPkUMaH94URoP6+F+v3sZTC3bLK4g0iY0KaUqbd3+E6MXdjZzlQ+SQDHodPu7fVign9WIe3tvIWUCuhI0KaUafBbuRd8cslDOyC2f5EDmyyGA/dGxYVShn6R7OAnIlfIcnTbhxpxBHL+YJ5Yzo3BCxkZzlQ+To/vVyJ+Gc179LlVAJaREbFdKE3h/vFIof/0RjbjhI5CSMbnpEta4llLMu6SI3LnQRbFTI7jYcycT563dUxxsNekzs0VxiRURka/MHtoO7Qf3MPQXim5WSY2KjQna1OS0L41clC+V81C+EU5GJnIxBr8O8/m2EcvLuFCKas4CcHhsVshuzRcH0DWKj9xvX8ESfEH9JFRGRPUUG+2FklwChnLSLeVifdEFSRaQFbFTIbg5m5CArV/0tHwD4YWI3OcUQkSbERgZifHexheBi1hzheBUnxkaF7ObVtWK3fF5+rCGnIhO5gIkRzWEUGK8CAP0WJ0qqhuzN7u/6mzZtQseOHeHp6Ylq1arhmWeesXdJZAN9FuzGhesFquOD63rjzT6c5UPkCgx6HT58PkQo5/DZ67htEluHiRyDXRuVdevW4cUXX8SwYcNw5MgR7N27F4MGDbJnSWQD761PE1ozpWuzGlg/rrPEiohIa55uUwfVPAXW1wfw2Mytkqohe7Jbo1JYWIgJEybgww8/xKhRo9CsWTMEBgaiX79+9iqJbCA+NRNLE88K5YzqKrYdPBE5h8TYHkLxObfu4r31xyRVQ/Zit0YlKSkJFy9ehF6vR5s2beDn54fevXsjLS2tzLyCggLk5eUVe5BjMFsUjF0pNi6l+kPu6BBQXVJFRKRlnkYDegTWFMpZmngG8amZkioie7Bbo/Lbb78BAN555x28+eab2LhxI6pVq4Zu3bohJyen1Ly4uDj4+PgUPerVq2erkqmCus/eCtFx+f98OohrphC5sCWD26NNXR+hnHGrkjkLyIlYvVGZOnUqdDpdmY8TJ07AYrm3odQbb7yB5557Du3atcOyZcug0+mwdu3aUl8/NjYWubm5RY/z589b+xBIgvc2HsPZHPWDZwHgpccaIjKYa6YQubpvxzwmFG9RgJiVhyVVQ7bmZu0XnDJlCoYOHVpmTKNGjZCVlQUACAz836ZyHh4eaNSoEc6dO1dqroeHBzw8PKxSK9mGqdCCpXvOCOU0fNgTb3OWDxHh3iyg+f1CEbMmRXXOprRLmFto4ZIGTsDqjYqvry98fX0fGNeuXTt4eHjgl19+weOPPw4AuHv3Ls6cOYMGDRpYuyyyo8FfHBDO2TrlCQmVEJGjeqptHSzZ+yuOXsxXnRM5bye28L3E4dmt1fT29saoUaMwbdo0/PTTT/jll18wevRoAEDfvn3tVRZZmanQgv1nSh9zVJKxTzTiuBQius+G8V1Q21v9FfXTV25xbRUnYNdrYh9++CEGDBiAF198Ee3bt8fZs2exbds2VKtWzZ5lkRVFz98lFK/XAZN7tJBUDRE5ul2vdReKH/mvnyVVQrZi10bF3d0ds2fPxqVLl5CXl4eEhAS0asVxCc5iw5FMnLx8Uyhn4cA2vJpCRKUyuunRsaH6P2Z3nbqKjSkXJVZEsnGUEUlhtiiYsFpszZSo1rU5y4eIHuhfLz8qFD9udQri4sV2aiftYKNCUoTFbYHIMgYebjrMH9hWXkFE5DSMbnoMe6yhUM7iXRmIT82SUxBJxUaFrG76hqO4lG8SypnbL5S3fIhItWl9WqGWt1Eo57V1qVwIzgGxUSGrMhVasGxv6evglGT441zYjYjEJU6NgLvAp9iNgkLs/+2avIJICjYqZFVR83YKxbep54O3ojmAmojEGfQ6LBgkdst40jdiY+fI/tiokNU8tXA3Tl25pTpeB+Db0WJLYxMR/VmvID98KtCsXM43YX0SZwE5EjYqZBXfp1xE6gWxnazn9QvhuBQiqrDIYD/EdG+sOn7KuiMcq+JA2KhQhZktCqZ8kyKUE/CwF55qW1dOQUTkciZENIfav3vumhXErEqSWxBZDRsVqrCYVckoFPjjRKcDtkzpJq0eInI9Br0OjzepoTp+09FsvLcxTWJFZC1sVKhC4uLTsemo2NoEE8Ob8pYPEVnd4hcfEYpfuucsZmziQnBax0aFys1UaMHnuzKEcip7GDCue1NJFRGRK/M0GhDRsqZQzpLdXAhO69ioULkNXrofosPRZj3HAbREJM8XQ9qjka+XUM6kNSkcXKthbFSoXOLi07E/43ehnBGdGyIy2E9SRURE9yRM6gYvgZXgCgot+HjLL/IKogpho0LCTIUWLNktdssnqnVtvBHFhd2ISD6DXofZfUOFchZs+xWb03gLSIvYqJCwf+07I7Th4ENGAzccJCKbigz2Q1Tr2kI541cm8RaQBrFRIWGfbD8tFP/h8xyXQkS2N39gW7gLvPfctQB9P0uUWBGVBxsVEjJ9wzHk3LqrOn5klwCOSyEiuzDodZjbL1QoJ+n8dU5Z1hg2KqRafGomlu09ozp+fr9QxEYGyiuIiOgBokP90SNQfMqyqdAiqSISxUaFVDFbFPz92yOq4+tUrYSn2taRWBERkTpLBrdHx4BqQjldZ22XVA2JYqNCqsSsSsItk/q/MLq3EPsLhohIpn8Nf1QoPivvDl5ecVBSNSSCjQo90L1l8rOFcl7nLR8i0hCjmx4jOjcUytly/Apum8xyCiLV2KhQmUyFFiwWXCY/oqUvPI0GSRUREZXPG1GtEN5C/caFAPDuhmOSqiG12KhQmaLm7RKKb1TDC18M6SCpGiKiilk6tCNqPGRUHb/q0HkuBGdnbFSoVCO+OoRTV26qjn/IaEDC5G7yCiIisoKRXRsLxY/6OonNih2xUaES3TaZkZB+WSjno35c2I2ItG9IWEPhnDH/5qq19sJGhUrUY84Oofhn2/ijVxAXdiMi7TO66fHyYw2FciwKMPbrQ3IKojKxUaH7bEzJxIXrd4RyZj4XIqkaIiLre7NPKwTX9RbK2Zx+BfGpvAVka2xUqBizRcGENclCOSO7BMDoxn9KRORY1o/rjMigWkI5Mat4C8jW+OlCxew5dQVmgZWjhz1Wn8vkE5HDWjCoHQwCQ+sKlXvNCtkOGxUqZtHOX1XHNvV9CNP6tJZYDRGRXAa9Dq90DRDK2XQ0m3sB2RAbFSoy7Mv92Pdbjur4TRO6SKyGiMg2/v5kS4jOV4z6eKeUWuh+bFQIAND6nc3YfvKa6viWtStzXAoROQWDXodPBrURyjl19RZmbEqXVBH9GT9pCJ0/2Ib8O2L7WXw35nFJ1RAR2V5ksD9GdBa7BbRkdwZvAdkAGxUX95/DF3D+99tCOT0Ca3IvHyJyOm9EBaJFrcpCOV1mbZNUDf2BjYoLM1sUTFp7RCinR2BNLBncXlJFRET29Z+xYleLs/MKcONOoaRqCGCj4tLGrRSbYjewfT02KUTk1DyNBkS0rCmUM/DzREnVEMBGxWWZCi34IS1bKOftPq0kVUNEpB1fDGmPgIe9VMcfzcxHXDwH1srCRsVFdfjnT0LxoXW9OS6FiFzGlindhOIX78pAfGqmnGJcHBsVFzR02QFcF5zls46zfIjIhRj0Oozt2lgoZ+zKZC6vLwEbFRczY1M6dvxyVShnwcA2MOhFl0MiInJsk3s2h0HgU1IB8Oj7W6TV46rYqLgQU6EFS3ZnCOWEt/BFnxB/SRUREWnXvYXg2grlXLlhwkvLDkiqyDWxUXEhKxLFmpQqHgYsHdpBUjVERNrXK8gPnwo2K9t+uYoNRzhexVrYqLiQfx84KxS/LzZCUiVERI4jMtgPjzepLpQz6ZsUjlexEjYqLuL7lIs4c039CrTBdbxRuZKbxIqIiBzHksFiV5cLLQrGrTwsqRrXwkbFBczYdAwTVqeojq9Z2R3rx3eWVxARkYPxNBrQvXkNoZwf0i5xfRUrYKPi5OLi07Fk9xnV8W56Hfa93kNeQUREDurLYR1R4yF3oZzPd3Hjwopio+LETIUWLN4lNoB2fPemnIpMRFSKn996Eg+5q//oVAAM/pKzgCqCjYoTe3HJPqH4yh4GjOveRFI1RETO4ee3nhSK3/9bDq+qVAAbFScVn5qFA2evC+XMei6YV1OIiB7A02hAj0CxjQu7ztomqRrnx0bFCZktCsavFtsZeWSXAEQGc2E3IiI1lgxuj/AW6gfXZuUVYPqGNIkVOS82Kk6o72eJMAtcZXwm1B+xkYHyCiIickJLh3ZEh4bVVMcv23uWGxeWAxsVJ7Mx5SKSzl8Xypn1fIicYoiInNzXLz8qFD+GGxcKY6PiRMwWBX//NlUoJ7q1H4xu/GdARFQeRjc9Amt7C+U8++keSdU4J35COZEJq5NxR2BkuUEHzBvYRmJFRETOb92YMKH4IxfycNtkllSN82Gj4iTiUzOxMTVLKOfjAW04y4eIqII8jQZEtBSbBRQxZ4ecYpwQGxUnYLYo+PvaI0I54S1qoE8IZ/kQEVnDF0Paw1NgIbiL1+9wh2WV2Kg4gQVbT+HWXfW3fFrX8cbSoR0lVkRE5Hr2C+44P+kbDqxVw66NysmTJ/H000+jRo0a8Pb2xuOPP47t27fbsySHszktCx9vPaU6vsHDXtjADQeJiKzOx8sdvpXV7wVUaAEiPtohryAnYddGJTo6GoWFhdi2bRsOHz6MkJAQREdHIzs7255lOQyzRcGUNWK3fN5/prWkaoiIaL/gpq4Z125h+HLuBVQWuzUqV69exalTpzB16lQEBwejadOmmDlzJm7duoW0NK7ep8bzn+3FTYGR45U93PBo44clVkRE5NoMeh0WCM6m3HriKjamcLxKaezWqDz88MNo3rw5vvrqK9y8eROFhYVYvHgxatasiXbt2pWaV1BQgLy8vGIPV/TyioNIPp8rlMO9fIiI5OsT4o+29X2Eciau4XiV0titUdHpdNiyZQuSk5NRpUoVVKpUCXPmzMHmzZtRrVrpSxLHxcXBx8en6FGvXj0bVq0NG45kYsvxK0I59/by8ZNUERER/dnaUY9B5O/CQguQeOqqvIIcmNUblalTp0Kn05X5OHHiBBRFwdixY1GzZk3s3r0bBw8exDPPPIM+ffogK6v09UBiY2ORm5tb9Dh//ry1D0HTzBYFr//nqFBOTPfG3MuHiMiGDHod5vcPFcpZc/icnGIcnE5RFKtea7py5QquXbtWZkyjRo2we/duPPnkk/j999/h7f2/5YebNm2K4cOHY+rUqap+Xl5eHnx8fJCbm1vsdZzVvC0nMXeL+lk+lT0MODKtJ2/5EBHZwbOf7BXaf61HYE0sGdxeXkEaovbz283aP9jX1xe+vr4PjLt16xYAQK8vflFHr9fDYhHY+teFbE7LEmpSAI5LISKyp7Wjw9D09Xio/VRLSL+MEV8dcplmRQ27jVHp1KkTqlWrhiFDhuDIkSM4efIkXn31VWRkZCAqKspeZWmW2aJg/KokoZyXHwtAZDBXnyUisheDXof5A8RmASWkX+ZeQH9it0alRo0a2Lx5M27cuIHu3bvjkUcewZ49e/D9998jJCTEXmVp1vOfJeKuwL/b8Ba+eLMPx6UQEdlbdKg/egSK7QXUffY2SdU4HquPUbE1VxijMmNTOpbszlAdH9HSF18M6SCxIiIiEiX6Xh7eoiaWDnXeW0BqP7+514/GmQotQv+wg+t4s0khItKgN6ICEdaouur4rScuY8amdIkVOQY2KhrX9UOxy399QupIqoSIiCpqZNfGQvFLdmfAVOjaE0zYqGjY9ykXkZVbIJQzJKyhnGKIiKjCHm/qCzfBmZgvLt0vqRrHwEZFo8qz4WDL2pVhdOMpJSLSKoNeh7mCC8EdyPgd8amlL4Tq7PipplELtp5EoeC+D9+NeVxSNUREZC339gKqKpQz6RvX3QuIjYoGmS0KFm4/LZTTI7AmPI0GSRUREZE1rR0VJhRfYFYwYVWypGq0jY2KBvVbnAiRsVNt6/lwFUMiIgdi0OuwcECoUM7Go1kueQuIjYrGbEzJxOGz11XH6wGsHf2YtHqIiEiO6NA6qFO1klDOa+tSXe4WEBsVDTFbFExYI3Zpb+GgNtzLh4jIQW2Z3E0o/kZBISauFttOxdGxUdGQeVtOwixwy2dEZ+7lQ0TkyDyNBkS0FFtef0NqNuJTMyVVpD1sVDTCbFGweNevquMfDaiGN6K4lw8RkaP7Ykh7BNTwEsqJWe06s4DYqGhEWNwWFBSq/0f31fBHJVZDRES2tGVyN3i6qb+NX2i5N/HCFbBR0YB27/6IS/km1fGPBlTjwm5ERE7EoNdh7oA2QjmHz17HbZNZUkXawU87O4uatwvXbhUK5fBqChGR8+kV5IeJ4U2Fcl756pCkarSDjYodvbziEI5l5QvljOjckFdTiIic1PjwpvCp5KY6fvfpa04/sJafeHZy22TGluOXhXJ6BNbEG1GtJFVERET2ZtDr8MHzwUI5Y1YmY3Oa8y4Ex0bFTp79ZI9QfKOHvbj6LBGRC+gV5Ifx3RoL5YxfmeS0s4DYqNiBqdCC45duCOX8Zyw3HCQichUTn2wOd4P6WUB3LfeaFWfERsUOXly6Xyi+QXVP+Hi5S6qGiIi0xqDXYV5/sVlA8WnOuRAcGxUbm7EpHQcyflcdX8kN2Plad4kVERGRFkUG+yE62E8oxxkXgmOjYkPxqVlYsjtDKCf1nd6SqiEiIq2bN6ANPAQXguu7aK/EimyPjYqNmC0Kxq0Su384/PEGnIpMROTCDHodZvcNFcpJOpeLDUec5xYQPwVtpMecHRC5GtfI1wtvRQfJK4iIiBxCnxB/RLT0FcqJWeU8t4DYqNjAjTuF+O3qLdXxbnogYVI3afUQEZFj+WJIB7St56M6XgEQMWeHtHpsiY2KDUTN3yUUP+6JJjDo1d+TJCIi57d29GMQGQ2QcfUWbtwR26JFi9ioSGYqtOBszm3V8R5uOowPbyaxIiIickQGvQ5zBacs9563U1I1tsNGRbKp644Ixc/t14ZXU4iIqER9QvzR1Pch1fHnf7+Dl5YdkFiRfGxUJHpv4zF8l6x+5PXQTvURKThnnoiIXMumCV2E4rf9chVPLdwtqRr52KhIMuKrQ1i654zq+Kqebnjn6dbyCiIiIqdgdNNjSFg9oZzUC3lYn3RBUkVysVGRYGPKRSSki+2MfPCNHpKqISIiZzP9qWA8ZBT7CJ/07RGHnLLMRsXKzBYF41anCOVEB/txYTciIhKS/HZPoXizBUg8dVVSNfLw09HKOsVtEYp/yGjAvAFio7iJiIiMbnqM7BIglLNg+ylJ1cjDRsWK3t1wDJfzTUI5H/UL4SwfIiIql9jIQLz0WEPV8QfP/I6NKY61vD4bFSsxFVrw5d4zQjkLBrZBryDO8iEiovJ7u08rNKnhpTp+3OpkzNiULrEi62KjYiWx34mtlxIVVBt9QvwlVUNERK4kfmJXofgluzMQF+8YzQobFSvYnJaFdUlil9LmD2orqRoiInI15RmvsnhXBkyFFkkVWQ8blQoyWxRM+iZFKOdvoX4cl0JERFYVGxmIaMFFQ//vi/2SqrEeNioVtOeXK7h9V6wj/eD5UDnFEBGRS5s3oA3cDer/ED545nfN3wJio1IBm9OyMGzFIaGcEZ0DuGYKERFJYdDr0KnRw0I5Wr8FxE/MctqcloVRXydB5NS2a1AVb0QFSquJiIho8YuPCOcMXqrdjQvZqJSD2aJg1NdJQjme7nqsGRkmqSIiIqJ7PI0GtK1XVShnf0aOZm8BsVEph7Ff/yycM+t5LuxGRES2sXZ0GEQ/cT7frc1bQGxUBJkKLdgsuOFguwZVuWYKERHZjEGvwwLB7VkUBViReEZOQRXARkVQ1PydQvEeBh1v+RARkc1Fh/qjbf2qQjnrUy7IKaYC2KgIuG0y49TlW0I5c/uH8pYPERHZxdpRYRCZaHo0Mx8vC85mlY2NioC/fbJbKH5YpwaIDOYtHyIisg+DXoe5/cVuAW05fhkjvtJOs8JGRaU+C3bhxKWbquN9q7hj2tNBEisiIiJ6sD4h/ohoWVMoJyH9Mm6bzJIqEsNGRYXhyw/i6MV8oZz9sT0kVUNERCTmiyHt0ba+j1BOxJwdcooRxEblATYcycTWE1eEcv4W6s9xKUREpClrRz2Gqp7uquMvXr+DGZvsv7YKG5UymC0KJn+TLJz3wfMhEqohIiIqP4Neh5nPtRbKWaKBtVXYqJRh4uokCO43iJFduJcPERFpU68gP4x7orFQTpdZ2yRVow4/UUthKrRgQ2q2UE5kUC3ERnIvHyIi0q5JPZrDXWB4QnZeAW7cKZRYUdnYqJSiywdbhOLd9TosGNROUjVERETWYdDrME9w1dpH/pkgqZoHY6NSgq6ztiE7/65Qztx+XNiNiIgcQ2SwH1rWrqw6/k6hBcO+3C+xotKxUfmLdzccw9mc20I5PQJrIjqUC7sREZHj+G7M40Lx209ew3sbbT8LiI3Kn5gKLfhy7xmhnOFhDbFkcHs5BREREUniaTTgiWbVhXKW7slAfGqWpIpKJq1RmTFjBsLCwuDl5YWqVauWGHPu3DlERUXBy8sLNWvWxKuvvorCQvsN2Fm+N0MoPjKoFt56qpWkaoiIiORa9lInob2AAGD86iSYLYqcgkogrVExmUzo27cvRo8eXeL3zWYzoqKiYDKZkJiYiBUrVmD58uV4++23ZZX0QD+lq5/lo9OBg2eJiMjhHX2nl1C82QLs+uWypGruJ61RmT59OiZNmoTWrUteXOann35Ceno6vv76a4SGhqJ3795477338Mknn8BkMskqq0x5d9QPoB3XtTEHzxIRkcPzNBoQ3kJsL6CPEk5KquZ+dhujsm/fPrRu3Rq1atUqeq5nz57Iy8vDsWPHSs0rKChAXl5esYe1tKhVRXXsxCebW+3nEhER2dPSoe3RoLqn6vjc22IzYyvCbo1KdnZ2sSYFQNHX2dml34KJi4uDj49P0aNevXpWq6lvu/qq4mKeaMKrKURE5FR2vtYd1T0NqmKbC0xtriihRmXq1KnQ6XRlPk6cOCGrVgBAbGwscnNzix7nz5+32muHNa0BL2PZJ8nopseEHs2s9jOJiIi0Ytc/IlTFfdy/reRK/sdNJHjKlCkYOnRomTGNGjVS9Vq1a9fGwYMHiz136dKlou+VxsPDAx4eHqp+hiiDXoc5/UIw6uukUmPmD+DCbkRE5JwqV3JDcF1vpF4ofVhFcF1vVK4k1D5UiNBP8vX1ha+vr1V+cKdOnTBjxgxcvnwZNWveG8STkJAAb29vBAbab7+cXkF+WPR/bTHt+zRcyv/foN5aVYyY/nQQegX52a02IiIi2daP64ynFu4usVkJruuN9eM627QeaS3RuXPnkJOTg3PnzsFsNiMlJQUA0KRJE1SuXBlPPvkkAgMD8eKLL2LWrFnIzs7Gm2++ibFjx0q7YqJWryA/9AisjYMZObicfwc1q1RCh4DqvJJCREQuYf24zrhxpxCTvknGud9vo341T8zt38amV1L+oFMURcqqLUOHDsWKFSvue3779u3o1q0bAODs2bMYPXo0duzYgYceeghDhgzBzJkz4eam/n9EXl4efHx8kJubC29vb2uVT0RERBKp/fyW1qjYChsVIiIix6P285t7/RAREZFmsVEhIiIizWKjQkRERJrFRoWIiIg0i40KERERaRYbFSIiItIsNipERESkWWxUiIiISLNsvxaulf2xXl1eXukbKBEREZG2/PG5/aB1Zx2+UcnPzwcA1KtXz86VEBERkaj8/Hz4+PiU+n2HX0LfYrEgMzMTVapUgU5nvU0D8/LyUK9ePZw/f94pl+Z39uMDnP8Ynf34AOc/Rmc/PsD5j9HZjw+Qd4yKoiA/Px/+/v7Q60sfieLwV1T0ej3q1q0r7fW9vb2d9h8f4PzHBzj/MTr78QHOf4zOfnyA8x+jsx8fIOcYy7qS8gcOpiUiIiLNYqNCREREmsVGpRQeHh6YNm0aPDw87F2KFM5+fIDzH6OzHx/g/Mfo7McHOP8xOvvxAfY/RocfTEtERETOi1dUiIiISLPYqBAREZFmsVEhIiIizWKjQkRERJrFRoWIiIg0y2UblRkzZiAsLAxeXl6oWrVqiTHnzp1DVFQUvLy8ULNmTbz66qsoLCws83VzcnLwwgsvwNvbG1WrVsXw4cNx48YNCUcgZseOHdDpdCU+Dh06VGpet27d7osfNWqUDStXr2HDhvfVOnPmzDJz7ty5g7Fjx+Lhhx9G5cqV8dxzz+HSpUs2qljMmTNnMHz4cAQEBMDT0xONGzfGtGnTYDKZyszT+jn85JNP0LBhQ1SqVAkdO3bEwYMHy4xfu3YtWrRogUqVKqF169aIj4+3UaVi4uLi0L59e1SpUgU1a9bEM888g19++aXMnOXLl993ripVqmSjisW9884799XbokWLMnMc5fwBJb+n6HQ6jB07tsR4Rzh/u3btQp8+feDv7w+dTof//ve/xb6vKArefvtt+Pn5wdPTExERETh16tQDX1f091iEyzYqJpMJffv2xejRo0v8vtlsRlRUFEwmExITE7FixQosX74cb7/9dpmv+8ILL+DYsWNISEjAxo0bsWvXLrzyyisyDkFIWFgYsrKyij1efvllBAQE4JFHHikzd8SIEcXyZs2aZaOqxb377rvFah0/fnyZ8ZMmTcKGDRuwdu1a7Ny5E5mZmXj22WdtVK2YEydOwGKxYPHixTh27Bjmzp2LRYsW4fXXX39grlbP4TfffIPJkydj2rRpSEpKQkhICHr27InLly+XGJ+YmIiBAwdi+PDhSE5OxjPPPINnnnkGaWlpNq78wXbu3ImxY8di//79SEhIwN27d/Hkk0/i5s2bZeZ5e3sXO1dnz561UcXl06pVq2L17tmzp9RYRzp/AHDo0KFix5aQkAAA6Nu3b6k5Wj9/N2/eREhICD755JMSvz9r1izMnz8fixYtwoEDB/DQQw+hZ8+euHPnTqmvKfp7LExxccuWLVN8fHzuez4+Pl7R6/VKdnZ20XOfffaZ4u3trRQUFJT4Wunp6QoA5dChQ0XP/fDDD4pOp1MuXrxo9dorwmQyKb6+vsq7775bZlzXrl2VCRMm2KaoCmrQoIEyd+5c1fHXr19X3N3dlbVr1xY9d/z4cQWAsm/fPgkVWt+sWbOUgICAMmO0fA47dOigjB07tuhrs9ms+Pv7K3FxcSXG9+vXT4mKiir2XMeOHZWRI0dKrdMaLl++rABQdu7cWWpMae9HWjVt2jQlJCREdbwjnz9FUZQJEyYojRs3ViwWS4nfd7TzB0D5z3/+U/S1xWJRateurXz44YdFz12/fl3x8PBQVq1aVerriP4ei3LZKyoPsm/fPrRu3Rq1atUqeq5nz57Iy8vDsWPHSs2pWrVqsSsUERER0Ov1OHDggPSaRaxfvx7Xrl3DsGHDHhj773//GzVq1EBQUBBiY2Nx69YtG1RYPjNnzsTDDz+MNm3a4MMPPyzzVt3hw4dx9+5dREREFD3XokUL1K9fH/v27bNFuRWWm5uL6tWrPzBOi+fQZDLh8OHDxf7/6/V6RERElPr/f9++fcXigXu/l45wvnJzcwHggefrxo0baNCgAerVq4enn3661PcbrTh16hT8/f3RqFEjvPDCCzh37lypsY58/kwmE77++mu89NJL0Ol0pcY52vn7s4yMDGRnZxc7Rz4+PujYsWOp56g8v8eiHH73ZFmys7OLNSkAir7Ozs4uNadmzZrFnnNzc0P16tVLzbGXpUuXomfPng/ceXrQoEFo0KAB/P39kZqain/84x/45Zdf8N1339moUvViYmLQtm1bVK9eHYmJiYiNjUVWVhbmzJlTYnx2djaMRuN9Y5Rq1aqlufNVktOnT2PBggWYPXt2mXFaPYdXr16F2Wwu8ffsxIkTJeaU9nup9fNlsVgwceJEPPbYYwgKCio1rnnz5vjyyy8RHByM3NxczJ49G2FhYTh27JjUXeLLq2PHjli+fDmaN2+OrKwsTJ8+HZ07d0ZaWhqqVKlyX7yjnj8A+O9//4vr169j6NChpcY42vn7qz/Og8g5Ks/vsSinalSmTp2KDz74oMyY48ePP3CwlyMpzzFfuHABP/74I9asWfPA1//z+JrWrVvDz88P4eHh+PXXX9G4cePyF66SyPFNnjy56Lng4GAYjUaMHDkScXFxmt6Hozzn8OLFi+jVqxf69u2LESNGlJlr73NIwNixY5GWllbm+A0A6NSpEzp16lT0dVhYGFq2bInFixfjvffek12msN69exf9d3BwMDp27IgGDRpgzZo1GD58uB0rs76lS5eid+/e8Pf3LzXG0c6fo3CqRmXKlClldrsA0KhRI1WvVbt27ftGLf8xG6R27dql5vx18FBhYSFycnJKzamo8hzzsmXL8PDDD+Opp54S/nkdO3YEcO+veVt8yFXknHbs2BGFhYU4c+YMmjdvft/3a9euDZPJhOvXrxe7qnLp0iVp56skoseYmZmJJ554AmFhYfj888+Ff56tz2FpatSoAYPBcN8sq7L+/9euXVsoXgvGjRtXNLBe9K9qd3d3tGnTBqdPn5ZUnXVVrVoVzZo1K7VeRzx/AHD27Fls2bJF+Cqko52/P87DpUuX4OfnV/T8pUuXEBoaWmJOeX6PhVllpIsDe9Bg2kuXLhU9t3jxYsXb21u5c+dOia/1x2Dan3/+uei5H3/8UVODaS0WixIQEKBMmTKlXPl79uxRAChHjhyxcmXW9/XXXyt6vV7Jyckp8ft/DKb99ttvi547ceKEpgfTXrhwQWnatKkyYMAApbCwsFyvoaVz2KFDB2XcuHFFX5vNZqVOnTplDqaNjo4u9lynTp00ORjTYrEoY8eOVfz9/ZWTJ0+W6zUKCwuV5s2bK5MmTbJydXLk5+cr1apVU+bNm1fi9x3p/P3ZtGnTlNq1ayt3794VytP6+UMpg2lnz55d9Fxubq6qwbQiv8fCdVrlVRzQ2bNnleTkZGX69OlK5cqVleTkZCU5OVnJz89XFOXeP7CgoCDlySefVFJSUpTNmzcrvr6+SmxsbNFrHDhwQGnevLly4cKFoud69eqltGnTRjlw4ICyZ88epWnTpsrAgQNtfnyl2bJliwJAOX78+H3fu3DhgtK8eXPlwIEDiqIoyunTp5V3331X+fnnn5WMjAzl+++/Vxo1aqR06dLF1mU/UGJiojJ37lwlJSVF+fXXX5Wvv/5a8fX1VQYPHlwU89fjUxRFGTVqlFK/fn1l27Ztys8//6x06tRJ6dSpkz0O4YEuXLigNGnSRAkPD1cuXLigZGVlFT3+HONI53D16tWKh4eHsnz5ciU9PV155ZVXlKpVqxbNtnvxxReVqVOnFsXv3btXcXNzU2bPnq0cP35cmTZtmuLu7q4cPXrUXodQqtGjRys+Pj7Kjh07ip2rW7duFcX89fimT5+u/Pjjj8qvv/6qHD58WBkwYIBSqVIl5dixY/Y4hAeaMmWKsmPHDiUjI0PZu3evEhERodSoUUO5fPmyoiiOff7+YDablfr16yv/+Mc/7vueI56//Pz8os87AMqcOXOU5ORk5ezZs4qiKMrMmTOVqlWrKt9//72SmpqqPP3000pAQIBy+/btotfo3r27smDBgqKvH/R7XFEu26gMGTJEAXDfY/v27UUxZ86cUXr37q14enoqNWrUUKZMmVKso96+fbsCQMnIyCh67tq1a8rAgQOVypUrK97e3sqwYcOKmh8tGDhwoBIWFlbi9zIyMor9Pzh37pzSpUsXpXr16oqHh4fSpEkT5dVXX1Vyc3NtWLE6hw8fVjp27Kj4+PgolSpVUlq2bKm8//77xa5+/fX4FEVRbt++rYwZM0apVq2a4uXlpfztb38r9sGvJcuWLSvx3+yfL4w64jlcsGCBUr9+fcVoNCodOnRQ9u/fX/S9rl27KkOGDCkWv2bNGqVZs2aK0WhUWrVqpWzatMnGFatT2rlatmxZUcxfj2/ixIlF/y9q1aqlREZGKklJSbYvXqX+/fsrfn5+itFoVOrUqaP0799fOX36dNH3Hfn8/eHHH39UACi//PLLfd9zxPP3x+fWXx9/HIfFYlHeeustpVatWoqHh4cSHh5+37E3aNBAmTZtWrHnyvo9riidoiiKdW4iEREREVkX11EhIiIizWKjQkRERJrFRoWIiIg0i40KERERaRYbFSIiItIsNipERESkWWxUiIiISLPYqBAREZFmsVEhIiIizWKjQkRERJrFRoWIiIg06/8BHEYaSyaMA/8AAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1851,7 +3221,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1863,18 +3233,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.2%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-9.95, -9.94)\": 98.9, \"(-9.94, -9.93)\": 98.69, \"(-9.93, -9.91)\": 98.47, \"(-9.91, -9.89)\": 98.22, \"(-9.89, -9.87)\": 97.58, \"(-9.87, -9.85)\": 97.2, \"(-9.85, -9.82)\": 96.64, \"(-9.82, -9.79)\": 95.99, \"(-9.79, -9.77)\": 95.58, \"(-9.77, -9.75)\": 95.34, \"(-9.75, -9.72)\": 94.8, \"(-9.72, -9.68)\": 94.28, \"(-9.68, -9.62)\": 92.96, \"(-9.62, -9.59)\": 92.24, \"(-9.59, -9.58)\": 91.81, \"(-9.58, -9.54)\": 91.43, \"(-9.54, -9.51)\": 90.71, \"(-9.51, -9.51)\": 90.46, \"(-9.51, -9.49)\": 90.26, \"(-9.49, -9.46)\": 89.76, \"(-9.46, -9.42)\": 89.05, \"(-9.42, -9.4)\": 88.54, \"(-9.4, -9.37)\": 88.03, \"(-9.37, -9.34)\": 87.34, \"(-9.34, -9.31)\": 87.09, \"(-9.31, -9.28)\": 86.35, \"(-9.28, -9.27)\": 86.05, \"(-9.27, -9.26)\": 85.85, \"(-9.26, -9.24)\": 85.58, \"(-9.24, -9.22)\": 85.21, \"(-9.22, -9.21)\": 84.95, \"(-9.21, -9.19)\": 84.73, \"(-9.19, -9.17)\": 84.29, \"(-9.17, -9.14)\": 83.98, \"(-9.14, -9.11)\": 83.14, \"(-9.11, -9.08)\": 82.83, \"(-9.08, -9.02)\": 81.84, \"(-9.02, -8.99)\": 81.09, \"(-8.99, -8.97)\": 80.74, \"(-8.97, -8.94)\": 80.19, \"(-8.94, -8.91)\": 79.7, \"(-8.91, -8.89)\": 79.34, \"(-8.89, -8.86)\": 78.87, \"(-8.86, -8.83)\": 78.15, \"(-8.83, -8.81)\": 77.75, \"(-8.81, -8.79)\": 77.5, \"(-8.79, -8.76)\": 77.06, \"(-8.76, -8.74)\": 76.6, \"(-8.74, -8.72)\": 76.25, \"(-8.72, -8.7)\": 75.97, \"(-8.7, -8.68)\": 75.7, \"(-8.68, -8.68)\": 75.32, \"(-8.68, -8.65)\": 75.03, \"(-8.65, -8.61)\": 74.31, \"(-8.61, -8.59)\": 73.98, \"(-8.59, -8.58)\": 73.76, \"(-8.58, -8.56)\": 73.43, \"(-8.56, -8.52)\": 73.13, \"(-8.52, -8.48)\": 72.2, \"(-8.48, -8.46)\": 71.78, \"(-8.46, -8.43)\": 71.31, \"(-8.43, -8.4)\": 70.82, \"(-8.4, -8.37)\": 70.42, \"(-8.37, -8.36)\": 70.05, \"(-8.36, -8.33)\": 69.74, \"(-8.33, -8.32)\": 69.39, \"(-8.32, -8.31)\": 69.08, \"(-8.31, -8.29)\": 68.86, \"(-8.29, -8.27)\": 68.61, \"(-8.27, -8.26)\": 68.31, \"(-8.26, -8.23)\": 68.07, \"(-8.23, -8.21)\": 67.57, \"(-8.21, -8.19)\": 67.36, \"(-8.19, -8.16)\": 66.88, \"(-8.16, -8.14)\": 66.55, \"(-8.14, -8.12)\": 66.2, \"(-8.12, -8.1)\": 65.72, \"(-8.1, -8.08)\": 65.46, \"(-8.08, -8.03)\": 64.9, \"(-8.03, -7.98)\": 64.02, \"(-7.98, -7.95)\": 63.48, \"(-7.95, -7.92)\": 63.09, \"(-7.92, -7.9)\": 62.63, \"(-7.9, -7.89)\": 62.42, \"(-7.89, -7.87)\": 62.16, \"(-7.87, -7.86)\": 61.87, \"(-7.86, -7.85)\": 61.67, \"(-7.85, -7.82)\": 61.43, \"(-7.82, -7.8)\": 60.95, \"(-7.8, -7.75)\": 60.62, \"(-7.75, -7.68)\": 59.33, \"(-7.68, -7.66)\": 58.93, \"(-7.66, -7.64)\": 58.72, \"(-7.64, -7.62)\": 58.36, \"(-7.62, -7.61)\": 58.08, \"(-7.61, -7.6)\": 57.87, \"(-7.6, -7.58)\": 57.62, \"(-7.58, -7.57)\": 57.4, \"(-7.57, -7.56)\": 57.15, \"(-7.56, -7.54)\": 56.92, \"(-7.54, -7.5)\": 56.63, \"(-7.5, -7.48)\": 56.07, \"(-7.48, -7.45)\": 55.78, \"(-7.45, -7.45)\": 55.52, \"(-7.45, -7.41)\": 55.31, \"(-7.41, -7.37)\": 54.48, \"(-7.37, -7.36)\": 54.2, \"(-7.36, -7.34)\": 53.99, \"(-7.34, -7.3)\": 53.6, \"(-7.3, -7.27)\": 53.13, \"(-7.27, -7.26)\": 52.87, \"(-7.26, -7.24)\": 52.59, \"(-7.24, -7.21)\": 52.29, \"(-7.21, -7.18)\": 51.86, \"(-7.18, -7.17)\": 51.53, \"(-7.17, -7.16)\": 51.3, \"(-7.16, -7.12)\": 50.93, \"(-7.12, -7.08)\": 50.36, \"(-7.08, -7.05)\": 50.01, \"(-7.05, -7.02)\": 49.51, \"(-7.02, -6.98)\": 49.11, \"(-6.98, -6.93)\": 48.27, \"(-6.93, -6.9)\": 47.88, \"(-6.9, -6.85)\": 47.22, \"(-6.85, -6.82)\": 46.62, \"(-6.82, -6.77)\": 46.25, \"(-6.77, -6.73)\": 45.56, \"(-6.73, -6.71)\": 45.3, \"(-6.71, -6.69)\": 45.05, \"(-6.69, -6.67)\": 44.7, \"(-6.67, -6.65)\": 44.5, \"(-6.65, -6.63)\": 44.2, \"(-6.63, -6.6)\": 43.82, \"(-6.6, -6.57)\": 43.4, \"(-6.57, -6.56)\": 43.17, \"(-6.56, -6.54)\": 42.97, \"(-6.54, -6.53)\": 42.75, \"(-6.53, -6.51)\": 42.55, \"(-6.51, -6.49)\": 42.26, \"(-6.49, -6.46)\": 41.86, \"(-6.46, -6.44)\": 41.6, \"(-6.44, -6.42)\": 41.37, \"(-6.42, -6.4)\": 41.14, \"(-6.4, -6.38)\": 40.85, \"(-6.38, -6.35)\": 40.61, \"(-6.35, -6.31)\": 40.12, \"(-6.31, -6.29)\": 39.76, \"(-6.29, -6.27)\": 39.36, \"(-6.27, -6.23)\": 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64.84, \"(8.07, 8.12)\": 65.29, \"(8.12, 8.17)\": 66.34, \"(8.17, 8.19)\": 66.88, \"(8.19, 8.21)\": 67.2, \"(8.21, 8.24)\": 67.58, \"(8.24, 8.28)\": 68.19, \"(8.28, 8.32)\": 68.9, \"(8.32, 8.33)\": 69.32, \"(8.33, 8.34)\": 69.54, \"(8.34, 8.36)\": 69.84, \"(8.36, 8.38)\": 70.09, \"(8.38, 8.4)\": 70.41, \"(8.4, 8.42)\": 70.69, \"(8.42, 8.44)\": 71.0, \"(8.44, 8.46)\": 71.42, \"(8.46, 8.47)\": 71.69, \"(8.47, 8.5)\": 72.0, \"(8.5, 8.54)\": 72.57, \"(8.54, 8.56)\": 73.12, \"(8.56, 8.59)\": 73.44, \"(8.59, 8.61)\": 73.89, \"(8.61, 8.65)\": 74.24, \"(8.65, 8.69)\": 75.15, \"(8.69, 8.7)\": 75.52, \"(8.7, 8.72)\": 75.79, \"(8.72, 8.72)\": 76.1, \"(8.72, 8.75)\": 76.32, \"(8.75, 8.8)\": 76.89, \"(8.8, 8.83)\": 77.51, \"(8.83, 8.84)\": 77.9, \"(8.84, 8.87)\": 78.43, \"(8.87, 8.92)\": 79.21, \"(8.92, 8.95)\": 79.81, \"(8.95, 8.98)\": 80.39, \"(8.98, 8.99)\": 80.76, \"(8.99, 9.03)\": 81.07, \"(9.03, 9.09)\": 82.25, \"(9.09, 9.12)\": 82.78, \"(9.12, 9.15)\": 83.39, \"(9.15, 9.17)\": 83.93, \"(9.17, 9.19)\": 84.26, \"(9.19, 9.22)\": 84.8, \"(9.22, 9.25)\": 85.21, \"(9.25, 9.26)\": 85.77, \"(9.26, 9.29)\": 86.0, \"(9.29, 9.32)\": 86.79, \"(9.32, 9.33)\": 87.03, \"(9.33, 9.35)\": 87.3, \"(9.35, 9.38)\": 87.69, \"(9.38, 9.4)\": 88.19, \"(9.4, 9.43)\": 88.75, \"(9.43, 9.45)\": 89.11, \"(9.45, 9.48)\": 89.42, \"(9.48, 9.51)\": 90.34, \"(9.51, 9.53)\": 90.57, \"(9.53, 9.55)\": 91.09, \"(9.55, 9.6)\": 91.43, \"(9.6, 9.64)\": 92.87, \"(9.64, 9.65)\": 93.07, \"(9.65, 9.69)\": 93.49, \"(9.69, 9.73)\": 94.31, \"(9.73, 9.77)\": 95.17, \"(9.77, 9.79)\": 95.61, \"(9.79, 9.81)\": 96.07, \"(9.81, 9.83)\": 96.39, \"(9.83, 9.84)\": 96.69, \"(9.84, 9.85)\": 96.91, \"(9.85, 9.93)\": 97.59}\n",
+      "Means: {\"(-9.97, -9.88)\": -9.968, \"(-9.88, -9.77)\": -9.86, \"(-9.77, -9.64)\": -9.756, \"(-9.64, -9.51)\": -9.62, \"(-9.51, -9.41)\": -9.508, \"(-9.41, -9.29)\": -9.395, \"(-9.29, -9.19)\": -9.286, \"(-9.19, -9.11)\": -9.182, \"(-9.11, -8.98)\": -9.076, \"(-8.98, -8.85)\": -8.956, \"(-8.85, -8.73)\": -8.835, \"(-8.73, -8.62)\": -8.723, \"(-8.62, -8.49)\": -8.579, \"(-8.49, -8.38)\": -8.473, \"(-8.38, -8.27)\": -8.369, \"(-8.27, -8.16)\": -8.265, \"(-8.16, -8.03)\": -8.125, \"(-8.03, -7.91)\": -8.011, \"(-7.91, -7.8)\": -7.899, \"(-7.8, -7.69)\": -7.795, \"(-7.69, -7.6)\": -7.678, \"(-7.6, -7.45)\": -7.569, \"(-7.45, -7.36)\": -7.449, \"(-7.36, -7.25)\": -7.349, \"(-7.25, -7.15)\": -7.239, \"(-7.15, -7.0)\": -7.099, \"(-7.0, -6.88)\": -6.983, \"(-6.88, -6.77)\": -6.866, \"(-6.77, -6.69)\": -6.76, \"(-6.69, -6.56)\": -6.66, \"(-6.56, -6.44)\": -6.535, \"(-6.44, -6.34)\": -6.431, \"(-6.34, -6.23)\": -6.329, \"(-6.23, -6.12)\": -6.225, \"(-6.12, -6.02)\": -6.123, \"(-6.02, -5.91)\": -6.009, \"(-5.91, -5.77)\": -5.888, \"(-5.77, -5.68)\": -5.771, \"(-5.68, -5.57)\": -5.667, \"(-5.57, -5.46)\": -5.562, \"(-5.46, -5.37)\": -5.459, \"(-5.37, -5.26)\": -5.357, \"(-5.26, -5.13)\": -5.246, \"(-5.13, -5.0)\": -5.111, \"(-5.0, -4.93)\": -5.005, \"(-4.93, -4.8)\": -4.9, \"(-4.8, -4.67)\": -4.787, \"(-4.67, -4.57)\": -4.669, \"(-4.57, -4.48)\": -4.564, \"(-4.48, -4.36)\": -4.464, \"(-4.36, -4.24)\": -4.336, \"(-4.24, -4.13)\": -4.235, \"(-4.13, -4.02)\": -4.122, \"(-4.02, -3.88)\": -3.98, \"(-3.88, -3.73)\": -3.847, \"(-3.73, -3.61)\": -3.692, \"(-3.61, -3.48)\": -3.579, \"(-3.48, -3.38)\": -3.476, \"(-3.38, -3.26)\": -3.373, \"(-3.26, -3.15)\": -3.237, \"(-3.15, -3.02)\": -3.122, \"(-3.02, -2.91)\": -3.016, \"(-2.91, -2.79)\": -2.898, \"(-2.79, -2.67)\": -2.765, \"(-2.67, -2.57)\": -2.664, \"(-2.57, -2.47)\": -2.561, \"(-2.47, -2.34)\": -2.455, \"(-2.34, -2.25)\": -2.34, \"(-2.25, -2.14)\": -2.235, \"(-2.14, -2.02)\": -2.134, \"(-2.02, -1.92)\": -2.004, \"(-1.92, -1.81)\": -1.903, \"(-1.81, -1.71)\": -1.798, \"(-1.71, -1.6)\": -1.697, \"(-1.6, -1.48)\": -1.581, \"(-1.48, -1.36)\": -1.468, \"(-1.36, -1.26)\": -1.362, \"(-1.26, -1.14)\": -1.25, \"(-1.14, -1.03)\": -1.125, \"(-1.03, -0.92)\": -1.022, \"(-0.92, -0.83)\": -0.917, \"(-0.83, -0.72)\": -0.816, \"(-0.72, -0.58)\": -0.69, \"(-0.58, -0.49)\": -0.562, \"(-0.49, -0.37)\": -0.455, \"(-0.37, -0.25)\": -0.352, \"(-0.25, -0.13)\": -0.23, \"(-0.13, 0.21)\": -0.112, \"(0.21, 0.33)\": -0.219, \"(0.33, 0.47)\": -0.372, \"(0.47, 0.57)\": -0.474, \"(0.57, 0.69)\": -0.577, \"(0.69, 0.8)\": -0.694, \"(0.8, 0.91)\": -0.813, \"(0.91, 1.02)\": -0.921, \"(1.02, 1.13)\": -1.035, \"(1.13, 1.24)\": -1.138, \"(1.24, 1.37)\": -1.267, \"(1.37, 1.5)\": -1.374, \"(1.5, 1.64)\": -1.535, \"(1.64, 1.78)\": -1.649, \"(1.78, 1.91)\": -1.819, \"(1.91, 2.03)\": -1.925, \"(2.03, 2.11)\": -2.031, \"(2.11, 2.22)\": -2.132, \"(2.22, 2.33)\": -2.234, \"(2.33, 2.45)\": -2.337, \"(2.45, 2.59)\": -2.493, \"(2.59, 2.72)\": -2.603, \"(2.72, 2.82)\": -2.714, \"(2.82, 2.92)\": -2.827, \"(2.92, 3.05)\": -2.94, \"(3.05, 3.21)\": -3.091, \"(3.21, 3.31)\": -3.209, \"(3.31, 3.41)\": -3.311, \"(3.41, 3.51)\": -3.414, \"(3.51, 3.63)\": -3.523, \"(3.63, 3.73)\": -3.636, \"(3.73, 3.86)\": -3.741, \"(3.86, 3.97)\": -3.878, \"(3.97, 4.09)\": -3.997, \"(4.09, 4.2)\": -4.099, \"(4.2, 4.31)\": -4.211, \"(4.31, 4.41)\": -4.323, \"(4.41, 4.53)\": -4.429, \"(4.53, 4.65)\": -4.573, \"(4.65, 4.79)\": -4.685, \"(4.79, 4.88)\": -4.798, \"(4.88, 5.01)\": -4.899, \"(5.01, 5.12)\": -5.015, \"(5.12, 5.24)\": -5.121, \"(5.24, 5.34)\": -5.246, \"(5.34, 5.42)\": -5.354, \"(5.42, 5.56)\": -5.456, \"(5.56, 5.66)\": -5.562, \"(5.66, 5.77)\": -5.663, \"(5.77, 5.88)\": -5.774, \"(5.88, 5.97)\": -5.881, \"(5.97, 6.09)\": -5.99, \"(6.09, 6.2)\": -6.1, \"(6.2, 6.31)\": -6.22, \"(6.31, 6.46)\": -6.33, \"(6.46, 6.59)\": -6.484, \"(6.59, 6.7)\": -6.585, \"(6.7, 6.79)\": -6.706, \"(6.79, 6.93)\": -6.813, \"(6.93, 7.03)\": -6.945, \"(7.03, 7.14)\": -7.051, \"(7.14, 7.24)\": -7.151, \"(7.24, 7.33)\": -7.255, \"(7.33, 7.45)\": -7.36, \"(7.45, 7.57)\": -7.469, \"(7.57, 7.68)\": -7.596, \"(7.68, 7.8)\": -7.698, \"(7.8, 7.91)\": -7.801, \"(7.91, 8.03)\": -7.931, \"(8.03, 8.13)\": -8.041, \"(8.13, 8.27)\": -8.164, \"(8.27, 8.38)\": -8.28, \"(8.38, 8.49)\": -8.383, \"(8.49, 8.61)\": -8.507, \"(8.61, 8.73)\": -8.627, \"(8.73, 8.83)\": -8.746, \"(8.83, 8.96)\": -8.855, \"(8.96, 9.05)\": -8.957, \"(9.05, 9.17)\": -9.072, \"(9.17, 9.28)\": -9.189, \"(9.28, 9.39)\": -9.302, \"(9.39, 9.5)\": -9.402, \"(9.5, 9.62)\": -9.505, \"(9.62, 9.74)\": -9.638, \"(9.74, 9.85)\": -9.742, \"(9.85, 9.94)\": -9.849, \"(9.94, 9.99)\": -9.957}\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1884,7 +3253,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1896,18 +3265,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.2%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-9.88, -9.87)\": -195.1, \"(-9.87, -9.85)\": -194.3, \"(-9.85, -9.82)\": -193.3, \"(-9.82, -9.79)\": -192.3, \"(-9.79, -9.76)\": -191.1, \"(-9.76, -9.72)\": -189.4, \"(-9.72, -9.71)\": -188.7, \"(-9.71, -9.7)\": -188.2, \"(-9.7, -9.67)\": -187.7, \"(-9.67, -9.65)\": -186.6, \"(-9.65, -9.64)\": -186.0, \"(-9.64, -9.63)\": -185.6, \"(-9.63, -9.61)\": -185.1, \"(-9.61, -9.59)\": -184.4, \"(-9.59, -9.56)\": -183.1, \"(-9.56, -9.54)\": -182.4, \"(-9.54, -9.52)\": -181.9, \"(-9.52, -9.51)\": -181.2, \"(-9.51, -9.5)\": -180.8, \"(-9.5, -9.49)\": -180.2, \"(-9.49, -9.47)\": -179.4, \"(-9.47, -9.44)\": -178.8, \"(-9.44, -9.4)\": -177.5, \"(-9.4, -9.38)\": -176.5, \"(-9.38, -9.37)\": -176.0, \"(-9.37, -9.35)\": -175.4, \"(-9.35, -9.33)\": -174.9, \"(-9.33, -9.29)\": -173.3, \"(-9.29, -9.26)\": -171.7, \"(-9.26, -9.23)\": -171.0, \"(-9.23, -9.21)\": -170.0, \"(-9.21, -9.19)\": -169.2, \"(-9.19, -9.17)\": -168.6, \"(-9.17, -9.16)\": -167.8, \"(-9.16, -9.14)\": -167.3, \"(-9.14, -9.11)\": -166.3, \"(-9.11, -9.08)\": -165.5, \"(-9.08, -9.07)\": -164.9, \"(-9.07, -9.06)\": -164.5, \"(-9.06, -9.03)\": -163.7, \"(-9.03, -9.0)\": -162.5, \"(-9.0, -8.99)\": -161.9, \"(-8.99, -8.96)\": -161.2, \"(-8.96, -8.93)\": -160.2, \"(-8.93, -8.91)\": -159.4, \"(-8.91, -8.89)\": -158.7, \"(-8.89, -8.88)\": -158.1, \"(-8.88, -8.86)\": -157.6, \"(-8.86, -8.85)\": -156.8, \"(-8.85, -8.83)\": -156.3, \"(-8.83, -8.83)\": -155.8, \"(-8.83, -8.82)\": -155.4, \"(-8.82, -8.75)\": -154.6, \"(-8.75, -8.69)\": -151.4, \"(-8.69, -8.67)\": -150.9, \"(-8.67, -8.66)\": -150.0, \"(-8.66, -8.63)\": -149.5, \"(-8.63, -8.6)\": -148.3, \"(-8.6, -8.56)\": -147.4, \"(-8.56, -8.53)\": -145.9, \"(-8.53, -8.49)\": -144.9, \"(-8.49, -8.46)\": -143.5, \"(-8.46, -8.43)\": -142.8, \"(-8.43, -8.4)\": -141.7, \"(-8.4, -8.4)\": -141.2, \"(-8.4, -8.37)\": -140.8, \"(-8.37, -8.34)\": -139.4, \"(-8.34, -8.32)\": -138.9, \"(-8.32, -8.31)\": -138.4, \"(-8.31, -8.29)\": -137.8, \"(-8.29, -8.28)\": -137.3, \"(-8.28, -8.25)\": -136.6, \"(-8.25, -8.23)\": -136.1, \"(-8.23, -8.22)\": -135.5, \"(-8.22, -8.21)\": -135.0, \"(-8.21, -8.19)\": -134.5, \"(-8.19, -8.17)\": -133.9, \"(-8.17, -8.15)\": -133.3, \"(-8.15, -8.13)\": -132.6, \"(-8.13, -8.12)\": -132.0, \"(-8.12, -8.08)\": -131.4, \"(-8.08, -8.06)\": -130.7, \"(-8.06, -8.06)\": -129.9, \"(-8.06, -8.03)\": -129.4, \"(-8.03, -8.0)\": -128.6, \"(-8.0, -7.98)\": -127.9, \"(-7.98, -7.93)\": -126.9, \"(-7.93, -7.87)\": -124.5, \"(-7.87, -7.87)\": -124.0, \"(-7.87, -7.83)\": -123.4, \"(-7.83, -7.78)\": -121.6, \"(-7.78, -7.76)\": -120.7, \"(-7.76, -7.73)\": -120.0, \"(-7.73, -7.7)\": -118.7, \"(-7.7, -7.68)\": -118.2, \"(-7.68, -7.66)\": -117.7, \"(-7.66, -7.63)\": -116.7, \"(-7.63, -7.61)\": -116.1, \"(-7.61, -7.57)\": -115.2, \"(-7.57, -7.52)\": -114.1, \"(-7.52, -7.5)\": -113.1, \"(-7.5, -7.49)\": -112.7, \"(-7.49, -7.48)\": -112.1, \"(-7.48, -7.47)\": -111.6, \"(-7.47, -7.44)\": -111.2, \"(-7.44, -7.41)\": -110.4, \"(-7.41, -7.39)\": -109.5, \"(-7.39, -7.37)\": -109.1, \"(-7.37, -7.36)\": -108.6, \"(-7.36, -7.34)\": -107.9, \"(-7.34, -7.32)\": -107.4, \"(-7.32, -7.29)\": -106.5, \"(-7.29, -7.21)\": -106.0, \"(-7.21, -7.13)\": -102.2, \"(-7.13, -7.11)\": -101.5, \"(-7.11, -7.09)\": -100.8, \"(-7.09, -7.07)\": -100.3, \"(-7.07, -7.04)\": -99.5, \"(-7.04, -7.01)\": -98.9, \"(-7.01, -7.0)\": -98.4, \"(-7.0, -6.98)\": -97.8, \"(-6.98, -6.96)\": -97.3, \"(-6.96, -6.95)\": -96.8, \"(-6.95, -6.94)\": -96.3, \"(-6.94, -6.91)\": -95.9, \"(-6.91, -6.88)\": -95.0, \"(-6.88, -6.86)\": -94.5, \"(-6.86, -6.83)\": -94.0, \"(-6.83, -6.8)\": -92.8, \"(-6.8, -6.76)\": -92.0, \"(-6.76, -6.72)\": -90.7, \"(-6.72, -6.69)\": -90.1, \"(-6.69, -6.67)\": -89.2, \"(-6.67, -6.63)\": -88.7, \"(-6.63, -6.57)\": -87.6, \"(-6.57, -6.5)\": -85.1, \"(-6.5, -6.48)\": -84.3, \"(-6.48, -6.45)\": -83.6, \"(-6.45, -6.44)\": -83.0, \"(-6.44, -6.42)\": -82.6, \"(-6.42, -6.39)\": -82.1, \"(-6.39, -6.35)\": -81.2, \"(-6.35, -6.32)\": -80.3, \"(-6.32, -6.29)\": 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\"(8.75, 8.76)\": -153.3, \"(8.76, 8.78)\": -153.7, \"(8.78, 8.8)\": -154.5, \"(8.8, 8.82)\": -155.1, \"(8.82, 8.87)\": -156.4, \"(8.87, 8.9)\": -157.8, \"(8.9, 8.92)\": -158.5, \"(8.92, 8.94)\": -159.6, \"(8.94, 8.95)\": -160.0, \"(8.95, 8.97)\": -160.6, \"(8.97, 8.98)\": -161.1, \"(8.98, 9.02)\": -161.8, \"(9.02, 9.06)\": -163.8, \"(9.06, 9.08)\": -164.4, \"(9.08, 9.12)\": -165.4, \"(9.12, 9.15)\": -167.1, \"(9.15, 9.16)\": -167.7, \"(9.16, 9.2)\": -168.1, \"(9.2, 9.23)\": -169.8, \"(9.23, 9.24)\": -170.5, \"(9.24, 9.25)\": -170.9, \"(9.25, 9.27)\": -171.5, \"(9.27, 9.29)\": -172.4, \"(9.29, 9.31)\": -173.0, \"(9.31, 9.33)\": -173.6, \"(9.33, 9.36)\": -174.5, \"(9.36, 9.39)\": -175.9, \"(9.39, 9.41)\": -176.4, \"(9.41, 9.43)\": -177.5, \"(9.43, 9.45)\": -178.2, \"(9.45, 9.46)\": -178.6, \"(9.46, 9.47)\": -179.0, \"(9.47, 9.49)\": -179.5, \"(9.49, 9.53)\": -180.9, \"(9.53, 9.59)\": -182.7, \"(9.59, 9.61)\": -184.5, \"(9.61, 9.62)\": -185.0, \"(9.62, 9.64)\": -185.5, \"(9.64, 9.68)\": -186.4, \"(9.68, 9.72)\": -188.4, \"(9.72, 9.74)\": -189.4, \"(9.74, 9.77)\": -190.6, \"(9.77, 9.8)\": -191.5, \"(9.8, 9.84)\": -193.0, \"(9.84, 9.87)\": -194.0, \"(9.87, 9.9)\": -195.8, \"(9.9, 9.91)\": -196.2, \"(9.91, 9.93)\": -196.9, \"(9.93, 9.96)\": -198.0, \"(9.96, 9.98)\": -198.6}\n",
+      "Means: {\"(-9.98, -9.86)\": 14.94, \"(-9.86, -9.72)\": 14.69, \"(-9.72, -9.58)\": 14.43, \"(-9.58, -9.42)\": 14.12, \"(-9.42, -9.28)\": 13.8, \"(-9.28, -9.16)\": 13.53, \"(-9.16, -9.0)\": 13.25, \"(-9.0, -8.87)\": 12.97, \"(-8.87, -8.73)\": 12.72, \"(-8.73, -8.61)\": 12.44, \"(-8.61, -8.47)\": 12.17, \"(-8.47, -8.33)\": 11.92, \"(-8.33, -8.2)\": 11.65, \"(-8.2, -8.08)\": 11.38, \"(-8.08, -7.91)\": 11.13, \"(-7.91, -7.76)\": 10.78, \"(-7.76, -7.66)\": 10.52, \"(-7.66, -7.51)\": 10.26, \"(-7.51, -7.41)\": 10.0, \"(-7.41, -7.23)\": 9.73, \"(-7.23, -7.09)\": 9.42, \"(-7.09, -6.95)\": 9.13, \"(-6.95, -6.79)\": 8.86, \"(-6.79, -6.63)\": 8.49, \"(-6.63, -6.49)\": 8.24, \"(-6.49, -6.36)\": 7.96, \"(-6.36, -6.25)\": 7.7, \"(-6.25, -6.09)\": 7.44, \"(-6.09, -5.96)\": 7.18, \"(-5.96, -5.82)\": 6.89, \"(-5.82, -5.68)\": 6.6, \"(-5.68, -5.53)\": 6.32, \"(-5.53, -5.39)\": 6.03, \"(-5.39, -5.25)\": 5.77, \"(-5.25, -5.1)\": 5.49, \"(-5.1, -5.0)\": 5.21, \"(-5.0, -4.84)\": 4.92, \"(-4.84, -4.69)\": 4.64, \"(-4.69, -4.57)\": 4.36, \"(-4.57, -4.43)\": 4.1, \"(-4.43, -4.31)\": 3.84, \"(-4.31, -4.16)\": 3.59, \"(-4.16, -4.03)\": 3.33, \"(-4.03, -3.91)\": 3.04, \"(-3.91, -3.77)\": 2.78, \"(-3.77, -3.64)\": 2.51, \"(-3.64, -3.49)\": 2.24, \"(-3.49, -3.36)\": 1.97, \"(-3.36, -3.23)\": 1.71, \"(-3.23, -3.09)\": 1.43, \"(-3.09, -2.96)\": 1.17, \"(-2.96, -2.83)\": 0.89, \"(-2.83, -2.69)\": 0.62, \"(-2.69, -2.55)\": 0.37, \"(-2.55, -2.31)\": 0.1, \"(-2.31, -2.2)\": 0.38, \"(-2.2, -2.05)\": 0.63, \"(-2.05, -1.93)\": 0.92, \"(-1.93, -1.77)\": 1.18, \"(-1.77, -1.62)\": 1.51, \"(-1.62, -1.48)\": 1.78, \"(-1.48, -1.33)\": 2.03, \"(-1.33, -1.18)\": 2.38, \"(-1.18, -1.04)\": 2.66, \"(-1.04, -0.89)\": 2.92, \"(-0.89, -0.77)\": 3.25, \"(-0.77, -0.6)\": 3.54, \"(-0.6, -0.46)\": 3.83, \"(-0.46, -0.33)\": 4.1, \"(-0.33, -0.21)\": 4.36, \"(-0.21, -0.06)\": 4.61, \"(-0.06, 0.1)\": 4.94, \"(0.1, 0.23)\": 5.22, \"(0.23, 0.38)\": 5.49, \"(0.38, 0.5)\": 5.76, \"(0.5, 0.65)\": 6.06, \"(0.65, 0.81)\": 6.35, \"(0.81, 0.94)\": 6.64, \"(0.94, 1.08)\": 6.89, \"(1.08, 1.21)\": 7.19, \"(1.21, 1.36)\": 7.46, \"(1.36, 1.5)\": 7.76, \"(1.5, 1.64)\": 8.01, \"(1.64, 1.78)\": 8.29, \"(1.78, 1.91)\": 8.57, \"(1.91, 2.04)\": 8.83, \"(2.04, 2.17)\": 9.08, \"(2.17, 2.31)\": 9.36, \"(2.31, 2.44)\": 9.62, \"(2.44, 2.58)\": 9.9, \"(2.58, 2.7)\": 10.17, \"(2.7, 2.82)\": 10.42, \"(2.82, 2.94)\": 10.67, \"(2.94, 3.05)\": 10.93, \"(3.05, 3.21)\": 11.18, \"(3.21, 3.35)\": 11.47, \"(3.35, 3.48)\": 11.72, \"(3.48, 3.63)\": 11.99, \"(3.63, 3.78)\": 12.28, \"(3.78, 3.91)\": 12.57, \"(3.91, 4.04)\": 12.84, \"(4.04, 4.16)\": 13.11, \"(4.16, 4.31)\": 13.37, \"(4.31, 4.45)\": 13.66, \"(4.45, 4.59)\": 13.91, \"(4.59, 4.73)\": 14.21, \"(4.73, 4.86)\": 14.49, \"(4.86, 5.0)\": 14.76, \"(5.0, 5.15)\": 15.05, \"(5.15, 5.29)\": 15.32, \"(5.29, 5.42)\": 15.6, \"(5.42, 5.55)\": 15.86, \"(5.55, 5.69)\": 16.14, \"(5.69, 5.85)\": 16.45, \"(5.85, 6.01)\": 16.75, \"(6.01, 6.13)\": 17.02, \"(6.13, 6.27)\": 17.3, \"(6.27, 6.42)\": 17.59, \"(6.42, 6.54)\": 17.85, \"(6.54, 6.69)\": 18.12, \"(6.69, 6.79)\": 18.38, \"(6.79, 6.93)\": 18.64, \"(6.93, 7.06)\": 18.89, \"(7.06, 7.19)\": 19.14, \"(7.19, 7.33)\": 19.4, \"(7.33, 7.46)\": 19.66, \"(7.46, 7.6)\": 19.91, \"(7.6, 7.75)\": 20.25, \"(7.75, 7.89)\": 20.52, \"(7.89, 8.02)\": 20.81, \"(8.02, 8.17)\": 21.08, \"(8.17, 8.32)\": 21.38, \"(8.32, 8.45)\": 21.64, \"(8.45, 8.57)\": 21.91, \"(8.57, 8.7)\": 22.17, \"(8.7, 8.84)\": 22.43, \"(8.84, 8.97)\": 22.68, \"(8.97, 9.08)\": 22.94, \"(9.08, 9.2)\": 23.19, \"(9.2, 9.34)\": 23.45, \"(9.34, 9.48)\": 23.71, \"(9.48, 9.62)\": 23.99, \"(9.62, 9.76)\": 24.26, \"(9.76, 9.89)\": 24.53, \"(9.89, 9.99)\": 24.82}\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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QCgoK0KlTJ3z++ecPPHfnzh0kJCTgvffeQ0JCAjZu3IgzZ85gyJAhZepGjRqF1NRU7Ny5E1FRUYiJicG4ceNMfxcWRKNWYdJjLUXXf73/vILdEBERWSeVIAgmn2NQqVTYtGkThg4dWmHN0aNH0bVrV1y6dAlNmjTBqVOn0L59exw9ehSdO3cGAGzfvh1hYWG4evUqvL29q3zdvLw8uLi4IDc3F87Ozqa2rxi9QUCnub8jv6hUVP1rvX0REdZe4a6IiIjMS8rfb8XnoOTm5kKlUsHV1RUAEBcXB1dXV2M4AYDQ0FCo1WocPny43H0UFRUhLy+vzMOSadQqLBruL7r+q5g0FJcaFOyIiIjIuigaUAoLC/H2229j5MiRxqSUkZGBRo0alanTarVwc3NDRkZGufuJjIyEi4uL8eHj46Nk27II8/fCa719RdUKAN7ZmKxsQ0RERFZEsYBSUlKCZ599FoIg4Msvv6zWviIiIpCbm2t8XLlyRaYulRUR1h69Ra4uuzX5Oq/oISIi+h9FAsq9cHLp0iXs3LmzzHkmT09PZGVllakvLS1FdnY2PD3Lv+Geo6MjnJ2dyzysxSOtG4qqKyo18CaCRERE/yN7QLkXTs6ePYtdu3bB3d29zPMhISHIyclBfHy8cduePXtgMBgQHBwsdztm92JIM9G1sedvKtcIERGRFdFKHZCfn49z584Zv05LS0NSUhLc3Nzg5eWFp59+GgkJCYiKioJerzfOK3Fzc4NOp0O7du3Qv39/jB07FsuXL0dJSQkmTpyIESNGiLqCx9rotGq0bFgH524UVFl7lGuiEBERATDhCMqxY8cQGBiIwMBAAMC0adMQGBiI2bNn49q1a9iyZQuuXr2KgIAAeHl5GR+xsbHGffzwww9o27Yt+vbti7CwMPTs2RNfffWVfO/KwjzRwUNU3ZGLf2F7ynWFuyEiIrJ81VoHxVwsfR2U+x08exOjVpV/CfX9vFxq4cDbj0EjcjVaIiIia2FR66DQ33c5dq3tIKr2em4hl78nIiK7x4BSAzRqFT4Y1lF0/e8ny18PhoiIyF4woNSQ/n5eeDqosajaX+Kvck0UIiKyawwoNahHS3GLtuUVlvI0DxER2TUGlBrk6eIkunYXT/MQEZEdY0CpQV193eBWR9xk2fXHrvA0DxER2S0GlBqkUasw/0k/UbX5RXp8tudc1YVEREQ2iAGlhoX5e+OxtuLuz/NVzHkeRSEiIrvEgGIGY3u1EFVXUKzHZ3vOKtwNERGR5WFAMYOuvm5wdRI3F+XT3Wd5FIWIiOwOA4oZaNQqvNyjmahagwA8uzy26kIiIiIbwoBiJhMfa4VaWnH//PGXc3C3WK9wR0RERJaDAcVMNGoV+rRtJLr+8U/2KtcMERGRhWFAMaMXujUVXXs1pxBRSekKdkNERGQ5GFDMqFtzdzhoVKLrp/6cxAmzRERkFxhQzEijVuHj4f6i60v0Apbt5mXHRERk+xhQzGxI0ENwddKKrv/8j3M8ikJERDaPAcUCLBsRJLq2xCAg9uxNBbshIiIyPwYUC9C9VQOIvOIYADB3a4pyzRAREVkABhQLoFGrMLFPK9H1527eQXGpQcGOiIiIzIsBxUJM6tsKEi7owdrYi4r1QkREZG4MKBZCo1ZhwqPibiIIAN8fvqhcM0RERGbGgGJB3ny8DVQij6JcunUX0cnXlW2IiIjITBhQLIhGrcIT7TxE17+5PpGXHBMRkU1iQLEwo7s3E11bohcw+ccE5ZohIiIyEwYUC9OtuTvqOopfuG3biQxe0UNERDaHAcXCaNQqLJKw/D0AfBd3UZlmiIiIzIQBxQKF+XthYEdP0fUxf95QsBsiIqKax4BioZaODBK9LsrRi9mcLEtERDaFAcVCadQqvNCtiajaOyUGHEnLVrgjIiKimsOAYsH6+3mLrp2/LVXBToiIiGoWA4oF6+rrBrc6DqJqU9NvY8iy/Qp3REREVDMYUCyYRq3C/Cf9RNcnX8vD/K08kkJERNaPAcXChfl7o2/bhqLrvz54keuiEBGR1WNAsQKv9hJ/E0EAiNiYrFAnRERENYMBxQp09XVDLQfxH9WmxGu87JiIiKya5IASExODwYMHw9vbGyqVCps3by7zvCAImD17Nry8vODk5ITQ0FCcPXu2TE12djZGjRoFZ2dnuLq6Ijw8HPn5+dV6I7ZMo1ZhXC9f0fUGAVi6+2zVhURERBZKckApKChAp06d8Pnnn5f7/KJFi7B06VIsX74chw8fRp06ddCvXz8UFhYaa0aNGoXU1FTs3LkTUVFRiImJwbhx40x/F3bgzdA2ELluGwDgiz/O8SgKERFZLZUgCCb/FVOpVNi0aROGDh0K4O+jJ97e3pg+fTr+9a9/AQByc3Ph4eGBNWvWYMSIETh16hTat2+Po0ePonPnzgCA7du3IywsDFevXoW3d9Vrf+Tl5cHFxQW5ublwdnY2tX2rs3jnn/hUwpGR717pil6txU+wJSIiUpKUv9+yzkFJS0tDRkYGQkNDjdtcXFwQHByMuLg4AEBcXBxcXV2N4QQAQkNDoVarcfjw4XL3W1RUhLy8vDIPezS5bys4asUfR5n4Y4KC3RARESlH1oCSkZEBAPDw8Ciz3cPDw/hcRkYGGjVqVOZ5rVYLNzc3Y839IiMj4eLiYnz4+PjI2bbV0KhVWPxsgOj63LulyC8sVa4hIiIihVjFVTwRERHIzc01Pq5cuWLulswmzN8bHnV1ouunrk9UsBsiIiJlyBpQPD09AQCZmZlltmdmZhqf8/T0RFZWVpnnS0tLkZ2dbay5n6OjI5ydncs87Fl4z+aia2PO3lCwEyIiImXIGlB8fX3h6emJ3bt3G7fl5eXh8OHDCAkJAQCEhIQgJycH8fHxxpo9e/bAYDAgODhYznZs1ks9xV9yXFQqYF7USQW7ISIikp/kgJKfn4+kpCQkJSUB+HtibFJSEi5fvgyVSoUpU6Zg/vz52LJlC06cOIHRo0fD29vbeKVPu3bt0L9/f4wdOxZHjhzBwYMHMXHiRIwYMULUFTwE6LRqjOneRHT9qgNpXP6eiIisiuTLjPfu3Ys+ffo8sH3MmDFYs2YNBEHAnDlz8NVXXyEnJwc9e/bEF198gdatWxtrs7OzMXHiRGzduhVqtRrDhw/H0qVLUbduXVE92OtlxvdrO+s3FIoMHsHN6mP9+O4Kd0RERFQxKX+/q7UOirkwoPxt1f4LmLftlOj6P+cPgE5rFfOiiYjIBpltHRSqWS+GNJNUH7HxuDKNEBERyYwBxYrptGoEN3MRXR91/DqXvyciIqvAgGLlvntV/LySIr2AI2nZCnZDREQkDwYUK6fTqhHm51F14f/M+IWneYiIyPIxoNiAZc8/DI3IT/JK9l28uvaosg0RERFVEwOKDdCoVVg2Ikh0/a5TWbhbrFewIyIiouphQLERYf5eCPIRP2H2te+OKdgNERFR9TCg2JDuLRuKro09f5NX9BARkcViQLEhIS3cRdeWGoBD528p2A0REZHpGFBsSLfm7nB1chBdP3VDooLdEBERmY4BxYZo1Cp8MLyj6Pqs28W80zEREVkkBhQb09/PC1P6thJdzzsdExGRJWJAsUGT+raCg0Ylun4mF28jIiILw4BigzRqFZ7s5C26fnNSOq/oISIii8KAYqMWDvMXXWsQgEMXeEUPERFZDgYUG6XTqtGigZPo+o92nFGwGyIiImkYUGzYvweLv6In8UoOopOvK9gNERGReAwoNqx7qwZw1IqfLDvjl2TORSEiIovAgGLDNGoVFj8bILo+v6iUc1GIiMgiMKDYuDB/bwzy9xJdP2vTCQW7ISIiEocBxQ58OiJQ9KmetFt38OraIwp3REREVDkGFDugUasw/pEWout3nbqBqKRrCnZERERUOQYUOzG5b2votOI/7qkbkjhhloiIzIYBxU5o1CpMeFT8UZQSAxB79qaCHREREVWMAcWOTHysFRzU4i87nhuVqmA3REREFWNAsSMatQqvSziKcu5GAe90TEREZsGAYmfeDG0NCVNRMOabQ8o1Q0REVAEGFDujUauw+LlA0fVxF/5CdHK6gh0RERE9iAHFDg3u5I3WjeqKrucS+EREVNMYUOxU1OReomvzi/Q4dJ5L4BMRUc1hQLFTOq1a0hL4U9YnKNgNERFRWQwoduzTEYHQiLzq+EZ+CebxsmMiIqohDCh2TKNW4fH2HqLrVx24yMuOiYioRjCg2LkXuzWTVB/xy3FlGiEiIvoHBhQ7162FO2o5iP822JiYzit6iIhIcbIHFL1ej/feew++vr5wcnJCixYtMG/ePAjC//9REwQBs2fPhpeXF5ycnBAaGoqzZ8/K3QqJoFGr8J+nO4muFwA8uyJWuYaIiIigQED58MMP8eWXX+Kzzz7DqVOn8OGHH2LRokVYtmyZsWbRokVYunQpli9fjsOHD6NOnTro168fCgsL5W6HRBjcyRvN3J1E18dfysHdYr2CHRERkb2TPaDExsbiySefxMCBA9GsWTM8/fTTeOKJJ3DkyBEAfx89WbJkCWbNmoUnn3wS/v7++Pbbb5Geno7NmzfL3Q6JtOApf0n1oR/vVaYRIiIiKBBQunfvjt27d+PPP/8EABw/fhwHDhzAgAEDAABpaWnIyMhAaGiocYyLiwuCg4MRFxdX7j6LioqQl5dX5kHy6tbcHa61HUTXX8stRFTSNQU7IiIieyZ7QJk5cyZGjBiBtm3bwsHBAYGBgZgyZQpGjRoFAMjIyAAAeHiUvbzVw8PD+Nz9IiMj4eLiYnz4+PjI3bbd06hV+GBYR0ljJv2UxAmzRESkCNkDyoYNG/DDDz9g3bp1SEhIwNq1a/HRRx9h7dq1Ju8zIiICubm5xseVK1dk7Jju6e/nhacCxK8uKwCIOZ2lXENERGS3ZA8ob731lvEoSseOHfHiiy9i6tSpiIyMBAB4enoCADIzM8uMy8zMND53P0dHRzg7O5d5kDI+fDpAUv2k9YnKNEJERHZN9oBy584dqNVld6vRaGAw/L0Cqa+vLzw9PbF7927j83l5eTh8+DBCQkLkbock0mnVCPMTv7psfpEeC7adVLAjIiKyR7IHlMGDB2PBggXYtm0bLl68iE2bNuGTTz7BU089BQBQqVSYMmUK5s+fjy1btuDEiRMYPXo0vL29MXToULnbIRMse/5hSfVf70/jEvhERCQrrdw7XLZsGd577z288cYbyMrKgre3N1577TXMnj3bWDNjxgwUFBRg3LhxyMnJQc+ePbF9+3bUqlVL7nbIBBq1Cm880hxf7Lsgql4AsDb2Isb2bq5sY0REZDdUwj+XeLUSeXl5cHFxQW5uLuejKERvENDynWiI/eZoWFeHo7MeV7QnIiKyblL+fvNePFQujVqFz58PFF1/I7+Yq8sSEZFsGFCoQmH+3hjoV/6VVeUZ9sVBBbshIiJ7woBClVr6fBBUImtPZdzGgm2pivZDRET2gQGFKqVRq9CrVQPR9Sv3X0R08nUFOyIiInvAgEJVWvFiZ0n1szaf4BL4RERULQwoVCUnnQadm7qKrs++U4IjadnKNURERDaPAYVEWf9ad2gkfLes3H9euWaIiMjmMaCQKBq1CpP6tBJdv/fMDa4uS0REJmNAIdEm9W2FOjqNqFqD8PfqskRERKZgQCHRNGoVPn62k+j6D347he0pvKKHiIikY0AhSfr7eeHpoMaiavUCMP77BIYUIiKSjAGFJFs4zB9qsau3AZi6PomXHRMRkSQMKCSZTqvG2F6+ouvvlhgQe/amgh0REZGtYUAhk0SEtUd7L/F3kl72x1kFuyEiIlvDgEImGy5yLgoAHLn4F+eiEBGRaAwoZLIXQ5qJvpEgAEzfcJxzUYiISBQGFDKZTqtGeI9mousLivWYtC5euYaIiMhmMKBQtcwa3AH+D4mfixKdkonI6JMKdkRERLaAAYWqbcvEXmhS30l0/YqYNC6DT0RElWJAIVm8GNJUUv3MX44r1AkREdkCBhSSxZjuvpImzG5KTOeEWSIiqhADCslCp1VjXG/xi7cJAD7d9adyDRERkVVjQCHZRIS1R7BvfdH1X+w9x6MoRERULgYUktV34d1E15YagMcX71WuGSIisloMKCQrnVaNbr5uousv3LiDV9ceUbAjIiKyRgwoJLtvw4Ml1e86dQN3i/UKdUNERNaIAYVk9/fdjptJGjM/KlWZZoiIyCoxoJAi3h3YAQ83dRVdvzHxmnLNEBGR1WFAIcVseK071CIXR7lbYuBcFCIiMysuNWBlzAWM+/YYpvyUiP1nbpjtakutWV6V7IJGrcKQTt7YnJQuqn7XqRuISkrHoABvhTsjIqL7RUafxFcxafhnHNmclI7aOg0+ebYT+vt51Wg/PIJCilr0dCdJ9ZPXJ3JtFCKiGhYZfRIr7gsn99wp1mP89wnYnnK9RntiQCFF6bRqDPb3FF1vEIBJ6xIU7IiIiP6puNSAr2LSqqyb82tKjf4PJAMKKW7JiCDoNOK/1aJTMni3YyKiGjLzl+PlHjm5X+btYhxJy1a8n3sYUEhxGrUKHz8r7VRP1/k7FeqGiIju2Z5yHRsTxc0TBICs24UKdlMWAwrViMGdvNG6UR3R9TmFpdiUwEuPiYiUojcImLnxhKQxjerVUqibBzGgUI2JmtxbUv3UDUmcMEtEpJBDF24h506J6Pq6OjW6SriVSXUpElCuXbuGF154Ae7u7nByckLHjh1x7Ngx4/OCIGD27Nnw8vKCk5MTQkNDcfbsWSVaIQui06oR3MxV0pjQj/cq0gsRkb2buj5RUn3k8E7QiF3cSgayB5S//voLPXr0gIODA3777TecPHkSH3/8MerXr2+sWbRoEZYuXYrly5fj8OHDqFOnDvr164fCwpo7t0Xm8d2rIZLq027dQX5hqULdEBHZp18TryHrdrHo+sfbN8LgTjW7RpVKEARZj6HPnDkTBw8exP79+8t9XhAEeHt7Y/r06fjXv/4FAMjNzYWHhwfWrFmDESNGVPkaeXl5cHFxQW5uLpydneVsn2rA+O+OYXtqpuj6BnUdcGzWEwp2RERkP/QGAW1mRUPsxZKdGtfDr5OknaKviJS/37IfQdmyZQs6d+6MZ555Bo0aNUJgYCBWrlxpfD4tLQ0ZGRkIDQ01bnNxcUFwcDDi4uLK3WdRURHy8vLKPMh6fT7qYUn1N/NLsPW4+FnmRERUsWW7z4oOJwAwo3975ZqphOwB5cKFC/jyyy/RqlUr7NixA6+//jomT56MtWvXAgAyMjIAAB4eHmXGeXh4GJ+7X2RkJFxcXIwPHx8fudumGqRRq/DZiABJY2b89zgnzBIRVZPeIOCzP8TP+XTQqNCthbuCHVVM9oBiMBgQFBSEhQsXIjAwEOPGjcPYsWOxfPlyk/cZERGB3Nxc4+PKlSsydkzmMCigMfq0Fj8b/G6JAYfO31KwIyIi2/fcilhJR08+Hu5foxNj/0n2gOLl5YX27cseDmrXrh0uX74MAPD0/HvZ88zMsnMQMjMzjc/dz9HREc7OzmUeZP1WvxKC2jqN6Pr9Z28o2A0RkW3bknANxy7liK73bVAbQ4IeUq6hKsgeUHr06IEzZ86U2fbnn3+iadOmAABfX194enpi9+7dxufz8vJw+PBhhIRIu8KDrF/SbPGTX5fHXMCra48q2A0RkW2KTr6OyRuSRNerVcCuaY8q1o+oHuTe4dSpU3Ho0CEsXLgQ586dw7p16/DVV19hwoQJAACVSoUpU6Zg/vz52LJlC06cOIHRo0fD29sbQ4cOlbsdsnA6rRoDO3pUXfg/u05lYchn5V8hRkRED9qech1vSLwJ62cjA812auce2QNKly5dsGnTJvz444/w8/PDvHnzsGTJEowaNcpYM2PGDEyaNAnjxo1Dly5dkJ+fj+3bt6NWrZpbQpcsx9KRD8NRK/5bMflqHrZwGXwioirpDQKmrk+SNKZbczeE+dfsmiflkX0dlJrAdVBsz/aU6xj/vbSEf35hmNkTPhGRJVuy808s2S1tpfY/5w+ATsL/NEph1nVQiEzR388Ly18IgpS88cyXsco1RERk5fQGAZ/vPSdpTDff+oqFE6ksowsi/B1SWjQUf8fjhCs5uFusV7AjIiLrNeWnBJTopZ0k+Ta8m0LdSMeAQhZleKC0S9p4M0EiogcVlxqwNbn8xU8rEt6zqcUcPQEYUMjCvNKruaT6a7mFeHXtEYW6ISKyTmtj0yTV+zd2xnuD/BTqxjQMKGRRdFo1Xu3hK2nMrlM3EJXEq3qIiO6JSr4uuvaxNg2xZVIvBbsxDQMKWZxZg9vD191J0ph//ZLMe/UQEQHYejwdx6/miqpt0cAJ37zcVeGOTMOAQhZp1/Q+kuoLSwz4bI+02epERLZmXlQqJv2YKLr+30M6KthN9TCgkEXSqFWY0reVpDHfHLjAoyhEZLfGfnsUqw5cFF1fx1GD7i0bKNdQNTGgkMWa1LcVnBzEf4vmFpbisz3SFiQiIrIFUUnp2HkyS9KYj5/pZNGLXTKgkMXSqFVY/FyApDGLd53F9hTxk8OIiKyd3iAgYvMJSWOmhrZCfz8vhTqSBwMKWbT+fl54WuLtvif8kMBTPURkN46kZeN2YanoelcnB0x8TNopdHNgQCGLt3BYR0lL4OsFIGDuduUaIiKyIF/vPy+p/uUevhZ9auceBhSyeDqtGmN7SVsb5XaRAYOX7VeoIyIiyzB/60nsPn1DdL2rkxYTH2upYEfyYUAhqxAR1h6v9ZYWUk5cy0O+hMOeRETWZF5UKr4+KG3F2A+G+1vF0ROAAYWsSERYe8zs30bSmJFf8Y7HRGR7IqNPSrqk2FGrxvIXgix+Yuw/MaCQVXmlp7R79ZxIv43I6JMKdUNEVPOKSw1YESP+yIn/Q844+X5/qwonAAMKWRmdVo3wntJO9ayISUO0hPtSEBFZskFLpc2ve7JTY6s5rfNPDChkdd4b1B7NJN6rZ9rPSbz0mIis3tbj6fgzK190vVoFvBjSTLmGFMSAQlZptwn36ok9d1OhboiIlKc3CJi2Xvx9dgBgbC9f6LTW+afeOrsmu6dRq7D8hSBJY+b8Km2lRSIiS/LMlwdRYhBfH+bniYiw9so1pDAGFLJa/f288MXzgaLrL9y6i0cW7VGwIyIiZby69igSruSKrteqgGXPS/ufOEvDgEJWLczfG8MCvUXXX8q+i0FLYxTsiIhIXlFJ6dh1StqNAJc8F2CVE2P/iQGFrN4HwztJqk9Jv405v6Yo1A0RkXz0BgH/+u9xSWOCmrhgUEBjhTqqOQwoZPV0WjUG+3tKGrM27hLGfntUoY6IiOTx2Z5zKCwVP/HEQa3Cz+N7KNhRzWFAIZuwZEQQakmcqb7zZBaikq4p1BERUfXoDQIW7/pT0phPRwRa/amdexhQyCZo1Cp88qy0Uz0A8K//Huf6KERkkUIW7pJUH+bXCGH+1rVabGUYUMhmhPl7Y0z3JpLGFJYKOHT+lkIdERGZJnzNUWTlF4uud9Sqsez5zgp2VPMYUMimzB3SEU3dpK0ye+C8+FuVExEp7W6xHrtPS7tq59MR1n/Vzv0YUMjm7JvxGJpJCClf7r2AeVG8qoeILEP3D6Sd2hke1NjqbgQoBgMK2aTd/+oDnYTv7lUHLmHIZ9JuwEVEJLdBS/fjrzulksZEDvNXqBvzYkAhm6RRq7BkhLRVFJOv5mFe1EmFOiIiqtzgpTFISc+TNObVHtZ7r52q2Oa7IgIQ5u+F13r7Shqz6kAaiiWsOUBEJId5USk4kX5b0hg/r7qYNdh677VTFQYUsmkRYe3xxfOBkr7RBy3jUvhEVHOKSw1YdeCSpDGuTlpEvfmIQh1ZBgYUsnlh/t5o6l5bdP2fmQVcwI2IakzA3B2Sxxx593EFOrEsDChkF7o3d5dUP/GnJC7gRkSKG7x0P+6USDutHN6zmc3OO/kn23+HRABmDe4geYzUVRyJiKTYknANJyROivV1d8J7g6T/PrNGigeUDz74ACqVClOmTDFuKywsxIQJE+Du7o66deti+PDhyMzMVLoVsmNOOg0eb99I0pis/GJsPHpFoY6IyJ7pDQImb0iSNEYFYNf0Por0Y4kUDShHjx7FihUr4O9f9hrtqVOnYuvWrfj555+xb98+pKenY9iwYUq2QoSVo7ugb9uGksZM+yUZ0cnXFeqIiOxV34/3Sh6zzAZXi62MYgElPz8fo0aNwsqVK1G/fn3j9tzcXKxatQqffPIJHnvsMTz88MNYvXo1YmNjcejQIaXaIQIArHqpK7o0dZE05o11CdiewpBCRPLYlHAVF2/dkTSmT9sGGBTQWKGOLJNiAWXChAkYOHAgQkNDy2yPj49HSUlJme1t27ZFkyZNEBcXV+6+ioqKkJeXV+ZBZKofxnaXPGb6z7zrMRFVX3RyOqZuOC5pTIM6Wqx+KVihjiyXIgHlp59+QkJCAiIjIx94LiMjAzqdDq6urmW2e3h4ICMjo9z9RUZGwsXFxfjw8fFRom2yEzqtWvJdjwuK9Hhm+UGFOiIie7A95TreWJcoedzhd59QoBvLJ3tAuXLlCt5880388MMPqFWrliz7jIiIQG5urvFx5QonLlL1zB3SEc61tJLGJFzOxftbUxXqiIhsmd4gYOIPCZLH2eJdisWSPaDEx8cjKysLQUFB0Gq10Gq12LdvH5YuXQqtVgsPDw8UFxcjJyenzLjMzEx4enqWu09HR0c4OzuXeRBVV+LsJyD15/6bgxcxL4ohhYik6bZwJ0olniX2866HJ+1s3sk/yR5Q+vbtixMnTiApKcn46Ny5M0aNGmX8bwcHB+zevds45syZM7h8+TJCQkLkboeoQhq1Cl+MknZDQQBYdeAiFmxjSCEicXov2o0b+SWSxrjX0SJqcm+FOrIO0o5xi1CvXj34+fmV2VanTh24u7sbt4eHh2PatGlwc3ODs7MzJk2ahJCQEHTr1k3udogq1d/PC8tfCMKUn5JQKOEmgSv3X0SgjxvC/L0U7I6IrN2mhGu4nF0oaUwz99rY+5b9rHdSEbOsJLt48WIMGjQIw4cPR+/eveHp6YmNGzeaoxUi9PfzQuJs6ZPQJq5L4JU9RFQhvUHAW/+VdsWOo1aF3dMfVaYhK6MSBMHqfsPm5eXBxcUFubm5nI9CslmwLRUr91+UNCbMzwNfvNBZmYaIyKrtP3MDL64+ImnMspGBGNzJW6GOzE/K32/ei4fof94d2AEPN5G2iFt0SiaKJZwaIiL7sD3lOsb/EC9pTGi7hjYdTqRiQCH6hw3je0Aj8cqeAUv2KtILEVmnrcfTMf77BBQU60WPebRVA3w9pquCXVkfBhSif9CoVVj8TICkMedv3sXoVbHKNEREVmXBtlRM+lHaYmwN6zhgTbj9rRRbFQYUovsMCWqMjt71JI2JOfsXuszfqVBHRGQNIqNPSp7HplEBh959XJmGrBwDClE5tk7ujYZ1dZLG3MgvxkvfHFaoIyKyZMWlBqyISZM87vNRQXa7UmxVGFCIKnDonVBI/bWx98+bmLclRZF+iMhyPfz+dkn1dR01WP5CEPr7cS2lijCgEFVAo1ZhyYgAyeNWxV5CZPRJ+RsiIovUdtZvuF0sbcWOL59/mOGkCgwoRJV4MqAx/B+SvtbOyv1pvPyYyA48PO93SatQA4BrbQd0b9VAoY5sBwMKURW2TOyFR9tI+2ViEIC3f5G2giQRWZd3Nx/HrQJp99gBgA+GdeS8ExEYUIhEWPNyMHq3qi9pzKbEdLyy+pBCHRGROc2LOokfDl2VPO6L5znvRCwGFCKRvg3vLvnKnj1nbqH3oj0KdURE5hAZfRKrDki/YuezEQG8wagEDChEEhyd9TgebS3tdM/l7LsYvGy/Qh0RUU0y9XLiMd2bYFBAYwU6sl0MKEQSrXklGOHdm0oac+JaHrYkSD8cTESWpeuC3yWPaeyiw9whHRXoxrYxoBCZ4L0hfhgk8VDt9F+O88oeIis2e/MJ5NwVf38dAKilVeNgBFeKNQUDCpGJPh0RiDo68T9CJXqg/Xu/ISopXcGuiEgJ86JS8e2hy5LG1HZQ4/T8AQp1ZPsYUIhMpFGr8OEwf0ljSgVg4k+JGPvtUYW6IiK5LdiWilUHLkoelzSnn/zN2BEGFKJqGBTQGI+1cZc8bufJLMyLSlWgIyKS069J1yTfABAAxvZqBp2Wf2Krg/96RNX0zcvd0MTNSfK4VQcuYutxnu4hslQLtp3Emz8lSR7Xp20DvDuwg/wN2RkGFCIZxMx4DB0bS18Sf9KPidiecl2BjoioOhZsS8XK/dIvJ/bzdsbql4IV6Mj+MKAQyWTrpF6Y0Ke55HGvf58AvUHajcaISDnRyddNOq3TxK0Woib3kr8hO8WAQiSjaY+3hZODtB8rAUA3E9ZWICL56Q0C3v5vouRxnZu4ImZGXwU6sl8MKEQy0qhV+PApaVf2AMCNglIMWLJX/oaISJKJP8TjdrH0I5rrxoUo0I19Y0AhktmQoMbwf0j6fJRTGQXo9eFuBToiIjF6RO7Eb6mZkseF9/TlFTsK4L8okQK2TOwl+Z49AHDlr0L0ZkghqnFt3t2Ga7nFksf5P+SM9wa1V6AjYkAhUsiaV4LR3rOO5HGX/yrEe7+eUKAjIipP85nbUCRtBXsAwKhuD2HLRE6KVQoDCpGCoqc8KnnSLAB8F3cZ87eeVKAjIvqnFhHbYModsjo2dsaCoZ1k74f+HwMKkcJS5vY3adzXB9OwYBtDCpFSBizeC70JV/j7P+SMrZN45ERpDChECtOoVVj+QpBJY1fuT8Oviddk7oiIxqw6hFOZBZLHJcx6nKd1aggDClEN6O/nheUvBEGjkj72zfVJWLCN9+0hkssji/Zg39lbkseFd28Kt7o6BTqi8jCgENWQ/n5e+HNBGBqa8Atu5f6LDClEMnjx61hcyr4reVxjFx3eG+KnQEdUEQYUohqkUatwdNbjJocU3lyQyHQ9P9iF/ef+kjzOUQMcjHhcgY6oMgwoRGZw6J1Qk8bx5oJE0ukNAtq99xuu5hRJHqsGcGbBQPmboioxoBCZgUatwtJnTbtE8V8/J6O41JQLI4nsz/aU62j1bjTulkj/mdGogAsfMJyYCwMKkZkMCXoIHRtLXxI/v6gUHef8huhknu4hqkx08nWM/z4BptwsvJ1HHZyPZDgxJwYUIjPaOqkXmrg5SR5XpAfeWJeIyGiuk0JUnl+OXcEb6xJMGvtIK3f8NvVRWfsh6RhQiMwsZsZj6GPCfXsAYEUM10khut8ji/Zg+n+TTRrb1M0Ja8O7ydwRmUL2gBIZGYkuXbqgXr16aNSoEYYOHYozZ86UqSksLMSECRPg7u6OunXrYvjw4cjMlH4HSSJbsfqVYIT3bGrS2DfXJ+H9rbwEmQj4O5yYchkxAPRqWR/7Zjwmc0dkKtkDyr59+zBhwgQcOnQIO3fuRElJCZ544gkUFPz/in1Tp07F1q1b8fPPP2Pfvn1IT0/HsGHD5G6FyKq8N8gPXzwfhFom3Lvnm4MXMWjpPgW6IrIem+KvmhxOHnJ1xHevdpe5I6oOlSAIJkwfEu/GjRto1KgR9u3bh969eyM3NxcNGzbEunXr8PTTTwMATp8+jXbt2iEuLg7dulV9aC0vLw8uLi7Izc2Fs7P0SYZElqy41ICgeb8j34Tbqzaqq0PcO6HQqE1YspbIit0t1qPd7O0mja3rqDH5nlkkjZS/34rPQcnNzQUAuLm5AQDi4+NRUlKC0ND/Xweibdu2aNKkCeLi4srdR1FREfLy8so8iGyVTqvGR8+YdglyVn4x2s3ezrVSyK5ERp80OZw0dnZgOLFQigYUg8GAKVOmoEePHvDz+3uJ4IyMDOh0Ori6upap9fDwQEZGRrn7iYyMhIuLi/Hh4+OjZNtEZtffzwufjQgwaWxxqQHjv09AVBInz5LtmxeVihUxaSaNre+kxcF3npC5I5KLogFlwoQJSElJwU8//VSt/URERCA3N9f4uHLlikwdElmuQQGNEd6zmcnjJ/6UhDlbUuRriMjCzP01BasOXDRprHttByTO6SdvQyQrrVI7njhxIqKiohATE4OHHnrIuN3T0xPFxcXIyckpcxQlMzMTnp6e5e7L0dERjo6OSrVKZLHeG9QBl27dwa5TWSaNXxt7CbtPZuLAzL4yd0ZkPnqDgNCP9yLt1h2Txnfwqottbz4ic1ckN9mPoAiCgIkTJ2LTpk3Ys2cPfH19yzz/8MMPw8HBAbt37zZuO3PmDC5fvoyQkBC52yGyel+P6YJXezQzefzVnEK0fGebfA0RmdH2lOto+U60yeGkR8v6DCdWQvareN544w2sW7cOv/76K9q0aWPc7uLiAienv1fMfP311xEdHY01a9bA2dkZkyZNAgDExsaKeg1exUP2KDo5HRPWJaI6P7AXeV8RsmLRyddNXh0W4NU6lkDK32/ZA4pKVf7ljatXr8ZLL70E4O+F2qZPn44ff/wRRUVF6NevH7744osKT/HcjwGF7JXeICBk4S5k5RebvA+GFLJGWxKuYvKG4yaPr+eowQmGE7Mza0CpCQwoZO/C1xzF7tOmzUtx0qqQ8v4ArpVCVuFusR6hH+/BtVzTQ/mLXX0wb5i/jF2RqRhQiOxAdn4xgubvNGmsCsDSkYEY3Mlb3qaIZPTq2qMmTxC/Z2yvZnh3YAeZOqLqsqiF2ohIGW51dXi8fSOTxgoAJv2YiMc/2YviUoO8jRHJYMhn+6sVTnQaFb54PojhxIoxoBBZsZWjuyC0nWkhBQDOZhWg9azfsGDbSRm7Iqqe9UcuI/mq6SuG+7o74dS8AQjz95KxK6ppDChEVu7rMV2w9NmAau1j5f40vLLmiDwNEVXDwE/34u2NJ0we38GrDv546zHOsbIBDChENmBIUGMsfyGoWvvYc/oG74hMZnO3WI9W72xD6vUCk/fh19gZ2958VL6myKwYUIhsRH8/L5xfGFatfaSk5yN4/u+cl0I16pXVR9Bu9naUVOPbrk8rd0RN6iVfU2R2DChENkSjVuHiBwNRV2f6j3Zmfglaz/oN87emytgZ0YP0BgEd52zHnjM3qrWfjo3rYXV4N5m6IkvBgEJkg1LeH4D2XvWqtY+vD15EyMJdPJpCiohOTkeLd6Jxu0hfrf2EtmuErZN6y9QVWRKug0Jkw+ZuTcHqg5eqvZ+xvXzx7sD2MnREBCzYdhIr96dVax/1nbSIjQiFk04jU1dUE7gOChEBAOYM9sMXzwfBQVO9KxpW7k/DsM8PQG+wuv+fIQuiNwh447tj1Q4nfds2ROKcfgwnNo5HUIjsgN4gYOK6ePyWklmt/ahVwOLnAvBkQGOZOiN7EZWUjsnrE1GdjKsGsHREIAYFcAVka8Wl7omoXHIcWgeA5g1rY+fUR7nWBFVJbxDwzPJYJFzOqdZ+ajuocWJuf37PWTme4iGicr07sD2+eD6w2vu5cOMO2syKRnTydRm6Ilv1y7GraPlOdLXDyaOtG+LkPN7g0t7wCAqRHdIbBDz95UEkXsmt9r4a1nHArul94FLbQYbOyBboDQIC3/8deYWl1d7X0mc7YUjQQzJ0RZaAR1CIqFIatQqbJvTE2F6+1d7XjYISdHr/d/RetFuGzsia6Q0C/vPbKbR4J7ra4UQFYPkLQQwndoxHUIjsXHGpAb0X7UZGXnG19+VcS4vE2U/wULydKS41IGJjMn5JuCbL/gZ08MBnox7m95EN4iRZIpJsXlQqVh24KMu+erV0x1eju/AyUDsg18RrAAhuVh/fvdoNOi0P7tsqBhQiMklxqQH9Fu9F2q27suzP29kRu//Vh0HFBukNAp5dEYv4Szmy7K9v2wZY9VKwLPsiy8U5KERkEp1WjT/eegxN6teSZX/peUVoN3s7wtcckWV/ZH56g4BPfj+DNrOiZQsnoe0aMZzQA3gEhYjKNXjZfpy4lifb/mppVTg26wnUraWVbZ9Uc/6eZ3IcvySky7ZPrVqFxc8FYHAnLrxmL3iKh4hk8WviNczcmIy7JfLdMJD3ULEueoOAST/EIzq1eqsQ329ynxZ48/E2nAhrZxhQiEg2eoOAI2nZmLAuHtkFJbLtN9DHBf99vQf/QFmw6OTrmPhjQrWWp7+fk4MaKVwR1m4xoBCRIrrM34kb+dW/HPmfHnKthXlP+qF3m0b8o2UBiksNWBubhu8OXcLlbHkmS98zulsTvD+0o6z7JOvCgEJEipmz5QTWxl6Wfb9qFfDZyECE+XM+gjkUlxow+uvDOHQxW/Z9ezk7Yt+Mx3j5MDGgEJGyiksNGPRpDP68USD7vkOa18faV7gWRk2aF5WCVQcuyb5fBzWQOLsfJ0aTEQMKEdWIrcfTMX1DEor18v8aqa1T48lOjTF7cAdOqFXA3WI95mw5gf/GX5N1jsk9Td2csG/GY/LvmKwaAwoR1Ri9QUDsuZv414YkZMo8P+Ue9zoO2PfWY/w/8WrKLyzF1PWJOHjuJu7IeGXWPzVvUBub3ujJm0dSuRhQiMgsopKuYfJPSVDmTx/gqFWjd6sGWPxcIMOKSHqDgEPnb2HKTwm4IeNVWPdz1Krx6YgA9PfzUuw1yPoxoBCR2egNAj7d9SeW7jmn+Gu1bOCEDeN7wq2uTvHXsiZ3i/VYsO0k9p3JwpWcQkVfS60CJvZpiTdDW/MqLKoSAwoRmZ3eIGDYFwdw/Kp8q9FWRA3g+eAmeHdge7udr6I3CDh04Rbe3XQCF2/dqZHXHNjRA0tH8q7DJB4DChFZjLvFevT9aA/S85SZn3K/eo4ajApuil6tGqJbC3eb/+N574jVl/vOo0SBycrleSrACx8+HcArrUgyBhQisjh3i/V47btjiDl7s0Zf17OeDl2bu+OZIB90b9XAqgNLcakBaw6m4ffUDOTeLUFhiR7XcgsVuQqnPEFNXPDzeK7+S6ZjQCEii6U3CNh35gbmbDmBK38pOz+iPB71HPBwUzeUGgR0aeaOMd2bWeSRgNw7JXh5zRFcvJkPAYBGpcJNBSe5VkQNoEdLd3w1uovdnj4j+TCgEJFV0BsEPLF4L87fqJk5ExWpq1Ohtk4LqABXJ0cMC2qMV3o2r5HgojcIiDmdhY92nsGVv+6gtNSAO6Xm/7Uc8JAL3urX1i5Ok1HNYUAhIqui1Eqm1aUF4OighgEC6ui0eKR1A5SUCkjPK4RnPUdcybmLtBsFKCzVQwPASaeBW51aaFBXh2s5d5CZVwSVSgWtWoWiUgMMAqDTADqtGkX/CyFFNTRvRCwvF0fse4vL0pMyGFCIyOoUlxrwzf4L+HT3WdwtVWolFapIXZ0ah955nOvLkKKk/P02a0T+/PPP0axZM9SqVQvBwcE4cuSIOdshIjPSadUY36clTs0fgFPv98eznRujPlcjVZSXsw6Pt2uElH/3Q8r7AxhOyKKY7btx/fr1mDZtGpYvX47g4GAsWbIE/fr1w5kzZ9CoUSNztUVEFsBJp8GipwOMX8/dkorVsRfN1o+tGdjRE0tHBnFuCVk0s53iCQ4ORpcuXfDZZ58BAAwGA3x8fDBp0iTMnDmzTG1RURGKioqMX+fl5cHHx4eneIjsSHGpAd8cuICPfz8DhW4jY9NaNKiNZ7v44OUeNTP5l6g8Uk7xmOUISnFxMeLj4xEREWHcplarERoairi4uAfqIyMjMXfu3JpskYgsjE6rxvhHW2L8oy2RX1iKKesTcep6LjLzisApKw9qVFeHkBbueNoG1n8h+2SWgHLz5k3o9Xp4eHiU2e7h4YHTp08/UB8REYFp06YZv753BIWI7FPdWlp8PaaL8eu7xXrM35aKLUnpuF2kN2Nn5tXUzQkvdGuKMd19eZSErJ5VzIhydHSEo6OjudsgIgvlpNNgwVP+WPCUP+4W6zEvKhWx524iu6AYeTYYWFQAPJ11cKujg5dLbQQ3t9wF54hMZZaA0qBBA2g0GmRmZpbZnpmZCU9PT3O0REQ2wkmnwcJh/sav9QYBf5zKwvzok7iZX4R8Kw0sKgCdm9XH5D6teMqG7IJZAopOp8PDDz+M3bt3Y+jQoQD+niS7e/duTJw40RwtEZGN0qhVCO3ggdAOf59S1hsExJ69iV8SruJydgGy7xTjVn4RbhdZxkQWBzVQW6dGXUcHNHGvgwCf+ujZsgFXdCW7Y7ZTPNOmTcOYMWPQuXNndO3aFUuWLEFBQQFefvllc7VERHZAo1ahV5uG6NWmYZnt9xaK+yXxKq7n3EWJ3gCdBjAIyq0kq1Wr0KpRHYwMboom7nXR1deNIYTof8wWUJ577jncuHEDs2fPRkZGBgICArB9+/YHJs4SEdWEewvFje/T0tytEBG41D0RERHVEKtZ6p6IiIioPAwoREREZHEYUIiIiMjiMKAQERGRxWFAISIiIovDgEJEREQWhwGFiIiILA4DChEREVkcq7ib8f3urS2Xl5dn5k6IiIhIrHt/t8WsEWuVAeX27dsAAB8fHzN3QkRERFLdvn0bLi4uldZY5VL3BoMB6enpqFevHlQq+W6slZeXBx8fH1y5csVml9C39fdo6+8P4Hu0Bbb+/gDbf4+2/v4AZd6jIAi4ffs2vL29oVZXPsvEKo+gqNVqPPTQQ4rt39nZ2Wa/4e6x9fdo6+8P4Hu0Bbb+/gDbf4+2/v4A+d9jVUdO7uEkWSIiIrI4DChERERkcRhQ/sHR0RFz5syBo6OjuVtRjK2/R1t/fwDfoy2w9fcH2P57tPX3B5j/PVrlJFkiIiKybTyCQkRERBaHAYWIiIgsDgMKERERWRwGFCIiIrI4DChERERkcewuoCxYsADdu3dH7dq14erqWm7N5cuXMXDgQNSuXRuNGjXCW2+9hdLS0kr3m52djVGjRsHZ2Rmurq4IDw9Hfn6+Au9Amr1790KlUpX7OHr0aIXjHn300Qfqx48fX4Odi9esWbMHev3ggw8qHVNYWIgJEybA3d0ddevWxfDhw5GZmVlDHUtz8eJFhIeHw9fXF05OTmjRogXmzJmD4uLiSsdZ+mf4+eefo1mzZqhVqxaCg4Nx5MiRSut//vlntG3bFrVq1ULHjh0RHR1dQ51KExkZiS5duqBevXpo1KgRhg4dijNnzlQ6Zs2aNQ98VrVq1aqhjqX797///UC/bdu2rXSMtXx+QPm/U1QqFSZMmFBuvTV8fjExMRg8eDC8vb2hUqmwefPmMs8LgoDZs2fDy8sLTk5OCA0NxdmzZ6vcr9SfYynsLqAUFxfjmWeeweuvv17u83q9HgMHDkRxcTFiY2Oxdu1arFmzBrNnz650v6NGjUJqaip27tyJqKgoxMTEYNy4cUq8BUm6d++O69evl3m8+uqr8PX1RefOnSsdO3bs2DLjFi1aVENdS/f++++X6XXSpEmV1k+dOhVbt27Fzz//jH379iE9PR3Dhg2roW6lOX36NAwGA1asWIHU1FQsXrwYy5cvxzvvvFPlWEv9DNevX49p06Zhzpw5SEhIQKdOndCvXz9kZWWVWx8bG4uRI0ciPDwciYmJGDp0KIYOHYqUlJQa7rxq+/btw4QJE3Do0CHs3LkTJSUleOKJJ1BQUFDpOGdn5zKf1aVLl2qoY9N06NChTL8HDhyosNaaPj8AOHr0aJn3tnPnTgDAM888U+EYS//8CgoK0KlTJ3z++eflPr9o0SIsXboUy5cvx+HDh1GnTh3069cPhYWFFe5T6s+xZIKdWr16teDi4vLA9ujoaEGtVgsZGRnGbV9++aXg7OwsFBUVlbuvkydPCgCEo0ePGrf99ttvgkqlEq5duyZ779VRXFwsNGzYUHj//fcrrXvkkUeEN998s2aaqqamTZsKixcvFl2fk5MjODg4CD///LNx26lTpwQAQlxcnAIdym/RokWCr69vpTWW/Bl27dpVmDBhgvFrvV4veHt7C5GRkeXWP/vss8LAgQPLbAsODhZee+01RfuUQ1ZWlgBA2LdvX4U1Ff0+slRz5swROnXqJLremj8/QRCEN998U2jRooVgMBjKfd7aPj8AwqZNm4xfGwwGwdPTU/jPf/5j3JaTkyM4OjoKP/74Y4X7kfpzLJXdHUGpSlxcHDp27AgPDw/jtn79+iEvLw+pqakVjnF1dS1zRCI0NBRqtRqHDx9WvGcptmzZglu3buHll1+usvaHH35AgwYN4Ofnh4iICNy5c6cGOjTNBx98AHd3dwQGBuI///lPpafk4uPjUVJSgtDQUOO2tm3bokmTJoiLi6uJdqstNzcXbm5uVdZZ4mdYXFyM+Pj4Mv/+arUaoaGhFf77x8XFlakH/v65tIbPKzc3FwCq/Lzy8/PRtGlT+Pj44Mknn6zw942lOHv2LLy9vdG8eXOMGjUKly9frrDWmj+/4uJifP/993jllVegUqkqrLO2z++f0tLSkJGRUeYzcnFxQXBwcIWfkSk/x1JZ5d2MlZSRkVEmnAAwfp2RkVHhmEaNGpXZptVq4ebmVuEYc1m1ahX69etX5d2gn3/+eTRt2hTe3t5ITk7G22+/jTNnzmDjxo011Kl4kydPRlBQENzc3BAbG4uIiAhcv34dn3zySbn1GRkZ0Ol0D8xB8vDwsLjPqzznzp3DsmXL8NFHH1VaZ6mf4c2bN6HX68v9OTt9+nS5Yyr6ubT0z8tgMGDKlCno0aMH/Pz8Kqxr06YNvvnmG/j7+yM3NxcfffQRunfvjtTUVEXv3G6q4OBgrFmzBm3atMH169cxd+5c9OrVCykpKahXr94D9db6+QHA5s2bkZOTg5deeqnCGmv7/O5373OQ8hmZ8nMslU0ElJkzZ+LDDz+stObUqVNVTuKyJqa856tXr2LHjh3YsGFDlfv/5/yZjh07wsvLC3379sX58+fRokUL0xsXScr7mzZtmnGbv78/dDodXnvtNURGRlr0fTJM+QyvXbuG/v3745lnnsHYsWMrHWvuz5CACRMmICUlpdL5GQAQEhKCkJAQ49fdu3dHu3btsGLFCsybN0/pNiUbMGCA8b/9/f0RHByMpk2bYsOGDQgPDzdjZ/JbtWoVBgwYAG9v7wprrO3zsxY2EVCmT59eaboFgObNm4val6en5wOzkO9d3eHp6VnhmPsnBZWWliI7O7vCMdVlyntevXo13N3dMWTIEMmvFxwcDODv/3uviT9u1flMg4ODUVpaiosXL6JNmzYPPO/p6Yni4mLk5OSUOYqSmZmp2OdVHqnvMT09HX369EH37t3x1VdfSX69mv4MK9KgQQNoNJoHrpqq7N/f09NTUr0lmDhxonHCvNT/i3ZwcEBgYCDOnTunUHfycnV1RevWrSvs1xo/PwC4dOkSdu3aJfmoo7V9fvc+h8zMTHh5eRm3Z2ZmIiAgoNwxpvwcSybLTBYrVNUk2czMTOO2FStWCM7OzkJhYWG5+7o3SfbYsWPGbTt27LCoSbIGg0Hw9fUVpk+fbtL4AwcOCACE48ePy9yZ/L7//ntBrVYL2dnZ5T5/b5Lsf//7X+O206dPW/Qk2atXrwqtWrUSRowYIZSWlpq0D0v6DLt27SpMnDjR+LVerxcaN25c6STZQYMGldkWEhJikZMsDQaDMGHCBMHb21v4888/TdpHaWmp0KZNG2Hq1Kkyd6eM27dvC/Xr1xc+/fTTcp+3ps/vn+bMmSN4enoKJSUlksZZ+ueHCibJfvTRR8Ztubm5oibJSvk5ltynLHuxIpcuXRISExOFuXPnCnXr1hUSExOFxMRE4fbt24Ig/P2N5efnJzzxxBNCUlKSsH37dqFhw4ZCRESEcR+HDx8W2rRpI1y9etW4rX///kJgYKBw+PBh4cCBA0KrVq2EkSNH1vj7q8iuXbsEAMKpU6ceeO7q1atCmzZthMOHDwuCIAjnzp0T3n//feHYsWNCWlqa8OuvvwrNmzcXevfuXdNtVyk2NlZYvHixkJSUJJw/f174/vvvhYYNGwqjR4821tz//gRBEMaPHy80adJE2LNnj3Ds2DEhJCRECAkJMcdbqNLVq1eFli1bCn379hWuXr0qXL9+3fj4Z401fYY//fST4OjoKKxZs0Y4efKkMG7cOMHV1dV49dyLL74ozJw501h/8OBBQavVCh999JFw6tQpYc6cOYKDg4Nw4sQJc72FCr3++uuCi4uLsHfv3jKf1Z07d4w197+/uXPnCjt27BDOnz8vxMfHCyNGjBBq1aolpKammuMtVGn69OnC3r17hbS0NOHgwYNCaGio0KBBAyErK0sQBOv+/O7R6/VCkyZNhLfffvuB56zx87t9+7bx7x0A4ZNPPhESExOFS5cuCYIgCB988IHg6uoq/Prrr0JycrLw5JNPCr6+vsLdu3eN+3jssceEZcuWGb+u6ue4uuwuoIwZM0YA8MDjjz/+MNZcvHhRGDBggODk5CQ0aNBAmD59epkE/ccffwgAhLS0NOO2W7duCSNHjhTq1q0rODs7Cy+//LIx9FiCkSNHCt27dy/3ubS0tDL/BpcvXxZ69+4tuLm5CY6OjkLLli2Ft956S8jNza3BjsWJj48XgoODBRcXF6FWrVpCu3bthIULF5Y52nX/+xMEQbh7967wxhtvCPXr1xdq164tPPXUU2X+4FuS1atXl/s9+88DoNb4GS5btkxo0qSJoNPphK5duwqHDh0yPvfII48IY8aMKVO/YcMGoXXr1oJOpxM6dOggbNu2rYY7Fqeiz2r16tXGmvvf35QpU4z/Fh4eHkJYWJiQkJBQ882L9NxzzwleXl6CTqcTGjduLDz33HPCuXPnjM9b8+d3z44dOwQAwpkzZx54zho/v3t/t+5/3HsfBoNBeO+99wQPDw/B0dFR6Nu37wPvvWnTpsKcOXPKbKvs57i6VIIgCPKcLCIiIiKSB9dBISIiIovDgEJEREQWhwGFiIiILA4DChEREVkcBhQiIiKyOAwoREREZHEYUIiIiMjiMKAQERGRxWFAISIiIovDgEJEREQWhwGFiIiILM7/AfcHEqt5XD2hAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1917,7 +3285,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1929,18 +3297,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.2%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-9.98, -9.98)\": 143.5, \"(-9.98, -9.95)\": 143.2, \"(-9.95, -9.92)\": 142.5, \"(-9.92, -9.89)\": 141.7, \"(-9.89, -9.87)\": 141.1, \"(-9.87, -9.83)\": 140.6, \"(-9.83, -9.79)\": 139.3, \"(-9.79, -9.77)\": 138.7, \"(-9.77, -9.74)\": 138.3, \"(-9.74, -9.71)\": 137.3, \"(-9.71, -9.69)\": 137.0, \"(-9.69, -9.67)\": 136.5, \"(-9.67, -9.65)\": 136.1, \"(-9.65, -9.63)\": 135.5, \"(-9.63, -9.58)\": 135.2, \"(-9.58, -9.51)\": 133.2, \"(-9.51, -9.47)\": 132.0, \"(-9.47, -9.44)\": 131.2, \"(-9.44, -9.41)\": 130.6, \"(-9.41, -9.39)\": 130.1, \"(-9.39, -9.38)\": 129.7, \"(-9.38, -9.36)\": 129.3, \"(-9.36, -9.34)\": 128.9, \"(-9.34, -9.31)\": 128.4, \"(-9.31, -9.3)\": 127.9, \"(-9.3, -9.29)\": 127.5, \"(-9.29, -9.27)\": 127.2, \"(-9.27, -9.22)\": 126.9, \"(-9.22, -9.16)\": 125.2, \"(-9.16, -9.13)\": 124.1, \"(-9.13, -9.11)\": 123.6, \"(-9.11, -9.08)\": 123.1, \"(-9.08, -9.07)\": 122.7, \"(-9.07, -9.04)\": 122.2, \"(-9.04, -9.01)\": 121.6, \"(-9.01, -8.97)\": 121.0, \"(-8.97, -8.9)\": 119.5, \"(-8.9, -8.87)\": 118.5, \"(-8.87, -8.86)\": 118.0, \"(-8.86, -8.82)\": 117.6, \"(-8.82, -8.77)\": 116.4, \"(-8.77, -8.75)\": 115.7, \"(-8.75, -8.7)\": 115.1, \"(-8.7, -8.65)\": 114.0, \"(-8.65, -8.61)\": 113.0, \"(-8.61, -8.58)\": 112.3, \"(-8.58, -8.57)\": 111.9, \"(-8.57, -8.55)\": 111.5, \"(-8.55, -8.53)\": 111.1, \"(-8.53, -8.51)\": 110.7, \"(-8.51, -8.49)\": 110.4, \"(-8.49, -8.47)\": 109.9, \"(-8.47, -8.45)\": 109.6, \"(-8.45, -8.43)\": 109.0, \"(-8.43, -8.42)\": 108.6, \"(-8.42, -8.38)\": 108.2, \"(-8.38, -8.36)\": 107.6, \"(-8.36, -8.34)\": 107.2, \"(-8.34, -8.32)\": 106.7, \"(-8.32, -8.3)\": 106.2, \"(-8.3, -8.27)\": 105.8, \"(-8.27, -8.24)\": 105.3, \"(-8.24, -8.22)\": 104.5, \"(-8.22, -8.17)\": 104.0, \"(-8.17, -8.13)\": 103.0, \"(-8.13, -8.1)\": 102.5, \"(-8.1, -8.09)\": 102.0, \"(-8.09, -8.06)\": 101.6, \"(-8.06, -8.04)\": 101.0, \"(-8.04, -8.02)\": 100.5, \"(-8.02, -7.99)\": 100.1, \"(-7.99, -7.97)\": 99.7, \"(-7.97, -7.96)\": 99.4, \"(-7.96, -7.94)\": 99.1, \"(-7.94, -7.94)\": 98.8, \"(-7.94, -7.91)\": 98.5, \"(-7.91, -7.86)\": 97.8, \"(-7.86, -7.85)\": 97.2, \"(-7.85, -7.83)\": 96.9, \"(-7.83, -7.82)\": 96.5, \"(-7.82, -7.8)\": 96.2, \"(-7.8, -7.78)\": 95.9, \"(-7.78, -7.74)\": 95.3, \"(-7.74, -7.71)\": 94.5, \"(-7.71, -7.69)\": 94.2, \"(-7.69, -7.68)\": 93.8, \"(-7.68, -7.65)\": 93.5, \"(-7.65, -7.62)\": 92.9, \"(-7.62, -7.6)\": 92.5, \"(-7.6, -7.57)\": 92.0, \"(-7.57, -7.54)\": 91.4, \"(-7.54, -7.52)\": 90.8, \"(-7.52, -7.51)\": 90.3, \"(-7.51, -7.46)\": 90.0, \"(-7.46, -7.41)\": 89.2, \"(-7.41, -7.38)\": 88.2, \"(-7.38, -7.34)\": 87.6, \"(-7.34, -7.31)\": 87.0, \"(-7.31, -7.3)\": 86.7, \"(-7.3, -7.27)\": 86.3, \"(-7.27, -7.26)\": 85.9, \"(-7.26, -7.24)\": 85.5, \"(-7.24, -7.22)\": 85.1, \"(-7.22, -7.18)\": 84.5, \"(-7.18, -7.15)\": 83.9, \"(-7.15, -7.11)\": 83.5, \"(-7.11, -7.08)\": 82.8, \"(-7.08, -7.05)\": 82.3, \"(-7.05, -7.03)\": 81.7, \"(-7.03, -7.0)\": 81.3, \"(-7.0, -6.97)\": 80.9, \"(-6.97, -6.95)\": 80.6, \"(-6.95, -6.94)\": 80.2, \"(-6.94, -6.92)\": 79.8, \"(-6.92, -6.89)\": 79.4, \"(-6.89, -6.88)\": 79.0, \"(-6.88, -6.87)\": 78.7, \"(-6.87, -6.85)\": 78.3, \"(-6.85, -6.82)\": 78.0, \"(-6.82, -6.8)\": 77.6, \"(-6.8, -6.76)\": 77.2, \"(-6.76, -6.73)\": 76.5, \"(-6.73, -6.71)\": 76.1, \"(-6.71, -6.68)\": 75.6, \"(-6.68, -6.65)\": 75.1, \"(-6.65, -6.63)\": 74.7, \"(-6.63, -6.6)\": 74.3, \"(-6.6, -6.52)\": 73.6, \"(-6.52, -6.46)\": 71.9, \"(-6.46, -6.46)\": 71.6, \"(-6.46, -6.43)\": 71.3, \"(-6.43, -6.39)\": 70.8, \"(-6.39, -6.35)\": 70.0, \"(-6.35, -6.33)\": 69.6, \"(-6.33, -6.3)\": 69.3, \"(-6.3, -6.27)\": 68.6, \"(-6.27, -6.24)\": 68.3, \"(-6.24, -6.22)\": 67.7, \"(-6.22, -6.2)\": 67.3, \"(-6.2, -6.16)\": 66.8, \"(-6.16, -6.12)\": 66.2, \"(-6.12, -6.09)\": 65.9, \"(-6.09, -6.08)\": 65.4, \"(-6.08, -6.04)\": 65.1, \"(-6.04, -6.01)\": 64.3, \"(-6.01, -5.99)\": 64.0, \"(-5.99, -5.95)\": 63.6, \"(-5.95, -5.92)\": 63.1, \"(-5.92, -5.9)\": 62.8, \"(-5.9, -5.88)\": 62.5, \"(-5.88, -5.86)\": 62.0, \"(-5.86, -5.82)\": 61.6, \"(-5.82, -5.8)\": 61.1, \"(-5.8, -5.77)\": 60.7, \"(-5.77, -5.77)\": 60.4, \"(-5.77, -5.75)\": 60.1, \"(-5.75, -5.7)\": 59.6, \"(-5.7, -5.68)\": 59.1, \"(-5.68, -5.61)\": 58.6, \"(-5.61, -5.54)\": 57.4, \"(-5.54, -5.53)\": 56.9, \"(-5.53, -5.5)\": 56.4, \"(-5.5, -5.48)\": 56.0, \"(-5.48, -5.46)\": 55.7, \"(-5.46, -5.41)\": 55.4, \"(-5.41, -5.38)\": 54.8, \"(-5.38, -5.36)\": 54.3, \"(-5.36, -5.33)\": 53.9, \"(-5.33, -5.31)\": 53.6, \"(-5.31, -5.3)\": 53.2, \"(-5.3, -5.24)\": 52.8, \"(-5.24, -5.18)\": 51.9, \"(-5.18, -5.14)\": 51.5, \"(-5.14, -5.12)\": 51.1, \"(-5.12, -5.11)\": 50.6, \"(-5.11, -5.1)\": 50.3, \"(-5.1, -5.05)\": 49.9, \"(-5.05, -5.0)\": 49.2, \"(-5.0, -4.96)\": 48.8, \"(-4.96, -4.93)\": 48.4, \"(-4.93, -4.9)\": 47.7, \"(-4.9, -4.86)\": 47.4, \"(-4.86, -4.83)\": 47.1, \"(-4.83, -4.8)\": 46.7, \"(-4.8, -4.75)\": 46.0, \"(-4.75, -4.72)\": 45.4, \"(-4.72, -4.66)\": 44.9, \"(-4.66, -4.58)\": 43.6, \"(-4.58, -4.55)\": 43.3, \"(-4.55, -4.52)\": 42.9, \"(-4.52, -4.49)\": 42.2, \"(-4.49, -4.44)\": 41.9, \"(-4.44, -4.42)\": 41.6, \"(-4.42, -4.39)\": 41.1, \"(-4.39, -4.35)\": 40.5, \"(-4.35, -4.31)\": 40.1, \"(-4.31, -4.25)\": 39.4, \"(-4.25, -4.21)\": 38.9, \"(-4.21, -4.17)\": 38.3, \"(-4.17, -4.12)\": 38.0, \"(-4.12, -4.09)\": 37.4, \"(-4.09, -4.06)\": 37.0, \"(-4.06, -4.05)\": 36.7, \"(-4.05, -4.0)\": 36.4, \"(-4.0, -3.97)\": 35.9, \"(-3.97, -3.94)\": 35.4, \"(-3.94, -3.91)\": 35.1, \"(-3.91, -3.85)\": 34.7, \"(-3.85, -3.84)\": 34.3, \"(-3.84, -3.79)\": 33.9, \"(-3.79, -3.75)\": 33.2, \"(-3.75, -3.69)\": 32.7, \"(-3.69, -3.62)\": 31.9, \"(-3.62, -3.58)\": 31.4, \"(-3.58, -3.53)\": 31.1, \"(-3.53, -3.47)\": 30.4, \"(-3.47, -3.39)\": 29.5, \"(-3.39, -3.36)\": 29.1, \"(-3.36, -3.33)\": 28.6, \"(-3.33, -3.28)\": 28.3, \"(-3.28, -3.26)\": 27.9, \"(-3.26, -3.24)\": 27.6, \"(-3.24, -3.2)\": 27.3, \"(-3.2, -3.17)\": 26.9, \"(-3.17, -3.11)\": 26.6, \"(-3.11, -3.07)\": 26.0, \"(-3.07, -3.05)\": 25.6, \"(-3.05, -3.0)\": 25.2, \"(-3.0, -2.94)\": 24.8, \"(-2.94, -2.92)\": 24.4, \"(-2.92, -2.88)\": 24.1, \"(-2.88, -2.84)\": 23.7, \"(-2.84, -2.81)\": 23.2, \"(-2.81, -2.77)\": 22.9, \"(-2.77, -2.7)\": 22.4, \"(-2.7, -2.63)\": 21.9, \"(-2.63, -2.58)\": 21.3, \"(-2.58, -2.55)\": 20.9, \"(-2.55, -2.5)\": 20.5, \"(-2.5, -2.46)\": 20.2, \"(-2.46, -2.41)\": 19.7, \"(-2.41, -2.38)\": 19.4, \"(-2.38, -2.35)\": 19.2, \"(-2.35, -2.32)\": 18.8, \"(-2.32, -2.22)\": 18.4, \"(-2.22, -2.2)\": 17.9, \"(-2.2, -2.15)\": 17.6, \"(-2.15, -2.11)\": 17.2, \"(-2.11, -2.08)\": 16.9, \"(-2.08, -2.04)\": 16.6, \"(-2.04, -2.01)\": 16.3, \"(-2.01, -1.97)\": 16.0, \"(-1.97, -1.9)\": 15.6, \"(-1.9, -1.86)\": 15.2, \"(-1.86, -1.83)\": 14.8, \"(-1.83, -1.76)\": 14.5, \"(-1.76, -1.73)\": 14.1, \"(-1.73, -1.66)\": 13.8, \"(-1.66, -1.64)\": 13.4, \"(-1.64, -1.55)\": 13.0, \"(-1.55, -1.5)\": 12.5, \"(-1.5, -1.43)\": 12.2, \"(-1.43, -1.37)\": 11.7, \"(-1.37, -1.29)\": 11.2, \"(-1.29, -1.24)\": 10.8, \"(-1.24, -1.2)\": 10.5, \"(-1.2, -1.12)\": 10.0, \"(-1.12, -1.03)\": 9.6, \"(-1.03, -1.0)\": 9.2, \"(-1.0, -0.93)\": 8.9, \"(-0.93, -0.86)\": 8.4, \"(-0.86, -0.78)\": 8.1, \"(-0.78, -0.72)\": 7.7, \"(-0.72, -0.65)\": 7.4, \"(-0.65, -0.57)\": 7.0, \"(-0.57, -0.54)\": 6.7, \"(-0.54, -0.47)\": 6.3, \"(-0.47, -0.41)\": 6.0, \"(-0.41, -0.33)\": 5.7, \"(-0.33, -0.27)\": 5.4, \"(-0.27, -0.15)\": 5.0, \"(-0.15, -0.05)\": 4.5, \"(-0.05, 0.01)\": 4.2, \"(0.01, 0.09)\": 3.9, \"(0.09, 0.17)\": 3.6, \"(0.17, 0.27)\": 3.3, \"(0.27, 0.38)\": 2.9, \"(0.38, 0.43)\": 2.6, \"(0.43, 0.55)\": 2.3, \"(0.55, 0.67)\": 2.0, \"(0.67, 0.8)\": 1.7, \"(0.8, 0.94)\": 1.4, \"(0.94, 1.1)\": 1.1, \"(1.1, 1.35)\": 0.8, \"(1.35, 1.51)\": 0.5, \"(1.51, 2.71)\": 0.2, \"(2.71, 2.84)\": 0.5, \"(2.84, 3.01)\": 0.8, \"(3.01, 3.16)\": 1.1, \"(3.16, 3.32)\": 1.4, \"(3.32, 3.44)\": 1.8, \"(3.44, 3.57)\": 2.1, \"(3.57, 3.68)\": 2.5, \"(3.68, 3.75)\": 2.8, \"(3.75, 3.85)\": 3.1, \"(3.85, 3.94)\": 3.5, \"(3.94, 4.02)\": 3.8, \"(4.02, 4.09)\": 4.1, \"(4.09, 4.18)\": 4.5, \"(4.18, 4.26)\": 4.8, \"(4.26, 4.32)\": 5.1, \"(4.32, 4.41)\": 5.4, \"(4.41, 4.49)\": 5.9, \"(4.49, 4.54)\": 6.2, \"(4.54, 4.62)\": 6.6, \"(4.62, 4.67)\": 6.9, \"(4.67, 4.76)\": 7.2, \"(4.76, 4.84)\": 7.8, \"(4.84, 4.89)\": 8.1, \"(4.89, 4.96)\": 8.5, \"(4.96, 5.03)\": 8.9, \"(5.03, 5.08)\": 9.3, \"(5.08, 5.14)\": 9.6, \"(5.14, 5.23)\": 10.0, \"(5.23, 5.3)\": 10.5, \"(5.3, 5.34)\": 10.9, \"(5.34, 5.43)\": 11.3, \"(5.43, 5.52)\": 12.0, \"(5.52, 5.57)\": 12.4, \"(5.57, 5.59)\": 12.7, \"(5.59, 5.67)\": 13.0, \"(5.67, 5.7)\": 13.4, \"(5.7, 5.77)\": 13.7, \"(5.77, 5.83)\": 14.5, \"(5.83, 5.89)\": 14.8, \"(5.89, 5.92)\": 15.2, \"(5.92, 5.97)\": 15.6, \"(5.97, 6.01)\": 15.8, \"(6.01, 6.06)\": 16.2, \"(6.06, 6.09)\": 16.5, \"(6.09, 6.13)\": 16.8, \"(6.13, 6.19)\": 17.2, \"(6.19, 6.25)\": 17.7, \"(6.25, 6.31)\": 18.3, \"(6.31, 6.35)\": 18.8, \"(6.35, 6.42)\": 19.2, \"(6.42, 6.45)\": 19.5, \"(6.45, 6.49)\": 19.8, \"(6.49, 6.52)\": 20.2, \"(6.52, 6.57)\": 20.6, \"(6.57, 6.61)\": 21.0, \"(6.61, 6.63)\": 21.3, \"(6.63, 6.69)\": 21.6, \"(6.69, 6.72)\": 22.1, \"(6.72, 6.77)\": 22.4, \"(6.77, 6.81)\": 22.9, \"(6.81, 6.84)\": 23.2, \"(6.84, 6.87)\": 23.6, \"(6.87, 6.92)\": 23.9, \"(6.92, 6.99)\": 24.6, \"(6.99, 7.03)\": 25.0, \"(7.03, 7.06)\": 25.3, \"(7.06, 7.1)\": 25.7, \"(7.1, 7.13)\": 26.2, \"(7.13, 7.19)\": 26.5, \"(7.19, 7.25)\": 27.2, \"(7.25, 7.27)\": 27.5, \"(7.27, 7.32)\": 28.0, \"(7.32, 7.36)\": 28.4, \"(7.36, 7.39)\": 28.9, \"(7.39, 7.43)\": 29.2, \"(7.43, 7.49)\": 29.8, \"(7.49, 7.53)\": 30.4, \"(7.53, 7.58)\": 30.7, \"(7.58, 7.6)\": 31.1, \"(7.6, 7.64)\": 31.5, \"(7.64, 7.69)\": 32.1, \"(7.69, 7.71)\": 32.4, \"(7.71, 7.74)\": 32.7, \"(7.74, 7.77)\": 33.0, \"(7.77, 7.79)\": 33.4, \"(7.79, 7.85)\": 33.7, \"(7.85, 7.89)\": 34.3, \"(7.89, 7.92)\": 34.9, \"(7.92, 7.98)\": 35.2, \"(7.98, 8.06)\": 36.5, \"(8.06, 8.1)\": 36.9, \"(8.1, 8.13)\": 37.2, \"(8.13, 8.18)\": 37.8, \"(8.18, 8.22)\": 38.2, \"(8.22, 8.26)\": 38.7, \"(8.26, 8.32)\": 39.6, \"(8.32, 8.36)\": 40.0, \"(8.36, 8.37)\": 40.4, \"(8.37, 8.41)\": 40.8, \"(8.41, 8.44)\": 41.1, \"(8.44, 8.46)\": 41.5, \"(8.46, 8.49)\": 41.8, \"(8.49, 8.51)\": 42.2, \"(8.51, 8.54)\": 42.5, \"(8.54, 8.57)\": 42.9, \"(8.57, 8.6)\": 43.2, \"(8.6, 8.65)\": 43.8, \"(8.65, 8.68)\": 44.4, \"(8.68, 8.7)\": 44.7, \"(8.7, 8.74)\": 45.1, \"(8.74, 8.76)\": 45.6, \"(8.76, 8.8)\": 46.0, \"(8.8, 8.83)\": 46.5, \"(8.83, 8.87)\": 46.8, \"(8.87, 8.89)\": 47.3, \"(8.89, 8.93)\": 47.8, \"(8.93, 8.95)\": 48.1, \"(8.95, 8.98)\": 48.4, \"(8.98, 9.0)\": 48.9, \"(9.0, 9.03)\": 49.3, \"(9.03, 9.08)\": 49.6, \"(9.08, 9.11)\": 50.4, \"(9.11, 9.17)\": 50.7, \"(9.17, 9.24)\": 52.0, \"(9.24, 9.28)\": 52.7, \"(9.28, 9.31)\": 53.0, \"(9.31, 9.37)\": 53.8, \"(9.37, 9.42)\": 54.9, \"(9.42, 9.44)\": 55.3, \"(9.44, 9.47)\": 55.6, \"(9.47, 9.51)\": 56.1, \"(9.51, 9.54)\": 56.6, \"(9.54, 9.58)\": 57.0, \"(9.58, 9.6)\": 57.5, \"(9.6, 9.63)\": 58.0, \"(9.63, 9.66)\": 58.3, \"(9.66, 9.7)\": 59.0, \"(9.7, 9.73)\": 59.6, \"(9.73, 9.77)\": 60.0, \"(9.77, 9.79)\": 60.4, \"(9.79, 9.82)\": 61.0, \"(9.82, 9.84)\": 61.4, \"(9.84, 9.88)\": 61.7, \"(9.88, 9.92)\": 62.4, \"(9.92, 9.94)\": 62.8}\n",
+      "Means: {\"(-9.98, -9.94)\": 992.1, \"(-9.94, -9.91)\": 981.3, \"(-9.91, -9.84)\": 966.9, \"(-9.84, -9.75)\": 941.2, \"(-9.75, -9.68)\": 921.0, \"(-9.68, -9.62)\": 903.2, \"(-9.62, -9.58)\": 889.3, \"(-9.58, -9.52)\": 871.0, \"(-9.52, -9.46)\": 859.2, \"(-9.46, -9.42)\": 840.9, \"(-9.42, -9.34)\": 830.0, \"(-9.34, -9.29)\": 810.8, \"(-9.29, -9.24)\": 798.9, \"(-9.24, -9.16)\": 786.8, \"(-9.16, -9.07)\": 759.6, \"(-9.07, -9.01)\": 742.5, \"(-9.01, -8.97)\": 731.2, \"(-8.97, -8.88)\": 712.4, \"(-8.88, -8.83)\": 700.8, \"(-8.83, -8.78)\": 687.3, \"(-8.78, -8.75)\": 675.9, \"(-8.75, -8.71)\": 665.8, \"(-8.71, -8.64)\": 655.7, \"(-8.64, -8.57)\": 641.1, \"(-8.57, -8.5)\": 623.1, \"(-8.5, -8.42)\": 610.7, \"(-8.42, -8.37)\": 592.9, \"(-8.37, -8.31)\": 581.4, \"(-8.31, -8.23)\": 570.8, \"(-8.23, -8.17)\": 554.0, \"(-8.17, -8.11)\": 544.1, \"(-8.11, -8.05)\": 531.6, \"(-8.05, -8.0)\": 520.7, \"(-8.0, -7.94)\": 510.0, \"(-7.94, -7.88)\": 498.9, \"(-7.88, -7.82)\": 487.5, \"(-7.82, -7.75)\": 474.8, \"(-7.75, -7.67)\": 462.0, \"(-7.67, -7.59)\": 448.4, \"(-7.59, -7.54)\": 435.9, \"(-7.54, -7.47)\": 426.0, \"(-7.47, -7.39)\": 415.3, \"(-7.39, -7.35)\": 404.8, \"(-7.35, -7.25)\": 393.2, \"(-7.25, -7.2)\": 380.0, \"(-7.2, -7.11)\": 369.5, \"(-7.11, -7.05)\": 358.8, \"(-7.05, -6.96)\": 346.5, \"(-6.96, -6.89)\": 336.4, \"(-6.89, -6.79)\": 323.9, \"(-6.79, -6.72)\": 311.4, \"(-6.72, -6.63)\": 301.3, \"(-6.63, -6.55)\": 291.2, \"(-6.55, -6.44)\": 277.2, \"(-6.44, -6.34)\": 265.9, \"(-6.34, -6.25)\": 253.9, \"(-6.25, -6.15)\": 242.7, \"(-6.15, -6.04)\": 231.3, \"(-6.04, -5.95)\": 219.6, \"(-5.95, -5.84)\": 208.6, \"(-5.84, -5.73)\": 198.7, \"(-5.73, -5.58)\": 185.3, \"(-5.58, -5.46)\": 173.0, \"(-5.46, -5.33)\": 161.3, \"(-5.33, -5.19)\": 150.9, \"(-5.19, -5.07)\": 140.7, \"(-5.07, -4.89)\": 127.5, \"(-4.89, -4.75)\": 116.6, \"(-4.75, -4.6)\": 106.2, \"(-4.6, -4.42)\": 96.0, \"(-4.42, -4.21)\": 84.8, \"(-4.21, -3.99)\": 74.2, \"(-3.99, -3.81)\": 64.2, \"(-3.81, -3.55)\": 54.3, \"(-3.55, -3.23)\": 43.7, \"(-3.23, -2.89)\": 33.6, \"(-2.89, -2.38)\": 23.5, \"(-2.38, -1.4)\": 13.1, \"(-1.4, 2.35)\": 3.1, \"(2.35, 2.82)\": 13.3, \"(2.82, 3.22)\": 23.3, \"(3.22, 3.5)\": 33.7, \"(3.5, 3.76)\": 43.8, \"(3.76, 3.99)\": 53.9, \"(3.99, 4.19)\": 64.0, \"(4.19, 4.39)\": 74.4, \"(4.39, 4.56)\": 85.5, \"(4.56, 4.74)\": 97.1, \"(4.74, 4.9)\": 107.4, \"(4.9, 5.08)\": 120.9, \"(5.08, 5.21)\": 131.0, \"(5.21, 5.33)\": 141.9, \"(5.33, 5.47)\": 152.6, \"(5.47, 5.6)\": 165.5, \"(5.6, 5.75)\": 178.6, \"(5.75, 5.86)\": 191.8, \"(5.86, 5.96)\": 202.0, \"(5.96, 6.09)\": 214.1, \"(6.09, 6.16)\": 225.7, \"(6.16, 6.27)\": 236.9, \"(6.27, 6.38)\": 249.6, \"(6.38, 6.48)\": 261.3, \"(6.48, 6.57)\": 273.0, \"(6.57, 6.64)\": 284.2, \"(6.64, 6.74)\": 295.6, \"(6.74, 6.85)\": 309.3, \"(6.85, 6.92)\": 321.3, \"(6.92, 6.99)\": 333.9, \"(6.99, 7.09)\": 346.5, \"(7.09, 7.16)\": 356.9, \"(7.16, 7.24)\": 368.1, \"(7.24, 7.3)\": 380.0, \"(7.3, 7.4)\": 391.5, \"(7.4, 7.48)\": 408.3, \"(7.48, 7.55)\": 418.4, \"(7.55, 7.64)\": 433.7, \"(7.64, 7.72)\": 449.1, \"(7.72, 7.78)\": 461.9, \"(7.78, 7.85)\": 474.0, \"(7.85, 7.9)\": 484.7, \"(7.9, 7.96)\": 495.7, \"(7.96, 8.03)\": 507.8, \"(8.03, 8.08)\": 519.1, \"(8.08, 8.17)\": 531.7, \"(8.17, 8.24)\": 546.6, \"(8.24, 8.28)\": 558.9, \"(8.28, 8.35)\": 568.9, \"(8.35, 8.42)\": 587.4, \"(8.42, 8.47)\": 599.5, \"(8.47, 8.52)\": 611.3, \"(8.52, 8.61)\": 628.5, \"(8.61, 8.64)\": 638.9, \"(8.64, 8.68)\": 648.9, \"(8.68, 8.74)\": 662.5, \"(8.74, 8.82)\": 673.4, \"(8.82, 8.88)\": 688.1, \"(8.88, 8.97)\": 701.6, \"(8.97, 9.06)\": 733.9, \"(9.06, 9.11)\": 745.8, \"(9.11, 9.14)\": 757.9, \"(9.14, 9.19)\": 769.2, \"(9.19, 9.25)\": 780.0, \"(9.25, 9.31)\": 796.1, \"(9.31, 9.35)\": 810.4, \"(9.35, 9.4)\": 825.3, \"(9.4, 9.47)\": 835.8, \"(9.47, 9.55)\": 858.9, \"(9.55, 9.6)\": 877.5, \"(9.6, 9.63)\": 887.9, \"(9.63, 9.69)\": 900.6, \"(9.69, 9.75)\": 913.3, \"(9.75, 9.8)\": 930.6, \"(9.8, 9.84)\": 941.0, \"(9.84, 9.88)\": 954.0, \"(9.88, 9.91)\": 968.1}\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1950,7 +3317,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1962,18 +3329,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.1%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-9.99, 0.26)\": 0.01, \"(0.26, 1.1)\": 0.04, \"(1.1, 1.69)\": 0.07, \"(1.69, 2.13)\": 0.1, \"(2.13, 2.41)\": 0.14, \"(2.41, 2.72)\": 0.17, \"(2.72, 2.93)\": 0.21, \"(2.93, 3.15)\": 0.25, \"(3.15, 3.3)\": 0.28, \"(3.3, 3.52)\": 0.32, \"(3.52, 3.62)\": 0.35, \"(3.62, 3.72)\": 0.39, \"(3.72, 3.9)\": 0.43, \"(3.9, 3.97)\": 0.46, \"(3.97, 4.04)\": 0.49, \"(4.04, 4.15)\": 0.52, \"(4.15, 4.19)\": 0.56, \"(4.19, 4.31)\": 0.6, \"(4.31, 4.4)\": 0.63, \"(4.4, 4.49)\": 0.67, \"(4.49, 4.55)\": 0.72, \"(4.55, 4.63)\": 0.77, \"(4.63, 4.71)\": 0.81, \"(4.71, 4.75)\": 0.85, \"(4.75, 4.87)\": 0.88, \"(4.87, 4.89)\": 0.91, \"(4.89, 4.96)\": 0.95, \"(4.96, 5.03)\": 0.98, \"(5.03, 5.08)\": 1.03, \"(5.08, 5.1)\": 1.07, \"(5.1, 5.17)\": 1.11, \"(5.17, 5.23)\": 1.15, \"(5.23, 5.25)\": 1.2, \"(5.25, 5.31)\": 1.24, \"(5.31, 5.39)\": 1.28, \"(5.39, 5.43)\": 1.33, \"(5.43, 5.47)\": 1.37, \"(5.47, 5.49)\": 1.41, \"(5.49, 5.53)\": 1.44, \"(5.53, 5.58)\": 1.48, \"(5.58, 5.64)\": 1.51, \"(5.64, 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       "\n"
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     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1983,7 +3349,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1995,18 +3361,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.2%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-10.0, -9.94)\": 98.2, \"(-9.94, -9.88)\": 97.32, \"(-9.88, -9.84)\": 96.23, \"(-9.84, -9.81)\": 95.51, \"(-9.81, -9.8)\": 95.18, \"(-9.8, -9.79)\": 94.85, \"(-9.79, -9.78)\": 94.6, \"(-9.78, -9.75)\": 94.37, \"(-9.75, -9.72)\": 93.55, \"(-9.72, -9.7)\": 93.29, \"(-9.7, -9.68)\": 92.88, \"(-9.68, -9.66)\": 92.5, \"(-9.66, -9.64)\": 92.16, \"(-9.64, -9.63)\": 91.87, \"(-9.63, -9.62)\": 91.56, \"(-9.62, -9.56)\": 91.18, \"(-9.56, -9.46)\": 89.39, \"(-9.46, -9.41)\": 87.75, \"(-9.41, -9.37)\": 87.36, \"(-9.37, -9.33)\": 86.39, \"(-9.33, -9.31)\": 86.02, \"(-9.31, -9.29)\": 85.5, \"(-9.29, -9.26)\": 85.0, \"(-9.26, -9.22)\": 84.64, \"(-9.22, -9.19)\": 83.49, \"(-9.19, -9.17)\": 83.24, \"(-9.17, -9.15)\": 82.94, \"(-9.15, -9.14)\": 82.65, \"(-9.14, -9.12)\": 82.37, \"(-9.12, -9.1)\": 81.97, \"(-9.1, -9.07)\": 81.59, \"(-9.07, -9.05)\": 81.16, \"(-9.05, -9.05)\": 80.9, \"(-9.05, -9.02)\": 80.59, \"(-9.02, -8.97)\": 80.05, \"(-8.97, -8.92)\": 78.79, \"(-8.92, -8.89)\": 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48.23, \"(7.03, 7.05)\": 48.45, \"(7.05, 7.06)\": 48.72, \"(7.06, 7.08)\": 48.92, \"(7.08, 7.1)\": 49.3, \"(7.1, 7.12)\": 49.63, \"(7.12, 7.15)\": 49.87, \"(7.15, 7.2)\": 50.52, \"(7.2, 7.24)\": 51.18, \"(7.24, 7.28)\": 51.63, \"(7.28, 7.31)\": 52.23, \"(7.31, 7.33)\": 52.47, \"(7.33, 7.35)\": 52.78, \"(7.35, 7.36)\": 53.03, \"(7.36, 7.38)\": 53.3, \"(7.38, 7.39)\": 53.5, \"(7.39, 7.42)\": 53.81, \"(7.42, 7.44)\": 54.27, \"(7.44, 7.47)\": 54.53, \"(7.47, 7.5)\": 54.95, \"(7.5, 7.53)\": 55.41, \"(7.53, 7.55)\": 55.77, \"(7.55, 7.56)\": 56.13, \"(7.56, 7.58)\": 56.38, \"(7.58, 7.61)\": 56.62, \"(7.61, 7.64)\": 57.11, \"(7.64, 7.66)\": 57.47, \"(7.66, 7.68)\": 57.93, \"(7.68, 7.7)\": 58.13, \"(7.7, 7.73)\": 58.59, \"(7.73, 7.81)\": 59.08, \"(7.81, 7.91)\": 61.1, \"(7.91, 7.95)\": 61.86, \"(7.95, 7.99)\": 62.38, \"(7.99, 8.03)\": 63.07, \"(8.03, 8.08)\": 63.86, \"(8.08, 8.13)\": 64.68, \"(8.13, 8.15)\": 65.19, \"(8.15, 8.17)\": 65.45, \"(8.17, 8.18)\": 65.76, \"(8.18, 8.19)\": 66.1, \"(8.19, 8.21)\": 66.35, \"(8.21, 8.23)\": 66.64, \"(8.23, 8.25)\": 66.9, \"(8.25, 8.26)\": 67.25, \"(8.26, 8.29)\": 67.59, \"(8.29, 8.33)\": 67.99, \"(8.33, 8.33)\": 68.36, \"(8.33, 8.35)\": 68.56, \"(8.35, 8.36)\": 68.89, \"(8.36, 8.38)\": 69.14, \"(8.38, 8.4)\": 69.39, \"(8.4, 8.42)\": 69.62, \"(8.42, 8.43)\": 69.89, \"(8.43, 8.45)\": 70.26, \"(8.45, 8.47)\": 70.55, \"(8.47, 8.51)\": 70.94, \"(8.51, 8.55)\": 71.92, \"(8.55, 8.56)\": 72.19, \"(8.56, 8.58)\": 72.4, \"(8.58, 8.61)\": 72.92, \"(8.61, 8.65)\": 73.31, \"(8.65, 8.67)\": 73.93, \"(8.67, 8.68)\": 74.14, \"(8.68, 8.69)\": 74.45, \"(8.69, 8.71)\": 74.79, \"(8.71, 8.74)\": 75.01, \"(8.74, 8.77)\": 75.59, \"(8.77, 8.79)\": 76.11, \"(8.79, 8.82)\": 76.65, \"(8.82, 8.84)\": 76.95, \"(8.84, 8.87)\": 77.4, \"(8.87, 8.88)\": 77.81, \"(8.88, 8.93)\": 78.3, \"(8.93, 8.99)\": 79.48, \"(8.99, 9.01)\": 79.99, \"(9.01, 9.04)\": 80.47, \"(9.04, 9.07)\": 81.01, \"(9.07, 9.08)\": 81.32, \"(9.08, 9.11)\": 81.64, \"(9.11, 9.15)\": 82.39, \"(9.15, 9.18)\": 82.87, \"(9.18, 9.19)\": 83.13, \"(9.19, 9.2)\": 83.46, \"(9.2, 9.21)\": 83.76, \"(9.21, 9.23)\": 84.06, \"(9.23, 9.24)\": 84.26, \"(9.24, 9.26)\": 84.49, \"(9.26, 9.28)\": 84.72, \"(9.28, 9.29)\": 85.15, \"(9.29, 9.29)\": 85.43, \"(9.29, 9.32)\": 85.64, \"(9.32, 9.34)\": 86.18, \"(9.34, 9.37)\": 86.5, \"(9.37, 9.4)\": 87.11, \"(9.4, 9.43)\": 87.65, \"(9.43, 9.45)\": 88.05, \"(9.45, 9.48)\": 88.57, \"(9.48, 9.49)\": 88.95, \"(9.49, 9.5)\": 89.24, \"(9.5, 9.53)\": 89.45, \"(9.53, 9.58)\": 90.4, \"(9.58, 9.61)\": 91.03, \"(9.61, 9.64)\": 91.68, \"(9.64, 9.67)\": 92.29, \"(9.67, 9.69)\": 92.7, \"(9.69, 9.7)\": 92.9, \"(9.7, 9.71)\": 93.14, \"(9.71, 9.73)\": 93.42, \"(9.73, 9.74)\": 93.78, \"(9.74, 9.75)\": 94.03, \"(9.75, 9.77)\": 94.24, \"(9.77, 9.8)\": 94.74, \"(9.8, 9.82)\": 95.32, \"(9.82, 9.83)\": 95.52, \"(9.83, 9.85)\": 95.72, \"(9.85, 9.88)\": 96.1, \"(9.88, 9.92)\": 97.0, \"(9.92, 9.94)\": 97.56, \"(9.94, 9.97)\": 98.03}\n",
+      "Means: {\"(-10.0, -9.96)\": -0.111, \"(-9.96, -9.94)\": -0.206, \"(-9.94, -9.89)\": -0.256, \"(-9.89, -9.81)\": -0.391, \"(-9.81, -9.76)\": -0.462, \"(-9.76, -9.67)\": -0.528, \"(-9.67, -9.6)\": -0.577, \"(-9.6, -9.53)\": -0.647, \"(-9.53, -9.46)\": -0.694, \"(-9.46, -9.37)\": -0.739, \"(-9.37, -9.28)\": -0.806, \"(-9.28, -9.21)\": -0.851, \"(-9.21, -9.12)\": -0.895, \"(-9.12, -9.05)\": -0.943, \"(-9.05, -8.94)\": -0.991, \"(-8.94, -8.83)\": -1.036, \"(-8.83, -8.74)\": -1.083, \"(-8.74, -8.64)\": -1.132, \"(-8.64, -8.52)\": -1.177, \"(-8.52, -8.39)\": -1.225, \"(-8.39, -8.26)\": -1.272, \"(-8.26, -8.12)\": -1.322, \"(-8.12, -7.98)\": -1.382, \"(-7.98, -7.83)\": -1.43, \"(-7.83, -7.71)\": -1.475, \"(-7.71, -7.57)\": -1.519, \"(-7.57, -7.41)\": -1.566, \"(-7.41, -7.25)\": -1.614, \"(-7.25, -7.1)\": -1.66, \"(-7.1, -6.97)\": -1.705, \"(-6.97, -6.8)\": -1.748, \"(-6.8, -6.62)\": -1.797, \"(-6.62, -6.41)\": -1.844, \"(-6.41, -6.24)\": -1.896, \"(-6.24, -6.05)\": -1.941, \"(-6.05, -5.86)\": -1.99, \"(-5.86, -5.66)\": -2.036, \"(-5.66, -5.48)\": -2.084, \"(-5.48, -5.27)\": -2.128, \"(-5.27, -5.07)\": -2.18, \"(-5.07, -4.87)\": -2.224, \"(-4.87, -4.64)\": -2.271, \"(-4.64, -4.45)\": -2.315, \"(-4.45, -4.23)\": -2.359, \"(-4.23, -4.0)\": -2.403, \"(-4.0, -3.78)\": -2.451, \"(-3.78, -3.53)\": -2.497, \"(-3.53, -3.28)\": -2.546, \"(-3.28, -3.09)\": -2.59, \"(-3.09, -2.81)\": -2.634, \"(-2.81, -2.59)\": -2.68, \"(-2.59, -2.36)\": -2.725, \"(-2.36, -2.11)\": -2.77, \"(-2.11, -1.8)\": -2.818, \"(-1.8, -1.56)\": -2.862, \"(-1.56, -1.28)\": -2.906, \"(-1.28, -1.02)\": -2.953, \"(-1.02, -0.74)\": -2.998, \"(-0.74, -0.47)\": -3.045, \"(-0.47, -0.16)\": -3.091, \"(-0.16, 0.13)\": -3.135, \"(0.13, 0.4)\": -3.181, \"(0.4, 0.69)\": -3.225, \"(0.69, 0.99)\": -3.27, \"(0.99, 1.3)\": -3.315, \"(1.3, 1.62)\": -3.364, \"(1.62, 1.92)\": -3.41, \"(1.92, 2.21)\": -3.454, \"(2.21, 2.54)\": -3.501, \"(2.54, 2.89)\": -3.546, \"(2.89, 3.2)\": -3.59, \"(3.2, 3.53)\": -3.634, \"(3.53, 3.86)\": -3.681, \"(3.86, 4.21)\": -3.725, \"(4.21, 4.55)\": -3.769, \"(4.55, 4.89)\": -3.813, \"(4.89, 5.22)\": -3.859, \"(5.22, 5.57)\": -3.903, \"(5.57, 5.91)\": -3.948, \"(5.91, 6.29)\": -3.993, \"(6.29, 6.65)\": -4.037, \"(6.65, 7.03)\": -4.081, \"(7.03, 7.42)\": -4.13, \"(7.42, 7.81)\": -4.175, \"(7.81, 8.18)\": -4.22, \"(8.18, 8.58)\": -4.267, \"(8.58, 8.97)\": -4.312, \"(8.97, 9.38)\": -4.357, \"(9.38, 9.77)\": -4.403, \"(9.77, 9.91)\": -4.448}\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2016,7 +3381,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2028,18 +3393,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.1%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-9.99, -9.99)\": -995.1, \"(-9.99, -9.97)\": -993.1, \"(-9.97, -9.94)\": -986.1, \"(-9.94, -9.94)\": -983.2, \"(-9.94, -9.93)\": -980.1, \"(-9.93, -9.92)\": -976.5, \"(-9.92, -9.9)\": -974.3, \"(-9.9, -9.87)\": -966.9, \"(-9.87, -9.85)\": -957.4, \"(-9.85, -9.83)\": -953.0, \"(-9.83, -9.82)\": -947.7, \"(-9.82, -9.81)\": -944.5, \"(-9.81, -9.79)\": -942.1, \"(-9.79, -9.77)\": -936.0, \"(-9.77, -9.75)\": -929.6, \"(-9.75, -9.73)\": -926.5, \"(-9.73, -9.7)\": -915.2, \"(-9.7, -9.68)\": -909.0, \"(-9.68, -9.67)\": -906.8, \"(-9.67, -9.66)\": -903.8, \"(-9.66, -9.65)\": -900.2, \"(-9.65, -9.64)\": -897.7, \"(-9.64, -9.62)\": -893.3, \"(-9.62, -9.61)\": -889.8, \"(-9.61, -9.58)\": -884.6, \"(-9.58, -9.56)\": -875.2, \"(-9.56, -9.54)\": -871.1, \"(-9.54, -9.51)\": -862.1, \"(-9.51, -9.49)\": -857.2, \"(-9.49, -9.48)\": -851.8, \"(-9.48, -9.46)\": -849.1, \"(-9.46, -9.45)\": -845.8, \"(-9.45, -9.44)\": -841.8, \"(-9.44, -9.43)\": -838.7, \"(-9.43, -9.41)\": -835.9, \"(-9.41, -9.38)\": -829.0, \"(-9.38, -9.36)\": -821.9, \"(-9.36, -9.34)\": -816.1, \"(-9.34, -9.31)\": -810.8, \"(-9.31, -9.27)\": -800.5, \"(-9.27, -9.24)\": -792.0, \"(-9.24, -9.21)\": -785.8, \"(-9.21, -9.17)\": -778.1, \"(-9.17, -9.14)\": -767.8, \"(-9.14, -9.13)\": -762.6, \"(-9.13, -9.11)\": -757.6, \"(-9.11, -9.1)\": -754.5, \"(-9.1, -9.08)\": -750.3, \"(-9.08, -9.07)\": -747.4, \"(-9.07, -9.06)\": -745.0, \"(-9.06, -9.04)\": -741.7, \"(-9.04, -9.03)\": -736.6, \"(-9.03, -9.01)\": -734.2, \"(-9.01, -9.0)\": -730.7, \"(-9.0, -8.99)\": -728.4, \"(-8.99, -8.97)\": -723.7, \"(-8.97, -8.96)\": -720.7, \"(-8.96, -8.94)\": -716.5, \"(-8.94, -8.93)\": -713.7, \"(-8.93, -8.91)\": -711.0, \"(-8.91, -8.89)\": -705.3, \"(-8.89, -8.89)\": -702.8, \"(-8.89, -8.88)\": -699.4, \"(-8.88, -8.85)\": -696.0, \"(-8.85, -8.81)\": -686.7, \"(-8.81, -8.78)\": -681.1, \"(-8.78, -8.75)\": -673.9, \"(-8.75, -8.74)\": -669.5, \"(-8.74, -8.73)\": -666.5, \"(-8.73, -8.72)\": -662.9, \"(-8.72, -8.68)\": -660.3, \"(-8.68, -8.62)\": -645.0, \"(-8.62, -8.59)\": -636.7, \"(-8.59, -8.56)\": -630.3, \"(-8.56, -8.53)\": -624.0, \"(-8.53, -8.51)\": -620.7, \"(-8.51, -8.51)\": -617.7, \"(-8.51, -8.5)\": -615.0, \"(-8.5, -8.46)\": -610.2, \"(-8.46, -8.44)\": -604.1, \"(-8.44, -8.43)\": -601.0, \"(-8.43, -8.42)\": -598.3, \"(-8.42, -8.4)\": -594.5, \"(-8.4, -8.38)\": -591.3, \"(-8.38, -8.37)\": -587.2, \"(-8.37, -8.35)\": -584.6, \"(-8.35, -8.32)\": -580.1, \"(-8.32, -8.29)\": -573.9, \"(-8.29, -8.28)\": -567.7, \"(-8.28, -8.24)\": -564.8, \"(-8.24, -8.19)\": -554.4, \"(-8.19, -8.14)\": -543.3, \"(-8.14, -8.1)\": -535.6, \"(-8.1, -8.07)\": -529.0, \"(-8.07, -8.06)\": -524.8, \"(-8.06, -8.04)\": -521.4, \"(-8.04, -8.01)\": -517.1, \"(-8.01, -7.99)\": -513.4, \"(-7.99, -7.98)\": -510.9, \"(-7.98, -7.97)\": -508.0, \"(-7.97, -7.96)\": -505.5, \"(-7.96, -7.93)\": -500.1, \"(-7.93, -7.9)\": -494.0, \"(-7.9, -7.88)\": -490.5, \"(-7.88, -7.83)\": -484.1, \"(-7.83, -7.77)\": -474.8, \"(-7.77, -7.74)\": -466.3, \"(-7.74, -7.73)\": -463.5, \"(-7.73, -7.7)\": -458.1, \"(-7.7, -7.68)\": -455.8, \"(-7.68, -7.66)\": -451.2, \"(-7.66, -7.65)\": -448.4, \"(-7.65, -7.63)\": -445.9, \"(-7.63, -7.6)\": -441.6, \"(-7.6, -7.58)\": -436.8, \"(-7.58, -7.55)\": -433.2, \"(-7.55, -7.54)\": -429.9, \"(-7.54, -7.52)\": -427.6, \"(-7.52, -7.51)\": -423.9, \"(-7.51, -7.49)\": -421.4, \"(-7.49, -7.46)\": -418.2, \"(-7.46, -7.45)\": -414.6, \"(-7.45, -7.41)\": -412.4, \"(-7.41, -7.38)\": -405.8, \"(-7.38, -7.37)\": -402.7, \"(-7.37, -7.36)\": -400.0, \"(-7.36, -7.34)\": -397.6, \"(-7.34, -7.32)\": -393.6, \"(-7.32, -7.28)\": -389.9, \"(-7.28, -7.26)\": -383.9, \"(-7.26, -7.23)\": -380.4, \"(-7.23, -7.2)\": -375.0, \"(-7.2, -7.18)\": -372.7, \"(-7.18, -7.17)\": -370.2, \"(-7.17, -7.15)\": -367.6, \"(-7.15, -7.13)\": -363.2, \"(-7.13, -7.1)\": -360.0, \"(-7.1, -7.09)\": -357.4, \"(-7.09, -7.05)\": -354.9, \"(-7.05, -7.02)\": -348.1, \"(-7.02, -6.99)\": -344.7, \"(-6.99, -6.96)\": -339.8, \"(-6.96, -6.93)\": -335.3, \"(-6.93, -6.92)\": -333.2, \"(-6.92, -6.91)\": -330.5, \"(-6.91, -6.88)\": -326.7, \"(-6.88, -6.85)\": -323.6, \"(-6.85, -6.83)\": -320.6, \"(-6.83, -6.8)\": -316.5, \"(-6.8, -6.77)\": -312.6, \"(-6.77, -6.74)\": -308.2, \"(-6.74, -6.69)\": -302.7, \"(-6.69, -6.68)\": -299.2, \"(-6.68, -6.64)\": -296.1, \"(-6.64, -6.6)\": -290.3, \"(-6.6, -6.58)\": -286.9, \"(-6.58, -6.56)\": -283.1, \"(-6.56, -6.52)\": -280.4, \"(-6.52, -6.47)\": -273.8, \"(-6.47, -6.44)\": -269.3, \"(-6.44, -6.4)\": -264.5, \"(-6.4, -6.36)\": -258.6, \"(-6.36, -6.32)\": -256.2, \"(-6.32, -6.29)\": -250.4, \"(-6.29, -6.27)\": -248.4, \"(-6.27, -6.21)\": -244.5, \"(-6.21, -6.15)\": -237.0, \"(-6.15, -6.13)\": -234.1, \"(-6.13, -6.11)\": -231.2, \"(-6.11, -6.1)\": -228.5, \"(-6.1, -6.09)\": -226.0, \"(-6.09, -6.06)\": -223.3, \"(-6.06, -6.02)\": -220.3, \"(-6.02, -6.0)\": -217.4, \"(-6.0, -5.97)\": -214.9, \"(-5.97, -5.93)\": -211.1, \"(-5.93, -5.89)\": -207.2, \"(-5.89, -5.87)\": -204.8, \"(-5.87, -5.86)\": -202.3, \"(-5.86, -5.84)\": -200.3, \"(-5.84, -5.8)\": -197.5, \"(-5.8, -5.79)\": -195.0, \"(-5.79, -5.76)\": -192.5, \"(-5.76, -5.74)\": -189.4, \"(-5.74, -5.71)\": -187.2, \"(-5.71, -5.69)\": -185.1, \"(-5.69, -5.66)\": -182.6, \"(-5.66, -5.62)\": -179.1, \"(-5.62, -5.58)\": -174.9, \"(-5.58, -5.56)\": -172.5, \"(-5.56, -5.49)\": -169.9, \"(-5.49, -5.42)\": -163.3, \"(-5.42, -5.42)\": -160.6, \"(-5.42, -5.39)\": -158.6, \"(-5.39, -5.36)\": -154.6, \"(-5.36, -5.32)\": -151.3, \"(-5.32, -5.27)\": -148.7, \"(-5.27, -5.24)\": -145.7, \"(-5.24, -5.2)\": -142.7, \"(-5.2, -5.17)\": -139.0, \"(-5.17, -5.14)\": -136.7, \"(-5.14, -5.08)\": -134.6, \"(-5.08, -5.02)\": -129.0, \"(-5.02, -4.99)\": -125.4, \"(-4.99, -4.96)\": -123.4, \"(-4.96, -4.93)\": -121.3, \"(-4.93, -4.87)\": -118.3, \"(-4.87, -4.83)\": -115.1, \"(-4.83, -4.8)\": -112.8, \"(-4.8, -4.74)\": -109.1, \"(-4.74, -4.68)\": -105.5, \"(-4.68, -4.62)\": -100.4, \"(-4.62, -4.55)\": -98.0, \"(-4.55, -4.48)\": -93.3, \"(-4.48, -4.47)\": -91.3, \"(-4.47, -4.41)\": -89.0, \"(-4.41, -4.37)\": -85.4, \"(-4.37, -4.35)\": -83.1, \"(-4.35, -4.27)\": -80.6, \"(-4.27, -4.23)\": -78.1, \"(-4.23, -4.19)\": -74.9, \"(-4.19, -4.15)\": -72.0, \"(-4.15, -4.06)\": -69.7, \"(-4.06, -4.0)\": -66.9, \"(-4.0, -3.94)\": -63.7, \"(-3.94, -3.89)\": -61.3, \"(-3.89, -3.84)\": -58.8, \"(-3.84, -3.81)\": -56.7, \"(-3.81, -3.74)\": -54.2, \"(-3.74, -3.7)\": -51.9, \"(-3.7, -3.6)\": -49.7, \"(-3.6, -3.59)\": -47.6, \"(-3.59, -3.5)\": -45.6, \"(-3.5, -3.42)\": -42.5, \"(-3.42, -3.4)\": -39.6, \"(-3.4, -3.27)\": -37.5, \"(-3.27, -3.21)\": -35.3, \"(-3.21, -3.1)\": -33.0, \"(-3.1, -3.06)\": -30.4, \"(-3.06, -2.97)\": -28.1, \"(-2.97, -2.9)\": -25.8, \"(-2.9, -2.74)\": -23.6, \"(-2.74, -2.68)\": -21.5, \"(-2.68, -2.58)\": -18.6, \"(-2.58, -2.43)\": -16.2, \"(-2.43, -2.26)\": -13.9, \"(-2.26, -2.13)\": -11.8, \"(-2.13, -1.98)\": -9.6, \"(-1.98, -1.81)\": -7.6, \"(-1.81, -1.49)\": -5.5, \"(-1.49, -1.05)\": -3.1, \"(-1.05, 0.93)\": -1.1, \"(0.93, 1.44)\": 0.9, \"(1.44, 1.68)\": 3.1, \"(1.68, 1.91)\": 5.2, \"(1.91, 2.08)\": 7.2, \"(2.08, 2.26)\": 9.5, \"(2.26, 2.39)\": 11.7, \"(2.39, 2.52)\": 14.2, \"(2.52, 2.65)\": 16.6, \"(2.65, 2.72)\": 18.6, \"(2.72, 2.88)\": 20.6, \"(2.88, 2.93)\": 23.1, \"(2.93, 3.02)\": 25.4, \"(3.02, 3.11)\": 28.0, \"(3.11, 3.14)\": 30.4, \"(3.14, 3.24)\": 32.5, \"(3.24, 3.31)\": 34.8, \"(3.31, 3.37)\": 36.9, \"(3.37, 3.47)\": 39.2, \"(3.47, 3.58)\": 43.1, \"(3.58, 3.67)\": 45.8, \"(3.67, 3.68)\": 48.2, \"(3.68, 3.71)\": 50.7, \"(3.71, 3.8)\": 53.3, \"(3.8, 3.88)\": 55.7, \"(3.88, 3.91)\": 58.1, \"(3.91, 3.98)\": 60.2, \"(3.98, 4.01)\": 62.7, \"(4.01, 4.06)\": 65.3, \"(4.06, 4.11)\": 67.9, \"(4.11, 4.15)\": 70.4, \"(4.15, 4.18)\": 72.6, \"(4.18, 4.26)\": 74.7, \"(4.26, 4.35)\": 80.5, \"(4.35, 4.41)\": 82.7, \"(4.41, 4.45)\": 85.6, \"(4.45, 4.51)\": 88.9, \"(4.51, 4.54)\": 92.0, \"(4.54, 4.59)\": 94.6, \"(4.59, 4.62)\": 97.0, \"(4.62, 4.68)\": 99.0, \"(4.68, 4.71)\": 102.2, \"(4.71, 4.74)\": 105.0, \"(4.74, 4.78)\": 108.1, \"(4.78, 4.83)\": 110.5, \"(4.83, 4.86)\": 113.4, \"(4.86, 4.89)\": 115.6, \"(4.89, 4.93)\": 117.9, \"(4.93, 4.95)\": 120.6, \"(4.95, 5.0)\": 123.1, \"(5.0, 5.05)\": 126.4, \"(5.05, 5.07)\": 128.8, \"(5.07, 5.11)\": 130.9, \"(5.11, 5.16)\": 134.7, \"(5.16, 5.18)\": 138.2, \"(5.18, 5.21)\": 140.6, \"(5.21, 5.26)\": 143.8, \"(5.26, 5.31)\": 148.0, \"(5.31, 5.34)\": 151.1, \"(5.34, 5.37)\": 153.7, \"(5.37, 5.41)\": 157.2, \"(5.41, 5.44)\": 159.8, \"(5.44, 5.49)\": 162.2, \"(5.49, 5.56)\": 167.1, \"(5.56, 5.58)\": 171.4, \"(5.58, 5.59)\": 173.6, \"(5.59, 5.61)\": 175.9, \"(5.61, 5.63)\": 177.9, \"(5.63, 5.68)\": 180.7, \"(5.68, 5.72)\": 185.0, \"(5.72, 5.76)\": 187.9, \"(5.76, 5.81)\": 192.6, \"(5.81, 5.83)\": 196.2, \"(5.83, 5.84)\": 199.3, \"(5.84, 5.89)\": 201.6, \"(5.89, 5.95)\": 208.1, \"(5.95, 5.97)\": 210.7, \"(5.97, 6.02)\": 213.7, \"(6.02, 6.07)\": 220.8, \"(6.07, 6.12)\": 224.9, \"(6.12, 6.13)\": 228.2, \"(6.13, 6.15)\": 230.7, \"(6.15, 6.18)\": 233.9, \"(6.18, 6.19)\": 235.9, \"(6.19, 6.2)\": 237.9, \"(6.2, 6.23)\": 240.5, \"(6.23, 6.28)\": 242.9, \"(6.28, 6.29)\": 247.0, \"(6.29, 6.31)\": 250.7, \"(6.31, 6.37)\": 254.6, \"(6.37, 6.42)\": 263.4, \"(6.42, 6.45)\": 265.5, \"(6.45, 6.49)\": 269.5, \"(6.49, 6.5)\": 271.9, \"(6.5, 6.52)\": 274.5, \"(6.52, 6.53)\": 277.0, \"(6.53, 6.55)\": 279.4, \"(6.55, 6.56)\": 282.0, \"(6.56, 6.61)\": 285.1, \"(6.61, 6.69)\": 295.1, \"(6.69, 6.71)\": 301.0, \"(6.71, 6.75)\": 303.1, \"(6.75, 6.78)\": 311.0, \"(6.78, 6.8)\": 313.1, \"(6.8, 6.84)\": 317.0, \"(6.84, 6.85)\": 319.6, \"(6.85, 6.88)\": 323.7, \"(6.88, 6.9)\": 326.9, \"(6.9, 6.92)\": 328.9, \"(6.92, 6.95)\": 332.8, \"(6.95, 6.97)\": 337.7, \"(6.97, 6.99)\": 339.8, \"(6.99, 7.01)\": 342.5, \"(7.01, 7.05)\": 345.9, \"(7.05, 7.1)\": 354.5, \"(7.1, 7.13)\": 359.6, \"(7.13, 7.13)\": 362.0, \"(7.13, 7.17)\": 365.3, \"(7.17, 7.19)\": 370.2, \"(7.19, 7.21)\": 372.4, \"(7.21, 7.23)\": 376.2, \"(7.23, 7.24)\": 379.0, \"(7.24, 7.25)\": 382.0, \"(7.25, 7.28)\": 384.8, \"(7.28, 7.32)\": 388.7, \"(7.32, 7.36)\": 394.4, \"(7.36, 7.39)\": 400.5, \"(7.39, 7.4)\": 404.1, \"(7.4, 7.42)\": 407.8, \"(7.42, 7.44)\": 410.4, \"(7.44, 7.47)\": 415.6, \"(7.47, 7.52)\": 419.5, \"(7.52, 7.55)\": 428.7, \"(7.55, 7.57)\": 431.8, \"(7.57, 7.59)\": 436.5, \"(7.59, 7.61)\": 438.7, \"(7.61, 7.62)\": 442.0, \"(7.62, 7.65)\": 445.6, \"(7.65, 7.67)\": 449.2, \"(7.67, 7.68)\": 451.7, \"(7.68, 7.7)\": 455.3, \"(7.7, 7.72)\": 457.6, \"(7.72, 7.73)\": 461.5, \"(7.73, 7.74)\": 463.6, \"(7.74, 7.75)\": 466.1, \"(7.75, 7.77)\": 469.1, \"(7.77, 7.84)\": 475.0, \"(7.84, 7.88)\": 488.2, \"(7.88, 7.9)\": 490.4, \"(7.9, 7.93)\": 496.5, \"(7.93, 7.97)\": 502.5, \"(7.97, 8.0)\": 508.7, \"(8.0, 8.02)\": 513.7, \"(8.02, 8.03)\": 516.3, \"(8.03, 8.05)\": 519.4, \"(8.05, 8.08)\": 524.5, \"(8.08, 8.09)\": 528.6, \"(8.09, 8.11)\": 531.6, \"(8.11, 8.13)\": 536.5, \"(8.13, 8.17)\": 539.0, \"(8.17, 8.21)\": 549.0, \"(8.21, 8.23)\": 555.1, \"(8.23, 8.26)\": 560.0, \"(8.26, 8.29)\": 566.2, \"(8.29, 8.32)\": 571.7, \"(8.32, 8.36)\": 580.4, \"(8.36, 8.39)\": 587.6, \"(8.39, 8.4)\": 591.5, \"(8.4, 8.41)\": 593.8, \"(8.41, 8.43)\": 597.3, \"(8.43, 8.47)\": 604.4, \"(8.47, 8.48)\": 608.4, \"(8.48, 8.49)\": 611.5, \"(8.49, 8.52)\": 615.4, \"(8.52, 8.54)\": 619.8, \"(8.54, 8.57)\": 622.7, \"(8.57, 8.61)\": 634.4, \"(8.61, 8.64)\": 642.6, \"(8.64, 8.66)\": 646.1, \"(8.66, 8.68)\": 651.1, \"(8.68, 8.69)\": 653.3, \"(8.69, 8.69)\": 656.5, \"(8.69, 8.7)\": 658.5, \"(8.7, 8.72)\": 661.0, \"(8.72, 8.76)\": 668.0, \"(8.76, 8.79)\": 675.1, \"(8.79, 8.8)\": 680.5, \"(8.8, 8.82)\": 684.0, \"(8.82, 8.87)\": 689.3, \"(8.87, 8.91)\": 703.6, \"(8.91, 8.93)\": 707.9, \"(8.93, 8.98)\": 717.2, \"(8.98, 9.03)\": 732.5, \"(9.03, 9.04)\": 736.4, \"(9.04, 9.05)\": 739.7, \"(9.05, 9.07)\": 743.2, \"(9.07, 9.1)\": 750.5, \"(9.1, 9.14)\": 755.7, \"(9.14, 9.17)\": 768.3, \"(9.17, 9.18)\": 770.5, \"(9.18, 9.19)\": 774.1, \"(9.19, 9.21)\": 777.5, \"(9.21, 9.23)\": 783.8, \"(9.23, 9.23)\": 785.9, \"(9.23, 9.25)\": 788.5, \"(9.25, 9.26)\": 793.3, \"(9.26, 9.29)\": 796.3, \"(9.29, 9.32)\": 804.7, \"(9.32, 9.35)\": 814.2, \"(9.35, 9.37)\": 819.7, \"(9.37, 9.4)\": 827.7, \"(9.4, 9.42)\": 833.1, \"(9.42, 9.44)\": 837.8, \"(9.44, 9.48)\": 846.0, \"(9.48, 9.51)\": 857.3, \"(9.51, 9.52)\": 860.5, \"(9.52, 9.54)\": 863.9, \"(9.54, 9.56)\": 870.3, \"(9.56, 9.56)\": 873.0, \"(9.56, 9.58)\": 875.8, \"(9.58, 9.62)\": 884.9, \"(9.62, 9.66)\": 893.1, \"(9.66, 9.68)\": 906.0, \"(9.68, 9.7)\": 909.7, \"(9.7, 9.71)\": 915.8, \"(9.71, 9.72)\": 918.4, \"(9.72, 9.77)\": 924.2, \"(9.77, 9.8)\": 939.2, \"(9.8, 9.81)\": 942.5, \"(9.81, 9.82)\": 946.1, \"(9.82, 9.86)\": 952.9, \"(9.86, 9.88)\": 962.3, \"(9.88, 9.89)\": 966.0, \"(9.89, 9.91)\": 970.3, \"(9.91, 9.93)\": 976.6, \"(9.93, 9.93)\": 978.7, \"(9.93, 9.95)\": 981.8, \"(9.95, 9.96)\": 986.6, \"(9.96, 9.97)\": 988.6}\n",
+      "Means: {\"(-9.99, -9.89)\": 8.651, \"(-9.89, -9.78)\": 8.549, \"(-9.78, -9.64)\": 8.41, \"(-9.64, -9.55)\": 8.31, \"(-9.55, -9.43)\": 8.18, \"(-9.43, -9.31)\": 8.075, \"(-9.31, -9.18)\": 7.957, \"(-9.18, -9.06)\": 7.849, \"(-9.06, -8.95)\": 7.743, \"(-8.95, -8.84)\": 7.611, \"(-8.84, -8.72)\": 7.508, \"(-8.72, -8.61)\": 7.402, \"(-8.61, -8.52)\": 7.301, \"(-8.52, -8.41)\": 7.198, \"(-8.41, -8.31)\": 7.098, \"(-8.31, -8.2)\": 6.988, \"(-8.2, -8.07)\": 6.879, \"(-8.07, -7.95)\": 6.771, \"(-7.95, -7.82)\": 6.652, \"(-7.82, -7.69)\": 6.505, \"(-7.69, -7.57)\": 6.405, \"(-7.57, -7.44)\": 6.279, \"(-7.44, -7.33)\": 6.156, \"(-7.33, -7.22)\": 6.053, \"(-7.22, -7.09)\": 5.942, \"(-7.09, -6.98)\": 5.828, \"(-6.98, -6.87)\": 5.725, \"(-6.87, -6.76)\": 5.614, \"(-6.76, -6.65)\": 5.51, \"(-6.65, -6.52)\": 5.4, \"(-6.52, -6.41)\": 5.285, \"(-6.41, -6.29)\": 5.171, \"(-6.29, -6.16)\": 5.052, \"(-6.16, -6.07)\": 4.948, \"(-6.07, -5.97)\": 4.847, \"(-5.97, -5.83)\": 4.742, \"(-5.83, -5.73)\": 4.642, \"(-5.73, -5.61)\": 4.521, \"(-5.61, -5.49)\": 4.403, \"(-5.49, -5.37)\": 4.304, \"(-5.37, -5.25)\": 4.202, \"(-5.25, -5.13)\": 4.097, \"(-5.13, -5.02)\": 3.975, \"(-5.02, -4.88)\": 3.863, \"(-4.88, -4.74)\": 3.734, \"(-4.74, -4.62)\": 3.629, \"(-4.62, -4.5)\": 3.526, \"(-4.5, -4.39)\": 3.42, \"(-4.39, -4.24)\": 3.309, \"(-4.24, -4.12)\": 3.199, \"(-4.12, -4.0)\": 3.095, \"(-4.0, -3.86)\": 2.988, \"(-3.86, -3.74)\": 2.876, \"(-3.74, -3.6)\": 2.769, \"(-3.6, -3.45)\": 2.66, \"(-3.45, -3.31)\": 2.553, \"(-3.31, -3.18)\": 2.449, \"(-3.18, -3.02)\": 2.348, \"(-3.02, -2.86)\": 2.241, \"(-2.86, -2.68)\": 2.137, \"(-2.68, -2.46)\": 2.018, \"(-2.46, -2.22)\": 1.912, \"(-2.22, -1.91)\": 1.808, \"(-1.91, -0.78)\": 1.706, \"(-0.78, -0.56)\": 1.81, \"(-0.56, -0.36)\": 1.915, \"(-0.36, -0.19)\": 2.018, \"(-0.19, -0.02)\": 2.132, \"(-0.02, 0.16)\": 2.234, \"(0.16, 0.31)\": 2.349, \"(0.31, 0.48)\": 2.473, \"(0.48, 0.64)\": 2.605, \"(0.64, 0.78)\": 2.716, \"(0.78, 0.92)\": 2.837, \"(0.92, 1.04)\": 2.939, \"(1.04, 1.19)\": 3.042, \"(1.19, 1.28)\": 3.154, \"(1.28, 1.42)\": 3.267, \"(1.42, 1.55)\": 3.367, \"(1.55, 1.68)\": 3.481, \"(1.68, 1.79)\": 3.587, \"(1.79, 1.9)\": 3.693, \"(1.9, 2.03)\": 3.794, \"(2.03, 2.12)\": 3.898, \"(2.12, 2.24)\": 4.008, \"(2.24, 2.36)\": 4.117, \"(2.36, 2.51)\": 4.22, \"(2.51, 2.63)\": 4.349, \"(2.63, 2.76)\": 4.464, \"(2.76, 2.87)\": 4.59, \"(2.87, 3.0)\": 4.693, \"(3.0, 3.09)\": 4.796, \"(3.09, 3.23)\": 4.908, \"(3.23, 3.36)\": 5.036, \"(3.36, 3.45)\": 5.141, \"(3.45, 3.58)\": 5.242, \"(3.58, 3.78)\": 5.417, \"(3.78, 3.87)\": 5.518, \"(3.87, 4.0)\": 5.643, \"(4.0, 4.12)\": 5.769, \"(4.12, 4.24)\": 5.872, \"(4.24, 4.37)\": 5.987, \"(4.37, 4.5)\": 6.12, \"(4.5, 4.6)\": 6.243, \"(4.6, 4.74)\": 6.347, \"(4.74, 4.83)\": 6.463, \"(4.83, 4.93)\": 6.564, \"(4.93, 5.05)\": 6.665, \"(5.05, 5.17)\": 6.764, \"(5.17, 5.31)\": 6.898, \"(5.31, 5.42)\": 7.001, \"(5.42, 5.54)\": 7.104, \"(5.54, 5.69)\": 7.27, \"(5.69, 5.81)\": 7.402, \"(5.81, 5.92)\": 7.508, \"(5.92, 6.05)\": 7.618, \"(6.05, 6.2)\": 7.73, \"(6.2, 6.35)\": 7.928, \"(6.35, 6.49)\": 8.041, \"(6.49, 6.62)\": 8.178, \"(6.62, 6.7)\": 8.29, \"(6.7, 6.83)\": 8.39, \"(6.83, 6.94)\": 8.504, \"(6.94, 7.1)\": 8.621, \"(7.1, 7.25)\": 8.799, \"(7.25, 7.37)\": 8.923, \"(7.37, 7.45)\": 9.023, \"(7.45, 7.57)\": 9.123, \"(7.57, 7.69)\": 9.239, \"(7.69, 7.83)\": 9.365, \"(7.83, 7.94)\": 9.478, \"(7.94, 8.05)\": 9.597, \"(8.05, 8.17)\": 9.698, \"(8.17, 8.3)\": 9.828, \"(8.3, 8.4)\": 9.944, \"(8.4, 8.51)\": 10.044, \"(8.51, 8.62)\": 10.159, \"(8.62, 8.74)\": 10.265, \"(8.74, 8.84)\": 10.374, \"(8.84, 8.96)\": 10.476, \"(8.96, 9.06)\": 10.607, \"(9.06, 9.18)\": 10.708, \"(9.18, 9.27)\": 10.81, \"(9.27, 9.4)\": 10.917, \"(9.4, 9.52)\": 11.038, \"(9.52, 9.63)\": 11.143, \"(9.63, 9.74)\": 11.26, \"(9.74, 9.86)\": 11.387, \"(9.86, 9.97)\": 11.49}\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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WqGuABzxqOZpc/81fV7AjhY+cICIiKg8DjxVSyGX47JUgUWOmbDrJ+TxERETlYOCxUv2D/RAaUM/k+iKtHsvjz0vYERERke1i4LFiG6K6iqpffYCPnCAiIioLA48VUzrIMbZngMn1RSV6rNjDozxERESPY+CxcjMi26BTI3eT69ceuMSjPERERI9h4LEBm8d1N/lmhAUaHY/yEBERPYaBxwYo5DJ8+ff2Jtev4VEeIiIiIww8NqJ/sB8GBJv2nK0HGh1W7LkgcUdERES2g4HHhiwd0hEujqbtstX7zvMoDxER0f/HwGNDFHIZ3nmuuUm1hVoBf1/D52wREREBDDw2Z8LzzeGiVJhUe+xKHuZtPy1xR0RERNaPgcfGKOQyvBPW1OT6dX9mQKPVS9gRERGR9ZM08CxYsABdunRBnTp14OXlhYEDB+Ls2bNGNUVFRYiOjka9evVQu3ZtvPrqq8jJyTGqyczMRGRkJFxcXODl5YUPPvgAWq3WqGbfvn3o1KkTVCoVmjdvjtjYWCk3rVpNeL4Fapl4lAcApv10UsJuiIiIrJ+kgWf//v2Ijo7GoUOHEBcXh5KSEvTt2xcFBQWGmsmTJ2Pr1q3YvHkz9u/fj6ysLAwaNMjwvk6nQ2RkJDQaDRISErBhwwbExsZi1qxZhpqMjAxERkaid+/eSE5OxqRJkzBmzBjs3r1bys2rNgq5DG+HNTO5/pfkLE5gJiKiGk0mCILFvglv3rwJLy8v7N+/H2FhYcjPz4enpyd++OEHvPbaawCA9PR0tG7dGomJiejWrRt27tyJAQMGICsrC97e3gCA1atXY9q0abh58yaUSiWmTZuG7du3IzU11fBZQ4YMQV5eHnbt2vXUvtRqNdzc3JCfnw9XV1dpNt7MdHoBzT7aYXK9Vx0lDs94QcKOiIiILEvM97dF5/Dk5+cDADw8PAAAx44dQ0lJCcLDww01rVq1QqNGjZCYmAgASExMRLt27QxhBwAiIiKgVquRlpZmqHl0HaU1pet4XHFxMdRqtdHL1ijkMgR61zK5PveeBluOXpWwIyIiIutlscCj1+sxadIk9OjRA0FBQQCA7OxsKJVKuLu7G9V6e3sjOzvbUPNo2Cl9v/S9imrUajUKCwuf6GXBggVwc3MzvPz9/c2yjZb20YttRNVP/imFp7aIiKhGsljgiY6ORmpqKn788UdLfWS5YmJikJ+fb3hdvWqbRz6ebekJEx+xBQAQAPx19qZk/RAREVkriwSeCRMmYNu2bdi7dy8aNmxoWO7j4wONRoO8vDyj+pycHPj4+BhqHr9qq/Tnp9W4urrC2dn5iX5UKhVcXV2NXrZIIZdh2ZBOosbEbDklUTdERETWS9LAIwgCJkyYgF9++QV79uxBQECA0fudO3eGo6Mj4uPjDcvOnj2LzMxMhIaGAgBCQ0Nx6tQp5ObmGmri4uLg6uqKNm3aGGoeXUdpTek67Fn/YF90aexmcn1WfhF2pNyQsCMiIiLrI+lVWv/4xz/www8/4Ndff0VgYKBhuZubm+HIy/jx47Fjxw7ExsbC1dUVEydOBAAkJDx8LIJOp0OHDh3g5+eHRYsWITs7G8OHD8eYMWMwf/58AA8vSw8KCkJ0dDTeeust7NmzB++++y62b9+OiIiIp/Zpi1dpPUqj1aPlzJ0m13u4OOLIzBegkMsk7IqIiEhaVnOV1qpVq5Cfn49evXrB19fX8Nq4caOhZsmSJRgwYABeffVVhIWFwcfHBz///LPhfYVCgW3btkGhUCA0NBRvvvkmRowYgblz5xpqAgICsH37dsTFxaF9+/b44osv8PXXX5sUduyB0kGOF9t6mVx/50EJn6ZOREQ1ikXvw2OtbP0ID/DwvjzNP9oBMTtz9Zud0C/IV7KeiIiIpGQ1R3jIchRyGVYO6yhqzJytp3mZOhER1QgMPHakf7AfxvZsYnL9jfwirNhzXrqGiIiIrAQDj52ZEdkWvQM9Ta5f8sd57ErlVVtERGTfGHjskJgHiwLAlE3JPLVFRER2jYHHDnUN8ICPq8rk+gcaPZbH89QWERHZLwYeO6SQy/DJy21FjVm57wKP8hARkd1i4LFT/YJ8MTm8hcn1JToB3Rf+IWFHRERE1YeBx45NeL4F6ro4mlyfo9YgKjZJwo6IiIiqBwOPHVPIZZg3MEjUmPj0WyjU6CTqiIiIqHow8Ng5sffmAYC/ffWXNM0QERFVEwaeGmBGZFt0C/AwuT49+z4W7DgtYUdERESWxcBTQ3wbFSKqfs2BDGi0eom6ISIisiwGnhpC6SBH1yZ1RY3puSheom6IiIgsi4GnBvluTDdR9TlqDZ5btEeiboiIiCyHgacGUTrI8U5YgKgxV+4UYs7WVIk6IiIisgwGnhompn8btPKuJWrM+oNXOJ+HiIhsGgNPDfRLdE/RY/ovPSBBJ0RERJbBwFMDOSsVCG/tKWrMhZsFmLedl6oTEZFtYuCpob4e2RVBvrVFjVn3Jy9VJyIi28TAU4Nte+851HV2EDVm+NeHJOqGiIhIOgw8NVxCTLio+qTLdzFve5pE3RAREUmDgaeGc1Yq0KmRm6gx6/68jB0pWRJ1REREZH4MPITN43pALhM35h8/nIBOL0jTEBERkZkx8BAUchlWDO0kelz4F3sl6IaIiMj8GHgIANA/2BcD2vmIGpNxuxD3i7QSdURERGQ+DDxksHRoJziK/BsRuuAPaZohIiIyIwYeMlDIZVjy946ixtwr1iGMT1UnIiIrx8BDRgZ08EOfQHF3Yc68U4RZW05J1BEREVHVMfDQE74Z3RUN3VWixnx7KBNjNhyRqCMiIqKqYeChMv01PRx1VApRY/44k8ubEhIRkVVi4KFynZrTD7WV4kLPuj8v83lbRERkdRh4qEKHPhL36AkAeHNdggSdEBERVR4DD1WotpMDAuq7iBpz+Eo+Ptt6WqKOiIiIxGPgoaf6Y0ov0WO+PpiBBTsYeoiIyDow8NBTKeQyfDVM3P15AGDtgQzO5yEiIqvAwEMm6R/sh7E9m4gaIwB4cel+SfohIiISg4GHTDYjsi3e6t5E1JiLNx+gJ+/ETERE1YyBh0SZ9XJbhLf2EjXm6p0i9JgfJ1FHRERET8fAQ6J9PbILujZ2EzXmulqDsH/ySA8REVUPBh6qlO/Gdhc9JvNuEeZu5Z2YiYjI8hh4qFKUDnK8ExYgety/D15GoUYnQUdERETlY+ChSovp3wajezQRPa7Tp79jV+oN8zdERERUDgYeqpLZL7VFx4auosYUlugx7rvjDD1ERGQxDDxUZT/949lKjZv+31PQ6QUzd0NERPQkBh6qMoVchhVDOogel1dYgo5zd5u/ISIiosdIGngOHDiAl156CX5+fpDJZNiyZYvR+4IgYNasWfD19YWzszPCw8Nx/vx5o5o7d+7gjTfegKurK9zd3REVFYX79+8b1aSkpKBnz55wcnKCv78/Fi1aJOVmURkGdGgg+v48AKAu0qH1xzsl6IiIiOh/JA08BQUFaN++PVauXFnm+4sWLcKyZcuwevVqJCUloVatWoiIiEBRUZGh5o033kBaWhri4uKwbds2HDhwAG+//bbhfbVajb59+6Jx48Y4duwYFi9ejE8++QRr166VctOoDF+P7II+rTxFjyss0eONrxMk6IiIiOghmSAIFplEIZPJ8Msvv2DgwIEAHh7d8fPzw9SpU/H+++8DAPLz8+Ht7Y3Y2FgMGTIEZ86cQZs2bXDkyBE888wzAIBdu3ahf//+uHbtGvz8/LBq1SrMmDED2dnZUCqVAIDp06djy5YtSE9PN6k3tVoNNzc35Ofnw9VV3ARcetKn21LxzV9XRI97PrA+/j06RIKOiIjIHon5/q62OTwZGRnIzs5GeHi4YZmbmxtCQkKQmJgIAEhMTIS7u7sh7ABAeHg45HI5kpKSDDVhYWGGsAMAEREROHv2LO7evVvmZxcXF0OtVhu9yHw+HhCE5UPFP119z9lbGP3vwxJ0RERENV21BZ7s7GwAgLe3t9Fyb29vw3vZ2dnw8jKeF+Lg4AAPDw+jmrLW8ehnPG7BggVwc3MzvPz9/au+QWTkpfZ++GqY+NCz99xNDFj2pwQdERFRTVYjr9KKiYlBfn6+4XX16tXqbsku9Q/2w+eD2okel5qlxoBl+yXoiIiIaqpqCzw+Pj4AgJycHKPlOTk5hvd8fHyQm5tr9L5Wq8WdO3eMaspax6Of8TiVSgVXV1ejF0njta6N4KIU/9csNes+Zm85JUFHRERUE1Vb4AkICICPjw/i4//3BG21Wo2kpCSEhoYCAEJDQ5GXl4djx44Zavbs2QO9Xo+QkBBDzYEDB1BSUmKoiYuLQ2BgIOrWrWuhraGKJM+KqNS4DYcyERXLOT1ERFR1kgae+/fvIzk5GcnJyQAeTlROTk5GZmYmZDIZJk2ahM8++wy//fYbTp06hREjRsDPz89wJVfr1q3Rr18/jB07FocPH8bBgwcxYcIEDBkyBH5+fgCAYcOGQalUIioqCmlpadi4cSOWLl2KKVOmSLlpJILSQY6xPcU/aBQA4tNvMvQQEVGVSXpZ+r59+9C7d+8nlo8cORKxsbEQBAGzZ8/G2rVrkZeXh2effRZfffUVWrZsaai9c+cOJkyYgK1bt0Iul+PVV1/FsmXLULt2bUNNSkoKoqOjceTIEdSvXx8TJ07EtGnTTO6Tl6Vbxlvrk7Dn7K1KjR3dowlmv9TWzB0REZEtE/P9bbH78FgzBh7LiVx2AGlZ9yo1dljXhpg/qL2ZOyIiIltlE/fhoZpp+7thaOThXKmxPxy+huBP+OwtIiISj4GHLO7Ah8+jV2C9So1VF2nRee7vZu6IiIjsHQMPVYvY0d0Q9WyTSo29/aAEw9fy2VtERGQ6Bh6qNh8PaItlf+9QqbF/XrqLkHlx0Olr/BQ0IiIyAQMPVauXOzXAWz2aVGpszj0NWs7cia0ns8zbFBER2R0GHqp2s15qi3Z+dSo1VqcXMPE/JxAVm2TmroiIyJ4w8JBV2PpuGNo1qFzoAYD49Ft8/hYREZWLgYesxtaJYYjq3qTS41Oz7qPX4r2c10NERE9g4CGr8vHLbbFiSIdKj798+wGafbQDu1JvmK8pIiKyeQw8ZHUGdGiA1W92gqwK6xj33XFsS+ZkZiIieoiBh6xSvyBfXJjfH151lJVex4QfT+DT31LN2BUREdkqBh6yWgq5DIdnvIDeLT0rvY5vEq4gciknMxMR1XQMPGT11r/VFaN7NK70+LQb99F21k4zdkRERLaGgYdswuyXgvBWFa7gKtDo0frjnbyCi4iohmLgIZsx6+W2CG/tVenxhSV6tOQVXERENRIDD9mUr0d2wdieAZUer8PDK7imbjwBjVZvvsaIiMiqyQRBqPHH+NVqNdzc3JCfnw9XV9fqbodMoNHqETR7FzS6qv31HdszADMi25ipKyIisiQx3988wkM2Sekgx7l5/VGvduUvWweAdX9moM/ne3m0h4jIzjHwkE07NvMFDA/1r9I6Lt56gJYzd+LTbbxnDxGRvWLgIZv36SvB+GpYxyqv55u/rvBZXEREdoqBh+xC/2A/XJzfH86OVfsrffn2AzT/aAd+Tb5ups6IiMgaMPCQ3VDIZTjz6Yvo1cKjSusRALz3YzI6zf0dhRqdeZojIqJqxcBDdic2KhQrhnSEyqEqjx8F7jwoQetZu9B3yX5OaiYisnEMPGSXBnTww+m5L2JSnxZVXte5nPtoOXMn5mw9ZYbOiIioOjDwkN1SyGWY9EJLrH6zE1QOVf+rvv5gJkLmxXFSMxGRDeKNB8EbD9YEOr2A0PlxyL1fYpb1+bqqMKJ7E0Q92xRKM4QpIiISjzceJHqMQi7D4Zl9EdzAPIH2hroY/9x1Fi1n7sS87afNsk4iIpIOAw/VKL9N7InUTyLgVMUJzY9a92cG+n7Jic1ERNaMp7TAU1o1VVRsEuLTb5l1nbWVMrzSviFmvtQWzkqFWddNRETGxHx/M/CAgacmK9To8Mxnv6NAY/6jM5383bB5fA8o5OY7mkRERP/DOTxEJnJWKpA290WM7tHI7Os+fjUfzT/agR0pWWZfNxERicMjPOARHnpIo9XjzXWHcPjKXbOv28VRjjdCGqFXK290a1qPR32IiMyAp7REYuChRy3YcRprDmRItn6ZDHgl2BeLXu/AS9qJiKqAgUckBh56nEarx7SfkvFL8g1JP6ehmxK7JvdGbScHST+HiMgeMfCIxMBD5dHpBSyLP4fley5AyhssK2RAK5/a6NCoLmZG8govIiJTMPCIxMBDT6PTC/jr3E3E/JyCLHWx5J/nWVuJt8OaYWT3JjztRURUDgYekRh4SIwdKVn4xw8nLPZ5KgdgwvMt8E5Yc4YfIqJHMPCIxMBDYun0AoJm7USh1rK/Ps6OMvRu5YWhXRqje/P6vNqLiGo0Bh6RGHiosm6qixEy/w9U10Ml3JwU8HZ1wt86NUDUs814BIiIahQGHpEYeKiq8h+UIHzJPty8p6nWPuQywMPFEe393bF0SCde/UVEdo2BRyQGHjIXjVaPDQkZ+DLuHApLrONhorUdZWjo4YyGHrUQElCfE6GJyG4w8IjEwENSyH9QgtGxh5GcmVdtp7zKo5QBzio5NDoBTo4O6NvGC5+83I6XwxORTWHgEYmBh6R2v0iL0AV/4F6xrrpbqZAMgK+rCq91aIBd6blQF5Wgaf1aeKdnMzwb6MlJ0kRkVRh4RGLgIUu5X6TFi0v34+rdoupuRTQZgM6N3KB0dECRRotb94tx+34xdAJQS6lAC6/a6NDYAz1bePJ5YURkEQw8IjHwkKVptHp889cl/HL8GnLuFSO/UFvdLUlCjofPDgMAF0egbi0nOChkuHVfA0EQ4O7sCAgyZOUXQQfAUQb4ujshyLcO8ot1cHJ0gCDokVegwX2NDnVrOULQC7h08wHua7SQywDoBcgVcjgrFVAqZLj7oAQarWC4M7ZSASjkMmj1AmQyOdydFNABKNJooRMAR4UMDnIFtNoSFOkAB7kctZwcUFspR5FGj2K9AL1OD2elA9ycHeCkdEChRovreYXQaPVwUMhRS6mAu4sSgzo1xFvPNuUcKSILqbGBZ+XKlVi8eDGys7PRvn17LF++HF27dn3qOAYeqm46vYBDF2/j20OXsTstp7rboSryd3eCVx0nCDLAzckBeQ9KkKMuRLFOgCAIqFdLifb+7rh2txCXbz9AiVaPui6OqFdHhYLih+G3g787ZvAxI0QVqpGBZ+PGjRgxYgRWr16NkJAQ/Otf/8LmzZtx9uxZeHl5VTiWgYesiU4vYG96LmJ+TsHN+9V7mTtVv0YeTngjpAnuPijG9btFyLh1H4AM7f3d+Nw1qvFqZOAJCQlBly5dsGLFCgCAXq+Hv78/Jk6ciOnTp1c4loGHrJVOL+DAmVyM+88xFFv4rs5kG+q6OKJJPReU6PTwcXNG1yYeaO3jijuFGnjVcULXAA/OpyK7VeMCj0ajgYuLC3766ScMHDjQsHzkyJHIy8vDr7/+alRfXFyM4uL/PQBSrVbD39+fgYes2v0iLd79z3Ecy7xrt3N+yPxqKRVo6+eGLk3qonuz+ujWjBPKyX6ICTx2cRvWW7duQafTwdvb22i5t7c30tPTn6hfsGAB5syZY6n2iMyitpMD/j36f3PS7hdp8d6Px5GceRf3irTQ6mF19/uh6leg0eHw5Ts4fPkOVu67CABQyABHB6ChmzPaNKiL1zs35LPZyO7ZReARKyYmBlOmTDH8XHqEh8iW1HZywDejjCfll1799dPRq7h2txA6/cOrlRiE6FE6AdCVABduFeLCrUL8djILAFDXxQH1ainR1s8Nr3X2Zwgiu2IXgad+/fpQKBTIyTG+uiUnJwc+Pj5P1KtUKqhUKku1R2QxSgc5xvdqjvG9mhstL9To8Nm2NBy8cBt3HhRDp9OhoKSamiSrdfeBFncfaHHh5gP8evIGAMDd2REvBvlg1kucIE22zS4Cj1KpROfOnREfH2+Yw6PX6xEfH48JEyZUb3NEVsBZqcC8QcFGy3R6AYcz7iBbXYSruffx70OXUViiQz0XJbxrO+BEVkE1dUvWJK+wBP85chX/OXIVLTxrYft7YbzPENkku5i0DDy8LH3kyJFYs2YNunbtin/961/YtGkT0tPTn5jb8zhepUX0pIcPQr2Mwxm3UFCsQ73aD4+K3i7QlHmnZQe5DDn3edioJqjr7Ij6dZRo41MHrz3TiKe+qNrUuKu0Sq1YscJw48EOHTpg2bJlCAkJeeo4Bh4i89Bo9fj6z4v47tAV5OQXQ4B932m5UKMH7xbwUN82nhgZ2pRXgZFF1djAU1kMPERUWTq9gITzt/DTsas4k6NGLaUD3F0coX6gFXWnZUeFDDn3NLD1f5DlAMb1aoqpfVsx+JDkGHhEYuAhImug0wv46/xNrD1wEdfzilBbpUCP5p7wqKU03Gk5OTMP1/MLDUewrJmzowytfVyxfnQI3Fwcq7sdskMMPCIx8BCRLSl99tpfF24i5Vo+Hmi0KNHpIZfJcOnmfdzXWN+NCFyUcqx58xnO9yGzYuARiYGHiOyF4eq7/ELcul+MuwUl+P1MNi7kWs9Vd92b1sU3o0J4mTtVGQOPSAw8RGTvNFo91u2/gPUJl6Eu0kKrF6r9tFiAhwq7pzzPy9yp0hh4RGLgIaKaSKPVY/3BS9idloNTV/NQUk3fBs3qu+CTl4N4uotEY+ARiYGHiOjh89kmbzyBy7cLcP3OAzyw8DX3jnLgy8Ed8VJ7P4t+LtkuBh6RGHiIiJ5UegRo09FryLhVYLFTYI5yGcJa1sfSIZ1Q28kuHghAEmHgEYmBh4ioYjq9gIQLt/DJb2m4eMtyE6Db+dXB1nfDLPZ5ZFsYeERi4CEiMp1Gq8c3f13CL8ev4dKtAmglvgq+llKOE7MiOLmZnsDAIxIDDxFR5d0v0mLSxhNIunQb94p1kn1Ol0Zu+P7t7gw+ZMDAIxIDDxGReWi0eryxNgFHMvMl+4zhIf749G/Bkq2fbAcDj0gMPERE5qXR6jH9vyn4+cR1SdYvlwFpc/rx5oU1HAOPSAw8RETS0OkF7D2Ti7H/d1SSB6N2buSOTeO68/49NRQDj0gMPERE0rupLkavxXtQUGLeWc4yAO/2aYF3+7Rg8KlhGHhEYuAhIrIcjVaPyKX7cf7mA7OuVw5gYEc/LHy1PSc21xAMPCIx8BARWV6hRodPt6Vh09GrZr+0fUyPJpj5UlvzrpSsDgOPSAw8RETVKyo2CfHpt8y6zqAGrtg2sadZ10nWRcz3N4/5ERFRtftmVAjOzO2HYV39Ya5pOKnX1Xh24R/mWRnZPAYeIiKyCs5KBeYPCsalBZFo18A8R9uv5RWj6fTtuHNfY5b1ke3iKS3wlBYRkTW6X6RFu092m+1ydpVChuTZEbx3jx3hKS0iIrJ5tZ0ckLEwEm928zfL+op1AlrP2oXh/z5klvWRbWHgISIiq/bZwGCc++xFDOzgZ5b1/XnuNlrO2GGWdZHtYOAhIiKrp3SQ419DOuLi/P6Y1KcFFFWc2KzRCQiavcs8zZFNYOAhIiKboZDLMOmFljg3rz86NXKv0rruF+vw2fZU6PQ1fiprjcDAQ0RENkchl+Hnf/TA8qEdq7Ser/+8guYf7cBPhzPN1BlZKwYeIiKyWS+198PF+f3hWduh0usQALz/8ykEzdppvsbI6jDwEBGRTVPIZTgyMwJ9Aj2rtJ77Gj2aTt/OU1x2ioGHiIjswjeju2LFkKqd4tIDaP7RDuxKvWGepshqMPAQEZHdGNDh4Smu2srKf70JAMZ9dxy/HLtmvsao2jHwEBGRXVHIZUid+yJGhzau0nombz6JF/+1zzxNUbVj4CEiIrs0+5UgnPvsRUS286r0Os5kF6BpDOf12AMGHiIisltKBzlWvtEFXw2r/NwevQA0+2gHtiVnmbEzsjQGHiIisnv9g/2w+s1OVVrHhB9PYOy3R8zUEVkaAw8REdUI/YJ8cXF+/yp98cWdzsVnW0+brSeyHAYeIiKqMRRyGS4tjISHs6LS6/j6YAa2HOUVXLaGgYeIiGqc47P7Vekqrkk/neTpLRvDwENERDVS6VVcXrUq91iKuNO5mLM11cxdkVQYeIiIqMZSOshx+OMIKGSVG7/+4BV8uo1zemwBAw8REdV4FxdEwsWhcqnnm78yMG87Q4+1Y+AhIiICcPqz/mjrU6dSY9f9yYnM1o6Bh4iI6P/bPikMfVpV7qnrk346iTEbOJHZWjHwEBERPeKbUV3x5avBlRr7x5lcvLX+sJk7InNg4CEiInrMoC7+CG9duSM9e87eRFRskpk7oqpi4CEiIirD1yO7ok+ryj14ND79Fj7bmmbmjqgqGHiIiIjK8c2oLojqXrkbFH598DJ+OcaJzNaCgYeIiKgCH78chNE9Khd6Jm8+iTEbOKfHGkgWeObNm4fu3bvDxcUF7u7uZdZkZmYiMjISLi4u8PLywgcffACtVmtUs2/fPnTq1AkqlQrNmzdHbGzsE+tZuXIlmjRpAicnJ4SEhODwYf7lIiIi85n9UhDCW1fu9NYfZ24iKpbfS9VNssCj0Wjw+uuvY/z48WW+r9PpEBkZCY1Gg4SEBGzYsAGxsbGYNWuWoSYjIwORkZHo3bs3kpOTMWnSJIwZMwa7d+821GzcuBFTpkzB7Nmzcfz4cbRv3x4RERHIzc2VatOIiKgG+npkF7xVydNb8ek38ek2zumpTjJBEAQpPyA2NhaTJk1CXl6e0fKdO3diwIAByMrKgre3NwBg9erVmDZtGm7evAmlUolp06Zh+/btSE3937NKhgwZgry8POzatQsAEBISgi5dumDFihUAAL1eD39/f0ycOBHTp08vs6fi4mIUFxcbflar1fD390d+fj5cXV3NuflERGRnPtt6Gl8fzKjU2K+GdUT/YD8zd1RzqdVquLm5mfT9XW1zeBITE9GuXTtD2AGAiIgIqNVqpKWlGWrCw8ONxkVERCAxMRHAw6NIx44dM6qRy+UIDw831JRlwYIFcHNzM7z8/f3NuWlERGTHZr7UBu+EBVRq7D9+OAGdXtLjDFSOags82dnZRmEHgOHn7OzsCmvUajUKCwtx69Yt6HS6MmtK11GWmJgY5OfnG15Xr141xyYREVENEdO/Dc7M7Qd5JR6/1efzPeZviJ5KVOCZPn06ZDJZha/09HSpejUblUoFV1dXoxcREZEYzkoFVgztJHrc5TtFmPVr6tMLyawcxBRPnToVo0aNqrCmadOmJq3Lx8fniaupcnJyDO+V/m/pskdrXF1d4ezsDIVCAYVCUWZN6TqIiIik0j/YF2OvNsG6Py+LGvdt4hWcyLyDrRPDpGmMniDqCI+npydatWpV4UupVJq0rtDQUJw6dcroaqq4uDi4urqiTZs2hpr4+HijcXFxcQgNDQUAKJVKdO7c2ahGr9cjPj7eUENERCSlGZFtMSq0kehxp67f4yMoLEiyOTyZmZlITk5GZmYmdDodkpOTkZycjPv37wMA+vbtizZt2mD48OE4efIkdu/ejZkzZyI6OhoqlQoAMG7cOFy6dAkffvgh0tPT8dVXX2HTpk2YPHmy4XOmTJmCdevWYcOGDThz5gzGjx+PgoICjB49WqpNIyIiMvLJK+0Q1ED89Ij49Fv45fh1CTqix0l2WfqoUaOwYcOGJ5bv3bsXvXr1AgBcuXIF48ePx759+1CrVi2MHDkSCxcuhIPD/8607du3D5MnT8bp06fRsGFDfPzxx0+cVluxYgUWL16M7OxsdOjQAcuWLUNISIjJvYq5rI2IiKg8PRfuwdW8QtHj3gkLQEz/NhJ0ZN/EfH9Lfh8eW8DAQ0RE5vLsgjhcy9eIHsd79IhnE/fhISIiskd/xbyAJh5OosdN/A/v0SMlBh4iIiIzi3//eShFfsPqBOCvszelaYgYeIiIiMxNIZfhX0PE36Nnyubj0Gj1EnREDDxEREQS6B/si6hnm4gac/uBDi1n7sSCHaelaaoGY+AhIiKSyMcD2uL5wPqix605kMHQY2YMPERERBL69+gQBPnWET1uzYEMnt4yIwYeIiIiiW17LwyNPJxFj3tuER80ai4MPERERBZw4MPn0dFf3L3ebqiL8fLyPyXqqGZh4CEiIrKQn8Y/i9pKhagxKdfV+I2Pn6gyBh4iIiILUchlWDionehx725K5k0Jq4iBh4iIyIIGdGiAF9p4iR7X9bPdEnRTczDwEBERWdi6EV3QP8hH1JjbD3To+U9OYq4sBh4iIqJqsHxYJziJm86Dq3cLERV7WJqG7BwDDxERUTVQyGX4crD4x0/Ep99EoUYnQUf2jYGHiIiomjx8/ERj0ePe/vaIBN3YNwYeIiKiavTxgCD0bukpasyfF25jR8oNiTqyTww8RERE1Wz9W13h7uQgasw/fjiOXakMPaZi4CEiIrICh2e+IHrM9J9P8f48JmLgISIisgJKBzneCQsQNSbvQQne/c8xiTqyLww8REREViKmfxuM7Sku9Gw/lYN5209L1JH9YOAhIiKyIjMi22Di881EjVn3ZwYnMT8FAw8REZGVmRQeCDdncZOYZ/6ayvk8FWDgISIisjIKuQz/fDVY1Jg7BRoczrgjUUe2j4GHiIjICvUL8sVXw8Tdifn309kSdWP7GHiIiIisVP9gX7zb2/T5POsPXua9ecrBwENERGTF3nshEEqFzOT66f9N4VyeMjDwEBERWTGFXIbo3i1Mrs8r1KLvkn3SNWSjGHiIiIis3ITnm8PdxdHk+os3H2DMhsMSdmR7GHiIiIisnEIuw8JB7USN+ePMTRRqdBJ1ZHsYeIiIiGzAw6u2OsL02TzAC1/sk6odm8PAQ0REZCP6B/vhvT6mz+e5ll+Esd8ekbAj28HAQ0REZEMm9mkBB7npx3niTufy1BYYeIiIiGyKQi7D+OeaihoT/uU+aZqxIQw8RERENmbSC4GijvJczyuq8VdtMfAQERHZGIVchhXDOooa88eZm9iWfF2ijqwfAw8REZEN6hfki6WDO4ga834NvgszAw8REZGNeqVjA3Rq5GZyfVGJHocu3pawI+vFwENERGTDNo/rAYWIb/PFv5+RrhkrxsBDRERkwxRyGZb+3fT5PMlX1TXy3jwMPERERDZuQAc/dGrkbnJ93OlcbD2ZJV1DVoiBh4iIyA5sHtcdKgfTv9Zn/HKqRk1gZuAhIiKyAwq5DEuHdDC5Xl2kxYo956VryMow8BAREdmJfkG+eK1TA5Prl/xxHrtSb0jYkfVg4CEiIrIj8wcFi6qf8MOJGnFqi4GHiIjIjigd5Bjbs4nJ9Vq9gIk/HJOuISshWeC5fPkyoqKiEBAQAGdnZzRr1gyzZ8+GRqMxqktJSUHPnj3h5OQEf39/LFq06Il1bd68Ga1atYKTkxPatWuHHTt2GL0vCAJmzZoFX19fODs7Izw8HOfP15zzkkRERI+aEdkWL7TxMrl+R2oONFq9hB1VP8kCT3p6OvR6PdasWYO0tDQsWbIEq1evxkcffWSoUavV6Nu3Lxo3boxjx45h8eLF+OSTT7B27VpDTUJCAoYOHYqoqCicOHECAwcOxMCBA5GammqoWbRoEZYtW4bVq1cjKSkJtWrVQkREBIqKiqTaPCIiIqu2bkQXBDdwNbn+w5+SpWvGCsgEQbDYibvFixdj1apVuHTpEgBg1apVmDFjBrKzs6FUKgEA06dPx5YtW5Ceng4AGDx4MAoKCrBt2zbDerp164YOHTpg9erVEAQBfn5+mDp1Kt5//30AQH5+Pry9vREbG4shQ4Y8tS+1Wg03Nzfk5+fD1dX0vxxERETWbN2BS5i3w7Q7K8sAXJjfHwoRT2GvbmK+vy06hyc/Px8eHh6GnxMTExEWFmYIOwAQERGBs2fP4u7du4aa8PBwo/VEREQgMTERAJCRkYHs7GyjGjc3N4SEhBhqHldcXAy1Wm30IiIisjcjuzcxuVYAMHhNgmS9VDeLBZ4LFy5g+fLleOeddwzLsrOz4e3tbVRX+nN2dnaFNY++/+i4smoet2DBAri5uRle/v7+VdgyIiIi66R0kKNRXWeT649eycO2ZPu8A7PowDN9+nTIZLIKX6Wno0pdv34d/fr1w+uvv46xY8earfnKiomJQX5+vuF19erV6m6JiIhIEjveCxNVP2mjfV6m7iB2wNSpUzFq1KgKa5o2bWr476ysLPTu3Rvdu3c3mowMAD4+PsjJyTFaVvqzj49PhTWPvl+6zNfX16imQ4cOZfanUqmgUqkq3AYiIiJ7UNvJAe0a1MGp6/dMqtcKwHs/nsCKYZ0k7syyRB/h8fT0RKtWrSp8lc7JuX79Onr16oXOnTtj/fr1kMuNPy40NBQHDhxASUmJYVlcXBwCAwNRt25dQ018fLzRuLi4OISGhgIAAgIC4OPjY1SjVquRlJRkqCEiIqrJtk4Mg4jHbGF7yg27u0xdsjk8pWGnUaNG+Pzzz3Hz5k1kZ2cbzasZNmwYlEoloqKikJaWho0bN2Lp0qWYMmWKoea9997Drl278MUXXyA9PR2ffPIJjh49igkTJgAAZDIZJk2ahM8++wy//fYbTp06hREjRsDPzw8DBw6UavOIiIhsyt+fMX2+qgBgQ8JlyXqpDpJdlh4bG4vRo0eX+d6jH5mSkoLo6GgcOXIE9evXx8SJEzFt2jSj+s2bN2PmzJm4fPkyWrRogUWLFqF///5G65s9ezbWrl2LvLw8PPvss/jqq6/QsmVLk3rlZelERGTvCjU6tJ61y+T6OioFTs3pJ2FHVSfm+9ui9+GxVgw8RERUE4z99gjiTueaXN+nlSe+GdVVwo6qxmrvw0NERETVZ92ILujo72ZyfXz6TWxLvi5hR5bDwENERFSD/DS+BxQi6t//b4pdXKbOwENERFSDKOQy/K1TA5Pri0r0OHTxtoQdWQYDDxERUQ0zf1CwqPrJG09I1InlMPAQERHVMEoHOcb0aGJyfe59De4XaaVryAIYeIiIiGqgmS+1RUB9F5Prbf0oDwMPERFRDfXHlF4m16ZlqaVrxAIYeIiIiGoohVyG4Aam3X8uK78Iu1JvSNyRdBh4iIiIarAfxpr+3Mmpm07a7CXqDDxEREQ1WG0nBwQ3NO0oT4FGh+Xx5yXuSBoMPERERDXcbxN6wqu20qTaVfsu2ORRHgYeIiIiwt+7mPY09WKdgMFrEiTuxvwYeIiIiAihTeubXHv0Sh7mbT8tYTfmx8BDRERE6NasHmqpTH/K1td/ZkCj1UvYkXkx8BAREREUchkWv2r6IycEACP+nSRdQ2bGwENEREQAgP7BfhgQ7Gty/aFLd7AjxTbuzcPAQ0RERAZLh3SEk4Pp8eDD/6bYxFVbDDxERERkoJDL8Plrpp/aul+sxaFLtyXsyDwYeIiIiMjIgA4N8Exjd5PrP999VrpmzISBh4iIiJ6w8Z3ucJDLTKo9cTUPO1KyJO6oahh4iIiI6AkKuQzRvZubXD/z11SrnsvDwENERERlerdPCyhNnMB8p6AEhzPuSNxR5THwEBERUZkUchmiezUzuT73XpGE3VQNAw8RERGVa8LzLVBb5WBSrVcdJ4m7qTwGHiIiIiqXQi7DIhPvwPz7aeuduMzAQ0RERBXqH+yLd8ICnlq3/mAmXl7xpwU6Eo+Bh4iIiJ4qpn8bLB3c4al1KdfU+HSb9T1JnYGHiIiITHLrfrFJdd/8ZX1PUmfgISIiIpNcufPA5NoNCRkSdiIeAw8RERGZpLGHi8m13yVlStiJeAw8REREZJLhoU1Mrr1y+wF2pNyQrhmRGHiIiIjIJEoHOfoHeZtc/+F/U6zmcRMMPERERGSy5cM6Q2HaM0Vxv1iLFXvOS9uQiRh4iIiIyGQKuQzv9mlpcv2aA5es4igPAw8RERGJMuH55qilUphU+0Cjw6GLtyXu6OkYeIiIiEgUhVyGxSY+bgIAvku6LF0zJmLgISIiItH6B/uho7+7SbX7z96s9tNaDDxERERUKe9HBJpU96BEj/d+PCFxNxVj4CEiIqJK6da0HlyUps3l2ZZyAwt2VN8zthh4iIiIqFIUchneCWtqcv3aA9X3jC0GHiIiIqq0Cc+3gLuLo0m1AoDYg9XzjC0GHiIiIqo0hVyGhYPamVz/++lsCbspHwMPERERVUm/IF80q1/LpNpzOfck7qZsDDxERERUZX/v0tCkOnWRDltPZknczZMYeIiIiKjKRvcwffLy1E3JFr8vj6SB5+WXX0ajRo3g5OQEX19fDB8+HFlZxqkuJSUFPXv2hJOTE/z9/bFo0aIn1rN582a0atUKTk5OaNeuHXbs2GH0viAImDVrFnx9feHs7Izw8HCcP28dDysjIiKqCZQOcrT1q2NSrUYnYNKPxyXuyJikgad3797YtGkTzp49i//+97+4ePEiXnvtNcP7arUaffv2RePGjXHs2DEsXrwYn3zyCdauXWuoSUhIwNChQxEVFYUTJ05g4MCBGDhwIFJTUw01ixYtwrJly7B69WokJSWhVq1aiIiIQFFRkZSbR0RERI8Y1NG001oAsC0l26KXqMsEQbDYMaXffvsNAwcORHFxMRwdHbFq1SrMmDED2dnZUCqVAIDp06djy5YtSE9PBwAMHjwYBQUF2LZtm2E93bp1Q4cOHbB69WoIggA/Pz9MnToV77//PgAgPz8f3t7eiI2NxZAhQ57al1qthpubG/Lz8+Hq6irBlhMREdk/jVaPwJk7YWqw+DiyNaJ6mn4q7HFivr8tNofnzp07+P7779G9e3c4Oj68Xj8xMRFhYWGGsAMAEREROHv2LO7evWuoCQ8PN1pXREQEEhMTAQAZGRnIzs42qnFzc0NISIih5nHFxcVQq9VGLyIiIqoapYMcY3o2Mbn+yp0H0jXzGMkDz7Rp01CrVi3Uq1cPmZmZ+PXXXw3vZWdnw9vb26i+9Ofs7OwKax59/9FxZdU8bsGCBXBzczO8/P39q7CFREREVGpGZFu09K5tUm1jDxeJu/kf0YFn+vTpkMlkFb5KT0cBwAcffIATJ07g999/h0KhwIgRI2DBs2hliomJQX5+vuF19erVau2HiIjInmyb2BOyp9TIZcDw0CaWaAcA4CB2wNSpUzFq1KgKa5o2/d/5uPr166N+/fpo2bIlWrduDX9/fxw6dAihoaHw8fFBTk6O0djSn318fAz/W1bNo++XLvP19TWq6dChQ5n9qVQqqFSqp28sERERiaZ0kOPtsACsOVD+YyTG9gyA0sFyd8cRHXg8PT3h6elZqQ/T6x/Oxi4uLgYAhIaGYsaMGSgpKTHM64mLi0NgYCDq1q1rqImPj8ekSZMM64mLi0NoaCgAICAgAD4+PoiPjzcEHLVajaSkJIwfP75SfRIREVHVxPRvAwBY92cGHr3ljlz2MOyUvm8pkl2llZSUhCNHjuDZZ59F3bp1cfHiRXz88cfIyclBWloaVCoV8vPzERgYiL59+2LatGlITU3FW2+9hSVLluDtt98G8PCy9Oeeew4LFy5EZGQkfvzxR8yfPx/Hjx9HUFAQAOCf//wnFi5ciA0bNiAgIAAff/wxUlJScPr0aTg5OT21V16lRUREJA2NVo//S7yMK3ceoLGHC4aHNjHbkR1R39+CRFJSUoTevXsLHh4egkqlEpo0aSKMGzdOuHbtmlHdyZMnhWeffVZQqVRCgwYNhIULFz6xrk2bNgktW7YUlEql0LZtW2H79u1G7+v1euHjjz8WvL29BZVKJfTp00c4e/asyb3m5+cLAIT8/PzKbSwRERFZnJjvb4veh8da8QgPERGR7bHK+/AQERERVRcGHiIiIrJ7DDxERERk9xh4iIiIyO4x8BAREZHdY+AhIiIiu8fAQ0RERHaPgYeIiIjsnuhnadmj0nsvqtXqau6EiIiITFX6vW3KPZQZeADcu3cPAODv71/NnRAREZFY9+7dg5ubW4U1fLQEHj7FPSsrC3Xq1IFMJjPrutVqNfz9/XH16lW7fGyFvW8fYP/byO2zffa+jfa+fYD9b6NU2ycIAu7duwc/Pz/I5RXP0uERHgByuRwNGzaU9DNcXV3t8i9xKXvfPsD+t5HbZ/vsfRvtffsA+99GKbbvaUd2SnHSMhEREdk9Bh4iIiKyeww8ElOpVJg9ezZUKlV1tyIJe98+wP63kdtn++x9G+19+wD730Zr2D5OWiYiIiK7xyM8REREZPcYeIiIiMjuMfAQERGR3WPgISIiIrvHwENERER2j4GniubNm4fu3bvDxcUF7u7uZdZkZmYiMjISLi4u8PLywgcffACtVlvheu/cuYM33ngDrq6ucHd3R1RUFO7fvy/BFoizb98+yGSyMl9Hjhwpd1yvXr2eqB83bpwFOzddkyZNnuh14cKFFY4pKipCdHQ06tWrh9q1a+PVV19FTk6OhToW5/Lly4iKikJAQACcnZ3RrFkzzJ49GxqNpsJx1rwPV65ciSZNmsDJyQkhISE4fPhwhfWbN29Gq1at4OTkhHbt2mHHjh0W6lS8BQsWoEuXLqhTpw68vLwwcOBAnD17tsIxsbGxT+wrJycnC3UszieffPJEr61atapwjC3tP6Dsf1NkMhmio6PLrLf2/XfgwAG89NJL8PPzg0wmw5YtW4zeFwQBs2bNgq+vL5ydnREeHo7z588/db1if4/FYuCpIo1Gg9dffx3jx48v832dTofIyEhoNBokJCRgw4YNiI2NxaxZsypc7xtvvIG0tDTExcVh27ZtOHDgAN5++20pNkGU7t2748aNG0avMWPGICAgAM8880yFY8eOHWs0btGiRRbqWry5c+ca9Tpx4sQK6ydPnoytW7di8+bN2L9/P7KysjBo0CALdStOeno69Ho91qxZg7S0NCxZsgSrV6/GRx999NSx1rgPN27ciClTpmD27Nk4fvw42rdvj4iICOTm5pZZn5CQgKFDhyIqKgonTpzAwIEDMXDgQKSmplq4c9Ps378f0dHROHToEOLi4lBSUoK+ffuioKCgwnGurq5G++rKlSsW6li8tm3bGvX6119/lVtra/sPAI4cOWK0fXFxcQCA119/vdwx1rz/CgoK0L59e6xcubLM9xctWoRly5Zh9erVSEpKQq1atRAREYGioqJy1yn297hSBDKL9evXC25ubk8s37FjhyCXy4Xs7GzDslWrVgmurq5CcXFxmes6ffq0AEA4cuSIYdnOnTsFmUwmXL9+3ey9V4VGoxE8PT2FuXPnVlj33HPPCe+9955lmqqixo0bC0uWLDG5Pi8vT3B0dBQ2b95sWHbmzBkBgJCYmChBh+a3aNEiISAgoMIaa92HXbt2FaKjow0/63Q6wc/PT1iwYEGZ9X//+9+FyMhIo2UhISHCO++8I2mf5pKbmysAEPbv319uTXn/Hlmj2bNnC+3btze53tb3nyAIwnvvvSc0a9ZM0Ov1Zb5vS/sPgPDLL78Yftbr9YKPj4+wePFiw7K8vDxBpVIJ//nPf8pdj9jf48rgER6JJSYmol27dvD29jYsi4iIgFqtRlpaWrlj3N3djY6YhIeHQy6XIykpSfKexfjtt99w+/ZtjB49+qm133//PerXr4+goCDExMTgwYMHFuiwchYuXIh69eqhY8eOWLx4cYWnII8dO4aSkhKEh4cblrVq1QqNGjVCYmKiJdqtsvz8fHh4eDy1ztr2oUajwbFjx4z+7OVyOcLDw8v9s09MTDSqBx7+TtrSvgLw1P11//59NG7cGP7+/njllVfK/ffGGpw/fx5+fn5o2rQp3njjDWRmZpZba+v7T6PR4LvvvsNbb70FmUxWbp0t7b9HZWRkIDs722gfubm5ISQkpNx9VJnf48rg09Illp2dbRR2ABh+zs7OLneMl5eX0TIHBwd4eHiUO6a6fPPNN4iIiHjq0+aHDRuGxo0bw8/PDykpKZg2bRrOnj2Ln3/+2UKdmu7dd99Fp06d4OHhgYSEBMTExODGjRv48ssvy6zPzs6GUql8Yg6Xt7e31e2vsly4cAHLly/H559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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2049,7 +3413,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2061,18 +3425,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.1%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-9.98, -9.98)\": 2978.1, \"(-9.98, -9.96)\": 2971.7, \"(-9.96, -9.95)\": 2961.6, \"(-9.95, -9.91)\": 2951.4, \"(-9.91, -9.88)\": 2900.5, \"(-9.88, -9.87)\": 2886.3, \"(-9.87, -9.8)\": 2867.0, \"(-9.8, -9.73)\": 2778.6, \"(-9.73, -9.71)\": 2754.1, \"(-9.71, -9.69)\": 2736.6, \"(-9.69, -9.67)\": 2718.1, \"(-9.67, -9.63)\": 2707.0, \"(-9.63, -9.57)\": 2654.0, \"(-9.57, -9.56)\": 2624.7, \"(-9.56, -9.55)\": 2615.8, \"(-9.55, -9.53)\": 2605.3, \"(-9.53, -9.52)\": 2593.8, \"(-9.52, -9.51)\": 2584.7, \"(-9.51, -9.5)\": 2578.5, \"(-9.5, -9.49)\": 2571.6, \"(-9.49, -9.48)\": 2560.7, \"(-9.48, -9.46)\": 2545.2, \"(-9.46, -9.4)\": 2526.9, \"(-9.4, -9.35)\": 2464.8, \"(-9.35, -9.35)\": 2452.6, \"(-9.35, -9.34)\": 2445.0, \"(-9.34, -9.33)\": 2438.3, \"(-9.33, -9.3)\": 2426.6, \"(-9.3, -9.28)\": 2403.7, \"(-9.28, -9.24)\": 2386.0, \"(-9.24, -9.2)\": 2344.8, \"(-9.2, -9.15)\": 2319.8, \"(-9.15, -9.13)\": 2284.9, \"(-9.13, -9.12)\": 2278.0, \"(-9.12, -9.09)\": 2267.2, \"(-9.09, -9.07)\": 2242.7, \"(-9.07, -9.05)\": 2232.5, \"(-9.05, -9.0)\": 2204.4, \"(-9.0, -8.95)\": 2164.8, \"(-8.95, -8.91)\": 2133.3, \"(-8.91, -8.86)\": 2107.7, \"(-8.86, -8.82)\": 2072.4, \"(-8.82, -8.81)\": 2055.0, \"(-8.81, -8.79)\": 2045.5, \"(-8.79, -8.78)\": 2034.4, \"(-8.78, -8.77)\": 2027.9, \"(-8.77, -8.74)\": 2021.1, \"(-8.74, -8.72)\": 1993.1, \"(-8.72, -8.7)\": 1983.1, \"(-8.7, -8.69)\": 1975.9, \"(-8.69, -8.68)\": 1967.4, \"(-8.68, -8.68)\": 1960.6, \"(-8.68, -8.66)\": 1953.8, \"(-8.66, -8.65)\": 1946.9, \"(-8.65, -8.64)\": 1938.5, \"(-8.64, -8.62)\": 1931.6, \"(-8.62, -8.59)\": 1911.9, \"(-8.59, -8.54)\": 1886.9, \"(-8.54, -8.5)\": 1847.8, \"(-8.5, -8.46)\": 1837.7, \"(-8.46, -8.41)\": 1795.0, \"(-8.41, -8.4)\": 1781.1, \"(-8.4, -8.37)\": 1770.8, \"(-8.37, -8.32)\": 1739.2, \"(-8.32, -8.3)\": 1718.4, \"(-8.3, -8.28)\": 1711.6, \"(-8.28, -8.26)\": 1695.4, \"(-8.26, -8.24)\": 1684.3, \"(-8.24, -8.21)\": 1673.9, \"(-8.21, -8.18)\": 1646.0, \"(-8.18, -8.16)\": 1635.4, \"(-8.16, -8.15)\": 1626.8, \"(-8.15, -8.14)\": 1618.3, \"(-8.14, -8.11)\": 1608.7, \"(-8.11, -8.1)\": 1596.2, \"(-8.1, -8.09)\": 1589.9, \"(-8.09, -8.06)\": 1582.2, \"(-8.06, -8.03)\": 1565.8, \"(-8.03, -8.02)\": 1548.8, \"(-8.02, -8.0)\": 1541.2, \"(-8.0, -7.98)\": 1529.5, \"(-7.98, -7.97)\": 1522.2, \"(-7.97, -7.96)\": 1514.0, \"(-7.96, -7.92)\": 1503.4, \"(-7.92, -7.89)\": 1478.1, \"(-7.89, -7.87)\": 1471.4, \"(-7.87, -7.86)\": 1461.1, \"(-7.86, -7.85)\": 1454.7, \"(-7.85, -7.84)\": 1446.4, \"(-7.84, -7.81)\": 1440.2, \"(-7.81, -7.79)\": 1427.6, \"(-7.79, -7.77)\": 1414.6, \"(-7.77, -7.76)\": 1404.9, \"(-7.76, -7.73)\": 1394.9, \"(-7.73, -7.68)\": 1367.6, \"(-7.68, -7.66)\": 1353.7, \"(-7.66, -7.64)\": 1342.1, \"(-7.64, -7.61)\": 1327.4, \"(-7.61, -7.59)\": 1315.8, \"(-7.59, -7.56)\": 1306.1, \"(-7.56, -7.55)\": 1294.0, \"(-7.55, -7.53)\": 1283.4, \"(-7.53, -7.5)\": 1273.4, \"(-7.5, -7.46)\": 1256.9, \"(-7.46, -7.43)\": 1236.8, \"(-7.43, -7.41)\": 1230.6, \"(-7.41, -7.4)\": 1219.7, \"(-7.4, 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\"(8.27, 8.31)\": -1714.1, \"(8.31, 8.33)\": -1726.9, \"(8.33, 8.37)\": -1753.7, \"(8.37, 8.39)\": -1769.6, \"(8.39, 8.41)\": -1776.0, \"(8.41, 8.44)\": -1795.4, \"(8.44, 8.49)\": -1816.8, \"(8.49, 8.54)\": -1857.6, \"(8.54, 8.55)\": -1870.9, \"(8.55, 8.61)\": -1886.3, \"(8.61, 8.65)\": -1938.3, \"(8.65, 8.66)\": -1948.2, \"(8.66, 8.68)\": -1955.2, \"(8.68, 8.71)\": -1969.5, \"(8.71, 8.72)\": -1979.6, \"(8.72, 8.72)\": -1990.0, \"(8.72, 8.73)\": -1996.4, \"(8.73, 8.76)\": -2005.3, \"(8.76, 8.8)\": -2030.4, \"(8.8, 8.81)\": -2043.6, \"(8.81, 8.82)\": -2050.9, \"(8.82, 8.83)\": -2064.7, \"(8.83, 8.86)\": -2071.8, \"(8.86, 8.88)\": -2095.8, \"(8.88, 8.89)\": -2104.1, \"(8.89, 8.92)\": -2116.4, \"(8.92, 8.94)\": -2138.8, \"(8.94, 8.95)\": -2145.1, \"(8.95, 8.97)\": -2160.3, \"(8.97, 8.99)\": -2173.7, \"(8.99, 9.01)\": -2190.5, \"(9.01, 9.04)\": -2204.6, \"(9.04, 9.07)\": -2226.2, \"(9.07, 9.07)\": -2237.3, \"(9.07, 9.07)\": -2243.6, \"(9.07, 9.12)\": -2251.5, \"(9.12, 9.17)\": -2310.5, \"(9.17, 9.19)\": -2319.3, \"(9.19, 9.23)\": -2340.3, \"(9.23, 9.27)\": -2379.0, \"(9.27, 9.28)\": -2395.3, \"(9.28, 9.3)\": -2405.3, \"(9.3, 9.34)\": -2426.6, \"(9.34, 9.36)\": -2453.7, \"(9.36, 9.4)\": -2475.6, \"(9.4, 9.43)\": -2507.1, \"(9.43, 9.5)\": -2531.5, \"(9.5, 9.56)\": -2606.6, \"(9.56, 9.57)\": -2623.4, \"(9.57, 9.59)\": -2639.6, \"(9.59, 9.6)\": -2651.1, \"(9.6, 9.63)\": -2663.9, \"(9.63, 9.66)\": -2700.2, \"(9.66, 9.69)\": -2715.9, \"(9.69, 9.72)\": -2747.2, \"(9.72, 9.75)\": -2769.9, \"(9.75, 9.81)\": -2800.6, \"(9.81, 9.84)\": -2847.8, \"(9.84, 9.85)\": -2860.2, \"(9.85, 9.86)\": -2871.6, \"(9.86, 9.89)\": -2886.1, \"(9.89, 9.92)\": -2920.4, \"(9.92, 9.94)\": -2936.3, \"(9.94, 9.97)\": -2954.7, \"(9.97, 9.99)\": -2983.8}\n",
+      "Means: {\"(-9.92, 5.37)\": 4.3, \"(5.37, 6.09)\": 234.0, \"(6.09, 6.48)\": 456.0, \"(6.48, 6.81)\": 688.8, \"(6.81, 7.06)\": 942.6, \"(7.06, 7.28)\": 1193.3, \"(7.28, 7.44)\": 1461.9, \"(7.44, 7.54)\": 1696.0, \"(7.54, 7.69)\": 1940.0, \"(7.69, 7.8)\": 2211.7, \"(7.8, 7.88)\": 2441.0, \"(7.88, 7.95)\": 2683.6, \"(7.95, 8.08)\": 2965.2, \"(8.08, 8.16)\": 3238.9, \"(8.16, 8.22)\": 3541.2, \"(8.22, 8.29)\": 3764.4, \"(8.29, 8.33)\": 4004.2, \"(8.33, 8.4)\": 4269.6, \"(8.4, 8.47)\": 4534.7, \"(8.47, 8.53)\": 4827.5, \"(8.53, 8.57)\": 5123.2, \"(8.57, 8.61)\": 5359.8, \"(8.61, 8.68)\": 5638.4, \"(8.68, 8.74)\": 6028.9, \"(8.74, 8.78)\": 6247.5, \"(8.78, 8.82)\": 6565.8, \"(8.82, 8.87)\": 6889.8, \"(8.87, 8.9)\": 7107.1, \"(8.9, 8.92)\": 7332.2, \"(8.92, 8.98)\": 7638.2, \"(8.98, 9.02)\": 8117.9, \"(9.02, 9.07)\": 8410.3, \"(9.07, 9.11)\": 8826.7, \"(9.11, 9.14)\": 9087.4, \"(9.14, 9.17)\": 9402.8, \"(9.17, 9.23)\": 9787.4, \"(9.23, 9.29)\": 10570.9, \"(9.29, 9.33)\": 10985.0, \"(9.33, 9.35)\": 11341.6, \"(9.35, 9.37)\": 11560.2, \"(9.37, 9.43)\": 11991.0, \"(9.43, 9.47)\": 12768.0, \"(9.47, 9.5)\": 13029.1, \"(9.5, 9.51)\": 13360.9, \"(9.51, 9.53)\": 13583.9, \"(9.53, 9.54)\": 13805.6, \"(9.54, 9.56)\": 14063.1, \"(9.56, 9.62)\": 14605.5, \"(9.62, 9.68)\": 15592.6, \"(9.68, 9.7)\": 16137.3, \"(9.7, 9.71)\": 16354.8, \"(9.71, 9.73)\": 16572.7, \"(9.73, 9.75)\": 16897.0, \"(9.75, 9.77)\": 17239.0, \"(9.77, 9.78)\": 17590.2, \"(9.78, 9.8)\": 17883.3, \"(9.8, 9.87)\": 18547.0, \"(9.87, 9.93)\": 20089.7, \"(9.93, 9.95)\": 20757.9, \"(9.95, 9.98)\": 21173.1, \"(9.98, 9.99)\": 21618.4}\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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SPHbYq06nQ1BQEEpLSxEYGCjb98nILcCMbw40umbOIif6oSAiIhJhNEnYpS3GuctVCA+4cmfCHldwRD+/eSXHDobGRQIWsuSMbw5w5WMiInIpKqUCSe1DcWePlkhqH+oUt6iuxpBjBzuPX0RJpaHRmpKKGuw8ftFOHREREbk/hhw7yDwhtn+HaB0RERFZxpBjF6KX75zrMh8REZErY8ixg0TBKeKidURERI5iNEnIPH4R3+f8gczjF516PKna0Q14AqVC7ArNnlPFGNiphczdEBERNU1GbgFe/SEPhbo/V+qPDPTGq3+9ySlnCPNKjh2IbtvwyVatUydiIiLyXBm5BXhixd46AQcACnXVeGLFXmTkFjios4Yx5NiB6LYNFXojZ1gREZHTMZokzPjmQKM1zrgUCkOOHfSLDYG/RiVUyxlWRETkbHYev9jograAcy6FwpBjByqlAoMEx9psP+ZcPyBERETv/nxYqM7ZflFnyLGTh/q3Earbd6YEeoNJ5m6IiIjE6A0m7DtTKljtXEuhMOTYSf92ofD1EvvjXr5DK3M3REREYv6VeRKiI22S2ofK2ou1GHLsRKVUoGN4M6HaXdpimbshIiISc6q4QqjOW6VA/3YMOR4rwFtsWaKK6sb3uSIiIrKXNiF+QnV3dG/JDTo9WUiAt03riIiI5DY+qS0sZReFAnjz7m72acgKDDl2pBJc+Vi0joiISG4atRKTBsY2WjN5YCw0aueLFNzWwY5aBvvatI6IiMgeZqbEAbiyMv/V6/0pFcCkgbHm550NQ44dDWgfho82HReqIyIiciYzU+IwfVgX/CvzJE4VV6BNiB/GJ7V1yis4tRhy7Kh/+1AE+3lZXDVy6Q4tbu7IoENERM5Fo1Zi4sB2jm5DmM3jl9FoxCuvvILY2Fj4+vqiffv2eP311yFJf17fkiQJs2bNQlRUFHx9fZGcnIyjR4/WeZ3i4mKMGzcOgYGBCA4OxsSJE1FWVlanZv/+/Rg4cCB8fHwQExODefPm2fp0bEqlVOCNv8ZbrPvl0Dn8+Fu+HToiIiJyXzYPOXPnzsXHH3+MhQsX4tChQ5g7dy7mzZuHBQsWmGvmzZuH+fPnY9GiRcjKyoK/vz+GDx+Oqqoqc824ceOQl5eH9evXY/Xq1diyZQsmT55sfl6n02HYsGFo06YNsrOz8fbbb+PVV1/FkiVLbH1KNlV0ucpyEYCXvnW+jc6IiIhciUK6+hKLDdx+++2IiIjAZ599Zn5szJgx8PX1xYoVKyBJEqKjozF9+nQ899xzAIDS0lJERERg2bJlGDt2LA4dOoS4uDjs3r0bffr0AQBkZGQgJSUFZ8+eRXR0ND7++GO89NJLKCwshEajAQDMmDED3333HQ4fFttjQ6fTISgoCKWlpQgMDLTlH0ODZn2fi39mnhKq/fek/k63eiQREXkeo0nCLm0xzl2uQniAD/rFhjh0TRzRz2+bX8kZMGAANmzYgN9//x0A8Ntvv2Hbtm0YOXIkAECr1aKwsBDJycnmY4KCgpCYmIjMzEwAQGZmJoKDg80BBwCSk5OhVCqRlZVlrhk0aJA54ADA8OHDceTIEVy6dKne3qqrq6HT6ep82ZvookoAcE7wqg8REZFcMnILcMvcjXjgk5145sscPPDJTtwydyMycgsc3ZpFNg85M2bMwNixY9GlSxd4eXmhZ8+emDp1KsaNGwcAKCwsBABERETUOS4iIsL8XGFhIcLDw+s8r1arERISUqemvte4+ntcKz09HUFBQeavmJiYGzxb641Paiu8fVl4gI+svRARETUmI7cAT67Yi4LSur90F5ZW4ckVe50+6Ng85KxatQpffPEFVq5cib1792L58uV45513sHz5clt/K6vNnDkTpaWl5q8zZ87YvQeNWonHBra1WNfczwv9YkPkb4iIiKgeRpOEGd8cqHdzztrH5vx40KnHj9o85Dz//PPmqzndunXD+PHj8eyzzyI9PR0AEBkZCQAoKiqqc1xRUZH5ucjISJw7d67O8waDAcXFxXVq6nuNq7/Htby9vREYGFjnyxFmjIyzuK6A8/7IEBGRJ1i48WijS55IAApKq5x6U2mbh5yKigoolXVfVqVSwWQyAQBiY2MRGRmJDRs2mJ/X6XTIyspCUlISACApKQklJSXIzs4212zcuBEmkwmJiYnmmi1btqCm5s83YP369ejcuTOaN29u69OyqV3aYugNpkZrSipqnPoHh4iI3JfRJGHxlhNCtc48ftTmIeeOO+7A3//+d6xZswYnT57Et99+i/feew933XUXAEChUGDq1Kl444038MMPP+DAgQOYMGECoqOjMXr0aABA165dMWLECEyaNAm7du3C9u3bkZaWhrFjxyI6OhoA8OCDD0Kj0WDixInIy8vDV199hQ8//BDTpk2z9SnZnOgPxPqD9Y8tIiIiktPO4xdRoTcK1Trz+FGbr3i8YMECvPLKK3jqqadw7tw5REdH4/HHH8esWbPMNS+88ALKy8sxefJklJSU4JZbbkFGRgZ8fP78g/riiy+QlpaGIUOGQKlUYsyYMZg/f775+aCgIPz8889ITU1F7969ERYWhlmzZtVZS8dZif5AfJ+Tj5dGxTnd1vVEROTeMk9cEKrz91Y59fhRm6+T40ocsU4OcOUyYN+/r0dxeePbOwBcK4eIiOzvnXVHsPDXYxbrRsZH4uOHetuho7octk4OWaZSKnBXj5ZCtb/wlhUREdmZ6C/XD/VvI3MnN4Yhx0GS4+qfAXatb3P+cOrpeURE5H76t7uyoXRjmvt5oX87577TwJDjIP1iQxDir7FYV1zOWVZERGRfKqUCb93drdGa9Lu7Of2YUYYcB1EpFbgjQexqTv6lCpm7ISIiqmtoXCSeTe6EIN+6V3Signyw6KFeGBEf5aDOxNl8dhWJMwjehvou5w+M6WP/LSiIiMgzZeQWYM6PB+ts5xDs64VHbm6LtMEdnf4KTi1eyXGgczq9UN0u7SWOyyEiIrtoaL+qksoafPDLUZdaw40hx4GaeauE6qqNJo7LISIi2RlNEub8eLDBrYUkOP9+VVdjyHGgu3u1Eq4tLK2UsRMiIqIr2w5dewXnWs6+X9XVGHIcaECHMHipxO5rFpeL3doiIiJqqp/zCoTqXOUXb4YcB1IpFXiwX2uh2uZ+lqebExERNZXRJOHr7LNCtRfKXOMXb4YcB2sd4idUd6nCNX6giIjINe3SFqOsWmxTzhIX+UxiyHEwkQUBrakjIiJqinOXGx+LczWFa8wgZ8hxtMggX6G6FVmnZO6EiIg8WXiAj3BtUrswGTuxHYYcB+sXG4KIAMtXabJPleDH3/Lt0BEREXmifrEhiAy0HHSCfdXoL7iBp6Mx5DiYSqnAwI4thGqnr8pxmbUJiIjItaiUCrz61ziLdW+NSeCKxyTOz1tsdw29UcKOYxdk7oaIiDzViPgoLHqoV707kAf5ql1mz6pa3LvKCbQRnGEFAN/sPYuBncSu/BAREVlrRHwUhsZFYueJi8g8fhGAhKR2YejfPtRlruDUYshxAuOT2uL1NYeEasv1YtP7iIiIrGU0SdilLca5y1UID/DBs0M7uVywuRpDjhPQqJXo37Y5dp68ZLG2b9vmduiIiIg8TX07j0cF+WD2HXEudYvqahyT4yT++Vh/oTrRKedERESiGtp5vLC0Ck+u2IuMXLHtHpwNQ46T0KiVmDSwrcW6N9ce4gwrIiKymcZ2Hq99zJV2Hr8aQ44TGdwl0mKNK+3+SkREzs/SzuMSXPezhyHHiYju6uoqu78SEZHzE93OwZptH5wFQ44TOX+52qZ1RERElpy8UCFUZ822D86CIceJHCzQ2bSOiIioMUaThH/vOm2xLjLQG/1iQ+zQkW0x5DiRCsE1cETriIiIGrNLW4xCneXbUA/0a+2S6+Uw5DgR0TVwuFYOERHZgug4m7Zh/jJ3Ig+GHCfy8IBYiOTkokZGwRMREYlan1ckVBfWzFvmTuTBkONENGolHhNYK+fT7Sexdn++/A0REZHb0htMWCO6yJ/rLZEDgCHH6YislQMAL3+f65ILMxERkXP4V+ZJSIIfIxfKXXNWL0OOkxG9P1pcXuOSCzMREZFzOFUsNnUccM3p4wBDjtOx5gfJFRdmIiIi51BRbRCqC/BRueT0cYAhx+n0iw1BiL9GqNZVkzURETmW0SRh69HzQrXpo7u55PRxgCHH6aiUCrxxZ7xQ7cbDYqPiiYiIrrZLW4yiy3qLdb3bBOP2Hi3t0JE8GHKcUEpCFCbe0sZi3adbtdAbTHboiIiI3InocIcJSW3lbURmDDlOKiLA12KNBGDZdq38zRARkVsJ8xdb90a0zlkx5Dipn/MKbVpHRERUyyQ4d1y0zlkx5DgpXVWNTeuIiIhqZZ64aNM6Z8WQ46REl9B21aW2iYjIcfJLKm1a56wYcpxUjxixTTgr9GLrHBAREdU6dbFMqK5lsOXxoc6MIcdJ3dwxTKgu56yOM6yIiEjY2v352HdGJ1Q7oIPYZ5GzYshxUv3bhcLXS+ztWb6DM6yIiMgyo0nCy9/nCtU281ajf7tQmTuSF0OOk1IpFegUESBUu3q/4C6yRETk0XZpi1FcLjZh5b4+rVx2peNaDDlOrF2Yv1DdoQIddyQnIiKLrNnzcGhcpIyd2AdDjhO7u1croTq9UcLO4649zY+IiOQnuudhqL/GZTflvBpDjhMb0CEMasErhduOi220RkREnutSueX9qgDg9TvjXf5WFcCQ49RUSgU6hIuNyzlwplTmboiIyJUZTRJeX3PQYt2kgW2RkhBlh47kx5Dj5GJCxNYo8NWoZO6EiIhc2S5tMQpKLY/JGdzF9cfi1GLIcXL9YsWm74nWERGRZ/KUVY6vxpDj5B4e0BYKC7dFFf+rIyIiasi+05dsWucKGHKcnEatxOSBsY3WSADe/fmwfRoiIiKXtPWo2ASVIp34NHNnx5DjAmamxOHxQY0HncVbtEhfa3lAGREReR69wYRTxWK3ofy91TJ3Yz8MOS5i+rAusDSZ75OtWu5jRURE11m+46Rw7RjBNdpcAUOOi/hX5klYWtPYJF2pIyIiutruk8VCdUqF62/KeTWGHBdxqrjCpnVEROQ5/ASXGenTprlbLAJYiyHHRbQJ8ROqq6g2yNwJERG5GtGhDFNu6yhzJ/bFkOMixie1tTgmBwDW5hZys04iIjLTG0zIyCu0WOejVmBAR/e5VQXIFHL++OMPPPTQQwgNDYWvry+6deuGPXv2mJ+XJAmzZs1CVFQUfH19kZycjKNHj9Z5jeLiYowbNw6BgYEIDg7GxIkTUVZWVqdm//79GDhwIHx8fBATE4N58+bJcTpOQaNWIiU+3GJdhd6IHUcv2KEjIiJyBf/KPAmR331vT4h2q1tVgAwh59KlS7j55pvh5eWFn376CQcPHsS7776L5s2bm2vmzZuH+fPnY9GiRcjKyoK/vz+GDx+Oqqo/5+aPGzcOeXl5WL9+PVavXo0tW7Zg8uTJ5ud1Oh2GDRuGNm3aIDs7G2+//TZeffVVLFmyxNan5DS81GLT+v6776zMnRARkasQHavp50ZTx2vZ/Izmzp2LmJgYLF261PxYbOyfa7xIkoQPPvgAL7/8Mu68804AwD//+U9ERETgu+++w9ixY3Ho0CFkZGRg9+7d6NOnDwBgwYIFSElJwTvvvIPo6Gh88cUX0Ov1+Pzzz6HRaHDTTTchJycH7733Xp0w5E4q9Eahun2nS+RthIiIXIboWE3RsZ+uxOZXcn744Qf06dMH9957L8LDw9GzZ0988skn5ue1Wi0KCwuRnJxsfiwoKAiJiYnIzMwEAGRmZiI4ONgccAAgOTkZSqUSWVlZ5ppBgwZBo9GYa4YPH44jR47g0qX6l6Surq6GTqer8+VK+rYNEao7VVzB9XKIiAhGk4SfD1oej6NUXBn76W5sHnJOnDiBjz/+GB07dsS6devw5JNP4umnn8by5csBAIWFV/6wIyIi6hwXERFhfq6wsBDh4XXHn6jVaoSEhNSpqe81rv4e10pPT0dQUJD5KyYm5gbP1r6s2Z+K6+UQEdHCjcegq7J8FyClWxQ0avebi2TzMzKZTOjVqxfefPNN9OzZE5MnT8akSZOwaNEiW38rq82cOROlpaXmrzNnzji6Jato1ErERQUK1W7h4GMiIo9mNElYul0rVDs0LsJykQuyeciJiopCXFxcnce6du2K06dPAwAiIyMBAEVFRXVqioqKzM9FRkbi3LlzdZ43GAwoLi6uU1Pfa1z9Pa7l7e2NwMDAOl+uZkyvlkJ1+05f4lRyIiIPtktbjJLKGqHa8AAfmbtxDJuHnJtvvhlHjhyp89jvv/+ONm3aALgyCDkyMhIbNmwwP6/T6ZCVlYWkpCQAQFJSEkpKSpCdnW2u2bhxI0wmExITE801W7ZsQU3Nn2/g+vXr0blz5zozudyN6Ho5uioDdmnFlvEmIiL3U1gqtiFnkI8a/WLFxny6GpuHnGeffRY7d+7Em2++iWPHjmHlypVYsmQJUlNTAQAKhQJTp07FG2+8gR9++AEHDhzAhAkTEB0djdGjRwO4cuVnxIgRmDRpEnbt2oXt27cjLS0NY8eORXR0NADgwQcfhEajwcSJE5GXl4evvvoKH374IaZNm2brU3IqGrUSg7u0EKo9d7nKchEREbmlC2V6obrkuHC3Wx+nls2nkPft2xfffvstZs6ciddeew2xsbH44IMPMG7cOHPNCy+8gPLyckyePBklJSW45ZZbkJGRAR+fPy+XffHFF0hLS8OQIUOgVCoxZswYzJ8/3/x8UFAQfv75Z6SmpqJ3794ICwvDrFmz3Hb6+NUeG9geGw6ft1jnrpcfiYjIspIKsZATGegrcyeOo5AkyWMHbuh0OgQFBaG0tNSlxucYTRJumbsRBaWNX6n5x4M9kZIQbaeuiIjImbyz7jAW/nrcYl3abe3x3PAudujIdkQ/v91vvpgHUCkVeGVUnMW619cc4uBjIiIPpVKKfcQntXOv/aquxpDjopr7ayzWFJRWcfAxEZEHMpokfLXb8jIpwb5q9G8faoeOHIMhx0UVlIiNml+8xfKlSiIici+7tMUo1FmefPLIzbFuO+gYYMhxWfvO1L91xbU2HTnPLR6IiDzMeoGtHACgbZi/zJ04FkOOi7JmpA23eCAi8hxGk4SVu04L1br7LFyGHBcVGyqevrcetTzdnIiI3MOCDUdRVWP5Cn6Iv5fbLgJYiyHHRVmzW2w2t3ggIvIIRpOEjzeLjcVMjA116/E4AEOOy9KolRgVH265EMDlKiNnWREReYCdxy+iWnAcZvsW7j0eB2DIcWnD4sUX+uMWD0RE7i/zxAXhWndeH6cWQ44Ls2bAWFgzbxk7ISIi5yB2+8lbrXTr9XFqMeS4sH6xIWju5yVWzCE5RERuTy34qf7kre3cfjwOwJDj0lRKBe7q2VKodsPhIpm7ISIiRzKaJCzbcdJinb+3ClOGdJK/ISfAkOPihsZFCtV9n5PPGVZERG5s5/GLKKk0WKx77BbPuIoDMOS4vH6xIQjxt3zL6mK5njOsiIjcmOigY6PJc1bBZ8hxcSqlAnf1ELtl9YvgMt9EROSKRK/OeMZVHIAhxy0kC96y+nLPGd6yIiJyUycvlAnVJXnArKpaDDlu4MotK43FuvJqI3Yev2iHjoiIyJ70BhPWHLB8tT7YV43+7RhyyIWolAr0i20uVLvDioWiiIjINUz4PEtopZDkrpEeM+gYYMhxG75qlVBd/qVKmTshIiJ70htM2HlCbGKJn7fYZ4W7YMhxE9HBvjatIyIi17BcYG2cWm1C/ORrxAkx5LiJAe3F9iARrSMiItewSys+1nJ8Ulv5GnFCDDluon/7UARb2OIhyFftEXuVEBF5krPFFUJ1LYN8oBHd98FNeNbZujGVUoG37u7WaE1ljQmr9+fbqSMiIpKb0SThuODU8TsF11RzJww5bmREfBQWPdQLwb7qep/XG0x45ssc/HXhVjt3RkREcth5/CL0RrHamzt63nAFhhw3MzQuEj5e9YecWvvP6vDY8l126oiIiOSy47jYsiDeaqVHrY9TiyHHzezSFqNQV2Wx7pdD51EpGv+JiMgp5ZeILQvSvVWQR62PU4shx82cu2w54NR6c+1BGTshIiK5RTcXWxakb2yIzJ04J4YcNxMe4CNcq70gNiKfiIic0ynBQccD2nneeByAIcft9IsNgbfgFEEfL779RESuKn3tQaw+UGSxLtjPy2OXD+GnnJtRKRUY1Ekssbdr4S9zN0REJAe9wYRPtmqFat+6u5tHjscBGHLcUueIAKG6PVqxvU6IiMi5/CvzJEwCO3Le06slRsRHyd+Qk2LIcUNJgvde954pRToHHxMRuZwtR88L1fl5N76kiLtjyHFDIls81PpkqxZ6g0nmjoiIyFaMJglZglfiPW1Dzmsx5LghkS0eapmkK5c9iYjINew8cRFVNZZ/OVUoPG9Dzmsx5LipEfFRuFVwAPJWwcueRETkeCt2nhKq69Eq2OM25LyWZ5+9mxvUsYVQ3fbjF2EUGcFGREQOZTRJwr+Y3tzBM6eNX40hx42JXqasMUrYcVRs/xMiInKcXdpilFWLbckjOgnFnTHkuDGNWon2LcQGnf1n7xmZuyEiohslunWPn0blsQsAXo0hx80FCk4fPH2xXOZOiIjoRolu3fP4oHYeuwDg1Rhy3NylSoNN64iIyHEulVdbrGnu54W0wR3t0I3zY8hxc828VTatIyIixzCaJDz3n/0W6/4+2nO3cbgWQ46b6xHT3KZ1RETkGAs2HEWF3vKg4yBfscVgPQFDjpt7aVScTeuIiMj+jCYJn247IVSbeYKzZWsx5Lg5X40KQ+PCG60ZGhcOXw1vVxEROStrpo4DvFVViyHHA3wyoW+DQWdoXDg+mdDXzh0REZE1fjlYKFybxKnjZp69PakH+WRCX1TqjXhz7UGcvFiBtqF+eHFEVxz4oxTf5/yB8AAf9IsN4WA1IiInYzRJ+DbnD6HaZt4q9G/HkFOLIceD+GpUeH30lY07M3ILMOTdTSi6/Od0xIgAb8y58yaMiI9yVItERHSNXdpiFJfXCNXOG5PAX1avwttVHigjtwBPrNhbJ+AAQNHlajyxYi8ycgsc1BkREV1LdJXjwV1aICUhWuZuXAtDjocxmiQ882VOozXPfJnDDTuJiJyE6CrHkwa2l7kT18OQ42G2/X4e1QZTozXVBhO2/S62yy0REcmrX2wIIgMbDzpRQVfGVVJdDDkeZsmWYzatIyIiea0/WIgqQ/3TxxX/+5p9RxzH4tSDA489zPELFTatIyIi+dSOoWxIsJ8X0u/uxgkjDeCVHA+jFkz6l6vERvITEZE8jCYJM7450GiNt1qJoXGRdurI9TDkeJiercX2qCrXm6C3MHaHiIjks3DjMZRUNP4LZ6GuGru0xXbqyPUw5HiY+3rHCNfO/O9vMnZCREQNMZokLN2uFaoVnWLuiRhyPMyAjmEQHZv2w28FnEpOROQAu7TFKKkUGzYgOsXcE8kect566y0oFApMnTrV/FhVVRVSU1MRGhqKZs2aYcyYMSgqKqpz3OnTpzFq1Cj4+fkhPDwczz//PAwGQ52aTZs2oVevXvD29kaHDh2wbNkyuU/H5amUCvRpI3bLqsYkYeeJizJ3RERE1yrUiV2dCfJVc+p4I2QNObt378bixYuRkJBQ5/Fnn30WP/74I77++mts3rwZ+fn5uPvuu83PG41GjBo1Cnq9Hjt27MDy5cuxbNkyzJo1y1yj1WoxatQo3HbbbcjJycHUqVPx2GOPYd26dXKekluYMrijcO2/Mk/K1wgREdWruKzachGAoV0jOHW8EbKFnLKyMowbNw6ffPIJmjf/88pBaWkpPvvsM7z33nsYPHgwevfujaVLl2LHjh3YuXMnAODnn3/GwYMHsWLFCvTo0QMjR47E66+/jo8++gh6vR4AsGjRIsTGxuLdd99F165dkZaWhnvuuQfvv/++XKfkNgZ0CBOeZbXpyHnesiIisrMzl8SW8bi5Q5jMnbg22UJOamoqRo0aheTk5DqPZ2dno6amps7jXbp0QevWrZGZmQkAyMzMRLdu3RAREWGuGT58OHQ6HfLy8sw117728OHDza9Rn+rqauh0ujpfnkilVGBI13Ch2iqDiSP3iYjsyGiS8J9ssV3HI4N8Ze7GtckScr788kvs3bsX6enp1z1XWFgIjUaD4ODgOo9HRESgsLDQXHN1wKl9vva5xmp0Oh0qKyvr7Ss9PR1BQUHmr5gY8ZlG7mZCUlvhWo7cJyKyn4Ubj6Ks2mCxLsTfi+NxLLB5yDlz5gyeeeYZfPHFF/Dxca4R3zNnzkRpaan568yZM45uyWH6twtFM2+xBa85cp+IyD6uTB0/KVR7V4+WHI9jgc1DTnZ2Ns6dO4devXpBrVZDrVZj8+bNmD9/PtRqNSIiIqDX61FSUlLnuKKiIkRGXlm1MTIy8rrZVrX/b6kmMDAQvr71X77z9vZGYGBgnS9PpVIqMG9MguVCAJ9tOyFzN0REBFg3dTyZKx1bZPOQM2TIEBw4cAA5OTnmrz59+mDcuHHm//by8sKGDRvMxxw5cgSnT59GUlISACApKQkHDhzAuXPnzDXr169HYGAg4uLizDVXv0ZtTe1rkGUpCVGYNLCtxbpfDp3D39cclL8hIiIPJzo8INiPt6pE2HyDzoCAAMTHx9d5zN/fH6GhoebHJ06ciGnTpiEkJASBgYGYMmUKkpKS0L9/fwDAsGHDEBcXh/Hjx2PevHkoLCzEyy+/jNTUVHh7ewMAnnjiCSxcuBAvvPACHn30UWzcuBGrVq3CmjVrbH1Kbm1wl0h8svWkxbpPt2nx/PAu0Ki5fiQRkVxOXigXqntkQCxvVQlwyCfW+++/j9tvvx1jxozBoEGDEBkZiW+++cb8vEqlwurVq6FSqZCUlISHHnoIEyZMwGuvvWauiY2NxZo1a7B+/Xp0794d7777Lj799FMMHz7cEafkskR/a5AkrplDRCQn0a0cgv28kDa4gx06cn0KSZI8dhEUnU6HoKAglJaWeuz4nMzjF/HAJzuFah/q3xpvjO4mc0dERJ5p+9ELGPdZlsW6Z4Z0xLNDO9mhI+cl+vnNew8erl9sCHy9xH4MeGGUiEg+7/58WKjOaDLJ3In7YMjxcCqlAvf3EVsvSKVgzCEiksPa/QXYe6ZUsJr/FotiyCEMj48SqluWeQoZuQUyd0NE5FmMJgkv/He/cH1S+1AZu3EvDDmEfrEhiAoSW/BvxjcHuJcVEZEN7TxxUWiFYwDw16jQvx1DjiiGHIJKqcDsO+KEaksqarDz+EWZOyIi8hwrdp4Srp08qD2njluBIYcAACPiozAiPsJyIYAdJy7I3A0RkWcwmiRsPXpeqNZbreTUcSsx5JCZr1olVPff7LMyd0JE5Bl2aYtRVm0Uqn3i1na8imMlhhwyiw6uf8+vaxXqqrnNAxGRDYguyOqtUuDpIZ69Nk5TMOSQ2YD2YcK1n2zVQm/gWg1ERDdCdBuHp27rwKs4TcCQQ2b924fCTyN2ywoAlgksP05ERPXLyC3A+78ctVjX3M8LaYM72qEj98OQQ2YqpQKPD2onXJ+Rmy9jN0RE7stokjDjmwMW6xQA0u/uxqs4TcSQQ3VY89vC4cIyGTshInJfO49fRElFjcW6p4d0xAjBBVvpegw5VIdKqUCAt9iPRUWNiQsDEhE1wT8zTwrV1XDs4w1hyKHrJMaKr6a5cOMxGTshInI/RpOEX4+IrY2z/48SeZtxcww5dJ0PxvYSrl3461FezSEissIubTH0RrErND5e4pNB6HoMOXSdZj5qtGvhJ1RbY5Tw4fojMndEROQ+fjlYKFybGBsiYyfujyGH6rX+2b9AdCz/km0neDWHiEiA0SRhRdZp4fqHB8TK2I37Y8iheqmUCiS0ChKqraqRsEtbLHNHRESub8fRC6gWHEw8aWBbaNT8mL4R/NOjBt2eID5tcb0Vl1+JiDzVgl8tL/4HAJGB3nhp1E0yd+P+GHKoQdZcJl215yxvWRERNcJokrD75CWh2oRWwfI24yEYcqhBGrUSkwaKBZ2yagMWbhT7DYWIyBNtO3Ieor8K9m3LAce2wJBDjXppVBziWwYK1S7ewgHIREQNWbLthHDtwwPayteIB2HIIYteSokTqqvQG7Hz+EWZuyEick1HCi8L1YX5azjg2Eb4p0gW9YsNgZ+X2I/KtqNiq3gSEXmS9LUHcaFcL1Q7Ij5S5m48B0MOWaRSKtA61F+o9kB+qczdEBG5Fr3BhMVbtML1L40Su3pOljHkkJCY5mIrIJ8prpC5EyIi1zLzv78J1w6NC4evhls52ApDDgnpF9tcqO5UcSXS1x6UuRsiItdgNEn4/rd8odoWzTT4ZEJfmTvyLAw5JOThAbHC2zx8slULveCKnkRE7mzhxqMQ/efwlg5h8jbjgRhySIhGrcTkQWJr5pgk4F+ZJ+VtiIjIyRlNEpZuPylcf3evVvI146EYckjYzJQ4xEUFCNV+s++szN0QETm3XdpilFTWCNX6eCkxgFdybI4hh6wyRvA3jbz8y1i7X+w+NBGRO/opV/zfwPfu7QGVUnRQAIliyCGrjE9qC4Xg38OXv8/lCshE5JGMJgkrdp4Wqo2LCkCKFRsikziGHLKKRq3EbZ1bCNUWl9dgl7ZY5o6IiJzPM//eB9Hf8Xq34T5VcmHIIatNGtheuHb9wUIZOyEicj56gwmrDxQI17cNFVuHjKzHkENW6xcbghB/jVDt59tPIiNX/C87EZGr+3Sr+EacClwZBkDyYMghq6mUCrxxZ7xw/cxvDnBsDhF5jE+t2G08pVskN+OUEf9kqUlSEqLwuOC6OZcqarDzBHcnJyL3ZzRJKC4XmzYOAPMf6CVjN8SQQ002MyUOI26KEKrNPM6QQ0Tu777FO4Rru0U347RxmTHk0A3pEN5MqO74+csyd0JE5Firc/5A9qkS4fp/T75ZvmYIAEMO3aCkdmIrdO48fpHjcojIbRlNEqb/R3y38XZhfmjmo5axIwIYcugG9W8fimbeKot1lyoNWLjxmB06IiKyv53HL6LaIPaLnALA+ml/kbUfuoIhh26ISqnA/X1ihGrf/+V3TicnIreUeeKCcG3fts05FsdOGHLohiXHRQrXTlv1G29bEZEbEg8tUwZ3lLEPuhpDDt2wfrEhiAryEaqt0Bsxf8NRmTsiIrKvvm2aC9Vxt3H7YsihG6ZSKjD7jjjh+n/8eoxXc4jIbaSvPYhHlu8WquVu4/bFkEM2MSI+CiPjxW5b1ZgkzN/wu8wdERHJL33tQSzeohXajPPxQbHcbdzOGHLIZh7q30a49uNNx3k1h4hcmt5gwpKtWot1wb5q/OPBnpiZIn7Fm2yDIYdspn+7UHipxC7D6o0SdnIVZCJyYct3nIQk8LvaU3/pgJSEaPkbousw5JDNqJQKDOkaLlz/8ncHZOyGiEheCzaKTaLYc6pY5k6oIQw5ZFPjE9sK12ovVmB1zh/yNUNEJJPHlu+GrsogVOun4crGjsKQQzZ1ZQVk8b/Qz/13P8fmEJFLqdQb8cuhc8L1Y3q1krEbagxDDtmUSqnAvDEJwvVVNSaOzSEilzJJcLo4AHipFFwXx4EYcsjmUhKikNRObGEswLrl0ImIHMlokrDNil/MkruGc10cB2LIIVksf7S/cO2xc2UydkJEZDtTVu61qv4hK8Ypku0x5JAsNGolbu8mtuhVRl4R0tcelLkjIqIbozeYsDa3ULherbgyTpEchyGHZPPhAz2hUYv9iC3eosXa/dyhnIic18z//mZVPbdwcDyGHJKNSqnA/LE9hOunfLmXM62IyCll5Bbgv/vyhesjA73x114tZeyIRNg85KSnp6Nv374ICAhAeHg4Ro8ejSNHjtSpqaqqQmpqKkJDQ9GsWTOMGTMGRUVFdWpOnz6NUaNGwc/PD+Hh4Xj++edhMNRdk2DTpk3o1asXvL290aFDByxbtszWp0M3aER8FB69ua1QrdEE3Ldou7wNERFZyWiSMOMb6xYv3fLCYJm6IWvYPORs3rwZqamp2LlzJ9avX4+amhoMGzYM5eXl5ppnn30WP/74I77++mts3rwZ+fn5uPvuu83PG41GjBo1Cnq9Hjt27MDy5cuxbNkyzJo1y1yj1WoxatQo3HbbbcjJycHUqVPx2GOPYd26dbY+JbpBQ+PENu4EgOzTpajUG2XshojIOgs3HkNJRY1wfUp8hPCtepKXQpJEdt5ouvPnzyM8PBybN2/GoEGDUFpaihYtWmDlypW45557AACHDx9G165dkZmZif79++Onn37C7bffjvz8fERERAAAFi1ahBdffBHnz5+HRqPBiy++iDVr1iA3N9f8vcaOHYuSkhJkZGQI9abT6RAUFITS0lIEBgba/uQJwJXfghLmrEN5tVh4Gd+/NV4f3U3mroiILDOaJHR++ScYBG+le6kUOPz6SI7FkZno57fsUbO0tBQAEBISAgDIzs5GTU0NkpOTzTVdunRB69atkZmZCQDIzMxEt27dzAEHAIYPHw6dToe8vDxzzdWvUVtT+xr1qa6uhk6nq/NF8lMpFZh0S6xw/YaDRZaLiIjs4Jl/7xMOOACw4IGeDDhORNaQYzKZMHXqVNx8882Ij48HABQWFkKj0SA4OLhObUREBAoLC801Vwec2udrn2usRqfTobKyst5+0tPTERQUZP6KiYm54XMkMVOGdILo1dt8XTX+umCrvA0REVmgN5iw+oD4rM9Hbm6LEfFiS2eQfcgaclJTU5Gbm4svv/xSzm8jbObMmSgtLTV/nTlzxtEteQyVUoF37+0hXL//Dx0eW75LvoaIiCyY8NlOq+qHWTH+kOxDtpCTlpaG1atX49dff0WrVn9uThYZGQm9Xo+SkpI69UVFRYiMjDTXXDvbqvb/LdUEBgbC19e33p68vb0RGBhY54vs586eLdE6pP73pj6/HDrPQchE5BB6gwk7tZeE60P8vNAvNkTGjqgpbB5yJElCWloavv32W2zcuBGxsXXHYvTu3RteXl7YsGGD+bEjR47g9OnTSEpKAgAkJSXhwIEDOHfuz11e169fj8DAQMTFxZlrrn6N2pra1yDnNHdMd6vq7/6It62IyP6svYrzxuhuHIvjhNS2fsHU1FSsXLkS33//PQICAsxjaIKCguDr64ugoCBMnDgR06ZNQ0hICAIDAzFlyhQkJSWhf/8r+x0NGzYMcXFxGD9+PObNm4fCwkK8/PLLSE1Nhbe3NwDgiSeewMKFC/HCCy/g0UcfxcaNG7Fq1SqsWbPG1qdENtQvNgQh/l4oLhebjnmoqBx6g4nTMYnIbtbuz7fqKs6o+HCkJHAsjjOy+RRyhaL+JLt06VL87W9/A3BlMcDp06fj3//+N6qrqzF8+HD84x//MN+KAoBTp07hySefxKZNm+Dv74+HH34Yb731FtTqP3PZpk2b8Oyzz+LgwYNo1aoVXnnlFfP3EMEp5I6xdn8+nlq5T7i+f2wIvnycV+iISH5Gk4Sur/wEvVHso1EJ4OibKbyKY2ein9+yr5PjzBhyHOe+j7dj16kS4fp/PNgTKQnR8jVERAQg9Yu9WGPFjKqnB3fAtGGdZeyI6uM06+QQ1WfFJOuuzDzz5T7ua0VEslq7v8CqgKNSAs8kd5KxI7pRDDnkEBq1Einx4tMta0zA0Pc3ydcQEXk0o0nCUyv3WnXMlNs68jaVk2PIIYdZ8GAvWPPPw4nzFXjjx4Oy9UNEnmvuT9b92+KtVmLKkI4ydUO2wpBDDqNSKvD0kA5WHfPpdi30BpNMHRGRJzKaJCzZetKqY96/rzuv4rgAhhxyqKeHdIKfRmXVMRM+a3h/MiIia93z8Tar6kd1i+RECBfBkEMOpVIq8N591i0QuFNbgrX782XqiIg8yY+/5WPfGes2a57/QC+ZuiFbY8ghhxsRH4UFD/S06pinOduKiG6Q0SRh+qocq46Zd1c8b1O5EIYccgp3dI/GiPgIy4X/YzABye9tkq8hInJ79y7aLrzoHwAoFcB9iW1k7IhsjSGHnMZHD/a26jck7YUKzPkxT8aOiMhdvfHjQew9XWrVMXlzRsjUDcmFIYechkqpwIKx1t22Wrr9JNbuF1+8i4hodU4+Pt2uteqYIV1awNfKSRLkeAw55FRSEqIwaWCs5cKrPPc1x+cQkZiM3AKkfSm+dx4AhAd44bO/9ZOpI5ITQw45nZdGxaFrRDPh+ooaCfcu2iFjR0TkDowmCU+ssG5VY7USyJw5VKaOSG4MOeSUvkm9xar6vadLMOmfu2XqhojcQY8566w+Zv7YXpxN5cIYcsgp+WpUCPP3suqY9QfPoVJvlKkjInJljyzdhcvV1v37MPGWtkhJiJKpI7IHhhxyWpueH2z1MXGzMmTohIhc2eqcP/DrkfNWHdO7dRBeuf0mmToie2HIIafVzEeNhJaBVh0jAej0f2vkaYiIXI7RJGH6179ZdYxKAax64maZOiJ7Ysghp/bDlIFoE+pr1TF6E/Dwpztl6oiIXMmH639HtRUL/gFA6l/acxyOm2DIIae3+fnBmHt3N+uOOXYRP+z9Q6aOiMgVpK89iPm/HrPqGG+1Es8M7SxTR2RvDDnkEu7v1xotrByI/PSqHGTkcqFAIk+0dn8+Fm+xbsE/APhwbA9exXEjDDnkMn5twkDktJV7uVAgkYcxmiQ8tdK6Bf8A4B8P9sKIeM6mcicMOeQymvmo0TbEuvE5BhMw5uPtMnVERM6o+6vWz7JcOLYHp4u7IYYccikbnrvN6mNyzpRi9g/7ZeiGiJzN8Hc3okxvsuqYxwfF4vYeLWXqiByJIYdcikqpwD8etG4TTwBYvuMMHl2aJUNHROQsur7yE46cr7TqmIVje2JmSpxMHZGjMeSQy0lJiMbEW6zbxBMANh65gL8u2CpDR0TkaAPn/oLKGuuu4AzsEIrbe0TL1BE5A4Ycckmv3B6H27qEWX3c/j90mP19rgwdEZGjFJfpceZStdXHLZnQV4ZuyJkw5JDLWvq3RNwU5W/1ccszT+ER3roicgs//paP3m+st/q4v3QKg69GJUNH5EwYcsilrXnmL4gM9Lb6uF+PXMCgeRtk6IiI7OXRZbsw5d/7YO0iEb5eSix7NFGWnsi5MOSQy9vygvXr5wDA6eIqPLKMV3SIXNGguRuw8bB1m24CQIivCodeHylDR+SMGHLI5WnUSkwa2LZJx/56+AK+5fYPRC5l1IebcfpSldXHtQrwwt7ZI2ToiJwVQw65hZdG3YTBna0fiAwAz67KwRwORiZyCeM/yUReQVmTjt08c6iNuyFnx5BDbuPzRxLRrWVgk45dmnkKA+dyjA6RM7tp1k/Yery4Scf+48Ge3JPKAzHkkFv5ccpADOkS3qRjz1yqQs851i8HT0Tyi5/1E8qtXMm41qSBsUhJ4Ho4noghh9zOZ3/ri/n3dW/SsZcqjeg1Z52NOyKiptIbTEh4NcPqrRpqTRoYi5dGcUVjT8WQQ27pr71a4R8P9mrSscWVBvSYsw56Q9P+USUi25jzYy46vfwTdFXGJh3/wT0JDDgejiGH3FZKQlST9rkCgJJKAzq9/BOeWpENo8naVTiI6Ebd+vZGLN1+qsnHD+nSAqP7xNiwI3JFDDnk1lISorHooV4I9G7aj/ra3EJ0+L+1WJ3DaeZE9qA3mJD87iacumjdRptXS2gViM/+1s+GXZGrUkiS5LG/pup0OgQFBaG0tBSBgU2blUOuwWiSkPrFHmTknWvyawzuHIbPH+EqqURySV97EEu2aK1ewfhqH97fA3f2bGmznsg5iX5+80oOeQSVUoFF4/siprlPk19j45ELGDh3ow27IqJa6WsPYvENBBwftQLH30xhwKE6GHLIo2x9cQia+zZ9U74zlyrRYeYalFbU2LArIs9VVmXAxGVZWLxF2+TXaKZR4vAbKVwHh67D21W8XeWRes1Zh+JKww29RpsQX2xu4r5ZRAT8deFW7D+ru6HXaBXsg20zhtioI3IVvF1F1Ii9s4cjJsT3hl7jVHEl2vOqDpFVjCYJmccvYuDcjTcccLpFBzDgUKN4JYdXcjza6z/k4rMdTZ+mWisiQIMdM5N5uZyoAUaThAUbjuLTbSdQVt20dW+u9sE9CZwi7sFEP78ZchhyPJ7eYMKgeRtRqKu+4dd6enAHPJPciWGH6Cpr9xfg2VU5qLbRApv/eLAXUhKibPJa5JoYcgQw5NDV7pi/BQfyL9vktRh2iK5cvXnmy31Yvb/AJq8X6K3EvHt7YEQ8A46nY8gRwJBD13p9dR4+23bSZq93c/sQfPpwP/hqmj6ji8iVGE0Stv1+Hm/+dAhHisps8poatRKpf2mPtMEd+YsDAWDIEcKQQ/XRG0zo+8bPKG3ifjn16RTuj9VPD4JGzbH+5J6MJgnzN/yOBRuPwVY7odzeLQL392mDAR3DGG6oDoYcAQw51Jjb529Bro1uX9WKDvJB+uhuuKVzC/6jTW5BbzDhhVU5+M5Gt6RqJbQKxA9pA236muQ+GHIEMOSQJW/8eBCfbm/6ImWNaR/mh/v6xuCRm9vxCg+5nEq9EXct3ILD5yps/toJLQPxwxQGHGoYQ44AhhwSoTeYMG7JDuw+XSrb90iJj8CCB3vz6g45Lb3BhGXbtPj5UCF+L7oMnQ1v517t/fu6465erWR5bXIfDDkCGHLIGmv35+Oplftk/R7NvFV44tb2mDyoPa/ukMMZTRJ2Hr+It38+jJwz8oV8AIgK8sHsO+I4c4qEMOQIYMghaxlNEt7OOIhFW07K/r38NErc2b0lZt1xE2dnkd1cPTvq93NlkPsTItTfCwsf7I1+sSG8kknCGHIEMORQUxlNEpLf2wTtBduPR6iPr5cSnSOawd9bje6tmuPmjmHo3y6UHwp0Q8qqDJiycjeytCWoMZmgggJVRvt9JHDsDTUVQ44Ahhy6Ua+vPojPtskzMNkSBYDwAC9UG0xQQIHYFv5Y+rdEBPl5OaQfcm56gwnLd5zE1t/PIfPERdTYZvFhq/molbilYxg+uL8nmvmoHdMEuTyGHAEMOWQLeoMJy7Zr8f4vv6PSUZ8c1wj0VsJokqBRq9A5KgBPDuqAWzpx2rq7M5ok7NIW49TFMny+TQtdlQEB3ipU1xhwukTv6PYwaWBbvDTqJke3QW6AIUcAQw7ZWlmVAWMX70BugW3X17EVLyUQHahBlVFCjUlCRZUBSgXg7eWFHq2DMaB9KB4eEMtBz06mdpzMkq0nUFxejXO6KlQZTJAkCQpI0BsBSQIMTvqveb+2wVjxWBJ/rshmGHIEMOSQXIwmCR/8cgQLNh53dCtNogCgUl754Az0VuG2ri3gpVIj948SXCjTw1uthEKhQLCPCgWXq2EwmGCSJGi81KjSG1BjMEH63wvVGAGTBEi4ErJ8NSr4qhWoMUr/u7KkwKWKGhilK9+3ZbA3lAolJIUELwVQWmVEM2812ob6oaLGiNPFlQjwViOhVSAOFVzGH6VVkCQJYf5e0KhVKK3Qo8ogQaEAFAoFmmlUCPLT4EJZNfQGE4J81fBVK3DiYhX0BhNUSiDAW40qowSjyYhmXmoE+nmhtPJKAAwP8EFibCj2nbmEIl01DCYTFDDBYJJQqZdgkiT4adQI9FGg2ggYjBICfLxQqTei2mCAUVLAT6NGsJ8aUYE+kABcuFwF7cVK6A1GGE2AWgm0CPBGRJA38kuqUaE3olJvhEohQalUokzvHFcIrTWmZ0ukj0lguCGbY8gRwJBDcjOaJKStzMZPuUWOboXILppplJj/QG/cylW9SUain98uH68/+ugjtG3bFj4+PkhMTMSuXbsc3RKRmUqpwMcP9cHvb4zEK6O6YmDHUPCffXJH0YHeOPTaCOS+NhKDu4Yz4JBTcOkrOV999RUmTJiARYsWITExER988AG+/vprHDlyBOHh4RaP55UccgSjScKOoxeQ9uVelFYaHN0OUZMoAHSKaIZ+sSH4v5Q4ruVEduURt6sSExPRt29fLFy4EABgMpkQExODKVOmYMaMGdfVV1dXo7q62vz/Op0OMTExDDnkMGVVBjy1cg+2/n4RLvsXkTyGWgkM7RqOh/rHon97rtNEjiMaclx2kQK9Xo/s7GzMnDnT/JhSqURycjIyMzPrPSY9PR1z5syxV4tEFjXzUeOfj/YHcGUq+udbT+C/2Wdw4mIF7LgmG1GDQv28cE/fVhjUIZzBhlyOy4acCxcuwGg0IiIios7jEREROHz4cL3HzJw5E9OmTTP/f+2VHCJnoFEr8cRtHfDEbR0AXNnl+Y01efjtTAmKdNW4UKbn1R6SXWSAGi2D/TH8pij87RYuJ0CuzWVDTlN4e3vD29vb0W0QCfHVqPD3uxLM/280Sdhx7AL+k30GBwtKkV9ShXIXnVpMziNIA/Rp1wIfju3FFYjJ7bjsT3RYWBhUKhWKiupOzS0qKkJkZKSDuiKSj0qpwMBOLTCwUwvzY7VL9WdpLyK/uBwllQZU1BhRVWNElbOuDEd2FRmoQY+WAThQUA6jSUL7Fs0weWA7roBNHsFlQ45Go0Hv3r2xYcMGjB49GsCVgccbNmxAWlqaY5sjshONWolJg9ph0qB21z1XO4vrqz2nsOXoBVTVmKBUSIhoVnfF4yqjAxqnG+atBFQqhXnFY5UC8FIr4eetRvuwZnj81va4pSODDHk2lw05ADBt2jQ8/PDD6NOnD/r164cPPvgA5eXleOSRRxzdGpHDqZQKDOzcAgM7t7BYW1ymx72LtuF0cSVM0pUPTBO44rEzrHjs6+2FDi38EeirwbC4CLQK8Ue/2BCGFyIBLh1y7r//fpw/fx6zZs1CYWEhevTogYyMjOsGIxNR40KaabDhucGOboOIyKZcep2cG8XFAImIiFyPx2zrQERERFQfhhwiIiJySww5RERE5JYYcoiIiMgtMeQQERGRW2LIISIiIrfEkENERERuiSGHiIiI3JJLr3h8o2rXQdTpdA7uhIiIiETVfm5bWs/Yo0PO5cuXAQAxMTEO7oSIiIisdfnyZQQFBTX4vEdv62AymZCfn4+AgAAoFLbZ7E6n0yEmJgZnzpxx260i3P0c3f38APc/R3c/P8D9z9Hdzw9w/3OU8/wkScLly5cRHR0NpbLhkTcefSVHqVSiVatWsrx2YGCgW/7QXs3dz9Hdzw9w/3N09/MD3P8c3f38APc/R7nOr7ErOLU48JiIiIjcEkMOERERuSWGHBvz9vbG7Nmz4e3t7ehWZOPu5+ju5we4/zm6+/kB7n+O7n5+gPufozOcn0cPPCYiIiL3xSs5RERE5JYYcoiIiMgtMeQQERGRW2LIISIiIrfEkENERERuiSGnCf7+979jwIAB8PPzQ3BwcL01p0+fxqhRo+Dn54fw8HA8//zzMBgMjb5ucXExxo0bh8DAQAQHB2PixIkoKyuT4Qyss2nTJigUinq/du/e3eBxf/nLX66rf+KJJ+zYubi2bdte1+tbb73V6DFVVVVITU1FaGgomjVrhjFjxqCoqMhOHVvn5MmTmDhxImJjY+Hr64v27dtj9uzZ0Ov1jR7nzO/hRx99hLZt28LHxweJiYnYtWtXo/Vff/01unTpAh8fH3Tr1g1r1661U6fWS09PR9++fREQEIDw8HCMHj0aR44cafSYZcuWXfde+fj42Klj67366qvX9dulS5dGj3Gl97C+f1MUCgVSU1PrrXeF92/Lli244447EB0dDYVCge+++67O85IkYdasWYiKioKvry+Sk5Nx9OhRi69r7d9lazDkNIFer8e9996LJ598st7njUYjRo0aBb1ejx07dmD58uVYtmwZZs2a1ejrjhs3Dnl5eVi/fj1Wr16NLVu2YPLkyXKcglUGDBiAgoKCOl+PPfYYYmNj0adPn0aPnTRpUp3j5s2bZ6eurffaa6/V6XXKlCmN1j/77LP48ccf8fXXX2Pz5s3Iz8/H3XffbadurXP48GGYTCYsXrwYeXl5eP/997Fo0SL83//9n8VjnfE9/OqrrzBt2jTMnj0be/fuRffu3TF8+HCcO3eu3vodO3bggQcewMSJE7Fv3z6MHj0ao0ePRm5urp07F7N582akpqZi586dWL9+PWpqajBs2DCUl5c3elxgYGCd9+rUqVN26rhpbrrppjr9btu2rcFaV3sPd+/eXefc1q9fDwC49957GzzG2d+/8vJydO/eHR999FG9z8+bNw/z58/HokWLkJWVBX9/fwwfPhxVVVUNvqa1f5etJlGTLV26VAoKCrru8bVr10pKpVIqLCw0P/bxxx9LgYGBUnV1db2vdfDgQQmAtHv3bvNjP/30k6RQKKQ//vjD5r3fCL1eL7Vo0UJ67bXXGq279dZbpWeeecY+Td2gNm3aSO+//75wfUlJieTl5SV9/fXX5scOHTokAZAyMzNl6ND25s2bJ8XGxjZa46zvYb9+/aTU1FTz/xuNRik6OlpKT0+vt/6+++6TRo0aVeexxMRE6fHHH5e1T1s5d+6cBEDavHlzgzUN/XvkrGbPni11795duN7V38NnnnlGat++vWQymep93tXePwDSt99+a/5/k8kkRUZGSm+//bb5sZKSEsnb21v697//3eDrWPt32Vq8kiODzMxMdOvWDREREebHhg8fDp1Oh7y8vAaPCQ4OrnNlJDk5GUqlEllZWbL3bI0ffvgBFy9exCOPPGKx9osvvkBYWBji4+Mxc+ZMVFRU2KHDpnnrrbcQGhqKnj174u2332709mJ2djZqamqQnJxsfqxLly5o3bo1MjMz7dHuDSstLUVISIjFOmd7D/V6PbKzs+v82SuVSiQnJzf4Z5+ZmVmnHrjyd9KV3isAFt+vsrIytGnTBjExMbjzzjsb/PfGWRw9ehTR0dFo164dxo0bh9OnTzdY68rvoV6vx4oVK/Doo49CoVA0WOdq79/VtFotCgsL67xHQUFBSExMbPA9asrfZWt59C7kciksLKwTcACY/7+wsLDBY8LDw+s8plarERIS0uAxjvLZZ59h+PDhFndwf/DBB9GmTRtER0dj//79ePHFF3HkyBF88803dupU3NNPP41evXohJCQEO3bswMyZM1FQUID33nuv3vrCwkJoNJrrxmRFREQ43ftVn2PHjmHBggV45513Gq1zxvfwwoULMBqN9f4dO3z4cL3HNPR30hXeK5PJhKlTp+Lmm29GfHx8g3WdO3fG559/joSEBJSWluKdd97BgAEDkJeXZ/HvqiMkJiZi2bJl6Ny5MwoKCjBnzhwMHDgQubm5CAgIuK7eld/D7777DiUlJfjb3/7WYI2rvX/Xqn0frHmPmvJ32VoMOf8zY8YMzJ07t9GaQ4cOWRwY50qacs5nz57FunXrsGrVKouvf/V4om7duiEqKgpDhgzB8ePH0b59+6Y3Lsia85s2bZr5sYSEBGg0Gjz++ONIT0936n1lmvIe/vHHHxgxYgTuvfdeTJo0qdFjHf0eEpCamorc3NxGx6sAQFJSEpKSksz/P2DAAHTt2hWLFy/G66+/LnebVhs5cqT5vxMSEpCYmIg2bdpg1apVmDhxogM7s73PPvsMI0eORHR0dIM1rvb+uQqGnP+ZPn16oykbANq1ayf0WpGRkdeNDq+ddRMZGdngMdcOtDIYDCguLm7wmBvVlHNeunQpQkND8de//tXq75eYmAjgylUEe3xA3sh7mpiYCIPBgJMnT6Jz587XPR8ZGQm9Xo+SkpI6V3OKiopke7/qY+055ufn47bbbsOAAQOwZMkSq7+fvd/D+oSFhUGlUl03k62xP/vIyEir6p1FWlqaeRKCtb/Ne3l5oWfPnjh27JhM3dlWcHAwOnXq1GC/rvoenjp1Cr/88ovVVz9d7f2rfR+KiooQFRVlfryoqAg9evSo95im/F22mk1G9ngoSwOPi4qKzI8tXrxYCgwMlKqqqup9rdqBx3v27DE/tm7dOqcaeGwymaTY2Fhp+vTpTTp+27ZtEgDpt99+s3FntrdixQpJqVRKxcXF9T5fO/D4P//5j/mxw4cPO/XA47Nnz0odO3aUxo4dKxkMhia9hrO8h/369ZPS0tLM/280GqWWLVs2OvD49ttvr/NYUlKS0w5aNZlMUmpqqhQdHS39/vvvTXoNg8Egde7cWXr22Wdt3J08Ll++LDVv3lz68MMP633e1d7DWrNnz5YiIyOlmpoaq45z9vcPDQw8fuedd8yPlZaWCg08tubvstV92uRVPMypU6ekffv2SXPmzJGaNWsm7du3T9q3b590+fJlSZKu/HDGx8dLw4YNk3JycqSMjAypRYsW0syZM82vkZWVJXXu3Fk6e/as+bERI0ZIPXv2lLKysqRt27ZJHTt2lB544AG7n19DfvnlFwmAdOjQoeueO3v2rNS5c2cpKytLkiRJOnbsmPTaa69Je/bskbRarfT9999L7dq1kwYNGmTvti3asWOH9P7770s5OTnS8ePHpRUrVkgtWrSQJkyYYK659vwkSZKeeOIJqXXr1tLGjRulPXv2SElJSVJSUpIjTsGis2fPSh06dJCGDBkinT17ViooKDB/XV3jKu/hl19+KXl7e0vLli2TDh48KE2ePFkKDg42z2gcP368NGPGDHP99u3bJbVaLb3zzjvSoUOHpNmzZ0teXl7SgQMHHHUKjXryySeloKAgadOmTXXeq4qKCnPNtec4Z84cad26ddLx48el7OxsaezYsZKPj4+Ul5fniFOwaPr06dKmTZskrVYrbd++XUpOTpbCwsKkc+fOSZLk+u+hJF35wG7durX04osvXvecK75/ly9fNn/eAZDee+89ad++fdKpU6ckSZKkt956SwoODpa+//57af/+/dKdd94pxcbGSpWVlebXGDx4sLRgwQLz/1v6u3yjGHKa4OGHH5YAXPf166+/mmtOnjwpjRw5UvL19ZXCwsKk6dOn10nyv/76qwRA0mq15scuXrwoPfDAA1KzZs2kwMBA6ZFHHjEHJ2fwwAMPSAMGDKj3Oa1WW+fP4PTp09KgQYOkkJAQydvbW+rQoYP0/PPPS6WlpXbsWEx2draUmJgoBQUFST4+PlLXrl2lN998s85Vt2vPT5IkqbKyUnrqqaek5s2bS35+ftJdd91VJzQ4k6VLl9b7M3v1xVxXew8XLFggtW7dWtJoNFK/fv2knTt3mp+79dZbpYcffrhO/apVq6ROnTpJGo1Guummm6Q1a9bYuWNxDb1XS5cuNddce45Tp041/3lERERIKSkp0t69e+3fvKD7779fioqKkjQajdSyZUvp/vvvl44dO2Z+3tXfQ0m6cjUegHTkyJHrnnPF96/2c+var9rzMJlM0iuvvCJFRERI3t7e0pAhQ6479zZt2kizZ8+u81hjf5dvlEKSJMk2N76IiIiInAfXySEiIiK3xJBDREREbokhh4iIiNwSQw4RERG5JYYcIiIicksMOUREROSWGHKIiIjILTHkEBERkVtiyCEiIiK3xJBDREREbokhh4iIiNzS/wPCZi9i+7vhVAAAAABJRU5ErkJggg==",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2082,7 +3445,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2094,18 +3457,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.1%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-9.97, -9.96)\": 9860.7, \"(-9.96, -9.96)\": 9833.2, \"(-9.96, -9.95)\": 9813.2, \"(-9.95, -9.93)\": 9753.3, \"(-9.93, -9.93)\": 9719.3, \"(-9.93, -9.91)\": 9705.4, \"(-9.91, -9.89)\": 9574.7, \"(-9.89, -9.87)\": 9541.3, \"(-9.87, -9.86)\": 9483.5, \"(-9.86, -9.86)\": 9446.6, \"(-9.86, -9.85)\": 9432.5, \"(-9.85, -9.84)\": 9393.5, \"(-9.84, -9.84)\": 9369.2, \"(-9.84, -9.81)\": 9342.2, \"(-9.81, -9.78)\": 9205.0, \"(-9.78, -9.77)\": 9148.5, \"(-9.77, -9.74)\": 9094.1, \"(-9.74, -9.7)\": 8903.0, \"(-9.7, -9.66)\": 8817.5, \"(-9.66, -9.63)\": 8624.5, \"(-9.63, -9.61)\": 8581.2, \"(-9.61, -9.6)\": 8507.2, \"(-9.6, -9.58)\": 8448.8, \"(-9.58, -9.57)\": 8404.1, \"(-9.57, -9.55)\": 8341.7, \"(-9.55, -9.55)\": 8314.0, \"(-9.55, -9.54)\": 8286.8, \"(-9.54, -9.53)\": 8262.4, \"(-9.53, -9.52)\": 8240.6, \"(-9.52, -9.5)\": 8191.7, \"(-9.5, -9.48)\": 8080.0, \"(-9.48, -9.47)\": 8059.1, \"(-9.47, -9.45)\": 8005.1, \"(-9.45, -9.41)\": 7892.2, \"(-9.41, -9.38)\": 7786.0, \"(-9.38, 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\"(-8.8, -8.79)\": 5964.5, \"(-8.79, -8.77)\": 5939.1, \"(-8.77, -8.72)\": 5856.3, \"(-8.72, -8.69)\": 5744.1, \"(-8.69, -8.67)\": 5687.6, \"(-8.67, -8.67)\": 5647.0, \"(-8.67, -8.65)\": 5631.5, \"(-8.65, -8.63)\": 5552.7, \"(-8.63, -8.61)\": 5517.9, \"(-8.61, -8.57)\": 5441.4, \"(-8.57, -8.54)\": 5352.2, \"(-8.54, -8.52)\": 5299.2, \"(-8.52, -8.51)\": 5254.7, \"(-8.51, -8.46)\": 5211.7, \"(-8.46, -8.41)\": 5034.5, \"(-8.41, -8.4)\": 4988.9, \"(-8.4, -8.39)\": 4964.4, \"(-8.39, -8.37)\": 4936.3, \"(-8.37, -8.34)\": 4895.4, \"(-8.34, -8.31)\": 4776.8, \"(-8.31, -8.29)\": 4751.1, \"(-8.29, -8.27)\": 4691.9, \"(-8.27, -8.26)\": 4666.8, \"(-8.26, -8.25)\": 4651.5, \"(-8.25, -8.24)\": 4622.6, \"(-8.24, -8.23)\": 4596.5, \"(-8.23, -8.23)\": 4573.6, \"(-8.23, -8.16)\": 4531.2, \"(-8.16, -8.09)\": 4317.5, \"(-8.09, -8.09)\": 4294.1, \"(-8.09, -8.08)\": 4282.0, \"(-8.08, -8.08)\": 4263.8, \"(-8.08, -8.08)\": 4248.5, \"(-8.08, -8.07)\": 4237.7, \"(-8.07, -8.04)\": 4201.0, \"(-8.04, -8.03)\": 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6246.5, \"(8.89, 8.91)\": 6282.6, \"(8.91, 8.92)\": 6317.8, \"(8.92, 8.93)\": 6337.4, \"(8.93, 8.96)\": 6383.9, \"(8.96, 8.99)\": 6506.6, \"(8.99, 9.01)\": 6553.0, \"(9.01, 9.04)\": 6638.6, \"(9.04, 9.04)\": 6680.5, \"(9.04, 9.06)\": 6712.4, \"(9.06, 9.09)\": 6779.4, \"(9.09, 9.11)\": 6859.3, \"(9.11, 9.12)\": 6907.7, \"(9.12, 9.14)\": 6952.6, \"(9.14, 9.18)\": 7032.6, \"(9.18, 9.25)\": 7172.7, \"(9.25, 9.3)\": 7415.5, \"(9.3, 9.3)\": 7473.1, \"(9.3, 9.31)\": 7503.2, \"(9.31, 9.36)\": 7589.3, \"(9.36, 9.41)\": 7784.0, \"(9.41, 9.42)\": 7836.8, \"(9.42, 9.49)\": 7935.6, \"(9.49, 9.55)\": 8288.3, \"(9.55, 9.56)\": 8329.0, \"(9.56, 9.57)\": 8367.5, \"(9.57, 9.64)\": 8414.5, \"(9.64, 9.71)\": 8817.1, \"(9.71, 9.72)\": 8897.1, \"(9.72, 9.73)\": 8939.5, \"(9.73, 9.75)\": 8966.4, \"(9.75, 9.77)\": 9092.1, \"(9.77, 9.78)\": 9126.1, \"(9.78, 9.78)\": 9137.7, \"(9.78, 9.79)\": 9169.1, \"(9.79, 9.81)\": 9199.6, \"(9.81, 9.82)\": 9284.0, \"(9.82, 9.86)\": 9372.0, \"(9.86, 9.91)\": 9576.3, \"(9.91, 9.92)\": 9664.7, \"(9.92, 9.94)\": 9721.2, \"(9.94, 9.96)\": 9815.4, \"(9.96, 9.96)\": 9839.2, \"(9.96, 9.98)\": 9891.3}\n",
+      "Means: {\"(-10.0, 5.31)\": -4.2, \"(5.31, 6.02)\": -212.5, \"(6.02, 6.43)\": -425.5, \"(6.43, 6.73)\": -640.0, \"(6.73, 6.97)\": -850.4, \"(6.97, 7.17)\": -1091.7, \"(7.17, 7.33)\": -1321.2, \"(7.33, 7.49)\": -1567.9, \"(7.49, 7.61)\": -1795.0, \"(7.61, 7.72)\": -2030.9, \"(7.72, 7.8)\": -2255.3, \"(7.8, 7.91)\": -2524.8, \"(7.91, 8.0)\": -2771.5, \"(8.0, 8.09)\": -3046.1, \"(8.09, 8.15)\": -3266.0, \"(8.15, 8.2)\": -3493.6, \"(8.2, 8.26)\": -3707.6, \"(8.26, 8.33)\": -3924.9, \"(8.33, 8.39)\": -4154.9, \"(8.39, 8.46)\": -4548.1, \"(8.46, 8.54)\": -4912.7, \"(8.54, 8.62)\": -5180.9, \"(8.62, 8.69)\": -5690.2, \"(8.69, 8.73)\": -5989.8, \"(8.73, 8.78)\": -6231.5, \"(8.78, 8.83)\": -6601.6, \"(8.83, 8.88)\": -6946.1, \"(8.88, 8.91)\": -7228.2, \"(8.91, 8.95)\": -7475.3, \"(8.95, 8.97)\": -7722.1, \"(8.97, 9.02)\": -7931.8, \"(9.02, 9.07)\": -8415.8, \"(9.07, 9.13)\": -8841.2, \"(9.13, 9.18)\": -9485.9, \"(9.18, 9.21)\": -9773.0, \"(9.21, 9.24)\": -10155.2, \"(9.24, 9.27)\": -10389.6, \"(9.27, 9.29)\": -10742.6, \"(9.29, 9.33)\": -10965.8, \"(9.33, 9.35)\": -11364.7, \"(9.35, 9.38)\": -11582.5, \"(9.38, 9.42)\": -12121.5, \"(9.42, 9.48)\": -12641.3, \"(9.48, 9.51)\": -13360.2, \"(9.51, 9.54)\": -13584.8, \"(9.54, 9.56)\": -13987.8, \"(9.56, 9.59)\": -14417.8, \"(9.59, 9.62)\": -14773.8, \"(9.62, 9.65)\": -15250.4, \"(9.65, 9.68)\": -15889.2, \"(9.68, 9.71)\": -16182.7, \"(9.71, 9.72)\": -16531.4, \"(9.72, 9.75)\": -16740.4, \"(9.75, 9.78)\": -17196.9, \"(9.78, 9.83)\": -18129.0, \"(9.83, 9.85)\": -18727.3, \"(9.85, 9.86)\": -19042.0, \"(9.86, 9.88)\": -19253.8, \"(9.88, 9.89)\": -19531.4, \"(9.89, 9.9)\": -19784.7, \"(9.9, 9.91)\": -20010.2, \"(9.91, 10.0)\": -20747.7}\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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djz/+uCoqKnTrrbd663vTavGdPbtK5GkdAABou1aFnIULF0qSvvOd77gtf/HFF3XLLbdIkh577DFZrVZdd911qq6uVnZ2tp599llXrc1m0/LlyzVt2jRlZmaqU6dOmjJlih5++GFXTWpqqlasWKF7771XTzzxhM477zz9+c9/VnZ2tqvm+uuv17Fjx/TQQw+psLBQF198sVauXNlgMLI/JcZEerUOAAC0Hc+u8uI8OQ6n0eV/eK/ZwcdJsZH6cNZVslkt5/x+AAB0RH6ZJwfubFaLcic0/6Tx3AlpBBwAAPyAkAMAAEISIceL6icDbIpFTAYIAIC/EHK8qKXJAI2YDBAAAH8h5HhR8anmZztubR0AAGg7Qo4X9ezi2a3hntYBAIC2I+R4UXpqvJJiI9XUvVMWnb2FPD013p9tAQDQIRFyvOjrt5A3FXS4hRwAAP8g5HjZ2EFJWnjjMMVGhzdY19gyAADgG4QcHymtrG2wrKyyVtNe3qaVuwoC0BEAAB0LIcfLmpsrp352HObKAQDA9wg5XsZcOQAAtA+EHC9jrhwAANoHQo6XMVcOAADtAyHHy4b37qqW7hC3Ws7WAQAA3yHkeNnWQyfV0phipzlbBwAAfIeQ42VHS894tQ4AALQNIcfLdnzp2RUaT+sAAEDbEHIAAEBIIuR4WZ9unbxaBwAA2oaQ42U3ZfZp8e4qi+VsHQAA8B1CjpfZw6yaOiq12RpjpPf2FfmpIwAAOiZCjg/8cuyFirbbmlxvEc+vAgDA1wg5PrA5v0SVNY4m1/P8KgAAfI+Q4wM8vwoAgMAj5PgAz68CACDwCDk+kJ4ar6TYSDV1k5VFUlJspNJT4/3ZFgAAHQohxwdsVotyJ6RJUoOgU/917oQ02Vq61xwAALQZIcdHxg5K0sIbhykx1v0jqcTYSC28cZjGDkoKUGcAAHQMYYFuIJSNHZSk76YlauOBE8r74rgkizL7ddOIvt0C3RoAACGPkONjq/YUat6yPSooO3sn1dPv71dSbKRyJ6RxNQcAAB/i4yofWrmrQNNe3uYKOPUKy6o07eVtWrmrIECdAQAQ+gg5PuJwGs1btkeNzWls/u/FrMcAAPgOIcdHNueXNLiC803MegwAgO8QcnyksNyz2Yw9rQMAAK1DyPGRktPVXq0DAACtQ8jxkbiocK/WAQCA1iHk+EjpmVqv1gEAgNYh5PhIfOcIr9YBAIDWIeT4SGKMZ08Y97QOAAC0DiHHR+qfRN6cuOhwnkQOAICPEHJ85OtPIm9KaWWtVu0p9FNHAAB0LIQcH/puWqLiopu/e4pZjwEA8A1Cjg9tzi9RaWXzd08x6zEAAL5ByPEhZj0GACBwCDk+xKzHAAAEDiHHh+I72b1aBwAAPEfI8aGeXTybA8fTOgAA4DlCji9ZvFwHAAA8RsjxoeMejrXxtA4AAHiOkONDfFwFAEDgEHJ8KD01vsXJAHm0AwAAvkHICTCG4wAA4BuEHB/yZMbjk5W1zHgMAIAPEHJ8qPiUZzMZe1oHAAA8R8jxIU8HFB88XuHjTgAA6HgIOT6UnhqvxJiWg86L6/N5EjkAAF5GyPEhm9WiSZeltFhXeqZOG7844YeOAADoOAg5Plbn4RWavAOEHAAAvImQ43OefgzFx1UAAHgTIcfHMvp082odAADwDCHH13hIJwAAAUHI8bFNHk7052kdAADwDCHHx5zG6VHd/uJyH3cCAEDHQsjxsa7RER7Vrd9/grlyAADwIkKOj3Xv4lnIOVXt4BlWAAB4ESHHxzyZ8bgez7ACAMB7CDk+lp4ar/hOdo9qPX3WFQAAaFmrQ866des0YcIEJScny2KxaOnSpW7rb7nlFlksFrfX2LFj3WpKSkp0ww03KCYmRnFxccrJydHp06fdanbu3KlRo0YpMjJSKSkpWrBgQYNelixZooEDByoyMlKDBw/WW2+91drD8Tmb1aLffH9Qi3Vdo8OVnhrvh44AAOgYWh1yKioqNHToUD3zzDNN1owdO1YFBQWu16uvvuq2/oYbbtDu3bu1atUqLV++XOvWrdMdd9zhWl9eXq4xY8aod+/e2rp1qx555BHNnTtXixYtctVs2LBBkydPVk5OjrZv366JEydq4sSJ2rVrV2sPyeeyByUq2m5rtoYhxwAAeJfFGNPmv68Wi0VvvPGGJk6c6Fp2yy23qLS0tMEVnnp79+5VWlqatmzZoksvvVSStHLlSo0bN05HjhxRcnKyFi5cqAceeECFhYWy289+1DN79mwtXbpU+/btkyRdf/31qqio0PLly137HjFihC6++GI999xzHvVfXl6u2NhYlZWVKSYmpg3fAc/kHTihyS9sbLHu1akjlNmPmY8BAGiOp3+/fTImZ82aNerZs6cGDBigadOm6cSJ/zx8Mi8vT3Fxca6AI0lZWVmyWq3atGmTq2b06NGugCNJ2dnZ+vTTT3Xy5ElXTVZWltv7ZmdnKy8vr8m+qqurVV5e7vbyB08HFDPwGAAA7/F6yBk7dqz+3//7f1q9erX+8Ic/aO3atbrmmmvkcDgkSYWFherZs6fbNmFhYYqPj1dhYaGrJiEhwa2m/uuWaurXN2b+/PmKjY11vVJSUs7tYD3k6YBiBh4DAOA9Yd7e4aRJk1z/Hjx4sIYMGaJ+/fppzZo1uvrqq739dq0yZ84czZw50/V1eXm5X4JOemq8kmIjVVhW1eTYmzgGHgMA4FU+v4W8b9++6t69u/bv3y9JSkxMVHFxsVtNXV2dSkpKlJiY6KopKipyq6n/uqWa+vWNiYiIUExMjNvLH2xWi3InpDU7uLi0slar9jR9FQoAALSOz0POkSNHdOLECSUlJUmSMjMzVVpaqq1bt7pq3nvvPTmdTmVkZLhq1q1bp9raWlfNqlWrNGDAAHXt2tVVs3r1arf3WrVqlTIzM319SG3y3bRExUWHN7neImnesj082gEAAC9pdcg5ffq0duzYoR07dkiS8vPztWPHDh0+fFinT5/W/fffr40bN+rgwYNavXq1vv/97+v8889Xdna2JOnCCy/U2LFjNXXqVG3evFnr16/XjBkzNGnSJCUnJ0uSfvKTn8hutysnJ0e7d+/Wa6+9pieeeMLto6a7775bK1eu1B//+Eft27dPc+fO1UcffaQZM2Z44dvifZvzS1RaWdvkeiOpoKyKRzsAAOAlrQ45H330kS655BJdcsklkqSZM2fqkksu0UMPPSSbzaadO3fqe9/7nvr376+cnBwNHz5cH3zwgSIi/vMMp7///e8aOHCgrr76ao0bN06XX3652xw4sbGx+t///V/l5+dr+PDhuu+++/TQQw+5zaUzcuRIvfLKK1q0aJGGDh2qf/7zn1q6dKkGDWp54r1A4A4rAAD865zmyQl2/ponR5LWf35cN/xlU4t1f8/J0Lcv6O7TXgAACGYBnScHjbB4uQ4AADSLkOMnx09Xe7UOAAA0j5DjJ0wICACAfxFy/CQ9Nb7ZW8glJgQEAMCbCDntSK3DGegWAAAIGYQcP2lpnhxJqqh26On3PvdTRwAAhDZCjp94Ov/N8+u+YNZjAAC8gJDjJ54OKK6scWjjgRM+7gYAgNBHyPGT9NR4dY6weVSb98VxH3cDAEDoI+T4ic1q0agLenhYzYyAAACcK0KOH904ordHdZn9uvm4EwAAQh8hx48u6xMvSwsXaSyWs3UAAODcEHL8aOuhk2rpcajGnK0DAADnhpDjR57eRu5pHQAAaBohx488vY384PEKH3cCAEDoI+T4UXpqvBJjWg46r24+zISAAACcI0KOH9msFk1O79ViXWF5tTbnl/ihIwAAQhchx8/6dI/2qI5xOQAAnBtCjp95Oi7H0zoAANA4Qo6fpafGKyk2ssk5jS2SkmIjlZ7KXDkAAJwLQo6f2awW5U5Ik9Tw4Q31X+dOSJPNyqMdAAA4F4ScABg7KEl3jE5tMPuxxSLdMTpVYwclBaYxAABCCCEnAFbuKtCidfn65l3iTiMtWpevlbsKAtMYAAAhhJDjZw6n0bxle9TcLDjzlu1hnhwAAM4RIcfPNueXqKCs6dvDjaSCsirmyQEA4BwRcvzM0/lv3t1T6ONOAAAIbYQcP/N0/ps3dnzFR1YAAJwDQo6fpafGK76TvcW6kopaPrICAOAcEHL8zGa1aOLFyR7V8mgHAADajpATAN9NS/Sojkc7AADQdoScAEhPjVdcdHizNXHR4TzaAQCAc0DICZDaOmfz6x3NrwcAAM0j5ATAxi9OqKLG0WxNRbVDG7844aeOAAAIPYScAMg74Fl4eXnjIR93AgBA6CLkBIRn89+s++wYc+UAANBGhJwAyOzb3aO6ihoHc+UAANBGhJwAGNGvm6LtNo9qC8vO+LgbAABCEyEnAGxWi8YN8myunJKKGh93AwBAaCLkBMi3L+jhUV185wgfdwIAQGgi5ARIYoxnsxl7WgcAANwRcgIkPTVeSbHNB5ik2EhmPQYAoI0IOQFis1qUOyFNlmZqvjc0STZrcxUAAKAphJwAGjsoSXeMTm1y/aJ1+Vq5q8CPHQEAEDoIOQHkcBr9++PmQ8y8ZXuYEBAAgDYg5ATQ5vwSFZRVNbneSCooq2JCQAAA2oCQE0DFp5oOOG2pAwAA/0HICaCeXTy7PdzTOgAA8B+EnABKT41XXHR4szVx0eHcRg4AQBsQcto5biAHAKBtCDkBtDm/RKWVtc3WnKysZeAxAABtQMgJIAYeAwDgO4ScAGLgMQAAvkPICaD651c1Ne7GIp5fBQBAWxFyAqj++VVSwwHG9V/nTkjj+VUAALQBISfAxg5K0sIbhynxG08kT4yN1MIbh2nsoKQAdQYAQHALC3QDOBt0vpuWqM35JSo+VaWeXc5+RMUVHAAA2o4rOe2EzWpRZr9uunZIsiRp+c6jyjtwgodzAgDQRlzJaUdW7irQvGV73B7amRQbqdwJaXxsBQBAK3Elp51YuatA017e1uCp5IVlVZr28jat3FUQoM4AAAhOhJx2wOE0mrdsjxr7YMr832vesj18dAUAQCsQctqBzfklDa7gfFNBWRWPdwAAoBUIOe1AYblnj23wtA4AABBy2oWS09VerQMAAIScdiG+k92rdQAAgJDTLiTGRnlUd7jkjI87AQAgdBBy2oH01HglxkS0WPePLYe5wwoAAA8RctoBm9Wiyem9WqzjDisAADxHyGkn+nTv5FFd8SnusAIAwBOtDjnr1q3ThAkTlJycLIvFoqVLl7qtN8booYceUlJSkqKiopSVlaXPP//craakpEQ33HCDYmJiFBcXp5ycHJ0+fdqtZufOnRo1apQiIyOVkpKiBQsWNOhlyZIlGjhwoCIjIzV48GC99dZbrT2cdqNnl8iWi1pRBwBAR9fqkFNRUaGhQ4fqmWeeaXT9ggUL9OSTT+q5557Tpk2b1KlTJ2VnZ6uq6j9XIG644Qbt3r1bq1at0vLly7Vu3TrdcccdrvXl5eUaM2aMevfura1bt+qRRx7R3LlztWjRIlfNhg0bNHnyZOXk5Gj79u2aOHGiJk6cqF27drX2kNqF4b27qqWHjlstZ+sAAEDLLMaYNo9ktVgseuONNzRx4kRJZ6/iJCcn67777tMvfvELSVJZWZkSEhK0ePFiTZo0SXv37lVaWpq2bNmiSy+9VJK0cuVKjRs3TkeOHFFycrIWLlyoBx54QIWFhbLbz942PXv2bC1dulT79u2TJF1//fWqqKjQ8uXLXf2MGDFCF198sZ577jmP+i8vL1dsbKzKysoUExPT1m+DV+QdOKHJL2xsse7VqSOU2a+bHzoCAKB98vTvt1fH5OTn56uwsFBZWVmuZbGxscrIyFBeXp4kKS8vT3Fxca6AI0lZWVmyWq3atGmTq2b06NGugCNJ2dnZ+vTTT3Xy5ElXzdffp76m/n0aU11drfLycrdXe1FY5tnt4Z7WAQDQ0Xk15BQWFkqSEhIS3JYnJCS41hUWFqpnz55u68PCwhQfH+9W09g+vv4eTdXUr2/M/PnzFRsb63qlpKS09hB9pqSixqO61z867ONOAAAIDR3q7qo5c+aorKzM9fryyy8D3ZJLfOeW58mRpLwvTqqmzunjbgAACH5eDTmJiYmSpKKiIrflRUVFrnWJiYkqLi52W19XV6eSkhK3msb28fX3aKqmfn1jIiIiFBMT4/ZqLxJjPL9r6m95B33XCAAAIcKrISc1NVWJiYlavXq1a1l5ebk2bdqkzMxMSVJmZqZKS0u1detWV817770np9OpjIwMV826detUW1vrqlm1apUGDBigrl27umq+/j71NfXvE2zSU+MVbbd5VHuopNLH3QAAEPxaHXJOnz6tHTt2aMeOHZLODjbesWOHDh8+LIvFonvuuUe/+c1v9O9//1uffPKJbr75ZiUnJ7vuwLrwwgs1duxYTZ06VZs3b9b69es1Y8YMTZo0ScnJyZKkn/zkJ7Lb7crJydHu3bv12muv6YknntDMmTNdfdx9991auXKl/vjHP2rfvn2aO3euPvroI82YMePcvysBYLNaNG5Q01ehvq53fLSPuwEAIPi1+hbyNWvW6Morr2ywfMqUKVq8eLGMMcrNzdWiRYtUWlqqyy+/XM8++6z69+/vqi0pKdGMGTO0bNkyWa1WXXfddXryySfVuXNnV83OnTs1ffp0bdmyRd27d9ddd92lWbNmub3nkiVL9OCDD+rgwYO64IILtGDBAo0bN87jY2lPt5BLUk2dUwMefFstnZDPfnON7GEdajgVAAAunv79Pqd5coJdews5kjT/rT16fl1+szU/HZ2qOePS/NQRAADtS0DmycG5u2/MQLUw8bFe+CCfO6wAAGgBIaed+VvewRY/rnIa7rACAKAlhJx2xtM7p7jDCgCA5hFy2hlP75ziDisAAJpHyGlnbsrs0+LTyC2Ws3UAAKBphJx2xh5m1dRRqc3WGCO9t6+o2RoAADo6Qk479MuxF7Y4D86cf30ih7PD3v0PAECLCDnt0MYvTrR4i/jJylpt/OKEnzoCACD4EHLaofX7j3u1DgCAjoiQ0w4dLT3j1ToAADoiQk47lBwb5dU6AAA6IkJOO9S1k92rdQAAdESEnHaoe2fPwsvRUmY9BgCgKYScdijRw4+h3vz4KLeRAwDQBEJOO5SeGq94Dz6KKqmo1eb8Ej90BABA8CHktEM2q0XfG5rkUW1hGXdYAQDQGEJOO5XS1bMHcJZU1Pi4EwAAghMhp52K7xzh1ToAADoaQk47lRgT6VHd4RMVPu4EAIDgRMhpp9JT4z0KOq9uPswdVgAANIKQ007ZrBZNTu/VYl1heTV3WAEA0AhCTjvWp7tng4+LT1X5uBMAAIIPIacd69nFs3E5ntYBANCREHLaseG9u8pqab7GYjlbBwAA3BFy2rGth06qpTHFxkgL1xzwT0MAAAQRQk475ulYmxc35HOHFQAA30DIacc8HWtTWskzrAAA+CZCTjuWnhqvuKhwj2q5wwoAAHeEnHbMZrXo1m/38aiWO6wAAHBHyGnnZlx1geKim76aY5GUFBup9NR4/zUFAEAQIOS0czarRb//wWA1did5/bLcCWmytXSvOQAAHQwhJwiMHZSkhTcOU1Ks+0dSibGRWnjjMI0dlBSgzgAAaL/CAt0APDN2UJK+m5aozfklKj5VpZ5dzn5ExRUcAAAaR8gJIjarRZn9ugW6DQAAggIfVwEAgJBEyAEAACGJj6uCkMNpGJsDAEALCDlBZuWuAs1btkcFZf+Z4Ti+k12/+f4gjRvCXVYAANTj46ogsnJXgaa9vM0t4EhSSUWNfvbKNs1/a0+AOgMAoP0h5AQJh9No3rI9au5Z48+vy9dbO4/6rScAANozQk6Q2Jxf0uAKTmMefHOXHM7mohAAAB0DISdIePqU8ZKKWm3OL/FxNwAAtH+EnCDRmqeMF5ad8WEnAAAEB0JOkEhPjVeXSM9uhiupqPFxNwAAtH+EnCBhs1r0w+HneVR75GSlj7sBAKD9I+QEkTFpiR7VvfnxUQYfAwA6PEJOEElPjVd8J3uLdQw+BgCAkBNUbFaLvjfUs1mNGXwMAOjoCDlBJqVrtEd1DD4GAHR0hJwgE985wqt1AACEKkJOkEmM8Wy+nMMnuMMKANCxEXKCTHpqvBJjWr5K8+ya/dxhBQDo0Ag5QcZmtejHw1NarKuuc+qJVZ/5oSMAANonQk4Q+tLDyf5e+PALruYAADosQk4QqqhxeFR3ptbJfDkAgA6LkBOELuvT1eNa5ssBAHRUhJwgNGVkqse16/ef8GEnAAC0X4ScIGQPs+o7/bt7VPvvHUcYlwMA6JAIOUFq1AU9PKqrcUob9h/3cTcAALQ/hJwg1ZoZjf9n2xEfdgIAQPtEyAlSns58LElHPLzlHACAUELICVLpqfGKCLN4VJsc63kgAgAgVBBygpTNatH4QUke1Q5KjvNtMwAAtEOEnCA2akBPj+p6cCUHANABEXKCmKfjclozfgcAgFBByAli6anxSmrhKk1SbKTSU+P91BEAAO0HISeI2awW5U5Ik0XSN4cg1y/LnZAmm9WzAcoAAIQSQk6QGzsoSQtvHKbEb1zRSYyN1MIbh2msh4OTAQAINV4POXPnzpXFYnF7DRw40LW+qqpK06dPV7du3dS5c2ddd911KioqctvH4cOHNX78eEVHR6tnz566//77VVdX51azZs0aDRs2TBERETr//PO1ePFibx9K0Bg7KEkfzrpKr04doScmXaxXp47Qh7OuIuAAADq0MF/s9KKLLtK77777nzcJ+8/b3HvvvVqxYoWWLFmi2NhYzZgxQz/4wQ+0fv16SZLD4dD48eOVmJioDRs2qKCgQDfffLPCw8P1u9/9TpKUn5+v8ePH684779Tf//53rV69WrfffruSkpKUnZ3ti0Nq92xWizL7dXN97XAa5R04oeJTVerZ5ey4HD62AgB0JBZjjFef3jh37lwtXbpUO3bsaLCurKxMPXr00CuvvKIf/vCHkqR9+/bpwgsvVF5enkaMGKG3335b1157rY4ePaqEhARJ0nPPPadZs2bp2LFjstvtmjVrllasWKFdu3a59j1p0iSVlpZq5cqVHvdaXl6u2NhYlZWVKSYm5twOvB1ZuatA85btUUFZlWtZUmykciekcXUHABD0PP377ZMxOZ9//rmSk5PVt29f3XDDDTp8+LAkaevWraqtrVVWVparduDAgerVq5fy8vIkSXl5eRo8eLAr4EhSdna2ysvLtXv3blfN1/dRX1O/j6ZUV1ervLzc7RVqVu4q0LSXt7kFHEkqKKvStJe3aeWuggB1BgCAf3k95GRkZGjx4sVauXKlFi5cqPz8fI0aNUqnTp1SYWGh7Ha74uLi3LZJSEhQYWGhJKmwsNAt4NSvr1/XXE15ebnOnDnTZG/z589XbGys65WSknKuh9uuOJxG85btUVOX5oykecv2yOH06sU7AADaJa+Pybnmmmtc/x4yZIgyMjLUu3dvvf7664qKivL227XKnDlzNHPmTNfX5eXlIRV0NueXNLiC800FZVXanF/iNn4HAIBQ5PNbyOPi4tS/f3/t379fiYmJqqmpUWlpqVtNUVGREhMTJUmJiYkN7raq/7qlmpiYmGaDVEREhGJiYtxeoaSwrOmrWG2pAwAgmPk85Jw+fVoHDhxQUlKShg8frvDwcK1evdq1/tNPP9Xhw4eVmZkpScrMzNQnn3yi4uJiV82qVasUExOjtLQ0V83X91FfU7+PjqqkosajuvX7T/i4EwAAAs/rIecXv/iF1q5dq4MHD2rDhg36r//6L9lsNk2ePFmxsbHKycnRzJkz9f7772vr1q269dZblZmZqREjRkiSxowZo7S0NN100036+OOP9c477+jBBx/U9OnTFRERIUm688479cUXX+iXv/yl9u3bp2effVavv/667r33Xm8fTlCJ7xzhUd2/th9hXA4AIOR5PeQcOXJEkydP1oABA/TjH/9Y3bp108aNG9WjRw9J0mOPPaZrr71W1113nUaPHq3ExET961//cm1vs9m0fPly2Ww2ZWZm6sYbb9TNN9+shx9+2FWTmpqqFStWaNWqVRo6dKj++Mc/6s9//nOHnSOnnqcP4nQa6bH/3efjbgAACCyvz5MTTEJtnhyH02jgf7+tWkfLp9RmlT77zTgmCAQABJ2AzpODwLBZLS0+lbyewyltPMDYHABA6CLkhJjRF3T3uPZRPrICAIQwQk6IeWD8RR7Xbv+yTDV1Th92AwBA4BByQkyU3aZhveI8rl+8Pt93zQAAEECEnBC05M6RHtf+755CH3YCAEDgEHJCkM1qUVKMZ3PmfHWS2Y8BAKGJkBOi+nSL9qiusLyaiQEBACGJkBOiLu7V1aM6I+np9z73bTMAAAQAISdEXX5BD49rX1x/kKs5AICQQ8gJUSP6dlPniDCPakvP1GpzfomPOwIAwL8IOSHKZrVowXVDPK5/Z3eBD7sBAMD/CDkhbNyQJGWmdvOo9l/bvuIjKwBASCHkhLgfp6d4VFdeVcdHVgCAkELICXGJMZ49sFOSik9V+bATAAD8i5AT4tJT4xXfye5R7cHjlT7uBgAA/yHkhDib1aLffH+QR7X/2HKYcTkAgJBByOkAxg1J0oQhiS3WFZRVMTEgACBkEHI6iKy0lkOOJD327udauYvbyQEAwY+Q00H07OL5AOTZ//qEj60AAEGPkNNBpKfGKynWs6BTWlmrp9/b7+OOAADwLUJOB2GzWpQ7Ic3j+ufXHeBqDgAgqBFyOpCxg5J0b1Z/j2oraxzaeOCEjzsCAMB3CDkdzIyrzldUmMWj2v+Xl+/jbgAA8B1CTgdjs1rUu1snj2rf2VPMR1YAgKBFyOmAUuKjPa798XMbfNgJAAC+Q8jpgNI9fDK5JG09XKozNQ4fdgMAgG8QcjqgKSP7tKr+p3/7yDeNAADgQ4ScDsgeZtW1g5M8rl+//zhjcwAAQYeQ00E9MfkSeXaPleQw0o+fZ2wOACC4EHI6KJvVop9d2dfj+q2HSvXbFXt82BEAAN5FyOnAZn53oKyeXs6R9MIH+aqpc/quIQAAvIiQ04HZrBY9PfmSVm0z5187fdQNAADeRcjp4MYNSdYlKbEe17+x7SsGIQMAggIhB/rFmIEe1zolffexNT7rBQAAbyHkQCP6dVN0uOc/Cl8cq9SvlzMIGQDQvhFyIJvVokd/dHGrtvnLhwxCBgC0b4QcSJLGDUnS1FF9WrXN7P/52DfNAADgBYQcuDww/iL16+HZE8ol6V/bj+q3K3b7sCMAANqOkAM3c6+9qFX1L3xwkEkCAQDtEiEHbkZe0F32Vv5UvPBBvt7aWeCbhgAAaCNCDtzYrBY9PmlYq7e7/587mD8HANCuEHLQwLghSZowJLFV21TUODXjla0+6ggAgNYj5KBRj08aprio8FZt8/auIn3vqQ981BEAAK1DyEGjbFaLfn/d4FZvt/Orct36100+6AgAgNYh5KBJYwcl6alWPsBTkt7/7LhyFm/xQUcAAHiOkINmTRiarJzL+7R6u9X7iplDBwAQUIQctOi/r71I/RM6t3q7Fz44qNNVdT7oCACAlhFy4JHld41q03aD5r7DZIEAgIAg5MAj9jCrfjo6tU3bvvBBPmN0AAB+R8iBx+aMS9PUUW0LOqv3FStnMXddAQD8h5CDVnlgfJoe/+HQNm27et9xXf/cBtXUOb3cFQAADRFy0GoTLz1PVw/s0aZtNx08qf4Pvq2Hl3HnFQDAtwg5aJO/3JKuQUld2rz9X9cfVPpvV/G8KwCAzxBy0GbL7x6t8+Ii2rx98aka9fvVW3pz+1de7AoAgLMIOTgnH87OUkp81Dnt4+7XduiKBau5qgMA8CpCDs7ZB7+8St/p3+2c9nGopEr9fvWWMn/3rkpO13ipMwBAR0bIgVcsvm2Erh7Y/Zz3U1BerWG/WaW+s1do7d5iru4AANqMkAOv+cstGW2eR+ebnJKmvLRF/R98S2/tLPDKPgEAHYvFGNNh/69yeXm5YmNjVVZWppiYmEC3EzJq6py68YUN2nyozGv77NEpXO/ed6Vio8O9tk8AQHDy9O83IYeQ4zNT/98WrdpT7NV99uxi12M/vkTHK6rVs0uk0lPjZbNavPoeAID2jZDjAUKO7y37+Kh+/up2+fKHzG6Vpl95gaZdeb7sYXwCCwChjpDjAUKOfzicRjP+/pHe3u3dqzqNSYyx6w/XDdXlF/TgCg8AhChCjgcIOf711s6j+tkr2/3yXlaLFB8VpspapyyS4qLDdUNGb90+uh9XewAgyBFyPEDI8T+H0+jqR9/XwZIzAeshtVuURvTppn1F5TpWUaOELpHKvihRt3w7lQAEAEGAkOMBQk7gnK6q009eyNPOr8oD3YqbCKsUFmZVuM2qmMgw2W1WWaxSWlKsfjg8RSPP787HYAAQYIQcDxByAs/hNPrBMx/q43YWdppjt0rdO9sVGWZVeXWdys7UyemUosKlb3WNllMWGWNUU+dUdZ1TYVaLLundVddf2ouQBABeQMjxACGn/ThT49DVj76vo+XVgW7F5+KiwnRhYhed1zVKO74sU0HZGRkjRYRZ1DU6XLVOqfRMjSqqnbJZpYSYCIVZpJOVdaqpdchpschikZJjIjUwsYsKyqtVWVWjY5V1OlPjkNNpFGW3yCKLIsJtchgjm8WiU1V1rveRxeL6t8MpVdU5ZLFYZLNINXUOOZwWxUaH64KETjpSUqXy6jr1iY/SnVdcoI+/KtXhExU6UHxKJZV1slksig6XCstrVOVwSkaSOfs/YVYpKiJcVw3ooeOnqrT9SJkcTqPocJuiIsJkkVG41aroCKuOl9foVI1DFosUY7fKarNJksJs0vGKWslIdXVORYZb1SkiTKeq6lRV51Ane7hiIm0qOFUth8MoJjJcPTqHq6yqTicra+V0GtnDLLLbrDKyqlt0mFK6dZLFYlFR+Rmdqa5VWZVDVXVOOZ1GEWFWDUjsoshwm/YVlut0VZ2q64xkkbp1susnl6XodJ1DeftLdLqqTp0jbbosNV4Hj1foQPFpHTtdrfrbCe1hFjmN5HRKRkaxUeFKiY9S+Zk6fXnyjGxWi/p276RL+3TTsVPVKjpVqUPHKlVZXafKOqfqv51RYVLvbp10xYAEZfbrpr1Hy/W/ewpUVF4tI4uq6+oUZrEo0m5T1052RYfb1K1zpI6dqtaXJypUJ6O+3Tvp8n49tLewXB8dPKFjp2vl/L+fgZSukaqsNbLKyGEkY4xOVTsUE2FTYly0Jl78LS3dfkTFp6pks1qVEBOhkooaVdY4VFXjUHWdU+FhFsVEhis+Olxn6hz66uQZVdUa1X3tL0xnuxQRFi5ZpW/FRqm2zqFDJWdUU+dURJhFMVHhslmlkxW1MpIiwmzqGROhTvYwxUXZVe1wqmuUTV+UnNGJU9Uqq6yR00j2cJuiwqyKCJPCbDbFRIWroLRK5VV1skjq3jlc53WNVrXDqZKKWlXVOuRwGkVanSo47ZDDnB3Hl9QlXGHhYYq229TFbtOXJZU6VeOU3WZR/4QuGtW/h06fqdNXpZXaX3xaJ05Xq7LWoYpqp4wkm0WKDpdq6qRaIxkjdbJb1atrpI5V1OpUVZ2cTqMwm0WR4TZFhNkUHiYdOVktx9kfMaXEhqtzpF11RooMt+qL4xWqqD47trCz3SIji6pqnTIWKcJ29udLxshmsyq1e7RKz9SqsKxatU4pzCL17Ralk1V1rveODLfKHmZTXHS44qPCdaikUmVn6hRus+hbcVGKsoepxulUhM2qb3WNUpfIMFktVlks0qDkGG3/slRbD57UsdNVqqx2SJIiwqyy2yRZzv73xGmMahxGYVaLEmMjdd3wFOVc3terwwEIOR4g5LQ/Z2ocum7hh9pTcDrQrQAAvOino1M1Z1yaV/bl6d/voB9l+cwzz6hPnz6KjIxURkaGNm/eHOiWcA6i7Da9dfcV+uw31+j+rAvUJdIW6JYAAF7w/Lp8zX9rj1/fM6hDzmuvvaaZM2cqNzdX27Zt09ChQ5Wdna3iYt/PxwLfsodZNT2rvz6ZO1YHfjdOr04doW6d7YFuCwBwDp5fl6+aOqff3i+oP67KyMjQZZddpqefflqS5HQ6lZKSorvuukuzZ89uUF9dXa3q6v+M+SgvL1dKSgofVwWRsspa3fLXjdr1Vblqg/YnFwA6rv8ef6FyRvU9p314+nFV2Dm9SwDV1NRo69atmjNnjmuZ1WpVVlaW8vLyGt1m/vz5mjdvnr9ahA/ERofrjRmjJJ29M2vd3mI98u4+HTxeKavVojBJpf83GA4A0P4cKqn023sFbcg5fvy4HA6HEhIS3JYnJCRo3759jW4zZ84czZw50/V1/ZUcBCeb1aIrL0rQlRe5/wycqXHo18t36cPPjulIabX8d2EUANCS3vHRfnuvoA05bREREaGIiIhAtwEfi7Lb9LsfDHV9fTb07NaGA8dV5zC6+Lw4nTxTo/UHSgLYJQB0TDdl9vHbewVtyOnevbtsNpuKiorclhcVFSkxMTFAXaE9Oht6hjRY7nAaffjZMT2/7oAOHDutOodTxkjVDqdrxuOaOqcKT9UEoGsACD0/He3fx+cEbcix2+0aPny4Vq9erYkTJ0o6O/B49erVmjFjRmCbQ1CwWS26YmBPXTGwZ7N1DqfR+/uK9evlu1VUXiWrRYqLCm9xxuOTFTU6UVnnp6MBgPbNm/PkeCpoQ44kzZw5U1OmTNGll16q9PR0Pf7446qoqNCtt94a6NYQQmxWi7LSEpSVltBy8Tc4nEYbvzih9Z8f1/ZDJ3T4ZJUcxii1WzQzHjPjMTMeM+MxMx77WFDfQi5JTz/9tB555BEVFhbq4osv1pNPPqmMjAyPtmXGYwAAgg+PdfAAIQcAgODTYR7rAAAA0BhCDgAACEmEHAAAEJIIOQAAICQRcgAAQEgi5AAAgJBEyAEAACGJkAMAAEJSUD/W4VzVz4NYXl4e4E4AAICn6v9utzSfcYcOOadOnZIkpaSkBLgTAADQWqdOnVJsbGyT6zv0Yx2cTqeOHj2qLl26yGKxeGWf5eXlSklJ0Zdffhmyj4oI9WMM9eOTQv8YQ/34JI4xFIT68Um+O0ZjjE6dOqXk5GRZrU2PvOnQV3KsVqvOO+88n+w7JiYmZH9o64X6MYb68Umhf4yhfnwSxxgKQv34JN8cY3NXcOox8BgAAIQkQg4AAAhJhBwvi4iIUG5uriIiIgLdis+E+jGG+vFJoX+MoX58EscYCkL9+KTAH2OHHngMAABCF1dyAABASCLkAACAkETIAQAAIYmQAwAAQhIhBwAAhCRCThv89re/1ciRIxUdHa24uLhGaw4fPqzx48crOjpaPXv21P3336+6urpm91tSUqIbbrhBMTExiouLU05Ojk6fPu2DI2idNWvWyGKxNPrasmVLk9t95zvfaVB/5513+rFzz/Xp06dBr7///e+b3aaqqkrTp09Xt27d1LlzZ1133XUqKiryU8etc/DgQeXk5Cg1NVVRUVHq16+fcnNzVVNT0+x27fkcPvPMM+rTp48iIyOVkZGhzZs3N1u/ZMkSDRw4UJGRkRo8eLDeeustP3XaevPnz9dll12mLl26qGfPnpo4caI+/fTTZrdZvHhxg3MVGRnpp45bb+7cuQ36HThwYLPbBNM5bOy/KRaLRdOnT2+0PhjO37p16zRhwgQlJyfLYrFo6dKlbuuNMXrooYeUlJSkqKgoZWVl6fPPP29xv639XW4NQk4b1NTU6Ec/+pGmTZvW6HqHw6Hx48erpqZGGzZs0EsvvaTFixfroYceana/N9xwg3bv3q1Vq1Zp+fLlWrdune644w5fHEKrjBw5UgUFBW6v22+/Xampqbr00kub3Xbq1Klu2y1YsMBPXbfeww8/7NbrXXfd1Wz9vffeq2XLlmnJkiVau3atjh49qh/84Ad+6rZ19u3bJ6fTqeeff167d+/WY489pueee06/+tWvWty2PZ7D1157TTNnzlRubq62bdumoUOHKjs7W8XFxY3Wb9iwQZMnT1ZOTo62b9+uiRMnauLEidq1a5efO/fM2rVrNX36dG3cuFGrVq1SbW2txowZo4qKima3i4mJcTtXhw4d8lPHbXPRRRe59fvhhx82WRts53DLli1ux7Zq1SpJ0o9+9KMmt2nv56+iokJDhw7VM8880+j6BQsW6Mknn9Rzzz2nTZs2qVOnTsrOzlZVVVWT+2zt73KrGbTZiy++aGJjYxssf+utt4zVajWFhYWuZQsXLjQxMTGmurq60X3t2bPHSDJbtmxxLXv77beNxWIxX331ldd7Pxc1NTWmR48e5uGHH2627oorrjB33323f5o6R7179zaPPfaYx/WlpaUmPDzcLFmyxLVs7969RpLJy8vzQYfet2DBApOamtpsTXs9h+np6Wb69Omurx0Oh0lOTjbz589vtP7HP/6xGT9+vNuyjIwM89Of/tSnfXpLcXGxkWTWrl3bZE1T/z1qr3Jzc83QoUM9rg/2c3j33Xebfv36GafT2ej6YDt/kswbb7zh+trpdJrExETzyCOPuJaVlpaaiIgI8+qrrza5n9b+LrcWV3J8IC8vT4MHD1ZCQoJrWXZ2tsrLy7V79+4mt4mLi3O7MpKVlSWr1apNmzb5vOfW+Pe//60TJ07o1ltvbbH273//u7p3765BgwZpzpw5qqys9EOHbfP73/9e3bp10yWXXKJHHnmk2Y8Xt27dqtraWmVlZbmWDRw4UL169VJeXp4/2j1nZWVlio+Pb7GuvZ3Dmpoabd261e17b7ValZWV1eT3Pi8vz61eOvs7GUznSlKL5+v06dPq3bu3UlJS9P3vf7/J/960F59//rmSk5PVt29f3XDDDTp8+HCTtcF8DmtqavTyyy/rtttuk8ViabIu2M7f1+Xn56uwsNDtHMXGxiojI6PJc9SW3+XW6tBPIfeVwsJCt4AjyfV1YWFhk9v07NnTbVlYWJji4+Ob3CZQ/vKXvyg7O7vFJ7j/5Cc/Ue/evZWcnKydO3dq1qxZ+vTTT/Wvf/3LT5167uc//7mGDRum+Ph4bdiwQXPmzFFBQYH+9Kc/NVpfWFgou93eYExWQkJCuztfjdm/f7+eeuopPfroo83WtcdzePz4cTkcjkZ/x/bt29foNk39TgbDuXI6nbrnnnv07W9/W4MGDWqybsCAAfrrX/+qIUOGqKysTI8++qhGjhyp3bt3t/i7GggZGRlavHixBgwYoIKCAs2bN0+jRo3Srl271KVLlwb1wXwOly5dqtLSUt1yyy1N1gTb+fum+vPQmnPUlt/l1iLk/J/Zs2frD3/4Q7M1e/fubXFgXDBpyzEfOXJE77zzjl5//fUW9//18USDBw9WUlKSrr76ah04cED9+vVre+Meas3xzZw507VsyJAhstvt+ulPf6r58+e36+fKtOUcfvXVVxo7dqx+9KMfaerUqc1uG+hzCGn69OnatWtXs+NVJCkzM1OZmZmur0eOHKkLL7xQzz//vH7961/7us1Wu+aaa1z/HjJkiDIyMtS7d2+9/vrrysnJCWBn3veXv/xF11xzjZKTk5usCbbzFywIOf/nvvvuazZlS1Lfvn092ldiYmKD0eH1d90kJiY2uc03B1rV1dWppKSkyW3OVVuO+cUXX1S3bt30ve99r9Xvl5GRIensVQR//IE8l3OakZGhuro6HTx4UAMGDGiwPjExUTU1NSotLXW7mlNUVOSz89WY1h7j0aNHdeWVV2rkyJFatGhRq9/P3+ewMd27d5fNZmtwJ1tz3/vExMRW1bcXM2bMcN2E0Nr/Nx8eHq5LLrlE+/fv91F33hUXF6f+/fs32W+wnsNDhw7p3XffbfXVz2A7f/XnoaioSElJSa7lRUVFuvjiixvdpi2/y63mlZE9HVRLA4+Liopcy55//nkTExNjqqqqGt1X/cDjjz76yLXsnXfeaVcDj51Op0lNTTX33Xdfm7b/8MMPjSTz8ccfe7kz73v55ZeN1Wo1JSUlja6vH3j8z3/+07Vs37597Xrg8ZEjR8wFF1xgJk2aZOrq6tq0j/ZyDtPT082MGTNcXzscDvOtb32r2YHH1157rduyzMzMdjto1el0munTp5vk5GTz2WeftWkfdXV1ZsCAAebee+/1cne+cerUKdO1a1fzxBNPNLo+2M5hvdzcXJOYmGhqa2tbtV17P39qYuDxo48+6lpWVlbm0cDj1vwut7pPr+ylgzl06JDZvn27mTdvnuncubPZvn272b59uzl16pQx5uwP56BBg8yYMWPMjh07zMqVK02PHj3MnDlzXPvYtGmTGTBggDly5Ihr2dixY80ll1xiNm3aZD788ENzwQUXmMmTJ/v9+Jry7rvvGklm7969DdYdOXLEDBgwwGzatMkYY8z+/fvNww8/bD766COTn59v3nzzTdO3b18zevRof7fdog0bNpjHHnvM7Nixwxw4cMC8/PLLpkePHubmm2921Xzz+Iwx5s477zS9evUy7733nvnoo49MZmamyczMDMQhtOjIkSPm/PPPN1dffbU5cuSIKSgocL2+XhMs5/Af//iHiYiIMIsXLzZ79uwxd9xxh4mLi3Pd0XjTTTeZ2bNnu+rXr19vwsLCzKOPPmr27t1rcnNzTXh4uPnkk08CdQjNmjZtmomNjTVr1qxxO1eVlZWumm8e47x588w777xjDhw4YLZu3WomTZpkIiMjze7duwNxCC267777zJo1a0x+fr5Zv369ycrKMt27dzfFxcXGmOA/h8ac/YPdq1cvM2vWrAbrgvH8nTp1yvX3TpL505/+ZLZv324OHTpkjDHm97//vYmLizNvvvmm2blzp/n+979vUlNTzZkzZ1z7uOqqq8xTTz3l+rql3+VzRchpgylTphhJDV7vv/++q+bgwYPmmmuuMVFRUaZ79+7mvvvuc0vy77//vpFk8vPzXctOnDhhJk+ebDp37mxiYmLMrbfe6gpO7cHkyZPNyJEjG12Xn5/v9j04fPiwGT16tImPjzcRERHm/PPPN/fff78pKyvzY8ee2bp1q8nIyDCxsbEmMjLSXHjhheZ3v/ud21W3bx6fMcacOXPG/OxnPzNdu3Y10dHR5r/+67/cQkN78uKLLzb6M/v1i7nBdg6feuop06tXL2O32016errZuHGja90VV1xhpkyZ4lb/+uuvm/79+xu73W4uuugis2LFCj937LmmztWLL77oqvnmMd5zzz2u70dCQoIZN26c2bZtm/+b99D1119vkpKSjN1uN9/61rfM9ddfb/bv3+9aH+zn0JizV+MlmU8//bTBumA8f/V/t775qj8Op9Np/vu//9skJCSYiIgIc/XVVzc49t69e5vc3Fy3Zc39Lp8rizHGeOeDLwAAgPaDeXIAAEBIIuQAAICQRMgBAAAhiZADAABCEiEHAACEJEIOAAAISYQcAAAQkgg5AAAgJBFyAABASCLkAACAkETIAQAAIen/A9MQlCbSPzW9AAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2115,7 +3477,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2127,18 +3489,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.1%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-10.0, -9.95)\": 1282.2, \"(-9.95, -9.88)\": 1239.3, \"(-9.88, -9.82)\": 1187.9, \"(-9.82, -9.76)\": 1129.5, \"(-9.76, -9.7)\": 1089.8, \"(-9.7, -9.64)\": 1037.5, \"(-9.64, -9.56)\": 995.8, \"(-9.56, -9.5)\": 950.3, \"(-9.5, -9.43)\": 909.9, \"(-9.43, -9.35)\": 858.4, \"(-9.35, -9.28)\": 815.5, \"(-9.28, -9.23)\": 765.2, \"(-9.23, -9.12)\": 725.5, \"(-9.12, -9.06)\": 679.5, \"(-9.06, -8.96)\": 640.0, \"(-8.96, -8.87)\": 600.0, \"(-8.87, -8.79)\": 558.3, \"(-8.79, -8.67)\": 517.2, \"(-8.67, -8.55)\": 470.8, \"(-8.55, -8.43)\": 420.6, \"(-8.43, -8.31)\": 380.6, \"(-8.31, -8.18)\": 339.5, \"(-8.18, -8.05)\": 301.1, \"(-8.05, -7.86)\": 261.4, \"(-7.86, -7.7)\": 222.3, \"(-7.7, -7.45)\": 180.2, \"(-7.45, -7.12)\": 134.0, \"(-7.12, -6.72)\": 95.4, \"(-6.72, -6.08)\": 55.2, \"(-6.08, -1.33)\": 15.4, \"(-1.33, -0.87)\": 58.1, \"(-0.87, -0.64)\": 99.8, \"(-0.64, -0.31)\": 143.1, \"(-0.31, -0.17)\": 186.6, \"(-0.17, 0.04)\": 230.4, \"(0.04, 0.17)\": 276.7, \"(0.17, 0.37)\": 321.4, \"(0.37, 0.49)\": 363.3, \"(0.49, 0.6)\": 407.1, \"(0.6, 0.7)\": 454.9, \"(0.7, 0.77)\": 500.1, \"(0.77, 0.85)\": 539.6, \"(0.85, 1.04)\": 587.8, \"(1.04, 1.11)\": 637.4, \"(1.11, 1.22)\": 688.9, \"(1.22, 1.25)\": 729.1, \"(1.25, 1.33)\": 772.4, \"(1.33, 1.39)\": 814.6, \"(1.39, 1.43)\": 858.3, \"(1.43, 1.52)\": 908.0, \"(1.52, 1.59)\": 949.1, \"(1.59, 1.66)\": 1000.0, \"(1.66, 1.74)\": 1041.6, \"(1.74, 1.79)\": 1085.6, \"(1.79, 1.84)\": 1151.3, \"(1.84, 1.93)\": 1197.0, \"(1.93, 2.0)\": 1242.6, \"(2.0, 2.08)\": 1299.5, \"(2.08, 2.14)\": 1360.2, \"(2.14, 2.2)\": 1425.9, \"(2.2, 2.25)\": 1473.5, \"(2.25, 2.29)\": 1526.1, \"(2.29, 2.31)\": 1569.0, \"(2.31, 2.33)\": 1608.6, \"(2.33, 2.41)\": 1658.1, \"(2.41, 2.48)\": 1726.8, \"(2.48, 2.51)\": 1765.4, \"(2.51, 2.57)\": 1805.2, \"(2.57, 2.59)\": 1871.0, \"(2.59, 2.62)\": 1929.0, \"(2.62, 2.72)\": 1970.2, \"(2.72, 2.77)\": 2054.0, \"(2.77, 2.8)\": 2098.7, \"(2.8, 2.84)\": 2144.6, \"(2.84, 2.9)\": 2211.4, \"(2.9, 2.92)\": 2264.7, \"(2.92, 2.97)\": 2312.2, \"(2.97, 2.99)\": 2359.1, \"(2.99, 3.01)\": 2401.2, \"(3.01, 3.04)\": 2441.2, \"(3.04, 3.07)\": 2489.6, \"(3.07, 3.08)\": 2542.5, \"(3.08, 3.12)\": 2588.2, \"(3.12, 3.2)\": 2629.0, \"(3.2, 3.25)\": 2709.2, \"(3.25, 3.28)\": 2768.4, \"(3.28, 3.32)\": 2817.9, \"(3.32, 3.36)\": 2874.2, \"(3.36, 3.37)\": 2941.9, \"(3.37, 3.4)\": 2988.1, \"(3.4, 3.43)\": 3047.5, \"(3.43, 3.49)\": 3101.3, \"(3.49, 3.52)\": 3164.7, \"(3.52, 3.53)\": 3220.7, \"(3.53, 3.57)\": 3261.6, \"(3.57, 3.6)\": 3320.3, \"(3.6, 3.64)\": 3366.8, \"(3.64, 3.69)\": 3447.3, \"(3.69, 3.73)\": 3522.9, \"(3.73, 3.74)\": 3564.7, \"(3.74, 3.77)\": 3620.5, \"(3.77, 3.81)\": 3692.7, \"(3.81, 3.86)\": 3756.5, \"(3.86, 3.9)\": 3819.4, \"(3.9, 3.92)\": 3870.1, \"(3.92, 3.95)\": 3910.3, \"(3.95, 3.96)\": 3976.5, \"(3.96, 3.99)\": 4039.4, \"(3.99, 4.03)\": 4123.2, \"(4.03, 4.06)\": 4195.1, \"(4.06, 4.1)\": 4252.5, \"(4.1, 4.15)\": 4360.2, \"(4.15, 4.17)\": 4456.9, \"(4.17, 4.19)\": 4497.0, \"(4.19, 4.23)\": 4541.7, \"(4.23, 4.25)\": 4590.4, \"(4.25, 4.28)\": 4667.8, \"(4.28, 4.31)\": 4746.5, \"(4.31, 4.35)\": 4817.8, \"(4.35, 4.38)\": 4874.7, \"(4.38, 4.4)\": 4948.8, \"(4.4, 4.43)\": 5027.0, \"(4.43, 4.44)\": 5066.2, \"(4.44, 4.47)\": 5117.2, \"(4.47, 4.5)\": 5206.1, \"(4.5, 4.53)\": 5245.9, \"(4.53, 4.56)\": 5346.8, \"(4.56, 4.59)\": 5398.5, \"(4.59, 4.62)\": 5484.1, \"(4.62, 4.65)\": 5554.9, \"(4.65, 4.68)\": 5614.6, \"(4.68, 4.71)\": 5699.4, \"(4.71, 4.73)\": 5755.7, \"(4.73, 4.75)\": 5803.2, \"(4.75, 4.77)\": 5904.2, \"(4.77, 4.79)\": 5947.0, \"(4.79, 4.81)\": 5998.3, \"(4.81, 4.84)\": 6044.8, \"(4.84, 4.85)\": 6085.7, \"(4.85, 4.86)\": 6132.0, \"(4.86, 4.87)\": 6176.8, \"(4.87, 4.9)\": 6226.7, \"(4.9, 4.91)\": 6300.1, \"(4.91, 4.94)\": 6338.5, \"(4.94, 4.97)\": 6425.9, \"(4.97, 4.99)\": 6493.2, \"(4.99, 5.02)\": 6568.9, \"(5.02, 5.05)\": 6611.5, \"(5.05, 5.05)\": 6686.7, \"(5.05, 5.06)\": 6759.5, \"(5.06, 5.11)\": 6797.9, \"(5.11, 5.15)\": 6897.4, \"(5.15, 5.15)\": 6939.2, \"(5.15, 5.17)\": 6988.3, \"(5.17, 5.18)\": 7085.8, \"(5.18, 5.19)\": 7139.6, \"(5.19, 5.23)\": 7180.2, \"(5.23, 5.25)\": 7253.7, \"(5.25, 5.27)\": 7331.5, \"(5.27, 5.28)\": 7389.4, \"(5.28, 5.29)\": 7436.6, \"(5.29, 5.31)\": 7483.9, \"(5.31, 5.33)\": 7550.0, \"(5.33, 5.34)\": 7606.7, \"(5.34, 5.36)\": 7654.2, \"(5.36, 5.38)\": 7726.1, \"(5.38, 5.39)\": 7814.8, \"(5.39, 5.42)\": 7886.2, \"(5.42, 5.45)\": 7946.0, \"(5.45, 5.5)\": 8038.5, \"(5.5, 5.55)\": 8233.1, \"(5.55, 5.58)\": 8373.0, \"(5.58, 5.62)\": 8468.2, \"(5.62, 5.66)\": 8612.5, \"(5.66, 5.68)\": 8699.5, \"(5.68, 5.69)\": 8772.9, \"(5.69, 5.7)\": 8823.3, \"(5.7, 5.72)\": 8889.2, \"(5.72, 5.74)\": 9007.2, \"(5.74, 5.76)\": 9062.6, \"(5.76, 5.8)\": 9131.1, \"(5.8, 5.84)\": 9284.3, \"(5.84, 5.86)\": 9426.4, \"(5.86, 5.87)\": 9477.0, \"(5.87, 5.9)\": 9520.0, \"(5.9, 5.93)\": 9628.5, \"(5.93, 5.95)\": 9670.2, \"(5.95, 5.96)\": 9749.6, \"(5.96, 5.96)\": 9809.0, \"(5.96, 5.97)\": 9863.9, \"(5.97, 5.98)\": 9930.5, \"(5.98, 5.99)\": 9971.8, \"(5.99, 5.99)\": 10019.9, \"(5.99, 6.06)\": 10070.9, \"(6.06, 6.14)\": 10476.8, \"(6.14, 6.16)\": 10587.5, \"(6.16, 6.17)\": 10661.3, \"(6.17, 6.23)\": 10799.9, \"(6.23, 6.29)\": 11054.7, \"(6.29, 6.29)\": 11191.0, \"(6.29, 6.3)\": 11270.2, \"(6.3, 6.31)\": 11337.3, \"(6.31, 6.37)\": 11413.6, \"(6.37, 6.42)\": 11721.4, \"(6.42, 6.47)\": 11829.2, \"(6.47, 6.52)\": 12122.0, \"(6.52, 6.53)\": 12250.5, \"(6.53, 6.53)\": 12298.7, \"(6.53, 6.55)\": 12348.1, \"(6.55, 6.57)\": 12446.4, \"(6.57, 6.58)\": 12503.0, \"(6.58, 6.6)\": 12602.8, \"(6.6, 6.62)\": 12656.9, \"(6.62, 6.64)\": 12761.7, \"(6.64, 6.66)\": 12871.2, \"(6.66, 6.68)\": 12976.2, \"(6.68, 6.71)\": 13098.5, \"(6.71, 6.78)\": 13258.2, \"(6.78, 6.85)\": 13772.0, \"(6.85, 6.88)\": 13907.2, \"(6.88, 6.91)\": 14128.2, \"(6.91, 6.93)\": 14255.0, \"(6.93, 6.94)\": 14304.4, \"(6.94, 6.96)\": 14372.9, \"(6.96, 6.98)\": 14463.9, \"(6.98, 7.0)\": 14555.3, \"(7.0, 7.01)\": 14622.2, \"(7.01, 7.02)\": 14712.6, \"(7.02, 7.06)\": 14834.6, \"(7.06, 7.11)\": 15132.0, \"(7.11, 7.13)\": 15254.4, \"(7.13, 7.16)\": 15428.6, \"(7.16, 7.19)\": 15614.8, \"(7.19, 7.22)\": 15779.2, \"(7.22, 7.26)\": 15939.4, \"(7.26, 7.29)\": 16170.0, \"(7.29, 7.3)\": 16256.0, \"(7.3, 7.33)\": 16385.9, \"(7.33, 7.36)\": 16565.6, \"(7.36, 7.37)\": 16669.5, \"(7.37, 7.39)\": 16784.8, \"(7.39, 7.42)\": 16922.5, \"(7.42, 7.43)\": 17040.8, \"(7.43, 7.45)\": 17141.8, \"(7.45, 7.51)\": 17314.0, \"(7.51, 7.57)\": 17773.1, \"(7.57, 7.59)\": 17929.7, \"(7.59, 7.6)\": 18070.3, \"(7.6, 7.62)\": 18158.9, \"(7.62, 7.64)\": 18317.9, \"(7.64, 7.66)\": 18366.7, \"(7.66, 7.68)\": 18601.6, \"(7.68, 7.71)\": 18685.7, \"(7.71, 7.73)\": 18808.8, \"(7.73, 7.73)\": 18899.7, \"(7.73, 7.74)\": 19007.8, \"(7.74, 7.75)\": 19091.5, \"(7.75, 7.8)\": 19232.0, \"(7.8, 7.85)\": 19574.7, \"(7.85, 7.86)\": 19706.1, \"(7.86, 7.87)\": 19789.8, \"(7.87, 7.87)\": 19883.0, \"(7.87, 7.92)\": 19985.7, \"(7.92, 7.99)\": 20505.2, \"(7.99, 8.0)\": 20641.6, \"(8.0, 8.0)\": 20747.6, \"(8.0, 8.03)\": 20848.5, \"(8.03, 8.06)\": 21082.5, \"(8.06, 8.08)\": 21189.2, \"(8.08, 8.09)\": 21271.0, \"(8.09, 8.09)\": 21349.6, \"(8.09, 8.1)\": 21448.3, \"(8.1, 8.11)\": 21523.9, \"(8.11, 8.14)\": 21619.8, \"(8.14, 8.16)\": 21841.2, \"(8.16, 8.18)\": 21915.6, \"(8.18, 8.2)\": 22079.6, \"(8.2, 8.22)\": 22228.0, \"(8.22, 8.25)\": 22417.3, \"(8.25, 8.27)\": 22599.2, \"(8.27, 8.28)\": 22692.3, \"(8.28, 8.33)\": 22854.3, \"(8.33, 8.38)\": 23370.6, \"(8.38, 8.38)\": 23492.4, \"(8.38, 8.39)\": 23557.0, \"(8.39, 8.41)\": 23640.4, \"(8.41, 8.43)\": 23760.5, \"(8.43, 8.45)\": 23960.7, \"(8.45, 8.47)\": 24062.6, \"(8.47, 8.51)\": 24339.2, \"(8.51, 8.53)\": 24564.2, \"(8.53, 8.54)\": 24670.7, \"(8.54, 8.56)\": 24821.0, \"(8.56, 8.57)\": 24950.8, \"(8.57, 8.59)\": 25014.1, \"(8.59, 8.6)\": 25171.1, \"(8.6, 8.65)\": 25455.3, \"(8.65, 8.69)\": 25854.5, \"(8.69, 8.71)\": 26040.2, \"(8.71, 8.73)\": 26184.4, \"(8.73, 8.75)\": 26317.3, \"(8.75, 8.77)\": 26564.6, \"(8.77, 8.81)\": 26645.1, \"(8.81, 8.86)\": 27163.1, \"(8.86, 8.88)\": 27445.3, \"(8.88, 8.9)\": 27595.2, \"(8.9, 8.92)\": 27813.7, \"(8.92, 8.94)\": 27929.7, \"(8.94, 8.95)\": 28077.1, \"(8.95, 8.95)\": 28117.9, \"(8.95, 8.97)\": 28258.8, \"(8.97, 8.99)\": 28408.8, \"(8.99, 9.0)\": 28487.7, \"(9.0, 9.02)\": 28578.4, \"(9.02, 9.03)\": 28708.8, \"(9.03, 9.03)\": 28805.7, \"(9.03, 9.04)\": 28851.0, \"(9.04, 9.04)\": 28967.9, \"(9.04, 9.05)\": 29027.4, \"(9.05, 9.06)\": 29102.8, \"(9.06, 9.09)\": 29191.2, \"(9.09, 9.12)\": 29524.2, \"(9.12, 9.14)\": 29639.0, \"(9.14, 9.18)\": 30005.1, \"(9.18, 9.2)\": 30291.2, \"(9.2, 9.24)\": 30556.4, \"(9.24, 9.3)\": 31144.2, \"(9.3, 9.32)\": 31326.7, \"(9.32, 9.32)\": 31448.3, \"(9.32, 9.33)\": 31550.8, \"(9.33, 9.34)\": 31609.3, \"(9.34, 9.36)\": 31762.4, \"(9.36, 9.37)\": 31858.8, \"(9.37, 9.38)\": 32033.0, \"(9.38, 9.39)\": 32109.2, \"(9.39, 9.41)\": 32181.7, \"(9.41, 9.43)\": 32451.9, \"(9.43, 9.45)\": 32630.7, \"(9.45, 9.49)\": 32791.1, \"(9.49, 9.54)\": 33455.3, \"(9.54, 9.56)\": 33704.4, \"(9.56, 9.57)\": 33834.9, \"(9.57, 9.58)\": 33983.0, \"(9.58, 9.58)\": 34040.3, \"(9.58, 9.6)\": 34106.4, \"(9.6, 9.62)\": 34317.4, \"(9.62, 9.64)\": 34481.9, \"(9.64, 9.7)\": 34777.4, \"(9.7, 9.74)\": 35501.3, \"(9.74, 9.75)\": 35697.5, \"(9.75, 9.78)\": 35872.0, \"(9.78, 9.81)\": 36249.2, \"(9.81, 9.82)\": 36386.7, \"(9.82, 9.83)\": 36575.2, \"(9.83, 9.85)\": 36699.0, \"(9.85, 9.89)\": 36968.8, \"(9.89, 9.91)\": 37410.6, \"(9.91, 9.92)\": 37466.0, \"(9.92, 9.94)\": 37727.1, \"(9.94, 9.96)\": 37912.3, \"(9.96, 9.96)\": 37954.5}\n",
+      "Means: {\"(-9.97, -9.94)\": 20907.1, \"(-9.94, -9.92)\": 20591.2, \"(-9.92, -9.89)\": 19998.3, \"(-9.89, -9.88)\": 19729.3, \"(-9.88, -9.85)\": 19300.1, \"(-9.85, -9.84)\": 18815.0, \"(-9.84, -9.82)\": 18587.4, \"(-9.82, -9.81)\": 18333.4, \"(-9.81, -9.78)\": 17992.0, \"(-9.78, -9.76)\": 17483.5, \"(-9.76, -9.74)\": 17270.1, \"(-9.74, -9.72)\": 16805.7, \"(-9.72, -9.7)\": 16434.0, \"(-9.7, -9.68)\": 16129.0, \"(-9.68, -9.66)\": 15916.5, \"(-9.66, -9.64)\": 15671.4, \"(-9.64, -9.63)\": 15344.9, \"(-9.63, -9.61)\": 15054.1, \"(-9.61, -9.57)\": 14704.3, \"(-9.57, -9.55)\": 14204.5, \"(-9.55, -9.54)\": 13938.9, \"(-9.54, -9.52)\": 13715.7, \"(-9.52, -9.49)\": 13436.7, \"(-9.49, -9.46)\": 12979.0, \"(-9.46, -9.45)\": 12758.0, \"(-9.45, -9.42)\": 12522.7, \"(-9.42, -9.39)\": 12294.8, \"(-9.39, -9.35)\": 11679.6, \"(-9.35, -9.31)\": 11364.2, \"(-9.31, -9.25)\": 10742.8, \"(-9.25, -9.23)\": 10328.0, \"(-9.23, -9.2)\": 10075.0, \"(-9.2, -9.17)\": 9791.6, \"(-9.17, -9.13)\": 9419.0, \"(-9.13, -9.09)\": 9096.2, \"(-9.09, -9.05)\": 8779.3, \"(-9.05, -9.03)\": 8447.6, \"(-9.03, -9.0)\": 8198.9, \"(-9.0, -8.91)\": 7981.2, \"(-8.91, -8.83)\": 7068.6, \"(-8.83, -8.78)\": 6727.3, \"(-8.78, -8.75)\": 6419.2, \"(-8.75, -8.7)\": 6187.2, \"(-8.7, -8.66)\": 5943.3, \"(-8.66, -8.56)\": 5697.0, \"(-8.56, -8.45)\": 4912.8, \"(-8.45, -8.39)\": 4618.4, \"(-8.39, -8.34)\": 4374.8, \"(-8.34, -8.28)\": 4136.7, \"(-8.28, -8.19)\": 3881.0, \"(-8.19, -8.12)\": 3479.6, \"(-8.12, -8.03)\": 3263.3, \"(-8.03, -7.95)\": 3038.3, \"(-7.95, -7.88)\": 2820.9, \"(-7.88, -7.79)\": 2599.6, \"(-7.79, -7.68)\": 2373.0, \"(-7.68, -7.55)\": 2126.9, \"(-7.55, -7.43)\": 1895.5, \"(-7.43, -7.29)\": 1675.3, \"(-7.29, -7.14)\": 1460.9, \"(-7.14, -6.94)\": 1249.3, \"(-6.94, -6.71)\": 1032.8, \"(-6.71, -6.42)\": 822.1, \"(-6.42, -5.98)\": 606.4, \"(-5.98, -5.21)\": 394.3, \"(-5.21, 9.98)\": 182.8}\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2148,7 +3509,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2160,18 +3521,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.1%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-9.98, -9.56)\": -1.002, \"(-9.56, -3.51)\": -1.0, \"(-3.51, -3.49)\": -0.997, \"(-3.49, -2.1)\": -1.0, \"(-2.1, -2.09)\": -1.002, \"(-2.09, -0.4)\": -1.0, \"(-0.4, -0.31)\": -0.998, \"(-0.31, -0.23)\": -0.994, \"(-0.23, -0.19)\": -0.991, \"(-0.19, -0.18)\": -0.989, \"(-0.18, -0.04)\": -0.987, \"(-0.04, -0.02)\": -0.983, \"(-0.02, -0.02)\": -0.953, \"(-0.02, 0.0)\": -0.851, \"(0.0, 0.05)\": 0.868, \"(0.05, 0.1)\": 0.96, \"(0.1, 0.22)\": 0.989, \"(0.22, 0.29)\": 0.991, \"(0.29, 0.33)\": 0.994, \"(0.33, 0.44)\": 0.996, \"(0.44, 9.74)\": 1.0, \"(9.74, 9.99)\": 1.002}\n",
+      "Means: {\"(-9.97, -2.16)\": -0.0003, \"(-2.16, -1.98)\": 0.0101, \"(-1.98, -1.84)\": 0.022, \"(-1.84, -1.76)\": 0.0357, \"(-1.76, -1.66)\": 0.0474, \"(-1.66, -1.58)\": 0.0719, \"(-1.58, -1.53)\": 0.0849, \"(-1.53, -1.48)\": 0.1008, \"(-1.48, -1.45)\": 0.1136, \"(-1.45, -1.42)\": 0.1254, \"(-1.42, -1.38)\": 0.1378, \"(-1.38, -1.36)\": 0.152, \"(-1.36, -1.33)\": 0.1632, \"(-1.33, -1.28)\": 0.1764, \"(-1.28, -1.25)\": 0.1996, \"(-1.25, -1.19)\": 0.2172, \"(-1.19, -1.12)\": 0.2664, \"(-1.12, -1.06)\": 0.3084, \"(-1.06, -1.01)\": 0.3489, \"(-1.01, -0.96)\": 0.3671, \"(-0.96, -0.93)\": 0.4151, \"(-0.93, -0.9)\": 0.427, \"(-0.9, -0.87)\": 0.4621, \"(-0.87, -0.83)\": 0.4739, \"(-0.83, -0.78)\": 0.5263, \"(-0.78, -0.76)\": 0.5535, \"(-0.76, -0.73)\": 0.5709, \"(-0.73, -0.68)\": 0.5946, \"(-0.68, -0.62)\": 0.6504, \"(-0.62, -0.58)\": 0.7061, \"(-0.58, -0.57)\": 0.7207, \"(-0.57, -0.53)\": 0.7308, \"(-0.53, -0.49)\": 0.7719, \"(-0.49, -0.46)\": 0.7993, \"(-0.46, -0.44)\": 0.8154, \"(-0.44, -0.42)\": 0.8257, \"(-0.42, -0.39)\": 0.8406, \"(-0.39, -0.35)\": 0.8746, \"(-0.35, -0.32)\": 0.8868, \"(-0.32, -0.26)\": 0.9201, \"(-0.26, -0.23)\": 0.9387, \"(-0.23, -0.19)\": 0.9507, \"(-0.19, -0.11)\": 0.9755, \"(-0.11, 0.14)\": 0.9886, \"(0.14, 0.19)\": 0.9774, \"(0.19, 0.23)\": 0.9604, \"(0.23, 0.27)\": 0.9395, \"(0.27, 0.3)\": 0.9291, \"(0.3, 0.32)\": 0.9083, \"(0.32, 0.35)\": 0.8929, \"(0.35, 0.37)\": 0.8803, \"(0.37, 0.39)\": 0.8683, \"(0.39, 0.41)\": 0.8496, \"(0.41, 0.44)\": 0.8348, \"(0.44, 0.49)\": 0.8056, \"(0.49, 0.52)\": 0.7727, \"(0.52, 0.54)\": 0.7565, \"(0.54, 0.56)\": 0.7393, \"(0.56, 0.59)\": 0.7197, \"(0.59, 0.61)\": 0.6971, \"(0.61, 0.63)\": 0.6811, \"(0.63, 0.67)\": 0.656, \"(0.67, 0.7)\": 0.625, \"(0.7, 0.73)\": 0.6018, \"(0.73, 0.79)\": 0.571, \"(0.79, 0.83)\": 0.5127, \"(0.83, 0.84)\": 0.498, \"(0.84, 0.86)\": 0.4866, \"(0.86, 0.88)\": 0.4756, \"(0.88, 0.91)\": 0.4479, \"(0.91, 0.95)\": 0.432, \"(0.95, 0.99)\": 0.3882, \"(0.99, 1.02)\": 0.3662, \"(1.02, 1.04)\": 0.3542, \"(1.04, 1.08)\": 0.3302, \"(1.08, 1.09)\": 0.3086, \"(1.09, 1.11)\": 0.2978, \"(1.11, 1.13)\": 0.2839, \"(1.13, 1.16)\": 0.2714, \"(1.16, 1.19)\": 0.2542, \"(1.19, 1.23)\": 0.2335, \"(1.23, 1.26)\": 0.2153, \"(1.26, 1.28)\": 0.2039, \"(1.28, 1.29)\": 0.1938, \"(1.29, 1.33)\": 0.1837, \"(1.33, 1.38)\": 0.1638, \"(1.38, 1.43)\": 0.1391, \"(1.43, 1.49)\": 0.1222, \"(1.49, 1.54)\": 0.1023, \"(1.54, 1.6)\": 0.0885, \"(1.6, 1.64)\": 0.0766, \"(1.64, 1.69)\": 0.0655, \"(1.69, 1.77)\": 0.0533, \"(1.77, 1.85)\": 0.0423, \"(1.85, 1.96)\": 0.0318, \"(1.96, 2.13)\": 0.0212, \"(2.13, 2.83)\": 0.0103, \"(2.83, 9.99)\": 0.0003}\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2181,7 +3541,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2193,18 +3553,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.1%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-9.98, -9.81)\": -1.004, \"(-9.81, -8.07)\": -1.001, \"(-8.07, -7.61)\": -0.999, \"(-7.61, -7.6)\": -1.002, \"(-7.6, -7.06)\": -0.999, \"(-7.06, -6.73)\": -1.002, \"(-6.73, -4.74)\": -0.999, \"(-4.74, -4.66)\": -1.002, \"(-4.66, -3.75)\": -0.999, \"(-3.75, -3.74)\": -1.002, \"(-3.74, -3.31)\": -1.0, \"(-3.31, -3.24)\": -0.995, \"(-3.24, -3.12)\": -0.991, \"(-3.12, -3.07)\": -0.988, \"(-3.07, -3.03)\": -0.985, \"(-3.03, -2.99)\": -0.976, \"(-2.99, -2.97)\": 0.824, \"(-2.97, -2.96)\": 0.925, \"(-2.96, -2.95)\": 0.955, \"(-2.95, -2.94)\": 0.984, \"(-2.94, -2.83)\": 0.989, \"(-2.83, -2.79)\": 0.992, \"(-2.79, -2.61)\": 0.997, \"(-2.61, -0.49)\": 0.999, \"(-0.49, -0.41)\": 1.002, \"(-0.41, -0.34)\": 0.998, \"(-0.34, 0.06)\": 1.001, \"(0.06, 0.22)\": 0.998, \"(0.22, 9.99)\": 1.001}\n",
+      "Means: {\"(-9.97, 3.27)\": 0.2, \"(3.27, 4.28)\": 10.0, \"(4.28, 4.89)\": 19.9, \"(4.89, 5.3)\": 30.0, \"(5.3, 5.61)\": 39.9, \"(5.61, 5.89)\": 50.0, \"(5.89, 6.12)\": 60.1, \"(6.12, 6.31)\": 70.8, \"(6.31, 6.52)\": 82.8, \"(6.52, 6.69)\": 94.0, \"(6.69, 6.83)\": 104.5, \"(6.83, 6.98)\": 115.5, \"(6.98, 7.08)\": 127.1, \"(7.08, 7.19)\": 137.3, \"(7.19, 7.32)\": 149.1, \"(7.32, 7.41)\": 160.2, \"(7.41, 7.5)\": 170.9, \"(7.5, 7.58)\": 182.2, \"(7.58, 7.66)\": 192.4, \"(7.66, 7.73)\": 203.5, \"(7.73, 7.79)\": 213.3, \"(7.79, 7.87)\": 223.7, \"(7.87, 7.95)\": 234.3, \"(7.95, 8.06)\": 255.8, \"(8.06, 8.12)\": 268.7, \"(8.12, 8.18)\": 280.7, \"(8.18, 8.23)\": 290.9, \"(8.23, 8.28)\": 301.1, \"(8.28, 8.33)\": 311.4, \"(8.33, 8.37)\": 323.2, \"(8.37, 8.43)\": 333.0, \"(8.43, 8.47)\": 347.0, \"(8.47, 8.52)\": 360.5, \"(8.52, 8.58)\": 373.9, \"(8.58, 8.65)\": 389.5, \"(8.65, 8.73)\": 415.6, \"(8.73, 8.76)\": 428.8, \"(8.76, 8.83)\": 438.9, \"(8.83, 8.88)\": 460.3, \"(8.88, 8.91)\": 470.6, \"(8.91, 8.95)\": 484.2, \"(8.95, 8.98)\": 497.9, \"(8.98, 9.03)\": 514.7, \"(9.03, 9.07)\": 530.5, \"(9.07, 9.12)\": 546.4, \"(9.12, 9.14)\": 558.5, \"(9.14, 9.18)\": 569.2, \"(9.18, 9.22)\": 582.5, \"(9.22, 9.24)\": 596.9, \"(9.24, 9.28)\": 611.6, \"(9.28, 9.3)\": 623.1, \"(9.3, 9.33)\": 636.5, \"(9.33, 9.37)\": 649.2, \"(9.37, 9.42)\": 672.2, \"(9.42, 9.45)\": 689.2, \"(9.45, 9.48)\": 705.5, \"(9.48, 9.51)\": 721.4, \"(9.51, 9.54)\": 734.3, \"(9.54, 9.57)\": 751.8, \"(9.57, 9.59)\": 761.6, \"(9.59, 9.63)\": 779.3, \"(9.63, 9.65)\": 793.6, \"(9.65, 9.69)\": 808.3, \"(9.69, 9.75)\": 852.2, \"(9.75, 9.78)\": 866.3, \"(9.78, 9.81)\": 889.7, \"(9.81, 9.83)\": 901.1, \"(9.83, 9.86)\": 911.4, \"(9.86, 9.89)\": 941.1, \"(9.89, 9.99)\": 976.5}\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2214,7 +3573,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2226,18 +3585,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.1%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-9.98, -7.84)\": -1.002, \"(-7.84, -2.48)\": -1.0, \"(-2.48, -2.35)\": -1.002, \"(-2.35, -2.01)\": -0.999, \"(-2.01, -1.93)\": -1.001, \"(-1.93, 0.58)\": -0.999, \"(0.58, 0.68)\": -0.997, \"(0.68, 0.79)\": -0.993, \"(0.79, 0.84)\": -0.991, \"(0.84, 0.88)\": -0.988, \"(0.88, 0.9)\": -0.985, \"(0.9, 0.91)\": -0.983, \"(0.91, 0.96)\": -0.777, \"(0.96, 1.0)\": 0.981, \"(1.0, 1.11)\": 0.986, \"(1.11, 1.2)\": 0.988, \"(1.2, 1.22)\": 0.991, \"(1.22, 1.27)\": 0.993, \"(1.27, 1.34)\": 0.997, \"(1.34, 1.57)\": 0.999, \"(1.57, 1.94)\": 1.001, \"(1.94, 2.12)\": 0.999, \"(2.12, 2.14)\": 1.001, \"(2.14, 3.16)\": 0.999, \"(3.16, 3.33)\": 1.001, \"(3.33, 3.99)\": 0.999, \"(3.99, 4.86)\": 1.001, \"(4.86, 4.94)\": 0.999, \"(4.94, 10.0)\": 1.001}\n",
+      "Means: {\"(-10.0, 5.82)\": 16.0, \"(5.82, 6.4)\": 621.9, \"(6.4, 6.81)\": 1222.7, \"(6.81, 7.08)\": 1847.7, \"(7.08, 7.25)\": 2425.8, \"(7.25, 7.43)\": 3015.5, \"(7.43, 7.62)\": 3623.8, \"(7.62, 7.71)\": 4284.3, \"(7.71, 7.84)\": 4888.2, \"(7.84, 7.93)\": 5568.5, \"(7.93, 8.03)\": 6301.5, \"(8.03, 8.14)\": 6899.3, \"(8.14, 8.23)\": 7834.9, \"(8.23, 8.31)\": 8656.9, \"(8.31, 8.38)\": 9331.4, \"(8.38, 8.42)\": 9934.1, \"(8.42, 8.48)\": 10537.7, \"(8.48, 8.52)\": 11199.3, \"(8.52, 8.6)\": 11940.5, \"(8.6, 8.64)\": 12767.8, \"(8.64, 8.69)\": 13392.6, \"(8.69, 8.73)\": 14034.4, \"(8.73, 8.77)\": 14804.7, \"(8.77, 8.81)\": 15461.8, \"(8.81, 8.89)\": 16179.1, \"(8.89, 8.95)\": 18128.8, \"(8.95, 9.0)\": 19011.0, \"(9.0, 9.05)\": 20245.5, \"(9.05, 9.1)\": 21005.3, \"(9.1, 9.15)\": 22690.0, \"(9.15, 9.19)\": 23617.7, \"(9.19, 9.24)\": 24893.2, \"(9.24, 9.27)\": 25730.4, \"(9.27, 9.29)\": 26566.2, \"(9.29, 9.32)\": 27300.8, \"(9.32, 9.36)\": 28775.0, \"(9.36, 9.37)\": 29406.4, \"(9.37, 9.41)\": 30172.3, \"(9.41, 9.45)\": 31796.4, \"(9.45, 9.47)\": 32627.7, \"(9.47, 9.5)\": 33364.2, \"(9.5, 9.54)\": 34915.4, \"(9.54, 9.56)\": 35811.1, \"(9.56, 9.6)\": 37508.8, \"(9.6, 9.61)\": 38120.9, \"(9.61, 9.63)\": 38835.5, \"(9.63, 9.64)\": 39570.0, \"(9.64, 9.68)\": 40172.9, \"(9.68, 9.72)\": 42652.6, \"(9.72, 9.73)\": 43418.2, \"(9.73, 9.75)\": 44275.9, \"(9.75, 9.76)\": 45130.5, \"(9.76, 9.79)\": 45921.4, \"(9.79, 9.82)\": 47788.2, \"(9.82, 9.84)\": 49180.7, \"(9.84, 9.86)\": 49786.5, \"(9.86, 9.92)\": 51790.1, \"(9.92, 9.96)\": 55473.7, \"(9.96, 9.97)\": 56813.4, \"(9.97, 9.98)\": 57606.1}\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2247,7 +3605,7 @@
     },
     {
      "data": {
-      "image/png": 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vD1tzAveSOfkYEZGxUasVtJi5B4lPswAAozrWxDtNvCWnMkxsPgyAhYUKHfLM8b/tXLzENEREVBLbz8fjYWqmqHs0rigxjWFj82Egvnu/Eeysc3fHpM3n8efpu5ITERFRUa0/GoNha06I+vTkTvBytZeYyLCx+TAQtlaW+O9rmqnXh/98Ek/SMgt5BhERGYqLccliPCbYFy721hLTGD6tNx85OTmYOHEifHx8YG9vj2rVqmH69OlQFEXbb2VyPgisghX9m4r67UUREtMQEVFRbD51BysjbgIAhrevjpB21eUGMgJabz5mzZqFRYsW4f/+7/9w8eJFzJo1C7Nnz8aCBQu0/VYmqV0tdzSs5AoAuH4/FVP+OC83EBERFSg1IxufrTsl6trlneWFMSJabz4iIiLQtWtXdOnSBVWqVEGPHj3QqVMnHDlyRNtvZbIW9WksxisjbiI9K0diGiIiKsi7Sw6L8Zwe9fFavfIS0xgPrTcfzZs3x+7du3HlyhUAwOnTpxEeHo7OnTs/d/uMjAwkJSXl+zJ3ni52+GlggKhrTdwmMQ0RET3PikM3cPZOIgBApQJ68rbaIrPS9guOGzcOSUlJqFWrFiwtLZGTk4OvvvoKffr0ee72oaGhmDp1qrZjGL0mVfJPxbv51B10bVBBUhoiIsrrSVompv55QdTHJ3SUmMb4aP3Ix4YNG7BmzRqsXbsWJ06cwKpVqzB37lysWrXquduPHz8eiYmJ4is2NlbbkYySnbUlbs7sIurP1p1CYlqWxERERAQA2TlqNJi2U9RL+zZBGUcbiYmMj9abjzFjxmDcuHF49913Ua9ePXzwwQcYOXIkQkNDn7u9ra0tnJ2d832RRt7TL/7TdiAzm2u/EBHJ9PMRzaJxpWyt0Na3nMQ0xknrzUdaWhosLPK/rKWlJdRq/tIsiZY1ysLf21XUn/96Wl4YIiIzl5WjxsTNmrsQj00IgpUlp8wqLq3/ib3xxhv46quv8Ndff+HmzZvYuHEjwsLC8NZbb2n7rczG5pAWYrzp1F3cefJUYhoiIvMVkmcW0wldasPO2lJiGuOl9eZjwYIF6NGjBz7++GPUrl0b//nPfzBkyBBMnz5d229lVhb1aSTGby4IR46ak7YREelLYloWfoy8iR15Vh7/sIWPxETGTaUY2NSjSUlJcHFxQWJiIq//+Jchq49h+/ncv/iXpr/KjpuISE/CdlzGt3uiRb12cACaVysrMZHhKc7vb56oMiJze/qL8Suhu3Hgyn2JaYiIzEfi09y7DWt5OmF0p5oIrOomOZFxY/NhRBxtrFDdvRQA4ElaFvouP4K7vP6DiEin/m/PVey7nPufvU51PDC8fQ2oVCrJqYwbmw8jYmGhwtbPWmFgS815xnG/n5WYiIjItO25lIC5O67g1qM0AEBpzuehFWw+jIy1pQXGBPuibClbAMCBK/dxMY5T0hMRaVt2jhofrjwm6q97+uPdppUkJjIdbD6MkJ21JTZ+3FzUg1YdK2RrIiIqiZlbL4nx1z390b1xRdjb8EJ/bWDzYaS8yzigxv+u/7jz5CmO3HgkORERkelIzcjG0vAbou7euKLENKaHzYcRW/S+Zu6Pd76PxH7e/UJEpBV1J28X4ylv1JGYxDSx+TBi1d2dMK1rXVH3W35EYhoiItOwM89EYgDQn5OJaR2bDyPXN7AKBrfS/GBM+eN8IVsTEVFhFEXB4B8119GdntRJYhrTxebDBHzctroYr4y4iWv3UySmISIyXsHzD4jxmGBfuDhYS0xjuth8mIDSjjbYlGfxuQ5f74eBzZpPRGTwxvxyGlcSNP95C2lXvZCt6WWw+TARDbxd8VmHGqL+5dhtiWmIiIzLzQep+OW45t/Nc1ODJaYxfWw+TMiQNlVhbZk75e/nv53BoegHkhMRERmHtnP3ifGRLzqglK2VvDBmgM2HCXGwscKSvk1E3WdpFNRqnn4hIirMyZjHYly/ogvcne0kpjEPbD5MTDtf93y33360mrOfEhEV5NydRLz1XYSoVw8MkJjGfLD5MEF9A6uI8a6L93AlIVleGCIiA5WSkY3XF4SL+vNXfeFiz7tb9IHNh4naM7qNGHead6CQLYmIzNO4386I8Uetq+abtoB0i82HiaparhR6N9OsvvjukkjefktE9D9PM3Ow5UycqMd3riUxjflh82HCvurmJ8aHrz/CsVuPC9maiMg8KIqC2pO2iXr9R69ApVJJTGR+2HyYMAsLFY7+N0jUPRdHSkxDRGQY3l8Wla8OqOomKYn5YvNh4so52SK4roeoP1l7QmIaIiK5Vh++hUPRD0V9fcZrEtOYLzYfZuC7Po3FeMuZOBy+/rCQrYmITNfETefE+OyUTrCw4OkWGdh8mAFLCxWivugg6neXHObFp0Rkdo7feiTGX3bzg5Mdb6uVhc2HmfBwtsOYYF9Rj9pwWmIaIiL9upeUju6LNNe9vZfnbkDSPzYfZiTvCo0bT97Bcd79QkRmQFEUNJuxW9SjO9bk6RbJ2HyYmb8+bSnG3RdFcO0XIjJ573yvOeLxdqMKGJ5nBXCSg82Hmanr5YK3G1UQ9dQ/z0tMQ0SkW+lZOTh6U3OUN+ydBvLCkMDmwwzN7eEvxqsib2H/lfsS0xAR6YaiKKg1UTOZ2IoBTSWmobzYfJghCwsVVvTX/BD2W34EGdk5EhMREWnfiPWnxNjGygLtfN3lhaF82HyYqXa13PFpnvOezUP3SExDRKRdWTlqbD51V9RXvuwsMQ39G5sPM/ZR66pi/DA1E2uibklMQ0SkHYqiwG/ydlHP6+VfyNYkA5sPM1bK1gpnp3QS9X83nuPkY0Rk9FYfvoWMbDUAoLyLHd5qWFFyIvo3Nh9mzsnOGl/mWf128f7rEtMQEb2cjOwcTNqsuYvv4OftJKahgrD5oHwz/c3adgn3ktIlpiEiKpl/n275ZWggrCz5a84Qca8QLCxU+PMTzeRjzWbsRvS9FImJiIiKb/If55GVk3vquEnl0mhapYzkRFQQNh8EAKhX0QVvN9RMPjZi/UmJaYiIiif2URp+jNRcNL9+SKDENPQibD5ICOvVAM2ruQEAzt1Jwvbz8ZITERG9WOyjNLSavVfUxycEwZJrtxg0Nh+Uz5yemlvShqw+jvvJGRLTEBG92PQtF8T47YYV4FbKVmIaKgo2H5RPBVd7TOhSW9SdvzkoMQ0RUeGirj/EjgsJAICq5RwR1quB3EBUJGw+6BkftvCBp7MdAOBBSgb2XEqQnIiI6FnpWTnoteSwqBe/31hiGioONh/0DAsLFfaNaSvqD1ce49ovRGRwhqw+LsY9G1dETQ8niWmoONh80HPZWVvi/95rKOrXvw2XmIaIKL+rCcn5VuSe2rWuxDRUXGw+qECv1/cS46v3UnD+bqLENEREuRRFQcd5B0R9eHwHONhYSUxExcXmgwp19L9BYtzl23Cu/UJE0o377awYd29UEZ4udhLTUEmw+aBClXOyhW+e86hf77giMQ0Rmbvk9CysPxYr6jk96ktMQyXF5oNeaEOemQL/b280tp2Lk5iGiMxVYloW6k3ZIerdo9vAgpOJGSU2H/RCLg7W+La35uLToT+dwK2HqRITEZE5Gv7zCTGuX9EF1cqVkpiGXgabDyqSTnU8ML5zLVG3mbMPajWv/yAi/Yi89hAHrj4AALg52mBzSAvJiehlsPmgIrGztsSQNtXwYQsf8divJ25LTERE5qT3D5rJxH4b1hwqFU+3GDM2H1Qsk96oI8af/3pGYhIiMhcLdl8V4/cCKqFKWUeJaUgb2HxQsXVtoJn/o8eiCIlJiMjUbT0bh693au6ym97VT2Ia0hY2H1Rs895pIMbHbj3GkgPX5IUhIpN158lTDFujucj0t2GBsOTdLSaBzQcVm4WFCscnaCYfm/H3JV58SkRa1zFsvxh/3dMfjSuXkZiGtInNB5WIWylbfNenkag/W39KXhgiMjmL919DWmbugpatapRF98YVJScibWLzQSXW2c9TjP88fRfR95IlpiEiU6EoCmZuvSTqvP/RIdPA5oNKTKVSYdeoNqIOCjsAv8nbsfrwLYmpiMiYKYqCGv/dKuqlfZvAyc5aYiLSBZ00H3fu3MH7778PNzc32Nvbo169ejh27Jgu3ookq+5eCkNaVxV1SkY2tp7l9OtEVDL7Lt9Hdp5ryILqeEhMQ7qi9ebj8ePHaNGiBaytrbF161ZcuHABX3/9NUqXLq3ttyIDMf612jg1qSMm55kDhIiouFIysjFg5VFRn5saLDEN6ZKVtl9w1qxZ8Pb2xooVK8RjPj4+hTyDTIGrgw3KONrIjkFERmzm1oti/GU3P5Sy1fqvKDIQWj/y8ccff6BJkybo2bMn3N3d0bBhQ/zwww8Fbp+RkYGkpKR8X0REZF5iHqbhp8Mxon7/lcoS05Cuab35uH79OhYtWoQaNWpg+/btGDZsGD799FOsWrXquduHhobCxcVFfHl7e2s7EulZ9L0UjP31DPZcSpAdhYiMQOLTLLSes1fUSz5oLDEN6YNKURStzg5lY2ODJk2aICJCM+32p59+iqNHjyIyMvKZ7TMyMpCRkSHqpKQkeHt7IzExEc7OztqMRjq29/I9DFhxNN9jZ6Z0gjOvVCeiQgTP24/LCSkAgO6NKuLrd/wlJ6KSSEpKgouLS5F+f2v9yEf58uVRp07+Cw9r166NmJiY525va2sLZ2fnfF9knFpVL4s5Perjozx3v7Sbs09eICIyeDsvJIjGw97aErN71JeciPRB681HixYtcPny5XyPXblyBZUr8/ydqbOytEDPJt744rXacHeyBQA8TM3Eon1c+4WInqUoCgb/qJmG4cDn7bh2i5nQevMxcuRIHD58GDNmzEB0dDTWrl2LJUuWICQkRNtvRQZs50jN5GOztl3Cw5SMQrYmInMUlGftls9f9UW5//2nhUyf1puPpk2bYuPGjfj555/h5+eH6dOnY/78+ejTp4+234oMmIuDNX4e/Iqo31gQjtuP0yQmIiJDEvsoDdfup4r647bVJaYhfdP6BacvqzgXrJDhe2dxJI7cfAQA+OCVypjezU9yIiKSLStHnW8K9XNTgzmnhwmQesEpUV4L3msIRxtLAMDqw7dw5MYjyYmISLbO3xwU4/7Nq7DxMENsPkinPJztsKx/U1G/8/2zt1sTkfnYe+keou+liHrKm3UlpiFZ2HyQzr1S1Q2DWmqm2D9+i0c/iMxRZrY639otZ6d0kpiGZGLzQXoxrnMtMe6+KBIZ2TkS0xCRvj1KzUSDaTtE/W3vhnDiBIRmi80H6YWVpQWGt9dcze47YRuyctQSExGRPu2+mIC0zNz/dNhYWuBNfy/JiUgmNh+kN6M7+aK0g+Z/OisP3ZQXhoj05kFKBubtvCLq4xODJKYhQ8Dmg/Tq2ISOYvzV3xdx62FqIVsTkbFTFAU9FkXgbmI6AKBbAy+ebiE2H6RflhYqrBkUIOo2c/ZBrTaoqWaISEsys9U4cPUBbj7UTDA4smNNiYnIULD5IL1rUb0sXq9fXtTLD92QmIaIdCE5PQstZu5Bv+VHxGNH/tsBld0cJaYiQ8Hmg6RY0LuhGH/510U8ScuUmIaItC3mURru/29NJ0uVCiHtqsHdyU5yKjIUbD5ICpVKhRV5Jh9rM2efvDBEpDPuTra4FvoaxgTXevHGZDbYfJA07Wq5o7xL7v+EEp9mYXk4T78QmYqnmZzLhwrG5oOk2jaitRhP23IBiWlZEtMQkTb0XX4EPRZzKQUqGJsPksrF3hpbhrcUtf+0HTCwhZaJqBh+ORaLA1fui7qdr7vENGSo2HyQdH4VXNCokquoF+yJlheGiEos+l4yxvx6RtQXp72KWT3qS0xEhorNBxmEX4Y2F+OwnVfyrXpJRIZPrVYQFHZA1KsHNoO9jaXERGTI2HyQQbC0UGHlAM3dL0Fh+zn5GJERmbPjshj7lHVEqxrlJKYhQ8fmgwxGW193BNf1EPW0LRckpiGiorqSkIxF+66JOu91XETPw+aDDMqcnv5ivDLiJk+/EBmB3ksOi/GW4S3haGslMQ0ZAzYfZFCc7ayx8WPN9R9BYfslpiGiFxn32xk8TM2dofj1+uXhV8FFciIyBmw+yOA0rFQa/ZtXEfV/fjnN6z+IDFBaZjbWHY0V9fSufhLTkDFh80EGafIbdcT41+O3ceDq/UK2JiJ9U6sV1Jm0XdQHxrRDaUcbiYnImLD5IIOkUqmwOaSFqPuvOIqbD1IlJiKivA5GPxBjO2sLVHJzkJiGjA2bDzJY/t6ueLept6jbzt0nLwwRCfGJ6ei3/Iioj/43SGIaMkZsPsigTXqjDppWKS3qmIdpEtMQkaIoGPzjMVGPCKoBJztriYnIGLH5IIPmYGOFDUMCRd16zl6u/UIk0Q8Hr+PsnUQAQO3yzhgRVFNyIjJGbD7I4KlUKpRzshV13cnbC9maiHRlw9FYzPj7kqiX9WsiMQ0ZMzYfZBR2jWojxmmZOTgV+0ReGCIz9CAlA5//plk0buWApvBytZeYiIwZmw8yCi721rg0/VVRd1t4SGIaIvOzcK9mtemwd/zR1tddYhoydmw+yGjYWVuidU3NYlVfcu0XIr3IyM7BikM3AQBOdlZ4099LbiAyemw+yKgs7as5x7w0/AYSktIlpiEyDz0XR4rx1DfrwsqSvzro5fBvEBkVGysLbB/RWtQBM3bj8f/WlSAi7Vuw+yrO3E4UdbcGFSSmIVPB5oOMjq+nE4Jqe4i634ojhWxNRCV1PzkDX++8IupjE4JgYaGSmIhMBZsPMkpL+zWBl4sdAODM7UQkpmVJTkRkWh6kZKDpV7tEvW1EK5QtZVvIM4iKjs0HGa0f8swx4D9th8QkRKZn5lbNfB5Nq5RGLU9niWnI1LD5IKNV18tFHP0AgNWHb0lMQ2Q6IqIf4NfjtwEAFipgab+mkhORqWHzQUZt12jN5GMTN53DPd79QvRSFEXBe0ujRP3HJy3hYs+1W0i72HyQUXOwscL6j14RdbMZu7n2C9FLWLBHM5nYp+2rw6+Ci8Q0ZKrYfJDRC6jqhi71y4u6y7fhEtMQGa+DV+8jLM/dLZ92qCExDZkyNh9kEha+10iML8Ql4dydxEK2JqLn+c8vp8X4t2GBnEyMdIZ/s8hknJsaLMavLwhHelaOxDRExuWbXVeRkJQBAJjQpTYaVy4jORGZMjYfZDJK2Vph4ut1RO03ebvENETGY/H+a5i3S3O6pU9AZYlpyByw+SCTMrClDwJ8cv/Hlq1WcDr2idxARAYuPSsHs7Zp5vT4rk8j2NtYSkxE5oDNB5mcdXnufum68JDEJESGLStHjZ0XEvDPDWJLPmiMV+t6yg1FZoHNB5kclUqFHo0rinrcb2ckpiEyXD0XR2L4zycBACoV0LpmOa7dQnrB5oNM0pwe9cV43dFYhF99IDENkeF5lJqJU3lOS459tRbsrHm6hfSDzQeZJJVKhQNj2on6/WVRyMpRS0xEZFg+XnNcjI/8twOGtqkmMQ2ZGzYfZLIquTng/VcqibrvsiPIyObtt0Rpmdk4fP2RqN2d7ArZmkj72HyQSZv6pp8YR15/iDnbLktMQ2QY6kzS3Ia+5IPGEpOQuWLzQSbN0kKFPXkWn1safgMX45IkJiKSa/Lmc2JctawjOvHuFpKAzQeZvKrlSuG3YYGiHrn+lLwwRBJdv5+CVZG3RL3l05YS05A5Y/NBZqFx5TKo6VEKAHApPhmxj9IkJyLSr+T0LLT/er+oV33YDA42VhITkTlj80FmI/TtemLcY3EEUjKyJaYh0q9Bq46J8dA21dCmZjmJacjcsfkgs9G4chk0r+YGAEhIykCPRRGSExHpR/S9FETd0NzdMq5zLYlpiNh8kJmZ29NfjC/FJ+PcnUSJaYj0IyhMc7pl7aAAiUmIcrH5ILPi5WqPkxM7ivr1BeE4EfNYYiIi3fr5SIwYV3FzQPPqZSWmIcql8+Zj5syZUKlUGDFihK7fiqhISjva4KPWVUW980KCxDREurPv8j2M//2sqHeNalPI1kT6o9Pm4+jRo/j+++9Rv379F29MpEfjO9dC+1ruAIBF+67hyy0XJCci0q7k9Cz0X3FU1GsHBcDKkge7yTDo7G9iSkoK+vTpgx9++AGlS5fW1dsQlYhKpcJbDSuIemn4DaRl8u4XMh31puwQ4wldavN0CxkUnTUfISEh6NKlC4KCgnT1FkQv5Q1/L4SP1Sw+131RpMQ0RNpz/m7+C6n7N68iJwhRAXQyw8y6detw4sQJHD169IXbZmRkICMjQ9RJSZz6mvSnYmkHMb4Yl4Tfjt9G98YVJSYienldvg0X43NTg3m6hQyO1v9GxsbG4rPPPsOaNWtgZ/filRJDQ0Ph4uIivry9vbUdiahQ+8e0FePRv5xGehZXviXj9fUOzeKJwXU9UMqWs5iS4VEpiqJo8wU3bdqEt956C5aWluKxnJwcqFQqWFhYICMjI9/3nnfkw9vbG4mJiXB2dtZmNKICRUQ/wHtLowAAjSq5Yu3gV2BnbfmCZxEZlsPXH+LdJYdFfX3Ga7CwUElMROYkKSkJLi4uRfr9rfWWuEOHDjh79my+xwYMGIBatWph7Nix+RoPALC1tYWtra22YxAVS/PqZVGtnCOu3U/FiZgn2Hf5Pl7142qfZDwuxyfnazw2h7Rg40EGS+unXZycnODn55fvy9HREW5ubvDz89P22xFpzcoBzcR46E/HcS8pXWIaoqLLylEjeP4BUY/vXAv+3q7yAhG9AK9CIvof7zIOeDvP7bfNZuyGls9KEunEsZuaWXpD2lXDkDbVJKYhejG9XIm0b98+fbwN0Uub/GZdbDkbh8xsNQDgr7NxeL2+l+RURIXr/YPmdMuYYC4aR4aPRz6I8nCxt8b5qcGi/mTtSU4+RgZt8uZzYty1ARtlMg5sPoj+xdrSAjPeqifqBlN3SkxDVLDIaw+xKvKWqOf3aiAvDFExsPkgeo73AirB0zl3nprMHDVmbbskORFRfulZOflOt+wf0xYqFe9uIePA5oOoAAc+10y9vmjfNWRkc/IxMhy1Jm4T4wldaqOym6PENETFw+aDqAA2VhbYHNJC1EFh+yWmIdL48/TdfPWgVlUlJSEqGTYfRIXw93aFm6MNACD20VMsPXhdciIyd08zczD855OiPjWpo8Q0RCXD5oPoBfbnOf3y5V8XufYLSaMoClrM2iPqb95tAFcHG4mJiEqGzQfRC5SytcKW4S1F3fTLXRLTkDn7KSoGj1IzAQDNq7nhTX/eWkvGic0HURH4VXBBBVd7AEByRjZWR96UG4jMzsmYx5i4STOnx5pBAby7hYwWmw+iIvr701ZiPHHzeZy9nSgxDZmbT9dprvOY8VY9Nh5k1Nh8EBWRi4M1Vn2oWXzujf8Ll5iGzMmGozGIffQUANCtgRd6NqkoORHRy2HzQVQMbWqWw4QutUW960KCxDRkDg5evY/Pfzsr6rGda8Hakv90k3Hj32CiYso7p8KgH4/hfnKGxDRk6gauOibG3/RqgPIu9hLTEGkHmw+iEhjSRtOAtJq9p5AtiUouIztHrLDcpHJpdG1YQXIiIu1g80FUAuM714Z/RRcAQHqWGhtP3paciExNZrYavhM0U6j/33uNJKYh0i42H0QltCnP1Osj159G7KM0iWnI1PhO3CrG1d1LwdPFTmIaIu1i80FUQiqVCus/ekXUrWbvlZiGTMlfZ+KgKJp658jW8sIQ6QCbD6KXEFDVDR1quYt669k4iWnIFERee4iQtSdEfWn6q5zTg0wOmw+ilxTWq4EYD1tzAjEPefqFSm7Bnqti/GU3P9hZW0pMQ6QbbD6IXpKLvTXWDAoQdes5PP1CJbMq4iYirj0EALzdsALebeotORGRbrD5INKCFtXLomElV1GH7bwiLwwZpfjEdEz+47yoh3eoAStOJkYmin+zibRkw5BAMf5291UkJKVLTEPGRFEUdAzbL+oVA5rCp6yjxEREusXmg0hLrC0tsGtUG1EHzNgNtVop5BlEufqtOIrkjGwAQP2KLmjn6/6CZxAZNzYfRFpU3b0UujXwEvXY385ITEPGICEpHQeu3Bf16oEBhWxNZBrYfBBp2bw8d7/8cvw20rNy5IUhg5aelYOgrzWnW/b9py1c7K0lJiLSDzYfRFqmUqnyzX5aa+I2KApPv9CzPl5zQpxu8fV0QhVe50Fmgs0HkQ408HZFOSdbUf8YeUtiGjJEd548xZ5L90T98+BXCtmayLSw+SDSkaP/DRLjyX+cx5ID1ySmIUPTef4BMd74cXOUcbSRmIZIv9h8EOnQjx82E+P5u64WsiWZk7/OxCEpPfd0S6saZeFf0VVuICI9Y/NBpEOta5bDygFNAQBpmTmY8fdFyYlItqsJyfnWblk1oBksLLh2C5kXNh9EOta0ShkxXnLgOm48SJWYhmTKUSv5Zr+d29OfjQeZJTYfRDrmaGuFXaM0S6K3m7tPXhiSKmznZWw9Fw8A8Pd2RY/GFSUnIpKDzQeRHlR3d8p3QeGC3bz+w9zcfpyGhXs1Fx1P71pXYhoiudh8EOlJ1BcdxPjrnVdwj2u/mA1FUdBylma14z8/aYn6vMiUzBibDyI9sba0wK9DNYvPNZuxG4lpWRITkb5sOBYrxm1qlkO9ii4S0xDJx+aDSI+aVCmDRpVcRT1h8zl5YUgvbjxIxdjfzor6+w8aS0xDZBjYfBDpWd6Fw/48fRdxiU8lpiFdUquVfBcYL+/fBHbWlvICERkINh9EeuZoa4W9/2kr6sDQPVz7xURN23JBjN9pUhHta3lITENkONh8EEngU9YRHetofhFx8jHTM/73M1gZcVPUM9+uLy8MkYFh80EkyYLeDcX4h4M3cO5OosQ0pG0bT94R40V9GnEyMaI82HwQSWJnbYkdIzWTj72+IJwNiIlIychGepYaALBleEt0rldeciIiw8Lmg0iimh5OGBPsK+rv9kVLTEPacCr2Cfwmbxe1q4O1xDREhonNB5FkIe2qo03NcgCAv8/GI3Qrr/8wZt0WHhLjRpVcUd7FXmIaIsPE5oPIAIzqWFOMv99/XWISehnT/tTc3fJx22r4/eMWsOS1HkTPYPNBZAD8vV2xemAzUb86/4DENFQS1+6nYPmhG6L+TyffQrYmMm9sPogMRKsa5cT4UnwyTsU+kReGikVRFATP0zSMawcF8O4WokKw+SAyIAc/byfG4347IzEJFcekzeeRrc6dKC6wqhua+ZSRnIjIsLH5IDIg3mUcMKilD4Dcox9T/jgvORG9yKX4JKw+fEvUKwY0hZUl/2klKgx/QogMzMftqovxyoib+O/Gs0jPypGYiAqSkZ2DV+cfFPWW4S25dgtREbD5IDIwZRxtsG1EK1GviYpBxLUHEhNRQfIemerRuCL8KrhITENkPNh8EBmgWp7OWDWgKbxL584RkZnNhecMzfm7ifj5SKyoZ3fn2i1ERcXmg8hAtfF1h7uzHQBg6E/H8deZOMmJ6B/3kzPQ5dtwUW8f0Zp3txAVA5sPIgNWxc1RjEPWnkCOmkdADMH8XVfEeERQDfh6OklMQ2R82HwQGbBZ3ethyht1RF3ti78lpiEg93TLmqgYAICbow1GBNV8wTOI6N/YfBAZMCtLC/Rv4ZPvsYNX70tKQ7cfp+U73TLx9TqFbE1EBWHzQWQEzk0NFuMPlh1BYlqWxDTmq++yI2L8ev3y6NawgsQ0RMaLzQeREShla4WReQ7v+0/bITGNeUpKz8L1B6kAgNIO1viym5/kRETGS+vNR2hoKJo2bQonJye4u7ujW7duuHz5srbfhsjsfBZUI98KqRHRnPtDX7Jz1Gg0baeod49uC1cHG4mJiIyb1puP/fv3IyQkBIcPH8bOnTuRlZWFTp06ITU1VdtvRWR2Tk/uJMbvLY1C4lOeftE1RVEQOHOPWLvFr4Izyjiy8SB6GVpvPrZt24b+/fujbt268Pf3x8qVKxETE4Pjx49r+62IzE4pW6t8k1n1XnJYYhrzcP1BKu4nZ4h67eBXJKYhMg06v+YjMTERAFCmzPNXeczIyEBSUlK+LyIq2DtNvVHK1goAcCEuCeuPxkhOZNo6fL1fjK9+1RnOdtYS0xCZBp02H2q1GiNGjECLFi3g5/f8i7NCQ0Ph4uIivry9vXUZicgk7B7dRozH/saF53Tl7e8OiXHHOh6w5mq1RFqh05+kkJAQnDt3DuvWrStwm/HjxyMxMVF8xcbGFrgtEeXycLbDwvcaibr+VN79om0xD9NwIuaJqH/o20ReGCITo7Pm45NPPsGWLVuwd+9eVKxYscDtbG1t4ezsnO+LiF6sS/3yYpyZrcaBK5x8TFty1Apaz9kr6r3/aSsvDJEJ0nrzoSgKPvnkE2zcuBF79uyBj4/Pi59ERCVybcZrYtx3+RFk56glpjEdA1cdFeNeTbzhU9axkK2JqLi03nyEhITgp59+wtq1a+Hk5IT4+HjEx8fj6dOn2n4rIrNnaaHCmGBfUb/6zUGJaUxD6N8Xse+y5ijSzO71JKYhMk1abz4WLVqExMREtG3bFuXLlxdf69ev1/ZbERGAkHbVxTj6Xgq2no2TmMb4/Xbijhgf/LwdVCpVIVsTUUno5LTL87769++v7bciov85M0Uz+diwNSeQkpEtMY3xGvvrGTxIyZ3T47dhgfAu4yA5EZFp4n1jRCbA2c4aawcFiLp56G6JaYxTzMM0rD+muduuuruTxDREpo3NB5GJaF69rJj2Oyk9G4e49kuRPc3MyXd3y6lJHeFiz8nEiHSFzQeRCQkf206M+yyNQlomT7+8SGa2Gu2/3ifqwKpuXDSOSMfYfBCZEAcbq3yTYQXPPyAxjXFIfJqFuMR0AECTyqWx6sNmkhMRmT42H0QmpmMdDzjYWAIAYh89xWfrTiLnfyuyUn53njxFp3m5a7eoVMCvw5rDxor/LBLpGn/KiExQ5LgOYrz51F1cuMsFG5+nzw+H8TgtCwBQ25OzKxPpC5sPIhPk4mCNiHHtRR2flC4xjWE6HfsENx+mAQBc7K2xKaSF5ERE5oPNB5GJ8nK1h3cZewDA4B+PYcmBa5ITGY7bj9PQdaFmxdqDY9vxdAuRHvGnjciEdW+kWdRxxt+XEJfIZQ4AYMbfF8V4WNtqcLbjbbVE+sTmg8iEjQiqme/ujcDQPRLTGIbNp+7g77PxAIBq5RwxppPvC55BRNrG5oPIxLWpWQ6V8kwT/v1+8z39kqNW8Nm6U6Je9H5jWFhw7RYifWPzQWQGdoxsLcahWy/haWaOxDTyBMzYJcZL+zZBTQ9OoU4kA5sPIjNgZ22Zb+2X2pO2QW1mc38cin6ABymZog6q4yExDZF5Y/NBZCYCq7mhWjlHUW89Fy8xjX6p1Qr6LI0S9YVpwRLTEBGbDyIzoVKpsGtUG1GHrD2B+8kZEhPpT8PpO8V46pt14WBjJTENEbH5IDIjKpUK83r5i7rpV7tMfur147ceIfFplqj7Na8iLwwRAWDzQWR23mpYEWVL2Yp6bdQtiWl0K/peMrovihT1mSmdJKYhon+w+SAyQ+Fj24nxxM3ncftxmsQ0uvPat+FiPCKoBicTIzIQbD6IzJCdtSUWvtdI1C1n7TW5u19+P3EbmdlqAEDrmuXwWYcakhMR0T/YfBCZqS71yyO4ruZ202/3XJWYRrsysnMwasNpUS/q0wgqFScTIzIUbD6IzNj8Xg01411XEX0vRWIa7Wk8XTOZ2OiONeFoy7tbiAwJmw8iM2ZvY4ll/ZqIuuO8/VAU4z79snBvNFIyskU9nKdbiAwOmw8iM9ehtge61CsPAFAUYOqfFyQnKjm1WsGc7ZdFfXJiR4lpiKggbD6ICLN61BfjlRE3cethqsQ0JZOjVjD+97Oi3jK8JUo72khMREQFYfNBRChla4Utw1uKumPYAYlpSiZs52WsPxYLAHCytUJ191KSExFRQdh8EBEAwK+CC3o2rggAyMxRI2TNCaO5/iNHrWDh3mui3vxJC9hZW0pMRESFYfNBRMKMt+uJ8V9n43A3MV1imqJrPXuvGC9+vzGqluNRDyJDxuaDiARrSwucnqyZgrzVrD0Gv/bLj5E3cefJU1G/6ucpMQ0RFQWbDyLKx8XeGq/Wzf0FrlaA9UdjJScqWGa2GpM2nxf16Ulcu4XIGLD5IKJnLP6gsRh/sfEsBv94NN/cGYZAURTUnLBV1L8ODYSLA9duITIGbD6I6LlGBtUU450X7iHq+kOJaZ615UycGFdwtUeTKmUkpiGi4mDzQUTPNbx9dawe2AxV3BwAwKCu/UhOz8Lwn0+K+tC49hLTEFFxsfkgoueysFChVY1yBjdR15O0TNSbskPU3/ZuWMjWRGSI2HwQUZF8tu4UAkN3Sz8C8ufpu2LsXcYeb/p7SUxDRCXB5oOIClW7vDMA4GlWDuIS0/HBsihpWc7fTcTEPHe37B7VVloWIio5Nh9EVKivuvlh73/aijri2kPEPEyTkqXLt+FiPOOterCx4j9hRMaIP7lEVCiVSgWfso7YP6ateKz1nL16n3p9WfgNMe7fvAreC6ik1/cnIu1h80FERVLZzRHta7mLOu/dJrp2+3Eapm+5IOpJr9fR23sTkfax+SCiIlvWr4kYbzkTh5sPUvXyvq99c1CM1w4KgIWFSi/vS0S6weaDiIpMpVLh4OftRN127j6dv+fc7ZeRlJ47u2qbmuXQvHpZnb8nEekWmw8iKhbvMg5o4O0q6pHrT+nsva7dT8H/7Y0W9Xd9GunsvYhIf9h8EFGxbfy4uWZ88g6u30/R+nvkqBV0+Hq/qH8bFghHWyutvw8R6R+bDyIqNpVKhd2j24i6/df7tX73y9c7Lmtev5Y7GlUqrdXXJyJ52HwQUYlUK1cKPRpXFHXI2hNae+1bD1Px3b5rov6uTyOoVLzIlMhUsPkgohKb29NfjP8+G49zdxJf+jUVRUGbOftE/denLWFnbfnSr0tEhoPNBxG9lKgvOojx6wvCC9myaCZuPifGbzeqgLpeLi/9mkRkWNh8ENFL8XC2w9A21UQ97c8LhWxduLnbL+OnwzGinvFWvZfKRkSGic0HEb20cZ1rifHyQzew9/K9Er1OePQDMV47KICnW4hMFJsPItKKvGu/DFhxtNjPX7TvGk7FPskd92nEycSITBibDyLSispujvjiNc0RkH7LjxT5uXGJTzFr2yVRe5dx0Go2IjIsbD6ISGv6BlYR4/1X7osjGS8SGLpHjP/8pCX8KvAiUyJTxuaDiLTGztoSh8a1F3W3hYeQnpVT6HPyzg/ypr8X6lVk40Fk6th8EJFWVXC1x5Q3NEve91pyuMBtbz9Ow19n4kQ9v1cDXUYjIgPB5oOItK5/Cx8xPh37BIfy3MXyD7VaQctZe0W9f0xbWFhwFlMic8Dmg4h04tiEIDHuszQKp/Nc/5H4NAvvfB8p6kplHFDZzVGf8YhIIjYfRKQTZUvZ5rv7ZeZWzd0sn6w9gWO3Hov6wOft9JqNiOTSWfOxcOFCVKlSBXZ2dggICMCRI0W/7Y6ITMPgVlXRoZY7ACDy+kPsvJAAALj75KnYJu/quERkHnTSfKxfvx6jRo3C5MmTceLECfj7+yM4OBj37pVs1kMiMk4qlQrj8xz9GPzjMUTfS8a1+6kAgHUfvYJq5UrJikdEkuik+QgLC8PgwYMxYMAA1KlTB4sXL4aDgwOWL1+ui7cjIgNW3d0Jk17X3P0SFHZAjC15gSmRWdJ685GZmYnjx48jKEhzsZmFhQWCgoIQGRn5zPYZGRlISkrK90VEpuXDlj5Q/avP6FKvPOpzTg8is2Sl7Rd88OABcnJy4OHhke9xDw8PXLp06ZntQ0NDMXXqVG3HICIDc33Ga/jj9F3cT87Am/5ecHe2kx2JiCSRfrfL+PHjkZiYKL5iY2NlRyIiHVCpVOjaoAIGtarKxoPIzGn9yEfZsmVhaWmJhISEfI8nJCTA09Pzme1tbW1ha2ur7RhERERkoLR+5MPGxgaNGzfG7t27xWNqtRq7d+9GYGCgtt+OiIiIjIzWj3wAwKhRo9CvXz80adIEzZo1w/z585GamooBAwbo4u2IiIjIiOik+ejVqxfu37+PSZMmIT4+Hg0aNMC2bdueuQiViIiIzI9KURRFdoi8kpKS4OLigsTERDg7O8uOQ0REREVQnN/f0u92ISIiIvPC5oOIiIj0is0HERER6RWbDyIiItIrNh9ERESkV2w+iIiISK/YfBAREZFesfkgIiIivWLzQURERHqlk+nVX8Y/E64mJSVJTkJERERF9c/v7aJMnG5wzUdycjIAwNvbW3ISIiIiKq7k5GS4uLgUuo3Bre2iVqtx9+5dODk5QaVSae11k5KS4O3tjdjYWJNdM8bUP6Opfz7A9D+jqX8+wPQ/o6l/PsD0P6OuPp+iKEhOToaXlxcsLAq/qsPgjnxYWFigYsWKOnt9Z2dnk/zLlJepf0ZT/3yA6X9GU/98gOl/RlP/fIDpf0ZdfL4XHfH4By84JSIiIr1i80FERER6ZTbNh62tLSZPngxbW1vZUXTG1D+jqX8+wPQ/o6l/PsD0P6Opfz7A9D+jIXw+g7vglIiIiEyb2Rz5ICIiIsPA5oOIiIj0is0HERER6RWbDyIiItIrk2o+vvrqKzRv3hwODg5wdXV97jYxMTHo0qULHBwc4O7ujjFjxiA7O7vQ13306BH69OkDZ2dnuLq6YuDAgUhJSdHBJyieffv2QaVSPffr6NGjBT6vbdu2z2w/dOhQPSYvuipVqjyTdebMmYU+Jz09HSEhIXBzc0OpUqXQvXt3JCQk6Clx0d28eRMDBw6Ej48P7O3tUa1aNUyePBmZmZmFPs/Q99/ChQtRpUoV2NnZISAgAEeOHCl0+19++QW1atWCnZ0d6tWrh7///ltPSYsvNDQUTZs2hZOTE9zd3dGtWzdcvny50OesXLnymf1lZ2enp8TFM2XKlGey1qpVq9DnGNP+A57/b4pKpUJISMhztzf0/XfgwAG88cYb8PLygkqlwqZNm/J9X1EUTJo0CeXLl4e9vT2CgoJw9erVF75ucX+Oi8ukmo/MzEz07NkTw4YNe+73c3Jy0KVLF2RmZiIiIgKrVq3CypUrMWnSpEJft0+fPjh//jx27tyJLVu24MCBA/joo4908RGKpXnz5oiLi8v3NWjQIPj4+KBJkyaFPnfw4MH5njd79mw9pS6+adOm5cs6fPjwQrcfOXIk/vzzT/zyyy/Yv38/7t69i7fffltPaYvu0qVLUKvV+P7773H+/HnMmzcPixcvxhdffPHC5xrq/lu/fj1GjRqFyZMn48SJE/D390dwcDDu3bv33O0jIiLQu3dvDBw4ECdPnkS3bt3QrVs3nDt3Ts/Ji2b//v0ICQnB4cOHsXPnTmRlZaFTp05ITU0t9HnOzs759tetW7f0lLj46tatmy9reHh4gdsa2/4DgKNHj+b7fDt37gQA9OzZs8DnGPL+S01Nhb+/PxYuXPjc78+ePRvffvstFi9ejKioKDg6OiI4OBjp6ekFvmZxf45LRDFBK1asUFxcXJ55/O+//1YsLCyU+Ph48diiRYsUZ2dnJSMj47mvdeHCBQWAcvToUfHY1q1bFZVKpdy5c0fr2V9GZmamUq5cOWXatGmFbtemTRvls88+00+ol1S5cmVl3rx5Rd7+yZMnirW1tfLLL7+Ixy5evKgAUCIjI3WQULtmz56t+Pj4FLqNIe+/Zs2aKSEhIaLOyclRvLy8lNDQ0Odu/8477yhdunTJ91hAQIAyZMgQnebUlnv37ikAlP379xe4TUH/HhmiyZMnK/7+/kXe3tj3n6IoymeffaZUq1ZNUavVz/2+Me0/AMrGjRtFrVarFU9PT2XOnDnisSdPnii2trbKzz//XODrFPfnuCRM6sjHi0RGRqJevXrw8PAQjwUHByMpKQnnz58v8Dmurq75jiQEBQXBwsICUVFROs9cHH/88QcePnyIAQMGvHDbNWvWoGzZsvDz88P48eORlpamh4QlM3PmTLi5uaFhw4aYM2dOoafJjh8/jqysLAQFBYnHatWqhUqVKiEyMlIfcV9KYmIiypQp88LtDHH/ZWZm4vjx4/n+7C0sLBAUFFTgn31kZGS+7YHcn0lj2FdA7v4C8MJ9lpKSgsqVK8Pb2xtdu3Yt8N8bQ3D16lV4eXmhatWq6NOnD2JiYgrc1tj3X2ZmJn766Sd8+OGHhS5kakz7L68bN24gPj4+3z5ycXFBQEBAgfuoJD/HJWFwC8vpUnx8fL7GA4Co4+PjC3yOu7t7vsesrKxQpkyZAp8jy7JlyxAcHPzChfnee+89VK5cGV5eXjhz5gzGjh2Ly5cv4/fff9dT0qL79NNP0ahRI5QpUwYREREYP3484uLiEBYW9tzt4+PjYWNj88w1Px4eHga3v/4tOjoaCxYswNy5cwvdzlD334MHD5CTk/Pcn7FLly499zkF/Uwa+r4CclfgHjFiBFq0aAE/P78Ct/P19cXy5ctRv359JCYmYu7cuWjevDnOnz+v00U0SyIgIAArV66Er68v4uLiMHXqVLRq1Qrnzp2Dk5PTM9sb8/4DgE2bNuHJkyfo379/gdsY0/77t3/2Q3H2UUl+jkvC4JuPcePGYdasWYVuc/HixRdeFGVMSvKZb9++je3bt2PDhg0vfP2816vUq1cP5cuXR4cOHXDt2jVUq1at5MGLqDifb9SoUeKx+vXrw8bGBkOGDEFoaKjBTn1ckv13584dvPrqq+jZsycGDx5c6HNl7z/KFRISgnPnzhV6TQQABAYGIjAwUNTNmzdH7dq18f3332P69Om6jlksnTt3FuP69esjICAAlStXxoYNGzBw4ECJyXRj2bJl6Ny5M7y8vArcxpj2nzEx+OZj9OjRhXalAFC1atUivZanp+czV+z+cxeEp6dngc/590U22dnZePToUYHPeVkl+cwrVqyAm5sb3nzzzWK/X0BAAIDc/3nr45fXy+zTgIAAZGdn4+bNm/D19X3m+56ensjMzMSTJ0/yHf1ISEjQ2f76t+J+vrt376Jdu3Zo3rw5lixZUuz30/f+K0jZsmVhaWn5zJ1Fhf3Ze3p6Fmt7Q/HJJ5+Ii8+L+79fa2trNGzYENHR0TpKpz2urq6oWbNmgVmNdf8BwK1bt7Br165iHzE0pv33z35ISEhA+fLlxeMJCQlo0KDBc59Tkp/jEtHa1SMG5EUXnCYkJIjHvv/+e8XZ2VlJT09/7mv9c8HpsWPHxGPbt283qAtO1Wq14uPjo4wePbpEzw8PD1cAKKdPn9ZyMu376aefFAsLC+XRo0fP/f4/F5z++uuv4rFLly4Z7AWnt2/fVmrUqKG8++67SnZ2dolew5D2X7NmzZRPPvlE1Dk5OUqFChUKveD09ddfz/dYYGCgwV6wqFarlZCQEMXLy0u5cuVKiV4jOztb8fX1VUaOHKnldNqXnJyslC5dWvnmm2+e+31j2395TZ48WfH09FSysrKK9TxD3n8o4ILTuXPniscSExOLdMFpcX6OS5RVa69kAG7duqWcPHlSmTp1qlKqVCnl5MmTysmTJ5Xk5GRFUXL/0vj5+SmdOnVSTp06pWzbtk0pV66cMn78ePEaUVFRiq+vr3L79m3x2Kuvvqo0bNhQiYqKUsLDw5UaNWoovXv31vvnK8iuXbsUAMrFixef+d7t27cVX19fJSoqSlEURYmOjlamTZumHDt2TLlx44ayefNmpWrVqkrr1q31HfuFIiIilHnz5imnTp1Srl27pvz0009KuXLllL59+4pt/v35FEVRhg4dqlSqVEnZs2ePcuzYMSUwMFAJDAyU8REKdfv2baV69epKhw4dlNu3bytxcXHiK+82xrT/1q1bp9ja2iorV65ULly4oHz00UeKq6uruMPsgw8+UMaNGye2P3TokGJlZaXMnTtXuXjxojJ58mTF2tpaOXv2rKyPUKhhw4YpLi4uyr59+/Ltr7S0NLHNvz/j1KlTle3btyvXrl1Tjh8/rrz77ruKnZ2dcv78eRkfoVCjR49W9u3bp9y4cUM5dOiQEhQUpJQtW1a5d++eoijGv//+kZOTo1SqVEkZO3bsM98ztv2XnJwsftcBUMLCwpSTJ08qt27dUhRFUWbOnKm4uroqmzdvVs6cOaN07dpV8fHxUZ4+fSpeo3379sqCBQtE/aKfY20wqeajX79+CoBnvvbu3Su2uXnzptK5c2fF3t5eKVu2rDJ69Oh8ne/evXsVAMqNGzfEYw8fPlR69+6tlCpVSnF2dlYGDBggGhpD0Lt3b6V58+bP/d6NGzfy/RnExMQorVu3VsqUKaPY2toq1atXV8aMGaMkJibqMXHRHD9+XAkICFBcXFwUOzs7pXbt2sqMGTPyHaX69+dTFEV5+vSp8vHHHyulS5dWHBwclLfeeivfL3RDsWLFiuf+fc17QNIY99+CBQuUSpUqKTY2NkqzZs2Uw4cPi++1adNG6devX77tN2zYoNSsWVOxsbFR6tatq/z11196Tlx0Be2vFStWiG3+/RlHjBgh/jw8PDyU1157TTlx4oT+wxdBr169lPLlyys2NjZKhQoVlF69einR0dHi+8a+//6xfft2BYBy+fLlZ75nbPvvn99Z//765zOo1Wpl4sSJioeHh2Jra6t06NDhmc9duXJlZfLkyfkeK+znWBtUiqIo2juJQ0RERFQ4s5rng4iIiORj80FERER6xeaDiIiI9IrNBxEREekVmw8iIiLSKzYfREREpFdsPoiIiEiv2HwQERGRXrH5ICIiIr1i80FERER6xeaDiHTu/v378PT0xIwZM8RjERERsLGxwe7duyUmIyIZuLYLEenF33//jW7duiEiIgK+vr5o0KABunbtirCwMNnRiEjP2HwQkd6EhIRg165daNKkCc6ePYujR4/C1tZWdiwi0jM2H0SkN0+fPoWfnx9iY2Nx/Phx1KtXT3YkIpKA13wQkd5cu3YNd+/ehVqtxs2bN2XHISJJeOSDiPQiMzMTzZo1Q4MGDeDr64v58+fj7NmzcHd3lx2NiPSMzQcR6cWYMWPw66+/4vTp0yhVqhTatGkDFxcXbNmyRXY0ItIznnYhIp3bt28f5s+fj9WrV8PZ2RkWFhZYvXo1Dh48iEWLFsmOR0R6xiMfREREpFc88kFERER6xeaDiIiI9IrNBxEREekVmw8iIiLSKzYfREREpFdsPoiIiEiv2HwQERGRXrH5ICIiIr1i80FERER6xeaDiIiI9IrNBxEREekVmw8iIiLSq/8HIYv3MXvJwfIAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2259,18 +3617,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.2%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-9.98, -9.95)\": 9.963, \"(-9.95, -9.91)\": 9.941, \"(-9.91, -9.86)\": 9.893, \"(-9.86, -9.8)\": 9.824, \"(-9.8, -9.77)\": 9.794, \"(-9.77, -9.73)\": 9.761, \"(-9.73, -9.71)\": 9.725, \"(-9.71, -9.68)\": 9.697, \"(-9.68, -9.64)\": 9.657, \"(-9.64, -9.61)\": 9.626, \"(-9.61, -9.58)\": 9.592, \"(-9.58, -9.55)\": 9.558, \"(-9.55, -9.52)\": 9.538, \"(-9.52, -9.5)\": 9.515, \"(-9.5, -9.47)\": 9.491, \"(-9.47, -9.44)\": 9.464, \"(-9.44, -9.4)\": 9.426, \"(-9.4, -9.35)\": 9.379, \"(-9.35, -9.33)\": 9.34, \"(-9.33, -9.3)\": 9.314, \"(-9.3, -9.24)\": 9.277, \"(-9.24, -9.18)\": 9.207, \"(-9.18, -9.17)\": 9.184, \"(-9.17, -9.14)\": 9.161, \"(-9.14, -9.13)\": 9.139, \"(-9.13, -9.11)\": 9.119, \"(-9.11, -9.07)\": 9.081, \"(-9.07, -9.04)\": 9.06, \"(-9.04, -9.03)\": 9.033, \"(-9.03, -9.0)\": 9.011, \"(-9.0, -8.97)\": 8.988, \"(-8.97, -8.96)\": 8.965, \"(-8.96, -8.93)\": 8.942, \"(-8.93, -8.89)\": 8.911, \"(-8.89, -8.86)\": 8.883, \"(-8.86, -8.84)\": 8.853, \"(-8.84, -8.81)\": 8.829, \"(-8.81, -8.78)\": 8.789, \"(-8.78, -8.72)\": 8.755, \"(-8.72, -8.66)\": 8.697, \"(-8.66, -8.62)\": 8.646, \"(-8.62, -8.61)\": 8.617, \"(-8.61, -8.58)\": 8.596, \"(-8.58, -8.57)\": 8.573, \"(-8.57, -8.54)\": 8.552, \"(-8.54, -8.5)\": 8.525, \"(-8.5, -8.46)\": 8.482, \"(-8.46, -8.43)\": 8.455, \"(-8.43, -8.41)\": 8.427, \"(-8.41, -8.36)\": 8.399, \"(-8.36, -8.32)\": 8.336, \"(-8.32, -8.27)\": 8.302, \"(-8.27, -8.24)\": 8.264, \"(-8.24, -8.23)\": 8.238, \"(-8.23, -8.19)\": 8.203, \"(-8.19, -8.14)\": 8.152, \"(-8.14, -8.11)\": 8.124, \"(-8.11, -8.08)\": 8.097, \"(-8.08, -8.04)\": 8.065, \"(-8.04, -7.99)\": 8.012, \"(-7.99, -7.97)\": 7.984, \"(-7.97, -7.93)\": 7.955, \"(-7.93, -7.88)\": 7.921, \"(-7.88, -7.87)\": 7.883, \"(-7.87, -7.85)\": 7.863, \"(-7.85, -7.81)\": 7.833, \"(-7.81, -7.74)\": 7.764, \"(-7.74, -7.7)\": 7.726, \"(-7.7, -7.66)\": 7.69, \"(-7.66, -7.65)\": 7.664, \"(-7.65, -7.62)\": 7.62, \"(-7.62, -7.57)\": 7.59, \"(-7.57, -7.51)\": 7.545, \"(-7.51, -7.49)\": 7.497, \"(-7.49, -7.45)\": 7.477, \"(-7.45, -7.4)\": 7.425, \"(-7.4, -7.38)\": 7.396, \"(-7.38, -7.35)\": 7.373, \"(-7.35, -7.31)\": 7.34, \"(-7.31, -7.28)\": 7.306, \"(-7.28, -7.25)\": 7.264, \"(-7.25, -7.22)\": 7.244, \"(-7.22, -7.19)\": 7.212, \"(-7.19, -7.16)\": 7.173, \"(-7.16, -7.13)\": 7.149, \"(-7.13, -7.1)\": 7.117, \"(-7.1, -7.08)\": 7.094, \"(-7.08, -7.04)\": 7.065, \"(-7.04, -6.99)\": 7.019, \"(-6.99, -6.97)\": 6.979, \"(-6.97, -6.93)\": 6.954, \"(-6.93, -6.89)\": 6.914, \"(-6.89, -6.85)\": 6.883, \"(-6.85, -6.82)\": 6.845, \"(-6.82, -6.79)\": 6.804, \"(-6.79, -6.77)\": 6.773, \"(-6.77, -6.73)\": 6.751, \"(-6.73, -6.69)\": 6.711, \"(-6.69, -6.66)\": 6.681, \"(-6.66, -6.62)\": 6.649, \"(-6.62, -6.6)\": 6.625, \"(-6.6, -6.57)\": 6.597, \"(-6.57, -6.55)\": 6.564, \"(-6.55, -6.51)\": 6.534, \"(-6.51, -6.49)\": 6.507, \"(-6.49, -6.45)\": 6.473, \"(-6.45, -6.39)\": 6.413, \"(-6.39, -6.34)\": 6.378, \"(-6.34, -6.32)\": 6.345, \"(-6.32, -6.3)\": 6.313, \"(-6.3, -6.25)\": 6.284, \"(-6.25, 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1.298, \"(-1.28, -1.25)\": 1.266, \"(-1.25, -1.23)\": 1.24, \"(-1.23, -1.2)\": 1.212, \"(-1.2, -1.17)\": 1.185, \"(-1.17, -1.14)\": 1.153, \"(-1.14, -1.11)\": 1.121, \"(-1.11, -1.08)\": 1.1, \"(-1.08, -1.05)\": 1.072, \"(-1.05, -1.03)\": 1.041, \"(-1.03, -0.98)\": 1.011, \"(-0.98, -0.94)\": 0.958, \"(-0.94, -0.92)\": 0.93, \"(-0.92, -0.84)\": 0.89, \"(-0.84, -0.74)\": 0.775, \"(-0.74, -0.71)\": 0.732, \"(-0.71, -0.67)\": 0.687, \"(-0.67, -0.64)\": 0.664, \"(-0.64, -0.61)\": 0.633, \"(-0.61, -0.59)\": 0.611, \"(-0.59, -0.56)\": 0.583, \"(-0.56, -0.53)\": 0.547, \"(-0.53, -0.5)\": 0.517, \"(-0.5, -0.46)\": 0.489, \"(-0.46, -0.43)\": 0.454, \"(-0.43, -0.4)\": 0.42, \"(-0.4, -0.38)\": 0.389, \"(-0.38, -0.33)\": 0.367, \"(-0.33, -0.29)\": 0.313, \"(-0.29, -0.26)\": 0.282, \"(-0.26, -0.24)\": 0.249, \"(-0.24, -0.2)\": 0.226, \"(-0.2, -0.17)\": 0.182, \"(-0.17, -0.11)\": 0.149, \"(-0.11, -0.07)\": 0.09, \"(-0.07, -0.04)\": 0.066, \"(-0.04, -0.02)\": 0.043, \"(-0.02, 0.09)\": 0.023, \"(0.09, 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1.366, \"(1.38, 1.41)\": 1.391, \"(1.41, 1.45)\": 1.425, \"(1.45, 1.48)\": 1.455, \"(1.48, 1.52)\": 1.489, \"(1.52, 1.56)\": 1.543, \"(1.56, 1.59)\": 1.564, \"(1.59, 1.62)\": 1.59, \"(1.62, 1.67)\": 1.637, \"(1.67, 1.72)\": 1.703, \"(1.72, 1.75)\": 1.731, \"(1.75, 1.77)\": 1.751, \"(1.77, 1.81)\": 1.785, \"(1.81, 1.88)\": 1.845, \"(1.88, 1.92)\": 1.886, \"(1.92, 1.95)\": 1.929, \"(1.95, 1.98)\": 1.953, \"(1.98, 2.02)\": 1.993, \"(2.02, 2.06)\": 2.051, \"(2.06, 2.09)\": 2.071, \"(2.09, 2.12)\": 2.097, \"(2.12, 2.14)\": 2.127, \"(2.14, 2.18)\": 2.162, \"(2.18, 2.23)\": 2.2, \"(2.23, 2.23)\": 2.228, \"(2.23, 2.26)\": 2.254, \"(2.26, 2.29)\": 2.28, \"(2.29, 2.32)\": 2.304, \"(2.32, 2.37)\": 2.334, \"(2.37, 2.41)\": 2.388, \"(2.41, 2.44)\": 2.422, \"(2.44, 2.48)\": 2.456, \"(2.48, 2.52)\": 2.502, \"(2.52, 2.54)\": 2.535, \"(2.54, 2.61)\": 2.564, \"(2.61, 2.67)\": 2.649, \"(2.67, 2.69)\": 2.679, \"(2.69, 2.72)\": 2.705, \"(2.72, 2.75)\": 2.736, \"(2.75, 2.78)\": 2.756, \"(2.78, 2.81)\": 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\"(4.22, 4.24)\": 4.221, \"(4.24, 4.28)\": 4.258, \"(4.28, 4.31)\": 4.28, \"(4.31, 4.33)\": 4.302, \"(4.33, 4.36)\": 4.332, \"(4.36, 4.39)\": 4.366, \"(4.39, 4.4)\": 4.391, \"(4.4, 4.42)\": 4.412, \"(4.42, 4.45)\": 4.445, \"(4.45, 4.51)\": 4.475, \"(4.51, 4.57)\": 4.545, \"(4.57, 4.6)\": 4.574, \"(4.6, 4.64)\": 4.63, \"(4.64, 4.68)\": 4.661, \"(4.68, 4.71)\": 4.695, \"(4.71, 4.75)\": 4.722, \"(4.75, 4.79)\": 4.776, \"(4.79, 4.81)\": 4.801, \"(4.81, 4.85)\": 4.823, \"(4.85, 4.89)\": 4.856, \"(4.89, 4.92)\": 4.896, \"(4.92, 4.93)\": 4.916, \"(4.93, 4.96)\": 4.937, \"(4.96, 5.01)\": 4.995, \"(5.01, 5.03)\": 5.015, \"(5.03, 5.06)\": 5.038, \"(5.06, 5.09)\": 5.074, \"(5.09, 5.12)\": 5.097, \"(5.12, 5.17)\": 5.137, \"(5.17, 5.19)\": 5.172, \"(5.19, 5.24)\": 5.203, \"(5.24, 5.26)\": 5.254, \"(5.26, 5.29)\": 5.28, \"(5.29, 5.33)\": 5.307, \"(5.33, 5.37)\": 5.335, \"(5.37, 5.4)\": 5.385, \"(5.4, 5.48)\": 5.417, \"(5.48, 5.56)\": 5.537, \"(5.56, 5.59)\": 5.578, \"(5.59, 5.65)\": 5.61, \"(5.65, 5.69)\": 5.675, \"(5.69, 5.71)\": 5.697, \"(5.71, 5.73)\": 5.721, \"(5.73, 5.77)\": 5.745, \"(5.77, 5.79)\": 5.778, \"(5.79, 5.84)\": 5.81, \"(5.84, 5.89)\": 5.859, \"(5.89, 5.9)\": 5.887, \"(5.9, 5.92)\": 5.909, \"(5.92, 5.95)\": 5.939, \"(5.95, 5.98)\": 5.969, \"(5.98, 6.03)\": 5.995, \"(6.03, 6.05)\": 6.03, \"(6.05, 6.07)\": 6.057, \"(6.07, 6.09)\": 6.078, \"(6.09, 6.13)\": 6.109, \"(6.13, 6.16)\": 6.135, \"(6.16, 6.17)\": 6.172, \"(6.17, 6.2)\": 6.193, \"(6.2, 6.26)\": 6.229, \"(6.26, 6.31)\": 6.279, \"(6.31, 6.35)\": 6.314, \"(6.35, 6.36)\": 6.351, \"(6.36, 6.4)\": 6.384, \"(6.4, 6.46)\": 6.419, \"(6.46, 6.5)\": 6.479, \"(6.5, 6.53)\": 6.508, \"(6.53, 6.55)\": 6.54, \"(6.55, 6.58)\": 6.564, \"(6.58, 6.61)\": 6.59, \"(6.61, 6.62)\": 6.613, \"(6.62, 6.65)\": 6.64, \"(6.65, 6.71)\": 6.681, \"(6.71, 6.73)\": 6.716, \"(6.73, 6.77)\": 6.75, \"(6.77, 6.8)\": 6.786, \"(6.8, 6.82)\": 6.806, \"(6.82, 6.86)\": 6.837, \"(6.86, 6.91)\": 6.873, \"(6.91, 6.92)\": 6.899, \"(6.92, 6.94)\": 6.924, \"(6.94, 6.98)\": 6.947, \"(6.98, 7.0)\": 6.978, \"(7.0, 7.01)\": 7.002, \"(7.01, 7.04)\": 7.027, \"(7.04, 7.07)\": 7.054, \"(7.07, 7.12)\": 7.09, \"(7.12, 7.13)\": 7.12, \"(7.13, 7.15)\": 7.141, \"(7.15, 7.18)\": 7.174, \"(7.18, 7.22)\": 7.195, \"(7.22, 7.24)\": 7.224, \"(7.24, 7.26)\": 7.246, \"(7.26, 7.29)\": 7.27, \"(7.29, 7.31)\": 7.311, \"(7.31, 7.37)\": 7.337, \"(7.37, 7.41)\": 7.379, \"(7.41, 7.42)\": 7.419, \"(7.42, 7.45)\": 7.442, \"(7.45, 7.49)\": 7.464, \"(7.49, 7.53)\": 7.501, \"(7.53, 7.55)\": 7.531, \"(7.55, 7.58)\": 7.555, \"(7.58, 7.61)\": 7.587, \"(7.61, 7.63)\": 7.611, \"(7.63, 7.65)\": 7.642, \"(7.65, 7.69)\": 7.676, \"(7.69, 7.76)\": 7.725, \"(7.76, 7.84)\": 7.808, \"(7.84, 7.87)\": 7.846, \"(7.87, 7.9)\": 7.878, \"(7.9, 7.94)\": 7.923, \"(7.94, 7.97)\": 7.955, \"(7.97, 8.01)\": 7.999, \"(8.01, 8.04)\": 8.025, \"(8.04, 8.06)\": 8.048, \"(8.06, 8.12)\": 8.073, \"(8.12, 8.18)\": 8.156, \"(8.18, 8.21)\": 8.2, \"(8.21, 8.25)\": 8.223, \"(8.25, 8.32)\": 8.282, \"(8.32, 8.37)\": 8.34, \"(8.37, 8.39)\": 8.37, \"(8.39, 8.43)\": 8.404, \"(8.43, 8.46)\": 8.439, \"(8.46, 8.49)\": 8.475, \"(8.49, 8.5)\": 8.496, \"(8.5, 8.52)\": 8.516, \"(8.52, 8.54)\": 8.538, \"(8.54, 8.6)\": 8.573, \"(8.6, 8.67)\": 8.645, \"(8.67, 8.72)\": 8.688, \"(8.72, 8.75)\": 8.722, \"(8.75, 8.77)\": 8.751, \"(8.77, 8.83)\": 8.779, \"(8.83, 8.88)\": 8.869, \"(8.88, 8.9)\": 8.894, \"(8.9, 8.94)\": 8.927, \"(8.94, 8.98)\": 8.963, \"(8.98, 9.03)\": 9.007, \"(9.03, 9.07)\": 9.058, \"(9.07, 9.1)\": 9.088, \"(9.1, 9.14)\": 9.111, \"(9.14, 9.17)\": 9.151, \"(9.17, 9.2)\": 9.182, \"(9.2, 9.21)\": 9.205, \"(9.21, 9.23)\": 9.225, \"(9.23, 9.27)\": 9.249, \"(9.27, 9.3)\": 9.278, \"(9.3, 9.34)\": 9.327, \"(9.34, 9.37)\": 9.347, \"(9.37, 9.4)\": 9.386, \"(9.4, 9.46)\": 9.425, \"(9.46, 9.51)\": 9.49, \"(9.51, 9.54)\": 9.514, \"(9.54, 9.57)\": 9.559, \"(9.57, 9.61)\": 9.584, \"(9.61, 9.66)\": 9.622, \"(9.66, 9.69)\": 9.674, \"(9.69, 9.72)\": 9.704, \"(9.72, 9.76)\": 9.736, \"(9.76, 9.78)\": 9.766, \"(9.78, 9.82)\": 9.8, \"(9.82, 9.86)\": 9.833, \"(9.86, 9.89)\": 9.872, \"(9.89, 9.92)\": 9.898, \"(9.92, 9.97)\": 9.947, \"(9.97, 9.99)\": 9.983}\n",
+      "Means: {\"(-9.99, 3.32)\": 0.01, \"(3.32, 4.34)\": 0.32, \"(4.34, 4.91)\": 0.64, \"(4.91, 5.34)\": 0.96, \"(5.34, 5.68)\": 1.3, \"(5.68, 5.96)\": 1.61, \"(5.96, 6.17)\": 1.93, \"(6.17, 6.36)\": 2.25, \"(6.36, 6.54)\": 2.59, \"(6.54, 6.69)\": 2.94, \"(6.69, 6.84)\": 3.26, \"(6.84, 6.96)\": 3.61, \"(6.96, 7.08)\": 3.94, \"(7.08, 7.2)\": 4.25, \"(7.2, 7.3)\": 4.63, \"(7.3, 7.4)\": 4.94, \"(7.4, 7.48)\": 5.26, \"(7.48, 7.56)\": 5.61, \"(7.56, 7.65)\": 5.96, \"(7.65, 7.73)\": 6.32, \"(7.73, 7.8)\": 6.66, \"(7.8, 7.87)\": 7.0, \"(7.87, 7.95)\": 7.35, \"(7.95, 8.04)\": 7.89, \"(8.04, 8.1)\": 8.24, \"(8.1, 8.16)\": 8.61, \"(8.16, 8.21)\": 8.97, \"(8.21, 8.27)\": 9.45, \"(8.27, 8.33)\": 9.77, \"(8.33, 8.39)\": 10.12, \"(8.39, 8.45)\": 10.5, \"(8.45, 8.54)\": 11.23, \"(8.54, 8.58)\": 11.65, \"(8.58, 8.62)\": 12.09, \"(8.62, 8.68)\": 12.43, \"(8.68, 8.73)\": 12.95, \"(8.73, 8.78)\": 13.37, \"(8.78, 8.86)\": 13.89, \"(8.86, 8.94)\": 14.92, \"(8.94, 8.98)\": 15.34, \"(8.98, 9.03)\": 15.91, \"(9.03, 9.06)\": 16.49, \"(9.06, 9.12)\": 16.95, \"(9.12, 9.18)\": 17.91, \"(9.18, 9.21)\": 18.27, \"(9.21, 9.24)\": 18.68, \"(9.24, 9.28)\": 19.22, \"(9.28, 9.34)\": 19.92, \"(9.34, 9.37)\": 20.54, \"(9.37, 9.41)\": 20.92, \"(9.41, 9.45)\": 21.51, \"(9.45, 9.48)\": 22.04, \"(9.48, 9.51)\": 22.41, \"(9.51, 9.54)\": 22.96, \"(9.54, 9.57)\": 23.37, \"(9.57, 9.59)\": 23.88, \"(9.59, 9.63)\": 24.45, \"(9.63, 9.65)\": 24.85, \"(9.65, 9.69)\": 25.21, \"(9.69, 9.72)\": 26.18, \"(9.72, 9.78)\": 26.65, \"(9.78, 9.83)\": 28.13, \"(9.83, 9.85)\": 28.46, \"(9.85, 9.9)\": 29.34, \"(9.9, 9.94)\": 30.32, \"(9.94, 9.98)\": 30.89}\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2280,7 +3637,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2292,18 +3649,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.2%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-9.98, -9.96)\": 14.96, \"(-9.96, -9.92)\": 14.91, \"(-9.92, -9.88)\": 14.82, \"(-9.88, -9.85)\": 14.74, \"(-9.85, -9.81)\": 14.68, \"(-9.81, -9.77)\": 14.59, \"(-9.77, -9.75)\": 14.53, \"(-9.75, -9.71)\": 14.46, \"(-9.71, -9.68)\": 14.4, \"(-9.68, -9.65)\": 14.33, \"(-9.65, -9.6)\": 14.27, \"(-9.6, -9.58)\": 14.19, \"(-9.58, -9.53)\": 14.14, \"(-9.53, -9.49)\": 14.03, \"(-9.49, -9.44)\": 13.96, \"(-9.44, -9.38)\": 13.82, \"(-9.38, -9.32)\": 13.69, \"(-9.32, -9.28)\": 13.61, \"(-9.28, -9.25)\": 13.56, \"(-9.25, -9.22)\": 13.49, \"(-9.22, -9.19)\": 13.42, \"(-9.19, -9.16)\": 13.37, \"(-9.16, -9.13)\": 13.28, \"(-9.13, -9.07)\": 13.21, \"(-9.07, -9.01)\": 13.1, \"(-9.01, -8.97)\": 12.97, \"(-8.97, -8.92)\": 12.9, \"(-8.92, -8.88)\": 12.8, \"(-8.88, -8.86)\": 12.75, \"(-8.86, -8.82)\": 12.7, \"(-8.82, -8.78)\": 12.63, \"(-8.78, -8.71)\": 12.49, \"(-8.71, -8.67)\": 12.37, \"(-8.67, -8.64)\": 12.31, \"(-8.64, -8.59)\": 12.25, \"(-8.59, -8.55)\": 12.16, \"(-8.55, -8.52)\": 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\"(6.88, 6.94)\": 18.82, \"(6.94, 6.98)\": 18.92, \"(6.98, 7.03)\": 18.98, \"(7.03, 7.1)\": 19.13, \"(7.1, 7.13)\": 19.23, \"(7.13, 7.17)\": 19.28, \"(7.17, 7.2)\": 19.38, \"(7.2, 7.25)\": 19.43, \"(7.25, 7.28)\": 19.49, \"(7.28, 7.32)\": 19.6, \"(7.32, 7.34)\": 19.66, \"(7.34, 7.4)\": 19.74, \"(7.4, 7.46)\": 19.87, \"(7.46, 7.49)\": 19.92, \"(7.49, 7.52)\": 19.99, \"(7.52, 7.59)\": 20.12, \"(7.59, 7.65)\": 20.24, \"(7.65, 7.7)\": 20.35, \"(7.7, 7.75)\": 20.41, \"(7.75, 7.78)\": 20.49, \"(7.78, 7.81)\": 20.57, \"(7.81, 7.85)\": 20.65, \"(7.85, 7.9)\": 20.74, \"(7.9, 7.96)\": 20.87, \"(7.96, 7.99)\": 20.95, \"(7.99, 8.04)\": 21.01, \"(8.04, 8.11)\": 21.15, \"(8.11, 8.17)\": 21.26, \"(8.17, 8.19)\": 21.35, \"(8.19, 8.25)\": 21.42, \"(8.25, 8.3)\": 21.53, \"(8.3, 8.33)\": 21.6, \"(8.33, 8.37)\": 21.67, \"(8.37, 8.4)\": 21.76, \"(8.4, 8.43)\": 21.82, \"(8.43, 8.46)\": 21.89, \"(8.46, 8.5)\": 21.96, \"(8.5, 8.53)\": 22.02, \"(8.53, 8.58)\": 22.09, \"(8.58, 8.6)\": 22.16, \"(8.6, 8.63)\": 22.22, \"(8.63, 8.65)\": 22.28, \"(8.65, 8.7)\": 22.35, \"(8.7, 8.72)\": 22.43, \"(8.72, 8.77)\": 22.49, \"(8.77, 8.83)\": 22.59, \"(8.83, 8.86)\": 22.67, \"(8.86, 8.9)\": 22.73, \"(8.9, 8.95)\": 22.85, \"(8.95, 9.01)\": 22.96, \"(9.01, 9.06)\": 23.06, \"(9.06, 9.09)\": 23.13, \"(9.09, 9.12)\": 23.2, \"(9.12, 9.15)\": 23.26, \"(9.15, 9.19)\": 23.32, \"(9.19, 9.21)\": 23.38, \"(9.21, 9.24)\": 23.43, \"(9.24, 9.26)\": 23.51, \"(9.26, 9.31)\": 23.56, \"(9.31, 9.34)\": 23.63, \"(9.34, 9.37)\": 23.7, \"(9.37, 9.4)\": 23.75, \"(9.4, 9.45)\": 23.84, \"(9.45, 9.47)\": 23.91, \"(9.47, 9.5)\": 23.96, \"(9.5, 9.53)\": 24.02, \"(9.53, 9.56)\": 24.07, \"(9.56, 9.6)\": 24.17, \"(9.6, 9.64)\": 24.25, \"(9.64, 9.67)\": 24.31, \"(9.67, 9.71)\": 24.38, \"(9.71, 9.75)\": 24.45, \"(9.75, 9.77)\": 24.5, \"(9.77, 9.8)\": 24.55, \"(9.8, 9.85)\": 24.62, \"(9.85, 9.9)\": 24.73, \"(9.9, 9.94)\": 24.81, \"(9.94, 9.97)\": 24.89, \"(9.97, 10.0)\": 24.95}\n",
+      "Means: {\"(-9.97, -9.95)\": -3.425, \"(-9.95, -9.94)\": -2.832, \"(-9.94, -9.89)\": -2.687, \"(-9.89, -9.84)\": -1.935, \"(-9.84, -9.83)\": -1.798, \"(-9.83, -9.81)\": -1.724, \"(-9.81, -9.79)\": -1.599, \"(-9.79, -9.78)\": -1.522, \"(-9.78, -9.76)\": -1.446, \"(-9.76, -9.72)\": -1.366, \"(-9.72, -9.68)\": -1.174, \"(-9.68, -9.65)\": -1.106, \"(-9.65, -9.63)\": -1.04, \"(-9.63, -9.6)\": -0.974, \"(-9.6, -9.57)\": -0.896, \"(-9.57, -9.55)\": -0.827, \"(-9.55, -9.49)\": -0.759, \"(-9.49, -9.42)\": -0.599, \"(-9.42, -9.38)\": -0.515, \"(-9.38, -9.3)\": -0.43, \"(-9.3, -9.24)\": -0.346, \"(-9.24, -9.19)\": -0.269, \"(-9.19, -9.09)\": -0.171, \"(-9.09, -9.02)\": -0.078, \"(-9.02, -8.92)\": -0.007, \"(-8.92, -8.83)\": 0.098, \"(-8.83, -8.74)\": 0.165, \"(-8.74, -8.64)\": 0.242, \"(-8.64, -8.55)\": 0.318, \"(-8.55, -8.43)\": 0.389, \"(-8.43, -8.31)\": 0.459, \"(-8.31, -8.19)\": 0.54, \"(-8.19, -8.04)\": 0.606, \"(-8.04, -7.87)\": 0.686, \"(-7.87, -7.71)\": 0.765, \"(-7.71, -7.54)\": 0.833, \"(-7.54, -7.38)\": 0.902, \"(-7.38, -7.2)\": 0.977, \"(-7.2, -6.98)\": 1.045, \"(-6.98, -6.77)\": 1.11, \"(-6.77, -6.54)\": 1.183, \"(-6.54, -6.28)\": 1.247, \"(-6.28, -6.05)\": 1.315, \"(-6.05, -5.75)\": 1.379, \"(-5.75, -5.48)\": 1.447, \"(-5.48, -5.16)\": 1.512, \"(-5.16, -4.82)\": 1.579, \"(-4.82, -4.46)\": 1.648, \"(-4.46, -4.1)\": 1.713, \"(-4.1, -3.7)\": 1.78, \"(-3.7, -3.25)\": 1.844, \"(-3.25, -2.77)\": 1.91, \"(-2.77, -2.31)\": 1.978, \"(-2.31, -1.75)\": 2.043, \"(-1.75, -1.19)\": 2.108, \"(-1.19, -0.6)\": 2.176, \"(-0.6, 0.08)\": 2.241, \"(0.08, 0.73)\": 2.308, \"(0.73, 1.48)\": 2.374, \"(1.48, 2.26)\": 2.439, \"(2.26, 3.05)\": 2.505, \"(3.05, 3.92)\": 2.569, \"(3.92, 4.89)\": 2.634, \"(4.89, 5.95)\": 2.701, \"(5.95, 7.02)\": 2.767, \"(7.02, 8.16)\": 2.832, \"(8.16, 9.38)\": 2.897, \"(9.38, 9.98)\": 2.962}\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2313,7 +3669,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2325,18 +3681,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.1%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-9.98, -9.94)\": 0.235, \"(-9.94, -9.9)\": 0.242, \"(-9.9, -9.88)\": 0.324, \"(-9.88, -9.84)\": 0.348, \"(-9.84, -9.8)\": 0.432, \"(-9.8, -9.79)\": 0.451, \"(-9.79, -9.78)\": 0.46, \"(-9.78, -9.77)\": 0.473, \"(-9.77, -9.76)\": 0.486, \"(-9.76, -9.73)\": 0.506, \"(-9.73, -9.69)\": 0.53, \"(-9.69, -9.65)\": 0.586, \"(-9.65, -9.65)\": 0.591, \"(-9.65, -9.63)\": 0.599, \"(-9.63, -9.62)\": 0.613, \"(-9.62, -9.61)\": 0.619, \"(-9.61, -9.6)\": 0.627, \"(-9.6, -9.57)\": 0.649, \"(-9.57, -9.55)\": 0.668, \"(-9.55, -9.54)\": 0.674, \"(-9.54, -9.53)\": 0.68, \"(-9.53, -9.5)\": 0.69, \"(-9.5, -9.48)\": 0.721, \"(-9.48, -9.47)\": 0.726, \"(-9.47, -9.46)\": 0.735, \"(-9.46, -9.43)\": 0.743, \"(-9.43, -9.4)\": 0.761, \"(-9.4, -9.39)\": 0.778, \"(-9.39, -9.38)\": 0.785, \"(-9.38, -9.36)\": 0.791, \"(-9.36, -9.34)\": 0.808, \"(-9.34, -9.33)\": 0.818, \"(-9.33, -9.3)\": 0.826, \"(-9.3, -9.27)\": 0.844, \"(-9.27, -9.25)\": 0.859, \"(-9.25, -9.24)\": 0.869, \"(-9.24, -9.22)\": 0.878, 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\"(3.38, 3.41)\": 3.66, \"(3.41, 3.45)\": 3.666, \"(3.45, 3.52)\": 3.67, \"(3.52, 3.61)\": 3.682, \"(3.61, 3.64)\": 3.69, \"(3.64, 3.67)\": 3.695, \"(3.67, 3.73)\": 3.7, \"(3.73, 3.78)\": 3.708, \"(3.78, 3.81)\": 3.713, \"(3.81, 3.83)\": 3.717, \"(3.83, 3.89)\": 3.722, \"(3.89, 3.93)\": 3.729, \"(3.93, 3.97)\": 3.734, \"(3.97, 4.04)\": 3.738, \"(4.04, 4.12)\": 3.754, \"(4.12, 4.17)\": 3.76, \"(4.17, 4.21)\": 3.764, \"(4.21, 4.25)\": 3.77, \"(4.25, 4.29)\": 3.775, \"(4.29, 4.31)\": 3.78, \"(4.31, 4.36)\": 3.785, \"(4.36, 4.41)\": 3.792, \"(4.41, 4.48)\": 3.8, \"(4.48, 4.54)\": 3.808, \"(4.54, 4.61)\": 3.817, \"(4.61, 4.66)\": 3.827, \"(4.66, 4.72)\": 3.832, \"(4.72, 4.76)\": 3.838, \"(4.76, 4.81)\": 3.843, \"(4.81, 4.85)\": 3.85, \"(4.85, 4.9)\": 3.855, \"(4.9, 4.96)\": 3.862, \"(4.96, 5.0)\": 3.869, \"(5.0, 5.05)\": 3.874, \"(5.05, 5.09)\": 3.881, \"(5.09, 5.16)\": 3.889, \"(5.16, 5.21)\": 3.893, \"(5.21, 5.24)\": 3.901, \"(5.24, 5.28)\": 3.906, \"(5.28, 5.34)\": 3.911, \"(5.34, 5.39)\": 3.92, \"(5.39, 5.44)\": 3.924, \"(5.44, 5.49)\": 3.931, \"(5.49, 5.51)\": 3.936, \"(5.51, 5.55)\": 3.94, \"(5.55, 5.6)\": 3.945, \"(5.6, 5.64)\": 3.952, \"(5.64, 5.7)\": 3.957, \"(5.7, 5.72)\": 3.962, \"(5.72, 5.77)\": 3.967, \"(5.77, 5.81)\": 3.972, \"(5.81, 5.84)\": 3.979, \"(5.84, 5.89)\": 3.983, \"(5.89, 5.93)\": 3.988, \"(5.93, 5.99)\": 3.995, \"(5.99, 6.05)\": 4.0, \"(6.05, 6.12)\": 4.007, \"(6.12, 6.14)\": 4.014, \"(6.14, 6.19)\": 4.019, \"(6.19, 6.21)\": 4.024, \"(6.21, 6.27)\": 4.028, \"(6.27, 6.34)\": 4.036, \"(6.34, 6.4)\": 4.045, \"(6.4, 6.45)\": 4.052, \"(6.45, 6.51)\": 4.056, \"(6.51, 6.56)\": 4.062, \"(6.56, 6.59)\": 4.068, \"(6.59, 6.6)\": 4.073, \"(6.6, 6.66)\": 4.079, \"(6.66, 6.67)\": 4.084, \"(6.67, 6.75)\": 4.089, \"(6.75, 6.8)\": 4.095, \"(6.8, 6.86)\": 4.1, \"(6.86, 6.88)\": 4.105, \"(6.88, 6.93)\": 4.109, \"(6.93, 6.99)\": 4.115, \"(6.99, 7.06)\": 4.123, \"(7.06, 7.09)\": 4.13, \"(7.09, 7.12)\": 4.135, \"(7.12, 7.19)\": 4.14, \"(7.19, 7.24)\": 4.146, \"(7.24, 7.31)\": 4.154, \"(7.31, 7.32)\": 4.159, \"(7.32, 7.36)\": 4.164, \"(7.36, 7.44)\": 4.17, \"(7.44, 7.49)\": 4.178, \"(7.49, 7.52)\": 4.183, \"(7.52, 7.58)\": 4.188, \"(7.58, 7.65)\": 4.194, \"(7.65, 7.73)\": 4.207, \"(7.73, 7.78)\": 4.212, \"(7.78, 7.85)\": 4.219, \"(7.85, 7.91)\": 4.228, \"(7.91, 7.96)\": 4.233, \"(7.96, 8.0)\": 4.239, \"(8.0, 8.08)\": 4.245, \"(8.08, 8.13)\": 4.253, \"(8.13, 8.16)\": 4.258, \"(8.16, 8.2)\": 4.263, \"(8.2, 8.24)\": 4.267, \"(8.24, 8.28)\": 4.272, \"(8.28, 8.33)\": 4.277, \"(8.33, 8.38)\": 4.284, \"(8.38, 8.42)\": 4.289, \"(8.42, 8.46)\": 4.294, \"(8.46, 8.52)\": 4.3, \"(8.52, 8.59)\": 4.305, \"(8.59, 8.64)\": 4.313, \"(8.64, 8.69)\": 4.319, \"(8.69, 8.73)\": 4.324, \"(8.73, 8.8)\": 4.329, \"(8.8, 8.84)\": 4.335, \"(8.84, 8.86)\": 4.34, \"(8.86, 8.93)\": 4.345, \"(8.93, 9.01)\": 4.355, \"(9.01, 9.06)\": 4.361, \"(9.06, 9.09)\": 4.366, \"(9.09, 9.13)\": 4.371, \"(9.13, 9.22)\": 4.376, \"(9.22, 9.31)\": 4.39, \"(9.31, 9.38)\": 4.395, \"(9.38, 9.46)\": 4.405, \"(9.46, 9.49)\": 4.411, \"(9.49, 9.55)\": 4.416, \"(9.55, 9.58)\": 4.42, \"(9.58, 9.62)\": 4.425, \"(9.62, 9.66)\": 4.43, \"(9.66, 9.71)\": 4.434, \"(9.71, 9.76)\": 4.44, \"(9.76, 9.82)\": 4.448, \"(9.82, 9.87)\": 4.453, \"(9.87, 9.93)\": 4.459, \"(9.93, 9.98)\": 4.465}\n",
+      "Means: {\"(-10.0, -9.96)\": 4.102, \"(-9.96, -9.96)\": 3.251, \"(-9.96, -9.95)\": 3.037, \"(-9.95, -9.94)\": 2.848, \"(-9.94, -9.91)\": 2.769, \"(-9.91, -9.86)\": 2.133, \"(-9.86, -9.83)\": 1.845, \"(-9.83, -9.82)\": 1.727, \"(-9.82, -9.79)\": 1.602, \"(-9.79, -9.76)\": 1.477, \"(-9.76, -9.73)\": 1.362, \"(-9.73, -9.69)\": 1.269, \"(-9.69, -9.65)\": 1.095, \"(-9.65, -9.61)\": 0.989, \"(-9.61, -9.56)\": 0.897, \"(-9.56, -9.49)\": 0.73, \"(-9.49, -9.46)\": 0.628, \"(-9.46, -9.39)\": 0.549, \"(-9.39, -9.32)\": 0.462, \"(-9.32, -9.24)\": 0.318, \"(-9.24, -9.18)\": 0.242, \"(-9.18, -9.09)\": 0.17, \"(-9.09, -8.96)\": 0.022, \"(-8.96, -8.87)\": -0.053, \"(-8.87, -8.76)\": -0.156, \"(-8.76, -8.66)\": -0.229, \"(-8.66, -8.55)\": -0.316, \"(-8.55, -8.42)\": -0.393, \"(-8.42, -8.3)\": -0.464, \"(-8.3, -8.17)\": -0.544, \"(-8.17, -8.03)\": -0.615, \"(-8.03, -7.9)\": -0.686, \"(-7.9, -7.7)\": -0.757, \"(-7.7, -7.5)\": -0.849, \"(-7.5, -7.29)\": -0.93, \"(-7.29, -7.08)\": -1.002, \"(-7.08, -6.88)\": -1.076, \"(-6.88, -6.65)\": -1.147, \"(-6.65, -6.38)\": -1.22, \"(-6.38, -6.08)\": -1.293, \"(-6.08, -5.81)\": -1.367, \"(-5.81, -5.45)\": -1.44, \"(-5.45, -5.16)\": -1.513, \"(-5.16, -4.79)\": -1.583, \"(-4.79, -4.35)\": -1.656, \"(-4.35, -3.97)\": -1.727, \"(-3.97, -3.49)\": -1.799, \"(-3.49, -2.98)\": -1.87, \"(-2.98, -2.41)\": -1.947, \"(-2.41, -1.97)\": -2.019, \"(-1.97, -1.35)\": -2.091, \"(-1.35, -0.63)\": -2.162, \"(-0.63, 0.07)\": -2.234, \"(0.07, 0.85)\": -2.306, \"(0.85, 1.65)\": -2.378, \"(1.65, 2.47)\": -2.45, \"(2.47, 3.43)\": -2.521, \"(3.43, 4.44)\": -2.593, \"(4.44, 5.59)\": -2.665, \"(5.59, 6.74)\": -2.736, \"(6.74, 8.11)\": -2.809, \"(8.11, 9.51)\": -2.881, \"(9.51, 9.98)\": -2.952}\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2346,7 +3701,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2358,18 +3713,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.2%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-9.99, -9.98)\": 8.651, \"(-9.98, -9.94)\": 8.622, \"(-9.94, -9.92)\": 8.596, \"(-9.92, -9.89)\": 8.571, \"(-9.89, -9.88)\": 8.549, \"(-9.88, -9.83)\": 8.516, \"(-9.83, -9.78)\": 8.467, \"(-9.78, -9.76)\": 8.443, \"(-9.76, -9.75)\": 8.417, \"(-9.75, -9.7)\": 8.397, \"(-9.7, -9.63)\": 8.331, \"(-9.63, -9.58)\": 8.279, \"(-9.58, -9.55)\": 8.242, \"(-9.55, -9.53)\": 8.212, \"(-9.53, -9.49)\": 8.186, \"(-9.49, -9.46)\": 8.145, \"(-9.46, -9.44)\": 8.118, \"(-9.44, -9.41)\": 8.094, \"(-9.41, -9.37)\": 8.073, \"(-9.37, -9.36)\": 8.038, \"(-9.36, -9.32)\": 8.015, \"(-9.32, -9.29)\": 7.982, \"(-9.29, -9.26)\": 7.955, \"(-9.26, -9.22)\": 7.928, \"(-9.22, -9.13)\": 7.855, \"(-9.13, -9.06)\": 7.767, \"(-9.06, -9.03)\": 7.729, \"(-9.03, -8.99)\": 7.705, \"(-8.99, -8.96)\": 7.67, \"(-8.96, -8.93)\": 7.631, \"(-8.93, -8.88)\": 7.598, \"(-8.88, -8.86)\": 7.564, \"(-8.86, -8.83)\": 7.539, \"(-8.83, -8.79)\": 7.497, \"(-8.79, -8.76)\": 7.455, \"(-8.76, -8.74)\": 7.435, \"(-8.74, 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8.154, \"(6.5, 6.53)\": 8.179, \"(6.53, 6.56)\": 8.2, \"(6.56, 6.6)\": 8.238, \"(6.6, 6.63)\": 8.281, \"(6.63, 6.66)\": 8.311, \"(6.66, 6.71)\": 8.346, \"(6.71, 6.72)\": 8.375, \"(6.72, 6.75)\": 8.395, \"(6.75, 6.77)\": 8.42, \"(6.77, 6.81)\": 8.443, \"(6.81, 6.82)\": 8.483, \"(6.82, 6.85)\": 8.504, \"(6.85, 6.89)\": 8.527, \"(6.89, 6.92)\": 8.567, \"(6.92, 6.97)\": 8.594, \"(6.97, 7.0)\": 8.638, \"(7.0, 7.04)\": 8.668, \"(7.04, 7.05)\": 8.7, \"(7.05, 7.08)\": 8.721, \"(7.08, 7.09)\": 8.746, \"(7.09, 7.13)\": 8.767, \"(7.13, 7.18)\": 8.807, \"(7.18, 7.21)\": 8.848, \"(7.21, 7.24)\": 8.881, \"(7.24, 7.28)\": 8.904, \"(7.28, 7.32)\": 8.961, \"(7.32, 7.37)\": 8.999, \"(7.37, 7.4)\": 9.028, \"(7.4, 7.45)\": 9.063, \"(7.45, 7.46)\": 9.094, \"(7.46, 7.48)\": 9.116, \"(7.48, 7.5)\": 9.145, \"(7.5, 7.53)\": 9.168, \"(7.53, 7.55)\": 9.192, \"(7.55, 7.58)\": 9.218, \"(7.58, 7.62)\": 9.242, \"(7.62, 7.67)\": 9.292, \"(7.67, 7.68)\": 9.33, \"(7.68, 7.74)\": 9.357, \"(7.74, 7.81)\": 9.432, \"(7.81, 7.85)\": 9.466, \"(7.85, 7.88)\": 9.492, \"(7.88, 7.93)\": 9.544, \"(7.93, 7.95)\": 9.574, \"(7.95, 7.98)\": 9.6, \"(7.98, 7.99)\": 9.63, \"(7.99, 8.03)\": 9.655, \"(8.03, 8.08)\": 9.688, \"(8.08, 8.14)\": 9.76, \"(8.14, 8.2)\": 9.795, \"(8.2, 8.24)\": 9.839, \"(8.24, 8.25)\": 9.869, \"(8.25, 8.27)\": 9.893, \"(8.27, 8.28)\": 9.915, \"(8.28, 8.32)\": 9.945, \"(8.32, 8.39)\": 9.99, \"(8.39, 8.43)\": 10.042, \"(8.43, 8.45)\": 10.064, \"(8.45, 8.47)\": 10.097, \"(8.47, 8.51)\": 10.12, \"(8.51, 8.55)\": 10.163, \"(8.55, 8.58)\": 10.196, \"(8.58, 8.62)\": 10.233, \"(8.62, 8.64)\": 10.261, \"(8.64, 8.65)\": 10.282, \"(8.65, 8.69)\": 10.311, \"(8.69, 8.75)\": 10.357, \"(8.75, 8.77)\": 10.385, \"(8.77, 8.79)\": 10.418, \"(8.79, 8.82)\": 10.439, \"(8.82, 8.84)\": 10.465, \"(8.84, 8.88)\": 10.49, \"(8.88, 8.93)\": 10.539, \"(8.93, 8.99)\": 10.59, \"(8.99, 9.04)\": 10.635, \"(9.04, 9.05)\": 10.67, \"(9.05, 9.09)\": 10.69, \"(9.09, 9.11)\": 10.726, \"(9.11, 9.15)\": 10.755, \"(9.15, 9.19)\": 10.798, \"(9.19, 9.22)\": 10.836, \"(9.22, 9.25)\": 10.859, \"(9.25, 9.27)\": 10.885, \"(9.27, 9.29)\": 10.909, \"(9.29, 9.33)\": 10.93, \"(9.33, 9.35)\": 10.959, \"(9.35, 9.38)\": 10.984, \"(9.38, 9.42)\": 11.026, \"(9.42, 9.45)\": 11.051, \"(9.45, 9.47)\": 11.074, \"(9.47, 9.51)\": 11.11, \"(9.51, 9.55)\": 11.154, \"(9.55, 9.59)\": 11.195, \"(9.59, 9.64)\": 11.223, \"(9.64, 9.68)\": 11.288, \"(9.68, 9.72)\": 11.315, \"(9.72, 9.74)\": 11.344, \"(9.74, 9.76)\": 11.368, \"(9.76, 9.78)\": 11.392, \"(9.78, 9.81)\": 11.418, \"(9.81, 9.84)\": 11.438, \"(9.84, 9.88)\": 11.482, \"(9.88, 9.93)\": 11.525, \"(9.93, 9.96)\": 11.556, \"(9.96, 9.97)\": 11.577}\n",
+      "Means: {\"(-10.0, -9.79)\": -9.99, \"(-9.79, -9.58)\": -9.79, \"(-9.58, -9.39)\": -9.57, \"(-9.39, -9.16)\": -9.36, \"(-9.16, -8.96)\": -9.15, \"(-8.96, -8.74)\": -8.94, \"(-8.74, -8.55)\": -8.74, \"(-8.55, -8.32)\": -8.52, \"(-8.32, -8.11)\": -8.32, \"(-8.11, -7.91)\": -8.1, \"(-7.91, -7.67)\": -7.88, \"(-7.67, -7.44)\": -7.66, \"(-7.44, -7.23)\": -7.43, \"(-7.23, -7.06)\": -7.23, \"(-7.06, -6.83)\": -7.02, \"(-6.83, -6.54)\": -6.8, \"(-6.54, -6.26)\": -6.47, \"(-6.26, -6.07)\": -6.25, \"(-6.07, -5.83)\": -6.05, \"(-5.83, -5.59)\": -5.82, \"(-5.59, -5.34)\": -5.55, \"(-5.34, -5.14)\": -5.35, \"(-5.14, -4.93)\": -5.14, \"(-4.93, -4.73)\": -4.93, \"(-4.73, -4.51)\": -4.71, \"(-4.51, -4.31)\": -4.51, \"(-4.31, -4.07)\": -4.3, \"(-4.07, -3.84)\": -4.06, \"(-3.84, -3.64)\": -3.84, \"(-3.64, -3.45)\": -3.63, \"(-3.45, -3.23)\": -3.43, \"(-3.23, -3.04)\": -3.23, \"(-3.04, -2.79)\": -3.01, \"(-2.79, -2.59)\": -2.79, \"(-2.59, -2.39)\": -2.58, \"(-2.39, -2.17)\": -2.37, \"(-2.17, -1.93)\": -2.15, \"(-1.93, -1.72)\": -1.93, \"(-1.72, -1.52)\": -1.73, \"(-1.52, -1.3)\": -1.5, \"(-1.3, -1.09)\": -1.28, \"(-1.09, -0.86)\": -1.08, \"(-0.86, -0.67)\": -0.87, \"(-0.67, -0.47)\": -0.65, \"(-0.47, -0.23)\": -0.44, \"(-0.23, -0.02)\": -0.22, \"(-0.02, 0.19)\": 0.01, \"(0.19, 0.42)\": 0.21, \"(0.42, 0.68)\": 0.47, \"(0.68, 0.89)\": 0.7, \"(0.89, 1.11)\": 0.91, \"(1.11, 1.3)\": 1.12, \"(1.3, 1.53)\": 1.33, \"(1.53, 1.76)\": 1.55, \"(1.76, 1.95)\": 1.76, \"(1.95, 2.17)\": 1.97, \"(2.17, 2.36)\": 2.18, \"(2.36, 2.58)\": 2.39, \"(2.58, 2.8)\": 2.6, \"(2.8, 3.0)\": 2.82, \"(3.0, 3.24)\": 3.02, \"(3.24, 3.43)\": 3.25, \"(3.43, 3.66)\": 3.45, \"(3.66, 3.86)\": 3.67, \"(3.86, 4.07)\": 3.88, \"(4.07, 4.27)\": 4.08, \"(4.27, 4.49)\": 4.29, \"(4.49, 4.73)\": 4.51, \"(4.73, 4.91)\": 4.73, \"(4.91, 5.14)\": 4.96, \"(5.14, 5.39)\": 5.17, \"(5.39, 5.61)\": 5.41, \"(5.61, 5.82)\": 5.61, \"(5.82, 6.01)\": 5.82, \"(6.01, 6.24)\": 6.04, \"(6.24, 6.48)\": 6.26, \"(6.48, 6.69)\": 6.48, \"(6.69, 6.89)\": 6.69, \"(6.89, 7.11)\": 6.9, \"(7.11, 7.31)\": 7.11, \"(7.31, 7.5)\": 7.32, \"(7.5, 7.7)\": 7.52, \"(7.7, 7.91)\": 7.74, \"(7.91, 8.14)\": 7.94, \"(8.14, 8.37)\": 8.16, \"(8.37, 8.58)\": 8.37, \"(8.58, 8.83)\": 8.63, \"(8.83, 9.05)\": 8.84, \"(9.05, 9.25)\": 9.06, \"(9.25, 9.47)\": 9.27, \"(9.47, 9.7)\": 9.5, \"(9.7, 9.91)\": 9.72, \"(9.91, 9.98)\": 9.92}\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2379,7 +3733,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2391,18 +3745,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.1%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2412,7 +3765,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2424,18 +3777,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.1%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-9.99, 3.23)\": 5.4, \"(3.23, 3.9)\": 27.0, \"(3.9, 4.24)\": 49.5, \"(4.24, 4.52)\": 71.2, \"(4.52, 4.73)\": 94.4, \"(4.73, 4.91)\": 116.4, \"(4.91, 5.04)\": 142.1, \"(5.04, 5.21)\": 163.3, \"(5.21, 5.35)\": 188.2, \"(5.35, 5.47)\": 216.7, \"(5.47, 5.56)\": 243.1, \"(5.56, 5.66)\": 272.2, \"(5.66, 5.75)\": 300.5, \"(5.75, 5.83)\": 327.7, \"(5.83, 5.91)\": 355.4, \"(5.91, 5.97)\": 386.7, \"(5.97, 6.02)\": 409.7, \"(6.02, 6.16)\": 437.9, \"(6.16, 6.24)\": 473.6, \"(6.24, 6.26)\": 497.7, \"(6.26, 6.28)\": 521.9, \"(6.28, 6.35)\": 546.0, \"(6.35, 6.39)\": 572.3, \"(6.39, 6.42)\": 603.6, \"(6.42, 6.49)\": 629.6, \"(6.49, 6.51)\": 658.8, \"(6.51, 6.56)\": 683.4, \"(6.56, 6.63)\": 726.4, \"(6.63, 6.66)\": 760.8, \"(6.66, 6.67)\": 782.9, \"(6.67, 6.69)\": 807.4, \"(6.69, 6.75)\": 829.1, \"(6.75, 6.78)\": 862.5, \"(6.78, 6.81)\": 889.3, \"(6.81, 6.84)\": 918.6, \"(6.84, 6.88)\": 945.3, \"(6.88, 6.9)\": 976.2, \"(6.9, 6.94)\": 1001.7, \"(6.94, 6.95)\": 1038.0, \"(6.95, 6.99)\": 1066.4, \"(6.99, 7.02)\": 1103.8, \"(7.02, 7.03)\": 1131.4, \"(7.03, 7.05)\": 1155.4, \"(7.05, 7.08)\": 1185.6, \"(7.08, 7.13)\": 1217.2, \"(7.13, 7.17)\": 1279.7, \"(7.17, 7.2)\": 1308.9, \"(7.2, 7.23)\": 1355.7, \"(7.23, 7.26)\": 1392.7, \"(7.26, 7.28)\": 1426.7, \"(7.28, 7.3)\": 1454.5, \"(7.3, 7.33)\": 1494.8, \"(7.33, 7.35)\": 1517.6, \"(7.35, 7.36)\": 1547.0, \"(7.36, 7.37)\": 1577.9, \"(7.37, 7.42)\": 1619.8, \"(7.42, 7.48)\": 1733.5, \"(7.48, 7.5)\": 1788.3, \"(7.5, 7.52)\": 1822.4, \"(7.52, 7.54)\": 1870.6, \"(7.54, 7.57)\": 1924.1, \"(7.57, 7.64)\": 2024.5, \"(7.64, 7.67)\": 2119.8, \"(7.67, 7.68)\": 2156.1, \"(7.68, 7.72)\": 2208.1, \"(7.72, 7.79)\": 2326.5, \"(7.79, 7.86)\": 2526.1, \"(7.86, 7.9)\": 2659.8, \"(7.9, 7.93)\": 2722.3, \"(7.93, 7.94)\": 2793.1, \"(7.94, 7.97)\": 2839.9, \"(7.97, 8.01)\": 2990.0, \"(8.01, 8.04)\": 3041.6, \"(8.04, 8.08)\": 3170.3, \"(8.08, 8.11)\": 3289.8, \"(8.11, 8.14)\": 3382.9, \"(8.14, 8.17)\": 3494.8, \"(8.17, 8.18)\": 3535.9, \"(8.18, 8.19)\": 3590.6, \"(8.19, 8.2)\": 3629.8, \"(8.2, 8.23)\": 3691.1, \"(8.23, 8.26)\": 3808.6, \"(8.26, 8.28)\": 3889.6, \"(8.28, 8.28)\": 3944.8, \"(8.28, 8.32)\": 4006.3, \"(8.32, 8.38)\": 4258.8, \"(8.38, 8.4)\": 4401.8, \"(8.4, 8.41)\": 4472.3, \"(8.41, 8.43)\": 4557.3, \"(8.43, 8.45)\": 4623.6, \"(8.45, 8.47)\": 4723.6, \"(8.47, 8.48)\": 4787.5, \"(8.48, 8.49)\": 4829.3, \"(8.49, 8.53)\": 4964.4, \"(8.53, 8.55)\": 5116.4, \"(8.55, 8.56)\": 5177.9, \"(8.56, 8.56)\": 5221.8, \"(8.56, 8.57)\": 5253.2, \"(8.57, 8.58)\": 5322.5, \"(8.58, 8.6)\": 5358.7, \"(8.6, 8.61)\": 5448.8, \"(8.61, 8.65)\": 5600.0, \"(8.65, 8.68)\": 5850.5, \"(8.68, 8.7)\": 5932.9, \"(8.7, 8.71)\": 6043.0, \"(8.71, 8.78)\": 6182.9, \"(8.78, 8.86)\": 6907.8, \"(8.86, 8.87)\": 7049.9, \"(8.87, 8.89)\": 7175.9, \"(8.89, 8.92)\": 7361.0, \"(8.92, 8.93)\": 7512.5, \"(8.93, 8.93)\": 7556.3, \"(8.93, 8.95)\": 7625.1, \"(8.95, 8.98)\": 7821.4, \"(8.98, 9.0)\": 8023.3, \"(9.0, 9.02)\": 8210.5, \"(9.02, 9.05)\": 8293.8, \"(9.05, 9.09)\": 8673.3, \"(9.09, 9.13)\": 9008.9, \"(9.13, 9.16)\": 9376.5, \"(9.16, 9.19)\": 9708.0, \"(9.19, 9.22)\": 9926.0, \"(9.22, 9.25)\": 10253.8, \"(9.25, 9.27)\": 10527.1, \"(9.27, 9.28)\": 10650.1, \"(9.28, 9.29)\": 10765.8, \"(9.29, 9.3)\": 10840.7, \"(9.3, 9.3)\": 10927.4, \"(9.3, 9.32)\": 11024.7, \"(9.32, 9.33)\": 11210.9, \"(9.33, 9.35)\": 11349.5, \"(9.35, 9.37)\": 11673.5, \"(9.37, 9.38)\": 11767.2, \"(9.38, 9.39)\": 11853.6, \"(9.39, 9.4)\": 12037.5, \"(9.4, 9.42)\": 12229.6, \"(9.42, 9.43)\": 12369.8, \"(9.43, 9.44)\": 12465.8, \"(9.44, 9.44)\": 12579.3, \"(9.44, 9.45)\": 12670.5, \"(9.45, 9.46)\": 12824.8, \"(9.46, 9.47)\": 12897.1, \"(9.47, 9.47)\": 12950.5, \"(9.47, 9.5)\": 13081.9, \"(9.5, 9.53)\": 13588.8, \"(9.53, 9.57)\": 13850.0, \"(9.57, 9.61)\": 14770.5, \"(9.61, 9.62)\": 15018.3, \"(9.62, 9.64)\": 15129.6, \"(9.64, 9.66)\": 15549.6, \"(9.66, 9.67)\": 15660.4, \"(9.67, 9.69)\": 16046.6, \"(9.69, 9.72)\": 16259.3, \"(9.72, 9.78)\": 17085.1, \"(9.78, 9.82)\": 18158.3, \"(9.82, 9.84)\": 18632.1, \"(9.84, 9.84)\": 18791.0, \"(9.84, 9.85)\": 18936.5, \"(9.85, 9.86)\": 19137.3, \"(9.86, 9.89)\": 19314.3, \"(9.89, 9.92)\": 20274.2, \"(9.92, 9.93)\": 20424.3, \"(9.93, 9.93)\": 20508.2, \"(9.93, 9.95)\": 20735.3, \"(9.95, 9.96)\": 21030.6, \"(9.96, 9.97)\": 21157.1, \"(9.97, 9.97)\": 21180.0}\n",
+      "Means: {\"(-9.98, -9.93)\": -0.518, \"(-9.93, -9.88)\": -0.47, \"(-9.88, -9.86)\": -0.436, \"(-9.86, -9.83)\": -0.415, \"(-9.83, -9.81)\": -0.389, \"(-9.81, -9.78)\": -0.363, \"(-9.78, -9.74)\": -0.326, \"(-9.74, -9.72)\": -0.304, \"(-9.72, -9.69)\": -0.28, \"(-9.69, -9.66)\": -0.254, \"(-9.66, -9.63)\": -0.225, \"(-9.63, -9.59)\": -0.183, \"(-9.59, -9.56)\": -0.15, \"(-9.56, -9.51)\": -0.122, \"(-9.51, -9.48)\": -0.079, \"(-9.48, -9.46)\": -0.055, \"(-9.46, -9.44)\": -0.027, \"(-9.44, -9.41)\": -0.006, \"(-9.41, -9.39)\": 0.021, \"(-9.39, -9.36)\": 0.044, \"(-9.36, -9.32)\": 0.072, \"(-9.32, -9.29)\": 0.12, \"(-9.29, -9.24)\": 0.157, \"(-9.24, -9.2)\": 0.206, \"(-9.2, -9.17)\": 0.233, \"(-9.17, -9.14)\": 0.257, \"(-9.14, -9.11)\": 0.291, \"(-9.11, -9.08)\": 0.32, \"(-9.08, -9.04)\": 0.346, \"(-9.04, -8.99)\": 0.4, \"(-8.99, -8.93)\": 0.436, \"(-8.93, -8.87)\": 0.503, \"(-8.87, -8.84)\": 0.527, \"(-8.84, -8.82)\": 0.554, \"(-8.82, -8.78)\": 0.575, \"(-8.78, -8.73)\": 0.615, 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2445,7 +3797,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2457,18 +3809,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.1%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-9.99, -9.96)\": 21013.8, \"(-9.96, -9.92)\": 20949.6, \"(-9.92, -9.91)\": 20354.5, \"(-9.91, -9.88)\": 19954.9, \"(-9.88, -9.83)\": 18925.8, \"(-9.83, -9.79)\": 18136.3, \"(-9.79, -9.77)\": 17576.5, \"(-9.77, -9.72)\": 17083.8, \"(-9.72, -9.69)\": 16243.5, \"(-9.69, -9.68)\": 16064.0, \"(-9.68, -9.66)\": 15929.9, \"(-9.66, -9.62)\": 15357.7, \"(-9.62, -9.6)\": 14921.7, \"(-9.6, -9.58)\": 14660.8, \"(-9.58, -9.57)\": 14410.9, \"(-9.57, -9.56)\": 14296.4, \"(-9.56, -9.56)\": 14221.9, \"(-9.56, -9.55)\": 14103.2, \"(-9.55, -9.53)\": 14020.9, \"(-9.53, -9.51)\": 13579.6, \"(-9.51, -9.51)\": 13507.6, \"(-9.51, -9.5)\": 13432.9, \"(-9.5, -9.49)\": 13311.7, \"(-9.49, -9.49)\": 13235.0, \"(-9.49, -9.46)\": 13112.0, \"(-9.46, -9.44)\": 12563.4, \"(-9.44, -9.39)\": 12329.9, \"(-9.39, -9.35)\": 11540.9, \"(-9.35, -9.32)\": 11437.5, \"(-9.32, -9.29)\": 10919.6, \"(-9.29, -9.27)\": 10679.9, \"(-9.27, -9.25)\": 10484.0, \"(-9.25, -9.24)\": 10380.7, \"(-9.24, -9.21)\": 10069.9, \"(-9.21, -9.16)\": 9995.4, \"(-9.16, -9.09)\": 9084.9, \"(-9.09, -9.07)\": 8754.8, \"(-9.07, -9.04)\": 8560.6, \"(-9.04, -8.99)\": 8238.0, \"(-8.99, -8.97)\": 7879.2, \"(-8.97, -8.96)\": 7811.2, \"(-8.96, -8.93)\": 7695.3, \"(-8.93, -8.87)\": 7327.9, \"(-8.87, -8.82)\": 6921.7, \"(-8.82, -8.78)\": 6604.2, \"(-8.78, -8.75)\": 6417.7, \"(-8.75, -8.73)\": 6254.2, \"(-8.73, -8.71)\": 6116.4, \"(-8.71, -8.71)\": 6082.5, \"(-8.71, -8.69)\": 6011.8, \"(-8.69, -8.66)\": 5855.7, \"(-8.66, -8.64)\": 5724.3, \"(-8.64, -8.61)\": 5564.7, \"(-8.61, -8.59)\": 5430.4, \"(-8.59, -8.58)\": 5375.0, \"(-8.58, -8.57)\": 5320.2, \"(-8.57, -8.57)\": 5263.6, \"(-8.57, -8.57)\": 5240.6, \"(-8.57, -8.55)\": 5203.8, \"(-8.55, -8.53)\": 5105.2, \"(-8.53, -8.5)\": 4991.3, \"(-8.5, -8.49)\": 4904.4, \"(-8.49, -8.48)\": 4849.3, \"(-8.48, -8.47)\": 4786.3, \"(-8.47, -8.46)\": 4732.7, \"(-8.46, -8.45)\": 4673.4, \"(-8.45, -8.43)\": 4639.5, \"(-8.43, -8.42)\": 4538.3, \"(-8.42, -8.41)\": 4497.9, \"(-8.41, -8.4)\": 4470.7, \"(-8.4, -8.37)\": 4385.4, \"(-8.37, -8.35)\": 4263.4, \"(-8.35, -8.33)\": 4193.5, \"(-8.33, -8.32)\": 4123.4, \"(-8.32, -8.27)\": 4042.4, \"(-8.27, -8.24)\": 3791.9, \"(-8.24, -8.22)\": 3757.3, \"(-8.22, -8.2)\": 3656.5, \"(-8.2, -8.17)\": 3587.5, \"(-8.17, -8.13)\": 3450.9, \"(-8.13, -8.11)\": 3343.2, \"(-8.11, -8.09)\": 3315.1, \"(-8.09, -8.07)\": 3225.2, \"(-8.07, -8.05)\": 3169.0, \"(-8.05, -8.04)\": 3121.3, \"(-8.04, -8.03)\": 3085.6, \"(-8.03, -8.01)\": 3043.8, \"(-8.01, -7.98)\": 2945.6, \"(-7.98, -7.96)\": 2908.3, \"(-7.96, -7.95)\": 2854.7, \"(-7.95, -7.94)\": 2820.9, \"(-7.94, -7.92)\": 2792.9, \"(-7.92, -7.9)\": 2719.4, \"(-7.9, -7.85)\": 2653.3, \"(-7.85, -7.81)\": 2510.0, \"(-7.81, -7.79)\": 2441.6, \"(-7.79, -7.76)\": 2366.8, \"(-7.76, -7.74)\": 2322.0, \"(-7.74, -7.73)\": 2288.3, \"(-7.73, -7.72)\": 2262.2, \"(-7.72, -7.69)\": 2203.0, \"(-7.69, -7.66)\": 2156.2, \"(-7.66, -7.65)\": 2111.4, \"(-7.65, -7.63)\": 2077.5, \"(-7.63, -7.61)\": 2040.5, \"(-7.61, -7.58)\": 2012.7, \"(-7.58, -7.53)\": 1897.9, \"(-7.53, -7.51)\": 1851.3, \"(-7.51, -7.5)\": 1817.2, \"(-7.5, -7.47)\": 1782.5, \"(-7.47, -7.44)\": 1740.5, \"(-7.44, -7.42)\": 1707.0, \"(-7.42, -7.4)\": 1671.2, \"(-7.4, -7.39)\": 1637.7, \"(-7.39, -7.38)\": 1602.6, \"(-7.38, -7.36)\": 1575.2, \"(-7.36, -7.34)\": 1550.3, \"(-7.34, -7.34)\": 1518.1, \"(-7.34, -7.31)\": 1486.1, \"(-7.31, -7.25)\": 1450.6, \"(-7.25, -7.22)\": 1397.5, \"(-7.22, -7.2)\": 1360.7, \"(-7.2, -7.18)\": 1332.5, \"(-7.18, -7.13)\": 1286.7, \"(-7.13, -7.12)\": 1242.5, \"(-7.12, -7.07)\": 1210.5, \"(-7.07, -7.03)\": 1148.8, \"(-7.03, -7.01)\": 1123.2, \"(-7.01, -6.99)\": 1096.6, \"(-6.99, -6.96)\": 1074.6, \"(-6.96, -6.95)\": 1052.1, \"(-6.95, -6.92)\": 1028.2, \"(-6.92, -6.88)\": 992.3, \"(-6.88, -6.85)\": 965.8, \"(-6.85, -6.81)\": 931.9, \"(-6.81, -6.77)\": 904.2, \"(-6.77, -6.72)\": 865.0, \"(-6.72, -6.68)\": 831.5, \"(-6.68, -6.65)\": 807.4, \"(-6.65, -6.63)\": 780.9, \"(-6.63, -6.58)\": 754.8, \"(-6.58, -6.53)\": 724.0, \"(-6.53, -6.5)\": 697.5, \"(-6.5, -6.44)\": 660.5, \"(-6.44, -6.39)\": 635.5, \"(-6.39, -6.35)\": 613.9, \"(-6.35, -6.29)\": 584.8, \"(-6.29, -6.26)\": 555.4, \"(-6.26, -6.22)\": 530.5, \"(-6.22, -6.16)\": 506.9, \"(-6.16, -6.12)\": 482.0, \"(-6.12, -6.03)\": 457.2, \"(-6.03, -6.0)\": 433.6, \"(-6.0, -5.94)\": 404.9, \"(-5.94, -5.86)\": 380.6, \"(-5.86, -5.77)\": 354.2, \"(-5.77, -5.73)\": 332.0, \"(-5.73, -5.62)\": 308.6, \"(-5.62, -5.55)\": 283.8, \"(-5.55, -5.46)\": 260.2, \"(-5.46, -5.36)\": 237.3, \"(-5.36, -5.24)\": 213.0, \"(-5.24, -5.1)\": 186.9, \"(-5.1, -4.91)\": 164.8, \"(-4.91, -4.76)\": 143.1, \"(-4.76, -4.56)\": 121.9, \"(-4.56, -4.33)\": 100.4, \"(-4.33, -4.0)\": 78.8, \"(-4.0, -3.44)\": 53.5, \"(-3.44, -2.41)\": 31.7, \"(-2.41, 9.99)\": 10.0}\n",
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2478,7 +3829,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2490,18 +3841,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.1%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-10.0, -10.0)\": -5.402, \"(-10.0, -9.99)\": -5.373, \"(-9.99, -9.95)\": -3.936, \"(-9.95, -9.92)\": -2.746, \"(-9.92, -9.91)\": -2.442, \"(-9.91, -9.88)\": -2.31, \"(-9.88, -9.86)\": -1.974, \"(-9.86, -9.84)\": -1.893, \"(-9.84, -9.83)\": -1.777, \"(-9.83, -9.83)\": -1.739, \"(-9.83, -9.8)\": -1.689, \"(-9.8, -9.76)\": -1.508, \"(-9.76, -9.71)\": -1.316, \"(-9.71, -9.68)\": -1.156, \"(-9.68, -9.68)\": -1.135, \"(-9.68, -9.68)\": -1.125, \"(-9.68, -9.67)\": -1.115, \"(-9.67, -9.66)\": -1.086, \"(-9.66, -9.65)\": -1.069, \"(-9.65, -9.64)\": -1.044, \"(-9.64, -9.63)\": -0.999, \"(-9.63, -9.6)\": -0.958, \"(-9.6, -9.58)\": -0.867, \"(-9.58, -9.52)\": -0.83, \"(-9.52, -9.47)\": -0.658, \"(-9.47, -9.45)\": -0.627, \"(-9.45, -9.43)\": -0.576, \"(-9.43, -9.4)\": -0.532, \"(-9.4, -9.39)\": -0.505, \"(-9.39, -9.37)\": -0.484, \"(-9.37, -9.32)\": -0.417, \"(-9.32, -9.27)\": -0.345, \"(-9.27, -9.23)\": -0.28, \"(-9.23, -9.21)\": -0.241, \"(-9.21, -9.18)\": -0.212, \"(-9.18, -9.13)\": -0.158, \"(-9.13, -9.09)\": -0.105, \"(-9.09, -9.08)\": -0.086, \"(-9.08, -9.03)\": -0.064, \"(-9.03, -8.98)\": 0.01, \"(-8.98, -8.96)\": 0.029, \"(-8.96, -8.92)\": 0.053, \"(-8.92, -8.85)\": 0.113, \"(-8.85, -8.81)\": 0.153, \"(-8.81, -8.8)\": 0.172, \"(-8.8, -8.8)\": 0.183, \"(-8.8, -8.79)\": 0.192, \"(-8.79, -8.77)\": 0.204, \"(-8.77, -8.75)\": 0.218, \"(-8.75, -8.72)\": 0.235, \"(-8.72, -8.7)\": 0.256, \"(-8.7, -8.69)\": 0.273, \"(-8.69, -8.66)\": 0.284, \"(-8.66, -8.65)\": 0.297, \"(-8.65, -8.64)\": 0.307, \"(-8.64, -8.62)\": 0.318, \"(-8.62, -8.62)\": 0.327, \"(-8.62, -8.58)\": 0.338, \"(-8.58, -8.53)\": 0.376, \"(-8.53, -8.52)\": 0.384, \"(-8.52, -8.51)\": 0.402, \"(-8.51, -8.47)\": 0.414, \"(-8.47, -8.41)\": 0.448, \"(-8.41, -8.36)\": 0.484, \"(-8.36, -8.33)\": 0.505, \"(-8.33, -8.32)\": 0.516, \"(-8.32, -8.26)\": 0.532, \"(-8.26, -8.19)\": 0.584, \"(-8.19, -8.16)\": 0.601, \"(-8.16, -8.13)\": 0.619, \"(-8.13, -8.07)\": 0.634, \"(-8.07, -8.02)\": 0.677, \"(-8.02, -8.0)\": 0.688, \"(-8.0, -7.96)\": 0.698, \"(-7.96, -7.93)\": 0.725, \"(-7.93, -7.9)\": 0.737, \"(-7.9, -7.88)\": 0.751, \"(-7.88, -7.86)\": 0.761, \"(-7.86, -7.82)\": 0.77, \"(-7.82, -7.77)\": 0.79, \"(-7.77, -7.75)\": 0.804, \"(-7.75, -7.73)\": 0.812, \"(-7.73, -7.7)\": 0.824, \"(-7.7, -7.67)\": 0.837, \"(-7.67, -7.65)\": 0.851, \"(-7.65, -7.61)\": 0.861, \"(-7.61, -7.57)\": 0.878, \"(-7.57, -7.55)\": 0.889, \"(-7.55, -7.52)\": 0.903, \"(-7.52, -7.49)\": 0.919, \"(-7.49, -7.45)\": 0.927, \"(-7.45, -7.42)\": 0.938, \"(-7.42, -7.4)\": 0.947, \"(-7.4, -7.35)\": 0.961, \"(-7.35, -7.33)\": 0.977, \"(-7.33, -7.28)\": 0.989, \"(-7.28, -7.26)\": 1.005, \"(-7.26, -7.23)\": 1.014, \"(-7.23, -7.18)\": 1.029, \"(-7.18, -7.15)\": 1.045, \"(-7.15, -7.08)\": 1.06, \"(-7.08, -7.03)\": 1.08, \"(-7.03, -6.99)\": 1.095, \"(-6.99, -6.96)\": 1.11, \"(-6.96, -6.91)\": 1.12, \"(-6.91, -6.85)\": 1.139, \"(-6.85, -6.81)\": 1.15, \"(-6.81, -6.79)\": 1.16, \"(-6.79, -6.75)\": 1.171, \"(-6.75, -6.73)\": 1.181, \"(-6.73, -6.69)\": 1.19, \"(-6.69, -6.66)\": 1.203, \"(-6.66, -6.62)\": 1.214, \"(-6.62, -6.57)\": 1.23, \"(-6.57, -6.51)\": 1.242, \"(-6.51, -6.43)\": 1.26, \"(-6.43, -6.37)\": 1.28, \"(-6.37, -6.33)\": 1.294, \"(-6.33, -6.27)\": 1.308, \"(-6.27, -6.21)\": 1.323, \"(-6.21, -6.14)\": 1.339, \"(-6.14, -6.09)\": 1.349, \"(-6.09, -6.06)\": 1.365, \"(-6.06, -6.02)\": 1.376, \"(-6.02, -5.94)\": 1.392, \"(-5.94, -5.91)\": 1.406, \"(-5.91, -5.87)\": 1.415, \"(-5.87, -5.82)\": 1.427, \"(-5.82, -5.73)\": 1.437, \"(-5.73, -5.7)\": 1.448, \"(-5.7, -5.64)\": 1.459, \"(-5.64, -5.59)\": 1.472, \"(-5.59, -5.56)\": 1.481, \"(-5.56, -5.51)\": 1.49, \"(-5.51, -5.44)\": 1.502, \"(-5.44, -5.37)\": 1.519, \"(-5.37, -5.35)\": 1.529, \"(-5.35, -5.29)\": 1.538, \"(-5.29, -5.26)\": 1.549, \"(-5.26, -5.21)\": 1.559, \"(-5.21, -5.17)\": 1.569, \"(-5.17, -5.12)\": 1.579, \"(-5.12, -5.07)\": 1.589, \"(-5.07, -5.03)\": 1.599, \"(-5.03, -4.98)\": 1.611, \"(-4.98, -4.89)\": 1.624, \"(-4.89, -4.82)\": 1.635, \"(-4.82, -4.73)\": 1.647, \"(-4.73, -4.68)\": 1.659, \"(-4.68, -4.65)\": 1.67, \"(-4.65, -4.6)\": 1.681, \"(-4.6, -4.53)\": 1.692, \"(-4.53, -4.47)\": 1.701, \"(-4.47, -4.42)\": 1.713, \"(-4.42, -4.37)\": 1.722, \"(-4.37, -4.32)\": 1.734, \"(-4.32, -4.24)\": 1.743, \"(-4.24, -4.17)\": 1.753, \"(-4.17, -4.13)\": 1.765, \"(-4.13, -4.08)\": 1.774, \"(-4.08, -4.0)\": 1.785, \"(-4.0, -3.89)\": 1.798, \"(-3.89, -3.86)\": 1.808, \"(-3.86, -3.78)\": 1.82, \"(-3.78, -3.74)\": 1.829, \"(-3.74, -3.64)\": 1.841, \"(-3.64, -3.58)\": 1.851, \"(-3.58, -3.51)\": 1.861, \"(-3.51, -3.45)\": 1.873, \"(-3.45, -3.37)\": 1.883, \"(-3.37, -3.33)\": 1.893, \"(-3.33, -3.26)\": 1.902, \"(-3.26, -3.18)\": 1.914, \"(-3.18, -3.08)\": 1.925, \"(-3.08, -3.02)\": 1.934, \"(-3.02, -2.94)\": 1.945, \"(-2.94, -2.88)\": 1.953, \"(-2.88, -2.81)\": 1.963, \"(-2.81, -2.75)\": 1.973, \"(-2.75, -2.68)\": 1.982, \"(-2.68, -2.62)\": 1.991, \"(-2.62, -2.56)\": 2.0, \"(-2.56, -2.5)\": 2.008, \"(-2.5, -2.38)\": 2.018, \"(-2.38, -2.31)\": 2.03, \"(-2.31, -2.23)\": 2.039, \"(-2.23, -2.15)\": 2.047, \"(-2.15, -2.09)\": 2.057, \"(-2.09, -2.02)\": 2.066, \"(-2.02, -1.91)\": 2.078, \"(-1.91, -1.87)\": 2.09, \"(-1.87, -1.78)\": 2.098, \"(-1.78, -1.69)\": 2.109, \"(-1.69, -1.63)\": 2.119, \"(-1.63, -1.55)\": 2.129, \"(-1.55, -1.48)\": 2.138, \"(-1.48, -1.37)\": 2.148, \"(-1.37, -1.26)\": 2.157, \"(-1.26, -1.23)\": 2.167, \"(-1.23, -1.13)\": 2.178, \"(-1.13, -1.02)\": 2.187, \"(-1.02, -0.93)\": 2.197, \"(-0.93, -0.85)\": 2.206, \"(-0.85, -0.78)\": 2.217, \"(-0.78, -0.66)\": 2.226, \"(-0.66, -0.58)\": 2.235, \"(-0.58, -0.47)\": 2.244, \"(-0.47, -0.39)\": 2.257, \"(-0.39, -0.25)\": 2.267, \"(-0.25, -0.16)\": 2.277, \"(-0.16, -0.03)\": 2.286, \"(-0.03, 0.06)\": 2.296, \"(0.06, 0.14)\": 2.306, \"(0.14, 0.26)\": 2.315, \"(0.26, 0.33)\": 2.325, \"(0.33, 0.43)\": 2.334, \"(0.43, 0.6)\": 2.344, \"(0.6, 0.64)\": 2.352, \"(0.64, 0.73)\": 2.361, \"(0.73, 0.82)\": 2.37, \"(0.82, 0.91)\": 2.379, \"(0.91, 1.03)\": 2.387, \"(1.03, 1.13)\": 2.398, \"(1.13, 1.18)\": 2.408, \"(1.18, 1.31)\": 2.416, \"(1.31, 1.42)\": 2.426, \"(1.42, 1.53)\": 2.436, \"(1.53, 1.64)\": 2.446, \"(1.64, 1.81)\": 2.455, \"(1.81, 1.92)\": 2.465, \"(1.92, 2.02)\": 2.476, \"(2.02, 2.09)\": 2.486, \"(2.09, 2.26)\": 2.495, \"(2.26, 2.4)\": 2.506, \"(2.4, 2.49)\": 2.516, \"(2.49, 2.62)\": 2.525, \"(2.62, 2.76)\": 2.536, \"(2.76, 2.89)\": 2.545, \"(2.89, 3.0)\": 2.556, \"(3.0, 3.15)\": 2.566, \"(3.15, 3.26)\": 2.575, \"(3.26, 3.37)\": 2.584, \"(3.37, 3.51)\": 2.593, \"(3.51, 3.59)\": 2.602, \"(3.59, 3.82)\": 2.611, \"(3.82, 3.88)\": 2.62, \"(3.88, 4.0)\": 2.629, \"(4.0, 4.11)\": 2.638, \"(4.11, 4.24)\": 2.647, \"(4.24, 4.32)\": 2.656, \"(4.32, 4.45)\": 2.665, \"(4.45, 4.62)\": 2.674, \"(4.62, 4.79)\": 2.685, \"(4.79, 4.96)\": 2.695, \"(4.96, 5.1)\": 2.704, \"(5.1, 5.18)\": 2.713, \"(5.18, 5.3)\": 2.722, \"(5.3, 5.48)\": 2.731, \"(5.48, 5.62)\": 2.74, \"(5.62, 5.78)\": 2.75, \"(5.78, 5.94)\": 2.758, \"(5.94, 6.06)\": 2.768, \"(6.06, 6.18)\": 2.777, \"(6.18, 6.34)\": 2.786, \"(6.34, 6.55)\": 2.796, \"(6.55, 6.67)\": 2.804, \"(6.67, 6.89)\": 2.814, \"(6.89, 7.09)\": 2.827, \"(7.09, 7.26)\": 2.835, \"(7.26, 7.39)\": 2.845, \"(7.39, 7.58)\": 2.854, \"(7.58, 7.68)\": 2.862, \"(7.68, 7.82)\": 2.871, \"(7.82, 7.97)\": 2.879, \"(7.97, 8.12)\": 2.888, \"(8.12, 8.31)\": 2.898, \"(8.31, 8.47)\": 2.906, \"(8.47, 8.74)\": 2.916, \"(8.74, 8.88)\": 2.925, \"(8.88, 9.04)\": 2.934, \"(9.04, 9.21)\": 2.943, \"(9.21, 9.4)\": 2.951, \"(9.4, 9.59)\": 2.96, \"(9.59, 9.78)\": 2.969, \"(9.78, 9.98)\": 2.978}\n",
+      "Means: {\"(-9.96, -9.66)\": 1.0, \"(-9.66, -9.56)\": 1.022, \"(-9.56, -9.48)\": 1.047, \"(-9.48, -9.42)\": 1.068, \"(-9.42, -9.34)\": 1.108, \"(-9.34, -9.29)\": 1.129, \"(-9.29, -9.23)\": 1.167, \"(-9.23, -9.19)\": 1.19, \"(-9.19, -9.14)\": 1.225, \"(-9.14, -9.09)\": 1.256, \"(-9.09, -9.04)\": 1.299, \"(-9.04, -8.99)\": 1.329, \"(-8.99, -8.97)\": 1.352, \"(-8.97, -8.93)\": 1.386, \"(-8.93, -8.9)\": 1.408, \"(-8.9, -8.86)\": 1.434, \"(-8.86, -8.83)\": 1.458, \"(-8.83, -8.8)\": 1.483, \"(-8.8, -8.78)\": 1.507, \"(-8.78, -8.76)\": 1.528, \"(-8.76, -8.73)\": 1.554, \"(-8.73, -8.69)\": 1.584, \"(-8.69, -8.67)\": 1.606, \"(-8.67, -8.63)\": 1.636, \"(-8.63, -8.6)\": 1.663, \"(-8.6, -8.58)\": 1.693, \"(-8.58, -8.55)\": 1.715, \"(-8.55, -8.52)\": 1.739, \"(-8.52, -8.47)\": 1.791, \"(-8.47, -8.44)\": 1.828, \"(-8.44, -8.42)\": 1.849, \"(-8.42, -8.4)\": 1.876, \"(-8.4, -8.36)\": 1.9, \"(-8.36, -8.34)\": 1.933, \"(-8.34, -8.32)\": 1.954, \"(-8.32, -8.25)\": 1.985, \"(-8.25, -8.19)\": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2511,7 +3861,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2523,18 +3873,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.4%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
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2.76)\": 0.383, \"(2.76, 2.77)\": 0.368, \"(2.77, 2.79)\": 0.355, \"(2.79, 2.83)\": 0.333, \"(2.83, 2.85)\": 0.291, \"(2.85, 2.87)\": 0.282, \"(2.87, 2.89)\": 0.258, \"(2.89, 2.91)\": 0.247, \"(2.91, 2.93)\": 0.216, \"(2.93, 2.96)\": 0.201, \"(2.96, 3.0)\": 0.162, \"(3.0, 3.07)\": 0.109, \"(3.07, 3.14)\": 0.042, \"(3.14, 3.19)\": -0.036, \"(3.19, 3.2)\": -0.059, \"(3.2, 3.24)\": -0.067, \"(3.24, 3.27)\": -0.122, \"(3.27, 3.28)\": -0.139, \"(3.28, 3.3)\": -0.147, \"(3.3, 3.34)\": -0.169, \"(3.34, 3.38)\": -0.222, \"(3.38, 3.46)\": -0.253, \"(3.46, 3.52)\": -0.359, \"(3.52, 3.53)\": -0.376, \"(3.53, 3.57)\": -0.384, \"(3.57, 3.61)\": -0.435, \"(3.61, 3.64)\": -0.462, \"(3.64, 3.68)\": -0.499, \"(3.68, 3.73)\": -0.531, \"(3.73, 3.76)\": -0.568, \"(3.76, 3.78)\": -0.588, \"(3.78, 3.8)\": -0.605, \"(3.8, 3.82)\": -0.614, \"(3.82, 3.83)\": -0.631, \"(3.83, 3.85)\": -0.641, \"(3.85, 3.86)\": -0.652, \"(3.86, 3.89)\": -0.672, \"(3.89, 3.9)\": -0.685, \"(3.9, 3.92)\": -0.695, \"(3.92, 3.94)\": -0.706, \"(3.94, 3.96)\": -0.725, \"(3.96, 3.99)\": -0.74, \"(3.99, 4.01)\": -0.752, \"(4.01, 4.03)\": -0.771, \"(4.03, 4.06)\": -0.78, \"(4.06, 4.08)\": -0.801, \"(4.08, 4.11)\": -0.812, \"(4.11, 4.14)\": -0.833, \"(4.14, 4.16)\": -0.846, \"(4.16, 4.18)\": -0.855, \"(4.18, 4.24)\": -0.873, \"(4.24, 4.29)\": -0.903, \"(4.29, 4.31)\": -0.915, \"(4.31, 4.35)\": -0.923, \"(4.35, 4.38)\": -0.94, \"(4.38, 4.43)\": -0.949, \"(4.43, 4.47)\": -0.962, \"(4.47, 4.5)\": -0.97, \"(4.5, 4.58)\": -0.98, \"(4.58, 4.88)\": -0.992, \"(4.88, 4.93)\": -0.984, \"(4.93, 4.97)\": -0.976, \"(4.97, 5.0)\": -0.962, \"(5.0, 5.08)\": -0.953, \"(5.08, 5.16)\": -0.91, \"(5.16, 5.19)\": -0.9, \"(5.19, 5.23)\": -0.877, \"(5.23, 5.24)\": -0.868, \"(5.24, 5.26)\": -0.859, \"(5.26, 5.28)\": -0.847, \"(5.28, 5.31)\": -0.836, \"(5.31, 5.34)\": -0.816, \"(5.34, 5.36)\": -0.805, \"(5.36, 5.38)\": -0.788, \"(5.38, 5.4)\": -0.779, \"(5.4, 5.42)\": -0.767, \"(5.42, 5.43)\": -0.757, \"(5.43, 5.46)\": -0.742, \"(5.46, 5.5)\": -0.712, \"(5.5, 5.54)\": -0.703, \"(5.54, 5.59)\": -0.654, \"(5.59, 5.62)\": -0.628, \"(5.62, 5.64)\": -0.604, \"(5.64, 5.66)\": -0.588, \"(5.66, 5.68)\": -0.574, \"(5.68, 5.71)\": -0.565, \"(5.71, 5.74)\": -0.522, \"(5.74, 5.8)\": -0.508, \"(5.8, 5.86)\": -0.428, \"(5.86, 5.88)\": -0.409, \"(5.88, 5.9)\": -0.382, \"(5.9, 5.92)\": -0.367, \"(5.92, 5.94)\": -0.344, \"(5.94, 5.95)\": -0.331, \"(5.95, 5.97)\": -0.317, \"(5.97, 5.98)\": -0.305, \"(5.98, 6.0)\": -0.287, \"(6.0, 6.03)\": -0.273, \"(6.03, 6.06)\": -0.234, \"(6.06, 6.11)\": -0.202, \"(6.11, 6.15)\": -0.142, \"(6.15, 6.16)\": -0.133, \"(6.16, 6.18)\": -0.11, \"(6.18, 6.2)\": -0.098, \"(6.2, 6.22)\": -0.074, \"(6.22, 6.24)\": -0.055, \"(6.24, 6.27)\": -0.026, \"(6.27, 6.29)\": -0.004, \"(6.29, 6.32)\": 0.022, \"(6.32, 6.35)\": 0.049, \"(6.35, 6.38)\": 0.086, \"(6.38, 6.4)\": 0.104, \"(6.4, 6.41)\": 0.126, \"(6.41, 6.44)\": 0.135, \"(6.44, 6.47)\": 0.171, \"(6.47, 6.48)\": 0.187, \"(6.48, 6.49)\": 0.199, \"(6.49, 6.51)\": 0.214, \"(6.51, 6.52)\": 0.227, \"(6.52, 6.54)\": 0.243, \"(6.54, 6.55)\": 0.257, \"(6.55, 6.56)\": 0.27, \"(6.56, 6.58)\": 0.279, \"(6.58, 6.62)\": 0.314, \"(6.62, 6.66)\": 0.348, \"(6.66, 6.68)\": 0.373, \"(6.68, 6.69)\": 0.386, \"(6.69, 6.7)\": 0.398, \"(6.7, 6.74)\": 0.409, \"(6.74, 6.78)\": 0.473, \"(6.78, 6.81)\": 0.483, \"(6.81, 6.84)\": 0.522, \"(6.84, 6.86)\": 0.535, \"(6.86, 6.87)\": 0.546, \"(6.87, 6.88)\": 0.556, \"(6.88, 6.91)\": 0.57, \"(6.91, 6.93)\": 0.595, \"(6.93, 6.96)\": 0.613, \"(6.96, 6.98)\": 0.635, \"(6.98, 7.0)\": 0.645, \"(7.0, 7.02)\": 0.658, \"(7.02, 7.03)\": 0.67, \"(7.03, 7.04)\": 0.683, \"(7.04, 7.09)\": 0.692, \"(7.09, 7.15)\": 0.75, \"(7.15, 7.2)\": 0.774, \"(7.2, 7.24)\": 0.809, \"(7.24, 7.26)\": 0.82, \"(7.26, 7.29)\": 0.834, \"(7.29, 7.33)\": 0.855, \"(7.33, 7.37)\": 0.878, \"(7.37, 7.4)\": 0.892, \"(7.4, 7.43)\": 0.904, \"(7.43, 7.46)\": 0.913, \"(7.46, 7.5)\": 0.926, \"(7.5, 7.53)\": 0.943, \"(7.53, 7.56)\": 0.951, \"(7.56, 7.61)\": 0.959, \"(7.61, 7.66)\": 0.974, \"(7.66, 7.73)\": 0.983, \"(7.73, 8.03)\": 0.993, \"(8.03, 8.1)\": 0.979, \"(8.1, 8.15)\": 0.963, \"(8.15, 8.19)\": 0.952, \"(8.19, 8.21)\": 0.943, \"(8.21, 8.23)\": 0.934, \"(8.23, 8.27)\": 0.924, \"(8.27, 8.31)\": 0.907, \"(8.31, 8.35)\": 0.886, \"(8.35, 8.38)\": 0.871, \"(8.38, 8.4)\": 0.861, \"(8.4, 8.42)\": 0.851, \"(8.42, 8.44)\": 0.841, \"(8.44, 8.49)\": 0.827, \"(8.49, 8.52)\": 0.792, \"(8.52, 8.55)\": 0.783, \"(8.55, 8.58)\": 0.756, \"(8.58, 8.6)\": 0.747, \"(8.6, 8.61)\": 0.732, \"(8.61, 8.64)\": 0.717, \"(8.64, 8.66)\": 0.7, \"(8.66, 8.68)\": 0.684, \"(8.68, 8.7)\": 0.667, \"(8.7, 8.73)\": 0.652, \"(8.73, 8.75)\": 0.629, \"(8.75, 8.79)\": 0.615, \"(8.79, 8.84)\": 0.573, \"(8.84, 8.89)\": 0.535, \"(8.89, 8.92)\": 0.488, \"(8.92, 8.93)\": 0.48, \"(8.93, 8.94)\": 0.467, \"(8.94, 8.99)\": 0.458, \"(8.99, 9.05)\": 0.383, \"(9.05, 9.06)\": 0.357, \"(9.06, 9.07)\": 0.347, \"(9.07, 9.11)\": 0.333, \"(9.11, 9.15)\": 0.285, \"(9.15, 9.22)\": 0.239, \"(9.22, 9.29)\": 0.163, \"(9.29, 9.33)\": 0.118, \"(9.33, 9.37)\": 0.07, \"(9.37, 9.4)\": 0.054, \"(9.4, 9.47)\": 0.006, \"(9.47, 9.54)\": -0.089, \"(9.54, 9.56)\": -0.124, \"(9.56, 9.57)\": -0.141, \"(9.57, 9.6)\": -0.157, \"(9.6, 9.66)\": -0.204, \"(9.66, 9.7)\": -0.26, \"(9.7, 9.72)\": -0.278, \"(9.72, 9.74)\": -0.299, \"(9.74, 9.76)\": -0.31, \"(9.76, 9.79)\": -0.341, \"(9.79, 9.82)\": -0.368, \"(9.82, 9.86)\": -0.399, \"(9.86, 9.88)\": -0.435, \"(9.88, 9.89)\": -0.443, \"(9.89, 9.9)\": -0.451, \"(9.9, 9.92)\": -0.465, \"(9.92, 9.95)\": -0.495, \"(9.95, 9.97)\": -0.511, \"(9.97, 10.0)\": -0.526}\n",
+      "Means: {\"(-10.0, -9.95)\": -0.847, \"(-9.95, -9.88)\": -0.876, \"(-9.88, -9.82)\": -0.9, \"(-9.82, -9.76)\": -0.93, \"(-9.76, -9.66)\": -0.951, \"(-9.66, -9.53)\": -0.975, \"(-9.53, -9.21)\": -0.995, \"(-9.21, -9.12)\": -0.975, \"(-9.12, -9.06)\": -0.951, \"(-9.06, -8.99)\": -0.924, \"(-8.99, -8.92)\": -0.898, \"(-8.92, -8.84)\": -0.864, \"(-8.84, -8.79)\": -0.826, \"(-8.79, -8.75)\": -0.804, \"(-8.75, -8.68)\": -0.762, \"(-8.68, -8.61)\": -0.715, \"(-8.61, -8.55)\": -0.662, \"(-8.55, -8.52)\": -0.637, \"(-8.52, -8.48)\": -0.61, \"(-8.48, -8.46)\": -0.58, \"(-8.46, -8.42)\": -0.556, \"(-8.42, -8.36)\": -0.527, \"(-8.36, -8.31)\": -0.463, \"(-8.31, -8.27)\": -0.429, \"(-8.27, -8.23)\": -0.381, \"(-8.23, -8.2)\": -0.352, \"(-8.2, -8.17)\": -0.331, \"(-8.17, -8.12)\": -0.297, \"(-8.12, -8.08)\": -0.243, \"(-8.08, -8.05)\": -0.212, \"(-8.05, -8.01)\": -0.178, \"(-8.01, -8.0)\": -0.156, \"(-8.0, -7.97)\": -0.131, \"(-7.97, -7.92)\": -0.105, \"(-7.92, -7.88)\": -0.045, \"(-7.88, -7.86)\": -0.018, \"(-7.86, -7.8)\": 0.013, \"(-7.8, -7.75)\": 0.068, \"(-7.75, -7.71)\": 0.13, \"(-7.71, -7.68)\": 0.152, \"(-7.68, -7.65)\": 0.179, \"(-7.65, -7.61)\": 0.222, \"(-7.61, -7.57)\": 0.264, \"(-7.57, -7.53)\": 0.297, \"(-7.53, -7.5)\": 0.328, \"(-7.5, -7.47)\": 0.352, \"(-7.47, -7.44)\": 0.391, \"(-7.44, -7.39)\": 0.414, \"(-7.39, -7.37)\": 0.448, \"(-7.37, -7.34)\": 0.469, \"(-7.34, -7.32)\": 0.496, \"(-7.32, -7.26)\": 0.526, \"(-7.26, -7.19)\": 0.58, \"(-7.19, -7.14)\": 0.635, \"(-7.14, -7.1)\": 0.663, \"(-7.1, -7.03)\": 0.703, \"(-7.03, -6.96)\": 0.757, \"(-6.96, -6.92)\": 0.781, \"(-6.92, -6.87)\": 0.813, \"(-6.87, -6.83)\": 0.835, \"(-6.83, -6.78)\": 0.858, \"(-6.78, -6.74)\": 0.88, \"(-6.74, -6.67)\": 0.905, \"(-6.67, -6.62)\": 0.925, \"(-6.62, -6.54)\": 0.947, \"(-6.54, -6.42)\": 0.969, \"(-6.42, -6.04)\": 0.991, \"(-6.04, -5.96)\": 0.97, \"(-5.96, -5.9)\": 0.947, \"(-5.9, -5.84)\": 0.923, \"(-5.84, -5.79)\": 0.902, \"(-5.79, -5.74)\": 0.871, \"(-5.74, -5.68)\": 0.846, \"(-5.68, -5.63)\": 0.821, \"(-5.63, -5.57)\": 0.787, \"(-5.57, -5.52)\": 0.736, \"(-5.52, -5.47)\": 0.707, \"(-5.47, -5.42)\": 0.676, \"(-5.42, -5.38)\": 0.645, \"(-5.38, -5.35)\": 0.609, \"(-5.35, -5.31)\": 0.585, \"(-5.31, -5.25)\": 0.551, \"(-5.25, -5.22)\": 0.502, \"(-5.22, -5.19)\": 0.48, \"(-5.19, -5.17)\": 0.458, \"(-5.17, -5.14)\": 0.433, \"(-5.14, -5.12)\": 0.41, \"(-5.12, -5.09)\": 0.388, \"(-5.09, -5.06)\": 0.362, \"(-5.06, -5.01)\": 0.325, \"(-5.01, -4.97)\": 0.266, \"(-4.97, -4.94)\": 0.245, \"(-4.94, -4.91)\": 0.219, \"(-4.91, -4.87)\": 0.176, \"(-4.87, -4.83)\": 0.137, \"(-4.83, -4.8)\": 0.106, \"(-4.8, -4.78)\": 0.08, \"(-4.78, -4.73)\": 0.051, \"(-4.73, -4.66)\": -0.018, \"(-4.66, -4.62)\": -0.071, \"(-4.62, -4.6)\": -0.097, \"(-4.6, -4.58)\": -0.121, \"(-4.58, -4.56)\": -0.142, \"(-4.56, -4.53)\": -0.171, \"(-4.53, -4.49)\": -0.198, \"(-4.49, -4.46)\": -0.238, \"(-4.46, -4.42)\": -0.262, \"(-4.42, -4.4)\": -0.298, \"(-4.4, -4.36)\": -0.325, \"(-4.36, -4.31)\": -0.363, \"(-4.31, -4.28)\": -0.404, \"(-4.28, -4.24)\": -0.431, \"(-4.24, -4.2)\": -0.47, \"(-4.2, -4.17)\": -0.496, \"(-4.17, -4.13)\": -0.534, \"(-4.13, -4.11)\": -0.555, \"(-4.11, -4.07)\": -0.587, \"(-4.07, -4.04)\": -0.608, \"(-4.04, -4.01)\": -0.636, \"(-4.01, -3.96)\": -0.66, \"(-3.96, -3.91)\": -0.683, \"(-3.91, -3.85)\": -0.733, \"(-3.85, -3.82)\": -0.766, \"(-3.82, -3.77)\": -0.789, \"(-3.77, -3.74)\": -0.815, \"(-3.74, -3.65)\": -0.836, \"(-3.65, -3.57)\": -0.894, \"(-3.57, -3.5)\": -0.917, \"(-3.5, -3.44)\": -0.938, \"(-3.44, -3.34)\": -0.959, \"(-3.34, -2.85)\": -0.981, \"(-2.85, -2.76)\": -0.948, \"(-2.76, -2.69)\": -0.921, \"(-2.69, -2.64)\": -0.896, \"(-2.64, -2.58)\": -0.872, \"(-2.58, -2.53)\": -0.831, \"(-2.53, -2.48)\": -0.807, \"(-2.48, -2.45)\": -0.787, \"(-2.45, -2.41)\": -0.757, \"(-2.41, -2.37)\": -0.736, \"(-2.37, -2.33)\": -0.712, \"(-2.33, -2.29)\": -0.673, \"(-2.29, -2.25)\": -0.649, \"(-2.25, -2.2)\": -0.608, \"(-2.2, -2.13)\": -0.56, \"(-2.13, -2.08)\": -0.514, \"(-2.08, -2.03)\": -0.468, \"(-2.03, -1.98)\": -0.424, \"(-1.98, -1.96)\": -0.395, \"(-1.96, -1.94)\": -0.375, \"(-1.94, -1.91)\": -0.355, \"(-1.91, -1.87)\": -0.318, \"(-1.87, -1.82)\": -0.28, \"(-1.82, -1.77)\": -0.23, \"(-1.77, -1.73)\": -0.188, \"(-1.73, -1.7)\": -0.149, \"(-1.7, -1.66)\": -0.113, \"(-1.66, -1.62)\": -0.071, \"(-1.62, -1.6)\": -0.033, \"(-1.6, -1.55)\": -0.006, \"(-1.55, -1.5)\": 0.056, \"(-1.5, -1.48)\": 0.083, \"(-1.48, -1.45)\": 0.104, \"(-1.45, -1.41)\": 0.134, \"(-1.41, -1.39)\": 0.167, \"(-1.39, -1.36)\": 0.194, \"(-1.36, -1.34)\": 0.215, \"(-1.34, -1.31)\": 0.236, \"(-1.31, -1.29)\": 0.266, \"(-1.29, -1.27)\": 0.287, \"(-1.27, -1.23)\": 0.31, \"(-1.23, -1.19)\": 0.35, \"(-1.19, -1.15)\": 0.387, \"(-1.15, -1.11)\": 0.428, \"(-1.11, -1.07)\": 0.459, \"(-1.07, -1.03)\": 0.494, \"(-1.03, -1.0)\": 0.517, \"(-1.0, -0.97)\": 0.548, \"(-0.97, -0.94)\": 0.569, \"(-0.94, -0.91)\": 0.593, \"(-0.91, -0.87)\": 0.617, \"(-0.87, -0.81)\": 0.65, \"(-0.81, -0.76)\": 0.706, \"(-0.76, -0.72)\": 0.728, \"(-0.72, -0.69)\": 0.763, \"(-0.69, -0.64)\": 0.785, \"(-0.64, -0.57)\": 0.816, \"(-0.57, -0.53)\": 0.844, \"(-0.53, -0.45)\": 0.878, \"(-0.45, -0.38)\": 0.902, \"(-0.38, -0.29)\": 0.937, \"(-0.29, -0.21)\": 0.961, \"(-0.21, 0.27)\": 0.984, \"(0.27, 0.35)\": 0.961, \"(0.35, 0.41)\": 0.938, \"(0.41, 0.47)\": 0.915, \"(0.47, 0.57)\": 0.886, \"(0.57, 0.67)\": 0.801, \"(0.67, 0.73)\": 0.766, \"(0.73, 0.77)\": 0.738, \"(0.77, 0.83)\": 0.697, \"(0.83, 0.86)\": 0.675, \"(0.86, 0.9)\": 0.65, \"(0.9, 0.94)\": 0.605, \"(0.94, 0.98)\": 0.583, \"(0.98, 1.01)\": 0.552, \"(1.01, 1.03)\": 0.526, \"(1.03, 1.06)\": 0.504, \"(1.06, 1.07)\": 0.483, \"(1.07, 1.13)\": 0.46, \"(1.13, 1.2)\": 0.384, \"(1.2, 1.24)\": 0.353, \"(1.24, 1.28)\": 0.304, \"(1.28, 1.32)\": 0.274, \"(1.32, 1.37)\": 0.237, \"(1.37, 1.41)\": 0.171, \"(1.41, 1.44)\": 0.147, \"(1.44, 1.47)\": 0.117, \"(1.47, 1.5)\": 0.095, \"(1.5, 1.52)\": 0.065, \"(1.52, 1.56)\": 0.041, \"(1.56, 1.6)\": -0.001, \"(1.6, 1.64)\": -0.047, \"(1.64, 1.67)\": -0.073, \"(1.67, 1.69)\": -0.11, \"(1.69, 1.72)\": -0.131, \"(1.72, 1.76)\": -0.161, \"(1.76, 1.8)\": -0.203, \"(1.8, 1.83)\": -0.232, \"(1.83, 1.86)\": -0.26, \"(1.86, 1.9)\": -0.308, \"(1.9, 1.94)\": -0.332, \"(1.94, 1.98)\": -0.371, \"(1.98, 2.01)\": -0.403, \"(2.01, 2.04)\": -0.427, \"(2.04, 2.06)\": -0.457, \"(2.06, 2.09)\": -0.477, \"(2.09, 2.12)\": -0.504, \"(2.12, 2.15)\": -0.527, \"(2.15, 2.19)\": -0.557, \"(2.19, 2.22)\": -0.588, \"(2.22, 2.26)\": -0.618, \"(2.26, 2.3)\": -0.648, \"(2.3, 2.36)\": -0.675, \"(2.36, 2.42)\": -0.727, \"(2.42, 2.46)\": -0.752, \"(2.46, 2.49)\": -0.787, \"(2.49, 2.54)\": -0.809, \"(2.54, 2.61)\": -0.839, \"(2.61, 2.64)\": -0.865, \"(2.64, 2.72)\": -0.887, \"(2.72, 2.8)\": -0.922, \"(2.8, 2.91)\": -0.951, \"(2.91, 3.03)\": -0.974, \"(3.03, 3.37)\": -0.994, \"(3.37, 3.47)\": -0.974, \"(3.47, 3.54)\": -0.934, \"(3.54, 3.64)\": -0.913, \"(3.64, 3.7)\": -0.865, \"(3.7, 3.75)\": -0.844, \"(3.75, 3.79)\": -0.819, 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",
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F+bPdqU6elTPKYs6/5PGubTtaJMt2lQobL4zHs+HAWZyQwdCorRdIy/K8tWhP4e/fH5ZluzkFlZxyr1EsVoFfjsnjIfkoPV+W7SoVNl4Yj8ZiFXjss/1kfbi/F8b2CSbrH/z3Ho9bi/YEzPVWh1z/9yT1giOdaT72sAeRp5CRW+zQ8vRHM64la6vMFvxtnecUr2PjhfFo/vnDUdQSZ7kGPbDj/12PFfclk7dvFcAfeRlAczz95QGyduLV3fD8zQPxj9sGkz8jhyeQcT//SqcnBegAjOrXBbcM7U7+zPvb8j3Ga8fGC+OxWKwC7/98nKz/89i+MOh1MOh1WHbXUPLn9p4q5yBMDWGxCqwjpqYadMCbdw4DANw0tAcignxIn+sd6t/u8THKxGIV2JRDr9z98HWxMOh1WDx1kEPfs8ABw1rNsPHCeCyZeSWoIhoVvt56zBvbt/H/qfERGNwjiPxdL67nvkda4a3NdG/dH67pDoP+8oLRT0+OJX2uoFkHc0b9ZOQWgxrfrQfwyPh+AACjlx5JMSHk7/n2wFmPWKpm44XxWAor6A+IB8bENnkIAcCNgyLJn99/uoysZZSLxSrw4Ta66/+lZrNmo5ce94+Jlvzc+9u40afWSD9OzwZqbvR+dF8i+bO19QKZeSUOjU2NsPHCeCzU1EKjQY+Hx/Vt8fqM5CjydwX6epO1jHLJzCtB+SVaE72kmBAYvVreYhdMjMOsUVGSn1/xMzf61BK5F6rI2sW3tDR6J8WbyJ8/X6l9zx0bL4xHYrEKrD94lqS9c0TPFl4XoOGGkhAVTNrGufJLjgyPUSiFDhzH24b3avO9yCA/yc9bBWcdaYW0rAL8L6uQpL1xYESrRu8/7xiKVm5DrdK1s68jw1MlbLwwHklGbjEu1dFmtddfHdHme9MTepO2cayoGi+u95w0Rq2yzYEaHSY7Bgo1m2iVA0tUjDKxWAXmf3mQpPXx0uMf069p9T2DXoe32nivOZsPnyOPT62w8cJ4JP/ekU/SBRgNSIgObfN9ew+o5qz42XPSGLWIxSqwMYf2UAj287Z73lCziU6V1eDbfTQPIaNMMo4Xo6y6jqT9x7QhrXp5bUwcFIlZo6QnTJ6w5MjGC+NxWKwCPxHjXcZc1cXuzSQhOhTBfvR4lo+255O1jLJoiHehPYRmjoy2e97cnRRF/t7/+2K/R2SPaJX0XJq37oZ4E1Lj2/by2jAF0gxfradMs/HCeByZeSWoqqWlSN+VaH+WY9DrMHNklAPf7VnN07TE+z/TmjD6Gw2YN7aPXY3RS4+4yM6k7dXUWT2u6Z6WyL1QSdLFdgkg6Xbm0zKJvtmv7ZRpNl4Yj2NjNi1wLsBoQGJMmKRu3ti+8CJG0p3iyqmqxFxvxQ+HL5C097eSVt8at1zTg/z91Nk7oywsVoGfiV7epJhwks7faCDp6ixC00YvGy+MR2GxCnxNjCGYQ3wIGfQ6jOvflbTN/OJqTc+GtMpH22mBs75eekmvi427k6Ic6HfE54wayThejIsEL2+AjwGJsdITJYCNXhtsvDAeRWZeCUqqzJK6Tj7Srv8ruYdY86Wm3ooMDd9QtMoOYtGvq7p1Ihm8QMPS0cSBtNod1Fk5oyxWZ5wg6cb0tR9bdyXJfcPJnl4tG71svDAexfeHaD1pbh3Wg3wzAYDEmDAYDTT9dgcqbTLK4HQpbbkv0IHgbQD45/Shrdb0aM6WX+k9cRhl0NDLiJadRo13ARo8vQ9dR5tYadnoZeOF8RgsVoHP95whaXuEONYYz6DXYUjPYJL2TCkXrFMTFqvAqRLaMRvUPdihbRv0OrxB6Da94uc8bCA2g2SUwfZjRagjNjNy1Mj487i+krEvQb5e5KUoNcLGCxGLVSA9txhr951Bem4xxy2okMy8ElTW0Eq7h3aidf+9kghizZeDp8sd3jbjPhxp4Dmyj+Mz3ZAA2rn2zNosvu+oiDc3HyXpvA06h40Mg16H1ySM3oraerySluPQdtWEl7sHoAbSsgqwaF02Csov94uICPLFwslxpLx8Rhk40u/DFOh4ee3uIbTP5BZVwVxvJS0XMO7nJWKDRH8jPejySqjnZXGVGZl5JUjS8GxaK1isAvuJk5S4iM4OLVHbSI2PwLK7huKxT/e1Wi1cCOC9rQ2B5gsmxjm8faXDd08J0rIKMHf1niaGCwAUlNdg7uo9SMtiV65aoPb7CA2wXx21LUbGdiFruVidOrhktuDgmQqS9v4xMe16CDnSh8YTGu5pgcy8EtQSK9w60p2+OWP7d0ONxPcs12i1XTZe7GCxCixal91mvLYAsGhdNrtyVUJCdCgigqQfFH+7Ob5dD6HE2DBQnSnrD9Bibxj3QvW6GA16zBvbsvM4hYToUIQG0AJ984vonYkZ9+GIkTkjObrd3/PR9nwIicePENqcLLHxYofMvJIWHpfmFJTXIJOYRsm4F4Neh6sjA+1q7h8TjYntnAkZ9DoM7RVC0h4qqGSjVwXkF9OyjGK7BLTL4AUazpu/3RxP0v4n8ySfNyognBgzN2mgqUPLx9Rqu1SdmmDjxQ6F5bQMA6qOcS+z/7UTm3LaTjlNGdClw2vDDxNn33UWwfVeVEBUGC3rbHgUzWhti4mDInHjIOn4ucKKWp4sqYAdxMq2067t1aHvoVbbLauWrm2lNth4sUPRRdoBp+oY9/HtvrPYmG2/Vsbmwxc6vDac3CccBuJVtT2X670onaeJxixVZw9qlWaeLCkbi1Xgva3HSVpq8cO2oFbbPXahSnMeO5cYL2+//TaioqLg6+uLESNGIDMzk/S5NWvWQKfTYcqUKfIOsA2o1qoWrVotYbEK/N8X+yV1VgF8nJ7foe8y6HUY1osW7Hu2jB9CSsfPaMCgHvaXGsfHdYUfcQZsD54saYOM3GJysG5HK+Am9w2HD2HZqeS3TDUtIbvx8umnn+Lxxx/HwoULsWfPHgwePBgTJkzA+fP2Z8H5+fn4v//7P4wePVruIbaJjriETdUx7iEjtxg1raQStsYJJzROvDaatoQQGUKrC8O4j7SsAhw83Xa20aAegVhxz7VO+a6ySzSjZPeJUqd8HyMP6Q5U0O5oBVyDXoc7EnqStIUV2spUk914ee211zB79mzMnDkTcXFxWLZsGfz9/fHhhx+2+RmLxYI777wTixYtQkxMjNxDbBPqiZVXxJ2ClYwjN5PeoY5V1m2NZOJ5Q9Ux7kEq2xAALlSaneaOp86BfjxyXnNLANqCdiR9vPROqYBLrQZecrG2w9+lJGQ1XsxmM3bv3o2UlJTLX6jXIyUlBenp6W1+7vnnn0fXrl0xa9Ysye+ora1FRUVFkz9nkRgbhmB/6RTG9QcLsJiYUsm4A7pr7O6kqA5/G/W8KSfOtBn34OpsQ+pkqZabeyoaahHBub9rX12g5oQGGJ2qUwuyGi9FRUWwWCzo1q1bk9e7deuGwsLCVj/zyy+/4IMPPsCKFStI37F48WIEBQU1/vXsSXOhUTDodVgydSBJu0KjhYC0wAhiwbmJ8d2cUvXWoNfhpSnS580L63N4Bq1gNmW3fo9qjrMKxyXGhpHiFwBu7qlkygkxkAE+Bjw87iqnfJ+J2JaEqlMLiso2qqysxN13340VK1YgPJw2C1mwYAHKy8sb/06dOuXUMaXGR+CPQ7tL6pwR7MnIwy5ijMCdI6Kc9p0hhFkO1whSLharwFf7aIUEHamQaw+DXofBPYJI2p15HPeiRCxWgRfWS/cTevWWwU7xugC04pumQJ92VQ1XMrL2NgoPD4fBYMC5c03bgp87dw4mk6mFPjc3F/n5+Zg8eXLja1ZrgzfDy8sLR44cQWxsbJPP+Pj4wMfH8SZ6juDvQ/uZnBHsyTgXi1WQq0sWVTlvTZg6G+dy78okM68EJVV1krqwAKNTHwrXRoUiM1/aMDl0tgwWq3DaA5BxDpSlRoA2uaFi0OuwcHIc5q7e02Z8Vk29FRuzCzXVi09Wz4vRaMSwYcPwww8/NL5mtVrxww8/ICkpqYW+f//+OHjwIPbt29f4d9NNN+G6667Dvn37nLok5Ag9iQFRVB3jOjLzSlB2SfohBDhvBu3Itpz5nYzzoBqVNw+JdKoBkRxL8zhXma3stVMgy7fmknTOrtWTGh+Bd+8a2masXXl1neZ68cneVfrxxx/HjBkzMHz4cCQkJOCNN95AVVUVZs6cCQC455570L17dyxevBi+vr6Ij29aJjs4OBgAWrzuSvqEBThVx7gOatxCsF/7mjG2hc2VW1he0+ZsSK8DSqs4aFeJ5BMzCMfHtfQgd4TE2DD4Gw2oNlsktey1Uxbmeit+PHKBpC2R4bofH2fCc99kA2g5WbPdgxaty8b4OJMmPHayx7zcfvvtWLp0KZ599lkMGTIE+/btQ1paWmMQ78mTJ1FQoGxrcO3BsyTdS2nSa52M63AkbmHmyCinXtA2V649rAJ46BNtzYa0gMUqsHJbnqROjjgCg16H+8fQykOw105ZONL8MJTY+8gRMvNKJGu5aCnOTnbPCwDMmzcP8+bNa/W9LVu22P3sqlWrnD8gB6HMggDg6IUqmOutTslYYToONW6hk49XuzsC2yM1PgJv33EN5v1nL+wlFWlpNqQFMnKLSUuNt1/bU5ZjNm9sX6zcno+y6rbHEOzvXE8h03Ey8+jp66ZA5xueBcSK3VSd0uGnLIFro+g3Cc44Ug5Ut/ptw3vIZjiEBPjYNVwEtDUb0gLUooZypblTSjSUVddhI3FJlHEN1Emu0aCTxfDce4qWgUbVKR02XgjMSI4iaznjSDmE+tEi+q+7itYQrz1w1pEaoRqy8nnKxseZECDRL2nBlwe5TpCC6ORLW8i4rn8XWSZL1DPhRHGV07/bHbDxQsDopUdSNK1qojPKyzPO4fC5Sqfq2gNnHakPL+KDhVpJtT1kHC9GlcRMvrS6DhnHudKuErBYBX76lRase09StCxjiCYmjOw7Va4Jo5eNFyIfzUqQ1Oh1zikvzziHU6U0LxhV1x5sWUdtPQ51ACKCfDl+QSFYrAJrdkoXugz280JijHzGSzqx/D9Vx8jL9mNFpOavAd562c6bu5OiSE2CK2rqNbFMzcYLEaOXHvePsW8xjxvQlYN1FQTVCyant0wq60gAuGlwBAfrKgRKxgYAzBwZLfMxo86M1T+D1gJf7jlN0l3dPUi288bopcd1V9HqBH1/SP0ZjvykdYAFE+MwPq7t+IiN2ec57VVBdCMsxbjCW5YaH4E5dgzf5Vvz+LxRCBTDBQB6yVzTidqkkapj5KWyllYIM9BP3gTfpNguJN1/d59S/dIRGy8OYLEKZJ2x37V6PgfRKQKLVeDF/0nX3Zk1Klp2b5nFKvDNfvvGyaJ12XzeKICiSlqLCKquvVA6kwf7eyNRxrgbho4gXrpUXXspI3aqv1ir/grNbLw4AKVvRVl1Hd7afMxFI2LagtpjZGz/bpIaucfC6dLKoZjY34qqay/UjvacLq0MdJRgEwd07R6HA1q1Zziy8eIA1IP9zpZjPIt2M0pKUVbSWBj77CV2IC8kGMYdJTU+AsvuGoqgNpYatNivRq10IjbvperaiyPLiGrPcGTjxQGoB7u23ortx2iFrhh5UFKKMvU78ou0UX9BrVisAntPlZG03YP95B3Mb4yPM8HPu/V6L1f2q+HJknu55ZoeTtW1l8TYMAT72V9uBIAgPy/VZziy8eIACdGh8PGiOeY+302LPmfkQUkpygnRoaRy4P/JPMkPITeScbwYZgvt90/u45pA2Ybsp7aXqHjJURkk9w2Hn7f9x6m/0YDkvvKeNwa9DktukV5uLL9Ur/olRzZeHMCg16FHMC2t9kwZV9p1Jwa9DjcNjrCbSLpwcpxLUpQNeh2mJ/SS1BVW1PJDyI1Qa6b4ylirozm85KgOXknLwSWJOi+v3TbYJfeb1PgIvHPHUEmd2j12bLw4yMDuQSQd1chh5CEtqwDLt7bdGXjOmGikxke4bDxR4bTzgR9C7oR2I7+unzzl3VsjPIDWfZiqY5zP4g3ZeM/OvcbPW4dldw116f0mJEC6NYraPXZsvDjILcOIa5tEHeN8LFaBReuy7T6Kvtlf4NJZBz+ElI9BT7sd3jUiSt6BXIGVmFtL1THOxVxvtTtJAoCaOuGSrMYrodYrouqUCBsvDpLcJxz+Eg3TAowGl62JMy2hpEm7fNbh/l5/jB0sVoGPtudL6oL9vFxaW2UbMfCfqmOcy0fb8yT9deI3nSspuUhL5afqlAgbLw5i0Ovw2m2D7WruSuzF5d7dCDUQzZVLNEXEmwRVxziXjNxilF2SrpI6IznKpdf2z0dpzf6oOsa57MynpdZTdc4ilLBs5IhOibDx0g5S4yPs9jl6j8u9uw2LVeCzXbRML1fWOVBS6jbTkvTjNM+FqwMcpTpLO6pjnEuAhBfeUZ2zMAXRUvmpOiXCxks7sFgFPpV4QC7gNgFuISO3GBdr6yV1If6urXMglbrdMCZv1ddeUC/KXNfrHkwzZqk6xrlMHUqLbaTqnIXtfmOPYJXfb9h4aQcZx4tRVm3fxVxaXYeM49yu3tVQZ9BJMWEudf/bukvbM2dLq+tUX3tBrVBPhREuvtnPGRNL0g3tGSLzSJjWqKyRXmoM8HF9DKRUN3ugoZWNmu83bLy0A2o9CKqOcSa0p1BMl84yj6Ml4+NMks32uLGn67FYBT7OOEHS6l0cyzaqbxf4EBqH/mvHCT5vXIzFKvDCeunmr6/e4pr6Ls0ZH2eSXK5S8woBGy/tgnqw1XlSqJkkYiYIVedMMvNKJD123NjT9WTmlaBU4rjYcHVAtUGvw9zfSXtfyi/VI4MnSy6F2vyVUnNFDjKOF0vGQql5hYCNl3ZAbX7lSJMsxjkkxoRJejdC/L1dViH1SqjZTSu356l2NqRGHMk6c0dAdb3VfuVWG3///rDMI2GuROnVj6n99dTah4+Nl3aQGCv9gAz293ZpPQimAYNeh9uH2w+OWzx1oFvcuNQHX1l1naorX6oN6nEJCzC6KcCRdq7uOVUOcz3N0GE6jtIzCM+UXSLpduWr817Dxks7MOh1WDLVfvOrJW56QHo6aVkFdkt13+/itgBXkhAdSur4CnCbAFcyrHcIKWB30eSr3XJNO7LE6epiaJ6Mkpq/tkYksfP53lOlqvT0svHSTlLjI7DsrqEwBTYt524K9HF5HwumAYtVYP6XB+1qPtt12m0XqkGvw8yRUSQt13txHbtPlIJySoR1dk/rhsSYMHgTjaYd7LFzGVdm9DQ/Orb/u6r5a2uMJGY4mS1QZbyUl7sHoGZS4yMwPs6EzLwSnK+sQdfODVY2e1zcgyMp7NQL29nMG9sXK7fnS45z8+Fzbgkq9kSUHrtg0OsQ2zUAhwsvSmpr6rhYnStJjY/Au3cNxaJ12U2Cd01Bvlg4Oc6tk9jEmDD4eulRQ1hKTD9ehJF91RWjycZLBzHodUiKDYPFKpCZV4JvD5xlI8ZNOJLC7i7jxaDX4W83xWPemr12dR/8kocnJ/SHkZAmy3QMpccuAMDYft1IxkuAkW/prkapk1iDXofr+nfF/7IotVzU96ziM90JpGUVYOHaQzhXeTmNsltnHyy6+WpePnIhuRcqiUr3ru+eI8zgrQL4OD0fs0bHuGBEnk1plXT6sztjF4CGJYB3fsqV1G3LLYbFKtz+4PQUbJNWm9Fy46BIRf32dyX2JhkvapwjsfHSQdKyCvDA6j0tXj9XWYsHVu/h+BcXYbEK7MijNT9zdwp7fnGVU3VM+7FYBZ7+OktS98wk98UuAA0Zjv5GParN9pcALtY21HtR2xKAGknLKmixXBShgOWiK7GVjpBapl6z8xQeHneVogwvKVRobykHi1Xg8c/229XM+2SvKiO51UZmXglKqsySugAfg9tT2AXxdKDqmPaTkSsdJwUAQcQsMbkw6HX4HdEg2ZbLHablJi2rAHNX72lRpK6wvAZzV+9RTGNeg16HmcltNxG2UVhRq7ryDGy8dIDtx4pQLVHBsN4q8PAnu100Is+FGkw5bXhPt88uOvvSHJ5UHdN+qL2wqDo58fGmnQ9nyzjNXk4sVoFF67JbXXy2vbZoXbZiJq1R4f4kndrKM7Dx0gG+2GO/s7SNDVnnuHiUzFCDKVPiTDKPRBqq8cTLRq5Amd2kGeUi1RZAACgor1GMJyM8gJbiT9UpBTZeOkC1uZ6sXbmNi0fJidILRl0JNeZmR546i0epCSX3wmpOd2LRMaqOaR9KT61vgUbtczZeOsC1UfQb2sbsczKOhFF6wagrSYwNQ4CP/W6vAFBSZVbM7E2rJMaESXbedVcvrOYkx9KMXqqOaR/hnYieDKJObqjNRF3ddLSjsPHSAWYkR5G1gqMvZSc1PgJv33ENQgKaBleagnzxroKyvgx6HaYN70nSKmb2plE2ZhdKdt51Vy+s5nBPNWVgtdDu5VSd3KihjlF7YOOlAxi99Bhg6kTS9jN1lnk0TFpWAV5Yn4OSqsvZI6EBRjwzSTmpizaosTdqu6GoCUo7iWB/b4xXQJwUwD3VlMInO0+QdDvylVFy37akLgWl3pGSYOOlg8waRSsiNrRXiMwj8WzaSl0srTLjoU+Uk7poIyE6VHIWHeLvrYgYHa3y1uZjkmnSSuvwbeupFuzXMvNI6nxiOo7FKvDjYWoqujKMSINeh2cmxUnqXlifo6oYOzZeOkj5JekaEY7oGMdRW+oilVrOUJMNi1WQg+iVuHRXfqllskBZdZ2iaoxokYzjxaReQYAygrxthAQYJTVKypCiwMZLBwklnBSO6BjHUVvqItAwZqlZf7XZgrc2H3PRiDyLzLwSlBEnFEpaurNnqAMN5/oT/92vOkNdLVD7p/l46RUR5G1DdRlSBNh46SCmIFpaIlXHOM77P0v3fAGUdWFSx7Jyex4/iGSA+vsH+ylr6U7KUAeAqloLHl3TsmUJ4wxo1+LY/l0UFXukxaBdNl46CCUYSin1RbSIud6KzcQ1aCVdmNSxKC3mQitQf/+ZI6MU9RCiGl3fHijkwpgyQK3RdNeIKHkH4iBSdbCAhpgpNT2n2HjpILb6IvaKoymlvogW+Tg9nzQXCvT1UtSFmRAdiqBWgi5bo7BCOR4jrUC5mYf4e2Pe2L4uGxMFqtEl0HBtMM5FrenqtueUvXtlWXUdNmZLd6BWCmy8OIHU+Ai8e9fQFh6YYD9vPJrSVzGpllrkREk1SXdNrxBFGZAGvQ7jB3QjaUtUVjxKDRj0Otw0OKLNm7kOyqnvciUJ0aHwlyiqZ4N6bTB01JyuPj7OBB8v+4/8Jz5TT7wUGy9OIjU+Ar88NRaPpVzV2IG27FIdXt90FCOXbOYMAJnoHUprOjaG2JHXlYzsQxsTB3s7n7SsAizf2na20Zwx0YqrDQQ0PDwnxtMmQ9Rrg3EMW7q6KbBpBV1ToA+WKagYZnO2HyuSzGCsMluw/Zj7m5BSYOPFiWzMLsTrm35tkRZdWFGDBziFURbuToqC1CRHr2vQKQ1qEPcPh8/LPBLPQipjBwC+2V+g2BnoC1Psz/xt3DGit8wj8VxS4yOwbf44/Gd2Iv4xbQj+MzsR2+aPU6zhAgBf7KY1Eqbq3A0bL07CYhV44rP9djWcwuh8jF56zB4dbVcze3Q0jBLuUneQEB2Kbp2lvSobDhZw8KUToWTsKC21/kp2EcdF1THtw6DXISk2DDcP6Y6k2DBFLhVdyeky2jIiVedulHdHVynbjxVJ9kipqlWPS05NXNMrpNU4AJ0OuH9MNBZMlK4u6Q4Meh1G9+0iqbMKDr50JoXll5yqczVf7CXOoIk6xjPoHkxbRqTq3A0bL07ii92nnKpjaNjaAlS3YjgK0WDYKBl/H1rGEQdfOo+SKrNTda6mtXO9IzrGM/jjsB5O1bkbNl6cxOkyWjorVcdIIxW7oIPy2wJQgyo5+NJ5hHbykRY5oHM110bRUv6pOsYxLFaB9NxirN13Bum5xYq+v1xJcp9wUqba1qPU3k3uhY0XJ9EjhBZ8SdUx0qixLUBzqEGVHHzpPEyBtFopVJ2rmZEcBZ1EeIVO16BjnEtaVgFGvbwZ01dk4JE1+zB9RQZGvayObFKDXodXpg6S1H3wS54qYuzYeHEStwyludqoOkYaLfTr2HeqjKRbvvW4vAPxINReFdvopccciSD1OQoNUlczbXWuLyyvUU1DzHOEe6FaYuz47HYSyX3CEUBwyVXWcHdpZ6GFfh3UoNCV27jHkbO4sip2cweG7TWlV8VeMDEO94+JblEmQK/wIHW1opXO9dTYOTXE2LHx4iQMeh1e/eNgSd0L63MUf4KrBakS7zooewYN0INCyy5xjyNnYquKbWrmgTEF+eJdBRcau5IFE+Nw+IUb8MykAbgnqTeemTQAh1+4gQ0XGdDCEjUA9CSGLVB17oSW6sCQCCFUQrWd4EkK632hRmwz6Lmr90CHpv1ebQaN0mfQjgSFco8j52GxCgT5GfGXCf1QUmVGaCcfmAIbDF0lny/NMXrpMWt0jLuHoXm0sEQNAP1NgU7VuRM2XpwI9eHCDyHnYZtBL1qX3WRmZAryxcLJcYqfQTsSFFpUyT2OnEFaVkGL8yXit/NFTYYL4zq0sEQNACXVNE/vj0fOY/RV0jWo3AkbL06E2kCPG+05l9T4CIyPMyEzrwTnK2vQtbN6ZtANjfb0qDZLR/cXV/F501FsQZfNF25tQZdqWTJqjsUqVHn+qwXbEnVheU2rcS86NEyYlLxEDdCNq7X7zuKvk5RtzLsk5uXtt99GVFQUfH19MWLECGRmZrapXbFiBUaPHo2QkBCEhIQgJSXFrl5JUBvocaM956O2Ut02DHod4iODSNpCiZL2jH20EnTZHDWn76oF2xI10HqQN6D8JWqgwQgLDfCW1BVXmRUfvyO78fLpp5/i8ccfx8KFC7Fnzx4MHjwYEyZMwPnzrTeb27JlC6ZPn44ff/wR6enp6NmzJ66//nqcOXNG7qF2GGqjvZMlyiw7zrgHajGxmjrl115QMloJurySttJ3C8q5GayzSY2PwJvTrkEn36YLFmoK8jbodfjDkO4krdLjd2Q3Xl577TXMnj0bM2fORFxcHJYtWwZ/f398+OGHrer//e9/48EHH8SQIUPQv39/vP/++7Barfjhhx/kHmqHSYgObdEmvTXW7DyputkdIx/JfcJJul+OXeDzpgNoJejSBqU79vwvD/I54yReXJ+Nhz/di8qa+sbXdDrgxkERqjBcbIzt342kCw9QZoVpG7IaL2azGbt370ZKSsrlL9TrkZKSgvT0dNI2qqurUVdXh9DQ1mentbW1qKioaPLnLgx6HaYn9JLUqW12p2TUWqr7ShJjwkg1gi7WWpCRW+yCEWkT6s1Y6TdtG5Tu2GXVdXhr8zEXjUi7zP7XTqz4OQ+i2e1FCGDFz3lYvCHbPQNrD8SVrZ35yr7XyGq8FBUVwWKxoFu3ppZet27dUFhYSNrGU089hcjIyCYG0JUsXrwYQUFBjX89e/bs8Lg7Qi9iDxqldqxVE2lZBRi5pOla/8gl6lvrN+h1GHMVzfuSfpy7krcXa/MnTwd17obqIVq5nQscdoRv953BxuzWwxxsLP9ZHSX1AeA8Mdt15bZ8RZ83ii5St2TJEqxZswZfffUVfH1bj5JesGABysvLG/9OnXJv1+aii7RUNKqOaZ20rAI8sHpPi7Tzwgp1rvXHdulMVCo7IFDJ7MijzSSpOndDzRwpq+YCh+3FYhWY/9UBSZ0QwEfb8+UfkBOgFsYsr6lX9Hkjq/ESHh4Og8GAc+fONXn93LlzMJlMdj+7dOlSLFmyBN9//z0GDWq7mZSPjw8CAwOb/LmTUuKJQdUxLbFYBeZ/edCuZoHK1vqpRQu5uGFHoBp+6jAQE6JDEewnnTkCqCeOR2lk5pXgYi3No6L0ZRYbjhTGVPJ5I6vxYjQaMWzYsCbBtrbg26SkpDY/98orr+CFF15AWloahg8fLucQnc5Z4nLQ7hPKtWiVTsbxYpRV2+8RVVpdh4zj6riZAA1xL8H+9h9EXnodEmPYeGkvI4g1OKg6d2PQ6zBzZBRJq/TiaUrFkYe3v1EdZdMcKYyp5PNG9mWjxx9/HCtWrMBHH32EnJwczJ07F1VVVZg5cyYA4J577sGCBQsa9S+//DKeeeYZfPjhh4iKikJhYSEKCwtx8eJFuYfqFCKDaenSe0+VqsozoCTSiUGrVJ0SMOh1WDJ1oF1NvVXggdW7XDQi7aHX0TwqVJ0SmDe2r6TRq/T+XkrGkeDtW4b2kHEkzqMhK1baKDEF+ij6vJHdeLn99tuxdOlSPPvssxgyZAj27duHtLS0xiDekydPoqDgcnzCu+++C7PZjD/+8Y+IiIho/Fu6dKncQ3UKI4lpr2YLOHOk3VCNPnUZh7+7qqukZmP2eVwyW1wwGu3xwbbjJF2RiioZ24xee+ZWfPdAxRdPUyzEn83XS08ueeBuDHodnrtJunnnczddrejzxiUBu/PmzcOJEydQW1uLHTt2YMSIEY3vbdmyBatWrWr8f35+PoQQLf6ee+45Vwy1wyTGhMHXi/azcuZI+6BeUEkx6riZ2HiJmG5J1TGXMddb8ePhCyStkl3lrZEaH4GUuLYN343Z59WVyqsgioitXO4c0UvRD/rmpMZHYNldQ9v02kl585SAorON1IhBr8N1/aVn0A2o52RXCharwKc7pTPKgv29kaiy4Na8oiqn6pjLfLQ9n+SH6+RjULSrvDXM9Vb8kGM/lXeFilJ5lQTVkE2Js5+AokRS4yPw0pT4Vt8rr67DXIVnbbLxIgN3JfYm6ThzxHEy80pQWCE9G5qZHK2qmRAA+HlLF6pzRMdcZmc+LUA+tksn1Z03H6fnQyp8zioadIxjDOsdAqnTQa9r0KkNi1XghfU5rb6nhl5fbLzIACVzJMDHwJkj7YAa/R8VTisWqCSuv5o2e6PqmMv4EyoYA0B0eIDMI3E+J0qqnapjLrP7RCnJMNx9otQ1A3Iiau/1xcaLDFAyR6pqLdiYTasyzFxGayXer6RHCM3gouqYy9xyDS0ThKpTEr2JVb2pOuYyWuuHdSVq3zc2XmRifJzJrvdFB2W75JQK1f2vxnCihOhQRATZX2PntNf2kdw3HEaJQHp/owHJfdUV5A0AdydFkZY27k6Kcsl4tAQ15kVtQd6A+veNjReZyMwrsVtITekuOSVisQq881MuSUvNElASBr0OCyfH2bW7Fk6OU11MhhLYmF0oGbD62m2DVfnbGr30mD062q5m9uhoSeONaUkpIW1erROKhOhQyfAGf6NyA9j5bJYJtbvklMj2Y0XkjAmlzhakSI2PwLttpDCqIX1RiVDaSQT4GDBehRkjNhZMjMP9Y6JbeGD0ugbDZcFE6boeTFPsBbReyTOTtDuhqDZb8J1CM47YeJGJcGL/CKqOAb7Yc5qk8/XWK3a2QKW8Fa9dmQrSF5UIpZ1EVa1FVe0kWmPBxDj88/Yh6ORzOTjZKoB1+wv4nGkHUgGtNkICjC4YjfORWh2w8f/WZikyvIGNF7nQZhFYt1JlrifpruqmvnRXGxarwKJ12W2eFgIcK+UoWmwn0RppWQWYt2YfLtY2rcCs1k7r7kbr3nPquEuqlNmVnI0XmaCWGFdTKXJ3060zzUs1qHuQzCORD8psj2OlHOPouUqSTs0GoRY7rbsbtQe0SuHIuJVooLHxIhNaP/HdAdWbck1P9RWMslFI7EpO1Xk6FqvAT7/arz5ro6LGLPNo5EOLndbdjVRAqw7qDdYFGvYvhBhHp8TSE2y8yIQt7bWtx63aT3xXY7EKrDtAc3tHqrgOSkkV7QFK1Xk6mXklqKmneRvU1E26OZ6yNOZKNmYXSmaMqjn7z6DX4W5iNXirUJ7Hjo0XmbClvQKtlxxR+4nvajLzSlBSJR1cFhZgVLVBGEoM4KbqPB1H3N1RYeqrrnsZDrJzJqQMNaO6M9QAoJ64jJiuQI8dGy8yYkt79WulNLlOB+w9qb6S0u6C+hC6eUikqg1CUyBtGfFkMTdnpEBdltWpvIgbtYO6l55v+RQycgkZamYLMlTuyTpbRlt+pupcCZ/JMrP3ZCmqzZYWrwsBvLc1j1vVE6E+hNQ+E0qIDiUZMP/JPMnBlwQoVYsB4E+j1F3ELTE2DEF+XpK6T3ed4vOGQPrxIqfqGOej3qtVBZjrrVjxc55dDbeqp6Hl7q5XYtDrMD2hl6SusKKWM44IXLl82xbj47rir5PUXcTNoNfhvpH2q+wCnKlGhWrfqd0O7B7s51SdK2HjRUa4Vb3z0HJ31+ZQO2IrMX1RqbSWNeLrrcdb067BinuudcOInE8UsSM2nzfShPjTCs9RdUolOZa43GhQnqmgvBFpCGoL+p+PXpB5JOpH6wWjroTT7J1HWlYBHli9p9X4hZo6K7y81Bsf1Rw+b5xHOLGmFFWnVBJjwxBMWG78KD1fccuNbLzICLUF/e6TpYo7MZQGtc6AEusROIpUmj3Q4ElQc1aVK/C0wm2URnt83tCgBs5TdUrFoNdhRnKUpK6suk5xwclsvMjI3UlRoJSOqKyx8Dq0FNQJsgYm0rY4DXuP1LLqOmzMLnTZmNQIF25riQYuD5dACfTWSp0uarr0doUFJ7PxIiNGLz2u69eFpNXCcoecFF0ktlsg6pTO+DiTZHVP7nFkH08r3EZptFdarcw+NUrDoNfhmTaCuHW//WmlTtfZUmK6NFHnKth4kZlZI2NIOi0sd8iJp63nSz2IBDhzRBrPKtzmSXFhcpOWVYCnv259yTHY3xvv3jUUqfERLh6VPEQSM4moOlfBxovceNByh5x4WrsFfhB1HGrhNqpO6XiagS8X9oK8gQbvlZagZhxRda6CjReZ8bTlDrkw6HW4aXCE3TmyVty4AD+InEFibBgpgDUxNsxFI5IXSqC3lgx8ObBYBZ775pCkTktLtpTrBADKLynLaGPjRWY8KUtGTtKyCrB8a9sF/+aMidaMGxfgjCNnYNDrsGTqQLuaJVMHasbgpRTki+8eqJn9lYPMvBIUVkhPJLW0ZGvQ6/DSlHhJ3QvrlWWwsfEiN7xs1GEsVoFF67Ltel2+2V+gqAuro3DGkXNIjY/AsruGwhTYdHJgCvTBMg3FLdhIjY9ASlzXNt/fmH2eW5LYwZFlWC0t2YYQJs9KM9ikq9MwHYKXjTpOZl4JCsrt3yhsF1aSRpYAgMsZR22tvdsyjsbHmXg2bYfxcSZ09vX+LatIICkmHImxYZr8zcz1VmzKOW9Xs/znPDxxfX9V93KSC0eWYbW0ZKvGGDs2XmSGYxc6jhovLGfgSMaRlow2Z5KWVYDnvslGYcXlc+Pz3Wfw3E1xmvO6AMBH2/MhJByQQjToZo+hZUJ6Eg2NUX0kl460FjukxucUm94y42lZMnKgxgvLGXiq0eYsbFkjVxouAFBYUYMHVu9BWlaBm0YmHzvzaTVrqDpPw6DX4bmbrpbUaSk5AFDnc4qNF5mRCqITAG4aHKGpC8HZlFZJL6kp7cJyBqHEpm9UnSfhaa0BbPgbac70A6fLZR6Juglqo99PiL+3JmOlrnxONX8S2f6vNIONjRcXkBofgTlj2m5X/97WPE3OAp2BxSrw9NdZkrpnJinrwnIG2QUVTtV5Ep7aGuCWoT1IusKKWlwyW2QejfqweevKL9W3+v6LUwZqznCxkRofgXfvGgpTs7YIpiBfRRbl45gXF2CxCnyz375xMv/Lgxx42QqUhxAABBHqFKiNXUTX/q78Ytz/u1iZR6Muln53hKRLzy3GyD7KKr7VEZL7hMOgByxWae2L67Pxtz/YTyX3JCjeuqe/PogJ8dq9T6fGR2B8nAmZeSU4X1mDrp0bPNpK3F/2vLgASrZMWXUd3tp8zEUjUg+rM06QdFrpT3Ml1WbCE8gBnadgrrdi36kyolpby0YGvQ7diWXc958uk3cwKiMjV3qipMTuys7GoNchKTYMNw/pjiQFZ+Wx8eICqAGV723N1dwafEewWAV+PGw/7fMy2vvdBvcIJuk6+bAD9Uo+Ts8nnw1aaQ1wJT1D/Em6QF/teSs7wvZcWtdkqo6RFzZeXAA1C6babNG8Ve8IGbnFqKmneRW0+BAa2Ze2T9tyi9novYITJdUkndGg00xrgCuZM4qWAj20Z7C8A1EZZ8uI3ZWJOkZe2HhxAQnRoQgwGkja9ONs1dug/hY+XnpNPoQSY8IQ4CN93lysrddc4GlH6B1K8zzcNDhSsS7xjjCqXxd4G6T36/O9Z9jovYKIINpyG1XHyAsbLy7AoNdhzFVdiGrt3UzbC/W+el2/Lpp8CBn0OozpSztvtBjz017uToqC1Omg0wEvTR3kmgG5GINeh3nX9ZHUKa3cu7sJCaAto1F1jLyw8eIi7kjoRdKN0Fitko4QQqxfMqy3dn+z2C4BRCXPoG0YvfSYPbrt0gQAMGd0tKbL40eF084bLnB4GepyUHgnbqKrBLR79SoN6rOFn0GNhHcmduQm6tTIiCjachhV5yksmBiH+8dEt/DA6HXA/WOisWCi/e7LasdTq1K3F4tVYK1EOQsbJg9ZNrJYBdJzi7F23xmkKzCujtMUXMQOYs2OHfnFGN2PusSkbUyBtBsrVadGrERrlqrzJBZMjMMT1/fHx+n5OFFSjd6h/rg7KUrTHhcbtnLv9ko0aLEqdXvJzCtBSZVZUhca4O0Rv1laVgEWrctucv5EBPli4WTl9ATT/lWsGKgxGdqL3WgvntoW4Eq+2nvGqTpPw+ilx6zRMXj+5njMGh3jEYYL0BD3ctNg+w8ZbktyGery2R+GdNf8b5aWVYC5q/e0MHwLy2swV0E9wTzjSlYA1K6/3B24AYtV4IX1OZI6LbYFuJLTpbS0X85Sa4nS3d5yYrEKfLrrtF3N6oyTHvWb2IO6fJYSZ5J5JO7FYhVYtC67VT+u7bVF67IVcd7wspGLSIwJQ7C/t90KjgE+BiTGsPEC0KoSA0BIgLabEnYP9sOuE2WSunMVZpjrrR7jWZBCDW5vOaG01agyW/DmD0fx6PirXDQq5cLLbA1I3XcFLmepuXuizXc6F2HQ67Bkqv0+IlW1FmzMLnTRiJQN1Y2r9WyJPw7rSdZ+nJ4v30BUhFrc3nJCTZ1//5c8Rcyi3Q0vszWgpvsuGy8uZHycCf4SxeoWfHmQbyagpyNqPW0xuU84CPXGAAD5xbQlJi2jJre3vND272JtPdd6Aa157jf7CzR/3qgpS42NFxeScbwY1RJt6Eur67haKsCp5b9h0Osc6Hqs8R+DgCNuby3jSLsMJcyi3Q1lmdoTzhvb8pm9+VKwvzIyrth4cSHUhl4fp9M6KWuZIkKmkSM6NTN5cCRJN4jYyFHLbCIuu2r9gZ0YS2stAShjFu1u1LRcIicGvQ4LJ8fZnQaVVdcpIryBjRcXcqaUVsFxy6/nNe+elIKXjS5Tccl+4KWjOq1isQp8tY+WMq71B7ZBr8Ort0i3P/CEIFQK+UVVJJ3WzxugIbwh2L/tFgg6KGPplY0XFxIZTDvxa+qsmndPSrGTuv8eYOOFEjOqqDqt0lBoTNqACwswesQDe+KgSNw/xn6bhIWTtV1qgILFKvCfzJOSOk8x9DLzSuxmqill6ZWNFxcyMpZeObewQtvuSXtYrAKrtueTtJ6wbEQtR36yhObZ0ypUl/7NQ7TZTbo1FkyMwzt3DEUnn5ZVMezNrj2JzLwSFFZI30emXdvLI84btSyhsfHiQhJjw+DrTfvJSy5q/6HcFpl5JSgjLoF4ghs3IToUpkDp5bFV2z077ZV6LozXeKGx5uj1QFVtfYvXy6vrPCZ13B7Uh3BUuL/MI1EGalmyZ+PFhRj0Oky/lla3w5OXAKg3k2A/ZUS9y41Br8M0wnnj6ZlqtkwJe3iK698Gp45Lk3eBFu/i7oe1y1BJpicbLy7m+qtp1T09pXNpa1Bn0DNHRnmEGxcA6q00HbU4mRbhQmMt4dRx+1isAh9Rizt6iH2nlkxPNl5cDGV2qJQ8endBqTUQ4u+NeWP7umxM7kcl0yE3woXGWqKW+AV3kZlXglKJNgo23P2wdhXUyWN+kXuLYrLx4mJsefT2UEoevbu48jdqzYDRAVg8daBHzaCv7U0zZqk6LcKFxlqipoqp7sARo81TfiNqjN2ane5t7MnGixsYH2eCj0QDvSf+u9+jZojNSY2PwLt3DYWpmZcqIsgX79411COa613JkcJKkm7tvrMyj0S5UA1+T/IyJESHSrYkCTAaPNbTSzVIQgM8xxtu0OswPaGXpM7dEwHuKu0Gth8rQq1EEENVrQXbjxVh9FX09GqtkRofgfFxJmTmleB8ZQ26dm4ItvQkj4uNXSdpN4n1WQV41TrY434ji1Xga6Lh5ikzaKDhd7kk0ZKk2myBxSo87pwBaN2kAeBvN8d71O8TFR5A0rlzIuASz8vbb7+NqKgo+Pr6YsSIEcjMzLSr/+9//4v+/fvD19cXAwcOxIYNG1wxTJfx+e5TTtVpFYtVsOHyG/5G2jyjtt4zCxw2FKgzS+o8aQYNAB9tz5eMghK/6TwRyjL+/WOiMXEQrUWHVlDDcqPsxsunn36Kxx9/HAsXLsSePXswePBgTJgwAefPn29Vv337dkyfPh2zZs3C3r17MWXKFEyZMgVZWVlyD9Vl5BRWOFWnRdKyCjDq5c2YviIDj6zZh+krMjDq5c0eW5PilqE9yFpPWhaxQd3nPwzp7lEG8M58WvbZ+gO0tgpapbWCfZ18vPDOHUOxYKJ940aLUBJLAKDUjUHMshsvr732GmbPno2ZM2ciLi4Oy5Ytg7+/Pz788MNW9f/4xz+QmpqKJ598EgMGDMALL7yAoUOH4q233mpVX1tbi4qKiiZ/SqezD62yJVWnNdKyCjB39Z4WrtzC8hqPLaqV3CdcMk7Khicti9ig7nOKhxWoo3rsDp2t8MgYu7SsAjywek+r5fAv1tZD76FRoQa9Ds9Mkjbanv46y23njayHxmw2Y/fu3UhJSbn8hXo9UlJSkJ6e3upn0tPTm+gBYMKECW3qFy9ejKCgoMa/nj1pReDcyYSraTdQqk5LcFGt1jHodXj9tsGSOk8rwmZj8+FzkhpP/G2oHrs6K5DhYTWCLFaB+V8etKtZ8OVBj7vX2Ajyk548l1XXue28kdV4KSoqgsViQbdu3Zq83q1bNxQWtp4ZUFhY6JB+wYIFKC8vb/w7dUr5cSL3joy2W8MEaEgHvnek/aZqWoSLarWNVKM9HTyz0Z653ooPfsmT1P31hgEe99sk9wmHF3Gff8m9IPNolEXG8WK7DQgBz65anX68yKk6Z6N6p5iPjw8CAwOb/Ckdo5cecyS6vabEdYWRuEygJbioln1sjfaat4/w1BRyAPg4PR+UyfE5DzxnDHod+nShZY4cPFUu82iUBbUatadWraY6nNzlmJI1VTo8PBwGgwHnzjV16Z47dw4mU+tLIiaTySG9WlkwMQ7Hi6qwMbv1wOWN2eeRllXgcQ8jNUS5u5uJgyIwIZ5TyG2cKKFV+qTqtEbPUH8cPndRUucnUQ9Ge3DVanuE+NP661F1zkbWqb3RaMSwYcPwww8/NL5mtVrxww8/ICkpqdXPJCUlNdEDwMaNG9vUqxWLVWBnfqldzXwPXG+VKqqlg2fGLjTHoNchIToUXTv74nxlwzKap50rNnqG0PqAUXVaIyE6zKk6rZAUE+5UndYI70zsLk3UORvZi9Q9/vjjmDFjBoYPH46EhAS88cYbqKqqwsyZMwEA99xzD7p3747FixcDAB555BH87ne/w9///ndMmjQJa9aswa5du7B8+XK5h+pSMnKl11ttwVAj+3rOxfNKWg6q7RTVEvDMuI7mpGUVYNG67CbxQRFBvlg4Oc7jvHX9TbSlYqpOa8xIjsJL/8uBsGPb6nQNOk/i2uhQ6GDfr6L7TeeJmAJp3m2qztnIHlRx++23Y+nSpXj22WcxZMgQ7Nu3D2lpaY1BuSdPnkRBweXU1+TkZHzyySdYvnw5Bg8ejM8//xxff/014uPj5R6qS1F6MJQ7MNdbseJn+4GXOh0wtn83uxqtw6nkTSmpli5O54hOaxi99JgzWiLGboDnxdjtPlFKKuC3+4R9D7lWodR6CXFjE2GXnK3z5s3DiRMnUFtbix07dmDEiBGN723ZsgWrVq1qor/11ltx5MgR1NbWIisrCxMnTnTFMF0M1XPgOR4GSuClEA06T4VTyVsS3ono3ibqtMiCiXEYH9e1zfc3/RZj50kUll9yqk5rUKoPl7qxibBnmdoKIimWtr5M1WkBDryUhlPJW4HjLiWxWAWyztgv4OlpRu+2YzSvNqXthFYZH2dqtfqwDR3cd96w8eImEmPC7J4UQINLLjHGc4yX3qH+TtVpEU4lb0kRsUQ5VadF2OhtisUqsDGn9UzP5oR6sMcuM6/EbmymO88bNl7chEGvw5KpA+1qFk8d6FGBqXcnRUFqd/W6Bp2nQl36yLtQJfNIlAOn10vDRm9TMvNKUH7JfsKEDXcFpCoBJZ83bLy4kdT4CCy7a2iLiyMiyBfLPLDgmNFLj3ED2l6XB4DZo6M9LrCwCUTv7L8yTnjMEoBUYCGn1wOhfrRaHFSd2qE+bIP9PKsLeXOUHE8me6o0Y5/U+AiMj+OCY0BDFs2mNor2AcD4uK4e2eH1SqhLHyVVZmTmlXhEzJRBr8NNgyPw3ta2M9U8Pb3+8LlKku7r/Wcwul8XmUfjfqheuJkjozz6vFFyPJkHT2GVg0GvQ1JsGG4e0h1JsWEeebHYy6KxkXXGMzvfXokjSx+esgSQllVg13CZMyba47yYzTlVSgtyX7vvrEdcYwnRoaSYw3lj+7poRMqEOlmiNEZ1Nmy8MIpAKqAQ8KyAwrZIiA5FiMRN14YnpAZTOgN/tuu0RzyQ7UENcq+3CmwnZuGomY3ZhZJFQj0t5rA1qJOlr/adcfk1xsYLowgKK2heAqpOqxj0OtyT1Jsm9oDnNXcGpuFIkPuXe07LNxAFYPPy2iPY3xvj47TVT689JESHtmgC2xolVXUun1iy8cIogpKLxFgOok7LRIV3IunOe8BvtTrjBEnnqZ2BbRi99GTvS5Wd9hxagOLlLat2/cNYiRj0OkwZEknSunqZmo0XRhFQrHtHdFqGasBtO6pt97/FKrD11wtEtQe4oSS4K7EXSXdtVIjMI3EvSk7/VSJUD5SrSxGw8cIoAlMQreMvVadlgv1oMS/fZxdoOtYjM6+E7CXw1M7AVzIjOVqy2YjuN52W4bpAjjGsd4hk/S2bzpWw8aIgLFaB9NxirN13Bum5xZp+8DSHEv3v6bU6bJQRi2tV1Fg07fqm9pzxNxqQ6AEp41IYvfSYM8a+YTJnjPbrKA3rHQKdxMNYp3P9w1ip7D5RKtlzDgAe/2yf7GO5Eq7zohDSsgqwaF12k7XYiCBfLJwc5xFpnpTof0+v1WHDkXLlWnZ9U3vOTIw38XnzG7Y6Scu35jVZSNOhwXDxhDpKO/NLIAgNYHfml2BkH/bYUe8h6w8U4LXbrC4zfrVtYquEtKwCzF29p0UQWWF5Deau3qP5bq+U6P8Qjv5vxJFy5Vp2fVONuJF9tV90zREWTIzDkb/dgL9O7I/r47rhD0Mi8a/7EvCX1AHuHppL2J5LiwWj6rQO9R4iAHy0PV/WsVwJGy9uxl5xNttrWu/2Son+L+Xo/0YSokPRrbP0g1sHbbu+qUacJ/emaYvNh8/hw235+D77HL7adxZ3f5iJUS9v1vxECQDOlNKWG6k6rZMQHQofL5rncme+67L62HhxM9ztlR67QNVpHYNeh+kJ0pkjAg2ub60i1dMI4Dip1vB0T29tXT1JFxnMRi/QcL8Z3COYpPU3ui4ShY0XN8Npe/TYBarOE6gneuK0XN/EoNdh4eQ46IAWWTS21zhOqime7um1WAUyiBPBkbG83GjjYWKbhFuG9pB5JJdh48XNcNoePXbBkUBV7UN7uOReoDXkUyup8RF4+46hCGlW/8cU5It3PbAzuxSe7unNzCtBabW056WTjxdnqF1Bcp9w+BsNdjUBRgOSXRjgzMaLm7G5vu3NDUP8td2W/WQxrWkcxy5chlq3ZMfxEs3OooGGJZDnvz3UxCsX4u+FZyYNYMOlFTzd00vdr9uG92CP3RUY9Dq8dttgu5q/3zbYpb8ZGy9uxub6tvd4Ka2uw8bsQpeNyZVYrAL/yTwpqTMF+mjagHOUxNgwdPKxPxMCgBINBzqnZRXggdV7UFjRtOJwaXU9Hvxkr+ZjN9qDp3t6qfvFmY0tSY2PwLK7hrZIFjAF+mCZG7ycXOdFAYyPM8HfaEC1nWqh8788iPFx2qtXkZlXQmq2OD2hl+b2vSMY9DrcPrwnPtiWL6nV4iya0k1aq9dMR7B5egvLa1qdMOnQsOSm1YlCEaG1hp4L1LVJanwExseZkJlXgvOVNejaueFcccc1xp4XBZCRW2zXcAEaGoVlaDD4kvpgjQoPkHkk6uP3V3Ul6UL9tNcPKiNXupu0Vq+ZjmDz9AItg5xtaDXI2WIVWPjNIUmdVTRUlWVax6DXISk2DDcP6Y6k2DC3nStsvCiA9OO0Ykird+TLOxA34Olu7I6QU1jhVJ2aoF4zVJ0nkRofgXfvGoqgVtpxtPaaVsjMKyFnLGrRW6k12HhRAFKlqm1szjmvueDLIT2DnarzJKg1XLRZ64U629OeB8FZtOa5Kq+u02ytF0fqRPFkSfmw8aIAgv1pbv1ai9CcG/yTHSecqvMkpJYaHdWpiSRiGitV50nYa8eh5VovVK9LZ1+DZmN+tAQbLwogvBM9JkFrbvATJbQ0aarOkxjYI8ipOjWx5cg5SU2IvzcSY9h4aY6n1nqh1on641BOk1YDbLwoAFOQnwNqbV1UvUP9narzJEYTGw76ekmnVKsJc70VH/ySL6l7/uZ4fgi1gqfWeqHWibr+aq4PpAbYeFEACdGhCCEGymnNDX53UhSkni96XYOOaUpiTBiCCefNqu15mloC+Dg9H5TdOU9IwfdEPDVInnthaQs2XhSAQa/Di1PiJXVadIMbvfSYPTrarmb26GgYvfhUbY5Br8MMglFXdqleU7FSecVVTtV5GpSq3lp8iHMvLG3BTwSFMHFQJMbH2a/bMTwqRJMX1oKJcbh/THQLD4xeB9w/JhoLJsa5Z2AqwGK1knRaipXiPKOOcWWtl7a4aXCEJu81tjRxUzMPDPfCUh9cYVchWKwCWWfs1+PYmH0eGw4UYOIg7V1gCybG4Ynr++Pj9HycKKlG71B/3J0UxR4XSTzvUX5NzxB8nCHdUuKanlwltS1S4yMwZ0w03tua1+r7723NwzW9QjT5MB8fZ0JnH+/fDPqGgmuJMe4rtsa0DzZeFIJUBoCNZ9ZmYUK8NkueG730mDU6xt3DUBVJsWF468djJJ1WOFVKq9cREexIILxnYbEKfLrrtF2NFtsrpGUVYNG67Cb32i/2nMbCyXGaNNS0DE9rFQI1sr+4yqy5FEam/VCCdg16aCZWiht5OgdPbK+QllWAuav3tJgkFpbXaLYwn5Zh40UhOBLZr7UURqb9GPQ6LJk60K7GYgX+8M4vLhqRvHAjT+fgae0VbIX5WktS03JhPi3DxotCaEiXpq3ihQfQii0xnsGoPtL1Xg6crsDFmnoXjEZeuJGns/CsWClPLcynZdh4UQjUtFcAWrmfME7isU/3OlWnZDy1RomzocZA5RdpI93cUwvzaRkO2FUQ0V06kXRFF2tlHonrsFgFMvNKcL6yBl07N9SWYHe/Y5wkBrBSdUpmWO8Q6HT2m5nqdQ06pm0SY8IQ6GtARY39vlcbsgpgrreqPuuPjV7twcaLgqAuBx2/oI3ZUFpWAZ775hAKKy4bY6ZAHzx309Uc+e8APYN9caSwkqRTO+9uyZXswm4VwO4TpZrKsHI2Br0O18eZ8PmeM3Z1VtFQ0VjtWYC2wnyF5TWtxr3o0FDrhYO81YO6zWmtQXQ4fJyRr/rAsrSsAjywek8TwwUACitq8QBH/jvEtITeTtUpFYtVYPnWXJKW3f/S+Blpc9f8YvU3Rb2yMF9r1XUBrq6rNth4URDU5aCSqjpVB5ZZrALzvzxoVzP/y4OqN9Bcxb5TZU7VKZWM48WoMttf5rDB7n8KtOvrZIk2PL1cXVdb8LKRgnDkhltYrt74BUdqTIzsG+6iUakZ2kMo94L00pKS2Z5LS9sN8DGw+5/AkB7B+BjSNXN25pfCYhWa8EqkxkdgfJyJ4+w0AHteFERCdCg6+9LsyaKLZplHIx+eVmNCbpJiaAbexuxzqvZmnS6hGexxEYH8MCIQGeJP0lWbLcg4rp1idQZ9Q0uAm4d0R1IstwVQK2y8KAiDXoeRxCDDsmr1Gi+eVmNCbhJjw+DnLf1b1VuBX45ccMGI5EEIWhPKiECug0QhIToUAT4GkjZdQ5V2GW3AxovCiCWmS6sZahYIZ4vQMOh16B1Km0Uv/4UW8KpE9Hra7Yqq83QMeh1+R16WVa/HjtEmfJUrjEA/+31qbFCaOCoVSj+eEH9vzfTjcQW1tDhWnC1Xb42g7iG0RotUHQNMJ2agjYjma5FRFmy8KIyKS/YDWW18n12o2vgFSj+exVMH8lq0A0QG0YK9qTolkkyM7aHqGECvo11jVB3DuAo2XhQG9R5xsdai6nTp1PgILLtrKEyBTR+mEUG+WMZpiw4zh1hEjKpTIomx0h67YH9vJPJyI5miKponjqpTMharQHpuMdbuO4P03GLVTv6YBjhVWmEkxYTjrR9pcQmU7rpKhtMWnceoq7rAx0uP2vq2g1q99DqMukq6iaNSsXnsHli9p03NEvbYOYSnlM1PyyrAonXZTZbbI4J8sXByHE+UVAp7XhRGYmwYfL1ph6VEAz2OOG3RORj0Ovxj2hC7mnqrwMbsQtcMSCYue+yaZhSZAn3YY9cObGXz27rqdGh4yKu5bk5aVgHmrt7TIk6wsLwGc7mat2phz4vCMOh1mH5tT6zcfkJSGxpgdMGIGLUwPs4Ef6MB1Xaq0C748iDGx5lUayRarAJBfkY8ldofJVVmhHbygSmQPXbtxVY2f+7qPdChaU6RFsrmW6wCi9Zlt5orJdCwj4vWZav6mvBU2POiQFLiTCRd10B1u3IZ55JxvNiu4QIApdV1qi04lpZVgFEvb8b0FRl47LP9eGF9Dl5JO4zyS2Z+8HSAtsrmd/I14M1pQ1TtzcrMK7GbmSnQkLmp5vhBT4WNFwVitdACyag6xjOgls+n6pQEu/7lJTU+AjcOimiSMFBZY8GfP92HxRuy3TewDkJt0MmNPNUHGy8KZEc+bWb8n53SS0uM53CmlFY+f6fKZplSrn+gwfXP2SPtZ/GGbKz4OQ+i2U9oFcB7W/NUa8B4SkCyJ8LGiyKhucB/Olqk2hs2py06n8hg2g04u6BCVb83u/7lxVxvxfKf8+xqlv+cB7OdTDal4gkByZ4KGy8KhFoWv0qltV6ujF14ZM0+TF+RgVEvb2bXfwcZGUtLg1ZbjSB2/cvLR9vzW3hcmiNEg05t2AKSgZZTQi0EJHsybLwokMSYMPgbaQ3T1HbD5tgF+Who0Ei7pNVUI4hd//KyM59myFJ1SmN8nAmPplyFoGatV0xBvniX0+tVi2zGS0lJCe68804EBgYiODgYs2bNwsWLF+3qH374YfTr1w9+fn7o1asX/vznP6O8vFyuISoWg16H+8fEkrRqumFLxS4IcOxCRzDodZgYT8tUK1KR0ZsQHUqqrMuu//ZBnSipsZO9zcv7+qZfUfZb65VgP288ltIXvzw1lg0XFSOb8XLnnXfi0KFD2LhxI7799lts3boVc+bMaVN/9uxZnD17FkuXLkVWVhZWrVqFtLQ0zJo1S64hKpp5Y/vYvWGrca1WKnYB4NiFjmIKojUlLKum9dBSC+z0bz+3XNODpDt6rlJVE4u2vLzll+rwxqajqi/Y6OnIYrzk5OQgLS0N77//PkaMGIFRo0bhzTffxJo1a3D27NlWPxMfH48vvvgCkydPRmxsLMaOHYsXX3wR69atQ319vRzDVDT2mheqda2WulShpiUNpaEjNsei6pRAZl6JpLFVWl3HRm87Se4bDl8v6UdB6aV61fzGnKGmfWQxXtLT0xEcHIzhw4c3vpaSkgK9Xo8dO3aQt1NeXo7AwEB4ebVdCLi2thYVFRVN/rSCrRR6RLPiUSEB3nj7jmtU5/KktjPQQtsDdzGC6Imj6pQAB+zKi0Gvw50jepG0avmNOUNN+8hivBQWFqJr165NXvPy8kJoaCgKC2muuqKiIrzwwgt2l5oAYPHixQgKCmr869mzZ7vHrURS4yPwzKS4Jq0ASqrq8ML6HNUFt54qrSbpuO1B+9ETPXFUnRLggF35IVf1VslvzAav9nHIeJk/fz50Op3dv8OHD3d4UBUVFZg0aRLi4uLw3HPP2dUuWLAA5eXljX+nTp3q8PcribSsAjz0yR6UVDUNllNbdo7FKvD57jMkLTVug2lJEdFrRdUpAa7VIT/DeoeAYs8WV6rjvGGDV/s41JjxiSeewL333mtXExMTA5PJhPPnzzd5vb6+HiUlJTCZ7Fv4lZWVSE1NRefOnfHVV1/B29t+loGPjw98fHzsatSKlpqKZeQW42KtdOxSiL8XP4Q6gBZv2ga9DjcNjsB7W9supKa2+C+lsftEKSjhHwvXHcINgyIU/1vbMtTsxUpxhpq6cch46dKlC7p0kS6ElZSUhLKyMuzevRvDhg0DAGzevBlWqxUjRoxo83MVFRWYMGECfHx88M0338DXVz03WDlwZN2WWtjOXaQfp/XTSYoJU/yNUcnYvBRSWV2lVeqYQQMN3sfldgyXOWOiVRf/pTSoyyfFVWZV3G8o8F1G3cgS8zJgwACkpqZi9uzZyMzMxLZt2zBv3jxMmzYNkZGRAIAzZ86gf//+yMzMBNBguFx//fWoqqrCBx98gIqKChQWFqKwsBAWi/1OuVpFW+u2tFtFTJfOMo9D2xj0OjwzKU5S98L6HFVkWtjzPtr4Zn+BKvZFyTjiiVPD/YYz1LSPbHVe/v3vf6N///4YN24cJk6ciFGjRmH58uWN79fV1eHIkSOorm4I4tyzZw927NiBgwcPok+fPoiIiGj801ocCxXqDSW/iBYI606oMzUtzOjcTQgh4FktmRZcG8g1JESHIkSiEKCN8E7KX6bX1sSPaQ2Hlo0cITQ0FJ988kmb70dFRUFc0VDj97//fZP/Mw03FFOgDwor7Lv41+w8iXlj+yh6uSUxJgwBRgOqzG170UL8vZEYw8ZLR9HSjZtaSEwN+6JkDHod7kmKwj9+OCotVsFtWouxX0xTuLeRgjHodZieIF1/QQ0zz43ZhXYNFwBYPHWgog0wtaCVG7fFKvD1vtaLWjZH6fuiBmK6BJB0RSqIl+IMNe3DxovCiQqn3VCUPPO0xS3YI9jfG+OJtSYY+2jlxp2ZV9KiREBrhAZw1ogz0NIyNXeT1j5svCgcLcyiKXELZRw85zS0cuOmGuR/GNJd8fuiBmzL1FKs2p6nigDp1PgIvHvXUJiaVSjnbtLaQLaYF8Y52GbRheU1rS4169BwMSp55sk9jVyP7ca9aF12E8PRFOSLhZPjVHHjphrk1OqwjH0Meh2mXdsTb/xwzK6utLoOGceLMbJPuItG1n5S4yMwPs6EzLwSnK+sQdfODfdKNnbVDxsvCsc2i567eg90aBorp5ZZNPc0cg9qv3FL1axRg+GuNsz1NI/KtqNFqjBegIZ7KGcxag9eNlIBbbk/1dKgkdqriHsaOR/bjfvGQQ31lb49cBbpucWqcPvbKuvaQ+mGu9rYf7rMqTp3YrEKpOcWY+2+M6o55xk67HlRCanxEbBagf+3NqsxiLGkqg7Pf5sDvV6naAOG2quIexrJQ1pWQYvlowgVLB+lZRXYbQnAlXWdj683bT5L1bkLtZ7zDB1ln4FMI2lZBXiwtQaNFTV4QOENGm3uf3uoIftFjaRlFWDu6j0tll6U3tjTYhWY/+VBu5rPdp3m2bSbOC9Re8qdqPWcZxyDjRcVQLmRL/jyoGJv5La4HXupu+z+dz5SjT2BhsaeSjxvMo4Xk8q7ZxwvdtGIPAPqFXiksEKR542az3nGMdh4UQFauJHb4naae2AiOG1RNhxp7Kk0tufSGnlSdQyNTr60FgFmKxR53qj5nGccg2NeVEB6Ls0oSc9VbvqixSoQ5GfEXyb0Q0mVGaGdfGAKVFf2i9pQc5uA0yWXnKpjaNwytAe5qrESzxs1n/OMY7DxogpoLs7cC5Uyj6N92AueY8NFPoL9aLNoqs6VXCA+XKg6hkZyn3D4eulRU2+V1CqxMCa1+q8Sx844Bi8bqYCkGJo3ZUdeqeLWcjl4zn18T2xqSNW5kgAfg1N1DA2DXofXbhsiqfM3GhQXYG+xCvwn86SkzhToo7ixM47DxosKSIwNI92kS6rMilrL5eA593KSuKRC1bmShGiawU7VMXQmDorA/WOi7WqqzRa8kpbjohHRyMwrIVXpnp7Qiz2+GoCNFxVg0Otw27AeJK2SSuxz8Jx7iQrzd6rOlcxIjpLMfNHpGnSM83ni+v6SmuVb82AmLC+5CmocC7XZLaNs2HhRCT1CaA8YJZXY5+A59/L0xDin6lzJ5sPnJCO95oyOhtGLb2FysOqXtosD2hBEnasI7yTdVNIRHaNs+MpXCWossc83E/fiZzRgfFxXu5rxcV3hZ1RW3AilrlGA0YC/pA5w0Yg8j+9ziPFSRJ0rsFpoy89UHaNs2HhRCaossU+9R/C9RDZW3HNtmwbMsN7BWHbXcBePSJq3Nh+TrGtUZbbwcqOMCOI1SdW5gh35tJISVB2jbNh4UQlqLLH/w+FzJF1RlXKWurTIinuuRc7zqfjdVeEwGi5f8rtPlGHkks2KyviyWAVWbqMtRfByo3z0M3V2qs41UINwOVhXC7DxohKkSuwDwE2DIxQTRW+xCny26zRJyzUX5OenX8/jp1+LYLY0DbBUWm+szLwSlF2y73WxweeNfAztFeJUnStIig1zqo5RNmy8qIjU+AjMsZPC+N7WPMU8hDJyi3Gxtl5SF+LvpShvkRZRU28sqjcl2M+bzxsZKScakFSdKyivNktqQvy9kRjDxosWYONFRVisAqsz7BdheuK/+xXxEEo/Tus5kxQTphhvkVZRU28sqjdl5sgoPm9kRG0JAharwAvrpevOvDhlIJ83GoGNFxWx/VgRqswWu5qqWgu2H1NCszraDSKmi5LWzLUJ9XxQwnlTSoh/CvH3xryxfV0wGs9FbQkCUjWlbIQoxNhiOg4bLyriyz20GJI3Nx+VeSTS8PqzcjhTRqugS9XJhcUq8PTXWZI6nj3Lj9oSBLimlOfBxouKkPK62Nh7ssztS0fXRoVCJ/F80ekadIy8RAbTZsdUnVxk5EovbwFAkAIbSWoNqQQBHaCoxqrU5UYO8tYObLyoiGujaJH9dVbh9viF3SdKJWtACNGgY+RlZB9a/58r06jdATVOiqpjOkZqfATevWtoCw9MsJ83Hk3pi/FxJjeNrCWlVdLBukryFDEdh40XFTEj2X6ztCtJz3Wv8cJuXOWQGBOGID8vSd2nu0652WPHdTqURmp8BH55aiweS7kKwb95vMou1eH1TUcx6mVl1AhqWG60n00HAM9MGqAYTxHTcdh4URFGLz2G9AwkaYWbS1+yG1c5GPQ63DdS2vB1d5PMEcRZMVXHOIeN2YV4Y9OvLervFJbXYK4CagS9tfkoabkxJIDbkGgJNl5UxqSBkSRdsL974wI++OW4pIbduK6D2knXnZ4wPXFWTNUxHcdiFVi0LrvVDh621xaty3abx66hInM+ScteXm3BxovKCCd6Kqg6ObhktmBTznlJ3YIJ/diN6yLU4AnbnENsJ6GgzulaRyoFWcC9HjuuyOy5sPGiMkyBtAuQqpODlzZkk3Q7T3Kwrquwpb7ayx5xpyfMYhX4at8ZkpYfQq5D6bFr5IrM/lyRWWuw8aIyKPUXAFr0vVzkF1c7Vcd0HFvqK9Ay3NX2f3emvmbmlaCkSnoGHRZg5IeQC6EaivlF7rmWyRWZk6PZy6sx2HhRGQa9Ds9MGiCpe/Jz97UJiArzd6qOcQ621FdTM+PXFOSLd+8aitT4CDeNjD6DvnlIJD+EXEhCdChMgdKBrmt2nnTL/SYhOlQyvi/Y3xvzxvZx0YgYVyGdP8kojs6+0sG4VeaGNgGjr+righE15emJcfhYogeTTce4ltT4CIyPMyHjePFv6fQCSTHhSHRzpePwTrRMkHEDusk8EuZKDHodpif0wuub7FfttsW9uLpi9sbsQslMoyVTuSKzFmHjRYV84UCbAHcYL35GA8bHdcXG7LaDdsfHdYWf0eDCUTE2NmYXYtG67MZAzLd+zEVogBF/uzkeEwe5x/tiJc7aqTrGeSg1U82WCWWPEH9vRRXTY5wHLxupkCpzPUm352Sp25aOVtxzLcbHdW31vfFxXbHinmtdPCIGANKyCjB39Z4WGSQlVWY8+MkeLCYGWzubdGJFaKqOcR5UrxhV5ywozRhLq+vcWruIkQ/2vKiQbp1pN4l6a0O/mJF9aeXhnYnFKnDfyBhcP6Abvs8+h0t1VkSH++PpiXHscXET9mp22Hhvax4G9wjGxEG0ekLO4iyxKSRVxzgR6vzHxfMkpWdCMfLCxosKuaZnCFbvOEXSph8vcrnxkpZVgOe+yUZhxeWbhinQF3cl9mLDxY1QZqoA8P/WZmFCfIRL4wRq6mhNR7u7uXmkJ1JURaurQ9U5i2BC7J8jOkZd8LKRCokMoWfpuHrRKC2rAA+s3tPEcAGAwooaPKCAUuKeDHUGWlLlWle7xSqQmU+r+ZNMbDLJOA+lFjj8LrvQqTpGXbDxokISokPRyYfmwQh04azDYhWY/6X9BmkLvjzo5uZ/nosjD5fCctctzzTUeJGuS9TJx4DEGPdmRXki9NpSrvW8HDhd5lQdoy7YeFEhBr0OI4kz0Api6WxnkHG8WDJtsbS6DhkcdOkWEqJD0dmXtlJMMSacRXMvXVvcOqwHp7y6gYbaUtJlDV5Yn+PSiUk5+d7G54wWYeNFpfTt2pmk0+lcd+E21A1xno5xLga9Dn8c1oOkDXVh5kgJsVdRDweWSxnnEhJglNS4sseRxSpwtoxm9I7ipUZNwsaLSqEWg3Jt0SiFpiUwjVxPrHnhyt5Yp0tppeVDCQ9QRh6Ultmz/WgRLMTbiDtqXTHyw8aLSkmMCZMsi+1v1Ls0RiAphjbDoeoY50OJX3Blg0aLVWDt/rMkrSmIM43cBb3HUZXMI2ngi720Qp3eBh3HSWkUNl5UikGvw5KpA+1qqs1WvJKW46IR0dagg/293V6K3pOxNWjUofUGjTq4tkEjN2RUBw09jqQNmP9kuqbHUVUtrVDngIjOHCelUdh4UTHj40yS3pf3tuZhwwHazLYjWKwC//f5fkndS1Pi+WbiZpTUoJEbMqoDW48jKQoral0S99KVWKhzYGSQzCNh3AUXqVMxmXklktk9APCXLw7IXnTskTV7UW2WLjQWEuDaEuJM69gaNGbmleBs2SXsO9VQZ+VM6SWY660werlmXkNdjuD+NO6nRzDtWJ0trQYgr3e1sx/t0UXVMeqDj6yKoc5aL9ZakHG8mJxe7SjmeivWH6AVn+NS3crBoNdhy5FzWPFzHq709L+4IQezR0djgQu6ftticArLa9oM43ZlDA7TNvuI9VL2nS7DLcN7yjoWLz2tzhVVx6gPXjZSMY4UHZMzPfnj9Hxy/pCrq3AybbN4Qzbe29rUcAEAq2hYbnRFk0ZbDA6gjBgcxh60Y3CyhJY91hGojkHXZlsyroSNFxWTEB2KAGKlXSHkC6I7QbxZ+RsNPINWCOZ6K1b8nGdXs+LnPJjrrbKPZXycCY+m9EWQX9P4LXfE4DBtExVGq7OzI69E1qBdi1VgzU7p3m4h/t6caaRh2HhRMQa9DtcP6ErSdvKVz33ak1g87IZ4E8+gFcLH6fktPC7NsYoGnZykZRVg1Mub8fqmoyj7LVst2M8bj6VchV+eGsuGi4K4OykKlJqXNXVWWatoZ+aVoLBCurDhvcnRfL/RMGy8qBxBdOVuPnxBtjFc1aUTSXfTwEjZxsA4BtVbRtW1h7SsAsxdvadFp+vyS3V4Y9Ov2MgN9RSF0UuPwd1p2TvbjhbJNg5q3FxUOFdk1jJsvKgcSoYPABw4XS6bK3fnSVpqJFXHyE/vUNqNvZpYT8NRLFaBReuyW42Vsr22aF02N/FUGAE+tByPr/edkW0MSu1yzbgWNl5UzrVRtBiS2nqrbPUXci9Qq2qyC1cp3J0URToamw6fl8WAyMwraeFxuRIB1/bKYWj4G2mPjLPlNbLFS9ky1No6f3XgDDVPgI0XlTMjmfYQAuRJU7ZYBXl9myP/lYPRS4+JA6Vrp5TJ1AWcuiTEqfXKIiGaXm5BrngpqQw1gDPUPAE2XlSO0UuPSYNoQY1yuFEz80pQSiiU18nHiyP/FUZ0OC1Wydlp9harwKeEbBEACOeihopiRnIUWStnvBRnqDGyGS8lJSW48847ERgYiODgYMyaNQsXL14kfVYIgRtuuAE6nQ5ff/21XEPUDP+Ydg38jW1nE8npRt1EnEHfNrwHz4QUh3u6gGccL0YVMVaLVxqVhdFLjyTifYQaV+UonKHGADIaL3feeScOHTqEjRs34ttvv8XWrVsxZ84c0mffeOMN6Cg5eQyABjfqa7cNbvN9AXncqBarwFfEwDwu76483NUF3BFPTtFF6ZRYxrV8NGuEpEaHhrgqZ8MZaowNWYyXnJwcpKWl4f3338eIESMwatQovPnmm1izZg3OnrXfJHDfvn34+9//jg8//FCOoWmW1PgILLtraKseGJ0O2Huy1OnfyR2B1U1ibJhkY89gPy+ndwG3CnogJ2eMKA+jlx73j4m2qxEANh8+59Tv5Qw15kpkMV7S09MRHByM4cOHN76WkpICvV6PHTt2tPm56upq3HHHHXj77bdhMtFm6rW1taioqGjy56nsPVnaauq0kKnce2H5JZJu8mB5m0Iy7cOg12HJ1IH2RTqd02ezIf60OJZAXy82ehXKX1IHIMDOUjXgfEOCM9SYK5HFeCksLETXrk0rv3p5eSE0NBSFhW3fCB977DEkJyfj5ptvJn/X4sWLERQU1PjXs6e8DcGUCqXc+3tbnVvu/dOdJ0m67sFcLEqp2Dx2QW103y2vrsPc1XuQlkVrvEnhLNHovWUYx0kplcy8Esm4JWcbEtT4Os5Q8wwcMl7mz58PnU5n9+/w4cPtGsg333yDzZs344033nDocwsWLEB5eXnj36lTtCwGrUEp9w4AMz7IdMr3meutyMijLUWVXTI75TsZeRgfZ4Kfd+vGi7Pd8RarwNp99peObVzPcVKKpbCCZiAs35rrlO9zJL6Olxo9A1q5xN944okncO+999rVxMTEwGQy4fz5801er6+vR0lJSZvLQZs3b0Zubi6Cg4ObvH7LLbdg9OjR2LJlS6uf8/HxgY8Pp1NS0xLT84phrrfCSG3L2gYfbbfv5bkSnjsrm4ZeMTR3fEdr9TTESUkbs6EB3rxkpGBKiIHUPx654JT7DcfXMc1xyHjp0qULunTpIqlLSkpCWVkZdu/ejWHDhgFoME6sVitGjGg9Un3+/Pn405/+1OS1gQMH4vXXX8fkyZMdGaZH4kha4oIv9+Pvt13Toe/b4YA72NnZKoxzobrZneGOp27jD0O685KRggkNMJK1H23Px+wxMR36Pup5c/OQSD5vPARZYl4GDBiA1NRUzJ49G5mZmdi2bRvmzZuHadOmITKyoTnfmTNn0L9/f2RmNixjmEwmxMfHN/kDgF69eiE62n5kO+NYWuK3+ws6vARQU0er02HQ65yercI4F1f2iqFuI4WXjBSNKciPrM3M63iRw/wiWgsSLsngOchW5+Xf//43+vfvj3HjxmHixIkYNWoUli9f3vh+XV0djhw5gupq+aowehJGLz26B9EeDLUW0eFAukHdg0m6lP5deSakcGy9YqQorep4zZWE6FB069z2Mi/3pVEHCdGh8CZe19TmsW1hsQr8J1M6OYDPG89CNuMlNDQUn3zyCSorK1FeXo4PP/wQnTpdLkceFRUFIQR+//vft7kNIQSmTJki1xA1x03XRJK1HV0CGNmHthR0jwyFqhjnYtDr8MykOEndC+tzOuyxeyUtB+cr7RtB3JdG+Rj0OoztLx1CAACDegR16LsaYrKkDedp1/bi88aD4N5GGmJULO1mAgD5RR3zeJUTMoiC/b15yUglhBBiGDqa+rp4Qzbe25pnt9nAnDHRXN5dJdyTTFvOP1VCS41vi+8P0dL0e4XSl7IY9cPGi4ZIjA1DsJ/9iqk2Vm3Pa/cs2mIVeGF9jqTupSkDeSakEqieuPYWrDPXW7F8q3SG2tp9Z7lCqkpIjAlDoK/9QnUAsP5gQbvrS1msAp/voaVIU7LYGO3AxouGMOh1WHKLRMXU3yitrkPG8fYF0klVurRBmc0zyoAaSNte4+Kj7fmk9o6FFbVcIVUlGPQ6Ui0eAeDpLw+06zsy80pQWVNP0oZ24pIZngQbLxojNT4CE+K6kbTbjha16zvmfLyLpONKl+ohIToUoQHSXrviKnO7jAtHMk74vFEP/j60ahvrDrQvw9GRc8EUyMXpPAk2XjRIVS1tpnLgdJnD2/5i1ynyTIgrXaoHg16HPwzpTtK2x7g4XEjvOcbnjXqg1peqrbe2y9NLbQnARQ09DzZeNIifRMM0R3U2LFaBJ7+guX+99eCbicqg1lZx1LiwWAUKymgGj9Gg4/NGRThSX+rj9BMObdtcb8W6AzTj5W83x3N8nYfBxosGod78A30dKrCMjNxiUv8kAPDxNvDNRGXY6r3YO2rtqaWRcbwY9cTzZvJgrpCqJoxeevTpEkDSbvn1vENLRyu30VqQjIgOwcRB9DIRjDZg40WDzEiOJvUT2phzzqGbyb935JO14Rw8pzoMeh0WTm6o99LW+XOpzuJwxtHqDPqMe/HUQQ5tm3E/119Ni7GrqbM6FC/1zo9HSbp6S/symRh1w8aLBjF66TFpoHStjIoaCzJyaevQFqvA99nnyGN4JnUAWcsoh9T4CLx711AE+bcevFtWXYe5q/cgLYtWe8NiFdj66wWSdmjPoA438GNcz0gH6ktR46UumS0or6FV5q0k6hhtwXcKjRIVTgukW030pmw/WgRHSjX8njgbY5TH+DgTfO0YEQLAonXZJK9dZl4Jqojl4Z+4vj91iIyCSIwNQ4APLX6OWhzzpQ3Z5O8fEBFI1jLagY0XzUKLG/hf1jnSLPqLPafJ3zwzuTfHLagYSjl2arVd6kzb32jgaswqxaDX4dVbaMt91OKYuecvkr//j8N6kLWMdmDjRaMkOfAgeO6bQ5I3lINnysnbu/5qLu+uZs6W0mbH7/10TFJD7QZ8/5gYNnhVzMRBkZg0UDpbjVocs+girQmoDkAysc8aoy3YeNEoiTF0V65UVVOLVSC/mPYQ8uZUV9Wzj1j/Z8uvRXbLvlusAh8SMkZC/L0xb2xf6vAYhdI7jJZ19HF6vqQmjBjwf1XXADZ6PRQ2XjSKQa/DtOE9yXp77v2M3GJYiElJY/uF881E9dCP3wI7Zd/f2nwU5ZekCxremxzN54wGOFtGa8C45dcLkp7ePl07kbaVEMNLjZ4KGy8ahlp0DLAfSJd+nN5G4J6kGLKWUSZRYbRgbwD49kDrvY4sVoE3N0svKwH04HJG2XQPpnV1pqRMPz0xjrQtqo7RHmy8aJiE6FByv4/XN/3aZuBu7gXakpGPl46DLjWAI1VTa+tFqw+iX45cQD2xhhC3A9AGjsSeSGUT+RkNGB/X1a5mfFxXh6uEM9qBjRcNY9Dr8NxN9JnJE5/tbzGLTssqwP+yaEXJ5v4ult3/GsDopcekeLrXrrUlx8VpOaTPcoyUdkiMCUMnYqPGg2cq8OL61g2YS2YLnvn6IGrqrOge3LphOz6uK1bcc227x8qoHzZeNE5qfASW3TUUAYQZSpXZgu3HLi8RWawC8z7ZS/qeAB8DHh53VbvHySiLf94xFFQ79PtDTT12FqvA4XO0VNeIIF82eDWCQa/DK8SUaQBY8XNei4Dv2f/aiQHPpuHjjJP4+WgRzvzWE6tHsC9G9w3H3Ym9kPN8KhsuDBsvnkBqfAR+34/m0v1s18nGfz/8yR6y6//vtw7mh5CGMOh1eHhsH5J2/cFzTR5Cf/7PHvL3jOlLr87KKJ+JgyLIvY4A4OkrAr5n/2snNmafb1V3uqwGvt56vDBlIC8VMQDYePEY9p+qIOl+PNxw8zDXW7GBuFx0be9gpMZzbRetEdOFlvEBXE5/Nddbsf4gvffRXydxwKXWcOS8+XLPGVisApfMljYNFxsbs8/jErFaM6N92HjxELwMNK/IRbMVf/ooE6Nf2UzetnAgtZZRD44E0n6z7zQsVoG7P8ggfyY6zJ9n0Rrk2ih6DJMVwLx/78K4v28h6duKk2E8DzZePIRkB7KANuVcwDmJ8vBX0iOEliLJqIuE6FB09qUFYO4/U4nYpzdgR14peft/mzKwvUNjFMyM5CiH9P87dB5ny2ltJPYTCygy2oeNFw/hmRuvlm3btwzl3iJaxKDX4aU/yGNg+HrrOa1eoxi99EiKlufYBvq23u2c8TzYePEQKHUT2oPRwL1FtMzkwZGI7+78rr1LbxnEAd4a5qNZCbJsd85oLoLJNMDGiwex4p5rEURcBqDyxu1D+SGkcf7q5Cqmw3oH48Yh3Z26TUZZGL30+NPIaKdu06ADRl3F2WlMA2y8eBgPXhfrtG3NHh2FiYM4y0jrJESHIiLIOVVwfbx0+Oz+ZKdsi1E2/29yHEL9nbfM84/bh/BEiWmEjRcPY+ZI57hdU6/uir9Oki+OhlEOBr0OCyc7x/vy+m38APIkts0f55TtDOvF3jqmKWy8eBhGLz1mjerdoW3oAbx953DnDIhRBanxEXjnjmug64DdkRgdgomDIp03KEbx+BkNSBnQsVg7o0GHzx5gbx3TFDZePJBnbozHoA4EYb51xzU8e/ZAJg6KxD+nXdOuz+oA/GtWonMHxKiC92dci0E92n+/+ed0vt8wLWHjxUP55uHRGNff8RnR/WOiefbswUweHIn7xzgeiDlnTDSMXny78VS+mTca/7xtsEOf8dLrsOyuoVy9m2kVnRCC1rxGJVRUVCAoKAjl5eUIDHR+iqfW+HbfGfzf5/tRU2//NOhk9MIrfxzEAboMAGDDgbN47LP9qG3WWK85OjQYLgucnLHEqBOLVSDltR+RV3TJru7h62Lx6Ph+7HHxMBx5frPxwsBiFcjILUb68SJYhEB5dR3OV9Sgpt6Kgd2DMapPOBJjw/hGwjTBYhXYfrQIn+06gb2nylFltkAPAaOXHpFBfpgQH4F7R7LHhWnJxZp6PLJmD/adKsPFmnr4eOnQMzQA/ze+H8b078r3Gg+FjRc2XhiGYRhGVTjy/OYpEcMwDMMwqoKNF4ZhGIZhVAUbLwzDMAzDqAo2XhiGYRiGURVsvDAMwzAMoyrYeGEYhmEYRlWw8cIwDMMwjKpg44VhGIZhGFXBxgvDMAzDMKrCy90DcDa2gsEVFRVuHgnDMAzDMFRsz21K4X/NGS+VlZUAgJ49e7p5JAzDMAzDOEplZSWCgoLsajTX28hqteLs2bPo3LkzdDrnNfeqqKhAz549cerUKc32TNL6Pmp9/wDt76PW9w/Q/j7y/qkfufZRCIHKykpERkZCr7cf1aI5z4ter0ePHj1k235gYKBmT0gbWt9Hre8foP191Pr+AdrfR94/9SPHPkp5XGxwwC7DMAzDMKqCjReGYRiGYVQFGy9EfHx8sHDhQvj4+Lh7KLKh9X3U+v4B2t9Hre8foP195P1TP0rYR80F7DIMwzAMo23Y88IwDMMwjKpg44VhGIZhGFXBxgvDMAzDMKqCjReGYRiGYVQFGy8MwzAMw6gKNl6u4MUXX0RycjL8/f0RHBzcqubkyZOYNGkS/P390bVrVzz55JOor6+3u92SkhLceeedCAwMRHBwMGbNmoWLFy/KsAeOsWXLFuh0ulb/du7c2ebnfv/737fQP/DAAy4cOZ2oqKgWY12yZIndz9TU1OChhx5CWFgYOnXqhFtuuQXnzp1z0Yjp5OfnY9asWYiOjoafnx9iY2OxcOFCmM1mu59T+vF7++23ERUVBV9fX4wYMQKZmZl29f/973/Rv39/+Pr6YuDAgdiwYYOLRuo4ixcvxrXXXovOnTuja9eumDJlCo4cOWL3M6tWrWpxvHx9fV00Ysd47rnnWoy1f//+dj+jpuMHtH5P0el0eOihh1rVK/34bd26FZMnT0ZkZCR0Oh2+/vrrJu8LIfDss88iIiICfn5+SElJwdGjRyW36+h17ChsvFyB2WzGrbfeirlz57b6vsViwaRJk2A2m7F9+3Z89NFHWLVqFZ599lm7273zzjtx6NAhbNy4Ed9++y22bt2KOXPmyLELDpGcnIyCgoImf3/6058QHR2N4cOH2/3s7Nmzm3zulVdecdGoHef5559vMtaHH37Yrv6xxx7DunXr8N///hc//fQTzp49i6lTp7potHQOHz4Mq9WK9957D4cOHcLrr7+OZcuW4emnn5b8rFKP36efforHH38cCxcuxJ49ezB48GBMmDAB58+fb1W/fft2TJ8+HbNmzcLevXsxZcoUTJkyBVlZWS4eOY2ffvoJDz30EDIyMrBx40bU1dXh+uuvR1VVld3PBQYGNjleJ06ccNGIHefqq69uMtZffvmlTa3ajh8A7Ny5s8n+bdy4EQBw6623tvkZJR+/qqoqDB48GG+//Xar77/yyiv45z//iWXLlmHHjh0ICAjAhAkTUFNT0+Y2Hb2O24VgWrBy5UoRFBTU4vUNGzYIvV4vCgsLG1979913RWBgoKitrW11W9nZ2QKA2LlzZ+Nr//vf/4ROpxNnzpxx+tg7gtlsFl26dBHPP/+8Xd3vfvc78cgjj7hmUB2kd+/e4vXXXyfry8rKhLe3t/jvf//b+FpOTo4AINLT02UYoXN55ZVXRHR0tF2Nko9fQkKCeOihhxr/b7FYRGRkpFi8eHGr+ttuu01MmjSpyWsjRowQ999/v6zjdBbnz58XAMRPP/3Upqat+5ESWbhwoRg8eDBZr/bjJ4QQjzzyiIiNjRVWq7XV99V0/ACIr776qvH/VqtVmEwm8eqrrza+VlZWJnx8fMR//vOfNrfj6HXcHtjz4gDp6ekYOHAgunXr1vjahAkTUFFRgUOHDrX5meDg4CaejJSUFOj1euzYsUP2MTvCN998g+LiYsycOVNS++9//xvh4eGIj4/HggULUF1d7YIRto8lS5YgLCwM11xzDV599VW7y3y7d+9GXV0dUlJSGl/r378/evXqhfT0dFcMt0OUl5cjNDRUUqfE42c2m7F79+4mv71er0dKSkqbv316enoTPdBwTarhWAENxwuA5DG7ePEievfujZ49e+Lmm29u836jBI4ePYrIyEjExMTgzjvvxMmTJ9vUqv34mc1mrF69Gvfddx90Ol2bOjUdvyvJy8tDYWFhk2MUFBSEESNGtHmM2nMdtwfNdZWWk8LCwiaGC4DG/xcWFrb5ma5duzZ5zcvLC6GhoW1+xl188MEHmDBhgmRX7jvuuAO9e/dGZGQkDhw4gKeeegpHjhzBl19+6aKR0vnzn/+MoUOHIjQ0FNu3b8eCBQtQUFCA1157rVV9YWEhjEZji5inbt26Ke54NefYsWN48803sXTpUrs6pR6/oqIiWCyWVq+xw4cPt/qZtq5JpR8rALBarXj00UcxcuRIxMfHt6nr168fPvzwQwwaNAjl5eVYunQpkpOTcejQIclr1dWMGDECq1atQr9+/VBQUIBFixZh9OjRyMrKQufOnVvo1Xz8AODrr79GWVkZ7r333jY1ajp+zbEdB0eOUXuu4/ageeNl/vz5ePnll+1qcnJyJIPK1ER79vn06dP47rvv8Nlnn0lu/8p4nYEDByIiIgLjxo1Dbm4uYmNj2z9wIo7s3+OPP9742qBBg2A0GnH//fdj8eLFiu090p7jd+bMGaSmpuLWW2/F7Nmz7X7W3cePaeChhx5CVlaW3ZgQAEhKSkJSUlLj/5OTkzFgwAC89957eOGFF+QepkPccMMNjf8eNGgQRowYgd69e+Ozzz7DrFmz3Dgyefjggw9www03IDIysk2Nmo6fmtC88fLEE0/YtYoBICYmhrQtk8nUImLaloViMpna/EzzIKX6+nqUlJS0+ZmO0p59XrlyJcLCwnDTTTc5/H0jRowA0DDzd8XDryPHdMSIEaivr0d+fj769evX4n2TyQSz2YyysrIm3pdz587Jdrya4+j+nT17Ftdddx2Sk5OxfPlyh7/P1cevLcLDw2EwGFpkdtn77U0mk0N6pTBv3rzG4H1HZ9/e3t645pprcOzYMZlG5zyCg4Nx1VVXtTlWtR4/ADhx4gQ2bdrksMdSTcfPdhzOnTuHiIiIxtfPnTuHIUOGtPqZ9lzH7cJp0TMaQipg99y5c42vvffeeyIwMFDU1NS0ui1bwO6uXbsaX/vuu+8UFbBrtVpFdHS0eOKJJ9r1+V9++UUAEPv373fyyJzP6tWrhV6vFyUlJa2+bwvY/fzzzxtfO3z4sGIDdk+fPi369u0rpk2bJurr69u1DSUdv4SEBDFv3rzG/1ssFtG9e3e7Abs33nhjk9eSkpIUG/BptVrFQw89JCIjI8Wvv/7arm3U19eLfv36iccee8zJo3M+lZWVIiQkRPzjH/9o9X21Hb8rWbhwoTCZTKKurs6hzyn5+KGNgN2lS5c2vlZeXk4K2HXkOm7XWJ22JQ1w4sQJsXfvXrFo0SLRqVMnsXfvXrF3715RWVkphGg46eLj48X1118v9u3bJ9LS0kSXLl3EggULGrexY8cO0a9fP3H69OnG11JTU8U111wjduzYIX755RfRt29fMX36dJfvX1ts2rRJABA5OTkt3jt9+rTo16+f2LFjhxBCiGPHjonnn39e7Nq1S+Tl5Ym1a9eKmJgYMWbMGFcPW5Lt27eL119/Xezbt0/k5uaK1atXiy5duoh77rmnUdN8/4QQ4oEHHhC9evUSmzdvFrt27RJJSUkiKSnJHbtgl9OnT4s+ffqIcePGidOnT4uCgoLGvys1ajp+a9asET4+PmLVqlUiOztbzJkzRwQHBzdm+N19991i/vz5jfpt27YJLy8vsXTpUpGTkyMWLlwovL29xcGDB921C3aZO3euCAoKElu2bGlyvKqrqxs1zfdx0aJF4rvvvhO5ubli9+7dYtq0acLX11ccOnTIHbtglyeeeEJs2bJF5OXliW3btomUlBQRHh4uzp8/L4RQ//GzYbFYRK9evcRTTz3V4j21Hb/KysrGZx0A8dprr4m9e/eKEydOCCGEWLJkiQgODhZr164VBw4cEDfffLOIjo4Wly5datzG2LFjxZtvvtn4f6nr2Bmw8XIFM2bMEABa/P3444+Nmvz8fHHDDTcIPz8/ER4eLp544okmlvePP/4oAIi8vLzG14qLi8X06dNFp06dRGBgoJg5c2ajQaQEpk+fLpKTk1t9Ly8vr8lvcPLkSTFmzBgRGhoqfHx8RJ8+fcSTTz4pysvLXThiGrt37xYjRowQQUFBwtfXVwwYMEC89NJLTbxkzfdPCCEuXbokHnzwQRESEiL8/f3FH/7whyYGgVJYuXJlq+frlQ5VNR6/N998U/Tq1UsYjUaRkJAgMjIyGt/73e9+J2bMmNFE/9lnn4mrrrpKGI1GcfXVV4v169e7eMR02jpeK1eubNQ038dHH3208ffo1q2bmDhxotizZ4/rB0/g9ttvFxEREcJoNIru3buL22+/XRw7dqzxfbUfPxvfffedACCOHDnS4j21HT/bM6v5n20frFareOaZZ0S3bt2Ej4+PGDduXIv97t27t1i4cGGT1+xdx85AJ4QQzluEYhiGYRiGkReu88IwDMMwjKpg44VhGIZhGFXBxgvDMAzDMKqCjReGYRiGYVQFGy8MwzAMw6gKNl4YhmEYhlEVbLwwDMMwDKMq2HhhGIZhGEZVsPHCMAzDMIyqYOOFYRiGYRhVwcYLwzAMwzCq4v8DptxouoVK1AYAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2544,7 +3893,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2556,18 +3905,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.4%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-9.9, -9.85)\": -0.899, \"(-9.85, -9.8)\": -0.922, \"(-9.8, -9.78)\": -0.93, \"(-9.78, -9.73)\": -0.942, \"(-9.73, -9.7)\": -0.956, \"(-9.7, -9.67)\": -0.964, \"(-9.67, -9.61)\": -0.975, \"(-9.61, -9.54)\": -0.986, \"(-9.54, -9.25)\": -0.994, \"(-9.25, -9.21)\": -0.985, \"(-9.21, -9.15)\": -0.974, \"(-9.15, -9.12)\": -0.96, \"(-9.12, -9.07)\": -0.949, \"(-9.07, -9.02)\": -0.928, \"(-9.02, -9.0)\": -0.917, \"(-9.0, -8.97)\": -0.908, \"(-8.97, -8.95)\": -0.897, \"(-8.95, -8.93)\": -0.887, \"(-8.93, -8.91)\": -0.878, \"(-8.91, -8.89)\": -0.87, \"(-8.89, -8.88)\": -0.859, \"(-8.88, -8.85)\": -0.849, \"(-8.85, -8.83)\": -0.835, \"(-8.83, -8.8)\": -0.825, \"(-8.8, -8.77)\": -0.803, \"(-8.77, -8.74)\": -0.785, \"(-8.74, -8.71)\": -0.763, \"(-8.71, -8.67)\": -0.747, \"(-8.67, -8.64)\": -0.72, \"(-8.64, -8.61)\": -0.701, \"(-8.61, -8.59)\": -0.683, \"(-8.59, -8.57)\": -0.664, \"(-8.57, -8.55)\": -0.652, \"(-8.55, -8.54)\": -0.637, \"(-8.54, -8.52)\": -0.627, \"(-8.52, -8.5)\": -0.608, \"(-8.5, -8.47)\": -0.597, \"(-8.47, -8.4)\": -0.554, \"(-8.4, -8.3)\": -0.474, \"(-8.3, -8.25)\": -0.397, \"(-8.25, -8.23)\": -0.378, \"(-8.23, -8.18)\": -0.346, \"(-8.18, -8.12)\": -0.288, \"(-8.12, -8.08)\": -0.248, \"(-8.08, -8.07)\": -0.216, \"(-8.07, -8.06)\": -0.205, \"(-8.06, -8.04)\": -0.196, \"(-8.04, -8.01)\": -0.161, \"(-8.01, -7.98)\": -0.142, \"(-7.98, -7.91)\": -0.107, \"(-7.91, -7.86)\": -0.019, \"(-7.86, -7.85)\": -0.007, \"(-7.85, -7.83)\": 0.008, \"(-7.83, -7.72)\": 0.065, \"(-7.72, -7.61)\": 0.207, \"(-7.61, -7.6)\": 0.246, \"(-7.6, -7.58)\": 0.255, \"(-7.58, -7.56)\": 0.286, \"(-7.56, -7.54)\": 0.298, \"(-7.54, -7.49)\": 0.337, \"(-7.49, -7.46)\": 0.377, \"(-7.46, -7.44)\": 0.392, \"(-7.44, -7.41)\": 0.408, \"(-7.41, -7.36)\": 0.45, \"(-7.36, -7.31)\": 0.509, \"(-7.31, -7.29)\": 0.521, \"(-7.29, -7.27)\": 0.542, \"(-7.27, -7.25)\": 0.565, \"(-7.25, -7.23)\": 0.577, \"(-7.23, -7.19)\": 0.597, \"(-7.19, -7.16)\": 0.629, \"(-7.16, -7.12)\": 0.65, \"(-7.12, -7.08)\": 0.692, \"(-7.08, -7.06)\": 0.702, \"(-7.06, -7.02)\": 0.728, \"(-7.02, -6.97)\": 0.753, \"(-6.97, -6.94)\": 0.782, \"(-6.94, -6.93)\": 0.792, \"(-6.93, -6.91)\": 0.804, \"(-6.91, -6.89)\": 0.814, \"(-6.89, -6.84)\": 0.832, \"(-6.84, -6.82)\": 0.856, \"(-6.82, -6.78)\": 0.867, \"(-6.78, -6.75)\": 0.886, \"(-6.75, -6.73)\": 0.895, \"(-6.73, -6.68)\": 0.907, \"(-6.68, -6.65)\": 0.927, \"(-6.65, -6.6)\": 0.936, \"(-6.6, -6.56)\": 0.955, \"(-6.56, -6.51)\": 0.963, \"(-6.51, -6.46)\": 0.978, \"(-6.46, -6.38)\": 0.987, \"(-6.38, -6.09)\": 0.996, \"(-6.09, -6.03)\": 0.975, \"(-6.03, -5.98)\": 0.966, \"(-5.98, -5.95)\": 0.953, \"(-5.95, -5.93)\": 0.944, \"(-5.93, -5.85)\": 0.931, \"(-5.85, -5.79)\": 0.89, \"(-5.79, -5.77)\": 0.876, \"(-5.77, -5.74)\": 0.867, \"(-5.74, -5.71)\": 0.846, \"(-5.71, -5.68)\": 0.836, \"(-5.68, -5.64)\": 0.81, \"(-5.64, -5.61)\": 0.798, \"(-5.61, -5.59)\": 0.773, \"(-5.59, -5.56)\": 0.763, \"(-5.56, -5.55)\": 0.748, \"(-5.55, -5.52)\": 0.739, \"(-5.52, -5.49)\": 0.713, \"(-5.49, -5.46)\": 0.692, \"(-5.46, -5.44)\": 0.67, \"(-5.44, -5.42)\": 0.654, \"(-5.42, -5.4)\": 0.644, \"(-5.4, -5.39)\": 0.635, \"(-5.39, -5.37)\": 0.623, \"(-5.37, -5.35)\": 0.6, \"(-5.35, -5.34)\": 0.592, \"(-5.34, -5.31)\": 0.578, \"(-5.31, -5.28)\": 0.56, \"(-5.28, -5.25)\": 0.516, \"(-5.25, -5.23)\": 0.502, \"(-5.23, -5.22)\": 0.494, \"(-5.22, -5.2)\": 0.481, \"(-5.2, -5.18)\": 0.461, \"(-5.18, -5.17)\": 0.448, \"(-5.17, -5.15)\": 0.435, \"(-5.15, -5.14)\": 0.423, \"(-5.14, -5.13)\": 0.41, \"(-5.13, -5.1)\": 0.386, \"(-5.1, -5.06)\": 0.356, \"(-5.06, -5.04)\": 0.331, \"(-5.04, -5.01)\": 0.307, \"(-5.01, -5.0)\": 0.287, \"(-5.0, -4.95)\": 0.271, \"(-4.95, -4.87)\": 0.194, \"(-4.87, -4.83)\": 0.129, \"(-4.83, -4.82)\": 0.113, \"(-4.82, -4.8)\": 0.1, \"(-4.8, -4.78)\": 0.089, \"(-4.78, -4.73)\": 0.048, \"(-4.73, -4.68)\": -0.018, \"(-4.68, -4.67)\": -0.035, \"(-4.67, -4.62)\": -0.057, \"(-4.62, -4.58)\": -0.115, \"(-4.58, -4.56)\": -0.143, \"(-4.56, 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\"(5.59, 5.66)\": 0.789, \"(5.66, 5.7)\": 0.825, \"(5.7, 5.72)\": 0.837, \"(5.72, 5.74)\": 0.848, \"(5.74, 5.77)\": 0.861, \"(5.77, 5.8)\": 0.879, \"(5.8, 5.83)\": 0.893, \"(5.83, 5.86)\": 0.903, \"(5.86, 5.87)\": 0.912, \"(5.87, 5.9)\": 0.921, \"(5.9, 5.92)\": 0.933, \"(5.92, 5.96)\": 0.941, \"(5.96, 6.0)\": 0.954, \"(6.0, 6.04)\": 0.963, \"(6.04, 6.09)\": 0.974, \"(6.09, 6.14)\": 0.983, \"(6.14, 6.48)\": 0.991, \"(6.48, 6.53)\": 0.976, \"(6.53, 6.56)\": 0.965, \"(6.56, 6.59)\": 0.957, \"(6.59, 6.65)\": 0.947, \"(6.65, 6.71)\": 0.926, \"(6.71, 6.75)\": 0.898, \"(6.75, 6.79)\": 0.89, \"(6.79, 6.84)\": 0.862, \"(6.84, 6.88)\": 0.839, \"(6.88, 6.89)\": 0.828, \"(6.89, 6.91)\": 0.813, \"(6.91, 6.93)\": 0.805, \"(6.93, 6.96)\": 0.796, \"(6.96, 6.99)\": 0.773, \"(6.99, 7.0)\": 0.759, \"(7.0, 7.03)\": 0.745, \"(7.03, 7.06)\": 0.722, \"(7.06, 7.07)\": 0.714, \"(7.07, 7.09)\": 0.7, \"(7.09, 7.11)\": 0.688, \"(7.11, 7.13)\": 0.675, \"(7.13, 7.15)\": 0.651, \"(7.15, 7.17)\": 0.639, \"(7.17, 7.19)\": 0.625, \"(7.19, 7.22)\": 0.604, \"(7.22, 7.24)\": 0.58, \"(7.24, 7.25)\": 0.571, \"(7.25, 7.27)\": 0.56, \"(7.27, 7.28)\": 0.548, \"(7.28, 7.29)\": 0.539, \"(7.29, 7.32)\": 0.52, \"(7.32, 7.33)\": 0.507, \"(7.33, 7.36)\": 0.496, \"(7.36, 7.4)\": 0.446, \"(7.4, 7.41)\": 0.438, \"(7.41, 7.43)\": 0.415, \"(7.43, 7.45)\": 0.403, \"(7.45, 7.51)\": 0.373, \"(7.51, 7.56)\": 0.307, \"(7.56, 7.59)\": 0.283, \"(7.59, 7.63)\": 0.235, \"(7.63, 7.67)\": 0.197, \"(7.67, 7.69)\": 0.176, \"(7.69, 7.71)\": 0.157, \"(7.71, 7.76)\": 0.123, \"(7.76, 7.79)\": 0.077, \"(7.79, 7.79)\": 0.065, \"(7.79, 7.81)\": 0.052, \"(7.81, 7.83)\": 0.028, \"(7.83, 7.86)\": 0.015, \"(7.86, 7.89)\": -0.029, \"(7.89, 7.9)\": -0.038, \"(7.9, 7.92)\": -0.052, \"(7.92, 7.93)\": -0.068, \"(7.93, 7.95)\": -0.089, \"(7.95, 7.97)\": -0.097, \"(7.97, 8.0)\": -0.135, \"(8.0, 8.02)\": -0.153, \"(8.02, 8.04)\": -0.184, \"(8.04, 8.06)\": -0.195, \"(8.06, 8.08)\": -0.216, \"(8.08, 8.12)\": -0.235, \"(8.12, 8.16)\": -0.282, \"(8.16, 8.17)\": -0.304, \"(8.17, 8.21)\": -0.323, \"(8.21, 8.24)\": -0.368, \"(8.24, 8.26)\": -0.381, \"(8.26, 8.3)\": -0.411, \"(8.3, 8.34)\": -0.454, \"(8.34, 8.36)\": -0.469, \"(8.36, 8.37)\": -0.49, \"(8.37, 8.41)\": -0.5, \"(8.41, 8.45)\": -0.552, \"(8.45, 8.47)\": -0.568, \"(8.47, 8.49)\": -0.584, \"(8.49, 8.51)\": -0.596, \"(8.51, 8.53)\": -0.621, \"(8.53, 8.56)\": -0.635, \"(8.56, 8.6)\": -0.664, \"(8.6, 8.63)\": -0.689, \"(8.63, 8.65)\": -0.704, \"(8.65, 8.66)\": -0.715, \"(8.66, 8.68)\": -0.723, \"(8.68, 8.72)\": -0.747, \"(8.72, 8.76)\": -0.77, \"(8.76, 8.79)\": -0.795, \"(8.79, 8.83)\": -0.815, \"(8.83, 8.87)\": -0.836, \"(8.87, 8.89)\": -0.853, \"(8.89, 8.91)\": -0.861, \"(8.91, 8.94)\": -0.876, \"(8.94, 8.99)\": -0.897, \"(8.99, 9.01)\": -0.91, \"(9.01, 9.07)\": -0.925, \"(9.07, 9.14)\": -0.95, \"(9.14, 9.18)\": -0.962, \"(9.18, 9.21)\": -0.972, \"(9.21, 9.31)\": -0.982, \"(9.31, 9.61)\": -0.992, \"(9.61, 9.65)\": -0.98, \"(9.65, 9.7)\": -0.968, \"(9.7, 9.75)\": -0.957, \"(9.75, 9.78)\": -0.943, \"(9.78, 9.82)\": -0.933, \"(9.82, 9.86)\": -0.916, \"(9.86, 9.9)\": -0.901, \"(9.9, 9.92)\": -0.889, \"(9.92, 9.94)\": -0.877, \"(9.94, 9.97)\": -0.866, \"(9.97, 9.99)\": -0.852}\n",
+      "Means: {\"(-9.98, -9.93)\": 0.4132, \"(-9.93, -9.89)\": 0.3973, \"(-9.89, -9.85)\": 0.3867, \"(-9.85, -9.82)\": 0.374, \"(-9.82, -9.76)\": 0.3576, \"(-9.76, -9.72)\": 0.3444, \"(-9.72, -9.65)\": 0.3244, \"(-9.65, -9.59)\": 0.2878, \"(-9.59, -9.53)\": 0.2758, \"(-9.53, -9.47)\": 0.2435, \"(-9.47, -9.42)\": 0.2197, \"(-9.42, -9.39)\": 0.2017, \"(-9.39, -9.36)\": 0.1904, \"(-9.36, -9.34)\": 0.1776, \"(-9.34, -9.32)\": 0.1635, \"(-9.32, -9.28)\": 0.1523, \"(-9.28, -9.25)\": 0.1344, \"(-9.25, -9.21)\": 0.1201, \"(-9.21, -9.18)\": 0.1011, \"(-9.18, -9.14)\": 0.0842, \"(-9.14, -9.12)\": 0.0721, \"(-9.12, -9.1)\": 0.06, \"(-9.1, -9.07)\": 0.0456, \"(-9.07, -9.03)\": 0.033, \"(-9.03, -9.01)\": 0.0126, \"(-9.01, -8.98)\": 0.0021, \"(-8.98, -8.96)\": -0.0157, \"(-8.96, -8.91)\": -0.0275, \"(-8.91, -8.84)\": -0.0682, \"(-8.84, -8.8)\": -0.0885, \"(-8.8, -8.78)\": -0.0992, \"(-8.78, -8.74)\": -0.1148, \"(-8.74, -8.71)\": -0.1303, \"(-8.71, -8.69)\": -0.1408, \"(-8.69, -8.67)\": -0.1515, \"(-8.67, -8.64)\": -0.1629, \"(-8.64, -8.62)\": -0.1772, \"(-8.62, -8.59)\": -0.1885, \"(-8.59, -8.54)\": -0.2108, \"(-8.54, -8.5)\": -0.2235, \"(-8.5, -8.45)\": -0.249, \"(-8.45, -8.4)\": -0.2642, \"(-8.4, -8.36)\": -0.2885, \"(-8.36, -8.31)\": -0.3003, \"(-8.31, -8.27)\": -0.3208, \"(-8.27, -8.24)\": -0.3331, \"(-8.24, -8.21)\": -0.346, \"(-8.21, -8.16)\": -0.3603, \"(-8.16, -8.13)\": -0.3734, \"(-8.13, -8.09)\": -0.3842, \"(-8.09, -8.01)\": -0.4048, \"(-8.01, -7.98)\": -0.417, \"(-7.98, -7.91)\": -0.4299, \"(-7.91, -7.85)\": -0.4479, \"(-7.85, -7.78)\": -0.4599, \"(-7.78, -7.67)\": -0.4738, \"(-7.67, -7.5)\": -0.4885, \"(-7.5, -7.23)\": -0.4986, \"(-7.23, -7.14)\": -0.4885, \"(-7.14, -7.09)\": -0.4776, \"(-7.09, -6.99)\": -0.465, \"(-6.99, -6.95)\": -0.4532, \"(-6.95, -6.91)\": -0.443, \"(-6.91, -6.86)\": -0.4293, \"(-6.86, -6.8)\": -0.4157, \"(-6.8, -6.76)\": -0.4036, \"(-6.76, -6.71)\": -0.3887, \"(-6.71, -6.67)\": -0.375, \"(-6.67, -6.64)\": -0.3607, \"(-6.64, -6.57)\": -0.3473, \"(-6.57, -6.51)\": -0.3167, \"(-6.51, -6.48)\": -0.2981, \"(-6.48, -6.45)\": -0.288, \"(-6.45, -6.42)\": -0.2748, \"(-6.42, -6.38)\": -0.2634, \"(-6.38, -6.31)\": -0.2378, \"(-6.31, -6.28)\": -0.2148, \"(-6.28, -6.26)\": -0.2013, \"(-6.26, -6.2)\": -0.1876, \"(-6.2, -6.1)\": -0.1472, \"(-6.1, -6.05)\": -0.1053, \"(-6.05, -6.01)\": -0.0951, \"(-6.01, -5.97)\": -0.0754, \"(-5.97, -5.95)\": -0.0518, \"(-5.95, -5.92)\": -0.0396, \"(-5.92, -5.88)\": -0.0243, \"(-5.88, -5.83)\": -0.0011, \"(-5.83, -5.78)\": 0.0256, \"(-5.78, -5.74)\": 0.0436, \"(-5.74, -5.7)\": 0.0645, \"(-5.7, -5.67)\": 0.0761, \"(-5.67, -5.63)\": 0.1011, \"(-5.63, -5.58)\": 0.1213, \"(-5.58, -5.54)\": 0.1406, \"(-5.54, -5.52)\": 0.1547, \"(-5.52, -5.49)\": 0.1652, \"(-5.49, -5.42)\": 0.1849, \"(-5.42, -5.35)\": 0.227, \"(-5.35, -5.31)\": 0.253, \"(-5.31, -5.27)\": 0.2648, \"(-5.27, -5.22)\": 0.2884, \"(-5.22, -5.18)\": 0.2993, \"(-5.18, -5.13)\": 0.3222, \"(-5.13, -5.09)\": 0.3353, \"(-5.09, -5.04)\": 0.3551, \"(-5.04, -4.98)\": 0.3696, \"(-4.98, -4.93)\": 0.3875, \"(-4.93, -4.9)\": 0.401, \"(-4.9, -4.85)\": 0.4122, \"(-4.85, -4.77)\": 0.4288, \"(-4.77, -4.69)\": 0.447, \"(-4.69, -4.61)\": 0.4632, \"(-4.61, -4.53)\": 0.4748, \"(-4.53, -4.42)\": 0.4852, \"(-4.42, -4.03)\": 0.4953, \"(-4.03, -3.96)\": 0.4833, \"(-3.96, -3.9)\": 0.4729, \"(-3.9, -3.83)\": 0.4605, \"(-3.83, -3.78)\": 0.4493, \"(-3.78, -3.74)\": 0.4358, \"(-3.74, -3.69)\": 0.4252, \"(-3.69, -3.65)\": 0.4127, \"(-3.65, -3.59)\": 0.394, \"(-3.59, -3.53)\": 0.3754, \"(-3.53, -3.49)\": 0.3565, \"(-3.49, -3.42)\": 0.345, \"(-3.42, -3.37)\": 0.3165, \"(-3.37, -3.32)\": 0.2971, \"(-3.32, -3.29)\": 0.2786, \"(-3.29, -3.24)\": 0.2643, \"(-3.24, -3.2)\": 0.2499, \"(-3.2, -3.17)\": 0.2281, \"(-3.17, -3.13)\": 0.2155, \"(-3.13, -3.1)\": 0.2001, \"(-3.1, -3.07)\": 0.1818, \"(-3.07, -3.04)\": 0.1718, \"(-3.04, -2.99)\": 0.1491, \"(-2.99, -2.96)\": 0.1301, \"(-2.96, -2.92)\": 0.1129, \"(-2.92, -2.88)\": 0.0967, \"(-2.88, -2.87)\": 0.085, \"(-2.87, -2.84)\": 0.0736, \"(-2.84, -2.82)\": 0.0615, \"(-2.82, -2.79)\": 0.0499, \"(-2.79, -2.76)\": 0.0358, \"(-2.76, -2.74)\": 0.0212, \"(-2.74, -2.7)\": 0.0101, \"(-2.7, -2.65)\": -0.0234, \"(-2.65, -2.63)\": -0.034, \"(-2.63, -2.6)\": -0.0461, \"(-2.6, -2.58)\": -0.0569, \"(-2.58, -2.54)\": -0.0707, \"(-2.54, -2.51)\": -0.0887, \"(-2.51, -2.47)\": -0.1083, \"(-2.47, -2.44)\": -0.127, \"(-2.44, -2.42)\": -0.1388, \"(-2.42, -2.37)\": -0.1492, \"(-2.37, -2.34)\": -0.1732, \"(-2.34, -2.3)\": -0.1866, \"(-2.3, -2.28)\": -0.2053, \"(-2.28, -2.22)\": -0.2206, \"(-2.22, -2.17)\": -0.249, \"(-2.17, -2.13)\": -0.2598, \"(-2.13, -2.07)\": -0.2871, \"(-2.07, -2.02)\": -0.3072, \"(-2.02, -1.96)\": -0.3323, \"(-1.96, -1.91)\": -0.3458, \"(-1.91, -1.85)\": -0.3716, \"(-1.85, -1.79)\": -0.3851, \"(-1.79, -1.73)\": -0.4108, \"(-1.73, -1.68)\": -0.4234, \"(-1.68, -1.62)\": -0.4342, \"(-1.62, -1.53)\": -0.4534, \"(-1.53, -1.47)\": -0.4654, \"(-1.47, -1.39)\": -0.4757, \"(-1.39, -1.17)\": -0.4883, \"(-1.17, -0.92)\": -0.4983, \"(-0.92, -0.83)\": -0.4836, \"(-0.83, -0.77)\": -0.4729, \"(-0.77, -0.68)\": -0.4612, \"(-0.68, -0.63)\": -0.4445, \"(-0.63, -0.57)\": -0.4343, \"(-0.57, -0.51)\": -0.4105, \"(-0.51, -0.46)\": -0.3991, \"(-0.46, -0.4)\": -0.3813, \"(-0.4, -0.33)\": -0.3579, \"(-0.33, -0.29)\": -0.3408, \"(-0.29, -0.27)\": -0.3275, \"(-0.27, -0.23)\": -0.3161, \"(-0.23, -0.19)\": -0.3006, \"(-0.19, -0.14)\": -0.2813, \"(-0.14, -0.09)\": -0.259, \"(-0.09, -0.07)\": -0.2477, \"(-0.07, -0.0)\": -0.2273, \"(-0.0, 0.04)\": -0.1997, \"(0.04, 0.09)\": -0.1817, \"(0.09, 0.13)\": -0.158, \"(0.13, 0.15)\": -0.1468, \"(0.15, 0.17)\": -0.1338, \"(0.17, 0.21)\": -0.1218, \"(0.21, 0.23)\": -0.106, \"(0.23, 0.28)\": -0.0917, \"(0.28, 0.33)\": -0.0619, \"(0.33, 0.36)\": -0.0501, \"(0.36, 0.4)\": -0.0273, \"(0.4, 0.43)\": -0.0097, \"(0.43, 0.45)\": 0.0047, \"(0.45, 0.48)\": 0.0167, \"(0.48, 0.5)\": 0.0335, \"(0.5, 0.55)\": 0.0464, \"(0.55, 0.6)\": 0.0779, \"(0.6, 0.63)\": 0.089, \"(0.63, 0.7)\": 0.1151, \"(0.7, 0.74)\": 0.1415, \"(0.74, 0.77)\": 0.1537, \"(0.77, 0.8)\": 0.1746, \"(0.8, 0.83)\": 0.1869, \"(0.83, 0.89)\": 0.1975, \"(0.89, 0.96)\": 0.2352, \"(0.96, 1.02)\": 0.2629, \"(1.02, 1.07)\": 0.2879, \"(1.07, 1.1)\": 0.3049, \"(1.1, 1.14)\": 0.3159, \"(1.14, 1.17)\": 0.3293, \"(1.17, 1.2)\": 0.3427, \"(1.2, 1.26)\": 0.3539, \"(1.26, 1.31)\": 0.3753, \"(1.31, 1.39)\": 0.3905, \"(1.39, 1.45)\": 0.4185, \"(1.45, 1.5)\": 0.4304, \"(1.5, 1.57)\": 0.4413, \"(1.57, 1.63)\": 0.4571, \"(1.63, 1.7)\": 0.4689, \"(1.7, 1.81)\": 0.4804, \"(1.81, 2.26)\": 0.4931, \"(2.26, 2.35)\": 0.4828, \"(2.35, 2.43)\": 0.462, \"(2.43, 2.5)\": 0.4515, \"(2.5, 2.57)\": 0.4303, \"(2.57, 2.63)\": 0.4153, \"(2.63, 2.67)\": 0.4007, \"(2.67, 2.71)\": 0.3874, \"(2.71, 2.77)\": 0.3731, \"(2.77, 2.83)\": 0.3464, \"(2.83, 2.87)\": 0.334, \"(2.87, 2.91)\": 0.3155, \"(2.91, 2.94)\": 0.305, \"(2.94, 2.96)\": 0.2938, \"(2.96, 3.0)\": 0.2803, \"(3.0, 3.05)\": 0.2599, \"(3.05, 3.11)\": 0.2445, \"(3.11, 3.19)\": 0.2037, \"(3.19, 3.24)\": 0.1768, \"(3.24, 3.29)\": 0.1503, \"(3.29, 3.32)\": 0.1313, \"(3.32, 3.36)\": 0.1166, \"(3.36, 3.39)\": 0.0985, \"(3.39, 3.42)\": 0.0871, \"(3.42, 3.45)\": 0.0701, \"(3.45, 3.48)\": 0.0586, \"(3.48, 3.5)\": 0.0445, \"(3.5, 3.54)\": 0.0209, \"(3.54, 3.58)\": 0.0082, \"(3.58, 3.61)\": -0.0042, \"(3.61, 3.64)\": -0.0273, \"(3.64, 3.66)\": -0.0374, \"(3.66, 3.69)\": -0.0488, \"(3.69, 3.75)\": -0.0747, \"(3.75, 3.79)\": -0.0973, \"(3.79, 3.83)\": -0.1177, \"(3.83, 3.87)\": -0.1339, \"(3.87, 3.89)\": -0.148, \"(3.89, 3.92)\": -0.1605, \"(3.92, 3.97)\": -0.1834, \"(3.97, 3.99)\": -0.1984, \"(3.99, 4.02)\": -0.2086, \"(4.02, 4.04)\": -0.2216, \"(4.04, 4.08)\": -0.2326, \"(4.08, 4.12)\": -0.2441, \"(4.12, 4.16)\": -0.2681, \"(4.16, 4.21)\": -0.2784, \"(4.21, 4.26)\": -0.3107, \"(4.26, 4.32)\": -0.3247, \"(4.32, 4.35)\": -0.3454, \"(4.35, 4.4)\": -0.3586, \"(4.4, 4.44)\": -0.3732, \"(4.44, 4.48)\": -0.3848, \"(4.48, 4.54)\": -0.402, \"(4.54, 4.58)\": -0.4125, \"(4.58, 4.65)\": -0.431, \"(4.65, 4.7)\": -0.4423, \"(4.7, 4.8)\": -0.4572, \"(4.8, 4.89)\": -0.4741, \"(4.89, 5.0)\": -0.4851, \"(5.0, 5.39)\": -0.4956, \"(5.39, 5.48)\": -0.4845, \"(5.48, 5.6)\": -0.4573, \"(5.6, 5.66)\": -0.4468, \"(5.66, 5.73)\": -0.4298, \"(5.73, 5.83)\": -0.4082, \"(5.83, 5.89)\": -0.3738, \"(5.89, 5.93)\": -0.3619, \"(5.93, 5.97)\": -0.3495, \"(5.97, 6.02)\": -0.3378, \"(6.02, 6.08)\": -0.312, \"(6.08, 6.11)\": -0.2887, \"(6.11, 6.17)\": -0.2745, \"(6.17, 6.22)\": -0.2522, \"(6.22, 6.28)\": -0.2184, \"(6.28, 6.33)\": -0.2018, \"(6.33, 6.41)\": -0.1736, \"(6.41, 6.49)\": -0.1297, \"(6.49, 6.55)\": -0.0984, \"(6.55, 6.58)\": -0.0759, \"(6.58, 6.6)\": -0.0629, \"(6.6, 6.63)\": -0.048, \"(6.63, 6.67)\": -0.0339, \"(6.67, 6.7)\": -0.0179, \"(6.7, 6.73)\": -0.0008, \"(6.73, 6.76)\": 0.0168, \"(6.76, 6.79)\": 0.0308, \"(6.79, 6.82)\": 0.0441, \"(6.82, 6.86)\": 0.0622, \"(6.86, 6.9)\": 0.0746, \"(6.9, 6.95)\": 0.1025, \"(6.95, 6.99)\": 0.1289, \"(6.99, 7.01)\": 0.1404, \"(7.01, 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       "\n"
      ]
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2577,7 +3925,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2589,18 +3937,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.1%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-10.0, -9.99)\": -10975.8, \"(-9.99, -9.98)\": -10882.9, \"(-9.98, -9.96)\": -10734.5, \"(-9.96, -9.94)\": -10505.0, \"(-9.94, -9.92)\": -10332.6, \"(-9.92, -9.89)\": -10074.8, \"(-9.89, -9.87)\": -9728.5, \"(-9.87, -9.85)\": -9603.4, \"(-9.85, -9.83)\": -9359.6, \"(-9.83, -9.82)\": -9242.4, \"(-9.82, -9.81)\": -9164.5, \"(-9.81, -9.78)\": -9026.7, \"(-9.78, -9.75)\": -8738.3, \"(-9.75, -9.74)\": -8525.6, \"(-9.74, -9.73)\": -8460.7, \"(-9.73, -9.71)\": -8398.8, \"(-9.71, -9.69)\": -8099.4, \"(-9.69, -9.68)\": -8049.7, \"(-9.68, -9.67)\": -7960.6, \"(-9.67, -9.65)\": -7818.0, \"(-9.65, -9.64)\": -7754.5, \"(-9.64, -9.64)\": -7667.2, \"(-9.64, -9.62)\": -7639.3, \"(-9.62, -9.61)\": -7481.1, \"(-9.61, -9.6)\": -7437.0, \"(-9.6, -9.59)\": -7302.0, \"(-9.59, -9.58)\": -7280.4, \"(-9.58, -9.55)\": -7233.8, \"(-9.55, -9.5)\": -6845.1, \"(-9.5, -9.48)\": -6631.6, \"(-9.48, -9.47)\": -6516.1, \"(-9.47, -9.45)\": -6393.8, \"(-9.45, -9.4)\": -6300.0, \"(-9.4, -9.32)\": -5843.7, \"(-9.32, -9.27)\": -5423.0, \"(-9.27, -9.26)\": -5289.3, \"(-9.26, -9.25)\": -5238.4, \"(-9.25, -9.24)\": -5185.9, \"(-9.24, -9.23)\": -5136.8, \"(-9.23, -9.22)\": -5103.0, \"(-9.22, -9.21)\": -5012.1, \"(-9.21, -9.2)\": -4964.4, \"(-9.2, -9.19)\": -4938.9, \"(-9.19, -9.17)\": -4823.3, \"(-9.17, -9.15)\": -4763.8, \"(-9.15, -9.14)\": -4675.0, \"(-9.14, -9.13)\": -4647.3, \"(-9.13, -9.13)\": -4613.4, \"(-9.13, -9.12)\": -4580.9, \"(-9.12, -9.09)\": -4520.1, \"(-9.09, -9.07)\": -4379.0, \"(-9.07, -9.02)\": -4335.8, \"(-9.02, -8.93)\": -3915.3, \"(-8.93, -8.89)\": -3679.6, \"(-8.89, -8.88)\": -3609.9, \"(-8.88, -8.86)\": -3559.0, \"(-8.86, -8.84)\": -3481.6, \"(-8.84, -8.83)\": -3441.3, \"(-8.83, -8.82)\": -3402.8, \"(-8.82, -8.78)\": -3337.3, \"(-8.78, -8.75)\": -3160.4, \"(-8.75, -8.71)\": -3131.4, \"(-8.71, -8.65)\": -2916.0, \"(-8.65, -8.63)\": -2834.9, \"(-8.63, -8.61)\": -2781.1, \"(-8.61, -8.58)\": -2704.2, \"(-8.58, -8.56)\": -2639.5, \"(-8.56, -8.55)\": -2612.8, \"(-8.55, -8.52)\": -2567.4, \"(-8.52, -8.48)\": -2450.0, \"(-8.48, -8.45)\": -2367.9, \"(-8.45, -8.43)\": -2322.1, \"(-8.43, -8.41)\": -2267.4, \"(-8.41, -8.39)\": -2240.9, \"(-8.39, -8.38)\": -2192.2, \"(-8.38, -8.36)\": -2151.8, \"(-8.36, -8.33)\": -2120.6, \"(-8.33, -8.28)\": -2035.6, \"(-8.28, -8.24)\": -1927.2, \"(-8.24, -8.22)\": -1883.5, \"(-8.22, -8.19)\": -1821.0, \"(-8.19, -8.17)\": -1785.4, \"(-8.17, -8.15)\": -1754.9, \"(-8.15, -8.12)\": -1712.3, \"(-8.12, -8.09)\": -1643.7, \"(-8.09, -8.05)\": -1615.8, \"(-8.05, -8.02)\": -1528.7, \"(-8.02, -7.98)\": -1500.0, \"(-7.98, -7.95)\": -1442.6, \"(-7.95, -7.92)\": -1413.2, \"(-7.92, -7.86)\": -1340.8, \"(-7.86, -7.83)\": -1282.8, \"(-7.83, -7.8)\": -1257.7, \"(-7.8, -7.77)\": -1210.6, \"(-7.77, -7.74)\": -1184.2, \"(-7.74, -7.74)\": -1149.1, \"(-7.74, -7.7)\": -1124.6, \"(-7.7, -7.66)\": -1079.5, \"(-7.66, -7.63)\": -1046.2, \"(-7.63, -7.61)\": -1017.3, \"(-7.61, -7.57)\": -995.1, \"(-7.57, -7.53)\": -960.7, \"(-7.53, -7.49)\": -914.4, \"(-7.49, -7.46)\": -889.4, \"(-7.46, -7.42)\": -864.8, \"(-7.42, -7.39)\": -835.5, \"(-7.39, -7.33)\": -796.2, \"(-7.33, -7.31)\": -763.8, \"(-7.31, -7.24)\": -729.4, \"(-7.24, -7.22)\": -698.7, \"(-7.22, -7.19)\": -673.5, \"(-7.19, -7.15)\": -648.2, \"(-7.15, -7.09)\": -616.7, \"(-7.09, -7.01)\": -583.6, \"(-7.01, -6.98)\": -561.0, \"(-6.98, -6.94)\": -538.1, \"(-6.94, -6.91)\": -515.7, \"(-6.91, -6.8)\": -487.5, \"(-6.8, -6.72)\": -441.4, \"(-6.72, -6.68)\": -418.6, \"(-6.68, -6.59)\": -394.4, \"(-6.59, -6.51)\": -361.6, \"(-6.51, -6.45)\": -339.8, \"(-6.45, -6.37)\": -317.6, \"(-6.37, -6.29)\": -291.4, \"(-6.29, -6.21)\": -269.1, \"(-6.21, -6.06)\": -241.7, \"(-6.06, -6.01)\": -220.1, \"(-6.01, -5.83)\": -194.0, \"(-5.83, -5.63)\": -171.5, \"(-5.63, -5.43)\": -144.5, \"(-5.43, -5.27)\": -120.2, \"(-5.27, -4.99)\": -96.4, \"(-4.99, -4.65)\": -74.9, \"(-4.65, -4.08)\": -51.8, \"(-4.08, -2.84)\": -30.1, \"(-2.84, 3.18)\": -8.0, \"(3.18, 4.25)\": 13.6, \"(4.25, 4.73)\": 35.5, \"(4.73, 5.05)\": 57.0, \"(5.05, 5.27)\": 79.9, \"(5.27, 5.52)\": 104.2, \"(5.52, 5.7)\": 127.7, \"(5.7, 5.82)\": 152.5, \"(5.82, 5.96)\": 176.2, \"(5.96, 6.05)\": 199.7, \"(6.05, 6.16)\": 222.8, \"(6.16, 6.29)\": 245.4, \"(6.29, 6.39)\": 273.2, \"(6.39, 6.46)\": 296.4, \"(6.46, 6.51)\": 321.0, \"(6.51, 6.57)\": 342.9, \"(6.57, 6.64)\": 364.5, \"(6.64, 6.68)\": 390.3, \"(6.68, 6.76)\": 417.6, \"(6.76, 6.8)\": 446.3, \"(6.8, 6.89)\": 469.6, \"(6.89, 6.95)\": 493.5, \"(6.95, 7.01)\": 515.8, \"(7.01, 7.04)\": 548.0, \"(7.04, 7.08)\": 575.5, \"(7.08, 7.12)\": 598.0, \"(7.12, 7.13)\": 621.1, \"(7.13, 7.17)\": 643.5, \"(7.17, 7.22)\": 675.9, \"(7.22, 7.28)\": 705.5, \"(7.28, 7.32)\": 729.5, \"(7.32, 7.35)\": 758.2, \"(7.35, 7.4)\": 794.3, \"(7.4, 7.44)\": 836.6, \"(7.44, 7.48)\": 868.4, \"(7.48, 7.51)\": 895.6, \"(7.51, 7.54)\": 924.7, \"(7.54, 7.57)\": 947.7, \"(7.57, 7.63)\": 984.6, \"(7.63, 7.67)\": 1038.4, \"(7.67, 7.71)\": 1082.9, \"(7.71, 7.76)\": 1139.3, \"(7.76, 7.78)\": 1173.1, \"(7.78, 7.81)\": 1207.9, \"(7.81, 7.84)\": 1244.5, \"(7.84, 7.89)\": 1284.9, \"(7.89, 7.93)\": 1366.5, \"(7.93, 7.95)\": 1398.9, \"(7.95, 7.98)\": 1438.0, \"(7.98, 8.01)\": 1461.6, \"(8.01, 8.04)\": 1509.9, \"(8.04, 8.05)\": 1553.4, \"(8.05, 8.06)\": 1578.4, \"(8.06, 8.08)\": 1607.3, \"(8.08, 8.12)\": 1635.2, \"(8.12, 8.14)\": 1695.8, \"(8.14, 8.16)\": 1718.5, \"(8.16, 8.17)\": 1747.6, \"(8.17, 8.19)\": 1789.8, \"(8.19, 8.2)\": 1822.9, \"(8.2, 8.24)\": 1849.2, \"(8.24, 8.28)\": 1945.7, \"(8.28, 8.31)\": 1979.4, \"(8.31, 8.34)\": 2070.4, \"(8.34, 8.35)\": 2100.3, \"(8.35, 8.38)\": 2130.1, \"(8.38, 8.44)\": 2234.6, \"(8.44, 8.48)\": 2391.5, \"(8.48, 8.53)\": 2430.5, \"(8.53, 8.59)\": 2620.9, \"(8.59, 8.6)\": 2713.6, \"(8.6, 8.63)\": 2746.0, \"(8.63, 8.65)\": 2826.4, \"(8.65, 8.65)\": 2851.3, \"(8.65, 8.67)\": 2875.0, \"(8.67, 8.68)\": 2931.6, \"(8.68, 8.69)\": 2953.7, \"(8.69, 8.72)\": 3031.6, \"(8.72, 8.74)\": 3105.3, \"(8.74, 8.76)\": 3128.1, \"(8.76, 8.79)\": 3248.4, \"(8.79, 8.85)\": 3318.4, \"(8.85, 8.9)\": 3606.5, \"(8.9, 8.93)\": 3699.1, \"(8.93, 8.96)\": 3859.3, \"(8.96, 8.98)\": 3901.1, \"(8.98, 8.99)\": 3991.8, \"(8.99, 9.0)\": 4029.7, \"(9.0, 9.01)\": 4061.5, \"(9.01, 9.01)\": 4097.4, \"(9.01, 9.03)\": 4122.3, \"(9.03, 9.04)\": 4190.6, \"(9.04, 9.05)\": 4236.6, \"(9.05, 9.07)\": 4282.9, \"(9.07, 9.08)\": 4369.1, \"(9.08, 9.09)\": 4409.0, \"(9.09, 9.12)\": 4498.3, \"(9.12, 9.15)\": 4680.2, \"(9.15, 9.15)\": 4721.0, \"(9.15, 9.18)\": 4759.2, \"(9.18, 9.2)\": 4897.9, \"(9.2, 9.23)\": 4971.9, \"(9.23, 9.25)\": 5167.4, \"(9.25, 9.25)\": 5209.8, \"(9.25, 9.27)\": 5265.0, \"(9.27, 9.31)\": 5396.5, \"(9.31, 9.34)\": 5668.9, \"(9.34, 9.35)\": 5732.9, \"(9.35, 9.36)\": 5770.9, \"(9.36, 9.36)\": 5811.8, \"(9.36, 9.37)\": 5845.9, \"(9.37, 9.38)\": 5905.9, \"(9.38, 9.39)\": 5979.6, \"(9.39, 9.4)\": 6037.4, \"(9.4, 9.41)\": 6069.7, \"(9.41, 9.42)\": 6117.8, \"(9.42, 9.43)\": 6195.8, \"(9.43, 9.44)\": 6262.0, \"(9.44, 9.45)\": 6318.5, \"(9.45, 9.46)\": 6354.2, \"(9.46, 9.47)\": 6449.6, \"(9.47, 9.48)\": 6527.0, \"(9.48, 9.48)\": 6563.9, \"(9.48, 9.5)\": 6630.1, \"(9.5, 9.52)\": 6745.2, \"(9.52, 9.53)\": 6857.4, \"(9.53, 9.54)\": 6924.5, \"(9.54, 9.55)\": 6974.9, \"(9.55, 9.56)\": 7036.9, \"(9.56, 9.58)\": 7219.5, \"(9.58, 9.58)\": 7251.0, \"(9.58, 9.59)\": 7288.0, \"(9.59, 9.62)\": 7355.9, \"(9.62, 9.66)\": 7672.6, \"(9.66, 9.67)\": 7870.0, \"(9.67, 9.68)\": 7952.9, \"(9.68, 9.7)\": 8078.5, \"(9.7, 9.73)\": 8276.6, \"(9.73, 9.77)\": 8551.7, \"(9.77, 9.79)\": 8851.6, \"(9.79, 9.8)\": 8935.0, \"(9.8, 9.83)\": 9078.3, \"(9.83, 9.87)\": 9573.3, \"(9.87, 9.88)\": 9711.4, \"(9.88, 9.88)\": 9741.9, \"(9.88, 9.89)\": 9806.1, \"(9.89, 9.92)\": 10026.2, \"(9.92, 9.98)\": 10450.9}\n",
+      "Means: {\"(-9.9, -9.8)\": -9943.0, \"(-9.8, -9.69)\": -8277.0, \"(-9.69, -9.66)\": -7967.0, \"(-9.66, -9.62)\": -7757.9, \"(-9.62, -9.59)\": -7475.9, \"(-9.59, -9.53)\": -7162.3, \"(-9.53, -9.49)\": -6726.9, \"(-9.49, -9.43)\": -6466.9, \"(-9.43, -9.39)\": -6171.1, \"(-9.39, -9.36)\": -5940.2, \"(-9.36, -9.3)\": -5632.6, \"(-9.3, -9.26)\": -5411.0, \"(-9.26, -9.2)\": -5169.0, \"(-9.2, -9.13)\": -4768.2, \"(-9.13, -9.08)\": -4506.7, \"(-9.08, -8.99)\": -4225.3, \"(-8.99, -8.92)\": -3991.9, \"(-8.92, -8.86)\": -3730.8, \"(-8.86, -8.76)\": -3376.6, \"(-8.76, -8.69)\": -3164.3, \"(-8.69, -8.61)\": -2941.5, \"(-8.61, -8.54)\": -2726.5, \"(-8.54, -8.43)\": -2469.3, \"(-8.43, -8.3)\": -2245.4, \"(-8.3, -8.19)\": -1995.6, \"(-8.19, -8.04)\": -1771.6, \"(-8.04, -7.88)\": -1522.1, \"(-7.88, -7.69)\": -1284.3, \"(-7.69, -7.45)\": -1068.7, \"(-7.45, -7.2)\": -861.8, \"(-7.2, -6.78)\": -650.5, \"(-6.78, -6.16)\": -444.2, \"(-6.16, -4.16)\": -237.4, \"(-4.16, 5.83)\": -32.2, \"(5.83, 6.64)\": 174.1, \"(6.64, 7.09)\": 396.5, \"(7.09, 7.39)\": 608.6, \"(7.39, 7.62)\": 820.2, \"(7.62, 7.82)\": 1028.0, \"(7.82, 7.99)\": 1275.7, \"(7.99, 8.12)\": 1484.8, \"(8.12, 8.24)\": 1693.4, \"(8.24, 8.36)\": 1909.4, \"(8.36, 8.46)\": 2140.7, \"(8.46, 8.54)\": 2349.1, \"(8.54, 8.66)\": 2650.1, \"(8.66, 8.73)\": 2912.7, \"(8.73, 8.8)\": 3190.7, \"(8.8, 8.9)\": 3419.1, \"(8.9, 8.97)\": 3678.2, \"(8.97, 9.06)\": 4049.3, \"(9.06, 9.15)\": 4369.4, \"(9.15, 9.23)\": 4858.2, \"(9.23, 9.26)\": 5111.3, \"(9.26, 9.32)\": 5331.8, \"(9.32, 9.36)\": 5612.1, \"(9.36, 9.41)\": 5826.7, \"(9.41, 9.45)\": 6262.7, \"(9.45, 9.54)\": 6520.0, \"(9.54, 9.6)\": 7187.3, \"(9.6, 9.66)\": 7439.4, \"(9.66, 9.7)\": 7942.5, \"(9.7, 9.72)\": 8198.8, \"(9.72, 9.76)\": 8499.6, \"(9.76, 9.79)\": 8766.2, \"(9.79, 9.82)\": 9034.5, \"(9.82, 9.85)\": 9252.5, \"(9.85, 9.89)\": 9647.8, \"(9.89, 9.91)\": 9885.1, \"(9.91, 9.94)\": 10115.0, \"(9.94, 9.98)\": 10540.9}\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2610,7 +3957,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2622,18 +3969,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.1%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-9.96, -9.95)\": 10370.7, \"(-9.95, -9.91)\": 10346.2, \"(-9.91, -9.87)\": 9857.3, \"(-9.87, -9.87)\": 9693.9, \"(-9.87, -9.86)\": 9627.2, \"(-9.86, -9.85)\": 9571.6, \"(-9.85, -9.84)\": 9393.4, \"(-9.84, -9.82)\": 9340.9, \"(-9.82, -9.81)\": 9098.0, \"(-9.81, -9.79)\": 9035.2, \"(-9.79, -9.76)\": 8844.8, \"(-9.76, -9.71)\": 8475.6, \"(-9.71, -9.68)\": 8051.3, \"(-9.68, -9.67)\": 7962.3, \"(-9.67, -9.66)\": 7898.4, \"(-9.66, -9.64)\": 7759.8, \"(-9.64, -9.6)\": 7543.2, \"(-9.6, -9.53)\": 7186.0, \"(-9.53, -9.48)\": 6614.4, \"(-9.48, -9.46)\": 6517.5, \"(-9.46, -9.41)\": 6287.4, \"(-9.41, -9.36)\": 5900.4, \"(-9.36, -9.34)\": 5733.7, \"(-9.34, -9.34)\": 5704.5, \"(-9.34, -9.34)\": 5681.4, \"(-9.34, -9.33)\": 5635.5, \"(-9.33, -9.32)\": 5582.5, \"(-9.32, -9.31)\": 5549.7, \"(-9.31, -9.29)\": 5440.1, \"(-9.29, -9.27)\": 5346.5, \"(-9.27, -9.24)\": 5225.4, \"(-9.24, -9.2)\": 4982.4, \"(-9.2, -9.19)\": 4894.1, \"(-9.19, -9.17)\": 4858.7, \"(-9.17, -9.15)\": 4750.2, \"(-9.15, -9.13)\": 4677.7, \"(-9.13, -9.1)\": 4569.8, \"(-9.1, -9.08)\": 4417.1, \"(-9.08, -9.07)\": 4357.2, \"(-9.07, -9.05)\": 4303.1, \"(-9.05, -9.03)\": 4202.7, \"(-9.03, -9.01)\": 4146.5, \"(-9.01, -9.0)\": 4071.8, \"(-9.0, -8.99)\": 4014.2, \"(-8.99, -8.96)\": 3985.1, \"(-8.96, -8.93)\": 3789.5, \"(-8.93, -8.92)\": 3763.5, \"(-8.92, -8.91)\": 3727.6, \"(-8.91, -8.89)\": 3686.3, \"(-8.89, -8.88)\": 3606.2, \"(-8.88, -8.87)\": 3567.1, \"(-8.87, -8.87)\": 3553.4, \"(-8.87, -8.85)\": 3522.2, \"(-8.85, -8.82)\": 3438.5, \"(-8.82, -8.8)\": 3351.2, \"(-8.8, -8.79)\": 3320.9, \"(-8.79, -8.75)\": 3215.9, \"(-8.75, -8.72)\": 3095.5, \"(-8.72, -8.71)\": 3052.4, \"(-8.71, -8.71)\": 3026.9, \"(-8.71, -8.69)\": 2993.3, \"(-8.69, -8.66)\": 2945.6, \"(-8.66, -8.63)\": 2848.1, \"(-8.63, -8.6)\": 2773.3, \"(-8.6, -8.58)\": 2682.2, \"(-8.58, -8.54)\": 2629.5, \"(-8.54, -8.5)\": 2503.4, \"(-8.5, -8.49)\": 2453.0, \"(-8.49, -8.48)\": 2429.2, \"(-8.48, -8.47)\": 2395.0, \"(-8.47, -8.47)\": 2379.9, \"(-8.47, -8.46)\": 2362.3, \"(-8.46, -8.45)\": 2335.5, \"(-8.45, -8.43)\": 2323.1, \"(-8.43, -8.38)\": 2249.4, \"(-8.38, -8.33)\": 2104.8, \"(-8.33, -8.32)\": 2068.9, \"(-8.32, -8.31)\": 2055.1, \"(-8.31, -8.29)\": 2012.2, \"(-8.29, -8.28)\": 1991.0, \"(-8.28, -8.27)\": 1971.4, \"(-8.27, -8.26)\": 1950.8, \"(-8.26, -8.25)\": 1922.8, \"(-8.25, -8.24)\": 1904.6, \"(-8.24, -8.23)\": 1889.9, \"(-8.23, -8.23)\": 1872.7, \"(-8.23, -8.21)\": 1847.7, \"(-8.21, -8.19)\": 1816.5, \"(-8.19, -8.18)\": 1793.5, \"(-8.18, -8.17)\": 1774.6, \"(-8.17, -8.14)\": 1738.9, \"(-8.14, -8.11)\": 1695.2, \"(-8.11, -8.08)\": 1637.6, \"(-8.08, -8.05)\": 1595.0, \"(-8.05, -8.05)\": 1569.8, \"(-8.05, -8.04)\": 1552.2, \"(-8.04, -8.01)\": 1538.6, \"(-8.01, -7.93)\": 1444.4, \"(-7.93, -7.89)\": 1345.9, \"(-7.89, -7.86)\": 1315.0, \"(-7.86, -7.83)\": 1275.0, \"(-7.83, -7.81)\": 1253.1, \"(-7.81, -7.78)\": 1210.9, \"(-7.78, -7.76)\": 1181.3, \"(-7.76, -7.73)\": 1156.1, \"(-7.73, -7.69)\": 1125.1, \"(-7.69, -7.65)\": 1079.0, \"(-7.65, -7.63)\": 1043.0, \"(-7.63, -7.61)\": 1025.1, \"(-7.61, -7.59)\": 1001.3, \"(-7.59, -7.57)\": 981.4, \"(-7.57, -7.56)\": 970.2, \"(-7.56, -7.56)\": 958.1, \"(-7.56, -7.53)\": 945.6, \"(-7.53, -7.5)\": 916.8, \"(-7.5, -7.48)\": 904.7, \"(-7.48, -7.46)\": 877.1, \"(-7.46, -7.45)\": 865.7, \"(-7.45, -7.43)\": 849.3, \"(-7.43, -7.41)\": 830.4, \"(-7.41, -7.4)\": 817.8, \"(-7.4, -7.37)\": 801.8, \"(-7.37, -7.32)\": 775.0, \"(-7.32, -7.28)\": 747.2, \"(-7.28, -7.24)\": 721.4, \"(-7.24, -7.23)\": 695.1, \"(-7.23, -7.2)\": 672.4, \"(-7.2, -7.18)\": 661.0, \"(-7.18, -7.15)\": 647.9, \"(-7.15, -7.1)\": 635.5, \"(-7.1, -7.09)\": 615.4, \"(-7.09, -7.08)\": 598.3, \"(-7.08, -7.06)\": 585.5, \"(-7.06, -7.04)\": 574.6, \"(-7.04, -7.0)\": 561.7, \"(-7.0, -6.98)\": 545.4, \"(-6.98, -6.96)\": 528.4, \"(-6.96, -6.93)\": 517.1, \"(-6.93, -6.88)\": 501.0, \"(-6.88, -6.84)\": 482.8, \"(-6.84, -6.8)\": 469.8, \"(-6.8, -6.77)\": 458.3, \"(-6.77, -6.74)\": 437.2, \"(-6.74, -6.7)\": 420.1, \"(-6.7, -6.68)\": 406.0, \"(-6.68, -6.64)\": 393.1, \"(-6.64, -6.59)\": 379.6, \"(-6.59, -6.56)\": 363.7, \"(-6.56, -6.5)\": 350.3, \"(-6.5, -6.46)\": 338.8, \"(-6.46, -6.43)\": 321.1, \"(-6.43, -6.39)\": 309.9, \"(-6.39, -6.35)\": 291.0, \"(-6.35, -6.27)\": 275.6, \"(-6.27, -6.19)\": 257.5, \"(-6.19, -6.1)\": 244.5, \"(-6.1, -6.08)\": 233.6, \"(-6.08, -6.01)\": 217.1, \"(-6.01, -5.94)\": 205.8, \"(-5.94, -5.89)\": 191.6, \"(-5.89, -5.79)\": 179.5, \"(-5.79, -5.7)\": 166.0, \"(-5.7, -5.61)\": 153.5, \"(-5.61, -5.56)\": 141.1, \"(-5.56, -5.46)\": 129.0, \"(-5.46, -5.38)\": 117.7, \"(-5.38, -5.2)\": 105.6, \"(-5.2, -5.05)\": 89.3, \"(-5.05, -4.89)\": 77.9, \"(-4.89, -4.69)\": 67.1, \"(-4.69, -4.51)\": 56.2, \"(-4.51, -4.21)\": 44.6, \"(-4.21, -3.75)\": 32.6, \"(-3.75, -3.0)\": 21.7, \"(-3.0, -2.03)\": 10.6, \"(-2.03, 2.96)\": -0.3, \"(2.96, 3.74)\": 11.6, \"(3.74, 4.17)\": 22.4, \"(4.17, 4.47)\": 33.4, \"(4.47, 4.67)\": 45.7, \"(4.67, 4.89)\": 56.6, \"(4.89, 5.08)\": 67.7, \"(5.08, 5.2)\": 81.0, \"(5.2, 5.36)\": 96.6, \"(5.36, 5.45)\": 107.6, \"(5.45, 5.52)\": 118.5, \"(5.52, 5.6)\": 129.3, \"(5.6, 5.71)\": 143.0, \"(5.71, 5.81)\": 154.5, \"(5.81, 5.86)\": 166.9, \"(5.86, 5.95)\": 179.9, \"(5.95, 6.0)\": 193.6, \"(6.0, 6.06)\": 206.2, \"(6.06, 6.14)\": 218.2, \"(6.14, 6.18)\": 229.9, \"(6.18, 6.21)\": 240.9, \"(6.21, 6.26)\": 251.8, \"(6.26, 6.32)\": 268.8, \"(6.32, 6.35)\": 281.0, \"(6.35, 6.37)\": 293.6, \"(6.37, 6.4)\": 305.6, \"(6.4, 6.48)\": 316.8, \"(6.48, 6.5)\": 331.8, \"(6.5, 6.54)\": 342.6, \"(6.54, 6.57)\": 359.7, \"(6.57, 6.63)\": 372.9, \"(6.63, 6.69)\": 388.8, \"(6.69, 6.73)\": 400.4, \"(6.73, 6.78)\": 420.4, \"(6.78, 6.8)\": 441.0, \"(6.8, 6.86)\": 460.8, \"(6.86, 6.9)\": 479.8, \"(6.9, 6.93)\": 494.0, \"(6.93, 6.94)\": 506.4, \"(6.94, 6.96)\": 523.3, \"(6.96, 6.99)\": 545.2, \"(6.99, 7.03)\": 562.9, \"(7.03, 7.05)\": 578.8, \"(7.05, 7.09)\": 590.9, \"(7.09, 7.13)\": 617.7, \"(7.13, 7.16)\": 632.9, \"(7.16, 7.18)\": 646.8, \"(7.18, 7.2)\": 659.8, \"(7.2, 7.25)\": 682.6, \"(7.25, 7.3)\": 721.1, \"(7.3, 7.31)\": 732.0, \"(7.31, 7.33)\": 751.4, \"(7.33, 7.36)\": 775.3, \"(7.36, 7.38)\": 796.4, \"(7.38, 7.41)\": 818.5, \"(7.41, 7.46)\": 847.8, \"(7.46, 7.48)\": 873.1, \"(7.48, 7.49)\": 890.9, \"(7.49, 7.5)\": 904.9, \"(7.5, 7.54)\": 931.7, \"(7.54, 7.6)\": 972.3, \"(7.6, 7.64)\": 1029.6, \"(7.64, 7.68)\": 1063.0, \"(7.68, 7.72)\": 1096.0, \"(7.72, 7.74)\": 1132.2, \"(7.74, 7.76)\": 1152.3, \"(7.76, 7.77)\": 1178.0, \"(7.77, 7.79)\": 1198.6, \"(7.79, 7.81)\": 1225.6, \"(7.81, 7.83)\": 1249.3, \"(7.83, 7.89)\": 1274.2, \"(7.89, 7.94)\": 1383.0, \"(7.94, 7.95)\": 1407.6, \"(7.95, 7.96)\": 1418.9, \"(7.96, 7.97)\": 1435.0, \"(7.97, 7.98)\": 1452.5, \"(7.98, 7.99)\": 1478.0, \"(7.99, 8.0)\": 1489.9, \"(8.0, 8.01)\": 1505.0, \"(8.01, 8.04)\": 1536.0, \"(8.04, 8.06)\": 1571.3, \"(8.06, 8.07)\": 1589.0, \"(8.07, 8.08)\": 1608.7, \"(8.08, 8.1)\": 1620.1, \"(8.1, 8.11)\": 1648.0, \"(8.11, 8.11)\": 1665.3, \"(8.11, 8.14)\": 1683.2, \"(8.14, 8.24)\": 1771.0, \"(8.24, 8.32)\": 2015.8, \"(8.32, 8.34)\": 2060.9, \"(8.34, 8.36)\": 2105.6, \"(8.36, 8.38)\": 2133.5, \"(8.38, 8.38)\": 2168.2, \"(8.38, 8.38)\": 2194.1, \"(8.38, 8.39)\": 2205.1, \"(8.39, 8.4)\": 2222.9, \"(8.4, 8.43)\": 2243.9, \"(8.43, 8.51)\": 2370.4, \"(8.51, 8.56)\": 2573.1, \"(8.56, 8.57)\": 2617.9, \"(8.57, 8.58)\": 2642.4, \"(8.58, 8.59)\": 2672.7, \"(8.59, 8.61)\": 2730.7, \"(8.61, 8.62)\": 2761.4, \"(8.62, 8.63)\": 2792.8, \"(8.63, 8.66)\": 2822.7, \"(8.66, 8.73)\": 2962.5, \"(8.73, 8.78)\": 3229.3, \"(8.78, 8.79)\": 3273.1, \"(8.79, 8.81)\": 3310.6, \"(8.81, 8.83)\": 3373.4, \"(8.83, 8.85)\": 3439.1, \"(8.85, 8.86)\": 3498.7, \"(8.86, 8.87)\": 3542.5, \"(8.87, 8.92)\": 3615.1, \"(8.92, 8.96)\": 3839.5, \"(8.96, 9.02)\": 3972.9, \"(9.02, 9.06)\": 4298.0, \"(9.06, 9.07)\": 4325.7, \"(9.07, 9.08)\": 4366.4, \"(9.08, 9.09)\": 4398.0, \"(9.09, 9.12)\": 4476.8, \"(9.12, 9.22)\": 4639.6, \"(9.22, 9.3)\": 5419.8, \"(9.3, 9.32)\": 5527.4, \"(9.32, 9.34)\": 5662.4, \"(9.34, 9.35)\": 5723.4, \"(9.35, 9.37)\": 5836.0, \"(9.37, 9.39)\": 5926.7, \"(9.39, 9.39)\": 5984.7, \"(9.39, 9.4)\": 6032.3, \"(9.4, 9.41)\": 6081.8, \"(9.41, 9.43)\": 6154.1, \"(9.43, 9.44)\": 6273.4, \"(9.44, 9.45)\": 6318.9, \"(9.45, 9.45)\": 6362.7, \"(9.45, 9.46)\": 6386.1, \"(9.46, 9.47)\": 6422.4, \"(9.47, 9.48)\": 6546.5, \"(9.48, 9.5)\": 6605.2, \"(9.5, 9.52)\": 6741.5, \"(9.52, 9.53)\": 6869.5, \"(9.53, 9.53)\": 6904.1, \"(9.53, 9.54)\": 6934.7, \"(9.54, 9.58)\": 7072.1, \"(9.58, 9.65)\": 7441.7, \"(9.65, 9.7)\": 8081.3, \"(9.7, 9.72)\": 8233.0, \"(9.72, 9.74)\": 8350.9, \"(9.74, 9.77)\": 8624.2, \"(9.77, 9.78)\": 8804.4, \"(9.78, 9.8)\": 8894.5, \"(9.8, 9.81)\": 9078.6, \"(9.81, 9.82)\": 9151.9, \"(9.82, 9.83)\": 9225.3, \"(9.83, 9.88)\": 9363.1, \"(9.88, 9.93)\": 10148.9, \"(9.93, 9.96)\": 10364.3, \"(9.96, 9.98)\": 10743.0, \"(9.98, 9.98)\": 10755.3}\n",
+      "Means: {\"(-9.99, -9.97)\": 10826.1, \"(-9.97, -9.94)\": 10590.0, \"(-9.94, -9.91)\": 10245.7, \"(-9.91, -9.89)\": 9981.2, \"(-9.89, -9.83)\": 9757.9, \"(-9.83, -9.75)\": 8942.9, \"(-9.75, -9.68)\": 8308.2, \"(-9.68, -9.64)\": 7859.4, \"(-9.64, -9.6)\": 7624.3, \"(-9.6, -9.58)\": 7392.2, \"(-9.58, -9.56)\": 7170.8, \"(-9.56, -9.52)\": 6956.0, \"(-9.52, -9.47)\": 6716.8, \"(-9.47, -9.43)\": 6407.5, \"(-9.43, -9.35)\": 6053.3, \"(-9.35, -9.27)\": 5569.4, \"(-9.27, -9.21)\": 5245.7, \"(-9.21, -9.17)\": 4947.0, \"(-9.17, -9.13)\": 4729.1, \"(-9.13, -9.05)\": 4484.4, \"(-9.05, -9.0)\": 4253.9, \"(-9.0, -8.9)\": 3964.3, \"(-8.9, -8.79)\": 3560.2, \"(-8.79, -8.7)\": 3185.2, \"(-8.7, -8.6)\": 2950.1, \"(-8.6, -8.51)\": 2693.6, \"(-8.51, -8.39)\": 2470.4, \"(-8.39, -8.28)\": 2173.3, \"(-8.28, -8.15)\": 1951.5, \"(-8.15, -8.0)\": 1692.2, \"(-8.0, -7.84)\": 1479.0, \"(-7.84, -7.64)\": 1250.9, \"(-7.64, -7.4)\": 1025.7, \"(-7.4, -7.09)\": 807.9, \"(-7.09, -6.63)\": 593.7, \"(-6.63, -5.77)\": 379.8, \"(-5.77, 4.55)\": 164.1, \"(4.55, 6.28)\": -49.2, \"(6.28, 6.86)\": -268.1, \"(6.86, 7.24)\": -487.9, \"(7.24, 7.52)\": -704.2, \"(7.52, 7.75)\": -931.7, \"(7.75, 7.91)\": -1160.4, \"(7.91, 8.06)\": -1381.0, \"(8.06, 8.2)\": -1607.4, \"(8.2, 8.33)\": -1879.5, \"(8.33, 8.44)\": -2095.6, \"(8.44, 8.56)\": -2351.0, \"(8.56, 8.66)\": -2663.1, \"(8.66, 8.74)\": -2912.8, \"(8.74, 8.82)\": -3126.9, \"(8.82, 8.9)\": -3457.8, \"(8.9, 8.97)\": -3695.9, \"(8.97, 9.05)\": -4046.4, \"(9.05, 9.08)\": -4288.6, \"(9.08, 9.16)\": -4532.5, \"(9.16, 9.21)\": -4773.1, \"(9.21, 9.26)\": -5104.8, \"(9.26, 9.34)\": -5447.6, \"(9.34, 9.4)\": -5800.4, \"(9.4, 9.45)\": -6103.2, \"(9.45, 9.51)\": -6454.8, \"(9.51, 9.55)\": -6891.7, \"(9.55, 9.59)\": -7123.5, \"(9.59, 9.64)\": -7501.0, \"(9.64, 9.7)\": -8012.7, \"(9.7, 9.75)\": -8240.7, \"(9.75, 9.8)\": -8717.9, \"(9.8, 9.86)\": -9274.7, \"(9.86, 9.89)\": -9610.1, \"(9.89, 9.92)\": -9902.3, \"(9.92, 9.93)\": -10240.2, \"(9.93, 9.98)\": -10480.6}\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2643,7 +3989,7 @@
     },
     {
      "data": {
-      "image/png": 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5K0/TJrITBhwiIjvbf6oIf/3vxbOk/h7TBS/e3U/BiojUj1/4EhHZUfofBTbhppOvF8MNURvgCA4RkZ1sOZqPB1amy8+n3dYDTw6LULAiItfBgENEZAc5pnKbcPOvOyMx5eYeClZE5FoYcIiI7CA2+Qd5esHIvnjwhq4KVkPkengMDhFRK/vp97PydFSYL8MNkQIYcIiIWtH5KgvGr9ohP9/wcKyC1RC5LgYcIqJWtH7HxRsIT76JN8kkUgrfeURErWR39jnM3fib/HzWHb0VrIbItTHgEBG1khWbj8nTy/9+Ha9STKQgnkVFRHSVqixWPPh2OrZnFQKouVLx8P4hCldF5No4gkNEdJWy8kvlcKPVSLh/UJjCFRERR3CIiFrR9qShCOygU7oMIpfHgENEdBXe+vkPvL45EwDg386D4YbIQfArKiKiq7Ah4yQKy6oAAL2C2itcDRHV4ggOEVErWHhvP9xzXWelyyCiCziCQ0TUQhv3ncHvuSUAgDB/b17Uj8iB8N1IRNQCQgg8+sEe+XmI3kvBaojocgw4REQtsHJLljz9f2MHIjygnYLVENHlGHCIiFog5YBRnr6zX6iClRBRfRhwiIiayWg6j10nzgEAku/pp3A1RFQfBhwiomZ69IPd8nTvEB8FKyGihjDgEBE1gxACO4/XjN4E++gQFearbEFEVC8GHCKiZpj9+UF5et1DNyhYCRE1hgGHiKgZNu4/I0/35JWLiRwWAw4RUTNoJAkA8Pm0PylcCRE1hgGHiKiJDp4xobC0EgDg7aFVuBoiakybBJzly5ejW7du8PT0RExMDHbs2NFg21tvvRWSJNV5DB8+XG4zYcKEOssTEhLaoitE5MJGv7ldntZ7uStYCRFdid1vtvnhhx8iMTERK1asQExMDJYuXYr4+HgcOXIEQUFBddp/8sknqKyslJ8XFBRgwIABuO+++2zaJSQkYPXq1fJznU5nv04QkcsTQqD4fDUAYFifYAT5eCpcERE1xu4jOEuWLMHkyZMxceJE9OnTBytWrIC3tzdWrVpVb3t/f38YDAb5sWnTJnh7e9cJODqdzqadn5+fvbtCRC5s7bbj8vTsu/ooVwgRNYldA05lZSUyMjIQFxd38QU1GsTFxSEtLa1J21i5ciVGjx6Ndu1s7/OyefNmBAUFISIiAlOnTkVBQUGD26ioqIDZbLZ5EBE1x9yNv8nTnf28FayEiJrCrgEnPz8fFosFwcHBNvODg4NhNBobWOuiHTt24MCBA3jooYds5ickJOCdd95BamoqFi5ciJ9++gl33HEHLBZLvdtJTk6GXq+XH2FhYS3vFBG5JE3NyVN4+d7+yhZCRE1i92NwrsbKlSvRr18/DB482Gb+6NGj5el+/fqhf//+6NGjBzZv3oyhQ4fW2U5SUhISExPl52azmSGHiFrk1shApUsgoiaw6whOQEAAtFotcnNzbebn5ubCYDA0um5paSnWr1+PSZMmXfF1unfvjoCAAGRmZta7XKfTwcfHx+ZBRNRU3x00wiqUroKImsOuAcfDwwPR0dFITU2V51mtVqSmpiI2NrbRdTds2ICKigo88MADV3ydU6dOoaCgACEhIVddMxHR5aa8myFP+3p5KFgJETWV3c+iSkxMxFtvvYW1a9fi0KFDmDp1KkpLSzFx4kQAwLhx45CUlFRnvZUrV2LkyJHo2LGjzfySkhI89dRT2L59O44fP47U1FSMGDECPXv2RHx8vL27Q0Qupvh8lTz94t194eHG66MSOQO7H4MzatQonD17FrNnz4bRaERUVBRSUlLkA4+zs7Oh0dj+wjhy5Ai2bNmC7777rs72tFot9u/fj7Vr16KoqAihoaEYNmwYFixYwGvhEFGr+2LfxXtP/S2ax+4ROQtJCOFy3yybzWbo9XqYTCYej0NEjRr79nZszSyAJAFZycOvvAIR2U1zPr851kpE1IAPd2Zja2bNNbbujuqkcDVE1BwMOEREDfj5aL48PeFP3ZQrhIiajQGHiOgK/n1nb/Tv7Kt0GUTUDAw4RERXwDOniJwP37VERA2w8up+RE6LAYeIqB4ZJ87hmwNXvmceETkmBhwionp8vve0PN21I+8eTuRsGHCIiOqxfsdJAMC913XCrRFBCldDRM3FgENEVI9KixUA0K+TXuFKiKglGHCIiC5z6lyZPM3RGyLnxIBDRHSZX0+Z5Gkef0PknBhwiIgu89znBwEAEcEdIEmSwtUQUUsw4BARXcZdWxNqeod0ULgSImopBhwioktYrQI5pvMAgEk3dle4GiJqKQYcIqJLvPnLH/I0v50icl4MOEREl7j0DKpIA7+iInJWDDhERJf48fBZAMDjQ3vBTctfkUTOiu9eIqILSiuqcbqoHADg7aFVuBoiuhoMOEREF+zJLpKn/xbdWblCiOiqMeAQEV3wwc5sedq/nYeClRDR1WLAISK6zJjBXXiBPyInx4BDRHSBxSIA8OwpIjVgwCEiAmAqr0LKQaPSZRBRK2HAISICcCjHLE9Hd/VTsBIiag0MOEREAI7nlwIAOujc0LeTXuFqiOhqMeAQEQEoKK0EAOi93RWuhIhaAwMOERGA//sxEwAQEcwDjInUgAGHiAhAQAcdACC2R0eFKyGi1sCAQ0Quz1RWhRMFNTfZHNiFBxgTqQEDDhG5vJ+OnpWnDXpPBSshotbCgENELm/mR3sBAAHtdejk66VsMUTUKtok4CxfvhzdunWDp6cnYmJisGPHjgbbrlmzBpIk2Tw8PW3/ohJCYPbs2QgJCYGXlxfi4uJw9OhRe3eDiFRK71Vz5tSIqFCFKyGi1mL3gPPhhx8iMTERc+bMwe7duzFgwADEx8cjLy+vwXV8fHyQk5MjP06cOGGz/OWXX8ayZcuwYsUKpKeno127doiPj8f58+ft3R0iUrH7B4UpXQIRtRK7B5wlS5Zg8uTJmDhxIvr06YMVK1bA29sbq1atanAdSZJgMBjkR3BwsLxMCIGlS5fi2WefxYgRI9C/f3+88847OHPmDD777DN7d4eIVEYIgfySSqXLIKJWZteAU1lZiYyMDMTFxV18QY0GcXFxSEtLa3C9kpISdO3aFWFhYRgxYgQOHjwoL8vKyoLRaLTZpl6vR0xMTIPbrKiogNlstnkQEQHA5iMXDzDW8AbiRKph14CTn58Pi8ViMwIDAMHBwTAa67+pXUREBFatWoXPP/8c7733HqxWK4YMGYJTp04BgLxec7aZnJwMvV4vP8LCOAxNRDU+zjglT/cIbK9gJUTUmhzuLKrY2FiMGzcOUVFRuOWWW/DJJ58gMDAQb7zxRou3mZSUBJPJJD9OnjzZihUTkTPzvXBrhqgwX2g4hEOkGnYNOAEBAdBqtcjNzbWZn5ubC4PB0KRtuLu7Y+DAgcjMrLmMeu16zdmmTqeDj4+PzYOISAiBdenZAIBbIwIVroaIWpNdA46Hhweio6ORmpoqz7NarUhNTUVsbGyTtmGxWPDrr78iJCQEABAeHg6DwWCzTbPZjPT09CZvk4gIAM4WV8jTXTt6K1gJEbU2N3u/QGJiIsaPH49BgwZh8ODBWLp0KUpLSzFx4kQAwLhx49CpUyckJycDAObPn48bbrgBPXv2RFFRERYtWoQTJ07goYceAlBzhtWMGTPw/PPPo1evXggPD8dzzz2H0NBQjBw50t7dISIVsYqL0yOjOilXCBG1OrsHnFGjRuHs2bOYPXs2jEYjoqKikJKSIh8knJ2dDY3m4kDSuXPnMHnyZBiNRvj5+SE6Ohrbtm1Dnz595DZPP/00SktLMWXKFBQVFeHGG29ESkpKnQsCEhE1JuPEOQCAm6bmoqJEpB6SEEJcuZm6mM1m6PV6mEwmHo9D5MKe2rAPGy6cRXX8peEKV0NEV9Kcz2+HO4uKiKitfPdbzckKD90YrnAlRNTaGHCIyGWZyqsAgKeHE6kQAw4Ruby7B/IAYyK1YcAhIpdUO3oD8BRxIjViwCEil7T9jwJ52tvD7ieUElEbY8AhIpe0fke20iUQkR0x4BCRSzpdVA4A6BXEG2wSqREDDhG5pN9zSwAAI3mAMZEqMeAQkcu59PqmvUM6KFgJEdkLAw4RuZys/FJ5un9nX+UKISK7YcAhIpdTVmmRpwPa6xSshIjshQGHiFzOhztPAgA6tvNQuBIishcGHCJyWbxFA5F6MeAQkUspKKnAu9tPAAD+PriLwtUQkb0w4BCRS9l14pw8fU0wz6AiUisGHCJyScE+OgzvH6J0GURkJww4ROSSOvvxBptEasaAQ0RERKrDgENERESqw4BDRC7FVFaldAlE1AYYcIjIZbybdhxP/2+/0mUQURtgwCEil7E7u0ie/nOfYOUKISK7c1O6ACKitvbs8N546KbuSpdBRHbEERwiIiJSHQYcIiIiUh0GHCIiIlIdBhwiIiJSHQYcInIZ1Rar0iUQURthwCEil1BUVomN+3OULoOI2ggDDhG5hP/tPi1Pt9fxChlEaseAQ0QuobyyWp7+a1SogpUQUVtok4CzfPlydOvWDZ6enoiJicGOHTsabPvWW2/hpptugp+fH/z8/BAXF1en/YQJEyBJks0jISHB3t0gIie2JTMfAHBfdGd4e3AEh0jt7B5wPvzwQyQmJmLOnDnYvXs3BgwYgPj4eOTl5dXbfvPmzRgzZgx+/PFHpKWlISwsDMOGDcPp06dt2iUkJCAnJ0d+fPDBB/buChE5MR9PdwBAQWmlwpUQUVuwe8BZsmQJJk+ejIkTJ6JPnz5YsWIFvL29sWrVqnrbr1u3Do888giioqIQGRmJt99+G1arFampqTbtdDodDAaD/PDz87N3V4jIif1wuOaPqj/1DFC4EiJqC3YNOJWVlcjIyEBcXNzFF9RoEBcXh7S0tCZto6ysDFVVVfD397eZv3nzZgQFBSEiIgJTp05FQUFBg9uoqKiA2Wy2eRCR60g5kINqqwAAuGslhashorZg14CTn58Pi8WC4GDbu/YGBwfDaDQ2aRvPPPMMQkNDbUJSQkIC3nnnHaSmpmLhwoX46aefcMcdd8BisdS7jeTkZOj1evkRFhbW8k4RkdNZ+v1Refq6LhztJXIFDn2k3UsvvYT169dj8+bN8PT0lOePHj1anu7Xrx/69++PHj16YPPmzRg6dGid7SQlJSExMVF+bjabGXKIXEjt6M3023ri2lAfhashorZg1xGcgIAAaLVa5Obm2szPzc2FwWBodN3FixfjpZdewnfffYf+/fs32rZ79+4ICAhAZmZmvct1Oh18fHxsHkTkem7sFQBJ4ldURK7ArgHHw8MD0dHRNgcI1x4wHBsb2+B6L7/8MhYsWICUlBQMGjToiq9z6tQpFBQUICQkpFXqJiIiIudm97OoEhMT8dZbb2Ht2rU4dOgQpk6ditLSUkycOBEAMG7cOCQlJcntFy5ciOeeew6rVq1Ct27dYDQaYTQaUVJSAgAoKSnBU089he3bt+P48eNITU3FiBEj0LNnT8THx9u7O0REROQE7H4MzqhRo3D27FnMnj0bRqMRUVFRSElJkQ88zs7OhkZzMWe9/vrrqKysxN/+9jeb7cyZMwdz586FVqvF/v37sXbtWhQVFSE0NBTDhg3DggULoNPp7N0dIiIicgKSEEIoXURbM5vN0Ov1MJlMPB6HyAXELfkJmXklWD/lBtzQvaPS5RBRCzXn85v3oiIiIiLVYcAhIiIi1WHAISLVc8Fv4olcHgMOEaleZbVV6RKIqI0x4BCRqr39yx84ea5c6TKIqI0x4BCRqr30zWF5OqgDLyVB5CoYcIhI1WrvQ/V/Y69D98D2CldDRG2FAYeIXMKAznqlSyCiNsSAQ0Sq9cfZEnna28PuF24nIgfCgENEqrX9j0J52q+dh4KVEFFbY8AhItUqLK1QugQiUggDDhGp1vqdJwEAt0YEKlwJEbU1BhwiUq3a08J7h/CmukSuhgGHiFTrwhniGBjmq2gdRNT2GHCISJUKSyux92SR0mUQkUIYcIhIlQ7nmOXpPqH8iorI1TDgEJEqlVdZAADhAe3Q2c9b4WqIqK0x4BCRKqUdKwAAVFl4J3EiV8SAQ0SqdCS3GADQjlcwJnJJDDhEpEqBF04R7xbAr6eIXBEDDhGp0oHTJgBAbPeOCldCREpgwCEi1amyWPF7bs2NNj3dtQpXQ0RKYMAhItW59MDim6/hbRqIXBEDDhGpztniizfZ9PV2V7ASIlIKAw4Rqc43B4zytLuWv+aIXBHf+USkOkcvHH8TaejAgEPkovjOJyLV+fV0EQAg1NdL2UKISDEMOESkOrVnUPUJ4T2oiFwVAw4RqY67VgIADLs2WOFKiEgpDDhEpFpBHTyVLoGIFNImAWf58uXo1q0bPD09ERMTgx07djTafsOGDYiMjISnpyf69euHr7/+2ma5EAKzZ89GSEgIvLy8EBcXh6NHj9qzC0TkJNKOFaDKIpQug4gUZveA8+GHHyIxMRFz5szB7t27MWDAAMTHxyMvL6/e9tu2bcOYMWMwadIk7NmzByNHjsTIkSNx4MABuc3LL7+MZcuWYcWKFUhPT0e7du0QHx+P8+fP27s7ROTgNh+5+LvFv52HgpUQkZIkIYRd/9SJiYnB9ddfj//+978AAKvVirCwMDz66KOYNWtWnfajRo1CaWkpvvzyS3neDTfcgKioKKxYsQJCCISGhmLmzJl48sknAQAmkwnBwcFYs2YNRo8efcWazGYz9Ho9TCYTfHx4ECKRmiR/fQhv/PwHxsd2xbwRfZUuh4haUXM+v+06glNZWYmMjAzExcVdfEGNBnFxcUhLS6t3nbS0NJv2ABAfHy+3z8rKgtFotGmj1+sRExPT4DaJyPXoeA8qIpfmZs+N5+fnw2KxIDjY9kyG4OBgHD58uN51jEZjve2NRqO8vHZeQ20uV1FRgYqKi5duN5vNzesIETmNsyUVV25ERKrnEmdRJScnQ6/Xy4+wsDClSyIiO7BYBT7ZfVrpMojIAdg14AQEBECr1SI3N9dmfm5uLgwGQ73rGAyGRtvX/r8520xKSoLJZJIfJ0+ebFF/iMixXXoX8bjevAYOkSuza8Dx8PBAdHQ0UlNT5XlWqxWpqamIjY2td53Y2Fib9gCwadMmuX14eDgMBoNNG7PZjPT09Aa3qdPp4OPjY/MgIvWxXnLORJ9Qvs+JXJldj8EBgMTERIwfPx6DBg3C4MGDsXTpUpSWlmLixIkAgHHjxqFTp05ITk4GADz++OO45ZZb8J///AfDhw/H+vXrsWvXLrz55psAAEmSMGPGDDz//PPo1asXwsPD8dxzzyE0NBQjR460d3eIyIFtzSyQp7WSpGAlRKQ0uwecUaNG4ezZs5g9ezaMRiOioqKQkpIiHyScnZ0NjebiQNKQIUPw/vvv49lnn8W//vUv9OrVC5999hn69r14uufTTz+N0tJSTJkyBUVFRbjxxhuRkpICT09etZTIlZ0rrZSnvTx4FhWRK7P7dXAcEa+DQ6ROD65Mxy9H83FTrwC8OylG6XKIqJU5zHVwiIja0h9nSwEAuWZe1ZzI1THgEJFqhPrWfE1973WdFa6EiJTGgENEqiCEwM7j5wAAXfy9Fa6GiJTGgENEqmA+Xy1Pd+3YTsFKiMgRMOAQkSpsOZovT3cPZMAhcnUMOESkChXVFgCAJAGevNEmkctjwCEiVbmpV6DSJRCRA2DAISJVSP7msNIlEJEDYcAhIlUor6z5isrXy13hSojIETDgEJEq1N556ok/X6NoHUTkGBhwiEhVeItNIgIYcIhIJayud1s9ImoEAw4ROb092edQeuEYHCIigAGHiFRg14VbNACAQe+pYCVE5CgYcIjI6Z06VwYA+OuAUF7kj4gAMOAQkZP7PbcYa9NOAADctDzEmIhqMOAQkVPLLiiTp+8e2EnBSojIkTDgEJEqDAjz5W0aiEjGgENERESqw4BDREREqsOAQ0RERKrDgENERESqw4BDREREqsOAQ0RO7djZEqVLICIHxIBDRE5r1/FCJH9zGACg4TX+iOgSDDhE5LSyCy9e5O+BmK4KVkJEjoYBh4ic3k29AnBvdGelyyAiB8KAQ0RERKrDgENERESqw4BDRE5rXXq20iUQkYNiwCEip2Qqr0LGiXMAgIpqq8LVEJGjsWvAKSwsxNixY+Hj4wNfX19MmjQJJSUNX7OisLAQjz76KCIiIuDl5YUuXbrgscceg8lksmknSVKdx/r16+3ZFSJyMP/3Y6Y8Pf22ngpWQkSOyM2eGx87dixycnKwadMmVFVVYeLEiZgyZQref//9etufOXMGZ86cweLFi9GnTx+cOHECDz/8MM6cOYOPP/7Ypu3q1auRkJAgP/f19bVnV4jIwRSWVsrTMd39FayEiByR3QLOoUOHkJKSgp07d2LQoEEAgNdeew133nknFi9ejNDQ0Drr9O3bF//73//k5z169MALL7yABx54ANXV1XBzu1iur68vDAaDvconIifxdEIEdG5apcsgIgdjt6+o0tLS4OvrK4cbAIiLi4NGo0F6enqTt2MymeDj42MTbgBg2rRpCAgIwODBg7Fq1SoIIVqtdiIiInJudhvBMRqNCAoKsn0xNzf4+/vDaDQ2aRv5+flYsGABpkyZYjN//vz5uP322+Ht7Y3vvvsOjzzyCEpKSvDYY4/Vu52KigpUVFTIz81mczN7Q0RERM6k2QFn1qxZWLhwYaNtDh061OKCapnNZgwfPhx9+vTB3LlzbZY999xz8vTAgQNRWlqKRYsWNRhwkpOTMW/evKuuiYiIiJxDswPOzJkzMWHChEbbdO/eHQaDAXl5eTbzq6urUVhYeMVjZ4qLi5GQkIAOHTrg008/hbu7e6PtY2JisGDBAlRUVECn09VZnpSUhMTERPm52WxGWFhYo9skIiIi59XsgBMYGIjAwMArtouNjUVRUREyMjIQHR0NAPjhhx9gtVoRExPT4Hpmsxnx8fHQ6XT44osv4OnpecXX2rt3L/z8/OoNNwCg0+kaXEZERETqY7djcHr37o2EhARMnjwZK1asQFVVFaZPn47Ro0fLZ1CdPn0aQ4cOxTvvvIPBgwfDbDZj2LBhKCsrw3vvvQez2SwfLxMYGAitVouNGzciNzcXN9xwAzw9PbFp0ya8+OKLePLJJ+3VFSIiInIydr0Ozrp16zB9+nQMHToUGo0G9957L5YtWyYvr6qqwpEjR1BWVgYA2L17t3yGVc+ethfuysrKQrdu3eDu7o7ly5fjiSeegBACPXv2xJIlSzB58mR7doWIHAzPmySixkjCBc+vNpvN0Ov18inoRORc0o4VYMxb2wHUXAfnkVt5JWMiV9Ccz2/ei4qInM5/fzwqT0d19lWuECJyWAw4ROR0vD1qvl0f0qMjhvQMULgaInJEDDhE5FTySyqw6bdcAMDtkUFXaE1ErooBh4icyu+5xfJ0THhHBSshIkfGgENETql7QDv066xXugwiclAMOETklLQaSekSiMiBMeAQERGR6jDgEBERkeow4BCRU3lv+wmlSyAiJ8CAQ0RO43h+Kb7+1QgAqLRYFa6GiBwZAw4ROY2N+87I028+OEjBSojI0THgEJHTqLbW3DpvQGc9IgwdFK6GiBwZAw4ROR1e/4aIroQBh4iIiFSHAYeIiIhUhwGHiIiIVIcBh4icxru8Bg4RNREDDhE5hRMFpSgsrQQA+Hi6K1wNETk6Bhwicgpf7L14DZyHbuquYCVE5AwYcIjIKRSVV8nT/u08FKyEiJwBAw4ROZWpt/ZQugQicgIMOETkFPaeLFK6BCJyIgw4ROTwThaWIePEOQCAu0ZSuBoicgYMOETk8AounD0FAPdc11nBSojIWTDgEJHDs4qam2x28vVCt4B2CldDRM6AAYeIHF7tKeKWC3cTJyK6EgYcInJ4v+cWA7g4kkNEdCUMOETk0I7nl2LbsQIAwH2DePwNETUNAw4ROTSj+bw8PTKqk4KVEJEzYcAhIqfQM6g9egV3ULoMInISDDhERESkOnYNOIWFhRg7dix8fHzg6+uLSZMmoaSkpNF1br31VkiSZPN4+OGHbdpkZ2dj+PDh8Pb2RlBQEJ566ilUV1fbsytERETkRNzsufGxY8ciJycHmzZtQlVVFSZOnIgpU6bg/fffb3S9yZMnY/78+fJzb29vedpisWD48OEwGAzYtm0bcnJyMG7cOLi7u+PFF1+0W1+IiIjIedgt4Bw6dAgpKSnYuXMnBg0aBAB47bXXcOedd2Lx4sUIDQ1tcF1vb28YDIZ6l3333Xf47bff8P333yM4OBhRUVFYsGABnnnmGcydOxceHrzLMJGaPPvZAaVLICInZLevqNLS0uDr6yuHGwCIi4uDRqNBenp6o+uuW7cOAQEB6Nu3L5KSklBWVmaz3X79+iE4OFieFx8fD7PZjIMHD9a7vYqKCpjNZpsHETk+i1UgM6/ma22Dj6fC1RCRM7HbCI7RaERQUJDti7m5wd/fH0ajscH1/v73v6Nr164IDQ3F/v378cwzz+DIkSP45JNP5O1eGm4AyM8b2m5ycjLmzZt3Nd0hIgXM23jxj5ZXR0cpVwgROZ1mB5xZs2Zh4cKFjbY5dOhQiwuaMmWKPN2vXz+EhIRg6NChOHbsGHr06NGibSYlJSExMVF+bjabERYW1uIaiahtHDhtkqf92/HrZyJqumYHnJkzZ2LChAmNtunevTsMBgPy8vJs5ldXV6OwsLDB42vqExMTAwDIzMxEjx49YDAYsGPHDps2ubm5ANDgdnU6HXQ6XZNfk4gcy5sPRkOSJKXLICIn0uyAExgYiMDAwCu2i42NRVFRETIyMhAdHQ0A+OGHH2C1WuXQ0hR79+4FAISEhMjbfeGFF5CXlyd/BbZp0yb4+PigT58+zewNERERqZHdDjLu3bs3EhISMHnyZOzYsQNbt27F9OnTMXr0aPkMqtOnTyMyMlIekTl27BgWLFiAjIwMHD9+HF988QXGjRuHm2++Gf379wcADBs2DH369MGDDz6Iffv24dtvv8Wzzz6LadOmcZSGiIiIANj5Qn/r1q1DZGQkhg4dijvvvBM33ngj3nzzTXl5VVUVjhw5Ip8l5eHhge+//x7Dhg1DZGQkZs6ciXvvvRcbN26U19Fqtfjyyy+h1WoRGxuLBx54AOPGjbO5bg4RqYOFdw8nohaShHC93yBmsxl6vR4mkwk+Pj5Kl0NE9dhyNB8PrKy5pMSbD0Zj2LVNP3aPiNSpOZ/fvBcVETmkT/eclqd7h/APESJqHgYcInJI/9t9CgDwt+jOCPP3vkJrIiJbDDhE5JD8vN0BALdFBF2hJRFRXQw4RORwqixWnCurAgBcE9xe4WqIyBkx4BCRw/n597PytLfObneUISIVY8AhIoeTevjiVdA7+XopWAkROSsGHCJyOFsz8wEAA8J8lS2EiJwWAw4ROZTTReU4UVBz8c87+vLaN0TUMgw4RORQCkoq5Om7B3ZSsBIicmYMOETkkDr5eiHYx1PpMojISTHgEJFDWbP1uNIlEJEKMOAQkUM5drYEAHC+yqJwJUTkzBhwiMhhnC2uwL5TJgBA8j39FK6GiJwZAw4ROYwDp03yNG+wSURXgwGHiByGxSoAAD2D2vMGm0R0VRhwiMghFJ+vwkPv7AIAeLlrFa6GiJwdAw4ROYQ/zpbK0zdfE6BgJUSkBgw4RORQAtrr8FR8pNJlEJGTY8AhIoeic+OvJSK6evxNQkQOoayS170hotbDgENEituTfQ5j3tqudBlEpCIMOESkuEuvfzPs2mAFKyEitWDAISKHcWc/A+bcda3SZRCRCjDgEBERkeow4BCRoqxWgVe+P6p0GUSkMgw4RKSofaeKUFhaCQDQe3koXA0RqQUDDhEp6mheiTw9c9g1ClZCRGrCgENEinr7lz8AAP7tPBDQXqdwNUSkFgw4RKQYU3kVfs+tGcG5sSfvP0VErYcBh4gU8+JXh+Tpuwd2UrASIlIbBhwiUkx+SQUAwMtdi5t6cQSHiFqPXQNOYWEhxo4dCx8fH/j6+mLSpEkoKSlpsP3x48chSVK9jw0bNsjt6lu+fv16e3aFiOxo3l+vhZuWf28RUetxs+fGx44di5ycHGzatAlVVVWYOHEipkyZgvfff7/e9mFhYcjJybGZ9+abb2LRokW44447bOavXr0aCQkJ8nNfX99Wr5+IiIick90CzqFDh5CSkoKdO3di0KBBAIDXXnsNd955JxYvXozQ0NA662i1WhgMBpt5n376Ke6//360b9/eZr6vr2+dtkTkPF79/nekHs5TugwiUim7jQmnpaXB19dXDjcAEBcXB41Gg/T09CZtIyMjA3v37sWkSZPqLJs2bRoCAgIwePBgrFq1CkKIBrdTUVEBs9ls8yAi5RSUVNhcvTigAy/wR0Sty24jOEajEUFBQbYv5uYGf39/GI3GJm1j5cqV6N27N4YMGWIzf/78+bj99tvh7e2N7777Do888ghKSkrw2GOP1bud5ORkzJs3r2UdIaJWV229+AfJW+Oices1QY20JiJqvmaP4MyaNavBA4FrH4cPH77qwsrLy/H+++/XO3rz3HPP4U9/+hMGDhyIZ555Bk8//TQWLVrU4LaSkpJgMpnkx8mTJ6+6PiK6em4aCX/uY4BGIyldChGpTLNHcGbOnIkJEyY02qZ79+4wGAzIy7P9fr26uhqFhYVNOnbm448/RllZGcaNG3fFtjExMViwYAEqKiqg09W9EqpOp6t3PhG1PatV4N+f/qp0GUSkcs0OOIGBgQgMDLxiu9jYWBQVFSEjIwPR0dEAgB9++AFWqxUxMTFXXH/lypX461//2qTX2rt3L/z8/BhiiJzAkdxifH+o5o+fYB9PhashIrWy2zE4vXv3RkJCAiZPnowVK1agqqoK06dPx+jRo+UzqE6fPo2hQ4finXfeweDBg+V1MzMz8fPPP+Prr7+us92NGzciNzcXN9xwAzw9PbFp0ya8+OKLePLJJ+3VFSJqRZZLjr/56OFYBSshIjWz63Vw1q1bh+nTp2Po0KHQaDS49957sWzZMnl5VVUVjhw5grKyMpv1Vq1ahc6dO2PYsGF1tunu7o7ly5fjiSeegBACPXv2xJIlSzB58mR7doWIWsl/vjsCADD4eKKTr5fC1RCRWkmisfOrVcpsNkOv18NkMsHHx0fpcohcRn5JBQY9/z0AIMzfC788fbvCFRGRM2nO5zevjU5Ebealby6eYfnxw0MaaUlEdHUYcIioTVRWW/FxxikAgH87Dx5gTER2xYBDRG3i24MXL/C5YERfBSshIlfAgENEdieE7bVvhvbmlYuJyL4YcIjI7jLzSmA+Xw0AeOCGLvB01ypcERGpHQMOEdnd279kydNPJ0QqWAkRuQoGHCKyuw93Xbz/m4+nu4KVEJGrYMAhIrs6WXjxQp4v3t1PwUqIyJUw4BCRXT3zv/3y9P2DOitYCRG5EgYcIrKbaosV244VAAC6B7SDm5a/coiobfC3DRHZzT2vb5On/3P/AAUrISJXw4BDRHaRlV+K/adMAAAPrQYDu/gpXBERuRIGHCKyi9sWb5andz4bp1whROSSGHCIqNU98/HFA4vHDA6D3ounhhNR22LAIaJWda600ua6Nzw1nIiUwIBDRK1q9bbj8vQHk2+AJEnKFUNELosBh4haTa75PJalHgVQc2DxDd39Fa6IiFwVAw4RtYqSimqMfTtdfr5ywiCO3hCRYtyULoCInJ/FKhD/ys84XVQOAOgd4oObegUqXBURuTKO4BDRVduRVSCHmy7+3lg2OkrZgojI5XEEh4iuyud7T+Px9XsBAJIEfPfEzfB01ypbFBG5PI7gEFGLnSkql8MNACTGXcNwQ0QOgSM4RNQiB06b8JfXtsjP/2/sdbizX4iCFRERXcSAQ0TNIoTAkk2/47UfMuV5cb2DGG6IyKEw4BBRk1msAr1np6Cy2irPu2tAKJLv4dWKicixMOAQUZNUWazo9e9vbOZ9/HAsBnXjxfyIyPEw4BDRFdUXbn5//g54uPE8BSJyTAw4RNSgKosV9/zfNvx62mQzP/OFO+CmZbghIsfFgENE9Xpqwz5syDhlM697QDukzryFt2AgIofHgENEsp3HC7Es9Sh+OZpfZ9nWWbejk6+XAlURETUfAw6RC7NaBXYeL8RLKYexJ7uo3jYrxw/C0N7BbVsYEdFVstuX6C+88AKGDBkCb29v+Pr6NmkdIQRmz56NkJAQeHl5IS4uDkePHrVpU1hYiLFjx8LHxwe+vr6YNGkSSkpK7NADIvURQmBrZj4eXJmOvnO+Rfd/fY1Rb26vE27iegdh0xM3448X72S4ISKnZLcRnMrKStx3332IjY3FypUrm7TOyy+/jGXLlmHt2rUIDw/Hc889h/j4ePz222/w9PQEAIwdOxY5OTnYtGkTqqqqMHHiREyZMgXvv/++vbpC5NCEEPgjvxRCCAT5eKKy2ooqixVllRZszczHj4fzcOxsKbQaCVn5pQ1up2tHbywbPRADwnzbrngiIjuRhBDCni+wZs0azJgxA0VFRY22E0IgNDQUM2fOxJNPPgkAMJlMCA4Oxpo1azB69GgcOnQIffr0wc6dOzFo0CAAQEpKCu68806cOnUKoaGhTarJbDZDr9fDZDLBx8fnqvrnyMorLfDyaNp9gcorLXDTSnDTSFd1AKnVKlBcUQ1JAiQAkiRBAqCRJGg0gM6t/nqEEMgvqUQHTzfo3DQ2NdT+E22sLiEEhAAEAKsQKD5fDasQ8HTXwttdC41GsmlnEQLW2mmrgEUI6Nw0NvWVVlSjrNICSQLaebjJP0shBKwCKCqrhLubBlpJgkaS4OleMyBau71Lt221ClisAhXVVpwpKkdRWRXC/L1rfmaX1WIVAqUVFuw/XQQ3jQQvDzf8dsaMMH8vHM4phqe7Btv/KERhaSVKKqpbuqsAAK+OjsKt1wRB7+1+VdshImoLzfn8dphjcLKysmA0GhEXFyfP0+v1iImJQVpaGkaPHo20tDT4+vrK4QYA4uLioNFokJ6ejrvvvrvebVdUVKCiokJ+bjab7dKHdekn8O9PD9jMq/1MluTnkvz88s/rS5fZrivV2dblH/a1T2s/HC+lc9PA20MLi7XmQ9QqLnzoippAUm0VNttx12hQabFC7+VeEwgA1MbgS58LXAwVuLDdS7fVGHetBDeNBlqNBEkCis9X11leZRHw83bHubIqAIBGAi7dvJtGqgkGl9TXEG1t2yaUV/va9bm8BkflrpVgvRCYtBoJfx/cBTdfE4jADjoEtvdAJz9vpUskIrIrhwk4RqMRABAcbPt9f3BwsLzMaDQiKCjIZrmbmxv8/f3lNvVJTk7GvHnzWrnippGDweUz6m9tlxoqqq2ouOTS+o0RAqi01LQ1lVfZpR4AqLIIVFksjS4HIIcboG6waGqYAmo+6JtTW0NaM9z4t/OARqoJqxrpwijXhZEujSThREEZACBE74lIQwecKCjDoG5+2JpZgE5+XvDQanDXgBB09vNGqK8XQn09IUGCu/bqRuGIiNSgWQFn1qxZWLhwYaNtDh06hMjIyKsqqrUlJSUhMTFRfm42mxEWFtbqr3PvdZ0Rf63hklBjm25sRkIuzLw8ANV+HXN5Drp0vQbXudCuqKwSgR108HDToKisCpIEaKWaDz2t5pIP0wvTWkmSv5qpqLagpKIaVlF3xKh21EmCdHGZZLsMAHy9POTpS0d6ckzlMJ+vhkaSEOyjQ7Wl5mubkopqVFsFuvp7QyNJqKi2oKi8ClUWqzzK006nhVaSUFZpwcELX9cE+3herO1Cn2rrcNNqIKHmqyhTWRU83DQXAkTdIFE7XXy+GpXVVrmPHm4a+LfzAADkmc+j2irgpqn5uWklCd46Lczl1ThXVonA9jrkFVfA19sdHlpNTZsL7TQayF9j1X5VRkRE9tWsgDNz5kxMmDCh0Tbdu3dvUSEGgwEAkJubi5CQi3clzs3NRVRUlNwmLy/PZr3q6moUFhbK69dHp9NBp9O1qK7m8HTXwtO9ace8tJUQfXOvW+KOoCs3apGeQR2aXoOPZ4NLuwW0a9br+ng27fgSXfuG911D9QR20CKwQ82/Lb8LYYiIiJTXrIATGBiIwMBAuxQSHh4Og8GA1NRUOdCYzWakp6dj6tSpAIDY2FgUFRUhIyMD0dHRAIAffvgBVqsVMTExdqmLiIiInI/droOTnZ2NvXv3Ijs7GxaLBXv37sXevXttrlkTGRmJTz/9FEDN1wwzZszA888/jy+++AK//vorxo0bh9DQUIwcORIA0Lt3byQkJGDy5MnYsWMHtm7diunTp2P06NFNPoOKiIiI1M9uBxnPnj0ba9eulZ8PHDgQAPDjjz/i1ltvBQAcOXIEJtPFm/g9/fTTKC0txZQpU1BUVIQbb7wRKSkp8jVwAGDdunWYPn06hg4dCo1Gg3vvvRfLli2zVzeIiIjICdn9OjiOyFWug0NERKQmzfn8tttXVERERERKYcAhIiIi1WHAISIiItVhwCEiIiLVYcAhIiIi1WHAISIiItVhwCEiIiLVYcAhIiIi1WHAISIiItVhwCEiIiLVsdu9qBxZ7d0pzGazwpUQERFRU9V+bjflLlMuGXCKi4sBAGFhYQpXQkRERM1VXFwMvV7faBuXvNmm1WrFmTNn0KFDB0iS1GrbNZvNCAsLw8mTJ1V7E0+191Ht/QPU30e19w9Qfx/V3j9A/X20V/+EECguLkZoaCg0msaPsnHJERyNRoPOnTvbbfs+Pj6q/Ad7KbX3Ue39A9TfR7X3D1B/H9XeP0D9fbRH/640clOLBxkTERGR6jDgEBERkeow4LQinU6HOXPmQKfTKV2K3ai9j2rvH6D+Pqq9f4D6+6j2/gHq76Mj9M8lDzImIiIideMIDhEREakOAw4RERGpDgMOERERqQ4DDhEREakOA04zvfDCCxgyZAi8vb3h6+tbb5vs7GwMHz4c3t7eCAoKwlNPPYXq6upGt1tYWIixY8fCx8cHvr6+mDRpEkpKSuzQg+bZvHkzJEmq97Fz584G17v11lvrtH/44YfbsPKm69atW51aX3rppUbXOX/+PKZNm4aOHTuiffv2uPfee5Gbm9tGFTfP8ePHMWnSJISHh8PLyws9evTAnDlzUFlZ2eh6jrwPly9fjm7dusHT0xMxMTHYsWNHo+03bNiAyMhIeHp6ol+/fvj666/bqNLmS05OxvXXX48OHTogKCgII0eOxJEjRxpdZ82aNXX2laenZxtV3Dxz586tU2tkZGSj6zjT/gPq/50iSRKmTZtWb3tH338///wz7rrrLoSGhkKSJHz22Wc2y4UQmD17NkJCQuDl5YW4uDgcPXr0ittt7vu4uRhwmqmyshL33Xcfpk6dWu9yi8WC4cOHo7KyEtu2bcPatWuxZs0azJ49u9Htjh07FgcPHsSmTZvw5Zdf4ueff8aUKVPs0YVmGTJkCHJycmweDz30EMLDwzFo0KBG1508ebLNei+//HIbVd188+fPt6n10UcfbbT9E088gY0bN2LDhg346aefcObMGdxzzz1tVG3zHD58GFarFW+88QYOHjyIV155BStWrMC//vWvK67riPvwww8/RGJiIubMmYPdu3djwIABiI+PR15eXr3tt23bhjFjxmDSpEnYs2cPRo4ciZEjR+LAgQNtXHnT/PTTT5g2bRq2b9+OTZs2oaqqCsOGDUNpaWmj6/n4+NjsqxMnTrRRxc137bXX2tS6ZcuWBts62/4DgJ07d9r0b9OmTQCA++67r8F1HHn/lZaWYsCAAVi+fHm9y19++WUsW7YMK1asQHp6Otq1a4f4+HicP3++wW02933cIoJaZPXq1UKv19eZ//XXXwuNRiOMRqM87/XXXxc+Pj6ioqKi3m399ttvAoDYuXOnPO+bb74RkiSJ06dPt3rtV6OyslIEBgaK+fPnN9rulltuEY8//njbFHWVunbtKl555ZUmty8qKhLu7u5iw4YN8rxDhw4JACItLc0OFba+l19+WYSHhzfaxlH34eDBg8W0adPk5xaLRYSGhork5OR6299///1i+PDhNvNiYmLEP//5T7vW2Vry8vIEAPHTTz812Kah30eOaM6cOWLAgAFNbu/s+08IIR5//HHRo0cPYbVa613uTPsPgPj000/l51arVRgMBrFo0SJ5XlFRkdDpdOKDDz5ocDvNfR+3BEdwWllaWhr69euH4OBgeV58fDzMZjMOHjzY4Dq+vr42IyJxcXHQaDRIT0+3e83N8cUXX6CgoAATJ068Ytt169YhICAAffv2RVJSEsrKytqgwpZ56aWX0LFjRwwcOBCLFi1q9CvFjIwMVFVVIS4uTp4XGRmJLl26IC0trS3KvWomkwn+/v5XbOdo+7CyshIZGRk2P3uNRoO4uLgGf/ZpaWk27YGa96Qz7SsAV9xfJSUl6Nq1K8LCwjBixIgGf984gqNHjyI0NBTdu3fH2LFjkZ2d3WBbZ99/lZWVeO+99/CPf/yj0Zs7O9P+u1RWVhaMRqPNPtLr9YiJiWlwH7XkfdwSLnmzTXsyGo024QaA/NxoNDa4TlBQkM08Nzc3+Pv7N7iOUlauXIn4+Pgr3qz073//O7p27YrQ0FDs378fzzzzDI4cOYJPPvmkjSptusceewzXXXcd/P39sW3bNiQlJSEnJwdLliypt73RaISHh0edY7CCg4Mdbn/VJzMzE6+99hoWL17caDtH3If5+fmwWCz1vscOHz5c7zoNvSedYV9ZrVbMmDEDf/rTn9C3b98G20VERGDVqlXo378/TCYTFi9ejCFDhuDgwYN2vbFwS8TExGDNmjWIiIhATk4O5s2bh5tuugkHDhxAhw4d6rR35v0HAJ999hmKioowYcKEBts40/67XO1+aM4+asn7uCUYcADMmjULCxcubLTNoUOHrnggnDNpSZ9PnTqFb7/9Fh999NEVt3/p8UP9+vVDSEgIhg4dimPHjqFHjx4tL7yJmtO/xMREeV7//v3h4eGBf/7zn0hOTnboy6i3ZB+ePn0aCQkJuO+++zB58uRG11V6HxIwbdo0HDhwoNFjVAAgNjYWsbGx8vMhQ4agd+/eeOONN7BgwQJ7l9ksd9xxhzzdv39/xMTEoGvXrvjoo48wadIkBSuzj5UrV+KOO+5AaGhog22caf85EwYcADNnzmw0XQNA9+7dm7Qtg8FQ50jw2rNrDAZDg+tcfmBVdXU1CgsLG1znarWkz6tXr0bHjh3x17/+tdmvFxMTA6Bm9KAtPhyvZp/GxMSguroax48fR0RERJ3lBoMBlZWVKCoqshnFyc3Ntdv+qk9z+3jmzBncdtttGDJkCN58881mv15b78P6BAQEQKvV1jljrbGfvcFgaFZ7RzF9+nT5hIPm/hXv7u6OgQMHIjMz007VtR5fX19cc801DdbqrPsPAE6cOIHvv/++2aOezrT/avdDbm4uQkJC5Pm5ubmIioqqd52WvI9bpNWO5nExVzrIODc3V573xhtvCB8fH3H+/Pl6t1V7kPGuXbvked9++61DHWRstVpFeHi4mDlzZovW37JliwAg9u3b18qVtb733ntPaDQaUVhYWO/y2oOMP/74Y3ne4cOHHfog41OnTolevXqJ0aNHi+rq6hZtw1H24eDBg8X06dPl5xaLRXTq1KnRg4z/8pe/2MyLjY112INUrVarmDZtmggNDRW///57i7ZRXV0tIiIixBNPPNHK1bW+4uJi4efnJ1599dV6lzvb/rvUnDlzhMFgEFVVVc1az5H3Hxo4yHjx4sXyPJPJ1KSDjJvzPm5Rra22JRdx4sQJsWfPHjFv3jzRvn17sWfPHrFnzx5RXFwshKj5h9m3b18xbNgwsXfvXpGSkiICAwNFUlKSvI309HQREREhTp06Jc9LSEgQAwcOFOnp6WLLli2iV69eYsyYMW3ev4Z8//33AoA4dOhQnWWnTp0SERERIj09XQghRGZmppg/f77YtWuXyMrKEp9//rno3r27uPnmm9u67Cvatm2beOWVV8TevXvFsWPHxHvvvScCAwPFuHHj5DaX908IIR5++GHRpUsX8cMPP4hdu3aJ2NhYERsbq0QXrujUqVOiZ8+eYujQoeLUqVMiJydHflzaxln24fr164VOpxNr1qwRv/32m5gyZYrw9fWVz1x88MEHxaxZs+T2W7duFW5ubmLx4sXi0KFDYs6cOcLd3V38+uuvSnWhUVOnThV6vV5s3rzZZl+VlZXJbS7v47x588S3334rjh07JjIyMsTo0aOFp6enOHjwoBJdaNTMmTPF5s2bRVZWlti6dauIi4sTAQEBIi8vTwjh/PuvlsViEV26dBHPPPNMnWXOtv+Ki4vlzzoAYsmSJWLPnj3ixIkTQgghXnrpJeHr6ys+//xzsX//fjFixAgRHh4uysvL5W3cfvvt4rXXXpOfX+l93BoYcJpp/PjxAkCdx48//ii3OX78uLjjjjuEl5eXCAgIEDNnzrRJ8D/++KMAILKysuR5BQUFYsyYMaJ9+/bCx8dHTJw4UQ5NjmDMmDFiyJAh9S7Lysqy+RlkZ2eLm2++Wfj7+wudTid69uwpnnrqKWEymdqw4qbJyMgQMTExQq/XC09PT9G7d2/x4osv2oy2Xd4/IYQoLy8XjzzyiPDz8xPe3t7i7rvvtgkMjmT16tX1/pu9dADX2fbha6+9Jrp06SI8PDzE4MGDxfbt2+Vlt9xyixg/frxN+48++khcc801wsPDQ1x77bXiq6++auOKm66hfbV69Wq5zeV9nDFjhvzzCA4OFnfeeafYvXt32xffBKNGjRIhISHCw8NDdOrUSYwaNUpkZmbKy519/9X69ttvBQBx5MiROsucbf/VfmZd/qjtg9VqFc8995wIDg4WOp1ODB06tE6/u3btKubMmWMzr7H3cWuQhBCi9b7wIiIiIlIer4NDREREqsOAQ0RERKrDgENERESqw4BDREREqsOAQ0RERKrDgENERESqw4BDREREqsOAQ0RERKrDgENERESqw4BDREREqsOAQ0RO7+zZszAYDHjxxRfledu2bYOHhwdSU1MVrIyIlMJ7URGRKnz99dcYOXIktm3bhoiICERFRWHEiBFYsmSJ0qURkQIYcIhINaZNm4bvv/8egwYNwq+//oqdO3dCp9MpXRYRKYABh4hUo7y8HH379sXJkyeRkZGBfv36KV0SESmEx+AQkWocO3YMZ86cgdVqxfHjx5Uuh4gUxBEcIlKFyspKDB48GFFRUYiIiMDSpUvx66+/IigoSOnSiEgBDDhEpApPPfUUPv74Y+zbtw/t27fHLbfcAr1ejy+//FLp0ohIAfyKioic3ubNm7F06VK8++678PHxgUajwbvvvotffvkFr7/+utLlEZECOIJDREREqsMRHCIiIlIdBhwiIiJSHQYcIiIiUh0GHCIiIlIdBhwiIiJSHQYcIiIiUh0GHCIiIlIdBhwiIiJSHQYcIiIiUh0GHCIiIlIdBhwiIiJSHQYcIiIiUp3/BxNfAF54HDFkAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2655,18 +4001,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.1%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-9.99, -3.46)\": -1.0, \"(-3.46, -3.08)\": -0.997, \"(-3.08, -2.72)\": -0.995, \"(-2.72, -2.65)\": -0.991, \"(-2.65, -2.51)\": -0.989, \"(-2.51, -2.43)\": -0.986, \"(-2.43, -2.34)\": -0.984, \"(-2.34, -2.26)\": -0.981, \"(-2.26, -2.19)\": -0.977, \"(-2.19, -2.13)\": -0.974, \"(-2.13, -2.05)\": -0.97, \"(-2.05, -2.01)\": -0.967, \"(-2.01, -1.98)\": -0.964, \"(-1.98, -1.94)\": -0.962, \"(-1.94, -1.89)\": -0.958, \"(-1.89, -1.86)\": -0.955, \"(-1.86, -1.85)\": -0.952, \"(-1.85, -1.82)\": -0.95, \"(-1.82, -1.8)\": -0.947, \"(-1.8, -1.77)\": -0.943, \"(-1.77, -1.73)\": -0.939, \"(-1.73, -1.68)\": -0.935, \"(-1.68, -1.62)\": -0.929, \"(-1.62, -1.59)\": -0.922, \"(-1.59, -1.57)\": -0.918, \"(-1.57, -1.52)\": -0.914, \"(-1.52, -1.46)\": -0.903, \"(-1.46, -1.44)\": -0.896, \"(-1.44, -1.41)\": -0.891, \"(-1.41, -1.38)\": -0.883, \"(-1.38, -1.37)\": -0.88, \"(-1.37, -1.36)\": -0.876, \"(-1.36, -1.34)\": -0.873, \"(-1.34, -1.33)\": -0.87, \"(-1.33, -1.3)\": -0.865, \"(-1.3, -1.27)\": -0.857, \"(-1.27, -1.25)\": -0.853, \"(-1.25, -1.24)\": -0.849, \"(-1.24, -1.23)\": -0.845, \"(-1.23, -1.21)\": -0.839, \"(-1.21, -1.2)\": -0.836, \"(-1.2, -1.19)\": -0.833, \"(-1.19, -1.18)\": -0.828, \"(-1.18, -1.17)\": -0.824, \"(-1.17, -1.14)\": -0.816, \"(-1.14, -1.11)\": -0.806, \"(-1.11, -1.09)\": -0.798, \"(-1.09, -1.07)\": -0.791, \"(-1.07, -1.04)\": -0.785, \"(-1.04, -0.99)\": -0.768, \"(-0.99, -0.93)\": -0.745, \"(-0.93, -0.89)\": -0.717, \"(-0.89, -0.88)\": -0.71, \"(-0.88, -0.87)\": -0.704, \"(-0.87, -0.83)\": -0.691, \"(-0.83, -0.78)\": -0.665, \"(-0.78, -0.73)\": -0.635, \"(-0.73, -0.71)\": -0.617, \"(-0.71, -0.69)\": -0.606, \"(-0.69, -0.66)\": -0.585, \"(-0.66, -0.65)\": -0.577, \"(-0.65, -0.65)\": -0.571, \"(-0.65, -0.63)\": -0.566, \"(-0.63, -0.61)\": -0.55, \"(-0.61, -0.58)\": -0.54, \"(-0.58, -0.55)\": -0.503, \"(-0.55, -0.51)\": -0.492, \"(-0.51, -0.48)\": -0.454, \"(-0.48, -0.47)\": -0.442, \"(-0.47, -0.44)\": -0.424, \"(-0.44, -0.42)\": -0.402, \"(-0.42, -0.39)\": -0.393, \"(-0.39, -0.36)\": -0.35, \"(-0.36, -0.33)\": -0.343, \"(-0.33, -0.31)\": -0.306, \"(-0.31, -0.3)\": -0.295, \"(-0.3, -0.26)\": -0.289, \"(-0.26, -0.23)\": -0.226, \"(-0.23, -0.21)\": -0.217, \"(-0.21, -0.16)\": -0.186, \"(-0.16, -0.12)\": -0.132, \"(-0.12, -0.1)\": -0.115, \"(-0.1, -0.07)\": -0.086, \"(-0.07, -0.06)\": -0.064, \"(-0.06, -0.05)\": -0.055, \"(-0.05, -0.04)\": -0.041, \"(-0.04, -0.03)\": -0.036, \"(-0.03, -0.02)\": -0.024, \"(-0.02, 0.01)\": -0.006, \"(0.01, 0.03)\": 0.022, \"(0.03, 0.05)\": 0.042, \"(0.05, 0.05)\": 0.05, \"(0.05, 0.07)\": 0.062, \"(0.07, 0.09)\": 0.078, \"(0.09, 0.09)\": 0.088, \"(0.09, 0.1)\": 0.094, \"(0.1, 0.11)\": 0.103, \"(0.11, 0.13)\": 0.118, \"(0.13, 0.15)\": 0.138, \"(0.15, 0.15)\": 0.148, \"(0.15, 0.16)\": 0.156, \"(0.16, 0.22)\": 0.176, \"(0.22, 0.26)\": 0.248, \"(0.26, 0.27)\": 0.257, \"(0.27, 0.28)\": 0.267, \"(0.28, 0.31)\": 0.283, \"(0.31, 0.37)\": 0.334, \"(0.37, 0.4)\": 0.374, \"(0.4, 0.42)\": 0.388, \"(0.42, 0.43)\": 0.402, \"(0.43, 0.44)\": 0.411, \"(0.44, 0.45)\": 0.419, \"(0.45, 0.46)\": 0.425, \"(0.46, 0.47)\": 0.429, \"(0.47, 0.47)\": 0.434, \"(0.47, 0.47)\": 0.438, \"(0.47, 0.48)\": 0.442, \"(0.48, 0.48)\": 0.449, \"(0.48, 0.49)\": 0.451, \"(0.49, 0.51)\": 0.463, \"(0.51, 0.54)\": 0.482, \"(0.54, 0.54)\": 0.495, \"(0.54, 0.57)\": 0.502, \"(0.57, 0.59)\": 0.525, \"(0.59, 0.61)\": 0.535, \"(0.61, 0.63)\": 0.55, \"(0.63, 0.64)\": 0.56, \"(0.64, 0.64)\": 0.564, \"(0.64, 0.67)\": 0.572, \"(0.67, 0.69)\": 0.594, \"(0.69, 0.71)\": 0.605, \"(0.71, 0.74)\": 0.621, \"(0.74, 0.77)\": 0.638, \"(0.77, 0.78)\": 0.649, \"(0.78, 0.79)\": 0.654, \"(0.79, 0.8)\": 0.661, \"(0.8, 0.8)\": 0.665, \"(0.8, 0.84)\": 0.673, \"(0.84, 0.89)\": 0.704, \"(0.89, 0.89)\": 0.708, \"(0.89, 0.9)\": 0.713, \"(0.9, 0.91)\": 0.717, \"(0.91, 0.93)\": 0.726, \"(0.93, 0.96)\": 0.733, \"(0.96, 0.99)\": 0.753, \"(0.99, 1.0)\": 0.758, \"(1.0, 1.01)\": 0.762, \"(1.01, 1.02)\": 0.768, \"(1.02, 1.05)\": 0.773, \"(1.05, 1.09)\": 0.792, \"(1.09, 1.1)\": 0.798, \"(1.1, 1.12)\": 0.804, \"(1.12, 1.15)\": 0.81, \"(1.15, 1.18)\": 0.822, \"(1.18, 1.19)\": 0.828, \"(1.19, 1.23)\": 0.834, \"(1.23, 1.31)\": 0.852, \"(1.31, 1.37)\": 0.87, \"(1.37, 1.41)\": 0.881, \"(1.41, 1.43)\": 0.885, \"(1.43, 1.46)\": 0.892, \"(1.46, 1.46)\": 0.896, \"(1.46, 1.48)\": 0.899, \"(1.48, 1.5)\": 0.903, \"(1.5, 1.51)\": 0.906, \"(1.51, 1.54)\": 0.91, \"(1.54, 1.54)\": 0.914, \"(1.54, 1.58)\": 0.916, \"(1.58, 1.63)\": 0.922, \"(1.63, 1.65)\": 0.926, \"(1.65, 1.69)\": 0.93, \"(1.69, 1.72)\": 0.934, \"(1.72, 1.72)\": 0.936, \"(1.72, 1.74)\": 0.939, \"(1.74, 1.77)\": 0.943, \"(1.77, 1.8)\": 0.945, \"(1.8, 1.83)\": 0.948, \"(1.83, 1.86)\": 0.95, \"(1.86, 1.91)\": 0.952, \"(1.91, 1.94)\": 0.958, \"(1.94, 1.97)\": 0.96, \"(1.97, 2.0)\": 0.962, \"(2.0, 2.02)\": 0.964, \"(2.02, 2.06)\": 0.966, \"(2.06, 2.12)\": 0.97, \"(2.12, 2.17)\": 0.973, \"(2.17, 2.26)\": 0.975, \"(2.26, 2.31)\": 0.978, \"(2.31, 2.39)\": 0.981, \"(2.39, 2.49)\": 0.983, \"(2.49, 2.56)\": 0.987, \"(2.56, 2.69)\": 0.989, \"(2.69, 2.82)\": 0.991, \"(2.82, 3.02)\": 0.993, \"(3.02, 3.35)\": 0.996, \"(3.35, 3.91)\": 0.998, \"(3.91, 9.97)\": 1.0}\n",
+      "Means: {\"(-9.98, -9.95)\": 10655.8, \"(-9.95, -9.95)\": 10525.1, \"(-9.95, -9.93)\": 10392.5, \"(-9.93, -9.92)\": 10223.9, \"(-9.92, -9.9)\": 10102.8, \"(-9.9, -9.88)\": 9809.9, \"(-9.88, -9.86)\": 9685.1, \"(-9.86, -9.84)\": 9494.3, \"(-9.84, -9.82)\": 9283.6, \"(-9.82, -9.81)\": 9159.7, \"(-9.81, -9.79)\": 9046.5, \"(-9.79, -9.78)\": 8906.3, \"(-9.78, -9.76)\": 8774.1, \"(-9.76, -9.75)\": 8631.5, \"(-9.75, -9.73)\": 8497.3, \"(-9.73, -9.7)\": 8319.5, \"(-9.7, -9.68)\": 8052.3, \"(-9.68, -9.64)\": 7909.7, \"(-9.64, -9.61)\": 7602.0, \"(-9.61, -9.54)\": 7340.8, \"(-9.54, -9.49)\": 6717.4, \"(-9.49, -9.47)\": 6589.9, \"(-9.47, -9.46)\": 6434.6, \"(-9.46, -9.41)\": 6322.6, \"(-9.41, -9.36)\": 5939.2, \"(-9.36, -9.34)\": 5777.1, \"(-9.34, -9.3)\": 5657.0, \"(-9.3, -9.26)\": 5351.5, \"(-9.26, -9.24)\": 5225.7, \"(-9.24, -9.22)\": 5083.4, \"(-9.22, -9.18)\": 4958.9, \"(-9.18, -9.15)\": 4790.2, \"(-9.15, -9.11)\": 4676.8, \"(-9.11, -9.08)\": 4526.4, \"(-9.08, -9.04)\": 4343.9, \"(-9.04, -8.98)\": 4177.3, \"(-8.98, -8.94)\": 3884.2, \"(-8.94, -8.89)\": 3747.1, \"(-8.89, -8.85)\": 3590.7, \"(-8.85, -8.79)\": 3416.8, \"(-8.79, -8.75)\": 3246.1, \"(-8.75, -8.71)\": 3127.2, \"(-8.71, -8.67)\": 3015.2, \"(-8.67, -8.64)\": 2882.0, \"(-8.64, -8.57)\": 2746.4, \"(-8.57, -8.51)\": 2594.9, \"(-8.51, -8.46)\": 2469.6, \"(-8.46, -8.4)\": 2334.7, \"(-8.4, -8.34)\": 2201.2, \"(-8.34, -8.27)\": 2082.8, \"(-8.27, -8.23)\": 1959.0, \"(-8.23, -8.15)\": 1832.0, \"(-8.15, -8.09)\": 1718.6, \"(-8.09, -8.02)\": 1604.9, \"(-8.02, -7.89)\": 1475.3, \"(-7.89, -7.77)\": 1304.5, \"(-7.77, -7.68)\": 1188.9, \"(-7.68, -7.56)\": 1072.0, \"(-7.56, -7.45)\": 960.8, \"(-7.45, -7.29)\": 838.4, \"(-7.29, -7.12)\": 726.7, \"(-7.12, -6.9)\": 605.2, \"(-6.9, -6.63)\": 491.2, \"(-6.63, -6.29)\": 373.9, \"(-6.29, -5.72)\": 265.0, \"(-5.72, -4.44)\": 154.2, \"(-4.44, 5.69)\": 44.8, \"(5.69, 6.25)\": 154.4, \"(6.25, 6.62)\": 266.9, \"(6.62, 6.88)\": 381.9, \"(6.88, 7.08)\": 493.0, \"(7.08, 7.27)\": 608.6, \"(7.27, 7.42)\": 727.0, \"(7.42, 7.55)\": 844.7, \"(7.55, 7.66)\": 960.5, \"(7.66, 7.75)\": 1071.6, \"(7.75, 7.86)\": 1182.3, \"(7.86, 7.94)\": 1299.1, \"(7.94, 8.01)\": 1419.2, \"(8.01, 8.07)\": 1545.5, \"(8.07, 8.18)\": 1661.4, \"(8.18, 8.25)\": 1803.5, \"(8.25, 8.32)\": 1942.2, \"(8.32, 8.43)\": 2064.1, \"(8.43, 8.53)\": 2469.3, \"(8.53, 8.6)\": 2605.6, \"(8.6, 8.63)\": 2728.5, \"(8.63, 8.7)\": 2864.2, \"(8.7, 8.74)\": 3006.2, \"(8.74, 8.79)\": 3178.0, \"(8.79, 8.83)\": 3322.3, \"(8.83, 8.89)\": 3523.4, \"(8.89, 8.92)\": 3644.9, \"(8.92, 8.96)\": 3789.2, \"(8.96, 8.99)\": 3916.2, \"(8.99, 9.04)\": 4119.5, \"(9.04, 9.1)\": 4330.1, \"(9.1, 9.13)\": 4490.0, \"(9.13, 9.2)\": 4781.8, \"(9.2, 9.23)\": 4990.1, \"(9.23, 9.26)\": 5117.9, \"(9.26, 9.28)\": 5246.7, \"(9.28, 9.31)\": 5428.0, \"(9.31, 9.36)\": 5590.8, \"(9.36, 9.4)\": 5899.2, \"(9.4, 9.43)\": 6092.1, \"(9.43, 9.44)\": 6245.1, \"(9.44, 9.49)\": 6425.1, \"(9.49, 9.55)\": 6865.9, \"(9.55, 9.56)\": 7049.2, \"(9.56, 9.58)\": 7167.9, \"(9.58, 9.61)\": 7311.0, \"(9.61, 9.64)\": 7624.9, \"(9.64, 9.66)\": 7753.2, \"(9.66, 9.68)\": 7897.0, \"(9.68, 9.72)\": 8114.0, \"(9.72, 9.76)\": 8526.9, \"(9.76, 9.78)\": 8741.5, \"(9.78, 9.8)\": 8896.9, \"(9.8, 9.83)\": 9110.8, \"(9.83, 9.87)\": 9405.4, \"(9.87, 9.9)\": 9899.9, \"(9.9, 9.91)\": 10024.6, \"(9.91, 9.95)\": 10165.3, \"(9.95, 9.98)\": 10696.4, \"(9.98, 9.99)\": 10855.0}\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2676,7 +4021,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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VZZddppycHLndbq92n3/+ufr376+YmBilpKRo7ty5Vfa1dOlSdenSRTExMerevbtWrlzp9boxRjNmzFBycrJiY2OVkZGhr776qlGPLxRtmJ7B0BkAhAjqiqoKWCgqKChQRUWFXnzxRW3btk1PPfWUFixYoIcfftjTxuVyadCgQWrfvr3y8/P1+OOPa+bMmXrppZc8bdauXas77rhDI0eO1KZNm5SZmanMzExt3brV02bu3Ll65plntGDBAq1bt06XXHKJnE6nvv/+e78ec7A5v5aIe5wBQHCrXLNIkmfNIvzAZiwUEx9//HG98MIL+vrrryVJL7zwgqZNm6aioiLZ7XZJ0pQpU7Rs2TIVFBRIkm677TadPHlSy5cv9+znxz/+sXr27KkFCxbIGKO2bdtqwoQJ+u1vfytJKikpUZs2bbRo0SLdfvvtdeqby+VSfHy8SkpK5HA4fHnYllRdLdH2WU4W/AKAIHfyTJmuzlktKTx+r9fn89tSNUUlJSVq0aKF53leXp4GDBjgCUSS5HQ6tXPnTh07dszTJiPD+2Z3TqdTeXln19TZs2ePioqKvNrEx8crLS3N06Y6Z86ckcvl8nqEE2qJACA0nXvB3zqXRazBMqFo165devbZZ/XAAw94thUVFalNmzZe7SqfFxUV1drm3NfPfV91baozZ84cxcfHex4pKSkNPLLgRy0RAIQm6oq8+TwUTZkyRTabrdZH5dBXpYMHD+qmm27S0KFDdf/99/u6Sw0ydepUlZSUeB4HDhwIdJcChloiAAgd1BXVzOcDiRMmTNDw4cNrbdOpUyfP/xcWFmrgwIHq27evVwG1JCUlJam4uNhrW+XzpKSkWtuc+3rltuTkZK82PXv2rLGP0dHRio6OrvU4AAAINpVrFlXWFeEHPg9FiYmJSkxMrFPbgwcPauDAgUpNTdXChQsVEeF94So9PV3Tpk1TaWmpoqKiJEm5ubnq3LmzEhISPG3WrFmjcePGed6Xm5ur9PSzN7/r2LGjkpKStGbNGk8IcrlcWrduncaMGXORRwsAQPA5/15osVGMCEgBrCk6ePCgbrjhBrVr105PPPGEjhw5oqKiIq86nzvvvFN2u10jR47Utm3btGTJEs2bN0/Z2dmeNg899JBWrVqlP/7xjyooKNDMmTO1YcMGjR07VtLZRDxu3DjNnj1bb731lr744gvdc889atu2rTIzM/192JZ3dhp+mddUfABA6OJeaD8I2Dy83Nxc7dq1S7t27dKll17q9VrliYmPj9c777yjrKwspaamqlWrVpoxY4ZGjRrladu3b1/99a9/1fTp0/Xwww/riiuu0LJly9StWzdPm0mTJunkyZMaNWqUjh8/rn79+mnVqlWKiYnxz8EGieqm4QMAQk/lvdA2/P/f95X3Qgv16fkXYql1iqwsHNYpOuUuU9cZ3mPMvdsnMPMMAEKQMcbrXmihumZRfT6/Q+/o4RMbpmcozh7JODMAhKjz74UGC61TBGuJs0cqzt6EQAQAYeKUuzzs64oIRQAAgIJrEYoAAAhblQXXlSoLrsMVoQiSKqfih+8PAgCEo8qFHDdMz7hw4zBAoTWYig8AYYyC6x9wpQg6XVruFYh6t09QbBQ/IAAQjsK4pIhQBG8bpmewLhEAhLFwLrYmFMFLnJ11iQAg3MRGRapr8tmFDbcfcoVtsTWhCACAMFdZcB3uCEUAAEDnDhKE6egZoSjcMRUfAHC+cK0rYkp+GGMqPgCgUmVd0fZDLk9dUSjeILY2XCkKY0zFBwBUoq6IK0X4/zZMz1DLS+zMPAOAMBbuHwFcKYIkpuIDALydcpeHXV0RoQgAAFTRe/a7YVdwTSgCAACSzhZb926f4Hm+Yd+xsFrIkVAUxsIo/AMA6qCy2HrD9IxAdyUgCEVhyhijoQvyAt0NAIDF2Gw2xdnDcyYyoShMnS4t1/ZDLklS12QHU/EBAGGPUAQtHZ3OzDMAQNgjFIWpc+uJyEMAgJqEU/0poSgMUU8EAKircJqWTygKQ9QTAQBqU3kfNEme+6CFA0JRmKOeCABwvnC9DxqhKMyRhwAA1QnHzwdCURgKk6FhAICPhMvnBqEozFBkDQCor3AptiYUhRmKrAEAdRGOxdaEojBGkTUAoCbhWGxNKAozLNoIAKircPucIBSFEeqJAAANFQYlRYSicEI9EQCgocKh2JpQFKaoJwIAXEi4FVsTisII9UQAgPoIt2JrQlGYoJ4IANAQ5/4RHeKjZ4SicEE9EQDgYoV6XRGhKAxRTwQAqKtwqisiFIUJ6okAAA0RTnVFhKIwQD0RAOBihEtdEaEoDFBPBADwlVCuKyIUhRnqiQAA9RUudUWEojBAPREA4GKES10RoSjEUU8EAPCFcPijmlAU4qgnAgCgbghFYYR6IgCAL5xyl4dksTWhKIyQhwAAvtB79rshOQuNUBTiQuzfKwAgQGKjItW7fYLn+YZ9x0JuFhqhKIRRZA0A8JXKGWgbpmcEuiuNxhKh6MyZM+rZs6dsNps2b97s9drnn3+u/v37KyYmRikpKZo7d26V9y9dulRdunRRTEyMunfvrpUrV3q9bozRjBkzlJycrNjYWGVkZOirr75qzEOyBIqsAQC+ZLPZFGcP3c8SS4SiSZMmqW3btlW2u1wuDRo0SO3bt1d+fr4ef/xxzZw5Uy+99JKnzdq1a3XHHXdo5MiR2rRpkzIzM5WZmamtW7d62sydO1fPPPOMFixYoHXr1umSSy6R0+nU999/75fjswKKrAEAqF3AQ9Hbb7+td955R0888USV1/7yl7/I7XbrlVde0dVXX63bb79dv/nNb/Tkk0962sybN0833XSTJk6cqKuuukq/+93v1KtXLz333HOSzl4levrppzV9+nTdcsstuuaaa/Tqq6+qsLBQy5Yt89dhBhx5CACA2gU0FBUXF+v+++/Xa6+9pri4uCqv5+XlacCAAbLb7Z5tTqdTO3fu1LFjxzxtMjK8xzedTqfy8s7W0uzZs0dFRUVebeLj45WWluZpU50zZ87I5XJ5PQAAQOgKWCgyxmj48OEaPXq0evfuXW2boqIitWnTxmtb5fOioqJa25z7+rnvq65NdebMmaP4+HjPIyUlpR5HBwAAgo3PQ9GUKVNks9lqfRQUFOjZZ5/ViRMnNHXqVF93wSemTp2qkpISz+PAgQOB7lK9MR0fANCYQu1zpomvdzhhwgQNHz681jadOnXSe++9p7y8PEVHR3u91rt3b911111avHixkpKSVFxc7PV65fOkpCTPf6trc+7rlduSk5O92vTs2bPGPkZHR1fpWzBhOj4AoLENXZCnFb/pFzITeXweihITE5WYmHjBds8884xmz57teV5YWCin06klS5YoLS1NkpSenq5p06aptLRUUVFRkqTc3Fx17txZCQkJnjZr1qzRuHHjPPvKzc1VevrZu/l27NhRSUlJWrNmjScEuVwurVu3TmPGjPHFIVsS0/EBAI0hNipSXZMd2n7Ipe2HXDpdWq44u8/jREAErKaoXbt26tatm+dx5ZVXSpIuu+wyXXrppZKkO++8U3a7XSNHjtS2bdu0ZMkSzZs3T9nZ2Z79PPTQQ1q1apX++Mc/qqCgQDNnztSGDRs0duxYSWfXVBg3bpxmz56tt956S1988YXuuecetW3bVpmZmX4/7kBgOj4AwFcqF3EMRZaOdvHx8XrnnXeUlZWl1NRUtWrVSjNmzNCoUaM8bfr27au//vWvmj59uh5++GFdccUVWrZsmbp16+ZpM2nSJJ08eVKjRo3S8ePH1a9fP61atUoxMTGBOCy/Iw8BAHwpVD9XbCbU7ubWSFwul+Lj41VSUiKHwxHo7lzQyTNlujpntSRp+yxnyFzaBAAE3il3mbrOCI7PmPp8fgd88Ub4HkXWAADUH6EoBFFkDQBA/RGKQhxF1gCAxhRKRTiEohBHHgIANKahC/IUKuXJhCIAAFAvlWsVSfKsVRQKCEUAAKBezl+rKEQuFBGKAABA/Z1bnhEqQ2iEohAUAv8uAQAWF4pDaISiEMMaRQAAfwjF230QikIMaxQBAPwl1GY4E4pCGGsUAQBQd4SiEEYeAgCg7ghFIYYiawAAGoZQFEIosgYABEoo/FFOKAohFFkDAAIlFNYqIhSFKIqsAQCNLdTWKiIUhZBzAzp5CADQ2EJtrSJCUYignggAEAjn/hEe5KNnhKJQQT0RACDQgr2uiFAUgqgnAgD4SyjVFRGKQhB5CADgL6FUV0QoAgAAFyVU/hgnFAEAAIhQBAAAIIlQFDKCuNgfAABLIBSFANYoAgBYRTD/kU4oCgGsUQQAsIpgXquIUBRiWKMIAOBvobJWEaEoxJCHAAD+FiprFRGKAADARQuFP8oJRQAAACIUAQAAH9t20BXoLjQIoQgAAECEIgAAAEmEIgAA4GPfl5YH5VpFhCIAAOBTd7/yWVAu4kgoCnLGGOXvPRbobgAAwlxsVKR6t0/wPN+w71jQLeLYJNAdQMMZY/RfC/KUv49QBAAIrMoFHD/+6qjufuWzQHenQbhSFMROl5Z7BaLe7RO47xkAIGBsNptigvhziCtFIeK1e/uo3xWtuO8ZAAANxJWiEJHaIYFABADARSAUAQAAn7n6R45Ad6HBCEUAAAAiFAEAAEgiFAEAAEgiFAEAAEgiFAW1IFs9HQAASyMUBSljjIYuyAt0NwAACBmEoiB1urRc2w+5JEldkx2sZA0AwEUiFIWApaPTWbgRAICLFPBQtGLFCqWlpSk2NlYJCQnKzMz0en3//v0aMmSI4uLi1Lp1a02cOFFlZWVebT744AP16tVL0dHRuvzyy7Vo0aIqX2f+/Pnq0KGDYmJilJaWps8+C86b1VWHPAQAwMULaCj6xz/+obvvvlsjRozQli1b9Mknn+jOO+/0vF5eXq4hQ4bI7XZr7dq1Wrx4sRYtWqQZM2Z42uzZs0dDhgzRwIEDtXnzZo0bN0733XefVq9e7WmzZMkSZWdnKycnRxs3blSPHj3kdDp1+PBhvx4vAACwLpsxgZnDVFZWpg4dOujRRx/VyJEjq23z9ttv6+abb1ZhYaHatGkjSVqwYIEmT56sI0eOyG63a/LkyVqxYoW2bt3qed/tt9+u48ePa9WqVZKktLQ0XXfddXruueckSRUVFUpJSdGDDz6oKVOm1Km/LpdL8fHxKikpkcMR+CXMT7nL1HXG2eC3fZZTcXbu7QsACDyrfT7V5/M7YFeKNm7cqIMHDyoiIkLXXnutkpOTNXjwYK9wk5eXp+7du3sCkSQ5nU65XC5t27bN0yYjI8Nr306nU3l5Z2dmud1u5efne7WJiIhQRkaGp00wYjo+AAC+FbBQ9PXXX0uSZs6cqenTp2v58uVKSEjQDTfcoO+++06SVFRU5BWIJHmeFxUV1drG5XLp9OnTOnr0qMrLy6ttU7mP6pw5c0Yul8vrYRVMxwcAwPd8HoqmTJkim81W66OgoEAVFRWSpGnTpunWW29VamqqFi5cKJvNpqVLl/q6W/U2Z84cxcfHex4pKSmB7pIH0/EBAPA9nw/0TZgwQcOHD6+1TadOnXTo0CFJUteuXT3bo6Oj1alTJ+3fv1+SlJSUVGWWWHFxsee1yv9Wbju3jcPhUGxsrCIjIxUZGVltm8p9VGfq1KnKzs72PHe5XJYKRpWYjg8AgG/4PBQlJiYqMTHxgu1SU1MVHR2tnTt3ql+/fpKk0tJS7d27V+3bt5ckpaen67HHHtPhw4fVunVrSVJubq4cDocnTKWnp2vlypVe+87NzVV6erokyW63KzU1VWvWrPFM96+oqNCaNWs0duzYGvsXHR2t6Ojo+h18AJCHAADwjYDVFDkcDo0ePVo5OTl65513tHPnTo0ZM0aSNHToUEnSoEGD1LVrV919993asmWLVq9erenTpysrK8sTWEaPHq2vv/5akyZNUkFBgZ5//nm98cYbGj9+vOdrZWdn609/+pMWL16sHTt2aMyYMTp58qRGjBjh/wMHAACWFNB5co8//riaNGmiu+++W6dPn1ZaWpree+89JSQkSJIiIyO1fPlyjRkzRunp6brkkks0bNgwzZo1y7OPjh07asWKFRo/frzmzZunSy+9VC+//LKcTqenzW233aYjR45oxowZKioqUs+ePbVq1aoqxdcAAMB3TrnLFRsVGTRlHgFbpyjYWGmdIqutAQEAQKVzP6MkqXf7hIDWvwbFOkUAACD0xEZFqnf7BM/zDfuO6XRpeQB7VHeEoiDEtT0AgFXZbDYtHZ2uDdMzLtzYYghFQYaFGwEAVmez2RRn/2ENvWD5Y55QFGRYuBEAEGyGLshTMJQwE4qCGAs3AgCsKjYqUl2TzxY2bz/kCoq6IkJRECMPAQCsqrK2KJgQigAAQKMItj/eCUUAAAAiFAWdIKhTAwAgKBGKggjT8QEAaDyEoiDCdHwAQLAKhpEOQlGQYjo+ACCYBMNaRYSiIEUeAgBYXbCtVUQoAgAAjSLY1ioiFAEAgEYTTCMbhCIAAAARigAAACQRioKKxYv2AQAIaoSiIMHCjQAANC5CUZBg4UYAABoXoSgIsXAjAAC+RygKQuQhAAB8j1AEAAAgQhEAAPATq8+iJhQBAAC/sPpNYQlFAACg0QTTTWEJRQAAoNEE001hCUUAAKBRnTtr2sKjZ4QiAADgP1auKyIUBQmL/vsBAOCCgqWuiFAUBLjvGQAgmAVLXRGhKAhw3zMAQLALhrsxEIqCDPc9AwCgcRCKggx5CACAxkEoCgIUWQMA0PgIRRZHkTUAAP5BKLI4iqwBAPAPQlEQocgaAIDGQygKIuQhAAAaD6EIAABAhCIAAABJhCIAAABJhCIAAABJhCIAAOBnVl2UmFAEAAD8auiCPBkLJiNCEQAAaHSxUZHqmuyQJG0/5NLp0vIA96gqQhEAAGh0NptNS0enB7obtSIUAQAAvzh3EWILjp4RiqzOiv9oAAC4WFasKyIUWZgxRkMX5AW6GwAA+ITV64oCGoq+/PJL3XLLLWrVqpUcDof69eun999/36vN/v37NWTIEMXFxal169aaOHGiysrKvNp88MEH6tWrl6Kjo3X55Zdr0aJFVb7W/Pnz1aFDB8XExCgtLU2fffZZYx6aT5wuLdf2Qy5JUtdkh2KjIgPcIwAAGs7qdUUBDUU333yzysrK9N577yk/P189evTQzTffrKKiIklSeXm5hgwZIrfbrbVr12rx4sVatGiRZsyY4dnHnj17NGTIEA0cOFCbN2/WuHHjdN9992n16tWeNkuWLFF2drZycnK0ceNG9ejRQ06nU4cPH/b7MTfU0tHpsnFHWABAkLPyR5nNBGhA7+jRo0pMTNRHH32k/v37S5JOnDghh8Oh3NxcZWRk6O2339bNN9+swsJCtWnTRpK0YMECTZ48WUeOHJHdbtfkyZO1YsUKbd261bPv22+/XcePH9eqVaskSWlpabruuuv03HPPSZIqKiqUkpKiBx98UFOmTKlTf10ul+Lj41VSUiKHw+HLb0WNTrnL1HXG2XC3fZZTcfYmfvm6AAA0Fn9/ttXn8ztgV4patmypzp0769VXX9XJkydVVlamF198Ua1bt1ZqaqokKS8vT927d/cEIklyOp1yuVzatm2bp01GRobXvp1Op/LyztbiuN1u5efne7WJiIhQRkaGpw0AAEDALj3YbDa9++67yszMVLNmzRQREaHWrVtr1apVSkhIkCQVFRV5BSJJnueVQ2w1tXG5XDp9+rSOHTum8vLyatsUFBTU2L8zZ87ozJkznucul6vhBwsAACzP51eKpkyZIpvNVuujoKBAxhhlZWWpdevW+te//qXPPvtMmZmZ+vnPf65Dhw75ulv1NmfOHMXHx3seKSkpge4SAABoRD6/UjRhwgQNHz681jadOnXSe++9p+XLl+vYsWOeMb7nn39eubm5Wrx4saZMmaKkpKQqs8SKi4slSUlJSZ7/Vm47t43D4VBsbKwiIyMVGRlZbZvKfVRn6tSpys7O9jx3uVwEIwAAQpjPQ1FiYqISExMv2O7UqVOSztb3nCsiIkIVFRWSpPT0dD322GM6fPiwWrduLUnKzc2Vw+FQ165dPW1WrlzptY/c3Fylp5+d8me325Wamqo1a9YoMzNT0tlC6zVr1mjs2LE19i86OlrR0dF1OGIAABAKAlZonZ6eroSEBA0bNkxbtmzRl19+qYkTJ3qm2EvSoEGD1LVrV919993asmWLVq9erenTpysrK8sTWEaPHq2vv/5akyZNUkFBgZ5//nm98cYbGj9+vOdrZWdn609/+pMWL16sHTt2aMyYMTp58qRGjBgRkGOvK4st9AkAQEgLWKF1q1attGrVKk2bNk0//elPVVpaqquvvlpvvvmmevToIUmKjIzU8uXLNWbMGKWnp+uSSy7RsGHDNGvWLM9+OnbsqBUrVmj8+PGaN2+eLr30Ur388styOp2eNrfddpuOHDmiGTNmqKioSD179tSqVauqFF9bCatZAwDgXwFbpyjY+HudonPXceia7NCK3/Rj8UYAQNBjnSJcFFazBgCEIqtdliEUBQHyEAAgFA1dkCcrDVgRigAAgN/ERkWqa/LZYazth1w6XVoe4B79gFAEAAD8xmazaeno9EB3o1qEIgAA4FdWLQshFAEAAIhQBAAAIIlQBAAAIIlQBAAAIIlQZFkWWrYBAICwQCiyIO57BgCA/xGKLOh0abm2H3JJOnvfs9ioyAD3CACA0EcosjjuewYAgH8QiiyOPAQAgH8QigAAAEQoAgAAAWSl2daEIgAAEDBDF+TJWCQZEYoAAIBfxUZFqmuyQ5K0/ZBLp0vLA9yjswhFAADAr2w2m5aOTg90N6ogFAEAAL+z4uxqQhEAAIAIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIIRZZkkTWsAAAIK4QiizHGaOiCvEB3AwCAsEMospjTpeXafsglSeqa7FBsVGSAewQAQOOyyggJocjClo5Ol82Kq1sBAOBDVrn/GaHIwshDAIBQZcX7nxGKAACA31nx/meEIouxwNVDAAD8wmojIoQiC2HmGQAAgUMoshBmngEAEDiEIoti5hkAAP5FKLIo8hAAAP5FKAIAAAFnhYlGhCIAABBwVljAkVAEAAACwmoLOBKKAABAQFhtAUdCEQAACBgrTSwiFAEAAIhQBAAAIIlQBAAAIIlQZClWWKMBAIBwRSiyCG4GCwBAYBGKLIKbwQIAEFiEIgviZrAAAPgfociCyEMAAPgfoQgAAECEIgAAYBGn3OUBvSlso4Wixx57TH379lVcXJyaN29ebZv9+/dryJAhiouLU+vWrTVx4kSVlZV5tfnggw/Uq1cvRUdH6/LLL9eiRYuq7Gf+/Pnq0KGDYmJilJaWps8++8zr9e+//15ZWVlq2bKlmjZtqltvvVXFxcW+OlQAAOADvWe/G9CbwjZaKHK73Ro6dKjGjBlT7evl5eUaMmSI3G631q5dq8WLF2vRokWaMWOGp82ePXs0ZMgQDRw4UJs3b9a4ceN03333afXq1Z42S5YsUXZ2tnJycrRx40b16NFDTqdThw8f9rQZP368/vnPf2rp0qX68MMPVVhYqF/+8peNdegAAKCOYqMi1bt9QqC7cZZpZAsXLjTx8fFVtq9cudJERESYoqIiz7YXXnjBOBwOc+bMGWOMMZMmTTJXX3211/tuu+0243Q6Pc/79OljsrKyPM/Ly8tN27ZtzZw5c4wxxhw/ftxERUWZpUuXetrs2LHDSDJ5eXl1Po6SkhIjyZSUlNT5PfVx8kypaT95uWk/ebk5eaa0Ub4GAABWVFFRYT77+ltz8kypqaio8Om+6/P5HbCaory8PHXv3l1t2rTxbHM6nXK5XNq2bZunTUZGhtf7nE6n8vLOLnLodruVn5/v1SYiIkIZGRmeNvn5+SotLfVq06VLF7Vr187TpjpnzpyRy+XyegAAAN+z2WyKiYpUnL1JQJekCVgoKioq8gpEkjzPi4qKam3jcrl0+vRpHT16VOXl5dW2OXcfdru9Sl3TuW2qM2fOHMXHx3seKSkpDTpOAABwYd0vjQ90F+oXiqZMmSKbzVbro6CgoLH66ldTp05VSUmJ53HgwIFAdwkAADSiJvVpPGHCBA0fPrzWNp06darTvpKSkqrMEqucEZaUlOT57/mzxIqLi+VwOBQbG6vIyEhFRkZW2+bcfbjdbh0/ftzratG5baoTHR2t6OjoOh0LAAAIfvW6UpSYmKguXbrU+rDb7XXaV3p6ur744guvWWK5ublyOBzq2rWrp82aNWu83pebm6v09HRJkt1uV2pqqlebiooKrVmzxtMmNTVVUVFRXm127typ/fv3e9oAAADU60pRfezfv1/fffed9u/fr/Lycm3evFmSdPnll6tp06YaNGiQunbtqrvvvltz585VUVGRpk+frqysLM8VmtGjR+u5557TpEmTdO+99+q9997TG2+8oRUrVni+TnZ2toYNG6bevXurT58+evrpp3Xy5EmNGDFCkhQfH6+RI0cqOztbLVq0kMPh0IMPPqj09HT9+Mc/bqzDBwAAwcan897OMWzYMCOpyuP999/3tNm7d68ZPHiwiY2NNa1atTITJkwwpaXe09Hff/9907NnT2O3202nTp3MwoULq3ytZ5991rRr187Y7XbTp08f8+mnn3q9fvr0afPrX//aJCQkmLi4OPOLX/zCHDp0qF7Hw5R8AACCT30+v23GBHA97SDicrkUHx+vkpISORwOn+//lLtMXWecXZRy+yyn4uyNdhEPAICwUZ/Pb+59BgAAIEIRAACAJEKRZTCICQBAYBGKLMAYo6ELar7lCAAAaHyEIgs4XVqu7YfO3luta7JDsVGRAe4RAADhh1BkMUtHpwf0ZngAAIQrQpHFkIcAAAgMQhEAAIAIRQAAAJIIRQAAAJIIRQAAAJIIRZbAwo0AAAQeoSjAWLgRAABrIBQFGAs3AgBgDYQiC2HhRgAAAodQZCHkIQAAAodQBAAAIEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJKlJoDsQ7mKjIrV9ltPz/wAAIDAIRQFms9kUZ+c0AAAQaAyfAQAAiFAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgSWoS6A4EC2OMJMnlcgW4JwAAoK4qP7crP8drQyiqoxMnTkiSUlJSAtwTAABQXydOnFB8fHytbWymLtEJqqioUGFhoZo1ayabzebTfbtcLqWkpOjAgQNyOBw+3bcVhPrxSRxjKAj145M4xlAQ6scn+f4YjTE6ceKE2rZtq4iI2quGuFJURxEREbr00ksb9Ws4HI6Q/Ucuhf7xSRxjKAj145M4xlAQ6scn+fYYL3SFqBKF1gAAACIUAQAASCIUWUJ0dLRycnIUHR0d6K40ilA/PoljDAWhfnwSxxgKQv34pMAeI4XWAAAA4koRAACAJEIRAACAJEIRAACAJEIRAACAJEKRXzz22GPq27ev4uLi1Lx582rb7N+/X0OGDFFcXJxat26tiRMnqqysrNb9fvfdd7rrrrvkcDjUvHlzjRw5Uv/+978b4Qjq54MPPpDNZqv2sX79+hrfd8MNN1RpP3r0aD/2vH46dOhQpb9/+MMfan3P999/r6ysLLVs2VJNmzbVrbfequLiYj/1uO727t2rkSNHqmPHjoqNjdVll12mnJwcud3uWt9n9XM4f/58dejQQTExMUpLS9Nnn31Wa/ulS5eqS5cuiomJUffu3bVy5Uo/9bT+5syZo+uuu07NmjVT69atlZmZqZ07d9b6nkWLFlU5XzExMX7qcf3NnDmzSn+7dOlS63uC6RxW9zvFZrMpKyur2vbBcP4++ugj/fznP1fbtm1ls9m0bNkyr9eNMZoxY4aSk5MVGxurjIwMffXVVxfcb31/luuKUOQHbrdbQ4cO1ZgxY6p9vby8XEOGDJHb7dbatWu1ePFiLVq0SDNmzKh1v3fddZe2bdum3NxcLV++XB999JFGjRrVGIdQL3379tWhQ4e8Hvfdd586duyo3r171/re+++/3+t9c+fO9VOvG2bWrFle/X3wwQdrbT9+/Hj985//1NKlS/Xhhx+qsLBQv/zlL/3U27orKChQRUWFXnzxRW3btk1PPfWUFixYoIcffviC77XqOVyyZImys7OVk5OjjRs3qkePHnI6nTp8+HC17deuXas77rhDI0eO1KZNm5SZmanMzExt3brVzz2vmw8//FBZWVn69NNPlZubq9LSUg0aNEgnT56s9X0Oh8PrfO3bt89PPW6Yq6++2qu/H3/8cY1tg+0crl+/3uvYcnNzJUlDhw6t8T1WP38nT55Ujx49NH/+/Gpfnzt3rp555hktWLBA69at0yWXXCKn06nvv/++xn3W92e5Xgz8ZuHChSY+Pr7K9pUrV5qIiAhTVFTk2fbCCy8Yh8Nhzpw5U+2+tm/fbiSZ9evXe7a9/fbbxmazmYMHD/q87xfD7XabxMREM2vWrFrbXX/99eahhx7yT6d8oH379uapp56qc/vjx4+bqKgos3TpUs+2HTt2GEkmLy+vEXroW3PnzjUdO3astY2Vz2GfPn1MVlaW53l5eblp27atmTNnTrXtf/WrX5khQ4Z4bUtLSzMPPPBAo/bTVw4fPmwkmQ8//LDGNjX9TrKqnJwc06NHjzq3D/Zz+NBDD5nLLrvMVFRUVPt6sJ0/Seb//u//PM8rKipMUlKSefzxxz3bjh8/bqKjo83f/va3GvdT35/l+uBKkQXk5eWpe/fuatOmjWeb0+mUy+XStm3banxP8+bNva68ZGRkKCIiQuvWrWv0PtfHW2+9pW+//VYjRoy4YNu//OUvatWqlbp166apU6fq1KlTfuhhw/3hD39Qy5Ytde211+rxxx+vdcgzPz9fpaWlysjI8Gzr0qWL2rVrp7y8PH9096KUlJSoRYsWF2xnxXPodruVn5/v9b2PiIhQRkZGjd/7vLw8r/bS2Z/LYDhX0tnzJemC5+zf//632rdvr5SUFN1yyy01/s6xiq+++kpt27ZVp06ddNddd2n//v01tg3mc+h2u/X666/r3nvvrfUm5MF2/s61Z88eFRUVeZ2j+Ph4paWl1XiOGvKzXB/cENYCioqKvAKRJM/zoqKiGt/TunVrr21NmjRRixYtanxPoPz5z3+W0+m84A1177zzTrVv315t27bV559/rsmTJ2vnzp363//9Xz/1tH5+85vfqFevXmrRooXWrl2rqVOn6tChQ3ryySerbV9UVCS73V6lrqxNmzaWO2fn27Vrl5599lk98cQTtbaz6jk8evSoysvLq/05KygoqPY9Nf1cWv1cSVJFRYXGjRunn/zkJ+rWrVuN7Tp37qxXXnlF11xzjUpKSvTEE0+ob9++2rZtW6PfALsh0tLStGjRInXu3FmHDh3So48+qv79+2vr1q1q1qxZlfbBfA6XLVum48ePa/jw4TW2Cbbzd77K81Cfc9SQn+X6IBQ10JQpU/Tf//3ftbbZsWPHBYsAg0lDjvmbb77R6tWr9cYbb1xw/+fWQ3Xv3l3Jycm68cYbtXv3bl122WUN73g91OcYs7OzPduuueYa2e12PfDAA5ozZ45ll+BvyDk8ePCgbrrpJg0dOlT3339/re+1wjmElJWVpa1bt9ZabyNJ6enpSk9P9zzv27evrrrqKr344ov63e9+19jdrLfBgwd7/v+aa65RWlqa2rdvrzfeeEMjR44MYM98789//rMGDx6stm3b1tgm2M5fMCAUNdCECRNqTfCS1KlTpzrtKykpqUrlfOWMpKSkpBrfc35RWVlZmb777rsa33OxGnLMCxcuVMuWLfWf//mf9f56aWlpks5epfDXB+rFnNe0tDSVlZVp79696ty5c5XXk5KS5Ha7dfz4ca+rRcXFxY12zs5X3+MrLCzUwIED1bdvX7300kv1/nqBOIfVadWqlSIjI6vM9Kvte5+UlFSv9lYxduxYz8SL+l4tiIqK0rXXXqtdu3Y1Uu98q3nz5rryyitr7G+wnsN9+/bp3XffrfcV1mA7f5Xnobi4WMnJyZ7txcXF6tmzZ7XvacjPcr1cdFUS6uxChdbFxcWebS+++KJxOBzm+++/r3ZflYXWGzZs8GxbvXq1pQqtKyoqTMeOHc2ECRMa9P6PP/7YSDJbtmzxcc8ax+uvv24iIiLMd999V+3rlYXWf//73z3bCgoKLFto/c0335grrrjC3H777aasrKxB+7DSOezTp48ZO3as53l5ebn50Y9+VGuh9c033+y1LT093bJFuhUVFSYrK8u0bdvWfPnllw3aR1lZmencubMZP368j3vXOE6cOGESEhLMvHnzqn092M5hpZycHJOUlGRKS0vr9T6rnz/VUGj9xBNPeLaVlJTUqdC6Pj/L9erjRe8BF7Rv3z6zadMm8+ijj5qmTZuaTZs2mU2bNpkTJ04YY87+Q+7WrZsZNGiQ2bx5s1m1apVJTEw0U6dO9exj3bp1pnPnzuabb77xbLvpppvMtddea9atW2c+/vhjc8UVV5g77rjD78dXk3fffddIMjt27Kjy2jfffGM6d+5s1q1bZ4wxZteuXWbWrFlmw4YNZs+ePebNN980nTp1MgMGDPB3t+tk7dq15qmnnjKbN282u3fvNq+//rpJTEw099xzj6fN+cdojDGjR4827dq1M++9957ZsGGDSU9PN+np6YE4hFp988035vLLLzc33nij+eabb8yhQ4c8j3PbBNM5/J//+R8THR1tFi1aZLZv325GjRplmjdv7pn1effdd5spU6Z42n/yySemSZMm5oknnjA7duwwOTk5JioqynzxxReBOoRajRkzxsTHx5sPPvjA63ydOnXK0+b8Y3z00UfN6tWrze7du01+fr65/fbbTUxMjNm2bVsgDuGCJkyYYD744AOzZ88e88knn5iMjAzTqlUrc/jwYWNM8J9DY85+wLdr185Mnjy5ymvBeP5OnDjh+cyTZJ588kmzadMms2/fPmOMMX/4wx9M8+bNzZtvvmk+//xzc8stt5iOHTua06dPe/bx05/+1Dz77LOe5xf6Wb4YhCI/GDZsmJFU5fH+++972uzdu9cMHjzYxMbGmlatWpkJEyZ4/ZXw/vvvG0lmz549nm3ffvutueOOO0zTpk2Nw+EwI0aM8AQtK7jjjjtM3759q31tz549Xt+D/fv3mwEDBpgWLVqY6Ohoc/nll5uJEyeakpISP/a47vLz801aWpqJj483MTEx5qqrrjK///3vva7snX+Mxhhz+vRp8+tf/9okJCSYuLg484tf/MIraFjFwoULq/03e+7F5WA8h88++6xp166dsdvtpk+fPubTTz/1vHb99debYcOGebV/4403zJVXXmnsdru5+uqrzYoVK/zc47qr6XwtXLjQ0+b8Yxw3bpzn+9GmTRvzs5/9zGzcuNH/na+j2267zSQnJxu73W5+9KMfmdtuu83s2rXL83qwn0Njzl7xl2R27txZ5bVgPH+Vn13nPyqPo6KiwjzyyCOmTZs2Jjo62tx4441Vjr19+/YmJyfHa1ttP8sXw2aMMRc/CAcAABDcWKcIAABAhCIAAABJhCIAAABJhCIAAABJhCIAAABJhCIAAABJhCIAAABJhCIAAABJhCIAAABJhCIAAABJhCIAYezIkSNKSkrS73//e8+2tWvXym63a82aNQHsGYBA4N5nAMLaypUrlZmZqbVr16pz587q2bOnbrnlFj355JOB7hoAPyMUAQh7WVlZevfdd9W7d2998cUXWr9+vaKjowPdLQB+RigCEPZOnz6tbt266cCBA8rPz1f37t0D3SUAAUBNEYCwt3v3bhUWFqqiokJ79+4NdHcABAhXigCENbfbrT59+qhnz57q3Lmznn76aX3xxRdq3bp1oLsGwM8IRQDC2sSJE/X3v/9dW7ZsUdOmTXX99dcrPj5ey5cvD3TXAPgZw2cAwtYHH3ygp59+Wq+99pocDociIiL02muv6V//+pdeeOGFQHcPgJ9xpQgAAEBcKQIAAJBEKAIAAJBEKAIAAJBEKAIAAJBEKAIAAJBEKAIAAJBEKAIAAJBEKAIAAJBEKAIAAJBEKAIAAJBEKAIAAJBEKAIAAJAk/T99cfgMGmqwTQAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2688,18 +4033,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.1%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-10.0, -9.94)\": -2.998, \"(-9.94, -9.88)\": -2.992, \"(-9.88, -9.83)\": -2.986, \"(-9.83, -9.73)\": -2.979, \"(-9.73, -9.67)\": -2.972, \"(-9.67, -9.63)\": -2.966, \"(-9.63, -9.58)\": -2.959, \"(-9.58, -9.5)\": -2.952, \"(-9.5, -9.42)\": -2.946, \"(-9.42, -9.34)\": -2.938, \"(-9.34, -9.28)\": -2.93, \"(-9.28, -9.22)\": -2.924, \"(-9.22, -9.16)\": -2.916, \"(-9.16, -9.1)\": -2.91, \"(-9.1, -8.99)\": -2.899, \"(-8.99, -8.93)\": -2.893, \"(-8.93, -8.87)\": -2.883, \"(-8.87, -8.77)\": -2.875, \"(-8.77, -8.7)\": -2.868, \"(-8.7, -8.64)\": -2.859, \"(-8.64, -8.57)\": -2.852, \"(-8.57, -8.52)\": -2.845, \"(-8.52, -8.46)\": -2.836, \"(-8.46, -8.37)\": -2.828, \"(-8.37, -8.32)\": -2.821, \"(-8.32, -8.26)\": -2.815, \"(-8.26, -8.2)\": -2.808, \"(-8.2, -8.17)\": -2.801, \"(-8.17, -8.12)\": -2.795, \"(-8.12, -8.03)\": -2.789, \"(-8.03, -7.98)\": -2.782, \"(-7.98, -7.93)\": -2.775, \"(-7.93, -7.87)\": -2.768, \"(-7.87, -7.82)\": -2.76, \"(-7.82, -7.78)\": -2.75, \"(-7.78, 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4.27)\": 2.153, \"(4.27, 4.32)\": 2.159, \"(4.32, 4.37)\": 2.178, \"(4.37, 4.4)\": 2.186, \"(4.4, 4.46)\": 2.192, \"(4.46, 4.48)\": 2.199, \"(4.48, 4.49)\": 2.205, \"(4.49, 4.56)\": 2.212, \"(4.56, 4.64)\": 2.228, \"(4.64, 4.67)\": 2.238, \"(4.67, 4.71)\": 2.247, \"(4.71, 4.73)\": 2.254, \"(4.73, 4.76)\": 2.262, \"(4.76, 4.82)\": 2.268, \"(4.82, 4.89)\": 2.285, \"(4.89, 4.94)\": 2.295, \"(4.94, 4.98)\": 2.303, \"(4.98, 5.03)\": 2.311, \"(5.03, 5.04)\": 2.319, \"(5.04, 5.09)\": 2.325, \"(5.09, 5.11)\": 2.332, \"(5.11, 5.16)\": 2.338, \"(5.16, 5.21)\": 2.348, \"(5.21, 5.3)\": 2.356, \"(5.3, 5.35)\": 2.371, \"(5.35, 5.41)\": 2.381, \"(5.41, 5.47)\": 2.391, \"(5.47, 5.51)\": 2.402, \"(5.51, 5.57)\": 2.412, \"(5.57, 5.63)\": 2.42, \"(5.63, 5.67)\": 2.43, \"(5.67, 5.73)\": 2.441, \"(5.73, 5.76)\": 2.448, \"(5.76, 5.81)\": 2.455, \"(5.81, 5.85)\": 2.463, \"(5.85, 5.9)\": 2.47, \"(5.9, 5.96)\": 2.479, \"(5.96, 6.01)\": 2.487, \"(6.01, 6.06)\": 2.495, \"(6.06, 6.11)\": 2.505, \"(6.11, 6.19)\": 2.514, \"(6.19, 6.25)\": 2.524, \"(6.25, 6.27)\": 2.531, \"(6.27, 6.36)\": 2.538, \"(6.36, 6.38)\": 2.551, \"(6.38, 6.46)\": 2.557, \"(6.46, 6.53)\": 2.567, \"(6.53, 6.58)\": 2.576, \"(6.58, 6.62)\": 2.585, \"(6.62, 6.68)\": 2.592, \"(6.68, 6.73)\": 2.598, \"(6.73, 6.76)\": 2.608, \"(6.76, 6.84)\": 2.614, \"(6.84, 6.89)\": 2.622, \"(6.89, 6.93)\": 2.629, \"(6.93, 6.97)\": 2.638, \"(6.97, 7.09)\": 2.65, \"(7.09, 7.16)\": 2.659, \"(7.16, 7.19)\": 2.667, \"(7.19, 7.27)\": 2.675, \"(7.27, 7.31)\": 2.682, \"(7.31, 7.37)\": 2.688, \"(7.37, 7.43)\": 2.699, \"(7.43, 7.49)\": 2.705, \"(7.49, 7.56)\": 2.713, \"(7.56, 7.59)\": 2.723, \"(7.59, 7.69)\": 2.729, \"(7.69, 7.74)\": 2.737, \"(7.74, 7.79)\": 2.746, \"(7.79, 7.84)\": 2.752, \"(7.84, 7.9)\": 2.759, \"(7.9, 7.96)\": 2.766, \"(7.96, 8.02)\": 2.773, \"(8.02, 8.07)\": 2.781, \"(8.07, 8.13)\": 2.788, \"(8.13, 8.22)\": 2.795, \"(8.22, 8.25)\": 2.802, \"(8.25, 8.29)\": 2.809, \"(8.29, 8.36)\": 2.815, \"(8.36, 8.43)\": 2.824, \"(8.43, 8.5)\": 2.83, \"(8.5, 8.55)\": 2.838, \"(8.55, 8.59)\": 2.844, \"(8.59, 8.69)\": 2.851, \"(8.69, 8.76)\": 2.86, \"(8.76, 8.85)\": 2.867, \"(8.85, 8.89)\": 2.875, \"(8.89, 9.0)\": 2.885, \"(9.0, 9.11)\": 2.895, \"(9.11, 9.15)\": 2.904, \"(9.15, 9.23)\": 2.911, \"(9.23, 9.29)\": 2.919, \"(9.29, 9.37)\": 2.928, \"(9.37, 9.45)\": 2.936, \"(9.45, 9.53)\": 2.944, \"(9.53, 9.57)\": 2.95, \"(9.57, 9.66)\": 2.958, \"(9.66, 9.72)\": 2.965, \"(9.72, 9.79)\": 2.971, \"(9.79, 9.86)\": 2.978, \"(9.86, 9.92)\": 2.987, \"(9.92, 9.99)\": 2.993}\n",
+      "Means: {\"(-10.0, -9.98)\": -10906.4, \"(-9.98, -9.97)\": -10783.2, \"(-9.97, -9.95)\": -10642.3, \"(-9.95, -9.92)\": -10362.8, \"(-9.92, -9.88)\": -9907.9, \"(-9.88, -9.86)\": -9712.8, \"(-9.86, -9.85)\": -9540.4, \"(-9.85, -9.84)\": -9427.7, \"(-9.84, -9.82)\": -9291.0, \"(-9.82, -9.78)\": -9085.6, \"(-9.78, -9.75)\": -8679.0, \"(-9.75, -9.73)\": -8563.9, \"(-9.73, -9.7)\": -8288.6, \"(-9.7, -9.68)\": -8157.7, \"(-9.68, -9.66)\": -7952.6, \"(-9.66, -9.64)\": -7829.1, \"(-9.64, -9.62)\": -7622.8, \"(-9.62, -9.59)\": -7393.1, \"(-9.59, -9.56)\": -7215.7, \"(-9.56, -9.53)\": -7017.9, \"(-9.53, -9.5)\": -6817.7, \"(-9.5, -9.47)\": -6549.1, \"(-9.47, -9.45)\": -6435.4, \"(-9.45, -9.43)\": -6324.2, \"(-9.43, -9.4)\": -6154.0, \"(-9.4, -9.37)\": -6026.0, \"(-9.37, -9.34)\": -5780.8, \"(-9.34, -9.32)\": -5606.5, \"(-9.32, -9.26)\": -5449.5, \"(-9.26, -9.2)\": -5077.2, \"(-9.2, -9.18)\": -4929.0, \"(-9.18, -9.16)\": -4808.3, \"(-9.16, -9.11)\": -4664.5, \"(-9.11, -9.08)\": -4512.7, \"(-9.08, -9.05)\": -4362.5, \"(-9.05, -9.02)\": -4245.6, \"(-9.02, -8.97)\": -4079.6, \"(-8.97, -8.91)\": -3841.0, \"(-8.91, -8.89)\": -3699.5, \"(-8.89, -8.84)\": -3557.0, \"(-8.84, -8.8)\": -3436.0, \"(-8.8, -8.77)\": -3304.3, \"(-8.77, -8.72)\": -3173.7, \"(-8.72, -8.68)\": -3008.7, \"(-8.68, -8.6)\": -2881.3, \"(-8.6, -8.52)\": -2650.8, \"(-8.52, -8.42)\": -2416.1, \"(-8.42, -8.31)\": -2183.5, \"(-8.31, -8.23)\": -1962.0, \"(-8.23, -8.14)\": -1845.2, \"(-8.14, -8.07)\": -1716.4, \"(-8.07, -7.98)\": -1586.0, \"(-7.98, -7.88)\": -1437.9, \"(-7.88, -7.8)\": -1317.5, \"(-7.8, -7.69)\": -1196.3, \"(-7.69, -7.56)\": -1071.9, \"(-7.56, -7.43)\": -944.0, \"(-7.43, -7.26)\": -830.6, \"(-7.26, -7.02)\": -677.6, \"(-7.02, -6.8)\": -563.2, \"(-6.8, -6.51)\": -448.2, \"(-6.51, -6.1)\": -336.9, \"(-6.1, -5.5)\": -226.7, \"(-5.5, -2.68)\": -116.0, \"(-2.68, 5.44)\": -6.4, \"(5.44, 6.12)\": -116.7, \"(6.12, 6.52)\": -232.7, \"(6.52, 6.81)\": -342.1, \"(6.81, 7.02)\": -453.1, \"(7.02, 7.22)\": -575.8, \"(7.22, 7.35)\": -687.1, \"(7.35, 7.5)\": -799.0, \"(7.5, 7.62)\": -912.6, \"(7.62, 7.73)\": -1026.6, \"(7.73, 7.81)\": -1141.0, \"(7.81, 7.9)\": -1252.1, \"(7.9, 8.0)\": -1371.8, \"(8.0, 8.09)\": -1503.4, \"(8.09, 8.16)\": -1638.8, \"(8.16, 8.24)\": -1763.0, \"(8.24, 8.3)\": -1906.6, \"(8.3, 8.35)\": -2019.0, \"(8.35, 8.42)\": -2144.3, \"(8.42, 8.49)\": -2315.8, \"(8.49, 8.56)\": -2492.5, \"(8.56, 8.61)\": -2603.7, \"(8.61, 8.68)\": -2787.0, \"(8.68, 8.74)\": -3022.9, \"(8.74, 8.78)\": -3180.3, \"(8.78, 8.83)\": -3349.1, \"(8.83, 8.87)\": -3475.5, \"(8.87, 8.91)\": -3608.4, \"(8.91, 8.98)\": -3779.1, \"(8.98, 9.05)\": -4070.1, \"(9.05, 9.09)\": -4337.6, \"(9.09, 9.11)\": -4452.7, \"(9.11, 9.14)\": -4570.0, \"(9.14, 9.16)\": -4698.8, \"(9.16, 9.2)\": -4833.2, \"(9.2, 9.23)\": -4996.9, \"(9.23, 9.26)\": -5109.8, \"(9.26, 9.27)\": -5267.8, \"(9.27, 9.35)\": -5383.8, \"(9.35, 9.44)\": -6194.5, \"(9.44, 9.46)\": -6328.7, \"(9.46, 9.48)\": -6473.0, \"(9.48, 9.51)\": -6611.3, \"(9.51, 9.54)\": -6869.6, \"(9.54, 9.56)\": -7035.0, \"(9.56, 9.59)\": -7148.6, \"(9.59, 9.6)\": -7312.6, \"(9.6, 9.62)\": -7425.6, \"(9.62, 9.65)\": -7685.2, \"(9.65, 9.69)\": -7804.7, \"(9.69, 9.73)\": -8271.5, \"(9.73, 9.76)\": -8465.3, \"(9.76, 9.78)\": -8773.1, \"(9.78, 9.8)\": -8890.8, \"(9.8, 9.83)\": -9152.5, \"(9.83, 9.87)\": -9433.3, \"(9.87, 9.9)\": -9902.0}\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2709,7 +4053,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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q448/1nvvvVfpeCEhIfriiy+0atUqFRYWKi4uToMGDdKcOXNYCwcAAEiSgow59/6AwOB0OhUZGamioiLm4wCo0anSM0qYsU6S9PXsFIWH+vS9GYCl1eX7m2dRAQAAyyHgAEA1zn/+FAD/wVgrAFSB508B/o0RHACoAs+fAvwbIzgAUIVzb7/Y+mCyLmseyho4gB9hBAcAznP+4xl4/hTgfwg4AHAeHs8A+D8CDgDUgMczAP6JgAMANSDbAP6JgAMAACyHgAMA52BxP8AauE0cAP6Nxf0A62AEBwD+jcX9AOtgBAcAqsDifoB/YwQHAKrA4n6AfyPgAMC/nft4BgD+jYADAKr8eAYA/o2AAwDi8QyA1RBwAOA8PJ4B8H8EHAA4D9kG8H8EHAAAYDkEHAAQd1ABVkPAARDwuIMKsB4CDoCAxx1UgPUQcADgHNxBBVgDAQdAwDt3/g3ZBrAGAg6AgMb8G8CaCDgAAhrzbwBrIuAAwL8x/wawDgIOAPwb2QawDgIOAACwnEYJOEuXLlW7du0UFhamxMREbd68udq6K1euVFBQkNsrLCzMrY4xRjNmzFDr1q3VrFkzJScn69tvv/V0NwAAgJ/weMB5+eWXlZaWpoyMDG3btk09e/ZUSkqKDh8+XO0+EREROnTokOu1b98+t+3z58/XkiVLlJmZqdzcXDVv3lwpKSn68ccfPd0dAADgBzwecBYtWqTx48dr7NixSkhIUGZmpsLDw7V8+fJq9wkKCpLdbne9YmNjXduMMVq8eLEefPBBDRkyRD169NBzzz2ngwcP6o033vB0dwAAgB/waMApLS1VXl6ekpOT//OBwcFKTk5WTk71606cPHlSbdu2VXx8vIYMGaIdO3a4tu3Zs0cOh8PtmJGRkUpMTKz2mCUlJXI6nW4vAJB4yCZgVR4NOEePHlV5ebnbCIwkxcbGyuFwVLlP586dtXz5cr355pt64YUXVFFRof79++vAgQOS5NqvLsecO3euIiMjXa/4+PiL7RoAC2CRP8C6fO4uqqSkJI0aNUq9evXSgAED9NprrykmJkZPP/10vY+Znp6uoqIi1+u7775rwBYD8Fcs8gdYl0cDTsuWLRUSEqKCggK38oKCAtnt9lodo2nTpurdu7d27dolSa796nJMm82miIgItxcAnItF/gBr8WjACQ0NVZ8+fZSdne0qq6ioUHZ2tpKSkmp1jPLycn355Zdq3bq1JKl9+/ay2+1ux3Q6ncrNza31MQHgfGQbwFqaePoD0tLSNHr0aPXt21f9+vXT4sWLVVxcrLFjx0qSRo0apcsvv1xz586VJM2ePVtXX321OnXqpMLCQi1YsED79u3T7bffLulfd1hNmTJFDz/8sK688kq1b99eDz30kOLi4jR06FBPdwcAAPgBjwecYcOG6ciRI5oxY4YcDod69eqlrKws1yTh/fv3Kzj4PwNJP/zwg8aPHy+Hw6Ho6Gj16dNHmzZtUkJCgqvO/fffr+LiYk2YMEGFhYW65pprlJWVVWlBQAAAEJiCjAm8mySdTqciIyNVVFTEfBwggBWXnNFPM9ZJkr6enaLwUI//zgfgItTl+9vn7qICgMbALeKAtRFwAAQkbhEHrI2AAyDgcYs4YD0EHAAB6dzZh2QbwHoIOAACDvNvAOsj4AAIOMy/AayPgAMgoDH/BrAmAg6AgMP8G8D6CDgAAgrzb4DAQMABEFCYfwMEBgIOgIDF/BvAugg4AAIW2QawLgIOgIASeI8XBgITAQdAwGCCMRA4CDgAAgYTjIHAQcABEJCYYAxYGwEHQEAi2wDWRsABAACWQ8ABEDC4gwoIHAQcAAGBO6iAwELAARAQuIMKCCwEHAABhzuoAOsj4AAIOGQbwPoIOAAAwHIIOAAAwHIIOAAAwHIIOAACAmvgAIGFgAPA8lgDBwg8BBwAlscaOEDgIeAACCisgQMEBgIOgIBCtgECQ6MEnKVLl6pdu3YKCwtTYmKiNm/eXG3dZ599Vtdee62io6MVHR2t5OTkSvXHjBmjoKAgt1dqaqqnuwEAAPyExwPOyy+/rLS0NGVkZGjbtm3q2bOnUlJSdPjw4Srrb9iwQSNGjNCHH36onJwcxcfHa9CgQfr+++/d6qWmpurQoUOu1z/+8Q9PdwUAAPgJjwecRYsWafz48Ro7dqwSEhKUmZmp8PBwLV++vMr6L774ou6880716tVLXbp00d///ndVVFQoOzvbrZ7NZpPdbne9oqOjPd0VAADgJzwacEpLS5WXl6fk5OT/fGBwsJKTk5WTU7tbNk+dOqWysjK1aNHCrXzDhg1q1aqVOnfurIkTJ+rYsWPVHqOkpEROp9PtBSBwsAYOEHg8GnCOHj2q8vJyxcbGupXHxsbK4XDU6hgPPPCA4uLi3EJSamqqnnvuOWVnZ2vevHn66KOPdNNNN6m8vLzKY8ydO1eRkZGuV3x8fP07BcCvsAYOEJiaeLsBNXnssce0evVqbdiwQWFhYa7y4cOHu/7cvXt39ejRQx07dtSGDRs0cODASsdJT09XWlqa673T6STkAAGCNXCAwOTREZyWLVsqJCREBQUFbuUFBQWy2+017rtw4UI99thjeu+999SjR48a63bo0EEtW7bUrl27qtxus9kUERHh9gIQeFgDBwgcHg04oaGh6tOnj9sE4bMThpOSkqrdb/78+ZozZ46ysrLUt2/fC37OgQMHdOzYMbVu3bpB2g3Amsg2QODw+F1UaWlpevbZZ7Vq1Srl5+dr4sSJKi4u1tixYyVJo0aNUnp6uqv+vHnz9NBDD2n58uVq166dHA6HHA6HTp48KUk6efKk7rvvPn366afau3evsrOzNWTIEHXq1EkpKSme7g4AAPADHp+DM2zYMB05ckQzZsyQw+FQr169lJWV5Zp4vH//fgUH/ydnPfXUUyotLdVvf/tbt+NkZGRo5syZCgkJ0RdffKFVq1apsLBQcXFxGjRokObMmSObzebp7gAAAD8QZEzg3UDpdDoVGRmpoqIi5uMAFneq9IwSZqyTJH09O0XhoT59bwWAGtTl+5tnUQGwtMD7FQ6ARMABYGGsgQMELgIOAMtiDRwgcBFwAAQE1sABAgsBB0BAINsAgYWAAwAALIeAA8CyuIMKCFwEHACWxB1UQGAj4ACwJO6gAgIbAQeA5XEHFRB4CDgALI9sAwQeAg4AALAcAg4AALAcAg4AALAcAg4AS2INHCCwEXAAWA5r4AAg4ACwHNbAAUDAAWBprIEDBCYCDgBLI9sAgYmAAwAALIeAAwAALIeAAwAALIeAA8ByWAMHAAEHgKWwBg4AiYADwGJYAweARMABYGGsgQMELgIOAMsi2wCBi4ADAAAsh4ADAAAsh4ADwFK4RRyARMABYCHcIg7grEYJOEuXLlW7du0UFhamxMREbd68ucb6a9asUZcuXRQWFqbu3bvr3XffddtujNGMGTPUunVrNWvWTMnJyfr222892QUAfoBbxAGc5fGA8/LLLystLU0ZGRnatm2bevbsqZSUFB0+fLjK+ps2bdKIESM0btw4ffbZZxo6dKiGDh2qr776ylVn/vz5WrJkiTIzM5Wbm6vmzZsrJSVFP/74o6e7A8BPcIs4ENiCjPHsFevExERdddVV+tvf/iZJqqioUHx8vO666y5Nnz69Uv1hw4apuLhYb7/9tqvs6quvVq9evZSZmSljjOLi4jRt2jTde++9kqSioiLFxsZq5cqVGj58+AXb5HQ6FRkZqaKiIkVERDRQTwF426nSM0qYsU6S9PXsFIWHNvFyiwA0pLp8f3t0BKe0tFR5eXlKTk7+zwcGBys5OVk5OVVfJ8/JyXGrL0kpKSmu+nv27JHD4XCrExkZqcTExGqPCQAAAotHf705evSoysvLFRsb61YeGxurb775psp9HA5HlfUdDodr+9my6uqcr6SkRCUlJa73Tqezbh0B4Be4gwrAWQFxF9XcuXMVGRnpesXHx3u7SQAaGHdQATiXRwNOy5YtFRISooKCArfygoIC2e32Kvex2+011j/737ocMz09XUVFRa7Xd999V6/+APBd3EEF4FweDTihoaHq06ePsrOzXWUVFRXKzs5WUlJSlfskJSW51Zek9evXu+q3b99edrvdrY7T6VRubm61x7TZbIqIiHB7AbAu7qAC4PFbDNLS0jR69Gj17dtX/fr10+LFi1VcXKyxY8dKkkaNGqXLL79cc+fOlSTdc889GjBggP76179q8ODBWr16tbZu3apnnnlGkhQUFKQpU6bo4Ycf1pVXXqn27dvroYceUlxcnIYOHerp7gDwA2QbAB4POMOGDdORI0c0Y8YMORwO9erVS1lZWa5Jwvv371dw8H8Gkvr376+XXnpJDz74oP785z/ryiuv1BtvvKFu3bq56tx///0qLi7WhAkTVFhYqGuuuUZZWVkKCwvzdHcAAIAf8Pg6OL6IdXAA62ENHMD6fGYdHAAAAG8g4AAAAMsh4AAAAMsh4ADwe8YYnSot93YzAPgQZuEB8GvGGP02M0d5+37wdlMA+BBGcAD4tdNl5W7hpm/baFYxBsAIDgDr2Ppgsi5rHsoqxgAYwQFgHeGhIYQbAJIIOAAAwIIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAD8mjHebgEAX0TAAeC3jDG6NTPH280A4IMIOAD81umycn19yClJSmgdwTOoALgQcABYwpo7knhMAwAXAg4ASyDbADgXAQcAAFgOAQcAAFgOAQcAAFgOAQeA32INHADVIeAA8EusgQOgJgQcAH6JNXAA1ISAA8DvsQYOgPMRcAD4PbINgPMRcAAAgOUQcAAAgOUQcAAAgOV4NOAcP35cI0eOVEREhKKiojRu3DidPHmyxvp33XWXOnfurGbNmqlNmza6++67VVRU5FYvKCio0mv16tWe7AoAH8MaOABq0sSTBx85cqQOHTqk9evXq6ysTGPHjtWECRP00ksvVVn/4MGDOnjwoBYuXKiEhATt27dPd9xxhw4ePKhXX33Vre6KFSuUmprqeh8VFeXJrgDwIayBA+BCPBZw8vPzlZWVpS1btqhv376SpCeffFI333yzFi5cqLi4uEr7dOvWTf/93//tet+xY0c98sgj+v3vf68zZ86oSZP/NDcqKkp2u91TzQfgw1gDB8CFeOwSVU5OjqKiolzhRpKSk5MVHBys3NzcWh+nqKhIERERbuFGkiZNmqSWLVuqX79+Wr58uQzj1UBAYg0cAFXx2AiOw+FQq1at3D+sSRO1aNFCDoejVsc4evSo5syZowkTJriVz549WzfeeKPCw8P13nvv6c4779TJkyd19913V3mckpISlZSUuN47nc469gaAryLbAKhKnQPO9OnTNW/evBrr5Ofn17tBZzmdTg0ePFgJCQmaOXOm27aHHnrI9efevXuruLhYCxYsqDbgzJ07V7NmzbroNgHwDQzYAriQOgecadOmacyYMTXW6dChg+x2uw4fPuxWfubMGR0/fvyCc2dOnDih1NRUXXrppXr99dfVtGnTGusnJiZqzpw5Kikpkc1mq7Q9PT1daWlprvdOp1Px8fE1HhOAb2KCMYDaqHPAiYmJUUxMzAXrJSUlqbCwUHl5eerTp48k6YMPPlBFRYUSExOr3c/pdColJUU2m01vvfWWwsLCLvhZ27dvV3R0dJXhRpJsNlu12wD4FyYYA6gNj83B6dq1q1JTUzV+/HhlZmaqrKxMkydP1vDhw113UH3//fcaOHCgnnvuOfXr109Op1ODBg3SqVOn9MILL8jpdLrmy8TExCgkJERr165VQUGBrr76aoWFhWn9+vV69NFHde+993qqKwB8FBOMAVTHo+vgvPjii5o8ebIGDhyo4OBg3XLLLVqyZIlre1lZmXbu3KlTp05JkrZt2+a6w6pTp05ux9qzZ4/atWunpk2baunSpZo6daqMMerUqZMWLVqk8ePHe7IrAHwQ2QZAdYJMAN5f7XQ6FRkZ6boFHYD/OFV6Rgkz1kmSvp6dovBQj/6eBsCH1OX7m2dRAQAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAPArgXdbBID6IOAA8BusYgygtgg4APwGqxgDqC0CDgC/xCrGAGpCwAHgl8g2AGpCwAEAAJZDwAEAAJZDwAEAAJZDwAHgF4wxOlVa7u1mAPATPIYXgM8zxui3mTnK2/eDt5sCwE8wggPA550uK3cLN33bRrMGDoAaMYIDwK9sfTBZlzUPZQ0cADViBAeAXwkPDSHcALggAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4An2eMt1sAwN8QcAD4NGOMbs3M8XYzAPgZAg4An3a6rFxfH3JKkhJaR7DAH4BaIeAA8Btr7khiDRwAtULAAeA3yDYAaouAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALMejAef48eMaOXKkIiIiFBUVpXHjxunkyZM17nP99dcrKCjI7XXHHXe41dm/f78GDx6s8PBwtWrVSvfdd5/OnDnjya4A8BIe0wCgPpp48uAjR47UoUOHtH79epWVlWns2LGaMGGCXnrppRr3Gz9+vGbPnu16Hx4e7vpzeXm5Bg8eLLvdrk2bNunQoUMaNWqUmjZtqkcffdRjfQHQ+HhMA4D68ljAyc/PV1ZWlrZs2aK+fftKkp588kndfPPNWrhwoeLi4qrdNzw8XHa7vcpt7733nr7++mu9//77io2NVa9evTRnzhw98MADmjlzpkJDQz3SHwCNj8c0AKgvj12iysnJUVRUlCvcSFJycrKCg4OVm5tb474vvviiWrZsqW7duik9PV2nTp1yO2737t0VGxvrKktJSZHT6dSOHTuqPF5JSYmcTqfbC4DvO/fyFI9pAFAXHhvBcTgcatWqlfuHNWmiFi1ayOFwVLvf7373O7Vt21ZxcXH64osv9MADD2jnzp167bXXXMc9N9xIcr2v7rhz587VrFmzLqY7ABrZ+ZenyDYA6qLOAWf69OmaN29ejXXy8/Pr3aAJEya4/ty9e3e1bt1aAwcO1O7du9WxY8d6HTM9PV1paWmu906nU/Hx8fVuIwDP4/IUgItR54Azbdo0jRkzpsY6HTp0kN1u1+HDh93Kz5w5o+PHj1c7v6YqiYmJkqRdu3apY8eOstvt2rx5s1udgoICSar2uDabTTabrdafCcC3cHkKQF3VOeDExMQoJibmgvWSkpJUWFiovLw89enTR5L0wQcfqKKiwhVaamP79u2SpNatW7uO+8gjj+jw4cOuS2Dr169XRESEEhIS6tgbAP6AbAOgrjw2ybhr165KTU3V+PHjtXnzZn3yySeaPHmyhg8f7rqD6vvvv1eXLl1cIzK7d+/WnDlzlJeXp7179+qtt97SqFGjdN1116lHjx6SpEGDBikhIUF/+MMf9Pnnn2vdunV68MEHNWnSJEZpAAth/RsAF8OjC/29+OKL6tKliwYOHKibb75Z11xzjZ555hnX9rKyMu3cudN1l1RoaKjef/99DRo0SF26dNG0adN0yy23aO3ata59QkJC9PbbbyskJERJSUn6/e9/r1GjRrmtmwPAv7H+DYCLFWRM4P2e5HQ6FRkZqaKiIkVERHi7OQDOc6r0jBJmrJP0rwnG79x9DXNwANTp+5tnUQHwaUwwBlAfBBwAPo1sA6A+CDgAAMByCDgAfE7gzQwE0NAIOAB8CndQAWgIBBwAPoVHNABoCAQcAD6LO6gA1BcBB4BPOXf+DdkGQH0RcAD4DObfAGgoBBwAPoP5NwAaCgEHgE9i/g2Ai0HAAeCTyDYALgYBB4DPYIE/AA2FgAPAJzDBGEBDIuAA8AlMMAbQkAg4AHwOE4wBXCwCDgCfwAJ/ABoSAQeA1zH/BkBDI+AA8Drm3wBoaAQcAD6F+TcAGgIBB4DXMf8GQEMj4ADwKubfAPAEAg4Ar2L+DQBPIOAA8BnMvwHQUAg4ALyK+TcAPIGAA8BrmH8DwFMIOAC8hvk3ADyFgAPAJzD/BkBDIuAA8AlkGwANiYADwGvOnWAMAA2JgAPAK5hgDMCTPBpwjh8/rpEjRyoiIkJRUVEaN26cTp48WW39vXv3KigoqMrXmjVrXPWq2r569WpPdgVAA2OCMQBPauLJg48cOVKHDh3S+vXrVVZWprFjx2rChAl66aWXqqwfHx+vQ4cOuZU988wzWrBggW666Sa38hUrVig1NdX1PioqqsHbD6BxMMEYQEPzWMDJz89XVlaWtmzZor59+0qSnnzySd18881auHCh4uLiKu0TEhIiu93uVvb666/rtttu0yWXXOJWHhUVVakuAP9gjNGp0nLXe7INgIbmsUtUOTk5ioqKcoUbSUpOTlZwcLByc3NrdYy8vDxt375d48aNq7Rt0qRJatmypfr166fly5fL1DBbsaSkRE6n0+0FwDuMMfptZo76Pvy+t5sCwMI8NoLjcDjUqlUr9w9r0kQtWrSQw+Go1TGWLVumrl27qn///m7ls2fP1o033qjw8HC99957uvPOO3Xy5EndfffdVR5n7ty5mjVrVv06AqBBnS4rV96+H1zv+7aNZv4NgAZX54Azffp0zZs3r8Y6+fn59W7QWadPn9ZLL72khx56qNK2c8t69+6t4uJiLViwoNqAk56errS0NNd7p9Op+Pj4i24jgIuz9cFkXdY8lPk3ABpcnQPOtGnTNGbMmBrrdOjQQXa7XYcPH3YrP3PmjI4fP16ruTOvvvqqTp06pVGjRl2wbmJioubMmaOSkhLZbLZK2202W5XlALwrPDSEcAPAI+occGJiYhQTE3PBeklJSSosLFReXp769OkjSfrggw9UUVGhxMTEC+6/bNky/epXv6rVZ23fvl3R0dGEGMAPsLgfgMbgsTk4Xbt2VWpqqsaPH6/MzEyVlZVp8uTJGj58uOsOqu+//14DBw7Uc889p379+rn23bVrlzZu3Kh333230nHXrl2rgoICXX311QoLC9P69ev16KOP6t577/VUVwA0EBb3A9BYPLoOzosvvqjJkydr4MCBCg4O1i233KIlS5a4tpeVlWnnzp06deqU237Lly/XFVdcoUGDBlU6ZtOmTbV06VJNnTpVxhh16tRJixYt0vjx4z3ZFQANgMX9ADSWIFPT/dUW5XQ6FRkZqaKiIkVERHi7OUDAOFV6Rgkz1kmSdsxKUXObR3/HAmAxdfn+5llUABoFi/sBaEz8+gTA484u7nfu+jcA4EmM4ADwOBb3A9DYGMEB4HHnzvRjcT8AjYERHAAedf6t4SzuB6AxEHAAeBS3hgPwBgIOgEaz5o4kRm8ANAoCDoBGQ7YB0FgIOAA85vy1bwCgsXAXFQCPYO0bAN7ECA4Aj2DtGwDexAgOAI9j7RsAjY0RHAAN7vy5N6x9A6CxMYIDoEEx9waAL2AEB0CDYu4NAF/ACA6ABnP+pSnm3gDwFgIOgAZR1aUp5t4A8BYuUQFoEFyaAuBLGMEB0OC4NAXA2wg4AC6KMUany8q5LRyATyHgAKg3bgkH4KuYgwOg3s6fdyMx9waAb2AEB0CdVXVZauuDyQoPDVGzplyeAuB9BBwAdVLdZanw0BCFh/JPCgDfwCUqAHXCZSkA/oBftwDUG5elAPgqAg6AGp2db3PW+beDc1kKgC/iXyYA1eI2cAD+ijk4AKpV1Xybs5h3A8CXMYIDoFbOzrc5i3k3AHwZAQcIYOfPrzkf820A+CuP/Wv1yCOP6J133tH27dsVGhqqwsLCC+5jjFFGRoaeffZZFRYW6uc//7meeuopXXnlla46x48f11133aW1a9cqODhYt9xyi5544gldcsklnuoKYEnMrwFgZR6bg1NaWqpbb71VEydOrPU+8+fP15IlS5SZmanc3Fw1b95cKSkp+vHHH111Ro4cqR07dmj9+vV6++23tXHjRk2YMMETXQAsrab5Nedjvg0AfxNkjDGe/ICVK1dqypQpFxzBMcYoLi5O06ZN07333itJKioqUmxsrFauXKnhw4crPz9fCQkJ2rJli/r27StJysrK0s0336wDBw4oLi6uVm1yOp2KjIxUUVGRIiIiLqp/5/ehpuF+wJecKi1X34ffl1R5fs35mG8DwBfU5fvbZy6o79mzRw6HQ8nJya6yyMhIJSYmKicnR8OHD1dOTo6ioqJc4UaSkpOTFRwcrNzcXP3617+u8tglJSUqKSlxvXc6nR7pw+myciXMWOeRYwOexPwaAFbjM7eJOxwOSVJsbKxbeWxsrGubw+FQq1at3LY3adJELVq0cNWpyty5cxUZGel6xcfHN3DrAf/F5ScAVlSnX9mmT5+uefPm1VgnPz9fXbp0uahGNbT09HSlpaW53judTo+EnGZNQ/T17JQGPy7gSVx+AmBFdQo406ZN05gxY2qs06FDh3o1xG63S5IKCgrUunVrV3lBQYF69erlqnP48GG3/c6cOaPjx4+79q+KzWaTzWarV7vqIigoiGF+AAB8QJ2+jWNiYhQTE+ORhrRv3152u13Z2dmuQON0OpWbm+u6EyspKUmFhYXKy8tTnz59JEkffPCBKioqlJiY6JF2AQAA/+OxOTj79+/X9u3btX//fpWXl2v79u3avn27Tp486arTpUsXvf7665L+NfoxZcoUPfzww3rrrbf05ZdfatSoUYqLi9PQoUMlSV27dlVqaqrGjx+vzZs365NPPtHkyZM1fPjwWt9BBQAArM9j11NmzJihVatWud737t1bkvThhx/q+uuvlyTt3LlTRUVFrjr333+/iouLNWHCBBUWFuqaa65RVlaWwsLCXHVefPFFTZ48WQMHDnQt9LdkyRJPdQMAAPghj6+D44s8tQ4OAADwnLp8f/vMbeIAAAANhYADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsx2PPovJlZ59O4XQ6vdwSAABQW2e/t2vzlKmADDgnTpyQJMXHx3u5JQAAoK5OnDihyMjIGusE5MM2KyoqdPDgQV166aUKCgpqkGM6nU7Fx8fru+++s+wDPK3eR6v3T6KPVmD1/kn00Qo81T9jjE6cOKG4uDgFB9c8yyYgR3CCg4N1xRVXeOTYERERlvyf9VxW76PV+yfRRyuwev8k+mgFnujfhUZuzmKSMQAAsBwCDgAAsBwCTgOx2WzKyMiQzWbzdlM8xup9tHr/JPpoBVbvn0QfrcAX+heQk4wBAIC1MYIDAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4BTS4888oj69++v8PBwRUVFVVln//79Gjx4sMLDw9WqVSvdd999OnPmTI3HPX78uEaOHKmIiAhFRUVp3LhxOnnypAd6UHcbNmxQUFBQla8tW7ZUu9/1119fqf4dd9zRiC2vvXbt2lVq62OPPVbjPj/++KMmTZqkyy67TJdccoluueUWFRQUNFKL62bv3r0aN26c2rdvr2bNmqljx47KyMhQaWlpjfv5+jlcunSp2rVrp7CwMCUmJmrz5s011l+zZo26dOmisLAwde/eXe+++24jtbRu5s6dq6uuukqXXnqpWrVqpaFDh2rnzp017rNy5cpK5yosLKyRWlx3M2fOrNTeLl261LiPv5y/s6r6dyUoKEiTJk2qsr6vn8ONGzfql7/8peLi4hQUFKQ33njDbbsxRjNmzFDr1q3VrFkzJScn69tvv73gcev6c1xXBJxaKi0t1a233qqJEydWub28vFyDBw9WaWmpNm3apFWrVmnlypWaMWNGjccdOXKkduzYofXr1+vtt9/Wxo0bNWHCBE90oc769++vQ4cOub1uv/12tW/fXn379q1x3/Hjx7vtN3/+/EZqdd3Nnj3bra133XVXjfWnTp2qtWvXas2aNfroo4908OBB/eY3v2mk1tbNN998o4qKCj399NPasWOHHn/8cWVmZurPf/7zBff11XP48ssvKy0tTRkZGdq2bZt69uyplJQUHT58uMr6mzZt0ogRIzRu3Dh99tlnGjp0qIYOHaqvvvqqkVt+YR999JEmTZqkTz/9VOvXr1dZWZkGDRqk4uLiGveLiIhwO1f79u1rpBbXz09/+lO39n788cfV1vWn83fWli1b3Pq3fv16SdKtt95a7T6+fA6Li4vVs2dPLV26tMrt8+fP15IlS5SZmanc3Fw1b95cKSkp+vHHH6s9Zl1/juvFoE5WrFhhIiMjK5W/++67Jjg42DgcDlfZU089ZSIiIkxJSUmVx/r666+NJLNlyxZX2f/8z/+YoKAg8/333zd42y9WaWmpiYmJMbNnz66x3oABA8w999zTOI26SG3btjWPP/54resXFhaapk2bmjVr1rjK8vPzjSSTk5PjgRY2vPnz55v27dvXWMeXz2G/fv3MpEmTXO/Ly8tNXFycmTt3bpX1b7vtNjN48GC3ssTERPOnP/3Jo+1sCIcPHzaSzEcffVRtner+TfJVGRkZpmfPnrWu78/n76x77rnHdOzY0VRUVFS53Z/OoSTz+uuvu95XVFQYu91uFixY4CorLCw0NpvN/OMf/6j2OHX9Oa4PRnAaSE5Ojrp3767Y2FhXWUpKipxOp3bs2FHtPlFRUW6jIcnJyQoODlZubq7H21xXb731lo4dO6axY8desO6LL76oli1bqlu3bkpPT9epU6caoYX189hjj+myyy5T7969tWDBghovK+bl5amsrEzJycmusi5duqhNmzbKyclpjOZetKKiIrVo0eKC9XzxHJaWliovL8/t7z84OFjJycnV/v3n5OS41Zf+9bPpD+erqKhIki54vk6ePKm2bdsqPj5eQ4YMqfbfHF/x7bffKi4uTh06dNDIkSO1f//+auv68/mT/vX/7AsvvKA//vGPNT7c2d/O4Vl79uyRw+FwO0eRkZFKTEys9hzV5+e4PgLyYZue4HA43MKNJNd7h8NR7T6tWrVyK2vSpIlatGhR7T7etGzZMqWkpFzwQaW/+93v1LZtW8XFxemLL77QAw88oJ07d+q1115rpJbW3t13362f/exnatGihTZt2qT09HQdOnRIixYtqrK+w+FQaGhopXlYsbGxPnnOzrdr1y49+eSTWrhwYY31fPUcHj16VOXl5VX+rH3zzTdV7lPdz6avn6+KigpNmTJFP//5z9WtW7dq63Xu3FnLly9Xjx49VFRUpIULF6p///7asWOHxx4qfDESExO1cuVKde7cWYcOHdKsWbN07bXX6quvvtKll15aqb6/nr+z3njjDRUWFmrMmDHV1vG3c3ius+ehLueoPj/H9RHQAWf69OmaN29ejXXy8/MvOAHO39Sn3wcOHNC6dev0yiuvXPD4584h6t69u1q3bq2BAwdq9+7d6tixY/0bXkt16V9aWpqrrEePHgoNDdWf/vQnzZ0716eXUK/POfz++++VmpqqW2+9VePHj69xX2+fQ0iTJk3SV199VeP8FElKSkpSUlKS633//v3VtWtXPf3005ozZ46nm1lnN910k+vPPXr0UGJiotq2batXXnlF48aN82LLPGPZsmW66aabFBcXV20dfzuH/iKgA860adNqTNWS1KFDh1ody263V5oBfvbOGrvdXu0+50+oOnPmjI4fP17tPg2hPv1esWKFLrvsMv3qV7+q8+clJiZK+tfoQWN8OV7MeU1MTNSZM2e0d+9ede7cudJ2u92u0tJSFRYWuo3iFBQUePScna+ufTx48KBuuOEG9e/fX88880ydP6+xz2F1WrZsqZCQkEp3rdX092+32+tU3xdMnjzZddNBXX+Db9q0qXr37q1du3Z5qHUNKyoqSj/5yU+qba8/nr+z9u3bp/fff7/OI5/+dA7PnoeCggK1bt3aVV5QUKBevXpVuU99fo7rpcFm8wSIC00yLigocJU9/fTTJiIiwvz4449VHuvsJOOtW7e6ytatW+dzk4wrKipM+/btzbRp0+q1/8cff2wkmc8//7yBW9bwXnjhBRMcHGyOHz9e5fazk4xfffVVV9k333zj05OMDxw4YK688kozfPhwc+bMmXodw5fOYb9+/czkyZNd78vLy83ll19e4yTjX/ziF25lSUlJPjlJtaKiwkyaNMnExcWZ//u//6vXMc6cOWM6d+5spk6d2sCt84wTJ06Y6Oho88QTT1S53Z/O3/kyMjKM3W43ZWVlddrPl8+hqplkvHDhQldZUVFRrSYZ1+XnuF5tbbAjWdy+ffvMZ599ZmbNmmUuueQS89lnn5nPPvvMnDhxwhjzr/8hu3XrZgYNGmS2b99usrKyTExMjElPT3cdIzc313Tu3NkcOHDAVZaammp69+5tcnNzzccff2yuvPJKM2LEiEbvX03ef/99I8nk5+dX2nbgwAHTuXNnk5uba4wxZteuXWb27Nlm69atZs+ePebNN980HTp0MNddd11jN/uCNm3aZB5//HGzfft2s3v3bvPCCy+YmJgYM2rUKFed8/tnjDF33HGHadOmjfnggw/M1q1bTVJSkklKSvJGFy7owIEDplOnTmbgwIHmwIED5tChQ67XuXX86RyuXr3a2Gw2s3LlSvP111+bCRMmmKioKNcdjH/4wx/M9OnTXfU/+eQT06RJE7Nw4UKTn59vMjIyTNOmTc2XX37prS5Ua+LEiSYyMtJs2LDB7VydOnXKVef8/s2aNcusW7fO7N692+Tl5Znhw4ebsLAws2PHDm904YKmTZtmNmzYYPbs2WM++eQTk5ycbFq2bGkOHz5sjPHv83eu8vJy06ZNG/PAAw9U2uZv5/DEiROu7zxJZtGiReazzz4z+/btM8YY89hjj5moqCjz5ptvmi+++MIMGTLEtG/f3pw+fdp1jBtvvNE8+eSTrvcX+jluCAScWho9erSRVOn14Ycfuurs3bvX3HTTTaZZs2amZcuWZtq0aW7J/cMPPzSSzJ49e1xlx44dMyNGjDCXXHKJiYiIMGPHjnWFJl8xYsQI079//yq37dmzx+3vYf/+/ea6664zLVq0MDabzXTq1Mncd999pqioqBFbXDt5eXkmMTHRREZGmrCwMNO1a1fz6KOPuo24nd8/Y4w5ffq0ufPOO010dLQJDw83v/71r90Cgy9ZsWJFlf/fnjt464/n8MknnzRt2rQxoaGhpl+/fubTTz91bRswYIAZPXq0W/1XXnnF/OQnPzGhoaHmpz/9qXnnnXcaucW1U925WrFihavO+f2bMmWK6+8iNjbW3HzzzWbbtm2N3/haGjZsmGndurUJDQ01l19+uRk2bJjZtWuXa7s/n79zrVu3zkgyO3furLTN387h2e+u819n+1BRUWEeeughExsba2w2mxk4cGClfrdt29ZkZGS4ldX0c9wQgowxpuEueAEAAHgf6+AAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAA8HtHjhyR3W7Xo48+6irbtGmTQkNDlZ2d7cWWAfAWnkUFwBLeffddDR06VJs2bVLnzp3Vq1cvDRkyRIsWLfJ20wB4AQEHgGVMmjRJ77//vvr27asvv/xSW7Zskc1m83azAHgBAQeAZZw+fVrdunXTd999p7y8PHXv3t3bTQLgJczBAWAZu3fv1sGDB1VRUaG9e/d6uzkAvIgRHACWUFpaqn79+qlXr17q3LmzFi9erC+//FKtWrXydtMAeAEBB4Al3HfffXr11Vf1+eef65JLLtGAAQMUGRmpt99+29tNA+AFXKIC4Pc2bNigxYsX6/nnn1dERISCg4P1/PPP63//93/11FNPebt5ALyAERwAAGA5jOAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADL+X9y8aU6RKzCyAAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2721,18 +4065,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.1%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-9.99, -9.72)\": -1.471, \"(-9.72, -9.41)\": -1.468, \"(-9.41, -9.1)\": -1.464, \"(-9.1, -8.86)\": -1.461, \"(-8.86, -8.6)\": -1.458, \"(-8.6, -8.39)\": -1.455, \"(-8.39, -8.15)\": -1.452, \"(-8.15, -7.92)\": -1.448, \"(-7.92, -7.69)\": -1.445, \"(-7.69, -7.57)\": -1.442, \"(-7.57, -7.35)\": -1.439, \"(-7.35, -7.15)\": -1.435, \"(-7.15, -7.0)\": -1.432, \"(-7.0, -6.81)\": -1.428, \"(-6.81, -6.67)\": -1.425, \"(-6.67, -6.5)\": -1.421, \"(-6.5, -6.39)\": -1.418, \"(-6.39, -6.25)\": -1.414, \"(-6.25, -6.07)\": -1.411, \"(-6.07, -5.93)\": -1.407, \"(-5.93, -5.84)\": -1.404, \"(-5.84, -5.68)\": -1.4, \"(-5.68, -5.63)\": -1.397, \"(-5.63, -5.48)\": -1.394, \"(-5.48, -5.4)\": -1.391, \"(-5.4, -5.3)\": -1.388, \"(-5.3, -5.15)\": -1.383, \"(-5.15, -5.04)\": -1.379, \"(-5.04, -4.93)\": -1.374, \"(-4.93, -4.85)\": -1.371, \"(-4.85, -4.81)\": -1.367, \"(-4.81, -4.72)\": -1.364, \"(-4.72, -4.6)\": -1.361, \"(-4.6, -4.57)\": -1.357, \"(-4.57, -4.46)\": -1.353, \"(-4.46, -4.37)\": 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1.47)\": 0.974, \"(1.47, 1.49)\": 0.979, \"(1.49, 1.54)\": 0.984, \"(1.54, 1.58)\": 0.997, \"(1.58, 1.58)\": 1.001, \"(1.58, 1.6)\": 1.008, \"(1.6, 1.64)\": 1.017, \"(1.64, 1.65)\": 1.024, \"(1.65, 1.66)\": 1.027, \"(1.66, 1.68)\": 1.032, \"(1.68, 1.7)\": 1.037, \"(1.7, 1.73)\": 1.045, \"(1.73, 1.76)\": 1.051, \"(1.76, 1.81)\": 1.059, \"(1.81, 1.85)\": 1.07, \"(1.85, 1.86)\": 1.074, \"(1.86, 1.87)\": 1.081, \"(1.87, 1.9)\": 1.085, \"(1.9, 1.93)\": 1.093, \"(1.93, 1.98)\": 1.097, \"(1.98, 2.06)\": 1.109, \"(2.06, 2.08)\": 1.115, \"(2.08, 2.09)\": 1.119, \"(2.09, 2.1)\": 1.123, \"(2.1, 2.12)\": 1.126, \"(2.12, 2.13)\": 1.131, \"(2.13, 2.15)\": 1.136, \"(2.15, 2.19)\": 1.144, \"(2.19, 2.23)\": 1.15, \"(2.23, 2.29)\": 1.154, \"(2.29, 2.33)\": 1.162, \"(2.33, 2.38)\": 1.168, \"(2.38, 2.43)\": 1.175, \"(2.43, 2.46)\": 1.183, \"(2.46, 2.48)\": 1.187, \"(2.48, 2.53)\": 1.191, \"(2.53, 2.58)\": 1.197, \"(2.58, 2.63)\": 1.201, \"(2.63, 2.68)\": 1.208, \"(2.68, 2.69)\": 1.212, \"(2.69, 2.71)\": 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+      "Means: {\"(-9.94, -2.33)\": -1.0, \"(-2.33, -1.95)\": -0.98, \"(-1.95, -1.71)\": -0.959, \"(-1.71, -1.58)\": -0.936, \"(-1.58, -1.46)\": -0.914, \"(-1.46, -1.35)\": -0.893, \"(-1.35, -1.28)\": -0.872, \"(-1.28, -1.19)\": -0.849, \"(-1.19, -1.13)\": -0.824, \"(-1.13, -1.05)\": -0.801, \"(-1.05, -1.0)\": -0.781, \"(-1.0, -0.93)\": -0.752, \"(-0.93, -0.89)\": -0.728, \"(-0.89, -0.82)\": -0.696, \"(-0.82, -0.78)\": -0.674, \"(-0.78, -0.74)\": -0.649, \"(-0.74, -0.7)\": -0.625, \"(-0.7, -0.67)\": -0.596, \"(-0.67, -0.61)\": -0.572, \"(-0.61, -0.58)\": -0.535, \"(-0.58, -0.54)\": -0.509, \"(-0.54, -0.49)\": -0.486, \"(-0.49, -0.46)\": -0.446, \"(-0.46, -0.41)\": -0.421, \"(-0.41, -0.36)\": -0.373, \"(-0.36, -0.34)\": -0.343, \"(-0.34, -0.31)\": -0.32, \"(-0.31, -0.29)\": -0.292, \"(-0.29, -0.25)\": -0.261, \"(-0.25, -0.23)\": -0.234, \"(-0.23, -0.19)\": -0.213, \"(-0.19, -0.17)\": -0.189, \"(-0.17, -0.14)\": -0.158, \"(-0.14, -0.1)\": -0.136, \"(-0.1, -0.05)\": -0.068, \"(-0.05, -0.02)\": -0.041, \"(-0.02, 0.02)\": -0.006, \"(0.02, 0.04)\": 0.026, \"(0.04, 0.06)\": 0.047, \"(0.06, 0.09)\": 0.067, \"(0.09, 0.11)\": 0.092, \"(0.11, 0.13)\": 0.113, \"(0.13, 0.15)\": 0.136, \"(0.15, 0.18)\": 0.161, \"(0.18, 0.22)\": 0.197, \"(0.22, 0.24)\": 0.224, \"(0.24, 0.26)\": 0.245, \"(0.26, 0.29)\": 0.268, \"(0.29, 0.34)\": 0.293, \"(0.34, 0.39)\": 0.342, \"(0.39, 0.42)\": 0.378, \"(0.42, 0.45)\": 0.404, \"(0.45, 0.48)\": 0.427, \"(0.48, 0.52)\": 0.455, \"(0.52, 0.57)\": 0.494, \"(0.57, 0.6)\": 0.521, \"(0.6, 0.64)\": 0.55, \"(0.64, 0.68)\": 0.575, \"(0.68, 0.74)\": 0.609, \"(0.74, 0.8)\": 0.636, \"(0.8, 0.85)\": 0.671, \"(0.85, 0.92)\": 0.693, \"(0.92, 0.99)\": 0.74, \"(0.99, 1.06)\": 0.762, \"(1.06, 1.1)\": 0.784, \"(1.1, 1.18)\": 0.806, \"(1.18, 1.26)\": 0.829, \"(1.26, 1.34)\": 0.852, \"(1.34, 1.43)\": 0.877, \"(1.43, 1.56)\": 0.897, \"(1.56, 1.72)\": 0.919, \"(1.72, 1.92)\": 0.939, \"(1.92, 2.33)\": 0.961, \"(2.33, 9.97)\": 0.983}\n",
       "\n"
      ]
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2742,7 +4085,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2754,12 +4097,75 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: The graph of feature x was simplified by 0.1%.\n",
       "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Means: {\"(-9.99, -8.37)\": -0.001, \"(-8.37, -6.54)\": 0.001, \"(-6.54, -5.8)\": 0.002, \"(-5.8, -5.5)\": 0.003, \"(-5.5, -5.22)\": 0.005, \"(-5.22, -4.96)\": 0.006, \"(-4.96, -4.79)\": 0.007, \"(-4.79, -4.68)\": 0.008, \"(-4.68, -4.51)\": 0.01, \"(-4.51, -4.48)\": 0.011, \"(-4.48, -4.34)\": 0.012, \"(-4.34, -4.2)\": 0.013, \"(-4.2, -4.15)\": 0.014, \"(-4.15, -4.07)\": 0.016, \"(-4.07, -4.03)\": 0.017, \"(-4.03, -3.94)\": 0.018, \"(-3.94, -3.84)\": 0.019, \"(-3.84, -3.83)\": 0.021, \"(-3.83, -3.76)\": 0.022, \"(-3.76, -3.69)\": 0.023, \"(-3.69, -3.61)\": 0.025, \"(-3.61, -3.53)\": 0.027, \"(-3.53, -3.49)\": 0.028, \"(-3.49, -3.44)\": 0.03, \"(-3.44, -3.37)\": 0.032, \"(-3.37, -3.31)\": 0.034, \"(-3.31, -3.28)\": 0.035, \"(-3.28, -3.24)\": 0.037, \"(-3.24, -3.21)\": 0.038, \"(-3.21, -3.18)\": 0.039, \"(-3.18, -3.16)\": 0.041, \"(-3.16, -3.13)\": 0.042, \"(-3.13, -3.08)\": 0.043, \"(-3.08, -3.06)\": 0.045, \"(-3.06, -3.01)\": 0.047, \"(-3.01, -2.95)\": 0.049, \"(-2.95, -2.92)\": 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1.06)\": 0.737, \"(1.06, 1.16)\": 0.752, \"(1.16, 1.22)\": 0.768, \"(1.22, 1.24)\": 0.773, \"(1.24, 1.25)\": 0.775, \"(1.25, 1.26)\": 0.778, \"(1.26, 1.29)\": 0.781, \"(1.29, 1.32)\": 0.786, \"(1.32, 1.32)\": 0.788, \"(1.32, 1.33)\": 0.79, \"(1.33, 1.34)\": 0.792, \"(1.34, 1.35)\": 0.794, \"(1.35, 1.37)\": 0.797, \"(1.37, 1.39)\": 0.8, \"(1.39, 1.4)\": 0.802, \"(1.4, 1.43)\": 0.803, \"(1.43, 1.45)\": 0.808, \"(1.45, 1.47)\": 0.811, \"(1.47, 1.47)\": 0.812, \"(1.47, 1.49)\": 0.814, \"(1.49, 1.51)\": 0.817, \"(1.51, 1.53)\": 0.819, \"(1.53, 1.54)\": 0.822, \"(1.54, 1.55)\": 0.824, \"(1.55, 1.57)\": 0.825, \"(1.57, 1.59)\": 0.828, \"(1.59, 1.6)\": 0.83, \"(1.6, 1.61)\": 0.833, \"(1.61, 1.62)\": 0.834, \"(1.62, 1.64)\": 0.837, \"(1.64, 1.66)\": 0.839, \"(1.66, 1.67)\": 0.841, \"(1.67, 1.69)\": 0.842, \"(1.69, 1.69)\": 0.844, \"(1.69, 1.7)\": 0.845, \"(1.7, 1.71)\": 0.847, \"(1.71, 1.72)\": 0.849, \"(1.72, 1.74)\": 0.85, \"(1.74, 1.77)\": 0.853, \"(1.77, 1.79)\": 0.857, \"(1.79, 1.82)\": 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\"(2.75, 2.76)\": 0.94, \"(2.76, 2.8)\": 0.942, \"(2.8, 2.85)\": 0.944, \"(2.85, 2.87)\": 0.945, \"(2.87, 2.89)\": 0.946, \"(2.89, 2.93)\": 0.948, \"(2.93, 2.94)\": 0.949, \"(2.94, 2.95)\": 0.95, \"(2.95, 2.99)\": 0.952, \"(2.99, 3.06)\": 0.954, \"(3.06, 3.11)\": 0.956, \"(3.11, 3.13)\": 0.957, \"(3.13, 3.16)\": 0.958, \"(3.16, 3.21)\": 0.96, \"(3.21, 3.23)\": 0.961, \"(3.23, 3.27)\": 0.963, \"(3.27, 3.33)\": 0.964, \"(3.33, 3.38)\": 0.966, \"(3.38, 3.4)\": 0.967, \"(3.4, 3.45)\": 0.969, \"(3.45, 3.52)\": 0.97, \"(3.52, 3.56)\": 0.971, \"(3.56, 3.61)\": 0.972, \"(3.61, 3.67)\": 0.974, \"(3.67, 3.7)\": 0.975, \"(3.7, 3.74)\": 0.976, \"(3.74, 3.83)\": 0.978, \"(3.83, 3.87)\": 0.979, \"(3.87, 3.97)\": 0.98, \"(3.97, 4.0)\": 0.982, \"(4.0, 4.11)\": 0.983, \"(4.11, 4.2)\": 0.984, \"(4.2, 4.3)\": 0.985, \"(4.3, 4.4)\": 0.986, \"(4.4, 4.47)\": 0.988, \"(4.47, 4.57)\": 0.989, \"(4.57, 4.69)\": 0.99, \"(4.69, 4.88)\": 0.991, \"(4.88, 5.0)\": 0.993, \"(5.0, 5.28)\": 0.994, \"(5.28, 5.51)\": 0.995, \"(5.51, 5.79)\": 0.996, \"(5.79, 6.4)\": 0.997, \"(6.4, 7.45)\": 0.999, \"(7.45, 9.98)\": 1.0}\n",
+      "Means: {\"(-10.0, -9.44)\": -3.0, \"(-9.44, -8.89)\": -2.939, \"(-8.89, -8.34)\": -2.879, \"(-8.34, -7.86)\": -2.817, \"(-7.86, -7.38)\": -2.756, \"(-7.38, -6.91)\": -2.695, \"(-6.91, -6.5)\": -2.628, \"(-6.5, -6.09)\": -2.568, \"(-6.09, -5.72)\": -2.505, \"(-5.72, -5.38)\": -2.444, \"(-5.38, -5.07)\": -2.384, \"(-5.07, -4.73)\": -2.32, \"(-4.73, -4.45)\": -2.257, \"(-4.45, -4.17)\": -2.196, \"(-4.17, -3.89)\": -2.131, \"(-3.89, -3.65)\": -2.067, \"(-3.65, -3.42)\": -2.005, \"(-3.42, -3.22)\": -1.942, \"(-3.22, -3.01)\": -1.881, \"(-3.01, -2.81)\": -1.817, \"(-2.81, -2.63)\": -1.751, \"(-2.63, -2.44)\": -1.686, \"(-2.44, -2.29)\": -1.625, \"(-2.29, -2.14)\": -1.559, \"(-2.14, -1.98)\": -1.494, \"(-1.98, -1.83)\": -1.423, \"(-1.83, -1.69)\": -1.36, \"(-1.69, -1.57)\": -1.294, \"(-1.57, -1.45)\": -1.227, \"(-1.45, -1.35)\": -1.166, \"(-1.35, -1.23)\": -1.105, \"(-1.23, -1.13)\": -1.035, \"(-1.13, -1.02)\": -0.959, \"(-1.02, -0.95)\": -0.889, \"(-0.95, -0.81)\": -0.811, \"(-0.81, -0.7)\": -0.719, \"(-0.7, -0.63)\": -0.635, \"(-0.63, -0.53)\": -0.571, \"(-0.53, -0.46)\": -0.49, \"(-0.46, -0.4)\": -0.43, \"(-0.4, -0.3)\": -0.365, \"(-0.3, -0.2)\": -0.265, \"(-0.2, -0.14)\": -0.179, \"(-0.14, -0.05)\": -0.109, \"(-0.05, 0.02)\": -0.042, \"(0.02, 0.09)\": 0.024, \"(0.09, 0.18)\": 0.115, \"(0.18, 0.26)\": 0.186, \"(0.26, 0.31)\": 0.264, \"(0.31, 0.41)\": 0.328, \"(0.41, 0.46)\": 0.404, \"(0.46, 0.55)\": 0.467, \"(0.55, 0.64)\": 0.534, \"(0.64, 0.72)\": 0.6, \"(0.72, 0.81)\": 0.691, \"(0.81, 0.88)\": 0.76, \"(0.88, 1.0)\": 0.822, \"(1.0, 1.11)\": 0.887, \"(1.11, 1.21)\": 0.964, \"(1.21, 1.32)\": 1.03, \"(1.32, 1.42)\": 1.098, \"(1.42, 1.56)\": 1.162, \"(1.56, 1.66)\": 1.23, \"(1.66, 1.8)\": 1.29, \"(1.8, 1.95)\": 1.358, \"(1.95, 2.08)\": 1.426, \"(2.08, 2.22)\": 1.487, \"(2.22, 2.41)\": 1.549, \"(2.41, 2.59)\": 1.614, \"(2.59, 2.77)\": 1.684, \"(2.77, 2.98)\": 1.745, \"(2.98, 3.17)\": 1.811, \"(3.17, 3.41)\": 1.88, \"(3.41, 3.62)\": 1.944, \"(3.62, 3.85)\": 2.005, \"(3.85, 4.15)\": 2.066, \"(4.15, 4.45)\": 2.137, \"(4.45, 4.74)\": 2.2, \"(4.74, 5.06)\": 2.262, \"(5.06, 5.39)\": 2.326, \"(5.39, 5.73)\": 2.387, \"(5.73, 6.14)\": 2.451, \"(6.14, 6.49)\": 2.513, \"(6.49, 6.93)\": 2.573, \"(6.93, 7.37)\": 2.637, \"(7.37, 7.83)\": 2.697, \"(7.83, 8.34)\": 2.758, \"(8.34, 8.88)\": 2.818, \"(8.88, 9.45)\": 2.879, \"(9.45, 9.99)\": 2.943}\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: x\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(-10.0, -7.71)\": -1.472, \"(-7.71, -6.33)\": -1.442, \"(-6.33, -5.19)\": -1.412, \"(-5.19, -4.51)\": -1.382, \"(-4.51, -3.93)\": -1.352, \"(-3.93, -3.5)\": -1.322, \"(-3.5, -3.14)\": -1.292, \"(-3.14, -2.83)\": -1.26, \"(-2.83, -2.53)\": -1.229, \"(-2.53, -2.33)\": -1.195, \"(-2.33, -2.16)\": -1.165, \"(-2.16, -1.98)\": -1.132, \"(-1.98, -1.85)\": -1.101, \"(-1.85, -1.71)\": -1.071, \"(-1.71, -1.59)\": -1.04, \"(-1.59, -1.44)\": -0.998, \"(-1.44, -1.33)\": -0.955, \"(-1.33, -1.24)\": -0.92, \"(-1.24, -1.18)\": -0.891, \"(-1.18, -1.08)\": -0.861, \"(-1.08, -1.02)\": -0.814, \"(-1.02, -0.94)\": -0.782, \"(-0.94, -0.89)\": -0.749, \"(-0.89, -0.81)\": -0.718, \"(-0.81, -0.76)\": -0.682, \"(-0.76, -0.73)\": -0.652, \"(-0.73, -0.64)\": -0.619, \"(-0.64, -0.59)\": -0.557, \"(-0.59, -0.53)\": -0.525, \"(-0.53, -0.46)\": -0.46, \"(-0.46, -0.42)\": -0.429, \"(-0.42, -0.37)\": -0.388, \"(-0.37, -0.31)\": -0.335, \"(-0.31, -0.25)\": -0.275, \"(-0.25, -0.21)\": -0.233, \"(-0.21, -0.17)\": -0.193, \"(-0.17, -0.12)\": -0.146, \"(-0.12, -0.07)\": -0.097, \"(-0.07, -0.01)\": -0.061, \"(-0.01, 0.07)\": 0.039, \"(0.07, 0.12)\": 0.101, \"(0.12, 0.15)\": 0.134, \"(0.15, 0.18)\": 0.165, \"(0.18, 0.27)\": 0.208, \"(0.27, 0.39)\": 0.33, \"(0.39, 0.44)\": 0.385, \"(0.44, 0.48)\": 0.416, \"(0.48, 0.52)\": 0.455, \"(0.52, 0.58)\": 0.485, \"(0.58, 0.66)\": 0.533, \"(0.66, 0.73)\": 0.605, \"(0.73, 0.79)\": 0.639, \"(0.79, 0.85)\": 0.673, \"(0.85, 0.91)\": 0.713, \"(0.91, 0.95)\": 0.744, \"(0.95, 1.04)\": 0.774, \"(1.04, 1.12)\": 0.809, \"(1.12, 1.21)\": 0.852, \"(1.21, 1.29)\": 0.882, \"(1.29, 1.41)\": 0.918, \"(1.41, 1.49)\": 0.95, \"(1.49, 1.57)\": 0.98, \"(1.57, 1.71)\": 1.01, \"(1.71, 1.84)\": 1.043, \"(1.84, 2.0)\": 1.074, \"(2.0, 2.18)\": 1.11, \"(2.18, 2.38)\": 1.144, \"(2.38, 2.6)\": 1.176, \"(2.6, 2.84)\": 1.206, \"(2.84, 3.18)\": 1.237, \"(3.18, 3.55)\": 1.268, \"(3.55, 4.04)\": 1.298, \"(4.04, 4.55)\": 1.329, \"(4.55, 5.41)\": 1.359, \"(5.41, 6.54)\": 1.389, \"(6.54, 8.14)\": 1.419, \"(8.14, 9.96)\": 1.449}\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: x\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(-9.97, -4.69)\": -0.0005, \"(-4.69, -3.92)\": 0.0096, \"(-3.92, -3.49)\": 0.0198, \"(-3.49, -3.19)\": 0.0304, \"(-3.19, -2.96)\": 0.0409, \"(-2.96, -2.74)\": 0.0513, \"(-2.74, -2.56)\": 0.0618, \"(-2.56, -2.4)\": 0.0727, \"(-2.4, -2.3)\": 0.0851, \"(-2.3, -2.12)\": 0.0956, \"(-2.12, -2.03)\": 0.1066, \"(-2.03, -1.92)\": 0.1174, \"(-1.92, -1.81)\": 0.1301, \"(-1.81, -1.73)\": 0.1413, \"(-1.73, -1.63)\": 0.1519, \"(-1.63, -1.55)\": 0.1655, \"(-1.55, -1.46)\": 0.1774, \"(-1.46, -1.39)\": 0.1892, \"(-1.39, -1.35)\": 0.1992, \"(-1.35, -1.27)\": 0.2093, \"(-1.27, -1.22)\": 0.22, \"(-1.22, -1.15)\": 0.2303, \"(-1.15, -1.08)\": 0.2415, \"(-1.08, -1.03)\": 0.254, \"(-1.03, -0.98)\": 0.2643, \"(-0.98, -0.93)\": 0.2746, \"(-0.93, -0.86)\": 0.2857, \"(-0.86, -0.79)\": 0.2989, \"(-0.79, -0.71)\": 0.3188, \"(-0.71, -0.65)\": 0.3316, \"(-0.65, -0.59)\": 0.3449, \"(-0.59, -0.53)\": 0.3564, \"(-0.53, -0.46)\": 0.3767, \"(-0.46, -0.42)\": 0.3894, \"(-0.42, -0.36)\": 0.4009, \"(-0.36, -0.31)\": 0.4127, \"(-0.31, -0.28)\": 0.4227, \"(-0.28, -0.23)\": 0.4336, \"(-0.23, -0.19)\": 0.445, \"(-0.19, -0.13)\": 0.4561, \"(-0.13, -0.08)\": 0.468, \"(-0.08, -0.04)\": 0.4839, \"(-0.04, 0.02)\": 0.4969, \"(0.02, 0.07)\": 0.5102, \"(0.07, 0.14)\": 0.5217, \"(0.14, 0.18)\": 0.5362, \"(0.18, 0.22)\": 0.5482, \"(0.22, 0.32)\": 0.5631, \"(0.32, 0.38)\": 0.587, \"(0.38, 0.45)\": 0.5999, \"(0.45, 0.5)\": 0.6125, \"(0.5, 0.56)\": 0.6245, \"(0.56, 0.6)\": 0.635, \"(0.6, 0.66)\": 0.6491, \"(0.66, 0.75)\": 0.6643, \"(0.75, 0.84)\": 0.6859, \"(0.84, 0.89)\": 0.6986, \"(0.89, 0.95)\": 0.7102, \"(0.95, 1.01)\": 0.723, \"(1.01, 1.07)\": 0.7347, \"(1.07, 1.14)\": 0.7481, \"(1.14, 1.21)\": 0.7598, \"(1.21, 1.29)\": 0.772, \"(1.29, 1.37)\": 0.7875, \"(1.37, 1.46)\": 0.8009, \"(1.46, 1.52)\": 0.8116, \"(1.52, 1.59)\": 0.8233, \"(1.59, 1.69)\": 0.8344, \"(1.69, 1.78)\": 0.8459, \"(1.78, 1.89)\": 0.8569, \"(1.89, 2.02)\": 0.8713, \"(2.02, 2.1)\": 0.8817, \"(2.1, 2.27)\": 0.8939, \"(2.27, 2.41)\": 0.9076, \"(2.41, 2.55)\": 0.918, \"(2.55, 2.72)\": 0.9287, \"(2.72, 2.92)\": 0.9391, \"(2.92, 3.16)\": 0.95, \"(3.16, 3.51)\": 0.9601, \"(3.51, 3.93)\": 0.9706, \"(3.93, 4.73)\": 0.9809, \"(4.73, 9.99)\": 0.9914}\n",
       "\n"
      ]
     }
@@ -2784,7 +4190,7 @@
     "    # add the intercept\n",
     "    graph.scores = [x + ebm.intercept_ for x in graph.scores]\n",
     "    #graphs.plot_graph(graph)\n",
-    "    #graph = t2ebm.graphs.simplify_graph(graph, min_variation_per_cent=0.01)\n",
+    "    graph = t2ebm.graphs.simplify_graph(graph, min_variation_per_cent=0.01)\n",
     "    graphs.plot_graph(graph)\n",
     "    plt.title(n)    \n",
     "    plt.show()\n",
@@ -2796,7 +4202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2806,7 +4212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2816,45 +4222,65 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
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+       " 'Feature Name: x\\nFeature Type: continuous\\nGraph: {\"(-10.0, -7.71)\": -1.472, \"(-7.71, -6.33)\": -1.442, \"(-6.33, -5.19)\": -1.412, \"(-5.19, -4.51)\": -1.382, \"(-4.51, -3.93)\": -1.352, \"(-3.93, -3.5)\": -1.322, \"(-3.5, -3.14)\": -1.292, \"(-3.14, -2.83)\": -1.26, \"(-2.83, -2.53)\": -1.229, \"(-2.53, -2.33)\": -1.195, \"(-2.33, -2.16)\": -1.165, \"(-2.16, -1.98)\": -1.132, \"(-1.98, -1.85)\": -1.101, \"(-1.85, -1.71)\": -1.071, \"(-1.71, -1.59)\": -1.04, \"(-1.59, -1.44)\": -0.998, \"(-1.44, -1.33)\": -0.955, \"(-1.33, -1.24)\": -0.92, \"(-1.24, -1.18)\": -0.891, \"(-1.18, -1.08)\": -0.861, \"(-1.08, -1.02)\": -0.814, \"(-1.02, -0.94)\": -0.782, \"(-0.94, -0.89)\": -0.749, \"(-0.89, -0.81)\": -0.718, \"(-0.81, -0.76)\": -0.682, \"(-0.76, -0.73)\": -0.652, \"(-0.73, -0.64)\": -0.619, \"(-0.64, -0.59)\": -0.557, \"(-0.59, -0.53)\": -0.525, \"(-0.53, -0.46)\": -0.46, \"(-0.46, -0.42)\": -0.429, \"(-0.42, -0.37)\": -0.388, \"(-0.37, -0.31)\": -0.335, \"(-0.31, -0.25)\": -0.275, \"(-0.25, -0.21)\": -0.233, \"(-0.21, -0.17)\": -0.193, \"(-0.17, -0.12)\": -0.146, \"(-0.12, -0.07)\": -0.097, \"(-0.07, -0.01)\": -0.061, \"(-0.01, 0.07)\": 0.039, \"(0.07, 0.12)\": 0.101, \"(0.12, 0.15)\": 0.134, \"(0.15, 0.18)\": 0.165, \"(0.18, 0.27)\": 0.208, \"(0.27, 0.39)\": 0.33, \"(0.39, 0.44)\": 0.385, \"(0.44, 0.48)\": 0.416, \"(0.48, 0.52)\": 0.455, \"(0.52, 0.58)\": 0.485, \"(0.58, 0.66)\": 0.533, \"(0.66, 0.73)\": 0.605, \"(0.73, 0.79)\": 0.639, \"(0.79, 0.85)\": 0.673, \"(0.85, 0.91)\": 0.713, \"(0.91, 0.95)\": 0.744, \"(0.95, 1.04)\": 0.774, \"(1.04, 1.12)\": 0.809, \"(1.12, 1.21)\": 0.852, \"(1.21, 1.29)\": 0.882, \"(1.29, 1.41)\": 0.918, \"(1.41, 1.49)\": 0.95, \"(1.49, 1.57)\": 0.98, \"(1.57, 1.71)\": 1.01, \"(1.71, 1.84)\": 1.043, \"(1.84, 2.0)\": 1.074, \"(2.0, 2.18)\": 1.11, \"(2.18, 2.38)\": 1.144, \"(2.38, 2.6)\": 1.176, \"(2.6, 2.84)\": 1.206, \"(2.84, 3.18)\": 1.237, \"(3.18, 3.55)\": 1.268, \"(3.55, 4.04)\": 1.298, \"(4.04, 4.55)\": 1.329, \"(4.55, 5.41)\": 1.359, \"(5.41, 6.54)\": 1.389, \"(6.54, 8.14)\": 1.419, \"(8.14, 9.96)\": 1.449}\\n',\n",
+       " 'Feature Name: x\\nFeature Type: continuous\\nGraph: {\"(-9.97, -4.69)\": -0.0005, \"(-4.69, -3.92)\": 0.0096, \"(-3.92, -3.49)\": 0.0198, \"(-3.49, -3.19)\": 0.0304, \"(-3.19, -2.96)\": 0.0409, \"(-2.96, -2.74)\": 0.0513, \"(-2.74, -2.56)\": 0.0618, \"(-2.56, -2.4)\": 0.0727, \"(-2.4, -2.3)\": 0.0851, \"(-2.3, -2.12)\": 0.0956, \"(-2.12, -2.03)\": 0.1066, \"(-2.03, -1.92)\": 0.1174, \"(-1.92, -1.81)\": 0.1301, \"(-1.81, -1.73)\": 0.1413, \"(-1.73, -1.63)\": 0.1519, \"(-1.63, -1.55)\": 0.1655, \"(-1.55, -1.46)\": 0.1774, \"(-1.46, -1.39)\": 0.1892, \"(-1.39, -1.35)\": 0.1992, \"(-1.35, -1.27)\": 0.2093, \"(-1.27, -1.22)\": 0.22, \"(-1.22, -1.15)\": 0.2303, \"(-1.15, -1.08)\": 0.2415, \"(-1.08, -1.03)\": 0.254, \"(-1.03, -0.98)\": 0.2643, \"(-0.98, -0.93)\": 0.2746, \"(-0.93, -0.86)\": 0.2857, \"(-0.86, -0.79)\": 0.2989, \"(-0.79, -0.71)\": 0.3188, \"(-0.71, -0.65)\": 0.3316, \"(-0.65, -0.59)\": 0.3449, \"(-0.59, -0.53)\": 0.3564, \"(-0.53, -0.46)\": 0.3767, \"(-0.46, -0.42)\": 0.3894, \"(-0.42, -0.36)\": 0.4009, \"(-0.36, -0.31)\": 0.4127, \"(-0.31, -0.28)\": 0.4227, \"(-0.28, -0.23)\": 0.4336, \"(-0.23, -0.19)\": 0.445, \"(-0.19, -0.13)\": 0.4561, \"(-0.13, -0.08)\": 0.468, \"(-0.08, -0.04)\": 0.4839, \"(-0.04, 0.02)\": 0.4969, \"(0.02, 0.07)\": 0.5102, \"(0.07, 0.14)\": 0.5217, \"(0.14, 0.18)\": 0.5362, \"(0.18, 0.22)\": 0.5482, \"(0.22, 0.32)\": 0.5631, \"(0.32, 0.38)\": 0.587, \"(0.38, 0.45)\": 0.5999, \"(0.45, 0.5)\": 0.6125, \"(0.5, 0.56)\": 0.6245, \"(0.56, 0.6)\": 0.635, \"(0.6, 0.66)\": 0.6491, \"(0.66, 0.75)\": 0.6643, \"(0.75, 0.84)\": 0.6859, \"(0.84, 0.89)\": 0.6986, \"(0.89, 0.95)\": 0.7102, \"(0.95, 1.01)\": 0.723, \"(1.01, 1.07)\": 0.7347, \"(1.07, 1.14)\": 0.7481, \"(1.14, 1.21)\": 0.7598, \"(1.21, 1.29)\": 0.772, \"(1.29, 1.37)\": 0.7875, \"(1.37, 1.46)\": 0.8009, \"(1.46, 1.52)\": 0.8116, \"(1.52, 1.59)\": 0.8233, \"(1.59, 1.69)\": 0.8344, \"(1.69, 1.78)\": 0.8459, \"(1.78, 1.89)\": 0.8569, \"(1.89, 2.02)\": 0.8713, \"(2.02, 2.1)\": 0.8817, \"(2.1, 2.27)\": 0.8939, \"(2.27, 2.41)\": 0.9076, \"(2.41, 2.55)\": 0.918, \"(2.55, 2.72)\": 0.9287, \"(2.72, 2.92)\": 0.9391, \"(2.92, 3.16)\": 0.95, \"(3.16, 3.51)\": 0.9601, \"(3.51, 3.93)\": 0.9706, \"(3.93, 4.73)\": 0.9809, \"(4.73, 9.99)\": 0.9914}\\n']"
+      ]
+     },
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3641,7 +5067,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [
     {
@@ -3659,7 +5085,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3670,12 +5096,12 @@
     "    question += \"The graph approximately depicts one of the following functions:\\n\\n\"\n",
     "    question += '\\n'.join([f'{case} f(x) = {fbench[option][1]}' for case, option in zip(cases, mc_options)])\n",
     "    question += \"\\n\\nWhich of these functions is depicted in the graph? Think step by step.\"\n",
-    "    llm_questions.append(question)"
+    "    llm_questions.append((question, cases[mc_options.index(solution)]))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [
     {
@@ -3686,943 +5112,282 @@
       "\n",
       "Feature Name: x\n",
       "Feature Type: continuous\n",
-      "Graph: {\"(-9.99, -9.97)\": -9.98, \"(-9.97, -9.95)\": -9.96, \"(-9.95, -9.91)\": -9.93, \"(-9.91, -9.88)\": -9.9, \"(-9.88, -9.86)\": -9.87, \"(-9.86, -9.82)\": -9.84, \"(-9.82, -9.77)\": -9.8, \"(-9.77, -9.75)\": -9.76, \"(-9.75, -9.72)\": -9.73, \"(-9.72, -9.68)\": -9.7, \"(-9.68, -9.64)\": -9.66, \"(-9.64, -9.61)\": -9.63, \"(-9.61, -9.56)\": -9.6, \"(-9.56, -9.51)\": -9.53, \"(-9.51, -9.48)\": -9.5, \"(-9.48, -9.45)\": -9.47, \"(-9.45, -9.43)\": -9.44, \"(-9.43, -9.4)\": -9.42, \"(-9.4, -9.37)\": -9.39, \"(-9.37, -9.35)\": -9.36, \"(-9.35, -9.32)\": -9.33, \"(-9.32, -9.28)\": -9.29, \"(-9.28, -9.22)\": -9.26, \"(-9.22, -9.16)\": -9.19, \"(-9.16, -9.14)\": -9.16, \"(-9.14, -9.14)\": -9.14, \"(-9.14, -9.09)\": -9.11, \"(-9.09, -9.03)\": -9.05, \"(-9.03, -9.01)\": -9.03, \"(-9.01, -8.98)\": -9.0, \"(-8.98, -8.97)\": -8.98, \"(-8.97, -8.95)\": -8.95, \"(-8.95, -8.91)\": -8.93, \"(-8.91, -8.88)\": -8.9, \"(-8.88, -8.86)\": -8.88, \"(-8.86, -8.85)\": -8.86, \"(-8.85, -8.81)\": 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\"(6.41, 6.46)\": 6.44, \"(6.46, 6.52)\": 6.49, \"(6.52, 6.57)\": 6.53, \"(6.57, 6.6)\": 6.58, \"(6.6, 6.63)\": 6.61, \"(6.63, 6.64)\": 6.64, \"(6.64, 6.67)\": 6.67, \"(6.67, 6.72)\": 6.69, \"(6.72, 6.75)\": 6.73, \"(6.75, 6.78)\": 6.76, \"(6.78, 6.8)\": 6.78, \"(6.8, 6.85)\": 6.82, \"(6.85, 6.93)\": 6.9, \"(6.93, 6.98)\": 6.95, \"(6.98, 7.03)\": 7.01, \"(7.03, 7.07)\": 7.04, \"(7.07, 7.09)\": 7.08, \"(7.09, 7.11)\": 7.1, \"(7.11, 7.17)\": 7.14, \"(7.17, 7.22)\": 7.2, \"(7.22, 7.27)\": 7.24, \"(7.27, 7.31)\": 7.29, \"(7.31, 7.34)\": 7.31, \"(7.34, 7.36)\": 7.33, \"(7.36, 7.38)\": 7.37, \"(7.38, 7.41)\": 7.39, \"(7.41, 7.45)\": 7.42, \"(7.45, 7.48)\": 7.45, \"(7.48, 7.49)\": 7.49, \"(7.49, 7.5)\": 7.51, \"(7.5, 7.55)\": 7.53, \"(7.55, 7.59)\": 7.57, \"(7.59, 7.61)\": 7.61, \"(7.61, 7.66)\": 7.63, \"(7.66, 7.69)\": 7.67, \"(7.69, 7.7)\": 7.69, \"(7.7, 7.75)\": 7.71, \"(7.75, 7.77)\": 7.76, \"(7.77, 7.79)\": 7.78, \"(7.79, 7.84)\": 7.8, \"(7.84, 7.87)\": 7.83, \"(7.87, 7.88)\": 7.87, \"(7.88, 7.9)\": 7.89, \"(7.9, 7.94)\": 7.92, \"(7.94, 7.96)\": 7.95, \"(7.96, 7.99)\": 7.98, \"(7.99, 8.03)\": 8.0, \"(8.03, 8.08)\": 8.07, \"(8.08, 8.13)\": 8.09, \"(8.13, 8.19)\": 8.16, \"(8.19, 8.25)\": 8.2, \"(8.25, 8.32)\": 8.3, \"(8.32, 8.34)\": 8.32, \"(8.34, 8.36)\": 8.34, \"(8.36, 8.38)\": 8.36, \"(8.38, 8.41)\": 8.4, \"(8.41, 8.44)\": 8.42, \"(8.44, 8.49)\": 8.46, \"(8.49, 8.52)\": 8.5, \"(8.52, 8.55)\": 8.53, \"(8.55, 8.58)\": 8.55, \"(8.58, 8.6)\": 8.58, \"(8.6, 8.63)\": 8.61, \"(8.63, 8.65)\": 8.63, \"(8.65, 8.67)\": 8.66, \"(8.67, 8.7)\": 8.68, \"(8.7, 8.73)\": 8.71, \"(8.73, 8.76)\": 8.74, \"(8.76, 8.82)\": 8.78, \"(8.82, 8.86)\": 8.83, \"(8.86, 8.9)\": 8.87, \"(8.9, 8.91)\": 8.9, \"(8.91, 8.94)\": 8.93, \"(8.94, 8.97)\": 8.96, \"(8.97, 8.99)\": 8.98, \"(8.99, 9.02)\": 9.01, \"(9.02, 9.06)\": 9.04, \"(9.06, 9.11)\": 9.1, \"(9.11, 9.15)\": 9.12, \"(9.15, 9.18)\": 9.16, \"(9.18, 9.2)\": 9.18, \"(9.2, 9.24)\": 9.21, \"(9.24, 9.28)\": 9.26, \"(9.28, 9.32)\": 9.3, \"(9.32, 9.36)\": 9.34, \"(9.36, 9.39)\": 9.38, \"(9.39, 9.43)\": 9.4, \"(9.43, 9.49)\": 9.44, \"(9.49, 9.55)\": 9.52, \"(9.55, 9.6)\": 9.55, \"(9.6, 9.65)\": 9.63, \"(9.65, 9.66)\": 9.65, \"(9.66, 9.68)\": 9.67, \"(9.68, 9.71)\": 9.7, \"(9.71, 9.74)\": 9.72, \"(9.74, 9.79)\": 9.77, \"(9.79, 9.87)\": 9.81, \"(9.87, 9.94)\": 9.91, \"(9.94, 9.98)\": 9.96}\n",
+      "Graph: {\"(-9.99, -9.79)\": -9.99, \"(-9.79, -9.59)\": -9.78, \"(-9.59, -9.38)\": -9.58, \"(-9.38, -9.19)\": -9.38, \"(-9.19, -8.97)\": -9.16, \"(-8.97, -8.76)\": -8.96, \"(-8.76, -8.53)\": -8.74, \"(-8.53, -8.29)\": -8.51, \"(-8.29, -8.07)\": -8.27, \"(-8.07, -7.84)\": -8.04, \"(-7.84, -7.65)\": -7.84, \"(-7.65, -7.43)\": -7.62, \"(-7.43, -7.19)\": -7.41, \"(-7.19, -6.99)\": -7.19, \"(-6.99, -6.79)\": -6.98, \"(-6.79, -6.58)\": -6.78, \"(-6.58, -6.38)\": -6.58, \"(-6.38, -6.15)\": -6.36, \"(-6.15, -5.94)\": -6.14, \"(-5.94, -5.74)\": -5.94, \"(-5.74, -5.53)\": -5.73, \"(-5.53, -5.31)\": -5.5, \"(-5.31, -5.1)\": -5.3, \"(-5.1, -4.91)\": -5.09, \"(-4.91, -4.69)\": -4.88, \"(-4.69, -4.48)\": -4.66, \"(-4.48, -4.28)\": -4.46, \"(-4.28, -4.08)\": -4.25, \"(-4.08, -3.86)\": -4.05, \"(-3.86, -3.63)\": -3.85, \"(-3.63, -3.43)\": -3.63, \"(-3.43, -3.2)\": -3.42, \"(-3.2, -3.0)\": -3.2, \"(-3.0, -2.78)\": -2.99, \"(-2.78, -2.52)\": -2.71, \"(-2.52, -2.32)\": -2.5, \"(-2.32, -2.07)\": -2.28, \"(-2.07, -1.84)\": -2.05, \"(-1.84, -1.63)\": -1.83, \"(-1.63, -1.43)\": -1.62, \"(-1.43, -1.25)\": -1.42, \"(-1.25, -1.02)\": -1.21, \"(-1.02, -0.84)\": -1.01, \"(-0.84, -0.62)\": -0.81, \"(-0.62, -0.4)\": -0.61, \"(-0.4, -0.16)\": -0.36, \"(-0.16, 0.03)\": -0.14, \"(0.03, 0.26)\": 0.06, \"(0.26, 0.46)\": 0.27, \"(0.46, 0.68)\": 0.48, \"(0.68, 0.88)\": 0.69, \"(0.88, 1.11)\": 0.9, \"(1.11, 1.33)\": 1.13, \"(1.33, 1.55)\": 1.35, \"(1.55, 1.76)\": 1.56, \"(1.76, 1.97)\": 1.77, \"(1.97, 2.16)\": 1.98, \"(2.16, 2.37)\": 2.18, \"(2.37, 2.59)\": 2.39, \"(2.59, 2.83)\": 2.61, \"(2.83, 3.06)\": 2.86, \"(3.06, 3.31)\": 3.09, \"(3.31, 3.53)\": 3.33, \"(3.53, 3.77)\": 3.56, \"(3.77, 3.95)\": 3.77, \"(3.95, 4.16)\": 3.97, \"(4.16, 4.38)\": 4.18, \"(4.38, 4.62)\": 4.42, \"(4.62, 4.85)\": 4.63, \"(4.85, 5.06)\": 4.88, \"(5.06, 5.29)\": 5.09, \"(5.29, 5.49)\": 5.31, \"(5.49, 5.7)\": 5.51, \"(5.7, 5.91)\": 5.71, \"(5.91, 6.1)\": 5.92, \"(6.1, 6.32)\": 6.12, \"(6.32, 6.53)\": 6.33, \"(6.53, 6.7)\": 6.53, \"(6.7, 6.91)\": 6.73, \"(6.91, 7.14)\": 6.94, \"(7.14, 7.37)\": 7.14, \"(7.37, 7.58)\": 7.38, \"(7.58, 7.79)\": 7.59, \"(7.79, 7.97)\": 7.79, \"(7.97, 8.2)\": 8.0, \"(8.2, 8.42)\": 8.21, \"(8.42, 8.66)\": 8.45, \"(8.66, 8.87)\": 8.67, \"(8.87, 9.09)\": 8.88, \"(9.09, 9.31)\": 9.12, \"(9.31, 9.52)\": 9.33, \"(9.52, 9.72)\": 9.53, \"(9.72, 9.94)\": 9.73, \"(9.94, 9.95)\": 9.93}\n",
       "\n",
       "The graph approximately depicts one of the following functions:\n",
       "\n",
       "a) f(x) = x\n",
-      "b) f(x) = sign(x)\n",
+      "b) f(x) = -sinh(x)\n",
       "c) f(x) = -3*x^3\n",
-      "d) f(x) = sign(x+3)\n",
-      "e) f(x) = 2^(x-5)\n",
+      "d) f(x) = -x^5\n",
+      "e) f(x) = -sin(x)\n",
       "\n",
-      "Which of these functions is depicted in the graph? Think step by step.\n"
+      "Which of these functions is depicted in the graph? Think step by step.\n",
+      "SOLUTION:  a)\n"
      ]
     }
    ],
    "source": [
-    "print(llm_questions[0])"
+    "print(llm_questions[0][0])\n",
+    "print('SOLUTION: ', llm_questions[0][1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "with open(\"../benchmark/function-recognition.json\", \"w\") as f:\n",
+    "    json.dump(llm_questions, f, indent=2)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Benchmark gpt-4-turbo-2024-04-09"
+    "# Benchmark an LLM"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Graph x\n",
-      "\n",
-      "To determine which function is depicted in the JSON graph, let's examine the behavior of the graph and compare it to the key features of the functions listed.\n",
-      "\n",
-      "### Step-by-step Analysis\n",
-      "\n",
-      "1. **Graph Overview**:\n",
-      "   The JSON object has keys as intervals (\"a, b\") and values as numbers which are approximately in the middle of the interval's start and end. This suggests an approximate relationship where y ≈ x within each interval.\n",
-      "\n",
-      "2. **Examining Functions**:\n",
-      "   - **f(x) = x**: This function directly maps x to y. A graph for this function would be a straight line with a slope of 1 passing through the origin.\n",
-      "   - **f(x) = sign(x)**: This function returns -1 for x < 0, 0 for x = 0, and +1 for x > 0, so its graph would be flat lines at -1, 0, and +1.\n",
-      "   - **f(x) = -3*x^3**: This cubic function would show curvature and asymmetric behavior around zero, with sharp turns dependent on the negative cubic power.\n",
-      "   - **f(x) = sign(x+3)**: This would shift the graph of f(x) = sign(x) to the left by 3 units. It would show -1 for x < -3, and +1 for x > -3.\n",
-      "   - **f(x) = 2^(x-5)**: An exponential function, growing very fast after crossing x=5, and slowly approaching zero as x goes towards negative infinity.\n",
-      "\n",
-      "3. **Matching with JSON Graph**:\n",
-      "   - From the given JSON, the values increase approximately by the change in the range, i.e., y ≈ middle(x), with the relationship being almost linear (x ≈ mid-point of the range is close to y).\n",
-      "   - This linear behavior with a slope of approximately 1 matches f(x) = x.\n",
-      "\n",
-      "### Conclusion\n",
-      "\n",
-      "Given the nature of the graph (where each y-value approximates the midpoint of the x-interval provided, indicating a linear relationship), the function that is most likely depicted in the graph is **f(x) = x**. \n",
-      "\n",
-      "This matches the consistent, proportionate increase seen in the mappings provided in the JSON object, which aligns closely with a linear function where y equals x. Hence, **Option a) f(x) = x** is the correct choice.\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph -2*x+5\n",
-      "\n",
-      "To determine which function the graph represents, we can analyze the pattern and behavior of the y-values with respect to the x-values. By looking at the values, we observe the intervals on the x-axis and corresponding y-axis values:\n",
-      "\n",
-      "- The graph is continuous and each interval on the x-axis shows small incremental changes.\n",
-      "- According to the provided data, y-values decrease as x-values increase, which suggests a possible linear or polynomial relationship where the x-values exert a significant negative effect on the function.\n",
-      "  \n",
-      "Let's evaluate against each of the given function options:\n",
-      "\n",
-      "a) \\( f(x) = \\text{sign}(x+3) \\) - This function would output values of -1, 0, or 1 depending on whether \\( x+3 \\) is negative, zero, or positive. The provided graph has y-values that range far beyond these outputs, with multiple unique values.\n",
-      "\n",
-      "b) \\( f(x) = -2x + 5 \\) - This is a linear function with a negative slope. As x increases, y decreases linearly. Given the vast range from approximately -15 to around 25 in the y-axis, this pattern could match, especially considering that the decrease appears consistent. We can test this with specific values: for instance, at middle point, \\( x = 0 \\), \\( y = 5 \\); as x moves to more negative, y increases, and as x moves to more positive, y gets dramatically negative.\n",
-      "\n",
-      "c) \\( f(x) = x \\) - This linear function does not modify the magnitude of x (other than changing possibly the sign when negative). The observed pattern with y-values between -15 to around 25 and not a proportionate increase or decrease relative to x indicates it's less likely to fit this pattern.\n",
-      "\n",
-      "d) \\( f(x) = \\sqrt{x + 10} \\) - This function involves a square root transformation, limiting y-values to non-negative numbers, as square roots of real numbers must be non-negative. This doesn’t fit the negative y-values shown as x increases.\n",
-      "\n",
-      "e) \\( f(x) = x^3 \\) - This cubic function would give a lot of curvature to the graph, especially extremes being very high or low as x moves away from 0. The graph here seems to display a more straightforward, monotonic shift rather than the higher-order polynomial curves typical with cubic functions.\n",
-      "\n",
-      "Comparison and Calculation:\n",
-      "- Let’s check the model \\( f(x) = -2x + 5 \\) at two random points:\n",
-      "  - Suppose \\( x = -9.8 \\), \\( f(-9.8) = -2(-9.8) + 5 = 19.6 + 5 = 24.6 \\) which reasonably approximates the \\( y = 24.97 \\) from the data.\n",
-      "  - Suppose \\( x = 9.8 \\), \\( f(9.8) = -2(9.8) + 5 = -19.6 + 5 = -14.6 \\) which is close to the \\( y = -14.62 \\).\n",
-      "\n",
-      "Conclusion:\n",
-      "Given the evaluation, the closest matching function to the graph data appears to be (**b) \\( f(x) = -2x + 5 \\)**). This function captures the linear behavior and magnitudes of change shown in the graph.\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph x^2\n",
-      "\n",
-      "To determine which function the graph represents, let's perform a qualitative analysis based on the general behavior of the values across the range of x values presented in the intervals.\n",
-      "\n",
-      "1. **Exponential Functions**:\n",
-      "   - **f(x) = exp(-x^2)** is the Gaussian function (bell-shaped curve), mostly centered around 0. It shows rapid decline as the value moves away from 0 on both sides (i.e., both positively and negatively).\n",
-      "   - **f(x) = exp(-x)** decays exponentially as x increases and approaches infinity more smoothly as x becomes more negative.\n",
-      "\n",
-      "2. **Polynomial Function**:\n",
-      "   - **f(x) = x^2** increases as x moves away from 0, symmetrically in both the negative and positive x directions.\n",
-      "\n",
-      "3. **Exponential Growth**:\n",
-      "   - **f(x) = 2^(x-5)** would grow exponentially as x increases, particularly having lower values when x < 5 and rapidly increasing when x > 5.\n",
-      "\n",
-      "4. **Sign Function**:\n",
-      "   - **f(x) = sign(x)** provides a constant output of -1 for negative x, +1 for positive x, and 0 at x = 0.\n",
-      "\n",
-      "Now looking at the data: the values start near 99.4 at x values just below -10 and gradually decrease to about 1.26 near x = 0. Then, it starts increasing back up to 99.55 as x approaches 10. This behavior suggests a symmetric function around x=0, characterized by high values at both extremes (near -10 and 10) and the lowest value near the middle (near 0).\n",
-      "\n",
-      "From the options provided:\n",
-      "- **f(x) = exp(-x^2)**: The described profile fits this function, as it's high near x = -10 and x = 10 and lowest near x = 0, mirroring the bell-shaped curve of a Gaussian function.\n",
-      "- **f(x) = exp(-x)**: Does not fit because it would not increase again after decreasing.\n",
-      "- **f(x) = x^2**: Does not fit because the values should lower near x = 0 and then increase in both positive and negative directions which conflicts with the data that shows a high value at both ends.\n",
-      "- **f(x) = 2^(x-5)**: This does not align as the function would need to have low values for x < 5 and only increase significantly beyond x = 5.\n",
-      "- **f(x) = sign(x)**: Clearly not fitting as the graph should only take values of -1, 0, or +1, which does not correspond to the given interval mapping.\n",
-      "\n",
-      "Thus, the graph most likely depicts the function **a) f(x) = exp(-x^2)**, a Gaussian distribution centered at x = 0. This conclusion is drawn from observing the symmetric decrease from high values at both extremes towards the center and back up, characteristic of the Gaussian function’s bell curve.\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph -2*x^2\n",
-      "\n",
-      "To determine which function the graph represents, let's analyze the provided data in relation to each of the given potential functions.\n",
-      "\n",
-      "### 1. Visualizing and Analyzing the Provided Information\n",
-      "We are given several key-value pairs where the key indicates a range of x-values and the value indicates the corresponding y-value.\n",
-      "\n",
-      "If we extract some specific example points:\n",
-      "\n",
-      "- For x around -10 (range `(-10.0, -9.84)`), y is approximately -199.9.\n",
-      "- For x around 0 (range `(-1.7, 2.46)`), y is approximately -5.8.\n",
-      "- For x around 10 (range `(9.83, 9.97)`), y is approximately -193.6.\n",
-      "\n",
-      "### 2. Comparing with Function Options:\n",
-      "a) **`f(x) = (x-2)^2`**\n",
-      "   - A parabolic function with the vertex at x=2. As x moves away from 2 in either direction, y should increase (positive slope parabola). The midpoint value around 0 should be higher, contradicting the provided data.\n",
-      "\n",
-      "b) **`f(x) = -2*x^2`**\n",
-      "   - An inverted parabolic curve centered at x=0. Y-values should decrease (become more negative) as x moves away from 0. This matches the pattern seen in the data: highest (least negative) value near x=0 and lower values as x increases or decreases.\n",
-      "\n",
-      "c) **`f(x) = exp(-x^2)`**\n",
-      "   - An exponential decay curve based on x². Starting high (near 1 if x is 0) and approaching 0 as x moves away from 0. However, y should never be negative, which contradicts the negative values across all x ranges in the data.\n",
-      "\n",
-      "d) **`f(x) = cos(x)`**\n",
-      "   - Oscillating between 1 and -1 regular intervals (every 2π). The substantial variations in y-values and the ranges offered (from near -200 to -5.8) are not reflective of the regular, bounded oscillation a cosine function would show.\n",
-      "\n",
-      "e) **`f(x) = |x|`**\n",
-      "   - Linear, with y increasing positively as x moves away from zero in either direction and being symmetrical about the y-axis. Importantly, the values are not negative, conflicting with the data's uniformly negative values.\n",
-      "\n",
-      "### 3. Conclusion\n",
-      "The provided data matches best with option **b) `f(x) = -2*x^2`**. This function describes a downward opening parabola passing through the origin, where the function decreases in value (getting more negative), the further x is from 0, consistent with the graph values showing deep negatives moving towards the extremes of the x-range and less negative near x=0. Therefore, option b) `f(x) = -2*x^2` is the function most likely represented by the provided JSON object data.\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph (x-2)^2\n",
-      "\n",
-      "To determine which of the provided functions is likely represented by the graph described in the JSON object, we can analyze the characteristics of the data and check which function’s behavior correlates with it. Let’s analyze each function:\n",
-      "\n",
-      "**a) \\( f(x) = \\sqrt{x+10} \\)**\n",
-      "- This function is defined for \\( x \\geq -10 \\) and represents a square root function, which generally increases at a decreasing rate as x increases. The function should start low (near zero if we include -10 within our valid x range) and gradually increase.\n",
-      "\n",
-      "**b) \\( f(x) = \\text{sign}(x-1) \\)**\n",
-      "- The sign function takes -1 for negative inputs, 0 at zero, and +1 for positive inputs. The function \\( \\text{sign}(x-1) \\) will be -1 for \\( x < 1 \\), 0 at \\( x = 1 \\), and +1 for \\( x > 1 \\). Graphically, this corresponds to two flat lines and a jump discontinuity at \\( x = 1 \\).\n",
-      "\n",
-      "**c) \\( f(x) = (x-2)^2 \\)**\n",
-      "- This function is a quadratic function with a minimum at \\( x = 2 \\). Quadratics are paraboloid and symmetric around the minimum point. It should decrease as x approaches 2, from either direction, reaching a minimum value of 0 at \\( x = 2 \\), then increasing symmetrically.\n",
-      "\n",
-      "**d) \\( f(x) = x^3 \\)**\n",
-      "- A cubic function has an 'S'-shaped curve, with negative values when \\( x < 0 \\) turning positive as \\( x \\) crosses 0, and the function value then rapidly increasing for \\( x > 0 \\).\n",
-      "\n",
-      "**e) \\( f(x) = \\sin(x) \\)**\n",
-      "- The sine function oscillates between -1 and 1 with a regular frequency, periodic in nature.\n",
-      "\n",
-      "From the high level data provided, the values initially decline slowly with decreasing changes, eventually decline more rapidly, reach a minimum perhaps somewhere in the middle, then switch to a rapid increase, progressing to a slower increase extending from negative x-values around -10 towards positive x-values around 10. This overall pattern and behavior closely resemble a quadratic function exhibited by option c, \\( f(x) = (x-2)^2 \\), which decreases towards a minimum at \\( x = 2 \\) and increases symmetrically on both sides of this point.\n",
-      "\n",
-      "**Conclusion:** Based on the description and behavior of the data described in the JSON object, the function \\( f(x) = (x-2)^2 \\) (option c) is likely being graphed, aligning with the statistical change in y-values surrounding \\( x = 2 \\) being close to the minimum and increasing symmetrically around it.\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph 2^(x-5)\n",
-      "\n",
-      "To determine which function from the choices a) through e) the data in the JSON object approximates, we need to analyze the nature of the function's behavior as illustrated by the given data points:\n",
-      "\n",
-      "1. **Qualitative Check**:\n",
-      "   - **Examine Growth**: The `y` values in the JSON data increase as `x` increases, suggesting a sort of exponential or fast-growing function rather than linear or simple polynomial growth.\n",
-      "   - **Examine Symmetry and Behavior across Negative and Positive Values**: The function values start from a certain minimum value and increase monotonically, likely a behavior of specific functions like exponential, hyperbolic sine, or highly manipulated polynomials.\n",
-      "   - **Bounded or Unbounded Functionality**: The sample shows the function rising steeply in positive regions of `x`. This hints towards functions that have exponential behaviors or specific polynomial forms.\n",
-      "   \n",
-      "2. **Comparing Function Forms**:\n",
-      "   - **a) Exponential Function — `f(x) = 2^(x-5)`**:\n",
-      "     - This function will have a slow start and then increase rapidly, which can align with the values provided that show a steep increase as `x` progresses. The function essentially shifts the standard exponential curve 5 units to the right.\n",
-      "   - **b) Square Root of a Polynomial — `f(x) = sqrt(x ** 2 + 3*x + 5)`**:\n",
-      "     - Typically, square root functions moderated by a quadratic polynomial will show a progressive, but not an explosive growth, as seen in exponential functions.\n",
-      "   - **c) Negative Cubic Polynomial — `f(x) = -3*x^3`**:\n",
-      "     - Cubic functions, especially with a leading negative coefficient, will typically dip down into negative values as `x` approaches negative infinity and rise up from negative values as `x` goes to positive infinity. This doesn't match the non-negative and increasing nature of our data.\n",
-      "   - **d) Sign Function Adjustment — `f(x) = sign(x-1)`**:\n",
-      "     - The sign function jumps between -1, 0, and 1, which clearly does not fit the continuous and variable increase seen in the data.\n",
-      "   - **e) Hyperbolic Sine — `f(x) = sinh(x)`**:\n",
-      "     - The hyperbolic sine function, similar to exponential functions, shows a quick growth as `x` increases, particularly steep after moving past the origin in positive direction, similar to the exponential function but symmetrical around the origin.\n",
-      "\n",
-      "3. **Matching with Data**:\n",
-      "   - An exponential function form like `f(x) = 2^(x-5)` would potentially match best considering the quickly increasing nature from a certain point (around `x > 5` sharply increases), corresponding to an exponential increase. The mathematical transformation `(x-5)` effectively delays the start of the steep increase until about `x=5`, aligning with the breakpoints in your data around `x=5` to `x=6`.\n",
-      "\n",
-      "Given the points above and without plotting the actual values for more precision, it is likely that the provided data best approximates **option a) `f(x) = 2^(x-5)`**.\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph (x-1)*(x+1)\n",
-      "\n",
-      "To identify the function depicted in the given JSON graph, let's analyze the general behavior and qualitative properties of the presented y-values as x changes. We can examine this information against the proposed functions:\n",
-      "\n",
-      "1. **f(x) = -2*x^2**: \n",
-      "   - This function is a downward-opening parabola with its vertex at x = 0.\n",
-      "   - As x increases or decreases from zero, the function's value should decrease (become more negative).\n",
-      "\n",
-      "2. **f(x) = x^3**:\n",
-      "   - A cubic function with no constant term and coefficients leading to symmetry about the origin.\n",
-      "   - This function decreases as x decreases from zero and increases as x increases from zero, passing through zero at x = 0.\n",
-      "\n",
-      "3. **f(x) = (x-1)*(x+1) = x^2 - 1**:\n",
-      "   - This is another parabola, but it opens upwards with a minimum value at x = 0 (y = -1).\n",
-      "   - The function decreases till x = 0 and then increases symmetrical about the y-axis.\n",
-      "\n",
-      "4. **f(x) = log(x+10)**:\n",
-      "   - A logarithmic function, translates to the right by 10 units.\n",
-      "   - It increases gradually as x increases but never decreases; the rate of increase diminishes as x becomes larger.\n",
-      "\n",
-      "5. **f(x) = -3*x^3**:\n",
-      "   - Similar to a cubic function (like in option b) but steeper and inverted.\n",
-      "   - Decreases as x moves from zero to negative and increases steeply as x moves from zero to positive.\n",
-      "\n",
-      "From your JSON object, the y-values:\n",
-      "   - Are symmetric around some central x value (approximating zero).\n",
-      "   - Show behavior that at the extremes (both positive and negative x), the function values are very high, decreasing toward the middle.\n",
-      "\n",
-      "Given these traits, we can note the following about each function against this behavior:\n",
-      "\n",
-      "- Functions **a**, **c**, and **e** are parabolic and their described curves do not match the pattern (both a and c have minimum values at or near x = 0, which is false with your graph and e behaves far too dramatically).\n",
-      "- Function **d** (logarithmic) grows indefinitely without respective symmetry or reaches vastly high values symmetrically on either side of an axis point.\n",
-      "\n",
-      "Function **b, f(x) = x^3**, is highly indicative of the mirrored changes around x = 0 and also producing high values at both positive and negative extremes, decreasing to cross the y-axis near y = 0, as depicted in your graph's y-values and behavior. \n",
-      "\n",
-      "Therefore, based on the given data and qualitative behavior analysis, **the graph most likely represents the function f(x) = x^3**.\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph x^3\n",
-      "\n",
-      "To determine which function from the given options closely matches the described graph, we can analyze the nature and behavior of each option as well as the data patterns from the JSON object.\n",
-      "\n",
-      "1. **Function Options:**\n",
-      "   a) \\( f(x) = \\text{sign}(x+3) \\)\n",
-      "   b) \\( f(x) = \\text{sign}(x) \\)\n",
-      "   c) \\( f(x) = \\sqrt{x^2 + 3x + 5} \\)\n",
-      "   d) \\( f(x) = x^3 \\)\n",
-      "   e) \\( f(x) = |2x + 4| \\)\n",
-      "\n",
-      "2. **Behavior of Options:**\n",
-      "   - \\( \\text{sign}(x) \\) or \\( \\text{sign}(x+3) \\) would result in outputs of -1, 0, or 1, which doesn't fit the wide range of continuous values in the graph.\n",
-      "   - \\( \\sqrt{x^2 + 3x + 5} \\) will always be positive and should increase as \\( x \\) increases, given its nature as a square root of a quadratic equation with positive coefficients.\n",
-      "   - \\( x^3 \\) suggests a cubic increase or decrease, passing through zero at \\( x = 0 \\) and showing symmetric behavior in positive and negative quadrants.\n",
-      "   - \\( |2x + 4| \\) results in a V-shaped graph, being zero at \\( x = -2 \\) and increasing linearly away from this point on either side.\n",
-      "\n",
-      "3. **Data Analysis:**\n",
-      "   - The graph data starts with negative values that decrease to a minimum and then increase, flatten around \\( x = 0 \\), and then increase significantly in the positive range. This doesn't fit the symmetric increase or singular inflection points typical of a cubic (\\( x^3 \\)) function, nor does it reflect the constant value that would be found with a sign function.\n",
-      "   - Points to consider:\n",
-      "      - There is a critical change around \\( x = 0 \\) in the graph where values start very negative and then become positive.\n",
-      "      - The function involves sharp decreases and increases, suggesting the potential absolute value format given the rebound on either side of a particular value.\n",
-      "      - The change seems centered roughly symmetrically around an x-value that could be calculated if it were the root of \\( |ax + b| \\).\n",
-      "\n",
-      "4. **Matching with a Potential Function:**\n",
-      "   - Given the symmetrical and linear reduction in negative values of the function, rebounding at a certain point and increasing linearly on both sides, the pattern aligns well with an absolute function \\( |2x + 4| \\). The critical point at \\( x = -2 \\) leads to \\( |2(-2) + 4| = 0 \\), and linearly increasing values on either side.\n",
-      "\n",
-      "Based on this analysis, the graph most likely represents the function:\n",
-      "\\[ e) f(x) = |2x + 4| \\]\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph -3*x^3\n",
-      "\n",
-      "To determine which function among the given options the graph depicts, I will assess the general characteristics and behavior of each function described and cross-reference them with the patterns deduced from the JSON object data:\n",
-      "\n",
-      "1. **Function Analysis**:\n",
-      "   - **f(x) = -3*x^3**: This cubic function is odd (symmetric about the origin), which implies that it is negative where \\( x \\) is positive and positive where \\( x \\) is negative. The function’s growth rate in the positive and negative x direction is not linear but rather cubic, increasing faster as \\( |x| \\) grows.\n",
-      "   - **f(x) = |2*x+4|**: This is an absolute value function that simulates linear growth with a vertex (the lowest point on a V-shaped graph), making it non-negative across its domain.\n",
-      "   - **f(x) = exp(-x^2)**: This Gaussian function peaks at \\( x = 0 \\) and symmetrically decreases towards both positive and negative infinity. It is always positive and tends towards zero as \\( |x| \\) increases.\n",
-      "   - **f(x) = x^4**: This quartic function is even (symmetric about the y-axis) and, like \\( x^2 \\), has a global minimum at \\( x = 0 \\), with rapid growth as \\( |x| \\) increases.\n",
-      "   - **f(x) = x**: This linear function is the simplest, growing positively in proportion to \\( x \\).\n",
-      "\n",
-      "2. **Graph Data Characteristics Analysis**:\n",
-      "   - Observing the JSON values, we can identify a symmetrical pattern: the y-values increase as x decreases from negative to zero and decrease as x increases from zero to positive.\n",
-      "   - The function must be symmetric around the origin or the y-axis based on the increasing then decreasing values, suggesting the function is either even or odd.\n",
-      "   - The function climbs steeply in magnitude as \\( |x| \\) increases, a characteristic typical for higher-powered polynomial functions or an exponential function.\n",
-      "   \n",
-      "3. **Matching Function to Pattern**:\n",
-      "   - **f(x) = exp(-x^2)** is a plausible match due to:\n",
-      "     - Symmetry observed around x = 0.\n",
-      "     - The clear maximum at x = 0 (near the center of the graph intervals in the data).\n",
-      "     - Rapid decrease to near zero values as x moves away from 0, though the negative y-values in the data when x is positive suggests the function descends below zero, which does not occur for \\( exp(-x^2) \\).\n",
-      "   - **f(x) = -3*x^3** also shows symmetry about the origin where negative values of x produce positive values, and vice versa, as seen in the data. The cubic nature allows steep climbs in positive or negative y-values as \\( |x| \\) increases.\n",
-      "\n",
-      "Given these considerations, **f(x) = -3*x^3** is the best fit among the options for the depicted graph. The function is both odd (mirroring behavior around origin) and cubic (steep increases and decreases), matching the pattern observed in the graph data.\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph x^4\n",
-      "\n",
-      "To determine which function the graph represents, we'll evaluate the characteristics of each proposed function and compare them with the behavior expressed in the JSON data. Here's a step-by-step analysis of each function option:\n",
-      "\n",
-      "### a) \\(f(x) = x^4\\)\n",
-      "- **Characteristics:** This function is always non-negative and symmetric around the y-axis. It grows rapidly as \\(x\\) moves away from zero in either direction.\n",
-      "- **JSON data**: Shows symmetry around \\(x = 0\\) and values are always non-negative.\n",
-      "\n",
-      "### b) \\(f(x) = -3x^3\\)\n",
-      "- **Characteristics:** This function is odd, meaning \\(f(x) = -f(-x)\\). It changes sign at \\(x = 0\\) and grows faster (cubically) as \\(x\\) moves away from zero.\n",
-      "- **JSON data**: Does not exhibit clear symmetry where one side is the negative of the other, hence unlikely to be the correct function.\n",
-      "\n",
-      "### c) \\(f(x) = \\tanh(x)\\)\n",
-      "- **Characteristics:** This function has horizontal asymptotes at \\(y = 1\\) and \\(y = -1\\), and it is smoothly increasing through \\(x = 0\\). The output values always fall between -1 and 1.\n",
-      "- **JSON data**: Does not fit as values exceed the range \\([-1, 1]\\).\n",
-      "\n",
-      "### d) \\(f(x) = -2x + 5\\)\n",
-      "- **Characteristics**: This is a linear function with a downwards slope, no symmetry about the origin.\n",
-      "- **JSON data**: Data shows symmetry and is not linear, ruling out this option.\n",
-      "\n",
-      "### e) \\(f(x) = x^3\\)\n",
-      "- **Characteristics**: This function is odd, like \\(f(x) = -3x^3\\), and suffers the same issues when compared against the symmetric JSON data.\n",
-      "\n",
-      "### Analysis of JSON Data:\n",
-      "- Visually symmetrical about \\(x = 0\\).\n",
-      "- Values increase as \\(x\\) deviates further away from zero, representing rapid growth typical of functions with higher powers of \\(x\\).\n",
-      "- All values are positive or zero, with larger absolute values of \\(x\\) leading to significantly higher y-values.\n",
-      "\n",
-      "### Conclusion:\n",
-      "Given the rapid increase and perfect symmetry, option (a) \\(f(x) = x^4\\) most closely fits the behavior described in the JSON data. The function \\(x^4\\) is always positive, symmetric around the y-axis, and increases rapidly as the absolute value of \\(x\\) increases, matching the profile shown in the data.\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph (x + 4)^4\n",
-      "\n",
-      "To determine which function the JSON graph approximately represents, we can analyze the general behavior and properties of each function in light of the given graph data. Here's how we'll approach the assessment:\n",
-      "\n",
-      "### **Step 1: Understand the Behavior of Each Function**\n",
-      "\n",
-      "a) **Linear Function** \\( f(x) = -2x + 5 \\)\n",
-      "   - This is a decreasing linear function; as \\( x \\) increases, \\( f(x) \\) decreases linearly without bounds.\n",
-      "\n",
-      "b) **Quartic Function** \\( f(x) = (x + 4)^4 \\)\n",
-      "   - This function grows very quickly as \\( x \\) increases due to the fourth power. It exhibits very rapid increases particularly for positive \\( x \\) values.\n",
-      "\n",
-      "c) **Quadratic Function** \\( f(x) = (x - 2)^2 \\)\n",
-      "   - This is a parabolic function with its vertex (minimum point) at \\( x = 2 \\). The function value increases as \\( x \\) moves away from 2.\n",
-      "\n",
-      "d) **Inverse Hyperbolic Sine Function** \\( f(x) = \\text{arcsinh}(x) \\)\n",
-      "   - This function increases gradually; it is somewhat linear for large \\( |x| \\) values, though it does increase at a slower rate than a strict linear function.\n",
-      "\n",
-      "e) **Logistic Function** \\( f(x) = \\frac{1}{1 + e^{-x}} \\)\n",
-      "   - This S-shaped function transitions from approaching 0 to approaching 1 as \\( x \\) goes from negative to positive. It has an inflection point around \\( x = 0 \\).\n",
-      "\n",
-      "### **Step 2: Compare with the Graph Behavior**\n",
-      "\n",
-      "- The JSON data suggests as \\( x \\) increases, \\( f(x) \\) also increases.\n",
-      "- Most key, the values associated with every interval are growing, and some intervals suggest rapid growth.\n",
-      "\n",
-      "### **Robust Growth Analysis**:\n",
-      "\n",
-      "- Comparing the function descriptions with the provided JSON ranges and the increasing nature of \\( f(x) \\), a linear function seems too uniform without explosive growth.\n",
-      "- The inverse hyperbolic sine grows too slowly compared to the suggested data.\n",
-      "- A logistic function might initially seem appealing due to its increasing nature, but it caps out as it approaches an asymptote near \\( f(x) = 1 \\), which does not align with the unbounded growth shown in the data.\n",
-      "- The quadratic function suggests increasing values too, but the nature of increase appears less dramatic than what the data might imply.\n",
-      "- The quartic function \\( f(x) = (x + 4)^4 \\), aligns well with large growth rates for positive x-values and the fact that values at negative x are significantly high may suggest the translation leftwards by 4 units.\n",
-      "\n",
-      "**Conclusion:** Given the rapid and unbounded increase in function values with increasing \\( x \\) as implied by the JSON data compared to the formulations of the potential functions, the function \\( f(x) = (x + 4)^4 \\) appears to be the best fit. This function exhibits high growth rates for \\( x \\) values slightly lower than zero and up, which matches well with the graphical data representation described in the JSON object.\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph sign(x)\n",
-      "\n",
-      "To determine which function the graph represents, we need to analyze how values change as x moves from negative to positive across the specified ranges.\n",
-      "\n",
-      "Here is a breakdown of the changes in y-values according to the given x-intervals:\n",
-      "\n",
-      "1. For x in the interval \\((-10.0, -0.05)\\), y is approximately \\(-1.002\\).\n",
-      "2. For x in the interval \\((-0.05, -0.02)\\), y is approximately \\(-0.982\\).\n",
-      "3. For x in the interval \\((-0.02, -0.0)\\), y is approximately \\(-0.853\\).\n",
-      "4. For x in the interval \\((-0.0, 0.02)\\), y is approximately \\(0.915\\).\n",
-      "5. For x in the interval \\((0.02, 0.37)\\), y is approximately \\(0.981\\).\n",
-      "6. For x in the interval \\((0.37, 9.99)\\), y is approximately \\(1.001\\).\n",
-      "\n",
-      "Analyzing the options:\n",
-      "\n",
-      "a) \\( f(x) = \\cos(x) \\): This function is periodic and oscillates between -1 and 1, with values decreasing towards 0 as x approaches \\( \\pm \\frac{\\pi}{2} \\), \\( \\pm \\frac{3\\pi}{2} \\), etc., but not jumping across the y-axis near zero as abruptly or changing signs linearly as in our graph.\n",
-      "\n",
-      "b) \\( f(x) = x^2 \\): This function is a parabola opening upwards with its vertex at the origin. Since it outputs only non-negative values and it increases as x moves away from zero, it doesn't match the negative y-values for negative x.\n",
-      "\n",
-      "c) \\( f(x) = \\text{sign}(x) \\): This function returns -1 for negative x, 0 for x equal to zero, and 1 for positive x. The graph quickly jumps from negative values to positive values around \\( x = 0 \\), a key characteristic of the sign function but not matching with exact 0 for \\( x = 0 \\) as defined by \\( \\text{sign}(x) \\).\n",
-      "\n",
-      "d) \\( f(x) = (x + 4)^4 \\): This function is a polynomial that rapidly increases as x moves away from -4; however, its values are positive for all \\(x\\) and increase dramatically, which does not match our data.\n",
-      "\n",
-      "e) \\( f(x) = x \\): This linear relationship increases or decreases with x and crosses through the origin. The change in values is proportional to x changes, but it fails to capture the abrupt jump from negative to positive values, nor do the constant values over ranges match the continuous growth/decline of a linear function.\n",
-      "\n",
-      "Based on the analysis, option (c) \\( f(x) = \\text{sign}(x) \\) comes closest to the representation of the described changes across the intervals. There's an abrupt transition from negative to positive at x around 0, consistent with the behavior of the Sign function. This matches the jump in y-values at around \\(x=0\\) and constant values elsewhere. Despite the lack of a precise match at \\(x=0\\), as can happen with empirical or approximated data, it best fits the pattern described in the graph.\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph sign(x+3)\n",
-      "\n",
-      "To determine which function the graph represents based on the given JSON object, let's go through the intervals and inspect the y-values, aligning them with the shapes that each potential function would show:\n",
-      "\n",
-      "1. Looking at the intervals and the corresponding y-values in the graph:\n",
-      "   - From x in (-9.98, -3.08), y = -1.004.\n",
-      "   - From x in (-3.08, -3.05), y = -0.983.\n",
-      "   - From x in (-3.05, -3.01), y = -0.798.\n",
-      "   - From x in (-3.01, -2.98), y = 0.803.\n",
-      "   - From x in (-2.98, 9.97), y = 0.984.\n",
-      "\n",
-      "2. Understanding the potential functions:\n",
-      "   - **f(x) = sign(x+3):** This function is the signum function shifted to the left by 3 units. It yields -1 when x+3 < 0 (x < -3) and 1 when x+3 > 0 (x > -3).\n",
-      "   - **f(x) = x:** This is a linear function increasing continuously over all x.\n",
-      "   - **f(x) = sinh(x):** The hyperbolic sine function increases gradually beyond zero and is an increasing function symmetrical around y=0.\n",
-      "   - **f(x) = cos(x):** The cosine function oscillates between -1 and 1 with periods.\n",
-      "   - **f(x) = |x|:** The absolute value function which reflects a V shape at x=0.\n",
-      "\n",
-      "3. Analyzing the alignment with these functions:\n",
-      "   - The jump from negative to positive occurs around -3, signifying a switching behavior rather than gradual or oscillating behavior.\n",
-      "   - The function stays at about -1 before transitioning abruptly to a value around 1, before jumping to a value near 1. This abrupt change corresponds with a signum function rather than a linear, hyperbolic, oscillating, or V-shape.\n",
-      "\n",
-      "4. Conclusion:\n",
-      "   The behavior of y-values given by the specified intervals and their consistency align closely with the behavior of **f(x) = sign(x+3)**, which is -1 for x values smaller than -3 and 1 for x values greater than -3. Furthermore, the sharp change near x=-3 also supports this identification.\n",
-      "\n",
-      "Thus, the function depicted in the graph is **(a) f(x) = sign(x+3)**.\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph sign(x-1)\n",
-      "\n",
-      "To determine which function the given graph represents, we should interpret the changes in the y-values across the different x-ranges. Let's examine the information provided:\n",
-      "\n",
-      "The graph is defined by various intervals on the x-axis, with respective y-values for each:\n",
-      "1. \\((-9.98, 0.91)\\): y = -1.003\n",
-      "2. \\( (0.91, 0.98) \\): y = -0.98\n",
-      "3. \\( (0.98, 0.98) \\): y = -0.922\n",
-      "4. \\( (0.98, 1.0) \\): y = -0.739\n",
-      "5. \\( (1.0, 1.02) \\): y = 0.839\n",
-      "6. \\( (1.02, 1.27) \\): y = 0.97\n",
-      "7. \\( (1.27, 9.97) \\): y = 0.994\n",
-      "\n",
-      "Next, let's evaluate each function choice in the context of the graph:\n",
-      "\n",
-      "a) \\(f(x) = x^3\\)\n",
-      "- This is a cubic function, characterized by smooth changes across x, with increasing or decreasing values depending on the sign of \\(x\\). This function will not settle into a fixed value for any interval, which contradicts the pattern in the provided graph.\n",
-      "\n",
-      "b) \\(f(x) = \\text{sign}(x-1)\\)\n",
-      "- The sign function returns -1 for negative inputs, 0 for zero input, and +1 for positive inputs. Here, when \\(x < 1\\), \\(x - 1 < 0\\) and \\(\\text{sign}(x-1) = -1\\); for \\(x = 1\\), \\(x-1 = 0\\) and \\(\\text{sign}(x-1) = 0\\); and for \\(x > 1\\), \\(x-1 > 0\\) and \\(\\text{sign}(x-1) = +1\\). Reflecting on the intervals and values provided in the graph, we see the transition around \\(x = 1\\), where the y-value changes from negative to positive, which aligns well with the character of the sign function.\n",
-      "\n",
-      "c) \\(f(x) = \\sinh(x)\\)\n",
-      "- The hyperbolic sine, \\(\\sinh(x)\\), exhibits exponential growth and decay, and does not show flat, constant y-values across wide x-intervals. The graph structure doesn't match with stable y-values at multiple intervals.\n",
-      "\n",
-      "d) \\(f(x) = x\\)\n",
-      "- This linear function will continuously increase or decrease; it will never show the flat, horizontal line segments as indicated in the graph.\n",
-      "\n",
-      "e) \\(f(x) = (x-1) \\cdot (x+1)\\)\n",
-      "- Given as \\(x^2 - 1\\), this quadratic function will have a parabolic shape, with no flat segments as indicated in the provided graph.\n",
-      "\n",
-      "**Conclusion:**\n",
-      "Based on analysis, option (b), \\(f(x) = \\text{sign}(x-1)\\), matches the graph most closely. There are significant changes around \\(x = 1\\), where the graph switches from negative values to positive values, consistent with the expected behavior of the sign function tied around \\(x-1\\).\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph |x|\n",
-      "\n",
-      "To determine which function the data corresponds to among the given options, let's evaluate properties and behaviors common to each function and see which one aligns with the data trends presented in the JSON object.\n",
-      "\n",
-      "1. **f(x) = arcsinh(x)** - This function, the hyperbolic arcsine, gradually increases without bound for positive x and decreases without bound for negative x, maintaining smooth, continuous change throughout since it is an odd function. \n",
-      "\n",
-      "2. **f(x) = sign(x+3)** - This function outputs `-1` if `x < -3`, `0` if `x = -3`, and `1` if `x > -3`.\n",
-      "\n",
-      "3. **f(x) = sign(x)** - This function provides output as `-1` for negative x values, `0` at `x=0`, and `1` for positive x values.\n",
-      "\n",
-      "4. **f(x) = log(x+10)** - The logarithmic function (with base e) shifted horizontally to the left by 10 units. As x ranges from negative values approaching -10 and onward, the function values start from negative infinity (at x=-10), increasing through 0 (at x=0).\n",
-      "\n",
-      "5. **f(x) = |x|** - The absolute value function has a \"V\" shape: it is 0 at x = 0 and increases linearly as x moves away from 0 in either direction.\n",
-      "\n",
-      "Now let's consider the data features:\n",
-      "\n",
-      "- The JSON object starts at a range close to `x = -10` and extends to a range close to `x = 10`.\n",
-      "- Values increase gradually from both ends of x towards the center, suggesting a symmetry around x = 0.\n",
-      "- The values at the extremes and around the center (near `x=0`) are notably different: at `x=0` the value is minimal, and values increase as x moves further away from zero, depicting a typical characteristic of an absolute value function.\n",
-      "\n",
-      "Now, let’s match the data features with the expected behavior of the provided function options:\n",
-      "\n",
-      "- **f(x) = |x|** is the only function with a symmetric pattern about x = 0, where values increase linearly as the distance from x = 0 increases. This feature is characteristic of the \"V\" shape in the absolute value function.\n",
-      "\n",
-      "From this analysis, it appears that option **(e) f(x) = |x|** best matches the description of the graph depicted by the JSON object data, showing values increasing symmetrically in both the positive and negative directions away from zero in a linear manner.\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph |2*x+4|\n",
-      "\n",
-      "To assess which function the graph approximates, let's evaluate the behavior of each provided equation based on what we know about their analytical forms:\n",
-      "\n",
-      "1. **f(x) = x^3**\n",
-      "   - This function is a cubic polynomial with a characteristic shape smoothly increasing in negative x-values, passing through zero at x = 0, and accelerating in positive x-values. \n",
-      "   \n",
-      "2. **f(x) = (x-2)^2**\n",
-      "   - A quadratic function with a minimum (vertex) at x = 2, symmetric about x = 2, and values increasing as x moves away from 2 on either side.\n",
-      "\n",
-      "3. **f(x) = |2*x+4|**\n",
-      "   - An absolute value function which is linear but mirrored to have only non-negative values. This graph will intersect the y-axis where x = -2 (the point that makes the inside of the absolute value zero), and increase linearly on either side.\n",
-      "\n",
-      "4. **f(x) = exp(-x)**\n",
-      "   - An exponential decay function starting at y = 1 when x = 0 and approaching zero as x increases. As x decreases into negative values, the function value increases exponentially.\n",
-      "\n",
-      "5. **f(x) = sign(x-1)**\n",
-      "   - This function returns 1 if x > 1, -1 if x < 1, and 0 at x = 1 (though in some definitions it can also be 1). It suffers an abrupt change at x = 1.\n",
-      "\n",
-      "Based on the JSON object:\n",
-      "- The values start relatively high when x is significantly negative and increase towards a maximum as x increases, with no sudden shifts or changes in symmetry evident from the intervals provided.\n",
-      "\n",
-      "Analysis:\n",
-      "- A cubic function (**f(x) = x^3**) would show a symmetric shift around 0 (negative values yielding negative outputs and positive values yielding positive outputs). However, the values in the graph are all positive, which makes this a less likely fit.\n",
-      "- A quadratic function (**f(x) = (x-2)^2**) presents symmetry regarding a vertex point, so its values would decrease to a minimum and then increase symmetrically, which does not match the steady increase shown in the graph.\n",
-      "- A mirrored linear function like **f(x) = |2*x+4|** would show an absolute minimum at x = -2, with all values positive; this matches the idea of increasing values from a specific negative x-value.\n",
-      "- An exponential decay function (**f(x) = exp(-x)**) should only decrease as x increases, contrary to the overall increasing trend observed here.\n",
-      "- A sign function (**f(x) = sign(x-1)**) would only exhibit two flat jumps which aren't observed.\n",
-      "\n",
-      "Given these analyses, the most likely candidate for the data described by the graph is **f(x) = |2*x+4|** based on:\n",
-      "- The steady increase on both sides of a certain x-value (in this case, around x = -2, which would turn the expression inside the absolute value to zero and result in a vertex of the graph).\n",
-      "- All y-values are positive, consistent with an absolute value function's output.\n",
-      "\n",
-      "Conclusively, option **c) f(x) = |2*x+4|** is the function that the graph approximates.\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph sqrt(x+10)\n",
-      "\n",
-      "To determine which function is depicted in the graph from the provided JSON object, we can analyze how the function changes across specific ranges of x. We also need to compare these changes to that expected from the five given functions. Let's look at each function's expected behavior and compare it to our graph's y-values as x progresses from negative to positive:\n",
-      "\n",
-      "**a) f(x) = x**\n",
-      "- This is a linear function with a constant slope of 1. As x increases, y would increase linearly at a constant rate directly proportional to x.\n",
-      "\n",
-      "**b) f(x) = sqrt(x + 10)**\n",
-      "- This function is defined for x ≥ -10. It increases as x increases, but the rate of increase slows down (the derivative decreases as x increases).\n",
-      "\n",
-      "**c) f(x) = sign(x - 1)**\n",
-      "- This function jumps between -1 and 1 at x = 1. It remains constant everywhere else, at -1 for x < 1, and at 1 for x > 1.\n",
-      "\n",
-      "**d) f(x) = exp(-x^2)**\n",
-      "- This Gaussian or \"bell curve\" function is highest at x = 0 and decays symmetrically towards both sides as x moves away from 0.\n",
-      "\n",
-      "**e) f(x) = (x + 4)^4**\n",
-      "- This is a polynomial function, growing very rapidly as x increases. Its graph would start from a very high value (if starting from x < -4, where it would have low values), increasing sharply as it crosses -4 and onwards.\n",
-      "\n",
-      "Checking the JSON data points, you provided:\n",
-      "- Values start relatively low, increase smoothly as you move from x ~ -10 to x ~ 10. \n",
-      "- No sudden changes or rapidly increasing values suggestive of a high-degree polynomial.\n",
-      "- No constant sections that would suggest a sign function.\n",
-      "- The smooth and progressive increase in y-values as x gets closer to zero and continuing as x moves to larger positive values contradict the Gaussian decay on either side of a central peak.\n",
-      "\n",
-      "Thus, function b) **f(x) = sqrt(x + 10)** seems to fit best, as this function starts at 0 at x = -10, rises, and approaches infinity as x increases, with the increase rate decreasing as x becomes larger (as the function's slope decreases), which aligns with the steady rise seen in the data values from the JSON object. This behavior matches most closely with the changes in y-values as described in the JSON data set.\n",
+     "data": {
+      "text/plain": [
+       "50"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# load quesitons\n",
+    "llm_questions = json.load(open(\"../benchmark/function-recognition.json\", \"r\"))\n",
+    "len(llm_questions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LLM RESPONSE:  The function depicted in the graph is:\n",
+      "e) f(x) = -sin(x)\n",
+      "SOLUTION: x\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph sqrt(x ** 2 + 3*x +5)\n",
-      "\n",
-      "To determine which of the given functions is depicted in the graph based on the JSON object, we can start by analyzing the structure and pattern of the data. The keys in the JSON object represent intervals on the x-axis, and the values represent corresponding y-values. Let's extract some key insights:\n",
-      "\n",
-      "1. **Behavior at Small Values of x**: Examine changes close to x = 0, as this may provide insights into the function's behavior around a critical point. \n",
-      "   - Right after x = 0, the values increase: \\( y(0.0, 0.48) = 2.256 \\) to \\( y(0.48, 0.77) = 2.585 \\) \n",
-      "   - This increase continues towards positive x values.\n",
-      "   \n",
-      "2. **Behavior at Large Values of x**:\n",
-      "    - At large positive x: \\( y(9.0, 9.24) = 10.652 \\) increasing to \\( y(9.58, 9.88) = 11.209 \\)\n",
-      "    - At large negative x: \\( y(-9.65, -9.43) = 8.317 \\) to \\( y(-10.0, -9.65) = 8.646 \\)\n",
-      "\n",
-      "3. **Behavior Across Zero**:\n",
-      "    - Noticeably, there is a smooth transition and consistent increase as x transitions from negative to positive.\n",
-      "    - The function value at \"close to zero\" (-2.45, 0.0) starts at 1.897 and rises from there.\n",
-      "\n",
-      "Given these observations, consider how each of the candidate functions behaves under similar conditions:\n",
-      "- **a) f(x) = exp(-x)**: This function decays exponentially as x increases. It would not exhibit the consistent increase in y-values seen across both negative and positive ranges.\n",
-      "- **b) f(x) = sqrt(x^2 + 3x + 5)**: This function is based on a quadratic under a square root, ensuring all output values are non-negative and it generally increases as |x| increases. This candidate should be considered given its potential to match the observed symmetry and growth.\n",
-      "- **c) f(x) = arctan(x)**: The arctan function tends to saturate (approach a horizontal asymptote) as x becomes very large or small, not matching the pattern where values keep increasing.\n",
-      "- **d) f(x) = (x + 4)^4**: This function has a steep increase due to the power of 4, especially moving away from -4. However, it is not symmetrical around zero but around x = -4; it's less likely to display the near symmetry we observed.\n",
-      "- **e) f(x) = x**: This is a simple linear function, which does not fit due to the y-values that are non-symmetric and non-linear in nature.\n",
-      "\n",
-      "Given this analysis, **b) f(x) = sqrt(x**2 + 3*x + 5)** is most likely to be the depicted function, as it matches the observed increase in y-values, the symmetric nature of growth around zero, and steady positive values even for negative x.\n",
+      "LLM RESPONSE:  The function depicted in the graph is most likely f(x) = -x^5.\n",
+      "SOLUTION: -2*x+5\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph exp(-x^2)\n",
-      "\n",
-      "To identify which function the graph closely represents among the given options, we can analyze the pattern of change in y-values as x-values change. Particularly, how y behaves over positive and negative ranges of x, will provide critical clues.\n",
-      "\n",
-      "### Step 1: Qualitative Observation\n",
-      "\n",
-      "By examining the changes in y as x varies:\n",
-      "- The function appears to have a peak (a maximum y-value) near \\(x = 0\\), as y-values are highest close to \\(x = 0\\), and decrease as \\(x\\) moves away in both the positive and negative directions.\n",
-      "  \n",
-      "### Step 2: Examine Functional Forms\n",
-      "Let's evaluate the properties of each function:\n",
-      "\n",
-      "a) **\\( f(x) = -2x + 5 \\)**\n",
-      "   - This is a linear function with a positive slope. The function does not have any peak; instead, it would continuously increase or decrease.\n",
-      "\n",
-      "b) **\\( f(x) = x^4 \\)**\n",
-      "   - This function is a polynomial with a minimum peak at \\(x = 0\\) (not a maximum), and it rises symmetrically on both sides as \\(|x|\\) increases.\n",
-      "\n",
-      "c) **\\( f(x) = \\exp(x) \\)**\n",
-      "   - This is an exponential growth function which increases as \\(x\\) becomes more positive, and approaches zero as \\(x\\) becomes more negative—no symmetry around zero.\n",
-      "\n",
-      "d) **\\( f(x) = \\exp(-x^2) \\)**\n",
-      "   - This function has a Gaussian (bell-shaped) form, symmetric around zero. It has a maximum at \\(x = 0\\) and decays on both sides as \\(|x|\\) increases.\n",
-      "\n",
-      "e) **\\( f(x) = \\sinh(x) \\)**\n",
-      "   - This hyperbolic sine function grows exponentially in positive and negative directions, not typical of decreasing in both directions away from the origin.\n",
-      "\n",
-      "### Step 3: Matching to Graph\n",
-      "From the JSON object, the peak at or close to \\(x = 0\\) and the symmetric decrease on both sides strongly suggest a bell-shaped curve typical of a Gaussian distribution. This aligns perfectly with **\\( f(x) = \\exp(-x^2) \\)**, as it is the only option among those provided that shows maximum at zero and symmetric decay in both positive and negative directions similar to a bell-shaped curve.\n",
-      "\n",
-      "### Conclusion\n",
-      "The graph most likely represents the function **\\( f(x) = \\exp(-x^2) \\)**. This selection is supported by the qualitative behavior of y-values around \\(x = 0\\) as they are highest and decrease similarly as \\(x\\) moves further from zero on both the positive and negative sides.\n",
+      "LLM RESPONSE:  The function depicted in the graph is most likely f(x) = x^5.\n",
+      "SOLUTION: x^2\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph exp(x)\n",
-      "\n",
-      "To determine which function the graph might be representing, let's first consider the general behavior of each function option given in the ranges mentioned:\n",
-      "\n",
-      "**a) f(x) = x** - This is a linear function, meaning the graph should look like a straight line with a constant rate of increase or decrease.\n",
-      "\n",
-      "**b) f(x) = exp(x)** - Exponential growth is characterized by increasingly steep slopes (or a rapidly increasing rate of increase in y-values) as x increases.\n",
-      "\n",
-      "**c) f(x) = sin(x)** - A sinusoidal function, with a repetitive cyclic graph that oscillates between fixed maximum and minimum values within each cycle (typically between -1 and 1).\n",
-      "\n",
-      "**d) f(x) = -3*x^3** - A cubic function which, since multiplied by -3, should show a decreasing value as x increases (negative cubic curve), dropping more sharply the further away x moves from zero.\n",
-      "\n",
-      "**e) f(x) = x^2** - A quadratic function that describes a parabola open upward, with y-values increasing by the square of x, resulting in an accelerating rate of increase in y-values as x gets larger.\n",
-      "\n",
-      "Let's analyze the y-values provided for increasing x-values from the JSON object:\n",
-      "\n",
-      "1. For smaller x-values (-9.99 to 5.39), y is nearly constant at 5.2.\n",
-      "2. As x increases from 5.39 to about 10.0, y-values rise significantly:\n",
-      "   - Initially relatively steady increments from small intervals in x result in a notable increase in y.\n",
-      "   - As x moves closer to 10, the increments in y become quite large, even though x increments are small.\n",
-      "\n",
-      "If we approximate by checking rate changes:\n",
-      "- In interval (5.39, 6.09), y rises to 225.1 from 5.2.\n",
-      "- By the interval (8.93, 8.96), y goes up to 7574.7.\n",
-      "- Towards the end of the intervals close to x = 10, y-values increase rapidly towards 21775.3.\n",
-      "\n",
-      "**Analysis and Conclusion**:\n",
-      "- **Option a), linear (f(x) = x)**: The y-increments are far from linear; they increase much more rapidly than a constant slope.\n",
-      "- **Option c), sinusoidal (f(x) = sin(x))**: With no periodic pattern or boundedness by maximum or minimum values, this is not a sinusoidal pattern.\n",
-      "- **Option d), negative cubic (f(x) = -3*x^3)**: The values of y increase instead of decreasing, which contradicts this function.\n",
-      "- **Option e), quadratic (f(x) = x^2)**: Quadratic growth doesn’t align with the enormous increases in the later stage; geometric growth (i.e., exponential) is faster than quadratic growth.\n",
+      "LLM RESPONSE:  The function depicted in the graph is:\n",
       "\n",
-      "Given the rapid and accelerating increase in y-values as x increases, this behaviour best resembles **Option b), exponential growth (f(x) = exp(x))**, which features sharp increases especially as x-values move higher. Hence, the graph most likely depicts an exponential function.\n",
+      "e) f(x) = -sign(x)\n",
+      "SOLUTION: -2*x^2\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph exp(-x)\n",
-      "\n",
-      "To determine which function the graph most likely represents, we'll analyze the trend in the y-values across the intervals for x-values given, and reason about the shapes and behaviors typically shown by each function within the proposed range.\n",
-      "\n",
-      "To start with, let’s focus on examining the core behavior of each proposed function within the domain from around -10 to +10:\n",
-      "\n",
-      "a) f(x) = sin(x),\n",
-      "   - The sine function oscillates between -1 and +1, repeating every \\(2\\pi\\) units.\n",
-      "\n",
-      "b) f(x) = sqrt(x+10),\n",
-      "   - The square root function defined here can start taking real values for x>= -10. This function would slowly increase as x increases.\n",
-      "\n",
-      "c) f(x) = exp(-x),\n",
-      "   - The exponential decay function decreases rapidly towards zero as x increases from a large initial value (when x is near zero).\n",
-      "\n",
-      "d) f(x) = x^3,\n",
-      "   - The cubic function has a symmetry about the origin with negative values for negative x diminishing in size as x decreases (more negative), and increasing rapidly for positive x.\n",
-      "\n",
-      "e) f(x) = x^2,\n",
-      "   - The square function is symmetric around the y-axis, increasing rapidly as x moves away from zero either to the positive or negative side.\n",
-      "\n",
-      "Let's analyze the given data:\n",
-      "- The values start at a high of around 21143.4 when x is around -10 and progressively decrease to about 374.2 as x approaches +10. \n",
-      "- Note the progressive and overall continuous decrease with no periodic or oscillating pattern, and no return towards large values as x moves towards positive values.\n",
-      "\n",
-      "Among the options:\n",
-      "- **sin(x)** would show an oscillating pattern, which doesn't match.\n",
-      "- **sqrt(x+10)** would start from zero at x = -10 (more precisely undefined just before that) and increase; hence, it doesn't match the decrease.\n",
-      "- **exp(-x)** starts high and exponentially decays, making it a potential candidate.\n",
-      "- **x^3** decreases from a negative high to deeper negatives as x decreases from zero and increases dramatically as x turns positive, which is not observed.\n",
-      "- **x^2** steadily increases as x moves away from zero, not matching with a decrease in values.\n",
-      "\n",
-      "Given the data trends:\n",
-      "- **Option c, f(x) = exp(-x)**, appears fitting as it would start high near x = -10, with exp(-(-10) -> exp(10) a large value) and decay towards exp(10) (a very small value near zero), which matches the observed pattern in y-values as x increases.\n",
-      "\n",
-      "Hence, the described graph most likely represents an exponential decay function, given by \\(f(x) = exp(-x)\\).\n",
+      "LLM RESPONSE:  The function depicted in the graph is most likely f(x) = x.\n",
+      "SOLUTION: (x-2)^2\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph log(x+10)\n",
-      "\n",
-      "To identify which function is depicted in the graph, let's evaluate the options by considering the change in y-values in relation to x-intervals and match the behavior pattern with the characteristics of each function type:\n",
-      "\n",
-      "1. **f(x) = log(x+10)**: This function reflects a logarithmic growth starting from values near zero (since `x+10` changes from a small positive number to a larger number as x increases from -10 to 0). The function should show gradual increases with the rate of increase slowing as x increases.\n",
-      "\n",
-      "2. **f(x) = 2^(x-5)**: An exponential function with rapid increases in value, especially as x goes from a negative to a more positive region, reflecting exponential growth.\n",
-      "\n",
-      "3. **f(x) = sign(x-1)**: This is the sign function, which typically shows only three values: -1, 0, and 1, changing abruptly at the defined point (x=1 here).\n",
-      "\n",
-      "4. **f(x) = sin(x)**: A periodic function with increases and decreases between -1 and 1, repeating every 2π units.\n",
-      "\n",
-      "5. **f(x) = sqrt(x+10)**: Since this is a square root function, it starts with an undefined value at x < -10 and then grows as `x+10` becomes positive, with the rate of growth decreasing as `x+10` keeps increasing.\n",
-      "\n",
-      "Considering the data:\n",
-      "- The values in the graph consistently increase over the range provided.\n",
-      "- The rate of increase in the y-values is relatively smooth, with no sudden jumps or periodic patterns—this rules out (c) sign and (d) sinusoidal functions. It also doesn't fit the rapid scaling expected in an exponential growth function like (b).\n",
-      "- Given the smooth increase and the nature of the intervals (positive growth as you move towards higher x-values), the candidates to consider are logarithmic and square root functions.\n",
-      "- A logarithmic growth, like **f(x) = log(x+10)**, would fit the criteria of starting from a low (negative or near-zero) value and increasing steadily at a decreasing rate as `x+10` increases, which corresponds well with the data shown.\n",
-      "\n",
-      "Thus, the most plausible function depicted by the graph should be **f(x) = log(x+10)**.\n",
+      "LLM RESPONSE:  The function depicted in the graph is:\n",
+      "e) f(x) = -sqrt(x+10)\n",
+      "SOLUTION: (x-1)*(x+1)\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph sin(x)\n",
-      "\n",
-      "To determine which of the listed functions is best depicted by the data given in the JSON object, let’s consider some characteristics visible in the mapped intervals and values:\n",
-      "\n",
-      "1. **Trend examination**: We can see the general trends the function follows through examining the values and their progression as x changes.\n",
-      "   \n",
-      "2. **Increasing and Decreasing Intervals**: Check where the function values increase, where they decrease, and if they show cyclic patterns or asymmetry resembling common functions like polynomial, exponential, logarithmic, trigonometric, etc.\n",
-      "\n",
-      "By observing the intervals and corresponding values:\n",
-      "- Starting from \"(-9.98, -9.78)\": 0.514 and going towards intervals like \"(-9.78, -9.58)\": 0.338, we see a decreasing pattern initially.\n",
-      "- The function value decreases until reaching a minimum around the interval \"(-2.19, -0.7)\": -0.82.\n",
-      "- After hitting a minimum, the function begins to increase, marking positive values and reaching high values like \"(7.5, 8.52)\": 0.941, and again decreasing.\n",
-      "\n",
-      "### Comparing to listed options:\n",
-      "a) **f(x) = -3*x^3**: This cubic function tends to decrease, increase sharply, and then decrease again, which doesn’t quite match the apparent symmetry seen in the data.\n",
-      "\n",
-      "b) **f(x) = (x + 4)^4**: As a quartic function shifted to the left by 4 units, this would generally have a U-shape with a minimum point at x = -4. However, there’s a symmetric increase and decrease observed around this point, which should be significantly different for this degree of polynomial without any additional transformations leading to symmetry.\n",
+      "LLM RESPONSE:  The function depicted in the graph is likely:\n",
       "\n",
-      "c) **f(x) = x**: This is a simple linear function, increasing throughout the domain, which doesn't correspond to the described data.\n",
-      "\n",
-      "d) **f(x) = exp(-x)**: The exponential decay function decreases continuously and does not display the symmetric increase and decrease showcased in the data.\n",
-      "\n",
-      "e) **f(x) = sin(x)**: The sinusoidal function increases and decreases in a regular and periodic way, showing waveform patterns. This behavior seems to coincide with the data's repeated cycles of rise and fall.\n",
-      "\n",
-      "### Conclusion:\n",
-      "Considering the nature of sinusoidal functions with cyclic increases and decreases, **Option E (f(x) = sin(x))** seems the most appropriate model that describes the data presented in the JSON object. The data follows a cycle that approximates the shape of a sine wave, showing the regular periodicity that neither the polynomial options nor the exponential or linear options offer.\n",
+      "e) f(x) = -3*x^3\n",
+      "SOLUTION: x^2+3*x-1\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph cos(x)\n",
-      "\n",
-      "To determine which function the graph represents, we can consider the domain, range, and patterns in the provided intervals and corresponding values. Let's examine the characteristics of each function and compare them with the provided JSON object.\n",
-      "\n",
-      "1. **f(x) = exp(x):**\n",
-      "   - This function is an exponential function characterized by rapid growth. It's always positive and increases as x increases.\n",
-      "\n",
-      "2. **f(x) = x:**\n",
-      "   - This function is a linear function with both positive and negative values in a proportional increase.\n",
-      "\n",
-      "3. **f(x) = cosh(x):**\n",
-      "   - The hyperbolic cosine function, which is symmetric about the y-axis, is always positive, and features exponential growth in both positive and negative directions of x.\n",
-      "\n",
-      "4. **f(x) = cos(x):**\n",
-      "   - The cosine function oscillates between -1 and 1 with periodic zeros and extrema. It is not unbounded and repeats its pattern every \\(2\\pi\\) units.\n",
-      "\n",
-      "5. **f(x) = sqrt(x^2 + 3x + 5):**\n",
-      "   - This function, based on the formula, will result in always positive values and is unbounded above. The squared term dominates causing the outputs to increase, particularly for large positive or large negative values of x, somewhat similar to an exponential growth pattern.\n",
-      "\n",
-      "Now let's analyze the pattern seen in the graph through the JSON data:\n",
-      "\n",
-      "- The values range from about -0.9 at the lowest to around 0.98 at the highest.\n",
-      "- The values gradually change from negative to positive and back to negative as x increases, showing a wave-like pattern but not strictly periodic.\n",
-      "- The endpoints do not show exponential growth as x gets large (toward either end); instead, they peak near mid-range (around x = 0) and decrease as x moves away in either direction.\n",
-      "\n",
-      "The observed pattern of values mirrors that of a hyperbolic cosine function:\n",
-      "- **cosh(x)** starts at a positive value for very negative x, increases to a peak at x = 0, and symmetrically decreases as x increases toward positive infinity. It is always positive, matching with the predominantly positive values in the JSON graph around the center, and the symmetry around x = 0 also matches nicely with **cosh(x)**.\n",
-      "\n",
-      "Given this analysis, the graph most closely depicts the function:\n",
-      "**c) f(x) = cosh(x).**\n",
+      "LLM RESPONSE:  The function depicted in the graph is most likely:\n",
+      "e) f(x) = exp(x)\n",
+      "SOLUTION: x^3\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph sinh(x)\n",
-      "\n",
-      "To determine which function the graph depicts from the given list of functions, we need to analyze the characteristics and behavior of the graphed data:\n",
-      "\n",
-      "1. **Graph Description**:\n",
-      "   - The x-values range from -10 to 10.\n",
-      "   - The y-values initially decrease, reach a minimum, then increase sharply.\n",
-      "   - The interval around where the function sharply increases centers around x=0.\n",
-      "\n",
-      "2. **Evaluating Each Option**:\n",
-      "   - a) **f(x) = |2x + 4|**: The absolute value function features a linear increase and decrease with a vertex (minimum or maximum point) where the expression inside the absolute value equals zero. The expression inside the absolute value (2x + 4) is 0 at x = -2, suggesting a vertex at x = -2.\n",
-      "   - b) **f(x) = x^4**: A quartic function with only even powers of x would have a single minimum at x = 0 and be symmetric around y-axis, but it typically grows very quickly as x moves away from 0, much faster than quadratic growth.\n",
-      "   - c) **f(x) = sinh(x)**: The hyperbolic sine function grows exponentially for large positive x and decreases exponentially for large negative x, passing through the origin (0,0).\n",
-      "   - d) **f(x) = arcsinh(x)**: The inverse hyperbolic sine function resembles a linear function that passes through the origin but grows slower than linearly at large absolute values of x.\n",
-      "   - e) **f(x) = x^2**: A quadratic function has a single global minimum at x = 0 and is symmetric about the y-axis, growing as the square of the distance from zero.\n",
-      "\n",
-      "3. **Analyzing the Data**:\n",
-      "   - Observing the transitions in y-values we note:\n",
-      "     - The function decreases as x approaches zero from the left and then increases as x moves away from zero to the right, suggesting symmetry around the origin.\n",
-      "     - Given the symmetric substantial quadratic-like increase when x moves away from 0 (but not too dramatically like that in the quartic function), it suggests a function similar to a quadratic function.\n",
-      "\n",
-      "4. **Conclusion Based on Analysis**:\n",
-      "   - The provided data decreases and increases symmetrically around x = 0, and based on the quadratic-like growth yet not extremely steep like x^4, option **e) f(x) = x^2** is the most likely graph being depicted based on the values provided in the JSON object.\n",
-      "\n",
-      "Thus, based on analyzing the characteristics of the plotted data from the JSON object and comparing them against the function behaviors, the graph most closely corresponds to **e) f(x) = x^2**.\n",
+      "LLM RESPONSE:  The function depicted in the graph is most likely f(x) = x.\n",
+      "SOLUTION: -3*x^3\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph cosh(x)\n",
-      "\n",
-      "To determine which function is depicted in the graph based on the given JSON object and the options provided, we need to analyze the behavior of these functions across the range of x-values given in the key intervals, and compare this behavior with the patterns in the y-values.\n",
-      "\n",
-      "Let's analyze the options one by one, focusing on general behavior since exact values over intervals are hard to match precisely without calculation:\n",
-      "\n",
-      "**a) \\( f(x) = \\text{sign}(x-1) \\)**\n",
-      "\n",
-      "This function results in three possible y-values (-1, 0, 1), corresponding to whether x is less than, equal to, or greater than 1. This is a piecewise constant function, which does not match the continuous and variable pattern seen in the JSON data.\n",
-      "\n",
-      "**b) \\( f(x) = \\cosh(x) \\)**\n",
-      "\n",
-      "The hyperbolic cosine function, \\( \\cosh(x) \\), is symmetric around the y-axis and exhibits exponential growth as \\( |x| \\) increases. Given the range and the growth pattern in the JSON object, this is a possible match because the values sharply increase from \\( x < -6 \\) and similarly as \\( x > 6 \\).\n",
-      "\n",
-      "**c) \\( f(x) = \\sqrt{x^2 + 3x + 5} \\)**\n",
-      "\n",
-      "This function, involving a square root of a quadratic, implies values are always positive, starts with a minimum value where the discriminant of \\( x^2 + 3x + 5 \\) (which is \\( 9 - 20 \\) = -11, thus the function has a minimum somewhere since the discriminant is negative) and increases as \\( x \\) moves away from this minimum on either side. The shape could potentially fit, but it needs clarification if it's appropriately symmetric or if the growth rate on one side is faster than the other which impacts its similarity to the JSON pattern.\n",
-      "\n",
-      "**d) \\( f(x) = \\text{arcsinh}(x) \\)**\n",
-      "\n",
-      "The function \\( \\text{arcsinh}(x) \\) is the inverse hyperbolic sine function, which behaves similarly to a linear function around zero and increases logarithmically as \\( |x|\\) increases. It's odd and not symmetric. The growth in the JSON shows symmetry around a central point, unlike the characteristic of \\( \\text{arcsinh} \\).\n",
-      "\n",
-      "**e) \\( f(x) = -3x^3 \\)**\n",
-      "\n",
-      "This cubic function is skewed heavily to one side because of the cube effect that dominates and it is odd symmetric. Given the JSON pattern, this kind of growth is at odds with the more-bilateral symmetry observed.\n",
-      "\n",
-      "From this analysis, the most likely candidate is:\n",
-      "\n",
-      "**b) \\( f(x) = \\cosh(x) \\)**\n",
-      "\n",
-      "This function is symmetric and experiences exponential increases on either side of \\( x = 0 \\), matching the pattern observed in the given JSON values where values start low at the center of the range and increase drastically as \\( x \\) moves away from zero toward both positive and negative extremes.\n",
+      "LLM RESPONSE:  The function depicted in the graph is most likely f(x) = x^3.\n",
+      "SOLUTION: x^4\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph tanh(x)\n",
-      "\n",
-      "To determine which function is depicted in the given graph, we should analyze how the function values change over the given intervals and compare this behavior to the typical behaviors of the listed functions.\n",
-      "\n",
-      "1. **f(x) = tanh(x)** - The hyperbolic tangent function, tanh(x), approaches -1 as x approaches negative infinity and approaches 1 as x approaches positive infinity. It is symmetric about the origin and smoothly transitions from -1 to 1 as x passes through 0.\n",
-      "\n",
-      "2. **f(x) = cos(x)** - The cosine function oscillates between -1 and 1 with a period of 2π. \n",
-      "\n",
-      "3. **f(x) = |x|** - The absolute value function |x| has a V shape, with its minimum at x = 0. The function is zero at x = 0 and increases linearly as x moves away from 0 in both the positive and negative directions.\n",
-      "\n",
-      "4. **f(x) = sign(x - 1)** - The sign function returns -1, 0, or 1 based on the sign of (x - 1). This function would be -1 for x < 1, and 1 for x > 1, with a jump at x = 1.\n",
-      "\n",
-      "5. **f(x) = sqrt(x + 10)** - The square root function shifted left by 10 units, defined only for x ≥ -10. The function has a minimum at x = -10 and increases as x increases.\n",
-      "\n",
-      "From the JSON data:\n",
-      "- The values begin at -1.001 for the range extending from a very negative number to -1.93, gradually increasing as x increases towards positive infinity (reaching up to 0.968).\n",
-      "- The increments grow smoother and maintain a consistent behavior, without any abrupt changes or oscillations.\n",
-      "- The function gradually transitions from negative to positive values near x = 0.\n",
-      "\n",
-      "Observing these properties, the behavior closely resembles f(x) = tanh(x), with the function values constrained between -1 and 1 and showing symmetry around the origin with a smooth \"S-shaped\" transition from -1 to 1. The data seem to increase from values very close to -1 to values very close to 1 as x moves from negative to positive, consistent with the characteristic behavior of tanh(x).\n",
-      "\n",
-      "**Conclusion**: The function depicted in the graph most likely corresponds to **a) f(x) = tanh(x)**.\n",
+      "LLM RESPONSE:  The function depicted in the graph is most likely **f(x) = -(x + 4)^4**.\n",
+      "SOLUTION: -(x + 4)^4\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph arcsinh(x)\n",
-      "\n",
-      "To determine which function the graph represents, we can analyze the behavior of the provided values relative to their corresponding x-axis intervals. Let's examine the characteristics of each function option with respect to the properties evident in the dataset.\n",
-      "\n",
-      "1. **f(x) = arcsinh(x):**\n",
-      "   - The arcsinh (inverse hyperbolic sine) function is defined for all real numbers.\n",
-      "   - It has an asymptote at infinity but grows slower than linear and exponential functions.\n",
-      "   - It is odd and symmetric about the origin, meaning f(-x) = -f(x) and the function should increase when x moves away from zero in either direction.\n",
-      "\n",
-      "2. **f(x) = x^2:**\n",
-      "   - This is a parabolic function that increases as \\( x \\) moves away from zero. It is symmetric about the y-axis.\n",
-      "   - For negative x values, y is positive and increases, consistent with the parabolic shape where both \\( x^2 \\) and \\( (-x)^2 \\) yield a non-negative result.\n",
-      "\n",
-      "3. **f(x) = exp(-x):**\n",
-      "   - This exponential decay function decreases monotonically as x increases.\n",
-      "   - It is not symmetric with respect to either axis and should have a rapidly decreasing behavior for positive x and approach a constant value as x becomes large and negative.\n",
-      "\n",
-      "4. **f(x) = 2^(x-5):**\n",
-      "   - This is an exponential growth function that has been horizontally shifted.\n",
-      "   - This function increases as x increases, speeding up particularly after surpassing the horizontal shift point (x = 5).\n",
-      "\n",
-      "5. **f(x) = |x|:**\n",
-      "   - The absolute value function features linear growth from zero, both in the positive and negative directions of x.\n",
-      "   - It shows mirror symmetry about the y-axis, with \\( f(-x) = f(x) \\).\n",
-      "\n",
-      "Given this information, let's evaluate the provided data characteristics:\n",
-      "- The y values are symmetric around the y-axis and demonstrate equivalent magnitudes but opposite signs at symmetrically opposite x values (e.g., intervals \\( (-3.03, -2.83) \\) and \\( (2.87, 3.28) \\)).\n",
-      "- The y values increase as x moves away from zero, in both directions. \n",
-      "\n",
-      "These traits - symmetry about the y-axis and increase in value with an increase in the absolute value of x - suggest that the graph belongs to function **e) f(x) = |x|**. The absolute value function is most consistent with these characteristics, showing a mirrored increase as x moves away from zero in a linear fashion on both sides.\n",
+      "LLM RESPONSE:  The function depicted in the graph is **f(x) = x**.\n",
+      "SOLUTION: x^5\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph arctan(x)\n",
-      "\n",
-      "To determine which function the graph depicts, we need to analyze the pattern of y-values in relation to changes in x-values and relate this pattern to the characteristics of the listed functions.\n",
-      "\n",
-      "1. **Understanding the Graph Data**:\n",
-      "   The JSON data essentially pairs x-value ranges with specific y-values. There is a distinct pattern likely evident from mapping these ranges and values. We'll need to consider negative and positive x-values separately due to the nature of the listed functions.\n",
-      "\n",
-      "2. **Function Characteristics**:\n",
-      "   - **f(x) = exp(-x)**: This function decreases exponentially from a high positive value towards zero as x increases from negative to positive values.\n",
-      "   - **f(x) = -2*x + 5**: A linear function with a negative slope; as x increases, y decreases linearly.\n",
-      "   - **f(x) = exp(x)**: This function increases exponentially from a very small positive value (near zero) towards higher values as x increases from negative to positive values.\n",
-      "   - **f(x) = x^2**: A quadratic function with a parabolic shape, showing minimum value at x = 0 and symmetrical rising values as x moves away from zero on both sides.\n",
-      "   - **f(x) = arctan(x)**: This function increases gradually, asymptotically approaching π/2 for large positive x and -π/2 for large negative x.\n",
-      "\n",
-      "3. **Analysis of JSON data pattern**:\n",
-      "   - For negative x-values, the y-values start from a lower value and tend to increase as x approaches zero. Specifically, from -1.472 to -0.053 as x goes from -9.97 to near 0. \n",
-      "   - For positive x-values, the y-values continue rising from 0.009 to 1.455 as x goes from close to 0 to 9.98.\n",
-      "\n",
-      "4. **Matching the Data to Function Characteristics**:\n",
-      "   - **f(x) = exp(-x)**: The values do not fit as this function would have high values at negative x decreasing towards zero, which is not reflected in the data.\n",
-      "   - **f(x) = -2*x + 5**: The values do not fit as the function is linear and the data shows a different, more complex pattern.\n",
-      "   - **f(x) = exp(x)**: The pattern matches because as x moves from negative to positive, the y-values increase slightly and then more rapidly, suggesting an exponential growth typical of the exponential function.\n",
-      "   - **f(x) = x^2**: The values do not fit this pattern as they should show symmetrical values around x=0, which does not occur in the data.\n",
-      "   - **f(x) = arctan(x)**: The values do not fit as they should be bounded between -π/2 and π/2 and the growth should be less steep.\n",
+      "LLM RESPONSE:  The function depicted in the graph is likely f(x) = 2^(x-5).\n",
+      "SOLUTION: -x^5\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is most likely f(x) = x^2.\n",
+      "SOLUTION: sign(x)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is f(x) = x^5.\n",
+      "SOLUTION: -sign(x)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is f(x) = -cosh(x).\n",
+      "SOLUTION: -sign(-x)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is f(x) = sign(x+3).\n",
+      "SOLUTION: sign(x+3)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is likely f(x) = exp(-x).\n",
+      "SOLUTION: sign(x-1)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is f(x) = x.\n",
+      "SOLUTION: |x|\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is f(x) = |x|.\n",
+      "SOLUTION: -|x|\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is most likely e) f(x) = sinh(x).\n",
+      "SOLUTION: -|-x|\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph provided in the JSON object is not one of the options provided (a, b, c, d, e).\n",
+      "SOLUTION: |2*x+4|\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is f(x) = x.\n",
+      "SOLUTION: |x^3|\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is f(x) = sqrt(x+10).\n",
+      "SOLUTION: sqrt(x+10)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is f(x) = -sqrt(x+10).\n",
+      "SOLUTION: -sqrt(x+10)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is most likely:\n",
       "\n",
-      "Given this analysis, the function that most closely fits the provided data pattern is **c) f(x) = exp(x)**. The y-values increase gradually at first (near zero or negative x) and then more markedly as x becomes more positive, depicting an exponential rise, characteristic of exp(x).\n",
+      "**f(x) = cos(x)**\n",
+      "SOLUTION: sqrt(x ** 2 + 3*x +5)\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph 1/(1+exp(-x))\n",
+      "LLM RESPONSE:  The function depicted in the graph is f(x) = exp(x).\n",
+      "SOLUTION: exp(x)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is:\n",
       "\n",
-      "To determine which function is depicted in the graph given the intervals and corresponding values provided in the JSON data, we need to assess the general behavior and pattern of the data across the ranges to match it against the functions listed (a-e).\n",
+      "d) f(x) = x\n",
+      "SOLUTION: -exp(x)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is f(x) = exp(-x).\n",
+      "SOLUTION: exp(-x)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is most likely f(x) = |x|.\n",
+      "SOLUTION: exp(-x^2)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is f(x) = x.\n",
+      "SOLUTION: 2^x\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is most likely d) f(x) = 3^x+1.\n",
+      "SOLUTION: 3^x+1\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is most likely f(x) = -sign(x).\n",
+      "SOLUTION: 2^(x-5)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is:\n",
+      "e) f(x) = -log(x+10)\n",
+      "SOLUTION: log(x+10)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is: **f(x) = -2*x^2**\n",
+      "SOLUTION: -log(x+10)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is likely to be e) f(x) = -(x + 4)^4.\n",
+      "SOLUTION: log(exp(x))\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is most likely **f(x) = -sign(x)**.\n",
+      "SOLUTION: sin(x)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is most likely: f(x) = x.\n",
+      "SOLUTION: -sin(x)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is most likely:\n",
       "\n",
-      "### Step 1: Evaluate the function descriptions\n",
-      "- **a) \\( f(x) = \\exp(-x^2) \\)**: This function is a Gaussian or normal distribution curve which is symmetric about the y-axis and decreases as x moves away from 0.\n",
-      "- **b) \\( f(x) = \\cos(x) \\)**: This is a periodic function with regular oscillations between 1 and -1.\n",
-      "- **c) \\( f(x) = (x + 4)^4 \\)**: A polynomial function which increases sharply as x increases due to the power of 4, particularly after shifting the curve to the left by 4 units.\n",
-      "- **d) \\( f(x) = -2x^2 \\)**: A downward opening quadratic function with a vertex at the origin, symmetric about the y-axis.\n",
-      "- **e) \\( f(x) = \\frac{1}{1+\\exp(-x)} \\)**: This is the logistic function, characterized by an S-shaped curve (sigmoid curve) that transitions smoothly from a lower asymptote to an upper asymptote as x increases.\n",
+      "b) f(x) = |x^3|\n",
+      "SOLUTION: -sin(-x)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is most likely:\n",
       "\n",
-      "### Step 2: Observing the provided data behavior\n",
-      "Without visualizing the data, it can be seen that the y-values are increasing as x increases from left (negative values) to right (positive values) based on the provided ranges and respective values.\n",
+      "e) f(x) = (x-2)^2\n",
+      "SOLUTION: sin(x+2)+2\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is **f(x) = tanh(x)**.\n",
+      "SOLUTION: cos(x)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is f(x) = tanh(x).\n",
+      "SOLUTION: 1/2*cos(x-2)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is f(x) = exp(x).\n",
+      "SOLUTION: sinh(x)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is:\n",
       "\n",
-      "Consider the behavior of each interval:\n",
-      "- From left (-9.97, -3.87) to right (4.74, 9.99), the y-values exhibit a clear pattern of increase (from almost 0 to almost 1).\n",
+      "**f(x) = -exp(x)**\n",
+      "SOLUTION: -sinh(x)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is: **f(x) = x**\n",
+      "SOLUTION: cosh(x)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is:\n",
       "\n",
-      "### Step 3: Matching data pattern to function options:\n",
-      "- **Option a, Gaussian**: The data does not revert to small values as x becomes large; hence, it's unlikely to be Gaussian.\n",
-      "- **Option b, Cosine**: No periodic behaviors or cycling of values through peaks and troughs are observed.\n",
-      "- **Option c, Polynomial**: This would typically show sharper increases toward one end (larger positive values), but the data does not favor extremely high values as x goes to large positive numbers.\n",
-      "- **Option d, Parabola**: The data continuously increases passing through the origin symmetrically, which does not match the behavior (does not decrease symmetrically).\n",
-      "- **Option e, Logistic function**: The smooth, monotonous increase from a near zero to near one resembles a logistic growth pattern.\n",
+      "f(x) = 2^(x-5)\n",
+      "SOLUTION: -cosh(x)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is likely to be f(x) = -x^5.\n",
+      "SOLUTION: tanh(x)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is most likely:\n",
       "\n",
-      "### Conclusion:\n",
-      "The graph most likely depicts **e) \\( f(x) = \\frac{1}{1+\\exp(-x)} \\)**. This function starts near zero for large negative x, crosses 0.5 around x = 0, and approaches 1 for large positive x, showing an S-shaped curve which matches the monotonous increase in the given data as x values proceed from negative towards positive.\n",
+      "f(x) = x\n",
+      "SOLUTION: arcsinh(x)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is likely to be f(x) = x^4.\n",
+      "SOLUTION: arctan(x)\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The function depicted in the graph is most likely f(x) = |x|.\n",
+      "SOLUTION: 1/(1+exp(-x))\n",
       "--------------------------------------------------------------------------------\n"
      ]
     }
    ],
    "source": [
     "for idx, question in enumerate(llm_questions):\n",
-    "    print(f'Graph {fbench[idx][1]}\\n')\n",
     "    messages = []\n",
     "    messages.append({'role': 'system', 'content': \"You are an expert statistician and data scientist.\"})\n",
-    "    messages.append({'role': 'user', 'content': question})\n",
-    "    response = t2ebm.utils.openai_completion_query('gpt-4-turbo-2024-04-09', messages)\n",
-    "    print(response)\n",
+    "    messages.append({'role': 'user', 'content': question[0]})\n",
+    "    response = t2ebm.utils.openai_completion_query('gpt-3.5-turbo-0125', messages, temperature=0)\n",
+    "    messages.append({\"role\": \"assistant\", \"content\": response})\n",
+    "    messages.append({\"role\": \"user\", \"content\": \"Thanks. Now summarize your response by answering with the function that is depicted in the graph.\"})\n",
+    "    response = t2ebm.utils.openai_completion_query('gpt-3.5-turbo-0125', messages, temperature=0.0)\n",
+    "    print('LLM RESPONSE: ', response)\n",
+    "    print(f'SOLUTION: {fbench[idx][1]}')\n",
     "    print('-'*80)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.7666666666666667"
+       "0.14"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "23 / 30"
+    "7 / 50"
    ]
   },
   {
@@ -5550,54 +6315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import google.generativeai as genai"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "genai.configure(api_key=os.environ['GENAI_API_KEY'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = genai.GenerativeModel(model_name=\"gemini-1.5-pro-latest\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def to_gemini(messages):\n",
-    "    gemini_messages = []\n",
-    "    for message in messages:\n",
-    "        if message[\"role\"] == \"system\":\n",
-    "            pass\n",
-    "        elif message[\"role\"] == \"user\":\n",
-    "            gemini_messages.append({'role':'user', 'parts': [message['content']]})\n",
-    "        elif message[\"role\"] == \"assistant\":\n",
-    "            gemini_messages.append({'role':'model', 'parts': [message['content']]})\n",
-    "        else:\n",
-    "            raise ValueError(\"Unknown message role: {}\".format(message[\"role\"]))\n",
-    "    return gemini_messages"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [
     {
@@ -5606,1297 +6324,1315 @@
      "text": [
       "Graph x\n",
       "\n",
-      "Let's analyze the graph step-by-step to determine the correct function:\n",
+      "LLM RESPONSE:  Here's how to determine the function depicted in the graph:\n",
+      "\n",
+      "**1. Analyze the Graph's Shape:**\n",
       "\n",
-      "1. **Observe the General Trend:** The graph seems to have a generally increasing trend. As x increases, y also increases. This eliminates options b) and d) since the sign function primarily outputs -1, 0, or 1 and wouldn't have a consistently increasing trend.\n",
+      "* **Symmetry:** The graph appears to be roughly symmetrical about the origin (0,0). This suggests it could be an odd function. Odd functions satisfy the condition f(-x) = -f(x).\n",
+      "* **End Behavior:** As x approaches negative infinity, y approaches positive infinity. As x approaches positive infinity, y approaches negative infinity. This behavior eliminates options c) and d) as they have opposite end behaviors. \n",
+      "* **General Trend:** The graph has a single inflection point around the origin and resembles a rotated \"S\" shape.\n",
       "\n",
-      "2. **Check for Symmetry:**  The graph is not symmetric about the y-axis or the origin. This eliminates option c), as cubic functions have specific symmetry properties.\n",
+      "**2. Evaluate the Functions:**\n",
       "\n",
-      "3. **Analyze the Rate of Increase:** The graph's rate of increase appears to become steeper as x increases. This suggests an exponential function rather than a linear function. This eliminates option a). \n",
+      "* **a) f(x) = x:** This is a straight line and doesn't match the graph's shape.\n",
+      "* **b) f(x) = -sinh(x):** The hyperbolic sine function grows exponentially, which is not consistent with the graph's behavior around x = 0.\n",
+      "* **c) f(x) = -3*x^3:**  While an odd function, its shape is more curved than the graph and doesn't have the characteristic \"S\" shape.\n",
+      "* **d) f(x) = -x^5:**  Similar to option c), it's an odd function but lacks the \"S\" shape.\n",
+      "* **e) f(x) = -sin(x):** This is an odd function with a sinusoidal shape.  While the standard sine function oscillates between -1 and 1, this one appears stretched and shifted, which can be achieved with transformations.\n",
       "\n",
-      "4. **Consider Option e):**  The function f(x) = 2^(x-5) is an exponential function that would indeed exhibit an increasing trend with a steeper rate of increase as x grows. This seems to fit the observed pattern of the graph. \n",
+      "**3. Conclusion:**\n",
       "\n",
-      "**Therefore, the function depicted in the graph is most likely (e) f(x) = 2^(x-5).** \n",
+      "The graph most closely resembles a transformed version of the sine function. Therefore, the function depicted in the graph is most likely **e) f(x) = -sin(x)**. \n",
       "\n",
+      "SOLUTION: x\n",
       "--------------------------------------------------------------------------------\n",
       "Graph -2*x+5\n",
       "\n",
-      "Let's analyze the graph and functions to find the best match.\n",
-      "\n",
-      "**1. General Shape:**\n",
+      "LLM RESPONSE:  Here's how to determine the function from the graph:\n",
       "\n",
-      "* The graph shows a decreasing trend overall. \n",
-      "* It doesn't have a sharp change in direction like a step function. \n",
-      "* It doesn't show a parabolic curve like a quadratic or cubic function.\n",
+      "**1. Analyze the Graph's Shape**\n",
       "\n",
-      "This eliminates options a), d) and e):\n",
+      "* The graph is generally decreasing as x increases. This eliminates options (c) and (e), which represent increasing functions.\n",
+      "* The graph appears to be curved rather than a straight line. This eliminates option (b), which is a linear function.\n",
       "\n",
-      "* **a) f(x) = sign(x+3)** is a step function, changing abruptly at x = -3.\n",
-      "* **d) f(x) = sqrt(x+10)**  has a curved shape and is always increasing.\n",
-      "* **e) f(x) = x^3** has a steeper, curved shape.\n",
+      "**2. Consider the Function Behavior**\n",
       "\n",
-      "**2. Linearity:**\n",
+      "* **Option (a) f(x) = -x^5:**  A negative odd power function would have a similar downward curve. This is a strong possibility.\n",
+      "* **Option (d) f(x) = sign(x+3):** The sign function produces only three possible outputs (-1, 0, 1) and would result in a horizontal line with a single step. This doesn't match the graph.\n",
       "\n",
-      "The remaining options are linear:\n",
+      "**3. Focus on Key Points**\n",
       "\n",
-      "* **b) f(x) = -2*x+5**\n",
-      "* **c) f(x) = x**\n",
+      "* Observe that the graph crosses the y-axis (x=0) at a positive value.  The function f(x) = -x^5 would equal 0 at x=0. This eliminates option (a).\n",
       "\n",
-      "**3. Slope and Intercept:**\n",
-      "\n",
-      "* The graph has a negative slope, meaning it goes downwards from left to right.\n",
-      "* It appears to intersect the y-axis somewhere around 5.\n",
-      "\n",
-      "This strongly suggests the function is **b) f(x) = -2*x+5**:\n",
-      "\n",
-      "* The slope of -2 matches the decreasing trend.\n",
-      "* The y-intercept of 5 aligns with the approximate intersection point on the graph.\n",
+      "**Conclusion**\n",
       "\n",
-      "**Conclusion:**\n",
+      "None of the provided functions perfectly match the graph. While option (a) had the closest shape, it doesn't align with the y-intercept. \n",
       "\n",
-      "The graph most likely depicts the function **b) f(x) = -2*x+5**. \n",
+      "**It's likely that the actual function represented by the graph is a variation or combination of the provided options.**  For example, a function like  f(x) = -0.2x^5 + 5  could potentially produce a similar shape with a positive y-intercept. \n",
       "\n",
+      "SOLUTION: -2*x+5\n",
       "--------------------------------------------------------------------------------\n",
       "Graph x^2\n",
       "\n",
-      "Here's how to determine the function represented by the graph:\n",
-      "\n",
-      "1. **Analyze the graph's shape:**\n",
-      "   - The graph is symmetrical around the y-axis. This means the function is even, i.e., f(x) = f(-x).\n",
-      "   - The graph has a peak at x=0 and decreases as we move away from the center in either direction.\n",
+      "LLM RESPONSE:  Here's how to determine the function:\n",
       "\n",
-      "2. **Eliminate incompatible functions:**\n",
-      "   - **b) f(x) = exp(-x):** This function is not symmetrical; it decays exponentially as x increases.\n",
-      "   - **c) f(x) = x^2:** This function is symmetrical but increases as we move away from the center.\n",
-      "   - **d) f(x) = 2^(x-5):** This function is not symmetrical; it grows exponentially as x increases.\n",
-      "   - **e) f(x) = sign(x):** This is a step function and doesn't resemble the smooth curve of the graph.\n",
+      "1. **Analyze the graph's shape:** The graph is symmetric, with the lowest point around x = 0. It increases as we move away from x = 0 in both directions.\n",
       "\n",
-      "3. **Focus on the remaining function:**\n",
-      "   - **a) f(x) = exp(-x^2):** This function is symmetrical due to the x^2 term. It has a maximum at x=0 and decays as |x| increases, matching the graph's features.\n",
+      "2. **Eliminate unlikely options:**\n",
+      "    * **a) f(x) = |x|:** This function has a sharp point at x=0, while our graph is smoother.\n",
+      "    * **b) f(x) = -|-x|:** This function is always negative and has a peak at x=0, unlike our graph. \n",
+      "    * **d) f(x) = -sin(x):** This is a periodic wave, and our graph doesn't show repeating patterns.\n",
+      "    * **e) f(x) = x^5:** This function doesn't have the same symmetry as our graph. It increases rapidly for positive x and decreases rapidly for negative x.\n",
       "\n",
-      "4. **Verify by comparing values:**\n",
-      "   -  Take a few x values from the graph and calculate the corresponding f(x) values using f(x) = exp(-x^2). You'll find a good match between the calculated and graphed values.\n",
+      "3. **Focus on the remaining option:**\n",
+      "    * **c) f(x) = x^2:** This function is symmetric around x=0, always positive, and its slope increases as we move away from x=0. This aligns perfectly with our graph.\n",
       "\n",
-      "**Therefore, the function depicted in the graph is a) f(x) = exp(-x^2).** \n",
+      "**Therefore, the function depicted in the graph is most likely (c) f(x) = x^2.** \n",
       "\n",
+      "SOLUTION: x^2\n",
       "--------------------------------------------------------------------------------\n",
       "Graph -2*x^2\n",
       "\n",
-      "Here's how to determine the function depicted in the graph:\n",
+      "LLM RESPONSE:  Let's analyze the graph and each function to find the match:\n",
       "\n",
-      "**1. Analyze the Graph's Shape:**\n",
+      "**1. Analyzing the Graph:**\n",
       "\n",
-      "* The graph is symmetric, with a minimum point. This eliminates options d) and e). \n",
-      "* The graph appears parabolic, suggesting a quadratic function. This supports options a) and b).\n",
+      "* **Overall Shape:** The graph has a downward-facing parabolic shape. \n",
+      "* **Symmetry:** It appears somewhat symmetrical around x = 2.\n",
+      "* **Negative Values:** The graph primarily exists in the negative y-value region.\n",
       "\n",
-      "**2. Consider the Sign and Magnitude:**\n",
+      "**2. Evaluating the Functions:**\n",
       "\n",
-      "* The y-values are negative and generally large in magnitude. This points towards b) f(x) = -2*x^2, as the negative coefficient and squaring of x will produce large negative values.\n",
+      "* **a) f(x) = 1/2*cos(x-2):** This is a cosine function, which has a repeating wave pattern. The graph doesn't match this.\n",
+      "* **b) f(x) = -2*x^2:** This is a downward-facing parabola, which seems promising. It's symmetrical around x = 0. We'll come back to this.\n",
+      "* **c) f(x) = (x-2)^2:** This is an upward-facing parabola. The graph doesn't match this.\n",
+      "* **d) f(x) = sqrt(x+10):** This is a square root function, which has a continuously increasing curve. The graph doesn't match this.\n",
+      "* **e) f(x) = -sign(x):** This function returns -1 for positive x, 1 for negative x, and 0 for x=0. This is a horizontal line with a jump at x=0. The graph doesn't match this.\n",
       "\n",
-      "**3. Test a Data Point:**\n",
+      "**3. Focusing on the Most Likely Candidate:**\n",
       "\n",
-      "* Let's take a point from the graph, for instance, (-4.0, -38.3).\n",
-      "    * For f(x) = -2*x^2:  f(-4) = -2 * (-4)^2 = -32. This is reasonably close to the graph's value.\n",
+      "Function **b) f(x) = -2*x^2** is the closest match so far, but its symmetry is off.  We need to shift it to the right by 2 units to match the graph's symmetry.\n",
       "\n",
-      "**Conclusion:**\n",
+      "**4. Adjusting for Symmetry:**\n",
+      "\n",
+      "To shift a function horizontally, we modify the x-value inside the function. To shift 2 units to the right, we replace 'x' with '(x-2)':\n",
       "\n",
-      "The graph most closely resembles the function **b) f(x) = -2*x^2**. \n",
+      " **f(x) = -2*(x-2)^2**\n",
       "\n",
-      "While it's not a perfect match to every data point, the overall shape, sign, magnitude, and test point all strongly suggest this function. \n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph most closely depicts the function **f(x) = -2*(x-2)^2**. \n",
       "\n",
+      "SOLUTION: -2*x^2\n",
       "--------------------------------------------------------------------------------\n",
       "Graph (x-2)^2\n",
       "\n",
-      "Here's how to determine the function represented by the graph:\n",
+      "LLM RESPONSE:  Let's analyze the graph and the function options step-by-step:\n",
       "\n",
-      "**1. Analyze the graph's shape:**\n",
+      "**1. Observing the Graph's Shape:**\n",
       "\n",
-      "* The graph is mostly decreasing until around x = -1, then it sharply increases.\n",
-      "* There's a point of symmetry around x = 4. \n",
+      "* The graph is symmetrical, with a minimum point around x = 2. \n",
+      "* It has increasing values as we move away from the minimum point in both directions.\n",
       "\n",
-      "**2. Eliminate unlikely options:**\n",
+      "**2. Eliminating Unlikely Options:**\n",
       "\n",
-      "* **a) f(x) = sqrt(x+10):** This function is always increasing and wouldn't have the decreasing section seen in the graph. \n",
-      "* **b) f(x) = sign(x-1):** This function only produces values of -1, 0, and 1, while the graph has a wider range of values.\n",
-      "* **d) f(x) = x^3:** This function is always increasing and wouldn't have the sharp turn around x = -1.\n",
-      "* **e) f(x) = sin(x):** This function is periodic and would have repeating peaks and valleys, unlike the single peak in the graph.\n",
+      "* **b) f(x) = sign(x):** This function only outputs -1, 0, or 1, and wouldn't create the smooth curve seen in the graph.\n",
+      "* **d) f(x) = x^4:** This function has a minimum at x=0 and increases much more rapidly than the graph.\n",
+      "* **e) f(x) = x:** This is a straight line and doesn't match the graph's curvature.\n",
       "\n",
-      "**3. Focus on the remaining option:**\n",
+      "**3. Analyzing the Remaining Options:**\n",
       "\n",
-      "* **c) f(x) = (x-2)^2:** This function is a parabola that opens upwards. Its vertex is at x = 2, which aligns with the symmetry point observed in the graph. The decreasing section before the vertex and the increasing section after are also consistent with a parabola.\n",
+      "* **a) f(x) = 1/2*cos(x-2):** \n",
+      "    * The cosine function is symmetrical and oscillates. \n",
+      "    * The (x-2) shifts the graph 2 units to the right, aligning with the graph's minimum.\n",
+      "    * The 1/2 scales the amplitude, making the oscillations smaller.\n",
+      "* **c) f(x) = (x-2)^2:**\n",
+      "    * This is a parabola with a minimum at x=2, matching the graph's shape.\n",
+      "\n",
+      "**4. Determining the Best Fit:**\n",
+      "\n",
+      "While both options (a) and (c) have characteristics that resemble the graph, the graph doesn't show the oscillating behavior of a cosine function. \n",
       "\n",
       "**Conclusion:**\n",
       "\n",
-      "The function depicted in the graph is most likely **(c) f(x) = (x-2)^2**. \n",
+      "The graph most closely depicts the function **c) f(x) = (x-2)^2**. \n",
       "\n",
+      "SOLUTION: (x-2)^2\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph 2^(x-5)\n",
+      "Graph (x-1)*(x+1)\n",
+      "\n",
+      "LLM RESPONSE:  Here's how to determine the function depicted in the graph:\n",
+      "\n",
+      "**1. Analyze the Graph's Shape:**\n",
+      "\n",
+      "* The graph is generally increasing from left to right.\n",
+      "* It has a distinct \"jump\" around x = 0.\n",
       "\n",
-      "Let's analyze the graph and functions step-by-step:\n",
+      "**2. Eliminate Unlikely Candidates:**\n",
       "\n",
-      "1. **Observe the graph's behavior:**\n",
-      "    - The graph is largely flat for x values less than 5. \n",
-      "    -  After x = 5, the graph starts increasing rapidly.\n",
-      "    -  The increase seems exponential.\n",
+      "* **a) f(x) = (x-1)*(x+1):** This is a parabola opening upwards. The graph doesn't resemble a parabola. **Eliminate.**\n",
+      "* **c) f(x) = -3*x^3:** This is a cubic function with a single inflection point. The graph doesn't have this shape. **Eliminate.**\n",
+      "* **e) f(x) = -sqrt(x+10):** This is a square root function reflected over the x-axis. The graph doesn't have this smooth, curved shape. **Eliminate.**\n",
       "\n",
-      "2. **Eliminate unlikely functions based on the observations:**\n",
-      "    - **d) f(x) = sign(x-1):** This function only outputs -1, 0, or 1, and wouldn't have the continuous increasing trend.\n",
-      "    - **c) f(x) = -3*x^3:** This is a cubic function. While it could increase, it would also decrease for some x values and wouldn't match the graph's trend.\n",
-      "    - **b) f(x) = sqrt(x ** 2 + 3*x +5):** This function would have a smoother, less steep increase than what the graph shows.\n",
+      "**3. Focus on Remaining Options:**\n",
       "\n",
-      "3. **Focus on potential candidates:** \n",
-      "    - **a) f(x) = 2^(x-5):**  This exponential function would be flat for x values below 5 (2 raised to a negative power gets very small), then rise rapidly as x increases past 5. This seems promising. \n",
-      "    - **e) f(x) = sinh(x):**  The hyperbolic sine function also increases rapidly. However, it wouldn't be as flat for x values less than 5 as the graph shows.\n",
+      "* **b) f(x) = sign(x-1):** The sign function returns -1 for negative values, 0 for 0, and 1 for positive values. This function would jump from -1 to 1 at x=1. \n",
+      "* **d) f(x) = sign(x):** This is similar to option b, but the jump occurs at x=0.\n",
       "\n",
-      "4. **Conclude based on the best fit:** \n",
-      "    - Function **a) f(x) = 2^(x-5)** aligns best with the provided graph. Its behavior of staying relatively flat before x=5 and then increasing exponentially matches the graph's trend.\n",
+      "**4. Determine the Jump Location:**\n",
       "\n",
-      "**Therefore, the function depicted in the graph is most likely f(x) = 2^(x-5).** \n",
+      "* The graph clearly shows a jump around x = 0, not x = 1.\n",
       "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph most closely depicts the function **d) f(x) = sign(x)**. \n",
+      "\n",
+      "SOLUTION: (x-1)*(x+1)\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph (x-1)*(x+1)\n",
+      "Graph x^2+3*x-1\n",
       "\n",
-      "Let's analyze the graph and the function options step by step:\n",
+      "LLM RESPONSE:  Here's how to determine the function depicted in the graph:\n",
       "\n",
-      "**1. Symmetry:**\n",
+      "**1. Analyze the Graph's Shape**\n",
       "\n",
-      "* The graph is roughly symmetric around the y-axis. This means the function is likely even. \n",
-      "*  Even functions satisfy f(x) = f(-x). \n",
-      "* Options (a), (c) are even. Options (b), (d), (e) are not.\n",
+      "* The graph is generally increasing from left to right.\n",
+      "* It has a steeper increase towards the right end, suggesting an exponential or higher-order polynomial function.\n",
+      "* It doesn't show symmetry like a parabola (x^2), ruling out options (c) and (d).\n",
       "\n",
-      "**2. Behavior at x=0:**\n",
+      "**2. Consider the Function Behavior**\n",
       "\n",
-      "* The graph seems to have a maximum value around x=0. \n",
-      "* Option (a) has a maximum at x=0. Option (c) has a minimum at x=0.\n",
+      "* **Option (a) f(x) = x^3:**  A cubic function would have both positive and negative values, but our graph seems to have primarily positive y-values.\n",
+      "* **Option (b) f(x) = 2^x:** An exponential function with a base greater than 1 will always be positive and exhibit increasing growth, matching our graph's behavior.\n",
+      "* **Option (e) f(x) = -3*x^3:** A negative cubic function would decrease as x increases, contradicting our graph.\n",
       "\n",
-      "**3. Overall shape:**\n",
+      "**3. Test a Point**\n",
       "\n",
-      "* The graph has a parabolic shape, decreasing as we move away from x=0.\n",
-      "* Option (a) has a parabolic shape, decreasing as we move away from x=0. \n",
+      "Let's test a point from the graph to confirm our suspicion about option (b).  Take the interval \"(2.0, 2.18)\": 9.1.  This means an x-value within that range should produce a y-value around 9.1. Let's try x = 2.1:\n",
       "\n",
-      "**4. Eliminating other options:**\n",
+      "* f(2.1) = 2^(2.1) ≈ 4.28 \n",
       "\n",
-      "* **(b) f(x) = x^3:** This is an odd function, not symmetric around the y-axis.\n",
-      "* **(c) f(x) = (x-1)*(x+1):** This has a minimum at x=0, not a maximum.\n",
-      "* **(d) f(x) = log(x+10):** Logarithmic functions don't have this parabolic shape.\n",
-      "* **(e) f(x) = -3*x^3:** This is an odd function, not symmetric around the y-axis.\n",
+      "This value is significantly lower than 9.1. This discrepancy suggests that while the graph's shape resembles an exponential function, a simple 2^x doesn't fit the data precisely.\n",
       "\n",
-      "**Conclusion:**\n",
+      "**Conclusion**\n",
       "\n",
-      "Based on the analysis, the function depicted in the graph is most likely **(a) f(x) = -2*x^2**. \n",
+      "None of the provided functions perfectly match the graph. While the shape most closely resembles an exponential function like **f(x) = 2^x**, the actual values don't align. It's likely that the graph represents a more complex function or a transformation of an exponential function (e.g., with a vertical stretch or shift). \n",
       "\n",
+      "SOLUTION: x^2+3*x-1\n",
       "--------------------------------------------------------------------------------\n",
       "Graph x^3\n",
       "\n",
-      "Gemini: Invalid response with parts [].\n",
+      "LLM RESPONSE:  Here's how to determine the function from the graph:\n",
       "\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph -3*x^3\n",
+      "1. **Analyze the graph's overall shape:**\n",
+      "    - The graph is not symmetrical about the y-axis, ruling out even functions like x^2 or cos(x).\n",
+      "    - The graph has a steep increase as x becomes positive, suggesting exponential growth or a positive power function.\n",
+      "    - The graph has a steep decrease as x becomes negative, suggesting a negative power function or a function with a similar behavior in the negative domain.\n",
       "\n",
-      "Let's analyze the graph and the functions to determine the best match.\n",
+      "2. **Eliminate unlikely candidates:**\n",
+      "    - **a) f(x) = -x^5:**  While this has a similar shape, the negative sign would flip the graph, making it increase for negative x and decrease for positive x. This doesn't match our graph.\n",
+      "    - **b) f(x) = -sinh(x):**  The hyperbolic sine function is symmetrical about the origin. Our graph isn't, so this is incorrect.\n",
+      "    - **c) f(x) = x:** This is a straight line, not matching our curved graph.\n",
       "\n",
-      "**1. Observe the Graph's Characteristics:**\n",
+      "3. **Compare remaining options:**\n",
+      "    - **d) f(x) = x^3:** This cubic function has the correct shape: increasing rapidly for positive x and decreasing rapidly for negative x.\n",
+      "    - **e) f(x) = exp(x):** The exponential function also increases rapidly for positive x. However, it approaches 0 for negative x, not decreasing rapidly like our graph.\n",
       "\n",
-      "* **Symmetry:** The graph appears roughly symmetric, with a steep descent on the left and a less steep ascent on the right. This rules out linear functions (e) and absolute value functions (b) which have distinctive symmetry.\n",
-      "* **Trend:** The graph shows a decreasing trend initially, reaches a minimum point, and then starts increasing. This suggests a function with a turning point, like a polynomial or exponential function. \n",
+      "**Conclusion:**\n",
       "\n",
-      "**2. Analyze the Functions:**\n",
+      "Based on the analysis, the function depicted in the graph is most likely **(d) f(x) = x^3**. \n",
       "\n",
-      "* **a) f(x) = -3*x^3:** This cubic function has a single inflection point and matches the general trend of the graph.  It's a strong contender.\n",
-      "* **c) f(x) = exp(-x^2):** This Gaussian function has a peak at x=0 and decays rapidly on either side. This doesn't match the graph's overall shape.\n",
-      "* **d) f(x) = x^4:** This function is always positive and increases rapidly on both sides of x=0. This doesn't match the graph's decreasing trend on the left.\n",
+      "SOLUTION: x^3\n",
+      "--------------------------------------------------------------------------------\n",
+      "Graph -3*x^3\n",
       "\n",
-      "**3. Focus on the Minimum Point:**\n",
+      "LLM RESPONSE:  Let's analyze the graph step-by-step to determine the correct function:\n",
       "\n",
-      "The graph seems to have a minimum around x=2. Let's see if this aligns with the potential function:\n",
+      "1. **Observe the overall shape:** The graph is not symmetrical around the y-axis, ruling out options (c) and (d). It also doesn't resemble a straight line, eliminating option (e).\n",
       "\n",
-      "* **a) f(x) = -3*x^3:**  The derivative of this function is f'(x) = -9x^2. Setting it to zero, we get x=0 as the only critical point, not x=2. This means the cubic function doesn't have a minimum at the location suggested by the graph.\n",
+      "2. **Analyze the behavior for positive and negative x:**\n",
+      "    - For negative x values, the function produces large positive outputs.\n",
+      "    - For positive x values, the function produces large negative outputs. \n",
       "\n",
-      "**Conclusion:**\n",
+      "3. **Consider the growth rate:** The magnitude of the output increases rapidly as the magnitude of x increases. This rapid growth suggests a higher-order polynomial function.\n",
       "\n",
-      "None of the provided functions perfectly match the graph. While the cubic function (a) initially seemed promising, its minimum point doesn't align with the graph's minimum. \n",
+      "4. **Compare with remaining options:**\n",
+      "    - Option (a), f(x) = -3*x^3, exhibits the observed behavior: large positive outputs for negative x, large negative outputs for positive x, and rapid growth due to the cubic term.\n",
+      "    - Option (b), f(x) = -sign(-x), only produces outputs of -1 or 1, not matching the graph.\n",
       "\n",
-      "It's likely that the graph represents a different function altogether, possibly a higher-degree polynomial or a combination of functions. \n",
+      "**Therefore, the function depicted in the graph is most likely (a) f(x) = -3*x^3.** \n",
       "\n",
+      "SOLUTION: -3*x^3\n",
       "--------------------------------------------------------------------------------\n",
       "Graph x^4\n",
       "\n",
-      "Let's analyze the graph and functions step by step:\n",
-      "\n",
-      "1. **Symmetry:** The graph appears symmetrical, with similar behavior for positive and negative x values. This eliminates options b) and d), as they are not symmetrical.\n",
+      "LLM RESPONSE:  Here's how to determine the function:\n",
       "\n",
-      "2. **End Behavior:**  As x moves towards positive or negative infinity, the graph seems to increase rapidly. This points towards a function with a higher degree polynomial, eliminating options c) (tanh has horizontal asymptotes) and d) (linear function).\n",
+      "1. **Analyze the graph's shape:** The graph has low values for negative x, increases rapidly around x=0, and then continues to increase at a slower rate for positive x. This shape suggests an exponential function.\n",
       "\n",
-      "3. **Specific Values:** \n",
-      "    * Around x=0, the function has a relatively low value, close to zero. This eliminates option a), as x^4 would have a minimum at x=0.\n",
-      "    * The graph increases rapidly for positive x values and for negative x values. This supports the idea of a cubic function.\n",
+      "2. **Eliminate unlikely options:**\n",
+      "    * **a) f(x) = x^4:**  This function would be symmetrical around the y-axis, and our graph is not.\n",
+      "    * **b) f(x) = cos(x):** This function is periodic and oscillates between -1 and 1. Our graph is always increasing.\n",
+      "    * **d) f(x) = x:** This is a straight line, not the curved shape we see.\n",
+      "    * **e) f(x) = x^3:** While this function increases, it doesn't have the same rapid growth around x=0 that the graph shows.\n",
       "\n",
-      "4. **Comparing a) and e):** Both x^4 and x^3 increase rapidly as x moves away from zero. However, x^4 would have a much steeper increase than what's shown in the graph.\n",
+      "3. **Conclusion:** The graph most closely resembles the behavior of an exponential function. \n",
       "\n",
-      "**Therefore, the function depicted in the graph is most likely (e) f(x) = x^3.** \n",
+      "**Therefore, the function depicted in the graph is most likely (c) f(x) = exp(x).** \n",
       "\n",
+      "SOLUTION: x^4\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph (x + 4)^4\n",
+      "Graph -(x + 4)^4\n",
       "\n",
-      "Here's how we can deduce the function depicted in the graph:\n",
+      "LLM RESPONSE:  Here's how to determine the function depicted in the graph:\n",
       "\n",
-      "**1. Analyze the Graph's Behavior:**\n",
-      "\n",
-      "* The graph is clearly non-linear. It shows a steep increase in y-values as x increases.\n",
-      "* The graph appears to have an inflection point around x=2, where the rate of increase changes.\n",
+      "**1. Analyze the Graph's Shape:**\n",
       "\n",
-      "**2. Eliminate Linear and Simple Quadratic Functions:**\n",
+      "* The graph is always **decreasing** as x increases.\n",
+      "* The rate of decrease seems to be **slowing down** as x increases. \n",
+      "* The graph appears to be **asymptotically approaching negative infinity** as x approaches positive infinity.\n",
       "\n",
-      "* **a) f(x) = -2*x+5:** This is a linear function and would produce a straight line, not the curved shape observed.\n",
-      "* **c) f(x) = (x-2)^2:** This is a simple quadratic function. While it has a curve, it's symmetrical around x=2. The graph provided doesn't show this symmetry.\n",
+      "**2. Eliminate Options Based on Shape:**\n",
       "\n",
-      "**3. Consider Exponential and Trigonometric Functions:**\n",
+      "* **a) f(x) = sin(x) and b) f(x) = -sin(x):**  Sine functions oscillate between positive and negative values. This graph does not oscillate. **Eliminate a) and b).**\n",
+      "* **d) f(x) = -|x|:** The absolute value function has a sharp point at its minimum and decreases at a constant rate on either side. This graph is curved. **Eliminate d).**\n",
       "\n",
-      "* **d) f(x) = arcsinh(x):**  The inverse hyperbolic sine function has a gentler slope than the graph, especially for larger x values. \n",
-      "* **e) f(x) = 1/(1+exp(-x)):** This is a sigmoid function. It has a characteristic \"S\" shape, gradually increasing then flattening out. The provided graph doesn't flatten, it continues to increase steeply.\n",
+      "**3. Compare Remaining Options:**\n",
       "\n",
-      "**4. Focus on Polynomial Functions:**\n",
+      "* **c) f(x) = -exp(x):** The negative exponential function decreases rapidly at first, then slows down as x increases, approaching zero but never reaching it.\n",
+      "* **e) f(x) = -(x + 4)^4:** This function is a fourth-degree polynomial with a negative leading coefficient. It will decrease as x approaches both positive and negative infinity.\n",
       "\n",
-      "* **b) f(x) = (x + 4)^4:** This function represents a quartic polynomial.  Quartic functions can exhibit the steep, non-symmetrical growth observed in the graph. The inflection point around x=2 further supports this.\n",
+      "**4. Determine the Best Fit:**\n",
       "\n",
-      "**Conclusion:**\n",
+      "The graph's shape more closely resembles the behavior of a **negative exponential function**. The slowing rate of decrease and the asymptotic behavior point towards this conclusion.\n",
       "\n",
-      "Based on the graph's characteristics, the function depicted is most likely **b) f(x) = (x + 4)^4**. \n",
+      "**Therefore, the function depicted in the graph is most likely c) f(x) = -exp(x).** \n",
       "\n",
+      "SOLUTION: -(x + 4)^4\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph sign(x)\n",
+      "Graph x^5\n",
       "\n",
-      "Let's analyze the graph step-by-step to determine the function:\n",
+      "LLM RESPONSE:  Let's analyze the graph and compare it to the function options:\n",
       "\n",
-      "1. **Negative x values:** The graph shows negative y values for negative x values. This eliminates options b) and d) as both x^2 and (x+4)^4 always result in positive values for any x.\n",
+      "**1. Observing the Graph's Behavior:**\n",
       "\n",
-      "2. **Zero crossing:**  The graph crosses from negative to positive y values around x=0. This eliminates option a) cos(x), which has a positive value at x=0.\n",
+      "* **Symmetry:** The graph doesn't seem symmetrical around the y-axis (like even functions) or the origin (like odd functions). This might rule out some options.\n",
+      "* **Trends:** The graph has a steep negative slope for negative x values, becomes close to zero around x=0, and then has a steep positive slope for positive x values.\n",
+      "* **Sharp Changes:** There's no abrupt jump or discontinuity in the graph.\n",
       "\n",
-      "3. **Sign change:**  The most significant feature of the graph is the abrupt change in y value from negative to positive around x=0. This strongly suggests the function is related to the sign of x.\n",
+      "**2. Eliminating Options:**\n",
       "\n",
-      "4. **Constant values:** The graph shows almost constant y values within each interval. This further supports the idea of a sign function, as it outputs a constant value for all positive x and another constant for all negative x.\n",
+      "* **a) f(x) = sqrt(x+10):** This function is only defined for x >= -10 and would have a gradual increase, not a steep change like the graph shows. **Ruled out.**\n",
+      "* **b) f(x) = sqrt(x ** 2 + 3*x +5):** This function would have a more symmetrical shape due to the x**2 term. **Ruled out.**\n",
+      "* **c) f(x) = -sign(-x):** This function would be a step function with a value of -1 for x < 0 and 1 for x > 0. It wouldn't have the steep slopes seen in the graph. **Ruled out.**\n",
+      "* **e) f(x) = x:** This is a simple linear function. The graph shows a much steeper increase for both positive and negative x values. **Ruled out.**\n",
       "\n",
-      "5. **Slight slope:** The graph shows a slight slope in the constant values within each interval. This minor deviation from a perfect step function suggests there might be a small linear component added to the sign function.\n",
+      "**3. The Best Fit:**\n",
       "\n",
-      "**Conclusion:**\n",
+      "* **d) f(x) = x^5:** This function aligns well with our observations:\n",
+      "    * **Steep Slopes:** x^5 creates very steep slopes as x moves away from zero.\n",
+      "    * **Near Zero:** The function is close to zero for small values of x.\n",
+      "    * **No Discontinuities:** The function is smooth and continuous.\n",
       "\n",
-      "Based on these observations, the function depicted in the graph is most likely **c) f(x) = sign(x)**, potentially with a small linear term added. \n",
+      "**Conclusion:**\n",
       "\n",
-      "The other functions don't exhibit the key characteristics of the graph, primarily the sign change at x=0 and the near-constant values for positive and negative x ranges. \n",
+      "The graph most likely depicts the function **d) f(x) = x^5**. \n",
       "\n",
+      "SOLUTION: x^5\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph sign(x+3)\n",
+      "Graph -x^5\n",
       "\n",
-      "Let's break down the graph and analyze each function:\n",
-      "\n",
-      "**1. Graph Analysis:**\n",
-      "\n",
-      "* **Central Point:** The graph seems to change behavior around x = -3. \n",
-      "* **Negative x:**  For x values less than -3, the function has a negative output.\n",
-      "* **Positive x:** For x values greater than -3, the function has a positive output.\n",
+      "Gemini: Invalid response with parts [].\n",
+      "LLM RESPONSE:  \n",
+      "SOLUTION: -x^5\n",
+      "--------------------------------------------------------------------------------\n",
+      "Graph sign(x)\n",
       "\n",
-      "**2. Function Analysis:**\n",
+      "LLM RESPONSE:  Let's analyze the graph and each function to determine the best fit.\n",
       "\n",
-      "* **a) f(x) = sign(x+3):**  The sign function returns -1 for negative values, 0 for 0, and 1 for positive values. This function shifts the sign change to x = -3, matching the graph's behavior.\n",
-      "* **b) f(x) = x:** This is a straight line increasing continuously. It doesn't fit the sudden change around x = -3.\n",
-      "* **c) f(x) = sinh(x):** The hyperbolic sine function is always increasing and doesn't have the abrupt shift seen in the graph.\n",
-      "* **d) f(x) = cos(x):** The cosine function oscillates between -1 and 1, not matching the consistent positive/negative behavior based on the x-value in the graph.\n",
-      "* **e) f(x) = |x|:** The absolute value function has a sharp change at x = 0, not x = -3 as in the graph.\n",
+      "**Graph Analysis:**\n",
       "\n",
-      "**Conclusion:**\n",
+      "* **Symmetry:** The graph appears mostly symmetrical around x=0, with slight variations.\n",
+      "* **Sign Change:** The function changes sign around x=0, going from negative to positive.\n",
+      "* **Magnitude:** The function's output is close to -1 for negative x values near 0 and close to 1 for positive x values near 0. \n",
       "\n",
-      "The function that most closely resembles the graph is **(a) f(x) = sign(x+3)**. \n",
+      "**Function Analysis:**\n",
       "\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph sign(x-1)\n",
+      "a) **f(x) = sign(x):** This function outputs -1 for x < 0, 0 for x = 0, and 1 for x > 0. It matches the sign change and approximate magnitudes near 0 but is a step function, unlike our graph.\n",
       "\n",
-      "Here's a step-by-step analysis to determine the function:\n",
+      "b) **f(x) = x^2 + 3x - 1:** This quadratic function doesn't exhibit symmetry around x=0 and wouldn't have the near-constant outputs observed in the graph for intervals near 0.\n",
       "\n",
-      "1. **Analyze the graph's behavior:** \n",
-      "    - The graph has a negative value for x < 1 and positive values for x > 1.\n",
-      "    - There's a sharp jump around x = 1.\n",
-      "    - The function seems relatively flat except for the jump.\n",
+      "c) **f(x) = x:** This linear function matches the sign change but doesn't have the near-constant outputs observed in the graph for intervals near 0.\n",
       "\n",
-      "2. **Eliminate unlikely candidates based on the behavior:**\n",
-      "    - **a) f(x) = x^3:** This function would have a smooth curve and wouldn't exhibit the sudden jump.\n",
-      "    - **c) f(x) = sinh(x):**  The hyperbolic sine function has a steeper curve and doesn't flatten out like the graph.\n",
-      "    - **d) f(x) = x:**  This is a straight line and doesn't have the jump observed in the graph.\n",
-      "    - **e) f(x) = (x-1)*(x+1):** This would have a parabolic shape, not the flat segments seen in the graph.\n",
+      "d) **f(x) = |2x + 4|:** This absolute value function is always non-negative, contradicting the negative outputs in the graph for x < 0.\n",
       "\n",
-      "3. **Focus on the remaining candidate:**\n",
-      "    - **b) f(x) = sign(x-1):**  The sign function returns -1 for negative input, 0 for 0 input, and 1 for positive input.  This aligns perfectly with the graph's behavior: \n",
-      "        - Negative values for x < 1.\n",
-      "        - A jump to positive values for x > 1.\n",
-      "        - Relatively flat segments before and after the jump.\n",
+      "e) **f(x) = x^2:** This quadratic function is always non-negative, contradicting the negative outputs in the graph for x < 0.\n",
       "\n",
       "**Conclusion:**\n",
       "\n",
-      "The graph most closely depicts the function **b) f(x) = sign(x-1).** \n",
+      "None of the functions perfectly match the graph. However, **f(x) = sign(x)** is the closest approximation. The graph likely represents a smoothed or slightly modified version of the sign function. \n",
       "\n",
+      "SOLUTION: sign(x)\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph |x|\n",
+      "Graph -sign(x)\n",
       "\n",
-      "Let's analyze the graph and the functions to find the best match.\n",
+      "LLM RESPONSE:  Let's analyze the graph step-by-step to determine the function:\n",
       "\n",
-      "1. **Examine the graph's general shape:** The graph is roughly symmetrical, with a steep slope around x=0 and gradually flattening out as x moves away from 0.\n",
+      "1. **Sign Changes:** The graph has positive y-values for negative x-values and negative y-values for positive x-values. This eliminates options a), d), and e) as they don't exhibit this sign change behavior.\n",
       "\n",
-      "2. **Eliminate unlikely options:**\n",
-      "   - **(b) f(x) = sign(x+3) and (c) f(x) = sign(x):**  The sign function produces only values of -1, 0, and 1. The graph clearly has a wider range of values.\n",
-      "   - **(d) f(x) = log(x+10):** The logarithmic function is not symmetrical and would have a vertical asymptote at x = -10. The graph doesn't show this.\n",
-      "   - **(e) f(x) = |x|:** The absolute value function has a sharp, pointed bottom at x = 0. The graph has a smoother transition.\n",
+      "2. **Constant Values within Intervals:** The graph shows constant y-values within each specified interval of x. This behavior aligns with the sign function (sign(x)), which outputs a constant value (-1, 0, or 1) depending on the sign of x.\n",
       "\n",
-      "3. **Focus on the remaining option:**\n",
-      "    - **(a) f(x) = arcsinh(x):** The inverse hyperbolic sine function (arcsinh) is indeed symmetrical and has a shape that gradually flattens out as x moves away from 0. This matches the graph's characteristics.\n",
+      "3. **Matching Values:**  \n",
+      "    - For negative x values, the graph shows positive y-values close to 1. This matches the output of `sign(x)` for negative x (which is -1).\n",
+      "    - For positive x values, the graph shows negative y-values close to -1. This matches the output of `-sign(x)` for positive x (which is -1).\n",
       "\n",
-      "**Therefore, the function depicted in the graph is most likely (a) f(x) = arcsinh(x).** \n",
+      "**Therefore, the graph most closely depicts the function c) f(x) = -sign(x).** \n",
       "\n",
+      "SOLUTION: -sign(x)\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph |2*x+4|\n",
+      "Graph -sign(-x)\n",
       "\n",
-      "Let's analyze the graph and the function options step-by-step:\n",
+      "LLM RESPONSE:  Let's analyze the graph and the function options step-by-step:\n",
       "\n",
-      "1. **Shape:** The graph has a clear V-shape. This eliminates options a), b), d) and e):\n",
-      "    * a) `x^3` has a cubic shape, not a V-shape.\n",
-      "    * b) `(x-2)^2` has a parabolic shape, not a V-shape. \n",
-      "    * d) `exp(-x)` is an exponential function with a decreasing curve, not a V-shape.\n",
-      "    * e) `sign(x-1)` is a step function, not a V-shape.\n",
+      "1. **Analyze the graph:**\n",
+      "    * The graph has negative y-values for negative x-values and positive y-values for positive x-values. \n",
+      "    * The graph seems to have a jump discontinuity around x=0.\n",
+      "    * For x values further away from zero, the graph seems to approach constant values (around -1 for negative x and around 1 for positive x).\n",
       "\n",
-      "2. **Absolute Value:** The V-shape strongly suggests an absolute value function. This supports option c) `f(x) = |2*x+4|`.\n",
+      "2. **Eliminate unlikely functions:**\n",
+      "    * **a) f(x) = x^3:** This function is always increasing and doesn't have a jump discontinuity. **Eliminate.**\n",
+      "    * **b) f(x) = sin(x+2)+2:** This function is periodic and oscillates. The graph doesn't show oscillations. **Eliminate.**\n",
+      "    * **d) f(x) = sin(x):** Similar to (b), this function is periodic and oscillates. **Eliminate.**\n",
+      "    * **e) f(x) = -cosh(x):** This function is always negative and symmetrical around the y-axis. The graph has both positive and negative values. **Eliminate.**\n",
       "\n",
-      "3. **Slope and Vertex:**  The graph's slope appears to be around 2 on both sides of the vertex, and the vertex seems to be around x = -2. This further supports option c), as  `|2*x+4|` has a slope of 2 for x > -2 and -2 for x < -2, and its vertex is at x = -2.\n",
+      "3. **Focus on the remaining function:**\n",
+      "    * **c) f(x) = -sign(-x):** The sign function returns -1 for negative values, 0 for 0, and 1 for positive values.  Let's break down this function:\n",
+      "        * `-x`: This inverts the sign of x.\n",
+      "        * `sign(-x)`: This returns -1 for positive x, 0 for x=0, and 1 for negative x.\n",
+      "        * `-sign(-x)`: This finally returns 1 for positive x, 0 for x=0, and -1 for negative x.\n",
       "\n",
-      "**Therefore, the function depicted in the graph is c) f(x) = |2*x+4|.** \n",
+      "4. **Conclusion:**\n",
+      "    The function **c) f(x) = -sign(-x)** accurately represents the graph. It has a jump discontinuity at x=0 and approaches -1 for negative x and 1 for positive x, matching the graph's behavior. \n",
       "\n",
+      "SOLUTION: -sign(-x)\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph sqrt(x+10)\n",
+      "Graph sign(x+3)\n",
       "\n",
-      "Let's analyze the graph and the functions step-by-step to find the best match:\n",
+      "LLM RESPONSE:  Here's how to determine the function represented by the graph:\n",
       "\n",
-      "**1. Observing the graph:**\n",
+      "**1. Analyze the Graph's Characteristics:**\n",
       "\n",
-      "* **Positive and increasing:** The graph's y-values are always positive and generally increase as x increases.\n",
-      "* **Curve shape:**  The graph doesn't appear linear, suggesting it's not a simple straight line function. It has a gradual curve, increasing more slowly at higher x-values.\n",
+      "* **Piecewise Constant:** The graph shows distinct horizontal segments, meaning the function's output remains constant within certain x-value intervals.\n",
+      "* **Sign Change:** The function takes on both positive and negative values.\n",
+      "* **Discontinuity:** There's likely a jump discontinuity where the function changes signs.\n",
       "\n",
-      "**2. Evaluating the functions:**\n",
+      "**2. Eliminate Unlikely Candidates:**\n",
       "\n",
-      "* **a) f(x) = x:** This is a linear function. It doesn't fit the graph's non-linear, always-positive nature.\n",
-      "* **b) f(x) = sqrt(x+10):** This function is only defined for x ≥ -10, matching the graph's starting point. It's also always positive and increases as x increases. This is a strong candidate.\n",
-      "* **c) f(x) = sign(x-1):**  This function only outputs -1, 0, or 1, not matching the graph's continuous and increasing nature.\n",
-      "* **d) f(x) = exp(-x^2):** This function is bell-shaped, symmetrical around x=0, and decreases as x moves away from 0.  It doesn't fit the graph's shape.\n",
-      "* **e) f(x) = (x + 4)^4:** This function generates very large y-values quickly, especially for positive x-values. It doesn't match the graph's slower increase.\n",
+      "* **a) f(x) = 2^(x-5):** Exponential functions are continuous and always increasing or decreasing. This doesn't match the piecewise constant nature of the graph.\n",
+      "* **c) f(x) = x:** A linear function would be a straight line, not horizontal segments.\n",
+      "* **d) f(x) = |x|:** The absolute value function has a V-shape, not horizontal segments.\n",
+      "* **e) f(x) = -(x + 4)^4:** This function is a transformed even-degree polynomial. It would have a smooth, continuous curve, not the abrupt changes seen in the graph.\n",
       "\n",
-      "**3. Conclusion:**\n",
+      "**3. Focus on the Most Likely Candidate:**\n",
       "\n",
-      "Based on the analysis, the function **b) f(x) = sqrt(x+10)** most closely matches the graph's characteristics:\n",
+      "* **b) f(x) = sign(x+3):** The sign function (also called signum function) returns:\n",
+      "    * -1 if the input is negative\n",
+      "    * 0 if the input is zero\n",
+      "    * +1 if the input is positive\n",
       "\n",
-      "* Defined for x ≥ -10\n",
-      "* Always positive\n",
-      "* Increasing with a gradual curve \n",
+      "This behavior aligns perfectly with the graph's characteristics: piecewise constant, a sign change, and a likely jump discontinuity at x = -3.\n",
       "\n",
-      "Therefore, the graph approximately depicts the function **f(x) = sqrt(x+10)**. \n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph most closely depicts the function **b) f(x) = sign(x+3)**. \n",
       "\n",
+      "SOLUTION: sign(x+3)\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph sqrt(x ** 2 + 3*x +5)\n",
+      "Graph sign(x-1)\n",
       "\n",
-      "Let's analyze the graph and functions to determine the best match.\n",
+      "LLM RESPONSE:  Let's analyze the graph and each function to find the best match:\n",
       "\n",
-      "**1. Observing the Graph's Characteristics**\n",
+      "**1. Analyzing the Graph:**\n",
       "\n",
-      "* **Symmetry:** The graph doesn't exhibit symmetry around the y-axis or the origin. This rules out even functions like `f(x) = exp(-x)` and `f(x) = (x + 4)^4`.\n",
-      "* **Monotonicity:**  The graph appears to be mostly increasing, with a slight dip around x = 0. This eliminates functions that are strictly decreasing, like `f(x) = exp(-x)`.\n",
-      "* **Asymptotes:** The graph doesn't seem to have any vertical asymptotes, which rules out functions like `f(x) = 1/x`.\n",
+      "* **Negative to Positive:** The graph transitions from negative y-values to positive y-values around x = 1.\n",
+      "* **Steep Change:** The change around x = 1 appears quite steep.\n",
+      "* **Relatively Flat:**  The graph seems relatively flat for x-values significantly less than 1 and significantly greater than 1.\n",
       "\n",
-      "**2. Analyzing the Functions**\n",
+      "**2. Evaluating the Functions:**\n",
       "\n",
-      "* **a) f(x) = exp(-x):**  This is an exponential decay function. It's always decreasing and doesn't match the graph's characteristics.\n",
-      "* **b) f(x) = sqrt(x ** 2 + 3*x +5):** This function is always positive and has a minimum point. It somewhat resembles the graph's shape.\n",
-      "* **c) f(x) = arctan(x):** The arctangent function has horizontal asymptotes at y = π/2 and y = -π/2. It's always increasing but has a much flatter shape than the given graph. \n",
-      "* **d) f(x) = (x + 4)^4:** This function is a fourth-degree polynomial that's symmetric around x = -4. It doesn't match the graph's lack of symmetry.\n",
-      "* **e) f(x) = x:** This is a simple linear function. It's increasing but doesn't have the slight curve observed in the graph.\n",
+      "* **a) f(x) = -|-x|:** This function is always negative or zero, which doesn't match our graph.\n",
+      "* **b) f(x) = sign(x-1):** This function has a sharp jump from -1 to 1 at x = 1, which aligns well with the graph's behavior.\n",
+      "* **c) f(x) = exp(-x):** This function is always positive and decays exponentially. It doesn't match the graph.\n",
+      "* **d) f(x) = -(x + 4)^4:** This function is always negative and doesn't have the sharp transition seen in the graph.\n",
+      "* **e) f(x) = 2^(x-5):** This function is always positive and increases exponentially. It doesn't match the graph.\n",
       "\n",
-      "**3. Conclusion**\n",
+      "**Conclusion:**\n",
       "\n",
-      "The function that most closely resembles the graph is **(b) f(x) = sqrt(x ** 2 + 3*x + 5)**. It's the only function that captures the increasing trend, the minimum point, and the general shape of the provided data. \n",
+      "Based on the analysis, the function that best matches the graph is **b) f(x) = sign(x-1)**. \n",
       "\n",
+      "SOLUTION: sign(x-1)\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph exp(-x^2)\n",
-      "\n",
-      "Let's analyze the graph and the functions to find the best match.\n",
+      "Graph |x|\n",
       "\n",
-      "**1. Symmetry:**\n",
+      "LLM RESPONSE:  Let's analyze the graph and the function options step-by-step:\n",
       "\n",
-      "* The graph appears symmetrical around x = 0. This eliminates options a), b) and e) which are not symmetrical around x = 0.\n",
+      "**1. Observing the Graph's Behavior:**\n",
       "\n",
-      "**2.  Behavior around x = 0:**\n",
+      "* **Overall Trend:** The graph generally increases as x increases. \n",
+      "* **Curvature:** The rate of increase seems to slow down as x increases.\n",
+      "* **Negative x-values:** The graph exists for negative x-values.\n",
       "\n",
-      "* The graph has a peak around x = 0 and decreases as we move away from x = 0 in both directions. This matches the behavior of  d) f(x) = exp(-x^2) and contradicts c) f(x) = exp(x) which increases monotonically. \n",
+      "**2. Eliminating Function Options based on Observations:**\n",
       "\n",
-      "**3. Asymptotic Behavior:**\n",
+      "* **d) f(x) = |x|:** This function has a sharp turn at x=0 and is symmetrical. Our graph doesn't have a sharp turn. **Eliminated.**\n",
+      "* **e) f(x) = x:** This is a straight line and doesn't match the curvature of our graph. **Eliminated.**\n",
+      "* **a) f(x) = 1/2*cos(x-2):** Cosine functions are periodic (repeating). Our graph doesn't show a repeating pattern. **Eliminated.**\n",
       "\n",
-      "* The graph seems to approach 0 as x approaches positive or negative infinity. This is consistent with d) f(x) = exp(-x^2). \n",
+      "**3. Analyzing Remaining Options:**\n",
       "\n",
-      "**Therefore, the function depicted in the graph is most likely d) f(x) = exp(-x^2). ** \n",
+      "* **b) f(x) = log(x+10):** Logarithmic functions are defined only for positive values of the argument (inside the logarithm). Our graph exists for negative x-values.  **Eliminated.**\n",
+      "* **c) f(x) = -sqrt(x+10):**  Square root functions with a negative sign in front will start at a certain x-value (where the term inside the square root becomes zero) and then decrease as x increases. This matches our graph's overall trend and curvature.\n",
       "\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph exp(x)\n",
+      "**4. Verifying the Fit:**\n",
       "\n",
-      "Here's how we can determine the function:\n",
+      "The function f(x) = -sqrt(x+10) is defined for x >= -10, which aligns with the graph's domain. The general shape and values in the graph also seem consistent with a negative square root function.\n",
       "\n",
-      "1. **Analyze the graph's trend:** The graph shows a clear upward trend where the y-value increases as the x-value increases. This eliminates options c) and d). \n",
+      "**Conclusion:**\n",
       "\n",
-      "    * c) `f(x) = sin(x)` is periodic and oscillates between -1 and 1.\n",
-      "    * d) `f(x) = -3*x^3`  would show a decreasing trend as x increases.\n",
+      "The graph most likely depicts the function **c) f(x) = -sqrt(x+10)**. \n",
       "\n",
-      "2. **Consider the rate of increase:** The graph shows a rapid increase in y-value as x increases. This makes option a) less likely.\n",
+      "SOLUTION: |x|\n",
+      "--------------------------------------------------------------------------------\n",
+      "Graph -|x|\n",
       "\n",
-      "    * a) `f(x) = x` would have a linear increase, not a rapid acceleration.\n",
+      "LLM RESPONSE:  Let's analyze the graph step-by-step to determine the correct function:\n",
       "\n",
-      "3. **Compare remaining options:** We're left with b) `f(x) = exp(x)` and e) `f(x) = x^2`.  Both exhibit upward trends with increasing rates of change. However, exponential functions (like exp(x)) increase at a much faster rate than quadratic functions (like x^2). \n",
+      "1. **Symmetry:** The graph appears to be symmetric around the y-axis. This means the function is likely an even function, where f(-x) = f(x). This eliminates options a) and e).\n",
       "\n",
-      "4. **The verdict:** The graph's rapid, accelerating growth strongly suggests an **exponential function**. \n",
+      "2. **Behavior around x=0:** The graph has a sharp point (non-differentiable) at x=0. This suggests an absolute value function. This eliminates option b).\n",
       "\n",
-      "**Therefore, the function depicted in the graph is most likely b) f(x) = exp(x).** \n",
+      "3. **Positive vs. Negative:** The graph has negative y-values for all x-values. This eliminates option d).\n",
       "\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph exp(-x)\n",
+      "4. **Matching the shape:** Option c), f(x) = -|x|, represents a downward-pointing V shape, which closely matches the provided graph.\n",
       "\n",
-      "Let's analyze the graph's behavior to determine the correct function.\n",
+      "**Therefore, the function depicted in the graph is most likely c) f(x) = -|x|.** \n",
       "\n",
-      "**Step 1: Observe the overall trend:**\n",
+      "SOLUTION: -|x|\n",
+      "--------------------------------------------------------------------------------\n",
+      "Graph -|-x|\n",
       "\n",
-      "* The graph shows a decreasing trend as x increases. This eliminates options (b), (d), and (e) because:\n",
-      "    *  sqrt(x+10) is an increasing function\n",
-      "    * x^3 and x^2 are increasing for positive x and decreasing for negative x, not consistently decreasing like the graph.\n",
+      "LLM RESPONSE:  Let's analyze the graph and each function option to determine the best fit.\n",
       "\n",
-      "**Step 2: Analyze the rate of change:**\n",
+      "**Graph Observations:**\n",
       "\n",
-      "* The graph decreases rapidly for larger values of x and slows down as x approaches 0. This suggests an exponential decay. Option (c), exp(-x), exhibits this behavior.\n",
+      "* **Symmetry:** The graph appears roughly symmetrical around the point (0,0). \n",
+      "* **Negative Slope (mostly):**  The graph generally has a negative slope for x < 0 and a positive slope for x > 0.\n",
+      "* **Constant Values:** The function takes on constant values within each specified interval.\n",
       "\n",
-      "**Step 3: Consider sin(x):**\n",
+      "Now let's consider each function:\n",
       "\n",
-      "* sin(x) oscillates between -1 and 1, producing a wave-like pattern. The graph doesn't display this oscillatory behavior, eliminating option (a).\n",
+      "* **a) f(x) = -sign(-x):** This function returns -1 for x > 0 and 1 for x < 0. It doesn't match the graph's shape.\n",
+      "* **b) f(x) = sign(x-1):** This function returns -1 for x < 1 and 1 for x > 1.  It doesn't have the negative slope for x < 0.\n",
+      "* **c) f(x) = -|-x|:** This function represents a negative absolute value, creating a V-shape with the vertex pointing downwards at (0,0). While it has symmetry, it doesn't match the graph's slopes.\n",
+      "* **d) f(x) = (x-1)*(x+1):** This simplifies to f(x) = x² - 1, a parabola opening upwards with the vertex at (0,-1). This doesn't match the graph's shape.\n",
+      "* **e) f(x) = sinh(x):** The hyperbolic sine function is odd (symmetric about the origin) and has a negative slope for x < 0 and a positive slope for x > 0. This aligns well with our observations.\n",
       "\n",
       "**Conclusion:**\n",
       "\n",
-      "Based on the analysis, the graph most likely depicts the function: **(c) f(x) = exp(-x)** \n",
+      "The graph most closely resembles the function **e) f(x) = sinh(x)**. While the graph is a piecewise constant approximation, the overall shape, symmetry, and slope behavior strongly suggest the hyperbolic sine function. \n",
       "\n",
+      "SOLUTION: -|-x|\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph log(x+10)\n",
+      "Graph |2*x+4|\n",
       "\n",
-      "Let's analyze the graph and the functions step-by-step:\n",
+      "LLM RESPONSE:  Let's analyze the graph and each function to determine the best fit.\n",
       "\n",
-      "1. **Examine the graph's general shape:**\n",
-      "    - The graph shows a generally increasing trend.\n",
-      "    - The increase appears to be gradual and continuous, not sharp or sudden.\n",
+      "**1. Analyzing the Graph:**\n",
       "\n",
-      "2. **Eliminate unlikely functions:**\n",
-      "    - **c) f(x) = sign(x-1):** The sign function would produce a graph with only two distinct values (-1 and 1), which doesn't match the continuous increase in our graph.\n",
-      "    - **d) f(x) = sin(x):** The sine function produces a wave-like graph oscillating between -1 and 1, not the consistently increasing pattern we observe.\n",
+      "* **Overall Shape:** The graph has a generally increasing trend. It starts with low values, gradually increases, and then seems to level off at higher x-values.\n",
+      "* **Symmetry:** The graph doesn't appear symmetrical around the y-axis or the origin.\n",
+      "* **Key Features:**  There's a noticeable \"bend\" or change in slope around x = -2 and x = 0.\n",
       "\n",
-      "3. **Consider remaining functions:**\n",
-      "    - **a) f(x) = log(x+10):** Logarithmic functions increase slowly initially and then flatten out. The graph shows a more consistent increase.\n",
-      "    - **b) f(x) = 2^(x-5):** Exponential functions show a rapid increase, accelerating as x increases.  The graph's increase seems more gradual.\n",
-      "    - **e) f(x) = sqrt(x+10):** Square root functions show a gradual increase that slows down as x increases. This seems to fit the graph's behavior. \n",
+      "**2. Evaluating the Functions:**\n",
       "\n",
-      "4. **Focus on the square root function (e):**\n",
-      "    - The graph starts around x = -10, which is where the square root function would begin (sqrt(0) = 0).\n",
-      "    - The graph increases steadily but slows down, matching the typical behavior of a square root function.\n",
+      "* **a) f(x) = 1/(1+exp(-x)) (Sigmoid Function):** This function is always positive, increasing, and has a characteristic \"S\" shape. It doesn't match the graph's bend around x = -2.\n",
+      "* **b) f(x) = -sin(x) (Sine Function):** This function is periodic and oscillates between -1 and 1. It doesn't match the overall increasing trend of the graph.\n",
+      "* **c) f(x) = |2*x+4| (Absolute Value Function):** This function has a \"V\" shape with its vertex at x = -2. This aligns with the bend in the graph. The function is also always positive and generally increasing, matching the graph's characteristics.\n",
+      "* **d) f(x) = sign(x) (Sign Function):** This function only takes on values of -1, 0, and 1. It doesn't match the continuous and gradually increasing nature of the graph.\n",
+      "* **e) f(x) = log(x+10) (Logarithmic Function):** This function is only defined for x > -10 and increases rapidly at first, then slows down. It doesn't match the graph's behavior for x < 0.\n",
       "\n",
       "**Conclusion:**\n",
       "\n",
-      "The function depicted in the graph is most likely **e) f(x) = sqrt(x+10)**. \n",
+      "Based on the analysis, the function that most closely resembles the graph is **c) f(x) = |2*x+4|**. The graph's shape, bend at x = -2, and overall increasing trend align well with the characteristics of an absolute value function. \n",
       "\n",
+      "SOLUTION: |2*x+4|\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph sin(x)\n",
-      "\n",
-      "Here's how to determine the function:\n",
+      "Graph |x^3|\n",
       "\n",
-      "**1. Analyze the graph's shape:**\n",
+      "LLM RESPONSE:  Let's analyze the graph and the function options step-by-step:\n",
       "\n",
-      "* The graph has peaks and valleys, suggesting a periodic function like a trigonometric function (e.g., sine or cosine).\n",
-      "* The graph is not symmetrical around the y-axis, ruling out even functions like  x^2 or x^4.\n",
-      "* The graph doesn't continuously increase or decrease, ruling out simple linear functions (like x) or exponential functions (like exp(-x)).\n",
+      "1. **Symmetry:** The graph appears to be roughly symmetrical around the y-axis. This eliminates options b) and e) which are linear and don't exhibit this symmetry.\n",
       "\n",
-      "**2. Consider the functions provided:**\n",
+      "2. **Behavior at Extremes:** As x goes to negative infinity, the y values increase rapidly. This eliminates options c) and d). Option c) approaches zero as x goes to negative infinity, and option d) oscillates within a bounded range.\n",
       "\n",
-      "* **a) f(x) = -3*x^3:**  A cubic function would have a single curve, not multiple peaks and valleys.\n",
-      "* **b) f(x) = (x + 4)^4:**  A fourth-degree polynomial would have a more symmetrical shape and fewer peaks and valleys.\n",
-      "* **c) f(x) = x:** A linear function would be a straight line.\n",
-      "* **d) f(x) = exp(-x):** An exponential function would continuously decrease and not have peaks and valleys.\n",
-      "* **e) f(x) = sin(x):** A sine function has the characteristic wave-like pattern with multiple peaks and valleys.\n",
+      "3. **Shape:** The graph resembles a \"V\" shape, especially for values close to zero. This strongly suggests an absolute value function.\n",
       "\n",
-      "**3. Conclusion:**\n",
+      "4. **Option a):** The function f(x) = |x^3| would indeed create a symmetrical \"V\" shape with rapidly increasing y values as x moves away from zero. \n",
       "\n",
-      "Based on the analysis, the graph most closely resembles the function **e) f(x) = sin(x)**. \n",
+      "**Therefore, the function depicted in the graph is most likely (a) f(x) = |x^3|.** \n",
       "\n",
+      "SOLUTION: |x^3|\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph cos(x)\n",
-      "\n",
-      "Here's how we can determine the function depicted in the graph:\n",
-      "\n",
-      "1. **Analyze the graph's shape:** The graph exhibits a repeating wave-like pattern with peaks and troughs. This suggests a trigonometric function like cosine.\n",
-      "\n",
-      "2. **Consider the function options:** \n",
-      "    - exp(x) grows exponentially and doesn't have a repeating pattern.\n",
-      "    - x is a linear function and doesn't have a repeating pattern. \n",
-      "    - cosh(x) has a single minimum and grows exponentially on either side.\n",
-      "    - sqrt(x ** 2 + 3*x +5)  would have a single minimum point and wouldn't exhibit a repeating pattern.\n",
-      "    - cos(x) produces a repeating wave pattern with peaks and troughs.\n",
-      "\n",
-      "3. **Compare the graph to cos(x):** The graph closely resembles a shifted and scaled version of cos(x). The peaks are not exactly at 1 and -1, and the period might be slightly different, but the overall shape matches.\n",
+      "Graph sqrt(x+10)\n",
       "\n",
-      "**Therefore, the function depicted in the graph is most likely (d) f(x) = cos(x).** \n",
+      "LLM RESPONSE:  Here's how to determine the function depicted in the graph:\n",
       "\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph sinh(x)\n",
+      "**1. Analyze the Graph's Behavior**\n",
       "\n",
-      "Let's analyze the graph and the function options step-by-step:\n",
+      "* **Increasing:** The y-values increase as x increases.\n",
+      "* **Continuous (approximately):** The graph shows a smooth transition between intervals, suggesting a continuous function.\n",
+      "* **Positive for x > -10:**  The y-values are positive for x-values greater than approximately -10.\n",
       "\n",
-      "1. **Symmetry:** The graph appears to be roughly symmetrical, with a steep increase on both sides of a central point. This eliminates options like `f(x) = 2*x + 4` which would be a straight line.\n",
+      "**2. Eliminate Options Based on Behavior**\n",
       "\n",
-      "2. **Minimum Point:** The graph has a minimum point around x = 0. This eliminates options like `f(x) = x^4` which has its minimum at x = 0, but continues to decrease for negative x values.\n",
+      "* **a) f(x) = sign(x):** This function is discontinuous, taking only values of -1, 0, and 1. Eliminate.\n",
+      "* **b) f(x) = -sign(x):**  Similar to (a), this is discontinuous. Eliminate.\n",
+      "* **c) f(x) = -sinh(x):** The hyperbolic sine function increases exponentially for positive x, and this graph does not. Eliminate.\n",
+      "* **d) f(x) = -|-x|:** This function is always negative or zero, but our graph has positive values. Eliminate.\n",
       "\n",
-      "3. **Shape:** The graph increases rapidly as we move away from the minimum point. This eliminates options like `f(x) = sinh(x)` which has a more gradual increase.\n",
+      "**3. Focus on the Remaining Option**\n",
       "\n",
-      "4. **Comparing remaining options:**  We are left with `f(x) = arcsinh(x)` and `f(x) = x^2`.  Let's consider their behavior:\n",
+      "* **e) f(x) = sqrt(x+10):** \n",
+      "    * This function is defined only for x >= -10, matching our graph.\n",
+      "    * It's a square root function, which increases steadily as x increases, aligning with the graph's behavior.\n",
       "\n",
-      "    * `f(x) = arcsinh(x)` increases more slowly than `f(x) = x^2` for values of x far from 0.\n",
-      "    * The graph shows a very sharp increase for values of x far from 0, suggesting a steeper curve.\n",
+      "**Conclusion**\n",
       "\n",
-      "**Conclusion:** Based on the symmetrical shape, minimum point, and the rate of increase, the function depicted in the graph is most likely **(e) f(x) = x^2**. \n",
+      "The graph most closely depicts the function **e) f(x) = sqrt(x+10)**. \n",
       "\n",
+      "SOLUTION: sqrt(x+10)\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph cosh(x)\n",
+      "Graph -sqrt(x+10)\n",
       "\n",
-      "Let's analyze the graph and the function options step-by-step:\n",
+      "LLM RESPONSE:  Here's how to determine the function:\n",
       "\n",
-      "1. **Symmetry:** The graph is symmetric around the y-axis. This eliminates options a), c), and e) as they are not symmetric around the y-axis.\n",
+      "**1. Analyze the Graph's Shape:**\n",
       "\n",
-      "2. **Shape:** The graph has a minimum point and increases rapidly as x moves away from zero in both directions. This eliminates d) as arcsinh(x) has a more gradual increase.\n",
+      "* **Negative x-values:** The graph has negative y-values for negative x-values. This eliminates options a) and d) since even powers always result in positive outputs for negative inputs.\n",
+      "* **Decreasing then Increasing:** The graph decreases as x approaches zero from the negative side and then increases as x becomes positive. This eliminates option b) as x^3 increases consistently.\n",
+      "* **Curve Shape:** The graph has a curve that resembles a square root function flipped horizontally.\n",
       "\n",
-      "3. **Function b):** cosh(x) is a hyperbolic cosine function, which is known for its symmetrical U-shape and rapid growth away from the minimum point. This matches the characteristics observed in the graph.\n",
+      "**2. Consider the Remaining Options:**\n",
       "\n",
-      "Therefore, the function depicted in the graph is most likely **(b) f(x) = cosh(x)**. \n",
+      "* **Option c) f(x) = -sqrt(x+10):** This function is a square root function shifted 10 units to the left and then flipped vertically. This matches the observed shape and behavior of the graph.\n",
+      "* **Option e) f(x) = (x-1)*(x+1):** This is a quadratic function (parabola) that opens upwards. The graph clearly doesn't fit this shape.\n",
       "\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph tanh(x)\n",
+      "**3. Conclusion:**\n",
       "\n",
-      "Let's analyze the graph and the functions to determine the best match.\n",
+      "Based on the analysis, the graph most closely depicts the function **c) f(x) = -sqrt(x+10)**. \n",
       "\n",
-      "**1. Analyze the Graph:**\n",
+      "SOLUTION: -sqrt(x+10)\n",
+      "--------------------------------------------------------------------------------\n",
+      "Graph sqrt(x ** 2 + 3*x +5)\n",
       "\n",
-      "* The graph is symmetrical around x = 1.\n",
-      "* The function output ranges from approximately -1 to 1.\n",
-      "* The function is mostly flat with a steep increase around x = 1.\n",
+      "LLM RESPONSE:  Let's analyze the graph and the function options step-by-step:\n",
       "\n",
-      "**2. Evaluate the Functions:**\n",
+      "**1. Observing the Graph's Behavior:**\n",
       "\n",
-      "* **a) f(x) = tanh(x):**  The hyperbolic tangent function is symmetrical around x = 0 and has a smooth transition, not a steep jump. This doesn't match the graph. \n",
+      "* **General Trend:** The graph has a generally increasing trend. It starts with lower values for negative x and increases as x becomes positive.\n",
+      "* **Shape:** The graph seems to flatten out for very negative values of x and approaches a limit as x increases. \n",
+      "* **Symmetry:** The graph doesn't appear to be symmetrical about the y-axis or the origin.\n",
       "\n",
-      "* **b) f(x) = cos(x):**  The cosine function is periodic and oscillates between -1 and 1.  The graph doesn't show periodic behavior.\n",
+      "**2. Eliminating Function Options Based on Observations:**\n",
       "\n",
-      "* **c) f(x) = |x|:**  The absolute value function has a sharp V shape centered at x = 0. This doesn't align with the symmetry point of the graph.\n",
+      "* **a) f(x) = sign(x):** This function only takes on values of -1, 0, and 1. The graph clearly has a wider range, so this option is incorrect.\n",
+      "* **d) f(x) = -exp(x):** This function is always negative and decreases exponentially. Our graph is mostly positive and increasing, so this option is incorrect.\n",
+      "* **e) f(x) = cos(x):** This function is periodic and oscillates between -1 and 1. Our graph doesn't show this behavior, so this option is incorrect.\n",
       "\n",
-      "* **d) f(x) = sign(x-1):** The sign function returns -1 for x < 1, 0 for x = 1, and 1 for x > 1. This matches the steep jump around x = 1 and the flat behavior elsewhere.\n",
+      "**3. Analyzing the Remaining Options:**\n",
       "\n",
-      "* **e) f(x) = sqrt(x+10):**  The square root function is not symmetrical and continuously increases. This doesn't fit the graph.\n",
+      "* **b) f(x) = sqrt(x ** 2 + 3*x +5):** This function represents a parabola that opens upwards. While it's always positive, it doesn't exhibit the flattening behavior for negative x values seen in the graph.\n",
+      "* **c) f(x) = 1/(1+exp(-x)):** This is the sigmoid function. It has a characteristic 'S' shape, starts to flatten for very negative x, approaches 1 as x increases, and is always positive. This closely matches our graph's behavior.\n",
       "\n",
       "**Conclusion:**\n",
       "\n",
-      "Based on the step-by-step analysis, the function depicted in the graph is most likely **(d) f(x) = sign(x-1).** \n",
+      "Based on the step-by-step analysis, the graph most likely depicts the function **c) f(x) = 1/(1+exp(-x))**, the sigmoid function. \n",
       "\n",
+      "SOLUTION: sqrt(x ** 2 + 3*x +5)\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph arcsinh(x)\n",
-      "\n",
-      "Let's analyze the graph and the function options step-by-step:\n",
+      "Graph exp(x)\n",
       "\n",
-      "1. **Shape:** The graph is symmetrical, meaning it's mirrored across a vertical line (appears to be the y-axis). This eliminates options b), c), and d) as they don't have this symmetry.\n",
+      "LLM RESPONSE:  Here's how to determine the function:\n",
       "\n",
-      "2. **Slope:**  The graph's slope starts relatively flat and gradually becomes steeper. This behavior is consistent with both a) arcsinh(x) and e) |x|.\n",
+      "1. **Analyze the graph's behavior:**\n",
+      "    * The y-values are always positive.\n",
+      "    * The y-values increase as x increases.\n",
+      "    * The rate of increase seems to accelerate as x increases.\n",
       "\n",
-      "3. **Domain:** The graph covers both positive and negative x values. This supports both a) and e).\n",
+      "2. **Eliminate unlikely candidates based on the behavior:**\n",
+      "    * **c) f(x) = -sign(x):** This function only takes on values of -1, 0, or 1, and doesn't match the graph's behavior.\n",
+      "    * **d) f(x) = -cosh(x):** The hyperbolic cosine function is always positive, and its negative would be always negative, not matching the graph.\n",
+      "    * **e) f(x) = sign(x+3):** Similar to option c, this only takes on values of -1, 0, or 1.\n",
       "\n",
-      "4. **Range:** The graph has only negative y values for negative x values and only positive y values for positive x values. This matches the behavior of the absolute value function, e) |x|.\n",
+      "3. **Compare the remaining options:**\n",
+      "    * **a) f(x) = exp(x):** The exponential function matches the observed behavior of always positive values and accelerating increase.\n",
+      "    * **b) f(x) = sqrt(x+10):** While this function is always positive and increasing, its rate of increase slows down as x increases, unlike the graph.\n",
       "\n",
-      "5. **Specific Points:** Consider x = -10. The graph shows f(-10) ≈ -3. This is much closer to |-10| = 10 than arcsinh(-10) ≈ -2.998. \n",
+      "4. **Conclusion:**\n",
       "\n",
-      "**Therefore, the graph most likely depicts function e) f(x) = |x|. ** \n",
+      "The graph most closely resembles the behavior of the exponential function. Therefore, the function depicted in the graph is most likely **a) f(x) = exp(x)**. \n",
       "\n",
+      "SOLUTION: exp(x)\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph arctan(x)\n",
+      "Graph -exp(x)\n",
       "\n",
-      "Let's analyze the graph and the function options step-by-step:\n",
+      "LLM RESPONSE:  Let's analyze the graph and the function options step-by-step:\n",
       "\n",
       "**1. Observing the Graph:**\n",
       "\n",
-      "* The graph is roughly symmetric around the y-axis. \n",
-      "* The function values are negative for negative x values and positive for positive x values.\n",
-      "* The function appears to approach a horizontal asymptote as x approaches negative infinity.\n",
+      "* **Negative Values:** The graph shows only negative y-values.\n",
+      "* **Sharp Change:** There's a sharp change in the y-value around x = 5.31, transitioning from a relatively small negative value to a much larger one.\n",
+      "* **Continuously Decreasing:** After the sharp change, the function continues to decrease as x increases.\n",
       "\n",
       "**2. Eliminating Options:**\n",
       "\n",
-      "* **b) f(x) = -2*x+5:** This is a linear function and wouldn't have the observed symmetry or asymptotic behavior. \n",
-      "* **d) f(x) = x^2:** This is a parabola, symmetric around the y-axis, but its values are always positive.\n",
-      "* **a) f(x) = exp(-x):** This function has an asymptote as x approaches positive infinity, not negative infinity, and would have rapidly decreasing values for positive x. \n",
+      "* **a) f(x) = -exp(x):**  The exponential function is always negative but decreases smoothly. The graph shows a sharp change, making this option unlikely.\n",
+      "* **b) f(x) = -sign(x):** The sign function only outputs -1, 0, or 1. It doesn't match the continuous and decreasing nature of the graph.\n",
+      "* **d) f(x) = x and e) f(x) = tanh(x):** Both of these functions can have positive and negative values, unlike the graph which only has negative values.\n",
       "\n",
-      "**3. Focusing on Remaining Options:**\n",
+      "**3. Focusing on the Remaining Option:**\n",
       "\n",
-      "* **c) f(x) = exp(x):** This function grows very rapidly for positive x, which doesn't match the graph's behavior.\n",
-      "* **e) f(x) = arctan(x):** This function is symmetric around the origin, has horizontal asymptotes as x approaches positive and negative infinity, and transitions smoothly from negative to positive values.\n",
+      "* **c) f(x) = x^2:** The square function is always positive. However, we can modify it to fit the graph: **f(x) = -x^2**. This modified function is always negative and has a continuously increasing slope in the negative direction, matching the graph's behavior.\n",
       "\n",
-      "**4. Conclusion:**\n",
+      "**4. The Sharp Change:**\n",
       "\n",
-      "The function that best matches the graph's characteristics is **e) f(x) = arctan(x).** \n",
+      "The sharp change around x = 5.31 suggests there might be a piecewise function involved. The graph could be a combination of two functions, one for x < 5.31 and another (resembling -x^2) for x >= 5.31.\n",
       "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "While none of the options perfectly match the graph, **the graph most closely resembles a modified version of option (c): f(x) = -x^2**, potentially as part of a piecewise function to account for the sharp change. \n",
+      "\n",
+      "SOLUTION: -exp(x)\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph 1/(1+exp(-x))\n",
+      "Graph exp(-x)\n",
       "\n",
-      "Let's analyze the graph and the functions to determine the best fit.\n",
+      "LLM RESPONSE:  Here's how to determine the function from the graph:\n",
       "\n",
-      "**1. Observing the Graph:**\n",
+      "**1. Analyze the Graph's Shape:**\n",
+      "\n",
+      "* The graph shows a generally decreasing trend as x increases.\n",
+      "* The decrease is very sharp for negative values of x and becomes much slower as x approaches 0 and beyond. \n",
       "\n",
-      "* **Shape:** The graph appears to be sigmoidal, meaning it starts low, increases gradually, and then plateaus at a higher value. \n",
-      "* **Symmetry:** The graph seems somewhat symmetrical, although not perfectly.\n",
-      "* **Range:** The y-values range from near zero to almost one.\n",
+      "**2. Eliminate Unlikely Options:**\n",
       "\n",
-      "**2. Analyzing the Functions:**\n",
+      "* **a) f(x) = -|-x|:** This function would be a straight line decreasing at a constant rate for x < 0 and then increasing at a constant rate for x > 0. This doesn't match our graph.\n",
+      "* **b) f(x) = log(x+10):**  The logarithmic function increases slowly and is only defined for x > -10. This doesn't match the sharp decrease in our graph.\n",
+      "* **c) f(x) = -|x|:** Similar to option (a), this is a V-shaped graph with a constant rate of decrease and increase. It doesn't match the curvature of our graph.\n",
+      "* **d) f(x) = x^2:** This is a parabola opening upwards. Our graph is clearly not a parabola.\n",
       "\n",
-      "* **a) f(x) = exp(-x^2):** This is a Gaussian function, symmetrical and bell-shaped.  It doesn't fit the sigmoidal shape of our graph.\n",
-      "* **b) f(x) = cos(x):** Cosine is periodic, oscillating between -1 and 1. Our graph doesn't have oscillations.\n",
-      "* **c) f(x) = (x + 4)^4:** This function is a polynomial, increasing rapidly as x moves away from -4. It doesn't plateau like our graph.\n",
-      "* **d) f(x) = -2*x^2:** This is a parabola opening downwards. Again, no sigmoidal shape.\n",
-      "* **e) f(x) = 1/(1+exp(-x)):** This is the logistic function, known for its sigmoidal shape. It starts low, increases gradually, and plateaus at 1. This matches our graph's characteristics.\n",
+      "**3. Focus on the Remaining Option:**\n",
+      "\n",
+      "* **e) f(x) = exp(-x):** This is an exponential decay function. It starts with a rapid decrease for negative x values and then the decrease slows down as x approaches 0 and becomes positive. This closely matches the shape of our graph.\n",
       "\n",
       "**Conclusion:**\n",
       "\n",
-      "By carefully analyzing the graph's shape, range, and comparing it with the properties of each function, we can conclude that the function depicted in the graph is most likely **e) f(x) = 1/(1+exp(-x))**. \n",
+      "The graph most likely depicts the function **f(x) = exp(-x)**. \n",
       "\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "import time\n",
-    "\n",
-    "for idx, question in enumerate(llm_questions):\n",
-    "    print(f'Graph {fbench[idx][1]}\\n')\n",
-    "    messages = []\n",
-    "    messages.append({'role': 'system', 'content': \"You are an expert statistician and data scientist.\"})\n",
-    "    messages.append({'role': 'user', 'content': question})\n",
-    "    response = model.generate_content(\n",
-    "                to_gemini(messages),\n",
-    "                generation_config=genai.types.GenerationConfig(\n",
-    "                    candidate_count=1,\n",
-    "                    max_output_tokens=500,\n",
-    "                    temperature=1),\n",
-    "            )\n",
-    "    try:\n",
-    "        response = response.text\n",
-    "    except:\n",
-    "        print(f\"Gemini: Invalid response with parts {response.parts}.\")\n",
-    "        response = \"\"\n",
-    "    print(response)\n",
-    "    print('-'*80)\n",
-    "    # sleep 30 sec to avoid rate limit\n",
-    "    time.sleep(30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.6333333333333333"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "19 / 30"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Benchmark gemini-1.5-flash-latest"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = genai.GenerativeModel(model_name=\"gemini-1.5-flash-latest\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Graph x\n",
+      "SOLUTION: exp(-x)\n",
+      "--------------------------------------------------------------------------------\n",
+      "Graph exp(-x^2)\n",
       "\n",
-      "Let's analyze the graph step-by-step and eliminate options:\n",
+      "LLM RESPONSE:  Here's how to determine the function depicted in the graph:\n",
+      "\n",
+      "**1. Analyze the Graph's Shape:**\n",
       "\n",
-      "1. **The graph is not a straight line:** This eliminates options **a) f(x) = x** and **d) f(x) = sign(x+3)**. The sign function is always either -1, 0, or 1, resulting in a horizontal line with jumps.\n",
+      "* **The graph is not symmetrical about the y-axis**, ruling out options (d) and (e) which are even functions.\n",
+      "* **The graph has a steep increase as x approaches zero from the negative side**, and then **decreases more gradually as x becomes positive**. This suggests an exponential function with a negative exponent.\n",
       "\n",
-      "2. **The graph is not strictly increasing or decreasing:** This eliminates options **c) f(x) = -3*x^3** and **e) f(x) = 2^(x-5)**.  A cubic function like -3x^3 would be decreasing for positive x and increasing for negative x. An exponential function like 2^(x-5) would be increasing throughout its domain.\n",
+      "**2. Consider the Exponential Options:**\n",
       "\n",
-      "3. **The graph has a constant value within intervals:** This is a key characteristic of the sign function. The sign function is either -1, 0, or 1. Within each interval, the graph is constant, which is a typical behavior for the sign function.\n",
+      "* **Option (a) f(x) = 3^x+1:** This function would be increasing for all x, not decreasing as our graph does for positive x values.\n",
+      "* **Option (b) f(x) = 2^x:**  Similar to option (a), this function would also be increasing for all x.\n",
+      "* **Option (c) f(x) = exp(-x):** This function has the desired behavior: a steep increase for negative x, reaching a maximum at x=0, and then a gradual decrease for positive x.\n",
       "\n",
-      "**Therefore, the graph approximately depicts the function b) f(x) = sign(x).** \n",
+      "**3. Conclusion:**\n",
       "\n",
-      "The graph shows that the function is -1 for negative x values, 0 for x = 0, and 1 for positive x values, which is the behavior of the sign function. \n",
+      "The graph most closely resembles the function **(c) f(x) = exp(-x)**. \n",
       "\n",
+      "SOLUTION: exp(-x^2)\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph -2*x+5\n",
+      "Graph 2^x\n",
       "\n",
-      "Let's analyze each function option and see if it matches the provided graph:\n",
+      "LLM RESPONSE:  Let's analyze the graph and the function options step-by-step:\n",
       "\n",
-      "**a) f(x) = sign(x+3)**\n",
+      "**1. Observing the Graph:**\n",
       "\n",
-      "* This function has a constant value of 1 for x > -3, -1 for x < -3, and 0 at x = -3.\n",
-      "* The provided graph has a decreasing trend, so this option is **incorrect**.\n",
+      "* **Positive Trend:** The graph generally shows an increasing trend as x increases.\n",
+      "* **Curvature:** The rate of increase seems to accelerate as x gets larger. This suggests a possible exponential or polynomial function with a degree greater than 1. \n",
+      "* **No Negative Values:** All y-values are positive.\n",
       "\n",
-      "**b) f(x) = -2*x+5**\n",
+      "**2. Eliminating Options based on Observations:**\n",
       "\n",
-      "* This is a linear function with a negative slope. \n",
-      "* The graph exhibits a decreasing trend, suggesting a negative slope. However, the provided graph has a steeper slope than this function would produce. Therefore, this option is **incorrect**.\n",
+      "* **a) f(x) = sign(x):** This function only outputs -1, 0, or 1, and wouldn't match the increasing trend of the graph. **Eliminated.**\n",
+      "* **c) f(x) = x:** This is a straight line and doesn't show the accelerating increase. **Eliminated.**\n",
+      "* **d) f(x) = -|-x|:** This function is always negative or zero, contradicting the positive y-values in the graph. **Eliminated.**\n",
+      "* **e) f(x) = -3*x^3:** While this is a cubic function and could show some curvature, it would have negative values for positive x, which the graph doesn't. **Eliminated.**\n",
       "\n",
-      "**c) f(x) = x**\n",
+      "**3. Remaining Option:**\n",
       "\n",
-      "* This is a linear function with a positive slope. \n",
-      "* The provided graph has a decreasing trend, so this option is **incorrect**.\n",
+      "* **b) f(x) = 2^x:** This exponential function has a positive trend, accelerates as x increases, and always produces positive y-values. This aligns well with our observations of the graph.\n",
       "\n",
-      "**d) f(x) = sqrt(x+10)**\n",
+      "**Conclusion:**\n",
       "\n",
-      "* This is a square root function, which would be defined only for x ≥ -10.\n",
-      "* The provided graph has a defined range for both positive and negative values of x. Therefore, this option is **incorrect**.\n",
+      "The graph most likely depicts the function **b) f(x) = 2^x**. \n",
       "\n",
-      "**e) f(x) = x^3**\n",
+      "SOLUTION: 2^x\n",
+      "--------------------------------------------------------------------------------\n",
+      "Graph 3^x+1\n",
       "\n",
-      "* This is a cubic function. \n",
-      "* The graph is not linear and exhibits a more pronounced curvature than a simple quadratic function, suggesting a cubic function. The graph also displays a decreasing trend for positive values of x and an increasing trend for negative values of x, which aligns with the behavior of a cubic function.\n",
-      "* This option appears to be the most likely candidate based on the graph's characteristics.\n",
+      "LLM RESPONSE:  Let's analyze the graph and the function options step-by-step:\n",
       "\n",
-      "**Therefore, the function depicted in the graph is most likely e) f(x) = x^3.** \n",
+      "**1. Observing the Graph's Behavior:**\n",
       "\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph x^2\n",
+      "* **Increasing:** The y-values are consistently increasing as x increases.\n",
+      "* **Sharp Rise:** The graph shows a very rapid increase in y-values within a relatively small range of x-values.\n",
+      "* **Positive Values:** All y-values are positive.\n",
       "\n",
-      "Let's analyze the graph step-by-step to identify the function:\n",
+      "**2. Eliminating Function Options Based on Observations:**\n",
       "\n",
-      "1. **Symmetry:** The graph is symmetrical around the y-axis. This eliminates options (b), (d), and (e).  Functions (b) and (d) are exponential and would be asymmetrical, and (e) is the sign function which is piecewise constant and not symmetrical.\n",
+      "* **a) f(x) = 1/(1+exp(-x)) (Logistic Function):**  This function is always bounded between 0 and 1, while our graph has values far exceeding 1. **Eliminated.**\n",
+      "* **b) f(x) = cosh(x) (Hyperbolic Cosine):** This function is always greater than or equal to 1 and has a more symmetrical shape. Our graph seems to rise much more sharply. **Eliminated.**\n",
+      "* **c) f(x) = -sqrt(x+10):** This function produces only negative y-values, while our graph has positive values. **Eliminated.**\n",
+      "* **e) f(x) = -|x|:** This function is always negative or zero, while our graph has positive values. **Eliminated.**\n",
       "\n",
-      "2. **Shape:** The graph has a bell-shaped curve, reaching its peak at x = 0 and decreasing on either side. This further supports the elimination of option (c), which is a parabola.\n",
+      "**3. Analyzing the Remaining Option:**\n",
       "\n",
-      "3. **Behavior:**  The function rapidly decreases as x moves away from 0, approaching zero as x approaches positive or negative infinity. This is consistent with the behavior of exponential decay functions.\n",
+      "* **d) f(x) = 3^x+1 (Exponential Function):** Exponential functions are known for their rapid growth. The \"+1\" shifts the graph upwards, ensuring all y-values are positive. This aligns well with our observations.\n",
       "\n",
-      "4. **Comparison:** Option (a) is f(x) = exp(-x^2), which is a standard Gaussian function with a bell-shaped curve. This matches the characteristics of our graph.\n",
+      "**Conclusion:**\n",
       "\n",
-      "**Therefore, the function depicted in the graph is most likely (a) f(x) = exp(-x^2).** \n",
+      "The graph most likely depicts the function **d) f(x) = 3^x + 1**. \n",
       "\n",
+      "SOLUTION: 3^x+1\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph -2*x^2\n",
+      "Graph 2^(x-5)\n",
       "\n",
-      "Let's analyze the graph step by step to determine the function it represents:\n",
+      "LLM RESPONSE:  Here's how to determine the function:\n",
       "\n",
-      "1. **Symmetry:** The graph appears to be symmetric around the y-axis. This eliminates options (a), (c), and (e) because they are not symmetric. \n",
+      "1. **Analyze the graph's shape:** The graph shows an increasing trend, starting with low values and becoming very steep as x increases. This eliminates options c, d, and e:\n",
+      "    * **c) f(x) = -|x|:** This is a V-shaped graph, decreasing then increasing.\n",
+      "    * **d) f(x) = -exp(x):** This is a rapidly decreasing exponential function.\n",
+      "    * **e) f(x) = -sign(x):** This is a horizontal line at -1 for negative x and +1 for positive x.\n",
       "\n",
-      "2. **Concavity:** The graph is concave down, meaning it has a maximum point. This eliminates option (d), as the cosine function oscillates between -1 and 1 without a clear maximum point.\n",
+      "2. **Compare remaining options:** We're left with:\n",
+      "    * **a) f(x) = 2^(x-5)**: An exponential function shifted 5 units to the right.\n",
+      "    * **b) f(x) = x:** A straight line with a slope of 1.\n",
       "\n",
-      "3. **Shape:** The graph is a parabola that opens downwards. This leaves us with option (b),  f(x) = -2*x^2.\n",
+      "3. **Exponential growth:** The graph's steep increase suggests exponential growth, making option **a) f(x) = 2^(x-5)** the most likely candidate. \n",
       "\n",
-      "**Therefore, the graph approximately depicts the function f(x) = -2*x^2.** \n",
+      "**Therefore, the graph approximately depicts the function f(x) = 2^(x-5).** \n",
       "\n",
+      "SOLUTION: 2^(x-5)\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph (x-2)^2\n",
+      "Graph log(x+10)\n",
       "\n",
-      "Here's how we can determine which function is depicted in the graph:\n",
+      "LLM RESPONSE:  Let's analyze the graph and the function options step-by-step:\n",
       "\n",
-      "**1. Analyze the General Shape**\n",
+      "**1. Analyzing the Graph:**\n",
       "\n",
-      "* **The graph starts low and rises:** This eliminates options b) and c). Option b) has a flat section and then jumps to a constant value. Option c) is a parabola, which has a single minimum point.\n",
-      "* **The graph doesn't have a clear peak or valley:**  This rules out option e) (sine wave), which oscillates periodically.\n",
+      "* **Shape:** The graph is smooth and S-shaped, symmetric around the origin (approximately). \n",
+      "* **Domain:** The x-values span both negative and positive ranges.\n",
+      "* **Range:** The y-values are also both negative and positive.\n",
       "\n",
-      "**2. Consider the Behavior at Extreme Values**\n",
+      "**2. Eliminating Options based on Shape and Domain:**\n",
       "\n",
-      "* **As x approaches negative infinity, the graph approaches zero:** This rules out option d) (x^3), which goes to negative infinity as x approaches negative infinity.\n",
+      "* **b) f(x) = log(exp(x)) = x:** This is a linear function, not S-shaped. **Eliminated.**\n",
+      "* **d) f(x) = log(x+10) and e) f(x) = -log(x+10):** Logarithmic functions have a vertical asymptote at x = -10 and are not defined for x <= -10. The graph provided has data points for x < -10. **Eliminated.**\n",
       "\n",
-      "**3. Check for Smoothness and Continuity**\n",
+      "**3. Comparing Remaining Options:**\n",
       "\n",
-      "* **The graph is relatively smooth and continuous:** This leaves us with option a) (square root). The square root function is smooth and continuous in its domain.\n",
+      "We are left with:\n",
       "\n",
-      "**4.  Confirm with Specific Points**\n",
+      "* **a) f(x) = arcsinh(x)**\n",
+      "* **c) f(x) = tanh(x)**\n",
       "\n",
-      "* **Let's check a few points in the JSON object:**\n",
-      "    *  For x close to -10, f(x) should be close to zero. The graph has a value close to 143.9 for x around -10.  This matches the square root function. \n",
-      "    * For x around 0, f(x) should be close to 3.16. The graph has a value close to 5.4 for x around 0, which aligns with the square root function.\n",
+      "Both of these functions are S-shaped and defined for all real numbers. Let's consider their behavior around the origin:\n",
+      "\n",
+      "* **arcsinh(x):**  The inverse hyperbolic sine function has a steeper slope near the origin compared to tanh(x).\n",
+      "* **tanh(x):** The hyperbolic tangent function has a more gradual slope near the origin and approaches -1 as x goes to negative infinity and +1 as x goes to positive infinity.\n",
+      "\n",
+      "**4. Matching Behavior to the Graph:**\n",
+      "\n",
+      "The provided graph has a more gradual slope near the origin, suggesting it's more likely to be **tanh(x)**. Additionally, the y-values in the graph seem to approach -3 and +3 as x goes towards negative and positive infinity, respectively, which aligns with the behavior of **tanh(x)**.\n",
       "\n",
       "**Conclusion:**\n",
       "\n",
-      "Based on the shape, behavior, and specific points, the graph most closely represents the function **a) f(x) = sqrt(x+10)**. \n",
+      "Based on the shape, domain, range, and behavior around the origin, the function depicted in the graph is most likely **(c) f(x) = tanh(x)**. \n",
       "\n",
+      "SOLUTION: log(x+10)\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph 2^(x-5)\n",
+      "Graph -log(x+10)\n",
       "\n",
-      "Let's analyze the graph and eliminate options step by step:\n",
+      "LLM RESPONSE:  Let's analyze the graph and compare it to the function options:\n",
       "\n",
-      "1. **Observing the shape:** The graph is mostly increasing with a steep slope in some areas and a flatter slope in others. This rules out options **d) f(x) = sign(x-1)** (which is a step function) and **c) f(x) = -3*x^3** (which is a cubic function with a negative leading coefficient and a decreasing shape).\n",
+      "**1. Overall Shape and Trend:**\n",
       "\n",
-      "2. **Exponential growth:** The graph shows a rapid increase in the y-values as x increases, suggesting exponential growth. This makes **a) f(x) = 2^(x-5)** a strong contender.\n",
+      "* The graph is generally decreasing as x increases. \n",
+      "* It has a steeper decline in the middle and flattens out towards the edges.\n",
       "\n",
-      "3. **Comparing to the remaining options:**\n",
-      "   - **b) f(x) = sqrt(x ** 2 + 3*x +5)** : This function represents a parabola shifted and its square root taken. While it can have increasing sections, it wouldn't show the same kind of rapid growth as observed in the graph.\n",
-      "   - **e) f(x) = sinh(x)** : The hyperbolic sine function has a shape similar to an exponential function, but it's more symmetrical. The graph provided shows a more pronounced asymmetry in its growth.\n",
+      "This eliminates:\n",
+      "* **b) f(x) = x:** This is a straight line increasing at a constant rate.\n",
+      "* **e) f(x) = -2*x^2:** This is a parabola opening downwards, symmetric around the y-axis.\n",
       "\n",
-      "4. **Verifying with values:** Looking at the graph, we see that the y-value is close to 0 for x values around 4.  Let's check option a) for x = 4:\n",
-      "   - f(4) = 2^(4-5) = 2^(-1) = 0.5 \n",
-      "   - This is close to the y-value of 0 shown for x = 4 in the graph.\n",
+      "**2. Symmetry:**\n",
       "\n",
-      "Therefore, based on the shape, growth pattern, and value verification, the function **a) f(x) = 2^(x-5)** is the most likely function depicted in the graph. \n",
+      "* The graph doesn't seem perfectly symmetrical around any vertical line.\n",
       "\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph (x-1)*(x+1)\n",
+      "This eliminates:\n",
+      "* **d) f(x) = -|x|:** This function is symmetrical around the y-axis.\n",
       "\n",
-      "Here's how we can analyze the graph and determine the function:\n",
+      "**3. Remaining Options:**\n",
       "\n",
-      "**1. Examine the overall shape:**\n",
+      "* **a) f(x) = -sin(x):** The sine function oscillates between -1 and 1. While our graph has a somewhat wave-like pattern, it doesn't repeat and keeps decreasing overall.\n",
+      "* **c) f(x) = -log(x+10):** The negative logarithmic function has a vertical asymptote at x = -10 and decreases rapidly initially, then flattens out.\n",
       "\n",
-      "* **The graph is not symmetric about the y-axis:** This rules out options (a) and (c), which are both even functions. \n",
-      "* **The graph appears to have a general upward trend:** This suggests a function that increases with increasing x values, ruling out option (e), which would be a decreasing cubic function.\n",
+      "**4. Best Fit:**\n",
       "\n",
-      "**2. Analyze the behavior at extreme values:**\n",
+      "Comparing the remaining options, **c) f(x) = -log(x+10)** seems to fit the graph best. The graph's shape, the steep decline in the beginning, and the flattening trend align well with the characteristics of a negative logarithmic function.\n",
       "\n",
-      "* **As x approaches negative infinity, the graph goes to negative infinity:** This indicates a function that grows increasingly negative as x decreases. This further rules out option (d) which would approach negative infinity as x approaches -10.\n",
+      "**Therefore, the function depicted in the graph is most likely (c) f(x) = -log(x+10).** \n",
       "\n",
-      "**3. Consider the remaining option:**\n",
+      "SOLUTION: -log(x+10)\n",
+      "--------------------------------------------------------------------------------\n",
+      "Graph log(exp(x))\n",
       "\n",
-      "* **Option (b), f(x) = x^3:** This function is an odd function (symmetric about the origin) and its general shape matches the graph. \n",
+      "LLM RESPONSE:  Here's how to determine the function depicted in the graph:\n",
       "\n",
-      "**Conclusion:**\n",
+      "**1. Analyze the Graph's Shape:**\n",
       "\n",
-      "The graph most closely resembles the function **f(x) = x^3**. \n",
+      "* The graph is generally increasing.\n",
+      "* It appears to have a slight curve, suggesting it's not perfectly linear.\n",
+      "* The curve seems to be getting less steep as x increases.\n",
       "\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph x^3\n",
+      "**2. Eliminate Options Based on Shape:**\n",
       "\n",
-      "Let's analyze the graph and each function to determine the best fit:\n",
+      "* **b) f(x) = x^2:** This is a parabola opening upwards, not matching our graph.\n",
+      "* **d) f(x) = log(exp(x))**: This simplifies to f(x) = x, a straight line, not matching our graph.\n",
+      "* **e) f(x) = -(x + 4)^4:** This is a steep curve opening downwards, not matching our graph.\n",
       "\n",
-      "**Analyzing the Graph:**\n",
+      "**3.  Consider Remaining Options:**\n",
       "\n",
-      "* **Shape:** The graph has a general \"V\" shape, indicating a sharp change in direction.\n",
-      "* **Symmetry:** The graph appears to be symmetric about a vertical line, suggesting an absolute value function might be involved.\n",
-      "* **Increasing/Decreasing:** The graph decreases on the left side of the vertical line of symmetry and increases on the right side.\n",
+      "* **a) f(x) = -log(x+10):**  Logarithmic functions tend to increase at a decreasing rate, which aligns with our graph's shape. The negative sign indicates a reflection across the x-axis, which also seems plausible.\n",
+      "* **c) f(x) = -sqrt(x+10):** Square root functions also increase at a decreasing rate. The negative sign indicates a reflection across the x-axis.\n",
       "\n",
-      "**Analyzing the Functions:**\n",
+      "**4. Test a Point:**\n",
       "\n",
-      "* **a) f(x) = sign(x+3):** This function is a step function, jumping between -1, 0, and 1. It doesn't match the smooth curve of the graph.\n",
-      "* **b) f(x) = sign(x):** This is also a step function, not a smooth curve.\n",
-      "* **c) f(x) = sqrt(x ** 2 + 3*x +5):** This function would result in a smooth, parabolic shape, not a sharp \"V\".\n",
-      "* **d) f(x) = x^3:** This function is a cubic function, resulting in a completely different shape.\n",
-      "* **e) f(x) = |2*x+4|:** This function is an absolute value function with a \"V\" shape. It's symmetric about a vertical line and decreases on the left, increases on the right.\n",
+      "Let's test a point from the graph to differentiate between options (a) and (c).  A point around x = -5 seems convenient.\n",
+      "\n",
+      "* **From the graph:** When x is approximately -5, y is approximately -5.\n",
+      "* **a) f(-5) = -log(-5 + 10) = -log(5):** This value is negative and closer to -5.\n",
+      "* **c) f(-5) = -sqrt(-5 + 10) = -sqrt(5):** This value is also negative but further from -5.\n",
       "\n",
       "**Conclusion:**\n",
       "\n",
-      "Based on the shape and symmetry of the graph, the function **e) f(x) = |2*x+4|** is the most likely representation of the provided data. \n",
+      "The graph most closely resembles the function **f(x) = -log(x+10)**. \n",
       "\n",
+      "SOLUTION: log(exp(x))\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph -3*x^3\n",
+      "Graph sin(x)\n",
       "\n",
-      "Here's how we can determine the function depicted in the graph:\n",
+      "LLM RESPONSE:  Here's how to determine the function depicted in the graph:\n",
       "\n",
-      "**1. Analyze the Graph's Characteristics:**\n",
+      "**1. Analyze the graph's behavior:**\n",
       "\n",
-      "* **Symmetry:** The graph appears to be symmetrical about the y-axis. This suggests an even function, where f(-x) = f(x). \n",
-      "* **Shape:** The graph has a distinct \"peak\" and then falls rapidly on either side. This suggests a function that increases rapidly at first, then reaches a maximum, and decreases rapidly.\n",
-      "* **Behavior at x = 0:** The value of the function at x = 0 is positive.\n",
+      "* **Overall Shape:** The graph has a wave-like pattern, increasing and decreasing periodically. \n",
+      "* **Symmetry:** The graph appears roughly symmetrical around the y-axis.\n",
+      "* **Range:** The y-values oscillate between approximately -1 and 1.\n",
       "\n",
-      "**2. Eliminate Possibilities based on the Characteristics:**\n",
+      "**2. Eliminate unlikely options:**\n",
       "\n",
-      "* **a) f(x) = -3*x^3:** This is an odd function (not symmetrical about the y-axis) and has a negative value at x = 0.\n",
-      "* **b) f(x) = |2*x+4|:** This is a V-shaped function, not the smooth curve we see in the graph.\n",
-      "* **e) f(x) = x:** This is a linear function, not a curve.\n",
+      "* **a) f(x) = log(exp(x))**: This simplifies to f(x) = x, which is a straight line, not a wave. **Eliminate.**\n",
+      "* **b) f(x) = x**:  A straight line, not a wave. **Eliminate.**\n",
+      "* **c) f(x) = sign(x-1)**: This function would be a step function with a value of -1 for x < 1 and 1 for x > 1.  **Eliminate.**\n",
+      "* **e) f(x) = -sign(x)**: This is a step function with a value of 1 for x < 0 and -1 for x > 0. **Eliminate.**\n",
       "\n",
-      "**3. Consider the Remaining Possibilities:**\n",
+      "**3. Focus on the remaining option:**\n",
       "\n",
-      "* **c) f(x) = exp(-x^2):** This function is symmetrical about the y-axis and has a bell-shaped curve, which aligns with the graph's shape. The function's maximum value occurs at x = 0, and it decreases rapidly as x moves away from 0. \n",
-      "* **d) f(x) = x^4:** This function is also symmetrical about the y-axis and has a shape similar to c). However, the graph doesn't increase as rapidly as x^4 would.\n",
+      "* **d) f(x) = sin(x)**: The sine function is known for its wave-like pattern, symmetry around the y-axis, and a range between -1 and 1.\n",
       "\n",
-      "**4. Conclusion:**\n",
+      "**Conclusion:**\n",
       "\n",
-      "Based on the graph's characteristics, the most likely function is **c) f(x) = exp(-x^2)**. It exhibits the symmetry, peak, and rapid decline that match the given graph. \n",
+      "The graph most closely resembles the function **d) f(x) = sin(x)**. \n",
       "\n",
+      "SOLUTION: sin(x)\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph x^4\n",
+      "Graph -sin(x)\n",
       "\n",
-      "Let's analyze the graph step by step:\n",
+      "LLM RESPONSE:  Let's analyze the graph and compare it to the function options:\n",
       "\n",
-      "1. **Symmetry:** The graph is symmetric about the y-axis. This eliminates options **b) f(x) = -3*x^3** and **d) f(x) = -2*x+5** as they are not symmetric about the y-axis.\n",
+      "**1. General Shape and Trends**\n",
       "\n",
-      "2. **Behavior at x = 0:** The graph has a minimum point around x = 0. This eliminates option **c) f(x) = tanh(x)**, as it has a maximum at x = 0.\n",
+      "* The graph is symmetrical, with a central point around x = 0. \n",
+      "* It has a peak around x = 0 and descends on both sides.\n",
+      "* The descent appears steeper than a simple linear decrease.\n",
       "\n",
-      "3. **Shape and Growth:** The graph resembles a curve that increases rapidly as x moves away from 0 in both positive and negative directions. This is characteristic of functions with an odd power.\n",
+      "**2. Eliminating Options**\n",
       "\n",
-      "4. **Comparing remaining options:** Both **a) f(x) = x^4** and **e) f(x) = x^3** have similar growth patterns, but **f(x) = x^4** is an even function and should be symmetric about the origin, not the y-axis. \n",
+      "* **a) f(x) = x^2+3*x-1:** This is a parabola, which opens upwards. Our graph has a peak, not a bottom point, so this is incorrect.\n",
+      "* **b) f(x) = sign(x):** This function only outputs -1, 0, or 1. Our graph has a continuous range of values, so this is incorrect.\n",
+      "* **d) f(x) = -(x + 4)^4:** This function is always negative and has a single peak at x = -4. Our graph has a peak near x = 0 and both positive and negative values, so this is incorrect.\n",
+      "* **e) f(x) = x:** This is a straight line. Our graph has curves, so this is incorrect.\n",
       "\n",
-      "**Therefore, the function depicted in the graph is most likely e) f(x) = x^3.** \n",
-      "\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph (x + 4)^4\n",
-      "\n",
-      "Gemini: Invalid response with parts [].\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph sign(x)\n",
+      "**3. Analyzing the Remaining Option**\n",
       "\n",
-      "Let's analyze each function and see if it matches the given graph:\n",
+      "* **c) f(x) = -sin(x):** This is a sine wave flipped vertically. It has a peak at x = 0, is symmetrical, and oscillates between -1 and 1, similar to our graph.\n",
       "\n",
-      "**a) f(x) = cos(x)**\n",
+      "**Conclusion**\n",
       "\n",
-      "* The cosine function oscillates between -1 and 1. \n",
-      "* Our graph shows a jump from negative values to positive values around x=0. \n",
-      "* While the cosine function also has jumps, its pattern doesn't match the graph.\n",
+      "Based on the shape, symmetry, and value range, the graph most closely resembles the function **c) f(x) = -sin(x)**. \n",
       "\n",
-      "**b) f(x) = x^2**\n",
+      "SOLUTION: -sin(x)\n",
+      "--------------------------------------------------------------------------------\n",
+      "Graph -sin(-x)\n",
       "\n",
-      "* This function is a parabola, always increasing as x moves away from 0. \n",
-      "* Our graph shows a sudden jump near x=0, not a smooth increase.\n",
+      "LLM RESPONSE:  Let's analyze the graph step-by-step to determine the function:\n",
       "\n",
-      "**c) f(x) = sign(x)**\n",
+      "1. **Symmetry:** The graph appears to be symmetrical around the y-axis. This means the function is likely even. An even function satisfies f(x) = f(-x).\n",
       "\n",
-      "* The sign function returns -1 for negative x, 0 for x=0, and 1 for positive x.\n",
-      "* This behavior closely matches our graph! It has a negative value for negative x, a sudden jump near 0, and a positive value for positive x.\n",
+      "2. **Behavior at x=0:** The graph seems to have a minimum point at x=0 with a positive y-value.\n",
       "\n",
-      "**d) f(x) = (x + 4)^4**\n",
+      "3. **Behavior as x approaches positive/negative infinity:**  As x moves away from zero in either direction, the y-values seem to increase.\n",
       "\n",
-      "* This function is a quartic function, meaning it has a smooth curve and doesn't have sudden jumps like the graph.\n",
+      "Now let's consider the options:\n",
       "\n",
-      "**e) f(x) = x**\n",
+      "* **a) f(x) = x:** This is a linear function and doesn't exhibit symmetry around the y-axis. **Ruled out.**\n",
+      "* **b) f(x) = |x^3|:** This function is symmetrical around the y-axis and increases as x moves away from zero. However, it has a sharp turn at x=0, while the graph appears smoother. **Possible, but let's investigate further.**\n",
+      "* **c) f(x) = cosh(x):** This function is symmetrical around the y-axis, has a minimum at x=0, and increases as x moves away from zero. It also has a smooth curve. **This is a strong candidate.**\n",
+      "* **d) f(x) = x:** This is the same as option a) and is already ruled out.\n",
+      "* **e) f(x) = -sin(-x):** This function simplifies to f(x) = sin(x), which is an odd function (symmetrical about the origin) and doesn't match the graph's symmetry. **Ruled out.**\n",
       "\n",
-      "* This is a linear function with a constant slope. Our graph shows a jump, not a linear relationship.\n",
+      "**Conclusion:**\n",
       "\n",
-      "**Therefore, the function depicted in the graph is (c) f(x) = sign(x).** \n",
+      "Comparing the characteristics, the graph most closely resembles the function **c) f(x) = cosh(x)**. \n",
       "\n",
+      "SOLUTION: -sin(-x)\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph sign(x+3)\n",
+      "Graph sin(x+2)+2\n",
       "\n",
-      "Let's analyze the graph step-by-step:\n",
+      "LLM RESPONSE:  Here's how to determine the function depicted in the graph:\n",
       "\n",
-      "1. **The graph is piecewise constant:** The function has a constant value within each interval. This eliminates options like **b) f(x) = x**, **c) f(x) = sinh(x)**, and **d) f(x) = cos(x)**, as these are continuous functions.\n",
+      "**1. Analyze the Graph's Shape:**\n",
       "\n",
-      "2. **The graph has a jump discontinuity:** The function jumps from a negative value to a positive value near x = -3. This eliminates option **e) f(x) = |x|**, as the absolute value function is continuous.\n",
+      "* The graph is not always increasing or decreasing. It has a peak around x = -8 and a trough around x = 6. This eliminates options (a) and (d) which are strictly decreasing and increasing respectively. \n",
       "\n",
-      "3. **The graph is symmetrical:** The function is close to -1 for x < -3 and close to 1 for x > -3. This points towards the **sign function**, which is 1 for positive values, -1 for negative values, and 0 for 0.\n",
+      "* The graph appears to have some periodicity, meaning it repeats a similar pattern. This makes option (c), a sine function, a likely candidate.\n",
       "\n",
-      "Therefore, the function depicted in the graph is **a) f(x) = sign(x+3)**. \n",
+      "**2. Consider Key Features:**\n",
       "\n",
-      "The graph represents the sign function shifted 3 units to the left, which explains the jump discontinuity and the constant values near -1 and 1. \n",
+      "* **Option (b) arcsinh(x):** The arcsinh function is always increasing and has a much gentler slope than the graph presented. This option is unlikely.\n",
       "\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph sign(x-1)\n",
+      "* **Option (c) sin(x+2)+2:**  Sine functions oscillate between -1 and 1. Adding 2 shifts the graph up, making it oscillate between 1 and 3. The graph's y-values generally fall within this range. The horizontal shift of +2 also seems consistent with the graph's peaks and troughs.\n",
       "\n",
-      "Let's analyze the graph and the functions step-by-step to find the best match:\n",
+      "* **Option (e) (x-2)^2:** This is a parabola opening upwards. While it has a minimum point, it doesn't exhibit the periodic behavior seen in the graph.\n",
       "\n",
-      "**Understanding the Graph:**\n",
+      "**3. Conclusion:**\n",
       "\n",
-      "* **Intervals:** The graph is defined by intervals, meaning the function has constant values within each interval.\n",
-      "* **Discontinuities:** There are clear jumps in the function's value at certain points, indicating potential discontinuities.\n",
+      "Based on the shape, periodicity, and y-value range, the graph most closely resembles a sine function shifted upwards by 2 units. \n",
       "\n",
-      "**Analyzing the Functions:**\n",
+      "**Therefore, the function depicted in the graph is most likely (c) f(x) = sin(x+2)+2.** \n",
       "\n",
-      "* **a) f(x) = x^3:**  This function is a smooth, continuous curve without any flat intervals or discontinuities. It doesn't match the graph.\n",
-      "* **b) f(x) = sign(x-1):** This function is a step function. It takes a value of -1 for x < 1, 0 at x = 1, and 1 for x > 1. This matches the behavior of the graph, with flat intervals and sudden jumps. **This is a strong candidate.**\n",
-      "* **c) f(x) = sinh(x):** The hyperbolic sine function is a continuous, smooth curve that increases monotonically. It doesn't match the graph's behavior.\n",
-      "* **d) f(x) = x:** This is a linear function, continuous and increasing at a constant rate. It doesn't match the graph.\n",
-      "* **e) f(x) = (x-1)*(x+1):** This is a quadratic function, which is continuous and has a parabolic shape. It doesn't match the graph's behavior.\n",
+      "SOLUTION: sin(x+2)+2\n",
+      "--------------------------------------------------------------------------------\n",
+      "Graph cos(x)\n",
       "\n",
-      "**Conclusion:**\n",
+      "LLM RESPONSE:  Here's how to determine the function depicted in the graph:\n",
       "\n",
-      "Based on the analysis, the function **b) f(x) = sign(x-1)** is the closest match to the graph provided. It exhibits the same step function behavior with flat intervals and discontinuities at the points where the function changes value. \n",
+      "**1. Analyze the Graph's Shape:**\n",
       "\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph |x|\n",
+      "* The graph is generally increasing.\n",
+      "* It has a smooth, S-like shape.\n",
+      "* It seems to level off as x approaches negative and positive infinity.\n",
       "\n",
-      "Let's analyze the graph step-by-step to determine which function it represents:\n",
+      "**2. Eliminate Unlikely Options:**\n",
       "\n",
-      "1. **Symmetry:** The graph exhibits a symmetry around the origin. This means the function is odd, i.e., f(-x) = -f(x). This eliminates options (b), (c), (d), and (e) as they are not odd functions. \n",
+      "* **a) f(x) = cos(x):**  Cosine is periodic and oscillates between -1 and 1. The graph doesn't show this behavior.\n",
+      "* **b) f(x) = -sign(-x):** This function is a step function with a single jump from -1 to 1 at x=0. The graph is smooth.\n",
+      "* **c) f(x) = sign(x+3):** This is also a step function, jumping from -1 to 1 at x=-3. The graph is smooth.\n",
+      "* **e) f(x) = x^3:**  A cubic function increases without bound as x goes to infinity and decreases without bound as x goes to negative infinity. The graph levels off.\n",
       "\n",
-      "2. **Shape:** The graph has a gradual increase in value as x moves away from the origin in either direction. This suggests a continuous function, eliminating option (b) and (c) which are step functions.\n",
+      "**3. Focus on the Remaining Option:**\n",
       "\n",
-      "3. **Growth Rate:**  The graph shows a slower increase in the value of y as x increases. This characteristic is consistent with the arcsinh function.\n",
+      "* **d) f(x) = tanh(x):** The hyperbolic tangent function has the following properties:\n",
+      "    * It's smooth and increasing.\n",
+      "    * It asymptotically approaches -1 as x goes to negative infinity.\n",
+      "    * It asymptotically approaches 1 as x goes to positive infinity.\n",
       "\n",
-      "4. **Range:** The range of the function seems to extend from negative infinity to positive infinity, which is consistent with the arcsinh function.\n",
+      "**Conclusion:**\n",
       "\n",
-      "**Therefore, the function depicted in the graph is most likely f(x) = arcsinh(x) (option a).** \n",
+      "The graph closely resembles the characteristics of the hyperbolic tangent function. \n",
       "\n",
-      "It's worth noting that the provided graph might not be a perfect representation of arcsinh(x). However, given the choices, it's the most accurate description based on the provided information. \n",
+      "**Therefore, the function depicted in the graph is most likely (d) f(x) = tanh(x).** \n",
       "\n",
+      "SOLUTION: cos(x)\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph |2*x+4|\n",
+      "Graph 1/2*cos(x-2)\n",
       "\n",
-      "Let's analyze the graph step by step to determine the correct function.\n",
+      "LLM RESPONSE:  Let's analyze the graph and each function to determine the best match.\n",
       "\n",
-      "1. **Shape:** The graph shows a general upward trend with a change in direction, suggesting a non-linear function. This eliminates options **e) f(x) = sign(x-1)** (which is a step function) and **d) f(x) = exp(-x)** (which is a decaying exponential).\n",
+      "**Graph Observations:**\n",
       "\n",
-      "2. **Symmetry:** The graph seems to be symmetric around the point x = -2. This characteristic is consistent with the quadratic function in option **b) f(x) = (x-2)^2**.\n",
+      "* **Shape:** The graph has a sigmoid shape, meaning it starts with a low slope, increases in slope in the middle, and then flattens out again. \n",
+      "* **Symmetry:** The graph appears roughly symmetrical around the point (0,0).\n",
+      "* **Range:** The y-values are bounded, staying within a specific range (approximately -0.5 to 0.5).\n",
       "\n",
-      "3. **Intervals:** The graph is defined in intervals, indicating that the function maintains a constant value within each interval. This is not typical for a smooth quadratic function like **b) f(x) = (x-2)^2**.\n",
+      "**Analyzing the Functions:**\n",
       "\n",
-      "4. **Absolute Value:** The graph's shape resembles an absolute value function, which has a sharp bend at the point of symmetry. This suggests that option **c) f(x) = |2*x+4|** is a strong contender.\n",
+      "* **a) f(x) = 1/2*cos(x-2):** This is a cosine function, which has a periodic, wave-like shape. This doesn't match our graph.\n",
+      "* **b) f(x) = |2*x+4|:** This is an absolute value function, resulting in a V-shaped graph. This doesn't match our graph.\n",
+      "* **c) f(x) = -|-x|:** This is a negated absolute value function, creating an upside-down V-shape. This doesn't match our graph.\n",
+      "* **d) f(x) = sqrt(x ** 2 + 3*x +5):** This function involves a square root, which typically produces a curve that starts at a point and increases. It doesn't have the sigmoid shape or symmetry we're looking for.\n",
+      "* **e) f(x) = tanh(x):** The hyperbolic tangent function (tanh) is known for its sigmoid shape, symmetry around (0,0), and bounded output (between -1 and 1). This closely aligns with our graph's characteristics.\n",
       "\n",
-      "5. **Verification:** Let's examine option **c) f(x) = |2*x+4|**. This function has a zero at x = -2, and the graph indeed shows a zero at approximately x = -2. The graph also demonstrates an increasing slope on both sides of x = -2, matching the behavior of an absolute value function.\n",
+      "**Conclusion:**\n",
       "\n",
-      "**Therefore, the function depicted in the graph is most likely c) f(x) = |2*x+4|.** \n",
+      "Based on the analysis, the function depicted in the graph is most likely **(e) f(x) = tanh(x)**. \n",
       "\n",
+      "SOLUTION: 1/2*cos(x-2)\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph sqrt(x+10)\n",
-      "\n",
-      "Gemini: Invalid response with parts [].\n",
+      "Graph sinh(x)\n",
       "\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph sqrt(x ** 2 + 3*x +5)\n",
+      "LLM RESPONSE:  Let's analyze the graph and the function options step-by-step:\n",
       "\n",
-      "Let's analyze the graph step by step and eliminate options:\n",
+      "**1. Observing the Graph's Behavior:**\n",
       "\n",
-      "1. **Symmetry:** The graph seems to have a point of symmetry around x = 0. This eliminates options **a) f(x) = exp(-x)** and **d) f(x) = (x + 4)^4** since these functions are not symmetric around x = 0.\n",
+      "* **General Trend:** The graph has a steep negative slope for negative x values, flattens out near zero, and then has a steep positive slope for positive x values. \n",
+      "* **Symmetry:**  The graph appears somewhat symmetrical, with the negative and positive x regions mirroring each other to some extent.\n",
       "\n",
-      "2. **Asymptotic Behavior:** The graph doesn't seem to have any horizontal asymptotes. This eliminates option **c) f(x) = arctan(x)** which has horizontal asymptotes at y = -pi/2 and y = pi/2.\n",
+      "**2. Eliminating Unlikely Functions:**\n",
       "\n",
-      "3. **Increasing/Decreasing:** The graph is decreasing for x < 0 and increasing for x > 0. This eliminates option **e) f(x) = x** which is always increasing.\n",
+      "* **a) f(x) = -x^5:** This function would have a similar general trend but wouldn't flatten out near zero. It would continue to have a steep slope.\n",
+      "* **b) f(x) = sqrt(x+10):** This function is only defined for x >= -10 and wouldn't have the negative x portion seen in the graph.\n",
+      "* **e) f(x) = x:** This is a simple linear function and doesn't match the curvature of the graph.\n",
       "\n",
-      "4. **Shape:** The graph has a steep increase around x = 0, suggesting a function with a quadratic-like behavior.  \n",
+      "**3. Focusing on Potential Matches:**\n",
       "\n",
-      "5. **Remaining Option:** This leaves us with **b) f(x) = sqrt(x ** 2 + 3*x +5)**.  This function has the following properties:\n",
+      "* **c) f(x) = exp(x):** The exponential function has a very steep positive slope for positive x and approaches zero for negative x. This matches a portion of the graph but not the steep negative slope for negative x.\n",
+      "* **d) f(x) = sinh(x):** The hyperbolic sine function is symmetric around the origin. It has a steep positive slope for positive x, flattens out near zero, and has a steep negative slope for negative x. This closely matches the observed behavior of the graph.\n",
       "\n",
-      "    - It's symmetric around a vertical line, not necessarily x = 0.\n",
-      "    - It doesn't have any horizontal asymptotes.\n",
-      "    - Its behavior resembles a quadratic, with a steep increase around its minimum point.\n",
+      "**Conclusion:**\n",
       "\n",
-      "**Therefore, the graph most likely depicts the function b) f(x) = sqrt(x ** 2 + 3*x +5).** \n",
+      "Based on the step-by-step analysis, the function that is most likely depicted in the graph is **d) f(x) = sinh(x)**. \n",
       "\n",
+      "SOLUTION: sinh(x)\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph exp(-x^2)\n",
+      "Graph -sinh(x)\n",
       "\n",
-      "Let's analyze the graph step by step to determine which function it best represents:\n",
+      "LLM RESPONSE:  Here's a step-by-step analysis to determine the function:\n",
       "\n",
-      "1. **Symmetry:** The graph appears to be symmetrical around the y-axis. This eliminates options a) and e) as they are not symmetrical. \n",
+      "1. **Analyze the graph's behavior:**\n",
+      "    * The graph is generally decreasing.\n",
+      "    * It seems to have a very steep decrease around x = 9.\n",
+      "    * It appears to approach positive infinity as x approaches -10.\n",
       "\n",
-      "2. **Peak:** The graph has a peak around x = 0. This eliminates option b) (x^4) which would have a minimum at x = 0. \n",
+      "2. **Eliminate unlikely candidates based on behavior:**\n",
+      "    * **d) f(x) = (x-1)*(x+1):** This is a parabola, not a continuously decreasing function. **Eliminated.**\n",
+      "    * **c) f(x) = arctan(x):** Arctangent has horizontal asymptotes at y = π/2 and y = -π/2, and wouldn't show such a steep decrease. **Eliminated.**\n",
       "\n",
-      "3. **Behavior at Infinity:** The graph approaches zero as x goes to both positive and negative infinity. This eliminates option c) (exp(x)) which grows exponentially as x goes to positive infinity.\n",
+      "3. **Examine remaining options:**\n",
+      "    * **a) f(x) = -log(x+10):**  Logarithms have a vertical asymptote at x = -10 and decrease as x increases. This aligns with the graph's behavior.\n",
+      "    * **b) f(x) = -sinh(x):** Hyperbolic sine is an odd function, symmetric about the origin. The graph doesn't show this symmetry. **Eliminated.**\n",
+      "    * **e) f(x) = -exp(x):**  Negative exponential decay decreases but approaches zero as x approaches negative infinity. The graph doesn't approach zero on the left side. **Eliminated.**\n",
       "\n",
-      "4. **Bell-shaped curve:** The graph resembles a bell curve. This strongly suggests option d) (exp(-x^2)) which is the standard form of the Gaussian function, known for its bell shape.\n",
+      "4. **Conclusion:**\n",
       "\n",
-      "**Therefore, the graph most closely resembles the function f(x) = exp(-x^2) (option d).** \n",
+      "The graph's behavior most closely matches the function **a) f(x) = -log(x+10)**. \n",
       "\n",
+      "SOLUTION: -sinh(x)\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph exp(x)\n",
-      "\n",
-      "Let's analyze the graph and eliminate options step by step:\n",
+      "Graph cosh(x)\n",
       "\n",
-      "1. **The graph is increasing:** This eliminates options a) f(x) = x (linear with a slope of 1) and d) f(x) = -3*x^3 (cubic function with a negative coefficient).\n",
+      "LLM RESPONSE:  Let's analyze the graph step-by-step to determine the function:\n",
       "\n",
-      "2. **The graph has a steep initial rise and then levels off:** This eliminates option c) f(x) = sin(x) (periodic function with oscillating values).\n",
+      "1. **Symmetry:** The graph appears to be symmetric around the y-axis. This eliminates options c) and d) which are not symmetrical.\n",
       "\n",
-      "3. **The graph is not symmetrical around the origin:** This eliminates option e) f(x) = x^2 (symmetrical parabola).\n",
+      "2. **Behavior as x approaches positive/negative infinity:** As x goes to both positive and negative infinity, the y-value increases rapidly. This eliminates option b) which approaches zero as x goes to positive infinity.\n",
       "\n",
-      "4. **The graph has a strong positive slope initially:** This leaves us with option b) f(x) = exp(x) (exponential function).\n",
+      "3. **Shape:** The graph has a parabolic shape, opening upwards. This eliminates option a) which has a U-shape.\n",
       "\n",
-      "**Therefore, the graph approximately depicts the function f(x) = exp(x).**\n",
+      "4. **Remaining Option:** The only remaining option is **e) f(x) = x^2**, which is symmetrical, increases rapidly as x moves away from zero, and has a parabolic shape.\n",
       "\n",
-      "The exponential function is characterized by its rapid growth initially and then a gradual leveling off, which matches the pattern observed in the provided graph. \n",
+      "**Therefore, the graph approximately depicts the function f(x) = x^2.** \n",
       "\n",
+      "SOLUTION: cosh(x)\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph exp(-x)\n",
+      "Graph -cosh(x)\n",
       "\n",
-      "Let's analyze the graph step by step and eliminate options:\n",
+      "LLM RESPONSE:  Here's how to determine the function:\n",
       "\n",
-      "1. **Shape:** The graph is decreasing and concave up. This eliminates options **(d) f(x) = x^3** and **(e) f(x) = x^2** because they have different shapes.\n",
+      "1. **Analyze the graph's shape:** The graph is symmetric, with a minimum point and increasing values as we move away from the minimum in either direction. This eliminates options a) and d), which are always increasing, and option b) which is always decreasing or constant.\n",
       "\n",
-      "2. **Asymptotic behavior:**  The graph appears to approach a horizontal asymptote at y=0 as x approaches infinity. This further eliminates **(b) f(x) = sqrt(x+10)**, which would grow without bound as x increases.\n",
+      "2. **Consider the remaining options:** We are left with:\n",
+      "    * c) f(x) = exp(x) - Exponential function\n",
+      "    * e) f(x) = -cosh(x) - Negative hyperbolic cosine function\n",
       "\n",
-      "3. **Decreasing rate:** The graph decreases at a decreasing rate. This is consistent with exponential decay.\n",
+      "3. **Differentiate between exponential and hyperbolic cosine:** \n",
+      "    * Exponential functions grow very rapidly. \n",
+      "    * Hyperbolic cosine functions have a more rounded bottom and grow slightly less rapidly than exponential functions.\n",
       "\n",
-      "4. **Positive values:** The function is always positive. This is consistent with **(c) f(x) = exp(-x)**, which is always positive and exponentially decays.\n",
+      "4. **Compare growth rates:** Looking at the graph, the function's values increase rapidly but not as explosively as a pure exponential function. The change in y-values seems more consistent with a hyperbolic cosine function.\n",
       "\n",
-      "5. **Sinusoidal pattern:** While the graph has a wavy pattern, it's not a periodic function like **(a) f(x) = sin(x)**. The graph does not repeat its pattern.\n",
+      "5. **Symmetry and minimum point:** The graph is symmetrical and has a minimum point, which aligns perfectly with the properties of a negative hyperbolic cosine function.\n",
       "\n",
-      "**Therefore, the graph most closely resembles the function (c) f(x) = exp(-x).** \n",
+      "**Therefore, the function depicted in the graph is most likely (e) f(x) = -cosh(x).** \n",
       "\n",
+      "SOLUTION: -cosh(x)\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph log(x+10)\n",
+      "Graph tanh(x)\n",
       "\n",
-      "Let's analyze the given graph and the functions to determine the best match.\n",
+      "LLM RESPONSE:  Here's how to determine the function from the graph:\n",
       "\n",
-      "**Understanding the Graph**\n",
+      "**1. Analyze the Graph's Shape:**\n",
       "\n",
-      "* **Monotonicity:** The graph shows a generally increasing trend as x increases. This means the function is likely monotonic.\n",
-      "* **Range:** The y-values range from approximately -3.8 to 2.97. This suggests the function has a limited range.\n",
-      "* **Shape:** The graph is relatively smooth and doesn't have sharp changes in direction.\n",
+      "* The graph is **symmetric** around the y-axis (approximately). This eliminates options (a) and (e) as they are not symmetric.\n",
+      "* The graph has **horizontal asymptotes** as x approaches positive and negative infinity. This eliminates options (b) and (d). Option (b) goes to negative infinity as x increases, and option (d) oscillates.\n",
       "\n",
-      "**Analyzing the Functions**\n",
+      "**2. Consider the Remaining Option:**\n",
       "\n",
-      "* **a) f(x) = log(x+10):**\n",
-      "    * Logarithmic functions generally have a vertical asymptote and increase very slowly. This doesn't match the graph's shape and range.\n",
-      "* **b) f(x) = 2^(x-5):**\n",
-      "    * Exponential functions grow rapidly and have an unlimited range. This doesn't match the graph's range.\n",
-      "* **c) f(x) = sign(x-1):**\n",
-      "    * The sign function is a step function with values of -1, 0, or 1. This doesn't match the graph's smooth shape.\n",
-      "* **d) f(x) = sin(x):**\n",
-      "    * The sine function oscillates between -1 and 1 and has a periodic pattern. This doesn't match the graph's limited range and lack of periodicity.\n",
-      "* **e) f(x) = sqrt(x+10):**\n",
-      "    * The square root function is defined only for x >= -10, has a limited range, and increases slowly. This matches the graph's overall shape and range.\n",
+      "* **Option (c): f(x) = tanh(x)** \n",
+      "    * The hyperbolic tangent function (tanh) is symmetric around the origin.\n",
+      "    * It has horizontal asymptotes at y = 1 and y = -1 as x approaches positive and negative infinity, respectively.\n",
       "\n",
-      "**Conclusion**\n",
+      "**Conclusion:**\n",
       "\n",
-      "Considering the shape, monotonicity, and range, **option (e) f(x) = sqrt(x+10)** is the function best represented by the graph. \n",
+      "The graph most closely resembles the function **f(x) = tanh(x)**. \n",
       "\n",
+      "SOLUTION: tanh(x)\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph sin(x)\n",
+      "Graph arcsinh(x)\n",
       "\n",
-      "Let's analyze the graph step by step to identify the function:\n",
+      "LLM RESPONSE:  Here's how to determine the function:\n",
       "\n",
-      "1. **Shape:** The graph exhibits multiple peaks and valleys, suggesting a function with a non-linear shape. This eliminates options (c) and (d). \n",
+      "1. **Analyze the graph's shape:** The graph is generally increasing. It has a steep slope in the middle that flattens out towards the edges. This shape suggests a function with a sigmoid-like curve.\n",
       "\n",
-      "2. **Symmetry:** The graph appears somewhat symmetrical around a central point. This hints at an even function. However, the symmetry isn't perfect, so it's not a definitive indicator.\n",
+      "2. **Eliminate unlikely options:**\n",
+      "    * **a) f(x) = sin(x):**  Sine is periodic and oscillates between -1 and 1. The graph doesn't show this behavior.\n",
+      "    * **b) f(x) = x^2 + 3x - 1:** This is a parabola, which has a U-shape. The graph doesn't match.\n",
+      "    * **d) f(x) = x:** This is a straight line, not a curve like the graph.\n",
+      "    * **e) f(x) = sign(x):** This function only outputs -1, 0, or 1. The graph has a continuous range.\n",
       "\n",
-      "3. **Behavior at extremes:** The graph doesn't seem to approach infinity at either end. This rules out option (a) because a cubic function would grow infinitely in both positive and negative directions.\n",
+      "3. **Focus on the remaining option:**\n",
+      "    * **c) f(x) = arcsinh(x):** The inverse hyperbolic sine function (arcsinh) has a sigmoid-like shape, increasing more slowly at its extremes, which aligns with the graph.\n",
       "\n",
-      "4. **Periodicity:** The graph doesn't repeat itself in a regular pattern. This rules out option (e) as a sine function is periodic.\n",
+      "**Therefore, the function depicted in the graph is most likely (c) f(x) = arcsinh(x).** \n",
       "\n",
-      "5. **Possible function:** We are left with option (b) -  f(x) = (x + 4)^4.  \n",
+      "SOLUTION: arcsinh(x)\n",
+      "--------------------------------------------------------------------------------\n",
+      "Graph arctan(x)\n",
       "\n",
-      "**Reasoning for option (b):**\n",
-      "* The fourth power function will have a characteristic shape with a flat bottom and a steeper rise at the edges. \n",
-      "* The graph's general shape and symmetry align with the expected behavior of a fourth power function shifted by 4 units to the left.\n",
+      "LLM RESPONSE:  Let's analyze the graph and function options step-by-step:\n",
       "\n",
-      "**Therefore, the graph most likely depicts the function f(x) = (x + 4)^4.** \n",
+      "**1. Analyzing the Graph:**\n",
       "\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph cos(x)\n",
+      "* **Overall Shape:** The graph is smooth and has a single inflection point, suggesting it might represent an inverse trigonometric function or a sigmoid function like arctangent or hyperbolic tangent. \n",
+      "* **Symmetry:** The graph appears roughly symmetric around the point (0,0), which is a characteristic of odd functions like arctangent and hyperbolic sine.\n",
+      "* **Asymptotes:** The graph seems to approach certain y-values as x goes to positive and negative infinity, hinting at horizontal asymptotes.\n",
       "\n",
-      "Let's analyze the graph step by step:\n",
+      "**2. Eliminating Options:**\n",
       "\n",
-      "1. **Periodic Behavior:** The graph exhibits a repeating pattern, suggesting a periodic function. This eliminates options **a) f(x) = exp(x)** and **e) f(x) = sqrt(x ** 2 + 3*x +5)**, which are not periodic.\n",
+      "* **a) f(x) = 3^x+1:** Exponential functions grow rapidly and don't have the same shape or symmetry as the graph. **Eliminated.**\n",
+      "* **b) f(x) = x^4:** This function is always positive and has a different shape than the graph. **Eliminated.**\n",
+      "* **c) f(x) = 1/2*cos(x-2):** Cosine functions are periodic and oscillate between fixed values. The graph doesn't show this behavior. **Eliminated.**\n",
+      "* **e) f(x) = sinh(x):** Hyperbolic sine grows exponentially for large positive and negative x-values. While the graph is symmetric, it doesn't exhibit this rapid growth. **Eliminated.**\n",
       "\n",
-      "2. **Symmetry:** The graph appears to be symmetric about the y-axis. This means the function is an even function.  Of the remaining options, **d) f(x) = cos(x)** is an even function.\n",
+      "**3. The Best Fit:**\n",
       "\n",
-      "3. **Shape:**  The graph resembles the general shape of a cosine function, with peaks and troughs. \n",
+      "* **d) f(x) = arctan(x):**  The arctangent function has all the characteristics observed in the graph:\n",
+      "    * **Shape:**  The smooth, increasing shape with a single inflection point matches.\n",
+      "    * **Symmetry:** Arctangent is an odd function, symmetric around the origin.\n",
+      "    * **Asymptotes:** It has horizontal asymptotes at y = π/2 and y = -π/2, which the graph seems to approach.\n",
       "\n",
-      "4. **Range:**  The range of the graph is approximately between -1 and 1, which aligns with the range of the cosine function.\n",
+      "**Conclusion:**\n",
       "\n",
-      "Therefore, the graph most likely represents **d) f(x) = cos(x)**. \n",
+      "Based on the step-by-step analysis, the graph most likely depicts the function **d) f(x) = arctan(x)**. \n",
       "\n",
+      "SOLUTION: arctan(x)\n",
       "--------------------------------------------------------------------------------\n",
-      "Graph sinh(x)\n",
-      "\n",
-      "Let's analyze the graph step by step to determine the best fit:\n",
+      "Graph 1/(1+exp(-x))\n",
       "\n",
-      "**1. Symmetry:**\n",
+      "LLM RESPONSE:  Let's analyze the graph and each function to find the match:\n",
       "\n",
-      "* The graph is symmetrical around the y-axis. This means the function is even, i.e., f(x) = f(-x). This eliminates options (a), (c), and (d), which are not even functions.\n",
+      "**1. Analyzing the Graph:**\n",
       "\n",
-      "**2. Shape:**\n",
+      "* **Shape:** The graph is roughly symmetrical, with a steep increase in the middle and flattening out towards both ends. \n",
+      "* **Range:** The y-values are always positive and seem to approach 0 on the left and 1 on the right.\n",
       "\n",
-      "* The graph has a steep increase in the y-values as the absolute value of x increases. This characteristic points towards a function with a power greater than 1. \n",
+      "**2. Eliminating Options:**\n",
       "\n",
-      "**3. Comparison with remaining options:**\n",
+      "* **b) f(x) = -x^5:** This function is always decreasing and becomes very large and negative for positive x. It doesn't match the graph.\n",
+      "* **d) f(x) = exp(-x^2):** This is a Gaussian function, symmetrical around x=0, and always positive. While it shares some similarities with the graph, it doesn't flatten out towards 1 on the right side.\n",
+      "* **e) f(x) = |x|:** This function has a sharp V-shape and doesn't match the smooth curve of the graph.\n",
       "\n",
-      "* **b) f(x) = x^4:** This function grows very quickly, with a very sharp increase as x increases. It could be a potential candidate.\n",
-      "* **e) f(x) = x^2:** This function also grows steadily as x increases, but the increase is not as drastic as in option (b).\n",
+      "**3. Comparing the Remaining Options:**\n",
       "\n",
-      "**4. Conclusion:**\n",
+      "* **a) f(x) = sinh(x):** The hyperbolic sine function is symmetrical but increases exponentially for both positive and negative x. It doesn't match the flattening behavior of the graph.\n",
+      "* **c) f(x) = 1/(1+exp(-x)):** This is the sigmoid function. It's symmetrical around (0, 0.5), increases monotonically, and asymptotically approaches 0 for negative x and 1 for positive x. This closely matches our graph's characteristics.\n",
       "\n",
-      "Based on the steep increase in y-values and the even function property, **option (b) f(x) = x^4** is the most likely function represented in the graph.\n",
+      "**Conclusion:**\n",
       "\n",
-      "**In conclusion, the graph most likely depicts the function f(x) = x^4.** \n",
+      "The graph most closely depicts the function **c) f(x) = 1/(1+exp(-x))**, the sigmoid function. \n",
       "\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph cosh(x)\n",
-      "\n"
-     ]
-    },
-    {
-     "ename": "ResourceExhausted",
-     "evalue": "429 Resource has been exhausted (e.g. check quota).",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mResourceExhausted\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[53], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m messages\u001b[38;5;241m.\u001b[39mappend({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrole\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msystem\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcontent\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou are an expert statistician and data scientist.\u001b[39m\u001b[38;5;124m\"\u001b[39m})\n\u001b[1;32m      5\u001b[0m messages\u001b[38;5;241m.\u001b[39mappend({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrole\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124muser\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcontent\u001b[39m\u001b[38;5;124m'\u001b[39m: question})\n\u001b[0;32m----> 6\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate_content\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m            \u001b[49m\u001b[43mto_gemini\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[43m            \u001b[49m\u001b[43mgeneration_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgenai\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtypes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mGenerationConfig\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[43m                \u001b[49m\u001b[43mcandidate_count\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[43m                \u001b[49m\u001b[43mmax_output_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m500\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     11\u001b[0m \u001b[43m                \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     12\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     14\u001b[0m     response \u001b[38;5;241m=\u001b[39m response\u001b[38;5;241m.\u001b[39mtext\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/google/generativeai/generative_models.py:232\u001b[0m, in \u001b[0;36mGenerativeModel.generate_content\u001b[0;34m(self, contents, generation_config, safety_settings, stream, tools, request_options)\u001b[0m\n\u001b[1;32m    230\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m generation_types\u001b[38;5;241m.\u001b[39mGenerateContentResponse\u001b[38;5;241m.\u001b[39mfrom_iterator(iterator)\n\u001b[1;32m    231\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 232\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate_content\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    233\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    234\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mrequest_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    235\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    236\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m generation_types\u001b[38;5;241m.\u001b[39mGenerateContentResponse\u001b[38;5;241m.\u001b[39mfrom_response(response)\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/google/ai/generativelanguage_v1beta/services/generative_service/client.py:566\u001b[0m, in \u001b[0;36mGenerativeServiceClient.generate_content\u001b[0;34m(self, request, model, contents, retry, timeout, metadata)\u001b[0m\n\u001b[1;32m    561\u001b[0m metadata \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(metadata) \u001b[38;5;241m+\u001b[39m (\n\u001b[1;32m    562\u001b[0m     gapic_v1\u001b[38;5;241m.\u001b[39mrouting_header\u001b[38;5;241m.\u001b[39mto_grpc_metadata(((\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m, request\u001b[38;5;241m.\u001b[39mmodel),)),\n\u001b[1;32m    563\u001b[0m )\n\u001b[1;32m    565\u001b[0m \u001b[38;5;66;03m# Send the request.\u001b[39;00m\n\u001b[0;32m--> 566\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mrpc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    567\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    568\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretry\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretry\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    569\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    570\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetadata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    571\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    573\u001b[0m \u001b[38;5;66;03m# Done; return the response.\u001b[39;00m\n\u001b[1;32m    574\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m response\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/google/api_core/gapic_v1/method.py:131\u001b[0m, in \u001b[0;36m_GapicCallable.__call__\u001b[0;34m(self, timeout, retry, compression, *args, **kwargs)\u001b[0m\n\u001b[1;32m    128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compression \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    129\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcompression\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m compression\n\u001b[0;32m--> 131\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mwrapped_func\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/google/api_core/retry/retry_unary.py:293\u001b[0m, in \u001b[0;36mRetry.__call__.<locals>.retry_wrapped_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    289\u001b[0m target \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mpartial(func, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    290\u001b[0m sleep_generator \u001b[38;5;241m=\u001b[39m exponential_sleep_generator(\n\u001b[1;32m    291\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_initial, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maximum, multiplier\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_multiplier\n\u001b[1;32m    292\u001b[0m )\n\u001b[0;32m--> 293\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mretry_target\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    294\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    295\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_predicate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    296\u001b[0m \u001b[43m    \u001b[49m\u001b[43msleep_generator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    297\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    298\u001b[0m \u001b[43m    \u001b[49m\u001b[43mon_error\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mon_error\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    299\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/google/api_core/retry/retry_unary.py:153\u001b[0m, in \u001b[0;36mretry_target\u001b[0;34m(target, predicate, sleep_generator, timeout, on_error, exception_factory, **kwargs)\u001b[0m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;66;03m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m    150\u001b[0m \u001b[38;5;66;03m# This function explicitly must deal with broad exceptions.\u001b[39;00m\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m    152\u001b[0m     \u001b[38;5;66;03m# defer to shared logic for handling errors\u001b[39;00m\n\u001b[0;32m--> 153\u001b[0m     \u001b[43m_retry_error_helper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    154\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    155\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdeadline\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    156\u001b[0m \u001b[43m        \u001b[49m\u001b[43msleep\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    157\u001b[0m \u001b[43m        \u001b[49m\u001b[43merror_list\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    158\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpredicate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    159\u001b[0m \u001b[43m        \u001b[49m\u001b[43mon_error\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    160\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexception_factory\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    161\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    162\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    163\u001b[0m     \u001b[38;5;66;03m# if exception not raised, sleep before next attempt\u001b[39;00m\n\u001b[1;32m    164\u001b[0m     time\u001b[38;5;241m.\u001b[39msleep(sleep)\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/google/api_core/retry/retry_base.py:212\u001b[0m, in \u001b[0;36m_retry_error_helper\u001b[0;34m(exc, deadline, next_sleep, error_list, predicate_fn, on_error_fn, exc_factory_fn, original_timeout)\u001b[0m\n\u001b[1;32m    206\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m predicate_fn(exc):\n\u001b[1;32m    207\u001b[0m     final_exc, source_exc \u001b[38;5;241m=\u001b[39m exc_factory_fn(\n\u001b[1;32m    208\u001b[0m         error_list,\n\u001b[1;32m    209\u001b[0m         RetryFailureReason\u001b[38;5;241m.\u001b[39mNON_RETRYABLE_ERROR,\n\u001b[1;32m    210\u001b[0m         original_timeout,\n\u001b[1;32m    211\u001b[0m     )\n\u001b[0;32m--> 212\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m final_exc \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msource_exc\u001b[39;00m\n\u001b[1;32m    213\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m on_error_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    214\u001b[0m     on_error_fn(exc)\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/google/api_core/retry/retry_unary.py:144\u001b[0m, in \u001b[0;36mretry_target\u001b[0;34m(target, predicate, sleep_generator, timeout, on_error, exception_factory, **kwargs)\u001b[0m\n\u001b[1;32m    142\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sleep \u001b[38;5;129;01min\u001b[39;00m sleep_generator:\n\u001b[1;32m    143\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 144\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43mtarget\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    145\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m inspect\u001b[38;5;241m.\u001b[39misawaitable(result):\n\u001b[1;32m    146\u001b[0m             warnings\u001b[38;5;241m.\u001b[39mwarn(_ASYNC_RETRY_WARNING)\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/google/api_core/timeout.py:120\u001b[0m, in \u001b[0;36mTimeToDeadlineTimeout.__call__.<locals>.func_with_timeout\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    117\u001b[0m     \u001b[38;5;66;03m# Avoid setting negative timeout\u001b[39;00m\n\u001b[1;32m    118\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_timeout \u001b[38;5;241m-\u001b[39m time_since_first_attempt)\n\u001b[0;32m--> 120\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/google/api_core/grpc_helpers.py:78\u001b[0m, in \u001b[0;36m_wrap_unary_errors.<locals>.error_remapped_callable\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     76\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m callable_(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m     77\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m grpc\u001b[38;5;241m.\u001b[39mRpcError \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[0;32m---> 78\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exceptions\u001b[38;5;241m.\u001b[39mfrom_grpc_error(exc) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mexc\u001b[39;00m\n",
-      "\u001b[0;31mResourceExhausted\u001b[0m: 429 Resource has been exhausted (e.g. check quota)."
+      "SOLUTION: 1/(1+exp(-x))\n",
+      "--------------------------------------------------------------------------------\n"
      ]
     }
    ],
    "source": [
+    "import google.generativeai as genai\n",
+    "import time\n",
+    "import os\n",
+    "\n",
+    "genai.configure(api_key=os.environ['GEMINI_API_KEY'])\n",
+    "\n",
+    "model = genai.GenerativeModel(model_name=\"gemini-1.5-pro-latest\")\n",
     "for idx, question in enumerate(llm_questions):\n",
     "    print(f'Graph {fbench[idx][1]}\\n')\n",
-    "    messages = []\n",
-    "    messages.append({'role': 'system', 'content': \"You are an expert statistician and data scientist.\"})\n",
-    "    messages.append({'role': 'user', 'content': question})\n",
+    "    messages = [{'role':'user', 'parts': [question[0]]}]\n",
     "    response = model.generate_content(\n",
-    "                to_gemini(messages),\n",
+    "                messages,\n",
     "                generation_config=genai.types.GenerationConfig(\n",
     "                    candidate_count=1,\n",
     "                    max_output_tokens=500,\n",
-    "                    temperature=1),\n",
+    "                    temperature=0.2),\n",
     "            )\n",
     "    try:\n",
     "        response = response.text\n",
     "    except:\n",
     "        print(f\"Gemini: Invalid response with parts {response.parts}.\")\n",
     "        response = \"\"\n",
-    "    print(response)\n",
-    "    print('-'*80)"
+    "    print('LLM RESPONSE: ', response)\n",
+    "    print(f'SOLUTION: {fbench[idx][1]}')\n",
+    "    print('-'*80)\n",
+    "    # sleep 20 sec to avoid rate limit\n",
+    "    time.sleep(20)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "models/gemini-1.0-pro\n",
-      "models/gemini-1.0-pro-001\n",
-      "models/gemini-1.0-pro-latest\n",
-      "models/gemini-1.0-pro-vision-latest\n",
-      "models/gemini-1.5-flash-latest\n",
-      "models/gemini-1.5-pro-latest\n",
-      "models/gemini-pro\n",
-      "models/gemini-pro-vision\n"
-     ]
+     "data": {
+      "text/plain": [
+       "0.66"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "for m in genai.list_models():\n",
-    "  if 'generateContent' in m.supported_generation_methods:\n",
-    "    print(m.name)\n",
-    "\n"
+    "33 / 50"
    ]
   }
  ],
diff --git a/benchmarks/notebooks/functions.ipynb b/benchmarks/notebooks/functions.ipynb
deleted file mode 100644
index 8db7177..0000000
--- a/benchmarks/notebooks/functions.ipynb
+++ /dev/null
@@ -1,3360 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import t2ebm\n",
-    "\n",
-    "import t2ebm.graphs as graphs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "invalid character '−' (U+2212) (442941767.py, line 7)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  Cell \u001b[0;32mIn[116], line 7\u001b[0;36m\u001b[0m\n\u001b[0;31m    (x−1)(x+1)\u001b[0m\n\u001b[0m      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid character '−' (U+2212)\n"
-     ]
-    }
-   ],
-   "source": [
-    "np.log(x+10)\n",
-    "np.sqrt(x+10)\n",
-    "np.abs(2*x+5)\n",
-    "1/(1+np.exp(-x))\n",
-    "2 ** (x-5)\n",
-    "np.sin(x) + np.sin(2*x)\n",
-    "(x−1)(x+1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "45"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "1/2 * (x+1) ** 4 - 5 * (x+2) **3 + 3 * x ** 2 + 2 * x + 1\n",
-    "\n",
-    "-1/2 * (x+1) ** 4 + 5 * (x+2) **3 + 3 * x ** 2 + 2 * x + 1\n",
-    "\n",
-    "np.sign(x ** 2 - 15)\n",
-    "np.abs(x ** 2 - 20)\n",
-    "np.abs(x) ** (1/10)\n",
-    "np.sin(x) + np.sin(3*x)\n",
-    "np.sin(x) + np.sin(0.5 * x)\n",
-    "np.abs(x) - np.sin(x)\n",
-    "np.abs(x) - np.cos(x)\n",
-    "np.abs(x) + np.sin(x)\n",
-    "np.abs(x) + np.cos(x)\n",
-    "np.sign(x) + np.cos(x)\n",
-    "x ** 3 + 250 * np.sin(x)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 226,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# for each, function draw 1000 samples from a uniform distribution and plot the function\n",
-    "function_points = []\n",
-    "x = np.linspace(-10, 10, 1000)\n",
-    "y = x ** 3 + 250 * np.sin(x)\n",
-    "plt.scatter(x, y)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# F-Bench"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "30\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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KMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwqlVhZPny5UpLS5PT6VRmZqaKi4ubbb9s2TINGTJE3bp1U2pqqmbPnq26urpWFQwAAKJLyGFk3bp1ysvLk9vtVmlpqYYPH66cnBxVVVU12f73v/+95s6dK7fbrZ07d+qZZ57RunXrdPfdd5928QAAIPKFHEaWLl2qW265RdOmTdPQoUO1cuVKde/eXatWrWqy/ebNm3XJJZfo+uuvV1pamq6++mpNmjTplGdTAABA5xBSGKmvr1dJSYmys7O/WUBMjLKzs1VUVNTkPGPHjlVJSYk/fOzevVsvv/yyrr322qDrOXbsmGpqagJeAAAgOnUJpfHBgwfl9XqVlJQUMD0pKUkffPBBk/Ncf/31OnjwoL73ve/JGKPjx4/rtttua/YyzaJFi5Sfnx9KaQAAIEKF/WmaTZs26YEHHtATTzyh0tJS/fGPf9SGDRt03333BZ1n3rx58ng8/te+ffvCXSYAALAkpDMjffr0UWxsrCorKwOmV1ZWKjk5ucl55s+frxtvvFE333yzJOnCCy/UkSNHdOutt+qee+5RTEzjPBQfH6/4+PhQSgMAABEqpDMjcXFxGj16tAoLC/3TfD6fCgsLlZWV1eQ8R48ebRQ4YmNjJUnGmFDrBdCBeX1GRbu+0EvbPlfRri/k9bXddzycywZgV0hnRiQpLy9PU6dO1ZgxY5SRkaFly5bpyJEjmjZtmiRpypQpGjBggBYtWiRJys3N1dKlSzVy5EhlZmbqk08+0fz585Wbm+sPJQAiX8H2cuWv36FyzzdjCKW4nHLnDtW4YSkddtkA7As5jEycOFEHDhzQggULVFFRoREjRqigoMB/U+vevXsDzoTce++9cjgcuvfee/X555+rb9++ys3N1f333992vQBgVcH2cs1YW6pvn6uo8NRpxtpSrZg8qtWhIZzLBtAxOEwEXCupqamRy+WSx+NRQkKC7XIAnMTrM/reg68FnLU4mUNSssupN+dcqdgYR4dZNoDwa+nxm9+mAXBaisuqg4YFSTKSyj11Ki6r7lDLBtBxEEYAnJaq2pb9zlRL27XXsgF0HIQRAKelXw9nm7Zrr2UD6DgIIwBOS0Z6olJcTgW7Y8OhE0++ZKQndqhlA+g4CCMATktsjEPu3KGS1Cg0NLx35w5t1Q2m4Vw2gI6DMALgtI0blqIVk0cp2RV4uSTZ5TztR2/DuWwAHQOP9gJoM16fUXFZtapq69Svx4nLJ2111iKcywYQHi09foc86BkABBMb41DW4N4Rt2wAdnGZBgAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFZ1sV0AgMjl9RkVl1WrqrZO/Xo4lZGeqNgYR6dZP4C2QRgB0CoF28uVv36Hyj11/mkpLqfcuUM1blhK1K8fQNvhMg2AkBVsL9eMtaUBQUCSKjx1mrG2VAXby6N6/QDaVqvCyPLly5WWlian06nMzEwVFxc32/7QoUOaOXOmUlJSFB8fr3PPPVcvv/xyqwoGYJfXZ5S/fodME581TMtfv0NeX1MtIn/9ANpeyGFk3bp1ysvLk9vtVmlpqYYPH66cnBxVVVU12b6+vl4/+MEPtGfPHr3wwgv68MMP9fTTT2vAgAGnXTyA9ldcVt3ojMTJjKRyT52Ky6qjcv0A2l7I94wsXbpUt9xyi6ZNmyZJWrlypTZs2KBVq1Zp7ty5jdqvWrVK1dXV2rx5s7p27SpJSktLO72qAVhTVRs8CLSmXaStH0DbC+nMSH19vUpKSpSdnf3NAmJilJ2draKioibn+fOf/6ysrCzNnDlTSUlJGjZsmB544AF5vd6g6zl27JhqamoCXgA6hn49nG3aLtLWD6DthRRGDh48KK/Xq6SkpIDpSUlJqqioaHKe3bt364UXXpDX69XLL7+s+fPn65FHHtGvf/3roOtZtGiRXC6X/5WamhpKmQDCKCM9USkup4I9QOvQiadaMtITo3L9ANpe2J+m8fl86tevn5566imNHj1aEydO1D333KOVK1cGnWfevHnyeDz+1759+8JdJoAWio1xyJ07VJIaBYKG9+7coWEb78P2+gG0vZDCSJ8+fRQbG6vKysqA6ZWVlUpOTm5ynpSUFJ177rmKjY31Tzv//PNVUVGh+vr6JueJj49XQkJCwAtAxzFuWIpWTB6lZFfgpZBkl1MrJo8K+zgfttcPoG2FdANrXFycRo8ercLCQk2YMEHSiTMfhYWFmjVrVpPzXHLJJfr9738vn8+nmJgT2eejjz5SSkqK4uLiTq96ANaMG5aiHwxNtjYCqu31A2g7IT9Nk5eXp6lTp2rMmDHKyMjQsmXLdOTIEf/TNVOmTNGAAQO0aNEiSdKMGTP0+OOP64477tDtt9+ujz/+WA888IB+8YtftG1PALS72BiHsgb37rTrB9A2Qg4jEydO1IEDB7RgwQJVVFRoxIgRKigo8N/UunfvXv8ZEElKTU3Vxo0bNXv2bF100UUaMGCA7rjjDs2ZM6ftegEAACKWwxjT4YcprKmpkcvlksfj4f4RAAAiREuP3/w2DQAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrutguAEBk8PqMisuqVVVbp349nMpIT1RsjMN2WQEioUYAjRFGAJxSwfZy5a/foXJPnX9aisspd+5QjRuWYrGyb0RCjQCaxmUaAM0q2F6uGWtLAw7yklThqdOMtaUq2F5uqbJvREKNAIIjjAAIyuszyl+/Q6aJzxqm5a/fIa+vqRbtIxJqBNA8wgiAoIrLqhudbTiZkVTuqVNxWXX7FfUtkVAjgOYRRgAEVVUb/CDfmnbhEAk1AmgeYQRAUP16ONu0XThEQo0AmkcYARBURnqiUlxOBXs41qETT6xkpCe2Z1kBIqFGAM0jjAAIKjbGIXfuUElqdLBveO/OHWp1LI9IqBFA8wgjAJo1bliKVkwepWRX4GWOZJdTKyaP6hBjeERCjQCCcxhjOvzzbjU1NXK5XPJ4PEpISLBdDtApRcLoppFQI9CZtPT4zQisAFokNsahrMG9bZfRrEioEUBjXKYBAABWEUYAAIBVrQojy5cvV1pampxOpzIzM1VcXNyi+Z599lk5HA5NmDChNasFAABRKOQwsm7dOuXl5cntdqu0tFTDhw9XTk6Oqqqqmp1vz549+vd//3ddeumlrS4WAABEn5DDyNKlS3XLLbdo2rRpGjp0qFauXKnu3btr1apVQefxer264YYblJ+fr7POOuu0CgYAANElpDBSX1+vkpISZWdnf7OAmBhlZ2erqKgo6HwLFy5Uv379NH369Bat59ixY6qpqQl4AQCA6BRSGDl48KC8Xq+SkpICpiclJamioqLJed58800988wzevrpp1u8nkWLFsnlcvlfqampoZQJAAAiSFifpqmtrdWNN96op59+Wn369GnxfPPmzZPH4/G/9u3bF8YqAQCATSENetanTx/FxsaqsrIyYHplZaWSk5Mbtd+1a5f27Nmj3Nxc/zSfz3dixV266MMPP9TgwYMbzRcfH6/4+PhQSgMAABEqpDMjcXFxGj16tAoLC/3TfD6fCgsLlZWV1aj9eeedp/fee0/btm3zv370ox/piiuu0LZt27j8AgAAQh8OPi8vT1OnTtWYMWOUkZGhZcuW6ciRI5o2bZokacqUKRowYIAWLVokp9OpYcOGBczfs2dPSWo0HQAAdE4hh5GJEyfqwIEDWrBggSoqKjRixAgVFBT4b2rdu3evYmIY2BUAALQMv9oLAADCoqXHb05hAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAq7rYLgBAx+T1GRWXVauqtk79ejiVkZ6o2BiH7bJCFi39AKIZYQRAIwXby5W/fofKPXX+aSkup9y5QzVuWIrFykITLf0Aoh2XaQAEKNherhlrSwMO4JJU4anTjLWlKthebqmy0ERLP4DOgDACwM/rM8pfv0Omic8apuWv3yGvr6kWHUe09APoLAgjAPyKy6obnUk4mZFU7qlTcVl1+xXVCtHSD6CzIIwA8KuqDX4Ab007W6KlH0BnQRgB4Nevh7NN29kSLf0AOgvCCAC/jPREpbicCvbgq0MnnkbJSE9sz7JCFi39ADoLwggAv9gYh9y5QyWp0YG84b07d2iHH6cjWvoBdBaEEQABxg1L0YrJo5TsCryEkexyasXkUREzPke09APoDBzGmA7/bFtNTY1cLpc8Ho8SEhJslwN0CtEycmm09AOIRC09fjMCK4AmxcY4lDW4t+0yTlu09AOIZlymAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFjVqjCyfPlypaWlyel0KjMzU8XFxUHbPv3007r00kvVq1cv9erVS9nZ2c22BwAAnUvIYWTdunXKy8uT2+1WaWmphg8frpycHFVVVTXZftOmTZo0aZJef/11FRUVKTU1VVdffbU+//zz0y4eAABEPocxxoQyQ2Zmpi6++GI9/vjjkiSfz6fU1FTdfvvtmjt37inn93q96tWrlx5//HFNmTKlReusqamRy+WSx+NRQkJCKOUCAABLWnr8DunMSH19vUpKSpSdnf3NAmJilJ2draKiohYt4+jRo/r666+VmJgYtM2xY8dUU1MT8AIAANEppDBy8OBBeb1eJSUlBUxPSkpSRUVFi5YxZ84c9e/fPyDQfNuiRYvkcrn8r9TU1FDKBAAAEaRdn6ZZvHixnn32Wf3pT3+S0+kM2m7evHnyeDz+1759+9qxSgAA0J66hNK4T58+io2NVWVlZcD0yspKJScnNzvvkiVLtHjxYr366qu66KKLmm0bHx+v+Pj4UEoDcBq8PqPismpV1dapXw+nMtITFRvjsF1WWHSmvgKRIqQwEhcXp9GjR6uwsFATJkyQdOIG1sLCQs2aNSvofA899JDuv/9+bdy4UWPGjDmtggG0rYLt5cpfv0Plnjr/tBSXU+7coRo3LMViZW2vM/UViCQhX6bJy8vT008/rd/+9rfauXOnZsyYoSNHjmjatGmSpClTpmjevHn+9g8++KDmz5+vVatWKS0tTRUVFaqoqNDhw4fbrhcAWqVge7lmrC0NODhLUoWnTjPWlqpge7mlytpeZ+orEGlCDiMTJ07UkiVLtGDBAo0YMULbtm1TQUGB/6bWvXv3qrz8my/1ihUrVF9fr3/9139VSkqK/7VkyZK26wWAkHl9Rvnrd6ipZ/sbpuWv3yGvL6Sn/zukztRXIBKFdJmmwaxZs4Jeltm0aVPA+z179rRmFQDCrLisutFZgpMZSeWeOhWXVStrcO/2KywMOlNfgUjEb9MAnVRVbfCDc2vadWSdqa9AJCKMAJ1Uvx7BH69vTbuOrDP1FYhEhBGgk8pIT1SKy6lgD7U6dOJJk4z04KMlR4rO1FcgEhFGgE4qNsYhd+5QSWp0kG54784dGhVjcHSmvgKRiDACdGLjhqVoxeRRSnYFXp5Idjm1YvKoqBp7ozP1FYg0If9qrw38ai8QXp1pVNLO1FfAtpYev1v1aC+A6BIb4+g0j7R2pr4CkYLLNAAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsKqL7QIAtD+vz6i4rFpVtXXq18OpjPRExcY4bJdlDdsDsIswAnQyBdvLlb9+h8o9df5pKS6n3LlDNW5YisXK7GB7APZxmQboRAq2l2vG2tKAA68kVXjqNGNtqQq2l1uqzA62B9AxEEaATsLrM8pfv0Omic8apuWv3yGvr6kW0YftAXQchBGgkyguq250BuBkRlK5p07FZdXtV5RFbA+g4yCMAJ1EVW3wA29r2kU6tgfQcRBGgE6iXw9nm7aLdGwPoOMgjACdREZ6olJcTgV7YNWhE0+RZKQntmdZ1rA9gI6DMAJ0ErExDrlzh0pSowNww3t37tBOM74G2wPoOAgjQCcybliKVkwepWRX4KWHZJdTKyaP6nTjarA9gI7BYYzp8M+t1dTUyOVyyePxKCEhwXY5QMRjxNFAbA8gPFp6/GYEVqATio1xKGtwb9tldBhsD8AuLtMAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKt4mgaIcjy2Gjq2GdC+CCNAFCvYXq789TsCfp02xeWUO3coA3oFwTYD2h+XaYAoVbC9XDPWlgYcVCWpwlOnGWtLVbC93FJlHRfbDLCDMAJEIa/PKH/9DjU1vHLDtPz1O+T1dfgBmNsN2wywhzACRKHisupG/3d/MiOp3FOn4rLq9iuqg2ObAfYQRoAoVFUb/KDamnadAdsMsIcwAkShfj2cp24UQrvOgG0G2EMYAaJQRnqiUlxOBXsY1aETT4hkpCe2Z1kdGtsMsIcwAkSh2BiH3LlDJanRwbXhvTt3KGNnnIRtBthDGAGiiNdnVLTrC7207XO5usVp+fWjlOwKvKyQ7HJqxeRRjJnRhHHDUrRictPbbPn1I+XqFqeXtn2uol1f8FQN0IYY9AyIEsEG65o//nz1OiOe0URbaNywFP1gaHLACKxfHqnXfRsYCA0IF4cxpsPH+5qaGrlcLnk8HiUkJNguB+hwGgbr+vaXuSFycCak9di2QOu19PjNZRogwjFYV/iwbYH2QRgBIhyDdYUP2xZoH9wzAkQwr8/orU8Otqgtg3WFrqXb7K///zdruB8HaB3CCBChmrphtTkM1hW6lm6z3xV9qt8VfcpNrUArEUaACOH1Gf8THnsOHtWyVz9q8l6Gb3PoxKOpDNYVuoaB0Co8dS3a1uWeOt22tlTTL0lT9tBkzpQALdRpn6Y5+Q97w+OOkgKmjR7USyWffnnabcK5bGrsHDXuOXhUfyjeq4qa0C618MTH6Wt4mkZSiwLJyZp6tDoa/j1SY3TX2JYBuqXH704ZRpo6vd2ze1dJ0qGjX/unxTikk2+Sb22bcC6bGjtPja3BZYO2EeolseZEy79HaozOGtv6b0ZYw8jy5cv18MMPq6KiQsOHD9djjz2mjIyMoO2ff/55zZ8/X3v27NE555yjBx98UNdee22L19eWYSTYmAFAtJl1xWDN/sEQLhO0kYazqX/dXq7fFX1quxwgLNr6bGrYxhlZt26d8vLy5Ha7VVpaquHDhysnJ0dVVVVNtt+8ebMmTZqk6dOna+vWrZowYYImTJig7du3h7rq09bcmAFAtLnk7L4EkTYUG+NQ1uDeuoazTIhitsbPCfnMSGZmpi6++GI9/vjjkiSfz6fU1FTdfvvtmjt3bqP2EydO1JEjR/SXv/zFP+273/2uRowYoZUrVza5jmPHjunYsWP+9zU1NUpNTT3tMyNFu77QpKffbvX8QCRouGH1zTlXEkbCwOsz+t6Dr7X4plYgUv3hlu8qa3Dv01pGWM6M1NfXq6SkRNnZ2d8sICZG2dnZKioqanKeoqKigPaSlJOTE7S9JC1atEgul8v/Sk1NDaXMoBhnAdGOX5cNv+Z+3ReIJu15zAwpjBw8eFBer1dJSUkB05OSklRRUdHkPBUVFSG1l6R58+bJ4/H4X/v27QulzKAYZwHRjl/kbR/Bft0XiCbteczskOOMxMfHKz4+vs2XG+qYAUBH5tCJ67uzs89RWp8z+EXednbyr/u+sqNCq97a498nQCSzMTZRSGGkT58+io2NVWVlZcD0yspKJScnNzlPcnJySO3DqeH06oy1pfzRQMRL5rFd6xpuas0a3FsZ6Ylt9vgvYIutS70hhZG4uDiNHj1ahYWFmjBhgqQTN7AWFhZq1qxZTc6TlZWlwsJC3Xnnnf5pr7zyirKyslpd9OloOL3KOCPUGGk1JifEa1LGQM6CdFAnnylpGEDqyyP1um9D4N+aaPn3SI3RWaOt/8kJ+TJNXl6epk6dqjFjxigjI0PLli3TkSNHNG3aNEnSlClTNGDAAC1atEiSdMcdd+jyyy/XI488ovHjx+vZZ5/Vli1b9NRTT7VtT0LQ1B+NSBkZjxo7b42Ej46v4UzJyXKGJUflv0dqjN4abfydadWgZ48//rh/0LMRI0boN7/5jTIzMyVJ3//+95WWlqY1a9b42z///PO69957/YOePfTQQ9YGPQMAAO2D4eABAIBVYRuBFQAAoC0RRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABY1SF/tffbGsZlq6mpsVwJAABoqYbj9qnGV42IMFJbWytJSk1NtVwJAAAIVW1trVwuV9DPI2I4eJ/Pp/3796tHjx5yONruB3xqamqUmpqqffv2Re0w89HeR/oX+aK9j/Qv8kV7H8PZP2OMamtr1b9/f8XEBL8zJCLOjMTExOg73/lO2JafkJAQlf/AThbtfaR/kS/a+0j/Il+09zFc/WvujEgDbmAFAABWEUYAAIBVnTqMxMfHy+12Kz4+3nYpYRPtfaR/kS/a+0j/Il+097Ej9C8ibmAFAADRq1OfGQEAAPYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVkV9GLn//vs1duxYde/eXT179myyzd69ezV+/Hh1795d/fr10y9/+UsdP3682eVWV1frhhtuUEJCgnr27Knp06fr8OHDYehBy23atEkOh6PJ17vvvht0vu9///uN2t92223tWHlo0tLSGtW7ePHiZuepq6vTzJkz1bt3b5155pn6l3/5F1VWVrZTxS23Z88eTZ8+Xenp6erWrZsGDx4st9ut+vr6Zufr6Ptw+fLlSktLk9PpVGZmpoqLi5tt//zzz+u8886T0+nUhRdeqJdffrmdKg3NokWLdPHFF6tHjx7q16+fJkyYoA8//LDZedasWdNoXzmdznaqOHS/+tWvGtV73nnnNTtPpOw/qem/Jw6HQzNnzmyyfUfff2+88YZyc3PVv39/ORwOvfjiiwGfG2O0YMECpaSkqFu3bsrOztbHH398yuWG+h0OVdSHkfr6ev3kJz/RjBkzmvzc6/Vq/Pjxqq+v1+bNm/Xb3/5Wa9as0YIFC5pd7g033KD3339fr7zyiv7yl7/ojTfe0K233hqOLrTY2LFjVV5eHvC6+eablZ6erjFjxjQ77y233BIw30MPPdROVbfOwoULA+q9/fbbm20/e/ZsrV+/Xs8//7z+9re/af/+/frnf/7ndqq25T744AP5fD49+eSTev/99/Xoo49q5cqVuvvuu085b0fdh+vWrVNeXp7cbrdKS0s1fPhw5eTkqKqqqsn2mzdv1qRJkzR9+nRt3bpVEyZM0IQJE7R9+/Z2rvzU/va3v2nmzJl6++239corr+jrr7/W1VdfrSNHjjQ7X0JCQsC++vTTT9up4ta54IILAup98803g7aNpP0nSe+++25A31555RVJ0k9+8pOg83Tk/XfkyBENHz5cy5cvb/Lzhx56SL/5zW+0cuVKvfPOOzrjjDOUk5Ojurq6oMsM9TvcKqaTWL16tXG5XI2mv/zyyyYmJsZUVFT4p61YscIkJCSYY8eONbmsHTt2GEnm3Xff9U/761//ahwOh/n888/bvPbWqq+vN3379jULFy5stt3ll19u7rjjjvYpqg0MGjTIPProoy1uf+jQIdO1a1fz/PPP+6ft3LnTSDJFRUVhqLBtPfTQQyY9Pb3ZNh15H2ZkZJiZM2f633u9XtO/f3+zaNGiJtv/27/9mxk/fnzAtMzMTPOzn/0srHW2haqqKiPJ/O1vfwvaJtjfoo7K7Xab4cOHt7h9JO8/Y4y54447zODBg43P52vy80jaf5LMn/70J/97n89nkpOTzcMPP+yfdujQIRMfH2/+8Ic/BF1OqN/h1oj6MyOnUlRUpAsvvFBJSUn+aTk5OaqpqdH7778fdJ6ePXsGnG3Izs5WTEyM3nnnnbDX3FJ//vOf9cUXX2jatGmnbPs///M/6tOnj4YNG6Z58+bp6NGj7VBh6y1evFi9e/fWyJEj9fDDDzd7Wa2kpERff/21srOz/dPOO+88DRw4UEVFRe1R7mnxeDxKTEw8ZbuOuA/r6+tVUlISsO1jYmKUnZ0ddNsXFRUFtJdOfCcjZV9JOuX+Onz4sAYNGqTU1FT9+Mc/Dvq3pqP4+OOP1b9/f5111lm64YYbtHfv3qBtI3n/1dfXa+3atfrpT3/a7C/ER9r+a1BWVqaKioqA/eNyuZSZmRl0/7TmO9waEfGrveFUUVEREEQk+d9XVFQEnadfv34B07p06aLExMSg89jwzDPPKCcn55S/eHz99ddr0KBB6t+/v/7xj39ozpw5+vDDD/XHP/6xnSoNzS9+8QuNGjVKiYmJ2rx5s+bNm6fy8nItXbq0yfYVFRWKi4trdM9QUlJSh9pfTfnkk0/02GOPacmSJc2266j78ODBg/J6vU1+xz744IMm5wn2nezo+8rn8+nOO+/UJZdcomHDhgVtN2TIEK1atUoXXXSRPB6PlixZorFjx+r9998P66+Tt1ZmZqbWrFmjIUOGqLy8XPn5+br00ku1fft29ejRo1H7SN1/kvTiiy/q0KFDuummm4K2ibT9d7KGfRDK/mnNd7g1IjKMzJ07Vw8++GCzbXbu3HnKm6wiRWv6+9lnn2njxo167rnnTrn8k+91ufDCC5WSkqKrrrpKu3bt0uDBg1tfeAhC6WNeXp5/2kUXXaS4uDj97Gc/06JFizrsb0e0Zh9+/vnnGjdunH7yk5/olltuaXbejrAPO7uZM2dq+/btzd5PIUlZWVnKysryvx87dqzOP/98Pfnkk7rvvvvCXWbIrrnmGv9/X3TRRcrMzNSgQYP03HPPafr06RYra3vPPPOMrrnmGvXv3z9om0jbf5EiIsPIXXfd1WxylaSzzjqrRctKTk5udFdww1MWycnJQef59o07x48fV3V1ddB5Tkdr+rt69Wr17t1bP/rRj0JeX2ZmpqQT/1feXgey09mnmZmZOn78uPbs2aMhQ4Y0+jw5OVn19fU6dOhQwNmRysrKsOyvpoTav/379+uKK67Q2LFj9dRTT4W8Phv7sCl9+vRRbGxsoyeXmtv2ycnJIbXvCGbNmuW/kT3U/zvu2rWrRo4cqU8++SRM1bWtnj176txzzw1abyTuP0n69NNP9eqrr4Z8NjGS9l/DPqisrFRKSop/emVlpUaMGNHkPK35DrdKm9190sGd6gbWyspK/7Qnn3zSJCQkmLq6uiaX1XAD65YtW/zTNm7c2GFuYPX5fCY9Pd3cddddrZr/zTffNJLM3//+9zauLDzWrl1rYmJiTHV1dZOfN9zA+sILL/inffDBBx32BtbPPvvMnHPOOea6664zx48fb9UyOtI+zMjIMLNmzfK/93q9ZsCAAc3ewPrDH/4wYFpWVlaHvAHS5/OZmTNnmv79+5uPPvqoVcs4fvy4GTJkiJk9e3YbVxcetbW1plevXuY///M/m/w8kvbfydxut0lOTjZff/11SPN15P2nIDewLlmyxD/N4/G06AbWUL7Draq1zZbUQX366adm69atJj8/35x55plm69atZuvWraa2ttYYc+If0rBhw8zVV19ttm3bZgoKCkzfvn3NvHnz/Mt45513zJAhQ8xnn33mnzZu3DgzcuRI884775g333zTnHPOOWbSpEnt3r+mvPrqq0aS2blzZ6PPPvvsMzNkyBDzzjvvGGOM+eSTT8zChQvNli1bTFlZmXnppZfMWWedZS677LL2LrtFNm/ebB599FGzbds2s2vXLrN27VrTt29fM2XKFH+bb/fRGGNuu+02M3DgQPPaa6+ZLVu2mKysLJOVlWWjC8367LPPzNlnn22uuuoq89lnn5ny8nL/6+Q2kbQPn332WRMfH2/WrFljduzYYW699VbTs2dP/xNsN954o5k7d66//VtvvWW6dOlilixZYnbu3Gncbrfp2rWree+992x1IagZM2YYl8tlNm3aFLCvjh496m/z7f7l5+ebjRs3ml27dpmSkhJz3XXXGafTad5//30bXTilu+66y2zatMmUlZWZt956y2RnZ5s+ffqYqqoqY0xk778GXq/XDBw40MyZM6fRZ5G2/2pra/3HOUlm6dKlZuvWrebTTz81xhizePFi07NnT/PSSy+Zf/zjH+bHP/6xSU9PN1999ZV/GVdeeaV57LHH/O9P9R1uC1EfRqZOnWokNXq9/vrr/jZ79uwx11xzjenWrZvp06ePueuuuwLS8euvv24kmbKyMv+0L774wkyaNMmceeaZJiEhwUybNs0fcGybNGmSGTt2bJOflZWVBfR/79695rLLLjOJiYkmPj7enH322eaXv/yl8Xg87Vhxy5WUlJjMzEzjcrmM0+k0559/vnnggQcCzmJ9u4/GGPPVV1+Zn//856ZXr16me/fu5p/+6Z8CDvAdxerVq5v893ryScxI3IePPfaYGThwoImLizMZGRnm7bff9n92+eWXm6lTpwa0f+6558y5555r4uLizAUXXGA2bNjQzhW3TLB9tXr1an+bb/fvzjvv9G+LpKQkc+2115rS0tL2L76FJk6caFJSUkxcXJwZMGCAmThxovnkk0/8n0fy/muwceNGI8l8+OGHjT6LtP3XcLz69quhDz6fz8yfP98kJSWZ+Ph4c9VVVzXq96BBg4zb7Q6Y1tx3uC04jDGm7S76AAAAhKbTjzMCAADsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAqv8HW10PvhE2EaMAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_288061/2008396885.py:34: RuntimeWarning: divide by zero encountered in log\n",
-      "  (lambda x: np.log(x+10), 'log(x+10)'),\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fbench = [\n",
-    "    # polynomials\n",
-    "    (lambda x: x, 'x'),\n",
-    "    (lambda x: -2*x+5, '-2*x+5'),\n",
-    "    (lambda x: x**2, 'x^2'),\n",
-    "    (lambda x: -2*x**2, '-2*x^2'),\n",
-    "    (lambda x: (x-2)**2, '(x-2)^2'),\n",
-    "    (lambda x: 2 ** (x-5), '2^(x-5)'),\n",
-    "    (lambda x: (x-1)*(x+1), '(x-1)*(x+1)'),\n",
-    "    (lambda x: x**3, 'x^3'),\n",
-    "    (lambda x: -3*x**3, '-3*x^3'),\n",
-    "    (lambda x: x ** 4, 'x^4'),\n",
-    "    (lambda x: (x + 4) ** 4, '(x + 4)^4'),\n",
-    "\n",
-    "    # sign\n",
-    "    (lambda x: np.sign(x), 'sign(x)'),\n",
-    "    (lambda x: np.sign(x+3), 'sign(x+3)'),\n",
-    "    (lambda x: np.sign(x-1), 'sign(x-1)'),\n",
-    "\n",
-    "    # abs\n",
-    "    (lambda x: np.abs(x), '|x|'),\n",
-    "    (lambda x: np.abs(2*x+5), '|2*x+4|'),\n",
-    "\n",
-    "    # root\n",
-    "    (lambda x: np.sqrt(x+10), 'sqrt(x+10)'),\n",
-    "    (lambda x: np.sqrt(x ** 2 + 3*x + 5), 'sqrt(x ** 2 + 3*x +5)'),\n",
-    "\n",
-    "    # exponential\n",
-    "    (lambda x: np.exp(-x**2), 'exp(-x^2)'),\n",
-    "    (lambda x: np.exp(x), 'exp(x)'),\n",
-    "    (lambda x: np.exp(-x), 'exp(-x)'),\n",
-    "\n",
-    "    # logarithm\n",
-    "    (lambda x: np.log(x+10), 'log(x+10)'),\n",
-    "\n",
-    "    # trigonometric\n",
-    "    (lambda x: np.sin(x), 'sin(x)'),\n",
-    "    (lambda x: np.cos(x), 'cos(x)'),\n",
-    "    (lambda x: np.sinh(x), 'sinh(x)'),\n",
-    "    (lambda x: np.cosh(x), 'cosh(x)'),\n",
-    "    (lambda x: np.tanh(x), 'tanh(x)'),\n",
-    "    (lambda x: np.arcsinh(x), 'arcsinh(x)'),\n",
-    "    (lambda x: np.arctan(x), 'arctan(x)'),\n",
-    "\n",
-    "    # logistic function\n",
-    "    (lambda x: 1/(1+np.exp(-x)), '1/(1+exp(-x))'),\n",
-    "]\n",
-    "\n",
-    "print(len(fbench))\n",
-    "\n",
-    "# for each, function draw 1000 samples from a uniform distribution and plot the function\n",
-    "x = np.linspace(-10, 10, 100)\n",
-    "for f, n in fbench:\n",
-    "    y = f(x)\n",
-    "    plt.scatter(x, y)\n",
-    "    plt.title(n)\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### mc with 5 choices each"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "([27, 2, 20, 0, 14], 27)"
-      ]
-     },
-     "execution_count": 142,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fbench_questions[27]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_288061/2008396885.py:34: RuntimeWarning: divide by zero encountered in log\n",
-      "  (lambda x: np.log(x+10), 'log(x+10)'),\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "7\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "8\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "9\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "11\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "12\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "13\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "14\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "15\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "16\n"
-     ]
-    },
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "17\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "18\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "19\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "20\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "21\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "22\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "23\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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V3L17t9K1x8cee0wYPHhwpecNN/WYc+nSJSElJUVITk4WXn75ZeGpp54yuSZ56tQp44DRrFmzhA0bNgiCIAhnzpwxbrdBTEyM0LBhQ6FFixZCy5YtKw2SVadi3SdPniw0bty40vNFRUU1vs7169eF4OBg4ZFHHqlyubtc/+S0YCQrf/zxB/Lz87Fu3To0atTI5DFixAgAMM456OXlhfXr1yMnJwc3b97Ehg0bqgwb9PDwQLNmzUyea9myJQDg0qVLJs8L1SRiat68OZKSknD06FG0adMG7777brXbIAhCrcIXiYhsTezxkojIUf744w8UFhYiPDy82jK//vorHnnkkUrPG6at+PXXXwEAU6dOxX333YcuXbqgRYsWSEhIqHaKgoceesjk74YNGwIAbty4YfJ8dX3CgIAArFixAidPnoRSqcSKFSuqrT/7hERkkT/+0E+tVcNxEb/+ClRxXMT/jov433ERU6cC992nn2KsRQsgIQGwdOqWyZP1U4Clp+vzsISFVV3OcLzk8Y6o1hYvXoysrCw0btwYXbp0QVJSEi5evGhcbuj7tGjRwmS9unXrVrruZRAaGmpxParrB1nzu7JOnTqIrpjbCYCPjw+6d+8OAOjevbvJtFkGTz31FOrUqSOqzrdv38bTTz+Nmzdv4ptvvqmUi6UqBw8erHTt8dChQ9i6dWul5y9fviyqHk2aNEF0dDTi4uKwZcsWNGvWDNHR0cZcNeHh4VXmkWnVqlWlpPOffvop/vrrL5w7dw4bN26s8jOqSsW6v//++7hy5Uql5z///PMaXycgIAAjRozA2bNn8dtvv1Va7i59XXF7IJFElJWVAQCGDh2K4cOHV1mmXbt2xv9/++23AICioiKcO3fOqpMGoE94BVT+YV2eYZ7Bq1ev4vr161CpVFWWy8/Pd9rcjkRE1bHV8ZKISGpat26Ns2fPYteuXdBoNNi2bRtWr16NmTNnVsqnUt184IaLCAEBAVAoFDX2CQ3H0xs3buC3336Dv79/leVu3LhR6eIHEZFDtG4NnD0L7NoFaDTAtm3A6tX6nCmG42JgIHDvnj6viq9v5de4eBE4d07//1Onqn8vw/GSv4GJau3FF1/EE088ga+//hrfffcd3n//fSxatAj//ve/0adPH6teU+wFeYPAwEBR/SBrflc2bdoU+/fvr7GMueVVuXv3Lp577jmcPHkS3377bY0375TXvn17pKSkmDz31ltvQaVSYfLkySbPV3cN0Jznn38eH3/8MQ4cOICYmBiTZUlJSTWuu3//fhQXFwMATp06hcjISFHvWXGbPvvsM3z33XfYvHmzyfNt2rQx+1qNGzcGAOTl5eHBBx80WeYu1z85uEKy0qhRI/j6+qK0tLTKke3yTp48iTlz5mDEiBHIzMzEa6+9hlOnTkGpVJqUKysrw8WLF43RKgDwyy+/AIBxVPihhx5CvXr1kJOTU+V7rV27FikpKZg/fz4WLFiA119/Hd98802lcr///jvu3r1rvKOSiEgKxB4viYgcpVGjRvDz80NWVla1ZZo0aYKzZ89Wev7nn382Ljdo0KABXnrpJbz00kvGH9jz58/H9OnT4ePjI7pederUQfPmzavtE2o0GnzyySeYMmUKtmzZguHDh+PIkSOV7qq8d+8erly5gmeeeUb0exORm2vUCPDz0yeHr06TJvpBk4r+d1xEueMiGjQAXnpJ/7h7F3juOWD+fGD6dMDHB2jVSl8uJwcodwMjAKCsDHj1VX19JkwA3nsPeP55/WtUlJMDeHgA5X5vE5H11Go1xo0bh3HjxuHatWuIiIjA/Pnz0adPH2Pf59y5c+jZs6dxnZKSEuTk5KB9+/ai3qOmaINWrVpV2w+S4u/KsrIyDBs2DHv37sWXX35pjIYRo2HDhpWuPTZs2BBqtdrsNUmxDBErBQUFFq2n1WrxxhtvoHfv3vDy8sLbb7+NmJgYk/5vdSrW/aeffoKPj49V22SInGrUqJHJ8+50/ZPTgpGseHp6YtCgQdi2bVuVP7b/+OMPAPoTx6uvvoqQkBAsX74cGzduRG5uLiZOnFjl665cudL4f0EQsHLlStStWxdPPfUUAH0IZefOnXHs2LFK6+bk5GDy5MkYNGgQ3nnnHXzwwQfYsWMHPvvss0pljx8/DgB47LHHLN94IiI7sPR4SUTkCB4eHhg4cCB27txZZf9LEAT07dsX6enpSEtLMz5/+/ZtrFu3Dk2bNkXY/6aouX79usm6Xl5eCAsLgyAIKCkpsbhukZGRVdYpPz8fr732Grp06YL33nsPn3zyCTIyMvDee+9VKpudnY2ioiL2CYlIPA8PYOBAYOdOoIpjEAQB6NtXP01XueMibt8G1q0Dmjb9e+quCsdFeHnplwkCYDguGu6Aruq9PvwQOHRI/7pz5wKPPQaMHQv8+WflssePA23aALxph6hWSktLK12ADwoKQkhIiDF6oXPnzmjUqBHWrl2Lu3fvGstt3LgR+fn5ot+rQYMGAFDlOpGRkcjKyjK+p4FUf1e+8cYb+OKLL7B69Wo8V9UAsIMYrldW9Omnn0KhUCAiIsKi1xs1ahTKysrw6aefYt26dahTpw7i4+OrnbKttqqq/++//47169ejXbt2UKvVJsvc6fonI1dIdhYuXIjvv/8eXbt2xahRoxAWFoa8vDxkZGQgNTUVeXl5mDdvHjIzM7F37174+vqiXbt2mDlzJmbMmIHnn38effv2Nb6ej48PNBoNhg8fjq5du+I///kPdu/ejXfeecdk5HXAgAH4f//v/6GwsBB+fn4A9D/sR44ciXr16mHNmjUAgNdffx3btm3D+PHjER0djZCQEONrpKSk4KGHHkLHjh0d9GkREdXMkuMlEZEjvffee/juu+/QvXt3jB49Gq1bt4ZWq8VXX32Fn376CdOmTcPnn3+OPn364M0330RAQAA2bdqEnJwcbNu2DR4e+vvIevfuDZVKhaioKAQHB+PMmTNYuXIl+vXrB9+qproxY8CAAfjXv/6FX375xSTyefz48bh+/TpSU1Ph6emJ2NhYvPbaa5g3bx4GDBhgcrdoSkoK6tevj169etX+gyIi9/Hee8B33wHduwOjR+un99Jqga++An76CZg2Dfj8c6BPH+DNN4GAAGDTJn30yLZt+gEaAOjdG1CpgKgoIDgYOHMGWLkS6Nfv7ynAmjXT53dJTQVGjvy7DmfOAO++q49c6d9f/9zGjUCHDsC4ccCXX/5dtqQE+OEH/fNEVCs3b97Egw8+iOeffx7t27fHfffdh9TUVBw9ehRLliwBoL8xeN68eXj99dfRs2dPvPTSS8jJycGGDRuqzblSlQ4dOsDT0xOLFi1CQUEBvL290bNnTwQFBWHAgAGYO3cufvjhB/Tu3du4jhR/Vy5btgyrV69GZGQk6tevX2naq2effdY4kGRv8+fPx8GDBxEbG4uHHnoIeXl52LZtG44ePYo33ngDDz/8sOjX2rBhA3bv3o2NGzcap+L66KOPMHToUKxZswbj7HDMnTJlCi5cuICnnnoKISEhuHTpEv75z3/i9u3bWL58eaXybnX9U2TieyJJyc3NFRISEoTGjRsLdevWFVQqlfDUU08J69atE44fPy7UqVNHeOONN0zWuXfvnvDoo48KISEhwo0bNwRBEIThw4cLDRo0EC5cuCD07t1bqF+/vhAcHCzMmjVLKC0trfSederUEf71r38Zn1u+fLkAQNi2bZtJ2cuXLwt+fn5C3759jc+VlpYKarVamDFjho0/DSIi61hyvCQicoZff/1VGDZsmNCoUSPB29tbaNasmZCQkCAUFxcLgiAIFy5cEJ5//nnB399f8PHxEbp06SLs2rXL5DX++c9/Ck8++aQQGBgoeHt7C82bNxcmT54sFBQUGMvMmjVLACD88ccfJutu2LBBACDk5OQYnysuLhbuv/9+Ye7cucbnvvnmGwGAsGTJEpP1CwsLhSZNmgjt27cX7t69a3y+a9euwtChQ2v9+RCRG/r1V0EYNkwQGjUSBG9vQWjWTBASEgThf8dF4cIFQXj+eUHw9xcEHx9B6NJFECocF4V//lMQnnxSEAID9a/RvLkgTJ4sCOWOi4IgCMKHHwrCffcJwl9/6f++d08QHn1UEB58UBDy803LLl8uCIAgfPHF38/95z/6586ds+1nQOSGiouLhcmTJwvt27cXfH19hQYNGgjt27cXVq9eXans6tWrhdDQUMHb21vo3LmzcODAAaF79+5C9+7djWW+//57AYDw1VdfVfl+H3/8sdCsWTPB09NTACB8//33xmXt2rUT4uPjjX9L9Xfl8OHDBQDVPsr378Tq3r27MHz4cIvX++6774Snn35aCAkJEerWrSv4+voKUVFRwoYNG4SysjLRr3PlyhVBqVQK/fv3r7Ts2WefFRo0aCBcvHjRorrNmjVLaNKkSY1lkpOThSeffFJo1KiRUKdOHeH+++8Xnn32WeH48eOVyrrb9U+FINgpXohIBl599VX83//9H27duiWqfHx8PH755Rf8+OOPFr/X9u3b8fLLL+PChQuVwuWIiIiISD7mzp2LDRs24Ny5c/D09LRo3czMTERERCAjIwMdOnSwTwWJiGyhoEAfwbJ4MRAfb/n6AwcCCgXw9dc2rxoRWaZHjx4ArEsIX9G//vUvJCQk4PLly/D396/165Frcbfrn8y5QmSBWbNm4ejRozh48KDF6y5atAiJiYlucWAhIiIicmUTJ07ErVu3sHXrVovXXbhwIZ5//nkOrBCR9CmVwJQpwPvv65PYW+LMGWDXLn1OFiJyKUOGDMFDDz2EVatWObsqJEHudv2TkSvk1iyNXCEiIiIiIiIiIpITW0auENHfGLlCRERERERERERERERkAUauEBERERERERERERERWYCRK0RERERERERERERERBbg4AoRERERERERERE51YEDB9C/f3+EhIRAoVBg+/btxmUlJSWYOnUq2rZtiwYNGiAkJATDhg3D1atXnVdhInJ7dZxdAWcqKyvD1atX4evrC4VC4ezqEJFECYKAmzdvIiQkBB4erjEmzeMfEYnFYyARuStXPP4BPAYSkTjOOAbevn0b7du3x8iRI/Hcc8+ZLPvrr7+QkZGBd999F+3bt8eNGzcwfvx4PPPMMzh27Jio1+fxj4jEEnsMdOucK7/99hsaN27s7GoQkUxcuXIFDz74oLOrYRM8/hGRpXgMJCJ35UrHP4DHQCKyjLOOgQqFAl9//TUGDhxYbZmjR4+iS5cu+PXXX/HQQw+ZfU0e/4jIUuaOgW4dueLr6wtA/yH5+fk5uTZEJFWFhYVo3Lix8ZjhCnj8IyKxeAwkInflisc/gMdAIhJHDsfAgoICKBQK+Pv7iyrP4x8RiSX2GOjWgyuGEEA/Pz8eVInILFcKG+bxj4gsxWMgEbkrVzr+ATwGEpFlpHoMLCoqwtSpUxEXF1ftsay4uBjFxcXGv2/evAmAxz8iEs/cMdB1Jo4lIiIiIiIiIiIil1ZSUoIXX3wRgiBgzZo11ZZbsGABlEql8cEpwYjI1ji4QkRERERERERERJJnGFj59ddfkZKSUmMEyvTp01FQUGB8XLlyxYE1JSJ34NbTghEREREREREREZH0GQZWzp07h++//x6BgYE1lvf29oa3t7eDakdE7oiRK0TkVkrLBKRduI5vMn9H2oXrKC0TnF2lKh04cAD9+/dHSEgIFAoFtm/fbrJcEATMnDkTarUa9erVQ3R0NM6dO+ecyhKRLMjl+Cc1/NyIXINU27K5Pl9V9u/fj4iICHh7e+Phhx/Gxo0b7V5PuTB8z19n/IZPf7yIr09I6/uWmvLt4uC5P3Hw/J+SayNSws/L/m7duoXMzExkZmYCAHJycpCZmYnLly+jpKQEzz//PI4dO4YtW7agtLQUOp0OOp0Od+/edW7FiUiy7N0HZOQKEbkNTZYWs3dmQ1tQZHxOrfTBrP5hiA1XO7Fmld2+fRvt27fHyJEj8dxzz1VavnjxYqxYsQKbNm1CaGgo3n33XcTExCA7Oxs+Pj5OqDERSZmcjn9Sws+NyDVIuS2b6/NVlJOTg379+mHMmDHYsmUL9u7di9deew1qtRoxMTEOqLF0VfU9G0jl+5aSmj4vgJ9ZRfy8HOPYsWP4xz/+Yfx70qRJAIDhw4cjKSkJO3bsAAB06NDBZL3vv/8ePXr0cFQ1iUgmHNEHVAiC4LbD64WFhVAqlSgoKKhxjkYikj9NlhZjN2eg4gFP8b9/1wyNqPbA6uxjhUKhwNdff42BAwcC0EethISE4K233sLbb78NACgoKEBwcDA2btyIwYMHm31NZ28TETnOnpNajEvOqPS8mOMf4JrHCzHbVJvzBhFJh5z6gBX7fFWZOnUqdu/ejaysLONzgwcPRn5+PjQajaj3ccXjenXfc3kK8NhtIPbzAviZAe77ebniscIVt4mIqlbb33NijxecFoyIXF5pmYDZO7Or7Awbnpu9M1s2odw5OTnQ6XSIjo42PqdUKtG1a1ekpaU5sWZEJDV7Tl5F4ueVB1YAeR7/HMXVzhtE7soV23JaWppJHxAAYmJiauwDFhcXo7Cw0OThSmr6niuS2/dtD2I/L7m2EVvj50VEJD+O7ANycIWIXF56Tl614duA/sCqLShCek6e4ypVCzqdDgAQHBxs8nxwcLBxWUWu/qOaiEyVlglYnnoO45JPoKb+otyOf47iaucNInflim1Zp9NV2QcsLCzEnTt3qlxnwYIFUCqVxkfjxo0dUVWHMfc9G8jx+7YHsZ8XwM8M4OdFRCRHjuwDcnCFiFzetZviOsNiy8mRq/+oJqK/abK0iFq4F0tTfxG9jisf/6zB8waRa2Bb1ps+fToKCgqMjytXrji7SjaVml31zUXV0RVUPQjlLiz9vAAgxYp1XIU1xwd3/ryIiKRA7LnOFn1ADq4QkcsL8hWX4F1sOWdTqVQAgNzcXJPnc3NzjcsqcvUf1USkt+ekFmM2Z0BXWGzRenI5/jmKq503iNyVK7ZllUpVZR/Qz88P9erVq3Idb29v+Pn5mTxchSZLi08PXrJonbm7z0CTpbVPhSTOms8LANYfvOS2n9mlP29bvI47f15ERM5mybnOFn1ADq4QkcvrEhoAtdLHmLSqIgUAtdIHXUIDHFktq4WGhkKlUmHv3r3G5woLC3HkyBFERkZWuY4r/6gmIr2a8qvURE7HP0dxtfMGkbtyxbYcGRlp0gcEgJSUlGr7gK7MMJ+6pW7cvouxmzPc7uK3tZ8XoG8r7phLRJOlxdLUcxav566fFxGRs4k919myD8jBFSJyeZ4eCszqHwYAlX5cG/6e1T8Mnh7V/fR2vFu3biEzMxOZmZkA9EnsMzMzcfnyZSgUCkyYMAHz5s3Djh07cOrUKQwbNgwhISEYOHCgU+tNRM6x56TWbH6V6kjt+CcFcjxvEFFlcmjLNfX5AH308bBhw4zlx4wZg4sXL2LKlCn4+eefsXr1anz55ZeYOHGiM6rvVJbkwijPXROPW/t5Ae6ZS6Q2g1Hu+HkREUmBJXnYbNUH5OAKEbmF2HA11gyNgEppGvKnUvpgzdAIxIarnVSzqh07dgwdO3ZEx44dAQCTJk1Cx44dMXPmTADAlClT8MYbb2D06NF49NFHcevWLWg0Gvj4yGdaCyKyDWsjVjwUwOqXpXf8kwq5nTeIqGpSb8vm+nxardY40ALoI5h3796NlJQUtG/fHkuWLMEnn3yCmJgYp9TfmWozT7o7Xvy2xbzyrp6fqLzaDEYZuNPnRUQkBWJzrYyMamqzPmAdm7wKEZEMxIar0StMhfScPFy7WYQgX30IoBTvPO7RowcEofo76RQKBebMmYM5c+Y4sFZEJCWlZQJW7jtvUeL68lbGdUTfdhwgqImczhtEVD0pt2Vzfb6NGzdWuc6JEyfsWCt5sMU86e508dsWn5ec8hPVli32DXf6vIiInM2SXCu9wqrOV2wNDq4QkVvx9FAgsnmgs6tBRFQrmiwtknactjhxPaCfW3ZW/zCn360tFzxvELkGtmXXY8ipoysogrWTe7nTxe/afF4K6KO95JSfqLZqs2+44+dFRORMluRasfXxmdOCEREREcmIJkuLsZszrBpYmRjdAj9N7cmBFSIicgmDH33IqoEVWyaylQsxOYhqWubs/ESOZhiMMrfFVS0XALzbr7VbfV5ERM50+OJ1h+daMeDgChEREZFMlJYJSNqRbfGFJEN+lfHRLflDn4iIZE+TpcXji/ZVOzWmWumD158MhQIcLDAoLROgrOeFEVFN0bCBl8kyldIHa4dGYG0V+YmU9epiQnQLm06hIgfmBqMUAF5/MrTS52Uwd/cZaLK0dq0jERHp+wQJW8TlILVlrhUDTgtGREREJBMr952DrtDyOcCZX4WIiFyFIYKzuhsNJka3QGLPFvD0UKDjQw0xe2e2yd2synp1MSKqqVsNFmiytJU+h4AGdfFshwcQHaYyyUHUK0yFlfvOY8PBHOTfKUH+nRIsTT2HrUevuNW0ouUHo7ZnXkXe7bvGZapyU6y2f7AhxiVXvqinKyjC2M0ZWDM0wm0+MyIiRzPXJ6jIHud+Dq4QERERSdzfyevPWbSehwJYGRfBgRUiInIJhjnVq7uIogCw9egVJPZsAQCIDVe7/WBBdReebtwuwfqDl/BouYEVAEjJ1mFZ6i+VyrvTYIHYwajSMgFzd1c9x78A/f44e2c2eoWp3CpKiojIEcz1CcqzZy4sTgtGREREJGGaLC2iFu6tduqTmjBihYiIXEl6Tl6Nc6oLALQFRUjPyTM+ZxgsyL9TYlLWMFjgylM31XThyfDc7J3ZKC0TrCrvigyDURX3M8NgVMGdu8aBEmv2RyIisg1zx+CK7DUdKAdXiIiIiCRqz0ktxliRvF79v7nT+7YLsVPNiIiIHO/aTXEXUQzl3H2wwNKL/+4+WGDp/mLp/khERLYj9tjqX7+uXaMuObhCREREJEF7Tl5F4ufiEvOVNzG6BX6a2tPlp+wgIiL3E+RbdfLw6sq5+2CBpRf/3X2wwNL9xdL9kYiIbOfSn7dFlVsVZ9/pLDm4QkRERCQhpWUClqeew7jkE7D0RtqJ0S0xProl5/UmIiKX1CU0AGqlD6o7yymgj940zKnu7oMFll78d/fBAkv3F0v3RyIisg1NltZsPlLDMbhb80C71sWiwZUFCxbg0Ucfha+vL4KCgjBw4ECcPXvWpExRURESEhIQGBiI++67D4MGDUJubq5JmcuXL6Nfv36oX78+goKCMHnyZNy7d8+kzP79+xEREQFvb288/PDD2LhxY6X6rFq1Ck2bNoWPjw+6du2K9PR0SzaHiIiISFJqk19F5eeNxJ4P26FWegcPHkT//v0REhIChUKB7du3mywXBAEzZ86EWq1GvXr1EB0djXPnTDu8eXl5GDJkCPz8/ODv74/4+HjcunXLpMzJkyfxxBNPwMfHB40bN8bixYsr1eWrr75Cq1at4OPjg7Zt22LPnj02314iIpIeTw8FZvUPA4BKF7QNf5efU93dBwssvfjv7oMFlu4vlu6PRERUe4YpHMVwxDHYosGVH374AQkJCTh8+DBSUlJQUlKC3r174/btv8NwJk6ciJ07d+Krr77CDz/8gKtXr+K5554zLi8tLUW/fv1w9+5dHDp0CJs2bcLGjRsxc+ZMY5mcnBz069cP//jHP5CZmYkJEybgtddew7fffmss88UXX2DSpEmYNWsWMjIy0L59e8TExODatWu1+TyIiIiInMLa/CqA/gd80jNt7Npx/Ouvv9C+fXusWrWqyuWLFy/GihUrsHbtWhw5cgQNGjRATEwMior+vgt0yJAhOH36NFJSUrBr1y4cOHAAo0ePNi4vLCxE79690aRJExw/fhzvv/8+kpKSsG7dOmOZQ4cOIS4uDvHx8Thx4gQGDhyIgQMHIisry27bTkRE0hEbrsaaoRFQKU0vhKuUPpXmVHf3wQJLL/67+2CBNfuLJfsjERHV3uGL10Ulsp8Q3dIhx2CFIAhWZ277448/EBQUhB9++AFPPvkkCgoK0KhRIyQnJ+P5558HAPz8889o3bo10tLS0K1bN/znP//B008/jatXryI4OBgAsHbtWkydOhV//PEHvLy8MHXqVOzevdvkR/LgwYORn58PjUYDAOjatSseffRRrFy5EgBQVlaGxo0b44033sC0adNE1b+wsBBKpRIFBQXw8/Oz9mMgIhfniscKV9wmIjnT51exfBowQP8jf1b/MLt1HKs6XigUCnz99dcYOHAgAH3USkhICN566y28/fbbAICCggIEBwdj48aNGDx4MM6cOYOwsDAcPXoUnTt3BgBoNBr07dsXv/32G0JCQrBmzRr8v//3/6DT6eDl5QUAmDZtGrZv346ff/4ZAPDSSy/h9u3b2LVrl7GO3bp1Q4cOHbB27Vqrt4mIqCJXPVa4ynaVlglIz8nDtZtFCPLVX/Cu6qK/JkuLsZv1OczKn2YNJd3hArgmS4vZO7NNLkbV1H+wtLwrsXZ/Ebs/yomrHCvKc8VtInInmiwtpm07hfw7JWbLLh/cAQM6PGD1e4k9XtSx+h2g/9EMAAEB+lH748ePo6SkBNHR0cYyrVq1wkMPPWQcXElLS0Pbtm2NAysAEBMTg7Fjx+L06dPo2LEj0tLSTF7DUGbChAkAgLt37+L48eOYPn26cbmHhweio6ORlpZWbX2Li4tRXPz33aCFhYXWbzwRERFRLZWWCVi577xV04AB+uT1iT1bOP3He05ODnQ6nUn/TalUomvXrkhLS8PgwYORlpYGf39/48AKAERHR8PDwwNHjhzBs88+i7S0NDz55JPGgRVA3wdctGgRbty4gYYNGyItLQ2TJk0yef+YmJhK05SVxz4gEZHrqHgR++l2ITWeBw2RBRUHC1RuMlgA6D+DXmEq0Rf/LS3vSqzdXzw9FIj837z+rjjQQkTkbIbBb7H3Izpqyk+rB1fKysowYcIEREVFITw8HACMdxn6+/ublA0ODoZOpzOWKT+wYlhuWFZTmcLCQty5cwc3btxAaWlplWUMdzVWZcGCBZg9e7blG0tERERkY5osLZJ2nLZqGjAPBbAyLgJ920njgpChD1dV36x8/y4oKMhkeZ06dRAQEGBSJjQ0tNJrGJY1bNiw2n6i4TWqwj4gEZFrsDaiwp0HCwzKX/y3R3lXUpv9xZ2jfoiI7MWQZ0XMwIoC+gFxR035aVHOlfISEhKQlZWFrVu32rI+djV9+nQUFBQYH1euXHF2lYiIiMgN1Sa/CgCsjOsomYEVOWAfkIhI/gx3rFacZ11XUISxmzOgydLWuL5hsGBAhwcQ2TzQbQZWSssEpF24jm8yf0fahesotWIOUlu8htxYs7/Udh8lIqKqpefkicqzYuDI/GBWRa4kJiYak5A++OCDxudVKhXu3r2L/Px8k+iV3NxcqFQqY5n09HST18vNzTUuM/xreK58GT8/P9SrVw+enp7w9PSssozhNari7e0Nb29vyzeYiIiIyEYM+VWsIdU7Hw39r9zcXKjVf9ctNzcXHTp0MJa5du2ayXr37t1DXl6e2T5g+feorgz7gERErqumO1YF6O9Snb0zG73CVKIuprjLtE22iKJwt0gMa/cNW++jRET0t9Ts6mcpKM+/fl0sfK6tQ89PFkWuCIKAxMREfP3119i3b1+laRs6deqEunXrYu/evcbnzp49i8uXLyMyMhIAEBkZiVOnTpn8uE5JSYGfnx/CwsKMZcq/hqGM4TW8vLzQqVMnkzJlZWXYu3evsQwRERGRlJSWCVieeg7jkq1LXD8xugV+mtpTkhcyQkNDoVKpTPpmhYWFOHLkiEkfMD8/H8ePHzeW2bdvH8rKytC1a1djmQMHDqCk5O8EhSkpKXjkkUfQsGFDY5ma+olEROR6zN2xKgDQFhQhPSfP7GtpsrR4fNE+xH18GOO3ZiLu48N4fNE+l4sqsEUUhbtFYtRm37DlPkpERH/TZGnx6cFLosquiotw+O9liwZXEhISsHnzZiQnJ8PX1xc6nQ46nQ537twBoE9cGh8fj0mTJuH777/H8ePHMWLECERGRqJbt24AgN69eyMsLAyvvPIK/vvf/+Lbb7/FjBkzkJCQYLyjcMyYMbh48SKmTJmCn3/+GatXr8aXX36JiRMnGusyadIkfPzxx9i0aRPOnDmDsWPH4vbt2xgxYoStPhsiIiIim9BkaRG1cK9Vies9FMDqlyMwPrqlU+90vHXrFjIzM5GZmQlAn8Q+MzMTly9fhkKhwIQJEzBv3jzs2LEDp06dwrBhwxASEoKBAwcCAFq3bo3Y2FiMGjUK6enpOHjwIBITEzF48GCEhIQAAF5++WV4eXkhPj4ep0+fxhdffIHly5ebJLAfP348NBoNlixZgp9//hlJSUk4duwYEhMTHf2REBGRg1y7KW4qEHPl3GWwwFwUBaCPoqhpei9bvIac1HbfsNU+SkREfzOci8xRQB9V2c0JucIsmhZszZo1AIAePXqYPL9hwwa8+uqrAIClS5fCw8MDgwYNQnFxMWJiYrB69WpjWU9PT+zatQtjx45FZGQkGjRogOHDh2POnDnGMqGhodi9ezcmTpyI5cuX48EHH8Qnn3yCmJgYY5mXXnoJf/zxB2bOnAmdTocOHTpAo9FUSnBKRERE5Ex7TmoxLjnD6vWlkl/lxIkTePrpp41/GwY8hg8fjo0bN2LKlCm4ffs2Ro8ejfz8fDz++OPQaDTw8fExrrNlyxYkJibiqaeeMvYXV6xYYVyuVCrx3XffISEhAZ06dcL999+PmTNnYvTo0cYyjz32GJKTkzFjxgy88847aNGiBbZv347w8HAHfApEROQMQb4+5guZKedO0zZZEkVRXdJ6W7yGXNhi37DFPkpERKYOX7wuKteKAMfmWSnPosEVQTB/R4KPjw9WrVqFVatWVVumSZMm2LNnT42v06NHD5w4UfN85ImJibxLkYiIiCTLlfKrPPHEEzX2BRUKBebMmWNyw0xFAQEBSE5OrvF92rVrhx9//LHGMi+88AJeeOGFmitMREQuo0toANRKH+gKiqq8AK4AoFLq82NUx50GC2wRReFOkRi22DdssY8SEdHfNFlaTNt2SlTZkVFNnfa72aJpwYiIiIhIHE2W1iXzqxARETmap4cCs/rrc7RWvCfV8Le5O1bdabDAFlEU7hSJYYt9wxb7KBER6Rmmasy/U2K+MIBeYSo716h6HFwhIiIisrHSMgFJO8zPDVuRVPKrEBERSU1suBprhkZApTS9mK9S+mDNUPMJbN1psMAQRVFdT8IwN31NURS2eA25sNW+Udt9lIiIap6qsSIpnIssmhaMiIiIiMxbue8cdIWW3/kqlfwqREREUhQbrkavMBXSc/Jw7WYRgnz1F1TE3JDgTtM2GaIoxm7OgAIw2V6xURS2eA25sOW+UZt9lIiIzE/VWJGzz0WMXCEiIiKykdIyActTz2Fp6jmL1lMrfbB2aAT6tguxU82IiIhcg6eHApHNAzGgwwOIbB4o+oKKu03bZIsoCneJxLD1vmHtPkrAgQMH0L9/f4SEhEChUGD79u0mywVBwMyZM6FWq1GvXj1ER0fj3DnL+t1EJG2p2TpR5fzr15XEuYiRK0REREQ2oMnSImnHaegKiy1ab2J0CyT2bMEf3kRERHZmGCyYvTPb5K5YldIHs/qHOf0Cja3ZIorCXSIx3G3fkKrbt2+jffv2GDlyJJ577rlKyxcvXowVK1Zg06ZNCA0NxbvvvouYmBhkZ2fDx0f+U/oRuTtNlhafHrwkquyquAhEtbjfvhUSgYMrRERERLW056QW45IzLFrHQwGsjIvgNGBEREQO5C6DBQaGKApnv4YcuNu+IUV9+vRBnz59qlwmCAKWLVuGGTNmYMCAAQCAzz77DMHBwdi+fTsGDx7syKoSkY0Zcq2YY5iqsZtEzkscXCEiIiKqhT0nryLx8xMWr8f8KkRERM7hLoMFZDnuG9KVk5MDnU6H6Oho43NKpRJdu3ZFWloaB1eIZE5srhUB0prGk4MrROTySssE3n1ERDZXWiZg5b7zWJr6i8XrToxuyfwqMsTzCZH8sN26Bn6PJEfcb21Lp9PnYQgODjZ5Pjg42LisouLiYhQX/z1lb2Fhof0qSES1oisUl8R+ZFRTSU3VyMEVInJpmixtpXlz1Zw3l4hqydr8KgCg8vNGYs+H7VArsieeT4jkh+3WNfB7JDnifisNCxYswOzZs51dDSIyQ5Olxdxdp0WV7RWmsnNtLOPh7AoQEdmLJkuLsZszKoUV6gqKMHZzBjRZWifVjIjkbM9JLcZszrBqYEUBIOmZNrxrUWZ4PiGSH7Zb18Dv0TqlZQLSLlzHN5m/I+3CdZSWCZJ+XVfD/dY+VCr9BdXc3FyT53Nzc43LKpo+fToKCgqMjytXrti9nkRkGcMxM+92SY3lFNAPUncJDXBMxURi5AoRuSRDIqyquvsC9Afl2Tuz0StMxYucRCSatflVAN6tKFc8nxDJD9uta+D3aB17RUwwEkMc7rf2ExoaCpVKhb1796JDhw4A9NN8HTlyBGPHjq1yHW9vb3h7ezuwlkRkiZqOmeUZjpZSyrViwMgVInJJ5hJhCQC0BUVIz8lzXKWISLZKywQsTz2HccknYM1NmhOjW+CnqT158UGGeD4hkh+2W9fgiO/R1SIx7BUx4eqRGLbcD3j8qZ1bt24hMzMTmZmZAPRJ7DMzM3H58mUoFApMmDAB8+bNw44dO3Dq1CkMGzYMISEhGDhwoFPrTUTWEZvEPqCBF9YMjZDk72lGrhCRS7p2U1wiLLHliMh91Sa/iocCWBkXgb7tpNcJJHF4PiGSH7Zb12Dv79HVIjHsFTHh6pEYtt4PePypnWPHjuEf//iH8e9JkyYBAIYPH46NGzdiypQpuH37NkaPHo38/Hw8/vjj0Gg08PHxcVaViagWUrN1osrN6NdasudmRq4QkUsK8hXXuRJbjojcU23yqwDAyriOHFiROZ5PiOSH7dY12PN7dMVIDHtFTLhyJIY99gMef2qnR48eEASh0mPjxo0AAIVCgTlz5kCn06GoqAipqalo2bKlcytNRFbRZGnx6cFLosqqlPXsW5la4OAKEbmkLqEBUCt9UN29U1JNhEVE0qHPr5Jh1bpqpQ/WDo1A33YhNq4VORrPJ0Tyw3brGuz1PZqLxAD0kRhymyLMXhETrhqJYa/9gMcfIiLzDMdgc+RwzOTgChG5JE8PBWb1DwOASh1bKSfCIiLnY34VKo/nEyL5Ybt1Dfb6Hl01EsNeEROuGolhr/2Axx8iIvMOX7wuKteKAOkfMzm4QkQuKzZcjTVDI6BSmnb0VUofySbCIiLn0mRpEbVwL5am/mLxuh4KYPXLERgf3VLSnT+yHM8nRPLDdusa7PE9umokhr0iJlw1EsOe+wGPP0RE1dNkaZGwRdwMESOjmkr+mMmE9kTk0mLD1egVpkJ6Th6u3SxCkK++488Ln0RUkWHebWsnAWF+FdfG8wmR/LDdugZbf4+uGolhiJgYuzkDCsCkP1ObiAl7va6z2Xs/4PGHiKgyS39z9wpT2bU+tsDBFSJyeZ4eCkQ2D3R2NYhIwkrLBCTtqHrebXPUSh/M6h8m+TtqqPZ4PiGSH7Zb12DL79EQiaErKKryvK+APsJAbpEYwN8RE7N3ZptMt6KqZV/FXq/rTI7YD3j8ISL6W025riqS07mYgytERETk9lbuOwddoeXTPkyMboHEni14FyIREZFMuGokhoG9IiZcLRLD1fcDIiKpMZfrqiK5HIM5uEJERERuq7RMwMp957E09ZxF63kogJVxEZwGjIiISIZcMRKjPHtFTLhaJIar7wdERFKSmq0TVc6/fl0sfK6tbI7BHFwhIiIit6TJ0iJpx2noCostXpf5VYiIiOTN1SIxyDrcD4iI7E+TpcWnBy+JKrsqLgJRLe63b4VsyMPZFSAiIsslJSVBoVCYPFq1auXsahHJxp6TWozZnGHxwIpa6YO1QyPQt12InWpGRETuZtWqVWjatCl8fHzQtWtXpKenV1t248aNlfqAPj7ySrwuJYZIjAEdHkBk80BeUHdT3A+IiOzHkGvFHAX0v7e7ySxCkpErREQy1aZNG6Smphr/rlOHh3QiMfacvIrEz09YvB7zqxARka198cUXmDRpEtauXYuuXbti2bJliImJwdmzZxEUFFTlOn5+fjh79qzxb4WC5yUiIiKSJrG5VgTIJ89KeYxcISKSqTp16kClUhkf998vn7BJImfZc1KLccknUCaYL1vexOiWGB/dUnYdPSIikrYPP/wQo0aNwogRIxAWFoa1a9eifv36WL9+fbXrKBQKkz5gcHCwA2tMREREJJ7YXCsjo5rKJs9KeRxcISKSqXPnziEkJATNmjXDkCFDcPny5WrLFhcXo7Cw0ORB5G70ESsZFq+n8vNGYs+H7VAjIiJyZ3fv3sXx48cRHR1tfM7DwwPR0dFIS0urdr1bt26hSZMmaNy4MQYMGIDTp0/X+D5y7geWlglIu3Ad32T+jrQL11Fq6d0RRBLBfZmI3JEluVZ6hansWxk74RwyREQy1LVrV2zcuBGPPPIItFotZs+ejSeeeAJZWVnw9fWtVH7BggWYPXu2E2pK5HylZQJW7juPpam/WLyuAkDSM20YsUJERDb3559/orS0tFLkSXBwMH7++ecq13nkkUewfv16tGvXDgUFBfjggw/w2GOP4fTp03jwwQerXEeu/UBNlhazd2abTCWiVvpgVv8wWd7ZSu6L+zIRuSNLcq2olD7oEhpg/0rZASNXiIhkqE+fPnjhhRfQrl07xMTEYM+ePcjPz8eXX35ZZfnp06ejoKDA+Lhy5YqDa0zkHJosLaIW7rVqYEWt9MGaoRH80StSaWkp3n33XYSGhqJevXpo3rw55s6dC0H4+85MQRAwc+ZMqNVq1KtXD9HR0Th37pzJ6+Tl5WHIkCHw8/ODv78/4uPjcevWLZMyJ0+exBNPPAEfHx80btwYixcvdsg2EhE5W2RkJIYNG4YOHTqge/fu+Pe//41GjRrhn//8Z7XryLEfqMnSYuzmjEpztOsKijB2cwY0WVon1UzaHBkdwUgMcbgvE5G7cvVcKwaMXCEicgH+/v5o2bIlzp8/X+Vyb29veHt7O7hWRM6lz69i+TRgAJPXW2PRokVYs2YNNm3ahDZt2uDYsWMYMWIElEol3nzzTQDA4sWLsWLFCmzatAmhoaF49913ERMTg+zsbPj4+AAAhgwZAq1Wi5SUFJSUlGDEiBEYPXo0kpOTAQCFhYXo3bs3oqOjsXbtWpw6dQojR46Ev78/Ro8e7bTtJyKy1P333w9PT0/k5uaaPJ+bmwuVStzUGHXr1kXHjh2r7QMC8usHGu50repSvQD9Ha6zd2ajV5iK5+lyHBkdwUgMcbgvE5E70xWaH1gB5JtrxYCRK0RELuDWrVu4cOEC1Gr5npCIbMna/CoeCmD1yxFMXm+FQ4cOYcCAAejXrx+aNm2K559/Hr1790Z6ejoAfdTKsmXLMGPGDAwYMADt2rXDZ599hqtXr2L79u0AgDNnzkCj0eCTTz5B165d8fjjj+Ojjz7C1q1bcfXqVQDAli1bcPfuXaxfvx5t2rTB4MGD8eabb+LDDz901qYTEVnFy8sLnTp1wt69e43PlZWVYe/evYiMjBT1GqWlpTh16pRL9QHN3ekqANAWFCE9J8+m7yvnSAxHRke4UiSGvb9zZ+3LRETOpsnSYu6umnPCGcg114oBI1eIiGTo7bffRv/+/dGkSRNcvXoVs2bNgqenJ+Li4pxdNSKnqk1+FQBYGdcRfdu5zgUqR3rsscewbt06/PLLL2jZsiX++9//4qeffjIOeuTk5ECn05kkblYqlejatSvS0tIwePBgpKWlwd/fH507dzaWiY6OhoeHB44cOYJnn30WaWlpePLJJ+Hl5WUsExMTg0WLFuHGjRto2LBhpboVFxejuLjY+LeckjkTkWubNGkShg8fjs6dO6NLly5YtmwZbt++jREjRgAAhg0bhgceeAALFiwAAMyZMwfdunXDww8/jPz8fLz//vv49ddf8dprrzlzM2zq2k1xd7qKLSeGnCMxHBkd4UqRGI74zp2xLxMROZthEN7ccLXcc60YcHCFiEiGfvvtN8TFxeH69eto1KgRHn/8cRw+fBiNGjVydtWInEaTpUXSjtPQFRabL1yBhwJYGRfBgZVamDZtGgoLC9GqVSt4enqitLQU8+fPx5AhQwAAOp0OAKpM3GxYptPpEBQUZLK8Tp06CAgIMCkTGhpa6TUMy6oaXJFrMmcicn0vvfQS/vjjD8ycORM6nQ4dOnSARqMxHtcuX74MD4+/J5y4ceMGRo0aZTzederUCYcOHUJYWJizNsHmgnx9bFrOnOouAhkiMaSef82S6IjI5oGyeS97ctR37uh9mWyvtExAek4ert0sQpCv/iKw1AcOiZyppkH48gytSM65Vgw4uEJEJENbt251dhWIJKU2+VUARqzYwpdffoktW7YgOTkZbdq0QWZmJiZMmICQkBAMHz7cqXWbPn06Jk2aZPy7sLAQjRs3dmKNiIj+lpiYiMTExCqX7d+/3+TvpUuXYunSpQ6olfN0CQ2AWukDXUFRlRdnbHmnqytEYjgyOsIVIjEc+Z07cl8m25NzRBuRs4hNYh/QwAvznw13ibbEnCtEREQka9bmVwH0P5DWDo1A33YhNq6V+5k8eTKmTZuGwYMHo23btnjllVcwceJE41Q2huTMNSVuVqlUuHbtmsnye/fuIS8vz6RMVa9R/j0q8vb2hp+fn8mDiIikydNDgVn99ZE4FS9t2/pOV1fIieHI6AhXiMRw5HfuyH2ZbMuVcgsROVJqtk5UuRn9WrvEwArAwRUiIiKSMU2WFuOST8Ca/KMTo1vgp6k9XaZT52x//fWXydQ1AODp6YmysjIAQGhoKFQqlUni5sLCQhw5csSYuDkyMhL5+fk4fvy4scy+fftQVlaGrl27GsscOHAAJSUlxjIpKSl45JFHqpwSjIiI5Cc2XI01QyOgUppepFcpfWw6TZcrRGIYoiOquzyvgP5mEltERzjyvezF0d+5o/Zlsh1z0U2APrqp1JofIEQuTJOlxacHL4kqq1LWs29lHIjTghEREZEs3b1Xhne+zrJ4PeZXsY/+/ftj/vz5eOihh9CmTRucOHECH374IUaOHAkAUCgUmDBhAubNm4cWLVogNDQU7777LkJCQjBw4EAAQOvWrREbG4tRo0Zh7dq1KCkpQWJiIgYPHoyQEH100csvv4zZs2cjPj4eU6dORVZWFpYvX+7y0+QQEbmb2HA1eoWp7JrvwBUiMQzREWM3Z0ABmFwQtnV0hCPfy16c8Z07Yl8m23GV3EJEjmQYlDTHFadDtDhy5cCBA+jfvz9CQkKgUCiwfft2k+WvvvoqFAqFySM2NtakTF5eHoYMGQI/Pz/4+/sjPj4et27dMilz8uRJPPHEE/Dx8UHjxo2xePHiSnX56quv0KpVK/j4+KBt27bYs2ePpZtDREREMqTJ0qLbglTk3b5r8brMr2IfH330EZ5//nmMGzcOrVu3xttvv43XX38dc+fONZaZMmUK3njjDYwePRqPPvoobt26BY1GAx+fvy9gbNmyBa1atcJTTz2Fvn374vHHH8e6deuMy5VKJb777jvk5OSgU6dOeOuttzBz5kyMHj3aodtLRET25+mhQGTzQAzo8AAimwfa/GK0K0RiAI6NjpB7JIazvnN778tkO2KjllJETn9E5A4OX7wuKteKAOkPwlvK4siV27dvo3379hg5ciSee+65KsvExsZiw4YNxr+9vb1Nlg8ZMgRarRYpKSkoKSnBiBEjMHr0aCQnJwPQTxHRu3dvREdHY+3atTh16hRGjhwJf39/4w/nQ4cOIS4uDgsWLMDTTz+N5ORkDBw4EBkZGQgPD7d0s4iIiEgmrE1ezwSU9uXr64tly5Zh2bJl1ZZRKBSYM2cO5syZU22ZgIAAY5+wOu3atcOPP/5obVWJiIgAuEYkhoEjoyPkHInhSt852YfYqKX1By+hS2gAf1uQ29NkaTFt2ylRZUdGNXW5NmPx4EqfPn3Qp0+fGst4e3tXm1D0zJkz0Gg0OHr0KDp37gxAf6dj37598cEHHyAkJARbtmzB3bt3sX79enh5eaFNmzbIzMzEhx9+aBxcWb58OWJjYzF58mQAwNy5c5GSkoKVK1di7dq1lm4WERERyYA+ef0Ji9ebGN0CiT1b8IcyERERmTBEYszemW1y161KhjdlGKIjXO29bM2VvnOyPUN0k7m78BXQ517pFabibwxyW5osLcZuzqgyR1FVeoVVPV4gZ3bJubJ//34EBQWhYcOG6NmzJ+bNm4fAQP1JNy0tDf7+/saBFQCIjo6Gh4cHjhw5gmeffRZpaWl48skn4eXlZSwTExODRYsW4caNG2jYsCHS0tIwadIkk/eNiYmpNE0ZEbmv0jJBlndTEVFlpWUCVu47j6Wpv1i0HvOrkD3w/EIkbWyjZCk5R2KQdfidU3UM0U1jNtccKc/cK+TuDHlWxAysuGKuFQObD67ExsbiueeeQ2hoKC5cuIB33nkHffr0QVpaGjw9PaHT6RAUFGRaiTp1EBAQAJ1OP1+hTqdDaGioSZng4GDjsoYNG0Kn0xmfK1/G8BpVKS4uRnFxsfHvwsLCWm0rEUmXJktb6U4kTglEJE+aLC2SdpyGrrDYfOEKmF+FbI3nFyJpYxsla8k5EoOsw++cqhMbrkZ8VFN8evCS2bJic7QQuZr0nDxReVYMXHXKRYsT2pszePBgPPPMM2jbti0GDhyIXbt24ejRo9i/f7+t38piCxYsgFKpND4aN27s7CoRkR0YwhIrHuR1BUUYuzkDmiytk2pGRJbac1KLMZszLB5YCWzghbVDI9C3XYidakbuiOcXImljGyUiIluJFjl90aU//7JzTYikKTW7+gCH8vzr18WaoREue5OLzQdXKmrWrBnuv/9+nD9/HgCgUqlw7do1kzL37t1DXl6eMU+LSqVCbm6uSRnD3+bKVJfrBQCmT5+OgoIC4+PKlSu12zgikpyawhINz83emY3SMrEzQhKRs+jzq1ieuD6gQV2kTX/KZTtv5Bw8vxBJG9soERHZkiH3irn77Jel/sLBe3I7miytqMguAFgV57oDK4ADBld+++03XL9+HWq1/kOMjIxEfn4+jh8/biyzb98+lJWVoWvXrsYyBw4cQElJibFMSkoKHnnkETRs2NBYZu/evSbvlZKSgsjIyGrr4u3tDT8/P5MHEbkWc2GJ5edFJSJpKi0TsDz1HMYln4A118Dee7YtvOrYvYtDbobnFyJpYxslIiJbMuReEfNzhIP35E4MN7SYo4B+atZuLj79osVXHm7duoXMzExkZmYCAHJycpCZmYnLly/j1q1bmDx5Mg4fPoxLly5h7969GDBgAB5++GHExMQAAFq3bo3Y2FiMGjUK6enpOHjwIBITEzF48GCEhOin7nj55Zfh5eWF+Ph4nD59Gl988QWWL19uksB+/Pjx0Gg0WLJkCX7++WckJSXh2LFjSExMtMHHQkRyJXa+U86LSiRNmiwtohbutThxPaBPXr/6Zde+K4ach+cXImljGyUiIluLDVdjYnSLGssYBu8PX7jumEoROdnhi9dF5VoR4Lp5VsqzeHDl2LFj6NixIzp27AgAmDRpEjp27IiZM2fC09MTJ0+exDPPPIOWLVsiPj4enTp1wo8//ghvb2/ja2zZsgWtWrXCU089hb59++Lxxx/HunXrjMuVSiW+++475OTkoFOnTnjrrbcwc+ZMjB492ljmscceQ3JyMtatW4f27dvj//7v/7B9+3aEh4fX5vMgIpkL8vWxaTkichxr86sYMHk92RPPL0TSxjbqOkrLBKRduI5vMn9H2oXrvBucXAr3b/lpen8DUeUSkpnbi1yfJkuLhC3ipu4eGdXULW58rGPpCj169IAgVH/w//bbb82+RkBAAJKTk2ss065dO/z44481lnnhhRfwwgsvmH0/InIfhnlRdQVFVYbvKgColD7oEhrg6KoRUQ30+VVOWLWuWumDWf3D3KLjRs7D8wuRtLGNugZNlhazd2ab3BHL8zy5Cu7ftVdaWoqkpCRs3rwZOp0OISEhePXVVzFjxgwoFPa5O17soHz+nRKM3Zzh0om7yb1psrQYuzlD1FR5ANArrPq86K6EE5ITkUsxzIsKoFLiOcPf7hCWSCQne05qrc6vMjG6BX6a2pM/YMjueH4hkja2UfkzXLSpONWIrqAIYzfzjvDqODsSwtnvLxfcv21j0aJFWLNmDVauXIkzZ85g0aJFWLx4MT766CO7vafYxPYGzL9CrsiQZ0XMnm3IteIuN7RYHLlCRCR1seFqrBkaUemuIBXvCiKSHGsjVjwUwMq4CE4DRg7F8wuRtLGNyldNF20E6C/UzN6ZjV5hKg6QlePsSAhnv79ccP+2nUOHDmHAgAHo168fAKBp06b4/PPPkZ6ebrf3NAzej91sfiokQ/6V9Jw8RLp4Em9yL+k5eaLyrBi40w0tHFwhIpcUG65GrzAV0nPycO1mEYJ89aPm7nJwJ5K60jIBK/edtypxPcD8KuQ8PL8QSRvbqDyZu2jjyAuWpWWCLPaf6qZnMURC2HtqIme/v7Wc8f1Kaf+Wu8ceewzr1q3DL7/8gpYtW+K///0vfvrpJ3z44YdVli8uLkZx8d/5HAsLC616X8Pg/bRtp5B/p8Rs+ZRsHb9Lcimp2TpR5fzr18XC59pK8vhvLxxcISKX5emhYIeGSII0WVok7ThtVeJ63g1JUsDzC5G0sY3Kz7Wb4u6GFVvOWnKJxHB2JISz399azvp+pbJ/u4Jp06ahsLAQrVq1gqenJ0pLSzF//nwMGTKkyvILFizA7NmzbfLeseFq+PrUxZBPjpgtu/7gJXQJDZDUcYPIWposLT49eElU2VVxEYhqcb99KyQxzLlCREREDmO4y9GagRXmVyEiInJNYhNGiy1nDTnlxLAkEsIV398azvx+pbB/u4ovv/wSW7ZsQXJyMjIyMrBp0yZ88MEH2LRpU5Xlp0+fjoKCAuPjypUrtXr/bs0CoVaa/54MA4zMvUJyZxhMN8eQZ6WbG97cwsEVIiIicojSMgFJO8QlwSvPQwGsfjkC46NbSuruRyIiIrINcwmj7Z0c11wkBiCtC6XOjoRw9vtbytnfr7P3b1cyefJkTJs2DYMHD0bbtm3xyiuvYOLEiViwYEGV5b29veHn52fyqA1D/hVzDAOMhy9cr9X7ETmb2FwrAtwrz0p5HFwhIiIih1i57xx0hZb/yGZ+FSIiItdW/oJlxcsyhr/tedFGbpEYzo6EcPb7W8rZ36+z929X8tdff8HDw/RSpqenJ8rKyhxWh9hwNeKjmooqm5Asrag3IkuJ/f0+Mqqp284wwcEVIiIisqvSMgHLU89haeo5i9YzRKz0bRdip5oRERGRVBgSRqsqTLmjUvrYPTm63CIxnB0J4ez3t5QUvl9n7t+upH///pg/fz52796NS5cu4euvv8aHH36IZ5991qH1iA5TiSqXf6dEctMKEomlydJi7q7Tosr2EtkmXBET2hMREZHd1CZ5PSNWiIiI3EtsuBq9wlRIz8nDtZtFCPLVX6C39x39covEMERCjN2cAQVgMt2VIyIhnP3+lpLK9+us/duVfPTRR3j33Xcxbtw4XLt2DSEhIXj99dcxc+ZMh9bDMMCoKygSNeXx7J3Z6BWm4ndNsmHIU2Vu/1ZAP0gslcF0Z2DkChEREdnFnpNajLEieb1a6YO1QxmxQkRE5I48PRSIbB6IAR0eQGTzQIdcjJRbJAbg/EgIZ7+/JaT0/Tpj/3Ylvr6+WLZsGX799VfcuXMHFy5cwLx58+Dl5eXQeojNvQJIb1pBInNqylNVnhQH052BkStERERkc3tOXkXi5ycsXm9idAsk9mzh1p0zIiIiciy5RWIYODsSwtnvL5Zcv1+SNsMA47Rtp5B/p8Rs+ZRsHSKbBzqgZkS1IzaJfUADL8x/NlxSg+nOwMgVIiIishlDfpVxySdQJiZGvpyJ0S0xProlf9gSERGRw8kpEqM8Z0dCOPv9xZLr90vSFhuuxqohEaLKrj94iblXSBZSs3Wiys3o15rHTjByhYiIiGykNvlVVH7eSOz5sB1qRURERCSOXCIxyDr8fskeujULhFrpY/ZOfwWYe4WkT5OlxacHL4kqq1LWs29lZIKDK0RERFRre05qMS45w6p1FQCSnmnDHxlERETkdIZIDHJN/H7J1gzTzo3ZXPNvIUPulcMXriOqxf2OqRyRBQy5VsxhEntTnBaMiIiIakWfX8W6gRU1p2IgIiIiIiIZiw1XIz6qqaiyCckZnB6MJOnwxeuicq0IYJ6q8ji4QkRERFapTX4VQJ+8/qepPTmwQkREREREshYdphJVLv9OCcZu5gALSYsmS4uELeJumBwZ1ZS/4cvhtGBERERksdrkV/FQACvjItC3HTtkREREREQkf11CA6BW+kBXUAQx950x/wpJhSZLi7GbM0TttwDQS+RAortg5AoRuZTSMgFpF67jm8zfkXbhOkqtuZ2eiGq056QWYzZnWDWwAgAr4zpyYIVcAs85RNLCNklERM5iyL0ihiH/SnpOnn0rRWSGIc+KmB6TAvppvZlrxRQHV4jIZWiytHh80T7EfXwY47dmIu7jw3h80T6G2xLZUG3zq6wdGoG+7UJsXCuSit9//x1Dhw5FYGAg6tWrh7Zt2+LYsWPG5YIgYObMmVCr1ahXrx6io6Nx7tw5k9fIy8vDkCFD4OfnB39/f8THx+PWrVsmZU6ePIknnngCPj4+aNy4MRYvXuyQ7SuP5xwiaWGbJCIiZ4sNV2PN0Aj416srqnxKts7ONSKqWXpOnqg8KwbMtVIZB1eIyCUYwhgrnhR0BUWcz5TIBphfhcy5ceMGoqKiULduXfznP/9BdnY2lixZgoYNGxrLLF68GCtWrMDatWtx5MgRNGjQADExMSgq+vvYPWTIEJw+fRopKSnYtWsXDhw4gNGjRxuXFxYWonfv3mjSpAmOHz+O999/H0lJSVi3bp3DtpXnHCJpYZt0DYw8InfDfd41xYarsWpIhKiy6w9e4jmKnCpV5ACff/26WDM0gr/nq8CcK0QkezWFMQrQhy5yPlMi6zG/ComxaNEiNG7cGBs2bDA+Fxoaavy/IAhYtmwZZsyYgQEDBgAAPvvsMwQHB2P79u0YPHgwzpw5A41Gg6NHj6Jz584AgI8++gh9+/bFBx98gJCQEGzZsgV3797F+vXr4eXlhTZt2iAzMxMffvihySCMvfCcQyQtbJOuQZOlxeyd2SYDZGqlD2b1D+OFHHJJ3OddW7dmgVArfcxGBPAcRc6kydLi04OXRJVdFReBqBb327dCMsXIFSKSPXNhjJzPlMh6zK9CYu3YsQOdO3fGCy+8gKCgIHTs2BEff/yxcXlOTg50Oh2io6ONzymVSnTt2hVpaWkAgLS0NPj7+xsHVgAgOjoaHh4eOHLkiLHMk08+CS8vL2OZmJgYnD17Fjdu3KiybsXFxSgsLDR5WIvnHCJpYZuUP0YeWUeKUQ9SrJMUcZ93fWLzr/AcRc5iuDnFHEOelW7NA+1fKZli5AoRyd61m+LmhxRbjoj09PlVTli1Lu+8cz8XL17EmjVrMGnSJLzzzjs4evQo3nzzTXh5eWH48OHQ6fQh58HBwSbrBQcHG5fpdDoEBQWZLK9Tpw4CAgJMypSPiCn/mjqdzmQaMoMFCxZg9uzZNtlOnnOIpIVtUt4YeWQdKUY9SLFOUsR93n3EhqsRH9VUVGSAruCO/StEVI7YXCsCmGfFHEauEJHsBfn62LQcEel/IDO/ClmirKwMEREReO+999CxY0eMHj0ao0aNwtq1a51dNUyfPh0FBQXGx5UrV6x+LZ5ziKSFbVLepB55JMVIDClGPUixThVJ5buU+j5PthUdphJVbu7uM5JoJ+Q+xOZaGRnVlL/rzWDkChHJXpfQAKiVPtAVFFV5B5ACgErpgy6hAY6uGpEslZYJSNphPkS4IuZXcW9qtRphYabTH7Ru3Rrbtm0DAKhU+h+Xubm5UKv/3kdyc3PRoUMHY5lr166ZvMa9e/eQl5dnXF+lUiE3N9ekjOFvQ5mKvL294e3tbeWWmeI5h0ha2CblTcqRR1KMxJBi1IMU61SRlL5LKe/zZHvmzlEGN27fxdjNGUwYTg5hSa6VXiIHCN0ZI1eISPbKz2dasbtu+JthjETirdx3DrpCy3/QMb+Ke4uKisLZs2dNnvvll1/QpEkTAPrk9iqVCnv37jUuLywsxJEjRxAZGQkAiIyMRH5+Po4fP24ss2/fPpSVlaFr167GMgcOHEBJSYmxTEpKCh555JEqpwSzNZ5ziKSFbVLepBp5JNVIDClGPUixTuVJ7buU6j5P9mFJ7hVAPxAphQg5cl2W5lrhzSnmcXCFiFxCbLgaa4ZGQKU07YSqlD68+4NIpNIyActTz2Fp6jmL1lMrfbB2aAT6tguxU81IDiZOnIjDhw/jvffew/nz55GcnIx169YhISEBAKBQKDBhwgTMmzcPO3bswKlTpzBs2DCEhIRg4MCBAPSRLrGxsRg1ahTS09Nx8OBBJCYmYvDgwQgJ0e9fL7/8Mry8vBAfH4/Tp0/jiy++wPLlyzFp0iSHbSvPOUTSwjYpX4a7uqsb+nLGxR1zkRiA8y6ASjHqQYp1MpDidynFfZ7sy3COCmhQt8Zyzh6IJPdw+OJ15lqxMU4LRkQuIzZcjV5hKqTn5OHazSIE+eo7pa58Mli1ahXef/996HQ6tG/fHh999BG6dOni7GqRDGmytEjacRq6wmKL1psY3QKJPVu4dDsjcR599FF8/fXXmD59OubMmYPQ0FAsW7YMQ4YMMZaZMmUKbt++jdGjRyM/Px+PP/44NBoNfHz+viC6ZcsWJCYm4qmnnoKHhwcGDRqEFStWGJcrlUp89913SEhIQKdOnXD//fdj5syZGD16tEO31x3POURSJuc2aWl/7quvvsK7776LS5cuoUWLFli0aBH69u3rwBrbjuGu7rGbM6AATC6COyvyyJJIjMjmgQ6rFyDNqAcp1slAit+lFPd5sr/YcDXulJRh4heZZsumZOscfmwh96DJ0mLatlOiyjLXingcXCEil+LpoXCbjsgXX3yBSZMmYe3atejatSuWLVuGmJgYnD17FkFBQc6uHsnInpNajEvOsGgd5lehqjz99NN4+umnq12uUCgwZ84czJkzp9oyAQEBSE5OrvF92rVrhx9//NHqetqKO51ziORAjm3S0v7coUOHEBcXhwULFuDpp59GcnIyBg4ciIyMDISHhzthC2rPcFd3xZwYKubEqESKOYakWCcDqX6XUtvnyTFUfuIGGNcfvIQuoQHcD8imDFMkio3TY64V8Ti4QkQkUx9++CFGjRqFESNGAADWrl2L3bt3Y/369Zg2bZqTa0dysefkVSR+fsLi9ZhfhYiIqPYs7c8tX74csbGxmDx5MgBg7ty5SElJwcqVK7F27VqH1t2WpBR5JOVIDClGPUixTgZS/i6ltM+TYxgGIs1NyaSAfrq6XmEq7g9kEzVNkViRMwfE5Yo5V4iIZOju3bs4fvw4oqOjjc95eHggOjoaaWlpTqwZyYUhv8q45BOwdJrpidEtmV+FiIiolqzpz6WlpZmUB4CYmBiX6P8ZIo8GdHgAkc0DnXZRUeo5MaSYY0iKdQKk/11KZZ8nx7AkuT1zr5AtmZsisSJOTWgZRq4QEcnQn3/+idLSUgQHB5s8HxwcjJ9//rlS+eLiYhQX/51Lo7Cw0O51JOmyNr8KAKj8vJHY82E71IqIiMi9WNqfAwCdTldleZ1OV+37sB9oGSlHYhhIMepBinWSw3dJ7iU2XI34qKb49OAls2WZe4VsJTW7+j5Cef7162Lhc205JZ2FGLlCROQGFixYAKVSaXw0btzY2VUiJ9lzUosxmzOsGlhRAEh6pg1/gBIREckI+4GWk2okRnlSjHqQYp3k8F2Se4kWmcti/cFL0GRp7VwbcnWaLK2owTwAWBXHY6I1GLlCRCRD999/Pzw9PZGbm2vyfG5uLlSqyp216dOnY9KkSca/CwsL+cPaDVmbXwXQT5nABJtERES2Y2l/DgBUKpVF5QH2A60lxUgMsg6/S5IS5l4hRzHkWjHHkGelGyOlrMLIFSIiGfLy8kKnTp2wd+9e43NlZWXYu3cvIiMjK5X39vaGn5+fyYPcR23yqwDAxOgW+GlqTw6sEBER2ZCl/TkAiIyMNCkPACkpKdWWB9gPrA0pRmKQdfhdklRYmnvl8IXr9q8UuSSxuVYEcIrE2uDgChGRTE2aNAkff/wxNm3ahDNnzmDs2LG4ffs2RowY4eyqkYRosrSIWrgXS1N/sXhdDwWw+uUIjI9uyY4WERGRHZjrzw0bNgzTp083lh8/fjw0Gg2WLFmCn3/+GUlJSTh27BgSExOdtQlERGQhQ+4VMRKSMzg9GFlFVyguif3IqKa8kbIWOC0YEZFMvfTSS/jjjz8wc+ZM6HQ6dOjQARqNplKSU3Jfe05qMS45w+r1V8Z1RN927GQRERHZi7n+3OXLl+Hh8fc9kY899hiSk5MxY8YMvPPOO2jRogW2b9+O8PBwZ20CERFZITpMJSoXRv6dEozdnMEcQWQRTZYWc3edFlW2l8g8QFQ1iyNXDhw4gP79+yMkJAQKhQLbt283WS4IAmbOnAm1Wo169eohOjoa586dMymTl5eHIUOGwM/PD/7+/oiPj8etW7dMypw8eRJPPPEEfHx80LhxYyxevLhSXb766iu0atUKPj4+aNu2Lfbs2WPp5hARyVpiYiJ+/fVXFBcX48iRI+jatauzq0QSoc+vYt3Ailrpg7VDI9C3XYiNa0VEREQV1dSf279/PzZu3GhS/oUXXsDZs2dRXFyMrKws9O3b18E1JiKi2jLkXhE7P8DsndkotWaOZ3I7miwtxm7OQN7tkhrLKaD/7d8lNMAxFXNRFg+u3L59G+3bt8eqVauqXL548WKsWLECa9euxZEjR9CgQQPExMSgqOjvUKQhQ4bg9OnTSElJwa5du3DgwAGMHj3auLywsBC9e/dGkyZNcPz4cbz//vtISkrCunXrjGUOHTqEuLg4xMfH48SJExg4cCAGDhyIrKwsSzeJiFxAaZmAtAvX8U3m70i7cJ2dDnJbzK9C5Bw8DxE5B9seERHJkdjcK8Df+VfSc/LsWymSPUMSe3O9IcOgHnOt1J7F04L16dMHffr0qXKZIAhYtmwZZsyYgQEDBgAAPvvsMwQHB2P79u0YPHgwzpw5A41Gg6NHj6Jz584AgI8++gh9+/bFBx98gJCQEGzZsgV3797F+vXr4eXlhTZt2iAzMxMffvihcRBm+fLliI2NxeTJkwEAc+fORUpKClauXIm1a9da9WEQkTxpsrSYvTPbJFGXWumDWf3DeIGY3IomS4ukHaehKyy2eF0PBbAyLoLTgBFZgechIudg2yMiIjmLDVdjzdAITNt2Cvl3ao4yAICUbB0imwc6oGYkV2KT2Ac08ML8Z8PZX7IBmya0z8nJgU6nQ3R0tPE5pVKJrl27Ii0tDQCQlpYGf39/48AKAERHR8PDwwNHjhwxlnnyySfh5eVlLBMTE4OzZ8/ixo0bxjLl38dQxvA+VSkuLkZhYaHJg4jkzRDuWPHkoSsowtjNTPxG7sPQFqwZWAGYX4XIWjwPETkH255rYOQREduBu4sNV2PVkAhRZdcfvMTzG9UoNVsnqtyMfq05sGIjNh1c0en0X2DFZMrBwcHGZTqdDkFBQSbL69Spg4CAAJMyVb1G+feoroxheVUWLFgApVJpfDRu3NjSTSQiCakp3NHwHOclJXdQWiYgaYf50N+qML8KkfV4HiJyDrY916DJ0uLxRfsQ9/FhjN+aibiPD+PxRft44ZDcCtuB5X7//XcMHToUgYGBqFevHtq2bYtjx445u1q10q1ZINRKH7PlFOD5jaqnydLi04OXRJVVKevZtzJuxKaDK1I3ffp0FBQUGB9XrlxxdpWIqBbMhTtyXlJyFyv3nYOu0Hzob0XMr0JUOzwPETkH2578MfLIOnKJcJBLPZ2N7cByN27cQFRUFOrWrYv//Oc/yM7OxpIlS9CwYUNnV61WxOZfMZzfDl+4bv9KkawYbjwxh0nsbc/inCs1UalUAIDc3Fyo1X9fqMnNzUWHDh2MZa5du2ay3r1795CXl2dcX6VSITc316SM4W9zZQzLq+Lt7Q1vb28rtoyIpOjaTXEXk8WWI5KjPSe1WJp6zqJ1mF+FyDZ4HiJyDrY9eTMXeWS4M7tXmIpJdsuRS44hudTT2dgOrLNo0SI0btwYGzZsMD4XGhrqxBrZTmy4GvFRTUVFHiQkZ2DhoLZsU2R0+OJ1UblWBDCJva3ZNHIlNDQUKpUKe/fuNT5XWFiII0eOIDIyEgAQGRmJ/Px8HD9+3Fhm3759KCsrQ9euXY1lDhw4gJKSv5M5paSk4JFHHjGORkdGRpq8j6GM4X2IyPUF+ZoPm7WkHJHc7Dl5FYmfZ1i8HvOrENkGz0NEzsG2J29yjDxydiSGXCIcpFhPZ3931ZFjO5CCHTt2oHPnznjhhRcQFBSEjh074uOPP662vNxyL0eHVX/DeHn5d0ok1fbJuTRZWiRsEXddYGRUUw7K2ZjFkSu3bt3C+fPnjX/n5OQgMzMTAQEBeOihhzBhwgTMmzcPLVq0QGhoKN59912EhIRg4MCBAIDWrVsjNjYWo0aNwtq1a1FSUoLExEQMHjwYISH6+d5ffvllzJ49G/Hx8Zg6dSqysrKwfPlyLF261Pi+48ePR/fu3bFkyRL069cPW7duxbFjx7Bu3bpafiREJBddQgOgVvpAV1BU5R0/CgAqhjuSCyotE7By33ksTf3FovV41yCRbfE8ROQcbHvyJrfII2dHYsglwkGK9XT2d1cTubUDqbh48SLWrFmDSZMm4Z133sHRo0fx5ptvwsvLC8OHD69UfsGCBZg9e7YTamodc+e3iqTQ9sm5DIPaYoeNe4kcwCPxLI5cOXbsGDp27IiOHTsCACZNmoSOHTti5syZAIApU6bgjTfewOjRo/Hoo4/i1q1b0Gg08PH5+66hLVu2oFWrVnjqqafQt29fPP744yaDIkqlEt999x1ycnLQqVMnvPXWW5g5cyZGjx5tLPPYY48hOTkZ69atQ/v27fF///d/2L59O8LDw63+MIhIXsrPS1qxK2H4m+GO5Go0WVpELdxr8cAK86sQ2R7PQ0TOwbYnb3KKPJJCJIZcIhykVk8pfHc1kVM7kJKysjJERETgvffeQ8eOHTF69GjjzdtVkVvuZbG5VwDptH1ynpoGtStirhX7sThypUePHhCE6r82hUKBOXPmYM6cOdWWCQgIQHJyco3v065dO/z44481lnnhhRfwwgsv1FxhInJpseFqrBkaUemOJJVE7kgisqU9J7UYl2z5NGATo1tifHQLO9SIiHgeInIOtj35kkvkkVQiMeQS4SClekrlu6uJXNqB1KjVaoSFmQ4+tG7dGtu2bauyvBxzLxvOb9O2nUL+nRKz5VOydYhsHuiAmpHUmBvUrog3ntiHTRPaExE5Q2y4Gr3CVEjPycO1m0UI8tV3QnnSIFeiz69ywuL1VH7eSOz5sB1qREQGPA8ROQfbnjwZ7sweuzkDCsDkwrKUIo8sicSw54VNuUQ4SKmeUvnuaiKXdiA1UVFROHv2rMlzv/zyC5o0aeKkGtlHbLgavj51MeSTI2bLrj94CV1CA3hTgRtKzdaJKudfvy4WPteW+4idcHCFiFyCp4eCd2uQS7I2v4pB0jNt+KOMyAF4HiJyDrY9eZJD5JFUIjHkEuEgpXpK5bszRw7tQGomTpyIxx57DO+99x5efPFFpKenY926dS6Zf7lbs0ColT5mIxOkEIlFjqfJ0uLTg5dElV0VF4GoFvfbt0JujIMrREREEqXJ0iJpx2noCostXtdDAayMi+CPMiIiIpIkqUceSSUSQy4RDlKqp1S+OzGk3g6k5tFHH8XXX3+N6dOnY86cOQgNDcWyZcswZMgQZ1fN5gxtaszmmqeFNkRiHb5wnRfQ3YRh6kNzDIPa3XgTil1ZnNCeiIiI7G/PSS3GbM6wamAFAFbGdUTfdhxYISIiIukyRB4N6PAAIpsHSuqCsiESo7oaOTI5sCHCQaU0HQxQKX2wZqh0bqaRSj2l9N2JIeV2IEVPP/00Tp06haKiIpw5cwajRo1ydpXsJjZcjfiopqLKJiRnQJOltW+FSBIOX7wuKteKAGkMvrs6Rq4QERFJjLX5VQD9D0VOI0BERERUO1KKxADkE+EghXpK7bsjqo3oMJWo6Z/y75Rg7OYMSQ24ku1psrSYtu2UqLIjo5pyX3AADq4QERFJRG3zq0yMboHEni34Q5GIiIjIBqSWE0MuOYakUE+pfXdE1jKXz6gi5l9xXZosLcZuzhC1HwBArzCVXetDehxcISIikgBb5FfhNGBEREREtiWFSAyyDr87cgXlI7HMMeRfSc/Jc/oAJ9mWIc+KmIEVQ64VqUx96Oo4uEJEslVaJrCjTC5hz0ktxiWb7yxXh/lViKSD5yYix2BbI0eSQiQGWYffHbkCQyTWtG2nkH+nxGz5lGwd93sXk56TJyrPigGnPnQcJrQnIlnSZGnx+KJ9iPv4MMZvzUTcx4fx+KJ9TOBGsqPPr2LdwIpa6YO1QyPQt12IjWtFVHsLFy6EQqHAhAkTjM8VFRUhISEBgYGBuO+++zBo0CDk5uaarHf58mX069cP9evXR1BQECZPnox79+6ZlNm/fz8iIiLg7e2Nhx9+GBs3bnTAFpnHcxORY7CtERGRu4kNV2PVkAhRZdcfvMRzootJzdaJKudfvy7z7jgYB1eISHYM80xWHLXXFRRh7OYMdiJINjRZWoxLPoEysZOmljMxugV+mtqTnSaSpKNHj+Kf//wn2rVrZ/L8xIkTsXPnTnz11Vf44YcfcPXqVTz33HPG5aWlpejXrx/u3r2LQ4cOYdOmTdi4cSNmzpxpLJOTk4N+/frhH//4BzIzMzFhwgS89tpr+Pbbbx22fVXhuYnIMdjWiIjIXXVrFgi10sdsOQX0uVdKrfmhSZKjydLi04OXRJVdFceBFUfj4AoRyUpN80wanmMnguTg7r0yvPN1lsXreSiA1S9HYHx0S4b5kiTdunULQ4YMwccff4yGDRsany8oKMCnn36KDz/8ED179kSnTp2wYcMGHDp0CIcPHwYAfPfdd8jOzsbmzZvRoUMH9OnTB3PnzsWqVatw9+5dAMDatWsRGhqKJUuWoHXr1khMTMTzzz+PpUuXOmV7AZ6biByFbY2IiNyZIf+KOYbcK4cvXLd/pciuDH0fcxTQz2zRjdPBORwHV4hIVszNM1k+gRuRVGmytOi2IBV5t+9avC7zq5DUJSQkoF+/foiOjjZ5/vjx4ygpKTF5vlWrVnjooYeQlpYGAEhLS0Pbtm0RHBxsLBMTE4PCwkKcPn3aWKbia8fExBhfwxl4biJyDLY1IiJyd7HhasRHNRVVNiGZEZ1yd/jidVG5VgQwz4qzMKE9EcnKtZviEniJLUfkaNYmr1crfTCrfxhDfEnStm7dioyMDBw9erTSMp1OBy8vL/j7+5s8HxwcDJ1OZyxTfmDFsNywrKYyhYWFuHPnDurVq1fpvYuLi1FcXGz8u7Cw0PKNqwHPTUSOwbbmOkrLBKTn5OHazSIE+fqgS2gALwgRgW2DxIkOU4maJir/TgnGbs5gDg6Z0mRpMW3bKVFlR0Y15XfsJBxcISJZCfI1P7+oJeWIHEmfvP6ExetNjG6BxJ4t+MOKJO3KlSsYP348UlJS4OMjrWPwggULMHv2bLu9Ps9NRI7BtuYaNFlazN6ZbXInLm8iIWLbIPG6hAZArfSBrqCoyqkyK5q9Mxu9wlT8PSkjhhxzYic67RWmsmt9qHqcFoyIZMXQiaiuS2CYZ7JLaIAjq0VUo9IyActTz1mcvJ75VUhOjh8/jmvXriEiIgJ16tRBnTp18MMPP2DFihWoU6cOgoODcffuXeTn55usl5ubC5VK/2NApVIhNze30nLDsprK+Pn5VRm1AgDTp09HQUGB8XHlyhVbbLIRz01EjsG2Jn+Gi0UVpzjRFRRh7GZOX0Pui22DLCE29wrAKTPlqKYccxWx7+N8HFwhIlkp34mo+MPa8DfnmSQp0WRpEbVwL5am/mLxusyvQnLy1FNP4dSpU8jMzDQ+OnfujCFDhhj/X7duXezdu9e4ztmzZ3H58mVERkYCACIjI3Hq1Clcu3bNWCYlJQV+fn4ICwszlin/GoYyhteoire3N/z8/EwetsRzE5FjsK3JW00XiwzPzd6ZjVJL7kRxE6VlAtIuXMc3mb8j7cJ1WX1Gcq67o7BtkDViw9VYMzQC/vXqiiqfkq2zc43IVszlmKuIfR/n4rRgRCQ7hk5ExZBpFUOmSWKsza8S2MAL858N575MsuLr64vw8HCT5xo0aIDAwEDj8/Hx8Zg0aRICAgLg5+eHN954A5GRkejWrRsAoHfv3ggLC8Mrr7yCxYsXQ6fTYcaMGUhISIC3tzcAYMyYMVi5ciWmTJmCkSNHYt++ffjyyy+xe/dux25wBTw3ETkG25p8mbtYVP7u6sjmgY6rmMTJeaooOdfdkdg2yFqx4Wr4+tTFkE+OmC27/uAldAkNYNuTgVSRA2H+9eti4XNt+Z06GQdXiEiWYsPV6BWmYrI/kixr86sENKiLtOlPwasOg0vJ9SxduhQeHh4YNGgQiouLERMTg9WrVxuXe3p6YteuXRg7diwiIyPRoEEDDB8+HHPmzDGWCQ0Nxe7duzFx4kQsX74cDz74ID755BPExMQ4Y5NM8NxE5Bhsa/J07aa4u3DFlnMWRyYcr27OfcNUUVJOUu3MusstKbyrtA1yjm7NAqFW+piNdFCAuVfkQJOlxacHL4kquyouAlEt7rdvhcgsDq4QkWx5eih45w5Jkj5ixfKBFQB479m2HFghl7F//36Tv318fLBq1SqsWrWq2nWaNGmCPXv21Pi6PXr0wIkT1rUxe+O5icgx2NbkJ8jXx6blnMGRkRjmpoqS8oVSZ9ZdjtEyrtA2yHkMU2aO2VzzjAmGCKjDF67zgrxEGY6d5iigj9jtxn6QJPDqDRERkQ3pI1YsnwrMkLxeqj/6iIiIiGqjS2gA1EqfSvlyDKSelNfRCcctmSpKapxVd7kmhZd72yDniw1XIz6qqaiyCcnSbQvuTmyuFQHMsyIlHFwhIiKygdIyActTz2Fc8glYk2uSyeuJiIjIlRnurgZQ6SKy4W+pXixyRsJxOU8V5Yy6yzkpvJzbBklHdJhKVLn8OyWSHmx0Z2JzrYyMasqbMiWEgytEJDulZQLSLlzHN5m/I+3CdUl2kMm9aLK0iFq4F0tTf7F4XbXSB2uHRqBvuxA71IyInInnKyLbYpuSv9hwNdYMjYBKaTq9kUrpI+n8Ic6IxJDzVFHOqLucI30A+bYNkg5zEVAVSXWw0V1Zkmull8iBNHIM5lwhIlmR4xy65Nr0+VUsnwYMACZGt0Bizxa8C43IBfF8RWRbbFOuIzZcjV5hKiYcN8NwoVRXUFRlNIZhzn0pThXljLrLOdLHQI5tg6TDEAE11kzuFcB0sJH5y5zP0lwrUjzuuzNGrhCRbMh1Dl1yXbXNrzI+uiV/LBG5IJ6viGyLbcr1eHooENk8EAM6PIDI5oGS7w85IxJDzlNFOaPuco70KU9ubYOkxRAB5V+vrqjyKSKnoSL7OnzxOnOtyBgHV4hIFuQ8hy65HuZXIaLq8HxFZFtsUyQFzko4LuepohxddyaFJ9KLDVdj1ZAIUWXXH7zEGxScTJOlRcIWcTdsMteKNHFaMCKSBUvm0GVYK9mTJkuLpB2noSsstnhdDwWwMi6CAytELoznKyLbYpsiKSg/3Y4CMBnss3cUiZyninJk3Z35HRFJTbdmgVArfcxGQyigv0GhV5iKbcMJDJG5Ym8PYa4VaWLkChHJgivMoUvyZ+j8WDOwAjBihcgd8HxFZFtsUyQVzowikfNUUY6su5wjfYhsqfzUfDUpf4MCOVZNkbkVMfJO2hi5QkSy4Cpz6JJ8lZYJSNohrvNTERPuErkPnq+IbIttiqREzlEk7oLfEZFebLga8VFN8enBS2bLpmTrGP3pYOYicyti5J10cXCFiGTBMIeurqCoyovbCujvSOJIPtnLyn3noCu0/K7YidEtkNizBTtCRG6C5ysi22KbIqkxRGKQdPE7ItKLDlOJGlxZf/ASuoQG8GZAB0rN1okq51+/LhY+15bfjYRxWjAikoXyYa0VL1FzDl2yJ0Py+qWp5yxaz0MBrH45AuOjW3K/JHIjPF8R2RbbFBERkXUMNyiYY8i9UlpmzTwNZClNllbUoBcArIrjlIZSx8EVIpINzqFLjqbJ0iJq4V4sTf3F4nWZX4XIffF8RWRbbFNERESWszT3yuEL1+1fKTdnyLVijiHPSjdG4UkepwUjIlnhHLrkKHtOajEuOcPi9ZhfhYgAnq+IbI1tioiIyHKW5F5JSM7AwkGcgsqeDl+8LirXigBG5soFI1eISHYMc+gO6PAAIpsHuuXJpmnTplAoFCaPhQsXOrtaLmPPyatI/NzygZWJ0S3w09Se7IwSEQCer4hszdXaVF5eHoYMGQI/Pz/4+/sjPj4et27dqnGdHj16VOoDjhkzxkE1rp3SMgFpF67jm8zfkXbhOqefIaoB2wvZUnSYSlS5/DslGLs5A5osrZ1r5J40WVokbBF3nWFkVFNeV5AJRq4QkWyUlgm8W7GcOXPmYNSoUca/fX19nVgb11BaJmDlvvNWTQM2Mbolxke3sEOtiEjueP4iqh1XbUNDhgyBVqtFSkoKSkpKMGLECIwePRrJyck1rjdq1CjMmTPH+Hf9+vXtXdVa02RpMXtntsnduoz2Jaoa2wvZmiH3iq6gCGKG6WbvzEavMJVLnGulQpOlxdjNGaI+fwDoJXJAjJyPgytEJAvsYFbm6+sLlYonXFvRZGmRtOM0dIXFFq+r8vNGYs+H7VArIpI7nr+IasdV29CZM2eg0Whw9OhRdO7cGQDw0UcfoW/fvvjggw8QEhJS7br169eXVR+wugtKuoIijN2cwbw5NXClgUVX2hZ7YnshezDkXhm72XzUhCH/SnpOHiKZ78MmDHlWxAysKKDPKdclNMDe1SIb4bRgRCR5hg5mxXkpDR1Mdw1ZXbhwIQIDA9GxY0e8//77uHfvXrVli4uLUVhYaPKgv+05qcWYzRlWDawoACQ904Y/DomoEp6/iGrHldtQWloa/P39jQMrABAdHQ0PDw8cOXKkxnW3bNmC+++/H+Hh4Zg+fTr++uuvGss7sx9Y0wUlw3Ozd2bLcsoje0/bpMnS4vFF+xD38WGM35qJuI8P4/FF+2S53ztqW+Q+lZYrtxd7WbhwIRQKBSZMmODsqkhebLgaa4ZGwL9eXVHlU7J1dq6R+0jPyROVZ8WAuVbkhZErRCRp5jqYCrhnyOqbb76JiIgIBAQE4NChQ5g+fTq0Wi0+/PDDKssvWLAAs2fPdnAt5UGfX+WEVeu6wp2zRGQfPH8R1Y6rtyGdToegoCCT5+rUqYOAgADodNVf0Hr55ZfRpEkThISE4OTJk5g6dSrOnj2Lf//739Wu48x+oLkLSnK9Q9reEVWuFL3gqG1xhSg3V20v9nL06FH885//RLt27ZxdFdmIDVfD16cuhnxS8yA+AKw/eAldQgNk036kLFXkQJV//bpY+FxbfuYyY/PIlaSkpEoJ9lq1amVcXlRUhISEBAQGBuK+++7DoEGDkJuba/Ialy9fRr9+/VC/fn0EBQVh8uTJle7I3r9/PyIiIuDt7Y2HH34YGzdutPWmEJEEWNLBlLtp06ZVOn5WfPz8888AgEmTJqFHjx5o164dxowZgyVLluCjjz5CcXHVkRfTp09HQUGB8XHlyhVHbpoklZYJWJ56DuOST8Cam7+YvJ6IauJO5y8ie5BrG7KkP2eN0aNHIyYmBm3btsWQIUPw2Wef4euvv8aFCxeqXceZ/cBrN8XdqSu2nBTYO6LKlaIXHLUtrhLl5ortxV5u3bqFIUOG4OOPP0bDhg2dXR1Z6dYsEGqlj9lyhpsY5HCskTJNlhafHrwkquyqOPkMnNPf7BK50qZNG6Smpv79JnX+fpuJEydi9+7d+Oqrr6BUKpGYmIjnnnsOBw8eBACUlpaiX79+UKlUOHToELRaLYYNG4a6devivffeAwDk5OSgX79+GDNmDLZs2YK9e/fitddeg1qtRkxMjD02iYicxJ06mG+99RZeffXVGss0a9asyue7du2Ke/fu4dKlS3jkkUcqLff29oa3t7ctqukSapNfxUMBrIyLQN927PQQUfXc6fxFZA9ybUNi+3MqlQrXrl0zef7evXvIy8uzKJ9K165dAQDnz59H8+bNqyzjzH5gkK/5C3iWlHM2R0RUuVL0giO2xZWi3FytvdhTQkIC+vXrh+joaMybN6/acsXFxSY3IHJ67L/zr4wxk3/F0D4PX7iOqBb3O6ZyLsZwfDLHkGelm8SP6VQ1uwyu1KlTp8oOYUFBAT799FMkJyejZ8+eAIANGzagdevWOHz4MLp164bvvvsO2dnZSE1NRXBwMDp06IC5c+di6tSpSEpKgpeXF9auXYvQ0FAsWbIEANC6dWv89NNPWLp0KQdXiFyMO3UwGzVqhEaNGlm1bmZmJjw8PCpNL0GV7Tmpxbhk84n8qrMyriMHVojILHc6fxHZg1zbkNj+XGRkJPLz83H8+HF06tQJALBv3z6UlZUZB0zEyMzMBACo1dLsm3QJDYBa6QNdQVGVF7/llrjXEYMFch1YrIojtsWVBqNcrb3Yy9atW5GRkYGjR4+aLcvpsasWG65GfFRTUREVCckZWDiIU1VZ4/DF66JyrQhgnhU5s0tC+3PnziEkJATNmjXDkCFDcPnyZQDA8ePHUVJSgujoaGPZVq1a4aGHHkJaWhoAfWK/tm3bIjg42FgmJiYGhYWFOH36tLFM+dcwlDG8RnWY0JlIfgwdzOpOMQro59J1pw5mWloali1bhv/+97+4ePEitmzZgokTJ2Lo0KEMiTZDn1/FuoEVtdIHa4dGoG+7EBvXiohcEc9fRLXj6m2odevWiI2NxahRo5Ceno6DBw8iMTERgwcPRkiIvq/x+++/o1WrVkhPTwcAXLhwAXPnzsXx48dx6dIl7NixA8OGDcOTTz4p2ZwDhjukAVT6Lg1/y+mCkiMGC+Q6sFgVR2yLKw1GuVp7sYcrV65g/Pjx2LJlC3x8zO83nB67etFh4qIk8++UyGp6PanQZGmRsEXctYeRUU05eCVjNh9c6dq1KzZu3AiNRoM1a9YgJycHTzzxBG7evAmdTgcvLy/4+/ubrBMcHGxM2qfT6UwGVgzLDctqKlNYWIg7d+5UW7cFCxZAqVQaH40bN67t5hKRnbGDWZm3tze2bt2K7t27o02bNpg/fz4mTpyIdevWObtqkqaPWGF+FSJyDJ6/iGrHHdrQli1b0KpVKzz11FPo27cvHn/8cZP+XElJCc6ePYu//voLAODl5YXU1FT07t0brVq1wltvvYVBgwZh586dztoEUWLD1VgzNAKqCnP8q5Q+skrMDjhmsMCVBhYdsS2uNBgFuFZ7sYfjx4/j2rVriIiIQJ06dVCnTh388MMPWLFiBerUqYPS0lKT8t7e3vDz8zN5kJ659lkR86+IZ8gDlX+nRFT5XiIHukiabD4tWJ8+fYz/b9euHbp27YomTZrgyy+/RL169Wz9dhaZPn06Jk2aZPy7sLCQAyxEEldaJkBZzwsjoppie+ZV5N2+a1ymUvpgVv8wt+tgRkRE4PDhw86uhqzoI1ZOWLwe86sQUW0YLpDM3pltMiVAwwZ18WyHB6Cs54XSMkHWF4eJ7MUd+oABAQFITk6udnnTpk0hCH9fyGrcuDF++OEHR1TN5mLD1egVpkJ6Th6u3SxCkK/+grrcjn+OmLbJMLA4dnMGFIDJ+8htYNER2+KKU2m5Snuxh6eeegqnTp0yeW7EiBFo1aoVpk6dCk9PTyfVTH7Kt09z5DS9nrOVlglI2lF1HqiK5Hh8osrsknOlPH9/f7Rs2RLnz59Hr169cPfuXeTn55tEr+Tm5hpztKhUKmPYc/nlhmWGfw3PlS/j5+dX4wAOEzoTyYsmS1vpglTA/y5IRYep2MEks0rLBKzcdx5LU3+xan3mVyGi2ip/gSQlW2e8SPzpwUv49OAlqF3kIjGRLbEP6Jo8PRSyvyjnqIGP6gbn5TiwaO9tcaXBqPJcob3Yg6+vL8LDw02ea9CgAQIDAys9T+YZ2ue0badERVmkZOu4X5qxct856ArFT0Mox+MTmbL74MqtW7dw4cIFvPLKK+jUqRPq1q2LvXv3YtCgQQCAs2fP4vLly4iMjASgT+w3f/58XLt2zZiYOSUlBX5+fggLCzOW2bNnj8n7pKSkGF+DiOTPEEZZcbT/xu0SrD94CY/yRzWZocnSImnHaegKiy1elxc7iciWPD0UKLhzFxsOXqp0XtMVFGHs5gxO9UH0P+wDktQ5auDDlaIX7L0trjQYReRoseFq+PrUxZBPjpgtu/7gJXQJDWCbqoYmS4ulqedElfWvXxcLn2vLz9IF2Hxw5e2330b//v3RpEkTXL16FbNmzYKnpyfi4uKgVCoRHx+PSZMmISAgAH5+fnjjjTcQGRmJbt26AQB69+6NsLAwvPLKK1i8eDF0Oh1mzJiBhIQEY9TJmDFjsHLlSkyZMgUjR47Evn378OWXX2L37t223hwicoLSMgGzd1YdRilAfwfS7J3Z6BWmkuWPC7K/6i7MiDExugUSe7bgvkVENsPzGpE4bCskF44a+HCl6AV7b4srDUaRZfbv3+/sKshet2aBUCt9TAYnq8LzcPUMfRixVsVFIKrF/XasETmKzRPa//bbb4iLi8MjjzyCF198EYGBgTh8+DAaNWoEAFi6dCmefvppDBo0CE8++SRUKhX+/e9/G9f39PTErl274OnpicjISAwdOhTDhg3DnDlzjGVCQ0Oxe/dupKSkoH379liyZAk++eQTxMTE2HpziMgJ0nPyajypl5/vk6giS+Y4Lc9DAax+OQLjo1uyo0hkhQULFuDRRx+Fr68vgoKCMHDgQJw9e9akTFFRERISEhAYGIj77rsPgwYNqjTV6+XLl9GvXz/Ur18fQUFBmDx5Mu7du2dSZv/+/YiIiIC3tzcefvhhbNy40d6bVys8rxGJw7ZCcmIYLBjQ4QFENg9k/1EC+J0QWccwvZ45hvPw4QvX7V8pmTl88brZwSkDtdIH3Vxk4JzsELmydevWGpf7+Phg1apVWLVqVbVlmjRpUmnar4p69OiBEycsT05MRNJ37aa4E5LYcuReLJ3j1Lge86sQ1coPP/yAhIQEPProo7h37x7eeecd9O7dG9nZ2WjQoAEAYOLEidi9eze++uorKJVKJCYm4rnnnsPBgwcBAKWlpejXrx9UKhUOHToErVaLYcOGoW7dunjvvfcAADk5OejXrx/GjBmDLVu2YO/evXjttdegVqsle6MNz2tE4rCtEBEROUdsuBrxUU3x6cFLZssmJGdg4SBOaWWgydJi2rZTosszz4prsXvOFSIiSwX5+ti0HLmHv5PXi5vj1MBDAayMi+DAClEtaTQak783btyIoKAgHD9+HE8++SQKCgrw6aefIjk5GT179gQAbNiwAa1bt8bhw4fRrVs3fPfdd8jOzkZqaiqCg4PRoUMHzJ07F1OnTkVSUhK8vLywdu1ahIaGYsmSJQCA1q1b46effsLSpUslO7jC8xqROGwrREREzhMdphI1uJJ/p4Q5A//H0inJJ0a3dPvPzNXYfFowIqLa6hIaALXSB9WN4yugD6PsEhrgyGqRhGmytIhauBdLU3+xeF1GrBDZR0FBAQAgIEB/rD5+/DhKSkoQHR1tLNOqVSs89NBDSEtLAwCkpaWhbdu2CA4ONpaJiYlBYWEhTp8+bSxT/jUMZQyvIUU8rxGJw7bimkrLBKRduI5vMn9H2oXrKC2zJisekftiGyJHMXceLk8AkLTjtFvvjzXliquKys8biT0ftmudyPEYuUJEkuPpocC7/cIwLjmj0jLDSZ5hlGSw56S2yn3FHLXSB7P6h/GuESI7KCsrw4QJExAVFYXw8HAAgE6ng5eXF/z9/U3KBgcHQ6fTGcuUH1gxLDcsq6lMYWEh7ty5g3r16lWqT3FxMYqLi41/FxYW1m4DLWSYx3rs5gwogEo/wAQA7/ZrzfMauT32AV2PJkuL2TuzTeahZx+sZqVlgssnZXeHbbQVtiFypPJ9VjF0hcVYue88xke3sHPNpMlcrrjyFACSnmnDY50L4uAKEUmOJkuLubuzq1ymYkeSytlz8ioSP7c8/9bE6BZI7NmCHRsiO0lISEBWVhZ++uknZ1cFALBgwQLMnj3bqXWIDVdjzdCIShdIDObuPgMPDwXPb+TW2Ad0LdVNlaIrKOJ0MtVwhwvp7rCNtsI2RM5g6LNO23YK+XdKzJZfmvoLHlHd55b7Ymq2TlQ5//p1sfA55qhxVZwWjIgkxdCBrG70/91+rXlCIpSWCVieeg7jkk/A0ijkidEtMT66JQdWiOwkMTERu3btwvfff48HH3zQ+LxKpcLdu3eRn59vUj43NxcqlcpYJjc3t9Jyw7Kayvj5+VUZtQIA06dPR0FBgfFx5cqVWm2jtWLD1Xi3X1iVywwXSjRZWgfXikga2Ad0LTVNlWJ4bvbObJeYTsZWUzZV1wZc6fxgj2101Smz3KkNkfTEhquxakiE6PLuuC9qsrSi8tMAwKo4DoS6Mg6uEJFkmJuvUgH9nb3udtImU7XJr8I5TonsRxAEJCYm4uuvv8a+ffsQGhpqsrxTp06oW7cu9u7da3zu7NmzuHz5MiIjIwEAkZGROHXqFK5du2Ysk5KSAj8/P4SFhRnLlH8NQxnDa1TF29sbfn5+Jg9nKC0Tqr0rnxdKyJ2xD+h6zE2VIgDQFhQhPSfPcZWyA02WFo8v2oe4jw9j/NZMxH18GI8v2mfxIIE7XEi3xzba6vOXIndpQyRd3ZoFQq30EVVWW1CEwxeu27lG0mE4npljyBXXrXmg/StFTsPBFSKSDHYgyZw9J7UYszkDusJi84Ur4BynRPaVkJCAzZs3Izk5Gb6+vtDpdNDpdLhz5w4AQKlUIj4+HpMmTcL333+P48ePY8SIEYiMjES3bt0AAL1790ZYWBheeeUV/Pe//8W3336LGTNmICEhAd7e3gCAMWPG4OLFi5gyZQp+/vlnrF69Gl9++SUmTpzotG0Xi+c5oqqxbbieazfFzUEvtpwU2TIKwx3agK230dUjfdyhDZG0GfKviJWQLP92J9bhi9dF5VoRwFxx7oCDK0QkGexAUk30+VUsT1wP6O8W4ZzERPa1Zs0aFBQUoEePHlCr1cbHF198YSyzdOlSPP300xg0aBCefPJJqFQq/Pvf/zYu9/T0xK5du+Dp6YnIyEgMHToUw4YNw5w5c4xlQkNDsXv3bqSkpKB9+/ZYsmQJPvnkE8TExDh0e63B8xxR1dg2XE+Qr7i7ncWWkxpbR2G4Qxuw5Ta6Q6SPq7chkofYcDUmikxWn3+nxCUGNs3RZGmRsEXcdYmRUU15DcINMKE9EUnG/fd5iyrHDqR7KS0TsHLfeaumAQOYvJ7IUQTB/AUMHx8frFq1CqtWraq2TJMmTbBnz54aX6dHjx44ceKExXV0NrHnr/sbiDsfErkKXkR0PV1CA6BW+kBXUFTlBXAFAJXSB11CAxxdNZuwJAojUsR0MO7QBmy5jbb+/KXI1dsQyUdizxb4PP0KdIXiBkhn78xGrzCVS/7+NkTMiR227RWmsmt9SBoYuUJEkqDJ0uKtLzNrLGOYr5IdSPdRm/wqHgpg9csRTF5PRJJhuFBi7oj01lf/dfm7/ojKu3G7GDWdqtkHlJ/y08lU/GoNf8t5qhRbR5qYOz+4Qhuw5Ta6Q6SPq7chkg9PDwWSngkz238FXGMKw+qUlglI2lF9frjyXOGYTeJxcIWInM4w+l9THg12IN1PbfKrAMDKuI7o244huEQkHTVdKCkvt9A15osnEkOTpUVC8gmYm72HfUD5iQ1XY83QCKgqJERWucB0rbaONHGHC+m23EZ3iPQBXLsNkbwY9kX/enVFlU/J1tm5Ro63ct850dE7gPyP2SQepwUjIqeqab7c8lRKH8zqH8YOpJvQ51exbsofNfcVIpIww4/TpB2nqx08FqC/0OTK0yoQAeL6gR4KYGUcLyLKVWy4Gr3CVEjPycO1m0UI8tXfySv345o9pmwynB9m78w2mfLKlX4H2Wob3WnKLFdtQyQ/seFq+PrUxZBPjpgtu/7gJXQJDXCJ4xagvxFkaeo5UWX969fFwufausy2k3kcXCEipzI3X67BB8+3R1SL+x1QI3Im5lchIncg5sepK8wXT2SOmH5gmQA0bODloBqRPXh6KFzuOGaIwhi7OQMKwOQCf20iTdzhQrotttFen79UuWIbInnq1iwQaqWPqGs473x9Cj1bBcOrjrwnTTLcCCLWqrgIXrtyM/Lew4lI9sTOg/vnbeumhiL5YH4VInInf94Sd16T83zxROaInV6D7YCkyF5TNhkupA/o8AAimwe6ZN/WFtvIKbOIHK/89H7m5N0uQbcFe2U/za3YG4IB/Swa3TgQ6nYYuUJETnXpz9uiysl9vlyqmSHvjpjkcFVhfhUikhux57VLf/5l55oQOYcmS4u5u06LKst+IEmVO0SaSBk/fyLHiw1XIz6qKT49eMls2bzbdzF2c4asBzxTLcgf40oRcyQeB1eIyGnEzFvpSvPlUtVKywQk7TCfd6cqzK9CRHJlbr54g2Wpv+AR1X08zpFLEXtTBfuB8lZaJrjFRW9O2eRc7vT5u0ubIumLDlOJGlwB9NP2Je04Lcs8gposrejtnBjdkv11N8XBFSJyCkvmreTov2tbue+c6GlBymN+FSKSM8O0CmM2Z5gty8T25ErEJLEHXDNvgjvRZGkrJS3nTTE1c8cL5+64zdZimyIpEXuTkIGusBgr953H+OgWdq+brdy9V4Z3vs4SVVbl543Eng/buUYkVRxcISKnOHzxuqh5Kydw9N+l7TlpPnqpIg8FsDIugtOAEZHsxYarMTG6RY3HQUNi+8MXrjM5JrkEsXOXBzTwwvxnw9kPlKHqIpN0BUWynx7GXtzxwrk7brO12KZIagw3CY0VcZOQwVIZRWNrsrR45+tTyLtdIqp80jNtODDsxpjQnogcTpOlRcIWcSfhpvfXt3NtyFn2nLyKxM/Fd8YMmF+FiFxJ0/sbiCqXkJwh+4SgRID4uctn9GstiwswZKqmyCTDc7N3ZqO0zNpMe9JWWiYg7cJ1fJP5O9IuXBe1nYYL5xUHHQ0Xzl3x2G+Lbbbms5Yjd29TJF2x4WqsGRqBgAZ1Ra8jh33VcHwSO7AyMqop+ytujpErRORQliYuZwJT11NaJmDlvvNYmvqLRevxTjYickViz3P5d0p4dyrJniVzl6uU9exbGRLF0mmbzEUmGaLx0nPyXC5PhjWRGOYunCvgelND2mKb3SnqxVZtilOwkT3EhqvRs1Uwui3Yi7zbd82Wl/rx35p8sL3CVHarD8kDB1eIyGHEzrENMIGpq9JkaZG04zR0hcUWrcf8KkTkqiyds9rVLrKR+xCbb499QOmw5gL2tZvi8uiJLScX1k7b5I6DUbXdZnebIssWbcqdBqPI8bzqeOC9Z8NF5REEgJRsnWSPZ5bkg2V/hQw4LRgROYzYObYNmMDUtew5qcWYzRlWDKy0xPjoltwXiMglGeasFqP8BSciuRGbb08A+4BSYO20TWKj8VwpOr020za542BUbbbZHafIqm2bcsdp5+j/t3f3QVFddx/Av7sElvdFVNwlGsWIWsTXWAiadqwhovExiZ1an7xYtJ10QiCtYtpGHxVNoxhtotUQnfZJ1IyjY55OzVsdWkNsUyORVkNTtDqaQEkqi7UUVomA7N7nD3oJ4gL33r27d/ee72eGP1iW5Zy77HfP3t+55wSfvI+gEq9+UBeS/3flNer3g+V4hQAWV4goiJSusZ0UG2m6GUei07q/iiPRhqLZYwLQIiKi0CGvWZ0Uo2zN6qMK30+JQoWa/fa4drnx/DmBLV+N19epJgu6ZsybaaavmisxehOxGOVPn/051uHKn9eUiMUoMk7R7HQ4EpW9vlcf/is6Or0BbpFyHZ1erD5co/j+g+OieM6KurG4QkQB5/FK+Pm7FxSvsV32MN+kzEJ+7p888BG0jNnXPzCBM0GISAhzM50oe3Saovu++kEdjnx8KcAtItKHfOVq83VlG8Ny7XLj+XMCu+fVeL1HcPL3Zpvp68+VGCIWo/zps4hX+vjzmhKxGEXGibBasP4BZVdjN7XewN2lFSFxBUt5TQPuLn1X0Z4xAJAcF4nKVffynBV1Y3GFiAKqvKYBMzdXKNq8XB5I3x2i62+SOmqe+96sFuDlR1hkIyKx3D16MJx2ZTP+ig5+hCMfG/+BlKg/aq5cNeNJ5HDl7wls+Wo8R688c9ijTTnT158rMUQsRvnTZxGv9AG0v6ZELEaRseZmOvG9maMU3beptcPwpenkZfOaWpVNAAGATQsnIuo2nk6nL3FDeyIKmL42G+wL19g2jyMfN+DJA+qXAZO99PBU3D/JXB+8iYgGIp9wUrIhqFcCnjxwGrut5jtRSeZQXtOAJw98pOp3OA4MDXqcwJ6b6cR9GQ5U1Tbh8tU2pCR0Fc7M+PzKV2K4Wtp8fu4ZaNNj+cR57w3HHSbecFxrn/091uFMy2tK1GIUGSs3w6F41RIJwPq3zuC+DEfQ3x88Xgnr3/K9bF5fVuSONWUmk39YXCGigNDyRsU1ts2ha5aqupMpMqeJP0QSESkhz/hT+qF09eG/Yvb4YZxBRyFF7drlSbGR2PzNiXz/DxH+nMD2eCUhCio9yYXxgv2nYQFuOmZKrz4RqRgl09JnPY51OIuwWpBz5+Du19k7H1/q97iJXIwi4wz0f9eby92Ol967iB/mpge8bT299N4FuNzKr9rifrDUFxZXiEhX8kDvtcpaVW9UANfYDncer4SX3ruoaRkwAFiRm46i2emm/TBERKSUmhl/8prVmxZm8sQ0hYTymgasPvxXVUtslD08DTPThwSwVaSG1hPY5TUNt1yJIMrEGa1XYohYjOpJbbEAEPNKn57UvM5EL0aRMXr+3ynVdQ5BCtr5gPKaBmx794Li+1vA/WCpbxZJkjRsMWwObrcbdrsdLS0tSExMNLo5RGHP10BPCXnGzPGfzA7JNyszZoXefSqvacD6t87A5W5X/btWC/DSw9O4DBhRiGIGBp/HK+Ge599T/X768iNTcf+k1AC1imhgWpYFdXIMGHRK+6XmJG5fywHLz6oZ91rxRS4SuFquo6m1A8nxNjgSfRcLRC5G9aTmOPQsRg2JswEW4Mq1dmEKU1pfZ1r/18yYgWbsU6jSMtnCkRiN9Q8ENgM7Or24u7RC8Qb2g+OisJGTmISkNC945QoR6cLfPTY4YyZ8cX8VIiJ9qdl7pafCAx9had2/MWeCQ4iTTBQa5JOdvz3TgH2Vf1f9+xwDhq6eyzb1LBbYY6Lg8Urdz5vHK2HD276XA5bQdeJ3w9tnDVlTP9girBa0XO/Alt+e7/dEdl8nyV0tbSjYf1qYYpSa49BfgSDnzsFBbLUxtLzO5Hxu7/TiZ9+abIpiVGlpKX7961/j3LlziImJwYwZM/D8889j3LhxRjeNepmb6cTs8cNUFTJc7sBmoNqCT3JcJCpX3cvld6lfLK4QkV88Xgk7Ki5gR4XySyp7EnF2lplwfxUiosCYm+nEy49MRdHBj+BVeJ25BGDPiTrsOVGHpJhILJs5isstUsDIy4Hu+aAWzdeVz0qVyVeuchwQ2pQUC6pqm/q90k4C0NDShqraJtOfBFdSLLgvw8FiFNQVC46edQlfjFL7OjNrMeoPf/gDCgsL8dWvfhWdnZ1YvXo15syZg7NnzyIuLs7o5lEvUbdZsWlhpqoJQ4Ha5F7LpNBNCyeysEIDYnGFiFSTZ8AcPevC63/+HNfaOzU9DvfYCG/lNQ148oC2wgqfeyKigd0/KRUvwaLp6sDm6zew7d0L+MUfP8V/Tx+B3AxezUL+02sMCPDK1XChpFjQ3ulV9FiXr6pb6jDcKC0WJERHshgF5cWCDz/5F4tRUP76uXy1zdRXRpWXl9/0/d69e5GSkoJTp07h61//ukGtov7MzXRiRW66qj1O9N7kXsuk0BW5Y8P2dULBxeIKESnm7wzFnlbkjtXtjZKCr6PTi9WHa1T/HvdXISJS5/5JTuy2TlO9ZrWstd2DVz6owysf8GoW0k7PMSDXLg8fSosFP1s0WdHjpSRE69m8kKO0WFD5yb8UPZ7Zi1FK+1f56RUWo6D89TMkzoanf/UXYYpRLS0tAIDk5GSDW0L9KZqdjoNVn8HlVp5remxyL49fuh5LOUeiDUWzx2j6myQeFleIyKfemzF+3nwd/+fnDEUZ36jCm5aN6WScpUpEpJ6WNat96Xk1y7fvGo7hg2L73WyZxNR7w+g/1TXhF3/8FF90ePx+bK5dHl6UFgsgdW1C3NdJMwsAh70rZ8xMeTFE2VqPZi9GKe2fpHBpTLMXo7LSkuG0R8PV0tbnf1ByXCS8kiRMMcrr9WL58uWYOXMmMjMzfd6nvb0d7e3t3d+73e5gNY96iLBasP6BDJ9XVPVn27sX8OoHdfiuhslB5TUNWP/WGbjc7QPfuQcLgPUPTOC4mBRjcYVIUL0/OMMCXHa3dRdS3qy+5NcJHF/ktya+UYUvrZvXc38VIiL/aFmzui+t7R7sOXHzxuPJcZF4cHIqhg+KRVJsFJq/6GDhxcR6T6JJjrchJb6rkLL3RJ3fV6f0hWuXhxelJ6vf/ZsLbZ2+i29ycpQsyDB9jigtFlgtFlgt6HM/LVGKUUqKBQBwoKpe0eOZvRgVYbWgZEHXyWkLfJfomlpv4CmFSx+ZoRhVWFiImpoaHD9+vM/7lJaWYsOGDUFsFfVlbqYTux6bprrg0fKfyUF7TtRh8zcnKjqnoPW8Ba+uJS1YXCEKQ/0VRuQPxz1v63mSJBgfnPvi4An2sKZ183rur0JEpA8tm9wr1dR645aCi6xn4cXXOGOg265ca0dKAos0euldGOk9zhvouQnUJJr+cPP68KT0ZHVf2QEASbGRKFV4MizcKS0W7Hjv4oCPJUIxSkmxAAD+/UX/n1lFKUYBX56c7r1RfU9KP+OHezGqqKgI77zzDt5//30MHz68z/utWrUKxcXF3d+73W6MGDEiGE0kH+ZmOnFfhkPTUl3NX9zAE/tP41vTbsfM9KG3TACSx0e/PdOAfZV9vy/1hVfXklZhX1wpKyvD1q1b4XK5MHnyZOzcuRNZWVm6/52+ZnepPamt5oOo2tv4t8T4W0YVRrSyAFg6YxTmTOBGukpt3LgRv/nNb1BdXY2oqCg0Nzffcp/6+noUFBTg2LFjiI+PR35+PkpLS3HbbfrHutZ1Srm/ChEFUrDGgKHGn03uteqv8KJGUkwk8meMRFba4LAae4XS3zKiMKIHLgvqm5IxX2+SJKGkpAS//OUv0dzcjJkzZ2LXrl1IT9d/L0OlxYL+2G6z4r4Mh67tClVKiwX9Ea0QqXUme28iFKNk/i4VGu7FKEmS8NRTT+Hw4cP4/e9/j7S0tH7vb7PZYLPZgtQ6UiLCavnP/ruSqk3uZb86/Q/86vQ/AHw5AehqWyeO/u0yWvw4T8ara0mrsC6uHDp0CMXFxdi9ezeys7Oxfft25OXl4fz580hJSdHt75TXNPQ7M4CI+lb2yFTcPynV6GaElY6ODixatAg5OTl45ZVXbvm5x+PB/Pnz4XA4cOLECTQ0NOA73/kOIiMjsWnTJl3bonWdUoAnUogocII1BgxV8ib34TY+bb5+Az+vuAhg4FnbZA5cFrR/A435fNmyZQt27NiBffv2IS0tDWvXrkVeXh7Onj2L6Gh9Z6L3LBZo5XK3m2JvB6X8LRZ4JWBQXFQAWha65mY6kRAdiUf/96Tq3xWtGCU79fd/+1VkD+diVGFhIQ4cOIA333wTCQkJcLlcAAC73Y6YmBiDW0dqaNnkvjc9JgCJmiOkn7Auyb344ot4/PHHsWzZMmRkZGD37t2IjY3Fq6++qtvfKK9pQMH+02H1wZUoFAyKjcTux6axsKLBhg0bsGLFCkycONHnz3/3u9/h7Nmz2L9/P6ZMmYJ58+bhpz/9KcrKytDRod9MVjn/1H4wHBwXxeeeiAIqGGPAUDc304njP5mNg4/fje/OHIV4W1jPmSITibdF4HszR+Hg43fj+E9m82RFPwYa8/UmSRK2b9+ONWvW4MEHH8SkSZPw2muv4dKlS3jjjTcC0ka5WJAcF6n5Mcywt4MaczOdeOHbUzT/vmjHC+haPlILEYtRgH//I8tzx4Z1Lu/atQstLS2YNWsWnE5n99ehQ4eMbhqpJG9yb3SZj5NCyV9hW1zp6OjAqVOnkJub232b1WpFbm4uKisrdfkbHq+EDW+f1XwJNJGIkmIisSI3HX9ec19YD9pCWWVlJSZOnIhhw4Z135aXlwe3240zZ87o8je05p+8TimfeyIKlGCMAcNFhNWCnDsHY92CCfhLyRysyB2LpBjtJ0CJ/CGPAf9Skoe1CyYg587BYTszOlTV1tbC5XLdlH92ux3Z2dkBzb+5mU6s/a8Jmn8/3Pd20EJrsQAQ83j502cRi1H+HK9RQ2J1bEnwSZLk82vp0qVGN400kAv4jsTgL93mtEdzUijpImynuF25cgUej+emk4sAMGzYMJw7d87n77S3t6O9/ctBjtvt7vdvVNU28YoVIgXibRFYPH0EcjO4r0owuFwun9kn/8yXYOUf1yklokALxhgwHMnrVxfNHoOq2iYcPevC63/+HNfaO41uGpkYx4DBJY/zfOVfX2NAQJ8MdCSqP5kb7ns7+EPLyW+Rj5e8v4+Wzx8iFqN4vMhM/NnkXqsVuekomp3OcQvpQqgzYKWlpbDb7d1fI0aM6Pf+Is6AIFKDMxSVe+aZZ2CxWPr96uukoB4CnX9WC/DyI1ynlIhCk9oMDGe8moWCIS4qgmPAPhg95vNFjwyUT+aqFc57O/iDx0sdeX8fNSzomnkuYjGKx4vMRp4ktPuxaUiKDdy4VT5v8cPcsUJmLQVG2F65MmTIEERERKCxsfGm2xsbG+FwOHz+zqpVq1BcXNz9vdvt7ndgyYo+0a2S4yKxcMrtnKGo0sqVKwe8VHn06NGKHsvhcKCqquqm2+QsNCr/uE4pEQVLMMaAZuHrapY3qi/5tQkuiS0pJhLLZo7ibM9+6Dnm603OuMbGRjidX467GhsbMWXKlD5/T48MlE/mPqFwc/vBcVHYuDBT2Ik3PF7qzc10YkVuOra9e0Hx74hajAJ4vMicel7F8urxT9HSpu8V2DxvQYEQtsWVqKgo3HXXXaioqMBDDz0EAPB6vaioqEBRUZHP37HZbLDZlK/j58+llkRmkBwXiQcnp2L4oFgkx9vgSIxmQUWjoUOHYujQobo8Vk5ODjZu3IjLly8jJSUFAHD06FEkJiYiI8P3DCat+edqaet33xWnPRolCzKE/iBIRMEVjDGg2chXs+TcORj/Mz8DVbVNcLVcR1NrBz5vvo43WXAhH5JiIpE/YySy0gbjyrV2pCRwHKiEnmO+3tLS0uBwOFBRUdFdTHG73Th58iQKCgr6/D29MnBuphMvPzIVRQc/grefAaK8B5/oS8XyeKlXNDsdB6s+g8vd/zkYfgbpwuNFZtRzcpBeS4XxNUCBFLbFFQAoLi5Gfn4+pk+fjqysLGzfvh2tra1YtmyZLo8vzzYp2H+am9qTafX84HzZ3Yam1g4WUgxWX1+PpqYm1NfXw+PxoLq6GgAwZswYxMfHY86cOcjIyMCSJUuwZcsWuFwurFmzBoWFhbqdPOyZfxbAZwZynVIiMkqgx4BmJhdaelrTq+CSFBuF5i9YeDG73pNoUuJtgAUspATRQGM+ABg/fjxKS0uxcOFCWCwWLF++HM899xzS09ORlpaGtWvXIjU1tbvYHGj3T0rFS7DgyQO3XpEh/7dwD74v8XipE2G1YP0DXZ9BAH4GGQiPF5mZXGQZ54jHhrfPqp74zn3hKFjCuriyePFi/POf/8S6devgcrkwZcoUlJeX37LBnz/mZjqx67Fpml7IRIHWV2FE/nAs3yafJOEH5/Cwbt067Nu3r/v7qVOnAgCOHTuGWbNmISIiAu+88w4KCgqQk5ODuLg45Ofn49lnn9W1HX3lH2d9EJHRgjEGFImvgousd+HF1zijv9v+VNeEvSfq0Hz9RpB7ZW49CyO+xnkDPTecRBMaBhrzAcD58+fR0tLSfZ8f//jHaG1txfe//300NzfjnnvuQXl5OaKjg7ek9f2TnNhtvXWM6OAY0SceL3X4GUQdHi8yO3mpMKVXXnMJUwo2iyRJwl6U4Xa7Ybfb0dLSgsTExH7v6/FKmj5Yavmwo/U2/i1x/hYLI8GlJivChZb8u3y1jf93RAISPQPJfz3fR4bEhefYK5T+FgsjwWPWrNCrXxwjqsPjpQ6PlzqBOF5mzEAz9klUvs7TcoxEelKaF2F95Uow9Tejj4jIzJh/RETkD76PEJkTX9vq8Hipw+OlDo8XiYb/8xQquLAnERERERERERERERGRCiyuEBERERERERERERERqSD0smDydjNut9vglhBRKJMzwkxbVDH/iEgpZiARicqM+QcwA4lIGTNmIPOPiJRSmoFCF1euXr0KABgxYoTBLSGicHD16lXY7Xajm6EL5h8RqcUMJCJRmSn/AGYgEaljpgxk/hGRWgNloEUyUwlaJa/Xi0uXLiEhIQEWi2XA+7vdbowYMQKfffYZEhMTg9BCY4nWX4B9Zp99kyQJV69eRWpqKqxWc6ymyPwbGPvMPpuRlv4yA8X7PwHYZxH6LFp/AY4BZczAgYnWZ9H6C7DPomag2vwDxPtfEa2/APssQp8D+TlY6CtXrFYrhg8frvr3EhMThfjHk4nWX4B9FoWaPptlpo6M+acc+ywG0fqstr/MwC6i/Z8A7LMIROsvIPYYEGAGqiFan0XrL8A+D8RsGag1/wDx/ldE6y/APosgEJ+DzVF6JiIiIiIiIiIiIiIiChIWV4iIiIiIiIiIiIiIiFRgcUUFm82GkpIS2Gw2o5sSFKL1F2CfRSFin/0l4jFjn8UgWp9F669eRDxu7LP5idZfQMw+60HE4yZan0XrL8A+k3KiHTfR+guwzyIIZH+F3tCeiIiIiIiIiIiIiIhILV65QkREREREREREREREpAKLK0RERERERERERERERCqwuEJERERERERERERERKQCiytEREREREREREREREQqsLii0MaNGzFjxgzExsYiKSnJ533q6+sxf/58xMbGIiUlBT/60Y/Q2dkZ3IYG0KhRo2CxWG762rx5s9HN0lVZWRlGjRqF6OhoZGdno6qqyugmBcz69etveT7Hjx9vdLN08/7772PBggVITU2FxWLBG2+8cdPPJUnCunXr4HQ6ERMTg9zcXFy4cMGYxoY45h/zz2zMnn8AM1BPzEBmoNkwA5mBajADmYFmwwxkBirF/Oti9gxk/jH//M0/FlcU6ujowKJFi1BQUODz5x6PB/Pnz0dHRwdOnDiBffv2Ye/evVi3bl2QWxpYzz77LBoaGrq/nnrqKaObpJtDhw6huLgYJSUlOH36NCZPnoy8vDxcvnzZ6KYFzIQJE256Po8fP250k3TT2tqKyZMno6yszOfPt2zZgh07dmD37t04efIk4uLikJeXh7a2tiC3NPQx/7ow/8zFzPkHMAP1xAzswgw0F2YgM1ApZmAXZqC5MAOZgUow/75k1gxk/jH/dMk/iVTZs2ePZLfbb7n9yJEjktVqlVwuV/dtu3btkhITE6X29vYgtjBwRo4cKW3bts3oZgRMVlaWVFhY2P29x+ORUlNTpdLSUgNbFTglJSXS5MmTjW5GUACQDh8+3P291+uVHA6HtHXr1u7bmpubJZvNJh08eNCAFoYH5t82o5sRMMw/c2MG6oMZuM3oZgQMM9DcmIH6YAZuM7oZAcMMNDdmoP9Ezj9JMncGMv/MLVj5xytXdFJZWYmJEydi2LBh3bfl5eXB7XbjzJkzBrZMX5s3b8bgwYMxdepUbN261TSXO3Z0dODUqVPIzc3tvs1qtSI3NxeVlZUGtiywLly4gNTUVIwePRqPPvoo6uvrjW5SUNTW1sLlct30fNvtdmRnZ5v6+Q4U5l94Y/6JlX8AM1BvzMDwxgxkBgLMQH8wA8MbM5AZCDADtRIl/wBzZiDzj/kH6JN/t+nROAJcLtdNgQqg+3uXy2VEk3T3gx/8ANOmTUNycjJOnDiBVatWoaGhAS+++KLRTfPblStX4PF4fD6H586dM6hVgZWdnY29e/di3LhxaGhowIYNG/C1r30NNTU1SEhIMLp5ASW/Jn0932Z5vQYT8y+8Mf/Eyj+AGag3ZmB4YwYyA2XMQG2YgeGNGcgMlDED1RMh/wDzZiDzj/kn8zf/hL5y5ZlnnrllI5/eX2Z9QcnUHIPi4mLMmjULkyZNwhNPPIEXXngBO3fuRHt7u8G9IC3mzZuHRYsWYdKkScjLy8ORI0fQ3NyM119/3eimURAw/5h/ImP+ETOQGSgyZiAxA5mBImMGio3514UZKCbmX2AIfeXKypUrsXTp0n7vM3r0aEWP5XA4UFVVddNtjY2N3T8LVf4cg+zsbHR2dqKurg7jxo0LQOuCZ8iQIYiIiOh+zmSNjY0h/fzpKSkpCWPHjsXFixeNbkrAyc9pY2MjnE5n9+2NjY2YMmWKQa0KLuYf80/G/BMr/wBmIMAMBJiBMmYgM1DGDLwZM5AZGMrPn56Ygej+XoQMZP51YQYy/wDmn8zf/BO6uDJ06FAMHTpUl8fKycnBxo0bcfnyZaSkpAAAjh49isTERGRkZOjyNwLBn2NQXV0Nq9Xa3d9wFhUVhbvuugsVFRV46KGHAABerxcVFRUoKioytnFBcu3aNXzyySdYsmSJ0U0JuLS0NDgcDlRUVHQHqNvtxsmTJ1FQUGBs44KE+cf8kzH/xMo/gBkIMAMBZqCMGcgMBJiB/mAGhjdmIDMQECsDmX9dmIHMP4D5B+iTf0IXV9Sor69HU1MT6uvr4fF4UF1dDQAYM2YM4uPjMWfOHGRkZGDJkiXYsmULXC4X1qxZg8LCQthsNmMbr4PKykqcPHkS3/jGN5CQkIDKykqsWLECjz32GAYNGmR083RRXFyM/Px8TJ8+HVlZWdi+fTtaW1uxbNkyo5sWEE8//TQWLFiAkSNH4tKlSygpKUFERAQefvhho5umi2vXrt1Ufa+trUV1dTWSk5Nxxx13YPny5XjuueeQnp6OtLQ0rF27Fqmpqd1vqvQl5h/zz2zMnn8AM1BPzEBmoNkwA5mBajADmYFmwwxkBiolev4B5s9A5h/zT5f8k0iR/Px8CcAtX8eOHeu+T11dnTRv3jwpJiZGGjJkiLRy5Urpxo0bxjVaR6dOnZKys7Mlu90uRUdHS1/5ylekTZs2SW1tbUY3TVc7d+6U7rjjDikqKkrKysqSPvzwQ6ObFDCLFy+WnE6nFBUVJd1+++3S4sWLpYsXLxrdLN0cO3bM52s2Pz9fkiRJ8nq90tq1a6Vhw4ZJNptNuvfee6Xz588b2+gQxfxj/pmN2fNPkpiBemIGMgPNhhnIDFSDGcgMNBtmIDNQKdHzT5LEyEDmH/PP3/yzSJIkaS/NEBERERERERERERERicVqdAOIiIiIiIiIiIiIiIjCCYsrREREREREREREREREKrC4QkREREREREREREREpAKLK0RERERERERERERERCqwuEJERERERERERERERKQCiytEREREREREREREREQqsLhCRERERERERERERESkAosrREREREREREREREREKrC4QkREREREREREREREpAKLK0RERERERERERERERCqwuEJERERERERERERERKQCiytEREREREREREREREQq/D/mY6pCZWVd0wAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "24\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "25\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "26\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "27\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "28\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "29\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# first, randomly select the other multiple choice options\n",
-    "np.random.seed(1)\n",
-    "fbench_questions = []\n",
-    "for idx, _ in enumerate(fbench):\n",
-    "    mc_options = [idx]\n",
-    "    # select 4 more random functions\n",
-    "    for _ in range(4):\n",
-    "        random_idx = np.random.randint(0, len(fbench))\n",
-    "        while random_idx in mc_options:\n",
-    "            random_idx = np.random.randint(0, len(fbench))\n",
-    "        mc_options.append(random_idx)\n",
-    "    # shuffle options\n",
-    "    np.random.shuffle(mc_options)\n",
-    "    # store the options and the correct answer\n",
-    "    fbench_questions.append((mc_options, idx))\n",
-    "\n",
-    "# assure that the shape of the correct answer is unique among the mc options\n",
-    "fbench_questions[4] = ([16, 13, 4, 7, 22], 4)\n",
-    "fbench_questions[7] = ([12, 11, 17, 7, 15], 7)\n",
-    "fbench_questions[9] = ([9, 8, 26, 1, 7], 9)\n",
-    "fbench_questions[10] = ([1, 10, 4, 27, 29], 10)\n",
-    "fbench_questions[13] = ([7, 13, 24, 0, 6], 13)\n",
-    "fbench_questions[19] = ([0, 19, 22, 8, 2], 19)\n",
-    "fbench_questions[22] = ([8, 10, 0, 20, 22], 22)\n",
-    "fbench_questions[27] = ([27, 2, 20, 5, 14], 27)\n",
-    "fbench_questions[29] = ([18, 23, 10, 3, 29], 29)\n",
-    "\n",
-    "# plot the 5 functions for each question\n",
-    "# make a 1x5 grid of plots\n",
-    "for idx, (options, correct) in enumerate(fbench_questions):\n",
-    "    fig, axes = plt.subplots(1, 5, figsize=(20, 3))\n",
-    "    print(idx)\n",
-    "    for i, ax in enumerate(axes):\n",
-    "        f, n = fbench[options[i]]\n",
-    "        y = f(x)\n",
-    "        ax.scatter(x, y)\n",
-    "        ax.set_title(n)\n",
-    "        # if it is the correct one, set the title color to red\n",
-    "        if options[i] == correct:\n",
-    "            ax.title.set_color('red')\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# F-Bench (Hard)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 229,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "23\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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5uQoKClKTJk0UEhIiPz+/cuuEhYXV5VCd/Bv56ne3RtVqn5N+3nDva9LE30+/7N7W08PwSg31uPHz9dHTw3p4ehheK/Gu7g3yfjh+vj6aO7ynp4fhlX7Zva1X3A+nXv01jI6OVmpqqktZSkqKoqOjJUn+/v7q16+fSx2Hw6HU1FRnHU9IuLN7rYQcH0m/434menXsTYScauC4keJ6hCv5/hvV1Av+6NYXzQL8lHz/jQ36PjiXj5uG+B+DmvL0fXCqw603+jt79qyysrIkSX379tXixYt1xx13qFWrVmrXrp0SEhJ07NgxrVmzRtKlj4n36NFDU6dO1W9/+1tt3rxZjz76qD788EPFxsZKuvQx8XHjxunll1/WgAEDtGTJEq1bt07ff/99mWtzKuKOG/1J3Mm4tnEnY+5kXF3cyZg7GdcEdzL2njsZV+f1260BZ8uWLbrjjjvKlI8bN06rVq3S+PHjdfDgQW3ZssWlzYwZM/Tdd9/p2muv1ezZszV+/HiX9i+99JIWLlwom82mPn36aOnSpbJarVUel7sCDgAAcJ96E3DqKwIOAADep958VQMAAIAnEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDp1EnAWb58uTp06KDAwEBZrVZt3769wrq33367fHx8yixDhw511hk/fnyZ9XFxcXUxFQAA4AUauXsDb7/9tuLj45WcnCyr1aolS5YoNjZW+/fvV9u2bcvUf+edd1RUVOR8fPr0afXu3Vv33nuvS724uDitXLnS+TggIMB9kwAAAF7F7QFn8eLFmjRpkiZMmCBJSk5O1ocffqgVK1Zo1qxZZeq3atXK5fHatWvVtGnTMgEnICBAYWFhVRpDYWGhCgsLnY/tdnt1pwEAALyIW9+iKioqUkZGhmJiYv63QV9fxcTEKC0trUp9vPbaaxo1apSaNWvmUr5lyxa1bdtWXbp00ZQpU3T69OkK+0hKSpLFYnEukZGRNZsQAADwCm4NOKdOnVJpaalCQ0NdykNDQ2Wz2a7Yfvv27dq7d68efPBBl/K4uDitWbNGqampmj9/vj7//HMNGTJEpaWl5faTkJCggoIC53LkyJGaTwoAANR7bn+L6mq89tpr6tmzpwYMGOBSPmrUKOfPPXv2VK9evdSpUydt2bJFgwYNKtNPQEAA1+gAANCAuPUMTkhIiPz8/JSbm+tSnpube8XrZ86dO6e1a9dq4sSJV9xOx44dFRISoqysrKsaLwAAMAe3Bhx/f3/169dPqampzjKHw6HU1FRFR0dX2nb9+vUqLCzU/ffff8XtHD16VKdPn1Z4ePhVjxkAAHg/t98HJz4+Xq+++qpWr16tzMxMTZkyRefOnXN+qmrs2LFKSEgo0+61117T8OHD1bp1a5fys2fP6vHHH9fXX3+tgwcPKjU1VcOGDVPnzp0VGxvr7ukAAAAv4PZrcEaOHKmTJ09qzpw5stls6tOnjzZt2uS88Pjw4cPy9XXNWfv379eXX36pf/7zn2X68/Pz0549e7R69Wrl5+crIiJCgwcP1rx587jOBgAASJJ8DMMwPD2Iuma322WxWFRQUKCgoCBPDwcAAFRBdV6/+S4qAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOnUScJYvX64OHTooMDBQVqtV27dvr7DuqlWr5OPj47IEBga61DEMQ3PmzFF4eLiaNGmimJgYHThwwN3TAAAAXsLtAeftt99WfHy8EhMTtXPnTvXu3VuxsbE6ceJEhW2CgoKUk5PjXA4dOuSyfsGCBVq6dKmSk5OVnp6uZs2aKTY2VhcvXnT3dAAAgBdwe8BZvHixJk2apAkTJqh79+5KTk5W06ZNtWLFigrb+Pj4KCwszLmEhoY61xmGoSVLlujJJ5/UsGHD1KtXL61Zs0bHjx/Xhg0byu2vsLBQdrvdZQEAAObl1oBTVFSkjIwMxcTE/G+Dvr6KiYlRWlpahe3Onj2r9u3bKzIyUsOGDdO+ffuc67Kzs2Wz2Vz6tFgsslqtFfaZlJQki8XiXCIjI2thdgAAoL5ya8A5deqUSktLXc7ASFJoaKhsNlu5bbp06aIVK1bovffe0+uvvy6Hw6GBAwfq6NGjkuRsV50+ExISVFBQ4FyOHDlytVMDAAD1WCNPD+CnoqOjFR0d7Xw8cOBAdevWTS+//LLmzZtXoz4DAgIUEBBQW0MEAAD1nFvP4ISEhMjPz0+5ubku5bm5uQoLC6tSH40bN1bfvn2VlZUlSc52V9MnAAAwN7cGHH9/f/Xr10+pqanOMofDodTUVJezNJUpLS3Vv/71L4WHh0uSoqKiFBYW5tKn3W5Xenp6lfsEAADm5va3qOLj4zVu3Dj1799fAwYM0JIlS3Tu3DlNmDBBkjR27Fhdc801SkpKkiTNnTtXN998szp37qz8/HwtXLhQhw4d0oMPPijp0iespk+frmeeeUbXXXedoqKiNHv2bEVERGj48OHung4AAPACbg84I0eO1MmTJzVnzhzZbDb16dNHmzZtcl4kfPjwYfn6/u9E0n//+19NmjRJNptNLVu2VL9+/bRt2zZ1797dWecPf/iDzp07p8mTJys/P1+33HKLNm3aVOaGgAAAoGHyMQzD8PQg6prdbpfFYlFBQYGCgoI8PRwAAFAF1Xn95ruoAACA6RBwAACA6RBwAACA6RBwAACA6RBwAACA6RBwAACA6RBwAACA6RBwAACA6RBwAACA6RBwAACA6RBwAACA6RBwAACA6RBwAACA6RBwAACA6RBwAACA6RBwAACA6RBwAACA6RBwAACA6RBwAACA6RBwAACA6RBwAACA6RBwAACA6RBwAACA6RBwAACA6RBwAACA6RBwAACA6RBwAACA6RBwAACA6RBwAACA6RBwAACA6RBwAACA6dRJwFm+fLk6dOigwMBAWa1Wbd++vcK6r776qn7+85+rZcuWatmypWJiYsrUHz9+vHx8fFyWuLg4d08DAAB4CbcHnLffflvx8fFKTEzUzp071bt3b8XGxurEiRPl1t+yZYtGjx6tzz77TGlpaYqMjNTgwYN17Ngxl3pxcXHKyclxLm+99Za7pwIAALyEj2EYhjs3YLVaddNNN+mll16SJDkcDkVGRuqRRx7RrFmzrti+tLRULVu21EsvvaSxY8dKunQGJz8/Xxs2bKjSGAoLC1VYWOh8bLfbFRkZqYKCAgUFBVV/UgAAoM7Z7XZZLJYqvX679QxOUVGRMjIyFBMT878N+voqJiZGaWlpVerj/PnzKi4uVqtWrVzKt2zZorZt26pLly6aMmWKTp8+XWEfSUlJslgsziUyMrJmEwIAAF7BrQHn1KlTKi0tVWhoqEt5aGiobDZblfp44oknFBER4RKS4uLitGbNGqWmpmr+/Pn6/PPPNWTIEJWWlpbbR0JCggoKCpzLkSNHaj4pAABQ7zXy9AAq8/zzz2vt2rXasmWLAgMDneWjRo1y/tyzZ0/16tVLnTp10pYtWzRo0KAy/QQEBCggIKBOxgwAADzPrWdwQkJC5Ofnp9zcXJfy3NxchYWFVdp20aJFev755/XPf/5TvXr1qrRux44dFRISoqysrKseMwAA8H5uDTj+/v7q16+fUlNTnWUOh0OpqamKjo6usN2CBQs0b948bdq0Sf3797/ido4eParTp08rPDy8VsYNAAC8m9s/Jh4fH69XX31Vq1evVmZmpqZMmaJz585pwoQJkqSxY8cqISHBWX/+/PmaPXu2VqxYoQ4dOshms8lms+ns2bOSpLNnz+rxxx/X119/rYMHDyo1NVXDhg1T586dFRsb6+7pAAAAL+D2a3BGjhypkydPas6cObLZbOrTp482bdrkvPD48OHD8vX9X87661//qqKiIt1zzz0u/SQmJuqpp56Sn5+f9uzZo9WrVys/P18REREaPHiw5s2bx3U2AABAUh3cB6c+qs7n6AEAQP1Qb+6DAwAA4AkEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDp1EnCWL1+uDh06KDAwUFarVdu3b6+0/vr169W1a1cFBgaqZ8+e+uijj1zWG4ahOXPmKDw8XE2aNFFMTIwOHDjgzikAAAAv4vaA8/bbbys+Pl6JiYnauXOnevfurdjYWJ04caLc+tu2bdPo0aM1ceJE7dq1S8OHD9fw4cO1d+9eZ50FCxZo6dKlSk5OVnp6upo1a6bY2FhdvHjR3dMBAABewMcwDMOdG7Barbrpppv00ksvSZIcDociIyP1yCOPaNasWWXqjxw5UufOndMHH3zgLLv55pvVp08fJScnyzAMRUREaObMmXrsscckSQUFBQoNDdWqVas0atSoMn0WFhaqsLDQ+dhutysyMlIFBQUKCgqq7SkDAAA3sNvtslgsVXr9dusZnKKiImVkZCgmJuZ/G/T1VUxMjNLS0sptk5aW5lJfkmJjY531s7OzZbPZXOpYLBZZrdYK+0xKSpLFYnEukZGRVzs1AABQj7k14Jw6dUqlpaUKDQ11KQ8NDZXNZiu3jc1mq7T+5X+r02dCQoIKCgqcy5EjR2o0HwAA4B0aeXoAdSEgIEABAQGeHgYAAKgjbj2DExISIj8/P+Xm5rqU5+bmKiwsrNw2YWFhlda//G91+gQAAA2LWwOOv7+/+vXrp9TUVGeZw+FQamqqoqOjy20THR3tUl+SUlJSnPWjoqIUFhbmUsdutys9Pb3CPgEAQMPi9reo4uPjNW7cOPXv318DBgzQkiVLdO7cOU2YMEGSNHbsWF1zzTVKSkqSJP3+97/XbbfdphdeeEFDhw7V2rVr9c033+iVV16RJPn4+Gj69Ol65plndN111ykqKkqzZ89WRESEhg8f7u7pAAAAL+D2gDNy5EidPHlSc+bMkc1mU58+fbRp0ybnRcKHDx+Wr+//TiQNHDhQb775pp588kn98Y9/1HXXXacNGzaoR48ezjp/+MMfdO7cOU2ePFn5+fm65ZZbtGnTJgUGBrp7OgAAwAu4/T449VF1PkcPAADqh3pzHxwAAABPIOAAAADTIeAAAADTIeAAAADTIeAAAADTIeAAAADTIeAAAADTIeAAAADTIeAAAADTIeAAAADTIeAAAADTIeAAAADTIeAAAADTIeAAAADTIeAAAADTIeAAAADTIeAAAADTIeAAAADTIeAAAADTIeAAAADTIeAAAADTIeAAAADTIeAAAADTIeAAAADTIeAAAADTIeAAAADTIeAAAADTIeAAAADTIeAAAADTIeAAAADTIeAAAADTcWvAycvL05gxYxQUFKTg4GBNnDhRZ8+erbT+I488oi5duqhJkyZq166dHn30URUUFLjU8/HxKbOsXbvWnVMBAABepJE7Ox8zZoxycnKUkpKi4uJiTZgwQZMnT9abb75Zbv3jx4/r+PHjWrRokbp3765Dhw7poYce0vHjx/WPf/zDpe7KlSsVFxfnfBwcHOzOqQAAAC/iYxiG4Y6OMzMz1b17d+3YsUP9+/eXJG3atEl33nmnjh49qoiIiCr1s379et1///06d+6cGjW6lMd8fHz07rvvavjw4VXqo7CwUIWFhc7HdrtdkZGRKigoUFBQUPUmBgAAPMJut8tisVTp9dttb1GlpaUpODjYGW4kKSYmRr6+vkpPT69yP5cncTncXDZ16lSFhIRowIABWrFihSrLaUlJSbJYLM4lMjKy+hMCAABew20Bx2azqW3bti5ljRo1UqtWrWSz2arUx6lTpzRv3jxNnjzZpXzu3Llat26dUlJSNGLECD388MNatmxZhf0kJCSooKDAuRw5cqT6EwIAAF6j2tfgzJo1S/Pnz6+0TmZmZo0HdJndbtfQoUPVvXt3PfXUUy7rZs+e7fy5b9++OnfunBYuXKhHH3203L4CAgIUEBBw1WMCAADeodoBZ+bMmRo/fnyldTp27KiwsDCdOHHCpbykpER5eXkKCwurtP2ZM2cUFxenFi1a6N1331Xjxo0rrW+1WjVv3jwVFhYSZAAAQPUDTps2bdSmTZsr1ouOjlZ+fr4yMjLUr18/SdLmzZvlcDhktVorbGe32xUbG6uAgAC9//77CgwMvOK2du/erZYtWxJuAACAJDd+TLxbt26Ki4vTpEmTlJycrOLiYk2bNk2jRo1yfoLq2LFjGjRokNasWaMBAwbIbrdr8ODBOn/+vF5//XXZ7XbZ7XZJl4KVn5+fNm7cqNzcXN18880KDAxUSkqKnnvuOT322GPumgoAAPAybr0PzhtvvKFp06Zp0KBB8vX11YgRI7R06VLn+uLiYu3fv1/nz5+XJO3cudP5CavOnTu79JWdna0OHTqocePGWr58uWbMmCHDMNS5c2ctXrxYkyZNcudUAACAF3HbfXDqs+p8jh4AANQP9eI+OAAAAJ5CwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKbj1oCTl5enMWPGKCgoSMHBwZo4caLOnj1baZvbb79dPj4+LstDDz3kUufw4cMaOnSomjZtqrZt2+rxxx9XSUmJO6cCAAC8SCN3dj5mzBjl5OQoJSVFxcXFmjBhgiZPnqw333yz0naTJk3S3LlznY+bNm3q/Lm0tFRDhw5VWFiYtm3bppycHI0dO1aNGzfWc88957a5AAAA7+FjGIbhjo4zMzPVvXt37dixQ/3795ckbdq0SXfeeaeOHj2qiIiIctvdfvvt6tOnj5YsWVLu+o8//li/+tWvdPz4cYWGhkqSkpOT9cQTT+jkyZPy9/cv06awsFCFhYXOx3a7XZGRkSooKFBQUNBVzhQAANQFu90ui8VSpddvt71FlZaWpuDgYGe4kaSYmBj5+voqPT290rZvvPGGQkJC1KNHDyUkJOj8+fMu/fbs2dMZbiQpNjZWdrtd+/btK7e/pKQkWSwW5xIZGXmVswMAAPWZ296istlsatu2revGGjVSq1atZLPZKmz3m9/8Ru3bt1dERIT27NmjJ554Qvv379c777zj7PfH4UaS83FF/SYkJCg+Pt75+PIZHAAAYE7VDjizZs3S/PnzK62TmZlZ4wFNnjzZ+XPPnj0VHh6uQYMG6YcfflCnTp1q1GdAQIACAgJqPCYAAOBdqh1wZs6cqfHjx1dap2PHjgoLC9OJEydcyktKSpSXl6ewsLAqb89qtUqSsrKy1KlTJ4WFhWn79u0udXJzcyWpWv0CAADzqnbAadOmjdq0aXPFetHR0crPz1dGRob69esnSdq8ebMcDocztFTF7t27JUnh4eHOfp999lmdOHHC+RZYSkqKgoKC1L1792rOBgAAmJHbLjLu1q2b4uLiNGnSJG3fvl1fffWVpk2bplGjRjk/QXXs2DF17drVeUbmhx9+0Lx585SRkaGDBw/q/fff19ixY3XrrbeqV69ekqTBgwere/fueuCBB/Ttt9/qk08+0ZNPPqmpU6fyNhQAAJDk5hv9vfHGG+ratasGDRqkO++8U7fccoteeeUV5/ri4mLt37/f+Skpf39/ffrppxo8eLC6du2qmTNnasSIEdq4caOzjZ+fnz744AP5+fkpOjpa999/v8aOHety3xwAANCwue0+OPVZdT5HDwAA6od6cR8cAAAATyHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA03FrwMnLy9OYMWMUFBSk4OBgTZw4UWfPnq2w/sGDB+Xj41Pusn79eme98tavXbvWnVMBAABepJE7Ox8zZoxycnKUkpKi4uJiTZgwQZMnT9abb75Zbv3IyEjl5OS4lL3yyitauHChhgwZ4lK+cuVKxcXFOR8HBwfX+vgBAIB3clvAyczM1KZNm7Rjxw71799fkrRs2TLdeeedWrRokSIiIsq08fPzU1hYmEvZu+++q/vuu0/Nmzd3KQ8ODi5TtyKFhYUqLCx0Prbb7dWdDgAA8CJue4sqLS1NwcHBznAjSTExMfL19VV6enqV+sjIyNDu3bs1ceLEMuumTp2qkJAQDRgwQCtWrJBhGBX2k5SUJIvF4lwiIyOrPyEAAOA13BZwbDab2rZt61LWqFEjtWrVSjabrUp9vPbaa+rWrZsGDhzoUj537lytW7dOKSkpGjFihB5++GEtW7aswn4SEhJUUFDgXI4cOVL9CQEAAK9R7beoZs2apfnz51daJzMzs8YDuuzChQt68803NXv27DLrflzWt29fnTt3TgsXLtSjjz5abl8BAQEKCAi46jEBAADvUO2AM3PmTI0fP77SOh07dlRYWJhOnDjhUl5SUqK8vLwqXTvzj3/8Q+fPn9fYsWOvWNdqtWrevHkqLCwkyAAAgOoHnDZt2qhNmzZXrBcdHa38/HxlZGSoX79+kqTNmzfL4XDIarVesf1rr72mX//611Xa1u7du9WyZUvCDQAAkOTGT1F169ZNcXFxmjRpkpKTk1VcXKxp06Zp1KhRzk9QHTt2TIMGDdKaNWs0YMAAZ9usrCxt3bpVH330UZl+N27cqNzcXN18880KDAxUSkqKnnvuOT322GPumgoAAPAybr0PzhtvvKFp06Zp0KBB8vX11YgRI7R06VLn+uLiYu3fv1/nz593abdixQpde+21Gjx4cJk+GzdurOXLl2vGjBkyDEOdO3fW4sWLNWnSJHdOBQAAeBEfo7LPV5uU3W6XxWJRQUGBgoKCPD0cAABQBdV5/ea7qAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOk08vQAGrqiEodWfvUfbfpXjg7lnVOpQ/LzkQIa+UkyVFjiUKlxqczfz1dFpf97XNM63tKuiX9jhVkCFXtDmMb/LEr+jcjjklTqMLTtwCmtzzis73LsOl9UIsNR//dnXYypqNRQ40Z+6tSmmSbf2km3XNdGfr4+HttX9cnl42bdN4eUcfi/OldYWu/3Z121C2zcSM0DG6lbuEX39LtWAzuHcNz8/y6/Rn2y1yab/UKV/tb4+vqoWYDnn08fwzCMOt+qh9ntdlksFhUUFCgoKMhj40j66Du9vDXbY9v3Jj6SJt8apYQ7u3t6KB61aW+O4td9q/NFpZ4eilcIaOSrP4/qo7ge4Z4eikdx3FRPM38/vXBf7wZ/3NTWa1RtPp/Vef1223+Jn332WQ0cOFBNmzZVcHBwldoYhqE5c+YoPDxcTZo0UUxMjA4cOOBSJy8vT2PGjFFQUJCCg4M1ceJEnT171g0zcC/CTfUYkl7emq2kj77z9FA8ZtPeHD30+k5epKqhsMShh17fqU17czw9FI/huKm+c0WlDf64qc3XKE89n24LOEVFRbr33ns1ZcqUKrdZsGCBli5dquTkZKWnp6tZs2aKjY3VxYsXnXXGjBmjffv2KSUlRR988IG2bt2qyZMnu2MKblNU4iDc1NCrX2SrqMTh6WHUuVKHocT39np6GF7rqfe/U6mjwZ2s5ri5Sk9vbJjHjbteo+r6+XRbwHn66ac1Y8YM9ezZs0r1DcPQkiVL9OSTT2rYsGHq1auX1qxZo+PHj2vDhg2SpMzMTG3atEl/+9vfZLVadcstt2jZsmVau3atjh8/XmHfhYWFstvtLosn/T3toEe3780cRsN8/rZn5yn3TJGnh+G1bPaL2p6d5+lh1DmOm6uTU9Awjxt3/Y2t6+ez3ly1mZ2dLZvNppiYGGeZxWKR1WpVWlqaJCktLU3BwcHq37+/s05MTIx8fX2Vnp5eYd9JSUmyWCzOJTIy0n0TqYJDeec9un1v1xCfvxNnLl65EirVEJ/Dhjjn2tYQn0N3/o2ty+ez3gQcm80mSQoNDXUpDw0Nda6z2Wxq27aty/pGjRqpVatWzjrlSUhIUEFBgXM5cuRILY++etq3aurR7Xu7hvj8tW0R6OkheL2G+Bw2xDnXtob4HLrzb2xdPp/VCjizZs2Sj49Ppcv333/vrrHWWEBAgIKCglwWT3oguoNHt+/NfH0a5vM3IKqVQlv4e3oYXissKFADolp5ehh1juPm6oRbGuZx466/sXX9fFYr4MycOVOZmZmVLh07dqzRQMLCwiRJubm5LuW5ubnOdWFhYTpx4oTL+pKSEuXl5TnreAP/Rr763a1Rnh6GV5r084Z5Pxw/Xx89PayHp4fhtZ76dfcGeV8Tjpurk3hXwzxu3PUaVdfPZ7VeKdq0aaOuXbtWuvj71+x/C1FRUQoLC1NqaqqzzG63Kz09XdHR0ZKk6Oho5efnKyMjw1ln8+bNcjgcslqtNdqupyTc2Z2QUw0+kn7XwO+DE9cjXMn336im/n6eHorXCGjkq+T7b2zQ9zPhuKm+ZgF+Df64qc3XKE89n2670d/hw4eVl5en999/XwsXLtQXX3whSercubOaN28uSeratauSkpJ09913S5Lmz5+v559/XqtXr1ZUVJRmz56tPXv26LvvvlNg4KX37YYMGaLc3FwlJyeruLhYEyZMUP/+/fXmm29WeWz15UZ/Ency5k7G1cedjLmTcU1wJ2PuZFwT9e1OxtV5/XZbwBk/frxWr15dpvyzzz7T7bfffmnjPj5auXKlxo8fL+nSR8UTExP1yiuvKD8/X7fccov+8pe/6Prrr3e2z8vL07Rp07Rx40b5+vpqxIgRWrp0qTM0VUV9CjgAAKBq6kXAqc8IOAAAeJ968VUNAAAAnkLAAQAApkPAAQAApkPAAQAApkPAAQAApkPAAQAApkPAAQAApkPAAQAAptPI0wPwhMv3NrTb7R4eCQAAqKrLr9tVuUdxgww4Z86ckSRFRkZ6eCQAAKC6zpw5I4vFUmmdBvlVDQ6HQ8ePH1eLFi3k41O7X6hmt9sVGRmpI0eOmPJrIJif9zP7HJmf9zP7HM0+P8l9czQMQ2fOnFFERIR8fSu/yqZBnsHx9fXVtdde69ZtBAUFmfbAlZifGZh9jszP+5l9jmafn+SeOV7pzM1lXGQMAABMh4ADAABMh4BTywICApSYmKiAgABPD8UtmJ/3M/scmZ/3M/sczT4/qX7MsUFeZAwAAMyNMzgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDjV9Oyzz2rgwIFq2rSpgoODy61z+PBhDR06VE2bNlXbtm31+OOPq6SkpNJ+8/LyNGbMGAUFBSk4OFgTJ07U2bNn3TCD6tmyZYt8fHzKXXbs2FFhu9tvv71M/YceeqgOR151HTp0KDPW559/vtI2Fy9e1NSpU9W6dWs1b95cI0aMUG5ubh2NuOoOHjyoiRMnKioqSk2aNFGnTp2UmJiooqKiStvV9/23fPlydejQQYGBgbJardq+fXul9devX6+uXbsqMDBQPXv21EcffVRHI62+pKQk3XTTTWrRooXatm2r4cOHa//+/ZW2WbVqVZn9FRgYWEcjrp6nnnqqzFi7du1aaRtv2n/l/T3x8fHR1KlTy63vDftu69atuuuuuxQRESEfHx9t2LDBZb1hGJozZ47Cw8PVpEkTxcTE6MCBA1fst7q/x9VFwKmmoqIi3XvvvZoyZUq560tLSzV06FAVFRVp27ZtWr16tVatWqU5c+ZU2u+YMWO0b98+paSk6IMPPtDWrVs1efJkd0yhWgYOHKicnByX5cEHH1RUVJT69+9fadtJkya5tFuwYEEdjbr65s6d6zLWRx55pNL6M2bM0MaNG7V+/Xp9/vnnOn78uP7f//t/dTTaqvv+++/lcDj08ssva9++fXrxxReVnJysP/7xj1dsW1/339tvv634+HglJiZq586d6t27t2JjY3XixIly62/btk2jR4/WxIkTtWvXLg0fPlzDhw/X3r1763jkVfP5559r6tSp+vrrr5WSkqLi4mINHjxY586dq7RdUFCQy/46dOhQHY24+m644QaXsX755ZcV1vW2/bdjxw6XuaWkpEiS7r333grb1Pd9d+7cOfXu3VvLly8vd/2CBQu0dOlSJScnKz09Xc2aNVNsbKwuXrxYYZ/V/T2uEQM1snLlSsNisZQp/+ijjwxfX1/DZrM5y/76178aQUFBRmFhYbl9fffdd4YkY8eOHc6yjz/+2PDx8TGOHTtW62O/GkVFRUabNm2MuXPnVlrvtttuM37/+9/XzaCuUvv27Y0XX3yxyvXz8/ONxo0bG+vXr3eWZWZmGpKMtLQ0N4ywdi1YsMCIioqqtE593n8DBgwwpk6d6nxcWlpqREREGElJSeXWv++++4yhQ4e6lFmtVuN3v/udW8dZW06cOGFIMj7//PMK61T096g+SkxMNHr37l3l+t6+/37/+98bnTp1MhwOR7nrvWnfGYZhSDLeffdd52OHw2GEhYUZCxcudJbl5+cbAQEBxltvvVVhP9X9Pa4JzuDUsrS0NPXs2VOhoaHOstjYWNntdu3bt6/CNsHBwS5nRGJiYuTr66v09HS3j7k63n//fZ0+fVoTJky4Yt033nhDISEh6tGjhxISEnT+/Pk6GGHNPP/882rdurX69u2rhQsXVvqWYkZGhoqLixUTE+Ms69q1q9q1a6e0tLS6GO5VKSgoUKtWra5Yrz7uv6KiImVkZLg8976+voqJianwuU9LS3OpL136nfSGfSVd2l+SrrjPzp49q/bt2ysyMlLDhg2r8O9NfXDgwAFFRESoY8eOGjNmjA4fPlxhXW/ef0VFRXr99df129/+Vj4+PhXW86Z991PZ2dmy2Wwu+8hischqtVa4j2rye1wTDfLbxN3JZrO5hBtJzsc2m63CNm3btnUpa9SokVq1alVhG0957bXXFBsbe8VvY//Nb36j9u3bKyIiQnv27NETTzyh/fv365133qmjkVbdo48+qhtvvFGtWrXStm3blJCQoJycHC1evLjc+jabTf7+/mWuwQoNDa13++unsrKytGzZMi1atKjSevV1/506dUqlpaXl/o59//335bap6Heyvu8rSXI4HJo+fbp+9rOfqUePHhXW69Kli1asWKFevXqpoKBAixYt0sCBA7Vv374r/q7WNavVqlWrVqlLly7KycnR008/rZ///Ofau3evWrRoUaa+N++/DRs2KD8/X+PHj6+wjjftu/Jc3g/V2Uc1+T2uCQKOpFmzZmn+/PmV1snMzLzihXDepCZzPnr0qD755BOtW7fuiv3/+Pqhnj17Kjw8XIMGDdIPP/ygTp061XzgVVSd+cXHxzvLevXqJX9/f/3ud79TUlJSvf2umJrsv2PHjikuLk733nuvJk2aVGlbT+8/XDJ16lTt3bu30mtUJCk6OlrR0dHOxwMHDlS3bt308ssva968ee4eZrUMGTLE+XOvXr1ktVrVvn17rVu3ThMnTvTgyGrfa6+9piFDhigiIqLCOt6077wNAUfSzJkzK03YktSxY8cq9RUWFlbmSvDLn64JCwursM1PL6wqKSlRXl5ehW2uVk3mvHLlSrVu3Vq//vWvq709q9Uq6dIZhLp4gbyafWq1WlVSUqKDBw+qS5cuZdaHhYWpqKhI+fn5LmdxcnNz3ba/fqq68zt+/LjuuOMODRw4UK+88kq1t1fX+68iISEh8vPzK/OJtcqe+7CwsGrVry+mTZvm/MBBdf8n37hxY/Xt21dZWVluGl3tCQ4O1vXXX1/hWL11/x06dEiffvpptc96etO+k/73upabm6vw8HBneW5urvr06VNum5r8HtdIrV3N08Bc6SLj3NxcZ9nLL79sBAUFGRcvXiy3r8sXGX/zzTfOsk8++aReXWTscDiMqKgoY+bMmTVq/+WXXxqSjG+//baWR1b7Xn/9dcPX19fIy8srd/3li4z/8Y9/OMu+//77enuR8dGjR43rrrvOGDVqlFFSUlKjPurT/hswYIAxbdo05+PS0lLjmmuuqfQi41/96lcuZdHR0fX2IlWHw2FMnTrViIiIMP7973/XqI+SkhKjS5cuxowZM2p5dLXvzJkzRsuWLY0///nP5a73tv13WWJiohEWFmYUFxdXq11933eq4CLjRYsWOcsKCgqqdJFxdX6PazTWWuupgTh06JCxa9cu4+mnnzaaN29u7Nq1y9i1a5dx5swZwzAuHZw9evQwBg8ebOzevdvYtGmT0aZNGyMhIcHZR3p6utGlSxfj6NGjzrK4uDijb9++Rnp6uvHll18a1113nTF69Og6n19FPv30U0OSkZmZWWbd0aNHjS5duhjp6emGYRhGVlaWMXfuXOObb74xsrOzjffee8/o2LGjceutt9b1sK9o27Ztxosvvmjs3r3b+OGHH4zXX3/daNOmjTF27FhnnZ/OzzAM46GHHjLatWtnbN682fjmm2+M6OhoIzo62hNTqNTRo0eNzp07G4MGDTKOHj1q5OTkOJcf1/Gm/bd27VojICDAWLVqlfHdd98ZkydPNoKDg52fXHzggQeMWbNmOet/9dVXRqNGjYxFixYZmZmZRmJiotG4cWPjX//6l6emUKkpU6YYFovF2LJli8v+On/+vLPOT+f49NNPG5988onxww8/GBkZGcaoUaOMwMBAY9++fZ6YQqVmzpxpbNmyxcjOzja++uorIyYmxggJCTFOnDhhGIb37z/DuPRi3a5dO+OJJ54os84b992ZM2ecr3WSjMWLFxu7du0yDh06ZBiGYTz//PNGcHCw8d577xl79uwxhg0bZkRFRRkXLlxw9vGLX/zCWLZsmfPxlX6PawMBp5rGjRtnSCqzfPbZZ846Bw8eNIYMGWI0adLECAkJMWbOnOmS4j/77DNDkpGdne0sO336tDF69GijefPmRlBQkDFhwgRnaKoPRo8ebQwcOLDcddnZ2S7PweHDh41bb73VaNWqlREQEGB07tzZePzxx42CgoI6HHHVZGRkGFar1bBYLEZgYKDRrVs347nnnnM52/bT+RmGYVy4cMF4+OGHjZYtWxpNmzY17r77bpfQUF+sXLmy3OP1xydvvXH/LVu2zGjXrp3h7+9vDBgwwPj666+d62677TZj3LhxLvXXrVtnXH/99Ya/v79xww03GB9++GEdj7jqKtpfK1eudNb56RynT5/ufD5CQ0ONO++809i5c2fdD74KRo4caYSHhxv+/v7GNddcY4wcOdLIyspyrvf2/WcYl87ASzL2799fZp037rvLr1k/XS7Pw+FwGLNnzzZCQ0ONgIAAY9CgQWXm3r59eyMxMdGlrLLf49rgYxiGUXtveAEAAHge98EBAACmQ8ABAACmQ8ABAACmQ8ABAACmQ8ABAACmQ8ABAACmQ8ABAACmQ8ABAACmQ8ABAACmQ8ABAACmQ8ABAACm8/8BtusNZIE2ubYAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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CwKPT04UQuHTpElxdXeHl5VXpPmridfL390dxcTEKCgrg7u6us17XtWce17BhQwDQ+m9l9+7dAB6d2n3nzh34+PhorJ88eTKGDBmi/js3NxdDhgzBF198oXHKav369Svcf0JCAqZMmYL3338fM2bMqLQdhv5b0ee6VYZcl6ZZs2YIDQ3FunXrdF7bauHChRqv6YkTJ/Dhhx/ihx9+0Jjf5+fnp/fzV7UtZfT9XDy5TVFREQDg1KlTCA8PN2h7Xa9vVa/d8u6772Lbtm1Yt24dnnvuOb228fHxKfdZXbBgAeRyORYuXKix3JBTravSljL6fJ9VRNdnsiI19Z1eI2osWtmgu3fvisTERI3Hw4cPy9Vp6+G5d++ecHBwED/99FO5dUqlUgwYMEDY29uL//znP6Y4hHIiIiJEaGhohetOnDghnJycxPDhw0VoaKjw9/fXOdwghGnm8Pzwww8CgME9Xsagz3/xvfLKKwbv1xjj4o/r37+/kEqlQqlUGm2fZYqLi4WLi4sYP358hevLhpE+/fRTUadOHb3OkjJkDs/mzZuFvb296N+/v87jGzFihPDw8NC5T1MKCQkRrVq1Mng7Y5+lVZ226CsnJ0fUr19f9O7dW7z44ouibt26ep0hamoffvih3vMOdanuHB5jtkVfVflMVoZzeKhS2gLPxo0bhYODQ4XB4YMPPhAAxNdff10DrXzk448/Fo6OjqKwsFBjeXFxsQgNDRVNmjQRCoVCI/zoYqrT0mUymd6njxtTQUGB+OWXX8o9evXqJaRSqfjll190zpWqSFW/RG7evFluWXp6unB0dKzWl5ou3bp1E926dSu3/OLFi6JOnTrqye+rVq0SAMTatWu17k/fwLN//34hlUpFr169yv07rUhoaKh46aWXdNZVV0lJiXqOyeNSU1OFvb19lSbIVjXwmKIt+urXr5+QyWTi6tWr6vDz/PPPm+WzWqZs6POjjz4yyv6qE3iM3RZ9VPUzWRlrDTwc0jKhOXPmAID61Onvv/8eBw8eBAB1F/z27dvRtWvXclc1XrRoEVasWIHw8HC4urrihx9+0Fj/6quvmuTKvq+88gpmz56N/fv3o3fv3hrHkp6ejqSkJNStWxft2rVDXFwcZsyYgddff73ap2Xn5+dj6dKlAKC+L9OyZctQr1491KtXr1z3e2JiIl566aVqnZZaVa6uroiKiiq3fPPmzTh8+HCF60xpwIABcHFxQZcuXeDl5YUzZ85g9erVcHV1xbx580z2vK+88gqmT58OhUKhHjITQuCdd96Bi4sLVq5cCeDR1Y7/85//YNy4cYiIiKjycAwAXL58GS+//DIkEglef/11bNq0SWN9u3bt0K5dO/XfN2/exMmTJxETE1Pl59TXgwcP4O/vjwEDBqBNmzZwc3PDqVOnsGbNGshkMoNvh2KNbVmzZg22b9+OhIQENGrUCMCjW+YMGTIEK1euxAcffGCS59Xml19+weTJk9GiRQu0atWq3HfpCy+8oPNyINbcFlN/Jg21bNky5OXlqa/2vXXrVly7dg0AMGbMGNNe4b/GolUtBB2Tu1QqlfDy8hLz588vt62uCZ3G7OJ+Urt27TQmLqelpQkHBwcxZswYjbrS0lLRuXNn4efnp/UsCX3+a0DbMNGTExjLztAy5tWgjcFcp6UvXrxYPP3006JBgwbCwcFB+Pr6iiFDhpS76rCx5ebmCgcHB42Jy4sXLxYAyg3BXrlyRbi7u4u+fftWuj99enjKejwqezz5+q1cuVK4urpW6eJyhioqKhLjxo0T7dq1E+7u7sLR0VEEBASI6OjoKn9eq9rDY4q26HL16lUhk8kq7E179dVXhZubW4UnOphafHy80SYal6lqD48p2qJLdT6TlalOD09AQIBZfteEEEIihBDGjVCkr8OHDyMsLAwZGRlo3bq1uZuj9v333yMmJgZXrlxBvXr1zN2ccmJjY3HgwAGkpaWZpYeH/ic6Ohp//fUX/vjjD3M3pUKhoaHo2bMnvvrqK3M3hYjMjFdaNrPPPvvMosIOALz55pto3Lgxli9fbu6mlHPnzh38+9//xpw5cxh2LEB8fDyOHDmiHoa0JDt37sS5c+cwbdo0czeFiCwAe3iIiIjI5rGHh4iIiGweAw8RERHZPAYeIiIisnkMPERERGTzeOFBACqVCjk5Oahbty7P/CEiIrISQgjcv38ffn5+sLPT3ofDwINHN1Lz9/c3dzOIiIioCq5evaq+undlGHgA1K1bF8CjF0yfu0oTERGR+SkUCvj7+6t/x7Vh4AHUw1ju7u4MPERERFZGn+konLRMRERENo+Bh4iIiGweAw8RERHZPAYeIiIisnkMPERERGTzGHiIiIjI5jHwEBERkc0zaeCZO3cuOnfujLp168LLywtRUVHIysrSqCksLERMTAwaNmyIOnXqoH///sjNzdWouXLlCvr16wdXV1d4eXlh0qRJKC0t1ajZt28fOnToAGdnZzRv3hwJCQmmPDQiIiKyIiYNPPv370dMTAwOHTqExMRElJSUoHfv3igoKFDXjB8/Hlu3bsWmTZuwf/9+5OTk4LXXXlOvVyqV6NevH4qLi5GcnIy1a9ciISEBcXFx6prs7Gz069cPvXr1Qnp6OmJjYzFixAjs2rXLlIdHRERkcYpLVfh6/3m8tvwgnp33O3p/uQ/jfjyOP/66BaVKmLt5ZiMRQtTY0d+6dQteXl7Yv38/unfvjvz8fHh6emL9+vV4/fXXAQCZmZlo1aoVUlJS8Mwzz+C3337Diy++iJycHHh7ewMAVq1ahSlTpuDWrVtwcnLClClTsH37dpw+fVr9XAMHDkReXh527typs10KhQIymQz5+fm80jIREVms4lIV1vx5ETtP3cDluwVQqgB7CeDsYA9A4N7DUhSWav9Zd3OUwMHeTr2dvb0dvN2liGzjg2HPBsLJwXpmuxjy+12jt5bIz88HADRo0AAAkJaWhpKSEkRERKhrWrZsicaNG6sDT0pKCtq2basOOwAQGRmJUaNGISMjA6GhoUhJSdHYR1lNbGxshe0oKipCUVGR+m+FQmGsQyQiIjIKpUog+dxtbEq7goycfFzPK9QSZpR677egRAAlZfWP/vdaXiHSruThs98y4eZkh5Y+7lYZgLSpscCjUqkQGxuLZ599FsHBwQAAuVwOJycn1KtXT6PW29sbcrlcXfN42ClbX7ZOW41CocDDhw/h4uKisW7u3Ln45JNPjHZsRERExlBcqsK3By9gbfIlyBXFZmlDQbEKaVfy1AHIz90Zc/u3Q9cWnrC3033PKktVY4EnJiYGp0+fxsGDB2vqKSs1bdo0TJgwQf132d1WiYiIatrDYiVmbTuN7SdvQFGof09NTclRFGHomiMAgM4B9TD2+afQpbmH1YWfGgk8o0ePxrZt23DgwAE0atRIvdzHxwfFxcXIy8vT6OXJzc2Fj4+Puubw4cMa+ys7i+vxmifP7MrNzYW7u3u53h0AcHZ2hrOzs1GOjYiIyFBlc3GW770ARWGp7g0sxJHLeXjru8OwAzC6VzOMeyHIaoKPSQfmhBAYPXo0fvnlF+zZsweBgYEa6zt27AhHR0ckJSWpl2VlZeHKlSsIDw8HAISHh+PUqVO4efOmuiYxMRHu7u5o3bq1uubxfZTVlO2DiIjIEjwsVqLPov14asZvmPtbllWFncepACzZewHNP9qBL3dlWsXZXyY9S+uDDz7A+vXr8euvvyIoKEi9XCaTqXteRo0ahR07diAhIQHu7u4YM2YMACA5ORnAo9PSQ0JC4Ofnh/nz50Mul+Ott97CiBEj8NlnnwF4dFp6cHAwYmJi8M4772DPnj0YO3Ystm/fjsjISJ3t5FlaRERkKtbam2MICYDF/2qPlzs00llrTIb8fps08EgkFXdzrVmzBsOGDQPw6MKDEydOxI8//oiioiJERkZixYoV6uEqALh8+TJGjRqFffv2wc3NDUOHDsW8efPg4PC/Ebl9+/Zh/PjxOHPmDBo1aoSPP/5Y/Ry6MPAQEZGxPSxW4tUVB5Epf2DuptSYJg2lSJr4XI0Nc1lM4LEWDDxERGQsxaUq9FtyAOduFugutlFja2h+DwOPgRh4iIioumpjj442dhJgyYAQvBjyD5M9h8VeeJCIiMjWmLNHx1EC1JXaq6+0XFSqgpDYwbOOE17r0AhDnmmCdamXsOu0HHLFQwjVoxqleHSF5gfFKhQrTdPvoRLA6A3p+CX9Gr4dFmaS5zAEe3jAHh4iIjJM2VWQZ247jQu3/q6x53VzssM/6rngtQ6N8E7Xpka5CnLZdYCSz99GrqJI560pqqJJAymSPjT+3B4OaRmIgYeIiPS19UQOJvyUjhIT9Yw8zs3JDs+39MYbnfxr7GJ/xaUqTP45Hb+m34Axj9AOwLLBoejbzs9o+2TgMRADDxER6VJTQ1dedZ0womtTs9/HylS9WO92a4Lp/doYZV+cw0NERGQkxaUqvPXtIaRm3zPZc3jVdcaIroFmDzmPs7eToFuQJ5KCeqG4VIWp/zmBX47nVLvX55s/LgGQYHq/1kZopf7YwwP28BARUXlKlcCY9cew47TcZM/RuUk9rBsRbjEhRxelSmBx4l9Ysvd8tfe1YnAH9G3nW619cEjLQAw8RERUpuxHfene80adw1LGEntzDKVUCby+8k8cv5pf5X00cHPCkekR1ZqXxCEtIiKiKthx8gbGbTiOEhPcG8raenO0sbeT4JeYrth6IgfjNhxHVV6uuwXFOJx9F+HNGhq/gRVg4CEiolrPlMNXYU3q4/sRz9hE0HnSS+390Letb5Vfu5v3C03Qqoox8BARUa1lyuErW+rR0cbeToIVQzpix8kbGL3+GFQGbOtVV2qydj2JgYeIiGolUw1f2XKPjjZ92/niXHBf9F9+EOnXFTrrfWVSPB3YoAZa9ggDDxER1SqmGr6qrUHncfZ2Emwe0w3RCUeQlHlTa238S61r7K7qAAMPERHVItWZZFsZBp3yvh3WGZ9uP4Nv/sgut87N2R4L32iPPsHVOyXdUDwtHTwtnYjI1hnjNOontfB0xfZxPRh0tCguVWFtcjaOXLoHNyd7vNahkVFvkcHr8BiIgYeIyHZtPZGDMT8eN9r+7AAsGRiCF0P+YbR9UtXwOjxERFTrmeLeV2N7NcO4F4JqdO4JGQcDDxER2ZzZ287g24Pl549UVQd/d2wa1ZVBx4ox8BARkc0oLlWhx/w9uKEoMsr+7CTAkgEcvrIFDDxERGT1THGqOYevbAsDDxERWbUdJ29gzI/HoDTSKTgcvrJNDDxERGSVjN2rw+Er28bAQ0REVsfYFxDk8JXtY+AhIiKros9tC/TVpKEUSROfY9CpBRh4iIjIKhSXqtD98yTI7xcbZX9L/tUeL3doZJR9keVj4CEiIotm7Lk6nJRcOzHwEBGRxTLmGVgSAEt5S4hai4GHiIgsjrF7dfoGe2Pp4I7s1anFGHiIiMiiGPMMLO+6jvhjSgTvaE4MPEREZDneSTiMPZm3jLKv51t64NthYUbZF1k/Bh4iIjK74lIVOs/ZjfxCZbX3xQsIUkUYeIiIyGw4V4dqCgMPERGZhTHPwLIDsGxwKPq286v+zsgmMfAQEVGNYq8OmYNJp60fOHAAL730Evz8/CCRSLB582aN9UIIxMXFwdfXFy4uLoiIiMC5c+c0au7evYs333wT7u7uqFevHqKjo/HgwQONmpMnT6Jbt26QSqXw9/fH/PnzTXlYRERURVtP5KDF9B1GCTvedR3x15x/YsWQTgw7pJNJA09BQQHat2+P5cuXV7h+/vz5WLJkCVatWoXU1FS4ubkhMjIShYWF6po333wTGRkZSExMxLZt23DgwAGMHDlSvV6hUKB3794ICAhAWloaFixYgJkzZ2L16tWmPDQiIjKAUiXw6vKDGPOjcU43f76lB1Kn9+bp5qQ3iRDCSPea1fFEEgl++eUXREVFAXjUu+Pn54eJEyfiww8/BADk5+fD29sbCQkJGDhwIM6ePYvWrVvjyJEj6NSpEwBg586d6Nu3L65duwY/Pz+sXLkS06dPh1wuh5OTEwBg6tSp2Lx5MzIzM/Vqm0KhgEwmQ35+Ptzd3Y1/8EREtdjWEzkY8+Nxo+yLZ2DR4wz5/TZbNM7OzoZcLkdERIR6mUwmQ1hYGFJSUgAAKSkpqFevnjrsAEBERATs7OyQmpqqrunevbs67ABAZGQksrKycO/evQqfu6ioCAqFQuNBRETGpVQJvLb8T6OFnbG9muHcp30ZdqhKzDZpWS5/NH7r7e2tsdzb21u9Ti6Xw8vLS2O9g4MDGjRooFETGBhYbh9l6+rXr1/uuefOnYtPPvnEOAdCRETl/Jp+HeM2pBtlX551HHHooxc4T4eqpVYOfk6bNg35+fnqx9WrV83dJCIim6BUCfRasNdoYWf4s41xZEZvhh2qNrP18Pj4+AAAcnNz4evrq16em5uLkJAQdc3Nmzc1tistLcXdu3fV2/v4+CA3N1ejpuzvsponOTs7w9nZ2SjHQUREj4LO4sS/sGTveaPsj/fAImMz27+kwMBA+Pj4ICkpSb1MoVAgNTUV4eHhAIDw8HDk5eUhLS1NXbNnzx6oVCqEhYWpaw4cOICSkhJ1TWJiIoKCgiocziIiIuPacfIGWs74zWhhh2dgkSmY9F/TgwcPkJ6ejvT0dACPJiqnp6fjypUrkEgkiI2NxZw5c7BlyxacOnUKb7/9Nvz8/NRncrVq1Qp9+vTBu+++i8OHD+PPP//E6NGjMXDgQPj5Pbqa5uDBg+Hk5ITo6GhkZGRg48aNWLx4MSZMmGDKQyMiqvWUKoEPfkjDB+uPocQI55rbSYBlA0N4w08yCZOelr5v3z706tWr3PKhQ4ciISEBQgjEx8dj9erVyMvLQ9euXbFixQo89dRT6tq7d+9i9OjR2Lp1K+zs7NC/f38sWbIEderUUdecPHkSMTExOHLkCDw8PDBmzBhMmTJF73bytHQiIv2VDV8t3XsexvoBGdurGca9EMS5OmQQQ36/a+w6PJaMgYeISD87Tt7AuA3HjdKjAwBNGkqRNPE5Bh2qEkN+v3kvLSIi0snY978CgCX/ao+XOzQy2v6ItGHgISKiSpli+KqDvzs2jerKXh2qUQw8RERUIWMPXwGPJiXzSslkDgw8RESkwRTDV6H+7viZvTpkRgw8RESk9mv6dcRuSDfa8JUEwFL26pAFYOAhIiIUl6rQY/4e3FAUGW2fnKtDloSBh4ioFisuVeGtbw8hNfue0fZpJwGWDGCvDlkWBh4iolpIqRIYvS4Nv2Xk6i42AC8gSJaKgYeIqBYx9k0+y3D4iiwdAw8RUS1QFnSW7TsPI55lzuErshoMPERENsxUQQfg8BVZFwYeIiIbZMqgw+ErskYMPERENmbriRzEbjwOpcq4++XwFVkzBh4iIhugVAkkn7uND/+TjlxFsdH3z+ErsnYMPEREVkypEli0OwvL918w+tAVAPQJ9sLywZ0YdMjqMfAQEVkhU9zF/HFhTerj+xHPwMnBzgR7J6p5DDxERFakrEdn2b4LJgk63u6O+GNyBIMO2RwGHiIiK1BcqsLU/5zAf4/nmGT/EgCL/9UeL3doZJL9E5kbAw8RkYVSqgQOZt3C1F9OGvWmnk/ihGSqDRh4iIgsTFlvzi/Hc0wybFWmb7A3lg7uyKBDtQIDDxGRBaip3hwAeC3ED/Neb895OlSrMPAQEZlJ2bVzFu/5C0cv55n8+dijQ7UZAw8RUQ0rG7LanJ5jkmvnPImnmBMx8BAR1YiHxUrM2nYa20/egKJQWSPPyaErov9h4CEiMoGy4apNaVfwe+ZN/F1s5BtbaTGmZzPE9uZZV0SPY+AhIjIS9ZycpCwcvZJf48/P08uJKsfAQ0RURWUB56ejl3Hwwh3c+7u0xtsgATCaPTpEOjHwEBHpqezU8VUHziPjhqLG5uJUpJmHG2a+3AZdmnsw6BDpgYGHiKgSZRONk8/fxp0HxXhQg/NwKvNqez98/gYnIhMZioGHiAiPThX/9uAF/CftGm7dL8LfxUqUmD/fAGBvDpExMPAQUa1RXKrCmj8vYtdpOW7k/42iEhWUAigqVaGwtAYuiGMg9uYQGQ8DDxHZhMdPA8/Iyce9v4uhVAH2EsDZwR75haX421K6bLRgbw6RaTDwEFm5x3st5IqHECqBotJHPRdlP/aA5jInezsUK6tfU9l2JSoBRwd7NPN0w8juzdC1hafGj/eTw0fVfb4HxUod82vMN7lYH151nTGiayCGPRvI3hwiE2HgIbIwj5/qnHblHgqKlJX+2BeWCj16LSr6sX9ymbFqHl9WCrmiCH9euAsAcHOUwMHeTsvwUXWfz7p41XXCiK5NGXKIaohNBZ7ly5djwYIFkMvlaN++PZYuXYqnn37a3M0iKufx4ZczNxQoKCpBUYlKR4Cxzh/2MgUlAiix7mOorqYerhjYuTFDDpEZ2Ezg2bhxIyZMmIBVq1YhLCwMixYtQmRkJLKysuDl5WXu5lEt9/iwU/adArNcoI7MI8irDj7q16rcsB4R1SyJEMLyTk2ogrCwMHTu3BnLli0DAKhUKvj7+2PMmDGYOnWq1m0VCgVkMhny8/Ph7u5eE80lG/d4wMnMvV+j91Ei82rg6oiuzT3wRid/TjwmMjFDfr9tooenuLgYaWlpmDZtmnqZnZ0dIiIikJKSUq6+qKgIRUVF6r8VCkWNtJNs1+PzbvZk3UIBA06t4ePujLDAhni9YyMGHCILZhOB5/bt21AqlfD29tZY7u3tjczMzHL1c+fOxSeffFJTzSMb9rBYiXf/7wj+PH8HNtFVSlq5OkrQqL4rWvnKGHCIrIxNBB5DTZs2DRMmTFD/rVAo4O/vb8YWkTUpG65avvcCFIWci2OrnOwAz7pS+MikiGzjw4nGRFbOJgKPh4cH7O3tkZubq7E8NzcXPj4+5eqdnZ3h7OxcU80jG1B208ipv5zEDUWR7g3IKtgDqCO1h70EcHFyZLghsmE2EXicnJzQsWNHJCUlISoqCsCjSctJSUkYPXq0eRtHVk2pElic+BeW7TsPFcesrI6LA+DiaK++hlGJCqgrdUSXZg0x48U2cHGyN3cTiaiG2ETgAYAJEyZg6NCh6NSpE55++mksWrQIBQUFGD58uLmbRlZIqRJYtDsLy/ZdsLq5OY4A6rrYm+1KyyoB5BUadr0dJ7tHZzdV9UrLxUoVVJCgjrMDOjSuzzOkiKgcmwk8AwYMwK1btxAXFwe5XI6QkBDs3Lmz3ERmIm3KenSW7j1vUUHHzVECd6kjKvqxLxWW12tR2YUVy9rN4SMiqmk2cx2e6uB1eMgShq6c7AAXJ3s42EnQwM0Zrf14JhARkTa17jo8RNWx9UQOYjceh7IGL53jJAG8ZFJ4u7OXg4ioJjDwUK1VXKpCvyUHcO5mgcmfS+pgB1+Z1KKGnYiIahMGHqp1lCqBMeuPYcdpuUmfx9fdGUO7NME7XZuy94aIyMwYeKhW2XoiB+M2HDfZPJ2mHq745OVgzrshIrIwDDxUKyhVAm+sTMaxq3lG37dXXWeM6BrIeThERBaMgYds3tYTORj743Gjn2b+ans/fP5Ge4YcIiIrwMBDNi064QiSMm8adZ9jezXDuBeCOGRFRGRFGHjIJilVAs9/sReX7j402j7H9GyG2N4MOkRE1oiBh2zOjpM3ELP+mNGGsNijQ0Rk/Rh4yKbM3nYG3x7MNsq++gZ7Y+ngjgw6REQ2gIGHbMY7aw5jT9atau8nrEl9fD/iGU5GJiKyIQw8ZBNeXHIAp3PuV2sf3u6O+GNyBIMOEZENYuAhq6ZUCTy3YA8u3yus1n6iuwbg4xeDjdQqIiKyNAw8ZLWMMTm5uacrdozrwV4dIiIbx8BDVmnujjP4+kD1JiezV4eIqPZg4CGrsy09p1phxw7AssGh6NvOz3iNIiIii8bAQ1ZlW3oORm84XuXtQ/3d8fOorjzVnIiolmHgIavx6fYz+OaPqvfsDH82APEvcQiLiKg2YuAhq/Dp9gx888elKm//brcmmN6vjfEaREREVoWBhyzejpM5VQ47nK9DREQAAw9ZOKVKYPT6qs3ZadJAiqQPn+N8HSIiYuAhy/b8F3ugqsJ2vZ7ywJp3wozeHiIisk682hpZrH6L9+PSXcOvoMywQ0RET2LgIYvUb/F+ZNx4YPB2bf3qMuwQEVE5DDxkcV6sYth5LsgDW8d2N0GLiIjI2nEOD1mU6IRUnK5C2BnaJQCfvMxr7BARUcXYw0MWY1v6dSRl3jZ4u+eCPBl2iIhIKwYesghKlcDYjekGbxfsVxffDX/a+A0iIiKbwsBDFuH5hXuhEoZt08a3DrZxzg4REemBgYfM7sXF+3HpzkODtmnjWwfbx/UwUYuIiMjWMPCQWVVlknJAfSnDDhERGYSBh8ymKpOUJQD2THrONA0iIiKbxcBDZqFUCYzdkG7wdssHd+C9sYiIyGAMPGQWr688aPA9sqK7BqJvO1+TtIeIiGybyQLPp59+ii5dusDV1RX16tWrsObKlSvo168fXF1d4eXlhUmTJqG0tFSjZt++fejQoQOcnZ3RvHlzJCQklNvP8uXL0aRJE0ilUoSFheHw4cMmOCIyltnbTuP4VYVB2zwX5ImPX2xtohYREZGtM1ngKS4uxhtvvIFRo0ZVuF6pVKJfv34oLi5GcnIy1q5di4SEBMTFxalrsrOz0a9fP/Tq1Qvp6emIjY3FiBEjsGvXLnXNxo0bMWHCBMTHx+PYsWNo3749IiMjcfPmTVMdGlXDjpM5+PbgZYO24bV2iIiouiRCCAOvfmKYhIQExMbGIi8vT2P5b7/9hhdffBE5OTnw9vYGAKxatQpTpkzBrVu34OTkhClTpmD79u04ffq0eruBAwciLy8PO3fuBACEhYWhc+fOWLZsGQBApVLB398fY8aMwdSpUytsU1FREYqKitR/KxQK+Pv7Iz8/H+7u7sY8fHqMUiUQNGMHSg0Yy/Ku44TUGS+YrlFERGS1FAoFZDKZXr/fZpvDk5KSgrZt26rDDgBERkZCoVAgIyNDXRMREaGxXWRkJFJSUgA86kVKS0vTqLGzs0NERIS6piJz586FTCZTP/z9/Y15aFSJsT+mGRR2AOCPqc+bpjFERFSrmC3wyOVyjbADQP23XC7XWqNQKPDw4UPcvn0bSqWywpqyfVRk2rRpyM/PVz+uXr1qjEMiLYpLVdh+KtegbaK7BsLJgfPqiYio+gz6NZk6dSokEonWR2ZmpqnaajTOzs5wd3fXeJBp9Vt8wKD6ph6unKRMRERG42BI8cSJEzFs2DCtNU2bNtVrXz4+PuXOpsrNzVWvK/vfsmWP17i7u8PFxQX29vawt7evsKZsH2R+s7edxrlbBXrX2wNInNDTZO0hIqLax6DA4+npCU9PT6M8cXh4OD799FPcvHkTXl5eAIDExES4u7ujdevW6podO3ZobJeYmIjw8HAAgJOTEzp27IikpCRERUUBeDRpOSkpCaNHjzZKO6l6qnJW1lJeXJCIiIzMZBMkrly5gvT0dFy5cgVKpRLp6elIT0/HgweP7pvUu3dvtG7dGm+99RZOnDiBXbt2YcaMGYiJiYGzszMA4P3338fFixcxefJkZGZmYsWKFfjpp58wfvx49fNMmDAB33zzDdauXYuzZ89i1KhRKCgowPDhw011aKSnR1dTPm7QNn2DfXhxQSIiMjqDengMERcXh7Vr16r/Dg0NBQDs3bsXPXv2hL29PbZt24ZRo0YhPDwcbm5uGDp0KGbNmqXeJjAwENu3b8f48eOxePFiNGrUCP/+978RGRmprhkwYABu3bqFuLg4yOVyhISEYOfOneUmMlPNe2PVnwadlWUvedS7Q0REZGwmvw6PNTDkPH7Sz7b06xht4L2ylg4KxUvt/UzTICIisjlWcR0esl1KlcD4n04YtE3HxvUYdoiIyGQYeMjolib9hRKV/h2HDhLgp/e7mLBFRERU2zHwkFEpVQLL9pw3aJslg3hWFhERmRYDDxnV2B/TUGrArLDoroE8K4uIiEyOgYeMZsfJHINuHxHQwIVXUyYiohrBwENGoVQJjN+YbtA2n73azjSNISIiegIDDxnF2B/TUKTUfyyrjrMDnmnW0IQtIiIi+h8GHqo2Q4eyAGB+/3acqExERDWGgYeqRakSmPSzYdfcebGtLycqExFRjWLgoWo5dPEOCor1v3+Es70EiweFmrBFRERE5THwULUs2HXWoPqvBoRyKIuIiGocAw9V2Y6TOUi/qtC7nkNZRERkLgw8VCWGnobuIAGHsoiIyGwYeKhKlib9ZdBp6KOfa8GhLCIiMhsGHjKYUiWwfO8Fvesd7SQY83wLE7aIiIhIOwYeMpihd0OP6dWcvTtERGRWDDxkEEN7d6QOduzdISIis2PgIYMY2rvz5b9C2LtDRERmx8BDelOqBJbtOa93PU9DJyIiS8HAQ3pbmvQXSvXs3LHnaehERGRBGHhIL4bO3Ylo7c2hLCIishgMPKQXQ+fuvP1ME9M1hoiIyEAMPKSTob07dZwd8EyzhiZsERERkWEYeEgnQ3t35vdvx+EsIiKyKAw8pJWhvTs8M4uIiCwRAw9pZUjvDm8QSkREloqBhyqlVAms3K9/7w5vEEpERJaKgYcqdejiHRTpeeEd3iCUiIgsGQMPVer7lEt61/IGoUREZMkYeKhCSpVA4plcvWod7MDeHSIismgMPFShpUl/QannmejPt+JVlYmIyLIx8FA5hp6KzqsqExGRpWPgoXIMORVd6mDHqyoTEZHFM1nguXTpEqKjoxEYGAgXFxc0a9YM8fHxKC4u1qg7efIkunXrBqlUCn9/f8yfP7/cvjZt2oSWLVtCKpWibdu22LFjh8Z6IQTi4uLg6+sLFxcXRERE4Ny5c6Y6NJtmaO/O+z2acTiLiIgsnskCT2ZmJlQqFb7++mtkZGTgq6++wqpVq/DRRx+paxQKBXr37o2AgACkpaVhwYIFmDlzJlavXq2uSU5OxqBBgxAdHY3jx48jKioKUVFROH36tLpm/vz5WLJkCVatWoXU1FS4ubkhMjIShYWFpjo8m2VI7w5PRSciImshEULof5OkalqwYAFWrlyJixcvAgBWrlyJ6dOnQy6Xw8nJCQAwdepUbN68GZmZmQCAAQMGoKCgANu2bVPv55lnnkFISAhWrVoFIQT8/PwwceJEfPjhhwCA/Px8eHt7IyEhAQMHDtTZLoVCAZlMhvz8fLi7uxv7sK2GUiXQOu43va+9E/t8C8S+8JSJW0VERFQxQ36/a3QOT35+Pho0aKD+OyUlBd27d1eHHQCIjIxEVlYW7t27p66JiIjQ2E9kZCRSUlIAANnZ2ZDL5Ro1MpkMYWFh6ponFRUVQaFQaDyIFxokIiLbVWOB5/z581i6dCnee+899TK5XA5vb2+NurK/5XK51prH1z++XUU1T5o7dy5kMpn64e/vX40jsx280CAREdkqgwPP1KlTIZFItD7KhqPKXL9+HX369MEbb7yBd99912iNr6pp06YhPz9f/bh69aq5m2R2vNAgERHZMgdDN5g4cSKGDRumtaZp06bq/5+Tk4NevXqhS5cuGpORAcDHxwe5uZo/smV/+/j4aK15fH3ZMl9fX42akJCQCtvn7OwMZ2dnrcdQ2/BCg0REZMsMDjyenp7w9PTUq/b69evo1asXOnbsiDVr1sDOTrNDKTw8HNOnT0dJSQkcHR0BAImJiQgKCkL9+vXVNUlJSYiNjVVvl5iYiPDwcABAYGAgfHx8kJSUpA44CoUCqampGDVqlKGHVyvxQoNERGTrTDaH5/r16+jZsycaN26ML774Ardu3YJcLteYVzN48GA4OTkhOjoaGRkZ2LhxIxYvXowJEyaoa8aNG4edO3di4cKFyMzMxMyZM3H06FGMHj0aACCRSBAbG4s5c+Zgy5YtOHXqFN5++234+fkhKirKVIdnU3ihQSIisnUG9/DoKzExEefPn8f58+fRqFEjjXVlZ8LLZDLs3r0bMTEx6NixIzw8PBAXF4eRI0eqa7t06YL169djxowZ+Oijj9CiRQts3rwZwcHB6prJkyejoKAAI0eORF5eHrp27YqdO3dCKpWa6vBsBi80SEREtUGNXofHUtXm6/AsSszCoqTzetU62kmQOeefDDxERGQRLPY6PGRZDO3d4anoRERkrRh4ajHeRoKIiGoLBp5aSqkSWLmfvTtERFQ7MPDUUryNBBER1SYMPLVU8oXbeteyd4eIiKwdA08tdST7rl51vI0EERHZAgaeWkipEjh2+Z5etbyNBBER2QIGnlpoadJf0HP6Dm8jQURENoGBp5Yx5No7vI0EERHZCgaeWsaQa+/0aunJ4SwiIrIJDDy1iKHX3hkS1sR0jSEiIqpBDDy1iCHX3uFwFhER2RIGnlrkh0OX9a7lXdGJiMiWMPDUEkqVwO9n5HrV8srKRERkaxh4aolHk5X1q+WVlYmIyNYw8NQChkxWZu8OERHZIgaeWsCQycoRrb3Yu0NERDaHgacWMORGoTwVnYiIbBEDTy2g741CnewlPBWdiIhsEgOPjTPkRqG9WnI4i4iIbBMDj43jjUKJiIgYeGyaIWdn8crKRERkyxh4bJghZ2fxRqFERGTLGHhsmCG3kuDZWUREZMsYeGyUIbeS4HAWERHZOgYeG2XIrSR4o1AiIrJ1DDw2iLeSICIi0sTAY4N4KwkiIiJNDDw2KOXCHb1rOVmZiIhqAwYeG3T+5n296jhZmYiIagsGHhujVAnszbqpV22/dr4cziIiolqBgcfGGDJ/p2tzDxO3hoiIyDIw8NgYQy426CNzMWFLiIiILAcDjw0x5GKDdZzt8XRgAxO3iIiIyDKYNPC8/PLLaNy4MaRSKXx9ffHWW28hJydHo+bkyZPo1q0bpFIp/P39MX/+/HL72bRpE1q2bAmpVIq2bdtix44dGuuFEIiLi4Ovry9cXFwQERGBc+fOmfLQLJIhFxsc0bUp5+8QEVGtYdLA06tXL/z000/IysrCf/7zH1y4cAGvv/66er1CoUDv3r0REBCAtLQ0LFiwADNnzsTq1avVNcnJyRg0aBCio6Nx/PhxREVFISoqCqdPn1bXzJ8/H0uWLMGqVauQmpoKNzc3REZGorCw0JSHZ1GUKoFvDmbrVcuLDRIRUW0jEULoN8PVCLZs2YKoqCgUFRXB0dERK1euxPTp0yGXy+Hk5AQAmDp1KjZv3ozMzEwAwIABA1BQUIBt27ap9/PMM88gJCQEq1atghACfn5+mDhxIj788EMAQH5+Pry9vZGQkICBAwfqbJdCoYBMJkN+fj7c3d1NcOSml3LhDgZ9c0iv2n8Ge2PlkE4mbhEREZFpGfL7XWNzeO7evYt169ahS5cucHR0BACkpKSge/fu6rADAJGRkcjKysK9e/fUNRERERr7ioyMREpKCgAgOzsbcrlco0YmkyEsLExd86SioiIoFAqNh7XbnXFD71pebJCIiGobkweeKVOmwM3NDQ0bNsSVK1fw66+/qtfJ5XJ4e3tr1Jf9LZfLtdY8vv7x7SqqedLcuXMhk8nUD39//2ocofkpVQI/H7umV62LIy82SEREtY/BgWfq1KmQSCRaH2XDUQAwadIkHD9+HLt374a9vT3efvtt1OAoWoWmTZuG/Px89ePq1atmbU91Hc6+i/uFSr1qB3b252RlIiKqdRwM3WDixIkYNmyY1pqmTZuq/7+Hhwc8PDzw1FNPoVWrVvD398ehQ4cQHh4OHx8f5Obmamxb9rePj4/6fyuqeXx92TJfX1+NmpCQkArb5+zsDGdnZ90HayUMGc7q3cZXdxEREZGNMTjweHp6wtPTs0pPplI9Ome6qKgIABAeHo7p06ejpKREPa8nMTERQUFBqF+/vromKSkJsbGx6v0kJiYiPDwcABAYGAgfHx8kJSWpA45CoUBqaipGjRpVpXZaE6VK4Mcj+vVQuUsdeO0dIiKqlUw2hyc1NRXLli1Deno6Ll++jD179mDQoEFo1qyZOqwMHjwYTk5OiI6ORkZGBjZu3IjFixdjwoQJ6v2MGzcOO3fuxMKFC5GZmYmZM2fi6NGjGD16NABAIpEgNjYWc+bMwZYtW3Dq1Cm8/fbb8PPzQ1RUlKkOz2IcungHhXpefKd/h39wOIuIiGolg3t49OXq6or//ve/iI+PR0FBAXx9fdGnTx/MmDFDPZwkk8mwe/duxMTEoGPHjvDw8EBcXBxGjhyp3k+XLl2wfv16zJgxAx999BFatGiBzZs3Izg4WF0zefJkFBQUYOTIkcjLy0PXrl2xc+dOSKVSUx2exUi5cEfvWg5nERFRbVWj1+GxVNZ8HZ73vz+KnRm5OuvqONvjRHwke3iIiMhmWOR1eMj4lCqBg+dv6VUb2caHYYeIiGotBh4rdjj7Lh4U6Td/p2tzDxO3hoiIyHIx8FgxQ05H95G5mLAlRERElo2Bx0rxdHQiIiL9MfBYKZ6OTkREpD8GHiv1w6HLetfydHQiIqrtGHiskFIlsCfzpl61dZztOZxFRES1HgOPFTp08Q6KSvUbzhrRtSmHs4iIqNZj4LFC+g5nOdpJMOb5FiZuDRERkeVj4LEyhgxnRbT2Yu8OERERGHisjiHDWUPCmpi2MURERFaCgcfK6DucJXWwwzPNGpq4NURERNaBgceKGDKc1aulJ4eziIiI/j8GHivC4SwiIqKqYeCxIikX7uhVx+EsIiIiTQw8VuT8zft61fXkcBYREZEGBh4roVQJHDx/S6/ajo3rm7g1RERE1oWBx0oczr6LB0X6zd/xqONs4tYQERFZFwYeKyFXFOpd6yNzMWFLiIiIrA8Dj5X485x+w1nuUgfeLJSIiOgJDDxWQKkS2H7qhl61/Tv8gxOWiYiInsDAYwUOXbyDhyX6zd/p3cbXxK0hIiKyPgw8VkDf20nUceZwFhERUUUYeCycIbeT6NaiIYeziIiIKsDAY+F4OwkiIqLqY+CxcLw7OhERUfUx8Fgw3h2diIjIOBh4LBiHs4iIiIyDgceC8e7oRERExsHAY8EEhF51vDs6ERGRdgw8Fkye91CvOt4dnYiISDsGHgulVAnsOC3Xq5Z3RyciItKOgcdCGXI7Cd4dnYiISDsGHgvF20kQEREZT40EnqKiIoSEhEAikSA9PV1j3cmTJ9GtWzdIpVL4+/tj/vz55bbftGkTWrZsCalUirZt22LHjh0a64UQiIuLg6+vL1xcXBAREYFz586Z8pBMSqkSOPDXLb1qeTsJIiIi3Wok8EyePBl+fn7llisUCvTu3RsBAQFIS0vDggULMHPmTKxevVpdk5ycjEGDBiE6OhrHjx9HVFQUoqKicPr0aXXN/PnzsWTJEqxatQqpqalwc3NDZGQkCgsLa+LwjO5w9l0UFCv1quX1d4iIiHQzeeD57bffsHv3bnzxxRfl1q1btw7FxcX47rvv0KZNGwwcOBBjx47Fl19+qa5ZvHgx+vTpg0mTJqFVq1aYPXs2OnTogGXLlgF41LuzaNEizJgxA6+88gratWuH//u//0NOTg42b95s6sMzid0ZN/Sqc3Wy5/V3iIiI9GDSwJObm4t3330X33//PVxdXcutT0lJQffu3eHk5KReFhkZiaysLNy7d09dExERobFdZGQkUlJSAADZ2dmQy+UaNTKZDGFhYeqaJxUVFUGhUGg8LIVSJfDzsWt61fYN9uFwFhERkR5MFniEEBg2bBjef/99dOrUqcIauVwOb29vjWVlf8vlcq01j69/fLuKap40d+5cyGQy9cPf39/AozOdw9l3cb9Qv+GsZ5t7mLg1REREtsHgwDN16lRIJBKtj8zMTCxduhT379/HtGnTTNHuapk2bRry8/PVj6tXr5q7SWpyhf7zjng6OhERkX4cDN1g4sSJGDZsmNaapk2bYs+ePUhJSYGzs+ZF8Tp16oQ333wTa9euhY+PD3JzczXWl/3t4+Oj/t+Kah5fX7bM19dXoyYkJKTC9jk7O5drl6X485x+Z2e5S3k6OhERkb4MDjyenp7w9PTUWbdkyRLMmTNH/XdOTg4iIyOxceNGhIWFAQDCw8Mxffp0lJSUwNHREQCQmJiIoKAg1K9fX12TlJSE2NhY9b4SExMRHh4OAAgMDISPjw+SkpLUAUehUCA1NRWjRo0y9PDMSqkS2H5KvwnL/Tv8g/N3iIiI9GRw4NFX48aNNf6uU6cOAKBZs2Zo1KgRAGDw4MH45JNPEB0djSlTpuD06dNYvHgxvvrqK/V248aNQ48ePbBw4UL069cPGzZswNGjR9WnrkskEsTGxmLOnDlo0aIFAgMD8fHHH8PPzw9RUVGmOjyTMOTqyr3b+OouIiIiIgAmDDz6kMlk2L17N2JiYtCxY0d4eHggLi4OI0eOVNd06dIF69evx4wZM/DRRx+hRYsW2Lx5M4KDg9U1kydPRkFBAUaOHIm8vDx07doVO3fuhFQqNcdhVRmvrkxERGQaEiGEMHcjzE2hUEAmkyE/Px/u7u5maYNSJdBu5i69Ljj4z2BvrBxS8ZlvREREtYUhv9+8l5aF4NWViYiITIeBx0Loezo6r65MRERkOAYeC3H3QZFedby6MhERkeEYeCzElbsFetWFN+PVlYmIiAzFwGMBlCqB/x6/rldt3t/FJm4NERGR7WHgsQCG3D+rgZuT7iIiIiLSwMBjAXj/LCIiItNi4LEAvH8WERGRaTHwmJlSJZB4Jld3IXj/LCIioqpi4DGzw9l3kV9Yqlct759FRERUNQw8Zqbv/J16Lo4cziIiIqoiBh4z03f+TkQrLw5nERERVREDjxkpVQLbT93Qq/bZ5rzgIBERUVUx8JjRoYt38LBEpVctT0cnIiKqOgYeM/rh0GW96uo483R0IiKi6mDgMROlSuDAX/rN3+nWoiHn7xAREVUDA4+ZHM6+i4Ji/W4nMSSsiWkbQ0REZOMYeMxE39PRXZ3s8UyzhiZuDRERkW1j4DETfU9H7xvsw+EsIiKiamLgMQNDbifB09GJiIiqj4HHDAy5nQRPRyciIqo+Bh4z4O0kiIiIahYDjxncfVCkVx1vJ0FERGQcDDxmcOVugV514c04f4eIiMgYGHhqmFIl8N/j1/Wqzfu72MStISIiqh0YeGrY4ey7uF+o3wUHG7g5mbg1REREtQMDTw3Td8IywDO0iIiIjIWBp4bpe8FBdylvGEpERGQsDDw1yJALDvbv8A+eoUVERGQkDDw1yJALDvZu42vi1hAREdUeDDw1iBccJCIiMg8Gnhqk7/wdXnCQiIjIuBh4aghvGEpERGQ+DDw1hDcMJSIiMh+TBp4mTZpAIpFoPObNm6dRc/LkSXTr1g1SqRT+/v6YP39+uf1s2rQJLVu2hFQqRdu2bbFjxw6N9UIIxMXFwdfXFy4uLoiIiMC5c+dMeWgG4/wdIiIi8zF5D8+sWbNw48YN9WPMmDHqdQqFAr1790ZAQADS0tKwYMECzJw5E6tXr1bXJCcnY9CgQYiOjsbx48cRFRWFqKgonD59Wl0zf/58LFmyBKtWrUJqairc3NwQGRmJwkL9L/Jnapy/Q0REZD4Opn6CunXrwsfHp8J169atQ3FxMb777js4OTmhTZs2SE9Px5dffomRI0cCABYvXow+ffpg0qRJAIDZs2cjMTERy5Ytw6pVqyCEwKJFizBjxgy88sorAID/+7//g7e3NzZv3oyBAweWe96ioiIUFf3vjuUKhcLYh62B83eIiIjMy+Q9PPPmzUPDhg0RGhqKBQsWoLT0f/NYUlJS0L17dzg5/e+eUZGRkcjKysK9e/fUNRERERr7jIyMREpKCgAgOzsbcrlco0YmkyEsLExd86S5c+dCJpOpH/7+/kY73opw/g4REZF5mTTwjB07Fhs2bMDevXvx3nvv4bPPPsPkyZPV6+VyOby9vTW2KftbLpdrrXl8/ePbVVTzpGnTpiE/P1/9uHr1ajWOUjfO3yEiIjIvg4e0pk6dis8//1xrzdmzZ9GyZUtMmDBBvaxdu3ZwcnLCe++9h7lz58LZ2dnw1hqJs7NzjT7/3QdFuovA+TtERESmYnDgmThxIoYNG6a1pmnTphUuDwsLQ2lpKS5duoSgoCD4+PggN1dzbkvZ32XzfiqreXx92TJfX1+NmpCQEL2Py5TquTrpLgIQ3ozzd4iIiEzB4MDj6ekJT0/PKj1Zeno67Ozs4OXlBQAIDw/H9OnTUVJSAkdHRwBAYmIigoKCUL9+fXVNUlISYmNj1ftJTExEeHg4ACAwMBA+Pj5ISkpSBxyFQoHU1FSMGjWqSu00tpQLt/Wqy/u72MQtISIiqp1MNocnJSUFixYtwokTJ3Dx4kWsW7cO48ePx5AhQ9RhZvDgwXByckJ0dDQyMjKwceNGLF68WGMobNy4cdi5cycWLlyIzMxMzJw5E0ePHsXo0aMBABKJBLGxsZgzZw62bNmCU6dO4e2334afnx+ioqJMdXh6M+QMrQZu+vUEERERkWFMdlq6s7MzNmzYgJkzZ6KoqAiBgYEYP368RpiRyWTYvXs3YmJi0LFjR3h4eCAuLk59SjoAdOnSBevXr8eMGTPw0UcfoUWLFti8eTOCg4PVNZMnT0ZBQQFGjhyJvLw8dO3aFTt37oRUKjXV4emNZ2gRERGZn0QIIczdCHNTKBSQyWTIz8+Hu7u7Uff9y/HrGL8xXWddPRdHpH38AictExER6cmQ32/eS8vEeIVlIiIi82PgMSFeYZmIiMgyMPCYEOfvEBERWQYGHhPiFZaJiIgsAwOPCXH+DhERkWVg4DERzt8hIiKyHAw8JsL5O0RERJaDgcdEOH+HiIjIcjDwmAjn7xAREVkOBh4T4PwdIiIiy8LAYwKcv0NERGRZGHhMgPN3iIiILAsDjwlw/g4REZFlYeAxMs7fISIisjwMPEbG+TtERESWh4HHyDh/h4iIyPIw8BjZ3QdFetVx/g4REVHNYeAxsgZuTnrVcf4OERFRzWHgMTJ95+Vw/g4REVHNYeAxsqcDG8BXJtVa4yuTcv4OERFRDWLgMTJ7OwniX2qNymbnSADEv9Sa83eIiIhqEAOPCfQJ9sXKIR3K9fT4yqRYOaQD+gT7mqllREREtZODuRtgq/oE++KF1j44nH0XN+8Xwqvuo2Es9uwQERHVPAYeE7K3kyC8WUNzN4OIiKjW45AWERER2TwGHiIiIrJ5DDxERERk8xh4iIiIyOYx8BAREZHNY+AhIiIim8fAQ0RERDaPgYeIiIhsHgMPERER2TxeaRmAEAIAoFAozNwSIiIi0lfZ73bZ77g2DDwA7t+/DwDw9/c3c0uIiIjIUPfv34dMJtNaIxH6xCIbp1KpkJOTg7p160IiMe7NPRUKBfz9/XH16lW4u7sbdd+WwNaPD7D9Y+TxWT9bP0ZbPz7A9o/RVMcnhMD9+/fh5+cHOzvts3TYwwPAzs4OjRo1MulzuLu72+Q/4jK2fnyA7R8jj8/62fox2vrxAbZ/jKY4Pl09O2U4aZmIiIhsHgMPERER2TwGHhNzdnZGfHw8nJ2dzd0Uk7D14wNs/xh5fNbP1o/R1o8PsP1jtITj46RlIiIisnns4SEiIiKbx8BDRERENo+Bh4iIiGweAw8RERHZPAYeIiIisnkMPNX06aefokuXLnB1dUW9evUqrLly5Qr69esHV1dXeHl5YdKkSSgtLdW637t37+LNN9+Eu7s76tWrh+joaDx48MAER2CYffv2QSKRVPg4cuRIpdv17NmzXP37779fgy3XX5MmTcq1dd68eVq3KSwsRExMDBo2bIg6deqgf//+yM3NraEWG+bSpUuIjo5GYGAgXFxc0KxZM8THx6O4uFjrdpb8Hi5fvhxNmjSBVCpFWFgYDh8+rLV+06ZNaNmyJaRSKdq2bYsdO3bUUEsNN3fuXHTu3Bl169aFl5cXoqKikJWVpXWbhISEcu+VVCqtoRYbZubMmeXa2rJlS63bWNP7B1T8nSKRSBATE1NhvaW/fwcOHMBLL70EPz8/SCQSbN68WWO9EAJxcXHw9fWFi4sLIiIicO7cOZ37NfRzbCgGnmoqLi7GG2+8gVGjRlW4XqlUol+/figuLkZycjLWrl2LhIQExMXFad3vm2++iYyMDCQmJmLbtm04cOAARo4caYpDMEiXLl1w48YNjceIESMQGBiITp06ad323Xff1dhu/vz5NdRqw82aNUujrWPGjNFaP378eGzduhWbNm3C/v37kZOTg9dee62GWmuYzMxMqFQqfP3118jIyMBXX32FVatW4aOPPtK5rSW+hxs3bsSECRMQHx+PY8eOoX379oiMjMTNmzcrrE9OTsagQYMQHR2N48ePIyoqClFRUTh9+nQNt1w/+/fvR0xMDA4dOoTExESUlJSgd+/eKCgo0Lqdu7u7xnt1+fLlGmqx4dq0aaPR1oMHD1Zaa23vHwAcOXJE4/gSExMBAG+88Ual21jy+1dQUID27dtj+fLlFa6fP38+lixZglWrViE1NRVubm6IjIxEYWFhpfs09HNcJYKMYs2aNUImk5VbvmPHDmFnZyfkcrl62cqVK4W7u7soKiqqcF9nzpwRAMSRI0fUy3777TchkUjE9evXjd726iguLhaenp5i1qxZWut69Oghxo0bVzONqqaAgADx1Vdf6V2fl5cnHB0dxaZNm9TLzp49KwCIlJQUE7TQ+ObPny8CAwO11ljqe/j000+LmJgY9d9KpVL4+fmJuXPnVlj/r3/9S/Tr109jWVhYmHjvvfdM2k5juXnzpgAg9u/fX2lNZd9Hlig+Pl60b99e73prf/+EEGLcuHGiWbNmQqVSVbjemt4/AOKXX35R/61SqYSPj49YsGCBelleXp5wdnYWP/74Y6X7MfRzXBXs4TGxlJQUtG3bFt7e3uplkZGRUCgUyMjIqHSbevXqafSYREREwM7ODqmpqSZvsyG2bNmCO3fuYPjw4Tpr161bBw8PDwQHB2PatGn4+++/a6CFVTNv3jw0bNgQoaGhWLBggdYhyLS0NJSUlCAiIkK9rGXLlmjcuDFSUlJqornVlp+fjwYNGuiss7T3sLi4GGlpaRqvvZ2dHSIiIip97VNSUjTqgUefSWt6rwDofL8ePHiAgIAA+Pv745VXXqn0+8YSnDt3Dn5+fmjatCnefPNNXLlypdJaa3//iouL8cMPP+Cdd96BRCKptM6a3r/HZWdnQy6Xa7xHMpkMYWFhlb5HVfkcVwXvlm5icrlcI+wAUP8tl8sr3cbLy0tjmYODAxo0aFDpNuby7bffIjIyUufd5gcPHoyAgAD4+fnh5MmTmDJlCrKysvDf//63hlqqv7Fjx6JDhw5o0KABkpOTMW3aNNy4cQNffvllhfVyuRxOTk7l5nB5e3tb3PtVkfPnz2Pp0qX44osvtNZZ4nt4+/ZtKJXKCj9jmZmZFW5T2WfSGt4rlUqF2NhYPPvsswgODq60LigoCN999x3atWuH/Px8fPHFF+jSpQsyMjJ0flZrWlhYGBISEhAUFIQbN27gk08+Qbdu3XD69GnUrVu3XL01v38AsHnzZuTl5WHYsGGV1ljT+/eksvfBkPeoKp/jqmDgqcDUqVPx+eefa605e/aszol11qQqx3zt2jXs2rULP/30k879Pz7/qG3btvD19cXzzz+PCxcuoFmzZlVvuJ4MOb4JEyaol7Vr1w5OTk547733MHfuXIu+z01V3sPr16+jT58+eOONN/Duu+9q3dbc7yEBMTExOH36tNY5LgAQHh6O8PBw9d9dunRBq1at8PXXX2P27NmmbqZB/vnPf6r/f7t27RAWFoaAgAD89NNPiI6ONmPLTOPbb7/FP//5T/j5+VVaY03vnzVh4KnAxIkTtaZvAGjatKle+/Lx8Sk307zs7B0fH59Kt3lyolZpaSnu3r1b6TbVVZVjXrNmDRo2bIiXX37Z4OcLCwsD8Kh3oSZ+LKvznoaFhaG0tBSXLl1CUFBQufU+Pj4oLi5GXl6eRi9Pbm6uyd6vihh6jDk5OejVqxe6dOmC1atXG/x8Nf0eVsTDwwP29vblzojT9tr7+PgYVG8pRo8erT6BwdD/ynd0dERoaCjOnz9votYZT7169fDUU09V2lZrff8A4PLly/j9998N7hW1pvev7H3Izc2Fr6+venlubi5CQkIq3KYqn+MqMdpsoFpO16Tl3Nxc9bKvv/5auLu7i8LCwgr3VTZp+ejRo+plu3btsqhJyyqVSgQGBoqJEydWafuDBw8KAOLEiRNGbpnx/fDDD8LOzk7cvXu3wvVlk5Z//vln9bLMzEyLnrR87do10aJFCzFw4EBRWlpapX1Yynv49NNPi9GjR6v/ViqV4h//+IfWScsvvviixrLw8HCLnfSqUqlETEyM8PPzE3/99VeV9lFaWiqCgoLE+PHjjdw647t//76oX7++WLx4cYXrre39e1x8fLzw8fERJSUlBm1nye8fKpm0/MUXX6iX5efn6zVp2ZDPcZXaarQ91VKXL18Wx48fF5988omoU6eOOH78uDh+/Li4f/++EOLRP9Tg4GDRu3dvkZ6eLnbu3Ck8PT3FtGnT1PtITU0VQUFB4tq1a+plffr0EaGhoSI1NVUcPHhQtGjRQgwaNKjGj68yv//+uwAgzp49W27dtWvXRFBQkEhNTRVCCHH+/Hkxa9YscfToUZGdnS1+/fVX0bRpU9G9e/eabrZOycnJ4quvvhLp6eniwoUL4ocffhCenp7i7bffVtc8eXxCCPH++++Lxo0biz179oijR4+K8PBwER4ebo5D0OnatWuiefPm4vnnnxfXrl0TN27cUD8er7GW93DDhg3C2dlZJCQkiDNnzoiRI0eKevXqqc+MfOutt8TUqVPV9X/++adwcHAQX3zxhTh79qyIj48Xjo6O4tSpU+Y6BK1GjRolZDKZ2Ldvn8Z79ffff6trnjzGTz75ROzatUtcuHBBpKWliYEDBwqpVCoyMjLMcQhaTZw4Uezbt09kZ2eLP//8U0RERAgPDw9x8+ZNIYT1v39llEqlaNy4sZgyZUq5ddb2/t2/f1/9WwdAfPnll+L48ePi8uXLQggh5s2bJ+rVqyd+/fVXcfLkSfHKK6+IwMBA8fDhQ/U+nnvuObF06VL137o+x8bAwFNNQ4cOFQDKPfbu3auuuXTpkvjnP/8pXFxchIeHh5g4caJGwt+7d68AILKzs9XL7ty5IwYNGiTq1Kkj3N3dxfDhw9UhyhIMGjRIdOnSpcJ12dnZGq/BlStXRPfu3UWDBg2Es7OzaN68uZg0aZLIz8+vwRbrJy0tTYSFhQmZTCakUqlo1aqV+OyzzzR64548PiGEePjwofjggw9E/fr1haurq3j11Vc1AoQlWbNmTYX/Zh/v8LW293Dp0qWicePGwsnJSTz99NPi0KFD6nU9evQQQ4cO1aj/6aefxFNPPSWcnJxEmzZtxPbt22u4xfqr7L1as2aNuubJY4yNjVW/Ht7e3qJv377i2LFjNd94PQwYMED4+voKJycn8Y9//EMMGDBAnD9/Xr3e2t+/Mrt27RIARFZWVrl11vb+lf1mPfkoOwaVSiU+/vhj4e3tLZydncXzzz9f7rgDAgJEfHy8xjJtn2NjkAghhPEGyIiIiIgsD6/DQ0RERDaPgYeIiIhsHgMPERER2TwGHiIiIrJ5DDxERERk8xh4iIiIyOYx8BAREZHNY+AhIiIim8fAQ0RERDaPgYeIiIhsHgMPERER2bz/B3blfTu71XV0AAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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SGzDhqzSMWbDZBb0irfnpjxNo9dIyLEvPUXRc6tECtHxxGZbtPKFSz0gNJiGEy0LOU6dOITg4GGvWrEGfPn0qbHPffffhwoULWLp0qf2+7t27o0uXLpg3b9417YuLi1FcXGz/uXxXyvz8fG7MSGQA6/adwoPztyg6pkNYfSx9sq9KPSKteeTTrVi552StzzO2d3O8NKS9E3pENVFQUICAgACp67dLs5by8/MBAIGBgZW2SUlJQWJiosN9AwYMQEpKSoXtZ8yYgYCAAPstIiLCeR0mIrdbvOOY4mPSswtx6ztrVegNac2YBVucErgAwIfrDmP60l1OORepy2XBi81mw1NPPYWePXuiQ4cOlbbLyclBSEiIw30hISHIyal4KDApKQn5+fn229GjR53abyJyr4sl1hodl37iPP6mcMSG9GX60nQk7z3l1HN+vP4wXvt5t1PPSc7nsuBl/PjxSE9Px1dffeXU8/r4+MDf39/hRkTGcWPzykdqq7Nq3ylM/YnfpI1o2c4T+Hj9EVXO/eG6TCzbma3Kuck5XBK8TJgwAUuXLsXq1atx/fXXV9k2NDQUubm5Dvfl5uYiNDRUzS4SkUY91KN5rY6fv4HfpI3GahN44qsdqj7GE1+mchWShqkavAghMGHCBHz//fdYtWoVIiMjqz0mPj4eycnJDvetWLEC8fHxanWTiDTM28uMx/pU/39HVfhN2ljueW8DymzqPkaZACYuSlX3QajGVA1exo8fj4ULF2LRokVo0KABcnJykJOTg0uXLtnbjBo1CklJSfafn3zySSxfvhwzZ87E3r178eqrr2Lbtm2YMGGCml0lIg1LGtyu1gHMM9+k8Zu0AUxfmo7Uo/kueaxl6TkMejVK1eDlvffeQ35+Pvr164ewsDD77euvv7a3ycrKQnb2Xx+OHj16YNGiRfjggw/QuXNn/Pe//8WSJUuqTPIlIuNLGtwO+/8xCEmD2qB5oJ/i44vKbJidfECFnpGr1DTPxdcC9GvVGA18lF/ynv56B4NeDXJpnRdXULJOnIj0a9nObPxd4bC+xQTsf20wtxHQIatNoNOry3GhRNl80cM9m2HKbX99+Z360y7M33BY0Tlu7RiGdx/oqugYUk6zdV6IiJxlcKcwzL0/RtExVgG8vWK/Sj0iNW06dEZx4DKmV3OHwAUAptzWHg/3bKboPEv/zOb0kcYweCEi3RrcKRzvDlcWwMxdk8FpAB36POWwovYP92yOV26tuFrulNs6oGtEgKLzPbd4Jz83GsLghYh07dYu4Rjbu7l0+zIbmPuiM1abwIrdudU3/J+uEQGYclvVZf6/HdcTXgqugIXFZdh08Iz8AaQqBi9EpHsvDWmPuOaNpNvPWc3RFz2ZnbwfVsm3y4zLgUl1LGYT3lE4avfZpsOK2pN6GLwQkSF8/kh36balNsHRF52w2gRmr8qQbv9EQivphOzBncIxpGNI9Q3/J3lPLoNejWDwQkSG4O1lVnQh4uiLPtwzb4P0qEsdswkTE1opOv87I7rBS3LxGacctYPBCxEZxjsjusEieSHi6Iv2LU07jtQs+YJ04/u3VLwM3mI2YcJNLaXbM+jVBgYvRGQYFrMJExVciOatOcgLkUZZbQJJS/6Ubl+TUZdyExNao45k0MOgVxsYvBCRoSi5EBWV2biCRKO2ZObhfJFVun1NRl3KWcwmjO/fQro9R1/cj8ELERmK0gsRV5BoU05BkXRbb0vNR13KcfRFXxi8EJHhTExoLZ37whUk2rT+wEnptjPv7VLrLR84+qIvDF6IyHAsZhNubie38ogrSLTHahP4YccJqbbNG9fFbZ3DnfK4HH3RDwYvRGRID8Y3l27Lb9HaMjt5P8ok345bO4U57XGVjr4w4dt9GLwQkSF1j2oMH8kCHvwWrR1Wm8Cc1Qel28dHNXHq4zPhWx8YvBCRIVnMJozry2/RejM7eT9KJd8HXy8zurdo7NTHVzr6suHgKac+Pslh8EJEhsVv0fpitQm8t0Z+1OXxvi1qnahbESUJ39sOn3X641P1GLwQkWEp/Ra9cPNh9TpD1dp06AyKJZNdalOUrjpKEr53ZJ3jiJ0bMHghIkNTMvqycvdJXojcaOGmI9Jta1OUToZswjfzpdyDwQsRGZqS0RdeiNzHahNYuTtHqq2aoy7lLid8y10imS/legxeiMjwLo++yLXlhcg9LifqyrVVe9QFuBz03hQdLNWW+VKux+CFiAzPYjYhsV2oVFteiFxPSaKuK0Zdyo3s3ky6LfOlXIvBCxF5BCUXopRDp1XsCV1NSaJuYrtg1UddyimpFcR8Kddi8EJEHqF7VGN4W+TaHjhZqG5nyEGKgpGukXHN1evIVZTUCmK+lGsxeCEij2Axm6T3wFm7/xS/RbtQxsnzUu3UKEpXHSX5Uh+tP8TPjYsweCEij9GrlVwC5qVS5r24itUmsHqf3A7SQzqFuWzKqJySfKnCYiu2ZOap3CMCGLwQkQcJ9feVbssETNdQku/Sq6Vz9zGSpSRf6rdd2Sr2hMoxeCEijxEbGYh6PnKJL0zAdA0lhelCA/xU7Enlukc1hq/k3NFXW4/yc+MCDF6IyGNYzCaM7RUp1ZYJmOqz2gRW7ZWbMqrvY0FsZKDKPaqYxWzCiBsjpNpyytE1GLwQkUdhAqZ2XJ4ykqtM90ivKJfnu1zplvZh0m251F59DF6IyKMwAVM7ZKeMXFmYrjKxkYGo6y13yeRSe/UxeCEij8METPdTMmXkysJ0lbGYTRjUQS7oTTl4hiN2KlM1eFm7di1uu+02hIeHw2QyYcmSJVW2//3332Eyma655eTIbdZFRCRDSQLm4tTjvBCpQMmUkSsL01VFdql9QVEZR+xUpmrwcuHCBXTu3Blz5sxRdNy+ffuQnZ1tvwUHy31giIhkKEnA5IVIHbJTRu4oTFcZJUvtOWKnLi81Tz5o0CAMGjRI8XHBwcFo2LChVNvi4mIUFxfbfy4oKFD8eETkeW5pH4b5G+UuoDn5l1TujWex2gRW7pYbUe8fHeT2KaNysZGBaOBrwfkia7Vtv9p6FC/f2l4zfTcaTea8dOnSBWFhYbj55puxYcOGKtvOmDEDAQEB9ltEhNy3KSLybLGRgajvI/df4OnC4uobkbTZyftRKjdjpJkpI+DyiN3dXa+Xassl0+rSVPASFhaGefPmYfHixVi8eDEiIiLQr18/pKamVnpMUlIS8vPz7bejR4+6sMdEpFcWswm9WgZJtd2edVbl3ngOq03gw/WZUm21NGVUTsmSaVZpVo+q00ZKtWnTBm3atLH/3KNHDxw8eBCzZs3C559/XuExPj4+8PHxcVUXichAWgY3AHblVtvu972XN2rkFEDtbcnMw4Xi6qddAG1NGZUrr9Is8xxW83OjGk2NvFQkNjYWGRkZ7u4GERlQvOS3+qIyTgE4S05BkXRbLU0ZlVNSpZmfG/VoPnhJS0tDWJj8MB0RkazuUY3h4yX33yCnAJzj9Hm5/CG/OtqbMiqnpEozPzfqUHXaqLCw0GHUJDMzE2lpaQgMDETTpk2RlJSE48eP47PPPgMA/Oc//0FkZCTat2+PoqIifPTRR1i1ahV+++03NbtJRB7KYjbhpuhg/JJe/coXTgE4x/YjcsvO+7TW3pRRufIqzfzcuI+qIy/btm1DTEwMYmJiAADPPPMMYmJiMHnyZABAdnY2srKy7O1LSkowadIkdOzYEX379sUff/yBlStXIiEhQc1uEpEHk622yymA2rPaBNYdkNv3p1VwfZV7Uzv83LiXqiMv/fr1gxCVV6ZcsGCBw8/PPfccnnvuOTW7RETkoHzqSKba68LNh9GzVRMX9MqYtmTm4UKJXLJufJS2X2cln5uUQ6f5uXEyzee8EBGpqXzqSEb5FADVjGzV2breFs3mu5SzmE3o30ZuqT0/Ms7H4IWIPB6nANRntQn8N/WYVNvBHUJ1kSPSrVmgVLtcBSusSA6DFyLyeFx1pL4tmXlSZfUBoGdLfUyxNGkgV2Psl/Qcjtg5GYMXIvJ4nDpSn5KNCkMD/FTsifPIbtR4scTKETsnY/BCRAROHalJyZSRv68XYiPlpmPcLTYyEPW8LVJtOWLnXAxeiIjAqSM1KZkyGtb1Ol3kuwCXR+z6tJZL2uWInXMxeCEigrKpo3UHzvBCpICSKSMlGx9qAUfs3IPBCxHR/8heiAqLy7AlU65SrKcz6pRROY7YuQeDFyKi/+ke1Rh+kpvW5ORfUrk3xmDUKaNyHLFzDwYvRET/YzGbMLhDqFTb04VyGwx6OiW7SOttyqgcR+xcj8ELEdEVQhvKLdPdnnVW5Z4YQ55kkKfHKaNySkbslOT/UOUYvBARXcEEuWmL9ZwCkJKVd0Gq3Z0x+psyKmcxmzCko9yo0eLU4/zcOAGDFyKiK8RL7qnDKYDqWW0C3+04LtW2aWBdlXujrp6t5JZMFxTxc+MMDF6IiK7AKQDnUZKsG1jPW+XeqEu22i7Az40zMHghIroCpwCcR0myrl62BKhMbGQgGvjKVdvl56b2GLwQEV2FUwDOseHAKal2ek7WLWcxm3B31+ul2vJzU3sMXoiIrsIpgNqz2gR+/lPutdFjfZeKKFnqzTpBtcPghYjoKpwCqL1Nh87gUqlNqq1e67tcTcnnJu9Cicq9MTYGL0REV+EUQO0t3HREql19H/1PGZWzmE24K+Y6qbZZeRdV7o2xMXghIqoApwBqzmoTWLtfLt+ld6vGhpgyKtc0sJ5Uu+93cMSuNhi8EBFVQMkUwIaM0yr3Rl+2ZObhQoncEumRcc3V7YyLBdb3kWrHEbvaYfBCRFQBJVNHy9Jz+C36CrJJzHW9LeguWRRQL5Qke3PEruYYvBARVUJ26uhiiRWbDp5RuTf6YLUJ/Df1mFTbwR1CDTVlBHDEzlUYvBARVSI2MhD1vOUuRAs3H1a3MzqhpKpuz5ZNVO6N63HEzjUYvBARVcJiNqFPa7mCdeu4USMAz6qqWxmO2KmPwQsRURVGdm8m1Y4bNV7mSVV1K8MRO/UxeCEiqoKSjRo9PQHTE6vqVoQjdupj8EJEVAWL2YTBHUKl2p4uLFa5N9rmiVV1K8MRO3UxeCEiqkZoQ7ncjO1ZZ1XuibalSOZvGKmqbmWUjNhxfyzlGLwQEVXDBLnpjfUePgUgIPfcexmsqm5FLGYThnSUG13i/ljKMXghIqpGvGQhNU+fAsg5J5fz061pI5V7og09W8nlvbDarnKqBi9r167FbbfdhvDwcJhMJixZsqTaY37//Xd07doVPj4+aNmyJRYsWKBmF4mIqsWk3epZbQLL0nOk2jaRLKGvd6y2qx5Vg5cLFy6gc+fOmDNnjlT7zMxMDBkyBP3790daWhqeeuopPPLII/j111/V7CYRUZWUTAF4atVUJcm6Rq3vcjVW21WPqsHLoEGD8I9//AN33nmnVPt58+YhMjISM2fORNu2bTFhwgTcfffdmDVrVqXHFBcXo6CgwOFGRORsslMAnlo1deGmI1LtPCFZtxyr7apHUzkvKSkpSExMdLhvwIABSElJqfSYGTNmICAgwH6LiIhQu5tE5IFkpwA8sWqq1Sawdr9ccbreHpCseyVW21WHpoKXnJwchISEONwXEhKCgoICXLpU8XxgUlIS8vPz7bejR4+6oqtE5GFYNbVyWzLzcKFEbj+jkXHN1e2Mxij53KQc4tSRLE0FLzXh4+MDf39/hxsRkbOxamrlZOuU1PW2oLvkyi2jsJhN6N1KbgNKD/rI1JqmgpfQ0FDk5uY63Jebmwt/f3/4+XlGghcRaRerpl7LahP4b+oxqbaDO4R61JRRuW7N5HJ8chVsaunpNBW8xMfHIzk52eG+FStWID4+3k09IiL6C6umXmtLZh7OF8lNGfVsKTcCYTRNGsgtDU/ec9KjRuxqQ9XgpbCwEGlpaUhLSwNweSl0WloasrKyAFzOVxk1apS9/eOPP45Dhw7hueeew969ezF37lx88803ePrpp9XsJhGRFFZNvVaOgtECT1kifTXZZO9zl0o9ZsSutlQNXrZt24aYmBjExMQAAJ555hnExMRg8uTJAIDs7Gx7IAMAkZGR+Pnnn7FixQp07twZM2fOxEcffYQBAwao2U0iImmsmupowwG5VUb+vp6zRPpqsZGBCPD1kmrrKSN2tSX3atZQv379IETl3zwqqp7br18/7NixQ8VeERHVHKum/sVqE/j5T7mL7bCu13lkvgtwecTu5nYh+G/q8WrbLk49jpdvbe+xr5UsTeW8EBFpnZKqqXkXSlTujXspqaorW+/EqDhi51wMXoiIFLCYTbgr5jqptll5F1XujXuxqq48jtg5F4MXIiKFmgbWk2r3/Q7jJu2yqq4y3OfIuRi8EBEpFCi5K7KRpwBYVVcZ7nPkXAxeiIgU4hSA/BJpT6yqWxnuc+Q8DF6IiBTiFABw+nyxVLtBHlpVtyLcH8t5GLwQESnEKQBg+xG56bAQBaNURsf9sZyHwQsRUQ148hSA1Saw7oDciBIHXRxxfyznYPBCRFQDnjwFoCRZNz7KM/czqoyS/bGMmi/lDAxeiIhqwJOnAGRL2DNZ91oWswmDO4RKtT1dKJdX5IkYvBAR1ZAnTgFYbQL/TT0m1XYwk3UrFNpQboPK7VlnVe6JfjF4ISKqISVTAEbZcG9LZh7OF8lNGfVsySmjipggF9D9vveUoUbsnInBCxFRDVnMJgzpKJe4uzjVGNV2Zeu7AEBogNwIg6eJl5xKKyqzGS7Z21kYvBAR1YKnbbi34YDclgD+vtzPqDLdoxrDx0vu8mu0ZG9nYfBCRFQLSqrt6n3qyGoT+PlPuecwrOt1zHephMVswk3RwVJtjZbs7SwMXoiIakFJtV29Tx1tOnQGl0ptUm1l6+B4Kk9M9nYmBi9ERLWgpNqu3qeOFm46ItWuvg+njKrjicnezsTghYiolpSMMui18JjVJrBq70mptr1bNeaUUTU8MdnbmRi8EBHVkpKpo7wLJSr3Rh2bDp1BcZnclNHIuObqdsYgPC3Z25kYvBAR1ZLFbMJdMddJtc3Ku6hyb9SRIrlk19fLzKq6kjwp2dvZGLwQETlB08B6Uu2+36HPKYCMk+el2vWLDuKUkSRPSvZ2NgYvREROEFjfR6qdHqcArDaB9Rly9V26NW2kcm+Mw5OSvZ2NwQsRkRMYeQpgS2YeCovl8l2aSAZxdJknJHurgcELEZETGHkKgFsCqEfJ52ZDxmmVe6MfDF6IiJzAyFMA3BJAPUo+N8vSc3QV9KqJwQsRkZMomQLQy9QRtwRQn+zn5mKJlRs1/g+DFyIiJzHi1BG3BFBfbGQg6nnLfW5SDnHqCGDwQkTkNEacOuKWAOqzmE3o3aqJVNsDJwtV7o0+MHghInIiI00dcUsA1+nWTC7wSznIXaYBBi9ERE5lpKkjbgngOk0aGLdOkBoYvBAROZGRpo5kp4y4JUDtGblOkBpcErzMmTMHzZs3h6+vL+Li4rBly5ZK2y5YsAAmk8nh5usr/6YSEbmbEaaOlEwZ9eeWALVmpBE7V1A9ePn666/xzDPPYMqUKUhNTUXnzp0xYMAAnDxZ+R+Fv78/srOz7bcjR+SifyIiLTDChYhTRq5lpBE7V1A9ePn3v/+NsWPH4uGHH0a7du0wb9481K1bF5988kmlx5hMJoSGhtpvISEhlbYtLi5GQUGBw42IyJ2McCHilJHrGWHEzlVUDV5KSkqwfft2JCYm/vWAZjMSExORkpJS6XGFhYVo1qwZIiIicMcdd2DXrl2Vtp0xYwYCAgLst4iICKc+ByKimtDznjWcMnIPJSN2X209qskRO1dRNXg5ffo0rFbrNSMnISEhyMnJqfCYNm3a4JNPPsEPP/yAhQsXwmazoUePHjh27FiF7ZOSkpCfn2+/HT161OnPg4hIqdjIQNT3kfsv9nRhscq9UYZTRu6hZMTuUqnNo6vtam61UXx8PEaNGoUuXbqgb9+++O677xAUFIT333+/wvY+Pj7w9/d3uBERuZvFbEKvlkFSbbdnnVW5N8psPChXxZVTRs6nZMRu4ebD6nVE41QNXpo0aQKLxYLc3FyH+3NzcxEaGip1jjp16iAmJgYZGRlqdJGISDUtgxtItUvefVJTUwBbJXNwOl0fwCkjJ4uNDEQ9H7mpo9V7T2nqc+NKqgYv3t7e6NatG5KTk+332Ww2JCcnIz4+XuocVqsVf/75J8LCuGcGEelLvOSoRKlNYHbyAZV7I8dqE0g7ek6q7Q3NG6nbGQ9kMZswtlekVNuiMs+dOlJ92uiZZ57Bhx9+iE8//RR79uzBuHHjcOHCBTz88MMAgFGjRiEpKcneftq0afjtt99w6NAhpKamYuTIkThy5AgeeeQRtbtKRORU3aMaw8dL7r/Zj9Yf0sS36E2HzqDEKtePni3kpsVImYkJrVFH8ursqRs1eqn9APfddx9OnTqFyZMnIycnB126dMHy5cvtSbxZWVkwm/96l86ePYuxY8ciJycHjRo1Qrdu3bBx40a0a9dO7a4SETmVxWzCTdHB+CW94gUKVyostmJLZp70aI1auETa/SxmExLahmD5rtxq23rqRo0mIYT7Q30nKigoQEBAAPLz85m8S0RutyHjNB74aLNU24d7NMOU2zuo3KPKWW0C0S8vQ6nEQqNBHULw3sgb1O+Uh/pw7SG8tmxPte386piRPnWgIXKPlFy/NbfaiIjISLpHNYav5ByAu2t3zE7eLxW4AFwirTbZjRo9dck0gxciIhVZzCaMuFGueKY7L0RWm8B7aw5KteWUkfqUbNToiUumGbwQEalMD7U7Lhemkxv1YVVd9SlZMr1SY0vtXYHBCxGRyvRQu0M2URfglJErKFkyraWl9q7C4IWISGVar91htQms3F39iiiAU0aupGTJ9Lw1Bz1q9IXBCxGRC0xMaA0vyZmWzzYdVrUvV1OSqPt43xacMnIRi9mExHZy1eg9rWAdgxciIhewmE3o2kyuIu3a/a6bOlKSqFvHbMLEhFYq94iuNLJ7M+m2npS4y+CFiMhFbowMlGp3qdSGLZL7C9WWkkTdxHbBHHVxsctVmuVec09K3GXwQkTkIj1aNJFu+9uubBV78pcUBVMNTNR1PYvZhHF9W0i19aTEXQYvREQuoqRg3Rebs1zyLTrj5HmpdkzUdR8m7l6LwQsRkYsoKVhXYlX/W/TlVUbV758DAEM6hXHKyE2YuHstBi9ERC6kpGDdnNUZqn6Lnp28H5LpLujVUn7Ki5xPSeKuq1eruQODFyIiF1JSsE7NHAarTWDOarlVRgAQGuCnSj9ITveoxtJTR6v3Gj9xl8ELEZELKSlYB6g3+nK5tovceev7WBAruVKK1GExm5DQNkSqbYlVGH7qiMELEZGLXU7AlMsfUWP0RemoyyO9opjvogEPxjeXbmv0mi8MXoiIXMxiNmF8f7nlr4DzV5AoGXVhYTrtUFLzZcWuXENPHTF4ISJyAyWjL85cQaKkoi4AjO/fkqMuGqGk5kuZAJ78cofT+2C1CaQcPIMf0o4j5eAZtwVIDF6IiNxA6eiLs1aQKKmoy1EX7VFS82Xpn9lYttN5xQ6X7czGja+txIgPN+HJr9Iw4sNN6PWvVVie7pqCildi8EJE5CYTE1rDIjmokbzHOdMAn6cclm7LURftUVLzBQCe+SbNKZ+bGct24++LUpF3ocTh/uz8IoxbmOryAIbBCxGRm1jMJtzcTm4FSZkNtU7ctdoEVkgWpfMyg6MuGqWk5ktRma3Wn5tlO0/g/bWZlf5eAJj6026XTiExeCEiciMlK0jeXXWgVheI2cn7YZU8PKFtCEddNEpJ4i5Qu4Rvq03gya/Sqm2XnV/kss1EAQYvRERupeRCVJskTKtNYPaqDOn2o7o3r9HjkPqUJO4CtUv4fuLL7dIr006eL6rRY9QEgxciIjdSeiGqaRLmPe9tkB514SaM2jcxoTV8ZBOmALz5217Fj1FSZsPPf8pNMwJAcANfxY9RUwxeiIjcTMmyaUB5Eub0pelIPZov3f7xvi04ZaRxFrMJs+7rIt0+7Wi+4qB31MebpNu6ugozgxciIjdTumxaSRLmsp0n8PH6I9Ln5vJo/RjcKRxdIvyl2z/xZap00Lts5wlsyjwrfW5XV2Fm8EJEpAFKR1/eTq4+eddqE3j66zRF/eDyaH15dkBb6bZlArh33sZq21ltAk98JZ9b5Y6VaQxeiIg0QOnoiwAwbM76KttMXLQdxbKJLgC8LRx10RulK4+2Z53DT3+cqLJNwszVKLPJ9+HvfV0f8DJ4ISLSCKVJmGnHC/DwJ5sr/N30pbuwLF0+2RIAZt7bhaMuOqM04RsAJn65AyWVRCe3vr0Gh89ckn98E/Dkza0VPb4zMHghItIIpUmYALB6/2nc+vYah/um/rQLH68/rOg83Zo2xG2dwxUdQ9qgNOgFgNYv/4KlacftP1ttAkNnr0N6dqGyx76plVsCXpMQwlDbThYUFCAgIAD5+fnw95dPZCLXsdoENh08g5RDpwGYEN+iMbpHNeY3PqqS1SawJTMPJ88XIbiBL2IjAw37mRn+/kZFyZLlAnwtuFBsheTWRXZmAAdeH2zY19MTLNt5An9fpLwGUB0TUM/HgnNFVsXH+nqZsWvaQKd9bpRcv72c8ohE1bDaBDYeOI23k/dhe1Y+rvy/9d3VGTABiGxSF8NvbIrRPSPh7cVBQbpcZ2L+hkP4astRHMm7iCvzU0P9ffHq7e0wsEOY+zqoks/GdEfrl39RfFx+DS5AAPBEgnu+PZPzDO4UjjFZeYpWlgFAqUCNAhcA+LcbpxldcoWYM2cOmjdvDl9fX8TFxWHLli1Vtv/2228RHR0NX19fdOzYEcuWLXNFN0klP/1xAtGv/IIH52/BtqsCl3ICwKHTF/H6L3vR+uVfMH1puqu7SRozfelutH75F8z4ZR8yzzgGLgCQU1CEx92wIZwreHuZMaaX/P41teHrZWaSrkG8cmsHtAqq55LHGtMrEoM7ue+Lg+rBy9dff41nnnkGU6ZMQWpqKjp37owBAwbg5MmTFbbfuHEjRowYgTFjxmDHjh0YOnQohg4divR0Xsz0aMyCrZj45Q6UKljxAAAfrz+Cfm8mu3SjL9KO22avw8frK98I7kpJ3/1pyM/JK7d2QGQTP9Ufx53fnsn5fn6yj+qPcVObILxyazvVH6cqque8xMXF4cYbb8S7774LALDZbIiIiMDEiRPxwgsvXNP+vvvuw4ULF7B06VL7fd27d0eXLl0wb968a9oXFxejuLjY/nNBQQEiIiKY86IBt72zFn+eOF+rc5gBvHt/DAZ3YiKhJ7DaBG56cxWOnFW2R8oXj8ShZ8smKvXKfaw2geiXl6FUwbJVJcb2jsRLQ9x7ESLnm740XfH0kazmgX74/bmbVDm3kpwXVUdeSkpKsH37diQmJv71gGYzEhMTkZKSUuExKSkpDu0BYMCAAZW2nzFjBgICAuy3iIgI5z0BqrG/zd9c68AFAGwA/r5oB177eVftO0Watjw9G61eXKY4cAGAlBpuOqd1FrMJs+/vqsq5x/RqzsDFoF65tQM6hNd3+nlNAJL/r7/Tz1sTqgYvp0+fhtVqRUhIiMP9ISEhyMnJqfCYnJwcRe2TkpKQn59vvx09etQ5nacam/pTOlbtO+3Uc3647jBe+3m3U89J2rE8PRuPL0xFzQcYjDdtVG5ghzDMvT/Gqecc06s5Xrm1vVPPSdqy9Im+6BDewKnnnHN/V81MMep+SYePjw/8/f0dbuQ+r/28C/M3qDNc+eG6zBrtpkvaZrUJTFyUWqtzxEcZb8roSoM7hWOuk0ZgGLh4jqVP9EFCdLBTzvVYH/cm6F5N1eClSZMmsFgsyM11rPKYm5uL0NDQCo8JDQ1V1J60Y9nOE/hw3WFVH0PpbrqkfffM21CrnI6Gdeuge4vGzuuQRg3uFIZ5I7vCqxbffMf2ZuDiaT4efSNmj4hBTT81XmZg7v0xSBqsrSlGVYMXb29vdOvWDcnJyfb7bDYbkpOTER8fX+Ex8fHxDu0BYMWKFZW2J21QupFXTSnZTZe0b2nacaRm5dfqHP+8q6NmhrLVNrBDGPb9YxAGd1D2Zc7bcvkC9NIQBi6e6LbO4ch4fTC6RjSUPsYE4In+LbDvH4M1uWBC9dVGX3/9NR566CG8//77iI2NxX/+8x9888032Lt3L0JCQjBq1Chcd911mDFjBoDLS6X79u2Lf/7znxgyZAi++uorvP7660hNTUWHDh2qfTxW2HWPu+aur/VFSBargRpDbVfS1PU249/3djFkkToZ5QX8fk3PQXb+RRSX2mAVl/ea8a3jhfq+XmgbFoC7u12PHi2b8O+FAACXSqyYtjQdGzNOo7CoFN4WCwCBEqtAHS8LWgTVw6N9WqBXqyCXf2Y0VWH3vvvuw6lTpzB58mTk5OSgS5cuWL58uT0pNysrC2bzXwNAPXr0wKJFi/Dyyy/jxRdfRKtWrbBkyRKpwIXco6bfnq/z90ZdXy8cOHlR0XE2XN7WffHfeyp+TNKOJ77cXqPA5cZmDfFEQmuPvyB7e5nxWN+WeKxvS3d3hXTEz9uCGXd1dnc3ao17G1GtXP72/AtKFeahvDs8Brd2Cbef4+65G7DjmLIAaPaIGG4kp1M12YeleaAvkv/vJo8OWIiMTDN1Xsj4ZifvVxy4jO0daQ9cgMu1LL6f0AsDO4RUcdS1JjF5V5esNoGnv05TdEyX6/zx+3MJDFyICACDF6oFq03g3VUZio6pqjDWnPu7KdrWvcQqmLyrQ7OT96NYwXYRFgCLx/dSr0NEpDsMXqjGZifvR5mCgY+He1a9TNNiNmHWfV0U9WHemoMcfdERq01gzuqDio6ZraHCWESkDQxeqEaUXoSaBfphym3VL9Mc3Clc0W66RWU2bDJoaXgjUjrNeGvHME0VxiIibWDwQjWi9CL0+p2dpNu+cmsHdI0IkG7/5m97pduS+1htAu+tkQ94fSwmvD3CuWXxicgYGLyQYkpHXer7eCmugPrtuJ7SH860o/ncNkAHNh06g2IF84yz7ovhdBERVYjBCymmdNTljWGdFF+ELGYTbmkvv/roucU7mfuicZ+nHJZuy+kiIqoKgxdSROmoS20uQg/GN5duW1hcxtwXDbPaBFbszq2+IS5XiOV0ERFVhcELKaJk1MWrlheh7lGN4eMlP2KzcPPhGj8WqWt28n7Iro5ObBfC6SIiqhKDF5KmNOFywk2tanURsphNGNe3hXT7lbtPcupIg5SO1o3q3ly9zhCRITB4IWlKEi7rmE2YmNCq1o85MaG1dOG6UhuL1mmRktE6Xy+z4uRuIvI8DF5ImpKEy/H9Wzpl6F9p4bo5qzM4+qIhSkddHu/bglNGRFQtBi8kRUnCpZcZThl1KTe4U7h03ReOvmiLklEXZ43WEZHxMXghKUoSLhPaOj/hctKAaOm23DJAG5TmSDlrtI6IjI/BC1XLahP4cH2mdHs1Ei6VrDzilgHa4I4cKSLyDAxeqFpbMvNwodgq1VathEulK48+23TY6X0gZTYePC3dlqMuRKQEgxeq1m+75Evvq5lwOTGhNSQXHmHt/lOcOnKzrZl5Uu2cnSNFRMbH4IWqZLUJLNyUJdXW26Lu0L/FbMLN7eS2DLhUasMWyYsnOZ/VJpB65KxUWzVypIjI2Bi8UJWUrBZ5IK6p6hchJVsGKBkxIueanbwfsnswsigdESnF4IUqpTRR95b26m+k1z2qMXzryH1sv9icxakjN1CyyohF6YioJhi8UKWUJOrW97EgNjJQ5R5dnjoacWOEVNsSK2u+uIOSVUb9o4M4ZUREijF4oUrlFBRJt32kV5TLLkJKRng+Wn+Ioy8utnDTEem2I+Oaq9cRIjIsBi9UqfUHTkq1UztR92qxkYGo52ORaltYbGXirgtZbQIrd+dIteWUERHVFIMXqpDVJvBLutxFqH90sEuH/i1mE8b2ipRuz8Rd17mc4C3XlvsYEVFNMXihCm3JzMPFErmrUKvg+ir35loTE1qjjuSF76utRzl15AJKErxZUZeIaoPBC1VIyWhFfFQTFXtSMYvZhJHdm0q1vVTK7QJcQUmCd2I7147WEZGxMHiha1htAl9uPSrV1q+O+/IWlCTuLtx8WL2OEABlAS8TdYmoNhi80DU2HTqDIsnEheE3RrjtG7SSxN3Ve7ldgJr0EvASkTEweKFrKFnq6orCdJVRkrjLnabVpZeAl4iMgcELObDaBFbtlVsi7arCdFW5nLgr1zblkPwux6RMioLA0J0BLxEZA4MXcnC5OqrcN2hXFqarjMVsQkJbuc0aD5wsVLk3nivj5HmpdloIeIlI/1QNXvLy8vDAAw/A398fDRs2xJgxY1BYWPUFpF+/fjCZTA63xx9/XM1u0hVkp4y0tNS1WzO5i2HKwTPMe1GB1SawPuOUVNsB7UPdHvASkf6pGrw88MAD2LVrF1asWIGlS5di7dq1ePTRR6s9buzYscjOzrbf3njjDTW7Sf+jpDqqlpa6NmngI9WuoKiM1XZVsCUzD4XFcqN1vVq6flk9ERmPl1on3rNnD5YvX46tW7fihhtuAADMnj0bgwcPxltvvYXw8PBKj61bty5CQ0OlHqe4uBjFxcX2nwsKCmrXcQ+mpDqqlpa6hvr7Srf9bVc24rnSxamULJEODfBTsSdE5ClUG3lJSUlBw4YN7YELACQmJsJsNmPz5s1VHvvFF1+gSZMm6NChA5KSknDx4sVK286YMQMBAQH2W0SE3I7D5EhJdVSt7UkTGxmIBr5yS6ZZbde5rDaB/6Yek2rr7+vFfBcicgrVgpecnBwEBwc73Ofl5YXAwEDk5FQ+NXH//fdj4cKFWL16NZKSkvD5559j5MiRlbZPSkpCfn6+/Xb0qFytCXKkpDpq/+ggzUwZAZeTdu/uer1UW1bbda4tmXk4XyT3uRnW9TpNfW6ISL8UTxu98MIL+Ne//lVlmz179tS4Q1fmxHTs2BFhYWFISEjAwYMH0aJFi2va+/j4wMdHLueBKpdTUCTdVktTRuVuaR+G+Rvlko0Xbj6Mnq2Ye+EMSqaMuESaiJxFcfAyadIkjB49uso2UVFRCA0NxcmTjvVCysrKkJeXJ53PAgBxcXEAgIyMjAqDF3KO0+eLq28E7VZHLa+2KzN6VF5tl6MAtcMpIyJyF8XBS1BQEIKCgqptFx8fj3PnzmH79u3o1q0bAGDVqlWw2Wz2gERGWloaACAsjN/a1LT9iNwqnD6ttTVlVK682u5/kjOqbVtebZejL7XDKSMichfVcl7atm2LgQMHYuzYsdiyZQs2bNiACRMmYPjw4faVRsePH0d0dDS2bNkCADh48CCmT5+O7du34/Dhw/jxxx8xatQo9OnTB506dVKrqx7PahNYvU+uTker4Poq96bmlFTb5UaNtccpIyJyF1XrvHzxxReIjo5GQkICBg8ejF69euGDDz6w/760tBT79u2zryby9vbGypUrccsttyA6OhqTJk3CsGHD8NNPP6nZTY+npKpufJR2RyssZhMS28lNSa47wIJ1tcEpIyJyJ9XqvABAYGAgFi1aVOnvmzdvDiH+uoBERERgzZo1anaJKiBbVVdrS6QrMrJ7M/ySXn2hvcLiywXrWPOlZjhlRETuxL2NPJzVJrB2v9yUkdaWSFeke1Rj+EnOHeXkX1K5N8bFKSMicicGLx5uS2YeLpTIfYPW4hLpq1nMJgzuIDd1dLpQboUVOeKUERG5G4MXDyf7Dbqut0XzU0blQhvKlaDfnnVW5Z4YE6eMiMjdGLx4MCXfoAd30M9uwCbI9fP3/9V7IWWUFDTklBERqYHBiwdT8g26p452A5ZNwi2v90LKyBY05JQREamFwYsHM+puwN2jGsPHS+6jzXovyskWNIxv0Vg3o3VEpC8MXjyUkZMuLWYTbooOrr4h/toqgORYbQLrDpyWaqvlgoZEpG8MXjyU0ZMuR3ZvJtWOU0fKKFmdpuWChkSkbwxePJTRky45daQOI65OIyL9YfDiofIka5zobcqonJKpI24VIMeoq9OISH8YvHiorLwLUu3ujNHflFE52amj8q0CqGpGXZ1GRPrD4MUDWW0C3+04LtW2aWBdlXujHiVbBShZeeWplEw16ml1GhHpD4MXD6TkG3RgPW+Ve6Mei9mEIR3l8nUWpx7n1FE1NhyQ2wNLr1ONRKQfDF48kFHru1SkZ6sgqXYFRZw6qorVJvDzn3KfGz2uTiMifWHw4mGMXN+lIqH+vtJtuct05TYdOoNLpTaptnpcnUZE+sLgxcMYvb7L1WIjA9HA1yLVdkOGXPE1T7Rw0xGpdvV99B/wEpH2MXjxMEav73I1i9mEu7teL9V2WXoO814qYLUJrNp7Uqpt71bcEoCI1MfgxcN4YtKlbBB2scTKarsV2HToDIrL5KaMRsY1V7czRERg8OJRPDXpMjYyEPW85aaOUg5x6uhqKZIBna+XmVV1icglGLx4EE9NurSYTejdSq5oGmeNriUg96L0iw4yTMBLRNrG4MWDeHLSZbdmcs8nV0FOkKfIOSe3Cqtb00Yq94SI6DIGLx7CahNYu18u38WISZdNGvhItfuFSbsOrDaBZek5Um2b1Jd7jYmIaovBi4fYkpmHCyVyS6SNmHQpW++FSbuOlEw16r2gIRHpB4MXDyFbVbeut8WQSZdKknYXbj6sbmd0xJOnGolIuxi8eAAlVXUHdwg13JQRcDlpt09rua0C1h04w6kjcKqRiLSLwYsHUFJVt2dLuVU5ejSyezOpdoXF3OcI4FQjEWkXgxcPoKSqrpHzFrpHNYZfHbmPvJLNK41K9nNj1KlGItIuBi8eIK+wWKqdkarqVsRiNmFIR7n6NYtTj3v81JFsNWajTjUSkXYxePEAWXkXpNrdGWOcqrqV6dlKLu+loMizp46UVGM28lQjEWkTgxeDs9oEvttxXKpt08C6KvfG/WSXTANATr5ccTYj4hJpItIyBi8GpyRZN7Cet8q9cb/YyEA08JVbMr0hw3P3OeISaSLSMtWCl9deew09evRA3bp10bBhQ6ljhBCYPHkywsLC4Ofnh8TERBw4cECtLnoEJus6sphNuLvr9VJtl3lotV0ukSYirVMteCkpKcE999yDcePGSR/zxhtv4J133sG8efOwefNm1KtXDwMGDEBREfebqSnZpEujJ+teSXbTSU+ttssl0kSkdV5qnXjq1KkAgAULFki1F0LgP//5D15++WXccccdAIDPPvsMISEhWLJkCYYPH17hccXFxSgu/ms1TUFBQe06biBKki6HdTV+sm658mq7MhfohZsPo6fkjtRG4enVmIlI+zST85KZmYmcnBwkJiba7wsICEBcXBxSUlIqPW7GjBkICAiw3yIiIlzRXV1QknQpOxphBKy2WzlWYyYiPdBM8JKTc3nn2pCQEIf7Q0JC7L+rSFJSEvLz8+23o0ePqtpPPWHSZeVYbbdirMZMRHqgKHh54YUXYDKZqrzt3btXrb5WyMfHB/7+/g43YtJldZRU2/WkJdNM8CYiPVCU8zJp0iSMHj26yjZRUVE16khoaCgAIDc3F2Fhf01h5ObmokuXLjU6pydj0mXVLGYTBncIxeIdJ6pte1qyQrERsBozEemBouAlKCgIQUFyuQJKRUZGIjQ0FMnJyfZgpaCgAJs3b1a0Yoku47401QttKDdysD3rLMaq3BetYDVmItID1XJesrKykJaWhqysLFitVqSlpSEtLQ2FhYX2NtHR0fj+++8BACaTCU899RT+8Y9/4Mcff8Sff/6JUaNGITw8HEOHDlWrm4bFfWmqZ4Lc817vIUm7rMZMRHqh2lLpyZMn49NPP7X/HBMTAwBYvXo1+vXrBwDYt28f8vPz7W2ee+45XLhwAY8++ijOnTuHXr16Yfny5fD1lS/pTtyXRlZ8i8Z4d3VGte3Kk3bjDT5CxWrMRKQXqgUvCxYsqLbGixCO32ZNJhOmTZuGadOmqdUtj8B9aeSUJ+3KvFa/7co2fPDCZF0i0gvNLJUm5+ESaTkWswlDOsrVt1mcetzwU0esxkxEesHgxWC4RFqZnq3kEtALioxd74XVmIlITxi8GAyXSCsT6i+fT2Xkei+sxkxEesLgxWC4RFqZ2MhANPC1SLXdkHFa5d64D6caiUhPGLwYzOnzckXGBnnwEukrWcwm3N31eqm2y9JzDJn3wqlGItIbBi8Gs/2IXF5GiILpEqOTnQa5WGLFpoNnVO6N63GqkYj0hsGLgVhtAusOyE1t8MvzX2IjA1HPW27qaOHmw+p2xg1+2yWXqMupRiLSCgYvBqLkG3R8lOcWp7uaxWxCn9Zyq47WGazartUm8N/UY1JtPbkaMxFpC4MXA+E36Job2b2ZVLvyartGoaSqridXYyYibWHwYhD8Bl075dV2ZRhpyTSr6hKRHjF4MQh+g64di9mEwR1CpdqeLpRb0aUHrKpLRHrE4MUgZKeMAH6DrkxoQ7nXZXvWWZV74hqsqktEesXgxQCUTBnxG3TlTJC7OK83SNIuq+oSkV4xeDEAJVNG/AZdOdldo42StJsiWbOGVXWJSGsYvBiAkqRLfoOunJKkXSXTdFqVcfK8VLterKpLRBrD4MUAmHTpHBazCUM6ygV3i1OP63rqyGoTWJ8h97np1rSRyr0hIlKGwYvOMenSuXq2kitWV1Ck76mjLZl5KCyWy3dpUt9H5d4QESnD4EXnmHTpXKEK9nzS89QR67sQkZ4xeNE5Jl06V2xkIBr4yu1zpOepI041EpGeMXjROSZdOpfFbMLdXa+XaqvXqSNONRKR3jF40TEmXapDyfSaHqeOONVIRHrH4EXHmHSpDqNPHS3cdESqHacaiUirGLzoGJMu1WHkqSOrTWDV3pNSbXtzqpGINIrBi44x6VI9SqZL9LTL9KZDZ1BcJjdaNzKuubqdISKqIQYvOsWkS3UpmTrakHFa5d44j+yUka+XGd0lt0sgInI1Bi86xaRLdSmZOlqWnqOLvBclU0b9o4MY8BKRZjF40SkmXapPNui7WGLFJsl6O+7EKSMiMgoGLzrEpEvXiI0MRD1vuamjlEPanzqSLWjIKSMi0joGLzrEb9CuYTGb0LtVE6m2B04Wqtyb2pMtaNiPU0ZEpHEMXnSISZeu062Z3JRbysEzms57YUFDIjISBi86w6RL12rSQK64n9brvbCgIREZiWrBy2uvvYYePXqgbt26aNiwodQxo0ePhslkcrgNHDhQrS7qEqeMXMsou0wr6RsLGhKR1qkWvJSUlOCee+7BuHHjFB03cOBAZGdn229ffvmlSj3UJ04ZuZYRtgqw2gT+m3pMqi0LGhKRHnipdeKpU6cCABYsWKDoOB8fH4SGhkq3Ly4uRnFxsf3ngoICRY+nJ5wycr3yei/zN1YfNJZPHcVrLGjckpmH80VWqbYsaEhEeqC5nJfff/8dwcHBaNOmDcaNG4czZ6pe3jljxgwEBATYbxERES7qqetxysg99L7LtJI+saAhEemBpoKXgQMH4rPPPkNycjL+9a9/Yc2aNRg0aBCs1sq/NSYlJSE/P99+O3r0qAt77FobD8rVEuGUkXMpmTr6autRTU0dccqIiIxIUfDywgsvXJNQe/Vt7969Ne7M8OHDcfvtt6Njx44YOnQoli5diq1bt+L333+v9BgfHx/4+/s73Ixqq+Rqlk7XB3Do34mUbBVwqdSmqWq7nDIiIiNSlPMyadIkjB49uso2UVFRtenPNedq0qQJMjIykJCQ4LTz6pHVJpB29JxU2xuas06Hs93SPkwq7wUAFm4+jJ6Sxe3UxikjIjIiRcFLUFAQgoKC1OrLNY4dO4YzZ84gLIz/qW46dAYlVrnpiJ4tXPceeYrYyEDU87HgQnH1oxir956C1SbcPophtQl8uVVuGpVTRkSkJ6rlvGRlZSEtLQ1ZWVmwWq1IS0tDWloaCgv/KqMeHR2N77//HgBQWFiIZ599Fps2bcLhw4eRnJyMO+64Ay1btsSAAQPU6qZucIm0e1nMJoztFSnVtqhMG1NHmw6dQZHkzuOcMiIiPVEteJk8eTJiYmIwZcoUFBYWIiYmBjExMdi2bZu9zb59+5Cfnw8AsFgs2LlzJ26//Xa0bt0aY8aMQbdu3bBu3Tr4+Hh2xU+rTWDl7hyptlwirZ6JCa1RR/IvRgsbNcoGvACnjIhIX1Sr87JgwYJqa7wI8dc0iJ+fH3799Ve1uqNrs5P3Q/ILNJdIq8hiNiGhbQiW78qttq27N2pUUhOovo+FU0ZEpCuaWipN17LaBD5cnynVllNG6pPdqHH13pNuXTKtpCbQI72iOFpHRLrC4EXjtmTmSSWJApwycgXZjRpLrAKzkw+o3JvKydYEqmM2YWJCK5V7Q0TkXAxeNE7JUldOGalPyUaNH60/5LbRF9maQDFNGzLgJSLdYfCiYVabwMJNWVJt/epwysgVypdMyygstmKLZBDhTFabQOqRs1JtWROIiPSIwYuGXU7UlfvmPvzGCH6DdgElS6YB9+x1NDt5P8okB3xYE4iI9IjBi0YpSdQFuNTVlS4vmZYLFL/YnOXSqSOrTeC9NQel2jLBm4j0isGLRilJ1OVSV9eymE0Y2b2pVFtXJ+5eXmUkFywxwZuI9IrBi0blFBRJt+VSV9dTMtLlysRdJYXpmOBNRHrF4EWj1h+QKzDmbeFSV3fQYuKukkrMnDIiIj1j8KJBVpvADztOSLXtHx3MURc30GLirpJKzI/3bcHPDRHpFoMXDVKyWqRVcH11O0OVUpK4+9XWo6pOHSlJ1GVhOiLSOwYvGmO1CcxZLXcRAoD4qCYq9oaqoiRx91KpujtNK0nUTWzH0Toi0jcGLxqjpLYL8xbcT0ni7mebDqvWj89T5M/NRF0i0jsGLxqitLYL8xbcLzYyEL515P6MkvfkqjJ1ZLUJrNhd/U7XwOUEbwa8RKR3DF40REltF+YtaIPFbEK/1nJVastsUKXmy+zk/bBKxkRM8CYiI2DwoiFKVqSM79+SFyGNeDC+uXTbOasznDr6ojRHalT35k57bCIid2HwIslqE0g5eAY/pB1HysEzTh/+V7IJI2u7aEv3qMbw8ZILJEttzq24yxwpIvJEXu7ugB4sT8/Gqz/uQk5Bsf2+UH8fvHp7ewzs4Jw9hZRchB6Ia8pRFw2xmE0Y17cF/pOcIdV+zuoMTExoVev3UOmoC3OkiMgoOPJSjeXp2Xh8YapD4AIAOQXFeHxhKpan1774mNUm8O4quQsfwE0YtUhJzRdnjb4oCXiZI0VERsLgpQpWm8AL3/1ZZZunv06r9RSSkqJ0fnXM3IRRgyxmE8b3byHdvra5L0pHXZgjRURGwuClCpsOnsG5i6VVtrlUasOTX+6o8WMovQj1ac2dgLXKlaMvHHUhIk/G4KUKKYdOS7Vb+mc2lu2s2fSRkosQwNUiWuaq0ReOuhCRp2PwUiX5//Cf/nqH4guR1SYwW0GuS30fL64W0ThXjL5w1IWIPB2DlyrEKwgUiq1C8fTRPfM2SBcXA4A3hnXiN2iNUzr68k7yAUVBr9Lkbo66EJERMXipgpL6HYCy6aOlaceRmpUvfe5bO4ZhcCeuMtKDiQmtYZH82NgA3PPeBvlzL9oundztZQZHXYjIkBi8VKG8focSMtNHVpvAk1+nSZ/TywS8PSJGUT/IfSxmE25uFyLdPvVoPqYv3V1tu9d+3oVl6XJ7GAFAQtsQjroQkSExeKnGxITW8JH9Go3L00dPLEqtss34RdsVTRdNuKn2Bc3ItZRsGQAAH6/PrHLUbtnOE/hw3WFF52RyNxEZFYOXaljMJsy6r4uiY35Oz8H0pbsq/N3Un3ZhuYJvz9wKQJ+6RzVGPW9lf17jF6VWOGpntQlMWKQsn4rJ3URkZAxeJAzuFI4hHeWnAQDg4/WHMfWndIf7Hp6/GfM3HFZ0npn3duGoiw5ZzCa8eXdnRccIAD1eX+EQwFhtAl2n/QqbwsdncjcRGZlJCOHcHQbdrKCgAAEBAcjPz4e/v7/Tzmu1CbR5aZl0smQ5C4D6vhYUFFmh9IVuHVwfvz3TV+FRpCXTl6bj4/VHFB8X4GuBCcC5IqviY2/tGIZ3H+iq+DgiIndScv3myIski9mECTe1VHycFUB+DQIXAFj6RO8aHEVa8sqtHdA1IkDxcflF1hoFLj4WE5O7icjwVAteDh8+jDFjxiAyMhJ+fn5o0aIFpkyZgpKSkiqPKyoqwvjx49G4cWPUr18fw4YNQ26ufI6ImpQm79bGrR3D4O3F2NIIvh3XE66awZl1Xwyni4jI8FS7Ou7duxc2mw3vv/8+du3ahVmzZmHevHl48cUXqzzu6aefxk8//YRvv/0Wa9aswYkTJ3DXXXep1U1FapK8WxN1zPz2bCQWswnvuOBzM7Z3JGsBEZFHcGnOy5tvvon33nsPhw4dqvD3+fn5CAoKwqJFi3D33XcDuBwEtW3bFikpKejevfs1xxQXF6O4uNj+c0FBASIiIpye83Kl137epXjZqhJz7+/Ki5ABjVmwGcl75fbLUmp0j2Z49fYOqpybiMgVNJvzkp+fj8DAwEp/v337dpSWliIxMdF+X3R0NJo2bYqUlJQKj5kxYwYCAgLst4iICKf3+2ovDWmPMb2aq3Jufns2ro9Hx6FDWH2nn7dDeAMGLkTkUVwWvGRkZGD27Nl47LHHKm2Tk5MDb29vNGzY0OH+kJAQ5OTkVHhMUlIS8vPz7bejR486s9uVeuXW9ni4ZzOnnnNs7+Z4aUg7p56TtGXpk33R3okBTEQjXyx9oo/TzkdEpAeKg5cXXngBJpOpytvevXsdjjl+/DgGDhyIe+65B2PHjnVa5wHAx8cH/v7+DjdXmXJbByS2Da71eUwA5t4fg5eGtK99p0jzfn6yLzqEN6j1edqH1sO65xOc0CMiIn3xUnrApEmTMHr06CrbREVF2f994sQJ9O/fHz169MAHH3xQ5XGhoaEoKSnBuXPnHEZfcnNzERoaqrSrLvHRQzfitZ9348N1mTU6Pqh+HWx68WauEPEwS5/ogzELtiJ578kaHZ8Q3QQfj45zcq+IiPRB1YTd48ePo3///ujWrRsWLlwIi8VSZfvyhN0vv/wSw4YNAwDs27cP0dHRlSbsXk2tInXVKSmz4cGPN2Fz5lnpYx7u2RRTbuuoYq9I63764wSe+noHrJIldM0m4J37uuDWLtep2zEiIhdTcv1WLXg5fvw4+vXrh2bNmuHTTz91CFzKR1GOHz+OhIQEfPbZZ4iNjQUAjBs3DsuWLcOCBQvg7++PiRMnAgA2btwo9bjuCl7KlZTZMH/DIfyanoPs/IsoLrXBKgCLCfDzroPQAF8MaB+K0T0jWceFAFyu3rx+3ynMW5uBg6cKUWa1wcfLAkCgxCpQx8uCFkH18GifFujVKoijdERkSEqu34qnjWStWLECGRkZyMjIwPXXX+/wu/J4qbS0FPv27cPFixftv5s1axbMZjOGDRuG4uJiDBgwAHPnzlWrm07n7WXGY31b4rG+yqvxkmeymE3o2zYYfZ2QP0VE5Am4txERERG5nWbrvBARERHVFoMXIiIi0hUGL0RERKQrDF6IiIhIVxi8EBERka4weCEiIiJdYfBCREREusLghYiIiHRFtQq77lJec6+goMDNPSEiIiJZ5ddtmdq5hgtezp8/DwCIiIhwc0+IiIhIqfPnzyMgIKDKNobbHsBms+HEiRNo0KABTCbnbmBXUFCAiIgIHD161JBbDxj9+QHGf458fvpn9OfI56d/aj1HIQTOnz+P8PBwmM1VZ7UYbuTFbDZfsxGks/n7+xv2QwkY//kBxn+OfH76Z/TnyOenf2o8x+pGXMoxYZeIiIh0hcELERER6QqDFwV8fHwwZcoU+Pj4uLsrqjD68wOM/xz5/PTP6M+Rz0//tPAcDZewS0RERMbGkRciIiLSFQYvREREpCsMXoiIiEhXGLwQERGRrjB4ISIiIl1h8HKF1157DT169EDdunXRsGHDCttkZWVhyJAhqFu3LoKDg/Hss8+irKysyvPm5eXhgQcegL+/Pxo2bIgxY8agsLBQhWegzO+//w6TyVThbevWrZUe169fv2vaP/744y7subzmzZtf09d//vOfVR5TVFSE8ePHo3Hjxqhfvz6GDRuG3NxcF/VYmcOHD2PMmDGIjIyEn58fWrRogSlTpqCkpKTK47T8Hs6ZMwfNmzeHr68v4uLisGXLlirbf/vtt4iOjoavry86duyIZcuWuainys2YMQM33ngjGjRogODgYAwdOhT79u2r8pgFCxZc8175+vq6qMfKvPrqq9f0NTo6uspj9PT+ARX/n2IymTB+/PgK22v9/Vu7di1uu+02hIeHw2QyYcmSJQ6/F0Jg8uTJCAsLg5+fHxITE3HgwIFqz6v071gpBi9XKCkpwT333INx48ZV+Hur1YohQ4agpKQEGzduxKeffooFCxZg8uTJVZ73gQcewK5du7BixQosXboUa9euxaOPPqrGU1CkR48eyM7Odrg98sgjiIyMxA033FDlsWPHjnU47o033nBRr5WbNm2aQ18nTpxYZfunn34aP/30E7799lusWbMGJ06cwF133eWi3iqzd+9e2Gw2vP/++9i1axdmzZqFefPm4cUXX6z2WC2+h19//TWeeeYZTJkyBampqejcuTMGDBiAkydPVth+48aNGDFiBMaMGYMdO3Zg6NChGDp0KNLT013cczlr1qzB+PHjsWnTJqxYsQKlpaW45ZZbcOHChSqP8/f3d3ivjhw54qIeK9e+fXuHvq5fv77Stnp7/wBg69atDs9vxYoVAIB77rmn0mO0/P5duHABnTt3xpw5cyr8/RtvvIF33nkH8+bNw+bNm1GvXj0MGDAARUVFlZ5T6d9xjQi6xvz580VAQMA19y9btkyYzWaRk5Njv++9994T/v7+ori4uMJz7d69WwAQW7dutd/3yy+/CJPJJI4fP+70vtdGSUmJCAoKEtOmTauyXd++fcWTTz7pmk7VUrNmzcSsWbOk2587d07UqVNHfPvtt/b79uzZIwCIlJQUFXrofG+88YaIjIysso1W38PY2Fgxfvx4+89Wq1WEh4eLGTNmVNj+3nvvFUOGDHG4Ly4uTjz22GOq9tNZTp48KQCINWvWVNqmsv+PtGjKlCmic+fO0u31/v4JIcSTTz4pWrRoIWw2W4W/19P7B0B8//339p9tNpsIDQ0Vb775pv2+c+fOCR8fH/Hll19Weh6lf8c1wZEXBVJSUtCxY0eEhITY7xswYAAKCgqwa9euSo9p2LChw0hGYmIizGYzNm/erHqflfjxxx9x5swZPPzww9W2/eKLL9CkSRN06NABSUlJuHjxogt6WDP//Oc/0bhxY8TExODNN9+scppv+/btKC0tRWJiov2+6OhoNG3aFCkpKa7obq3l5+cjMDCw2nZaew9LSkqwfft2h9febDYjMTGx0tc+JSXFoT1w+W9ST+8VgGrfr8LCQjRr1gwRERG44447Kv3/RgsOHDiA8PBwREVF4YEHHkBWVlalbfX+/pWUlGDhwoX429/+BpPJVGk7Pb1/V8rMzEROTo7DexQQEIC4uLhK36Oa/B3XhOF2lVZTTk6OQ+ACwP5zTk5OpccEBwc73Ofl5YXAwMBKj3GXjz/+GAMGDKh2V+77778fzZo1Q3h4OHbu3Innn38e+/btw3fffeeinsp74okn0LVrVwQGBmLjxo1ISkpCdnY2/v3vf1fYPicnB97e3tfkPIWEhGju/apIRkYGZs+ejbfeeqvKdlp8D0+fPg2r1Vrh39jevXsrPKayv0k9vFc2mw1PPfUUevbsiQ4dOlTark2bNvjkk0/QqVMn5Ofn46233kKPHj2wa9euav9WXS0uLg4LFixAmzZtkJ2djalTp6J3795IT09HgwYNrmmv5/cPAJYsWYJz585h9OjRlbbR0/t3tfL3Qcl7VJO/45owfPDywgsv4F//+leVbfbs2VNtUpme1OQ5Hzt2DL/++iu++eabas9/Zb5Ox44dERYWhoSEBBw8eBAtWrSoecclKXl+zzzzjP2+Tp06wdvbG4899hhmzJih6b1HavIeHj9+HAMHDsQ999yDsWPHVnmsu99DAsaPH4/09PQqc0IAID4+HvHx8fafe/TogbZt2+L999/H9OnT1e6mIoMGDbL/u1OnToiLi0OzZs3wzTffYMyYMW7smTo+/vhjDBo0COHh4ZW20dP7pyeGD14mTZpUZVQMAFFRUVLnCg0NvSZjunwVSmhoaKXHXJ2kVFZWhry8vEqPqa2aPOf58+ejcePGuP322xU/XlxcHIDL3/pdceGrzXsaFxeHsrIyHD58GG3atLnm96GhoSgpKcG5c+ccRl9yc3NVe78qovQ5njhxAv3790ePHj3wwQcfKH48V7+HFWnSpAksFss1K7uqeu1DQ0MVtdeKCRMm2JP3lX77rlOnDmJiYpCRkaFS75ynYcOGaN26daV91ev7BwBHjhzBypUrFY9W6un9K38fcnNzERYWZr8/NzcXXbp0qfCYmvwd14jTsmcMpLqE3dzcXPt977//vvD39xdFRUUVnqs8YXfbtm32+3799VdNJezabDYRGRkpJk2aVKPj169fLwCIP/74w8k9c76FCxcKs9ks8vLyKvx9ecLuf//7X/t9e/fu1XTC7rFjx0SrVq3E8OHDRVlZWY3OoZX3MDY2VkyYMMH+s9VqFdddd12VCbu33nqrw33x8fGaTfi02Wxi/PjxIjw8XOzfv79G5ygrKxNt2rQRTz/9tJN753znz58XjRo1Em+//XaFv9fb+3elKVOmiNDQUFFaWqroOC2/f6gkYfett96y35efny+VsKvk77hGfXXamQzgyJEjYseOHWLq1Kmifv36YseOHWLHjh3i/PnzQojLH7oOHTqIW265RaSlpYnly5eLoKAgkZSUZD/H5s2bRZs2bcSxY8fs9w0cOFDExMSIzZs3i/Xr14tWrVqJESNGuPz5VWblypUCgNizZ881vzt27Jho06aN2Lx5sxBCiIyMDDFt2jSxbds2kZmZKX744QcRFRUl+vTp4+puV2vjxo1i1qxZIi0tTRw8eFAsXLhQBAUFiVGjRtnbXP38hBDi8ccfF02bNhWrVq0S27ZtE/Hx8SI+Pt4dT6Fax44dEy1bthQJCQni2LFjIjs72367so1e3sOvvvpK+Pj4iAULFojdu3eLRx99VDRs2NC+wu/BBx8UL7zwgr39hg0bhJeXl3jrrbfEnj17xJQpU0SdOnXEn3/+6a6nUKVx48aJgIAA8fvvvzu8VxcvXrS3ufo5Tp06Vfz666/i4MGDYvv27WL48OHC19dX7Nq1yx1PoUqTJk0Sv//+u8jMzBQbNmwQiYmJokmTJuLkyZNCCP2/f+WsVqto2rSpeP7556/5nd7ev/Pnz9uvdQDEv//9b7Fjxw5x5MgRIYQQ//znP0XDhg3FDz/8IHbu3CnuuOMOERkZKS5dumQ/x0033SRmz55t/7m6v2NnYPByhYceekgAuOa2evVqe5vDhw+LQYMGCT8/P9GkSRMxadIkh8h79erVAoDIzMy033fmzBkxYsQIUb9+feHv7y8efvhhe0CkBSNGjBA9evSo8HeZmZkOr0FWVpbo06ePCAwMFD4+PqJly5bi2WefFfn5+S7ssZzt27eLuLg4ERAQIHx9fUXbtm3F66+/7jBKdvXzE0KIS5cuib///e+iUaNGom7duuLOO+90CAa0ZP78+RV+Zq8cVNXbezh79mzRtGlT4e3tLWJjY8WmTZvsv+vbt6946KGHHNp/8803onXr1sLb21u0b99e/Pzzzy7usbzK3qv58+fb21z9HJ966in76xESEiIGDx4sUlNTXd95Cffdd58ICwsT3t7e4rrrrhP33XefyMjIsP9e7+9fuV9//VUAEPv27bvmd3p7/8qvWVffyp+DzWYTr7zyiggJCRE+Pj4iISHhmufdrFkzMWXKFIf7qvo7dgaTEEI4bxKKiIiISF2s80JERES6wuCFiIiIdIXBCxEREekKgxciIiLSFQYvREREpCsMXoiIiEhXGLwQERGRrjB4ISIiIl1h8EJERES6wuCFiIiIdIXBCxEREenK/wOJ67NWuNlM5gAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fbench_hard = [\n",
-    "    # composite functions\n",
-    "    (lambda x: -np.tanh(x) + 1/4 * x, '-tanh(x) + 1/4 * x'),\n",
-    "    (lambda x: np.arctan(x) + np.sin(x), 'arctan(x) + sin(x)'),\n",
-    "    (lambda x: np.exp(-x**2+1) + 1/3*np.abs(x), 'exp(-x^2+1)+ 1/3 * |x|'),\n",
-    "    (lambda x: np.exp(-x+1) + 2000* np.abs(x+1), 'exp(-x+1)+ 2000 * abs(x+1)'),\n",
-    "    (lambda x: np.exp(x) + 2000* np.abs(x), 'exp(x)+ 2000 * abs(x)'),\n",
-    "    (lambda x: np.exp(x) + 4000* np.sign(x), 'exp(x)+ 4000 * sign(x)'),\n",
-    "    (lambda x: np.sin(x) + np.cos(x), 'sin(x)+cos(x)'),\n",
-    "    (lambda x: np.abs(np.sin(x/2)), '|sin(x/2)|'),\n",
-    "    (lambda x: np.exp(x) + 4000* np.sin(x), 'exp(x) + 4000* sin(x)'),\n",
-    "    (lambda x: np.sign(np.sin(x)), 'sign(sin(x))'),\n",
-    "    (lambda x: np.sign(np.cos(x)), 'sign(cos(x))'),\n",
-    "    (lambda x: np.sin(x) + np.sin(2*x), 'sin(x)+sin(2*x)'),\n",
-    "\n",
-    "\n",
-    "    (lambda x: 1/2 * (x+1) ** 4 - 5 * (x+2) **3 + 3 * x ** 2 + 2 * x + 1, '1/2 * (x+1) ** 4 - 5 * (x+2) **3 + 3 * x ** 2 + 2 * x + 1'),\n",
-    "    (lambda x: -1/2 * (x+1) ** 4 + 5 * (x+2) **3 + 3 * x ** 2 + 2 * x + 1, '-1/2 * (x+1) ** 4 + 5 * (x+2) **3 + 3 * x ** 2 + 2 * x + 1'),\n",
-    "    (lambda x: np.sign(x ** 2 - 15), 'sign(x ** 2 - 15)'),\n",
-    "    (lambda x: np.abs(x ** 2 - 20), 'abs(x ** 2 - 20)'),\n",
-    "    (lambda x: np.abs(x) ** (1/10), 'abs(x) ** (1/10)'),\n",
-    "    (lambda x: np.sin(x) + np.sin(3*x), 'sin(x) + sin(3*x)'),\n",
-    "    (lambda x: np.sin(x) + np.sin(0.5 * x), 'sin(x) + sin(0.5 * x)'),\n",
-    "    (lambda x: np.abs(x) + np.sin(x), 'abs(x) + sin(x)'),\n",
-    "    (lambda x: np.abs(x) + np.cos(x), 'abs(x) + cos(x)'),\n",
-    "    (lambda x: np.sign(x) + np.cos(x), 'sign(x) + cos(x)'),\n",
-    "    (lambda x: x ** 3 + 250 * np.sin(x), 'x ** 3 + 250 * sin(x)'),\n",
-    "]\n",
-    "\n",
-    "print(len(fbench_hard))\n",
-    "\n",
-    "# for each, function draw 1000 samples from a uniform distribution and plot the function\n",
-    "function_points = []\n",
-    "x = np.linspace(-10, 10, 1000)\n",
-    "for f, n in fbench_hard:\n",
-    "    y = f(x)\n",
-    "    plt.scatter(x, y)\n",
-    "    plt.title(n)\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 230,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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wV65o7jtxAnj/fc3f8tCh5m2XuIzc3Fz07dsXO3fuRHx8PBYvXozWrVtj0qRJ+OzfY6G3tzfWrVuHzMxM/LfasTAuLg7FxcVYu3YtFNWOhSqVCkOGDEGTJk0wb9489OjRA7NmzcKsasdCRzsXe++99xAcHIyXX37Z4M/VajWOHz9e6xoUAHr37o0LFy7orvN4r1eFbLMucrlcdz2q7cViSmJiIt5//30MHDgQS5cuxX//+1/cd999eosfDZkwYQKWLFmCoUOH4pNPPoG3tzeGGZlsfuqpp3D79m3MnTsXTz31FNauXYv3339fL4bOQYkrMHfszBHHFE+dOoXhw4ejoqICs2fPxsKFC/Hoo49yfT5VKhViY2MREBCABQsWoH///li4cCFWrlypi6nru6Uu2l4jgTXOd6sfOwH9cT17/Z5yGbZNdCFC+Pr6sri4OKMx48eP16uDp01ZCwgI0Eu93bx5MwPAfv/9d919zz//PAsICDC43e3btzMA7IMPPmBZWVmsQYMGbNSoUQZjP/roIwaA5ebmCnh1GuaW57LVexMXF8cUCoXB52vcuDEbM2aM0X06cOAAGz16NFu9ejXbvHkzmzt3LgsICGBeXl7syJEjRh/7448/svvuu48tX75cV55r9uzZXOW5Tpw4wTw8PNiLL77Ibt26xZo2bcp69uxpsN7s+vXrGQB28OBBo9skxBFUrx+qlZKSwgCwb775RneftpxVjx49mFKp1N0/b948BoBt3ryZMcbYpk2bTJbxyszMZADYkiVLav1MpVKx+++/nzVp0oTl5+ezuLg45ubmVuf2PDw82Kuvvsr9emu+HqHOnz/P5HI5e+yxx5iqRt18bcpvXl4e8/DwYIMHD9aLWbp0KQPA1qxZwxhj7OjRoybLEVZWVjKZTMbefPNNgz9/5plnWL169dg///zD5s+fzwCwX3/91WBs27Zt2SOPPCLo9RIimUmTGAsJYaxmTfgxYxjz9WWs+rEpIYExuZyx/fvvlZf67DP9x40fr7n/9dfv3adWa3qKeHhoykAxxlhmpibOwPGnTmVljLVuzVinTozdvavZpo8PY5cuCXnF/AoKNP07pCiRxVOe6/p1Tcwnn4j73IbwlOcaO1bzXDWlpGgeq721a2f8dTGmKc8VE6O5ZWXpl+d69lnzynPduMGYv79mmxUVjEVGMnbffYwVF+vHUXkuYsCkSZNYSEhIrf4YY8aMYb6+vnrnaQkJCUwul7P9+/ezn376iQFgn9U4Fo4fP54BYK9XOxaq1Wo2bNgw5uHhoStpZe1zMUvKcx07dowpFAq2fft2xpimdyVqlOe6efMmA8Bmz55d6/HLli1jANjZs2cZY/zXq0K2aciMGTNqled65plnTJbnioiIqFU6rCbte6CVnp7OALBp06bpxU2YMIEB0BtP0D72hRde0It97LHH9K7v6RyUuApTY2eOMqbIU57r008/NVje0NB2vq52vqP9bql5PIyMjGQ9evTQ/dvYd4shkyZNYgqFgv3zzz+MMc33lbaMYfXyXDExMXrluaw1ZkBqo0wTB+Ln54eDBw/qpa7xevrpp9GoUSPdvx944AEAQFa1MggFBQV6MdUNHjwYL7/8MmbPno3HH38cXl5e+PLLLw3GareRn59vdJ8qKiqQn5+vd1Or1bhz506t+02x1XtTXl4ODw8Pg9v18vLSSyk3JDo6Gj///DNeeOEFPProo3jnnXeQmpoKmUyGhIQEo4/t1KkTkpOT8eqrr0Imk8Hd3R3vvfcetm7divDwcKOP7dy5M95//32sWrUKsbGxyM/Px7p16+Dm5lYrlvf3SYgj8Pb21v1/ZWUlCgoK0Lp1a/j5+Rlc0fbSSy/B3d1d9+9XX30Vbm5u2PpvCRJtyYQtW7borbKprqCgAAAMHkPkcjnWrl2L0tJSPPLII1i+fDkSEhIMrvLTboPns3jr1i29Y2hpaSkA1Dq2Gsquqe7XX3+FWq3GzJkz9bLagHsrYHbu3AmlUolp06bpxUyePBk+Pj66sg++vr4ANJltdT1vYWEhGGN1fhctXboUvr6+eOKJJ/Dee+9h7NixGDlypMFY3veKEMkxBvzyCzBihOb/8/Pv3WJjNVkk1Y8/iYma0l3jx2uyPvr3B6ZMMbzt+Ph7/y+Taf6tVGoyWgDg3+MP6vhMGVSvniar5cwZ4MEHgT/+AD79FLjvPiGvmo9aDTz3nKbE1L8reHUqK/Xfq/x8zX0VFbXvV6vN3wfte1P9eGGt565JrQa2bTNcmqtjRyApSVNu7a23NCXZ/j2216lBA01WyI4dmtJoABARAaSkaLKAzMk4CQ4Gli3T7MsDDwAZGZpyXT4+wrdFXApjDL/88gtGjBgBxpje+UhsbCyKi4v1zsUSExPRqVMnjB8/Hq+99hr69++PKXUcC+OrHQtlMhni4+OhVCqx899joZTnYmq1utb5VUVFBSorK2vdX9e5YnVTpkzBI488gsE1s82q0V5jenp61vqZl5eXXgzv9aqQbRoybNgwHDlyRFeSpnHjxli/fj0+/vhjNGnSpM7H+fn54dSpUzh//nydMTVt27YNAGqVHHv99dfrfMwrNTIiH3jgARQUFOiqKdA5KHEV5o6d2XpMsbS0VO94qi2ZXVxcrHd/9bLP2mv1zZs3Q23G+Zqh40bN11t9f41Zv349Vq9ejTfffBNt2rQBoPm+mjBhAlJTU3UVX8LDw7Fjxw5Mnz5dV/JLqjEDYhpNmjiQefPm4eTJkwgLC0Pv3r2RmJio94E15r4aF7raD3XN2vysZh3sahYsWAB/f39kZGTg888/R1BQkME47TZMpeP+8MMPaNy4sd7typUrmD9/fq37TbHVe+Pt7Q2lUmlwu3fv3tUboOXVunVrjBw5Env27KlVL7G6zp07G6wP2759e70alHWZMWMGIiIikJaWhlmzZqFjx44G43h/n4Q4gvLycsycORNhYWHw9PREYGAgGjdujKKiIr0TLC3tCY1WgwYNEBISouuB0r9/f4wePRrvv/8+AgMDMXLkSHz99deoqKiota26jq+tWrVCYmIiDh06hE6dOuG9996rc/8ZY1yfxcjISL1jqPYisuaxdd68eUa3c+HCBcjl8jqPD4CmTwwAtGvXTu9+Dw8PtGzZUvfz8PBwTJ8+HatWrUJgYCBiY2OxbNkyg+97Xe+Vv78/Pv/8cxw/fhy+vr74/PPP69wv3veKEMndvKmZFFi5EmjcWP82caImJi/vXryHh2YQOjsbuH37Xh+JmuRyoGVL/fvattX8t2afppqfqdJSTUks7e3mTf2f9+sHvPoqkJammdip0adJNK+/rpkkWLVKM5hf3YEDtd+v5GTgxx9r33/5svn7oH1vqr/H1nrumg4d0vwuDE2a+PhoSqSNHAl88gnw5pua/6/Wi86gIUNq36dQaHq5mGvMGM0+pqUBkycDDz9s/raIy7h58yaKioqwcuXKWucjE/89FuZVOxZ6eHhgzZo1yM7Oxu3bt/H1118b/F6Xy+VoWeNY2PbfY2HNnnVSnItdvny51uv58ccfkZycXOt+U+VSNmzYgOTkZCxcuNBonPYa09D55t27d/VieK9XhWzTkP79+xv8+cMPP2xwYZ7W7NmzUVRUhLZt26JLly6YMWMGjh8/Xmc8oDn3lMvltRYKtm7dus7HWDoeQuegxFmYO3Zm6zHF+Ph4veOptiTWqFGj9O6vPpn59NNPo1+/fnjxxRfRpEkTjBkzBv/73/+4JlC8vLxqjUU2atSo1us19ZoB4K+//sKkSZMQGxuLDz/8UO9nMTExeiUntYbUOH+TYsyAmFb3txexO0899RQeeOABbNq0CTt27MD8+fPxySefYOPGjXjkkUeMPtbQhxDQ/3AHBAQYPABoHT16VHcie+LECTzzzDMG47TbqFmnr6bY2NhaDdKef/55DB48uFaTI1Ns9d6EhIRApVIhLy9P74CvVCpRUFBQqzYjr7CwMCiVSpSVlcGHY+Ve9WZ3vLKysnQrek6cOFFnHO/vkxBH8Prrr+Prr7/GtGnTEBUVBV9fX8hkMowZM8as1ScymQw///wzUlNT8fvvv2P79u144YUXsHDhQqSmpqJBgwYICAgAUPuEsjptU/Xr16+joKAAwcHBBuOKioq4Povff/+93mpA7XGx5jG35iCD1BYuXIgJEyZg8+bN2LFjB6ZMmYK5c+ciNTUVzZo1g7+/P2QymdH3avv27QA07+fVq1frbJB669atWpNehNiE9tjy/POa7BFDunbV//e/f+e4exc4f/5eloBQ/x5/UPMztWCBpg+FVvPm+hMtFRXA3r2a/79wAbhzR5OBIqb339c0kP/4Y2Ds2No/j4jQZDNU9+abmkyHGTP076/jmMlF+95UP7Za67lr2rpV0+DdyES1zuOPa963H3+sPeFUl5qTaeYqKAC0/fdOn9b8jctpLR4xTnue9fzzz2N8HcfCrjWOhdrv/Lt37+L8+fMms+nrIuW5WHBwcK3zq/nz5yMnJ6fW5EfN3pE1zZgxA08++SQ8PDx0Ez7avm9XrlyBUqlEaGgo/P394enpiRs3btTahvY+7XUo7/WqkG2aslf7/cHhwQcfxIULF3TnhqtWrcKnn36KL774Ai+++CL3dkwxdc1P56DEVZg7dmbrMcW33noLzz//vO7fubm5eP7557FgwQK9Y2v1rA9vb2/s378fe/bswR9//IFt27Zhw4YNeOihh7Bjx446X5Ox11sdz3fLsWPH8Oijj6Jz5874+eefjU4i15zor0nsMQPCwXqVwIjYcnNzWdOmTVm/fv1099VVf3D+/Pm1Ho8a9T4/+OADJpPJWFFRUa3Y0tJS1qpVK9a5c2f20ksvMYVCwdLS0gzu14svvsgCAwPNek3m9jSpyVrvzZYtWxgA9scff+jdf+DAgVo9EoQYPXo08/LyqtVDQCwqlYpFR0ez4OBg9p///IcBYL/88ovB2A8++IDJ5XKDfxeEOBpfX182ceJEvfvKy8uZQqFg46vVqtf2APnyyy/1Ym/fvs3c3NzYyy+/XOdzfP/99wwA++qrrxhjjCmVSubt7c3eeOMNg/ErVqxgANiHH37IGjRowB599FGDcVevXhVUM7U6c3uaaGs2Hz16tM4Ybd+jrVu36t1fUVHBfH192ejRo+t8rPZY+d///ld3X+vWrdljjz1mMP7PP/9kANhbb73FmjZtyrp3726wF1NlZSXz8vKqsy41IVZVVcVYw4aMPfMMX/yxY5q+JBMnavpFhIUxVvM7WNvT5Nw5/fv//FNz/w8/aP6tVDLm7c1YzePPhQuMJSXdu/39t/7P335b01dlwQLGFAr93iliWLpUs5816tGbJEVPk7//1sRUq8ktynMbYqqnSY8ejL32Gt+2ioo025KyZrWhniaMMfb004zVq8fY3LmamIULa8dQTxNSQ1VVFWvYsCF7hvNYeOzYMebh4cEmTpzIIiMjWVhYWK3rEW3d+XM1joXa84Uf/j0WWvtczNyeJgCM3iIiInSxPXv2ZL169aq1jZiYGNayZUvdv4Vcr/JuU0q3b99mkZGRrGnTprr7avY0+fDDDxkAXV8ALW2vE0M9TWr2NNCeG2dnZ+vuo3NQ4opqjp05ypgiT08TQ7THj6SkJL3t1OxpUr9+/VqPrXksMvXdkpmZyYKDg1nbtm1ZXl6eoP2syRpjBqQ2WhLkIFQqVa0SJkFBQQgNDTWYQmuOqKgoMMaQnp5e62dvv/02Ll++jHXr1mHRokVo0aIFxo8fb/C509PTERUVJco+8bDle/PQQw/B398fK1as0Lt/xYoVqFevHoZVK6+Qn5+Ps2fP6tXyv1mzHAY0M9G//fYbBg8eXKuHgFgWLVqE5ORkrFy5EnPmzEF0dDReffVVg3UP09PT0alTJ10/AkIcmUKhqJU+u2TJkjpL4a1cuVKv/vSKFStQVVWlW4Vz69atWtvr1q0bgHvlDdzd3dGzZ08c1q7KrSY7OxszZszA6NGj8Z///AcLFizAb7/9hm+++aZWrPb4Ex0dzflqLTdq1CjI5XLMnj27ViaO9nUPGjQIHh4e+Pzzz/Xei9WrV6O4uFh3HCwpKUFVVZXeNrp06QK5XK53rI6KijL4XhUVFeHFF19E79698dFHH2HVqlU4cuQIPvroo1qxp0+fxt27d636XhFSJ4UCGD1a09fk5MnaP69+LlBZqek1ERoKLF6s6S2Smwu88YbhbS9deu//GdP82939Xrkkd3egZ897WQFaLVtqSj1pb/363fvZwYOaTJRp0zTZFTNmaLa7b58ZL96ADRs0PVqeew5YtEicbVoiPV1TmsuK564G5eZqetvULM1VVKT5u6hp1SrNf+uoZy2Zn3/W/A4//hh45x1Nqa533wX++ce6+0EcjkKhwOjRo/HLL7/gpIFjYfXrosrKSkyYMAGhoaFYvHgx1q5di9zcXLxRx7FwabVjIWMMS5cuhbu7Ox7+91joKOdimzZtqnV7+umnAQDffPMNPv30U13sE088gUOHDum9pnPnzmH37t148skndfcJuV7l3aaYtD0BtBo0aIDWrVsbvY6PjY0FACxfvlzv/iU1e2MJROegxNlJPXZmb2OKhYWFte6rea1uCWPfLTk5Oboxve3bt3O1HaiLPX1PuRzbzdcQIW7dusXq16/Pxo8fzxYtWsRWrlzJnnrqKQaALay2usuSWeGKigoWEBDAEhIS9OJ27drFZDIZS0xM1N23f/9+JpfL2YwZM/Ric3NzmUKhYKtWrTLrdZqTaWLL94YxxpYtW8YAsCeeeIJ99dVXbNy4cboZ4Oq0s9LVZ8IHDhzIhg4dyj744AO2cuVKNm3aNFavXj3m6+vLTp8+Leh94HX69Gnm5eXFJkyYoLvvn3/+YfXq1WNPPvmkXqxSqWT+/v7s3XfflWRfCLG2cePGMYVCwaZOncq+/PJLNmHCBNasWTMWEBBgMNOkS5cu7IEHHmBLlixh8fHxTC6Xs/vvv5+p1WrGGGOffvopa9OmDXvrrbfYl19+yRYsWMDatWvHfHx8WFZWlm57CxYsYJ6enqy4uFh3n1qtZgMGDGCNGzfWW3kSExPD/Pz82LVr1/T2PT4+nt1333265xbC3EwTxhh77733GAAWHR3NFixYwJYsWcLGjRvH3nnnHV2M9vg2ePBgtnTpUvb6668zhULBevXqxZRKJWOMsU2bNrGmTZuyadOmseXLl7PPP/+c9erVi7m7u7OUlBTdtn7++WeDq0bHjRvHvLy82JkzZ3T3vfjii8zd3Z1lZGToxS5YsIDVq1ePlZSUmPWaCRFdTo5m1X29eoxNncrYl19qVuk/+SRjjRrdi5s5kzGZjLHdu+/d98EHmtX81VcJjx/PmJcXY23aMDZuHGPLljE2fLgm7j//0X/uBQsY8/RkrNrxp07l5Yy1a8dY+/aa/2eMsYoKxjp1Yiw8nLHSUjPfgH8dPKjJomncmLE1axj79lv924ULxh/Pm+0xZ47mNmaM5j154YV799U0fDhj999vepuWZJp8843muRMSNPszcOC9/bl4UROzZo0mK+jOHf3HbtqkyTZ64w3Gli9n7LPPGBs9WvN30rOn5vcjlZqZJrm5jAUGavZf+12Un89YkyaMRUUxVj1DmjJNiAE5OTmsefPmrF69erpzsblz57Inn3ySNap2LJw5cyaTyWRsd7Vj4QcffFArY2L8+PHMy8uLtWnTho0bN44tW7aMDR8+nAFg/6lxLLTmuZi5mSaG1JUpUVJSwlq1asWCgoLYvHnz2KeffsrCwsJYaGhorRXNvNerQrYplqCgIPbUU0+xTz75hH311Vfs5ZdfZjKZjL1eLcOx5upuxjSVGQCwsWPHsmXLlrGnnnqKdevWjQHQG7cQkmlC56DE2fGMnTnKmCJPpsnUqVNZZGQke/fdd9lXX33FPvzwQ9a0aVPWrFkzXTaMJZkmjBn+bmGMsYiICF122rfffqt327Fjh9HXVp01xwxIbTRp4iAqKirYjBkzWEREBGvYsCGrX78+i4iIYMuXL9eLs+QAxxhjU6ZMYa1bt9b9u6SkhDVv3txg+ukbb7zB5HK53mDXihUrLDpJMGfSxFbvTXUrV65k7dq1Yx4eHqxVq1bs008/rXWQMjRpsnjxYta7d2/m7+/P3NzcWEhICHv++efZ+fPnBb0HvKqqqlivXr30viSq7wsAtmHDBt192jRkqfaHEGu7desWmzhxIgsMDGQNGjRgsbGx7OzZs6x58+YGJ0327dvHXnrpJdaoUSPWoEED9txzz7GCggJd3JEjR9gzzzzD7rvvPubp6cmCgoLY8OHD2eHDh/WeNzc3l7m5ubFvv/1Wd5/2M1ezNN7ly5eZj48PGzp0qO4+lUrFQkJCzJ7AtGTShDHG1qxZwyIjI5mnpydr1KgR69+/vy6lWWvp0qWsffv2zN3dnTVp0oS9+uqr7NatW7qfZ2VlsRdeeIG1atWKeXl5MX9/fzZw4EC2c+dOve1UVFSwwMBANqfa4ObmzZtrTYQzdu87KiIiQjc5wxhjffr0Yc8//7zZr5cQSeTmMhYXpxkAd3dnLDiYsYcfZmzlSs3P09MZc3OrXQqrqoqxXr0YCw1lTPuZGj+esfr1NZMMgwdrJmOaNNEMVNcs7Zmbq9luteNPnd54Q1OO6+BB/fsPH9Zsw9JSUF9/rRmIr+tmqBRUdbwTF8aeo7qiIs0kDs9iH0smTfr3r3t/tOeFTzzBWLXjvk5mpmZirGVLzaSKl5dmEmvWLMsnsUyp+Tt5/HFNqTntRI/W5s2a2E8+uXcfTZqQOuTm5rK4uDgWFhbG3N3dWXBwMHv44YfZyn+Phenp6czNzU1v0Jyxe9cxoaGhuvML7cDWhQsX2ODBg1m9evVYkyZN2KxZs2qVObbmuZg1Jk0YY+zKlSvsiSeeYD4+PqxBgwZs+PDhdV638VyvCt2mGD744APWu3dv5ufnx7y9vVn79u3Zhx9+qHdeZ2igsqysjMXFxTF/f3/WoEEDNmrUKHbu3DkGgH388ce1HsszaULnoMTZ8YydOcqYIs+kya5du9jIkSNZaGgo8/DwYKGhoeyZZ57RK+1n6aSJoe8WxoyXWxTy/WDNMQNSm4yxGnVFiEvLyspC+/bt8eeff+rSmYWIjIzEgAED9FKHnYWl740jGjVqFGQyGTZt2mTrXSHE4U2aNAn//PMP/vrrL8GP/fXXX/Hss8/iwoULCAkJkWDv7MucOXPw9ddf4/z581xN+KrLyMhA9+7dceTIEV36NSFOZ8IETZmk0lK++EmTNOWTzDj+OLXPPgPmzdM0u/f2tt1+VFUBAQHA3LnAa6/Zbj9qksmAr7/W/L0JlZioKS8nVvN5QgyYMGECfv75Z5RyHgvpXMy5ZWRkIDIyEt999x2ee+45s7ZB56CEWMYVxxQt+W4RE31PiY96mhA9LVu2xKRJk/Dxxx8Lfuy2bdtw/vx5JCQkSLBntmfJe+OIzpw5gy1btmDOnDm23hVCnMKsWbNw6NAhHDhwQPBjP/nkE8THx7vMyc8bb7yB0tJS/Pjjj4If+/HHH+OJJ56gi1VCqps1Czh0CDDj+OO0Kis1PVXefde2EyYAUFio6Vvz2GO23Q9CnBydizmP8vLyWvd99tlnkMvlePDBB83eLp2DEmIZVxxTtOS7RUz0PSU+yjQhhBBCCCHEkQjNNCHEHJRpQuyc0EwT4jzef/99pKenY+DAgXBzc8Off/6JP//8Ey+99BK+/PJLW+8eIYQQJ+Bm6x0ghBBCCCGEEEIIIYRHdHQ0kpKSMGfOHJSWluK+++5DYmIi/vvf/9p61wghhDgJyjQhhBBCCCGEEEIIIYQQQggB9TQhhBBCCCGEEEIIIYQQQggBQJMmhBBCCCGEEEIIIYQQQgghAJywp4larcb169fRsGFDyGQyW+8OIcSOMcZw+/ZthIaGQi53jjlkOgYSQng44/EPoGMgIYSPMx4D6fhHCOFFx0BCiKsScvxzukmT69evIywszNa7QQhxIFeuXEGzZs1svRuioGMgIUQIZzr+AXQMJIQI40zHQDr+EUKEomMgIcRV8Rz/nG7SpGHDhgA0L97Hx8fGe0MIsWclJSUICwvTHTecAR0DCSE8nPH4B9AxkBDCxxmPgXT8I4TwomMgIcRVCTn+Od2kiTYNz8fHhw6UhBAuzpS+S8dAQogQznT8A+gYSAgRxpmOgXT8I4QIRcdAQoir4jn+OUfxQkIIIYQQQgghhBBCCCGEEAvRpAkhhBBCCCGEEEIIIYQQQgho0oQQQgghhBBCCCGEEEIIIQSAE/Y0IcQaypUqzPztOLYdv4HbSgY5AG8PBXqH+2PJM93RwIs+WsT+qdQMadmFyLt9F0ENvdA73B8KufPUtSXEVdBn2TzKKjW+TbmIS4V30Ny/HsZGtYCHG60nIsTR0GeZEOLK6DyQ1KQdr0o6lYdKlRrNA+rj/wa3R/92jelvwwCVmmH/mTzMTzqLa0V30dDLHc/2uQ8vPtCSzifqUHq3ClPWH0b65WIo5DIM7hSMWSM6wdtDYetdE5WMMcZsvRNiKikpga+vL4qLi6n5E5HEpLWHsOtsntGYjsH1sHXaQCvtETGXMx4veF/TtpM3kPjbKeSUVOjua1TPHR+O6oyhXUOtsauEEBFoPsunkVNyV3dfsI8XEh/tiCGdQ+p8nDMe/wD+1zV362ms3J+NmifBkx9ogf8O6yTtThJCRDNny2ms+Vv/syyXAZMfCEfC0I51Ps4Zj4HO+JoIIcZtO3kD7/9+GjeK750Hhvh6YdYI1zsPdMbXZI4X1x3CzjOGx6sUMmDZc92N/m24mm0nbyB+/VFUqQ0Pjb/8oPHzCVc0YslfOHGtxODPBnVojFXje1t5j4QRcqygKTNCBOg/f7fJCRMAOJ1zB23/84cV9ogQ4badvIFXvjuiN2ECALfuVOK19Ucxd+tpG+0ZIUSIe5/lu3r355TcxSvfHcG2kzdstGf2be7W0/jSwIQJAHz110VM/uaQ1feJECLco0v/wuq/a3+W1Qz4cn82nc8QQpzatpM38Op3R/QmTAAgp/guXqXzQJf06NK/6pwwAQAVA10jVKO9lqprwgSg84maen2QVOeECQDsPHMTjy79y4p7JC2aNCGE0/u/n8ClgnLueKUa6DRzm4R7RIhwKjXDOxtPGI35cn82th6nEylC7JlKzTD1xwyjMW/+dAwqIxcBrkhZpcbK/dlGY5JO5+H3Y9ettEeEEHPM2XISx6/WfdEOAF/9lQ1lldpKe0QIIdajUjO8//tpgwtAtPe9//tpOg90Ib8duWrye1Fr+v/oGkGlZpi6/ghX7Jf76XwCAGb9dhw3S5Um445fLcHmjGtW2CPp0aQJIRyUVWp8feCy4MeVKVUY/rnzzLISx5d6oQBFdypNxr31y3GXP5EixJ4tTjqHChMn72UVKiRn5ltpjxzDumTDGSY1vfm/DDoGEmKnlFVqrP77ksk4NQO+Tbko/Q4RQoiVpWUX1sowqY4BuFF8F2nZhdbbKWIzKjXDmz8f446/o1Qh+bxrXyMkZ+ajQsA8yDu/8L+/zkhZpca65Cvc8W86ycQcTZoQwuGBeTvNfuzJ684zy0ocX0oW38lRaUUVUi8USLw3hBBzqNQMK/ZnccVuPHJV4r1xLIcu3uKKU6qYy19MEmKv3hEwMHSp8I6Ee0IIIbax83QOV1ze7bonVojzSM0qQKXARIjPd/8jzc44iCUCX//mY9edYhLAXAkbhU0aVamd41qKJk0IMeG3I1eRW2J6Zb4xb2ygFavEXsi4I79JvSjdbhBCzJaaVYBKFd93SplSJfHeOJb6Hgru2MQtJyXcE0KIOVRqho0Z/OXzmvvXk3BvCCHE+lRqhk2cizKDGnpJvDfEHiRfED44feRKkcuOUanUDIcvFQl8DFx2UalKzbD5qPDSxc4wMUeTJoQYoVIzvPGT5Wl4agYsTnL8AwZxfFGtArhj95zNc9kTKULs2TfJxntyVNerRSMJ98TxPN69GXfshZt3qH4xIXZmcdI5QfFjo1pIsyOEEGIjadmFKCwzvagzoL4Heof7W2GPiK2ZU4bNlScBUrMKYM4wx4ELN8XfGQeQmlWAKjPeL2eYmKNJE0KMSM7MB+diXpOW7s10+AMGcXx9WwbAnfPIr1Qxlz2RIsReqdQMO8/mccePjw6XcG8cT3TrQCj4E+4wdnWqdDtDCBFEpWZYsucCd3yf8EbwcDN+0rNw4UL06tULDRs2RFBQEEaNGoVz5/QnZgYMGACZTKZ3e+WVV/RiLl++jGHDhqFevXoICgrCjBkzUFVVpRezd+9edO/eHZ6enmjdujXWrl1ba3+WLVuGFi1awMvLC3369EFaWhr36yWEuAbeklsju4VCIRdw0kMckkrNcPQSX/nZmlx1EsCczBwAOOSiPYLMfb+cYWKOJk0IMeL930+Jti3KNiH2QCGX4eEOTbjjqUQXIfYlNasAKs7kh+b+3iYHDF2NQi5D3IBW3PEHs29RtgkhdmJx0jkIWX707aS+JmMOHDiAuLg4pKamIikpCZWVlRg8eDDKysr04iZPnowbN27obvPmzdP9TKVSYdiwYVAqlUhOTsa6deuwdu1azJw5UxeTnZ2NYcOGYeDAgcjIyMC0adPw4osvYvv27bqYDRs2YPr06Zg1axaOHDmCiIgIxMbGIi+Pf6KcEOL8Aht4csUJueYjjsvcLADAdScBzMnMAYBjV4tdciH0tVvlZj/W0Sfm6EqakDooq9TIvFlmOlCAL/ZfcMmDLLEvQkpVUIkuQuyLkNJcz/dtLuGeOK6pMe0ExVO2CSG2JzTLpFXjelyTxhs3bsSECRPQqVMnREREYO3atbh8+TLS09P14urVq4fg4GDdzcfHR/ezHTt24PTp0/juu+/QrVs3PPLII5gzZw6WLVsGpVIJAPjiiy8QHh6OhQsXokOHDoiPj8cTTzyBTz/9VLedRYsWYfLkyZg4cSI6duyIL774AvXq1cOaNWu4XzchxAXwXprRJZxLMDcLAHDNSQBLMnNctRJHubLKdFAdLJlwsQc0aUJIHRI2Wt7LpCZXPcgS+0IlughxTCo1Q9JpKs1lKYVchse7hXLHU7YJIbYnNMskcXhns56nuLgYAODvr98H4Pvvv0dgYCA6d+6MhIQE3LlzR/ezlJQUdOnSBU2a3FvVHRsbi5KSEpw6dUoXM2jQIL1txsbGIiUlBQCgVCqRnp6uFyOXyzFo0CBdTE0VFRUoKSnRuxFCnF9+WYWoccSxWTIo7YrX+pZk5gCOnzkhlErNsPcf81/zjWK+coL2iiZNCDFApWbYfPS6JNtel8K/SpgQKQgt0UV/s4TYh+TMfPAO3VNpLuM+fiJCUPw7v4i/kIIQwkelZli2lz/LxE0ORLcJFPw8arUa06ZNQ79+/dC5871Jl2effRbfffcd9uzZg4SEBHz77bd4/vnndT/PycnRmzABoPt3Tk6O0ZiSkhKUl5cjPz8fKpXKYIx2GzXNnTsXvr6+ultYWJjg10wIcTy85bl444hjY8yyTBFXmwRIsXCS6HqRY08CCJWaVYAKC2aZjjt4NhNdTRNigJDZZxmA1wXUR995hsodEdsTUqKL/mYJsQ9C+mxRaS7jPNzk6NOiEXf85mPX6ThIiI0kZ+ZDJeDj91r/1mY1P46Li8PJkyfx448/6t3/0ksvITY2Fl26dMFzzz2Hb775Bps2bcKFC/wTOVJISEhAcXGx7nblyhWb7g8hxEqoPBep5m6lyqLHu9okALPwg1Fu4fvtaCydZLpbpXbobCaaNCHEgG9TLnLHxnQMwrTB7cB7aaZmQPJ58+tOEiKGvi0D4Mb5R0t/s4TYntA+W1Say7RvXzTdJFpLpYZDn/AT4siETBjLZcDUmLaCnyM+Ph5btmzBnj170KxZM6Oxffr0AQBkZmYCAIKDg5Gbm6sXo/13cHCw0RgfHx94e3sjMDAQCoXCYIx2GzV5enrCx8dH70YIcX5UnotoqdQMByzoaQK43iSAj5e7RY9PuVDgUgupLJ1kAhw7m8mlJ01UaoaUCwXYnHHN5f7wSd1UaoZdZ3JNB/5rfFS4pj56d/766D8foZVgxLYUchlGRvL/zSZuOSnh3hBCTFmXfJE7lrcBsqvzcJOjVWB97vh5289IuDeEEEOEThjHDxCWZcIYQ3x8PDZt2oTdu3cjPNz0hHNGRgYAICQkBAAQFRWFEydOIC/vXs+ppKQk+Pj4oGPHjrqYXbt26W0nKSkJUVFRAAAPDw/06NFDL0atVmPXrl26GEIIAag8F7knLbsQpRWW9d1ztbHQo5fNawKvVXK3CmnZhSLtjf3z87Zskglw7GwmSa+o9+/fjxEjRiA0NBQymQy//vqr0fi9e/dCJpPVutVVx9US207ewP2f7MYzX6Vi6o8ZeOarVPT6MAlbj0vTx4I4jtSsAlRyfu94KGTo2yoAADD3cf766CeuFZuza4SISsjf7IWbd6gRMiE29F3qRe5Ycxsgu6LERztxxx67WkLHQUKsbOyqVO5Yc7JM3nzzTXz33XdYv349GjZsiJycHOTk5KC8XNNY98KFC5gzZw7S09Nx8eJF/Pbbbxg3bhwefPBBdO3aFQAwePBgdOzYEWPHjsWxY8ewfft2vPvuu4iLi4Onp2bQ8pVXXkFWVhbeeustnD17FsuXL8f//vc/vPHGG7p9mT59Or766iusW7cOZ86cwauvvoqysjJMnDhR0GsihDi3tGzOzFcbjoPb81igM8kpsXww2pUmAVRqhr9EqKCRU1wuwt44Bv/6lk++OnI2k6STJmVlZYiIiMCyZcsEPe7cuXO4ceOG7hYUFCTqfm07eQOvfncEN4r1DzCFZZV4bf1RzN16WtTnI44lWUB648D2QbrVbB5ucoT6enE97mLBHZeazSf2ycNNjtaN+VdZUyNkQmxDWaXGpUK+k3M5zGuA7KqiWwcKOhkeu5p/AJcQYhlllRoHL/KvCBWaZQIAq1evRnFxMQYMGICQkBDdbcOGDQA0GSA7d+7E4MGD0b59e7z55psYPXo0fv/9d902FAoFtmzZAoVCgaioKDz//PMYN24cZs+erYsJDw/HH3/8gaSkJERERGDhwoVYtWoVYmNjdTFPP/00FixYgJkzZ6Jbt27IyMjAtm3bajWHJ4S4LpWaYV3yJa5YW5bnstexQGdTWCrO79hVJgHSsgtRprR8AD9fpPfdERSWKS3ehiNnM7lJufFHHnkEjzzyiODHBQUFwc/PT/wdguZL5v3fTxuddP9yfzYimjXC0K4hkuwDsW9CZtnH9W2h9+9R3Zpi+T7TTSG1PSIeaNdY6O4RIqpZIzph7Jo0rthNR69j/pPdzGquSggxX8JG/gnLzk196DMqgEIuw2PdQ/HLEb5M44PZt6CsUlP5M0Ks4J2f+Y99MpjXy6S4uNhoL5CwsDDs27fP5HaaN2+OrVu3Go0ZMGAAjh49ajQmPj4e8fHxJp+PEOKa0rILUVReyRUb1JBvQacU7HEs0Bn51fMQZTtiDIw7AjEycwBwfwadQfoly7OQtNlMUf9W6XEkdnnF161bN4SEhCAmJgYHDhwwGltRUYGSkhK9mzFp2YW1MkwMeeuX4w47E0bMp1IzHL3Et6KtemkurX4CVvd+vvsfQftGiBSiWweCd3iVAVicRH+3hFiTSs2wkXNAHwBGRPD3KiIaQkoVApRtQog1qNQMGzP4j32PRYbShDEhxOnl3eYb9PWr547e4f4S7434pBwLdEZFd8SZ7BBr8sXe5d8WJ0OEuchQsVjlzADHzWayq0mTkJAQfPHFF/jll1/wyy+/ICwsDAMGDMCRI0fqfMzcuXPh6+uru4WFhRl9Dt4vmdKKKqRe4KwVSZxGalYBqjgPgNVLc2n1bRkA3uu1I1eKaGKO2JxCLsNj3fgHWZfvy6S/W0KsaHHSOUElqcdHm25iTPR5uMnRp0Uj7nhttgkhRDqLk84Jiv94tLDJT0IIcUS8zd0nRLVwqIlka4wFOiPxMk1co9zULZEmmXJFylixd2KVMwMcN5vJriZN2rVrh5dffhk9evRAdHQ01qxZg+joaHz66ad1PiYhIQHFxcW625UrV4w+h5AUxQMXbnLHEucgpJ9JzdJcgGYAumdzP67Hq9SgiTliFz5+gn+goUqtKS1HCJGeSs2wZI/pko9aEc18qGyUmb59sa+geOrxRIh0VGqGFRzlbrVaNa5Hxz5CiGvgXEnTq4VjZZlYYyzQGaUIGL8yJv0yf/8wRyYTaR7xz5M5LrGQVKxyZoDjZjPZ/dll7969kZmZWefPPT094ePjo3czur1wf3i68X1Stp/KEbSvxPHx9jMxVJpL6/WH+Osp08QcsQdCG8Inbjkp4d4QQrSEZpm8FdtBsn1xdkKzTTYdve4SF0uE2EJqVgEqBSRzJQ7vLN3OEEKIHeFt7m7LJvBiEXss0Nmo1AxJp3NF2dbf5x23UbcQOUXilIi6o1S5xALoQhEb3otVSs7a7H7SJCMjAyEh4jVkV8hl6NrMjyv2ws07VH7BhQjpZxLRzLfOdNfo1oHcH6xDAprOEyKlWSM6ccfSsZEQ6QnNMjE2mU/4CMk2oR5PhEhn/rYz3LFuciBaQE9BQghxZLzluXjj7JnYY4HOJi27EMV3q0TZVmlFFfcCYkelUjPsPJMn2vZSspy/+oaY2SGUaWJAaWkpMjIykJGRAQDIzs5GRkYGLl++DECTTjdu3Dhd/GeffYbNmzcjMzMTJ0+exLRp07B7927ExcWJul9CGmKtS84W9bmJ/RLSz6SXkb8hhVyGHpyrVY9dLXaJGX1i/4Q0hAeAYZ/vl2xfCCHCs0xeebCVQ9WutkdCs02W7qUeT4SITVmlRsZV/ma+r/VvTcc+Qojr4D3tsPHpib2OBToTMUsnAY7bqJuXmJNMAOAKlwBiZoeIVUrO2iSdNDl8+DAiIyMRGRkJAJg+fToiIyMxc+ZMAMCNGzd0B00AUCqVePPNN9GlSxf0798fx44dw86dO/Hwww+Lul/RrfhXI/1+7Lqoz03sl5B+Jv1aNTb6c96JOaWKuURaH7F/QhvCn88ro+MjIRJRqRk+F5BlIpcBU2P4S0OSugnJNlEzyjYhRGxjV6Vyx8pAxz5CiGtxlPJc9joW6Ex4Syc1b+TNtz0HbdTNK+823ySTF2c7h0YOmjkhhJjZIVsdtA+Mm5QbHzBgABir+01Zu3at3r/feustvPXWW1LuEgCgb8sAKOSaRtymnL5xGyo1oxVMLkCMfiZa0a0CsYxzwOvAhZvoR2UFiB34+IkIbMzgnwiZ8sNRDO0SQsdHQkT2xIq/BcXHD6CV1mLR9njKvFnGFb90byamxrSl958QESir1Dh4kb8Z7ePdm9JnjxDiUhylPJe9jgU6E94B7ajWAbh06Kpo23NUvJ+JB9s0xg6OMl7+9Z37/QLEzQ7R9oFxtLFPu+9pIgWFXIZB7YO4YqvUlAngCsTqZ6LVt2UAOCeoqa8JsRtCS9MwAE+uSJZuhwhxQVsyruHoFf7SNPaaZbJixQp07dpV15gzKioKf/75p9HH/PTTT2jfvj28vLzQpUsXbN261Up7q09IjyfKNiFEPEKyTABg7uNdJdoTQgixT2nZnGNTjregmwjEWzqpoJQvzlEbdXPj/EwE+XhxxTlquSleKjVD0ulcrlgPBd/UgiP2gXHJSRMAGBcdzh174MJNCfeE2AOx+ploKeQyRDanvibE8QgpTQMAR64UUZkuQkSiUjPE/5gh6DH2mmXSrFkzfPzxx0hPT8fhw4fx0EMPYeTIkTh16pTB+OTkZDzzzDOYNGkSjh49ilGjRmHUqFE4efKklfdc0+NJIeAtXb6PepsQYimhWSZ9whvBw81lL2UJIS5IpWZYl3yJK9bW5bmI9HgzQwIa8MU5e6ZJHmc5M94z+p1n8pz6/F9ID5iIZr5ccY74drnsmSZlApDqUgRkE5nqZ6JFfU2IIxKabQIAr/9w1KlPGAixlj4f7hAUb69ZJgAwYsQIDB06FG3atEHbtm3x4YcfokGDBkhNNbySfPHixRgyZAhmzJiBDh06YM6cOejevTuWLl1q5T3XLHyIG9CKO75KDSSfd7yVU4TYk3d+PiYo/ttJwhZ5EEKIo0vLLkRReSVXbFBDvtXyxHHxZjrwZpo4e+YEbw8Y3rVoReWV3CX+HRFvDxi/eu6I6diEK9YR+8C47KQJZQKQ6hjnfLKXm9xkPxOt6Fb8tfoom4kAgEqlwnvvvYfw8HB4e3ujVatWmDNnjtF6sFIQmm0CAF1m2qaMDiHOYsLqVOSX8a3m0fr06W52mWVSk0qlwo8//oiysjJERUUZjElJScGgQYP07ouNjUVKSorRbVdUVKCkpETvJoapMe0g5J1N3GL9jBhCnIVKzQT1VKMsE0KIKxIyiMm7gJM4JiGlk4Ia8vXycPbMCd5Mmq7N/ODrxdf+O6e43JJdsmu8PWAmRLVAY86SZoGcf4v2xKXPNikTgGj5eLlzxQlpek3ZTESoTz75BCtWrMDSpUtx5swZfPLJJ5g3bx6WLFli1f3wcJNjaGe+1QJad6qANv/5w6lPtAiRyvjVqdh7Xth5RoiPJ0Z2ayrRHonjxIkTaNCgATw9PfHKK69g06ZN6Nixo8HYnJwcNGmif9xp0qQJcnJyjD7H3Llz4evrq7uFhYWJsu8KuQyvD+TPNrlw8w6UVWpRnpsQV7M46ZygeMoyIYS4IiGDmI6wqIaYT0jppJaNG3DFOXvmBG/PlpLySu7MicIyJ+4Dw9u+oIU/gjiPTbxx9sSlJ00oE4BoHbnE9+XQxJf/Q07ZTESo5ORkjBw5EsOGDUOLFi3wxBNPYPDgwUhLS7P6vix5toegVdYAUKkGWv1nK+ZvPUN/z4RwKL1bhdYJf2CfwAkTANj31kMS7JG42rVrh4yMDBw8eBCvvvoqxo8fj9OnT4v6HAkJCSguLtbdrly5Itq2hWabvPOLsPJChBDNatkley5wx1OWCSHEZQkYxCTOTUjW0dioFpQ5AeDqrTtccf71PRDFOVbszH1geHvA5JVWgPuCyQHncvk+OU6qb8sAuMs1A32mXC/iOygRx6NSM+w+m8cVe0Pg30HvcH8c4mhqqc1m6teGfyKPOJ/o6GisXLkS//zzD9q2bYtjx47h77//xqJFi6y+Lwq5DEvGdBPclBoAlu3PwrL9WXX+3F0uQ5CPJ57r0xwvPtCSBj+IU1GpGfafycMn208jM+8OhBXc4vNCvxYO8bnx8PBA69atAQA9evTAoUOHsHjxYnz55Ze1YoODg5Gbq19mIDc3F8HBwUafw9PTE56e0qxa0mabfM45oLvp6HXMf9IxSqYRYi8WJ53jbroKUJYJIcR1CRrEJE5NSNaRh5scMR2b4Ocj10zGO2vmhErNsPkYXxnQYF9vnL5ezBXLm73iiHh7wBSWVgCc5eTzShxvXN2lJ00Uchkeah+E7adND5iXV6qssEfEFlKzCrgmzgAg1E9YQ7XoVoFYxjnYcuDCTZo0cXHvvPMOSkpK0L59eygUCqhUKnz44Yd47rnn6nxMRUUFKirufaGJVc8fAIZ3a4qNR69g9zlxyxNWqhmuFd3FvO3nMG+7piQHTaQQR1V6twqvf38IB84XwhqnzUENPDBzRCcrPJP41Gq13vGquqioKOzatQvTpk3T3ZeUlFRnDxRrmRrTjnvShAFYnPQPpse2k3anCHESQrNMWjWuR+cHhBCXJWgQkzg3gVlHUa0CuSZNnDVzIi27EIVllSbjAup7oHe4P64X8WXcOOv7BWgybnjjeCfbDmTm47HuzSzZLatz+bPOni34mnrv/+cmlZtxUikC+tX0a9VY0LaprwkR4n//+x++//57rF+/HkeOHMG6deuwYMECrFu3rs7HSFXPX2vNxL4IrC/9/Hr1iZS27/6Jl79No2MusWuld6vQeeY2dE7cjj1WmjABgJT/DDIdZAcSEhKwf/9+XLx4ESdOnEBCQgL27t2rmwQeN24cEhISdPFTp07Ftm3bsHDhQpw9exaJiYk4fPgw4uPjbfUSAGgW2PRq7scdv3RvJh27COEkNMskcXhnyfaFEELsnZBBTOLchGYd8WZEOGvmRA5nhsOj3UKhkMtc/v0CgCDO5u5BPl7w58x82nkmz+Guk1x+0iSwId8vt7xSTc3gnRTjvFzzcpOjbyu+STYt6mtChJgxYwbeeecdjBkzBl26dMHYsWPxxhtvYO7cuXU+Rsp6/loH/ztY9G2asv3UTbT6z1b8duSq1Z+bEGOUVWr0+SgJnRO3o1Rp3SzUxWMcp/RTXl4exo0bh3bt2uHhhx/GoUOHsH37dsTExAAALl++jBs3bujio6OjsX79eqxcuRIRERH4+eef8euvv6JzZ9sPkk55uC13rJppsk0IIcap1AzL9vJnmbjJgWjKyCaEuDAhg5jEuQnNOuLNiHDWzAne96uZnzcAcE8CXOXMSHFEadmc498MCOY85hSVVyLNwRaLu3R5LoD/lwtQ+SRndaOIryHU0C4hZg1WUV8TwuvOnTuQy/XnshUKBdTquuvHSVnPX7cPchmWPxuJ19YflfR5DJnyv2P48q9M/DF1gNWfm5Ca5mw5jdV/Z9vkuTs39cHIbk1t8tzmWL16tdGf7927t9Z9Tz75JJ588kmJ9sh80a0DoZABKs51DV/sv4CpMW0dZoKLEFtIzszn/kwBwGv9W9NnihDi0oQMYhLnJjTryNUzJ4S+X7zjxL9lXMe7wzo63fmJSs2wLvkSV2x+WQWGdw2Fr5cbiu+a7uaZU+xYE00un2nSO9wfnpz1k6h8kvNRqRm2nco1HQgg2Ne8FRvRrfgnQQ5cuGnWcxDnMGLECHz44Yf4448/cPHiRWzatAmLFi3CY489Zutdw9CuoXj5wXCbPPepG2Xo/v42mzw3IVojlvxlswmTgPru2PL6AzZ5bqKZOI4b0Io7XrsIghBStyW7+TOy5DJgagx/xhchhDgboYOYxLkJzTrizZzgjXM0Qt+v3uH+8K/vbjK+oEzpcJkTPNKyC1FUbroHDAAENfSCQi5DTMcmXPG8/U/shctPmijkMnRt5scVS+WTnE9adiHuKPm6wJs7eSykr8m1W44160rEtWTJEjzxxBN47bXX0KFDB/zf//0fXn75ZcyZM8fWuwYASBjaEcuf7W6T5y4sV6EbTZwQGxm2eB9OXCuxyXM38/NA+nvWL5FH9E2NaQchpwHztp+RbF8IcXQqNUPaxSLu+PgBlGVCCHFtQgcxiZPjHZb8Ny6IczKEN87hCHy/FHIZRkaEcj3E0TIneOTd5usB41fPHb3D/QEAUZyLxR2tBJzLT5oA0P2STaGVg86HtyEUAES1NK9slpC+JjeK+feHOJ+GDRvis88+w6VLl1BeXo4LFy7ggw8+gIeH/XyxDO0aggsfDYWHwvqDF0XlKkTO3m715yWubfjn+3DqRqlNnntiv/vw9zsxNnluok8hl+H1gfzZJseulkBZxbcogxBXszjpHHesDJRlQgghvOMWft7u3ONbxHHtPstXLUWXdcR56X7oovNlTQBAHmdPk+pxzRrV43qMo2VO8AjknDybENVCt6jFWUvA0aQJqHySK8u/zXfw9HYX3gS+Ot4Tl+OUzUQcgEIuwz8fDsWAto2t/ty37lTh/k92Wf15iWuas+UkTl63/oRJkwbu+OeDRzBrRBerPzepm9Bsk7GrUyXbF0IclUrNsGQPfwP4mI5BlGVCCHF5vI2sB3WgY6azU6kZNmVc44rVZh3lc/79rE256JTjUbyfn+pxvBkRjpY5wYXzT6BXi3vjnM5aAo4mTaApn+TO+U5cL6JMAGdSeIfv4Plg28YWnXzwTszdrVJTNhNxGGtf6I0zs4cgxNe6X3xXb93FC2vTrPqcxPUoq9RY/Tdf7WixNHCX42RiLA6+OxgebnSKZm8Uchke786Xqg8AB7NvUbYJITUsTjonqEfx+Chp+6ktXLgQvXr1QsOGDREUFIRRo0bh3Dn9TJi7d+8iLi4OAQEBaNCgAUaPHo3cXP1VvpcvX8awYcNQr149BAUFYcaMGaiq0m+IunfvXnTv3h2enp5o3bo11q5dW2t/li1bhhYtWsDLywt9+vRBWhqd7xBC+BtZ92ttXnUM4jjSsgtRWGa6VFtAfQ/d4l3ekm1FdyqdskfH1Vt3uOKqf86cNXOCB29fpOpxzloCjq7IobkIfqh9EFdseaVK4r0h1pR+8RZXnLe7wqLn6dsygLucEWUzEUfi7aFASsIgnJk9BE/2DEVDD+usbNp99iZ+P3bdKs9FXFPPD6QvBScHUN9DgYHtGuNkYixOznkEDbzcJH9eYr65j0cIiqdsE0LuEZpl4qGQWZTpzePAgQOIi4tDamoqkpKSUFlZicGDB6OsrEwX88Ybb+D333/HTz/9hH379uH69et4/PHHdT9XqVQYNmwYlEolkpOTsW7dOqxduxYzZ87UxWRnZ2PYsGEYOHAgMjIyMG3aNLz44ovYvv3ed82GDRswffp0zJo1C0eOHEFERARiY2ORl5cn6XtACLF/QhtZE+fF229iZLdQ3cLf3uH+8OW8xnC2Hh0qNcNmznGDYF9v3f+7cqYJb3kuvTgnLQFHV+b/6tkiANtPmz4h3f/PTajUjFIenYBKzZBxpYgrNtTPspMPhVyGiGa+OHTJ9PMdcsKZfeL8vD0UmP9EJOY/EWk0Tlmlxld/ZeLb5Gzk3q4StNq0pqk/HsXQLiF0PCaie//3Eyi5a36GgByAj7c7hnQOxqwRneDtYdnEO7EfHm5y9GnRCAc5F11os00oc4gQ4VkmrzzYSvLv+I0bN8LHx0f377Vr1yIoKAjp6el48MEHUVxcjNWrV2P9+vV46KGHAABff/01OnTogNTUVPTt2xc7duzA6dOnsXPnTjRp0gTdunXDnDlz8PbbbyMxMREeHh744osvEB4ejoULFwIAOnTogL///huffvopYmNjAQCLFi3C5MmTMXHiRADAF198gT/++ANr1qzBO++8I+n7QAixcwIbWRPnxTug/XCHJrr/V8hliOnYBD8fMV3Wy9l6dJiTmQO4dqaJOccbISXgXn+4jcOM4dAV3L8CG/IdeMorqXySs0jNKoBSxXc06NfK8t4NTTkbSR2jvibEiXm4yRE3sC1S/xuL7I+H4eLHw3Bm9hD4egsfVFYz4LMd/M1kCeGhrFLj6wOXBT9OIQNOJsbi4sfDkPXxMGTMGoyPR3elCRMn9O2LfQXFv/PLMYn2hBDHoVIzrNjHn2Uil9mmAXxxcTEAwN9fM3CSnp6OyspKDBo0SBfTvn173HfffUhJSQEApKSkoEuXLmjS5N4AVWxsLEpKSnDq1CldTPVtaGO021AqlUhPT9eLkcvlGDRokC6GEOK6zGlkTZyUmRNoUZwl450tc8KczByAv/fG1SLnyswBzDveOGsJOEknTfbv348RI0YgNDQUMpkMv/76q8nH8NR6lUKwgDRGKp/kHFI4J7+83CxrAq/VtJG36SAAShWjiTniUrw9FDg2awgmRDcX/Ngley/QJCMR1bDF+wQ/pkOTergwdxiV1nIR2mwTXpuOXqfjFHF5qVkFqBSQwBc/oLXVVyGq1WpMmzYN/fr1Q+fOnQEAOTk58PDwgJ+fn15skyZNkJOTo4upPmGi/bn2Z8ZiSkpKUF5ejvz8fKhUKoMx2m3UVFFRgZKSEr0bIcQ5HTjPNwbF2/Baao40FuhozJ1Ac9XMCXMycwD+MeLfMpzvPJ/3OFI9zllLwEk6aVJWVoaIiAgsW7aMK56n1qtUeof7w9ON78Scyic5B8Y5RT+gvWVN4LV4m8EDQEpWvsXPR4ijSXy0Mybd30Lw415ff0T8nSEuaUvGNZy/ydcoUKtjSAP8+cZAifaI2Csh2SYMwOKkf6TbGUIcwPxtZ7hjbZVlEhcXh5MnT+LHH3+0+nObY+7cufD19dXdwsLCbL1LhBAJqNQMSadzuWJ5G8ZLzZHGAh2NOQPaAH/mBG+cwzAzM6d3uD/867ubfFhBmdKhMid4XL3Fdz1c/XijLQHHw5FKwEm6JPKRRx7BI488wh3PU+tVKgq5DF2b+eEQR43qk9dLqK+JE/DxMn0ABIDIMP7VpMZomsEDSpXpWCebqCaE23vDO0HNGL4+cIn7MVtP5lDPAGIxlZphyo8Zgh4TWM8NW6f2l2aHiF0T2ttk6d5MTI1pS+eOVqQptZeFP49fR1bBHXi6KRDRzBefjelOWWFWpqxSI+MqfxaELbJM4uPjsWXLFuzfvx/NmjXT3R8cHAylUomioiK9bJPc3FwEBwfrYtLS0vS2l5ubq/uZ9r/a+6rH+Pj4wNvbGwqFAgqFwmCMdhs1JSQkYPr06bp/l5SU0MQJIU4oLbsQxXeruGKrN7K2JUcaC3Q0vBNjNeOCOCdDeOMcRX4Z3yRTzTiFXIaREaH4Otn0uIQjZU6YolIzbD52nSu25vGmX5vGXH1zHGlizq5GmEzVejVEzLTk6k1/jCmvVDvdTKIrOnqZb6CjuNx00ygeCrkMw7uGcMXmFPPVXSTEGc0a0RndmvmYDqxm7OpUifaGuIrFSecgtPX7wXcHS7IvxDEIyTZRM8o2saa5W0+j7bt/Yu6f55Bx7TZK7qpws1SJnWdvonPidgz/XHgZPmK+sav4v6NlsG6WCWMM8fHx2LRpE3bv3o3w8HC9n/fo0QPu7u7YtWuX7r5z587h8uXLiIqKAgBERUXhxIkTyMvL08UkJSXBx8cHHTt21MVU34Y2RrsNDw8P9OjRQy9GrVZj165dupiaPD094ePjo3cjhDifnBK+sQE/b3fuMS17Y+uxQEcSxFk2qlYc51qEQxeda6yTtzyXobhmnH2JHSlzwpS07EIUlpkeAw2o71HreOOME3N2NWliqtarIWKmJQspn+RMM4muSKVm2HOOry6omAvdQvz4DrpbT9xwurqIhAjxy2v3C4o/mH0LyiqhQ96EaKjUDEv28DcoBoAlz0RS1oCL83CTo3Xj+tzxy/dl0ne7Fczdehpf7s82GnPyeil6fZBkpT1ybcoqNXdGFgA83r2pVY+tb775Jr777jusX78eDRs2RE5ODnJycnTXnr6+vpg0aRKmT5+OPXv2ID09HRMnTkRUVBT69tVMnA4ePBgdO3bE2LFjcezYMWzfvh3vvvsu4uLi4OmpGRh45ZVXkJWVhbfeegtnz57F8uXL8b///Q9vvPGGbl+mT5+Or776CuvWrcOZM2fw6quvoqysDBMnTrTa+0EIsT+85ZgGdQhy2HNTW48FOhQzy03lc/4drU256Fznq2a+X4BrljTLu803STuyW2jt4w3v4ceBDlN2NWlijoSEBBQXF+tuV65cMXtbfVsGcPc14T3gEPuUmlWACs4B1qiW/JNppsg4jw53q9TUDJ64NIVchikDWwl6DGWbEHMtTjrHfT4NAG2C6mNERKhk+0Mcx6wRnbhjq9RA8nnqWSYlZZXa5ISJ1s1SJd7/7ZTEe0SEZJkAwNzHu0q0J4atXr0axcXFGDBgAEJCQnS3DRs26GI+/fRTDB8+HKNHj8aDDz6I4OBgbNy4UfdzhUKBLVu2QKFQICoqCs8//zzGjRuH2bNn62LCw8Pxxx9/ICkpCREREVi4cCFWrVqlV3bm6aefxoIFCzBz5kx069YNGRkZ2LZtW62BREKIa+Etx9SvtXjjFo5AzLFAR7L7LF9/m5rlpoIa8mWoFN2pdKrKOuaW5wKcM3PCFN7MnIc71D43yePMiuONswd2VdDXVK1XQzw9PXUreCylkMswoG1jbD+dZzL28KVbmCzKsxJbSOGckPByk6NvqwDRnjeqVQCW7snkij1w4Sb6tXGtEx9Cqpsa0w5L91zgLpmkzTah3iZECJWaYdleYVkmf0x5UKK9IY4munUgFDJAxTnrlrjlJHa1GyjtTrmwtQf4Jky0vk6+iIShHeh7QyJCs0z6hDey+u+iuLjYZFkrLy8vLFu2zGhD4+bNm2Pr1q1GtzNgwAAcPXrUaEx8fDzi4+ONxhBCXIvZ5ZgciK3HAh2FSs2wKcN0zwig9iRJ73B/+Hm7o4ij/DxvtoEjsKQ8lzNmTphkQWYOb5myA5n5eKx7M9OBdsCurhBM1Xq1Bm8PvnmkPWfznCtlzcUwziPBgPaNRU1x7dsyAO6cn7rrRc7zRUWIORRyGT4f003QY9755Zg0O0OcVnJmPveANwAM7RxMA6xERyGXIW4Af1bchZt3qJSghDYcuiz4MeuShU20EH7v/CzsO/nbSfx9ggghxGVYMIjpKOxhLNARWNJvQiGXYXx0c67nCazvRJNRFnx+nDFzwhRLMnN4y5TtPOM44+mSXvWXlpYiIyMDGRkZAIDs7GxkZGTg8mXNBU1CQgLGjRuni+ep9Sq1po0Mz2LXpFQxKp/kwHy83LniIsMaifq8CrkMD7UP4ooN8XPclSKEiGV4t6Zo05ivFxAAbDp63WG+gIl9eP93/vI8MgBLnu0u3c4QhzQ1pp2g+ISNxyXaE9emUjNk5d8R/Ljfj12XYG+IZjUs/3triywTQghxBHmcpeF546zBEccCHYFF/SYA9A7nrKLiRJkT5pYzA4RlTjgLSzJzgjmz3YrKHacEnKRnpocPH0ZkZCQiIyMBaJrbRUZGYubMmQCAGzdu6A6aAF+tV6kJaQafkuU8HwxXc/QyX6mAYo7URaG6N/c3HQQgt9h+TnoIsaU/pvbnjmUAFif9I93OEKeirFIj82YZd/zrA1s7bINNIh2FXIbHu/H3uNl45BpN7kogNavArEW2p2/cpt+HBJIz8wX9PijLhBBCDDtw/iZXHG/DeGtwxLFAR2BJvwnA9TInLClnBjhn5oRJFmTmaEvA8XCUEnCS9jQZMGAAGKv7HV+7dq3Bx5iq9Sqlvi0D4KEAlCrTsc7ymXA1KjXDnnN8Jx5SjI2V3OWbiNl64gbmPxlBA3TE5Xm4ydGnRSPuuuhf7L+AqTFt6bNDTBJSOkYuA6bGtJVwb4gj+/iJCGzkXFWvndydHissQ4UYl3zBvMVMVWpN9jj1kRNX4m/8WXyUZUIIIYap1AxJp/lWyvM2jLcGRxwLdAgWlmrjzZzgjbN3lpQzA4RnTkSJ2A/ZViwpz6UtAbd4l+k+zo5SAo7OTmtQyGUY3jWEK9aXcwaN2JfUrAJUcNYTj2op/gW0jDPX8W6VmkrAEfKvb1/kX4FK5RMJD6GlY+IHUJYJqZuHmxzdmhlvJl3d0r2ZzrMizU5YkuZ/4ALfYhrCR1mlxoV8/iw+yjIhhBDD0rILUXy3iis22Jev1DxxXJYMaAP8mRO8cfbO8nJmzpc5YYol5bkA5ysBR5MmBjTh/LI5wlniidiXFM7BVC83OfpKMFMsZPaZSsARouHhJkfrxvW549elUGNfYpyQ0jEyUJYJMW3GkA7csWpGpQTFpFIzHL9aZPbjr90qF29nCMauSuWObdLQg7JMCCGkDjmcZZL8vN0NrpQnzsXSAe0gzsfzxtk7S8uZaTMnuJ7LQTInTLIwm8nZSsDRGaoBN4r4fnn7/7lJqwQdEOM8Cgxo31iSVcXaEnA86M+LkHtmjejEHetUdUWJJIQ0gH8s0vDqI0Kq69syAO4Czqwp20Q8admFqKgy/728UewYF26OQFml5i6nCQAv3B8u4d4QQohj4+1TMqhDEJ2rugILB7R5V/cfuugYTbpNsvT9gvNlTphiaTaTs5WAo0kTA5o24ss0Ka9UW1QKgNiGjxdfel1kWCNJnl9ICbgcuognRCe6dSD3l5aaAcnnKVOLGCa0AfzHoyMk3BviLBRyGV7t34o7nrJNxMO7Ercup2+U0ASWSIRkmQDAxH4tJdoTQghxfH71+PqURLWivlyuwNIB7XzOSbi1KRed4rzI0vcLcL7MCVMucpZXDWpouN+Ls5WAo0kTA6IFfOHkFFM6v6M5yllWrbicr2G7OUL86nHFbT1xwym+rAgRg0Iuw2PdQ7njE7eclHBviCNbl3yRO7ZV43pUOoZwmxrTTtBCsy/2X6DveRHwrsStS2mFihZCiUBolgk1gCeEEOOK7vCtxuaNI46troFq3jjexxfdqXSK8yJLy5kBzpc5YYxKzfBD2mWTcSG+XnWWA3S2EnB0lmpA35YB8HTju+Tlnakl9kGlZviLc/W5lNmt1AyeEPPMfZx/xf+Fm3egrFJLuDfEUf1+7Bp3bOLwzhLuCXE2CrkMrw/kzzZRqhh9z4uAdyWuMbQQynIJG48JiqcG8IQQYtzlQr5V32J8DxL716N5I5PjVHKZJs6Q3uH+8PVy43oupzgvEqE8l7NlThiTll2InBLTY9xjet1XdzlAJysBR5MmBijkMgxo25gr9vAlagbvSNKyC1GmVHHFRrWULsVVSDP4AxduSrYfhDgaoQ3hEzYel3BviCNSqRlOXCvhipUDiG5D5Q6IMEKzTeZtPyPZvrgKMVbYOsMKQVtSqRk2HrnOHU9ZJoQQYpxKzbDxKN9CH8o0cQ3pl26Z7HurZpo4QxRyGWI6Gm56XpMznBeJUZ7L2TInjMm7zVdirEVg3ZVznK0EHJ2p1sHbg2/2lZrBOxbemtf1PBToK2BiQyghzWKvFzlHbURCxCKkIfzmjOt0jCZ6kjPzuRcddW7qQ001iWBCs02OXS2hrDgLibHCllbpWmZx0jnuYytAWSaEEGJKWnYhbt/lW/DpX5++w1zBztM5XHHGBr/7teFbIO4MmRNilOdytswJY8R4v5ytBBxNmtSBmsE7p/zbfLOej3QOlnSgTCGX4aH2QVyx5ZV8J0qEuAohDeGr1FT6huhbspu/8faICP4eOoRUJzTbZOxqYc2zib6UC3ylV42hVbrmU6kZluy5wB0f0cyHskwIIcQE3gWfABDsyzd+RRyXSs2wKYMv88jYwLUrZU6IUZ7L2TInjBLh/eod7g8/b3euzfBmttgSna3WgZrBO6dbnBfETXz4Zkct0bMFXyYLZTMRok+TVsw36QhQiTtyj0rNcPhiEXf8+Ohw6XaGODWFXIbHu/NPuh3MvkXZJmZSqRmSTudavB3KNDGf0CyTt2I7SLYvhBDiLAo5B2t9vNzqbMpMnEdadiEKyypNxgXU9zD+98C7qscJku1FKc/lZJkTxojxfinkMoyPbs61ncD69j8xR5MmdaBm8M4p62YpV5zMCl8QgQ35DhDlldQMnpCaxgkYzHb0kxcinuTMfPAOS7dqXI9WQltg7ty56NWrFxo2bIigoCCMGjUK586dM/qYtWvXQiaT6d28vKRfxCCVuY9HCIqnbBPzpGUXovhulcXbESNbxRUJzTLxUMgkLYFLCCHOgncy/7HIplRO1gXwrsof2S3U6N9DHmcGE2+cPeOd8DAW52yZE8aIUs4MQO9wzvM8Bzhs0WhAHagZvPNRqRn+zuRbcc57ULREsIBsFlopT4i+vi0DoOD8ks24UkzZWgSAsNJcicM7S7gnzm/fvn2Ii4tDamoqkpKSUFlZicGDB6OsrMzo43x8fHDjxg3d7dKlS1baY/F5uMnRp0Uj7njKNjGPkPIlxuw8k0ffFWYQmmXyyoOtaHCPEEI48JaNvM+/7qbMxHnwDmg/3MF4o3feBu/O0Ai+R/NGMHXKIZdp4uribJkTRolQngtwrok5mjQxgrcZ/J6zdJHlCNKyC1FawTcYwfuFZIne4f7w4uwGT83gCdGnkMvQqakPVyz1NSHAv6W5LhVxxcplQHQb/jKdpLZt27ZhwoQJ6NSpEyIiIrB27VpcvnwZ6enpRh8nk8kQHBysuzVpYvzCz959+6KwZtfv/HJMoj1xXrzlS0wpKnf8sgrWplIzrNjHn2UilwFTY9pKuEeEEOI8eDNNqLykixBpQJu3wfvVIsdvQ5B+6RZMDdWqmSbOGGfKnDBGjPJcgHNNzNGkiRG8zeCVKhqQcwT21khNIZehP+egHDWDJ6S2EV2bcsd+k3pRuh0hDiE1q8DkSbNWj/v8aCW0yIqLiwEA/v7Ga26XlpaiefPmCAsLw8iRI3Hq1Cmj8RUVFSgpKdG72ROh2Sa/ZlynhTgCiTlYRH0KhUnNKkClgOSo+AGt6dhKCCGceMtG8makEMcm1oA2b8WT35zgnHTn6RyuOFNltZwpc8IYscpz8U7M8cbZEk2aGCGkGXxKFtVBtnf22EiNmsETYr7x0S24YykjkHyTnM0dO+UhWgktJrVajWnTpqFfv37o3Lnusmft2rXDmjVrsHnzZnz33XdQq9WIjo7G1atX63zM3Llz4evrq7uFhYVJ8RIsIiTbRM2A5PN0TimEmINFjrDizZ7M33aGO5ayTAghhJ9KzfDHiRtcsf71KdPEFYjRnwPQVDzxr2+6HH1BmdKhM3BVaoZNGde4Yk29Z86UOWGUSNlMQZyTIbxxtkSTJkb0bRkADwVfLI3F2T97bKRGzeAJMZ+HmxytGvPV8KWMQNemUjPsPJvHFUulucQXFxeHkydP4scffzQaFxUVhXHjxqFbt27o378/Nm7ciMaNG+PLL7+s8zEJCQkoLi7W3a5cuSL27ltMaLZJ4paTEu6N8xEz04RKnPBTVqmRcZU/s4uyTAghhF9qVgHKOVP5rFElg9ieGP05AE3Fk5ERoVzP6cgZuGnZhSgsqzQZF1Dfw+SiaWfKnDBGrGwm3jJlhy7a/6QcTZoYoZDLMLxrCFdsTrFjp2G5AntspEbN4AmxzJDOfMdogD5Driw1qwAqzhIynUN9aGBPRPHx8diyZQv27NmDZs2aCXqsu7s7IiMjkZmZWWeMp6cnfHx89G72SEi2yYWbd6ghvABiZppQiRN+Y1elcsfKQFkmhBAiRArnYq8GntarkkFsS6z+HADQrBHfmJcjZ06YKrmlNbJbqMlrP2fKnDDmYn4ZV5ypzJx8zio/a1Mu2n01EJo0MSHEj+9gsvXEDbv/Zbu6y4V8BwBrrjLsHe4PTze+wblrtxx3lp8QqQgpo3jIgdOLiWWSOWtCA8AIzpVXxDjGGOLj47Fp0ybs3r0b4eHhgrehUqlw4sQJhITwT47aKw83OZpwZpcCwNjV/APSru7qrTuibcvRVwhai7JKjYMXTQ/KaD3e3XpZ3IQQ4gwYZ52c+9sE0PHVRfBOAvDEuULmBG9/joc7NDEdxPsRc+CPokrN8EPaZZNxIb5eJidqeUvJFd2ptPsScDRpYoKM86/+bhWVT7JnKjXDxqN89QytucpQIZehazM/rtgblM1ESC19WwbAnfOb7OiVIprcdlHbTvLVhAaA8dHCB/dJbXFxcfjuu++wfv16NGzYEDk5OcjJyUF5+b0FAOPGjUNCQoLu37Nnz8aOHTuQlZWFI0eO4Pnnn8elS5fw4osv2uIliG7S/fx/Wwezb1G2CQeVmmHzseuibc/RVwhayzs/HxMUP/fxrhLtCSGEOCc/b9M9JwCgx3385T+JYxOrSTfgIpkTIvXnAFyjEXxadiFySkxniIzpdZ/Jidre4f7w9XLjel57LwFnlUmTZcuWoUWLFvDy8kKfPn2QlpZWZ+zatWshk8n0bl5e/CWMxBbViq9RN0DN4O1ZWnYhbt9VccVau5Eabzrt8avFNOBLSA0KuYxvdQiAKjU1WHZFyio1LtzkW4neqnE9eLjRehIxrFixAsXFxRgwYABCQkJ0tw0bNuhiLl++jBs37k1o3bp1C5MnT0aHDh0wdOhQlJSUIDk5GR07drTFSxDdhH7CJuQo28Q03nrVDT35mhQ6Qm1lW9M0VuWfqOoT3oiOq4QQIhBvFqV/ffsd1HbkcUC7JOIkgCtkTojWnwOu0QieN5OpRaDpakwKuQwxHfnGaOz9PZP8DHbDhg2YPn06Zs2ahSNHjiAiIgKxsbHIy6u7IauPjw9u3Lihu126dEnq3awTNYN3DjkCZnyt3UiNt7wQZTMRYtjYqBbcsZ/v/ke6HSF2aV3yRe7Y2E7B0u2Ii2GMGbxNmDBBF7N3716sXbtW9+9PP/0Uly5dQkVFBXJycvDHH38gMjLS+jsvEaEN4SnbxDTe87vuJpqiajlCbWVbS87M5x63AYBvJ/H387GFAwcOYMSIEQgNDYVMJsOvv/6q9/MJEybUGsgbMmSIXkxhYSGee+45+Pj4wM/PD5MmTUJpaalezPHjx/HAAw/Ay8sLYWFhmDdvXq19+emnn9C+fXt4eXmhS5cu2Lp1q+ivlxBi/+y1SoYQjj4OaI92n83liuOZBHCFzAneElE8cbxlyq4W2XfWhDFiZjIBQL82jbni7L0EnOSTJosWLcLkyZMxceJEdOzYEV988QXq1auHNWvW1PkYmUyG4OBg3a1JE74ZKilQM3jnUMjZiMjHy/qN1DQTc3xT+NTImpDa+rYMAG8p3yNUosvl/H6c76ITAPq14ju5I8RcQhrCA0DCxuMS7Ylz4D2/u8+fr0ehI9RWtrX3fz/FHesI2Xt37txBREQEli1bVmfMkCFD9AbyfvjhB72fP/fcczh16hSSkpKwZcsW7N+/Hy+99JLu5yUlJRg8eDCaN2+O9PR0zJ8/H4mJiVi5cqUuJjk5Gc888wwmTZqEo0ePYtSoURg1ahROnjwp/osmhNg1e66SwcvRxwHtjSbLk++ahmcSwBUyJ3o0b2RyjEAu08SZEuzDNwHzW8Z1xx1rEDOTCc5TAk7Ss1ilUon09HQMGjTo3hPK5Rg0aBBSUlLqfFxpaSmaN2+OsLAwjBw5EqdO1X1yXlFRgZKSEr2b2KgZvOPjbe7+WKT1G1Uq5DJENPPliqVG1oTUppDL0LO5H1esSg3K2HIhKjXDqWt85wVuchn6CijJSYg5hGabbDxyjc4tjeAdLOrWzI+7PjxveQJXpKxSI/NmGXd84vDOEu6NOGJiYvDBBx/gscceqzPG09NTbyCvUaN7n+EzZ85g27ZtWLVqFfr06YP7778fS5YswY8//ojr1zVlzL7//nsolUqsWbMGnTp1wpgxYzBlyhQsWrRIt53FixdjyJAhmDFjBjp06IA5c+age/fuWLp0qXQvnhBil+y5SgYPa4wDAtYZC7QXvOVIA+p7cC0CdoXMifRLt0xWA1IzTZwpvcP94V/f9HlkQZnSYRffiFnODIDTlICTdNIkPz8fKpWq1gxxkyZNkJOTY/Ax7dq1w5o1a7B582Z89913UKvViI6OxtWrVw3Gz507F76+vrpbWFiY6K+DmsE7Pt4Z8maN+CbIxNaU83lPXi+hwRNCDHj9obbcsZSx5TpSswqg4jxkPtS+sdUnzYlrEpJtwgAsTqKygnUJ4lz5F+znjfHRzbliA+24PrytjV3F32fHTQ5Et+ErQWvv9u7di6CgILRr1w6vvvoqCgruXe+lpKTAz88PPXv21N03aNAgyOVyHDx4UBfz4IMPwsPj3iRfbGwszp07h1u3buliqg8wamOMDTASQpxT/m37rZLBwxrjgIB1xgLtBe+CjpHdQrmuZ1whc4L3PeOJU8hlGBkRyrU9e29sXhcxy5kBzlMCzu7ypaOiojBu3Dh069YN/fv3x8aNG9G4cWN8+eWXBuMTEhJQXFysu125ckX8fRKw8pQG4+xT+iW+2d5bNqoJ2rQR3wqR8kq1w85cEyKl6NaB3F9olLHlOpIv5HPHjo8S1qSbEHN5uMnRrZkPd/zSvZkOe8EqOQGlBHqHc57P09ypQcoqNQ5eNL0aU+u1/q2dYiJ6yJAh+Oabb7Br1y588skn2LdvHx555BGoVJrSOTk5OQgKCtJ7jJubG/z9/XWDgzk5OQYHD7U/MxZT1wAj4FqrrAlxJbxjElGtApziOAsIHwcErDMWaC94B6pjOvL1Z3SFzAmxe3TwLrB21JJmYpYzA5ynBJykkyaBgYFQKBTIzdVvWJSbm4vgYL4Ps7u7OyIjI5GZmWnw556envDx8dG7ia1vywC4cX4XXbvlmLOKzkylZvjrPN/Ama3OOXibwQOOO3NN+Fy7dg3PP/88AgIC4O3tjS5duuDw4cO23i27p5DL0IOz5M2xq8U0AOkieE/yPRRUmotY14whHbhj1YyyTeoipJRAPmf/E944VyMky0QGYGoMfwaoPRszZgweffRRdOnSBaNGjcKWLVtw6NAh7N2719a75lKrrAlxJTLOMYnWQQ2k3REzWWMcELDOWKC9EHtA2xUyJ8Tu0cFb8p83zt6IWc4McJ4ScG5SbtzDwwM9evTArl27MGrUKACAWq3Grl27EB8fz7UNlUqFEydOYOjQoRLuqXEKuQyRzRvhEMfqqht21gxepWbYfyYPn2w/jcy8O6iC5kLG002Olo3r4/8Gt0f/ds5dkiQtuxBlSr5GalEtbVNGoG/LAHi6yVBRZfqIbW8X88oqNb76KxPfJmcj93YVGACFDPDxdkdsp2DMGtEJ3h4KW++mQ7h16xb69euHgQMH4s8//0Tjxo1x/vx5vdrZpG69w/25jtNKFUPqhQL0c5KyIcQwlZohnXNVdEQzX6f+HiT2p2/LALjLgUo1X/zSvZmYGtOW/k5rELSKkPOimMpz1SY0y+SxSL7yII6oZcuWCAwMRGZmJh5++GEEBwcjLy9PL6aqqgqFhYW6wcHg4GCDg4fanxmLMTbAmJCQgOnTp+v+XVJSQhMnhDgBHy++Hly8cdbmLOOANWnHPb5LuYhbd6pQz8MNg6003iFkQJu3Uo41MieK71Ri3KpknMktg1ymOf9d+mwPNPCSdCgagPg9Ooo4M8B44wwpV6ow87fj2HEyFxVVagQ08MRzfZrjxQdawsNN2kJRYpYzA4SVgHt3WEezzhuVVWqs/vsCNh29jkqVGtEtA/DucHE/j5L/pU6fPh3jx49Hz5490bt3b3z22WcoKyvDxIkTAQDjxo1D06ZNMXfuXADA7Nmz0bdvX7Ru3RpFRUWYP38+Ll26hBdffFHqXTWKdzDu+L8rmO3hQuH3Y9cx9YejqHk9zqDpv3L6xm28sO4QZACWPRuJoV35ZpodDW8jtXoeCputNFbIZRjQtjG2n84zGXv40i1MtsI+8Zj9+ymsOXCx1v0qBty6U4kfD13Bj4euYGBbf3z9QpT1d9DBfPLJJwgLC8PXX3+tuy88nEoG8YpuFYhley5wxa5LyaZJEyeXnJlf6/uvLr3ssB40cW4KuQyv9m+FzzmPWdpsk+mx7STeM8eSls3ZS5DBaRpS2oKQLBMA+Hh0hER7YntXr15FQUEBQkJCAGhKyhQVFSE9PR09evQAAOzevRtqtRp9+vTRxfz3v/9FZWUl3N01g5xJSUlo166dbmFMVFQUdu3ahWnTpumeKykpCVFRdZ8/e3p6wtOTJvkIcTZHL/NNUheXm24MbivOMg6oNXfraXy5P1vvvrtV98Y7BnVojFXje0v2/GIPaAPSZ048MG83rhTqZxHs/ScfnRO3o3NoA2yZ0t+s7fK6mF/GFcdb+ow3c4I3rqZJaw9h11n98cBrRXcxb/s5zNt+Di8/GI6EoR3N2jYPscuZaUvAFZYZP05pS8AJaYsBAB/+cRpf/aX/mczOv4Pv064gpmMQvhrXS9D26iJ5T5Onn34aCxYswMyZM9GtWzdkZGRg27Ztupqtly9fxo0bN3Txt27dwuTJk9GhQwcMHToUJSUlSE5ORseO0v1x8OAtn2QvzeAnrT2E1w1MmBjCALy2/ijmbj0t9W7ZRCFnZsbQzsE2nezy9uCbw9xzNs8uSgs9MG+3wQkTQ/b8U4jOM/+UdoecwG+//YaePXviySefRFBQECIjI/HVV18ZfQzVs76nb8sAKDg/wrvP3rSLzxGRzpLd/OWM+rVqLOGeEGLY1Jh2gsbnv9h/gY5b1ajUDOuSL3HF5pdVOE1DSmsTmmXSJ7yR5KshxVRaWoqMjAxkZGQAALKzs5GRkYHLly+jtLQUM2bMQGpqKi5evIhdu3Zh5MiRaN26NWJjYwEAHTp0wJAhQzB58mSkpaXhwIEDiI+Px5gxYxAaqlmQ9uyzz8LDwwOTJk3CqVOnsGHDBixevFgvS2Tq1KnYtm0bFi5ciLNnzyIxMRGHDx/mXpVNCHEOKjXDnnN8fXLtYJ1unZxlHBAwPGFS084zN/Ho0r8k2wexB7QBaTMnOrz3Z60Jk+pOXi9Frw+SBG+Xl0rN8EPaZZNxIb5e6M25eC6I873ljauu//zdtSZMavpyf7a0Y7YilzOTsgTc5G8O1ZowqS7pdB4mf3NI0DbrYpUz2vj4eFy6dAkVFRU4ePCgbtUNAOzduxdr167V/fvTTz/Vxebk5OCPP/5AZGSkNXbTqL4tA+DBORpn62bwIz7fb/IDZ8iX+7Ox9fgN04EOhndmPEpAXxEp8DaD15YWsqXus7cb/RI0pFSpRvc5OyTaI+eQlZWFFStWoE2bNti+fTteffVVTJkyBevWravzMVTP+h6FXIZOTflq2Vapbf85ItJRqRkOXyriilXIQf1MiE0o5DK8PrAVd7w9fP/bk7TsQhRxrrINaujlNA0prS1h4zFB8d9O6ivRnkjj6NGjiIyM1F1vTp8+HZGRkZg5cyYUCgWOHz+ORx99FG3btsWkSZPQo0cP/PXXX3oZHt9//z3at2+Phx9+GEOHDsX999+PlStX6n7u6+uLHTt2IDs7Gz169MCbb76JmTNn4qWXXtLFREdHY/369Vi5ciUiIiLw888/49dff0Xnzp2t92YQQmwuNasAFVV8udK2Ki3OyxnGAZVVapMTJlrHr5Zgc8Y1aXZE5AFtQLrMiQmrk1HOUX/2ZqkS7/92StC2eaVlFyKnxPTi6TG97uNfOM0ZdugiX09Nrfd/P4FLBXxja1/uz4aS8/gglNjlzABpSsBtybiGJI4KPUmn81DO2abBGMdZBmRjCrkMEc18uWIPcTaelcKktQdx4vptsx//2vojTreK0VEukoU0g7flxNz9c5NQeKfKrMcWllVi4tdpIu+R81Cr1ejevTs++ugjREZG4qWXXsLkyZPxxRdf1PmYhIQEFBcX625Xrlyx4h7bnxFdm3LH2nqCm0gnNavAZN1fre5hfnZRUpO4JqHZJvO2n5FsXxwNbwkKv3rumhIBTtKQ0ppUaoaNR65zxztalgkAPPDAA2CM1bqtXbsW3t7e2L59O/Ly8qBUKnHx4kWsXLlSt1Jay9/fH+vXr8ft27dRXFyMNWvWoEED/QbNXbt2xV9//YW7d+/i6tWrePvtt2vty5NPPolz586hoqICJ0+etKta/oQQ60jhXBzh5SanRT9WILQ85Zv/OybJeJoUA9pSZE6UK1XYe54/O/Xr5IuSTALwniO2COQb1Af4ewuvTbnI/TegrFLj6wOmM2KqG7ta2N8kL7HLmQHil4BTqRmmbcjgfv6PRMjMcayzWhtryjlLduzfvibWtiXjGnadzbd4Ow8v3Gv5ztiRW5zphLxxUtE2heVxvcg2pSNe+DoVV4ste5/2nLuJ34/xX4C7kpCQkFopyB06dMDly3V/kXp6esLHx0fv5srGR7fgjrXlBDeRVvIF/u/CKQ+1lXBPCDFOaLbJsaslkq0wczS8JSgmRLWAQi4T1JDS2RYQmWtx0jkhi1YdLsuEEELsDeM86g5o35gW/UhMaHlKQFPNIPm85WNyNfEOVAsZ0JYic+Lx5X/zP/+/3vlFWEYrDynKmfG+t0V3KpHGOc7wzs/CX/vB7FuiXwtIUc4MEL8EXHJmPqoEnJheLLjDH1wHmjQRwJ7LJ6nUDK//mCHKti4W3JEurdAGZLzZdjY+51DIZXiofRBXbHml5WlmQm3JuIbd58T5u57yw1EakDCgX79+OHfunN59//zzD5o3b26jPXI8Hm5ytGps3xPcRHq8J6puciC6jX2XNiDOT2i2iVQrzBwO5+G7VwvNxZ22IaUp2oaUrk6lZliy5wJ3fEQzH4fLMiGEEHtzo4hvkC8yrJHEe0LMGdAGgMQtJ0XeE6BH80Yme9jIZZo4XmJnTiir1DiTU8r9/Fq/SrFYRYJyZr3D/eHrxdeHmKdHh0rNsCnDvMXE65L5SsbxkqScGcQvASekZykAtAjgzySqC53ZCiCkfFJKlvizy8Z8tkPYSjBT3tiQ4TSDiTmcZRb8vE1fSEutZwu+FNv9/1i3ibWYk3KA5rsp/vt00bbnLN544w2kpqbio48+QmZmpq62dVxcnK13zaEM6RzCFUf9AZyTSs1w9BLfqrBIKs1F7IBCLsPj3fkaJQLSrDBzREJLVSjkMjzWja+EI29ZB2cmNMvkrdgOku0LIYS4ApWaYdupXK7YYs6eXsQ8lgxoX7h5R/TztPRLt0yWHlYzTRwvsTMnhPZA01IziJ6dI0U5M4VchpiOTUwHgq/0f3JmvtljuGJXbpGinBkgbgk4lZrh8MUiQc//n6EdTQeZQJMmAmiawfPFWnO+QaVmWLKXfyUYDzUDFicJm8WzRyo1w84zppsEAcJS86QS2JBvH8or1VYd7BV7Ug4A/jyVS4M+NfTq1QubNm3CDz/8gM6dO2POnDn47LPP8Nxzz9l61xyKo/QHItJIzSrgTtvtJSC9mBApzX08QlC8FKUMHI05pRceas93sRtY3/bnhLYkNMvEQyGj2vqEEGKhtOxC3FHyXR/Tmh9pWTKgDYifCbDzdA5XnJBFH2JmTqjUDJuPmj+Q/7nADAJTJClnBiCKc5yBp0eH0KyJ6k7fuC3qImopypkBELUEXGpWAYSMHj7cPgjevAP4RtCkiQAKuQzDu/KtYM4ptt4Ktbj1hyXZ7tK9mQ6fbZKWXYjiu3xNy4N9+cqvSYm33jZgvcFeKSbltKjESG3Dhw/HiRMncPfuXZw5cwaTJ0+29S45nL4tA+DG+wVNJVicjpB+Jv1aNZZwTwjh5+EmR58W/CUVNh2lvhtmlV7gHWRy8cEooVkmrzzYirL2CCHEQjkl/GNIUS2pvKyULBnQBsTNBNBkvfCVzxcyCSBm5oSQRWuGHLlSJOp5rRTlzADxenSo1AyHLxUJeu7qqtQiV8yQoJwZIG4JOCHX+D5eblg9oRd3vDE0aSJQiB9fOtLWEzescjGrrFJj20m+TAqhnCHbhPfEw8/bXVBDI6n0DveHJ+dor7UGexcnnTMdZCYqMUKkoJDLEMl5AkR9TZzPtVt8JRlpZTSxN9++yN9Em8Hxz9EsZU7pBd6LN944Z6RSMywTsFhGLgOmxrSVcI8IIcQ15N/m++7xdpfTOayELB3QBsTNBEjLLkRhmelybAH1PQSPaYmVOSFkQNsQlRqiTgJIUc4MEK9HR2pWgcXVicRcRC1FOTNA3BJwQvoNrniuB3esKTRpIpCMc+nZ3SrrlE8au0rYSv0wX9NpYtUt3+fY2Sa8Jx4PdwiyixVyCrkMXZv5ccVaY7BXaHkGAAhpKKw3DJUYIVLgPWGkvibO5+otvgaaEc187eK4T4iWh5scrQLrc8d/sf+CQ5+jWcqcUgK8ZbdcuTxXcmY+VAL+rOIHtKZjKSGEiODwRb5rkgfbNqbjroTEGNAWMxOAt+TWyG6hgv8uxMqcEDKgXRcx+0LzvmdCe9iJ1aPD0kkmgH+hIA+pypmJVQJOSM9SsRdG0qSJQFEC3nypyycpq9Q4eFHYzOjetwdhysBW3PFVavGbMlnTLc4vgSYCymJJzZ4Ge4WWZ+jWzAcp/x0MXy/+2oG/ZlCJESI+6mvimlRqhozLRVyxoX62L8lISE2Jj3bijnX5SV8qzyUJISVJKMuEEELEoVIz7OMcd/F2t7xOP6mbGAPagHiTALwD1TEdgwVvm6f3hqk4IQPaxog5JGTvPTrEmGS6XiTepIlU5czEKgEnpPzbwPbiLoinSROB7KlWvtAsk8VjukEhl2FqTDtB14KJW04K2zE7IuN8obxx1mAvg73mZJn88tr9AIBD7w7mfoyaOfbEHLFP9nSsJtYj5ISqaSOaNCH2J7p1IBQCzknmbT8j3c7Yud1nc7niqDwXP5WaIe1iEXc8ZZkQQog40rILcbeSr2x1qJ/9LPh0RmIMaAPiTQJINaANiJNpYmk/Ey1R+0LbcY8OsSaZxKw8I1U5M0CcEnBCJjLH9W3BHcuDJk0Espda+UKzTEJ8PDGyW1MAmtfwuoBskws37zhs34kcztlXP29hJaWkJGSwV8yUvJqEZplMGXjvwlloQ1tHnpgj9slejtXEuqgJPHF0CrkMcQP4z9GOXS1x2HM0S5jbFJXKcxknpI+dDJRlQgghYhHSBJ7OYaUj1oA2APiKNMYk5YA2b4+Oq0bG1cTKzBGzL7Q99+gQa5JJzIxzqcqZAeJMzPFOZErRs5QmTcxgD+WThGaZ7HvrIb1/C802Sdh4XNDz2QOVmmHnmTyuWMFpeRJSyGXodp8fV6yYKXnVCc0yMVSeQUhDW0eemCP2yx6O1cS6bHlCRYhYhJ6jjV0t7JzQGZjdFJXKc9VJ6LlfTEf76AdICCHOgJrA2wexBrQBoKicb7DYFCkHtIM5y9T/ZqSkuliZOWL2hb6YX8YVZ4seHWJNMgHilYCTrJwZLC8BJ2QiU4qepTRpYgZbl08SmmXSJ7wRPNz0f9VCs002O2DfibTsQhTfreKKDfa1rzItzRrV44qTaoW80CwTQ+UZPNzkaN2Yv6GtI07MEftm62M1sS5bn1ARIhaFXIbHu4dyxx/MvuVyCw/MbYrKW1Zh1xm+0l/OROi53/iocMn2hRBCXA01gbcPYg5opwvsP1wXKQe0e4f7w7++6YyYgjKlwckRMTNzAHEmAVRqhh/SLpuMC/H14l5kqSVGjw6xJpkAEfvASFTODODPNEmp47MnZCKzl8DfJw+aNDGDrcsnJWw8Jij+20mGV/xPjWnHvY0qteOtxOZNcfXzdhd8sJQab619KVbIq9QMy/ZalmWiNWsEf0NbR5yYI/bN1sdqYl22PqEiRExzH48QFO9q2Sa8AwMPd9C/sOVdUehq5yRCs0woW48QQsSjUjPsPstXIYOawEtLzGvC42ItcJVwQFshl2FkBN9CHUOZE2Jm5gDiTAKkZRcip8T0Ipkxve4zawLSkh4dYk8yiVUCTqpyZgB/CbidZ/IMfl5sXX6bJk3MYMvySSo1w8Yj17njDWWZaCnkMsR2DOLe1jepF7lj7UEh52rCQR3sr7yALVfIJ2fmQyXgy8pYE9Do1oHcBxlHnJgj9s0eSt0R67H1CRUhYhLaG8zlsk3MHECwdEWlsxKaZfLKg63s7tyZEEIcVWpWATh7wFMTeIkxJt4MgFjlpqQc0Ab4q5wYypwQMzMHEGcSgDcbuUUg3+uuyZIeHWJPMh29Is4EjFTlzAD+EnBF5Yb7wNi6/DZNmpjJVuWThF7U1JVlojUumj+1fs9ZwzN/9oq3dh7vTLE1CVkhf0jki/r3fz/FHWssywTQpi/yT8xRiSQiNluXuiPWY+sTKkLEJqQ3GAC884uwTGRHZu4AgkIuw2PdmnI91pza4I5IpWZYsU+cDGNCCCHC0cIf+3G3UsUVx7tuQIxyU7wD1eYMaAOW9ZzgzczhHXgWYxJAynJmgGXvl9iTTHvP3rR4DEPKcmaAZX1g7KH8Nk2amMkW5ZOEXtRENPOpM8tEq2/LALhz/hU4WrPkumoImhtnTQq5DJHN+VaYijnYq6xSI/Mm3ywzYDzLREvIxNz2UzncsYTwsGWpO2I99nBCRYjYhGabbDrqOiWlLLkgfqg9Xy3qwPrmXUw7GiErnAG+cz9CCCH8eAeeaeGPtFRqhr3/8C3ibBPUgCtOjNOyHs0bmZykkcs0ceawJHOiXMnXQ7hFIF+v27/PF1h+LithOTPAsh4dvJ91BedplhjZTFKXM7OkD4w9lN+mSRMz2aJ8ktCLmrdiO5iMUchlteo9G+NImQDpl/hWHd/iPOhZG+8srpiDve/8zL9KlXeloZCJuQs377hWeREiOWoG7xrs4YSKECkIyTZhABYn/SPdztgTSy6Iea/3XGReYP62M9yxlGVCCCHi4x14Htje/sqKO5PUrAJUcF5QtOWcNBGj3FT6pVsmJ1/UTBNnDt7MicuFd/T+LWSSqW9Lvuuv0ooqi8ujSl3OzJIeHSGc5fUe7hAETxML4LUszWaSupwZYH4fmBQB45xSZeFZZdJk2bJlaNGiBby8vNCnTx+kpaUZjf/pp5/Qvn17eHl5oUuXLti6das1dlMQW5RPEnJRI2QVwtioFtzbFbsUlFRUaoa/zvMdPOz1vMPag70qNcPGDP5+ObwrDYVOzK1LzuaOJcQUW5a6I9ZDZQ2IsxKabbJ0b6ZLZJtYckGcz9nzjjfOkSmr1Mi4WsIdT1kmhBAiLiEDzz3NzCSwFUcbBxRyPaGQ8w2lilFuindQ29yyoryZE5uOXtM7xxQyydTjvkbw5lxJa6jhvBBSlzOzpEdHLudr69HcHwPb8V2zWnraL3U5M8D8bCbGuUrKy00uWRae5JMmGzZswPTp0zFr1iwcOXIEERERiI2NRV5ensH45ORkPPPMM5g0aRKOHj2KUaNGYdSoUTh58qTUuyqItcsnCb2oEdKgUUgmgKPU/U/LLkSZkq8eZVRL++tpAlh/sHdx0jnuWBmErTQUMjHnSo1XifRsVeqOWBf1MyHOTEi2iZq5RraJJRd4vGW3XKE819hVqdyxQs/9CCGEmCZk4NmSQUtrc8RxQN7rCW93OZr685WAFqPclJRNugH+zImSu/pZIEImmUIb1cPQzsFcsZYuWpG6nJm5PTpUaoYtx29wPa64vBKR9/Htn8XZTBKXMwPM7wNzo+hOHZH6hnYJkWxRj+STJosWLcLkyZMxceJEdOzYEV988QXq1auHNWvWGIxfvHgxhgwZghkzZqBDhw6YM2cOunfvjqVLl0q9q4JZs3xSwkb+sklCL2qEZAI4St3/nBK+WfZ6Hgq7HUCz5mCv0H45j0WGCjoo9W0ZwJ3Rc6WQ78BICC9blLoj1kP9TOzT3Llz0atXLzRs2BBBQUEYNWoUzp0zPTlv61WG9sjDTY7WjflqQQMukm1C5bkspqxS4+BF/hWwj3dvSsdPQggRmZCB52BfvoF6e+Bo44AqNcPxq0VcsZ1DfbirglhabkrqJt0Af+YEoD8JIGSSqXe4P4L9+P5+i8oruffHEKnLmZnboyM1qwCc67ohlwEld/neB0uzmaQuZwaY1wdGyCRTsK95E4Y8JJ00USqVSE9Px6BBg+49oVyOQYMGISUlxeBjUlJS9OIBIDY2ts54W7JW+SSVmmHTEf6ySeZc1AjJBFiXYv/lkwo5Z6eHdg626wtAaw32Cu2X8/HoCEHbV8hl6HGfH1dsVn6Z8w/2EKuivibOjfqZ2Kd9+/YhLi4OqampSEpKQmVlJQYPHoyysrpXy9nDKkN7NWtEJ+5YV8g2ofJclhOSZQIAcx/vKtGeEEKI6xI68OwIHHEcMC27kDvjp1e4P/q2DLBKuSmpm3QDmjGnhl4KrljtJIDQSSaFXAbGeb3GG1cXqcuZAeb16BAyQRrVMhAyztU7lmYzSV3ODDCvD4zQSSapSDppkp+fD5VKhSZN9GfhmjRpgpycHIOPycnJERRfUVGBkpISvZu1CCmftP2U4f3nkZyZDyGtsc25qOnbMgAKztey++xNux/U5k3/4j3Y2Yq1BnuF9Mtp1bgePDibUlXXuyVfRg+t9idio74mzo36mdinbdu2YcKECejUqRMiIiKwdu1aXL58Genp6XU+xtarDO1ZdOtA7vM0AFi+z7mzTSwpVWGNi0N7JzTLpE94I7PO/QghhNRNpWZI5zwWaweeHYE1xgEBcccCeSuVAJrrCYVchmFdQrjiq2cbCGWNJt0KuQyPRzblitWOswmdZAKARpxjdLkCfheGWOM8r5Bz8U71uGu3+CbPtOWkozgr4liazSR1OTPAvD4wQieZpOLwZ79z586Fr6+v7hYWFma15xZSPunCzTtQVgmZ+rhnyW7+1YIRzXzMuqhRyGXo1NSHK7ZKbf+D2rxfTJZ8gVmDNSbmhPbLSRze2aznodX+xFaor4lzo34mjqG4uBgA4O9f9ypJc1YZ2nLxjDUp5DLEDWjFHV+lBpLP819sOBJLS1VY4+LQ3r3zM3/ZXwD4dhJ/Xx1CCCF8hCyOpWzp2sQcC8y/zTcI7u1+r+G0OdkGQlmjSTcA3OfPVwZWW2ZJ6CQTAAQ25NvHXdWyDcxhjfM83hJi6ZfvTYpeu8VXhl5bTtpa2UxSlzMDzOsDI3SSSSqSTpoEBgZCoVAgNzdX7/7c3FwEBxtuAhQcHCwoPiEhAcXFxbrblStXxNl5TkJSJNclCy9rpVIzpF0s4o5/K7aD4OfQGtGVb3YZsP9B7fRLfINotzhr69mKNSbm1iVf5I6Vy4DoNubN4lorM4sQQ6iviXOifiaOQa1WY9q0aejXrx86d6574t2cVYa2XDxjbVNj2glqs/G5gEU3jsTSUhXWuDi0Zyo1w8YM/rK/lGVCCCHSELI41pGypa0xDgiIOxZ4+CLf9d+DbRvrzi3MyTYQzApNugH+iZ3L//afNWeSyZxsA3NY4zxPaOkslZrh2NVirsdoJ0jtLZvJknJmCrkMgzoEccVqy+MKnWSSiqRnwB4eHujRowd27dqlu0+tVmPXrl2Iiooy+JioqCi9eABISkqqM97T0xM+Pj56N2sSsnr+92P8Fyhai5NMN03VcpPDohm28dEtuGN5Z/1sQaVm+ItzdaUjjJ9JPTH3XepF7tjHIs1vAmqtzCxCDKFMJ+dE/UwcQ1xcHE6ePIkff/xR9G3bevGMNSnkMrw+kD/b5PClIqfMnLO0VIU1Lg7tmZBrC8D5s0wOHDiAESNGIDQ0FDKZDL/++qvezxljmDlzJkJCQuDt7Y1Bgwbh/PnzejGFhYV47rnn4OPjAz8/P0yaNAmlpaV6McePH8cDDzwALy8vhIWFYd68ebX25aeffkL79u3h5eWFLl26YOvWraK/XkKIfVCpGQ5fKuKKVVg4zmNt1hgHBMQbC1SpGXafzeOK9Xa/1/vDnGwDoazRpBvgb9S96eg1zd+uGZNM5mQbmGPnab4FuJb1NBFWOkvTn4PvnLz6BKk1spmsVbY22M+bK66ovNKsSSapSL5saPr06fjqq6+wbt06nDlzBq+++irKysowceJEAMC4ceOQkJCgi586dSq2bduGhQsX4uzZs0hMTMThw4cRHx8v9a6apW/LACg438WT10sEXbyq1Awr9l3gjn+ofZBFM2webnK0DOT7Q75eZL+TJmnZhSjj7BgkZe07sUg5MaesUuNSIf/v0tImoFJPABFSF+pr4pyon4n9i4+Px5YtW7Bnzx40a9bMaKw5qwxtvXjG2qbGtOOOZXDOhvCWlqoIrM/5eM44R6JSMyzby39t4QpZJnfu3EFERASWLVtm8Ofz5s3D559/ji+++AIHDx5E/fr1ERsbi7t37w22PPfcczh16hSSkpKwZcsW7N+/Hy+99JLu5yUlJRg8eDCaN2+O9PR0zJ8/H4mJiVi5cqUuJjk5Gc888wwmTZqEo0ePYtSoURg1ahROnjwp3YsnhNhMalaBydXwWt3D/BwuW9qRxgFTswpQybleM9Tv3sCxNRp1W2tAm7dRd8ndKqReKDBrkkkhlyGmYxMj0feYmzmhUjNsyrjGFWvJeya0dBbvNauXm1xvgpR3Mos3zhBrla1lnB8BxmD2JJMUJD8Lfvrpp7FgwQLMnDkT3bp1Q0ZGBrZt26Yrv3D58mXcuHFDFx8dHY3169dj5cqViIiIwM8//4xff/3VaDkHW1LIZRjUni/NSM2E1ZcWcvAGgPFR4fzBdejazPHr/vPWV6znoXCIFRtSTswJqWnd3N/b4gtnqTOzCKkL9TVxTtTPxH4xxhAfH49NmzZh9+7dCA83fY5izipDV6OQyxDbke+8EwCW7nXChvCWlqrgHXdyrPEpLsmZ+eC8BgXg/FkmABATE4MPPvgAjz32WK2fMcbw2Wef4d1338XIkSPRtWtXfPPNN7h+/bouI+XMmTPYtm0bVq1ahT59+uD+++/HkiVL8OOPP+L6dc257Pfffw+lUok1a9agU6dOGDNmDKZMmYJFixbpnmvx4sUYMmQIZsyYgQ4dOmDOnDno3r07li5dapX3gRBiXUIW/kx5qK2EeyINRxoHTBFQmlk/C0D6Rt3WGtDmLZ0FAH+fv2nWJBMgfeZEWnYhCstMZwAF1PcQtKC3JoVchqGd617UVV1+aQX3779rjVJTvO+DJZkm1ipb6+vtzh1n7iSTFKyydCg+Ph6XLl1CRUUFDh48iD59+uh+tnfvXqxdu1Yv/sknn8S5c+dQUVGBkydPYujQodbYTbONi+afrBBSX3r+tjPcsWINCDVtxJdpYs91/wtL+VITh3YOdogVG1JNzAmtaf183+bcsXURMgF0+sZt5xvoITZFfU2cC/UzsW9xcXH47rvvsH79ejRs2BA5OTnIyclBefm97EZ7WmXoSIScd6qZ82WbWFqqIp/zPHHXmVzTQQ4m8bdT3LGtGtdz+iwTU7Kzs5GTk4NBgwbp7vP19UWfPn2QkpICAEhJSYGfnx969uypixk0aBDkcjkOHjyoi3nwwQfh4XFvUCM2Nhbnzp3DrVu3dDHVn0cbo30eQohz4R1EdZOb31PU1hxlHJBxrsaoOUBrjUbd1hrQ7h3uj4ZeCtOBAI5fK+Lebs0sAKkzJ3hLbo3sFmrxtSFvuanCO0rua9YQX/1JJt73IUXAJGxN1ihnBgAld/nK2R29csvsSSYpuPaZsEj6tgzg7o2RfpmvvrSySo2MqyXc+/DKg61E+WNxhrr/vLOsvLPc9kCKiTmhNa3HC9iHugiZAKpS08A1EZczHN/IPdTPxL6tWLECxcXFGDBgAEJCQnS3DRs26GLsaZWhI+nbMgCc1+gAnC/bxNLyXLzlGDZnXHeq901ZpcaF/DLu+MTh9LnLydEMJGhXRms1adJE97OcnBwEBemf27q5ucHf318vxtA2qj9HXTHanxtSUVGBkpISvRshxP6p1AzpF/kGUSMdsDSXo7lRxNdwemiXEL3fhTUadVurD5tCLsPjkU25Yss500wMLeyWOnOC9xwvpiNflogxvOWmDmUXcl+z1lzEzls2beeZPLPOWa1VzgzgL2e350ye2ZNMUqBJExEo5DL0bO7HFcubCTB2VSr388sATI0RJ2XTGer+W6Pun7WJPTGnUjMs2WObmtZCJoBSssyfMSekJmc4vpF7qJ+JfWOMGbxNmDBBF2NPqwwdiUIuw6v9+RvCO122iYXluXqH+8O/vukSAQVlSrPLadgjIdcWcpnjrmx2JXPnzoWvr6/uFhYWZutdIoRwSM7MB28Vdlr4Iy2VmmHL8RumAwEEGxiglbrclKULRYRo1qgeV1zpXb5xtIEGei5LnTlhrXJmANCI83d6nLOhOVD7mpW3bFpReaVZ56zWKmcG8Jezq1AxsyeZpECTJiJ5XUCdycQtxhv6KavUOMi58gAAHu/eVLTVB85Q9/9yId8qOkvq/lmb2BNzi5POcY85AOLWtO7bMgAefJmf3M3xCOHhDMc3cg/1MyGubGpMO0EtN77Yf8FpjmmWludSyGV4rBvfakpLV27aC6HXFqNEKFvhDIKDNStRc3P1S7Xl5ubqfhYcHIy8PP2GuFVVVSgsLNSLMbSN6s9RV4z254YkJCSguLhYd7ty5YrQl0gIsYElAkq208IfaWkaTvPFGvpalHzBrqV93AQo5NzHzDy+zJyeBq67pc6csFY5MwAIbMj3Wngbmhvqz9E73B++Xm5cjzenBJw1y5n1bRkAT5HLvlrj+EiTJiKJbh3I/WZeuHkHyqq61xYkbORvzg0Acx/vKijeFEeu+69SM2w8ypde5kiZJoB4E3NCs0zErmmtkMswvCtfGmtOsXMMVhD74cjHN3IP9TMhrk4hl+H1gfzZJs50TBNj1eVD7ZvU+TO9bdS3fOWmPRB6bfHx6AiJ9sSxhIeHIzg4GLt27dLdV1JSgoMHDyIqKgoAEBUVhaKiIqSnp+tidu/eDbVaravfHxUVhf3796Oy8t5qzqSkJLRr1w6NGjXSxVR/Hm2M9nkM8fT0hI+Pj96NEGLfVGqGwxeLuGIVctDCH4kJyVyPalk7q0TqclOWLhQR4kYR39gLb5aUofMwqTMnrFXODOB/Lbxqln8DNOf7MR35zlnNKQFnzXJmCrkMA9uJN8lhrYWRNGkiEoVchp4t/LjjEzYeN3i/Ss3wyxH+5txilk3ScuS6/2nZhbh9l2+pgH99x8k0AcSbmBOaZSJFTesQP77Uz60nbjjNylhiHxz5+EbuoX4mhAjPNpm3/Yxk+2JVYqy65H3jnGC+VaVm2Gjjawt7VlpaioyMDGRkZADQNH/PyMjA5cuXIZPJMG3aNHzwwQf47bffcOLECYwbNw6hoaEYNWoUAKBDhw4YMmQIJk+ejLS0NBw4cADx8fEYM2YMQkNDAQDPPvssPDw8MGnSJJw6dQobNmzA4sWLMX36dN1+TJ06Fdu2bcPChQtx9uxZJCYm4vDhw4iPj7f2W0IIkZCQ0lzdqZ+J5CzNXJc604R3UNvSfhOA+KWOgn1rb0/qzAlrljPrHe6PBp7inS8ZKv8GSFsCzprlzAAg8j5xtgNYb2Gk65wRW4GQTICNR64ZHAyOW39Y0HOKWTZJy5Hr/ueU8M8YGzqI2zOhE3NjV9euXS00y8RNLk1Na94mUHer1E6zMpbYB0c+vpF7qJ8JIcKzTY5dLTGa6ewodp/NNR0E46su80s5V25yxtkzW5ZkdQRHjx5FZGQkIiMjAQDTp09HZGQkZs6cCQB466238Prrr+Oll15Cr169UFpaim3btsHL697gxvfff4/27dvj4YcfxtChQ3H//fdj5cqVup/7+vpix44dyM7ORo8ePfDmm29i5syZeOmll3Qx0dHRWL9+PVauXImIiAj8/PPP+PXXX9G5s/iLlwghtvPLkavcsVMEjC8R4cTIXOctN3W1SPgEAGDdQW0hiwtN8XaXG6zwIHXmhDXLmSnkMvQT8T2r6/cs5cScNcuZAUDJXdP9U3hZa2EkTZqISEgmAEPtppzKKjW2ncwz/AADIpr5SLISTEjd/5PXS+wqE6CQ8+LWx8vN4kZGtiBkYu5g9q1agyNCL5xf699aktlb3iZQAK32J+KivibOgfqZEKIhNNvE0IIKR6JSM2zK4CvDamzVJW/ZLUcvzyV0sYxU1xb27IEHHgBjrNZt7dq1AACZTIbZs2cjJycHd+/exc6dO9G2rf75uL+/P9avX4/bt2+juLgYa9asQYMGDfRiunbtir/++gt3797F1atX8fbbb9falyeffBLnzp1DRUUFTp48iaFDh0r2ugkhtsHb4Fouk2bxIrlHjMx13hJNv2Vct/seHZres+KM/XQO9alzHEnKzAlrljMDAG8PvqwZHobKvwHSloCzZjkzgH/xNA9rLYx0rbNiiWlmTYO445fuzdQ7cA5bvE/Q870V20FQvBC8EwrllWqzag1KhfdA8VhkU4dMdY1uHWj24IhKzfC5gAtnuQyYGiPN6hZa7U9sifqaODbqZ0LIPQq5DI93D+WON7SgwpGkZReisMz0KrWA+h7Gj/UuUp5L6GIZKa8tCCHE1Smr1Mi9zbcavE1QAzqHldi3KRe5Y+saoO0d7g//+u4mH19QpjRr3Gzn6RyuODEGtRVyGSKa+Vq8HQAI9au7qouUmRPWLGcGiFfSzE1e90I/3veBd0K2OmuWMwOELZ42xpoLI2nSRGTjosO5Y9XsXrbJloxrOH/zDvdjPdzkkv6RCEnNM6fWoFR4Dyj3+fP11LA3CrkMj3Uzb3Bk9PK/BT1X/ABpskwAWu1PbIv6mjg26mdCiL65jwtr2u3I2Sa8gwIju4UaPYdxhfJcQrNMKDOPEEKklbDxGHfswPZUXlZKKjXDrjN85T6NfT8q5DKMjOAbnxE6biZWdq0QTRuJM05mbDJByswJa/foEKukWceQhnWet/KWgNt5Jk/4uJkVy5kBwhZPG2PNhZE0aSKyvi0D4C7gXf18TyaUVWrE/5gh6HnmP9FV0j+Svi0D4Mn512xPF5SXC8u44sw5ANuLj58QNjgy7PP9mLPlJDKulnA/RsosEy1a7U9sRchx+nqROKmoRDwpAo4H1M+EuAIPNzn6tOC/+HPkbBPeQYGYjsGibEesQQhbEJpl8sqDrWhVMyGESESlZth05Dp3/AOt+SuYEOFSswpQyXkqZGqAthnnRIPQHh2iZdcKIFbmhLFrMCkzJ6zdo0OsSYARRibeeEvAFZVXCs5msnY5MyGLp42x5sJImjQRmUIuw6v9+ZtyAkDbd/8UFB/i44mR3ZoKeoxQCrkMA9ryDTYdFumAYymVmmHjUb6ZeHNS/eyF0MGR83llWP33JUHPIWWWiRat9ie2opDL8FB7vguR8kqVxHtDhGKcw4BeEmdkEmJPvn1RWPPud37hX+1qT8RaQWjtlYjWplIzrNhnHyVZCSGEAMmZ+eBdrmCsVA8Rh5DSXKYGaKXKnBAru1YIMTInTP39Spk5Yc1yZoB4kwDjjVQs6h3uDz9v0yXgAOGvyxaLiMSY4LPmwkiaNJGA0KacQu176yEJt34Pb1OjPWfNSAOTQFp2IW7f5Rvg9K/vuJkmgPDBESGsdeFMfU2ILfVswXchsv+fm3ZxfCP33CjiK2U5tEsIrZomLkPogopNR81rSGprYq0gtPZKRGsTsoIWsM5iGUIIcWXv/36KO/ah9o3pmCwhlZph2ym+0lyA6QFaqXp0iJVdK4QYmROm/n6lypywRTkzwPJJgFaN68HDre6heYVchvHRzbm2FVhfWO8RWywisnRiztqTyjRpIgGFXIbXBwrLNuHVJ7yR0Q+UmHhT8+ylfFJOCf+sarCvOGmHtuLhJkerwPqSbPvTp7tZ5SRNyKz8yeslDjm4Q+xXYEO+E4rySrVdHN+IhkrNsOX4Da7YYF/HLatDiDmELKhguNdXz5HwrqAzFce7nSTOFYv2Zv62M9yxlGVCCCHSUlapkXmTr4w4AIyP4u+TS4R7eOFu7liefl9SZZrYYkBbjMwJU3+/UmVO2KKcGWD5JEBsJ9OTXr3DOScJBA7j2WIRUd+WAVBYMNx4n7+3VSeVadJEIlNj2kmy3W8nSZdhUJOjlU8q5Oyt4uPlJupB0lYSH+0k+jatUfqtOt7fQ3mlWnB9RkKM4V3hAtjH8Y1opGYVQMlZMY0W6BFXIzTbZPm+TIdbkBDIWdLBVBzvCsPNGY6XkaOsUgvqY0dZJoQQIq2xq1K5Y3kG6Yn55mw5iYsF/APxA9sHmfyOlCrTxFZZsZaMlfFkAUiVOWGLcmaAZhLAks3xlJrK41wgzhunixdpMZIQCrkMnZr6mP34Lk19RdsXHjRpIhGFXIbXB4ibbfJCvxZWyzIBhKXmXbtVLu3OcOCduX8ssqlTXBxGtw4UpelUddYq/aYlZGIup9j2f2PEefQO94cn5weIysPZj2QBDQGjWlpek5cQRyMk26RKDSSfF95k06Z45y9MxPUO94d/fdOrHAvKlA63aEPI4JwMlGVCCCFSUlapcfAi/6D2Kw+2coqxCnukrFIL7vU6rm8LkzG8PTquFgkbz7DFgDZgWeYEb2k5KTInbFHODNCM/fZs7mfWY3lLTRWW8U248cZpibUYSagRXc1fqP1E9zAR98Q0mjSR0LTB4vU28fN2w8wR4mcWGCMkNe9GsbgHanPwztzf519P4j2xDoVchs+e7iba9qw9KQdoJuZ4B67zOTOJCOGhkP9/e/ceF2WZ9gH898zAAKKAgAh4AjyAJw5WIpp5Fg+Z2mE7mJVrbblqmm4Wm3nosLq2bVlZbkfNbbdtd+2or77ksQTFVDRNTRDEA+CBAFEBmZn3D15IFJj7mXmeOTzP7/v5zKdPcs/MPaNc88x939d1SYhvHyQ0dv+pMo87aaxVohv0PKVHemXyMqBLG/HynYu+OajibJR3/pLYtYCtcUaDhImCmbVKL0aoSe7i3J19tHGQiIjIXXEj233Iaf4OiH+fEK1g8JXM7FVXLWj3iwmBt53LQqKl5dTInHBFObM6M4fa93srmvWiVgk4pQ4jyfVw/yi77meQgP5dnXswkpsmKjIaJLx5X6Iij5X13AhFHkcu0dS8A26wqFhQIlYnVHYgcWO3J7bD0FjHFwY7tPZz+qYcUPs7Mrib7XREAPjBQ5uxkvsSjW/u0reJgFO/iDWBT2gfyIVA0q2FMj7Pc89dRnWNjI7hLqbkAsLQuLZijyWzqaYryVmcA4Ald8arNBMiIpK7kT0xSdmyQdTQiRKx7xF1RLN+VMteddGCttEgYdog+VVzTF4G4UNramROuKqcGVBbBcae31zR6zC1SsApdRhJLrklhetMH+T8TDxumqjs9sR2GBbn2E7YYwOjnZ4BUEc0Na+yxrXNks0WK9buOy00Vm4gcXcfTumHdgFijbQa08LbgO+ecW5Zrmv5mbyExm05ctblG3POtnTpUkiShNmzZ7t6KprkaX2b9M5ssSK7oFRobGSQn7qTIXJjcr+4Tf5A3kK7Sym5gCD6JnnI+pXcxbnk6NYu+35BRKQHcjeyl96VoNJMCAA6yag4YjSIZ/0YDRLGJ0QKjZVTctxVC9pAbY9muVcIr9wdL7ygrUbmhKvKmQG1/wZmyGzPMKW/eKUXtUrA5Z8XO3guWvpMDjklhYHaLJPZI9XpHd7s8zr9GXXog0eSEW9no5sRPcLw3NgeCs9IXL+YEJiMYoHPlYuKWXkluFgp1h042F87mSZ1dvxxJFr6GGXfz9sA/PTiaBVmJK5da7HFTb2d9t+9ezf+9re/IT6ep0DVIqdvE/uauN7O4xdQI7hgKhpXiLSotvSU2Jd3ANiV94vHZJtsPlIsNE5kAUG07KenlAddtSNP1vg1U+V9WSUiInHcyHY/k1OihMe+fm+SrBPt7VuLbcjIyZwQXahWY0HbaJDwhoyqOb0iAzBesOwpoE7mhKvKmdWR054hxN+EhXeIZ4arUQLObLHin1kFNsdFBPoKV+iQw+RlwJQBHYXHL79P3u+kUlSNyiUlJZg0aRICAgIQFBSEqVOnoqKiotn7DB48GJIkNbg98cQTak7TKb6aORBTbxWr71fnrfsS8d5Dt6g0IzFGg4SE9oFCY125qFgko9ZheKA2F9MOLh6FDsHir621rwHH/jRWxRmJ4Wn/G1VUVGDSpEl477330Lq18jU3qZacvk3sa+J6cprAD+gsVvaPSKuW3i3vtGra2gMqzUQ5ZosVn2eLZRWLLCCIlt3ylPJcH3wvvmnCxTkiInUNXPatrPFa2sh213VAk5cBj99me01uRI8wjBPMHKmjRuaEK3t0ALVVc0b0CLM5rpWPEd88OVDWY4tmToiOA+CycmZ1jAYJ7zzYx+Y4HyOw53l57RfUKAGXlVeConLbB4Puu6WjapsVC8f1RqcQ22uY9vxOKkXVq+VJkybh0KFDSE9PxzfffIPt27fjd7/7nc37PfbYYygsLKy/LVu2TM1pOs3zt/fAzy+NRtuA5gNlYvtWyP3TGNwuY6dWTe0Ed81duahYIngKMMDXS5VdUnfx3byheO03iTbHPdy/A/Ytcm2GSR05jcbOlHpOM1ZHTJ8+HWPHjsXw4cNdPRXNY18TzyF6Acgm8ETyawWv3Xva7TeGs/JKUHLpqs1xIf4msdiuofJc1TUWFF8Uz4jR0uIcEZG7+WrvKRSX2/68qqO1jWx3XgdMG9Oj2Y2TxwZG2XVwWY3MCVf26Kjz3kO34LGBTb9fPSP88ePiUbIfN0xwM0R0HODacmZ1RvWKwMoH+6CpX+ce4S1w9GX5B5fVKAEnWqYsKlS8rJ09tj09FMPimt6cs/d3UilizQTscPjwYWzYsAG7d+/GzTffDAB48803MWbMGPzlL39BZGTTf+EtWrRAeHi4WlNzKZOXAbv+OAIVlTWY+cluZOaW4KoVCPDzRmrPcCwc1xN+JvllltQkt3zSgK6O9XCxh+iO/cSkdppvrjaxTzvckRiJ7YfP4s8bf8Lxc5dhlSSEBfhgUnInPDowxq0uyowGCUPjwrDxp7M2x165KlaCzZN9+umn2Lt3L3bv3i00vqqqClVVv37wl5eXqzU1TerfORQrtuQKjd2Re84l8Y1qT5jvE/xCwCbwRLXWPNoP3eb/j9BYK4Dl6T9jTqrzawWLEv1yNz5RrJGulspzyamb37lNC7e6DiQi0hKzxYonP9sv6z5a2sj2hHXAtDE9MHdkHD7acRzpP50FYMXIHuF4ZID9vYTVyJz49qcioXFq9Oi41nNje+Dp1Di8910O/pl1ElU1FiS0C8Tr9/VBS187l5RFS2Tnlwh//3ZlObNrjeoVgaMvjcH2w2fx6qafUXblKmLDW+H1e5Psf7+gfAk4V5czu9YHj9yCK9VmvPDNQew8XgKT0YCJSe3w21tdv3ap2qZJZmYmgoKC6gMlAAwfPhwGgwG7du3CxIkTm7zvJ598gr///e8IDw/HuHHj8Pzzz6NFC3V3t5ytpa8XPpqa4uppCJGzqJh5/LxLFhVFd+w7ymj+5cmMBglDerbFkJ5tXT0VITdHhQhtmmz/+RzMFqtmF0RPnjyJWbNmIT09Hb6+Yh/mS5YsweLFi1WemXbV9TUR6ZVx+hd5jdVIOXL6mdyi4WxCIjlMXgYktg9A9imxzfS3tuZg1ohubvsZK/qlbVh3sWsfrZTnkls3f9HtvVScDRGRvg37y2ZZ47WWZeIp64AmLwMeH9QFjw/qosjjKZ05oXRJUkeZvAyYPqQbpg/ppsjjiR5IWZWZj5nDugpdm9aVM2suO0fNcmbXUmM9TvEScC4uZ3Y9P5MRS+6UV17YGVSLzkVFRQgLa5hi4+XlheDgYBQVNb1j+sADD+Dvf/87tmzZgrS0NKxZswYPPvhgk+OrqqpQXl7e4EbKqm0GLzbWVZUdCkouCY2TU0OSnCe0ldjFw5WrFk2XSNqzZw/Onj2LPn36wMvLC15eXti2bRveeOMNeHl5wWy+MdMmLS0NZWVl9beTJ0+6YOaey2iQkNgxSGjsmVJumrgK+5kQ2efpUd2Fx1qstdkmbkvpL3caKc8lJ8vEIAH9mTFJRKSKr/aeQn6JvFP/WsoyAZy3Dgi42VqgjMwJEYqXJHUzohs9pZevCpdododyZmpSugScO5Qz8wSyN02effbZGxo0XX87cuSI3RP63e9+h9TUVPTu3RuTJk3Cxx9/jM8//xy5uY1nOixZsgSBgYH1tw4dOtj93NQ4o0HC7fERQmOLypzfc8JssWLtPrFdeDk1JMl5wgPET0douRn8sGHD8OOPPyI7O7v+dvPNN2PSpEnIzs6G0Xjj7qWPjw8CAgIa3Ege0VRXNoN3HfYzIbKPnL5hQG22ibvGOaW/3Imectx0uFhonCvIzTKZIFi6jIiI5LGnLNfonm09JsvE3dYBAfdaC5STOSFynaV0SVJ30zc6GIGCpapEe3S4SzkztShdAs5dypm5O9kReu7cuTh8+HCzt5iYGISHh+Ps2YbldmpqalBSUiKrTmFycjIAICcnp9Gf85S1c0QEiS0qrv+x0OlftrPySnCxUqzXRbA/M03cUd/oYPh4iX3Ya7lEUqtWrdCrV68GN39/f4SEhKBXL5bTUIvcvk3kXOxnQmQ/o0HCtEGdhce7c7aJ0rWXRb8Efpl9xm03kuRkmQDA0rvcr+wBEZEWxC/aIPs+b026SYWZqMPd1gEB91oLVDpzQvTxRvTwzF7QRoOEET3ESleJ9Ohwt3JmalC6BFxdObPmOKucmTuT3dOkTZs2aNPGdumLlJQUlJaWYs+ePbjpptoPg82bN8NisdQHQBHZ2dkAgIiIxjMdfHx84OPj3rWGtUASzDesrLE4vRl8Ubn4TnF4oNjiKDmX0SAhvn0Qdguclix0QTYTaRubwbs39jMhcsysEbF4c0uucNWqldtz3bO3icLlufpGByPY39tm+YsLl6qRlVeCFDfLYpObZaK1uvlERO4icfEGXKq2yLrP8vsS3e9zthnutg4IuNdaYN/oYAT5eaP0iu2SWiKZDu7Un0MtA7q2wX/22t7oEMmc0Ho5MwCySsCJrFfIKWfmbtfAzqTalXP37t0xatQoPPbYY8jKysKOHTswY8YM3HfffYiMjAQAnD59GnFxccjKygIA5Obm4sUXX8SePXuQn5+Pr776Cg899BBuu+02xMfHqzVVEiDnlyTzuHjteSWUCKZCBvh6eW6A1AHRv5sDOiuRtHXrVrz++uuunoam1TWDF7HxkFjaLylnTWa+8Fj2MyG6kdEgYeYQ8WwTd82qU7o8l9EgYWJiO6Gx7ljKIW2tvDIwWqubT0TkDhIXb0DpFbGqF3XCA0wYL/j542n0ug5oNEh4uH8nobGh/rY3AbTenwNQNnNC6+XMANeVgHPHa2BnUvW40SeffIK4uDgMGzYMY8aMwa233op33323/udXr17F0aNHcfnyZQCAyWTCt99+i5EjRyIuLg5z587FXXfdha+//lrNaZIAd24GL9rcfWJSO48NkHrQv7PY6f26bCYipRgNEpIET+nknruM6hp5J8nIfmaLVbifAPuZEDVt1ohYWf3Ml208rNpc7KV0eS4AGBonVhpCZIHDmcwWK9buPSM8nlkmRETKMlus6Pn8etkbJgCwfd4wFWbkPvS6Dtg3WvB7iMAFmS4WtEUvTAXGab2cGaB8CTg1rqu1SHZ5LjmCg4Pxj3/8o8mfR0VFwWr9dYW9Q4cO2LZtm5pTIjvVNYNfu6/Q5lhnN4MXqXEIiDd7Jteo3ZiTUG22vevGEkmktL7RwULl4QBgdUYeHrtN/NQ22W/n8Qu4KrhHxX4mRE2ryzZ5Q7AU4f5T5aiusbjXQrvC5bkAKPqF3ZmWpx+V9TKZZUJEpJyv95/BzH/us+u+vx0Q5V6frSrQ6zrgWcGy8SLj9LCgreT7pYdyZkqXgFPlulqDtB2tSVHu2gx+zwnbu6gA8Mtlsc0Vcg2jQUJC+0ChsVpuBk+uIZrpBNR+USLnyMgVL/fIfiZEzZObbTL5A3lNxtWmdHkuQLzUgeg4ZzBbrHhTcPMLABLaB2h+gY6IyFmmfJRl94ZJWEsTFozrqfCMyF2IHuYVGqeDBW0l3y89lDNTugTc5iNi1RzkXFdrEa+gSZjcZvDOYLZY8d0xsUU1HkB2f+0Es4EkiX+ZpKx+MSEwCn4i/lR4UVd9dVxJJLW4DvuZuJ/t27dj3LhxiIyMhCRJ+OKLL5odv3XrVkiSdMOtqIi9hJRgNEi4s0+k8Phdeb+4VTlCNU5dipbdcqfyXHKzTOaldldtLnqxaNGiG+JSXFxc/c8rKysxffp0hISEoGXLlrjrrrtQXNxwMaKgoABjx45FixYtEBYWhqeffho1NTUNxmzduhV9+vSBj48PunTpglWrVjnj5RGRgCvVZnR9bh22HD1n92Nk/nG4gjMidyNaNl5knBoHRdyNSIN3ADhVavvArC7KmUG5EnBmixWfZ58WeijRsmBaxU0TEianGfyOXPsvJuTIyivBpWqxOqIpMSzn5O4igsQC8pWr8mvHEjXHaJAwPC5MaGyNxT2bJGuN2WLFPsHTQOxn4p4uXbqEhIQErFixQtb9jh49isLCwvpbWJjY7ybZtuTOBFnj3SrbhOW5ZGeZMDYqp2fPng3i0vfff1//s6eeegpff/01/v3vf2Pbtm04c+YM7rzzzvqfm81mjB07FtXV1cjIyMDq1auxatUqLFiwoH5MXl4exo4diyFDhiA7OxuzZ8/Go48+io0bNzr1dRJRQxWVNbjphf9F9wUb4MhX0Lcf6MMyshpXKljZRGScHspzhQeIrf18lX3G5oFFPbxfgHIlzbLySlByyXaZrxB/E/rqvJqDqj1NSFv6xYTA2wCh+vJnSp2zg1skGDRamIz80ugBgluIfYht//kczBYrLzxJUQ/1j8bGn84KjWVfHfXtPH4BNYKLn0PiwhgP3NDo0aMxevRo2fcLCwtDUFCQ8hMimLwMSI5qjV2CPZzqsk3cobyTGmUEPK08l9wskydu68zYqBAvLy+Eh9/YQLasrAwffPAB/vGPf2Do0KEAgI8++gjdu3fHzp070a9fP/zv//4vfvrpJ3z77bdo27YtEhMT8eKLL+KZZ57BokWLYDKZsHLlSkRHR+PVV18FAHTv3h3ff/89XnvtNaSmpjr1tRLpXXWNBe99l4PX0o9BiYTLxwZGY0x8hOMPRG5NycwJPZTn6hsdjGB/b5uL9xcuVSMrr6T5Q9w6eL8A5UqaiWbcjE+M1P11pOu/AZHHMBokDBU8ie2sTIDzF8W+xI7uFa77X3ZPENpK7ELjylXnlYAj/egXEwIvwTCxW0bZKLKPnH4mD/WLUm8i5HSJiYmIiIjAiBEjsGPHjmbHVlVVoby8vMGNmrfmUXlNwT/acVylmYhTq4yAJ5XnMluseGebeJaJQQJmjeim4oz05dixY4iMjERMTAwmTZqEgoICAMCePXtw9epVDB/+a9mduLg4dOzYEZmZmQCAzMxM9O7dG23btq0fk5qaivLychw6dKh+zLWPUTem7jGISF3VNRas2PIzejy/Ht3m/w9e2ajMhsnUW6Pw3Ngejj8QuT0lMyf0UJ7LaJAwPkGsbGxRWfMbTXp4vwDxjTlb40SvlUf0uPGwiN5w04RkuTlKLFujLhNAbaLN3dsKfoCRa4leaADOKwFH+mE0SEjq1Fpo7P5TZexrojLRfiYsP6MdERERWLlyJf773//iv//9Lzp06IDBgwdj7969Td5nyZIlCAwMrL916NDBiTP2THXZJqI+/D5PxdmIUa2MgAeV59p5/IJQtnedGYO78MCQQpKTk7Fq1Sps2LAB77zzDvLy8jBw4EBcvHgRRUVFMJlMN2THtW3btr4fU1FRUYMNk7qf1/2suTHl5eW4cqXxxSJuGhPZr6KyBlM+yERs2jpEPbuufqPk8lXlru+n3hqF529n43e9qMucsKUuc6I5+ecvCT2np/ebaC/Y09ZW5oReynOFCc7f1ribOrW22fPZINWO0zuW5yJZ5GYCqF2+RrQfOPuGe4a+0cHw8ZJQJVCT5/QvAmmtRDL1jQ7GboGyNdVmq1NinF7J6WeS0D6QC4MaERsbi9jY2Pr/79+/P3Jzc/Haa69hzZo1jd4nLS0Nc+bMqf//8vJybpwIWPNoP3Sb/z9CY4svVru8RJdaZQREy25tOlyMAV1cG+9f2XBYeCyzTJR1bZnB+Ph4JCcno1OnTvjss8/g5+fnsnktWbIEixcvdtnzE7m7K9VmLPjqADYcKMTFaucednpsYBSeG8sNEz2py5z4KOOEzbHNZU6YLVb8M6vA5mNEBPp6fL8JpTIn9FKeS/QQz+78kmbXKfac+AW2zn9arLXj5PS21iJumpAscjMB1F5QDPKzvZMvZxy5ltEgIb59kNCidWGZc/rmkL707xyKFYJNdtnXRD1y+pnc4uFfFqh5ffv2bdBw+Xo+Pj7w8fHsU2OuYPIyoHOoP3IFTzJO/mAn/vV4f5Vn1TTRk4HDure1Pegaoic0v8w+g+fG9nDZBm11jQXZp8SzCJhloq6goCB069YNOTk5GDFiBKqrq1FaWtog26S4uLi+B0p4eDiysrIaPEZxcXH9z+r+W/dn144JCAhocmOGm8akZa7c8HDU2w8kYUy8WNkh0hYlMiey8kpQVG77UMd9t3T0+M96pTIn1Oh7545ED/usyszHzGFdm/z3IXoYSXSclnHThGTpGx0MX28DKgXqAzijGfypXy4LjQt2g1rUJEb0pP+B/y+P5OkXCuRe6vqaiCzYs6+JeuT0MxnQuY2KMyFXy87ORkQEm6eqYdEdPTH5wyzbA+EGDeFVOkGoaBNSFT37n/3CYyUwy0RtFRUVyM3NxeTJk3HTTTfB29sbmzZtwl133QUAOHr0KAoKCpCSkgIASElJwcsvv4yzZ88iLKy2P2R6ejoCAgLQo0eP+jHr169v8Dzp6en1j9EYbhqTLXUNzddk5KH4Yo3HH7J2dy29JexfPJrfT3VMicwJ0YXqqFCxDRq3pkCZVLX63rkj0fmXXr7a7HWrXsqZKYE9TUgWo0HCIMGT1RFB6gYks8WKtfvEgmOpYO8Tcr3+ncX+fVXWsBk8KU9OX5N9J0vZ10QlGw4WCo1jPxP3VlFRgezsbGRnZwMA8vLykJ2dXd9AOS0tDQ899FD9+Ndffx1ffvklcnJycPDgQcyePRubN2/G9OnTXTF9zevfJVRWq47JH+xUbS62qNXg02iQMDGxndBYV522M1usWJt9Rnj8nX3accFOYX/4wx+wbds25OfnIyMjAxMnToTRaMT999+PwMBATJ06FXPmzMGWLVuwZ88eTJkyBSkpKejXrx8AYOTIkejRowcmT56M/fv3Y+PGjZg/fz6mT59ev+nxxBNP4Pjx45g3bx6OHDmCt99+G5999hmeeuopV7508mBL1v9U36ejiBsmqhsaG4KDL45h/NU5JTIn9LSgfbZcMOOhmXGq9b1zQ32jg4Wr6DR73aqXcmYK4KYJydank1igKS5TN/UtK68EFyvNQmOD/U2qzoWU0y8mBCaj2MUmm8GTGkQvpmosQMYx8YwIElNdY0HuObEsQvYzcW8//PADkpKSkJSUBACYM2cOkpKSsGDBAgBAYWFh/QYKAFRXV2Pu3Lno3bs3Bg0ahP379+Pbb7/FsGHDXDJ/ravdMBAvH1KXbeIKai4gDI0TK+kV6qKs5eXpR2WNX3JnvEoz0a9Tp07h/vvvR2xsLH7zm98gJCQEO3fuRJs2tZmOr732Gm6//XbcdddduO222xAeHo61a9fW399oNOKbb76B0WhESkoKHnzwQTz00EN44YUX6sdER0dj3bp1SE9PR0JCAl599VW8//77SE1NdfrrJc+3ZP1P+Nv2PFdPQxdaeEs4/MIofDiln6unQu5AgcwJPS1o22rwLjJOrb537shokPBw/05CY5u7blXrMJIWsTwXyVZeaXsXFwDW/1iIV+5JUC0wFQnuSgNAeKDrmjSSPEaDhIT2gdh9otTmWDaDJzXI6WvyxuafMTCW5aGUtDojX3gs+5m4t8GDB8Nqbfob3apVqxr8/7x58zBv3jyVZ0XXWnp3gqwsBpf1NlFzAUGJBQ6VmC1WrNgq9nkEAJ3btHBdCTUN+/TTT5v9ua+vL1asWIEVK1Y0OaZTp043lN+63uDBg7Fv3z675khUp7rGwg0TJ3n97nhMuJl9hOhXSmRO6GlBW7Sc2anSptd9REtWjegRLjTO3fWNDgGQY3tgM9etespmchSvqkk2SfBbo9rlk85fFPuQCPD18vg0PL1pJ9hAjc3gSQ39YkIgute7lyW6FPf1AbGyiwD7mRA5yuRlQHKUWElCwHXZJmouIIg21RQdp6SMnPMwy/iIWXR7L/UmQ0QeYU1mvqunoGk+RgkfPnwLcv80hhsmdAMlMif0tKAdHiC24fFV9pkmv3Pf1Km1ze/uBql2nBYosTGnp2wmR3HThGST0wRTzfJJvwj2KUnpHOLxaXh60661WGZQXTN4IiUZDRJu7hQkNNZsAXvrKMhsseLQ6XKhsV4G9jMhUsKaR+WVFElbe0ClmTRNzQUE0bJbrijPtfjrQ8JjvQxAf8G+g0SkXSdKxEqckjwdWvvi4KJUHH15DIZ2D+P6AjVKicwJPS1o940ORrC/7R4dFy5VIyuvpNGf7TnxC2wtCVmsteO0QImNuc1HioUeQwvZTI7ipgnJ1i8mBN6C/3LOlKqXCXD8XIXQuC5hLVWbA6mDzeDJ1WYO7SY8lr11lLPz+AXhU9VD49rwCyuRAuRmm6zde9rpBxay8gQ/6zVUnqu6xoKcc5eEx/9+UBfGRCJCp2CxjH2yzddLwh9GdsPPL43Gd88MQ0tfVren5imROaGn8lxGg4TxCWL99YrKGt9oEu1pIjrO3QW1EOvX3NQ4s8WKz7PFKjuIlj7TMm6akGxGg4ShcWFCY69cFWvULpfZYsX3OWILlUF+tneuyb2wGTy5Wv8uocIfkBsPFak6Fz2RU1Li4ZRo9SZCpDNysk2sAJan/6zeZK5jtlixOuOE0Fh7FhAUKXOggsnv7xQea5CAWSPEN/uJSLsmp0S5egoey9sgoV2QL+alxuLnl0bjyEtjMGNoV/aKImFKZE7oqTwXALQXLM3eVOaE3t6vUsGKO02Ny8orQckl232qQ/xNbHMAbpqQnW6OEiuJsv3nc6qcRszKK0FFlVhNba0ERz2pawYvYncTFxtEjjAaJNwkePI699xll9T41xqzxYpNh8VShU1GluYiUpLJy4DE9gHC49/amuO0bJOsvBKUXrH95Q6w70ScEmUOlFZdY8GufPEyEjMGM8uEiGqZvAx4/DYeLLFFAtDC24ghsW1wcFEq8peOxbE/jcGOZ4fh90O6cKOE7KJE5oSeynMB4iXNmhzH96tRTZWAE824GZ8YyWtLAMwvJLuEthL7Rb1ytbZ80gCFaywXyTjtFx4o1h+D3Eu71i2w+0SpzXEHz5TDbLEyoJPi+kYHY7fgotXqjDw8dltnlWekbTuPX8BVwb2nhPaB/J0nUtjTo7pj0vu7hMZarLXZJnNSY1WelfiXu6AW3nadiFOk/rjC5GSZSGCWCRE1lDamBwDgb9vzXDwT15IA+HgZENPGH38YGYdBsSztSupzNHNCb/0mwgSvw5oap6dyZoC8EnDzx/a4IeaJHiof1r2t7LlpETdNyC6iv6hAbfkkpTdNzl8UC3gBvl5MKfNQos3gr1y1ICuvBCk8dU4K6985FCu25AqN/Xr/GW6aOCgj97zw2FsY14kUV9ezTnTz8q2tOZg1opvqC1CiX+4eSYmyay6OfvlUmtwsk4lJPAlIRDdKG9MDc0fG4b3vcrAmIw/FF2s86qA1NzzIUzmSOaHLfhMO9pbTW3muuhJwtkps1ZWAu2GdTGeZOY5SbdPk5Zdfxrp165CdnQ2TyYTS0lKb97FarVi4cCHee+89lJaWYsCAAXjnnXfQtWtXtaZJduobHQxfbwMqBb5Zq9EM/hfBOn4pnUN4ceWh5CxYN5naSuSAfjEhMBoAs8ACIjOeHNdUXd/GDOjcRsWZEOmT0SBh2qDOeEPws9dp2SaCX9puibJvM9XhL58Kk5NlAgBL70pQaSZE5OlMXgZMH9IN04cwG42cR+9rgY5kTuix34TDveV0tglQVwLuI4F+f42tk+ktM8dRqhVqrK6uxj333INp06YJ32fZsmV44403sHLlSuzatQv+/v5ITU1FZaVzGy+SbUaDhEGC2SNqNIM/fq5CaFyXsJaKPzc5R7+YEPh4iS1An69gQCflGQ0ShseFCY21WIGMY+KZEtSQ2WLFvhNiJ6vZz4RIPbNGxAof+AOAldtzVe9tovaXO6NBwsTEdkJjRUuF2UtulklydGvW3SciIrei+7VABzIn9NhvQrRn3I6cxr9r662cGeBYCTi9ZeY4SrWr7MWLF+Opp55C7969hcZbrVa8/vrrmD9/PsaPH4/4+Hh8/PHHOHPmDL744gu1pkkOcFUzeLPFiu9zzgmNDfLzVux5ybmMBgmDu4mdJv9BcLGVSK6H+os30nxj888qzkTbdh6/gBrBjwn2MyFSj9EgYeYQ8VKD1WYrduZeUHFGzvlyNzROrG5zqL+6XyDT1u6XNX7N1H4qzYSIiMg+el8LdCRzQo/9JkTLmX17+OwN64q6LGcGx0rA6S0zx1FuczQpLy8PRUVFGD58eP2fBQYGIjk5GZmZmU3er6qqCuXl5Q1u5Bxym8ErJSuvBBVVYgW3uTvq2fxMYhUEtxy58QOUSAn9YkIguj6/p6CU/w7ttCYzX3gs+5kQqUtutsmyjYdVmwsA53y5c7CethLMFivW7j0jPJ5ZJkREpAVaWwt0KHNChwvaor3lSq9cvaGcsx7LmQGOlYBjeS553OZKu6ioCADQtm3DHdO2bdvW/6wxS5YsQWBgYP2tQ4cOqs6TfiW3GbxSigR37gEgPFCsmTi5J9Fm8M446Ur6ZDRIuLlTkNBYluiyj9lixabDYmnVAPuZEKlNbrbJ/lPlqK4R7B5vh7OCJThFxzV6X0fraStgefpRWWsgzDIhIiIt0NpaoCOZE3pc0O4bHSxcIeb68mV6LGcGwKHDPizPJY+sTZNnn30WkiQ1ezty5Ihac21UWloaysrK6m8nT5506vPrWd/oYOGeE7tlNPi15fxFsQ+IAF8vTe0m61H/zmJ9cwAg8zgXq0kdM4eKN8/8z15+Bsm18/gFXBVcb2U/EyLnkJtt8ux/5ZWVkqNEcDNEdFyj9xU8FSo6Ti6zxYo3t+QKj09oH8AsEyIichquBYpzJHNCjwvaRoOEh/t3Ehp7fZlUPZYzAxw87KPDbCZHiNW++X9z587FI4880uyYmJgYuyYSHh4OACguLkZERET9nxcXFyMxMbHJ+/n4+MDHRzsBw5MYDRLi2wdht0DDyoNnymG2WBXZ3f3lstgX1pTOIdraTdahfjEhMBmBarPtsayKRGrp3yUUEsSuG3YeZ8aTXHJKcw2JC2NcJ3ICo0HCnX0i8V/BclGf7zuDV+5JVOX389Qvl4XGBfub7H4Oh2pDK0Bulsm81O6qzIOIiKgxXAsUV5c5UXrFdtmoGzIldLqg3Tc6BECO7YHXX2bq9P1y5LDP5iNiFR60lM3kCFmbJm3atEGbNuqUxYiOjkZ4eDg2bdpUHxjLy8uxa9cuTJs2TZXnJMf1jQ4W2jS5ctWCrLwSpChwQvj4uQqhcV3CWjr8XORaRoOE2+MjsHZfoc2xRWXqlcwgfTMaJMS29ceR4ks2xxZfrEZ1jYUngAXJLc31UL8o9SZDRA0suTNBeNPECmB5+s+Ykxqr6BzMFiu+3C82B0dKsjpSG9pRZosVK7aKZ5kw446IiJyNa4Hi6jInlm+yvQlwfeaEHstzAfZnTjijhKs7CmohdlDo+nFmixWfZ58Wum9YK/F2DFqm2qpOQUEBsrOzUVBQALPZjOzsbGRnZ6Oi4tcF77i4OHz++ecAAEmSMHv2bLz00kv46quv8OOPP+Khhx5CZGQkJkyYoNY0yUFyyicVlV1x+PnMFiu+zxHrjyJaF5HcW0RQC6Fx638sZBNuUs3QuHDhsWlrD6g4E21haS4i92XyMqBzqL/w+Le35Sj+Oey0Bp+CCTK785UrN1snI+c8zDLetidu68yMOyIicltcC6zLnBBw3ce5HstzAfZnTjijhKs7KhWsvnP9OKddV2uIapsmCxYsQFJSEhYuXIiKigokJSUhKSkJP/zwQ/2Yo0ePoqysrP7/582bh5kzZ+J3v/sdbrnlFlRUVGDDhg3w9eUOl7vqFxMi3NfkvAKBKiuvBBVVYitsWvsg0StJcCWjssbCZvCkmgFdxTeIv8w+ww08QR9n5AmPZWkuIudbdEdP4bE1FiDjmLL9xYoETx7e4WCDT9Fr1FWZ+YrH9zc2/Sw81iABs0aI99kiIiJyNq4FOtBzQqflpuzNnBAtzepICVd3JFou9lRpw4PrN5SDa8J4B6+rtUS1TZNVq1bBarXecBs8eHD9GKvV2qAuoiRJeOGFF1BUVITKykp8++236NaNXwzcmdEgYXA3sTTNH07YLuNli+iXZ8CxMg3kPuSUdNuRK5aFRCRXv5gQGAU/MWssVm7gCTBbrEj/6azweJbmInK+/l1CYZTxnWnRNwcVfX7Rk4Htgxy75hMtQVB6+camrY4wW6zYfaJUePyMwV34JZaIiNwa1wLtz5zQa78JezMnwgLErt9Ex3mKcMHX89V1hzlFD5YP697WrnlpEYuuk8P8TGKtcbYcOevw6bzzF8U+HAJ8vZhOphH9YkIgmMyE0784XgKOqDFGg4ThcWHC41dnimdQ6FVGznkIVuaCl4GluYhcwWiQMH1wZ+Hxuecuo7pG9DfbNntPHsrVNzoYgb5i17NKlJutszz9qPBYCcwyISIi8gT2XL/oud+EvZkTes3M6RsdjGB/2+0ILlyqbnjYR6fvlyO4aUIOa9da7HRftdnx09cll8U2TVI6h/AknkYYDRKSOrUWGlvIZvCkoof6RwuPTf/J8U1irVv89SHhsUwRJnKdWSNiRVt+AAAmf7BTsee29+ShXEaDhBE9xE7ViZ4etcVsseLNLeIN4CcmMQ4SERF5AnuuX/Tcb8LezAm9ZuYYDRLGJ0QKjb32sI/o+6C198sR3DQhh8lpBu9o+aQ9+WIlvvy8jQ49D7kX0YuCA6fKuFBNqpGT9WQFsDxdvE693lTXWJBz7pLw+CV3xqs4GyJqjtEgYeYQ8WyTXXm/KJZt4sxa1QO6ipWbFT0Nacvy9KOyDvItvStBkeclIiIidYleK1w7zll93NyRPZkTes7MAYD2rVsIjbv2sI9oeS72h/4VN03IYc4qn2S2WJF9slRobGSQ9oKinoluzLEZPKnJaJAwPknsRAcArNyey028Jjz7n/3CYzu3aQGTFy9XiFxp1ohYWeOVyjZxZq3qMMEviKLjmiM3y4RxkIiIyHPYc03hrD5u7siezAk9Z+YAdpawZXku2Xj1TQ5zVvmknccvoNos9ts7oLPYaUHyDP1iQmAS7ETLZvCkpiV3ip/0VaIkoRaZLVaszT4jPH7R7b1UnA0RiTAaJNyZKL5prFS2SVaeYAxV4sud6KFNBQ53ys0yYRwkIiLyIILXCrvzf+034czsWnckN3Pi7EWxtUWtlnm2pwTcWcGNOdFxesBNE1KEM8onZeSeFxrn62Vgw2CNMRokJLQPFBq7+9pGV0QKM3kZ0KWNv/D4ZRsPqzgbzySn8bFBAvp3FS8BSUTqWXq3vPJQjmabmC1WrM44ITRWidrLZwXLYoiOa4rZYsWKreJZJl4GxkEiIiJPcl5w0XlVZn79+pgzs2vdkdzMCdESUsO6i/Ws8zT2ZJqIZjOJjtMDbpqQIpxRPkm0tFd8+0BN7iTrXTvBkwf72deEVLZwXE/hsftPlStW218L5JakmaDRk0FEnsjkZUBylFhmMeB4tklWXglKr9guuwAoU6tatMH7jhyxQzxNycg5D8HEaQDA7wd1YRzUmRUrViAqKgq+vr5ITk5GVlaWq6dEREQyiF6XlF6+Wt+jQ++lk2RnTvD9kj3u1C+Xhe6j1Wwme3DThBThjPJJpwV/wSMCtbnzrnftWovV7mRJJFJb/y6hsj48lartrwVsfEzk2dY82k/W+LS1B+x+LtGGqEF+3orUqhZt2vrt4bMOHc5Y/PUh4bEGCZg1opvdz0We51//+hfmzJmDhQsXYu/evUhISEBqairOnj3r6qkREZGgvtHBCPT1Ehpb16ND76WT5GZO6P39Er1uPVVa++/LbLHiy/1iJbLDA7XXN8de3DQhRahdPslssWL/qTKhsaKL6+RZRLOZAPY1IXUZDRIm9nF+bX9PJ7ckDRsfE7kfudkma/eetnuDQbQ0wPDuYYpkYoQLlrsovXLNqVCZqmssyDl3SXj8jMHMMtGbv/71r3jssccwZcoU9OjRAytXrkSLFi3w4YcfunpqREQkyGiQMKKHWFmoukxXvZdOEs2cyPz/sv16f79Er1u/yj4Ds8WKrLwSlFyyncEd4m9S5DCSVnA1ghSjZvkkNoGnfjEh8BJtqObBfU2WLFmCW265Ba1atUJYWBgmTJiAo0fFe0CQc8hpCA8w2wSQX5KGjY+J3JOcbBMrgOXpP9v1PKInDlNkHKpojj2nQuWa/L74ZwGzTPSnuroae/bswfDhw+v/zGAwYPjw4cjMzHThzIiISC7R65O66x29l06Sm/FrT08PLekbHYxgf2+b4y5cqkZWXolwBvcdLI/dADdNSDFqlk9iE3gyGiQkdRI73erJfU22bduG6dOnY+fOnUhPT8fVq1cxcuRIXLokfjKV1Ofs2v5a8PS/s4XHsvExkfsyeRmQ2D5AePxbW3Ps+ky2p1azI+w5FSpHdY0Fu/J/ER7PLBP9OX/+PMxmM9q2bfjvsG3btigqKrphfFVVFcrLyxvciIjIPci5jmHpJPkZv86+TnQ3RoOE8Qli1S+Kyq4IZ9y0D9Lmvy97cdOEFKNm+STRMghsAq9tommCntzXZMOGDXjkkUfQs2dPJCQkYNWqVSgoKMCePXtcPTW6jtza/oOWbVZpJu7vm+zTKLoofsHKxsfasH37dowbNw6RkZGQJAlffPGFzfts3boVffr0gY+PD7p06YJVq1apPk+S7+lR3YXHWqz2ZZu44gSh3FOhcsjJMpHALBOybcmSJQgMDKy/dejQwdVTIiKi/yfnOoalk+Rn/Ipm3Gg1MwcA2gtW+ym5VK37zBx7cdOEFKNW+SSzxYp9J8RO5rEJvLbpsa9JWVltL5/g4MYvjnjK0HXkZpsUllfhy+zTKs7IPZktVsz8NFt4PBcLtePSpUtISEjAihUrhMbn5eVh7NixGDJkCLKzszF79mw8+uij2Lhxo8ozJbn6xYTAW8a3iLe3yc82yRTMMlbyBKFapxblZplMTGJpBD0KDQ2F0WhEcXFxgz8vLi5GeHj4DePT0tJQVlZWfzt58qSzpkpERDbI6dHB0knyM37DBDNTRMd5IjkbIXrPzLEXN01IMWqVT9p5/AJqBL9nswm8tsnZmDv9i331xt2JxWLB7NmzMWDAAPTq1Xh/B54ydC252SazPs322NJx9rrnne8h5xVzsVA7Ro8ejZdeegkTJ04UGr9y5UpER0fj1VdfRffu3TFjxgzcfffdeO2111SeKcllNEiYNqiz8PgaC5BxTGwTBKjdbF33Y6HQWCVPEIp++SwoEas7XkdOlgkALL1LXt8s0gaTyYSbbroJmzZtqv8zi8WCTZs2ISUl5YbxPj4+CAgIaHAjIiL3IKdHx/mLLJ0EyMv4zcoTrCyi4a/ecjZCmJljH26akKLUKJ+UKaPMEpvAa5vRICGxY5DQ2DOlnr9pMn36dBw8eBCffvppk2N4ytC15GabAMBdb3+v0mzczzfZp7H3pLzsJy4W6ldmZmaDBsgAkJqa2mwDZGbbuc6sEbGQs725fLN4ia6dxy/gylWxPlBK1vYW/fL5+b7TwhvgcrNMkqNbw+TFr2h6NWfOHLz33ntYvXo1Dh8+jGnTpuHSpUuYMmWKq6dGREQyyOnRUXJJbNNE66WTRK/DSi5VYXXGCaGx5wXfW08kJ9OEmTn24RU5KUqN8klWwa1hNoHXB9G6jZ7cDB4AZsyYgW+++QZbtmxB+/btmxzHU4auJzfbJPtUORZ/fUil2bgPs8WKGTLKcgFcLNS7oqKiRhsgl5eX48qVxjfCmW3nOkaDhJlDxLNNfjhRKvy5LHpgpqWPl6K1vUVPhZZX1gj320tbu1/WHNZMlfeZQtpy77334i9/+QsWLFiAxMREZGdnY8OGDTfERiIicm9yenQUloqV59J66STRTYCSS1dResV2DxgACGul3U0AOSXgmJljH65MkKLU6GtSWCpWAmFM7wiWdNEB0RJsntoM3mq1YsaMGfj888+xefNmREdHu3pKZIPJy4BRvcJk3eejHflY/PVBlWbkemaLFV3/uF72/bhYSHIx2861Zo2IlTVetCG86IGZW7uGKHrtJ3oqFKhtQmqL2WLF53vPCD8mN44JqD04c+LECVRVVWHXrl1ITk529ZSIiEgmOT06RFeqtV46SXQTYE++2DpPkJ+3oodr3I3oYZ/0n4qxipk5duFVOSlK6b4mZosV3xwQq2kdzibwuqD1ZvDTp0/H3//+d/zjH/9Aq1atUFRUhKKioiZPWZN7WPHAzbLv89GOE3jkg6bLDnmqL7NPo/Mf10OssM6vxvQK52KhzoWHhzfaADkgIAB+fo1vmDPbzrWMBgl3JkYKj39rq1hD+CA/b6HHu6mjvPKItvSNDkYrX6PQ2JJLtr/YZ+SclxULuXFMRESkHaI9OkRLjWq9dJLoJsCB02LleId3D9P0wWrRwz5llTUoY2aOXbg6QYpTsq/JzuMXUG0We14Nx0K6hhrZTO7knXfeQVlZGQYPHoyIiIj627/+9S9XT42aYTRImDlYvExNna3HShD97DpsPnzWo8vJAcCVajMSX9iIWTJLcgG18fvNB/ooPynyKCkpKQ0aIANAenp6ow2QyX0svVu8D5HFKpZtcuoXsSzjYH+xL9eijAYJdya1ExorUkJi0VfipRgT2gdw45iIiEhDRDMnDp0pE3tAz/66aJPoJkBljdiRFNFNK08lpwScCK1n5thDuXf3Oi+//DLWrVuH7OxsmEwmlJaW2rzPI488gtWrVzf4s9TUVGzYsEGlWZIa+ncOxYotuUJjv8s5iwFdmw5kGbnnhZ83JUbbAZFq1WUz7RZoqlqXzeRJpwusVo1fCWnY7JGxeGtrruxrWSuA367ebXOctwHoEtYKT6fGYVBsG6f8u66orMHMT3YjI6cEVSr+01x+X5JH/Z6SmIqKCuTk5NT/f15eHrKzsxEcHIyOHTsiLS0Np0+fxscffwwAeOKJJ/DWW29h3rx5+O1vf4vNmzfjs88+w7p161z1EkiAycuALm38kXPuktD4t7bmYNaIbk3+zpstVqzdd1rosdSo7S3aO81W09bqGgtyz4u9JwAwL7W78FgiIiJ3xbXAX4n26Mg8LnbYU+ulk+o2AcoqaxR5PK33gDEaJAzvHob/7hMvBdscrWfm2EO140zV1dW45557MG3aNFn3GzVqFAoLC+tv//znP1WaIamlX0yI8D+sLUeaL58k2mTTZJTYBF5HlMxmIlKK0SDhzfsSVXv8qxbgcNFF/Hb1bnT+43rEPrceUz7KQoVCF5VAbbbI0//Zh94L1iPq2XXotWgjthxTd8NkaFwbjEsQL+9DnuOHH35AUlISkpKSAABz5sxBUlISFixYAAAoLCxEQUFB/fjo6GisW7cO6enpSEhIwKuvvor3338fqampLpk/iVs4rqfwWFvZJll5JbhYKZZmrEZtb9HGonsKmj+8Mfn9ncLPaTSA17FERKQJXAv8leiifY1gxQGtl06S1wfGNtFNK08WHiRW2k2E1jNz7KFapsnixYsBAKtWrZJ1Px8fH4SHh6swI3IWo0FCVGgLHD9vu7RCzrmKJjMBzBYr9ghkEwBAQvtA7ojqiJLZTERKuj2xHdbuO4nNR9XfrKsyW7Hl6Dn0WrQRLb0N2PncCLS0Mz23usaC0cu3IfecWEkcpbQP8sWHj/R16nOS8wwePLjZ7LnGrhEHDx6Mffv2qTgrUkP/LqEwSoBZcIO1uWyTovJK4ecVrQEuhwSx68mtR841eQ1bXWPBLsFrWAAYnxDJ61giItIErgX+SrRHh4iWPl66KJ2U0jkU/9krlnFsi9YzTQBAyUIleni/5HK7wrlbt25FWFgYYmNjMW3aNFy40PzCU1VVFcrLyxvcyPXi2wcJjTNb0GQmgJzmmbfo4MODftUvJgRGwbWFH08xJpBzfTilH9oHOvdUS8VVC3ot2ohBf/5Wdm+UF74+hG7z/8fpGyYtvA34/tlhTn1OIlKH0SBhuoy+Ts1lm5y/KFZ6IsBXncWDFMGMj8oaS5PXsHKyTABg6V3ifWGIiIi0SItrgaI9OkTc2jVEFwcsbJU/lUONjGR301rBbBo9ZObI5VabJqNGjcLHH3+MTZs24c9//jO2bduG0aNHw2xuOkV/yZIlCAwMrL916NDBiTOmptzVp73w2I935jf65//de0r4MQZ0biM8ljyf0SChZ7sAobEtfIwqz4boRt+njUBrP+f/2zvxSxU6/3E91h8Qq2s68M+b8eGOfHUn1QhvCfjpxdFOf14iUs+sEbGCORq13tqa0+gmb8llsS/LKZ3VWTzoFxMCk+DJjB25N5aZlZtlkhzdmg3giYhI17S6Ftg3OhitfJX5TnhTx9aKPI67Ey2TKkKNjGR3E9pKuWwmZprcSNYV+rPPPgtJkpq9HTlyxO7J3HfffbjjjjvQu3dvTJgwAd988w12796NrVu3NnmftLQ0lJWV1d9Onjxp9/OTcvp3CZXR1+Rso1+aD5wqFbo/60Dr07j4dkLj+kbx3wa5xr6Fo9C6hWpVMJv1+3/sw8vrDjU7psfz/4OTv1xx0ox+5SUBx5aMdfrzEpG6jAYJM4c4nm0iWprVz1udjWmjQUJihyChsWdKbywlJjfLZM3UfrLGExERORvXAu1jNEi4M0ls3cKWYH/lFsfdmWiZVFuC/Lx1Uc5MyWwmPWTmyCVr02Tu3Lk4fPhws7eYmBjFJhcTE4PQ0FDk5OQ0OcbHxwcBAQENbuR6RoOEm6LEdsIba9ZttliRL9ATBQC6tGmpizRFaujh/lGQbPy1S1LtOCJX2bcgFR2CXXPC5b3v8vHyup8a/VnXP67D5auiBRCV4+cF5HDDhEizHM02MVusyD5ZKnTfyCD1mqGKXsNGXDcHZpkQEZEWcS3Qfu1bt1DkcfSSBSBaJtWWYd3DdLFO2Dc6GP4mZQ4S6SEzRy5ZR2DbtGmDNm2cVwbp1KlTuHDhAiIiIpz2nKScvtHB2C34xfHjnfkNmnXvPH5BuJ/JkDiW5tIjk5cBvxsYjb9tz2tyzO8GRnMxglzuu3lD8cLXh1xSBuu97/KQ1KE1xsT/+jnaff46uGC/BO0CvbEjbaTzn5iInKYu2+SNLblC4+uyTeakxgKovf6rFuwmr2Zp1iA/sZN2xWUNS4kxy4SIiLSIa4H2U6rclF6yAPrFhMDHy4CqGse+sLZVMAPDnRkNEgZ2DcWGQ8UOPU5LH3V6BXo61VYTCwoKkJ2djYKCApjNZmRnZyM7OxsVFRX1Y+Li4vD5558DACoqKvD0009j586dyM/Px6ZNmzB+/Hh06dIFqampak2TVNS/c6jtQf/v+hJdH2c0vRB+vYFdwmTNi7QjbUwPPH5bNK4/QGCQgMdvi0bamB6umRjRdRaM64mfXxqNtgHOv9j9/T/21sfXAX/6X1ypcfoUMGVAR26YEOmEI9kmGbnnhe7j62VQtTRreaXYAsf6Hwvr584sEyIiIq4FXk+pclN6yQIwGiQMiXV8g85WVRItSVKg382tXdXpFejpVCu2vmDBAqxevbr+/5OSkgAAW7ZsweDBgwEAR48eRVlZGQDAaDTiwIEDWL16NUpLSxEZGYmRI0fixRdfhI+PPmr3aU2/mBB4GyB0ormuRNeArqEwW6xI/+ms0HN4GST2M9G5tDE9MHdkHNZk5uNEyWV0Cm6BySlRXIggt2PyMmDXH0egorIG/f70LSqqm25sqLR+L6ejd/sAnC5XrrGeiAnxEVj2m0T+PhLpiCPZJll5JUL3iW8fqOoXO9EFjsoaS/31K7NMiIiIuBZ4vZTOIXhrS9NlxkToLQsgqWNrhzMngvy8FZqN+xM97NOcpA6Ob7xokWqbJqtWrcKqVauaHWO1/ppZ4Ofnh40bN6o1HXIBo0HCsO5thYPdso2H8WXXgcjIOS9cmqtHRCvuhhJMXgZMHahcDVUiNbX09cLBF0ahorIGMz/ZjYycElSJVaOx27lLV7H56AXbAx0gAfDxMiCmjT/+MDIOg2LbMD4T6dSsEbF4c0suREPbm1tyMGNYV+w7IZapERGobskFOQscO3LP4ZboYGaZEBERgWuB1+sXEwKTURIuP9oYvWUBlF5xvH9LaEvP33ATpUQ2U5lCZeS0RrVNEyIAmJwSJbxpsv9UOaprLFj89SHhxx+XEGnv1IiIXKqlrxc+mpoiNPZKtRkLvjqADQcKcbFa5R2W6/gYJfTvEoo37++Dlr68bCAi2+Rmm1gBPPj+TtQIhrd2rdUtUdEvJgReEoTmszuvBAOXfSvr8ZllQkREpA9Gg4SE9oHYfaLU7sfQWxZAYWmlw4+hl3JmgDLZTDrak5OFqx+kKjlfOgFg0nsZyDl3SfjxH+4fbefMiIg8h5/JiFfuTsIrd9emt1dU1qDXInVPZCW0a4m102/T1akmIlKO3GyTLBmZGmo2gQdqFziSOrXGboE5yV0EYZYJERGRvrRr3cKhTRO9ZQE4ejgmwFdf5cz6xYTAx0tClejCayNSYsR7UusJr9hJVUaDhPFJ4tkgu0+UCY/t3KYFv3QSkS619PVC/tKx8PNSZ0PjrfsS8eXMQdwwISK71WWbKM1kdE4/O7W+bDPLhIiISF8c3QTQ21ey/p0dW8BP6ayvcmZGg4TB3ew/UOTnbWCv6CZwxZlUt+TOBFUed9HtvVR5XCIiT3H4pTHw91b2o/znl0bj9sR2ij4mEenTrBGxClRZbigm1N8pX4Qd/cLeGGaZEBER6Y/DmwA6ywKo6wNjLz9vo4Kz8Qx+JvsLSd3Wjb1Im8LyXKQ6k5cByVGtZTXItMUgAf276uuDg4ioMQcWj0LnP65X5LHevD+JC3pEpBi5vU1EdAhuodhjNadfTAgMEmBRsI0Us0zInVRU1mDmJ7ux41gJHG+5S+7K2yAhLMAHk5I74dGBMbzOI3IBuWXrr+WsDFt34mgfmMggX2Un5AEcyWbS4yaTKH5iklOseVTZL4kTEiO5E0pEhNqLyrfuS3T4cYbGtcG4BPFyikREIpTONnFWjWqjQcLNnYIUe7zRPdtysZLcxh1vfYdeizZiCzdMNO+qxYrTpZVYtvEous3/HyxZ/5Orp0SkO0aDhE4h/nbdN6F9oC7Xvtq1tv+QjNq979yRI9lMetxkEsUrd3KKumwTpSy9S52SX0REnuj2xHbo0yHA7vsH+hrx4SN9FZwREVEtpXubPNw/WrHHsmXm0G6KPdZbk25S7LGIHHHHW9/hwKlyV0+DXORv2/O4cULkAr3bB9p1v8ggx/qheCp7Mye8DPrLzAF+zZC2hx43mURx04ScRqlsE9aDJiK60b+n3Wr3ae7d80cqOhciomvNGhGryJeOzm1aOPUasH+XUEWyZJbfl6jLU6Lkfioqa7hhQnjvuzxU11hcPQ0iXbmrT3u77udoE3lPZW/mRI+IVrq85rI3Q9ooQZebTKK48kxOY/IyYFSvMIcfh/WgiYhuZDRIeNOOMl3ciCYitRkNEt5QoIzgott7OT4ZGYwGCRMTHStbGB5gwvjEdgrNiMgxT/1rn6unQG7AYgXWZOa7ehpEumLvQQy9ZgHU9YGRS8/lpu3JkO4ZGaDLTSZRXCUhp1rxwM0O3f+3A6K4uEdE1ITbE9uhaxt59V+5EU1EznB7YjsMjbX/JJu3UUL/rvbXa7bX0rsdKwm7fd4whWZC5LiCX664egrkJk6UXHb1FIh0xZ6DGHpsAl/HaJAwPkn+Bogzy7i6G3s25vS8ySSCq8/kVI40LO7Q2g8LxvVUdkJERBqzbtYg4bHMMiEiZ/pwSj+0C/C2675//Y1rSlyZvAyYMqCjXfflYR9yNx11WuaFbtQp2P4my0RkH7kHMZ64rbOuswCW3Cnv/Rrds62ur7uMBgkzBsvrI6jnTSYR+v3XRC5ze2I7DO8uL8WwhcmA754ZqtKMiIi0w+RlwNRbOwmNZZYJETnbjj+OhL9J3leQYXFhLj0Jt3Bcb7QPMsm6T6dgHvbRiqioKEiS1OC2dOnSBmMOHDiAgQMHwtfXFx06dMCyZctueJx///vfiIuLg6+vL3r37o3169c3+LnVasWCBQsQEREBPz8/DB8+HMeOHVP0tbx2b5Kij0eeySABk1OiXD0NIt0xeRnwcP8OwmNnjZBfbklL5BxcMUjAW5NuUnlG7m/2SPE+gjzcYxvfHXKJ9x/ui+Hdxfqb+HtL+OmF0SrPiIhIO56/vRfi2wc0O+bx26J5kURELnHohdEIaSm2CdG7XSt88MgtKs/Itu+fHYFQwTn3imyJbfN42EdLXnjhBRQWFtbfZs6cWf+z8vJyjBw5Ep06dcKePXvwyiuvYNGiRXj33Xfrx2RkZOD+++/H1KlTsW/fPkyYMAETJkzAwYMH68csW7YMb7zxBlauXIldu3bB398fqampqKysVOx1tPT1snl9QNr32EBeAxK5yuI74tFG4Hrijftck2HrbhaO641OIbazJN+e1IfvF2qzTd5+sI/NcZ1CeLhHBD8pyWXef/gWvHl/UrM194Z0C8ahF8c4bU5ERFrx1YyBmHpr4+m2j98WjbQxPZw8IyKiX+2ZPwJT+kc1O2bqrZ3w9czbnDMhAT/MH4Ghcc1nS7/xmwR886R4mUTyDK1atUJ4eHj9zd/fv/5nn3zyCaqrq/Hhhx+iZ8+euO+++/Dkk0/ir3/9a/2Y5cuXY9SoUXj66afRvXt3vPjii+jTpw/eeustALVZJq+//jrmz5+P8ePHIz4+Hh9//DHOnDmDL774QtHX8tWMgdw40TFeAxK53u75I9C7XeNx2CgBKx/sg1G9Ipw8K/e17emhGBbX+KFrL75fNxjVKwIrH+wDk1fjq63D4kKx7Wke7hEhWa1Wq6snoaTy8nIEBgairKwMAQG8GPQEZosVWw8VY9G6gyi+WA0fLwNG947A4jt6wc9kdPX0SMO0GC+0+JrIMdU1FqzJzMeJksvoFNwCk1OYhkvajRVafV1aVl1jwXvf5eCTnSdQdqUGrVuY8EByJzw6MMZtY9WVajMWfHUA6YfO4qrZgk4h/vjDyDgMim3DU44eQk6siIqKQmVlJa5evYqOHTvigQcewFNPPQUvLy8AwEMPPYTy8vIGmxtbtmzB0KFDUVJSgtatW6Njx46YM2cOZs+eXT9m4cKF+OKLL7B//34cP34cnTt3xr59+5CYmFg/ZtCgQUhMTMTy5csVfU0AUFFZg5mf7MaOYyWotjmaPJW3QUJYgA8muXlcJefS4vWSJ76misoazP50L44WVyDQzxtzRsTyWqIZV6rNeGndIRw4VYZAP288NjAGt3bl+9UUs8WK74+ew7vfH0d55VUktA/Cc2N76H6dVU6s8HLSnIiaZDRIGNY7HMN6h7t6KkREmmPyMmDqwBhXT4OIqFEmLwOmD+mG6UM8p263n8mIV+5OAu529UzIGZ588kn06dMHwcHByMjIQFpaGgoLC+szSYqKihAd3TCzs23btvU/a926NYqKiur/7NoxRUVF9eOuvV9jY65XVVWFqqqq+v8vLy+X9bpa+nrho6kpsu5DRETKaenrhfcf6evqaXgMP5MRL0+Md/U0PIbRIGFQ9zAMEmyNQDfipgkRERERERHpxsKFCwEAgYGBTY45fPgw4uLiMGfOnPo/i4+Ph8lkwuOPP44lS5bAx8dH9bk2ZcmSJVi8eLHLnp+IiIhIy5ibSURERERERLpR18h99+7dOHz4cKO3mJjGszSTk5NRU1OD/Px8AEB4eDiKi4sbjKn7//Dw8GbHXPvza+/X2JjrpaWloaysrP528uRJ0ZdPRERERDZoLtOkrkWL3PRkItKfujihpdZOjIFEJEKL8Q9gDCQiMSaTCQDQtWvXZrNNGpOdnQ2DwYCwsNpyFykpKXjuuedw9epVeHt7AwDS09MRGxuL1q1b14/ZtGlTg54m6enpSEmpLY8VHR2N8PBwbNq0qb6nSXl5OXbt2oVp06Y1Og8fH58GmS6Mf0QkSovXgYyBRCRCTvzT3KbJxYsXAQAdOnRw8UyIyFNcvHhR9hdmd8UYSERyaCn+AYyBRCSPrRiYmZmJXbt2YciQIWjVqhUyMzPx1FNP4cEHH6zfEHnggQewePFiTJ06Fc888wwOHjyI5cuX47XXXqt/nFmzZmHQoEF49dVXMXbsWHz66af44Ycf8O677wIAJEnC7Nmz8dJLL6Fr166Ijo7G888/j8jISEyYMEH4tQCMf0QkTkvXgYyBRCSHSPyTrFraWgZgsVhw5swZtGrVCpIk2RxfXl6ODh064OTJkwgICHDCDF2Pr5mvWYvseb1WqxUXL15EZGQkDAZtVCtkDLSNr1n7r1lvrxeQ/5q1GP8AxkARenvNenu9AF+zkjFw7969+P3vf48jR46gqqoK0dHRmDx5MubMmdMgy+PAgQOYPn06du/ejdDQUMycORPPPPNMg8f697//jfnz5yM/Px9du3bFsmXLMGbMmAZzWrhwId59912Ulpbi1ltvxdtvv41u3boJvQdy4x+gv38renu9AF8zX3PjtHgdyGtA2/iatf+a9fZ6AXXjn+Y2TeQqLy9HYGAgysrKdPUPiq9Z+/T2mvX2epWix/eNr1n7r1lvrxfQ52tWgh7fN729Zr29XoCvWS+vWQl6e9/09noBvma+ZmqKHt8zvmbtv2a9vV5A3desjS1lIiIiIiIiIiIiIiIiB3HThIiIiIiIiIiIiIiICNw0gY+PDxYuXNigJq3W8TXrg95es95er1L0+L7xNWuf3l4voM/XrAQ9vm96e816e70AXzOJ09v7prfXC/A164UeX7Oj9Pie8TVrn95eL6Dua9Z9TxMiIiIiIiIiIiIiIiKAmSZEREREREREREREREQAuGlCREREREREREREREQEgJsmREREREREREREREREALhpQkREREREREREREREBEDnmyYvv/wy+vfvjxYtWiAoKKjRMQUFBRg7dixatGiBsLAwPP3006ipqXHuRFUWFRUFSZIa3JYuXerqaSlmxYoViIqKgq+vL5KTk5GVleXqKalm0aJFN/xdxsXFuXpaitq+fTvGjRuHyMhISJKEL774osHPrVYrFixYgIiICPj5+WH48OE4duyYaybr5hgDtR//AMZAxkDGwKYwBjIGag1jIGOgKMa/WoyB2sH4x/gnB2Mg45/WMAaqEwN1vWlSXV2Ne+65B9OmTWv052azGWPHjkV1dTUyMjKwevVqrFq1CgsWLHDyTNX3wgsvoLCwsP42c+ZMV09JEf/6178wZ84cLFy4EHv37kVCQgJSU1Nx9uxZV09NNT179mzwd/n999+7ekqKunTpEhISErBixYpGf75s2TK88cYbWLlyJXbt2gV/f3+kpqaisrLSyTN1f4yBtbQa/wDGQMZAxsDmMAbWYgzUFsZAxkARjH+/YgzUDsY/xj9RjIG1GP+0hTFQhRhoJetHH31kDQwMvOHP169fbzUYDNaioqL6P3vnnXesAQEB1qqqKifOUF2dOnWyvvbaa66ehir69u1rnT59ev3/m81ma2RkpHXJkiUunJV6Fi5caE1ISHD1NJwGgPXzzz+v/3+LxWINDw+3vvLKK/V/VlpaavXx8bH+85//dMEMPYOeY6CW45/VyhiodYyBymAMfM3V01ANY6C2MQY6Ts/xz2plDNQSxj/GP3voOQYy/mkLY6A6MVDXmSa2ZGZmonfv3mjbtm39n6WmpqK8vByHDh1y4cyUt3TpUoSEhCApKQmvvPKKJtIOq6ursWfPHgwfPrz+zwwGA4YPH47MzEwXzkxdx44dQ2RkJGJiYjBp0iQUFBS4ekpOk5eXh6KiogZ/54GBgUhOTtb037la9BIDtRj/AMZAxsBajIH2Ywz0bIyBjIEAY6C99BL/AMZALWH8Y/xTil5iIOOftjAGKh8DvZSYnFYVFRU1CJIA6v+/qKjIFVNSxZNPPok+ffogODgYGRkZSEtLQ2FhIf7617+6emoOOX/+PMxmc6N/h0eOHHHRrNSVnJyMVatWITY2FoWFhVi8eDEGDhyIgwcPolWrVq6enurqfi8b+zvX0u+ss+ghBmo1/gGMgYyBv2IMtA9joGdjDGQMrMMYKJ8e4h/AGKgljH+Mf0rSQwxk/NMWxkB1YqDmMk2effbZG5rfXH/T6i/JteS8D3PmzMHgwYMRHx+PJ554Aq+++irefPNNVFVVufhVkFyjR4/GPffcg/j4eKSmpmL9+vUoLS3FZ5995uqpkZMwBjL+6RljIDEGMgbqGWOgvjH+1WIM1CfGP2IMZPzTM8ZAdWgu02Tu3Ll45JFHmh0TExMj9Fjh4eHIyspq8GfFxcX1P3NnjrwPycnJqKmpQX5+PmJjY1WYnXOEhobCaDTW/53VKS4udvu/P6UEBQWhW7duyMnJcfVUnKLu77W4uBgRERH1f15cXIzExEQXzcq5GAMZ/+owBjIG1mEMbIgxkDHQnf/+lMQYiPr/10MMZPyrxRhYS+8xkPEP9f+vh/gHMAYCjH919B7/AMbAOo7GQM1tmrRp0wZt2rRR5LFSUlLw8ssv4+zZswgLCwMApKenIyAgAD169FDkOdTiyPuQnZ0Ng8FQ/5o9lclkwk033YRNmzZhwoQJAACLxYJNmzZhxowZrp2ck1RUVCA3NxeTJ0929VScIjo6GuHh4di0aVN9YCwvL8euXbswbdo0107OSRgDGf/qMAYyBgKMgY5gDPRsjIGMgYC+YiDjXy3GwFp6j4GMf/qKfwBjIMD4V0fv8Q9gDASUiYGa2zSRo6CgACUlJSgoKIDZbEZ2djYAoEuXLmjZsiVGjhyJHj16YPLkyVi2bBmKioowf/58TJ8+HT4+Pq6dvEIyMzOxa9cuDBkyBK1atUJmZiaeeuopPPjgg2jdurWrp+ewOXPm4OGHH8bNN9+Mvn374vXXX8elS5cwZcoUV09NFX/4wx8wbtw4dOrUCWfOnMHChQthNBpx//33u3pqiqmoqGiwW56Xl4fs7GwEBwejY8eOmD17Nl566SV07doV0dHReP755xEZGVn/YUm/0nsM1Hr8AxgDGQMZA5vDGMgYqDWMgYyBovQe/wDGQK1h/GP8k0PvMZDxT3sYA1WKgVYde/jhh60Abrht2bKlfkx+fr519OjRVj8/P2toaKh17ty51qtXr7pu0grbs2ePNTk52RoYGGj19fW1du/e3fqnP/3JWllZ6eqpKebNN9+0duzY0Woymax9+/a17ty509VTUs29995rjYiIsJpMJmu7du2s9957rzUnJ8fV01LUli1bGv29ffjhh61Wq9VqsViszz//vLVt27ZWHx8f67Bhw6xHjx517aTdlN5joB7in9XKGMgYyBjYFMZAxkCtYQxkDBSl9/hntTIGag3jH+OfHHqPgYx/2sMYqE4MlKxWq9X+LRciIiIiIiIiIiIiIiJtMLh6AkRERERERERERERERO6AmyZERERERERERERERETgpgkREREREREREREREREAbpoQEREREREREREREREB4KYJERERERERERERERERAG6aEBERERERERERERERAeCmCREREREREREREREREQBumhAREREREREREREREQHgpgkREREREREREREREREAbpoQEREREREREREREREB4KYJERERERERERERERERAG6aEBERERERERERERERAQD+D4VGaYoMgfJ5AAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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zzJw506R9u3btAMDp9/8ZM2bgueeeg7e3N2bOnIkZM2agXr162FpprHz77bcxZswYxMTEYN68eXjsscfw2WefoXfv3sbn5PLyciQnJ2P37t145ZVXsGjRIrzwwgvIyMgwEVd27dpV7VhFRERgyZIl+PPPP4119XQ6HYYNG4aaNWtWsy2juRJByINr166hd+/eOH/+PP773/9i4cKFeOaZZ8xaOJvjiSeewM2bNzFnzhw88cQTSElJwYwZM0zaiDFmbNq0CSqVCr1797baH3PP4AZcERMgJMTFmS6ExAQHB7MxY8ZYbTN06FAT25DMzEwGgIWHh5tYrqxdu5YBYOvWrTO+9+yzz7Lw8HCz+920aRMDwGbPns0yMjJYjRo12MCBA822fffddxkAlpOTI+DX6bE37c+Vx4Yxxr799lsGwPi677772JEjR4yfb9u2jcXGxrKZM2ca7bkWL15czZ7LWceZ8AxGjBjBoqOj2Y0bN0zef+qpp1hwcDC7deuW8b3JkycztVrNduzYwX744QcGgH388ccm2w0dOpQBYK+88orxPZ1Ox/r168d8fHzY9evXGWOMnT17lgFgCxcu5O5rSUkJa9y4MWvZsiW7ffs269evHwsKCmIXLlywup09Flm27LkYY+yFF15g/v7+on+3NZKSklhQUBDLz883vjdmzBim0WjMtq9duzZ76qmnGGN3LaBmzpxZrd2iRYsYAHbq1ClB+zTHkSNHWPPmzdkrr7xitOf64YcfWGxsbLXzpSqVzzcDL774IgsICGC3b982vte9e3cGgM2bN8/4XllZGWvTpg2LiIhg5eXljDHGBgwYYNPGa8qUKQyAWSuHo0ePMh8fHzZy5EiWn5/P6tSpw+677z52584dq/vktecywGPPxRhjTZs2ZQ899BD3fgmCi+BgxqzNj4YOZazy+WywwQoPZ6yyVd/atfr3K82N2LPP6tuZY9MmffvZsxnLyGCsRg3GLMxn2Lvv6ttam8/w2HOdOcOYWs3YoEGM/WvHaMRgfXPtGmM+Poz17m3a5tNP9ftfuVL/70OH9P/+4QfL33fnDmMqFWOvvWb+86efZiwggLF//mHsww/1+/v1V/NtmzZljK5/grCJPc98P/30U7V5rVarZQ888AADwL744guTbc3NpxISEli7du2M/7Y219VqtaxLly4sMjKS3bhxg40ZM4Z5eXmxffv2me2vj48PGz16tNXfZA57LWLOnDnD1Go1GzRokNG61oDBJuzatWvMx8eH9e7d26TNp59+ygCwlf+OlYcOHWIA2A9Wxso7d+4wlUrFXrMwVj799NMsICCA/fPPP+zDDz9kANivFsZKmisRhGuoHKf75ZdfGACLY5qBqs8/06dPZwDY888/b9Ju0KBBJrE2scaM5557zuYzf25uLouIiGBdu3Y1+7m5mADZcykXyjRxc0JCQrBnzx4TmyZennzySZPix127dgUAkxS03NxciwWSe/fujRdffBEzZ87Eo48+Cj8/P3z22Wdm2xr2cePGDat9Kisrw40bN0xeOp0Ot27dqva+LVx5bACgZ8+eSE1NxQ8//ICXXnoJ3t7eKCkpMX4eGxuL9evXY+rUqfD29oZKpcLo0aOxa9cuxMXFGdtJcZwJz4Qxhp9++gn9+/cHY8zkekpOTkZhYaGJlcHbb7+Nli1bYujQoXj55ZfRvXt3jBs3zuy+x44da/x/lUqFsWPHory8HJs3bwYAY9Fxa9dMVQICApCSkoKTJ0+iW7duWL9+PT766CPcc889Ju0KCwtNfosh9T8/P9/k/eLiYu7vNkdoaChKS0tNUoWl/O53330XmzdvxnvvvYeQkBDj+6WlpfDx8TG7jZ+fn9FSzfBfX19fs+0qt+HdpznuuecefPHFF/jkk0+MdhSDBw/GwYMH0alTJ6u/0d/f3/j/N2/exI0bN9C1a1fcunULp06dMmnr5eWFF1980fhvHx8fvPjii7h27RoOHDgAQD/uX758uZqdYmVyc3Ph5eVl1iauVatWmDFjBlasWIHk5GTcuHEDq1atgpeXl0m7qvejW7duQafTVXu/rKzM6u+3RWhoKI3nhPiEhAB79phaYvHw5JN6aysD/86NUNm6IDfXtE1levfWZ6nMnKnPUPHz02dzmMOwj8rnf3Gx/t+GV36+/v3CQtP3q1pl6XTAtGlAVTtGQzbc5s36LJLx403bjBoFBAUBBnvC4GD9fzdtAqpYRhjJy9Pbb1k6Bp9+qt/P4MHA1Kn6DJgBAywfA7r+CcIm9jzzbdy4Ed7e3hg1apTxPbVajTFjxljcpmqWc9euXas9GwLm57pqtRopKSkoLi7GQw89hMWLF2Py5Mlms4EN++C5/1uab5qbp1jj119/hU6nw7Rp06pZ1xoyhzdv3ozy8nKMHz/epM2oUaMQFBRktHIN/nes3LRpk8XvzcvLA2PM4nPBp59+iuDgYAwePBhTp07Fc889hwEWxkqaKxGE6zE8q/7+++8m7iy8mBtfc3NzUVRUBECcMUOn02Hjxo1Gay5z6HQ6PPPMMygoKDBmrlTFXEyAUC4kmrg5H3zwAY4dO4Z69eqhQ4cOePvtt6v57lmiauDRMADlGx5C/4VVqUtQmblz5yIsLAzp6en45JNPEBERYbadYR+27Fq+/fZb1K5d2+R16dIlfPjhh9Xet4Wrj01kZCSSkpIwePBgLFmyBA8//DB69eplTPWrX78+WrRoUW27mJgY3HvvvSbviX2cCc/k+vXrKCgowLJly6pdT8OHDwegT6014OPjg5UrVyIzMxM3b97EF198YfbcUqvVaNiwocl7TZs2BaCvE1SZqtdMcXExsrOzja/r16+bfH7//fdj9OjR2Lt3L5KTk6v5ogLAgAEDTH7LwIEDAehTZyu/X1nYsQdz15dU371mzRpMmTIFI0aMwOjRo00+8/f3R3l5udntbt++bRQiDP81F7i/ffu2SRvefZojODjYrDgSEhKCjh07WtwOAI4fP45BgwYhODgYQUFBqF27Np599lkAMPG9BvRjY2BgoMl7Vc+zN954AzVq1ECHDh3QpEkTjBkzRnD69Ouvv474+Hjs3bsX06dPNxGxDVS9fj788ENcunSp2vvffvutoO+uCmOMxnNCfD74ADh2TG9R1aGD3pqKZ35UZW5kFAaqzI1gZW6EuXOBsDAgPR345BO9bZU5DPuofP6PHau3+DK8DBYNAweavl/5IfncOb0QYuY6NnLhgv6/zZqZvu/jAzRsePfz2Fhg4kRgxQqgVi29VdeiRaYiTdX+VyUsTP+7jxzRiyeffGK5X4yZ/n6CIMxizzPfhQsXEB0djYCAAJP3K9soV8bPz6/a82doaGi1Z0PA8vNho0aN8Pbbb2Pfvn1o2bIlpk6darF/vPf/hIQEk3nHK6+8AqD6POWDDz6wup9z585BrVabnfMYuPDvWNisyljp4+ODhg0bGj+PjY3FxIkTsWLFCtSqVQvJyclYtGhRtXmd4XeaIywsDJ988gmOHDmC4OBgfGJlrKS5EkG4nu7du+Oxxx7DjBkzUKtWLQwYMABffPEF9wIyR+NvPGPGvn37cP36dauiySuvvIKNGzdixYoVFq2ZKebmXnjZbkIomSeeeAJdu3bFL7/8gj/++AMffvgh3n//ffz888946KGHrG5buSZBZSoPROHh4WYngwYOHTpkDLIePXoUTz/9tNl2hn2Y8wSsTHJycrVCbs8++yx69+5drXieLVx9bKoyePBgvPXWW1i7dq3JamlAX2DQGmIfZ8Iz0el0APTXlCXP5tatW5v8e9OmTQD0gfMzZ84gNjbWru8ODw8HUH3iM3fuXBO/0vr165sILWVlZdi+fTsA/QPdrVu3qj3gzps3z2S/hw8fxv/93//h66+/RmRkpPH9qjWAhJKfn4+AgAATAUGK705NTcWQIUPQr18/s4XOo6OjodVqce3aNRMBtby8HLm5ucbvCgsLg6+vL7Kysqrtw/CeoS3vPm3Ro0cP9OjRg6ttQUEBunfvjqCgIMycORONGjWCn58fDh48iDfeeMN4vgqhRYsWOH36NH7//Xds3LgRP/30ExYvXoxp06YZz7Pw8HBUVFTg5s2bqFmzZrV9ZGRk4MyZMwD04605qt6nvvzyS/zxxx/4+uuvTd5v2bKl4N9Qmfz8fDRp0sShfRBENZ54Qp8l8ssv+poiH34IvP8+8PPPgLX5kYW5kYlAEB5eXUSpzKFD+qLoAHD0KGBhPmPcR+X5zKRJwL+iKgAgJ0f/77lzgcoPtgIyGgUzb56+DsnatfpjN24cMGeOvs5J3bp6UUSlsn4M/r2vIj8fuHxZn/ljjvx8gK5/grCJI898vFh6NqyMpbluZQxF1a9evYrc3FxERUWZbVdQUMD1PPfNN9+YZAMbfn/VeUrVxU1SM2/ePAwbNgxr167FH3/8gXHjxmHOnDnYvXs36tati7CwMKhUKqvHyvAMkp+fj8uXL5tkXVeG5koE4XpUKhV+/PFH7N69G+vWrcOmTZvw/PPPY968edi9e7fZDP/K2Iq/iTFmbNiwAQ0aNLAoDs+YMQOLFy/Ge++9h+eee87i95iLCRAKxrluYISrycnJYXXq1GH333+/8T1LdTs+/PDDatujisfg7NmzmUqlYgUFBdXaFhcXs0aNGrFWrVqxF154gWk0GrZ3716z/Ro5ciSrVauWXb/J3pomVXHmsTFHeno6A8Def/99Qf121nEm3J+KigpWs2ZN9vTTT3O1P3z4MPPx8WHDhw9nCQkJrF69etXOd4PP8+nTp03e/9///scAsG+//ZYxxlh5eTnz9/dnEyZMMGl37tw5lpqaanz9/fffJp+/8cYbTK1Ws7lz5zKNRmNSO8USUtU0SUpKMvGuFuu7K7N7924WGBjIOnfubLbeB2OM/f777wwAW79+vcn7O3fuZADYl19+aXzvvvvuY+3bt6+2j169erGGDRvatU+xMHjf/vnnnybvL1u2jAFg2yrVKejevTvz8vJixcXFJm2XLFnCALC0tDSz31FWVsb69evHNBoNKy0tZYwx9vXXXzMA7PDhw9Xaa7Va1rlzZxYVFcXefPNNBoD99NNPNn+LFDVN7ty5w/z8/Cx69xKEaOTkMFanDmOG+ZGlmiZm5kYMYKzyeTx7tr6mh7m5UXExY40aMdaqFWMvvMCYRsOYhfkMGzmSMVvzGZ6aJoa6IYcOWW6zerW+zYYNpu+Xlenrvzz2mOVtd+7Ub/vWW3ffa9xYX0PFHP/7n779pEn6Y962rb4OSlXu3GHMz89ybRSCICzC88w3atQo5u3tzUpKSky2NdQ6qVrTJDAwsNr3GLz4DVia6xowzFneeecdVqNGDfbII4+YbXf58mXBdQAN2Ourb6gBcMjKWLl69WoGgG2oMlaWlZWx4OBg9piVsdIwn3yr0ljZuHFjNsjCWGl4jpg0aRKrU6cOa9u2rdn6cjRXIgjXYStO98033zAAbPny5cb3qj7/GMZRQx1UA4axrPIztaNjRrt27djLL79sdntDbabx48db/D0GzMUEqKaJciF7LjdGq9VWS3ONiIhATEyMwz7qBhITE8EYM/rFV+aNN97AxYsXsWrVKsyfPx8NGjTA0KFDzX73gQMHkJiYKEqfeHDlsblx44bZtMEVK1YAgEXvWkvI+TgTykKj0eCxxx7DTz/9hGPHjlX7vLI11p07dzBs2DDExMRgwYIFSElJQU5ODiZMmGB2359++qnx/xlj+PTTT+Ht7Y0HH3wQAODt7Y377rsP+/fvN9muYcOGSEpKMr7uv/9+42d79uzB3LlzMX78eLz22mt4/fXX8emnn+LPP/906DjYy8GDB9G5c2fJ9n/y5En069cPDRo0wO+//25x9coDDzyAsLAwLFmyxOT9JUuWICAgwCTlePDgwdi3b5/JcT99+jS2bt2Kxx9/3K59ioVhRVHl8bK8vByLFy82276iosKknlN5eTk+++wz1K5dG+3atQNw10/cgI+PD+Li4sAYM/rrGsbIquciAMyfPx+7du3CsmXLMGvWLHTu3BmjR492iVf2iRMncPv2bUnPOcID0Wqr20lFRAAxMYAY86PERH3miZl5I954A7h4EVi1Cpg/H2jQABg61Pz3Hjig35ejDByot+eaOVNf26QyhrEnKUlvxfXJJ6ZZM59/rj9WhvGvqAioqDDdx7336vdf+TckJgJmxhcUFAAjR+ot0d59V2/zdfCg/v+rcuIEcPs2QNc/QVjF3me+5ORk3LlzB8uXLze+p9PpsGjRIrv7YmmuCwCZmZl4/fXX8dhjj+HNN9/E3Llz8dtvv+HLL7+s1tbwbOnM+//AgQOhVqsxc+bMapm+hnlaUlISfHx88Mknn5jM3T7//HMUFhYa54pFRUWoqDJW3nvvvVCr1SZ/k8TERLPHqqCgACNHjkSHDh3w7rvvYsWKFTh48CDeNTNW0lyJIORBfn5+tRhYmzZtAJi3irYHR8aMnJwcHDx40Owz7Zo1azBu3Dg888wzmD9/vs1+SB0TIJwL2XO5MTdv3kTdunUxePBgxMfHo0aNGti8eTP27duHefPmifIdXbp0QXh4ODZv3owHHnjA+P7WrVuxePFiTJ8+HW3/9ZT+4osv0KNHD0ydOtXEN/XatWs4cuSI1cJ6YuPKY/P1119j6dKlGDhwIBo2bIibN29i06ZNSE1NRf/+/U3a2kLux5lQHu+99x62bduGjh07YtSoUYiLi0NeXh4OHjyIzZs3Iy8vDwAwe/ZspKenY8uWLahZsyZat26NadOmYcqUKRg8eDD69u1r3Kefnx82btyIoUOHomPHjvjf//6H9evX48033zTxfx4wYADeeustFBUVISgoyGo/b9++jaFDh6JJkyZ45513AOhTZtetW4fhw4fj6NGj1epbCGXHjh3YsWMHAL1gVFJSgtmzZwMAunXrhm7duhnbHjhwAHl5eRaLUDrKzZs3kZycjPz8fLz++uvGYpoGGjVqZAz2+/v7Y9asWRgzZgwef/xxJCcn46+//sLXX3+Nd955B2FhYcbtXn75ZSxfvhz9+vXD//3f/8Hb2xvz589HZGQkXnvtNWM7IfsUi86dOyM0NBRDhw7FuHHjoFKp8NVXX1n0qo2JicH777+P8+fPo2nTplizZg3S09OxbNkyeHt7AwB69+6NqKgo3H///YiMjMTJkyfx6aefol+/fkYrroYNG6JVq1bYvHmzSY2ckydPYurUqRg2bBj69+8PAEhJSUGbNm3w8ssv4/vvv3f4N3/11Ve4cOGCsXDgjh07jOfcc889h/r16xvbpqamIiAgAL169XL4ewnCyM2behupwYP1llY1augLoe/bp7eecpQuXfQWXZs3A5XnO1u3AosXA9On361F8sUXQI8e+oLolf32r13T1/wQYz7TuDHw1lvArFl6S7JHHwV8ffW/NyZGb61VuzYweTIwYwbQpw/wyCPA6dP6/rZvf9cSbOtWfV2Vxx8HmjbVCyhffaW3LXvssbvfOWCA/v1//tG3M/Dqq0Burv7YaDT67xo5Epg9W79NZYux1FQgIACg658grGLvM9/AgQPRoUMHvPbaazh79iyaN2+O3377zTgPtter3txclzGG559/Hv7+/sbFKS+++CJ++uknvPrqq0hKSjKxQU1NTcU999yDhIQEu/pgD40bN8Zbb72FWbNmoWvXrnj00Ufh6+uLffv2ISYmBnPmzEHt2rUxefJkzJgxA3369MEjjzyC06dPY/HixWjfvr2xJt3WrVsxduxYPP7442jatCkqKirw1VdfGRdvVT5WX331Ff755x9jjToAePXVV5Gbm4vNmzdDo9GgT58+GDlyJGbPno0BAwaY1BmguRJByINVq1Zh8eLFGDRoEBo1aoSbN29i+fLlCAoKMokbOIIjY8aGDRvg5+eHnj17muxz7969GDJkCMLDw/Hggw/im2++Mfm8c+fOJvaGUscECBfgogwXwgmUlZWx119/ncXHx7OaNWuywMBAFh8fzxYvXmzSzhELKsYYGzduHGvcuLHx30VFRax+/fpmU94mTJjA1Gq1iVXKkiVLWEBAACsqKrLrd9pjz+WqY8MYY/v27WOPP/44u+eee5ivry8LDAxkbdu2ZfPnzzebVmwJZx9nwnPIyclhY8aMYfXq1WPe3t4sKiqKPfjgg2zZsmWMMcYOHDjAvLy8qllhVVRUsPbt27OYmBiWn5/PGLtrWXDu3DnWu3dvFhAQwCIjI9n06dOZVqut9r1eXl7sq6++stnHCRMmMI1Gw/bs2WPy/v79+5mXlxcbPXq0xW15LbIM6cDmXlWv9TfeeIPdc889TKfTWd2nvfZchrHH0mvo0KHVtlm2bBlr1qwZ8/HxYY0aNWIfffSR2f5dunSJDR48mAUFBbEaNWqwhx9+mJ05c8ZsP3j3KRY7d+5knTp1Yv7+/iwmJoZNmjSJbdq0yaw9V8uWLdn+/ftZYmIi8/PzY/Xr12effvqpyf4+++wz1q1bNxYeHs58fX1Zo0aN2Ouvv84KCwtN2s2fP5/VqFHDaIFmOLfr1q1bzYJuwYIFDABbs2aNxd/Ba8/VvXt3i3/jbVUshjp27MieffZZm/skCEGUlTH2+uuMxcczVrMmY4GB+v+vPD9yxJ6LMcbGjdNbVBkoKtLvz5wV1YQJjKnVjFW22FuyhLGAAP121uCx5zKwciVjCQmM+foyFhrKWPfujKWmmrb59FPGmjdnzNubschIxkaPZuzfex1jjLGMDMaef15vMebnx1hYGGM9ezK2ebPpfsrK9NZis2bdfW/tWn1f580zbWs4NvHxjJWX332/Y0fG6PonCJvY+8zHmN4q8z//+Q+rWbMmCw4OZsOGDTPaSH333Xcm2/LYczFmfq5rmEdUtfu8ePEiCwoKYn379jW+p9VqWXR0NJsyZYrgY8GY4xYxK1euZAkJCczX15eFhoay7t27s9QqY+Wnn37Kmjdvzry9vVlkZCQbPXq08bmAMcYyMjLY888/zxo1asT8/PxYWFgY69mzJ9tcZawsKytjtWrVYrMqjZVr165lANi8KmOl4bk4Pj6elVcaK2muRBCuo3Kc7uDBg+zpp582xsAiIiLYww8/zPbv32+yTdXnbCH2XI6MGYMHDzYZa6t+j6VXZatGxizHBMieS7nQX41wmHPnzjFvb+9qEx1e2rRpw+UNqEQcPTZi4s7HmZAvlh4kLfH888+zLl26SNgj8bl9+zaLiopiH3/8sau7QohEQUEBCwsLYytWrHB1V8xy6NAhplKprHqLE4RsOXdOLzzYOzdq04Yxpc9nZs5kLDaWsYoK4dseOqSvC0PXP0E4HUO9tao19oTgyFz3l19+Yf7+/uzq1at2f7+SmDlzJouNjWUVdoyVNFciCNciVu1hIdgzZty5c4cFBQWxRYsWOfTd1mICJJooF6ppQjhMw4YNMWLECLz33nuCt924cSPOnDmDyZMnS9Az1+PIsRETdz/OhPswffp07Nu3Dzt37nR1V7j54osv4O3tjZdeesnVXSFEIjg4GJMmTcKHH35YzbtbDrz33nsYPHiw0QuYIBRFw4bAiBGAPXOjjRuBM2f0dllKZsIEoLgY+O474du+957ePo2uf4KQlNLSUpN/a7VaLFy4EEFBQUZbZHtwZK77/vvvY+zYsYiOjrb7+5XEhAkTUFxcjO/sGCtprkQQnoc9Y0ZeXh4mTJiAQYMGOfTdFBNwT1SMWTAHJwiCIAgHGTZsGH788UcUFxe7uisEQRAEQRAEwcXIkSNRWlqKxMRElJWV4eeff8auXbvw7rvv0kI0giAIGzRo0ADDhg3D22+/7equuJyUlBQMHz7cYm1OQr5QIXiCIAiCIAiCIAiCIIh/eeCBBzBv3jz8/vvvuH37Nho3boyFCxdi7Nixru4aQRAEQRBOgDJNCIIgCIIgCIIgCIIgCIIgCIIgAFBNE4IgCIIgCIIgCIIgCIIgCIIgCJBoQhAEQRAEQRAEQRAEQRAEQRAEAcANa5rodDpcvXoVNWvWhEqlcnV3CIKQMYwx3Lx5EzExMVCr3UNDpjGQIAge3HH8A2gMJAiCD3ccA2n8IwiCFxoDCYLwVISMf24nmly9ehX16tVzdTcIglAQly5dQt26dV3dDVGgMZAgCCFIOf7t2LEDH374IQ4cOICsrCz88ssvGDhwoMX227dvR8+ePau9n5WVhaioKK7vpDGQIAgh0ByQIAhPhsZAgiA8FZ7xz+1Ek5o1awLQ//igoCAX94YgCDlTVFSEevXqYcGCBfjxxx+RnZ2NmJgYDBs2DFOmTDGuUGGMYfr06Vi+fDkKCgpw//33Y8mSJWjSpIlxX3l5eXjllVewbt06qNVqPPbYY1iwYAFq1KhhbHPkyBGMGTMG+/btQ+3atfHKK69g0qRJJn364YcfMHXqVJw/fx5NmjTB+++/j759+3L/JhoDCYLgwTD+GcYMKSgpKUF8fDyef/55PProo9zbnT592mT8ioiI4N6WxkCCIHhwxhjobGj8IwiCFxoDCYLwVISMf24nmhiCnEFBQTRQEgTBxcqVK/Hll1+iZcuW2L9/P4YPH47g4GCMGzcOAPDBBx/gk08+wapVqxAbG4upU6ciOTkZJ06cgJ+fHwDgmWeeQVZWFlJTU3Hnzh0MHz4cL7zwAlavXg1APzD37t0bSUlJWLp0KY4ePYrnn38eISEheOGFFwAAu3btwtNPP405c+bg4YcfxurVqzFw4EAcPHgQrVq14votNAYSBCEEKe0LHnroITz00EOCt4uIiEBISIhd30ljIEEQQnAnCxca/wiCEAqNgQRBeCo84597mBcSBEE4QN++fdGvXz80aNAAgwcPRu/evbF3714A+iyTjz/+GFOmTMGAAQPQunVrfPnll7h69Sp+/fVXAMDJkyexceNGrFixAh07dkSXLl2wcOFCfPfdd7h69SoA4JtvvkF5eTlWrlyJli1b4qmnnsK4ceMwf/58Yz8WLFiAPn364PXXX0eLFi0wa9YstG3bFp9++qnTjwlBEISraNOmDaKjo9GrVy/s3LnTatuysjIUFRWZvAiCIAiCIAiCIAjCEUg0IQjC49mxYwf++ecfAMDhw4fx999/G1dHZ2ZmIjs7G0lJScb2wcHB6NixI9LS0gAAaWlpCAkJwX333Wdsk5SUBLVajT179hjbdOvWDT4+PsY2ycnJOH36NPLz841tKn+PoY3he8xBAUOCINyF6OhoLF26FD/99BN++ukn1KtXDz169MDBgwctbjNnzhwEBwcbX+RlTRAEQRAEQRAEQTiK29lzEQRBCOXRRx9F8+bNodFooNVq8c477+CZZ54BAGRnZwMAIiMjTbaJjIw0fpadnV3Nc9/LywthYWEmbWJjY6vtw/BZaGgosrOzrX6POebMmYMZM2YI/ckEQRCyo1mzZmjWrJnx3507d8a5c+fw0Ucf4auvvjK7zeTJkzFx4kTjvw0etQRBEARBEARBEARhLx4tmmh1DHsz83Dt5m1E1PRDh9gwaNTK9nQsLddi9vrjOHK5EMH+3hjVtSG6NKktyu8qLddi5u/HsDsjDz4aNQYl1MHzXRrCx0u5CUtS/qbyCh0+//scfjl0FXe0OnRuGI4pD7eEv49GhJ67Bq2OYcfJa5i35R8Ult5Bs6ia+PjJBNTwE2coKb5dgQlrDuFifinuCfXHRyLu2xo//PADVq9ejZYtWyI9PR3jx49HTEwMhg4dKvl3O4ojAUN3HAMJgnAvOnTogL///tvi576+vvD19XVij+SN4T79/qYTyLh+C0ylQkSQL57pWB8juyp7ziYFlY/XuWu3oFMBQf7eSG4Zhen9lT1nk4rSci2m/XYEG49koaScwcdLjYa1A/F/vZujezNxnjk8lTlz5uDnn3/GqVOn4O/vj86dO+P99983EZMJQgjlFTos/+ssvtl9AYWlFQgN8MF/6H5gluLbFXjlm33YeSYP5dBbsgT6avDQvdGY8Ugruh8IZMeOHfjwww9x4MABZGVl4ZdffsHAgQOtbrN9+3ZMnDgRx48fR7169TBlyhQMGzbMKf0FTOcEZ6/dQgX054G/jwYdYsOw8Om2TolNKAnDdbPrbB7KGB0vWxjG5K92ZSL7ZgUAwFsNxIT4Y+rDLdGzeQTNoypR+Xjl3KwAA6Bx8lzdY8/gjceyMGPdCWQV3ja+FxbojdkDWqFv6xgX9sx+Rq7ah80nr5m89/fZXHirgYX/aYs+raLt3veoL/ch9YTpvt/beBrvbTyNF7vFYnLfOLv37Sqk/E1zNpzAZzsyTd7LvHEL3+y9hF5xEVg+pL3d+3YVG49lYezqQ6jQMeN7l/JL0ertTWhdNwi/je3q0P77L/wLR6/ctZY6nX0Trd7ehFYxNfD7uO4O7dsWEyZMwFNPPQUAuPfee3HhwgXMmTMHQ4cORVRUFAAgJycH0dF3r6GcnBy0adMGABAVFYVr10zPpYqKCuTl5Rm3j4qKQk5Ojkkbw79ttTF8bg57A4bmxsDoYD9M7x/n0FhBEAQhJunp6SZjL2GZDUeyMPbbg6h0mwYYw5WC2/hg02l8sOk0RnVtgLf6tXRZH+WEfl5zEBW6Sm8yIP/WHXy37xK+23cJDzavhc+HdXRZH+XGiJR92HLKdL5zu0KHE1k38fyqfVADWPysY88cnsyff/6JMWPGoH379qioqMCbb76J3r1748SJEwgMDHR19wiFYe55tKT87v1g+P33YHr/e13UO/mg1TE8OG87zufeMnlfB+BmmRbf77+M7/dfRs+mYfji+UTXdFKBlJSUID4+Hs8//zweffRRm+0zMzPRr18/vPTSS/jmm2+wZcsWjBw5EtHR0UhOTpa8v+sOX8Wr3x6Crsr7OgAl5VpsO30drd7ehJbRgVj/ag/J+6ME+i34C8ezTK3Bqx6v+qG+2Pr6gyQEAJi57jhW7jxf7f07OuBCXilGfrkfAPDpU23wcJs6Tu6d/Jj1+wl8/ndmtfe1VebqUo/NHrm8YOOxLIz++qBJsBAA8kru4OXVhzBnwwkX9cx+Hvn0r2qCiYE7OuClrw9i47Esu/ZtTlyozGc7MhV3zKT8TeYmqJVJPXENo77cZ9e+XcXGY1l46euDJoJJZY5cLsIjn/5l9/7bz041EUwqc+xqMdrPTrV73zyoVKY3cY1GA51OP2WKjY1FVFQUtmzZYvy8qKgIe/bsQWKifnBOTExEQUEBDhw4YGyzdetW6HQ6dOzY0dhmx44duHPnjrFNamoqmjVrhtDQUGObyt9jaGP4HrGwNAZmF97GaAfGCoIgiMoUFxcjPT0d6enpAPQPxOnp6bh48SIAfabckCFDjO0//vhjrF27FmfPnsWxY8cwfvx4bN26FWPGjHFF9xXFnA0n8PLqKoKJGZb/dV5xcxApuDuvsd5uy6kb6PbBFuuNPITuH26tJphURQfHnjk8nY0bN2LYsGFo2bIl4uPjkZKSgosXL5rMLwmCB1vPowDwxc6L6Pr+Zif1SJ5sPJaFRm9uqCaYmGPbP3loOXWDE3rlHjz00EOYPXs2Bg0axNV+6dKliI2Nxbx589CiRQuMHTsWgwcPxkcffSRxT/WLj18xI5iY43hWCVpN+5/kfZI7Td7aUE0wMceF/DI0enODx88Lun2w1axgYo6x36Vj5Kq90nZI5jyy8C+zgok5pB6bJRVNduzYgf79+yMmJgYqlQq//vqr1fbbt2+HSqWq9rLm5y8UrY5hxroTsPZM+dmOTGw4opyL+reDl3Hksu0Ba+L3h6G19TRdhdJyrVVxwcCyHZkot/XkKROk/E3lFTqbE1RAL5yUlmsF7dtVaHUM//f9IZvtjlwuQvHtCsH7n/7bEVwvLrfa5npxOWb8dlzwvnmZN28e1q9fj/Pnz+OXX37B/PnzjRM8lUqF8ePHY/bs2fjtt99w9OhRDBkyBDExMcYU4xYtWqBPnz4YNWoU9u7di507d2Ls2LF46qmnEBOjz1z7z3/+Ax8fH4wYMQLHjx/HmjVrsGDBAhNrrVdffRUbN27EvHnzcOrUKbz99tvYv38/xo4dK9pvtTYGGt6bse6E4LGCIAiiKvv370dCQgISEhIAABMnTkRCQgKmTZsGAMjKyjIKKABQXl6O1157Dffeey+6d++Ow4cPY/PmzXjwwQdd0n+lsOHIVa65h4HUE9ew7vBVCXskb7Q6htFfH+RufzHvNp5P8eyH1xnrjuJCbil3+5e/OUjzCBEoLCwEAISFhbm4J4SS4H0eBYBL+WXo+r5nCsMG8VwIJXcY4ihgLglpaWlISkoyeS85ORlpaWmSfu/IVXstLj62RHG5Dl3e88zrBgAaT16PO1ph93hPXlDR9b3NuJjHP4cCgM0nr2PkKs9c5DQiZQ+OWFhQbQkpx2ZJRRNDSt6iRYsEbXf69GlkZWUZX1ULLDvC3sy8aqurzTHppyOKmOxrdQyv/XiYq+2tci12nbkhaP8vfsV3oTIAq3bxP7C7Eil/U8pO/vYvcPbD1ezOyEVxOd+18J/lwiY15RU6rNp1iavtF7vOSybMDRgwAC+//DJatGiB//u//8OLL76IWbNmGT+fNGkSXnnlFbzwwgto3749iouLsXHjRvj5+RnbfPPNN2jevDkefPBB9O3bF126dMGyZcuMnwcHB+OPP/5AZmYm2rVrh9deew3Tpk3DCy+8YGzTuXNnrF69GsuWLUN8fDx+/PFH/Prrr2jVqpVov9XWGMgAZBXext7MPNG+kyAIz6RHjx5gjFV7paSkAABSUlKwfft2Y/tJkybh7NmzKC0tRW5uLrZt24aePXu6pvMKQatjGLva9sKGqoz79pAi5rlS8PEfp60unjLH1lPXPVZoKq/Q4YudF203rISOAWO/oewIR9DpdBg/fjzuv/9+i/PAsrIyFBUVmbwI4qu084LaX8r3PGFYq2OCBRMDt8p16Pr+VpF7RGRnZyMyMtLkvcjISBQVFaG01HzA2dEx8Pf0K9h88rpd/b1c4HnXDQB0fncTKuycPr70tectqHj+i924VFBm17abT3reIqff069gyylhMWsDUo3NkoomQlPyDERERCAqKsr4UqvF6+a1m7YFEwAoLqvA7nO5on2vVOzOyMUdAXHkHw/yBagB/WTi77P8x0AJF7TUv+mPE/xZUbvO5SriprHrHP+gdexKkaDftGrXeUF9kUqYe++993DhwgWUlpbi3LlzmD17Nnx8fIyfq1QqzJw5E9nZ2bh9+zY2b96Mpk2bmuwjLCwMq1evxs2bN1FYWIiVK1eiRo0aJm1at26Nv/76C7dv38bly5fxxhtvVOvL448/jtOnT6OsrAzHjh1D3759Rf2tvGMgbzuCIAjCdSxIPc1lJ1EVBs8Mamt1DAu3n7Nr24nfpyti3iY2z63Ybdd2/zueo5gsdDkyZswYHDt2DN99953FNnPmzEFwcLDxVa9ePSf2kJArF/JsW01VxdOE4Y7v/OHQ9pfySzFznXQuCAQfjoyBWh3D2O/SHfp+T7tuZqw7iqtFwp1FKpM0b7s4nVEAv6dfwdbTjsWUPWmRk1bH8IqD16QUY7Msa5q0adMG0dHR6NWrF3bu3Gm1rVB1OaKmn9XPK7PznH2qszNJEyjsXCngD4Tuzsi16Y1dmVPZxbK/oIX+phNZNwX9pqv5/MdXq4MihLkr+fyphDoI+017zwv7/fvO5wtqT1SnViBf0XjedgRBEIRr0OoYFtkpAACeGdRekHra7m3vaJngjG2lU16hwx4H5l7//YkvG54wZezYsfj999+xbds21K1b12K7yZMno7Cw0Pi6dIl/cRzhvtQPC7Bru1e/84zg3Ix1R3GjxLHALwCs3CmdC4InEhUVhZycHJP3cnJyEBQUBH9/f7PbODIGjlm936H+GvCU68aerFNzZObewtr0KyL0SN5odQzjHBQAAM9a5GRPJrg5xB6bZSWaREdHY+nSpfjpp5/w008/oV69eujRowcOHrScOilUXe4QGwZfL5XVNgaEBItdBRN4WqkEtBeSYQAA5Vqd7C19hIpMFTrGLQJodQw5RcJW56dlyP/h+0q+sNVKQsTGSxxF9ypTUGJfaiNRCb7hj78dQRAE4RJ2nb0BgZbS1VCKtaoYaHUMS/60X2QCgLd/PyZSb5TBfzktgC3xa/pVjwgmiQVjDGPHjsUvv/yCrVu3IjY21mp7X19fBAUFmbwI4rnEBnZtp2PAK6vts6xSCmIFfg0897l9mXhEdRITE7Fli2mdkNTUVCQmJlrcxt4xsLxCh43HhNUxsYQnXDeA/Vmn5piwxv0zd+3NBDeHJyxyciQT3BxiLtqRlWjSrFkzvPjii2jXrh06d+6MlStXonPnzvjoo48sbiNUXdaoVWhdN4SrPzy1T1xNkJ+3oPanc/izQewRjbIL5S00CRWZAH4RYHdGLoSWdpf7vUKrYzh8uVDQNlc5s5m0OoYLuSWC9n1KwPlLmOdGMZ/wtOVkju1GBEEQhMuYIUL6uSfZSgi1tDXHueu33P7B1YBWx/BLumPnh47B47JzHGHMmDH4+uuvsXr1atSsWRPZ2dnIzs626OdPEObw8VLjxW7WBTdLbDiW7dZjnJiBXwDYk5nv1sfLEYqLi5Geno709HQAQGZmJtLT03Hxol60mjx5MoYMGWJs/9JLLyEjIwOTJk3CqVOnsHjxYnz//feYMGGC6H0T+zxw9+vG0azTqugYsCD1H9H2Jze0OoaF28QTAAD3F2jFyvwy8Msh8RbtyEo0MUeHDh1w9uxZi5/boy53iA3j+u4TWcLqM7iCQxeFDV5Ftyu4s0GEZhgA/AFZVxHiL0xkAvhFAKFZLAAQGuBju5EL2Z2Ri3KBy1hL7/BJR3sz81AqsIqYkPOXMA+vReFaWh1KEAQhW8ordDh7XdjCA3MIrUWmZL4UKatm8s9HRNmP3Nl19oYoNgmfbHXfwIjYLFmyBIWFhejRoweio6ONrzVr1ri6a4TCmNw3zm7h5IudGSL3Rh6IHfg14O7BTHvZv38/EhISkJCQAACYOHEiEhISMG3aNABAVlaWUUABgNjYWKxfvx6pqamIj4/HvHnzsGLFCiQnJ4vaL6nOA3e2oxRbZAKAxX+eddv554JUcWymKuPOAq2YmV8GGMQT5mQvmqSnpyM6OlrUfXZuVIurXXGZVtYBWq2O4S87Vm/xZINodQzHr1qvD2OOgtI7grdxJmF21GmIDuELMtuTxRIWKG/RxB4hKI2zwH22QCsz43Yyz2aSOx1iwxAWaFs8zC0pl/X4RxAE4cms2nVelP3o4BmZAFodw+ZT4jyQecqigoUiiR0HLhZ4xPESA8aY2dewYcNc3TVCgUzuG4d/Zj+E2HD+mq4AsPJv97RtdNRu0BLuHMx0hB49epgdz1JSUgAAKSkp2L59e7VtDh06hLKyMpw7d06SsU8KAQAQd2W7nJBKZKrQuef8U4osEwPuKtBO/lmasVksYU5S0URoSt7HH3+MtWvX4uzZszh27BjGjx+PrVu3YsyYMaL2q1PDcPh78/10OQdo92bmoaRcqCEUkFdSzrXvW3Z4GDCZ3yfSBNZpAYBrRXzZM/ZksdjTH2dijxDEmw1y46Z9WUk85y9hGY1ahUFt6nC1vXZT/haFBEEQnsi6I+IV0fSETIDdGbnQihTTElLvTqlodQz7zxeIsi+y6CII1+Hjpca21x9Em7r89W5ybpa7nQig1TH87KDdoDXcNZjpbkglAADirmyXE1KJjYB71okTmmUipIysOwq0Wh3DLwelGZvFEuYkFU2EpuSVl5fjtddew7333ovu3bvj8OHD2Lx5Mx588EFR+6VRq9DvXr7sFTkHaO1dqR/CYQll777lbDel1TGknhBep2HLyWtcCqU9WSybOfftKuwRggA+sTH/ln3XFs/5S1gnKS6Kqx2vlRdBEAThPLQ6huNXhGcDW+LgJffPBPgq7byo++Otd6dUdp29IVoBU8AzhDmCkDM/vdxFUHt3sxpakHpaUPuFTyegSe0A7vbuGMx0R4TOBU7O7CMoqL10xzm3mk8JrW3WJCIQ43o24m7vbnXitDqGJX8KyzI5MbOPoPbuZhErdL750RPxgq7Jnw5dFtqlakgqmghNyZs0aRLOnj2L0tJS5ObmYtu2bejZs6ckfUvktOiSc4A2z876IQUcwWp7931V5pk5hbcrBG9XUHqHK3PCnqwR3n27CnuEIIBPbFQJGe0qwXP+EtZpVz8UahvHX63StyMIgiDkxe6MXAgsN2YVrQ5unTmh1TFsOSl80Yw19sl47iYGYllzGfAEYY4g5IxGrRIUzHQnqyGhgcwmEYHoHx+D9a92F/Q97hbMdEcu5PHX7O0YGwp/Hw0WPtWGe5tyrXtlogqtbbZ+XDe82quZoO9wp+tmd0YuhJj1jOvZGP4+GnRswB9zcTeL2BnrjnO3rV3DB4Pa1sUrAu5lt+xwZqqK7GuaSAVv4FXOAVp7BR2e7ezd928yvojtzZ4BbGdO2JvFwrNvV2Lv+c91jtmZxSJnIVMpHLiQD1uXqY7p2xEEQRDyYpcE1p7unDkh9CGWh8OXC2U733UUrY5h/4UCkffp3sIcQSgBIcFMd7IaEnoPWD+uGwC9tZknBzPdkfph/NlDX43oBAB4uE0dRAXxLyRdleY+NYGEBLQ7xobCx0sNjVqFR9vEcG/nTtfNTgFWUGoV8GqvpgCAr0Z24t7OnSxiyyt0OHu9hLv9x0/qHaxe7dXM5gJgA+0bhNnTNRM8VjQJq8E38PG2cwX2BrR5MiLs3beci0fbmz0D2M6csDeLhWffruRyPv9qjMrwnD9XC+wTi+QsZCoF3lolVNOEIAhCflzJ57t/eqv5J/runDkhRGSKrcUXUHG31aSV2Z2Ra3NhhYGmAuxr3FmYIwglIDSY6S5WQ0ICmY1qB8DH6+6d01ODme7Kc4kNuIKtI7rEmpwHHw6O5/6Oraeuu8V1IzSgbRCZAOA9AcfLna6braeyuduO7dEYmn9PRh8vtaC6U+4izK3adZ67rY9GhU6NwgHo72WfPNnG5jYqFTC0cwP7OlcJjxVNIjjFEN52riAs0L4V9zx1NBxZzS/XzAlHfpOtbR3JYpFr5oRWx7D2sH1FmWyJjY7s+7KdYgtxl1qctmu87QiCIAjnwbugoU29ELTjXCXrzpkTQhbzPH3fPdxt3VUEECIyvdW3JQlzBKEghAQz3UUcFhLIfPvhVib/9tRgprvi46XGqK6xVtu0rhuEqQ/HmbzXuXEt7nudu4gAQgLaVcVGodfNl7v5v0uuaHUMp3P4RCYV7maZGHi9Twvu73IXYe5rAX/3l7o1MopMgD4DrFdchNVtXuhqKn7ai8eKJrzVY/adl+8EPyLIviLNPHU0HFnNL9fMCUd+k61tHclikWvmxN7MPOSV3LFrW1tioyP7lrMFnGLgrSdjZ90ZgiAIQhq0Oob0iwVcbWNC/NEhli8t3V2CY1XR6hiOXC7gauujUWFYl1h48T4juKkIwJvJ5KUGujSrTcIcQSgIHy81GtcO5G6v9GCmkECmWgV0blK97q2QYCbP4lTCtUzuG4cXu8WazTgZ0aU+fhvbtdr7GrUKg9ryZ2m5g3gmJKBdVWwEhF03204p/7oRUv/lvvohJgIAAHRqGM49/3QHYa68QocLeXzzTXMiEwAsH9Ieo7rGVquVrFYBL3aLxeS+cdW2sQePFU1ucAa5U9LOy/YC3ptp/4ViKxvEXlsmQL6WZvZm5gC2f5Mj2SJyPV6OWDPZEhsdycyRswWcUuAd/3jbEQRBEM5hd0YuKjinpXVC/dG5UfUAkCXcMXNib2YeyjgPWM/mEfDxUiOhvmeLALzPAAn19A/9ni7MEYTSmN6/JXdbpQczhQQy291TPZAJCAtm6hiwS4AdGOEaJveNw6lZD2FqvxYYklgfU/u1wD+zH8JUM8F/A3Me9RyLLiEBbUtiY6eG4fDmjDa7w/xg4Vb+GlDjHqguAGjUKgxI8BxhTkgmU6+4CLNjMwC81S8Op6tcy6dmPSSaYAJ4sGgSUZMvS6Pglu2sDFeg1TGs2nXB7u2tZYM4Yp0EyNfSzN7MHMD2b3IkW0Sux6uWA/2yJTY6kpkDUK0NRyF7LoIgCGUixDrp/ka1BQV73DFzQsgijSGdGgCAR4sAWh3D0SuFXG1jQvwBwOOFOYJQGkKshpQ+zjkayASEBzN/PHiJuy3hOny81BjRtSFmDmiFEV0b2rTx8fFSo36YP9e+lZ4JICSgndTCfEBbo1bhwRaR3PtR8vxAq2PYf6GAq60lkQnwLGFu3ZEr3G2HJlq31BN6LQvFY0WTDrFhCPH35morxwDt3sw8FJTaZ28EWM9ucMQ6CYB8LX0cGFNsZU44ksXijsfLltjo0PECBfMdhuy5CIIgFAnvQh5DwUSNWuXRmRM3bvIt0vD3VhsLTHqyCCAkM6dOqD54JESY47X+IghCOjRqFXq35A9mKtWiS6tj2H++gKuttUAmICyYySs8E8rj2X8XV/Cg1OsGEC+g/VxiA+79bDrOX3tIbuzOyAXv9NlSRhvgOcKcVsdw7HIRV1sv9d0C8K7CY0UTjVqFoZ3rc7WVY4DWUSHHWnaDI9ZJgHwtfbaeyrF7W1uZE45ksWw5aX+/pOSahNkgjhwvABTMdxDea1Su5yZBEIQnotUxHLqQz9U2vm6w8aHMkzMn9p/n+z3dmtY2Hi9PFgGEPAPc36g2AAgS5rIK5bcQjSA8ESHBTKVadO06ewM6zrbWApmAPpgZE8z3/Ho+95Yijxdhm6GdG3C3Vep1o9UxHL8iTkBbiEXXueu3UF7Be8XKiy938VtlWcpoMyBEmFPqwh0hY/MDzWtbHZudgceKJgDQIZZTsZJhgNYR6yQAVn+To9ZJchSZtDqGX9L5FfOq2LRpc+B+uFamhc0lPQ8c/LlyFeaUAq89oVzPTYIgCE9ESD2T9pWEEk/NnNDqGLaeusbV1t9bY/x/ISKAqmr1SYVjT2YOwC/MHXHDbCaCUCKeUG9ADGuuygxsU4drX1TXxH3x8VKjUe0ArrZKvW52Z+RCy3mbthXQFmrRtUqA+CAXtDqGzZxzTVsZbYAwYU6ptrpCxmZb1lzOwKNFE0UXQ3bweeOalZVkjlonyVFkcthyDNYzJxzJYpFrYXMpzwNHs1jkKMwpiQ6xYQgLtG1PKNdzkyAIwhMRWs/EgJDg2NUC98kE2J2RizucS9liQkwXE9zXgE80Kb2jFdotWWNPZg7AL8zdrtApMohEEO6Gu9cbEKvGQGXu52hj4BMBQUFCWfRpFc3dVmnXDSAsa4InoC0kq22dA3WVXcXujFxoOeearWKCbGZN+Hip0bAWn0WXEm11hYzNGjVcbs0FeLhoouRiyDdKHAs6WysE76h1khwtfcSoS2PpPHA0iwWQZ92cqGC+wdoS1oQ5R7NY5CjMKQmNWoVBnKul5HhuEgRBeCK8VlCGeiYGNGoVHmgewbWtO4kAaQKC85VFJgAIC+Cb++/4R9mFOCuj1TH8ybk6unJmDqAX5nw0fJMzJQaRCMIdERLMVNqKZrFqDFSmU8Nw8LrEHLxU4Db3BsIUIdm7SrPwFJI1wVtrolPDcGg4o84nsm4q7roRsqCpf3wMV7vWdfkW7igxm0nI2Ny2Ht/YLDUeLZoouRgyr72OJS4XWBnAHRyn5Gjp47CdGWDxPBAji0WOwly7+qHcE0NzWBPmLuffsn/HkGn2l8J4oDnf6jI5npsEQRCeSGl5BVe7ns0jqj1k3NeAb6WWO4kAjHNC6+elrvbgX6sm372v9I77ZE7szczDbc7UnKqZORq1CvF1g7m2VVoQiSDcFSFZiEpb0fxV2nnutjzWXIB+nLuvfghXW60ObnNvIEwRUvfsqrWYmwwRkjURF12TK6CtUauQxLlwR4nFzYW4cgztzGc1VSeUf/Gy0haiCBGZeMdmqfFo0UTJ9lyOBrR/syJsOJrFIktLHxHmeJbOA1FW4stQmDtwIZ9bBTZHmAWhSqtjWOtg6iUF8kVAwaIxQRCEp6HVMWz/h+/B6D4z9Tg8UQTIKuBboNH33uhqD/5RArKu0zLcw7veniLwlakTyufzTsXgCUIeCLHoUtKKZq2OcTtfeKn5rLkMvCIgiKe0YCbBh5C6Z0oTG6XImgCAIZxiAaCs60arYzh0IZ+rbaPaAfDx4gu/C8lmUloWIG+cWOjYLCUeLZrwZms4mtUhBY4GtK0JG2JkZcjN0sdRIQiwHKgX43jJUZjbfCLboe0jLBwXMTJzKJDvOEoWjQmCIDyN3Rm5KOOsAm9uXuJpIoBWx7DxOF/QLCq4+rHpEBuGAB++xyQFxUOsYm8ReAO8KyOpGDxByAchFl2r0pRRpFlIPasEgfYvnRvX4g6gbTru2LM0IV86xIZxtVOS2AgIywTlzZoAhGXnKEkE2J2RC86pOZJbRnHv112zAIWITELHZinxaNGEJ1tDrdK3kxtiiBIW9yHCNSe3TABRhC9L54oIx0tuwpwYdVosHS8xzl0K5DuOkms6EQRBeBpC6nOYq0nmaSLA3sw83Crni5qZexbQqFXo05JvBXa2m2RO5N3im1tVLQJvgIrBE4Ty6NQwHJzliLD1lDLsG4Wslm/PGfw2oFGr0K4BX2zo3PVbKK/gVG8IRSEkE0BJmRO8FupCsiYA983OETLWmMvQtYS7ZgEKEZmEjs1S4tGiCU+2ho7p28kNMbIbLAVDt54SoZC7PERBI47amQGWA/WOZrHIUZgTIxvEUlq0lOcuIQCy5yIIglAMvPU5avhqzK6A9DQRQIjVVGJD88GP6BA+u6k/TmQr5gHfGgfO8z3vVC0Cb4CKwROE8tCoVWhZJ4irrVLqDQixCRcSyDTAm2UAAKt2KSM7hxCGO2ZOaHUM6RcLuNq2iuGrYVYZd8zO4R1rfDQqsxm61hCSBaiUOZVUIpPUeLRowrviPdVBmyJJEOPZzMxAL0qGAeSXCeConRkgnT2XHIU5MbJB1lqqmyPRuUsI4xpnQIm3HUEQBCEdQX7eXO16x0VZTGf3JBHAUaspAFBxTjaKy7Tyq+UnEK2OIf1SAVfbqkXgDWjUKrSpF8K1j6sFNLcgCLnQv3Ud7rZyD84JsX+xJ5AJCMsyWOdgHU9Cnrhj5oSQLAAhhcoNuFt2jpCxJr5usGCrKXcU5njt3+wdm6XCo0UTXkski8FfFyJGjQ5zwoYo9SYgP7spSYu1i3BqyK0GjBh/P0t1c6Q6dwlh5JWUi9qOIAiiKjt27ED//v0RExMDlUqFX3/91eY227dvR9u2beHr64vGjRsjJSVF8n4qgUMX+R7MzNXnMOBJIoCjVlMAkCjggS27kN8HXI7szshFuZZvQmtt9R+vbU3pHS1XO4IgpGdo5wbcbeUenBMS+O3ZPMIuz/xODcOh4Yyinci6Kbs4EiEO7pY5IXUWgLuJAFJbTbmjMMdr/2aPyCQlHi2adIgNQ1ig7ZV71oqmu4rzN0oc3oe5zAkxgvdytJsSwxLKkt2UGCKA3EQmMezMAPPnkxh/i/M3+AZcwjJhnH+HywXKDgQRBOE6SkpKEB8fj0WLFnG1z8zMRL9+/dCzZ0+kp6dj/PjxGDlyJDZt2iRxT+WNVsew7TTfqjtr925PEgEctZoC9A/4vpxP+EpfzMFbM8fPy3JmDgCEBfDNLdLO5SriAZ8gPAEfLzUa1ebLRJR7cE5I4HdIpwZ2fYdGrUJS8wiutkqxNCOE426ZE1JaTQHuJwI4w2rKnYQ5IfZvMSHCM5mkxKNFE41ahUFt+NJR5ZQJoNUxfLv3ouM7MvMcKEZAW452U2Jkg1jKOHL0mMlRZBLDzgywYGkmwn6/23dR9jdSuRMVxCfU/SbDTDuCIJTBQw89hNmzZ2PQoEFc7ZcuXYrY2FjMmzcPLVq0wNixYzF48GB89NFHEvdU3uzOyEUZZzFZS/U5AM8RAcSwmgL0zwk9m/EFxgpKHc/SdiW8NXN6NLecmQMAtWryzYmLblfIbkEaQXgyfVpFc7WTe3BO6sCvgSGdY7nbKiFgTgjHnTIntDqGI5cLuNo6kgXgTiKAM8YadxLmpLZ/kxKPFk0AICkuiqudnDIB9mbmIbtIIosjkWKjchKZAHGyQSxmHDl4zOQoMon29zNzPxXjb5FVeJseth1EyZl2BEG4J2lpaUhKSjJ5Lzk5GWlpaS7qkTwQKwvAU0QAsaymAKBh7Rpc+zl7rZirnVzhrZmTUM/6Ih/eBRmA8rOZCMKdcIfgnNQ1BirjTgFzwj7cKXNib2Yeyjgj2vZYTRlwh3EGcJ7I1KlhOLw5I/ZyrxWn1CLwAIkmXDZEcssEECugbU4IEiOgbWnfrkQMOzPA/LHfesq8bZcQUk9kO7wPMREj4wgwL8yJtW+5CXNKQ6mZdgRBuC/Z2dmIjIw0eS8yMhJFRUUoLTUfYC0rK0NRUZHJy90QKwsA8AwRQCyRCQBCA3y4v1POARFb8NbMKbQhpnWIDUMNX77HSyVnMxGEu+EOIoDUNQYq404Bc8J+3CVzIruI/1nfkYC2u4gAzhKZNGoVHuC0ApR7rThnZQFKgceLJjw2RHLLBBBDkLAkBIkR0JabyCSanRmq201pdQy/pF9xeL+WrL9chkhdMXuuSrlvQhAPNI+03QgWbNYIgiBkwJw5cxAcHGx81atXz9VdEh2xsgAAzxABxBSZPMFuSqyaOYD+Ab9LY76AipKzmQjC3XAHEcDZK5ndJWBO2I+7ZE7cuMm3iMHf2/ZiE2u4iwjgLJEJAO5rwHe8d/xzXZbjMuC8zBypkFQ02bFjB/r374+YmBioVCr8+uuvNrfZvn072rZtC19fXzRu3BgpKSlSdpF7BbWcVlqLUaTbohAkwnUmN5FJLDszANXspvZm5iGvxPGHPrlZIImRcWRJPJNy34RAeMcRed23CIJwU6KiopCTY5q9mZOTg6CgIPj7m/e3nTx5MgoLC42vS5cuOaOrTkWsLADAM0QAMUUmT7CbEqtmjgFPyGYiCHdE6SKAs1cyu0vAnLAfd8mc2H+e73ru1tT2YhNbuIMI4CyRCeCft5fe0clyXAacl5kjFZKKJiUlJYiPj8eiRYu42mdmZqJfv37o2bMn0tPTMX78eIwcORKbNm2SrI+8q9XltKpdrCLd5iyhxLLnkpPIJGZfqloJiLlvOR0zMc53S+KZGFZpchPmlAqvNQZZaBAE4QwSExOxZcsWk/dSU1ORmJhocRtfX18EBQWZvNwJMbMAAM8QAcQUmTzBbkpMOzPAM7KZCMIdUbII4Mx6JgbcwdKMcAx3yJzQ6hi2nrrG1dbfW+Pw97mDCOBMkUnIvF1u47IBZ2bmSIGkoslDDz2E2bNnY9CgQVztly5ditjYWMybNw8tWrTA2LFjMXjwYHz00UeS9dGTa5qYs4QSSxw6f+OWKPsRAzEFr6o2RWLV5wDkJcyJkc0EVD9XxbRKk5PIpFR4bbfInosgCHsoLi5Geno60tPTAegXx6Snp+PiRf19YPLkyRgyZIix/UsvvYSMjAxMmjQJp06dwuLFi/H9999jwoQJrui+LBA7C6BDbBhq+vE99OaVlHO1kxNaHcNfZ/gsWnjmOZ5gNyWmnRngGdlMBOGOCBEBruTLS1R3Zj0TA+5gaUY4jtIzJ3Zn5OIO3zQTMSGOx6uULgI4W2TqEBsGX86BWa7irDMzc6RAVjVN0tLSkJSUZPJecnIy0tLSJPtOT61pApi3hBIrWP7dvouyuSmI9ZsAVLcp4vyJKoUJc2JlM1UNtotplSYnkUmxkD0XQRASsn//fiQkJCAhIQEAMHHiRCQkJGDatGkAgKysLKOAAgCxsbFYv349UlNTER8fj3nz5mHFihVITk52Sf/lgNhZABq1Co8m1OHaZwhnxoCc2JuZh5JyvtWcPCIT4P52U2LamQGekc1EEO6IRq1Cm3tCuNpeLZDXtevseiYGlG5pRjiO0jMnXFELSMkigLNFJo1ahdZ1Q7jaHrtaJJsYbGWcmZkjBbISTbKzsxEZaVqYODIyEkVFRSgtNX9jLisrQ1FRkclLCJ5a08RA1d8lVrA8q/C2bFaPifWbgOrWC7x2Zkxhwpxo53uV85R3v7ZOb7mJTErlGmeqJG87giCIyvTo0QOMsWovQ726lJQUbN++vdo2hw4dQllZGc6dO4dhw4Y5vd9yQuwsAACoGxrA1S5PJMtWZ8JrARDgo+FezebudlNi2pkB7p/NRBDuDO/9QW6ZE86uZ2JAyZZmhDgoPXOCN2tMrGtH6SKAKwRaXnG29I5ONjFYA87OzJECWYkm9jBnzhwEBwcbX/Xq1RO0vSfXNAGqZwK4Y40OMftR9TwQ055LLscLEO9833LStKAv7/GydXrLTWRSKrzBCgpqEARBuAaxswAAfhupA5zBdDnBawHwUKsobpHJne2mxLYzA9w/m4kg3Jk6of5c7eSUOeGKeiYGqK6JdRYtWoQGDRrAz88PHTt2xN69e622//jjj9GsWTP4+/ujXr16mDBhAm7flk+MxBxCMifkZmsHAKXlFVztejaPEO3aUbII4GyRCRAmzsotg9fZmTlSICvRJCoqCjk5pkHWnJwcBAUFwd/f/A188uTJKCwsNL4uXbok6Ds9uaYJgGpL+t2xRgdvP+yy0BJR+JbL8QKAfJFWl1armyPi8ZKTyKRUwjiv98syS8EnCILwFMTOAgAAFafn4vZT8vTftkb+LT6RP1LAylB3tpuSws4McO9sJoJwZ5SYOeGKeiYGqK6JZdasWYOJEydi+vTpOHjwIOLj45GcnIxr18yvOl+9ejX++9//Yvr06Th58iQ+//xzrFmzBm+++aaTey4MIZkTWYXyil9odQzb/+G7ju8TMR6qZBHAFSJTp4bh3MJcVWccV8NrMwzIswg8IDPRJDExEVu2bDF5LzU1FYmJiRa38fX1RVBQkMlLCJ5c0wQwc1G5YY0OHmFMBfsstHjtuQJ8rKeahQR4cyvuUqPVMcxaf9Jmu0Bf2+lzVevm8B4vHuQkMikV3kDQb1XFL4IgCEJypMgCAIBEzpVvtyvk6b9tDVvzU6HtAPe2m5LCzgxw72wmgnBnlJg54ap6Jgaorol55s+fj1GjRmH48OGIi4vD0qVLERAQgJUrV5ptv2vXLtx///34z3/+gwYNGqB37954+umnbWanyAHec+CIzISz3Rm5KONUHMVcXK1UEcBVIpNGrUKPpnxj134Zxa0Bfpth3tqMrkBS0aS4uBjp6elIT08HAGRmZiI9Pd1Y9HPy5MkYMmSIsf1LL72EjIwMTJo0CadOncLixYvx/fffY8KECZL10dNrmlS159p6KsdCS1OUVKODRxjjvXVVPQ94A/dqG0/ncip3tDczj2sVRLcmfAN35WPGe7NVkiinZDrEhiEs0Lb1S1XxiyAIgpAeqbIA9A+rfI8AaRn8wSg5wGtnxtsOcG+7KSnszAD+bKa/zyizDgxBuCtKzJxwVT0TA0rMzpGa8vJyHDhwAElJScb31Go1kpKSkJaWZnabzp0748CBA0aRJCMjAxs2bEDfvn2d0mdH4D0H5LYYRYjgGBXMZ93HgxARII8zg9gZuEpkAgB/Hy+udjv+kVeWeFbBLa52fe+NlmUReEBi0WT//v1ISEhAQkICAGDixIlISEjAtGnTAABZWVlGAQUAYmNjsX79eqSmpiI+Ph7z5s3DihUrkJycLFkfPb2mSeVnGq2O4Zf0KyLtWD5Ck5j9qCoy8WaxFJdZT+PLv3VHNkFp3uMVW4vPesHkmHGet0oS5ZSMRq3CgPgYrrZyS40lCIJwd6TKAtCoVejZjO9hVUbPXVxIYWcGuK/dlBR2ZgB/NlNxmfLqwBCEu6OkzAlX1jMxoMTsHKm5ceMGtFotIiMjTd6PjIxEdna22W3+85//YObMmejSpQu8vb3RqFEj9OjRw6o9V1lZGYqKikxerqBTw3D4aPhOAjkJZ7z1Ofy91aK7ovhxFv2+KqM6MK4SmQD+elNyqgOj1TH8fiSLq21UsHzi7VWRVDTp0aMHGGPVXikpKQCAlJQUbN++vdo2hw4dQllZGc6dO4dhw4ZJ2UV0iA1DNMcfKF9G6fZiigCV0932ZuYhr8T2Q2SQH5/KKRehSdR+VLkXSpnF4ip4j1doAKd6XumYiWnPJZfjpXT4A0HyGQMJgiA8AamyAACgXX2+h99QBWVOSGVnBriv3ZQUdmaAPoDk7833mEmLMghCXigpc8KV9UwMKDE7R45s374d7777LhYvXoyDBw/i559/xvr16zFr1iyL28yZMwfBwcHGV7169ZzY47to1CrE1w3maiunYvC89Tm6Na0tuuCo4pxYyKkOjCtFJiXWgdmdkQvOhHnRnJSkQFY1TVyBRq3C1H4tbLabtf6EbG5w52+UiLavygFy3iD0wIQYmye1nOyTOsSGISTAug1DoI2aIwa2nDS1L9t8wvxKCXuQi8jEkz2jVgHhgXyBlMrCnJi/US7HS+nwFoPnbUcQBEGIg1RZAAAQxnkP520nB6SyMwPc124qu4DvwTrEn9/ODNA/X/VtFcXVVk5+5QRBKCtzwtX1TAwoKTvHGdSqVQsajQY5Oaaxk5ycHERFmb83TJ06Fc899xxGjhyJe++9F4MGDcK7776LOXPmQKfTmd1m8uTJKCwsNL4uXbok+m/hpQ7nQkS5iACuqs9hgDdzQk51YFwpMimxDoyQIvBC5+XOxONFEwAIDbQdDMwqvC2LNCetjuHbvRdttous6SNY2OANQtcPC7SZXaE4+yTOMW1tpYLYQuzMlFSjgyd7Rsf4gzmVzyteOzOeAuViZn9dvXoVzz77LMLDw+Hv7497770X+/fvN37OGMO0adMQHR0Nf39/JCUl4cyZMyb7yMvLwzPPPIOgoCCEhIRgxIgRKC4uNmlz5MgRdO3aFX5+fqhXrx4++OCDan354Ycf0Lx5c/j5+eHee+/Fhg0bRPud5ojgFEN42xEEQRDiIFUWAAAUcN7D0wQEpFyNVHZmgHvaTWl1DBuO8S3+scebOyqELyDCm8VDEIRzUFLmBO/Kb6nqmRhQUnaOM/Dx8UG7du2wZcsW43s6nQ5btmxBYmKi2W1u3boFtdo0PKnR6Be2Mgve3b6+vggKCjJ5uQqliQCurM8BKK8OjKtFJiXWgXGHIvAAiSYAlFUMfm9mHrKLbCuHXZtECBY2eDMMeK0a5HC8AP0xK7hl/YGspEyLmhy2Y5ULYguxM1NSjQ7ev1uIv7dgYY7XzuzJ9rZTa8XM/kpOToa3tzf+97//4cSJE5g3bx5CQ+/2+4MPPsAnn3yCpUuXYs+ePQgMDERycjJu3757rJ555hkcP34cqamp+P3337Fjxw688MILxs+LiorQu3dv1K9fHwcOHMCHH36It99+G8uWLTO22bVrF55++mmMGDEChw4dwsCBAzFw4EAcO3ZMlN9pFt5gm4xTJgmCINwRqbIAAP7swQ3HsmXxcM9DHufKur522Jm5o93U7oxclN4xv3q3KvZ4c9ua+wptRxCE81BK5sTlfL4iw1LVMzGgpOwcZzFx4kQsX74cq1atwsmTJzF69GiUlJRg+PDhAIAhQ4Zg8uTJxvb9+/fHkiVL8N133yEzMxOpqamYOnUq+vfvbxRP5IzSRAAhWQBi1+cAlFcHxtUiE8BfDP7AeXnEFd2hCDxAogkAZRWD5w1oB/jy3VhSK9lLSZlh4Ep4j1nbe0IE7Y93vwkC9+tqeP9uBaV3BAtzvHZmWgspuJURM/urTp06+OKLL9ChQwfExsaid+/eaNSoEQD9ypaPP/4YU6ZMwYABA9C6dWt8+eWXuHr1Kn799VcAwMmTJ7Fx40asWLECHTt2RJcuXbBw4UJ89913uHr1KgDgm2++QXl5OVauXImWLVviqaeewrhx4zB//nxjPxYsWIA+ffrg9ddfR4sWLTBr1iy0bdsWn376qSi/0xzXOFfm8rYjCIIgHEerY9h88hpXW7uyADgtvW6Va2XxcM9DCOeinkQBK4INaNQq9Ls3mqutUmqA8QZMavh62eXNzbvISkl1cwjCU1BC5oRWx5B+sYCrbQxn5pu9KCk7x1k8+eSTmDt3LqZNm4Y2bdogPT0dGzduNBaHv3jxIrKy7haJnjJlCl577TVMmTIFcXFxGDFiBJKTk/HZZ5+56icIQmkiAG8WQA1fjej1OQD9NdOmXghX26sFro9DuFpkApSVzaTVMWw8nmO7IeRdBB4g0QQAf4aFHOyT+C20+DwVK9tN8QbtwwJ9bNYICQnwlmRwtQfeY9atCV+6m2F/Uu3X1fDUgAkN8OZepWo4r4TYmfGmNYglNCUkJODxxx9HREQEEhISsHz5cuNnmZmZyM7ORlJSkvG94OBgdOzYEWlpaQCAtLQ0hISE4L777jO2SUpKglqtxp49e4xtunXrBh+fu8GB5ORknD59Gvn5+cY2lb/H0MbwPeYoKytDUVGRyUsIvMEdpQSBCIIg3IG9mXkovM3nnWzPw1mH2DDuem5pGcqw6JL6fsYrtvCKN66GN2DSpUm4XSsA3bFuDkF4CkrInBBSBJ432OgISsnOcSZjx47FhQsXUFZWhj179qBjx47Gz7Zv346UlBTjv728vDB9+nScPXsWpaWluHjxIhYtWoSQkBDnd9wOlCYC8GYB9I4Tnp3LS7sGfPHV0juc1cQlxNUiE6CsbKa9mXm4Vc6XzSzjJBMAJJoA4M+wkIN9Em9A+7nEBggLtG3XUNluinelIk87OZ33vKLYk+3vESSe8e73Px3rI5pDPRWzRofUMAC1OB9ya/1bM4jXziw80IfbO1wsoenzzz9HkyZNsGnTJowePRrjxo3DqlWrAADZ2frsGMOqGAORkZHGz7KzsxEREWHyuZeXF8LCwkzamNtH5e+w1MbwuTnmzJmD4OBg46tePdvWZpXhFb8uc9rEEARBEI7DW58jxN++RSoatQpdm/A9fCllQeyBC3yBO96M6arklfDZf/G2czW8tm7t7rFv0Zg71s2Rmh07dqB///6IiYmBSqUyZjQThLNRQuaEXIrAG1BCdg4hLUoRAeSSBRAWwBeH2PHPdZdnTgT58c2ZpBSZlJTNxPscA8i7CDxAogkAZdU04YFBP9EZ1KYOV3vj7+Ich05l37RZIyT/1h3ZFMLkFcXW7LsoSDzj3W/6pQJM7dfCZj/FrNHhCDw1YApu3cGp7Jt8O/x3XOe9fga0iUH7BmFOzf6Kj4/Hu+++i4SEBLzwwgsYNWoUli5dKsq+pWby5MkoLCw0vi5duiRoe16Llt8qZaURBEEQ0sJbnyOpRYTdD2ft6vOJLUqwT9LqGP46wxdAs/dZlrdg+YGLrl9kxUNYIF+wgrddte3csG6O1JSUlCA+Ph6LFi1ydVcIQvaZE7yxBqmLwBsQkp3DW8CeUBa8IkDauVyX3vfkkgVQqybf8Sq94/rMiYJSvoUgkcHS1DMBlJXNdOMm33OMv7e8i8ADJJoAUFZNE96A9t7MPCTFRXHt0/C7bnCujLvEWXBNLiITbz8u5An7XULEtlCOB04xa3Q4Au/v4j0Pbvwb+OHNZHqwRaTTs7+aNWtm8u8WLVrg4sWLAICoKP11lJNjuhojJyfH+FlUVBSuXTP1nq+oqEBeXp5JG3P7qPwdltoYPjeHr68vgoKCTF5C6BAbJjgrjSAIgpAWKetzGHAn+6S9mXkoKedbuWnvijYVZx7132dcGwzhhTfDgzdjpCruWDdHah566CHMnj0bgwYNcnVXCELWmRNaHcMhzudAqYvAG9CoVWjDWcuUcE94RYCi2xUufa6WSxYA7zwBcL1VLG9x9SyJxQqlZDPxZnV3a1pb1kXgARJNAPBZXsmlRoeQQL3QWi28Qe16oXz1UuQgMgH8/ajH6XVqsJsSYmempGwmsevmnL/xr7jCGz9gzs/+Onv2rMm///nnH9SvXx8AEBsbi6ioKGzZssX4eVFREfbs2YPExEQAQGJiIgoKCnDgwAFjm61bt0Kn0xm9WxMTE7Fjxw7cuXNX9ExNTUWzZs0QGhpqbFP5ewxtDN8jBRq1CgPiY7jaZhfSqiiCIAhn4Ix6U7zBcHuD5s6ENwAQ4KOxe0Ubr3VocZlrgyE8aHUMqSf4rDnsFc3csW6O3HC0rh1BWEPOdU2E1DNp78QYTl3OOElWoeuf+QnxESICuPK5Wi5ZAB1iwxDgwxeSduVaFK2OIf1SAVfbmBBpY6BKsTTLuF7M1a5xRA2Je+I4JJpwIhftS0hWjODV+pzXVPPImooRmQAgnyODJjrYD82jOFfoG04GASKAkrKZhNRqiQqyPWh/t+8itDrGncl0o6TM6cdr3759ePfdd3H27FmsXr0ay5Ytw5gxYwAAKpUK48ePx+zZs/Hbb7/h6NGjGDJkCGJiYjBw4EAA+syUPn36YNSoUdi7dy927tyJsWPH4qmnnkJMjF6Q+M9//gMfHx+MGDECx48fx5o1a7BgwQJMnDjR2I9XX30VGzduxLx583Dq1Cm8/fbb2L9/P8aOHSvK77QE7wSfisETBEE4B6nrcwD82SxKKGzOa2fWt5X9XtOdGobD35vv0Unuiwz2Zuah8HYFV9uoYPsKKLtj3Ry54WhdO4KwhpzrmsitnokB3oLzR1xUB4aQlg6xYajhyzdPuME5b5GCvFt83y11FoBGrUKflpG2GwII5qzDJgW7M3JRruW7XqUeb5RgaabVMWw7fc12Q/DX13MlJJqAz/JKLjU6eAvBd4gNE7xaX0hQ2xZyEZm0OoZZ60/abDe1XxzyOAMPhhuckOMlNOvHlQip1fJ0h3ts7s9gOyZECHF29tc333yDb7/9Fq1atcKsWbPw8ccf45lnnjF+PmnSJLzyyit44YUX0L59exQXF2Pjxo3w8/Mz2Ufz5s3x4IMPom/fvujSpQuWLVtm/Dw4OBh//PEHMjMz0a5dO7z22muYNm0aXnjhBWObzp07G0Wb+Ph4/Pjjj/j111/RqlUrUX6nJdwpcEYQBKF0nFGfA3CvQt3OsDPTqFXod280V1u5LzLgzcwJ8XdsruVOdXPkiKN17QjCFnKtayK3eiYGeC3Nble4vkYDIT4atQr3c54DvLEnKeC1mvL35ssWdYToEL7Fm4cuua5eXBrnternJX19DiVYmu3OyEUZZyogr3uPK/FydQfkgJKsk3gwnJ61OAs3Gtqdv1HC1T6vpJxbZOK1MpCKvZl5XOmvoQKsBwzBfyH2XEKyflx9zIRcDw1qBXK3fahVNNQq66sJhQhHYgpzffr0wRNPPGH5u1QqzJw5EzNnzrTYJiwsDKtXr7b6Pa1bt8Zff/1ltc3jjz+Oxx9/3HqHRcadLFoIgiCUjjPqcwDCCnW/P5jJ2nPYWfexxEa18OPBKzbbyX2RAa81x4MtIhz6u7tT3Rw54uvrC19f+QccCOXSuVEtLNp2jqttWsYN3M+ZXeYIcqxnYqBTw3D4aFRcq9J3nrvulONFOBd/H74QK69wITZyspoC+OvFbT+lt5tyxVyUcVrM9GgufX0Og6XZrXKdzbauSmbjFZkA+7OZnQllmkBZ1klCCsFzR5VV+sHz270XbTaNDvbjfsiWg8gkZQ0YOdfocAQh14MQYU6IcKSk7C93gDJNCIIg5IMz6nMA7lWo21n3MXdZZMBr6xYpYEWjOdwpm4kgPBG9CMDX1lkBOrnWMwH0mQbxdYO52jq7DgzhHORu0SYnqymAv16cK7OzsgpucbVLqCe9a4wSLM14RaYavhrZlHSwBokmcN9C8Lw+iTeKy7A3Mw/ZRbbbP9X+Hu6HbDmITFLWgJFzjQ5H4K0B0yE2TJAwJ+TcVZLI5A64SxCIIAjCHXBGfQ7AvQp18wbdHb2PucsiAxXnacPbzhK8C602n7xG/v4AiouLkZ6ejvT0dABAZmYm0tPTcfGi7YVtBCEFGrUKD7fmsyXMdlJxc7nWMzFQh7NWpLPrwBDOQe4WbXKymgLuZmfxsPPcdYl7Ux2tjmHj8RyutoWl1hf9ioXcLc2C/PjEmt5xjj3HOAsSTTiRy59SSPBdSFve4HODWgHoEBuG6GDb+86XgZ+zFDVgUk9kA+C3M3NFjQ57EVIDRqNWCRLmhNiZKUlkcgd4gxqXC+Rd2JYgCMIdcEZ9DsB9CnVrdQypJ/geaB21gXKXzIlszvu5owU6eRdaFZRS9jAA7N+/HwkJCUhISAAATJw4EQkJCZg2bZqLe0Z4MrwBug1Hs5wiAlzJ5xu/nF3PxABvpoGz68AQzqFTw3D4evGFWV2xIEVOVlOAfi7apl4IV9urBc5fMLs3M4/LCgtwrM6gEIRamjmbQxf5xJoojpiyHCDRBO5bCF6I3ZSQgLZGrcLUfi1stp21/oQiVk8YesgbgF+bfhXlFTpuOzNeIUQOwpzQGjCCxA0BdmZKEZncBd6gxm/pVxVxTRMEQSgZZ2b/uUOh7r2ZeSi8XcHV1lHvZHfInNDqGDafvMbV1tECnR1iwxDsx+fvnl1ICzN69OgBxli1V0pKiqu7RngwvAE6Z62cv5LPZ5Xj7HomBngzDQDXrJwnpEWjVqFnM74MJ1dME3izAJxhNWWgXQO+7yq9w1fvT0x4LXMBx+oMCkHOlmZaHcO203zjmgKSTACQaAJAWfUmeDCMvYLspgQEtAEglKOWRVbhbZcLTUJqwHSIDUNYoO2bSG5JOb5KO89tZ6ZRqxQjzAm9FoQIc0LszHhQyBirCISc+64+RwmCINwdZ1pAuUOhbt4H2hB/xxdbuEPmhDNFJo1ahV5xfN7beTLIUCcIojq8ATpAehFAq2M4fLmQq62z65kY6NQwHF6cD6pU18Q9SbiHTwRwRc0J3iwAZ1lNAUBYAN8CjbRzuU5fkHLjJl9syt/bOXZmgLyzmXZn5KKsgi8zx1kik6OQaAL3LQQvxG5KaEBbKUKTkH5q1CoMalOHq/2FPL4VLg1qBQjuhysRei0IEeaE7FspIpO7oFGrMCA+hqstrQQlCIKQFmfV5xCyDznbTfE+0D7YIsLhVcfukDnhTJEJ4LeRk3sdGILwVOQkAsitiLU5NGoVEurzBc2prol7UnSbT3Bwds0JuWYB1KrJJ5oU3a5wevwnn3Oe3K2pc+zMAHlnM8mtZo4YkGgCKKpGh5DguxC7Kd70e8M+lSI0Ce3nA835VsPV5Vx5ZziuSjleQuzfAGHno5CsFKWITO5EXc6ihbQSlCAIQjqcWZ8DcA+7Kd4H2kjOLBFraNQqJLWI4GrLW/fN2eRx9itJBJEJcK7dHEEQ4iMnEYC3CLyrA3K8gjPVNXFP5FpzQq5ZALxZvIDzF6RkXC/matc4oobEPTFFrtlMcquZIwYkmgCKqtEhJPguxHJHp2XcAW1AmC2TKxEqAvB6PjHe65vx90MpNToqXwG1OGzaDO2kykohxMGZdjAEQRCEeZxpnQS4h92UinNOxtvOFlEhfMe9wInWFkLgvY/zZoiI9X00vyAI+SIXEYD3PtTaRfVMDFBdE89GrjUn5JoF0CE2DDV8+ULTzlyQotUx/H2W7/oMcbI4wZvNxNtOLORYM8dRSDT5F6XU6MjnsNEyFB8XYje153wuf/0TCKyXInMq/wzeQfhKAZ/CzWt7BsijRocQ+zcA/J1WAZtPZHM1vXbztqKyv9wFWglKEIQULFq0CA0aNICfnx86duyIvXv3WmybkpIClUpl8vLz8yxx3NnWSW5hN8U5JxPrgZZxrp/ibedsnH2/dwcLOILwdOQgAmh1DIc4Yws8z5FSIidLM8L5yLXmhFyzADRqFbo05rObcuaClL2ZeSgu48vM4XXuEQveOebZa3yZMmIhx5o5jkKiyb8owQ5Iq2OYtf6kzXZT+8UZBzleuynei87w+5VwvADhIgBv5kI9Tisjw+CplBodQv+uvCJT6ols/JJ+hattRE0/RWV/uQu0EpQgCLFZs2YNJk6ciOnTp+PgwYOIj49HcnIyrl27ZnGboKAgZGVlGV8XLlxwYo9dj7Otk5ReqFurY9h80vL5VBmxHmhDOe+DOZwCmLNx9v3eHSzgCMLTkYMIsDsjFxWcQ0SdUMczMR1BTpZmhPORa80JOWcBNKzNZ2/lzAUpvAuZAHGyv4XAOxdNO5frtPFFrjVzHIVEk39Rgh3Q3sw8ZBXavnBDK3tcc56MvF53SqvRIVQE4LUda8rrWcjs64erEPp35W3/88EryCuxrSaHB/oYV84qJfvLXaCVoARBiM38+fMxatQoDB8+HHFxcVi6dCkCAgKwcuVKi9uoVCpERUUZX5GRfAF9d8HZ1klC9iVH0dzZdmYAf8HSLTIVAXjv42JlmriDBRxBeDpyEAF465kArisCXxm5WJoRrkGONSfknAUgxwUpvAuZgvy8nG6zzzsXLbpd4bS5lVxr5jiKU0QTJVgzKKHmhD2Bd95MgIJSzgejSjU6bKW9GmzCXIlQEYDXdmwfZ2qwwZ5LKSKT0BowvHVzijgDGgPaxBhXzipFaHIXaCUoQRBiUl5ejgMHDiApKcn4nlqtRlJSEtLS0ixuV1xcjPr166NevXoYMGAAjh8/7ozuygZXWCUq2Z7R2XZmgLJFAK2OIfVEDlfbsEBxRDJ3sIAjCML1IgDveOqjUbm0CLwBOViaEa5DbjUn5J4FwCsC/O9YttNiEbyLhQYl1HF6DSXeuSjgvLmVXGvmOIrkook7WTO4OoPInsA7b6HujOu3uNoZRACNWoVH4qOttn0kPtqlBdgA4SIAfwCeb6A2/C3cqUZH5V8upG4ODw+2uLuiWClCk7ug5CAQQRDy48aNG9BqtdUyRSIjI5Gdbb7GVbNmzbBy5UqsXbsWX3/9NXQ6HTp37ozLly9b/J6ysjIUFRWZvJSMK6wSlWzP6Gw7M0DZIoArMnOUbgFHEIQeV4oAQuqZxLu4CLwBOViaEa5DbjUn5J4FwBuLuFWudVpmFu+cpC6ndb+YdIgNQw1fvnA+70J6R5FrzRxHkVw0UYo1gxJqTggVAABwKz07OdNdDQFqrY7ht8NZVtv+djhLESvSK/eQV2Rqf08Yl41Xu3/TmJVSo0NwIXjw183hotJPV0L2lzuh5CAQQRDuQWJiIoYMGYI2bdqge/fu+Pnnn1G7dm189tlnFreZM2cOgoODja969eo5scfiI+dMEznaM7rCzkzJIoArMnMAZVvAEQShx5UigJB6Ju1l8mwoB0szwnXIreaE3LMAOsSGIdBHw9U2LcM589EDF/jGsXwXZGJr1Cp0acxnQ1jgJLs1OdfMcQRJRRMlWTO4ixVQ1eGWV1W8ybHqrHK9CZ76KnKoNyFYBOCcCP5zvZjLxutApRUxSqjRYdd1IKJIbMhk4kU5+rT8UXIQiCAI+VGrVi1oNBrk5JhaAeXk5CAqKoprH97e3khISMDZs2cttpk8eTIKCwuNr0uXLjnUb1fjiqwPJdszuspaTKkigCsycwBlW8ARBKHHlSKA0uqZGHC1pRnhOuRWc0LuWQAatQpdm/DNrZwxFZW7nRkANKzNV2fZWdlMcq6Z4wiSiibOsGYQy5ZBCVZA9mQBiNlfJdabENpPXpHpl3TLViGWvl8Jx8ye60DMdL9alQI3Ssj+cjeUGgQiCEJ++Pj4oF27dtiyZYvxPZ1Ohy1btiAxMZFrH1qtFkePHkV0tGU7UF9fXwQFBZm8lIyzi3QDyrZndJW1mFJFAFdk5gj5XppfEIS8cZUIoLR6JgaoronnIreaE0rIAki4h++7g/35fosjyN3ODJBXNpNWx/DXGb5nGAU5cwFwUiF4IQi1ZhDLlkEJVkD2BN3b1Q8V7aRUYr0Jof3kbX/y6k3B36+EY2aPBZyo/a00litBZHI3lBoEIghCnkycOBHLly/HqlWrcPLkSYwePRolJSUYPnw4AGDIkCGYPHmysf3MmTPxxx9/ICMjAwcPHsSzzz6LCxcuYOTIka76CU7FFUW6AWXbM7pCZAKUKwK46j6vZAs4giDu4goRQIn1TAxQXRPPpUNsGGr68dlNOcPFgXd1vyuzAIpu8333oUt844EjyN3ODJBXNtPezDyUlGu52rpKZLIXSUUTZ1gzONOWwdW3X3uC7gcu5IuXvqbAehP5HHZP0cF+xn52iA1DWKBt5ZrnkFa2MzPs21Yx+Mp9kStVfzuPMMd77VS251KCyORuKDUIRBCEPHnyyScxd+5cTJs2DW3atEF6ejo2btxozEC+ePEisrLu1kfLz8/HqFGj0KJFC/Tt2xdFRUXYtWsX4uLiXPUTnIorinQDyrVndJXIBChXBHDVfV7JFnAEQdxFiAhwJV8ckV2J9UwMUF0Tz0WjVuHRhDpcbZ3xbK3ivG5520mBijNqtP3UdcmvFbnbmQHyymbirZkX4KORVTYgD5KKJs6wZhDLlkEJVkD2ZAGIuQpfafUmtDqGWetP2mw3tV+ccaDTqFUY1Ibv5maLynZmhn0/Em/ZYgQAHomPdunqGHss4HiEOd5bWmUBRCnCnDtBmSYEQYjN2LFjceHCBZSVlWHPnj3o2LGj8bPt27cjJSXF+O+PPvrI2DY7Oxvr169HQkKCC3rtGlxVpBtQpj2jq0QmQLkigKvu80q2gCMI4i4atQpt7gnhanu1QJwg3Ze7MrnbyqmeiQGqa+K51A0N4GqXJzDOZg/ZnNdjiBOsryyRyBlMv12hk/xaUYKdmZyymW7c5DuHH2oVJatsQB4kt+dSijWDu1gBVX0sE3MVvtLqTfAUqweA0CqrDx9ozrfa0haV7cwAvYjz2+EsC631/HY4y6UP1/ZcB7zb2Boa1Sp91ooQlDXcyh/KNCEIgnAdrirSDShTNHelyKRUEcBV93klW8ARBGEKbyBYjMwJrY5h86lrXG291PKqZ2KA6pp4LgWcVlcHOAto24tWx7DhmPma0lWpxbkoRAo6NQyHrxdfiDotQ9pMXiUUNZdTNlM+5/NBpIDsGLkguWiiFGsGJVgB2ZMFwGs3xYXC6k3Y3Uex4hBV5og8Ik5W4W2XPlzbcx3UCuS7sdqaMuuYPmvFgBKEOXdDqXYjBEEQ7oCrinQL+W45ieauFJmUKgK4qgaMUi3gCIKoTp1Qvsw9MTIndmfkQstXixlx0TVluYKZ6pp4LnKxm9qdkYvSO3wXktiZuULQqFXo2YwvW0zKdcZKKmoul2wmJdi/2YtTCsErwZpBCVZA9ogAYtpNKa3ehL19vMH5EG6LqnZmShCabF0HKpipuyLiwGdPBovcs7+UhFLtRgiCINwBV2Z7KFE0d6XIpEQRwJU1YABlWsARBFEdIZkTq9L4rbXMsUvAPad/fIxD3yUVQuqaHLpUQM9YboRc7KZ4i5rX8PVyufV5u/p83x8q4VxBSUXN5ZLNxGtnxttOTjhFNHEXXC2K2SsCiGU3VTlVj6eoOQDku/BB0Z4aMAB/5oQtqqY2KkFoSj2RbTW7gwGY3j/OZBWPWCITYPrblXC83A2l2o0QBEG4A67M9lCiaO5qSzGliQCurAEDuP7vRRCEOHRqGA4NZ2Bkq4Mr6IU8bwztHGv390gNbyC6Qgfs4lzhTsgfudhN8RY179Ik3OXZWryLNi7n35KsD0oqas6bzfT3mVxJ5+9KsDOzFxJN/kUJVkD2igBS2E1p1CpM7dfC5iaz1p+QzcO1Ocz2TCJ7LrlnM2l1DDPWnbDaJiTAG73iokzeE0u0CA/0MfntPMJctawXwiGUajdCEAThDrjKOglQpmjuaksxpYkArqwBA7j+70UQhDho1Cq0rBPE1bZCZ79Fl1bHcOgCXyCuUe0A+HAGp12BkOycT7b+I2FPCGciF7sp3uLu7e5xXVFzA7xzpl8OXZEszqikoua82UzFZRWSzd+VZGdmD/K9szgZd7ECMjdsSGU3FcqRkeHKGh321IABpDtePLhyDOGpuWLueIlVN+eRNjEmNx2NWoVH4qOtbxMf7fIblTuhUauQ1CKCq62YGUYEQRCejqutk5QomrtSZAKUJwK4sgYMoEwLOIIgzNO/Nb/9t73FzXdn5KKCMyaa3DLKdiMX0qlhOHew8CBZdLkVcrCb4s3KCBPJccUReDOfi25LJwIoqah5p4bh8PfmC+tLNX9Xkp2ZPZBo8i9KsAKyVwQQy26q6m+Xu9Bkb/+ksueSezaTvcdLrLo5dUNMrSC0OobfDmdZ3ea3w1k0qRSZqBA+Sw5e/0yCIAjCNq62TlJajQ5Xi0yA8jJNXFkDBlCmBRxBEOYZ2rkBd1t7i5sLqWdyfyO+1fyuQqNW4b76IVxttTpIWt/CmSxatAgNGjSAn58fOnbsiL1791ptX1BQgDFjxiA6Ohq+vr5o2rQpNmzY4KTeSoOr7aa0OoafD13haiuH+Qpv5jMgnQigpKLmGrUKfVvxicZSLXpVkp2ZPZBo8i8GKyBr570SC8EDECV9oap1EiB/ocnu/klkzyV3kcmRv6cYdXOqTih4Ml9cmcnkrjDOOAVvO4IgCMI2rrZOApRVo8PVIhPALwLwtpMaV4s8SrSAIwjCPD5eajSqHcDV9vDlQruEUN5xwEejUkQg7pUHmnK3tTc7R06sWbMGEydOxPTp03Hw4EHEx8cjOTkZ165dM9u+vLwcvXr1wvnz5/Hjjz/i9OnTWL58OerUcXxxpitxtd3U3sw83LzNlwUg1SITIXSIDUNNPw1XW6kW8WQX8IkxvLZnUuPqRa9KsjOzBxJN/kWjVmF6/zirJZIKbt1B6olsp/WpKvYGta9xPohbY0AV6yRA/jU67K2JIZU9l9xFJrtr5gCiCE0RQcrKZHJXeFODc0QYVwiCIAg9rrZOAlwfVBeCHESmCE4xhLed1LjaTkyJFnAEQVimTyvrNsoGyrXC65oIqWcSXzdYEYG4zo1rcQffNh13XcxJLObPn49Ro0Zh+PDhiIuLw9KlSxEQEICVK1eabb9y5Urk5eXh119/xf33348GDRqge/fuiI+Pd3LPxcXVdlO88yVAukUmQtCoVXg0gU8ok2K+otUxbDjGd/1VdZZxFa5e9KokOzN7INGkEr3ioqwGjVUAZqxzXWFzW0FtFcyLAGIosA+2sC+TwJXTF3trYoglWlTdj9xFJh4snfliCHNVdy53kcldqVWT7+a/hewzCIIgRMPV1klC+iCHTBM5iEy8k9x95+WRNeHqGjBKs4AjCMI6Qoqbr0rLFLRvIfVMYjhXWbsajVqFdg34Cm2fu34L5RU6iXskHeXl5Thw4ACSkpKM76nVaiQlJSEtLc3sNr/99hsSExMxZswYREZGolWrVnj33Xeh1VrOkigrK0NRUZHJS2642m6Kd74U5OclmzhU3VC+LLY8O2oI22J3Ri5K7/Bde3IQmQDXL3pVkp2ZPZBoUglbNScYXGsHlHoi22b/pvePq/ZwKIotgJlJi9xrdNhbE0OMwubm7Mx4cHUheHtq5gDiPNxWzczhyRQCgHx6sBYVss8gCIJwPnLI8lBSoW5eC4n7G0snMvFmJqeknXf5IgM51IABlGUBRxCEdTo1DIeG8+F166nrgsZBIfVM6oTKI3DJg5D4wKpdwoQmOXHjxg1otVpERpoK5ZGRkcjONr+KPyMjAz/++CO0Wi02bNiAqVOnYt68eZg9e7bF75kzZw6Cg4ONr3r16on6O8TA1XZTvPfTQQl1ZJOxxWsjdeAiXzaaENI4s+Jq+MpHZOJd9Pq/Y9mSzEeVZmcmFBJNKiFnOyCtjmHGuhNW24QEeKNXXPUiQELUbUtUDWgD8j5egP01MTRqFQbExzj03Y+YsTOTu8jkyN9TDGGuanqjRq3C1H4tbG43a73rsr/cEbLPIAiCcD5yyPJQUqHuqpaejrazqw+cma6WFpw4EznUgAHkIQ4SBCEOGrUKLesEcbWt0Amz6Np4zPrCx8rIvQh8ZYRk57j6vuFsdDodIiIisGzZMrRr1w5PPvkk3nrrLSxdutTiNpMnT0ZhYaHxdenSJSf2mA9X203xCjG82R3OQMW5lPjvM7miz0eZ1YINd+nSJFw2IhNvvPdWuVawVaIttDqGzSfN1ymqilzszIRCokkl5GwHxCMAWHooEyNzwtwJLufjBTgmAjh606hrJk1Y7iKTI39PUfy6zdyfQgNt75eKwYuLRq1CUosIrrZi1f8hCILwdFxtnQQoLNOQ9xlZQm1HSYsM5FADBpCHOEgQhHj0b81fpPvL3ee52pVX6HDu+i2utkopAm+gU8Nw8MZZT1yVn9UUL7Vq1YJGo0FOjmmGY05ODqKiqi/yBYDo6Gg0bdoUGs3drIwWLVogOzsb5eXm5z6+vr4ICgoyecmRe8ICudpJMcc7cIFvvsZbl8IZJHJe08Vl4teB4c2GaHcPn9WeM+gQG4ZAH75sprQMcbPF5bIoR0pINKmEnGtOOBJwFyNzwtxDp72F1p2FIyKAo5kT5qwN5C4yuboQvBKzmdyVKE5vYN7UWYIgCMIycrFOUpIIcI1TtOdtZw9KqtEhixowUJYFHEEQthnauQF3222n+LIUV+06z73Pns2lHbPERqNWod09IVxtrxTeVmxdEx8fH7Rr1w5btmwxvqfT6bBlyxYkJiaa3eb+++/H2bNnodPd/c3//PMPoqOj4eOjbCGddyHAxTw+sZAXrY7hrzN891M5XUadGobD35svVC32fDSMY9GukHbOQKNWoWsTviw2sRPF5bIoR0pINBGIq8YSRwPujmZOmHvotLfQurNwRARwNHPCnB2EQWSydjSUWghejIwDsxksMhea3BXGeTPlbUcQBEFYRi6rtJQkAuw8c52rHa9YYC9KqdHB+/28v8delGQBRxCEbXy81GhUmy/OUK7ls+j6mjMjBQCGdGrA3VYudGjInxmj5LomEydOxPLly7Fq1SqcPHkSo0ePRklJCYYPHw4AGDJkCCZPnmxsP3r0aOTl5eHVV1/FP//8g/Xr1+Pdd9/FmDFjXPUTRIN3wcAvh66Ieu/bm5mHknItV9vEhtLe/4WgUavQ717rcUYDYs9H5ZD5bQ/t6vPFEHmLxvNy4ybfPPtBiRflSAmJJpWQc80Jh7IA4HjmhLmHTnsLrcsJiz1z9Ho2s2ONWoXp/eOsOkUU3LqD1BPmi6NJjSOF4B0VLSyJRXLO/nJneG+mOZwrCwiCIAjLyGmVlhJEALlk5gDKqdEhl34qygKOIAgu+rTiC24CwKo06yJAeYUOF/L4Vo57qZVlzWVASF2Tr3dfkLAn0vLkk09i7ty5mDZtGtq0aYP09HRs3LjRWBz+4sWLyMq6G0uqV68eNm3ahH379qF169YYN24cXn31Vfz3v/911U8QDd5YXNFtce2meOeXAT4a2V1LrpiPanUM64/y1VOSen4pFN7+iN1vXlu3SAlrDEoNiSaVULoVkLVgvKOZE+YuLnsLrTsLR0SAaw4Ggy3ZQfSKi7IqAqgAzFjnmsLmjpz/HWLDuP0fzTEssYHdyrMy9Wp5U6sm33jxv2PZshZFCYIglIBcrJMA+QTXrSGXzBxAOTU65NJPJVnAEQTBhxARwFYW2eSfD3Pv64HmtRW5crlTw3BoOKNwF/JKFWvRBQBjx47FhQsXUFZWhj179qBjx47Gz7Zv346UlBST9omJidi9ezdu376Nc+fO4c033zSpcaJUeBcMAOLe+3jnl31bRcnuWsozY93uSDsedmfkovQO3/Umt/ocrrI/VXGeNrzt5AiJJpWQsxWQIwIAAIcjy+YGBbmLTI70z9E0P0s3KFt/RwbXCU2OnP8atQpDO9e3+7vbNzC/clbO2V/uDO/E7la5livNniAIgrCMXKyTAP7VkI5mMDuCnDJzlFKjQy5imJIs4AiC4KNTw3B4ccYadAzYZaHGglbHsPbQVe7vHZoYy91WTmjUKiQ1j+BuP/nnIzbbaHUMaedysTb9CtLO5dKiNpnRITYMNf34xB8x731yml8Khbd2qpg1VtM44xo1fL1k53TCOy/fIPKi1+wCPpHPkQXWroZEk0rI2QrIUYHCkcwJSw+dchaZhHyvswrBA/IWmgw1V6wRHexn8fzvEGt/SqelzBw5Hy93pkNsGAJ9+CZ2aRlUrJUgCMIR5BLQBvgzkx3NYHYEOWXmKKVGx+V8vuKyzrCbUIIFHEEQ/GjUKgxIiOFu//bvx8y+vzsjFxWcw6RSrbkMDOnML/jYqnOx7vBVtJn5B55evhuvfpeOp5fvRpf3t2LjMT6bIUJ6NGoVHk2ow9VWzHsfrwAjx0UKrqixyqx699ylS5Nw2WXmuGLRq1bHsPnkNa62tVz43OAoJJoIRKmF4B0ZCC09dMpZZAJs908FyyKAkBRKc1hK15Oz0KRRq/BIvHVP2kfioy3eIBwR5iwFQOR8vNwZjVqFrk34ghq0kIkgCMIx5GKdBIB7orvvvOsyPOW0clIJNTq0Ooa1h/lWbzvDbkJOIiFBEOIw59F47rbnrt8yazn14caT3PtQqjWXASEWXdayc0Z9uQ+vfHsIN6tYVmYV3sborw+ScCIj7gkL5Gon5r3vwAW+eQdvXQpn4ooaq7zZEO3uCRXtO8XCFYte5WSXKyUkmlRCzlZAriwE78hDpyunMqknsm1aYU3vH2dREOL1XK6KNTsIOQtNWh3Db4etT6x+O5xlcaWLI8KcpQCIo9kvhP0kcE4GghWcakkQBCEHeK2bnBFEvsGZxZGSdt5lmRNyCroroUbH3sw85JXYtq8ID/RxynxKViIhQRCi4OOlRuPafEFhoLrlVHmFDumXi7i3V6o1lwGhFl3msnPeWX8cqScsr/JmcF2tVKI6vPe0i3l8maG20OoY/rIgtlVFjvojb43VLSJm8oYFclrUcrZzJq5Y9Conu1wpIdGkEkq3ArJ27juSOWHpoVPOIpNWxzBj3QmrbUICvNErLsrsZ0I8l6viqB2Eq+5ZezPzkFVo/dy2Vm/FkYdbS+eYo9kvhP0U3ebzBz10KV/inhAEQbgvWh1D6okcrrbOsE7izdy0WkdPYuQUdFdCjQ7eh9pH2sQ4ZT6llDowBEEIY3r/ltxtfz5oajklpAC80q25DAix6KqanVNeocPyv87b3M5VtVKJ6vDe+2zZsfGyNzMPJeVarraJDeVX08QVmbxyWsRkD+3q8wkTvFk8trhxk2+h1YNOsMuVEhJNKiFnKyBHC8E7lDlh4aKSs8jEIwDYeuC3N8PG2nZyFpoc/XvmlfANmuawFAhyNPuFsB8Vp3y3/dR1Ov4EQRB2IrfUdiVkTsjtoVbuNTp4a8DUDXGOdYJS6sAQBCGMzo1rcQeXGIAFqf8A0D/v/XSQvwD8ACcJvFLTqWE4vAVE4577fPfd/1+x20pLU+S64NfT4L33Fd2uECUWxLtgIsBHI0sR0tnzUa2OYf1RPjs7ZyxisgfefonVf15bt0gHSx+4GhJNKiFnKyBHA9oatQpJLfhTQCtj6aFTziKTGIKOvQ/b1lYSyllocvTvWVDKl5lgDkuBIEezXwj7SeScPN2u0IlWTIwgCMLTkFtqu9wzJ+SWmQPIyy7MHHLKzAGUUQeGIAjh6O8f/PGGhdvOQqtjeGX1AUHfM+fR1kK7Jks0ahVGd2/E3X5PZj7KK3Qor9Bhz3n+TH+q/SkPhDi/iCEC8C6Y6NsqSpYipJD4Ja+1rDV2Z+Si9E71WkvmkGt9Dmdn8qo4TxvednKFRJNKyNkKSAyBIsrOFWSWHjoNIpO1o+Gq+hxiHC97Hx6tKa5yFpryOTJFrImGvJkJVanh62Vxn64Qmd577z2oVCqMHz/e+N7t27cxZswYhIeHo0aNGnjssceQk2MatLl48SL69euHgIAARERE4PXXX0dFhenq4e3bt6Nt27bw9fVF48aNkZKSUu37Fy1ahAYNGsDPzw8dO3bE3r17RfttQujUMBy+Xny3CLGKiREEQXgavA+1jlp/CkHOmRNyy8wB5CdKVEVuoo4SspkIgrAPIZZTDMDcjSex4RifEA4A8XWD4MP5fKIEXu3VTFD7+2ZtQt8Ff3K399aoFF1LwJ3oEBuGmn58hbrFWJTCO+dwpH6x1PDGLx1ZvGsgjXMRqLXYlavhzWbacCxblEze7AK+OVqIwmvgus8dRwTkbAUkRhYMs7Pblh46NWoVpvePs1pLpeDWHaSeyLbvix2Ap+B6qA1Bx96HR2tKqlwLwWt1DLPWn7TZbmq/OItBG97MhKp0aRJucZ/OFpn27duHzz77DK1bm65gmjBhAtatW4cffvgBf/75J65evYpHH33U+LlWq0W/fv1QXl6OXbt2YdWqVUhJScG0adOMbTIzM9GvXz/07NkT6enpGD9+PEaOHIlNmzYZ26xZswYTJ07E9OnTcfDgQcTHxyM5ORnXrlku8icVGrUKPZvV5mpL7hkEQRD2IceHWrkF2Ssjt8wcQP41OuQm6sg9m4kgCPsRajm1ZEemoP1PSm4hsEfyRqNW4dE2Mdzti8p0OHudv1B4/9ZU+1MuaNQqPJpQh6utGPdj3vunnO+zvPFLe+OcJvuwGtW8i7XYlavhzWa6Va512ClEq2PYfJIvRlWLU8yRK04RTYSunP7hhx/QvHlz+Pn54d5778WGDRuc0U3JrIDKK3RYsv0Men/0J3rO3Ya3fj6CUs6iTAbEyIIJtkPhq+GrsfrQ2SsuyqoIoAIwY90JQUKTVsew7XgOHv7kL3R9fytGrtqHYs5VhUKw1SNepbYqjiqp9gzBxbcrMDJlL7q+vxUPf/IXtgr0geY59wEg1IrVRaeG4fDRCO99Qr1Qi585U2QqLi7GM888g+XLlyM09G6fCgsL8fnnn2P+/Pl44IEH0K5dO3zxxRfYtWsXdu/W+8n+8ccfOHHiBL7++mu0adMGDz30EGbNmoVFixahvFw/EVm6dCliY2Mxb948tGjRAmPHjsXgwYPx0UcfGb9r/vz5GDVqFIYPH464uDgsXboUAQEBWLlypcO/zx4S7rH8t6lMNse5QxCEZ6KUOaCrkKNAIbcge2XkmJkj9xodcqsBAwD3N+FblGHvXFzpyCXrWE6Ulmvx+o+HcO+0DWjw3/WI/e96NHtrAwYu+huFNupFeiLlFTos2vYPOs9JRctp/0OX97Zg8bazJsXEpUCo5ZQQfDTuUQC+Ku8Njpdu349Jt29COPeEBXK1E+N+fOACX8ySty6FK+AtWJ7DuaDGGrwxvHac8RFX0CE2DIE+fNlMjjqFyDHzWyokF02ErpzetWsXnn76aYwYMQKHDh3CwIEDMXDgQBw7dkzqrkpiBTRnwwk0nfI/vL/xH/yTU4zMG7fwzd5LaDFtI0Z9uY97P2JkwRTdFj6hjIsOsvrQaauwOYMwoWnjsSw0m/I/DP9qP45dLcKl/FJsPnkNrd7ehIc/4U9F5Sm4bqsQvBDfycpYU1KlKAT/yMK/0OrtTdh86jou5Zfi2NUiPL9qH5pN2YCNx/iKWYlx7mvUKsTXDebaT2UKHUynFCskMmbMGPTr1w9JSUkm7x84cAB37twxeb958+a45557kJaWBgBIS0vDvffei8jIuysnk5OTUVRUhOPHjxvbVN13cnKycR/l5eU4cOCASRu1Wo2kpCRjG3OUlZWhqKjI5CUWvGPGHyfESfEkCMK9UNIc0FU4u2gjD3LOnJBjZo6ca3TIsQYMAERwiiG87dwJOWUdy4XhX+xFi2kb8cP+q7hZrp9vMgBlWob0S4WIn/kH7pu1keai/zJz3XE0nfI/fLjpDK4WlqOkXIfLBbfxwabTaDrlf5iz4YSk3y/UcoqXl7o1ku0Kb0fw8VKjYwPxA7EdY0PdysrMHXDWohStjuGvM3xzNDlfUrVq8s0BtoiwKCUskO+7eNu5Ao1aha5N+Oa/jt4u5Zj5LRWSj6JCV04vWLAAffr0weuvv44WLVpg1qxZaNu2LT799FOpuyq6FdCcDSfwmZWU09QT17iFEzGyYOypOWHLEkxMoWnjsSy89PVBVFi4go9dLUb72amifZ+tdh1iw+DrJfyYWVNSxRbmun+4FUeumA+SV+iAl74+yCWciHXu1wkN4NpPZazdqKUQmczx448/4uDBg5gzZ061z7Kzs+Hj44OQkBCT9yMjI5GdnW1sU1kwMXxu+Mxam6KiIpSWluLGjRvQarVm2xj2YY45c+YgODjY+KpXrx7fj+aAd8woLtNSsVaCIKqhpDmgq4jgDLjzthMDOWdOyDEzR841OmS7EpB3ei3jYI5UyC3r2NW0ensTtp2+brPdjRItGr3Jv2DMXen+wVas3HneapvPdmRKKpxo1Cq80kPcbBONGni1V1NR9yknvhrZSfx9jhB/n4RjOGtRyt7MPJRwOtskNpRxTRMnLkqRY1auPbSrzydQ8GbxWEKOmd9SIaloYs/KaVursaWEp24IAORz+P6VV+isCiYGUk9c47LqEiPYbk/NiTqh1h+gxAq2a3UM//f9IZv7uV5cjhm/HXf4+3jaadQqtK4bwrUfAz4atVUlVUxh7tf9l3Ah1/bD939/OmozqCFGzRzA9vliDms3amcVgv/vf/+Lb775Bn5+zgtKicXkyZNRWFhofF26dEm0fQsZM6hYK0EQlVHaHNBV7M3k9BR2ojYh58wJOVqHyblGh1xXAl7j7BdvO3fB3qxjdyVu6gbBFs28C8bckYc/+RMX8vjm48v/ypTUqmt8b3GzTT5+MkHxgTdr+Hip0adVhGj7oywTeeKsQt289/4AH42sLe+ctShFrlm59uCsDHY5Zn5LhaQjqT0rpy2txrbUXkxrGo1ahan9bBcXm7Xedo2OVbvOc38vT7aJGMH2Tg3DBaff3d/IuuewWDUndmfkoric78bwxa7zNid5YokAQh8gm0fVsDqhE6tfWh3Daz8d4epTQekdm4WexKiZAwCdBQ6KGjWs3qidVQj++vXraNu2Lby8vODl5YU///wTn3zyCby8vBAZGYny8nIUFBSYbJOTk4OoqCgAQFRUFHJycqp9bvjMWpugoCD4+/ujVq1a0Gg0ZtsY9mEOX19fBAUFmbzEolPDcO5sqxucqw0IgvAMnDEHBKS1KJQarY5h1a4LXG1vlDhvjJVz5oRcVwLyPhQ6uw6MXFcC8opHO8863wLOlQgdN5U8/tni/jl/4NYd+4KGL3190OOsun47eBnHrhZzt9cx4Ku085L1R6NW4ZMnxKmn0bh2APrH8xdLVyqL/nOfaPuiLBN54qxC3bz3/r6tomQtRjprUYpss3LtwFnZTLzH29mLhaRA8fKz2NY0oRwedTw1OtYducL9nbvO5dqc2OVzPCzbCrZr1CrcVz+Eu1+2Atq88AzDuwRetKt2Wc/icZUIYGtCJ1a/dmfkQivgWWDnOetp7WLUzAH0QXYhteDb1gvhEpms7VKMQvBpaWlIT083vu677z4888wzxv/39vbGli1bjO1Pnz6NixcvIjExEQCQmJiIo0ePmvhNp6amIigoCHFxccY2lfdhaGPYh4+PD9q1a2fSRqfTYcuWLcY2zkajVqFHU75irXkyT1UlCMI9kdKiUGr2ZuahgLOul6OLA4Qg18wJOa8ElKNtGCDflYBytoBTEkoe/6wxY91RXCl0rOZh4rubReqN/NHqGMZ9f1jwdhfybknQm7s80rYu4qKEWzdXZcOr3UXojfzRqFX49Kk2Du/n+fsbUJaJTHFWoW653vvtwRmLUuSalWsPzppfHbjAl2me7wYxIklHU3tWTltajW2pvdjWNGLYAWl1DMct1Jkwh47BqpKs1THMWn/S5n6m9ouzGWx/5QF+L9AB8TE29ydWzQmh9g77zudb/VxMEcBbwFUytHOsU/qVJnDlwT6O4+9ozRxAP9kbIGAl0Dgb56NGrcL0/nFWXUkKbt1B6gnLq5B5iIuLQ6tWrYyvwMBAhIeHo1WrVggODsaIESMwceJEbNu2DQcOHMDw4cORmJiITp30q3h69+6NuLg4PPfcczh8+DA2bdqEKVOmYMyYMfD11d+4XnrpJWRkZGDSpEk4deoUFi9ejO+//x4TJkww9mPixIlYvnw5Vq1ahZMnT2L06NEoKSnB8OHDHfp9juDvw7fa+ICNa5IgCM/CGXNAQFqLQqmR80OaHDMn5LwSkPchlbedWMhVzJGzBZwrETpuKnn8s0R5hQ5f7Lzo8H6uFZdj5jrbls7uwOAlf9u1Xf0wxwUNW2wY3xMBfI8SZhnRJdajBICH29TBg83tD2TXC/PHtP4tRewRISbOKtQt13u/PeRxZlrztjPHjZt82z6ogPoczphfaXUMf53hE/Vkfri4kPQOZM/KaVursasitjWNGHZAQrMAAOtKMk9AGwBCOVbVdW5cC/6cdjvvPWY7pVYskenQBWEB1wIbg6KYIsC8wXypxTyrOsTqFxNobn74cqFVIUbM2iHvcR6vAB8NOnNMGnrFRVm1gFMBmLHOtmWeI3z00Ud4+OGH8dhjj6Fbt26IiorCzz//bPxco9Hg999/h0ajQWJiIp599lkMGTIEM2fONLaJjY3F+vXrkZqaivj4eMybNw8rVqxAcnKysc2TTz6JuXPnYtq0aWjTpg3S09OxcePGajYNzoS3Ts0RG+cYQRCehTPmgIC0FoVSI1frJECeD9y8cxUxMlCFEsEphvC2Ews51oAB5G0B50qEjptKHv8s8dyK3aLta+VO25bOSuf39Cs4dEm4LZtaBTyX2ED8DpnhxOx+4Fxcb0LrukGY+nCc+B2SOZ8P64h7Y2oK3i48wAt/TXpAgh4RYuKMQt1yvffbA29G9oGL9i/g5M2GiOQUJFyJM+ZXezPzUMJRlxuwXr9YKUgu29taOT1kyBBMnjzZ2P7VV1/Fxo0bMW/ePJw6dQpvv/029u/fj7Fjx0rdVQDi1JwQajUFWFeSxQxoa9QqfPRUgs12L3bjW9UhlshUITDWeiqn2GkiwCNt66J1XesPIfXD+VZ1iNWvrAJh6dTlWmY1m0nM2iE+Xmq82M16xg0AzH8inisIZCubiYFPaBLC9u3b8fHHHxv/7efnh0WLFiEvLw8lJSX4+eefq634q1+/PjZs2IBbt27h+vXrmDt3Lry8TG9YPXr0wKFDh1BWVoZz585h2LBh1b577NixuHDhAsrKyrBnzx507NhRtN9lD7wWdbcrdA55rxIE4X4obQ7obORsnyDHB+5anILDsMQGzl8JyPl1+847N2tCrjVg5GoBJwfkmHXsLMordNgjcubyc5+LJ8LIDa2OYfyadLu2HdXVuRkc/7zTD+ECbBNHdKmP38Z2lbBH8mbduG54sDl/YfiW0YE4MC3ZdkPC5TijULdc7/32oOKcYP19xnbJA4vfwTmH423nSpwxv+LNlA/w0YhS7sHVSH6ntLVy+uLFi8jKumtX1LlzZ6xevRrLli1DfHw8fvzxR/z6669o1aqV1F0FIE7NiSv5whW7YH/LK+nFLobdp1U0lj7bFgEWlny82C0Wk/vyreoQQ2QSajUFAEW3K6wGycU+Zr+N7YqkFuYnLg82r4U/X+db1SFGv7Q6ho3H+Ty9K2Mtm6lDbJjVbA4ACBWwcnNy3zi82C3W7C3O31uFpc+2RZ9W1q8zA2IKYIRwOjUMhw9noRpbtXMIgvAslDYHdDZyzOYQ+p2OFpIUBOezcPsGzvebvsGZNZSSdt5pWZlyrgEDyNMCTg7IMevYWYiZZWJgT2a+22ab7Dp7Q/DCQ0DYs76YHJjaC8M7N7DaJrKGN/6Z/RCmPuye930hfD6sPRY+nWAzYPfJE/FY/2oPZ3SJEAGp51daHcP6o9bt4A244t4vlETOoHtxmfX4oDVCrMRi7WnnaqSeX/FmyvdtFSV7OzMeHHCY5Gfs2LEWVwlu37692nuPP/44Hn/8cYl7ZR7emhOT+rSweAJEhwhP2zp0yfKqGoMwYc3WyZYwUZU+raLRKy4Ku87cwE+HLuNWuRbtG4RhaGdhhcMMItNnOywXZrclMgm1mjJgLZ3MIAJYy1AQIgIAwIqh7VFarsW7G07gfO4tNAgPwJt94+AvIN+Y528JAPlWVN+9mXm4VS588u/oM7rQzSf3jcNrvZtj1a5M7Dufj0AfDR5tWxedG9cSNHiKLYARwtCoVYivG4x9FwpstrVHMCYIwr1R0hzQ2cgxm8OA0EKSzngousb5kMbbTkx45yAF/9b54w0COIKca8AA8hYNXY21cdNdkSLLxMB/fzqM+U/adlpQGjME1mypXcMbO/+b5NIaIdMfaYnJfVtg+V9n8dWuTOSWVMDHS4MOsWFY+HRb1OC0lfEU+sfHoO+90dhx8hre33QCmTduQaVWo2HtQPxf7+bo3qy2WwQlPQmp51e7M3JReocvVuSKe79QOjUMh7+3mus32Ws3FRbIWZeOs52rkboOjJwz5aWA7kpVEFJzwtIDzzWO+iNVMaSTmRsUxRAmzKFRq9C1WW10bVZbcH8NiCEyBfnZp9jyruqzhD0agr+PBrMG3mv3d2rUKkzt1wIvrz5ktd2s9SeQbEGZ5U2Hq4o1X0xbFliAfQ/6Pl5qjOrWCKO6cW9SDR4BzBX+5Z5EndAALtGEp/YSQRAEoUfO9glCC0k6QwTgXdnG205MDB7SPCKFs2p08M4XQ/xdM4eSs2hIOJ///nhYsn3/cugqPny8jVsFl8srdDh7vYS7fZCvGvum9JawR/z4eKkxpmdTjOnZ1NVdUQQatQo9W0aiZ0v3zzbzBKSeX/G6uNTw9VJE/ESjVqHfvdH48eAVm23ttZuS83zcHoTUgRllz/49bNGL65YZyBRHrYC0OobNJ68J/l5r6WS8woQrijCLUdj8kJ1Fm6wNBkJEAGcTyqFQWztm9gYDrKVfKt0Cy30egeQJFYMnCIIQF7lbJ3WIDeO2IXDW3EDOQXY51ujgnS8mtYhwSTDZ0x66CctodQxrD1/lbt8kIhCfPtWGuz0DsCD1H+EdkzFCRab9U6neBUHIAakLdfO6uHRpEq4YIVlKuym5z8ftQeo6MHKej0sBiSZVcNQKSEgqfFUsDYpiCBNSIYbI9NcZ+/wamZXrW84igKN9s3fwsaagy9kCi0cAy3eRAOYpUDF4giAIcZG7dZJGrcLQzvW52tZykl2B3IPscqvRwftwf39j19gneNpDN2GZ3Rm50AqI26wf1w0Pt6mDJrUDuLdZuuOc2yzs0eoYfk7nF5n6topyqSUXQRB3kXqRBe+Cl3b3hAret6uQcv4n9/m4PUhdB8bdMnNsQXfPKvAUxLZmBWSvdRJgeVCUswAghshUUq6167tzrBxrOYsAjvbN3sHH4ItpDkOtFWsIrZsjFnI+/z0FKgZPEAQhLnK3TgKADrGclhBOWqgo9yC73ESdCE4LEN52YiN1MVxCOXy5y7IFdVUa1Q4wCgDrX+3OvV25lrnNwp4FqacFtV/4n7YS9YQgCHuQcpGFu9XnAKSd/ylhPi4UQx0YHoRmM7ljZo4tSDSxA2vPho74KFu6yOUsADgqMjkS6N5iRQTI5yhq5CoRwFGBwt5ggMEX0xyGujnWsKdujhjI+fz3FAzF4HnYRxk/BEEQNpG7dRIAXON8kORt5yhyX9kmO1GHd1G9ixbfCy2GS7gn+gAMv7X12w+3Mv6/j5cajWsHcm+7Ko1fnJErWh3Dkj/Pcbd/NCFGMRY8BOEpSLnIQu5zJXuQcpGFEubjQjHUgeFBaDaTO2bm2IJEkyo4agXkyIOQpcGAR5gIlXEhbGtDSy3OByZzWBIBtDqGWetP2tx+ar84lwx8jgoUjtzgLCnJcq6bI+csGE+iTiifBcJhqmtCEARhE975Iu9qRCngfZByRo0OJaxsk1vmxDXOQABvO7ERWgyXcE92nb0BHWdbtQro3MR0TJzevyX3d209dV3xc9TdGbm4w3vAALz3WLx0nSEIwi5454AX824J2q8S5kr2IOUiCyXMx+1Bqmwmd8zMsQWJJlVw1ArIkYC2I4KLq6Z/DtebcLDj5v4OPDVgACDURTcKRwWKy/nCbp6VsRTYkHPdHDlnwXgSvMXg3cn+gCAIQirkZuVkDjllTihhZZvcMid2nuGzy3QkS94RpC6GSyiDGeuOc7cdlFCn2ny/c+Na3AGNCp3y56hfpZ3nblvZyowgCPnAO7f75dAVQfMFJcyV7EHKRRZKmI/bg1S/68ZNvjnjgwrKzLEF3UWr4KgVkBSZJjzCRIGLCmE7KjI5urrNXPFRudfAcESg0OoY1h7mL/xXFUvnp5yPmZyzYDwJ3mLwANU1IQiCsIWcBAlLyClzQgkr2+SUOaGE1aZSF8Ml5E95hQ5nr5dwt5/zaOtq72nUKgxqG8O9DyVbdGl1DFtO8l3XgKmVGUEQ8oF3kUXRbWGFupUwV7IHKRdZKGE+bg9S/a58zmeDSBfVy5MCEk2q4KgVkCMK5OUC8xe4nAPajopMDq9uMyNeyr0GhiN/z72ZecgrsS6gWcPS+SnnYybnLBhPolPDcHhxLhaguiYEQRDWUYLntJwyJ5TgOS2nzAmlrDaVshguIX9W7TrP3dZa1sScR/ktqJRs0SXEmstLXd3KjCAIecC7yAIQNl9QwlzJHqRcZKGE+bg9SLXwScV52vC2UwIkmlTBUSsgR6yTfku/anYSJ+eANo/IBAD5FgYvRx+CzBUflXsNDEf+nryrByxhKQCSX2L7BuuqYyZn0dCT0KhVSKgfytWW6poQBEFYRglZAIC8MieU4Dktp8wJpaw2dVdbDIKPdUeucLe1ljXh46X+//bOPDyq8uz/35kJ2YDsO1sSFkPYEhACcSlKJCwvSLX81FpZXouVF2wFqgVfDJuIRatWS0uLS1CraPuKdUEoIliVsBiIiixCIAYhCySQECAZMjO/P3DGLJOZ+2xztvtzXXNdGp4585wzM888516+X/SKoSX/9CzRJUSaa0z/RF0FRRnGTIxIi0HXUBtprJD9gh72SmJRoshCL/txMShV+FTZQaF/W6LCfHty6wlOmrRBihSQVOmkmot2rzedWk4C2KwWPDqxv99xKz446PWaSb0J8vYjonUPjBFpMYgK972IRIV7v4mV2pmT4GXxdDhdWPHBIb/PfXRipirXTMtJQ7NBXWPY14RhGKZj9NIFoKXOCb0E17XSOaGXalPqTT11HKMfHE4XDnxfTxrrzQC+Lb8YmUp+7U+PVZPHagWh0lzTBFwPhmECi81qwW3Z3UhjhewX9LJXEoMS56aX/bgYlCh8cjhd+OgQ7fczzkD7Nk6atEGKFJBU6STA+02n1pMA0V58RdrS0TWT0pkDeL+JMoIHRkfvpNQM994y759bf595AIhWKbsuJcnEyAv7mjAMYyQcTheKSmvwr5JTKCqtCdi+QC9dAFrqnNCL5rRWAhZ6qTb1VswjZRyjH3YeOwui0hQpuTc9N5X82tsP62+PKkSaK9hmwcjescpOiGEIrFmzBqmpqQgNDUVOTg727NlDet6GDRtgsVgwZcoUZSeoIj1jOpPGCdkv6GWvJAbqnMtr6fFFvezHxaBE4ZORk0y+4KRJG6RIAUmVTgK833RqPQkg9ppJ7cwBvN9Ead0DY8+JWpy/5Du5du6S94xvgkRDpcKisnafEyPIX3HzeWAQ4mty6pyyVccMwzBS2HygAtf//mPctW4XfrOhBHet24Xrf/8xNh/wvd+SA710AQDa6ZzQi+a0VgIWWkne+IXqleal6IfRN89//C157PRRaX7HBAdZkdiV9r06dqZB08Vz3tgpQHf+pgz1fzsY5s0338T8+fOxZMkS7Nu3D0OGDEF+fj6qq31XqpeVleG3v/0tbrjhhgDNVB2U2C/oZa8kBuqcN+4/RV7fz16g7cfHaGA/LhQlCp+MnGTyBSdN2iBFCkiyqTm8d05oPQkg9prJ0Znj7SZK60kASfOTuL8/7yUZo3X5KylJJkZebFYLsnpGkcaeJupdMgzDBJrNByow+7V97fZWFXWNmP3aPsUTJ9Su0ev6qK85rYXgu540p5Uy3hSKVpI3/jhLvHfyVvTD6BeH04UvvjtPGmuzgtw1QU3yOpzQnYyskPscluZitMDTTz+NWbNmYebMmcjMzMTatWsRHh6Ol156qcPnOBwO3H333Vi2bBnS09MDONvAI/d+QU97JTFQZTrrG5vJ6+U54nuQKLFwWS3kLnzSU9GXnHDSpA1SjM3lWHy8dU5oPQkg1nNFjvl6u4nSehJAyvyqZUjMtb3uFPmraBXlr7T++Tcb3aPDSePYDJ5hGC3icLqw7L2DHdYguAAse8+7D5tcULtGpXaXyoEWgu96kgNQynhTKHqpNqXuib0V/TD6ZdfxGlA//kN7RJGDL7cP7U6ewyu7yshj1cbhdGH/d+dIY1mai9ECdrsdxcXFyMvL8/zNarUiLy8PRUVFHT5v+fLlSEhIwL333kt6naamJtTX17d66AW59wt62iuJgerRAdDlpizEuD51nNaQu/BJL9KvcsNJkzZIMTaX5ebWyxdS60kAsZ4rcpgDebuJ0noSQGySCZCnmymO4EHTFjVD31r//JuNbtG0TRabwTMMo0U00b1L/VHVQN5ZC50mepIDUMJ4Uyh6qjZVQnOb0T5CpKZ+fXM/8tjcPnHk4Mb2w8omLuVk1/EaNBOnytJcjBY4e/YsHA4HEhNbywMlJiaisrLS63M+++wzvPjii1i3bh35dVatWoXIyEjPo0ePHpLmHUjk3i/oaa8khhFpMegaaiONJctNEZUxosJ8xxa1ityFT1q4J1ADTpp4QbSxuQz7Lm9t6lpPAoj2XJFpnyqmw0DNLbLYJBMAfH+ObmzVIW0OS5G/UrPCT0r3FyM/bAbPMIyeoQZelQzQUrtG5egulYoWOk30JAeghSSAnqpNldDcZrQPVQIxyArk9qXvO21WC4alRpPG6qm45/Oj9CQTS3MxeuTChQu45557sG7dOsTF0b/zixYtQl1dnedx8uRJBWcpL3LvF/S0VxKDzWrBbdndSGMpe1KH04WPDvn213EjR7G3GqjVaaK29KvccNLEC2LlgM5eVKcLAFA3CSC2alOO6wW0v2ZaTwKITTI5nC7868vTkl+/bWJO6/JXUrq/GPkRYga/l6U0GIbRGNTAq5IB2s+P0hLKcnSXSkULHh16kgPQQhJAb9WmcmtuM9rG3uxE6RlaEVi2AGkuN0I+03op7vn4sPfK/LYEWVmai9EGcXFxsNlsqKpq3fVYVVWFpKSkduNLS0tRVlaGSZMmISgoCEFBQXjllVfw7rvvIigoCKWlpV5fJyQkBBEREa0eekHu/YKe9kpi6RnTmTSOsnfVU4GJWOROcuhF+lVuOGniBbFyQLJkIL3sC7WeBBAbdJctY9vmmmk9CSA2ybTnRC1qL/r+HFBom2TSg/yV6O4vRnZsVguye9Gq+NjXhGEYraF2lZSepJMAbXh06E0OQO0kgN6qTfX2/jLSWL+zjDx2uIiknpCOaD0U9zicLhypukga2zMmTBPfaYYJDg7GsGHDsG3bNs/fnE4ntm3bhlGjRrUbn5GRga+//holJSWex+TJk3HTTTehpKREV7JbQpBzv2CG31I59/B6KzARg5yFT3q7f5ETTpp4gSKHFeVNDkuGe8Vth9p/ELWeBBAddJfp3rpt54TWkwBi30/Z3t82e2kpHiuBQuvfAbNB/SzoSfqAYRhzoHbnhN4q27Tg0aF2oksoagcu9FZtqrf3l5HGe1+dIo+9rne84OOPTI9FJ2KEQw/FPbuO15BvmQd1i1R0LgwjhPnz52PdunVYv349Dh06hNmzZ+PixYuYOXMmAGDatGlYtGgRACA0NBQDBw5s9YiKikLXrl0xcOBABAcbc/2Xc79ght9SOffweiswEYOchU96u3+RE06aiMTb1+bjw7TMmy/+VXK6vcG8xpMAYpNMSslzad0DRtVOJrRPMknxWAkUWv8OmA0hVXzri04oOBOGYRhhqN05obfKNi14dOhNDkDtwIXaSRuhqJ3IZAKHw+nCN6fqSWPFSk3ZrBaM6U+TvNFDcc9OAZ/7nw01ZjU+o0/uuOMOPPXUUygoKEBWVhZKSkqwefNmjzl8eXk5Kipo/kZGRc79gt72SmKQcw+vtwITMchZ+KS3+xc54aSJFyhyWOfayGE5nC5sLKFXznREzUV7uw/sOUJyQe1OAH94C7crJc9FQc2aIjU7mYD2SSaxHiuBRA/dMGZiZHosbMTv3ceHz2i+io9hGPOgdueE3irb1Pbo0KMcgNpJALWTNkJRO5HJBI5dx2vgIL6FN2fEi14D7xmVSh6rdV8T6u9QkBXI7avf4B5jTObOnYvvvvsOTU1N2L17N3Jycjz/tmPHDhQWFnb43MLCQrzzzjvKT1JF5Cpy0ONeSQxy7uH1VmAiBjkLn/R2/yInnDTxghgpILn8Jtoe1+F0YcUHh/w+59GJmap9OMUkmQAoJs+ldQ8YCt7eSbk6c9oeXKzHSiDRQzeMmbBZLRjQjWa01+zUfhUfwzDmQe3OCT1Wtqnp0aFHOQC1kwB6qzZVO5HJBA4hXRPTR6WJfp2R6bEIIt4SaNnXxOF0Yf9350hjs3tE8X0Qw+gMuYoc9LhXEoOce3i9FZiIQc7CJz3ev8iFokmT2tpa3H333YiIiEBUVBTuvfdeNDQ0+HzO6NGjYbFYWj3uv/9+JafZDjFSQHJ6KbTsBKAEtAEgWsWMsVi/CaXkubTufyE2ySRXZ05b3xytXy9AH90wZmPS4G7ksa/sKlNuIgzDMAJQu3NCj5Vtas5Zj3IAaiYB9FhtqnYikwkc1M97sE2cNJcbm9WC7F7RpLFa9jXZdbwGzcSpDdfI+scwDB25OlP1uFcSg5x7eL0VmIhFrsInPd6/yIWiSZO7774b33zzDbZu3Yr3338f//nPf3Dffff5fd6sWbNQUVHheaxevVrJabZDjHySbFJTQKtOAD0EtNX26GjbOaF1/wvR76lM+/m2vjlav16APrphzMb03FTy2O2HWVKDYRjtoGbnhB4r29Scsx7lANRMAuix2lTtRCYTGIR0TQzpHin5+0wNDGrZ10RIZ851veMVnAnDMEogV2fq2Qu0vdIYDe2VxCLHHl6PBSZiqSUWqvsbp8f7F7lQLGly6NAhbN68GS+88AJycnJw/fXX4/nnn8eGDRtw+vRpn88NDw9HUlKS5xERQZOBCSTtlhoZ44Et5ab0ENBW26OjrTyXv/lYoK7/hdj3VK7OnLa+OZT3L9rb+xdA9JA8NBvBQVb0jg8njdXyDSnDMOZDzWopPVa2qenRoUc5ADWTAHqtNlUzkckEhkB3TeQKWBO06msSqM4chmHUQa7O1HPEfVoi8fW0jBx7eD0WmIjl/GWahURxue+iBj3ev8iFYkmToqIiREVF4dprr/X8LS8vD1arFbt37/b53L///e+Ii4vDwIEDsWjRIly6dEmpaXpFjHySbH4TaC03pYeANgVv+eyPD9Oyu/5om1zYerDS5/vnArBkknoeMBRTcwA41+ZGWs5uJqHJBbV7BPSQPDQj4wb69plpCUt0MYy50LJEq1rVUnqtbFPTo0OvcgBqJQH02JkD6Pd9ZugEumtC774mge7MYRgm8MjVmWohfv2p47SMHHt4vRaYiMHiNRLbns+O1nS4h9fr/YtcKJY0qaysREJCQqu/BQUFISYmBpWVlR0+7+c//zlee+01bN++HYsWLcKrr76KX/ziFx2Ob2pqQn19fauHVMRUtSslz0VB7YC2mCSTw+nCxpJTkl/bagGGtdCsdThdWPbeQZ/PiQrvhFsykyS/tlhsVgsendjf77gVHxxsvXDJ+Ea39c3x9/6d9+KxEkhEdzMxiiKkio8luhjGXGhZolWtzgm9Vrap6dGhVzkAtZIAeuzMAfT7PjN0At01oXdfE/YzYRjjI1dnalSY7ziJ0HFaRo49vF4LTMQwivh72tDU3OHvtF7vX+RCcNJk4cKF7aoA2z4OHz4sekL33Xcf8vPzMWjQINx999145ZVXsHHjRpSWlnodv2rVKkRGRnoePXr0EP3abkRVtSskz6WHgLaYJNOeE7WovUhrFfOF0wUUt6jCoXhfqH29ACC6s/8kW1uPDjm7mfTmm0NB3z9n+mRkeiw6EX9FWKKLYcyD1iVa1eqc0Gtlm5oeHXqVA1ArCaDXjg01JeAY5VGra0LPvibsZ8Iw5kCOztQYQmxJyDgtI8ceXq8FJmIYmR6LMGLApqM9vF7vX+RCcNJkwYIFOHTokM9Heno6kpKSUF1d3eq5zc3NqK2tRVISvco/JycHAHDs2DGv/75o0SLU1dV5HidPnhR6Su0QU9WulDyXHgLaYpJMcs5368EfO5f0cL2EvH7LcWVnL8r2+nrzzRHTzcQoj81qwZj+tOoYAFhfdELB2TAMoxW0LtGqVueEXivb1PLo0LMcgFpJAL12bKgpAccoj1pdE3r2NWE/E4YxB3IUO+i1wEQMcuzh9VpgIgab1YKJg2iS6h3t4fV6/yIXtLKxFsTHxyM+3n81w6hRo3D+/HkUFxdj2LBhAICPP/4YTqfTkwihUFJSAgBITvb+RoeEhCAkJPAZ07YfBVkDyi0OroeAttujw1+HR0uPDjnlzP5Vchr/O/GqR4kerpeQ13ePczhdeGNPud/x0eFBOHfJf+ucN98cX0kJtX1z9JIMMyP3jErF5m9oQS13sMOIP6YMw/yIFInWXr16ISUlBV999RV+97vf4ciRI3j77bc7fE5TUxOamn7czFNkWt2dE5RWczk7J/Rc2Taqdxz+uc+/rKqcwXg9ywEITQLI9buo18CJ0CAIVW6C0QZqdU24fU0oCRst+ZqwnwnDmAepxQ56LjARgxx7eL0WmIhF6h5ez/cvcqCYp0n//v0xbtw4zJo1C3v27MHnn3+OuXPn4s4770RKSgoA4NSpU8jIyMCePXsAAKWlpVixYgWKi4tRVlaGd999F9OmTcONN96IwYMHKzXVdoipah/WKxr+9ivU7UzLLgA9GMGL8uggVht1JchB1Fy0e94LPVwvQHg3054Ttais95/hvTmDWPWvM98cvSTDzIgQo02nC9h5lKU1GEavaE2iFRAn06pW54SeK9vUmLue5QDU6GbSc+BETQk4RnlOnaO9Z3J3TejV14T9TBjGPEjtTNVzgYkY5NjD67XARCxS9/B6vn+RA8WSJsBViYWMjAyMGTMGEyZMwPXXX4+//e1vnn+/cuUKjhw54pFeCA4OxkcffYSxY8ciIyMDCxYswO2334733ntPyWm2Q0xVe/F35+Bvn0XdhpWdFSZFoYXtnVCPDqqc2dCeUaRxQjoMtHC9KLSMQ1PPr3MI7YZTb745bASvXWxWC27NTiGPX/r+AQVnwzCMkmhNohUQL9Mqh4a0UPRc2abG3PUsB6BGEkDPgRO1EplMYPj+HO3eVomuCT36mrCfCcOYB6nylHouMBGLlD28ngtMxCJ1D6/n+xc5ECzPJYSYmBi8/vrrHf57amoqXK4fv/g9evTAJ598ouSUSIipav/oYMdyEy2htJJt2FuOuTf3gc1qERTQVrNVXWiiiSrPlds7Dp9863/j6Jab0sv1EtLNNKp3LPl69YgOJ40T4y+jdekrbYVLzMWq24bg//b5Nnd2U3rmEuzNTgQHKZqzZxhGAbQm0QqIl2lVo2pKz5VaQiohbx/WXZbX1LMcgDsJQJFDkCsJoPfAiRoScIzyOJwulJSfJ41NiZI/mZfbOw5rtnfcrdiSz0vP4Lq+6q8n7GfCMOZBqjylngtMxCJlP63nAhOxSN3D6/n+RQ44auUFt0eHL5IjQz03HA6nCxtL/G/yAeCWTP8Vli07MvQS0BacaCK2e1iobSE/rP96uV6C50m8Dv3iu/iVibNarsrJudGD9BUbwWub4CAr+sR3Jo9f+H9fKjgbhmHURg8SrWpUTZXXXgz4a8qFGkbder9JC3Q3k94DJ3p/vxnvCJGa6hYtf4BKiIysFnxN2M+EYcyF1M5UPReYiEXKHl7vBSZikLqHN3unCSdNvGCzWjB5SMdVjQAweUiyZ5Oy50Qtai/6DugCQGznYHL1ijtYroeANiBcPokqz/U9UbLALTell+sldJ7U67W3vNavTJzTdVVOzs05wrFbJgnVQC/JMDOzZNIA8tiN+09rRjeaYfTMZbsDD/1zPwYv+RB9H/kA2cv/jYX/9xUu2x1qT03zEq1SNaSF4nC68PZ+WoGNFoPCanh06P0mLdBJAL0HTqjzL68VJmPMqIvaUlN68zVhPxOGMRdS5SnNWHAgZQ+v9wITMUjdw5vNA6YtnDTxgsPpwrtfVvgc8+6XFZ5NFTVQe2tWChKIWT633JRejM0ptFxyqEmDHsSKI71dL6FJJqo8F3Wbv/UHOTmH04UVHxzyO/7RiZmq/mjoJRlmZnL7xJEl0lwA/rj1WyWnwzCGpqGxGcOW/xv9CzbjH1+cRn2TE1ecVzvuNuw9if4FmzHrlb2qztEt0XrhwgXU1dXhpZdeQpcuXTz/7pZoHT16NIAfJVpramrQ2NiIo0ePYvXq1YiIiFBmfsTf1U0HKmUJou05UYsLjbRklhY1lNXw6ND7TVqgkz56D5xQ57Vx/ynVA9sMHS1ITenJ10TtJBPDMIFHSmeq3gtMxCClc0LvBSZikLKHN6MHTFs4aeKFPSdqUVHnOxHSUkKLGtAe0z+RbrwgID6thdsGofJJw3pFk2Sk+iV2pU1AZ9eLQqtTIk46Koy2UP2r5GqlP+WzDgDRKi+AFMk8ADjH5qCqYbNa8NMsuiH8nz85xkEPhhFIQ2MzBhZsxsClW1Dj5zd368Fq1RMnWoZadXXJ7pAliEaVAwC0qaEcaKNuI9ykBbqbSe+BE2oQpL6xmeVYdYJWpKZyBQS/Pi89o8gcqGghycQwTGCRUvSg94IJMUjpnDDj9ZKyhzejB0xbOGniBaX8JuD6UUbKH+5xQozN1UToNSv+7hxJRmpvGW2jrbfrJTTJRJXniusSjJjOvjtYAKDmoh17TtTqRvbKZrXg0Yn9/Y5b8cFBDsSryBM/G0Ie2+wEdh6VJ1DEMEbG3uzEmu3fos8jH2Dg0i1oECC9tfVgtSakurTIiLQYdA62kcYWHZe+VlHlACJCg1Tvhu2IQHp0GOEmLdA+MHoPBFCDIIB83UyMsmhFakovviZaSTIxDBNYpBQ9fH+OJlmp1QITMUjpnNB7gYlYrutL60xsu3c1owdMWzhp4gWqxE/Z2asLFDWgffZik0dGyh/ucXoJaguVT/roB3ko/9B22u7j6uV6CZ0n9fomRYbhp1ndyMfWk+xVNOG707IDjAk8Qg3hl75/QMHZMIy+uWx3YOwzO9Bv8Yd4cstRNDvFHefxTQflnZhBsFktuIHoMydHLp568/XT7G6aDYQFMihvhJu0QPvA6D0QMCItBl1DaYlMObqZGOXRitSUXnxNtJJkYhgmsIjtTHU4XfjXl6dJz9VqgYkYpHRO6F36VSxUm4i248zoAdMWTpp4YURaDJIi/H+oNuwth8PpEhZ4FijPpZegthCPDofThY0lNDPUnNRYkozXsB82wnq5XkLnSZUzG9YrGjdn0H5A4jqH6MYDBlA2IfaHP/wBw4cPR9euXZGQkIApU6bgyJEjrcY0NjZizpw5iI2NRZcuXXD77bejqqq1dEh5eTkmTpyI8PBwJCQk4KGHHkJzc+tK2R07dmDo0KEICQlBnz59UFhY2G4+a9asQWpqKkJDQ5GTk4M9e/YIPie1EGIIX3rmEuxiI8EMY1Ba+pV8W3VR8vHKatg0uSOG9aL9tkXLEGSm3nz1jAmX/FpKEcigvBFu0gLtA6P3QIDNasFt2bTCH60mfpjWaElqSg++JlpJMjEME1jEdqbuOVGL2ou+1UsAILZzsCbiOXIipvvZCNKvohFpE2FGD5i2cNLECzarBXeN6Ol3nLuqXUhAW6g81zlCF0tyZKguFkH3JRKyuFttFpKMV/EPrcx6SQIINYKnypkVf3dOdt8crYhdKZkQ+/zzzzFnzhzs2rULW7duxZUrVzB27FhcvPhjwHLevHl477338I9//AOffPIJTp8+jdtuu83z7w6HAxMnToTdbsfOnTuxfv16FBYWoqCgwDPmxIkTmDhxIm666SaUlJTgwQcfxC9/+Uts2bLFM+bNN9/E/PnzsWTJEuzbtw9DhgxBfn4+qqurBZ+XGggxhAeAe17cpdhcGEZPCPErEUJqrHaD8GpDvSGS48ZJ710AQGA9OoxwkxZIHxiH04UPvq4gjdVyIKBnDK1bVauJH+ZHtCY1pQdfEy0lmRiGCRxiO1OpXbmTs1I0W2AiFjHdz0aQfhVLNfGz0nac3qVf5YCTJh2QGkfbtFdfaBQU0BYS+HU4XVjxwSG/Yx+dmKn6IijEo4PaCXBrVgo5ybSVLPelnSSAP1q+o1Q5s+oLjYISc3rxgAGEJ5qE8Pbbb2PGjBkYMGAAhgwZgsLCQpSXl6O4uBgAUFdXhxdffBFPP/00br75ZgwbNgwvv/wydu7ciV27rgb9//3vf+PgwYN47bXXkJWVhfHjx2PFihVYs2YN7ParPyJr165FWloa/vCHP6B///6YO3cufvazn+GZZ57xzOXpp5/GrFmzMHPmTGRmZmLt2rUIDw/HSy+9JPi81ECoIfzuE+e424QxLVL8Sqg8MiFT9mMahUAmAfTeBQDQKyE3HaiULG9jlJu0QPnA7Dpeg8tXaL+lWg4EGCG5KIWVK1ciNzcX4eHhiIqKUns6ktCa1JTWfU20lmRiGCZwiO1MpXbldo/S7u++WMTsF4wg/SoWanHO58da36+YfV8GcNKkQ4QkN4TIBgnpStlzohYVdf6PHa2BijEh14B6bW/JTCKP/VfJaTicLt0kAYQkmYTImSV0DVXss6sH5Lp9qKurAwDExPzQ6VNcjCtXriAvL88zJiMjAz179kRRUREAoKioCIMGDUJi4o9Vpfn5+aivr8c333zjGdPyGO4x7mPY7XYUFxe3GmO1WpGXl+cZoweEGMIDwMTn/qPQTBhGm8jlV+KPWzITEEY0OzcjgTLqNoocALUS8pLdIVnepryWJk2n9Zu0QCV/iojXu0tIkKYDAYFMZGoRu92OqVOnYvbs2WpPRTJak5rSuq+J1pJMDMMEDrGdqWYOaIvZLxhB+lUsYu95jFD0JRVOmnSAkKr2OOIHMK5LiKCuFD0FtIUE6oUkjkakxSCms+/3AQBqLtoFdbGofc2EzFOoVqWQ66sXDxhAWKJJCk6nEw8++CCuu+46DBw4EABQWVmJ4ODgdlV/iYmJqKys9IxpmTBx/7v733yNqa+vx+XLl3H27Fk4HA6vY9zHaEtTUxPq6+tbPdQmOMiKnFTajSkAHK2+iPeIJnYMo2fk9ivxxS2ZCVg3bbiir6F3AmXUbRQ5gBFpMehMTMIVHRcf1HY4XXh7P61YROs3aYEKaLiIfdTX943VdCAgUIlMrbJs2TLMmzcPgwYNUnsqkjl1jubTE0ipKS37mmgtycQwTGAR05lqlK5cMYjZLxhB+lUsYu55jFL0JRVOmkjAc8tB3bO76MHyrQcrdRXQFpJkEpI4uir1QzOFrL7QiLKztCCU2tdMiW6QW3/QqhRyffXiAQMoawTfkjlz5uDAgQPYsGGDpOMEilWrViEyMtLz6NGjh9pTAgC8+suRgsb/ZsN+QwZAGAZQzq/EG9elx+DQ8nGcMCEQKKNuo8gB2KwW3NCXdiMpZTnfc6IWFxppUnVav0mrJXgTChnXEVFh/guMAGBYT3pBgxoEKpFpFLRYOOPm1LlLpHGBlJrSsq8J+5kwjLkRkwAxc6eJmP2CmZNMYu55jFL0JRVOmnSAkKr2s8QbnbMXmwTJTQ3rFa2bgDYF93ZYaPA7LzOJND6ucwje2FPud1xyZKjq12xEWgySI/1/Fs5dtJM7mcb0v9qdIHdyQSth7EAkEefOnYv3338f27dvR/fu3T1/T0pKgt1ux/nz51uNr6qqQlJSkmdMVVVVu393/5uvMREREQgLC0NcXBxsNpvXMe5jtGXRokWoq6vzPE6ePCn8xBVAaLeJ0wU88Po+BWfEMIElEH4lLZlzYzpKH5+Av983iiW5iATKqNtIcgDDetH2T9ESbtCpSSZA+zdp5y/TkqTF5TQvg46I6UzbK1LHqUWgEplGQauFMw6nC19+X0caG0ipKSG+JtROGTlgPxOGYcQkQMwsnSRmv2DmJJOYex6jFH1JhZMmHSAk8Cyku0GI3BTFhE4rAW0hSSYhcmYAyHJTTpcLlfX+AxN3Du+p+mbTZrXg0Yn9/Y5b8cFBOKnlmj8MiyPeEMd1DtGNBwxA/xwMI+oVt8TlcmHu3LnYuHEjPv74Y6SlpbV+7WHD0KlTJ2zbts3ztyNHjqC8vByjRo0CAIwaNQpff/01qqurPWO2bt2KiIgIZGZmesa0PIZ7jPsYwcHBGDZsWKsxTqcT27Zt84xpS0hICCIiIlo9tILQbpNNByrZFJ7RPYHyKwGAEJsFL00fjtLHJ+ChCf1V/23TI4Ew6jaSHAC1s0NKBwg1yRQRqm1/DgCwEN3WPjtaI6nb0iiBk0AlMgPJwoULYbFYfD4OHz4s6thaLZzZdbwGdgft8xxIqSmb1YKsnlGksafPBy5pwn4mDMMI9egwu3SSmP2CUfZKYhF6z2Okoi8pcNKkA6jV6kK7G4TITRUdP6ubgLag7gYBcmYAyHJTu4nXITUunDgBZYkmJDcq6hrJ5+XpeKKuV5bASV7JgRDZMaEsWLAAr732Gl5//XV07doVlZWVqKysxOXLV2+YIiMjce+992L+/PnYvn07iouLMXPmTIwaNQojR15NDIwdOxaZmZm455578OWXX2LLli1YvHgx5syZg5CQq+/1/fffj+PHj+Phhx/G4cOH8ec//xlvvfUW5s2b55nL/PnzsW7dOqxfvx6HDh3C7NmzcfHiRcycOVPweamN0G4TABj+2L8Vmg3DKEsg/Up6RIfiwNJ8HFk5ATcbfKOqNIGQT6IGd/UQBA6EUTc1yfTT7G6a/+yPIsroNDQ1i97PGy1wEohEZiBZsGABDh065PORnp4u6thaLZyh+nOEBlkDLjXVPZp2HxhIM/giAf4p7GfCMMZEqEcHSycJ2y8Yba8kBqHyZEYq+pICrZ/JhLi9HnwlLaLCOwEWCO5uyMtMwouflxFmQbsR1EJAm5pkKjtL07cFfkwC0M+PtrFV28/EjVLndZaYET7bQJeL08I1UzLB8+KLLwIARo8e3ervL7/8MmbMmAEAeOaZZ2C1WnH77bejqakJ+fn5+POf/+wZa7PZ8P7772P27NkYNWoUOnfujOnTp2P58uWeMWlpafjggw8wb948/PGPf0T37t3xwgsvID8/3zPmjjvuwJkzZ1BQUIDKykpkZWVh8+bN7czh9cKrvxyJfos/JI+va3Tgvwv34KUZIxScFcPIR0NjM0Y+/pHi8lsAMGVwMlb/vywEB3HNi1xQ5ZOo47xR/B0tGH5OB5VtQm/qxSQ1qDd1PWO0UQTji5HpsQjrZMXlK/5bzsTKTRktcBIoH5hAER8fj/h4cwW6qQnAwSpITXWLpn0H3Gbw1xF9nKTgIt7rqZFkYhgmMAj16GDpJGFJAKPtlcQgVJ7MzB4wLeGkiQQsAKov0DbsLbsb3DJDvopXrBYgJy0Gf9ru/9haCGiPSItBUkSI3wTShr3lWH37YNIx3fJcVLmpnNRYvBZe7jPRpSUPGOr7lpMaiz9bSv1+XtyyVEISWOMHJpM+i2Ikr+RGyQRPXV2d3+q80NBQrFmzBmvWrOlwTK9evbBp0yafxxk9ejT279/vc8zcuXMxd+5cn2P0QnCQFRMGJmLTAVplBwB8fPgM3vvyNCYNSVFwZgwjHnuzE+s+PYZntiorv+Vmzo3pmD8uQ/NV9XrERVXAFFlw7HC68OlRWtW1Ht5eoTf11E6LlhhJc9pmtWDioGT8c98pv2PFdhoZLXAixAdmlsJzCTTl5eWora1FeXk5HA4HSkpKAAB9+vRBly5d1J0cESH+HBR/R7nJ7R2HNdtLSWM/Lz0TkKRJxXlaUeGEQcm8D2AYg+L26KAE9ivrLpP3DEaWThKyXzTaXkkMQrrFbx/W3VD7cSlwqWIHUD06zhKTJi19PKgyQ3D5v4HWSkDbZrXgrhE9/Y6rqGvE4cp62kHd10iA3BT1kFqA6tEBP0kNoLUslTuB5Y8Ne8uxt6xWMckruXF3f/kiSkNJMeZHnv/5MPLX2M0Db+wPmCwCw1BhvxLjQTUsrxJgTt6SPSdqcZHYhTQqXfvt7YEw6jaa5rTSclPUe5ExOgmcBMoHRosUFBQgOzsbS5YsQUNDA7Kzs5GdnY0vvvhC7amREeLPQe36kBOtmcE7nC68/1UFaWySCkkmhmECg1CPDpZOEtYJwf4cwrvFjbYfFwsnTTqAKvFz7hKxNbzF5pF67N1lNboJaANAalxn0riTxA2oW56LKje1+0SNbjxgAPm9WtyfKyEJLKqGrhYk4CgY8+dN/9isFjx/Z5bg5415itBqxzABgP1KjEtcV9oNxLYfbiCEQq1sCw+26UJ2xWa1IK9/Amksdf/WEofThQ++pgUQ9aI5rbS8AVXWLZHYJaQ2gfCB0SqFhYVwuVztHm3lY7UM1c8EUMefw2a1IJtYcFhRp/z9z67jNaCqe/J2gGGMjZAiC5ZOoicBYrqEkPeM1/UxbpJJSLf4rtIa03vAuOGkSQdQJX6sFtolPNtCd5cqN0WVgtBKQJt6zS410XaG7uPR5Zb04wED0OfhIn4QWnYzURNYevKBoXZ/Ge0G2ij8V1Y3ZPcQZlBaVnsZS989oNCMGMY/DY3NGFiwGQOXbkGNn/VHKlMGJ+Pbx8bj09+NQRdiJT8jHaFyU0KhVrZNGJikmwRZUhStOlyMD8yu4zUk/w9AP5rT1GrQ8lq6719LLMSPDXWc2rh9YCiI7WZilIO6TgbbLKoliqld6V8FwAxeSJJJD92IDNOSNWvWIDU1FaGhocjJycGePXs6HLtu3TrccMMNiI6ORnR0NPLy8nyONyJCEiEsnQQkEJMmCV1CkEDc71PH6REh3eI7S8+a3gPGDSdNOoAqnTQ8lVap0jKgTS2Hpy5wWghoAzT5pMiwIHx69IzfYyVHhno2tNT3Ioe4AdbK9aLOw9819dBiT09NzFkJd9At3ws1UdIIngkM/5x9veDnFO78Dis/OKjAbBjGO/ZmJ9Zs/xZ9HvkAA5duUdzgfc6N6Sh9fAKe/flQNnhXAaXlpowon6CkDwy1A7ZLSJAm9iYUqEGQjftPietmOk/7XEaFEfeTKmOzWjBhYBJprJhuJkY5hPiZDFHBBN5NLnG9bWx2YhdxTRILVQJMzSQTw4jhzTffxPz587FkyRLs27cPQ4YMQX5+Pqqrq72O37FjB+666y5s374dRUVF6NGjB8aOHYtTp/x7ghkFIYkQlk4COa66t6yWrtNvLNXPVgiRgDslYG+pl/24WPjuvAOo0knfVl2gHbDFsagb/LguwX4D5loyNqfgcLpQRdBevnN4T89G2ogeMADdoyOOmFxp2c1E/QF5bXe53zGPTszURPWrEIN7RpvYrBb8SYRM17pPT2ATUe+ZYcTCfiXmRGm5KapRp1gTcDVQ0gfGRbxbvb5vrG6+N1T5iPpG4XJTDqcLmw5UksbGEeehBZTsZmKUQ4ifyXAV719Hpsci2EZbPz4v9V/sJ4Xvz9HuW9RMMjGMGJ5++mnMmjULM2fORGZmJtauXYvw8HC89NJLXsf//e9/x//8z/8gKysLGRkZeOGFF+B0OrFt27YAz1w9qAmOncfOsHQS6PvywqIy8p602uDFGNQiLWoNj5E9YNxw0qQDqNXqQv05AHoXAGWclhKhFPmkBqI0V2pcuOe/qe/F63u/05UHDAULgO/O0vTzWyYVqD8glCBNtEZ+aIUY3BvNGNRI/FdWN9x8jfBKuf95fR/sSkaxGdPCfiWMkgHa4u9oQXCqL4UWoPrAfHigUvDvMbUbYlhPbRTAUKBKwAHCu5mMKGcGKNvNxCiH1v1M3NisFgzpHkkau1dB2V+H04WS8vOksSnE3ymG0QJ2ux3FxcXIy8vz/M1qtSIvLw9FRUWkY1y6dAlXrlxBTEzHCdampibU19e3eugZapHF5m+qWDoJ9KLa85eu4J0SWscSVVZXr1ATcxZipFlPnfJi4aRJByjlzwGA3AVwuOqCrozN5ZRFalkNR30vPj1K26hrRb6J6tGxvqjM77HaSmhRE3MUtHK9hBjca+U7wXjnpZkjEREq/Oen3+IPsemr0wrMiDEj7FfCuFEqQOtwush7Ez3lz6hJgEt2h2BpG2rVdYyM+xylGZEWg66hNtJYoR1HRpQzA5TtZmKUQw9+Jm66RYf7HwTgSwV9TYR05nSLNm7gkzEeZ8+ehcPhQGJiaymgxMREVFbSuiN/97vfISUlpVXipS2rVq1CZGSk59GjRw9J81Yb6v7qIlE62OjSSUIkdqm/T0buzAHoEnAuYtDa0PJvP6BY0mTlypXIzc1FeHg4oqKiSM9xuVwoKChAcnIywsLCkJeXh6NHjyo1RZ9Qq9o/PVotWBKK2gVwknjjqJWgtqxeIS02kCPSYhDT2X/lIbWLRSueJtT37RwhmNdSzgwAOTFHQSvXC6Ab3GvlO8F0zBeL80U9739e348V77M5PCMO9ithvKFUgHbPiVryja2eDH5HpMWgczAtCVB0nF557nC68PZ+WiWgnm7SbFYLbsvuRhor1LDViHJmgLLdTIwy6MXPxA01CWF3uBTzNdFLZw7DBJonnngCGzZswMaNGxEa2nEsYtGiRairq/M8Tp48GcBZyo+QJAAFo0snCfHoaCQqVhi5Mweg758rztHUF4TuW/WIYnfwdrsdU6dOxezZs8nPWb16NZ577jmsXbsWu3fvRufOnZGfn4/GxsAHQKlV7VUX7IIloahBaKpfilaC2hTDduqS3VLOzGa14KdZtJtNf8fXkqeJnO9bSzkzgJ6Y09P1AujXTCvfCaZjgoOsmHmd/zXWGy9+9h3uLdwj84wYI8N+JYwvlArQVhKTLOHBNtWrroVgs1pwQ195NZGBq0mmC420JJPeKgG7E6vaay8Kk4UwopwZoGw3E6MMevEzcUM1gweU8zXRU2cOwwghLi4ONpsNVVWtfTeqqqqQlJTk87lPPfUUnnjiCfz73//G4MGDfY4NCQlBREREq4eeEZIEoGAG6SQ5z9HonTkAXQKu5Hua1J2eipjEoljSZNmyZZg3bx4GDRpEGu9yufDss89i8eLFuPXWWzF48GC88sorOH36NN555x2lpukTalU7hZaV79Qult3H/W+k2soyqQnFsJ1679w26H1zBu3Hw9/xteRpQkkyUWlr7klNGujpegG0a6a1RA/TMUsmDUL3KHGBr22Hz+C/X94t84wYo8F+JQwFpQK0Zy/QAuDjBybp7vMyrBdt70nt4gHoSSZAf5WAVD+c4nJhey4jypkBynUzMcqht66JkemxCCIuu0r4muitM4dhhBAcHIxhw4a1MnF3m7qPGjWqw+etXr0aK1aswObNm3HttdcGYqqaQ84kgBkC2nKeo9E7cwD6Pc8VYtWT3oqYxKAZrYgTJ06gsrKylWZhZGQkcnJyfJpFKWn+JGe1estjUbtYKB/TdrJMKkKVRBLV3SDjKWpFuomSZCLT5jhydv1o5XoBtGumtUQP45vPFt6C6DBaYKQtHx85i+kv0MwEGXPBfiWMEJQK0FLN3RMFGIVrBepNEjWoD9CTTBGh+vLnAAALcde14/AZcjeTUeXMAOW6mRjl0FvXhM1qQTaxyEoJXxO9deYwjFDmz5+PdevWYf369Th06BBmz56NixcvYubMmQCAadOmYdGiRZ7xv//97/Hoo4/ipZdeQmpqKiorK1FZWYmGhga1TkEV5Py9NoN0kpznaIbOHLkl4PRWxCQGzSRN3IZQQs2ilDR/GpEWg6hwWtu7L2I7B7e7uZOri6WtLJOaKNndQJWboqAV6SY5kxFn28g5KNn1oybUa6alRA/jn/1LxiFShDE8AHxyrBYDH93EmuYM+5UwolEqQHv8DO1G36KN2hdBUG/qN+4/RV6fqUmmUb315c8BXJ0zhcZmJ7mbychyZoAy3UyMMui1a4KafFXC10RvnTkMI5Q77rgDTz31FAoKCpCVlYWSkhJs3rzZE+8rLy9HRUWFZ/xf/vIX2O12/OxnP0NycrLn8dRTT6l1CqpAlU+ioLeCCTHIeY5muF5ySsBFhRtfzgwQmDRZuHAhLBaLz8fhw4eVmqtX9GD+dGtWSrvNYZxMbfJaCmgr2d0g13lqSc5MzveurTyXol0/KsKeJsbly6XjIbbooeGKC70f2YR3930v76QYXcB+JYwcZBM9HyKJHhIOpwufHaPp4FN9KbQE9aa+vrGZXIFOTR71SehCG6ghRqbHIoSYXKV2MxlZzgxQppuJUQa9dk2o6Wuit84chhHD3Llz8d1336GpqQm7d+9GTk6O59927NiBwsJCz/+XlZXB5XK1eyxdujTwE1eRBBmTJnosmBCKnEkmM3TmAPJ11MwYlWqKe2FBSZMFCxbg0KFDPh/p6emiJuI2hBJqFqWk+dOeE7U4L4Osx5j+XjJ5Mny2tBbQlrO7oW0SQC7/Dy3JmVG9bUi0ubDsaaKd7wVD5/BjE9FJQsH+r9/6Ej/5/UfcdWIS2K9E26xcuRK5ubkIDw9HVFQU6TkulwsFBQVITk5GWFgY8vLycPToUWUn+gP1jbT93v6TtN/EPSdq0dBEy+C13fPoAaomMgBU1l0mjaMmj/SYZLJZLbjpGlq1OPUnzMhyZoAy3UyMMui1a0ItXxO9duYwDBMAZPy667FgQihC9qP+MEOnCSDfeQ5P1d/eUgyCwlPx8fHIyMjw+QgOFpedS0tLQ1JSUiuzqPr6euzevdunWZSSyCbx42UfL4fclNYC2rJKIrW5ZnL5f2hJzozqbUOhrTzXiLQYxHSWJ6igJakr9jQxPkcfn4gw6h2sF74718RdJwbG4XRh+zdVyFi8if1KNI7dbsfUqVMxe/Zs8nNWr16N5557DmvXrsXu3bvRuXNn5Ofno7FR+d8huT0njN4FMCItBl1CaLcR1D2vUU3N3cjdzWRkOTNAmW4mRhn02jWhlq+JXjtzGIZRHrlk6aPCzCGdJKdHhxk6cwD5OmqqZbRQ0DKKiXCXl5ejpKQE5eXlcDgcKCkpQUlJSSsjp4yMDGzcuBEAYLFY8OCDD+Kxxx7Du+++i6+//hrTpk1DSkoKpkyZotQ0fSKXhFbbgLacx9ZSQFtOSaS210yu89SabJNc3jZtq1RtVgt+mtVNlmNr6Zqxp4k5OPTYBESFijOHd8NdJ8bC3uzEbzbsQ+9HNmHmq1+gkRptEAn7lUhn2bJlmDdvHgYNGkQa73K58Oyzz2Lx4sW49dZbMXjwYLzyyis4ffo03nnnHWUnC/k9J4zeBWCzWnB9H1rF+PnL/pObRjY1dyN3NxPVM0ePcmaAMt1MjPzovWtCDV8TvXbmMAyjPHLFXvJM0hkvp0eHHouYxCDXPrqWkybSKCgoQHZ2NpYsWYKGhgZkZ2cjOzsbX3zxhWfMkSNHUFdX5/n/hx9+GA888ADuu+8+DB8+HA0NDdi8eTNCQ1UK2sq0xniVXZDp2FoKaMspN9X2vORIMmlRtkm2989L/PDmDOk/Hlq7ZuxpYh5Klo5DVJi0xAl3neifln4l/yqp8P8ECbBfibqcOHEClZWVyMvL8/wtMjISOTk5KCoq6vB5TU1NqK+vb/UQg9yeE0bvAgCA9HhaMP5Ytf/gvtFNzQF5u5mM7pkDKNPNxMiP3rsmhPiarC86Ictr6rUzh2EY5ZGrc0Iu3wo9IMe5mqUzB5DPB8YsHjCKJU0KCwu9GjmNHj3aM8blcmHGjBme/7dYLFi+fDkqKyvR2NiIjz76CP369VNqin6RbQOukDyX1gLacslNxXYObr9gyRBP0KJsk1xeLd66mYx4zdjTxFyULBmH6HDpm8Zfv/Ulch77N+xKOoQzssJ+JeajsrISAJCY2Drhn5iY6Pk3b6xatQqRkZGeR48ePUS9vtyeE0bvAgCAaOLNUlFpjd8kgNHlzAB5u5mM7pkDyN/NxCiD3rsmRqbHwkb82f+YKM/oC7135jAMoyxydU7otStXDHKcq1k6cwD5fGDM8hljzQkfyFWtrpQ8l9YC2oA8clO3ZqW0W7DkSmBpTbZJLq8Wb59VI14z9jQxH/sL8tEjRnqArKrhCvot/hBT137GyRONwn4l2mfhwoWwWCw+H4cPHw7onBYtWoS6ujrP4+TJk6KPJZfnhBm6AAAgrqt8nhNGlzMD5O1mMkOSCaB3M7lYiVM1Tp2jSaNptWvCZrVgQLcI0thmp3SJLr135jAMozyydE6YpAsAkOdczdSZI1c3k147v4XCUQIfuKvapQa1lZTn0lJAG5An0TSmf/vMulwJLK3JNsnx/iVHhnoNIMjlm6Ola8aeJubk04dvxvL3vsFLn5dJPtbesjr0W/wh8gfE4893DzdNRYmWsTc78dA/SxSX33Iz58Z0zB+Xwe+9CBYsWNCqQ9gb6enpoo6dlJQEAKiqqkJycrLn71VVVcjKyurweSEhIQgJkef3Ti7PCTN0AQDyek7UXqIlTfQsZ+buZtr8TZXfsf7uPcyQZAL8JyjdVNbxvk8tvvpe/10TkwZ3w1ff06QdPy89g+v6ig+uvbKTLvGlxc4chmGUR44KfrN0AQB8vYTi7mb65z6al2BH6LkoRwicNPGBXF0ASslzAdq72ZYl0eTluSPSYhDTuRNqL4qvPNaibJMciY07h/f0fhMiw32JV6k0FaEmcMrOXlJ4JkygKZg0AAvH98eAJR/iCk323idbvjmD3o9swrM/G4wp14qT82GkcdnuwK1rPlVcfgu46lfyl19ci59cE6/ZoI0eiI+PR3y8MkGctLQ0JCUlYdu2bZ4kSX19PXbv3o3Zs2cr8pptEeo50dFnySxdAG7PCUqCyN++t7iMFngN6yTN60ptsntGk5Im/pIFZvDMAeiJzH8frPT5nWSUwd7sxPGztE6TlCjtrnXTc1OxctMh0ti9RD8SbzicLnx0uJo0Nsiqzc4chmGUR47OCbN0AQDyeHSYqTMHAK7rGy8paWImDxiW5/KBXNXq3uS5lDQAVxM5Ek3erpfNasFPs7pJOq4mZZtkuLdLjQv3+nc5EnPepNLUZERaDJIi/P8obthbLllzmNEewUFWHF05EQNTaDIKFB7851e45n8/QENjs2zHZHxz2e7AqFVb2a/E4JSXl6OkpATl5eVwOBwoKSlBSUkJGhp+9PrIyMjAxo0bAVz1tXvwwQfx2GOP4d1338XXX3+NadOmISUlBVOmTAnInOXynDBLF4DNasF1RDmDWh9BfofThZKT50nHSYnSTverGOTqZjKDZw5AT2Q2NDnI5tqMfKzfWUYe2y1au0mT4CAresd7v59qy5ff14m+x9h1vAYOokJsZnJX3rMwjEmRo+tBz0U5QpHDo8NMnSYAkCAx0WQmDxhOmvhASUkod+eEVLwagKuIHImmjrpnbs6QboilNdkmORIbHV0vObpYbslMknwMObFZLbhrRE+/4yrqGvnm2cC8/+sb8Mc7s2Q7XpMDGLh0CwY++iEnTxTC7VcysOBD9C/YjIo6ZTem7FeiPgUFBcjOzsaSJUvQ0NCA7OxsZGdn44svvvCMOXLkCOrq6jz///DDD+OBBx7Afffdh+HDh6OhoQGbN29GaGhgAuUj02MRTHQE/ry0Y88SM0hNuQkLpn2/fHWS7DpeA7uDFoTUu1yN0G4mbzicLmw/QqtW17NnDkBPZAL+JeAY+XnvK3qVqta/u+MGJvsfBMDuEO9rsrPUt1dRSyYNSRH1GgzD6B+pnRNaUwtRGjk8OszWaSK1eNtMHjCcNPEBtardFx0tWHJ0TgDa8psAZPLR6Oi+WYa4gtbkzGR5/xS6XlqUMwOA1LjOpHFaS5Ax8nJrVjeUPj4B8V3k2+A0XHFy8kRm7M1O/GbDPvR+ZBNmvvoFGuzEEkuRzLkxHaWPT8CzPx+KYKLhMqMMhYWFcLlc7R6jR4/2jHG5XK08UiwWC5YvX47Kyko0Njbio48+Qr9+/QI2Z5vVgqweUaSxp893/BtjFqkpgF49/pWP6mxqIDE0yKp7uRo5upl2Ha9BE9FJWmv7XqGMTI9FSBBtQyuX9DFDw+F04ZtTNB8QPUhN5QoIAPlKmvtCSEHX9Nw0Ua/BMIz+kdo5MVljaiFK4/bokILZOk2qBUgJe8NM14sjCj6gVrX7wpe8kdTOiY4MwFVFhrW5o+4ZWW6GNKbY5PaAkUJ1B9dF6vXSpJwZ6Ik5WRJ4jKaxWS3Yu/gW/Pd1qbIel5Mn0rlsd2DsMzvQb/GHihu8dwm24qXpw1H6+AQ8NKG/qW4SGPkZlkorFrjcgbmSmaSmAHqg0VcS4NQ5WofAYA0bSVORo5upSECVu97lOWxWC0b3o3Uo+JKAY+Rn1/EaEBvEcHOG9v3ERqbHgpifw5ZvKgUf3+F0YT/xvqp3fDgXfjCMiZHaOdFdwx5SSiG188FMHjAAUHtR2p7JTJ05/GvsB2pVe0eM6e8jMSJx79ihAbiKaF1uSmtyZnJ4wNR2cM3l6GLRZLcG9SOvra8GoyAFkwbg28fGIzFC3h9vTp4IJ5B+Jdckdsah5eNwYPl49ithZCMmnLbX+M+33uWTzCQ1BciTBLhsp62v1ISWlrFZLRjSPZI0tqNkkotYAdQlxKa94ioRyCEBx8jPq0Vl5LHTR2m/a8JmtSCb2GFfeuYS7M3COmd3Ha8BsUEM+QO0JY/MMExgkdo5YaaAthupnQ96LzIRilQJOO40YTxIDtT72BxJTTB0ZACuJlqX59KanJkcSYmOsuJy+OZoUdaB+r1hmQZzERxkxe5HbpHV68QNJ098o5ZfyZZ5oxEWrH95I0ZbxHWl/e5dvuK9c8JMUlOA9CSAw+nCjm9pUjcxBgkCdIum7d8r6rzvESvOXyI9f2xmkiGSyXJIwDHy4nC6sO1QFWlssE370lxuhCQZ1+88IejYrwgYb4SEOsMw0pDSOWGmgLYbKYmiqLBOhigyEYJUCTgzdeZw0sQfEu81fHU2SE0waDGgraQ8l1TdPS3KmcmRxEnoYMGTxTdHg/eeLM/F+MLtdTJOoq6pN9zJk2v+dxM+PlRt+uAM+5UwRkTITYS3zgkzSU25oSYBLJb252smfw43UpIADqcL739FkzxMitRWoZBY5JCAY+Rl1/EaXCH+5A/R0VonxNfkvS9Pk8c6nC58dLiaNFYP/i8MwyiPlMQHd5oII8+EigVSJeDM1JnDUQc/SK1W93mDJ/V7qcF4nRzV/R0lEqTq7mlRzkyObhBfnwOpvjlakzMDwPJcjF9sVgvWTrsW3z42HsNTo2Q/fpPDhf9evxe9H9mEB1/fJ1iiQe+wXwljZEakxSC0E2177M0M/tQ5WhdAskEC2gCQTPRm8eYDQ+3MAYxzgyYlCXBV/o32OkZZLuWQgGPkRcj3drjGCtZ8MTI9FjZidOTA6Xpy8cyu4zVwELeKmcldea/DMIykxIcZO02kyE1d10eaH4oekSIBZ7bOHE6a+EHL8lxaDGhL7ZyI7Rzc4RdQqu6eFuXM5OgG8fk5kLjn1mJVJ8tzMVSCg6z4x/3XKeJ34uadryrQb/GHmLr2M8MnT9ivhDEDNqsFP+lLu3lqmwRwOF348vs60nOp3QZ6QIoPDLUzJ6yT1TA3aFKSAEKC1aPSjREEECIBt/dErcKzYQBg8wF6wYSepKZsVgvyMhJIY50uYOdR2vdRiP/LpCEp5LEMwxgXKYkPM0knuZEiN9WRcovRESsBZ7bOHE6a+IPluQQhtXNiclZKh19Aqbp7WrxegPRuEF+JKqmSZlrsZmJ5LkYobr+TA0vz0UUhD4y9ZXXot/hD5D+zA5epZcA6gP1KGDNybSpNGqVtEsBsJvBupPjAUDtzBqZEGOYGTUoSgJpk0pOPBAWqBNyX7GuiOPZmJ0rP0L63evwcTsulm9b/c99Jv2OE+L8AwHQBr88wjHGRUjBslM5cIUiKQ5p02yA2MSfFb0ePcNLEDyzPJQypnRPdozpe4CVLWWnwegGQ9Dnw1ZkDSJc002I3E8tzMWLpEhqEA8vHKZo8OVJ1Ef0LNiP38a26Tp4E2q/kf25MY78SRjOITQKYzQTejVgfGCGdOXqS+KEgNglATTLpyUeCArUzy+5wsa+JwqzfWUYee1OG/qpRR6bHkm8hdh33/1kT4v/SOz6c90AMwwAQXzBsNukkNzarBbeK7NSrNqlCiVgJOLPJv/Gvsh9Ynks4UjonfLUSSlkIAe0uhlK6QXx15gDSJc3kMKqXG5bnYqQSiOTJ6Xo7+hdsxrDlW9DQ2KzIayiBWn4lD0/I1F1ghTEuYpMAe4jSQEYygQeuFrWEBNHOp2XnhFk7cwBxSQAzJ5mEGHSzr4myvLarjDx22shUxeahFDarBdckdiaNrbpg9yvN+srOE+TXzh+QRB7LMIyxEWvUbTbppJZ0JxaktKXWpHEjsckPKX47eoSTJv6QuN74CtRLDUhrVW5KyjXzpycodiEEtLsYSukG8dWZAwAJEj4j/rpY1ILluRi5aJk8CVWosq/mUjMGLt2Ca/73A3x8qFqzsiHsV8IwPyImCeBwurD/u3Ok5xjJBB64GmQc3D2KNLZl54RZO3MAcUkAMyeZRqbHgviVZF8TBbE3O/FdLU0iLsiqP2kuNzdn0JMXi97+qsN/czhd2Hqwmnwso31vGYYRj1ijbrNJJ7VEbDDfjB4wgPgCa+40YVohtVrdV6BebPbYgzZjb9KumZ9zktI5odXFUMo5+f1hkBCL9NfFohrUm+YyvmlmaHQJDcLhx8bjmf+XpdhrNDmA/16/F70f2YQHX9+nCdN49ithGO+ISQLsOl6DZuK+zEgm8G6oRRYtOyfM2pkDiEsCUM2kjZhkslktyO4VTRrLviZ0HE4Xikpr8K+SUygqrfF73Ra9/SX52DdnxOv2e3tdX3rQ8V8lpzu8bjuPnQV1t6fnJBPDMMpwXV/hiVSzBbRbIvbczegBA4iXgNNqXFUpOGniB6ndIP7kpsRkj91oVZ5LSoW/PwktKZ0T/rpY1ELKOfn7YZAi/eWvi0UtqEm5wqIyvmlmBPHTod1Q+vgEvHzPtQilRrNE8M5XFaqaxrNfCcP4R2gSgNo1ARizmlho54TD6UJxmTk7cwDhSQB7s5NsJm3EJBMgLjHHdMzmAxXIXfUR7lq3C7/ZUIK71u1C7qqPsPmAd2lOh9OFjftOk48/fZR+Dc1HpsfCRtyuNDs7/rw9//G35Ne8VavFagzDqIaYOJHZpJNaIubczeoBA4gv4jdbkomjF34YkRaDqDDx5uP+PlBS2ue06DcBQFJ3g18JLSl7Sa3GzyWck78srxTpL61mkKmf+/OXrpCrWBnGjc1qwU0DEnH4sQmKep4AP5rGB8r3hP1KGIaO0CQA9fcm2GbMamKhnRNCKrCN2JkDCEsCFH52gmwmbcQkE8C+JnKy+UAF7n9tH6outL5PqLpgx/2v7fOaODFT14TNakFeRgJ5/Ooth9r9zeF04Yuy8+RjrLptMHkswzAmQcTtE3eaCMPMHjBiivi1KuGvJJw08YPNasH03F6inkvJWoo239FwRlRKd4O/QL2UY2u1M0eKnJm/7hkp0l9a7cwRksisviD+88IwLT1PYsPFJ8/94fY9GfDoJkWSJw2NzRi2fAv7lTCMAIQkATYfqCD7mQwxaBeAkM6J/SfP47lt9ApsI3bmAMKSAJsP0hPdRk0ysa+JPDicLsx/y7fM1q83lLTr1l723jfk19CzNJebabn0Tpkvv69vJ7u663gNOcmUEhnKnbgMw7RDTOxLq4WvgUBM7MvMHjCA8PPXrIS/gvCvM4ERaeIqZShZS7Htc1rOiErpbvDXmSPl2FrtzJEyL6cf+SmxOoUANNuZIySRyWbwjBx0CQ1CccFYHFo+DsmRyn2mLl5x/WAav0myabzbryRj8SYMXLoFNZeU7WRhvxLGaAhJAhw/e5nsZ5KiUelLOaAW8zQ7geLvzpPG2qzQdcW6L4QkAY5UXiAf16hJJqGJOZZo9c7Oo2dxyY80qL3ZiT9u/bbV/x87Qy+60LM0lxsh30+gvSH8k5vbd590xOSsZPoLMQxjGsTEvswmndQSMbEvM3fmAMLPX6sS/krCSRMCYrsbKFk7sV9SLWdExXY3ULpnlDy2WojVEgSA3X4q6aQc25+/jJpc24v2XjpdfMPMyEdYsA1Fi/JwaPk49EvsrNjrNDlcok3j2/qVNFIjuSJhvxLGyCixbzBqFwAgrHOCuqoN7RGl2SIhqQhJAlwkek8ZVf7NjZDE3M6jdJ8hM/HPfSdJ4/6045gn8XTPC7vIxzfKZ9BmteDW7BTy+P/bd8pzvezNTpR8X09+7g196FJgDMOYB6GxLy3HvAKBmNiXmT1gAOHnb8brxREOAmLlkygJEbEfOi1nRMV2N4whdM+IPbaWO3PEaAn+iO+AqJRj+/WXUZHdJ2gGn9RxDCOEsGAb/j1vNL59bDxuVbg60G0af8Pvt/mU7mK/EoaRHyFJACpG7QIArlZmy70c/PrmfvIeUGPIHdwwqvybGyHfyaXvH1BwJvrl1HlaMaDTBfxx67ewNzuxu4wmPwgA99/Y2zCfwVW3DRE0/oHX9wEAblj9Efk5evd/YRhGOYTGvrQc8woEYmJfWo6rBgKh52/G66VY0mTlypXIzc1FeHg4oqKiSM+ZMWMGLBZLq8e4ceOUmiKZ85eviHoeJSEi2tNEwxk+sd0NiYQfhRFpMegsQvpFy505gPj5jUr3/zyxx9ayHiZVcYGVGRglCQ6y4o93DkXp4xMw5ye9FX2tk+cavfqesF8JwyjHyPRY2GTcKRulArsjbFYLru0VJdvxrBYgt6+2929SkTsxN9zgFaZCEnOlZy4J6tQ0C90FdLs9v/0Ybvg9PQFgAfCbW4yT6AwOsiKrewR5/KYDlfi/L06iqp4eO7jVhPrwDMPQEBpXu66PsfdMFITGvrQc8woEgruZNByHVgrFkiZ2ux1Tp07F7NmzBT1v3LhxqKio8DzeeOMNhWZIxwJxGxlKQkSs3JSWM3w2qwV5/YW3GVsIl9lmteAGETfQUrxQAkGtCJP68GAbKfgi5tiAtvUwo4mLdZVIaT2GEYLNasFD4zNQ+vgEvHzPteikYA+n2/ckbeEH6PPIB+xXwjAKYrNakJchn2zKTRnGTzY+IGNnyMCUCMNfL7m7c4zcyQQIT8xNe3G3cpPRKbcP7U4e6wJQdYGeAPhptvESAA+N6y9o/IJ/fuV/UAtW3TZY0HiGYcyD0M6JBCl+tgZBaJxUyzGvQJAgMB6t5Ti0UigW2lm2bBnmzZuHQYMGCXpeSEgIkpKSPI/oaJrWr5KMElkVSMlaipWb0npGNCFS+HlFhXUijcvuKfwzUXtJu1JTgLhupvEDk0g3JmKO3SXEpmk9zLiutMX9wwOVujQCXbNmDVJTUxEaGoqcnBzs2bNH7SkxBGxWC24akIijj0/ElwVjERGiXILBhaua7UrCfiUMA0zLlc/QeNrIVNmOpVVy+8TJdnMxaQjdT0Cv2KwWDEyhV7L7PhYM3cnkRkhibteJWt10m5SVleHee+9FWloawsLC0Lt3byxZsgR2u7wBitw+cbAplNd44nZhclZ6YGR6rGLFMDlp0by/YhjGJ4I6J/QX9pAdIZ0QZveAAQCh/QFaj0MrgeZ+pXfs2IGEhARcc801mD17NmpqfHsSNDU1ob6+vtVDbkamxyJYxO6SkrUckRaDcBE7Ma1nRE+fuyz4OXHELGd9o/AkQAVRv1ctxHQzJUTQrpeYY2cma7u6k5psvGR3YFepvnxN3nzzTcyfPx9LlizBvn37MGTIEOTn56O6ulrtqTECiAzvhK+WjVPcNF5u2K+EYVozMj1Wls2yWXTrbVYLrk2NkuVY02VMWGmZSUO6yXKcW4cYr8rfG0ITc68WlSk1FVk5fPgwnE4n/vrXv+Kbb77BM888g7Vr1+KRRx6R9XVsVgvmjJZfUtSoCQCb1YLZCkmwvnrvSEWOyzBaQWgh4D/+8Q9kZGQgNDQUgwYNwqZNmwI0U+0ipLK/WsOetIFCyPUyuwcMINy/W+txaCXQ1M5m3LhxeOWVV7Bt2zb8/ve/xyeffILx48fD4XB0+JxVq1YhMjLS8+jRo4fs87JZLRjSPVLQc8I6WUlZS5vVggECK8wiQoM0nxG1ULS22kD9AopJAqREabtVUUw3EzURJObYySI6hQLJiLQYhAXRPgefHtNXsuHpp5/GrFmzMHPmTGRmZmLt2rUIDw/HSy+9pPbUGBEE0jReCuxXwjDekSsJYCbdejkkunrHhxsyAOuN6bmpshzHiFX+3rgqV0KXzfuu9pKCs5GPcePG4eWXX8bYsWORnp6OyZMn47e//S3efvtt2V/rN7dcI/sxjZwA+M0t18geNBk/INE0axxjToQWAu7cuRN33XUX7r33Xuzfvx9TpkzBlClTcODAgQDPXFsI6Zyo5aSJIPuDkenGL2byR0JXetxP62o0SiHol3rhwoXtjNrbPg4fPix6MnfeeScmT56MQYMGYcqUKXj//fexd+9e7Nixo8PnLFq0CHV1dZ7HyZMnRb++L7pFhwsaL0SHWeixMxK7aP7Gu5sAk0EACLbRkkyAuCSA1jWeR6bHCm6VpyaCxBxbCsxwTwAAFxFJREFU6PsXaGxWC3rF0qr3v/5e/u4zpbDb7SguLkZeXp7nb1arFXl5eSgqKlJxZoxUWprGK+17IgT2K2EY/8iRBDCTbr0c8j9L/2ugPJPRAcFBVuSkSpMjNlOSCRAmm9crRth9lpaoq6tDTIz8AQqb1YLbsuSTvxuRGmXoz5/NasFzd2bJesw/3T1M1uMxjNYQWgj4xz/+EePGjcNDDz2E/v37Y8WKFRg6dCj+9Kc/BXjm2kKIP60ZpZPaIsT+4Mvvzys3EZ0wIi0GocRNe2aSttVolELQ7mbBggU4dOiQz0d6erpsk0tPT0dcXByOHTvW4ZiQkBBERES0eiiB0CDycAEZOKHHjuoszjw+kOQK0V4EkJFETwQJNc3UgySGzWpB34Qugp5DTQTZrBYM6Cbse6H1JBMA9Iil3QSHK+grITdnz56Fw+FAYmJrw7fExERUVlZ6fU4gJAoZ+WjrexKmQpChkwV4Ydq17FfCMESkJgGMKlvTEVLlf4KsFuT2FbaP1Duv/lJalb6ZkkzA1XuBiFD/+zurBbhnVKryE1KAY8eO4fnnn8evfvWrDsdI2QM+8TP5OpNe++Uo2Y6lVf4rqxuye8gTZ/jjnVmmDDwx5kFMIWBRUVGr8QCQn59v+sJBIf60ZpROastVRRLanpstYK7u2QcRVZW0rt6jFILu4OLj45GRkeHzERwsX3bz+++/R01NDZKT1Zc0EZoEEBJ0Fnrs4RKr0QKB0MSGELNPm9WCa3tFkcdnJnfVxcb05oxE/4N+QKjZ56TBdL1svRiJjkilzZE6Tq8EQqKQUYbI8E449Nh4HFiaj9jwToq/Xmx4EA4szcfRVRORl5moi3WRYbSAzWrBM1PFBxiNLFvTEVLkbP7w/4aYbn2S0m3SyWa+JJPNasFqQtB/1g1pqicsxSg1nDp1CuPGjcPUqVMxa9asDo8tZQ8YHGTFzOt6ij4vN/99Xarq1zhQ/HP29ZKPMTAlArdmyeNjxDBaRUwhYGVlpaDxgDmKB6nS9F1CtC/hHwhsVgsmDqbFj9OIyiVGZwRRpqybjjt3paDYDqe8vBwlJSUoLy+Hw+FASUkJSkpK0NDQ4BmTkZGBjRs3AgAaGhrw0EMPYdeuXSgrK8O2bdtw6623ok+fPsjPz1dqmmRGpseCuh8MDrIKCjqPTI8VJNWiB2NMm9WC6/vQr4HQcxIilSEkIaMm1wm44RVq9ilELzu3d6wughXTc1PhzzrHYpFPKzwQxMXFwWazoaqqqtXfq6qqkJSU5PU5gZIoZJSjS2gQigvGKmYa7/YrKS7IR5fQINmPzzBmYPLQ7shMEn6zYKaAYkvEytmkxoabNqAottvk6f9nzqr1cQOTsfYXQxHuRVrSYgF+dWMaFk3IVGFmrRGq1HD69GncdNNNyM3Nxd/+9jefx5a6B1wyaRC6R4kveOwRE4aCSQNEP19v2KwW/Pnn2aKf3znYhvd/fYOMM2IYc2OG4kGqNP0vr08z5V7AG4/fNthvnEjPnahyk5tOi0NSxxkNxe7iCgoKkJ2djSVLlqChoQHZ2dnIzs7GF1984Rlz5MgR1NXVAQBsNhu++uorTJ48Gf369cO9996LYcOG4dNPP0VIiPpyVDarBU8T25if/NlgQQuWzWrBH4jH1tPN91/vGU4aJ+accvvEkYzALdBHkgmgSw0Aws0+hVST/Y34vqlNcJAV993g+729TwMVhkIIDg7GsGHDsG3bNs/fnE4ntm3bhlGjvEsfBEqikFGelqbxD+X3FdSt5w32K2HUZOXKlcjNzUV4eDiioqJIz5kxY0a7Cuxx48YpO1EBbHrwJoQLyDuaLaDYlv/K6oYxGcJusLYtGK3MZHRAcJAVs25IFfScMRkJuikOUoJxA5Px9dJ8vDpzBKZkpWBsZiL+d0J/HFkxXhMJE0CYUsOpU6cwevRoDBs2DC+//DKsVt97WDn2gJ8tvAWxnYV3u8aGB+HTh28W/Dy9M2FwCn51o/B7y05W4Jvl2vk9YxglEVMImJSUJGg8YI7iwZHpsYjyo0jQOdiGB8b0DdCMtA8lTqSFTlStMLK3/89YVHgnXajRKIFin5LCwkK4XK52j9GjR3vGuFwuzJgxAwAQFhaGLVu2oLq6Gna7HWVlZfjb3/7WrkVPTSYP7Y7B3X1vRgd3F9dySzl2r1h93XyHBdtwS2aCzzFiz8lmteCZO/1X+tx3o34WQ6rUwK9EntOSSYPQK9a3zuUtmQm6Cq4umpCJX92Y1i64bNVQhaFQ5s+fj3Xr1mH9+vU4dOgQZs+ejYsXL2LmzJlqT40JEMFBVsy5qR+OrxLue8J+JYxWsNvtmDp1KmbPni3oeePGjUNFRYXn8cYbbyg0Q3EcfGwiQgntwWYNKLblxRk5GEz0VVv7i6Gmr5L834kD/O6d3Qzq1hUvztBHoYuS2KwW3HBNPJ69Mxt/m3YtZt2YrsvfPnfCpGfPnnjqqadw5swZVFZW+pSmkYviR8dioAD/wwHJnVFcoL4ShFosmpCJP/98KHl8dKgVRx+fqOCMGEZbiCkEHDVqVKvxALB169YOxwPmKB60WS144rZBPseYUdbUH0aMEykF5TP2xG2DTPsZs7hcLkP539TX1yMyMhJ1dXWKLZq/XL8XHx2qbvf3vP7xeGH6CEWOPSYjDi/OyJF0bLWY9cpebD2ozDltPlCB+W99iUt2R7t/0+tiqPQ53Vu4F9sOt38/bslMwLpp+rz5tjc78WpRGb6rvYReMeG4ZxSteykQ64UY/vSnP+HJJ59EZWUlsrKy8NxzzyEnh/Zd0eo5MdJoaGzGT1Z/jJpL3s0AY8OD8MnDY1h+iyETqLWisLAQDz74IM6fP+937IwZM3D+/Hm88847ol8vUOf1X899igOnvWtn33xNLF6aaT4fE1+seP8gXvzshNd/swD4yy+GYtxA9T0MtcJ7X57Gb97YD2cH/37v9b3wqMnM3+VGa/ulwsLCDgtkqLfrUs/pXyWn8OCGEp/muM/9vyGYPLS74GMbEYfThTF/2IGymksdjpme2wPLJg8O4KwYhobSa+Cbb76J6dOn469//StGjBiBZ599Fm+99RYOHz6MxMRETJs2Dd26dcOqVasAADt37sRPfvITPPHEE5g4cSI2bNiAxx9/HPv27cPAgbTfO62t63Ky+UAFlr57EJX1jZ6/JUeGYsmkTN4/+UBsnMiMXP2MfYPK+ibP35IiQrB08gDDfcaErBWcNBHJZbsDj286iLKaS0iNDccjEzJlq9BX8thqoeQ5OZwu7Dx6Fv+3/3tcsjswPDUG03P1vRgqfU5G/IyJwYgbKyOeE/Mjl+0OLH3va3xy5CwsAEZfk4CCSQNM+f1lpKHVpMk777yD4OBgREdH4+abb8Zjjz2G2NiO28GbmprQ1PTj5r6+vh49evQIyBrY0NiMX7/+BYrL62CzWjB2QBKW8PexQ+zNTqz79Bhe312OhiYHekSHYf4tGfjJNfGmrV7zhcPpwn8OVePJrYdx6nwjuoZ2ws9zeuKXN+izk0JrGHG/JMc5uT93v99yECfOXoLFakV6fGf8dix/VzuiobEZD/x9L3Ydr0Wzy4KEiBDcndOLv6uMpgnEGuirEHD06NFITU1FYWGhZ/w//vEPLF68GGVlZejbty9Wr16NCRMmkF/PiOt6SxxOF/acqEX1hUYkdA3FiLQYXpMZWTHLZ4yTJgZeKBmGkQ8jrhdGPCeGYeRHi0mTDRs2IDw8HGlpaSgtLcUjjzyCLl26oKioCDab90TE0qVLsWzZsnZ/5zWQYRhfGHG/ZMRzYhhGGYy4XhjxnBiGkR8hawWXPjAMwzAMwzB+WbhwYTuj9raPw4cPiz7+nXfeicmTJ2PQoEGYMmUK3n//fezduxc7duzo8DlmMAFlGIZhGIZhGIZhAguLnzMMwzAMwzB+WbBgAWbMmOFzTHp6umyvl56ejri4OBw7dgxjxozxOiYkJAQhISGyvSbDMAzDMAzDMAzDGC5p4lYbq6/3bs7JMAzjxr1OGEmlkNdAhmEoiFn/4uPjER8fr9SU2vH999+jpqYGycl080FeAxmGocB7QIZhzAyvgQzDmBUh65/hkiYXLlwAAPTo0UPlmTAMoxcuXLiAyMhItachC7wGMgwjBKXWv/LyctTW1qK8vBwOhwMlJSUAgD59+qBLly4AgIyMDKxatQo//elP0dDQgGXLluH2229HUlISSktL8fDDD6NPnz7Iz88XdD4Ar4EMw9DgPSDDMGaG10CGYcwKZf0znBG80+nE6dOn0bVrV1gsFr/j6+vr0aNHD5w8edI0ZlF8znzORkTM+bpcLly4cAEpKSmwWo1h8cRroH/4nI1/zmY7X0D4OSu9/s2YMQPr169v9/ft27dj9OjRAACLxYKXX34ZM2bMwOXLlzFlyhTs378f58+fR0pKCsaOHYsVK1YgMTGR/Lq8BvrHbOdstvMF+Jy1sAaqgdD1DzDfZ8Vs5wvwOfM5e4fXQP6c8DkbE7OdL6Ds+me4ThOr1Yru3bsLfl5ERIRpPlBu+JzNgdnOWej5GqWyxg2vgXT4nI2P2c4XEHbOSq5/hYWFKCws9DmmZd1OWFgYtmzZIvl1eQ2kY7ZzNtv5AnzO/uA94I+Y7bNitvMF+JzNAq+BvAekwOdsfMx2voAy658xUsoMwzAMwzAMwzAMwzAMwzAMwzAS4aQJwzAMwzAMwzAMwzAMwzAMwzAMOGmCkJAQLFmyBCEhIWpPJWDwOZsDs52z2c5XLsx43ficjY/Zzhcw5znLgRmvm9nO2WznC/A5M3TMdt3Mdr4An7NZMOM5S8WM14zP2fiY7XwBZc/ZcEbwDMMwDMMwDMMwDMMwDMMwDMMwYjB9pwnDMAzDMAzDMAzDMAzDMAzDMAzASROGYRiGYRiGYRiGYRiGYRiGYRgAnDRhGIZhGIZhGIZhGIZhGIZhGIYBwEkThmEYhmEYhmEYhmEYhmEYhmEYACZPmqxcuRK5ubkIDw9HVFSU1zHl5eWYOHEiwsPDkZCQgIceegjNzc2BnajCpKamwmKxtHo88cQTak9LNtasWYPU1FSEhoYiJycHe/bsUXtKirF06dJ272VGRoba05KV//znP5g0aRJSUlJgsVjwzjvvtPp3l8uFgoICJCcnIywsDHl5eTh69Kg6k9U4vAYaf/0DeA3kNZDXwI7gNZDXQKPBayCvgVR4/bsKr4HGgdc/Xv+EwGsgr39Gg9dAZdZAUydN7HY7pk6ditmzZ3v9d4fDgYkTJ8Jut2Pnzp1Yv349CgsLUVBQEOCZKs/y5ctRUVHheTzwwANqT0kW3nzzTcyfPx9LlizBvn37MGTIEOTn56O6ulrtqSnGgAEDWr2Xn332mdpTkpWLFy9iyJAhWLNmjdd/X716NZ577jmsXbsWu3fvRufOnZGfn4/GxsYAz1T78Bp4FaOufwCvgbwG8hroC14Dr8JroLHgNZDXQAq8/v0Ir4HGgdc/Xv+o8Bp4FV7/jAWvgQqsgS7G9fLLL7siIyPb/X3Tpk0uq9Xqqqys9PztL3/5iysiIsLV1NQUwBkqS69evVzPPPOM2tNQhBEjRrjmzJnj+X+Hw+FKSUlxrVq1SsVZKceSJUtcQ4YMUXsaAQOAa+PGjZ7/dzqdrqSkJNeTTz7p+dv58+ddISEhrjfeeEOFGeoDM6+BRl7/XC5eA40Or4HywGvgM2pPQzF4DTQ2vAZKx8zrn8vFa6CR4PWP1z8xmHkN5PXPWPAaqMwaaOpOE38UFRVh0KBBSExM9PwtPz8f9fX1+Oabb1Scmfw88cQTiI2NRXZ2Np588klDtB3a7XYUFxcjLy/P8zer1Yq8vDwUFRWpODNlOXr0KFJSUpCeno67774b5eXlak8pYJw4cQKVlZWt3vPIyEjk5OQY+j1XCrOsgUZc/wBeA3kNvAqvgeLhNVDf8BrIayDAa6BYzLL+AbwGGgle/3j9kwuzrIG8/hkLXgPlXwOD5JicUamsrGy1SALw/H9lZaUaU1KEX//61xg6dChiYmKwc+dOLFq0CBUVFXj66afVnpokzp49C4fD4fU9PHz4sEqzUpacnBwUFhbimmuuQUVFBZYtW4YbbrgBBw4cQNeuXdWenuK4v5fe3nMjfWcDhRnWQKOufwCvgbwG/givgeLgNVDf8BrIa6AbXgOFY4b1D+A10Ejw+sfrn5yYYQ3k9c9Y8BqozBpouE6ThQsXtjO/afsw6pekJUKuw/z58zF69GgMHjwY999/P/7whz/g+eefR1NTk8pnwQhl/PjxmDp1KgYPHoz8/Hxs2rQJ58+fx1tvvaX21JgAwWsgr39mhtdAhtdAXgPNDK+B5obXv6vwGmhOeP1jeA3k9c/M8BqoDIbrNFmwYAFmzJjhc0x6ejrpWElJSdizZ0+rv1VVVXn+TctIuQ45OTlobm5GWVkZrrnmGgVmFxji4uJgs9k875mbqqoqzb9/chEVFYV+/frh2LFjak8lILjf16qqKiQnJ3v+XlVVhaysLJVmFVh4DeT1zw2vgbwGuuE1sDW8BvIaqOX3T054DYTn/82wBvL6dxVeA69i9jWQ1z94/t8M6x/AayDA658bs69/AK+BbqSugYZLmsTHxyM+Pl6WY40aNQorV65EdXU1EhISAABbt25FREQEMjMzZXkNpZByHUpKSmC1Wj3nrFeCg4MxbNgwbNu2DVOmTAEAOJ1ObNu2DXPnzlV3cgGioaEBpaWluOeee9SeSkBIS0tDUlIStm3b5lkY6+vrsXv3bsyePVvdyQUIXgN5/XPDayCvgQCvgVLgNVDf8BrIayBgrjWQ17+r8Bp4FbOvgbz+mWv9A3gNBHj9c2P29Q/gNRCQZw00XNJECOXl5aitrUV5eTkcDgdKSkoAAH369EGXLl0wduxYZGZm4p577sHq1atRWVmJxYsXY86cOQgJCVF38jJRVFSE3bt346abbkLXrl1RVFSEefPm4Re/+AWio6PVnp5k5s+fj+nTp+Paa6/FiBEj8Oyzz+LixYuYOXOm2lNThN/+9reYNGkSevXqhdOnT2PJkiWw2Wy466671J6abDQ0NLTKlp84cQIlJSWIiYlBz5498eCDD+Kxxx5D3759kZaWhkcffRQpKSmeH0vmR8y+Bhp9/QN4DeQ1kNdAX/AayGug0eA1kNdAKmZf/wBeA40Gr3+8/gnB7Gsgr3/Gg9dAhdZAl4mZPn26C0C7x/bt2z1jysrKXOPHj3eFhYW54uLiXAsWLHBduXJFvUnLTHFxsSsnJ8cVGRnpCg0NdfXv39/1+OOPuxobG9Wemmw8//zzrp49e7qCg4NdI0aMcO3atUvtKSnGHXfc4UpOTnYFBwe7unXr5rrjjjtcx44dU3tasrJ9+3av39vp06e7XC6Xy+l0uh599FFXYmKiKyQkxDVmzBjXkSNH1J20RjH7GmiG9c/l4jWQ10BeAzuC10BeA40Gr4G8BlIx+/rncvEaaDR4/eP1TwhmXwN5/TMevAYqswZaXC6XS3zKhWEYhmEYhmEYhmEYhmEYhmEYxhhY1Z4AwzAMwzAMwzAMwzAMwzAMwzCMFuCkCcMwDMMwDMMwDMMwDMMwDMMwDDhpwjAMwzAMwzAMwzAMwzAMwzAMA4CTJgzDMAzDMAzDMAzDMAzDMAzDMAA4acIwDMMwDMMwDMMwDMMwDMMwDAOAkyYMwzAMwzAMwzAMwzAMwzAMwzAAOGnCMAzDMAzDMAzDMAzDMAzDMAwDgJMmDMMwDMMwDMMwDMMwDMMwDMMwADhpwjAMwzAMwzAMwzAMwzAMwzAMA4CTJgzDMAzDMAzDMAzDMAzDMAzDMAA4acIwDMMwDMMwDMMwDMMwDMMwDAOAkyYMwzAMwzAMwzAMwzAMwzAMwzAAgP8PAMFTzYAcSZ8AAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "7\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "8\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "9\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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qj0Pn4gIya54LMYnaykdVlJiqoFarufbt23N9+vTRux8AZ2dnp1fy5cyZMxwAbvny5br7KspzjR8/Xm/7YcOGce7u7rp/l5aWcgqFgnvnnXcMzuvVV1/lnJycuMuXL3NffvklB4Dbtm2bwbFPPfUU9/zzzxv8WW3qU56rsrrKc6WnpxssDfGf//yHCwwM5B49esRxHMdcnuvrr7/mAgICuP/+97+68lzTpk1jKs+1d+9eDgD36aefcunp6VzDhg25oUOH1v0iCSFGqVyea+vWrUypzqhSusXU+9VRo0ZxPXv2rHWOubm5nJeXV40lbV5//XXO0dFR7z4qz0Ws2YQJEzhfX18uJydH7/4RI0ZwLi4uesdjUVFRnFKp5A4fPqwrL/X111/rbTdmzBgOADdt2jTdfVqtlhs4cCBnZ2enK5OalpZW7VitLsXFxVzLli25du3acY8ePeIGDhzIOTs7c9euXTPmpdeprv1JZXxLZLGU57p9+zYHgPviiy8EfW5DWMpz1bQPTkxM5ADobq1atar1dXFceXmufv36cf369ePS09P1SlT8+9//Nqo8V2ZmJufm5sb169ePKykp4UJCQrhmzZrplYzgOCrPRWTIxYXjpkyp+edjxnBc5ff85s3lpacq7781Go7r06f8/sqfrTFjyu9bsED/MUNCOK5Tp8f/TksrH1d1n15WxnGBgeXPf++e/s8qf86DgznOy4vj/lfCkOM4jjtzhuOUSo4bPZr9tXIcxz3zjP7cKvvuu/J5/vwzxyUlcZxKxXEzZxoe+/rr5eXGqrp9u/wx6tg3E0LYVL32x3GPjy1++ukn3X0V52ydOnXi1Gq17v5FixZxALjff/+d4zi2c9vajkM1Gg33zDPPcN7e3lxOTg43ZcoUzsbGps5z5QMHDnCBgYHcggULdOW5Vq5cqVeeqz7HPlVVlJ01RbkrQ89VcSx7+PDhauNffvllzsfHp9r9hs7FiXEo08TKVS4xde/ePRQUFKBHjx661YWVRURE6JV86dChA5ydnQ2WUqi64rBHjx7Izc1FYWEhgPJSExzHoXHjxgbn9e2338LFxQUvvfQSPvroI4waNQpDhgwxOLZx48bIycmp87UWFBQgJydHdysoKNC97sr3FxUV1flYfOzatQsuLi545plndPddvnwZy5Ytw5dffgl7e3tej9etWzckJyfjpZdeAlBeeuGbb75BbGws/Pz8at22f//+eOONN7BgwQIMHz4cDg4O+O677/i/KEIIb66urgCAnTt3orS0lPf2ptivarVa7Nmzp9ayMFqtFiNHjkR+fr4uc6Wqxo0b4+HDhwZXLRFibTiOw+bNmzF48GBwHKd3zBEZGYmCggK946758+ejXbt2GDNmDN566y307NmzxsbqU6dO1f1/RSlVtVqta3RZkQ1c037BECcnJ8TGxiI1NRXPPvssdu3aha+++grNmjUz5uXXqrb9SWlpqd7vKicnB6WlpSgpKal2f13N0GtT8bupfCxpqueuqrZ9cNu2bREXF4dt27bhvffeQ4MGDeo8Zm3YsCFmzZqFP//8E4GBgQCAoKAgJCYmYuzYsUatuvTx8cGKFSsQFxeHHj16ICUlBWvXrtUrGUGILLm6lpe/Yi1pvGcPYGsLTJr0+D6lEpgypeZtqjZK7tEDqHyuXVHhoeo+/fRpICOjvKTW/443dSo+55mZQEpKeSmwyiUMO3QoL4VVubwey2vNza0+jwqvv16e0TJtWnl5shYtgM8+Mzy2cWPg4UOg6jFjxWMznOcTQupW+dpfaWkpcnNz0bJlS7i6uhq8/vf666/D1tZW9+/JkyfDxsZGV4qT5dy2tuNQpVKJ2NhYFBUV4fnnn8fKlSsRFRVVLaOiqsDAQOzatQsfffQRbG1toVAoMHnyZCQkJKBt27aCHvscPnwYH3/8Mf71r3+hT58+zNsZo6bnqijZa+i6oYODg15J3wp0Li4cCprIhFqtRlZWlt5NU7X+qQE7d+5Et27d4ODgADc3N3h6emLVqlW6gEJlhk6WGzduXK2uoaGxFTvJqmO5KnW3K7i5ueGbb77B33//DRcXF3zzzTc1vgaO45h2fEOGDIGnp6fuNnToUADlqWuV7698AUIIu3btQv/+/WFjY6O7b8aMGQgPD8eLL77I+/FCQ0MNfumEhobqvrhqs3jxYri5uSElJQXffPMNvLy8eM+BEMJfz5498eKLL+Ljjz+Gh4cHhgwZgpiYGJSUlDBtb4r96okTJ3D37t1agybTpk3Dnj178MMPPyAoKMjgmIo5GJsGTYic3L17F/n5+fj+++/1jjc8PT0xbtw4AOUlDirY2dlh7dq1yMjIwP3793V9JKpSKpVo3ry53n1PPfUUAFSrT111v1BUVKR3zHj37l29n3fv3h2TJ0/G8ePHERkZWa0+tVBq258cPXq02u8rISEBGzZsqHb/9evXjZ6Dof2VqZ67qtr2wc7OzoiIiMCQIUPwxRdf4J133sGQIUNw5syZWh/zueeeq3afSqXSle81xogRIzBw4EAcP34ckyZNQt++fY1+LEIsxqJFwLlzgJ8f0LVreXmr2vowXbsG+PoCTk7697dsaXi8g0N5L5DKGjcGDJxro+qx3pUr5f9t3772+QBAq1bVf9amTXlwoqLfCOtrreGYEwDw44/lgZB//inv31JTP9CKx6j6PVfT/YQQozx8+BBz586Fn58f7O3t4eHhAU9PT+Tn5xu8/vfkk0/q/bthw4bw9fXVHWPyObet6fy0RYsWmD9/Pk6cOIF27drho48+qvN1+Pv7o02bNtXub9Kkia7HrxDHPhcvXsSwYcPQvn17/PDDD3WOr+vY2tjnqgh2Gfq9Pnr0yGCvZToXF45N3UOIJUhISEDv3r317svIyKi18eRff/2FF154Ac8++yxWrlwJX19f2NraIiYmBr/++mu18ZXraFdmaAdY11g3NzcoFAqDAZcKe/fuBVB+QfDmzZs1BgTu3btXbYduyJIlS/Se78yZM/jPf/6Dn3/+Gd7e3rr7hezt8eDBAxw8eFCvcer+/fuxZ88ebNmyRe+iRllZGR4+fIirV6/Czc2NacVebOUGgoxOnz6tuzhz9uxZvPrqq7wfgxDCn0KhwKZNm5CUlIQdO3Zg7969GD9+PJYsWYKkpKQ6mzSbYr+6e/duBAQEoG3btga3//jjj7Fy5Up8/vnnGDVqVI3Pc+/ePTg5ORk8iCPE2lRkIrz22msYU0Mt+w4dOuj9u+Kz+ujRI/zzzz+6lXJ8ubu7A6geXF28eLFeTyR/f3+9Y5KSkhIcPHgQAHDlyhU8ePAATlUv/NVTXfuToKAgxMXF6d33zjvvwMfHB++++67e/Xx7w1VW8bupXD/aVM9dVV374MqGDx+OUaNGYcOGDTUGsKuqqdkrX7m5uTh58iQA4MKFC9BqtdWamRIiO//6V3nmx9atwJ9/Al9+CXzxBbBlC/D88/V//BqO8/T8b59uMJAiJJbX6u5e+zwOHgQqLvKdPQuEhRked+9eeWCp6jFjxWObodcVIXI0bdo0xMTEYObMmQgLC4OLiwsUCgVGjBhhVNYsy7ltTcehlf35558AgNu3byM3N5fXcdXYsWPrHGPMsc+NGzfQv39/uLi4YPfu3WjUqFGd29R1bG3sc/n6+gIo7zFYVWZmpsHrl3QuLhwKmsiEoZO7ip1NTdHFzZs3w8HBAXv37tVL9YqJiRFvov9jY2ODFi1aICMjw+DPK1Ydvvfee/jll18wZswYHDt2TC9bAygPNNy4cQMvvPBCnc/ZqVOnanMAyldT1hZcqo/9+/ejpKQEz1c6kK5YkTh8+PBq42/duoXAwEB89dVXmDlzpuDzKS4uxrhx49C2bVuEh4dj0aJFGDZsGLp06SL4cxFCDOvWrRu6deuGhQsX4tdff8XIkSOxYcMGTJw4sV6PK8R+ddeuXTU28l2xYgXmz5+PmTNn4v333691LhkZGQZXABFijTw9PdGoUSNoNBpERETUOf7vv//GggULMG7cOKSkpGDixIk4e/YsXFxc9MZptVqkp6frskuA8vKfAHTHNc2aNYOjo2O1/cLo0aP1yoZWPamaN28eUlNTsXjxYrz//vuYPXt2rVm/fLHsTxo3blzt99W4cWP4+voy/R5ZVfxuKu+zTPXcVdW2D66qpKQEWq3W4OpQsU2ZMgX3799HdHQ0oqKi8PXXX2PWrFkmnwchJufrC7z1Vvntzp3yJuYLFxoOmvj7AwcOlGdbVA46p6UZ//zNmpUHF6oe61WU0D53DqhpH1XRsP3Speo/u3ixPDhRuUl7Xa+1dWtg82bDz5WZWV6aq39/wM6uvJF8ZKR+0/gKGRnlmS6G7gcM/4wQwtumTZswZswYLFmyRHffo0ePkJ+fb3D8P//8o7cwu6ioCJmZmdWOU2o7t63pOLTC6tWrERcXh4ULFyI6OhpvvPEGfv/99/q/2HrIzc1F//79UVJSgvj4eF3Qoi51HVsb+1zt27eHjY0NTp48iX/961+6+9VqNVJSUvTuq0Dn4sKhJUEyUXFyV/nm4OAAAGjwv4OfqjtDlUoFhUKhV8br6tWr2LZtm0nmHBYWplulVll+fj4mTpyIrl274rPPPsMPP/yA5ORkfGagDuqFCxfw6NEjhIeHm2LKvO3evRudO3fWy2Tp06cPtm7dWu3m6emJzp07Y+vWrRg8eLAo83n//fdx/fp1rFu3DkuXLkVAQADGjBnDXB6IEGK8e/fuVcvMCw4OBmA43dYY9dmvZmdnIzk52WBZmI0bN2L69OkYOXIkli5dWuc8kpOTJbtfJsTUVCoVXnzxRWzevBnnzp2r9vPK6fulpaUYO3YsmjRpgmXLliE2NhbZ2dl4++23DT72t99+q/t/juPw7bffwtbWVlcuydbWFp07d662X2jevLneMWP37t11Pzt27BgWL16MmTNn4p133sG7776Lb7/9FocOHarX76EC3/2J2E6dOgWFQoGwmlZBm0hN++D8/HyDtcIryjfUVftbaJs2bcLGjRvx+eefY/bs2RgxYgTmzJmjC9gRIksaDVA1QOnlBTRp8jiboqrISKC0FFiz5vF9Wi2wYoXx87C1BTp3Bqoe63XsCAQGAl9/DVS9AFpx7OnrCwQHA+vW6Y85d648m6TiQijraw0LK88GMVS2a9Kk8tf644/A998DNjbAhAmGy3klJwOGjhlPnSovzWXmfTMhcqFSqaqdiy5fvrzGsv7ff/+93vHHqlWrUFZWplsQzHJuW9NxKFB+Yf/dd9/Fiy++iA8++ACLFy/G9u3b8dNPPxn9GuuruLgYAwYMwK1bt7B7926mijYVaju2rs9zubi4ICIiAj///DPu37+vu3/9+vUoKirCyy+/XG0bOhcXDmWaWIGKDIvp06cjMjISKpVKV4t46dKleO655/Dvf/8bd+7cwYoVK9CyZUv8/fffos9ryJAhWL9+PS5fvqy3SnLGjBnIzc3Fvn37oFKp8Nxzz2HixIn49NNPMWTIEL0SBHFxcXBycqpXXWZjXLt2DevXrwcA3RfAp59+CqA8Da+izMTu3bt19corNGvWzGB/mJkzZ8Lb21vXa0Vo+/fvx8qVKzFv3jx07NgRQHlWUa9evfDRRx9h0aJFojwvIaTcunXrsHLlSgwbNgwtWrTA/fv3sWbNGjg7OzOvLK5Lffaru3fvhoODQ7VSj8ePH8fo0aPh7u6Ovn374pdfftH7eXh4uF5fhVOnTiEvL69ak3lCrNnnn3+OAwcOIDQ0FJMmTULbtm2Rl5eH5ORk7Nu3D3l5eQDKjyVSUlIQHx+PRo0aoUOHDpg7dy7mzJmDl156SW9f4eDggD179mDMmDEIDQ3FH3/8gV27duGDDz6AZ6W6+EOGDMGHH36IwsLCOkt/Pnr0CGPGjMGTTz6JhQsXAigvo7Vjxw6MGzcOZ8+e1S3GMQbf/YmxKo7Jzp8/D6D8xPLIkSMAgDlz5uiNjYuLQ/fu3XUlJMSwfv16XLt2TdeQ8/Dhw7o5jho1Cv7+/jXugw8ePIjp06fjpZdewpNPPgm1Wo2//voLW7ZsQefOnfHaa6+JNu+q7ty5g8mTJ6N37966HoDffvstDhw4gLFjx+LIkSNUpovI0/37QNOmwEsvAUFBQMOGwL59wIkTQKVV23qGDi3vB/LOO+XZJa1bA9u3A//b3xvdq2PIEODDD4HCQqBin65UAqtWAYMHlwdGxo0rD5JcvAicPw/8r+QjvvyyPFMkLKw8iPHwIbB8OeDiUt63hM9rHTiwPBiyb1954/cKMTHArl3lfUyaNi2/b/ly4LXXyuf41luPx546Vf77MHTMGBcHdO/+uCQZIaReBg0ahPXr18PFxQVt27ZFYmIi9u3bV+Pxj1qtRt++ffGvf/0Lly5dwsqVK/HMM8/oqrywntsaOg7lOA7jx4+Ho6OjrpT9G2+8gc2bN2PGjBmIiIgQtGw+q5EjR+L48eMYP348UlNTkZqaqvtZw4YNBb1Wx+e5Fi5ciPDwcPTs2ROvv/46bt68iSVLlqB///7V+rfQubjAOCJ7ZWVl3LRp0zhPT09OoVBwlf/sP/74I/fkk09y9vb2XOvWrbmYmBhu3rx5XNW3BgBuypQp1R7b39+fGzNmjO7fFdvevXtXb1xMTAwHgMvIyNDdV1JSwnl4eHCffPKJ7r7ff/+dA8AtWbJEb/vCwkLO39+fCwoK4tRqte7+0NBQ7rXXXuP1+6hw4MCBanPiu62hW8+ePTmO47hz585xALjjx48zPaa/vz83cOBA3nNhUfH769ixI1daWqr3s7fffptTKpVcYmKiKM9NiDXz9/fn5s2bx3EcxyUnJ3Ovvvoq16xZM87e3p7z8vLiBg0axJ08eVJvGwC6bTjOdPvVl156iRswYEC111DxPDXdYmJi9Ma///77XLNmzTitVmvwcQixVtnZ2dyUKVM4Pz8/ztbWlvPx8eH69u3Lff/99xzHcdypU6c4Gxsbbtq0aXrblZWVcV26dOGaNGnC3bt3j+M4jhszZgzXoEED7sqVK1z//v05Jycnztvbm5s3bx6n0WiqPa+NjQ23fv36Ouf49ttvcyqVijt27Jje/SdPnuRsbGy4yZMn1+M3wH9/UlXPnj31jjtrUttzVJafn8/Z2dlxP/zwQ52PyfrcNW1b03wOHDjAcRxX4z44LS2NGz16NNe8eXPO0dGRc3Bw4Nq1a8fNmzePKyoqMmo+rKr+TYYPH841atSIu3r1qt64iu+ZL774QnffvHnzOH9/f1HnR4jJlJRw3LvvclxQEMc1asRxDRqU///KlY/HjBnDcVXf83fvcty//12+jYsLx40dy3FHj3IcwHEbNuhv26BB9eedN698bGXZ2RxnY8NxhvbpR45wXL9+j+fYoQPHLV+uP2bfPo7r3p3jHB05ztmZ4wYP5rgLF/i91govvMBxffs+/veNG+Wvc/Dg6mOHDSt/rPT0x/e9/z7HNWvGcVWOGbn8fI6zs+M4hn0zIYTNvXv3uHHjxnEeHh5cw4YNucjISO7ixYvVrulVHKsdOnSIe/3117nGjRtzDRs25EaOHMnl5ubqxrGe2xo6Dl22bBkHgNu8ebPe2OvXr3POzs4Gj4dMwd/fv8bjNaGPafg+119//cWFh4dzDg4OnKenJzdlyhSusLCw2riazsWJcRQcZyhHkhDT+OSTTxATE4N//vmnxibHNUlJSUHHjh2RnJysSwOUkkWLFmHp0qXIzMyssa8MIUTeAgICMHbsWMyvWL1nAsbsV8vKyuDu7o7o6Gi8VXkFIE8lJSUICAjA7NmzMWPGDL2fxcbGYty4cdXSuAkh/I0dOxabNm1CUVER0/gJEybg8uXL+Ouvv0SemWX5+uuvsWjRIly5csWszTKF2gcLTaFQICYmhqnRalXz589HbGysYM3nCZGNbduAYcOAI0fKMymMMWECcPkyYO59+l9/Ab16lWe08ChjA6C81FdAADB7NlDlmBFffw0sWgRcuVK9QTwhxOLQcahp1HYuToxD+dPErN5++20UFRVhw4YNvLf9/PPP8dJLL0kyYAKUXyz96quvKGBCCDEpY/areXl5ePvttzFs2LB6PXdMTAxsbW3x5ptv1utxCCHCmjdvHk6cOIGjR4+aeyqSUVpaiqVLl2LOnDlmDZgAwu2DCSES8/Ch/r81mvJSVc7O5X1IjDVvXnm5LHPv03v0KG/2bkyZ55iY8h4tVY8ZS0uBpUuBOXMoYEKITNBxqGnQubjwKNOEEEIIEYk5Mk2kijJNCBEO30wTQoxBmSaE1NPEieWBk7Cw8syKLVuAhATgs8+AqChzz44QQgghtaBG8IQQQgghhBBCCCFC6tOnvHn6zp3Ao0dAy5blmSZTp5p7ZoQQQgipA2WaEEIIIYQQQgghhBBCCCGEgHqaEEIIIYQQQgghhBBCCCGEAKCgCSGEEEIIIYQQQgghhBBCCAAZ9jTRarW4ffs2GjVqBIVCYe7pEEIkjOM43L9/H02aNIFSKY8YMu0DCSEs5Lj/A2gfSAhhI8d9IO3/CCGsaB9ICLFWfPZ/sgua3L59G35+fuaeBiHEgty4cQNNmzY19zQEQftAQggfctr/AbQPJITwI6d9IO3/CCF80T6QEGKtWPZ/sguaNGrUCED5i3d2djbzbAghUlZYWAg/Pz/dfkMOaB9ICGEhx/0fQPtAQggbOe4Daf9HCGFF+0BCiLXis/+TXdCkIg3P2dmZdpSEECZySt+lfSAhhA857f8A2gcSQviR0z6Q9n+EEL5oH0gIsVYs+z95FC8khBBCCCGEEEIIIYQQQgipJwqaEEIIIYQQQgghhBBCCCGEgIImhBBCCCGEEEIIIYQQQgghAETuaXL48GF8+eWXOHXqFDIzM7F161YMHTq0xvEHDx5E7969q92fmZkJHx8fEWdKCLFkD9UafLb7Aq7mPkCAuxM+GNAWjnYqc0+L9z4QKN8Pzpo1C+fPn4efnx/mzJmDsWPHmmS+AKDRcjicegef7zmPy3cfgkN5dN3Z0RbPtffBvMHtJPG7lZKiR2WY9ssJJKTloYQr/3052qnQNdANy1/tiIYOsmsfVi/qMi2+O/wPfjx8BfmPOACAvUqJFl4N8J/+rdGzlSdUSvnUV64vdZkWa/5Kw/qEDGTfLwMHQKUo/0xGtqPPZH2oy7RYn3gV1/IewN/NCaPCAmBnQ+uJCLE0UvwsW+IxoFgeqjWYu/1v7Pk7E/fVnLmno8dWqYCXsz1GhvpjYo/mZn/fAIa/96VCir8vQ+cuUkHnBI9pNBrMnz8fP//8M7KystCkSROMHTsWc+bM0fUV4DgO8+bNw5o1a5Cfn4/u3btj1apVePLJJ4Wfj5bD8Yw83Ln/CF6NHNA10I3OPwixQGJ/lkXdaxcXFyMoKAjjx4/H8OHDmbe7dOmSXuMmLy8vMaZHCJGBietOYF/qHd2///oHWJ90Hf3aemHN6C5mnBn/fWBGRgYGDhyIN998E7/88gvi4+MxceJE+Pr6IjIyUvT57jhzGzP+7zS0Ve7XAsh/WIoNJ25gw4kb6NPKHWvHdRN9PlKn0XLou+QgruY+0LtfC6BYrcGBS3fRfv5e+De2x/53+9KBOIAFO85j7dGr1e4v0WhxIfM+xq87AQD4dkQwBgU/YeLZSc8nOy/gxyMZ1e7XcMC9B48/k72fckPM+DAzzNByRe++gO8PZ+hdXPlkVyom9QjAhwPbmW1ehBB+Ptl5AWuP6H+WF+5OxaQegYga0NZs87K0Y0CxTPrpBOIu3Kl7oJmUajncyn+ERXsvYdHeS3jjWfO+b6J3X8B3h6t/70uF1H5fe85lYuqvp1GmlVKo5LGq5wQdmjpj+9Qe5p6WWXzxxRdYtWoV1q1bh3bt2uHkyZMYN24cXFxcMH36dADAokWL8M0332DdunUIDAzERx99hMjISFy4cAEODg6CzWXPuUzM334eWYUluvt8nO0x/4V2eK69r2DPQwgRV/ln+QKyCh/p7vNxdsD8F9oK9llWcBxnkm8YhULBnGly7949uLq6GvU8hYWFcHFxQUFBgV7ghRAiPy98+xf+vllY48/rCpyYcn/Bsg98//33sWvXLpw7d05334gRI5Cfn489e/YwPY+xr6lq8Kku7k42ODXXck/i62vPuUy8+XMyr21Wv9bRqg/En120H9fzHjKPj2jjiR/GdBVxRtL2wvK/8PetmvdvVTW0U+LcgueZx8v1eInlddV1UUoKQXdCSN3qOg6s7YIuHQOKT+oBk5qYKxAg9YBJTcz1+zLmWFwKpBI4MfX+YtCgQfD29saPP/6ou+/FF1+Eo6Mjfv75Z3AchyZNmuCdd97Bf/7zHwBAQUEBvL29ERsbixEjRtT5HCyvqa73jbWfrxFiKerzWeaz/zN/PqUBwcHB8PX1Rb9+/XD06FFzT4cQIkHbk2/WeqIMAHEX7uChWmOiGdVfYmIiIiIi9O6LjIxEYmKiqM87cd1xXgETAMh9UIZnvogXaUbSZuxJ2ps/J2PPuUwRZiR9PT7fxytgAgD7Uu9i4v8yT6zNhNhjvAImAFCk1qLjJ3+KNCP5UJdp8X0dF6XiLtzBjjO3TTQjQogxPtl5rs7jwDV/ZUBdVjV/VprMdQwolodqjUUGTADzvG/KS5daXsAEMM/vS6Pl8NHWv036nEL5+2Yhih6VmXsaJhceHo74+HhcvnwZAHDmzBkcOXIEzz9fvuAnIyMDWVlZevtBFxcXhIaGCrYf1Gg5zN5yttYxs347A41EM5cIIeVYPstRW84K8lmWVNDE19cXq1evxubNm7F582b4+fmhV69eSE6u+eJUSUkJCgsL9W6EEHnTaDlM/+0M09iFuy6IPBvhZGVlwdvbW+8+b29vFBYW4uFDwxec67sP3JlyC/tS7xo135v3HmF87HGjtrVUGi1Xr1Vtb/2SbHUH4uNjknAjv6TugQbsS7W+i9c7U24h/mKOUdvmFZdiXIx1fSb5WpeQwVTv/J3fUqzus0qIpVCXafHjkWt1jtNywPrEq+JPSADmOAYU02e7Lef4uypzvG8s5X1qiDl+X8cz8nC32HIDD29vPG3uKZjc7NmzMWLECLRu3Rq2trYICQnBzJkzMXLkSADl+0AABveDFT+riu8+MOlKLvIflNY65oFag+Xx/7C+LEKIGSSl1/1ZvvegFEnpufV+LkkFTVq1aoU33ngDnTp1Qnh4ONauXYvw8HB89dVXNW4THR0NFxcX3c3Pz8+EMyaEmEPfxfuZx565mS/eRCSgPvtAjZbD1A0p9Xr+/RfvWtVFbT7vPUO0HDD1l1MCzUb6dqbcwv5L9TtYmf5/p63m4rUQn8kDl6zrM8nXiav3mMapNRwS/jEueEUIEdfsTWwLZwDgWt6DugdZKCmfB1ft92ZpTP2+sfT3qannf+f+o7oHSdj1e/yyr+Xgt99+wy+//IJff/0VycnJWLduHRYvXox169YZ/Zh894GJ6WzHdasPXbGacw9CLBFroD7hSv3P5SQVNDGka9euSEtLq/HnUVFRKCgo0N1u3LhhwtkRQkxte/JNXM1jP1B2drAVcTbC8vHxQXZ2tt592dnZcHZ2hqOjo8Ft6rMPnPLryXrNt8KMDdZxUZvve68mf5zPtphyIfWh0XKYXs8AAABwsJ5A00urjgjyODM3Wsdn0hgN7FTMY+fvPFf3IEKISWm0HLaksAeG/d2cRJyNcEx9DCi2AHfL+L3XxNTvG0t5n9bE1PP3aiRcU3BzaNbY8Gdazt59911dtsnTTz+NUaNG4e2330Z0dDSA8n0gAIP7wYqfVcV/H6hgmuujMi2SrtR/hTohRHgaLYf41Oy6BwK4JUCAWvJBk5SUFPj61tyIyd7eHs7Ozno3Qog88SnLVeH1Z5qLNBvhhYWFIT5ev09IXFwcwsLCatzG2H2gukyLPeeEqTWt5YBpv1peI0Y+NFoOb/+X33uvNqN+TBLssaRqWdwlCBUasoZA086UWzh9Q5jSKhotsCzusiCPJTfDOzZlHnvl7gPZv+8IsTTL4i7xGj8qLECciQjMlMeApvCBGRqDC0WpMP37xlLep4aY4/fVNdANng1sTPqcQvrqlRBzT8HkHjx4AKVS//KjSqWCVlt+nBUYGAgfHx+9/WBhYSGOHTtW436Q7z4wrIU783yPXjGufDUhRFxJ6bkoZTw9a+Ja/wC7qEGToqIipKSkICUlBUB5c6eUlBRcv34dQHlkePTo0brxX3/9NX7//XekpaXh3LlzmDlzJvbv348pU6aIOU1CiIUIXcivybFSATzTylOk2dSN7z7wzTffRHp6Ot577z1cvHgRK1euxG+//Ya3335b8LmN+kHYi/a7z2XJ+uJiQloONAIu3D+WcU/Wvy+NlsPyA1cEfUw5B5o0Wg4zN6YI+pjfHkyjbBMDwlt6QMW20BCAvN93hFgavt8toYGNYWdjnjWCUj4GNAVHOxX6tfUy9zSMMqlHoMnfN3Y2SrzxbKBJn1Mo5vh9qZQKfDKsg0mfUygdmjqjoYPlBnyMNXjwYCxcuBC7du3C1atXsXXrVixduhTDhg0DACgUCsycOROffvoptm/fjrNnz2L06NFo0qQJhg4dKsgcujV3hw3jMeCJjDxBnpMQIiw+Jbe6t6j/tUBRv91OnjyJkJAQhISUR9JnzZqFkJAQzJ07FwCQmZmpO3AEALVajXfeeQdPP/00evbsiTNnzmDfvn3o27evmNMkhFiAj3ecRQ7Phn9fvRIMlZLH1TGB8d0HBgYGYteuXYiLi0NQUBCWLFmCH374AZGRkYLOS12mxTHGuv58zN4sXCaG1Mzffl7wx5TzxdhlcZeYmm3zIedAU0JaDsoE/oVpOco2MUSlVGBKrxbM4+X8viPE0vD9blk/oZtoc6mLVI8BTWnN6C4WFzh549lARJkpSyZqQFuLC5yY8/f1XHtfrH6tI2zMeK7HV4emztg+tYe5p2EWy5cvx0svvYS33noLbdq0wX/+8x+88cYb+OSTT3Rj3nvvPUybNg2vv/46unTpgqKiIuzZswcODsKUY1MpFQjxb8w09szNAlp8RIgEHWcMaNqpFOjGI7usJgqO42S1JygsLISLiwsKCgoklaJMCDGeukyLp+b8wWsbH2c7JH3Qr9YxctxfsLymV1YniBI0UQBI+2yAWQNVYjDm/cfq8qfPm20VrFg0Wg4tP9gteNAEKF81vPGNcBEe2bz+tfoojl/NF/xxbZTApU8NfybluP8D2F6XRsuhxQe7mR9Tru87QiwJ3++WFp5OiH+nd40/l+M+UKqv6aFag7nb/8aevzNxXy2tSw+2SgW8nO0xMtQfE3s0l8QxmbpMizV/pWF9Qgay75eJcjxlLCn+vjRaDodT7+DzPedx+e5DSf2+lCjPuuoa6Iblr3aUVIaJVPcX9cHymr7cexErGDMWf5kQiu5Pegg5RUJIPWi0HFp9uJtpsWEXf1f8d3J3gz/js/+Tzl6bEEJq0OVTfmW5AODwe5ShZohYWSZAecPuZXGXMSuylSiPby6zN4mXQTPqxyTZXYzluxJYATCPr1j1L4WTdKFotBxOiBAwAYAyLZDwTw56mLFMoRSplAoMD27C3Exaju87QiwN3++W+YPaizYXwo+jnQpfvhSCL1+yvj4OxrCzUWJK76cwpfdT5p6KRVApFejdzhu923mbeyrEAoS38GAOmhy9cpeCJoRISFJ6LnN1hi6BboI8J539EUIkbXxMEgoeafht0z2ALm7VYH3iVV7jUxc8Bz55I6sPX5FVKrNGy2Er44VVABge0gRPejoxj5db6R+NlsOqQ/x6mVxY8Byv8esSMniNl7qEtBxeFwJTef6+Np++yW9CVuLzl4J4jZdz+UFCpE6j5bDiIPt3i40SCKcLXYQQQqqgviaEWC5T9zMBKGhCCJGwnSm3sP9SLq9tvBraYe7gdiLNyPJdy3vAPDY0sDEc7VRYPiKYeRu1hkPSFX5/Mynje0H78xeDsGtGT17PEbXlb36TkrCk9FyU8ogBTe/dEo52KoQGsNUXBoAdZ9iDWJbg4x3s/XKCmjrD0U6F6b3Ze3I8UPMLOlsLOxslr/fd72duyyogTIglSUjLgYbHx++tni1lVyqUEEJI/VFfE0Is1617D5nGCdXPBKCgCSFEojRaDlM3pPDeLvGDCOEnIyP+buxZEBUNVAcFPwEfZ3vm7dYlyicTgM8F7dDAxrCzUfK/GJsin4uxP/HIAlEqgBn9yktPrJ/I3qz33K1C2fy+1GVapN0tZh7/XmQbAMCMfq3Aej2wS4AwqclyxOd9p9FCVgFhQiwJn+/iyt8thBBCSFVdGcv2yG0xICGW7uY9tgXAQU1dBFs8Q0ETQogkhS7k38dk+ashtLKwDqPCApgutk54JlCvxNmXPErZ7L94VxYXtfle0K4IMgH8LsaWaeVxQK7Rcth38Q7z+Km9Hq8EtrNRwt/NkWk7Lcr7dMjBuoSrzGMrr5hRKRX45pXgOrdRKIAx4QHGTc4K2Nko0cKjAfP4RXtTRZwNIcQQvt/Flb9bCCGEkKrCW7CXbzx65a6IMyGEsNJoOaRcz2ca28SV7boCCwqaEEIk5+MdZ5FTXMZrmxA/FwwOaiLSjOTDzkaJST0Cax3ToakzPhrUVu++8JYezF8YcgkC8Lmg3cLTSS/IZGejRHBTZ+btf0pify6pSkrPhYaxNJcC1VcCv9YtgPm5vtl/mX1iEvYzj7/7m8+20LsQOCj4CfRr61XrNq/3CKT+TnWY/wJ7OcczNwtl1YOIEEsw6ock5rGUZUIIIaQu1NeEEMvDpwn8E40paEIIkSl1mRYxR6/z3m7T5O4izEaeoga0xRvPBhrMOJnwjD+2T+1R7X6VUoFhHdmDUn+lsWccSBWfC9rzB7Wvdt+7z7Vh3v7AxTsWn53DpzTX8I5PVFsJzCcj4tT1fIv/fanLtLiWx1aX1VCQCQDWjO6CST0CoajyWVYqgDeeDUTUgLbVtiH6+ASEAWDUj+wXcAkh9aMu0+LY1XvM4ynLhBBCSF2orwkhlsccTeABwEawRyKEEAH0WLSP9zZUlou/qAFt8U7/1lifeBXX8h7A380Jo8ICal2VHj08CJuT2ZpwH7h4F7OfF2q2psfngrZSAYQ/WT3Nu1tzd9gqwdQYvaJmbncDj2MJ+Jbmih7eodp9djZKtPRswFSGRcuVl+jq0Uq4AyJT45PJ1K+tV437uA8HtsW7kfw+y+SxioAw677tWMY9qMu09PslxARmbzrDPLam4DIhhBBSVddAN5xgCMpb+jkaIXJxnDHrS8gm8ABlmhBCJGR78k1kF5by2qZPa08qy2UkOxslJvRojgVD2mNCj+Z1XgS0s1HCu5Ed02On3S2y6FU5fC5oR7QxfEFbpVSgbxtv5sex5Jq5fEpz+bs51vhemzeYvVSSpZfo2vH3LeaxY8JqL6nH97NM9EUPZ+/ZBFC2CSGmoNFy2JLCFswEgGEhTWgBDSGEECbU14QQy6HRcjh9jS3zWMgm8AAFTQghEqHRcpj+G/uKQgDwbGCLtWO7ijQjYkgY4wGmRguL7msi1AXtUWEBzI+z93wW81ip4ZMu+1o3/xp/xqdUUvINyy3RpdFyOHezkGmsjVLY1TKkOjsbJUID2Mo0AI+zTQgh4lkWd4nX+M9f5Bf8JIQQYr2orwkhloNPP5MugW6CPjcFTQghkhC68E/e2yR92E+EmZDavNixKfPYdYnsPS6kRKPlcP6WMBe0K0p0sbhy94HFXog9ns4eIBsTXnOQSaVUoHOAK9PjWHJgLiEtB6x/6T6tPWn1tAmsn9iN1/jZm/kF+Qkh7DRaDqsOXWEe38LTiTLsCCGEMKO+JoRYDnP1MwEoaEIIkYDxMUnIKS7jtQ31MTEPPpkA+y/etcgDzKT0XGgYp13XBW2+JbrW8WimLhUaLYdT1/OZxj7h4lDnha1pfdhr0v+UdJV5rJQs51FarK7SXEQYfLNNtp6+bZH7N0IsQVJ6LlM/sArzB7UXbzKEEEJkqSvjivSKviaEEPMwVz8TgIImhBAz25lyC/sv8TsIaenpRH1MzESlVKB9U2emsWVayzzA/IlH4ILlgjafEl07zrDXb5eKpPRcsF47btuk7vcOn8DcgYt3LO7CtUbL4eS1fKaxKiWoNJcJ8ck24QAsi7PsvjqESNWXe1KZx9oogXBq0EsIIYQn6mtCiPSZs58JQEETQogZabQcpm1I4b3d7hk9hZ8MYTa4wxPMYy3tAFOj5bDv4h2msay9Jro1d4eK8dv2QuZ9iwsC8EmXZVnRpVIq0Ilxxb8lrvziE2Tq6OdKGXUmxDfb5NuDaRb3eSVE6tRlWqQw9nwCgLd6tqT9JCGEEN6orwkh0mfOfiYABU0IIWb08qoj4Hu5aXz3AKpbbWZjwgOYx1raAWZSei40jCVB2vo2YrpQo1IqENHai+kxLTE7hzVdFqi9n0llrOnygOUF5vgEmabzKFVGhMEn20TLUbYJIUIb9UMS81gFgBn9aD9JCCGEP+prQoj0mbOfCUBBE0KImexMuYXkG+wrCQHAq6Ed5g5uJ9KMCCs7GyVaeDoxjbW0A0w+X8p8SsSNZgwWAJYVBOCTLsunUS+fdHlLC8yxBpmo5Ix52Nko0dKzAfN4yjYhRDjqMi2OXWX7TgGA4R2foCwTQgghRqO+JoRImzn7mQAUNCGEmIFGy2GqEWW5Ej+IEH4yxCjPtfdlGmdpB5hiZE0A8k3/5pMuG9nOh/lxuzV3hy3jEYolBeb4BJlCLLw016pVq9ChQwc4OzvD2dkZYWFh+OOPP2rd5r///S9at24NBwcHPP3009i9e7eJZqtvHo/gPGWbECIcPlkmABA9vINIMyGEEGINqK8JIdJl7n4mAAVNCCFmELrwT97bLH81xKIvIMqNHA8wxcqaAOSb/i1WuqxKqUDfNt5MYy0pMGfumqym1LRpU3z++ec4deoUTp48iT59+mDIkCE4f/68wfEJCQl49dVXMWHCBJw+fRpDhw7F0KFDce7cORPPHAhv6QEVj6+blYco24SQ+uKbZRIa2JjKtRJCCKkXuS5sI0QOpHDuTEeahBCT+njHWeQUl/HapqOfK69SSER8cjzAFCtrooIc07/FTJcdFRbAPNZSAnPmrslqSoMHD8aAAQPw5JNP4qmnnsLChQvRsGFDJCUZXkm+bNkyPPfcc3j33XfRpk0bfPLJJ+jYsSO+/fZbE8+8PGg3pVcL5vFlWiDhH/a/LSGkutmbzvAav34Ce/8hQgghxBC5LmwjRA6kcO5MQRNCiMmoy7SIOXqd1zYKAP+dHC7OhIjR5HiAKfaXstyyc8ROl5VjYM7cNVnNRaPRYMOGDSguLkZYWJjBMYmJiYiI0C/BGBkZicTExFofu6SkBIWFhXo3Iczo1wp83rHzd5o+I4YQudBoOWxJuc08nrJMCCGECEWOC9sIkQMpnDvT0SYhxGR6LNrHe5tvqCyXZMntAFPsL2U+QYBb9x7yfnxTEztdVm6BOSnUZDW1s2fPomHDhrC3t8ebb76JrVu3om3btgbHZmVlwdtbvySbt7c3srKyan2O6OhouLi46G5+fn6CzF2lVGBab/Zskyt3H0BdphXkuQmxNsviLvEaT1kmhBBChCK3hW2EyIFUzp0paEIIMYntyTeRXVjKa5s+rT2pLJeEyekA0xRfyiqlAsHNXJnG3s6XftDEFOmycgrMSaEmq6m1atUKKSkpOHbsGCZPnowxY8bgwoULgj5HVFQUCgoKdLcbN24I9th8s01mb+ZXXogQUv79u/zAFebxlGVCCCFESHLMbifE0knl3JmOOAkhotNoOUz/jd/FJM8Gtlg7tqtIMyJCkNMBpqm+lJs2dmIaZwmZE6ZIl5VTYE4KNVlNzc7ODi1btkSnTp0QHR2NoKAgLFu2zOBYHx8fZGdn692XnZ0NH5/a+wfZ29vD2dlZ7yYUvtkmW0/flvznlhCpWRZ3CXw+NZRlQgghREhyy24nRA6kcu5MQRNCiOhCF/7Je5ukD/uJMBMiJDkdYCbyyFKoz5fyE40dmcZJPXNCo+Xw9818prH1SZft1twdtoxHKrfzHxn1HKYihZqs5qbValFSUmLwZ2FhYYiPj9e7Ly4ursYeKKYyo18r5rEcgGVxl8WbDCEywzfLpIWnE2WZEEIIEZycstsJkQOpnDuLetR5+PBhDB48GE2aNIFCocC2bdvq3ObgwYPo2LEj7O3t0bJlS8TGxoo5RUKIyMbHJCGnuIzXNsupj4nFkMsBJse4ztXBRlmvL2U+mROJ6eyrK0zteEYeShhTc+qTmaNSKtCntRfT2IelGqOfR2xSqclqSlFRUTh8+DCuXr2Ks2fPIioqCgcPHsTIkSMBAKNHj0ZUVJRu/IwZM7Bnzx4sWbIEFy9exPz583Hy5ElMnTrVXC8BQPl7sIu/K/P4bw+mSTpATIiU8M0ymT+ovWhzIYQQYr3klN1OiKWT0rmzqEGT4uJiBAUFYcWKFUzjMzIyMHDgQPTu3RspKSmYOXMmJk6ciL1794o5TUKISHam3ML+S/wulLf0dKI+JhZELgeYmfkPmMYNeNq3Xl/K3Zq7w07FNlbK112zCtmzOuqbLts5gC1IdfjyXclerJZKTVZTunPnDkaPHo1WrVqhb9++OHHiBPbu3Yt+/cqzCK9fv47MzEzd+PDwcPz666/4/vvvERQUhE2bNmHbtm1o3978F0mn932KeayWo2wTQlhotBxWHGTPMrFRAuFPsh9zEEIIIazkVHaaEEsnpXNnGzEf/Pnnn8fzzz/PPH716tUIDAzEkiVLAABt2rTBkSNH8NVXXyEyMlKsaRJCRKDRcpi2IYX3drtn9BR+MkQ0FeWTSrV1j5Vq+SSNlsPOvzPrHgjAx8WhXs+lUiowqIMvtpyu+/myCqT5+wKAnPuGSyxV5Whbv8wcAPBoZM807mGpFklXctFdghfVTFX+TUp+/PHHWn9+8ODBave9/PLLePnll0WakfHCW3pApQA0jAfvqw9fwYx+T8kiY4gQsSSk5TB/pgDgrZ4t6TNFCCFEFBVlp09crXt1e0XZafpOIkQcUulnAkisp0liYiIiIiL07ouMjERiYqKZZkQIMdbLq47wKrkAAOO7B1Ctagsjh/JJSem5UDNOTYhjY19Xtmbwu89mSjZz4uRVtiDAs0951vuEwseZPVAl1WwmU5V/I+JQKRWY0ou9IbzUyxESIgXL97NnZCkVwIx+7BlfhBBCCF9yKTtNiKWTSj8TQGJBk6ysLHh7e+vd5+3tjcLCQjx8+NDgNiUlJSgsLNS7EULMa2fKLSTf4PdZ9Gpoh7mD24k0IyImSy+fxGclQ1jz+mcxKMAWRHhUppXkAblGy2H/xTtMYx1tGWuR1aJroBvsGfPlpZoub6ryb0Q8M/q1Yvzkllu0N1W0uRBi6TRaDsev5jOPn9qLskwIIYSISy5lpwmxZFLqZwJILGhijOjoaLi4uOhufn5+5p4SIVZNo+Uw1YiyXIkfRNQ9iEgS3/JJUnPrnuGgfFVCrWQI4/EYUjwgT0rPZSrHBgBNXOtXzgwoX+Xfoakr09hztwslF5gzZfk3Ih6VUoFpvdmzTc7cLIS6jPGDQoiVWRZ3iXmsApRlQgghRHx8+pqwnj8SQviRUj8TQGJBEx8fH2RnZ+vdl52dDWdnZzg6OhrcJioqCgUFBbrbjRs3TDFVQkgNQhf+yXub5a+G0ApCC2bp5ZM4ju1bWaiVDJZ+QG6OGqOs6fIPS7XM6bymYuryb0Q8fLNNRv2YJNpcCLFUGi2H5QfYG8D3a+tFx4iEEEJEp1IqENzMlWns7XzpnaMRIgdS6mcCSCxoEhYWhvj4eL374uLiEBYWVuM29vb2cHZ21rsRQszj4x1nkVNcxmubjn6uGBzURKQZmd+KFSsQEBAABwcHhIaG4vjx47WO//rrr9GqVSs4OjrCz88Pb7/9Nh49km5DcIBf+SQpBgEeMfZa6SzQSoaKRoMsMiXYDN7UmTkAv3T5rAJpvcdMXf6NiEelVGB4R/bvq2MZ9yjbhJAqlsVd4tXzbkxYoGhzIYQQQipr2pit92RFM3hCiLCk1M8EEDloUlRUhJSUFKSkpAAAMjIykJKSguvXrwMozxIZPXq0bvybb76J9PR0vPfee7h48SJWrlyJ3377DW+//baY0ySECEBdpkXM0eu8tlEA+O/kcHEmJAEbN27ErFmzMG/ePCQnJyMoKAiRkZG4c8dwP4hff/0Vs2fPxrx585Camooff/wRGzduxAcffGDimfPDp3yS1IIAGi2Hg5fZsl/cnOwEe17WzIm/JXhA/lDNFhjt3Vq41cHdmrszB+ZyikoEeU6hmCPIRMQTPTyI13jKNiHkMb5ZJrRfJIQQYkpPNDZc4aYqagZPiPCk1s8EEDlocvLkSYSEhCAkJAQAMGvWLISEhGDu3LkAgMzMTF0ABQACAwOxa9cuxMXFISgoCEuWLMEPP/yAyMhIMadJCBHAgGWHeG/zjczLci1duhSTJk3CuHHj0LZtW6xevRpOTk5Yu3atwfEJCQno3r07/v3vfyMgIAD9+/fHq6++Wmd2ihRYahAgKT0XJYxFMz0asvVuYcGaOSG1ZvB8gkydGbNpWKiUCvR6ii399iTjgZap+DL2dREyyETEY2ejRGgA+3ubsk0IeYxvlsmbz7ag/SIhhBCToWbwhJiP1PqZACIHTXr16gWO46rdYmNjAQCxsbE4ePBgtW1Onz6NkpISXLlyBWPHjhVzioQQAexMuYW0uw94bdOntaesy3Kp1WqcOnUKERGPG9wrlUpEREQgMTHR4Dbh4eE4deqULkiSnp6O3bt3Y8CAASaZc31YahCAT+kkHxe2lUcsujV3h52K7UKQlA7IzRVkAgBHOxumcYcv35VUYC6bsVxYx2bCBZmIuNZP7MZr/OzNZ0SaCSGWQ6PlsOoQe5aJUkEN4AkhhJgWn96TJyTWR5EQSye1fiaAxHqaEEIsj0bLYeqGFF7beDawxdqxXcWZkETk5ORAo9HA29tb735vb29kZWUZ3Obf//43FixYgGeeeQa2trZo0aIFevXqVWt5rpKSEhQWFurdzMFSgwCspZMcbZXM2TQsVEoFgpq6MI2VUh8YcwWZAPZ0eSk1g9doOez8O5NpbMHDUpFnQ4TCN9tk6+nbkgrkEWIOSem5KOWRdDW1V0vKMiGEEGJSfHpPUl8TQoQltX4mAAVNCCH1FLrwT97bJH3YT4SZWL6DBw/is88+w8qVK5GcnIwtW7Zg165d+OSTT2rcJjo6Gi4uLrqbn5+fCWf8mKUGAVj7czz7lKfgF2+eYGw0KKU+MOYKMgGW2Qw+KT0Xag3bWLo2aFn4ZJtwAJbFXRZvMoRYgC/3pDKPpSwTQggh5sJ6DkN9TQgRjhT7mQAUNCGE1MP4mCTkFLNddK6wbESwVawc9PDwgEqlQnZ2tt792dnZ8PHxMbjNRx99hFGjRmHixIl4+umnMWzYMHz22WeIjo6GVmt4eWZUVBQKCgp0txs3bgj+WlhZWhDAXP05KrBmTkipD4w5g0yW2Aw+kceJVFhz9qAQMT++2SbfHkyTzOfYWqjLtPjuUBqGLj+MDvP3oMuncZgYexxFj/gdt5D6U5dpkXKTPROWskwIIYSYC/U1IcT0pNjPBKCgCSHESDtTbmH/JX4rK3yd7TEk+AmRZiQtdnZ26NSpE+Lj43X3abVaxMfHIywszOA2Dx48gFKpv1tWqVQAAI4z/A1ib28PZ2dnvZu5WFoQwJz9OQDL6wNj7iATn2bweQ/Ugj+/MTjGlscONkqTpRgT4fDJNtFylG1iStG7L+CpOX8g+o9LSLl1H4WPNLhbpMa+i3fRfv5eDPrmkLmnaFVG/ZDEPFYByjIhhBBiPtTXhBDTk2I/E4CCJoQQIxjTxwQADr3XR/jJSNisWbOwZs0arFu3DqmpqZg8eTKKi4sxbtw4AMDo0aMRFRWlGz948GCsWrUKGzZsQEZGBuLi4vDRRx9h8ODBuuCJlFlaEIBPFoDQ/TkAy+sDY+4gE8DeDD4zXxrZTJn5D5jGDXjal1ZVWyA7GyVaejZgHr/yEGWbmEL07gv47nBGrWPO3S5Cl0/jTDQj66Yu0+LYVbZyCwAwvOMTtD8khBBiNtTXhBDTYy0Dbsp+JgAFTQghRnhp1RHe24zvHgA7G+va5bzyyitYvHgx5s6di+DgYKSkpGDPnj265vDXr19HZubjJtFz5szBO++8gzlz5qBt27aYMGECIiMj8d1335nrJfBiaUEA1iyAhvYqwftzAOUH5MF+rkxjb0sgCGDuIBMA+Lo6MI17WMrYSEREGi2HPeez6x4IwMeF7XUR6Zk3uB3z2DItkPAP+yoqwp+6TFtnwKTC3SI1Pt5+XuQZET5ZJgAQPbyDSDMhhBBC2FBfE0JM6+Y9tsWGpuxnAlDQhBDC086UWzh9g70uNQB4NbTDXB4XluRk6tSpuHbtGkpKSnDs2DGEhobqfnbw4EHExsbq/m1jY4N58+YhLS0NDx8+xPXr17FixQq4urqafuJGsLQgAGsWQP+2PqJ9MXdi7IkghSCAuYNMAODmxJbBcvjyXbOv+jqekYcHasO9iKqiRdWWK7ylBxhjxQCA+TvPiTcZgtijbAGTCjEJV6EuY/ucEv74ZpmEBja2ugU2hBBCpIf6mhBiOhoth5Tr+Uxjm7iKszizJnRUSghhZmxZrsQPIoSfDJEkSwkCSCULwJKCAFIIMnk0Yvt9PSw1fwm4rEL2wCA1gbdcKqUCU3q1YB5/5e4Dukgvoo0nrvPeZl0Cv0ALYTd70xle49dPYO8TZClWrFiBgIAAODg4IDQ0FMePH69xbGxsLBQKhd7NwYEyEQkh8nDr1i289tprcHd3h6OjI55++mmcPHlS93OO4zB37lz4+vrC0dERERER+Oeff8wyV+prQojp8GkCz9pHVygUNCGEMAtd+CfvbZa/GkK1qa0IaxAg8UquWYMAUskCsJQggFSCTD7O7I+dmG7eMkg590uYxjnaUhN4SzejXyte46O2/C3STKybRsshPYctuFvZjjO3RZgN0Wg5bE1h/93KMctk48aNmDVrFubNm4fk5GQEBQUhMjISd+7cqXEbZ2dnZGZm6m7Xrl0z4YwJIUQc9+7dQ/fu3WFra4s//vgDFy5cwJIlS9C48eMFd4sWLcI333yD1atX49ixY2jQoAEiIyPx6JHpKxRQXxNCTEeqTeABCpoQQhh9vOMscorLeG3Tp7UnBgc1EWlGRIpYgwCFj8pw3IyrcqSSBWApQQCpBJm6BrrByY7t0MXc5y73HqiZxj37lCcFli2cSqnA8GD277otybfo5FoESem5jEUE9V3IvE9/DxEkpOXw+nvIMctk6dKlmDRpEsaNG4e2bdti9erVcHJywtq1a2vcRqFQwMfHR3er6INHCCGW7IsvvoCfnx9iYmLQtWtXBAYGon///mjRojxbl+M4fP3115gzZw6GDBmCDh064KeffsLt27exbds2s8yZ+poQYhp7zmXWPQimbwIPUNCEEMJAXaZFzFF+JS9cHFRYO7arSDMiUsUnCJBV8FDEmdROKlkAlhIEkEqQSaVU4Ll2bBeQsgrM2zcn/W4R07iWXg1Fngkxhc9fCmIeywFYFndZvMlYKT6r1Cor09LFDjHM336eeawcs0zUajVOnTqFiIjHJWqVSiUiIiKQmJhY43ZFRUXw9/eHn58fhgwZgvPna/49lpSUoLCwUO9GCCFStH37dnTu3Bkvv/wyvLy8EBISgjVr1uh+npGRgaysLL19pouLC0JDQ2vcZ4q9D+TT12RdIpX6JMQY6jItrtyVZhN4gIImhBAGPb7Yx3ubE3P6izATInVdA93Q0J7tqyWniC1wIYa8B2zPLXYWAJ8ggIujrWjzqItUgkwA4OvqxDTuzwtZZls9rtFyOHCp5vIrlbma8e9KhGNno0RwU2fm8d8eTKPsBoHVJ3uRmrgKS12mxZWcYubxcswyycnJgUajqZYp4u3tjaysLIPbtGrVCmvXrsXvv/+On3/+GVqtFuHh4bh586bB8dHR0XBxcdHd/Pz8BH8dhBAihPT0dKxatQpPPvkk9u7di8mTJ2P69OlYt24dAOj2i3z2mWLvA7s1d4eK8TRw/0Xz958kxBKtS7jKPLYLY/aXkChoQgip1fbkm8i+X8prm/HdA2S3YpCwUSkV6M64KiePsXyRGE5dvcc0ztFWJfJM2IMAp2+wzVkMUio1pQDb4xeVaMxWAi4pPRcljN3sPBqylbQj0vfuc22Yx2o5yjYRkkbL4e+b+UZvf+ue+TIf5WjUD0nMY70b2dEx4/+EhYVh9OjRCA4ORs+ePbFlyxZ4enriu+++Mzg+KioKBQUFutuNGzdMPGNCCGGj1WrRsWNHfPbZZwgJCcHrr7+OSZMmYfXq1UY/ptj7QJVSgXZPsC2IoaxVQoyz4+9bzGNN3c8EoKAJIaQWGi2H6b+d4bWNV0M7zB3cTqQZEUvgaGfDNC4z3zzlkzRaDik38pnGNnEVr6l5BdYgwEEzrmCSUqmpMB6ZLOYqAZfI46TJx8VRxJkQU+rW3B22PI6sKdtEOMcz8pgDlYZkmrmcn5yoy7Q4xrgwAQDGPxMo4mzMx8PDAyqVCtnZ2Xr3Z2dnw8fHh+kxbG1tERISgrS0NIM/t7e3h7Ozs96NEEKkyNfXF23bttW7r02bNrh+vbwEeMV+kc8+0xT7wMEdnmAeS1mrhPCj0XI4f4utrJ6N0vT9TAAKmhBCahG68E/e2yR+EFH3ICJrvoyBhoelGpFnYlhSei7UGraLa6ZYzcAaBHhUpjXLCiaplZrq1twd9jZsgSZzlYDjGNsfN7RXMTeZJNKnUiowuWcL5vGUbSIcPn2XDLmQWUgBLIHwyTIBgHHdm4s0E/Oys7NDp06dEB8fr7tPq9UiPj4eYWFhTI+h0Whw9uxZ+Pr6ijVNQggxie7du+PSpUt6912+fBn+/v4AgMDAQPj4+OjtMwsLC3Hs2DHmfaYYxoQHMI89YaYMd0IsVVJ6Lhgvy6BPa/ErWhhCQRNCiEHjY5KQU1zGa5tlI4LNsiMj0uLmxFZuKPFKrlkuUrFmATjYiN+fAygPAtgxFsw1xwomqZWaUikV6N3Ki2ls/kN+pQWF4uzAFjzq39aH9pkyM6NfK8bcsXKrD1+hi/UCyKtngNSc5fzkhG+WiRwbwFc2a9YsrFmzBuvWrUNqaiomT56M4uJijBs3DgAwevRoREVF6cYvWLAAf/75J9LT05GcnIzXXnsN165dw8SJE831EgghRBBvv/02kpKS8NlnnyEtLQ2//vorvv/+e0yZMgUAoFAoMHPmTHz66afYvn07zp49i9GjR6NJkyYYOnSo2eZtZ6NEC0+2UspnbhbQMR0hPCRcyWEeOybMPJnJ8j1KJYQYbWfKLey/xG9Fe6C7E4YEs6evEvnyaMR24bzwUZlZLlKxZgH0MtFqBpVSgWA/V6axt81Q0kyKpaaae7KVAUu7w1ZWTGinr7NdNPRxEb/8GzEtlVKBab3Zs03UGqqBLQRXJ7t6P4a5yvnJSdQWfiVd5dgAvrJXXnkFixcvxty5cxEcHIyUlBTs2bNH1+j4+vXryMzM1I2/d+8eJk2ahDZt2mDAgAEoLCxEQkJCtZI2hBBiabp06YKtW7fi//7v/9C+fXt88skn+PrrrzFy5EjdmPfeew/Tpk3D66+/ji5duqCoqAh79uyBg4N5j5efa8+W7UfHdITww3otyE5lntJcAMBWeJ4QYjU0Wg5TN6Tw3m7fO70EnwuxTD7O7Ae25rhIxZoFEOLXWOSZPNYpoDGOM6zONUdJMymWmmrMeIG0IpvJlNkc5eXM2DKCKMlEnmb0a4XlB64wfnKARXtT8fuTPUSdk9zlP1DX+zHyiuv/GNZMo+WwJfk283i5Z5lUmDp1KqZOnWrwZwcPHtT791dffYWvvvrKBLMihBDTGzRoEAYNGlTjzxUKBRYsWIAFCxaYcFZ1C2/hgRUHrjCNPXrlLro/6SHyjAixfBoth9PX2BYaBjV1MVt1BvkfqRJCeOm7eD/vbagsF6msa6AbGtqzfb2Yo+cEaxZAgQlLO0m5pJkUS01JOZupvJyZlmlsWHM6qZIjvtkmZ24WQs34niGGCZFpIsRjWLNlcZeYA4WA/LNMCCGEyEO35u5gbKdIfU0IYZSUngvGCuDoYsYeoBQ0IYTobE++iat5/Mr/UFkuUpVKqcAzLdkaqJu654RUswCkHARgDR6ZstSUlLOZpNYzh5gH394mo37k1zyb6EvkURO5JkJkq1grjZbDcsZVuAAQ1NTZKrJMCCGEWD6VUoEQf7YKBNTXhBA2fPqZdG/Bdm1JDHS0SggBUH7CO/03frWoASrLRQyTas8JqWYBSDkIkH6X7W+kMGGQScrZTFLrmUPMQ6VUYHjHJszjj2Xco2wTI2m0HOIuZNf7cSjTxHh8s0zei2wj2lwIIYQQobGWIKa+JoSwsYR+JgAFTYiEabQcDpzPxnNLD6DF7F0InL0LrT7cjaErjqDggWlXp1uD0IV/8t5m+ashdNGPGMS354SpSDULQKpBAI2Ww5E0tswcV0e2Ml5CkHI2kxR75phDdHQ0unTpgkaNGsHLywtDhw7FpUuXat0mNjYWCoVC72bu5p/1ET08iNd4yjYxzvGMPBQ8Kqv34wiRrWKN+GaZmPvklxBCCOErvAX7Yrqfkq6KNxFCZECj5XCKoZ8rYN5+JgAFTYgEqcu0mLEhGS0+2I1x60/i4p0H0ADgAJRoOKTcKEDQgj/R9qPdeKg2fVNkOfp4x1nkFPO74NCntScGB7GvoiXWRarlpqSaBSDVIMDxjDwUlbCtfvdoyPY3F4pUs5mk2DPHHA4dOoQpU6YgKSkJcXFxKC0tRf/+/VFcXFzrds7OzsjMzNTdrl27ZqIZC8/ORonQAPbgGGWbGCerkF9Z0ZrsS71DJTWMwDfL5M1nW9CCG0IIIRaFT1+TAxfpeIKQ2iSk5YD1jMec/UwACpoQiVmw4zyemvMHfk/JrHPsg1IObebuwfgYWplZH+oyLWKOXue1jWcDW6wd21WkGRE5kGq5KSlnAbAGATgTHoPzuRjp4+Io4kyqk2I2k1R75pjDnj17MHbsWLRr1w5BQUGIjY3F9evXcerUqVq3UygU8PHx0d28vb1NNGNxrJ/Ir9n17M38y2RauzyBsu/yH5aavGeUpdNoOaw6xJ5lolQAM/o9JeKMCCGEEOHx6WtCJboIqd3y/ZeZx5qznwlgoqDJihUrEBAQAAcHB4SGhuL48eM1jpVbaQbCrscX+7H26FXe2+2/lItOC/YKPyEr0flT/r+7pA/7iTATIidSLTcl5SwA1iBAtkCrqlmwXox0drBhrvUrFClmM0m1Z44UFBQUAADc3Gp/nxQVFcHf3x9+fn4YMmQIzp8/X+v4kpISFBYW6t2khG+2ybaU27Q6kSche5GYumeUpUtKz0Upj+Soqb1aUpYJIYQQi8TnXOfoFbZFVIRYG42Ww8lr+UxjVUqYvaSr6EGTjRs3YtasWZg3bx6Sk5MRFBSEyMhI3Llzp8Zt5FSagbBpO+cP3Lhn/Ilq7oMyPPNFvIAzsg4f7ziLwkf8SoEsGxFMJ7ykTlIsNyX1LADWIMAf57JMdlGV9WLksJAnTL5fkGI2k1R75pibVqvFzJkz0b17d7Rv377Gca1atcLatWvx+++/4+eff4ZWq0V4eDhu3rxZ4zbR0dFwcXHR3fz8/MR4CfXCJ9tEywEJ/1BvDT7yH6gFe6y8YuEeyxp8uSeVeSxlmRBCCLFkfPqanKDMVUIMSkrPBeuljI5+rma/9ih60GTp0qWYNGkSxo0bh7Zt22L16tVwcnLC2rVra9xGbqUZSO2az96FBwLU8L557xHGx9acxUT0GVOWK9DdCUOCnxBpRkRupNZzQupZAKxBgAdqjclSvlkvIDZt7CTyTKqTYjaTVHvmmNuUKVNw7tw5bNiwodZxYWFhGD16NIKDg9GzZ09s2bIFnp6e+O6772rcJioqCgUFBbrbjRs3hJ5+vfHNNpm/85yIs5EfITNNhHwsuVOXaZFykz2zi7JMCCGEWLJuzd1hy3gF9czNAsocJsSAhCvsi8Om9zH/YhtRgyZqtRqnTp1CRETE4ydUKhEREYHExMQat+NbmoFYrsDZu5gbALHYf/Eudpy5LeAjyleXT//kvc2+d3oJPxEiW1LrOSH1LICugW5oYKdiGpuYbpqV6Keusa2SuifgSm9WUsxmknLPHHOZOnUqdu7ciQMHDqBp06a8trW1tUVISAjS0tJqHGNvbw9nZ2e9mxTxyTa5cvcBNYTnQchMEyEfS+5G/cDeU1AByjIhhBBi2VRKBfq2YVvQTX1NCDGMtWy2jRIIf9L85axFDZrk5ORAo9FUyxTx9vZGVlaWwW34lmaQei1rUrMWs3cxrsnlZ/r/naaofh3GxySh4JGG1zZUlovwJbWeE1LPAlApFejBeGBgil2c1MuZAdLLZpJyzxxT4zgOU6dOxdatW7F//34EBgbyfgyNRoOzZ8/C19dXhBmalp2NEt6M+0QAGPUj+wVpa3fz3gPBHsutIfvfyJqpy7Q4dpVtfwcAwzuavoQjIYQQIrRRYQHMY6mvCSH6NFoOp6+xHT+GSKA0F2CiRvB88C3NYAm1rEl1refsAr9L9uw4AFN/OSXSo1u+nSm3sP8Sv1UPVJaLGENqPScsIQsgpBnbc7s4sr2W+pB6OTNAWtlMGi2Hvxh7UUjg+E90U6ZMwc8//4xff/0VjRo1QlZWFrKysvDw4ePP+ujRoxEVFaX794IFC/Dnn38iPT0dycnJeO2113Dt2jVMnDjRHC9BcBOeYQ8cHcu4R9kmDDRaDr8LmGHsRUETJrM3neE1Pnp4B5FmQgghhJhOt+busGE8jr9Vj569hMhRUnouyhhPybsEuok7GUaiBk08PDygUqmQnZ2td392djZ8fHyYHqOu0gyWUMua6Aue/wcelYn7HH+cz6aLDQZotBymbkjhvR2V5SLG6BrohkYObOWmTNF81xKyAAofsT336RvsK3yNJfVyZoC0spmOZ+ShWM22HMBcQSZTWrVqFQoKCtCrVy/4+vrqbhs3btSNuX79OjIzM3X/vnfvHiZNmoQ2bdpgwIABKCwsREJCAtq2bWuOlyC4sd35ZdtQtkndjmfkIa+47v1mI3u276ITV6lxa100Wg5bU9gDVaGBjWFnI7l1eoQQQghvKqUCwc1cmcaeu10g7mQIsTDrE68yj+3egq0Mt9hEPYK1s7NDp06dEB8fr7tPq9UiPj4eYWFhTI9RV2kGS6llTco9Ex2H/EemCWbM3sxvFZw16Lt4P+9tlr8aIom0OGJ5VEoFhoewZSiJ3XzXUrIAFGB78oMX74qeOSH1cmaAtLKZsgofMY1zslOZLchkShzHGbyNHTtWN+bgwYOIjY3V/furr77CtWvXUFJSgqysLOzatQshISGmn7xI+DaEp2yTurF+7jr6s/3eYxOvUonXOiSk5fAqr7t+Ans/H0IIIUTqmjZ2YhpHPeoIeUyj5RCfml33QAB2KoVkzpdFX/Yza9YsrFmzBuvWrUNqaiomT56M4uJijBs3DoD1lWawZuPWJuFmgXGryZs42+HVLvxKr209fZtOfCvZnnwTV/PYLi5UCPFzweCgJiLNiFgD1oPKvOISUedhKVkAYYwHB4/KtKI3F7SEcmZSymbKuc/2Hn6+vQ8Foq0Yn4bwABC15W+RZiIPeUVsn7tmbmzfRfkPSk3SY8uSfbzjPPPYFp5OlGVCCCFEVp5o7Mg8dl1ChogzIcRyJKXnopQxhhjU1EUy58uiH8W+8sorWLx4MebOnYvg4GCkpKRgz549uubw1laawVp9svMcDlw27gLftyOCkfBBP0S/2AG9n2KPNnIAlsVdNuo55Uaj5TD9N/6ZN5smdxdhNsSa5DOWujrFWDrLWJaSBdCtuTvsGS8wJaazZc4YyxLKmUkpm+neA7agjDeP7BgiP3yzTbYk36IFILVwa8D2uQ5u6gpXxl5Qd+7zW2BiTdRlWqTdLWYeP39QexFnQwghhJheeAv2BXY7BOy7RoglS7jCfu1CKv1MABM1gp86daqu3MKxY8cQGhqq+5m1lWawRrv/vo0fj1wzatsrnw3AoEoNyGPGd4ML46piAFh9+ApdbAAQuvBP3ttQWS4iBKmUm7KULACVUoHerdjqd4q5a7OUcmaAdLKZ0u8WMY1T0G7V6vHJNqEFILXzYgxC+rg6Yky4P9NYjwbUDL4mo35g77NjowTCn5R//yZCCCHWpVtzd6gYr6ReyLxP16MIAXDrHnupbKn0MwFMFDQh1kuj5fDWr6d5b6cAcPXzgQYvXp6Y05/5cdQaTvQSNlL38Y6zyCku47VNn9aeVJaLCEIq5aYsKQugkz/byorGImZOWEo5M0Aa2UwaLYcjaXeZxrKudifyZWejRHBT9h583x5MoxPumrD+WjigayBjFiEFNg1Sl2lx7Cr7fvStni1p8Q0hhBDZUSkViGjtxTS2TEvXowgBgJv3HjCNk1I/E4CCJkRkxmQ4AEDaZwNq/JmdjRItPRswP9a6ROutI6ku0yLm6HVe23g2sMXasV1FmhGxNlIpN8W6ul8KWQCs5WZYxxnDUsqZAezZTEf+yRXtwvPxjDwUlbAVafVoSKvYCfDuc22Yx2o5yjapSQ5jBllOcQlyGPufsI6zNnyyTBQAZvR7SrzJEEIIIWY0OjyQeezRK2wLqwiRK42WQ8r1fKaxUupnAlDQhIjImAwHAFj57451fkjmDW7H/Hj7RS77I2WdP93Le5ukD/uJMBNiraRSbiorny0dVApZAKxlpMQsN2Up5cwA9mymopIy0Ro8swaZAMDHhb15JJGvbs3dYcvjKJyyTQxjDUJ6NLRnLrtF5bmq45tlMiykidm/GwghhBCxdGvuDhvGr7kTIp1/EGIpktJzUcZ4GiOlfiYABU2ISIzJcACACc8EYkAH3zrHhbf0YH7zWmtK5Mc7zqLwEdvK5wrLRgTTSS4RnLnLTWm0HHafy2IaK4UsACmUm7KkcmbdmrvDkfHqc1YBey1VPvIYV6Y7O9igq8QOBIl5qJQKTO7Zgnk8ZZsYdjyD8fiOA3vZLToMqoZPlgkAfP5ikEgzIYQQQsxPpVQgxL8x09jTN/Jp4QuxausTrzKPlVI/E4CCJkQkxmQ49G7lgY8GtWUaq1IqMKwje8+Nv9Lu8J6PJTMmaBXo7oQhwU+INCPrtWLFCgQEBMDBwQGhoaE4fvx4rePz8/MxZcoU+Pr6wt7eHk899RR2795totmKg7WMFGudS76S0nPxsJQtgCiFLAAplJuypHJmKqUCA9r7MI0Vq+yOK2PAb1jIExSYJjoz+rXidX1+9eErdNJdiUbLYV3CNaaxOcUluMOYEcY6zlrwzTIJDWwMO8aynIQQQoilYl0IVaYFEv4Rrww1IVKm0XKIT81mGiu1fiYABU2ICAZ+fZB3hkPTxg6IGRfKa5vo4eyr2A5ctK46kl0+5d9LZt87vYSfiJXbuHEjZs2ahXnz5iE5ORlBQUGIjIzEnTuGg3hqtRr9+vXD1atXsWnTJly6dAlr1qzBE09YdjArnzFrYevpW6JcEExkzDRraC+NLABJlJuyoHJmAODjyhbsYs3i4SuvmO093rSxkyjPTyyTSqnAtN7s2SZqjXVmztbkeEYe82faq5ED8+eUdZy1iNpyhtf49RO6iTQTQgghRDrCW3gwj/1mP2ULE+uUlJ4LxvWrkutnAlDQhAhsQuwxnM8q5rVNQzsljrzfl/dz2dko4d2IbXVv2t0iq1mdOT4mCQWPNLy2obJc4li6dCkmTZqEcePGoW3btli9ejWcnJywdu1ag+PXrl2LvLw8bNu2Dd27d0dAQAB69uyJoCDLLnPhxljyqvCROEEADmyf/WeedJfE58Dc5aYsrZwZAHCMu3fWcXydusb2vmUte0asB99sk0V7U0Wbi6W5c58tI8TVyRZdA92Yv4tuMgaNrYFGy2FL8m3m8ZRlQgghxFp0a+4O1lPHZCrRRaxUwhX2LCup9TMBKGhCBLQz5RbiL/JPOzwz/zmjnzOMMbqv0cIqVmfuTLmF/Zf4vU4qyyUOtVqNU6dOISIiQnefUqlEREQEEhMTDW6zfft2hIWFYcqUKfD29kb79u3x2WefQaOpOQhWUlKCwsJCvZvU+PDoeyFGEIA1G6JTM7a6tGIzd7kpSytnBrD3w8kWoeyORsvhL8aUewnE5IjE8M02OXOzEOoyftm8csUatB0bFgCVUsH8XbQ95TZd2PifZXGXGJcdlKMsE0IIIdZCpVSgs78r01hruR5FSFV8FsVKrZ8JQEETIhCNlsO0DSm8t6tvhsOLHZsyj12XmGH081gCjZbDVCP+BlSWSxw5OTnQaDTw9vbWu9/b2xtZWYZX8aenp2PTpk3QaDTYvXs3PvroIyxZsgSffvppjc8THR0NFxcX3c3Pz0/Q1yGEroFuaOSgYhorRlkU1l4pbg2kkTUBmLfclKWVMwMAj0Zsf7s/zmUJfjH0eEYeitVs2X1hzdnT+In14JttMupHfk25ZYvxo9wloHw/1TXQDW4N6g6i5xarRSt9aEk0Wg7LD1xhHh/U1JmyTAghhFiVaX2eYh579Ip1lYwnRKPlcPoaW188KfYzAShoQgTy8qojvFaiAcJkOIS39GB+E++/eFfWKwdDF/LvY7L81RBJlCMi5bRaLby8vPD999+jU6dOeOWVV/Dhhx9i9erVNW4TFRWFgoIC3e3GjRsmnDEblVKB4SFsn3XWhtqsNFoOW07fYhrL2nvFFMxZbsrSypkB7NlMD9QawVd5ZTFmrzjZqSR5IEjMT6VUYHjHJszjj2Xco2wTlDd35zNOpVRgGONxJ2vpLznjm2XyXmQb0eZCCCGESBGf61F7z7OVPyZELpLSc1HGeDDZu7WXZK4tVEZBE1JvO1NuIfkG/5JAQmQ4qJQKtG/qzDS2TCvfBqof7ziLnOIyXtuE+LlgcBD7RRrCj4eHB1QqFbKzs/Xuz87Oho+P4dJLvr6+eOqpp6BSPc7KaNOmDbKysqBWG76gb29vD2dnZ72bFDVza8A0TujAxfGMPNxn7PHj1kDYgE19mLPclKWVMwPKV5A3sGPLZkpM519GsjZ5jCXSBrT3keSBIJGG6OH8elfN3syvObccsZbnqjyuT2vvWkZW2kZCmYfmwDfLRKqrAwkhhBAxqZQKdApgOye6cvcBLXohVuWnBPZqP6O7BYg3kXqgoAmpF2NLQgmZ4TC4A3u2ihxTItVlWsQcvc57u02Tu4swG1LBzs4OnTp1Qnx8vO4+rVaL+Ph4hIWFGdyme/fuSEtLg1b7+GDq8uXL8PX1hZ2ddC7oG4M1g+R6HlspLVasWQCAdPpzAOzlpuJT7wieQcdapkxK5cxUSgV6PMlW+krohEPW9zZrDy5inexslAhlPOkGgK2nqe8GcxpE5XGsh55WHt/km2Xy5rMtKChMCCHEKvEpV7yOx0VkQiyZRsth38U7TGNtlNJdfENBE1IvxpSE6tvaS9AMhzHhAcxjT8iwRnWXT6ksl1TNmjULa9aswbp165CamorJkyejuLgY48aNAwCMHj0aUVFRuvGTJ09GXl4eZsyYgcuXL2PXrl347LPPMGXKFHO9BMGwZpBsPX1L0AuBrFkAzg7S6c8BsJebyn9YKnjt/cQrbJkYUipnBgCd/Nn+fqxZPKxY+/CI0a+HyMv6iexNtDkAy+IuizcZC8C3PBcA5DB+J7COkyONlsOKg+xZJkoFMKMfe013QgghRE7CeSyM2nHmtogzIUQ6ktJzoWFMrGrr20iy1ycpaEKMNj4miXdJqAA3R/w4toug87CzUaKFpxPT2DM3C2S1MvPjHWdRwFh6qEKf1p5UlstEXnnlFSxevBhz585FcHAwUlJSsGfPHl1z+OvXryMzM1M33s/PD3v37sWJEyfQoUMHTJ8+HTNmzMDs2bPN9RIE48ZYRqXwUZmgQQDWLIBhIU9I6ou6a6AbXBxsmMZmFTwU7Hk1Wg67zmbWPRDSKmcGsM9H6Hmfusb2fr0nsSATkR47GyVaeLCVMgSA1YevyOqYhi9jynOxlt2y5vJcCWk50PB4W03t1VJS35+EEEKIKXVr7g4V45XVC5n3rfrYjViPBMaFmAAkfX2SgibEKDtTbmH/Jf79QeL/01uE2QDPtfdlGqfWyKeviTFluTwb2GLt2K4izYgYMnXqVFy7dg0lJSU4duwYQkNDdT87ePAgYmNj9caHhYUhKSkJjx49wpUrV/DBBx/o9TixVKyZE4CwQQDWbIhmbmyBV1NRKRWIaOPFNFbIFdFJ6bl4WMq2JERK5cwA9r81ayYNC42Ww1//sD0eXVMkLOa/0I55rJyOaYxC5blEsXw/ewYTZZkQQgixdiqlAhGt2c7b5Nxnl5DK+CyEHRMeKOJM6oeCJoQ3KfQxqYpPSqRc+pp0/nQv722SPuwnwkwIqVvXQDc0cmAL/ghZxog104R1nCn5uLIFJfIflgr2nImMB/EN7aVVzgxgz2bafS5LsBVexzPyUKxmy/YLa049TUjdwlt6QMXjUGnR3lTxJiNx+y9mM42j8lzsNFoOx6/mM4+nLBNCCCEEGM3jou9PSVfFmwghEqDRcjh19R7T2BaeTrCzkW5oQrozI5L10qojvLcRuyRUt+busGE8Z5NDX5PxMUkofMRYIPB/qI8JMSeVUoHhIU8wjRUygGHJ/SY4xuv6rOOYHotx6fYzT7pLbn/Cms30QK0RbIVXVuEjpnFOdirJNrcj0qJSKjClVwvm8WduFkJdxu94QA40Wg5bU24xjfVq9HjfQOW5arcs7hLzWAUoy4QQQggB+F2POnDxDpXoIrKWkJYD1rOTyHY+os6lvihoQnjZmXILp28U8trGFCWhVEoFQvwbM4219L4mxpRGC/FzkXSdQGIdmrmx1eoXssG4JfebYG1Yns144Z6Fq6Mt07hOzdj2t6bUNdANDezYspkS04Up0ZXHuBp9QHsfyQWZiHTN6NeKV3WoUT8miTYXqTqekYe84rqz7Nwb2OlnxVF5rhpptByWH2BvAN+vrRft1wghhBDwux5l9eVViezxKfXavYWniDOpPwqaEGbGluUyVUko1lIxlvwlpdFymGbE32DT5O7CT4YQnlgzSK7nPRDk+Sy934RHI7aVzvGpwq1WcmNcXc06zpRUSgV6PMlWAkuouDnrezqMRwlJQlRKBYZ3ZF/ocCzjntVlm9y5zxYsHhLcRO/CPmvZrfhUttJfcrIs7hJzmxgAGBMm3frThBBCiKnxKV0sl5LxhFSl0XI4eS2faaxKCclXY6CgCWHWd/F+3tuYsiSUNfQ1eXnVEV4ntACwbEQwrQQkksCaQbL19C1BggCW3m+CtdxU/sNSXo3WasPaJF3IbCAhdfJnO1lhzeKpiyWXfyPSFj08iNd4a8s28WDsYdS3jbfevyuX6qrN7ym3LTormS++WSZ2KoXkT3IJIYQQU+JzPUoOJeMJMSQpPZd5gWJHP1fJX6ukoAlhsj35Jq7m8SsBY+qSUHLva7Iz5RaSeZZGC3R3wpBgtj4ShIiNtVF34aMyQYIAlt5vomugG1wcbJjGZhU8rPfzabQcdp3NZBrr1kC4vjNCYp2XUPNnLesmxfJvRNrsbJQIDWAvg2d12Sas8Ywq47oGusGtQd1lCHOL1YIFoy0B3yyTN59tIfmTXEIIIcSUujV3hy3jFdbTN/KtanEGsR7rE68yj53eR/q98ShoQuqk0XKY/tsZ3tuZuiSUnPuaGFsabd87vQSfCyHGYs2cAIQJAlh6vwmVUoGINl5MY1lLztQmKT0XD0vZLrr6uDjW+/nEwJoBw5pRUxcF49uGdRwhla2f2I3X+Nmb+R+rWaqcYrZ9XtVxKqUCwxgXk7CWALN0Gi2HVYfYs0yUCmoATwghhFSlUiqqZbjWpEwLJDCWkSbEUmi0HHOJWxslEM5YWtucKGhC6hS68E/e25iyLFdlcu1rYkl/A0Jq0jXQDY0c2Bp1C1HOSA79Jnxc2YIT+Q/rbohcl0TGfWJDexteNXtNiTWbafe5LEEC51n5bME9V8e6V7YTUhXfbJOtp62npBRreS5D4/q0Zrug4SHB3k1iSErPBWO8HAAwtVdLOr4khBBCDBgVFsA89hsezbIJsQR8jilDLKA0F0BBE1KH8TFJyCku47VNn9aeJi3LVZkc+5oY8zfo6Odqtr8BITVRKRUYHsK2wpc14FEbOfSb4Bivf7KOq/UxGIuzPPOku2QPcFizmR6oNfUOnGu0HPal3mEay3qBl5Cq+GSbcACWxVnJCbiR5bkAAKy7L2nu5gT35Z5U5rGUZUIIIYTUrFtzd7CeJiVTiS4iMwk8qjl0kegizKpMEjRZsWIFAgIC4ODggNDQUBw/frzW8f/973/RunVrODg44Omnn8bu3btNMU1Sxc6UW9h/id9FJc8Gtlg7tqtIM6qb3PqaGPM3UAD47+RwcSZESD01c2vANE6IRuOnrrF9xqXcb4K1YXk2Y/+W2rBmQ3Rqxr7y3dS6BrqhgR1bNlNiev1S4o9n5KHgEVtAW6rlzIj08c02+fZgmlWcgBtbngtgL2coRNlDqVOXaZFyk71fHmWZ8EfnwYQQUt3nn38OhUKBmTNn6u579OgRpkyZAnd3dzRs2BAvvvgisrPZSv1IhUqpQGd/V6axGi0sqvoJIXXh0w+wewtPEWciHNGDJhs3bsSsWbMwb948JCcnIygoCJGRkbhzx/DqzISEBLz66quYMGECTp8+jaFDh2Lo0KE4d+6c2FMllRjbQyPpw37CT4YHOfU1MfZv8A2V5SISxppBUt9ME42Ww1+MdWKl/HHxaMSWoRCfeqfe+zM3xlI0rOPMQaVUoAdjbdT67v6zGANVro62ki1nRiwDn2wTLWcd2Sb1Kc/FWnbLGspzjfohiXmsApRlwhedBxNCSHUnTpzAd999hw4dOujd//bbb2PHjh3473//i0OHDuH27dsYPny4mWZpvGk8mltbSvUTQuqi0XI4dfUe01g7lQLdWriLPCNhiB40Wbp0KSZNmoRx48ahbdu2WL16NZycnLB27VqD45ctW4bnnnsO7777Ltq0aYNPPvkEHTt2xLfffiv2VEklxvTQWDYiWBIX6+XS12TKryd5b2PO0miEsDBVo+7jGXkoVmuYxoY1l3BPE8ZyU/kPS3mt7DCE9XcuRBaQmDr5s30HsGbx1CSPcRV6RBsvSXw3mlp0dDS6dOmCRo0awcvLC0OHDsWlS5fq3I5WWVdnZ6NES0+2LD3ASrJNqDxXvanLtDjGeHILAMM7PmGV+7L6oPNgQgjRV1RUhJEjR2LNmjVo3PjxYteCggL8+OOPWLp0Kfr06YNOnTohJiYGCQkJSEpiD/BLQXhLD+ZDiPqevxEiFQlpOWBtkde7teWcH4saNFGr1Th16hQiIiIeP6FSiYiICCQmJhrcJjExUW88AERGRtY4ngjv4x1neffQCHR3wpBgtl4FYpNDXxN1mRZ7zrHVyq9g7tJohLAwVaNu1iwAJzuVpFc5dA10g4uDDdPYrAK2puSGaLQc4i6wpb+7Nah/vxkxsc6vvq+DNRsqjMd3kpwcOnQIU6ZMQVJSEuLi4lBaWor+/fujuLi4xm1olXXN5g1uxzzWGrJNqDxX/fHJMgGA6OEd6h5EdOg8mBBCqpsyZQoGDhxYbV936tQplJaW6t3funVrNGvWzOL2gSqlAk95N2Qam3JD2tVPCGG1fD/7ucfobgHiTURgogZNcnJyoNFo4O3trXe/t7c3srKyDG6TlZXFa3xJSQkKCwv1bsR46jItYo5e573dvnd6CT8ZI8mhr0nnT/fy3sbcpdEIYWGqRt2sWQAD2vtIepWDSqlAv7bedQ9E/Rray6k/B2smTH0zZlh/3/X5u1iyPXv2YOzYsWjXrh2CgoIQGxuL69ev49SpUzVuQ6usaxbe0gMqHruqlYfknW1yNafm4FtlXo2qf+cYuo91W7ngm2USGtgYdjYmaYUpG3QeTAgh+jZs2IDk5GRER0dX+1lWVhbs7Ozg6uqqd7+l7gObuTsxjSvTSrv6CSEsNFoOJ6/lM41VKSHpRatVWfzRb3R0NFxcXHQ3Pz8/c0/Jog1cdoj3Nssl1kPD0vuajI9JQuEj1sS2clL7GxBSE1M16pZTFgDrHOvTB0ZO/TlM1Tfn1DW2oPs9iZczM5WCggIAgJtbze8fY1ZZS/mEWUgqpQJTerVgHl+mBRIY+zpZGo2Ww/8dr3uBj6+Lg8H9VSf/xnX2slIqysfJ1exNZ3iNXz+Bva8OMR06DyaEWIobN25gxowZ+OWXX+DgIMyiBCnvA7sGsF8U/inpqngTIcQEktJzmfuFdvRztahrl6IGTTw8PKBSqZCdrV/yIzs7Gz4+Pga38fHx4TU+KioKBQUFutuNGzeEmbwV2plyC//cfcBrG6n20LDUviY7U25h/yV+85Hq34AQQ0zVqNtU2QamkMdYhoZ1nCE599m27WsB/TlM0TdHo+XwF+MFaYn/ukxCq9Vi5syZ6N69O9q3b1/jOL6rrAFpnzALbUa/VrzabHzDI03ekhzPyENWYd37rBFdmhncX526dq/O7xctVz5OjjRaDltSbjOPpywT49B5MCGEPHbq1CncuXMHHTt2hI2NDWxsbHDo0CF88803sLGxgbe3N9RqNfLz8/W2s9R94JjwAOaxBy7ekdxCXkL4OMpjodb0Pk+JOBPhiXoEbGdnh06dOiE+Pl53n1arRXx8PMLCwgxuExYWpjceAOLi4mocb29vD2dnZ70b4U+j5TB1QwqvbaTcQ8MS+5oY8zdoYKuU7N+AkJqYolG3qbINTCH/YSnTuFPXjb/Ax5oN4c1YXs2cWPvm7Es1/gTleEYeitUaprFhzaWfzSS2KVOm4Ny5c9iwYYPgjy3lE2ahqZQKTOvNnm1y8lq+LE/C79xny4wL8DBcGoN1e9ZxlmZZ3CVe4ynLxDh0HkwIIY/17dsXZ8+eRUpKiu7WuXNnjBw5Uvf/tra2evvAS5cu4fr16xa5D7SzUaKFJ1uJLqkt5CWEr/0Xa17gVplSAYQzLqCVCrbusvUwa9YsjBkzBp07d0bXrl3x9ddfo7i4GOPGjQMAjB49Gk888YSuruGMGTPQs2dPLFmyBAMHDsSGDRtw8uRJfP/992JP1aqFLvyT9zZS7qHRrbk7bJVAKUOVq9v50jgpNuZvcPKj/iLMhBBxmaJRN2sWgSVkmigY15Yf+ScXGi1nVCaIgnET1nHmxNo3J/9hKY5n5CHMiJqqrOXMnOxUFlWzVQxTp07Fzp07cfjwYTRt2rTWsXxXWQPlJ8z29myBMjmY0a8VvjlwhWksh/KG8LMiW4k7KRPzYAyM1jTOowHj9ozjLIlGy2HFQbb3D0BZJvVF58GEEFKuUaNG1bKNGzRoAHd3d939EyZMwKxZs+Dm5gZnZ2dMmzYNYWFh6NbNMoP3z7X3xQrGY7ajV+6iu4VdTCYEKD+2vJTN1mswwN1J8lUrqhL9KPiVV17B4sWLMXfuXAQHByMlJQV79uzRlV+4fv06MjMzdePDw8Px66+/4vvvv0dQUBA2bdqEbdu21VrOgdTP+Jgk5BSzNQCuML13S0m/2VVKBfq09mIa+7CUbbWwmD7ecZb33yDEzwWOjL0hCJESscsnabQcdp3NrHsg6heYMRXWi/pFJWU4nsHWZ6OqrPyHTONcHW2NenxT6hroBhcHtjUhWQVsr7uqvCK2cmYD2vtI+rtSTBzHYerUqdi6dSv279+PwMDAOrfhu8raGqmUCkS2ZTu+AYBvD8qwITzry6lpHOtHUoYf3YS0HGh4vB0oy6R+6DyYEELYffXVVxg0aBBefPFFPPvss/Dx8cGWLVvMPS2j8al+svc820p9QqQmKT2X+dD86SdcRJ2LGETPNAHKVxlOnTrV4M8OHjxY7b6XX34ZL7/8ssizIoBxPTRUSmBGP+nXoesc4I69F+7UOe7w5btGr84WgrpMi5ijdTc0rWrT5O4izIYQ8fEtn8T3s5mUnouHLGlmAHxcHHk9tjl0a+4OR1sl02syJgig0XLYl1r3vhJgX+FtTiqlAhFtvLD5dN01+3MYgx9VsZZ1C+NxsiQ3U6ZMwa+//orff/8djRo10vUlcXFxgaNj+eeOVlkbZ3R4INPxDVDem0Nu2SY5jP2bahrH+rmPT81G95by+gzP336eeWwLTyfKMhEAnQcTQohhVfeBDg4OWLFiBVasWGGeCQmMT/WTK3cfQF2mpe9dYnF+SshgHvtSR8vrPUmfSCtmTA8NAPj6lRCLWDnr0Yjt4t7DUq1Za0h2+ZR/Wa7lr1rG34AQQ/iWT+IrkfHz3NDeBl0D2fqrmJNKqcDAp32ZxuYV8y83djwjDwWP2DLdLCHIBAA+rmzzZO0XU207xmwpSyj/JpZVq1ahoKAAvXr1gq+vr+62ceNG3RhaZW2cipNwVnLLNqlveS6vRmzfQb+n3JbV701dpsWVHLbyCQAwfxB97gghhBBjqZQK9G3jzTx+HY+Lz4RIgUbLYd9FtoVcltjPBKCgiVV7adUR3tv0ae2JwUFNRJiN8FgvzALmawY/PiYJBY/4lQezpL8BIYaIXT6JY0wQfeZJd4sJPrJmLBjT2J61P4ero61FBJkAgGO8zsk6rirW37Mxfw+54DjO4G3s2LG6MQcPHkRsbKzedi+//DIuXbqEkpISnDt3DgMGDDDtxC2ASqnA5J7sDeErsk1ko57luboGusGtQd2lBnOL1UaXPJSiUT8kMY+11BNbQgghREpGhQUwj91xpu4seUKkJCk9Fxq2Ah9o38TZYq69VEZBEyu1M+UWTt8o5LWNZwNbrB3bVaQZCa9roBvsbdg+lCfMcFJsTGk0S/sbEGKISqlAv7Zsq26MyZxg7bvRqVlj3o9tLmJmNrD254ho42UxBzqNGYMV2YwBo6pY++1Yc6YJEdeMfq14tdxYffiKbLIm6lueS6VUYFjwE0yPcee+cfsIqVGXaXHs6j3m8UODm1jM/p4QQgiRqm7N3aFivOp67nahbI7ViHVYn3iVeaylLvymoIkVMrYsV9KH/YSfjIhUSgU6NHVlGnvmZoFJv6Cs5W9ASE3EzJxwa8BWuoV1nBSImdkgx/4crOUZ4//XN4cPjZZD3IVsprFuDaw304SIS6VUYFpv9mwTtYYzaylSIdW3PBcA9GnNFrj3sKDvidpEbTnDa/znLwaJNBNCCCHEeqiUCkS09mIaq+WAhH/YFmYRYm4aLYf4VLZzYgAYEx4o4mzEQ0ETKxS60Hp6aLCWkjH1xYS+i/fz3sZS/waEGCJm5oQcswBY58r62o15bEv6fYnZN0eOPWCIZeKbbbJob6poczGpepbnAgDmX5wMDrs0Wg5bktlLfoQGNqZGtIQQQohARvO4WPzNfhmVUyWylpSei1LG0lwtPJ0s9tjSMmdNjPbxjrPIKWa72FPBkntohPNYGW2qvibbk2/iah6/cg8hfi4W+zcgxBCxMifkmgXgxriyep8RmRNy7M8hZt8cOfaAIZaJb7bJmZuFUJcxnt1I2P6LbPv42sp45TCWJWQdJ2XL4i4xx5kAYP2EbqLNhRBCCLE23Zq7g3Xt66nr+VSii1gEPqW5Itv5iDcRkVHQxIqoy7SIOXqd1zYuDiqL7qHRrbk7VIxjTdHsU6PlMP03fiUSAGDT5O4izIYQ88ljrEnPOq6CXLMAxMyckGNmjph9c3Lus70n+1pQDxhiufhmm4z6kb0ZuBRptBy2ptxiGuvVqOb9JmvZLUsvz6XRclh+4Arz+KCmzha7EpAQQgiRIpVSgc7+rkxjqUQXsQR8S3N1b+Ep4mzERUfFVqTHF/t4b3NiTn8RZmI6KqUCLb0bMo1NuSF+XxNrKo1GSG3yH5YyjTt1nb1xLSDfLACxMifkmpkDiNc35x5j8MibMdBFSH2olAoM78ieiXos455FZ5scz8hDXnHd3x/uDexq38dbSXkuvlkm70W2EW0uhBBCiLWa1ucp5rFUootIHZ/SXHYqBbq1cBd3QiKioImV2J58E9n32S5SVhjfPUAWq82auTsxjSvTitvXZHxMklWVRiOkNgrGK1FH/snlFczMYyylEmFhWQBiZU7INTMHEK9Xi4LxbcM6jpD6ih7Or2m3JWeb3LnPFhgfEtyk1n28NZTn4ptlYukntYQQQohUhbf0YL74mnyDSnQRaUvg0Ue1d2vLuu5SleVfESd1MqYklFdDO8wd3E6kGZlW1wD2E0Cx+prsTLmF/Zf4BWQsvTQaIbUJY7wwU1RSxqvcFGvWAGsWgpSIkTkh18wcQLxeLVn5bJk8ro62vB6XEGPZ2SgRGtCYebwlZ5vUVnKrsn5ta6+dzPo4rOOkiG+WyZvPtrDok1pCCCFEqlRKBToHuDKN1Wgh6mJeQuprz7lM5rGjuwWINxEToKCJFTCmJFTiBxEizMQ8xoQHMI89IUJfE42Ww9QNKby3s/TSaITUpltzdzjasn0F8Sk3JVZ2gRSI8drkmpkDsP8eWHu6AOX7832pd5jGejS07F4IxLKsn8iveffszfz7q0lBJ//GdTZTVSrKx5nicaRKo+Ww6hB7lolSAczox146hBBCCCH88CnRtS4xQ8SZEGI8dZkWV+4+YBorhyxmCprInDEloZaNCLa4i2O1sbNRooUnW4muMzeF72tiTNBKLqXRCLBixQoEBATAwcEBoaGhOH78ONN2GzZsgEKhwNChQ8WdoJmolAoMfNqXaSyfclNiZRdIAeucr+exHcTweUxLzMxxYwxa7Eu9w7zfl3M5M2LZ+GabbD192yJLP5y6dg91TVvLlY8zxeNIFZ9a0wAwtVdLWR37E0IIIVLDp0TX/ot3LfI4jcjfuoSrzGMtvTQXQEETWTOmJFSguxOGBD8h0ozM57n2bBdn1Rph+5oYE7SSU2k0a7dx40bMmjUL8+bNQ3JyMoKCghAZGYk7d2pfqX716lX85z//QY8ePUw0U/MQo9wUa9aAnDNNtp6+xXyQzRqQ4hO4kgofxkbs+Q9LmUvAybmcGbF8fLJNOADL4iyv0ShrT5O6xrE+TtyFLKZxUvPlnlTmsZRlQgghhIhPpVSgfVNnprFi99slxFg/J11lHmvppbkACprIlrElofa900vwuUhBOI9V0kKlQhoTtALkVRrN2i1duhSTJk3CuHHj0LZtW6xevRpOTk5Yu3ZtjdtoNBqMHDkSH3/8MZo3b27C2Zqe0OWmNFoOcReymca6NbC8TBPWzInCR+x9YO4x/m5Zx0lJ10A3uDjYMI1lLQEn53JmxPLxzTZZeSjN4lYxspa9q2sca6+S31MsLyNHXaZFys1C5vGUZUIIIYSYxuAO7AuUqUQXkRp1mRbX8tjOm22Ull+aC6CgiWz1Xbyf9zZyK8tVWbfm7lAxvjQhUiGNDVotfzVEtn8Da6NWq3Hq1ClERDwOgimVSkRERCAxMbHG7RYsWAAvLy9MmDCB6XlKSkpQWFiod7MUQpfSknvpJNbMCYA9CKBg3N2wjpMSlVKBfm29mcayZtLIuZwZkQc+2SZlWiDhH/aePpLAenhWx7iugW5wa2Bb58PkFquZg9BSMeqHJOaxClCWCSGEEGIqfPrtUokuIjV8SnP1ae0pi2ubFDSRoe3JN3E1j63sQAW5luWqoFIq0O4J06VCGtPHpE9rTwwOalKv5yXSkZOTA41GA29v/Yu23t7eyMoyXO7jyJEj+PHHH7FmzRrm54mOjoaLi4vu5ufnV695m5LQjbrlXjqpa6AbGjmomMayBgGy8tmCK66OdV9clCKhS8AJnR1FiNDsbJRo6dmAefz8nedEnI3wcorZsr3qGqdSKjCM8biXtZSXFKjLtDh2lb0Py/COT8jihJYQQgixBHY2Svi7sS3eoxJdRGr4lOYaExYo3kRMiIImMqPRcpj+2xne28m1LFdlpkqFNKaPiWcDW6wd29Xo5ySW7/79+xg1ahTWrFkDDw/2VepRUVEoKCjQ3W7cuCHiLIUldKNuuZdOUikVGB7Cth9jCQJotBz2pdbeX6cCa0kcqRE6yCF0dhQhYpjHoy/albsPoC7j0THczIQqzwUAfVqzZaJ5NLCc/R+fLBMAiB7eQaSZEEIIIcSQ13j0eaASXUQqrLE0F0BBE9kxJsPBWkpCmSIV0tg+Jkkf9uO9DZE2Dw8PqFQqZGfr99jIzs6Gj49PtfFXrlzB1atXMXjwYNjY2MDGxgY//fQTtm/fDhsbG1y5csXg89jb28PZ2VnvZimEbtRtDaWTmrmxrSBnCQLIvZwZIHyQgzXriTJNiDmFt/QAn6O6UT/yu9BuVgKV5wIA5l+ShRwi880yCQ1sDDsbOhUkhBBCTIlKdBFLZI2luQAKmsjKxzvO8s5wsKaSUGKnQlIfE1KZnZ0dOnXqhPj4eN19Wq0W8fHxCAsLqza+devWOHv2LFJSUnS3F154Ab1790ZKSopFld1iJXSjbmsonSRkEEDu5cwAYUvAabQc4i5k1zkOANwaUKYJMZ/y0lPsx3bHMu5ZTLbJ/otsn0GWMl45jNmJrOPMLfYov9Wo6yew978hhBBCiDCoRBexRNZYmgugoIlsqMu0iDl6ndc21lgSik8q5KK9qbweO2j+Hp6zsa6glTWaNWsW1qxZg3Xr1iE1NRWTJ09GcXExxo0bBwAYPXo0oqKiAAAODg5o37693s3V1RWNGjVC+/btYWcnv4uwQjfqtobSSUIGAeRezgwQtgScNWTmEPn4/KUgXuOjtvwt0kyEo9Fy2Jpyi2msV6O6MxlZy25ZSnmuH4+wB00oy4QQQggxHz7Xpf5KYyunTIhYrLU0F0BBE9no8cU+3ttYY0koPqmQZ24WMq+87B4dhyI1v1Wa1hi0sjavvPIKFi9ejLlz5yI4OBgpKSnYs2ePrjn89evXkZmZaeZZmpeQjbqtoXSSkEEAayhnJmQJOGvIzCHyYWejRGhAY+bxW5JvSb78w/GMPOQVl9Y5zr2BHdtnUEbludRlWmTfZ8+IoSwTQgghxHz4XJc6cPGueBMhhIG1luYCKGgiC9uTbyL7ft0nkZUtGxEsqzcyKz6pkADbyssBXx/ErQL+F2GtMWhljaZOnYpr166hpKQEx44dQ2hoqO5nBw8eRGxsbI3bxsbGYtu2beJP0oyEKqllLaWThAwCWEM5MyFLwFlDZg6Rl/UT2S+McwCWxV0WbzICuHOfLXA5JLgJ02dQTuW5+DSAb+HpRFkmhBBCiBnZ2Sjh3YjtnPSfO0WSX9hC5M1aS3MBFDSxeBoth+m/neG1TaC7E4YEPyHSjKSPTyrk5jpWXg5cdggXsop5z8Fag1aEVCVUSS1rKZ0kZBDAGsqZCVkCzhoyc4i82NkoEdzUmXn8twfTJH1S7sGYade3DdtnXi7lufg2gJ8/qL2IsyGEEEIIC9ZzBi0HJPzDVlGBEKFZc2kugIImFi904Z+8t9n3Ti/hJ2JB+KRCAsC0X5MN3j9g2SGczyzi/fztn3C26qAVIZUJ1aPDWkonCRkEsIZyZoBwJeCsITOHyM+7z7VhHqvlJJ5twhrPYR0nk/JcfLJMlAog/EkK7BJCCCHm9mLHpsxj5+88J+JMCKmZNZfmAkQOmuTl5WHkyJFwdnaGq6srJkyYgKKi2i8y9+rVCwqFQu/25ptvijlNizU+Jgk5xWwrqytQhgP/lZe7z2VV623SPXofLhgRMGlkr8LOaT14b0eIXAnVoyOHsZZ7XxmUThIiCGAt5cwAIK+Y7b1R1zhryMwh8tOtuTtseRztSznbJIfxs8w8jrHsVnwq277SHPhmmQxlLF1GCCGEEHGFt/RgviB75e4D5n67hAhpx5lbzGPlVpoLEDloMnLkSJw/fx5xcXHYuXMnDh8+jNdff73O7SZNmoTMzEzdbdGiRWJO0yLtTLmF/ZdyeW1j7WW5KuOz8hIAnp73B4Dyk9OWH+zCrQLj6lunzIs0ajtC5EqoHh33GFf3ezM+n5QJkfFgLeXMgPL3DotT12u/8GgtmTlEXlRKBSb3bME8XsrZJqzluVjHeTVi+z74PeW2ZANJfLJMAODzF4NEmgkhhBBC+FApFegc4Mo8fl1ChniTIcQAjZbDuduFTGNVCsiuNBcgYtAkNTUVe/bswQ8//IDQ0FA888wzWL58OTZs2IDbt2/Xuq2TkxN8fHx0N2dn9qwAa6DRcpi6IYX3dtZelqsyvisvSzRAwOxdeGrOHzA2wL/y3x1pdR8hVQjVo0PB+NFiHSdlQmQ8WEs5MwBQMNbWOfJPbo0XRq0pM4fIz4x+rXhVmFp9+Io0gwQCl+fqGugGtwa2dY7LLVbXGrQ3F75ZJqGBjakBPCGEECIh0/o8xTz256RrIs6EkOqS0nPBekrQromzLK93inbknJiYCFdXV3Tu3Fl3X0REBJRKJY4dO1brtr/88gs8PDzQvn17REVF4cGDB2JN0yL1Xbyf9zZUlksf35WX9TXhmUAM6OBrsucjxFII1aMjK5+tOZmrY90XyKROiD4weYxlaSJkUc6MbcVLUUlZjRdGrSkzh8iPSqnAtN7sxzxqDYekK/yymU1B6PJcKqUCwxgzsO/cZws0m1LUljO8xq+f0E2kmRBCCCHEGHxKdF3Le0gluohJ/cQju2lwUBMRZ2I+ogVNsrKy4OXlpXefjY0N3NzckJWVVeN2//73v/Hzzz/jwIEDiIqKwvr16/Haa6/VOL6kpASFhYV6NznbnnwTV/P4nbhRWS7DZvRrJW59uv/p08oTHw1qa4JnIsQy1bdHh0bLYV/qHabHYC3bImVC9IFhzVZh/dtIWbfm7nBkTC2sKZvJmjJziDzxzTZZtDdVtLkYS+jyXADQpzVb0N6jgbS+OzRaDluSa8/cr4yyTAghhBDpKV9A6FX3wP+J2vK3iLMh5LHySgts11gAYEy4/PqZAEYETWbPnl2tUXvV28WLF42e0Ouvv47IyEg8/fTTGDlyJH766Sds3boVV65cMTg+OjoaLi4uupufn5/Rzy11Gi2H6b/xW1UGUFmumqiUCnwzIljU52jfpBHWjusq6nMQYunq26PD2rIAhOgDI0RfFEuhUiow8Gm2TL+aspmsKTOHyBPfbJMzNwult5pR4PJcAMAcSZLYx3pZ3CVeL5OyTAghhBBpGs3jYrOU+6wReUlIywHrmYC/m6NsF+fwflXvvPMOUlNTa701b94cPj4+uHNHPypVVlaGvLw8+Pj4MD9faGgoACAtLc3gz6OiolBQUKC73bhxg+9LshihC//kvc3yV0PoAk4tBgU/gRA/cXrmtPNtiJ3TnxXlsQmRk/r26LC2LAAh+sAI0RfFktQ3m8maMnOIfPHNNhn1I78m42ITujwXAOQwBkRZx5mCRsth+QHDi8kMCWrqLNsTWUIIIcTSdWvuDhXj13SZVpolVIn8LN9/mXnsa938RZyJefE+gvb09ETr1q1rvdnZ2SEsLAz5+fk4deqUbtv9+/dDq9XqAiEsUlJSAAC+voZXidrb28PZ2VnvJkcf7ziLnGK2ldQV+rT2lG1dOSFtmvyM4I/Z3rchds3oKfjjEiJH9e3RYW1ZAEL0gamt30llcsg0AeqfWWNNmTlCOnz4MAYPHowmTZpAoVBg27ZttY4/ePCgwQzm2sq6EnYqpQLDO7IfFx7LuCepbBMxynOxlt2SUnkuvlkm70W2EW0uhBBCCKkflVKBiNbsJbp+Sroq3mQIQfkCnZNX85nHy7U0FyBiT5M2bdrgueeew6RJk3D8+HEcPXoUU6dOxYgRI9CkSfkJ261bt9C6dWscP34cAHDlyhV88sknOHXqFK5evYrt27dj9OjRePbZZ9GhQwexpip56jItYo5e57WNZwNbrB1LZaFYqJQKfCtgma4+rTywkwImhDCrb48Oa8wCqE/mRHl90mym7d0ayCPTpL6ZNdaWmSOU4uJiBAUFYcWKFby2u3TpEjIzM3W3qj3yiPGihwfxGi+pbBMqz8U7y8ROpUC3Fu4izogQQggh9cWnRNeBizX3rSRECHxKc7XwdJJ1RrOor+yXX35B69at0bdvXwwYMADPPPMMvv/+e93PS0tLcenSJTx48AAAYGdnh3379qF///5o3bo13nnnHbz44ovYsWOHmNOUvM6f7uW9TdKH/USYiXwNCn4CEW086/04E57xx9px7JlUhJD69+iwxiyA+rxma+sBA9Q/m8naMnOE8vzzz+PTTz/FsGHDeG3n5eUFHx8f3U2plO+BuKnZ2SgRGtCYebyUsk32X2QL9sq5PBffLJM3n20hiwxLQgghRM66NXeHLePhrlpDJbqIuD7ecZ557PxB7UWcifmxFUU3kpubG3799dcafx4QEACOe3zo7+fnh0OHDok5JYvz8Y6zKHzE72R12YhgOkEywg9jumLiuhPYl3qn7sEGrPx3CAZ0oHJohPBV0aOD5UK+oR4d1pgFUJ/XbG09YAD+2UyVv0OtMTPH3IKDg1FSUoL27dtj/vz56N69e41jS0pKUFLy+IJ2YWGhKaZo0dZP7Ian5vzBPD7maDre6NlSxBnVTaPlsDXlFtNYr0ZsgXjAsspzabQcVh1izzJRKoAZ/Z4ScUaEEEIIEYJKqUDfNt7Yc57tnGNdYga6PymfKgpEOtRlWqTdLWYaq1QA4TJ/H9LSPQkzpixXoLsThgQ/IdKM5O+HMV2w/NUQXh+MyHaeuPLZAAqYEGKk+vbosMYsgPpkmlhbDxigftlM1piZYy6+vr5YvXo1Nm/ejM2bN8PPzw+9evVCcnJyjdtER0fDxcVFd/Pz8zPhjC0T32yTtUcyRJwNm+MZecgrLq1znHsDO37BXgsqz5WUnotSHuuopvZqKZt9OCGEECJ3o8ICmMfWVLaakPqK2nKGeWynZq6yP9akoImEdfn0T97b7Hunl/ATsTKDg5rgn88GIGZUZ7T2cqqWjmWrVOAJVwe8F9kKlz99Ht+N6ir7HQUhYjO2R4e1ZgHUJ9PEGnvAVGQzsaiazWSNmTnm0qpVK7zxxhvo1KkTwsPDsXbtWoSHh+Orr76qcZuoqCgUFBTobjdu3DDhjC3X+ondmMdm31ebvUTXnftsn8MhwU14HZOxlt2KT2X7nhHTl3tSmcdSlgkhhBBiWbo1d4cN4yGMlgMS/mFbOEgIK42Ww++nbzOPn95H/seaopbnIsYbH5OEgkcaXttQWS7hqJQK9G7njd7t2Fa/E0Lqx9jMCWvNAuDTo+PFTk2N2lZOmTkV2Uybkusu71M1m8kaM3OkpGvXrjhy5EiNP7e3t4e9vflLJ1kaOxslWng0wJUctvT7UT8mYeMb4SLPqmYejCX2+rbhd9zGWsrr95Tb+HBgW7N9xtVlWqTcZC89R1kmhBBCiGVRKRUYEtIEm5PZLlp/s/8yerSqf19eQiokpeeijDGByRpKcwGUaSJJO1NuYf8lfo2dqCwXIcSSGZs5wbr62NVJXlkAfHt0VGaNPWAAoPuTbCcVVX+31piZIyUpKSnw9fU19zRkaf4L7ZjHmr0hPGsFCp6VKroGusGtgW2d43KL1dVK95nS7E3spRIUoCwTQgghxBJFDw9iHnvqej6V6CKCWp94lXmstSwYpKCJxGi0HKZuSOG9HZXlIoRYMj6ZE5Wxrj4eGxYgqy/1+vTosMYeMADgxfheqTrOGjNzhFJUVISUlBSkpKQAADIyMpCSkoLr18v7tUVFRWH06NG68V9//TV+//13pKWl4dy5c5g5cyb279+PKVOmmGP6shfe0oNXq45RPyaJNpe65BSzZXyxjqugUiowjHHREWuQXmgaLYctKeylEoZ3fEJW33eEEEKItbCzUaKlZwOmsVSiiwhJo+V4laMdExYo4mykg4ImEhO6kH8fk+WvhtDJESHEohmdOcG4uKZLgHyyTADje3RYaw8YAEY3fLbWzBwhnDx5EiEhIQgJCQEAzJo1CyEhIZg7dy4AIDMzUxdAAQC1Wo133nkHTz/9NHr27IkzZ85g37596Nu3r1nmL3flAYMmzOPNmW3CGiBnHVdZn9ZsJb08GpinDNyyuEu8xkcP7yDSTAghhBAitnmD2TOB5+88J+JMiDVJSs9FKeNhvp1KgW4t3MWdkERQTxMJ+XjHWeQUs9XmrxDi54LBQewnvIQQIkV8MyfC/vclfYex3wTrOEthbI8Oa+0BAwB3GBu6Vx1nrZk5QujVqxc4rubIZmxsrN6/33vvPbz33nsiz4pU9vlLQbyyGMzW20Sk8lwAjA6omoJGy2HFwSvM41t4OuH/27v3uKiqvX/gn5nBAVEBLyCiJOANTQXURLTMDMNLlt1Od9Osnjxqlp56pMdbZemxq5rp81Rqdbqd+mXHzDAjzVLwgk5lqUcIwwsDEQmCIjKzf39wIFEua83sPTPs/Xm/XvMHsPbstWeG7+y91/p+l9WPc+KIiIiaq6HdO8AMQOT+dc5vZ1BZ5eR3/wXKKqow49092JldjHMXnRe2MJsQFuSPuxO74oGrYvi6XeDtnbnCba+JNUZpLoCZJj6jssqJtTvymm54kY+nDtOgN0REnuVq5oToIt2i7ZoT0TU0LsyAsAsOHIS01NcaMMClC7w3ZEf2n4Mkhs7MIUOw+pmRGNVWuL23sk20Ks8FAEWC3w+i7dS0M7sIDomBoIXX99WuMySluLgYd999N4KCghASEoIpU6agrKys0W1GjBgBk8lU5/Hwww97qMdERNpZvHgxrrjiCrRp0wZhYWGYMGECDh+um0lZUVGBadOmoX379mjdujVuueUWFBSIlwvSC4vZhEFRIcLtUz/5QbvO+LizlQ48/vF+9J33OaLmVD/6LtyMrUcuHTABgPNOBSdOVWDp5sPoOfcLRM35HLFzv8DYZdvxdT1rgRpF9TVvoXD7iUOitOuMj+GgiY+4YhHLchGRcdVkToi48Oa3kUsnubLWhujgkR4XdnOlBJyRM3PION55YIhUe29cnGtZnku07JY3ynM99dlPwm39zMDQHmKD6aS9u+++Gz/99BO2bNmCjRs3Yvv27XjooYea3O7BBx9Efn5+7WPp0qUe6C0Rkba++eYbTJs2DZmZmdiyZQvOnz+P6667DuXl5bVtHnvsMXz22Wf46KOP8M033+DkyZO4+eabvdhr75kxsqdw23/ZThrqZn9ZRRUmv5mB6Dmfo/f8NHy09yTKzrv+fBVVTvycfxr3v7UH3Z7chAFPf4nXtmZ7rSStN+zMLhLKbAIAP7NxSnMBLM/lE5767EeUVDikthkZG8qyXESkK0ndOgiVm7pwAMTIpZNcGTAS3UY0i6U5caUEnJEzc8g4arJNdh39Q6j9J/tOYOmtcR4dWN2d+7tYQx2V56qsciL7t/KmG/7HX6/urrvB7ubq4MGDSEtLw549ezBo0CAAwIoVKzB27Fi88MILiIho+BouMDAQ4eHhnuoqEZFHpKWl1fl53bp1CAsLQ1ZWFoYPH46SkhK8+eabeO+99zBy5EgAwNq1a9G7d29kZmZiyBC5CR7NnUyJriqngsyc3zFMxxMnHE4F2w8WYur7Waio0naAqPjMeSzdfBhLNx/GwK5BeP/BYbov4yUzSefG+AhDnW/q+51vBlwpyxXaqgXWTBqsUY+IiLxDNnPC6KWTRF+vCweWXMlO0QtXSsAZOTOHjEUm20QBsGzLv7XrzEUcTgVv7fxVqK0r5blcXe9Ia/e+kSnc1mwCZo4Sn5VK2srIyEBISEjtgAkAJCcnw2w2Y9euXY1u++6776JDhw7o27cvUlNTcebMmQbbnjt3DqWlpXUeRETNQUlJCQCgXbvqSUdZWVk4f/48kpOTa9vExsbisssuQ0ZGRr3PoecYWF2FIUy4/bfZ4qWVmpv/l3Uc3Z7chMnv7NV8wORiWb+WoufcL3Db6u90m3kiO0ln8c39NeyN7+GgiZcNWrRZepvM/xmlQU+IiLxLNnPC6KWTXCk3ZeRyZq6UgDNyZg4Zi9XPjPguQcLtX92W7bFSELtzi3HqrFjdhbA2YhllFxJd70i0nRoqq5zCmT8AMH0Es0x8id1uR1hY3Ztdfn5+aNeuHex2e4Pb3XXXXfjHP/6BrVu3IjU1Fe+88w7uueeeBtsvXrwYwcHBtY/IyEjVjoGISCtOpxOPPvoohg0bhr59q9fistvtsFqtCAkJqdO2Y8eODcZNvcfAiUOjhdtusOVr2BPvOFvpQM+5mzD7o++93RXsOVqCnnO/wH+9s1t3pdBSPxF/fbuFBuo+6+ZixjpaH3P/2kyUVsiNVnIdEyLSK9nMCaOXTpItNwUYu5wZID64UTNYYuTMHDKex0f3Fm7rVDyXbVJ4WjDWB7oW60UHoI+fOiv93K6SyTIxgVkmnjJnzpxLFmq/+HHo0CGXn/+hhx5CSkoK+vXrh7vvvhtvv/021q9fj5ycnHrbp6amoqSkpPZx7Ngxl/dNROQp06ZNw4EDB/DBBx+49Tx6j4FDYtoL37A9WVKhm0yIyionrn1xK3rPT0OlhzNLmrL5p9/Q7clN+HSvPj5rDqeCf+0/Kdx+4fV9NeyNb+KgiZdstJ3A14cF6zP/R0JkMNcxISLdks2cMHrpJNlyU0YvZwbID4IYOTOHjGdITHu0kLgy8FS2ieji7pOSolyK9aID0Bs8tNCqbJbJTQnGqi3tTbNnz8bBgwcbfcTExCA8PByFhXVLpVRVVaG4uFhqvZLExEQAQHZ2dr1/9/f3R1BQUJ0HEZEvmz59OjZu3IitW7eiS5cutb8PDw9HZWUlTp06Vad9QUFBg3FT7zHQYjZhUFSIcPvUT37QrjMe8tSGn9Bz7hfI+a3h0pS+4NGPf0D/hV80+4GqzF9+h+i4lNkEDNXxujkN4aCJFzicCmZ8YJPe7uOpw9TvDBGRj5DNnDB66STZclNGL2cGyA+CGD0zh4zFYjZh6tXdhNt7LNtE8GLuiijXMgoHR7dDu1Ytmmz3+3/iqNZkskwAYMktcRr1hC4WGhqK2NjYRh9WqxVJSUk4deoUsrKyarf9+uuv4XQ6awdCRNhsNgBAp06d1D4UIiKPUhQF06dPx/r16/H1118jOrpu6amBAweiRYsWSE9Pr/3d4cOHkZeXh6SkJE9312fMGCmeSbp+/4lmWzrK4VTQf+FmrN151NtdEVZa4UTPuV/gqc9+9HZXXPb2zlzhtnqdiNoUDpp4wW2rvhO9/qu17I54Q35Aicg4ZDMnWDpJrtyU0cuZAXIl4JiZQ0Y0c1QvyJxtrt6eo/kFuuji7q4sAg9UDxbdFN9ZqK1oqTBXyWaZJEa3NVxt6eagd+/eGD16NB588EHs3r0bO3bswPTp03HHHXcgIqK6asCJEycQGxuL3bt3AwBycnLwzDPPICsrC0ePHsWGDRswceJEDB8+HP37G2vRVSLSn2nTpuEf//gH3nvvPbRp0wZ2ux12ux1nz1aXvgwODsaUKVMwa9YsbN26FVlZWZg8eTKSkpIwZMgQL/fee4Z27yB809apADuPiE348iWffX8S3Z7chFLByX2+Zu2OPFy5ZIu3uyHN4VTw1cHCphv+x31J4mvs6AnPsj1so+0E9h0rldomun0gbhS8mCMiaq5kMydYOkmu3JTRy5kBciXgMn/53fCZOWQ8FrMJM64RzzapdCjIzJErNytLtDyXaLv6jIwV++7p0Mr1fYiQWYwTAN6ZYtwbSb7u3XffRWxsLK699lqMHTsWV155Jf7v//6v9u/nz5/H4cOHceZMdQkSq9WKr776Ctdddx1iY2Mxe/Zs3HLLLfjss8+8dQhERKpZtWoVSkpKMGLECHTq1Kn28eGHH9a2efnll3H99dfjlltuwfDhwxEeHo5PPvnEi732vurr4zDh9gs3HtCwN+qbsm4PZry/39vdcNvxU5WIX/iFt7shJfOX3+EQnPdkMQNDurXXtkM+SmxKL6nC4VQw3YWyXF/NHqF6X4iIfFFStw74eN+JJtuFBFqZaQLxQYB2rf0BReysaFh3fZYzA+RKwO3MFpuppefMHDKmmaN6YcXWHOGs6KWbD+JfPa7SrkOiHXEn4UV0nFjD8WSHU8En+8QX42SWiW9r164d3nvvvQb/HhUVBeWC7+XIyEh88803nugaEZHHKQLXIQEBAVi5ciVWrlzpgR41HxOHRmPzz2IZATm/nUFllbNZnB+MW74dP508rclzB7awIDGmHVbcOQCt/1PJ4mylA/M3/IC0H/JxulL9LOlTFU70ePJzHFo0tllMQJQpzTUgMqRZHJMWfP8/SUcSn/1SepsVdyYY9sNJpAcrV65EVFQUAgICkJiYWFuGoT6vv/46rrrqKrRt2xZt27ZFcnJyo+31SGYghJkmQJjgoElYa3+ECQ4YiLZrjmRKwJ3446xQOz1n5pAxyWabfH+8VNOFMAsFs+RE29W7rWD5QtF2rli25bDUuA+zTIiIiPRvSEx7tJC4c9scFoQftvgrVQdM+nRqgzX3XYGc58bi6JJx+PmZ0Vg7eXDtgAkAtLRa8PytCfjx6eo2R5eMw8GnR+O2QRFSr29jzjuBbk9uwkZb05NAvam6DLV4aa5HJNbW0RsOmnjI/WszUVQuV6NvQGQIxsdFaNQjItLahx9+iFmzZmHBggXYt28f4uLikJKSgsLC+r+gtm3bhjvvvBNbt25FRkYGIiMjcd111+HECd/+0lWTzEAIF+mG8KznPUeLPTNb28fJlIBTBF8I0XVliJoT2bVN5vw/ubJSMkRLC4q2q3fbcrHvCdF2shxOBSu25gi3j+sS1CxmkRIREZF7LGYTpl4tPpnlX7aTPr0gfJ95X+BEievnbDXaB/rhwMIUHF0yDptmDsdIFyay1QykHHluHP69aAwGdQ1xu18AMP0DG6as26XKc2lhZ3YRRKc7mU3A0B7Gvd7l2bYHbLSdwNeH5eo9mwB8NHWoNh0iIo946aWX8OCDD2Ly5Mno06cPVq9ejcDAQKxZs6be9u+++y7++te/Ij4+HrGxsXjjjTfgdDqRnp7u4Z57j+gAx87s37hIN4AiwZuE6zKOokB0JrUbNx6bA9FBDkXwlrGuB+XIsCxmE24eID5xZ/1+7S7Qj/9xRqidO7FeqtShBmSzTJ5I6a1JP4iIiMj3zBzVS7htlVP79eZc1SP1c5w57152cmAL4ODTo5E1P6VOJom7rH5mfDx1GP69aAyuiApx+/nSDxXh+mW+WXbzqc9+Em57U0JnQ1dV4KCJxhxOBTNcWMdkOctyETVrlZWVyMrKQnJycu3vzGYzkpOTkZGRIfQcZ86cwfnz59GuXcPrJZw7dw6lpaV1Hs2Z6A2ptJ8KuEg3gLA2gmt0nDmPTwXThN2Zrd0ciA5ymARvYeq5/BsZ2+Kb44TbKgCWbfm36n1wOBX863uxdT7cifUypQ7V5nAqWLlNPMvEajEZdjFOIiIiI7KYTUiRWBB+6eaDGvbGNd3nfI7zbs6veeXW/vj5mXFoabWo06l6WP3M+Ojh6sGTbqGBbj3XgfwyjPOxgZPKKieyfysXbr/45v4a9sb3cdBEYzPey5KudDIyNpRluYiauaKiIjgcDnTsWLcUUMeOHWG324We47//+78RERFRZ+DlYosXL0ZwcHDtIzIy0q1+e5voQt3llQ6hdnpfpFtmjY7ducVC7fScmQOID3Iw04SMzupnRrcOrYTbv/ZNturZJrtzi1Fcfr7Jdu1bWd2L9TKlDlW2M7sIDomX7eHh3TixioiIyGAmDo0Wbqv1enOyus/5HHKLFdSVcnkocp4biwmDPHevw+pnRvrsa3Dw6dFwpyLqTz42cDLnY/GSut1CAw1fDtbYR6+xyionNh0QKx9TI7RVC6yZNFijHhFRc7FkyRJ88MEHWL9+PQICGh5ISE1NRUlJSe3j2LFjHuyl+mQGAUTofZFumTU6KgRPnPWcmQOID3Lk/yE2A4eZJqRnC2+4XLhtlRPYeURsrSlRdsGygjfER7gV62VKHao9MLQ8XTxDx2wCZo4y7mKcRERERjUkpj38JE51fGVBeHcGTCwm4N+LxuB/7x3stWv6llYLsp8bh8s7tXH5OX7KL8P1y7er2CvXOJwK1tvEMrgBYOH1fTXsTfOg2aDJs88+i6FDhyIwMBAhISFC2yiKgvnz56NTp05o2bIlkpOTceTIEa26qLlBizZLb5P5P6M06AkReVqHDh1gsVhQUFB34LSgoADh4eGNbvvCCy9gyZIl+PLLL9G/f+PpkP7+/ggKCqrzaM5kBgFEGGGRbjWPUe+ZOYB4CTjbcbFSd8w0IT0b2r0DLBLXqAs3HlB1/6LlAruEuDfYK1PqUDRrT4TDqWDPr6eE208f0V3XEwGIiIiofhazCTcmiFek8YUF4bunuj5gEhJgRs7icT6T6fD5zOG4Nla8RNrFDpw8jSnr9qjYI3k7s4uEKyEZfQH4Gpp9+iorK3Hbbbdh6tSpwtssXboUy5cvx+rVq7Fr1y60atUKKSkpqKgQm2XmS+5fm4nSCrl0uBVcx4RIN6xWKwYOHFhnEfeaRd2TkpIa3G7p0qV45plnkJaWhkGDBnmiqz5HzUEAI9zQVvMY9Z6ZA4iXgDsveJGh93JmZGwWswnTRnQTbp/z2xlVy0GIZnK5m/Elk+VoLznr1r4utGzLYeG2JjDLhIiIyMhk1pvz9oLwved+jioXx2y6BFthWzhG3Q6p4M1JV2DFnQkub59+qBCfCa7VpwWZBeAnuJnFrReaDZo89dRTeOyxx9CvXz+h9oqi4JVXXsHcuXNx4403on///nj77bdx8uRJfPrpp1p1UxMbbSfw9WG54DQgMoTrmBDpzKxZs/D666/jrbfewsGDBzF16lSUl5dj8uTJAICJEyciNTW1tv3f//53zJs3D2vWrEFUVBTsdjvsdjvKysq8dQheoeYggBFKJ6l5jEbIzFG7BJzey5kRzRzVS3TJDwDAvW9mqrZv0e8Dd783ZLIci8vV+Y5yOBWs2Cq+APxNCbx4JSIiMjKrnxnxXcQrS3hrQfhhz32Jsy6mmIzs1R7fpfpuBZ7xcRHIeW4s/F1ci37G+/u9kgEkuwD8klvEB+j0zDfynADk5ubCbrfXWfA4ODgYiYmJyMjI8GLP5DicCqZ/YJPe7qOpQ9XvDBF51e23344XXngB8+fPR3x8PGw2G9LS0moXh8/Ly0N+fn5t+1WrVqGyshK33norOnXqVPt44YUXvHUIXiFaPkkEM02891y+Ss0ScCGB+i9nRmQxmzDjGvFsk125f6iWbSKayaVGxtewHqFi+1LpO2rZlsPCJRIAXrwSERER8Pjo3sJtvbEg/OQ1mThRet6lbV+9Ix5rJg9RuUfqs5hNOPzsOHQOdu2csPfcTSr3qGlcAN41PvMq2O12AKi9mVijY8eOtX+rz7lz51BaWlrn4U23rvpOeptld8Rz5hiRTk2fPh2//vorzp07h127diExMbH2b9u2bcO6detqfz569CgURbnksXDhQs933IvCVBw0MULpJDUHmYyQmQOol1EzKSmK399kCDNH9ZJqr1a2SZhgOT3Rdo0+h2AsVeM7SjbLhBevREREBPj2gvDPbDyArf+WLwlmBpDz3FhcH99Z/U5paEdqMvp0ai29XaUTSHhKfg1sVzmcCj7hAvAukTr7njNnDkwmU6OPQ4cOadXXei1evBjBwcG1j8jISI/u/0IbbSew/5jcoE10+0Dc2MwCAxGRplS8B22E0kmia3SIMEKmCaDecV4RxSwTMgaL2YSb48XLyKqVbbI7V/DCW40qB6LfPSp8R8lmmfDilYiIiAD5BeHX7z/hkXJQm344iTe/+1V6Oz8AvywZ12wnom2aeTUud2Hg5I+zVbjy7+lNN1SBzBp6XAC+LqlBk9mzZ+PgwYONPmJiYlzqSHh4OACgoKCgzu8LCgpq/1af1NRUlJSU1D6OHTvm0v7d5WpZrq9mj1C9L0REzVlR2TlVniekpTFKJ6m5RocRMnMA9TJqClX6rBI1B0tulSsP5W62icOp4K2dYhffReXu/y8Wllao2q4hDqeCldvEs0z8zLx4JSIioj/JLAjvVICdR4o07E31uc1f39svvZ0fgOwl49TvkId9PvNq9AlvJb3d8T8qMHntLg169CeHU8Gqb8TPO7kAfF1SgyahoaGIjY1t9GG1unYjIjo6GuHh4UhP/3OkrbS0FLt27UJSUlKD2/n7+yMoKKjOwxsSn/1SepsVdybww0hEdJGwNupkTiT3DjNEjFVzjQ4jZOYA6mWaFHPQhAzE6mdGYlRb4fbuZpvszi3GqbNiNbHV+N4QXeB9R7Z7Nx52ZhfBITHh869XdzfEdxkRERGJkV0QfuHGAxr2BohbmObSdnoYMKmx6dER6Bwsfz986+EiPLPxZw16VC3zl99xXuJ0nGvo1aVZcdy8vDzYbDbk5eXB4XDAZrPBZrOhrKystk1sbCzWr18PADCZTHj00UexaNEibNiwAT/++CMmTpyIiIgITJgwQatuquL+tZkoKq+S2mZkbCjGx4mn1BERGYVamRNqrVvRHKhxrEbJzAHUyzQxyhowRDXeeUBucU536mjbBTM61IpdoutDfXWw0K0yF0999pNwW7MJmDmqp8v7IiIiIn2SWRA+57czmi0If+XiLSirlH/ufy8ao0FvvGtH6iiEBMjfZn/zu1xs+iFfgx4Bz6cdFG7LNfQupdmrMX/+fCQkJGDBggUoKytDQkICEhISsHfv3to2hw8fRklJSe3PTzzxBGbMmIGHHnoIV1xxBcrKypCWloaAAPXqtatto+0Evj4st9BRcIAFayYN1qhHRETNm1qZE0ZZnwNQ51iNkpkDqPfZMNJnjAiQzzb5ZJ/rdbRFM7nUil2i60OdOnseu3OLXdpHZZUT2b+VC7efPoJZJkRERHSpITHt0ULiju6c//e96n24f20mjpfIXw9NuTJatzfnbQvHwGqR3+6v7+1Tfe2ZyionbMfF193mGnqX0uxTum7dOiiKcsljxIgRtW0URcGkSZNqfzaZTHj66adht9tRUVGBr776Cj17+u7sKodTwQwX1jHZM/c69TtDRKQjqmROGCgLQI1jNVJmjuiM8iafxyBrwBBdSCbbRAGwbMu/XdqPaFxTK3bJZDnaS866tI973xBf54VZJkRERNQQi9mEqVd3E27/r+9PqnpT3pUJ5ADQt3MQ5l3fR7V++KKDz4x1abuk575StR9zPhYfKOMC8PXT59Ceh8x4LwuyIef+YVG6HVElIlKLGjP4jZQFwNdLjuiM8iafxyBrwBBdSLaO9qvbsl26SBeNSWrFLpksR9H1Ty5UWeXErqN/CLdnlgkRERE1ZuaoXsJtHU4gM0d+kKP+51Iw3YUJ5J1D/LFxxlWq9MGXWcwmvHZXgvR2hWWVeFqijGtjHE4F620nhdvflNCZ55314N17F1VWObHpQIHUNmGtrZg//nKNekREpB9qZE4YKQtAjcwJI2XmDI5uh5CWLdx6DiOtAeOq7du3Y/z48YiIiIDJZMKnn37a5Dbbtm3DgAED4O/vj+7du2PdunWa95PkydTRdiquZZuIxiQ1Y5do1oor+5TJMjGBWSZERETUOIvZhCu6hgi3X7pZfH2LxiQ++6X0Nq2tZuyYk6zK/puDsf0jMOXKrtLbrdlxVJX1Z3ZmF0lN8l98c3+396lHHDRx0aBFm6W3yXjSOAGCiMgdaswcNlIWgBqZE0bKNLGYTbhvqPxJ7IWMtAaMq8rLyxEXF4eVK1cKtc/NzcW4ceNwzTXXwGaz4dFHH8UDDzyAzZvlz7lIW7J1tF/7Rj7bJCOnSKidmrFLq+wW2SyTmxIiGF+IiIioSY9cKz7J4vvjpW7fkH/qsx9RVF4lvd33C0e7td/maN71fXFNz/bS2w1fmu72vhduEM9YSYxuy4pIDeCr4oL712aitEIu0Ky4M4EXP0REgtydOdy+ldVQWQAytfgbYqTMHAAYHC1/AnshI60B46oxY8Zg0aJFuOmmm4Tar169GtHR0XjxxRfRu3dvTJ8+HbfeeitefvlljXtKsmTraFc5gZ1HxAZBgOqSAp//mC/UVs3YJfrdk1d8Rup5ZbJMAGDJLXFS7YmIiMiYhnbvIHVj150F4SurnFi7I096u9fuGmDY+6Fr7x+CLsFy56r20kr8y3bC5X1WVjmRU1Qu3P6dKeLrFRoNB00kubLY0YDIEIyPi9CoR0RE+uPuzOEb4o01S1emFn9DjJSZAwCFpRVubW+kzBxPycjIQHJy3azclJQUZGRkNLjNuXPnUFpaWudBnjFzVC/IRNllX4uX6Mr85XecPS82QUnN2CX6f71+/wnhzBnZLBPO9iMiIiJRFrMJNw0Qv9+4fr/rC8KPW/aN9DZTrozG2P6dXNqfXnyXOgqtrXLndjM/sLn8PqV+Ij4w1rGNleedjeArI8HVxY4+mjpU/c4QEemYu2t0dAkx1gAA4F7mgxHX53BlIecLGWkNGE+x2+3o2LHu4F/Hjh1RWlqKs2fP1rvN4sWLERwcXPuIjIz0RFcJ1RfpM64RzzbZ++sp4Yu/DMGFSlv7+6kau0S/e0orqrA7t1iorcyFK8DZfkRERCRn8c3iGaoKXFtrbqPtBI78Jpdpe02vDph3fR/pfemRK+XJkp77Snobh1PBJ/vEF4C//8po6X0YCQdNJNy66jvpbZbdEW+o2c5ERGpwd40OI97QdifzwYjrc7j7GWGmiW9ITU1FSUlJ7ePYsWPe7pKhzBzVS6q96EW6Irh05ZU92qsau2S+e+wl9Q/kXcjhVLBe4sKVWSZEREQky+pnRmJUW+H2r26TW2vOlQnkoa1aYO3kRKlt9MxiNmH5X+TKrxaWVeLpz8TXJgGAZVsOSy0AP3lYjNTzGw3PygVttJ3A/mNyJR+i2wfixvjOGvWIiEi/3F2jw4g3tN0ZBDDi+hzufkaMtgaMJ4SHh6OgoKDO7woKChAUFISWLevPHvP390dQUFCdB3mOxWzCzfHiJSFEL9JDWrYQer6Bl4nfIBAxOLod2gRYhNqKZKvtzC6CzCqIzDIhIiIiV7zzgPg5hFORyzZxZQJ55v+Mkt5G724Y0AVR7eQmh67ZcRSVVWJnkw6ngpXbcoSfO65LECfrNIGvjgBXy3J9NXuE6n0hIjICd9foYKaJ57ZtrtwtAWe0NWA8ISkpCenp6XV+t2XLFiQlJXmpRyRiya3is+ZEL9KP/yFW/qFdK/f+jy9mMZtwc4LYhCeR75mFG8RnB/LClYiIiFxl9TOje2gr4fart+cITWRxZQL5I9d0N1wVA1Hpfxspvc3VS78WarczuwgOiTSTJ1J6S/fFaHhmLiDx2S+lt1lxZwKDBBGRG9zJfuAggOS2BsyacKcEnBHXgHFFWVkZbDYbbDYbACA3Nxc2mw15eXkAqktrTZw4sbb9ww8/jF9++QVPPPEEDh06hNdeew3//Oc/8dhjj3mj+yRI9iK9qWwTh1PBJ/tPCD2XFrG+S9tAoXbF5eca/XtllRM5ReXC++WFKxEREbljwfjLhdtWOhRkNrGGnMOpYIbkBHKLGZg5qqfUNkbiSpmu/NJz+Jet6XPjpyRKeVktJgzp1l6qH0bEQZMm3L82E0XlVVLbjIwNxfg48VIFRER0KXduhnEQQE6Ym2vINEfulIAz4howrti7dy8SEhKQkJAAAJg1axYSEhIwf/58AEB+fn7tAAoAREdH4/PPP8eWLVsQFxeHF198EW+88QZSUlK80n8SJ3OR3lS2ye7cYpyucAg9lxax/tTZ80LtsvL+aPTv976RKbxPixm8cCUiIiK3DO3eARaJS5Slmw82+vcZ72VJrY8BAK/czgnkTXGlTNfMD2yNTjqqrHIi+zfxyToPD+/G90kAB00asdF2Al8fbnzk9WLBARasmTRYox4RERmHOyW2jFg6aXB0O7RrJbYOwCVkz4Z1wJ0ScEZcA8YVI0aMgKIolzzWrVsHAFi3bh22bdt2yTb79+/HuXPnkJOTg0mTJnm83yRP9iK9sWwTe2mF8PNoEetNEDuQbYd+a/AYKquc2HW08UGVC90YF8ELVyIiInKLxWzCtBHdhNt/f7y0wfUyKquc2HSgoN6/NYQTyMW5Uqbr1lU7GvybzGQds4nZQKI4aNIAV9cx2TP3OvU7Q0RkQK5mmhi1dJLFbMKNLp6kFjVRZkavXB38MGL5N6LGyF6kN5ZtUnRaLB4FBfhpEuuTBDM+KqqcDZa1kLlwBYAlt8iVaSAiIiKqz8xRvQSnf1S79836z1muWvqV1H45gVyOxWzCq3fES22z/1gJPvv+5CW/l52sM30E15wRxUGTBkx7b6/0NvcPi+ICjkREKnF1jQ4jl04SrcV/sbA2xivPBbgxMOdGFhSRXslepDeUbVJ8RmzQJKlbe01i/ZCY9rAKps3syPntkt/JXrgmRrfl9QMRERGpwmI24eYB4hPpduX+cUm2yYZ9x1FQKlautAYnkMu7Pr4zEiKDpLaZ+cH+S86fZSbrmMAsExk8Q69HZZUTaQcKpbYJa23FfIl6zkRE1DhX1+gwcukkV27mGzUzB3B98IOZJkSXsphNmHGN+9kmWYIDDi1bWIT3JcNiNiE+MkSo7clTl5YSk80yeWfKEKn2RERERI1ZfLNcBuuF2SYOp4JH/vm91PZj+4ZzAoiLPp56pVR7pwLMeG9f7c+yk3VuSmBJWBn8VNdj0KLN0ttkPJmsQU+IiIxrcHQ7hLSUX6PDyDe0XTl2I2fmuPpZ0WLxaSI9cDfbxOFUYDt2SmjbiBDtMuQGRrUVatfpoj4wy4SIiIi8zepnRqLguQxQN9vk1lXfSe3LbAJW3DVAahv6k8VswvK/yA1ybTpgr32/WBJWWzxLv8j9azNRWlH/QkgNWXFngmFvOBERacViNuG+oV2ltzNy6SRXjt3ImTmuloDTYvFpIj1wN9sk85ffUemof3H1iw3rFirdP1EhLcViaUFJ3VJizDIhIiIiX/DOA3LnGPe+mYmNthPYf6xUartld/B+qLtuGNAFHYPkJove+2YmJ+t4AF+tC2y0ncDXh+tf0LEhI2NDMd7FhXeJiKhxg6PFFuS9EDNNtN9GL1wpAde+ldWw5cyIRLiTbbIzp0homwA/M4YILtjuitIKsTrem37Mr+07L1yJiIjIV7iSbfLohzapfSREBvN+qEq+fUKuetGu3D9wNyfraI5n6v/hcCqY/oFNaptWLcxYM2mwNh0iIiIUll5aL74pRi6d5ErmhJEzcwZHt0NwgJ/UNjfEsw4sUWPcyTbZnVsstE3/LsGa/h+aBId9KqqcyMypnnDFLBMiIiLyJbLZJlViyb61Pp46TG4DapDVz4yxfTtKbbOHk3U0x1fsP6594WvpbfbOu06DnhARUY3icvksCCOXTnIlc8LImSYWswmj+sidnHYJMe7ni0iUbLbJiq3ZqKxyYv+vYhd/nYK1W88EAJIkslh25PzGLBMiIiLyObLZJjKW3RHPiWQqW3HXQM2em5N1XMOzdQAb9h3H0WK52cwJkcFoabVo1CMiIgLksyBCWrYwdOkkVzInjJxpAsiv6WL014tIhGy2iQLgnjcyhWc4dm6r7eDlkJj28BO8D7AntxhXLf1K6vl54UpERESeIJttIqJTkD9ujO+s+vMancVswiMS58+iOFnHdYZ/1RxOBY/883vp7ZiGRkSkPdksiOTeYYae8eJK5oSRM00A+eM3+utFJEo222S3RKaGlovAA9WxNKGr2MzMPb+eQkGp2BooAC9ciYiIyHOsfmZ069BK1ef85omRqj4f/WnmqF6q36jnZB3XGf6MPfHZL6W3WXFngqFvyhEReYrsGh3DustlDeiRbOaEkdeAAeQ/Y8w0IRIjm20iymoxaboIfA2tshZ54UpERESetPCGy1V7Lk7+0JbFbMLyO+JVez6+X+4x9Ct3/9pMFJVXSW0zMjYU4+MiNOoRERFdSHaNjjAX1vTQG9lMCCOvAQMAYZKDJsw0IRInm20iIqZDK49MXhoqOQAtgheuRERE5GlDu3eARaVTJ07+0N718Z3RIzRQlefi++UeucLnEp599ll8/vnnsNlssFqtOHXqVJPbTJo0CW+99Vad36WkpCAtLU31/m20ncDXh3+X2ia0VQusmTRY9b4QEVH9atboKKkQHOAWrIevZzKZEEZfAwYAZO/oGj0zh0hGTbbJ8q05qj1nZDt1LiKbMiSmPcwmwKni9wovXI3DlWthRVGwYMECvP766zh16hSGDRuGVatWoUePHpr0sayiCjPe3YMdR4qh5XQAEwB/PzNiQlvhb9fF4upeoT5RteFspQPzN/yAtB/ycbrSt04gLSagY1AA7hnSFQ9cFeMTg62VVU68/m023tmZi4LTVT51ym0xAUEtWyDl8nAsGH+5T6w963Aq2H6wEH/f/DOyC89AbqquHF/9H3PVypUr8fzzz8NutyMuLg4rVqzA4MG8D+cOi9mEaSPcPx/j5A/P+Xzm1eg59wu3nqNbaCDfLzdp9upVVlbitttuw9SpU6W2Gz16NPLz82sf77//vup9czgVzPjAJr1d5v+MUr0vRKRvK1euRFRUFAICApCYmIjdu3c32v6jjz5CbGwsAgIC0K9fP2zatMlDPfVNsmt0FJad07A3zYNMJoTR14ABgCLJz4zRM3OIZKmdbeKpgV6L2YRBXUNUe74xl3fkhauBuHItvHTpUixfvhyrV6/Grl270KpVK6SkpKCiokL1/t3w6rfou3Aztmo8YAJUz2epqHLi5/zTuP+tPYid9wXSDuRrvNfGPfj2HvSen4aP9p70uQETAHAowMmSCizdfBg9536BxZt+9mp/Fm/6GT3nfoHnNx+B3ccGTIDq1+uPM+fxwZ5j6D0/DQ++vcer/Uk7kI9ec7/A5Hf24pDGAyaAb/6PuerDDz/ErFmzsGDBAuzbtw9xcXFISUlBYWGht7vW7KlxPsbJH55j9TNjdN8wt55j4fV9VeqNcWl25v7UU0/hscceQ79+/aS28/f3R3h4eO2jbVuxRRhlvPLlYekv+mV3xBv+xhIRyZE96du5cyfuvPNOTJkyBfv378eECRMwYcIEHDhwwMM99y0ya3QUc9BEao2OITHarwvg68LaiJd0a+1vYWYOkSS11za5b2i0as/VlBkje6r2XK/ePVC15yLfJ3strCgKXnnlFcydOxc33ngj+vfvj7fffhsnT57Ep59+qmrfbnj1W/xwvFTV55Rx3qHg4X/s89pN3Qff3oMtPzevG7D/uz3XawMnizf9jP/dnuuVfbtqy8+FXhs4STuQj4f/sQ9VaqYpSvL2/5g7XnrpJTz44IOYPHky+vTpg9WrVyMwMBBr1qzxdteaPXfPx8b2DefkDw9bedcgl7f1MwNDe3C9V3f53Cd+27ZtCAsLQ69evTB16lT8/rtcCa2mOJwKVn/7i9Q20e0DcWN8Z1X7QUT6J3vSt2zZMowePRqPP/44evfujWeeeQYDBgzAq6++6uGe+5bicvGBEJZOklsH5vvjp7TrSDMxOLodAgSL/PYJD+IECiIXzBzVS5WLDk+XGRjavYMqWTKcfEVNyc3Nhd1uR3Jycu3vgoODkZiYiIyMDNX2U1ZR5dUBkwst3PAzHB6+sXy20tHsBkxqvP5tLiqrnB7dZ2WVs9kNmNTY8nMhzlY6PLpPh1PBvPU/eHSfjfHG/5g7KisrkZWVVScOms1mJCcnqxoHjczVbBMTgBV3DVC7O9QEi9mER1wc6Prr1d157qkCnxo0GT16NN5++22kp6fj73//O7755huMGTMGDkfDX3bnzp1DaWlpnUdjducW47xD7ovjq9kjpNoTEbly0peRkVGnPVC9rpPRTxJPnT0v3Jalk6oHAVoK3lRsPpdR2rGYTejXJViobUSI+IAUEf3JYjZh+R3xbj+Pp8sMWMwm3BQf4dZzhAdZOfmKmmS32wEAHTvWLUnasWPH2r9dTPY6GAAe+3C/+51Vib20Artziz26z+e8XObKHU4FeCfjqEf36en9qc3T7/fu3GL8Vq51MS5x3vgfc0dRUREcDodwHHQlBhqdq9kmM67hDXhvcWWgy2wCZo5SL1vayKQGTebMmQOTydTo49ChQy535o477sANN9yAfv36YcKECdi4cSP27NmDbdu2NbjN4sWLERwcXPuIjIxsdB+Fp+Vqwq64M4HBgYikyZ70AdUXzDLtAWOcLJoETxNa+/uxdBKqT4bH9e8k1Da6fSuNe9M8DBYsU9bZQwtQE+nR9fGdMbKX6yUBW1hMXikzsOTWOLe23/7EtSr1hLxN62thWbLXwQCQ98dZD/RMnOy1ubuO/n7Go/tT26/Fnu2/p/enNk+/357+PIvwxT6pxZUYSPI34S1m3oD3JovZhGV/kTsXnT6Cg1xqkRo0mT17Ng4ePNjoIyYmRrXOxcTEoEOHDsjOzm6wTWpqKkpKSmofx44da/Q5ZWqXj4wNxfg492aXERFpyQgni0ndxG6yPXBlNE8O/uO5m/vD1MRLYTYB9yZFeaQ/vm5ojNiNWNF2RFS/NZOHoHNQC5e2fekv3ilxZfUzY/Kwy1za9v5hUaz/rSNaXguHh4cDAAoKCur8vqCgoPZvF5O9DgaAy9r6VkauzLW5GqLaN+/JD109PHnD0/tTm6ffb09/nkX4Yp8a0qFDB1gsFuE46EoMJPlsk1du50Ryb7thQBd0bSe2bmkLs4mDXCqSOosPDQ1FbGxsow+rVb168sePH8fvv/+OTp0anjHr7++PoKCgOo/GDI5uh07BTX9xXNY2AGsmDZbuMxERIH/SB1RfMMu0B4xxsjgkpj1CAhu/ydbKasGMa3t4qEe+z+pnxkNXNb5Y8oNXRfNm3n8M6db0ZywksAWGCA7gEVHDdjx5HVpZ5WLPtbFhXp3ItGB8P3QJkbvG6dquJeaPv1yjHpE3aHktHB0djfDwcKSnp9f+rrS0FLt27UJSUlK928heBwPAy7cnuNQ/LYQHBXg8Q/jJsX08uj81eWOyS3OfXOPp93twdDuEtvLz6D4b443/MXdYrVYMHDiwThx0Op1IT0+vNw66EgOp2sxRveAvcB2Y3Nu751/0p6//dq3QDfxld3CQS02a3S3Jy8uDzWZDXl4eHA4HbDYbbDYbysrKatvExsZi/fr1AICysjI8/vjjyMzMxNGjR5Geno4bb7wR3bt3R0pKimr9sphNWDC+T6PpaH0jWmP7fzOVnohcJ3vSBwBJSUl12gPAli1bGmwPGONk0WI2YcnN/Rpt8+Jf4nhycJHUsX3wX8OjcfHLYjYB/zU8GqnN+MaB2kQ+Y0tu7sfPGJFKfnp6DNq3Fru53K9zG7w56QqNe9S07+aMQgfBPveNaI1vnhipcY/Il8leC5tMJjz66KNYtGgRNmzYgB9//BETJ05EREQEJkyYoFq/Wgf4oX8X3zhXXHhDH49/r7a0WjCqT5hH96kWb0x2sfqZ8V/DG5+E46tG9QlDS6vFo/u0mE145qb+Ht1nY7zxP+auWbNm4fXXX8dbb72FgwcPYurUqSgvL8fkyZO93TVdsZhNWNbEWnPJvUPxxn3eP/+iahazCa/dM6DRNv81PBpjBct0kxjNvnXnz5+PhIQELFiwAGVlZUhISEBCQgL27t1b2+bw4cMoKSkBAFgsFvzwww+44YYb0LNnT0yZMgUDBw7Et99+C39/sTQkUaP7dsKqewZcknHSsoUZy/8Sh42PXK3q/ojImJo66Zs4cSJSU1Nr28+cORNpaWl48cUXcejQISxcuBB79+7F9OnTvXUIPmN0305Yfc8AhAfVjdudggOw+p4BGN2XJwf1SR3bB4eeGYN543pjYlJXzBvXG4eeGcMBk3r8+Rmre84RHuTPzxiRBrLmjsLkoVGNtplyZVd8NmO4ZzokYO/cURgZG9poG15LECB/LQwATzzxBGbMmIGHHnoIV1xxBcrKypCWloaAAHXL62yYfpVXB05aWExe/V59feIVzW7gxJuTXWom4TQno/qE4fWJ3rnZW3M+6efFwQpv/4+54/bbb8cLL7yA+fPnIz4+HjabDWlpaZes+0nua+j6uk2ABa/eEY837mPlHV9T8551bFP3erVtYAu8dlcCr/E1YFIURfF2J9RUWlqK4OBglJSUNDnj2uFUsDu3GIWnKxDWpjp1sbmNxBOR62TihateffVVPP/887Db7YiPj8fy5cuRmJgIABgxYgSioqKwbt262vYfffQR5s6di6NHj6JHjx5YunQpxo4dK7w/TxyTNzFuk9aM8hnTa6zQ63HpWWWVE69/m413M39FydkqtA204q7ErnjgqhifLSF4ttKB+Rt+wJafCnHe4UTX9q3wt+ticXWvUF3GCz3SY6yQPaayiirMeHcPdhwpRqWG/TIB8PczIybUt/5Pav6P037Ix+lK37olYjEBHYMCcM8Q34mFNbH6nZ25KDhdBV96xSwmIKhlC6RcHo4F4y/3eIZJfRxOBdsPFuLvm39GduEZVGm4L1f+xxgDqYZRrn30hO+Ze2RihaEHTYjI2PQYL/R4TESkPr3GCr0eFxGpS4+xQo/HRETa0GO80OMxEZH6ZGKF96csEBERERERERERERER+QAOmhAREREREREREREREQHw83YH1FZTbay0tNTLPSEiX1cTJ/RUpZAxkIhE6DH+AYyBRCRGjzGQ8Y+IRDEGEpFRycQ/3Q2anD59GgAQGRnp5Z4QUXNx+vRpBAcHe7sbqmAMJCIZeop/AGMgEcnRUwxk/CMiWYyBRGRUIvFPdwvBO51OnDx5Em3atIHJZGqyfWlpKSIjI3Hs2DHDLBbFY+Yx65Erx6soCk6fPo2IiAiYzfqoVsgY2DQes/6P2WjHC8gfsx7jH8AYKMJox2y04wV4zEaNgbLxDzDeZ8VoxwvwmHnM9WMM5OeEx6xPRjteQNv4p7tME7PZjC5dukhvFxQUZJgPVA0eszEY7Zhlj1cvM2tqMAaK4zHrn9GOF5A7Zr3FP4AxUIbRjtloxwvwmJuitxjoavwDjPdZMdrxAjxmo2AM5DmgCB6z/hnteAFt4p8+hpSJiIiIiIiIiIiIiIjcxEETIiIiIiIiIiIiIiIicNAE/v7+WLBgAfz9/b3dFY/hMRuD0Y7ZaMerFiO+bjxm/TPa8QLGPGY1GPF1M9oxG+14AR4ziTPa62a04wV4zEZhxGN2lxFfMx6z/hnteAFtj1l3C8ETERERERERERERERG5wvCZJkRERERERERERERERAAHTYiIiIiIiIiIiIiIiABw0ISIiIiIiIiIiIiIiAgAB02IiIiIiIiIiIiIiIgAGHzQ5Nlnn8XQoUMRGBiIkJCQetvk5eVh3LhxCAwMRFhYGB5//HFUVVV5tqMai4qKgslkqvNYsmSJt7ulmpUrVyIqKgoBAQFITEzE7t27vd0lzSxcuPCS9zI2Ntbb3VLV9u3bMX78eERERMBkMuHTTz+t83dFUTB//nx06tQJLVu2RHJyMo4cOeKdzvo4xkD9xz+AMZAxkDGwIYyBjIF6wxjIGCiK8a8aY6B+MP4x/slgDGT80xvGQG1ioKEHTSorK3Hbbbdh6tSp9f7d4XBg3LhxqKysxM6dO/HWW29h3bp1mD9/vod7qr2nn34a+fn5tY8ZM2Z4u0uq+PDDDzFr1iwsWLAA+/btQ1xcHFJSUlBYWOjtrmnm8ssvr/Nefvfdd97ukqrKy8sRFxeHlStX1vv3pUuXYvny5Vi9ejV27dqFVq1aISUlBRUVFR7uqe9jDKym1/gHMAYyBjIGNoYxsBpjoL4wBjIGimD8+xNjoH4w/jH+iWIMrMb4py+MgRrEQIWUtWvXKsHBwZf8ftOmTYrZbFbsdnvt71atWqUEBQUp586d82APtdW1a1fl5Zdf9nY3NDF48GBl2rRptT87HA4lIiJCWbx4sRd7pZ0FCxYocXFx3u6GxwBQ1q9fX/uz0+lUwsPDleeff772d6dOnVL8/f2V999/3ws9bB6MHAP1HP8UhTFQ7xgD1cEY+LK3u6EZxkB9Ywx0n5Hjn6IwBuoJ4x/jnyuMHAMZ//SFMVCbGGjoTJOmZGRkoF+/fujYsWPt71JSUlBaWoqffvrJiz1T35IlS9C+fXskJCTg+eef10XaYWVlJbKyspCcnFz7O7PZjOTkZGRkZHixZ9o6cuQIIiIiEBMTg7vvvht5eXne7pLH5Obmwm6313nPg4ODkZiYqOv3XCtGiYF6jH8AYyBjYDXGQNcxBjZvjIGMgQBjoKuMEv8AxkA9Yfxj/FOLUWIg45++MAaqHwP91OicXtnt9jpBEkDtz3a73Rtd0sQjjzyCAQMGoF27dti5cydSU1ORn5+Pl156ydtdc0tRUREcDke97+GhQ4e81CttJSYmYt26dejVqxfy8/Px1FNP4aqrrsKBAwfQpk0bb3dPczX/l/W953r6n/UUI8RAvcY/gDGQMfBPjIGuYQxs3hgDGQNrMAbKM0L8AxgD9YTxj/FPTUaIgYx/+sIYqE0M1F2myZw5cy5Z/Obih17/SS4k8zrMmjULI0aMQP/+/fHwww/jxRdfxIoVK3Du3DkvHwXJGjNmDG677Tb0798fKSkp2LRpE06dOoV//vOf3u4aeQhjIOOfkTEGEmMgY6CRMQYaG+NfNcZAY2L8I8ZAxj8jYwzUhu4yTWbPno1JkyY12iYmJkboucLDw7F79+46vysoKKj9my9z53VITExEVVUVjh49il69emnQO8/o0KEDLBZL7XtWo6CgwOffP7WEhISgZ8+eyM7O9nZXPKLmfS0oKECnTp1qf19QUID4+Hgv9cqzGAMZ/2owBjIG1mAMrIsxkDHQl98/NTEGovZnI8RAxr9qjIHVjB4DGf9Q+7MR4h/AGAgw/tUwevwDGANruBsDdTdoEhoaitDQUFWeKykpCc8++ywKCwsRFhYGANiyZQuCgoLQp08fVfahFXdeB5vNBrPZXHvMzZXVasXAgQORnp6OCRMmAACcTifS09Mxffp073bOQ8rKypCTk4N7773X213xiOjoaISHhyM9Pb02MJaWlmLXrl2YOnWqdzvnIYyBjH81GAMZAwHGQHcwBjZvjIGMgYCxYiDjXzXGwGpGj4GMf8aKfwBjIMD4V8Po8Q9gDATUiYG6GzSRkZeXh+LiYuTl5cHhcMBmswEAunfvjtatW+O6665Dnz59cO+992Lp0qWw2+2YO3cupk2bBn9/f+92XiUZGRnYtWsXrrnmGrRp0wYZGRl47LHHcM8996Bt27be7p7bZs2ahfvuuw+DBg3C4MGD8corr6C8vByTJ0/2dtc08be//Q3jx49H165dcfLkSSxYsAAWiwV33nmnt7ummrKysjqj5bm5ubDZbGjXrh0uu+wyPProo1i0aBF69OiB6OhozJs3DxEREbVflvQno8dAvcc/gDGQMZAxsDGMgYyBesMYyBgoyujxD2AM1BvGP8Y/GUaPgYx/+sMYqFEMVAzsvvvuUwBc8ti6dWttm6NHjypjxoxRWrZsqXTo0EGZPXu2cv78ee91WmVZWVlKYmKiEhwcrAQEBCi9e/dWnnvuOaWiosLbXVPNihUrlMsuu0yxWq3K4MGDlczMTG93STO333670qlTJ8VqtSqdO3dWbr/9diU7O9vb3VLV1q1b6/2/ve+++xRFURSn06nMmzdP6dixo+Lv769ce+21yuHDh73baR9l9BhohPinKIyBjIGMgQ1hDGQM1BvGQMZAUUaPf4rCGKg3jH+MfzKMHgMZ//SHMVCbGGhSFEVxfciFiIiIiIiIiIiIiIhIH8ze7gAREREREREREREREZEv4KAJEREREREREREREREROGhCREREREREREREREQEgIMmREREREREREREREREADhoQkREREREREREREREBICDJkRERERERERERERERAA4aEJERERERERERERERASAgyZEREREREREREREREQAOGhCREREREREREREREQEgIMmREREREREREREREREADhoQkREREREREREREREBICDJkRERERERERERERERACA/w82RP0ncWck1gAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "11\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "12\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "13\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "14\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "15\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "16\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "17\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "18\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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IRmzSsWPHEB8fj9GjR+Orr77C1KlTsXfvXgQFBZW7EQWAadOm4cKFC1iwYAFCQ0Px888/4/nnn9dfmPK8b4uKinDs2LFy77327dtj0aJF2LBhA/78808AQF5eHiZOnIh27dph4cKFBuO7du0KAFzvvdLnHwDlzj/GPhuqUtHPYYro6GgEBwfDwcFB0PNu3bqFu3fvljs3A0D37t2Nnpu7du2K+Ph4k+dKiGxMnQqsWQO88AKwejXwf/8HuLiUD24Ys2kT8OmnunJbixfrAggjRwJFRf+OiY/XBRXKlhlYsQJo0EAXTCnZuPPtt8Du3bryVY0bG47v2lV3LEuy1u+m5JxT9pzUtasuUFHF9aLepEmAm5suWNWvH2Ckr59RCoXudUr/d1XmzgX8/YHJk/8tu7Zrl67c2bx5QNmSil276kqH5ebyzYkQYpSt3Ovt2rULCoUCAwcOrHQ+JX36jN1vd+nShe4VCSnjzp07+pLz06ZNw4oVK9CqVStMnjwZX375JQDh638ajQaDBg1Co0aN8Mknn6Br166YP3++QVksofdw/fv3x5tvvomIiAic+N9mlPT0dEyfPh0DBgwwKAn2559/YubMmfjmm2/KlQr74IMPsGTJEkycOBH79++v8nUrO6dIrWvXrmCMyeaesaLzcHFxMe7fv4/bt29j9+7dmDt3LmrXro3u3btXecyq1gd4TZo0CcOGDcPs2bNx48YNALr+LR999BEmT56MIUOGmHRcWbNiFgwxUXh4OFMqlezgwYP6dLQvv/yy0ufcvHmTdevWjY0ZM8agnFe7du3YO++8wxhj7J133ilXzmvMmDHc5bwiIiIYAP3XM888w9LS0kz6Gc1JjSvr4MGDTKFQsA/LlB/48ssv2bRp09jPP//Mtm7dymbOnMkcHBxY69atWU5Ojn5cWFgYU6lURo/doEEDNnr06Apf+/Tp06xdu3Zs+vTp+nJev/76K/P19a3032z79u1c5YNQJvWvpETZq6++ajBuxIgRrF69evr/LioqYgqFgr399ttGjztmzBjm6urKLl++rC/H8/vvvxsd26ZNGzZ48OBK50mIHD1+/LjcYwkJCQwA++mnn/SPlZQc6Nq1K1Or1frHP/nkEwaA/fHHH4wxvvft1atXGQD29ddfl/ueRqNhTz31FGvUqBG7f/8+CwsLYw4ODhUez9HRkb3xxhsVvpZWq9WXZCxdzis4OFhfzmvz5s2sWbNmbPXq1fpyXgsXLqyynFdlP0dpVZXzysvLY87OzhWmBVdWzqvke6X/rUq88847DAArKCgweHzp0qUMALtz506l8yZE9tzdGQsLq3zMhAm6klglUlN15aLq1TMsLfXHH7rHd+z497Fx43TjjNm1Szd+8WLGUlIYq1WLseefNz526VLdWFPec6aW87LW7yYsTFe+zJgGDRir5HqRMcbYoUOMvfCCrvzZH38wFhGhex1nZ8ZOnKj8uZs3M9asGWOrV/9bzmvhQr5yXmfOMOboyNiUKYw9eMBYkyaMdevGWFFR+bGbNul+H0eOVH5MQkg5pct52cq93vjx41nfvn0rnWNmZiZr2LBhheVv/vOf/zAXFxeDx6icF6nuJk+ezLy8vNj9+/cNHh89ejRzd3c3uE/lWf+bMGECA8CmT5+uf0yr1bKhQ4cyR0dHfQl73nu40vLy8lirVq1Yx44dWUFBARs6dChzc3MzKKcspqrOKaUJXTfkKed1+/ZtBoB9/PHHor62MTzlvCo6D5esW5R8tW3bttKfizFdOa+StYCUlBSDcl5jx441qZxXeno68/DwYMHBwaywsJAFBASwZs2aGaypMmY/5byEbfsksrBgwQLs3LkTEyZMwKNHj9C3b98KGzWVaNSoESIiIjBgwAB9qZqgoCCcPHlS3+B86NCh+Oijj/SZIw0aNMCmTZuwd+9eNGrUqMp5jRkzBt26dcO9e/ewc+dO3LlzxyDtsCJFRUXIyckp91hhYSHu379v8LiHh4fBjumq3L17F2PHjoWvry/effddg+/NnDnT4L9feOEFdO/eHa+88gpWr16NOXPmANBl2Dg6Oho9vrOzc6U/Y7NmzbBu3Tr07NkTcXFxAIBRo0ZhwIABuFRJQ9M6deoAAHbu3Ak/Pz/UqFGjqh/VQNnIf58+fbB9+3bk5ubCzc0NWVlZYIyhbt26Rp+/cuVKxMXFYdSoUbh8+TLGjx+P4cOHGx1bt27dcv9OhNiC0llyRUVFyM3NRatWrVCnTh2cOHEC48ePNxj/n//8x+C9+MYbb+D9999HdHQ0nnvuOa73bWZmJgAYfe8plUpERkbCz88PgwcPxvHjxzF37lyjmRYlx6jsvadQKDBx4kT0799fv0vJ19cXu3fvRkxMDGrVqoWOHTsiPj4eTZo0wYIFC1CjRg18+OGHePHFF+Hs7FzhsSv7OYTYt28fCgsLMXjwYMHPLTn3Ojk5lfteydzz8/MNvl8y3/v376Nhw4amTJkQeahTBzhyRFc+q2z2R1VefllXeqtEnz66/y1dMiEz03BMaQMH6jI1Fi4Etm7VZUx8+63xsSXHuH9fV1arIoWF5RvQa7XA48e655ZW1a5Ea/1u8vN1JbCMcXbWfb8yvXrpvko89xwwapSuJFp4OBATU/FzO3bUZcg0aQIsWKArvfXhh8CLL+peuzKdOgEffaR7jdOndb/v3bsBY9mBpf89CSEms4V7Pa1Wi5iYGLzzzjsVzkOr1eKVV15Bdna2PrOlrLp16yI/Px+PHz+Gq6uroJ+TEHvEGMNvv/2Gl156CYwxg/u5kJAQbN68GSdOnEDv3r0BCFv/mzZtmv7/KxQKTJs2DVFRUdizZw9Gjx5t0j2cq6srIiMj8fTTT+Ppp5/G0aNH8eOPPxqUUxZLZecUKdcNSyt9v2jp1y6rsvNwhw4dEBsbi7y8PMTHx2PPnj149OhRpcerVasWZs+ejUGDBukf8/PzQ0JCAvbt22dSRoqnpydWrVqFMWPGoE+fPkhKSkJsbCzc3NwEH8sWUDkvG+To6Ii1a9ciNTUVDx8+1Nehq4yDgwMGlK3dDN1CU9++fQEAffv2NVp665lnnuEqs+Lj44MBAwZgzJgx+Pnnn9GiRQsMGDCgykDKoUOH0KBBA4Ov+Ph4bN68udzjaWlpVc6jRF5eHoYNG4aHDx/ijz/+4Kr5OHbsWHh6emLPnj36x1xcXCrs61JQUFBpuTJ3d3f07Nmz3ON16tRBjx49Knxe37598cILL+Cjjz5C/fr1MXz4cKxbtw6FhYVV/gwAyn2glXwQlO3fwCroA+Ph4YGvvvoKp0+fhru7O7766qsKX4sxZnL6HyHWlJ+fj3nz5sHb2xtOTk6oX78+GjRogOzs7HIXSQDQunVrg/+uVasWvLy89IFpIe/bit57LVu2xIIFC3Ds2DF07NgRH374YYXz53nvBQcHGy3zWHLh1KlTJ6M9r9q1a8dVq7Win4NXVFQUunXrxhWoL6vk3Gvs91tQUGAwpkTJfOmcRWzeJ5/oyip5ewPdu+sWznnrRpe96S25kS5zjYDK3t/LlwMeHroeHF99VXGApOQYVb3n/vtfXZmw0l83buhKa5V9vCrW+t24uAAV9QEsKNB9X6hWrYDhw3X9ViroewhAFwgx1r+wXTvD3i8VeecdXemuo0d1fVI6dDA+jvffkxBSKVu41zt27Bju3btXaR3+6dOnIyYmBj/88AP8ypb/KzMHuvYiROfevXvIzs7Gd999V269a9KkSQB0m4FL8K7/KZVKtGjRwuCxNm3aAEC5np9lzw2PHj1CRkaG/uvevXsG3+/duzfeeOMNHD16FCEhIeX6N4mlsnOKVOuGZRk7Z1nqtcuq7Dzs5uaGAQMGYPjw4fj444/x9ttvY/jw4ThVup+dEaUDKCVUKhWCg4NNnufo0aMxdOhQHD16FK+99hqeeeYZk48ld5SJYqN27doFQLdQdOXKFfj6+nI/t3nz5vqsiIpU9X0eo0aNwvfff4+DBw8iJCSkwnF+fn7lGpO//fbb8PT0LBdx5W02rFarMXLkSJw+fRq7du1Cp06duOft7e1t0ETKy8sLGo0Gd+/eNdi5rFarkZmZicacuxyDgoIQFBTENVahUGDr1q04fPgwduzYgV27duHVV1/FZ599hsOHD1cZEKqoN07JB4KHhwcUCkW5C+3SSv7GHjx4gJs3b+p3TJX14MGDcovLhNiC6dOnY926dXjrrbcQGBgId3d3KBQKjB49GlqtVvDxeN639erVA1D+Jre03bt3AwBu376NzMzMCs972dnZgurElr14Lqt049Kq8PwcPKKjo/U3C0J5eXkB0NXlLSs9PR0eHh7lslRK5muN+rqEiOqll3RZEtu367IGPv0U+PhjYNs2oKrMror655W+oa5Xr3zgoLSTJ4GSG/wzZ3SNzY0pOUZV77mQEKDMtSDGjdNlvYSGVv7csqz1u/Hy0gU67t41DCqp1brsFaFZMSW8vXXHyMvT9UqpioBzuV5KCnDliu7/nzlT8Tjef09CSKVs4V4vOjoazZs3R4cKgqofffQRVq9ejWXLlpXL3i7twYMHcHV1lUWfVELkoOQ+c9y4cZgwYYLRMZ07dzb4b3PW/0qr6B5u+fLlBn2VfHx8DO4dCwsL9WuEycnJkmSWVXVOkWLd0Bhj94uWeu2yqjoPlzZy5EiMHz8emzdvrjCoXVZV6wO8MjMzcfx/PfzOnz8PrVYrWjaO3FAQxQadPn0aCxcuxKRJk5CUlIQpU6bgzJkzcHd3t/bUDJRkoBjb0V1a3bp1y2XJ1K1bF15eXkazZ6qi1WoRGhqKvXv34pdfftFn2vBgjOHatWsIKNUs1N/fHwBw/Phxg8ZIx48fh1ar1X9fCj179kTPnj2xZMkSbNq0Ca+88go2b96MKVOmmHVcBwcHtGzZEqmpqUa/XxL9f/fdd/Hzzz9jwoQJOHLkSLmMpOLiYty4cQPPPfecWfMhxBq2bt2KCRMm4LPPPtM/VlBQgOzsbKPjr1y5gn79+un/+9GjR0hPTy/XMK2y922zZs3g4uJS4Xvvm2++QWxsLJYsWYKIiAi8/vrr+OOPP8qNu3XrFtRqNdq3b2/CT26+qn4OHmfPnkVaWlqlOxwr06RJEzRo0EB/wVba0aNHjZ6bU1NT9RlHhNg8Ly/gzTd1X3fvAl26AEuWVB0o4NGuHfDzz0BODlD2+jIvT9f8vEMHXfmpTz4BRowAnnyy/HFSU3UL7lW957y8dF+lOTsDLVoAJlwLWuV3U3LOOX4cKP25cPy4rjSZqdeLKSm63wVHRrVJtFpg4kRdgOatt4ClS3VlxEaOLD82NVXXvP5/O1sJIeaR871eVFRUhU2BV61ahQULFuCtt97Ce++9V+lcUlNTrXa9SogcNWjQALVr14ZGo+Fa7+Jd/9NqtUhJSdFnnwDA5cuXAUBfYaCie7jQ0FA89dRT+v8uG/ScP38+Lly4gOXLl+O9997DnDlzKq1WIhTPOUXsdcOKlPxuSp+3LPXaZVV2Hi6rsLAQWq22yvVXKYSFheHhw4eIiIhAeHg4vvzyS8yePdvi87AE+wwN2bGioiJMnDgRjRs3xooVKxAZGYk7d+5g1qxZVptT2VS/Ej/++CMUCgW6dOli0flMnz4dW7ZswerVqzHS2A3g/xib95o1a3Dv3j2DFLf+/fvDw8MDa9asKTfW1dXV5AXAyjx48KBcimXJgiBvmndVAgMDjS4+ZmdnY8qUKejevTuWLl2KH374ASdOnMDSpUvLjT1//jwKCgrQq3QNb0JshEqlKvc++/rrr6GpoGTKd999h6KiIv1/r1mzBsXFxfp+Hjzv2xo1aqBbt25G33upqal455138MILL+D999/H8uXL8eeff+Knn34qNzYxMREArPbeq+zn4BUdHY1GjRpV2POFxwsvvICdO3fixo0b+sf27t2Ly5cv48UXXyw3PjExEYGBgSa/HiGyoNHoFvBLa9hQl+kg0jUCAgN12Rf/O9cYeO89IC0NWL8e+PxzXbmoCROMv3Ziou5YlmLN303//roSZ2WuF7FmDeDqCpS+Xrx/H7h4UdfzpYSx6+lTp4A//9Rl5Ei1o+/zz3X9VL77Dli0SBcYe+MN431PEhN1/VdktnGLEFsj93u9O3fu4MSJE0bvc7ds2YIZM2bglVdeweeff17lPE6cOEH3ioSUolKp8MILL+C3337D2bNny32/9DqV0PW/lStX6v8/YwwrV65EjRo19OWVKrqHKynFX/JV0o8FAI4cOYLly5fjrbfewttvv4133nkHK1euxIEDB8z6PZQQek6RWmJiIhQKhdXvGSs6D2dnZxusSZT44YcfAMCse2tTbN26FVu2bMGyZcswZ84cjB49GnPnztUH8OwNZaLYmMWLFyMpKQl79+5F7dq10blzZ8ybNw9z587FqFGjuKOUYlqyZAkOHTqEQYMGoVmzZsjKysJvv/2GY8eOYfr06WjVqpXF5vLll19i9erVCAwMhKurKzZu3Gjw/REjRqBmzZoAdCmKL7/8Mp544gk4Ozvjn3/+webNm+Hv74/XX39d/xwXFxcsWrQIYWFhePHFFxESEoK///4bGzduxJIlS+Dh4SH6z7F+/XqsXr0aI0aMQMuWLfHw4UN8//33cHNzE+3fePjw4diwYQMuX75ssFth5syZyMzMxJ49e6BSqTBo0CBMmTIFixcvxvDhww1SA2NjY+Hq6mpW/URCrGXYsGHYsGED3N3d0aFDByQkJGDPnj36NOey1Go1nnnmGbz00ku4dOkSVq9ejaeeekqficX7vh0+fDg++OADffNPQHeR++qrr8LFxUUfsH399dfx22+/YebMmRgwYIBB6cDY2Fg0a9bMIGvO0oz9HIAu+7CkEeChQ4cA6C7o69Spgzp16ugbHkZFRWHw4MFGa/quXLkS2dnZuH37NgBgx44duHnzJgBdoLxk59X777+PX3/9Ff369cPMmTPx6NEjfPrpp3jiiSfKlQm7e/cuTp8+jbCwMJF/E4RY2MOHQNOmumwBPz9dhsKePcCxY0CpzDqzPPWUrmzVnj264ECJffuA1at1fTNKNsmsWwcEBekamX/yyb9j797VNSq35HvOmr8bFxddECIsTNfQPSQE+PtvYONGXRZM6evFlSt1zdz379f97gBdU3sXF10Qo2FD4Px5XWDD1RVYtkycuZd14YLu323iRODZZ3WPRUbqsmbefBP45Zd/xxYVAQcO6B4nhJhF7vd60dHRcHZ2NsjABnSZvqGhoahXrx6eeeYZ/Pzzzwbf79Wrl0FPhsTERGRlZZVrWk9Idbds2TLs378fPXr0wGuvvYYOHTogKysLJ06cwJ49e/Tl5YWs/zk7OyMmJgYTJkxAjx498NdffyEqKgrvv/++QRZ+RfdwxhQUFGDChAlo3bo1lixZAkBXdmvHjh2YNGkSzpw5o19fM4XQc4qpFi9eDAA4d+4cAGDDhg34559/AABz5841GBsbG4vevXtXuCYghg0bNuD69et4/L/NNAcPHtTPcfz48fDx8anwPBwXF4cZM2Zg1KhRaN26NdRqNf7++29s27YN3bp1w7hx4ySbd1l3797FG2+8gX79+unv8VeuXIn9+/dj4sSJ+Oeff+yvrBcjNiMxMZE5ODiw6dOnGzxeXFzMnnzySda4cWP24MEDi89r9+7dbNiwYaxx48asRo0arHbt2qx3795s3bp1TKvVmnTMvn37sgkTJgh+3oQJExiACr9SU1P1Y6dMmcI6dOjAateuzWrUqMFatWrF3nvvPZabm2v02N999x1r27Ytc3R0ZC1btmRffPGFyT9fVU6cOMHGjBnDmjVrxpycnFjDhg3ZsGHD2PHjxw3GAWDz58/X//f8+fMZAHbv3j2DcevWrSv38xcWFrL69euzRYsW6R/7448/GAD22WefGTw/NzeX+fj4MD8/P6ZWq/WP9+jRg40bN06En5gQy3vw4AGbNGkSq1+/PqtVqxYLCQlhFy9eZD4+Pgbnn5L3z4EDB9h//vMfVrduXVarVi32yiuvsMzMTP043vftnTt3mIODA9uwYYP+sRUrVjAA7LfffjMYm5aWxtzc3NiQIUP0j2k0Gubl5cXmzp0r8m9EGGM/B2OMpaamVngO9vHxYYwxlp2dzRwcHNgvv/xi9Ng+Pj5c53HGGDt79iwbOHAgc3V1ZXXq1GGvvPIKy8jIKHfMNWvWMFdX1wrP8YTYjMJCxt55hzE/P8Zq12asZk3d/1+92nDchAmM/e89xxhjLDWVMYCxTz8tf0yAsVLXE4wxxmbMYKxVq3//OzdXd7wuXRgrKjIcO2sWY0olYwkJ/z62Zg1jrq6655nCx6f8nKpird9Nad99x1jbtow5OjLWsiVjX3zBWNnrxfnzdcfdv//fx1asYKx7d8Y8PBhzcGDMy4uxceMYu3Klyh/bJMXFjD35JGNNmzKWnW34vRUrdPPbsuXfx/76S/eYVPMhxM75+Pjo79vkfq83atQog2vPsq9T0de6desMxr/33nusWbNm5e6ZS45DSHV2584dFhYWxry9vVmNGjWYp6cne+aZZ9h3333HGBO2/jdhwgRWs2ZNlpycrL8vatSoEZs/fz7TaDTlXtfYPZwxs2bNYiqVih05csTg8ePHjzMHBwf2xhtvmPEbEH5OKYt33bCy1ygtOzubOTo6sh9++KHKY5q6Zlny3Irms/9/14YVnYevXr3KQkNDWYsWLZiLiwtzdnZmHTt2ZPPnz2ePHj0yaT68yv6bjBw5ktWuXZtdu3bNYFzJZ83HH3+sf2z+/Pn6tQBbpmCsTB4pIaTaWLRoEdatW4crV65U2KCwIklJSejSpQtOnDghaV8YQuzR5MmTcfnyZfz999+Cn/v7779j7NixSE5O1jdXtxZTf45ffvkFr7zyCu7fv2+xfl4BAQEICgrCF198YZHXI8TmpaTo+n/89RfwvzIQggQE6LIs7PE9Z+7vxhY9/zygUADbt1t7JoTYpObNm2PixIlYsGCBxV7TlHu94uJi1KtXDxEREXjTjMyzwsJCNG/eHHPmzMHMmTMNvhcZGYlJkyaVK2lGCDHNxIkTsXXrVjx69IhrvDn3ovbsyy+/xCeffILk5ORyfWEsSazzsNgUCgXWrVuHiRMnCn7uggULEBkZKVoze2uxs7waQogQs2bNwqNHj7B582bBz122bBlGjRpFARRCTDB//nwcO3ZMX+5KiI8//hjTpk2zegAFMP3nqFOnDr766iuLBVBiYmJw5coVhIeHW+T1CLELLVoAkyebVkoqJga4cgWw1/ecOb8bW3ThArBzp65cGSHEZphyr5eVlYVZs2ZhxIgRZr32unXrUKNGDUydOtWs4xBCxGfOvai9Kioqwueff465c+daNYACiHceJuKjTBRCCCGEEEIIIYQQiVgjE0WuKBOFEHEJzUQhxBSUiUKZKIQQQgghhBBCCCGEEEIIIUZRJgohhBBCCCGEEEIIIYQQQogRlIlCCCGEEEIIIYQQQgghhBBiBAVRCCGEEEIIIYQQQgghhBBCjHCw9gQsQavV4vbt26hduzYUCoW1p0MIkSnGGB4+fIjGjRtDqbSPGDOd/wghvOgcSAipruzx/AfQOZAQwscez4F0/iOE8OI9B1aLIMrt27fh7e1t7WkQQmzEjRs30LRpU2tPQxR0/iOECEXnQEJIdWVP5z+AzoGEEGHs6RxI5z9CiFBVnQOrRRCldu3aAHS/DDc3NyvPhhAiV7m5ufD29tafM+wBnf8IIbzoHEgIqa7s8fwH0DmQEMLHHs+BdP4jhPDiPQdWiyBKSeqem5sbnTwJIVWyp3RfOv8RQoSicyAhpLqyp/MfQOdAQogw9nQOpPMfIUSoqs6B9lHskBBCCCGEEEIIIYQQQgghRGQURCGEEEIIIYQQQgghhBBCCDGCgiiEEEIIIYQQQgghhBBCCCFGVIueKISQ6iVfrcHiqHM4fTMH7i418FqfFniqdQOolPZT41UKGi3DwQt38fGu87iWmQ+VSonuvh74ekwX1HKmjwtC5I7ew4RYx6OCYkz/+Rjir2ahkAEqBeDmUgMhHT0x/9mOcHFUWXuKRAYOHjyITz/9FImJiUhPT8f27dvx/PPPV/qcuLg4zJ49G+fOnYO3tzfmzp2LiRMnWmS+gO6aet6fpxFzOh0P1QwAUEOpQEM3J7zSwwdT+rSAowPtyyyh0TLEnbuD+TvP4GaOGgD9vqpS9vypBODiqKLrlwqU/X0pADg5KNGiQU3838B26NuW7nlNpS7WYkPCNVzPegwfD1eMD2xO71dCbIzU72MFY4yJdjSZys3Nhbu7O3JycqihFCF2buLao4i7fK/c404OSqwY7Y9BnbwqfK49nit4f6aYs+mYtukkirXGPxIUAILaNqCbGUJkKOdxEZ77+iCuPyiocEznpm74c1qfSo9Tnc+BhJhq2Fd/4+zt3ErH9G9bD2sn9bTQjIgpLHGu+Ouvv3Do0CF07doVI0eOrDKIkpqaik6dOmHq1KmYMmUK9u7di7feegtRUVEICQnhek1zfq5J645i/6Xy19RlTX7KBx8O6yTo2PZox6nbmPHfk6hqcWVS72aY/+wTFpmTnGm0DM98FodrmY8rHedT1wn73nmGAgMAhn39N87eqvzzBgBWjvbHMP8mgo5tj9dLQn6miOjz+PZgarnHX3/aF+FDOkg1RUKIiCKiz+P7v1NReklLqQBe61P1+5j3fEFBFEKIzVMXa/H931fx6a4rVY79ZlyXCgMp9niu4PmZYs6mY+rGE9zHVAGYNbAN/vN0S9qdQ4iVlOwO3nr8dpULNiWqCqRU13MgIaZq9+FfKCjSco2tX7MGjn84UOIZEVNZ+lyhUCiqDKK89957iIqKwtmzZ/WPjR49GtnZ2YiJieF6HVN/rk4LduFRQTH3+Cea1MaO6U9zj7c3U9Yfw54Ld7nHe9d1wt/vDZBwRvIm9N4DqPwerjp4YsEuPBTwnhzQvgF+mNCde7w9Xi/x/kwVBVBKUCCFEPkz933Me76g1S9CiM3IV2vwztaTeGJeNJrPidJ/tZn7F1cABQAW/HkemgqyLaojjZbhw+2nhT0HwPLdl9Fm7l/osmg39l24S79TQixAXazFqv2X0eaDKLSfF4NfBQRQAOD0zVxBi2KEkIoFLOAPoADA/bwiDPv6bwlnROxNQkICBgwwXGgPCQlBQkKCpK/b4cNowZ8VZ249xOTIYxLNSN6mrD8qKIACADceFKLPx3slmpG8mRJAAYCpG08g5my6BDOSv95LdwsKoADAngv3MGV99XxPCqEu1la68AoA3x5MhbqY//OeEGJZPO/j70R6H1MQhRAiSxotw/5zdzDo8/1o9b9gScmiYUlNZlNk5BbgaGqWiDO1bUdTs3Avz/RF1ay8Iry6/hhavh+NYSsO0gItISIrORd2+WiXPmCs1ph+vFlbToo3OUKqqUlrE/CgQPiN2Nlbufgj6ZYEMyL2KCMjA40aNTJ4rFGjRsjNzUV+fr7R5xQWFiI3N9fgS4jeEbvxuMi06+y9F+9ix6nbJj3XVu1MuoU9F6oueWbMjQcFeDXyqMgzkjeNlpkUQCnx5s8nqt3GrVfXHcat3CKTnrvnQvV7Twq17lAK17g5v52SeCaEEFON/+FwlWMYgMhDlQdaeFAQhRAiK+piLWZuPoGW70dj0objuHj3McRelr/7sOK+AdWNmL+Ls+kP0WnBLrQKj8Lq/Vdpxw4hZih7LszKF+dMmPbA+MIbIYTPzqRb2H/Z9M0Ys7YkVbtFQGI5ERERcHd31395e3tzP/ejHWdwK8e0xdoSMzefrDZ/3xotw7TNSWYdY9/Fe9VqkfuZ5fvMer6WAdN+ThRpNvK3M+kW9l3KNOsYM/5bfd6Tpth97g7XuD9O3abfIyEypC7W4si1B1xjd5/PMPv1KIhCCJGNj/48hzZz/8IfSdKmajes7Szp8W2JFL+LYgZ8susS2sz9C30+3kvZKYRwKpt1IsW5sFldF9GPSUh1odEyzDBz0VTLgBWxl8WZELFrnp6euHPHcIHvzp07cHNzg4uL8XN5eHg4cnJy9F83btzgei11sRbrDqWZPWctA77cfcns49iCsE3HRTnOW1uqxyL3nydu4lqW+Zu3/jp3p1ps1NJoGaab+XkD6HZfV6fAk1APC/kCxxotcDjZvIAWIUR86+OvCRitMPv1KIhCCJGFrgtjsU7QCdA0Hq6O6O7rIfnr2Iruvh5oUNNBsuPfeFBA2SmEVCFfrcGY7+NFzzox5ouXAyQ7NiH2bkXsJYjxKbYy7mq1WDQl5gkMDMTevYZ9M2JjYxEYGFjhc5ycnODm5mbwxWPOVvFK1Xwdl2z3f9/qYi1izgrrg1IRjdb+A6saLcOsX8X7Gxv/Y9WlW2zd9E2JgvreVaa6BJ5M0d7TnXvsJ7suSDgTQogpNh6+xj02uENDs1+PgiiEEKtrOScKmY/VFnmtxc93gkppfgTaXqiUCiwa0Vny16HsFELKy3lchM7zY9B+XgwSkvnSkM3RuakbajlLFzQlxJ5ptAxrDiSLcizKRqmeHj16hKSkJCQlJQEAUlNTkZSUhLQ0XQZIeHg4QkND9eOnTp2KlJQUvPvuu7h48SJWr16NX375BbNmzRJ1Xhotw7YkcUtK2fvOd57660LYe2A1/up9aET88Y6kPrDroIC6WIvos3xlpnhVh8CTKUZ1bco99tTNXLv+uyPE1qiLtbiexV+qelLvFma/JgVRCCFW5TsnCmb0SBbk9ad9MaSzl4VezXYM6uSFb8Z1gYOFgksl2Smtw6Ow5/wdu75pJKQsdbEWq/ZfRovwKPgt3I3cQsucATs3dcOf0/pY5LUIsUeHUzJRJOLayeoD9r1oSso7fvw4AgICEBCgywicPXs2AgICMG/ePABAenq6PqACAL6+voiKikJsbCz8/Pzw2Wef4YcffkBISIio81oRK375LXve+S6k/jovew+sLvjznOjHtOeggJiZYSXsPfBkql6t6gtaFLXnvztCbI2QDQ0+Hi5wdDA/BELbEQkhVtP2gyjR0pSrsnpsAIZ0bmyhV7M9gzp54dJiTxy8cBdv/DcRBcXS/8sUMWDKT7p60s939sInL/mL8sFGiBzlqzUYvupvXL6TZ9HXrefqgAPvPkMZKISY6af4VFGPV6wF4q/cR5+2DUQ9LpGvoKAgMFbx9VVkZKTR55w8eVKyOWm0DKvixMmwKit822l89pK/JMe2JrGzUEqsjLuKmcFt7C5jXl2sRfJ98a99SoIC9nbvIEVmWInxPx7Gltd7SXJsW6VSKjCiS2P8doLvd26vf3eE2BqhGxrG9fQR5XXpnU8IsQr/BX/BEhuwm9V1RPLSIRRA4aBSKtCvYyNcXDwEZxeEoF9ry/WO+f10OtrM/QshX8QhX22p3CRCpPeooBhdF+5G+3kxFgugKAGMftIbFxYOQuK8EAqgEGImjZYh9rw4/Q9KW7DzrOjHJEQIscsslbb95C27y7aSIgulhL1mo0gVdAKAOb+Jn7FhbUIzw4SE3CgbxbiIkX6CxlM2CiHWJ/SzZUIvX1Fel4IohBCLe2pZLLILpL2A867rjLMLQnDwvWC729FlCbWcHbBuciCuLRuKswtC4F3HySKve+lOHtrPi0HXhbuobwqxaTmPi9B+7l/otGAXMh8XWeQ1O3nVxtkFIUhZNhTLXugMF0eVRV6XEHsXf/W+KA3ly0q+95gWtIhVbTtxk3usUgF8OYp/sVHLdNlW9iR8m7BF+wsLBwka/83BZLsKPAkNOo0MaIzWDVy5x/+edNuufl9Ce28pAJwX+De2IeGasElVA44OSvRoXpd7PAWjCLEuoZ8tPXzripY9RkEUQohFfbTjDG5mi99EvoZSgSZ1nPFuSFtcXjwYf79H5WvEUsvZAX/PGYDLiwfjnZDWsMS6bObjYnRasAvtPohGjoUWoAkxl0bLsP/cHbR+X9fvJN8CN1geNWtg7YQnkbx0CHbOfJrOe4RI4DcBC80jAxoL2hkcvu208AkRIpI8Adm/04Ja4fluTVG/Vg3u59hTtpVGy/DHSf4ySz1868LFUYUZ/VpyP0etYTicnGnK9GRJaNBp2Qt+iJrZl3u8vQXqhPbe+mpMAFwcVYICANezHpswM/u3YUpPQePtMQuKEFshtG/UhsnC3t+VkTSI0rx5cygUinJfYWFhAHQ1Xst+b+rUqQbHSEtLw9ChQ+Hq6oqGDRvinXfeQXEx7U4mxBapi7VYdyit6oGVKBssubZsKK4tG4orS4fg0Jxn8Ga/VlSjVCKODkqE9WuDy0uG4sLCQXixW2PJI/EFGga/hbvRKjwK+y7ctavdZsR+qIu1mLn5BFq+H41JG46L2nzaGAcF9OfAEx8ORP/2DSnjjhAJJSTzL9Ite8EP0wUsmv5hZzupiW15knPx1UEJzAxuAwBYMboL9/HtKdvqcEomhLQMLFm0mRncVlBg9e+r4pcOtAZTgk6ODkrBWQH2FKgT0nurkZsjnvXTlasWEgDw8eDP9KlOhP7dbT9Jn92EWIPQvlFiZqEAEjeWP3bsGDSaf3e3nD17FsHBwXjxxRf1j7322mtYuHCh/r9dXf89qWs0GgwdOhSenp6Ij49Heno6QkNDUaNGDSxdulTKqRNCJNDnkz0mPa9to5r4PawPlaaRERdHFT4dFYBPRwXgUUEx+n6yT9KSRcUMeHX9MQDUhJ7Ih6WbxXvXdcZfM/tStolENFqGo6lZuPuwAA1rO6O7rwcFpwjUxVrceciXQdvY3RmODkrMDG6Lr/bzlWQp1up2nvduXd+caRJikgm9fLE0+iKqWgr84uUA/fmwZ4t6qKEE94aB9fGpeO1p/sCiXAlZ4PZr6qa/TlUpFZjeryX3OWH/xXuYM9ikKcqKqUEnQBcUaDP3L67nlQTqbP2+QKNl2HORP4C2/AV//f93dFBi8lM++PGf65U+R6kAxgc2N3GG9k/I3x2DrofR7JC20k6KEGJAaN8oMbNQAIkzURo0aABPT0/9186dO9GyZUv07ftviqarq6vBGDc3N/33du/ejfPnz2Pjxo3w9/fH4MGDsWjRIqxatQpqtfjlgAgh0vnzxE3cyRW+yH558WDsmhVEARQZq+XsgMR5Ay2WnVLShP7Fb/6xm92NxLbkqzUIjNhjkWbxTiqF3ZQpPHjwIJ599lk0btwYCoUCv//+e6Xj4+LijGY0Z2RkiD63mLPp6L1sL8Z8fxgzNydhzPeH0XvZXsScTRf9tYhtWR9/jXvsc/5eAHSLpiEdGnI/71DyPaHTIkQUjg5K/OfpyputBndoqN/xDuj+vt/oyx8U2Xi48oVdWyB0gfvdkPYG/z0zmH+h9crdR3axw11I743SQSdA93fZqkFN7uevFxDgkqvDKZnQcN7WKBVArzKB9w+HdULnpm4VPEPntT6+Nh9skpLQbJSVcVft4r1KiK3QaBm+5tyQAIifhQJYsCeKWq3Gxo0b8eqrr0Kh+HdX388//4z69eujU6dOCA8Px+PH/9ZoTEhIwBNPPIFGjRrpHwsJCUFubi7OnTtnqakTQsyk0TLM+EV43dDkpUPoQs+GlGSnpOib0TtL+nrHruVQMIVYlLpYi2c+24/282KQnlMo6Wv1buGBCwsH4dKSIXZTpjAvLw9+fn5YtWqVoOddunQJ6enp+q+GDfkXp3nEnE3H1I0nkJFr+G+akVuIqRtPUCClmttx+hb32D6t/v3bDO1V+cJ0abvOiR8YJIRX+JAOeP1pX6Mlp17r0xzfhz5Z7nEhQYHrWfk2f50mZIHbQalAz5b1DB5TKRXo3rwO1/Ptoc+HRsuw98Id7vFlg04AMP/ZjtzPt4dAnZBMpxEBTYxmyv45rQ8mP1X+vaxUAK8/7YvwIR3MnKX9E1IaTct02SiEEMtYEXupyszZ0sTOQgEkLudV2u+//47s7GxMnDhR/9jYsWPh4+ODxo0b4/Tp03jvvfdw6dIlbNu2DQCQkZFhEEABoP/vynYhFhYWorDw3xvh3NxcEX8SQohQYZuOC37O6rFdqIyKDdM1o38G6mItvv/7Kr6IvQKp7p9LgikhHRtg9StP0t8NEZ26WItXfkjAsWvZkr6OkwpYM+5J9G3bwC7/jgcPHozBg4XXKGnYsCHq1Kkj/oSgW+iZs+1MpWNm/3IKwR087fLfhFROo2U4d4vvPqLswqmQkkf2Uo6G2K7wIR3w9sB22JBwDdezHsPHwxXjA5tX+DdZkm216zxfdoatl/Q6JCCoMdy/sdHPi+n922D82qNcx/hq32X0aduA+zXlRkiDdEdV+aATAPRqVR9KADyHKQnU2eo5VGimU8TIzhV+78NhHfDeIP73MjFUkgV19R5fpvnKuKuYGdyGrhEJkZjQLJSWDVwlOe9Z7Ez6448/YvDgwWjc+N9U4P/85z8ICQnBE088gVdeeQU//fQTtm/fjuRk/l+MMREREXB3d9d/eXt7mzt9QoiJ1MVaxJwV1iBx8lO+GNLZS6IZEUsqaUZ/dakuO6Weq3Sx+13n7qHl+9H4NPoCpVYTUaiLtXjxm0NoM/cvSQMo3nWdcXZBCC4tGUpN4o3w9/eHl5cXgoODcejQoUrHFhYWIjc31+CrMoeTM5FdRT+nx2oNvt57RfC8ie07nJIJDefHSf92hsFPlVKBZ9o3quQZhuyhHA2xbY4OSkzu0wILh3fC5D4tqlx8EJJtteMUfxNYOdp3kT9brKIF7pKgAI8TN7Jt+lo2Ppk/6NSvnfHrHpVSgWABZRFt+RwqJNPJx8Olyvem0PcyMSQkC4qyUQixDKFZKAuGdZJkHhY5m16/fh179uzBlClTKh3Xo0cPAMDVq1cBAJ6enrhzxzANtOS/PT09KzxOeHg4cnJy9F83btwwZ/qEEDOM/+GwoPH+Td3w4TD7TDUW2g9g27ZtCA4ORoMGDeDm5obAwEDs2rXLMpOVgK53SgguLByENo346xwLtepgClq+H43fj9O5n5hGo2WY+tNxyYMnz3f2soteJ1Lx8vLCN998g99++w2//fYbvL29ERQUhBMnTlT4HKEbaRJS+BZ6vjmQbNMLWsQ0QhYCJwSWX1AW0sDX1heZSfXTs0U9qDhXE87ezrXZc6hGy3CJs/9Zo9qOlWbvdOMs6aXR6oL8tkpIGczQns0r/l41CdQJ6R8zrqePdBMhAHQBT5WA/UyrD1BvFEKkpNEyrIrjT7ZwUJbvGyUWiwRR1q1bh4YNG2Lo0KGVjktKSgKgu2kGgMDAQJw5cwZ37/67iz02NhZubm7o0KHiRVYnJye4ubkZfBFCLE9drMWRaw8EPee3N5+SaDbWJ7QfwMGDBxEcHIzo6GgkJiaiX79+ePbZZ3Hy5EmJZyotF0cVds8KwuXFgzHcX7qMo7e2nkbbD6LwqKBYstcg9ue3xJto+X40Ys7z1/IWwkmlwNoJTyJ56RB8ObYL7Q6sRNu2bfH666+ja9eu6NWrF9auXYtevXrhiy++qPA5wjfS8N0lFxRrbXpBi5jmaGoW17iKytEIWWQ+n/6QFmGITVEpFRjQji9TwJb7fBxOyeTe/dqzRfnzQGnT+7fhft31CbaZWaEu1iL53uOqB6Lic2eJ6nAOFdo/ZoKAwBIxjUqpQFgQf/nBYq3tnt8IsQXxV+9zZ4YDwJt9W0lW2UHyO3etVot169ZhwoQJcHD4d5dlcnIyFi1ahMTERFy7dg1//vknQkND8fTTT6NzZ10K7MCBA9GhQweMHz8ep06dwq5duzB37lyEhYXByclJ6qkTQswkNAtlxWh/uy5jM3jwYCxevBgjRozgGv/ll1/i3XffxZNPPonWrVtj6dKlaN26NXbs2CHxTC3D0UGJFaO7IHnpEKwb3w3ODuL/2xdqgE4LdiFgQQzy1RrRj0/sR75agzZzo/H2r6ckOX7NGor/lewaQiW7zNC9e3d9xrIxQjfSBFayeFPWT4evcY8ltk+jZTh5nW8jiF9T9wrL0fAuMhdrGQXqiM0Rkinw1T7bLHkjJEtgVJfKsx+FlPTad/GeTQYF1sdf4x5bUSmvEtXhHCqkf4xUNf5JeTOD23Jus9FZsPOsZHMhpLr7aMc57rFKBTAzmH/DglCSn4H37NmDtLQ0vPrqqwaPOzo6Ys+ePRg4cCDatWuHt99+Gy+88ILB4qBKpcLOnTuhUqkQGBiIcePGITQ0FAsXLpR62oQQMwnNQvFyc8Jw/yYSzsj2abVaPHz4EB4eHhWOEdoPQA5USgX6dWyEi4uH4OyCENRyFP+j6UGBBu3nxeD5lQdt8oaUSEddrMUzn+1H+3kxUBeL/7dRz9UBZxeE4NyiIVSySwRJSUn6jGUx9GxRD7zx2/0X79L5oxo5nJIJ3lPCk74Vfy4LWWQ+lHyPeywhctCzRT3w7gmwxT4fQrIEeMqHqJQKdGrKVyXDVoMCO07f4h5bWSkv/RgB51BbzN4RUjYypGPFJe2JuFRKBab3489GSb73GOpizmgYIYSbuliLq/f4SmoCwLQg6bJQAEDyu/mBAweCsfIXS97e3jhw4ECVz/fx8UF0dLQUUyOESEjILiQAOPBuf2kmYkeWL1+OR48e4aWXXqpwTEREBD766CMLzkpctZwdcHbhYDwqKEbfT/Yhs4qGz0Il3XyIlu9H48tRnfF8t8p3CxL7ptEyhG1MlKxsV9tGNfF7WB+4OKokOb4tevTokUEWSWpqKpKSkuDh4YFmzZohPDwct27dwk8//QRAl43n6+uLjh07oqCgAD/88AP27duH3bt3izYnlVKBAJ+6OMYR9FdrdAtavSWqsUvkRcjCVu+WDSr8Xkmgjicgc4yzfBghcqFSKtDNpw6OcvQPK+nzYUvnUCFZAgHedbgWbp7t3ASnb/JtcjqUfM+mfl8aLcO5W3w/m4Oy8lJeJXq2qAeVAlylXEqyd2wp2/fWg3zusZV91hDxzQxui6/3J3OX85vz2yl8/nKApHMipLoRUt1G6iwUwEI9UQgh1c9GAWVPevjWpdTkKmzatAkfffQRfvnlFzRsWHFau/B+APKka0I/ULIm9G9tPY0OH0ZTia9qavuJW5L1PSlpFr9rVhAFUMo4fvw4AgICEBCgu8GcPXs2AgICMG/ePABAeno60tLS9OPVajXefvttPPHEE+jbty9OnTqFPXv24JlnnhF1Xt0rySIoizIFqg9z+6GUKAnU8Th1M8fmduoTIqTPh62dQ4UEUyvLSCttQq/m3Me0tcDq4ZRM7rr1/ds14Ap2qJQKdGxiv9k7Nx+I0z+GiE9oNsr2k7fpM5wQEQmtbiN1FgpggUwUQkj1oy7W4noW/66aDZN7Sjgb27d582ZMmTIFv/76KwYMGFDpWCcnJ7vqGVXShF5drMUrPyTgGMdOR16Pixjaz4tBy/ou+OutIArkVQPqYi2eXBKLnPxi0Y8d0rEBVr/ypE3tfrS0oKAgo9nJJSIjIw3++91338W7774r8ayAXi3rY9X+ZK6xtragRUwjRj+U0rr7elC2E7FbJX0+eBI2bO0cyhtMBfizBBwdlGjZwJWr+XpJYNVWri2EBJ0mBPKX6bLX7B2NliEpLZtrLM9nDRHfzOC2+IrzGpEBWBF7GbND2ko7KUKqiTlb+XuVKiB9FgpAmSiEEAkIOdn5eLjQ4nUl/vvf/2LSpEn473//i6FDh1p7Olbj6KDEr1N74/LiwWjZwFXUYyffz0ebuX9h/p+nRT0ukZeP/jyHNnP/Ej2A8mRzd1xePBjfju9ON7c2qmeLeqjB+TFEmQLVg1j9UEr0asm/oGdrO/UJUSkV6Nrc/rKthARThWYJDOrE19urJLBqK8TK4CvLXrN3xP6skbM1a9agc+fOcHNzg5ubGwIDA/HXX39V+pxff/0V7dq1g7OzM5544gmrlPlXKRV40qcO9/iVcVdt5hxHiJxptAzbkm5zjx8R0Ngi9+K0ckkIEZVGy7BdwMluXE8fCWcjL48ePUJSUhKSkpIA/NsPoKR8TXh4OEJDQ/XjN23ahNDQUHz22Wfo0aMHMjIykJGRgZycHGtMXxYcHZTY+3Y/XFg4CF7u4mbcrI+/gXYfRFFTQDuTr9agzdxorBPYp6kqJcGTX6c+RYFgG6dSKvBM+0ZcY21tQYuYRqx+KCVK+qLwsKUFQEJK8JZFtKVzqJAF7n7tGgpavLHHwKrYGXyllWTv8LClQJ3YnzVy1rRpUyxbtgyJiYk4fvw4+vfvj+HDh+PcuXNGx8fHx2PMmDGYPHkyTp48ieeffx7PP/88zp49a+GZAzOe4d/drmW6bBRCiHlWxF4SNH7ZC34SzcQQ3fUTQkQVf/U+d/M1AJjQiz+V29YJ7Qfw3Xffobi4GGFhYfDy8tJ/zZw50yrzlxMXRxUSwgeIHkwp0ICyUuyERsvw/Mp/0H5eDNS8qyAcKHhin8YHNuceaysLWsR0vI1+eXdTU18UYu/sMSggZIE7tGdzQce2x8Cq1FkV9pi9I1Xmjhw9++yzGDJkCFq3bo02bdpgyZIlqFWrFg4fNt40esWKFRg0aBDeeecdtG/fHosWLUKXLl2wcuVKC89cV7JQJWCD+zcHk+lznBAzaLQMaw7wldEDgJYNXC12X053/4QQUX20w/huEmOqW0P5kn4AZb9K+gBERkYiLi5OPz4uLq7S8cQwmFLHVbw2X5SVYtv+SNI1jk+6KV7WVsv6LhQ8sWP2uKBFTJev5iv7J2T3uT3u1CekhD2eQ6Vc4LbHwKrUWRX2FqiTMnNH7jQaDTZv3oy8vDwEBgYaHZOQkFCuF2hISAgSEhIqPG5hYSFyc3MNvsSgUioQFsTfYJ4+xwkxz+GUTBQJWIZZMKyTdJMpg1YBCCGiURdrcfVeHvd4aihPxOLiqELSvBCcXRCCGiLdZFBWiu3RaBmCPt2PmZuTRDumaw3gwsJB2Pt//Sl4YsfscUGLmEajZYi7zLcA143zbwawvwVAQkqzt3OoJRa47S2wKnVWhZBAHW82oTVVp34oJc6cOYNatWrByckJU6dOxfbt29GhQwejYzMyMtCokWGp1UaNGiEjI6PC40dERMDd3V3/5e3tLdrcZwa3hZB3+Se7Loj22oRUN5/G8L9/HJRAr9b819jmotUAQohowrfxN5S3ZModqT5qOTvgytIhmCSgAWVVKCvFNuw4dRst34/GtczHoh3zy1GdcX7RULg4qkQ7JpEve1vQIqY5nJKJQs6Vrfq1+MtJ2tsCICFl2dM51BIL3PYUWLVE0EmlVMC/WR2usbez5X8OrU79UEq0bdsWSUlJOHLkCN544w1MmDAB58+fF+344eHhyMnJ0X/duHFDtGOrlApM78efjXLqZi7dOxJiAnWxFkk3+bPI3uzbyqKZerSCSQgRhUbL8MdJ/obylky5I9XP/Oc64vLiwdxNKKtSkpXy0Y4zohyPiOvVyKOY/t+Toh0vpGMDJC8dgue7ibeDjcifkAWthBT+xQ9iW4QsbHm6u3CPtbcFQELKsqeggCUWuO2pBJqlsiqa1uW7rj97O1f22U7VqR9KCUdHR7Rq1Qpdu3ZFREQE/Pz8sGLFCqNjPT09cefOHYPH7ty5A09PzwqP7+TkBDc3N4MvMQnNRhn/o/F+L4SQio3/gf99owAwM7iNdJMxgoIohBBRCLl4Viosm3JHqidHByX2vt0PFxYOgqujOB936w6lodfSXaIci5hPo2Xotmg39l0UZzGmpO/Jt+O721XtacKnZ4t64E06kvnaDDEDbxaISw0l9877ErwLgLZQ7oiQsuwp24p3fuYscAspgSb3oIClsiqa1OULXOcXabmDFNZQnfuhlKbValFYWGj0e4GBgdi7d6/BY7GxsRX2ULEElVKBkV0ac48/kvqAslEIEUBdrMWRa3znRgAY2aWJxc+PFEQhhIjip/hU7rED2vM3YiXEXC6OKpxfOBhfvOQvyvFu5xajxZwoWd/MVgcl5bvu5xWZfSwVqO8J0d0cD+vsxTU2I6dA4tkQa7n1gK8kYKfGboKvZXgXAG2h3BEhZdlTtlW+uphrXL925t3T8AZi5R4UsETQCRCW7ZSRI9+/serYDyU8PBwHDx7EtWvXcObMGYSHhyMuLg6vvPIKACA0NBTh4eH68TNnzkRMTAw+++wzXLx4EQsWLMDx48cxbdo0a/0IAICIkX6CxlM2CiH8hGShAEDEyM4SzaRitFJACDGbRsuw5+Jd7vETAn0lnA0hxo3o0gTJS4dgUIdGVQ+ughZAy/ejsTPplvkTI4KJWb5rfGBTJC+jvidEx6sOX6ZA9Jl0CqTaIY2W4dTNHK6xpixs2VO5I0KMsYdySxotQ9xlvvdfN85MkorYS1DAUkGnni3qwYkz3en+I+MZDnJQHfuh3L17F6GhoWjbti2eeeYZHDt2DLt27UJwcDAAIC0tDenp6frxvXr1wqZNm/Ddd9/Bz88PW7duxe+//45OnaxbEtzRQYkezfnf95SNQggfoVkoPXzrWmXzo4PFX5EQYncOp2RCw3lt4KC0n7quxPaolAp8E9oN6mItOi2IgZp3G1gFpm1Owm8n0rDuVeulllcnGi1DjyWxomSfuDkrcXxuCGWeEAMKzmrXBcVaHE7ORG8qTWlXDqdkQq3h+1wwZWGrpNwRz0eP3HsgEGKM0HJLgTK8JzickolCzuvD+rWczHqtkqAAz+vJNShgyaCTSqlAUJsG2HW+6s17x68/wGtmvZp0LJW5Iyc//vhjpd+Pi4sr99iLL76IF198UaIZmW7DlJ5oM/cv7vFzfjuFz18OkHBGhNi+OVtPCRq/YXJPiWZSOVo5IISYTUgpr+H+jamUF7E6RwclLi8eggmBPmYfa//lLHT6MFq2OyrthZjlu74c1RmnFwymAAopR8iCHmUK2B/e3cHODkqTFraE9ECgvijEFtlDZoWQLAFPd76gUUVKggI8jnP20LA0SwadAMDFkW8f8P6Ld2V7DrVU5g6RhtBslO0nb8v2b5EQOdBoGbYl3eYeb60sFICCKIQQMwkt5WWNuoWEVOSj4Z1wefFgsxvPPypiVN5LQpMjj4lSvsunrhOSlw7B8928RZgVsUf21BiZCMf7b9rZjEa/vD0QqC8KsUX2UG6Jt/eISw0l9/u50uPYeFDAkkEnwPZ7S1kyc4dIZ8MU/l3wDMCK2MvSTYYQG7ci9pKg8dbKQgEoiEIIMZOQUl4+Hi6085vIjqODEucXDsakXs3NPta0zUmYHHnE/EkRvWFfHcReAYHainz1kh8OvDeAdvSRStlTY2QiHO/u4K4CdqCWJWSnfkIK/+IkIXJg65kVGi3D6ZvZXGM7NXYT5ZrC1oMCvMFnsYJOtt5bytKZO0QaQrNRVh+4KssgKCHWptEyfL0/mXu8NbNQAAqiEELMJKSU17ie5pdOIkQq85/riMuLB8Olhnk3xHsv3sewFQdEmlX1NmTFAZy9/dCsY9SqoUDy0iF4rktTkWZF7J09NEYmwgnZHezh6mjy6/RsUQ+OKr6x9OdFbJEtZ1YcTc3iXuB+UoSAAGD7QQHG+H5fYgWderaohxqcq1i3swvMfj2xWTpzh0hHSDZKsRaIv0IbIwgpa0XsJQi5ErBmFgpAQRRCiBmElvKa0MtXwtkQYj5HByUuLBqCfm2FNwwu7Wz6Iwz5Mk6cSVVTvSP24Hz6I7OO0a+NB84uGkLZJ0QQoY2RiX2w1O5glVKBYZ29uMZm5MhvAZCYZ9WqVWjevDmcnZ3Ro0cPHD16tNLxX375Jdq2bQsXFxd4e3tj1qxZKCiQ99+FLWdWZOTy/257tzTvWrGErQcFeLMyG9cRJyCgUirQv11DrrH5RRpRXlNMls7cIdJxdFCiVYOa3OMX7Dwr4WwIsT1Cs1BaNnC1emUbCqIQQkxGpbyIvVo3qTu+HhNg1jHOZ+Shd0SsSDOqXjp8+Bdu5ZhXK33laH+sezVQpBmR6sQeGiMT4RIELOaauzvYqw5ftlP0mXTZ7dQnptuyZQtmz56N+fPn48SJE/Dz80NISAju3jW+IWnTpk2YM2cO5s+fjwsXLuDHH3/Eli1b8P7771t45sLYcsm6+w/5rj1caijRs2U9UV7TloMCGi3DqZs5XGN5g2s8ujXn+90fvHxPdufQWw8ec40TK3OHSGv+sx25xybfewx1MefiCSHVgNAslAXDOkk2F160okkIMdmGhGvcY6mUF7E1z/o1RvLSIXDiLLtizK0cNfwX/CXepOycRsvQck4UHheZfoNR31WF5KVDMMy/iYgzI9WJPTRGJsIxztu4Wk4qs3cHK8D391VQrJXdTn1ius8//xyvvfYaJk2ahA4dOuCbb76Bq6sr1q5da3R8fHw8evfujbFjx6J58+YYOHAgxowZU2X2irXZcsm649f43m9Pt2kg6gK3rQYFDqdkQq3hm49YmTsAUL82XzZgfpG8zqFCgk5ilYsj0urVqj7nJ7rO+B8PSzYXQmyJRsuwKo4/C8VBCfRqzb9JQyqSBlEWLFgAhUJh8NWuXTv99wsKChAWFoZ69eqhVq1aeOGFF3Dnzh2DY6SlpWHo0KFwdXVFw4YN8c4776C4mK/pIyFEOhotw94Ld6oe+D9UyovYIpVSgUtLhsLbjN1z2QVatJsbJeKs7FP06XS0fD8a5uyx7N+2Ho7PG0Q794hZbL0xMjGNm3MNrnEDO3iafY4JFLCDXY49EIhwarUaiYmJGDBggP4xpVKJAQMGICEhwehzevXqhcTERH3QJCUlBdHR0RgyZIhF5mwqWy1Zp9EyHODsWeBSw4wdNkbYalCAt7+Hs4N4mTsA4OnmzD1WTudQawWdiHRUSgVG+DfmHn8k9QFloxACIP7qfXCeDgEAb/ZtJYt7fMkzUTp27Ij09HT91z///KP/3qxZs7Bjxw78+uuvOHDgAG7fvo2RI0fqv6/RaDB06FCo1WrEx8dj/fr1iIyMxLx586SeNiGkCodTMsG7WVwOtQsJMcff7/XHq72bm/z8gmKgVTgFUiqyJOo83tx0wqxjrBztj7WTrNtojlTu4MGDePbZZ9G4cWMoFAr8/vvvVT4nLi4OXbp0gZOTE1q1aoXIyEjJ5wnYdmNkYpqTaXwBMU93/sW7ivRsUQ+cyU7c9fOJvN2/fx8ajQaNGjUyeLxRo0bIyMgw+pyxY8di4cKFeOqpp1CjRg20bNkSQUFBlZbzKiwsRG5ursGXNdhiybqjqVko4Ly5aVzH/PNAabYaFOA9P3Vu6i7q4ld3Xw84czaSkVMfGWsFnYi0lo3yEzQ+fNtpiWZCiO34et9l7rFKBTAzuI2Es+En+aqmg4MDPD099V/16+vSb3JycvDjjz/i888/R//+/dG1a1esW7cO8fHxOHxYl+K2e/dunD9/Hhs3boS/vz8GDx6MRYsWYdWqVVCr1VJPnRBSCd6LQAAI6egp4UwIsYx5z3bE5cWDYWo8sJgBreZQIKWsJVHn8P3fqSY/v2YNBZXvshF5eXnw8/PDqlWruManpqZi6NCh6NevH5KSkvDWW29hypQp2LVrl8Qzte3GyEQ4jZZh/yW+hUkx1gFVSgUCfOpyjU2X0U59YllxcXFYunQpVq9ejRMnTmDbtm2IiorCokWLKnxOREQE3N3d9V/e3t4WnPG/bLFknTWaypew1aAAb38PLxGCz6WplAr05SzrIqc+MtYKOhFpOToo0aM532c6AGw7cUs2wWNCrEGjZTh6LZt7/LQgeWShABYIoly5cgWNGzdGixYt8MorryAtLQ0AkJiYiKKiIoOU5nbt2qFZs2b6lOaEhAQ88cQTBjt2QkJCkJubi3PnzlX4mnLZgUOIPTuamsU9ltKRib1wdFDi6tKhaOzmaNLzi0GBlNKiT9/G939fM/n5TdwdcW7RENlcVJHKDR48GIsXL8aIESO4xn/zzTfw9fXFZ599hvbt22PatGkYNWoUvvjiC4lnKqwxspx2BRPTHE7JRCFneY3AFuLUY+btq3L6Zg4tttiB+vXrQ6VSlStdfefOHXh6Gt9s9OGHH2L8+PGYMmUKnnjiCYwYMQJLly5FREQEtFrjf6/h4eHIycnRf924cUP0n4WHkJJ1cmkub42m8iVsMShgrabyJWyxj4y1gk5Eehum8GfDMwArYvl34RNx5Ks1CN92Ck9/vAd+C2IQ9Ml+fLDtNPLV8jinVicrYi9xj1VAPlkogMRBlB49eiAyMhIxMTFYs2YNUlNT0adPHzx8+BAZGRlwdHREnTp1DJ5TOqU5IyPDaMpzyfcqIpcdOITYK42WIfEaX9kLR5WC0pGJ3Yl/PxhN6vDVry6LAik6Gi3Dm5tOmvz8jp41cSg8WMQZEblJSEgw2GwD6DbTVNQ/ABBvI03PFvXAuSlYVruCiWkSOHfCi1lihTdQJ6ed+sR0jo6O6Nq1K/bu3at/TKvVYu/evQgMDDT6nMePH0OpNDwRqVS6XhyMGV8UdnJygpubm8GXNdhic/msx3xBFLGbypewtaCAtft72FofGWsHnYi0HB2U8G/Kf75dGXdVFu/j6mLK+mNoPy8G/z16E2kPCpFToMG1rMf4+egNtJ8Xgynrj1p7itWGRsvw9X7+hvLBHRrKasOkpEGUwYMH48UXX0Tnzp0REhKC6OhoZGdn45dffpHyZWWzA4cQexV/9T5426H1ayevkx4hYjk0ZwC865q2U6wYQOtq3iOl9fvRJj/3mXb1EfVWkHiTIbJU0Waa3Nxc5OcbL4kh1kYalVKB/u0aco2Vy65gYjoGvoWMoHbiLZ7qFpn5jkXZTvZh9uzZ+P7777F+/XpcuHABb7zxBvLy8jBp0iQAQGhoKMLDw/Xjn332WaxZswabN29GamoqYmNj8eGHH+LZZ5/VB1Pkyhaby/NuEBO7qXwJWwsKWLu/h5A+MnLIdrJ20IlI751B7bnHahllo1jKcyv/xp4Ldysds+fCPTy38m8Lzah6WxF7ifOqW2dCoK9kczGFRTs916lTB23atMHVq1fh6ekJtVqN7OxsgzGlU5o9PT2NpjyXfK8ictmBQ4i9+u3ETe6xoT2bSzcRQqzs7/eeQb+2ppV2KWJAhw//EnlGtqFVeBR3ILaslaP98ePEHqLOh9gPMTfS2NquYGI6N+caXOMCvPlrnldFpVTAr6k719hjAkqoEvl6+eWXsXz5csybNw/+/v5ISkpCTEyMPliclpaG9PR0/fi5c+fi7bffxty5c9GhQwdMnjwZISEh+Pbbb631IwjC21x+9/kMq59DNVqGpBvZXGPFbipfwtaCAtbu79Hd1wOujnzLWXL4iLZ20IlIT0gWM0DZKJbw54mbOH2TLyv99M1c/JF0S+IZVW9Cs1DkWNXGokGUR48eITk5GV5eXujatStq1KhhkNJ86dIlpKWl6VOaAwMDcebMGdy9+2/UMDY2Fm5ubujQoYMlp04IKeX0zWyucSolZHfSI0Rs6yb1wOSnTNsh8bhIi94Re0Sekby1mxuFYhPuF5QANZCvZiraTOPm5gYXF+OlLsTcSGNru4KJ6XLyi0Qdx6tJXb5F5rO3c2mhxU5MmzYN169fR2FhIY4cOYIePf7dFBAXF4fIyEj9fzs4OGD+/Pm4evUq8vPzkZaWhlWrVpUrhy1XvM3lHxVqBPValIIcsgRsLSiQry7mGtdVQMNtIVRKBQZ1bFT1QMgj28naQSciPZVSgTf6tuQeT9ko0tJoGd7eekrQc97ZepqutyQkNAtl6tMtZXc+lDSI8n//9384cOAArl27hvj4eIwYMQIqlQpjxoyBu7s7Jk+ejNmzZ2P//v1ITEzEpEmTEBgYiJ49dU2ZBg4ciA4dOmD8+PE4deoUdu3ahblz5yIsLAxOTqbVoieEmEejZbh2n68pXqsGtWR30rOmgwcP4tlnn0Xjxo2hUCjw+++/Vzo+PT0dY8eORZs2baBUKvHWW29ZZJ5EuA+HdcDqsV1Meu6tnEIM/eqgyDOSJ/8Ff6GA757bgLMDkLJsKJ1PqpnAwECDzTaAbjNNRf0DxCZkVzCVW7JtKfcecY1TiHwK4q17n1+ktfoiMyFCCWkun5HDt8AsFTlkCQgJCri78GXPSUWjZYi7zPe55+HqKNk8bCnbydpBJ2IZM4PbcoaPdb45mGz1v017dTglE0UCSx+oqQ+dZDRahjUH+LNQlAp5NZQvIWkQ5ebNmxgzZgzatm2Ll156CfXq1cPhw4fRoIFu98YXX3yBYcOG4YUXXsDTTz8NT09PbNu2Tf98lUqFnTt3QqVSITAwEOPGjUNoaCgWLlwo5bQJIZU4nJIpoB8K1XMtLS8vD35+fli1ahXX+MLCQjRo0ABz586Fn5+fxLMj5hrS2QvJS4eY9MF67vZDvLrOvhva9Y6IRXaB8CJe7k5KXFw8VIIZEUt79OgRkpKSkJSUBABITU1FUlIS0tLSAOhKcYWGhurHT506FSkpKXj33Xdx8eJFrF69Gr/88gtmzZplkfl29/WAkwPfrTDvDlMiPxotwz9X+RYD64i8cMnbXB6w/iIzIUL1bFGP+xx6/xFfU3epyCVLoJE7X2A1O18t2Rx4HE7JRCFnWnH9WtJtfrWVbCeNluEQZ6BOyqATkZ5KqcD0fvzZKGoNo0V7ifAGx8uijVHSEBrUmhbUSpYbKB2kPPjmzZsr/b6zszNWrVpV6YKij48PoqNNbz5LCBHXhoRr3GP7tOJryltdDB48GIMHD+Ye37x5c6xYsQIAsHbtWqmmRUSkUiqQsmwoWs2JgtCEi32X7mHRzvP4cJj9lasc+mUcbuUIv+F3cVDg1Ef87xkib8ePH0e/fv30/z179mwAwIQJExAZGYn09HR9QAUAfH19ERUVhVmzZmHFihVo2rQpfvjhB4SEhFhkviqlAp2b1sExjmbD6TIoFUJMczQ1C48K+e7qxF4MLFlk5lmMtPYiMyFCqZQK9GvbEDHn7lQ5NlvkUnlCySVLID2b77MkkeNzSUoJAhZ9PTkDQ6YIbFkPK/df5RprzUC0NT9niOXNDG6Lr/cnc5ct+mTXBfzRuo+kc6qOTA2cUh86aXwac4F7rFyzUAAL90QhhNg2jZZh74Wqb4QAeTaBIsRSri4batIH7I//pCL6dHrVA23I5MgjOJeRJ/h5NRTAhcVDJJgRsZagoCAwxsp9lfQAiIyMRFxcXLnnnDx5EoWFhUhOTsbEiRMtOufuvh5c407fzKFyDDYqI5c/ACb2YqBKqUBQG76s3ePXrbtoSogpWjSoxTXu6l2+knpSkEtpKoC/xJ+1P3MY5/JwLScV9+eoKWwl28manzPE8oRmo5y6mQt1sfBsfVIxjZbhpInXTafoml506mItkm7mco+XaxYKQEEUQogAQlLw/KgpnlUUFhYiNzfX4ItYx5WlpgUA3tx0wm4u3HYm3cLei8JTqR0AXImgEl7E+njLLRVQDWWbdf8h38Kam7ODJIuBLo58hQEOXr5nN58NpPqoyxl0sObft1xKUwG285nj5sxX2nBgB09J7weFBKKzHluvBJq1P2eI5QntjTL+x8OSzaU6OpySCc7TejlUYk1843/g//tWQL5ZKAAFUQghAgipK/kkXQBaRUREBNzd3fVf3t7e1p5StaVSKrB6bIBJz/VbECPybCxPo2WYtjlJ8POU0GXyECIHPVvUg6OK7zaYaijbpgecC2uBLetJshhIzeWJPatfmy/okF9kvaCAXEpTASWZFXxLNAkpptX7F8PJNL4d3p7uzhLPhD8Qbc0SaFmP+YIoUn3OEMtTKRUY2aUx9/gjqQ8oG0VEpvZDKUHX9OJRF2txRMD5d2SXJrI+D1IQhRDCTcjNe++W1FTeGsLDw5GTk6P/unHjhrWnVK0N6dwYk5/yEfy8R2otnvp4rwQzshx/EwNBpmbwECIFlVIBv6buXGOpubxtSrnHV0aoVUO+skRCUXN5Ys883fgX0a0VFJBLaSqgpI8M3z2UtRLTNFqG/Zf4FhgtsQ5mCyXQeAM4LjVUEs+EWFLESD9B4ykbRTzmXpPf5uxPRaomJAsFACJGdpZoJuKgIAohhIuQupLUD8V6nJyc4ObmZvBFrOvDYZ3Qvy3/IlmJmw8K8GrkUQlmJL1JaxPwUC18N9XqsV1kvfOEVE9N6rpyjaPm8rZHo2X45yrfYmAdF77yNULZSk1/QkzR3dcDro58Sw7WCgrIpTRViYBmfM3rM6z0maMrf8Z3jRfYQvj1r1ByL4Gm0TIk3cjmGtu4jvSZO8RyHB2U6NGc7/0MUDaKmLzMfC+Z+3yiIzQLpYdvXThyZmNai7xnRwiRDSF1Jfu1a0gLoUY8evQISUlJSEpKAgCkpqYiKSkJaWlpAHRZJKGhoQbPKRn/6NEj3Lt3D0lJSTh//rylp07MtHZSD3T0rCn4efsu3sOOU7clmJF0dibdwv7LwkvOTH7KF0M6e0kwI0LMYwu7XIlpjqZm4VEh34KFVL0QqLk8sWcqpQKDOjbiGusuUaCyKjn5RVzjLFGaCgByC/jms/t8hlU+c3jL5Dg7KC2yqU7uJdAOp2RCreH7d6JKDvZnw5SegsavO5Qi0Uyql7tmBpnv5tKmFTHM2XpK0PgNk4W9X6yBgiiEEC5C6kqG9mwu3URs2PHjxxEQEICAAF2fjNmzZyMgIADz5s0DAKSnp+sDKiVKxicmJmLTpk0ICAjAkCFU7sgWRb0VhCbufA1WS5vx35M2szBrah+U/m0b4MNhHcSfECEikPsuV2K6jFz+m2wpeyHw1vTff/GuzXweEFLCqw5fNt/JG9YJEvKW9FNYaH+YgrMd9aNCjVX6JPGWyenc1N0im+rkXgKNt+eOpYJOxLKEZqOs/SdVwtlUDxotw54Ld806xt4LdL1lLo2WYVsS/2ZQW8hCASiIQgjhFHM2nWsclfKqWFBQEBhj5b4iIyMBAJGRkYiLizN4jrHx165ds/jciTgOhQejFmdZixIMwLSfE6WZkMieWb5P8HM6Nq6NtZO6SzAbQsRBzeXtVxZneSw3ZwdJeyHwZjupNYwCdcTm8AYF4i7es/iilRxK+pUVKOA+yhp9kvLVxVzjugpYODYXbwk0a2Q78fbcCWrXgCo52Ckh2Sh3HqqppJeZjqZmIaeA7zxVkez8IqsEqe3JithLgsbbQhYKQEEUQggHdbEWyfcec431s9CuI0Js1akFgwQ/569zd2R/Qf3niZu4liUsdbqeqwOiZjwt0YwIEQc1l7dfdVz5sgNHBDSR9NpGSHN5azXfJsRUvEEBa2TzyaGkX1ly7pOk0TIc4qxO4MF5fhUDbwk0a2Q78fbcCfC2XNCJWJajgxIt6/OXdaYG8+YRkmVc6XGsEKS2Fxotw6q4ZO7xtpKFAlAQhRDCYX38Ne6xT0q4U5MQe6BSKrB6bIDg53VfEivBbMSh0TLM+EVYzVMAODp3oASzIUR81FzePmXlqbnGNeX89zeVLtuJbyxVlyC2Rs49K+RS0q80XXmqhlxjszn7uYhFjkEnQN7ZTifT+AI3vL15iG1a8FxH7rHUYN48vFnGVR6H8xqRlBd/9T44W0EBsJ0sFICCKIQQDjtO3+IeSw3xCKnakM6NMfkpH0HPyc4vxsId5ySakXlMKeP19ZgAylojNoOay9unxOt8pRoePJb2RlqlVGBYZy+usRkUqCM2Rs49K+4/lEdJv7JaNKjFNe7qXb5+LmKRY9AJkG+2k0bLsP8SX7k4uiS2b71a1ecM9elQNorpeLOMLXWc6mjBn/xrFi0buNpMFgpAQRRCSBU0WoZzt3K5xjooqR8KIbw+HNYJ/k3dBD1n7aFrstuZZEoZr/7tGuBZv8YSzYgQ8VFzefsjt8Ut3ubb0WfSKVBHbI5ce1bwBkgDW9az6MaPupyLdwnJmRY9H8g16CTXbKfDKZko5LxuD2zBX9bRFkVERODJJ59E7dq10bBhQzz//PO4dKnyngmRkZFQKBQGX87OzhaasbhUSgVG+PPf+1A2iumyRdr4ItZxqht1sRbJ9/O4xy8Y1knC2YiPgiiEkEodTsnkTsXrTw3xCBHktzefEvwcOZX1MqWMV4OaNbB2IjWSJ7aFmsvbH7ktbvGWo6FAHbFFcu1ZkXKPL5OjVUO+zBCx1K/NVwort6DYos2P5Rp0kmu2UwLnudrZQWn3GxEPHDiAsLAwHD58GLGxsSgqKsLAgQORl1f5YqubmxvS09P1X9evX7fQjMW3bJSfoPGUjWIaykSxrvE/8P/dKhVAr9a2FUCmIAohpFLxnM0DAWBCoK+EMyHE/qiUCqwc7S/oOXIq62VKGa/DHwRLMBNCpCWkufwxCy5oEdPJbXGLtxwNQM3lie2RY88KjZbhn6t8Qe86Fs6Q8XTj321vyebHCs64iKWDToA8s50Y+P6Wg6rBRsSYmBhMnDgRHTt2hJ+fHyIjI5GWlobExMRKn6dQKODp6an/atSokYVmLD5HByV6NOf7OwUoG8VUCQLWrypDmSjCqYu1OHKNfzPE8/6Nbe7cR0EUQkileHc3OaqolBchphjm3wQB3rZX1suUMl4rRvvb3IUSISV4m8ufvZ1L5ZZsgNwWt6i5PLFncuxZIdcm6QDQ3dcDtZ35TgiWbH6ckc0XsLF00AmQZ7aTmzPf7yHAm39h3V7k5OQAADw8Ki/79ujRI/j4+MDb2xvDhw/HuXMVbyQrLCxEbm6uwZfcbJgirIF2+LbTEs3EPmm0DLHn74hyLMpEES58m7AKFcteEJadJQcURCGEVEijZTh5ne9C06+pOy2OEmKirW8IL+s19KuDEsyEjyllvHzruWK4fxOJZkSI9Hiby+cXaS1aXoWYhneRryvn7mZzUXN5Ys/k2LNCrk3SAd35YGQA3zWTpRb6NFqG6LMZXGMtHXQC5JntlJPPF9jhHWcvtFot3nrrLfTu3RudOlXcD6Ft27ZYu3Yt/vjjD2zcuBFarRa9evXCzZs3jY6PiIiAu7u7/svb21uqH8FkQrNRtp24RRtzBDiamoWcgmJRjiVWRkt1odEybDtxm3t8D9+6NtVQvoTtzZgQYjGHUzJRzPmZ/aQFmwcSYm9MKet15W4edpziv1AR0/RNlafeG7Pn7SDxJ0KIBfE2lwcsW16FmMajJt8iH+84MVBzeWKv5NizQq5N0ks05cx+zMrj+znMdTglE/lFfJk7lg46AfLMduLtucNbJs1ehIWF4ezZs9i8eXOl4wIDAxEaGgp/f3/07dsX27ZtQ4MGDfDtt98aHR8eHo6cnBz9140bN6SYvtmEZKMwACtiL0s3GTsjJDhelT0X7tK1lgArYi9x5njrbJgsLCtLLiiIQgip0E/xqdxje7fkuzEihBhnSlmvt7actPjFnbpYi+izwtKkqYwXsQe6ndR8f8f3H1lmUYuYjneHoSVrYlNzeWLP5NazQq5N0ktkc2YnJKZZpjwVbx+pWk7WCTrJLdtJzj13rGnatGnYuXMn9u/fj6ZNmwp6bo0aNRAQEICrV68a/b6TkxPc3NwMvuTI0UEJ/6b8c1sZd5UW8zlliXj9nZ1fRJnlnDRahq/3J3OP92vqZpNZKAAFUQgxiUbLsP/cHQz6fD9azYlC8/99tQyPQrdFu7Fy3xWr9yswl0bLsOfiXa6xDkrqh0KIGISW9dJoLb87ac5WKuNFqieVUoGgNnwbBo5zlsIk1qHRMkSdSeca61HTcjWxhTSXP5TMtzhHiFzIrWcFb5aANZqkA/IrT8XbR+qp1tYJOskt20nOPXesgTGGadOmYfv27di3bx98fX0FH0Oj0eDMmTPw8uIrfSln7wxqzz1WyygbhRfvNZtLDb6eU3cfUvlUHkKzUN4N4f/7lxtJgygRERF48sknUbt2bTRs2BDPP/88Ll26ZDAmKCgICoXC4Gvq1KkGY9LS0jB06FC4urqiYcOGeOedd1BcLE6dO0KE2n7iFlq+H41JG47j4t3HKP2XqGHA/bwiLN99GW3m/oXXNxy12V0Dh1MyoeGMA3Xwqk27zAkRgSllvSy5O0mjZdiWJKyEGJXxIvbExdGBa9zBy5aruU6Ek2tZmp4t6qEG593Z7Wy6sSe2hTco8M+VTMnPn7aQJSC38lS20CRdTtlOcu65Yw1hYWHYuHEjNm3ahNq1ayMjIwMZGRnIz/+3/GloaCjCw8P1/71w4ULs3r0bKSkpOHHiBMaNG4fr169jypQp1vgRRCXk8x6gbBReDd2cucYN7tSIa1x9C5Z0tVVCs1AcVba9AVvSIMqBAwcQFhaGw4cPIzY2FkVFRRg4cCDy8vIMxr322mtIT0/Xf33yySf672k0GgwdOhRqtRrx8fFYv349IiMjMW/ePCmnTohRfT7eh1m/JHGP33XuHlq+H43o09bpW2COeAGNtJ71ayzhTAipXob5N0HrBnx1sAHL7k5aEXup6kGlzOjXigKsxMCqVavQvHlzODs7o0ePHjh69GiFYyMjI8tttHF25rs5kgo1l7cPci1Lo1Iq0L9dQ66xXnWs+14gRCjeoMCjwmLJz5+2kCUgt/JUJznLhlmzSbqcsp14ywpZq+eOpa1ZswY5OTkICgqCl5eX/mvLli36MWlpaUhP/zdL9MGDB3jttdfQvn17DBkyBLm5uYiPj0eHDh2s8SOISqVU4I2+LbnHUzYKJ844U7tGnOXU6Da2SkKzUKY+3dKm1wckDaLExMRg4sSJ6NixI/z8/BAZGYm0tDQkJho2pHV1dYWnp6f+q3Ttwt27d+P8+fPYuHEj/P39MXjwYCxatAirVq2CWm25GsWEtP0gGjcemNYo9s1NJ7Ek6pzIM5KWkJuXCb2Ep+MSQioWNbOvoPGrD0i/O0noLhOlApgZ3EbCGRFbs2XLFsyePRvz58/HiRMn4Ofnh5CQENy9W3HpSDc3N4ONNtevX7fgjMuj5vL2Qc5labr48C2m3cmhvjvEtvRsUQ8unFuvpT5/2kKWgJzKU2m0DPsv8WXuWHNtTE7ZTnVc+coKjQhoYtMLirwYY0a/Jk6cqB8TFxeHyMhI/X9/8cUXuH79OgoLC5GRkYGoqCgEBARYfvISmRncVtAa/TcHkykbpQp3OYOX1x885jueiI3q7ZFGy7DmQPVaH7BoT5ScnBwAgIeH4c3Bzz//jPr166NTp04IDw/H48f//kEnJCTgiSeeQKNG/6ZbhYSEIDc3F+fOGV+ULiwsRG5ursEXIeZoFR6FQo15H1jf/30Ni3baRiBFo2U4yVnPvWUDV5ttCkWIXDk6KDGEM80YAIq1QPwVaXchCt1lMi2IslCIoc8//xyvvfYaJk2ahA4dOuCbb76Bq6sr1q5dW+FzFAqFwUab0teD1kDN5e0Db3merpylYcTEu5M6+kw6LaYQm6JSKjCkkyfXWKnPn7aSJSCX8lSHUzJRyNnvM7AF/2YDsckp2ykrj2/Db9O6/NnnxL6olApM78efjaLWMIuU7rNlvOd23uAI7/u4ujqckgnO6rgA7GN9wGIrn1qtFm+99RZ69+6NTp066R8fO3YsNm7ciP379yM8PBwbNmzAuHHj9N/PyMgod8Nc8t8ZGRlGXysiIgLu7u76L29vbwl+IlJdtJ8bhWKR7lF//OcalkSdF+dgEjqcksn9M4d05LsZIoQI8/XYroLGL9h5VqKZUBYKMZ9arUZiYiIGDBigf0ypVGLAgAFISEio8HmPHj2Cj48PvL29MXz48Ao30JSQeiONkObyWY/pxkuubnLuQPSwQi1s3p3UluqDQIiYPOvwZXVkS1wSylayBORSnoq3BKKzg9Kqte7llO2UeJ0vSPOArhWqNaHZKJ/suiDZXOwBb2P5hrX5ru88rFTO0VZ8GsP/92gv6wMWC6KEhYXh7Nmz2Lx5s8Hj//nPfxASEoInnngCr7zyCn766Sds374dycn8izVlhYeHIycnR/9148YNc6dPqqneEbuRX1z1OCG+/zsV0afTqx5oRUL6ofRuybeYRAgRRqVUYIaA3UnJ9x5DzblLUCjKQiHmun//PjQajdGNMRVtimnbti3Wrl2LP/74Axs3boRWq0WvXr1w8+bNCl/HEhtpnGuouMYdp54osqTRMmw7eYtrbLYVFrd4d1IDwKFkvvI6hMgF47yYuHr3kaTzsJUsAbmUp+ItgRjUroFVr//kku1kK+XPiPUJzUY5dTNXsvs9e8DbWL5ZvZp8x6MgSoXUxVok3eTfrGYv6wMWCaJMmzYNO3fuxP79+9G0adNKx/bo0QMAcPXqVQCAp6cn7ty5YzCm5L89PY1/QDo5OcHNzc3gixChPtpxBrdypNkFFbbphKxLMMSc5QvyOKoUVt1tRIi9mxncVtD48T8eFn0OlIVCrCUwMBChoaHw9/dH3759sW3bNjRo0ADffvtthc+xxEYahYLvBuDUzRxZf9ZXV0dTs/CwQMM1lndHo5h6tqgHzo3UuJ1NtbqJbanLmQGSkCxtUMBWsgTkUp5KziUQy5JDtpOtlD8j8iA0G2XOb6ckm4vN4/zYUPB+vNj+mr9kxv/Av+6ggP2sD0gaRGGMYdq0adi+fTv27dsHX9+qm08nJSUBALy8vADobqDPnDlj0HQ0NjYWbm5u6NChgyTzJkRdrMW6Q2mSHZ8BGLXmkGTHN4e6WIvke3xlLvyauttFNJkQuVIpFRjp35h7/JHUB6LvTqIsFCKG+vXrQ6VSGd0YU9GmmLJq1KiBgIAA/UYbYyyxkaZJXb4FGqpdLU9ybyitUirQv11DrrH5RXzBIELkoj5nCZXcAumCAhotw9+cfeSsfTkjl/JUci6BWBZvthPvOFPYSvkzIg8qpQIju/Df720/eZs26VRg38U7VQ8CcJPzfLn3At/xqht1sRZHrvGXkRzZxbqlMcUkaRAlLCwMGzduxKZNm1C7dm1kZGQgIyMD+fm6P9jk5GQsWrQIiYmJuHbtGv7880+Ehobi6aefRufOnQEAAwcORIcOHTB+/HicOnUKu3btwty5cxEWFgYnJ+t/SBP71OfjPZK/xskbOdhx6rbkryPU+vhr3GOftGKjRUKqi2Wj/ASNF3N3kkbLsOYAZaEQ8zk6OqJr167Yu3ev/jGtVou9e/ciMDCQ6xgajQZnzpzRb7Sxll4t+XeNJqTwl8e0BHWxFl/vuwT/BdFoPicKzedEoWV4FAIW7sac304jX23/i/K20FC6W3O+RbWDl+/JbiHlUUExJv2YgLbhur8v3zlRaDf3LwxZcRD7LtyV3XyJZXlylloBpAsKHE3NQh7nuc7aWQJyKE8l9xKIZfFmO/GOM4WtlD8j8hExkv9+jwFYEXtZusnYKI2WYXsS37nKx4OvVOMfSRSwMkZIFgoARIzsLNFMLE/SIMqaNWuQk5ODoKAgeHl56b+2bNkCQHdDvWfPHgwcOBDt2rXD22+/jRdeeAE7duzQH0OlUmHnzp1QqVQIDAzEuHHjEBoaioULF0o5dVKN/XniJu48lLaZYYkZ/z0pu5Py0Wv8u2apHwoh0nN0UKJHc/7yCGLuTjqckokiAYktlIVCKjN79mx8//33WL9+PS5cuIA33ngDeXl5mDRpEgAgNDQU4eHh+vELFy7E7t27kZKSghMnTmDcuHG4fv06pkyZYq0fAYBuZ7AjX1sUyOkjfuGOc2gz9y98tvsqsgv+nZiGAQ8eF2HzsRtoPy8Gr64TvyygnNhCQ2ne3fr5RfJqLj/sq7/RacEu7L+ShcL//YkxAAXFWpxPf4hX1x9Dy/ejsZNzkYPYn+6+HqjtzHcC5e1bIhRvNpqro0oWWQLWLk8l9xKIps6BN7vGFLZU/ozIg6ODEi3r8/XpAIDVB67Kbh3J2o6mZiErr+rzYL2ajhgf2BweNat+n2bmqSUtlWiLhGah9PCtC0cHi7Vjl5zk5byMfU2cOBEA4O3tjQMHDiAzMxMFBQW4cuUKPvnkk3KlF3x8fBAdHY3Hjx/j3r17WL58ORwcHKScOqmmNFqGGb8I38V9YeEgXF48GE6ciyolGIBpPycKfj0ppWXyXVA6KKkfCiGWsmFKT+6xYu5O+jTmAvdYykIhVXn55ZexfPlyzJs3D/7+/khKSkJMTIy+2XxaWhrS0//tyfXgwQO89tpraN++PYYMGYLc3FzEx8dbvZyrSqnAsM582TAZOfLoWdHnk31Ye+ga19h9lzLR5aMYaSdkRbw7pZtx7lKUgpDd+nJpLt9u7l84e5uvwei0zUmYsv6oxDOSp1WrVqF58+ZwdnZGjx49cPRo5b+H7OxshIWFwcvLC05OTmjTpg2io6MtNFvxqZQKjAxowjWWN+Ap1P2HfBkbgzt5ymJjiLXLU8m9BGJZvOf47SdvSbYIbUvlz4h8LHiuI/fYYi0Qz1mWsLrgPVc9598Yjg5KDPfjK6EmZalEWxR5KFXQ+A2T+dcxbIH9hIOIaNTFWqzafxk9l+yCbzVLw5++SXhAI3npELg4quDooMSlJUNRx0VYJOWvc3dE72FgKo2W4eqdR1xj/b2pHwohliI0G+Wbg8lmn6vVxVok3eRbEAMoC4XwmTZtGq5fv47CwkIcOXIEPXr00H8vLi4OkZGR+v/+4osv9GMzMjIQFRWFgIAAK8y6PK86fAvs0WfSrX7d1GXhLtzIEnYDmJWvQcDCXRLNyLrSsvK4xkm1gMuju68HnBz4zqfHZLBDslV4FAoEXsvuuXAPU9Yfk2hG8rRlyxbMnj0b8+fPx4kTJ+Dn54eQkBCD3p+lqdVqBAcH49q1a9i6dSsuXbqE77//Hk2a8AUh5KppXb7zZ1aeNOWpeJvFNxIQzJQSb9mpOwKCHULYQgnE0jxqWbfvjq2VPyPy0atVfagE3Eot2HlWusnYIN5zVdP/ZffxfxbR+7S0H//hD6LYWxYKQEEUUopGyzD1p+NoM/cvfLrrCjIeFoPBeBr+78dvWHu6olMXaxF9VljjqBWj/cstGibNHwTnGsIWEsf/KI/SGYdTMsFbDV0OF8m25uDBg3j22WfRuHFjKBQK/P7771U+Jy4uDl26dIGTkxNatWplsMBIqhch2ShiNLQO38aflacAZaGQ6kUBvs/5gmLrllt6KiIWWY+LTXrug8fF6PPJPpFnZF22srilUirQuWkdrrFnb+daNVDXfm4Uik18+T0X7sqyP6BUPv/8c7z22muYNGkSOnTogG+++Qaurq5Yu3at0fFr165FVlYWfv/9d/Tu3RvNmzdH37594ecnrFea3PCWnZKqPJWC8zaNd5zUeMv7/XU2Q5JzgS2UQCzN2n13bK38GZEPlVKBsKCW3OOT7z2WzWZcOeB9P5WM4w248o6rDtTFWtzhzOYE7C8LBaAgCvmf6NPpaPl+NGLO8wUR3tp6Gr2W2tcORaHNkXzruWK4v/GdYKfnDxJ0rCOpD2TxAfhTPH9UmfqhCJeXlwc/Pz+sWrWKa3xqaiqGDh2Kfv36ISkpCW+99RamTJmCXbvs671H+Dg6KNGqAX+t3PUJwlJtS9NoGf44yb+wNbKLPG6cCbGUQAHlLK1VbunVdYdxM8e8QMCNrHws3HFOpBlZny0tbvFuVskv0lqtXnfvpbuRb1qMTm+6DPsDSkGtViMxMREDBgzQP6ZUKjFgwAAkJCQYfc6ff/6JwMBAhIWFoVGjRujUqROWLl0Kjabiv+HCwkLk5uYafMmN1ctTZfMtnPP2tZAab1DgsVojSdCedxc2765uqVm7746tlT8j8jIzuC3nNh0duWzGlYOGnOfKknENOYMjvOOqAyFrpi0buNpdFgpAQRQCICL6PN7cdELw827nFqPFnCgJZmR5QpsjAcCet4Mq/J6jgxKTejcTdDxrfwBqtAx7LhovJ1AW9UMxzeDBg7F48WKMGDGCa/w333wDX19ffPbZZ2jfvj2mTZuGUaNG4YsvvpB4pkSu5j/LXyt3jxnlFw+nZAraWRwxsrNJr0OIrerZoh44qy3h1gPL11LemXQL+y6Js5i29tA1WWz0EIMtLW71almfe6w16nW/uu4wbuWKkykw4LM4UY4jZ/fv34dGo9H3gCrRqFEjZGRkGH1OSkoKtm7dCo1Gg+joaHz44Yf47LPPsHjx4gpfJyIiAu7u7vovb29vUX8OMVizPJVGyxB91vjvu6z6Mlk46+7rgZqOfEGBhBTxeyQkXucL0vKWSZOatfvu2Fr5MyIvKqUC0/vxZ6PIZTOuLPDeu5aM441W0T5BAMLXTBcM6yThbKyHgijVXPTp2/j2oOm7lbWAXQRShGahzOhXde3/+c8+ARcBZb2s/QF4OCUTGs6X7+BVm3adW0BCQoLBjkUACAkJqXDHImAbOxCJ6Xq1qs/9wa1lpjccFJKV5tfUzS53mRBSGZVSgQAfvj5F6RZuLq/RMkzfnCTqMa290UMstrS41bNFPe6+KPc5fy6xiBmkA4DUzMf4I4mvzFp1otVq0bBhQ3z33Xfo2rUrXn75ZXzwwQf45ptvKnxOeHg4cnJy9F83bsivBDNveaq9EvTiPJySifwivhseawdSS6iUCvRpzRdUFTupS6Nl+JvzWlJOt4bW7Ltja+XPiPzMDG4raLy9XKOZ6z7n+7lkHO+1k6WvseRKyJqpUgH04vzcsjW06lGNabQMb246afZxtAA6zP3L/AlZidCIqkrJX/v/u/FPCpqLNT8A45P5F1uf9Wss4UxIiYyMDKM7FnNzc5Gfb3zXqS3sQCSmUykVGNGF//1nSsNBjZYh9jxfVhoAvBvSXvBrEGIPeBfZT9/MsWi5oumbErk34/Gy9kYPsdjS4pZKqUBQG77SqcevC8umNodGyzBD5CAdAMzakmTXZb3q168PlUqFO3cMSyffuXMHnp6eRp/j5eWFNm3aQKX6Nwuhffv2yMjIgFptfNe/k5MT3NzcDL7khrc8VXZ+keil6hI4y13VcrJ+ILW0rj58c+HN8uF1NDULeWq+EoiBLeSzYMbbTycxTfxzJ28/rWYe8ih/RuRHpVRgpD///Z69XKOZizd7sGRc/Zqc4znH2TOha6bP+ze2+nW0VCiIUo31WCJeX4XHxVo8tWyvaMezJKFZKF++HMB9QujVqj53uQ/Auh+AQm5SJvTylXAmxBy2sAORmCdiJH9DWVMaDsZfvQ/eZ1BpP1Kd8ZZbsmRzeXWxFtFn+frbCWUPOx1tbXHLxdGBa9z+i+Lv2K/IithL3J8RQmgZsCL2sgRHlgdHR0d07doVe/f+e7+k1Wqxd+9eBAYGGn1O7969cfXqVWi1//7GL1++DC8vLzg62m5D6u6+HnB35vvbFrtUHeMMMT/Vup6sFoCENkwWC28JRFdHlayuBxWc9Xf+uZIp+rkzLSuPa5wUpcSI/Vg2iv9+D7CPazSzUTkvyQhdM132grC/X1tCQZRq6qMdZ3A/j29XCa+b2QV4NfKoqMeUmtCIauuGNQVlYaiUCnz5sr+gOVnjA1CjZUjk/D3Ya4MoOfL09DS6Y9HNzQ0uLsZLDNjCDkRiHsEN5gWU5gKAr/fxL2INt+NdJoRUpWeLenBU8f39W6q5vNCbHCHsYaejrS1uNanLV05IrWEWCdRptAxf70+W7PirD1y162yU2bNn4/vvv8f69etx4cIFvPHGG8jLy8OkSZMAAKGhoQgPD9ePf+ONN5CVlYWZM2fi8uXLiIqKwtKlSxEWFmatH0EUKqUCA9o35BordhkV3mbxXZvxlWu0FN4AcIKAygI8eEsgDunkKavrwUDOgM6jwmJRs500WoZtJ/lKE/L+m5LqydFBiR7N+c9D9nCNZq59F/k2EQkt57X3gjSbk2yF0DXTHr517Xq90H5/MlIhdbEW6w6lSXLsfRfvYcep25IcWwrh204JGh8142nBrzHMvwlaN+Df0WiND0AhO89DOhovOUDEFxgYaLBjEQBiY2Mr3LFIqg8hDeaFnJM1Wobj17O5x1NDeVKdqZQK+DV15xp7TOSSNMYIvckBgHaN+AOygG3vdLTFxS0hzeUtEahbEXtJcKm4ei58GQcAUKw1vZeXLXj55ZexfPlyzJs3D/7+/khKSkJMTIy+dGtaWhrS09P14729vbFr1y4cO3YMnTt3xowZMzBz5kzMmTPHWj+CaDzr8AUIecsy8fLgLMvCO85SPDjL1ESfzRA1EMkbUA4UcK6yhJ4t6sGlBt9Sl5jZTkdTs/CwgG+jqthZQ8T+bJjSU9D4Ob8JW9uyJxotw3bO3moNazsb/G9V/ki6bdcbPKoyZ6uwv6sNk4X93doaCqJUQ0NXHJD0+NP/e9ImTjIaLcO2E/yLi+ZEVKNm9hU03tKLFEJ2nvduyVefm5T36NEjJCUlISkpCQCQmpqKpKQkpKXpgprh4eEIDQ3Vj586dSpSUlLw7rvv4uLFi1i9ejV++eUXzJo1yxrTJzIipMH82du53OfkwymZ3E1JKSuNEKAJZ/PaUxboiyI0CyWgqTtiZgWhlcw3eojFFhe3eraox10WVupAnSlZKF+PCcDRDwcKes5XAq5JbdG0adNw/fp1FBYW4siRI+jRo4f+e3FxcYiMjDQYHxgYiMOHD6OgoADJycl4//33DXqk2CrGeTrkHceLN1NDLoHUErx9ZB6rNaJmpWXl8f0eeMdZikqpwNAnvLjGijl33vJnAODpzhdIJNWX0GyU7Ser72L/0dQsZOVVHXSvV9NR3++qu68HPGpWnZ2YmacWvT+XrdBoGbYlWWbN1FbY909HytmZdAtX7j2W/HVGrTkk+WuYS+huOnMiqnJOxxSy81ylhKzq3dqa48ePIyAgAAEBAQB0ZR0CAgIwb948AEB6ero+oAIAvr6+iIqKQmxsLPz8/PDZZ5/hhx9+QEhIiFXmT+RDpVQguANfKQwt49/Ze0jADuAFwzpxjyXEXsml3JLQLBQFgK1v9gYARAvc6CG0RKBc2OLilkqpQIAP3/Wj1IE6odfNXbzr4Fk/XcnHGf1acj/vxI3sarsIVJ3wNkC/I+B9WxWNliHqTHrVAyGfQGqJ7r4eqOnIFzxLSBEvm+sBZzCJd5wl8WbHiFm+kbf8mZuzg34hl5DKCMlGYbDv3mKVufuQ77OidClqlVKBEf5NRD2+vVkRe0nQeHvPQgEoiFKtaLQMMzYnCXqOEsCpecJ2kAHAyRs5si7rpdEyrIrj303n19TN7Iiq0HRMS2WjCNl53sW7jqzq3dqaoKAgMMbKfZXsOoyMjERcXFy555w8eRKFhYVITk7GxIkTLT5vIk+hvXy5x/Lu7N13MYNrnFIB9Gotr9INhFiDXMotCc1C+WpMgP7zXOhGDzlf31XGVhe3eOciZaDOlCyUX9/opf//M4PbCngtWKS/C7Gu+rX5ylPtvXBXtKDa4ZRM5BfxbVKTSyC1hEqpQB/O6y4xY5AKzts+3nGWlJXHd87nHceDNyAzIqAJ3VMTLkKv0VbG2XdvsYrU5yx5+Ez7Rgb/3b9dowpGljm+zEo8WoLQa7/qkIUCUBClWlkRe4m770WJK0uHwN21BpKXDhH8em9tkW9Zr/ir96ERMLV3Q9qb/ZpyzUb5ScCO0hn920g4E0KIED1b1APv/RfPzl6NluHSHb6my83rudLNHyGQR7kloVkorRvWxLN+jQ0eE7LR4+wt/hKBcmKri1tyCNQJzUKZ0a+Vwe9QpVQghDN7EgDWJ9hmthPhx1ueKju/SLQyKgmcwblaTvIKpJbo6sM3J94sHx4Z2Xz9Quq4VF0Sx9J4++kkpgnrJVbpa3Jm5DTz4C+jSYiQazQtq6bZKLwXKWXH8V7uyeey0GIsWbnHllAQpZoQmnkB6OoYl051WznaX+BryvcELqQHiKNKIVoJK7k1B9NoGfZcvMs1lnaeEyIvKqUC3XzqcI3l2dl7OCWT+0LpiSZ8zbQJsXdyKLcUeUjYgnPUjKfLPebooISPB9/Oay1ss/m3rS5uWTtQJ/QeQqkAZgaX33QjJHtyj4jZB0Seuvt6wN3ZgWusWI2/GedVzlOt68kqkFqCt8SYWKXINFqGPRf47hN5d4FbkoJz1fOfK5minW/Ssvg2I4lZQozYP0cHJVo1qMk9fvWB6peNcp8zo6zsuPucWcq84+yF0CyU6tQrtXr8lERw5oWxXYrD/Jugf1thwYSv98vvBK7RMhy7ls09furTLUW7kJZbc7DDKZnQcCa7dGrsJssbCkKqs+kCssOq2qEcz9lsFQBGdfHmHkuIvbN2uaUf/+EPolSWaj+uZ3Pu49hi82/eRSu5LW5ZO1An9B5iWlAro9eLQoJBQnp5EdukUiowoD1fdpJYi1e82RJdm/Hfq1kSbyA4QcD1XGWOpmYhp6CYa6zcyp8BQCDnJshHhcWiZDtptAzbTt7iGsv7b0lIifnPduQeW6ytfp+hvIHcsuN4y3RVt3JeQrNQqlOvVAqiVBMf7TgnaLyxXYoAsHZST7g78zW1A3TZctM3nRD02lKLv3qf+4SggPHddOaQU3MwIaW8ygbVCCHW16tVfe4P8l3nKu93EnOWr9mqg5Ky0ggpzZrlltTFWtx5yL/AWFmq/YRezbmPk5hme82/eRcW5bi4Zc1A3Vd7+a9DK8pCAXSL5sMD+K8lbTFQR4TxrMO38M5blqkqHpyLYLzjLM2Dc5Ew+myGKOfnjFy+Rsp1XGrIsvxZzxb14FKD7ypZjGyno6lZeFig4RorVrYQqT56taoPlYD9rAt2npVuMnJE5bxEIzQDubqtDVAQpRpQF2tx9R5faikADOnkWWkq1rG5whrNR5/NsEhvD15CAkrBHRqKnn0hl+ZgQkp5AcAEAWUYCCGWoVIq0JXzfJJ873GF52J1sRbJ9x5zHSfAuw5lpRFSijXLLQlpKF9Vqr2QchG2limg0TJEneELFMtxcctagTqNluHY9Wzu8RVloZSIGOnHfSyeXl7EtjHOf17ecVWx5UAqwN9H5rFaI0ow9T5ngP6Z9uLfL4tBpVRg6BNeXGOz8sz/N+cNOgHyzNwh8qZSKhAW1JJ7fGX3ffaIynmJR2gG8pt9K7/2szcURKkG5mzl76uhAPD12C6VjnF0UGJIp0aC5jD+R/6bfCkJDShNCJQmcCCH5mBCSnn5eLhUmxqHhNgaIbv/1leQfbY+/hr3MZ6U4W5DQqzJWuWWhDaU50m1F1IuwpYyBQ6nZCK/iO+iR46LW9YK1K2IvcQ9lid7W0igjqeXF7FtvA3Q7whYnK6IrQdSAd31Xk1HvooQCSnmB7kfcAaTGnEGd6whkDMALUYZxyzORVY3ZwdZZu4Q+ZsZ3FbQ+PBtpyWaifxQOS/xCNl0XlkGsr2iVVE7p9EybEu6zT1+ej++KOLXY7sKmseR1AeyiIQLCSiJ2VC+3LFl0BxMSCmvcT19RH1tQoh4hOxQrqjm847TfDWcAaB3ywbcYwmpLqxRbil8G/81jVLBl2ovpESgLWUKJHD+zms5yXNxyxqBOo2WYc0B/nIOIwIac91DCAnUrU/gv1Yltqd+bb5Fqb0X7pr9N23rgVRAdx7ow1kyRYxTs4IzcMs7zhqyOHen846rDG8gZkRAk2q1a5uIR6VUYKQ/f1nMbSdu2cx1mtmonJcohG46ryoD2R7ZTBBl1apVaN68OZydndGjRw8cPXrU2lOyCWLvICuhUiowox9/OiEAzPmN/2ZfCkIDSmI2lDfGms3BNFqG2PNUyosQe9CzRT3wnqoeF5ZvEKrRMlxIf8j1fAeldMFlUj0IvZ779ddf0a5dOzg7O+OJJ55AdHS0hWYqjKXLLWm0DNtP8F/TPO/Pt8CtUirQrXkdzjnYTqYA47y7fqp1PdneDFo6UHc4JROca84AgGUv8JXqEhKo23fxXvVZAKqGeMtTZecXmd3429YDqSW6+vDNjTfLpzIZ2Xx9Quq41DD7taTC208nMY0/q7PC1+LM3Gnm4Wr2a5Hqa9ko/rKYUvfXlZN9F+9wjTO1nNfeC3zHt3VCNp1XxywUwEaCKFu2bMHs2bMxf/58nDhxAn5+fggJCcHdu/yLwNWRVDvISswMbivoD+iPU7eteiMkVUDJVEKbg60QsWxG/NX74L0vplJehMibSqlA12Z1uMaqjRQ4PZqahSLOwqf92zWQ7QIjkT+h13Px8fEYM2YMJk+ejJMnT+L555/H888/j7Nn5dcs09LlloR8jgP8C9wAML0///WPmP03pMS7yNe1GX/POkuzdKDu05gL3GOr6rdTmkqpQKemblxji7XiZW4R+enu6wF3ZweuseY2/raHQCrAX2rM3JJkGi3Dngt8ay28ZXSsQcG5ffyfK5lmr1PwZqKIUTqMVF+ODkr4c36GAtL115UTjZZhexJfVYWGtZ0r/e+K/JFk3bVMSxC66bw6ZqEANhJE+fzzz/Haa69h0qRJ6NChA7755hu4urpi7dq11p6arEm1g6yESqnAV6P9ucdbc8ei1AElUwhtDnb8unhlM4TUOaRSXoTIX/cWfNkhZ26VL/MipBGmVH2iSPUg9HpuxYoVGDRoEN555x20b98eixYtQpcuXbBy5UoLz7xqli63JORz3K+pm6DNEEIyBXady+A+rjV5cNay5h1nDZYM1KmLtUi6mcs9nqffTmnPdm7CPdZWAnVEOJVSgeAOfH02zW38bQ+BVIA/2yEh2bwKBkdTs5BTUD572Ri5lj8DgEDO7OlHhcUiZDvx/c55/w3tTUREBJ588knUrl0bDRs2xPPPP49Ll6re5GorGcmW9M6g9txjpeqvKydHU7OQlVd11lm9mo7lMg27+3rAo2bVnw+ZeWqzzxFyJ2TTOVA9s1AAGwiiqNVqJCYmYsCAAfrHlEolBgwYgISEBCvOTP6E9LwQsoOstGH+TeBZm383xSe7+He1iUnqgJKphDYHE+MDUGidQyrlRYj88e5QLijWlgtm33/Il8bsUkNJpbyIyUy5nktISDAYDwAhISGyvf6zVLkloZ/j74bw32wD/8tua863kJh877Eset5VxR4WtywZqJOi305pE3o15x4rRuYWkS9LNf62h0AqAHhwZn3sMbOPDO8GmzouNWRd/qxni3pwqcG3xmFOtpNGyxB1Jp1rrLlZQrbqwIEDCAsLw+HDhxEbG4uioiIMHDgQeXkVX8/YUkayJfVsUQ+cf9YA7D8b5e5DvvPVcCOlbVVKBUb4823s4H0dW6TRMny9n3/TeTefOtUyCwWwgSDK/fv3odFo0KiR4S6VRo0aISPD+O63wsJC5ObmGnxVN0J7XgjdQVbapy/6c489dTPXKjfblggomUJoczAxPgCF3Bhb8ndBCDFdzxb14MhZH7Dsrt7j1/gWc59uQ6W8iOlMuZ7LyMgQNB6w7jWgkHJLPx2+ZvLrCKlX7KgyrY+RkEWx9QKusazBnha3LBGoE9pvx5QmyY4OSrRswNcXQIzMLSJflmr8bQ+BVMByfWSyOPsEDGjfUNbXhiqlAkOf8OIaa0620+GUTORz7tiUc+aOlGJiYjBx4kR07NgRfn5+iIyMRFpaGhITEyt8ji1lJFuSSqnAG335K5rYezYKb0mu4A6eRh8fUMHjpr6OLVoRe4mz6KXOTAGlf+2NXa6ORkREwN3dXf/l7e1t7SlZnJBa2absICtNSNkHABj/42GTX8sUGi3DLgsFlEwhpDmYuR+AGi3DHyf5b4wt/bsghJhGpVTAr6k719jSu3o1WoZ9F/nOjy41VCbNjRBLsuY1oJByS/svmrZLWGi94qlPtzRpgUtIQGjHKf75WIM9LW5ZIlAntN9OxMjOJr3OoE58C5vmZm4RebNE42/d5kK+psC2EEi1RB8Z3swf3kwia7JEtlMC5zmqlpODrDN3LCknJwcA4OFR8e9DaEZyddpMPTO4LWfHH51vDibb7YaErj51UdWlrlKhGyfF822d0NYHDkrz1o9tneyDKPXr14dKpcKdO4YXPnfu3IGnp/GIYXh4OHJycvRfN27csMRUZeW3Eze5x5qyg6w0lVKBEV34symOpD6waDaKkNp+5gaUTOHooESrBjW5x5uTjXI4JRPFnE+1xu+CEGK6JnWF7+oVUuqwcR373X1DpGfK9Zynp6eg8YB1rwGFlFsydWFYyDWNAqbXK+7Zoh5UnHcJ59MfyvrG3J4WtywRqJOy305plsrcIvJmicbf9tLfA9B9zgxo35Br7H3ObBJjeLMyzO1VYwm82UXmZCExzj3cT7WuJ+vMHUvRarV466230Lt3b3TqVPGmTaEZydVpM7VKqcD0fvzZKPa8ISHx+gNU9fGgZbpxUjzf1gltffBm3+rZUL6E7IMojo6O6Nq1K/bu3at/TKvVYu/evQgMDDT6HCcnJ7i5uRl8VTdCmsmZuoPM8BjCeohYKhtFeEN58wJKppr/bEfuseZkowgpayb39GxCiKEmdflu/EtfRMcL+Kzo3bKBSfMiBDDtei4wMNBgPADExsZWOB6w/jWgkEV4oQ2zNVqGVXFCrmnK137mpVIqMKAd30JdsVbeN+b2tLgldaBO6n47pVkiIETkzxKNv+2lv0cJzzp813u8WT7GJF7n+10/kHn5M4A/w8ScTJQ6LlU3pgaArs3scye7UGFhYTh79iw2b94s6nGr22Zqodko1upPLDXeXiUVjTP3+bbu0xj+vwulovo2lC8h+yAKAMyePRvff/891q9fjwsXLuCNN95AXl4eJk2aZO2pyZK6WIs7D/kuaBq7O4vS88LRQYkenE1IActlowiNqooRUDJFr1b1wdnOAADw1X7h2SgaLcOeC/xlzSYEUkN5QmyJkF29CSm64MmtB3ylHkztq0BIaVVdz4WGhiI8PFw/fubMmYiJicFnn32GixcvYsGCBTh+/DimTZtmrR+hSkLeh7vOVdzbxZj4q/ehEfDRv+wFYRtcygrtxX8dIDQgZEn2trglZaBu/A/8m5zM/VywROYWkT9LNP62l/4eJRjn5wDvuLI0Woa/r/BtsrGBXxd3homQTahledR0EnWcPZs2bRp27tyJ/fv3o2nTppWOFZqRbO2NNJYmNBvFWv2JpVa/Ft/7qqJx9Tnfl7zjbIm6WIukm/xl76YFVe8sFMBGgigvv/wyli9fjnnz5sHf3x9JSUmIiYkpl9pHdNbHX+Me+5w/Xz1iHhum9BQ0fs5v/I1RTSUkqmrNJuoqpQJhQfwfgAAw7eeKm7AZczglk3vxRaUELZgSYmN0zeX5xpbEYPPVfOUt+rWzjYUFIm9VXc+lpaUhPf3fBuC9evXCpk2b8N1338HPzw9bt27F77//XmnpB2vr2aIeONcDkXzvsaCbWSFllsS4phGSKXDMjAbGUrO3xS0hgToh/y7qYi2OXOMvVWFqv53SpAwIEdtgicbf9tTfAwDqcv48vOPKOpqahTy1hmtsYAv5/848OBdYo89mmJzxxhuAMadkmK1jjGHatGnYvn079u3bB1/fqjdqmJKRXN0IzUaxdH9ii+B921Y0jvcXaIe3wkI2z5hTJtie2EQQBdBFrK9fv47CwkIcOXIEPXr0sPaUZGujgLrBfVrxlWrgITQbZfvJ25Km5guNqlq7ibrQD8C/zt0RtPgiJKDUxbsOLZgSYmNUSgWGdeZbiMjIKYBGyxB3mW9RqpudNtIjllfZ9VxcXBwiIyMNxr/44ou4dOkSCgsLcfbsWQwZMsTCMxZGpVTgmfb8m3zWc5bZFFpmSYxrGiGZAqV7LcmNJerhW5KQ4JaQf5fwbfybm8S6kZYqIERsi9SNv+2pvwcAeNTk+z3wjiuLt/yZq6PKJjbdebrx9fR7rNaYlPGm0TLEnr9T9UCY/m9iD8LCwrBx40Zs2rQJtWvXRkZGBjIyMpCf/2+GmT1kJFuaSqnASBn3J7aE+3l82YYVjePtH2VOnyk5Erp5ZmQX67Q+kBubCaIQPupiLa5n8aU6OyjFL88iJBuFwfTeHjzmbOW/GZRDE3Wh6ZgAMPSrg1zjhAaUZvSnCDMhtsirDl9z+d3nMxB/9T4Ki/kW13jTpAkhwPjA5txjd5y6zXdMATvFHJTiXdPwZgrIudySJerhW5IUZbA0WoZtJ/j+FgHz+u2UJiRz6+ztXNkG6oh5pA502lN/D0D68lS85c+GdPK0iQW17r4eqMmZql1S7laIo6lZyCngy+z2dOfrZ2OP1qxZg5ycHAQFBcHLy0v/tWXLFv0Ye8hItga59ie2FCrnZRoh9xaA9VofyA0FUeyMkFJe/ds1EP3CR2g2yso44b09eGi0DNuShNwMyiOqKjQb5crdPK4FmKErDnAfUw4BJUKIaRScZ5BHhRpsO3GT+7jV+aaPEKF6tqjHXSf+fHrVC8NCd4q92Ve8esVCMgXkWm7JHsusCCmD9RNHhvqK2Evc1TAA8/vtlBCSuZVfpDW5sTiRNykDnfbW3wPgL0+158Jdk+6z7a38mUqpQB/Oe1tTliV4M3fquNQQdO62N4wxo18TJ07Uj7GHjGRrkGt/Youhcl6CCb236OFb12qtD+SGfgt2ZsfpW9xjpWocLiQbRcukyUZZEXtJ0Hi5RFVVSgVWvCTsxnTGf09WeoG8M+kWrtx7zH08uQSUCCHCBQrILnxYUMQ1rpaTqlrf9BEilEqpQNdmdbjGFmtRZaaAkMxasesV23pfFI2WIepMetUDYVtlVoQEt/ZfrHwhVaNl+Hp/Mvfx/Jq6iXojLSRzy9TG4kTepMyssLf+HgB/ears/CKTAo/2VgIRALr68F3HmtJHhjdzZ0B76i9IpCPH/sSWsu8iXzk9c8t57b3A9zq2QMi9BQBsmCzs78ueURDFjmi0DGc5SzZJUcqrhKODEq0a1OQeL3Y2ikbLsCqO/2ZQblHV57o0RSO3GtzjGYAX18Qb/Z5GyzB9c5Kg15dLQIkQIlzPFvXgxLniqWV8592BHWyjXAMhctK9Bf811vqEivuiCM+sFafMUglb74tyOCUT+UV8uy1tKeNOSBmsqkp6Cc1CeTekvYDRVRPyuWVv9ciJjpSZFfbW3wPQZaK5OztwjTUl8GhvJRABafvI2FvmDrFNQrNRfk+Stj+xpWi0DNuT+DaSN6xtPABd0eNl/WFHvzMh9xZyWy+1NvpN2JH4q/fBm5QnRSmv0uY/25F7rNjZKPFX70Mj4Nwmx6jq3+8OEDT+xI1so2W9Xlzzj6AbYzpBSm/VqlVo3rw5nJ2d0aNHDxw9erTCsUVFRVi4cCFatmwJZ2dn+Pn5ISYmxoKzJbZGpVSgX9uGXGNT/7+9u4+Lqkz/B/6ZGRwQFZBnUBJQEx8BLRHU0sIwzdI2vz1r/MzKzXLDb620KpYVbqabmeVmmtbW5rfWyi3XlkizAiUfqDXFQkFNeQhRUFSQmfn94TKJ8nCfmXNm5pzzeb9e83rlcM/MfSBuzjnXfV1XlViGWri/2IklEf1GSqZAzr7WbwxKzayVq8zSpdTcFyVfcD6dvb1UlXEnpQwWALz4+f4Wn5e68chskn8TlslowKirQ4TG7jwsXnqC1EPJzAqt9fcALv7OjOkn9vtfXSc9W0SLJRCVzHbSYuYOqZPUijB5gqUOPVlBSTWq69qvrhDUydzqed7QmEAEdmp/E/OJugZNlBWVem3hifdL3Yl3SzVk+ZfigQilSnk1SekVDJOE81A5s1GkfB96hvh6ZNBA6k4CAHjssrJenxYew+6j4s3kAS6QSlu/fj0yMjKQlZWF3bt3Iz4+HmlpaaisrGxx/Ny5c/HXv/4Vy5cvx759+/DII49g0qRJ2LNnj4tnTmoSG9JZaNwvJ8WCKCq5p0DkUaSUwbKh5c0kUsssKbURQs19UWyCW0lG9A5SzQ3UJlLKYH3/S22L9c+lbjx65LqeinyfOprFdtW3V5qM1EnJzAqtZgmIzldqtojFakPOPrGSNWoqgahkttOR6jqhcWrK3CF1knoPacGnexWcjWtUnhbLNrwtofVMbZPRgEkJ3WT9PE/lKdcWasbvhkZYrDbsPHxKaKzJCMXTlU1GAx4d1VN4vFzZKBarDQWlp4THL7hlgNOfqRSpdS0BYOD8TQAuNoqaKbGMFxdI5S1duhTTp09Heno6+vXrh5UrV8LX1xdr1qxpcfw777yDp59+GuPGjUNsbCxmzJiBcePGYcmSJS6eOamJaD1nwQo3qqkRTuRJTEYDbkuMFB7f0mYSqWWWlNoIISUgdOykZ/WsCOgoVh51yFXSNq54Aik/FwC4f/X2K5578oNC4dcbDfL227lUt65ipdQ8MduJnGcyGpDaVyyLVmpJN61mCSh1XAUl1ag53yg0Vk0lEJXKdrJYbdiwR6yUkNr+HyN1knIP6eCvZ1XfYD5YMEDaXvbuDXFi2X3BncQ+z1N5yrWFmvGOqUZsP3QCopsmBkcFuGS33awxfSDlU177yvlsFCmpaUYDkNLbc28Omr2MGDdAvFQDAJxtBKLnfIar5/5L8udxgVRWQ0MDdu3ahdTU30q1GY1GpKamIj8/v8XX1NfXw8en+Ul/x44d8c033yg6V1K34C7yndypqUY4kafJvl28tNblm0ksVhtekbBTLKyLWbGNEFL6opTVeNYOvUDBi13RcZ5EaqBuR8nJZjdLPi08hvLT4jf1Zo7qpdj1g5qznUge4QFiN+RPnWu/bMultNjfA1DuuER7yAR07KCqEohKZTsVlFTj9HmL0Fg1Ze6Qepm9jAiTcC3Y0gYLVRG9fdjeONHTG3UlLTcjtYSrp1btcTd+RzTinfxS4bGP36DMLrLLmYwGPDZaPBul0epcXUapqWmpfUM9vnTD8nuGuGSdHjcgnAukwqqqqmCxWBAW1jwwFhYWhvLy8hZfk5aWhqVLl+Lnn3+G1WpFTk4ONmzYgLKyslY/p76+HrW1tc0epC+iu+1E3KyiGuFEnsbsZUSvkE7C41/Z8ttmksmvSwuW/78RypZpFb1Z9oOHNZfXYm3/S0kJ1AHA+Fe2Abh4zvyYhIxlA5TLQgGkZdV8p4F65HQlm+CyITquid4zUaT2+BDtIaOG6+hLKdVHRjToBKgrc4fUbZqEc8LLN1ioTVWd2JrV3jjRLEep2ZCeRGoJV0+u2uNOvGuqARarDbn7xWqXehldm30hNRvFmbqMUlPTlO4LIweT0YDldyUo+hlGA7D8nsGKfgY5ZtmyZejduzfi4uJgNpsxc+ZMpKenw2hsfenOzs6Gv7+//REVFeXCGZMnGBoTiE5mkyzvFSZjQIZIj7Im9Jc0/o7XvsXG3dJ7mqUPj5U0XirRTIHzjVaPKbdksdrw2X9a33RwKbXuEJZa//znyjr88/vjuPGlLyWdM09KbL2WuBykZDt972GBOpKHaCnSCgk3rQHtZqIo1eNDqz1kAGX6yIgGnfx8vFSVuUPq9sBwafe51JyNEtpF7Fq1vXFyvY8nW7DxR+Gxrr5vrCYMomjA9kMnhGvbJ7qolFcTqdkojtZllJqFYjYZVFOi5paEbkiM8lPs/ZfdlaiqnURqFRwcDJPJhIqK5gHPiooKhIeHt/iakJAQfPzxx6irq8Phw4dRVFSEzp07Iza29ZtlmZmZqKmpsT+OHj0q63GQ5zMZDRgp00mPgUsDkVNSegXDJOH3aM8vNXj8/wolfYYrskmHxQbBLHggnlJuafuhEzgneIKs5h3CUnvoPfb3PSitlnYjetHvpGW8OEL0BiP7omiTaCnSXIlBAa1moynV40OrmTuAMscmGnCZlNiN19vkMlI3WKg5G2VIj65o71fLaLg4zhXv46kaGq04WFUnPP731ytXwlXtGETRgDwJabrXumEHxKwxfSSNv/7FLyV/htQslEeu66mqReHDGSMUed8b4kIwIV68njY5zmw2Y8iQIcjNzbU/Z7VakZubi+Tk5DZf6+Pjg27duqGxsRH/+Mc/cNttt7U61tvbG35+fs0epD9Desiz1os2ZSailpmMBjw6SnwziVQGuCab1GQ0IL67v9BYTym3lC94o72zt7p3CEu9WSJVUkxXl5R81UtflBUrViA6Oho+Pj5ISkpCQUGB0Ovef/99GAwGTJw4UdkJuokSQQGL1YacfWLVGtSWjaZUj48j1WI32dSWuQMok5UkGnC5KtBX+D2J5CB1g0Xmhh8Umomydh0+2W5vaKvt4jhXvI+nytzwvfBYpUu4qh2DKBogZXfJ8J4hCs6kZSajAbcniN+oL6utxyeFx4THS22+ajSob1EwGQ147Z5EWd+ze4AP1jwwVNb3pLZlZGRg1apVWLduHfbv348ZM2agrq4O6enpAIApU6YgMzPTPn7Hjh3YsGEDDh06hK+//hpjx46F1WrFU0895a5DIJWQ62ZAsGC5CCJqndTSplI8Ntp1O8W6dRW7CeQp5ZZsgttrRvQOUtXGmpZIvVki6b2nKffel5LSF+XYSfGbwp5k/fr1yMjIQFZWFnbv3o34+HikpaWhsrKyzdeVlpbif//3fzFy5EgXzdT1lAgKFJRUo+Z8o9BYtWWjmYwGpPYNFRorWsPfYrVhwx6xa3AtZ6JI6SOj1XJxpH5SN1hs2H3MI87dpKo8LZZZ2944ud7HE1msNmzYfVx4vNIlXNWOQRSVs1ht2CMYDXVnCatFd0grAzDr/ULhRfwOic1XZ45SZ2rauEGRmD4yWpb38u1gxDdzbpTlvUjcnXfeiZdeegnz589HQkICCgsLsXnzZnuz+SNHjjRrGn/+/HnMnTsX/fr1w6RJk9CtWzd88803CAgIcNMRkFrIdXGrtpsKRJ5IamlT8fd17aaQbl3F1gNPKbckmkk35Cp1lma4lFLZKK4oFddESl8Ug0prTS5duhTTp09Heno6+vXrh5UrV8LX1xdr1qxp9TUWiwX33nsvnnnmmTbLuaqdEo2/RZt+B3TsoMpstPAAsTX51LkLQuMKSqpx+rxFaKzaMncAZfrIaLVcHGmDlA0WNgDLcn5SbjIKEd3w19644E6C7yM4zpNIrdrjihKuasYgisptP3QCjYK/EaPjQt0WPHDk4m7y63ntjvm08Bj2SGi+qsYslEv9aXx/TB8prVHY5ToYgH0Lb5ZpRiTVzJkzcfjwYdTX12PHjh1ISkqyf23r1q1Yu3at/d/XX3899u3bh/Pnz6Oqqgpvv/02IiNZfo3aJ3qh2Ba13lQg8kSzxvSR/aT75Ttd29NMbeWWAgUvdEXHeTq5s1GMBteUirvUNYLXCucuiN3o9SQNDQ3YtWsXUlNT7c8ZjUakpqYiPz+/1dc9++yzCA0NxbRp04Q+p76+HrW1tc0eaiF342/Rpt+pfd13jewMm+A9ANFxokEnQJ2bbOQuGaflcnGkDWYvIxK6i5f2fnVrsfqyUUSn29440T8BKvtTIbV3dHx3P5dtnlErfndU7u28EuGxU4ZFKzcRAVIv7nYfPYV/ft962pnFasNj7xdKek+1ZqFc6k/j++E1By9qOxiAn7PHyzwjIvI0oheKbVHrTQUiT2QyGvDKXQmyvZ87epoNiw1CB8Erh+On3F/uQG87hM1eRqQPv0q291t2l2uDdAAQ6CsW0Nr206+qu9FTVVUFi8Vizz5uEhYWhvLy8hZf880332D16tVYtWqV8OdkZ2fD39/f/oiKinJq3q4kd+Nv0WCLaPDG03QVPL4KweCIaNDJz0edfaTkLhmn5XJxpB1Pju0rPNZqU182ypdFYoHMqrq21zfRsoe5+8U+z1NIzUJ5Kk38/xe9YhBFxSxWG74oaruGbhMvo/tKeTUxexkxdoBY7dYmj/19T6sXSXe8/o2kBUHtWSiXGjcoAgdfGIfoIPEmdf3CfRlAIdKJoTGBTjeFH95LnTcViDzVLQndcEMf58/F3NXTzGQ04IY4sfO4iADnA7nOsFht+Ow/Ze0PhLZ2CGdNGIjgTmI3CdtyY1yoy4N0ABDcRSyIcu6C1SNKxinp9OnTuP/++7Fq1SoEB4v/Pc7MzERNTY39cfToUQVnKS+5+0vIHZTxNKK/L7mC5alEv6+TErupcpON3CXjtF4ujrRBygYYQF3ZKBarDR8J9lIO7dL2eWl7X2/ySeFxVX1/pGShuLP9g5owiKJi2w+dgMUqNrZfRBePONlZcc81kl8zdOHnVzy38NO9ksp4AcBf7kzwiO+BXExGA7Y+ORp7F6RhdO9AeF92aEYAncwmjO4Tgr0L0rDpD6PdMk8icj2T0YCpKT2ceo9QGbJZiKi5NenD0N3f8Zv2vmb39jQb3EPsRlBFjdiOPqVsP3QC5y6InSRrbYfwjj/d5NTrB0R2weoHrpVpNtJIyaL0hJJxUgQHB8NkMqGiovku1oqKCoSHh18x/uDBgygtLcWECRPg5eUFLy8vvP3229i4cSO8vLxw8GDLN0a8vb3h5+fX7KEWcjf+1nrTb7nLU4l+/68KFN/E52nkLBmn9XJxpA0mowEzrhfvy6embJSCkmpU17Xf8ymok7ndQObQmEAEdmp/A+KJugah9dQTSM1CeeS6nlyrBDCIomJ5gieQANyym6wlJqMBj0tsrnrinAXJL/zb/u9PC49j9TeHJb1HhJ83bkvoJuk1atHZxwtvTUvGgezxKF302+PQovH48dmxeCt9KDoLpi4TkXYMjXFyJ4k6NtkQqc43mWMQJHChdrlOHQzY96x7e5rVnhdrULzpP2Vu3amXL5il0NlbnWVp2mIyGrDyPsfKvg6I7IxPH79O5hmJGxoTCG8vsQv471RyE6OJ2WzGkCFDkJuba3/OarUiNzcXycnJV4yPi4vDf/7zHxQWFtoft956K0aPHo3CwkJVlekSJXfjb62X9JO7PJXWg06AvNlJWi8XR9oxa0wfSa08Vm47qIpsi8rTYtlgtyVEthscMBkNmCR4v1D0c93JYrXh9a/Es1C0VLVHaQyiqJiUCOjUFOeakcvJkeaqZbUXED3nM8z423eY+f4eyZ/51VM3SH4NEZGaVUpoENri6wV32BGJqK6uxr333gs/Pz8EBARg2rRpOHPmTJuvGTVqFAwGQ7PHI4884qIZK2vXvJswoJv4DvF+4b74ceE4BWckxiB4GX6+0b3llmyCUeARvYM0uetu7IAIyYGUaSN64NPHr1doRmJMRgMGdQ8QGrv3eK0qbvJcKiMjA6tWrcK6deuwf/9+zJgxA3V1dUhPTwcATJkyBZmZmQAAHx8fDBgwoNkjICAAXbp0wYABA2A2q/dGdmvkzKzQQ9NvuctTaT3oBMgbKNJ6uTjSDpPRgMckbGRusNhUUTIzWDDwfmNfsXXyhjixccGdxD7XnbYfOgHBhGwA2ugd7SqKBFFKS0sxbdo0xMTEoGPHjujZsyeysrLQ0NDQbMzlF8YGgwHbt29v9l4ffPAB4uLi4OPjg4EDB2LTpk1KTFl1LFYb9hw+KTS2Z4gvzF6eEy9zprnqv/aK9YC51LgB4R51/EREriBywdzm6xlEIRnde++9+PHHH5GTk4NPP/0U27Ztw0MPPdTu66ZPn46ysjL748UXX3TBbF3j08dGYpnA+dAr/xPvMSU5kyXUSnZnuSXRnlBDruqq8EzcZ+yAi/3zErr7tzkurHMH/PTczZh3ywAXzaxtoplB5y5YVVNSo8mdd96Jl156CfPnz0dCQgIKCwuxefNme7P5I0eOoKxMrJePFsmZWaGXpt9ylafSQ9AJkLdknB4yd0g7pGajvPj5fsXmIhvRfRSi40S/QSqINSzeLP7zYxaKNIrU+CkqKoLVasVf//pX9OrVC3v37sX06dNRV1eHl156qdnYL774Av3797f/Oyjot4uzvLw83H333cjOzsYtt9yC9957DxMnTsTu3bsxYIBnnOi7y/ZDJ9AouBik9b+yzq673ZLQDRv2HMWXB5SNcBsNwPJ7HCtpQESkZqJlMVp9vYovksmz7N+/H5s3b8Z3332Ha6652Btt+fLlGDduHF566SVERrZectTX17fFfgFacVtCN9wyKBJbf6xA1qf/QVltA4wGoFdoFzyZFofr+4R41M6wYbFB8DJA6Bz02Mn2y8coJVBwl6DoOLUyGQ34eOYInGuwYP7GH7D5hzKcvWCDTwcThsYEYvndgz2u5GtKz2CsEGyEKlKiyNPMnDkTM2fObPFrW7dubfO1a9eulX9CHqQps+LD3e03Cm5vo4hemn7LlQ2hl6CT1JJxbf39ZSYKqUlTNsorgn9fv/+lFg2NVo/ejFxVJ7bhT3ic4AZC0XHu0tBoReEv4v2jmYUijSJnzWPHjsXYsWPt/46NjcWBAwfw+uuvXxFECQoKavXieNmyZRg7diyefPJJAMDChQuRk5ODV199FStXrlRi6qrxTn6p8NjhPUOUm4gT1qQPwzULP0dVndgJmyOW3ZXIBYGIdCnUySCKmi+SybPk5+cjICDAHkABgNTUVBiNRuzYsQOTJk1q9bXvvvsu/va3vyE8PBwTJkzAvHnz4Our3qa2LTEZDbhxYDhuHOj5wSKT0YDEHl3xXWn72dBlNe6rGa2HsjRSdDSbsPiORCy+I9HdU2nXsNggeHsZUC8QqfP0GxkkXXLPYKEgSnu7+/XS9FuubAi9BJ2kloxrK/uSmSikNrPG9MHyLQeFEzPuX70d6x9OUXROzhAt5yU8TnBjjaeX87r/ze3tD/ovA5iFIpXLwoo1NTUIDLzyD+6tt96K0NBQjBgxAhs3bmz2tfz8fKSmpjZ7Li0tDfn5+W1+Vn19PWpra5s9tMRitSF3v1i6rdlkwDAJpRdcbcefblLsvW+IC8GE+NZ3txIRaZoT9wSCOplVfZFMnqW8vByhoaHNnvPy8kJgYCDKy8tbfd0999yDv/3tb9iyZQsyMzPxzjvv4L777mvzs7R+DugJRNeGH36pcUvPCovVhs/+I1YSiRl3nsdkNGDU1WIbwHYKljYm9ZBrd79emn7LVZ5KL0EnOUvGMVhPamMyGnD7YPH7YztKTqKhUUJjDVdjOa8rNDRasUNgo1OT2wd3U/Wa7g4uCaIUFxdj+fLlePjhh+3Pde7cGUuWLMEHH3yAzz77DCNGjMDEiRObBVLKy8vtNWKbhIWFtXnBDQDZ2dnw9/e3P6KiouQ9IDeT0iQovru/R/9SmIwGvOpgf5S2dA/wwZoHhsr+vkREauHMDt3bEiI9+m8HeYY5c+a02N/u0kdRUZHD7//QQw8hLS0NAwcOxL333ou3334bH330EQ4ebL0UgdbPAT1BiuBNR3c1l99+6ATOCZ4oM+POM3U0i93k3FJUqbrm8tQ2uXb3i/aFc7Z/nLtJLU/VGr0EnUxGA1L7hrY/EG2fR+ulhwxpT/bt8ZLGz/nH9wrNxHks53UlKVkoAJB9+yCFZqJdkoIojlwsHzt2DGPHjsXkyZMxffp0+/PBwcHIyMhAUlISrr32WixatAj33XcfFi9e7PRBZWZmoqamxv44evSo0+/pSfIEdz0AwLUq2El8S0I3pPaVr+SYbwcjvplzo2zvR0SkRqFdxEoWtOTGvmHtDyLdmz17Nvbv39/mIzY2FuHh4aisrGz22sbGRlRXV0vqd5KUlATg4uac1mj9HNATDIsNgtkkFmR1R3P5fMHATWdvL2bceahuXcWCWw0Wm1sCdaQcuTIrdh2uFnqfkyrPEpBanqrVr+uov0d4gNj6curchVa/ppceMqQ9Zi8jkqK7Co//uPC4x25WYDmv5qRmoSTFdPXonjeeSlJPlNmzZ+OBBx5oc0xsbKz9v48fP47Ro0cjJSUFb7zxRrvvn5SUhJycHPu/w8PDUVHRPMJfUVHR7gW3t7c3vL09839sOUhp1Omp/VAu9+bUoZi2tgC5Rc5dbHcwAvsW3izTrIiI1GtoTCACO3VAdV3rF4Gt8sxzZfIwISEhCAlp/zwjOTkZp06dwq5duzBkyBAAwJdffgmr1WoPjIgoLCwEAERERLQ6RuvngJ7AZDQgvrs/vjt8qt2x7mgubxNcwEb0DmLGnYeS0lw+/1AVhvdW9+54+o0cjb8tVhu+/lls06Hal4Cm8lQiN/TbKk+lp/4eNsFz3LbG6aWHDGnTOw8Ow9Vz/yU01moD8n6uwsg+HnhfkeW8mpnzobSsoXemDVNoJtomKewUEhKCuLi4Nh9m88U/rMeOHcOoUaMwZMgQvPXWWzAa2/+owsLCZhfGycnJyM3NbTYmJycHycnJUqatOb+cPCs0ztP7oVxu9QNDMW1EjMOv9/UCfn5hvIwzIiJSL5PRgEkJ3Rx6rWjaM5GIvn37YuzYsZg+fToKCgrw7bffYubMmbjrrrsQGXmxNvOxY8cQFxeHgoICAMDBgwexcOFC7Nq1C6Wlpdi4cSOmTJmC6667DoMGMfXc3bp19RUa547m8gEdOwiNG3KV+E5Mcq2L2U5iYz10gyw5SI7MioKSatQ1WITeJzlW3QE4k9GAMf3EsofbKl2mp/4eXQUDQRVtBEr00kOGtMnsZUTP4E7C4xd8ulfB2TjuyyKxknpyl/MS7U/tSharDR8VHhcezywUxynyXWsKoFx11VV46aWX8Ouvv6K8vLxZL5N169bh73//O4qKilBUVIQXXngBa9aswWOPPWYfM2vWLGzevBlLlixBUVERFixYgJ07d2LmzJlKTFsVLFYbCo+cEhrr6f1QWjLvln547Z7BkncFjb46EPueYwCFiOhSN8Q5VpZLNO2ZSNS7776LuLg43HjjjRg3bhxGjBjRLEv5woULOHDgAM6evbhRxGw244svvsBNN92EuLg4zJ49G7/73e/wz3/+012HQJcQLbfkjubyopuNAj20HANdvDF8y6DWM84uVe6GQB0pR47G36JZAr5mk6o2HLZGtE9Ja1kkeuvvEdxFbO3PbaOPjF56yJB2Lbi1v/DYg7+e9bgG8xeDBseExoqWuBYd94kHljjLK66SVEiCWSiOk1TOS1ROTg6Ki4tRXFyM7t27N/ua7ZK8yIULF+Lw4cPw8vJCXFwc1q9fjzvuuMP+9ZSUFLz33nuYO3cunn76afTu3Rsff/wxBgwYoMS0VWH7oRNoFPztUEM/lJaMGxSBnweMw7b9lZjx910438YB9wnrhI8fHYmOotvViIj0xNE4umedF5IGBAYG4r333mv169HR0c3OEaOiovDVV1+5YmrkANFyS03N5V1VbslitWHDHrGLai3sqNayiACxbKdN/ynD4snxqts4Ri1ryqz4cHf7v8etZVaIZgmMGxCuif9vnO1norf+HlKznZJbCLTpqYcMaVNKr2CYDIBF8Jrv/tXbsf7hFGUnJUFBSbVQyeqgTmbhknqipbBP1DW0uja4y4KNPwqPZRaKcxT5zj3wwAOw2WwtPppMnToV+/btQ11dHWpqarBjx45mAZQmkydPxoEDB1BfX4+9e/di3LhxSkxZNaQ0lVdLP5SWmIwGjO4fhqLnxmHvgjSM7h2Ijiagg9GAbgE+eCqtD3567mZ8/sQoBlCIiFohmpZ8xetYzouI2uCpzeULSqpx+rxYGR8t7KjWMoPgLoCmQB1ph7OZFXrLEnC2n4ne+nvIke2kpx4ypE0mowGPjuopPH5HyUmPykapPC22bt2WECkcLJdSClv0812hodGKg1V1wuOZheIchp9UZvPeMqFxauuH0pbOPl54a1oy9j8/Hj+/MA7fzrkRvx/di9FTIqJ2BDtYroblvIioLU3N5UV810rfAiWI3gwEtLGjWsuk7PDMPyS+yYw8n7O7/PWWJSB6HK31PdFbfw85+sjoqYeMXLZt24YJEyYgMjISBoMBH3/8cZvjt27dCoPBcMXj0hYB5JxZY/pIGn//6u0KzUQ60WvVG/tKK20tWgrb0WtsJdz/pvjPJayLmfdRncTvnoo0NFpx8FexOs9q7IdCREQyYzkvIlKIaHP5vcdrXVY7WvRmoJ+PlyZ2VGsZm8vrl7O7/PWWJRAoeDPxi1Z6fOgtcwdwLttJbz1k5FJXV4f4+HisWLFC0usOHDiAsrIy+yM0NFShGeqPyWjA7QmRwuM9KhtF9O++1PMD0WtnD7nV2tBoxY7Sk8Lj/9+IGAVnow8MoqjIurxS4bFq7YdCRETyYTkvIlKKaHP5cxesKHBRNorozcBJid242cjDsbm8fjmbWaG3LAGpPT6ueF5nmTuAc8estx4ycrn55pvx3HPPYdKkSZJeFxoaivDwcPvDaOQtTDktuiNe0nhPyUYRvVaVek0reu3s6DW23KRkoQBA+vBYhWaiH1yBVOSfP4g1ygTU3Q+FiIjkEdpF7MJartcRkX6kSNiV3Fpdebm1Vnrlct0Fs2jIvaQ0l3dVthMpz5nMCj1mCTjb40NvmTuAc8estx4y7paQkICIiAiMGTMG3377bZtj6+vrUVtb2+xBbTN7GZEU3VV4vKdko4iW85Jaolq0TJcnlPOSmoXChvLy4HdQJSxWG348JvZHwMuonX4oRETkuCE9ukLqZusIfx9e9BFRu4bFBsHbS2yBcdWOvV2HxTJeTmpoR7WWsbm8PjmTWaHHLAFne3zoLXMHcC4TRW89ZNwlIiICK1euxD/+8Q/84x//QFRUFEaNGoXdu3e3+prs7Gz4+/vbH1FRUS6csXq986C0RuOZG35QaCYSsJwXMjd8L2k8G8rLg0EUldh+6AQsggvADXEh/INNRETYdfik5Frxd117Ff+GEFG7TEYDRl0tlvm887D4TjlHWaw2fP2z2M1ALnHqIKW5/LcHf1VwJuRKzmRW6DVLwNEeH3rM3AGcy0TRYw8Zd+jTpw8efvhhDBkyBCkpKVizZg1SUlLwl7/8pdXXZGZmoqamxv44evSoC2esXlKzUTbsPub27E+9l/OyWG3YsPu48HhmociH30WVyBPcIQIAU5PZLIiIiIDK09LrxEcHs8wNEYnpaBa70bntp18Vv+AuKKlGXYNFaGxyLG9uqcGw2CB0ELxaPX6KfVG0wpnMCr1mCTiaWaHHzB3AuUwUPfaQ8RRDhw5FcXFxq1/39vaGn59fsweJkZKNYgOwLOcn5SYjQO/lvJblHJCUZMMsFPkwiKISog05zSaW8iLyZCtWrEB0dDR8fHyQlJSEgoKCNse//PLL6NOnDzp27IioqCg88cQTOH+eNwpIjCO9TdgPhYhEeVJzedEd6L5mE8+VVcJkNOCGuFChsecuiAXQSB0czawQzZYY3ktbgVRHMyv0mrkj2nfnl1PsIeNJCgsLERER4e5paJLZy4iE7uJBp1e3Frs3G0XH5bwsVhuWbzkoPD6+ux+zUGTE76QKWKw27BJsGBTf3V9Tu2qItGT9+vXIyMhAVlYWdu/ejfj4eKSlpaGysrLF8e+99x7mzJmDrKws7N+/H6tXr8b69evx9NNPu3jmpFZDYwIR4S8eFOnqq62LZCJSlic1l686LbYD/eYB4TxXVpFrosUCXq7IdiLXcXS3f6hgPxXRcWoh+v26vP+JXjN3RPvubCw8fsW6osceMnI4c+YMCgsLUVhYCAAoKSlBYWEhjhw5AuBiKa4pU6bYx7/88sv45JNPUFxcjL179+IPf/gDvvzySzz66KPumL4uPDm2r/BYq8292ShfFomVIVSqnFfufrHPV4LULJSn0sR/rtQ+BlFUIK+4ClbBsdfy5heRx1q6dCmmT5+O9PR09OvXDytXroSvry/WrFnT4vi8vDwMHz4c99xzD6Kjo3HTTTfh7rvvbjd7haiJyWjAvPHiJ068/UREUnhSc3nRZvFhGrt5qnXBXcR2jJ+7wObyWuLwbn+ldid7ONHMii/2VzYLCui1v8fQmEAEdurQ7rgTdQ3Nsij12kNGDjt37kRiYiISExMBABkZGUhMTMT8+fMBAGVlZfaACgA0NDRg9uzZGDhwIK6//np8//33+OKLL3DjjTe6Zf56IKWEJuC+bBSL1YaPCo8JjZVaYUF0/CctBFhdQWoWCisVyY9BFBVY/qV4hHd4T7EGn0TkWg0NDdi1axdSU1PtzxmNRqSmpiI/P7/F16SkpGDXrl32oMmhQ4ewadMmjBs3rtXPqa+vR21tbbMH6VtXCTVbT529oHjJHSLSDk9qLn/o1zNC4wza2UytC6I7xgE2l9cSRzNRKgWDtaLj1EL09+TUuebneXrt72EyGnBbfKTQ2EuzKPXaQ0YOo0aNgs1mu+Kxdu1aAMDatWuxdetW+/innnoKxcXFOHfuHE6cOIEtW7Zg9OjR7pm8TpiMBsy4vqfweHdloxSUVKO67kK744I6mSVXWHA0wOoqUrNQHrmup6ayCD0BgygezmK1YefhU0JjTUYwykjkoaqqqmCxWBAW1rxRZlhYGMrLy1t8zT333INnn30WI0aMQIcOHdCzZ0+MGjWqzXJe2dnZ8Pf3tz+ioqJkPQ5SH6nN5R1pRk9E+iXaXH5LUaViu/YsVhu+KRa7gR7Qsf2LY/IcQ2MC4SO4NZbN5bXD0UyUb38WWwdEy1ipxdCYQPj7iK3FlwYF9Nzfo3tXX6Fx1XW/BZD02kOG9GPWmD6S2n2s3HbQ5RkZoteqtyVESg4gmIwGTEroJus85GKx2vD6V+JZKEYDMGvM1QrOSJ8YRPFw2w+dgOiaNDgqgFFGIg3ZunUrXnjhBbz22mvYvXs3NmzYgM8++wwLFy5s9TWZmZmoqamxP44ePerCGZMnUiqNmYgIEG8u32CxKVZuqaCkGmfqxYrfBguWvSHPYDIacH1vsVJCbC6vHY70+NBzqSWT0YAx/cLaH4jmQQE99/dwJICk1x4ypB8mowGPjRbPRlHy3K41oudxN/YVWxMvd0Oc2OuCJVR7kMP2QydwQbTPA4CZo3pxHVIAgygeLk/wxAYAHr+BUUYiTxUcHAyTyYSKiuYXdxUVFQgPD2/xNfPmzcP999+PBx98EAMHDsSkSZPwwgsvIDs7G1Zry39Bvb294efn1+xB+jY0JhABvmI7r9lYnoikktJcPv+Q+HmtFKK7gwGWWFEjNpfXH0d6fOi91JJo35KmoICeg06AY6XM9NpDhvRFajbKuvwSxebSIqV7X4kevIvjE4s37xceyywU5TCI4uFE6+x5GYEUwV1aROR6ZrMZQ4YMQW5urv05q9WK3NxcJCcnt/ias2fPwmhsvkybTCYAgM3GmwQkP/5fRURSDYsNgtkkNlap+9tVp8V2B/v5eDFQrEJsLq8/jvT40HupJalBAb0HnRzJRNFrDxnSF6nZKDn7lCvX2hKle19VCv4tER0nh4ZGKwp/Ee91yywU5TCI4sEsVht2lYo14UxkKS8ij5eRkYFVq1Zh3bp12L9/P2bMmIG6ujqkp6cDAKZMmYLMzEz7+AkTJuD111/H+++/j5KSEuTk5GDevHmYMGGCPZhC1J6CkmqcOtt+8z2AjeWJSDqT0YBbBkUIjS2vUeaC86TgDavknkE8X1YhNpfXH0d6fOi91JLUoACDTtJLxum5hwzpy6wxfYTH2uDaBvOia72jva8uLXkoxzg53P/mduGxBjALRUliZybkFnnFVRAteXetBk9siLTmzjvvxK+//or58+ejvLwcCQkJ2Lx5s73Z/JEjR5plnsydOxcGgwFz587FsWPHEBISggkTJuD555931yGQCrGxPBEpLSJArEHvpv+UYfHkeNlvYB769YzQuF6hnWX9XHKNoTGB8PYyoL6x/Z2ux06ea3cMeb6mHh8f7j7W7timG1l6L7UkJSjwuyHddR90kloyzmQ06LqHDOmLyWjA0OgAFJSeEhr/6tZizBpztUvWCtHygo6WIRRdG0THOauh0YodgpvrAeD2wd00uWZ7CmaieLDlX4pHc4f3DFFwJkQkl5kzZ+Lw4cOor6/Hjh07kJSUZP/a1q1bsXbtWvu/vby8kJWVheLiYpw7dw5HjhzBihUrEBAQ4PqJk2qxsTwRKc0gWBj6fKP85ZYsVhu+KRbLPgjoKNYfijyLyWjAoO4BQmPLFMp2IteT2uND76WWpAYF9B50kloyTu89ZEh/HpPQc9lqc102Sqjg767ouCteJ7iWio5z1pwPv5c0Pvv2QQrNhAAGUTyWxWrDzsOnhMaajMCwnmINF4mISF+GxgQiwl/sJDLC30eTJRvI/Z5//nmkpKTA19dXOBBss9kwf/58REREoGPHjkhNTcXPP/+s7ETJIckSzkPlbi5fUFKNM/ViudvBLrrgJfmJ/m364ZcaNpfXCKlBEb2XWpIaFNB70ElqyTi995Ah/UnpFQyThISG174qds3fXx01lrdYbfio8Ljw+KSYrjB78Ta/khT77kZHR8NgMDR7LFq0qNmYH374ASNHjoSPjw+ioqLw4osvXvE+H3zwAeLi4uDj44OBAwdi06ZNSk3Zo2w/dEK4+eZg9kMhIqJWmIwG3Bov1q/g1vgI/j0hRTQ0NGDy5MmYMWOG8GtefPFFvPLKK1i5ciV27NiBTp06IS0tDefPc6e5p3Fnc3nRuv4Ab2ypWYrgbnglsp3IPaQGRfReaklqUEDvQaemknEiqusadN9DhvTHZDTg0VHiDeYbrUDez/JulGnJl0ViGWFVdY71RKkSLHWYu19sHs7IK66SFAt6Z9owxeZCFykaonr22WdRVlZmfzz22GP2r9XW1uKmm25Cjx49sGvXLixevBgLFizAG2+8YR+Tl5eHu+++G9OmTcOePXswceJETJw4EXv37lVy2h7hnfxS4bGPS0izIyIifbFYbdj4fZnQ2I3fl3EHLynimWeewRNPPIGBAwcKjbfZbHj55Zcxd+5c3HbbbRg0aBDefvttHD9+HB9//LGykyXJ3Nlcvuq02MWun48Xb2yp2MVAnViQn83ltUFKjw+WWpIeFNB7JgogrWSc3nvIkD7NGtNHUsLFgk+VvVd7MTOj/V5ZgOMlqkVf90nhccWvm5/554/CY3uG+DILxQUU/Q536dIF4eHh9kenTp3sX3v33XfR0NCANWvWoH///rjrrrvw+OOPY+nSpfYxy5Ytw9ixY/Hkk0+ib9++WLhwIQYPHoxXX31VyWm7ncVqE45qehmBlN7arFNKRETOKyipFq4RX1ZzHgUl1QrPiKh9JSUlKC8vR2pqqv05f39/JCUlIT8/v9XX1dfXo7a2ttmDXENKc3k5LzpPCt7gS+4ZxBtbKmYyGhDf3V9o7Hf8O6YJUnp8bD90gqWWAAzvLdYnNbCzt+LNmdVASiBJ7z1kSJ9MRgMeGy2ejXLw17NoaBQrseqIgpJqVNddaHdcUCezwxtnhsYEIrBT+z30TtQ1KHrd3NBoRfGvdcLjF9wyQLG50G8UDaIsWrQIQUFBSExMxOLFi9HY+NuJTX5+Pq677jqYzb/9MUpLS8OBAwdw8uRJ+5hLL56bxrR18awF2w+dwAXBdSeRpbyIiKgNlael7fqWOp5ICeXl5QCAsLDmu2rDwsLsX2tJdnY2/P397Y+oqChF50m/cVdz+UO/nhEa1yu0s2yfSe7RratYoG7v8VqPzapcsWIFoqOj4ePjg6SkJBQUFLQ6dtWqVRg5ciS6du2Krl27IjU1tc3xWiOlx0desVgJGa2XWpLSEFnp5sxqIKWkGTN3SK+kZqPcv3q7YnMRLat3a0Kkw/dJTUYDbouPFJtPzTmHPkPE/W+Kfx+5ud51FAuiPP7443j//fexZcsWPPzww3jhhRfw1FNP2b9eXl7e4oVx09faGtPWxTOg/l2IeYL1XAHgWg2fBBIRkfOkpjI7mvpM+jNnzpwr+t9d/igqKnLpnDIzM1FTU2N/HD161KWfr2dSmsvLVW7JYrXhm2Kx9wro2P6uQvJs3bqKZRCcu2D1yKzK9evXIyMjA1lZWdi9ezfi4+ORlpaGysrKFsdv3boVd999N7Zs2YL8/HxERUXhpptuwrFjYqVM1E5Kj49jJ8VuZGm+1JKEhsgFJYLBbM+MR8pCSsk4vfeQIf2Smo2yo+SkYtkoomX1ugc4l3HYXXDTRnWdMkHThkYrdpSeFB7/++t7aftvmweRFESRcrGckZGBUaNGYdCgQXjkkUewZMkSLF++HPX1jjX3kULtuxClnPQP7ymWsktERPo0NCYQEf7tB0YMACL8fTS9Q5PkNXv2bOzfv7/NR2xsrEPvHR4eDgCoqGhe3rSiosL+tZZ4e3vDz8+v2YNcY1hsELwEr99Eb3i2p6CkGmfqxS7UgwV3aJPnEm0uDyi7O9RRS5cuxfTp05Geno5+/fph5cqV8PX1xZo1a1oc/+677+L3v/89EhISEBcXhzfffBNWqxW5ubkunrl7SOnxYRO806/1UkuVgru0y0+dw7q8w0JjHW3OrAZSSsaJbnRlJgpp0awxfSSNVyobxVVlCEXXBtFxUknJQjEAmDWGfbJdRWxrx3/Nnj0bDzzwQJtjWrtYTkpKQmNjI0pLS9GnTx+Eh4e3eGEM/Hbh3NqYti6egYu7EDMyMuz/rq2tVU0gxWK1YZdgxNFsMmCYhF1/RESkPyajAVkT+mHG33a3e4sha0I/7mIhYSEhIQgJUWYzR0xMDMLDw5Gbm4uEhAQAF8/nduzYgRkzZijymeQck9GAxB5d8Z3Aeaxon6b2iJZ1ALTdB0EvhsUGwdvLgPrG9m+YVwnuVnWVhoYG7Nq1C5mZmfbnjEYjUlNThUtVnz17FhcuXEBgYOubHerr65ttWlRbRYbLJfcMxoe728+8sQmmYGj9BrforuhPvj+OU+fa7ysAaDtDWUrJuH/tLRMaq+UeMqRfJqMBtydEYkPhcaHxTdkocjc6d1UZQimlEeUmNQtlUqLjpctIOkn/R4eEhCAuLq7Nx6U9Ti5VWFgIo9GI0NBQAEBycjK2bduGCxd+++Odk5ODPn36oGvXrvYxl++0ycnJQXJycpvzVPMuxLziKogmvo2O03g6MhERyWLsgAi8ft9ghPu1fKIX4e+D1+8bjLEDIlw8M9KLI0eOoLCwEEeOHIHFYkFhYSEKCwtx5sxv/Szi4uLw0UcfAQAMBgP+8Ic/4LnnnsPGjRvxn//8B1OmTEFkZCQmTpzopqOg9ohmsv3wS40sPSuqTovdKPfz8WKWnQaYjAaMuloscFvtYTfLq6qqYLFYHCpV3eSPf/wjIiMjr+gZeim1V2S4nGjQwyCYiaL1Ukuiu6K/KxWrfKH1HjJSSsadbRC7S8OAPWnVojviJY1XJBtF9NTR2VNMCaUR5SYlCwUAFv1O2s+FnKNIT5T8/Hy8/PLL+P7773Ho0CG8++67eOKJJ3DffffZAyT33HMPzGYzpk2bhh9//BHr16/HsmXLmmWQzJo1C5s3b8aSJUtQVFSEBQsWYOfOnZg5c6YS0/YI/9j9i/DYKcOilZsIERFpUMtnejabhgtek0eYP38+EhMTkZWVhTNnziAxMRGJiYnYuXOnfcyBAwdQU1Nj//dTTz2Fxx57DA899BCuvfZanDlzBps3b4aPj3Z3xaqdaLkluZrLnxS8wZrcM4gbjzTCp4NJaNxOD+yJ4oxFixbh/fffx0cffdTmGqi1vlCiQY/jp8Sy0rSeiSKaWXHuglhAQOs9ZKSUjBOh9aAT6ZvZy4ik6K7C45XojVIpmGUqOq7V1wtmOouOEyU1CyUppqvs2T7UNkW+297e3nj//fdx/fXXo3///nj++efxxBNP4I033rCP8ff3x7///W+UlJRgyJAhmD17NubPn4+HHnrIPiYlJQXvvfce3njjDcTHx+PDDz/Exx9/jAEDBigxbY+QL1hr02QES3kREZGQzXvLMONvu1stfVNeW48Zf9uNzYKlCoikWrt2LWw22xWPUaNG2cfYbLZmZWMNBgOeffZZlJeX4/z58/jiiy9w9dWs+evJhsUGwWwSu+EmR3N5g+C9vV6hnZ3+LPIMBsEf+vcyZTvJJTg4GCaTyaFS1S+99BIWLVqEf//73xg0aFCbY9VckaElokGPPUfFbjppvdSSlMwKEVrvIQPIe4xaDzoRvfPgMEnjMzf8IOvnf/uz2LmjaAP6Vl8vWBpR7sbymRu+lzT+nWnSfh7kPPn+wl5i8ODB2L69/RSkQYMG4euvv25zzOTJkzF58mS5pubRGhqtqDgt9kvYK6Qz/0ATEVG7LFYbnvnnPqGs5mf+uQ9j+oXz7wsROcRkNCC+uz++O3yq3bFyNJcvO3VWaJyfTwenP4s8Q7euYqVyGiw2bD94AsN7e8ZNYLPZjCFDhiA3N9dekrCpSXxbVRZefPFFPP/88/j8889xzTXXuGi2nkO0PJXoZmetl1pqyqwQ6SMjQuuZO4C8x6iHoBPpW1M2imi2xIbdx/DiHfGyXFtarDbk7KtofyCcD5iLZkHKWSLSYrVhw26xnjMAs1Dchd9xD7Iur1R47Og4ZRq5EhGRthSUVAs1cbbhYrPnAo2VQCEi14oMELtJefyUc0EUi9WGzT+KXUzXCDZQJs8nWjIOAPIPiWX4u0pGRgZWrVqFdevWYf/+/ZgxYwbq6uqQnp4OAJgyZUqzxvN//vOfMW/ePKxZswbR0dEoLy9HeXl5s15SWidankqEXkotyXkjX+s9ZAB5j1EPQSciKdkoNgDLcn6S5XMLSqpRc75RaKyzAXPR32U5f+eX5RyQ1MqFWSjuwSCKB/nb9lLhsSN7hSo3ESIi0ozK09JqtUodT0R0KVeVWyooqRZu9MvkOu24WDJObKwHVfMCANx555146aWXMH/+fCQkJKCwsBCbN2+2N5s/cuQIysp+K6v5+uuvo6GhAXfccQciIiLsj5deesldh+ByQ2MCEdBRnkwyvZRakvOmnh6CAnIeo9bLxREBF7NRErqLl4p87atiWcpril6jBvg6HzAXzYL8xckNQU0sVhuWbzkoPD6+ux+zUNyE33UP0dBoxeFqsV9AL6OB/VCIiEhIaBdpuziljiciupTUckuOaq3HU0uSY1liRStMRgNuGRQhNLZcIAvT1WbOnInDhw+jvr4eO3bsQFJSkv1rW7duxdq1a+3/Li0tbbGX1IIFC1w/cTcxGQ2YmtJDlvfSS6klOTMr9BAUEL1ZKiJUxswpIk/25Ni+wmMbrUDez85nhgYL/q4+kBztdMBcNAtyY+FxWQJEUrNQnkoT//6TvBhE8RBSSnndEBeii100RETkvKExgYjwb/9E0AAgwt9HF6UuiEg5riq3VHVarGloxw5Gbj7SmIgAX6Fxm/5T5lHN5ckxQ2Pk+f3VQ1YFIO9xar2HDCBvyThJd0GJVGxYbBA6SLibvODTvc5/qODv17XRzl/LDo0JRGCn9rMgT9Q1OF0KW2oWitnETfXuxCCKh5BSymtqcoxyEyEiIk0xGQ3ImtAPIqH3rAn9GKQnIqe4qtzSzlKxLJbrrubmI60xCP1FA843Wp3KdiLPUCkh66wteujvAciXWaGXHjJDYwLh7+Mly3tV1YkF94nUzmQ0YMb1PYXHH/z1LBoaxUqwtqbyjNjvl+i4tpiMBtwWHyk0trzGuZJeUrNQHrmuJ89r3YhBFA/AUl5ERKSksQMi8Pp9g1vNSInw98Hr9w3G2AFiJVKIiFrjinJLFqsNXwmWhujYQTCiQ6qRLOFa6NuDvyo4E3KF6jp5Miv0kokiV2aFXnrImIwGjOkXJst7sSQu6cmsMX0kjZ/zj++d+rxqweCI6Lj2dO8qlvXqzN8oi9WG178Sz0IxGoBZY652+PPIefKE3MkpmRvEFxOW8iIiIkeMHRCBMf3CUVBSjfKac6iua0BgZ2+E+10s4cW/LUQkFynllhZPjpe8/hSUVOP8BbEdjZEBvKmlNcNig+BlABoFtm5+52SZDXI/uTJI9NDfA/gts6LmfKNT76OXHjLAxWP9cPcxp95DL5k7RE1MRgNuT4jEhsLjQuM/2nMciycnOHzNKfq3QK6/Ga74vO2HTkDwdBYAMHNUL16zuxkzUdzMYrXhkz1iiw7AUl5EROQ4k9GA5J5BmDS4O6aNjMWkxG5I7hnEkzEikpXS5ZakNJUf3jNE8vuTZzMZDUjs0VVo7Pe/1LAvisrJlUGih/4egHyZFXrJ3AHkOVa9ZO4QXWrRHfHCY20AluX85PBnif6eyrV2ueLz3s4rER7LLBTPwCCKm20/dEJoFxXAUl5EROS8hkYrVn99CPM/2YvVXx9yuj4tEdHllC63xKbyJLrju8FiY18UlZOjx4fesgTkyCLRSw8ZQJ5j1VPmDlETs5cRSdFimxoA4NWtxQ5vbNBaJorFasPn+yqFxzMLxTMwiOJmizfvFx7LUl5EROSM7E37EDfvX1j42X68nX8YCz/bj7h5/0L2pn3unhoRaUhTuSURjpRbYlN5SpFww5J9UdRNjh4fessSkGMnNjNRXP8eRGr0zoPDhMdabY5no+QfFOuF5+pMFNF5XW5ZzgHhsQYwC8VTMIjiRg2NVhT+Uis8nqW8iLRhxYoViI6Oho+PD5KSklBQUNDq2FGjRsFgMFzxGD9+vAtnTFqQvWkf/rqtBJdv/rHagL9uK2EghYhko2S5JYvVhi+LxHbusam8dikdqCPP0dTjwxnDe+krS0COndh66SEDyJTtpKPMHUds27YNEyZMQGRkJAwGAz7++ON2X7N161YMHjwY3t7e6NWrF9auXav4PEk6s5cRvUI6CY93JBvFYrUhZ1+F0Fi51i7RdeGL/ZUOHc/yLeIN5a/pEaCrjQCejEEUN5LSUJ6lvIi0Yf369cjIyEBWVhZ2796N+Ph4pKWlobKy5RtCGzZsQFlZmf2xd+9emEwmTJ482cUzJzVraLRi1ddt11xd9XUJS3sRkWyUKrckpQknm8prF/ui6IccPT5CZchmURM5dmLrpYcMIE+2EzNR2lZXV4f4+HisWLFCaHxJSQnGjx+P0aNHo7CwEH/4wx/w4IMP4vPPP1d4puSIrAn9hcc6ko1SUFKNmvONQmPlWrtE14VT5y6gQOJmjWU5ByDlrOTxG5iF4ikYRHETi9WGj3aLN5S/LSGSkUciDVi6dCmmT5+O9PR09OvXDytXroSvry/WrFnT4vjAwECEh4fbHzk5OfD19WUQhSR5J7/0igyUy1ltF8cREclBqXJLeRLKJrCpvLaxL4p+ON1vQmcxNGezIvTWQ2ZoTCACO3Vw6j30lLnjiJtvvhnPPfccJk2aJDR+5cqViImJwZIlS9C3b1/MnDkTd9xxB/7yl78oPFNyREqvYJgk3K587Stp2SjlteeFxsm5dknJgiyvOSf8vharDSu2imeheBmBlN76yqb0ZAyiuElecRWk7PfNvn2QYnMhItdoaGjArl27kJqaan/OaDQiNTUV+fn5Qu+xevVq3HXXXejUSTxlluhw9VlZxxERtUepckvHTopdqJpNzOLWOvZF0Q9nd/lXnqmXaSbq4Oz3S289ZExGA26Lj3TqPfSUueMK+fn5za6ZASAtLa3Na+b6+nrU1tY2e5BrmIwGPDqqp/D4RiuQ97P4ppiq02Jr+I0yrl0mowGpfUOFxlZJ+BuTV1wFi4TA/u+vZ0N5T8Igips8888fhcfGd/eD2Ys/KiK1q6qqgsViQVhY85IEYWFhKC8vb/f1BQUF2Lt3Lx588MFWx/DkkVrSI9BX1nFERO1RqtzSuQaxcg6j4/R1E1CP2BdFP5zNrKjWWRDF2R4fTmf+qFD3ro6fA+stc8cVysvLW7xmrq2txblzLW+myM7Ohr+/v/0RFRXliqnSf80a0wdSzroWfLpXeOxJwcBwmMylG8MDxIKjp85dEH5PKfeCjQY2lPc0vDPvBg2NVhT/Wic8/qm0vgrOhojUYvXq1Rg4cCCGDh3a6hiePFJL7k+ORnv3Eo2Gi+OIiOQid7kli9WGrT+JZRRcIxjAIfViXxT9cDazQm+llpzt8aHH/h7OBOr0lrnjqTIzM1FTU2N/HD161N1T0hWT0YDHRotnoxz89axwP06D4K+X6DhRNsHTBtFxUu8FzxzFLBRPwyCKG9z/5nbhsSxFQKQdwcHBMJlMqKioaPZ8RUUFwsPD23xtXV0d3n//fUybNq3NcTx5pJaYvYyYPjKmzTHTR8Yw65GIZCV3uaXth06gvlHsSjXYyZ3YpA7si6IPzmZW6K3UkpRa/i1xNvNHjZwJHOkxc0dp4eHhLV4z+/n5oWPHln+fvb294efn1+xBriU1G+X+1WL3RstPiZVyDejoXG+jy3UVXAsrBHu2SLkXzCwUz8S7JS7W0GjFjtKTwuMfua4nI49EGmE2mzFkyBDk5uban7NarcjNzUVycnKbr/3ggw9QX1+P++67r81xPHmk1mSO64eHr4u5IiPFaAAevi4GmeP6uWdiRKRZUsotff5j+2UtpTSV19tNU71iXxR9cCazQo+llkxGA8b0C2t/YCuYiSKNHr9fSktOTm52zQwAOTk57V4zk3tJzUbZUXKy3WwUi9WGTXvbP0cE5N9AE9xF7P3+tbe83WxXqfeCmYXimRhEcTEpkUcDGHkk0pqMjAysWrUK69atw/79+zFjxgzU1dUhPT0dADBlyhRkZmZe8brVq1dj4sSJCApiZho5LnNcPxQtvBnzxvfFlOQemDe+L4oW3swACinu+eefR0pKCnx9fREQECD0mgceeAAGg6HZY+zYscpOlGQlpdySSFmHAsG+Fh07GHV301Sv2BdFH5zJrNBrqaXhvUMcfq3eyp8BzgVC9Ji5I9WZM2dQWFiIwsJCAEBJSQkKCwtx5MgRABerKUyZMsU+/pFHHsGhQ4fw1FNPoaioCK+99hr+7//+D0888YQ7pk8SzBrTR9L49rJRth86gXMXxMp+yb2BRjSAf7bB0m62K+8Fa4MiQZStW7decdHb9Pjuu+8AAKWlpS1+ffv25v9jffDBB4iLi4OPjw8GDhyITZs2KTFll5AaeZyUGKnLEz4iLbvzzjvx0ksvYf78+UhISEBhYSE2b95sb5x35MgRlJWVNXvNgQMH8M0337RbyotIhNnLiGkjY/HsbQMwbWQsS3iRSzQ0NGDy5MmYMWOGpNeNHTsWZWVl9sff//53hWZISpESzFiXV9Lq1yxWG/YcFjuPHhDpx3NonZASqNtz9BT7oqiUM5kVei21FOrEjmw9ZvI5UzKOmSjt27lzJxITE5GYmAjg4sbCxMREzJ8/HwBQVlZmD6gAQExMDD777DPk5OQgPj4eS5YswZtvvom0tDS3zJ/EmYwG3J4QKTy+vWyUfMFSnJ29vWTfQDM0JhCdzCahsfmHWs+W5r1g7XC8UGYbUlJSrrgJOG/ePOTm5uKaa65p9vwXX3yB/v372/996S7rvLw83H333cjOzsYtt9yC9957DxMnTsTu3bsxYMAAJaauKCmRRwBY9Lt4hWZCRO40c+ZMzJw5s8Wvbd269Yrn+vTpA5totzIiIg/0zDPPAADWrl0r6XXe3t7t9owiz5bSMxgrthwUGvvP749j+nUtl4HYfugEBNuh4FpmoejK0JhAfCdwc6LRCuT9XIWRfRzfoU/uk9wzGB/uPib5dbq9we3g/begTmZdZvI5UzJOj5k7Uo0aNarN69mWzg9HjRqFPXv2KDgrUsqiO+KxofC48Pj7V2/H+odTWvyaDWInfyN6B8keeDAZDRjZOxibf6xod2xbezR4L1g7FNl+ajabER4ebn8EBQXhk08+QXp6OgyG5v9TBwUFNRvbocNvjYCWLVuGsWPH4sknn0Tfvn2xcOFCDB48GK+++qoS01aU1MhjUkxX7g4mIiIiXdu6dStCQ0PRp08fzJgxAydOtL0brb6+HrW1tc0e5F7DYoNgEjyl3Vd2utVMgbfbyFK53PCevEmuJ1L6ovxjzy8KzoSU5GgwRK+llioFGx1f7tYEfe6AdqZknB4zd4jaYvYyIilaLEsUaDsbpezUWaH3SIwS/zwpEq8Se9/ympbXXN4L1haX/GQ2btyIEydO2Gv+X+rWW29FaGgoRowYgY0bNzb7Wn5+PlJTU5s9l5aWhvz8fEXnq4Rrn/u3pPHvTBum0EyIiIiIPN/YsWPx9ttvIzc3F3/+85/x1Vdf4eabb4bFYmn1NdnZ2fD397c/oqKiXDhjaonJaEBqXKjQ2EarrcWa0harDV8UVQq9h5fRgGE92T9MT4bFBkH0fsPZhtbXD/JsjgZD9JqJUl3n2HF3D9BnQMDRknEBHTvoMnOHqD3vPCjtnuacf3x/xXMWq00oCwQAas5dkPR5omrPi73vv/e13FxeahYK7wV7NpcEUVavXo20tDR0797d/lznzp2xZMkSfPDBB/jss88wYsQITJw4sVkgpby83N4noElYWBjKy8vb/DxP24X4/97ajprz4ifsPUN8GXkkIiIijzZnzpxWe+A1PYqKihx+/7vuugu33norBg4ciIkTJ+LTTz/Fd99912LZwyaZmZmoqamxP44ePerw55N8pqTECI9dl39lxsn2QydgEespin4RXXS5i1rPTEYDbhOsv35tNG92qhUzUaRxtMeHXr9fgGP9c1L7hvJvDlELpGajfLTn+BVBiIKSapxtEDsBVOrX0CBYG/FMvQUFJdXNnmMWivZI+uk4crH8yy+/4PPPP7+iIXJwcDAyMjKQlJSEa6+9FosWLcJ9992HxYsXO31QnrQL8dPCY/jygFgjpCYLblFfvxciIiLSl9mzZ2P//v1tPmJjY2X7vNjYWAQHB6O4uLjVMd7e3vDz82v2IPcbFhsEk+DF7ZdFv15xEZ13sPVmnZebEC/ezJS0I/v2+HZvcxgMwNSUaFdMhxTgaFBAr5kojvb40Ov3C3Ds2If3kh54IdILKdkoNgDLcn5q9ly5hLKEybHK/C4mS8huLq851+zfzELRHklFH2fPno0HHnigzTGXXyy/9dZbCAoKwq233tru+yclJSEnJ8f+7/DwcFRUNE/dqqioaLfBaGZmJjIyMuz/rq2tdUsgxWK1Yeb7hZJe08FkQEpv/iEmIiIizxYSEoKQENf1nvjll19w4sQJREREuOwzSR4mowH9u/nhh1/azw5vKuk1/JLz4c17y4Q/a6qErBfSDrOXEQ9dF4O/bmu9d85DI2O4w1PFHA0K6LXpd1OPj5rzjZJep+dMFEeOPdSJhvREWmf2MqJncCccrKoTGv/q1mLMGnO1Pbur6nS90Os6djAqVsp1WGwQvL0MqG9sv8F91Znf5sssFG2S9BMKCQlBXFxcmw+z+bc/PDabDW+99RamTJnSrGF8awoLC5tdGCcnJyM3N7fZmJycHCQnJ7f5Pp6wC7Gh0YqeT2+S/Lql/5PAdFAiIiLSlCNHjqCwsBBHjhyBxWJBYWEhCgsLcebMGfuYuLg4fPTRRwCAM2fO4Mknn8T27dtRWlqK3Nxc3HbbbejVqxfS0tLcdRjkhAmDugmPvbSkV0OjFQd/FWsqypK4+pY5rh8evi7mipIeRgPw8HUxyBzXzz0TI1kMjQlEYKf27ylcTq9Nvx3t8cFMFInav69KpGsLbu0vPNZqa56NsrNUrKrPdVeHKHYf1WQ0YNTVYpvGdh7+LWgy58Mre7y0hVko6iApE0WqL7/8EiUlJXjwwQev+Nq6detgNpuRmJgIANiwYQPWrFmDN9980z5m1qxZuP7667FkyRKMHz8e77//Pnbu3Ik33nhDyWk77ZmNP+KtvFLJr0uM8mcJAiIiItKc+fPnY926dfZ/N53/bdmyBaNGjQIAHDhwADU1NQAAk8mEH374AevWrcOpU6cQGRmJm266CQsXLoS3t2MlXci9pqZE4/lN+4XG5uyrhMVqg8loQOYG8YvQtP5tZ6uT9mWO64fZN8XhnfxSHK4+ix6Bvrg/OZrBNQ0wGQ24LT4Sb+UdFn6N3pt+J/cMxoe7j0l6jV4zdwDHSsZVnhHbKU+kVym9gmEyABbBgGNTNgoAfFlUKfSajh1Mjk5P7P3NYrfOtxRV2kvSbig8Lvz+zEJRD0WDKKtXr0ZKSgri4uJa/PrChQtx+PBheHl5IS4uDuvXr8cdd9xh/3pKSgree+89zJ07F08//TR69+6Njz/+GAMGeF7PkHMNFszf+AM+2Cn+i3K5D2cMl3FGRERERJ5h7dq1WLt2bZtjbLbfrq46duyIzz//XOFZkSuZvYzoEdgRh6vPtTu2qS72rDFX45M94ufWw3u6rrwceS6zlxHTRsrXj4k8R/euvpLG673ptyOZFXrN3AEcKxlXzSAKUZtMRgMeHdUTr2w5KDS+KRslqWcQLoj1lEdkgLJl9bp1FVsXGywXS9LuOCTeyw9gFoqaKBpEee+991r92tSpUzF16tR232Py5MmYPHmynNNyWEOjFX/d9jNWbzuIU+flzdtcfneirk/wiIiIiEjb7hsmno3y2lfFuDYmEAIlqAEAXkaDYvWwicgzSO1ZkdxT371GpX6/9J6540gfGT1n7hCJmjWmD5ZvOShc/e6VLcX4vVX8nqvSm2hSegZjhWAQ6OviSqz8qvX+bJdjKVp1UTSIonYWqw3b9lfiz5/vw4HKs4qVu7whLoRlvIiIiIhI06SU9Gq0Alkb9wq/9w1xytXDJiLPIDWzQs/9PQDpx6/3zJ2mPjJSSqDpOXOHSJTJaMBjo8WzUQDgzW/ExppNym+iGRYbBC8DhDb2rP5aPIACAAtu8bxKS9Q6hrtaYLHasPhfRej59Cakv7MTRQoGULoH+GDNA0MVenciIiIiIs9g9jKiV0gn4fGHqsQaygPA1OQYR6ZERCoiNbNC71kCUnt86D1zBwCG9xbf0a73zB0iKWaN6QMpIdoGi9i4+O7+igd/TUYDEnt0FRorWoIMALyMQEpvrrtqwiDKZf75/XH0enoTVnwlHiF1lG8HI76Zc6Pin0NERERE5AmyJvSX/T1ZyotIH6RmVoQ60ONCS6T2+NB75g4AhEoIPOk9c4dIiqZsFLld66JAphIB099f34triMowiHKJ6W9/h8f+vkexrJNLdTAA+xbe7IJPIiIi+k1DoxWrvz6E+Z/sxeqvD6GhUcJ2GSIiJ6X0Cpa0E1HEbQmRvAgl0gGpmRUuubD3YE09PkRJzfTRJAl/Spi5QySN1GwUEUr3Q2mSIvPvu9EAzBpztazvScpjEOW/nv/sR+Tsq3TJZ3kZgJ+zx7vks4iIiJpkb9qHuHn/wsLP9uPt/MNY+Nl+xM37F7I37XP31IhIJ0xGAyYlyNsLMPv2QbK+H5FSVqxYgejoaPj4+CApKQkFBQVtjv/ggw8QFxcHHx8fDBw4EJs2bXLRTD2T1MyKqrp6hWaiDk09PkQxEwWorD0vPJbfLyJpTEYDlv1PvGzv58pM5GGxQTDJeAf9L3cmcAOQCjGIgou7cld9XeqSz+rqY0QxAyhERORi2Zv24a/bSmC9bFem1Qb8dVsJAylE5DKL7pDvArpniC/MXrykIc+3fv16ZGRkICsrC7t370Z8fDzS0tJQWdnyRr68vDzcfffdmDZtGvbs2YOJEydi4sSJ2Lt3r4tn7jmGxgSiUwfx3/fQLvou5wUASbHiNxj13kMGAKrrxAMjzNwhku7Wwd0R5tdBlve6IS7EZYEIk9GA1LhQWd4rws8btyV0k+W9yLV4xQFg7bclLvmc0VcHYs8ClvAiIiLXurhZoO2/dau+LmFpLyJyCbOXEUnRYg0627PglgGyvA+R0pYuXYrp06cjPT0d/fr1w8qVK+Hr64s1a9a0OH7ZsmUYO3YsnnzySfTt2xcLFy7E4MGD8eqrr7p45p7DZDSgf6Sf0NiOHYxs+g3gh19OCY8N9++o3ERUQkrJOCkBFyL6zddPpcryPlOTY2R5H1FTUuT5vK+eukGW9yHXYxAFwL/3lSv+Ga/elYC3/l+y4p9DRER0uXfyS6/IQLmc1XZxHBGRK7zz4DCn36ODyYCU3qxJT56voaEBu3btQmrqbzeOjEYjUlNTkZ+f3+Jr8vPzm40HgLS0tFbH60VkV1+hcQO7+bNUCgDRJh++ZhODTpBWMu4ky3kROcTsZUT68Kucfg9XlfJqMiw2CBKSIVv0/4ZHM4NaxfiTAyCpe5gE3QN8sGbqtTj4wjjcwlQtIiJyk8PVZ2UdR0TkLDkuoJf+D+tJkzpUVVXBYrEgLKx5f4qwsDCUl7e8oa+8vFzSeACor69HbW1ts4fWRAaIZUtcE82AAABEB4kFncYNCOd6iv+WjDObhMby20XkuKwJAxHcycvh1y++Y5DL1yyT0YAlTpSkDe1sxvwJ/WWcEbkagygAxvRzvq6dAYCPlxH9IrrYAyffzLkRN/QN5ckIERG5VY9AsQto0XFERHLImjAQ3fwdq4t9Q1wIJsTL26CeSO2ys7Ph7+9vf0RFRbl7SrIb3kss+0x0nNbdnxzd7s1+A4AXbh/kkvl4OpPRgAdHipXsSY7l/2NEztjxp5scet2ASD+39RS5dXB39OgqXvbvUvlPy1PGjNyHQRQA6cNjJY3vFuCDp9L64KfnbkbpovEoXTQeJYvGo+i5m7Fp1nUMnBARkUcRuYA2Gi6OIyJypW8zb4JvB2nnzVFdfbDmgaEKzYhIfsHBwTCZTKioqGj2fEVFBcLDw1t8TXh4uKTxAJCZmYmamhr74+jRo85P3sMMiw1CgG/bwdeuvh0wTEJDdS0zexkxvZ2gwEPXxbC8zCUev/Fq+LaTjRLg28HlpYSItMZkNGDlfYMlvaaT2YRPHx+p0IzEfPnkjZJfs/K+wbxPrAH8S4mLJxYPX9f2iYVvBwP2PzsWpYvG49s5N+L3o3vxRIOIiFRB5AJ6+kheQBORe+xbOA6dfcRKOkR19cbXf5R+8UrkTmazGUOGDEFubq79OavVitzcXCQnt9w3Mzk5udl4AMjJyWl1PAB4e3vDz8+v2UNrTEYDFt0+sM0x2bcP5M2qS2SO64eHr4u5YkON0QA8fF0MMsf1c8/EPJTJaMDS/2m7ZM8i/j9GJIuxAyKEAylmI/Djs2MVnlH7pAZ/Vt43GGMHRCg4I3IV3i35r6YTi5akD78K+xaOQ0fB2phERESehhfQROTJ9i5Iw+g+IW2OSR9+Fb7+I0shkDplZGRg1apVWLduHfbv348ZM2agrq4O6enpAIApU6YgMzPTPn7WrFnYvHkzlixZgqKiIixYsAA7d+7EzJkz3XUIHqPpptvlTcAj/H14s6oVmeP6oWjhzZg3vi+mJPfAvPF9UbTwZp7/teK3/8eal+0J9/Pm/2NEMhs7IAIHXxjXZg+nfuG++OmF8S6cVdua1oi29iAmdO+Cgy+M43qhIQabzWZz9ySUVltbC39/f9TU1LS7G6eh0Yp38ktxuPosegT64v7kaO7MJdIJKWuFWmjxmMg5/DtHrdHieqHFY9K6cw0WzN/4A/69twL1jVYEdfbGvUk98ODIWK5VpBhXrRWvvvoqFi9ejPLyciQkJOCVV15BUlISAGDUqFGIjo7G2rVr7eM/+OADzJ07F6WlpejduzdefPFFjBs3TvjztL4GWqw2FJRUo/L0eYR28cHQmEBmB5Cs9PL/mBbXCi0ekx6cOd+Ix979DgWlpwCDAUNjArH87sHCGcuuZrHasG1/JV78934cqT4H7w4m3NQ/HFkT+nMjvoqIrhcMohAR/Zcr1ooVK1bYL57j4+OxfPlyDB3ael33U6dO4U9/+hM2bNiA6upq9OjRAy+//LLwBTTXPyISpcX1QovHRETy0+paodXjIiJ5aXGt0OIxEZEyRNcLzwzlERFp0Pr165GRkYGVK1ciKSkJL7/8MtLS0nDgwAGEhoZeMb6hoQFjxoxBaGgoPvzwQ3Tr1g2HDx9GQECA6ydPRERERERERESkQwyiEBG5yNKlSzF9+nR77euVK1fis88+w5o1azBnzpwrxq9ZswbV1dXIy8tDhw4dAADR0dGunDIREREREREREZGu6SKI0lSxrLa21s0zISJP1rRGKFHlsKGhAbt27WrWMNRoNCI1NRX5+fktvmbjxo1ITk7Go48+ik8++QQhISG455578Mc//hEmU8v1Nevr61FfX2//d01NDQCuf0TUPiXXQHfhOSARidDi+gdwDSQiMVpcA7n+EZEo0TVQF0GU06dPAwCioqLcPBMiUoPTp0/D399f1vesqqqCxWJBWFhYs+fDwsJQVFTU4msOHTqEL7/8Evfeey82bdqE4uJi/P73v8eFCxeQlZXV4muys7PxzDPPXPE81z8iEqXEGuguPAckIim0tP4BXAOJSBotrYFc/4hIqvbWQF00lrdarTh+/Di6dOkCg8HQ7vja2lpERUXh6NGjumhApbfjBXjMPOaW2Ww2nD59GpGRkTAajbLO5fjx4+jWrRvy8vKQnJxsf/6pp57CV199hR07dlzxmquvvhrnz59HSUmJPfNk6dKlWLx4McrKylr8nMszUaxWK6qrqxEUFMT1rxU8Zh6zFjlyvEquge7Cc8D28Zi1f8x6O17As84B3YlrYPv0dsx6O16Ax6zXNVDq+gfo7/8VvR0vwGPWwzEreR2si0wUo9GI7t27S36dn5+fLv4Ha6K34wV4zHoh5ZiV2nkTHBwMk8mEioqKZs9XVFQgPDy8xddERESgQ4cOzUp39e3bF+Xl5WhoaIDZbL7iNd7e3vD29m72nCON6Pn/iT7wmLVP6vFqZfdhE54DiuMxa5/ejhfwjHNAd+IaKE5vx6y34wV4zO3R2hro6PoH6O//Fb0dL8Bj1gMlroO1EWImIvJwZrMZQ4YMQW5urv05q9WK3NzcZpkplxo+fDiKi4thtVrtz/3000+IiIhoMYBCRERERERERERE8mIQhYjIRTIyMrBq1SqsW7cO+/fvx4wZM1BXV4f09HQAwJQpU5o1np8xYwaqq6sxa9Ys/PTTT/jss8/wwgsv4NFHH3XXIRAREREREREREemKLsp5SeXt7Y2srKwrSuJold6OF+Ax64WnHfOdd96JX3/9FfPnz0d5eTkSEhKwefNme7P5I0eONKu/GBUVhc8//xxPPPEEBg0ahG7dumHWrFn44x//qNgcPe175go8Zn3Q2zHr7XjlosfvG49Z+/R2vIA+j1kOevy+6e2Y9Xa8AI+ZxOnt+6a34wV4zHqg5PHqorE8ERERERERERER7qaYDgAACF9JREFUERGRVCznRURERERERERERERE1AIGUYiIiIiIiIiIiIiIiFrAIAoREREREREREREREVELGEQhIiIiIiIiIiIiIiJqAYMol3n++eeRkpICX19fBAQEtDjmyJEjGD9+PHx9fREaGoonn3wSjY2Nrp2ogqKjo2EwGJo9Fi1a5O5pyWrFihWIjo6Gj48PkpKSUFBQ4O4pKWbBggVX/Dzj4uLcPS3ZbNu2DRMmTEBkZCQMBgM+/vjjZl+32WyYP38+IiIi0LFjR6SmpuLnn392z2Q9HNc/rn9ao/X1D+AaKCeugVwDtYZrINdAKbgGcg3UGq6BXANFcf27SOtrINc/rn/Orn8MolymoaEBkydPxowZM1r8usViwfjx49HQ0IC8vDysW7cOa9euxfz58108U2U9++yzKCsrsz8ee+wxd09JNuvXr0dGRgaysrKwe/duxMfHIy0tDZWVle6emmL69+/f7Of5zTffuHtKsqmrq0N8fDxWrFjR4tdffPFFvPLKK1i5ciV27NiBTp06IS0tDefPn3fxTD0f17+LuP5pi5bXP4BroJy4Bl7ENVBbuAZyDRTFNfAiroHawjWQa6AIrn+/0eoayPWP658s65+NWvTWW2/Z/P39r3h+06ZNNqPRaCsvL7c/9/rrr9v8/Pxs9fX1Lpyhcnr06GH7y1/+4u5pKGbo0KG2Rx991P5vi8Vii4yMtGVnZ7txVsrJysqyxcfHu3saLgHA9tFHH9n/bbVabeHh4bbFixfbnzt16pTN29vb9ve//90NM1QHrn9/cfc0FMP1T9u4BsqDa+Bf3D0NxXAN1DaugfLgGvgXd09DMVwDtY1roPP0vP7ZbNpeA7n+aZur1j9mokiUn5+PgQMHIiwszP5cWloaamtr8eOPP7pxZvJatGgRgoKCkJiYiMWLF2smTbGhoQG7du1Camqq/Tmj0YjU1FTk5+e7cWbK+vnnnxEZGYnY2Fjce++9OHLkiLun5BIlJSUoLy9v9vP29/dHUlKSpn/eSuH6p25c//S1/gFcA+XGNVDduAZyDQS4BjqDa6C6cQ3kGghwDXSUXtY/QJtrINc/rn+APOuflxyT05Py8vJmCycA+7/Ly8vdMSXZPf744xg8eDACAwORl5eHzMxMlJWVYenSpe6emtOqqqpgsVha/BkWFRW5aVbKSkpKwtq1a9GnTx+UlZXhmWeewciRI7F371506dLF3dNTVNPvZEs/b638vroS1z914/qnr/UP4BooN66B6sY1kGtgE66BjuEaqG5cA7kGNuEaKJ0e1j9Au2sg1z+uf02cXf90kYkyZ86cKxrqXP7Q6i9OEynfg4yMDIwaNQqDBg3CI488giVLlmD58uWor69381GQI26++WZMnjwZgwYNQlpaGjZt2oRTp07h//7v/9w9NXIBrn9c//SM6x9xDeQaqGdcA4lrINdAPeMaqG9c/y7iGqhPXP+UoYtMlNmzZ+OBBx5oc0xsbKzQe4WHh6OgoKDZcxUVFfaveSpnvgdJSUlobGxEaWkp+vTpo8DsXCc4OBgmk8n+M2tSUVHh0T8/OQUEBODqq69GcXGxu6eiuKafaUVFBSIiIuzPV1RUICEhwU2zci2uf1z/mnD909f6B3ANBLgGAlwDm3AN5BrYhGtgc1wDuQZ68s9PTlwDYf+3HtZArn8XcQ3k+gdw/Wvi7PqniyBKSEgIQkJCZHmv5ORkPP/886isrERoaCgAICcnB35+fujXr58sn6EEZ74HhYWFMBqN9uNVM7PZjCFDhiA3NxcTJ04EAFitVuTm5mLmzJnunZyLnDlzBgcPHsT999/v7qkoLiYmBuHh4cjNzbUvlLW1tdixYwdmzJjh3sm5CNc/rn9NuP7pa/0DuAYCXAMBroFNuAZyDQS4BjqDa6C6cQ3kGgjoaw3k+ncR10CufwDXP0Ce9U8XQRQpjhw5gurqahw5cgQWiwWFhYUAgF69eqFz58646aab0K9fP9x///148cUXUV5ejrlz5+LRRx+Ft7e3eycvg/z8fOzYsQOjR49Gly5dkJ+fjyeeeAL33Xcfunbt6u7pySIjIwNTp07FNddcg6FDh+Lll19GXV0d0tPT3T01Rfzv//4vJkyYgB49euD48ePIysqCyWTC3Xff7e6pyeLMmTPNouklJSUoLCxEYGAgrrrqKvzhD3/Ac889h969eyMmJgbz5s1DZGSk/Y8n/YbrH9c/rdH6+gdwDZQT10CugVrDNZBroBRcA7kGag3XQK6BovS+/gHaXwO5/nH9k2X9s1EzU6dOtQG44rFlyxb7mNLSUtvNN99s69ixoy04ONg2e/Zs24ULF9w3aRnt2rXLlpSUZPP397f5+PjY+vbta3vhhRds58+fd/fUZLV8+XLbVVddZTObzbahQ4fatm/f7u4pKebOO++0RURE2Mxms61bt262O++801ZcXOzuaclmy5YtLf7OTp061Waz2WxWq9U2b948W1hYmM3b29t244032g4cOODeSXsorn9c/7RG6+ufzcY1UE5cA7kGag3XQK6BUnAN5BqoNVwDuQaK0vv6Z7PpYw3k+sf1z9n1z2Cz2WyOh2CIiIiIiIiIiIiIiIi0yejuCRAREREREREREREREXkiBlGIiIiIiIiIiIiIiIhawCAKERERERERERERERFRCxhEISIiIiIiIiIiIiIiagGDKERERERERERERERERC1gEIWIiIiIiIiIiIiIiKgFDKIQERERERERERERERG1gEEUIiIiIiIiIiIiIiKiFjCIQkRERERERERERERE1AIGUYiIiIiIiIiIiIiIiFrAIAoREREREREREREREVELGEQhIiIiIiIiIiIiIiJqwf8HAzvUhSZXr4UAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "19\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "20\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "21\n"
-     ]
-    },
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "22\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# first, randomly select the other multiple choice options\n",
-    "np.random.seed(1)\n",
-    "fbench_hard_questions = []\n",
-    "for idx, _ in enumerate(fbench_hard):\n",
-    "    mc_options = [idx]\n",
-    "    # select 4 more random functions\n",
-    "    for _ in range(4):\n",
-    "        random_idx = np.random.randint(0, len(fbench_hard))\n",
-    "        while random_idx in mc_options:\n",
-    "            random_idx = np.random.randint(0, len(fbench_hard))\n",
-    "        mc_options.append(random_idx)\n",
-    "    # shuffle options\n",
-    "    np.random.shuffle(mc_options)\n",
-    "    # store the options and the correct answer\n",
-    "    fbench_hard_questions.append((mc_options, idx))\n",
-    "\n",
-    "# plot the 5 functions for each question\n",
-    "# make a 1x5 grid of plots\n",
-    "for idx, (options, correct) in enumerate(fbench_hard_questions):\n",
-    "    fig, axes = plt.subplots(1, 5, figsize=(20, 3))\n",
-    "    print(idx)\n",
-    "    for i, ax in enumerate(axes):\n",
-    "        f, n = fbench_hard[options[i]]\n",
-    "        y = f(x)\n",
-    "        ax.scatter(x, y)\n",
-    "        ax.set_title(n)\n",
-    "        # if it is the correct one, set the title color to red\n",
-    "        if options[i] == correct:\n",
-    "            ax.title.set_color('red')\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Target function: [1.         0.97979798 0.95959596 0.93939394 0.91919192 0.8989899\n",
-      " 0.87878788 0.85858586 0.83838384 0.81818182 0.7979798  0.77777778\n",
-      " 0.75757576 0.73737374 0.71717172 0.6969697  0.67676768 0.65656566\n",
-      " 0.63636364 0.61616162 0.5959596  0.57575758 0.55555556 0.53535354\n",
-      " 0.51515152 0.49494949 0.47474747 0.45454545 0.43434343 0.41414141\n",
-      " 0.39393939 0.37373737 0.35353535 0.33333333 0.31313131 0.29292929\n",
-      " 0.27272727 0.25252525 0.23232323 0.21212121 0.19191919 0.17171717\n",
-      " 0.15151515 0.13131313 0.11111111 0.09090909 0.07070707 0.05050505\n",
-      " 0.03030303 0.01010101 0.01010101 0.03030303 0.05050505 0.07070707\n",
-      " 0.09090909 0.11111111 0.13131313 0.15151515 0.17171717 0.19191919\n",
-      " 0.21212121 0.23232323 0.25252525 0.27272727 0.29292929 0.31313131\n",
-      " 0.33333333 0.35353535 0.37373737 0.39393939 0.41414141 0.43434343\n",
-      " 0.45454545 0.47474747 0.49494949 0.51515152 0.53535354 0.55555556\n",
-      " 0.57575758 0.5959596  0.61616162 0.63636364 0.65656566 0.67676768\n",
-      " 0.6969697  0.71717172 0.73737374 0.75757576 0.77777778 0.7979798\n",
-      " 0.81818182 0.83838384 0.85858586 0.87878788 0.8989899  0.91919192\n",
-      " 0.93939394 0.95959596 0.97979798 1.        ]\n",
-      "1. Most similar function (index 3) with distance 1.8165902030029581: [1.00000000e+00 9.60004081e-01 9.20824406e-01 8.82460973e-01\n",
-      " 8.44913784e-01 8.08182838e-01 7.72268136e-01 7.37169677e-01\n",
-      " 7.02887460e-01 6.69421488e-01 6.36771758e-01 6.04938272e-01\n",
-      " 5.73921028e-01 5.43720029e-01 5.14335272e-01 4.85766758e-01\n",
-      " 4.58014488e-01 4.31078461e-01 4.04958678e-01 3.79655137e-01\n",
-      " 3.55167840e-01 3.31496786e-01 3.08641975e-01 2.86603408e-01\n",
-      " 2.65381084e-01 2.44975003e-01 2.25385165e-01 2.06611570e-01\n",
-      " 1.88654219e-01 1.71513111e-01 1.55188246e-01 1.39679625e-01\n",
-      " 1.24987246e-01 1.11111111e-01 9.80512193e-02 8.58075707e-02\n",
-      " 7.43801653e-02 6.37690032e-02 5.39740843e-02 4.49954086e-02\n",
-      " 3.68329762e-02 2.94867871e-02 2.29568411e-02 1.72431385e-02\n",
-      " 1.23456790e-02 8.26446281e-03 4.99948985e-03 2.55076013e-03\n",
-      " 9.18273646e-04 1.02030405e-04 1.02030405e-04 9.18273646e-04\n",
-      " 2.55076013e-03 4.99948985e-03 8.26446281e-03 1.23456790e-02\n",
-      " 1.72431385e-02 2.29568411e-02 2.94867871e-02 3.68329762e-02\n",
-      " 4.49954086e-02 5.39740843e-02 6.37690032e-02 7.43801653e-02\n",
-      " 8.58075707e-02 9.80512193e-02 1.11111111e-01 1.24987246e-01\n",
-      " 1.39679625e-01 1.55188246e-01 1.71513111e-01 1.88654219e-01\n",
-      " 2.06611570e-01 2.25385165e-01 2.44975003e-01 2.65381084e-01\n",
-      " 2.86603408e-01 3.08641975e-01 3.31496786e-01 3.55167840e-01\n",
-      " 3.79655137e-01 4.04958678e-01 4.31078461e-01 4.58014488e-01\n",
-      " 4.85766758e-01 5.14335272e-01 5.43720029e-01 5.73921028e-01\n",
-      " 6.04938272e-01 6.36771758e-01 6.69421488e-01 7.02887460e-01\n",
-      " 7.37169677e-01 7.72268136e-01 8.08182838e-01 8.44913784e-01\n",
-      " 8.82460973e-01 9.20824406e-01 9.60004081e-01 1.00000000e+00]\n",
-      "2. Most similar function (index 10) with distance 4.4866131268832925: [1.00000000e+00 8.17078422e-01 6.67617148e-01 5.45495566e-01\n",
-      " 4.45712658e-01 3.64182197e-01 2.97565416e-01 2.43134283e-01\n",
-      " 1.98659780e-01 1.62320624e-01 1.32628684e-01 1.08368042e-01\n",
-      " 8.85451964e-02 7.23483788e-02 5.91143107e-02 4.83010418e-02\n",
-      " 3.94657563e-02 3.22466391e-02 2.63480590e-02 2.15284623e-02\n",
-      " 1.75904809e-02 1.43728500e-02 1.17438039e-02 9.59568018e-03\n",
-      " 7.84051060e-03 6.40641896e-03 5.23467758e-03 4.27730228e-03\n",
-      " 3.49508744e-03 2.85600047e-03 2.33387001e-03 1.90731422e-03\n",
-      " 1.55886514e-03 1.27425340e-03 1.04182380e-03 8.52058081e-04\n",
-      " 6.97185129e-04 5.70862712e-04 4.67917782e-04 3.84134624e-04\n",
-      " 3.16082223e-04 2.60973754e-04 2.16552460e-04 1.80999240e-04\n",
-      " 1.52858148e-04 1.30976773e-04 1.14459047e-04 1.02628552e-04\n",
-      " 9.50008151e-05 9.12634706e-05 9.12634706e-05 9.50008151e-05\n",
-      " 1.02628552e-04 1.14459047e-04 1.30976773e-04 1.52858148e-04\n",
-      " 1.80999240e-04 2.16552460e-04 2.60973754e-04 3.16082223e-04\n",
-      " 3.84134624e-04 4.67917782e-04 5.70862712e-04 6.97185129e-04\n",
-      " 8.52058081e-04 1.04182380e-03 1.27425340e-03 1.55886514e-03\n",
-      " 1.90731422e-03 2.33387001e-03 2.85600047e-03 3.49508744e-03\n",
-      " 4.27730228e-03 5.23467758e-03 6.40641896e-03 7.84051060e-03\n",
-      " 9.59568018e-03 1.17438039e-02 1.43728500e-02 1.75904809e-02\n",
-      " 2.15284623e-02 2.63480590e-02 3.22466391e-02 3.94657563e-02\n",
-      " 4.83010418e-02 5.91143107e-02 7.23483788e-02 8.85451964e-02\n",
-      " 1.08368042e-01 1.32628684e-01 1.62320624e-01 1.98659780e-01\n",
-      " 2.43134283e-01 2.97565416e-01 3.64182197e-01 4.45712658e-01\n",
-      " 5.45495566e-01 6.67617148e-01 8.17078422e-01 1.00000000e+00]\n",
-      "3. Most similar function (index 7) with distance 5.202712318042597: [2.06115362e-09 2.52258972e-09 3.08732877e-09 3.77849750e-09\n",
-      " 4.62440006e-09 5.65967713e-09 6.92672452e-09 8.47742926e-09\n",
-      " 1.03752945e-08 1.26980401e-08 1.55407850e-08 1.90199430e-08\n",
-      " 2.32779896e-08 2.84892967e-08 3.48672734e-08 4.26731052e-08\n",
-      " 5.22264499e-08 6.39185279e-08 7.82281434e-08 9.57412917e-08\n",
-      " 1.17175156e-07 1.43407479e-07 1.75512503e-07 2.14804966e-07\n",
-      " 2.62893941e-07 3.21748726e-07 3.93779493e-07 4.81935984e-07\n",
-      " 5.89828309e-07 7.21874785e-07 8.83482865e-07 1.08127059e-06\n",
-      " 1.32333759e-06 1.61959679e-06 1.98218035e-06 2.42593648e-06\n",
-      " 2.96903751e-06 3.63372405e-06 4.44721579e-06 5.44282614e-06\n",
-      " 6.66132650e-06 8.15261587e-06 9.97776426e-06 1.22115136e-05\n",
-      " 1.49453385e-05 1.82911923e-05 2.23860915e-05 2.73977270e-05\n",
-      " 3.35313310e-05 4.10380817e-05 5.02253892e-05 6.14694843e-05\n",
-      " 7.52308257e-05 9.20729562e-05 1.12685581e-04 1.37912809e-04\n",
-      " 1.68787727e-04 2.06574696e-04 2.52821138e-04 3.09420897e-04\n",
-      " 3.78691799e-04 4.63470567e-04 5.67228989e-04 6.94216093e-04\n",
-      " 8.49632147e-04 1.03984162e-03 1.27263380e-03 1.55754181e-03\n",
-      " 1.90623295e-03 2.33298653e-03 2.85527860e-03 3.49449762e-03\n",
-      " 4.27682035e-03 5.23428381e-03 6.40609723e-03 7.84024772e-03\n",
-      " 9.59546540e-03 1.17436285e-02 1.43727066e-02 1.75903638e-02\n",
-      " 2.15283666e-02 2.63479808e-02 3.22465753e-02 3.94657042e-02\n",
-      " 4.83009992e-02 5.91142759e-02 7.23483504e-02 8.85451733e-02\n",
-      " 1.08368023e-01 1.32628669e-01 1.62320611e-01 1.98659770e-01\n",
-      " 2.43134276e-01 2.97565410e-01 3.64182192e-01 4.45712654e-01\n",
-      " 5.45495564e-01 6.67617146e-01 8.17078421e-01 1.00000000e+00]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7c73f4b837a0>"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from scipy.spatial.distance import euclidean\n",
-    "\n",
-    "# divide function values by the maximum value to normalize them\n",
-    "function_points = [f / np.max(f) for f in function_points]\n",
-    "\n",
-    "# Function to find the top 3 most similar functions to a given function\n",
-    "def find_top_3_similar_functions(target_function, functions):\n",
-    "    distances = []\n",
-    "    \n",
-    "    for i, func in enumerate(functions):\n",
-    "        if np.array_equal(func, target_function):\n",
-    "            continue\n",
-    "        distance = euclidean(target_function, func)\n",
-    "        distances.append((distance, i, func))\n",
-    "    \n",
-    "    # Sort by distance\n",
-    "    distances.sort(key=lambda x: x[0])\n",
-    "    \n",
-    "    # Get the top 3 most similar functions\n",
-    "    top_3 = distances[:3]\n",
-    "    return top_3\n",
-    "\n",
-    "\n",
-    "# Target function (e.g., the first function in the list)\n",
-    "target_function = function_points[8]\n",
-    "\n",
-    "# Find the most similar function\n",
-    "top_3_similar = find_top_3_similar_functions(target_function, function_points)\n",
-    "\n",
-    "print(f\"Target function: {target_function}\")\n",
-    "for rank, (dist, idx, func) in enumerate(top_3_similar, 1):\n",
-    "    print(f\"{rank}. Most similar function (index {idx}) with distance {dist}: {func}\")\n",
-    "\n",
-    "# plot the top 3 most similar functions\n",
-    "for rank, (_, _, func) in enumerate(top_3_similar, 1):\n",
-    "    plt.scatter(x, func, label=f\"Rank {rank}\")\n",
-    "\n",
-    "# plot the target function\n",
-    "plt.scatter(x, target_function, label=\"Target function\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fbench = fbench_hard\n",
-    "fbench_questions = fbench_hard_questions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Fit EBMs and convert functions to text"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: The graph of feature x was simplified by 2.2%.\n",
-      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
-      "\n",
-      "Feature Name: x\n",
-      "Feature Type: continuous\n",
-      "Means: {\"(-9.99, -9.6)\": -1.492, \"(-9.6, -9.23)\": -1.398, \"(-9.23, -8.97)\": -1.305, \"(-8.97, -8.59)\": -1.239, \"(-8.59, -8.19)\": -1.145, \"(-8.19, -7.84)\": -1.033, \"(-7.84, -7.58)\": -0.96, \"(-7.58, -7.29)\": -0.893, \"(-7.29, -7.04)\": -0.822, \"(-7.04, -6.62)\": -0.754, \"(-6.62, -6.34)\": -0.651, \"(-6.34, -5.98)\": -0.582, \"(-5.98, -5.58)\": -0.49, \"(-5.58, -5.21)\": -0.394, \"(-5.21, -4.83)\": -0.299, \"(-4.83, -4.56)\": -0.203, \"(-4.56, -4.19)\": -0.135, \"(-4.19, -3.78)\": -0.042, \"(-3.78, -3.41)\": 0.054, \"(-3.41, -3.12)\": 0.149, \"(-3.12, -2.78)\": 0.218, \"(-2.78, -2.32)\": 0.311, \"(-2.32, -1.76)\": 0.409, \"(-1.76, -0.73)\": 0.506, \"(-0.73, -0.56)\": 0.438, \"(-0.56, -0.37)\": 0.358, \"(-0.37, -0.23)\": 0.227, \"(-0.23, -0.11)\": 0.154, \"(-0.11, -0.02)\": 0.076, \"(-0.02, 0.1)\": 0.007, \"(0.1, 0.21)\": -0.084, \"(0.21, 0.33)\": -0.163, \"(0.33, 0.41)\": -0.238, \"(0.41, 0.62)\": -0.303, \"(0.62, 0.81)\": -0.399, \"(0.81, 2.35)\": -0.469, \"(2.35, 2.75)\": -0.395, \"(2.75, 3.16)\": -0.302, \"(3.16, 3.47)\": -0.2, \"(3.47, 3.75)\": -0.13, \"(3.75, 4.14)\": -0.057, \"(4.14, 4.4)\": 0.037, \"(4.4, 4.84)\": 0.114, \"(4.84, 5.22)\": 0.216, \"(5.22, 5.52)\": 0.315, \"(5.52, 5.8)\": 0.383, \"(5.8, 6.19)\": 0.458, \"(6.19, 6.47)\": 0.552, \"(6.47, 6.73)\": 0.619, \"(6.73, 7.02)\": 0.689, \"(7.02, 7.28)\": 0.758, \"(7.28, 7.56)\": 0.823, \"(7.56, 7.84)\": 0.893, \"(7.84, 8.11)\": 0.965, \"(8.11, 8.39)\": 1.033, \"(8.39, 8.77)\": 1.102, \"(8.77, 9.04)\": 1.195, \"(9.04, 9.43)\": 1.265, \"(9.43, 9.7)\": 1.361, \"(9.7, 9.95)\": 1.428}\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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VE5g7Vy9y+5Bd2BSIzJgxQ6+88oreffddde/evd79srOzlZ2dbUeTAARcqC4k8k45tBQ8/MPtQ3YTUV5V4+saJEv/4owxuvHGG/Xiiy/q7bffVu/eva18OwBIiFV3yX6/YHidW9aVSVbBvGJf1yBZWiMyffp0PfHEE3rqqafUunVr7du3T/v27VNFRYWVbwsADYpXF5KOO+WgDLf0itjaEC8M2Q0JjZ4J8XMNkqUZkUWLFkmSRo0aFbV96dKlmjx5spVvDQAJCdWFpJrJCOJwSy/wem1IkEbPWN41AwBuYUVdSLwLBl00zvNDbUjs6Bm/fq8I2QEEgpV3yLEXDL/36XuNV2tDYvn1e8WidwACwaq6kJAg9el7QWRC3ku1IbGC8L0iIwIgcJpaFxJPkPr03S52XRkvC8L3iowIgECIvUPOyTol7XfJ8fr0qZWzn1fWlUmU32daJRAB4HtO3SEznNd5fqunkKKDaj8gEAHge3beIQehT9/NvLauTCr8FtxSIwLA12IvTFbfIQehT9+tvD53SENaNs/UwK652ri3TBv3lvlqrhoyIgB8K3RhigwI7LhDplbEGX6YO6Q+oQDXj/wRTgFAHG65MPl1/gc388vcIZEiD8VPk5uREQEQCKVzCm0NBKgVcZaX5w5JhJ8KoQlEAASC3RemUCq9dE6hbe8Jf/NrcEsgAsC3nL5Z9Pv8D7CXX4NbAhEAvuTG2TUpWrVWED7a2ODWD8dMIALAl9w4u6af+vXdxo2Bpx388H0iEAHge06OVvFrv77buDHwtEpoThFJ4TlFvIxABIAvRd4kOjl4Il6/vsdvYF3P78OkY+cU8fr3iUAEgO+4LU0f26/vh3S6mwRhWvdYkcfo9e8TgQgA33Fjmt5v6XS3iDd7bhD46ftEIALAV+xeWyZRfp6i20lumT3Xbn76PjHFOwDfiLfomQtikLDYKbol+Waabjfw47TuDfHLlO8EIgB8w0t3x6GuBNagaZrI0gi/T+veEC+vZ0TXDABfsnttmUTEDuWVGM7bFG4rSrabX4aGkxEB4BtuvzsO9etXVNeovKomcAWW6ebGomQ7hb5PB49Xefq7RCACwBe8cnd8cigv//Smm9uyX3bxw5TvdM0A8IWg3x0HTRDnDkmEF+cUISwH4Dteuzv22HXDcfFGRwVZaE6RjXvLwnOKeCnrRkYEgC+4ZUr3VHjxLtZJXhodZQevzyninZAJAOrhlfqQSF6/i3WLoM0dUh8vHz4ZEQCe58X6EK/fxTrJ7aOjnOa15BqBCABf8VJ9SOzMmHTPNM6L2S+7ea2rj0AEgOd5uT4kpGBesecuIE7wYvbLDl5eBI9ABICnefkO2S8zYzrFS9kvq8V29XkpniUQAeBpXr5DDl08SucUhrd56QJiN+YOaVjk5+Gl7BqBCADf8OIdcuzMmF66gNgpNHeIl6cyt5pXu2cIRAB4mh/qQ7x6AbETc4c0LrZ7xisF0AQiADzLy/UhkRjKmxw3rqzsFpEfiVcKoAlEAHiWl+tDYnFNTRxzh9TPiwXQBCIAfIE7ZH9z+U29a8QrgHY7AhEAnuWH+pB4vNK3bxe/dMHZJbYA2u1fJQIRAJ7k54uTV/r27eKnLjgnuP27RCACwJP8dnHyYt++E+iCS4yXRmIRiADwPD9cnLzYt28Xv3bBWclLI7EIRAB4kh8vTrF9+9SK+LsLzmqRfxdu/hoRiADwnKBcnKgV8V8XnFPc/D0iEAHgOX6+OFErEi3y2umHLjg7eaVOhEAEgOf4+eJErci3YjNfPjrNtvDKirwEIgA8JQgXJ2pFTvJz5ssuXliRl0AEgKcE8eJErYj/Ml928UL3DIEIAM/y88WJWpFoPj3NlvPCMF4CEQCe4sdhu/EEvVbEGKPyquAGXukU+Xfixm6+U5xuAAAkKijDdkNia0WCwhijyxaXaN2Or51uiu8UzCtWQX5bV2UTyYgA8Iwg1ocEUUV1TVQQUpDflnPdBG7v5iMjAsCT3HRHZ5fyqhq1bJ7p++OO7DkonVOo9qdm+f6YrRTq5jt4vEoF84oluWsoLxkRAJ4UxOtSEEbPxHa/5WT5P/CyQ2w3n5u+RwQiADzDJf9u2srtafV0o/vNOm4dyksgAsATglaoGhJKqz9+zTCnm2K7IHa/WcmtQ3kJRAB4QpDvlDMyMnR2r7aN7+hxsUN2iUHSz40r8hKIAPAc7pT9JzRkN1RMCeu5pU7E0kDk3Xff1UUXXaRu3bopIyNDL730kpVvByAggh6DuODakXYM2bWHG+tELA1Ejh8/rsGDB+vhhx+28m0AIFDccidrldI5hWS9LOLGOhFL5xEZN26cxo0bZ+VbAEAghO5kN+4tC9/J5mT5cyoohuxay20fratqRCorK1VWVhb1AwCoeyfrxjVD4D3lVTUqrzrh6HfJVYHI/PnzlZeXF/7p0aOH000C4BJcc6PvZIMwuRmsVzCvWAPnrnC0VsRVgcjs2bN15MiR8M+uXbucbhIAFwjqHCKx/Dq5GSvt2iv2e+Q0V3UwZmdnKzs72+lmAHCZIM8hEinemiFex0q79gt9j0q3n/zMT/uOs39TrgpEAKAxQR9NEbtmiNcxbNcZGRkZGtq7ndPNkGRxIHLs2DFt3bo1/Hjbtm368MMP1a5dO/Xs2dPKtwbgUwGOQeLy04q8rLQbTJbWiJSWlmrIkCEaMmSIJGnWrFkaMmSI5s6da+XbAkBg+KlolWG7wWRpRmTUqFG++OMAADcJFRuW/rNLI1S06rV5RShShUSNCACX42JVlx+KVilSRQiBCADX4mJVP68XrVKkihACEQCuxcUqcV4uWqVINdgIRAB4AherhhXMK1ZBfltPDm+mSDXYXDWzKgDUh4tVXX6daRXBQiACAB4VniFzTmF4GwMV4TUEIgDgYbFFq36ZUwTBQSACAB7XsnmmBnbNlSRt3FtG9ww8hUAEADwu1EUDeBGjZgC4Fj0MifNKHa8xRhXVNUxShzACEQCuZIzRhMUlTjfDk9w6pwgT1CEeumYAuFJFdY027i2TJA3smstEZklw60J4sRPUSUxSBzIiADzAi5N02c1rC+GVzilUTlamKzM3sBcZEQCux3WqcfHmFHGT2MULc7IylZN1CkEIyIgAcCeX9Sp4QuycIm6pFaE2BA0hIwLAdShUTQ+31IqweCEaQkYEgOtQqJo6t9eKsHghYpERAeBqFKomJ16tSHlVjeNZkRAWL0QsAhEArsY1K3mxtSJOdtHEFqkCsdyRqwOACC65efc0N3TRUKSKRJARAeAqFKqmhxuG81KkikSQEQHgKhSqpo+bhvNSpIr6kBEB4FoUqqaXnbUi8SYw41wiHjIiAFyL61bTOVErQm0IkkFGBAB8zIlaEWpDkAwyIgDgc/FqRSTZUi9CbQgaQyACwDWYc8IeBfOKT/43v21a63CMMaqorqE2BEkhEAHgCtQVWCu2VkRKb70I5w+pIhAB4ArUFVgrVCsSyliEsiLpEnv+JM4hEkMgAsB1qCuwxslakeh/9iO7UVKtGYntUiudU6icrEzH5iyBtxCIAHAd6grsE5kZSaVmJF6XTE5WpmtW+4X7MXwXAAImVC8SK1Qzkgy61NBUhKwAEDCR9SKS0lYzQpcaUkEgAgABFK9eRFLSw6cZqoumIhABAISlezQN0BhqRAAg4OqrGUkGtSFIFRkRAAi42JqRVDBUF6kiEAHgCjasTI8G1FczAliNrhkAjjPGaMLiEqebAcABBCIAHFdRXaONe8skSQO75lJrAAQIgQgAx0V2y6RzNVgA7kcgAsBRsd0yxCBAsBCIAHAU3TJAsBGIAHANumWA4CEQAeCoyPoQYhAgeAhEADiGYbsACEQAOIb6EAAEIgBcgfoQIJgIRAC4AjEIEEwEIgAAwDEEIgAAwDEEIgAAwDEEIgAAwDEEIgAAwDEEIgAcEzmrKoBgIhAB4AhmVQUgEYgAcAizqgKQbApEHn74YfXq1UstWrTQ8OHD9f7779vxtgA8gllVgeCyPBB55plnNGvWLBUVFemDDz7Q4MGDNWbMGB04cMDqtwbgEcQgQHBZHojcf//9mjp1qqZMmaKBAwdq8eLFysnJ0aOPPmr1WwMAAJezNBCpqqrSunXrVFhY+O0bNmumwsJClZRQpAYAQNCdYuWLf/XVV6qpqVHnzp2jtnfu3FmbN2+us39lZaUqKyvDj8vKyqxsHgCHGGNUXlXjdDMAuICrRs3Mnz9feXl54Z8ePXo43SQAaWaM0WWLS1Qwr9jppgBwAUsDkQ4dOigzM1P79++P2r5//3516dKlzv6zZ8/WkSNHwj+7du2ysnkAHFBRXaN1O74OPy7Ib8vQXSDALA1EsrKydPbZZ+uNN94Ib6utrdUbb7yhESNG1Nk/Oztbubm5UT8A/Kt0TiFDd4GAs7RGRJJmzZqlSZMmqaCgQMOGDdODDz6o48ePa8qUKVa/NQCXy8nKJAgBAs7yQOSKK67Ql19+qblz52rfvn0688wztXz58joFrAAAIHgsD0QkacaMGZoxY4YdbwUAADzEVaNmAABAsBCIAAAAxxCIAAAAxxCIAAAAxxCIAAAAxxCIALCVMU63AICbEIgAsI0xRhMWs/I2gG8RiACwTUV1jTbuPbmq9sCuuawxA4BABIAzWGMGgEQgAsBGkfUhxCAAJAIRADahPgRAPAQiAGxBfQiAeAhEANiO+hAAIQQiAGxHDAIghEAEAAA4hkAEAAA4hkAEAAA4hkAEAAA4hkAEAAA45hSnGwA0hTFGFdU1KT+/ZfNMhpHawBij8qrUzxMA/yIQgWcZY3TZ4hKt2/F1yq9RkN+WOS0slo7zBMC/6JqBZ1VU1zT54la64+smZVTQuNjzVJDflllVAYSREYEvlM4pVE5W4he38qoaFcwrtrBFiKd0TqHan5pFBgpAGIEIPCWyJiSy5iAnK1M5Wal9nUOvQ72I9XKy+IwBRCMQgWdYVWsQyoxQLwIA9qNGBJ5RX01IKjUHLZtnqiC/bdQ26kUAwH5kROAJscM/I2tCUulSycjI0HPXj1BFdU1UvUh5VQ1dNABgIwIRuF68Lpmm1ISEZGRk1HmNgnnFdNEAgI3omoHrWT38M7abhi6a9DLG6RYAcDMyIvAUK4Z/hrppDh6vYkhvmhljNGFxidPNAOBiZETgWifrQk7UGaZrRZfJyW6ab7Ms5VU1MtzKN1lFdY027i2TJA3smstEZgDqICMCV3J6WnBqRdKPzxJAPGRE4ErxhupaPTU4tSLWIgYBEA8ZEbheaKiu1cNqqRUBAPuREYHrxM4ZEhqqa0dan1oRALAXGRG4itO1IbGoFWkaYjgAjSEjAldxw5Lx1IqkB0N3ASSCjAhcy6kl46kVSQ+G7gJIBBkRuEa82hCnukOoFUkvurYA1IeMCFzBbbUhsagVaRo+MgD1ISMCV3BDbUgsakUAwHpkROA6TtWGxIpXK0LvTOL4rAAkgowIXCHyouVkbUis2FqRCYtLqBVJACNmACSKQASOc/tFq2XzTA3smitJ2ri3jO6ZBDBiBkCiCETgOLdftEJdNEgNBb4AGkIgAldx60UrskkM5W1c5MfjwtMJwEUIROAqXrhoFcwrplakAW7vagPgLgQicMzJCcxORE1i5lYM5U2c27vaALgLw3fhCLdPYBaLad9T49auNgDuQUYEjoidwExyxyRmDYkdyovGEYMAaAwZETiudE6hcrIy1bK5e+YPSUR5VY3n2gwAbkNGBI6IncAsJ+sUz13QKVqNj48DQDIIRGA7L4+qoGi1YV4+twCcQSAC23l5VEWoaLV0TqHTTXElL59bAM4gEIGjvDiqIrZolQnO4vPiuQVgPwIROMoP1ylqRU4yxmjd9m9HQvnh3AKwHqNmYBtjjCqqazwxgVljQrUipf8cghyqFcnJCuaflNfmhQHgHsH8VxO289uFignOosXOC+P2OWEAuIdlXTN33XWXRo4cqZycHLVp08aqt0nJt1OLnwh8Ot0uXpzArDFMcBbf49cMoz4EQMIsy4hUVVVpwoQJGjFihB555BGr3iYlFdU1Gjh3haSTF0P+0bSXVycwawwx7Uln92rrq/MKwFqWZUTuuOMO3XTTTTr99NOteou0YB4I+3l1ArPGULAKAMkL5KiZls0zmQcCadGyeaYGds2VJG3cW0ZQCwBJclUgUllZqbKysqgfKzAPhP38+vGGilZD/HqcAGCVpAKR2267TRkZGQ3+bN68OeXGzJ8/X3l5eeGfHj16pPxayWAeCGv5fdrvyB4mvkcAkJykilVvvvlmTZ48ucF9+vTpk3JjZs+erVmzZoUfl5WVWRaMMA+Effw+7Xeoe2bj3rJw9wzfIwBITFL/Wnbs2FEdO3a0qi3Kzs5Wdna2Za8fiXkgnOHHEUqh79JpRSucbgoAeI5lt207d+7UoUOHtHPnTtXU1OjDDz+UJPXr10+tWrWy6m2TElsrQkbdGpGfq89ikDC/HhcAWM2yYtW5c+dqyJAhKioq0rFjxzRkyBANGTJEpaWlVr1lk9G/n35+rw+JJ2jFz7FrzABAMiwLRJYtWyZjTJ2fUaNGWfWWKWH4pbX8Xh8ST5CKn0NT9//80fedbgoAj3LV8F0nxA6/hHX8WB8SEip+DgnKRHmsMQOgqSjtV3T/fnlVje+mHncLP3+kFD+fXGPmh9/twN8OgKQEPiMSK0hpdaudXFzQ/1mBkKBPlNeCAB5ACghEFNy0upVCtQNBzQ5IwQtqT/tOrtNNAOBBBCL6Nq3O+jPpE9TaAYJaAEgONSL/FJtWX7f9a/q706R0TqHan5oViM+SWhEASA4ZkXr8/NH3A5VWT6fY2pCcrGDVDsQGtQCA+pERicD6M00Xqg2J7JaBfxGnA2gqMiIRqBVpuqDWhjRk3favfZlZC+KsuQDSj1v9GKTV0ydItSEN+fmj76sgv63vJnQL4qy5ANKPjAgsE7TakEhBGz3jtyALgH0IRJBWPuyBSEm8bj4/fzbEIABSRSCCtKFmIFpsN5/fRmH56FAAOIhABGlDzUBdfl3dmaATQLoQiDQiaOuFpAs1Ayf5dXVngk4A6UIg0oigrRfSFJEfETHIt/z+WRB0AmgKApE4gjbiIR1I1SfGL/EsQSeAdCEQiYOJzZJHqj4xfsiuEXQCSCcCkXowsVnqSNVH81vBKkEngHQiEEkQRav1i13kjhgkml8LViWCTgBNxxTvCSqYV+zLabqbikXuEhP5lQkFbS2be3/mWY83H4ALEIg0gNV4G8cid8krmFd88r8eDGxjs18A0FRcURsQSqkfPF4VvnigfixyV7/YoFbyXmBL9guAFbzxL6CDYotWKROpX5AXuWtMKKitqK5ReVWNJwNbsl8ArECxapL8MPwSzjgZ1J7ii9FYpXMKPdetBMCdCEQS4Lfhl3APr8a0ZL8ApAuBSAL8PPwSzvJShs0jzQTgMQQiCYodfumVi4eVGEGRGi9m2JhNFYBVCERSwEJ4346g8GLRpdNiM2xeCGyZTRWAVQhEEsRCeNEYQdE0kRk2rwW2FKkCSCeG7yaIOUXqx/whyfPaZHmstgvAKmREksCcIvExgiJ5XlrhmfoQAFYiEGkCL6XT04ki1fTwygrP1IcAsJI788AuFhrxsHFvWXjEg1vT6VZgmu9goz4EQLqREUlS0OcUoUjVOl4YPUMMAiDdgnMrn0aR/xi7/LphKYpU06tgXrErV+QN8nccgPXIiDRR0OpEIg+VItWmc/uwcApVAViNQCQFXpwZMx24KKVfvNEzboprKVQFYDUCkRQEtU6Ei5I1YkfPuCXLFjs6ym1dRgD8gRqRFMWuPdOyebC6KbgopZfbRmPFGx3F6QZgBTIiaeC1KbpTxeya1nFblo3RUQDsQkYkRV6borupqA+xnluzbIyOAmAlMiIp8tIU3elAfYi93JRlY3QUACsRiDRBUNeeoT7EGm4fygsAViAQSSO33MFajRjEGm7KsgXgawzAJQhEmigIc4qwyJ193JBlox4IgJ0IRJrIbaMd0i00jLNgXrHTTQkkJ7Js1AMBsBOBSBr4uauCYZz2c1OWjXogAFYjEEHCSucUcmGygVNZtpNdcCeiuuE41QCs5s9JLxzkpvkfmiq2NoRhnPaJnVNEkqXfq3gzqQKAHQhE0sytS7kniwuTe4Tqc6z8XsV2wYXej244AFYjEEkDP86ySm2Is2K/U5J936vSOYXKycr0TWYPgLt590rpIqE+/YPHq3wzuiRyoAZTfNsv9J2qqK5ReVWN5d+ryPOdk5Xp6SAagLfwr02axM7/4OVakdh5JKgNccbJ71T0n6gV9SLMGwLASQQiFvFyrQjzSLiXFfUinG8ATmL4bhr5ca0QLwZSfhP7vZKs+25xvgHYjYxIGvmxVoRrkvOsqhcxxoRf89v3SstLA0DCCETSLLZWxGtYV8ad0l0vwvBsAG5hWdfM9u3bde2116p3795q2bKl+vbtq6KiIlVVVVn1lmgi1pXxloJ5xRo4d0VK69EwbwgAt7AsI7J582bV1tZqyZIl6tevnzZs2KCpU6fq+PHjWrhwoVVv6zpeGj3D3CHuZ8X8IswbAsBJlgUiY8eO1dixY8OP+/Tpoy1btmjRokWBCkS8OnqGuUPcqb56kWS702Kn7mfeEABOsfVfnyNHjqhdu3Z2vqUj/DDTKnOHuFe8ehG60wB4lW3Dd7du3aqHHnpI1113Xb37VFZWqqysLOrHi0J3raVzCp1uSsIoUvWeeMN6k0X3GwCnJX2Lftttt+nee+9tcJ9NmzZpwIAB4cdffPGFxo4dqwkTJmjq1Kn1Pm/+/Pm64447km2SK8WOnkmyltBWjKDwpshumlRRFwLAaRkmyXL7L7/8UgcPHmxwnz59+igrK0uStGfPHo0aNUr/8i//omXLlqlZs/qTMJWVlaqsrAw/LisrU48ePXTkyBHl5uYm00xXKK86oYFzV0g6OWPlq7/+oSv/0Y9sp2TtKq8AAP8rKytTXl5eQtfvpDMiHTt2VMeOHRPa94svvtCPfvQjnX322Vq6dGmDQYgkZWdnKzs7O9kmuVbL5pka2DVXG/eWaePeMk/UiVCkCgCwk2U1Il988YVGjRqlnj17auHChfryyy+1b98+7du3z6q3dJ1Q6tztYldeJQgBANjFstvzlStXauvWrdq6dau6d+8e9btkJ1/yMrdf01l5FQDgJMsyIpMnT5YxJu5PUJVX1ai86oSrPgNWXgUAOMndBQs+Y8US7unkxjYBAPzNtnlEgsrOJdxTEZmcIQYBANiNjIjFrFrCPR2oDwEAOI1AxAb1LeHu9GRS1IcAAJxG14xDCuYVp7R8ezqcnM79RNSU7tSHAACcQEbERm5YDK++6dyJQQAATiAjYqN4i+GVV9XYmhWpqK6pE4Sw8BkAwClkRGwWuxhewbxix4bzls4pVE5WpuO1KgCA4CIj4oDYIb1ODefNycpUTtYpBCEAAMcQiDggXhcNAABBRCDikNguGjtqRU6OlnHHRGoAAEjUiLiG1bUi9Y2WAQDASWREHGRnrUjsaBlGygAA3ICMiINCtSIHj1eFp363Y8bV0jmFan9qFkWqAADHkRFxWLzhvOmccTXeLKo5WQzXBQC4AxkRF7BqxlXqQgAAbkdGxAWsGs7LLKoAALcjI+IS8YbzRmpq3QizqAIA3IhAxKVCxavhx0kM7TXGqKK6pk5diJ2L6wEAkAiuTC4SWysSKdG6EepCAABeQiDiIqFakci5RMqraupkRxpCXQgAwEsIRFzmZK1Iek4LdSEAALcjEPGQRNaJoS4EAOAlXKU8JJkuGgAAvIB5RFwudj2aRFEXAgDwAjIiLhevgDUR1IUAALyAQMQD0lnACgCAm9A1AwAAHEMgAgAAHEMgAgAAHEMgAgAAHEMgAgAAHEMgAgAAHEMgAgAAHEMgAgAAHEMgAgAAHEMgAgAAHEMgAgAAHEMgAgAAHEMgAgAAHOPqJV2NMZKksrIyh1sCAAASFbpuh67jDXF1IHL06FFJUo8ePRxuCQAASNbRo0eVl5fX4D4ZJpFwxSG1tbXas2ePWrdurYyMjLS9bllZmXr06KFdu3YpNzc3ba/rJn4/Rr8fn+T/Y/T78Un+P0a/H5/k/2O06viMMTp69Ki6deumZs0argJxdUakWbNm6t69u2Wvn5ub68svViS/H6Pfj0/y/zH6/fgk/x+j349P8v8xWnF8jWVCQihWBQAAjiEQAQAAjglkIJKdna2ioiJlZ2c73RTL+P0Y/X58kv+P0e/HJ/n/GP1+fJL/j9ENx+fqYlUAAOBvgcyIAAAAdyAQAQAAjiEQAQAAjiEQAQAAjvFtIHLXXXdp5MiRysnJUZs2beLus3PnTl1wwQXKyclRp06ddMstt+jEiRMNvu6hQ4d09dVXKzc3V23atNG1116rY8eOWXAEyXn77beVkZER92ft2rX1Pm/UqFF19r/++uttbHnievXqVaet99xzT4PP+eabbzR9+nS1b99erVq10k9/+lPt37/fphYnZ/v27br22mvVu3dvtWzZUn379lVRUZGqqqoafJ6bz+HDDz+sXr16qUWLFho+fLjef//9Bvd/7rnnNGDAALVo0UKnn366XnvtNZtamrz58+dr6NChat26tTp16qTx48dry5YtDT5n2bJldc5VixYtbGpxcm6//fY6bR0wYECDz/HS+ZPi/5uSkZGh6dOnx93fC+fv3Xff1UUXXaRu3bopIyNDL730UtTvjTGaO3euunbtqpYtW6qwsFD/+Mc/Gn3dZP+Wk+HbQKSqqkoTJkzQDTfcEPf3NTU1uuCCC1RVVaXVq1frscce07JlyzR37twGX/fqq6/Wp59+qpUrV+qVV17Ru+++q1/96ldWHEJSRo4cqb1790b9/PKXv1Tv3r1VUFDQ4HOnTp0a9bwFCxbY1Ork3XnnnVFtvfHGGxvc/6abbtJf//pXPffcc3rnnXe0Z88eXXrppTa1NjmbN29WbW2tlixZok8//VQPPPCAFi9erN/+9reNPteN5/CZZ57RrFmzVFRUpA8++ECDBw/WmDFjdODAgbj7r169WhMnTtS1116r9evXa/z48Ro/frw2bNhgc8sT884772j69Ol67733tHLlSlVXV2v06NE6fvx4g8/Lzc2NOlc7duywqcXJO+2006LaumrVqnr39dr5k6S1a9dGHd/KlSslSRMmTKj3OW4/f8ePH9fgwYP18MMPx/39ggUL9Ic//EGLFy/WmjVrdOqpp2rMmDH65ptv6n3NZP+Wk2Z8bunSpSYvL6/O9tdee800a9bM7Nu3L7xt0aJFJjc311RWVsZ9rY0bNxpJZu3ateFtr7/+usnIyDBffPFF2tveFFVVVaZjx47mzjvvbHC/8847z/zmN7+xp1FNlJ+fbx544IGE9z98+LBp3ry5ee6558LbNm3aZCSZkpISC1qYfgsWLDC9e/ducB+3nsNhw4aZ6dOnhx/X1NSYbt26mfnz58fd//LLLzcXXHBB1Lbhw4eb6667ztJ2psuBAweMJPPOO+/Uu099/x65UVFRkRk8eHDC+3v9/BljzG9+8xvTt29fU1tbG/f3Xjp/xhgjybz44ovhx7W1taZLly7mvvvuC287fPiwyc7ONk8//XS9r5Ps33KyfJsRaUxJSYlOP/10de7cObxtzJgxKisr06efflrvc9q0aROVYSgsLFSzZs20Zs0ay9ucjL/85S86ePCgpkyZ0ui+Tz75pDp06KBBgwZp9uzZKi8vt6GFqbnnnnvUvn17DRkyRPfdd1+DXWnr1q1TdXW1CgsLw9sGDBignj17qqSkxI7mNtmRI0fUrl27Rvdz2zmsqqrSunXroj77Zs2aqbCwsN7PvqSkJGp/6eTfpJfOlaRGz9exY8eUn5+vHj166OKLL6733xs3+Mc//qFu3bqpT58+uvrqq7Vz58569/X6+auqqtITTzyha665psFFVr10/mJt27ZN+/btizpPeXl5Gj58eL3nKZW/5WS5etE7K+3bty8qCJEUfrxv3756n9OpU6eobaeccoratWtX73Oc8sgjj2jMmDGNLhp41VVXKT8/X926ddPHH3+sW2+9VVu2bNELL7xgU0sT9+tf/1pnnXWW2rVrp9WrV2v27Nnau3ev7r///rj779u3T1lZWXVqhDp37uy68xXP1q1b9dBDD2nhwoUN7ufGc/jVV1+ppqYm7t/Y5s2b4z6nvr9JL5yr2tpazZw5Uz/4wQ80aNCgevfr37+/Hn30UZ1xxhk6cuSIFi5cqJEjR+rTTz+1dIHPVAwfPlzLli1T//79tXfvXt1xxx0655xztGHDBrVu3brO/l4+f5L00ksv6fDhw5o8eXK9+3jp/MUTOhfJnKdU/paT5alA5LbbbtO9997b4D6bNm1qtKDKS1I55t27d2vFihV69tlnG339yPqW008/XV27dtX555+vzz77TH379k294QlK5vhmzZoV3nbGGWcoKytL1113nebPn+/q6ZdTOYdffPGFxo4dqwkTJmjq1KkNPtfpcwhp+vTp2rBhQ4M1FJI0YsQIjRgxIvx45MiR+v73v68lS5boP//zP61uZlLGjRsX/v8zzjhDw4cPV35+vp599llde+21DrbMGo888ojGjRunbt261buPl86fl3gqELn55psbjFYlqU+fPgm9VpcuXepU/YZGU3Tp0qXe58QW55w4cUKHDh2q9zlNlcoxL126VO3bt9e//du/Jf1+w4cPl3TybtyOi1hTzunw4cN14sQJbd++Xf3796/z+y5duqiqqkqHDx+Oyors37/fsvMVT7LHuGfPHv3oRz/SyJEj9ac//Snp97P7HMbToUMHZWZm1hmh1NBn36VLl6T2d4sZM2aEC9eTvStu3ry5hgwZoq1bt1rUuvRp06aNvve979XbVq+eP0nasWOHiouLk84ieun8Sd9e2/bv36+uXbuGt+/fv19nnnlm3Oek8rectLRUmrhYY8Wq+/fvD29bsmSJyc3NNd98803c1woVq5aWloa3rVixwlXFqrW1taZ3797m5ptvTun5q1atMpLMRx99lOaWpd8TTzxhmjVrZg4dOhT396Fi1eeffz68bfPmza4uVt29e7f57ne/a6688kpz4sSJlF7DLedw2LBhZsaMGeHHNTU15jvf+U6DxaoXXnhh1LYRI0a4ttixtrbWTJ8+3XTr1s383//9X0qvceLECdO/f39z0003pbl16Xf06FHTtm1b8/vf/z7u7712/iIVFRWZLl26mOrq6qSe5/bzp3qKVRcuXBjeduTIkYSKVZP5W066nWl5FRfasWOHWb9+vbnjjjtMq1atzPr168369evN0aNHjTEnv0CDBg0yo0ePNh9++KFZvny56dixo5k9e3b4NdasWWP69+9vdu/eHd42duxYM2TIELNmzRqzatUq893vftdMnDjR9uOrT3FxsZFkNm3aVOd3u3fvNv379zdr1qwxxhizdetWc+edd5rS0lKzbds28/LLL5s+ffqYc8891+5mN2r16tXmgQceMB9++KH57LPPzBNPPGE6duxofvGLX4T3iT0+Y4y5/vrrTc+ePc2bb75pSktLzYgRI8yIESOcOIRG7d692/Tr18+cf/75Zvfu3Wbv3r3hn8h9vHIO/+d//sdkZ2ebZcuWmY0bN5pf/epXpk2bNuGRaj//+c/NbbfdFt7/73//uznllFPMwoULzaZNm0xRUZFp3ry5+eSTT5w6hAbdcMMNJi8vz7z99ttR56q8vDy8T+wx3nHHHWbFihXms88+M+vWrTNXXnmladGihfn000+dOIQG3Xzzzebtt98227ZtM3//+99NYWGh6dChgzlw4IAxxvvnL6Smpsb07NnT3HrrrXV+58Xzd/To0fD1TpK5//77zfr1682OHTuMMcbcc889pk2bNubll182H3/8sbn44otN7969TUVFRfg1fvzjH5uHHnoo/Lixv+Wm8m0gMmnSJCOpzs9bb70V3mf79u1m3LhxpmXLlqZDhw7m5ptvjoqI33rrLSPJbNu2Lbzt4MGDZuLEiaZVq1YmNzfXTJkyJRzcuMHEiRPNyJEj4/5u27ZtUZ/Bzp07zbnnnmvatWtnsrOzTb9+/cwtt9xijhw5YmOLE7Nu3TozfPhwk5eXZ1q0aGG+//3vm7vvvjsqexV7fMYYU1FRYaZNm2batm1rcnJyzCWXXBJ1YXeTpUuXxv3ORiYuvXYOH3roIdOzZ0+TlZVlhg0bZt57773w78477zwzadKkqP2fffZZ873vfc9kZWWZ0047zbz66qs2tzhx9Z2rpUuXhveJPcaZM2eGP4/OnTubf/3XfzUffPCB/Y1PwBVXXGG6du1qsrKyzHe+8x1zxRVXmK1bt4Z/7/XzF7JixQojyWzZsqXO77x4/kLXrdif0HHU1taa3/3ud6Zz584mOzvbnH/++XWOPT8/3xQVFUVta+hvuakyjDEmPZ08AAAAyQnsPCIAAMB5BCIAAMAxBCIAAMAxBCIAAMAxBCIAAMAxBCIAAMAxBCIAAMAxBCIAAMAxBCIAAMAxBCIAAMAxBCIAbPXll1+qS5cuuvvuu8PbVq9eraysLL3xxhsOtgyAE1hrBoDtXnvtNY0fP16rV69W//79deaZZ+riiy/W/fff73TTANiMQASAI6ZPn67i4mIVFBTok08+0dq1a5Wdne10swDYjEAEgCMqKio0aNAg7dq1S+vWrdPpp5/udJMAOIAaEQCO+Oyzz7Rnzx7V1tZq+/btTjcHgEPIiACwXVVVlYYNG6YzzzxT/fv314MPPqhPPvlEnTp1crppAGxGIALAdrfccouef/55ffTRR2rVqpXOO+885eXl6ZVXXnG6aQBsRtcMAFu9/fbbevDBB/X4448rNzdXzZo10+OPP67//d//1aJFi5xuHgCbkREBAACOISMCAAAcQyACAAAcQyACAAAcQyACAAAcQyACAAAcQyACAAAcQyACAAAcQyACAAAcQyACAAAcQyACAAAcQyACAAAcQyACAAAc8/97RWk1J8HugAAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: The graph of feature x was simplified by 3.7%.\n",
-      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
-      "\n",
-      "Feature Name: x\n",
-      "Feature Type: continuous\n",
-      "Means: {\"(-9.98, -9.72)\": -0.957, \"(-9.72, -9.52)\": -1.187, \"(-9.52, -9.27)\": -1.397, \"(-9.27, -9.07)\": -1.626, \"(-9.07, -8.83)\": -1.828, \"(-8.83, -8.58)\": -2.028, \"(-8.58, -8.06)\": -2.209, \"(-8.06, -7.17)\": -2.434, \"(-7.17, -6.86)\": -2.184, \"(-6.86, -6.59)\": -1.949, \"(-6.59, -6.4)\": -1.696, \"(-6.4, -6.17)\": -1.508, \"(-6.17, -5.97)\": -1.276, \"(-5.97, -5.74)\": -1.079, \"(-5.74, -5.5)\": -0.861, \"(-5.5, -5.1)\": -0.673, \"(-5.1, -3.87)\": -0.449, \"(-3.87, -3.56)\": -0.666, \"(-3.56, -3.36)\": -0.896, \"(-3.36, -3.12)\": -1.09, \"(-3.12, -2.91)\": -1.287, \"(-2.91, -2.69)\": -1.481, \"(-2.69, -2.35)\": -1.662, \"(-2.35, -1.07)\": -1.891, \"(-1.07, -0.9)\": -1.677, \"(-0.9, -0.74)\": -1.496, \"(-0.74, -0.62)\": -1.297, \"(-0.62, -0.46)\": -1.097, \"(-0.46, -0.34)\": -0.862, \"(-0.34, -0.22)\": -0.634, \"(-0.22, -0.11)\": -0.392, \"(-0.11, 0.02)\": -0.192, \"(0.02, 0.13)\": 0.083, \"(0.13, 0.23)\": 0.286, \"(0.23, 0.37)\": 0.497, \"(0.37, 0.5)\": 0.739, \"(0.5, 0.61)\": 0.963, \"(0.61, 0.77)\": 1.153, \"(0.77, 0.98)\": 1.375, \"(0.98, 1.22)\": 1.608, \"(1.22, 2.74)\": 1.828, \"(2.74, 2.99)\": 1.593, \"(2.99, 3.19)\": 1.398, \"(3.19, 3.45)\": 1.204, \"(3.45, 3.68)\": 0.98, \"(3.68, 4.01)\": 0.787, \"(4.01, 5.68)\": 0.562, \"(5.68, 5.98)\": 0.884, \"(5.98, 6.19)\": 1.107, \"(6.19, 6.43)\": 1.338, \"(6.43, 6.63)\": 1.572, \"(6.63, 6.92)\": 1.79, \"(6.92, 7.16)\": 2.024, \"(7.16, 7.7)\": 2.222, \"(7.7, 8.57)\": 2.431, \"(8.57, 8.9)\": 2.203, \"(8.9, 9.12)\": 1.945, \"(9.12, 9.34)\": 1.755, \"(9.34, 9.57)\": 1.534, \"(9.57, 9.8)\": 1.319, \"(9.8, 9.97)\": 1.106}\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: The graph of feature x was simplified by 4.6%.\n",
-      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
-      "\n",
-      "Feature Name: x\n",
-      "Feature Type: continuous\n",
-      "Means: {\"(-9.98, -9.54)\": 3.323, \"(-9.54, -9.1)\": 3.175, \"(-9.1, -8.68)\": 3.024, \"(-8.68, -8.3)\": 2.892, \"(-8.3, -7.89)\": 2.763, \"(-7.89, -7.53)\": 2.631, \"(-7.53, -7.16)\": 2.501, \"(-7.16, -6.72)\": 2.379, \"(-6.72, -6.35)\": 2.234, \"(-6.35, -5.88)\": 2.113, \"(-5.88, -5.46)\": 1.955, \"(-5.46, -5.06)\": 1.817, \"(-5.06, -4.6)\": 1.679, \"(-4.6, -4.2)\": 1.534, \"(-4.2, -3.76)\": 1.396, \"(-3.76, -3.37)\": 1.248, \"(-3.37, -2.96)\": 1.108, \"(-2.96, -2.58)\": 0.983, \"(-2.58, -1.24)\": 0.848, \"(-1.24, -1.11)\": 1.026, \"(-1.11, -1.01)\": 1.205, \"(-1.01, -0.92)\": 1.352, \"(-0.92, -0.86)\": 1.479, \"(-0.86, -0.77)\": 1.611, \"(-0.77, -0.69)\": 1.781, \"(-0.69, -0.59)\": 1.975, \"(-0.59, -0.5)\": 2.162, \"(-0.5, -0.39)\": 2.365, \"(-0.39, -0.28)\": 2.488, \"(-0.28, 0.37)\": 2.614, \"(0.37, 0.48)\": 2.471, \"(0.48, 0.61)\": 2.253, \"(0.61, 0.68)\": 2.052, \"(0.68, 0.76)\": 1.923, \"(0.76, 0.83)\": 1.747, \"(0.83, 0.91)\": 1.62, \"(0.91, 0.97)\": 1.488, \"(0.97, 1.09)\": 1.337, \"(1.09, 1.19)\": 1.179, \"(1.19, 1.38)\": 1.013, \"(1.38, 1.8)\": 0.855, \"(1.8, 2.45)\": 0.709, \"(2.45, 2.92)\": 0.829, \"(2.92, 3.32)\": 0.974, \"(3.32, 3.71)\": 1.11, \"(3.71, 4.13)\": 1.241, \"(4.13, 4.46)\": 1.378, \"(4.46, 4.92)\": 1.501, \"(4.92, 5.38)\": 1.642, \"(5.38, 5.78)\": 1.801, \"(5.78, 6.25)\": 1.932, \"(6.25, 6.65)\": 2.091, \"(6.65, 7.12)\": 2.223, \"(7.12, 7.55)\": 2.377, \"(7.55, 7.96)\": 2.52, \"(7.96, 8.39)\": 2.667, \"(8.39, 8.77)\": 2.8, \"(8.77, 9.12)\": 2.927, \"(9.12, 9.5)\": 3.048, \"(9.5, 9.89)\": 3.178, \"(9.89, 10.0)\": 3.304}\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: The graph of feature x was simplified by 1.3%.\n",
-      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
-      "\n",
-      "Feature Name: x\n",
-      "Feature Type: continuous\n",
-      "Means: {\"(-10.0, -9.97)\": 77629.6, \"(-9.97, -9.92)\": 75483.3, \"(-9.92, -9.88)\": 72339.5, \"(-9.88, -9.84)\": 70397.0, \"(-9.84, -9.8)\": 67393.0, \"(-9.8, -9.75)\": 66047.6, \"(-9.75, -9.71)\": 63231.7, \"(-9.71, -9.63)\": 61069.8, \"(-9.63, -9.58)\": 57163.5, \"(-9.58, -9.54)\": 55787.7, \"(-9.54, -9.51)\": 54497.2, \"(-9.51, -9.49)\": 53361.9, \"(-9.49, -9.43)\": 52076.6, \"(-9.43, -9.35)\": 49437.3, \"(-9.35, -9.29)\": 47029.3, \"(-9.29, -9.24)\": 45407.7, \"(-9.24, -9.19)\": 44261.8, \"(-9.19, -9.12)\": 42586.3, \"(-9.12, -9.09)\": 40835.6, \"(-9.09, -9.04)\": 39667.4, \"(-9.04, -8.96)\": 38096.4, \"(-8.96, -8.87)\": 36561.8, \"(-8.87, -8.81)\": 34852.9, \"(-8.81, -8.71)\": 33372.7, \"(-8.71, -8.59)\": 31554.1, \"(-8.59, -8.49)\": 29831.5, \"(-8.49, -8.37)\": 28079.5, \"(-8.37, -8.21)\": 26157.3, \"(-8.21, -8.07)\": 24287.9, \"(-8.07, -7.9)\": 22682.8, \"(-7.9, -7.68)\": 20993.7, \"(-7.68, -7.43)\": 19195.6, \"(-7.43, -7.17)\": 17424.8, \"(-7.17, -6.83)\": 15802.0, \"(-6.83, -6.43)\": 14204.7, \"(-6.43, -5.92)\": 12519.8, \"(-5.92, -5.35)\": 10825.2, \"(-5.35, -4.65)\": 9193.3, \"(-4.65, -3.91)\": 7577.4, \"(-3.91, -3.21)\": 5950.4, \"(-3.21, -2.39)\": 4375.4, \"(-2.39, -1.6)\": 2784.8, \"(-1.6, 0.36)\": 1187.6, \"(0.36, 1.17)\": 2753.6, \"(1.17, 1.96)\": 4362.3, \"(1.96, 2.77)\": 5955.3, \"(2.77, 3.56)\": 7539.5, \"(3.56, 4.41)\": 9189.1, \"(4.41, 5.22)\": 10844.1, \"(5.22, 5.99)\": 12429.0, \"(5.99, 6.78)\": 14001.2, \"(6.78, 7.6)\": 15585.3, \"(7.6, 8.42)\": 17220.3, \"(8.42, 9.2)\": 18816.3, \"(9.2, 9.97)\": 20429.2}\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: The graph of feature x was simplified by 2.2%.\n",
-      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
-      "\n",
-      "Feature Name: x\n",
-      "Feature Type: continuous\n",
-      "Means: {\"(-10.0, -9.38)\": 20007.1, \"(-9.38, -8.72)\": 18721.4, \"(-8.72, -8.06)\": 17371.5, \"(-8.06, -7.42)\": 16101.2, \"(-7.42, -6.79)\": 14835.2, \"(-6.79, -6.15)\": 13565.8, \"(-6.15, -5.49)\": 12268.5, \"(-5.49, -4.9)\": 11043.4, \"(-4.9, -4.26)\": 9762.5, \"(-4.26, -3.82)\": 8491.6, \"(-3.82, -3.35)\": 7576.4, \"(-3.35, -2.71)\": 6677.2, \"(-2.71, -2.04)\": 5378.3, \"(-2.04, -1.6)\": 4074.3, \"(-1.6, -1.15)\": 3180.3, \"(-1.15, -0.53)\": 2283.4, \"(-0.53, 1.08)\": 1034.9, \"(1.08, 1.72)\": 2231.9, \"(1.72, 2.36)\": 3472.0, \"(2.36, 2.92)\": 4768.9, \"(2.92, 3.56)\": 5881.7, \"(3.56, 4.2)\": 7179.7, \"(4.2, 4.81)\": 8490.1, \"(4.81, 5.41)\": 9786.6, \"(5.41, 5.96)\": 11096.8, \"(5.96, 6.49)\": 12349.7, \"(6.49, 6.81)\": 13701.8, \"(6.81, 7.12)\": 14599.6, \"(7.12, 7.41)\": 15507.3, \"(7.41, 7.76)\": 16565.8, \"(7.76, 7.95)\": 17853.4, \"(7.95, 8.25)\": 18952.5, \"(8.25, 8.43)\": 20547.8, \"(8.43, 8.53)\": 21457.3, \"(8.53, 8.7)\": 22348.8, \"(8.7, 8.81)\": 23523.8, \"(8.81, 8.93)\": 24428.8, \"(8.93, 9.05)\": 25709.0, \"(9.05, 9.17)\": 26942.5, \"(9.17, 9.27)\": 28147.7, \"(9.27, 9.36)\": 29590.1, \"(9.36, 9.45)\": 30604.2, \"(9.45, 9.52)\": 31765.4, \"(9.52, 9.59)\": 32729.2, \"(9.59, 9.65)\": 33847.6, \"(9.65, 9.72)\": 34872.4, \"(9.72, 9.79)\": 36169.4, \"(9.79, 9.87)\": 37900.2, \"(9.87, 9.94)\": 39516.5, \"(9.94, 9.95)\": 40602.2}\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
-      "\n",
-      "Feature Name: x\n",
-      "Feature Type: continuous\n",
-      "Means: {\"(-9.99, -0.02)\": -4033.1, \"(-0.02, -0.01)\": -3463.5, \"(-0.01, 0.0)\": -2878.5, \"(0.0, 0.01)\": 3168.7, \"(0.01, 0.24)\": 3601.3, \"(0.24, 5.2)\": 3902.3, \"(5.2, 6.14)\": 4194.4, \"(6.14, 6.69)\": 4486.9, \"(6.69, 6.95)\": 4801.1, \"(6.95, 7.25)\": 5095.5, \"(7.25, 7.42)\": 5394.2, \"(7.42, 7.61)\": 5741.0, \"(7.61, 7.75)\": 6060.0, \"(7.75, 7.88)\": 6370.5, \"(7.88, 8.01)\": 6719.7, \"(8.01, 8.09)\": 7018.4, \"(8.09, 8.19)\": 7318.2, \"(8.19, 8.27)\": 7633.6, \"(8.27, 8.34)\": 7931.9, \"(8.34, 8.43)\": 8243.3, \"(8.43, 8.51)\": 8715.0, \"(8.51, 8.59)\": 9025.6, \"(8.59, 8.63)\": 9455.6, \"(8.63, 8.71)\": 9781.1, \"(8.71, 8.76)\": 10095.1, \"(8.76, 8.81)\": 10402.4, \"(8.81, 8.85)\": 10769.3, \"(8.85, 8.93)\": 11205.3, \"(8.93, 8.98)\": 11657.2, \"(8.98, 9.02)\": 11978.3, \"(9.02, 9.04)\": 12274.0, \"(9.04, 9.11)\": 12620.0, \"(9.11, 9.16)\": 13195.6, \"(9.16, 9.2)\": 13622.6, \"(9.2, 9.27)\": 14170.2, \"(9.27, 9.33)\": 14922.7, \"(9.33, 9.4)\": 15653.1, \"(9.4, 9.43)\": 16179.2, \"(9.43, 9.46)\": 16623.1, \"(9.46, 9.51)\": 16925.6, \"(9.51, 9.55)\": 17773.3, \"(9.55, 9.59)\": 18134.4, \"(9.59, 9.64)\": 19003.0, \"(9.64, 9.66)\": 19407.0, \"(9.66, 9.69)\": 19852.4, \"(9.69, 9.73)\": 20519.5, \"(9.73, 9.79)\": 21280.6, \"(9.79, 9.83)\": 22288.4, \"(9.83, 9.86)\": 22741.4, \"(9.86, 9.89)\": 23336.0, \"(9.89, 9.92)\": 24073.7, \"(9.92, 9.95)\": 24600.7, \"(9.95, 9.97)\": 24976.1}\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: The graph of feature x was simplified by 8.7%.\n",
-      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
-      "\n",
-      "Feature Name: x\n",
-      "Feature Type: continuous\n",
-      "Means: {\"(-9.94, -9.72)\": -0.404, \"(-9.72, -9.46)\": -0.682, \"(-9.46, -9.11)\": -0.997, \"(-9.11, -7.87)\": -1.27, \"(-7.87, -7.62)\": -0.989, \"(-7.62, -7.42)\": -0.725, \"(-7.42, -7.22)\": -0.469, \"(-7.22, -7.02)\": -0.195, \"(-7.02, -6.81)\": 0.096, \"(-6.81, -6.62)\": 0.383, \"(-6.62, -6.39)\": 0.639, \"(-6.39, -6.11)\": 0.885, \"(-6.11, -4.66)\": 1.193, \"(-4.66, -4.4)\": 0.91, \"(-4.4, -4.2)\": 0.622, \"(-4.2, -4.01)\": 0.348, \"(-4.01, -3.79)\": 0.086, \"(-3.79, -3.63)\": -0.216, \"(-3.63, -3.37)\": -0.486, \"(-3.37, -3.11)\": -0.783, \"(-3.11, -2.74)\": -1.051, \"(-2.74, -1.64)\": -1.322, \"(-1.64, -1.44)\": -1.061, \"(-1.44, -1.17)\": -0.789, \"(-1.17, -0.97)\": -0.519, \"(-0.97, -0.76)\": -0.237, \"(-0.76, -0.53)\": 0.08, \"(-0.53, -0.32)\": 0.373, \"(-0.32, -0.11)\": 0.637, \"(-0.11, 0.17)\": 0.898, \"(0.17, 1.68)\": 1.158, \"(1.68, 1.88)\": 0.88, \"(1.88, 2.1)\": 0.617, \"(2.1, 2.31)\": 0.348, \"(2.31, 2.51)\": 0.059, \"(2.51, 2.72)\": -0.259, \"(2.72, 2.93)\": -0.528, \"(2.93, 3.2)\": -0.802, \"(3.2, 3.57)\": -1.06, \"(3.57, 4.66)\": -1.33, \"(4.66, 4.92)\": -1.031, \"(4.92, 5.12)\": -0.763, \"(5.12, 5.33)\": -0.496, \"(5.33, 5.51)\": -0.224, \"(5.51, 5.7)\": 0.038, \"(5.7, 5.9)\": 0.3, \"(5.9, 6.12)\": 0.584, \"(6.12, 6.37)\": 0.853, \"(6.37, 6.87)\": 1.117, \"(6.87, 7.71)\": 1.388, \"(7.71, 7.99)\": 1.124, \"(7.99, 8.21)\": 0.836, \"(8.21, 8.38)\": 0.582, \"(8.38, 8.57)\": 0.318, \"(8.57, 8.79)\": 0.065, \"(8.79, 8.96)\": -0.208, \"(8.96, 9.23)\": -0.459, \"(9.23, 9.52)\": -0.837, \"(9.52, 9.96)\": -1.106}\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: The graph of feature x was simplified by 8.7%.\n",
-      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
-      "\n",
-      "Feature Name: x\n",
-      "Feature Type: continuous\n",
-      "Means: {\"(-9.96, -8.41)\": 0.9674, \"(-8.41, -8.08)\": 0.8654, \"(-8.08, -7.8)\": 0.7739, \"(-7.8, -7.58)\": 0.682, \"(-7.58, -7.37)\": 0.5948, \"(-7.37, -7.13)\": 0.5034, \"(-7.13, -6.94)\": 0.4023, \"(-6.94, -6.73)\": 0.3139, \"(-6.73, -6.51)\": 0.207, \"(-6.51, -6.32)\": 0.1087, \"(-6.32, -6.07)\": 0.0155, \"(-6.07, -5.86)\": 0.1195, \"(-5.86, -5.68)\": 0.2152, \"(-5.68, -5.5)\": 0.3026, \"(-5.5, -5.27)\": 0.4029, \"(-5.27, -5.03)\": 0.4919, \"(-5.03, -4.8)\": 0.5859, \"(-4.8, -4.53)\": 0.681, \"(-4.53, -4.21)\": 0.7715, \"(-4.21, -3.74)\": 0.8639, \"(-3.74, -2.15)\": 0.9597, \"(-2.15, -1.82)\": 0.8725, \"(-1.82, -1.55)\": 0.7845, \"(-1.55, -1.33)\": 0.6985, \"(-1.33, -1.12)\": 0.6107, \"(-1.12, -0.89)\": 0.5251, \"(-0.89, -0.68)\": 0.4241, \"(-0.68, -0.49)\": 0.3266, \"(-0.49, -0.31)\": 0.2389, \"(-0.31, -0.12)\": 0.1463, \"(-0.12, 0.28)\": 0.0522, \"(0.28, 0.49)\": 0.1495, \"(0.49, 0.69)\": 0.2555, \"(0.69, 0.9)\": 0.3423, \"(0.9, 1.12)\": 0.438, \"(1.12, 1.35)\": 0.5324, \"(1.35, 1.6)\": 0.6278, \"(1.6, 1.88)\": 0.7211, \"(1.88, 2.3)\": 0.8173, \"(2.3, 4.35)\": 0.9139, \"(4.35, 4.65)\": 0.8171, \"(4.65, 4.9)\": 0.7235, \"(4.9, 5.1)\": 0.6375, \"(5.1, 5.33)\": 0.5493, \"(5.33, 5.54)\": 0.4531, \"(5.54, 5.75)\": 0.3541, \"(5.75, 5.94)\": 0.2524, \"(5.94, 6.14)\": 0.1613, \"(6.14, 6.58)\": 0.0664, \"(6.58, 6.77)\": 0.1566, \"(6.77, 6.97)\": 0.2437, \"(6.97, 7.18)\": 0.34, \"(7.18, 7.41)\": 0.4415, \"(7.41, 7.64)\": 0.5351, \"(7.64, 7.91)\": 0.6295, \"(7.91, 8.21)\": 0.7344, \"(8.21, 8.54)\": 0.8247, \"(8.54, 9.99)\": 0.9145}\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: The graph of feature x was simplified by 3.6%.\n",
-      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
-      "\n",
-      "Feature Name: x\n",
-      "Feature Type: continuous\n",
-      "Means: {\"(-9.97, -9.68)\": 1911.5, \"(-9.68, -9.43)\": 813.0, \"(-9.43, -9.21)\": -41.1, \"(-9.21, -8.93)\": -893.6, \"(-8.93, -8.65)\": -1956.4, \"(-8.65, -8.11)\": -2806.5, \"(-8.11, -7.14)\": -3878.5, \"(-7.14, -6.85)\": -3002.8, \"(-6.85, -6.56)\": -2094.7, \"(-6.56, -6.34)\": -1029.5, \"(-6.34, -6.09)\": -154.8, \"(-6.09, -5.8)\": 794.7, \"(-5.8, -5.54)\": 1909.3, \"(-5.54, -5.01)\": 2771.0, \"(-5.01, -3.93)\": 3822.1, \"(-3.93, -3.62)\": 2815.5, \"(-3.62, -3.37)\": 1688.0, \"(-3.37, -3.13)\": 810.0, \"(-3.13, -2.86)\": -104.0, \"(-2.86, -2.61)\": -1178.3, \"(-2.61, -2.21)\": -2154.9, \"(-2.21, -0.62)\": -3240.3, \"(-0.62, -0.29)\": -2206.4, \"(-0.29, -0.06)\": -1113.2, \"(-0.06, 0.2)\": -161.9, \"(0.2, 0.47)\": 806.3, \"(0.47, 0.81)\": 1843.8, \"(0.81, 1.25)\": 2932.5, \"(1.25, 2.38)\": 3813.1, \"(2.38, 2.69)\": 2747.9, \"(2.69, 3.0)\": 1554.0, \"(3.0, 3.28)\": 480.4, \"(3.28, 3.53)\": -651.9, \"(3.53, 3.79)\": -1522.0, \"(3.79, 4.13)\": -2396.1, \"(4.13, 5.61)\": -3293.6, \"(5.61, 5.82)\": -2225.1, \"(5.82, 6.1)\": -1305.4, \"(6.1, 6.28)\": -247.9, \"(6.28, 6.49)\": 648.0, \"(6.49, 6.72)\": 1584.1, \"(6.72, 6.96)\": 2534.5, \"(6.96, 7.19)\": 3599.5, \"(7.19, 7.51)\": 4532.3, \"(7.51, 7.83)\": 5612.0, \"(7.83, 8.26)\": 6521.8, \"(8.26, 8.65)\": 7552.2, \"(8.65, 8.93)\": 8598.2, \"(8.93, 9.14)\": 9550.7, \"(9.14, 9.32)\": 10647.4, \"(9.32, 9.44)\": 11785.6, \"(9.44, 9.54)\": 12708.6, \"(9.54, 9.63)\": 13573.4, \"(9.63, 9.72)\": 14553.8, \"(9.72, 9.8)\": 15654.9, \"(9.8, 9.87)\": 16766.6, \"(9.87, 9.92)\": 17928.2, \"(9.92, 10.0)\": 18818.0}\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
-      "\n",
-      "Feature Name: x\n",
-      "Feature Type: continuous\n",
-      "Means: {\"(-9.99, -9.69)\": 1.013, \"(-9.69, -9.53)\": 0.992, \"(-9.53, -9.47)\": 0.971, \"(-9.47, -9.43)\": 0.827, \"(-9.43, -9.38)\": -0.849, \"(-9.38, -9.19)\": -0.973, \"(-9.19, -6.42)\": -0.993, \"(-6.42, -6.28)\": -0.971, \"(-6.28, -6.16)\": 0.949, \"(-6.16, -5.92)\": 0.971, \"(-5.92, -3.25)\": 0.993, \"(-3.25, -3.16)\": 0.972, \"(-3.16, -3.14)\": 0.847, \"(-3.14, -3.12)\": -0.849, \"(-3.12, -2.74)\": -0.965, \"(-2.74, -1.99)\": -0.986, \"(-1.99, -0.24)\": -1.006, \"(-0.24, -0.03)\": -0.985, \"(-0.03, -0.01)\": -0.926, \"(-0.01, 0.01)\": -0.763, \"(0.01, 0.05)\": 0.717, \"(0.05, 0.27)\": 0.966, \"(0.27, 1.49)\": 0.987, \"(1.49, 2.65)\": 1.007, \"(2.65, 3.05)\": 0.986, \"(3.05, 3.08)\": 0.966, \"(3.08, 3.12)\": 0.827, \"(3.12, 3.15)\": -0.761, \"(3.15, 3.25)\": -0.963, \"(3.25, 3.92)\": -0.984, \"(3.92, 6.08)\": -1.004, \"(6.08, 6.22)\": -0.982, \"(6.22, 6.25)\": -0.962, \"(6.25, 6.25)\": -0.924, \"(6.25, 6.26)\": -0.889, \"(6.26, 6.28)\": -0.788, \"(6.28, 6.31)\": 0.801, \"(6.31, 6.33)\": 0.914, \"(6.33, 6.53)\": 0.955, \"(6.53, 6.86)\": 0.977, \"(6.86, 9.31)\": 0.998, \"(9.31, 9.38)\": 0.971, \"(9.38, 9.42)\": 0.756, \"(9.42, 9.47)\": -0.681, \"(9.47, 9.58)\": -0.969, \"(9.58, 9.95)\": -0.994, \"(9.95, 9.97)\": -1.015}\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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vmruUZkO4abhvDzp17TOfNXcZrY5PA05xcbEGDhyo5cuX16t/VlaWxowZo6uuukrbtm3TPffco9tuu03vv/++p8+rr76qWbNmad68edq6dasGDhyo5ORk5eXl+WoaluZyGz30Vtv9ZXsm0r7LU2kbDYUnj5sdzV1Gq/XCZ1mqOPVeaBtSVFZFuGmkbw86VVRW1dxltCo+DTijRo3Sn/70J1133XX16p+amqr4+Hg98cQT6tu3r2bOnKlf/epXeuqppzx9nnzySU2bNk1TpkxRv379lJqaqpCQEK1cubLWccvLy+V0Or0WnLQ5K185zrLmLqPVerSN/iV+8rgpb+4yWi23kf43fW9zl9Hk7n316+YuoVXj59cwLepst/T0dCUlJXm1JScnKz09XZJUUVGhzMxMrz52u11JSUmePjVZuHChwsLCPEtcXJxvJtAK5Z0g3JyJvcdKmruEZsFxc+b25be9Y2f/8dLmLqFV4+fXMC0q4OTk5CgqKsqrLSoqSk6nU6WlpTp69KhcLleNfXJycmodNyUlRYWFhZ7lwIEDPqm/NYrsENTcJbRq3TuHNHcJzYLj5sx169T2jp2uHYObu4RWjZ9fw7SogOMrDodDoaGhXgtOGhrfSdGhPFk11h/a6KdhTh43juYuo9Wy26RbE7s3dxlN7qmbLmruElo1fn4N06ICTnR0tHJzc73acnNzFRoaquDgYEVERMjPz6/GPtHR0U1ZqmX42W166Nq2+SR9pq7uF9lmr4dz8ri5oLnLaLWmXdY2r4fTPshfA87lD8zGGHBuKNfDaaAW9QhLTEzUhg0bvNrS0tKUmJgoSQoMDNTgwYO9+rjdbm3YsMHTBw038sIYpd5ysdq10SfrxuA6OP85bsJDApq7lFbDJum3bfw6OG/NvIyQ00BcB6dxfBoHi4qKtHv3bs/trKwsbdu2TZ06dVLXrl2VkpKiQ4cO6cUXX5Qk3X777XrmmWf0+9//Xr/5zW/00Ucf6bXXXtO7777rGWPWrFmaNGmShgwZoqFDh2rp0qUqLi7WlClTfDkVyxt5YYyu7hfNlYy5knGDnDpuuJIxVzJuiLdmXsaVjMWVjH3Np1cy3rhxo6666qpq7ZMmTdLq1as1efJk7d27Vxs3bvTa5t5779V3332nc889Vw8++KAmT57stf0zzzyjJUuWKCcnR4MGDdLTTz+thISEetfFlYwBAGh9GvL8zVc1EHAAAGgVWsxXNQAAADQHAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALCcJgk4y5cvV/fu3RUUFKSEhARt3ry51r5XXnmlbDZbtWXMmDGePpMnT662fuTIkU0xFQAA0Ar4+/oOXn31Vc2aNUupqalKSEjQ0qVLlZycrF27dikyMrJa/zfeeEMVFRWe28eOHdPAgQN1ww03ePUbOXKkVq1a5bntcDh8NwkAANCq+DzgPPnkk5o2bZqmTJkiSUpNTdW7776rlStXas6cOdX6d+rUyev22rVrFRISUi3gOBwORUdH16uG8vJylZeXe247nc6GTgMAALQiPn2LqqKiQpmZmUpKSvrPHdrtSkpKUnp6er3GWLFihcaPH6927dp5tW/cuFGRkZHq3bu37rjjDh07dqzWMRYuXKiwsDDPEhcX17gJAQCAVsGnAefo0aNyuVyKioryao+KilJOTs5pt9+8ebO2b9+u2267zat95MiRevHFF7VhwwYtWrRIn3zyiUaNGiWXy1XjOCkpKSosLPQsBw4caPykAABAi+fzt6jOxIoVK9S/f38NHTrUq338+PGe//fv318DBgxQz549tXHjRg0fPrzaOA6Hg3N0AABoQ3z6Ck5ERIT8/PyUm5vr1Z6bm3va82eKi4u1du1aTZ069bT306NHD0VERGj37t1nVC8AALAGnwacwMBADR48WBs2bPC0ud1ubdiwQYmJiXVu+/rrr6u8vFy33HLLae/n4MGDOnbsmGJiYs64ZgAA0Pr5/Do4s2bN0gsvvKA1a9Zo586duuOOO1RcXOz5VNXEiROVkpJSbbsVK1Zo3Lhx6ty5s1d7UVGR7r//fn355Zfau3evNmzYoLFjx6pXr15KTk729XQAAEAr4PNzcG666SYdOXJEc+fOVU5OjgYNGqT169d7Tjzev3+/7HbvnLVr1y59/vnn+uCDD6qN5+fnp2+//VZr1qxRQUGBYmNjNWLECC1YsIDzbAAAgCTJZowxzV1EU3M6nQoLC1NhYaFCQ0ObuxwAAFAPDXn+5ruoAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5TRJwFm+fLm6d++uoKAgJSQkaPPmzbX2Xb16tWw2m9cSFBTk1ccYo7lz5yomJkbBwcFKSkrSDz/84OtpAACAVsLnAefVV1/VrFmzNG/ePG3dulUDBw5UcnKy8vLyat0mNDRU2dnZnmXfvn1e6xcvXqynn35aqampysjIULt27ZScnKyysjJfTwcAALQCPg84Tz75pKZNm6YpU6aoX79+Sk1NVUhIiFauXFnrNjabTdHR0Z4lKirKs84Yo6VLl+qPf/yjxo4dqwEDBujFF1/U4cOHtW7duhrHKy8vl9Pp9FoAAIB1+TTgVFRUKDMzU0lJSf+5Q7tdSUlJSk9Pr3W7oqIidevWTXFxcRo7dqx27NjhWZeVlaWcnByvMcPCwpSQkFDrmAsXLlRYWJhniYuLOwuzAwAALZVPA87Ro0flcrm8XoGRpKioKOXk5NS4Te/evbVy5Uq9+eabeumll+R2uzVs2DAdPHhQkjzbNWTMlJQUFRYWepYDBw6c6dQAAEAL5t/cBfxUYmKiEhMTPbeHDRumvn376rnnntOCBQsaNabD4ZDD4ThbJQIAgBbOp6/gREREyM/PT7m5uV7tubm5io6OrtcYAQEBuuiii7R7925J8mx3JmMCAABr82nACQwM1ODBg7VhwwZPm9vt1oYNG7xepamLy+XSP/7xD8XExEiS4uPjFR0d7TWm0+lURkZGvccEAADW5vO3qGbNmqVJkyZpyJAhGjp0qJYuXari4mJNmTJFkjRx4kSdc845WrhwoSRp/vz5+vnPf65evXqpoKBAS5Ys0b59+3TbbbdJOvkJq3vuuUd/+tOfdN555yk+Pl4PPvigYmNjNW7cOF9PBwAAtAI+Dzg33XSTjhw5orlz5yonJ0eDBg3S+vXrPScJ79+/X3b7f15IOn78uKZNm6acnBx17NhRgwcP1qZNm9SvXz9Pn9///vcqLi7W9OnTVVBQoEsvvVTr16+vdkFAAADQNtmMMaa5i2hqTqdTYWFhKiwsVGhoaHOXAwAA6qEhz998FxUAALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALCcJgk4y5cvV/fu3RUUFKSEhARt3ry51r4vvPCCLrvsMnXs2FEdO3ZUUlJStf6TJ0+WzWbzWkaOHOnraQAAgFbC5wHn1Vdf1axZszRv3jxt3bpVAwcOVHJysvLy8mrsv3HjRt188836+OOPlZ6erri4OI0YMUKHDh3y6jdy5EhlZ2d7lr/85S++ngoAAGglbMYY48s7SEhI0M9+9jM988wzkiS32624uDjdddddmjNnzmm3d7lc6tixo5555hlNnDhR0slXcAoKCrRu3bp61VBeXq7y8nLPbafTqbi4OBUWFio0NLThkwIAAE3O6XQqLCysXs/fPn0Fp6KiQpmZmUpKSvrPHdrtSkpKUnp6er3GKCkpUWVlpTp16uTVvnHjRkVGRqp379664447dOzYsVrHWLhwocLCwjxLXFxc4yYEAABaBZ8GnKNHj8rlcikqKsqrPSoqSjk5OfUa44EHHlBsbKxXSBo5cqRefPFFbdiwQYsWLdInn3yiUaNGyeVy1ThGSkqKCgsLPcuBAwcaPykAANDi+Td3AXV57LHHtHbtWm3cuFFBQUGe9vHjx3v+379/fw0YMEA9e/bUxo0bNXz48GrjOBwOORyOJqkZAAA0P5++ghMRESE/Pz/l5uZ6tefm5io6OrrObR9//HE99thj+uCDDzRgwIA6+/bo0UMRERHavXv3GdcMAABaP58GnMDAQA0ePFgbNmzwtLndbm3YsEGJiYm1brd48WItWLBA69ev15AhQ057PwcPHtSxY8cUExNzVuoGAACtm88/Jj5r1iy98MILWrNmjXbu3Kk77rhDxcXFmjJliiRp4sSJSklJ8fRftGiRHnzwQa1cuVLdu3dXTk6OcnJyVFRUJEkqKirS/fffry+//FJ79+7Vhg0bNHbsWPXq1UvJycm+ng4AAGgFfH4Ozk033aQjR45o7ty5ysnJ0aBBg7R+/XrPicf79++X3f6fnPXss8+qoqJCv/rVr7zGmTdvnh566CH5+fnp22+/1Zo1a1RQUKDY2FiNGDFCCxYs4DwbAAAgqQmug9MSNeRz9AAAoGVoMdfBAQAAaA4EHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDlNEnCWL1+u7t27KygoSAkJCdq8eXOd/V9//XX16dNHQUFB6t+/v9577z2v9cYYzZ07VzExMQoODlZSUpJ++OEHX04BAAC0Ij4POK+++qpmzZqlefPmaevWrRo4cKCSk5OVl5dXY/9Nmzbp5ptv1tSpU/X1119r3LhxGjdunLZv3+7ps3jxYj399NNKTU1VRkaG2rVrp+TkZJWVlfl6OgAAoBWwGWOML+8gISFBP/vZz/TMM89Iktxut+Li4nTXXXdpzpw51frfdNNNKi4u1jvvvONp+/nPf65BgwYpNTVVxhjFxsZq9uzZuu+++yRJhYWFioqK0urVqzV+/PhqY5aXl6u8vNxz2+l0Ki4uToWFhQoNDT3bUwYAAD7gdDoVFhZWr+dvn76CU1FRoczMTCUlJf3nDu12JSUlKT09vcZt0tPTvfpLUnJysqd/VlaWcnJyvPqEhYUpISGh1jEXLlyosLAwzxIXF3emUwMAAC2YTwPO0aNH5XK5FBUV5dUeFRWlnJycGrfJycmps/+pfxsyZkpKigoLCz3LgQMHGjUfAADQOvg3dwFNweFwyOFwNHcZAACgifj0FZyIiAj5+fkpNzfXqz03N1fR0dE1bhMdHV1n/1P/NmRMAADQtvg04AQGBmrw4MHasGGDp83tdmvDhg1KTEyscZvExESv/pKUlpbm6R8fH6/o6GivPk6nUxkZGbWOCQAA2hafv0U1a9YsTZo0SUOGDNHQoUO1dOlSFRcXa8qUKZKkiRMn6pxzztHChQslSXfffbeuuOIKPfHEExozZozWrl2rr776Ss8//7wkyWaz6Z577tGf/vQnnXfeeYqPj9eDDz6o2NhYjRs3ztfTAQAArYDPA85NN92kI0eOaO7cucrJydGgQYO0fv16z0nC+/fvl93+nxeShg0bpldeeUV//OMf9Yc//EHnnXee1q1bpwsvvNDT5/e//72Ki4s1ffp0FRQU6NJLL9X69esVFBTk6+kAAIBWwOfXwWmJGvI5egAA0DK0mOvgAAAANAcCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsByfBpz8/HxNmDBBoaGhCg8P19SpU1VUVFRn/7vuuku9e/dWcHCwunbtqt/97ncqLCz06mez2aota9eu9eVUAABAK+Lvy8EnTJig7OxspaWlqbKyUlOmTNH06dP1yiuv1Nj/8OHDOnz4sB5//HH169dP+/bt0+23367Dhw/rr3/9q1ffVatWaeTIkZ7b4eHhvpwKAABoRWzGGOOLgXfu3Kl+/fppy5YtGjJkiCRp/fr1Gj16tA4ePKjY2Nh6jfP666/rlltuUXFxsfz9T+Yxm82mv/3tbxo3bly9xigvL1d5ebnnttPpVFxcnAoLCxUaGtqwiQEAgGbhdDoVFhZWr+dvn71FlZ6ervDwcE+4kaSkpCTZ7XZlZGTUe5xTkzgVbk6ZMWOGIiIiNHToUK1cuVJ15bSFCxcqLCzMs8TFxTV8QgAAoNXwWcDJyclRZGSkV5u/v786deqknJyceo1x9OhRLViwQNOnT/dqnz9/vl577TWlpaXp+uuv15133qlly5bVOk5KSooKCws9y4EDBxo+IQAA0Go0+BycOXPmaNGiRXX22blzZ6MLOsXpdGrMmDHq16+fHnroIa91Dz74oOf/F110kYqLi7VkyRL97ne/q3Esh8Mhh8NxxjUBAIDWocEBZ/bs2Zo8eXKdfXr06KHo6Gjl5eV5tVdVVSk/P1/R0dF1bn/ixAmNHDlSHTp00N/+9jcFBATU2T8hIUELFixQeXk5QQYAADQ84HTp0kVdunQ5bb/ExEQVFBQoMzNTgwcPliR99NFHcrvdSkhIqHU7p9Op5ORkORwOvfXWWwoKCjrtfW3btk0dO3Yk3AAAAEk+/Jh43759NXLkSE2bNk2pqamqrKzUzJkzNX78eM8nqA4dOqThw4frxRdf1NChQ+V0OjVixAiVlJTopZdektPplNPplHQyWPn5+entt99Wbm6ufv7znysoKEhpaWl69NFHdd999/lqKgAAoJXx6XVwXn75Zc2cOVPDhw+X3W7X9ddfr6efftqzvrKyUrt27VJJSYkkaevWrZ5PWPXq1ctrrKysLHXv3l0BAQFavny57r33Xhlj1KtXLz355JOaNm2aL6cCAABaEZ9dB6cla8jn6AEAQMvQIq6DAwAA0FwIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHJ8GnDy8/M1YcIEhYaGKjw8XFOnTlVRUVGd21x55ZWy2Wxey+233+7VZ//+/RozZoxCQkIUGRmp+++/X1VVVb6cCgAAaEX8fTn4hAkTlJ2drbS0NFVWVmrKlCmaPn26XnnllTq3mzZtmubPn++5HRIS4vm/y+XSmDFjFB0drU2bNik7O1sTJ05UQECAHn30UZ/NBQAAtB42Y4zxxcA7d+5Uv379tGXLFg0ZMkSStH79eo0ePVoHDx5UbGxsjdtdeeWVGjRokJYuXVrj+r///e/65S9/qcOHDysqKkqSlJqaqgceeEBHjhxRYGBgtW3Ky8tVXl7uue10OhUXF6fCwkKFhoae4UwBAEBTcDqdCgsLq9fzt8/eokpPT1d4eLgn3EhSUlKS7Ha7MjIy6tz25ZdfVkREhC688EKlpKSopKTEa9z+/ft7wo0kJScny+l0aseOHTWOt3DhQoWFhXmWuLi4M5wdAABoyXz2FlVOTo4iIyO978zfX506dVJOTk6t2/36179Wt27dFBsbq2+//VYPPPCAdu3apTfeeMMz7o/DjSTP7drGTUlJ0axZszy3T72CAwAArKnBAWfOnDlatGhRnX127tzZ6IKmT5/u+X///v0VExOj4cOHa8+ePerZs2ejxnQ4HHI4HI2uCQAAtC4NDjizZ8/W5MmT6+zTo0cPRUdHKy8vz6u9qqpK+fn5io6Orvf9JSQkSJJ2796tnj17Kjo6Wps3b/bqk5ubK0kNGhcAAFhXgwNOly5d1KVLl9P2S0xMVEFBgTIzMzV48GBJ0kcffSS32+0JLfWxbds2SVJMTIxn3EceeUR5eXmet8DS0tIUGhqqfv36NXA2AADAinx2knHfvn01cuRITZs2TZs3b9YXX3yhmTNnavz48Z5PUB06dEh9+vTxvCKzZ88eLViwQJmZmdq7d6/eeustTZw4UZdffrkGDBggSRoxYoT69eunW2+9Vd98843ef/99/fGPf9SMGTN4GwoAAEjy8YX+Xn75ZfXp00fDhw/X6NGjdemll+r555/3rK+srNSuXbs8n5IKDAzUhx9+qBEjRqhPnz6aPXu2rr/+er399tuebfz8/PTOO+/Iz89PiYmJuuWWWzRx4kSv6+YAAIC2zWfXwWnJGvI5egAA0DK0iOvgAAAANBcCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsByfBpz8/HxNmDBBoaGhCg8P19SpU1VUVFRr/71798pms9W4vP76655+Na1fu3atL6cCAABaEX9fDj5hwgRlZ2crLS1NlZWVmjJliqZPn65XXnmlxv5xcXHKzs72anv++ee1ZMkSjRo1yqt91apVGjlypOd2eHj4Wa8fAAC0Tj4LODt37tT69eu1ZcsWDRkyRJK0bNkyjR49Wo8//rhiY2OrbePn56fo6Givtr/97W+68cYb1b59e6/28PDwan1rU15ervLycs9tp9PZ0OkAAIBWxGdvUaWnpys8PNwTbiQpKSlJdrtdGRkZ9RojMzNT27Zt09SpU6utmzFjhiIiIjR06FCtXLlSxphax1m4cKHCwsI8S1xcXMMnBAAAWg2fBZycnBxFRkZ6tfn7+6tTp07Kycmp1xgrVqxQ3759NWzYMK/2+fPn67XXXlNaWpquv/563XnnnVq2bFmt46SkpKiwsNCzHDhwoOETAgAArUaD36KaM2eOFi1aVGefnTt3NrqgU0pLS/XKK6/owQcfrLbux20XXXSRiouLtWTJEv3ud7+rcSyHwyGHw3HGNQEAgNahwQFn9uzZmjx5cp19evTooejoaOXl5Xm1V1VVKT8/v17nzvz1r39VSUmJJk6ceNq+CQkJWrBggcrLywkyAACg4QGnS5cu6tKly2n7JSYmqqCgQJmZmRo8eLAk6aOPPpLb7VZCQsJpt1+xYoWuvfbaet3Xtm3b1LFjR8INAACQ5MNPUfXt21cjR47UtGnTlJqaqsrKSs2cOVPjx4/3fILq0KFDGj58uF588UUNHTrUs+3u3bv16aef6r333qs27ttvv63c3Fz9/Oc/V1BQkNLS0vToo4/qvvvu89VUAABAK+PT6+C8/PLLmjlzpoYPHy673a7rr79eTz/9tGd9ZWWldu3apZKSEq/tVq5cqXPPPVcjRoyoNmZAQICWL1+ue++9V8YY9erVS08++aSmTZvmy6kAAIBWxGbq+ny1RTmdToWFhamwsFChoaHNXQ4AAKiHhjx/811UAADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcvybuwArKSqr0t1rt+qbg4WSMeoc4q9jJZWqcEsOP5sqKipVXCG5JJkfbWe3SUF+UqCfTaVVUoCfXXEdHbr0/Eh1Cg7Up7uPak/eCZVWuOTnZ5PbSEH+NgUF+CmifZC6dm6n0RfG6OWMLG3OOq4qt5HdZlN7h5/iOgYrOsyhLXsLVVxRJX+7XXGdgnTZeZHq0sGhTiGBOlpcoe0HC7Ur16niiipFdwhS8gUxmnxpvAL9ycCSVFHl1qovsvT+9mzlnahQkL9NAQE2VVYalVa6VFrpUlnFyT3rMjbZbCf/eggJtMsR4K+4jiGSTdp/vFTOkgr52WwKC/ZXRGiQYkODdaCgRAfzS1Ra4VI7h//JYyLQX+2D/NW9czvZJe05ckJHiytUVulWkJ9NQYH+8rfbdLSoXFUut9z/vl+bzaYOgXYVlFWp3CXZJAX5S+0C/SW7TeFBAYoMdahTu0DlOcv07YFClblPztP/38diuVsKDvRTl/YBCg0KkJ+fn0b0i9atid21df9xfbbriD7bfURHnKUqcxn52WyKaO/Qfw0+R1Mv7clx82+FJZX6zerNOni8RFUutyI6OJTrrJDbuNXB4a9Lz+uii7p2VNr2bH29/5jyy07+ZrBJCg6wKcBuU6BdKqww8rNLHYP8FRzor0qXUaXbrUqXkTFGwYF+Ol5coUqXkSPArqgOwQrwt6nK5Va7QH/Z7DaVVbqUX1Sh0spKudyS223klk3+dqlTe4cC7DblOE8eSwF+dnWLaKf+MWE6VFCkLfsKVe4ysv17Xv72k8e3W1Kgn13nhDt0qKBUxZUn10e089etCfEqrXRp26HjOnCsVBVVVSqpcMnlNjKyKcjfpsiwYP3XxRwzP3Xy982/9MGOXJ0oq1Tn9g51DPbXd9kndLy0Qv42m0IC7DpR4VKV2ygsKEAD48LVzuGvbQeOq6C4UkVlJ5973Obk/gryt8nIJrtNCgmwKzAgQCWVVQoL8pe/TXKWVamgzCW326i9w0/Dekbouovj9I/Dx/Xipr0qqXCrQ5CfunUO0eHjZXIZKb5TkLp16aA8Z5n+dbRExm1kbEaBdj/Z/aS+0aH61cVxGnZehPzsttNP/CyzGWPM6btZi9PpVFhYmAoLCxUaGnpWxrz2mc/07UHnWRmrpbBJmn55vFJG92vuUprVwve+03OfZjV3Ga3KbzludMWSj7TvWGlzl9FqcMycZMXfNyGBfnryxoEaeWHMGY/VkOdvn0XmRx55RMOGDVNISIjCw8PrtY0xRnPnzlVMTIyCg4OVlJSkH374watPfn6+JkyYoNDQUIWHh2vq1KkqKirywQzqz4rhRjr5KtNzn2Zp4XvfNXcpzcaKv2yaQls/bgg3DdfWjxnJur9vSipcuv2lrVq/PbtJ79dnAaeiokI33HCD7rjjjnpvs3jxYj399NNKTU1VRkaG2rVrp+TkZJWVlXn6TJgwQTt27FBaWpreeecdffrpp5o+fbovplAvRWVVlgw3P/bCZ1mqqHI3dxlNrqLKrect+MumqTz3ads8bgpLKgk3jdRWjxnp5O8bK4abH5v35na53E33ppHPAs7DDz+se++9V/37969Xf2OMli5dqj/+8Y8aO3asBgwYoBdffFGHDx/WunXrJEk7d+7U+vXr9T//8z9KSEjQpZdeqmXLlmnt2rU6fPhwrWOXl5fL6XR6LWfLva9+fdbGaqncRvrf9L3NXUaT+9/0vWpz79+eZW3xuPnN6s3NXUKr1haPGaltzDv3RIU2Z+U32f21mLO6srKylJOTo6SkJE9bWFiYEhISlJ6eLklKT09XeHi4hgwZ4umTlJQku92ujIyMWsdeuHChwsLCPEtcXNxZq3v/8bbxl9q+/JLmLqHJtcU5n21t8Wd4uLDs9J1Qq7Z4zEhtZ955J5ru8dFiAk5OTo4kKSoqyqs9KirKsy4nJ0eRkZFe6/39/dWpUydPn5qkpKSosLDQsxw4cOCs1d21Y/BZG6sl69YppLlLaHJtcc5nW1v8GcaGBTV3Ca1aWzxmpLYz78gOTff4aFDAmTNnjmw2W53L999/76taG83hcCg0NNRrOVueuumiszZWS2W3Sbcmdm/uMprcrYnd1fQfbLSWtnjcrJw8tLlLaNXa4jEjtY15R3UI1ND4Tk12fw26Ds7s2bM1efLkOvv06NGjUYVER0dLknJzcxUT85+PkuXm5mrQoEGePnl5eV7bVVVVKT8/37N9U2sf5K8B54Za+kTjaZe1zevhBPrbNf3yeMuf+Ocrv728bR43YSEB6tY5mBONG6GtHjPSyd83v7X475uHx17YpNfDadCR1KVLF/Xp06fOJTAwsFGFxMfHKzo6Whs2bPC0OZ1OZWRkKDExUZKUmJiogoICZWZmevp89NFHcrvdSkhIaNT9ng1vzbxMA849e68KtRQ2cW2KlNH99NvL45u7jFanrR83n9z/C3Xr3Dbevj5b2voxI1n3901IoJ9Sb7n4rFwHpyF8dqG//fv3Kz8/X2+99ZaWLFmizz77TJLUq1cvtW/fXpLUp08fLVy4UNddd50kadGiRXrssce0Zs0axcfH68EHH9S3336r7777TkFBJ9+3GzVqlHJzc5WamqrKykpNmTJFQ4YM0SuvvFLv2nxxoT+JKxlbGVcy5krGjcGVjLmScWNwJePaNeT522cBZ/LkyVqzZk219o8//lhXXnnlyTu32bRq1SrP217GGM2bN0/PP/+8CgoKdOmll+rPf/6zzj//fM/2+fn5mjlzpt5++23Z7XZdf/31evrppz2hqT58FXAAAIDvtIiA05IRcAAAaH1axFc1AAAANBcCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsJwGfdmmVZy6tqHTad0vyAQAwGpOPW/X5xrFbTLgnDhxQpIUFxfXzJUAAICGOnHihMLCwurs0ya/qsHtduvw4cPq0KGDbLaz+9XtTqdTcXFxOnDggCW/BoL5tX5Wn6PV5ydZf45Wn59k/Tn6an7GGJ04cUKxsbGy2+s+y6ZNvoJjt9t17rnn+vQ+QkNDLXnQnsL8Wj+rz9Hq85OsP0erz0+y/hx9Mb/TvXJzCicZAwAAyyHgAAAAyyHgnGUOh0Pz5s2Tw+Fo7lJ8gvm1flafo9XnJ1l/jlafn2T9ObaE+bXJk4wBAIC18QoOAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwHAJOAz3yyCMaNmyYQkJCFB4eXmOf/fv3a8yYMQoJCVFkZKTuv/9+VVVV1Tlufn6+JkyYoNDQUIWHh2vq1KkqKirywQwaZuPGjbLZbDUuW7ZsqXW7K6+8slr/22+/vQkrr7/u3btXq/Wxxx6rc5uysjLNmDFDnTt3Vvv27XX99dcrNze3iSpumL1792rq1KmKj49XcHCwevbsqXnz5qmioqLO7VryPly+fLm6d++uoKAgJSQkaPPmzXX2f/3119WnTx8FBQWpf//+eu+995qo0oZbuHChfvazn6lDhw6KjIzUuHHjtGvXrjq3Wb16dbV9FRQU1EQVN8xDDz1UrdY+ffrUuU1r2n9Szb9TbDabZsyYUWP/lr7/Pv30U11zzTWKjY2VzWbTunXrvNYbYzR37lzFxMQoODhYSUlJ+uGHH047bkMfxw1FwGmgiooK3XDDDbrjjjtqXO9yuTRmzBhVVFRo06ZNWrNmjVavXq25c+fWOe6ECRO0Y8cOpaWl6Z133tGnn36q6dOn+2IKDTJs2DBlZ2d7Lbfddpvi4+M1ZMiQOredNm2a13aLFy9uoqobbv78+V613nXXXXX2v/fee/X222/r9ddf1yeffKLDhw/rv/7rv5qo2ob5/vvv5Xa79dxzz2nHjh166qmnlJqaqj/84Q+n3bYl7sNXX31Vs2bN0rx587R161YNHDhQycnJysvLq7H/pk2bdPPNN2vq1Kn6+uuvNW7cOI0bN07bt29v4srr55NPPtGMGTP05ZdfKi0tTZWVlRoxYoSKi4vr3C40NNRrX+3bt6+JKm64Cy64wKvWzz//vNa+rW3/SdKWLVu85peWliZJuuGGG2rdpiXvv+LiYg0cOFDLly+vcf3ixYv19NNPKzU1VRkZGWrXrp2Sk5NVVlZW65gNfRw3ikGjrFq1yoSFhVVrf++994zdbjc5OTmetmeffdaEhoaa8vLyGsf67rvvjCSzZcsWT9vf//53Y7PZzKFDh8567WeioqLCdOnSxcyfP7/OfldccYW5++67m6aoM9StWzfz1FNP1bt/QUGBCQgIMK+//rqnbefOnUaSSU9P90GFZ9/ixYtNfHx8nX1a6j4cOnSomTFjhue2y+UysbGxZuHChTX2v/HGG82YMWO82hISEsxvf/tbn9Z5tuTl5RlJ5pNPPqm1T22/j1qiefPmmYEDB9a7f2vff8YYc/fdd5uePXsat9td4/rWtP8kmb/97W+e226320RHR5slS5Z42goKCozD4TB/+ctfah2noY/jxuAVnLMsPT1d/fv3V1RUlKctOTlZTqdTO3bsqHWb8PBwr1dEkpKSZLfblZGR4fOaG+Ktt97SsWPHNGXKlNP2ffnllxUREaELL7xQKSkpKikpaYIKG+exxx5T586dddFFF2nJkiV1vqWYmZmpyspKJSUledr69Omjrl27Kj09vSnKPWOFhYXq1KnTafu1tH1YUVGhzMxMr5+93W5XUlJSrT/79PR0r/7Sycdka9pXkk67v4qKitStWzfFxcVp7Nixtf6+aQl++OEHxcbGqkePHpowYYL2799fa9/Wvv8qKir00ksv6Te/+Y1sNlut/VrT/vuxrKws5eTkeO2jsLAwJSQk1LqPGvM4bow2+W3ivpSTk+MVbiR5bufk5NS6TWRkpFebv7+/OnXqVOs2zWXFihVKTk4+7bex//rXv1a3bt0UGxurb7/9Vg888IB27dqlN954o4kqrb/f/e53uvjii9WpUydt2rRJKSkpys7O1pNPPllj/5ycHAUGBlY7BysqKqrF7a+a7N69W8uWLdPjjz9eZ7+WuA+PHj0ql8tV42Ps+++/r3Gb2h6TrWFfud1u3XPPPbrkkkt04YUX1tqvd+/eWrlypQYMGKDCwkI9/vjjGjZsmHbs2HHax2pTS0hI0OrVq9W7d29lZ2fr4Ycf1mWXXabt27erQ4cO1fq35v0nSevWrVNBQYEmT55ca5/WtP9+6tR+aMg+aszjuDEIOJLmzJmjRYsW1dln586dpz0RrjVpzJwPHjyo999/X6+99tppx//x+UP9+/dXTEyMhg8frj179qhnz56NL7yeGjK/WbNmedoGDBigwMBA/fa3v9XChQtb9PfENGYfHjp0SCNHjtQNN9ygadOm1bltc+9DSDNmzND27dvrPEdFkhITE5WYmOi5PWzYMPXt21fPPfecFixY4OsyG2TUqFGe/w8YMEAJCQnq1q2bXnvtNU2dOrUZK/ONFStWaNSoUYqNja21T2vaf60JAUfS7Nmz60zXktSjR496jRUdHV3tTPBTn66Jjo6udZufnlhVVVWl/Pz8Wrc5U42Z86pVq9S5c2dde+21Db6/hIQESSdfPWiKJ8cz2acJCQmqqqrS3r171bt372rro6OjVVFRoYKCAq9XcXJzc322v2rS0DkePnxYV111lYYNG6bnn3++wffX1PuwJhEREfLz86v2ibW6fvbR0dEN6t9SzJw50/OBg4b+FR8QEKCLLrpIu3fv9lF1Z094eLjOP//8WmttrftPkvbt26cPP/ywwa96tqb9d2o/5ObmKiYmxtOem5urQYMG1bhNYx7HjXLWzuZpY053knFubq6n7bnnnjOhoaGmrKysxrFOnWT81Vdfedref//9FnWSsdvtNvHx8Wb27NmN2v7zzz83ksw333xzlis7+1566SVjt9tNfn5+jetPnWT817/+1dP2/ffft+iTjA8ePGjOO+88M378eFNVVdWoMVrKPhw6dKiZOXOm57bL5TLnnHNOnScZ//KXv/RqS0xMbLEnqbrdbjNjxgwTGxtr/vnPfzZqjKqqKtO7d29z7733nuXqzr4TJ06Yjh07mv/+7/+ucX1r238/Nm/ePBMdHW0qKysbtF1L3n+q5STjxx9/3NNWWFhYr5OMG/I4blStZ22kNmLfvn3m66+/Ng8//LBp3769+frrr83XX39tTpw4YYw5eWBeeOGFZsSIEWbbtm1m/fr1pkuXLiYlJcUzRkZGhundu7c5ePCgp23kyJHmoosuMhkZGebzzz835513nrn55pubfH61+fDDD40ks3PnzmrrDh48aHr37m0yMjKMMcbs3r3bzJ8/33z11VcmKyvLvPnmm6ZHjx7m8ssvb+qyT2vTpk3mqaeeMtu2bTN79uwxL730kunSpYuZOHGip89P52eMMbfffrvp2rWr+eijj8xXX31lEhMTTWJiYnNM4bQOHjxoevXqZYYPH24OHjxosrOzPcuP+7SWfbh27VrjcDjM6tWrzXfffWemT59uwsPDPZ9cvPXWW82cOXM8/b/44gvj7+9vHn/8cbNz504zb948ExAQYP7xj3801xTqdMcdd5iwsDCzceNGr31VUlLi6fPTOT788MPm/fffN3v27DGZmZlm/PjxJigoyOzYsaM5plCn2bNnm40bN5qsrCzzxRdfmKSkJBMREWHy8vKMMa1//53icrlM165dzQMPPFBtXWvbfydOnPA810kyTz75pPn666/Nvn37jDHGPPbYYyY8PNy8+eab5ttvvzVjx4418fHxprS01DPGL37xC7Ns2TLP7dM9js8GAk4DTZo0yUiqtnz88ceePnv37jWjRo0ywcHBJiIiwsyePdsrwX/88cdGksnKyvK0HTt2zNx8882mffv2JjQ01EyZMsUTmlqCm2++2QwbNqzGdVlZWV4/g/3795vLL7/cdOrUyTgcDtOrVy9z//33m8LCwiasuH4yMzNNQkKCCQsLM0FBQaZv377m0Ucf9Xq17afzM8aY0tJSc+edd5qOHTuakJAQc91113kFhpZk1apVNR6zP34Bt7Xtw2XLlpmuXbuawMBAM3ToUPPll1961l1xxRVm0qRJXv1fe+01c/7555vAwEBzwQUXmHfffbeJK66/2vbVqlWrPH1+Osd77rnH8/OIiooyo0ePNlu3bm364uvhpptuMjExMSYwMNCcc8455qabbjK7d+/2rG/t+++U999/30gyu3btqraute2/U89ZP11OzcHtdpsHH3zQREVFGYfDYYYPH15t3t26dTPz5s3zaqvrcXw22Iwx5uy94QUAAND8uA4OAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwnP8PoX1HGgB1eXQAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
-      "\n",
-      "Feature Name: x\n",
-      "Feature Type: continuous\n",
-      "Means: {\"(-10.0, -8.26)\": -1.013, \"(-8.26, -7.92)\": -0.992, \"(-7.92, -7.87)\": -0.972, \"(-7.87, -7.86)\": -0.915, \"(-7.86, -7.83)\": 0.856, \"(-7.83, -7.69)\": 0.966, \"(-7.69, -5.83)\": 0.987, \"(-5.83, -4.89)\": 1.007, \"(-4.89, -4.74)\": 0.986, \"(-4.74, -4.72)\": 0.703, \"(-4.72, -4.71)\": -0.827, \"(-4.71, -4.26)\": -0.974, \"(-4.26, -1.68)\": -0.994, \"(-1.68, -1.59)\": -0.974, \"(-1.59, -1.58)\": -0.911, \"(-1.58, -1.56)\": -0.794, \"(-1.56, -1.54)\": 0.893, \"(-1.54, -1.3)\": 0.963, \"(-1.3, -0.62)\": 0.983, \"(-0.62, 1.36)\": 1.004, \"(1.36, 1.56)\": 0.983, \"(1.56, 1.56)\": 0.871, \"(1.56, 1.57)\": 0.773, \"(1.57, 1.57)\": -0.891, \"(1.57, 1.76)\": -0.966, \"(1.76, 2.8)\": -0.986, \"(2.8, 4.49)\": -1.008, \"(4.49, 4.67)\": -0.986, \"(4.67, 4.7)\": -0.831, \"(4.7, 4.72)\": 0.776, \"(4.72, 5.04)\": 0.972, \"(5.04, 7.76)\": 0.993, \"(7.76, 7.83)\": 0.969, \"(7.83, 7.85)\": 0.892, \"(7.85, 7.87)\": -0.728, \"(7.87, 7.89)\": -0.915, \"(7.89, 8.07)\": -0.963, \"(8.07, 9.76)\": -0.984, \"(9.76, 9.98)\": -1.005}\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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psfnYRZFGREidq1od/rhUZ/Vx23Iv4afsCyKMiJADWZoKVF21vDVtii0nSqBtkwhOCAs5L4QsuXSFbbuoPf/+4QSFc92UOz7YZ/Ox//ruOF03bsq23BKbj33g4wwBR0K0hZwXQpYUltXbdFxNQzOyNBUCj4aQOtpmPXKLr9h8fEOTHpn5HUvxCddGp+ewMdv2aO0BTRVFX0SCnBdCdqTnFOO/2/+w+fiS6qsCjoaQA59lFNp9joyCMvsHQsiKLE0FKursS/Rft48tQZywDnJeCFmh03NYujkX9gTw9+XRTcjdOFthW6SuLbRp5H7Yuj3dll9O2r7tRJiHnBdCVmRpKlBcbd+EsuVEMeUvuBk9Q/zsPkeJndcdIT+225HvYuAiRXpFgZwXQlYIsRK62qTHuzvzBBgNIRceSIqGvart20+VktPrRmib9fjpuP3OS3mtlq4bESDnhZAV23JLBTnP2v0amlDcCC8PJWaPirHrHNVXKdnbnfgso1CQrUKtjqNkbxEg54WQDdpmPbYcF0Z2u6q+iW5EbkbaxDjMGR3TIQKjULT0v2JBiMgfIQ+EyJMy8FlmoWDnIlrwcPYACIKV9fvZVkJKBcASVCmpoRuRO6HTcxjTNwLXRQQg+1wlACA61B8PJEXjg115eHP7n7znKCyzXuSOkCf1jTrBzrXrj8vQ6TnqOC0gFHkhZMPBQrbQq7qzD5Pdvj+p6shdSM8pxsiXd2LGmkws+t8xfJZZhO2nLqFrsC+8PJRYMLYPgvw8ec+zdh9tN7oDOj1nlzhdexqa9BTpFRhyXgjZ4OfFFiiMDGJzXrbmXKQbkRuQnlOMeRuOdKhSK6luwLy/OgCrlAo8lBTNe66qq82Uv+AGvLszD9U83eoBoFcYexUbbTkKCzkvhGzoEeLLZHfL9Womu3otqaa6OpZ0gQzPLd2cC52eg07PpoS64UChUMMjJIhOz+ETRmG5CddHMp83rJO3rUMiTEDOCyELdHoO3xw6z2vn76XCzBEx8PdSMZ2XVFNdGz5dIA5AcXXDXyF9tnyEPX+WU8TOhcnSVKD6ahOTbbC/F/y9GG+jdMkICjkvhCzI0lSgpKaR1+7R0b3g5aHE6Ou6MJ6ZEuhcGdZQ/aUrDUjqFcpkW9tIJdOujDXbO2GdvHDPjT2YbD/eV2DrkAgTkPNCyALW5Lnov/ag701km1CGxYTYPCZC+rBWB4V39sHw2FD4MUbsqFLNdQlnTPgHAHWgL8bHsW1T7zpzmZo0Cgg5L4Tksaazq2HiUSrYIiqsdoT80Ok5fJlVxGsXGeiDxJgQqJQK3BrPdiMqu8IfBSTkSWJMCNQB/A6MOsAbiTEhSIwJQWcffqdXzwnTIJRogZwXQvKwdnYN8fdE4l+RlLI6tpsLqx0hP1i3Gu+5sUer/kZEAFtS5eGzlOjtqqiUCkwdxJ+I+/yU66FSKqBSKjC0RzDTuYUUvnN3yHkhJA9riH5qQtfWmxBr6NeaEDEhL1hzF6LblLsqFWxT4t48Stp1VdJzivHhbvPVRn5eKqy6fwhS4685OKP6sOXYCdEglGiBnBdC8lTUskVHugVfK6VOjAlBZKCPxXTcYL9rkRrC9Qjy5heda2/HnrSro6RdF8RSab2BQF9PpLTLc2Ft/BnRmcqlhUJU52X37t2YPHkyoqKioFAosHHjRov2u3btgkKh6PAoKRFO6ZCQH0G+jDehNnYqpQJLJsdZnIQq65vwSvopO0dHSJVtp9maeLa1Gx4bylxmT6JjrgdfaT3QtrT+Gl4eSswayd/4c8XPpyliJxCiOi91dXVISEjAe++9Z9VxZ86cQXFxcesjPDxcpBEScqCKUXOhvV1KnJpX8n31bg22CtTskZAWrPkFbe1USgUeHd2L6TjacnQ9rCmtb8/YfhG8x5lyfAjbELUx46233opbb73V6uPCw8MRFBQk/IAIWRLCqEzZ3i5LU4Gqen7H56nvjmNCvJqaprkY0aF+2MPfaxHRocZ5CAvG9sba/RqL146fl4q2HF0Qe3Ll7HF8COuRZM7LoEGDEBkZiZSUFOzbt8+ibWNjI2pqaowehGvBUrZoyo51kqhtpH41rsgNPdmci2cmxhn9X6VU4KXpAyweU6/V0ZajCzK0ZzD41BOUiha79lCRgGORlPMSGRmJVatW4bvvvsN3332H7t27Y8yYMThy5IjZY1auXInAwMDWR/fu3R04YsIRVDKUMxu0OtpizSRBbQJcC52ew5LNJ3ntxvfvAl8TOS5j+0Xw3sTW7NGQ6JiL8cGuPHA8KSl6Djh8trLD8yxFAqbmKcI2JOW89O3bF3PmzMHQoUORnJyMTz75BMnJyXjzzTfNHpOWlobq6urWx7lz5xw4YkJsdHoOy7fwr3CfnRTXYdsnMSYEvp5sW0HNlETnUmQWlDNtGc4cEWvy+c8yCpluYiQ65jro9BzW7itksjUV1TUUCVhiSkIkbU8LhKScF1MkJiYiLy/P7Ove3t4ICAgwehCuA0v2P9DSIK09KqUCN0azrXJqG5qtHhshXTIYtwHN2dmS7EvImyxNBXNxgLmobmp8JB4dbb7qaPVuDdJzqEBACCTvvGRnZyMykr3tOOFa2JsE1zO0E9Px5+gm5GKwRtJM27GKiZHomOvAOtcEWdCH0uk5fJZpuSXFom+OUbm0AIjqvNTW1iI7OxvZ2dkAAI1Gg+zsbBQVtXy5aWlpePDBB1vt//vf/2LTpk3Iy8tDTk4OHn/8cezcuRPz588Xc5iEhLE3Ca59JYk5DhRW0ITiQiTFhtllxyI6plC02BGuAetcMzM5xuzWz/68MtRrdRaPr9PqsD+PcuzsRVTn5dChQxg8eDAGDx4MAFi4cCEGDx6M5557DgBQXFzc6sgAgFarxaJFizBgwADcdNNNOHbsGLZv345x48aJOUxCwvAlwSlgOQnugaRo3sRLAGho0lPFkQsxvFcor8ZPkJ8nhptR1PXyUGL2KMuiYxwH7GQUwiOkT2JMCDx4PFaloqWU3hzfHTnP9F6sdoR5RHVexowZA47jOjzWrVsHAFi3bh127drVav/UU08hLy8PV69eRXl5OX777TfcfPPNYg6RkDhtk+DaTyuG/y+Z3DFZ14CXhxKDugUyvRdVHLkOLOXOL00fYDF58qnU/vCzoLarALB0cy5F7FyEq1odb+K+nmuxM0edli13jtWOMI/kc14IIjU+Eh/cPwTqQOOwrjrQBx+0a5BmiuTebFsIdAtyLVLjI7Hq/iFQt+sUrQ7w7tBYzxRZmgqLWwAcSDHVlXj8K/OSHKx2rL2LqMeR/YiqsEsQQpEaH4mUODWyNBW4dKUB4Z1btopYyg6DfDtWItljR8gHe64bUkx1L46eq2KyO1Naa/a1wd2DseEAv1wHlUvbDzkvhGxQKRXMXX/bEtaJzSlhtSPkha3XDSmmug86PcekCwS0dJU2R1QwW4HAT8dL8NzkeHJi7IC2jQiXRx3oK6gdIQ+0zXp8vKcAz23Kwcd7CqxWw7U3WZyQD1maCugY940XpvQ1+1piTAhC/C0nigNAeZ2WthvthCIvhMtjuAnxid2xtCEg5MHKrblYs0eDtvmXK7aewuxRMUibaFkF1YAhWXzehiNQwDgniiVZnJAPrFt/KgVwU98u5l9XKnD7oK74mEGpl7Yb7YMiL4TLo1Iq8Owk/hvW8i2nqHLEBVi5NRerdxs7LkBLpcjq3Rqs3JrLfC57k8UJecC69ffPcdfxOqvj49SCvidhGoq8EG6BqfYB7TFUjtiSH0FIA22zHmv2aCzarNmjwaJb+sHLg23tZk/SLyEPBnUPYrJ7dLTpXlhtoUivY6DICyF5dHoOGfnl2JR9ARn55TZFR6hyxD34LKOwQ8SlPbY0VDQk/U4d1BVJvULJcXExvjhwVjA7ivQ6Boq8EJImPacYSzfnGq1iIgN9sGRynFUh+zB/Nl0FVjtCmojZUFGn5yj64qLs/vMykx3rdUORXvEh54WQLOk5xZi34UgH8biS6gbM23DEqpwDPce2wjmgKceIPmyidoT0EKuholBONCE9dHoOR4qqmGxZrxuK9IoPbRsRkkSn57B0c65J1VvDc9ZIs2cWsPUt+nivhkK5Moall5XSyoaKBie6fQ5DcXUD5m44gvScYhtGSkiFLE0FrjTwy/Vb04iTNILEh5wXQpJkaSosJrxZK81+oeoqk12dVkf6CzJm5+lS8AXZZo+KYU7WteREG1j8/QlyeGUMa/RjbN8uzNcNaQSJDzkvhCQROuwaFcQuQEehXHlicDQs4eelwlOp/ZnPyedEA0BVfRPe3ZnHfE5CWrBGPx4Z1Yv5nPY2lCX4IeeFkCRCh11HMDZntOachLRgcTTqrYyssTqya/fTdqNc4YuSALZFSUgjSFwoYZeQJIYJpaS6wWTIXoGWSYB1QhkeG4ogP0/e/iXqAG8K5coUMZIkWR3ZqvomqhyRKW2VlNtjb5TEoBGUmV+OjIIyAC0l98Nj6TqxF4q8EJJE6LCrSqnAS9MH8No9P+V6CuXKlLBOjOXwjHZAixMdZKERX1tou1HeBPp1/J6D/DztjpJsyy3Bv749hnd/y8e7v+Xhvo8OYOTLOynR207IeSEki9Bh19T4SMwZHSPkEAkpwbprY8XujkqpwMwR0Uy2tN0oTwzVZKaispWMnab5zt1+O9Mg90AOjO3QthEhaVLi1Ojs7dkh5GpLdESn5/DjMcuTxeLvTyAlTk3RFxlSxii3zmpnYMHYPli7v9DslqO1W5iEdOCrJlOgRZLBljmBT+7BnnMTFHkhJEx6TjFGvrwT9318oDXk+q//HcO23BKbzkeVI65NYRmb+qm1ERLDlqO52wsHYEpCJN2AZIjQkgyOOjdBzgshUcQIt1LliOui03P4MquI187WhOzU+Eg8amHL8cPdGtoCkCFiKuGSyq64kPNCSA6h1XUNWFs5QsiHLE0FSmr4bwIzEnuItuVoyzVJOBcxlXBJZVdcyHkhJIdY4VaqHHFdWL+v6DB/m85PWwCuiZhKuKSyKy7kvBCSQ6xwK1WOuC5ir3JpC8A1EVMJl1R2xYWcF0JyiHkjmjemN1PjvqE9g60+N+E8hvYMBt89wJ7vlbYAXBcxlXBJZVc8qFSakBw7T5fy2tgabj18tpK3cZ+ea7EjtVT5cPhsJfjSTez5XoVWfCakhUEJN0tTgUtXGhDeueW7FCIqIua53RlyXghJoW3W4+O9Gl67f9/az6YfP4X/XROxv1fDFsBcExLyQEvOC20ByBuVUiHagkXMc7srtG1ESIrPMgp5V9AAUHrFOqExAxT+d03C/BlbAzDameJoUaVdrxMEIRzkvBCS4mwFm9AYq117qALARWENeNgYGNE267Fmj+WI4Jo9Gmib9ba9AUEQVkHOCyEpeob4CWrXHqoAcE3KahlbAzDatYclIqjnWuwIghAfcl4ISfFAUjRT1cgDSdE2vwdVALgeYm8Hskb69vx52abzE85Fp+eQkV+OTdkXkJFfTmKDMoASdglJ4eWhxOxRMVi923yIfvaoGHh52Od3GyoAMvPLOzR9JORHYkwIgvw8RWueyBrpO1xUCZ2eo8idjEjPKcbSzblGIoSRgT5YMjmOFjIShiIvhORImxiHOaNjOkRglApgzugYpE2ME+R9tuWW4F/fHmtt+njfRwcw8uWd1KNGhmzLLTHruAD2VwM9kBTNqw8EAFcadKSyKyPE6KFGOAaKvBCSJG1iHBbd0g+fZRTibEU9eob44YGkaLsjLgYMk1b74LBh0qLtI/lg6IVliWA/T6TEqW1+Dy8PJW7u2wU7T/NvC1GZvTzg66GmQEu/qpQ4NUXSJAhFXgjJ4uWhxKxRsVg2NR6zRsUK5rjwTVocqMmenODrOwQAlQI025w9qheTHZXZywPqVyVvyHkh3A6Wmx1NWvLBUcKDg7oHCWpHOBcSrJQ35LwQbgfrZLQtt0TkkRBC4CjhwS8OnBXUjnAuzhCs1Ok57PuzDK/9chqv/XIG+/LKKMJrI5TzQrgdrJPRN4fO49+TSPNF6hiEBy1F04QQHhRbQJFwLI7uV5WeU4zF358wSix/97c8BPp64OU7BlKOnZWIGnnZvXs3Jk+ejKioKCgUCmzcuJH3mF27dmHIkCHw9vZG7969sW7dOjGHSLghiTEhCPbz5LWrbWxGZkG5A0ZE2INKqcCUBMsT/5SESLudULEFFAnHYhCsNOe4AMIJVqbnFGPuhiMmK+KqrzZjLlU2WY2ozktdXR0SEhLw3nvvMdlrNBpMmjQJN998M7Kzs/H444/jkUcewS+//CLmMAkJIqZolEqpQGJ0MJPtvj/LBHtfQhx0eg4/HrM88f94rNjua8gRAoqE4wkysZAJ9PMUrOJQp+fw/I8nee0Wf3+CtpCsQNRto1tvvRW33nors/2qVasQExOD119/HQDQv39/7N27F2+++SYmTJgg1jAJieEI0ajaxmYmu2Pnqdme1LEmAduezr6OElAkHIM5uQQAqLagGWQtWZoKlNTwt6Woqm9CZn45RvQJE+y9XRlJ/coyMjIwfvx4o+cmTJiAjIwMs8c0NjaipqbG6EHIF0eJRjU06QS1I5yHI6tGLAkozh4lnIAiIS6W5BIAYeUSrLnuWtS+CRYk5byUlJQgIiLC6LmIiAjU1NTg6tWrJo9ZuXIlAgMDWx/du3d3xFAJEeDTXwGEm1C6BfsLakc4D0dXjaRNjMPbdw9CZx9V63N6DvjpeDHlLcgER8olWHfdUXEAK5JyXmwhLS0N1dXVrY9z5845e0iEjThSNOr6yABB7QjnYagaMYcCwlQbGUjPKcY/vsrGlQbjqBxJysuHkhq2aAirnSVYCwQA2LWt6W5IynlRq9UoLS01eq60tBQBAQHw9fU1eYy3tzcCAgKMHoQ8cWT4v0uAt6B2hPNgqTYSqmqE1Jldg4pa/hwUa+wsoVIqsGJaPK+dv7eKGsNagaScl6SkJOzYscPouW3btiEpKclJIyIciSPD/+pA086wrXaE80jPKbaYRPvo6BjBEr1Jndk1CPJli4Sw2vExIT4Sfl4qizaeKkndjiWPqH+t2tpaZGdnIzs7G0BLKXR2djaKiooAtGz5PPjgg632c+fORUFBAZ566imcPn0a77//Pr755hs88cQTYg6TkAiJMSEmyxYNCBn+59tqgIDvRYiHTs9h8fcnLNp8fei8YJEQUmd2DaquslUTsdrxkaWpQL3WcvJ/lQD9t9wJUZ2XQ4cOYfDgwRg8eDAAYOHChRg8eDCee+45AEBxcXGrIwMAMTEx2LJlC7Zt24aEhAS8/vrr+Oijj6hM2k3YlltiUsTJAAfhwv8GgSpzZ1II+F6EeGTml1u8ZoBrJahCwBr125R9kbaOJEywn5egdnxQHyXhEVXnZcyYMeA48z9gU+q5Y8aMwdGjR0UcFSFFDLkElgj280RKnFqw90yNj8QH9w8RXVOGEI/9+WylpfvzywTRz0iMCUGIvycq6iw7TOV1Wrt1ZQjxqKzXCmrHhzP6KLk61NuIkAQsuQSVf4VVhbwhpMZHIiVOjSxNBS5daUB455atIoq4yIOLVaYlFGy140OlVGDKwCisy+Bvvlgs0HsSwhPizxZRYbXjg6X/FgDsPF1KDi8jlCFESAJnhlVVSgWSeoVi6qCuSOoVSo6LjIgKZkuoZrVjoZlxO+hIEakzSxVHJ+yrlAr8+9b+vHYf79VA26wX5D1dHXJeCElAYVXCFjwYHc3kWOEk10tr2CIqrHaE4+ErDgCET9gvZVh46Tngs4xCwd7TlSHnhZAEhrCqpQRaqv4h2qLTc/j6IL8oZZCfJ4YLGIr392Yrn2W1IxwPX3EAIHzC/tmKekHt3B1yXghJYKj+AToKZAvdnp5wDVgb3s1MjhH0url9UFdB7QjHwlIcECRwcQAA9AzxE9TO3SHnhZAMqfGReO/eIQhulySnDvQRrD29JXR6Dhn55diUfQEZ+eVU6ipxWPOfosOEvRl4MIqJHTlXJej7EsLAUhwghubKvcN6Cmrn7lC1ESEZ0nOKsXxLLirqrpUnhvh74tlJ/UV3XNJziqlkWmY4K0+qrI5NMn59RiH+Oa4PRQslRkk1Wy4Sqx0rR86yJXAfOVspSFm/q0ORF0ISpOcUY96GIx1WRJV1TZj/xVFRm92Ze29qtCdtnJUnxeoMkWKqNCmrZdNuYbVjJaOATZPo8wOFgr6vq0LOC+F0+JrdAeI1u3PmexP2YciTMvfNCKnI3JbEmBDmnjekmCo9qq6yOSWsduywXYe//1lG8w0D5LwQTodvD5qDeM3unPnehDxRKRV4cHgPJtsQgeTlCeFgdWWF3uxjFZ+ra9TRfMMAOS+E03GmQB31HJEvfFUjCogXNevMGHk5XVIj+HsT9lFYVstklySgNhAADI8N5e0sbYDmG37IeSGcjjMF6sL8vQW1IxyHM6Nm5yrZkjlZ7QjHoG3W46cTpbx2QmsDAS0Ru0dHxTLZ0nzDDzkvhNNxqkCds2LIhN04M2pGmh3yZO0+DZPd2L5dRKkSu5F1DqP5hhdyXgin40yBurJatrJXVjvCcTgzYvdAUjT4LkelosWOkA7bcvmjLgBQVCFOxIzmG+Eg54WQBKnxkfjg/iFQBxrfaMQWqKOeSvLFmRE7Lw8lZo+KsWgze1QMvDxoipUWrPlP4lT70HwjHCRSR0iG1PhIpMSpkaWpwKUrDQjv3HLjEVPky3ADLKluMDldKdDiQFFPJelhiNjN23AEChjfbhzRUiJtYku0cM0eDdrmBCsVLY6L4XVCOtzSX41DZ6uY7MSA5hvhoGUBIRl0es6hjgtAPZXkjrMidgbSJsbh9PJb8e+J/XBLXARuHxSF9TMT8VRqf1Hfl7CNh0fG8KaTKP6yEwOab4RDwXGcS6nh1NTUIDAwENXV1QgICHD2cAhG0nOK8fyPJ40a7akDvPH8lOsdIs9P7QHkjTMcXwN07ciLlVtzsXq3+cTdOaPFj5rRNWMaa+7f5LwQTic9pxhzNxwx+/oqB6ygAefeAAl5Ymgt0X4SNVw1joj+ENazcmuu07f7aL7pCDkv5LzIBp2ew9AXtqGqvsmsTZCfJw7/J8Xtf9iEaZx1E9DpOYx8eadZrRlD/sLep8fStStBtM16fJZRiLMV9egZ4ocHkqIpwdrJWHP/poRdwqlk5pdbdFyAlgZ3mfnl1GmV6IAzw+/WiOSxSsMTjsPLQ4lZjKJxhPQgN5NwKqydVlntCPfB2d3AqbWEfNHpOWTkl2NT9gVk5JdTI0QZQpEXwsmQxC1hPXzdwA19jVLi1KJt2ZBmhzyhZFnXgCIvhFNhDadT2J1oixS6gRs0O/jYebpEtDEQ1mEuWlfsoGgdIRzkvBBOZXhsKPx5Oq36e6swPNZxzguFlKXP9lw2h0DMLRuVUoFnJvLruXy0txDaZr1o4yDYsBStA1ocXrG6kBPCQ9tGhNPx9FACWp3511WO87EppCx9dHoOXx06x2Qr9pZNiYXojwGOA9bvL8Ts0ZQc6kz4onUAJVjLCYq8EE4lS1PBVG0kZvjfgLMTQAk2MvPLUddo3tk1EOLvJbrM+sFCtuuS1Y4Qj5JqtmaLrHaEcyHnhXAqUplQ+BJAAQopSwXWyrNhMcGi66v48Wx5WmtHiEdFnVZQO6GgbWrboG0jwqlIZUIhzQ45weaQ9OrSWeRxAHcM7oaN2ReZ7AjnEtLJW1A7IWhpi5KLkpprc486wAfPT6Ftaj4o8kI4FalMKKTZIR+kVKGW3CeMN6ri56VCMgksOh11AFv+E6udvRjaorR1XACgpKYBc2mbmhdyXginIpUJhTQ75MON0SFQ8ARfFIoWO7FRKRV4464EizYPDO9B7QEkQGJMCJOjKXaeFNCyVbT4+xMWbdK+P0FbSBYg54VwKixaGZGBPqJPKIZxmLvFKBw0DoKfw2crwdeRjeNa7BxBanwk5oyOMfv6h7s1tIqWADo9h6sWqhoB4KpW5xCHIbOAvy1KZX0TMgvKRR+LXCHnhXAqKqUCSybHQYGOmQyG55ZMjhN95WoYh+F9248DDhoHwY9UkrwN6PQcfjxm2TmhZG/ns35/oVmNFwPcX3Zisz+fLemc1c4dIeeFcDqp8ZH44P4hULeLwKgDffDB/UMclrgmlXEQlpFKkrcBKaj9EvxkadiiGKx29nChks2xZrVzR6jaiJAEqfGRSIlTI0tTgUtXGhDeuWWLxtGRjrbjKKlpQEVtI0L8vRDo6wWdnqPIiwQI8vMS1M5eKNlbHpyrqGeyq+fZWhKCqCC23DlWO3eEnBdCMqiUCkmUIauUClRf1eKV9NOktCtBKuoaBbWzlzB/tko4VjtCeHR6DkWVbM7LwG6BIo8GGNGrC97fVcBkR5iGto0Ioh2ktCttqq5aTnS01s5uqDG65MnSVKBey9ZfamQf8R2G4b1CEeTnadEmyM8TwyWwmJMqDnFe3nvvPURHR8PHxwfDhg1DVlaWWdt169ZBoVAYPXx8KHRGOAZS2pU+CkYvgNXOXspq2SI8rHaE8LBu2fl7OaYJrEqpwEvTB1i0eWn6ANqmtoDozsvXX3+NhQsXYsmSJThy5AgSEhIwYcIEXLp0yewxAQEBKC4ubn2cPXtW7GESBABKvpQDUhKpA0gjSA6w/u0fHR3rMIchNT4Sq+4fAnWA8XaiOsAbq6hAgBfRc17eeOMNzJ49GzNnzgQArFq1Clu2bMEnn3yCxYsXmzxGoVBArVaLPTRCIuj0nNMTdQ1Q8qX0GR7bEnK3pJMR7OfpkBU0AAztGQylArAUjFMqWuwI52DQcSqpbjBbLh3s54kFY/s4dFxSKVSQI6JGXrRaLQ4fPozx48dfe0OlEuPHj0dGRobZ42pra9GzZ090794dU6dOxcmTJ83aNjY2oqamxuhByIf0nGKMeGknZqzJxGNfZWPGmkyMeGmn0/JKaBUtfVhC7isdGHI/fLbSouMCtDg2jhLNIzpi0HGy9DU58pppi6FQ4baBUQCAn45fpAaNDIjqvJSVlUGn0yEiIsLo+YiICJSUlJg8pm/fvvjkk0+wadMmbNiwAXq9HsnJyTh//rxJ+5UrVyIwMLD10b17d8E/ByEOUuztYVhFW4JW0c7nWsjd2ImMDPRxeMidonWEvaTnFGPky8aLuJEvO28RJwckVyqdlJSEpKSk1v8nJyejf//+WL16NZYvX97BPi0tDQsXLmz9f01NDTkwMoC1t0dKnNqhqyFrVtFSKOt2Z6QScqdonfRhmW8WO2G+Aa5VN7afdgzVjSSQaRpRnZewsDCoVCqUlpYaPV9aWsqc0+Lp6YnBgwcjLy/P5Ove3t7w9ib9BLlhTW+PEb0d15GXVtHyQgraQIZ8CkuJ3gBQ6SDdGaIjmfn8801VfRMy88sxwoEdwPmqGxVoqW50hlMldUTdNvLy8sLQoUOxY8eO1uf0ej127NhhFF2xhE6nw4kTJxAZSZ6nK5GRzybBzWonFLSKlg86PYeM/HJsyr7g1BwBlVKBZyfF8dot33KK8hicREYBW48gVjuhoOpG2xF922jhwoV46KGHcMMNNyAxMRH//e9/UVdX11p99OCDD6Jr165YuXIlAGDZsmUYPnw4evfujaqqKrz66qs4e/YsHnnkEbGHSjgU1kncsZM9X1WCAi29jqi7tHNJzynG0s25klFADvbnb0VguAk5O1LknkhTSZAivbYjus7L3Xffjddeew3PPfccBg0ahOzsbKSnp7cm8RYVFaG4+FpSUmVlJWbPno3+/ftj4sSJqKmpwf79+xEXx7+yIeTDsGi2CZzVTiiou7T0kaICMt2EpI3UtIEMUKTXdhySsLtgwQIsWLDA5Gu7du0y+v+bb76JN9980wGjIpyJUsV282e1ExJDd+n2K/tgf0+8MDWekueciFRzBOgmJG2kpg1kgCK9tiO5aiN3p7ahGU98fRRFlVfRI9gXb949GJ18XO9rkrqkemp8JPR64D+bclBRpwUAVNQ1YfmWU1AqFZJyYHR6Dnv/uIwP9xSgpqEJCd2C8O9JcfD1Ujl7aIJjTY6AI1fRcrsJaZv1WLtPg19PluBKQzP6RnbGXUO7I7l3mEtGFQ3aQHM3HDFr4wydF0Okd96GI1DAeJNcipHe2oZmPPbVERy/UANvDwXuvbEHHhndC14ejm+TqOA4zqUyyGpqahAYGIjq6moEBAQ4ezjM1DY0Y/iL21BronlYTJgfti8cI5kLWAgy8ssxY00mr92Xs4c7JUfAXPmi4RuQSvliek4x/vlVNrTNHa+b8f3D8dFDNzphVOKxbPNJfLKvkNfurXsGYeqgruIPqA2GawYwfROSyjWzYksu1uzRmHzNz0uFN+5KkMQ4xWDr8WKjBQkgjW7xUsvhMsXkd/bgxAXTIrBzRscgbaL9qR3W3L/JeZEAt739O3Iu1lq0USqA9++TxuQnBDo9h5Ev7+Rdqe59eqzDnTbD2Myt8J05trYYRP4sMaBrADb/Y5SDRiQuOj2HG1dsN7rxmMOZTm/7m1CovxeWT43HxIHO/+3O/vQgtuWa7ytn4N17BuE2Bzt/YmPquwn5ayt44l/qts5ESm1S2nPjC9twudby704IB8aa+7fjYz2EEQOf/4XXcQFahNGcpTorBlJOjJVD+aJOz+G5jTm8dicu1GDmJ+ZbcciJLE0Fk+MS4u/ptO2Z1PhIPDupP0L8PVufK6/TYvmWXKf/dn/KvsDkuADAgq+ysWJLrsgjchzmkrwr65ow/4ujTv9ugGuaRVMHdUVSr1DJOC4zP8nkdVwAYPVujckIsFiQ8+JEZq49gJqGZquOWfzdcZfRijAkxqoDjZMY1YE+Tg2xy6FyJDO/HJcYJhQA+O2PCjyy/qDIIxIf1r/37YO6Om3iT88pxvwvjqKizjgx1JmVUECLs/uPr7KtOmbNHg1WbpW/A8OX5A20JHm7yrwqJD9lX8Bvf7BrbX2WUSjeYNrhepmgMuGqVoffzlgviFR1tdnhqrNiIhWJ97aEdWJTbNZcrhN5JKZJzynG4u8sS523Z/upS9h87CImJzg/PG4rrJU64+Oc05FeqpVQAHDnqv02KSat3q3Bolv6OSUhUyikmuQtdXR6Dv/67rhVx5ytqBdpNB2R7xUpc160Y0Xz+FdHBRyJ85FcuJRxlv8086zDV2uG8HfVVctS56b49w8nZL26NFT0mLs6FGhJcnTWlpFUtxs3H7uII0VVNh//zPfW3cCkhhwiqVIkM78cDU3WbQP1DPETaTQdIefFSRwqrLT52Mu1WjyyPkvA0RBtKWPsQVNRp3XojcjQXM5W96OmoVnWMuNSzpMCpHmT1Ok5PPXtMbvOsfnYRVk7vSF+/OrH1ti5C6/9etrqYx5IihZ+IGYg58UJpOcU41TJFbvOsf3UZVzV6gQaEdEWa4TEHHkjevyrI7zN5fjYnlsi0GicgyFPKiJAWnlSgDSF6jILynHVytVzexp1HDILHNtjTEhOM861rHbuwMqtuTh6rtqqY+aMjnHo9iI5Lw6GpTU7K3M+OyTIeQhjEmNCjKpFLOGoG9HW4xex+bj9jsfnB4pkvYq+hvFnkILiA9+2FgAE+Tm2EkqoxqYbMs8Kch5nsDfvMpNdUYVzctikhrZZb1YHyBxC6bxYAzkvDubdnXl2r54NZBY4r5OuK6NSKvDC1HheO0flV+j0HP6zib8smoWGZj3e3vGHIOdyBoacn5Ia46290ppGp1bzANe2tSz9Iqvqm7DNodEvYeaH3X9eluVco9NzOGjHFr078llGIVi/apUCOLUs1eGOC0DOi0PR6Tms3WedR2sJrY6TdQ6DlJk4MApzRseYfV0Bx+VXtOibCOPwAsBHuwtkeyOSeslrSpwaQX7mo3aGiiNHjVFTJkw0oa5RJ8u5JktTgXrG7fVB3YNFHo08+PbwOWbb9+4b4rQ2JOS8OJAsTYVNVSKWKKmRd4a8Ts8hI78cm7IvICNfWpGktIlxeP/eIQjxN07ki3RwfoXQ33Fdk162NyIpVvO0JUtTYTGy6sgxrtyaiy0nhIvyyLEax5oxRwX5ijgSeaBt1uNUCb9oKgAM7Brg1Bwz0nlxICu2nGSyC/TxgI+XEqU1/CJkl2qu2jsspyGHfh4TB0ZiQrxzdWj2/cm2Z++hBFgFLuV4I2LdbnHmZ5NKxZG2WY8PGfMWJg9UM+VTybEjNuuYnanK3B5ntglYv599Z2BAtyDxBsIARV4cxFWtDjkX2bLZU+IikBTDJkJ3ivGcUsOcXLezlUhNoVIqkBgTgvDOPrh0pWXV7KgIkU7PYVtuKZPtuH7hzOctLHOcmJQQ6PQcvjl0nsnWmTdZVoFDVjtbWb+/ECw5zHcM6Yr/3jPE4laXgUpGCQEpYUii5uOFqfHO15dCy7w48uWdmLEmE499lY0ZazIx8uWdDpsPrckPig3zF3Ek/JDz4iCsEaUb0acL6pvY9mmPnq+ycUTOQw65C21x5oSSpalANWMLiQeTYhDky1Yl9f6uPMn8fVnILChHbSP/38HpK2jGP6nY20YHC9mqjK40NEGlVODFaQN4bZdvOSWrawZoWXhMSbAcxU2JC5dEY0YpLOiq6tlajgCO1XQxBTkvDsIaUTp1gA9ujGabgIvK6x3aDEsI5JC7YMDZEwrr9kKQnyeG9wrFS3fw34QAoLFZj71/sG1HSQHWkt9hMc5VaGYVOFyfUSiqI8Ba0ejn1ZI5EOzPL9Amld+kNej0HH48Zvk3mnOhxulOmRQWdOk5xchivE9NjI9wessIcl4cgE7PIe8yWxKUn6cSiTEheCg5msmeg2ObYQmBVPIC+OCbUDiIP6GwboHMTI6BSqlAanwk+kV0ZjpmzZ4Ce4bmYNj+xr26ODeUzfp9VdU3ieYIWHMTumNINwDsvzW5iRzyLZQAaThlzl7QGeY6FjxVCrxz71BRxmEN5Lw4gMyCcjTp2CbfldMHQqVUwMtDibgotpvQd0fYcgGkghSVSE0hhYmPJc8gyM8TC8b2bv2/pwdb5KGoUj55L0mxbDlgrHZikRgTgkDGrTsxKgWtuQn5e6mQ/FeDV9bf2g/ZF5wepbAGuSyUnD1OlrnOwDszBksiP4icFwfwKWNkpEsnL0wd3LX1/3cM7sZ0XG7xFVltHUm9wZ6B4iq2Si5WO2vR6Tk8s5FfnO7FaQOMJpMBXQOZzl9cdVU2N6LhvUJ5k0oNW2fORKVUIKV/BJNtRa3wCbDW3IRevyuh9bppUZXm3zqqqBMvYiQGclkoOXucrE7R/42IlkwlKDkvIqPTc9h5+hKT7YhexqtGaxKirClxczZtG+y1RwoN9gwcLmKbpFntrIVVjbl9vsKE/mqm8zfpIZueNSqlAi9Nt5zP89L0AU6/ZgBgRG82B4rFWbAWW29CKqUC0waxJa06O0phDXJZKDl7nGH+bNVv4/qxOeaOgJwXkbFmy6j9levloUR0KJtwkhwlsAN8O8oMBfl5Or3BnoFSxrD+CSsbmLGg03N4b1cek237m0lVI7sQolC9bxxBanwkVt0/BOoA44lWHeCNVRK5ZgBAHcj2m2W1swbWEviUuI4Orqnn7HkPKSD1TuQGnD3Og4WMCzDnrw1aIZE6kbHm5tA1uONkNqhbEArL+bcl/Dzl44em5xRj7oYjJl+rFKjvkxA0MHbjPVV6BTo9J+jE8vaOP5i3AtuHkq0LLctj28hAanwkUuKcKxrIh2EVbWn7RoxVtE7P4cusIl47dYC3yfdOjAmBOsC7Q9+o9nx1sAgLxvaW1N/cEoZO5O0FMdUSE8R01jh1eg4fMibvl4mw1Wkr5LyIjDU5Bckmkg3jogKxkafUz2AnB1i6ai/+/gRS4tROnxwHdA3C3jx+57Pprx5TSQLlW+j0HN77jS3q4u+l6nAjSowJQbCfByrr+XVRnJ3gagsqpUKwv7UYGFbR8zYcMekaitUXK0tTwZQEPCOxh8n3VikVmJHYA29u/9Pi8YYkdSl/B+1JiVOjs4/nX4tJDkmxYRjey7ll9aZwhnOemV/O3P/J2blBbZHPcl2m1DSwif54KmEy2bBLANvFwmrnbDLzy3nzOKrqm5Apge2Mkb3Zb+xC5gHs/7OMWeZ/9HVdOkxsKqUCKxhEx6SQ4GotUu6F1RbDKrq9uquYfbFKqtkSx3uE+Jl9LZpRNVVOJdMGkcn7PjqAd3/Lw7u/5eNf3x5zcHdvdgzO+W1/Cef9dPyiqNd6RkEZk52phZIzociLyJyvZJtQknqFmfSu1YxOCauds2H9oWQUlGFEH+dGBYb3CkUnbw8mZVchVyTfHWUvfb9/eE+Tz08cGIU556uwerf5RG6pJLiyIodeWG1x9Cr6m4Ns3YAr6swvqKwpmX5mkvNzRfgwiEy2v+0bRCalkl/XnvScYjz/40mjLTx1gDeen3K9CONl+w5NLZScCUVeRESn57CPYdsBAG66rovJ51l6cygVcuo7wnrxO/9HolIq8ModA3nthM5fOHquisnOU6XA8FjzkRNDV+zgdiXGUktwZcHZSse24qi+WNpmPTIYS5hDLPRVcqWSaSmo1tqCISewfe5RSU0j5opwrWvK2PrjmVsoOQuKvIjI3j8vo5nxh2GuLJpv/xwA9Bzw9y+OYtVfCqtSJqlXKN5lyOeQyn76xIGRSMkOx7Zc8+XuXTp7CbYi0TbrUVTOVs0xJSGK932l0BXbXvhuQgq03ISkkCfVnpYVdK5RLoo6wAfPTxE2WrR2H7tUgqUoraFk+pN9hbznkXrJtDWqtVKZbxydE6ht1mPrCf7Gr0F+nhYXSs6AIi8i8swPli9CA1FBPhb7RKTGR+K9e4dAwXOtpn1/QnKriPYMj+UXGwuW0A9Fp+eQc6HGos3x8zVYsYW98aYlPssoZK7/WTmdPyoEXNtDnzqoK5IkmKTIh7Ol023l2gq6XbSopkHwFfSvjPkbnkrwRglZS6allLxpCmer1tqCo3MCWeeb8f3CJTdvkPMiEtpmPS5Usf0oQhnCtIF+nrwt7ivrmyQvOsYiNrZSQrkYrIqlH+3VCKJyXMgYdYmL6mx1YzRtsx4f7ynAc5ty8PGeAtmoMsvxJsSyghZysXGFUWIgzJ8/SpgYE8KkZiyl5E1TOFu11hasyQkUgrMVbPONn7f0NmnIeREJa5olDuzGX+a8L4/tYmW1cybXxMY6VmJILReD9YbIccI0yNTp2RyKQd2CrDrvyq256Pfsz1i+5RQ+zTiL5VtOod+zP2PlVmEiRmLCqv7JaucIMgv4V9BCLjYUjM5+THgnYd5PkLOIi7NVa23DsTmB9QzFCADQ00KFmrOQnjvlIuz+8zKz7X8mXc9rc5Gxfw6rnbORg9gYYN2qjHUVY4naBrbJhNUOaHFcTFUd6Tm0Pp820XS7BkkgnxzvVljFKTPyyzHCipJ8U+j0HArK6phsB3UL5rXJ0lQwOV5SyhUxRdt8QQWM5RilpK7bFkfmBOr0HLbm8G83KmBdqxpHQZEXEdDpORwpqmKyvS7CH75eKl67rkFsUuKsdlJADrkYiTEh6OzD5uMLsTppaGYTi2K10zbrsWaP5WTOD3cLs+UlFqyqnlJS/2RXLrZ/22h/XhlzCxIW+QE5btOZw6C3o25XsakWUW/HHm6MDuHNbTTY2cvbO/5gEqebNCDC6i1qRyC9EbkAWZoKXGFcGf/0j9FMdsmMq7PCcrYVGMGGSqnAiinxTLb3DrO/lLCzj+VcAwM3RrOtvD7LKARfWgUH4JnvjzOdzxnIMXeBVblYCIXj746w6QLxldYbkOPf2xKp8ZHY+/RYfDl7ON66ZxC+nD0ce58eKznHBQAOn63kzW0EgEXfHLXrfXR6Dqt+Z2sJEB0mzFaj0JDzIgIsEt0AcHPfLswe7fDYUAT58t/Ytp4okfQqWo504dHZMZDNqM9iDp2ew/ZT/GWLCgAPJUcznZN1K2vz8WLJVqrJMXdheC/+qjqhFI5PnK9isusR7MsU3ZTj35sPOUR5AfZo1ubj9s3zmQXlaGQ+Xpp/K3JeROAyo/OSZEU5sEqpwPj+4bx2HIRJHCWu4agw+rs7/0T1Vf6I3aSBkcxOL+tWVmOzXrKVaobcBXOuFQfp5S6wVNUJoXCs03PQMHZ5ju/K1v/M2R2OhUYuLSUA66JZ6/eza/u0x5qGwVLNayLnRQRYe2bkXqy26rys5WpCJI4S13BEGF2n57CWQRgMAFLiIpjPa02inTUTGsHPtao64yooIRWO9/9ZBtb189+Gdmc+r7lcEV9PFe4Y0hVj+7Ffg84kPacYI17agRlrMvHYV9mYsSYTI17aIVlF5sSYEHgzLkyyNJV2vBObA+fjqZSM5lZ7HOK8vPfee4iOjoaPjw+GDRuGrKwsi/b/+9//0K9fP/j4+GDAgAHYunWrI4YpCDo9h+M8omYG6pusC/t1D2ZLxmW1I9hwRBg9S1OBqqtsWh3WOEleHkoM7h7AZMuxbLY7AYPCrjkMCrtSXFGnxkdi3+JxRvkW+xaPEyzfgrUPloeSPW/OgCFX5LaBka3Xfn2TDt8euSCLMntHy+wLgUqpQCxjc8yrTewVh+1hzbWaO7qXZCNsojsvX3/9NRYuXIglS5bgyJEjSEhIwIQJE3Dpkmm59f3792PGjBmYNWsWjh49imnTpmHatGnIyckRe6iCkKWpYN5LvDGav2yxLf3UbDchVjuCDUeE0S9UskXLgnytFwebOCCK7dw8ORrOQq4KuwbEzLdgqRYBgOujAm1631fST+Gn48Ud1umGMnupOjCsMvtSdHhHMzakvT7S9nm+mmGh5Oelwj/G9bH5PcRGdOfljTfewOzZszFz5kzExcVh1apV8PPzwyeffGLS/q233kJqaiqefPJJ9O/fH8uXL8eQIUPw7rvvmrRvbGxETU2N0cOZsLalB4CHkmOsOndFvflusG3ZdcZ8Hx7CNsQuufzlJNtWY48QtqTLtoQxRmpY7RyNK5XuCk3NVbY54baBbA5sW1jK7NfskWaZvaNl9oVE7N+rTs/hmY38rWte+9tAyUZdAJGdF61Wi8OHD2P8+PHX3lCpxPjx45GRkWHymIyMDCN7AJgwYYJZ+5UrVyIwMLD10b07+76uGLAq3CbFBltdO29Nu3oprijkTtuSyzfvHoRnJ/XHUxP6ItDXy+6/9znGyEsAQ8VZeyw14rPFztEUMgqwSb10V+j2DNpmPTIZ8h4UCvbqtLawlNnrBVKWFhpHy+wLSVhnRkVpRrv2sKg/A0CgH3/bGmciqsJuWVkZdDodIiKMk7siIiJw+vRpk8eUlJSYtC8pMb0yTUtLw8KFC1v/X1NT4zQHRqfnsOUE2z7qXTdarwliaFdfUWd5tWVoVy/VLHE5o1IqUH1Vi1fSTxttZUQG+mDJZNs6Bev0HPIusd2gYxj3w9tiyNmxtPUi1dJXnZ7Dl1lFvHZSHb+BlVtzsWaPxsgZWLH1FGaPirFZ3ZjVaRgeHWKTyBhr4v/uP8swa1Ss1ecXFxnKMv+F2IuN/flsDtv+/DK71Z/FRPbVRt7e3ggICDB6OIvM/HJcZUzCteXCUykVmJrAdnO0ZvuKYCc9pxjzNhzp4AiUVDdgno1JgPvzytDMGLl5xoYbnSFnx9I0PSUhUpIh4ixNRYeES1Pcc2MPSY4fuNaeof1XbG/eyO4/2W5CfdSdbTo/a5l9lqZCcpFe1oWbFBd4hsUGH5V1tilKX6hkuzew2jkLUZ2XsLAwqFQqlJYaC2+VlpZCrTbddl2tVltlLyVYQ5CdvFU2rxIjGEPjpQydkJ2FnHQX2mKoejE1WsNztlS9fM+okKoO8GZqJWGK1PhIPDrafI7V6t0aSVZfsOaxRIdJr3EcIF7eSEsLErZSWVvbVjyQFM0Ul7japJOcRtCQHmzFEKx2jkSlVODZSfyLlH99e9ymuTMqiO0ewmrnLER1Xry8vDB06FDs2LGj9Tm9Xo8dO3YgKSnJ5DFJSUlG9gCwbds2s/ZSgjX0P7JPmM2rxD2MOTWsdo4mPacYI1/eaaS7MPLlnZK8cbZHrKqXgrJaJruBVnaSbotOz+HrQ5adpDQJVl/IXaperLwR1hYk9jTV8/JQYmBXtkj2Hisa0TqCLw6cFdTO0QQy5LbVa3V4e8efVp/bS8W2ABrRq4vV53Ykom8bLVy4EGvWrMH69etx6tQpzJs3D3V1dZg5cyYA4MEHH0RaWlqr/WOPPYb09HS8/vrrOH36NJ5//nkcOnQICxYsEHuodqHTc9j1B1uVz9Aetu/Ns5S4AcC5Cun1OBJjy8WRiFH1otNzyL/M5rxYW1rfFpYkvcr6JsmtoBNjQphk9qWa78KaN2KtsCTrNTa2H3sLElN0Yuy1deK8dYKbYiPW391RsOalfLQn36oFh07P4cM9/D2NAn09BGldISaiOy933303XnvtNTz33HMYNGgQsrOzkZ6e3pqUW1RUhOLiazet5ORkfPHFF/jwww+RkJCAb7/9Fhs3bkR8PFtzPGeRWVCOBsZ8l7BOtmdxt1frNEdxdYOkVtFibbk4EjGiAFmaCtQ28l83Lf2MrCutbwureq4cVXalmenSAuuWjbVbO6wVWI+M6mXVedvjx7hNyWrnKLoHs/09We0czcUqtnyTOq3eqkjvY18dZdIG+r8RMZLNITPgkITdBQsW4OzZs2hsbMSBAwcwbNiw1td27dqFdevWGdnfeeedOHPmDBobG5GTk4OJEyc6Yph2sd+KbRp1oO0KuKzVLE16SEq0S+5CY4A1iXRs2hsA+wr6ZjtX0Kxy4AWXr9jxHsKTpalgihhJ9bp5ICkafPcAa7d2HFmBlRjDtvpmtXMU/RiTlFntHE2UFSrprHOItlmPn46zRbejbahqdDSyrzaSCgcL2SZPH0+lXRNKVytWClIS7XIFobGWRLr+vHbLt7BHkAoZm+rNtnMFzSoHnlEgrQRquV83Xh5KzB5lOWLGAXj9V9PSEaZwZAXW/cPZJB1Y7RwFq6Anq52jSWb8vQJAWCe2aPy6veyNHKWaQ9YWcl4EQKfnkH2uisl2zHVd7JpQEmNCEMwo4x4iIZEhuSdeGgj2558oWCNIrCtodYC33Svo4b1C0YmhsWdlfbOkohiucN08ldof/jzbKqt3a7D1+EWm87HKIPQIsb/HGeu8xmrnKOR+3QzvFcp7zRhoZqxU+/ow/1wDAJ4qhWRzyNpCzosAZBaUQ6tjW63amvlvQKVUYGy/cCbb0yXS2QJwRHNDRyBkJKBlBc1vNyPR/hW0SqnAXTd0Y7KVUhTDFa6bLE0F6hjyDP6zKYcp6rXnD7bKnssM0Rk+5Br5kvt1o1IqEBfJtqX1PUNzTp2eQ1EFo9Mb7Cf5fBeAnBdB2JvHNpl4qxSCtBf3Z1hBA+yS847AEc0NHYGQKzoWxwUAeoQKs/88rn8EvxHYw9COwBWuG9Ybu0EZ2xI6PYetjH2wckvs7/Mm1whG2+umPXK5blg3by8wJPdmaSrQxLjAvutG57bYYYWcFwE4fo6tTDAmzF+QH4tYFQxiI3ZzQ0cwtGcwbwKmUtFix0dFLdvKmNWOF9bZUDopLwDkf91Yc2Pnc3SyNBXMVY2sHactwRfBAIBgCZeqB5rYYg/y85TFddONMb+RxY61egkAZo6wvarRkYja28hdYC0T7C6QM/FAUjRWbD3FK37FqsbrSFLjI5ESp0aWpgKXrjQgvHNL6FbKK6C2HD5bySQ6dvhsJa/0+D7GiF2IvzC5S2WMcuKsdo5EztcNa08ygN/RsWZ7xh5dIAOGCMbcDUfM2lTWN2FbbomknAGDppSpn2olQ1NCKXDHkG7YmM2fB9WDwXnJPsemxtw/srOdVY2OQx6jlDisnX6FKif08lBi1kh+73jFz6ckVTliQKVUIKlXKKYO6oqkXqGyuAEZECoHQNusx29n2Mrr7SmtbwtrBIBVQ8TRyPW6USkVeGEqm04VX5m9Nd+NPbpAbUmJU1sUClRAWhpNljSlAOmN1xzJvcOYknbXZxYK9lluYIgYSwVyXuwkPacY3x25wGtna1t6c4ztx5+/IHXdFDkiVA7AZxmFTLszAT4egoXkE2NCmBqCrt2nkfzELjcmDozkLZkGgGc2mm/RoNNz+GQfW7nr7FHRgq2g+bR2pKbR5AqaUkCL0zuboVt39dVmZPKIS17Vsm01RguUX+cIyHmxA52ew+LvjjPZPjJSuMkEkG8VgNxhyQFQKvg7vrJ2BB7cI1iwCINKqcDdDMl4VQyTIWE9Y67jrxKsqm8y+7fPLChH9VX+fkZDewTh35Out3p85pDbXCO38VqiWc/mdHx+oNDsazo9hy0n+LeflAr7q2EdCTkvdpCZX44qhskEAMb2E7YrtlyrAOSOpSoGA3oOmP/FUbO9mnR6jnnVN7oPu1gVCzrGyZC1QzrBDuvf1Jwda+uGpF7CXjNym2vkNl7LsC1cfs0tNRux259XhqsMSd6p10fIJt8FIOfFLqyZ4IX28oWseiGsIzU+Eu/dO4T3729uXz2zoBxXm/grQezpCGz5rELaOQadnkNGfjk2ZV9ARr60VIBZaWYsVTVv55xyMblpprC08ZDSeC3Bl/RvoFkP7DcTzX1nJ1vnaS8PafWn4oOcF7tgn+CF9vKtqXohhCfY38vi39/SvjrrCnpQ9yDBV0KskyGrnSNIzynGyJd3YsaaTDz2VTZmrMnEyJd3Sr4LeXvyGLuH/2zmc6mUbNcCaysIVviijRyAKQmRkkmgVikVmJJgufJJSuO1xPDYUHgyjvM7E2J1Oj2HI0VVTMfXa9l2EaQCOS92wHpfCfEXXgeBVeCM1Y6wDvv21dlWxiN6C+9ADI8NtVg5ArTodgghpigEhpLX9gmYJdUNmLfhiKwcGJZoGwCcrbgKbTvJd52ew/r9hbzHBvl6YLgIjmdqfCQeHW0+4fjD3RrJfBc6PYcfj1key4/HimURvVMpFegfxaa0W9vY0fl4d2cemhk/543R0vjNs0LOi43o9BzWMkwmAPDC1HjBvXyHC5wRRoQx9DgyZ8e6MhZ6BQ20TIYvTR9g0Wbl9AGSWJVaKnk1PCeHklcDsVZ06v0so9Do/+/uzEPVVX59kpkjYkT57lgcAql8F3zVRoA8qo0M2Bq11+k5vLcrj8lWAWGrYR0BOS82kpnPlvl/24BITBwYJfj7swqXCSVwJhSukLsAAHqObdwHNB23iPgqkYAWFVAxVtBAyyp61f1DTEZg+KIyjsRVSl4NPDPRcqJ3W85WXGvtodNz+HB3PtNx0VY4SNYgp+/ClaqNACAigG2hpG0X2dufV9YhgmeOSQMjZZWsC5DCrs2wJutGh4kj0c8qXCaUwJkQpOcUY+nmXKNJMDLQB0smx0lKnZOFA4yT9OrdBXhs/HWtq+GVW3Oxeje/VsdLDoh+mNLuqK5vwrwNRyQhn+5qNyFfLxWui/DHH6X8QnNtW3tkFpQzNXYExKugkdN34VrVRkBsWCcmu6yzldDpudZ547sj/A0bAUClAN66Z7DN43MW8nK1JET+ZValS3FuQHLLqHel3IUW2CIvjc16ZBa0RF+2Hi/mdVwUAN6/V1zHwbAdYwopbce42k0IAH76x2hem/Z6GxsyzzKdu5O3SrTfu5y+i52n+RtXSmlu5OOBpGgoGG4jDU16vNumskhTxpYgPqBrgCS2ia2FnBcb0Ok57DhdymQrVtWGoQLAUvmiVLqmulruAmBdPophe+wpBkFDDi2VTGIily0AuZXosuDlocQcC4mvAODjqcTOv+YXnZ7Dtly2uWZUnzDRfu8siyWAv72B2Gib9fh4byGv3TMT+0tibmTBy0OJsX27MNmu3dfSKkCn53CmhM15mTSwqz3DcxrkvNjAXav3Q9vMf6Pt5O0hatWGodtu+0klUmLdduVys7SG4b1C4c24R7wv7zIy88tNVgOYQuzQu1y2ANqW6La/zRj+LxUH3RrSJsZhzugYs6vpeq0ec/+KRu7/s4y5WuT+YdHCDbIdKqUCz07qz2u3fItzFyGfZRTySkgAwCWZVWE+MqoXk13V1SZkaSrw7s4/0cCQ7yLHRF0DlPNiJT9lX8Dhs1VMtnfd0E30iVUO3XblcrO0BpVSgbk39cJbO/gFoI6eq8bePHZBQ7FD72GdGCulGO3ExCAI+J9NOUZdmdUyzZUy8FRqf3yVdQ7VDeYd2oXfHENnH7Yp2lOlEC3B20AwQ4WdYRHiLJ2gtonOQthJhcSYEAT5ejJVnP16shhfH2LLdxnbr4vsEnUNyHPUTkKn5/DY19nM9ilxwrYEMIfUu+3Kab/cGv45rg+z1s+aPWzVIv4i5i204hyhVptIzynG8i25Ro5LiL8nnp3UX7aOC/BXtaIFxwUA6rU6lNawSR3ERYqftyCHRUjbRGch7KSCSqnAzBFsXcI/P3AO9YwJ3qwRHSlCzosVvLPjTzAqfMNTCVntxYuJK+YuAC0TSnxUAJMtY8UiZo8UR6ejLWUMpdoAmPO6xMJckndlXZPF3lFyQOjeUbeJIMfQHjksQu4d1lNQOymxYGxv+Hvx37K1OrbJJshPePFUR0LOCyM6PYd3f2MT/AGAHiH+Do+ASFVDxVVzFwAgmrGMkQUPpQL/GHedYOczB+vNZVP2RaddQ66Y5G2MsNe6I/IWWDqqBzv5hph9rkpQOymhUiowmqEzOSszk8VfKIkJOS+MjHv9N+bEOQC4+4buIo6mI1Lv/2JILla3Sy5WSyy52FruGNJNsHP1iejkkMkkMSYEIf78YnTldVqnJVG7YpJ3W4TMCekf2dkheQuGRYilWbCyvgnbcvlLlcWipPqqoHZSo1cXYUQIlYqWSI6coYRdBia/sxuF5dZd7A+PZNufFAJDeL39pGLQUJGKcyCH5GJrSe4dBk+VAk2s+4kWuMFBHcBVSgVuH9QVH+8r5LV1Vv6CHPIr7GF4bCg8lAqrFkTm+H7eCAFGxEZKnBp+XiqLORWLvz+BlDi1U37XFYyl2qx2UiMpNgzv/saWP2eJYD9PWc+7AEVeeHlh80mcuHDFqmNmj4pxWAa33MLrUk8uthaVUoE37xwkyLmskY+3l/GMyeTOyl+QQ36FPaiUCkwdZH+eyoCuAfD1UgkwIjYy88t5k0Gr6puQydg5XWhCGCvkWO2kxvBeoVAJMGUO7h5k/0mcDDkvFtA265lWp20Z3z8c/57kuJuQq4fX5cBtg6IQ6GPfDSQ+yrE3IaknUfOJojl7fEKwcvpAu8/hSIcXYE80FjohmRV1AJszy2onNVRKBUb0tr9h63/vGSLAaJwLOS8W+Cyj0Kpq0c7eSnz00I2ijccUrh5elwt/v9m+/WNHOrwAf/4CB+cmUauUCkxJsLzVKdckbwNeHkok2eF8hfp7OcF5Y/17O+d7kVvbFFtY/cANdh0f28UPnRj1g6QMOS8WsFbIaHKC42WWWcPmhWXyEmWSGzNHxNp8bGcfD0lOpkeLKp323uk5xfjQQh+oR0fHSCKPy17Wzxpm87HLp8Y73HljTTR2lkhd28rG9iggrbYptuLrpcLQnkE2H7/tiTFCDcWpkPNiAWuFjJ697XqRRmKexJgQqBlapn91sEgyeS+uiJeHErNH2Zak/eLt4neQbo+l5owGVu/WYOtxx1erWcrjAlpuQD8eK3aJ69nLQ4lHGMXH2jJrZAwmDnS883ZjdAhvk0CFosXOmQT5daymC/TzlEzxgr18MycZXjYkv/z3bwmydtzaQs6LBR5Iigbr9zy+f7hDcxYMqJQKzEjswWtHeS/i8+9JcUiJs06HISUuHJMTxBcYaw9frpSBp7477nAnwd3yuP4zOQ4DurKJHQLAzdeF4NnbHLvNaODw2UpwPJcDx7XYOQND5WVVfUcZ/WoTz8kVlVKBt2cMtuqY7sE+mHaDcNIOzoacFwuwrqYHdgtweK5LW6LD2Gr/Ke9FfNY8eCPevWcQPBlWRbNHRWPNg865blivhdrGZodXjrhjHtfmf4zCgKjOvHY9Q3yx9v+SHDAi00hZR4UvYsdBWpWX9pIaH4lV9w+BB8MKO8DHA3ueHueAUTkOcl54sNQBVqkA3r4rAT8uGOX4gbXB1ctK5cZtg7ri9PJb8di43vBvF43zVCpw++Ao/PHCrfj3JMdvMxqw5lpwdOWIu17Pm/85GrNGRpt9fVy/cPz+1FjHDcgEUtZRYYkmulLEDmhxYM68cCsmxpuXPrj5ulAcf36CA0flGOSfcuwA0ibGYdEt/bB+fyEOFlbAz0uFOwZ3Q3KfMEnsHxoy7EuqG0yuOhRoUbKVYlKoq6JSKvBESl/8c9x1khTlS4wJgb+3CnWNLA3cHDted76en73tejyd2h+f7CnA90cvoEmvR1JsKJ697XqnbEu3R8o6KiU1bJE4Vju5oFIq8P79Q6Ft1uOTvQX44egFaHV6DI8NwXO3xUviuhEDcl4Y8fJQYvboWMwebXtViVgYMuznbTgCBYybAcu9d5DcMYjySQ2VUoHZI2Pw3x38/bocPX53v569PJSYe3NvzLWz/F4MpKyjUlHL1nCU1U5ueHkoMXdMb8wdI73rRgxo28hFcNXeQYR4/P3mPrw2CjinciQ1PhLj48I7RF44AOPjwul6dhIsOioAUMnYuVxIQvy9BLUjpI2ozktFRQXuu+8+BAQEICgoCLNmzUJtba3FY8aMGQOFQmH0mDt3rpjDdBlS4yOx9+mx+HL2cLx1zyB8OXs49j49liZ6wiQHGfb+OUY7oVm5NRfbci+ZfG1b7iWs3Gq5zJsQB5VSgWcZBBWXbznl8MRYdaCvoHaEtBHVebnvvvtw8uRJbNu2DT/99BN2796NRx99lPe42bNno7i4uPXxyiuviDlMl8LVegcR4iFVqXdtsx5r9pgXqAOANXs00DbrHTQioi3BDJELZyTGuoO6LnEN0ZyXU6dOIT09HR999BGGDRuGkSNH4p133sFXX32FixcvWjzWz88ParW69REQwK6BQBAEG6zrYkcXln6WUQi+Rbuea7EjHI9US9kNuVKW+nW5cq6UuyGa85KRkYGgoCDccMO1Pgzjx4+HUqnEgQMHLB77+eefIywsDPHx8UhLS0N9vXlp+8bGRtTU1Bg93B2dnkNGfjk2ZV9ARn65y+gaEMIS5Mu2989qJxSsbTmsbd9BCIOUS9kNuX/tIzCRlPvncohWbVRSUoLwcGO1UQ8PD4SEhKCkpMTscffeey969uyJqKgoHD9+HE8//TTOnDmD77//3qT9ypUrsXTpUkHHLmfSc4qxdHOukd5BZKAPlkyOox8uYURYJzanhNVOKFjbcljbvoMQBr5SdsC52zOp8ZFIiVNLUqKAEA6rIy+LFy/ukFDb/nH69GmbB/Too49iwoQJGDBgAO677z58+umn+OGHH5Cfn2/SPi0tDdXV1a2Pc+fO2fzecscgjd1eqKmkugHzNhxBeo7j+9QQ0kWqCY4sbTmUihY7wvFYan5oYEpCpFOdBcr9c32sjrwsWrQIDz/8sEWb2NhYqNVqXLpkXC3Q3NyMiooKqNXm1QDbM2xYS9fVvLw89OrVq8Pr3t7e8PZ2vCCS1LAkjc2hZb936eZcpMSp6YdMALi2grakSuqMFbShLcdqC12lZ4+KgZcHKT04i9T4SDw62vx3tHq3BoN7BDsl2qvTcxR1cQOsdl66dOmCLl268NolJSWhqqoKhw8fxtChQwEAO3fuhF6vb3VIWMjOzgYAREbSloclrGlmJ0XRNMLxtBWDM6dk66wEx7SJLSv7NXs0Rsm7SkWL42J4nXAOOj2Hrw+dt2iT9v0Jhy+WaNvcfRAt56V///5ITU3F7NmzsWrVKjQ1NWHBggW45557EBXV0kX3woULGDduHD799FMkJiYiPz8fX3zxBSZOnIjQ0FAcP34cTzzxBEaPHo2BAweKNVSXQKoVAEBL6etnGYU4W1GPniF+eCApmlbNEsGQ4CjFCf+p1P4Y2bsLvj9yHnVaHW6MDsZDyRRxkQKZBeUmOze3pbK+CZkF5RjRO8whYzJsm7d3xA3b5pSw61qI2h7g888/x4IFCzBu3DgolUrccccdePvtt1tfb2pqwpkzZ1qriby8vLB9+3b897//RV1dHbp374477rgD//nPf8QcpkvAmtlfWObYCo2VW3M7rJ5XbD1Fq2cJIcUEx/ScYizZlIPSK9ca/B07V4nuIX50A5IAGYydxjPyHeO80La5+yGq8xISEoIvvvjC7OvR0dHguGuXW/fu3fH777+LOSSXJTEmBOoAb5TUWJblXrdfgwVjezvkB7xya67JPXE9h9bnyYGRBlLqwZSeU4y5G450eL70ihZzNxzBKlpBSwBpqQTRtrn7QfFXF0GlVOCeG3vw2hlCuWJDKqmELej0HBZ+c8yizcJvjpF2kZMZFs3mALDa2YuUt80JcSDnxYVoZpzQWUO+9kAqqYQt7P+zDPVanUWbeq0O+/90bMsCwhilii1y+/WhIpFH0kJhWR2TnTOE8whxIOfFpZBOKJdUUglb+PYIm04Tqx0hDpdq2CIYW06UiB5d1ek5fJnF7yRRXyPXgpwXF0JKodzuwWzCZqx2hGNwdmuJC1VXBbUjxKGiTstvhJZl0vr9haKOJUtTwZvrBwD33NiDknVdCFETdgnHwhrKZbWzh35qtmaarHaE+EhBI6NbsB8Ona1isiOcR0gndmHQg4UVmD06VrSxlDBGgXqE0jXjSlDkxYUoq+VffVhjZw8V9WwrM1Y7Qlyk0lrijiHdBLUjxEEdwJ474uelEnEkQAXjfMZqR8gDcl5ciDDG1RCrnT1IufMsYQyfRgbQopHhiC2kKw2Whc8AwN9LhWQHCZ8RpkmMCUGwnyeT7R2DxXU0Q/zZGoey2hHygJwXV4Lx3pKlqRB3HLjWN8fcBpUClEAnFazRyBATnZ7D8i2neO1e/VsC5S44GZVSgWVT45lsh4msqyLVBqOEuJDz4kKU1bGFRdfsKRB9Fd2282z724zh/87qm0MYIxWNDD4nykAwraAlAWsE9/DZSlHHYVgoWYIWSq4HOS8uBOsWTL1Wh0wHaL2kxkfivXsHI9jfOLysDvShPiMSQiqtJaTiRBFsSOX7MiyULEV5aaHkepDz4kIkxoTAnzE5LqNAfJGv9JxiLN9yChV11/IYQvy98Owk6vAqJQytJfj46mCRqBE7ypOSF1L6vgwNRtsnEkfSQsllIefFhVApFRh9XRdGa3FXIeaqVyrrtJj/heOqVwh+VEoFZiTyt5YQO++F8qTkBct2DdDym3ccxs512955hGtBzouLcf/wnkx2YjYnk1L1CsFGdJg/k52YWwCUJyUvVEoFnp3Un9du+Rbxf+uGZp7txepKaxodWupPOA5yXlyM4bGhCOIpYQz288TwWPGcF6lUrxDsSGULoDX8325FT3lS0iTYn3+7UezfuqVmnrRYcl1IYdfFUCkVeGn6AMzdcMSszcrpA0RdvUolkY9gx7AFUFLdYDJipkCLA+GILZvU+EikxKmRpanApSsNCO/c8r4UcZEeUvitP/bVUYvNPNsulsSMOBOOhSIvLkhqfCRWmUleW+WA1WsYw2rMGjtCfCxt2QAtNwBHbtmolAok9QrF1EFdkdQrlBwXieLsiJ22WY8tx9m2hGix5FpQ5MVFcebqVc+YJMdqRzgGw5bNwm+OdVjJKgAcLaqkbRvCCEOFY52FyIe/t0q0iN36/YWs2pxUpeZikPPiwhhWr44mU8OmIZOpKcco5uoowhEcLao0GYLnAKzerQEApE2Mc/CoCKmi03MWHRcAqGvUQafnRFk4HSxky6Xx9lBQlZqLQdtGhOAcZEzOu1B5VeSRENagbdbjwz0aizYf7tFA26wXdRw6PYeM/HJsyr6AjPxySrSUMOv3FwpqZy2sTR8TugXR1qOLQZEXQlB0eg45F6qZbKOCKIwrJdbvLwTfTh7HtdjNHh0ryhjSc4rx/I+5KKm5lp+gDvDB81NI2FCKZDFGWbM05aJcM3cM7oaN2Rd57f5xcx/B35twLhR5IQQlS1OB+ia2lfmIXrRlJCVYQ/CsdtZyTavDOLGypKYBc0mrQ5JYqvKxxc5akvuE8UZf/LxUSO5DXchdDXJeCEFhzej381JhOJUtSgpfT7bpgNXOGnR6Dou/P2HRJu37E7SFJDEGdAsU1M5aVEoF3rgrwaLNG3dRF3JXhJwXQlBYM/rnjI6lCUVixEWx3WBY7awhs6AcVfVNFm0q65uQWSB+Q1GCnVF92KKnrHa2YJCGiOhs3G08orOXQ6QhCOdAOS+EoPCJnQEtCr8LxtIetNQID2DU7GC0s4YMxi7nGfnlGNGbtgCkgkHRm8/xrOZ53V5I2ND9oMgLISh8YmcKiK/wS9hGe1FDe+2sQc+x5Umx2hGOQaVU4MVp8bx2/94o/pYfCRu6F+S8EIJjrj8NtaeXNixdgoP9PEXRywj09eI3ssKOcByBfvzfCW35EUJD20aEKFAYV34YomaW+mJV1jdhW26J4A4o67aC2NsPhPXQlh/hDMh5cQO0zXp8llGIsxX16BnihweSouHlIX7QzVkKv4TtpMSpLeYwKNDSoTclTi2oI1pczSZYyGpHOBLW7SDxto10eo4WSm4GOS8uzsqtufhwt8Zo2nhhyyk8OjqGZN6JDmRpKiwmX4rVofdqE5sOSFSQr2DvSQhDUmwY3v0tn8lODNJzirF0cy6Kq6/JNEQG+mDJZBI2dGUo58WFWbk1F6vbOS7AtT41K7fmOmNYhIRh1ekRskOvTs/hAKNSa3Iv2naQGsN7tVQcWSLIz1MUXaf0nGLM23DEyHEBgJLqBswjYUOXhpwXF0XbrMeHu3n61OwWv08NIS9YdXqE7NCbpalAZX0zr10nbw8SNpQgKqUCL00fYNHmxuhgwbdxdHoOSzfnmtyMMjy3dHMuCRu6KOS8uCjr93eMuLSH+8uOIAwkxoQwraKFrDhijeLcdUM3ymOQKC0J+uFmX9+We0nwSG+WpqJDxKUtbbc4CdeDnBcXhfUH+9Nx/qZmBNEWod0H1ihOSpxa4HcmhELbrMeOU5cs2qwRuCO5M7Y4CelAzouLwpoAefLiFQqrEq3wJewCLeXSQq5mDfoylpyiyEAfUfRlCGH4LKMQfNOInmuxE4qwTt6C2hHygpwXF2VA1yAmu2Y9R+JRRCvOWM1aUmVW/PVYMjmOtowkzNmKekHtmHB+hTbhRMh5cVFGWiEGxSoyRbg+zkjYBVpyJt67dzCC/Y3zbdSkyiwLeob4CWrHwo7TpUx2ZXWNgr0nIR3IeXFRhvcKhTezEB0tTYgW+LZwFBBnCyc9pxjLfjqFirprW1Yh/l54dhJpdciBe4f1FNSOD52ew8Zstnw9oR1tQhqI5rysWLECycnJ8PPzQ1BQENMxHMfhueeeQ2RkJHx9fTF+/Hj8+eefYg3RpVEpFXhkVAyT7Y09KJeAaIFvCwcQfgsnPacYczccQUmN8VZURZ0Wf/+CtDrkQPa5KkHt+MjSVKCiTstrF+IvTi8uwvmI5rxotVrceeedmDdvHvMxr7zyCt5++22sWrUKBw4cgL+/PyZMmICGBsoWt4VAH8slrwbOXLoi8kgIOWGusaYYWzg6PYfF35+waJP2vfgdiQn7cHSuFOt5bh/UlXKlXBTR2gMsXboUALBu3Tome47j8N///hf/+c9/MHXqVADAp59+ioiICGzcuBH33HOPWEN1WQ6dZasIOXS2Ao+il8ijIeSEoxprZhaUM1U3ZRZQUz8p4+hcKdbzjKfyepdFMjkvGo0GJSUlGD9+fOtzgYGBGDZsGDIyMswe19jYiJqaGqMH0YKfF5tvympHuBeGxppTB3VFUq9QUVaw1nQkJqQLS7m7n5dKsC0cw/tZgsrrXRvJOC8lJSUAgIiICKPnIyIiWl8zxcqVKxEYGNj66N69u6jjlBP9IwMEtSMI4aF6V1fAkCtl6Vuq1+rwSvopwd4vvqvleSu+awBtGbkwVjkvixcvhkKhsPg4ffq0WGM1SVpaGqqrq1sf586dc+j7S5nwzmziTKx2BCE0rJ2GxepITAjH2H4RUPD4CkKp7Gqb9djOo+i7/dQl6t3mwli1X7Bo0SI8/PDDFm1iY2NtGoha3bI3WVpaisjIawmBpaWlGDRokNnjvL294e1NN19TqAN9meyKKq6KPBKCMI2hI7GlvBexOhITwvJZRiE4RpXdWaNsu08YWL+f/704rsVu9mj73ouQJlZFXrp06YJ+/fpZfHh5edk0kJiYGKjVauzYsaP1uZqaGhw4cABJSUk2ndPdSYwJgTqA37H76mARVXMQToGlI/FL0wdQ+F8GOFJl92AhWzECqx0hP0TLeSkqKkJ2djaKioqg0+mQnZ2N7Oxs1NbWttr069cPP/zwAwBAoVDg8ccfxwsvvIAff/wRJ06cwIMPPoioqChMmzZNrGG6NCqlAjMSe/DaUedVwpmkxkdi1f1DOjja6gBvrCJ1XdngSJVdPy+VoHaE/BCtzOS5557D+vXrW/8/ePBgAMBvv/2GMWPGAADOnDmD6urqVpunnnoKdXV1ePTRR1FVVYWRI0ciPT0dPj6kkGgr0WH+THbUeZUwh07PiV4y7ajSbEI8HkiKxoqtpyw2aFQqWuzs5faErkwKu7cndLX7vQhpIprzsm7dOl6NF67dpqVCocCyZcuwbNkysYbldjirVw3hGqTnFOP5H0+ipOZafxh1gDeen3K94BERQ2k2IU+8PJSYPSoGq3drzNrMHhUDL+a2Jebx8GQ7B6sdIT/om3VxEmNCEORnWWk32E9YCW2dnkNGfjk2ZV9ARn455dPIlGuy/caN7UpqGjF3A8n2Ex1JmxiHOaNj0D5gplQAc0bHIG1inCDvU1bL1myR1Y6QH6RORgiqoJGeU4ylm3NRXH1tGyoy0AdLJlODPTnBItu/+PsTSIlT09YOYUTaxDgsuqUf1u/X4GBhJfy9VJg+pBuSBVRIpogyQZEXFydLU8Erv15V3yRIwm56TjHmbThi5LgAQEl1A+bRSl1WZObzy/ZX1Tchk5RvCRPsPF2Kj/Zo8GtuKX7IvogHPslC8sodgs0Bzup+TkgHcl5cHEc1TNPpOSzdnGsyimN4bunmXNpCkgkZBWVMdq/9KowoJW01ug6G7cbSK8ZbNqVXhNtudEb3c0JakPPi4rCGTbfnmm/BwEKWpqJDxKUtHKgkW16wTfrZ56rtVjFNzynGiJd2YsaaTDz2VTZmrMnEiJd2UqROhuj0HB77KtuizYIvjgrinKbGR+K9ewcj2N84p0+M7ueE9CDnxcVpEarjd2B+Ol5i103IUREewjGwVv1waFFMtZVrScHtthprGigpWIbs/eMyGnnmkWY9hze3nbH7vdJzirF8yylU1F3b3gzx98Kzkyi/zh0g58XFUSkVGNmb/0Zk702osIxNNZMS6OTB8NhQeDOWtNqqmMqSFJz2/QnaQpIRa/YUMNl98Hu+Xd+rufy6yjot5n9BTq87QM6LG+DnzVZUZs9N6MusIl47dYA3JdDJBJVSgckD2VavtiqmZhbwJwVX1jchs4CSguVCdYPl79OATg+bv1fKryMAcl7cgu7BbDcXVrv2ZGkqOoT9TTEjsQcl0MmIF6cP5LVR2KGYuuePy4LaEc4noVsgs+2+PLak8PZQfh0BkPPiFlwX3klQu/awOC4A0COUrVUBIQ1USgWvGqqnSmmzQ7qX8ebFakc4n39Pup7Z9mKVbd3sKb+OAMh5cQsOnmXswMpo154KRhVLVjtCGmTml/MmcWub9aT1QrTi66VCdChbBLdrkK9N70ECdQRAzoubwLoytm0FHeTnJagdIQ1YtV4+P1Bo0/n1HFtOwqDuQTadn3AOK24fwGRnq+JuYkwI/C10iyaBOveAnBc3gLXs1cPG8H9VvVZQO0IqsF0Pv/9ZZnVypLZZj9MlV5hs/z1JmH44hGMYHhvK1E9teKxtTThfST+FOq3O7OscSKDOHSDnxQ0YHhuKQF/+iqOvD52zKUM/xJ8tosJqR0gDVqe3rlFndXLkZxmFYLnUrovwh6+FVTYhPVRKBV6abjn6snL6AJucC22zHh/uMd+12sDYfhFWn5uQF+S8uAEqpQL/NyKG187WDH11INveNasdIQ2Gx4bCj9FxKKm2LvmStSx/eKxwzfwIx5EaH4lV9w8xGYHhi8pYYv3+QrDsNq7fX2jzexDygJwXNyE6jK3Sx5YM/cSYEN4Jifag5YdKqcDEeDWTbUWddVuCrNowtmrIENLAlI5PdX2TzY1aszRseVisdoR8IefFTQjz92ayK7hca/W5t+WW8IqN0R60PBnRpwuTXUgntuvLwANJ0eC7HJR2aMgQzsUgJGcKe4TkzlWwRfjqtfb12yKkDzkv7gKj3/BpxlmrJhQWiXd/LxVS4thW8IS0YOmLZY2dAS8PJWaPsryVOXtUDK/ODCFNxBCS0+k5FJTVMdkmdAtiPi8hT2hmcBPKGDVWKuubrJpQMvP5Jd7rtDrSApEpiTEhiAy07JgE+XnatCWYNjEOj4yI7uBXKxXAnNExSJtIVUZyRQwhucz8cmh1bAurEX0oV8rVIefFTbBGsMmaCYVVC4TVjpAWKqUCSyZbdiKq6puwLbfE6nOv3JqLT/YXduhRM3NENDkuMkcMITnWOcTHU2lzGTYhH8h5cRMSY0KYS5WtU6YUVwCPcD4pcWrehGxr8xdWbs3F6t0ak+XSH+8txMqtpvMlCHlgiNiZ+9XbJiTHNofc3Dec8uvcAHJe3ASVUoEXpsbz2lk7oQxjtGW1I6RHlqaCd2vQmvwFbbMea3i0Otbs0fC2JiCkS9uIXXs3wvB/a5P4WXWH7h/ek/mchHwh58WNmDgwEnNGm0+SVMD6CUWpYLNltSOkB2vjTVY7FoE6PddiR8iX1PhIfHD/EKjb5UypA33wwf1DkBofadX5xFbuJeQFv+wq4VKkTYxDQrcg/GdTDirqrq2mIwN9sGRynNUTSlkdWyIwqx0hPYRuvMkqUMdqR0iX1PhIpMSpkaWpwKUrDQjv3BLZtWVbR6VU4O4bumH1bvNRO1uVewn5Qc6LGzJxYBQmxEcKMqFQh1fXR+j2DyRQ516olAokxoS0zjdZmgqb5pv0nGJ8aMFxmTM6xurFFyFfyHlxU1RKBfMesiUM6rrmciIUaAkTk7qufBG6/cMDSdFYsfWUxa0jEqhzHdJzirF0c66R7ou1kV6D6J2l3cYfjxXjqdT+FHlxEyjnhbALPnVd6vAqf1i0XqxJ9PbyUGJc/3CLNiRQ5xqk5xRj3oYjHQTriqsbrGoRwCd6ZzinLb3ZCHlCs4Mbo9NzyMgvx6bsC8jIL7daqtuSBLiBID9PUteVOYbKEUtlr9Y4qOk5xdiee8ns6ylx4aTz4gLwRUs4AGnfn2Cad8QQvSPkDW0buSlChHJZVkNVfyn2CrFFRTgPQ+WI2OF/BYCcCzXQ6TmK1skclvmhsr4J7+78E4+Nv86iHeXWEe0h58UNMYRy299ADKFc1jJGVlVVWg25BkJUjljT84YcXnnD+rtfu68QC8b2sXgdGbYuLV071LnevaBtIzdDqFCuTs9hY/ZFpvek1ZDrYKgcCe/s01o5Ys12I4X/3QfW333VVf5+aiqlAlMSLC+opiREUrTOjaDIi5shVCg3S1OBijot7/uF+NvWtI+QJqa2Gzv7eGDltAG4bVAU7/EU/ncfEmNCEOTriaqrltWZAX5nVafn8OMxy8m9VG3kXlDkxc2wJpRraUXNep7bB3WlycRFMFc5cqWhGQu+OorZnx7kPYc4PW8IKaJSKvBwcjSTbVgnb4uvU7UR0R5yXtwMoUK5rOcZT5VGLgGLzsa23EtYscVy9ZkYPW8I6XJjNKMTyrPzSNuNRHvIeXEzEmNCEOhruT+IAUu9avi0P2gF7VqwrHwB4KO9/A0VU+Mj8d69gxHsb3wd2trzhpAuQrUPoe1Goj3kvLgZKqUCKTwCYQYs9aphSaCjFbTrwLqi5RgaKqbnFGPZT6eMemuF+Hvh2UnW99YipI1QTodBydsctFhyP8h5cUNG9A5jsrPUqyY9p9hig7RHqc+IS2HNitZSQ8X0nGLM3XCkQ1Svok6Lv3/BrrhKyAO+HCegpRM0n9NBSt5Ee0RzXlasWIHk5GT4+fkhKCiI6ZiHH34YCoXC6JGamirWEN0W1h40RRVXTT6v03NY9M0xi8duOFBktWIvIV0SY0LQ2YetONFcQ0WdnsPi709YPJZVcZWQB4YcJ0vfaGV9k0XNKFLyJkwhmvOi1Wpx5513Yt68eVYdl5qaiuLi4tbHl19+KdII3ZfEmBCoAyxn9wPAVwdNOyD788pQp9VZPLauUYf9eWU2j5GQFiqlAiunDWCyDQ8wHaXJLCi3uHoGWm5kmQXlVo+PkC4pcWqLWz4AsHRzrlmn1Rolb8J9EM15Wbp0KZ544gkMGMA24Rnw9vaGWq1ufQQHB4s0QvdFpVRgRmIPXjtzpYffHTnP9D6sdoQ8uG1QFMb378Jr9+LWUyZvRBn5bE4Jqx0hD7I0FbxOq6UyZ6o0IkwhuZyXXbt2ITw8HH379sW8efNQXm55ImtsbERNTY3Rg+AnOsyfyc7UhHC+0vR2kq12hHyYNbIXr435GxHrdhBtG7kSrE6Fua0jqjQiTCEp5yU1NRWffvopduzYgZdffhm///47br31Vuh05rcoVq5cicDAwNZH9+7dHThi+WLPhNA1iO1YVjtCPlgqn+ezY02mTIplSygn5AHrXPPNofMmI3YkbEiYwirnZfHixR0Sats/Tp8+bfNg7rnnHkyZMgUDBgzAtGnT8NNPP+HgwYPYtWuX2WPS0tJQXV3d+jh37pzN7+9ODOoeZLNdk45tZfy3IeRIuhplVxh1O9rZ6fQcvj7I/9sM8vPEcGrI6FIkxoQgmCfnBQBqG5tN5juRsCFhCqucl0WLFuHUqVMWH7GxsYINLjY2FmFhYcjLyzNr4+3tjYCAAKMHwc+GzEImu/9sNK4O0TbrkX6Sv5u0j4cCyX1oBe1qVNbz97MyZZelqUBJDb/jMzM5hm5CLoZKqcDwWDaH1Fy+U2p8JD64fwjU7YQxSdjQfbGqMWOXLl3QpQt/wp5QnD9/HuXl5YiMpAtTaA4WVjLZ/XS8GK/8LaH1hvJZRiFYKllvGxhFNyEXhPUrbW9XUs2W/9QjhK2Mn5AXvbqw5dhZyndKjY9ESpwaWZoKXLrSgPDOLVtFNM+4J6LlvBQVFSE7OxtFRUXQ6XTIzs5GdnY2amtrW2369euHH374AQBQW1uLJ598EpmZmSgsLMSOHTswdepU9O7dGxMmTBBrmG6Lv5eKya6xWW+UfFlYbl6ArC2+XtSw3BVhzUdpb8fSgdwaO0JesF43HkrTtySdnkNGfjl+On4RQMviKKlXKDkuboxod5jnnnsO69evb/3/4MGDAQC//fYbxowZAwA4c+YMqqurAQAqlQrHjx/H+vXrUVVVhaioKNxyyy1Yvnw5vL35NUkI65g+pBt+yL7IZNs2+ZLjLPetsdaOkBfDe4UiyM+Tt/S1+qqxExLC0zXYWjtCXgzvFYpAXw9UX222aLcuoxD/GNfHyClJzynG0s25RlovkYE+WDKZ2km4M6JFXtatWweO4zo8DI4LAHAch4cffhgA4Ovri19++QWXLl2CVqtFYWEhPvzwQ0RERIg1RLcmuXcYPFVsq5a2PY78GSMqrHaEvFApFXiRQaxu+RZjrZei8jqm86vNCNwR8kalVODh5Gheu6r6JmS2yXtJzynGvA1HOojUlVQ3YN4GaifhzkiqVJpwHCqlAvckslUDBbTpQl3KWG3CakfIj2ALPa8MtNV60ek5fJlVxHsMlbu6Nk06tmjsvvzLAK61BTCVBWN4zpIyL+HakPPizjD+5o+frxJ1GIS8YE2+NdixVhrdc2MPymFwYS4wilYa7PjaAnCwrMxLuDbkvBC8FLXpEhwVxFYNwmpHyA9rk29ZFVajw0w3dCRcA4WCzTE12H20J5/JntoCuCfkvLgx0aFs5YtHi6paQ7MjerNVDbDaEfKDNal271+NOUN8+beZrLEj5EnXYLYFTddgX2ib9dh5+jKTPbUFcE/IeXFjHkiKNiu53ZaahubW0Ozw2FDeDrHBfp7MolSE/GBNqt115jK0zXpsPMbWoPN06RV7hkVInORebAuawrI6rN9fyLSr3dnHg/Kk3BRyXtwYLw8lxvZjEx00hGbNNU9ry8rpAyh3wYVJjAlBJ29+nSAOwLp9Gvx8opTpvOcq2TSECHkyPDYUQb78bQK2nChBZkEZ0zljwvxornFTyHlxcx4Zxd8lGAC255a0li2a0/gI9vPEKpLqdnlUSgWzYuqvuSWobzLfWLUtPUMo58WVUSkVGNsvnMk27xJbFK6zN78zRLgmJMbh5iTGhEAd4MPbLXjz8RJkaSoshnK9PZRIiVMLO0BCksSEdcKx8zW8ducq2KIpCkXLNibh2hQxXg+XrrAlhQ/oFmjPcAgZQ5EXN0elVGBkb7b8lFKeCaWkppHKFt2EO4Z0Y7Lju2YMTBoQCS8Pmo5cHzZ9hqtNbJowo/o4rtceIS1otiDg5y1cAI7KFt2D5N5h8BHI2fDxUOKtewYLci5C2tzSX7jIrL+XigoD3BhyXghBcw2obNE9UCkVuJkx2ZuPO2/oSkmXbsLDI2MEO9eo68LounFjyHkhmEum+Qj196KyRTeiV5fOgpynpJpaSbgLXh5KJMUIEy3x9eCveCNcF3JeCHh5KDEs1n6nY+qgKFoJuRFJvYS5CV1lrEYiXIO7GHuq8UEq3u4NOS8EAODuG+yfUKjSyL0YHhuKTgLkS8WEsZVdE66BUJ3DWUXvCNeEnBcCAKAOtG8VE+LvSVtGboZKqcArdwy0+zzPTIwTYDSEXEiMCUEIQ2dyS3ipFBguUOSPkCfkvBAAWiYUT5XtWz63D6KkS3dk4sBIZpVmU3QN8oGvF+UuuBMqpQLTBkXZdY65o3vRfOPmkPNCAGiZUCYPtH1CGU9bRm7LbEaVZlNsXzhGuIEQssHeLebHUq4TaCSEXCHnhWjlJRu3AIJ8acvInUmMCYGfp/XRkxt6BlHUxU1JjAmBj4dtkZPRfahEmiDnhWiDl4cSw6KDrD5u5ohomkzcGJVSgYkDrFtJKxXA13OSRRoRIXVUSgVemZ5g07GrH7hB4NEQcoScF8KIzx5Jsso+yNcTC8b2EWk0hFx4cbp1Ubs37x5EDq+bM2VIV8RFdLLqmJS4cIrWEQDIeSHa4eWhxJzR7CqYL90xgG5ChFXXzcBuAZg6qKvIIyLkwNYnboKK8S6UEheONQ/eKO6ACNlAzgvRgbSJcbw3Ik+VAqvuH4LU+EgHjYqQOizXzfj+XfDjglEOGhEhB/JfnIROXuZvRd2CfXBqWSo5LoQRCo7j2Np8yoSamhoEBgaiuroaAQEBzh6OrNE267FmTx4+zziLy7VaqBQKxHTxx5MT+uOmvl0o4kKYRNusxyd7C/Dt4fMorWmAn5cKY/uF47nJ8RTyJ8xyuaYRU9/djdIrWqiUCkwZqMby2xPomnEjrLl/k/NCEARBEITTseb+TdtGBEEQBEHICnJeCIIgCIKQFeS8EARBEAQhK8h5IQiCIAhCVpDzQhAEQRCErCDnhSAIgiAIWUHOC0EQBEEQsoKcF4IgCIIgZAU5LwRBEARByAoPZw9AaAyCwTU1NU4eCUEQBEEQrBju2yzC/y7nvFy5cgUA0L17dyePhCAIgiAIa7ly5QoCAwMt2rhcbyO9Xo+LFy+ic+fOUCiEbRxYU1OD7t2749y5cy7ZN8nVPx/g+p+RPp/8cfXP6OqfD3D9zyjW5+M4DleuXEFUVBSUSstZLS4XeVEqlejWrZuo7xEQEOCSF6QBV/98gOt/Rvp88sfVP6Orfz7A9T+jGJ+PL+JigBJ2CYIgCIKQFeS8EARBEAQhK8h5sQJvb28sWbIE3t7ezh6KKLj65wNc/zPS55M/rv4ZXf3zAa7/GaXw+VwuYZcgCIIgCNeGIi8EQRAEQcgKcl4IgiAIgpAV5LwQBEEQBCEryHkhCIIgCEJWkPNCEARBEISsIOelDStWrEBycjL8/PwQFBRk0qaoqAiTJk2Cn58fwsPD8eSTT6K5udnieSsqKnDfffchICAAQUFBmDVrFmpra0X4BNaxa9cuKBQKk4+DBw+aPW7MmDEd7OfOnevAkbMTHR3dYawvvfSSxWMaGhowf/58hIaGolOnTrjjjjtQWlrqoBFbR2FhIWbNmoWYmBj4+vqiV69eWLJkCbRarcXjpPwdvvfee4iOjoaPjw+GDRuGrKwsi/b/+9//0K9fP/j4+GDAgAHYunWrg0ZqPStXrsSNN96Izp07Izw8HNOmTcOZM2csHrNu3boO35WPj4+DRmwdzz//fIex9uvXz+Ixcvr+ANNzikKhwPz5803aS/372717NyZPnoyoqCgoFAps3LjR6HWO4/Dcc88hMjISvr6+GD9+PP7880/e81r7O7YWcl7aoNVqceedd2LevHkmX9fpdJg0aRK0Wi3279+P9evXY926dXjuuecsnve+++7DyZMnsW3bNvz000/YvXs3Hn30UTE+glUkJyejuLjY6PHII48gJiYGN9xwg8VjZ8+ebXTcK6+84qBRW8+yZcuMxvqPf/zDov0TTzyBzZs343//+x9+//13XLx4EdOnT3fQaK3j9OnT0Ov1WL16NU6ePIk333wTq1atwjPPPMN7rBS/w6+//hoLFy7EkiVLcOTIESQkJGDChAm4dOmSSfv9+/djxowZmDVrFo4ePYpp06Zh2rRpyMnJcfDI2fj9998xf/58ZGZmYtu2bWhqasItt9yCuro6i8cFBAQYfVdnz5510Iit5/rrrzca6969e83ayu37A4CDBw8afb5t27YBAO68806zx0j5+6urq0NCQgLee+89k6+/8sorePvtt7Fq1SocOHAA/v7+mDBhAhoaGsye09rfsU1wRAfWrl3LBQYGdnh+69atnFKp5EpKSlqf++CDD7iAgACusbHR5Llyc3M5ANzBgwdbn/v55585hULBXbhwQfCx24NWq+W6dOnCLVu2zKLdTTfdxD322GOOGZSd9OzZk3vzzTeZ7auqqjhPT0/uf//7X+tzp06d4gBwGRkZIoxQeF555RUuJibGoo1Uv8PExERu/vz5rf/X6XRcVFQUt3LlSpP2d911Fzdp0iSj54YNG8bNmTNH1HEKxaVLlzgA3O+//27Wxtx8JEWWLFnCJSQkMNvL/fvjOI577LHHuF69enF6vd7k63L6/gBwP/zwQ+v/9Xo9p1aruVdffbX1uaqqKs7b25v78ssvzZ7H2t+xLVDkxQoyMjIwYMAAREREtD43YcIE1NTU4OTJk2aPCQoKMopkjB8/HkqlEgcOHBB9zNbw448/ory8HDNnzuS1/fzzzxEWFob4+HikpaWhvr7eASO0jZdeegmhoaEYPHgwXn31VYvbfIcPH0ZTUxPGjx/f+ly/fv3Qo0cPZGRkOGK4dlNdXY2QkBBeO6l9h1qtFocPHzb62yuVSowfP97s3z4jI8PIHmj5TcrpuwLA+33V1taiZ8+e6N69O6ZOnWp2vpECf/75J6KiohAbG4v77rsPRUVFZm3l/v1ptVps2LAB//d//weFQmHWTk7fX1s0Gg1KSkqMvqPAwEAMGzbM7Hdky+/YFlyuq7SYlJSUGDkuAFr/X1JSYvaY8PBwo+c8PDwQEhJi9hhn8fHHH2PChAm8Xbnvvfde9OzZE1FRUTh+/DiefvppnDlzBt9//72DRsrOP//5TwwZMgQhISHYv38/0tLSUFxcjDfeeMOkfUlJCby8vDrkPEVEREju+zJFXl4e3nnnHbz22msW7aT4HZaVlUGn05n8jZ0+fdrkMeZ+k3L4rvR6PR5//HGMGDEC8fHxZu369u2LTz75BAMHDkR1dTVee+01JCcn4+TJk7y/VUczbNgwrFu3Dn379kVxcTGWLl2KUaNGIScnB507d+5gL+fvDwA2btyIqqoqPPzww2Zt5PT9tcfwPVjzHdnyO7YFl3deFi9ejJdfftmizalTp3iTyuSELZ/5/Pnz+OWXX/DNN9/wnr9tvs6AAQMQGRmJcePGIT8/H7169bJ94IxY8/kWLlzY+tzAgQPh5eWFOXPmYOXKlZLuO2LLd3jhwgWkpqbizjvvxOzZsy0e6+zvkADmz5+PnJwcizkhAJCUlISkpKTW/ycnJ6N///5YvXo1li9fLvYwreLWW29t/ffAgQMxbNgw9OzZE9988w1mzZrlxJGJw8cff4xbb70VUVFRZm3k9P3JCZd3XhYtWmTRKwaA2NhYpnOp1eoOGdOGKhS1Wm32mPZJSs3NzaioqDB7jL3Y8pnXrl2L0NBQTJkyxer3GzZsGICWVb8jbnz2fKfDhg1Dc3MzCgsL0bdv3w6vq9VqaLVaVFVVGUVfSktLRfu+TGHtZ7x48SJuvvlmJCcn48MPP7T6/Rz9HZoiLCwMKpWqQ2WXpb+9Wq22yl4qLFiwoDV539rVt6enJwYPHoy8vDyRRiccQUFBuO6668yOVa7fHwCcPXsW27dvtzpaKafvz/A9lJaWIjIysvX50tJSDBo0yOQxtvyObUKw7BkXgi9ht7S0tPW51atXcwEBAVxDQ4PJcxkSdg8dOtT63C+//CKphF29Xs/FxMRwixYtsun4vXv3cgC4Y8eOCTwy4dmwYQOnVCq5iooKk68bEna//fbb1udOnz4t6YTd8+fPc3369OHuuecerrm52aZzSOU7TExM5BYsWND6f51Ox3Xt2tViwu5tt91m9FxSUpJkEz71ej03f/58Lioqivvjjz9sOkdzczPXt29f7oknnhB4dMJz5coVLjg4mHvrrbdMvi63768tS5Ys4dRqNdfU1GTVcVL+/mAmYfe1115rfa66upopYdea37FNYxXsTC7A2bNnuaNHj3JLly7lOnXqxB09epQ7evQod+XKFY7jWi66+Ph47pZbbuGys7O59PR0rkuXLlxaWlrrOQ4cOMD17duXO3/+fOtzqamp3ODBg7kDBw5we/fu5fr06cPNmDHD4Z/PHNu3b+cAcKdOnerw2vnz57m+fftyBw4c4DiO4/Ly8rhly5Zxhw4d4jQaDbdp0yYuNjaWGz16tKOHzcv+/fu5N998k8vOzuby8/O5DRs2cF26dOEefPDBVpv2n4/jOG7u3Llcjx49uJ07d3KHDh3ikpKSuKSkJGd8BF7Onz/P9e7dmxs3bhx3/vx5rri4uPXR1kYu3+FXX33FeXt7c+vWreNyc3O5Rx99lAsKCmqt8HvggQe4xYsXt9rv27eP8/Dw4F577TXu1KlT3JIlSzhPT0/uxIkTzvoIFpk3bx4XGBjI7dq1y+i7qq+vb7Vp/xmXLl3K/fLLL1x+fj53+PBh7p577uF8fHy4kydPOuMjWGTRokXcrl27OI1Gw+3bt48bP348FxYWxl26dInjOPl/fwZ0Oh3Xo0cP7umnn+7wmty+vytXrrTe6wBwb7zxBnf06FHu7NmzHMdx3EsvvcQFBQVxmzZt4o4fP85NnTqVi4mJ4a5evdp6jrFjx3LvvPNO6//5fsdCQM5LGx566CEOQIfHb7/91mpTWFjI3XrrrZyvry8XFhbGLVq0yMjz/u233zgAnEajaX2uvLycmzFjBtepUycuICCAmzlzZqtDJAVmzJjBJScnm3xNo9EY/Q2Kioq40aNHcyEhIZy3tzfXu3dv7sknn+Sqq6sdOGI2Dh8+zA0bNowLDAzkfHx8uP79+3MvvviiUZSs/efjOI67evUq9/e//50LDg7m/Pz8uNtvv93IGZASa9euNXnNtg2qyu07fOedd7gePXpwXl5eXGJiIpeZmdn62k033cQ99NBDRvbffPMNd91113FeXl7c9ddfz23ZssXBI2bH3He1du3aVpv2n/Hxxx9v/XtERERwEydO5I4cOeL4wTNw9913c5GRkZyXlxfXtWtX7u677+by8vJaX5f792fgl19+4QBwZ86c6fCa3L4/wz2r/cPwGfR6Pffss89yERERnLe3Nzdu3LgOn7tnz57ckiVLjJ6z9DsWAgXHcZxwm1AEQRAEQRDiQjovBEEQBEHICnJeCIIgCIKQFeS8EARBEAQhK8h5IQiCIAhCVpDzQhAEQRCErCDnhSAIgiAIWUHOC0EQBEEQsoKcF4IgCIIgZAU5LwRBEARByApyXgiCIAiCkBXkvBAEQRAEISv+H5M+9jZAXvPIAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: The graph of feature x was simplified by 8.6%.\n",
-      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
-      "\n",
-      "Feature Name: x\n",
-      "Feature Type: continuous\n",
-      "Means: {\"(-10.0, -9.48)\": -0.369, \"(-9.48, -9.17)\": -0.053, \"(-9.17, -8.34)\": 0.251, \"(-8.34, -8.16)\": -0.064, \"(-8.16, -7.95)\": -0.422, \"(-7.95, -7.74)\": -0.901, \"(-7.74, -7.54)\": -1.235, \"(-7.54, -6.74)\": -1.537, \"(-6.74, -6.62)\": -1.214, \"(-6.62, -6.43)\": -0.817, \"(-6.43, -6.27)\": -0.28, \"(-6.27, -6.14)\": 0.134, \"(-6.14, -6.02)\": 0.449, \"(-6.02, -5.9)\": 0.82, \"(-5.9, -5.74)\": 1.133, \"(-5.74, -4.81)\": 1.466, \"(-4.81, -4.66)\": 1.159, \"(-4.66, -4.5)\": 0.832, \"(-4.5, -4.31)\": 0.519, \"(-4.31, -4.1)\": 0.19, \"(-4.1, -2.98)\": -0.145, \"(-2.98, -2.0)\": 0.159, \"(-2.0, -1.82)\": -0.166, \"(-1.82, -1.67)\": -0.507, \"(-1.67, -1.51)\": -0.82, \"(-1.51, -1.36)\": -1.131, \"(-1.36, -0.39)\": -1.434, \"(-0.39, -0.25)\": -1.014, \"(-0.25, -0.12)\": -0.681, \"(-0.12, -0.0)\": -0.333, \"(-0.0, 0.11)\": 0.017, \"(0.11, 0.25)\": 0.383, \"(0.25, 0.4)\": 0.797, \"(0.4, 0.55)\": 1.136, \"(0.55, 1.49)\": 1.467, \"(1.49, 1.66)\": 1.127, \"(1.66, 1.81)\": 0.814, \"(1.81, 2.0)\": 0.5, \"(2.0, 2.26)\": 0.111, \"(2.26, 3.24)\": -0.209, \"(3.24, 4.31)\": 0.102, \"(4.31, 4.47)\": -0.237, \"(4.47, 4.66)\": -0.547, \"(4.66, 4.83)\": -0.926, \"(4.83, 5.04)\": -1.268, \"(5.04, 5.82)\": -1.576, \"(5.82, 5.96)\": -1.239, \"(5.96, 6.08)\": -0.898, \"(6.08, 6.2)\": -0.565, \"(6.2, 6.31)\": -0.221, \"(6.31, 6.42)\": 0.13, \"(6.42, 6.53)\": 0.45, \"(6.53, 6.65)\": 0.776, \"(6.65, 6.82)\": 1.077, \"(6.82, 7.05)\": 1.406, \"(7.05, 7.63)\": 1.71, \"(7.63, 7.79)\": 1.392, \"(7.79, 8.0)\": 1.034, \"(8.0, 8.18)\": 0.615, \"(8.18, 8.44)\": 0.244, \"(8.44, 9.65)\": -0.097, \"(9.65, 9.99)\": 0.224}\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from interpret.glassbox import ExplainableBoostingRegressor\n",
-    "\n",
-    "fbench_functions_as_text = []\n",
-    "for f, n in fbench:\n",
-    "    x = np.random.uniform(-10, 10, 1000)\n",
-    "    y = f(x)\n",
-    "    ebm = ExplainableBoostingRegressor(feature_names=['x'])  \n",
-    "    ebm.fit(x.reshape(-1, 1), y)\n",
-    "    ebm_global = ebm.explain_global()\n",
-    "\n",
-    "    # plot the function\n",
-    "    plt.scatter(x, y)\n",
-    "    plt.title(n)\n",
-    "    plt.show()\n",
-    "\n",
-    "    graph = graphs.extract_graph(ebm, 0)\n",
-    "    # add the intercept\n",
-    "    graph.scores = [x + ebm.intercept_ for x in graph.scores]\n",
-    "    #graphs.plot_graph(graph)\n",
-    "    graph = t2ebm.graphs.simplify_graph(graph, min_variation_per_cent=0.01)\n",
-    "    graphs.plot_graph(graph)\n",
-    "    plt.title(n)    \n",
-    "    plt.show()\n",
-    "\n",
-    "    graph_as_text = graphs.graph_to_text(graph, max_tokens=1000, x_axis_precision=2, confidence_bounds=False)\n",
-    "    print(graph_as_text)\n",
-    "    fbench_functions_as_text.append(graph_as_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-9.99, -9.57)\": -9.99, \"(-9.57, -9.17)\": -9.56, \"(-9.17, -8.76)\": -9.16, \"(-8.76, -8.53)\": -8.75, \"(-8.53, -8.07)\": -8.49, \"(-8.07, -7.89)\": -8.07, \"(-7.89, -7.44)\": -7.85, \"(-7.44, -7.22)\": -7.42, \"(-7.22, -6.8)\": -7.2, \"(-6.8, -6.37)\": -6.77, \"(-6.37, -6.12)\": -6.33, \"(-6.12, -5.9)\": -6.09, \"(-5.9, -5.45)\": -5.87, \"(-5.45, -5.03)\": -5.45, \"(-5.03, -4.6)\": -5.03, \"(-4.6, -4.18)\": -4.6, \"(-4.18, -3.73)\": -4.16, \"(-3.73, -3.26)\": -3.69, \"(-3.26, -2.87)\": -3.26, \"(-2.87, -2.44)\": -2.85, \"(-2.44, -2.03)\": -2.43, \"(-2.03, -1.6)\": -2.01, \"(-1.6, -1.18)\": -1.6, \"(-1.18, -0.77)\": -1.19, \"(-0.77, -0.35)\": -0.76, \"(-0.35, -0.1)\": -0.31, \"(-0.1, 0.33)\": -0.09, \"(0.33, 0.75)\": 0.35, \"(0.75, 1.19)\": 0.77, \"(1.19, 1.6)\": 1.2, \"(1.6, 2.02)\": 1.61, \"(2.02, 2.43)\": 2.03, \"(2.43, 2.87)\": 2.45, \"(2.87, 3.07)\": 2.87, \"(3.07, 3.53)\": 3.09, \"(3.53, 3.74)\": 3.52, \"(3.74, 4.16)\": 3.76, \"(4.16, 4.57)\": 4.18, \"(4.57, 4.99)\": 4.58, \"(4.99, 5.41)\": 5.01, \"(5.41, 5.62)\": 5.42, \"(5.62, 6.05)\": 5.65, \"(6.05, 6.28)\": 6.06, \"(6.28, 6.68)\": 6.29, \"(6.68, 6.93)\": 6.72, \"(6.93, 7.36)\": 6.95, \"(7.36, 7.76)\": 7.37, \"(7.76, 8.18)\": 7.78, \"(8.18, 8.4)\": 8.19, \"(8.4, 8.83)\": 8.43, \"(8.83, 9.05)\": 8.85, \"(9.05, 9.45)\": 9.07, \"(9.45, 9.91)\": 9.49, \"(9.91, 9.97)\": 9.92}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-9.98, -9.54)\": 24.92, \"(-9.54, -9.14)\": 24.09, \"(-9.14, -8.93)\": 23.27, \"(-8.93, -8.47)\": 22.75, \"(-8.47, -8.04)\": 21.91, \"(-8.04, -7.8)\": 21.02, \"(-7.8, -7.39)\": 20.57, \"(-7.39, -7.17)\": 19.76, \"(-7.17, -6.96)\": 19.31, \"(-6.96, -6.49)\": 18.85, \"(-6.49, -6.08)\": 17.97, \"(-6.08, -5.65)\": 17.13, \"(-5.65, -5.23)\": 16.29, \"(-5.23, -4.8)\": 15.45, \"(-4.8, -4.37)\": 14.54, \"(-4.37, -3.97)\": 13.71, \"(-3.97, -3.52)\": 12.89, \"(-3.52, -3.29)\": 12.02, \"(-3.29, -2.87)\": 11.57, \"(-2.87, -2.65)\": 10.71, \"(-2.65, -2.21)\": 10.26, \"(-2.21, -1.8)\": 9.4, \"(-1.8, -1.36)\": 8.57, \"(-1.36, -0.96)\": 7.7, \"(-0.96, -0.52)\": 6.89, \"(-0.52, -0.08)\": 6.03, \"(-0.08, 0.37)\": 5.1, \"(0.37, 0.77)\": 4.26, \"(0.77, 1.18)\": 3.42, \"(1.18, 1.61)\": 2.6, \"(1.61, 1.83)\": 1.75, \"(1.83, 2.29)\": 1.3, \"(2.29, 2.67)\": 0.44, \"(2.67, 3.09)\": -0.4, \"(3.09, 3.53)\": -1.23, \"(3.53, 3.77)\": -2.09, \"(3.77, 4.18)\": -2.57, \"(4.18, 4.61)\": -3.39, \"(4.61, 5.0)\": -4.23, \"(5.0, 5.43)\": -5.04, \"(5.43, 5.84)\": -5.9, \"(5.84, 6.25)\": -6.7, \"(6.25, 6.48)\": -7.54, \"(6.48, 6.89)\": -7.99, \"(6.89, 7.31)\": -8.81, \"(7.31, 7.75)\": -9.68, \"(7.75, 8.17)\": -10.52, \"(8.17, 8.59)\": -11.37, \"(8.59, 9.02)\": -12.18, \"(9.02, 9.47)\": -13.12, \"(9.47, 9.88)\": -13.95, \"(9.88, 9.96)\": -14.79}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-9.99, -9.87)\": 99.48, \"(-9.87, -9.68)\": 97.03, \"(-9.68, -9.49)\": 92.61, \"(-9.49, -9.32)\": 90.01, \"(-9.32, -9.11)\": 86.25, \"(-9.11, -8.96)\": 82.68, \"(-8.96, -8.81)\": 80.12, \"(-8.81, -8.68)\": 77.37, \"(-8.68, -8.52)\": 74.85, \"(-8.52, -8.3)\": 72.19, \"(-8.3, -8.11)\": 68.38, \"(-8.11, -7.85)\": 65.01, \"(-7.85, -7.69)\": 61.5, \"(-7.69, -7.46)\": 59.1, \"(-7.46, -7.24)\": 55.4, \"(-7.24, -7.07)\": 52.29, \"(-7.07, -6.83)\": 49.85, \"(-6.83, -6.64)\": 46.46, \"(-6.64, -6.4)\": 43.79, \"(-6.4, -6.13)\": 40.67, \"(-6.13, -5.82)\": 37.11, \"(-5.82, -5.62)\": 33.69, \"(-5.62, -5.3)\": 31.31, \"(-5.3, -5.08)\": 27.92, \"(-5.08, -4.73)\": 25.54, \"(-4.73, -4.34)\": 22.23, \"(-4.34, -3.92)\": 18.79, \"(-3.92, -3.49)\": 15.37, \"(-3.49, -2.98)\": 12.14, \"(-2.98, -2.4)\": 8.81, \"(-2.4, -1.64)\": 5.7, \"(-1.64, 2.38)\": 2.62, \"(2.38, 2.96)\": 5.73, \"(2.96, 3.47)\": 8.86, \"(3.47, 3.93)\": 12.25, \"(3.93, 4.3)\": 15.54, \"(4.3, 4.67)\": 18.71, \"(4.67, 5.03)\": 22.12, \"(5.03, 5.28)\": 25.36, \"(5.28, 5.57)\": 27.84, \"(5.57, 5.86)\": 31.24, \"(5.86, 6.13)\": 34.43, \"(6.13, 6.41)\": 37.71, \"(6.41, 6.69)\": 41.63, \"(6.69, 6.85)\": 44.87, \"(6.85, 7.13)\": 47.33, \"(7.13, 7.36)\": 51.02, \"(7.36, 7.55)\": 54.35, \"(7.55, 7.78)\": 57.13, \"(7.78, 7.97)\": 60.82, \"(7.97, 8.2)\": 63.91, \"(8.2, 8.37)\": 67.27, \"(8.37, 8.57)\": 70.45, \"(8.57, 8.72)\": 73.72, \"(8.72, 8.88)\": 76.31, \"(8.88, 9.08)\": 79.06, \"(9.08, 9.25)\": 82.48, \"(9.25, 9.42)\": 85.8, \"(9.42, 9.58)\": 89.11, \"(9.58, 9.75)\": 92.07, \"(9.75, 9.9)\": 95.73, \"(9.9, 9.99)\": 98.21}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-9.98, -9.78)\": -198.1, \"(-9.78, -9.59)\": -190.9, \"(-9.59, -9.41)\": -183.6, \"(-9.41, -9.22)\": -176.4, \"(-9.22, -9.09)\": -169.8, \"(-9.09, -8.92)\": -164.6, \"(-8.92, -8.74)\": -158.3, \"(-8.74, -8.56)\": -152.3, \"(-8.56, -8.42)\": -146.5, \"(-8.42, -8.21)\": -141.3, \"(-8.21, -8.08)\": -134.6, \"(-8.08, -7.86)\": -129.6, \"(-7.86, -7.7)\": -123.4, \"(-7.7, -7.46)\": -117.5, \"(-7.46, -7.21)\": -110.5, \"(-7.21, -7.06)\": -103.9, \"(-7.06, -6.8)\": -98.8, \"(-6.8, -6.58)\": -91.8, \"(-6.58, -6.33)\": -86.0, \"(-6.33, -6.06)\": -79.7, \"(-6.06, -5.85)\": -73.2, \"(-5.85, -5.63)\": -68.1, \"(-5.63, -5.31)\": -62.7, \"(-5.31, -4.99)\": -56.0, \"(-4.99, -4.64)\": -49.7, \"(-4.64, -4.29)\": -43.2, \"(-4.29, -3.89)\": -36.5, \"(-3.89, -3.43)\": -29.7, \"(-3.43, -2.93)\": -23.6, \"(-2.93, -2.32)\": -17.1, \"(-2.32, -1.47)\": -10.7, \"(-1.47, 2.29)\": -4.3, \"(2.29, 2.89)\": -10.5, \"(2.89, 3.39)\": -16.8, \"(3.39, 3.85)\": -23.3, \"(3.85, 4.27)\": -29.9, \"(4.27, 4.65)\": -36.6, \"(4.65, 4.99)\": -43.0, \"(4.99, 5.22)\": -50.3, \"(5.22, 5.48)\": -55.4, \"(5.48, 5.82)\": -61.2, \"(5.82, 6.09)\": -67.8, \"(6.09, 6.31)\": -74.4, \"(6.31, 6.49)\": -79.7, \"(6.49, 6.74)\": -84.7, \"(6.74, 6.92)\": -91.2, \"(6.92, 7.17)\": -96.1, \"(7.17, 7.39)\": -102.9, \"(7.39, 7.64)\": -109.6, \"(7.64, 7.87)\": -117.2, \"(7.87, 8.01)\": -123.9, \"(8.01, 8.16)\": -128.7, \"(8.16, 8.36)\": -133.5, \"(8.36, 8.51)\": -140.1, \"(8.51, 8.68)\": -145.0, \"(8.68, 8.86)\": -150.9, \"(8.86, 9.05)\": -157.6, \"(9.05, 9.25)\": -164.3, \"(9.25, 9.43)\": -171.6, \"(9.43, 9.62)\": -179.4, \"(9.62, 9.84)\": -186.4, \"(9.84, 9.99)\": -195.4}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-10.0, -9.83)\": 143.9, \"(-9.83, -9.69)\": 139.9, \"(-9.69, -9.52)\": 136.2, \"(-9.52, -9.37)\": 132.7, \"(-9.37, -9.24)\": 129.1, \"(-9.24, -9.09)\": 126.0, \"(-9.09, -8.94)\": 122.8, \"(-8.94, -8.72)\": 119.3, \"(-8.72, -8.54)\": 114.7, \"(-8.54, -8.35)\": 110.4, \"(-8.35, -8.21)\": 107.1, \"(-8.21, -8.01)\": 103.2, \"(-8.01, -7.85)\": 100.2, \"(-7.85, -7.67)\": 96.5, \"(-7.67, -7.53)\": 93.4, \"(-7.53, -7.35)\": 90.4, \"(-7.35, -7.17)\": 87.2, \"(-7.17, -6.97)\": 83.6, \"(-6.97, -6.79)\": 80.3, \"(-6.79, -6.6)\": 76.9, \"(-6.6, -6.4)\": 73.6, \"(-6.4, -6.21)\": 70.4, \"(-6.21, -6.01)\": 67.0, \"(-6.01, -5.8)\": 64.0, \"(-5.8, -5.5)\": 60.6, \"(-5.5, -5.19)\": 56.1, \"(-5.19, -4.97)\": 51.6, \"(-4.97, -4.74)\": 48.4, \"(-4.74, -4.5)\": 45.2, \"(-4.5, -4.24)\": 42.1, \"(-4.24, -3.99)\": 39.0, \"(-3.99, -3.73)\": 36.0, \"(-3.73, -3.44)\": 32.8, \"(-3.44, -3.1)\": 29.2, \"(-3.1, -2.8)\": 26.0, \"(-2.8, -2.29)\": 23.0, \"(-2.29, -1.92)\": 18.4, \"(-1.92, -1.49)\": 15.3, \"(-1.49, -0.73)\": 11.9, \"(-0.73, 0.24)\": 7.5, \"(0.24, 4.72)\": 3.0, \"(4.72, 5.44)\": 7.5, \"(5.44, 5.86)\": 12.0, \"(5.86, 6.27)\": 15.1, \"(6.27, 6.61)\": 18.3, \"(6.61, 7.07)\": 21.7, \"(7.07, 7.4)\": 26.1, \"(7.4, 7.72)\": 29.4, \"(7.72, 8.1)\": 32.8, \"(8.1, 8.45)\": 37.4, \"(8.45, 8.7)\": 41.8, \"(8.7, 8.96)\": 45.0, \"(8.96, 9.23)\": 48.8, \"(9.23, 9.43)\": 52.2, \"(9.43, 9.63)\": 55.3, \"(9.63, 9.85)\": 58.5, \"(9.85, 9.99)\": 61.7}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-9.96, 4.31)\": 0.0, \"(4.31, 5.33)\": 0.63, \"(5.33, 5.91)\": 1.26, \"(5.91, 6.16)\": 1.91, \"(6.16, 6.38)\": 2.25, \"(6.38, 6.59)\": 2.63, \"(6.59, 6.85)\": 2.99, \"(6.85, 6.98)\": 3.66, \"(6.98, 7.11)\": 4.02, \"(7.11, 7.23)\": 4.37, \"(7.23, 7.35)\": 4.78, \"(7.35, 7.51)\": 5.12, \"(7.51, 7.61)\": 5.77, \"(7.61, 7.71)\": 6.13, \"(7.71, 7.79)\": 6.63, \"(7.79, 7.88)\": 6.99, \"(7.88, 7.96)\": 7.45, \"(7.96, 8.03)\": 7.84, \"(8.03, 8.1)\": 8.21, \"(8.1, 8.16)\": 8.65, \"(8.16, 8.22)\": 9.06, \"(8.22, 8.29)\": 9.53, \"(8.29, 8.37)\": 9.92, \"(8.37, 8.43)\": 10.48, \"(8.43, 8.55)\": 10.84, \"(8.55, 8.67)\": 12.38, \"(8.67, 8.72)\": 12.82, \"(8.72, 8.75)\": 13.24, \"(8.75, 8.84)\": 13.65, \"(8.84, 8.87)\": 14.32, \"(8.87, 8.92)\": 14.8, \"(8.92, 8.98)\": 15.5, \"(8.98, 9.02)\": 15.99, \"(9.02, 9.07)\": 16.4, \"(9.07, 9.13)\": 17.17, \"(9.13, 9.16)\": 17.57, \"(9.16, 9.2)\": 18.02, \"(9.2, 9.25)\": 18.7, \"(9.25, 9.32)\": 19.35, \"(9.32, 9.35)\": 20.04, \"(9.35, 9.4)\": 20.45, \"(9.4, 9.45)\": 21.4, \"(9.45, 9.48)\": 22.13, \"(9.48, 9.51)\": 22.55, \"(9.51, 9.54)\": 22.96, \"(9.54, 9.57)\": 23.44, \"(9.57, 9.61)\": 23.79, \"(9.61, 9.65)\": 24.65, \"(9.65, 9.67)\": 25.15, \"(9.67, 9.7)\": 25.54, \"(9.7, 9.75)\": 26.28, \"(9.75, 9.79)\": 27.29, \"(9.79, 9.82)\": 27.88, \"(9.82, 9.86)\": 28.65, \"(9.86, 9.9)\": 29.3, \"(9.9, 9.92)\": 30.17, \"(9.92, 9.98)\": 30.86}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-9.99, -9.85)\": 98.77, \"(-9.85, -9.66)\": 95.7, \"(-9.66, -9.54)\": 92.11, \"(-9.54, -9.39)\": 89.54, \"(-9.39, -9.24)\": 86.77, \"(-9.24, -9.05)\": 84.17, \"(-9.05, -8.85)\": 80.71, \"(-8.85, -8.66)\": 77.1, \"(-8.66, -8.47)\": 73.64, \"(-8.47, -8.26)\": 70.4, \"(-8.26, -8.04)\": 67.09, \"(-8.04, -7.87)\": 63.27, \"(-7.87, -7.64)\": 60.76, \"(-7.64, -7.42)\": 57.07, \"(-7.42, -7.24)\": 53.82, \"(-7.24, -7.0)\": 51.24, \"(-7.0, -6.81)\": 47.9, \"(-6.81, -6.6)\": 44.87, \"(-6.6, -6.41)\": 42.33, \"(-6.41, -6.12)\": 39.62, \"(-6.12, -5.82)\": 36.39, \"(-5.82, -5.52)\": 32.82, \"(-5.52, -5.21)\": 29.32, \"(-5.21, -4.88)\": 26.05, \"(-4.88, -4.53)\": 22.77, \"(-4.53, -4.18)\": 19.53, \"(-4.18, -3.77)\": 16.36, \"(-3.77, -3.33)\": 13.16, \"(-3.33, -2.86)\": 10.05, \"(-2.86, -2.21)\": 7.04, \"(-2.21, -1.3)\": 3.79, \"(-1.3, 2.21)\": 0.7, \"(2.21, 2.84)\": 4.03, \"(2.84, 3.39)\": 7.18, \"(3.39, 3.86)\": 10.54, \"(3.86, 4.27)\": 13.98, \"(4.27, 4.66)\": 17.33, \"(4.66, 5.0)\": 20.86, \"(5.0, 5.31)\": 24.2, \"(5.31, 5.53)\": 27.35, \"(5.53, 5.82)\": 29.85, \"(5.82, 6.14)\": 33.25, \"(6.14, 6.38)\": 36.85, \"(6.38, 6.67)\": 40.15, \"(6.67, 6.84)\": 43.61, \"(6.84, 7.1)\": 46.22, \"(7.1, 7.36)\": 49.56, \"(7.36, 7.61)\": 53.6, \"(7.61, 7.78)\": 56.99, \"(7.78, 8.0)\": 59.9, \"(8.0, 8.2)\": 63.16, \"(8.2, 8.43)\": 66.42, \"(8.43, 8.62)\": 70.19, \"(8.62, 8.77)\": 73.4, \"(8.77, 8.98)\": 76.23, \"(8.98, 9.12)\": 79.68, \"(9.12, 9.33)\": 82.45, \"(9.33, 9.5)\": 86.07, \"(9.5, 9.7)\": 89.66, \"(9.7, 9.86)\": 93.09, \"(9.86, 9.98)\": 96.2}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-9.96, -9.82)\": -988.3, \"(-9.82, -9.76)\": -946.2, \"(-9.76, -9.67)\": -921.8, \"(-9.67, -9.58)\": -900.0, \"(-9.58, -9.4)\": -871.3, \"(-9.4, -9.28)\": -828.3, \"(-9.28, -9.09)\": -792.8, \"(-9.09, -8.99)\": -748.1, \"(-8.99, -8.82)\": -725.9, \"(-8.82, -8.7)\": -684.1, \"(-8.7, -8.55)\": -648.9, \"(-8.55, -8.35)\": -621.6, \"(-8.35, -8.14)\": -580.4, \"(-8.14, -7.94)\": -538.3, \"(-7.94, -7.7)\": -496.5, \"(-7.7, -7.44)\": -455.5, \"(-7.44, -7.3)\": -409.2, \"(-7.3, -7.01)\": -385.5, \"(-7.01, -6.84)\": -341.4, \"(-6.84, -6.69)\": -319.0, \"(-6.69, -6.49)\": -295.9, \"(-6.49, -6.1)\": -269.6, \"(-6.1, -5.71)\": -226.8, \"(-5.71, -5.24)\": -185.0, \"(-5.24, -4.67)\": -142.7, \"(-4.67, -3.94)\": -101.1, \"(-3.94, -2.75)\": -60.9, \"(-2.75, 2.67)\": -20.4, \"(2.67, 3.9)\": 19.4, \"(3.9, 4.68)\": 59.5, \"(4.68, 5.24)\": 102.2, \"(5.24, 5.7)\": 144.2, \"(5.7, 6.1)\": 186.3, \"(6.1, 6.46)\": 227.5, \"(6.46, 6.81)\": 273.9, \"(6.81, 7.13)\": 322.4, \"(7.13, 7.29)\": 365.7, \"(7.29, 7.42)\": 387.4, \"(7.42, 7.56)\": 410.8, \"(7.56, 7.67)\": 432.9, \"(7.67, 7.82)\": 454.6, \"(7.82, 7.96)\": 483.3, \"(7.96, 8.18)\": 507.7, \"(8.18, 8.38)\": 550.0, \"(8.38, 8.5)\": 594.0, \"(8.5, 8.62)\": 616.4, \"(8.62, 8.74)\": 644.6, \"(8.74, 8.92)\": 669.5, \"(8.92, 9.09)\": 710.7, \"(9.09, 9.29)\": 752.5, \"(9.29, 9.46)\": 808.8, \"(9.46, 9.57)\": 849.4, \"(9.57, 9.68)\": 885.2, \"(9.68, 9.76)\": 908.7, \"(9.76, 9.84)\": 930.4, \"(9.84, 9.9)\": 955.1, \"(9.9, 9.99)\": 977.1}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-9.97, -9.84)\": 2925.0, \"(-9.84, -9.76)\": 2851.0, \"(-9.76, -9.66)\": 2769.6, \"(-9.66, -9.59)\": 2700.3, \"(-9.59, -9.49)\": 2628.1, \"(-9.49, -9.39)\": 2558.0, \"(-9.39, -9.21)\": 2461.7, \"(-9.21, -9.03)\": 2338.1, \"(-9.03, -8.92)\": 2200.1, \"(-8.92, -8.81)\": 2118.7, \"(-8.81, -8.68)\": 2030.4, \"(-8.68, -8.49)\": 1958.2, \"(-8.49, -8.3)\": 1835.0, \"(-8.3, -8.2)\": 1711.7, \"(-8.2, -7.97)\": 1646.4, \"(-7.97, -7.76)\": 1519.5, \"(-7.76, -7.52)\": 1398.2, \"(-7.52, -7.28)\": 1273.3, \"(-7.28, -7.14)\": 1147.8, \"(-7.14, -6.98)\": 1080.9, \"(-6.98, -6.67)\": 1013.9, \"(-6.67, -6.34)\": 888.5, \"(-6.34, -6.15)\": 759.4, \"(-6.15, -5.75)\": 693.4, \"(-5.75, -5.31)\": 571.3, \"(-5.31, -5.03)\": 447.3, \"(-5.03, -4.41)\": 377.2, \"(-4.41, -3.57)\": 252.9, \"(-3.57, -1.61)\": 132.6, \"(-1.61, 3.28)\": 12.6, \"(3.28, 4.2)\": -105.6, \"(4.2, 4.88)\": -228.7, \"(4.88, 5.4)\": -351.1, \"(5.4, 5.83)\": -472.6, \"(5.83, 6.22)\": -598.1, \"(6.22, 6.57)\": -729.5, \"(6.57, 6.75)\": -860.0, \"(6.75, 7.05)\": -928.7, \"(7.05, 7.31)\": -1051.6, \"(7.31, 7.47)\": -1173.7, \"(7.47, 7.62)\": -1267.3, \"(7.62, 7.75)\": -1333.0, \"(7.75, 7.88)\": -1404.0, \"(7.88, 8.08)\": -1469.7, \"(8.08, 8.29)\": -1594.0, \"(8.29, 8.42)\": -1727.7, \"(8.42, 8.61)\": -1798.5, \"(8.61, 8.72)\": -1934.2, \"(8.72, 8.93)\": -2009.9, \"(8.93, 9.02)\": -2140.2, \"(9.02, 9.1)\": -2212.0, \"(9.1, 9.29)\": -2278.0, \"(9.29, 9.38)\": -2408.1, \"(9.38, 9.55)\": -2490.2, \"(9.55, 9.69)\": -2637.0, \"(9.69, 9.79)\": -2761.9, \"(9.79, 9.88)\": -2827.0, \"(9.88, 9.98)\": -2893.5}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-9.99, -9.84)\": 9750.2, \"(-9.84, -9.74)\": 9350.3, \"(-9.74, -9.66)\": 8906.2, \"(-9.66, -9.56)\": 8618.5, \"(-9.56, -9.45)\": 8311.2, \"(-9.45, -9.37)\": 7932.1, \"(-9.37, -9.26)\": 7668.4, \"(-9.26, -9.15)\": 7318.4, \"(-9.15, -9.04)\": 6966.3, \"(-9.04, -8.93)\": 6674.2, \"(-8.93, -8.77)\": 6318.3, \"(-8.77, -8.59)\": 5817.1, \"(-8.59, -8.47)\": 5448.5, \"(-8.47, -8.34)\": 5080.5, \"(-8.34, -8.19)\": 4777.9, \"(-8.19, -8.01)\": 4465.9, \"(-8.01, -7.84)\": 4082.9, \"(-7.84, -7.61)\": 3676.2, \"(-7.61, -7.4)\": 3345.2, \"(-7.4, -7.17)\": 3004.9, \"(-7.17, -6.92)\": 2622.5, \"(-6.92, -6.65)\": 2264.4, \"(-6.65, -6.31)\": 1930.7, \"(-6.31, -5.94)\": 1572.1, \"(-5.94, -5.52)\": 1236.4, \"(-5.52, -5.0)\": 915.6, \"(-5.0, -4.16)\": 603.6, \"(-4.16, -0.58)\": 297.8, \"(-0.58, 4.15)\": -3.1, \"(4.15, 4.99)\": 305.2, \"(4.99, 5.52)\": 623.6, \"(5.52, 5.98)\": 933.4, \"(5.98, 6.36)\": 1304.4, \"(6.36, 6.64)\": 1639.6, \"(6.64, 6.9)\": 1958.0, \"(6.9, 7.12)\": 2291.1, \"(7.12, 7.35)\": 2630.8, \"(7.35, 7.54)\": 2941.6, \"(7.54, 7.72)\": 3239.5, \"(7.72, 7.9)\": 3570.9, \"(7.9, 8.11)\": 3996.1, \"(8.11, 8.27)\": 4347.1, \"(8.27, 8.41)\": 4690.7, \"(8.41, 8.55)\": 5047.3, \"(8.55, 8.68)\": 5341.7, \"(8.68, 8.83)\": 5713.2, \"(8.83, 8.91)\": 6090.4, \"(8.91, 9.02)\": 6366.4, \"(9.02, 9.15)\": 6667.5, \"(9.15, 9.25)\": 7029.7, \"(9.25, 9.38)\": 7351.2, \"(9.38, 9.53)\": 7840.9, \"(9.53, 9.62)\": 8347.3, \"(9.62, 9.73)\": 8615.8, \"(9.73, 9.85)\": 8996.0, \"(9.85, 9.97)\": 9535.2, \"(9.97, 9.99)\": 9917.4}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-9.99, -8.67)\": 1258.2, \"(-8.67, 1.98)\": 470.6, \"(1.98, 2.76)\": 1286.8, \"(2.76, 3.32)\": 2084.1, \"(3.32, 3.79)\": 2873.9, \"(3.79, 4.17)\": 3689.2, \"(4.17, 4.51)\": 4488.4, \"(4.51, 4.82)\": 5299.8, \"(4.82, 5.1)\": 6103.5, \"(5.1, 5.39)\": 6973.1, \"(5.39, 5.63)\": 7802.3, \"(5.63, 5.87)\": 8685.1, \"(5.87, 6.07)\": 9503.9, \"(6.07, 6.18)\": 10330.5, \"(6.18, 6.4)\": 10851.0, \"(6.4, 6.49)\": 11686.6, \"(6.49, 6.66)\": 12177.9, \"(6.66, 6.75)\": 13002.4, \"(6.75, 6.9)\": 13496.6, \"(6.9, 7.1)\": 14304.2, \"(7.1, 7.2)\": 15313.1, \"(7.2, 7.35)\": 15779.7, \"(7.35, 7.48)\": 16612.8, \"(7.48, 7.64)\": 17497.2, \"(7.64, 7.72)\": 18474.5, \"(7.72, 7.87)\": 18947.7, \"(7.87, 8.01)\": 20065.7, \"(8.01, 8.13)\": 20887.3, \"(8.13, 8.2)\": 21744.3, \"(8.2, 8.34)\": 22329.3, \"(8.34, 8.41)\": 23278.8, \"(8.41, 8.49)\": 23924.9, \"(8.49, 8.56)\": 24403.2, \"(8.56, 8.64)\": 24927.7, \"(8.64, 8.74)\": 25857.6, \"(8.74, 8.84)\": 26474.1, \"(8.84, 8.95)\": 27349.1, \"(8.95, 9.03)\": 28298.7, \"(9.03, 9.1)\": 28978.1, \"(9.1, 9.21)\": 29559.9, \"(9.21, 9.29)\": 30826.9, \"(9.29, 9.36)\": 31397.3, \"(9.36, 9.41)\": 31938.5, \"(9.41, 9.46)\": 32430.9, \"(9.46, 9.5)\": 32905.3, \"(9.5, 9.59)\": 33364.1, \"(9.59, 9.67)\": 34408.1, \"(9.67, 9.74)\": 35145.8, \"(9.74, 9.79)\": 35717.5, \"(9.79, 9.83)\": 36173.7, \"(9.83, 9.89)\": 36843.8, \"(9.89, 9.99)\": 37520.4}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-9.97, -0.05)\": -1.003, \"(-0.05, -0.01)\": -0.982, \"(-0.01, -0.0)\": -0.91, \"(-0.0, 0.01)\": 0.704, \"(0.01, 0.29)\": 0.976, \"(0.29, 9.97)\": 0.997}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-10.0, -3.0)\": -1.001, \"(-3.0, -3.0)\": 0.921, \"(-3.0, 9.9)\": 0.99}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-10.0, 0.87)\": -1.004, \"(0.87, 0.95)\": -0.983, \"(0.95, 0.96)\": -0.926, \"(0.96, 1.0)\": -0.773, \"(1.0, 1.05)\": 0.715, \"(1.05, 1.2)\": 0.96, \"(1.2, 1.49)\": 0.981, \"(1.49, 9.97)\": 1.002}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-10.0, -9.73)\": 9.98, \"(-9.73, -9.37)\": 9.718, \"(-9.37, -9.03)\": 9.364, \"(-9.03, -8.78)\": 9.027, \"(-8.78, -8.43)\": 8.759, \"(-8.43, -8.12)\": 8.416, \"(-8.12, -7.86)\": 8.108, \"(-7.86, -7.6)\": 7.85, \"(-7.6, -7.23)\": 7.567, \"(-7.23, -6.85)\": 7.196, \"(-6.85, -6.5)\": 6.827, \"(-6.5, -6.16)\": 6.489, \"(-6.16, -5.9)\": 6.152, \"(-5.9, -5.58)\": 5.893, \"(-5.58, -5.22)\": 5.561, \"(-5.22, -4.97)\": 5.202, \"(-4.97, -4.6)\": 4.929, \"(-4.6, -4.24)\": 4.579, \"(-4.24, -3.87)\": 4.207, \"(-3.87, -3.53)\": 3.857, \"(-3.53, -3.22)\": 3.524, \"(-3.22, -2.9)\": 3.194, \"(-2.9, -2.55)\": 2.876, \"(-2.55, -2.19)\": 2.529, \"(-2.19, -1.88)\": 2.196, \"(-1.88, -1.49)\": 1.862, \"(-1.49, -1.1)\": 1.448, \"(-1.1, -0.73)\": 1.093, \"(-0.73, -0.38)\": 0.719, \"(-0.38, -0.06)\": 0.365, \"(-0.06, 0.36)\": 0.041, \"(0.36, 0.71)\": 0.373, \"(0.71, 1.03)\": 0.726, \"(1.03, 1.35)\": 1.043, \"(1.35, 1.69)\": 1.358, \"(1.69, 2.06)\": 1.705, \"(2.06, 2.35)\": 2.064, \"(2.35, 2.67)\": 2.367, \"(2.67, 2.98)\": 2.684, \"(2.98, 3.33)\": 2.994, \"(3.33, 3.65)\": 3.341, \"(3.65, 3.99)\": 3.675, \"(3.99, 4.33)\": 3.995, \"(4.33, 4.64)\": 4.334, \"(4.64, 4.99)\": 4.641, \"(4.99, 5.33)\": 4.99, \"(5.33, 5.67)\": 5.339, \"(5.67, 6.02)\": 5.694, \"(6.02, 6.4)\": 6.071, \"(6.4, 6.73)\": 6.395, \"(6.73, 7.06)\": 6.741, \"(7.06, 7.37)\": 7.066, \"(7.37, 7.74)\": 7.393, \"(7.74, 8.06)\": 7.752, \"(8.06, 8.41)\": 8.073, \"(8.41, 8.67)\": 8.41, \"(8.67, 8.97)\": 8.68, \"(8.97, 9.33)\": 8.994, \"(9.33, 9.67)\": 9.343, \"(9.67, 9.94)\": 9.666, \"(9.94, 9.98)\": 9.947}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-9.97, -9.72)\": 14.93, \"(-9.72, -9.4)\": 14.38, \"(-9.4, -9.01)\": 13.78, \"(-9.01, -8.68)\": 13.01, \"(-8.68, -8.27)\": 12.3, \"(-8.27, -7.99)\": 11.52, \"(-7.99, -7.58)\": 10.95, \"(-7.58, -7.32)\": 10.15, \"(-7.32, -6.91)\": 9.61, \"(-6.91, -6.65)\": 8.81, \"(-6.65, -6.25)\": 8.27, \"(-6.25, -5.93)\": 7.44, \"(-5.93, -5.63)\": 6.78, \"(-5.63, -5.21)\": 6.23, \"(-5.21, -4.8)\": 5.4, \"(-4.8, -4.52)\": 4.55, \"(-4.52, -4.12)\": 3.98, \"(-4.12, -3.72)\": 3.2, \"(-3.72, -3.47)\": 2.43, \"(-3.47, -3.05)\": 1.85, \"(-3.05, -2.64)\": 1.08, \"(-2.64, -1.99)\": 0.27, \"(-1.99, -1.69)\": 1.05, \"(-1.69, -1.43)\": 1.62, \"(-1.43, -1.13)\": 2.2, \"(-1.13, -0.84)\": 2.78, \"(-0.84, -0.54)\": 3.37, \"(-0.54, -0.16)\": 3.95, \"(-0.16, 0.26)\": 4.71, \"(0.26, 0.65)\": 5.53, \"(0.65, 0.91)\": 6.32, \"(0.91, 1.24)\": 6.87, \"(1.24, 1.63)\": 7.51, \"(1.63, 1.93)\": 8.27, \"(1.93, 2.2)\": 8.88, \"(2.2, 2.48)\": 9.42, \"(2.48, 2.87)\": 9.97, \"(2.87, 3.15)\": 10.75, \"(3.15, 3.44)\": 11.31, \"(3.44, 3.83)\": 11.91, \"(3.83, 4.25)\": 12.7, \"(4.25, 4.65)\": 13.53, \"(4.65, 4.92)\": 14.33, \"(4.92, 5.32)\": 14.87, \"(5.32, 5.7)\": 15.64, \"(5.7, 6.11)\": 16.45, \"(6.11, 6.51)\": 17.22, \"(6.51, 6.78)\": 18.04, \"(6.78, 7.08)\": 18.62, \"(7.08, 7.47)\": 19.17, \"(7.47, 7.77)\": 20.0, \"(7.77, 8.17)\": 20.55, \"(8.17, 8.44)\": 21.34, \"(8.44, 8.82)\": 21.89, \"(8.82, 9.11)\": 22.66, \"(9.11, 9.47)\": 23.22, \"(9.47, 9.76)\": 24.0, \"(9.76, 9.99)\": 24.55}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-9.99, -9.95)\": 0.124, \"(-9.95, -9.9)\": 0.271, \"(-9.9, -9.85)\": 0.337, \"(-9.85, -9.82)\": 0.392, \"(-9.82, -9.77)\": 0.442, \"(-9.77, -9.67)\": 0.491, \"(-9.67, -9.49)\": 0.622, \"(-9.49, -9.33)\": 0.731, \"(-9.33, -9.17)\": 0.823, \"(-9.17, -8.99)\": 0.915, \"(-8.99, -8.78)\": 1.015, \"(-8.78, -8.66)\": 1.112, \"(-8.66, -8.43)\": 1.161, \"(-8.43, -8.19)\": 1.263, \"(-8.19, -7.95)\": 1.352, \"(-7.95, -7.78)\": 1.443, \"(-7.78, -7.51)\": 1.491, \"(-7.51, -7.22)\": 1.583, \"(-7.22, -7.04)\": 1.673, \"(-7.04, -6.74)\": 1.724, \"(-6.74, -6.35)\": 1.815, \"(-6.35, -6.19)\": 1.909, \"(-6.19, -5.98)\": 1.956, \"(-5.98, -5.58)\": 2.012, \"(-5.58, -5.39)\": 2.102, \"(-5.39, -4.96)\": 2.151, \"(-4.96, -4.59)\": 2.241, \"(-4.59, -4.35)\": 2.33, \"(-4.35, -3.92)\": 2.379, \"(-3.92, -3.44)\": 2.47, \"(-3.44, -2.96)\": 2.565, \"(-2.96, -2.75)\": 2.655, \"(-2.75, -2.47)\": 2.703, \"(-2.47, -1.92)\": 2.751, \"(-1.92, -1.38)\": 2.844, \"(-1.38, -0.85)\": 2.935, \"(-0.85, -0.26)\": 3.027, \"(-0.26, 0.34)\": 3.124, \"(0.34, 0.92)\": 3.214, \"(0.92, 1.53)\": 3.303, \"(1.53, 2.16)\": 3.397, \"(2.16, 2.79)\": 3.489, \"(2.79, 3.44)\": 3.578, \"(3.44, 4.11)\": 3.668, \"(4.11, 4.82)\": 3.76, \"(4.82, 5.53)\": 3.851, \"(5.53, 6.24)\": 3.94, \"(6.24, 7.0)\": 4.031, \"(7.0, 7.7)\": 4.121, \"(7.7, 8.46)\": 4.209, \"(8.46, 9.25)\": 4.297, \"(9.25, 9.99)\": 4.388}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-10.0, -9.76)\": 8.644, \"(-9.76, -9.39)\": 8.393, \"(-9.39, -9.08)\": 8.062, \"(-9.08, -8.8)\": 7.743, \"(-8.8, -8.57)\": 7.477, \"(-8.57, -8.23)\": 7.234, \"(-8.23, -7.89)\": 6.918, \"(-7.89, -7.65)\": 6.585, \"(-7.65, -7.39)\": 6.351, \"(-7.39, -7.14)\": 6.117, \"(-7.14, -6.8)\": 5.883, \"(-6.8, -6.47)\": 5.541, \"(-6.47, -6.2)\": 5.235, \"(-6.2, -5.87)\": 4.983, \"(-5.87, -5.62)\": 4.661, \"(-5.62, -5.25)\": 4.417, \"(-5.25, -4.99)\": 4.095, \"(-4.99, -4.65)\": 3.854, \"(-4.65, -4.24)\": 3.528, \"(-4.24, -3.86)\": 3.176, \"(-3.86, -3.44)\": 2.862, \"(-3.44, -3.11)\": 2.538, \"(-3.11, -2.56)\": 2.28, \"(-2.56, 0.05)\": 1.968, \"(0.05, 0.36)\": 2.278, \"(0.36, 0.65)\": 2.505, \"(0.65, 0.94)\": 2.731, \"(0.94, 1.23)\": 2.969, \"(1.23, 1.48)\": 3.196, \"(1.48, 1.85)\": 3.426, \"(1.85, 2.1)\": 3.742, \"(2.1, 2.35)\": 3.97, \"(2.35, 2.71)\": 4.217, \"(2.71, 3.09)\": 4.539, \"(3.09, 3.32)\": 4.888, \"(3.32, 3.58)\": 5.114, \"(3.58, 3.89)\": 5.34, \"(3.89, 4.24)\": 5.662, \"(4.24, 4.49)\": 5.994, \"(4.49, 4.82)\": 6.22, \"(4.82, 5.04)\": 6.53, \"(5.04, 5.38)\": 6.757, \"(5.38, 5.69)\": 7.076, \"(5.69, 6.06)\": 7.387, \"(6.06, 6.42)\": 7.776, \"(6.42, 6.76)\": 8.113, \"(6.76, 7.09)\": 8.437, \"(7.09, 7.43)\": 8.759, \"(7.43, 7.76)\": 9.087, \"(7.76, 8.07)\": 9.411, \"(8.07, 8.31)\": 9.719, \"(8.31, 8.65)\": 9.953, \"(8.65, 8.99)\": 10.295, \"(8.99, 9.23)\": 10.646, \"(9.23, 9.47)\": 10.872, \"(9.47, 9.72)\": 11.106, \"(9.72, 9.98)\": 11.355}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-9.98, -1.86)\": -0.0004, \"(-1.86, -1.66)\": 0.0324, \"(-1.66, -1.56)\": 0.065, \"(-1.56, -1.47)\": 0.0935, \"(-1.47, -1.4)\": 0.1157, \"(-1.4, -1.35)\": 0.1419, \"(-1.35, -1.29)\": 0.1676, \"(-1.29, -1.22)\": 0.197, \"(-1.22, -1.18)\": 0.2263, \"(-1.18, -1.13)\": 0.2553, \"(-1.13, -1.09)\": 0.2829, \"(-1.09, -1.03)\": 0.3217, \"(-1.03, -1.0)\": 0.3547, \"(-1.0, -0.96)\": 0.3767, \"(-0.96, -0.94)\": 0.4042, \"(-0.94, -0.89)\": 0.4273, \"(-0.89, -0.84)\": 0.4689, \"(-0.84, -0.8)\": 0.5014, \"(-0.8, -0.75)\": 0.5401, \"(-0.75, -0.7)\": 0.592, \"(-0.7, -0.65)\": 0.6219, \"(-0.65, -0.6)\": 0.6611, \"(-0.6, -0.55)\": 0.7235, \"(-0.55, -0.52)\": 0.7471, \"(-0.52, -0.48)\": 0.7816, \"(-0.48, -0.44)\": 0.8115, \"(-0.44, -0.39)\": 0.8366, \"(-0.39, -0.36)\": 0.8635, \"(-0.36, -0.31)\": 0.8859, \"(-0.31, -0.26)\": 0.9213, \"(-0.26, -0.18)\": 0.943, \"(-0.18, 0.24)\": 0.9737, \"(0.24, 0.31)\": 0.9402, \"(0.31, 0.38)\": 0.8935, \"(0.38, 0.42)\": 0.8558, \"(0.42, 0.51)\": 0.8321, \"(0.51, 0.61)\": 0.7247, \"(0.61, 0.65)\": 0.6823, \"(0.65, 0.68)\": 0.6527, \"(0.68, 0.73)\": 0.6252, \"(0.73, 0.82)\": 0.5507, \"(0.82, 0.88)\": 0.4795, \"(0.88, 0.92)\": 0.4518, \"(0.92, 0.97)\": 0.4238, \"(0.97, 1.01)\": 0.3838, \"(1.01, 1.06)\": 0.345, \"(1.06, 1.11)\": 0.3194, \"(1.11, 1.2)\": 0.2885, \"(1.2, 1.26)\": 0.2184, \"(1.26, 1.32)\": 0.1944, \"(1.32, 1.4)\": 0.1659, \"(1.4, 1.51)\": 0.1285, \"(1.51, 1.57)\": 0.1019, \"(1.57, 1.71)\": 0.0789, \"(1.71, 1.85)\": 0.0534, \"(1.85, 2.7)\": 0.0314, \"(2.7, 9.93)\": 0.001}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-9.99, 5.38)\": 3.1, \"(5.38, 6.09)\": 225.6, \"(6.09, 6.49)\": 449.6, \"(6.49, 6.81)\": 678.9, \"(6.81, 7.03)\": 905.8, \"(7.03, 7.22)\": 1142.3, \"(7.22, 7.39)\": 1368.9, \"(7.39, 7.51)\": 1610.3, \"(7.51, 7.62)\": 1825.6, \"(7.62, 7.72)\": 2053.8, \"(7.72, 7.82)\": 2281.9, \"(7.82, 7.93)\": 2515.7, \"(7.93, 8.01)\": 2777.7, \"(8.01, 8.08)\": 3041.9, \"(8.08, 8.15)\": 3278.3, \"(8.15, 8.2)\": 3502.4, \"(8.2, 8.29)\": 3725.7, \"(8.29, 8.36)\": 4089.3, \"(8.36, 8.44)\": 4354.8, \"(8.44, 8.49)\": 4706.8, \"(8.49, 8.58)\": 5018.0, \"(8.58, 8.64)\": 5471.8, \"(8.64, 8.69)\": 5703.9, \"(8.69, 8.74)\": 6000.7, \"(8.74, 8.78)\": 6294.1, \"(8.78, 8.82)\": 6564.5, \"(8.82, 8.86)\": 6859.6, \"(8.86, 8.88)\": 7114.2, \"(8.88, 8.93)\": 7359.3, \"(8.93, 8.99)\": 7760.8, \"(8.99, 9.04)\": 8177.5, \"(9.04, 9.08)\": 8568.4, \"(9.08, 9.12)\": 8941.6, \"(9.12, 9.15)\": 9167.9, \"(9.15, 9.19)\": 9496.2, \"(9.19, 9.22)\": 9892.5, \"(9.22, 9.25)\": 10150.4, \"(9.25, 9.29)\": 10515.6, \"(9.29, 9.31)\": 10903.9, \"(9.31, 9.36)\": 11209.0, \"(9.36, 9.41)\": 11969.5, \"(9.41, 9.45)\": 12354.5, \"(9.45, 9.48)\": 12824.5, \"(9.48, 9.51)\": 13360.2, \"(9.51, 9.53)\": 13580.4, \"(9.53, 9.57)\": 13884.2, \"(9.57, 9.6)\": 14627.4, \"(9.6, 9.64)\": 15020.3, \"(9.64, 9.69)\": 15878.3, \"(9.69, 9.73)\": 16412.1, \"(9.73, 9.75)\": 17031.9, \"(9.75, 9.77)\": 17251.8, \"(9.77, 9.8)\": 17700.7, \"(9.8, 9.83)\": 18283.9, \"(9.83, 9.86)\": 18651.7, \"(9.86, 9.91)\": 19615.7, \"(9.91, 9.95)\": 20605.3, \"(9.95, 9.96)\": 20918.3, \"(9.96, 10.0)\": 21413.4}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-10.0, -9.98)\": 21803.4, \"(-9.98, -9.96)\": 21554.9, \"(-9.96, -9.94)\": 20875.2, \"(-9.94, -9.93)\": 20614.6, \"(-9.93, -9.91)\": 20322.7, \"(-9.91, -9.88)\": 19793.9, \"(-9.88, -9.86)\": 19382.1, \"(-9.86, -9.83)\": 19002.2, \"(-9.83, -9.8)\": 18265.5, \"(-9.8, -9.79)\": 18001.2, \"(-9.79, -9.75)\": 17703.6, \"(-9.75, -9.72)\": 16875.9, \"(-9.72, -9.69)\": 16518.5, \"(-9.69, -9.65)\": 15793.3, \"(-9.65, -9.61)\": 15347.3, \"(-9.61, -9.57)\": 14548.7, \"(-9.57, -9.55)\": 14304.5, \"(-9.55, -9.54)\": 14010.1, \"(-9.54, -9.5)\": 13744.2, \"(-9.5, -9.43)\": 13125.8, \"(-9.43, -9.36)\": 11738.3, \"(-9.36, -9.33)\": 11492.4, \"(-9.33, -9.27)\": 11060.3, \"(-9.27, -9.22)\": 10302.3, \"(-9.22, -9.17)\": 10002.8, \"(-9.17, -9.13)\": 9476.7, \"(-9.13, -9.1)\": 9191.1, \"(-9.1, -9.06)\": 8888.7, \"(-9.06, -8.96)\": 8310.2, \"(-8.96, -8.91)\": 7694.4, \"(-8.91, -8.86)\": 7361.0, \"(-8.86, -8.82)\": 6925.4, \"(-8.82, -8.77)\": 6687.3, \"(-8.77, -8.68)\": 6311.6, \"(-8.68, -8.62)\": 5770.6, \"(-8.62, -8.57)\": 5451.0, \"(-8.57, -8.5)\": 5197.0, \"(-8.5, -8.37)\": 4800.8, \"(-8.37, -8.31)\": 4270.9, \"(-8.31, -8.24)\": 4030.1, \"(-8.24, -8.17)\": 3755.5, \"(-8.17, -8.1)\": 3511.1, \"(-8.1, -7.94)\": 3271.9, \"(-7.94, -7.83)\": 2768.2, \"(-7.83, -7.73)\": 2493.0, \"(-7.73, -7.49)\": 2246.8, \"(-7.49, -7.19)\": 1780.6, \"(-7.19, -6.78)\": 1320.4, \"(-6.78, -6.05)\": 869.6, \"(-6.05, 9.98)\": 431.6}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-9.99, -9.97)\": -3.99, \"(-9.97, -9.94)\": -3.106, \"(-9.94, -9.91)\": -2.777, \"(-9.91, -9.88)\": -2.279, \"(-9.88, -9.87)\": -2.099, \"(-9.87, -9.84)\": -1.909, \"(-9.84, -9.81)\": -1.745, \"(-9.81, -9.78)\": -1.577, \"(-9.78, -9.73)\": -1.498, \"(-9.73, -9.66)\": -1.166, \"(-9.66, -9.63)\": -1.055, \"(-9.63, -9.59)\": -0.976, \"(-9.59, -9.54)\": -0.846, \"(-9.54, -9.5)\": -0.724, \"(-9.5, -9.44)\": -0.648, \"(-9.44, -9.4)\": -0.57, \"(-9.4, -9.35)\": -0.481, \"(-9.35, -9.28)\": -0.397, \"(-9.28, -9.22)\": -0.311, \"(-9.22, -9.11)\": -0.195, \"(-9.11, -9.03)\": -0.101, \"(-9.03, -8.96)\": -0.015, \"(-8.96, -8.86)\": 0.063, \"(-8.86, -8.75)\": 0.144, \"(-8.75, -8.63)\": 0.24, \"(-8.63, -8.53)\": 0.322, \"(-8.53, -8.4)\": 0.398, \"(-8.4, -8.26)\": 0.479, \"(-8.26, -7.98)\": 0.563, \"(-7.98, -7.8)\": 0.715, \"(-7.8, -7.43)\": 0.803, \"(-7.43, -7.02)\": 0.945, \"(-7.02, -6.55)\": 1.097, \"(-6.55, -6.03)\": 1.239, \"(-6.03, -5.42)\": 1.384, \"(-5.42, -4.7)\": 1.525, \"(-4.7, -3.87)\": 1.672, \"(-3.87, -2.91)\": 1.815, \"(-2.91, -1.85)\": 1.958, \"(-1.85, -1.17)\": 2.102, \"(-1.17, 0.17)\": 2.18, \"(0.17, 1.72)\": 2.322, \"(1.72, 3.53)\": 2.462, \"(3.53, 5.56)\": 2.604, \"(5.56, 7.93)\": 2.745, \"(7.93, 9.95)\": 2.887}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-9.99, -9.76)\": 0.524, \"(-9.76, -9.54)\": 0.315, \"(-9.54, -9.35)\": 0.101, \"(-9.35, -9.18)\": -0.088, \"(-9.18, -8.97)\": -0.259, \"(-8.97, -8.75)\": -0.442, \"(-8.75, -8.53)\": -0.631, \"(-8.53, -8.03)\": -0.811, \"(-8.03, -7.24)\": -0.988, \"(-7.24, -6.99)\": -0.812, \"(-6.99, -6.77)\": -0.637, \"(-6.77, -6.56)\": -0.463, \"(-6.56, -6.36)\": -0.262, \"(-6.36, -6.14)\": -0.063, \"(-6.14, -5.92)\": 0.183, \"(-5.92, -5.7)\": 0.363, \"(-5.7, -5.48)\": 0.561, \"(-5.48, -5.17)\": 0.732, \"(-5.17, -3.95)\": 0.908, \"(-3.95, -3.69)\": 0.69, \"(-3.69, -3.48)\": 0.513, \"(-3.48, -3.28)\": 0.324, \"(-3.28, -3.05)\": 0.106, \"(-3.05, -2.88)\": -0.109, \"(-2.88, -2.67)\": -0.285, \"(-2.67, -2.43)\": -0.478, \"(-2.43, -2.16)\": -0.652, \"(-2.16, -0.7)\": -0.834, \"(-0.7, -0.48)\": -0.636, \"(-0.48, -0.29)\": -0.451, \"(-0.29, -0.1)\": -0.277, \"(-0.1, 0.09)\": -0.074, \"(0.09, 0.27)\": 0.102, \"(0.27, 0.5)\": 0.281, \"(0.5, 0.76)\": 0.511, \"(0.76, 1.07)\": 0.692, \"(1.07, 2.31)\": 0.892, \"(2.31, 2.59)\": 0.713, \"(2.59, 2.8)\": 0.501, \"(2.8, 3.01)\": 0.322, \"(3.01, 3.23)\": 0.102, \"(3.23, 3.42)\": -0.107, \"(3.42, 3.61)\": -0.286, \"(3.61, 3.83)\": -0.457, \"(3.83, 4.12)\": -0.65, \"(4.12, 5.57)\": -0.842, \"(5.57, 5.79)\": -0.648, \"(5.79, 5.98)\": -0.458, \"(5.98, 6.15)\": -0.281, \"(6.15, 6.36)\": -0.106, \"(6.36, 6.58)\": 0.098, \"(6.58, 6.77)\": 0.298, \"(6.77, 6.99)\": 0.481, \"(6.99, 7.24)\": 0.655, \"(7.24, 8.73)\": 0.83, \"(8.73, 8.97)\": 0.614, \"(8.97, 9.18)\": 0.426, \"(9.18, 9.39)\": 0.227, \"(9.39, 9.63)\": 0.008, \"(9.63, 9.82)\": -0.224, \"(9.82, 9.99)\": -0.395}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-9.99, -8.65)\": -0.86, \"(-8.65, -8.41)\": -0.689, \"(-8.41, -8.19)\": -0.511, \"(-8.19, -8.04)\": -0.325, \"(-8.04, -7.87)\": -0.161, \"(-7.87, -7.66)\": 0.026, \"(-7.66, -7.47)\": 0.209, \"(-7.47, -7.26)\": 0.387, \"(-7.26, -7.04)\": 0.569, \"(-7.04, -6.78)\": 0.734, \"(-6.78, -5.54)\": 0.898, \"(-5.54, -5.3)\": 0.718, \"(-5.3, -5.11)\": 0.545, \"(-5.11, -4.93)\": 0.376, \"(-4.93, -4.75)\": 0.205, \"(-4.75, -4.57)\": 0.032, \"(-4.57, -4.37)\": -0.154, \"(-4.37, -4.19)\": -0.339, \"(-4.19, -3.97)\": -0.508, \"(-3.97, -3.71)\": -0.687, \"(-3.71, -2.33)\": -0.862, \"(-2.33, -2.07)\": -0.671, \"(-2.07, -1.85)\": -0.46, \"(-1.85, -1.67)\": -0.262, \"(-1.67, -1.49)\": -0.093, \"(-1.49, -1.28)\": 0.11, \"(-1.28, -1.09)\": 0.292, \"(-1.09, -0.84)\": 0.476, \"(-0.84, -0.56)\": 0.676, \"(-0.56, 0.81)\": 0.861, \"(0.81, 1.02)\": 0.686, \"(1.02, 1.22)\": 0.507, \"(1.22, 1.43)\": 0.313, \"(1.43, 1.62)\": 0.124, \"(1.62, 1.8)\": -0.058, \"(1.8, 1.97)\": -0.241, \"(1.97, 2.16)\": -0.413, \"(2.16, 2.45)\": -0.584, \"(2.45, 2.85)\": -0.781, \"(2.85, 3.83)\": -0.96, \"(3.83, 4.1)\": -0.753, \"(4.1, 4.31)\": -0.565, \"(4.31, 4.5)\": -0.383, \"(4.5, 4.7)\": -0.203, \"(4.7, 4.89)\": -0.005, \"(4.89, 5.06)\": 0.192, \"(5.06, 5.26)\": 0.364, \"(5.26, 5.5)\": 0.53, \"(5.5, 5.8)\": 0.719, \"(5.8, 7.05)\": 0.887, \"(7.05, 7.28)\": 0.718, \"(7.28, 7.49)\": 0.537, \"(7.49, 7.68)\": 0.347, \"(7.68, 7.84)\": 0.165, \"(7.84, 8.03)\": 0.001, \"(8.03, 8.21)\": -0.183, \"(8.21, 8.4)\": -0.357, \"(8.4, 8.62)\": -0.526, \"(8.62, 8.91)\": -0.7, \"(8.91, 9.96)\": -0.877}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-10.0, -9.96)\": -10883.3, \"(-9.96, -9.93)\": -10565.3, \"(-9.93, -9.86)\": -10069.6, \"(-9.86, -9.83)\": -9507.8, \"(-9.83, -9.78)\": -9155.0, \"(-9.78, -9.72)\": -8605.9, \"(-9.72, -9.66)\": -8125.4, \"(-9.66, -9.58)\": -7712.8, \"(-9.58, -9.51)\": -6896.7, \"(-9.51, -9.4)\": -6615.3, \"(-9.4, -9.33)\": -5839.9, \"(-9.33, -9.26)\": -5544.6, \"(-9.26, -9.15)\": -5162.0, \"(-9.15, -9.07)\": -4586.9, \"(-9.07, -9.02)\": -4312.6, \"(-9.02, -8.92)\": -3997.4, \"(-8.92, -8.79)\": -3703.0, \"(-8.79, -8.71)\": -3243.3, \"(-8.71, -8.61)\": -2967.9, \"(-8.61, -8.51)\": -2716.5, \"(-8.51, -8.39)\": -2420.1, \"(-8.39, -8.14)\": -2157.6, \"(-8.14, -8.0)\": -1697.0, \"(-8.0, -7.62)\": -1454.8, \"(-7.62, -7.0)\": -1010.0, \"(-7.0, -5.41)\": -548.8, \"(-5.41, 6.44)\": -111.6, \"(6.44, 7.33)\": 326.3, \"(7.33, 7.8)\": 771.9, \"(7.8, 8.13)\": 1230.6, \"(8.13, 8.37)\": 1701.2, \"(8.37, 8.57)\": 2187.8, \"(8.57, 8.74)\": 2663.1, \"(8.74, 8.81)\": 3123.4, \"(8.81, 8.88)\": 3382.2, \"(8.88, 8.94)\": 3627.1, \"(8.94, 9.02)\": 3971.8, \"(9.02, 9.13)\": 4210.2, \"(9.13, 9.2)\": 4719.4, \"(9.2, 9.32)\": 5087.5, \"(9.32, 9.36)\": 5635.8, \"(9.36, 9.42)\": 5893.6, \"(9.42, 9.48)\": 6202.6, \"(9.48, 9.55)\": 6749.7, \"(9.55, 9.58)\": 7089.7, \"(9.58, 9.62)\": 7354.0, \"(9.62, 9.66)\": 7632.2, \"(9.66, 9.76)\": 7903.6, \"(9.76, 9.82)\": 8972.6, \"(9.82, 9.87)\": 9403.6, \"(9.87, 9.92)\": 9997.9, \"(9.92, 9.95)\": 10241.0, \"(9.95, 9.99)\": 10500.1}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-9.98, -9.92)\": 10549.0, \"(-9.92, -9.88)\": 10094.9, \"(-9.88, -9.83)\": 9639.7, \"(-9.83, -9.79)\": 9117.2, \"(-9.79, -9.74)\": 8823.7, \"(-9.74, -9.69)\": 8355.4, \"(-9.69, -9.66)\": 8077.9, \"(-9.66, -9.6)\": 7697.7, \"(-9.6, -9.53)\": 7334.0, \"(-9.53, -9.46)\": 6667.2, \"(-9.46, -9.41)\": 6355.9, \"(-9.41, -9.36)\": 6049.5, \"(-9.36, -9.3)\": 5757.5, \"(-9.3, -9.21)\": 5375.9, \"(-9.21, -9.11)\": 4895.0, \"(-9.11, -9.02)\": 4317.6, \"(-9.02, -8.86)\": 3950.6, \"(-8.86, -8.76)\": 3454.8, \"(-8.76, -8.62)\": 3173.5, \"(-8.62, -8.5)\": 2752.4, \"(-8.5, -8.34)\": 2397.1, \"(-8.34, -8.12)\": 1952.2, \"(-8.12, -7.87)\": 1667.8, \"(-7.87, -7.61)\": 1296.1, \"(-7.61, -7.18)\": 1004.1, \"(-7.18, -6.47)\": 656.0, \"(-6.47, -1.54)\": 322.7, \"(-1.54, 6.5)\": -0.0, \"(6.5, 7.18)\": 329.9, \"(7.18, 7.59)\": 662.7, \"(7.59, 7.9)\": 1010.0, \"(7.9, 8.13)\": 1373.1, \"(8.13, 8.33)\": 1716.8, \"(8.33, 8.51)\": 2088.5, \"(8.51, 8.66)\": 2498.2, \"(8.66, 8.78)\": 2972.9, \"(8.78, 8.9)\": 3318.6, \"(8.9, 9.02)\": 3697.3, \"(9.02, 9.13)\": 4284.1, \"(9.13, 9.18)\": 4668.2, \"(9.18, 9.25)\": 4962.3, \"(9.25, 9.31)\": 5258.4, \"(9.31, 9.37)\": 5609.4, \"(9.37, 9.42)\": 5922.8, \"(9.42, 9.48)\": 6211.0, \"(9.48, 9.54)\": 6622.0, \"(9.54, 9.61)\": 7058.8, \"(9.61, 9.63)\": 7516.4, \"(9.63, 9.68)\": 7799.1, \"(9.68, 9.73)\": 8123.9, \"(9.73, 9.78)\": 8507.5, \"(9.78, 9.83)\": 8937.0, \"(9.83, 9.89)\": 9465.6, \"(9.89, 9.92)\": 9904.4, \"(9.92, 9.97)\": 10331.4, \"(9.97, 9.98)\": 10665.7}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-9.99, -1.95)\": -1.0, \"(-1.95, -1.57)\": -0.96, \"(-1.57, -1.47)\": -0.917, \"(-1.47, -1.28)\": -0.894, \"(-1.28, -1.19)\": -0.853, \"(-1.19, -1.11)\": -0.827, \"(-1.11, -1.05)\": -0.802, \"(-1.05, -0.99)\": -0.779, \"(-0.99, -0.94)\": -0.757, \"(-0.94, -0.89)\": -0.733, \"(-0.89, -0.82)\": -0.694, \"(-0.82, -0.79)\": -0.67, \"(-0.79, -0.7)\": -0.646, \"(-0.7, -0.61)\": -0.578, \"(-0.61, -0.57)\": -0.531, \"(-0.57, -0.53)\": -0.508, \"(-0.53, -0.51)\": -0.479, \"(-0.51, -0.48)\": -0.455, \"(-0.48, -0.43)\": -0.429, \"(-0.43, -0.4)\": -0.404, \"(-0.4, -0.37)\": -0.376, \"(-0.37, -0.34)\": -0.341, \"(-0.34, -0.31)\": -0.314, \"(-0.31, -0.26)\": -0.292, \"(-0.26, -0.22)\": -0.24, \"(-0.22, -0.2)\": -0.218, \"(-0.2, -0.16)\": -0.194, \"(-0.16, -0.14)\": -0.153, \"(-0.14, -0.1)\": -0.127, \"(-0.1, -0.07)\": -0.079, \"(-0.07, -0.01)\": -0.055, \"(-0.01, 0.03)\": 0.007, \"(0.03, 0.06)\": 0.033, \"(0.06, 0.09)\": 0.069, \"(0.09, 0.12)\": 0.094, \"(0.12, 0.2)\": 0.137, \"(0.2, 0.27)\": 0.216, \"(0.27, 0.32)\": 0.277, \"(0.32, 0.36)\": 0.319, \"(0.36, 0.39)\": 0.356, \"(0.39, 0.43)\": 0.382, \"(0.43, 0.46)\": 0.408, \"(0.46, 0.48)\": 0.433, \"(0.48, 0.56)\": 0.46, \"(0.56, 0.62)\": 0.513, \"(0.62, 0.69)\": 0.572, \"(0.69, 0.77)\": 0.595, \"(0.77, 0.82)\": 0.654, \"(0.82, 0.87)\": 0.677, \"(0.87, 0.93)\": 0.711, \"(0.93, 1.0)\": 0.738, \"(1.0, 1.1)\": 0.761, \"(1.1, 1.23)\": 0.804, \"(1.23, 1.32)\": 0.846, \"(1.32, 1.53)\": 0.869, \"(1.53, 1.7)\": 0.913, \"(1.7, 2.18)\": 0.935, \"(2.18, 9.99)\": 0.977}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-9.99, -8.83)\": -2.995, \"(-8.83, -7.77)\": -2.875, \"(-7.77, -6.9)\": -2.747, \"(-6.9, -6.12)\": -2.626, \"(-6.12, -5.37)\": -2.506, \"(-5.37, -4.74)\": -2.383, \"(-4.74, -4.16)\": -2.258, \"(-4.16, -3.66)\": -2.129, \"(-3.66, -3.37)\": -2.0, \"(-3.37, -2.98)\": -1.933, \"(-2.98, -2.6)\": -1.805, \"(-2.6, -2.26)\": -1.68, \"(-2.26, -1.96)\": -1.551, \"(-1.96, -1.83)\": -1.428, \"(-1.83, -1.68)\": -1.361, \"(-1.68, -1.54)\": -1.282, \"(-1.54, -1.41)\": -1.205, \"(-1.41, -1.16)\": -1.113, \"(-1.16, -1.05)\": -0.985, \"(-1.05, -0.95)\": -0.914, \"(-0.95, -0.8)\": -0.846, \"(-0.8, -0.62)\": -0.717, \"(-0.62, -0.53)\": -0.579, \"(-0.53, -0.4)\": -0.496, \"(-0.4, -0.26)\": -0.375, \"(-0.26, -0.12)\": -0.242, \"(-0.12, -0.03)\": -0.102, \"(-0.03, 0.05)\": -0.016, \"(0.05, 0.12)\": 0.051, \"(0.12, 0.17)\": 0.129, \"(0.17, 0.36)\": 0.206, \"(0.36, 0.46)\": 0.384, \"(0.46, 0.52)\": 0.45, \"(0.52, 0.68)\": 0.516, \"(0.68, 0.84)\": 0.649, \"(0.84, 0.93)\": 0.772, \"(0.93, 1.03)\": 0.839, \"(1.03, 1.16)\": 0.914, \"(1.16, 1.39)\": 1.009, \"(1.39, 1.63)\": 1.137, \"(1.63, 1.89)\": 1.273, \"(1.89, 2.19)\": 1.403, \"(2.19, 2.38)\": 1.54, \"(2.38, 2.75)\": 1.608, \"(2.75, 3.1)\": 1.735, \"(3.1, 3.54)\": 1.858, \"(3.54, 4.06)\": 1.98, \"(4.06, 4.33)\": 2.105, \"(4.33, 4.87)\": 2.173, \"(4.87, 5.26)\": 2.3, \"(5.26, 5.95)\": 2.366, \"(5.95, 6.8)\": 2.488, \"(6.8, 7.69)\": 2.615, \"(7.69, 8.73)\": 2.739, \"(8.73, 9.85)\": 2.865, \"(9.85, 9.97)\": 2.987}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-9.98, -6.3)\": -1.472, \"(-6.3, -4.49)\": -1.412, \"(-4.49, -3.5)\": -1.351, \"(-3.5, -2.83)\": -1.29, \"(-2.83, -2.37)\": -1.231, \"(-2.37, -2.15)\": -1.17, \"(-2.15, -1.84)\": -1.133, \"(-1.84, -1.56)\": -1.066, \"(-1.56, -1.37)\": -1.0, \"(-1.37, -1.18)\": -0.936, \"(-1.18, -1.06)\": -0.873, \"(-1.06, -0.93)\": -0.81, \"(-0.93, -0.81)\": -0.734, \"(-0.81, -0.69)\": -0.664, \"(-0.69, -0.59)\": -0.577, \"(-0.59, -0.53)\": -0.52, \"(-0.53, -0.49)\": -0.474, \"(-0.49, -0.4)\": -0.44, \"(-0.4, -0.32)\": -0.339, \"(-0.32, -0.29)\": -0.3, \"(-0.29, -0.23)\": -0.263, \"(-0.23, -0.21)\": -0.225, \"(-0.21, -0.15)\": -0.181, \"(-0.15, -0.12)\": -0.146, \"(-0.12, -0.08)\": -0.112, \"(-0.08, -0.05)\": -0.074, \"(-0.05, 0.02)\": -0.038, \"(0.02, 0.11)\": 0.066, \"(0.11, 0.16)\": 0.13, \"(0.16, 0.21)\": 0.164, \"(0.21, 0.26)\": 0.233, \"(0.26, 0.3)\": 0.269, \"(0.3, 0.38)\": 0.302, \"(0.38, 0.42)\": 0.368, \"(0.42, 0.46)\": 0.405, \"(0.46, 0.51)\": 0.44, \"(0.51, 0.55)\": 0.475, \"(0.55, 0.61)\": 0.512, \"(0.61, 0.71)\": 0.557, \"(0.71, 0.83)\": 0.632, \"(0.83, 0.87)\": 0.695, \"(0.87, 0.97)\": 0.733, \"(0.97, 1.06)\": 0.784, \"(1.06, 1.2)\": 0.821, \"(1.2, 1.39)\": 0.882, \"(1.39, 1.56)\": 0.944, \"(1.56, 1.82)\": 1.004, \"(1.82, 2.12)\": 1.071, \"(2.12, 2.52)\": 1.132, \"(2.52, 3.09)\": 1.195, \"(3.09, 3.87)\": 1.258, \"(3.87, 5.06)\": 1.318, \"(5.06, 7.39)\": 1.377, \"(7.39, 9.97)\": 1.437}\\n',\n",
-       " 'This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: x\\nFeature Type: continuous\\nMeans: {\"(-9.98, -3.9)\": -0.0006, \"(-3.9, -3.21)\": 0.0197, \"(-3.21, -2.72)\": 0.0407, \"(-2.72, -2.54)\": 0.0625, \"(-2.54, -2.25)\": 0.075, \"(-2.25, -2.03)\": 0.096, \"(-2.03, -1.85)\": 0.117, \"(-1.85, -1.68)\": 0.1372, \"(-1.68, -1.61)\": 0.1588, \"(-1.61, -1.43)\": 0.1726, \"(-1.43, -1.33)\": 0.1943, \"(-1.33, -1.18)\": 0.2148, \"(-1.18, -1.11)\": 0.2376, \"(-1.11, -0.99)\": 0.2509, \"(-0.99, -0.93)\": 0.2726, \"(-0.93, -0.86)\": 0.2851, \"(-0.86, -0.8)\": 0.2987, \"(-0.8, -0.69)\": 0.3124, \"(-0.69, -0.65)\": 0.335, \"(-0.65, -0.57)\": 0.3491, \"(-0.57, -0.51)\": 0.3667, \"(-0.51, -0.45)\": 0.3805, \"(-0.45, -0.35)\": 0.3925, \"(-0.35, -0.3)\": 0.4145, \"(-0.3, -0.26)\": 0.4292, \"(-0.26, -0.17)\": 0.4425, \"(-0.17, -0.07)\": 0.4661, \"(-0.07, 0.04)\": 0.4879, \"(0.04, 0.09)\": 0.5112, \"(0.09, 0.19)\": 0.5244, \"(0.19, 0.24)\": 0.5487, \"(0.24, 0.29)\": 0.5627, \"(0.29, 0.36)\": 0.5754, \"(0.36, 0.4)\": 0.5903, \"(0.4, 0.46)\": 0.6025, \"(0.46, 0.55)\": 0.6162, \"(0.55, 0.6)\": 0.6375, \"(0.6, 0.71)\": 0.6496, \"(0.71, 0.77)\": 0.6728, \"(0.77, 0.85)\": 0.6893, \"(0.85, 0.93)\": 0.7039, \"(0.93, 1.03)\": 0.717, \"(1.03, 1.18)\": 0.7394, \"(1.18, 1.31)\": 0.7694, \"(1.31, 1.52)\": 0.7973, \"(1.52, 1.68)\": 0.8206, \"(1.68, 1.86)\": 0.8427, \"(1.86, 2.06)\": 0.8665, \"(2.06, 2.3)\": 0.8873, \"(2.3, 2.57)\": 0.9086, \"(2.57, 2.99)\": 0.9303, \"(2.99, 3.57)\": 0.9513, \"(3.57, 5.05)\": 0.9731, \"(5.05, 10.0)\": 0.9936}\\n']"
-      ]
-     },
-     "execution_count": 103,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fbench_functions_as_text"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Benchmark an LLM"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['a)', 'b)', 'c)', 'd)', 'e)', 'f)', 'g)', 'h)', 'i)', 'j)', 'k)', 'l)', 'm)', 'n)', 'o)', 'p)', 'q)', 'r)', 's)', 't)', 'u)', 'v)', 'w)', 'x)', 'y)', 'z)']\n"
-     ]
-    }
-   ],
-   "source": [
-    "cases = ['a)', 'b)', 'c)', 'd)', 'e)', 'f)', 'g)', 'h)', 'i)', 'j)', 'k)', 'l)', 'm)', 'n)', 'o)', 'p)', 'q)', 'r)', 's)', 't)', 'u)', 'v)', 'w)', 'x)', 'y)', 'z)']\n",
-    "print(cases)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Graph -tanh(x) + 1/4 * x\n",
-      "\n",
-      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
-      "\n",
-      "Feature Name: x\n",
-      "Feature Type: continuous\n",
-      "Means: {\"(-9.99, -9.6)\": -1.492, \"(-9.6, -9.23)\": -1.398, \"(-9.23, -8.97)\": -1.305, \"(-8.97, -8.59)\": -1.239, \"(-8.59, -8.19)\": -1.145, \"(-8.19, -7.84)\": -1.033, \"(-7.84, -7.58)\": -0.96, \"(-7.58, -7.29)\": -0.893, \"(-7.29, -7.04)\": -0.822, \"(-7.04, -6.62)\": -0.754, \"(-6.62, -6.34)\": -0.651, \"(-6.34, -5.98)\": -0.582, \"(-5.98, -5.58)\": -0.49, \"(-5.58, -5.21)\": -0.394, \"(-5.21, -4.83)\": -0.299, \"(-4.83, -4.56)\": -0.203, \"(-4.56, -4.19)\": -0.135, \"(-4.19, -3.78)\": -0.042, \"(-3.78, -3.41)\": 0.054, \"(-3.41, -3.12)\": 0.149, \"(-3.12, -2.78)\": 0.218, \"(-2.78, -2.32)\": 0.311, \"(-2.32, -1.76)\": 0.409, \"(-1.76, -0.73)\": 0.506, \"(-0.73, -0.56)\": 0.438, \"(-0.56, -0.37)\": 0.358, \"(-0.37, -0.23)\": 0.227, \"(-0.23, -0.11)\": 0.154, \"(-0.11, -0.02)\": 0.076, \"(-0.02, 0.1)\": 0.007, \"(0.1, 0.21)\": -0.084, \"(0.21, 0.33)\": -0.163, \"(0.33, 0.41)\": -0.238, \"(0.41, 0.62)\": -0.303, \"(0.62, 0.81)\": -0.399, \"(0.81, 2.35)\": -0.469, \"(2.35, 2.75)\": -0.395, \"(2.75, 3.16)\": -0.302, \"(3.16, 3.47)\": -0.2, \"(3.47, 3.75)\": -0.13, \"(3.75, 4.14)\": -0.057, \"(4.14, 4.4)\": 0.037, \"(4.4, 4.84)\": 0.114, \"(4.84, 5.22)\": 0.216, \"(5.22, 5.52)\": 0.315, \"(5.52, 5.8)\": 0.383, \"(5.8, 6.19)\": 0.458, \"(6.19, 6.47)\": 0.552, \"(6.47, 6.73)\": 0.619, \"(6.73, 7.02)\": 0.689, \"(7.02, 7.28)\": 0.758, \"(7.28, 7.56)\": 0.823, \"(7.56, 7.84)\": 0.893, \"(7.84, 8.11)\": 0.965, \"(8.11, 8.39)\": 1.033, \"(8.39, 8.77)\": 1.102, \"(8.77, 9.04)\": 1.195, \"(9.04, 9.43)\": 1.265, \"(9.43, 9.7)\": 1.361, \"(9.7, 9.95)\": 1.428}\n",
-      "\n",
-      "MC OPTIONS:\n",
-      "a) f(x)=sign(sin(x))\n",
-      "b) f(x)=sin(x)+sin(2*x)\n",
-      "c) f(x)=-tanh(x) + 1/4 * x\n",
-      "d) f(x)=exp(x)+ 4000 * sign(x)\n",
-      "e) f(x)=exp(x) + 4000* sin(x)\n",
-      "To determine which function among the options given approximates the graph, we can analyze the behavior of each function and match it to the pattern described by the graph.  \n",
-      "\n",
-      "Let's step through each choice:\n",
-      "\n",
-      "**a) f(x) = sign(sin(x))**  \n",
-      "This function would output values of -1, 0, or 1, depending on the sine of x. The output switches between -1 and 1 at intervals determined by the zeros of the sinusoid. This would create a pattern of sharp jumps, which doesn't resemble the smooth and gradual pattern described by the graph.\n",
-      "\n",
-      "**b) f(x) = sin(x) + sin(2*x)**  \n",
-      "This function involves a combination of sinusoidal functions with different frequencies. The resulting wave pattern would be periodic with consistent peaks and troughs. It is more complex than a simple sinusoidal function because it superimposes two frequencies, but it still would not account for the smooth, large-scale transition from strongly negative to positive seen in the graph.\n",
-      "\n",
-      "**c) f(x) = -tanh(x) + 1/4 * x**  \n",
-      "Tanh(x) smoothly transitions from -1 to 1 as x moves from negative to positive infinity, while -tanh(x) will do the opposite. Adding 1/4 * x introduces a linearly increasing function. This combination would start low, increase as x increases, reach a neutral point close to zero, and then keep increasing at a modest rate, which is more akin to the shape described in the graph.\n",
-      "\n",
-      "**d) f(x) = exp(x) + 4000 * sign(x)**  \n",
-      "This function combines the exponential growth of exp(x) with a significant jump (either adding or subtracting 4000) depending on the sign of x. This would create a response dominated by very high positive or negative values, again not fitting the smooth transition described.\n",
-      "\n",
-      "**e) f(x) = exp(x) + 4000 * sin(x)**  \n",
-      "Similar to (d), exp(x) grows very fast as x increases, and adding a 4000-scaled sinusoidal function would create fluctuations superimposed on a rapidly growing exponential curve. This model would be too aggressive in its growth and too fluctuating compared to the smooth and gradual changes in the graph.\n",
-      "\n",
-      "Given the analysis, **choice c (f(x) = -tanh(x) + 1/4 * x)** seems to fit the best with the pattern described in the graph. It has the smooth transition through zero and continuous growth, both increasing and decreasing at a modest rate as described.\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph arctan(x) + sin(x)\n",
-      "\n",
-      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
-      "\n",
-      "Feature Name: x\n",
-      "Feature Type: continuous\n",
-      "Means: {\"(-9.98, -9.72)\": -0.957, \"(-9.72, -9.52)\": -1.187, \"(-9.52, -9.27)\": -1.397, \"(-9.27, -9.07)\": -1.626, \"(-9.07, -8.83)\": -1.828, \"(-8.83, -8.58)\": -2.028, \"(-8.58, -8.06)\": -2.209, \"(-8.06, -7.17)\": -2.434, \"(-7.17, -6.86)\": -2.184, \"(-6.86, -6.59)\": -1.949, \"(-6.59, -6.4)\": -1.696, \"(-6.4, -6.17)\": -1.508, \"(-6.17, -5.97)\": -1.276, \"(-5.97, -5.74)\": -1.079, \"(-5.74, -5.5)\": -0.861, \"(-5.5, -5.1)\": -0.673, \"(-5.1, -3.87)\": -0.449, \"(-3.87, -3.56)\": -0.666, \"(-3.56, -3.36)\": -0.896, \"(-3.36, -3.12)\": -1.09, \"(-3.12, -2.91)\": -1.287, \"(-2.91, -2.69)\": -1.481, \"(-2.69, -2.35)\": -1.662, \"(-2.35, -1.07)\": -1.891, \"(-1.07, -0.9)\": -1.677, \"(-0.9, -0.74)\": -1.496, \"(-0.74, -0.62)\": -1.297, \"(-0.62, -0.46)\": -1.097, \"(-0.46, -0.34)\": -0.862, \"(-0.34, -0.22)\": -0.634, \"(-0.22, -0.11)\": -0.392, \"(-0.11, 0.02)\": -0.192, \"(0.02, 0.13)\": 0.083, \"(0.13, 0.23)\": 0.286, \"(0.23, 0.37)\": 0.497, \"(0.37, 0.5)\": 0.739, \"(0.5, 0.61)\": 0.963, \"(0.61, 0.77)\": 1.153, \"(0.77, 0.98)\": 1.375, \"(0.98, 1.22)\": 1.608, \"(1.22, 2.74)\": 1.828, \"(2.74, 2.99)\": 1.593, \"(2.99, 3.19)\": 1.398, \"(3.19, 3.45)\": 1.204, \"(3.45, 3.68)\": 0.98, \"(3.68, 4.01)\": 0.787, \"(4.01, 5.68)\": 0.562, \"(5.68, 5.98)\": 0.884, \"(5.98, 6.19)\": 1.107, \"(6.19, 6.43)\": 1.338, \"(6.43, 6.63)\": 1.572, \"(6.63, 6.92)\": 1.79, \"(6.92, 7.16)\": 2.024, \"(7.16, 7.7)\": 2.222, \"(7.7, 8.57)\": 2.431, \"(8.57, 8.9)\": 2.203, \"(8.9, 9.12)\": 1.945, \"(9.12, 9.34)\": 1.755, \"(9.34, 9.57)\": 1.534, \"(9.57, 9.8)\": 1.319, \"(9.8, 9.97)\": 1.106}\n",
-      "\n",
-      "MC OPTIONS:\n",
-      "a) f(x)=sign(sin(x))\n",
-      "b) f(x)=arctan(x) + sin(x)\n",
-      "c) f(x)=|sin(x/2)|\n",
-      "d) f(x)=sin(x)+cos(x)\n",
-      "e) f(x)=exp(-x^2+1)+ 1/3 * |x|\n",
-      "We can analyze how the graph you described earlier aligns with each of the given function options step by step:\n",
-      "\n",
-      "a) **f(x) = sign(sin(x))**\n",
-      "   - The sign function of sin(x) pivots sharply at multiples of π (where the sine function crosses zero), switching abruptly between -1 and 1. The gradual and continuous change observed in the graph does not match this pattern.\n",
-      "\n",
-      "b) **f(x) = arctan(x) + sin(x)**\n",
-      "   - Combining arctan(x), which increases gradually from negative to positive values and has an S-shape, with sin(x), which oscillates regularly, could lead to a complex pattern. However, arctan(x) converges to its limits gradually, and sin(x) would introduce periodic local oscillations. This does not completely reflect the pattern described, where there is a consistent non-periodic trend upwards and then downwards without local periodic changes.\n",
-      "\n",
-      "c) **f(x) = |sin(x/2)|**\n",
-      "   - This function yields the absolute value of the sine function evaluated at half the rate, leading to a waveform pattern that has positive peaks every π (but with a reduced frequency compared to sin(x)). It dictates a consistent wave-like pattern without going negative, resembling the valley and peak pattern outlined in your description, but might be too regular and periodic compared to what you described.\n",
-      "\n",
-      "d) **f(x) = sin(x) + cos(x)**\n",
-      "   - This function would provide an oscillatory pattern as both sin(x) and cos(x) are periodic and phased out by π/2. Their sum would still exhibit periodic behavior, which does not match the description of continuously increasing values followed by a peak and a decrease.\n",
-      "\n",
-      "e) **f(x) = exp(-x² + 1) + 1/3 * |x|**\n",
-      "   - The exponential term exp(-x² + 1) peaks at x = 0 and decays symmetrically as x moves away from 0, either upward or downward. The term 1/3 * |x| adds a linear component that increases as x moves away from zero. This combination could closely approximate the pattern described - a minimum around x = 0 with values rising to either side, reaching a peak, and eventually declining more gently due to the absolute value's linear increase. This pattern aligns well with a rapid initial increase, a peak around 9, and a subsequent fall with a less steep slope.\n",
-      "\n",
-      "Given the explanations and based on the pattern described, option **e) f(x) = exp(-x² + 1) + 1/3 * |x|** appears to match best with your description of the graph pattern among the choices given.\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph exp(-x^2+1)+ 1/3 * |x|\n",
-      "\n",
-      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
-      "\n",
-      "Feature Name: x\n",
-      "Feature Type: continuous\n",
-      "Means: {\"(-9.98, -9.54)\": 3.323, \"(-9.54, -9.1)\": 3.175, \"(-9.1, -8.68)\": 3.024, \"(-8.68, -8.3)\": 2.892, \"(-8.3, -7.89)\": 2.763, \"(-7.89, -7.53)\": 2.631, \"(-7.53, -7.16)\": 2.501, \"(-7.16, -6.72)\": 2.379, \"(-6.72, -6.35)\": 2.234, \"(-6.35, -5.88)\": 2.113, \"(-5.88, -5.46)\": 1.955, \"(-5.46, -5.06)\": 1.817, \"(-5.06, -4.6)\": 1.679, \"(-4.6, -4.2)\": 1.534, \"(-4.2, -3.76)\": 1.396, \"(-3.76, -3.37)\": 1.248, \"(-3.37, -2.96)\": 1.108, \"(-2.96, -2.58)\": 0.983, \"(-2.58, -1.24)\": 0.848, \"(-1.24, -1.11)\": 1.026, \"(-1.11, -1.01)\": 1.205, \"(-1.01, -0.92)\": 1.352, \"(-0.92, -0.86)\": 1.479, \"(-0.86, -0.77)\": 1.611, \"(-0.77, -0.69)\": 1.781, \"(-0.69, -0.59)\": 1.975, \"(-0.59, -0.5)\": 2.162, \"(-0.5, -0.39)\": 2.365, \"(-0.39, -0.28)\": 2.488, \"(-0.28, 0.37)\": 2.614, \"(0.37, 0.48)\": 2.471, \"(0.48, 0.61)\": 2.253, \"(0.61, 0.68)\": 2.052, \"(0.68, 0.76)\": 1.923, \"(0.76, 0.83)\": 1.747, \"(0.83, 0.91)\": 1.62, \"(0.91, 0.97)\": 1.488, \"(0.97, 1.09)\": 1.337, \"(1.09, 1.19)\": 1.179, \"(1.19, 1.38)\": 1.013, \"(1.38, 1.8)\": 0.855, \"(1.8, 2.45)\": 0.709, \"(2.45, 2.92)\": 0.829, \"(2.92, 3.32)\": 0.974, \"(3.32, 3.71)\": 1.11, \"(3.71, 4.13)\": 1.241, \"(4.13, 4.46)\": 1.378, \"(4.46, 4.92)\": 1.501, \"(4.92, 5.38)\": 1.642, \"(5.38, 5.78)\": 1.801, \"(5.78, 6.25)\": 1.932, \"(6.25, 6.65)\": 2.091, \"(6.65, 7.12)\": 2.223, \"(7.12, 7.55)\": 2.377, \"(7.55, 7.96)\": 2.52, \"(7.96, 8.39)\": 2.667, \"(8.39, 8.77)\": 2.8, \"(8.77, 9.12)\": 2.927, \"(9.12, 9.5)\": 3.048, \"(9.5, 9.89)\": 3.178, \"(9.89, 10.0)\": 3.304}\n",
-      "\n",
-      "MC OPTIONS:\n",
-      "a) f(x)=sign(cos(x))\n",
-      "b) f(x)=exp(-x^2+1)+ 1/3 * |x|\n",
-      "c) f(x)=sin(x)+sin(2*x)\n",
-      "d) f(x)=|sin(x/2)|\n",
-      "e) f(x)=exp(x)+ 2000 * abs(x)\n",
-      "Let’s evaluate each suggested function by considering their expected behavior over the range given and match it with the general pattern described in the graph from the model.\n",
-      "\n",
-      "**Option a: f(x)=sign(cos(x))**\n",
-      "- This function alternates between -1, 0, and 1 depending on the cosine of the value, leading to rapid sign changes. This discrete behavior does not align with the smooth pattern and local minima and maxima shown in the graph.\n",
-      "\n",
-      "**Option b: f(x)=exp(-x^2+1) + 1/3 * |x|**\n",
-      "- This form involves an exponential decay centered roughly around x=0, which would typically lead to a peak near x=0, modulated by a linearly increasing term affinity |x|. Although the inclusion of |x| might introduce a consistent change with x's increase, the description points to a pattern of behavior that is clearly non-monotonic and complex rather than a simple peak and linear modulation.\n",
-      "\n",
-      "**Option c: f(x)=sin(x)+sin(2*x)**\n",
-      "- This function combines the sin of x with twice the frequency of sin(2*x), leading potentially to an overlapping wave pattern that contains multiple local minima and maxima within each cycle of π. The multiple changes in the mean value of the graph suggest a possibility of periodic behavior with frequency variations.\n",
-      "\n",
-      "**Option d: f(x)=|sin(x/2)|**\n",
-      "- The magnitude of the sine function, halved in frequency, will produce a wave with peaks at multiples of 2π (every 2π) and no negative values. The graph's complexity and the number of changes suggest this might be too simple to capture the observed behavior.\n",
-      "\n",
-      "**Option e: f(x)=exp(x) + 2000 * abs(x)**\n",
-      "- This function would lead to rapid, unbounded increases due to the exponential and linear absolute functions. The resulting outputs would not reflect the cyclic patterns we see in the graph.\n",
-      "\n",
-      "Given these evaluations, **Option c: f(x)=sin(x) + sin(2*x)** is the most plausible match for the graph. It produces a waveform pattern with multi-frequency components that are more likely to exhibit the rising and dipping trends with multiple local maxima and minima, aligning with the described behavior in the graph.\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph exp(-x+1)+ 2000 * abs(x+1)\n",
-      "\n",
-      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
-      "\n",
-      "Feature Name: x\n",
-      "Feature Type: continuous\n",
-      "Means: {\"(-10.0, -9.97)\": 77629.6, \"(-9.97, -9.92)\": 75483.3, \"(-9.92, -9.88)\": 72339.5, \"(-9.88, -9.84)\": 70397.0, \"(-9.84, -9.8)\": 67393.0, \"(-9.8, -9.75)\": 66047.6, \"(-9.75, -9.71)\": 63231.7, \"(-9.71, -9.63)\": 61069.8, \"(-9.63, -9.58)\": 57163.5, \"(-9.58, -9.54)\": 55787.7, \"(-9.54, -9.51)\": 54497.2, \"(-9.51, -9.49)\": 53361.9, \"(-9.49, -9.43)\": 52076.6, \"(-9.43, -9.35)\": 49437.3, \"(-9.35, -9.29)\": 47029.3, \"(-9.29, -9.24)\": 45407.7, \"(-9.24, -9.19)\": 44261.8, \"(-9.19, -9.12)\": 42586.3, \"(-9.12, -9.09)\": 40835.6, \"(-9.09, -9.04)\": 39667.4, \"(-9.04, -8.96)\": 38096.4, \"(-8.96, -8.87)\": 36561.8, \"(-8.87, -8.81)\": 34852.9, \"(-8.81, -8.71)\": 33372.7, \"(-8.71, -8.59)\": 31554.1, \"(-8.59, -8.49)\": 29831.5, \"(-8.49, -8.37)\": 28079.5, \"(-8.37, -8.21)\": 26157.3, \"(-8.21, -8.07)\": 24287.9, \"(-8.07, -7.9)\": 22682.8, \"(-7.9, -7.68)\": 20993.7, \"(-7.68, -7.43)\": 19195.6, \"(-7.43, -7.17)\": 17424.8, \"(-7.17, -6.83)\": 15802.0, \"(-6.83, -6.43)\": 14204.7, \"(-6.43, -5.92)\": 12519.8, \"(-5.92, -5.35)\": 10825.2, \"(-5.35, -4.65)\": 9193.3, \"(-4.65, -3.91)\": 7577.4, \"(-3.91, -3.21)\": 5950.4, \"(-3.21, -2.39)\": 4375.4, \"(-2.39, -1.6)\": 2784.8, \"(-1.6, 0.36)\": 1187.6, \"(0.36, 1.17)\": 2753.6, \"(1.17, 1.96)\": 4362.3, \"(1.96, 2.77)\": 5955.3, \"(2.77, 3.56)\": 7539.5, \"(3.56, 4.41)\": 9189.1, \"(4.41, 5.22)\": 10844.1, \"(5.22, 5.99)\": 12429.0, \"(5.99, 6.78)\": 14001.2, \"(6.78, 7.6)\": 15585.3, \"(7.6, 8.42)\": 17220.3, \"(8.42, 9.2)\": 18816.3, \"(9.2, 9.97)\": 20429.2}\n",
-      "\n",
-      "MC OPTIONS:\n",
-      "a) f(x)=sign(sin(x))\n",
-      "b) f(x)=|sin(x/2)|\n",
-      "c) f(x)=sin(x)+cos(x)\n",
-      "d) f(x)=arctan(x) + sin(x)\n",
-      "e) f(x)=exp(-x+1)+ 2000 * abs(x+1)\n",
-      "To identify the function that fits the pattern described by the graph for feature \"x\", we need to match the characteristics of the functions listed to the distinct behavior in the graph. Specifically, we have noticed symmetry around \\( x = 0 \\) and a general shape resembling a bell curve with minimal value at \\( x = 0 \\) and high values toward the extremes of the graph.\n",
-      "\n",
-      "Let's evaluate each function choice:\n",
-      "\n",
-      "**a) \\( f(x) = \\text{sign}(\\sin(x)) \\)** \n",
-      "- This function would oscillate between +1 and -1 based on the sign (positive or negative) of \\(\\sin(x)\\). This does not match the continuous, bell curve-like behavior observed.\n",
-      "\n",
-      "**b) \\( f(x) = |\\sin(x/2)| \\)**\n",
-      "- This function represents the absolute value of \\(\\sin(x/2)\\), giving non-negative values. This would produce periodic peaks at regular intervals, not showing the bell curve pattern seen in the graph.\n",
-      "\n",
-      "**c) \\( f(x) = \\sin(x) + \\cos(x) \\)**\n",
-      "- Since both sine and cosine functions oscillate between -1 and 1, their sum would also oscillate roughly between -2 and 2. This wouldn't provide the symmetric, bell-curve-like form rising to very high values at the extremes as seen in the graph.\n",
-      "\n",
-      "**d) \\( f(x) = \\arctan(x) + \\sin(x) \\)**\n",
-      "- Here, \\(\\arctan(x)\\) ranges from \\(-\\pi/2\\) to \\(\\pi/2\\) as \\(x\\) ranges from negative to positive infinity, combined with the oscillating \\(\\sin(x)\\). The outcome does not provide a symmetric pattern that would approximate the observed graph pattern centering the minimal value around \\( x = 0 \\) and rising to similar high values at both extremes.\n",
-      "\n",
-      "**e) \\( f(x) = \\exp(-x+1) + 2000 \\times |\\x + 1|\\)**\n",
-      "- This equation considers \\(\\exp(-x+1)\\), which is an exponential decay shifted right by 1, and \\(2000 \\times |\\x + 1|\\), which depicts a linear relationship with absolute value, providing a V-shaped curve shifted by 1 to the left. The combination of these two terms would likely create a curve that is minimal near \\( x = -1 \\) and increases symmetrically as \\( x \\) moves away from -1 on both sides, showing significant growth as \\( x \\) reaches the extremes of its range.\n",
-      "\n",
-      "Considering each function's distinct characteristics and behavior, option **e** \\( f(x) = \\exp(-x+1) + 2000 \\times |\\x + 1|\\) appears to be the best fit for describing the graph. It demonstrates a symmetric increase from a central point (around \\( x = -1 \\)) outward towards both low and high extremes along the x-axis, aligning better with the observed bell curve pattern in the provided graph, even though the minimal axis value in the graph description was near \\( x = 0 \\). The slight shift in the minimum could be due to approximations or specific conditions in the application or data that resulted in slightly different behavior in practice.\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph exp(x)+ 2000 * abs(x)\n",
-      "\n",
-      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
-      "\n",
-      "Feature Name: x\n",
-      "Feature Type: continuous\n",
-      "Means: {\"(-10.0, -9.38)\": 20007.1, \"(-9.38, -8.72)\": 18721.4, \"(-8.72, -8.06)\": 17371.5, \"(-8.06, -7.42)\": 16101.2, \"(-7.42, -6.79)\": 14835.2, \"(-6.79, -6.15)\": 13565.8, \"(-6.15, -5.49)\": 12268.5, \"(-5.49, -4.9)\": 11043.4, \"(-4.9, -4.26)\": 9762.5, \"(-4.26, -3.82)\": 8491.6, \"(-3.82, -3.35)\": 7576.4, \"(-3.35, -2.71)\": 6677.2, \"(-2.71, -2.04)\": 5378.3, \"(-2.04, -1.6)\": 4074.3, \"(-1.6, -1.15)\": 3180.3, \"(-1.15, -0.53)\": 2283.4, \"(-0.53, 1.08)\": 1034.9, \"(1.08, 1.72)\": 2231.9, \"(1.72, 2.36)\": 3472.0, \"(2.36, 2.92)\": 4768.9, \"(2.92, 3.56)\": 5881.7, \"(3.56, 4.2)\": 7179.7, \"(4.2, 4.81)\": 8490.1, \"(4.81, 5.41)\": 9786.6, \"(5.41, 5.96)\": 11096.8, \"(5.96, 6.49)\": 12349.7, \"(6.49, 6.81)\": 13701.8, \"(6.81, 7.12)\": 14599.6, \"(7.12, 7.41)\": 15507.3, \"(7.41, 7.76)\": 16565.8, \"(7.76, 7.95)\": 17853.4, \"(7.95, 8.25)\": 18952.5, \"(8.25, 8.43)\": 20547.8, \"(8.43, 8.53)\": 21457.3, \"(8.53, 8.7)\": 22348.8, \"(8.7, 8.81)\": 23523.8, \"(8.81, 8.93)\": 24428.8, \"(8.93, 9.05)\": 25709.0, \"(9.05, 9.17)\": 26942.5, \"(9.17, 9.27)\": 28147.7, \"(9.27, 9.36)\": 29590.1, \"(9.36, 9.45)\": 30604.2, \"(9.45, 9.52)\": 31765.4, \"(9.52, 9.59)\": 32729.2, \"(9.59, 9.65)\": 33847.6, \"(9.65, 9.72)\": 34872.4, \"(9.72, 9.79)\": 36169.4, \"(9.79, 9.87)\": 37900.2, \"(9.87, 9.94)\": 39516.5, \"(9.94, 9.95)\": 40602.2}\n",
-      "\n",
-      "MC OPTIONS:\n",
-      "a) f(x)=sign(sin(x))\n",
-      "b) f(x)=exp(x) + 4000* sin(x)\n",
-      "c) f(x)=sign(cos(x))\n",
-      "d) f(x)=exp(-x+1)+ 2000 * abs(x+1)\n",
-      "e) f(x)=exp(x)+ 2000 * abs(x)\n",
-      "Let's analyze each of the given functions step-by-step in relation to the described pattern in the graph for feature \"x\", keeping its progression from -10 to approximately 10 in mind:\n",
-      "\n",
-      "**a) f(x) = sign(sin(x))**\n",
-      "- This function outputs only -1, 0, or 1 depending on the sign of the sine of x. It oscillates between these values with the period of \\(2\\pi\\). Given the continuous progression and variation of values in the graphical data, this option is inconsistent with the data, which shows steady increases and decreases rather than static, bounded outputs.\n",
-      "\n",
-      "**b) f(x) = exp(x) + 4000 * sin(x)**\n",
-      "- The primary term here is \\(exp(x)\\), which increases exponentially as x increases, potentially explaining the steep upward trend towards the end of the provided graph. The term \\(4000 * sin(x)\\) introduces regular oscillations, but these may not significantly alter the general exponentially increasing pattern of \\(exp(x)\\). This function considers both progressive increase and elements of fluctuation, which makes it a plausible representation. \n",
-      "\n",
-      "**c) f(x) = sign(cos(x))**\n",
-      "- Similar to sign(sin(x)), this outputs -1, 0, or 1, based on the cosine of x, and also alternates between these values with a periodicity of \\(2\\pi\\). This does not describe the continuous numeric range depicted in the graph.\n",
-      "\n",
-      "**d) f(x) = exp(-x+1) + 2000 * abs(x+1)**\n",
-      "- The first term, \\(exp(-x+1)\\), results in a rapidly decreasing exponential function as x increases, which contradicts the overall pattern of the graph that shows an initial decrease but followed by a substantial increase. The term \\(2000 * abs(x+1)\\) adds a gradually increasing factor, but it does not fit the significant exponential rise seen in the data.\n",
-      "\n",
-      "**e) f(x) = exp(x) + 2000 * abs(x)**\n",
-      "- The exponential term \\(exp(x)\\) increases rapidly, similar to option b. The addition of \\(2000 * abs(x)\\) will also continuously increase and ensure values always remain positive, reinforcing the increase especially as x grows. However, this function lacks oscillatory behavior that the sin term adds in b, making it a more straightforward, unvarying increase.\n",
-      "\n",
-      "Considering all functionalities, option **b) f(x) = exp(x) + 4000 * sin(x)** best matches the overall behavior of the graph: starting at a high, initially declining, reaching a low point, and then rapidly increasing with some oscillations incorporated. It suits well the initial steep increase, a region of minimal value, and then a strong increasing trend, especially near the ending regions where the exp(x) contribution dominates.\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph exp(x)+ 4000 * sign(x)\n",
-      "\n",
-      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
-      "\n",
-      "Feature Name: x\n",
-      "Feature Type: continuous\n",
-      "Means: {\"(-9.99, -0.02)\": -4033.1, \"(-0.02, -0.01)\": -3463.5, \"(-0.01, 0.0)\": -2878.5, \"(0.0, 0.01)\": 3168.7, \"(0.01, 0.24)\": 3601.3, \"(0.24, 5.2)\": 3902.3, \"(5.2, 6.14)\": 4194.4, \"(6.14, 6.69)\": 4486.9, \"(6.69, 6.95)\": 4801.1, \"(6.95, 7.25)\": 5095.5, \"(7.25, 7.42)\": 5394.2, \"(7.42, 7.61)\": 5741.0, \"(7.61, 7.75)\": 6060.0, \"(7.75, 7.88)\": 6370.5, \"(7.88, 8.01)\": 6719.7, \"(8.01, 8.09)\": 7018.4, \"(8.09, 8.19)\": 7318.2, \"(8.19, 8.27)\": 7633.6, \"(8.27, 8.34)\": 7931.9, \"(8.34, 8.43)\": 8243.3, \"(8.43, 8.51)\": 8715.0, \"(8.51, 8.59)\": 9025.6, \"(8.59, 8.63)\": 9455.6, \"(8.63, 8.71)\": 9781.1, \"(8.71, 8.76)\": 10095.1, \"(8.76, 8.81)\": 10402.4, \"(8.81, 8.85)\": 10769.3, \"(8.85, 8.93)\": 11205.3, \"(8.93, 8.98)\": 11657.2, \"(8.98, 9.02)\": 11978.3, \"(9.02, 9.04)\": 12274.0, \"(9.04, 9.11)\": 12620.0, \"(9.11, 9.16)\": 13195.6, \"(9.16, 9.2)\": 13622.6, \"(9.2, 9.27)\": 14170.2, \"(9.27, 9.33)\": 14922.7, \"(9.33, 9.4)\": 15653.1, \"(9.4, 9.43)\": 16179.2, \"(9.43, 9.46)\": 16623.1, \"(9.46, 9.51)\": 16925.6, \"(9.51, 9.55)\": 17773.3, \"(9.55, 9.59)\": 18134.4, \"(9.59, 9.64)\": 19003.0, \"(9.64, 9.66)\": 19407.0, \"(9.66, 9.69)\": 19852.4, \"(9.69, 9.73)\": 20519.5, \"(9.73, 9.79)\": 21280.6, \"(9.79, 9.83)\": 22288.4, \"(9.83, 9.86)\": 22741.4, \"(9.86, 9.89)\": 23336.0, \"(9.89, 9.92)\": 24073.7, \"(9.92, 9.95)\": 24600.7, \"(9.95, 9.97)\": 24976.1}\n",
-      "\n",
-      "MC OPTIONS:\n",
-      "a) f(x)=exp(-x+1)+ 2000 * abs(x+1)\n",
-      "b) f(x)=sign(sin(x))\n",
-      "c) f(x)=exp(x)+ 2000 * abs(x)\n",
-      "d) f(x)=arctan(x) + sin(x)\n",
-      "e) f(x)=exp(x)+ 4000 * sign(x)\n",
-      "To determine which function closely approximates the behavior described in the graph, we can analyze each option based on the characteristics of the functions and how they align with the observed trends in the data:\n",
-      "\n",
-      "### Analyze Each Function:\n",
-      "1. **f(x) = exp(-x + 1) + 2000 * abs(x + 1)**\n",
-      "   - The exponential term exp(-x + 1) decreases as x increases.\n",
-      "   - The absolute value term 2000 * abs(x + 1) increases as the absolute distance between x and -1 increases. This function will not result in significant initial negative values transitioning sharply to large positive values as x crosses zero, conflicting with the graph behavior.\n",
-      "\n",
-      "2. **f(x) = sign(sin(x))**\n",
-      "   - This function oscillates between -1, 0, and 1, depending on the sign of sin(x), which does not align with the observed progressive and significant increase in model outputs.\n",
-      "\n",
-      "3. **f(x) = exp(x) + 2000 * abs(x)**\n",
-      "   - The term exp(x) significantly increases as x increases, which mimics the sharp rise seen in the graph.\n",
-      "   - 2000 * abs(x) increases linearly with the absolute value of x. Thus, as x increases or decreases from zero, this term increases, yielding higher values consistently as x grows. This matches closely with the graph data.\n",
-      "\n",
-      "4. **f(x) = arctan(x) + sin(x)**\n",
-      "   - arctan(x) increases slowly and saturates as x increases, which does not reflect sharp, large incremental changes.\n",
-      "   - sin(x) is periodic and does not contribute to a monotonously increasing function.\n",
-      "\n",
-      "5. **f(x) = exp(x) + 4000 * sign(x)**\n",
-      "   - exp(x) increases rapidly as x increases, fitting the right-side growth seen in the data.\n",
-      "   - However, the term 4000 * sign(x) will only contribute a constant value of 4000 or -4000, depending on the sign of x, which does not provide a continuous increase seen from negative x through zero to positive values.\n",
-      "\n",
-      "### Conclusion:\n",
-      "Based on the above analyses, option **c) f(x) = exp(x) + 2000 * abs(x)** best fits the data pattern from the graph. This function combines an exponential growth as x increases with a consistent increase depending on the magnitude of x, covering both negative and positive ranges of x, with significant changes particularly noticeable as x transitions through zero to positive values. This closely mirrors the trend observed in the graph where the output rapidly increases once x becomes slightly positive and continues to grow as x increases.\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph sin(x)+cos(x)\n",
-      "\n",
-      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
-      "\n",
-      "Feature Name: x\n",
-      "Feature Type: continuous\n",
-      "Means: {\"(-9.94, -9.72)\": -0.404, \"(-9.72, -9.46)\": -0.682, \"(-9.46, -9.11)\": -0.997, \"(-9.11, -7.87)\": -1.27, \"(-7.87, -7.62)\": -0.989, \"(-7.62, -7.42)\": -0.725, \"(-7.42, -7.22)\": -0.469, \"(-7.22, -7.02)\": -0.195, \"(-7.02, -6.81)\": 0.096, \"(-6.81, -6.62)\": 0.383, \"(-6.62, -6.39)\": 0.639, \"(-6.39, -6.11)\": 0.885, \"(-6.11, -4.66)\": 1.193, \"(-4.66, -4.4)\": 0.91, \"(-4.4, -4.2)\": 0.622, \"(-4.2, -4.01)\": 0.348, \"(-4.01, -3.79)\": 0.086, \"(-3.79, -3.63)\": -0.216, \"(-3.63, -3.37)\": -0.486, \"(-3.37, -3.11)\": -0.783, \"(-3.11, -2.74)\": -1.051, \"(-2.74, -1.64)\": -1.322, \"(-1.64, -1.44)\": -1.061, \"(-1.44, -1.17)\": -0.789, \"(-1.17, -0.97)\": -0.519, \"(-0.97, -0.76)\": -0.237, \"(-0.76, -0.53)\": 0.08, \"(-0.53, -0.32)\": 0.373, \"(-0.32, -0.11)\": 0.637, \"(-0.11, 0.17)\": 0.898, \"(0.17, 1.68)\": 1.158, \"(1.68, 1.88)\": 0.88, \"(1.88, 2.1)\": 0.617, \"(2.1, 2.31)\": 0.348, \"(2.31, 2.51)\": 0.059, \"(2.51, 2.72)\": -0.259, \"(2.72, 2.93)\": -0.528, \"(2.93, 3.2)\": -0.802, \"(3.2, 3.57)\": -1.06, \"(3.57, 4.66)\": -1.33, \"(4.66, 4.92)\": -1.031, \"(4.92, 5.12)\": -0.763, \"(5.12, 5.33)\": -0.496, \"(5.33, 5.51)\": -0.224, \"(5.51, 5.7)\": 0.038, \"(5.7, 5.9)\": 0.3, \"(5.9, 6.12)\": 0.584, \"(6.12, 6.37)\": 0.853, \"(6.37, 6.87)\": 1.117, \"(6.87, 7.71)\": 1.388, \"(7.71, 7.99)\": 1.124, \"(7.99, 8.21)\": 0.836, \"(8.21, 8.38)\": 0.582, \"(8.38, 8.57)\": 0.318, \"(8.57, 8.79)\": 0.065, \"(8.79, 8.96)\": -0.208, \"(8.96, 9.23)\": -0.459, \"(9.23, 9.52)\": -0.837, \"(9.52, 9.96)\": -1.106}\n",
-      "\n",
-      "MC OPTIONS:\n",
-      "a) f(x)=exp(x)+ 2000 * abs(x)\n",
-      "b) f(x)=sign(sin(x))\n",
-      "c) f(x)=sin(x)+cos(x)\n",
-      "d) f(x)=exp(-x+1)+ 2000 * abs(x+1)\n",
-      "e) f(x)=-tanh(x) + 1/4 * x\n",
-      "To identify the function that best represents the pattern observed in the graph, let's evaluate each option step-by-step based on the characteristics of the mathematical functions and their relationships to the described cyclical behavior of the graph:\n",
-      "\n",
-      "a) **f(x) = exp(x) + 2000 * abs(x)**\n",
-      "   - This function would show exponential growth as x increases due to the `exp(x)` term, with an additional linear growth factor multiplied by 2000, especially amplified for larger positive or negative values of x due to `abs(x)`. This function does not inherently represent a periodic or oscillating behavior.\n",
-      "\n",
-      "b) **f(x) = sign(sin(x))**\n",
-      "   - This function would output values of -1, 0, or 1 depending on the sign of `sin(x)`. It represents a discontinuous function jumping between these three values as the sine function crosses zero. This could look periodic but would not explain the smooth sinusoidal-like variation seen in the graph.\n",
-      "\n",
-      "c) **f(x) = sin(x) + cos(x)**\n",
-      "   - Both sine and cosine are periodic functions with a period of \\(2\\pi\\). Their sum also results in a periodic function. This equation would indeed produce a sinusoidal pattern with peaks and troughs as seen in the graph. This function seems initially promising considering the observed cyclic behavior.\n",
-      "\n",
-      "d) **f(x) = exp(-x+1) + 2000 * abs(x+1)**\n",
-      "   - Similar to the first option, the negative exponent in `exp(-x+1)` would result in exponential decay, but the presence of 2000 multiplied by the absolute value would dominate at larger values of x (both positive and negative), leading to overall growth in magnitude rather than oscillation. This function would not produce a periodic pattern.\n",
-      "\n",
-      "e) **f(x) = -tanh(x) + 1/4 * x**\n",
-      "   - The hyperbolic tangent function `tanh(x)` has a smooth transition between -1 and 1 as x varies, and the linear term would introduce a tilt or increasing trend. This function would not normally be repetitive/periodic, instead showing a smooth, monotonic behavior altered by a linear term.\n",
-      "\n",
-      "Comparing these options, the only function that inherently produces a periodic, smooth, oscillating behavior aligned with the description of the graph's mean values is **c) f(x) = sin(x) + cos(x)**, which naturally combines two periodic functions to produce a resultant wave-like graph. Therefore, **option c** is the most likely mathematical representation of the graph given the periodic nature of the described patterns.\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph |sin(x/2)|\n",
-      "\n",
-      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
-      "\n",
-      "Feature Name: x\n",
-      "Feature Type: continuous\n",
-      "Means: {\"(-9.96, -8.41)\": 0.9674, \"(-8.41, -8.08)\": 0.8654, \"(-8.08, -7.8)\": 0.7739, \"(-7.8, -7.58)\": 0.682, \"(-7.58, -7.37)\": 0.5948, \"(-7.37, -7.13)\": 0.5034, \"(-7.13, -6.94)\": 0.4023, \"(-6.94, -6.73)\": 0.3139, \"(-6.73, -6.51)\": 0.207, \"(-6.51, -6.32)\": 0.1087, \"(-6.32, -6.07)\": 0.0155, \"(-6.07, -5.86)\": 0.1195, \"(-5.86, -5.68)\": 0.2152, \"(-5.68, -5.5)\": 0.3026, \"(-5.5, -5.27)\": 0.4029, \"(-5.27, -5.03)\": 0.4919, \"(-5.03, -4.8)\": 0.5859, \"(-4.8, -4.53)\": 0.681, \"(-4.53, -4.21)\": 0.7715, \"(-4.21, -3.74)\": 0.8639, \"(-3.74, -2.15)\": 0.9597, \"(-2.15, -1.82)\": 0.8725, \"(-1.82, -1.55)\": 0.7845, \"(-1.55, -1.33)\": 0.6985, \"(-1.33, -1.12)\": 0.6107, \"(-1.12, -0.89)\": 0.5251, \"(-0.89, -0.68)\": 0.4241, \"(-0.68, -0.49)\": 0.3266, \"(-0.49, -0.31)\": 0.2389, \"(-0.31, -0.12)\": 0.1463, \"(-0.12, 0.28)\": 0.0522, \"(0.28, 0.49)\": 0.1495, \"(0.49, 0.69)\": 0.2555, \"(0.69, 0.9)\": 0.3423, \"(0.9, 1.12)\": 0.438, \"(1.12, 1.35)\": 0.5324, \"(1.35, 1.6)\": 0.6278, \"(1.6, 1.88)\": 0.7211, \"(1.88, 2.3)\": 0.8173, \"(2.3, 4.35)\": 0.9139, \"(4.35, 4.65)\": 0.8171, \"(4.65, 4.9)\": 0.7235, \"(4.9, 5.1)\": 0.6375, \"(5.1, 5.33)\": 0.5493, \"(5.33, 5.54)\": 0.4531, \"(5.54, 5.75)\": 0.3541, \"(5.75, 5.94)\": 0.2524, \"(5.94, 6.14)\": 0.1613, \"(6.14, 6.58)\": 0.0664, \"(6.58, 6.77)\": 0.1566, \"(6.77, 6.97)\": 0.2437, \"(6.97, 7.18)\": 0.34, \"(7.18, 7.41)\": 0.4415, \"(7.41, 7.64)\": 0.5351, \"(7.64, 7.91)\": 0.6295, \"(7.91, 8.21)\": 0.7344, \"(8.21, 8.54)\": 0.8247, \"(8.54, 9.99)\": 0.9145}\n",
-      "\n",
-      "MC OPTIONS:\n",
-      "a) f(x)=|sin(x/2)|\n",
-      "b) f(x)=exp(x) + 4000* sin(x)\n",
-      "c) f(x)=exp(-x^2+1)+ 1/3 * |x|\n",
-      "d) f(x)=sign(cos(x))\n",
-      "e) f(x)=sign(sin(x))\n",
-      "To identify which function from the list familiarly corresponds to the behavior observed in the graph (a U-shaped pattern), let's review each provided function logic and match it with the behavior identified earlier:\n",
-      "\n",
-      "a) **f(x) = |sin(x/2)|**  \n",
-      "This function will exhibit a pattern oscillating between 0 and 1 following the sine curve but with halved frequency due to the divisor of 2. The absolute value ensures all values are non-negative. However, this does not naturally suggest a clear U-shaped relationship.\n",
-      "\n",
-      "b) **f(x) = exp(x) + 4000 * sin(x)**  \n",
-      "This function combines an exponential growth term which will predominantly increase as x increases, heavily influenced by the exp(x) term. The sin(x) term would cause periodic fluctuations. The pattern would be increasing and not U-shaped.\n",
-      "\n",
-      "c) **f(x) = exp(-x^2+1) + 1/3 * |x|**  \n",
-      "Considering the first term exp(-x^2+1), this would peak at x=0 and decrease as x moves away from 0, thus leading to a bell-shaped curve. The additional term 1/3 * |x| adds a linear component that increases as |x| increases, which may modify the side slopes, potentially contributing to a U-shaped pattern by increasing the values symmetrically away from x=0.\n",
-      "\n",
-      "d) **f(x) = sign(cos(x))**  \n",
-      "This step function would yield values of -1 or 1 depending on the sign of cos(x), creating a pattern of alternating positive and negative values with each half cycle of cosine. This doesn’t fit a continuous U-shaped pattern.\n",
-      "\n",
-      "e) **f(x) = sign(sin(x))**  \n",
-      "Similar to the previous, this function would provide values of -1 or 1 based on the sign of sin(x), leading to a square wave pattern. This also does not represent a continuous U-shaped pattern.\n",
-      "\n",
-      "From this analysis, function **c) f(x) = exp(-x^2+1) + 1/3 * |x|** is the most likely match to the U-shaped pattern described. This function's first part exp(-x^2+1) contributes a bell-shaped (inverse U-shaped) core, and the addition of 1/3 * |x| modifies this into exhibiting higher values further away from the center, leading to the U-shaped behavior seen in the graph.\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph exp(x) + 4000* sin(x)\n",
-      "\n",
-      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
-      "\n",
-      "Feature Name: x\n",
-      "Feature Type: continuous\n",
-      "Means: {\"(-9.97, -9.68)\": 1911.5, \"(-9.68, -9.43)\": 813.0, \"(-9.43, -9.21)\": -41.1, \"(-9.21, -8.93)\": -893.6, \"(-8.93, -8.65)\": -1956.4, \"(-8.65, -8.11)\": -2806.5, \"(-8.11, -7.14)\": -3878.5, \"(-7.14, -6.85)\": -3002.8, \"(-6.85, -6.56)\": -2094.7, \"(-6.56, -6.34)\": -1029.5, \"(-6.34, -6.09)\": -154.8, \"(-6.09, -5.8)\": 794.7, \"(-5.8, -5.54)\": 1909.3, \"(-5.54, -5.01)\": 2771.0, \"(-5.01, -3.93)\": 3822.1, \"(-3.93, -3.62)\": 2815.5, \"(-3.62, -3.37)\": 1688.0, \"(-3.37, -3.13)\": 810.0, \"(-3.13, -2.86)\": -104.0, \"(-2.86, -2.61)\": -1178.3, \"(-2.61, -2.21)\": -2154.9, \"(-2.21, -0.62)\": -3240.3, \"(-0.62, -0.29)\": -2206.4, \"(-0.29, -0.06)\": -1113.2, \"(-0.06, 0.2)\": -161.9, \"(0.2, 0.47)\": 806.3, \"(0.47, 0.81)\": 1843.8, \"(0.81, 1.25)\": 2932.5, \"(1.25, 2.38)\": 3813.1, \"(2.38, 2.69)\": 2747.9, \"(2.69, 3.0)\": 1554.0, \"(3.0, 3.28)\": 480.4, \"(3.28, 3.53)\": -651.9, \"(3.53, 3.79)\": -1522.0, \"(3.79, 4.13)\": -2396.1, \"(4.13, 5.61)\": -3293.6, \"(5.61, 5.82)\": -2225.1, \"(5.82, 6.1)\": -1305.4, \"(6.1, 6.28)\": -247.9, \"(6.28, 6.49)\": 648.0, \"(6.49, 6.72)\": 1584.1, \"(6.72, 6.96)\": 2534.5, \"(6.96, 7.19)\": 3599.5, \"(7.19, 7.51)\": 4532.3, \"(7.51, 7.83)\": 5612.0, \"(7.83, 8.26)\": 6521.8, \"(8.26, 8.65)\": 7552.2, \"(8.65, 8.93)\": 8598.2, \"(8.93, 9.14)\": 9550.7, \"(9.14, 9.32)\": 10647.4, \"(9.32, 9.44)\": 11785.6, \"(9.44, 9.54)\": 12708.6, \"(9.54, 9.63)\": 13573.4, \"(9.63, 9.72)\": 14553.8, \"(9.72, 9.8)\": 15654.9, \"(9.8, 9.87)\": 16766.6, \"(9.87, 9.92)\": 17928.2, \"(9.92, 10.0)\": 18818.0}\n",
-      "\n",
-      "MC OPTIONS:\n",
-      "a) f(x)=exp(x)+ 2000 * abs(x)\n",
-      "b) f(x)=sign(sin(x))\n",
-      "c) f(x)=exp(x) + 4000* sin(x)\n",
-      "d) f(x)=sin(x)+sin(2*x)\n",
-      "e) f(x)=exp(x)+ 4000 * sign(x)\n",
-      "To determine which function best corresponds to the graph of feature \"x\" described earlier, let's evaluate each proposed function with respect to the graph behavioral pattern:\n",
-      "\n",
-      "1. **a) f(x) = exp(x) + 2000 * abs(x)**:\n",
-      "   - This function is composed of two parts: `exp(x)` which exponentially grows as x increases and `2000 * abs(x)` which linearly scales with the magnitude of x but is always positive.\n",
-      "   - The combination of these two would result in large positive values for both negative and positive x, continually increasing without returning to negative or minimal values. This doesn't match the initial high positive, deep negative, and then high positive cycle seen in the graph.\n",
-      "\n",
-      "2. **b) f(x) = sign(sin(x))**:\n",
-      "   - The `sign(sin(x))` will oscillate between -1 and 1, specifically, it returns -1, 0, or 1 based on the sign of the sine of x. This function would produce a graph made up of sharp jumps between -1, 0, and 1, which does not match the continuous and smooth U-shaped pattern seen in the graph.\n",
-      "\n",
-      "3. **c) f(x) = exp(x) + 4000 * sin(x)**:\n",
-      "   - This function has a growing exponential part `exp(x)` which ensures ever-increasing values as x increases.\n",
-      "   - The `4000 * sin(x)` part adds oscillations, but due to the exponential growth in the `exp(x)`, these oscillations will not dominant enough to drive the function negative which is required especially in the negative x range, as seen in the deep negative values in the graph.\n",
-      "\n",
-      "4. **d) f(x) = sin(x) + sin(2*x)**:\n",
-      "   - This function is purely based on sine functions and would produce a repeating, periodic oscillation. The resulting graph would show a sinusoidal pattern, a regular up and down motion, which does not resemble the deep dip and sharp rise we observe in the graph.\n",
-      "\n",
-      "5. **e) f(x) = exp(x) + 4000 * sign(x)**:\n",
-      "   - `exp(x)` ensures an exponential increase as x becomes larger.\n",
-      "   - `4000 * sign(x)` adds a step change of 4000 units whenever x crosses zero: it would be -4000 for x < 0 and +4000 for x > 0, affecting a sharp offset change but not altering the fundamental increase driven by the exponential part as x becomes large. This would reflect a graph that, while starting lower in negatives due to the -4000 offset, increases and then jumps up around 0 but continues increasing mostly driven by the exponential part.\n",
-      "   \n",
-      "Considering these functions, option **e) f(x) = exp(x) + 4000 * sign(x)** seems the most plausible to describe the graph as it encompasses the decrease to a deeply negative, scale-back to close zero, and explosive growth to positive values, identifying the behavior from the negative to positive part of x-axis, especially noting the sharp change around x = 0 as the sign changes.\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph sign(sin(x))\n",
-      "\n",
-      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
-      "\n",
-      "Feature Name: x\n",
-      "Feature Type: continuous\n",
-      "Means: {\"(-9.99, -9.69)\": 1.013, \"(-9.69, -9.53)\": 0.992, \"(-9.53, -9.47)\": 0.971, \"(-9.47, -9.43)\": 0.827, \"(-9.43, -9.38)\": -0.849, \"(-9.38, -9.19)\": -0.973, \"(-9.19, -6.42)\": -0.993, \"(-6.42, -6.28)\": -0.971, \"(-6.28, -6.16)\": 0.949, \"(-6.16, -5.92)\": 0.971, \"(-5.92, -3.25)\": 0.993, \"(-3.25, -3.16)\": 0.972, \"(-3.16, -3.14)\": 0.847, \"(-3.14, -3.12)\": -0.849, \"(-3.12, -2.74)\": -0.965, \"(-2.74, -1.99)\": -0.986, \"(-1.99, -0.24)\": -1.006, \"(-0.24, -0.03)\": -0.985, \"(-0.03, -0.01)\": -0.926, \"(-0.01, 0.01)\": -0.763, \"(0.01, 0.05)\": 0.717, \"(0.05, 0.27)\": 0.966, \"(0.27, 1.49)\": 0.987, \"(1.49, 2.65)\": 1.007, \"(2.65, 3.05)\": 0.986, \"(3.05, 3.08)\": 0.966, \"(3.08, 3.12)\": 0.827, \"(3.12, 3.15)\": -0.761, \"(3.15, 3.25)\": -0.963, \"(3.25, 3.92)\": -0.984, \"(3.92, 6.08)\": -1.004, \"(6.08, 6.22)\": -0.982, \"(6.22, 6.25)\": -0.962, \"(6.25, 6.25)\": -0.924, \"(6.25, 6.26)\": -0.889, \"(6.26, 6.28)\": -0.788, \"(6.28, 6.31)\": 0.801, \"(6.31, 6.33)\": 0.914, \"(6.33, 6.53)\": 0.955, \"(6.53, 6.86)\": 0.977, \"(6.86, 9.31)\": 0.998, \"(9.31, 9.38)\": 0.971, \"(9.38, 9.42)\": 0.756, \"(9.42, 9.47)\": -0.681, \"(9.47, 9.58)\": -0.969, \"(9.58, 9.95)\": -0.994, \"(9.95, 9.97)\": -1.015}\n",
-      "\n",
-      "MC OPTIONS:\n",
-      "a) f(x)=exp(-x^2+1)+ 1/3 * |x|\n",
-      "b) f(x)=sign(sin(x))\n",
-      "c) f(x)=|sin(x/2)|\n",
-      "d) f(x)=sign(cos(x))\n",
-      "e) f(x)=exp(-x+1)+ 2000 * abs(x+1)\n",
-      "Let's match the graph pattern to the listed function options by considering their mathematical behavior:\n",
-      "\n",
-      "### Option a) \\( f(x) = e^{-(x^2+1)} + \\frac{1}{3} |x| \\)\n",
-      "- This function combines a Gaussian-like exponential decay with a linear function that depends on the absolute value of x. The Gaussian component \\( e^{-(x^2 + 1)} \\) quickly tends toward 0 as x moves away from 0, effectively disappearing for large values of x, while the \\( \\frac{1}{3}|x| \\) component grows linearly. This doesn't closely match the cyclic and periodic pattern described in the graph data.\n",
-      "\n",
-      "### Option b) \\( f(x) = \\text{sign}(\\sin(x)) \\)\n",
-      "- The \\( \\text{sign} \\) function returns -1 for negative values, 0 for zero, and 1 for positive values. \\( \\sin(x) \\) is cyclic with period \\( 2\\pi \\). The combination would result in a step-function-like behavior, swapping between -1, 0, and 1 as x transitions through multiples of \\( \\pi/2 \\). The cyclic nature fits, but the step-like behavior is abrupt without the smooth transitions described in the graph.\n",
-      "\n",
-      "### Option c) \\( f(x) = |\\sin(x/2)| \\)\n",
-      "- The \\( |\\sin(x/2)| \\) adjusts the period of \\( \\sin(x) \\) to be twice as long (period of \\( 4\\pi \\)) while the absolute value removes negative parts, making the function always non-negative and reflective across the x-axis. This leads to a cyclical, wave-like pattern with smooth transitions between peaks and troughs, matching well with the graph data's description.\n",
-      "\n",
-      "### Option d) \\( f(x) = \\text{sign}(\\cos(x)) \\)\n",
-      "- Since \\( \\cos(x) \\) is similar in behavior to \\( \\sin(x) \\) but leads it by \\( \\pi/2 \\), the cyclic behavior similar to option b would appear but phaseshifted. Like option b, the abrupt changes between -1, 0, and 1 still don't mimic the smoother transitions observed in the graph.\n",
-      "\n",
-      "### Option e) \\( f(x) = e^{-(x+1)} + 2000 \\cdot \\text{abs}(x+1) \\)\n",
-      "- This function decreases exponentially from x = -1 and onwards, while the \\( 2000| x+1 | \\) term ensures a rapid escalation as x equals -1 and beyond, leading to very large values, not exhibiting periodic behavior.\n",
-      "\n",
-      "### Conclusion:\n",
-      "The description and patterns from your graph fit best with **Option c) \\( f(x) = |\\sin(x/2)| \\)**. This function captures the steady periodic nature with non-negative values and smooth transitions between peaks and valleys, aligning well with the cyclic, wave-like patterns you described.\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph sign(cos(x))\n",
-      "\n",
-      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
-      "\n",
-      "Feature Name: x\n",
-      "Feature Type: continuous\n",
-      "Means: {\"(-10.0, -8.26)\": -1.013, \"(-8.26, -7.92)\": -0.992, \"(-7.92, -7.87)\": -0.972, \"(-7.87, -7.86)\": -0.915, \"(-7.86, -7.83)\": 0.856, \"(-7.83, -7.69)\": 0.966, \"(-7.69, -5.83)\": 0.987, \"(-5.83, -4.89)\": 1.007, \"(-4.89, -4.74)\": 0.986, \"(-4.74, -4.72)\": 0.703, \"(-4.72, -4.71)\": -0.827, \"(-4.71, -4.26)\": -0.974, \"(-4.26, -1.68)\": -0.994, \"(-1.68, -1.59)\": -0.974, \"(-1.59, -1.58)\": -0.911, \"(-1.58, -1.56)\": -0.794, \"(-1.56, -1.54)\": 0.893, \"(-1.54, -1.3)\": 0.963, \"(-1.3, -0.62)\": 0.983, \"(-0.62, 1.36)\": 1.004, \"(1.36, 1.56)\": 0.983, \"(1.56, 1.56)\": 0.871, \"(1.56, 1.57)\": 0.773, \"(1.57, 1.57)\": -0.891, \"(1.57, 1.76)\": -0.966, \"(1.76, 2.8)\": -0.986, \"(2.8, 4.49)\": -1.008, \"(4.49, 4.67)\": -0.986, \"(4.67, 4.7)\": -0.831, \"(4.7, 4.72)\": 0.776, \"(4.72, 5.04)\": 0.972, \"(5.04, 7.76)\": 0.993, \"(7.76, 7.83)\": 0.969, \"(7.83, 7.85)\": 0.892, \"(7.85, 7.87)\": -0.728, \"(7.87, 7.89)\": -0.915, \"(7.89, 8.07)\": -0.963, \"(8.07, 9.76)\": -0.984, \"(9.76, 9.98)\": -1.005}\n",
-      "\n",
-      "MC OPTIONS:\n",
-      "a) f(x)=|sin(x/2)|\n",
-      "b) f(x)=exp(x) + 4000* sin(x)\n",
-      "c) f(x)=sign(cos(x))\n",
-      "d) f(x)=arctan(x) + sin(x)\n",
-      "e) f(x)=-tanh(x) + 1/4 * x\n",
-      "To determine which function the provided graph most closely approximates, we can analyze each given function's expected behavior and see which matches the observed characteristics of the graph. Here’s the breakdown:\n",
-      "\n",
-      "a) **f(x) = |sin(x/2)|**\n",
-      "   - This function should produce a graph with a periodic pattern that repeats every \\(2\\pi\\) units (since it is related to sine), but always remains non-negative due to the absolute value.\n",
-      "   - It has peaks at the multiples of \\(2\\pi\\) and troughs at odd multiples of \\(\\pi\\).\n",
-      "\n",
-      "b) **f(x) = exp(x) + 4000 * sin(x)**\n",
-      "   - The dominant term here is \\(exp(x)\\), which grows exponentially as x increases. The \\(4000 * sin(x)\\) term introduces periodic fluctuations, but these will be dwarfed by the exponential growth as \\(x\\) becomes large in the positive, and negligible as \\(x\\) goes negative.\n",
-      "\n",
-      "c) **f(x) = sign(cos(x))**\n",
-      "   - This function produces discrete jumps between 1 and -1 based on the cosine wave's sign. \\(cos(x)\\) changes its sign on \\((2k+1)\\pi/2\\), \\(k\\) being an integer, creating a pattern repeating about every \\(\\pi\\) unit with rapid transitions between 1 and -1.\n",
-      "\n",
-      "d) **f(x) = arctan(x) + sin(x)**\n",
-      "   - Here we have the gradual, bounded increase of \\(arctan(x)\\) modulated by the sinusoidal wave of \\(sin(x)\\), leading to a pattern that fluctuates around the curve of \\(arctan(x)\\). \n",
-      "\n",
-      "e) **f(x) = -tanh(x) + 1/4 * x**\n",
-      "   - This function combines a hyperbolic tangent, which moves from -1 to 1 as \\(x\\) goes from negative to positive infinity, offset slightly upwards by a linear term \\(1/4 * x\\). This would not typically show periodic behavior.\n",
-      "\n",
-      "Given the descriptions:\n",
-      "- **Option a** suggests a periodic and symmetric pattern but always positive, not matching the alternating signs in the graph.\n",
-      "- **Option b** would not show cyclicity due to the dominance of exponential growth.\n",
-      "- **Option c** most closely matches the observed behavior in the graph. This function would switch between -1 and 1 at intervals defined by the cosine function's periodicity, which closely resembles the sharp transitions from negative to positive values observed in the graph.\n",
-      "- **Option d** wouldn’t match the clear periodic flipping between positive and negative seen in the graph.\n",
-      "- **Option e** does not provide the regular periodic alternation seen in the graph.\n",
-      "\n",
-      "Therefore, the graph described most closely approximates **option c: \\(f(x) = sign(cos(x))\\)**.\n",
-      "--------------------------------------------------------------------------------\n",
-      "Graph sin(x)+sin(2*x)\n",
-      "\n",
-      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
-      "\n",
-      "Feature Name: x\n",
-      "Feature Type: continuous\n",
-      "Means: {\"(-10.0, -9.48)\": -0.369, \"(-9.48, -9.17)\": -0.053, \"(-9.17, -8.34)\": 0.251, \"(-8.34, -8.16)\": -0.064, \"(-8.16, -7.95)\": -0.422, \"(-7.95, -7.74)\": -0.901, \"(-7.74, -7.54)\": -1.235, \"(-7.54, -6.74)\": -1.537, \"(-6.74, -6.62)\": -1.214, \"(-6.62, -6.43)\": -0.817, \"(-6.43, -6.27)\": -0.28, \"(-6.27, -6.14)\": 0.134, \"(-6.14, -6.02)\": 0.449, \"(-6.02, -5.9)\": 0.82, \"(-5.9, -5.74)\": 1.133, \"(-5.74, -4.81)\": 1.466, \"(-4.81, -4.66)\": 1.159, \"(-4.66, -4.5)\": 0.832, \"(-4.5, -4.31)\": 0.519, \"(-4.31, -4.1)\": 0.19, \"(-4.1, -2.98)\": -0.145, \"(-2.98, -2.0)\": 0.159, \"(-2.0, -1.82)\": -0.166, \"(-1.82, -1.67)\": -0.507, \"(-1.67, -1.51)\": -0.82, \"(-1.51, -1.36)\": -1.131, \"(-1.36, -0.39)\": -1.434, \"(-0.39, -0.25)\": -1.014, \"(-0.25, -0.12)\": -0.681, \"(-0.12, -0.0)\": -0.333, \"(-0.0, 0.11)\": 0.017, \"(0.11, 0.25)\": 0.383, \"(0.25, 0.4)\": 0.797, \"(0.4, 0.55)\": 1.136, \"(0.55, 1.49)\": 1.467, \"(1.49, 1.66)\": 1.127, \"(1.66, 1.81)\": 0.814, \"(1.81, 2.0)\": 0.5, \"(2.0, 2.26)\": 0.111, \"(2.26, 3.24)\": -0.209, \"(3.24, 4.31)\": 0.102, \"(4.31, 4.47)\": -0.237, \"(4.47, 4.66)\": -0.547, \"(4.66, 4.83)\": -0.926, \"(4.83, 5.04)\": -1.268, \"(5.04, 5.82)\": -1.576, \"(5.82, 5.96)\": -1.239, \"(5.96, 6.08)\": -0.898, \"(6.08, 6.2)\": -0.565, \"(6.2, 6.31)\": -0.221, \"(6.31, 6.42)\": 0.13, \"(6.42, 6.53)\": 0.45, \"(6.53, 6.65)\": 0.776, \"(6.65, 6.82)\": 1.077, \"(6.82, 7.05)\": 1.406, \"(7.05, 7.63)\": 1.71, \"(7.63, 7.79)\": 1.392, \"(7.79, 8.0)\": 1.034, \"(8.0, 8.18)\": 0.615, \"(8.18, 8.44)\": 0.244, \"(8.44, 9.65)\": -0.097, \"(9.65, 9.99)\": 0.224}\n",
-      "\n",
-      "MC OPTIONS:\n",
-      "a) f(x)=sin(x)+sin(2*x)\n",
-      "b) f(x)=exp(x)+ 2000 * abs(x)\n",
-      "c) f(x)=sin(x)+cos(x)\n",
-      "d) f(x)=exp(x)+ 4000 * sign(x)\n",
-      "e) f(x)=exp(-x^2+1)+ 1/3 * |x|\n",
-      "To determine which function the graph approximates, let's analyze and compare each option against the observed patterns:\n",
-      "\n",
-      "1. **f(x) = sin(x) + sin(2*x):**\n",
-      "   - This function would generate a wave with harmonics at both the frequency of sin(x) and twice that frequency due to sin(2*x). The function should show periodic behavior, but with changes in amplitude that could overlap to produce a generally complex pattern.\n",
-      "   - Based on the description of the observed graph showing periodic-like behavior with symmetry around `x`=0, this function could be a plausible match.\n",
-      "\n",
-      "2. **f(x) = exp(x) + 2000 * abs(x):**\n",
-      "   - This function would exhibit exponential growth as `x` increases and a linear shift proportional to the absolute value of `x`, making it rise sharply and symmetrically around zero. The behavior should not show a periodic pattern or symmetry but would rather show monotonically increasing values with `x` exponentially.\n",
-      "   - Given that this function would not exhibit declines or multiple peaks and troughs as observed, it would not likely match the described pattern.\n",
-      "\n",
-      "3. **f(x) = sin(x) + cos(x):**\n",
-      "   - This would result in a trigonometric function where both sine and cosine are summed up, leading to a straightforward periodic form with a combined frequency and phase. The function would generally move smoothly between its maximum and minimum values without abrupt changes.\n",
-      "   - The symmetry and wave-like nature align with the pattern in the graph. However, it typically lacks the more complex harmonic content that might explain the higher peaks and sharper troughs seen.\n",
-      "\n",
-      "4. **f(x) = exp(x) + 4000 * sign(x):**\n",
-      "   - This function implies exponential growth with an abrupt jump (or drop) at `x`=0 due to the `sign(x)` function. It would not exhibit the periodic or wave-like pattern that decays over intervals.\n",
-      "   - As such, it does not fit the observed behavior well.\n",
-      "\n",
-      "5. **f(x) = exp(-x^2 + 1) + 1/3 * |x|:**\n",
-      "   - This would demonstrate a bell-like form due to the exp(-x^2 + 1) component, which peaks at `x`=0 and decays symmetrically. The added 1/3 * |x| would cause a linear modification but is not enough to create the observed multiple peaks and troughs.\n",
-      "   - The symmetry is present, but the specific shape with repetitive waves does not align perfectly with this potential function.\n",
-      "\n",
-      "From this analysis, while **option c) f(x) = sin(x) + cos(x)** shows a basic periodic form, it is actually **option a) f(x) = sin(x) + sin(2*x)** that is more likely to be correct due to the presence of additional harmonics creating a more complex periodic pattern with sharper and more varied peaks and troughs, similar to what is described for the graph. This complex wave pattern better fits the behavior illustrated in your graph.\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "for mc_options, solution in fbench_questions:\n",
-    "    print(f'Graph {fbench[solution][1]}\\n')\n",
-    "    # initial description message\n",
-    "    graph_as_text = fbench_functions_as_text[solution]\n",
-    "    print(graph_as_text)\n",
-    "    prompt = t2ebm.prompts.describe_graph(\n",
-    "        graph_as_text, include_assistant_response=False\n",
-    "    )\n",
-    "    messages = t2ebm.utils.parse_guidance_query(prompt)\n",
-    "    # send query\n",
-    "    response = t2ebm.utils.openai_completion_query('gpt-4-turbo-2024-04-09', messages)\n",
-    "    # prepare next query\n",
-    "    messages.append({'role': 'assistant', 'content': response})\n",
-    "    mc_options = '\\n'.join([f'{case} f(x)={fbench[option][1]}' for case, option in zip(cases, mc_options)])\n",
-    "    print('MC OPTIONS:')\n",
-    "    print(mc_options)\n",
-    "    mc_question = f\"\"\"It turns out that the graph closely approximates one of the following functions:\n",
-    "\n",
-    "{mc_options}\n",
-    "\n",
-    "Which of these functions is depicted in the graph? Think step by step.\n",
-    "\"\"\"    \n",
-    "    messages.append({'role': 'user', 'content': mc_question})\n",
-    "    # send query\n",
-    "    response = t2ebm.utils.openai_completion_query('gpt-4-turbo-2024-04-09', messages) \n",
-    "    print(response)\n",
-    "    print('-'*80)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "a) x\n",
-      "b) x^2\n",
-      "c) x^3\n",
-      "d) sin(x)\n",
-      "e) cos(x)\n",
-      "f) exp(x)\n",
-      "g) |x|\n",
-      "h) sinh(x)\n",
-      "i) cosh(x)\n",
-      "j) tanh(x)\n",
-      "k) arcsinh(x)\n",
-      "l) arctan(x)\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'{{#system~}}\\nYou are an expert statistician and data scientist.\\n    \\nYou interpret global explanations produced by a generalized additive model (GAM). GAMs produce explanations in the form of graphs that contain the effect of a specific input feature.\\n\\nYou will be given graphs from the model, and the user will ask you questions about the graphs. \\n    \\nAnswer all questions to the best of your ability, combining both the data contained in the graph and your knowledge about the real world.\\n\\nGraphs will be presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.  \\n    \\nThe user will provide graphs in the following format:\\n    - The name of the feature depicted in the graph\\n    - The type of the feature (continuous, categorical, or boolean)\\n    - Mean values\\n    - Lower bounds of confidence interval\\n    - Upper bounds of confidence interval\\n\\n\\n{{~/system}}\\n\\n{{#user~}}\\nConsider the following graph from the model. This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: feature_0000\\nFeature Type: continuous\\nMeans: {\"(-9.96, -6.32)\": -1.527, \"(-6.32, -4.55)\": -1.467, \"(-4.55, -3.51)\": -1.408, \"(-3.51, -3.15)\": -1.347, \"(-3.15, -2.56)\": -1.314, \"(-2.56, -2.17)\": -1.252, \"(-2.17, -2.0)\": -1.188, \"(-2.0, -1.82)\": -1.154, \"(-1.82, -1.58)\": -1.122, \"(-1.58, -1.38)\": -1.058, \"(-1.38, -1.29)\": -0.998, \"(-1.29, -1.2)\": -0.958, \"(-1.2, -1.12)\": -0.922, \"(-1.12, -0.95)\": -0.889, \"(-0.95, -0.88)\": -0.801, \"(-0.88, -0.79)\": -0.767, \"(-0.79, -0.73)\": -0.711, \"(-0.73, -0.67)\": -0.678, \"(-0.67, -0.6)\": -0.644, \"(-0.6, -0.51)\": -0.564, \"(-0.51, -0.47)\": -0.517, \"(-0.47, -0.4)\": -0.471, \"(-0.4, -0.36)\": -0.424, \"(-0.36, -0.31)\": -0.391, \"(-0.31, -0.24)\": -0.314, \"(-0.24, -0.17)\": -0.26, \"(-0.17, -0.09)\": -0.184, \"(-0.09, -0.05)\": -0.139, \"(-0.05, 0.0)\": -0.076, \"(0.0, 0.05)\": -0.028, \"(0.05, 0.11)\": 0.024, \"(0.11, 0.18)\": 0.058, \"(0.18, 0.21)\": 0.133, \"(0.21, 0.26)\": 0.173, \"(0.26, 0.31)\": 0.21, \"(0.31, 0.39)\": 0.268, \"(0.39, 0.44)\": 0.33, \"(0.44, 0.48)\": 0.367, \"(0.48, 0.51)\": 0.402, \"(0.51, 0.63)\": 0.441, \"(0.63, 0.67)\": 0.513, \"(0.67, 0.75)\": 0.551, \"(0.75, 0.81)\": 0.6, \"(0.81, 0.88)\": 0.634, \"(0.88, 0.94)\": 0.669, \"(0.94, 1.0)\": 0.704, \"(1.0, 1.09)\": 0.739, \"(1.09, 1.16)\": 0.774, \"(1.16, 1.32)\": 0.81, \"(1.32, 1.42)\": 0.872, \"(1.42, 1.63)\": 0.907, \"(1.63, 1.77)\": 0.971, \"(1.77, 1.92)\": 1.005, \"(1.92, 2.09)\": 1.039, \"(2.09, 2.49)\": 1.072, \"(2.49, 3.01)\": 1.135, \"(3.01, 3.74)\": 1.196, \"(3.74, 4.92)\": 1.255, \"(4.92, 7.04)\": 1.317, \"(7.04, 9.99)\": 1.377}\\n\\nPlease describe the general pattern of the graph.\\n{{~/user}}\\n\\n{{#assistant~}}{{gen \\'graph_description\\' temperature=0.7 max_tokens=2000}}{{~/assistant}}'"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "prompt = t2ebm.prompts.describe_graph(\n",
-    "    graph_as_text, include_assistant_response=True\n",
-    ")\n",
-    "prompt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "messages = t2ebm.utils.parse_guidance_query(prompt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'role': 'system',\n",
-       "  'content': 'You are an expert statistician and data scientist.\\n    \\nYou interpret global explanations produced by a generalized additive model (GAM). GAMs produce explanations in the form of graphs that contain the effect of a specific input feature.\\n\\nYou will be given graphs from the model, and the user will ask you questions about the graphs. \\n    \\nAnswer all questions to the best of your ability, combining both the data contained in the graph and your knowledge about the real world.\\n\\nGraphs will be presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.  \\n    \\nThe user will provide graphs in the following format:\\n    - The name of the feature depicted in the graph\\n    - The type of the feature (continuous, categorical, or boolean)\\n    - Mean values\\n    - Lower bounds of confidence interval\\n    - Upper bounds of confidence interval'},\n",
-       " {'role': 'user',\n",
-       "  'content': 'Consider the following graph from the model. This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: feature_0000\\nFeature Type: continuous\\nMeans: {\"(-9.99, -6.39)\": -1.417, \"(-6.39, -4.58)\": -1.357, \"(-4.58, -3.53)\": -1.298, \"(-3.53, -3.13)\": -1.236, \"(-3.13, -2.53)\": -1.201, \"(-2.53, -2.15)\": -1.139, \"(-2.15, -1.97)\": -1.073, \"(-1.97, -1.68)\": -1.039, \"(-1.68, -1.56)\": -0.975, \"(-1.56, -1.35)\": -0.94, \"(-1.35, -1.25)\": -0.875, \"(-1.25, -1.15)\": -0.831, \"(-1.15, -1.06)\": -0.792, \"(-1.06, -1.0)\": -0.756, \"(-1.0, -0.9)\": -0.712, \"(-0.9, -0.82)\": -0.66, \"(-0.82, -0.76)\": -0.625, \"(-0.76, -0.66)\": -0.575, \"(-0.66, -0.59)\": -0.514, \"(-0.59, -0.55)\": -0.474, \"(-0.55, -0.47)\": -0.425, \"(-0.47, -0.41)\": -0.372, \"(-0.41, -0.37)\": -0.324, \"(-0.37, -0.31)\": -0.278, \"(-0.31, -0.25)\": -0.233, \"(-0.25, -0.2)\": -0.166, \"(-0.2, -0.14)\": -0.133, \"(-0.14, -0.09)\": -0.071, \"(-0.09, -0.05)\": -0.035, \"(-0.05, -0.0)\": 0.015, \"(-0.0, 0.03)\": 0.057, \"(0.03, 0.1)\": 0.092, \"(0.1, 0.17)\": 0.177, \"(0.17, 0.23)\": 0.253, \"(0.23, 0.28)\": 0.292, \"(0.28, 0.33)\": 0.337, \"(0.33, 0.37)\": 0.384, \"(0.37, 0.41)\": 0.417, \"(0.41, 0.48)\": 0.463, \"(0.48, 0.52)\": 0.513, \"(0.52, 0.57)\": 0.545, \"(0.57, 0.61)\": 0.582, \"(0.61, 0.7)\": 0.617, \"(0.7, 0.77)\": 0.683, \"(0.77, 0.83)\": 0.718, \"(0.83, 0.91)\": 0.76, \"(0.91, 1.01)\": 0.811, \"(1.01, 1.1)\": 0.851, \"(1.1, 1.18)\": 0.891, \"(1.18, 1.25)\": 0.923, \"(1.25, 1.42)\": 0.956, \"(1.42, 1.57)\": 1.019, \"(1.57, 1.79)\": 1.055, \"(1.79, 2.05)\": 1.119, \"(2.05, 2.46)\": 1.179, \"(2.46, 2.94)\": 1.241, \"(2.94, 3.68)\": 1.301, \"(3.68, 4.81)\": 1.361, \"(4.81, 6.85)\": 1.422, \"(6.85, 9.98)\": 1.482}\\n\\nPlease describe the general pattern of the graph.'}]"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The graph represents the effect of the continuous feature \"feature_0000\" on the output of the generalized additive model. Here's a detailed description of the general pattern from the data provided:\n",
-      "\n",
-      "1. **Negative Values (Left Side of the Graph)**:\n",
-      "    - For highly negative values of \"feature_0000\", we observe mean values starting from around -1.527 for the interval (-9.96, -6.32).\n",
-      "    - As \"feature_0000\" increases towards less negative values, the mean values gradually increase (become less negative) until around -0.391 for the interval (-0.36, -0.31).\n",
-      "\n",
-      "2. **Transition Around Zero**:\n",
-      "    - Around zero, we see the values transitioning from negative to positive. For example:\n",
-      "        - (-0.09, -0.05): -0.139\n",
-      "        - (-0.05, 0.0): -0.076\n",
-      "        - (0.0, 0.05): -0.028\n",
-      "        - (0.05, 0.11): 0.024\n",
-      "    - This indicates a crossing over to positive effects on the output.\n",
-      "\n",
-      "3. **Positive Values (Right Side of the Graph)**:\n",
-      "    - For positive values of \"feature_0000\", we observe an increasing trend in the mean values:\n",
-      "        - It starts from around 0.024 for the interval (0.05, 0.11) and continues to rise.\n",
-      "        - The values gradually increase with positive intervals, showing a positive impact on the output as \"feature_0000\" increases further, reaching about 1.377 for the interval (7.04, 9.99).\n",
-      "\n",
-      "### Summary of the General Pattern:\n",
-      "- **Negative Correlation**: Strong negative values of \"feature_0000\" contribute negatively to the model's output, with decreasing negative effect as the feature value becomes less negative.\n",
-      "- **Near Zero/Transition**: Around zero, the effect transitions from negative to positive.\n",
-      "- **Positive Correlation**: Positive values of \"feature_0000\" exhibit an increasing positive effect on the model's output, with this positive contribution becoming larger as the feature value increases.\n",
-      "\n",
-      "This pattern suggests that \"feature_0000\" has a nonlinear but smooth increasing effect across its range, with a significant change in the contribution happening near zero.\n"
-     ]
-    }
-   ],
-   "source": [
-    "response = t2ebm.utils.openai_completion_query('gpt-4o-2024-05-13', messages[:-1])\n",
-    "print(response)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "new_messages = messages[:-1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'role': 'system',\n",
-       "  'content': 'You are an expert statistician and data scientist.\\n    \\nYou interpret global explanations produced by a generalized additive model (GAM). GAMs produce explanations in the form of graphs that contain the effect of a specific input feature.\\n\\nYou will be given graphs from the model, and the user will ask you questions about the graphs. \\n    \\nAnswer all questions to the best of your ability, combining both the data contained in the graph and your knowledge about the real world.\\n\\nGraphs will be presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.  \\n    \\nThe user will provide graphs in the following format:\\n    - The name of the feature depicted in the graph\\n    - The type of the feature (continuous, categorical, or boolean)\\n    - Mean values\\n    - Lower bounds of confidence interval\\n    - Upper bounds of confidence interval'},\n",
-       " {'role': 'user',\n",
-       "  'content': 'Consider the following graph from the model. This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: feature_0000\\nFeature Type: continuous\\nMeans: {\"(-9.96, -6.32)\": -1.527, \"(-6.32, -4.55)\": -1.467, \"(-4.55, -3.51)\": -1.408, \"(-3.51, -3.15)\": -1.347, \"(-3.15, -2.56)\": -1.314, \"(-2.56, -2.17)\": -1.252, \"(-2.17, -2.0)\": -1.188, \"(-2.0, -1.82)\": -1.154, \"(-1.82, -1.58)\": -1.122, \"(-1.58, -1.38)\": -1.058, \"(-1.38, -1.29)\": -0.998, \"(-1.29, -1.2)\": -0.958, \"(-1.2, -1.12)\": -0.922, \"(-1.12, -0.95)\": -0.889, \"(-0.95, -0.88)\": -0.801, \"(-0.88, -0.79)\": -0.767, \"(-0.79, -0.73)\": -0.711, \"(-0.73, -0.67)\": -0.678, \"(-0.67, -0.6)\": -0.644, \"(-0.6, -0.51)\": -0.564, \"(-0.51, -0.47)\": -0.517, \"(-0.47, -0.4)\": -0.471, \"(-0.4, -0.36)\": -0.424, \"(-0.36, -0.31)\": -0.391, \"(-0.31, -0.24)\": -0.314, \"(-0.24, -0.17)\": -0.26, \"(-0.17, -0.09)\": -0.184, \"(-0.09, -0.05)\": -0.139, \"(-0.05, 0.0)\": -0.076, \"(0.0, 0.05)\": -0.028, \"(0.05, 0.11)\": 0.024, \"(0.11, 0.18)\": 0.058, \"(0.18, 0.21)\": 0.133, \"(0.21, 0.26)\": 0.173, \"(0.26, 0.31)\": 0.21, \"(0.31, 0.39)\": 0.268, \"(0.39, 0.44)\": 0.33, \"(0.44, 0.48)\": 0.367, \"(0.48, 0.51)\": 0.402, \"(0.51, 0.63)\": 0.441, \"(0.63, 0.67)\": 0.513, \"(0.67, 0.75)\": 0.551, \"(0.75, 0.81)\": 0.6, \"(0.81, 0.88)\": 0.634, \"(0.88, 0.94)\": 0.669, \"(0.94, 1.0)\": 0.704, \"(1.0, 1.09)\": 0.739, \"(1.09, 1.16)\": 0.774, \"(1.16, 1.32)\": 0.81, \"(1.32, 1.42)\": 0.872, \"(1.42, 1.63)\": 0.907, \"(1.63, 1.77)\": 0.971, \"(1.77, 1.92)\": 1.005, \"(1.92, 2.09)\": 1.039, \"(2.09, 2.49)\": 1.072, \"(2.49, 3.01)\": 1.135, \"(3.01, 3.74)\": 1.196, \"(3.74, 4.92)\": 1.255, \"(4.92, 7.04)\": 1.317, \"(7.04, 9.99)\": 1.377}\\n\\nPlease describe the general pattern of the graph.'},\n",
-       " {'role': 'assistant',\n",
-       "  'content': 'The graph represents the effect of the continuous feature \"feature_0000\" on the output of the generalized additive model. Here\\'s a detailed description of the general pattern from the data provided:\\n\\n1. **Negative Values (Left Side of the Graph)**:\\n    - For highly negative values of \"feature_0000\", we observe mean values starting from around -1.527 for the interval (-9.96, -6.32).\\n    - As \"feature_0000\" increases towards less negative values, the mean values gradually increase (become less negative) until around -0.391 for the interval (-0.36, -0.31).\\n\\n2. **Transition Around Zero**:\\n    - Around zero, we see the values transitioning from negative to positive. For example:\\n        - (-0.09, -0.05): -0.139\\n        - (-0.05, 0.0): -0.076\\n        - (0.0, 0.05): -0.028\\n        - (0.05, 0.11): 0.024\\n    - This indicates a crossing over to positive effects on the output.\\n\\n3. **Positive Values (Right Side of the Graph)**:\\n    - For positive values of \"feature_0000\", we observe an increasing trend in the mean values:\\n        - It starts from around 0.024 for the interval (0.05, 0.11) and continues to rise.\\n        - The values gradually increase with positive intervals, showing a positive impact on the output as \"feature_0000\" increases further, reaching about 1.377 for the interval (7.04, 9.99).\\n\\n### Summary of the General Pattern:\\n- **Negative Correlation**: Strong negative values of \"feature_0000\" contribute negatively to the model\\'s output, with decreasing negative effect as the feature value becomes less negative.\\n- **Near Zero/Transition**: Around zero, the effect transitions from negative to positive.\\n- **Positive Correlation**: Positive values of \"feature_0000\" exhibit an increasing positive effect on the model\\'s output, with this positive contribution becoming larger as the feature value increases.\\n\\nThis pattern suggests that \"feature_0000\" has a nonlinear but smooth increasing effect across its range, with a significant change in the contribution happening near zero.'}]"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "new_messages.append({'role': 'assistant', 'content': response})\n",
-    "new_messages"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mc_question = \"\"\"It turns out that the depicted graph is one of the following functions:\n",
-    "\n",
-    "a) f(x) = sin(x)\n",
-    "b) f(x) = cos(x)\n",
-    "c) f(x) = exp(x)\n",
-    "d) f(x) = |x|\n",
-    "e) f(x) = arctan(x)\n",
-    "f) f(x) = sinh(x)\n",
-    "g) f(x) = cosh(x)\n",
-    "h) f(x) = tanh(x)\n",
-    "i) f(x) = arcsinh(x)\n",
-    "j) f(x) = arctan(x) \n",
-    "\n",
-    "Your task now is to identify which of the functions is the one that generated the graph. Think step by step.\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'role': 'system',\n",
-       "  'content': 'You are an expert statistician and data scientist.\\n    \\nYou interpret global explanations produced by a generalized additive model (GAM). GAMs produce explanations in the form of graphs that contain the effect of a specific input feature.\\n\\nYou will be given graphs from the model, and the user will ask you questions about the graphs. \\n    \\nAnswer all questions to the best of your ability, combining both the data contained in the graph and your knowledge about the real world.\\n\\nGraphs will be presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.  \\n    \\nThe user will provide graphs in the following format:\\n    - The name of the feature depicted in the graph\\n    - The type of the feature (continuous, categorical, or boolean)\\n    - Mean values\\n    - Lower bounds of confidence interval\\n    - Upper bounds of confidence interval'},\n",
-       " {'role': 'user',\n",
-       "  'content': 'Consider the following graph from the model. This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\\n\\nFeature Name: feature_0000\\nFeature Type: continuous\\nMeans: {\"(-9.96, -6.32)\": -1.527, \"(-6.32, -4.55)\": -1.467, \"(-4.55, -3.51)\": -1.408, \"(-3.51, -3.15)\": -1.347, \"(-3.15, -2.56)\": -1.314, \"(-2.56, -2.17)\": -1.252, \"(-2.17, -2.0)\": -1.188, \"(-2.0, -1.82)\": -1.154, \"(-1.82, -1.58)\": -1.122, \"(-1.58, -1.38)\": -1.058, \"(-1.38, -1.29)\": -0.998, \"(-1.29, -1.2)\": -0.958, \"(-1.2, -1.12)\": -0.922, \"(-1.12, -0.95)\": -0.889, \"(-0.95, -0.88)\": -0.801, \"(-0.88, -0.79)\": -0.767, \"(-0.79, -0.73)\": -0.711, \"(-0.73, -0.67)\": -0.678, \"(-0.67, -0.6)\": -0.644, \"(-0.6, -0.51)\": -0.564, \"(-0.51, -0.47)\": -0.517, \"(-0.47, -0.4)\": -0.471, \"(-0.4, -0.36)\": -0.424, \"(-0.36, -0.31)\": -0.391, \"(-0.31, -0.24)\": -0.314, \"(-0.24, -0.17)\": -0.26, \"(-0.17, -0.09)\": -0.184, \"(-0.09, -0.05)\": -0.139, \"(-0.05, 0.0)\": -0.076, \"(0.0, 0.05)\": -0.028, \"(0.05, 0.11)\": 0.024, \"(0.11, 0.18)\": 0.058, \"(0.18, 0.21)\": 0.133, \"(0.21, 0.26)\": 0.173, \"(0.26, 0.31)\": 0.21, \"(0.31, 0.39)\": 0.268, \"(0.39, 0.44)\": 0.33, \"(0.44, 0.48)\": 0.367, \"(0.48, 0.51)\": 0.402, \"(0.51, 0.63)\": 0.441, \"(0.63, 0.67)\": 0.513, \"(0.67, 0.75)\": 0.551, \"(0.75, 0.81)\": 0.6, \"(0.81, 0.88)\": 0.634, \"(0.88, 0.94)\": 0.669, \"(0.94, 1.0)\": 0.704, \"(1.0, 1.09)\": 0.739, \"(1.09, 1.16)\": 0.774, \"(1.16, 1.32)\": 0.81, \"(1.32, 1.42)\": 0.872, \"(1.42, 1.63)\": 0.907, \"(1.63, 1.77)\": 0.971, \"(1.77, 1.92)\": 1.005, \"(1.92, 2.09)\": 1.039, \"(2.09, 2.49)\": 1.072, \"(2.49, 3.01)\": 1.135, \"(3.01, 3.74)\": 1.196, \"(3.74, 4.92)\": 1.255, \"(4.92, 7.04)\": 1.317, \"(7.04, 9.99)\": 1.377}\\n\\nPlease describe the general pattern of the graph.'},\n",
-       " {'role': 'assistant',\n",
-       "  'content': 'The graph represents the effect of the continuous feature \"feature_0000\" on the output of the generalized additive model. Here\\'s a detailed description of the general pattern from the data provided:\\n\\n1. **Negative Values (Left Side of the Graph)**:\\n    - For highly negative values of \"feature_0000\", we observe mean values starting from around -1.527 for the interval (-9.96, -6.32).\\n    - As \"feature_0000\" increases towards less negative values, the mean values gradually increase (become less negative) until around -0.391 for the interval (-0.36, -0.31).\\n\\n2. **Transition Around Zero**:\\n    - Around zero, we see the values transitioning from negative to positive. For example:\\n        - (-0.09, -0.05): -0.139\\n        - (-0.05, 0.0): -0.076\\n        - (0.0, 0.05): -0.028\\n        - (0.05, 0.11): 0.024\\n    - This indicates a crossing over to positive effects on the output.\\n\\n3. **Positive Values (Right Side of the Graph)**:\\n    - For positive values of \"feature_0000\", we observe an increasing trend in the mean values:\\n        - It starts from around 0.024 for the interval (0.05, 0.11) and continues to rise.\\n        - The values gradually increase with positive intervals, showing a positive impact on the output as \"feature_0000\" increases further, reaching about 1.377 for the interval (7.04, 9.99).\\n\\n### Summary of the General Pattern:\\n- **Negative Correlation**: Strong negative values of \"feature_0000\" contribute negatively to the model\\'s output, with decreasing negative effect as the feature value becomes less negative.\\n- **Near Zero/Transition**: Around zero, the effect transitions from negative to positive.\\n- **Positive Correlation**: Positive values of \"feature_0000\" exhibit an increasing positive effect on the model\\'s output, with this positive contribution becoming larger as the feature value increases.\\n\\nThis pattern suggests that \"feature_0000\" has a nonlinear but smooth increasing effect across its range, with a significant change in the contribution happening near zero.'},\n",
-       " {'role': 'user',\n",
-       "  'content': 'It turns out that the depicted graph is one of the following functions:\\n\\na) f(x) = sin(x)\\nb) f(x) = cos(x)\\nc) f(x) = exp(x)\\nd) f(x) = |x|\\ne) f(x) = arctan(x)\\nf) f(x) = sinh(x)\\ng) f(x) = cosh(x)\\nh) f(x) = tanh(x)\\ni) f(x) = arcsinh(x)\\nj) f(x) = arctan(x) \\n\\nYour task now is to identify which of the functions is the one that generated the graph. Think step by step.\\n'}]"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "new_messages.append({'role': 'user', 'content': mc_question})\n",
-    "new_messages"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "To identify which of the provided functions is the one that generated the graph, we need to consider the general pattern steps I previously outlined in the data.\n",
-      "\n",
-      "### Step-by-Step Analysis:\n",
-      "1. **Examine the Ending Values (For High Magnitudes of x)**:\n",
-      "   - For large positive and negative values of \"feature_0000\", whether \\(x\\) or \\(-x\\), the function seems to diverge (negative values become more negative and positive values become more positive).\n",
-      "   - This behavior primarily rules out functions that are periodic or bounded, such as \\( \\sin(x) \\), \\( \\cos(x) \\), \\( \\text{arctan}(x) \\), \\( \\text{tanh}(x) \\).\n",
-      "\n",
-      "2. **Crossing Point Near Zero**:\n",
-      "   - There is a smooth crossing through zero, which matches functions like \\(e^x\\) and those with smooth transitions from negative to positive or those defined in such a way.\n",
-      "   - This crossing point further narrows down functions. Functions like \\(e^x\\) show exponential growth, but \\(e^x\\) doesn't cross from negative to positive.\n",
-      "\n",
-      "3. **Behavior Around Zero** and **Monoticity**:\n",
-      "   - The slope indicates the way \\(y\\) values change as \\(x\\) values surround 0. Given the function crosses zero and the nature remains increasing or decreasing continuously, the hyperbolic functions (\\(\\sinh(x)\\) and \\(\\cosh(x)\\)) showing direct monotonicity fit well.\n",
-      "\n",
-      "### Matching the Remaining Candidate Functions:\n",
-      "   - \\( f(x) = |x| \\) is not truly valid here as it would depict a V-shaped turn at 0 without smooth transition.\n",
-      "   - Functions \\( \\sinh(x) \\), \\( \\cosh(x) \\), and \\( arcsinh(x) \\) are hyperbolic. They demonstrate the gradual smooth slope we observed, but Hyperbolic \\( \\cosh(x) \\ngeq value <= 1 \\) for middle-interaction.\n",
-      "\n",
-      "### Checking Few Mathematical Functions' Grid Flat Analysis Could Apply:\n",
-      "- **Graph Reanalysis with Data Values Support**:\n",
-      "    1. **\\( \\sinh(x) \\)** describes how \\(y\\)-values when extended (+/- x yielding same modulus slope increasing up |1.255|-|1.37)|)\n",
-      "       - Fits both positive-negative correlation seamlessly.\n",
-      " \n",
-      "### Conclusion:\n",
-      "- Recognizing the data trends' behavior across the analyzed range, it seems most probable the function generated by this GAM is \\( f(x) = \\sinh(x) \\), hyperbolic-sine equally combining feature-graph pattern correlation seamlessly matching relatively unified data behavior noticed & the variance check to confidence levels.\n"
-     ]
-    }
-   ],
-   "source": [
-    "response = t2ebm.utils.openai_completion_query('gpt-4o-2024-05-13', new_messages)\n",
-    "print(response)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'You are an expert statistician and data scientist.\\n    \\nYou interpret global explanations produced by a generalized additive model (GAM). GAMs produce explanations in the form of graphs that contain the effect of a specific input feature.\\n\\nYou will be given graphs from the model, and the user will ask you questions about the graphs. \\n    \\nAnswer all questions to the best of your ability, combining both the data contained in the graph and your knowledge about the real world.\\n\\nGraphs will be presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.  \\n    \\nThe user will provide graphs in the following format:\\n    - The name of the feature depicted in the graph\\n    - The type of the feature (continuous, categorical, or boolean)\\n    - Mean values\\n    - Lower bounds of confidence interval\\n    - Upper bounds of confidence interval'"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t2ebm.utils.parse_guidance_query(prompt)[0]['content']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO\n",
-    "\n",
-    "- convert queries to the openai format\n",
-    "- write a cot execution function\n",
-    "\n",
-    "- later port everything to the newest version of guidance? not required for the benchmarks at this point"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import guidance"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from guidance import models, gen\n",
-    "\n",
-    "gpt = models.OpenAI('gpt-4-turbo-2024-04-09')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "token_ids must contain some tokens.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[20], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mgpt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mgen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/guidance/models/_model.py:1171\u001b[0m, in \u001b[0;36mModel.__add__\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m   1169\u001b[0m \u001b[38;5;66;03m# run stateless functions (grammar nodes)\u001b[39;00m\n\u001b[1;32m   1170\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(value, GrammarFunction):\n\u001b[0;32m-> 1171\u001b[0m     out \u001b[38;5;241m=\u001b[39m \u001b[43mlm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_stateless\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1173\u001b[0m \u001b[38;5;66;03m# run stateful functions\u001b[39;00m\n\u001b[1;32m   1174\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1175\u001b[0m     out \u001b[38;5;241m=\u001b[39m value(lm)\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/guidance/models/_model.py:1376\u001b[0m, in \u001b[0;36mModel._run_stateless\u001b[0;34m(self, stateless_function, temperature, top_p, n)\u001b[0m\n\u001b[1;32m   1374\u001b[0m delayed_bytes \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1375\u001b[0m \u001b[38;5;66;03m# last_is_generated = False\u001b[39;00m\n\u001b[0;32m-> 1376\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mgen_obj\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m   1377\u001b[0m \n\u001b[1;32m   1378\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;66;43;03m# we make everything full probability if we are not computing uncertainty\u001b[39;49;00m\n\u001b[1;32m   1379\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;66;43;03m# if not self.engine.compute_log_probs:\u001b[39;49;00m\n\u001b[1;32m   1380\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;66;43;03m#     chunk.new_bytes_prob = 1.0\u001b[39;49;00m\n\u001b[1;32m   1381\u001b[0m \n\u001b[1;32m   1382\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;66;43;03m# convert the bytes to a string (delaying if we don't yet have a valid unicode string)\u001b[39;49;00m\n\u001b[1;32m   1383\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtoken_count\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnew_token_count\u001b[49m\n\u001b[1;32m   1384\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchunk\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnew_bytes\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mdelayed_bytes\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnew_bytes\u001b[49m\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/guidance/models/_model.py:768\u001b[0m, in \u001b[0;36mEngine.__call__\u001b[0;34m(self, parser, grammar, ensure_bos_token)\u001b[0m\n\u001b[1;32m    766\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m logits_state \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    767\u001b[0m     token_ids, forced_bytes, current_temp \u001b[38;5;241m=\u001b[39m logits_state\n\u001b[0;32m--> 768\u001b[0m     logits \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_logits\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtoken_ids\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mforced_bytes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcurrent_temp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    770\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_done:\n\u001b[1;32m    771\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n",
-      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/guidance/models/_grammarless.py:227\u001b[0m, in \u001b[0;36mGrammarlessEngine.get_logits\u001b[0;34m(self, token_ids, forced_bytes, current_temp)\u001b[0m\n\u001b[1;32m    224\u001b[0m logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstart Grammarless._get_logits(token_ids=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtoken_ids\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    226\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(token_ids) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 227\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtoken_ids must contain some tokens.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    229\u001b[0m \u001b[38;5;66;03m# compute the prompt bytes\u001b[39;00m\n\u001b[1;32m    230\u001b[0m whole_token_prompt \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin([\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtokenizer\u001b[38;5;241m.\u001b[39mtokens[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m token_ids])\n",
-      "\u001b[0;31mValueError\u001b[0m: token_ids must contain some tokens."
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "gpt + gen(prompt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "_Guidance.__call__() takes from 1 to 2 positional arguments but 3 were given",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[13], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mguidance\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mgpt-4o-2024-05-13\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m()\n",
-      "\u001b[0;31mTypeError\u001b[0m: _Guidance.__call__() takes from 1 to 2 positional arguments but 3 were given"
-     ]
-    }
-   ],
-   "source": [
-    "response = guidance(prompt, )()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "tmcd",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/benchmarks/notebooks/jump.ipynb b/benchmarks/notebooks/jump.ipynb
new file mode 100644
index 0000000..b77fe6a
--- /dev/null
+++ b/benchmarks/notebooks/jump.ipynb
@@ -0,0 +1,350 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Largest Jump Benchmark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add parent directory to path\n",
+    "import sys\n",
+    "sys.path.append('..')\n",
+    "\n",
+    "import copy\n",
+    "import random\n",
+    "import numpy as np\n",
+    "\n",
+    "import t2ebm\n",
+    "from t2ebm import graphs\n",
+    "from t2ebm import prompts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the benchmark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load graphs (pickle)\n",
+    "import pickle\n",
+    "\n",
+    "with open(\"all_graphs.pkl\", \"rb\") as f:\n",
+    "    all_graphs = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def graph_largest_jump(graph):\n",
+    "    \"\"\"returns the position and the size of the largest jump in the graph\"\"\"\n",
+    "    jumps = [\n",
+    "        np.abs(graph.scores[idx] - graph.scores[idx - 1])\n",
+    "        for idx in range(1, len(graph.x_vals))\n",
+    "    ]\n",
+    "    largest_jump_idx = np.argmax(jumps)\n",
+    "    return graph.x_vals[largest_jump_idx][1], jumps[largest_jump_idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "questions = []\n",
+    "for graph, graph_as_text in all_graphs:\n",
+    "    # only continuous graphs\n",
+    "    if graph.feature_type != \"continuous\":\n",
+    "        continue\n",
+    "    question = \"\"\"Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n",
+    "        \n",
+    "    The graph is provided in the following format:\n",
+    "        - The name of the feature depicted in the graph\n",
+    "        - The type of the feature (continuous, categorical, or boolean)\n",
+    "        - Mean values\n",
+    "        - Lower bounds of confidence interval (optional)\n",
+    "        - Upper bounds of confidence interval (optional)\\n\\n\"\"\"\n",
+    "    question += f\"Here is the graph:\\n\\n{graph_as_text}\\n\"\n",
+    "    question += \"\"\"Within the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n",
+    "\n",
+    "We are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n",
+    "\n",
+    "What is the x-axis position of the largest jump in the graph? Think step by step.\"\"\"\n",
+    "    graph_ = graphs.text_to_graph(graph_as_text)\n",
+    "    pos, size = graph_largest_jump(graph_)\n",
+    "    questions.append((question, str(pos)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# subset 100 random questions\n",
+    "random.shuffle(questions)\n",
+    "questions = questions[:100]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n",
+      "        \n",
+      "    The graph is provided in the following format:\n",
+      "        - The name of the feature depicted in the graph\n",
+      "        - The type of the feature (continuous, categorical, or boolean)\n",
+      "        - Mean values\n",
+      "        - Lower bounds of confidence interval (optional)\n",
+      "        - Upper bounds of confidence interval (optional)\n",
+      "\n",
+      "Here is the graph:\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: compactness_worst\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.02729, 0.049945)\": -0.0578, \"(0.049945, 0.06971)\": -0.0099, \"(0.06971, 0.099305)\": -0.0565, \"(0.099305, 0.10635)\": -0.1408, \"(0.10635, 0.1243)\": -0.1882, \"(0.1243, 0.14795)\": -0.2357, \"(0.14795, 0.1507)\": -0.1883, \"(0.1507, 0.1861)\": -0.1381, \"(0.1861, 0.20124999999999998)\": -0.0918, \"(0.20124999999999998, 0.3358)\": -0.0443, \"(0.3358, 0.3456)\": 0.0027, \"(0.3456, 0.35755000000000003)\": 0.0649, \"(0.35755000000000003, 0.3703)\": 0.1151, \"(0.3703, 0.39235)\": 0.1642, \"(0.39235, 0.4087)\": 0.2124, \"(0.4087, 0.4229)\": 0.2605, \"(0.4229, 0.4486)\": 0.3109, \"(0.4486, 0.48865000000000003)\": 0.3586, \"(0.48865000000000003, 0.54825)\": 0.4132, \"(0.54825, 0.5892999999999999)\": 0.4651, \"(0.5892999999999999, 0.65835)\": 0.5154, \"(0.65835, 0.7680499999999999)\": 0.572, \"(0.7680499999999999, 0.99795)\": 0.6264, \"(0.99795, 1.058)\": 0.6748}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.02729, 0.049945)\": -0.8125, \"(0.049945, 0.06971)\": -0.7624, \"(0.06971, 0.099305)\": -0.6001, \"(0.099305, 0.10635)\": -0.4033, \"(0.10635, 0.1243)\": -0.4448, \"(0.1243, 0.14795)\": -0.4969, \"(0.14795, 0.1507)\": -0.4446, \"(0.1507, 0.1861)\": -0.2722, \"(0.1861, 0.20124999999999998)\": -0.1924, \"(0.20124999999999998, 0.3358)\": -0.2305, \"(0.3358, 0.3456)\": -0.1741, \"(0.3456, 0.35755000000000003)\": -0.068, \"(0.35755000000000003, 0.3703)\": 0.0047, \"(0.3703, 0.39235)\": 0.0473, \"(0.39235, 0.4087)\": 0.1107, \"(0.4087, 0.4229)\": 0.1686, \"(0.4229, 0.4486)\": 0.2243, \"(0.4486, 0.48865000000000003)\": 0.2736, \"(0.48865000000000003, 0.54825)\": 0.2405, \"(0.54825, 0.5892999999999999)\": 0.2819, \"(0.5892999999999999, 0.65835)\": 0.3155, \"(0.65835, 0.7680499999999999)\": 0.3513, \"(0.7680499999999999, 0.99795)\": 0.3892, \"(0.99795, 1.058)\": 0.4487}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.02729, 0.049945)\": 0.6969, \"(0.049945, 0.06971)\": 0.7425, \"(0.06971, 0.099305)\": 0.487, \"(0.099305, 0.10635)\": 0.1218, \"(0.10635, 0.1243)\": 0.0684, \"(0.1243, 0.14795)\": 0.0254, \"(0.14795, 0.1507)\": 0.068, \"(0.1507, 0.1861)\": -0.0039, \"(0.1861, 0.20124999999999998)\": 0.0087, \"(0.20124999999999998, 0.3358)\": 0.1418, \"(0.3358, 0.3456)\": 0.1794, \"(0.3456, 0.35755000000000003)\": 0.1979, \"(0.35755000000000003, 0.3703)\": 0.2255, \"(0.3703, 0.39235)\": 0.2811, \"(0.39235, 0.4087)\": 0.314, \"(0.4087, 0.4229)\": 0.3524, \"(0.4229, 0.4486)\": 0.3975, \"(0.4486, 0.48865000000000003)\": 0.4436, \"(0.48865000000000003, 0.54825)\": 0.5859, \"(0.54825, 0.5892999999999999)\": 0.6484, \"(0.5892999999999999, 0.65835)\": 0.7153, \"(0.65835, 0.7680499999999999)\": 0.7927, \"(0.7680499999999999, 0.99795)\": 0.8637, \"(0.99795, 1.058)\": 0.9008}\n",
+      "\n",
+      "Within the different intervals, the graph predicts the same mean value. This means that there are discontinuous 'jumps' in the mean of the graph in between the intervals.\n",
+      "\n",
+      "We are now looking for the position of the largest jump in the graph. We are looking for both positive and negative jumps, that is we care about the absolute magnitude of the jump.\n",
+      "\n",
+      "What is the x-axis position of the largest jump in the graph? Think step by step.\n",
+      "SOLUTION:  0.099305\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print a random question\n",
+    "import random\n",
+    "random.shuffle(questions)\n",
+    "print(questions[0][0])\n",
+    "print('SOLUTION: ', questions[0][1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save the questions to json\n",
+    "import json\n",
+    "with open(\"../benchmark/jumps.json\", \"w\") as f:\n",
+    "    json.dump(questions, f, indent=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Run the benchmark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load the json file\n",
+    "with open(\"../benchmark/jumps.json\", \"r\") as f:\n",
+    "    questions = json.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(questions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LLM RESPONSE:  To identify the position of the largest jump in the graph, we need to compare the mean values within each interval and calculate the absolute difference between adjacent intervals. The largest jump will be the one with the highest absolute difference in mean values.\n",
+      "\n",
+      "Let's calculate the absolute differences between adjacent intervals:\n",
+      "\n",
+      "1. Absolute difference between intervals (1.0, 1.5) and (1.5, 2.5):\n",
+      "   Absolute difference = |0.534 - (-0.009)| = 0.543\n",
+      "\n",
+      "2. Absolute difference between intervals (1.5, 2.5) and (2.5, 3.0):\n",
+      "   Absolute difference = |(-0.532) - 0.534| = 1.066\n",
+      "\n",
+      "Based on these calculations, the largest jump in the graph occurs between the intervals (1.5, 2.5) and (2.5, 3.0) with an absolute difference of 1.066.\n",
+      "\n",
+      "Therefore, the x-axis position of the largest jump in the graph is between Pclass values of 1.5 and 2.5.\n",
+      "SOLUTION:  2.5\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute difference in mean values between adjacent intervals. The largest jump will be the interval with the largest absolute difference in mean values.\n",
+      "\n",
+      "Given the data provided for the \"smoking\" feature:\n",
+      "- Mean value for the interval (0.0, 0.5): 0.01522\n",
+      "- Mean value for the interval (0.5, 1.0): -0.03391\n",
+      "\n",
+      "The absolute difference in mean values between these two intervals is:\n",
+      "|0.01522 - (-0.03391)| = 0.04913\n",
+      "\n",
+      "Therefore, the largest jump in the graph occurs between the intervals (0.0, 0.5) and (0.5, 1.0) with an absolute difference of 0.04913.\n",
+      "\n",
+      "The x-axis position of the largest jump is at the boundary between these two intervals, which is at 0.5 on the x-axis.\n",
+      "SOLUTION:  0.5\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.\n",
+      "\n",
+      "Let's calculate the absolute differences between consecutive mean values:\n",
+      "\n",
+      "1. Absolute difference between -0.759 and -0.662 = 0.097\n",
+      "2. Absolute difference between -0.662 and -0.567 = 0.095\n",
+      "3. Absolute difference between -0.567 and -0.464 = 0.103\n",
+      "4. Absolute difference between -0.464 and -0.368 = 0.096\n",
+      "5. Absolute difference between -0.368 and -0.271 = 0.097\n",
+      "6. Absolute difference between -0.271 and -0.173 = 0.098\n",
+      "7. Absolute difference between -0.173 and -0.076 = 0.097\n",
+      "8. Absolute difference between -0.076 and 0.309 = 0.385\n",
+      "9. Absolute difference between 0.309 and 0.405 = 0.096\n",
+      "10. Absolute difference between 0.405 and 0.51 = 0.105\n",
+      "11. Absolute difference between 0.51 and 0.607 = 0.097\n",
+      "12. Absolute difference between 0.607 and 0.707 = 0.1\n",
+      "13. Absolute difference between 0.707 and 0.806 = 0.099\n",
+      "14. Absolute difference between 0.806 and 0.911 = 0.105\n",
+      "15. Absolute difference between 0.911 and 1.01 = 0.099\n",
+      "16. Absolute difference between 1.01 and 1.109 = 0.099\n",
+      "\n",
+      "The largest absolute difference is 0.385, which occurs between the intervals \"(606.0, 696.25)\" and \"(696.25, 806.1500000000001)\".\n",
+      "\n",
+      "Therefore, the x-axis position of the largest jump in the graph is within the interval \"(606.0, 696.25)\".\n",
+      "SOLUTION:  696.25\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[64], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m question \u001b[38;5;129;01min\u001b[39;00m questions:\n\u001b[1;32m      3\u001b[0m     messages \u001b[38;5;241m=\u001b[39m [{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrole\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msystem\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent\u001b[39m\u001b[38;5;124m\"\u001b[39m: system_msg}, {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrole\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muser\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent\u001b[39m\u001b[38;5;124m\"\u001b[39m: question[\u001b[38;5;241m0\u001b[39m]}]\n\u001b[0;32m----> 4\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[43mt2ebm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mutils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopenai_completion_query\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mgpt-3.5-turbo-0125\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.0\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      5\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLLM RESPONSE: \u001b[39m\u001b[38;5;124m'\u001b[39m, response)\n\u001b[1;32m      6\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSOLUTION: \u001b[39m\u001b[38;5;124m'\u001b[39m, question[\u001b[38;5;241m1\u001b[39m])\n",
+      "File \u001b[0;32m~/Documents/GitHub/TalkToEBM/t2ebm/utils.py:33\u001b[0m, in \u001b[0;36mopenai_completion_query\u001b[0;34m(model, messages, **kwargs)\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mopenai_completion_query\u001b[39m(model, messages, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m     32\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Catches exceptions and retries, good for deployment / running experiments\"\"\"\u001b[39;00m\n\u001b[0;32m---> 33\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchat\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompletions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     34\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m response\u001b[38;5;241m.\u001b[39mchoices[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mmessage\u001b[38;5;241m.\u001b[39mcontent\n",
+      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/openai/_utils/_utils.py:275\u001b[0m, in \u001b[0;36mrequired_args.<locals>.inner.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    273\u001b[0m             msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMissing required argument: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquote(missing[\u001b[38;5;241m0\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    274\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[0;32m--> 275\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/openai/resources/chat/completions.py:667\u001b[0m, in \u001b[0;36mCompletions.create\u001b[0;34m(self, messages, model, frequency_penalty, function_call, functions, logit_bias, logprobs, max_tokens, n, presence_penalty, response_format, seed, stop, stream, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)\u001b[0m\n\u001b[1;32m    615\u001b[0m \u001b[38;5;129m@required_args\u001b[39m([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m], [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m    616\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcreate\u001b[39m(\n\u001b[1;32m    617\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    665\u001b[0m     timeout: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m httpx\u001b[38;5;241m.\u001b[39mTimeout \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m|\u001b[39m NotGiven \u001b[38;5;241m=\u001b[39m NOT_GIVEN,\n\u001b[1;32m    666\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ChatCompletion \u001b[38;5;241m|\u001b[39m Stream[ChatCompletionChunk]:\n\u001b[0;32m--> 667\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_post\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    668\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/chat/completions\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    669\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaybe_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    670\u001b[0m \u001b[43m            \u001b[49m\u001b[43m{\u001b[49m\n\u001b[1;32m    671\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmessages\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    672\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmodel\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    673\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfrequency_penalty\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrequency_penalty\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    674\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfunction_call\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunction_call\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    675\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfunctions\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunctions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    676\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlogit_bias\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogit_bias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    677\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlogprobs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogprobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    678\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmax_tokens\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    679\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mn\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    680\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpresence_penalty\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mpresence_penalty\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    681\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mresponse_format\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    682\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mseed\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mseed\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    683\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstop\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    684\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstream\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    685\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtemperature\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    686\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtool_choice\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtool_choice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    687\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtools\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtools\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    688\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtop_logprobs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtop_logprobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    689\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtop_p\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtop_p\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    690\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43muser\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43muser\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    691\u001b[0m \u001b[43m            \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    692\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcompletion_create_params\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mCompletionCreateParams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    693\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    694\u001b[0m \u001b[43m        \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmake_request_options\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    695\u001b[0m \u001b[43m            \u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_headers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_query\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_query\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_body\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_body\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\n\u001b[1;32m    696\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    697\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mChatCompletion\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    698\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    699\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mStream\u001b[49m\u001b[43m[\u001b[49m\u001b[43mChatCompletionChunk\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    700\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/openai/_base_client.py:1213\u001b[0m, in \u001b[0;36mSyncAPIClient.post\u001b[0;34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1199\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost\u001b[39m(\n\u001b[1;32m   1200\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   1201\u001b[0m     path: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1208\u001b[0m     stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   1209\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ResponseT \u001b[38;5;241m|\u001b[39m _StreamT:\n\u001b[1;32m   1210\u001b[0m     opts \u001b[38;5;241m=\u001b[39m FinalRequestOptions\u001b[38;5;241m.\u001b[39mconstruct(\n\u001b[1;32m   1211\u001b[0m         method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m\"\u001b[39m, url\u001b[38;5;241m=\u001b[39mpath, json_data\u001b[38;5;241m=\u001b[39mbody, files\u001b[38;5;241m=\u001b[39mto_httpx_files(files), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions\n\u001b[1;32m   1212\u001b[0m     )\n\u001b[0;32m-> 1213\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m)\u001b[49m)\n",
+      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/openai/_base_client.py:902\u001b[0m, in \u001b[0;36mSyncAPIClient.request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m    893\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrequest\u001b[39m(\n\u001b[1;32m    894\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    895\u001b[0m     cast_to: Type[ResponseT],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    900\u001b[0m     stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    901\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ResponseT \u001b[38;5;241m|\u001b[39m _StreamT:\n\u001b[0;32m--> 902\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    903\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    904\u001b[0m \u001b[43m        \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    905\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    906\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    907\u001b[0m \u001b[43m        \u001b[49m\u001b[43mremaining_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mremaining_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    908\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/openai/_base_client.py:931\u001b[0m, in \u001b[0;36mSyncAPIClient._request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m    928\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauth\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcustom_auth\n\u001b[1;32m    930\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 931\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    932\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    933\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_should_stream_response_body\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    934\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    935\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    936\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m httpx\u001b[38;5;241m.\u001b[39mTimeoutException \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m    937\u001b[0m     log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEncountered httpx.TimeoutException\u001b[39m\u001b[38;5;124m\"\u001b[39m, exc_info\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
+      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/httpx/_client.py:914\u001b[0m, in \u001b[0;36mClient.send\u001b[0;34m(self, request, stream, auth, follow_redirects)\u001b[0m\n\u001b[1;32m    906\u001b[0m follow_redirects \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    907\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfollow_redirects\n\u001b[1;32m    908\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(follow_redirects, UseClientDefault)\n\u001b[1;32m    909\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m follow_redirects\n\u001b[1;32m    910\u001b[0m )\n\u001b[1;32m    912\u001b[0m auth \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_request_auth(request, auth)\n\u001b[0;32m--> 914\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_handling_auth\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    915\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    916\u001b[0m \u001b[43m    \u001b[49m\u001b[43mauth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mauth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    917\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    918\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhistory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    919\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    920\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    921\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m stream:\n",
+      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/httpx/_client.py:942\u001b[0m, in \u001b[0;36mClient._send_handling_auth\u001b[0;34m(self, request, auth, follow_redirects, history)\u001b[0m\n\u001b[1;32m    939\u001b[0m request \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m(auth_flow)\n\u001b[1;32m    941\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m--> 942\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_handling_redirects\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    943\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    944\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    945\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhistory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhistory\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    946\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    947\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    948\u001b[0m         \u001b[38;5;28;01mtry\u001b[39;00m:\n",
+      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/httpx/_client.py:979\u001b[0m, in \u001b[0;36mClient._send_handling_redirects\u001b[0;34m(self, request, follow_redirects, history)\u001b[0m\n\u001b[1;32m    976\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m hook \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_event_hooks[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrequest\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m    977\u001b[0m     hook(request)\n\u001b[0;32m--> 979\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_single_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    980\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    981\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_event_hooks[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresponse\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n",
+      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/httpx/_client.py:1015\u001b[0m, in \u001b[0;36mClient._send_single_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m   1010\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m   1011\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAttempted to send an async request with a sync Client instance.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1012\u001b[0m     )\n\u001b[1;32m   1014\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m request_context(request\u001b[38;5;241m=\u001b[39mrequest):\n\u001b[0;32m-> 1015\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[43mtransport\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1017\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(response\u001b[38;5;241m.\u001b[39mstream, SyncByteStream)\n\u001b[1;32m   1019\u001b[0m response\u001b[38;5;241m.\u001b[39mrequest \u001b[38;5;241m=\u001b[39m request\n",
+      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/httpx/_transports/default.py:233\u001b[0m, in \u001b[0;36mHTTPTransport.handle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    220\u001b[0m req \u001b[38;5;241m=\u001b[39m httpcore\u001b[38;5;241m.\u001b[39mRequest(\n\u001b[1;32m    221\u001b[0m     method\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mmethod,\n\u001b[1;32m    222\u001b[0m     url\u001b[38;5;241m=\u001b[39mhttpcore\u001b[38;5;241m.\u001b[39mURL(\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    230\u001b[0m     extensions\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mextensions,\n\u001b[1;32m    231\u001b[0m )\n\u001b[1;32m    232\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m map_httpcore_exceptions():\n\u001b[0;32m--> 233\u001b[0m     resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_pool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    235\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(resp\u001b[38;5;241m.\u001b[39mstream, typing\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m    237\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Response(\n\u001b[1;32m    238\u001b[0m     status_code\u001b[38;5;241m=\u001b[39mresp\u001b[38;5;241m.\u001b[39mstatus,\n\u001b[1;32m    239\u001b[0m     headers\u001b[38;5;241m=\u001b[39mresp\u001b[38;5;241m.\u001b[39mheaders,\n\u001b[1;32m    240\u001b[0m     stream\u001b[38;5;241m=\u001b[39mResponseStream(resp\u001b[38;5;241m.\u001b[39mstream),\n\u001b[1;32m    241\u001b[0m     extensions\u001b[38;5;241m=\u001b[39mresp\u001b[38;5;241m.\u001b[39mextensions,\n\u001b[1;32m    242\u001b[0m )\n",
+      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/httpcore/_sync/connection_pool.py:216\u001b[0m, in \u001b[0;36mConnectionPool.handle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    213\u001b[0m         closing \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_assign_requests_to_connections()\n\u001b[1;32m    215\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_close_connections(closing)\n\u001b[0;32m--> 216\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exc \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    218\u001b[0m \u001b[38;5;66;03m# Return the response. Note that in this case we still have to manage\u001b[39;00m\n\u001b[1;32m    219\u001b[0m \u001b[38;5;66;03m# the point at which the response is closed.\u001b[39;00m\n\u001b[1;32m    220\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(response\u001b[38;5;241m.\u001b[39mstream, Iterable)\n",
+      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/httpcore/_sync/connection_pool.py:196\u001b[0m, in \u001b[0;36mConnectionPool.handle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    192\u001b[0m connection \u001b[38;5;241m=\u001b[39m pool_request\u001b[38;5;241m.\u001b[39mwait_for_connection(timeout\u001b[38;5;241m=\u001b[39mtimeout)\n\u001b[1;32m    194\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    195\u001b[0m     \u001b[38;5;66;03m# Send the request on the assigned connection.\u001b[39;00m\n\u001b[0;32m--> 196\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[43mconnection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    197\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpool_request\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\n\u001b[1;32m    198\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    199\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ConnectionNotAvailable:\n\u001b[1;32m    200\u001b[0m     \u001b[38;5;66;03m# In some cases a connection may initially be available to\u001b[39;00m\n\u001b[1;32m    201\u001b[0m     \u001b[38;5;66;03m# handle a request, but then become unavailable.\u001b[39;00m\n\u001b[1;32m    202\u001b[0m     \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m    203\u001b[0m     \u001b[38;5;66;03m# In this case we clear the connection and try again.\u001b[39;00m\n\u001b[1;32m    204\u001b[0m     pool_request\u001b[38;5;241m.\u001b[39mclear_connection()\n",
+      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/httpcore/_sync/connection.py:101\u001b[0m, in \u001b[0;36mHTTPConnection.handle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m     98\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_connect_failed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m     99\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[0;32m--> 101\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_connection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/httpcore/_sync/http11.py:143\u001b[0m, in \u001b[0;36mHTTP11Connection.handle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    141\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m Trace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresponse_closed\u001b[39m\u001b[38;5;124m\"\u001b[39m, logger, request) \u001b[38;5;28;01mas\u001b[39;00m trace:\n\u001b[1;32m    142\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_response_closed()\n\u001b[0;32m--> 143\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\n",
+      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/httpcore/_sync/http11.py:113\u001b[0m, in \u001b[0;36mHTTP11Connection.handle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    102\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m    104\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m Trace(\n\u001b[1;32m    105\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreceive_response_headers\u001b[39m\u001b[38;5;124m\"\u001b[39m, logger, request, kwargs\n\u001b[1;32m    106\u001b[0m ) \u001b[38;5;28;01mas\u001b[39;00m trace:\n\u001b[1;32m    107\u001b[0m     (\n\u001b[1;32m    108\u001b[0m         http_version,\n\u001b[1;32m    109\u001b[0m         status,\n\u001b[1;32m    110\u001b[0m         reason_phrase,\n\u001b[1;32m    111\u001b[0m         headers,\n\u001b[1;32m    112\u001b[0m         trailing_data,\n\u001b[0;32m--> 113\u001b[0m     ) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_receive_response_headers\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    114\u001b[0m     trace\u001b[38;5;241m.\u001b[39mreturn_value \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    115\u001b[0m         http_version,\n\u001b[1;32m    116\u001b[0m         status,\n\u001b[1;32m    117\u001b[0m         reason_phrase,\n\u001b[1;32m    118\u001b[0m         headers,\n\u001b[1;32m    119\u001b[0m     )\n\u001b[1;32m    121\u001b[0m network_stream \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_network_stream\n",
+      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/httpcore/_sync/http11.py:186\u001b[0m, in \u001b[0;36mHTTP11Connection._receive_response_headers\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    183\u001b[0m timeout \u001b[38;5;241m=\u001b[39m timeouts\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mread\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    185\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m--> 186\u001b[0m     event \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_receive_event\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    187\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(event, h11\u001b[38;5;241m.\u001b[39mResponse):\n\u001b[1;32m    188\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
+      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/httpcore/_sync/http11.py:224\u001b[0m, in \u001b[0;36mHTTP11Connection._receive_event\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    221\u001b[0m     event \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_h11_state\u001b[38;5;241m.\u001b[39mnext_event()\n\u001b[1;32m    223\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m event \u001b[38;5;129;01mis\u001b[39;00m h11\u001b[38;5;241m.\u001b[39mNEED_DATA:\n\u001b[0;32m--> 224\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_network_stream\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    225\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mREAD_NUM_BYTES\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\n\u001b[1;32m    226\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    228\u001b[0m     \u001b[38;5;66;03m# If we feed this case through h11 we'll raise an exception like:\u001b[39;00m\n\u001b[1;32m    229\u001b[0m     \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m    230\u001b[0m     \u001b[38;5;66;03m#     httpcore.RemoteProtocolError: can't handle event type\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    234\u001b[0m     \u001b[38;5;66;03m# perspective. Instead we handle this case distinctly and treat\u001b[39;00m\n\u001b[1;32m    235\u001b[0m     \u001b[38;5;66;03m# it as a ConnectError.\u001b[39;00m\n\u001b[1;32m    236\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;241m==\u001b[39m \u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_h11_state\u001b[38;5;241m.\u001b[39mtheir_state \u001b[38;5;241m==\u001b[39m h11\u001b[38;5;241m.\u001b[39mSEND_RESPONSE:\n",
+      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/site-packages/httpcore/_backends/sync.py:126\u001b[0m, in \u001b[0;36mSyncStream.read\u001b[0;34m(self, max_bytes, timeout)\u001b[0m\n\u001b[1;32m    124\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m map_exceptions(exc_map):\n\u001b[1;32m    125\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sock\u001b[38;5;241m.\u001b[39msettimeout(timeout)\n\u001b[0;32m--> 126\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmax_bytes\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/ssl.py:1233\u001b[0m, in \u001b[0;36mSSLSocket.recv\u001b[0;34m(self, buflen, flags)\u001b[0m\n\u001b[1;32m   1229\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m flags \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m   1230\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   1231\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnon-zero flags not allowed in calls to recv() on \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[1;32m   1232\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m)\n\u001b[0;32m-> 1233\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbuflen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1234\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1235\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mrecv(buflen, flags)\n",
+      "File \u001b[0;32m~/anaconda3/envs/tmcd/lib/python3.12/ssl.py:1106\u001b[0m, in \u001b[0;36mSSLSocket.read\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sslobj\u001b[38;5;241m.\u001b[39mread(\u001b[38;5;28mlen\u001b[39m, buffer)\n\u001b[1;32m   1105\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1106\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sslobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1107\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m SSLError \u001b[38;5;28;01mas\u001b[39;00m x:\n\u001b[1;32m   1108\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39margs[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m==\u001b[39m SSL_ERROR_EOF \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msuppress_ragged_eofs:\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "system_msg = \"You are an expert statistician and data scientist. You interpret global explanations produced by a generalized additive model (GAM). You answer all questions to the best of your ability, combining the data contained in the graph, any data set description you are given, and your knowledge about the real world.\"\n",
+    "for question in questions:\n",
+    "    messages = [{\"role\": \"system\", \"content\": system_msg}, {\"role\": \"user\", \"content\": question[0]}]\n",
+    "    response = t2ebm.utils.openai_completion_query('gpt-3.5-turbo-0125', messages, temperature=0.0)\n",
+    "    print('LLM RESPONSE: ', response)\n",
+    "    print('SOLUTION: ', question[1])\n",
+    "    print('-'*80)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 24 mistakes for gpt-4-turbo-2024-04-09"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#  mistakes for gpt-3.5-turbo-0125"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tmcd",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/benchmarks/notebooks/monotonicity.ipynb b/benchmarks/notebooks/monotonicity.ipynb
new file mode 100644
index 0000000..c62afb0
--- /dev/null
+++ b/benchmarks/notebooks/monotonicity.ipynb
@@ -0,0 +1,2614 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Monotonicity Benchmark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add parent directory to path\n",
+    "import sys\n",
+    "sys.path.append('..')\n",
+    "\n",
+    "import copy\n",
+    "import random\n",
+    "\n",
+    "import t2ebm\n",
+    "from t2ebm import graphs\n",
+    "from t2ebm import prompts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the benchmark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load graphs (pickle)\n",
+    "import pickle\n",
+    "\n",
+    "with open(\"all_graphs.pkl\", \"rb\") as f:\n",
+    "    all_graphs = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def is_monotone_increasing(graph):\n",
+    "    \"\"\"Return: True/False\"\"\"\n",
+    "    for idx, x_bin in enumerate(graph.x_vals):\n",
+    "        if idx > 0 and idx < len(graph.x_vals):\n",
+    "            if graph.scores[idx] < graph.scores[idx - 1]:\n",
+    "                return False\n",
+    "    return True\n",
+    "\n",
+    "def is_monotone_decreasing(graph):\n",
+    "    \"\"\"Return: True/False\"\"\"\n",
+    "    for idx, x_bin in enumerate(graph.x_vals):\n",
+    "        if idx > 0 and idx < len(graph.x_vals):\n",
+    "            if graph.scores[idx] > graph.scores[idx - 1]:\n",
+    "                return False\n",
+    "    return True\n",
+    "\n",
+    "def invert_graph(graph: t2ebm.graphs.EBMGraph):\n",
+    "    \"\"\"Returns a new graph with the y-axis inverted.\"\"\"\n",
+    "    new_graph = copy.deepcopy(graph)\n",
+    "    new_graph.scores = -new_graph.scores\n",
+    "    return new_graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "questions = []\n",
+    "num_non_monotone = 0\n",
+    "for graph, graph_as_text in all_graphs:\n",
+    "    # only continuous graphs\n",
+    "    if graph.feature_type != \"continuous\":\n",
+    "        continue\n",
+    "    question = \"\"\"Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n",
+    "        \n",
+    "    The graph is provided in the following format:\n",
+    "        - The name of the feature depicted in the graph\n",
+    "        - The type of the feature (continuous, categorical, or boolean)\n",
+    "        - Mean values\n",
+    "        - Lower bounds of confidence interval (optional)\n",
+    "        - Upper bounds of confidence interval (optional)\\n\\n\"\"\"\n",
+    "    question += f\"Here is the graph:\\n\\n{graph_as_text}\\n\\n\"\n",
+    "    question += f\"Your task is to determine if the graph is\\na) monotone increasing\\nb) monotone decreasing\\nc) not monotone.\\n\\nWhat is the correct answer? Think step by step.\"\n",
+    "    graph_ = graphs.text_to_graph(graph_as_text)\n",
+    "    if is_monotone_increasing(graph_):\n",
+    "        questions.append((question, \"Increasing\"))\n",
+    "    elif is_monotone_decreasing(graph_):\n",
+    "        questions.append((question, \"Decreasing\"))\n",
+    "    elif num_non_monotone < 62:\n",
+    "        num_non_monotone += 1\n",
+    "        questions.append((question, \"Not monotone\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# subset 100 random questions\n",
+    "random.shuffle(questions)\n",
+    "questions = questions[:100]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n",
+      "        \n",
+      "    The graph is provided in the following format:\n",
+      "        - The name of the feature depicted in the graph\n",
+      "        - The type of the feature (continuous, categorical, or boolean)\n",
+      "        - Mean values\n",
+      "        - Lower bounds of confidence interval (optional)\n",
+      "        - Upper bounds of confidence interval (optional)\n",
+      "\n",
+      "Here is the graph:\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: FoodCourt\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 593.5)\": -0.177, \"(593.5, 779.5)\": 0.043, \"(779.5, 1341.5)\": 0.27, \"(1341.5, 2175.5)\": 0.543, \"(2175.5, 3125.0)\": 0.863, \"(3125.0, 3637.0)\": 1.13, \"(3637.0, 4078.5)\": 1.479, \"(4078.5, 5218.5)\": 2.076, \"(5218.5, 6031.5)\": 1.81, \"(6031.5, 6171.5)\": 1.439, \"(6171.5, 8753.0)\": 2.236, \"(8753.0, 8824.0)\": 2.746, \"(8824.0, 10094.5)\": 3.43, \"(10094.5, 12683.5)\": 3.888, \"(12683.5, 27723.0)\": 4.131}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 593.5)\": -0.307, \"(593.5, 779.5)\": -0.11, \"(779.5, 1341.5)\": -0.04, \"(1341.5, 2175.5)\": -0.06, \"(2175.5, 3125.0)\": 0.404, \"(3125.0, 3637.0)\": 0.707, \"(3637.0, 4078.5)\": 0.742, \"(4078.5, 5218.5)\": 1.52, \"(5218.5, 6031.5)\": 1.485, \"(6031.5, 6171.5)\": 0.477, \"(6171.5, 8753.0)\": 1.548, \"(8753.0, 8824.0)\": 1.95, \"(8824.0, 10094.5)\": 2.626, \"(10094.5, 12683.5)\": 2.361, \"(12683.5, 27723.0)\": 2.558}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 593.5)\": -0.047, \"(593.5, 779.5)\": 0.196, \"(779.5, 1341.5)\": 0.58, \"(1341.5, 2175.5)\": 1.145, \"(2175.5, 3125.0)\": 1.322, \"(3125.0, 3637.0)\": 1.554, \"(3637.0, 4078.5)\": 2.216, \"(4078.5, 5218.5)\": 2.631, \"(5218.5, 6031.5)\": 2.135, \"(6031.5, 6171.5)\": 2.4, \"(6171.5, 8753.0)\": 2.925, \"(8753.0, 8824.0)\": 3.543, \"(8824.0, 10094.5)\": 4.234, \"(10094.5, 12683.5)\": 5.416, \"(12683.5, 27723.0)\": 5.705}\n",
+      "\n",
+      "\n",
+      "Your task is to determine if the graph is\n",
+      "a) monotone increasing\n",
+      "b) monotone decreasing\n",
+      "c) not monotone.\n",
+      "\n",
+      "What is the correct answer? Think step by step.\n",
+      "SOLUTION:  Not monotone\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print a random question\n",
+    "import random\n",
+    "random.shuffle(questions)\n",
+    "print(questions[0][0])\n",
+    "print('SOLUTION: ', questions[0][1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "62"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# print the number of non-monotone graphs\n",
+    "len([q for q in questions if q[1] == \"Not monotone\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save the questions to json\n",
+    "import json\n",
+    "with open(\"../benchmark/monotonicity.json\", \"w\") as f:\n",
+    "    json.dump(questions, f, indent=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Run the benchmark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load the json file\n",
+    "with open(\"../benchmark/monotonicity.json\", \"r\") as f:\n",
+    "    questions = json.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Decreasing\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Decreasing\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Decreasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Decreasing\n",
+      "SOLUTION:  Decreasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Decreasing\n",
+      "SOLUTION:  Decreasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Decreasing\n",
+      "SOLUTION:  Decreasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Decreasing\n",
+      "SOLUTION:  Decreasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Decreasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Decreasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Decreasing\n",
+      "SOLUTION:  Decreasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Increasing\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Not monotone.\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Decreasing\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "system_msg = \"You are an expert statistician and data scientist. You interpret global explanations produced by a generalized additive model (GAM). You answer all questions to the best of your ability, combining the data contained in the graph, any data set description you are given, and your knowledge about the real world.\"\n",
+    "for question in questions:\n",
+    "    messages = [{\"role\": \"system\", \"content\": system_msg}, {\"role\": \"user\", \"content\": question[0]}]\n",
+    "    response = t2ebm.utils.openai_completion_query('gpt-3.5-turbo-0125', messages, temperature=0.0)\n",
+    "    messages.append({\"role\": \"assistant\", \"content\": response})\n",
+    "    messages.append({\"role\": \"user\", \"content\": \"Thanks. Now summarize your response by answering with 'Increasing', 'Decreasing', or 'Not monotone'.\"})\n",
+    "    response = t2ebm.utils.openai_completion_query('gpt-3.5-turbo-0125', messages, temperature=0.0)\n",
+    "    print('LLM RESPONSE: ', response)\n",
+    "    print('SOLUTION: ', question[1])\n",
+    "    print('-'*80)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Gemini"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the \"Means\" data:**  We need to see if the mean values consistently increase or decrease as the input intervals increase. Here's a breakdown:\n",
+      "\n",
+      "   | Interval        | Mean     | \n",
+      "   |-----------------|----------|\n",
+      "   | (0.0, 593.5)     | -0.177   | \n",
+      "   | (593.5, 779.5)    | 0.043    | \n",
+      "   | (779.5, 1341.5)   | 0.27     |\n",
+      "   | (1341.5, 2175.5)  | 0.543    |\n",
+      "   | ...             | ...      | \n",
+      "\n",
+      "3. **Identify the Trend:** As you move from left to right across the intervals, the mean values are generally increasing. There's a small dip between the intervals (5218.5, 6031.5) and (6031.5, 6171.5), but the overall trend is upwards.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph is **not monotone** because while it's mostly increasing, there's a small dip in the mean values, breaking the strict definition of monotone increasing. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Analyze the Graph Data:**\n",
+      "   - We are given a series of age ranges and the corresponding mean output of the GAM.\n",
+      "   -  Let's look at the trend of the mean values:\n",
+      "      - Starts around -1.489, increases to 1.245, then decreases, then increases again, and so on.\n",
+      "\n",
+      "3. **Determine Monotonicity:**\n",
+      "   - The mean output value does not consistently increase or decrease as age increases. There are both upward and downward trends within the data.\n",
+      "\n",
+      "**Therefore, the correct answer is (c) not monotone.** \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone increasing:**  A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "   - **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the Mean Values:**\n",
+      "   - Look at the mean values provided in the JSON object.  We need to see if these values consistently increase or decrease as the \"Spa\" feature range increases.\n",
+      "   - The mean values are: 0.521, 0.118, -0.285, -0.907, -1.309, -1.712, -3.029, -2.456, -2.956, -2.512, -3.402, -2.902, -4.077, -4.481, -5.377, -4.36, -7.183\n",
+      "\n",
+      "3. **Identify the Trend:**\n",
+      "   - As the \"Spa\" range increases, the mean values predominantly decrease. There are a couple of instances where the decrease isn't strictly maintained (e.g., between -3.029 and -2.456), but the overall trend is clearly downward.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph is **not monotone** because while it mostly decreases, there are small intervals where it doesn't strictly follow a decreasing trend. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Analyze the \"Means\" data:**\n",
+      "   -  Look at the values in the \"Means\" dictionary. They represent the average predicted value of the output for each interval of the input feature \"id\".\n",
+      "   - We need to see if these values consistently increase or decrease as the \"id\" intervals increase.\n",
+      "\n",
+      "3. **Identify the Trend:**\n",
+      "    - Initially, the mean values increase as \"id\" increases.\n",
+      "    - However, around the interval \"(859643.0, 864727.5)\", the mean values start to decrease.\n",
+      "    - This switch from increasing to decreasing means the function is not monotone.\n",
+      "\n",
+      "**Answer:** The graph is **c) not monotone**. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the \"Means\" data:**  We need to see if the mean values consistently increase or decrease as the intervals of \"Watersheds\" increase.\n",
+      "\n",
+      "   - (0.0, 0.5): -0.02526\n",
+      "   - (0.5, 1.5): -0.02147 \n",
+      "   - (1.5, 2.5): -0.01542\n",
+      "   - ... and so on\n",
+      "\n",
+      "   Notice that as the intervals of \"Watersheds\" increase, the mean values are consistently increasing.\n",
+      "\n",
+      "3. **Consider Confidence Intervals:** While the means show an increasing trend, it's important to briefly check if the confidence intervals significantly overlap, which might introduce ambiguity. However, in this case, the confidence intervals are relatively tight around the means and don't suggest a deviation from the increasing trend.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph represents a **monotone increasing** function. As the value of \"Watersheds\" increases, the predicted output of the GAM also increases. \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "**1. Understand Monotonicity:**\n",
+      "\n",
+      "* **Monotone Increasing:**  A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "* **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "**2. Analyze the \"Means\" data:**\n",
+      "\n",
+      "* Look at the mean values provided for each interval of \"Deforestation.\" \n",
+      "* We want to see if these values consistently increase or decrease as the deforestation interval increases.\n",
+      "\n",
+      "**3.  Observations:**\n",
+      "\n",
+      "*  As deforestation increases from the interval \"(0.0, 0.5)\" to \"(14.5, 17.0)\", the mean values generally increase. There are a few instances where the increase is very small, but there's no clear decreasing trend.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph is **(a) monotone increasing**.  While the rate of increase might vary, the general trend of the mean values is upwards as deforestation increases. \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the \"Means\" data:**  We need to see if the mean values consistently increase or decrease as the input intervals increase.\n",
+      "\n",
+      "   -  Let's list the intervals and their corresponding mean values:\n",
+      "\n",
+      "     ```\n",
+      "     Interval               | Mean\n",
+      "     -----------------------|-------\n",
+      "     (6.802, 11.184...)      | -0.919\n",
+      "     (11.184..., 12.765)     | -0.814\n",
+      "     (12.765, 13.35...)     | -0.704\n",
+      "     (13.35..., 15.3)       | -0.596\n",
+      "     ...                     | ...\n",
+      "     (153.25, 542.2)        | 1.082 \n",
+      "     ```\n",
+      "\n",
+      "   -  As we move from left to right (increasing input intervals), we can see that the mean values are consistently **increasing**.\n",
+      "\n",
+      "3. **Consider Confidence Intervals:** While the means show an increasing trend, it's important to briefly check if the confidence intervals significantly overlap, which might introduce ambiguity. In this case, while there's some overlap, the overall trend is still clearly increasing.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph represents a **monotone increasing** function. \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the Provided Data:**\n",
+      "   - We have a continuous feature \"IsActiveMember\" with two intervals: (0.0, 0.5) and (0.5, 1.0).\n",
+      "   - The mean value for the interval (0.0, 0.5) is -0.555.\n",
+      "   - The mean value for the interval (0.5, 1.0) is 0.568.\n",
+      "\n",
+      "3. **Determine Monotonicity:**\n",
+      "   - As we move from the interval (0.0, 0.5) to (0.5, 1.0), the mean value increases from -0.555 to 0.568. \n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "Since the output increases as the input increases, the graph represents a **monotone increasing** function. \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Analyze the Mean Values:**\n",
+      "   - Look at the mean values provided for each interval of the \"Relationship\" feature:\n",
+      "      - (0.0, 0.5): 0.511\n",
+      "      - (0.5, 1.5): -0.233 \n",
+      "      - (1.5, 2.5): -0.666\n",
+      "      - (2.5, 3.5): -1.006\n",
+      "      - (3.5, 4.5): -0.529\n",
+      "      - (4.5, 5.0): 1.753\n",
+      "\n",
+      "3. **Identify the Pattern:**\n",
+      "   - As the \"Relationship\" feature increases from 0.0 to 3.5, the mean value decreases.\n",
+      "   - However, from 3.5 to 5.0, the mean value increases.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "Since the function does not strictly increase or decrease as the input increases, the graph is **c) not monotone**. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Analyze the Means:**\n",
+      "   - Look at the \"Means\" values in the JSON data. We need to see if these values generally increase or decrease as the intervals of \"ShoppingMall\" increase.\n",
+      "   - The values are: -0.032, -0.211, 0.034, 0.213, -0.042, 0.401, 0.585, 0.948, 1.235, 0.871, 1.066, 2.339, 2.909, 3.236\n",
+      "\n",
+      "3. **Identify the Pattern:**\n",
+      "   - While the values fluctuate slightly, there's a clear overall trend: as the \"ShoppingMall\" interval increases, the mean values generally increase as well. \n",
+      "   - Although there are a few small dips (e.g., from 1.235 to 0.871), the overall trend is upwards.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph is **not monotone** because there are minor dips in the mean values as the input increases. However, it's important to note that the graph exhibits a generally increasing trend. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the Mean Values:**\n",
+      "   - Look at the \"Means\" values in the JSON object.  We want to see if these values consistently increase or decrease as the interval for \"Siltation\" increases.\n",
+      "   -  Here's the trend:\n",
+      "      - (0.0, 1.5): -0.02643\n",
+      "      - (1.5, 2.5): -0.01529\n",
+      "      - (2.5, 3.5): -0.01037\n",
+      "      - ... and so on, with the values generally increasing.\n",
+      "\n",
+      "3. **Consider Confidence Intervals:**\n",
+      "   - While the means show an increasing trend, it's important to briefly check if the confidence intervals significantly overlap.  Significant overlap might introduce ambiguity.\n",
+      "   - In this case, while there's some overlap, the overall trend across intervals is still consistently increasing.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph representing the \"Siltation\" feature is **a) monotone increasing**. As the value of \"Siltation\" increases, the predicted output of the GAM model also generally increases. \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone increasing:**  A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "   - **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Examine the \"Means\" values:**\n",
+      "   -  We need to see if the mean values consistently increase or decrease as the \"area_worst\" intervals increase.\n",
+      "   -  Let's list the intervals and their corresponding means:\n",
+      "\n",
+      "     | Interval               | Mean    |\n",
+      "     |------------------------|---------|\n",
+      "     | (185.2, 357.5)        | -1.345  |\n",
+      "     | (357.5, 413.15)       | -1.192  |\n",
+      "     | (413.15, 471.9)      | -1.038  |\n",
+      "     | ...                   | ...     |\n",
+      "     | (1979.0, 4254.0)     |  1.557  |\n",
+      "\n",
+      "3. **Observe the Trend:** As we move from the lowest \"area_worst\" interval to the highest, the mean values are consistently increasing. \n",
+      "\n",
+      "**Conclusion:** The graph represents a **monotone increasing** function. \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the \"Means\" data:**  We'll focus on the mean values as they represent the general trend of the function. Look at the y-values (means) as the x-value intervals increase:\n",
+      "\n",
+      "   - (0.0, 0.5): -0.02484\n",
+      "   - (0.5, 1.5): -0.02089\n",
+      "   - (1.5, 2.5): -0.01739\n",
+      "   - ...\n",
+      "   - (10.5, 13.5): 0.03305\n",
+      "   - (13.5, 16.0): 0.02016 \n",
+      "\n",
+      "3. **Identify the Trend:**  We can see that as the x-value intervals increase, the mean values generally increase. However, there's a dip at the end between the intervals (10.5, 13.5) and (13.5, 16.0).\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "Since the function increases for most of the range but decreases at the end, it is **not monotone**. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here is the solution:\n",
+      "\n",
+      "1. **Understanding Monotonicity:**\n",
+      "    - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "    - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Analyzing the GAM Graph:**\n",
+      "    - We need to look at the 'Means' values across the intervals of the 'RiverManagement' feature. \n",
+      "    - Observe how the mean value changes as we move from the lowest interval to the highest:\n",
+      "        - \"(0.0, 0.5)\": -0.0273\n",
+      "        - \"(0.5, 1.5)\": -0.02345 \n",
+      "        - ...\n",
+      "        - \"(11.5, 12.5)\": 0.03673\n",
+      "        - \"(12.5, 13.5)\": 0.01311 \n",
+      "        - \"(13.5, 16.0)\": 0.03206\n",
+      "\n",
+      "3. **Identifying the Pattern:**\n",
+      "    - As we move from left to right across the intervals, the mean values generally increase. \n",
+      "    - However, there's a dip in the mean value between the intervals \"(11.5, 12.5)\" and \"(12.5, 13.5)\". \n",
+      "\n",
+      "4. **Conclusion:**\n",
+      "    - Since the mean value does not strictly increase across the entire range of 'RiverManagement', the graph is **not monotone**.\n",
+      "\n",
+      "**Therefore, the correct answer is (c) not monotone.** \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the \"Means\" data:**  We'll look at the mean values across the BMI intervals:\n",
+      "\n",
+      "   - (0.0, 9.1): -0.7 \n",
+      "   - (9.1, 22.55): -0.961 \n",
+      "   - (22.55, 23.65): -0.856 \n",
+      "   - ... (and so on)\n",
+      "\n",
+      "3. **Look for Violations of Monotonicity:**\n",
+      "   - Initially, the mean values decrease (from -0.7 to -0.961). \n",
+      "   - However, as BMI increases, the mean values start to increase (from -0.961 to -0.856 and onwards).\n",
+      "\n",
+      "**Conclusion:** Since the mean values do not consistently increase or decrease as BMI increases, the graph is **not monotone**. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Examine the \"Means\" Values:**\n",
+      "   -  Look at the mean values provided for each interval of \"texture_worst\".  We need to see if these values consistently increase or decrease as the intervals progress from lower to higher values.\n",
+      "   - The mean values are: -1.885, -1.717, -1.55, -0.851, -0.612, -0.44, -0.272, -0.1, 0.078, 0.279, 0.451, 0.619, 0.878, 1.044, 1.256, 1.423\n",
+      "\n",
+      "3. **Observe the Trend:**\n",
+      "    - As the intervals of \"texture_worst\" increase, the mean values consistently increase. \n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph is **a) monotone increasing**.  The mean predicted value increases as the value of the \"texture_worst\" feature increases. \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "Gemini: Invalid response with parts [].\n",
+      "LLM RESPONSE:  \n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "    - **Monotone increasing:**  A function is monotone increasing if, as the input increases, the output never decreases.\n",
+      "    - **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Analyze the Means:**\n",
+      "    - Observe how the mean values change as the petal_width intervals increase:\n",
+      "        - (0.1, 0.35): 8.07\n",
+      "        - (0.35, 0.45): 7.27 \n",
+      "        - (0.45, 0.75): 6.18\n",
+      "        - (0.75, 1.25): -2.64\n",
+      "        - (1.25, 1.75): -3.46\n",
+      "        - (1.75, 2.5): -4.19\n",
+      "\n",
+      "3. **Identify the Trend:**\n",
+      "    - Initially, as petal_width increases, the mean value decreases.\n",
+      "    - After a certain point (around petal_width 0.75), the mean value continues to decrease.\n",
+      "\n",
+      "4. **Conclusion:**\n",
+      "    - The graph is **not monotone increasing** because the mean values decrease as petal_width increases.\n",
+      "    - The graph is **not monotone decreasing** because while the mean values initially decrease, they don't decrease for the entire range of petal_width.\n",
+      "\n",
+      "**Therefore, the correct answer is (c) not monotone.** \n",
+      "\n",
+      "SOLUTION:  Decreasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone increasing:**  A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "   - **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Analyze the Intervals:**\n",
+      "   - **(0.0, 0.5):** Mean = -0.0751\n",
+      "   - **(0.5, 2.5):** Mean = 0.1633 \n",
+      "   - **(2.5, 3.0):** Mean = -0.7301\n",
+      "\n",
+      "3. **Compare Mean Values:**\n",
+      "   - From interval (0.0, 0.5) to (0.5, 2.5), the mean value increases.\n",
+      "   - From interval (0.5, 2.5) to (2.5, 3.0), the mean value decreases.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "Since the mean value increases in one interval and then decreases in the next, the graph is **not monotone**. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the \"Means\" data:**\n",
+      "   -  Look for instances where the mean value decreases and then increases again as you move from left to right across the x-axis intervals. \n",
+      "   -  We see this happening: the mean decreases from the interval \"(0.0, 3.5)\" to \"(28.5, 30.5)\" and then increases afterwards.\n",
+      "\n",
+      "3. **Conclusion:**\n",
+      "   Since the mean values do not consistently increase or decrease, the graph is **not monotone**. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Analyze the \"Means\" data:**  We'll focus on the \"Means\" values as they represent the model's prediction for each interval. Look at how the mean value changes as the input (DiabetesPedigreeFunction) increases:\n",
+      "\n",
+      "   - Starts at -0.528 in the interval (0.078, 0.1265)\n",
+      "   - Jumps to -0.218, then -0.342, then -0.168... there's some fluctuation here.\n",
+      "   - Continues to increase up to 1.283 in the interval (1.275, 1.3925)\n",
+      "   - Drops sharply to 1.168, then to 0.065, and continues decreasing.\n",
+      "\n",
+      "3. **Conclusion:** Since the mean value both increases and decreases as the input increases, the graph is **not monotone**. \n",
+      "\n",
+      "**Therefore, the correct answer is (c).** \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here is the solution:\n",
+      "\n",
+      "1. **Understanding Monotonicity:**\n",
+      "    - **Monotone increasing:** A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "    - **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Analyzing the GAM Graph:**\n",
+      "    - We need to look at the \"Means\" values across the different intervals of the \"Occupation\" feature. \n",
+      "    - Observe the trend of these mean values as the interval (representing the input) increases.\n",
+      "\n",
+      "3. **Observations:**\n",
+      "    -  \"(0.0, 0.5)\": -0.297\n",
+      "    -  \"(0.5, 3.5)\": -0.074  -> Increase from previous interval\n",
+      "    -  \"(3.5, 4.5)\": 0.644   -> Increase from previous interval\n",
+      "    -  \"(4.5, 6.5)\": -0.723  -> Decrease from previous interval \n",
+      "\n",
+      "    We can stop here. We've found instances where the mean value increases and then decreases as the input interval increases.\n",
+      "\n",
+      "4. **Conclusion:**\n",
+      "    Since the graph shows both increasing and decreasing trends in mean values as the input increases, the GAM graph is **not monotone**. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here is the step-by-step reasoning:\n",
+      "\n",
+      "1. **Understanding Monotonicity:**\n",
+      "    - **Monotone increasing:**  A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "    - **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Analyzing the GAM Graph:**\n",
+      "    - Look at the \"Means\" values across the intervals of the 'ejection_fraction'.\n",
+      "    - From (14.0, 16.0) to (32.5, 36.5), the mean values decrease (4.55 -> 3.26 -> 1.89 -> -0.42 -> -1.76).\n",
+      "    - There's an increase from (32.5, 36.5) to (36.5, 39.0) (-1.76 -> 0.48).\n",
+      "    - Further on, there are both increases and decreases in the mean values.\n",
+      "\n",
+      "3. **Conclusion:**\n",
+      "    Since the graph shows both increasing and decreasing trends in mean values as the 'ejection_fraction' increases, the function is **not monotone**. \n",
+      "\n",
+      "**Therefore, the correct answer is (c).** \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The correct answer is **(c) not monotone**. Here's why:\n",
+      "\n",
+      "* **Monotone increasing** means the graph's y-values only increase or stay the same as the x-values increase. \n",
+      "* **Monotone decreasing** means the graph's y-values only decrease or stay the same as the x-values increase.\n",
+      "\n",
+      "Let's look at the provided means:\n",
+      "\n",
+      "* The mean starts at -0.8769 in the interval (15565796.0, 15566519.0).\n",
+      "* It then increases to -0.1763 in the interval (15567333.5, 15567844.5). \n",
+      "* However, it decreases again to -0.2283 in the interval (15568343.5, 15571612.0).\n",
+      "\n",
+      "This up-and-down pattern continues throughout the graph.  Since the y-values (means) don't consistently increase or decrease, the graph is **not monotone**. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the \"Means\" data:**\n",
+      "   - Look at the mean values provided for each interval of \"WetlandLoss\". \n",
+      "   -  We need to see if these values consistently increase or decrease as the intervals progress.\n",
+      "\n",
+      "3. **The Trend:**\n",
+      "    - (0.0, 1.5): -0.02419\n",
+      "    - (1.5, 2.5): -0.01693 \n",
+      "    - (2.5, 3.5): -0.01069\n",
+      "    - ... and so on\n",
+      "    - Notice that as the \"WetlandLoss\" intervals increase, the mean values are also increasing.\n",
+      "\n",
+      "4. **Consider Confidence Intervals:**\n",
+      "   - While the mean values show an increasing trend, it's important to briefly check if the confidence intervals significantly overlap.  Significant overlap could make the increasing trend less certain. In this case, the overlap is not substantial enough to change the overall increasing pattern.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph represents a **monotone increasing** relationship between \"WetlandLoss\" and the target variable. As wetland loss increases, the model's prediction also tends to increase. \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:**  A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the \"Means\" data:**\n",
+      "   - Look at the y-values (means) across the x-axis intervals.  \n",
+      "   - We need to see if these values consistently increase or decrease.\n",
+      "\n",
+      "3. **The Data:**\n",
+      "    - (0.0, 22.0): -0.728\n",
+      "    - (22.0, 86.5): -1.069 \n",
+      "    - (86.5, 94.5): -0.907 \n",
+      "    - ... and so on\n",
+      "\n",
+      "4. **Observation:**\n",
+      "    - Initially, the mean decreases from -0.728 to -1.069. \n",
+      "    - Then, it starts increasing and continues to increase as the x-value intervals increase.\n",
+      "\n",
+      "5. **Conclusion:** Since the mean values do not consistently increase or decrease, the graph is **not monotone**. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input value increases, the output value either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input value increases, the output value either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the \"Means\" data:**\n",
+      "   - Look at the mean values provided for each interval of the \"ClimateChange\" feature. \n",
+      "   -  We need to see if these values consistently increase or decrease as the intervals progress.\n",
+      "\n",
+      "3. **Observations:**\n",
+      "    - \"(0.0, 1.5)\": -0.02549\n",
+      "    - \"(1.5, 2.5)\": -0.01575 \n",
+      "    - \"(2.5, 3.5)\": -0.01061\n",
+      "    - ... and so on, with the final mean being 0.04423 for the \"(12.5, 14.0)\" interval.\n",
+      "\n",
+      "    Notice that the mean values are generally increasing as the intervals of \"ClimateChange\" increase. While there might be slight fluctuations, the overall trend is upwards.\n",
+      "\n",
+      "4. **Consider Confidence Intervals:**\n",
+      "    - While the means show an increasing trend, it's essential to glance at the confidence intervals to ensure there's no significant overlap that might contradict the trend. \n",
+      "    - In this case, the confidence intervals are relatively tight around the means, and they don't suggest a reversal of the increasing trend.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph representing the \"ClimateChange\" feature is **a) monotone increasing**.  As the value of \"ClimateChange\" increases, the predicted output of the GAM generally increases as well. \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "    - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "    - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Analyze the Mean Values:**\n",
+      "    - Look at the mean values provided for each interval of \"Pregnancies\".  \n",
+      "    - The values are: -0.1506, -0.2484, -0.1873, -0.0302, 0.0211, 0.1013, 0.1489, 0.264, 0.3553, 0.4117, 0.2996, 0.6729\n",
+      "\n",
+      "3. **Identify the Trend:**\n",
+      "    - Initially, the mean values decrease (from -0.1506 to -0.2484).\n",
+      "    - Then, the mean values generally increase (from -0.2484 to 0.6729) with a small dip at the end.\n",
+      "\n",
+      "4. **Conclusion:**\n",
+      "    - Since the mean values both increase and decrease over the range of \"Pregnancies\", the graph is **not monotone**. \n",
+      "\n",
+      "**Therefore, the correct answer is (c) not monotone.** \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The correct answer is **c) not monotone**. Here's why:\n",
+      "\n",
+      "* **Monotone increasing** means the graph's y-values only increase or stay the same as the x-values increase. \n",
+      "* **Monotone decreasing** means the graph's y-values only decrease or stay the same as the x-values increase.\n",
+      "\n",
+      "Let's look at the provided data:\n",
+      "\n",
+      "* The mean value for the interval (32.0, 49.5) is 0.68.\n",
+      "* The mean value for the interval (49.5, 56.5) is -4.31. \n",
+      "\n",
+      "This shows a **decrease** in the mean value as the x-value increases. However, if we look at other intervals, we see increases as well. For example:\n",
+      "\n",
+      "* The mean value for the interval (121.5, 126.0) is 1.25.\n",
+      "* The mean value for the interval (127.5, 145.5) is 1.51.\n",
+      "\n",
+      "This shows an **increase** in the mean value as the x-value increases.\n",
+      "\n",
+      "**Therefore, the graph is not monotone because it exhibits both increasing and decreasing trends.** \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Analyze the Means:**\n",
+      "   - Look at the mean values provided for each interval of \"EducationNum.\" \n",
+      "   - The means are: -4.746, -1.252, -0.882, -0.483, -0.093, 0.276, 0.863, 1.487\n",
+      "\n",
+      "3. **Observe the Trend:**\n",
+      "    - As the value of \"EducationNum\" increases across the intervals, the mean value consistently increases.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "Since the mean values of the function consistently increase as \"EducationNum\" increases, the graph is **a) monotone increasing**. \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The correct answer is **c) not monotone**. Here's why:\n",
+      "\n",
+      "* **Monotone increasing** means the function's output (y-value) always increases or stays the same as the input (x-value) increases. \n",
+      "* **Monotone decreasing** means the function's output always decreases or stays the same as the input increases.\n",
+      "\n",
+      "Looking at the provided data:\n",
+      "\n",
+      "1. **Initial Increase:** The mean value increases from -0.195 for the interval (0.0, 0.5) to 1.333 for (0.5, 1.5). This suggests an increase.\n",
+      "\n",
+      "2. **Subsequent Decrease:** However, the mean value then decreases to -0.02 for the interval (1.5, 2.5). This breaks the pattern of a strictly increasing function.\n",
+      "\n",
+      "3. **Fluctuations:**  As we continue across the intervals, we see further fluctuations with both increases and decreases in the mean value.\n",
+      "\n",
+      "**Conclusion:**  Since the function does not consistently increase or decrease across the entire domain of the input feature \"NativeCountry\", it is **not monotone**. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Examine the Mean Values:**\n",
+      "   - Look at the mean values provided in the JSON object.  \n",
+      "   - Notice that as the intervals for \"PoliticalFactors\" increase (e.g., from \"(0.0, 0.5)\" to \"(0.5, 1.5)\" and so on), the corresponding mean values generally increase as well. \n",
+      "\n",
+      "3. **Consider Confidence Intervals:**\n",
+      "   - While the general trend is increasing, it's important to check if the confidence intervals overlap. Overlapping confidence intervals might indicate that the relationship is not strictly monotonic.\n",
+      "   - In this case, there might be slight overlaps between some confidence intervals. However, the overall trend of the means is still clearly increasing.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph is **not strictly monotone increasing** due to the potential slight overlaps in confidence intervals. However, the graph exhibits a **general upward trend**, indicating a strong positive association between \"PoliticalFactors\" and the target variable. \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Analyze the \"Means\" data:**  We need to see if the mean values consistently increase or decrease as the \"Balance\" intervals increase.\n",
+      "\n",
+      "   - Looking at the initial values:\n",
+      "      - (0.0, 50418.515): -0.132\n",
+      "      - (50418.515, 53570.93): -0.285  \n",
+      "      - (53570.93, 54249.445): -0.826 \n",
+      "      ... and so on\n",
+      "\n",
+      "   - We see fluctuations. The mean value decreases, then increases again later in the intervals.\n",
+      "\n",
+      "3. **Conclusion:** Since the mean values don't consistently increase or decrease, the graph is **not monotone**. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the Mean Values:**\n",
+      "   - Look at the mean values provided in the JSON object.  We want to see if these values consistently increase or decrease as the interval for \"DrainageSystems\" increases.\n",
+      "   -  Here's the trend of the mean values:\n",
+      "      - Starts at -0.02593\n",
+      "      - Generally increases as the \"DrainageSystems\" interval increases.\n",
+      "      - Ends at 0.04564 \n",
+      "\n",
+      "3. **Consider Confidence Intervals:**\n",
+      "   - While the means show an increasing trend, it's important to briefly check if the confidence intervals significantly overlap.  Significant overlap could mean the trend is not definitively increasing.\n",
+      "   - In this case, while there's some overlap, it's not substantial enough to negate the clear increasing trend of the means.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph represents a function that is **a) monotone increasing**. As the value of \"DrainageSystems\" increases, the predicted output generally increases. \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the \"Means\" data:**\n",
+      "   - Look at the y-values (means) across the x-axis intervals. \n",
+      "   -  We need to see if the values consistently increase or decrease as we move from left to right on the x-axis.\n",
+      "\n",
+      "3. **The Data:**\n",
+      "    - (0.0, 0.5): -0.02565\n",
+      "    - (0.5, 1.5): -0.02133 \n",
+      "    - (1.5, 2.5): -0.01683\n",
+      "    - ... and so on\n",
+      "\n",
+      "4. **Observation:** As the x-values increase, the mean values are consistently increasing. \n",
+      "\n",
+      "**Conclusion:** The graph represents a **monotone increasing** function. \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the Mean Values:**\n",
+      "   - Look at the mean values provided in the JSON object.  We want to see if these values generally increase or decrease as the intervals of \"Landslides\" increase.\n",
+      "   -  Here's a simplified view of the means:\n",
+      "      - (0.0, 0.5): -0.02593\n",
+      "      - (0.5, 1.5): -0.02172\n",
+      "      - (1.5, 2.5): -0.01544\n",
+      "      - ... and so on, with the last interval having a positive mean.\n",
+      "\n",
+      "3. **Observe the Trend:**  Notice that as the intervals of \"Landslides\" increase, the mean values are also increasing. \n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph represents a function that is **a) monotone increasing**. Even though the increase is slight in some intervals, the overall trend of the mean values is upwards as the value of \"Landslides\" increases. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:**  A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Examine the Mean Values:**\n",
+      "   - Look at the mean values provided in the JSON object.  We want to see if these values consistently increase or decrease as the intervals of the feature increase.\n",
+      "   -  Notice that the mean values generally increase as we move from the interval \"(0.0, 1.5)\" to \"(14.0, 15.0)\":\n",
+      "      - (0.0, 1.5): -0.02463\n",
+      "      - (1.5, 2.5): -0.01694\n",
+      "      - ...\n",
+      "      - (14.0, 15.0): 0.05506\n",
+      "\n",
+      "3. **Consider Confidence Intervals:**\n",
+      "   - While the mean values suggest an increasing trend, it's essential to look at the confidence intervals.  There might be overlap in the confidence intervals, which could mean the relationship isn't strictly increasing.\n",
+      "   - In this case, there is some minor overlap between the confidence intervals of consecutive intervals, particularly in the lower range of the feature. However, the overall trend still points towards an increasing relationship.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph is **not strictly monotone increasing** due to the slight overlap in confidence intervals. However, the general trend of the mean values strongly suggests that the relationship between the feature \"AgriculturalPractices\" and the output of the GAM is **predominantly increasing**. \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand the data representation:** The graph represents the effect of the \"diabetes\" feature on the model's prediction.  The x-axis represents different ranges of the \"diabetes\" feature, and the y-axis represents the corresponding effect on the prediction.\n",
+      "\n",
+      "2. **Analyze the mean values:**\n",
+      "    - For values of \"diabetes\" between 0.0 and 0.5, the mean effect is 0.3225.\n",
+      "    - For values of \"diabetes\" between 0.5 and 1.0, the mean effect is -0.415.\n",
+      "\n",
+      "3. **Compare the mean values:** The mean effect decreases from 0.3225 to -0.415 as the value of \"diabetes\" increases.\n",
+      "\n",
+      "4. **Consider the confidence intervals:**  Notice that the confidence intervals for both ranges don't overlap and stay entirely within their respective positive and negative regions. This strengthens our conclusion.\n",
+      "\n",
+      "**Conclusion:** Since the mean effect decreases as the value of the \"diabetes\" feature increases, the graph is **b) monotone decreasing**. \n",
+      "\n",
+      "SOLUTION:  Decreasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "**Understanding Monotonicity**\n",
+      "\n",
+      "* **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. It can stay flat or increase.\n",
+      "* **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases. It can stay flat or decrease.\n",
+      "* **Not Monotone:** If the function sometimes increases and sometimes decreases as the input increases, it's not monotone.\n",
+      "\n",
+      "**Analyzing the GAM Graph**\n",
+      "\n",
+      "1. **Focus on the Means:**  We'll primarily look at the \"Means\" values of the graph, as they represent the general trend of the function.\n",
+      "\n",
+      "2. **Trace the Trend:**  Examine the mean values as the 'id' intervals increase:\n",
+      "    * The values initially fluctuate slightly around -0.1 and -0.2.\n",
+      "    * There's a jump to a positive value (0.0378) in the interval (7170.5, 7335.5).\n",
+      "    * The values then increase further, reaching 0.1773.\n",
+      "    * After this, there's a general downward trend, but with some minor fluctuations.\n",
+      "\n",
+      "**Conclusion**\n",
+      "\n",
+      "The graph shows that the function does not consistently increase or decrease as the 'id' feature increases. There are both increasing and decreasing sections. \n",
+      "\n",
+      "**Therefore, the correct answer is (c) not monotone.** \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Examine the Mean Values:**\n",
+      "   -  Look at the \"Means\" values in the JSON object. We need to see if the values consistently increase or decrease as the interval of the input feature increases.\n",
+      "   -  The values initially decrease, then increase as the input \"smoothness_mean\" increases.\n",
+      "\n",
+      "3. **Conclusion:**\n",
+      "    Since the mean values do not consistently increase or decrease, the graph is **not monotone**. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The provided graph and description indicate a potential inconsistency. Let's break down why:\n",
+      "\n",
+      "* **The Problem:** The description states that \"Race\" is a **continuous** feature. However, the provided intervals and the very nature of race suggest it should be a **categorical** feature. Continuous variables can take on any value within a range (like height or temperature), while categorical variables represent distinct groups.\n",
+      "\n",
+      "* **Analyzing the Data:**  Even if we treat the \"Race\" feature as continuous for the sake of this example, the trend is not clearly monotone:\n",
+      "\n",
+      "    * It decreases from the first interval to the second  (-0.8604 to -0.0173).\n",
+      "    * Then it fluctuates, decreasing again, then increasing (-0.0173 to -0.2499 to -0.3026 to 0.0414).\n",
+      "\n",
+      "* **Conclusion:** Due to the likely misclassification of the \"Race\" feature and the fluctuating values, the graph is **c) not monotone**.\n",
+      "\n",
+      "**Important Note:**  It's crucial to use appropriate data types for features in any model. Misrepresenting categorical data as continuous can lead to inaccurate and potentially harmful interpretations. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here is the step-by-step reasoning:\n",
+      "\n",
+      "1. **Understanding Monotonicity:** A monotone function is a function that either never decreases (monotone increasing) or never increases (monotone decreasing). \n",
+      "\n",
+      "2. **Analyzing the Means:**  We need to look at the mean values of the function for each interval and see if they consistently increase or decrease. Here's the breakdown:\n",
+      "\n",
+      "    * (0.0, 0.5): -0.013\n",
+      "    * (0.5, 1.5): 0.434  (Increases from previous)\n",
+      "    * (1.5, 4.5): -0.066 (Decreases from previous)\n",
+      "    * (4.5, 5.5): 0.167  (Increases from previous)\n",
+      "    * (5.5, 7.5): -0.464 (Decreases from previous)\n",
+      "    * (7.5, 8.0): -2.54 (Decreases from previous)\n",
+      "\n",
+      "3. **Conclusion:** Since the mean values sometimes increase and sometimes decrease, the function is **not monotone**. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Analyze the Graph Data:**\n",
+      "   - Look at the \"Means\" values. We need to see if these values generally increase or decrease as the Age intervals increase.\n",
+      "   -  - The means initially increase (from age 2 to around age 17.5).\n",
+      "      - Then the means fluctuate a bit between ages 17.5 and 35.5.\n",
+      "      - Finally, the means generally decrease from age 35.5 onwards.\n",
+      "\n",
+      "3. **Conclusion:**\n",
+      "   Since the graph initially increases and then decreases, it is **not monotone**. \n",
+      "\n",
+      "**Therefore, the correct answer is (c).** \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the \"Means\" data:**  We'll focus on the mean values as they represent the general trend of the function. Look at the mean values in the provided JSON and see if they consistently increase or decrease as the input intervals increase:\n",
+      "\n",
+      "   -  The values initially decrease (from -5401.6 to -53732.5), then increase (up to 30854.6), and then generally decrease again.\n",
+      "\n",
+      "3. **Conclusion:** Since the mean values do not show a consistent increase or decrease as the input intervals increase, the graph is **not monotone**. \n",
+      "\n",
+      "**Therefore, the correct answer is (c).** \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand the data:** The graph represents the effect of the feature \"anaemia\" on the model's output. The x-axis represents the value of anaemia (ranging from 0.0 to 1.0), divided into two intervals: (0.0, 0.5) and (0.5, 1.0). The y-axis represents the model's prediction (mean, lower bound, and upper bound).\n",
+      "\n",
+      "2. **Compare the means:**\n",
+      "    - For the interval (0.0, 0.5), the mean is -0.0818.\n",
+      "    - For the interval (0.5, 1.0), the mean is 0.0917.\n",
+      "    - The mean is higher for the interval (0.5, 1.0) compared to (0.0, 0.5).\n",
+      "\n",
+      "3. **Consider the confidence intervals:**\n",
+      "    - While the means suggest an increase, it's crucial to check if the confidence intervals overlap. \n",
+      "    - The upper bound of the confidence interval for (0.0, 0.5) is -0.0173, which is lower than the lower bound of the confidence interval for (0.5, 1.0), which is 0.0194. This indicates a clear separation and strengthens the observation that the effect is likely increasing.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "Since the mean value of the function increases as the value of the \"anaemia\" feature increases, and the confidence intervals don't overlap, the graph is **a) monotone increasing**. \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here is the solution:\n",
+      "\n",
+      "1. **Understanding the graph:** The graph represents the effect of the \"smoking\" feature on the model's prediction. The x-axis represents the value of the \"smoking\" feature, and the y-axis represents the contribution of that value to the prediction.\n",
+      "\n",
+      "2. **Analyzing the means:**  \n",
+      "    - For the interval \"(0.0, 0.5)\", the mean effect is 0.01522.\n",
+      "    - For the interval \"(0.5, 1.0)\", the mean effect is -0.03391.\n",
+      "\n",
+      "3. **Determining monotonicity:**  Since the mean effect decreases as the value of the \"smoking\" feature increases, the graph is **monotone decreasing**.\n",
+      "\n",
+      "**Therefore, the correct answer is (b).** \n",
+      "\n",
+      "SOLUTION:  Decreasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone increasing:**  A function is monotone increasing if, as the input (x-value) increases, the output (y-value) never decreases. It can stay the same or go up.\n",
+      "   - **Monotone decreasing:** A function is monotone decreasing if, as the input (x-value) increases, the output (y-value) never increases. It can stay the same or go down.\n",
+      "\n",
+      "2. **Analyze the Graph Data:**\n",
+      "   - Look at the \"Means\" values in the JSON data. These represent the predicted output of the GAM for different ranges of the 'platelets' feature. \n",
+      "   - We need to see if these values consistently increase, consistently decrease, or fluctuate.\n",
+      "\n",
+      "3. **Observe the Trend:**\n",
+      "   - As the platelet count increases initially, the mean values increase (going from -1.004 to 2.956).\n",
+      "   - However, the mean values then start to decrease and fluctuate up and down across the entire range of platelet counts.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph is **c) not monotone**. The relationship between platelet count and the output of the GAM is not consistently increasing or decreasing. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the Mean Values:**\n",
+      "   - Look at the mean values provided in the JSON object.  We need to see if these values consistently increase or decrease as the input intervals increase.\n",
+      "   -  Here's a breakdown:\n",
+      "      - (0.0, 0.5): -0.02443\n",
+      "      - (0.5, 1.5): -0.02088 \n",
+      "      - (1.5, 2.5): -0.01613\n",
+      "      - ... (and so on)\n",
+      "      - (13.5, 15.0): 0.03345\n",
+      "      - (15.0, 16.0): 0.02926\n",
+      "\n",
+      "3. **Identify the Trend:**\n",
+      "   - As we move from the lowest interval to the highest interval, we can see that the mean values are generally increasing. However, there's a slight decrease from (13.5, 15.0) to (15.0, 16.0).\n",
+      "\n",
+      "4. **Conclusion:**\n",
+      "   - Since there's a small decrease in the mean values near the end, the graph is **not monotone**. Even a single decrease means it doesn't strictly adhere to the rules of monotone increasing or decreasing. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Examine the \"Means\" data:**  We need to see if the mean values consistently increase or decrease as the input intervals increase.\n",
+      "\n",
+      "    -  Let's list the intervals and their corresponding mean values:\n",
+      "\n",
+      "       | Interval                   | Mean Value |\n",
+      "       |----------------------------|------------|\n",
+      "       | (0.0, 0.02814)           | -0.771     |\n",
+      "       | (0.02814, 0.08293)        | -0.653     |\n",
+      "       | (0.08293, 0.08555)        | -0.533     |\n",
+      "       | (0.08555, 0.093225)       | -0.403     |\n",
+      "       | ...                       | ...         |\n",
+      "       | (0.26865, 0.291)          | 1.494      | \n",
+      "\n",
+      "3. **Analyze the Trend:** As we move from left to right across the intervals, the mean values are consistently increasing. \n",
+      "\n",
+      "**Conclusion:** The graph represents a **monotone increasing** function. \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The correct answer is **c) not monotone**. Here's why:\n",
+      "\n",
+      "* **Monotone Increasing:** A monotone increasing function means that as the x-value increases, the y-value never decreases. \n",
+      "* **Monotone Decreasing:** A monotone decreasing function means that as the x-value increases, the y-value never increases.\n",
+      "\n",
+      "Looking at the provided data, we can see multiple instances where the predicted value (mean) increases and then decreases as the CapitalLoss value increases. For example:\n",
+      "\n",
+      "* The mean is -1.147 for the interval (845.0, 1448.0) and 0.416 for the interval (1448.0, 1551.5). The mean increased.\n",
+      "* The mean is 3.928 for the interval (1551.5, 1568.5) and -3.752 for the interval (1568.5, 1748.0). The mean decreased.\n",
+      "\n",
+      "**Therefore, the graph is not monotone.** \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:**  A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the Mean Values:**\n",
+      "   - Look at the mean values provided in the JSON object.  \n",
+      "   - Do the values generally increase or decrease as the intervals of \"MonsoonIntensity\" increase?\n",
+      "     - We see the mean values generally increase as MonsoonIntensity increases (with a small dip at the end).\n",
+      "\n",
+      "3. **Consider Confidence Intervals:**\n",
+      "   - While the means show a generally increasing trend, it's important to consider the confidence intervals. \n",
+      "   - Notice that the confidence intervals are quite narrow, and even with the intervals, the trend remains generally increasing. The dip at the end is within the margin of error.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph is **not strictly** monotone increasing due to the slight dip in mean value at the high end of the \"MonsoonIntensity\" range. However, considering the confidence intervals and the overall trend, it's more accurate to say the graph demonstrates a **generally increasing** relationship between \"MonsoonIntensity\" and the response variable. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone increasing:**  A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "   - **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Analyze the Provided Data:**\n",
+      "   - We have three intervals for the feature \"Pclass\". Let's arrange them in increasing order of input:\n",
+      "      - (1.0, 1.5): Mean = -0.009\n",
+      "      - (1.5, 2.5): Mean = 0.534\n",
+      "      - (2.5, 3.0): Mean = -0.532\n",
+      "\n",
+      "3. **Check for Monotonicity:**\n",
+      "   - From (1.0, 1.5) to (1.5, 2.5) the mean value increases.\n",
+      "   - From (1.5, 2.5) to (2.5, 3.0) the mean value decreases.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "Since the function does not consistently increase or decrease as the input increases, the graph is **c) not monotone**. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The provided information indicates that the feature \"sex\" is a **continuous** variable. However, the provided data represents the feature with intervals \"(0.0, 0.5)\" and \"(0.5, 1.0)\", which implies a binary or categorical nature. \n",
+      "\n",
+      "This presents a contradiction:\n",
+      "\n",
+      "1. **Continuous Variable:**  A continuous variable would typically have a smooth curve representing the relationship with the output, not distinct intervals like these.\n",
+      "2. **Categorical Representation:** The way the data is presented suggests a categorical variable (perhaps representing two genders) rather than a continuous one.\n",
+      "\n",
+      "**Due to this inconsistency, it's impossible to determine the monotonicity of the graph.** \n",
+      "\n",
+      "Here's why we need clarification:\n",
+      "\n",
+      "* **Continuous \"sex\" variable:**  This doesn't make logical sense. We need to understand what this continuous variable represents.\n",
+      "* **Categorical \"sex\" variable:** If it's categorical, the intervals are unusual. We'd typically expect distinct categories like \"Male\" and \"Female\".\n",
+      "\n",
+      "**To determine monotonicity, we need a clear understanding of the feature and its representation.** \n",
+      "\n",
+      "SOLUTION:  Decreasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "    - **Monotone increasing:**  A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "    - **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Analyze the Means:**\n",
+      "    -  Let's look at the mean values provided in the JSON:\n",
+      "        - (1.0, 1.5): -0.918\n",
+      "        - (1.5, 2.5): 0.96 \n",
+      "        - (2.5, 3.5): -3.104\n",
+      "        - (3.5, 4.0): -2.768\n",
+      "\n",
+      "    - As the input (NumOfProducts) increases from (1.0, 1.5) to (1.5, 2.5), the mean output increases. However, as the input increases further to (2.5, 3.5), the mean output decreases.  \n",
+      "\n",
+      "3. **Conclusion:** Since the mean output does not consistently increase or decrease as the input increases, the graph is **not monotone**. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Let's analyze the graph step-by-step to determine its monotonicity:\n",
+      "\n",
+      "1. **Understanding Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Examining the \"Means\" data:**  We'll focus on the mean values as they represent the general trend of the function. Here's a simplified look at the means:\n",
+      "\n",
+      "   ```\n",
+      "   total_bedrooms:  (2, 4.5] -> -10633   (4.5, 9.5] -> -19829  (9.5, 12.5] -> -33356 ...  (2865.5, 6445] -> 51586 \n",
+      "   ```\n",
+      "\n",
+      "3. **Initial Trend:**  The function initially shows a decreasing trend. As the number of bedrooms increases from 2 to around 12.5, the mean value decreases.\n",
+      "\n",
+      "4. **Shift in Trend:**  However, as the number of bedrooms continues to increase beyond 12.5, the mean values start to increase. This shift is evident as we move towards the higher end of the 'total_bedrooms' range.\n",
+      "\n",
+      "5. **Conclusion:** Since the function initially decreases and then increases, it is **not monotone**. \n",
+      "\n",
+      "**Therefore, the correct answer is (c) not monotone.** \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand the data representation:** The graph represents the effect of the \"high_blood_pressure\" feature on the model's prediction.  The x-axis represents the value of \"high_blood_pressure\" (divided into two intervals: 0.0-0.5 and 0.5-1.0). The y-axis represents the model's prediction (mean, lower bound, and upper bound).\n",
+      "\n",
+      "2. **Compare the means:**\n",
+      "    - For the interval (0.0, 0.5), the mean is -0.1077.\n",
+      "    - For the interval (0.5, 1.0), the mean is 0.1864.\n",
+      "\n",
+      "3. **Analyze the trend:** The mean increases as the value of \"high_blood_pressure\" increases. \n",
+      "\n",
+      "4. **Consider the confidence intervals:** Notice that the confidence intervals for both intervals are entirely above (for the second interval) or below (for the first interval) zero, indicating a statistically significant difference between the two groups.\n",
+      "\n",
+      "**Conclusion:** Since the mean increases as the feature value increases, the graph is **a) monotone increasing**. \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:**  A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Analyze the Means:**\n",
+      "   - Look at the \"Means\" values in the JSON data.  \n",
+      "   - Notice that as the intervals of `sepal_length` increase, the corresponding mean values generally decrease:\n",
+      "      - (4.3, 4.55): 3.328 \n",
+      "      - (4.55, 4.75): 2.995\n",
+      "      - ...\n",
+      "      - (6.85, 7.7): -1.718\n",
+      "\n",
+      "3. **Consider Confidence Intervals:**\n",
+      "   - While the general trend is decreasing, it's important to check if the confidence intervals overlap significantly. Significant overlap could indicate that the relationship might not be strictly monotone.\n",
+      "   - In this case, while there's some overlap between adjacent intervals, the overall trend of the means and the confidence intervals suggests a decreasing relationship.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph is **b) monotone decreasing**.  The mean values of the function decrease as the `sepal_length` increases, and the confidence intervals, while showing some overlap, support this overall trend. \n",
+      "\n",
+      "SOLUTION:  Decreasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the \"Means\" data:**  We need to see if the mean values consistently increase or decrease as the area_mean intervals increase.\n",
+      "\n",
+      "   -  Let's list the intervals and their corresponding mean values:\n",
+      "\n",
+      "     ```\n",
+      "     Interval            | Mean\n",
+      "     ---------------------|-------\n",
+      "     (143.5, 259.35)     | -0.759\n",
+      "     (259.35, 289.4)    | -0.662 \n",
+      "     (289.4, 319.15)    | -0.567\n",
+      "     ...                 | ...\n",
+      "     (1801.0, 2501.0)    | 1.109 \n",
+      "     ```\n",
+      "\n",
+      "3. **Observe the Trend:** As we move from the lowest area_mean interval to the highest, the mean values are consistently increasing. \n",
+      "\n",
+      "**Conclusion:** The graph represents a **monotone increasing** function. \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "    - **Monotone Increasing:** A function is monotone increasing if, as the x-value increases, the y-value never decreases. It can stay the same or increase.\n",
+      "    - **Monotone Decreasing:** A function is monotone decreasing if, as the x-value increases, the y-value never increases. It can stay the same or decrease.\n",
+      "\n",
+      "2. **Analyze the Mean Values:**\n",
+      "    - Look at the mean values provided in the JSON object.  We need to see if the values generally increase or decrease as the serum_sodium intervals increase.\n",
+      "    - The values initially increase, then decrease, then increase again. This tells us the graph is not strictly increasing or decreasing.\n",
+      "\n",
+      "3. **Consider Confidence Intervals:**\n",
+      "    - Even though the mean values show some fluctuation, we need to consider the confidence intervals. If the confidence intervals for neighboring intervals overlap significantly, it makes it harder to definitively say the relationship is purely increasing or decreasing.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph is **c) not monotone**. The mean values do not show a consistent increasing or decreasing trend, and the confidence intervals likely overlap, further indicating the relationship between serum_sodium and the target variable is not strictly monotone. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The correct answer is **c) not monotone**. Here's why:\n",
+      "\n",
+      "1. **Understanding Monotonicity:** A monotone increasing function always has a positive slope (or 0), meaning the output (y-value) increases or stays the same as the input (x-value) increases. Conversely, a monotone decreasing function always has a negative slope (or 0), meaning the output decreases or stays the same as the input increases.\n",
+      "\n",
+      "2. **Analyzing the Graph Data:** Let's look at the mean values of the graph:\n",
+      "\n",
+      "   * (0.0, 0.5): -0.368\n",
+      "   * (0.5, 1.5): 0.724  \n",
+      "   * (1.5, 2.5): 0.587 \n",
+      "   * (2.5, 3.5): -0.221 \n",
+      "   * (3.5, 4.5): -0.631\n",
+      "   * (4.5, 5.5): -0.545\n",
+      "   * (5.5, 6.0): 0.179\n",
+      "\n",
+      "   We can see that the function increases from the first interval to the second, then decreases, then increases again towards the end. This up-and-down pattern clearly indicates the function is not always increasing or always decreasing.\n",
+      "\n",
+      "3. **Conclusion:** Since the graph doesn't exhibit a consistently increasing or decreasing trend, it is **not monotone**. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Analyze the Mean Values:**\n",
+      "   -  Look at the \"Means\" data in the JSON.  We need to see if the values generally increase or decrease as the population range increases.\n",
+      "   - The values initially decrease as population increases (e.g., from the interval \"(3.0, 14.5)\" to \"(837.5, 1019.5)\").\n",
+      "   - Then there's a fluctuation: an increase followed by a decrease around the intervals centered on population \"1268\".\n",
+      "   - After that, the values generally continue to decrease as the population increases.\n",
+      "\n",
+      "3. **Consider Confidence Intervals:**\n",
+      "   - While the means show some fluctuations, it's important to look at the confidence intervals. There are overlaps between the confidence intervals of some consecutive intervals, suggesting that the fluctuations might not be statistically significant. However, the general trend is still not strictly increasing or decreasing.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph is **c) not monotone**. While it shows a general trend of decreasing values as population increases, the fluctuations and overlapping confidence intervals indicate that it's not strictly monotone decreasing. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the x-value increases, the y-value either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the x-value increases, the y-value either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Examine the \"Means\" values:**  We'll focus on the mean values since they represent the function's overall trend. Look at the y-values (means) as the x-value intervals increase:\n",
+      "\n",
+      "   - (43.79, 60.035): -0.884\n",
+      "   - (60.035, 63.379999999999995): -0.783\n",
+      "   - (63.379999999999995, 66.67): -0.681\n",
+      "   - ... and so on\n",
+      "\n",
+      "   Notice that as the x-value intervals increase, the corresponding y-values are also increasing. \n",
+      "\n",
+      "3. **Check for Violations:**  Scan through all the mean values. Is there any instance where the y-value decreases as the x-value interval increases? No.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "Since the mean values of the function consistently increase as the x-value intervals increase, the graph represents a **monotone increasing** function. \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Analyze the \"Means\" data:**  We'll focus on the mean values as they represent the general trend of the function. Look at the mean values in each interval and see if they consistently increase or decrease as the serum_creatinine level (the input) increases.\n",
+      "\n",
+      "3. **Observations:**\n",
+      "   -  Initially, the mean values decrease (from -0.26 to -3.77).\n",
+      "   - Then, they generally increase (from -3.77 to 6.97) with some minor fluctuations.\n",
+      "\n",
+      "**Conclusion:** Since the mean values do not consistently increase or decrease as serum_creatinine increases, the graph is **not monotone**. \n",
+      "\n",
+      "**Therefore, the correct answer is (c).** \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases (it can stay the same or increase).\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases (it can stay the same or decrease).\n",
+      "\n",
+      "2. **Analyze the \"Means\" Values:**\n",
+      "   - Look at the mean values provided for each interval of the \"CoastalVulnerability\" feature. \n",
+      "   -  Notice that as the intervals increase (0.0-0.5, 0.5-1.5, 1.5-2.5, etc.), the corresponding mean values also increase:\n",
+      "      - -0.03259, -0.02272, -0.0157, -0.00983,... 0.02903, 0.03437, 0.04826 \n",
+      "\n",
+      "3. **Conclusion:**\n",
+      "   Since the mean values consistently increase as the input intervals increase, the graph represents a **monotone increasing** function. \n",
+      "\n",
+      "**Therefore, the correct answer is (a).** \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The correct answer is **c) not monotone**. Here's why:\n",
+      "\n",
+      "* **Monotone Increasing:** A monotone increasing function means that as the x-value increases, the y-value either increases or stays the same. It never decreases. \n",
+      "* **Monotone Decreasing:** A monotone decreasing function means that as the x-value increases, the y-value either decreases or stays the same. It never increases.\n",
+      "\n",
+      "Looking at the provided data, we can see several instances where the function changes direction:\n",
+      "\n",
+      "1. **Initial Increase, then Decrease:** The function initially increases from the interval \"(0.0, 57.0)\" to \"(3048.0, 3120.0)\" but then decreases in the interval \"(3120.0, 4243.5)\".\n",
+      "\n",
+      "2. **Fluctuations:**  The function continues to fluctuate throughout the rest of the intervals, with periods of both increasing and decreasing values.\n",
+      "\n",
+      "**Therefore, because the function does not consistently increase or decrease, it is not monotone.** \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the Mean Values:**\n",
+      "   - Observe the mean values provided in the JSON object.  We need to see if there's a consistent trend of increasing or decreasing as the tenure range increases.\n",
+      "   -  Let's list the mean values in order of increasing tenure:\n",
+      "      - (0.0, 0.5): -0.3765\n",
+      "      - (0.5, 1.5): -0.0692\n",
+      "      - (1.5, 4.5): -0.016\n",
+      "      - (4.5, 5.5): 0.0109\n",
+      "      - (5.5, 6.5): 0.0432\n",
+      "      - (6.5, 7.5): 0.0871\n",
+      "      - (7.5, 9.5): 0.0554\n",
+      "      - (9.5, 10.0): -0.0599 \n",
+      "\n",
+      "3. **Identify the Trend:**\n",
+      "   - Initially, the mean values increase as tenure increases. \n",
+      "   - However, there's a drop in the mean value at the end, from the interval (7.5, 9.5) to (9.5, 10.0).\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "Since the mean values do not strictly increase or decrease, the graph is **not monotone**. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone increasing:**  A function is monotone increasing if, as the input increases, the output never decreases. It can stay flat or increase.\n",
+      "   - **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output never increases. It can stay flat or decrease.\n",
+      "\n",
+      "2. **Examine the \"Means\" data:**  We'll focus on the mean values as they represent the general trend of the GAM function. Look at the y-values (means) as the x-values (intervals) increase:\n",
+      "\n",
+      "   -  Interval (9.71, 13.24): -1.121\n",
+      "   -  Interval (13.24, 14.075): -1.023 \n",
+      "   -  ...\n",
+      "   -  Interval (21.285, 33.81): 0.68\n",
+      "\n",
+      "   Notice that as the intervals increase, the mean values generally increase as well. There are a few instances where the increase is very small, but there's no point where the mean value decreases as the interval increases.\n",
+      "\n",
+      "3. **Consider Confidence Intervals:** While the means show a generally increasing trend, the confidence intervals do overlap slightly in some areas. However, the overall trend still points towards an increasing relationship.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph is **not strictly monotone increasing** due to the slight overlaps in the confidence intervals. However, it exhibits a **generally increasing trend**. \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone increasing:**  A function is monotone increasing if, as the input increases, the output never decreases. It can stay the same or increase.\n",
+      "   - **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output never increases. It can stay the same or decrease.\n",
+      "\n",
+      "2. **Analyze the Graph Data:**\n",
+      "   - We are given a series of latitude ranges and their corresponding mean values. \n",
+      "   - We need to check if these mean values consistently increase or decrease as latitude increases.\n",
+      "\n",
+      "3. **Look for Violations:**\n",
+      "   - Starting from the lowest latitude range, examine the mean values. Are there any instances where the mean value increases after a decrease, or decreases after an increase?\n",
+      "   - **Example:**  The mean value for the latitude range (33.555, 33.565) is lower than the mean value for the preceding range (33.504999999999995, 33.555). This indicates the function does not strictly increase. Similarly, there are other instances where the mean value increases after a decrease.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph is **c) not monotone**. The mean values do not consistently increase or decrease as latitude increases. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:**  A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the Mean Values:**\n",
+      "   - Look at the mean values provided in the JSON object.  We want to see if these values generally increase or decrease as the intervals of \"DamsQuality\" increase.\n",
+      "   -  Here's the trend:\n",
+      "      - (0.0, 1.5): -0.02325\n",
+      "      - (1.5, 2.5): -0.01532 \n",
+      "      - (2.5, 3.5): -0.01073\n",
+      "      - ...\n",
+      "      - (12.5, 13.5): 0.03961\n",
+      "      - (13.5, 14.0): 0.01644 \n",
+      "\n",
+      "3. **Identify the Pattern:**\n",
+      "   - We can see that the mean values generally increase as the \"DamsQuality\" intervals increase. However, there's a dip at the end between (12.5, 13.5) and (13.5, 14.0).\n",
+      "\n",
+      "4. **Consider Confidence Intervals:**\n",
+      "   - While there's a dip in the mean at the end, notice that the confidence intervals for the (12.5, 13.5) and (13.5, 14.0) intervals overlap significantly. This overlap suggests that the dip might not be statistically significant.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph is **not strictly monotone increasing** due to the dip at the end. However, considering the confidence intervals, the relationship is **almost monotone increasing**. The dip at the end could be due to random variation in the data or a slight non-linear effect within that range. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases (it can stay the same or increase).\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases (it can stay the same or decrease).\n",
+      "\n",
+      "2. **Analyze the \"Means\" data:**\n",
+      "   - Look at the mean values provided for each interval of the feature \"IneffectiveDisasterPreparedness.\" \n",
+      "   -  As the intervals increase (0.0-1.5, 1.5-2.5, 2.5-3.5, etc.), observe the corresponding mean values.\n",
+      "\n",
+      "3. **Observe the Trend:**\n",
+      "   - We see that as the \"IneffectiveDisasterPreparedness\" value increases, the mean value consistently increases as well. \n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "Since the mean values consistently increase as the input feature increases, the graph represents a **monotone increasing** function. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here is the step-by-step reasoning:\n",
+      "\n",
+      "1. **Understanding the graph:** The graph represents the effect of the feature \"HasCrCard\" on the model's prediction. The x-axis represents the value of \"HasCrCard\", and the y-axis represents the contribution to the prediction.\n",
+      "\n",
+      "2. **Interpreting the intervals:** The feature is continuous, and the JSON object provides intervals.  This means the model predicts the same effect for any value of \"HasCrCard\" within a given interval.\n",
+      "\n",
+      "3. **Analyzing the means:**\n",
+      "    - For the interval \"(0.0, 0.5)\", the mean effect is -0.004421.\n",
+      "    - For the interval \"(0.5, 1.0)\", the mean effect is 0.001379.\n",
+      "\n",
+      "4. **Monotonicity:**  Since the mean effect increases as the value of \"HasCrCard\" increases (from -0.004421 to 0.001379), the graph is **monotone increasing**.\n",
+      "\n",
+      "**Therefore, the correct answer is (a) monotone increasing.** \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "Gemini: Invalid response with parts [].\n",
+      "LLM RESPONSE:  \n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The correct answer is **c) not monotone**. Here's why:\n",
+      "\n",
+      "* **Monotone increasing** means the function's output (y-value) either increases or stays the same as the input (x-value) increases. \n",
+      "* **Monotone decreasing** means the function's output (y-value) either decreases or stays the same as the input (x-value) increases.\n",
+      "\n",
+      "Looking at the provided data, we can see:\n",
+      "\n",
+      "1. **Initial Increase:** The mean value increases from the first interval \"(4.0, 11.5)\" to \"(18.0, 28.5)\".\n",
+      "2. **Decrease:** Then, the mean value decreases from \"(18.0, 28.5)\" to \"(73.5, 76.5)\".\n",
+      "3. **Fluctuations:**  The mean value continues to fluctuate up and down throughout the rest of the intervals.\n",
+      "\n",
+      "**Therefore, the graph is not consistently increasing or decreasing, making it not monotone.** \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "Gemini: Invalid response with parts [].\n",
+      "LLM RESPONSE:  \n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here is the solution:\n",
+      "\n",
+      "1. **Understanding the graph:** The graph represents the effect of the \"Gender\" feature on the model's prediction.  The feature is continuous, which is unusual for a variable like gender. This suggests that \"Gender\" might be coded as a numerical variable (e.g., on a scale from 0 to 1).\n",
+      "\n",
+      "2. **Analyzing the means:** The mean effect for the interval \"(0.0, 0.5)\" is -0.4751, while the mean effect for the interval \"(0.5, 1.0)\" is 0.2339.  \n",
+      "\n",
+      "3. **Monotonicity:** Since the mean effect increases as the value of the \"Gender\" feature increases, the graph is **monotone increasing**.\n",
+      "\n",
+      "**Important Note:** While the graph shows a monotone increasing trend, it's crucial to remember that labeling \"Gender\" as a continuous variable is unusual. Gender is typically a categorical variable (male/female/other). Representing it as continuous might indicate an underlying coding scheme that needs further investigation. \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone increasing:**  A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "   - **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Analyze the \"Means\" data:**\n",
+      "   - Look at the mean values provided for each interval of \"Fare\".  We need to see if these values consistently increase or decrease as the Fare intervals increase.\n",
+      "   -  Here's a simplified look at the means:\n",
+      "      - -1.425, -1.303, -0.472, -0.602, -0.14, 0.225, 0.355, 0.207, -0.238, 0.051, -0.075,...\n",
+      "\n",
+      "3. **Identify Non-Monotonic Behavior:**\n",
+      "    - We can see that the mean values do not consistently increase or decrease. For example, the mean increases from -1.425 to -0.472, then decreases to -0.602. This up-and-down pattern continues throughout the data.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph is **c) not monotone**. The relationship between \"Fare\" and the target variable is not strictly increasing or decreasing. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The correct answer is **c) not monotone**. Here's why:\n",
+      "\n",
+      "* **Monotone increasing** means the graph's y-values only increase or stay the same as the x-values increase. \n",
+      "* **Monotone decreasing** means the graph's y-values only decrease or stay the same as the x-values increase.\n",
+      "\n",
+      "Let's look at the provided data:\n",
+      "\n",
+      "1. **Initial Increase:**  The mean values initially increase, starting from 0.3865 and rising to 0.3462, 0.2048, and so on.\n",
+      "\n",
+      "2. **Subsequent Decrease:** However, as the 'EstimatedSalary' (x-values) continues to increase, we see the mean values start to decrease. For example, from the interval starting at 48226.81, the mean value is -0.0771, and it continues to fluctuate between negative and small positive values.\n",
+      "\n",
+      "3. **Fluctuations:** The presence of both increasing and decreasing trends in the mean values across the different salary ranges indicates that the graph is not strictly going up or down. \n",
+      "\n",
+      "**Therefore, the graph is not monotone.** The GAM model captures a non-linear relationship between 'EstimatedSalary' and the target variable. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the x-value increases, the y-value either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the x-value increases, the y-value either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the \"Means\" data:**  We'll focus on the mean values as they represent the general trend of the function. Look at the y-values (means) as the x-value intervals increase:\n",
+      "\n",
+      "   - (7.93, 10.585): -1.149\n",
+      "   - (10.585, 11.305): -1.016\n",
+      "   - (11.305, 11.965): -0.883\n",
+      "   - ... and so on\n",
+      "\n",
+      "   Notice that as the x-value intervals increase, the corresponding y-values are also increasing. \n",
+      "\n",
+      "3. **Check for Violations:**  Scan through all the mean values. Is there any instance where the y-value decreases as the x-value interval increases? No.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "Since the mean values of the function consistently increase as the x-value intervals increase, the graph represents a **monotone increasing** function. \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the \"Means\" data:**\n",
+      "   - Look at the mean values provided for each interval of \"Education\".  We need to see if these values generally increase or decrease as the education level goes up.\n",
+      "   -  Here's the trend:\n",
+      "      - Starts around -0.40\n",
+      "      - Decreases to around -0.54\n",
+      "      - Fluctuates slightly around -0.48 to -0.40\n",
+      "      - Increases from -0.45 to a peak around 0.18\n",
+      "      - Dips slightly, then increases again to 0.19\n",
+      "\n",
+      "3. **Conclusion:**\n",
+      "   The function is not strictly increasing or decreasing. It initially decreases, then generally increases, showing a somewhat curved relationship. \n",
+      "\n",
+      "**Therefore, the correct answer is (c) not monotone.** \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. It can stay flat or increase.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases. It can stay flat or decrease.\n",
+      "\n",
+      "2. **Analyze the Graph Data:**  We need to look at the \"Means\" values in the JSON object. Since the x-axis represents \"longitude\" (a continuous variable), we need to see if the mean values consistently increase or decrease as longitude increases.\n",
+      "\n",
+      "3. **Step through the Intervals:**\n",
+      "   -  Let's look at a few consecutive intervals:\n",
+      "      - `(-124.35, -124.10499999999999)`: -50430.1\n",
+      "      - `(-124.10499999999999, -124.08500000000001)`: -38925.6 \n",
+      "      - `(-124.08500000000001, -124.07499999999999)`: -23742.3\n",
+      "      - ... and so on\n",
+      "\n",
+      "    - In these initial intervals, as longitude increases, the mean value is also increasing. \n",
+      "\n",
+      "    - However, if we continue down the list, we'll find places where the trend doesn't hold. For example:\n",
+      "      - `(-122.42500000000001, -122.405)`: 89733.4\n",
+      "      - `(-122.405, -122.39500000000001)`: 78586.0\n",
+      "\n",
+      "      - Here, the longitude increases, but the mean value decreases.\n",
+      "\n",
+      "4. **Conclusion:** Since the mean values do not consistently increase or decrease as longitude increases, the graph is **not monotone**. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The correct answer is **c) not monotone**. Here's why:\n",
+      "\n",
+      "**Understanding Monotonicity**\n",
+      "\n",
+      "* **Monotone increasing:**  A function is monotone increasing if, as the input increases, the output never decreases. It can stay the same or go up.\n",
+      "* **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output never increases. It can stay the same or go down.\n",
+      "\n",
+      "**Analyzing the GAM Graph**\n",
+      "\n",
+      "Let's look at the provided means for the \"Age\" feature:\n",
+      "\n",
+      "* The mean output is higher for the interval \"(0.0, 0.5)\" than for \"(0.5, 3.5)\". This suggests a decrease.\n",
+      "* The mean output is higher for the interval \"(73.5, 74.5)\" than for \"(74.5, 77.5)\". This suggests a decrease.\n",
+      "* However, there are intervals where the mean output increases as \"Age\" increases, for example, between \"(25.5, 39.5)\" and \"(39.5, 44.5)\".\n",
+      "\n",
+      "**Conclusion**\n",
+      "\n",
+      "Since the graph shows both increasing and decreasing trends in the mean output as \"Age\" increases, the function represented by this GAM is **not monotone**. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the \"Means\" data:**  We'll focus on the \"Means\" values since they represent the function's output for each interval of 'radius_mean'. \n",
+      "   -  Observe how the mean values change as we move from the leftmost interval to the rightmost interval:\n",
+      "      - (-0.762, -0.659, -0.56, ..., 0.891, 0.99, 1.093)\n",
+      "\n",
+      "3. **Identify the Trend:** The mean values consistently *increase* as the 'radius_mean' increases. \n",
+      "\n",
+      "**Conclusion:** The graph represents a **monotone increasing** function. \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Analyze the Means:**\n",
+      "   - Look at the \"Means\" values in the JSON data. We need to see if these values generally increase or decrease as the input intervals increase.\n",
+      "   - Initially, the mean values decrease (e.g., from -70808.9 to -78966.6).\n",
+      "   - However, as the input intervals grow larger, the mean values eventually start to increase (e.g., from -1233.9 to 4370.8 and onwards).\n",
+      "\n",
+      "3. **Conclusion:**\n",
+      "   Since the mean values initially decrease and then increase, the graph is **not monotone**. \n",
+      "\n",
+      "**Therefore, the correct answer is (c).** \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone increasing:**  A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "   - **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Analyze the Means:**\n",
+      "   - Look at the mean values provided for each interval of the \"Parch\" feature:\n",
+      "      - (0.0, 0.5): 0.085\n",
+      "      - (0.5, 1.5): -0.055\n",
+      "      - (1.5, 3.0): -0.299\n",
+      "      - (3.0, 4.0): -1.704\n",
+      "\n",
+      "   - As the value of \"Parch\" increases, the mean value of the function generally decreases. \n",
+      "\n",
+      "3. **Consider Confidence Intervals:**\n",
+      "   - While the means show a decreasing trend, it's essential to check if the confidence intervals overlap.  Overlapping confidence intervals might indicate that we cannot be certain about the direction of the relationship.\n",
+      "   - In this case, there's a slight overlap between the confidence intervals of (0.0, 0.5) and (0.5, 1.5). However, the overall trend across all intervals is still downward.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph is **b) monotone decreasing**. Despite a minor overlap in confidence intervals, the general trend shows that as \"Parch\" increases, the output of the GAM model decreases. \n",
+      "\n",
+      "SOLUTION:  Decreasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Analyze the Mean Values:**\n",
+      "   - Look at the mean values provided in the JSON object.  We want to see if these values consistently increase or decrease as the interval of \"InadequatePlanning\" increases.\n",
+      "   -  Here's a breakdown:\n",
+      "      - (0.0, 0.5): -0.02553\n",
+      "      - (0.5, 2.5): -0.02038 \n",
+      "      - (2.5, 4.5): -0.0099\n",
+      "      - (4.5, 6.5): 0.00082\n",
+      "      - (6.5, 7.5): 0.01088\n",
+      "      - ... and so on\n",
+      "\n",
+      "   - Notice that the mean values are consistently increasing as we move from lower intervals of \"InadequatePlanning\" to higher intervals.\n",
+      "\n",
+      "3. **Consider Confidence Intervals:**\n",
+      "   - While the mean values show an increasing trend, it's important to briefly consider the confidence intervals. There's a tiny overlap between the upper bound of (13.5, 15.0) and the lower bound of (15.0, 16.0). However, this overlap is very small and doesn't change the overall increasing trend.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph is **(a) monotone increasing**. As the value of \"InadequatePlanning\" increases, the predicted output of the GAM generally increases as well. \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The correct answer is **c) not monotone**. Here's why:\n",
+      "\n",
+      "* **Monotone Increasing:** A monotone increasing graph means the y-value never decreases as the x-value increases.  Looking at the \"Means\" values, we see the graph initially decreases (from -0.765 to -1.909), then increases (from -1.909 to 0.977), and then decreases again. This up-and-down pattern means it's not monotone increasing.\n",
+      "\n",
+      "* **Monotone Decreasing:** A monotone decreasing graph means the y-value never increases as the x-value increases.  As we already established, the graph both increases and decreases, so it's not monotone decreasing either.\n",
+      "\n",
+      "**Therefore, the graph is not monotone.** \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here is the step-by-step reasoning:\n",
+      "\n",
+      "1. **Understanding Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyzing the GAM Graph:**\n",
+      "   - We need to look at the \"Means\" values of the graph. These represent the average predicted value of the output for each interval of the input feature (Insulin).\n",
+      "   - Observe the trend of the mean values as Insulin increases. \n",
+      "\n",
+      "3. **Observations:**\n",
+      "   - Initially, from \"(0.0, 20.0)\" to \"(87.5, 97.5)\", the mean values generally decrease.\n",
+      "   - Then, from \"(87.5, 97.5)\" to \"(190.5, 192.5)\", there's a slight increase.\n",
+      "   - After \"(190.5, 192.5)\", the mean values consistently increase until \"(526.5, 680.0)\".\n",
+      "\n",
+      "4. **Conclusion:**\n",
+      "   Since the mean values do not show a strictly increasing or decreasing pattern across the entire range of Insulin values, the graph is **not monotone**.\n",
+      "\n",
+      "**Therefore, the correct answer is (c).** \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the Mean Values:**\n",
+      "   -  Look at the mean values provided in the JSON object. We need to see if there's a consistent trend of increasing or decreasing values as the housing_median_age increases.\n",
+      "   -  Here's a rough trend of the mean values:\n",
+      "      - Starts negative, becomes less negative.\n",
+      "      - Becomes positive and generally increases.\n",
+      "      - Dips back negative briefly around \"(41.5, 45.5)\" and \"(45.5, 47.5)\".\n",
+      "      - Then increases again significantly.\n",
+      "\n",
+      "3. **Conclusion:**\n",
+      "    - The function is not strictly increasing or decreasing. There are dips and rises in the mean values as the housing_median_age increases. \n",
+      "\n",
+      "**Therefore, the correct answer is (c) not monotone.** \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "Gemini: Invalid response with parts [].\n",
+      "LLM RESPONSE:  \n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Analyze the Means:**\n",
+      "   -  Look at the mean values provided for each interval of 'petal_length'.  \n",
+      "   - The means are: 8.05, 7.28, -1.17, -2.4, -3.03, -3.73, -4.38\n",
+      "\n",
+      "3. **Identify the Trend:**\n",
+      "    -  As 'petal_length' increases, the mean values initially decrease (from 8.05 to -1.17) and then continue to decrease.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "Since the mean values consistently decrease as 'petal_length' increases, the graph is **b) monotone decreasing**. \n",
+      "\n",
+      "SOLUTION:  Decreasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases (it can stay the same or increase).\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases (it can stay the same or decrease).\n",
+      "\n",
+      "2. **Analyze the \"Means\" data:**  We need to see if the mean values consistently increase or decrease as the input intervals increase. \n",
+      "\n",
+      "   -  Let's look at the first few mean values:\n",
+      "      - (0.07117, 0.09376000000000001): -1.298\n",
+      "      - (0.09376000000000001, 0.099705): -1.161\n",
+      "      - (0.099705, 0.10519999999999999): -1.024 \n",
+      "      - ...\n",
+      "\n",
+      "   - We can see that the mean values are increasing as the intervals increase.\n",
+      "\n",
+      "3. **Check for Violations:** Continue examining the mean values. Notice that as the intervals progress, the mean values continue to increase. There are no instances where the mean value decreases as the interval increases.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph represents a **monotone increasing** function. \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here is the solution:\n",
+      "\n",
+      "1. **Understanding Monotonicity:**\n",
+      "    - **Monotone increasing:** A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "    - **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Analyzing the Graph Data:**\n",
+      "    - We are given a series of intervals on the x-axis (\"concave points_mean\") and the corresponding mean predicted value on the y-axis.\n",
+      "    - We need to check if the mean values consistently increase or decrease as we move from left to right across the intervals.\n",
+      "\n",
+      "3. **Examining the Mean Values:**\n",
+      "    - Initially, the mean values increase as the \"concave points_mean\" increases. For example, the mean value for the interval (0.0, 0.0074145) is -1.054, and it increases for subsequent intervals.\n",
+      "    - However, after the interval (0.026115, 0.042455), where the mean is -0.235, the mean value for the next interval (0.042455, 0.048235) jumps to -0.115. This indicates a change from increasing to decreasing.\n",
+      "\n",
+      "4. **Conclusion:**\n",
+      "    - Since the mean values do not consistently increase or decrease across all intervals, the graph is **not monotone**.\n",
+      "\n",
+      "**Therefore, the correct answer is (c).** \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Analyze the \"Means\" data:**  We need to see if the mean values consistently increase or decrease as the RoomService value increases. Here's a breakdown:\n",
+      "\n",
+      "   - The mean starts at 0.328 for the interval \"(0.0, 105.5)\".\n",
+      "   - It then decreases to 0.028 for \"(105.5, 296.5)\". \n",
+      "   - It continues to decrease as the RoomService value increases.\n",
+      "   - However, there's a small increase from -1.446 to -1.136 between the intervals \"(734.0, 800.0)\" and \"(800.0, 816.0)\". \n",
+      "\n",
+      "3. **Consider Confidence Intervals:** While there's a slight increase in the mean value in one section, notice that the confidence intervals in that area still largely overlap with the intervals surrounding it. This overlap suggests the slight increase might not be statistically significant.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph is **not strictly monotone** due to the small increase in mean value. However, it demonstrates a predominantly decreasing trend with increasing RoomService values. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The correct answer is **c) not monotone**. Here's why:\n",
+      "\n",
+      "* **Monotone Increasing:** A monotone increasing graph means the y-value never decreases as the x-value increases.  We can see this is not true, for example, the mean y-value for the interval (22448.0, 23794.0) is lower than the mean y-value for the interval (20043.5, 22448.0).\n",
+      "\n",
+      "* **Monotone Decreasing:** A monotone decreasing graph means the y-value never increases as the x-value increases. This is also not true. For example, the mean y-value for the interval (40007.0, 41128.5) is higher than the mean y-value for the interval (37439.5, 40007.0).\n",
+      "\n",
+      "* **Not Monotone:** Since the graph is neither consistently increasing nor consistently decreasing, it is not monotone. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the Mean Values:**\n",
+      "   - Look at the \"Means\" values in the JSON data.  \n",
+      "   - As the intervals of \"TopographyDrainage\" increase (0.0-0.5, 0.5-1.5, 1.5-2.5, ...), observe the corresponding mean values.\n",
+      "   - We see that the mean values are consistently increasing: -0.0274, -0.02381, -0.01602,... 0.03564\n",
+      "\n",
+      "3. **Consider Confidence Intervals:**\n",
+      "   - While the means show an increasing trend, it's important to check if the confidence intervals overlap significantly. Significant overlap might suggest the trend isn't strictly increasing.\n",
+      "   - In this case, while there's some overlap, the overall trend within the confidence intervals remains largely increasing.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "Based on the analysis of mean values and considering the confidence intervals, the graph representing the \"TopographyDrainage\" feature is **a) monotone increasing**. \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "    - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "    - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Examine the Means:**\n",
+      "    - Look at the mean values provided in the JSON object.  \n",
+      "    - Notice that as the intervals for \"DeterioratingInfrastructure\" increase (0.0-0.5, 0.5-1.5, 1.5-2.5, etc.), the corresponding mean values also increase:\n",
+      "        - -0.02508, -0.01897, -0.01452,... 0.02782, 0.03175, 0.03686, 0.04451 \n",
+      "\n",
+      "3. **Consider Confidence Intervals:**\n",
+      "    - While the means show an increasing trend, it's important to briefly check if the confidence intervals significantly contradict this. In this case, the confidence intervals are relatively narrow and don't suggest a deviation from the increasing pattern.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "The graph representing \"DeterioratingInfrastructure\" is **a) monotone increasing**. As the value of \"DeterioratingInfrastructure\" increases, the model's prediction also tends to increase. \n",
+      "\n",
+      "SOLUTION:  Increasing\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the \"Means\" data:**  We'll focus on the \"Means\" values as they represent the general trend of the GAM function for the \"Age\" feature. \n",
+      "\n",
+      "   -  Look for instances where the mean value decreases and then subsequently increases as age increases. This would indicate the function is not monotone.\n",
+      "\n",
+      "3. **Identify Key Intervals:**\n",
+      "    - From the \"Means\" data:\n",
+      "        - The mean decreases from age 17 to around 29.5.\n",
+      "        - The mean generally increases from age 29.5 to 61.5.\n",
+      "        - There's a fluctuation between 61.5 and 79.5, with means increasing and decreasing.\n",
+      "\n",
+      "**Conclusion:**\n",
+      "\n",
+      "Since the mean values do not consistently increase or decrease as age increases, the graph is **not monotone**. \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:**  A function is monotone increasing if, as the input increases, the output never decreases. It can stay flat or go up.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases. It can stay flat or go down.\n",
+      "\n",
+      "2. **Analyze the \"Means\" data:**\n",
+      "   -  Look for instances where the mean value decreases as the age interval increases. \n",
+      "   - We see that the mean value increases from age 21.0 to around 54.5.  \n",
+      "   - After 54.5, the mean value starts to decrease.\n",
+      "\n",
+      "3. **Conclusion:**\n",
+      "   - Since the mean value both increases and decreases over the range of ages, the graph is **not monotone**. \n",
+      "\n",
+      "**Therefore, the correct answer is (c).** \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. \n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.\n",
+      "\n",
+      "2. **Analyze the \"Means\" data:**  We need to see if the mean values consistently increase or decrease as the CreditScore intervals increase.\n",
+      "\n",
+      "   -  Let's look at the initial values:\n",
+      "      - (350.0, 416.5): 0.62\n",
+      "      - (416.5, 421.5): 0.5698\n",
+      "      - (421.5, 427.5): 0.3799 \n",
+      "      - ... and so on\n",
+      "\n",
+      "   - We see that the mean values initially *decrease*. \n",
+      "\n",
+      "   - However, as we move further down:\n",
+      "      - ...\n",
+      "      - (661.5, 684.5): 0.0186\n",
+      "      - (684.5, 741.5): 0.0696\n",
+      "      - ...\n",
+      "      - (806.5, 850.0): -0.1138 \n",
+      "\n",
+      "    - We see fluctuations - the mean values increase and then decrease again.\n",
+      "\n",
+      "3. **Conclusion:**  Since the mean values do not consistently increase or decrease as the CreditScore increases, the graph is **not monotone**. \n",
+      "\n",
+      "**Therefore, the correct answer is (c).** \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  Here's how to determine the monotonicity of the graph:\n",
+      "\n",
+      "1. **Understand Monotonicity:**\n",
+      "   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.\n",
+      "   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.\n",
+      "\n",
+      "2. **Analyze the \"Means\" data:**  We'll focus on the mean values as they represent the general trend of the function. Look at the mean values in each interval:\n",
+      "\n",
+      "   - (0.0, 135.5): 0.445\n",
+      "   - (135.5, 215.5): 0.073 \n",
+      "   - (215.5, 500.5): -0.294\n",
+      "   - ... (and so on)\n",
+      "\n",
+      "3. **Identify the Trend:**  As the input (VRDeck value) increases, the mean output (y-axis value) generally decreases. There might be small fluctuations, but the overall trend is downwards.\n",
+      "\n",
+      "**Therefore, the correct answer is (b) monotone decreasing.** \n",
+      "\n",
+      "SOLUTION:  Not monotone\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "import google.generativeai as genai\n",
+    "import os\n",
+    "\n",
+    "genai.configure(api_key=os.environ['GEMINI_API_KEY'])\n",
+    "\n",
+    "import time\n",
+    "\n",
+    "model = genai.GenerativeModel(model_name=\"gemini-1.5-pro-latest\")\n",
+    "\n",
+    "for question in questions:\n",
+    "    messages = [{'role':'user', 'parts': [question[0]]}]\n",
+    "    response = model.generate_content(\n",
+    "                messages,\n",
+    "                generation_config=genai.types.GenerationConfig(\n",
+    "                    candidate_count=1,\n",
+    "                    max_output_tokens=500,\n",
+    "                    temperature=0.2),\n",
+    "            )\n",
+    "    try:\n",
+    "        response = response.text\n",
+    "    except:\n",
+    "        print(f\"Gemini: Invalid response with parts {response.parts}.\")\n",
+    "        response = \"\"\n",
+    "    print('LLM RESPONSE: ', response)\n",
+    "    print('SOLUTION: ', question[1])\n",
+    "    print('-'*80)\n",
+    "    # sleep 20 sec to avoid rate limit\n",
+    "    time.sleep(20)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tmcd",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/benchmarks/notebooks/preprocess-graphs.ipynb b/benchmarks/notebooks/preprocess-graphs.ipynb
new file mode 100644
index 0000000..bf87856
--- /dev/null
+++ b/benchmarks/notebooks/preprocess-graphs.ipynb
@@ -0,0 +1,2593 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Extract the graphs that are being used in the benchmarks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add parent directory to path\n",
+    "import sys\n",
+    "sys.path.append('..')\n",
+    "\n",
+    "import benchmark_utils\n",
+    "\n",
+    "import t2ebm\n",
+    "from t2ebm import graphs\n",
+    "from t2ebm import prompts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### plot the different graphs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "California-Housing longitude\n",
+      "California-Housing latitude\n",
+      "California-Housing housing_median_age\n",
+      "California-Housing total_rooms\n",
+      "California-Housing total_bedrooms\n",
+      "California-Housing population\n",
+      "California-Housing households\n",
+      "California-Housing median_income\n",
+      "California-Housing ocean_proximity\n",
+      "OpenML-Diabetes Pregnancies\n",
+      "OpenML-Diabetes Glucose\n",
+      "OpenML-Diabetes BloodPressure\n",
+      "OpenML-Diabetes SkinThickness\n",
+      "OpenML-Diabetes Insulin\n",
+      "OpenML-Diabetes BMI\n",
+      "OpenML-Diabetes DiabetesPedigreeFunction\n",
+      "OpenML-Diabetes Age\n",
+      "Iris sepal_length\n",
+      "Iris sepal_width\n",
+      "Iris petal_length\n",
+      "Iris petal_width\n",
+      "Titanic Pclass\n",
+      "Titanic Sex\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/sebastian/Documents/GitHub/TalkToEBM/t2ebm/graphs.py:147: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.\n",
+      "  fig = plt.figure()\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Titanic Age\n",
+      "Titanic SibSp\n",
+      "Titanic Parch\n",
+      "Titanic Fare\n",
+      "Titanic Embarked\n",
+      "Spaceship-Titanic HomePlanet\n",
+      "Spaceship-Titanic CryoSleep\n",
+      "Spaceship-Titanic Cabin\n",
+      "Spaceship-Titanic Destination\n",
+      "Spaceship-Titanic Age\n",
+      "Spaceship-Titanic VIP\n",
+      "Spaceship-Titanic RoomService\n",
+      "Spaceship-Titanic FoodCourt\n",
+      "Spaceship-Titanic ShoppingMall\n",
+      "Spaceship-Titanic Spa\n",
+      "Spaceship-Titanic VRDeck\n",
+      "Adult-Income Age\n",
+      "Adult-Income WorkClass\n",
+      "Adult-Income Education\n",
+      "Adult-Income EducationNum\n",
+      "Adult-Income MaritalStatus\n",
+      "Adult-Income Occupation\n",
+      "Adult-Income Relationship\n",
+      "Adult-Income Race\n",
+      "Adult-Income Gender\n",
+      "Adult-Income CapitalGain\n",
+      "Adult-Income CapitalLoss\n",
+      "Adult-Income HoursPerWeek\n",
+      "Adult-Income NativeCountry\n",
+      "Kaggle-Flood id\n",
+      "Kaggle-Flood MonsoonIntensity\n",
+      "Kaggle-Flood TopographyDrainage\n",
+      "Kaggle-Flood RiverManagement\n",
+      "Kaggle-Flood Deforestation\n",
+      "Kaggle-Flood Urbanization\n",
+      "Kaggle-Flood ClimateChange\n",
+      "Kaggle-Flood DamsQuality\n",
+      "Kaggle-Flood Siltation\n",
+      "Kaggle-Flood AgriculturalPractices\n",
+      "Kaggle-Flood Encroachments\n",
+      "Kaggle-Flood IneffectiveDisasterPreparedness\n",
+      "Kaggle-Flood DrainageSystems\n",
+      "Kaggle-Flood CoastalVulnerability\n",
+      "Kaggle-Flood Landslides\n",
+      "Kaggle-Flood Watersheds\n",
+      "Kaggle-Flood DeterioratingInfrastructure\n",
+      "Kaggle-Flood PopulationScore\n",
+      "Kaggle-Flood WetlandLoss\n",
+      "Kaggle-Flood InadequatePlanning\n",
+      "Kaggle-Flood PoliticalFactors\n",
+      "Kaggle-Heart-Failure age\n",
+      "Kaggle-Heart-Failure anaemia\n",
+      "Kaggle-Heart-Failure creatinine_phosphokinase\n",
+      "Kaggle-Heart-Failure diabetes\n",
+      "Kaggle-Heart-Failure ejection_fraction\n",
+      "Kaggle-Heart-Failure high_blood_pressure\n",
+      "Kaggle-Heart-Failure platelets\n",
+      "Kaggle-Heart-Failure serum_creatinine\n",
+      "Kaggle-Heart-Failure serum_sodium\n",
+      "Kaggle-Heart-Failure sex\n",
+      "Kaggle-Heart-Failure smoking\n",
+      "Kaggle-Heart-Failure time\n",
+      "Kaggle-Bank-Churn id\n",
+      "Kaggle-Bank-Churn CustomerId\n",
+      "Kaggle-Bank-Churn CreditScore\n",
+      "Kaggle-Bank-Churn Geography\n",
+      "Kaggle-Bank-Churn Gender\n",
+      "Kaggle-Bank-Churn Age\n",
+      "Kaggle-Bank-Churn Tenure\n",
+      "Kaggle-Bank-Churn Balance\n",
+      "Kaggle-Bank-Churn NumOfProducts\n",
+      "Kaggle-Bank-Churn HasCrCard\n",
+      "Kaggle-Bank-Churn IsActiveMember\n",
+      "Kaggle-Bank-Churn EstimatedSalary\n",
+      "Wisconsin-Cancer id\n",
+      "Wisconsin-Cancer radius_mean\n",
+      "Wisconsin-Cancer texture_mean\n",
+      "Wisconsin-Cancer perimeter_mean\n",
+      "Wisconsin-Cancer area_mean\n",
+      "Wisconsin-Cancer smoothness_mean\n",
+      "Wisconsin-Cancer compactness_mean\n",
+      "Wisconsin-Cancer concavity_mean\n",
+      "Wisconsin-Cancer concave points_mean\n",
+      "Wisconsin-Cancer symmetry_mean\n",
+      "Wisconsin-Cancer fractal_dimension_mean\n",
+      "Wisconsin-Cancer radius_se\n",
+      "Wisconsin-Cancer texture_se\n",
+      "Wisconsin-Cancer perimeter_se\n",
+      "Wisconsin-Cancer area_se\n",
+      "Wisconsin-Cancer smoothness_se\n",
+      "Wisconsin-Cancer compactness_se\n",
+      "Wisconsin-Cancer concavity_se\n",
+      "Wisconsin-Cancer concave points_se\n",
+      "Wisconsin-Cancer symmetry_se\n",
+      "Wisconsin-Cancer fractal_dimension_se\n",
+      "Wisconsin-Cancer radius_worst\n",
+      "Wisconsin-Cancer texture_worst\n",
+      "Wisconsin-Cancer perimeter_worst\n",
+      "Wisconsin-Cancer area_worst\n",
+      "Wisconsin-Cancer smoothness_worst\n",
+      "Wisconsin-Cancer compactness_worst\n",
+      "Wisconsin-Cancer concavity_worst\n",
+      "Wisconsin-Cancer concave points_worst\n",
+      "Wisconsin-Cancer symmetry_worst\n",
+      "Wisconsin-Cancer fractal_dimension_worst\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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iIiKSGRMsIiIiIpkxwSIiIiKSGRMsIiIiIpkxwSIiIiKSGRMsIiIiIpkxwSIiIiKSGRMsIiIiIpkxwSIiIiKSGRMsIiIiIpkxwSIiIiKSGRMsIiIiIpkxwSIiIiKSGRMs6tA8HlHpEIiIKAQxwaIO7WBhhdIhEBFRCGKCRR2amz1YREQUAEywiIiIiGTGBIuIiIhIZkywiIiIiGTGBIs6rG935uGvy3bj+935SodCREQhRqN0AERKue/9rQCADYeKcPj5y6FSCQpHREREoYI9WEQAOJeQiIjkxASLiIiISGZMsIiIiIhkxgSLiIiISGZMsIiIiIhkxgSLiIiISGZMsIiIiIhkxgSLiIiISGZMsIiIiIhkxgSLiIiISGZMsIiIiIhkxgSLiIiISGZMsIiIiIhkFtAEa926dbjyyivRqVMnCIKApUuX+uwXRRFPP/00kpOTYTQaMWbMGBw4cMCnTXFxMW655RaYzWZERUVh6tSpqKio8Gnz+++/Y9SoUTAYDEhJScGLL75YJ5bPPvsMffr0gcFgwIABA7BixYpmx0JERETUFAFNsCorKzFo0CAsWLDA7/4XX3wR//znP7Fo0SJs2rQJJpMJ2dnZsNlsUptbbrkFu3btwqpVq7Bs2TKsW7cO99xzj7TfYrHg0ksvRWpqKrZu3YqXXnoJs2fPxr///W+pzYYNGzBp0iRMnToV27dvx8SJEzFx4kTs3LmzWbEQERERNYnYRgCIX375pfTc4/GISUlJ4ksvvSRtKy0tFfV6vfjRRx+JoiiKu3fvFgGImzdvltp88803oiAI4smTJ0VRFMWFCxeK0dHRot1ul9o88cQTYu/evaXnN9xwgzh+/HifeDIzM8V77723ybE0RVlZmQhALCsra/JrSDmpTyyTvlxuj9LhEBGRQgLx+a3YGKwjR44gLy8PY8aMkbZFRkYiMzMTGzduBABs3LgRUVFRGDZsmNRmzJgxUKlU2LRpk9TmggsugE6nk9pkZ2dj3759KCkpkdrUPE91m+rzNCUWIiIioqbSKHXivLw8AEBiYqLP9sTERGlfXl4eEhISfPZrNBrExMT4tElPT69zjOp90dHRyMvLa/Q8jcXij91uh91ul55bLJYG3jEFk8Jy3volIqLA4SzCVpg7dy4iIyOlr5SUFKVDoiZa/nuu0iEQEVEIUyzBSkpKAgDk5+f7bM/Pz5f2JSUloaCgwGe/y+VCcXGxTxt/x6h5jvra1NzfWCz+zJo1C2VlZdLX8ePHG3nXFCwcbo/SIRARUQhTLMFKT09HUlISVq9eLW2zWCzYtGkTsrKyAABZWVkoLS3F1q1bpTZr1qyBx+NBZmam1GbdunVwOp1Sm1WrVqF3796Ijo6W2tQ8T3Wb6vM0JRZ/9Ho9zGazzxcRERFRQBOsiooK7NixAzt27ADgHUy+Y8cO5OTkQBAEPPzww/jb3/6Gr776Cn/88Qduv/12dOrUCRMnTgQA9O3bF+PGjcPdd9+NX3/9FevXr8f06dNx0003oVOnTgCAm2++GTqdDlOnTsWuXbvwySef4LXXXsPMmTOlOB566CF8++23mDdvHvbu3YvZs2djy5YtmD59OgA0KRYKHWVWJ55fsVfpMIiIKJTJNh/Rj7Vr14oA6nxNnjxZFEVveYS//OUvYmJioqjX68VLLrlE3Ldvn88xioqKxEmTJonh4eGi2WwW77jjDrG8vNynzW+//Saef/75ol6vFzt37iy+8MILdWL59NNPxV69eok6nU7s16+fuHz5cp/9TYmlMSzT0D6s3JnrU6KBZRqIiDq2QHx+C6IoigrmdyHFYrEgMjISZWVlvF0YxL7dmYf73t/qs+3Q85dDrRIUioiIiJQUiM9vziKkDisl2qh0CEREFKKYYFGHpRLYY0VERIHBBIs6LOZXREQUKEywqMNxebw1sDjmioiIAoUJFnU4nNZBRESBxgSLOhSHy4Nym0vpMIiIKMQpttgzUVsrrnTgVKkVFTZn442JiIhagT1Y1GGUVDl4e5CIiNoEEyzqMFxub3b13W7vot5CjWmEq3bnKRITERGFJiZY1KGcrrBjy7ESAEDNSYT3vb9NoYiIiCgUMcGiDqWowiE9XnjLUDx6aS8AQFy4TqmQiIgoBDHBog7leHEVACDJbECPhHCM6ZuocERERBSKOIuQOoQf9xdi8tu/Ss9tTreC0RARUahjDxZ1CDWTKwCYlNlVoUiIiKgjYA8WdQgxJh2KKx2Yen46Jg7ujHADv/WJiChw2INFHYLnTAGswV2ifLYLXPGZiIgCgAkWdThajYCYMN9ZgyxASkREcmKCRR2OWhAQGab12cb8ioiI5MQEi4iIiEhmTLCow+G4KyIiCjQmWNShdE8woUdCuNJhEBFRiGOCRR1CaZUTAKBSsfeKiIgCjwkWhby/f7tXeiyACRYREQUeEywKeav35EuPu8ebFIyEiIg6CiZYFPI0Ku+3+euTBkOj9v8tX1zpgMXmbMuwiIgohDHBopAnnqlyZdSp6+yLNp2th/XjvsI2i4mIiEIbEyzq0BIiDIg0epMsl8ejcDRERBQqmGBRhzegs1npEIiIKMQwwaKQV1LFsVVNVcZrRUQkCyZYFNJOlVqRV2YDwBINTeFw8zYpEZEcmGBRSDtaVCk9HpQSqWAkRETUkTDBog4hLTYMUUad0mEQEVEHwQSLOgSNWlVvDSwiIiK58ROHiIiISGZMsKjDEwQOficiInkxwaIOgSkUERG1JSZY1CGwk4qIiNoSEywiIiIimTHBIiIiIpIZEywiIiIimTHBIiJq57iGJFHwYYJFRNTOcQ1JouDDBIuIiIhIZkywiIiIiGTGBIuIKIR4PCJsTrfSYRB1eEywiIjasc1Hi/HAR9vw5fYTAIA9eRZU2l0KR0VEGqUDICKilvvbst347UQZfjlcjM5RYXj2611IjTXhmSszkGA2KB0eUYfFBIuIqB3Ls9ikxze8sREAsPOUBUNTo3Hn+elKhUXU4fEWIRFRO+app0KDq74dRNQmmGAREbVjhRV2pUMgIj+YYBERtVMFNW4PVlOrBAUiIaLamGAREbVTP+wvrLMtLTZMgUiIqDYmWERE7dTOk2V1tqXHmRSIhIhqY4JFRNROqQTf24F3nZ8Os1GrUDREVBPLNBARtXP3j+6OCYM6oWdiBB77/DelwyEisAeLiKjdUwsCYsP1HOBOFESYYBERhYBwPW9IEAUTJlhERCHAqFMrHQIR1cAEi4iIiEhmTLCISGJzupUOgYgoJDDBIiIAwBs/HsKoF9fimf/tVDoUIqJ2jwkWEQEAFv5wCADwzsZjCkdCRNT+McEiIgCAw+VROgQiopCheII1e/ZsCILg89WnTx9pv81mw7Rp0xAbG4vw8HBce+21yM/P9zlGTk4Oxo8fj7CwMCQkJOCxxx6Dy+XyafPDDz9gyJAh0Ov16NGjB5YsWVInlgULFiAtLQ0GgwGZmZn49ddfA/KeiYgCrbDcrnQIRB2a4gkWAPTr1w+5ubnS188//yztmzFjBr7++mt89tln+PHHH3Hq1Clcc8010n63243x48fD4XBgw4YNeOedd7BkyRI8/fTTUpsjR45g/PjxuOiii7Bjxw48/PDDuOuuu7By5UqpzSeffIKZM2fimWeewbZt2zBo0CBkZ2ejoKCgbS4CEZEMXG4RAPDmT0cUjoSoYwuKBEuj0SApKUn6iouLAwCUlZXhP//5D1555RVcfPHFGDp0KBYvXowNGzbgl19+AQB899132L17N95//30MHjwYl112Gf76179iwYIFcDgcAIBFixYhPT0d8+bNQ9++fTF9+nRcd911+Mc//iHF8Morr+Duu+/GHXfcgYyMDCxatAhhYWF4++232/6CEBE1wfI/cutsG9kjFgAQH6Fv63CIqIagSLAOHDiATp06oVu3brjllluQk5MDANi6dSucTifGjBkjte3Tpw+6du2KjRs3AgA2btyIAQMGIDExUWqTnZ0Ni8WCXbt2SW1qHqO6TfUxHA4Htm7d6tNGpVJhzJgxUhsKfU6XqHQIRE3m9ojSbUC95uyv8oFdohSKiIhqUnxthczMTCxZsgS9e/dGbm4u5syZg1GjRmHnzp3Iy8uDTqdDVFSUz2sSExORl5cHAMjLy/NJrqr3V+9rqI3FYoHVakVJSQncbrffNnv37q03drvdDrv97DgHi8XSvDdPQcUDJljUfnjEs9+v1wztomAkROSP4gnWZZddJj0eOHAgMjMzkZqaik8//RRGo1HByBo3d+5czJkzR+kwqAFf7TildAhEAReuU/xXORHVEhS3CGuKiopCr169cPDgQSQlJcHhcKC0tNSnTX5+PpKSkgAASUlJdWYVVj9vrI3ZbIbRaERcXBzUarXfNtXH8GfWrFkoKyuTvo4fP96i90yBk1tmAwAUVzoUjoSIiDqSoEuwKioqcOjQISQnJ2Po0KHQarVYvXq1tH/fvn3IyclBVlYWACArKwt//PGHz2y/VatWwWw2IyMjQ2pT8xjVbaqPodPpMHToUJ82Ho8Hq1evltr4o9frYTabfb4ouAiC99/HsnsrGwgREXUoiidYjz76KH788UccPXoUGzZswNVXXw21Wo1JkyYhMjISU6dOxcyZM7F27Vps3boVd9xxB7KysnDeeecBAC699FJkZGTgtttuw2+//YaVK1fiqaeewrRp06DXe2fR3HfffTh8+DAef/xx7N27FwsXLsSnn36KGTNmSHHMnDkTb775Jt555x3s2bMH999/PyorK3HHHXcocl1IXkJ1ptUAt4djsIiISB6K37g/ceIEJk2ahKKiIsTHx+P888/HL7/8gvj4eADAP/7xD6hUKlx77bWw2+3Izs7GwoULpder1WosW7YM999/P7KysmAymTB58mQ8++yzUpv09HQsX74cM2bMwGuvvYYuXbrgrbfeQnZ2ttTmxhtvRGFhIZ5++mnk5eVh8ODB+Pbbb+sMfKfQUz1YeO6KvbglM1XhaIiIKBQonmB9/PHHDe43GAxYsGABFixYUG+b1NRUrFixosHjjB49Gtu3b2+wzfTp0zF9+vQG21Do6RzlnUxRYXchr8yGpEiDwhERNe5YUZXSIRBRAxS/RUgUSNVVrRvyf5f3lR4fLqwIZDhEsrny9bMrXhh1agUjISJ/mGBRyLI53fj54OlG20WF6ZAeZ2qDiIjkU51UXTEwGToNf5UTBRv+VFLIOllqlR4P6RqtYCRE8quetvHAxT0UjYOI/GOCRSEvwqBBj4RwpcMgIqIOhAkWhbzGCzQQERHJiwkWEQU1u8utdAhERM3GBIuIgprF6lI6BCKiZmOCRQRAq+aNRGqf6lulQBS5MgGRkphgUchryjI5AkdqUYjhyk9EymKCRUQUgoorHbA5OX6NSClMsIiIQkiE4ewKaNcv2qhgJEQdGxMsIqIQ0iU6THosgvcJiZTCBIuIKMS8eftQpUMg6vCYYBHV4ObMKwoBnLRBpDwmWEQ4m1gtXHtI4UgCx+Z0w82pZR2ChmVHiBSnabwJUeiLDtMCAAza0P2bo7DcjnKbC2E6NVweEfHhekSeed9ERCSv0P00IWqGG4alKB1CQFQ5XMgpqsLOk2Ww2Jxwe0SU21ywOtwN3g6tsLN6OhFRazDBIlJIcaUDxZWOgJ7D6RJRZnVCFAGPp+G2KTFG6fG2YyUBjYuIKNQxwaKQVW51Kh1Cg1xuD06WWJFTVBUUY6N8p/dTsONSOETBjQkWhRyHy4OcoirkWWxKh9IkZVYn9uRaUFYVuIRQFEUcL65CaVVge8yo7TC9IgpuHOROIaW0yoE8iw1Ol4h1+08rHU69iisdKKvRwyaKQJXThUjIO+jcYnPiYEEF/r3uEPbklQMANCoBA7tE4spBnXBzZlfoNWpZz0lEREywKIScrrAjt9Tba7U3z4IPf80BAJ9EJpjYnI0MimqlP06U4eqF6+GqdfvR5RGxLacU23JKEWHQ4rqhXQIaB8nL7RFxvLgSJQHs8SSi1mOCRe2e2yMiz2JDcYX39tcL3+zB+kNF0v7nrx6gVGiK2pNn8UmuIgwa3HRuCo4XW/H7iVKcKrPxlmE75PaI+H5PgfQ8KdKgYDREVB8mWNSuudweHDldKfUG3fDGRlidbmn/1ed0ajc9NFaHu/FGLTAsNRrPXNnPZ9vL3+3DqbL2MUaN6qq0e79XBnWJhNnAWmZEwYgJFrVbDpcHeWU22JweiKKIqxas99n/7h3DkRCph07TPuZyVH9oEjXm16PFAACzkckVUbBqH588RDWIogiLzYkjpytRZnWiwu6qk1x9fl8Wok06RBl1CkXZuGCeZr8jp7TB/SdKqpBXZkMlC5K2OZvLjaOnKwEAFTZef6JgxR4saldEUURhhR35ZXaIoogn/vu7NDuu2v+mjYRaJSDBrEecSa9QpA3bcOg0Hvvsd1idbtw9qhuuGtQJAODxiHB5RHhEEQatvLP7hDPL0zUlr/vpQCEeGtOz3v02pwdWhxMqFWDS89dIW3p3w1GcLLUCAKaOSlc4GiKqD38zUrshiiIKy+3It9jhEUVMqNVr1SXaiIU3D4FKJSAlJgyRAbp9cqyoEpFGLSKNWgiC/0V1K+wuqAQgTFf3R2znyTJM/3C79PzNnw7jzZ8O40+ju0OjFqDTqJBolm/gsl6rQrRJi6gwHVSCd6yXRtXwYsBGXdOSu9PlDiREcJB1W3r5u/3S43PTYhSMhIgawgSL2g2H24N8ix2nSq249/2tPvvevWM4ok06CALQOcoYsOQKAKocblisLpwQrIgwaBBl1CHCoIFKJUAURZwosaLC7oJaJSAqTItYkx7qGgnNL4eL/B534Q+HYHW4cc0QeQblv/3zEQCAXqP2qdIeptMg3OD90Xe4PPB4RNSTJ1KQOXWm5woAZl+ZIWsiTkTyYoJF7YbN6YHD5amTXP1v2kioBEFKrqJNgRt35fGIOF1hh0GjhkGrhsXqgsXqgkYtIC5cjwq7CxarEwIAlyAgv8wOvUbtk/A53WfrX31093l4c91hrNnnnXZ/osYHaGtVL7/TUO5UVOnA7lwLAMDm5CD7YFflODvmqvq2MhEFJyZY1G4cK6rEtYs2SM8v6BmPx7J7A/COL0qONAQ0uQKA297ehPUHz/ZAje4Vj5vO7YrO0Ubkldnwt+W7semId4bXiO6xuPcC720/k04Njdp3TsmonnEI12swY2wvJEUa8OGvOVi1Ox/TL+ohS6zVvVKNjdOpHpPlcJ1N/DyNrI3o9ogoqXL49MwFgih6F6uOjwjOsXRKiTBooJd5jB4RyYsJFrULFXYXHvvsd+l5pFGLx7J7I9ygQbheA7NR0yZLvvx2vMzn+Q/7C/HD/kK/bTccKsKGQ0W46dwUPDuhPwBv4uJ0101e+iRFSI+LKhzoniBj0A34z89H8J+fj6BrTBhyiqua9Jq5K/bgjXWHpeeLbh2Ccf2TAxLfAx9tx7Lfc/HU+L64a1S3gJyjPan+/hMAaNSNJ7dBsIY4UYfFMg0U9KwON/blWbAv3ztbMMakw/tTM2HUqZASbUR8hL7N19O7dkhnmA31/33SPd4kPf5483G8vuYAXG4PNh8rxiurvIOUa3749esUKT2+853NGPnCGmkqfiCkxoT5PG9qcgUAi9cf9Xn++4ky/w1lsOz3XADA+78cC9g52pP1h7zra1psrqZ9zzPBIlIMe7Ao6B0vqcLJkrNVx+dePQCx4Tp0ijLKfq4qhxtWh7vRWXSXZiRhygjvrbc9uRY889UuxJh0GNI1CneN6gaVIGDH8VL85X87AQCvrzmIO0akYfuxUukY56RESY91GhUy02Ok24tlVid+3F+IrjFhUAXgNtzDY3ohLc6EA/kVOFpUiW925kGrFvz2rtWWHmeSkl3AOzj/8XF9ZI+xJvbEeGlV3r+Jb83sqnAkRNQYJljULlQP7jUbNOgUZURceGDG5Lg9Ig4WVCAlxoiosPrHc8WF66DXqmB3etA32YxP782CIABatQqpsWEoLLdjcEoU/nnTYDz48Q4AwEvf7cNHvx4HACSZDcjul+RzzKfGZ8Dp9uCaf3nHmTU2Dqo1VCoBw9Nj0DPBe2vyT6O9477+OFmG//vyjyYdY2T3WKw/VAQjxwK1uZgAjzUkotZjgkXtwjc78wAALo+I1LgwaJsw/iSQYsP1SIszwe5yo9LuhtXphscjIuXMrbdOUUZozwxqNxs0sNhcUnIFoN4yElq1Cj0SwnGwoCLg78GgVcOgdZ8ZDC80eRah58yo+HO6Rvssqk1tpynjrwBgd64FVQ6X33psRBRY/KmjoGd1uvHjmYHk3eJNAV3cdsuxEuw8WYaUmKbdftRr1H7HwqhVApIiDTBoVXVubw3pGoW/jM9AVJgWZVZnvZXV5yzbjbEZiehSa7yUXOLC9T49gU63B/vyLI2+zsX7dYpTqxoePpsWe3YM4O8nynBet9hAh0REtXCQOwU1URQxYf7Ziu0zxvQKyHl615jFN+vLP3Duc9/D7mp9XaioMB0yks1nnxu1+NvEAUiMNCAlJgwpMWEw6nx/DMNq3HI7/8W1rY6hqbRqVaMf3NQ+pMWZEBXm/UMkiJe8JApp/G1KQe2hM+OXAKBbnAmjewemfsHALlF4+ooM6bnTLeK7XfmyHPvT+7Lwf5f3xR0j0vD36wYixqRDwpm6TpFGLXokRKBXUji6xoQhwazH368bgM41BvCXVDqafU6b0+1T0DQQaieGFHhiM6YFxnKcFpGi+BuSgtrGGsvK/OvWoQEtbHntkC74/L4s6bnF5pTt2LdnpeKm4V1xUe8EJEUa6hQd1WvUiAzTItFsQJfoMPzrliHSPnczuyA8HhEH8ivQmvyqobFVrjMHjjLyA7yt2ZyBTZqJSD5MsCio6TXeb9G/TezfRudTIysA41UMWjV6JIRDp2n8Ry7CoIWpRo2tQwUVKG9GsldS5cDXv51qVm2rajXLU5woqfv6Q4UVOF7iXc4n0FXcyVdRhb1FvZLbckoCEA0RNYYJFrULRq1aSrYCdg6dOqC3vZqTkNRs+cBH27HrlAXHi6tQWuW9XZhbZsXJUivEGr1bVQ4XDhWU4/y/r8W/fzpbad3QjDIKF/aKlx5X2F3weETkW2yosLvgcHnw6Ge/Sfv71hhbFgqOnq4M+G3V5nJ7RJRUOnCsqBKbjhRLs2mboujMreWNnOlJpAgmWNRuRDRQOV0OOo0K3ePDoQ1wItcUyZFnx2AVlNtx079/wedbT+B4sRWHCytQXOFAUbkd+/MrcKrUitIqB04UW/HtznxYz5RbMGrVePCSHhjcJarJ5zVo1T5jdxxuDwosdhwprMS+vHIp8RubkRBytZhcHjHoBoSfrrDjRIkVFqtLmkkLABmdGk9u7zrfWwg30H+YEJF/LNNA7YJGLdRbO0pOgiDAqFX+A8moU+O/94/Atf86u7j1a6sP4LXVBxBr0km9EwDw9uRzUWl34YGPt/sc4+N7zkPPxPAWV4IXRRFr9hRg0bpDKKpwYGSPOGzLKQUAXNInsUXHpJbZeLgIn2z21lE7Ny0aFzVhskdChCHQYRFRA5hgUbuQEh1WZ2B4qBuaGo1lD5yP30/4VlcvqjWr8M53Ntd57eSsVJj0agho+TgpUQQWbzgirTX46ZazhVL1QZCEdiTv1ViLcWSPOAUjIaKmYoJF7UIg1uNrD3olRiAp0oABnc34z89HsXTHSQCASvC/Pl+P+HD848ZBGNCM24INqT5HktmAqDAtkswGxIbrMKpnHKyO4BqvFMqOn5mwcNt5XXHPqG4KR0NETcEEi6gBNqcbeo0KgqBMgqfTqBAXrodBq8Z9o7th6vnpsDndMOq8MbndIv44WYbNR0ug0wi4+pzOiJVpnUa3R8TWY94ZaHeMTMOI7t6eE0EATDotrA67LOch/3KKqnD725twtOjsbM4LeyUgTN+8X9vNLfNBRPJggkXUgCqH21vhXOEOtHC9Br0TI1BmdcLqdCM6TAe9RgWL1YVwgwaDUqJg0muQHmdq/GBNtCe3XHp8Ttco9EwMh9Pt8Y5T03GB50D7eHOOT3IFAH2TI+ppXT+ro/UrEhBR8zHBImqAzeluk8H1TSEIAqLCdIiqsS0yTIvIMC1EUZS9l23LsWLp8blpMRAEoVklHzoCq8MNrVoIyPhAh+tMQdcwLZ6fOAAje8a16HvRwx4sIkVwpCpRAyrsLqVDaBI5k6vqYqifbjkBAOgcZVDsFmmwc7g9Ab8Fd0mfRKTEhEHXwiRu89ESTP9wm8xREVFjmGBRcFPwj2+70wOHy9PhegCevKyP0iEowuX2+BRuDSXLfs9FZTv5Y4EoVDDBoqDWnMVt5VZUaccrq/bjte8PKBaDEq4c2KlZ7ZX8P5LL8yv2YNxrP+Hyf/4Euys0xixpNex1JFISEywKav5KEbSVBWsPYc3eAsxfexAltWpPdSyh/0H9w74CAMChwkocL7YqHI0vjUqA2aiBppkzLS7uk4irBjUvWSYi+TDBoqBUYXdhT64FbiUzrBqCbY26tsThV8qKNumQGmuCtpljsCKNWvz58r4BioqIGsMEi4JSXpkVLndwJFdA3erpREREDWGZBgoKDpcHeWU2aUZWdZXwYBlgvnT7SfRNbnyB3VBUXS6Azqq0u2CxOVFpdyElJkzpcOrFJY2IlMOfPgoKeWU2lFmdqLC5UGHzznbKt9hwuiI4eo7sHTjJ6BJtbHC/zenxKWfhcHmQU6tAZqgpszqRW2rDB7/k4LMz5SyCEeuWESmHCRYpqtLuwqHCCpRZnT7bPaKIL7aflJ53j5evQnlLLNlwVNHzK+nJy/yP4zHVWLJl5c486bEIEU5P+0pIW3I7euuxEizecBRPLd0Z8gklETUfbxGSok6VWmFz+n4YF1XYMWXJZul5z4RwRBjarpp6z4QIrNyV32bnCza1B7UPT4/x2y7G5F2ux+7ywOpsH6UNXG4Pym0uRJt00jZRFJt1K/p0uR1XL1yPfMvZtRgrHawxRUS+mGCRIkRRRHGlA84zPQcHCyqwL88CAFi07rBP28fH9W7T2B7N7g2jVo3tx0tw07ldcde7W6R9oVqIsiZBELDiwVH4fOtxXNY/qcG2F/aKx3e7208y6vAzG9Th9jSrHMjcb/b6JFdERP4wwSJF2F0enCq1wSOKeOunw/j699w6bUZ2j8WTl/VFz8TwNo/vqsGdMH5gMsINvj8ioZ9eeWV0MuMasQsSIvRKhyIri9UFvaZ1IyOCZeIFEQU3JlikmN9PlOLPS3f6bBueFgONWkCfpAhcfU4XhSLz3v6qvQ7h7ydKUeVoH7fC5CAIgFEn/yDpQCxM3RxOtweF5XbE10geqydWNGbnyTJ8eWZs4K2ZXbHsj1yUVjkbeRURdURMsEgxr6za7/N88ZRzERceHD0mJr0GJr0GpyvO3gq6av566XFMuM7fy0KKShBkHftWYXfhVKkVDpcHapUAjyhCrRIQF673+X+vcrhQWG5HjEm+a2x3uVFS6YTF5oReo4JHhJRgzV9zEMVVTZutunpPgfS4R2I4hD9kC5GIQgxnEdayYMECpKWlwWAwIDMzE7/++qvSIYUct9uDzOdXS8U7bzw3BV9NG+k3udJrVc2uYC2ncH3dv0EGdo6EuQ0H3Sslvpm3B60ON46crsTx4ioUVdjrjFdze0TYnR6IonfWnscDOF3esXhHTlfiYEEFDhZU4FhRFSxWF46elm9mns3p7bWyOz2wWF2ocrhw9Eysn2w+3uTjVK+7eEGvONx2XhpULHNPRPVgD1YNn3zyCWbOnIlFixYhMzMTr776KrKzs7Fv3z4kJCQoHV7I2H681Kcsw4RBnfzeMtJrVYgO00GtUu5DzKBV48s/jcDVCzecea7CW1OGKRZPW2pOb6LN6YbF5kR+mQ33vLcFFpsLt2el4tkJ/aU2FpsTeWU2FJbboFGroFEJ0KhVMOnVSIgwAPDWl/p4cw5KqpywyzwzscLmwqS3fkF8hB7PXJGB1FgTSqscKChv/oD1+HCDot+XRBT8mGDV8Morr+Duu+/GHXfcAQBYtGgRli9fjrfffhtPPvmkwtGFBqfbg69+OyU9/+L+EVIPVc0cKy5cj/gIfVB8iA1OicJ3My5Apc2FcIMG8UFyGzOY/G35Hvxt+R6fbTuOl0qP9+RacNX8n6VZo7UlROgxskecNL6pttZ+HxSW2/Hs8t3S4+kfbcew1GiM6ZvYouMFulO13OasUxuOiNoXJlhnOBwObN26FbNmzZK2qVQqjBkzBhs3bvT7GrvdDrv97F+/Fosl4HG2d6t25+PdjccAAJ2jjNBpVDBo1YgP18Ns1Cg6+Lk+giCgZ0I4rE43nG5lB2gHmxMl1nr31Zxttz+/3HvtcHYmZly4XhrjVlBur5NcDU6JkpK0PkkRqHK44HR5i5jGhOmgUglwe8RGk6/fT5Ti6gUbpGWYqm05VoItx0oAABF6DQSVd5ZhU+g0df8oaA2b040yqxNFFXb8Z/0R/Hfr2Wshdpi5q0ShhQnWGadPn4bb7UZiou9ftImJidi7d6/f18ydOxdz5sxpi/DaDVEUUVTpgNXhht3lgdPtHXNT/WH74EfbpbZ/Gt0dKTFhiDQG/3gmQRAQpuOPS20PXtID//flThRXOjCieyw2Hy32jrHyiH6row/sEom/TRwgPS+qsOPzbSfgcHmgVasgAhAATBreFZFGLb7+7RT+/dNhWJ1uHCqolF4XadRCBQHFlY5Gx4odKqyAWxSh06igEry3fWvP/MvqHotfjhS14Aq0PMNyuT2wuzz442Qpnlu+F3+cLPPbbnQvDk8gao/4idEKs2bNwsyZM6XnFosFKSkpCkbUdhwuj5Q0WWxO2M9UY3e6Pai0+46dsTndsDrcmL/2IFxnKjqO6B6LC3rFIywAZQCo7Yzrn4whqdEw6TSw2Jw4Xe7AlqPFmLNsN/bmleOPE2UY0CWy3tfHhutx7wXdfbZFGDTS90WE0fsryl2rEmhRhQNatYCSKgc8oohEs6Hec1R3XPVLNvuMCQO8A/P1WhXCdGpct8h/T3WgrD90GpPf3ux3n0GrwqzL+uKyAUnS+DQial+YYJ0RFxcHtVqN/HzfqtT5+flISvJfzVqv10OvD+3xON6EyQVRBNyit1fC5nSj0uFCfcvNWaxOvPnTYRwsrECVw43iyrpT4J+9qh9SYsICHD21heoEQKdRQa9R41jR2Z+JRT8ewoJbhkjP1SrBm0Dp1cCZnq7qnk4BgEolwGzUSiUaIs7M4vzpwGmE670JutsjIj3WhInndAYAWNUND4b3V729WnWdr4QGErRA+Pq3U3igRm8uAJgNGkwa3hU3npuCTlFGLtRM1M4xwTpDp9Nh6NChWL16NSZOnAgA8Hg8WL16NaZPn65scG3I7RFRbnOi3OZCmdWJxopWVzlc2HqsBE63iJOlVny6pf4p7yoBiDHp8eFdmeiRGCFz5KQ0rVqFGJMOYzISkJkeg01HimF3+SY3Rp0aaXFNX7hbW6Pq+jc1FpQGgBE9Yhvt3XF7xDrL2oTp1fB4RMSF62F1uiEC0DVh1LooivA0Z02dBnyw6Zj0OMqoxRfTRiAuXA+TThMUEzuIqPWYYNUwc+ZMTJ48GcOGDcPw4cPx6quvorKyUppVGMrsLjdOlljhcHvgdPn/ECmtcmBPrgUf/pqDfIsdapVQp9p5TWE6NSYO7ozkSAOGpcVINaXYcxXaBEHAuWneBKu1xvVLwpajJThVakVplRPD02PwyebjcLg92H3KAnVnb49YfQosNvzjTEFbnUaF5CgD1IIgLfYc3YQYTpRUQRSBKocbuWW2Vr8nAPjlsPfaXDu0M/46oT/H9xGFIP5U13DjjTeisLAQTz/9NPLy8jB48GB8++23dQa+hxqn24PTFY46Y6dcbg9W7s7HoYIKbDh8us7+mgxaFTKSzcgts+HyAck+ta0EAdCovY87RxkDsvwKBRddK9f7qxYbrsdfrsjAyRqzFat7f+adSZz+edNgv71idpcbWS+skZ5fOagTYk26Zi3sbHW4UVrl7ck9VlSJz7aeAIBmHcMfjUqAyyPitvPSmFwRhSj+ZNcyffr0DnVL8FSpFcWVDp9bgadKrVj4w0H8dsL/rCYAyEyPwW3npUKtEhCu1yAqzHdZk+qxNmqVAKNWLfUYUOhTCYJsCZY/Ewd3xhfbT0olH44W+a/4frz4bFJ27ZDOuPW8VACAugl34DweEacr7Kiwu7D+4GnM/cZ3JvElfeSZ2ZccGVwD2KscLpwssSLBbGgXs3uJghkTrA7O4fJIyZXD5cFDn2z3W9vo1vNSYdSqcXHvBIQ3cEtGp1Eh0qhFuEHjd5kZCn1atQpRMn44Rxq1iDJq4Twzq2LOhH64a1Q3PPPVTmw+WoJXVu3Hg5f0rPf1GpWAhy7p1axz2l0e5FvseO+XY3XGFd51fjouaWGB0mAmiiI+2XwcOUVVuKhPAi7oFa90SETtGj8BO5jiSgdKaixsazuzHMnzK/Zg42HfOkCZ6TG4Z1Q3vzOsEsx6aWBwmF4NvYa3/Sgwqgd961Xe7zG9Rg1BsPoMcP/lcBHO6xbr9/VhOjVSYowtOnfN5GpUzzjcmpmK7P7+ZxW3R44zkxB0GhVmf7UL75wpArx4w1G8fP0gXDe0i5LhEbVrTLA6GIfLg6paY6m+353vk1yZDRosuWN4nUWWzUYNok06hOs0UHGmEzVJYKqQq1UC7hyZjuV/5AIA/rpsN5Y/OMpvW5VKaFb1favDhQq7C0dOV0jb/nHDYPRICEdUWPu9bVZU4YCpVq+y3eXG5iMliAvXSclVtQ0HTzPBImoFJlgd3C+Hi/DamgPS8xeuGYA+SWafqeJpcWFQCQK0alVAx9ZQ6GmszEdLdY8Ph83pxrh+Sfh2Vx7MBvkSn+PFVvxzzUGs2n22Jt45XSMRYdDKMiC9pMohFdwNtJo/xxe8tBZHXxgvPS8ot2H4c6vrvKZvshl7ci2yLQNE1FHx07ID+3zrCTy34uwCvf93eV/06xQJtUqAQatCfIQeA7p4P1hMeg2TK2q21XsL4Gqg0GdLqVUCTHoNMrvFyH7sY8VVPsnV+AHJSIsLR2y4vlUzYB0uD8qqnLj3va3StkAXE9WqVdKA/NpjIv+oZxLLuH6hN76MSAnsweoALDYnRI93TbZXVu1Duc1bu6rmLMG/jO+L4emxEATvoOL4CD0rSVOL9Uk2S48bWhA6GFXWqO02Y0wv3JzZ8uWvnG4PSqocyC214bfjpXj6q13SPq1aaJOZek9fmYHVewvqXTQ6PlyHPslmlFmduOncFFjO/H7477aTePn6QVzcnKiFmGCFKI/Hu+hylcOFcpt3qZv3fjmGnw/WXdD2bxP7Y1CXKADev6hZCJRaa3BKlM/z0qq6yyUFu85RRlzcJwExppYth+VwebDzZBlmffEH9uWX19m/op4xYwFTz13J5Cgj5k8agsIKG3okRGDd/kJpX5nVWacECxE1DROsEOX0eJBXq+p09YK5mekxGNXTOwU7xqRD/07e3oausWEwsteKZBKu10iV/gNwlzBg/r3usPRYp1GhJf03bo+IaR9u87nVWO3ctGg8fUUGusWHtyJK+QiCgMgwLSLPDOAf2SNO4YiIQgMTrA6oa0wYLqxV4yYuQsfCgiSrtrixpJLx9lX1OoPVi0NbrE6Y9OoWzZg9XFhRJ7n61y1DMLhrFGJNekXGM1Y63MgrsyGpGcVN3W00GJ8oFDHBCnFVDhcOFVYCAIoq7H7baDUC4sNbdhuEqDE/7i/Es8t2AwBsTnm7siKN8v0KG54eg9V7CwAA6XEmzLt+IOJa+HNRMy158/Zh6JUYji7RYYos5BxTYxWFH/cX4MZzuzb5teU2F2L5u4GoRZhghSiHy4Pvdufh9TUH6+yrOWhVoxaQFmuCRs0ZghQY1WsHAr4f9nJQqeT7vn1r8jBsOlIEg0aDcIMaPRIimvV6j0dEud0FiEBhuff2fHSYFiN7xCq63mCEQYsBnSPxx8myZq+h+NvxUr/rPBJR45hghaBKuwsDZn/ns02nUSExQo8IgwbXnNMZXaK9la1ZfoECrfpDfWCXSPz92oHKBtMAQRAQFaaD09X822Iej4g9eRacWc0HJZVOAN5bmMGwmHOiWY8/Tjatbc0+toc+2YGU2DAM6RodkLiIQpnyP/kku9rT4u8YkYZrhngrMiea9X6XviEKtHH9kmTvwQoWxVUORBl1KLM6sWZvPt5ef1TpkFpMpRLwyNhemLdqPwBg58kyJlhELcAEK4RFGbV4b2qm0mEQhbzXvj+ALcdK6mzvnhA8MwVrq700Tk0PXNITm44U4+eDpwMZFlFIY4JFRG2i9jp4cqqviGZLRIVpm93T5i+5mjIiDX8e31eusGRnP7PQu9Plf+KBEgPyiUIJE6wOQKUChDMjK1iVmdpa9VR/gzZwY/3kWvOwa0xYs8ZMWR0un+cLbx4iFerVaoQ6C6YrrczqlB5X/yr400Xd/batvbQOETUPf4JCnE6jQrd4U9D9oqfQ5zwz4vvI6crAn8tPJVOxBVlXcwekVzrc0uPnrx6AlBhvKYZok7bFJR4Cofr6zF9zEPdd6D+hIiJ58VM3hKlVAnonRTC5IkVEGJQtXLvoR29F9kAWy3z00l6INGpxSZ8EXNwnAV1jw9A7KQLJkcag+rk7J8U7SD3WpMOJkiqpwj4RBQ57sEIY7waSkm7NTMU/vt+v2PldZ3rQ9AEsQzL94p4Ym5GERLM+qNfsy+oei398DxwrrsLDH+9Al2gjKu3uxl9IRC3GBIuIQs6xokr8b8cpAMAdI9MDeq6EiOBOrgCg5nj1LcdK/A7KJyJ5BU8fNhGRTKqTKwBIbsbaey0R3Q5qe/XvHFnvvuRIYxtGQtRxsAeLiEKO68y4qx4J4bj6nM4KR6M8g1aNXbOz8c7Go+gcbcSGg0XwiCL6JpsxpGuU0uERhSQmWETU7lmdvuOJFv1wCABwTtcoliY5Q6dV4YqBnZAYqceFveJRXOlAl+gwXh+iAOEtQiJqE8YArsm3J7ccW48VS8+rcwazwjMZg4lWrULX2DDoNWpEhenQLT6c65ASBRB/uoioTYztmyj7MYelnl0j7z8/H5Eeq85kWJOz0mQ/JxFRUzDBIqKAMOnVPs+NOnU9LVuuU5QR1w31LmTur+4U734RkVI4BouIAuL6YSkornTgcGEl7hoVuFIJabFhPs9dbk+dMVlERG2NCVYIKii3KR0CESKNWtwxMh15ZTakxLRdKYDv9+RLj4OpmnqoqF6CiIPjiRrG3z4h6L9bTwDwXdiVSEnVi423haJKh/Q4KcA1sDqCSrvbZ13HKocbuWX8I46oMezBCkGaM3+1j+gep3AkRIBGLSAyrO1n843NkH9QfUdUVuXArlMWqAQBOo2Av3+7D9tzSvDOncOREGFAVJiWPYVEfjDBCmFZ3WOVDoE6OEHgbbr2ynZmHNuidYfx1s9HcPmAZOw+ZcHBwgoAwPPL9+DJy/qizOpAj4QIJUMlCkr8zUdEARMXrkePhPA2Odf/dpxCUYVdeq7iEKFW2ZtXLj12eUR89dspKbkCgJIqJ46crvC5fUhEZzHBCkn8hUcdR0rM2VmEJVUcdyiX3klne6V6J0YgIULvs393rgUPfrwDY//xE0pqjHsjIi/eIgxB/IOSOpLL+ifjIewAAPamyGj2lf3QJdqIQV0i0TvJDK1aBavDhbX7CvHa6gM+bS955Uds+8tYhSIlCk5MsIioXau53MtbPx9B93iTgtGEjq6xYXjg4p6IMekgAFCpBFgdbmw/XlqnrdnAjxKi2vhTQUTtnkYlwOUR8cnm4+idyAHXclLXGMxm1KkRrq/7seH2sOeQqDaOwSKidm/2Vf2kx1UOl4KRhJZYk65J7ZheEdXFBCvEiKKI/247qXQYRG3qsv5JuKBnvNJhhByVn6mYYbqzPViv3Ti4DaMhal94izDE1KzenpFsVjASImUcL7EqHUJIu7hPAqaN7gFBAJKjvJXyK+3sNSSqjQlWCBvZg5XcqWOIDtOh9tJ4MSa9/8bUKkadGvdc0A0nSqtQ5fAWIy2pcuJgQUWb1Twjag94i5CI2j2VSkDPWh/uj2X3ViiajqFrTJhPL/kDH21XMBqi4MMeLCIKCY9c2hs9EsOhFgRkpscgpokDtKll9Bo1dBoVLu6TgDV7C1iqgagW9mARUUgw6tQ4v0cc+iSZIdS+X0gBc8XAZKVDIApKTLCIKKRo1AJMfmo1ERG1Jf4WIqKQYjZqeXswwMINGi6mTdQIJlhEFDLiwvXQqtkxH2g1q7vzehP5xwSLiEKGQatWOoQOpynD3dweEaIoQsNkjDoQfrcTEVFA2V1u7M+vQAULklIHwgSLiIgCzu0RcaSwEgcLypUOhahNMMEiIqKAyimqkpbTsbs8TX5dhd0Fu8sdqLCIAopjsELM6j0FSodARCTJt9gwYcF62F0eDOwcieevGYAqh8tn0ej6eEQRpyuc6BxlbINIieTFHqwQs/NkmfRYzXnURKSww4WVUq/V7yfLcMXrP+OLbSdRYLHB4xEbfb3T5YEoNt6OKNgwwQoh5TanNIj0jpFpygZDRASgoNxWZ9vzK/Yg32KHw9347cJymwtNyMOIgg4TLCIiCpiPfs0BAJgNGgzsEgkAqHK4sWZvfoOvq7C7MHD2d7jhjY34+rdTAY+TSG5MsEJIUYVD6RCIiHyYzoy1GpIajWeu6Cdt/8f3B/DmusNw19M9teHgaQCA1enGh5tyAh8okcyYYIWQKocLn209oXQYREQA4JM8jewRB71Whecm9pe2zVu1H6VV/v8wdNV47a9Hi+FoxuxDomDABCtE2F1uXP7Pn6Xn53SNVjAaIiKguNIBq9NbZiHS4F0jclhaDF64ZoDUZv3B07A53Y0WIS2pJxEjClYs0xAiNh4qkh6f1y0WVw3qpGA0RESAxeaEs8ZA9k5RRrg9IlweDxIi9Cgot+NwYSUOFlTApNcgXM+PJAod7MEKEQcLKqTHr944WLlAiIjO8HdbT60S0D0+XFok+tXVB1Bpd6HK4cKJkqq2DrFeNqcb+/PLm1RKgsgfJlgh4oMzg0DT40wKR0JE1DCDVo37R3eXnk/7cDv+u/UEjp0+m2ApPebK4fbA7vSA6RW1FBOsECCKIkw6NQBgKMdeEVE7MGl4V+nx6Qo73l5/FLO+/EPaVl3eQWlVDi5QTS3DBKud83hE5JbZ4D7zZ1af5AhEhWmVDYqIOjyb04V1+wtxrMj/bT+1SsDbk4f5bMspPtvW5vRdg3DbsRL5g2yCKgfXQqSWYYLVDnk8IpxuD6wON/LLbT71r2LDdTBo1QpGR0QELF5/FHO/2YuCcnu9bVJiwvD19PNx47AUadtTS/+AzenGbyfKfNre/8G2gMVKFAiKJlhpaWkQBMHn64UXXvBp8/vvv2PUqFEwGAxISUnBiy++WOc4n332Gfr06QODwYABAwZgxYoVPvtFUcTTTz+N5ORkGI1GjBkzBgcOHPBpU1xcjFtuuQVmsxlRUVGYOnUqKioqECyKKx04kF+OA/nl2JtXjr255ThYUIHT5d7te3ItAACjlrNwiKjt1V72pqTK6fPc39qoqbEmqFTAdUO7SNuOF1ux65RFel7zViJRe6J4D9azzz6L3Nxc6euBBx6Q9lksFlx66aVITU3F1q1b8dJLL2H27Nn497//LbXZsGEDJk2ahKlTp2L79u2YOHEiJk6ciJ07d0ptXnzxRfzzn//EokWLsGnTJphMJmRnZ8NmO7tG1i233IJdu3Zh1apVWLZsGdatW4d77rmnbS5CE7jcHtic3q+axfsq7S7M/Ow36XmfpAglwiOiDq72eszLf8+VHo/NSMRFfRLqvEanUSHWpEeEUYNHL+1V82gAAK1aqLW97TVWn4uoPoonWBEREUhKSpK+TKazs+A++OADOBwOvP322+jXrx9uuukmPPjgg3jllVekNq+99hrGjRuHxx57DH379sVf//pXDBkyBPPnzwfg7b169dVX8dRTT2HChAkYOHAg3n33XZw6dQpLly4FAOzZswfffvst3nrrLWRmZuL888/H66+/jo8//hinTgXnGliiKGLL0WLc9OYv0rbHx/VGGmcRElEQyLN4/4AdPyAZL103EDEmnd92SZEGpMWaEGGoO3Y0yWwIaIxNYeUYLGohxROsF154AbGxsTjnnHPw0ksvweU6+9fCxo0bccEFF0CnO/uDmZ2djX379qGkpERqM2bMGJ9jZmdnY+PGjQCAI0eOIC8vz6dNZGQkMjMzpTYbN25EVFQUhg07O+ByzJgxUKlU2LRpU72x2+12WCwWn69AsDrcKK1ywmJ1Ym+eBa+s2oerFqzHnGW7pTZZ3WJx23mpATk/EVFz6TXej5dx/ZIabWvQqqFR172FSNSeKTpg58EHH8SQIUMQExODDRs2YNasWcjNzZV6qPLy8pCenu7zmsTERGlfdHQ08vLypG012+Tl5Untar6uvjYJCb7d1xqNBjExMVIbf+bOnYs5c+Y092032+trDmDhD4fq3X/3qHRcNahzwOMgIqrPjuOlKLc5pZ6o6tl3em3T/o7X+hmjJQi+22xON7Rqld/xXE3lcntQaXfDbNTAI/ofG0YkB9l7sJ588sk6A9drf+3duxcAMHPmTIwePRoDBw7Efffdh3nz5uH111+H3V7/rJNgMmvWLJSVlUlfx48fb7NzZ3WLxazL+uCraSOZXBGRYuLD9dLjP3/pHfu64eBpaVvtJKk+KlXdj6PaL8232GB3te6WncsjIqe4CrllNuzNs3jXS3S4UW5zYn9+OU6VWlFpd+FwQQWKKtrHZxEFJ9l7sB555BFMmTKlwTbdunXzuz0zMxMulwtHjx5F7969kZSUhPz8fJ821c+TkpKkf/21qbm/eltycrJPm8GDB0ttCgoKfI7hcrlQXFwsvd4fvV4PvV5f7365PHppb9x2XiryLd4fdkEAVDV+8wgCkBChR5iOMwiJqG0NT4+ps+1QYaX0OMkc+N+RLVFd3uZkiRUqFWDSaWB3emB3OrDxUBEeOTN56JyUKHzxpxFNThSDgcvtQW6ZDREG7/qOKkGQktX29D7aO9l7sOLj49GnT58Gv2qOqappx44dUKlU0u26rKwsrFu3Dk7n2em+q1atQu/evREdHS21Wb16tc9xVq1ahaysLABAeno6kpKSfNpYLBZs2rRJapOVlYXS0lJs3bpVarNmzRp4PB5kZmbKcFVaR6USoK7xVTO5MurUSI8zIcFsYFc3EbU5QRDwWHZvn21fbj8BALigZxzS48OhUSs+3LdBHg9Qbjs7/jffcnaG+fbjpfhhX6ESYbWYWxRRWuXE8WIr9uSWY9cpC3aetGDXKQuKKuxcX7GNKPZdv3HjRrz66qv47bffcPjwYXzwwQeYMWMGbr31Vil5uvnmm6HT6TB16lTs2rULn3zyCV577TXMnDlTOs5DDz2Eb7/9FvPmzcPevXsxe/ZsbNmyBdOnTwfg/eF/+OGH8be//Q1fffUV/vjjD9x+++3o1KkTJk6cCADo27cvxo0bh7vvvhu//vor1q9fj+nTp+Omm25Cp06d2vzaNEalAjRqAd0TTOiREA4TV6AnoiBSXVzU5nQjXO/tRZFDuc0Fm7PpaxSKtWpHuD0i1u4twE8HCnG6Gbf/Pt92osltg5koAqdKbdiTF5gJWeRLsU9mvV6Pjz/+GLNnz4bdbkd6ejpmzJjhkzxFRkbiu+++w7Rp0zB06FDExcXh6aef9qlPNWLECHz44Yd46qmn8H//93/o2bMnli5div79+0ttHn/8cVRWVuKee+5BaWkpzj//fHz77bcwGM5OAf7ggw8wffp0XHLJJVCpVLj22mvxz3/+s20uRhNo1SqE6b0V2uNMekRyORwiClLaMz1Wt8g8s/n1NQfRNcaIWZf1haoJPfZ5FhuMWjUMWjXsLg8O5JfXqQifaNbjjVuH4cjpSpRZnTBoVXDV6uFZ/nsunp/olH7vutyeoO+VI+UplmANGTIEv/zyS6PtBg4ciJ9++qnBNtdffz2uv/76evcLgoBnn30Wzz77bL1tYmJi8OGHHzYaj1KiTTpE11NHhohISfVVWIg0tuwPwdpV4aut3OWd1T1+YCcMTolq9DiiCJwstUqPj5yurNMm32LHxIXrGzyOSvAex6BTQa9R41hxFbrHhzd6/mDl7cmyNqmtIADJkcYARxSaeG+JiIhaRas5u/6p0+2REhltC3t5TpbYGtxfeyHohnhq5GrVtxcFABf1TsCafQX+X3TGgM6R+OOkd01Et0fE8WIrusaEwVlPAtheiCJ81rBtiErFBKulmGAREZEsRFHE5iPF0vPoFg5lqDl2yl+F95ZwuDz4y/+8ZSQSzQbMGNsLM8b2QpXDhbd/PoL8cjtuGJaCLtFGbM8pgSAI6BZnwvSPtqP6jqHV4ca+vHJoNZxQRI3jTWQiIpKNrUadqoxOka0+nk6jwvIHzkdEjYHyn25puOagzelGvsWGt346jPc3HYPbI6LUerbH5pK+ZwtLh+k0mH5xT/x1Qn8M6ByJ6DAdLu6TiIt6J8BcI7nbW2NguMst4lhRZbN60trS7lMW/HSgkOsoKow9WEREJDs5F57vm2zGh3efh8mLf0VxpQNlVc4G2x8vrsK8Vfvx7U7vmK1PNp9NyLRqAbdnpcHhavw2n7nGGLJ53+3Hv28bCkEQIIqAxepChb0CabGmoJrJXWCx4eqFG6TnEQYNbjo3BWqVChqVgN6JEVyzto0Ez3cFERGFDE0L6/L9uL8QP+73X3fqlsyueH3NwToV3v1xu/3XeooP16N3UgSqHC54ROBIYd2B79XUKgEje8Rh/cHTyLPYcNWC9fjs3ixU2l2IDdfD4wEOn3m9UadCpyij4gWfT9caW1Vuc+HNn474bLtzZBomDu7MoqMBxgSLiIhkkVvW8OD05jpWVAXAO5PNoFVBp2nGqJZauUNGshlZ3WMxrp93XdrqRKhHQjgcLg8qHC6YdN7B+harC1VOF5wuEXeOTMP6Gkv/XP/GRgDABT3jfQqsWh0eHC6shFolwGzUonOUsgPDDVoVLuqdAJvTDbdHhN3lwaYz4+PeXn8U23JK8dcJ/Rs5CrUGEywiImqV6s6qLcdKmlUItDH3XOBdVk0QBKTHmRDbglI1t5+XiuuHpZw5jneAe01GnRpGndqntmBUmA4WmxPHTlchIcKApX8aWaeUw7oDhXj00l4+vUCi6B2fVVzhgEGjQmy4cssEGbVq/Gl0D59t+/LK8ejn3iWAfj9RqkBUHQsHuRMRUauMzUiUHlus3vFRrb399OX9I/B/l/eVnmvUqlYvB6YSBMRHNC3pMek00GoEJJj10KgFfDVtJM7r5rvu4lUL1sNVT8mG3DIbyqwNjxVra72TIrBkyrlKh9FhMMEiIqJW6RIdhtq5jxLDezweER9uOoaHPt6BVbvzW3UstUpAaowJiWYDosK0EAQBf748A1/ePwIm/dm6X1f/awOK/Cy7I4rAiZKqRgfky6328kC1cdxV22GCRUREsrE6lCtd4HB78H9f7sTu3LMlFUx6DSKN2hYlfEadus42jVqFj+46z+d25ZQlm/HNztw6bT0eIKe4qlnrHrZWpT04S0d0REywiIhINjXrYAWKp4FOmpr1suIj9BjXPwldY8OQkWxG7xaWjqhdkV4QBCy5Yzj6JpulbQt/OIQr5/+MLUeL6ySZTSkJIReHmwlWsOAgdyIiko1cg9y7xLRsFl64QYNyuwt/v3YgLumbgLgzA82bsjh0fRIi9FAJAsptTp8eotlXZuDr33Px/i/HpG1zlu0GAFzcOwEzxvZq8TkDzSMCD368vUltrxrUKajfS7BigkVERK1W3an0y+EiWY5n0Na9PacU4czg+PgIPQrKbSiw2CGK3lIPNw5LwfVDu+C55Xvw69GzywSt2VeAk6VWvHz9INnjcXtEuGossqhTq+qMrVKrBEQatXUG2pv0aoTp1KhyuP0ufu3PgrUHmWC1ABMsIiJqteqx1SdLra0+1sjusS1ag3DnyTLZa3HVlhBhgFGrRkmlU0peVIKAv1yRAcA7sP3+D7YBAPbll+OTzTn400U96j1eSxRXOpBX432ajRoYdWrEmc7OkBQEAZ2iDIgN18EtivB4RO8iz5UOLLx5CHKKqxo9T4XdhRdX7oOnkYHz5B8TLCIiarV7LuiGf687jBMl3gSrNevgPXNlRrNfU1Buw3WLNkrPO0UaGmjdOhEGLcJ0Gjg9HlTVGlTeJToMH96ViZvf2gQAeH9TDnokhGPKyPSAxWOxuqSv/247CcCb8GrUKmhqjR/ziCKsDn2TanSVVDoabUP14yB3IiJqtdojnHJLm9eT1DUmDIC3aGmCueHkaJOf25A1e3T+NLo7osKaX5S0OdQqAd3iTIg21e1pizBo8fzVA6Tns7/eja3Hiuu0k5vV4cZPB7zLDFXY/ZeHiA3XI6OT2e8+khcTLCIikt1L1w9sVvvh6TFY+qeReHvyuYg0+r89qNd4x2VVOtz1lj6IC9fjvtHd0SXaCH1zltZpAUEQYKxnrNiAzpF4+oqzPXEfbjrut52cbE63tBZhzSKtpAzeIiQiolarOcj6/gu74YqBnZp9jPQ4E0qt9Y+9uqRvgvS43OaSZgjWplWpYDa0zSD52HA9osN0OFBQUaccw7lpMbg0IxHf7c6HRh34Ap+lNQa0t+T6N+S4nzFb8RF66NRNS2LtLg/KrE6I8I4Fiw3XSQlzqGKCRURErTZ5RCoW/XgIAJDYivFPyZHGequNRxi0CNdrWjW+KxBUKgHd400oqXLC6nDDYnNKg/6r1z70NFS8Syar95ytXh8d1vxJAg0p9VOR3t+2piqpcqBHQnhIJ1lMsIiIqNWSI41Y8eD5OJBfgf6dlRvjIwCtXrOwJTRqlbTOYbnNiXyLDVbH2R6tcpsLOUVVSImpP4FsCqvDjaXbTyK37OxszUijDsNSo+F0e5O4GJMu6JfE8XjOzjwNVUywiIhIFnHherg9yq53JwiALsBjrxoTYdDCpNPgcI06Ux5RRJnViS6iUVq2x+MRUelwNaskxadbjuO5FXsabDO6V3yL4iZ5McEiIiLZJJj10CjQg1RNyXPXpFJ561DV15t2stQKt9ubdGk1Z9uYdN6PZaNOjXC970e0XqOSbst1ijSgd1IE1u4rrHPsYO+96iiYYBERkWw0KqFJNZb8MerUQZMgySFMp6mTJO06ZYFaJcBdY0yW03X2canLm0D5G98UYdCgzOqdJTiwSxSmXdQDM8f2hiiK+PDXHHy82TtT8YZhXWR/L9R8TLCIiCgoNOXWXnvrnDHqvIO4a1a4d7dwwHu5zeV3rUdBEHBLZipuyUz1OScpi3WwiIhIFuZ66lcFgtsjorjSAZvTW0m9oNx/XSylVSeEu05Z4HTLsxB2Y+SeQUgtwx4sIiKShUGrhraJdZFaSxRFnCyxQqsRkBpjQrm15SUDAmlcvyS8+O0+AMCbPx3GlBFpCNMF7qNXEIBwQ8PHV6uEJs30LAzSpLW9YA8WERHJpq1LJDhdIo4VVwbtlP+0WJP0+Judebj/g21wBbgnqym1pQRBaPSrzvpH1CxMsIiIqF2rOUg82KhUAv57f5Y02L240oFKh7uRV7WcEjXAWsruaptbpkrhLUIiImp3Pvo1BwUWO27LSsX6g6fxzzUHASCgyUtLpcaa8MFdmZiwYL1sxww3aNA11rtAts3phkcUYbG6pGKnchJFYNuxkjrb4yP0SDmzSHdDiisdeOGbPSiuckCAABEikiON+Ps1A+pddzIUMMEiIqJ2w35mFt3b648CAJb9keuzvyxIx2LVtPz3U7h2aJdWLROj06ik5KT631iTB1oZ1zxUnRmhLwJ45utd9bZrrLSGy8+syXyLHdmv/YS9fx0HQz0LZrd3vEVIRETtRudoY4P7514zoI0iab7qMhQfbT6O9QdPB+T4chYZjQvX47bzUtEt3lTnqyaXR2zwq9rglCi8cM0AGGskVHtyLbLFG2zYg0VERO3G1ed0xiur9vvdt3TaCAxOiW7jiJpGJQh4dGwvPP/NXgBApT34bmX6M/uqfth9qm4SJIoiSqqcEJs4u0CtEhAVpgMAfHpvFq5/YwNsTg+Cd/Rc6zHBIiKidilMp8aSKcOx/I9cpMQYA1r+oDWqb6FldY/D+T3i8PPB01ixMxdXDuqkcGQtJwgCYky6Fr8+yqhDntMmY0TBJzi/G4mIiPyoeQPM7RFh1Klx3dDgXhomKkyHcpsLpVVORJypUXWixIpym7NZCz1T+8IxWERE1G6MyUiUHtesjB5p1LZZkdOWSDQb0CXaiFvPS5W2+Vv2hkJH8H43EhER1VKzcKcgCDBovR9jXaKNSI8z1fcyxek0KkSbdIg0alE96e7OdzZj/tqDygZGAcNbhERE1G7UnCSnVQvokRCOkipnu1oEun/nSPx+ogwAsHJXHv44USr1zEUbdTinaxS0alWbru1I8mOCRURE7YZBq8a9F3TDj/sLce2QLq0ebK2Ev03oj+MlVkz7cBsA4FSZDe9uPOa3be/ECOlxuc3ZpMKe7UlZVfDXLWspJlhERNSuTL+4Byae0xl9kxtfsDgYCYKArjFhmD/pHHy+7QS0Ku9tzlV78iEAPqUL9uWX+7z2VJl35p0uiMebNYXN5S1T8f4vx3BRnwSFowkMJlhERNSueGfeWZUOo0X6JEUgt8yG0ionUmNNeGRsb2nfg5f0BOCtMbUnrxwVtrO9O2v2FcLu9CYlZqMWN2d2bdvAZdY9Phxbj5VAr23fiWJDmGARERG1EU0Tep4EQUBGrd654emx0uO4CB2SIxuuaB9sBAHQnFnGx+UWcW5aDLb6Wd8wlDDBIiIiooCKMGiQemYGqMPlgV6Tp3BEgccEi4iIiPxSqwT0TAxv9XFUNaZ56jQqxJr0rT5msGOCRURE7U51RXQKPEONxZnl0p7KarRU6I4uIyKikNUluv2WKzDq1B0iwejomGARERG1obhwPROsDoAJFhEREZHMmGARERERyYwJFhEREZHMmGARERERyYwJFhERURszG7QIZ6mJkMYEi4iIqI2lxIQhOdKgdBgUQEywiIiIiGTG/kkiIiIFqAQBeq0KdqdH6VAU4xGBwnI7AECE2Gj7CL0WRp38leUDgQkWERGRAnQaFXolRsDjOZtYFFU64BFFuD0iXG7fhMNic0JsPAdpVypsLuSV2ZrcXhUlMMEiIiKixqlUZ8u6x0eE/iLINYmhljHWwDFYRERERDJjgkVEREQkMyZYRERERDJjgkVEREQkMyZYRERERDJjgkVEREQkMyZYRERERDILWIL13HPPYcSIEQgLC0NUVJTfNjk5ORg/fjzCwsKQkJCAxx57DC6Xy6fNDz/8gCFDhkCv16NHjx5YsmRJneMsWLAAaWlpMBgMyMzMxK+//uqz32azYdq0aYiNjUV4eDiuvfZa5OfnNzsWIiIioqYIWILlcDhw/fXX4/777/e73+12Y/z48XA4HNiwYQPeeecdLFmyBE8//bTU5siRIxg/fjwuuugi7NixAw8//DDuuusurFy5UmrzySefYObMmXjmmWewbds2DBo0CNnZ2SgoKJDazJgxA19//TU+++wz/Pjjjzh16hSuueaaZsVCRERE1GRigC1evFiMjIyss33FihWiSqUS8/LypG3/+te/RLPZLNrtdlEURfHxxx8X+/Xr5/O6G2+8UczOzpaeDx8+XJw2bZr03O12i506dRLnzp0riqIolpaWilqtVvzss8+kNnv27BEBiBs3bmxyLE1RVlYmAhDLysqa/BoiIqKO5t0NR8TUJ5aJN/97o/j78dImfxWW2wISTyA+vxUbg7Vx40YMGDAAiYmJ0rbs7GxYLBbs2rVLajNmzBif12VnZ2Pjxo0AvL1kW7du9WmjUqkwZswYqc3WrVvhdDp92vTp0wddu3aV2jQlFn/sdjssFovPFxERETWNCMDt8a69KIbYsjmKrUWYl5fnk9AAkJ7n5eU12MZiscBqtaKkpARut9tvm71790rH0Ol0dcaBJSYmNnqemrH4M3fuXMyZM6cpb5eIiIhq2XCoCBMXrpeep8WGQRCEettr1QLuOr8bbjg3pS3Ca5Vm9WA9+eSTEAShwa/qxKYjmDVrFsrKyqSv48ePKx0SERFR0OueEO53+9GiKhw5XVnv1/78CpyutLdxtC3TrB6sRx55BFOmTGmwTbdu3Zp0rKSkpDqz/apn9iUlJUn/1p7tl5+fD7PZDKPRCLVaDbVa7bdNzWM4HA6Ulpb69GLVbtNYLP7o9Xro9R1r5XMiIqLWGtE9Dkv/NAJfbDuJbvHhSDDrcbq88cQpJlyHQV2iAh+gDJqVYMXHxyM+Pl6WE2dlZeG5555DQUEBEhISAACrVq2C2WxGRkaG1GbFihU+r1u1ahWysrIAADqdDkOHDsXq1asxceJEAIDH48Hq1asxffp0AMDQoUOh1WqxevVqXHvttQCAffv2IScnRzpOU2IhIiIi+aTGmnD9sLO3+lKiwxp9TXKUAXHh7aNjI2BjsHJyclBcXIycnBy43W7s2LEDANCjRw+Eh4fj0ksvRUZGBm677Ta8+OKLyMvLw1NPPYVp06ZJvUL33Xcf5s+fj8cffxx33nkn1qxZg08//RTLly+XzjNz5kxMnjwZw4YNw/Dhw/Hqq6+isrISd9xxBwAgMjISU6dOxcyZMxETEwOz2YwHHngAWVlZOO+88wCgSbEQERERNZls8xFrmTx5sgjvBAGfr7Vr10ptjh49Kl522WWi0WgU4+LixEceeUR0Op0+x1m7dq04ePBgUafTid26dRMXL15c51yvv/662LVrV1Gn04nDhw8Xf/nlF5/9VqtV/NOf/iRGR0eLYWFh4tVXXy3m5ub6tGlKLI1hmQYiIqKmKa6wN6tEQ3sr0yCIYojNi1SQxWJBZGQkysrKYDablQ6HiIgoaJVUOnCixNqs1wTqFmEgPr+5FiERERGRzJhgEREREcmMCRYRERGRzJhgEREREcmMCRYRERGRzJhgEREREcmMCRYRERGRzJhgEREREcksYEvlEBEREdUnTK9GolmP6mrnRRWORl+jEoTABiUjJlhERETU5vQaNRLMaul5otmgYDTy4y1CIiIiIpkxwSIiIiKSGRMsIiIiIpkxwSIiIiKSGRMsIiIiIpkxwSIiIiKSGRMsIiIiIpkxwSIiIiKSGRMsIiIiIpkxwSIiIiKSGRMsIiIiIpkxwSIiIiKSGRMsIiIiIpkxwSIiIiKSGRMsIiIiIplplA4glIiiCACwWCwKR0JERERNVf25Xf05LgcmWDIqLy8HAKSkpCgcCRERETVXeXk5IiMjZTmWIMqZrnVwHo8Hp06dQkREBARBUDocicViQUpKCo4fPw6z2ax0OCGL17lt8Dq3DV7ntsHrHHhNucaiKKK8vBydOnWCSiXP6Cn2YMlIpVKhS5cuSodRL7PZzB/gNsDr3DZ4ndsGr3Pb4HUOvMausVw9V9U4yJ2IiIhIZkywiIiIiGTGBKsD0Ov1eOaZZ6DX65UOJaTxOrcNXue2wevcNnidA0+pa8xB7kREREQyYw8WERERkcyYYBERERHJjAkWERERkcyYYBERERHJjAlWiHnuuecwYsQIhIWFISoqqs7+3377DZMmTUJKSgqMRiP69u2L1157rd7jrV+/HhqNBoMHDw5c0O2QHNf5iy++wNixYxEfHw+z2YysrCysXLmyjd5B8JPre/mHH37AkCFDoNfr0aNHDyxZsiTwwbcjjV1nAHjwwQcxdOhQ6PX6en8XrFy5Eueddx4iIiIQHx+Pa6+9FkePHg1Y3O2NXNdZFEW8/PLL6NWrF/R6PTp37oznnnsucIG3M3Jd52oHDx5EREREvcdqCBOsEONwOHD99dfj/vvv97t/69atSEhIwPvvv49du3bhz3/+M2bNmoX58+fXaVtaWorbb78dl1xySaDDbnfkuM7r1q3D2LFjsWLFCmzduhUXXXQRrrzySmzfvr2t3kZQk+MaHzlyBOPHj8dFF12EHTt24OGHH8Zdd93FRLaGxq5ztTvvvBM33nij331HjhzBhAkTcPHFF2PHjh1YuXIlTp8+jWuuuSYQIbdLclxnAHjooYfw1ltv4eWXX8bevXvx1VdfYfjw4XKH227JdZ0BwOl0YtKkSRg1alTLghEpJC1evFiMjIxsUts//elP4kUXXVRn+4033ig+9dRT4jPPPCMOGjRI3gBDhBzXuaaMjAxxzpw5MkQWOlpzjR9//HGxX79+Pm1uvPFGMTs7W84QQ0JTrnN9vws+++wzUaPRiG63W9r21VdfiYIgiA6HQ+ZI27fWXOfdu3eLGo1G3Lt3b2CCCyGtuc7VHn/8cfHWW29t1u+gmtiDRSgrK0NMTIzPtsWLF+Pw4cN45plnFIoq9Pi7zjV5PB6Ul5c32IYaVvsab9y4EWPGjPFpk52djY0bN7Z1aCFt6NChUKlUWLx4MdxuN8rKyvDee+9hzJgx0Gq1SocXMr7++mt069YNy5YtQ3p6OtLS0nDXXXehuLhY6dBCzpo1a/DZZ59hwYIFLT4GF3vu4DZs2IBPPvkEy5cvl7YdOHAATz75JH766SdoNPwWkYO/61zbyy+/jIqKCtxwww1tGFno8HeN8/LykJiY6NMuMTERFosFVqsVRqOxrcMMSenp6fjuu+9www034N5774Xb7UZWVhZWrFihdGgh5fDhwzh27Bg+++wzvPvuu3C73ZgxYwauu+46rFmzRunwQkZRURGmTJmC999/v1ULcLMHqx148sknIQhCg1979+5t9nF37tyJCRMm4JlnnsGll14KAHC73bj55psxZ84c9OrVS+63EtTa8jrX9uGHH2LOnDn49NNPkZCQ0Nq3ErSUvMYdSaCuc33y8vJw9913Y/Lkydi8eTN+/PFH6HQ6XHfddRBDeLGQtr7OHo8Hdrsd7777LkaNGoXRo0fjP//5D9auXYt9+/bJdp5g09bX+e6778bNN9+MCy64oFXHYfdEO/DII49gypQpDbbp1q1bs465e/duXHLJJbjnnnvw1FNPSdvLy8uxZcsWbN++HdOnTwfg/aEWRREajQbfffcdLr744ma/h/agLa9zTR9//DHuuusufPbZZ3VuZ4Watr7GSUlJyM/P99mWn58Ps9kc0r1XgbjODVmwYAEiIyPx4osvStvef/99pKSkYNOmTTjvvPNkO1cwaevrnJycDI1G4/PHb9++fQEAOTk56N27t2znCiZtfZ3XrFmDr776Ci+//DIA78xNj8cDjUaDf//737jzzjubdBwmWO1AfHw84uPjZTverl27cPHFF2Py5Ml1pveazWb88ccfPtsWLlyINWvW4PPPP0d6erpscQSbtrzO1T766CPceeed+PjjjzF+/HjZzh2s2voa+7tNtWrVKmRlZckWQzCS+zo3pqqqCiqV7w0RtVoNwPsHWqhq6+s8cuRIuFwuHDp0CN27dwcA7N+/HwCQmpraZnG0tba+zhs3boTb7Zae/+9//8Pf//53bNiwAZ07d27ycZhghZicnBwUFxcjJycHbrcbO3bsAAD06NED4eHh2LlzJy6++GJkZ2dj5syZyMvLA+D9ZRgfHw+VSoX+/fv7HDMhIQEGg6HO9o6stdcZ8N4WnDx5Ml577TVkZmZKbYxGIyIjIxV5X8FEjmt83333Yf78+Xj88cdx5513Ys2aNfj0008bHAvX0TR2nQFvLaCKigrk5eXBarVKbTIyMqDT6TB+/Hj84x//wLPPPotJkyahvLwc//d//4fU1FScc845Cr2z4CLHdR4zZgyGDBmCO++8E6+++io8Hg+mTZuGsWPHdrghHfWR4zpX9wpW27Jli9/PxkY1e94hBbXJkyeLAOp8rV27VhRF77RUf/tTU1PrPSbLNNQlx3W+8MIL/baZPHmyIu8p2Mj1vbx27Vpx8ODBok6nE7t16yYuXry4zd9LMGvsOoti/d+rR44ckdp89NFH4jnnnCOaTCYxPj5evOqqq8Q9e/a0/RsKUnJd55MnT4rXXHONGB4eLiYmJopTpkwRi4qK2v4NBSm5rnNNLS3TIIhiCI9AJCIiIlIAZxESERERyYwJFhEREZHMmGARERERyYwJFhEREZHMmGARERERyYwJFhEREZHMmGARERERyYwJFhG1G6NHj8bDDz8cMuecMmUKJk6cGJBjE5GyuFQOEVEDvvjiC2i1Wul5WloaHn744TZP9IiofWGCRUTUgJiYGKVDIKJ2iLcIiahdKikpwe23347o6GiEhYXhsssuw4EDB6T9S5YsQVRUFFauXIm+ffsiPDwc48aNQ25urtTG5XLhwQcfRFRUFGJjY/HEE09g8uTJPrftat4iHD16NI4dO4YZM2ZAEAQIggAAmD17NgYPHuwT36uvvoq0tDTpudvtxsyZM6VzPf7446i9UpnH48HcuXORnp4Oo9GIQYMG4fPPP5fnghFRm2KCRUTt0pQpU7BlyxZ89dVX2LhxI0RRxOWXXw6n0ym1qaqqwssvv4z33nsP69atQ05ODh599FFp/9///nd88MEHWLx4MdavXw+LxYKlS5fWe84vvvgCXbp0wbPPPovc3FyfZK0x8+bNw5IlS/D222/j559/RnFxMb788kufNnPnzsW7776LRYsWYdeuXZgxYwZuvfVW/Pjjj02/MEQUFHiLkIjanQMHDuCrr77C+vXrMWLECADABx98gJSUFCxduhTXX389AMDpdGLRokXo3r07AGD69Ol49tlnpeO8/vrrmDVrFq6++moAwPz587FixYp6zxsTEwO1Wo2IiAgkJSU1K+ZXX30Vs2bNwjXXXAMAWLRoEVauXCntt9vteP755/H9998jKysLANCtWzf8/PPPeOONN3DhhRc263xEpCwmWETU7uzZswcajQaZmZnSttjYWPTu3Rt79uyRtoWFhUnJFQAkJyejoKAAAFBWVob8/HwMHz5c2q9WqzF06FB4PB5Z4y0rK0Nubq5PvBqNBsOGDZNuEx48eBBVVVUYO3asz2sdDgfOOeccWeMhosBjgkVEIavm7D8AEAShzrgnOahUqjrHrXmrsikqKioAAMuXL0fnzp199un1+tYFSERtjmOwiKjd6du3L1wuFzZt2iRtKyoqwr59+5CRkdGkY0RGRiIxMRGbN2+Wtrndbmzbtq3B1+l0Orjdbp9t8fHxyMvL80myduzY4XOu5ORkn3hdLhe2bt0qPc/IyIBer0dOTg569Ojh85WSktKk90REwYM9WETU7vTs2RMTJkzA3XffjTfeeAMRERF48skn0blzZ0yYMKHJx3nggQcwd+5c9OjRA3369MHrr7+OkpISaXagP2lpaVi3bh1uuukm6PV6xMXFYfTo0SgsLMSLL76I6667Dt9++y2++eYbmM1m6XUPPfQQXnjhBfTs2RN9+vTBK6+8gtLSUml/REQEHn30UcyYMQMejwfnn38+ysrKsH79epjNZkyePLlF14qIlMEeLCJqlxYvXoyhQ4fiiiuuQFZWFkRRxIoVK+rcFmzIE088gUmTJuH2229HVlYWwsPDkZ2dDYPBUO9rnn32WRw9ehTdu3dHfHw8AG+P2sKFC7FgwQIMGjQIv/76q89sRQB45JFHcNttt2Hy5MnIyspCRESENLi+2l//+lf85S9/wdy5c9G3b1+MGzcOy5cvR3p6ejOuDBEFA0EMxIAEIqJ2yOPxoG/fvrjhhhvw17/+VelwiKgd4y1CIuqwjh07hu+++w4XXngh7HY75s+fjyNHjuDmm29WOjQiaud4i5CIOiyVSoUlS5bg3HPPxciRI/HHH3/g+++/R9++fZUOjYjaOd4iJCIiIpIZe7CIiIiIZMYEi4iIiEhmTLCIiIiIZMYEi4iIiEhmTLCIiIiIZMYEi4iIiEhmTLCIiIiIZMYEi4iIiEhmTLCIiIiIZPb/WAa8oAh1DtEAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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DJEn5+fkaMGCAHn30UUnSoUOHXAFHkrKysvT222+roKBA/fr109NPP63nn3+e6dwAAKBeNmOMCXUj/Km0tFTJyckqKSlRUlJSqJsDAJbGFOjoFIh7P/nj93dYDRQGAABoKkINAACwBEINAACwBEINAACwBEINAACwBEINAACwBEINAACwBEINAACwBEINAACwBEINAACwBEINAACwBEINAACwhLhQNwAAAH8yxqjytKNJx9rjYmSz2fzcIgQLoQYAEDHqCyzGSFOXbdauY+VNOn/P9CQ9OaJP2Aab5gQ2f/rm1Gm1iI8Nu+tEqAEARARjjB54bbO2FpUF7D22HipV5WmHEuNjA/YeTRWMz98YXzyRq5YJ4RUjwqs1AICo1JAKREVVdYN/oXdNaaWZI/qqoYWEiqpq3bJgfcN2DpHK046wCTThilADAAipplQgXho3uM5qitXHxtT3+QPtwnOT1CIMq1mEGgBAQNVXhWlMBUY6M+4luUV8wEJLRVV1QM7bXO7tSoyPDWmoCbduJ6fwbBUAIGxsOVDS6GOcQaaxA3cbUoEIdBUm3LuhUDtCDQDAr5o6oDXQFZi62ONi1DM9SVsPlQb9vRurZ3qS7HEsM+cLoQYA4KEplRl3vga0NmTgbijHwdhsNj05ok9YTJeuj9XHCzUHoQYA4DfGGI+xH87upEj4RWyz2cJyKjcajlADAPALX91OoR7QiuhCqAEANEnNWU01ZzEx9gPBRqgBALg0dDxNfYOBXxo3OGSDfhG9iNAAgEara3XbUM5iQnSjUgMAaJaaa8tEwqBgWBOhBgDQrGncDAZGuKD7CQAAWAKhBgAAWAKhBgAAWAKhBgAAWAKhBgAAWAKhBgAAWAKhBgCi0JYDJc2+GzcQbgg1ABDFCDawEkINAESZmkGGYAOrINQAQBQhwMDKCDUAAMASCDUAAMASCDUAAMASuEs3AEQBxtIgGhBqAACIIH06JYe6CWGLUAMAFtbcCo0xRpWnHV7bK6qqm3VeIBAINQAAn4wxeuC1zdpaVBbqpgANQqgBALi4V2YqqqrrDTQ905Nkj2POCcIDoQYALKQh3U01u5TscTGy2Wx1VmZeGjdYifGxXtudx1oF41UiG6EGACJcY8bNOIzRlCWF2nWs3LWta0orzcrrr1OnHT4DTc/0JCW3iLdUeIE1EWoAIEI1dhCwwxjdtWijDpZUeGzfdaxcU5YUasaIPq5t7pUZq1VjYF10hAJABGpsoDHfVmicgSYjOVFLxl+ijORESWeDjVNifKzrQaBBpCDUAECEaco07crTDleXU0ZyouaOGqiWCXGanTfAtY8z8HRNacXgX0QkfmoBIMrMzhugmG+rL76KMDNH9KU6g4jEmBoAiBBNXUjPGOOxWF59ecUKeYZZTNGJUAMAEaA5gcZqC+gRWFAbQg0AWJQxRiUnqzwCTc3F8uxxMeqZnqSth0p9vh5MhBU0F6EGAMJYU2Y5VZ52yBhp6rLNHuvRvDRusNd6MzabTU+O6ONajC9Q07cJLAgGQg0AhKnGrg7sK8g41bWAns1m87lacHMRZBBshBoAiEBnBv86ag0xTl1TWmnmiL5KjGcBPVgfoQYAIoyvWx24cwYZm43VgBFdCDUAEEF83erAPcRIoQ8ydDshVAg1ABABnN1Nk5ds8rjVwey8Ac3qWurTKbnJ08WBcEOoAYAw52utGeetDmKaEWbc/9zUYENVBuEkKIsRzJkzR126dFFiYqKGDBmi9evX17rvwoULZbPZPB6JiYnBaCYAhA33kFFR5fAINF1TWjUr0ABWFfBKzZIlS5Sfn6958+ZpyJAhmj17tnJzc7Vt2zadc845Po9JSkrStm3bXM8Z5AYgWhljNHXZZtdzX2vNNAaVFVhZwCs1v//973XHHXfo1ltvVa9evTRv3jy1bNlSCxYsqPUYm82mtLQ01yM1NTXQzQSAsOGs0jhXBHbOcuqa0qpZgQawuoBWak6dOqWNGzdq2rRprm0xMTHKycnRunXraj3uxIkT6ty5sxwOhy666CL95je/0YUXXuhz38rKSlVWVrqel5aW+u8DAEAQuXc5+Zq2zd2zgboFtFJz7NgxVVdXe1VaUlNTVVRU5POYCy64QAsWLNAbb7yhRYsWyeFw6NJLL9WBAwd87j9jxgwlJye7HpmZmX7/HAAQTMZHoOmZnqTE+NDckwmIFGE3+yk7O1vZ2dmu55deeql69uypP//5z/rlL3/ptf+0adOUn5/vel5aWkqwARBRas48qjztcAUaf0zblhhLg+gQ0FCTkpKi2NhYHT582GP74cOHlZaW1qBzxMfHa8CAAdqxY4fP1+12u+x2e7PbCgDhaHbeALVIaPp9mQgziCYBDTUJCQkaOHCgVq1apRtuuEGS5HA4tGrVKk2cOLFB56iurtaWLVs0fPjwALYUAIKvIWvDNKQ4Q3ABzgh491N+fr7GjBmjQYMGafDgwZo9e7bKy8t16623SpJGjx6tc889VzNmzJAkPfHEE7rkkkvUvXt3FRcX66mnntLevXt1++23B7qpABAWjGnYfoQZwFPAQ01eXp6OHj2qRx99VEVFRerfv79WrFjhGjy8b98+xcScHfx2/Phx3XHHHSoqKlK7du00cOBArV27Vr169Qp0UwEgaGqr0tRcl6Y2BBrAm82Yhv6fIDKUlpYqOTlZJSUlSkpKCnVzAMCn2kLNyVPV+un8M0tedE1ppdl5/b0GCAci0HCbBISaP35/h93sJwCwuoZWaWquSxOqAEFwQaQg1ABAGPC1erD7ujTBDBaEGEQqQg0ABJGvKo2vu3CzejDQeIQaAAixytOed+F2Xz042FUTqjSIZIQaAAiS2qo0FVXVrufNvQs3EM0INQAQBDUDzZkw49DUZZs97vGUGB/rCjRUTYDGIdQAQD2cgaSxIaOuWU41x9BIZ7qd7HHctBJoKkINANRQWxipL9w0dK2XiirPMTRdU1pp5oi+zb5pZXNQFYIVEGoARK2mLjjXHDXXomEMDeA/1DkBRKXmBJqax245UNKoKo37WjQEGsB/qNQAiCr+qs405Tz1rRjsju4goPEINQAinq+AUTMUhKKrqaaaVRr3FYPdEWiApqH7CUBEa0hYCYdA05gqDYCmoVIDICLVF1TCIci4a2iVBkDTEWoAhERT135xPzYYjDGqPO2QJNnjGj7l2nlcQlyMKqscmrxkk+s1qjRAYBBqAESMYIeZmiv+NnQ9mdoW13Oeg7E0QGAQagAEXVPCSTACjbO6Yoy8bl8gSbuOleun89epa0orzcrrr5hvg417NUeSKqqqaw00s/L6U6UBAoRQAyCowmGsS80Qcmab7yAjSVkprSRJu799bdexct25aKNm5/VXYnyspiwp9HmcU30VHio0gH8QagCEjS0HSjx+wQciADmMqTeEOLmHEenMYN/JSzbpYEmFDpVUKG/+J3Ue3zOtjR6/rjdhBggSQg2AsBSoQHPXoo06WFJR6z7OIGOzeQ8MbpEQq7mjBurWhZ/p6/JTHsdlJCdqdt4AuWeXxgwsBtB8hBoAYcE9xDR1td6KqjNdSjUDifM1Z5VF8h1CpPqDSIzNphfGXqzr53zs2lZzjE1DUKUB/I9QAyCkfN1HqaHcB/Y+sGyza8yLdLbiInmPlclITtTcUQMbFULcxdhsemPCZZKkU6cdVGSAMEGoARBxfE23rsk5U6mmplRVfHEenxgf2+hjqdIAgUGoARBRGjLQNyullUfVRmr4GjMAIhehBkBE8DUuRvIc2JsQd2aWkk3ymrIdLl1EVGmAwCHUAAh7vqozzoG+tVVemtItBCCyEWoAhCX3QcC+qjP+GBfTUM7qSnOnmVOlAQKLUAMg7NQ2bqa+6oy/1QwhjQ03hBgguHzfVQ0AguzMmJlq1wJ5NQNN15RWmjtqoFokxAY80PTplFxnIPH1Wm0BCEDwUKkBEDS1VThqu6u1+wJ5gRro6159aUwQaUiwARBchBoAQeEeaNxvKGmPi1FFlcNnoGnOAnn1obICWA+hBkBQ1Rwvk5XSShVV1R77BHIgMOEFsC5CDYCAc1ZpfN1Q0n2RvKyUVnoyQAvkEWYA6yPUAAgoZ6Ax31Zo3G8oaY+PdYUaf1Zn+nRKbvQYGQCRj1ADIGDcx9FUnna4upyc42XcV/5t6kDg2oILgQaIPoQaAEE3O29As24IKRFaAHhjnRoAAVHXAnXN7WEi0ADwhUoNAL/zFWiMad45CTIA6kOoARBwxhhNXba5SccSZgA0FKEGQMBVVJ0dJNw1pZXscfX3fBNmADQWY2oABFTNKs3MEX3rneVEoAHQFIQaAAFVs0qTGF/31w6BBkBTEWoA+JX7IGGHMZq8ZJPreUOqNADQVIypARAQNVcQrq9KQ4UGQHMRagAEhHu3U0Zyombl9fdZpSHMAPAXup8A+F3Nbif3FYQBIFAINQD8qrHdTgDgL3zTAPCrhnY7AYC/EWoA+E3NNWnodgIQTAwUBuA3G/Ycb/CaNAwQBuBvVGoA+I37TSt9rUlDkAEQSIQaAH6xeX+xR9dTzV4n90BDuAEQCIQaAH5RebphN60k0AAIFEINgGZzvzWCxO0QAIQGoQaAX7iPp6mr6wkAAoVQA6BZthwo8ZrK7Y5AAyBYCDUAmszZ7dTQ8TQAEEh88wBotvqmcgNAMBBqADSJs0pTs+vJPc/Q9QQgmAg1ABrNfbaT+72e3LueCDQAgo1QA6DJHMZo8pJNrud0PQEIJUINgCYxxmjKkkIdLKmQ5HmvJ6o0AEIhKKFmzpw56tKlixITEzVkyBCtX7++zv2XLl2qHj16KDExUX369NE777wTjGYCaAT3bqeM5ETNyutPlQZASAU81CxZskT5+fmaPn26/v3vf6tfv37Kzc3VkSNHfO6/du1a/d///Z9uu+02bdq0STfccINuuOEGff7554FuKoB6bDlQoi0HSry6nWbnDVCMzaY+nZKp0gAIGZsx7pMx/W/IkCG6+OKL9cc//lGS5HA4lJmZqXvuuUdTp0712j8vL0/l5eV66623XNsuueQS9e/fX/Pmzav3/UpLS5WcnKySkhIlJSX574MAcC20N3lJocfg4NnfVmkINACayh+/vwNaqTl16pQ2btyonJycs28YE6OcnBytW7fO5zHr1q3z2F+ScnNza92/srJSpaWlHg8AgUO3E4BwFdBQc+zYMVVXVys1NdVje2pqqoqKinweU1RU1Kj9Z8yYoeTkZNcjMzPTP40H4FLbmjTObicACAcRP/tp2rRpKikpcT32798f6iYBlrTlQInX7RCcs50kZjwBCL24QJ48JSVFsbGxOnz4sMf2w4cPKy0tzecxaWlpjdrfbrfLbrf7p8EA6sTtEACEs4BWahISEjRw4ECtWrXKtc3hcGjVqlXKzs72eUx2drbH/pJUUFBQ6/4AAsvZ9VRzxhO3QwAQbgJaqZGk/Px8jRkzRoMGDdLgwYM1e/ZslZeX69Zbb5UkjR49Wueee65mzJghSbr33nv1/e9/X08//bSuvvpqLV68WBs2bND8+fMD3VQAtfC10B63QwAQbgIeavLy8nT06FE9+uijKioqUv/+/bVixQrXYOB9+/YpJuZswejSSy/VK6+8oocfflgPPvigzj//fC1fvly9e/cOdFMB1MJ9LA0zngCEq4CvUxNsrFMD+Nfm/cUqOVmlWxacWQn8b+Oz1SIhVhJVGgD+44/f3wGv1ACIXMYYPfDaZm0tKnNtcxZoCDQAwk3ET+kGEDgb9hz3CDQ905Nkj4sh0AAIS1RqADTIS+MGK7lFPGNpAIQtKjUAfDpzn6ezzxPjYwk0AMIaoQaIMs51Z+pT85YITnQ9AQhXhBoAXnzdEoGxNADCHaEGgE/cEgFApCHUAPBwZiyNZ9cTeQZAJCDUAHBxjreh6wlAJCLUAPBC1xOASESoAeDBV9cTVRoAkYBQA0ShuqZ1V1R5dz0BQCTg2wqAS80qDV1PACIJoQaAJN9r0yTGM0AYQOTg3k9AlHPvimKAMIBIRqUGgCTfA4QBIJIQaoAo5l6l8TVAmK4nAJGEUAOAAcIALIFQA0Qp9yqNrwHCABBp+OYC4MFZpaHrCUCkIdQA8Jj1RK8TgEhFqAGiXM3xNBK3RQAQmQg1QJRxGCOHW2mG2yIAsAoW3wOiiMNhdP2cjyVJb0y4TJI0eckm1+vMegIQyfgvGRBFvv7mlOvPJSerNGVJoQ6WVEhi1hOAyMc3GBClKqqqXd1OGcmJmpXXn1lPACIaoQaIIu6znMa/tNH159l5AxRDtxOACEeoAaKEMUajF6z32u7e7USVBkAkI9QAUeJkVbW2Hir12s7gYABWQagBolBWSitJUs/0JAYHA7AMpnQDUejJEX1ls0n2uBiqNAAsg1ADRCGbTUqMj/XYxngaAJGOujMAALAEQg0AqjQALIFQAwAALIFQA0Q5qjQArIJQAwAALIFQAwAALIFQA0Qxup4AWAmhBgAAWAKhBohSVGkAWA2hBgAAWAKhBohCF56bFOomAIDfEWoAAIAlEGqAKPHfr0pD3QQACChCDQAAsARCDRAFthwoCXUTACDgCDUAAMASCDUAAMASCDUAAMASCDWAxTGeBkC0INQAAABLINQAFkaVBkA0IdQAFkWgARBtCDWABRFoAEQjQg0AALAEQg0AALAEQg0AALAEQg1gMYynARCtCDWAhRBoAEQzQg0AALAEQg1gEVRpAES7gIaar7/+WjfffLOSkpLUtm1b3XbbbTpx4kSdx/zgBz+QzWbzeNx5552BbCYQ8Qg0ACDFBfLkN998sw4dOqSCggJVVVXp1ltv1fjx4/XKK6/Uedwdd9yhJ554wvW8ZcuWgWxmo0TjL48+nZJD3QQAAOoVsFCzdetWrVixQp999pkGDRokSXr22Wc1fPhw/e53v1NGRkatx7Zs2VJpaWmBahoAALCggHU/rVu3Tm3btnUFGknKyclRTEyMPv300zqPffnll5WSkqLevXtr2rRp+uabb2rdt7KyUqWlpR4PIJpEY/UQAHwJWKWmqKhI55xzjuebxcWpffv2KioqqvW4n/3sZ+rcubMyMjK0efNmPfDAA9q2bZuWLVvmc/8ZM2bo8ccf92vbAQBA5Gl0qJk6daqefPLJOvfZunVrkxs0fvx415/79Omj9PR0XXHFFdq5c6e6devmtf+0adOUn5/vel5aWqrMzMwmvz8QSajSAMBZjQ419913n8aOHVvnPl27dlVaWpqOHDnisf306dP6+uuvGzVeZsiQIZKkHTt2+Aw1drtddru9wecDAADW1OhQ07FjR3Xs2LHe/bKzs1VcXKyNGzdq4MCBkqT3339fDofDFVQaorCwUJKUnp7e2KYCAIAoErCBwj179tRVV12lO+64Q+vXr9fHH3+siRMn6qabbnLNfPrqq6/Uo0cPrV+/XpK0c+dO/fKXv9TGjRu1Z88evfnmmxo9erS+973vqW/fvoFqKgAAsICALr738ssvq0ePHrriiis0fPhwXX755Zo/f77r9aqqKm3bts01uykhIUHvvfeefvSjH6lHjx667777NHLkSP3jH/8IZDOBiMR4GgDwFNDF99q3b1/nQntdunSRMcb1PDMzU2vWrAlkkwAAgEVx7ycgAlGlAQBvhBoAAGAJhBogwlClAQDfCDVABCHQAEDtCDVAhCDQAEDdCDVAlHCbaAgAlkSoASJAc6s0xhhNXbbZT60BgPBEqAHCnD+6nSpPO7TrWLkkqVd6klrExzb7nAAQbgg1QJRZeme2bDZbqJsBAH5HqAHCWCAGB5NnAFgVoQYAAFgCoQYIU/6s0jDzCUA0INQAFsfMJwDRglADWJgxRsUnq1wzn7qmtGLmEwDLigt1AwAEhjFGD7y2WVuLylzbZo7oy8wnAJZFpQYIQ/4YT1NR5fAINBnJiUqM5588AOviGw4IM/4INL7G0cwcSZUGgLURagALqqg6u4KwU3KL+BC1BgCCgzE1QJjw1xRuhzGavGST6/mS8ZcoMT5WMVRpAFgcoQawEGOMpiwp1MGSCklnZzvR7QQgGtD9BFiI+40rM5ITNSuvP4EGQNSgUtNAxhidrKpWRVV1qJsSdMYYfjFGCPeVg2fnDaDLCUBUIdQ00MmqavV6dGWomxESgzq3487OAeaP8TQ1x9Lw1wUg2tD9hHpt2HtcJ6OwQhVJfI2lscfxzxtAdKFS00At4mP1xRO5+u9XpaFuStBUVFXrlgXrQ90MNID7FG7G0gCIVoSaBrLZbGqZEKdE7psDP2tu11PNbifG0gCIVtSngRBqbqDx1e3ErRAARCu+/YAIxhRuADiLUAOESGOqNMYYVVRVy3w7Z9v53OE2h5tuJwDRjjE1QAg0NtA88NpmbS0qU8/0JM0c0UdTlhR63duJPAMg2hFqgCBq7BgaY4xKTlZpa1GZJGnroVLdtWijawyNE1O4AYBQAwRFUwYEu1do3PkKNIylAQBCDRrov1+VRt109j6dkv1ynqbOcKqocngFGndLxl+iGJtN9rgYAg0AiFAD1Mo9jDQ24PhjqvbUZZvr3CfGZou6oAkAdSHUAM3kj/s21eS+QnBWSis9ft2FGu22ujNjaADAG6EGaABfwcVf3VM11azSPDmirxLjY9QzPUlbD5UyhgYAakGoAZooEBUayXNBPecKwTabTU+O6KPK0w7G0ABALQg1QJhxW09PM0f0dQUYG2NoAKBOdMoDYaRm1xMFGQBoOEINEEbcBwgzGBgAGodvTCBM1KzSuHc9AQDqR6gBwoSvAcIAgIbjWxMIE7UNEAYANAyhBggDDmM0eckm13PyDAA0HqEGCDFjjKYsKXTdqJIBwgDQNHxzAiHmPpYmIzmR1YIBoIkINUAYmZ03QDEEGgBoEkINEEbIMwDQdIQaIMTcZz0BAJqOUAOEUM0F9wAATUeoAUIo2LdF6NMpOaDnB4BQItQAIcJtEQDAvwg1QIjUrNJwWwQAaB6+RYEQCGaVhi4nANGCUAOEQLCqNAQaANEkLtQNiDTR9Evim1OnQ90ES6p5nyfG0gCAf1CpAYLI132eGlqlaW6gjqZADiA6EWqAADPGqKKqWhVV1So5WdXo+zw1NYwQYgBEG7qfgAByfFuZcQYZd4G8z1PNQEPAARANCDVokIqq6lA3IejscTHNGuti6gg0PdOTGtTt5AwjhBIAqB+hBg1yy4L1oW5C0HVNafXtIN7GBRxjjCpPO1RRVe3R1TQ7b4DrhpXNDUwAAG+EGtSqRXysBnVupw17j4e6KSGx61i5fjp/nSTPgOPkvKVB5WmHa5sx0tRlm72qM7PzBqhFQmyj3p+xNADQOAELNb/+9a/19ttvq7CwUAkJCSouLq73GGOMpk+frueee07FxcW67LLLNHfuXJ1//vmBaibqYLPZtPTObG3YE12hxlcwcQ84TlkprSRJu310L7lraFdTY/XplKwtB0q8tgFAtApYqDl16pRuvPFGZWdn6y9/+UuDjvntb3+rZ555Ri+++KKysrL0yCOPKDc3V1988YUSExMD1VTUwWazKTG+cRUGK5id11+Vpx21Vl6kusNMU7uuGorwAgDeAhZqHn/8cUnSwoULG7S/MUazZ8/Www8/rOuvv16S9Ne//lWpqalavny5brrppkA1FfDiHuacAcepZtCprWuqOUGmKaGFoAMg2oXNmJrdu3erqKhIOTk5rm3JyckaMmSI1q1bV2uoqaysVGVlpet5aWlpwNuK6OKrWuUedII96NdXeCHQAEAYLb5XVFQkSUpNTfXYnpqa6nrNlxkzZig5Odn1yMzMDGg7Aels0EmMjw1q1xLhBQBq16hQM3XqVNlstjofX375ZaDa6tO0adNUUlLieuzfvz+o7w8AAMJDo7qf7rvvPo0dO7bOfbp27dqkhqSlpUmSDh8+rPT0dNf2w4cPq3///rUeZ7fbZbfbm/SegBVQvQGAMxoVajp27KiOHTsGpCFZWVlKS0vTqlWrXCGmtLRUn376qe66666AvCcQ6Qg0AHBWwMbU7Nu3T4WFhdq3b5+qq6tVWFiowsJCnThxwrVPjx499Prrr0s6M0Zh8uTJ+tWvfqU333xTW7Zs0ejRo5WRkaEbbrghUM0EAAAWEbDZT48++qhefPFF1/MBAwZIkj744AP94Ac/kCRt27ZNJSVnFw+7//77VV5ervHjx6u4uFiXX365VqxYwRo1iBpUXgCg6WzGGBPqRvhTaWmpkpOTVVJSoqSkpFA3xxJqrlqLwCDQAIhm/vj9HTZTugEAAJqDUAMAACyBUAMAACyBUAMAACyBUAOEAQYJA0DzEWoAAIAlEGqAEKNKAwD+QagBAACWQKgBAACWQKgBQoiuJwDwH0INAACwBEINECJUaQDAvwg1AADAEgg1QAhQpQEA/4sLdQOAaECIAYDAo1IDAAAsgUoN6hWtVYYtB0r8cp5ovX4AEGyEGqAW7mGkqQGHQAMAwUOoARrAVzipK+gQZgAg+Ag1QBMRXAAgvDBQGAAAWAKhBgAAWAKhBgAAWAKhBgAAWAKhBgAAWAKhBgAAWAKhBgAAWAKhBgAAWAKhBgAAWAKhBgAAWAKhBgAAWAKhBgAAWAKhBgAAWAKhBgAAWAKhBgAAWEJcqBvgb8YYSVJpaWmIWwIAABrK+Xvb+Xu8KSwXasrKyiRJmZmZIW4JAABorLKyMiUnJzfpWJtpTiQKQw6HQwcPHlSbNm1ks9n8eu7S0lJlZmZq//79SkpK8uu5Iw3X4iyuxVlcC09cj7O4FmdxLc5yvxZt2rRRWVmZMjIyFBPTtNExlqvUxMTEqFOnTgF9j6SkpKj/QXTiWpzFtTiLa+GJ63EW1+IsrsVZzmvR1AqNEwOFAQCAJRBqAACAJRBqGsFut2v69Omy2+2hbkrIcS3O4lqcxbXwxPU4i2txFtfiLH9fC8sNFAYAANGJSg0AALAEQg0AALAEQg0AALAEQg0AALAEQk0DzZkzR126dFFiYqKGDBmi9evXh7pJQfHhhx/q2muvVUZGhmw2m5YvX+7xujFGjz76qNLT09WiRQvl5ORo+/btoWlsgM2YMUMXX3yx2rRpo3POOUc33HCDtm3b5rFPRUWFJkyYoA4dOqh169YaOXKkDh8+HKIWB87cuXPVt29f14JZ2dnZevfdd12vR8t18GXmzJmy2WyaPHmya1u0XI/HHntMNpvN49GjRw/X69FyHZy++uorjRo1Sh06dFCLFi3Up08fbdiwwfV6NH1/dunSxetnw2azacKECZL897NBqGmAJUuWKD8/X9OnT9e///1v9evXT7m5uTpy5EiomxZw5eXl6tevn+bMmePz9d/+9rd65plnNG/ePH366adq1aqVcnNzVVFREeSWBt6aNWs0YcIEffLJJyooKFBVVZV+9KMfqby83LXPlClT9I9//ENLly7VmjVrdPDgQY0YMSKErQ6MTp06aebMmdq4caM2bNigH/7wh7r++uv13//+V1L0XIeaPvvsM/35z39W3759PbZH0/W48MILdejQIdfjo48+cr0WTdfh+PHjuuyyyxQfH693331XX3zxhZ5++mm1a9fOtU80fX9+9tlnHj8XBQUFkqQbb7xRkh9/NgzqNXjwYDNhwgTX8+rqapORkWFmzJgRwlYFnyTz+uuvu547HA6TlpZmnnrqKde24uJiY7fbzauvvhqCFgbXkSNHjCSzZs0aY8yZzx4fH2+WLl3q2mfr1q1Gklm3bl2omhk07dq1M88//3zUXoeysjJz/vnnm4KCAvP973/f3HvvvcaY6Pq5mD59uunXr5/P16LpOhhjzAMPPGAuv/zyWl+P9u/Pe++913Tr1s04HA6//mxQqanHqVOntHHjRuXk5Li2xcTEKCcnR+vWrQthy0Jv9+7dKioq8rg2ycnJGjJkSFRcm5KSEklS+/btJUkbN25UVVWVx/Xo0aOHzjvvPEtfj+rqai1evFjl5eXKzs6O2uswYcIEXX311R6fW4q+n4vt27crIyNDXbt21c0336x9+/ZJir7r8Oabb2rQoEG68cYbdc4552jAgAF67rnnXK9H8/fnqVOntGjRIo0bN042m82vPxuEmnocO3ZM1dXVSk1N9diempqqoqKiELUqPDg/fzReG4fDocmTJ+uyyy5T7969JZ25HgkJCWrbtq3Hvla9Hlu2bFHr1q1lt9t155136vXXX1evXr2i7jpI0uLFi/Xvf/9bM2bM8Hotmq7HkCFDtHDhQq1YsUJz587V7t279d3vfldlZWVRdR0kadeuXZo7d67OP/98rVy5UnfddZcmTZqkF198UVJ0f38uX75cxcXFGjt2rCT//hux3F26gWCYMGGCPv/8c4/xAtHmggsuUGFhoUpKSvT3v/9dY8aM0Zo1a0LdrKDbv3+/7r33XhUUFCgxMTHUzQmpYcOGuf7ct29fDRkyRJ07d9bf/vY3tWjRIoQtCz6Hw6FBgwbpN7/5jSRpwIAB+vzzzzVv3jyNGTMmxK0Lrb/85S8aNmyYMjIy/H5uKjX1SElJUWxsrNco7MOHDystLS1ErQoPzs8fbddm4sSJeuutt/TBBx+oU6dOru1paWk6deqUiouLPfa36vVISEhQ9+7dNXDgQM2YMUP9+vXTH/7wh6i7Dhs3btSRI0d00UUXKS4uTnFxcVqzZo2eeeYZxcXFKTU1Naquh7u2bdvqO9/5jnbs2BF1Pxfp6enq1auXx7aePXu6uuOi9ftz7969eu+993T77be7tvnzZ4NQU4+EhAQNHDhQq1atcm1zOBxatWqVsrOzQ9iy0MvKylJaWprHtSktLdWnn35qyWtjjNHEiRP1+uuv6/3331dWVpbH6wMHDlR8fLzH9di2bZv27dtnyetRk8PhUGVlZdRdhyuuuEJbtmxRYWGh6zFo0CDdfPPNrj9H0/Vwd+LECe3cuVPp6elR93Nx2WWXeS358L///U+dO3eWFH3fn04vvPCCzjnnHF199dWubX792fDzgGZLWrx4sbHb7WbhwoXmiy++MOPHjzdt27Y1RUVFoW5awJWVlZlNmzaZTZs2GUnm97//vdm0aZPZu3evMcaYmTNnmrZt25o33njDbN682Vx//fUmKyvLnDx5MsQt97+77rrLJCcnm9WrV5tDhw65Ht98841rnzvvvNOcd9555v333zcbNmww2dnZJjs7O4StDoypU6eaNWvWmN27d5vNmzebqVOnGpvNZv75z38aY6LnOtTGffaTMdFzPe677z6zevVqs3v3bvPxxx+bnJwck5KSYo4cOWKMiZ7rYIwx69evN3FxcebXv/612b59u3n55ZdNy5YtzaJFi1z7RNP3pzFnZg6fd9555oEHHvB6zV8/G4SaBnr22WfNeeedZxISEszgwYPNJ598EuomBcUHH3xgJHk9xowZY4w5My3xkUceMampqcZut5srrrjCbNu2LbSNDhBf10GSeeGFF1z7nDx50tx9992mXbt2pmXLlubHP/6xOXToUOgaHSDjxo0znTt3NgkJCaZjx47miiuucAUaY6LnOtSmZqiJluuRl5dn0tPTTUJCgjn33HNNXl6e2bFjh+v1aLkOTv/4xz9M7969jd1uNz169DDz58/3eD2avj+NMWblypVGks/P6K+fDZsxxjSjkgQAABAWGFMDAAAsgVADAAAsgVADAAAsgVADAAAsgVADAAAsgVADAAAsgVADAAAsgVADAAAsgVADIOTGjh0rm83menTo0EFXXXWVNm/e7NrH+donn3zicWxlZaU6dOggm82m1atXe+y/fPnyIH0CAOGAUAMgLFx11VU6dOiQDh06pFWrVikuLk7XXHONxz6ZmZl64YUXPLa9/vrrat26dTCbCiBMEWoAhAW73a60tDSlpaWpf//+mjp1qvbv36+jR4+69hkzZowWL16skydPurYtWLBAY8aMCUWTAYQZQg2AsHPixAktWrRI3bt3V4cOHVzbBw4cqC5duui1116TJO3bt08ffvihbrnlllA1FUAYIdQACAtvvfWWWrdurdatW6tNmzZ68803tWTJEsXEeH5NjRs3TgsWLJAkLVy4UMOHD1fHjh1D0WQAYYZQAyAsDB06VIWFhSosLNT69euVm5urYcOGae/evR77jRo1SuvWrdOuXbu0cOFCjRs3LkQtBhBuCDUAwkKrVq3UvXt3de/eXRdffLGef/55lZeX67nnnvPYr0OHDrrmmmt02223qaKiQsOGDQtRiwGEG0INgLBks9kUExPjMSjYady4cVq9erVGjx6t2NjYELQOQDiKC3UDAEA6s95MUVGRJOn48eP64x//qBMnTujaa6/12veqq67S0aNHlZSUFOxmAghjhBoAYWHFihVKT0+XJLVp00Y9evTQ0qVL9YMf/MBrX5vNppSUlCC3EEC4sxljTKgbAQAA0FyMqQEAAJZAqAEAAJZAqAEAAJZAqAEAAJZAqAEAAJZAqAEAAJZAqAEAAJZAqAEAAJZAqAEAAJZAqAEAAJZAqAEAAJZAqAEAAJbw/wERi4JIluFoTwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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AatX5/ra05+VceXm/fmk3bdq0wjZpaWmaOnWqx7aUlBStW7fOa/uCggIVFBS437tcrtoXCg/VCVsEHQCA1eosvJSUlGjKlCnq169fpbd/cnJyFBsb67EtNjZWOTk5XtunpqZqzpw5Pq21tuzWswIAgJ3U2WyjiRMnas+ePVq1apVPzztz5kzl5eW5X4cPH/bp+QEAQGCpk56XSZMmacOGDfroo4900UUXVdo2Li5Oubm5Httyc3MVFxfntb3T6ZTT6fRZraidinqduJ0EAPAVS3tejDGaNGmS1q5dqy1btqhNmzbnPSY5OVmbN2/22LZx40YlJydbVSYAALARS3teJk6cqJUrV+rtt9/WBRdc4B63EhMTo6ioKEnSmDFj1LJlS6WmpkqSJk+erP79+2v+/PkaNmyYVq1apZ07d+qll16yslRYjB4ZAICvWNrz8sILLygvL08DBgxQfHy8+/X666+72xw6dEjZ2dnu93379tXKlSv10ksv6dJLL9Wbb76pdevWBc0aLwAAoHbqdJ2XuhAI67ww26jq6HkBAEgBvM5LsCGk1J633yGBBgBQGR7MCAAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbIXwAgAAbMXS8PLRRx9p+PDhSkhIkMPh0Lp16ypt/+GHH8rhcJR75eTkWFkmAACwEUvDS35+vi699FItXLiwWsdlZGQoOzvb/WrRooVFFQIAALsJs/LkQ4cO1dChQ6t9XIsWLdS4cWPfFwQAAGwvIMe8dO/eXfHx8brmmmv08ccfV9q2oKBALpfL4wUAAIJXQIWX+Ph4LVq0SG+99ZbeeustJSYmasCAAfrss88qPCY1NVUxMTHuV2JiYh1WDAAA6prDGGPq5Ac5HFq7dq1GjhxZreP69++vpKQkvfrqq173FxQUqKCgwP3e5XIpMTFReXl5io6Ork3J5/Xl93mWnr++6npRjL9LAADUMZfLpZiYmCp9f1s65sUXevfurW3btlW43+l0yul01mFFAADAnwLqtpE36enpio+P93cZAAAgQFja83Lq1CllZma632dlZSk9PV1NmzZVUlKSZs6cqR9++EGvvPKKJGnBggVq06aNunTpojNnzugvf/mLtmzZor///e9WlgkAAGzE0vCyc+dOXX311e73U6dOlSSNHTtWy5YtU3Z2tg4dOuTeX1hYqGnTpumHH35QgwYN1K1bN23atMnjHAAAoH6rswG7daU6A35qiwG71mDALgDUP9X5/g74MS8AAABlEV4AAICtEF4AAICtEF4AAICtEF4AAICtEF4AAICtEF4AAICtEF4AAICtEF4AAICtEF4AAICtEF4AAICtEF4AAICtEF4AAICtEF4AAICtEF4AAICtEF4AAICtEF4AAICtEF4AAICtEF4AAICthPm7gPrGGKOCsyX+LqNanGEhcjgc/i4DAABJhJc6ZYzR9Ld2a1/OSX+XUi2d4qM1b1RXAgwAICAQXnyssp6VM0XFtgsukrQv26W800WKDA89b1t6aQAAViO8+FB1elZevbN3lcKAP50pKtbvl2yXJPf/ng+9NAAAqxFefKjgbEmVgkun+GjFRIUH/Be8MyxEneKjtS/bVeVjKuqloUcGAOArhBeLVNazYpcvcofDoXmjulZpgPH5emnokQEA+ArhxSKR4aEBf1uoKhwOR5XHulTWS0OPDADAVwgv8ImKemnokQEA+BqL1MFnSntpyr5iosLVKT66wmP2Zbtst+4NAMC/6HmBparSI3OmqNhjnzGGnhgAQIUIL7Dc+cbNnHs7qVerJlp9bzIBBgDgFbeNasgYozNFxeVeqJrSAb7e7Dz4s07zuwQAVICelxowxuimRWnadfBnf5diW95uJ5W9lQQAQEUILzVwuqi40uDSKT5azjA6tc6nqtOwAQAoy9Lw8tFHH+nJJ5/Url27lJ2drbVr12rkyJGVHvPhhx9q6tSp+uqrr5SYmKiHH35Y48aNs7LMWvG2GB1rlwAAYB1Luwfy8/N16aWXauHChVVqn5WVpWHDhunqq69Wenq6pkyZovHjx+v999+3ssxaOXdqcGR4KMEFAAALWdrzMnToUA0dOrTK7RctWqQ2bdpo/vz5kqROnTpp27Zt+u///m+lpKRYVSYAALCRgBqYkZaWpkGDBnlsS0lJUVpaWoXHFBQUyOVyebwAAEDwCqjwkpOTo9jYWI9tsbGxcrlcOn36tNdjUlNTFRMT434lJibWRakAAMBPAiq81MTMmTOVl5fnfh0+fNjfJQEAAAsF1FTpuLg45ebmemzLzc1VdHS0oqKivB7jdDrldDrrojwAABAAAqrnJTk5WZs3b/bYtnHjRiUnJ/upIgAAEGgsDS+nTp1Senq60tPTJf06FTo9PV2HDh2S9OstnzFjxrjb33vvvfr222/1xz/+Ufv379fzzz+vN954Q3/4wx+sLBMAANiIpeFl586d6tGjh3r06CFJmjp1qnr06KFZs2ZJkrKzs91BRpLatGmjd955Rxs3btSll16q+fPn6y9/+QvTpAEAgJulY14GDBggY0yF+5ctW+b1mM8//9zCqgAAgJ0F1JgXAACA8yG8AAAAWyG8AAAAWyG8AAAAWyG8AAAAWyG8AAAAWyG8AAAAWyG8AAAAWyG8AAAAWyG8AAAAWyG8AAAAWyG8AAAAWyG8AAAAWyG8AAAAWyG8AAAAWyG8AAAAWyG8AAAAWyG8AAAAWwnzdwGAHX35fZ7X7V0viqnjSgCg/iG8AD5EqAEA63HbCAAA2ArhBQAA2Aq3jYA64O12EreSAKBmCC9AEGLsDYBgxm0jAABgK/S8AH5C7wgA1Aw9LwAAwFYILwAAwFa4bQTUI9yqAhAM6HkBAAC2Qs8LAHpkANgKPS8AAMBW6HlBvVNRL4M39b3ngR4ZAIGoTnpeFi5cqNatWysyMlJ9+vTR9u3bK2y7bNkyORwOj1dkZGRdlAkAAGzA8vDy+uuva+rUqZo9e7Y+++wzXXrppUpJSdGRI0cqPCY6OlrZ2dnu18GDB60uEwAA2ITl4eWpp57ShAkTdMcdd6hz585atGiRGjRooCVLllR4jMPhUFxcnPsVGxtrdZkAAMAmLB3zUlhYqF27dmnmzJnubSEhIRo0aJDS0tIqPO7UqVNq1aqVSkpKdNlll+mJJ55Qly5dvLYtKChQQUGB+73L5fLdBcB2qjOeBQBgT5b2vBw7dkzFxcXlek5iY2OVk5Pj9ZgOHTpoyZIlevvtt/Xaa6+ppKREffv21ffff++1fWpqqmJiYtyvxMREn18HAAAIHAE3VTo5OVljxoxR9+7d1b9/f61Zs0bNmzfXiy++6LX9zJkzlZeX534dPny4jisGAAB1ydLbRs2aNVNoaKhyc3M9tufm5iouLq5K5wgPD1ePHj2UmZnpdb/T6ZTT6ax1rQAAwB4s7XmJiIhQz549tXnzZve2kpISbd68WcnJyVU6R3Fxsb788kvFx8dbVSYAALARyxepmzp1qsaOHatevXqpd+/eWrBggfLz83XHHXdIksaMGaOWLVsqNTVVkvTYY4/piiuuULt27XTixAk9+eSTOnjwoMaPH291qUCVGGN0pqjY6z5nWIgcDkcdVwQA9Yvl4WX06NE6evSoZs2apZycHHXv3l3vvfeeexDvoUOHFBLyzw6gn3/+WRMmTFBOTo6aNGminj176pNPPlHnzp2tLhU4L2OMblqUpl0Hf/a6v22zhvrzqG46N78QagDAdxzGGOPvInzJ5XIpJiZGeXl5io6OtuRn/FJ4Vp1nvS9JWn1PsiLDQy35OfXNmaJi3fzir1Po9z6WogYR1c/Wvp4qfe4y+GU/++roFB+teaO61irAVGdJfqunjPN4AAC+Vp3vb55thID01Q+ucqEw0L4wX72zt7tGY6QZa3br22P5Xtvuy3Yp73SRxzXRGwMANUN4Qb1ijFHB2ZJqta8oYESGh3qEkQWju5c795miYv1+ya/P8ir931K+6I0BgPqI8IJ6wxij6W/t1r6ck1U+5tyAUdFAXenXx1qc21vkDAtRp/ho7csuv/LzvmyXCs6WcNsRAKqJ8IJ6o+BsSbWCi1T7gOFwODRvVFePHpmyvTEAgOojvKBeKjtexRtfBgxvPTJlf865GAsDAJUjvMA2fDmD5tzxKv7iLSAxFgYAKkd4QdA432Dcysar1KXKxsFItb9VxSJ6AIId4cUPfD3l1+o1PeygJoNx/cXbOBjJN7eqzreIHr06AIIB4SUIWL3+iR3CUXUG43aKj5YzrOqP9Srbi+Gr3pvKxsHUxumi4gqDi8QMJwDBgfCC8/LHyq61Gch6vsG41b11YteZQWV/D2V7dRgkDMDuCC8WCLSVYO2oNgNZfTEY93zjUqrbe+MPFf0evP1ueSYTADshvMCnKgpuVemRsXoga3VUNC6llN2+1M/3u/32WL5ueSmt3HbGyAAIRISXavry+7yAmbUSbKoykLWi370Vn4lV41JqqioBsKLfQ0W/25o8k+nX4yp+bAIAWI3wgjpR1R6Z8wUGu44/CQQV/W6r+0wmSerVqolW35tMgAHgF4F90x7QP295VIUdxqIEmtJQU/YVExVe6e9858GfdZoeSAB+Qs8L/Mpbj4y33pjKxp+UZbexKIHKyrVoAKC2CC+whUAbf1If8DsHEKgIL0AlajN7CgBgDcIL6h3W4Qls3oIhnxmAsggvAPyC3isANUV4qaUuLaPVIIJfIwAAdYVvXQQcxpkAACpDeIFtVGXcAwEnOFX0uTIWBqifCC+ADRhjguY5SwBQW4QXBJW6+n/idXlryxij6W/t1r6ck173l30idH177hY9MkD9RHgBfMiKUFNwtqTC4CJV/ERoq331g8vrInYEh/qtKv/W+TeC2iK8AHWgKo9BqIpX7+ztDgyVPRGaZzwBCGaEF8BGSh+cWMrbE6ElxsEACG6EF8DGAvH5Q+f2KHGLAICvEV4ABB0G8gLBjfAC+Mm5X6S/FJ71UyUAYC+EFwA1UnZaNmNsANQlwguAGvn9ku3u/+4UH615o7p6DTCsegzA1+pkLuXChQvVunVrRUZGqk+fPtq+fXul7VevXq2OHTsqMjJSXbt21bvvvlsXZQIB40xRsccrUDjDQtQpPrrc9n3ZrgpXAAYAX7O85+X111/X1KlTtWjRIvXp00cLFixQSkqKMjIy1KJFi3LtP/nkE912221KTU3Vddddp5UrV2rkyJH67LPPdMkll1hdLhAQyvZqBBKHw6F5o7q6g8qZouKArRVA8LK85+Wpp57ShAkTdMcdd6hz585atGiRGjRooCVLlnht//TTT2vIkCF64IEH1KlTJz3++OO67LLL9Nxzz1ldKuBXUeGh6tWqSYX7A2XhudLp2eeuOQMAdcXSnpfCwkLt2rVLM2fOdG8LCQnRoEGDlJbmfTnztLQ0TZ061WNbSkqK1q1b57V9QUGBCgoK3O9dLlftCwf8wOFwaPW9ydr53c9e91dnUGxdPnsJAOqapeHl2LFjKi4uVmxsrMf22NhY7d+/3+sxOTk5Xtvn5OR4bZ+amqo5c+b4pmDAz6xedC5QQk1lT8n2htlMAMqy/WyjmTNnevTUuFwuJSYm+rEiwH4C6SnZ3lQ2mwlA/WNpeGnWrJlCQ0OVm5vrsT03N1dxcXFej4mLi6tWe6fTKafT6ZuCgQAWKL0m1eGth+VMUXG1gov062ymvNNF5Xql6JEB6idLw0tERIR69uypzZs3a+TIkZKkkpISbd68WZMmTfJ6THJysjZv3qwpU6a4t23cuFHJyclWlgrYlpVL3tcmMFWlh6XsU7K9KTubydusJnpkgPrJ8ttGU6dO1dixY9WrVy/17t1bCxYsUH5+vu644w5J0pgxY9SyZUulpqZKkiZPnqz+/ftr/vz5GjZsmFatWqWdO3fqpZdesrpUAD5UcLak0uDSKT5aMVHhlQaP0nVl9mV7H4hfur4Ms56A+sXy8DJ69GgdPXpUs2bNUk5Ojrp376733nvPPSj30KFDCgn55/TPvn37auXKlXr44Yf14IMPqn379lq3bh1rvAABpLRHpqrPY/LWw1KVWz7nritTqmyPjLdF/LidBAS3OhmwO2nSpApvE3344Yfltt188826+eabLa4KgC+VhghvwaE2a8KcbwYWt5OA+sf2s40ABIbSEFEaHKzE7SSgfiO8AKix0lWBdx7858J6dfGco6rcTgIQvAgvAGqs7KrAdR0cznc7ydtYmN2HT3i9lWTljC0Avkd4AVAr3kJEIDwJm7EwQPAivADwOX/dumEsDFA/EF4A+ERFwaEun4bNWBigfiC8APCJioJDr9ZNvN6mseqxBjV5uOW5tTAGBghshBcAPuMtODC+BICvEV4A4Bxle2LohQECT93ciAYAAPARel4AoBKMhwECD+EFAKqhugONCTuA73HbCAAA2ArhBQAA2Aq3jQAgQFR0S4pbT4AnwgsAv6jOF7JVC9r5S7BdD1DXCC8AAJ8wxlTpoZzGGBYvtEhdBWN/9wYSXgAEvIr+UNbkD7W3L1dnWIhlX6a++DLxxTms/rIxxuimRWnadfDn87bt1aqJVt+bTIBBjRFeANQr3h7Q2Ck+WvNGdeXLtBZOFxVXKbhI0s6DP+t0UbEaRPAVhJrhXw6AoFfRE69L7ct2qeBsSbUf6AjvXr2zt9ffJU/3hq8QXgDYVlVvJ1X0xGtffZkaY8qdu5SVt6QCVWR4KEEQliK8AKgXvD3x2heMMZr+1m7tyznpdT+3pADfI7wAqLWyPSD1bRpwwdmSCoOLxC0pwAqEFwDwkbJjPQJtfEegLYD31Q+ucoHO39NvYR+EFwBBx5dTq6vDjmM9qvI7IVQg0BBeAPgUX3SoqUDrHULgIrwAAYY/1MHn3IXx7DYDqSq9M1VZWRfwFcILAFjs3LEvzECqHm/hiZBfXlUfz+ALvxSeVVR4qN/+DRNeAMAClS2Mxwwk+Fp1Hs/gK3sfS/HbKsmEFwCwgLeF8QJtBhKCR3UezxAMCC8AYBGrFsYDKlPR4xl8qUvLaEX58d824QUAAAvU5YKNZce61MWUfX8/VJPwAgA+0qVldLk/6lX9Aqvs+UhVYbcZTEBtWBpejh8/rn//93/X3/72N4WEhOjGG2/U008/rUaNGlV4zIABA7R161aPbffcc48WLVpkZakA4Dfnez5SVTCDCfWJpeHl9ttvV3Z2tjZu3KiioiLdcccduvvuu7Vy5cpKj5swYYIee+wx9/sGDRpYWSYA+NX5no9UFcxgQn1iWXjZt2+f3nvvPe3YsUO9evWSJD377LO69tpr9V//9V9KSEio8NgGDRooLi7OqtIAIGBVd7BlbWYwVfVWVSDekmI13vrNsvCSlpamxo0bu4OLJA0aNEghISH69NNPdcMNN1R47IoVK/Taa68pLi5Ow4cP1yOPPOL33hdjjE4XFevM/78AwAq1GWxZnb9Nxkgz1uzWt8fyz9uWW1IINJaFl5ycHLVo0cLzh4WFqWnTpsrJyanwuN/+9rdq1aqVEhIStHv3bk2fPl0ZGRlas2aN1/YFBQUqKChwv3e5yi8I5Quni4rVedb7lpwbQN049/+V/1J41k+VWMOqNWT2ZbuUd7qo0lDF/6lDXap2eJkxY4bmzZtXaZt9+/bVuKC7777b/d9du3ZVfHy8Bg4cqAMHDujiiy8u1z41NVVz5syp8c+rjV6tmvh1njsAVLaSb1W0bdZQfx7VTd46VcrekrLL4np1OT0Z/lPt8DJt2jSNGzeu0jZt27ZVXFycjhw54rH97NmzOn78eLXGs/Tp00eSlJmZ6TW8zJw5U1OnTnW/d7lcSkxMrPL5qyoqPFR7H0vRVz/88w9Er9ZN6EYFUKmyvT1W9PR4W8m3Oiobz1KTYNQpPlrOsJAa1QJUVbXDS/PmzdW8efPztktOTtaJEye0a9cu9ezZU5K0ZcsWlZSUuANJVaSnp0uS4uPjve53Op1yOp1VPl9NORwONYgI8+g2JbgACARWreRbk2AUiIN7EXwsi8edOnXSkCFDNGHCBG3fvl0ff/yxJk2apFtvvdU90+iHH35Qx44dtX37r92RBw4c0OOPP65du3bpu+++0/r16zVmzBhdddVV6tatm1WlAkCdO1NmAkAgjxcpDUZVfRFcUBcsXedlxYoVmjRpkgYOHOhepO6ZZ55x7y8qKlJGRoZ++eUXSVJERIQ2bdqkBQsWKD8/X4mJibrxxhv18MMPW1kmANQ5u4whgf14W+k52Fh6dU2bNq10QbrWrVvLGON+n5iYWG51XQAIFlHhoerVqol2VvD0X8aLAFUT3NEMAAKIw+HQ6nuTtfM77+GF8SLBo7bPqqquQL71aAXCCwDI84+/lSHCqsG1CBy+eFYVKkd4AQB5jkFhRVnUhi+eVVVT9WX9McILgHqrojEoPOQQvlLdZ1XVVn1Zf4zwAqDeOncMStkVZUtvIzEOBbVRm2dV1UR9+bdKeAFQr1U0BqU0xHALCQg8zMkDgP9Xuhx+WaUPJSxdTK7s8g4A/IOeFwD4f2WXw6/ooYTnPsjQ7lNUz33Stjc87BCBhvACAGWU3kaq6KGE3x7L1y0vpdVpTQQMwBPhBQC8OPehhMZIM9bs1rfH8r22D/QpqlUJQL44lhCFukB4AYAKnDuYd8Ho7hWumuqLKaq1CRhAfUJ4AYAqqmx1XGYjAXWH8AIA8JnKeo9+KTxbh5UgmDFVGgAA2ArhBQAA2Aq3jQAgCDDYF/UJPS8AAMBW6HkBAMAiXVpGq0EEX7W+Rs8LAACwFcILAACwFcILAACwFcILAACwFcILAACwFcILAACwFcILAACwFcILAACwFcILAACwFZb9A4A6xnOIgNqh5wUAANgK4QUAANgKt40AAHXuTFFxuW3OsBA5HA4/VAO7IbwAAOrc75dsL7etU3y05o3qSoDBeXHbCABQJ6LCQ9WrVZMK9+/LdqngbEkdVgS7sqznZe7cuXrnnXeUnp6uiIgInThx4rzHGGM0e/Zsvfzyyzpx4oT69eunF154Qe3bt7eqTABAHXE4HFp9b7J2fvezx/YzRcXunhhuJ6EqLAsvhYWFuvnmm5WcnKzFixdX6Zj//M//1DPPPKPly5erTZs2euSRR5SSkqK9e/cqMjLSqlIBAHXE4XAoMjy0wv3cTkJVWBZe5syZI0latmxZldobY7RgwQI9/PDDGjFihCTplVdeUWxsrNatW6dbb73VqlIBAH7kDAtRp/ho7ct2ed2/L9ulvNNFHqGH3pj6LWAG7GZlZSknJ0eDBg1yb4uJiVGfPn2UlpZGeAGAIOVwODRvVNdy413K3k46t0eG3pj6LWDCS05OjiQpNjbWY3tsbKx7nzcFBQUqKChwv3e5vCd3AEDg8nY7qbIemdLBvZXdgkLwqlZ4mTFjhubNm1dpm3379qljx461Kqo6UlNT3beoAADBw1uPTNneGNRf1Qov06ZN07hx4ypt07Zt2xoVEhcXJ0nKzc1VfHy8e3tubq66d+9e4XEzZ87U1KlT3e9dLpcSExNrVAMAILCcb4Av6qdqhZfmzZurefPmlhTSpk0bxcXFafPmze6w4nK59Omnn+q+++6r8Din0ymn02lJTQCA+scYU6v1ZrxN94ZvWTbm5dChQzp+/LgOHTqk4uJipaenS5LatWunRo0aSZI6duyo1NRU3XDDDXI4HJoyZYr+9Kc/qX379u6p0gkJCRo5cqRVZQIA4GaM0fS3dmtfzkl/l4JKWBZeZs2apeXLl7vf9+jRQ5L0wQcfaMCAAZKkjIwM5eXludv88Y9/VH5+vu6++26dOHFCv/nNb/Tee++xxgsAoE4UnC3xWXDp1aqJorjlZQnLwsuyZcvOu8aLMcbjvcPh0GOPPabHHnvMqrIAAKiSV+/sXavxNr1aN2Eqt0UCZqo0AADVYfWjBCLDQ2sVXggu1iG8AKj3ul4U4/H+y+/zKmiJQMKjBOovnioNALCN0oXrKsKTqesHel4AALZRlUcJIPgRXgDgHGVvI3ELKfCcb+G62qyzwhot9kB4AQAEFXpggh/hBQAqwWBee6jsIY410Sk+Ws4whoUGKsILAFTDuWEGgaGisTA15csp1/A9wgsAICjwEMf6gz4xAABgK4QXAABgK4QXAABgK4QXAABgK4QXAABgK4QXAABgK4QXAABgK4QXAABgK4QXAABgK4QXAABgKzweoJp4rgkAAP5FzwsAALAVwgsAALAVwgsAALAVwgsAALAVwgsAALAVwgsAALAVwgsAALAVwgsAALAVwgsAALAVwgsAALAVwgsAALAVwgsAALAVwgsAALAVy8LL3Llz1bdvXzVo0ECNGzeu0jHjxo2Tw+HweA0ZMsSqEgEAgA2FWXXiwsJC3XzzzUpOTtbixYurfNyQIUO0dOlS93un02lFeQAAwKYsCy9z5syRJC1btqxaxzmdTsXFxVlQEQAACAYBN+blww8/VIsWLdShQwfdd999+umnn/xdEgAACCCW9bzUxJAhQzRq1Ci1adNGBw4c0IMPPqihQ4cqLS1NoaGhXo8pKChQQUGB+73L5aqrcgEAgB9Uq+dlxowZ5QbUnvvav39/jYu59dZbdf3116tr164aOXKkNmzYoB07dujDDz+s8JjU1FTFxMS4X4mJiTX++QAAIPBVq+dl2rRpGjduXKVt2rZtW5t6yp2rWbNmyszM1MCBA722mTlzpqZOnep+73K5CDAAAASxaoWX5s2bq3nz5lbVUs7333+vn376SfHx8RW2cTqdzEgCAKAesWzA7qFDh5Senq5Dhw6puLhY6enpSk9P16lTp9xtOnbsqLVr10qSTp06pQceeED/+Mc/9N1332nz5s0aMWKE2rVrp5SUFKvKBAAANmPZgN1Zs2Zp+fLl7vc9evSQJH3wwQcaMGCAJCkjI0N5eXmSpNDQUO3evVvLly/XiRMnlJCQoMGDB+vxxx+nZwUAgkjXi2K8bv/y+7w6rgR25TDGGH8X4Usul0sxMTHKy8tTdHS0v8sBAFRRsIWXikIavKvO93fArfMCAABQmYBa5wUAUH9xOwlVRXgBAOAc3PIJbNw2AgAAtkLPCwAgoHnrBeFWUv1GzwsAALAVwgsAALAVwgsAALAVwgsAALAVwgsAALAVwgsAALAVwgsAALAVwgsAALAVFqkDANgOz0Gq3+h5AQAAtkJ4AQAAtkJ4AQAAtsKYFwBA0KhoLAyCCz0vAADAVggvAADAVggvAADAVggvAADAVggvAADAVggvAADAVggvAADAVggvAADAVggvAADAVggvAADAVggvAADAVggvAADAVggvAADAVggvAADAVggvAADAVsL8XYCvGWMkSS6Xy8+VAACAqir93i79Hq9M0IWXkydPSpISExP9XAkAAKiukydPKiYmptI2DlOViGMjJSUl+vHHH3XBBRfI4XBU2tblcikxMVGHDx9WdHR0HVVY97jO4MJ1Bo/6cI0S1xlsrLpOY4xOnjyphIQEhYRUPqol6HpeQkJCdNFFF1XrmOjo6KD+h1aK6wwuXGfwqA/XKHGdwcaK6zxfj0spBuwCAABbIbwAAABbqdfhxel0avbs2XI6nf4uxVJcZ3DhOoNHfbhGiesMNoFwnUE3YBcAAAS3et3zAgAA7IfwAgAAbIXwAgAAbIXwAgAAbKVeh5eFCxeqdevWioyMVJ8+fbR9+3Z/l1QrH330kYYPH66EhAQ5HA6tW7fOY78xRrNmzVJ8fLyioqI0aNAgffPNN/4ptoZSU1N1+eWX64ILLlCLFi00cuRIZWRkeLQ5c+aMJk6cqAsvvFCNGjXSjTfeqNzcXD9VXDMvvPCCunXr5l4EKjk5Wf/zP//j3h8M1+jNn//8ZzkcDk2ZMsW9LRiu9dFHH5XD4fB4dezY0b0/GK5Rkn744Qf97ne/04UXXqioqCh17dpVO3fudO8Phr9BrVu3LvdZOhwOTZw4UVLwfJbFxcV65JFH1KZNG0VFReniiy/W448/7vHcIb9+nqaeWrVqlYmIiDBLliwxX331lZkwYYJp3Lixyc3N9XdpNfbuu++ahx56yKxZs8ZIMmvXrvXY/+c//9nExMSYdevWmS+++MJcf/31pk2bNub06dP+KbgGUlJSzNKlS82ePXtMenq6ufbaa01SUpI5deqUu829995rEhMTzebNm83OnTvNFVdcYfr27evHqqtv/fr15p133jFff/21ycjIMA8++KAJDw83e/bsMcYExzWea/v27aZ169amW7duZvLkye7twXCts2fPNl26dDHZ2dnu19GjR937g+Eajx8/blq1amXGjRtnPv30U/Ptt9+a999/32RmZrrbBMPfoCNHjnh8jhs3bjSSzAcffGCMCY7P0hhj5s6day688EKzYcMGk5WVZVavXm0aNWpknn76aXcbf36e9Ta89O7d20ycONH9vri42CQkJJjU1FQ/VuU754aXkpISExcXZ5588kn3thMnThin02n++te/+qFC3zhy5IiRZLZu3WqM+fWawsPDzerVq91t9u3bZySZtLQ0f5XpE02aNDF/+ctfgvIaT548adq3b282btxo+vfv7w4vwXKts2fPNpdeeqnXfcFyjdOnTze/+c1vKtwfrH+DJk+ebC6++GJTUlISNJ+lMcYMGzbM3HnnnR7bRo0aZW6//XZjjP8/z3p526iwsFC7du3SoEGD3NtCQkI0aNAgpaWl+bEy62RlZSknJ8fjmmNiYtSnTx9bX3NeXp4kqWnTppKkXbt2qaioyOM6O3bsqKSkJNteZ3FxsVatWqX8/HwlJycH5TVOnDhRw4YN87gmKbg+z2+++UYJCQlq27atbr/9dh06dEhS8Fzj+vXr1atXL918881q0aKFevTooZdfftm9Pxj/BhUWFuq1117TnXfeKYfDETSfpST17dtXmzdv1tdffy1J+uKLL7Rt2zYNHTpUkv8/z6B7MGNVHDt2TMXFxYqNjfXYHhsbq/379/upKmvl5ORIktdrLt1nNyUlJZoyZYr69eunSy65RNKv1xkREaHGjRt7tLXjdX755ZdKTk7WmTNn1KhRI61du1adO3dWenp60FyjJK1atUqfffaZduzYUW5fsHyeffr00bJly9ShQwdlZ2drzpw5uvLKK7Vnz56gucZvv/1WL7zwgqZOnaoHH3xQO3bs0P3336+IiAiNHTs2KP8GrVu3TidOnNC4ceMkBc+/V0maMWOGXC6XOnbsqNDQUBUXF2vu3Lm6/fbbJfn/O6VehhcEh4kTJ2rPnj3atm2bv0uxRIcOHZSenq68vDy9+eabGjt2rLZu3ervsnzq8OHDmjx5sjZu3KjIyEh/l2OZ0v+3KkndunVTnz591KpVK73xxhuKioryY2W+U1JSol69eumJJ56QJPXo0UN79uzRokWLNHbsWD9XZ43Fixdr6NChSkhI8HcpPvfGG29oxYoVWrlypbp06aL09HRNmTJFCQkJAfF51svbRs2aNVNoaGi5EeC5ubmKi4vzU1XWKr2uYLnmSZMmacOGDfrggw900UUXubfHxcWpsLBQJ06c8Ghvx+uMiIhQu3bt1LNnT6WmpurSSy/V008/HVTXuGvXLh05ckSXXXaZwsLCFBYWpq1bt+qZZ55RWFiYYmNjg+Zay2rcuLH+5V/+RZmZmUHzecbHx6tz584e2zp16uS+PRZsf4MOHjyoTZs2afz48e5twfJZStIDDzygGTNm6NZbb1XXrl31+9//Xn/4wx+Umpoqyf+fZ70MLxEREerZs6c2b97s3lZSUqLNmzcrOTnZj5VZp02bNoqLi/O4ZpfLpU8//dRW12yM0aRJk7R27Vpt2bJFbdq08djfs2dPhYeHe1xnRkaGDh06ZKvr9KakpEQFBQVBdY0DBw7Ul19+qfT0dPerV69euv32293/HSzXWtapU6d04MABxcfHB83n2a9fv3LLFnz99ddq1aqVpOD5G1Rq6dKlatGihYYNG+beFiyfpST98ssvCgnxjAihoaEqKSmRFACfp+VDggPUqlWrjNPpNMuWLTN79+41d999t2ncuLHJycnxd2k1dvLkSfP555+bzz//3EgyTz31lPn888/NwYMHjTG/Tmtr3Lixefvtt83u3bvNiBEjbDdN8b777jMxMTHmww8/9Jiu+Msvv7jb3HvvvSYpKcls2bLF7Ny50yQnJ5vk5GQ/Vl19M2bMMFu3bjVZWVlm9+7dZsaMGcbhcJi///3vxpjguMaKlJ1tZExwXOu0adPMhx9+aLKysszHH39sBg0aZJo1a2aOHDlijAmOa9y+fbsJCwszc+fONd98841ZsWKFadCggXnttdfcbYLhb5Axv85OTUpKMtOnTy+3Lxg+S2OMGTt2rGnZsqV7qvSaNWtMs2bNzB//+Ed3G39+nvU2vBhjzLPPPmuSkpJMRESE6d27t/nHP/7h75Jq5YMPPjCSyr3Gjh1rjPl1atsjjzxiYmNjjdPpNAMHDjQZGRn+LbqavF2fJLN06VJ3m9OnT5t/+7d/M02aNDENGjQwN9xwg8nOzvZf0TVw5513mlatWpmIiAjTvHlzM3DgQHdwMSY4rrEi54aXYLjW0aNHm/j4eBMREWFatmxpRo8e7bH+STBcozHG/O1vfzOXXHKJcTqdpmPHjuall17y2B8Mf4OMMeb99983krzWHiyfpcvlMpMnTzZJSUkmMjLStG3b1jz00EOmoKDA3cafn6fDmDLL5QEAAAS4ejnmBQAA2BfhBQAA2ArhBQAA2ArhBQAA2ArhBQAA2ArhBQAA2ArhBQAA2ArhBQAA2ArhBUBASEtLU2hoqMezYgDAG1bYBRAQxo8fr0aNGmnx4sXKyMhQQkKCv0sCEKDoeQHgd6dOndLrr7+u++67T8OGDdOyZcs89q9fv17t27dXZGSkrr76ai1fvlwOh0MnTpxwt9m2bZuuvPJKRUVFKTExUffff7/y8/Pr9kIA1AnCCwC/e+ONN9SxY0d16NBBv/vd77RkyRKVdgpnZWXppptu0siRI/XFF1/onnvu0UMPPeRx/IEDBzRkyBDdeOON2r17t15//XVt27ZNkyZN8sflALAYt40A+F2/fv10yy23aPLkyTp79qzi4+O1evVqDRgwQDNmzNA777yjL7/80t3+4Ycf1ty5c/Xzzz+rcePGGj9+vEJDQ/Xiiy+622zbtk39+/dXfn6+IiMj/XFZACxCzwsAv8rIyND27dt12223SZLCwsI0evRoLV682L3/8ssv9zimd+/eHu+/+OILLVu2TI0aNXK/UlJSVFJSoqysrLq5EAB1JszfBQCo3xYvXqyzZ896DNA1xsjpdOq5556r0jlOnTqle+65R/fff3+5fUlJST6rFUBgILwA8JuzZ8/qlVde0fz58zV48GCPfSNHjtRf//pXdejQQe+++67Hvh07dni8v+yyy7R37161a9fO8poB+B9jXgD4zbp16zR69GgdOXJEMTExHvumT5+uLVu26I033lCHDh30hz/8QXfddZfS09M1bdo0ff/99zpx4oRiYmK0e/duXXHFFbrzzjs1fvx4NWzYUHv37tXGjRur3HsDwD4Y8wLAbxYvXqxBgwaVCy6SdOONN2rnzp06efKk3nzzTa1Zs0bdunXTCy+84J5t5HQ6JUndunXT1q1b9fXXX+vKK69Ujx49NGvWLNaKAYIUPS8AbGfu3LlatGiRDh8+7O9SAPgBY14ABLznn39el19+uS688EJ9/PHHevLJJ1nDBajHCC8AAt4333yjP/3pTzp+/LiSkpI0bdo0zZw5099lAfATbhsBAABbYcAuAACwFcILAACwFcILAACwFcILAACwFcILAACwFcILAACwFcILAACwFcILAACwFcILAACwlf8D/dnDrJ9ZXt8AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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atGmKiorSwYMHValSpTz9s7Ky9Pjjj6tSpUr629/+pipVquj48eMKCAhwZpkAAMAgTg0vU6ZMUb9+/dSrVy9J0qxZs7Ry5UrNmzdPcXFxefrPmzdPqamp2rJli0qXLi1JCgsLc2aJAADAME47bZSVlaVdu3YpMjLy3xtzc1NkZKS2bt2a75gVK1YoIiJCAwYMUFBQkOrVq6d3331XOTk5zioTAAAYxmkzLykpKcrJyVFQUJBDe1BQkA4cOJDvmF9//VXr169Xly5dtGrVKh05ckQvvfSSsrOzNXr06HzHZGZmKjMz0/44PT29+HYCAACUOCXqbqPc3FxVqlRJs2fPVnh4uDp16qSRI0dq1qxZBY6Jj4+Xv7+/fQkNDb2NFQMAgNvNaeElMDBQ7u7uSk5OdmhPTk5WcHBwvmMqV66s2rVry93d3d5Wp04dJSUlKSsrK98xI0aMUFpamn1JTEwsvp0AAAAljtPCi4eHh8LDw7Vu3Tp7W25urtatW6eIiIh8xzRv3lxHjhxRbm6uve3QoUOqXLmyPDw88h3j6ekpPz8/hwUAANy5nHraKDY2VnPmzNHChQu1f/9+9e/fXxkZGfa7j7p3764RI0bY+/fv31+pqakaPHiwDh06pJUrV+rdd9/VgAEDnFkmAAAwiFNvle7UqZPOnj2rUaNGKSkpSY0aNdLq1avtF/GeOHFCbm7/zk+hoaFas2aNXnnlFTVo0EBVqlTR4MGD9dprrzmzTAAAYBCbZVmWq4soTunp6fL391daWhqnkHBHCotb6eoS4GIJ46NdXQJQ7Iry/l2i7jYCAAC4EcILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGuS3hZebMmQoLC5OXl5eaNWum7du3F2rckiVLZLPZ1KFDB+cWCAAAjOH08LJ06VLFxsZq9OjR2r17txo2bKioqCidOXPmuuMSEhI0bNgwtWjRwtklAgAAgzg9vEyZMkX9+vVTr169VLduXc2aNUtlypTRvHnzChyTk5OjLl26aOzYsapevbqzSwQAAAZxanjJysrSrl27FBkZ+e8NurkpMjJSW7duLXDcW2+9pUqVKqlPnz433EZmZqbS09MdFgAAcOdyanhJSUlRTk6OgoKCHNqDgoKUlJSU75hNmzbp448/1pw5cwq1jfj4ePn7+9uX0NDQW64bAACUXCXqbqOLFy+qW7dumjNnjgIDAws1ZsSIEUpLS7MviYmJTq4SAAC4UilnrjwwMFDu7u5KTk52aE9OTlZwcHCe/kePHlVCQoLatWtnb8vNzf2j0FKldPDgQdWoUcNhjKenpzw9PZ1QPQAAKImcOvPi4eGh8PBwrVu3zt6Wm5urdevWKSIiIk//++67T3v37tWePXvsy1NPPaVWrVppz549nBICAADOnXmRpNjYWPXo0UNNmzbVgw8+qGnTpikjI0O9evWSJHXv3l1VqlRRfHy8vLy8VK9ePYfxAQEBkpSnHQAA3J2cHl46deqks2fPatSoUUpKSlKjRo20evVq+0W8J06ckJtbibr0BgAAlGA2y7IsVxdRnNLT0+Xv76+0tDT5+fm5uhyg2IXFrXR1CXCxhPHRri4BKHZFef9mygMAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFFuS3iZOXOmwsLC5OXlpWbNmmn79u0F9p0zZ45atGihcuXKqVy5coqMjLxufwAAcHdxenhZunSpYmNjNXr0aO3evVsNGzZUVFSUzpw5k2//jRs3qnPnztqwYYO2bt2q0NBQtW7dWidPnnR2qQAAwAA2y7IsZ26gWbNmeuCBBzRjxgxJUm5urkJDQ/Xyyy8rLi7uhuNzcnJUrlw5zZgxQ927d79h//T0dPn7+ystLU1+fn63XD9Q0oTFrXR1CXCxhPHRri4BKHZFef926sxLVlaWdu3apcjIyH9v0M1NkZGR2rp1a6HWcfnyZWVnZ6t8+fL5Pp+Zman09HSHBQAA3LmcGl5SUlKUk5OjoKAgh/agoCAlJSUVah2vvfaaQkJCHALQteLj4+Xv729fQkNDb7luAABQcpXou43Gjx+vJUuWaNmyZfLy8sq3z4gRI5SWlmZfEhMTb3OVAADgdirlzJUHBgbK3d1dycnJDu3JyckKDg6+7thJkyZp/Pjx+v7779WgQYMC+3l6esrT07NY6gUAACWfU2dePDw8FB4ernXr1tnbcnNztW7dOkVERBQ4buLEiRo3bpxWr16tpk2bOrNEAABgGKfOvEhSbGysevTooaZNm+rBBx/UtGnTlJGRoV69ekmSunfvripVqig+Pl6SNGHCBI0aNUqLFy9WWFiY/doYX19f+fr6OrtcAABQwjk9vHTq1Elnz57VqFGjlJSUpEaNGmn16tX2i3hPnDghN7d/TwD99a9/VVZWlp555hmH9YwePVpjxoxxdrkAAKCEc/rnvNxufM4L7nR8zgv4nBfciUrM57wAAAAUN8ILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAFEJGRoZsNptsNpsyMjJcXc5djfACAACMQngBAABGIbwAAACjEF4AAIBRSrm6AACAWcLiVrq6BJfIzfrd/nOdN1fLzcPLhdW4VsL4aJdun5kXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABG4W4jAAAKwc3DS9Ve+8bVZUDMvAAAAMMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFFuS3iZOXOmwsLC5OXlpWbNmmn79u3X7f/555/rvvvuk5eXl+rXr69Vq1bdjjIBAIABnB5eli5dqtjYWI0ePVq7d+9Ww4YNFRUVpTNnzuTbf8uWLercubP69Omjn376SR06dFCHDh30yy+/OLtUAABgAKeHlylTpqhfv37q1auX6tatq1mzZqlMmTKaN29evv2nT5+uNm3aaPjw4apTp47GjRunJk2aaMaMGc4uFQAAGMCp4SUrK0u7du1SZGTkvzfo5qbIyEht3bo13zFbt2516C9JUVFRBfbPzMxUenq6wwIAAO5cpZy58pSUFOXk5CgoKMihPSgoSAcOHMh3TFJSUr79k5KS8u0fHx+vsWPHFk/BhRAWt/K2bQslU8L46Lt6+wDHIFzN+LuNRowYobS0NPuSmJjo6pIAAIATOXXmJTAwUO7u7kpOTnZoT05OVnBwcL5jgoODi9Tf09NTnp6exVMwAAAo8Zw68+Lh4aHw8HCtW7fO3pabm6t169YpIiIi3zEREREO/SVp7dq1BfYHAAB3F6fOvEhSbGysevTooaZNm+rBBx/UtGnTlJGRoV69ekmSunfvripVqig+Pl6SNHjwYD366KOaPHmyoqOjtWTJEu3cuVOzZ892dqkAAMAATg8vnTp10tmzZzVq1CglJSWpUaNGWr16tf2i3BMnTsjN7d8TQA8//LAWL16sN954Q6+//rpq1aql5cuXq169es4uFQAAGMBmWZbl6iKKU3p6uvz9/ZWWliY/P79iXz93G4E7LQCg+BXl/dv4u40AAMDdhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjOC28pKamqkuXLvLz81NAQID69OmjS5cuXbf/yy+/rHvvvVfe3t6qWrWqBg0apLS0NGeVCAAADOS08NKlSxft27dPa9eu1TfffKMffvhBzz//fIH9T506pVOnTmnSpEn65ZdftGDBAq1evVp9+vRxVokAAMBANsuyrOJe6f79+1W3bl3t2LFDTZs2lSStXr1abdu21W+//aaQkJBCrefzzz9X165dlZGRoVKlShVqTHp6uvz9/ZWWliY/P7+b3oeChMWtLPZ1wiwJ46NdXQIA3HGK8v7tlJmXrVu3KiAgwB5cJCkyMlJubm7atm1boddzdQcKG1wAAMCdzympICkpSZUqVXLcUKlSKl++vJKSkgq1jpSUFI0bN+66p5okKTMzU5mZmfbH6enpRS8YAAAYo0gzL3FxcbLZbNddDhw4cMtFpaenKzo6WnXr1tWYMWOu2zc+Pl7+/v72JTQ09Ja3DwAASq4izbwMHTpUPXv2vG6f6tWrKzg4WGfOnHFov3LlilJTUxUcHHzd8RcvXlSbNm1UtmxZLVu2TKVLl75u/xEjRig2Ntb+OD09nQADAMAdrEjhpWLFiqpYseIN+0VEROjChQvatWuXwsPDJUnr169Xbm6umjVrVuC49PR0RUVFydPTUytWrJCXl9cNt+Xp6SlPT8/C7wQAADCaUy7YrVOnjtq0aaN+/fpp+/bt2rx5swYOHKhnn33WfqfRyZMndd9992n79u2S/ggurVu3VkZGhj7++GOlp6crKSlJSUlJysnJcUaZAADAQE67jWfRokUaOHCgHnvsMbm5ualjx456//337c9nZ2fr4MGDunz5siRp9+7d9juRatas6bCuY8eOKSwszFmlAgAAgzgtvJQvX16LFy8u8PmwsDBd+xEzLVu2lBM+cgYAANxh+G4jAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QWFkpv1u45PeFLHJzyp3KzfXV0OAOAuRngBAABGIbwAAACjEF4AAIBRCC8AAMAoNsuyLFcXUZzS09Pl7++vtLQ0+fn5ubqcO0ZGRoZ8fX0lSZcuXZKPj4+LKwIA3EmK8v7NzAsAADAK4QUAABiF8AIAAIxCeAEAAEYp5eoCYAYfHx/dYdd2AwAMxcwLAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUp4WX1NRUdenSRX5+fgoICFCfPn106dKlQo21LEtPPPGEbDabli9f7qwSAQCAgZwWXrp06aJ9+/Zp7dq1+uabb/TDDz/o+eefL9TYadOmyWazOas0AABgMKd8PcD+/fu1evVq7dixQ02bNpUkffDBB2rbtq0mTZqkkJCQAsfu2bNHkydP1s6dO1W5cmVnlAcAAAzmlJmXrVu3KiAgwB5cJCkyMlJubm7atm1bgeMuX76s5557TjNnzlRwcHChtpWZman09HSHBQAA3LmcEl6SkpJUqVIlh7ZSpUqpfPnySkpKKnDcK6+8oocffljt27cv9Lbi4+Pl7+9vX0JDQ2+6bgAAUPIVKbzExcXJZrNddzlw4MBNFbJixQqtX79e06ZNK9K4ESNGKC0tzb4kJibe1PYBAIAZinTNy9ChQ9WzZ8/r9qlevbqCg4N15swZh/YrV64oNTW1wNNB69ev19GjRxUQEODQ3rFjR7Vo0UIbN27Md5ynp6c8PT0LuwsAAMBwRQovFStWVMWKFW/YLyIiQhcuXNCuXbsUHh4u6Y9wkpubq2bNmuU7Ji4uTn379nVoq1+/vqZOnap27doVukbLsiSJa18AADDI1fftq+/j12U5SZs2bazGjRtb27ZtszZt2mTVqlXL6ty5s/353377zbr33nutbdu2FbgOSdayZcuKtN3ExERLEgsLCwsLC4uBS2Ji4g3f651yq7QkLVq0SAMHDtRjjz0mNzc3dezYUe+//779+ezsbB08eFCXL18u1u2GhIQoMTFRZcuW5bNiill6erpCQ0OVmJgoPz8/V5eDuxDHIFyNY9B5LMvSxYsXr/txKlfZ/n+GA7ih9PR0+fv7Ky0tjX+0cAmOQbgax2DJwHcbAQAAoxBeAACAUQgvKDRPT0+NHj2aW9PhMhyDcDWOwZKBa14AAIBRmHkBAABGIbwAAACjEF4AAIBRCC9wioSEBNlsNu3Zs8fVpQAA7jCEl7tIz5498/0m8DZt2tzyejt06FA8ReKO4KxjDSguV4/RF198Mc9zAwYMkM1mu+EXEcN1nPb1ACiZ2rRpo/nz5zu03ewtfzk5OXwFAwpUnMeaZVnKyclRqVL8yULxCQ0N1ZIlSzR16lR5e3tLkn7//XctXrxYVatWvaV1Z2dnq3Tp0sVRJvLBzMtdxtPTU8HBwQ5LuXLlJElTpkxR/fr15ePjo9DQUL300ku6dOmSfeyCBQsUEBCgFStWqG7duvL09FTv3r21cOFCffXVV/b/XW/cuNE+5tdff1WrVq1UpkwZNWzYUFu3br3duwwXKehYy++U4oULFxyOnY0bN8pms+nbb79VeHi4PD09tWnTJmVmZmrQoEGqVKmSvLy89Mgjj2jHjh329Vwdt3LlSjVo0EBeXl566KGH9Msvv9j7nDt3Tp07d1aVKlVUpkwZ1a9fX59++unt+rWgBGnSpIlCQ0P15Zdf2tu+/PJLVa1aVY0bN7a3rV69Wo888ogCAgJUoUIFPfnkkzp69Kj9+avH9NKlS/Xoo4/Ky8tLixYt0vHjx9WuXTuVK1dOPj4+uv/++7Vq1arbuo93KsIL7Nzc3PT+++9r3759WrhwodavX69XX33Voc/ly5c1YcIEzZ07V/v27dP777+vmJgYtWnTRqdPn9bp06f18MMP2/uPHDlSw4YN0549e1S7dm117txZV65cud27BkPFxcVp/Pjx2r9/vxo0aKBXX31VX3zxhRYuXKjdu3erZs2aioqKUmpqqsO44cOHa/LkydqxY4cqVqyodu3aKTs7W9If/7MODw/XypUr9csvv+j5559Xt27dtH37dlfsIlysd+/eDjOE8+bNU69evRz6ZGRkKDY2Vjt37tS6devk5uamp59+Wrm5uQ794uLiNHjwYO3fv19RUVEaMGCAMjMz9cMPP2jv3r2aMGGCfH19b8t+3fFu+L3TuGP06NHDcnd3t3x8fByWd955J9/+n3/+uVWhQgX74/nz51uSrD179uRZb/v27R3ajh07Zkmy5s6da2/bt2+fJcnav39/8e0USqTrHWtXj42ffvrJ3v/8+fOWJGvDhg2WZVnWhg0bLEnW8uXL7X0uXbpklS5d2lq0aJG9LSsrywoJCbEmTpzoMG7JkiX2PufOnbO8vb2tpUuXFlhvdHS0NXTo0GLae5jg6t+tM2fOWJ6enlZCQoKVkJBgeXl5WWfPnrXat29v9ejRI9+xZ8+etSRZe/futSzr33/vpk2b5tCvfv361pgxY5y9K3clTiDfZVq1aqW//vWvDm3ly5eXJH3//feKj4/XgQMHlJ6eritXruj333/X5cuXVaZMGUmSh4eHGjRoUOjtXdu3cuXKkqQzZ87ovvvuu9VdQQlX0LGWnp5e6HU0bdrU/vPRo0eVnZ2t5s2b29tKly6tBx98UPv373cYFxER4bDNe++9194nJydH7777rj777DOdPHlSWVlZyszMtB/juLtUrFhR0dHRWrBggSzLUnR0tAIDAx36HD58WKNGjdK2bduUkpJin3E5ceKE6tWrZ+937fEqSYMGDVL//v313XffKTIyUh07dizS308UjPByl/Hx8VHNmjXztCckJOjJJ59U//799c4776h8+fLatGmT+vTpo6ysLPsfdm9v7yJdpHvtBWtXx/3nVCvuTAUda1evo7Ku+WaSq6d08ltHcXvvvfc0ffp0TZs2zX6N15AhQ5SVlVXs24IZevfurYEDB0qSZs6cmef5du3aqVq1apozZ45CQkKUm5urevXq5Tlm/vN47du3r6KiorRy5Up99913io+P1+TJk/Xyyy87b2fuElzzAknSrl27lJubq8mTJ+uhhx5S7dq1derUqUKN9fDwUE5OjpMrxJ2iYsWKkqTTp0/b2wrzeUA1atSQh4eHNm/ebG/Lzs7Wjh07VLduXYe+P/74o/3n8+fP69ChQ6pTp44kafPmzWrfvr26du2qhg0bqnr16jp06NCt7BIM16ZNG2VlZSk7O1tRUVEOz507d04HDx7UG2+8occee0x16tTR+fPnC73u0NBQvfjii/ryyy81dOhQzZkzp7jLvysx83KXyczMVFJSkkNbqVKlVLNmTWVnZ+uDDz5Qu3bttHnzZs2aNatQ6wwLC9OaNWt08OBBVahQQf7+/s4oHYYp6FgLDAzUQw89pPHjx+uee+7RmTNn9MYbb9xwfT4+Purfv7+GDx+u8uXLq2rVqpo4caIuX76sPn36OPR96623VKFCBQUFBWnkyJEKDAy0fxZRrVq19Le//U1btmxRuXLlNGXKFCUnJ+cJQLh7uLu7208ruru7OzxXrlw5VahQQbNnz1blypV14sQJxcXFFWq9Q4YM0RNPPKHatWvr/Pnz2rBhgz1E49Yw83KXWb16tSpXruywPPLII2rYsKGmTJmiCRMmqF69elq0aJHi4+MLtc5+/frp3nvvVdOmTVWxYkWH/xnj7lXQsSb9cUfHlStXFB4eriFDhujtt98u1DrHjx+vjh07qlu3bmrSpImOHDmiNWvW2G/3v7bf4MGDFR4erqSkJH399dfy8PCQJL3xxhtq0qSJoqKi1LJlSwUHB/Mhi5Cfn5/8/PzytLu5uWnJkiXatWuX6tWrp1deeUXvvfdeodaZk5OjAQMGqE6dOmrTpo1q166tDz/8sLhLvyvZrGtPPAOAwTZu3KhWrVrp/PnzCggIcHU5AJyEmRcAAGAUwgsAADAKp40AAIBRmHkBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAd4wxY8aoUaNGri4DgJMRXgDY9ezZM99Pm924caNsNpsuXLhw22u6VsuWLWWz2WSz2eTl5aW6deuWiE8sbdmypYYMGeLqMoC7BuEFgFH69eun06dP61//+pdiYmI0YMAAffrpp64uC8BtRHgBUGRffPGF7r//fnl6eiosLEyTJ092eD4sLExvv/22unfvLl9fX1WrVk0rVqzQ2bNn1b59e/n6+qpBgwbauXOnw7hNmzapRYsW8vb2VmhoqAYNGqSMjAyHPmXKlFFwcLCqV6+uMWPGqFatWlqxYkW+de7YsUOPP/64AgMD5e/vr0cffVS7d+926GOz2TR37lw9/fTTKlOmTL7r++WXX/TEE0/I19dXQUFB6tatm1JSUiT9MVv197//XdOnT7fPCiUkJNzMrxVAIRFeABTJrl27FBMTo2effVZ79+7VmDFj9Oabb2rBggUO/aZOnarmzZvrp59+UnR0tLp166bu3bura9eu2r17t2rUqKHu3bvr6udkHj16VG3atFHHjh31888/a+nSpdq0aZMGDhx43Xq8vb2VlZWV73MXL15Ujx49tGnTJv3444+qVauW2rZtq4sXLzr0Gzt2rGJiYvTzzz+rbdu26tKli1JTUyVJFy5c0J///Gc1btxYO3fu1OrVq5WcnKyYmBhJ0vTp0xUREWGfETp9+rRCQ0Nv5lcLoLAsAPh/PXr0sNzd3S0fHx+HxcvLy5JknT9/3nruueesxx9/3GHc8OHDrbp169ofV6tWzeratav98enTpy1J1ptvvmlv27p1qyXJOn36tGVZltWnTx/r+eefd1jvP/7xD8vNzc363//9X8uyLOvRRx+1Bg8ebFmWZV25csX65JNPLEnWjBkzLMuyrNGjR1sNGzYscP9ycnKssmXLWl9//bW9TZL1xhtv2B9funTJkmR9++23lmVZ1rhx46zWrVs7rCcxMdGSZB08eDBPXQCcj5kXAA5atWqlPXv2OCxz5861P79//341b97cYUzz5s11+PBh5eTk2NsaNGhg/zkoKEiSVL9+/TxtZ86ckST985//1IIFC+Tr62tfoqKilJubq2PHjtnHffjhh/L19ZW3t7f69eunV155Rf379893X5KTk9WvXz/VqlVL/v7+8vPz06VLl3TixAmHftfW6uPjIz8/P4e6NmzY4FDXfffdJ+mP2SIAt18pVxcAoGTx8fFRzZo1Hdp+++23Iq+ndOnS9p9tNluBbbm5uZKkS5cu6YUXXtCgQYPyrKtq1ar2n7t06aKRI0fK29tblStXlptbwf8H69Gjh86dO6fp06erWrVq8vT0VERERJ7TTNfWdbW2a+tq166dJkyYkGf9lStXLnDbAJyH8AKgSOrUqaPNmzc7tG3evFm1a9eWu7v7Ta+3SZMm+te//pUnOP0nf3//G/a5tq4PP/xQbdu2lSQlJibaL7QtSl1ffPGFwsLCVKpU/n8yPTw8HGadADgXp40AFMnQoUO1bt06jRs3TocOHdLChQs1Y8YMDRs27JbW+9prr2nLli0aOHCg9uzZo8OHD+urr7664QW711OrVi198skn2r9/v7Zt26YuXbrI29u7SOsYMGCAUlNT1blzZ+3YsUNHjx7VmjVr1KtXL3tgCQsL07Zt25SQkKCUlBT7rA0A5yC8ACiSJk2a6LPPPtOSJUtUr149jRo1Sm+99ZZ69ux5S+tt0KCB/v73v+vQoUNq0aKFGjdurFGjRikkJOSm1/nxxx/r/PnzatKkibp166ZBgwapUqVKRVpHSEiINm/erJycHLVu3Vr169fXkCFDFBAQYD9lNWzYMLm7u6tu3bqqWLFinmtqABQvm2X9/32KAAAABmDmBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACj/B+cmrPaMWKnWQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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2bElwgdFY8wIYJDs7W2vWrNHy5cv1448/uv0eEn6zcuXKsi6h3Jo2bVpZlwBcMsILYJBdu3YpLi5OtWvX1uzZs4t9bgoAXM1Y8wIAAIzCmhcAAGAUwgsAADDKVbfmxel06ocfflCVKlX40TEAAAxhWZZOnTqlunXrFvpR2T+66sLLDz/8UOgXXQEAgBkyMjJ07bXXltjnqgsv5x97npGRIX9//zKuBgAAlIbD4VBwcLDre7wkV114OX+pyN/fn/ACAIBhSrPkgwW7AADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADCKx8PL3LlzFRISIh8fH0VERGjLli0l9p81a5aaNm2qSpUqKTg4WOPHj9fZs2c9XSYAADCER8PL0qVLlZCQoKSkJG3btk2tWrVSTEyMsrOzi+y/ZMkSJSYmKikpSbt379aCBQu0dOlSPfroo54sEwAAGMSj4WXmzJkaPny4hg4dqubNm2v+/PmqXLmyFi5cWGT/TZs2qV27dho4cKBCQkLUqVMnDRgw4IKzNQAA4K/DY+ElPz9fW7duVXR09G8n8/JSdHS00tLSihxzyy23aOvWra6wcuDAAa1YsUJdu3Yt9jx5eXlyOBxuGwDAfLm5ubLZbLLZbMrNzS3rclCOVPDUgY8fP66CggIFBga6tQcGBio9Pb3IMQMHDtTx48d16623yrIs/fLLL/rnP/9Z4mWj5ORkTZky5bLWDgAAyq9ydbfRunXrNHXqVL344ovatm2b3nnnHaWkpOjJJ58sdszEiROVk5Pj2jIyMq5gxQAA4Erz2MxLzZo15e3traysLLf2rKwsBQUFFTnm8ccf16BBg3TvvfdKklq2bKnc3FyNGDFCjz32mLy8Cmctu90uu91++d8AAAAolzw281KxYkWFh4crNTXV1eZ0OpWamqrIyMgix5w5c6ZQQPH29pYkWZblqVIBAIBBPDbzIkkJCQkaPHiw2rRpo7Zt22rWrFnKzc3V0KFDJUnx8fGqV6+ekpOTJUk9evTQzJkzdeONNyoiIkL79u3T448/rh49erhCDAB4WkhiSlmXAEnO/N+e8RX2+Cp5VfQpw2rwe4emdSvT83s0vMTGxurYsWOaNGmSMjMz1bp1a61atcq1iPfIkSNuMy3/+te/ZLPZ9K9//UtHjx5VrVq11KNHDz399NOeLBMAABjEZl1l12McDocCAgKUk5Mjf3//si4HgIGYeSkfnPlnlfHcXZKk4PHLmXkpRzwx83Ix39/l6m4jAACACyG8AAAAoxBeAACAUTy6YBcAgD/Lq6KP6j/yUVmXgXKImRcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAo3g8vMydO1chISHy8fFRRESEtmzZUmL/kydPauTIkapTp47sdruaNGmiFStWeLpMAABgiAqePPjSpUuVkJCg+fPnKyIiQrNmzVJMTIz27Nmj2rVrF+qfn5+vO+64Q7Vr19by5ctVr149HT58WFWrVvVkmQAAwCAeDS8zZ87U8OHDNXToUEnS/PnzlZKSooULFyoxMbFQ/4ULF+rEiRPatGmTrrnmGklSSEiIJ0sEAACG8dhlo/z8fG3dulXR0dG/nczLS9HR0UpLSytyzAcffKDIyEiNHDlSgYGBatGihaZOnaqCggJPlQkAAAzjsZmX48ePq6CgQIGBgW7tgYGBSk9PL3LMgQMHtHbtWsXFxWnFihXat2+fHnjgAZ07d05JSUlFjsnLy1NeXp7rtcPhuHxvAgAAlDvl6m4jp9Op2rVr6+WXX1Z4eLhiY2P12GOPaf78+cWOSU5OVkBAgGsLDg6+ghUDAIArzWPhpWbNmvL29lZWVpZbe1ZWloKCgoocU6dOHTVp0kTe3t6utrCwMGVmZio/P7/IMRMnTlROTo5ry8jIuHxvAgAAlDseCy8VK1ZUeHi4UlNTXW1Op1OpqamKjIwscky7du20b98+OZ1OV9v333+vOnXqqGLFikWOsdvt8vf3d9sAAMDVy6OXjRISEvTKK6/otdde0+7du3X//fcrNzfXdfdRfHy8Jk6c6Op///3368SJExo7dqy+//57paSkaOrUqRo5cqQnywQAAAbx6K3SsbGxOnbsmCZNmqTMzEy1bt1aq1atci3iPXLkiLy8fstPwcHB+vjjjzV+/HjdcMMNqlevnsaOHatHHnnEk2UCAACD2CzLssq6iMvJ4XAoICBAOTk5XEIC8KeEJKaUdQlAuXZoWrfLfsyL+f4uV3cbAQAAXAjhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxyRcLL3LlzFRISIh8fH0VERGjLli2lGvfWW2/JZrOpV69eni0QAAAYw+PhZenSpUpISFBSUpK2bdumVq1aKSYmRtnZ2SWOO3TokB588EG1b9/e0yUCAACDeDy8zJw5U8OHD9fQoUPVvHlzzZ8/X5UrV9bChQuLHVNQUKC4uDhNmTJFoaGhni4RAAAYxKPhJT8/X1u3blV0dPRvJ/TyUnR0tNLS0ood98QTT6h27doaNmzYBc+Rl5cnh8PhtgEAgKuXR8PL8ePHVVBQoMDAQLf2wMBAZWZmFjlmw4YNWrBggV555ZVSnSM5OVkBAQGuLTg4+JLrBgAA5Ve5utvo1KlTGjRokF555RXVrFmzVGMmTpyonJwc15aRkeHhKgEAQFmq4MmD16xZU97e3srKynJrz8rKUlBQUKH++/fv16FDh9SjRw9Xm9Pp/LXQChW0Z88eNWzY0G2M3W6X3W73QPUAAKA88ujMS8WKFRUeHq7U1FRXm9PpVGpqqiIjIwv1b9asmb799ltt377dtfXs2VO33367tm/fziUhAADg2ZkXSUpISNDgwYPVpk0btW3bVrNmzVJubq6GDh0qSYqPj1e9evWUnJwsHx8ftWjRwm181apVJalQOwAA+GvyeHiJjY3VsWPHNGnSJGVmZqp169ZatWqVaxHvkSNH5OVVrpbeAACAcsxmWZZV1kVcTg6HQwEBAcrJyZG/v39ZlwPAQCGJKWVdAlCuHZrW7bIf82K+v5nyAAAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFGuSHiZO3euQkJC5OPjo4iICG3ZsqXYvq+88orat2+vatWqqVq1aoqOji6xPwAA+GvxeHhZunSpEhISlJSUpG3btqlVq1aKiYlRdnZ2kf3XrVunAQMG6NNPP1VaWpqCg4PVqVMnHT161NOlAgAAA9gsy7I8eYKIiAjddNNNmjNnjiTJ6XQqODhYo0ePVmJi4gXHFxQUqFq1apozZ47i4+Mv2N/hcCggIEA5OTny9/e/5PoB/PWEJKaUdQlAuXZoWrfLfsyL+f726MxLfn6+tm7dqujo6N9O6OWl6OhopaWlleoYZ86c0blz51S9evUi9+fl5cnhcLhtAADg6uXR8HL8+HEVFBQoMDDQrT0wMFCZmZmlOsYjjzyiunXrugWg30tOTlZAQIBrCw4OvuS6AQBA+VWu7zaaNm2a3nrrLb377rvy8fEpss/EiROVk5Pj2jIyMq5wlQAA4Eqq4MmD16xZU97e3srKynJrz8rKUlBQUIljZ8yYoWnTpmnNmjW64YYbiu1nt9tlt9svS70AAKD88+jMS8WKFRUeHq7U1FRXm9PpVGpqqiIjI4sdN336dD355JNatWqV2rRp48kSAQCAYTw68yJJCQkJGjx4sNq0aaO2bdtq1qxZys3N1dChQyVJ8fHxqlevnpKTkyVJ//73vzVp0iQtWbJEISEhrrUxfn5+8vPz83S5AACgnPN4eImNjdWxY8c0adIkZWZmqnXr1lq1apVrEe+RI0fk5fXbBNC8efOUn5+vu+66y+04SUlJmjx5sqfLBQAA5ZzHn/NypfGcFwCXiue8ACW7qp/zAgAAcLkRXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRrkh4mTt3rkJCQuTj46OIiAht2bKlxP7Lli1Ts2bN5OPjo5YtW2rFihVXokwAAGAAj4eXpUuXKiEhQUlJSdq2bZtatWqlmJgYZWdnF9l/06ZNGjBggIYNG6avv/5avXr1Uq9evfTdd995ulQAAGAAm2VZlidPEBERoZtuuklz5syRJDmdTgUHB2v06NFKTEws1D82Nla5ubn66KOPXG0333yzWrdurfnz51/wfA6HQwEBAcrJyZG/v//leyMA/jJCElPKugSgXDs0rdtlP+bFfH97dOYlPz9fW7duVXR09G8n9PJSdHS00tLSihyTlpbm1l+SYmJiiu0PAAD+Wip48uDHjx9XQUGBAgMD3doDAwOVnp5e5JjMzMwi+2dmZhbZPy8vT3l5ea7XDofjEqsGAADlmUfDy5WQnJysKVOmXLHzMZ0MFM8TU8ll4Wp5H8DVyqOXjWrWrClvb29lZWW5tWdlZSkoKKjIMUFBQRfVf+LEicrJyXFtGRkZl6d4AABQLnk0vFSsWFHh4eFKTU11tTmdTqWmpioyMrLIMZGRkW79JWn16tXF9rfb7fL393fbAADA1cvjl40SEhI0ePBgtWnTRm3bttWsWbOUm5uroUOHSpLi4+NVr149JScnS5LGjh2rqKgoPfvss+rWrZveeustffXVV3r55Zc9XSoAADCAx8NLbGysjh07pkmTJikzM1OtW7fWqlWrXItyjxw5Ii+v3yaAbrnlFi1ZskT/+te/9Oijj6px48Z677331KJFC0+XCgAADODx57xcaZ5+zgsLdoHisdAVwJ9Vbp7zAgAAcLkRXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAoHgsvJ06cUFxcnPz9/VW1alUNGzZMp0+fLrH/6NGj1bRpU1WqVEnXXXedxowZo5ycHE+VCAAADOSx8BIXF6edO3dq9erV+uijj/TZZ59pxIgRxfb/4Ycf9MMPP2jGjBn67rvv9Oqrr2rVqlUaNmyYp0oEAAAGslmWZV3ug+7evVvNmzfXl19+qTZt2kiSVq1apa5du+p//ud/VLdu3VIdZ9myZbr77ruVm5urChUqlGqMw+FQQECAcnJy5O/v/6ffQ3FCElMu+zGBq8Whad3KugQAhrqY72+PzLykpaWpatWqruAiSdHR0fLy8tLmzZtLfZzzb6Ck4JKXlyeHw+G2AQCAq5dHwktmZqZq167t1lahQgVVr15dmZmZpTrG8ePH9eSTT5Z4qUmSkpOTFRAQ4NqCg4P/dN0AAKD8u6jwkpiYKJvNVuKWnp5+yUU5HA5169ZNzZs31+TJk0vsO3HiROXk5Li2jIyMSz4/AAAov0q3kOT/TZgwQUOGDCmxT2hoqIKCgpSdne3W/ssvv+jEiRMKCgoqcfypU6fUuXNnValSRe+++66uueaaEvvb7XbZ7fZS1Q8AAMx3UeGlVq1aqlWr1gX7RUZG6uTJk9q6davCw8MlSWvXrpXT6VRERESx4xwOh2JiYmS32/XBBx/Ix8fnYsoDAAB/AR5Z8xIWFqbOnTtr+PDh2rJlizZu3KhRo0apf//+rjuNjh49qmbNmmnLli2Sfg0unTp1Um5urhYsWCCHw6HMzExlZmaqoKDAE2UCAAADXdTMy8VYvHixRo0apY4dO8rLy0t9+vTR7NmzXfvPnTunPXv26MyZM5Kkbdu2ue5EatSokduxDh48qJCQEE+VCgAADOKx8FK9enUtWbKk2P0hISH6/SNmOnToIA88cgYAAFxl+G0jAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMIrHwsuJEycUFxcnf39/Va1aVcOGDdPp06dLNdayLHXp0kU2m03vvfeep0oEAAAG8lh4iYuL086dO7V69Wp99NFH+uyzzzRixIhSjZ01a5ZsNpunSgMAAAar4ImD7t69W6tWrdKXX36pNm3aSJJeeOEFde3aVTNmzFDdunWLHbt9+3Y9++yz+uqrr1SnTh1PlAcAAAzmkZmXtLQ0Va1a1RVcJCk6OlpeXl7avHlzsePOnDmjgQMHau7cuQoKCvJEaQAAwHAemXnJzMxU7dq13U9UoYKqV6+uzMzMYseNHz9et9xyi+68885SnysvL095eXmu1w6H4+ILBgAAxriomZfExETZbLYSt/T09D9VyAcffKC1a9dq1qxZFzUuOTlZAQEBri04OPhPnR8AAJjhomZeJkyYoCFDhpTYJzQ0VEFBQcrOznZr/+WXX3TixIliLwetXbtW+/fvV9WqVd3a+/Tpo/bt22vdunVFjps4caISEhJcrx0OBwEGAICr2EWFl1q1aqlWrVoX7BcZGamTJ09q69atCg8Pl/RrOHE6nYqIiChyTGJiou699163tpYtW+q5555Tjx49ij2X3W6X3W6/iHcBAABM5pE1L2FhYercubOGDx+u+fPn69y5cxo1apT69+/vutPo6NGj6tixo15//XW1bdtWQUFBRc7KXHfddWrQoIEnygQAAAby2HNeFi9erGbNmqljx47q2rWrbr31Vr388suu/efOndOePXt05swZT5UAAACuQh6ZeZGk6tWra8mSJcXuDwkJkWVZJR7jQvsBAMBfD79tBAAAjEJ4gZGc+Wd1+N/ddfjf3eXMP1vW5QAAriDCCwAAMArhBQAAGMVmXWWrYh0OhwICApSTkyN/f/+yLgcekpubKz8/P0nS6dOn5evrW8YVAQAuxcV8fzPzAgAAjEJ4AQAARvHYc14AT/L19eU5QADwF8XMCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAoFcq6gMvNsixJksPhKONKAABAaZ3/3j7/PV6Sqy68nDp1SpIUHBxcxpUAAICLderUKQUEBJTYx2aVJuIYxOl06ocfflCVKlVks9nKuhx4kMPhUHBwsDIyMuTv71/W5QDwAP6d/3VYlqVTp06pbt268vIqeVXLVTfz4uXlpWuvvbasy8AV5O/vz39qwFWOf+d/DReacTmPBbsAAMAohBcAAGAUwguMZbfblZSUJLvdXtalAPAQ/p2jKFfdgl0AAHB1Y+YFAAAYhfACAACMQngBAABGIbzASK+++qqqVq1a1mUAAMoA4QVlasiQIbLZbIW2ffv2lXVpAC6zov6t/36bPHlyWZcIQ1x1T9iFeTp37qxFixa5tdWqVauMqgHgKf/7v//r+vPSpUs1adIk7dmzx9Xm5+fn+rNlWSooKFCFCnxNoTBmXlDm7Ha7goKC3Lbnn39eLVu2lK+vr4KDg/XAAw/o9OnTxR5jx44duv3221WlShX5+/srPDxcX331lWv/hg0b1L59e1WqVEnBwcEaM2aMcnNzr8TbA/D/fv9vPCAgQDabzfU6PT1dVapU0cqVKxUeHi673a4NGzZoyJAh6tWrl9txxo0bpw4dOrheO51OJScnq0GDBqpUqZJatWql5cuXX9k3hyuK8IJyycvLS7Nnz9bOnTv12muvae3atXr44YeL7R8XF6drr71WX375pbZu3arExERdc801kqT9+/erc+fO6tOnj7755hstXbpUGzZs0KhRo67U2wFQSomJiZo2bZp2796tG264oVRjkpOT9frrr2v+/PnauXOnxo8fr7vvvlvr16/3cLUoK8zHocx99NFHbtPFXbp00bJly1yvQ0JC9NRTT+mf//ynXnzxxSKPceTIET300ENq1qyZJKlx48aufcnJyYqLi9O4ceNc+2bPnq2oqCjNmzdPPj4+HnhXAP6MJ554QnfccUep++fl5Wnq1Klas2aNIiMjJUmhoaHasGGDXnrpJUVFRXmqVJQhwgvK3O2336558+a5Xvv6+mrNmjVKTk5Wenq6HA6HfvnlF509e1ZnzpxR5cqVCx0jISFB9957r9544w1FR0erb9++atiwoaRfLyl98803Wrx4sau/ZVlyOp06ePCgwsLCPP8mAZRKmzZtLqr/vn37dObMmUKBJz8/XzfeeOPlLA3lCOEFZc7X11eNGjVyvT506JC6d++u+++/X08//bSqV6+uDRs2aNiwYcrPzy8yvEyePFkDBw5USkqKVq5cqaSkJL311lvq3bu3Tp8+rfvuu09jxowpNO66667z6HsDcHF8fX3dXnt5eemPv2Jz7tw515/Pr4VLSUlRvXr13Prxe0hXL8ILyp2tW7fK6XTq2WeflZfXr8uy/vu///uC45o0aaImTZpo/PjxGjBggBYtWqTevXvrb3/7m3bt2uUWkACYoVatWvruu+/c2rZv3+5a09a8eXPZ7XYdOXKES0R/ISzYRbnTqFEjnTt3Ti+88IIOHDigN954Q/Pnzy+2/88//6xRo0Zp3bp1Onz4sDZu3Kgvv/zSdTnokUce0aZNmzRq1Cht375de/fu1fvvv8+CXcAAf//73/XVV1/p9ddf1969e5WUlOQWZqpUqaIHH3xQ48eP12uvvab9+/dr27ZteuGFF/Taa6+VYeXwJMILyp1WrVpp5syZ+ve//60WLVpo8eLFSk5OLra/t7e3fvzxR8XHx6tJkybq16+funTpoilTpkiSbrjhBq1fv17ff/+92rdvrxtvvFGTJk1S3bp1r9RbAvAnxcTE6PHHH9fDDz+sm266SadOnVJ8fLxbnyeffFKPP/64kpOTFRYWps6dOyslJUUNGjQoo6rhaTbrjxcTAQAAyjFmXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8ALhqdOjQwfXr4QCuXoQXABclMzNTo0ePVmhoqOx2u4KDg9WjRw+lpqZ69LwFBQWaNm2amjVrpkqVKql69eqKiIjQf/7zH4+eF0D5ww8zAii1Q4cOqV27dqpataqeeeYZtWzZUufOndPHH3+skSNHKj09vdCYc+fOuX5E71JMmTJFL730kubMmaM2bdrI4XDoq6++0k8//XTJxwZgFmZeAJTaAw88IJvNpi1btqhPnz5q0qSJrr/+eiUkJOiLL76QJNlsNs2bN089e/aUr6+vnnrqKTVq1EgzZsxwO9b27dtls9m0b98+SdKRI0d05513ys/PT/7+/urXr5+ysrJc/T/44AM98MAD6tu3rxo0aKBWrVpp2LBhevDBB4utNy8vTw8++KDq1asnX19fRUREaN26dW59NmzYoPbt26tSpUoKDg7WmDFjlJub69ofEhKiJ598UgMGDJCvr6/q1aunuXPnXupHCeASEF4AlMqJEye0atUqjRw5Ur6+voX2V61a1fXnyZMnq3fv3vr22281bNgw3XPPPVq0aJFb/0WLFum2225To0aN5HQ6deedd+rEiRNav369Vq9erQMHDig2NtbVPygoSGvXrtWxY8dKXfOoUaOUlpamt956S99884369u2rzp07a+/evZKk/fv3q3PnzurTp4+++eYbLV26VBs2bCj0i+PPPPOMWrVqpa+//lqJiYkaO3asVq9eXeo6AFxmFgCUwubNmy1J1jvvvFNiP0nWuHHj3NqOHj1qeXt7W5s3b7Ysy7Ly8/OtmjVrWq+++qplWZb1ySefWN7e3taRI0dcY3bu3GlJsrZs2eJ6HRYWZnl5eVktW7a07rvvPmvFihVu54mKirLGjh1rWZZlHT582PL29raOHj3q1qdjx47WxIkTLcuyrGHDhlkjRoxw2//5559bXl5e1s8//2xZlmXVr1/f6ty5s1uf2NhYq0uXLiV+DgA8h5kXAKViXcQP0Ldp08btdd26ddWtWzctXLhQkvThhx8qLy9Pffv2lSTt3r1bwcHBCg4Odo1p3ry5qlatqt27d7tef/fdd/riiy90zz33KDs7Wz169NC9995bZA3ffvutCgoK1KRJE/n5+bm29evXa//+/ZKkHTt26NVXX3XbHxMTI6fTqYMHD7qOFRkZ6XbsyMhIV10ArjwW7AIolcaNG8tmsxW5KPePirqsdO+992rQoEF67rnntGjRIsXGxqpy5coXVYOXl5duuukm3XTTTRo3bpzefPNNDRo0SI899pgaNGjg1vf06dPy9vbW1q1b5e3t7bbPz8/P1ee+++7TmDFjCp3ruuuuu6jaAFw5hBcApVK9enXFxMRo7ty5GjNmTKGAcvLkSbd1L3/UtWtX+fr6at68eVq1apU+++wz176wsDBlZGQoIyPDNfuya9cunTx5Us2bNy/2mOf3/X6B7Xk33nijCgoKlJ2drfbt2xc5/m9/+5t27dqlRo0aFXsOSa7FyL9/HRYWVuIYAJ7DZSMApTZ37lwVFBSobdu2evvtt7V3717t3r1bs2fPLnRp5Y+8vb01ZMgQTZw4UY0bN3brHx0drZYtWyouLk7btm3Tli1bFB8fr6ioKNclqLvuukvPPfecNm/erMOHD2vdunUaOXKkmjRpombNmhU6X5MmTRQXF6f4+Hi98847OnjwoLZs2aLk5GSlpKRIkh555BFt2rRJo0aN0vbt27V37169//77hRbsbty4UdOnT9f333+vuXPnatmyZRo7duylfpwA/iTCC4BSCw0N1bZt23T77bdrwoQJatGihe644w6lpqZq3rx5Fxw/bNgw5efna+jQoW7tNptN77//vqpVq6bbbrtN0dHRCg0N1dKlS119YmJi9OGHH6pHjx5q0qSJBg8erGbNmumTTz5RhQpFTyIvWrRI8fHxmjBhgpo2bapevXrpyy+/dF0SuuGGG7R+/Xp9//33at++vW688UZNmjRJdevWdTvOhAkT9NVXX+nGG2/UU089pZkzZyomJuZiPz4Al4nNuphVeABwCT7//HN17NhRGRkZCgwMLOtySiUkJETjxo3jZweAcoQ1LwA8Li8vT8eOHdPkyZPVt29fY4ILgPKJy0YAPO6//uu/VL9+fZ08eVLTp08v63IAGI7LRgAAwCjMvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAo/wfbNk4Ifkhw1sAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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Tp8rPz++Sv/8rJiZGX375pSZOnKiQkBBFREQoLi7ukvo6q0uXLqpXr54GDhyoJ554Qq6urpo1a5YCAgK0d+9ea7uAgACNGjVKY8eOVVJSkv75z39q+/btev3113Xdddfpvvvus7Z94IEH9P777yspKUk9e/bUzp079fbbb1uD1LljBwUFqX379goMDNS2bdv02muvKTk5WdWqVbO2y8zM1NGjR9W9e/fLOlagTA66SwuoNM69Ffx8yroVfObMmUajRo0MDw8Po2nTpsbs2bPLvE1XkjF06NAy+1UZt02PGzfOqFOnjuHi4mJza+8nn3xiREVFGZ6enkZ4eLgxfvx4Y9asWaVuKf777bs//vij8cQTTxht2rQxatSoYbi5uRnBwcHGXXfdZWzatMlm7JMnTxr33HOPUb16dUOS9RbtkpIS48UXXzTCwsIMDw8Po3Xr1sbSpUtL3cZtGIbx3XffGTExMYa7u3up43v77beN+vXrG+7u7kZ0dLSxfPnyUn28//77RpcuXYzatWsb7u7uRr169YyHHnrIOHDgQJnv4bkOHjxouLm5GfPnzy+17dtvvzX+8Y9/GNWqVTN8fHyMqKioUreNf/nll0b79u0NLy8vw9fX1+jWrZuxdetWmzZnf8eHDh2yWV/W7d0///yzceONNxpeXl6GJOvt1uXdCv73vzHDKPsW+szMTCMuLs76/kycOLHMPg3jr1u/mzZtalSpUsUIDAw0Hn744VK3xBuGYUyYMMGoU6eO4eHhYbRv397YuHFjqbHfeOMN48YbbzRq1qxpeHh4GA0aNDCeeOIJIzc316avkSNHGvXq1bO51R64UiyGwRd7ALi2DBw4UL/88ov+85//OLqUa1JBQYHCw8P11FNPafjw4Y4uBybENTcArjljxozRhg0beDqug8yePVtVqlRxmi8vhfkwcwMAAEyFmRsAAGAqhBsAAGAqhBsAAGAqhBsAAGAq1+RD/EpKSrR//35Vq1aNL2wDAKCSMAxDJ06cUEhIiM032f/dNRlu9u/fr9DQUEeXAQAALsG+fftUt27dcrdfk+Hm7CPA9+3bZ338OgAAcG55eXkKDQ21+SqPslyT4ebsqShfX1/CDQAAlcyFLinhgmIAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqbo4uAFfHT7/nOrqESqNlXT9HlwAAuAzM3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFOxa7j55ptv1K1bN4WEhMhiseijjz664D6rVq1SmzZt5OHhoYYNG2rOnDml2kybNk3h4eHy9PRUXFyc1q9ff+WLBwAAlZJdw01+fr5atWqladOmVaj9rl27lJycrJtuuklZWVkaMWKEHnjgAS1fvtzaZvHixUpJSdGYMWO0adMmtWrVSomJicrJybHXYQAAgErEYhiGcVUGslj04YcfqkePHuW2GTlypD777DNt3rzZuq537946fvy4li1bJkmKi4vTddddp9dee02SVFJSotDQUP3rX//SU089VaFa8vLy5Ofnp9zcXPn6+l76QVUifCt4xfGt4ADgnCr6+e1U19ysXbtWCQkJNusSExO1du1aSVJhYaEyMzNt2ri4uCghIcHapiwFBQXKy8uzWQAAgDk5VbjJzs5WYGCgzbrAwEDl5eXp9OnTOnz4sIqLi8tsk52dXW6/aWlp8vPzsy6hoaF2qR8AADieU4Ubexk1apRyc3Oty759+xxdEgAAsBM3RxdwrqCgIB08eNBm3cGDB+Xr6ysvLy+5urrK1dW1zDZBQUHl9uvh4SEPDw+71AwAAJyLU83cxMfHKyMjw2bdypUrFR8fL0lyd3dXTEyMTZuSkhJlZGRY2wAAgGubXcPNyZMnlZWVpaysLEl/3eqdlZWlvXv3SvrrdFGfPn2s7QcPHqzffvtNTz75pH7++We9/vrrevfdd/Xoo49a26SkpGjGjBmaO3eutm3bpocfflj5+fnq37+/PQ8FAABUEnY9LbVx40bddNNN1tcpKSmSpL59+2rOnDk6cOCANehIUkREhD777DM9+uijmjx5surWrau33npLiYmJ1ja9evXSoUOHNHr0aGVnZys6OlrLli0rdZExAAC4Nl2159w4E55zg/PhOTcA4Jwq5XNuAAAALhfhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmMpVCTfTpk1TeHi4PD09FRcXp/Xr15fbtlOnTrJYLKWW5ORka5t+/fqV2p6UlHQ1DgUAADg5N3sPsHjxYqWkpCg9PV1xcXGaNGmSEhMTtX37dtWuXbtU+yVLlqiwsND6+siRI2rVqpXuuusum3ZJSUmaPXu29bWHh4f9DgIAAFQadp+5mThxogYNGqT+/fsrMjJS6enp8vb21qxZs8psX6NGDQUFBVmXlStXytvbu1S48fDwsGnn7+9v70MBAACVgF3DTWFhoTIzM5WQkPC/AV1clJCQoLVr11aoj5kzZ6p3797y8fGxWb9q1SrVrl1bTZo00cMPP6wjR46U20dBQYHy8vJsFgAAYE52DTeHDx9WcXGxAgMDbdYHBgYqOzv7gvuvX79emzdv1gMPPGCzPikpSfPmzVNGRobGjx+v1atXq2vXriouLi6zn7S0NPn5+VmX0NDQSz8oAADg1Ox+zc3lmDlzplq2bKm2bdvarO/du7f155YtWyoqKkoNGjTQqlWr1Llz51L9jBo1SikpKdbXeXl5BBwAAEzKrjM3tWrVkqurqw4ePGiz/uDBgwoKCjrvvvn5+Vq0aJEGDhx4wXHq16+vWrVqaceOHWVu9/DwkK+vr80CAADMya4zN+7u7oqJiVFGRoZ69OghSSopKVFGRoaGDRt23n3fe+89FRQU6L777rvgOL///ruOHDmi4ODgK1G26RiGoT+Lyj5lh9IMw5DFYnF0GQCAS2T301IpKSnq27evYmNj1bZtW02aNEn5+fnq37+/JKlPnz6qU6eO0tLSbPabOXOmevTooZo1a9qsP3nypMaOHas77rhDQUFB2rlzp5588kk1bNhQiYmJ9j6cSscwDN2ZvlaZe445upRKIzbMX+8NjifgAEAlZfdw06tXLx06dEijR49Wdna2oqOjtWzZMutFxnv37pWLi+3Zse3bt+vbb7/VihUrSvXn6uqqH3/8UXPnztXx48cVEhKiLl26aNy4cTzrpgyni4oJNhdp455jOl1ULG93p74kDQBQDothGIaji7ja8vLy5Ofnp9zcXNNff3Oq8IwiRy+XJM0f0FaeVVwdXJHz+rOoWPfP+uvp2VufSyTcAICTqejnN//vfQ3xrOJKuAEAmB5fnAkAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEzFzdEFAM5oyx958qzi6ugynF7Lun6OLgEASmHmBgAAmAozN0AZ/iwqdnQJlYJhGLJYLI4uAwBsEG6AMtw/a72jS6gUYsP89d7geAIOAKfCaSng//Nwc1GzYF9Hl1GpbNxzTKeZ5QLgZJi5Af4/i8Wi8be3VMGZEkeX4vT+LCpmdguA0yLcAOewWCzcJQUAlRynpQAAgKkwcwP8Dc9uubBThWccXQIAlIuZGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCpXJdxMmzZN4eHh8vT0VFxcnNavL/+x7XPmzJHFYrFZPD09bdoYhqHRo0crODhYXl5eSkhI0K+//mrvwwAAAJWA3cPN4sWLlZKSojFjxmjTpk1q1aqVEhMTlZOTU+4+vr6+OnDggHXZs2ePzfaXX35ZU6ZMUXp6utatWycfHx8lJibqzz//tPfhAAAAJ2f3cDNx4kQNGjRI/fv3V2RkpNLT0+Xt7a1Zs2aVu4/FYlFQUJB1CQwMtG4zDEOTJk3SM888o+7duysqKkrz5s3T/v379dFHH9n7cAAAgJOza7gpLCxUZmamEhIS/jegi4sSEhK0du3acvc7efKkwsLCFBoaqu7du2vLli3Wbbt27VJ2drZNn35+foqLiztvnwAA4Npg13Bz+PBhFRcX28y8SFJgYKCys7PL3KdJkyaaNWuWPv74Y7399tsqKSlRu3bt9Pvvv0uSdb+L6bOgoEB5eXk2CwAAMCenu1sqPj5effr0UXR0tDp27KglS5YoICBAb7zxxiX3mZaWJj8/P+sSGhp6BSsGAADOxK7hplatWnJ1ddXBgwdt1h88eFBBQUEV6qNKlSpq3bq1duzYIUnW/S6mz1GjRik3N9e67Nu372IPBQAAVBJ2DTfu7u6KiYlRRkaGdV1JSYkyMjIUHx9foT6Ki4v1008/KTg4WJIUERGhoKAgmz7z8vK0bt26cvv08PCQr6+vzQIAAMzJzd4DpKSkqG/fvoqNjVXbtm01adIk5efnq3///pKkPn36qE6dOkpLS5MkPffcc7r++uvVsGFDHT9+XK+88or27NmjBx54QNJfd1KNGDFCzz//vBo1aqSIiAg9++yzCgkJUY8ePex9OAAAwMnZPdz06tVLhw4d0ujRo5Wdna3o6GgtW7bMekHw3r175eLyvwmkY8eOadCgQcrOzpa/v79iYmL03XffKTIy0trmySefVH5+vh588EEdP35cN9xwg5YtW1bqYX8AAODaYzEMw3B0EVdbXl6e/Pz8lJuba/pTVKcKzyhy9HJJ0nsPxcuziquDK3J+Lev6OboEp3fu39XW5xLl7W73fycBQIU/v53ubikAAIDLQbgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACm4uboAnD1NK/jK293fuUAAHNj5gYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJjKVQk306ZNU3h4uDw9PRUXF6f169eX23bGjBnq0KGD/P395e/vr4SEhFLt+/XrJ4vFYrMkJSXZ+zAAAEAlYPdws3jxYqWkpGjMmDHatGmTWrVqpcTEROXk5JTZftWqVbr77rv19ddfa+3atQoNDVWXLl30xx9/2LRLSkrSgQMHrMs777xj70MBAACVgN3DzcSJEzVo0CD1799fkZGRSk9Pl7e3t2bNmlVm+wULFmjIkCGKjo5W06ZN9dZbb6mkpEQZGRk27Tw8PBQUFGRd/P397X0oAACgErBruCksLFRmZqYSEhL+N6CLixISErR27doK9XHq1CkVFRWpRo0aNutXrVql2rVrq0mTJnr44Yd15MiRcvsoKChQXl6ezQIAAMzJruHm8OHDKi4uVmBgoM36wMBAZWdnV6iPkSNHKiQkxCYgJSUlad68ecrIyND48eO1evVqde3aVcXFxWX2kZaWJj8/P+sSGhp66QcFAACcmpujCzifl156SYsWLdKqVavk6elpXd+7d2/rzy1btlRUVJQaNGigVatWqXPnzqX6GTVqlFJSUqyv8/LyCDgAAJiUXWduatWqJVdXVx08eNBm/cGDBxUUFHTefV999VW99NJLWrFihaKios7btn79+qpVq5Z27NhR5nYPDw/5+vraLAAAwJzsOnPj7u6umJgYZWRkqEePHpJkvTh42LBh5e738ssv64UXXtDy5csVGxt7wXF+//13HTlyRMHBwVeqdAAAKoWffs91dAmltKzr59Dx7X63VEpKimbMmKG5c+dq27Ztevjhh5Wfn6/+/ftLkvr06aNRo0ZZ248fP17PPvusZs2apfDwcGVnZys7O1snT56UJJ08eVJPPPGEvv/+e+3evVsZGRnq3r27GjZsqMTERHsfDgAAcHJ2v+amV69eOnTokEaPHq3s7GxFR0dr2bJl1ouM9+7dKxeX/2Ws6dOnq7CwUHfeeadNP2PGjFFqaqpcXV31448/au7cuTp+/LhCQkLUpUsXjRs3Th4eHvY+HAAA4OSuygXFw4YNK/c01KpVq2xe7969+7x9eXl5afny5VeoMgAAYDZ8txQAADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVp/76BQAAUD7DMPRnUdnfq+hIpwrPyKuKqywWi0PGJ9wAAFAJGYahO9PXKnPPMUeXUqatzyXK290xMYPTUgAAVEKni4qdNtg4GjM3AABUcvMHtJVnFVdHl2HVvI6vvBxYD+EGAIBKzrOKq1OFG0edjjqL01IAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUeIgfAACVXPM6vg5/cJ4zYeYGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYylUJN9OmTVN4eLg8PT0VFxen9evXn7f9e++9p6ZNm8rT01MtW7bU559/brPdMAyNHj1awcHB8vLyUkJCgn799Vd7HgIAAKgk7B5uFi9erJSUFI0ZM0abNm1Sq1atlJiYqJycnDLbf/fdd7r77rs1cOBA/fe//1WPHj3Uo0cPbd682drm5Zdf1pQpU5Senq5169bJx8dHiYmJ+vPPP+19OAAAwMnZPdxMnDhRgwYNUv/+/RUZGan09HR5e3tr1qxZZbafPHmykpKS9MQTT6hZs2YaN26c2rRpo9dee03SX7M2kyZN0jPPPKPu3bsrKipK8+bN0/79+/XRRx/Z+3AAAICTs2u4KSwsVGZmphISEv43oIuLEhIStHbt2jL3Wbt2rU17SUpMTLS237Vrl7Kzs23a+Pn5KS4urtw+CwoKlJeXZ7MAAABzsmu4OXz4sIqLixUYGGizPjAwUNnZ2WXuk52dfd72Z//3YvpMS0uTn5+fdQkNDb2k4wEAAM7vmrhbatSoUcrNzbUu+/btc3RJAADATuwabmrVqiVXV1cdPHjQZv3BgwcVFBRU5j5BQUHnbX/2fy+mTw8PD/n6+tosAADAnOwabtzd3RUTE6OMjAzrupKSEmVkZCg+Pr7MfeLj423aS9LKlSut7SMiIhQUFGTTJi8vT+vWrSu3TwAAcO1ws/cAKSkp6tu3r2JjY9W2bVtNmjRJ+fn56t+/vySpT58+qlOnjtLS0iRJw4cPV8eOHTVhwgQlJydr0aJF2rhxo958801JksVi0YgRI/T888+rUaNGioiI0LPPPquQkBD16NHD3ocDAACcnN3DTa9evXTo0CGNHj1a2dnZio6O1rJly6wXBO/du1cuLv+bQGrXrp0WLlyoZ555Rk8//bQaNWqkjz76SC1atLC2efLJJ5Wfn68HH3xQx48f1w033KBly5bJ09PT3ocDAACcnMUwDMPRRVxteXl58vPzU25urumvvzlVeEaRo5dLkrY+lyhvd7vnWVwD+LsCHO9a/O+wop/f18TdUgAA4NpBuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKbi5ugCAFRuW/7Ik2cVV0eXUSm0rOvn6BKAawIzNwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFR4iB+Ay/JnUbGjS6g0DMOQxWJxdBmA6RFuAFyW+2etd3QJlUZsmL/eGxxPwAHsjNNSAC6aVxVXxYb5O7qMSmfjnmM6zUwXYHfM3AC4aBaLRe8NjtfG3cccXUql8GdRMTNcwFVkt5mbo0eP6t5775Wvr6+qV6+ugQMH6uTJk+dt/69//UtNmjSRl5eX6tWrp0ceeUS5ubk27SwWS6ll0aJF9joMAOWwWCzyrOLKUsEFwNVjt5mbe++9VwcOHNDKlStVVFSk/v3768EHH9TChQvLbL9//37t379fr776qiIjI7Vnzx4NHjxY+/fv1/vvv2/Tdvbs2UpKSrK+rl69ur0OAwAAVDJ2CTfbtm3TsmXLtGHDBsXGxkqSpk6dqltuuUWvvvqqQkJCSu3TokULffDBB9bXDRo00AsvvKD77rtPZ86ckZvb/0qtXr26goKC7FE6AACo5OxyWmrt2rWqXr26NdhIUkJCglxcXLRu3boK95ObmytfX1+bYCNJQ4cOVa1atdS2bVvNmjVLhmGct5+CggLl5eXZLAAAwJzsMnOTnZ2t2rVr2w7k5qYaNWooOzu7Qn0cPnxY48aN04MPPmiz/rnnntPNN98sb29vrVixQkOGDNHJkyf1yCOPlNtXWlqaxo4de/EHAgAAKp2Lmrl56qmnyryg99zl559/vuyi8vLylJycrMjISKWmptpse/bZZ9W+fXu1bt1aI0eO1JNPPqlXXnnlvP2NGjVKubm51mXfvn2XXSMAAHBOFzVz89hjj6lfv37nbVO/fn0FBQUpJyfHZv2ZM2d09OjRC14rc+LECSUlJalatWr68MMPVaVKlfO2j4uL07hx41RQUCAPD48y23h4eJS7DQAAmMtFhZuAgAAFBARcsF18fLyOHz+uzMxMxcTESJK++uorlZSUKC4urtz98vLylJiYKA8PD33yySfy9PS84FhZWVny9/cnvAAAAEl2uuamWbNmSkpK0qBBg5Senq6ioiINGzZMvXv3tt4p9ccff6hz586aN2+e2rZtq7y8PHXp0kWnTp3S22+/bXPhb0BAgFxdXfXpp5/q4MGDuv766+Xp6amVK1fqxRdf1OOPP26PwwAAAJWQ3Z5zs2DBAg0bNkydO3eWi4uL7rjjDk2ZMsW6vaioSNu3b9epU6ckSZs2bbLeSdWwYUObvnbt2qXw8HBVqVJF06ZN06OPPirDMNSwYUNNnDhRgwYNstdhAACASsZu4aZGjRrlPrBPksLDw21u4e7UqdMFb+lOSkqyeXgfAMdqWdfP0SVUCqcKzzi6BOCawhdnAgAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAU7Hbd0tdy376PdfRJVj9WVTs6BIAALiqmLkBAACmQrgBAACmwmmpK8gwDJ0uKnaqU0HOVAsAAFcD4eYKOl1UrMjRyx1dBgAA1zROS10jYsP85VXF1dFlAABgd8zcXEFeVVy19blEbfkjz9GllBIb7i+LxeLoMgAAsDvCzRVksVjk7e4mTyecISHYAACuFZyWAgAApkK4AQAApsJpKTtoWdfP0SUAAHDNYuYGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYipujCwAA4O9++j3X0SU4vT+Lih1dgtOy28zN0aNHde+998rX11fVq1fXwIEDdfLkyfPu06lTJ1ksFptl8ODBNm327t2r5ORkeXt7q3bt2nriiSd05swZex0GAACoZOw2c3PvvffqwIEDWrlypYqKitS/f389+OCDWrhw4Xn3GzRokJ577jnra29vb+vPxcXFSk5OVlBQkL777jsdOHBAffr0UZUqVfTiiy/a61AAAEAlYpdws23bNi1btkwbNmxQbGysJGnq1Km65ZZb9OqrryokJKTcfb29vRUUFFTmthUrVmjr1q368ssvFRgYqOjoaI0bN04jR45Uamqq3N3d7XE4AACgErHLaam1a9eqevXq1mAjSQkJCXJxcdG6devOu++CBQtUq1YttWjRQqNGjdKpU6ds+m3ZsqUCAwOt6xITE5WXl6ctW7aU22dBQYHy8vJsFgAAYE52mbnJzs5W7dq1bQdyc1ONGjWUnZ1d7n733HOPwsLCFBISoh9//FEjR47U9u3btWTJEmu/5wYbSdbX5+s3LS1NY8eOvdTDAQAAlchFhZunnnpK48ePP2+bbdu2XXIxDz74oPXnli1bKjg4WJ07d9bOnTvVoEGDS+531KhRSklJsb7Oy8tTaGjoJfcHAACc10WFm8cee0z9+vU7b5v69esrKChIOTk5NuvPnDmjo0ePlns9TVni4uIkSTt27FCDBg0UFBSk9evX27Q5ePCgJJ23Xw8PD3l4eFR4XAAAUHldVLgJCAhQQEDABdvFx8fr+PHjyszMVExMjCTpq6++UklJiTWwVERWVpYkKTg42NrvCy+8oJycHOtpr5UrV8rX11eRkZEXcygAAMCk7HJBcbNmzZSUlKRBgwZp/fr1WrNmjYYNG6bevXtb75T6448/1LRpU+tMzM6dOzVu3DhlZmZq9+7d+uSTT9SnTx/deOONioqKkiR16dJFkZGRuv/++/XDDz9o+fLleuaZZzR06FBmZgAAgCQ7PsRvwYIFatq0qTp37qxbbrlFN9xwg958803r9qKiIm3fvt16N5S7u7u+/PJLdenSRU2bNtVjjz2mO+64Q59++ql1H1dXVy1dulSurq6Kj4/Xfffdpz59+tg8FwcAAFzb7PYQvxo1apz3gX3h4eEyDMP6OjQ0VKtXr75gv2FhYfr888+vSI0AAMB8+G4pAIBTMQyD702qAN6j8hFuAABOwzAM3Zm+Vpl7jjm6FFRidrvmBgCAi3W6qJhgc5Fiw/zlVcXV0WU4FWZuAABOaf6AtvLkQ/uCYsP9ZbFYHF2GUyHcAACckmcVV8JNBRBsSuO0FAAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBWeUAwAV9GWP/J46u558E3XuBKYuQEAAKbCzA0AXEXMTJzfue9P8zq+8nbnYwoXj78aALiK7p+13tElAKbHaSkAsDOvKq6KDfN3dBmVSmyYv7y4NgmXiJkbALAzi8Wi9wbHa+PuY44updKIDfeXxWJxdBmopAg3AHAVWCwW7pK6CAQbXA5OSwEAAFMh3AAAAFPhtBQAXCUt6/o5ugTgmsDMDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBU3RxfgCIZhSJLy8vIcXAkAAKios5/bZz/Hy3NNhpsTJ05IkkJDQx1cCQAAuFgnTpyQn59fudstxoXijwmVlJRo//79qlatmiwWyxXtOy8vT6Ghodq3b598fX2vaN9mw3tVcbxXFcd7VXG8VxXHe1Vx9nyvDMPQiRMnFBISIheX8q+suSZnblxcXFS3bl27juHr68t/ABXEe1VxvFcVx3tVcbxXFcd7VXH2eq/ON2NzFhcUAwAAUyHcAAAAUyHcXGEeHh4aM2aMPDw8HF2K0+O9qjjeq4rjvao43quK472qOGd4r67JC4oBAIB5MXMDAABMhXADAABMhXADAABMhXADAABMhXBzBU2bNk3h4eHy9PRUXFyc1q9f7+iSnNI333yjbt26KSQkRBaLRR999JGjS3JKaWlpuu6661StWjXVrl1bPXr00Pbt2x1dltOaPn26oqKirA8Oi4+P1xdffOHospzeSy+9JIvFohEjRji6FKeUmpoqi8ViszRt2tTRZTmtP/74Q/fdd59q1qwpLy8vtWzZUhs3brzqdRBurpDFixcrJSVFY8aM0aZNm9SqVSslJiYqJyfH0aU5nfz8fLVq1UrTpk1zdClObfXq1Ro6dKi+//57rVy5UkVFRerSpYvy8/MdXZpTqlu3rl566SVlZmZq48aNuvnmm9W9e3dt2bLF0aU5rQ0bNuiNN95QVFSUo0txas2bN9eBAwesy7fffuvokpzSsWPH1L59e1WpUkVffPGFtm7dqgkTJsjf3//qF2Pgimjbtq0xdOhQ6+vi4mIjJCTESEtLc2BVzk+S8eGHHzq6jEohJyfHkGSsXr3a0aVUGv7+/sZbb73l6DKc0okTJ4xGjRoZK1euNDp27GgMHz7c0SU5pTFjxhitWrVydBmVwsiRI40bbrjB0WUYhmEYzNxcAYWFhcrMzFRCQoJ1nYuLixISErR27VoHVgYzyc3NlSTVqFHDwZU4v+LiYi1atEj5+fmKj493dDlOaejQoUpOTrb5/y2U7ddff1VISIjq16+ve++9V3v37nV0SU7pk08+UWxsrO666y7Vrl1brVu31owZMxxSC+HmCjh8+LCKi4sVGBhosz4wMFDZ2dkOqgpmUlJSohEjRqh9+/Zq0aKFo8txWj/99JOqVq0qDw8PDR48WB9++KEiIyMdXZbTWbRokTZt2qS0tDRHl+L04uLiNGfOHC1btkzTp0/Xrl271KFDB504ccLRpTmd3377TdOnT1ejRo20fPlyPfzww3rkkUc0d+7cq17LNfmt4EBlM3ToUG3evJlz/RfQpEkTZWVlKTc3V++//7769u2r1atXE3DOsW/fPg0fPlwrV66Up6eno8txel27drX+HBUVpbi4OIWFhendd9/VwIEDHViZ8ykpKVFsbKxefPFFSVLr1q21efNmpaenq2/fvle1FmZuroBatWrJ1dVVBw8etFl/8OBBBQUFOagqmMWwYcO0dOlSff3116pbt66jy3Fq7u7uatiwoWJiYpSWlqZWrVpp8uTJji7LqWRmZionJ0dt2rSRm5ub3NzctHr1ak2ZMkVubm4qLi52dIlOrXr16mrcuLF27Njh6FKcTnBwcKl/SDRr1swhp/EIN1eAu7u7YmJilJGRYV1XUlKijIwMzvfjkhmGoWHDhunDDz/UV199pYiICEeXVOmUlJSooKDA0WU4lc6dO+unn35SVlaWdYmNjdW9996rrKwsubq6OrpEp3by5Ent3LlTwcHBji7F6bRv377U4yp++eUXhYWFXfVaOC11haSkpKhv376KjY1V27ZtNWnSJOXn56t///6OLs3pnDx50uZfPbt27VJWVpZq1KihevXqObAy5zJ06FAtXLhQH3/8sapVq2a9fsvPz09eXl4Ors75jBo1Sl27dlW9evV04sQJLVy4UKtWrdLy5csdXZpTqVatWqnrtnx8fFSzZk2u5yrD448/rm7duiksLEz79+/XmDFj5OrqqrvvvtvRpTmdRx99VO3atdOLL76onj17av369XrzzTf15ptvXv1iHH27lplMnTrVqFevnuHu7m60bdvW+P777x1dklP6+uuvDUmllr59+zq6NKdS1nskyZg9e7ajS3NKAwYMMMLCwgx3d3cjICDA6Ny5s7FixQpHl1UpcCt4+Xr16mUEBwcb7u7uRp06dYxevXoZO3bscHRZTuvTTz81WrRoYXh4eBhNmzY13nzzTYfUYTEMw7j6kQoAAMA+uOYGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGgMPNmTNH1atXd5p+AFRuhBsA59WvXz9ZLBYNHjy41LahQ4fKYrGoX79+lzVGr1699Msvv1hfp6amKjo6+rL6lKTi4mK99NJLatq0qby8vFSjRg3FxcXprbfesrbp1KmTRowYcdF99+vXTz169LjsGgFceXy3FIALCg0N1aJFi/Tvf//b+r1Wf/75pxYuXHjZ3wdWVFQkLy8vu3xf1tixY/XGG2/otddeU2xsrPLy8rRx40YdO3bsio8FwHkwcwPggtq0aaPQ0FAtWbLEum7JkiWqV6+eWrdubV23bNky3XDDDapevbpq1qypW2+9VTt37rRu3717tywWixYvXqyOHTvK09NTCxYssDmdNGfOHI0dO1Y//PCDLBaLLBaL5syZI0maOHGiWrZsKR8fH4WGhmrIkCE6efJkuXV/8sknGjJkiO666y5FRESoVatWGjhwoB5//HFJf82+rF69WpMnT7aOtXv3bhUXF2vgwIGKiIiQl5eXmjRposmTJ1v7TU1N1dy5c/Xxxx9b91u1apVWrVoli8Wi48ePW9tmZWVZ+5WkPXv2qFu3bvL395ePj4+aN2+uzz///FJ/NQDKQLgBUCEDBgzQ7Nmzra9nzZpV6lvv8/PzlZKSoo0bNyojI0MuLi667bbbVFJSYtPuqaee0vDhw7Vt2zYlJibabOvVq5cee+wxNW/eXAcOHNCBAwfUq1cvSZKLi4umTJmiLVu2aO7cufrqq6/05JNPlltzUFCQvvrqKx06dKjM7ZMnT1Z8fLwGDRpkHSs0NFQlJSWqW7eu3nvvPW3dulWjR4/W008/rXfffVfSX98U3bNnTyUlJVn3a9euXYXex6FDh6qgoEDffPONfvrpJ40fP15Vq1at0L4AKobTUgAq5L777tOoUaO0Z88eSdKaNWu0aNEirVq1ytrmjjvusNln1qxZCggI0NatW9WiRQvr+hEjRuj2228vcxwvLy9VrVpVbm5uCgoKstl27rUx4eHhev755zV48GC9/vrrZfY1ceJE3XnnnQoKClLz5s3Vrl07de/eXV27dpUk+fn5yd3dXd7e3jZjubq6auzYsdbXERERWrt2rd5991317NlTVatWlZeXlwoKCkrVeCF79+7VHXfcoZYtW0qS6tevf1H7A7gwZm4AVEhAQICSk5M1Z84czZ49W8nJyapVq5ZNm19//VV333236tevL19fX4WHh0v66wP9XLGxsZdUw5dffqnOnTurTp06qlatmu6//34dOXJEp06dKrN9ZGSkNm/erO+//14DBgxQTk6OunXrpgceeOCCY02bNk0xMTEKCAhQ1apV9eabb5Y6jkvxyCOP6Pnnn1f79u01ZswY/fjjj5fdJwBbhBsAFTZgwADNmTNHc+fO1YABA0pt79atm44ePaoZM2Zo3bp1WrdunSSpsLDQpp2Pj89Fj717927deuutioqK0gcffKDMzExNmzatzP7P5eLiouuuu04jRozQkiVLNGfOHM2cOVO7du0qd59Fixbp8ccf18CBA7VixQplZWWpf//+5x3n7FiSZBiGdV1RUZFNmwceeEC//fab7r//fv3000+KjY3V1KlTL3j8ACqOcAOgwpKSklRYWKiioqJS18ocOXJE27dv1zPPPKPOnTurWbNml3xXkru7u4qLi23WZWZmqqSkRBMmTND111+vxo0ba//+/Rfdd2RkpKS/rg8qb6w1a9aoXbt2GjJkiFq3bq2GDRvaXBhd3n4BAQGSpAMHDljXZWVllaohNDRUgwcP1pIlS/TYY49pxowZF30cAMrHNTcAKszV1VXbtm2z/nwuf39/1axZU2+++aaCg4O1d+9ePfXUU5c0Tnh4uHbt2qWsrCzVrVtX1apVU8OGDVVUVKSpU6eqW7duWrNmjdLT08/bz5133qn27durXbt2CgoK0q5duzRq1Cg1btxYTZs2tY61bt067d69W1WrVlWNGjXUqFEjzZs3T8uXL1dERITmz5+vDRs2KCIiwqbG5cuXa/v27apZs6b8/PzUsGFDhYaGKjU1VS+88IJ++eUXTZgwwaamESNGqGvXrmrcuLGOHTumr7/+Ws2aNbuk9wlA2Zi5AXBRfH195evrW2q9i4uLFi1apMzMTLVo0UKPPvqoXnnllUsa44477lBSUpJuuukmBQQE6J133lGrVq00ceJEjR8/Xi1atNCCBQuUlpZ23n4SExP16aefqlu3bmrcuLH69u2rpk2basWKFXJz++vfdo8//rhcXV0VGRmpgIAA7d27Vw899JBuv/129erVS3FxcTpy5IiGDBli0/egQYPUpEkTxcbGKiAgQGvWrFGVKlX0zjvv6Oeff1ZUVJTGjx+v559/3ma/4uJiDR06VM2aNVNSUpIaN25c7gXRAC6NxTj35DAAAEAlx8wNAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwlf8HQodxILZLMBAAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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4oJo1a6a8vDy9//778vX1Ve/evZ3aJicnq3PnzqpRo8Y1vVfgqlh1ug5glZ9++skMHjzYREdHG39/f1OtWjXTuXNnM2XKFJOVleVo98knn5h27dqZgIAAEx0dbSZOnOg4tfTiUyejoqJMjx49zBdffGHatWtn7Ha7adGiRZHTJos7fffs2bPmD3/4gwkLCzOSHKfyFhQUmL/97W8mKirK2O12c/3115ulS5ea/v37FzndVyWcvlvS4/Dhw8YYY3788UeTmJhogoODTdWqVU23bt3MmjVrnLb9yiuvmLi4OBMWFmYCAwNNixYtzF//+leTk5NjjDHm1KlTJikpybRo0cIEBQWZ0NBQ07FjR/Ovf/2rVMdixIgRpkmTJkWWnz9/3jz//POmUaNGxs/Pz9StW9c88MADTqcbZ2RkmKeeesrUq1fP+Pn5maZNm5rXXnvN6XTawt9PUlJSkX1cevqsMcb85S9/MfXr1zc+Pj5Ox7mk03cvPUW6uGOcn59vnn32WVOzZk1TtWpVk5iYaPbs2VPs/vfu3WseeOABExYWZgICAkxcXJxZunRpkdr37t1rEhISjN1uN3Xq1DHPPfecSU5Odtr3vn37zKBBg0zjxo1NQECAqV69uunWrZv58ssvnbZ15swZ4+/vb2bOnFlkP0BFsBlzjbO1AC8XHR2tNm3aaOnSpVaX4nb27dunFi1aaNmyZerevbvV5XilyZMn69VXX9XevXtLnAMFuBJzRABYJiYmRo8++qgmTJhgdSleKTc3V5MmTdILL7xACIFlmCMCwFLF3b8HFcPPz0+HDh2yugx4OXpEAACAZZgjAgAALEOPCAAAsAxBBAAAWKZST1YtKCjQsWPHVK1aNW7GBACAmzDGKCMjQ/Xq1XNcjK8klTqIHDt2zOmeFwAAwH0cPnxYDRo0uGybSh1ECm/xffjwYce9LwAAQOWWnp6uyMhIx+f45VTqIFI4HBMSEkIQAQDAzZRmWgWTVQEAgGUIIgAAwDIEEQAAYBmCCAAAsAxBBAAAWIYgAgAALEMQAQAAliGIAAAAyxBEAACAZQgiAADAMgQRAABgGYIIAACwDEEEAABYhiACAAAsU8XqAgDAG209klbm17RtEOqCSgBrEUQAwE1cTXixGuEJV8LQDAAAsAxBBAAAWIYgAgAALEMQAQAAliGIAAAAyxBEAACAZQgiAADAMgQRAABgGYIIAACwDEEEAABYhiACAAAsQxABAACWIYgAAADLEEQAAIBlqlhdAABURluPpDl+5lb2gOvQIwIAACxDEAEAAJZxaRA5evSo/vjHP6pGjRoKDAxU27ZttWHDBlfuEgAAuBGXzRE5ffq0OnfurG7dumnZsmWqVauWdu/erfDwcFftEgAAuBmXBZGJEycqMjJSs2fPdixr1KiRq3YHAADckMuGZj755BPFxsbqwQcfVO3atXX99dfr3XffvexrsrOzlZ6e7vQAAE9jjFFWbn6ZH8YYq0sHyp3LekT27dunadOmadSoUXruuee0fv16jRgxQv7+/urfv3+xrxk/frxefvllV5UEAJYzxuiB6WuVcvB0mV8bUzNIE+5vJ5vNBYW5iDFGNncqGBXOZlwUsf39/RUbG6s1a9Y4lo0YMULr16/X2rVri31Ndna2srOzHc/T09MVGRmptLQ0hYSEuKJMACiWq64jcj4nT61e/KLctlfZxUaFa+GQeMKIl0lPT1doaGipPr9d1iMSERGhVq1aOS1r2bKlFi1aVOJr7Ha77Ha7q0oCgErl/UFxCvDzvWI7Y6Qxi7do36lzFVBV+dpw8LQyc/NV1Z/rZ6J4LvuX0blzZ6Wmpjot++mnnxQVFeWqXQKAWwnw8y1VEJGkyX3aKzuvwMUVlZ+s3Hw9PGud1WXADbgsiDz11FPq1KmT/va3v+mhhx7SunXrNGPGDM2YMcNVuwQAj2Wz2UodWgB34rKzZm688UYtWbJEH3zwgdq0aaO//OUvmjx5svr16+eqXQIAADfj0kG7nj17qmfPnq7cBQAAcGPcawYAAFiGIAIAACxDEAEAAJYhiAAAAMsQRAAAgGUIIgAAwDJccxcALlF4d9yLn3OvFMA1CCIAcJHi7o7LjdsA12FoBgAukpmb7xRCpP/euA1A+SOIAEAJ3n0k1uoSAI9HEAGAEgRU4U8k4Gr8LwMAAJYhiAAAAMsQRAAAgGUIIgAAwDIEEQAAYBmCCODlth5J09YjaVaXAcBLEUQAAIBlCCIAAMAy3GsGACzSun6IqvqX/s8wQ2jwRAQRAHATbRuEWl1CqZ3PybO6BLgJhmYAAIBlCCIAAMAyBBEAHo9TlIHKiyACAAAsw2RVAB6JHhDAPdAjAgAALEOPCACPQk8I4F7oEQEAAJahRwTwYsYYZeXmO3622WwWVwTA2xBEAC9ljNED09cq5eBpSVJsVLgWDoknjACoUAzNAF4qMzffEUIkacPB08r8T+8ILmhZL8TqEgCPRxABAACWIYgAAADLEEQAAIBlCCIAAMAyBBEAAGAZTt8F4NGMMcrOK3D8zOnJQOVCEAHgsYwxenbRFu08kSGJa6UAlRFDMwA8TuEVY9Mycx0hROJaKUBlRI8IAI9yaS8IgMqNHhHAIluPpHGn2HJmjCnSCyJJMTWDLKoIwJXQIwLAI1x67xxJen9QnAL8fGWM9NCMtRZWB6Ak9IgA8AiX3junZUSIQgP9FODnK+amApUXPSIAPM77g+IUGujH2TGAG6BHBIDHudALQggB3AFBBPBCTJIFUFkQRAAAgGUIIgAAwDIEEQAAYBmCCAAAsEyFBZEJEybIZrNp5MiRFbVLAABQyVVIEFm/fr3eeecdtWvXriJ2BwAA3ITLg8jZs2fVr18/vfvuuwoPD3f17gC4QEGBUUGBsboMAB7I5UEkKSlJPXr0UEJCwhXbZmdnKz093ekBwFoFBUYxz32umOc+J4wAKHcuvcT7/Pnz9eOPP2r9+vWlaj9+/Hi9/PLLriwJqBSMMcrKzXf8XJmvAvrb+Rynn2sG2y2spvyUdFG3wuMCoGK4LIgcPnxYTz75pJKTkxUQEFCq14wdO1ajRo1yPE9PT1dkZKSrSgQsceldYmOjwrVwSHylDiPurnX9EKtLAFAClwWRlJQU/fzzz7rhhhscy/Lz8/XNN9/o7bffVnZ2tnx9fZ1eY7fbZbd7xrctoCSX3iV2w8HTyszNV1V/7kEJwPu47C9f9+7dtXXrVqdlAwcOVIsWLfTss88WCSEAKkbhsJDx8OkebRuEOn4+n5NnYSUALsdlQaRatWpq06aN07KgoCDVqFGjyHIAFePiYaFWEQxXALAeV1YFvMjFw0I7jnvuWWnMCQHcR4UOSq9evboidwcAACo5ekQAAIBlCCIAAMAyBBEAAGAZgggAALAMQQQAAFiGIAIAACxDEAEAAJYhiAAAAMsQRAAAgGUIIgAAwDIEEQAAYBmCCAAAsAxBBAAAWKZC774LoHLbfjRdAX6+kqS2DUItrsZ1jDHKys0vdl1JywG4BkEEgFcxRnpg+lqlHDxtdSkAxNAMgEpo65E0bT2S5pJtZ+bmlyqExEaFK/A/vUMAXIceEQBe6/1BcY6hqEvFRocrk2EawOUIIgAcLp4fYYyRzWazsBrXC/DzLTGIePp7ByoLgggAh4dnrXP8HBsVroVD4i2sBp7i4knQ7syTJ3BbiTkiANQqIqTIsg0HTzM0AcDl6BEBoIVD4rXjWLqkC8MzF/eMAIArEUQAyGaT5V3nrjpLBkDlxtAMAACwDEEEAABYhqEZAJL+e0bA+Zw8iysB4E3oEQEAAJYhiAAAAMsQRAAAgGUIIgAAwDJMVgVQqRhjlJ1X4PiZe74Ano0gAqDSMMbo2UVbtPNEhqT/3u+GMAJ4LoZmAFQa2XkFjhAicb8bwBsQRAAAgGUIIgAsZ4xRVm6+suj9ALwOc0QAWMoYowemr1XKwdNWlwLAAvSIALBUZm5+kRASUzPIomoAVDR6RABUGu8PilOAn6+MkR6asdbl+2tdP0RV/fkzCFiJHhEAlUaAn68C/HzF2bqA9yCIAAAAyxBEAACAZQgiAADAMgQRAABgGYIIAACwDEEEAABYhiACAAAsQxABAACWIYgAqNSMKd/t7TyWXr4bBHBNCCIAKp3W9UMcPz84fa1MeacRAJUGN1kAUOkE+vmqVUSIdhxP147j6crMzeeeMG4sKzff6hLKhTFGNu4/UO74nw2g0rj4JnQLh8Sr9UtfWFwRysPDs9ZZXUK5iI0K18Ih8YSRcsbQDIBKib/17i3Qz1exUeFWl1GuNhw8rUwP6d2pTFzaIzJ+/HgtXrxYu3btUmBgoDp16qSJEyeqefPmrtwtAMBiNptNC4fEa8OB01aXcs2ycvM9plenMnJpEPn666+VlJSkG2+8UXl5eXruued0++23a8eOHQoKCnLlrgEAFrPZbArw87W6DFRyLg0iy5cvd3o+Z84c1a5dWykpKeratasrdw0AANxAhU5WTUtLkyRVr1692PXZ2dnKzs52PE9P53x/AAA8WYVNVi0oKNDIkSPVuXNntWnTptg248ePV2hoqOMRGRlZUeUBAAALVFgQSUpK0rZt2zR//vwS24wdO1ZpaWmOx+HDhyuqPAAAYIEKGZoZNmyYli5dqm+++UYNGjQosZ3dbpfdbq+IkgAAQCXg0iBijNHw4cO1ZMkSrV69Wo0aNXLl7gAAgJtxaRBJSkrSvHnz9PHHH6tatWo6ceKEJCk0NFSBgYGu3DUAAHADLp0jMm3aNKWlpemWW25RRESE47FgwQJX7hYAALgJlw/NALDO1iNpTs895eZjADwH95oBAACWIYgAAADLEEQAAIBlCCIAAMAyBBEAAGAZgggAALAMQQSAV8nKK7C6BAAXIYgA8CqD39tgdQkALkIQAeDxAv18FRsV7rQsNipcgX6+FlUEoFCF3H0XQMUzxhS5kqq3XlnVZrNp4ZB4bThw2rEsNjpcNpvNwqoASAQRwCMZY/TA9LVKOXj6yo0ttv1oeoXsx2azKeCiHhBCCFA5MDQDeJDCe8tk5ua7RQgBAHpEAA+z9Uia0xDM+4PinHoCsnLz9fCsdVaUBgBFEEQADxfg5+sURACgMmFoBoBliptQC8C70CMCwBLlPaG2oia9Aihf9IgAsMSlE2q5rgfgnegRAWC59wfFqUvTmpxSC3ghggjgIS6eb+Fu8y4C/HwJIYCXIogAHsCdLmDmyQqv43I57hYSAVcjiAAeoKQLmLWMCJG9iudPBePsm8qrbYNQq0u4Zudz8qwuwaMRRAAPc/EFzOxVfDx+yIPeIMC9EUQAD+OOFzBrXT/kql/L2TeAeyOIAB6suG5xT+5m5uwbwP14/uAxAK/B2TeA+yGIAAAAyxBEAACAZQgiAADAMkxWBTxM6/ohqurvnf+1r+XsGwDWoEcEAABYhiACAAAsQxABAACW8c6BZABeyRPuewJ4GoIIgApz8d1puUkdAIkgAuAyth9NJzD8x/aj6W53Dx/AHTBHBAAAWIYgAgAALMPQDFDOLp4HURyGOgDgv+gRAQAAliGIAAAAyzA0A5QjY8wVh14YmgGA/yKI4IpzGq6GN144yhijB6avVcrB01aXAgBug6EZoJxk5uaXKYS0ivCuO8UW9hZd/AAAekQAF3h/UNwVL37Vql6IWr/0RQVVZC1v6C0yxig7r+CK7QhggDOCCOACAX6+VwwiNlsFFXMNsnLzlVWKD9crKWtvkbsxxujZRVu080SG1aUAbocgAqBED89aV+7bfH9QnMu2bZXsvIIyh5DYqHAFcsl4gCACwFmgn69io8K1oZgejPL44PT0+7WUZlhOkmKjw2Vzh24xOPHEew5ZfXIBQQRwgdb1Q1TV//L/vc7n5FVQNWVjs9m0cEi8Nhy4EESycvMdvRd8bl5ZaYblJBFCgP8giAAowmazVapvfZd+C7X6GxyA8sPpuwAAwDIEEQAAYBmXB5GpU6cqOjpaAQEB6tixo9at85yZ8gAA4Nq4dI7IggULNGrUKE2fPl0dO3bU5MmTlZiYqNTUVNWuXduVuy53rrgMOgAA3s6lPSKTJk3S4MGDNXDgQLVq1UrTp09X1apVNWvWLFfuFmVQ3GW3y+NhjLH6rQEA3IDLekRycnKUkpKisWPHOpb5+PgoISFBa9euLfY12dnZys7OdjxPT093SW3GGGWW8TLLxhiPO93OlVeDjI0K18Ih8R73O8O1a13fu+6xA+DyXBZETp06pfz8fNWpU8dpeZ06dbRr165iXzN+/Hi9/PLLrirJITM3X61eLNs9PlpGhGji/W096oP1aq4GWVobDp5WZm7+Fa+lAQDwbpXqU2Ls2LEaNWqU43l6eroiIyMtrOi/dh5PV3ZeQaW6tkJ5Ku3VIK/k4otfAQBwJS4LIjVr1pSvr69OnjzptPzkyZOqW7dusa+x2+2y2+2uKskh0M9XO/43sVRtz+fkK/aVL11ckfVKezVIeI/Ci4ZV1ivAAvAMLpus6u/vrw4dOuirr75yLCsoKNBXX32l+Ph4V+22VGw2m6r6Vynlgw9nAABcxaVDM6NGjVL//v0VGxuruLg4TZ48WefOndPAgQNduVsAXmT7UddMar9U1mUmuF9uHYDLc2kQ6dOnj3755Re9+OKLOnHihNq3b6/ly5cXmcDq6Ywxys4rsLqMIvjjCZQec58A13D5ZNVhw4Zp2LBhrt5NhbiaD25jpDGLt2jfqXMuqAiAKwX6+So2KlwbDp4uVfuWESGyV+HOGUBZVKqzZio7T/1GxB9PoHg2m00Lh8Rrw4HSBRF7FR+POsUfqAgEkSso6zeiksTUDNKE+9upMv6N4o8nUDKbzcYZZXDwxCHt8zl5CvTztexzgCByBWX9RlQSPuyB8sO9n2AVT+0Z3/G/iZZdgJIgUgp8IwIA71VePeMoHkEEAIDLKK+e8cqqdf0QBVr4ZZsgAlyjwmECTxw7BnCBJ/eMW31PME6VAAAAlqFHBI57ipQH7ksCACgLekQAAIBl6BEBgHJSnr2LgLcgiHgg/hjC01w6EZjr8gCegyBSSny4A9a59CJSLSNCNPH+toQRwAMwRwS4SluPpHGFTxcqvIhUcXYeT6+Ud7QGUHb0iMAyfIjjcoq7iFRWbr7HXmIb8FYEEbjM9qPpHnsBIFQMT76IFIALGJoBAACWIYgAAADLEEQAuC1jDPf4Adwcc0QAuCVjpGcXbdHOExlWlwLgGtAjAsAtZeflO4WQ2KhwS29lDuDq0CMCXANjjON6FgwRWOf9QXHq0rQmFzgD3BBBBLhKxhiGBiqJAD9fQgjgphiaAa5Sdl5BsSGEIQIAKD16RIBy8P6gOMeFt2Kjw/l2Dngg7jnmGgQR4CpcetpogJ+vI4gQQgCg9AgiQClcPCnVGGnM4i3ad+qcxVUBgPsjiMASF3+wV3ZXCh4tI0Jkr8J0q4qWVYH/fuiSB1yHIAKXKel0Vk/pUYipGaQJ97dTgJ8PwzFlFOjnq9iocG04ePqqJ/cOfm+DCyoDUNEIInAZT7tde2HwKMwc9ioEkKtls9m0cEi8Nhw4XabJvfYqPmoZEaKdx9Mdy+iRAtwbQQTl6uJvuldy6Qd7ZUfwKF82m63M1/+w2WyaeH9bp2E9jgvg3ggiKFcXf9O9Ej5AcDUKAwwAz0AQQbnjgwIAUFoMrAIAAMsQRAAAgGUIIgAAwDIEEcDLFJ7ZVIib9AGwEpNVAS9z6ZlN3KQPgJUIIoAXuvjMJkIIACsRRIBrxH1IXKu43+/WI2kWVALAFZgjAgAALEMQAQAAlmFoBrhKDMlUDhwHwL0RRIBL8MEGABWHIALL8IEPACCIwCUIGQCA0mCyKgAAsAxBBAAAWIYgAgAALEMQAWAZ5hIBIIgAAADLEEQAAIBlXBJEDhw4oEcffVSNGjVSYGCgGjdurJdeekk5OTmu2B2Aq9C2QShDIwAs55LriOzatUsFBQV655131KRJE23btk2DBw/WuXPn9Prrr7tilwAAwA25JIjccccduuOOOxzPY2JilJqaqmnTphFEAACAQ4VdWTUtLU3Vq1e/bJvs7GxlZ2c7nqenp7u6LAAAYKEKmay6Z88eTZkyRU888cRl240fP16hoaGOR2RkZEWUB8DNFM5vYY4L4P7KFETGjBkjm8122ceuXbucXnP06FHdcccdevDBBzV48ODLbn/s2LFKS0tzPA4fPlz2dwQAANxGmYZmnn76aQ0YMOCybWJiYhw/Hzt2TN26dVOnTp00Y8aMK27fbrfLbreXpSQAAODGyhREatWqpVq1apWq7dGjR9WtWzd16NBBs2fPlo8PlywBAADOXDJZ9ejRo7rlllsUFRWl119/Xb/88otjXd26dV2xSwAA4IZcEkSSk5O1Z88e7dmzRw0aNHBaZ4xxxS4BAIAbcsl4yYABA2SMKfYBAABQiIkbAADAMgQRAABgGYIIAACwDEEEAABYhiACAAAsQxABAACWIYgAAADLEEQAlNr2o+lWlwDAwxBEAJTJ1iNpVpcAwIMQRAAAgGUIIgDKbOuRNHpGAJQLl9z0DoB3KG0YycrNd3ElANwVPSIAAMAyBBEAAGAZggiAUsvKzZcxxuoyAHgQggiAUnt41jo9u3grYQRAuSGIALisQD9fxUaFO57vPJ6urNwCCysC4EkIIgAuy2azaeGQeL0/KM6xbOSCjcrMYZgGwLUjiAC4IpvNptBAP8XUDJIkHUvL0kMz1mrkgk3KzMlXgTHKys2/7AMAisN1RACUis1m0xt92uupBZu079Q5SdK+U+f00Iy1FlcGwJ3RIwKg1HxsNk3u017/ejze0TtSFrFR4Qr083VBZQDcFT0iAMrEZrMp0N9Xk/u0V1ZugcYs3qJ9p84ppmaQJtzfTjZbya+NjQ6X7XINAHgdggiAq3JxIMnOK5C9is8VQwYhBMClCCIAronNZlMAwy0ArhJzRAAAgGUIIoAH2H403eX7aNsgVG0bhLp8PwC8C0EEAABYhiACuLmtR9IqdH/0igAoTwQRAABgGYIIgDJjvgiA8sLpu4AbqujhmJIQRgBcK3pEAA/ATXABuCt6RAA3UlxPiDFGYxZvsaAaALh29IgAbi47r8BxN9xWESHcVA6AW6FHBHADl5sTcvGwzMIh8dzPBYBboUcEcGMFxmjkgo2O52QQAO6GHhGgErt8T4jRUws26VhaliQppmYQwzIA3A5BBKhkSntq7sVzQ+qFBuiNPu0ZlgHgdggiQCWw/Wi6Aq6hN2Nyn+vlQwgB4IYIIoCbMcYoO69AWbn5jmVkEADuiiACuBFjjJ5dtEU7T2RYXQoAlAvOmgHchDFGaZm5RUJIy4gQ2avwXxmAe6JHBHADBf85Q6ZwcqokvT8oTgF+vrJX8WGSKgC3RRABKrkCY/Q//5fiOE1XutALEhro5xRAuAEdAHdEEAEqocIJqcZIIxdsdISQeqEBmtznegX40QsCwDMQRIBKoPAy7cYYZeUWaMziLU7DMNKFEDLtjx04TReARyGIAJXAyAUb9Uaf9hq7eGuRACJduGrqG33aE0IAeByCCGCRQD9ftYoI0Y7j6TqWlqU+M753Wh9TM0gT7m8nm01XnJDK/BAA7oogAljEZrNp6fAuSpj0tVMvSGEAKe08EEIIAHdGEAEs5ONj0+Q+7ZWdV+BYxum4ALwJQQSwmM1mu6b7zACAO3P55Rizs7PVvv2Fu4Ju2rTJ1bsDAABuxOU9Is8884zq1aunzZs3u3pXgNdgXggAT+HSHpFly5ZpxYoVev311125G8BrtG0QSggB4FFc1iNy8uRJDR48WB999JGqVq1aqtdkZ2crOzvb8Tw9Pd1V5QFuhwACwBO5pEfEGKMBAwZoyJAhio2NLfXrxo8fr9DQUMcjMjLSFeUBbocQAsBTlSmIjBkzRjab7bKPXbt2acqUKcrIyNDYsWPLVMzYsWOVlpbmeBw+fLhMrwc8SeEwDCEEgCezGVN4l4sr++WXX/Trr79etk1MTIweeughffrpp07XQsjPz5evr6/69eunuXPnlmp/6enpCg0NVVpamkJCQkpbJuBWth5Jc3pO8ADg7sry+V2mIFJahw4dcprfcezYMSUmJurDDz9Ux44d1aBBg1JthyACAID7Kcvnt0smqzZs2NDpeXBwsCSpcePGpQ4hAADA87n8gmYAAAAlqZBLvEdHR8sFI0AAAMDN0SMCAAAsQxABAACWIYgAAADLEEQAAIBlCCIAAMAyBBEAAGAZgggAALAMQQQAAFiGIAIAACxDEAEAAJYhiAAAAMsQRAAAgGUq5KZ3V6vwRnnp6ekWVwIAAEqr8HO7NDe8rdRBJCMjQ5IUGRlpcSUAAKCsMjIyFBoaetk2NlOauGKRgoICHTt2TNWqVZPNZivXbaenpysyMlKHDx9WSEhIuW4b14ZjU7lxfCovjk3l5k3HxxijjIwM1atXTz4+l58FUql7RHx8fNSgQQOX7iMkJMTj/0G4K45N5cbxqbw4NpWbtxyfK/WEFGKyKgAAsAxBBAAAWMZrg4jdbtdLL70ku91udSm4BMemcuP4VF4cm8qN41O8Sj1ZFQAAeDav7REBAADWI4gAAADLEEQAAIBlCCIAAMAyXhlEpk6dqujoaAUEBKhjx45at26d1SV5nG+++Ua9evVSvXr1ZLPZ9NFHHzmtN8boxRdfVEREhAIDA5WQkKDdu3c7tfntt9/Ur18/hYSEKCwsTI8++qjOnj3r1GbLli26+eabFRAQoMjISL366quufmtub/z48brxxhtVrVo11a5dW/fee69SU1Od2mRlZSkpKUk1atRQcHCwevfurZMnTzq1OXTokHr06KGqVauqdu3aGj16tPLy8pzarF69WjfccIPsdruaNGmiOXPmuPrtub1p06apXbt2jotexcfHa9myZY71HJvKY8KECbLZbBo5cqRjGcfnKhgvM3/+fOPv729mzZpltm/fbgYPHmzCwsLMyZMnrS7No3z++efm+eefN4sXLzaSzJIlS5zWT5gwwYSGhpqPPvrIbN682dx9992mUaNGJjMz09HmjjvuMNddd535/vvvzb///W/TpEkT8/vf/96xPi0tzdSpU8f069fPbNu2zXzwwQcmMDDQvPPOOxX1Nt1SYmKimT17ttm2bZvZtGmTueuuu0zDhg3N2bNnHW2GDBliIiMjzVdffWU2bNhgbrrpJtOpUyfH+ry8PNOmTRuTkJBgNm7caD7//HNTs2ZNM3bsWEebffv2mapVq5pRo0aZHTt2mClTphhfX1+zfPnyCn2/7uaTTz4xn332mfnpp59Mamqqee6554yfn5/Ztm2bMYZjU1msW7fOREdHm3bt2pknn3zSsZzjU3ZeF0Ti4uJMUlKS43l+fr6pV6+eGT9+vIVVebZLg0hBQYGpW7euee211xzLzpw5Y+x2u/nggw+MMcbs2LHDSDLr1693tFm2bJmx2Wzm6NGjxhhj/vGPf5jw8HCTnZ3taPPss8+a5s2bu/gdeZaff/7ZSDJff/21MebCsfDz8zMLFy50tNm5c6eRZNauXWuMuRA0fXx8zIkTJxxtpk2bZkJCQhzH45lnnjGtW7d22lefPn1MYmKiq9+SxwkPDzczZ87k2FQSGRkZpmnTpiY5Odn87ne/cwQRjs/V8aqhmZycHKWkpCghIcGxzMfHRwkJCVq7dq2FlXmX/fv368SJE07HITQ0VB07dnQch7Vr1yosLEyxsbGONgkJCfLx8dEPP/zgaNO1a1f5+/s72iQmJio1NVWnT5+uoHfj/tLS0iRJ1atXlySlpKQoNzfX6fi0aNFCDRs2dDo+bdu2VZ06dRxtEhMTlZ6eru3btzvaXLyNwjb8Xyu9/Px8zZ8/X+fOnVN8fDzHppJISkpSjx49ivwOOT5Xp1Lf9K68nTp1Svn5+U7/ACSpTp062rVrl0VVeZ8TJ05IUrHHoXDdiRMnVLt2baf1VapUUfXq1Z3aNGrUqMg2CteFh4e7pH5PUlBQoJEjR6pz585q06aNpAu/O39/f4WFhTm1vfT4FHf8Ctddrk16eroyMzMVGBjoirfkEbZu3ar4+HhlZWUpODhYS5YsUatWrbRp0yaOjcXmz5+vH3/8UevXry+yjv87V8ergggAZ0lJSdq2bZu+/fZbq0vBRZo3b65NmzYpLS1NH374ofr376+vv/7a6rK83uHDh/Xkk08qOTlZAQEBVpfjMbxqaKZmzZry9fUtMoP55MmTqlu3rkVVeZ/C3/XljkPdunX1888/O63Py8vTb7/95tSmuG1cvA+UbNiwYVq6dKlWrVqlBg0aOJbXrVtXOTk5OnPmjFP7S4/PlX73JbUJCQnxuG905c3f319NmjRRhw4dNH78eF133XV68803OTYWS0lJ0c8//6wbbrhBVapUUZUqVfT111/rrbfeUpUqVVSnTh2Oz1XwqiDi7++vDh066KuvvnIsKygo0FdffaX4+HgLK/MujRo1Ut26dZ2OQ3p6un744QfHcYiPj9eZM2eUkpLiaLNy5UoVFBSoY8eOjjbffPONcnNzHW2Sk5PVvHlzhmUuwxijYcOGacmSJVq5cmWR4a0OHTrIz8/P6fikpqbq0KFDTsdn69atTmExOTlZISEhatWqlaPNxdsobMP/tbIrKChQdnY2x8Zi3bt319atW7Vp0ybHIzY2Vv369XP8zPG5ClbPlq1o8+fPN3a73cyZM8fs2LHDPP744yYsLMxpBjOuXUZGhtm4caPZuHGjkWQmTZpkNm7caA4ePGiMuXD6blhYmPn444/Nli1bzD333FPs6bvXX3+9+eGHH8y3335rmjZt6nT67pkzZ0ydOnXMww8/bLZt22bmz59vqlatyum7V/A///M/JjQ01KxevdocP37c8Th//ryjzZAhQ0zDhg3NypUrzYYNG0x8fLyJj493rC88BfH22283mzZtMsuXLze1atUq9hTE0aNHm507d5qpU6d69CmI5WXMmDHm66+/Nvv37zdbtmwxY8aMMTabzaxYscIYw7GpbC4+a8YYjs/V8LogYowxU6ZMMQ0bNjT+/v4mLi7OfP/991aX5HFWrVplJBV59O/f3xhz4RTeP//5z6ZOnTrGbreb7t27m9TUVKdt/Prrr+b3v/+9CQ4ONiEhIWbgwIEmIyPDqc3mzZtNly5djN1uN/Xr1zcTJkyoqLfotoo7LpLM7NmzHW0yMzPN0KFDTXh4uKlataq57777zPHjx522c+DAAXPnnXeawMBAU7NmTfP000+b3NxcpzarVq0y7du3N/7+/iYmJsZpHyjeoEGDTFRUlPH39ze1atUy3bt3d4QQYzg2lc2lQYTjU3Y2Y4yxpi8GAAB4O6+aIwIAACoXgggAALAMQQQAAFiGIAIAACxDEAEAAJYhiAAAAMsQRAAAgGUIIgDKzbhx49S+fftKsx0AlR9BBPASJ06c0PDhwxUTEyO73a7IyEj16tWryD0trsWf/vQnp+0NGDBA99577zVvl2ACeK4qVhcAwPUOHDigzp07KywsTK+99pratm2r3NxcffHFF0pKStKuXbvKZT/BwcEKDg4ul20B8A70iABeYOjQobLZbFq3bp169+6tZs2aqXXr1ho1apS+//57SdKkSZPUtm1bBQUFKTIyUkOHDtXZs2cd25gzZ47CwsL00UcfqWnTpgoICFBiYqIOHz7saHNxz8W4ceM0d+5cffzxx7LZbLLZbFq9erUk6dlnn1WzZs1UtWpVxcTE6M9//rPTXZTLauvWrbr11lsVGBioGjVq6PHHH3eqffXq1YqLi1NQUJDCwsLUuXNnHTx4UJK0efNmdevWTdWqVVNISIg6dOigDRs2XHUtAMqGIAJ4uN9++03Lly9XUlKSgoKCiqwPCwuTJPn4+Oitt97S9u3bNXfuXK1cuVLPPPOMU9vz58/rr3/9q9577z199913OnPmjPr27Vvsfv/0pz/poYce0h133KHjx4/r+PHj6tSpkySpWrVqmjNnjnbs2KE333xT7777rt54442ren/nzp1TYmKiwsPDtX79ei1cuFBffvmlhg0bJknKy8vTvffeq9/97nfasmWL1q5dq8cff1w2m02S1K9fPzVo0EDr169XSkqKxowZIz8/v6uqBUDZMTQDeLg9e/bIGKMWLVpctt3IkSMdP0dHR+uVV17RkCFD9I9//MOxPDc3V2+//bY6duwoSZo7d65atmypdevWKS4uzml7wcHBCgwMVHZ2turWreu07oUXXnDa15/+9CfNnz+/SPApjXnz5ikrK0vvvfeeI2i9/fbb6tWrlyZOnCg/Pz+lpaWpZ8+eaty4sSSpZcuWjtcfOnRIo0ePdvx+mjZtWuYaAFw9ekQAD1faG2x/+eWX6t69u+rXr69q1arp4Ycf1q+//qrz58872lSpUkU33nij43mLFi0UFhamnTt3lqmmBQsWqHPnzqpbt66Cg4P1wgsv6NChQ2XaRqGdO3fquuuuc+rt6dy5swoKCpSamqrq1atrwIABSkxMVK9evfTmm2/q+PHjjrajRo3SY489poSEBE2YMEF79+69qjoAXB2CCODhmjZtKpvNdtkJqQcOHFDPnj3Vrl07LVq0SCkpKZo6daokKScnp1zrWbt2rfr166e77rpLS5cu1caNG/X888+X+34uNnv2bK1du1adOnXSggUL1KxZM8fcmHHjxmn79u3q0aOHVq5cqVatWmnJkiUuqwWAM4II4OGqV6+uxMRETZ06VefOnSuy/syZM0pJSVFBQYH+/ve/66abblKzZs107NixIm3z8vKcJnKmpqbqzJkzTkMdF/P391d+fr7TsjVr1igqKkrPP/+8YmNj1bRpU8fE0avRsmVLbd682em9fffdd/Lx8VHz5s0dy66//nqNHTtWa9asUZs2bTRv3jzHumbNmumpp57SihUrdP/992v27NlXXQ+AsiGIAF5g6tSpys/PV1xcnBYtWqTdu3dr586deuuttxQfH68mTZooNzdXU6ZM0b59+/T+++9r+vTpRbbj5+en4cOH64cfflBKSooGDBigm266qcj8kELR0dHasmWLUlNTderUKeXm5qpp06Y6dOiQ5s+fr7179+qtt94qVQ9EZmamNm3a5PTYu3ev+vXrp4CAAPXv31/btm3TqlWrNHz4cD388MOqU6eO9u/fr7Fjx2rt2rU6ePCgVqxYod27d6tly5bKzMzUsGHDtHr1ah08eFDfffed1q9fX2KwAuACBoBXOHbsmElKSjJRUVHG39/f1K9f39x9991m1apVxhhjJk2aZCIiIkxgYKBJTEw07733npFkTp8+bYwxZvbs2SY0NNQsWrTIxMTEGLvdbhISEszBgwcd+3jppZfMdddd53j+888/m9tuu80EBwcbSY59jR492tSoUcMEBwebPn36mDfeeMOEhoaWuJ2XXnrJSCry6N69uzHGmC1btphu3bqZgIAAU716dTN48GCTkZFhjDHmxIkT5t577zURERHG39/fREVFmRdffNHk5+eb7Oxs07dvXxMZGWn8/f1NvXr1zLBhw0xmZma5//4BFM9mTClnsgHwanPmzNHIkSN15swZq0sB4EEYmgEAAJYhiAAAAMswNAMAACxDjwgAALAMQQQAAFiGIAIAACxDEAEAAJYhiAAAAMsQRAAAgGUIIgAAwDIEEQAAYBmCCAAAsMz/B/SmedOaHCceAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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sWZd+vr6+mjFjhkaMGKGuXbtq4MCBys7O1uzZsxUVFeU8vChJI0eO1AsvvKCEhASNGjVKOTk5SklJUZs2bVxOrr777rt17Ngxde/eXQ0aNND+/fv10ksvKTo6Wq1atXL2y8nJ0datWzV69OjLmitwQSZdvQVUOecuvz33sNvtRnh4uHHTTTcZs2fPdl5a/Xs///yzceeddxo1a9Y0goODjbvuuss4fPiwIcmYPn26s9+5y66PHDnisv2wYcOMatWqldpv165dXS4Lf+WVV4wuXboYderUMfz8/IyrrrrKePTRR43c3NxS23733XfG3/72NyM8PNzw8vIyJBn+/v7G9u3b3c77k08+Mdq2bWvY7XajRYsWxltvveX2MvEVK1YYV199teHv729ERUUZM2bMMF5//fVSlyyX5TLxs2fPGg8++KAREhJi2Gw2t5eMP/DAA4YkY8mSJW7rdqegoMCoW7eu8dRTT5V6bdu2bc6flb+/v9GiRQtj6tSpLn2ysrKMhIQEo3r16kZgYKBx4403GuvXr3fpc77bCbi79NvhcBi9e/c2atSoYUhyvi/nu0zc3a0A3F3K/+OPPxrx8fGGn5+fERYWZkyePNlIS0srtU/DMIylS5ca11xzjeHn52fUrl3bGDRokPHzzz+XGuett94ymjRpYtjtdiM6Otr4+OOPS439zjvvGD179jRCQ0MNu91uNGzY0LjvvvuMX375xWVf8+fPNwIDA93+NwOUF76LCrjCvfHGGxo+fLgGDx6sN954w+xyymz8+PF67bXX5HA4FBgYWObtnnrqKS1cuFB79uw570m78KxrrrlG3bp104svvmh2KbAwzsEBrnBDhw5VcnKy3nzzTedl3ZXdmTNn9NZbb6lPnz4XFW6k/wajkydPKjU11UPV4UJWr16tPXv2VIovO4W1sYIDoMrIycnRp59+qnfeeUfLly9XVlaWoqOjzS4LQCXEScYAqowdO3Zo0KBBCg0N1Zw5cwg3AM6LFRwAAGA5nIMDAAAsh4ADAAAs54o8B6ekpESHDx9WjRo1+KI3AACqCMMw9OuvvyoiIkJeXhdeo7kiA87hw4cv6XtjAACA+Q4ePKgGDRpcsM8VGXDO3br+4MGDl/T9MQAAoOLl5eUpMjLS+Tl+IVdkwDl3WCooKIiAAwBAFVOW00s4yRgAAFgOAQcAAFgOAQcAAFgOAQcAAFgOAQcAAFgOAQcAAFgOAQcAAFgOAQcAAFgOAQcAAFgOAQcAAFgOAQcAAFgOAQcAAFgOAQcAAFgOAQcAAFiOj9kFAAAAV9/9nGvq+O0aBJs6fnmokBWcefPmKSoqSv7+/oqNjdWGDRsu2H/ZsmVq2bKl/P391a5dO3344Yfn7Xv//ffLZrNp1qxZ5Vw1AACoqjwecJYuXaqkpCRNnz5dWVlZat++vRISEpSTk+O2//r16zVw4ECNGjVKmzdvVmJiohITE7Vt27ZSfd977z19/fXXioiI8PQ0AABAFeLxgPPCCy/onnvu0YgRI9S6dWulpKQoMDBQr7/+utv+s2fPVq9evfToo4+qVatWeuqpp3Tttddq7ty5Lv0OHTqkBx98UIsXL5avr6+npwEAAKoQjwacwsJCZWZmKj4+/rcBvbwUHx+vjIwMt9tkZGS49JekhIQEl/4lJSUaMmSIHn30UbVp0+ZP6ygoKFBeXp7LAwAAWJdHTzI+evSoiouLFRYW5tIeFhamXbt2ud3G4XC47e9wOJzPZ8yYIR8fH40dO7ZMdSQnJ+uJJ564yOoBAKh4hmHoTFGx6TXYbDZTa7hcVe4qqszMTM2ePVtZWVllfvMnTZqkpKQk5/O8vDxFRkZ6qkQAAC6JYRjqm5KhzP3HTa0jplEtLbs/rkqHHI8eoqpbt668vb2VnZ3t0p6dna3w8HC324SHh1+w/5dffqmcnBw1bNhQPj4+8vHx0f79+/Xwww8rKirK7T79/PwUFBTk8gAAoLI5XVRseriRpE37j+u0yatIl8ujKzh2u10dOnRQenq6EhMTJf33/Jn09HSNGTPG7TZxcXFKT0/XQw895GxLS0tTXFycJGnIkCFuz9EZMmSIRowY4ZF5AABQ0d4c2VH+vt4VOuaZomINef3Ct3KpKjx+iCopKUnDhg1TTEyMOnbsqFmzZunUqVPOMDJ06FDVr19fycnJkqRx48apa9eumjlzpnr37q3U1FRt2rRJCxYskCTVqVNHderUcRnD19dX4eHhatGihaenAwBAhfD39a7wgGMlHg84/fv315EjRzRt2jQ5HA5FR0dr9erVzhOJDxw4IC+v346UXX/99VqyZImmTJmiyZMnq1mzZlq+fLnatm3r6VIBAIBF2AzDMMwuoqLl5eUpODhYubm5nI8DAKg08gvPqvW0jyVJy+6LM+UQ1V2v/Pe2LDueTFCgvXJdi3Qxn9982SYAALAcAg4AALCcyrX2BAAAJElt6gdV+CGi/MKzFTqeJ7GCAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALMfH7AIAAKhsvvs515RxzxQVmzKuFbGCAwAALIeAAwAALIeAAwAALIeAAwAALIeTjAEAlY5ZJ/lKkmEYKjhbYsrYnGRcfgg4AAD8j2EYmvDvrdrp+NXsUnCZOEQFAMD/FJwtqRThJqZRLQX4eptdRpXGCg4AAG68ObKj/E0KGTFRtWSz2UwZ2yoIOACASsUwDNPORfn9uP6+3qYFHMLN5SPgAAAqDcMw1DclQ5n7j5tdCqo4zsEBAFQap4uKK0W4aVUvSH4+fERWZazgAAAqJTPPgfHz8eIwURVHwAEAVEpmngODqo/1NwAAYDms4AAA8AftGgSbXQIuU4UEnHnz5un555+Xw+FQ+/bt9dJLL6ljx47n7b9s2TJNnTpV+/btU7NmzTRjxgzdcsstztcff/xxpaam6uDBg7Lb7erQoYOeeeYZxcbGVsR0AAAVoE39IAXa+Xc4Lo3HD1EtXbpUSUlJmj59urKystS+fXslJCQoJyfHbf/169dr4MCBGjVqlDZv3qzExEQlJiZq27Ztzj7NmzfX3Llz9d133+mrr75SVFSUevbsqSNHjnh6OgAAoAqwGYZheHKA2NhYXXfddZo7d64kqaSkRJGRkXrwwQc1ceLEUv379++vU6dOadWqVc62Tp06KTo6WikpKW7HyMvLU3BwsD799FP16NHjT2s61z83N1dBQUGXODMAQHnLLzyr1tM+liTteDKBFZwKVtnf/4v5/PboCk5hYaEyMzMVHx//24BeXoqPj1dGRobbbTIyMlz6S1JCQsJ5+xcWFmrBggUKDg5W+/bt3fYpKChQXl6eywMAAFiXRwPO0aNHVVxcrLCwMJf2sLAwORwOt9s4HI4y9V+1apWqV68uf39/vfjii0pLS1PdunXd7jM5OVnBwcHOR2Rk5GXMCgAAVHZV9jLxG2+8UVu2bNH69evVq1cv9evX77zn9UyaNEm5ubnOx8GDByu4WgAAUJE8GnDq1q0rb29vZWdnu7RnZ2crPDzc7Tbh4eFl6l+tWjU1bdpUnTp10muvvSYfHx+99tprbvfp5+enoKAglwcAALAujwacc5dwp6enO9tKSkqUnp6uuLg4t9vExcW59JektLS08/b//X4LCgouv2gAAFDlefz06KSkJA0bNkwxMTHq2LGjZs2apVOnTmnEiBGSpKFDh6p+/fpKTk6WJI0bN05du3bVzJkz1bt3b6WmpmrTpk1asGCBJOnUqVN65plndPvtt6tevXo6evSo5s2bp0OHDumuu+7y9HQAAEAV4PGA079/fx05ckTTpk2Tw+FQdHS0Vq9e7TyR+MCBA/Ly+m0h6frrr9eSJUs0ZcoUTZ48Wc2aNdPy5cvVtm1bSZK3t7d27dqlf/3rXzp69Kjq1Kmj6667Tl9++aXatGnj6ekAAIAqwOP3wamMuA8OAFROlf0+LFZX2d//SnMfHAAAADNUrmgGAKgUvvs515RxzxQVmzIurIcVHAAAYDms4AAAXBiGYdpKCis4KC8EHACAk2EY6puSocz9x80uBbgsHKICADidLiquFOEmplEtBfh6m10GqjBWcAAAbr05sqP8TQoZMVG1ZLPZTBkb1kDAAQC45e/rbVrAIdzgchFwAAButakfVOlu9AaUFefgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAy+H6PwCoRAzD0KZ95t1JmO+CglUQcACgkuB7oIDywyEqAKgkKsv3QEl8FxSqPlZwAKASMvN7oCS+CwpVHwEHACqhDlG1+JoE4DJwiAoAAFgOAQcAAFgOAQcAAFgOAQcAAFgOAQcAAFgOAQcAAFgOAQcAAFgOAQcAAFgOd5ECAAClbD+Ud1l3027XILgcq7l4rOAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLqZCAM2/ePEVFRcnf31+xsbHasGHDBfsvW7ZMLVu2lL+/v9q1a6cPP/zQ+VpRUZEmTJigdu3aqVq1aoqIiNDQoUN1+PBhT08DAABUER4POEuXLlVSUpKmT5+urKwstW/fXgkJCcrJyXHbf/369Ro4cKBGjRqlzZs3KzExUYmJidq2bZskKT8/X1lZWZo6daqysrL07rvvavfu3br99ts9PRUAAFBF2AzDMDw5QGxsrK677jrNnTtXklRSUqLIyEg9+OCDmjhxYqn+/fv316lTp7Rq1SpnW6dOnRQdHa2UlBS3Y2zcuFEdO3bU/v371bBhwz+tKS8vT8HBwcrNzVVQUNAlzgwAyld+4Vm1nvaxJGnHkwkKtPuYXBGuNL//HVx2X1yl+zbxi/n89ugKTmFhoTIzMxUfH//bgF5eio+PV0ZGhtttMjIyXPpLUkJCwnn7S1Jubq5sNptq1qzp9vWCggLl5eW5PAAAgHV5NOAcPXpUxcXFCgsLc2kPCwuTw+Fwu43D4bio/mfOnNGECRM0cODA86a55ORkBQcHOx+RkZGXMBsAAFBVVOmrqIqKitSvXz8ZhqH58+eft9+kSZOUm5vrfBw8eLACqwQAABXNowd469atK29vb2VnZ7u0Z2dnKzw83O024eHhZep/Ltzs379fa9asueCxOD8/P/n5+V3iLAAAQFXj0RUcu92uDh06KD093dlWUlKi9PR0xcXFud0mLi7Opb8kpaWlufQ/F2727NmjTz/9VHXq1PHMBAAAQJXk8VP0k5KSNGzYMMXExKhjx46aNWuWTp06pREjRkiShg4dqvr16ys5OVmSNG7cOHXt2lUzZ85U7969lZqaqk2bNmnBggWS/htu+vbtq6ysLK1atUrFxcXO83Nq164tu93u6SkBAIBKzuMBp3///jpy5IimTZsmh8Oh6OhorV692nki8YEDB+Tl9dtC0vXXX68lS5ZoypQpmjx5spo1a6bly5erbdu2kqRDhw5pxYoVkqTo6GiXsdauXatu3bp5ekoAAKCSq5CbLIwZM0Zjxoxx+9pnn31Wqu2uu+7SXXfd5bZ/VFSUPHzrHgAAUMVV6auoAAAA3CHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAy6mQr2oAgKrCMAydLio2Zez8QnPGBdw5c5n/HeQXnlWAr7dsNls5VXRxCDgA8D+GYahvSoYy9x83uxTAdENe33DZ+9jxZIIC7eZEDQ5RAcD/nC4qrhThJqZRLQX4eptdBq5AAb7eimlUy+wyygUrOADgxqYp8Qq0mxMyzFzWx5XNZrNp2f1x2rTv8oN+m/pBpgZ1Ag4AuBFo9zZtaR0wk81mk385BBOz//vhEBUAALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcH7MLAIDfMwxDp4uKTRk7v9CccQGUPwIOgErDMAz1TclQ5v7jZpcCoIrjEBWASuN0UXGlCDcxjWopwNfb7DIAXAZWcABUSpumxCvQbk7ICPD1ls1mM2VsAOWDgAOgUgq0eyvQzp8oAJeGQ1QAAMByCDgAAMByCDgAAMByCDgAAMByKiTgzJs3T1FRUfL391dsbKw2bNhwwf7Lli1Ty5Yt5e/vr3bt2unDDz90ef3dd99Vz549VadOHdlsNm3ZssWD1QMAgKrG4wFn6dKlSkpK0vTp05WVlaX27dsrISFBOTk5bvuvX79eAwcO1KhRo7R582YlJiYqMTFR27Ztc/Y5deqUOnfurBkzZni6fAAAUAXZDMMwPDlAbGysrrvuOs2dO1eSVFJSosjISD344IOaOHFiqf79+/fXqVOntGrVKmdbp06dFB0drZSUFJe++/btU+PGjbV582ZFR0eXuaa8vDwFBwcrNzdXQUFBlzYxAOUuv/CsWk/7WJK048kELhMHTPLdz7mXvY92DYLLoRJXF/P57dEVnMLCQmVmZio+Pv63Ab28FB8fr4yMDLfbZGRkuPSXpISEhPP2BwAA+COP/vPo6NGjKi4uVlhYmEt7WFiYdu3a5XYbh8Phtr/D4bjkOgoKClRQUOB8npeXd8n7AgAAld8VcRVVcnKygoODnY/IyEizSwIAAB7k0RWcunXrytvbW9nZ2S7t2dnZCg8Pd7tNeHj4RfUvi0mTJikpKcn5PC8vj5ADAMB5eOL8mYrm0RUcu92uDh06KD093dlWUlKi9PR0xcXFud0mLi7Opb8kpaWlnbd/Wfj5+SkoKMjlAQAArMvjlygkJSVp2LBhiomJUceOHTVr1iydOnVKI0aMkCQNHTpU9evXV3JysiRp3Lhx6tq1q2bOnKnevXsrNTVVmzZt0oIFC5z7PHbsmA4cOKDDhw9Lknbv3i3pv6s/l7PSAwAArMHjAad///46cuSIpk2bJofDoejoaK1evdp5IvGBAwfk5fXbQtL111+vJUuWaMqUKZo8ebKaNWum5cuXq23bts4+K1ascAYkSRowYIAkafr06Xr88cc9PSUAAFDJefw+OJUR98EBKifugwPgQirNfXAAAADMQMABAACWQ8ABAACWQ8ABAACWQ8ABAACWQ8ABAACWQ8ABAACWQ8ABAACWQ8ABAACWQ8ABAACWQ8ABAACWwxe9AHBhGIY27TtuythniopNGReA9RBwADgZhqG+KRnK3G9OwAGA8sIhKgBOp4uKK0W4iWlUSwG+3maXAaAKYwUHgFubpsQr0G5OyAjw9ZbNZjNlbADWQMAB4Fag3VuBdv5EAKiaOEQFAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsp0ICzrx58xQVFSV/f3/FxsZqw4YNF+y/bNkytWzZUv7+/mrXrp0+/PBDl9cNw9C0adNUr149BQQEKD4+Xnv27PHkFAAAQBXi8YCzdOlSJSUlafr06crKylL79u2VkJCgnJwct/3Xr1+vgQMHatSoUdq8ebMSExOVmJiobdu2Ofs899xzmjNnjlJSUvTNN9+oWrVqSkhI0JkzZzw9HQAAUAXYDMMwPDlAbGysrrvuOs2dO1eSVFJSosjISD344IOaOHFiqf79+/fXqVOntGrVKmdbp06dFB0drZSUFBmGoYiICD388MN65JFHJEm5ubkKCwvTokWLNGDAgD+tKS8vT8HBwcrNzVVQUFA5zRSo+vILz6r1tI8lSTueTFCg3cfkigDgNxfz+e3Rv16FhYXKzMzUpEmTnG1eXl6Kj49XRkaG220yMjKUlJTk0paQkKDly5dLkvbu3SuHw6H4+Hjn68HBwYqNjVVGRobbgFNQUKCCggLn87y8vMuZFuBRhmHodFGxKWPnF5ozLgCUN48GnKNHj6q4uFhhYWEu7WFhYdq1a5fbbRwOh9v+DofD+fq5tvP1+aPk5GQ98cQTlzQHoCIZhqG+KRnK3H/c7FIAoEq7Iq6imjRpknJzc52PgwcPml0S4NbpouJKEW5iGtVSgK+32WUAwCXz6ApO3bp15e3trezsbJf27OxshYeHu90mPDz8gv3P/W92drbq1avn0ic6OtrtPv38/OTn53ep0wBMsWlKvALt5oSMAF9v2Ww2U8YGgPLg0RUcu92uDh06KD093dlWUlKi9PR0xcXFud0mLi7Opb8kpaWlOfs3btxY4eHhLn3y8vL0zTffnHefQFUUaPdWoN3HlAfhBkBV5/FLJJKSkjRs2DDFxMSoY8eOmjVrlk6dOqURI0ZIkoYOHar69esrOTlZkjRu3Dh17dpVM2fOVO/evZWamqpNmzZpwYIFkiSbzaaHHnpITz/9tJo1a6bGjRtr6tSpioiIUGJioqenAwAAqgCPB5z+/fvryJEjmjZtmhwOh6Kjo7V69WrnScIHDhyQl9dvC0nXX3+9lixZoilTpmjy5Mlq1qyZli9frrZt2zr7/P3vf9epU6d077336sSJE+rcubNWr14tf39/T08HAABUAR6/D05lxH1wUFlxHxoAOL+L+fy+Iq6iAgAAVxYCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwfswsAKhPDMHS6qNi08fMLzRsbAKyEgAP8j2EY6puSocz9x80uBQBwmThEBfzP6aLiShNuYhrVUoCvt9llAECVxQoO4MabIzvK38SAERNVSzabzbTxAaCqI+AAbnSIqqVAO/95AEBVxSEqAABgOQQcAABgOQQcAABgOQQcAABgOQQcAABgOQQcAABgOQQcAABgOQQcAABgOQQcAABgOQQcAABgOQQcAABgOQQcAABgOQQcAABgOQQcAABgOQQcAABgOQQcAABgOQQcAABgOQQcAABgOQQcAABgOQQcAABgOR4LOMeOHdOgQYMUFBSkmjVratSoUTp58uQFtzlz5oxGjx6tOnXqqHr16urTp4+ys7Nd+owdO1YdOnSQn5+foqOjPVU+AACowjwWcAYNGqTt27crLS1Nq1at0hdffKF77733gtuMHz9eK1eu1LJly/T555/r8OHD+utf/1qq38iRI9W/f39PlQ4AAKo4H0/sdOfOnVq9erU2btyomJgYSdJLL72kW265Rf/85z8VERFRapvc3Fy99tprWrJkibp37y5JWrhwoVq1aqWvv/5anTp1kiTNmTNHknTkyBFt3brVE+UDAIAqziMrOBkZGapZs6Yz3EhSfHy8vLy89M0337jdJjMzU0VFRYqPj3e2tWzZUg0bNlRGRsZl1VNQUKC8vDyXBwAAsC6PBByHw6HQ0FCXNh8fH9WuXVsOh+O829jtdtWsWdOlPSws7LzblFVycrKCg4Odj8jIyMvaHwAAqNwuKuBMnDhRNpvtgo9du3Z5qtZLNmnSJOXm5jofBw8eNLskAADgQRd1Ds7DDz+s4cOHX7BPkyZNFB4erpycHJf2s2fP6tixYwoPD3e7XXh4uAoLC3XixAmXVZzs7OzzblNWfn5+8vPzu6x9oGIYhqHTRcWmjJ1faM64AIDyd1EBJyQkRCEhIX/aLy4uTidOnFBmZqY6dOggSVqzZo1KSkoUGxvrdpsOHTrI19dX6enp6tOnjyRp9+7dOnDggOLi4i6mTFRRhmGob0qGMvcfN7sUAEAV55FzcFq1aqVevXrpnnvu0YYNG7Ru3TqNGTNGAwYMcF5BdejQIbVs2VIbNmyQJAUHB2vUqFFKSkrS2rVrlZmZqREjRiguLs55BZUk/fDDD9qyZYscDodOnz6tLVu2aMuWLSosLPTEVFCBThcVV4pwE9OolgJ8vc0uAwBwGTxymbgkLV68WGPGjFGPHj3k5eWlPn36OC/xlqSioiLt3r1b+fn5zrYXX3zR2begoEAJCQl6+eWXXfZ799136/PPP3c+v+aaayRJe/fuVVRUlKemgwq2aUq8Au3mhIwAX2/ZbDZTxgYAlA+bYRiG2UVUtLy8PAUHBys3N1dBQUFml4P/yS88q9bTPpYk7XgyQYF2j+VvAEAVdDGf33wXFQAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwfswtA5WIYhk4XFZsydn6hOeMCAKyHgAMnwzDUNyVDmfuPm10KAACXhUNUcDpdVFwpwk1Mo1oK8PU2uwwAQBXGCg7cenNkR/mbFDJiomrJZrOZMjYAwBoIOHCrQ1QtBdr59QAAVE0cogIAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJZDwAEAAJbj0YBz7NgxDRo0SEFBQapZs6ZGjRqlkydPXnCbM2fOaPTo0apTp46qV6+uPn36KDs72/n6t99+q4EDByoyMlIBAQFq1aqVZs+e7clpAACAKsajAWfQoEHavn270tLStGrVKn3xxRe69957L7jN+PHjtXLlSi1btkyff/65Dh8+rL/+9a/O1zMzMxUaGqq33npL27dv12OPPaZJkyZp7ty5npwKAACoQmyGYRie2PHOnTvVunVrbdy4UTExMZKk1atX65ZbbtHPP/+siIiIUtvk5uYqJCRES5YsUd++fSVJu3btUqtWrZSRkaFOnTq5HWv06NHauXOn1qxZU6ba8vLyFBwcrNzcXAUFBV3iDK0nv/CsWk/7WJK048kEBdp9TK4IAIDfXMznt8dWcDIyMlSzZk1nuJGk+Ph4eXl56ZtvvnG7TWZmpoqKihQfH+9sa9mypRo2bKiMjIzzjpWbm6vatWuf9/WCggLl5eW5PAAAgHV5LOA4HA6Fhoa6tPn4+Kh27dpyOBzn3cZut6tmzZou7WFhYefdZv369Vq6dOkFD30lJycrODjY+YiMjLy4yQAAgCrlogPOxIkTZbPZLvjYtWuXJ2otZdu2bbrjjjs0ffp09ezZ87z9Jk2apNzcXOfj4MGDFVIfAAAwx0WfZPHwww9r+PDhF+zTpEkThYeHKycnx6X97NmzOnbsmMLDw91uFx4ersLCQp04ccJlFSc7O7vUNjt27FCPHj107733asqUKResx8/PT35+fhfsAwAArOOiA05ISIhCQkL+tF9cXJxOnDihzMxMdejQQZK0Zs0alZSUKDY21u02HTp0kK+vr9LT09WnTx9J0u7du3XgwAHFxcU5+23fvl3du3fXsGHD9Mwzz1zsFCq9737ONWXcM0XFpowLAEB589g5OK1atVKvXr10zz33aMOGDVq3bp3GjBmjAQMGOK+gOnTokFq2bKkNGzZIkoKDgzVq1CglJSVp7dq1yszM1IgRIxQXF+e8gmrbtm268cYb1bNnTyUlJcnhcMjhcOjIkSOemgoAAKhiPHod8OLFizVmzBj16NFDXl5e6tOnj+bMmeN8vaioSLt371Z+fr6z7cUXX3T2LSgoUEJCgl5++WXn6++8846OHDmit956S2+99ZazvVGjRtq3b58np1MhDMMwbSWFFRwAgFV47D44lVllvQ+OYRjqm5KhzP3HzS6F++AAACqdSnEfHFy800XFlSLcxDSqpQBfb7PLAADgkvFP9ErqzZEd5W9SyIiJqiWbzWbK2AAAlAcCTiXl7+ttWsAh3AAAqjoCjgdc6mXenOQLAED5IOBUUm3qB3GSLwAAl4iTjAEAgOUQcAAAgOUQcAAAgOVwkkc5MgxDp4uKL/lkYU4yBgCgfBBwytHpomK1nvax2WUAAHDF4xBVJcSdhAEAuDys4JSjAF9v7XgyQdsP5V3WfriTMAAAl4eAU45sNpsC7T6XfQdiwg0AAJeHQ1QAAMByCDgAAMByCDgAAMByCDgAAMByOMnYA9o1CDa7BAAArmis4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMvxMbsAMxiGIUnKy8szuRIAAFBW5z63z32OX8gVGXB+/fVXSVJkZKTJlQAAgIv166+/Kjg4+IJ9bEZZYpDFlJSU6PDhw6pRo4ZsNlu57jsvL0+RkZE6ePCggoKCynXfVQHzv7LnL/EeXOnzl3gPrvT5S557DwzD0K+//qqIiAh5eV34LJsrcgXHy8tLDRo08OgYQUFBV+wvtsT8r/T5S7wHV/r8Jd6DK33+kmfegz9buTmHk4wBAIDlEHAAAIDlEHDKmZ+fn6ZPny4/Pz+zSzEF87+y5y/xHlzp85d4D670+UuV4z24Ik8yBgAA1sYKDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCTjmaN2+eoqKi5O/vr9jYWG3YsMHskipMcnKyrrvuOtWoUUOhoaFKTEzU7t27zS7LNM8++6xsNpseeughs0upMIcOHdLgwYNVp04dBQQEqF27dtq0aZPZZVWY4uJiTZ06VY0bN1ZAQICuuuoqPfXUU2X6zpyq6IsvvtBtt92miIgI2Ww2LV++3OV1wzA0bdo01atXTwEBAYqPj9eePXvMKdZDLvQeFBUVacKECWrXrp2qVaumiIgIDR06VIcPHzav4HL2Z78Dv3f//ffLZrNp1qxZFVYfAaecLF26VElJSZo+fbqysrLUvn17JSQkKCcnx+zSKsTnn3+u0aNH6+uvv1ZaWpqKiorUs2dPnTp1yuzSKtzGjRv1yiuv6Oqrrza7lApz/Phx/eUvf5Gvr68++ugj7dixQzNnzlStWrXMLq3CzJgxQ/Pnz9fcuXO1c+dOzZgxQ88995xeeukls0vziFOnTql9+/aaN2+e29efe+45zZkzRykpKfrmm29UrVo1JSQk6MyZMxVcqedc6D3Iz89XVlaWpk6dqqysLL377rvavXu3br/9dhMq9Yw/+x0457333tPXX3+tiIiICqrsfwyUi44dOxqjR492Pi8uLjYiIiKM5ORkE6syT05OjiHJ+Pzzz80upUL9+uuvRrNmzYy0tDSja9euxrhx48wuqUJMmDDB6Ny5s9llmKp3797GyJEjXdr++te/GoMGDTKpooojyXjvvfecz0tKSozw8HDj+eefd7adOHHC8PPzM95++20TKvS8P74H7mzYsMGQZOzfv79iiqpA55v/zz//bNSvX9/Ytm2b0ahRI+PFF1+ssJpYwSkHhYWFyszMVHx8vLPNy8tL8fHxysjIMLEy8+Tm5kqSateubXIlFWv06NHq3bu3y+/ClWDFihWKiYnRXXfdpdDQUF1zzTV69dVXzS6rQl1//fVKT0/X999/L0n69ttv9dVXX+nmm282ubKKt3fvXjkcDpf/DoKDgxUbG3vF/k2U/vt30WazqWbNmmaXUiFKSko0ZMgQPfroo2rTpk2Fj39FftlmeTt69KiKi4sVFhbm0h4WFqZdu3aZVJV5SkpK9NBDD+kvf/mL2rZta3Y5FSY1NVVZWVnauHGj2aVUuJ9++knz589XUlKSJk+erI0bN2rs2LGy2+0aNmyY2eVViIkTJyovL08tW7aUt7e3iouL9cwzz2jQoEFml1bhHA6HJLn9m3jutSvNmTNnNGHCBA0cOPCK+QLOGTNmyMfHR2PHjjVlfAIOyt3o0aO1bds2ffXVV2aXUmEOHjyocePGKS0tTf7+/maXU+FKSkoUExOjf/zjH5Kka665Rtu2bVNKSsoVE3D+7//+T4sXL9aSJUvUpk0bbdmyRQ899JAiIiKumPcA7hUVFalfv34yDEPz5883u5wKkZmZqdmzZysrK0s2m82UGjhEVQ7q1q0rb29vZWdnu7RnZ2crPDzcpKrMMWbMGK1atUpr165VgwYNzC6nwmRmZionJ0fXXnutfHx85OPjo88//1xz5syRj4+PiouLzS7Ro+rVq6fWrVu7tLVq1UoHDhwwqaKK9+ijj2rixIkaMGCA2rVrpyFDhmj8+PFKTk42u7QKd+7vHn8Tfws3+/fvV1pa2hWzevPll18qJydHDRs2dP5N3L9/vx5++GFFRUVVSA0EnHJgt9vVoUMHpaenO9tKSkqUnp6uuLg4EyurOIZhaMyYMXrvvfe0Zs0aNW7c2OySKlSPHj303XffacuWLc5HTEyMBg0apC1btsjb29vsEj3qL3/5S6nbAnz//fdq1KiRSRVVvPz8fHl5uf5J9fb2VklJiUkVmadx48YKDw93+ZuYl5enb7755or5myj9Fm727NmjTz/9VHXq1DG7pAozZMgQbd261eVvYkREhB599FF9/PHHFVIDh6jKSVJSkoYNG6aYmBh17NhRs2bN0qlTpzRixAizS6sQo0eP1pIlS/T++++rRo0azuPswcHBCggIMLk6z6tRo0ap842qVaumOnXqXBHnIY0fP17XX3+9/vGPf6hfv37asGGDFixYoAULFphdWoW57bbb9Mwzz6hhw4Zq06aNNm/erBdeeEEjR440uzSPOHnypH744Qfn871792rLli2qXbu2GjZsqIceekhPP/20mjVrpsaNG2vq1KmKiIhQYmKieUWXswu9B/Xq1VPfvn2VlZWlVatWqbi42Pl3sXbt2rLb7WaVXW7+7Hfgj4HO19dX4eHhatGiRcUUWGHXa10BXnrpJaNhw4aG3W43OnbsaHz99ddml1RhJLl9LFy40OzSTHMlXSZuGIaxcuVKo23btoafn5/RsmVLY8GCBWaXVKHy8vKMcePGGQ0bNjT8/f2NJk2aGI899phRUFBgdmkesXbtWrf/zQ8bNswwjP9eKj516lQjLCzM8PPzM3r06GHs3r3b3KLL2YXeg71795737+LatWvNLr1c/NnvwB9V9GXiNsOw6G02AQDAFYtzcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcADgIj3++OOKjo52Ph8+fLil7tALWAEBB0CZDB8+XDabTTabTb6+vgoLC9NNN92k119/vVJ839LBgwc1cuRIRUREyG63q1GjRho3bpz+85//eHzs2bNna9GiRc7n3bp100MPPeTxcQGcHwEHQJn16tVLv/zyi/bt26ePPvpIN954o8aNG6dbb71VZ8+eNa2un376STExMdqzZ4/efvtt/fDDD0pJSXF+4e2xY8c8On5wcLBq1qzp0TEAXBwCDoAy8/PzU3h4uOrXr69rr71WkydP1vvvv6+PPvrIuYLxwgsvqF27dqpWrZoiIyP1wAMP6OTJk859LFq0SDVr1tSqVavUokULBQYGqm/fvsrPz9e//vUvRUVFqVatWho7dqyKi4ud27388stq1qyZ/P39FRYWpr59+zpfGz16tOx2uz755BN17dpVDRs21M0336xPP/1Uhw4d0mOPPebsa7PZtHz5cpd51axZ02UFZsKECWrevLkCAwPVpEkTTZ06VUVFRed9X35/iGr48OH6/PPPNXv2bOeK1969e9W0aVP985//dNluy5YtstlsLl9YCKB8EHAAXJbu3burffv2evfddyVJXl5emjNnjrZv365//etfWrNmjf7+97+7bJOfn685c+YoNTVVq1ev1meffaY777xTH374oT788EO9+eabeuWVV/TOO+9IkjZt2qSxY8fqySef1O7du7V69Wp16dJFknTs2DF9/PHHeuCBB0p9c314eLgGDRqkpUuX6mK+dq9GjRpatGiRduzYodmzZ+vVV1/Viy++WKZtZ8+erbi4ON1zzz365Zdf9Msvv6hhw4YaOXKkFi5c6NJ34cKF6tKli5o2bVrm2gCUjY/ZBQCo+lq2bKmtW7dKksu5J1FRUXr66ad1//336+WXX3a2FxUVaf78+brqqqskSX379tWbb76p7OxsVa9eXa1bt9aNN96otWvXqn///jpw4ICqVaumW2+9VTVq1FCjRo10zTXXSJL27NkjwzDUqlUrt7W1atVKx48f15EjRxQaGlqm+UyZMsVlDo888ohSU1NLBTV3goODZbfbFRgYqPDwcGf78OHDNW3aNG3YsEEdO3ZUUVGRlixZUmpVB0D5IOAAuGyGYchms0mSPv30UyUnJ2vXrl3Ky8vT2bNndebMGeXn5yswMFCSFBgY6Aw3khQWFqaoqChVr17dpS0nJ0eSdNNNN6lRo0Zq0qSJevXqpV69eunOO+907u9cDRdit9vLPJ+lS5dqzpw5+vHHH3Xy5EmdPXtWQUFBZd7enYiICPXu3Vuvv/66OnbsqJUrV6qgoEB33XXXZe0XgHscogJw2Xbu3KnGjRtr3759uvXWW3X11Vfr3//+tzIzMzVv3jxJUmFhobO/r6+vy/bnrsz6Y9u5q7Nq1KihrKwsvf3226pXr56mTZum9u3b68SJE2ratKlsNpt27tx53tpCQkKcJwHbbLZSYej359dkZGRo0KBBuuWWW7Rq1Spt3rxZjz32mEv9l+ruu+9WamqqTp8+rYULF6p///4uIQ1A+SHgALgsa9as0Xfffac+ffooMzNTJSUlmjlzpjp16qTmzZvr8OHD5TKOj4+P4uPj9dxzz2nr1q3at2+f1qxZozp16uimm27Syy+/rNOnT7ts43A4tHjxYg0fPtzZFhISol9++cX5fM+ePcrPz3c+X79+vRo1aqTHHntMMTExatasmfbv339RtdrtdpcTpM+55ZZbVK1aNc2fP1+rV6/WyJEjL2q/AMqOQ1QAyqygoEAOh0PFxcXKzs7W6tWrlZycrFtvvVVDhw7Vtm3bVFRUpJdeekm33Xab1q1bp5SUlMsed9WqVfrpp5/UpUsX1apVSx9++KFKSkrUokULSdLcuXN1/fXXKyEhQU8//bQaN26s7du369FHH1Xz5s01bdo05766d++uuXPnKi4uTsXFxZowYYLL6lGzZs104MABpaam6rrrrtMHH3yg995776LqjYqK0jfffKN9+/apevXqql27try8vOTt7a3hw4dr0qRJatasmeLi4i77vQHgHis4AMps9erVqlevnqKiotSrVy+tXbtWc+bM0fvvvy9vb2+1b99eL7zwgmbMmKG2bdtq8eLFSk5Ovuxxa9asqXfffVfdu3dXq1atlJKSorfffltt2rSR9N9QsnHjRjVp0kT9+vVTo0aNdPPNN6t58+Zat26dy7k9M2fOVGRkpG644Qb97W9/0yOPPOJymOj222/X+PHjNWbMGEVHR2v9+vWaOnXqRdX7yCOPyNvbW61bt1ZISIgOHDjgfG3UqFEqLCzUiBEjLvNdAXAhNuNirp0EgCpi+vTpeuGFF5SWlqZOnTqZXY7Tl19+qR49eujgwYMKCwszuxzAsgg4ACxr4cKFys3N1dixY+XlZe6CdUFBgY4cOaJhw4YpPDxcixcvNrUewOoIOABQARYtWqRRo0YpOjpaK1asUP369c0uCbA0Ag4AALAcTjIGAACWQ8ABAACWQ8ABAACWQ8ABAACWQ8ABAACWQ8ABAACWQ8ABAACWQ8ABAACWQ8ABAACW8/8BGoFxrLXTJVgAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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2+vTpY+Tk5DiNXVBQYEyYMMFo27atUbt2baNmzZpG27Ztjfnz55eqOTY21vjrX/9aqh0wg80wLvPsOAAwWXFxscLDw9WnTx89+eSTZpdzRdqyZYuuv/56ZWRklLpZJWAGAg4AS1i2bJkefPBB7dmzx/RfFL8S9evXTyUlJXrzzTfNLgWQRMABAAAWxFVUAADAcgg4AADAcgg4AADAcgg4AADAcq7IG/2VlJRo//79ql27Nj8KBwBANWEYho4fP67g4GC5uZ1/jeaKDDj79+9XSEiI2WUAAIBLsHfvXjVu3Pi8fa7IgHP2x/327t0rX19fk6sBAAAXIy8vTyEhIU4/0nsuV2TAOXtYytfXl4ADAEA1czGnl3CSMQAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwPswsAAADOvv8t19TxIxr7mTp+RaiUFZx58+apWbNm8vb2VmxsrDZs2HDe/suXL1dYWJi8vb0VERGhDz744Jx9H3jgAdlsNs2aNauCqwYAANWVywPOsmXLlJiYqKlTpyojI0Nt27ZVQkKCcnJyyuz/9ddfq3///ho+fLg2b96sHj16qEePHtq6dWupvm+//ba++eYbBQcHu3oaAACgGnF5wHn++ed17733aujQoQoPD1dycrJq1KihV199tcz+L774orp27aoJEyaoVatWevLJJ3X99ddr7ty5Tv327dun0aNH64033pCnp6erpwEAAKoRlwacwsJCpaenKz4+/vcB3dwUHx+vtLS0MrdJS0tz6i9JCQkJTv1LSko0cOBATZgwQa1bt75gHQUFBcrLy3N6AAAA63JpwDl06JCKi4sVGBjo1B4YGKisrKwyt8nKyrpg/+nTp8vDw0MPPfTQRdWRlJQkPz8/xyMkJKScMwEAANVJtbtMPD09XS+++KIWL14sm812UdtMmjRJubm5jsfevXtdXCUAADCTSwNOvXr15O7uruzsbKf27OxsBQUFlblNUFDQeft/8cUXysnJUZMmTeTh4SEPDw/t3r1b48ePV7Nmzcrcp5eXl3x9fZ0eAADAulwacOx2u6KiopSamupoKykpUWpqquLi4srcJi4uzqm/JKWkpDj6Dxw4UN999522bNnieAQHB2vChAn66KOPXDcZAABQbbj8Rn+JiYkaPHiwoqOjFRMTo1mzZik/P19Dhw6VJA0aNEiNGjVSUlKSJGnMmDHq1KmTZs6cqW7dumnp0qXatGmTFi5cKEkKCAhQQECA0xienp4KCgrSNddc4+rpAACAasDlAadv3746ePCgpkyZoqysLEVGRmrNmjWOE4n37NkjN7ffF5I6dOigJUuW6LHHHtMjjzyi0NBQrVq1Sm3atHF1qQAAwCJshmEYZhdR2fLy8uTn56fc3FzOxwEAVDn8VEPZyvP9zW9RAQDwJ2YHDFy+aneZOAAAwIUQcAAAgOUQcAAAgOVwDg4AAFWIYRgqOFNieg0X+2sBVRUBBwCAKsIwDD381nfannXc1Dqim9bR8gfiqnXI4RAVAABVRMGZEtPDjSRt2n1Up4qKzS7jsrCCAwBAFfSfYTHy9nSv1DFPFxVr4KsbKnVMVyHgAADwB4Zh6LRJqxd/HNfb073SA46VEHAAAPgfwzDUOzlN6buPml0KLhPn4AAA8D+nioqrRLhp1dBXXh58RV8OVnAAACiDGefAnOXl4Vatr2CqCgg4AACUgXNgqjfWvwAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOUQcAAAgOVwHxwAAMrQupGvatjN+Zr8/rdcU8a1ElZwAACA5RBwAACA5XCICgAAlLJtX95l/VRFRGO/Cqym/FjBAQAAlsMKDgAAVYxZqx8nC8+YMq4rsIIDAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAshzsZAwCqnO9/yzVl3NNFxaaMi4rHCg4AALAcAg4AALAcDlEBAKoUwzBMO1TEISrrqJSAM2/ePD377LPKyspS27ZtNWfOHMXExJyz//LlyzV58mTt2rVLoaGhmj59uu644w7H648//riWLl2qvXv3ym63KyoqSk8//bRiY2MrYzoAABcxDEO9k9OUvvuo2aWgmnP5Iaply5YpMTFRU6dOVUZGhtq2bauEhATl5OSU2f/rr79W//79NXz4cG3evFk9evRQjx49tHXrVkefq6++WnPnztX333+vL7/8Us2aNVOXLl108OBBV08HAOBCp4qKq0S4iW5aRz6e7maXgctgMwzDcOUAsbGxateunebOnStJKikpUUhIiEaPHq2JEyeW6t+3b1/l5+dr9erVjrb27dsrMjJSycnJZY6Rl5cnPz8/ffLJJ+rcufMFazrbPzc3V76+vpc4MwBARTtZeEbhUz6SJP1nWIy8TQoZ0c3qyGazmTK2mSry/W/dyFc+nu4V+j6W5/vbpYeoCgsLlZ6erkmTJjna3NzcFB8fr7S0tDK3SUtLU2JiolNbQkKCVq1adc4xFi5cKD8/P7Vt27bMPgUFBSooKHA8z8vLK+dMAACVzdvT3bSAcyWGmz8b+OqGy97HD08kqIbdnNN9XXqI6tChQyouLlZgYKBTe2BgoLKyssrcJisr66L6r169WrVq1ZK3t7deeOEFpaSkqF69emXuMykpSX5+fo5HSEjIZcwKAABr8vF0V3TTOmaXUSGq7VVUt9xyi7Zs2aJDhw7ppZdeUp8+fbR+/Xo1aNCgVN9JkyY5rQrl5eURcgAA+BObzablD8Rp067LPw/q7CEqs7g04NSrV0/u7u7Kzs52as/OzlZQUFCZ2wQFBV1U/5o1a+qqq67SVVddpfbt2ys0NFSvvPKK0+Gws7y8vOTl5XWZswEAVKbWjXxNO7xxJbPZbBVyaNDsz86lh6jOXsKdmprqaCspKVFqaqri4uLK3CYuLs6pvySlpKScs/8f9/vH82wAAMCVy+XxKjExUYMHD1Z0dLRiYmI0a9Ys5efna+jQoZKkQYMGqVGjRkpKSpIkjRkzRp06ddLMmTPVrVs3LV26VJs2bdLChQslSfn5+Xr66ad15513qmHDhjp06JDmzZunffv26Z577nH1dAAAQDXg8oDTt29fHTx4UFOmTFFWVpYiIyO1Zs0ax4nEe/bskZvb7wtJHTp00JIlS/TYY4/pkUceUWhoqFatWqU2bdpIktzd3bVjxw699tprOnTokAICAtSuXTt98cUXat26taunAwAAqgGX3wenKuI+OABQNf3xPixmXmJ8pauIX3OPaOxXAZU4K8/3Nz+2CQAALIeAAwAALIeAAwAALIeAAwAALIeAAwAALIfT0wEApVTEVTSX4nRRsSnjwnpYwQEAAJbDCg4AwIlhGKatpLCCg4pCwAEAOBiGod7JaUrfffm/Jg2YiUNUAACHU0XFVSLcRDetI58K+EVrXLlYwQEAlOk/w2LkbVLIiG5WRzabzZSxYQ0EHABAmbw93U0LOIQbXC4CDgAAcOKKH8qsbAQcAECZWjfy5de8UW1xkjEAALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcD7MLAAD8zjAMbdp11LTxTxcVmzY2UJEIOABQRRiGod7JaUrfbV7AAayCQ1QAUEWcKiquMuEmumkd+Xi6m10GcMlYwQGAKug/w2LkbWLAiG5WRzabzbTxgctFwAGAKiiqWR3VsPMnGrhUlXKIat68eWrWrJm8vb0VGxurDRs2nLf/8uXLFRYWJm9vb0VEROiDDz5wvFZUVKSHH35YERERqlmzpoKDgzVo0CDt37/f1dMAAADVhMsDzrJly5SYmKipU6cqIyNDbdu2VUJCgnJycsrs//XXX6t///4aPny4Nm/erB49eqhHjx7aunWrJOnkyZPKyMjQ5MmTlZGRoZUrVyozM1N33nmnq6cCAACqCZthGIYrB4iNjVW7du00d+5cSVJJSYlCQkI0evRoTZw4sVT/vn37Kj8/X6tXr3a0tW/fXpGRkUpOTi5zjI0bNyomJka7d+9WkyZNLlhTXl6e/Pz8lJubK19f30ucGQBUrJOFZxQ+5SNJ0g9PJHCICviT8nx/u3QFp7CwUOnp6YqPj/99QDc3xcfHKy0trcxt0tLSnPpLUkJCwjn7S1Jubq5sNpv8/f3LfL2goEB5eXlODwAAYF0uDTiHDh1ScXGxAgMDndoDAwOVlZVV5jZZWVnl6n/69Gk9/PDD6t+//znTXFJSkvz8/ByPkJCQS5gNAACoLqr1fXCKiorUp08fGYahBQsWnLPfpEmTlJub63js3bu3EqsEAACVzaUHeOvVqyd3d3dlZ2c7tWdnZysoKKjMbYKCgi6q/9lws3v3bq1du/a8x+K8vLzk5eV1ibMAAADVjUtXcOx2u6KiopSamupoKykpUWpqquLi4srcJi4uzqm/JKWkpDj1PxtufvrpJ33yyScKCAhwzQQAAEC15PJT9BMTEzV48GBFR0crJiZGs2bNUn5+voYOHSpJGjRokBo1aqSkpCRJ0pgxY9SpUyfNnDlT3bp109KlS7Vp0yYtXLhQ0n/DTe/evZWRkaHVq1eruLjYcX5O3bp1ZbfbXT0lAABQxbk84PTt21cHDx7UlClTlJWVpcjISK1Zs8ZxIvGePXvk5vb7QlKHDh20ZMkSPfbYY3rkkUcUGhqqVatWqU2bNpKkffv26d1335UkRUZGOo21bt063Xzzza6eEgAAqOJcfh+cqoj74ACoirgPDnB+VeY+OAAAAGYg4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMvhNpkA8AeGYehUUbEpY58sNGdcwIoIOADwP4ZhqHdymtJ3HzW7FACXiUNUAPA/p4qKq0S4iW5aRz6e7maXAVRrrOAAQBk2PRavGnZzQoaPp7tsNpspYwNWQcABgDLUsLvza95ANcYhKgAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkEHAAAYDkeZhcAAH9kGIZOFRWbMvbJQnPGBVDxCDgAqgzDMNQ7OU3pu4+aXQqAao5DVACqjFNFxVUi3EQ3rSMfT3ezywBwGVjBAVAlbXosXjXs5oQMH0932Ww2U8YGUDEIOACqpBp2d9Ww8ycKwKXhEBUAALAcAg4AALAcAg4AALCcSgk48+bNU7NmzeTt7a3Y2Fht2LDhvP2XL1+usLAweXt7KyIiQh988IHT6ytXrlSXLl0UEBAgm82mLVu2uLB6AABQ3bg84CxbtkyJiYmaOnWqMjIy1LZtWyUkJCgnJ6fM/l9//bX69++v4cOHa/PmzerRo4d69OihrVu3Ovrk5+frhhtu0PTp011dPgAAqIZshmEYrhwgNjZW7dq109y5cyVJJSUlCgkJ0ejRozVx4sRS/fv27av8/HytXr3a0da+fXtFRkYqOTnZqe+uXbvUvHlzbd68WZGRkRddU15envz8/JSbmytfX99LmxiACney8IzCp3wkSfrhiQSuogLgpDzf3y5dwSksLFR6erri4+N/H9DNTfHx8UpLSytzm7S0NKf+kpSQkHDO/hejoKBAeXl5Tg8AAGBdLg04hw4dUnFxsQIDA53aAwMDlZWVVeY2WVlZ5ep/MZKSkuTn5+d4hISEXPK+AABA1XdFXEU1adIk5ebmOh579+41uyQAAOBCLj3AXa9ePbm7uys7O9upPTs7W0FBQWVuExQUVK7+F8PLy0teXl6XvD0AAKheXLqCY7fbFRUVpdTUVEdbSUmJUlNTFRcXV+Y2cXFxTv0lKSUl5Zz9AQAA/szllygkJiZq8ODBio6OVkxMjGbNmqX8/HwNHTpUkjRo0CA1atRISUlJkqQxY8aoU6dOmjlzprp166alS5dq06ZNWrhwoWOfR44c0Z49e7R//35JUmZmpqT/rv5czkoPAACwBpcHnL59++rgwYOaMmWKsrKyFBkZqTVr1jhOJN6zZ4/c3H5fSOrQoYOWLFmixx57TI888ohCQ0O1atUqtWnTxtHn3XffdQQkSerXr58kaerUqXr88cddPSUAAFDFufw+OFUR98EBqibugwPgfKrMfXAAAADMQMABAACWQ8ABAACWQ8ABAACWQ8ABAACWQ8ABAACWQ8ABAACWQ8ABAACWQ8ABAACWQ8ABAACWQ8ABAACWQ8ABAACWwy/ZAXBiGIZOFRWbMvbJQnPGBWA9BBwADoZhqHdymtJ3HzW7FAC4LByiAuBwqqi4SoSb6KZ15OPpbnYZAKoxVnAAlGnTY/GqYTcnZPh4ustms5kyNgBrIOAAKFMNu7tq2PkTAaB64hAVAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwHAIOAACwHA+zCwDgzDAMnSoqNmXsk4XmjAsAFa1SAs68efP07LPPKisrS23bttWcOXMUExNzzv7Lly/X5MmTtWvXLoWGhmr69Om64447HK8bhqGpU6fqpZde0rFjx9SxY0ctWLBAoaGhlTEdwGUMw1Dv5DSl7z5qdikAUK25/BDVsmXLlJiYqKlTpyojI0Nt27ZVQkKCcnJyyuz/9ddfq3///ho+fLg2b96sHj16qEePHtq6daujz4wZMzR79mwlJydr/fr1qlmzphISEnT69GlXTwdwqVNFxVUi3EQ3rSMfT3ezywCAS2YzDMNw5QCxsbFq166d5s6dK0kqKSlRSEiIRo8erYkTJ5bq37dvX+Xn52v16tWOtvbt2ysyMlLJyckyDEPBwcEaP368/v73v0uScnNzFRgYqMWLF6tfv34XrCkvL09+fn7Kzc2Vr69vBc0UuHwnC88ofMpHkqRNj8Wrht2ckOHj6S6bzWbK2ABwLuX5/nbpCk5hYaHS09MVHx//+4BuboqPj1daWlqZ26SlpTn1l6SEhARH/507dyorK8upj5+fn2JjY8+5T6A6qmF3Vw27hykPwg2A6s6l5+AcOnRIxcXFCgwMdGoPDAzUjh07ytwmKyurzP5ZWVmO18+2navPnxUUFKigoMDxPC8vr3wTAQAA1coVcZl4UlKS/Pz8HI+QkBCzSwIAAC7k0oBTr149ubu7Kzs726k9OztbQUFBZW4TFBR03v5n/7c8+5w0aZJyc3Mdj717917SfAAAQPXg0oBjt9sVFRWl1NRUR1tJSYlSU1MVFxdX5jZxcXFO/SUpJSXF0b958+YKCgpy6pOXl6f169efc59eXl7y9fV1egAAAOty+X1wEhMTNXjwYEVHRysmJkazZs1Sfn6+hg4dKkkaNGiQGjVqpKSkJEnSmDFj1KlTJ82cOVPdunXT0qVLtWnTJi1cuFCSZLPZNHbsWD311FMKDQ1V8+bNNXnyZAUHB6tHjx6ung4AAKgGXB5w+vbtq4MHD2rKlCnKyspSZGSk1qxZ4zhJeM+ePXJz+30hqUOHDlqyZIkee+wxPfLIIwoNDdWqVavUpk0bR59//OMfys/P13333adjx47phhtu0Jo1a+Tt7e3q6QAAgGrA5ffBqYq4Dw6qqj/eB+eHJxJUw86vqQDAWVXmPjgAAABmIOAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADLIeAAAADL8TC7AKAqMQxDp4qKTRv/ZKF5YwOAlRBwgP8xDEO9k9OUvvuo2aUAAC4Th6iA/zlVVFxlwk100zry8XQ3uwwAqLZYwQHKsOmxeNWwmxcwfDzdZbPZTBsfAKo7Ag5Qhhp2d9Ww838PAKiuOEQFAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsx2UB58iRIxowYIB8fX3l7++v4cOH68SJE+fd5vTp0xo5cqQCAgJUq1Yt9erVS9nZ2U59HnroIUVFRcnLy0uRkZGuKh8AAFRjHq7a8YABA3TgwAGlpKSoqKhIQ4cO1X333aclS5acc5tx48bp/fff1/Lly+Xn56dRo0apZ8+e+uqrr5z6DRs2TOvXr9d3333nqvJhEsMwdKqo2JSxTxaaMy4AoOK5JOBs375da9as0caNGxUdHS1JmjNnju644w4999xzCg4OLrVNbm6uXnnlFS1ZskS33nqrJGnRokVq1aqVvvnmG7Vv316SNHv2bEnSwYMHCTgWYxiGeienKX33UbNLAQBUcy45RJWWliZ/f39HuJGk+Ph4ubm5af369WVuk56erqKiIsXHxzvawsLC1KRJE6WlpV1WPQUFBcrLy3N6oOo5VVRcJcJNdNM68vF0N7sMAMBlcMkKTlZWlho0aOA8kIeH6tatq6ysrHNuY7fb5e/v79QeGBh4zm0uVlJSkqZNm3ZZ+0Dl2vRYvGrYzQkZPp7ustlspowNAKgY5VrBmThxomw223kfO3bscFWtl2zSpEnKzc11PPbu3Wt2SbiAGnZ31bB7mPIg3ABA9VeuFZzx48dryJAh5+3TokULBQUFKScnx6n9zJkzOnLkiIKCgsrcLigoSIWFhTp27JjTKk52dvY5t7lYXl5e8vLyuqx9AACA6qNcAad+/fqqX7/+BfvFxcXp2LFjSk9PV1RUlCRp7dq1KikpUWxsbJnbREVFydPTU6mpqerVq5ckKTMzU3v27FFcXFx5ygQAAFc4l5xk3KpVK3Xt2lX33nuvNmzYoK+++kqjRo1Sv379HFdQ7du3T2FhYdqwYYMkyc/PT8OHD1diYqLWrVun9PR0DR06VHFxcY4rqCTp559/1pYtW5SVlaVTp05py5Yt2rJliwoLC10xFQAAUA257D44b7zxhkaNGqXOnTvLzc1NvXr1clziLUlFRUXKzMzUyZMnHW0vvPCCo29BQYESEhI0f/58p/2OGDFCn332meP5ddddJ0nauXOnmjVr5qrpAACAasRmGIZhdhGVLS8vT35+fsrNzZWvr6/Z5eB/ThaeUfiUjyRJPzyRoBp2l+VvAEA1VJ7vb36LCgAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWI6H2QWgajEMQ5t2HTVl7NNFxaaMCwCwHgIOHAzDUO/kNKXvNifgAABQUThEBYdTRcVVItxEN60jH093s8sAAFRjrOCgTP8ZFiNvk0JGdLM6stlspowNALAGAg7KFNWsjmrY+ecBAKieOEQFAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4ADAAAsx6UB58iRIxowYIB8fX3l7++v4cOH68SJE+fd5vTp0xo5cqQCAgJUq1Yt9erVS9nZ2Y7Xv/32W/Xv318hISHy8fFRq1at9OKLL7pyGgAAoJpxacAZMGCAtm3bppSUFK1evVqff/657rvvvvNuM27cOL333ntavny5PvvsM+3fv189e/Z0vJ6enq4GDRro9ddf17Zt2/Too49q0qRJmjt3riunAgAAqhGbYRiGK3a8fft2hYeHa+PGjYqOjpYkrVmzRnfccYd+++03BQcHl9omNzdX9evX15IlS9S7d29J0o4dO9SqVSulpaWpffv2ZY41cuRIbd++XWvXrr2o2vLy8uTn56fc3Fz5+vpe4gyt52ThGYVP+UiS9MMTCaph9zC5IgAAflee72+XreCkpaXJ39/fEW4kKT4+Xm5ublq/fn2Z26Snp6uoqEjx8fGOtrCwMDVp0kRpaWnnHCs3N1d169atuOIBAEC15rL/RM/KylKDBg2cB/PwUN26dZWVlXXObex2u/z9/Z3aAwMDz7nN119/rWXLlun9998/Zy0FBQUqKChwPM/Ly7vIWQAAgOqo3Cs4EydOlM1mO+9jx44drqi1lK1bt+quu+7S1KlT1aVLl3P2S0pKkp+fn+MREhJSKfUBAABzlHsFZ/z48RoyZMh5+7Ro0UJBQUHKyclxaj9z5oyOHDmioKCgMrcLCgpSYWGhjh075rSKk52dXWqbH374QZ07d9Z9992nxx577Lz1TJo0SYmJiY7neXl5hBwAACys3AGnfv36ql+//gX7xcXF6dixY0pPT1dUVJQkae3atSopKVFsbGyZ20RFRcnT01Opqanq1auXJCkzM1N79uxRXFyco9+2bdt06623avDgwXr66acvWIuXl5e8vLwuZnoAAMACXHaScatWrdS1a1fde++92rBhg7766iuNGjVK/fr1c1xBtW/fPoWFhWnDhg2SJD8/Pw0fPlyJiYlat26d0tPTNXToUMXFxTmuoNq6datuueUWdenSRYmJicrKylJWVpYOHjzoqqkAAIBqxqXXAb/xxhsaNWqUOnfuLDc3N/Xq1UuzZ892vF5UVKTMzEydPHnS0fbCCy84+hYUFCghIUHz5893vL5ixQodPHhQr7/+ul5//XVHe9OmTbVr1y5XTgcAAFQTLrsPTlXGfXDKxn1wAABVWXm+v/kGq2IMw9CmXUdNGft0UbEp4wIAUNEIOFWIYRjqnZym9N3mBBwAAKyCXxOvQk4VFVeJcBPdtI58PN3NLgMAgEvGCk4V9Z9hMfI2KWREN6sjm81mytgAAFQEAk4V5e3pblrAIdwAAKo7Ak4V1bqRL1cxAQBwiTgHBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4BBwAAWA4/V+0C3/+We0nbnS4qruBKAAC4MrGCAwAALIcVnApkGIZOFRVf8koMKzgAAFQMAk4FOlVUrPApH5ldBgAAVzwOUVVB0U3ryMfT3ewyAACotljBqUA+nu764YkEbduXd1n7iW5WRzabrYKqAgDgykPAqUA2m0017B7yvszVF8INAACXh0NUAADAcljBcYGIxn5mlwAAwBWNFRwAAGA5BBwAAGA5BBwAAGA5BBwAAGA5BBwAAGA5BBwAAGA5BBwAAGA5BBwAAGA5BBwAAGA5BBwAAGA5BBwAAGA5Lg04R44c0YABA+Tr6yt/f38NHz5cJ06cOO82p0+f1siRIxUQEKBatWqpV69eys7Odrx++PBhde3aVcHBwfLy8lJISIhGjRqlvLw8V04FAABUIy4NOAMGDNC2bduUkpKi1atX6/PPP9d999133m3GjRun9957T8uXL9dnn32m/fv3q2fPnr8X7Oamu+66S++++65+/PFHLV68WJ988okeeOABV04FAABUIzbDMAxX7Hj79u0KDw/Xxo0bFR0dLUlas2aN7rjjDv32228KDg4utU1ubq7q16+vJUuWqHfv3pKkHTt2qFWrVkpLS1P79u3LHGv27Nl69tlntXfv3ouqLS8vT35+fsrNzZWvr+8lzhAAAFSm8nx/u2wFJy0tTf7+/o5wI0nx8fFyc3PT+vXry9wmPT1dRUVFio+Pd7SFhYWpSZMmSktLK3Ob/fv3a+XKlerUqdM5aykoKFBeXp7TAwAAWJeHq3aclZWlBg0aOA/m4aG6desqKyvrnNvY7Xb5+/s7tQcGBpbapn///nrnnXd06tQpde/eXS+//PI5a0lKStK0adNKtRN0AACoPs5+b1/MwadyB5yJEydq+vTp5+2zffv28u623F544QVNnTpVP/74oyZNmqTExETNnz+/zL5nXz9r3759Cg8PV0hIiMvrBAAAFev48ePy8/M7b59yB5zx48dryJAh5+3TokULBQUFKScnx6n9zJkzOnLkiIKCgsrcLigoSIWFhTp27JjTKk52dnapbYKCghQUFKSwsDDVrVtXN954oyZPnqyGDRuW2q+Xl5e8vLwcz2vVqqW9e/eqdu3astlsF5hx+eTl5SkkJER79+69Is/vYf5X9vwl3oMrff4S78GVPn/Jde+BYRg6fvx4mefx/lm5A079+vVVv379C/aLi4vTsWPHlJ6erqioKEnS2rVrVVJSotjY2DK3iYqKkqenp1JTU9WrVy9JUmZmpvbs2aO4uLhzjlVSUiLpv+faXAw3Nzc1btz4ovpeKl9f3yv2H7bE/K/0+Uu8B1f6/CXegyt9/pJr3oMLrdyc5bJzcFq1aqWuXbvq3nvvVXJysoqKijRq1Cj169fPkbz27dunzp0769///rdiYmLk5+en4cOHKzExUXXr1pWvr69Gjx6tuLg4xxVUH3zwgbKzs9WuXTvVqlVL27Zt04QJE9SxY0c1a9bMVdMBAADViMsCjiS98cYbGjVqlDp37iw3Nzf16tVLs2fPdrxeVFSkzMxMnTx50tH2wgsvOPoWFBQoISHB6dwaHx8fvfTSSxo3bpwKCgoUEhKinj17auLEia6cCgAAqEZcGnDq1q2rJUuWnPP1Zs2alToT2tvbW/PmzdO8efPK3OaWW27R119/XaF1ViQvLy9NnTrV6ZyfKwnzv7LnL/EeXOnzl3gPrvT5S1XjPXDZjf4AAADMwo9tAgAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgVKB58+apWbNm8vb2VmxsrDZs2GB2SZUmKSlJ7dq1U+3atdWgQQP16NFDmZmZZpdlmmeeeUY2m01jx441u5RKs2/fPv31r39VQECAfHx8FBERoU2bNpldVqUpLi7W5MmT1bx5c/n4+Khly5Z68sknL+o3c6qjzz//XN27d1dwcLBsNptWrVrl9LphGJoyZYoaNmwoHx8fxcfH66effjKnWBc533tQVFSkhx9+WBEREapZs6aCg4M1aNAg7d+/37yCK9iF/g380QMPPCCbzaZZs2ZVWn0EnAqybNkyJSYmaurUqcrIyFDbtm2VkJBQ6ucqrOqzzz7TyJEj9c033yglJUVFRUXq0qWL8vPzzS6t0m3cuFH/+te/dO2115pdSqU5evSoOnbsKE9PT3344Yf64YcfNHPmTNWpU8fs0irN9OnTtWDBAs2dO1fbt2/X9OnTNWPGDM2ZM8fs0lwiPz9fbdu2PectPWbMmKHZs2crOTlZ69evV82aNZWQkKDTp09XcqWuc7734OTJk8rIyNDkyZOVkZGhlStXKjMzU3feeacJlbrGhf4NnPX222/rm2++uaifV6hQBipETEyMMXLkSMfz4uJiIzg42EhKSjKxKvPk5OQYkozPPvvM7FIq1fHjx43Q0FAjJSXF6NSpkzFmzBizS6oUDz/8sHHDDTeYXYapunXrZgwbNsyprWfPnsaAAQNMqqjySDLefvttx/OSkhIjKCjIePbZZx1tx44dM7y8vIz/+7//M6FC1/vze1CWDRs2GJKM3bt3V05Rlehc8//tt9+MRo0aGVu3bjWaNm1qvPDCC5VWEys4FaCwsFDp6emKj493tLm5uSk+Pl5paWkmVmae3NxcSf+92eOVZOTIkerWrZvTv4Urwbvvvqvo6Gjdc889atCgga677jq99NJLZpdVqTp06KDU1FT9+OOPkqRvv/1WX375pW6//XaTK6t8O3fuVFZWltP/D/z8/BQbG3vF/k2U/vt30WazOf2YtJWVlJRo4MCBmjBhglq3bl3p47v0TsZXikOHDqm4uFiBgYFO7YGBgdqxY4dJVZmnpKREY8eOVceOHdWmTRuzy6k0S5cuVUZGhjZu3Gh2KZXu119/1YIFC5SYmKhHHnlEGzdu1EMPPSS73a7BgwebXV6lmDhxovLy8hQWFiZ3d3cVFxfr6aef1oABA8wurdJlZWVJUpl/E8++dqU5ffq0Hn74YfXv3/+K+QHO6dOny8PDQw899JAp4xNwUOFGjhyprVu36ssvvzS7lEqzd+9ejRkzRikpKfL29ja7nEpXUlKi6Oho/fOf/5QkXXfdddq6dauSk5OvmIDz5ptv6o033tCSJUvUunVrbdmyRWPHjlVwcPAV8x6gbEVFRerTp48Mw9CCBQvMLqdSpKen68UXX1RGRoZsNpspNXCIqgLUq1dP7u7uys7OdmrPzs5WUFCQSVWZY9SoUVq9erXWrVunxo0bm11OpUlPT1dOTo6uv/56eXh4yMPDQ5999plmz54tDw8PFRcXm12iSzVs2FDh4eFOba1atdKePXtMqqjyTZgwQRMnTlS/fv0UERGhgQMHaty4cUpKSjK7tEp39u8efxN/Dze7d+9WSkrKFbN688UXXygnJ0dNmjRx/E3cvXu3xo8fr2bNmlVKDQScCmC32xUVFaXU1FRHW0lJiVJTUxUXF2diZZXHMAyNGjVKb7/9ttauXavmzZubXVKl6ty5s77//ntt2bLF8YiOjtaAAQO0ZcsWubu7m12iS3Xs2LHUbQF+/PFHNW3a1KSKKt/Jkyfl5ub8J9Xd3V0lJSUmVWSe5s2bKygoyOlvYl5entavX3/F/E2Ufg83P/30kz755BMFBASYXVKlGThwoL777junv4nBwcGaMGGCPvroo0qpgUNUFSQxMVGDBw9WdHS0YmJiNGvWLOXn52vo0KFml1YpRo4cqSVLluidd95R7dq1HcfZ/fz85OPjY3J1rle7du1S5xvVrFlTAQEBV8R5SOPGjVOHDh30z3/+U3369NGGDRu0cOFCLVy40OzSKk337t319NNPq0mTJmrdurU2b96s559/XsOGDTO7NJc4ceKEfv75Z8fznTt3asuWLapbt66aNGmisWPH6qmnnlJoaKiaN2+uyZMnKzg4WD169DCv6Ap2vvegYcOG6t27tzIyMrR69WoVFxc7/i7WrVtXdrvdrLIrzIX+Dfw50Hl6eiooKEjXXHNN5RRYaddrXQHmzJljNGnSxLDb7UZMTIzxzTffmF1SpZFU5mPRokVml2aaK+kyccMwjPfee89o06aN4eXlZYSFhRkLFy40u6RKlZeXZ4wZM8Zo0qSJ4e3tbbRo0cJ49NFHjYKCArNLc4l169aV+f/5wYMHG4bx30vFJ0+ebAQGBhpeXl5G586djczMTHOLrmDnew927tx5zr+L69atM7v0CnGhfwN/VtmXidsMw6K32QQAAFcszsEBAACWQ8ABAACWQ8ABAACWQ8ABAACWQ8ABAACWQ8ABAACWQ8ABAACWQ8ABUG0NGTLkou+M++e+N998s8aOHXvebZo1a6ZZs2Zdcn0AzMNPNQCoEEOGDNGxY8e0atUqs0u5KCtXrpSnp6fZZQBwEQIOgCtS3bp1zS4BgAtxiAqAyz3//POKiIhQzZo1FRISor/97W86ceKE4/XFixfL399fH330kVq1aqVatWqpa9euOnDggKNPcXGxEhMT5e/vr4CAAP3jH//Qn39pZsWKFYqIiJCPj48CAgIUHx+v/Pz8Mmv68yGqnJwcde/eXT4+PmrevLneeOONUtscO3ZMI0aMUP369eXr66tbb71V3377reP1b7/9Vrfccotq164tX19fRUVFadOmTZf6tgG4DAQcAC7n5uam2bNna9u2bXrttde0du1a/eMf/3Dqc/LkST333HP6z3/+o88//1x79uzR3//+d8frM2fO1OLFi/Xqq6/qyy+/1JEjR/T22287Xj9w4ID69++vYcOGafv27fr000/Vs2fPUiHoXIYMGaK9e/dq3bp1WrFihebPn6+cnBynPvfcc49ycnL04YcfKj09Xddff706d+6sI0eOSJIGDBigxo0ba+PGjUpPT9fEiRM5DAaYpdJ+1hOApQ0ePNi46667Lqrv8uXLjYCAAMfzRYsWGZKMn3/+2dE2b948IzAw0PG8YcOGxowZMxzPi4qKjMaNGzvGTE9PNyQZu3btuqj6/vhr75mZmYYkY8OGDY7Xt2/fbkhy/PrxF198Yfj6+hqnT5922m/Lli2Nf/3rX4ZhGEbt2rWNxYsXX9R7AMC1WMEB4HKffPKJOnfurEaNGql27doaOHCgDh8+rJMnTzr61KhRQy1btnQ8b9iwoWMFJTc3VwcOHFBsbKzjdQ8PD0VHRzuet23bVp07d1ZERITuuecevfTSSzp69OhF1bd9+3Z5eHgoKirK0RYWFiZ/f3/H82+//VYnTpxQQECAatWq5Xjs3LlTv/zyiyQpMTFRI0aMUHx8vJ555hlHO4DKR8AB4FK7du3SX/7yF1177bV66623lJ6ernnz5kmSCgsLHf3+fCjHZrNd9OElSXJ3d1dKSoo+/PBDhYeHa86cObrmmmu0c+fOCpnHiRMn1LBhQ23ZssXpkZmZqQkTJkiSHn/8cW3btk3dunXT2rVrFR4e7nQYDUDlIeAAcKn09HSVlJRo5syZat++va6++mrt37+/XPvw8/NTw4YNtX79ekfbmTNnlJ6e7tTPZrOpY8eOmjZtmjZv3iy73X5RASMsLKzU/jIzM3Xs2DHH8+uvv15ZWVny8PDQVVdd5fSoV6+eo9/VV1+tcePG6eOPP1bPnj21aNGics0VQMXgMnEAFSY3N1dbtmxxaqtXr56Kioo0Z84cde/eXV999ZWSk5PLve8xY8bomWeeUWhoqMLCwvT88887BZD169crNTVVXbp0UYMGDbR+/XodPHhQrVq1uuC+r7nmGnXt2lX333+/FixYIA8PD40dO1Y+Pj6OPvHx8YqLi1OPHj00Y8YMR1B7//33dffdd6t169aaMGGCevfurebNm+u3337Txo0b1atXr3LPFcDlI+AAqDCffvqprrvuOqe24cOH6/nnn9f06dM1adIk3XTTTUpKStKgQYPKte/x48frwIEDGjx4sNzc3DRs2DDdfffdys3NlST5+vrq888/16xZs5SXl6emTZtq5syZuv322y9q/4sWLdKIESPUqVMnBQYG6qmnntLkyZMdr9tsNn3wwQd69NFHNXToUB08eFBBQUG66aabFBgYKHd3dx0+fFiDBg1Sdna26tWrp549e2ratGnlmieAimEzynOQGwAAoBrgHBwAAGA5BBwAAGA5BBwAAGA5BBwAAGA5BBwAAGA5BBwAAGA5BBwAAGA5BBwAAGA5BBwAAGA5BBwAAGA5BBwAAGA5BBwAAGA5/w+XK90ZXp1qhwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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kncLt9OjRQ5I0a9Ysp+dnzpwpSY6fc2kUnl4sLoz8Hk2bNtWmTZuUm5vraFuxYkWRW+l79OihtLQ0LVmyxNGWl5enV199VbVq1VKXLl0k/Rp03N3dr/izy87O1oULF4rU4u3tXeRW+sJrxG666aYy7iUqA05RoUp48skn9f777+v222/XqFGjHLeJN2rUSGfOnHHpufi2bdsqLi7O6TZxScW+oRWqV6+exo8fr0mTJql79+7685//rN27d2vu3Lm64YYb9OCDD0r69aLMqVOnatCgQerSpYv69evnuE08NDRUjz32mGPMxMRE3XnnnerUqZP+8pe/6MyZM3r11VfVpk2bYkPGlQQHB2vq1Kk6cOCAWrRooSVLlig1NVXz58+/4ocsurm56a233tIdd9yhNm3aaNCgQWrQoIGOHj2qzz//XD4+Pvrkk0909uxZNWzYUPfdd5/at2+vWrVqae3atfrmm28cf5WvW7dOI0eO1P33368WLVooLy9Pf//73+Xu7q5evXo5tjlkyBBNmTJFQ4YMUVRUlDZs2OA44nQ1mjZtqhdeeEHjx4/XgQMH1LNnT3l7e2v//v1atmyZhg0bpscff7zE9cPCwtS2bVutXbvW6Tb6W2+9VQ899JBmz56tPXv2qHv37iooKNCXX36pW2+9VSNHjlT79u01YMAAzZ8/XxkZGerSpYu2bNmid999Vz179tStt9561ftRKCIiQu7u7po6daoyMzNlt9t12223qX79+qUe67eGDBmijz76SN27d1fv3r21b98+vf/++46jdoWGDRumN954QwMHDlRKSopCQ0P10UcfaePGjZo1a5bjNJ6vr6/uv/9+vfrqq7LZbGratKlWrFhR5JqnH3/8Ud26dVPv3r3VunVrVatWTcuWLVN6err69u3r1HfNmjVq1KgRt4hbXcXcvAWUXuHtuydPnnRqv/QWa2OKv1V127ZtpnPnzsZut5uGDRuaxMREM3v2bCPJpKWlOa175513Ftn+pbe5Xg1JZsSIEeb99983zZs3N3a73XTo0KHILa/F7YMxv94WHh4ebqpXr24CAgLM8OHDzc8//1xkO0uWLDEdOnQwdrvd+Pv7m/79+5sjR44U6ffPf/7TtGrVytjtdtO6dWvz8ccfmwEDBpTpNvE2bdqYb7/91sTExBhPT0/TuHFj89prrzn1K7zFt6RbuLdt22buvfdeU6dOHWO3203jxo1N7969TVJSkjHm11vPn3jiCdO+fXvj7e1tatasadq3b2/mzp3rGOOnn34yf/nLX0zTpk2Np6en8ff3N7feeqtZu3at07ays7PN4MGDja+vr/H29ja9e/c2J06cKPE28UtfZ7+dw06dOpmaNWuamjVrmvDwcDNixAize/fuK87bzJkzTa1atZxumTfGmLy8PDN9+nQTHh5uPDw8TL169cwdd9xhUlJSHH0uXrxoJk2aZJo0aWKqV69uQkJCzPjx482FCxecxirN6/fNN980YWFhxt3d3elW7JJuE7/051jS7fcvv/yyadCggbHb7ebmm2823377bbHbT09PN4MGDTJ169Y1Hh4epl27dsXeyn/y5EnTq1cvU6NGDVO7dm3zyCOPmO+//95p26dOnTIjRoww4eHhpmbNmsbX19dER0ebDz/80Gms/Px8ExQUZJ599tki24G12IzhqklUXWPGjNEbb7yhc+fOlekCyKqqa9euOnXqlL7//vuKLqVSyczMVFhYmKZNm6bBgwdXdDlV0vLly/XAAw9o3759CgoKquhy4EJcg4Mq45dffnF6fPr0af39739Xp06dCDcoF76+vnryySc1ffr0Svd9XFYxdepUjRw5knBTBXAEB1VGRESEunbtqlatWik9PV1vv/22jh07pqSkJN1yyy2lGqukz9Yo5OXlVeavCKhIZ86ccbo49FLu7u6qV68eR3AA/OFxkTGqjB49euijjz7S/PnzZbPZdP311+vtt98udbiRdMW//gYMGFDsFzv+0d17772X/QTZxo0bF/vFpADwR8MRHKAM1q5de9nng4ODy/wVCBUpJSXlsp9K6+XlpZtvvrkcKwKAsiHgAAAAy+EiYwAAYDlV8hqcgoICHTt2TN7e3nzZGgAAlYQxRmfPnlVwcHCRL2q9VJUMOMeOHVNISEhFlwEAAMrg8OHDatiw4WX7VMmAU/gR4IcPH5aPj08FVwMAAK5GVlaWQkJCHO/jl1MlA07haSkfHx8CDgAAlczVXF7CRcYAAMByCDgAAMByCDgAAMByCDgAAMByCDgAAMByCDgAAMByCDgAAMByCDgAAMByCDgAAMByCDgAAMByCDgAAMByCDgAAMByCDgAAMByCDgAAMByqlV0AQBwrW0/klnRJQBVXruGvhW6fY7gAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyymXgDNnzhyFhobK09NT0dHR2rJlS4l933zzTXXu3Fm1a9dW7dq1FRsbW6S/MUYTJkxQUFCQvLy8FBsbqz179rh6NwAAQCXh8oCzZMkSxcfHKyEhQVu3blX79u0VFxenEydOFNt//fr16tevnz7//HMlJycrJCREf/rTn3T06FFHn2nTpmn27NmaN2+eNm/erJo1ayouLk4XLlxw9e4AAIBKwGaMMa7cQHR0tG644Qa99tprkqSCggKFhIRo1KhRGjdu3BXXz8/PV+3atfXaa6/p4YcfljFGwcHBGjt2rB5//HFJUmZmpgICArRw4UL17dv3imNmZWXJ19dXmZmZ8vHx+X07COAPZ/uRzIouAajy2jX0veZjlub926VHcHJzc5WSkqLY2Nj/btDNTbGxsUpOTr6qMbKzs3Xx4kX5+/tLkvbv36+0tDSnMX19fRUdHV3imDk5OcrKynJaAACAdbk04Jw6dUr5+fkKCAhwag8ICFBaWtpVjfHUU08pODjYEWgK1yvNmImJifL19XUsISEhpd0VAABQifyh76KaMmWKFi9erGXLlsnT07PM44wfP16ZmZmO5fDhw9ewSgAA8EdTzZWD161bV+7u7kpPT3dqT09PV2Bg4GXXnTFjhqZMmaK1a9fquuuuc7QXrpeenq6goCCnMSMiIoody263y263l3EvAABAZePSIzgeHh6KjIxUUlKSo62goEBJSUmKiYkpcb1p06Zp8uTJWrVqlaKiopyea9KkiQIDA53GzMrK0ubNmy87JgAAqDpcegRHkuLj4zVgwABFRUWpY8eOmjVrls6fP69BgwZJkh5++GE1aNBAiYmJkqSpU6dqwoQJ+uCDDxQaGuq4rqZWrVqqVauWbDabxowZoxdeeEHNmzdXkyZN9Nxzzyk4OFg9e/Z09e4AAIBKwOUBp0+fPjp58qQmTJigtLQ0RUREaNWqVY6LhA8dOiQ3t/8eSHr99deVm5ur++67z2mchIQETZw4UZL05JNP6vz58xo2bJgyMjLUqVMnrVq16nddpwMAAKzD5Z+D80fE5+AA1sbn4AAVz9KfgwMAAFARCDgAAMByCDgAAMByXH6RMQCUJ2OMLlzMr+gygCovOzdPXtXdZbPZKmT7BBwAlmGM0X3zkpVy8OeKLgWApJ3Px6mGR8VEDU5RAbCMXy7mE24ASOIIDgCL+vtfOsqzuntFlwFUWW0a+MirAv8NEnAAWJJndXcCDlCBKurUVCFOUQEAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMspl4AzZ84chYaGytPTU9HR0dqyZUuJfXfs2KFevXopNDRUNptNs2bNKtJn4sSJstlsTkt4eLgL9wAAAFQmLg84S5YsUXx8vBISErR161a1b99ecXFxOnHiRLH9s7OzFRYWpilTpigwMLDEcdu0aaPjx487lq+++spVuwAAACoZlwecmTNnaujQoRo0aJBat26tefPmqUaNGlqwYEGx/W+44QZNnz5dffv2ld1uL3HcatWqKTAw0LHUrVvXVbsAAAAqGZcGnNzcXKWkpCg2Nva/G3RzU2xsrJKTk3/X2Hv27FFwcLDCwsLUv39/HTp0qMS+OTk5ysrKcloAAIB1uTTgnDp1Svn5+QoICHBqDwgIUFpaWpnHjY6O1sKFC7Vq1Sq9/vrr2r9/vzp37qyzZ88W2z8xMVG+vr6OJSQkpMzbBgAAf3yV8i6qO+64Q/fff7+uu+46xcXFaeXKlcrIyNCHH35YbP/x48crMzPTsRw+fLicKwYAAOWpmisHr1u3rtzd3ZWenu7Unp6eftkLiEvLz89PLVq00N69e4t93m63X/Z6HgAAYC0uPYLj4eGhyMhIJSUlOdoKCgqUlJSkmJiYa7adc+fOad++fQoKCrpmYwIAgMrLpUdwJCk+Pl4DBgxQVFSUOnbsqFmzZun8+fMaNGiQJOnhhx9WgwYNlJiYKOnXC5N37tzp+P+jR48qNTVVtWrVUrNmzSRJjz/+uO6++241btxYx44dU0JCgtzd3dWvXz9X7w4AAKgEXB5w+vTpo5MnT2rChAlKS0tTRESEVq1a5bjw+NChQ3Jz+++BpGPHjqlDhw6OxzNmzNCMGTPUpUsXrV+/XpJ05MgR9evXT6dPn1a9evXUqVMnbdq0SfXq1XP17gAAgErAZowxFV1EecvKypKvr68yMzPl4+NT0eUAuEayc/PUesJnkqSlj8TIs7p7BVcEVF3tGvpe8zFL8/7t8iM4AFAR2jTwUQ0PfsUBVVWlvE0cAADgcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcgg4AADAcsol4MyZM0ehoaHy9PRUdHS0tmzZUmLfHTt2qFevXgoNDZXNZtOsWbN+95gAAKBqcXnAWbJkieLj45WQkKCtW7eqffv2iouL04kTJ4rtn52drbCwME2ZMkWBgYHXZEwAAFC1uDzgzJw5U0OHDtWgQYPUunVrzZs3TzVq1NCCBQuK7X/DDTdo+vTp6tu3r+x2+zUZEwAAVC0uDTi5ublKSUlRbGzsfzfo5qbY2FglJyeX25g5OTnKyspyWgAAgHW5NOCcOnVK+fn5CggIcGoPCAhQWlpauY2ZmJgoX19fxxISElKmbQMAgMqhStxFNX78eGVmZjqWw4cPV3RJAADAhaq5cvC6devK3d1d6enpTu3p6eklXkDsijHtdnuJ1/MAAADrcekRHA8PD0VGRiopKcnRVlBQoKSkJMXExPxhxgQAANbi0iM4khQfH68BAwYoKipKHTt21KxZs3T+/HkNGjRIkvTwww+rQYMGSkxMlPTrRcQ7d+50/P/Ro0eVmpqqWrVqqVmzZlc1JgAAqNpcHnD69OmjkydPasKECUpLS1NERIRWrVrluEj40KFDcnP774GkY8eOqUOHDo7HM2bM0IwZM9SlSxetX7/+qsYEAABVm80YYyq6iPKWlZUlX19fZWZmysfHp6LLAXCNZOfmqfWEzyRJO5+PUw0Pl/8NB6Acleb9u0rcRQUAAKoWAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALAcAg4AALCccgk4c+bMUWhoqDw9PRUdHa0tW7Zctv/SpUsVHh4uT09PtWvXTitXrnR6fuDAgbLZbE5L9+7dXbkLAACgEnF5wFmyZIni4+OVkJCgrVu3qn379oqLi9OJEyeK7f/111+rX79+Gjx4sLZt26aePXuqZ8+e+v777536de/eXcePH3cs//jHP1y9KwAAoJJwecCZOXOmhg4dqkGDBql169aaN2+eatSooQULFhTb/5VXXlH37t31xBNPqFWrVpo8ebKuv/56vfbaa0797Ha7AgMDHUvt2rVdvSsAAKCScGnAyc3NVUpKimJjY/+7QTc3xcbGKjk5udh1kpOTnfpLUlxcXJH+69evV/369dWyZUsNHz5cp0+fLrGOnJwcZWVlOS0AAMC6XBpwTp06pfz8fAUEBDi1BwQEKC0trdh10tLSrti/e/fueu+995SUlKSpU6fqiy++0B133KH8/Pxix0xMTJSvr69jCQkJ+Z17BgAA/siqVXQBZdG3b1/H/7dr107XXXedmjZtqvXr16tbt25F+o8fP17x8fGOx1lZWYQcAAAszKVHcOrWrSt3d3elp6c7taenpyswMLDYdQIDA0vVX5LCwsJUt25d7d27t9jn7Xa7fHx8nBYAAGBdLg04Hh4eioyMVFJSkqOtoKBASUlJiomJKXadmJgYp/6StGbNmhL7S9KRI0d0+vRpBQUFXZvCAQBApebyu6ji4+P15ptv6t1339WuXbs0fPhwnT9/XoMGDZIkPfzwwxo/fryj/9/+9jetWrVKL7/8sn744QdNnDhR3377rUaOHClJOnfunJ544glt2rRJBw4cUFJSku655x41a9ZMcXFxrt4dAABQCbj8Gpw+ffro5MmTmjBhgtLS0hQREaFVq1Y5LiQ+dOiQ3Nz+m7NuuukmffDBB3r22Wf19NNPq3nz5lq+fLnatm0rSXJ3d9d3332nd999VxkZGQoODtaf/vQnTZ48WXa73dW7AwAAKgGbMcZUdBHlLSsrS76+vsrMzOR6HMBCsnPz1HrCZ5Kknc/HqYZHpbyPAkAJSvP+zXdRAQAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyyHgAAAAyymXgDNnzhyFhobK09NT0dHR2rJly2X7L126VOHh4fL09FS7du20cuVKp+eNMZowYYKCgoLk5eWl2NhY7dmzx5W7AAAAKhGXB5wlS5YoPj5eCQkJ2rp1q9q3b6+4uDidOHGi2P5ff/21+vXrp8GDB2vbtm3q2bOnevbsqe+//97RZ9q0aZo9e7bmzZunzZs3q2bNmoqLi9OFCxdcvTsAAKASsBljjCs3EB0drRtuuEGvvfaaJKmgoEAhISEaNWqUxo0bV6R/nz59dP78ea1YscLRduONNyoiIkLz5s2TMUbBwcEaO3asHn/8cUlSZmamAgICtHDhQvXt2/eKNWVlZcnX11eZmZny8fG5RnsKoKJl5+ap9YTPJEk7n49TDY9qFVwRgGupNO/fLj2Ck5ubq5SUFMXGxv53g25uio2NVXJycrHrJCcnO/WXpLi4OEf//fv3Ky0tzamPr6+voqOjSxwzJydHWVlZTgsAALAulwacU6dOKT8/XwEBAU7tAQEBSktLK3adtLS0y/Yv/G9pxkxMTJSvr69jCQkJKdP+AACAyqFK3EU1fvx4ZWZmOpbDhw9XdEkAAMCFXBpw6tatK3d3d6Wnpzu1p6enKzAwsNh1AgMDL9u/8L+lGdNut8vHx8dpAQAA1uXSgOPh4aHIyEglJSU52goKCpSUlKSYmJhi14mJiXHqL0lr1qxx9G/SpIkCAwOd+mRlZWnz5s0ljgkAAKoWl99iEB8frwEDBigqKkodO3bUrFmzdP78eQ0aNEiS9PDDD6tBgwZKTEyUJP3tb39Tly5d9PLLL+vOO+/U4sWL9e2332r+/PmSJJvNpjFjxuiFF15Q8+bN1aRJEz333HMKDg5Wz549Xb07AACgEnB5wOnTp49OnjypCRMmKC0tTREREVq1apXjIuFDhw7Jze2/B5JuuukmffDBB3r22Wf19NNPq3nz5lq+fLnatm3r6PPkk0/q/PnzGjZsmDIyMtSpUyetWrVKnp6ert4dAABQCbj8c3D+iPgcHMCa+BwcwNr+MJ+DAwAAUBEIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHIIOAAAwHJcGnDOnDmj/v37y8fHR35+fho8eLDOnTt32XUuXLigESNGqE6dOqpVq5Z69eql9PR0pz42m63IsnjxYlfuCgAAqERcGnD69++vHTt2aM2aNVqxYoU2bNigYcOGXXadxx57TJ988omWLl2qL774QseOHdO9995bpN8777yj48ePO5aePXu6aC8AAEBlU81VA+/atUurVq3SN998o6ioKEnSq6++qh49emjGjBkKDg4usk5mZqbefvttffDBB7rtttsk/RpkWrVqpU2bNunGG2909PXz81NgYKCrygcAAJWYy47gJCcny8/PzxFuJCk2NlZubm7avHlzseukpKTo4sWLio2NdbSFh4erUaNGSk5Oduo7YsQI1a1bVx07dtSCBQtkjCmxlpycHGVlZTktAADAulx2BCctLU3169d33li1avL391daWlqJ63h4eMjPz8+pPSAgwGmd559/Xrfddptq1Kih1atX69FHH9W5c+c0evToYsdNTEzUpEmTft8OAQCASqPUR3DGjRtX7EW+v11++OEHV9Tq8Nxzz+nmm29Whw4d9NRTT+nJJ5/U9OnTS+w/fvx4ZWZmOpbDhw+7tD4AAFCxSn0EZ+zYsRo4cOBl+4SFhSkwMFAnTpxwas/Ly9OZM2dKvHYmMDBQubm5ysjIcDqKk56eftnrbaKjozV58mTl5OTIbrcXed5utxfbDgAArKnUAadevXqqV6/eFfvFxMQoIyNDKSkpioyMlCStW7dOBQUFio6OLnadyMhIVa9eXUlJSerVq5ckaffu3Tp06JBiYmJK3FZqaqpq165NiAEAAJJceA1Oq1at1L17dw0dOlTz5s3TxYsXNXLkSPXt29dxB9XRo0fVrVs3vffee+rYsaN8fX01ePBgxcfHy9/fXz4+Pho1apRiYmIcd1B98sknSk9P14033ihPT0+tWbNGL730kh5//HFX7QoAAKhkXBZwJGnRokUaOXKkunXrJjc3N/Xq1UuzZ892PH/x4kXt3r1b2dnZjrb//d//dfTNyclRXFyc5s6d63i+evXqmjNnjh577DEZY9SsWTPNnDlTQ4cOdeWuAACASsRmLnd/tUVlZWXJ19dXmZmZ8vHxqehyAFwj2bl5aj3hM0nSzufjVMPDpX/DAShnpXn/5ruoAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5RBwAACA5VSr6AKsaPuRzIouAaiSLlzMr+gSAPxBcAQHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYjssCzpkzZ9S/f3/5+PjIz89PgwcP1rlz5y67zvz589W1a1f5+PjIZrMpIyPjmowLAACqFpcFnP79+2vHjh1as2aNVqxYoQ0bNmjYsGGXXSc7O1vdu3fX008/fU3HBQAAVYvNGGOu9aC7du1S69at9c033ygqKkqStGrVKvXo0UNHjhxRcHDwZddfv369br31Vv3888/y8/O7ZuMWysrKkq+vrzIzM+Xj41O2nbwMvk0cqBgXLubr/jeSJUk7n49TDY9qFVwRgGupNO/fLjmCk5ycLD8/P0cIkaTY2Fi5ublp8+bN5T5uTk6OsrKynBYAAGBdLgk4aWlpql+/vlNbtWrV5O/vr7S0tHIfNzExUb6+vo4lJCSkzDUAAIA/vlIFnHHjxslms112+eGHH1xVa5mNHz9emZmZjuXw4cMVXRIAAHChUp2gHjt2rAYOHHjZPmFhYQoMDNSJEyec2vPy8nTmzBkFBgaWushCZR3XbrfLbreXebsAAKByKVXAqVevnurVq3fFfjExMcrIyFBKSooiIyMlSevWrVNBQYGio6PLVqkLxwUAANbikmtwWrVqpe7du2vo0KHasmWLNm7cqJEjR6pv376OO52OHj2q8PBwbdmyxbFeWlqaUlNTtXfvXknS9u3blZqaqjNnzlz1uAAAAC67h3LRokUaOXKkunXrJjc3N/Xq1UuzZ892PH/x4kXt3r1b2dnZjrZ58+Zp0qRJjse33HKLJOmdd95xnBq70rgVyRijXy7m68LF/IouBaiS+LcHoJBLPgfnj85Vn4OTnZun1hM+u2bjASg7PgcHsJ4K/xwcAKhIUY1ry6u6e0WXAaAC8efNNeRV3V07n4/TjqN8kCBQkaJCa8tms1V0GQAqEAHnGrLZbKrhUU2e/OUIVCjCDQBOUQEAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMsh4AAAAMupVtEFWFG7hr4VXQIAAFUaR3AAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDlEHAAAIDluCzgnDlzRv3795ePj4/8/Pw0ePBgnTt37rLrzJ8/X127dpWPj49sNpsyMjKK9AkNDZXNZnNapkyZ4qK9AAAAlZHLAk7//v21Y8cOrVmzRitWrNCGDRs0bNiwy66TnZ2t7t276+mnn75sv+eff17Hjx93LKNGjbqWpQMAgErOJV+2uWvXLq1atUrffPONoqKiJEmvvvqqevTooRkzZig4OLjY9caMGSNJWr9+/WXH9/b2VmBg4LUsGQAAWIhLjuAkJyfLz8/PEW4kKTY2Vm5ubtq8efPvHn/KlCmqU6eOOnTooOnTpysvL++y/XNycpSVleW0AAAA63LJEZy0tDTVr1/feUPVqsnf319paWm/a+zRo0fr+uuvl7+/v77++muNHz9ex48f18yZM0tcJzExUZMmTfpd2wUAAJVHqQLOuHHjNHXq1Mv22bVr1+8q6Eri4+Md/3/dddfJw8NDjzzyiBITE2W324tdZ/z48U7rZWZmqlGjRhzJAQCgEil83zbGXLFvqQLO2LFjNXDgwMv2CQsLU2BgoE6cOOHUnpeXpzNnzlzza2eio6OVl5enAwcOqGXLlsX2sdvtTuGncIJCQkKuaS0AAMD1zp49K19f38v2KVXAqVevnurVq3fFfjExMcrIyFBKSooiIyMlSevWrVNBQYGio6NLs8krSk1NlZubW5FTYpcTHBysw4cPy9vbWzab7ZrWk5WVpZCQEB0+fFg+Pj7XdGz8F/NcPpjn8sE8lw/mufy4aq6NMTp79myJNyv9lkuuwWnVqpW6d++uoUOHat68ebp48aJGjhypvn37Ooo6evSounXrpvfee08dO3aU9Ou1O2lpadq7d68kafv27fL29lajRo3k7++v5ORkbd68Wbfeequ8vb2VnJysxx57TA8++KBq16591fW5ubmpYcOG137Hf8PHx4d/QOWAeS4fzHP5YJ7LB/Ncflwx11c6clPIZZ+Ds2jRIoWHh6tbt27q0aOHOnXqpPnz5zuev3jxonbv3q3s7GxH27x589ShQwcNHTpUknTLLbeoQ4cO+r//+z9Jv55qWrx4sbp06aI2bdroxRdf1GOPPeY0LgAAgM1czZU6uGpZWVny9fVVZmYmfyG4EPNcPpjn8sE8lw/mufz8Eeaa76K6xux2uxISEkq8owvXBvNcPpjn8sE8lw/mufz8EeaaIzgAAMByOIIDAAAsh4ADAAAsh4ADAAAsh4ADAAAsh4BTBnPmzFFoaKg8PT0VHR2tLVu2XLb/0qVLFR4eLk9PT7Vr104rV64sp0ort9LM85tvvqnOnTurdu3aql27tmJjY6/4c8GvSvt6LrR48WLZbDb17NnTtQVaRGnnOSMjQyNGjFBQUJDsdrtatGjB746rUNp5njVrllq2bCkvLy+FhIToscce04ULF8qp2sppw4YNuvvuuxUcHCybzably5dfcZ3169fr+uuvl91uV7NmzbRw4UKX1ymDUlm8eLHx8PAwCxYsMDt27DBDhw41fn5+Jj09vdj+GzduNO7u7mbatGlm586d5tlnnzXVq1c327dvL+fKK5fSzvMDDzxg5syZY7Zt22Z27dplBg4caHx9fc2RI0fKufLKpbTzXGj//v2mQYMGpnPnzuaee+4pn2IrsdLOc05OjomKijI9evQwX331ldm/f79Zv369SU1NLefKK5fSzvOiRYuM3W43ixYtMvv37zefffaZCQoKMo899lg5V165rFy50jzzzDPm448/NpLMsmXLLtv/p59+MjVq1DDx8fFm586d5tVXXzXu7u5m1apVLq2TgFNKHTt2NCNGjHA8zs/PN8HBwSYxMbHY/r179zZ33nmnU1t0dLR55JFHXFpnZVfaeb5UXl6e8fb2Nu+++66rSrSEssxzXl6euemmm8xbb71lBgwYQMC5CqWd59dff92EhYWZ3Nzc8irREko7zyNGjDC33XabU1t8fLy5+eabXVqnlVxNwHnyySdNmzZtnNr69Olj4uLiXFiZMZyiKoXc3FylpKQoNjbW0ebm5qbY2FglJycXu05ycrJTf0mKi4srsT/KNs+Xys7O1sWLF+Xv7++qMiu9ss7z888/r/r162vw4MHlUWalV5Z5/r//+z/FxMRoxIgRCggIUNu2bfXSSy8pPz+/vMqudMoyzzfddJNSUlIcp7F++uknrVy5Uj169CiXmquKinofdMmXbVrVqVOnlJ+fr4CAAKf2gIAA/fDDD8Wuk5aWVmz/tLQ0l9VZ2ZVlni/11FNPKTg4uMg/KvxXWeb5q6++0ttvv63U1NRyqNAayjLPP/30k9atW6f+/ftr5cqV2rt3rx599FFdvHhRCQkJ5VF2pVOWeX7ggQd06tQpderUScYY5eXl6a9//auefvrp8ii5yijpfTArK0u//PKLvLy8XLJdjuDAcqZMmaLFixdr2bJl8vT0rOhyLOPs2bN66KGH9Oabb6pu3boVXY6lFRQUqH79+po/f74iIyPVp08fPfPMM5o3b15Fl2Yp69ev10svvaS5c+dq69at+vjjj/Xpp59q8uTJFV0argGO4JRC3bp15e7urvT0dKf29PR0BQYGFrtOYGBgqfqjbPNcaMaMGZoyZYrWrl2r6667zpVlVnqlned9+/bpwIEDuvvuux1tBQUFkqRq1app9+7datq0qWuLroTK8noOCgpS9erV5e7u7mhr1aqV0tLSlJubKw8PD5fWXBmVZZ6fe+45PfTQQxoyZIgkqV27djp//ryGDRumZ555Rm5uHAO4Fkp6H/Tx8XHZ0RuJIzil4uHhocjISCUlJTnaCgoKlJSUpJiYmGLXiYmJceovSWvWrCmxP8o2z5I0bdo0TZ48WatWrVJUVFR5lFqplXaew8PDtX37dqWmpjqWP//5z7r11luVmpqqkJCQ8iy/0ijL6/nmm2/W3r17HQFSkn788UcFBQURbkpQlnnOzs4uEmIKQ6XhaxqvmQp7H3TpJcwWtHjxYmO3283ChQvNzp07zbBhw4yfn59JS0szxhjz0EMPmXHjxjn6b9y40VSrVs3MmDHD7Nq1yyQkJHCb+FUo7TxPmTLFeHh4mI8++sgcP37csZw9e7aidqFSKO08X4q7qK5Oaef50KFDxtvb24wcOdLs3r3brFixwtSvX9+88MILFbULlUJp5zkhIcF4e3ubf/zjH+ann34yq1evNk2bNjW9e/euqF2oFM6ePWu2bdtmtm3bZiSZmTNnmm3btpmDBw8aY4wZN26ceeihhxz9C28Tf+KJJ8yuXbvMnDlzuE38j+rVV181jRo1Mh4eHqZjx45m06ZNjue6dOliBgwY4NT/ww8/NC1atDAeHh6mTZs25tNPPy3niiun0sxz48aNjaQiS0JCQvkXXsmU9vX8WwScq1faef76669NdHS0sdvtJiwszLz44osmLy+vnKuufEozzxcvXjQTJ040TZs2NZ6eniYkJMQ8+uij5ueffy7/wiuRzz//vNjft4VzO2DAANOlS5ci60RERBgPDw8TFhZm3nnnHZfXaTOG43AAAMBauAYHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHAABYDgEHqMS6du2qMWPGlPi8zWbT8uXLr3q89evXy2azKSMjo0z1TJw4UREREZftM3DgQPXs2bNM45fGleYGgLXxZZuAhR0/fly1a9eu6DIAoNxxBAewsMDAQNnt9oouw5IuXrxY0SUU8UesCagoBBygkisoKNCTTz4pf39/BQYGauLEiY7nLj1F9fXXXysiIkKenp6KiorS8uXLZbPZlJqa6jRmSkqKoqKiVKNGDd10003avXt3qWp64403FBISoho1aqh3797KzMwssW9OTo5Gjx6t+vXry9PTU506ddI333zj1OeLL75Qx44dZbfbFRQUpHHjxikvL8/x/Pnz5/Xwww+rVq1aCgoK0ssvv1yqekNDQzV58mT169dPNWvWVIMGDTRnzhynPjabTa+//rr+/Oc/q2bNmnrxxRclSf/61790/fXXy9PTU2FhYZo0aZKjNmOMJk6cqEaNGslutys4OFijR492jDl37lw1b95cnp6eCggI0H333edU06xZs5xqiIiIKPLzLW1NQJXh8m+7AuAyXbp0MT4+PmbixInmxx9/NO+++66x2Wxm9erVxhhjJJlly5YZY4zJzMw0/v7+5sEHHzQ7duwwK1euNC1atDCSzLZt24wx//0SvejoaLN+/XqzY8cO07lzZ3PTTTddVT0JCQmmZs2a5rbbbjPbtm0zX3zxhWnWrJl54IEHHH0u/YLO0aNHm+DgYLNy5UqzY8cOM2DAAFO7dm1z+vRpY4wxR44cMTVq1DCPPvqo2bVrl1m2bJmpW7eu0xepDh8+3DRq1MisXbvWfPfdd+auu+4y3t7e5m9/+9tV1d24cWPj7e1tEhMTze7du83s2bONu7u7Yx4L57J+/fpmwYIFZt++febgwYNmw4YNxsfHxyxcuNDs27fPrF692oSGhpqJEycaY4xZunSp8fHxMStXrjQHDx40mzdvNvPnzzfGGPPNN98Yd3d388EHH5gDBw6YrVu3mldeecWppv/93/91qrN9+/ZO+12WmoCqgoADVGJdunQxnTp1cmq74YYbzFNPPWWMcQ44r7/+uqlTp4755ZdfHH3ffPPNYgPO2rVrHX0+/fRTI8lpvZIkJCQYd3d3c+TIEUfbv//9b+Pm5maOHz9ujHEOOOfOnTPVq1c3ixYtcvTPzc01wcHBZtq0acYYY55++mnTsmVLU1BQ4OgzZ84cU6tWLZOfn2/Onj1rPDw8zIcffuh4/vTp08bLy6tUAad79+5ObX369DF33HGH47EkM2bMGKc+3bp1My+99JJT29///ncTFBRkjDHm5ZdfNi1atDC5ublFtvnPf/7T+Pj4mKysrBJrupqAU9qagKqCU1RAJXfdddc5PQ4KCtKJEyeK9Nu9e7euu+46eXp6Oto6dux4xTGDgoIkqdgxi9OoUSM1aNDA8TgmJkYFBQXFnubat2+fLl68qJtvvtnRVr16dXXs2FG7du2SJO3atUsxMTGy2WyOPjfffLPOnTunI0eOaN++fcrNzVV0dLTjeX9/f7Vs2fKq6v1tnZc+LqyhUFRUlNPj//znP3r++edVq1YtxzJ06FAdP35c2dnZuv/++/XLL78oLCxMQ4cO1bJlyxynim6//XY1btxYYWFheuihh7Ro0SJlZ2eXquay1ARUFQQcoJKrXr2602ObzaaCgoJrNmZhsPi9Y1pBzZo1nR6fO3dOkyZNUmpqqmPZvn279uzZI09PT4WEhGj37t2aO3euvLy89Oijj+qWW27RxYsX5e3tra1bt+of//iHgoKCNGHCBLVv395xi76bm5uMMU7bK+4i4tLWBFQVBBygimjZsqW2b9+unJwcR9ulF/NeC4cOHdKxY8ccjzdt2iQ3N7dij6g0bdpUHh4e2rhxo6Pt4sWL+uabb9S6dWtJUqtWrZScnOz0Zr9x40Z5e3urYcOGatq0qapXr67Nmzc7nv/555/1448/lqruTZs2FXncqlWry65z/fXXa/fu3WrWrFmRxc3t11+vXl5euvvuuzV79mytX79eycnJ2r59uySpWrVqio2N1bRp0/Tdd9/pwIEDWrdunSSpXr16On78uGNbWVlZ2r9//xX342pqAqoCPgcHqCIeeOABPfPMMxo2bJjGjRunQ4cOacaMGZLkdPrn9/L09NSAAQM0Y8YMZWVlafTo0erdu7cCAwOL9K1Zs6aGDx+uJ554Qv7+/mrUqJGmTZum7OxsDR48WJL06KOPatasWRo1apRGjhyp3bt3KyEhQfHx8XJzc1OtWrU0ePBgPfHEE6pTp47q16+vZ555ptRv5hs3btS0adPUs2dPrVmzRkuXLtWnn3562XUmTJigu+66S40aNdJ9990nNzc3/ec//9H333+vF154QQsXLlR+fr6io6NVo0YNvf/++/Ly8lLjxo21YsUK/fTTT7rllltUu3ZtrVy5UgUFBY4geNttt2nhwoW6++675efnpwkTJsjd3f2K+3GlmoCqgoADVBE+Pj765JNPNHz4cEVERKhdu3aaMGGCHnjggWt66qJZs2a699571aNHD505c0Z33XWX5s6dW2L/KVOmqKCgQA899JDOnj2rqKgoffbZZ44PKGzQoIFWrlypJ554Qu3bt5e/v78GDx6sZ5991jHG9OnTde7cOd19993y9vbW2LFjL3trenHGjh2rb7/9VpMmTZKPj49mzpypuLi4y64TFxenFStW6Pnnn9fUqVNVvXp1hYeHa8iQIZIkPz8/TZkyRfHx8crPz1e7du30ySefqE6dOvLz89PHH3+siRMn6sKFC2revLn+8Y9/qE2bNpKk8ePHa//+/brrrrvk6+uryZMnX9URnCvVBFQVNnPpSV4AVcaiRYs0aNAgZWZmysvLq6LLqTChoaEaM2YMX+0AWAhHcIAq5L333lNYWJgaNGig//znP3rqqafUu3fvKh1uAFgTV5wBVUhaWpoefPBBtWrVSo899pjuv/9+zZ8//6rXb9OmjdPtx79dFi1a5MLKy+7LL78sseZatWpVdHkAXIRTVACu2sGDB0v8vqOAgAB5e3uXc0VX9ssvv+jo0aMlPt+sWbNyrAZAeSHgAAAAy+EUFQAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsBwCDgAAsJz/B/eq5g2Aw12BAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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Paq9988034te//rVybfv06VNtWPXmzZvF8OHDhdlsFlarVUyePFn8/PPPPmW8Q5XPnTtX43WqPPz58OHD4pprrhFms9lnuHdtQ6UnTZpUrd5VhysLIcS+ffvEkCFDhMFgEImJieLFF1+s8ZhCeIZGd+/eXej1ehETEyPuvffeakPGhRDihRdeEB06dBBGo1EMHz5c7N27t9q5//a3v4lrrrlGRERECKPRKLp06SIeffRRUVBQ4HOsxx9/XCQmJvoMRSdqCZIQXJSCiNRn1qxZOHLkCP773/8Guirtkt1uR1JSEubPn++39b2IasM+L0SkSk8++aQygyy1vFWrVkGv17eaxTGpfWHLCxEREakKW16IiIhIVRheiIiISFUYXoiIiEhVGF6IiIhIVdrcJHWyLCMzMxMhISFcKIyIiEglhBAoKipCfHx8veuktbnwkpmZWW21XCIiIlKHjIyMeleob3PhxTt1dUZGhjJtOBEREbVuhYWFSEhIaNASJ20uvHhvFVmtVoYXIiIilWlIlw922CUiIiJVYXghIiIiVWF4ISIiIlVheCEiIiJVYXghIiIiVWF4ISIiIlVheCEiIiJVYXghIiIiVWF4ISIiIlVheCEiIiJVYXghIiIiVWF4ISIiIlVheCEiIiJVYXi5BLIsIMsi0NUgIiJqVxheLsGFUgfOF9sDXQ0iIqJ2heGFiIiIVIXhhYiIiFSF4YWIiIhUpUXCy6uvvoqkpCSYTCYMGTIEu3fvrrXsm2++iREjRiAsLAxhYWEYM2ZMneWJiIioffF7ePnggw/w8MMP48knn8T+/fvRt29fjBs3Djk5OTWW3759O2699VZs27YNqampSEhIwNixY3HmzBl/V5WIiIhUQBJC+HWs75AhQ3DllVfilVdeAQDIsoyEhATcf//9mD9/fr37u91uhIWF4ZVXXsGMGTPqLV9YWAibzYaCggJYrdZLrn9dcovtcMsC0VaTX89DRETU1jXm+9uvLS8OhwP79u3DmDFjLp5Qo8GYMWOQmpraoGOUlpbC6XQiPDzcX9UkIiIiFdH58+Dnz5+H2+1GTEyMz/aYmBgcPny4Qcd4/PHHER8f7xOAKrPb7bDbL861UlhY2PQKExERUavXqkcbPffcc3j//ffx6aefwmSq+dbM4sWLYbPZlEdCQkIL15KIiIhakl/DS2RkJLRaLbKzs322Z2dnIzY2ts59ly1bhueeew5ff/01+vTpU2u5BQsWoKCgQHlkZGQ0S92JiIiodfJreDEYDBg4cCC2bNmibJNlGVu2bMHQoUNr3W/p0qV45plnsHHjRgwaNKjOcxiNRlitVp8HERERtV1+7fMCAA8//DBmzpyJQYMGYfDgwVi+fDlKSkpwxx13AABmzJiBDh06YPHixQCAJUuWYOHChVi3bh2SkpKQlZUFAAgODkZwcLC/q0tEREStnN/Dy80334xz585h4cKFyMrKQr9+/bBx40alE+/p06eh0VxsAHr99dfhcDhw0003+RznySefxKJFi/xdXSIiImrl/D7PS0vjPC9ERETq02rmeSEiIiJqbgwvREREpCoML0RERKQqDC9ERESkKgwvREREpCoML0RE1G7JssCFEgccLjnQVaFGYHghIqJ2Kz23BL9cKEO5yx3oqlAjMLwQEVG75XSzxUWNGF6IiIiowYrKnYGuAsMLERERNVxeiSPQVWB4ISIiInVheCEiIiJVYXghIiIiVWF4ISKidq/E7gp0FRrELQu4OEKK4YWIiNonWRaQK3JAbnHgO6E2RGZ+GbIKy6ttl2UBtyxq3a+w3ImCUifKnW7IdZRTC12gK6BWJXYXckscCDXrA10VIiJqAodbrvMLXw0y8kphd7nhqghiXaODYdBVb5fIK3agqNzTuiRJQGJEEKwm9X5/seWliVxuAbuTTXdERGol1J1bAADlTjfKHDKcLk/Li9yANyWEJ/SUO9U7qzDDCxERUTsjy1B1qxPDCxEREakKwwsRERGpCsMLERERqQrDi59caAVrPxARUdsl2kKP4ybiUGk/cam4IxQRUXuRmV+Gw1lFCDHp0DPOCo1GCnSV6lXulHG2oKzG0VJHs4uh00rKqCOdRgOdVoLDVf/o2PPFdgQbdTBoNa3+OjC8EBFRu3Tf2n3YlnZOed49LgTDukQGsEYNU+Zwo8zhRrCp5q9wl/tiqnHIMhwNnDy4oMyJs/nl0GgAo06DcIsR4RZDc1S52TG8EBFRu7T/dL7P84y8MuTFOpT5UoQABARcbgGNRoIEQKeVYDPrYdRpA1Lnyvy1pIEsA2UOGSU6F8MLERFRa+K9MxJq1iO/zIn9py+gZ5y1QfvazIAECXqtBEkKzC2WdtzlheGFiIiaj8stw+GWodVIjW6dyC4sh0mnhU4rQauRWqzvRXFFC8YHezIwfXBivWEku8CO7AK78tyo18Bi1CHEpING8rTQAIC2ou6S5AkaQnj+7uWdJM67TafRQJI827UaCRpJUo5BvhheiIio2RSUOZGZXw6rWYdOEZZG7ZtTaK+2TaPxhACdRgIgIcigRZBBixCT/pK+2MudbngbLkZ3j8ZXP2c3+Vh2pwy704G8Zl7cUaeVEGzUIcxigFlfPQgezCzAN8fOQytJ0Gs9HXM7RVhwdVfffjvHzxVjy6Fs6LUaRIcYMaBTGOJsZuSVOGDSa1UZkBheiIio1ZJlz4rJzoqoUeZwIxeAxeiAUa+FTuP54hbC01ohQYJOK0GSPC0ZLlmG3Sl7WkQ08LRmSBLOF9uV1ZVHXX4xvPxzbwZMFcfVVGr90Go8+7lkge6xIYgPNfv9vbvcAvmlTuSXOmt8feV/juNkbmm17Z2nD0SHsIv1W/PtKew9daFauQ/vHooShwuRwUZEBhubr+ItgOGFiKgFiYqOoE5ZhhCeaRWEENBrL0675ZZFnb8RV57fozH9LYQQcLoFSuwuaLWe2zKeOniG0criYj8QAUDIng6rkuQJA0KGMgRXAJAAT0dWCdBWfMl7b4WU2N1IP1+C+FCTXzq3ltjdKLE3z8KClW9Nrdl1ukH7TOwdh8l94hBnMwek5eJIdpESXEZdFgWrWY9NP2ejzOnGml2nEBlshL5iiHRNwQUApv4tFZ/cOwznhB1BhsB3QG4MhhciomYghIB3eidvSCh3uuFwy3C6ZZQ7PX+6ZdGgjpaSBOi1GmgkwC08KwZrKkKEyy2U/hPe/hTaihBRvV6ehxI6/NjJ80KJA+9+exLFdhd0Gg2m9I3HFR1skCTP+b39YHQaT+uITqOBTiNBq5Wg1wRuztRgow43DuiAQ2eLYDZokZFXiq7RwRDCEyTdQkCWBQ5nFaGsYiXmL348iy9+PAsACA3SI7/UiUWTr0C/hFC/hpnM/DIs2XgYJ86XKNsm9Y5D9zgrfjpTgBPnS/DNsfM17vvYuMsxolsU5n3wHY6f8+x/4lwJLo8Nwanc0morUjvcMhwuGQZd65vPluGFiKgedpcbTvfF4bMAlOG0ZQ437C43yp1yrcFACIGTuaUodbjgcgvklthh0GkhV3wxumWhHC+7sByRwUYYdBqUO2VEhRghV7zmlgWyi+yICjZAkjxDdt2ywJn8MuSXOqCpSC+yqGjhwcWWnuyicpQ7Zfx4pgBxNpNyTu8jv8wJg9bTYdQbkiQJ0ECqsk2CpuJPCZ7tWYXlSAgPQkae7y2Mw1mFGNMjBjqtBkadBm5ZoEecFV2iLK1iqHFlKcM6N6jcf4+ew9Kv0ny2eW/rLPr3QQCAXivB6RaIs5lQYndBp9Ug1mqCpuI6em5Hef4uV4TebtHB0Gok9IyzwmLUQa/VwGLQItpq8jnXvlMXfIILAGU480NjLsOu9Fw43QJOt4zvf8lHVkE5LEYdggxaXBFvAwAsv7k/bnhtJ1wVP1eA79wwXqV2N0rsLhh0rW+4NMMLEbVbLrcMp1vAJcuQZU8Lh0uWAeH5ci53ulFQ5qw1lNhdbmTmlyOroAxnC8qh02rgcss4k18Glyyw92QeCsv9MxfHpfjlQlmN2x3u+mdhrU3V4AIA54sdeH9PRq37aDUSuseGoLDMifhQM8b3isWgTuFNrkNLGNEtCiO6RQHwBNifMwvw+o4TPu/fWREEzhaUK9vy6lky5kBGfq2vhQXp0buDDfGhZvz3qKdVZUBiKCb2jkN0iFEJOEmRFiRFNqyTdFSIEWcLytGYhjhZFnC4aw/pLYnhhYjaNIfLM3TX7fa0cpQ6XLC7PM3hVX/b9LZueG+z7D99Ad//UoAjFdPHn8kvQ6nDDYtRi+waRsY0RGJ4EASA80V2dIsJvtghVJKg0XjmDjl+rhhlTjdirSals6imosUDAL7/pQCJ4UE+HUnPF9vRp6MNISY9JEDpp+LZx7NvmdONyGAjIiwGxFQ6tre/iraik6q3tUYIQIb370Jp0ancslNecRvFJXt+29dInn4Wz208rLznrtHBKLW74JIFcoouXje3LHAwsxAAkHGhDLvS8/DpvcOg07bMbYpLDZZajYTeHUPx2u8GAIDSAldU7kS5U0aZw4X8Mid0Wo3SeiZX/GzJskC5S0ZGXinKnG4cyMiHWa/FmfwyhFsMPmHnQqkTO4763gqyGHUY0jmiyXX33thqzPpIJQ4XTp4vhdkQ+NtIDC9EpHrePiaF5U5lDRe3LCBJnqG7Ff1RIQuBQ2cLcSynGMdyinE6rxQCwKncEqW/SkMUV5nZNMSkg1YjwazXokesFXqdBsXlTkQEGxFs1OHKpHB0DDPDVMNw1+YiSVACCHDxy0mu6BsjCwEJ3tcFdBpNxS0MKOHI22dGqgg7QjmGJ9RV3i7BE1h8Z6MFnG4ZpXY37rkmGT+fLUKPuBBM6h2ndCwWQqDE7saFUgeyCstx/Fwx0s+X4H/HcwEAmw5lIyEsCCa9FgnhZr/dXsouvNgq4h2GbDZooNVooJUuzs9id8lwyTIkSD7vtSZGnRZGnRY2s77Z6rjz2PmKVj0Je07mNTk0V1W1o/eGHzLxtx0n8Ju+8bjz6s4Bm3ivoRheiEhRuQ+EwMUOqFX7R3gpHVDh+VKSJKniN3PP15v3tzqdVgN9xegWo04Lg04Dg06j9J3wdGj1zLshywKlDrfyG73n2BW/rVb61vDWQ650pyOnsNxzm6dinw0/ZuJ0XinyShy1DjdtiAGJoegSFYzOkRbIAogMNiDIoIXFoENkiFHpa1ITSfKsE2PSeyZfU7ajegdbreZiP5LKLSDe0TDair4STpdAqdN1cUI0Ca1myvoLJQ5kyeW4rm88JvXxtLrotJ4vfpcsUFTuRJhbIMpqROcoC65MCofTLWPa31LhkgVe23682jEtFfO6lDvd0Gk16BYdjF9dHoWhl7AOUZnj4kilqBAjJAnoGh3SoH1zi+3IK3HA4ZZ9fv6aW4zVhN8O6Kg8P19sb8bw4vnT+1/qbztOAAA++z4Tn32fieRIC8b2jMHsa5Kb5XzNjeGFqA3ztkg43Z5bJLIQyhBdp1tGmcOtdBj13iaoyQ+/5CO/1ImE8CAU211wVoxCOFdkh8WoU25nVO7U6fkSBlARaGxmPWKtJkRUmk/C+2Ve9fZNqcOFzPxyFJY5ca7ii0KjkeB2yxW3J3xv/6SeyG30tekaFYyIYAN6xFnRKTwIVrMeISYdQox6aCrmA/GMivE0kXtH83jnFfG2cnhC2MVOrICnjFYjwe6SEWLUNf8ssQbAhub57b65hVkMCLMYUFDmxOmKobx6rQbeRqdgo+/XjhCeULNsWl98vO8X5BY7cCyn2Kf/TYnDjZJKYeN8sR2pJ3LxfxN7YGhy026deINweFDjO6NGBBuVn+NShwsldjcKyhwoc/gxyTSzqreNLEatz9DzE+dLsHLHCRTZXXh6Sq9WN5EdwwtRKybLAqVOtzJKpfIwW1HRqH9xhImnWR8AHG437C7ZZ5hsudONCyVOlDhcyC4sx4GMfJj0WpQ73ThxvgQhRp1yG8U7MsQtC+TW09HwUsRYjZAgIT7UjF8ueO79FzVDB9fokIsBKafIjjmjuiI+1IToEBMigw0+gUSr8axPY9BpoNNofK6rd9ZSi0HXpA9vf94mau2MOg1CgzxBsC6S5Ln+1/frgC6Rwcp2WXjmo7lQ6rkVWOZ0IzO/DAczC5SVoF/4Og1T+nVAkEGL4Coh2mbWw6DTIL/UCbkiIHlbf9yyUPqUXOrdkSCDDkEGHaJCjChzuOFwyXDKsnIuWRawu9yeYddCQCtJSotm5T5WLc57G6/iaXSICen2EtxzTTJO5ZXiy5+yAEC5vZoYHuTTXynQWiS8vPrqq3j++eeRlZWFvn37YsWKFRg8eHCt5T/88EM88cQTOHnyJLp164YlS5Zg4sSJLVFVqofDJSO/1IEwi8FnUi2qmbd3vvdeufcDTe/pmYkyh1sZ6eIt571lU7klRAjPrZSswnK4KkbHuGUBh0tGkd2FrIJyaDUSjmR75qk4klWEcIsBP1V0hmwuNrMeQQatMoGa3SWjpNyFxIigi508gYt9IIQn/NT2oedtAs+q1P+gsoQwM4JNepwtKIPFoEOvDjYYdRdvQQUZdRXPPY+BiWEINvl+rOl1FSGkosXEoNMgyKCFQatpsY6h7ZGnz0pQk/fXSBJCTHqEmC62MPXuYMO4K2Kx99QFFJV7Wt7+ubf20UwNOk8ztiiYDVqYGznZmywLlDndcMmesOZwySh3uWu8LVqVxaj1zCPkuph+vIFcufVby/5Ky0uV7fGhZkzqE4/kyGC8uv0YAM/nlBACpc00KWBz8Ht4+eCDD/Dwww9j5cqVGDJkCJYvX45x48YhLS0N0dHR1cr/73//w6233orFixfjuuuuw7p163D99ddj//796NWrl7+rS/UodbiQXWiH1axHO/6lskYut+xpJbF75v3w3tJo6G9VbtnTOlLicHmmQC924JPvfsH3vxQ0qT6ZBTUHAgDoGGZGQakTwSYdhnWJRKhZj3KXG1HBRmg1EqJCjBdvjUgSwoP1CA0y1PhBWPk31/rea4ndhazCcnyfkY/Cchf0FWu3lLtkdAg1QyMBYUEGRIcYfW4vVT5X5UXsvGveePqVaD1BRqepGG3j6Vdi1LXM4n7Ucu4b1RVLKkYzDUgMg1GnUebcOX6+2NOKcL4EkcEGGHWeETwDO4Upt/N+OlOgjDTy/mQEqpVMo5FgqbiVVrWjrxDeDtGeUGKtFOS0GgnRVhPO5pcpSycAQHKUxee9lDs9/cfOFdlR7rz4H1hTW3qpgSw88wC1Jn4PLy+++CJmz56NO+64AwCwcuVKfP7553j77bcxf/78auVfeukljB8/Ho8++igA4JlnnsGmTZvwyiuvYOXKlf6uLlGtvHOC2F1u5fZMucvzwVBU7vL54vZOHHYkuwjnKlodjuUUQ5KAwjInypye36yCDDqkZRc1ui5xNpPyQeyd8VOn0WBgpzDklzqRFBEEvU6DeJsZOq2EeJsZYRZ9xSRkF7/IDTqNT18Ob0dRvUby+UzzrvUCePqpaCTfjqWVuSumu/eOSJEr+te4KlqKOoab0S8hFO6KC6avWH/Gez4Jnj4SxopOvbqKVhOjTtPqR0BQy7i6aySunnt1k/ef/8kPyhBt723XaGvrW9tHkiSfTt6V/x4WZKjWf6gmJr2npTS/1OkTXryqzqpbEyGAs/m1/zIUCH4NLw6HA/v27cOCBQuUbRqNBmPGjEFqamqN+6SmpuLhhx/22TZu3DisX7/en1VttJO5JXjsox8gScCyqX3Rq4Mt0FUiXJyDwumWlVsX5RVNsjaz58vbLUTFb+u1rxvjlgWK7S4UlbtQ7rzYf6Q2ZQ43dqXn4t8/ZOJYTnGjht1W5RnI6um3ca7IDq1Gwl3XJOOqzhEIDdLXWG/viBZva4l3Bk8JnqGwhkodTL0tFpU7mDYnbcXqv5W1xunFqf2qPGJHkoAg48Vw3prNGJqEc0V26LUazByWBADKbSqnW1SsQ9WwY0lV+rzUZld6Hsoc7kbfDvM3v4aX8+fPw+12IyYmxmd7TEwMDh8+XOM+WVlZNZbPysqqsbzdbofdfvEHsbCwee/x1+Y/R84pvzF/8eNZhpcAKXO4cSbfM6LB6a6781tOoV1ZB0aSLjYTe1sdvP1R7DX8dlLqcOFkbilOVkzL7XTLOJhZiFO5JXXenjFUdHzt3cGGTuFBKHG4lCnBY6wmlNjdiA81Ic5mRnyoCWFBhmrByjt/R4hJ5xlmrPX0l9FUmtejNQyRJVKLaYM6KkOyL4sJQZeo4Hr2aB2uSo7Ai9P6QZKAjmGe/kTeP+sTXjG8v7DiFzLlE6aWz0uL8eJnSuqJ87i2e0zNBQNE9aONFi9ejKeeeqrlT1zpH/xSfsumugkhPDNVOt1wuj1L27uFUFpWyh0yiu2e/4z5pc6KIbaeqdoNOg2cboG07CJEhxjhcssY3DkCkRXrwgQbdbCadMqIkmK7C4VlLuSVOPDz2QIcyS7GobOFcDXyH3hAYiim9OuAPh1sDe4Q6g0oLrdn8TqzQat0MI0IZudoouY0oVccRnSNgkYD9E8MC3R1WoTVpIfVpEe01fPce7v3yX8fxIpb+lcrX3n23ppuNwWaX8NLZGQktFotsrOzfbZnZ2cjNja2xn1iY2MbVX7BggU+t5kKCwuRkJBwiTWnQCmxu1BaMZ9DXolDmS211OFCfqkTv1wog7Ni7ZiDmYXYf7rmpd6rOuRZ/BU7jzd+PpCqgo06DOwUBr1WQondjeQoC/p2DEWPOKtSRqPxfFhIkmf2Tl2lzq8aDZRWHiE8i7gFGVT/ewTRJQuz6OF0e/pGeUfh+UvVUWntTU6lEX73v/9dtdcNOg2GdYlQZj5ubfz6r2cwGDBw4EBs2bIF119/PQBAlmVs2bIFc+fOrXGfoUOHYsuWLZg3b56ybdOmTRg6dGiN5Y1GI4zG1tfRiupW5vAseOeutCqvEJ7JyuwuN747nY+DmQVIPZGL88X1zzOi00hwyQKRwUboNJ4ZWxPDg9A50gKdVoOvD2ZBwNObXwiBjAtlMOg8i+hVbViJs5lQWOZEaJABVyaFoX9iGLpFB/sM2azK2+fEatYj3FL91g8R1a/qLRBPyPf0N/PO5OysWKsqv/TiUhDUeLv/OAaDn93cKhcObQi/R8+HH34YM2fOxKBBgzB48GAsX74cJSUlyuijGTNmoEOHDli8eDEA4MEHH8TIkSPxwgsvYNKkSXj//fexd+9evPHGG/6uKvmZd7heicOFUrtnKPDRnCL89+h5WE06fPVzdv0HgWeYb1SwEUV2F2xmPW7s3wG9O4bWuc/tV3Wq9TV3xYJy3s6uNZEkVExiJsGo10JfEU50GglarQRTxZT3RNR8PH26qvTnqvhd1TvLMzWNSa/Fp3OG41h2Me5esy/Q1Wk0v4eXm2++GefOncPChQuRlZWFfv36YePGjUqn3NOnT0NTaQrGYcOGYd26dfjTn/6E//u//0O3bt2wfv16zvHSCrgrJlOqzLueTVXOimXTPTOmOlFU7sLhs0X4+ucsbE87V+04tZnYOw5xVhOGJIcj1mqqdq7Ko2x0WqlibRjPbKlaSfL0vocESBcnjJPgmenSM9mbp5Ovd00ZfUUA0VdMC+9dbI/BhIjaovhQM+b+qite2XYMoWY9kiItga5Sg7TITb+5c+fWepto+/bt1bZNnToVU6dO9XOtqC7eWR/LnZ61b4TwrCdS9R50fqkTYZaLa4N4JkLydJ4VQuCX/DK8tPlovXOZRFgMuL5fB8hCYEKvOBj1GmXdGLNeq3Rg9a5+e3HYL2/PELVHJr0WoUEX+49Jku9SGZ4pDwCt5uJSD96ZZ3VayTNDbyvsiBoI466IxZgeMbAF6VBidwdmuYJGat89lqhWR3OK622SLXe64XDLcLll5JY4UFjmmQRJFgLPfXm41sXyukYHY3BSOG7o30EZrmyomPLdYtTBVDHsN9jUtPVkiKjtCw8yQBPc9M8Hu7OE4aUSrUZCdIgJhjANzhfbUVDLjLpCCJwvtiM0yIDIGmbBbikML1QjUc/URXklDuSVOBBs1OGUvVRZ80IWAl/8eLZacEkMD8LNgxJwVXIErGadZw2QihYVjSS16wXsiKjxuOSDf+i1GsTZzIi1mmCs4Xb5M5//jD0nLyA50oKtfxjV8hWswPBCTZJbMQKo2O6ZFj+/1IHHPv4BZ6tM2PbC1L7oEReCYKMeZoNWmWOFiIhaL6ni9nxVh7M8XQBO5pa0dJV8MLzQJSm1uzHtjZqXenhs3GUY0zMGFoOWq/cSEbUhXz90TUDPz/DShjndMuwuGfaKvilxNnOzHv/k+ZJqkxvFWI1YfEMfdAw347KYkGY9HxEREcDw4hdZBeV4N/UkJvaOa/E1j4QQKLK7kF1Q7jOls1GvgVnvgCyA8Eqjgy7FzuPnLx5fp8F7s69SprHX8NYQEbViZoMWZU63KhZkDJTWPGCCbfl+8NS/D+K17cdx2993tcj53LJAfqkDp3NLcSS7GKfOl1Zbi8LulJGRV1ZrD/Km8M590ruDDR/dMwx6rQYGnQZWsw5RAeyFTkTUEHqtxI6/dfD+EnowswCilY2fZsuLH/x81rOydX5p8wWFmlwocSCnyO73NUDqE2szIcSkQ2iQHkEGHSd0I6JWL8Skg8XIr8C6eFtedhw9j45hQbh1cGKAa3QRv2VUqqDMs0ihw9W8wUV4V2xuxDFNeg2SIi0IDTIwuBCRKgQZdAhmeKnTHcM7K39ft/t0AGtSHf/l1KoZWvBk2dM/xu50wyl7VnItc7jhrrpSYT3MnKOFiKjNGdgpDC9O64uH//k9AGB3el6Aa3QRw0s745YFCkqdKCx3oqDMeUnTQG/8Kav5KkZERK3O5ZVGjS7ffARoJV2E2MbfzpQ53DidV1qx9tClHSunyA4ASD8f2MmKiIio8RqSQ6xmPe4Z2QUAUGR3oajc5d9KNRDDSztwKrcE/zpwBofOFuJgZgHK61nRubjcheI6fkBLHS5MfuUb5fm8MZc1W12JiKhldIqwIC7UM+CiLqO7R7dQjRqOt43aoLSsIrybehI/nCmo8fVRl0XhkbGXAwCOZhdh+5FzcLhkbD2cA4fb01PXrNdiVcqVNfbG997/9BqaHNHM74CIiPxNq5EQGWxEsFEHh7u01nImvRZzf9UVr2w71oK1qxvDSysihEBhmQu5JXbEWE11DuNzVRoOJITAT2cKsP5AJnafrL9D1fYj57Dv1AUU2WtvXSlzupFb4qixDnKl+007HhvFeRKIiFTMpNciJsRU56R0NrO+BWtUP4aXABBCoLDcBbvLjeJyF2Qh4JY90/l7c8HZgjIYdVpEBhthNmghywK5JQ4U212eaf+dMvaduoBVO9NxKq/mxNw1Khg3X5mArtHBiLAYkHoiF4u/PAwANQaXCb1iYdBq8K/vMxv0Ppbe2Ac6De88EhGpnS2odYWT+jC8tLDcYjvOFzvgcNU9kUqZQ0aZQ0ZhuRNajQSXWyjBxumW8dvX/1fjfgM7heHqrpEY3T262urNyVHBMOk1yuy7M4Z2woiuUYi1mXzKbU3LaTWdsoiIiKpiePGDyqN4yhxulDndCDJoYdJrUVjuqje4VCbLnvlYKtt36oLP87E9YzChVxy6RFmqBZbKYq0mvHfnVbUudU5ERKQGDC9+duJ8MWQZSIoMgqmZJnMrdVxsFfnXnOGNWgRRp+VtHiIitbKa9K1mrpVAYnjxM2+/WrtLxoXcUpTU0Um2xO7C/tMXUGJ3o9ThQreYEHSJsiDIUPM/04DEsGZZvVmSgGCjDnqdBnqNBC1XhCYiapXU1jfFXxheWsjZ/HKf56fzSvGPb0/C5RYocbhxqGIxx5rcf21XjO0Z22x1kSQgNEgPg06DIIMOFoPW53YTswsREbVmDC8tSBYCWw/n4KUtRxu1385juT7hpczZ9JUYTXoNukQFc3gzERE1WNfoYIRbDMgrcSApIggJ4UEBrQ/Di59lXChF6vFcxFhNSD1+HjuP5/q8rtdKGH9FLBLCgxAVbET/xDClM+1n35/Bm/9NB+BZk2hbmm/wkRsxv79Jr0FcqBl6rcTgQkREjRIZbMTqlCshAAQZNDDqArsgL8NLMyoocyKroByiUqi4b+3+GstO6h2Hu69JrnN0kHe59qPZRbj+tZ3VXu/T0VbrvpLkmXjIYtRCp9EgxKRrtg7DlekYhIiI2gVJkiBV/BloDC/NpKDMidO5nsniXHLdLSJrZg1p0GyF3s64VSeUG9gpDP83oQcMOt+RQ3qdBLNeC4tRhxCTrkWSsbWVzbpIRERtH8NLM8gttiOj0iy3Ve/m/H54Ev51IBNuIbBy+sA6p/2vrH9iGIYmR6DE4YLTLfCry6MwoVecTxmzQQujToNgow42s75FbwlFW41+ac0hIiKqC8NLM5CFb2DpFhOMrELP6KKP7hkKo06LG/p3bNQxQ4P06BBmxtKb+iC/1FljmYhgA+JspoA14dW3EikREZE/8NvHD7xRYvaI5Dpv3Rh0Gpj1Wmi1EvQaCRajDhajDrIslBYUrSTVGF6sZh3iQ83+qD4REREAIMigRbHdBZe74QNEWgLDSwuSJM8S5G5ZIDLYiOgQY423eRpy60dq4SkWT54vwV83H0GXqGCcLSivfwciIlK9iGAj3LJAdqE90FXxwfDSArQaCcFGHaJCjBAQ0Gs10Ktgmv456zwjpXQaSemEfOJ8SSCrRERELSzcYmB4aU8kCUgIN8Nm1jdrvxRJArRa/7W8VF20sb7RU0RE1HZpNRISws3IyCsLdFUUDC9+FGrWIzTI0KzHNOg0SAwPgtngv1E+91/bDf/cmwGDVoPusSEYkBgGo16Dn88WYeV/jvvtvERE1PpIkqTMO9ZatK7atDHNGTB0WgkJ4UHV1iHyh5nDkjAkORxOl2+Ly5HsYr+el4iIqCEYXlq5IKMWnaMsMGg11SalC7zAz7JIRETtD8NLK9eaOvd2jQ6GWa9FmdONOJsJV8RbA10lIiJqhxhemsjpbvrKzmrVJSoYa+8cAgDoGW/l7LpERBQQreNXepVJyyrCim3HAl2NgGhNLUFERNQydFoNEiOCAl0NBVtemuCHX/KrbZOFwLupJ1FY5kJ+Wc3T+RMREalVCy6dVy+Gl2aSfr4EH+8/E+hqEBERtXl+bf/Py8vD9OnTYbVaERoailmzZqG4uPbhtnl5ebj//vtx+eWXw2w2IzExEQ888AAKCgr8Wc1m0drWfSAiImqr/Bpepk+fjoMHD2LTpk3YsGEDduzYgbvuuqvW8pmZmcjMzMSyZcvw008/YfXq1di4cSNmzZrlz2oSERGRivjtttGhQ4ewceNG7NmzB4MGDQIArFixAhMnTsSyZcsQHx9fbZ9evXrh448/Vp536dIFzz77LG677Ta4XC7odLzLRURE1N75reUlNTUVoaGhSnABgDFjxkCj0WDXrl0NPk5BQQGsVmutwcVut6OwsNDnQURERG2X38JLVlYWoqOjfbbpdDqEh4cjKyurQcc4f/48nnnmmTpvNS1evBg2m015JCQkXFK9iYiIqHVrdHiZP38+JEmq83H48OFLrlhhYSEmTZqEnj17YtGiRbWWW7BgAQoKCpRHRkbGJZ+biIiIahYVYgp0FRrf5+WRRx5BSkpKnWWSk5MRGxuLnJwcn+0ulwt5eXmIjY2tc/+ioiKMHz8eISEh+PTTT6HX62stazQaYTQaG1x/IiIiajpzK5hdvdHhJSoqClFRUfWWGzp0KPLz87Fv3z4MHDgQALB161bIsowhQ4bUul9hYSHGjRsHo9GIzz77DCZT4BMeERERtR5+6/PSo0cPjB8/HrNnz8bu3buxc+dOzJ07F7fccosy0ujMmTPo3r07du/eDcATXMaOHYuSkhL8/e9/R2FhIbKyspCVlQW32+2vqjYDgRVbjwa6EkRERO2CX8cer127FnPnzsXo0aOh0Whw44034uWXX1ZedzqdSEtLQ2lpKQBg//79ykikrl27+hwrPT0dSUlJ/qxuk9U2s26/hNCWrQgREVE74NfwEh4ejnXr1tX6elJSEoS4ODPtqFGjfJ6r2Uf3DEX/xLBAV4OIiKjN4fLAlyDCYkC/BBsiLAbcM7ILhiZHKK9x5WUiIiL/4JS1l6BbTAheuqUfcgrtAIBDZzlBHhERkb+xeYCIiIhUheGlGUlSoGtARETU9jG8NCMNwwsREZHfMbw0o8qzDpoNgZ+BkIiIqC1ih91mdMuViQizGBAVbERkMJcsICIi8geGl2YUazPhnpFdcKHEGeiqEBERtVkML0RERFSvYKMO3eNCoGsFHTwZXvzAbGBXIiIialskSYJeG/jgAjC8+EWM1QSnW/3LHEiSZ6ZgnVaCRpIQEWyAwyXjbH55oKtGRETtGMML1Sg6xASDToNgo++PSH6pI0A1IiIi8mB4oRqFWwyBrgIREVGN2DmDiIiIVIXhhYiIiFSF4YWIiIhUheGFiIiIVIXhhYiIiFSF4YWIiIhUheGFiIiIVIXhhYiIiFSF4YWIiIhUheGlGWlbwUqbREREbR3DSzMw6DSIsRmRHBUc6KoQERG1eVzb6BKZDVpEWAzQaZkDiYiIWgLDyyWymvSBrgIREVG7wuYCIiIiUhWGFyIiIlIV3jbyA0mSoG2jA4+CDDokRgRBzz4+REQUIAwvfmDUaaA3ts0vd4NOA4Oubb43IiJSB34LERERkaowvBAREZGqMLwQERGRqjC8EBERkaowvBAREZGqMLwQERGRqjC8EBERkaowvDQzg1aDNjo/HRERUavg1/CSl5eH6dOnw2q1IjQ0FLNmzUJxcXGD9hVCYMKECZAkCevXr/dnNZtVtNXEFaaJiIj8yK/fstOnT8fBgwexadMmbNiwATt27MBdd93VoH2XL18OSWIbBhEREfny2/IAhw4dwsaNG7Fnzx4MGjQIALBixQpMnDgRy5YtQ3x8fK37HjhwAC+88AL27t2LuLg4f1WRiIiIVMhvLS+pqakIDQ1VggsAjBkzBhqNBrt27ap1v9LSUvzud7/Dq6++itjY2HrPY7fbUVhY6PMgIiKitstv4SUrKwvR0dE+23Q6HcLDw5GVlVXrfg899BCGDRuGKVOmNOg8ixcvhs1mUx4JCQmXVG8iIiJq3RodXubPnw9Jkup8HD58uEmV+eyzz7B161YsX768wfssWLAABQUFyiMjI6NJ5yYiIiJ1aHSfl0ceeQQpKSl1lklOTkZsbCxycnJ8trtcLuTl5dV6O2jr1q04fvw4QkNDfbbfeOONGDFiBLZv315tH6PRCKPR2Ji3QERERCrW6PASFRWFqKioessNHToU+fn52LdvHwYOHAjAE05kWcaQIUNq3Gf+/Pm48847fbb17t0bf/3rXzF58uTGVpWIiIjaIL+NNurRowfGjx+P2bNnY+XKlXA6nZg7dy5uueUWZaTRmTNnMHr0aLz77rsYPHgwYmNja2yVSUxMROfOnf1VVSIiIlIRv87zsnbtWnTv3h2jR4/GxIkTcfXVV+ONN95QXnc6nUhLS0Npaak/q0FERERtiN9aXgAgPDwc69atq/X1pKQkCCHqPEZ9rxMREVH7wnnsiYiISFUYXoiIiEhVGF6IiIhIVRheiIiISFUYXoiIiEhVGF6IiIhIVRheiIiISFUYXoiIiEhVGF4ugVYKdA2IiIjaH4aXS6DX8fIRERG1NH77EhERkaowvBAREZGqMLwQERGRqjC8EBERkaowvBAREZGqMLwQERGRqjC8EBERkaowvBAREZGqMLwQERGRqjC8EBERkaowvBAREZGqMLwQERGRqjC8NEFRuTPQVSAiImq3GF6aoMzpDnQViIiI2i2GFyIiIlIVhhciIiJSFYYXIiIiUhWGFyIiIlIVhhciIiJSFYYXIiIiUhWGFyIiIlIVhhciIiJSFYYXIiIiUhWGFyIiIlIVhhciIiJSFYYXIiIiUhWGFyIiIlIVhhciIiJSFb+Fl7y8PEyfPh1WqxWhoaGYNWsWiouL690vNTUV1157LSwWC6xWK6655hqUlZX5q5pERESkMn4LL9OnT8fBgwexadMmbNiwATt27MBdd91V5z6pqakYP348xo4di927d2PPnj2YO3cuNBo2EBEREZGHzh8HPXToEDZu3Ig9e/Zg0KBBAIAVK1Zg4sSJWLZsGeLj42vc76GHHsIDDzyA+fPnK9suv/xyf1SRiIiIVMovTRqpqakIDQ1VggsAjBkzBhqNBrt27apxn5ycHOzatQvR0dEYNmwYYmJiMHLkSHzzzTd1nstut6OwsNDnQURERG2XX8JLVlYWoqOjfbbpdDqEh4cjKyurxn1OnDgBAFi0aBFmz56NjRs3YsCAARg9ejSOHj1a67kWL14Mm82mPBISEprvjRAREVGr06jwMn/+fEiSVOfj8OHDTaqILMsAgLvvvht33HEH+vfvj7/+9a+4/PLL8fbbb9e634IFC1BQUKA8MjIymnR+IiIiUodG9Xl55JFHkJKSUmeZ5ORkxMbGIicnx2e7y+VCXl4eYmNja9wvLi4OANCzZ0+f7T169MDp06drPZ/RaITRaGxA7YmIiKgtaFR4iYqKQlRUVL3lhg4divz8fOzbtw8DBw4EAGzduhWyLGPIkCE17pOUlIT4+HikpaX5bD9y5AgmTJjQmGoSERFRG+aXPi89evTA+PHjMXv2bOzevRs7d+7E3Llzccsttygjjc6cOYPu3btj9+7dAABJkvDoo4/i5ZdfxkcffYRjx47hiSeewOHDhzFr1ix/VJOIiIhUyC9DpQFg7dq1mDt3LkaPHg2NRoMbb7wRL7/8svK60+lEWloaSktLlW3z5s1DeXk5HnroIeTl5aFv377YtGkTunTp4q9qNorTLePw2SI4XCLQVSEiImq3/BZewsPDsW7dulpfT0pKghDVQ8D8+fN95nkhIiIiqoxT1xIREZGqMLwQERGRqjC8EBERkaowvBAREZGqMLwQERGRqjC8EBERkaowvBAREZGqMLwQERGRqjC8EBERkaowvBAREZGqMLwQERGRqjC8EBERkaowvBAREZGqMLwQERGRqjC8EBERkaowvBAREZGqMLwQERGRqjC8EBERkaowvBAREZGqMLwQERGRqjC8EBERkaowvBAREZGqMLwQERGRqjC8EBERkaowvBAREZGqMLwQERGRqjC8EBERkaowvBAREZGqMLwQERGRqjC8EBERkaowvBAREZGqMLwQERGRqjC8EBERkaowvBAREZGqMLwQERGRqjC8EBERkaowvBAREZGqMLwQERGRqvgtvOTl5WH69OmwWq0IDQ3FrFmzUFxcXOc+WVlZuP322xEbGwuLxYIBAwbg448/9lcViYiISIX8Fl6mT5+OgwcPYtOmTdiwYQN27NiBu+66q859ZsyYgbS0NHz22Wf48ccf8dvf/hbTpk3Dd999569qEhERkcr4JbwcOnQIGzduxFtvvYUhQ4bg6quvxooVK/D+++8jMzOz1v3+97//4f7778fgwYORnJyMP/3pTwgNDcW+ffv8UU0iIiJSIb+El9TUVISGhmLQoEHKtjFjxkCj0WDXrl217jds2DB88MEHyMvLgyzLeP/991FeXo5Ro0bVuo/dbkdhYaHPg4iIiNouv4SXrKwsREdH+2zT6XQIDw9HVlZWrfv985//hNPpREREBIxGI+6++258+umn6Nq1a637LF68GDabTXkkJCQ02/sgIiKi1qdR4WX+/PmQJKnOx+HDh5tcmSeeeAL5+fnYvHkz9u7di4cffhjTpk3Djz/+WOs+CxYsQEFBgfLIyMho8vmJiIio9dM1pvAjjzyClJSUOsskJycjNjYWOTk5PttdLhfy8vIQGxtb437Hjx/HK6+8gp9++glXXHEFAKBv377473//i1dffRUrV66scT+j0Qij0diYt0FEREQq1qjwEhUVhaioqHrLDR06FPn5+di3bx8GDhwIANi6dStkWcaQIUNq3Ke0tBQAoNH4NgZptVrIstyYahIREVEb5pc+Lz169MD48eMxe/Zs7N69Gzt37sTcuXNxyy23ID4+HgBw5swZdO/eHbt37wYAdO/eHV27dsXdd9+N3bt34/jx43jhhRewadMmXH/99f6oZpM53QxTREREgeK3eV7Wrl2L7t27Y/To0Zg4cSKuvvpqvPHGG8rrTqcTaWlpSouLXq/HF198gaioKEyePBl9+vTBu+++i3feeQcTJ070VzUb7Y0dx/Ha9uOBrgYREVG71ajbRo0RHh6OdevW1fp6UlIShBA+27p169bqZ9Tdfzpf+fvwLpGBqwgREVE75bfw0tYtu6kvbhrUMdDVICIiane4MGMTGfW8dERERIHAb2AiIiJSFYYXIiIiUhWGFyIiIlIVhhciIiJSFYYXIiIiUhWGFyIiIlIVhhciIiJSFYYXIiIiUhWGlwZyywJ5JQ4UljsDXRUiIqJ2jcsDNNCp3BJc+8J/Al0NIiKido8tL00QYzUiOdIS6GoQERG1S2x5aaDOkRYc+fN4HD5bBAAIMekDXCMiIqL2ieGlgSRJUh5EREQUOLxtRERERKrC8EJERESqwvBCREREqsLwQkRERKrC8EJERESqwvBCREREqsLwQkRERKrC8EJERESqwvBCREREqsLwQkRERKrC8EJERESqwvBCREREqsLwQkRERKrC8EJERESqwvBCREREqsLwQkRERKrC8EJERESqwvBCREREqsLwQkRERKrC8EJERESqwvBCREREqsLwQkRERKrC8EJERESq4rfw8uyzz2LYsGEICgpCaGhog/YRQmDhwoWIi4uD2WzGmDFjcPToUX9VkYiIiFTIb+HF4XBg6tSpuPfeexu8z9KlS/Hyyy9j5cqV2LVrFywWC8aNG4fy8nJ/VZOIiIhURuevAz/11FMAgNWrVzeovBACy5cvx5/+9CdMmTIFAPDuu+8iJiYG69evxy233OKvqhIREZGKtJo+L+np6cjKysKYMWOUbTabDUOGDEFqamqt+9ntdhQWFvo8iIiIqO1qNeElKysLABATE+OzPSYmRnmtJosXL4bNZlMeCQkJfq0nERERBVajwsv8+fMhSVKdj8OHD/urrjVasGABCgoKlEdGRkaLnp+IiIhaVqP6vDzyyCNISUmps0xycnKTKhIbGwsAyM7ORlxcnLI9Ozsb/fr1q3U/o9EIo9HYpHMSERGR+jQqvERFRSEqKsovFencuTNiY2OxZcsWJawUFhZi165djRqxRERERG2b3/q8nD59GgcOHMDp06fhdrtx4MABHDhwAMXFxUqZ7t2749NPPwUASJKEefPm4c9//jM+++wz/Pjjj5gxYwbi4+Nx/fXX+6uaREREpDJ+Gyq9cOFCvPPOO8rz/v37AwC2bduGUaNGAQDS0tJQUFCglHnsscdQUlKCu+66C/n5+bj66quxceNGmEwmf1WTiIiIVEYSQohAV6I5FRYWwmazoaCgAFartVmP7XTLOHy2CABgM+uRGBHUrMcnIiJqrxrz/d1qhkoTERERNQTDCxEREakKwwsRERGpCsMLERERqQrDCxEREakKwwsRERGpCsMLERERqQrDCxEREakKwwsRERGpCsMLERERqQrDCxEREakKwwsRERGpCsMLERERqQrDCxEREakKwwsRERGpCsMLERERqQrDCxEREakKwwsRERGpCsMLERERqQrDCxEREakKwwsRERGpCsMLERERqQrDCxEREakKwwsRERGpCsNLE4SYdLAYtYGuBhERUbukC3QF1EarkRBrM8GkZ3ghIiIKBIaXRtBrNegZbw10NYiIiNo13jYiIiIiVWF4ISIiIlVheCEiIiJVYXghIiIiVWF4ISIiIlVheCEiIiJVYXghIiIiVWF4ISIiIlVheCEiIiJVYXghIiIiVWF4ISIiIlXxW3h59tlnMWzYMAQFBSE0NLTe8k6nE48//jh69+4Ni8WC+Ph4zJgxA5mZmf6qIhEREamQ38KLw+HA1KlTce+99zaofGlpKfbv348nnngC+/fvxyeffIK0tDT85je/8VcViYiISIUkIYTw5wlWr16NefPmIT8/v9H77tmzB4MHD8apU6eQmJjYoH0KCwths9lQUFAAq5UrQBMREalBY76/dS1UpyYpKCiAJEl13nay2+2w2+3K88LCwhaoGREREQVKqw0v5eXlePzxx3HrrbfWmcAWL16Mp556qtp2hhgiIiL18H5vN+iGkGiExx9/XACo83Ho0CGffVatWiVsNltjTiMcDoeYPHmy6N+/vygoKKizbHl5uSgoKFAeP//8c7115IMPPvjggw8+WucjIyOj3pzQqJaXRx55BCkpKXWWSU5Obswhq3E6nZg2bRpOnTqFrVu31nvfy2g0wmg0Ks+Dg4ORkZGBkJAQSJJ0SXUhj8LCQiQkJCAjI4P9iPyI17ll8Dq3DF7nltNWrrUQAkVFRYiPj6+3bKPCS1RUFKKioppcsfp4g8vRo0exbds2RERENPoYGo0GHTt29EPtyGq1qvo/hlrwOrcMXueWwevcctrCtbbZbA0q57eh0qdPn8aBAwdw+vRpuN1uHDhwAAcOHEBxcbFSpnv37vj0008BeILLTTfdhL1792Lt2rVwu93IyspCVlYWHA6Hv6pJREREKuO3DrsLFy7EO++8ozzv378/AGDbtm0YNWoUACAtLQ0FBQUAgDNnzuCzzz4DAPTr18/nWJX3ISIiovbNb+Fl9erVWL16dZ1lRKUexUlJSQ3rYUwtzmg04sknn/TpW0TNj9e5ZfA6twxe55bTHq+13yepIyIiImpOXJiRiIiIVIXhhYiIiFSF4YWIiIhUheGFiIiIVIXhpQ3bsWMHJk+ejPj4eEiShPXr19dZfvv27ZAkqdojKyvLp9yZM2dw2223ISIiAmazGb1798bevXuV14UQWLhwIeLi4mA2mzFmzBgcPXrUH2+x1QjUtU5JSal2jPHjx/vjLbYK/rjOSUlJNZaZM2eOUqa8vBxz5sxBREQEgoODceONNyI7O9tfbzPgAnWdR40aVe31e+65x19vM+D8cZ3dbjeeeOIJdO7cGWazGV26dMEzzzzjM5q3LXxGM7y0YSUlJejbty9effXVRu2XlpaGs2fPKo/o6GjltQsXLmD48OHQ6/X48ssv8fPPP+OFF15AWFiYUmbp0qV4+eWXsXLlSuzatQsWiwXjxo1DeXl5s7231iZQ1xoAxo8f73OM9957r1neU2vkj+u8Z88en9c2bdoEAJg6dapS5qGHHsK///1vfPjhh/jPf/6DzMxM/Pa3v22eN9UKBeo6A8Ds2bN9yi1duvTS31Ar5Y/rvGTJErz++ut45ZVXcOjQISxZsgRLly7FihUrlDJt4jO6USsmkmoBEJ9++mmdZbZt2yYAiAsXLtRa5vHHHxdXX311ra/LsixiY2PF888/r2zLz88XRqNRvPfee42ttiq11LUWQoiZM2eKKVOmNL6SbUBzXeeqHnzwQdGlSxchy7IQwvPzq9frxYcffqiUOXTokAAgUlNTm1J1VWmp6yyEECNHjhQPPvhg0yqqcs11nSdNmiR+//vf+2z77W9/K6ZPny6EaDuf0Wx5oWr69euHuLg4/PrXv8bOnTt9Xvvss88waNAgTJ06FdHR0ejfvz/efPNN5fX09HRkZWVhzJgxyjabzYYhQ4YgNTW1xd6DWlzKtfbavn07oqOjcfnll+Pee+9Fbm5uS1VfNeq6zpU5HA6sWbMGv//975WFXfft2wen0+nzM929e3ckJibyZ7qKS7nOXmvXrkVkZCR69eqFBQsWoLS01N/VVp26rvOwYcOwZcsWHDlyBADw/fff45tvvsGECRMAtJ3PaIYXUsTFxWHlypX4+OOP8fHHHyMhIQGjRo3C/v37lTInTpzA66+/jm7duuGrr77CvffeiwceeEBZCsJ77zUmJsbn2DExMdX6c7RnzXGtAc8to3fffRdbtmzBkiVL8J///AcTJkyA2+0OxNtqdRpynStbv3498vPzkZKSomzLysqCwWBAaGioT1n+TF/UHNcZAH73u99hzZo12LZtGxYsWIB//OMfuO2221rgHahDQ67z/Pnzccstt6B79+7Q6/Xo378/5s2bh+nTpwNoQ5/RgW76oZaBBjRJ1uSaa64Rt912m/Jcr9eLoUOH+pS5//77xVVXXSWEEGLnzp0CgMjMzPQpM3XqVDFt2rTGV1yFWupa1+T48eMCgNi8eXOjz682zXWdKxs7dqy47rrrfLatXbtWGAyGamWvvPJK8dhjjzX6/GrTUte5Jlu2bBEAxLFjxxp9frVpruv83nvviY4dO4r33ntP/PDDD+Ldd98V4eHhYvXq1UKItvMZzZYXqtPgwYNx7Ngx5XlcXBx69uzpU6ZHjx44ffo0ACA2NhYAqo3EyM7OVl6jmjX2WtckOTkZkZGRPschX1Wvs9epU6ewefNm3HnnnT7bY2Nj4XA4kJ+f77OdP9N1a+x1rsmQIUMAgD/Pdah6nR999FGl9aV37964/fbb8dBDD2Hx4sUA2s5nNMML1enAgQOIi4tTng8fPhxpaWk+ZY4cOYJOnToBADp37ozY2Fhs2bJFeb2wsBC7du3C0KFDW6bSKtXYa12TX375Bbm5uT7HIV9Vr7PXqlWrEB0djUmTJvlsHzhwIPR6vc/PdFpaGk6fPs2f6To09jrXdgwA/HmuQ9XrXFpaCo3G96tdq9VClmUAbegzOtBNP+Q/RUVF4rvvvhPfffedACBefPFF8d1334lTp04JIYSYP3++uP3225Xyf/3rX8X69evF0aNHxY8//igefPBBodFofG5B7N69W+h0OvHss8+Ko0ePirVr14qgoCCxZs0apcxzzz0nQkNDxb/+9S/xww8/iClTpojOnTuLsrKylnvzLSwQ17qoqEj84Q9/EKmpqSI9PV1s3rxZDBgwQHTr1k2Ul5e37AVoIf64zkII4Xa7RWJionj88cdrPO8999wjEhMTxdatW8XevXvF0KFDq93Sa0sCcZ2PHTsmnn76abF3716Rnp4u/vWvf4nk5GRxzTXX+PfNBpA/rvPMmTNFhw4dxIYNG0R6err45JNPRGRkpM8tzrbwGc3w0oZ5h9VVfcycOVMI4fkhHzlypFJ+yZIlokuXLsJkMonw8HAxatQosXXr1mrH/fe//y169eoljEaj6N69u3jjjTd8XpdlWTzxxBMiJiZGGI1GMXr0aJGWlubPtxpwgbjWpaWlYuzYsSIqKkro9XrRqVMnMXv2bJGVleXvtxsw/rrOX331lQBQ689pWVmZuO+++0RYWJgICgoSN9xwgzh79qw/3mKrEIjrfPr0aXHNNdeI8PBwYTQaRdeuXcWjjz4qCgoK/PU2A84f17mwsFA8+OCDIjExUZhMJpGcnCz++Mc/CrvdrpRpC5/RkhCVpt0jIiIiauXY54WIiIhUheGFiIiIVIXhhYiIiFSF4YWIiIhUheGFiIiIVIXhhYiIiFSF4YWIiIhUheGFiOgSrV69utqq00Rt0Y4dOzB58mTEx8dDkiSsX7++UfsvWrQIkiRVe1gslkYdh+GFiGqVlZWF+++/H8nJyTAajUhISMDkyZN91kVpqpMnT0KSJGX9mtZq+/btkCSp2sKMRO1RSUkJ+vbti1dffbVJ+//hD3/A2bNnfR49e/bE1KlTG3UcXZPOTkRt3smTJzF8+HCEhobi+eefR+/eveF0OvHVV19hzpw5OHz4cKCr6HdOpzPQVSBqVSZMmIAJEybU+rrdbscf//hHvPfee8jPz0evXr2wZMkSjBo1CgAQHByM4OBgpfz333+Pn3/+GStXrmxUPdjyQkQ1uu+++yBJEnbv3o0bb7wRl112Ga644go8/PDD+Pbbb2tsOcnPz4ckSdi+fTsA4MKFC5g+fTqioqJgNpvRrVs3rFq1CoBndVsA6N+/PyRJUj7cZFnG008/jY4dO8JoNKJfv37YuHGjcg7vef/5z39ixIgRMJvNuPLKK3HkyBHs2bMHgwYNQnBwMCZMmIBz5875vKe33noLPXr0gMlkQvfu3fHaa69VO+4HH3yAkSNHwmQyYe3atTVem9WrVyMxMRFBQUG44YYbkJube6mXm6hNmDt3LlJTU/H+++/jhx9+wNSpUzF+/HgcPXq0xvJvvfUWLrvsMowYMaJxJwr04kpE1Prk5uYKSZLEX/7yl1rLpKenCwDiu+++U7ZduHBBABDbtm0TQggxZ84c0a9fP7Fnzx6Rnp4uNm3aJD777DMhhGfVbABi8+bN4uzZsyI3N1cIIcSLL74orFareO+998Thw4fFY489JvR6vThy5IjPebt37y42btwofv75Z3HVVVeJgQMHilGjRolvvvlG7N+/X3Tt2lXcc889St3WrFkj4uLixMcffyxOnDghPv74YxEeHi5Wr17tc9ykpCSlTGZmprJ43oULF4QQQnz77bdCo9GIJUuWiLS0NPHSSy+J0NBQYbPZmunqE6kDAPHpp58qz0+dOiW0Wq04c+aMT7nRo0eLBQsWVNu/rKxMhIWFiSVLljT+3I3eg4javF27dgkA4pNPPqm1TEPCy+TJk8Udd9zR4P2FECI+Pl48++yzPtuuvPJKcd999/ns99Zbbymvv/feewKA2LJli7Jt8eLF4vLLL1eed+nSRaxbt87nuM8884wYOnSoz3GXL1/uU6ZqeLn11lvFxIkTfcrcfPPNDC/U7lQNLxs2bBAAhMVi8XnodDoxbdq0avuvW7dO6HQ6kZWV1ehzs88LEVUjmmmx+XvvvRc33ngj9u/fj7Fjx+L666/HsGHDai1fWFiIzMxMDB8+3Gf78OHD8f333/ts69Onj/L3mJgYAEDv3r19tuXk5ADwdDI8fvw4Zs2ahdmzZytlXC4XbDabz3EHDRpU53s6dOgQbrjhBp9tQ4cO9bm1RdQeFRcXQ6vVYt++fdBqtT6vVe7n4vXWW2/huuuuU/7/NgbDCxFV061bN0iSVGenXI3G02WuctCp2sF1woQJOHXqFL744gts2rQJo0ePxpw5c7Bs2bJLrqNer1f+LklSjdtkWQbg+VAFgDfffBNDhgzxOU7VD9nGDtkkIo/+/fvD7XYjJyen3j4s6enp2LZtGz777LMmnYsddomomvDwcIwbNw6vvvoqSkpKqr2en5+PqKgoAMDZs2eV7TUNe46KisLMmTOxZs0aLF++HG+88QYAwGAwAADcbrdS1mq1Ij4+Hjt37vQ5xs6dO9GzZ88mv5+YmBjEx8fjxIkT6Nq1q8/D23G4oXr06IFdu3b5bPv222+bXDciNSkuLsaBAweU/+vp6ek4cOAATp8+jcsuuwzTp0/HjBkz8MknnyA9PR27d+/G4sWL8fnnn/sc5+2330ZcXFydI5fqwpYXIqrRq6++iuHDh2Pw4MF4+umn0adPH7hcLmzatAmvv/46Dh06hKuuugrPPfccOnfujJycHPzpT3/yOcbChQsxcOBAXHHFFbDb7diwYQN69OgBAIiOjobZbMbGjRvRsWNHmEwm2Gw2PProo3jyySfRpUsX9OvXD6tWrcKBAwdqHfnTUE899RQeeOAB2Gw2jB8/Hna7HXv37sWFCxfw8MMPN/g4DzzwAIYPH45ly5ZhypQp+Oqrr3jLiNqNvXv34le/+pXy3Pt/Z+bMmVi9ejVWrVqFP//5z3jkkUdw5swZREZG4qqrrsJ1112n7CPLMlavXo2UlJRqLZ8N1vSuOkTU1mVmZoo5c+aITp06CYPBIDp06CB+85vfKB1yf/75ZzF06FBhNptFv379xNdff+3TYfeZZ54RPXr0EGazWYSHh4spU6aIEydOKMd/8803RUJCgtBoNGLkyJFCCCHcbrdYtGiR6NChg9Dr9aJv377iyy+/VPapqaNv1U61QgixatWqap1o165dK/r16ycMBoMICwsT11xzjdIpubYOxDUd++9//7vo2LGjMJvNYvLkyWLZsmXssEvUgiQhmqlnHhEREVELYJ8XIiIiUhWGFyIiIlIVhhciIiJSFYYXIiIiUhWGFyIiIlIVhhciIiJSFYYXIiIiUhWGFyIiIlIVhhciIiJSFYYXIiIiUhWGFyIiIlIVhhciIiJSlf8HCJNLNIyaC4AAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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eAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAA1WaWmpbDabbDabSktL67scNBCEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARvGr7wIAoKGJnPZefZeA/89Vfsb9c9c/ZcqnqX89VoPz8mYn1+v8rLwAAACjEF4AAIBRCC8AAMAohBcAAGAULtgFADRYPk391fEPq+q7DDQwrLwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwyiUJL/Pnz1dkZKT8/f0VFxennJycavu+/PLL6t+/v1q2bKmWLVsqISGhxv4AAODy4vXwsnz5cqWlpWnGjBnasmWLevToocTERBUWFlbZf8OGDbrzzjv14YcfKjs7WxERERo0aJDy8/O9XSoAADCAzbIsy5sTxMXFqW/fvnrhhRckSS6XSxEREZo0aZKmTZt2weMrKirUsmVLvfDCCxo7duwF+zudTgUFBam4uFgOh+MX1w/g8hM57b36LgFo0PJmJ9f5mLV5/fbqykt5eblyc3OVkJDw7wl9fJSQkKDs7OyLGuP06dM6e/asQkJCvFUmAAAwiJ83Bz9+/LgqKioUFhbm0R4WFqY9e/Zc1Bh/+MMf1K5dO48A9GNlZWUqKytzP3Y6nT+/YAAA0OA16E8bzZ49W8uWLdPbb78tf3//KvtkZGQoKCjIvUVERFziKgEAwKXk1fDSunVr+fr66ujRox7tR48eVXh4eI3Hzp07V7Nnz9batWt1zTXXVNsvPT1dxcXF7u3w4cN1UjsAAGiYvBpemjZtqj59+igrK8vd5nK5lJWVpfj4+GqPe/rppzVr1ixlZmYqNja2xjnsdrscDofHBgAAGi+vXvMiSWlpaRo3bpxiY2N17bXXat68eSotLVVqaqokaezYsWrfvr0yMjIkSXPmzNH06dO1dOlSRUZGqqCgQJIUGBiowMBAb5cLAAAaOK+Hl1GjRunYsWOaPn26CgoK1LNnT2VmZrov4j106JB8fP69APTiiy+qvLxcI0aM8BhnxowZeuKJJ7xdLgAAaOC8fp+XS437vAD4pbjPC1CzRn2fFwAAgLpGeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjXJLwMn/+fEVGRsrf319xcXHKycmptu+uXbt0++23KzIyUjabTfPmzbsUJQIAAEN4PbwsX75caWlpmjFjhrZs2aIePXooMTFRhYWFVfY/ffq0oqKiNHv2bIWHh3u7PAAAYBivh5e//OUvmjBhglJTU9WtWze99NJLat68uV555ZUq+/ft21fPPPOMRo8eLbvd7u3yAACAYbwaXsrLy5Wbm6uEhIR/T+jjo4SEBGVnZ9fJHGVlZXI6nR4bAABovLwaXo4fP66KigqFhYV5tIeFhamgoKBO5sjIyFBQUJB7i4iIqJNxAQBAw2T8p43S09NVXFzs3g4fPlzfJQEAAC/y8+bgrVu3lq+vr44ePerRfvTo0Tq7GNdut3NtDAAAlxGvrrw0bdpUffr0UVZWlrvN5XIpKytL8fHx3pwaAAA0Ul5deZGktLQ0jRs3TrGxsbr22ms1b948lZaWKjU1VZI0duxYtW/fXhkZGZJ+uMj3iy++cP+cn5+vbdu2KTAwUDExMd4uFwAANHBeDy+jRo3SsWPHNH36dBUUFKhnz57KzMx0X8R76NAh+fj8ewHoyJEj6tWrl/vx3LlzNXfuXN10003asGGDt8sFAAANnM2yLKu+i6hLTqdTQUFBKi4ulsPhqO9yABgoctp79V0C0KDlzU6u8zFr8/pt/KeNAADA5YXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxyScLL/PnzFRkZKX9/f8XFxSknJ6fG/m+++aa6dOkif39/de/eXatXr74UZQIAAAN4PbwsX75caWlpmjFjhrZs2aIePXooMTFRhYWFVfbftGmT7rzzTo0fP15bt27V8OHDNXz4cH3++efeLhUAABjAZlmW5c0J4uLi1LdvX73wwguSJJfLpYiICE2aNEnTpk2r1H/UqFEqLS3VqlWr3G39+vVTz5499dJLL11wPqfTqaCgIBUXF8vhcNTdiQC4bEROe6++SwAatLzZyXU+Zm1ev7268lJeXq7c3FwlJCT8e0IfHyUkJCg7O7vKY7Kzsz36S1JiYmK1/cvKyuR0Oj02AADQePl5c/Djx4+roqJCYWFhHu1hYWHas2dPlccUFBRU2b+goKDK/hkZGZo5c2bdFHwR+B8ZUD1v/G+sPjSW8wAaK+M/bZSenq7i4mL3dvjw4fouCQAAeJFXV15at24tX19fHT161KP96NGjCg8Pr/KY8PDwWvW32+2y2+11UzAAAGjwvLry0rRpU/Xp00dZWVnuNpfLpaysLMXHx1d5THx8vEd/SVq3bl21/QEAwOXFqysvkpSWlqZx48YpNjZW1157rebNm6fS0lKlpqZKksaOHav27dsrIyNDkvTQQw/ppptu0rPPPqvk5GQtW7ZMmzdv1qJFi7xdKgAAMIDXw8uoUaN07NgxTZ8+XQUFBerZs6cyMzPdF+UeOnRIPj7/XgC67rrrtHTpUj3++OP64x//qE6dOumdd97R1Vdf7e1SAQCAAbx+n5dLzdv3eeHTRkD1+JQOgJ+rwdznBQAAoK4RXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRvBZeioqKNGbMGDkcDgUHB2v8+PEqKSmp8ZhFixZpwIABcjgcstlsOnnypLfKAwAAhvJaeBkzZox27dqldevWadWqVfr44491zz331HjM6dOnNXjwYP3xj3/0VlkAAMBwft4YdPfu3crMzNRnn32m2NhYSdLzzz+vpKQkzZ07V+3atavyuMmTJ0uSNmzY4I2yAABAI+CVlZfs7GwFBwe7g4skJSQkyMfHR59++mmdzlVWVian0+mxAQCAxssr4aWgoEBt2rTxaPPz81NISIgKCgrqdK6MjAwFBQW5t4iIiDodHwAANCy1Ci/Tpk2TzWarcduzZ4+3aq1Senq6iouL3dvhw4cv6fwAAODSqtU1L1OmTFFKSkqNfaKiohQeHq7CwkKP9nPnzqmoqEjh4eG1LrImdrtddru9TscEAAANV63CS2hoqEJDQy/YLz4+XidPnlRubq769OkjSVq/fr1cLpfi4uJ+XqUAAADy0jUvXbt21eDBgzVhwgTl5ORo48aNmjhxokaPHu3+pFF+fr66dOminJwc93EFBQXatm2b9u/fL0nauXOntm3bpqKiIm+UCQAADOS1+7y8/vrr6tKliwYOHKikpCTdcMMNWrRokXv/2bNntXfvXp0+fdrd9tJLL6lXr16aMGGCJOnGG29Ur169tHLlSm+VCQAADGOzLMuq7yLqktPpVFBQkIqLi+VwOOp8/Mhp79X5mEBjkTc7ub5LAGCo2rx+891GAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBSvhpeioiKNGTNGDodDwcHBGj9+vEpKSmrsP2nSJF111VVq1qyZOnTooAcffFDFxcXeLBMAABjEq+FlzJgx2rVrl9atW6dVq1bp448/1j333FNt/yNHjujIkSOaO3euPv/8cy1ZskSZmZkaP368N8sEAAAGsVmWZXlj4N27d6tbt2767LPPFBsbK0nKzMxUUlKSvvnmG7Vr1+6ixnnzzTf129/+VqWlpfLz87tgf6fTqaCgIBUXF8vhcPyic6hK5LT36nxMoLHIm51c3yUAMFRtXr+9tvKSnZ2t4OBgd3CRpISEBPn4+OjTTz+96HHOn0R1waWsrExOp9NjAwAAjZfXwktBQYHatGnj0ebn56eQkBAVFBRc1BjHjx/XrFmzanyrKSMjQ0FBQe4tIiLiF9UNAAAatlqHl2nTpslms9W47dmz5xcX5nQ6lZycrG7duumJJ56otl96erqKi4vd2+HDh3/x3AAAoOG68EUkPzFlyhSlpKTU2CcqKkrh4eEqLCz0aD937pyKiooUHh5e4/GnTp3S4MGD1aJFC7399ttq0qRJtX3tdrvsdvtF1w8AAMxW6/ASGhqq0NDQC/aLj4/XyZMnlZubqz59+kiS1q9fL5fLpbi4uGqPczqdSkxMlN1u18qVK+Xv71/bEgEAQCPmtWteunbtqsGDB2vChAnKycnRxo0bNXHiRI0ePdr9SaP8/Hx16dJFOTk5kn4ILoMGDVJpaan+/ve/y+l0qqCgQAUFBaqoqPBWqQAAwCC1Xnmpjddff10TJ07UwIED5ePjo9tvv13//d//7d5/9uxZ7d27V6dPn5Ykbdmyxf1JpJiYGI+xDh48qMjISG+WCwAADODV8BISEqKlS5dWuz8yMlI/vs3MgAED5KXbzgAAgEaC7zYCAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAo3g1vBQVFWnMmDFyOBwKDg7W+PHjVVJSUuMx9957r6Kjo9WsWTOFhoZq2LBh2rNnjzfLBAAABvFqeBkzZox27dqldevWadWqVfr44491zz331HhMnz59tHjxYu3evVvvv/++LMvSoEGDVFFR4c1SAQCAIWyWZVneGHj37t3q1q2bPvvsM8XGxkqSMjMzlZSUpG+++Ubt2rW7qHF27NihHj16aP/+/YqOjr5gf6fTqaCgIBUXF8vhcPyic6hK5LT36nxMoLHIm51c3yUAMFRtXr+9tvKSnZ2t4OBgd3CRpISEBPn4+OjTTz+9qDFKS0u1ePFiXXnllYqIiKiyT1lZmZxOp8cGAAAaL6+Fl4KCArVp08ajzc/PTyEhISooKKjx2AULFigwMFCBgYFas2aN1q1bp6ZNm1bZNyMjQ0FBQe6tupADAAAah1qHl2nTpslms9W4/dILbMeMGaOtW7fqo48+UufOnTVy5EidOXOmyr7p6ekqLi52b4cPH/5FcwMAgIbNr7YHTJkyRSkpKTX2iYqKUnh4uAoLCz3az507p6KiIoWHh9d4/PlVlE6dOqlfv35q2bKl3n77bd15552V+trtdtnt9tqeBgAAMFStw0toaKhCQ0Mv2C8+Pl4nT55Ubm6u+vTpI0lav369XC6X4uLiLno+y7JkWZbKyspqWyoAAGiEvHbNS9euXTV48GBNmDBBOTk52rhxoyZOnKjRo0e7P2mUn5+vLl26KCcnR5L01VdfKSMjQ7m5uTp06JA2bdqkO+64Q82aNVNSUpK3SgUAAAbx6n1eXn/9dXXp0kUDBw5UUlKSbrjhBi1atMi9/+zZs9q7d69Onz4tSfL399cnn3yipKQkxcTEaNSoUWrRooU2bdpU6eJfAABwear120a1ERISoqVLl1a7PzIyUj++zUy7du20evVqb5YEAAAMx3cbAQAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcYyVV+Rl/PGaKv5wyRq/xMfZcDALiECC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEaxWZZl1XcRdcnpdCooKEjFxcVyOBz1XQ68pLS0VIGBgZKkkpISBQQE1HNFAIBfojav36y8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACM4lffBQA/R0BAgBrZLYoAABeJlRcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUfzqu4C6ZlmWJMnpdNZzJQAA4GKdf90+/zpek0YXXk6dOiVJioiIqOdKAABAbZ06dUpBQUE19rFZFxNxDOJyuXTkyBG1aNFCNputvsuBFzmdTkVEROjw4cNyOBz1XQ4AL+F3/fJgWZZOnTqldu3aycen5qtaGt3Ki4+Pj6644or6LgOXkMPh4A8acBngd73xu9CKy3lcsAsAAIxCeAEAAEYhvMBYdrtdM2bMkN1ur+9SAHgRv+v4qUZ3wS4AAGjcWHkBAABGIbwAAACjEF4AAIBRCC+47ERGRmrevHn1XQaAnykvL082m03btm2r71JQTwgv8KqUlBTZbLZK2/79++u7NACX0Pm/Bffdd1+lfQ888IBsNptSUlIufWEwEuEFXjd48GB9++23HtuVV15Z32UBuMQiIiK0bNkyff/99+62M2fOaOnSperQoUM9VgbTEF7gdXa7XeHh4R6br6+v3n33XfXu3Vv+/v6KiorSzJkzde7cOfdxNptNCxcu1JAhQ9S8eXN17dpV2dnZ2r9/vwYMGKCAgABdd911OnDggPuYAwcOaNiwYQoLC1NgYKD69u2rDz74oMb6Tp48qd/97ncKDQ2Vw+HQLbfcou3bt3vt+QAuV71791ZERIRWrFjhbluxYoU6dOigXr16udsyMzN1ww03KDg4WK1atdKQIUM8fs+r8vnnn+tXv/qVAgMDFRYWpv/8z//U8ePHvXYuqF+EF9SLTz75RGPHjtVDDz2kL774QgsXLtSSJUv0X//1Xx79Zs2apbFjx2rbtm3q0qWL7rrrLt17771KT0/X5s2bZVmWJk6c6O5fUlKipKQkZWVlaevWrRo8eLCGDh2qQ4cOVVvLHXfcocLCQq1Zs0a5ubnq3bu3Bg4cqKKiIq+dP3C5uvvuu7V48WL341deeUWpqakefUpLS5WWlqbNmzcrKytLPj4+uu222+Ryuaoc8+TJk7rlllvUq1cvbd68WZmZmTp69KhGjhzp1XNBPbIALxo3bpzl6+trBQQEuLcRI0ZYAwcOtJ566imPvq+99prVtm1b92NJ1uOPP+5+nJ2dbUmy/v73v7vb/vnPf1r+/v411vAf//Ef1vPPP+9+3LFjR+u5556zLMuyPvnkE8vhcFhnzpzxOCY6OtpauHBhrc8XQNXGjRtnDRs2zCosLLTsdruVl5dn5eXlWf7+/taxY8esYcOGWePGjavy2GPHjlmSrJ07d1qWZVkHDx60JFlbt261LMuyZs2aZQ0aNMjjmMOHD1uSrL1793rztFBPGt23SqPhufnmm/Xiiy+6HwcEBOiaa67Rxo0bPVZaKioqdObMGZ0+fVrNmzeXJF1zzTXu/WFhYZKk7t27e7SdOXNGTqdTDodDJSUleuKJJ/Tee+/p22+/1blz5/T9999Xu/Kyfft2lZSUqFWrVh7t33///QWXqQHUXmhoqJKTk7VkyRJZlqXk5GS1bt3ao8++ffs0ffp0ffrppzp+/Lh7xeXQoUO6+uqrK425fft2ffjhhwoMDKy078CBA+rcubN3Tgb1hvACrwsICFBMTIxHW0lJiWbOnKnf/OY3lfr7+/u7f27SpIn7Z5vNVm3b+T9ujzzyiNatW6e5c+cqJiZGzZo104gRI1ReXl5lbSUlJWrbtq02bNhQaV9wcPDFnSCAWrn77rvdb/fOnz+/0v6hQ4eqY8eOevnll9WuXTu5XC5dffXVNf4eDx06VHPmzKm0r23btnVbPBoEwgvqRe/evbV3795KoeaX2rhxo1JSUnTbbbdJ+uGPWl5eXo11FBQUyM/PT5GRkXVaC4CqDR48WOXl5bLZbEpMTPTYd+LECe3du1cvv/yy+vfvL0n6v//7vxrH6927t9566y1FRkbKz4+XtcsBF+yiXkyfPl2vvvqqZs6cqV27dmn37t1atmyZHn/88V80bqdOnbRixQpt27ZN27dv11133VXtRX6SlJCQoPj4eA0fPlxr165VXl6eNm3apMcee0ybN2/+RbUAqJqvr692796tL774Qr6+vh77WrZsqVatWmnRokXav3+/1q9fr7S0tBrHe+CBB1RUVKQ777xTn332mQ4cOKD3339fqampqqio8OapoJ4QXlAvEhMTtWrVKq1du1Z9+/ZVv3799Nxzz6ljx46/aNy//OUvatmypa677joNHTpUiYmJ6t27d7X9bTabVq9erRtvvFGpqanq3LmzRo8era+//tp9jQ2AuudwOORwOCq1+/j4aNmyZcrNzdXVV1+thx9+WM8880yNY7Vr104bN25URUWFBg0apO7du2vy5MkKDg6Wjw8vc42RzbIsq76LAAAAuFhEUgAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvABqdAQMGaPLkyfVdBgAvIbwA8IqCggI99NBDiomJkb+/v8LCwnT99dfrxRdf1OnTp+u7PAAG4xusANS5r776Stdff72Cg4P11FNPqXv37rLb7dq5c6cWLVqk9u3b69e//nV9l1mtiooK2Ww2bi0PNFD8ZgKoc/fff7/8/Py0efNmjRw5Ul27dlVUVJSGDRum9957T0OHDpUknTx5Ur/73e8UGhoqh8OhW265Rdu3b3eP88QTT6hnz5567bXXFBkZqaCgII0ePVqnTp1y9yktLdXYsWMVGBiotm3b6tlnn61UT1lZmR555BG1b99eAQEBiouL04YNG9z7lyxZouDgYK1cuVLdunWT3W7XoUOHvPcEAfhFCC8A6tSJEye0du1aPfDAAwoICKiyj81mkyTdcccdKiws1Jo1a5Sbm6vevXtr4MCBKioqcvc9cOCA3nnnHa1atUqrVq3SRx99pNmzZ7v3T506VR999JHeffddrV27Vhs2bNCWLVs85ps4caKys7O1bNky7dixQ3fccYcGDx6sffv2ufucPn1ac+bM0d/+9jft2rVLbdq0qcunBUBdsgCgDv3rX/+yJFkrVqzwaG/VqpUVEBBgBQQEWI8++qj1ySefWA6Hwzpz5oxHv+joaGvhwoWWZVnWjBkzrObNm1tOp9O9f+rUqVZcXJxlWZZ16tQpq2nTptYbb7zh3n/ixAmrWbNm1kMPPWRZlmV9/fXXlq+vr5Wfn+8xz8CBA6309HTLsixr8eLFliRr27ZtdfMkAPAqrnkBcEnk5OTI5XJpzJgxKisr0/bt21VSUqJWrVp59Pv+++914MAB9+PIyEi1aNHC/bht27YqLCyU9MOqTHl5ueLi4tz7Q0JCdNVVV7kf79y5UxUVFercubPHPGVlZR5zN23aVNdcc03dnCwAryK8AKhTMTExstls2rt3r0d7VFSUJKlZs2aSpJKSErVt29bj2pPzgoOD3T83adLEY5/NZpPL5broekpKSuTr66vc3Fz5+vp67AsMDHT/3KxZM/fbWQAaNsILgDrVqlUr3XrrrXrhhRc0adKkaq976d27twoKCuTn56fIyMifNVd0dLSaNGmiTz/9VB06dJAkfffdd/ryyy910003SZJ69eqliooKFRYWqn///j9rHgANCxfsAqhzCxYs0Llz5xQbG6vly5dr9+7d2rt3r/7xj39oz5498vX1VUJCguLj4zV8+HCtXbtWeXl52rRpkx577DFt3rz5ouYJDAzU+PHjNXXqVK1fv16ff/65UlJSPD7i3LlzZ40ZM0Zjx47VihUrdPDgQeXk5CgjI0Pvvfeet54CAF7EyguAOhcdHa2tW7fqqaeeUnp6ur755hvZ7XZ169ZNjzzyiO6//37ZbDatXr1ajz32mFJTU3Xs2DGFh4frxhtvVFhY2EXP9cwzz6ikpERDhw5VixYtNGXKFBUXF3v0Wbx4sZ588klNmTJF+fn5at26tfr166chQ4bU9akDuARslmVZ9V0EAADAxeJtIwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACM8v8ArMfCgd7K+KQAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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bZrKzs01+fr4ZO3as+dvf/hZxrmAwaPr06WNuv/32ZtcBuguLMczDDSB1lJeXq6SkRPfdd59mzpyZ7OJkpBdeeEE//vGPtWPHDvXp0yfZxQE6hYADIOXce++9WrFihT7++OOkryieicaNG6fvfe97uu+++5JdFKDTCDgAdPjw4YgOr03ZbDYVFhYmsEQA0DUEHACaMGFCq7PiDhgwoNlijACQygg4ALRp06ZWZ9r1eDw688wzE1giAOgaAg4AAEg79N4DAABpJ+0m+guFQtq7d69yc3NZJA4AgG7CGKPKykr17ds3JqMn0y7g7N27V/379092MQAAQCd88cUX6tevX5fPk3YBp2Hq8i+++EJ5eXlJLg0AAGiPiooK9e/fP/wc76q0CzgNzVJ5eXkEHAAAuplYdS+hkzEAAEg7BBwAAJB2CDgAACDtEHAAAEDaIeAAAIC0Q8ABAABph4ADAADSDgEHAACkHQIOAABIOwQcAACQdgg4AAAg7RBwAABA2iHgAACAtJN2q4kDAJCJ6vxBhYyJ+3WsFovcDlvcr9NVBBwAANLAF4drVOcPxf06HqdVQ3rnxv06XUXAAQBIkgLBkL6u9iW7GOikQCj+tTfdCQEHQMrwBoIqr/EnuxgZyx8yOlxFwEF6IOAAiImKOr+8Xawe9waCOlJNwAHQdQQcAB3iD0YPMUeqfaqoDSS4NAAQHQEHQLsZY/TJvspkFwMA2sQ8OAAAIO0QcAC0W7UvmOwiAEC7EHAAtFudn4ADoHugDw6QwQ5WeqNuP1rjkzcQ/wnDACBeCDhABisrr0t2EQAgLmiiAjLU3qO1yS4CAMQNAQfIUIEg07oDSF80UQEZZO/R2nBHYfrYAEhnBBwgg9T6g6rxMhIKQPqjiQoAAKQdanCANFdR51fdNxP0tbSOFACkGwIOkObKa/w6WsMK3QAyC01UAAAg7RBwgDRW5w/KG6BTMYDMQ8AB0lidP6haH/1uAGQe+uAAaaLaG9D+isilF4IhJvMDkJkIOECaCASNqpnjBgAkEXCAbscXCOlAZfNFMv0svQAAYQQcoJswpj7ABEIhHalm2DcAtIaAA3QDh6t9+uoIq38DQHsRcIAUFQoZhb6ptaGzMAB0DAEHSFFHanzae7R5XxsAQNuYBwcAAKQdanCAFNDQgViSav1B1flDqvEx5BsAOouAAyTZvvJaHar0JbsYAJBWaKICAABphxocIAkOVNSpoVGKpigAiL241uC8/fbb+uEPf6i+ffvKYrHohRdeaPOYN998U6eeeqpcLpeGDBmilStXxrOIQEJVeQPadaha+yu8OvDNq4blFQAg5uIacKqrqzVy5EgtW7asXfvv2rVLF110kSZOnKjS0lLNmTNHs2bN0iuvvBLPYgIJEQiGdLDSq6q6QLKLAgBpL65NVBdccIEuuOCCdu+/fPlyDRo0SIsXL5YkDR8+XP/85z91//33a/LkyfEqJhB3xhj5g4ZwAwAJklKdjNevX69JkyZFbJs8ebLWr1/f4jFer1cVFRURLyDVVHkD+uxAVbKLAQAZI6UCTllZmYqKiiK2FRUVqaKiQrW10dfhWbRokfLz88Ov/v37J6KoQLvsPVqr7WWV+pJ1pAAgoVIq4HTG/PnzVV5eHn598cUXyS4SEBYIGvkCIQWCrCUFAImUUsPEi4uLtX///oht+/fvV15enjweT9RjXC6XXC5XIooHtFtlnb8+3ARDyS4KAGSklAo448aN08svvxyxbe3atRo3blySSgR0zsFKr6oZ/g0ASRPXJqqqqiqVlpaqtLRUUv0w8NLSUu3Zs0dSffPSNddcE97/hhtu0M6dO3Xrrbfqk08+0UMPPaS//e1v+vnPfx7PYgIAgDQT14CzceNGjR49WqNHj5YkzZ07V6NHj9Ydd9whSdq3b1847EjSoEGD9NJLL2nt2rUaOXKkFi9erMcee4wh4uhW6vxB+elzAwBJZTGNlzFOAxUVFcrPz1d5ebny8vKSXRykqVDIqC4Q2QR1pMavI9X1i2am1/9VAPAtj9OqIb1zY37eWD+/U6oPDtBd1AWC2nGgOtnFAAC0gIADtIMxRt7AtyOifAFGRwFAKiPgAC3wB0M6UOmVVN8kdbTGn+QSAQDai4ADNHGw0qv9FXWS6EsDAN0VAQdoZF95raq9QYINgG6laTN6XFmManwBeRw2WSyWxFyzEwg4gOqHdn9d7dORah/hBkC3YozRbc9t0bayyoRe9+PfTlaWM3VjROqWDEigQMjocJUv2cUAkiqhtQCImTp/MOHhpjsg4ACSymvpQJwKeMAmjzHSvOe3aOchpj/ozp6ccZrcDltcr+F2WjW4MEeeOF+nqwg4yHiBYCg8QR86JxbBhAcs0DXD++Qp3+OIe78Yj8Oa0k1TDVK/hECc+YOGfjftFC3IEEzSS0lBtu655BSlcN9RtMBlt6Z0p99EI+AAaJdEdWTkAZtcPCSRLgg4yGjBkNGhKm+yi9EteAOhVsNNrIIJD1gAsUDAQcYKBEP64kitquoCyS5KSmmpP02d/9vFRaN1ZCSYAEglBBxkrP2V3owON13pT+N22OI+UgMAuoKAg4ziDQRV4w2qLhDM6JFTIWP081WlneoYPLxPnlx2axxKBQCxQ8BBRgiFjOoCQVXUBnSwMjP73DTU2BgjzVm1WXvL61rct7X+NDRFAegOCDhIS19XeRU0RnW+kCrq6ifxy+Sh4C2NgOqb79bSqaObBRlCDIDujoCDtFPlDWh/hVfBUAYnmm801NpEm8q9pCBb908dJStBBkAaIuAg7Ryu8hFu1HKtTcMIKGppAKQzAg7Shi8Q0v6KunCTVKaLNm9NoqZyB4BkI+AgLYRCRvsr6nS0JnPDTdNh39HmraHWBkCmIOCg2wuGjHZ/Xa0ab7DtndNUW8soMG8NgExDwEG3FgwZ7T1am1HhJtoEfdE6ETdg3hoAmYiAg24tU5qlGs9h09ZMw02XUaBZCkAmIuCg2/EFQgqGjA5WelXlTf+lFjqyijediAGgHgEH3UqdP6gvj9Sq1pf+TVJtzWETbaZhamsAoB4BB91KrS+Y1uGmraYoRkMBQPsQcNCtHKlJrwUyG3cYbqt/Dc1PANB+BBx0G95AUDVpVHvTnr41jZuiqLUBgPYj4KDbqPUF02LBzNb61kiEGgCIBQIOuo2vq7t/81TIGP18VWmLfWskQg0AxAIBBynNH6zvn7LncE23n8wvZIxufGqT9pbXRWynbw0AxB4BBympzh9UyBjtPFidNs1SP19VGg43ffPdWjp1NM1QABAnBBykHF8gpJ0HqxUMpUGy+UadPxRuluqb79af/mOMrIQaAIgbAg5SSo0voMq6QFqEm8Zz2sxZtTm8fenU0YQbAIgzAg5Sgi8Q0s5DVQoETbdvkjLGqM4fijqnTUlBttwOFr4EgHgj4CDp6vz1sxP7A9082aj1uW1KCrJ1/9RR9LcBgAQg4CCpdh2qVp0/qEAwPcJNea0/Itwwpw0AJAcBB0lTUedXVV33XA288RIL9e+bL7Pw5IzTGP4NAElCwEFSHK3x6csjtckuRqe0Z4kF5rYBgOQi4CDhqrwBVXezZRca19i0tMSC9G2TlNtBcxQAJBMBBwljjFFZRZ2OVPu7xTDwxsO8W1rlu/ESCxL9bAAgVRBwkDDVvqAOVab+elKtDfNujGYoAEhdBBwkjL9Rp9xU057amsYjoiRqawAglRFwkDDltf5kFyGqllb4lhjmDQDdFQEHCXGoyqvKFBwSbloIN3QWBoDujYCDhKhI0dobbyByEUxW+AaA9EDAQdztK69VjS+Y7GI0U9+Z+NtyLZ06Wh6nrZUjAADdBQEHcVdZF0iZOW9a60xMhQ0ApA8CDjJCW0O/h/fJk8vOKt8AkC4IOIirg5Veef2JHR7ennWiGtCZGADSEwEHcXOgsk4HK70JvWZrQ74bMPQbANIfAQdx4Q+GVFHrVyiBlTchY3TjU5u0t7wu6ufU1gBA5iDgIC58gZBqfYlLN03DTeMh3w2orQGAzEHAQVw0Hn4dTw2dh+es2hwRbv70H2NkJcwAQMYi4CDm6vxBHayKf9+baP1tCDcAAImAgxir8QW053CN/IH4TnwTrb9NSUG27p86inADICP16+mRMwHTXXSX37EEHMSMMSbu4aalJqmlU0fTeRjoIpvVop7ZjmQXA52U47bLYWM+rwYEHMRMyCju4ea257ZoW1lleBtNUunFYbco38MDNlnsVqsKc13JLgYQEwQcxEyVN76rhdf5QxHhhiap9OO0WdUn35PsYgBIAwQcxEw8R04ZYzTv+S3h90/OOE35HgdNUikk122Xy9G16nEn1esAYoSAg5iprItfDU6dPxQeLVVSkE24SRKLpb4jYzRZTntCOjgCQHsQcBAzwVB8+t+EjNGcVZvD7+uXWSDcJEuPLGeyiwAAbUrIP7eWLVumgQMHyu12a+zYsXr//fdb3HflypWyWCwRL7fbnYhiIsUYY1TrC0YMBy8pyJa7i80gaFuex65jcpzNXv17ZSW7aADQLnGvwVm1apXmzp2r5cuXa+zYsVq6dKkmT56s7du3q3fv3lGPycvL0/bt28Pv+dd6ZmkYCt50BfC++W7dP3UU34cY6d/Lozx39BFLViv3GED3FveAs2TJEl133XW69tprJUnLly/XSy+9pMcff1zz5s2LeozFYlFxcXG8i4YU01KwkRgx1RU9sx3Kdtb/r+4PhtTQkuh22AgyANJWXAOOz+fTpk2bNH/+/PA2q9WqSZMmaf369S0eV1VVpQEDBigUCunUU0/V7373O5100klR9/V6vfJ6v10WoKKiInY/ABIm2rILEiuAd1W2y6beuW46/wLIOHENOIcOHVIwGFRRUVHE9qKiIn3yySdRjxk6dKgef/xxnXLKKSovL9cf/vAHnXHGGfroo4/Ur1+/ZvsvWrRICxcujEv5kRgmSrgh2HScx2lTYU7kJG0Ou4VwAyAjpdwoqnHjxmncuHHh92eccYaGDx+uhx9+WHfeeWez/efPn6+5c+eG31dUVKh///4JKSu6xhgjbyCkOn8wHG5YdqFtVmt981JTOS678rOYBRgApDgHnIKCAtlsNu3fvz9i+/79+9vdx8bhcGj06NH67LPPon7ucrnkcjG1eHfTUpPU0qmj5XE2f3hnOpvVIqe9PvC57DZGMwFAG+Jad+10OjVmzBitW7cuvC0UCmndunURtTStCQaD2rp1q/r06ROvYiLBojVJSdLwPnkMAVd9mHE5rMpy2ZTvcSjf41BxvltDeudqSO9cwg0AtEPcm6jmzp2radOm6Tvf+Y5OO+00LV26VNXV1eFRVddcc42OPfZYLVq0SJL029/+VqeffrqGDBmio0eP6ve//70+//xzzZo1K95FRQIYY1Re62/WJGWxSC47zVKSVJDrVO9c5n4CgK6Ie8CZOnWqDh48qDvuuENlZWUaNWqU1qxZE+54vGfPHlmt3/6r/ciRI7ruuutUVlamnj17asyYMXr33Xd14oknxruoiLNozVI0SUnH5DjVs9HswHYbIQ8AuspijInP/PpJUlFRofz8fJWXlysvLy/Zxcko28sq5QuEon5mjNGcJuFmeJ883XvJiIystenX06OGH9vtsEXtNAwAmSTWz++UG0WF9NR4scxMHyl13DFZyvcw2gkA4omAg7gzxmje81vC7zO1WcpikYry3IQbAEgAhqwgJvzBkPzB6M1TjWtvMnmxTIfNqsJcpjQAgESgBgddVuUNqLLOr2i9uZrW3txzySkZ2SzltFtVkONse0cAQEwQcNBpwZDR11VeHa31y+un9qYxj9Mmm9WiPLddTrtVdqs1I5vlACBZCDjotLKKOh2u8rX4eabV3lgs9ZP02a0W9evpYWQUACQRAQedVusLtPhZ0wn90q32xmKp71PT2DE5ThXk0McGAFIBAQcdZoxRtS8obytz3tz23BZtK6sMb0u32hu3w6YhvXOSXQwAQAsIOOiQGl9Ae4/WqtYXPdxIkjcQigg36bDGVMNSEg0a/xkAkHoIOGgXY4y+PFKr8troo6Ui9/32z0/OOE35Hke3r72x2yw6vig32cUAALQTAQctCoaMjDGqrAuovNavyrqW+9w0aNqx2O2wpXy4aShe0+Bms1rUI6t+Uj67NbV/BgBAJAIOovq6yqt95XWSmj/4W9N0WHh3aMrxOG06todH1d6APE6b7FarHN8seJnq4QwAEB0BB82U1/q1r7yuQ8FGql8tfM6qzeH3qdSx2GJpWNTSqlyXQ4FQSG6HTU67VcbUT8THsG4ASB8EHESo8gb01ZHaDocbY4x+vqpUe7+p9UnmsHCH3aJct0NWi2S1WGS1WOS0WZWfxRpQAJApCDgI8wdD2nWwulPHegORq4XfP3VUUmpvsl029e3BJHsAkOkIOJBUH24+O1AVk3MtnTpa1gSHG6tVKinIkdthTZlmMQBA8hBwIEkqK69TINjBdqlGGjdpJTpfOO1WFee5WesJABBGwIEq6to3BLwlTYeGJ5LHaVNhjov+NQCACAQc6Osqn4KhztfeNO5/k4ih4fkeh7Jc9SOgPA5bszWhAAAg4GQ4fzCkam/na2+aivfQcI/TpqJ8l1x2mqMAAC3jn74Z7usqX4eHhLcmnv1vLBapdx7hBgDQNgJOhjtc7Ut2EdrN47Qpz01fGwBA2wg4Gc6oa9U3xhjV+YMxKk3rju3hSch1AADdH31wMtjXVd4uNU+Fvpm9uKGDcTwV5rqYvA8A0G4EnAx1pNqnvUfrOn18yBjd+NSm8NIMkjS8T17MR1AN7p0tq8XSLRbtBACkDgJOhqrqwsipputO9c13a+nU0TGbRdhikQYWZMtutVBrAwDoFAJOhvIGQp0+ts4fue7Un/5jTMyWZijOdyvPY2ekFACgSwg4GcgXCKnW1/GOwfUdikOas2pzeFus1p1y2C0aVJAth9Uqq5W1pAAAXUPAyTDeQFBfHK7p8HHROhSXFGTL7eh635gct13ZThu1NgCAmCHgZJhDVT7V+tpunjLGhJuxjJHmrNoc0aG4pCBb908d1ek+Ny6HVVaLRUV59aOjWG4BABBLBJwMEgiGdLSm7Yn9Whv+HasOxR6HTf16euK6rAMAIHMRcDJIea1foTYqb6IN/27QUGvT1T43vfNcynM7CDcAgLgh4GSQ2jZmHG4abhpqaxpyiMve9WHg2S6b8j0Ohn8DAOKKgJMh6vxBHa3xt/h5tHATy+HfkuRxWjXgmGzZGCUFAIgzenZmiGpvoMVlGaJN3BfrcCNJeR4H4QYAkBAEnAwRCLW86FQ8J+5r4LBblO9hJXAAQGIQcDJEZV30pRlCxsRl4r7GLBapX88s5rkBACQMAScDHK3xqS5KB+OmTVOxmrivqYIcl3JcdPcCACQOAScD1PlDUfvfeAORTVNdmbivJT2zHSrKc8X0nAAAtIWAkwH8weiT3zQOPfFomnLarcp22pnvBgCQcLQbpDlvIKjy2ubDw40xmvf8lvD7WGcQu82iAcdkMd8NACApqMFJc7W+YNTmqcYjp0oKsuWyx/ar4LRbCTcAgKQh4KS5r6ubrz3VdOTUPZecEvNmpIJs+t0AAJKHgJPG6vxB1XgjR08lYuRUrxyn8rOY8wYAkDz0wUljh6PU3sRj5JTHWd8cVe0NKsdt17E9PF06HwAAXUXASVPBkFFFXWTnYmNMxHw4XR05ZbVKhbku5Xscctlt+vf+ShXkODt9PgAAYoWAk6bKKurkD3zbu9gYo9ue26JtZZXhbV3tduNx2NQ71x3xntmKAQCpgD44aarpzMXeQCgi3Azvk9flkVNFee6I94yaAgCkCmpw0lRLk/tJ0pMzTlO+x9GlvjcWi5oFJFcclnkAAKAzCDhpyBsIKhCMnPym8Vw4boet0+HG47Qpx2VXjyyH7LbIQJPnZuQUACA1EHDSUCBoIgJN01mLO8pikbKcNuW47eqZ5ZTDRk0NACC1EXAyQOOh4e2dtTjLZVMwZFSU61aO2y6blfWkAADdBwEnDTXtf9O4Nqe9sxb36+lhRBQAoNuirSENVdQGwn/uzKKaxfluOWmGAgB0Y9TgpJlQyKjG/23A6eiimv17eZTn7toIKwAAko2Ak2Yq6wLhCf6a1t601TzVv5dHPbKYiRgA0P3RDpFmGi/P0LRzcWuLavbMdhBuAABpg4CTZspr/VG3t1Z7Y7FIBTmueBYLAICEIuCkkSpvIGLEVGOtdakpznezzAIAIK0QcLqZYKiFBCOp2hto8bOWOOwWHZNN0xQAIL0QcLqZsoq6qOtMBYIhVdZ1POD0ynYyYgoAkHYION2MPxDSzoPVzbYfrPKq1heMckTrWD8KAJCOGCaewmp9Qbkd1mY1LIFQSFXegPyBkKwWi2SRDlX6Onx+m9XChH4AgLSUkKfbsmXLNHDgQLndbo0dO1bvv/9+q/s/88wzGjZsmNxut0aMGKGXX345EcVMOUdrffq6unlwCYWkXQer9eWRWu0tr9WBiroOn9tmtWjAMVmyssYUACANxT3grFq1SnPnztWCBQv0wQcfaOTIkZo8ebIOHDgQdf93331XV111lWbOnKnNmzdrypQpmjJlij788MN4FzUltdVxOBA0qvM375PTGqfdqmN7eJTtogIPAJCe4h5wlixZouuuu07XXnutTjzxRC1fvlxZWVl6/PHHo+7/wAMP6Pzzz9cvf/lLDR8+XHfeeadOPfVU/d//+3/jXdSUVOUN6EBlnQ5HqcnpDLvNopLCbOVn0fcGAJC+4hpwfD6fNm3apEmTJn17QatVkyZN0vr166Mes379+oj9JWny5Mkt7p/uQiFpf7lXXx2p1Y6DVQq1NNFNO/XvlSUH/W4AAGkurm0Uhw4dUjAYVFFRUcT2oqIiffLJJ1GPKSsri7p/WVlZ1P29Xq+8Xm/4fUVFRRdLnbpqvMF2rQbeoGkWslktymJCPwBABuj2/5RftGiR8vPzw6/+/fsnu0hx1d4KnKYLbUpSltNGp2IAQEaIa8ApKCiQzWbT/v37I7bv379fxcXFUY8pLi7u0P7z589XeXl5+PXFF1/EpvDdXNOFNl12K8sxAAAyRlwDjtPp1JgxY7Ru3brwtlAopHXr1mncuHFRjxk3blzE/pK0du3aFvd3uVzKy8uLeHUnnVleoT0a1/Tcc8kpcjtt6plNx2IAQGaI+zjhuXPnatq0afrOd76j0047TUuXLlV1dbWuvfZaSdI111yjY489VosWLZIk/exnP9P48eO1ePFiXXTRRfrrX/+qjRs36pFHHol3UZOivNYf8+HaIWM0Z9Xm8HuLRcpx2eWyU4MDAMgMcQ84U6dO1cGDB3XHHXeorKxMo0aN0po1a8Idiffs2SOr9duKpDPOOEP//d//rdtvv12/+tWvdPzxx+uFF17QySefHO+iJkWtPyhvIBiz8GGM0c9XlWpvef3kfw3NUy57t+9uBQBAu1mM6eK44xRTUVGh/Px8lZeXd4vmqk/3VyrP41BRnrvZZ/vKazu8BEOdP6jLH64fUt83360//ccYZTltKinMkY0OxgCAFBXr5zdT2SaZNxDSgQqv/MGQnHareuc2DzqdtXTqaFktFhXmugg3AICMQsBJokAwFO4MfKTaL6tV8geNGqJIRZ2/S+e3WCSH3cKSDACAjMOTL4nq/EHV+YNy2etXDA+FpMNVsVmSoYHHYWPmYgBAxiHgJIkxRlc9ukFbvyrX8D55uveSEbJ0ZJpiAADQIgJOktT6g9r6Vbkkadu+CtX5Q7JYFK7NAQAAnUfASZIaX+QEf3NWbdbe8jqVFGRryRWjZLFIVoIOAACdQueMJKmojQw4DfPW7DxUrSkPvaOLl73T5ZXDAQDIVAScJPEH2g4vNz61SWk2TREAAAlBE1USBIIhVXq/HQL+5IzT5HbY5LRbNWdVqXZ9s0jm3vI6eQMhFskEAKCDqMFJgjp/ULc+uyX83u2wye2wyWqx6IGpo/TnGaclsXQAAHR/BJwkKK/1a+c3tTQNa0U1sFgs8lBjAwBAlxBwEswYo+kr/hV+//vLRjIsHACAGCPgJNiRGp8+PVAlqb725rhjPJKkHLddvfNcySwaAABpg07GCVbtDYb/fM8lp6ggxyWn3aZclz080V/ZN0PGAQBA5xBwEuxwzbdrTTkdFlksFuV7HOFtPbKcctqpWAMAoCt4kiaYzx8K/7mnx5nEkgAAkL6owUkQY4yO1vjVeN6+bHfbo6WY5w8AgI6jBidBKr0BfXG4RvOe/3b+myxn2/lyzqrNLNkAAEAHEXASIBAM6esqn+r8ofD8NycU5bQ4343HYdOJffIk1c9m/PNVpSzZAABABxBwEsAfNKrzByJqb1ZM/26L899YLBa9+NOz1L9n/RDynYeqVdeo7w4AAGgdASfOjDEqq6hTRW0gYvbiY3Ja72BstVq0csZ3w+/nrNqsWl+wzZocKnoAACDgxF3ISBW1fk195L3wtqVTR8ntaLv/jcdhU0lBtqT6pqorHlmv257f2mLIMcZE1BJJkkXMkgwAyDwEnDgLGaOKWn/ENoetfbfdYrHo/qmjwiFHkrbtq5A3EL25yhsINVvjyu3krxgAkHl4+sWZL0oYcXRgIj+rxaKlU0fpyXasMN64YueeS06R1Ro5iSAAAJmCgBNnXx2tlcv+7WipkoJsFbTR/6Ypi8UidxsrjDdtnrJY9M3SD6xMDgDIPAScOAqFjLz+kBoPlnrwqtFdrlWJ1gUnWvNUn3xPl64DAEB3RcCJI3+oefNUr2xni8PD2yva5H9Nm6c8Tpt6ZbMUBAAgMxFw4ihKvpHH2bkmI5fdGjGiqvHkf9Gap47JcXXqOgAApAMCThwFoiSczq4U3jCiqm++W1L95H8No6kaz5Dc0DzlsDE8HACQuQg4cdTQahSryffqR1SNjrxGk9qbey45RRaLpdNBCgCAdMBq4nEUDJqok+91RePuO7X+oGp9wYjaG7ejPtgwwR8AIJMRcOKoLhCMGN10Yp+8FhfY7IxrHn8/4n3j2hu7lYADAMhctGPEUTBkIpqnnrlhXJdHULnsVg3/ZqXxxvrmu8O1NzarRVYCDgAggxFw4ija6KauslgsuveSEVp1/ekR2//0H2O6HJ4AAEgXNFHFUa3v2+apIYU5MWueslgsynLa9T83nSlj6oOTlXADAEAYASeOGk/Gt/TKUTGvYbFaLKIvMQAAzdFEFUcRMxkTRAAASBgCThzV+aJMZQwAAOKOgBMnNb5AsosAAEDGIuDESY0vmOwiAACQsQg4cVLnJ+AAAJAsBJw4CQQjF6Cy0ssYAICEIeDESSAUGXCynLFbogEAALSOgBMn/iAjqAAASBYCThyEQkbBJjU4Dhu3GgCAROGpGwchE7nIpiRlu2iiAgAgUQg4ceCL0jzltHOrAQBIFJ66cVAbozlw8tyOmJwHAIBMQ8CJA28gNh2Ms112Fea6YnIuAAAyCQEnDnwxCjiS1CvbqRgvQg4AQNoj4MRR047GneG0W3XcMVldPxEAABmEgBMHdYGgjDGa9/yWmJwvz+2Qx8lfFQAA7cVTM8ZqfAEFgkbeQEg7D1VLkoYV58rj6Now8SG9c1VSmK0shpsDANAmAk6M+QPN58B54trvyhKDjjTZLrtsdMgBAKBNBJwYizYHDutsAgCQWAScGKvzx2YOHAAA0HkEnBgKhYwq6vzJLgYAABmPgBND1b6AQiwiDgBA0hFwYihWSzQAAICuIeDEEM1TAACkBgJOjFR5A6r1fds+FYtZjAEAQOcQcGLkSLUv/OdYzmIMAAA6joATI41XEG88i3FJQXaXZzEGAAAdQ8CJgVpfsMX5b+655JSYzGIMAADaL64B5/Dhw/rJT36ivLw89ejRQzNnzlRVVVWrx0yYMEEWiyXidcMNN8SzmF1ijNG+8toW+9yQbQAASDx7PE/+k5/8RPv27dPatWvl9/t17bXX6vrrr9d///d/t3rcddddp9/+9rfh91lZWfEsZpccrfGr2ptaw8N7ZjmSXQQAAJIqbgFn27ZtWrNmjf71r3/pO9/5jiTpwQcf1IUXXqg//OEP6tu3b4vHZmVlqbi4OF5Fi6lDVd6EXq89NUIeJ31+AACZLW5NVOvXr1ePHj3C4UaSJk2aJKvVqg0bNrR67NNPP62CggKdfPLJmj9/vmpqalrc1+v1qqKiIuKVKF8crlGdP7FTFxfkuNoMOW47AQcAkNniVoNTVlam3r17R17MblevXr1UVlbW4nE//vGPNWDAAPXt21dbtmzRbbfdpu3bt+v555+Puv+iRYu0cOHCmJa9LcGQUY0voPLa6BP7Ne2PY7fGLkdmu+wqzndr39G6qJ/nuu2yWun4AwDIbB0OOPPmzdO9997b6j7btm3rdIGuv/768J9HjBihPn366JxzztGOHTs0ePDgZvvPnz9fc+fODb+vqKhQ//79O339tgRDRp+UVbS45lS0OXBi3dE43+OIGnDsNouO7emJ7cUAAOiGOhxwbrnlFk2fPr3VfUpKSlRcXKwDBw5EbA8EAjp8+HCH+teMHTtWkvTZZ59FDTgul0sul6vd5+uq8lp/qwtqNp0Dx2W3ypagGpUeWQ45bIz8BwCgwwGnsLBQhYWFbe43btw4HT16VJs2bdKYMWMkSa+//rpCoVA4tLRHaWmpJKlPnz4dLWpcHK3xtb3TN+655BRZrRbZExRwmFAQAIB6cfvn/vDhw3X++efruuuu0/vvv6933nlHs2fP1pVXXhkeQfXVV19p2LBhev/99yVJO3bs0J133qlNmzZp9+7d+t///V9dc801Ovvss3XKKafEq6jtFgoZ1bYwoV80FkvDK/4BJ9tlU56b4eEAAEhxnujv6aef1rBhw3TOOefowgsv1FlnnaVHHnkk/Lnf79f27dvDo6ScTqdee+01nXfeeRo2bJhuueUWXXrppfp//+//xbOY7RY0ptXmqWgsin+4sVikvj08dC4GAOAbcZ3or1evXq1O6jdw4ECZRkOO+vfvr7feeiueRUpLOS673DRPAQAQFteAg/gs1WC1WJTrtisQMvIFQirKc8f+IgAAdGMEnG7IZrVoYEG2qrwB2a0Wam8AAGiCgBNjTSf5s8axg3GOi78+AACiYdKUGIo2yV+ehxACAECiEXBiKOokfwkYIg4AACIRcOLknktOkcViUZ6HuWkAAEg0Ak6cUHEDAEDyEHDizMnaUAAAJBxP3zhjdmEAABKPgBNDTYeI00wFAEByEHBiJNoQcQfNUwAAJAVP4BiJNkQcAAAkB0/hOGgYIm6j/w0AAElBwImRxv1vGvrekG8AAEgOAk4MROt/I8V3HSoAANAyAk4MtNT/hoADAEByEHBirKH/jSQ56WgMAEBS8ASOgWj9byTJYaMGBwCAZCDgdFFL/W8khWtyAABAYhFwuqi1+W8YRQUAQHIQcLqocfNU4/43EjU4AAAkCwGnC5o2TzXNM9TgAACQHAScLqjzt748AzMZAwCQHAScTmpae9O0eUqS3HZboosFAABEwOm0prU3bkfkrcx22WSlBgcAgKQg4HRCe2pvct2ORBcLAAB8g4DTCW3V3kiSK8o2AACQGDyFOyhkjOas2hx+H632RpLsNE8BAJA0BJwOMMbo56tKtbe8TlLLtTcSC20CAJBMBJwOqPEFw01TffPdun/qqBYn83PYuLUAACQLT+F2MsboqkffC79fOnV0i7U0NquFOXAAAEgiAk471fqD2ravUlLrTVOS5LQTbgAASCYCTie01LG4Ac1TAAAkF0/iTmir/zAdjAEASC57sgvQXXgcNv1/C87Vv8uqmq051ZTdRsABACCZCDjtZLFYlOW0y+1oe30piwg4AAAkE01UcUANDgAAyUXAiQNmMQYAILkIOHHAKuIAACQXAScOGEUFAEByEXDiwN3GKCsAABBfPIljLNdtl52J/gAASCqexDGW73EkuwgAAGQ8Ak6M0cEYAIDkI+DEkM1qUa6LuRMBAEg2Ak4MeZw2anAAAEgBBJwYYoI/AABSAwEnhjzOttepAgAA8UfAiaFsJ/1vAABIBQScGLJyNwEASAk8kmPEYpFsLNEAAEBKIODEiNthZQZjAABSBE/kGMn3OJNdBAAA8A0CTozkMMEfAAApg4ATI24HtxIAgFTBUzkGjjsmSxY6GAMAkDIIOF2U57Gz/hQAACmGgNNFrD8FAEDqIeB0Uc8sRk8BAJBqCDhdcGxPjxzMfQMAQMrh6dwF2S4W1wQAIBURcDrJ47TKSe0NAAApiSd0J1gsUq7bwdBwAABSVNwCzt13360zzjhDWVlZ6tGjR7uOMcbojjvuUJ8+feTxeDRp0iR9+umn8Spip/XIcqgoz53sYgAAgBbELeD4fD5dfvnluvHGG9t9zH333ac//vGPWr58uTZs2KDs7GxNnjxZdXV18Spmh1ksUu9cwg0AAKksbjPULVy4UJK0cuXKdu1vjNHSpUt1++236+KLL5Yk/fnPf1ZRUZFeeOEFXXnllfEqaofkuOxy2mnZAwAglaXMk3rXrl0qKyvTpEmTwtvy8/M1duxYrV+/vsXjvF6vKioqIl7x1DObeW8AAEh1KRNwysrKJElFRUUR24uKisKfRbNo0SLl5+eHX/37949rOVlUEwCA1Nehp/W8efNksVhafX3yySfxKmtU8+fPV3l5efj1xRdfxO1aFklWRk4BAJDyOtQH55ZbbtH06dNb3aekpKRTBSkuLpYk7d+/X3369Alv379/v0aNGtXicS6XSy6Xq1PX7Cg7894AANAtdCjgFBYWqrCwMC4FGTRokIqLi7Vu3bpwoKmoqNCGDRs6NBILAAAgblUSe/bsUWlpqfbs2aNgMKjS0lKVlpaqqqoqvM+wYcO0evVqSZLFYtGcOXN011136X//93+1detWXXPNNerbt6+mTJkSr2ICAIA0FLdh4nfccYeeeOKJ8PvRo0dLkt544w1NmDBBkrR9+3aVl5eH97n11ltVXV2t66+/XkePHtVZZ52lNWvWyO1m3hkAANB+FmOMSXYhYqmiokL5+fkqLy9XXl5esosDAADaIdbPb3rNAgCAtEPAAQAAaYeAAwAA0g4BBwAApB0CDgAASDsEHAAAkHYIOAAAIO0QcAAAQNoh4AAAgLRDwAEAAGmHgAMAANJO3BbbTJaGpbUqKiqSXBIAANBeDc/tWC2RmXYBp7KyUpLUv3//JJcEAAB0VGVlpfLz87t8nrRbTTwUCmnv3r3Kzc2VxWKRVJ8K+/fvry+++IIVxjuJexgb3MfY4D52HfcwNriPsdFwHz/++GMNHTpUVmvXe9CkXQ2O1WpVv379on6Wl5fHF7CLuIexwX2MDe5j13EPY4P7GBvHHntsTMKNRCdjAACQhgg4AAAg7WREwHG5XFqwYIFcLleyi9JtcQ9jg/sYG9zHruMexgb3MTbicR/TrpMxAABARtTgAACAzELAAQAAaYeAAwAA0g4BBwAApJ20CTjLli3TwIED5Xa7NXbsWL3//vut7v/MM89o2LBhcrvdGjFihF5++eUElTR1deQerly5UhaLJeLldrsTWNrU9Pbbb+uHP/yh+vbtK4vFohdeeKHNY958802deuqpcrlcGjJkiFauXBn3cqayjt7DN998s9l30WKxqKysLDEFTkGLFi3Sd7/7XeXm5qp3796aMmWKtm/f3uZx/F6M1Jn7yO/G5v70pz/plFNOCU+GOG7cOP39739v9ZhYfBfTIuCsWrVKc+fO1YIFC/TBBx9o5MiRmjx5sg4cOBB1/3fffVdXXXWVZs6cqc2bN2vKlCmaMmWKPvzwwwSXPHV09B5K9TN37tu3L/z6/PPPE1ji1FRdXa2RI0dq2bJl7dp/165duuiiizRx4kSVlpZqzpw5mjVrll555ZU4lzR1dfQeNti+fXvE97F3795xKmHqe+utt3TTTTfpvffe09q1a+X3+3Xeeeepurq6xWP4vdhcZ+6jxO/Gpvr166d77rlHmzZt0saNG/X9739fF198sT766KOo+8fsu2jSwGmnnWZuuumm8PtgMGj69u1rFi1aFHX/K664wlx00UUR28aOHWv+8z//M67lTGUdvYcrVqww+fn5CSpd9yTJrF69utV9br31VnPSSSdFbJs6daqZPHlyHEvWfbTnHr7xxhtGkjly5EhCytQdHThwwEgyb731Vov78Huxbe25j/xubJ+ePXuaxx57LOpnsfoudvsaHJ/Pp02bNmnSpEnhbVarVZMmTdL69eujHrN+/fqI/SVp8uTJLe6f7jpzDyWpqqpKAwYMUP/+/VtN42gZ38XYGTVqlPr06aNzzz1X77zzTrKLk1LKy8slSb169WpxH76LbWvPfZT43diaYDCov/71r6qurta4ceOi7hOr72K3DziHDh1SMBhUUVFRxPaioqIW2+DLyso6tH+668w9HDp0qB5//HH9z//8j5566imFQiGdccYZ+vLLLxNR5LTR0nexoqJCtbW1SSpV99KnTx8tX75czz33nJ577jn1799fEyZM0AcffJDsoqWEUCikOXPm6Mwzz9TJJ5/c4n78Xmxde+8jvxuj27p1q3JycuRyuXTDDTdo9erVOvHEE6PuG6vvYtqtJo7EGDduXET6PuOMMzR8+HA9/PDDuvPOO5NYMmSaoUOHaujQoeH3Z5xxhnbs2KH7779fTz75ZBJLlhpuuukmffjhh/rnP/+Z7KJ0a+29j/xujG7o0KEqLS1VeXm5nn32WU2bNk1vvfVWiyEnFrp9DU5BQYFsNpv2798fsX3//v0qLi6OekxxcXGH9k93nbmHTTkcDo0ePVqfffZZPIqYtlr6Lubl5cnj8SSpVN3faaedxndR0uzZs/Xiiy/qjTfeUL9+/Vrdl9+LLevIfWyK3431nE6nhgwZojFjxmjRokUaOXKkHnjggaj7xuq72O0DjtPp1JgxY7Ru3brwtlAopHXr1rXYvjdu3LiI/SVp7dq1Le6f7jpzD5sKBoPaunWr+vTpE69ipiW+i/FRWlqa0d9FY4xmz56t1atX6/XXX9egQYPaPIbvYnOduY9N8bsxulAoJK/XG/WzmH0XO9kBOqX89a9/NS6Xy6xcudJ8/PHH5vrrrzc9evQwZWVlxhhjrr76ajNv3rzw/u+8846x2+3mD3/4g9m2bZtZsGCBcTgcZuvWrcn6EZKuo/dw4cKF5pVXXjE7duwwmzZtMldeeaVxu93mo48+StaPkBIqKyvN5s2bzebNm40ks2TJErN582bz+eefG2OMmTdvnrn66qvD++/cudNkZWWZX/7yl2bbtm1m2bJlxmazmTVr1iTrR0i6jt7D+++/37zwwgvm008/NVu3bjU/+9nPjNVqNa+99lqyfoSku/HGG01+fr558803zb59+8Kvmpqa8D78XmxbZ+4jvxubmzdvnnnrrbfMrl27zJYtW8y8efOMxWIxr776qjEmft/FtAg4xhjz4IMPmuOOO844nU5z2mmnmffeey/82fjx4820adMi9v/b3/5mTjjhBON0Os1JJ51kXnrppQSXOPV05B7OmTMnvG9RUZG58MILzQcffJCEUqeWhiHLTV8N927atGlm/PjxzY4ZNWqUcTqdpqSkxKxYsSLh5U4lHb2H9957rxk8eLBxu92mV69eZsKECeb1119PTuFTRLT7Jyniu8XvxbZ15j7yu7G5GTNmmAEDBhin02kKCwvNOeecEw43xsTvu2gxxpiO1fkAAACktm7fBwcAAKApAg4AAEg7BBwAAJB2CDgAACDtEHAAAEDaIeAAAIC0Q8ABAABph4ADIGlWrlypHj16hN//5je/0ahRo5JWHgDpg4ADIGX84he/aLYGDQB0hj3ZBQDQ/fl8Pjmdzi6fJycnRzk5OTEoEYBMRw0OgA6bMGGCZs+erTlz5qigoECTJ0/WkiVLNGLECGVnZ6t///76r//6L1VVVUUct3LlSh133HHKysrSj370I3399dcRnzdtopowYYLmzJkTsc+UKVM0ffr08PuHHnpIxx9/vNxut4qKinTZZZe162d49tlnNWLECHk8Hh1zzDGaNGmSqqurw58/9thjGj58uNxut4YNG6aHHnqofTcHQEog4ADolCeeeEJOp1PvvPOOli9fLqvVqj/+8Y/66KOP9MQTT+j111/XrbfeGt5/w4YNmjlzpmbPnq3S0lJNnDhRd911V5fKsHHjRt1888367W9/q+3bt2vNmjU6++yz2zxu3759uuqqqzRjxgxt27ZNb775pi655BI1LM339NNP64477tDdd9+tbdu26Xe/+51+/etf64knnuhSeQEkDk1UADrl+OOP13333Rd+P3To0PCfBw4cqLvuuks33HBDuObjgQce0Pnnnx8OPSeccILeffddrVmzptNl2LNnj7Kzs/WDH/xAubm5GjBggEaPHt3mcfv27VMgENAll1yiAQMGSJJGjBgR/nzBggVavHixLrnkEknSoEGD9PHHH+vhhx/WtGnTOl1eAIlDDQ6AThkzZkzE+9dee03nnHOOjj32WOXm5urqq6/W119/rZqaGknStm3bNHbs2Ihjxo0b16UynHvuuRowYIBKSkp09dVX6+mnnw5frzUjR47UOeecoxEjRujyyy/Xo48+qiNHjkiSqqurtWPHDs2cOTPcJygnJ0d33XWXduzY0aXyAkgcAg6ATsnOzg7/effu3frBD36gU045Rc8995w2bdqkZcuWSarvgNxZVqs13GzUwO/3h/+cm5urDz74QH/5y1/Up08f3XHHHRo5cqSOHj3a6nltNpvWrl2rv//97zrxxBP14IMPaujQodq1a1e439Cjjz6q0tLS8OvDDz/Ue++91+mfBUBiEXAAdNmmTZsUCoW0ePFinX766TrhhBO0d+/eiH2GDx+uDRs2RGxrKzAUFhZq37594ffBYFAffvhhxD52u12TJk3Sfffdpy1btmj37t16/fXX2yyzxWLRmWeeqYULF2rz5s1yOp1avXq1ioqK1LdvX+3cuVNDhgyJeA0aNKjN8wJIDfTBAdBlQ4YMkd/v14MPPqgf/vCH4Y7Hjd18880688wz9Yc//EEXX3yxXnnllTb733z/+9/X3Llz9dJLL2nw4MFasmRJRO3Miy++qJ07d+rss89Wz5499fLLLysUCkX0B4pmw4YNWrdunc477zz17t1bGzZs0MGDBzV8+HBJ0sKFC3XzzTcrPz9f559/vrxerzZu3KgjR45o7ty5nbtJABKKGhwAXTZy5EgtWbJE9957r04++WQ9/fTTWrRoUcQ+p59+uh599FE98MADGjlypF599VXdfvvtrZ53xowZmjZtmq655hqNHz9eJSUlmjhxYvjzHj166Pnnn9f3v/99DR8+XMuXL9df/vIXnXTSSa2eNy8vT2+//bYuvPBCnXDCCbr99tu1ePFiXXDBBZKkWbNm6bHHHtOKFSs0YsQIjR8/XitXrqQGB+hGLKZpAzcAAEA3Rw0OAABIOwQcAGlnz549EUO8m7727NmT7CICiDOaqACknUAgoN27d7f4+cCBA2W3M8YCSGcEHAAAkHZoogIAAGmHgAMAANIOAQcAAKQdAg4AAEg7BBwAAJB2CDgAACDtEHAAAEDaIeAAAIC08/8DdVcSYeU0ZGIAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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VV4hvv/22wb779+8XN910k4iPjxcmk0kMGjRI/Oc//2lQ7tNPPxW9evUSOp3Oa3hqY3WtP/zVM1S6/rDlxoa77tixQ4wcOVJ06NBBGI1G0aVLFzF69Gixdu1aIYR/5/LAgQPirrvuEt26dRMmk0kkJiaKK664Qnz99dcN6tuY0tJSMWnSJNGjRw8RExMjLBaLyMrKEh988EGDsuvXrxc5OTnCYrEIk8kkunXrJu68806xdevWZl9n1apVQpIkceTIkQbPrVy5UvTv318YjUaRkJAghg4dKtasWeNV5qWXXhI9evQQer1epKSkiPvvv1+cOnXKq4y/n5UQjX/ejQ2V9jWUHPWGKwshxDvvvCO6du0qDAaD6Nevn/jyyy99vn5FRYWYOnWqSE9PF3q9Xpx77rniueee8xo+L0TtEPq7775bWCwWERcXJ0aPHi1KSkq8Xtvf3zshhMjKyhK33XZbg+1EzZGECGHPQyKiEHC73ejVqxdGjx6NJ598MtTVaZd27tyJCy+8ENu3b0e/fv1CXR1SGYYXImqX8vLycP/99+PIkSMhX1m6PRo7dixkWcYHH3wQ6qqQCjG8EFGbKC8vR01NTZNlUlNT26g2RKRmDC9E1CbuvPNOvPXWW02W4Z8jIvIHwwsRtYmff/652eniG1vpmYioLoYXIiIiUhVOUkdERESqEnGT1MmyjMLCQsTFxXGxLyIiIpUQQqCiogLp6ekNVnavL+LCS2FhYYOVgImIiEgdjh49is6dOzdZJuLCi2dZ96NHjypryhAREVF4s1qtyMjIUK7jTYm48OK5VWQ2mxleiIiIVMafLh/ssEtERESqwvBCREREqsLwQkRERKrC8EJERESqwvBCREREqsLwQkRERKrC8EJERESqwvBCREREqsLwQkRERKrC8EJERESqwvBCREREqsLwQkRERKrC8EJERESqEnGrSoeawyXDJctNlok28LQTERGdKV5FA+x4pR0nKx1Nlkm1mCBJQIxBhyiDto1qRkREFBkYXlrI6W66VUUI0ewxisptAGpDDMMLERFRyzC8tIDTLWPPsYpQV4OIiKhdY4fdELI53SirdqCsuunbTERERHQaW15CqKzaibJqJyQJiI82hLo6REREqsCWFyIiIlIVhpcwUe1whboKREREqsDwEgaEAArLakJdDSIiIlVgeCEiIiJVYXgJE3aXjBOV9lBXg4iIKOwxvIQJWQZOVHHINBERUXMYXsKI3dn07L1ERETE8EJEREQqw/ASZmoc7lBXgYiIKKwxvBAREZGqMLwQERGRqrRJeHn55ZeRmZkJk8mErKwsFBQUNFl+6dKl6N69O6KiopCRkYGpU6fCZrO1RVWJiIgozAU9vOTl5WHatGmYNWsWtm/fjr59+yInJwclJSU+y7/33nuYMWMGZs2ahd27d+P1119HXl4e/vrXvwa7qkRERKQCQQ8vixcvxoQJE5Cbm4tevXph2bJliI6OxsqVK32W//bbbzFkyBDceuutyMzMxPDhw3HLLbc021pDRERE7UNQw4vD4cC2bduQnZ19+gU1GmRnZyM/P9/nPhdffDG2bdumhJUDBw7g888/xzXXXOOzvN1uh9Vq9XoQERFR5NIF8+ClpaVwu91ISUnx2p6SkoI9e/b43OfWW29FaWkpLrnkEggh4HK5cN999zV622j+/PmYM2dOwOseKjVON6IM2lBXg4iIKGyF3WijDRs2YN68eXjllVewfft2rFq1Cp999hmefPJJn+VnzpyJ8vJy5XH06NE2rnFgVdpcoa4CERFRWAtqy0tSUhK0Wi2Ki4u9thcXFyM1NdXnPo8//jjGjx+Pe+65BwDQp08fVFVVYeLEifjb3/4GjcY7bxmNRhiNxuC8gRCocjC8EBERNSWoLS8GgwEDBgzA2rVrlW2yLGPt2rUYPHiwz32qq6sbBBSttvY2ihAieJUlIiIiVQhqywsATJs2DXfccQcGDhyIQYMGYenSpaiqqkJubi4A4Pbbb0enTp0wf/58AMB1112HxYsXo3///sjKysKvv/6Kxx9/HNddd50SYiKZWxaosDkRZ9KHuipERERhKejhZcyYMTh+/DieeOIJFBUVoV+/fli9erXSiffIkSNeLS2PPfYYJEnCY489ht9//x0dO3bEddddh6effjrYVQ0LQgDVDjfDCxERUSMkEWH3YqxWKywWC8rLy2E2mwN6bKdbxp5jFQE9pi/JZiNSzKagvw4REVG4aMn1O+xGGxERERE1heGFiIiIVIXhhYiIiFSF4YWIiIhUheGFiIiIVIXhhYiIiFSF4YWIiIhUheGFiIiIVIXhhYiIiFSF4YWIiIhUheGFiIiIVIXhhYiIiFSF4YWIiIhUheGFiIiIVIXhhYiIiFSF4YWIiIhUheGFiIiIVIXhhYiIiFSF4YWIiIhUheGFiIiIVIXhhYiIiFSF4YWIiIhUheGFiIiIVIXhhYiIiFSF4YWIiIhUheGFiIiIVIXhhYiIiFSF4YWIiIhUheGFiIiIVIXhhYiIiFSF4YWIiIhUheGFiIiIVIXhhYiIiFSF4YWIiIhUpU3Cy8svv4zMzEyYTCZkZWWhoKCgyfJlZWWYNGkS0tLSYDQacd555+Hzzz9vi6oSERFRmNMF+wXy8vIwbdo0LFu2DFlZWVi6dClycnKwd+9eJCcnNyjvcDhw1VVXITk5GR999BE6deqEw4cPIz4+PthVJSIiIhWQhBAimC+QlZWFiy66CC+99BIAQJZlZGRk4MEHH8SMGTMalF+2bBmee+457NmzB3q9vsWvZ7VaYbFYUF5eDrPZ3Or61+V0y9hzrCKgx/Ql2WxEitkU9NchIiIKFy25fgf1tpHD4cC2bduQnZ19+gU1GmRnZyM/P9/nPv/6178wePBgTJo0CSkpKTj//PMxb948uN1un+XtdjusVqvXg4iIiCJXUMNLaWkp3G43UlJSvLanpKSgqKjI5z4HDhzARx99BLfbjc8//xyPP/44Fi1ahKeeespn+fnz58NisSiPjIyMgL8PIiIiCh9hN9pIlmUkJydj+fLlGDBgAMaMGYO//e1vWLZsmc/yM2fORHl5ufI4evRoG9eYiIiI2lJQO+wmJSVBq9WiuLjYa3txcTFSU1N97pOWlga9Xg+tVqts69mzJ4qKiuBwOGAwGLzKG41GGI3GwFeeiIiIwlJQW14MBgMGDBiAtWvXKttkWcbatWsxePBgn/sMGTIEv/76K2RZVrb98ssvSEtLaxBciIiIqP0J+m2jadOmYcWKFXjrrbewe/du3H///aiqqkJubi4A4Pbbb8fMmTOV8vfffz9OnjyJhx9+GL/88gs+++wzzJs3D5MmTQp2VYmIiEgFgj7Py5gxY3D8+HE88cQTKCoqQr9+/bB69WqlE++RI0eg0ZzOUBkZGfjyyy8xdepUXHDBBejUqRMefvhhTJ8+PdhVJSIiIhUI+jwvbY3zvBAREalP2MzzQkRERBRoDC9ERESkKgwvREREpCoML0RERKQqDC9ERESkKgwvREREpCoML0RERKQqDC9ERESkKgwvREREpCoML0RERKQqDC9ERESkKgwvREREpCoML0RERKQqDC9ERESkKgwvREREpCoML0RERKQqDC9ERESkKgwvREREpCoML0RERKQqDC9ERESkKgwvREREpCoML0RERKQqDC9ERESkKgwvREREpCoML0RERKQqDC9ERESkKgwvREREpCoML0RERKQqDC9ERESkKrpQV4CoJWRZ4NfjlUiINkCvlRBn0kOrkUJdLSIiakMML6Qq1U437E4ZReW2P7bUIC3ehKRYY0jrRUREbYfhhVTlVJWjwbZjZTYUlduQGGNAmsUESWJLDBFRJGN4IdVwumWU1zh9PicEcKLSgWqHG2aTDh3jjE2GGCEEQw4RkUq1SYfdl19+GZmZmTCZTMjKykJBQYFf+73//vuQJAk33nhjcCtIquBwyRCi6TI1DjeKrXb8VGjFiUo7KmwNw86RE9XYV1KJA8crcfRkNSrtriDVmIiIgiHo4SUvLw/Tpk3DrFmzsH37dvTt2xc5OTkoKSlpcr9Dhw7hL3/5Cy699NJgV5FUwt1ccqlDCKCwzIbDJ6pRVu2A0y3D6ZZx9GQ1ymucsDtlVNndKKt24vCJKrjcchBrTkREgRT08LJ48WJMmDABubm56NWrF5YtW4bo6GisXLmy0X3cbjfGjRuHOXPmoGvXrsGuIqlE1Rm0kAgBHD1Zgz3HKrC3qAJl1Q1bYmQZOFhaBbvLHYhqEhFRkAU1vDgcDmzbtg3Z2dmnX1CjQXZ2NvLz8xvdb+7cuUhOTsbdd9/d7GvY7XZYrVavB0WeYqsNJyobdtZtiaYabmxOGYVltsYLEBFR2AhqeCktLYXb7UZKSorX9pSUFBQVFfnc57///S9ef/11rFixwq/XmD9/PiwWi/LIyMhodb0p/JRW2pvt79Ja1Q4XHC7ePiIiCndhNcNuRUUFxo8fjxUrViApKcmvfWbOnIny8nLlcfTo0SDXktrasfIayG2QKWQZ2FdSgUOlVTjpY0g2ERGFh6AOlU5KSoJWq0VxcbHX9uLiYqSmpjYov3//fhw6dAjXXXedsk3+46ql0+mwd+9edOvWzWsfo9EIo5ETlEUKtyzgkmWUVzvhaWipsLXdaCBZrn29CpsL1honMhKjOYMvEVGYCWp4MRgMGDBgANauXasMd5ZlGWvXrsXkyZMblO/Rowd++OEHr22PPfYYKioq8Pzzz/OWUDtwrLwG5TXONmlpaU6FzQWXLEOr0Ya6KkREVEfQJ6mbNm0a7rjjDgwcOBCDBg3C0qVLUVVVhdzcXADA7bffjk6dOmH+/PkwmUw4//zzvfaPj48HgAbbKfLUzsviCovg4lFhc8EYy/BCRBROgh5exowZg+PHj+OJJ55AUVER+vXrh9WrVyudeI8cOQKNJqy63lCInKp2wOUOcq/cFiqvcXLdJCKiMCMJEewxHG3LarXCYrGgvLwcZrM5oMd2umXsOVYR0GP6kmw2IsVsCvrrhJufCsvDqtXFIzHWgDSzCRr2fSEiCpqWXL+5thFRM05WOmCtccIcpUeUXosYoxZGHW8lERGFCsMLhQVZFkGfx6U1XG6Bk39MkqfXSegUH4U4kz7EtSIiap8YXigsVNhcYR1e6nK6BH47VQOgBkJAGUodbdAiIzE6tJUjImoHGF4oLJTVqGtSuLodi92yUP57qsoBS5Se/WOIiIKIw3woLNgjYFp+t1zbIlNSYecq1UREQcTwQmEh3IZIt8bxCjsOnagKdTWIiCIWwwuFnMstQ1ZLhxc/1Thk7D7GFc6JiIKB4YVCrtrpVk1n3ZZwywKHSqtQXuMMdVWIiCIKwwuFXFlVZF7chagdRVXtaLuFJYmI2gOGFwopl1uG1RaZ4YWIiIKDQ6WpzQkhaocVVztRXuOMyFtGdTldEf4GiYjaGMMLtblKuwuHSqtDXY02U17jxM+FVnTtGAONJEGnkTgPDBFRKzC8UJuqdrhQWGYLdTXanFsW2FdcCQA4q0M0LFFcWoCI6EwxvASQEMKvydaMOg0kqX39y7vK7kKx1YZqR2SOLCIiorbD8BIgQghM/+cu7C6qaLZszzQznhnZp10FmCKrDdV2d6irERasNU443TKSYo2hrgoRkSoxvPhJCIFqhws2p+8LsM3p9iu4AMDuY1aU1zhhidK3mwAjy2xu8SirdqLC5mJ4IYog+49XtptWZaNOE/JFaBle/FTjdKPvnDV+lX37rkEw6bUNttucboxfWQAAGL+yoF22wFAttyyUGXijDVp06RAT4hoRUWvUtKtb4qF/owwvAdYzzdxoi4pRp0HPNLNy0dp9zAq7S/YZdCjyedZzqnG6cay8RtmulSQkm02hqhaRT8cr7I22PBPaUXAJDwwvforSa/H9rKvwS1Flk+Wa6owrSRKeGdkH5TVOpQXG88egPXbipVpOl0BphUP5WZJqV9nWaiSkx0eFsGYUCgdLq8JyVXKHW4YcftWidorhxU+SJCHaoGt1K4kkSV7H8IQY3kIiDyFq+8XodRLSwfDS3thdbk5sSNQMLg8QAp7bR3V5biEReThdAhVcOoGIqAG2vISA5/aR3SV7deIlqq+00gFJkhBr5K9qMBVbbXCEyT8ePH2hiKhx/IsYIvVvHwHs8EUNVdpciDFqYdRpoNeyobS+YqsNxyvsrT4Of/eI1IV/DcPIjFW7IPhXlOopLrdjz7EKHD1Zjd9OVXMV7jqECMyDiNSFLS8hZtRp0DUpBgdKq3CgtIr9XqhRZdW1ocXmlFFpcynbU82miFro0S0Lv0O8zORB1C4xvISYJElYMPICjF6eD4D/CqTm1TjcqHGcnm/jZJUDnkFqOo0G3VPjQlSzwDhYWokaB0M8ETWOt43CQN3R0bx1RC0lBCDLtQ+nW8bB0iocPVkd6moREQUNw0sY8Nw6AoADpVWwOfmvTjozQtR28rXanFxPiogiFsNLGPDcOqLmCSFgc7qVB1upfJNloKJOv5hw5XTLOFZe4/VwcqgwETWDfV7CBCfWbZ4QAtP/uctr9e6uSTFYMqYfNDyBDRSW16CsxoHkuNPrJOm0kteQa0/4C9XMzm7Ze2kEIiJ/MLyQaticsldwAWpvs93/zjYsHdMfksQ1oupyuQWsNS5Ya06vx1X/1Hgarox6DQx/hBpLlB4JMYYzes3GRgrZXTKKrDblZ40kQaeROFqIiM4IwwupghACM1btUn7++12DMOOfu1BYbkNhuU0ZrcWWmKY1lhXsThn2P/paVdpdStCIM+kQYzj9Z8Ko1yDa0PifjV+KKzhDLBEFHcMLqYLdJeNAaRWA2oASH6XHq7cNwNS8ncp2wLslxqRv2AojhPA5lw5bbE4T4vQU9aeqnDhV5XtSPKNeg8R6LTRsSSGitsDwQqqzYOQFkCQJEoClY/rB7pIhBDAlb4dXS0zXpBgsGHmBEmJkIRqEHQ+22LSc3SnjWJmt+YJERAHWJqONXn75ZWRmZsJkMiErKwsFBY0vRLhixQpceumlSEhIQEJCArKzs5ssT+1D3X/Q180XnjWiogxavHrbAGXIOVDbCjN6eT6m5O1EtcOF+9/Z5jO4eMre/842thwQEalA0MNLXl4epk2bhlmzZmH79u3o27cvcnJyUFJS4rP8hg0bcMstt2D9+vXIz89HRkYGhg8fjt9//z3YVaUwJQuBKXk7mi2nkSQsHdMPH0wc3CDEjFn+HQrLa1sJ0i0mfDBxMD68dzA+mDgY6Zba0TiF5TZMzdvJ4ddERGEu6OFl8eLFmDBhAnJzc9GrVy8sW7YM0dHRWLlypc/y7777Lh544AH069cPPXr0wGuvvQZZlrF27dpgV5XCkPjjVo8neHRNioFR1/jXVpIkRBm0PkOMZ/9XbxuAKIPWq8XGE2AOlFahvMbJAENEFMaC2ufF4XBg27ZtmDlzprJNo9EgOzsb+fn5fh2juroaTqcTiYmJPp+32+2w2+3Kz1artXWVprBic57uqJtuMWHJmH5+daytG2LqdtD11TG3tsWmvzJiafzKggb9ZYiIKHwEteWltLQUbrcbKSkpXttTUlJQVFTk1zGmT5+O9PR0ZGdn+3x+/vz5sFgsyiMjI6PV9abQ8syiW+Nwe90uWjqmf4s71Hr6xHgejQURk16Dnmlm5ee6/WXYD4aIKLyE9WijBQsW4P3338eGDRtgMpl8lpk5cyamTZum/Gy1WlUfYATa78WysRFBXZNiYNIHL2tLkoRnRvaBzSljxqpdyusfKK3C1LydWOpniw8REQVfUFtekpKSoNVqUVxc7LW9uLgYqampTe67cOFCLFiwAF999RUuuKDxdX+MRiPMZrPXQ+3ufXtbu+xzIQvhc0SQZxhzsMND/f4ydfvB+JobhoiIQiOo4cVgMGDAgAFenW09nW8HDx7c6H7PPvssnnzySaxevRoDBw4MZhXDRt2VpX8prkSN0x3iGjVUYfM9WVkgeIKLrxFBS9t4/pXTIaa/so2LQBIRhY+gjzaaNm0aVqxYgbfeegu7d+/G/fffj6qqKuTm5gIAbr/9dq8Ovc888wwef/xxrFy5EpmZmSgqKkJRUREqKysbe4mIoIaVpSvtwVmluP6IonSLyWtEUKhu19R92fErCzAlbydqHAwxREShFvQ+L2PGjMHx48fxxBNPoKioCP369cPq1auVTrxHjhyBRnM6Q7366qtwOBy46aabvI4za9YszJ49O9jVDalw71JR4whOa1Ddqf89wSUcZro16mo78e4+VjuCzdOJl7PxEhGFVpt02J08eTImT57s87kNGzZ4/Xzo0KHgV0gFwvEf9w538Pt9nMmIomBpqhNv3ZWs6+IaSUREwRfWo43as5uX5eOzhy5pdxfCcHu7dTvx2pxyg/WT6mOrDBFR8LXJ2kbkn7qddn8+Zg3LTrvBEI6tTPV5Qkz99ZPq4xpJRETBx5aXMOLptOvrX/SRSgiBGat2hboafvOsn1R/6HT9Va3vf2db2PTdISKKNGx5CTPheK2zOd347VQ1XO7AtybU7azb3LpF4aL+rL2+1kjyBBi2wBARBV74Xyko5Eor7ThV5Qz67Z0FIy9QdR8fjSQxwBARtQGGF2qS0y2jrDp4k9PVpeLcomCAISIKPoYXapTLLeNQaVWrW1yEEHA2csspEq/pvgLM1LydnNyOiChAGF6oUVV2N2zO1s/t4nQLuOWGF261ddZtifoB5kBpVUDOJRERMbxQE6qdgVkO4ESV3ed2m1N9nXVbonZk0un1kWas2sXWFyKiAIisqwUFjM3pRmmFo9XHsbvcKKt2QggBm9OtPGocbkzJ26GUU3tn3caY9Kfn7jlQWoXyGicDDBFRK3GeF/IpUOsYVdpccLhkTM3bqbSy1Nc1KQYmfWTm6Ppz94xfWYCuSTFYMPICmPRcSoCI6ExE5hWDWs0pB6Z/hj/BZcmYfhF9ETfpaxd49PAs8Dh91Q9shSEiOgNseSGfAjEhncsto6TS7rVidP3FDNvDQoaNLfC4+5gV5TVOmPTadnEeiIgCheGFfLIFYF0lm0uGo84Im6Vj+iPKoG31cdWo7gKP5TVOjF9ZAADKfz23kjz5hWGGiKhxDC/UgMMlw+VjaHN9siyg0TR+ga12eI9W4rW4NsRYovTomWbG7mNWZbvnVpIH+8UQETWO4YUaOFZeA3szc5I43TKKrTZ0io9qcHEVQqDC7sKJytaPVopEnttIdpcMIeB1K8nDE2Z6ppnxzMg+DDBERHUwvJAXl1tGha35+V2q7C6cqnLC6RZIs5hg0tfeDnK6ZZRU2FFe7fQ5MR3V8izuCMBrler6YWb3MStsTrnd3m4jIvKFo43IS5XD7deU/VV/DKWutLlw4HgV3LKAyy3j8IlqnKx0MLi0QN1Vqj39Yt6+a5DyPCe3IyLyxvASxkIRAKw1zS/CWF7txKmq07eE3LJAkdWGfSWVAZsfpj3z9Ivh5HZERL4xvISxtlrN2UMIgUp707eMahxuHD1V3aB15mSlIyDDq6mWZ3I7j/ErCzAlbydqHG6vmYptTjdDDRG1O+zzEsbkNm55sbvkZgPIwQCsMk3+8Uxu5xmVVH9EkgeHWRNRe8PwEsZk0fxw5EBqboQREJpbWe1VY5Pb1cdh1kShJYRQOt23C5JAtcOFKL02ZH9jGF7CWGmlHQICqRYTog3B/6hsLvZXCTd1J7er/8eRw6yJQk8Igen/3IXdRRWhrkqb+3luTptcm3xheAlzVfba1Z3P6hD8j8qfIdItxVtMgVF3aHVdzQ2ztrtkn/sRtUS7a1loAZvT3S6DS6gxvKhAhd0Ju8sNoy54FyGXWw74SCEhBGas2hXQY5K3+qGm/vIDNqebfWBUJBxDQmMtfNTQ23cNahf/WDAZNOjWMRZRIXyvDC8qIMvA0ZM16JwQFbRfjJoArGVUn80pK3/wuibFwKjj4LZgqx9mxq8sYIdelWjPtx8iQc80MyxR+nbxuxWl14TsdpEHw4tK1Djc2H+8El06xCDWGPiPzeZHZ926PP9CbOxCWL/VpfbiGfm/1OHAqGt6lFL9MOPZR22fTzi2UrRGuN9+8PW9odPU+DukZgwvKiLLwG+nqpESZ0JCjCFgx3W4ZJyosvtfDyEwNW8nDpRWoWtSDJaM6QdNvV/a+q0uJj1bXdpKc6OUfA25DpfWGX8DSaTfygjH2w+8OFM4YXhRGadL4LdTNThV7UCnhKgz7gcjywKnqh0wR+lxrLwGTpd/PWtFneAC1F4I739nG169bQA0kgQhBGxOGVPydij7sNWl7fkapdTUBT8chlvztkmt9nT7gehMMbyoVJXdjRKrHRmJ0We0v1OWUVhmw7FyW4tGBNVtUfEoLLdhat5OLBnTzyvYAMFrdYmP1kMWAtaawI+QiiS+OvTWbdlobrh1S24xtfQ2jqcPlGefM7ltEom3MtjCQdQ8hhcVK6t2Ij7aiTiTvsX7Our8a9xfshBeLSp5E/+EqXk7UVhuw4HSKuS+uQUn66x55LmlFOg/xClmIzrGGSEEUBHl8rlcAfnma8h1U60z/txi8rWfP87+Y+2mgz728fe2CS/0RO0Tw4vK1TjcLQ4vNqcbpZWO5gvW4bldVFhuA1B7AYvSa7F0TH/l4uYJLukWE5aO6R+UWw7RRi06xhkhSRIkCbBE6+GSTSgsswX0ddoTX60zTc3q29gyBS3lK7QAvG1CRM1jeFE5h7vloy2OV9hR2cIJ6eyu07eL0i0mpUWl/vo7jXXgDQSNBshIiG5wUesQa0RijAElFXaUWP3veOw5DFttvDU2q68/rSv+3Mapf5xw6SxMROrB8KJy/vYxEEJAkiScrHKgvKZ1q1UvHdNfCSeekS2eegTzwmPUaWBoZK4YSZKQYjbBLQucaKZVyRKlh1GvQVKsERqp9vbbb6dqglFlVWvuFpMv/n7+dY/DsEJELdUm41dffvllZGZmwmQyISsrCwUFBU2W//DDD9GjRw+YTCb06dMHn3/+eVtUU5X8bTU4UFqFonIbilrYQdeX+tcZz0XOFORFurSa5r+u6fFR6NPZgmSzEXqdBJ3Wuz6SBKTFm5BiNkGrkSBJEhJiDNDrePH0R93P2tfD38+/rb4zRBSZgh5e8vLyMG3aNMyaNQvbt29H3759kZOTg5KSEp/lv/32W9xyyy24++67sWPHDtx444248cYb8eOPPwa7qqpkc7qbXenZ7nKj2u7G8Qq7qleFjjH4Pyw8xWxCj1QzuqfEIcVihCVKjyiDBt1T46DXNvzad+sYizgTGyKJiNQg6OFl8eLFmDBhAnJzc9GrVy8sW7YM0dHRWLlypc/yzz//PEaMGIFHH30UPXv2xJNPPokLL7wQL730UrCrqkpCABW2xm8DCSFw5ER1G9YoOOJMOnSINbZ4P41GQnKcCWd1iMY5yb6DCwDotRqkWkytrSYREbWBoIYXh8OBbdu2ITs7+/QLajTIzs5Gfr7v0Qr5+fle5QEgJyen0fJ2ux1Wq9Xr0d40NddJtcPd4qn/w41WIyE9PgpaTXBvL5j0WpyXGouzOkQjLd7UaP8aIiIKraD+dS4tLYXb7UZKSorX9pSUFBQVFfncp6ioqEXl58+fD4vFojwyMjICU3kVqbS7Gr0dFAlrv3SKj2qzIGHUaWGJ0iMp1oguf4QYIiIKL6r/p+XMmTNRXl6uPI4ePRrqKrU5tyxQaW/Y+iLLQrV9XAw6DeJMOsRH6xHVgr4ugWTSa5EUa+S6TEREYSaoPRSTkpKg1WpRXFzstb24uBipqak+90lNTW1ReaPRCKOx5X0hIk2l3QVLlPdkdTaXG8XW4EzeZtRroNVIqLa7/SrrlgVcbv+CVHy0Hp3io6AJ8m0if4VLPYiIqFZQ/0lpMBgwYMAArF27VtkmyzLWrl2LwYMH+9xn8ODBXuUBYM2aNY2Wp1qVNhdKrDZl2n+gdhHHYEzAZtBpkJEQjTQ/OrhqNEDnhCjo/AwAMUYtMhKjwyowZHaICXUViIiojqCPDZ02bRruuOMODBw4EIMGDcLSpUtRVVWF3NxcAMDtt9+OTp06Yf78+QCAhx9+GEOHDsWiRYtw7bXX4v3338fWrVuxfPnyYFdV1RwuGcVWO4w6LQw6DRwuGccr/Z9ttiWSYo3KrZxksxGllXbIPrrWeIJIYyN8fImPNgSqmgGj1UjokRZXOzOx3QW7yjtAExGpXdDDy5gxY3D8+HE88cQTKCoqQr9+/bB69WqlU+6RI0egqTP52MUXX4z33nsPjz32GP7617/i3HPPxSeffILzzz8/2FWNCFUOFyzRepRW2lHjaP6WzplIiDl9eyrFbIJBq/GaoVaSgA6xBqRZolp0XEkCokPUv6U5eq0G6fFREELgt1M1KKtu3SzFRER05tpkVq7Jkydj8uTJPp/bsGFDg20333wzbr755iDXKjKVVTuRGGNAjTM4wcWX+Gg9jpXbICAQbdChQ6wBMYaWf7U6xUf5tZJwKEmShGSzETqtVHtbDgI2pwxZ1HaO5jpJRETBxylFI4xbFii22oLW6uKLJEk4NyUWGkmCWxZnPKw5VKOKWsqo0/psVSoqt+F4RXBu1RER0WkMLxGowuZq8xYAT7+W1kwk15K+MeEoOa52WHVhmU21Q9SJiNSA4SUCBSO4BDsMSRIQPuOLzoxGIykdjousNjhdDDBERMGg7n/qUpsQQmDGql1BfY1ogzashke3Rny0AV0SYzi5HRFRkPCvKzXL7pJxoLQKANArzYyoIHSqTQjDIdKtEWXQomvH2FBXg4goIjG8UIt8eN9gSFLgW0jU0lm3JbQaCeelxkLD3zIiooDin1VqkSDkFgDwewZetTHqtEiO4+KORESBxPBCYUGn8pFGTTFH6Vo1CouIiLxF7hWDAibYI40i8ZZRXUadFr3SzQwwREQBwqHS1CRZCEzJ29Hq40QbdUiIqV1d+niF3SsQpfqxwGMkyEyKhiyA0go7qh1uzgVDRHSGGF6oUUIITM3bicJyGwCga1LMGY80SreYlI6+Wo2EEqsdWo2E+Gg9osN8SYBAif5jyYRYow4ut4zdxypCXCMiInVieKFG2Zynh0inW0xYMqbfGY80qrtfUqwRSbHGgNRRrTSShI5xtefA6Za50CMRUQswvJBP9SemWzqmPzTBGmrUDmk0knK7zOGSvdaDcssCJyodoaoaEVHYY3ghn+pOTNc1ibPFBpNBp0GK+XS/HyEEzFF6uNwyjp6sCWHNiIjCE8ML+VS3Q+2CkRcEZWI68k2SJKVfjE4rweVmx14iorr4z2lqoP4tI+aW0NBpNejaMQaWKH2oq0JEFFYYXqiB+reMjDp+TULFqNNCq2V6JCKqi1claoC3jMJLQrQenROioNNKkCS2hBERsc8LeeEto/ATbdAh2gAkxNSuvC3LAnKdhHnoRBVcdSa8c7rYR4aIIhvDC3nhLaPwp9FI0OB0qjwnOU75f05+R0TtAa9M1CjeMlIfSZJgjtIh1sR/lxBR5GJ4oUYxt6iPViOhS4cYnJUYzc+PiCIWwwt5CfYK0tQ2tBoJei1/vYkoMvGvGynqd9YldeuUEIUUS/teQ4qIIhNvjJOCnXUjS6zR8+ttD2k9iIgCjeGFfGJn3cgQa9Th/E5m5edKuwsVNhcAoMrugs0ph6pqRERnjOGFfGJuiRx1Q2icSY84U+1yA7Is4OniVFJhQ2kFV7ImInVgeCEFO+u2LxrN6VCTGGNQQk15jRMnKxlkiCh8MbyEsbYME+ys274ZdVp4usjUONyhrQwRUTPYIzOMzVi1C6KNEgw765JHxzgj+nS2oE9nC5LiDKGuDhFRA2x5CTNGnQZdk2JwoLQKB0qrYHfJMOm1QX9dLsZIviREGxBV5/tXaXfhVJUzhDUiImJ4CTuSJGHByAswenl+m70mF2Okxpj0Wq/wHG3QwRxV2zfG5nSjxOo9DFvTyJdHQEDmwCaKYNHG4P8jM1yEQ8s8w0sYauvwwFtG5C+DTgPDH98Ps0mP5DiTX/sJIeCus/J1pd2FoydrglJHolDomhTDFus2FNTwcvLkSTz44IP497//DY1Gg1GjRuH5559HbGxso+VnzZqFr776CkeOHEHHjh1x44034sknn4TFYglmVekPvGVEwSBJEnTa09+rKIMWafEmyELA5vBukrG53F5BpylC1LbqtJYQHG3XnGSzsU1uYasV/262raCGl3HjxuHYsWNYs2YNnE4ncnNzMXHiRLz33ns+yxcWFqKwsBALFy5Er169cPjwYdx3330oLCzERx99FMyqtmt1/2jz94/aglGnhTE2fC6ENQ435DBJLyUVdr/DW1uKM+kQbWBjPYWHoH0Td+/ejdWrV2PLli0YOHAgAODFF1/ENddcg4ULFyI9Pb3BPueffz7++c9/Kj9369YNTz/9NG677Ta4XC7odO3vF8fmdMOo0wQ81QshYHfJMOg0mJK3I6DHJlKbKEP4BKmzje3v7xxRSwXttyQ/Px/x8fFKcAGA7OxsaDQabN68GX/+85/9Ok55eTnMZnOjwcVut8NuP91p0Gq1tq7iYWb8ygL0TDPjmZF9AhJghBCwOWXMWLVL6efiwf4uRESkBkG7UhUVFSE5Odlrm06nQ2JiIoqKivw6RmlpKZ588klMnDix0TLz58+HxWJRHhkZGa2qdzgw6jTomXZ6PZrdx6wBWYNGCIHp/9yF0cvzGwSXdIsJS8b0431bIiIKey0OLzNmzIAkSU0+9uzZ0+qKWa1WXHvttejVqxdmz57daLmZM2eivLxceRw9erTVrx1qkiThmZF98PZdg5RtgZiwzuaUsbuoosH2rkkxePW2AY0OcyUiIgonLb5t9Mgjj+DOO+9sskzXrl2RmpqKkpISr+0ulwsnT55Eampqk/tXVFRgxIgRiIuLw8cffwy9Xt9oWaPRCKPR6Hf91UKSJFii9AGZsM5zq6hu35a37xoEc5QeDpcclD41REREwdLi8NKxY0d07Nix2XKDBw9GWVkZtm3bhgEDBgAA1q1bB1mWkZWV1eh+VqsVOTk5MBqN+Ne//gWTyb95JCJR/QnrzqTzrudWUd0Wl65JMbBE6SFJEoc+EhGR6gStz0vPnj0xYsQITJgwAQUFBdi0aRMmT56MsWPHKiONfv/9d/To0QMFBQUAaoPL8OHDUVVVhddffx1WqxVFRUUoKiqC290+F4urm1PGryzA9FU/tOj2Uf1bRV2TYti3hYiIVC2oY/LeffddTJ48GcOGDVMmqXvhhReU551OJ/bu3Yvq6moAwPbt27F582YAwDnnnON1rIMHDyIzMzOY1Q1Lns67u4/VjqLafcyK8hqn0nLSlPrT/r991yC/9iMiIgpnkmirZYvbiNVqhcViUYZYB5LTLWPPsYYdXoNNCIHyGifGryxQtnlaUJrqZGtzunHz/+Yr5ZcGoMWlT2fOdExERIHXkus3J/VQAU/n3brDpw+UVuH+d7b5PSsop/0nIqJIwfCiEp7h0x9MHIx0S20n5sJyG6bm7Wy0Dwyn/SciokjE8KIikiQhyqDFq7cNUALMgdIqnxPY1e/vQkREFCkYXlRII0lYOqa/8rOvCezsLlmZRZfT/hMRUSThFU2lTHoNuibFAIAygV1j2N+FiIgiCcOLSnkmsPOvbJArQ0RE1IYYXlSMoYSIiNojhpcIEVmz9RARETWO4SVCBGLVaSIiIjVgeFExo87/TrtERESRguFFxVrSaZeIiChSMLyoXN1Ou8G+axRt1Ab3BYiIiPzA8BJBgt3vpUOMIWjHJiIi8pcu1BVQE40koUNs0xfwaIMWhmZmsz1wvCpgrSSefi8HSquUpQKiDMFpITHq2PJCREShx/DSAlqNhPT4qFYfp0OsAbIAahwu1Dha18nW0+9l9PJ8ALWtL0vH9AvKLSSthhPLEBFR6PG2UQikWaLQKT4KMcbAZMf6SwXYnDIXZSQioojF8BIB6o86sjndAV+UMcaohY4tL0REFAYYXkJII0nQaqSA3I6pO+ro9jcKlP8PxKKMkgSclRgNDcMLERGFAYaXEEoxm9Ar3YyeaXGtPlbdCes8uibFwKRv/Ucca9RBp+VXhYiIwgOvSBHC14R180f2aXWrCwDER+tbfQwiIqJAYXiJICa9BukWk/KzI0DLBQQiABEREQUKw0sEkSQJC0YFdrmAOJMOcQEaFUVERBQIvCpFGEvU6Vs85qjW3e7RaiRk1utHQ0REFGoMLxFGI0n4dNIQ5f9bI1gz9RIREbUGbxuFAUmScHbHwLVwaCSp1cEFAMwmZlsiIgo/DC9hItymUNHrJK9bUEREROGC4SVMaDVSQOZkCRTO7UJEROGKV6cwYdRpkRDT9IrVbcnA4EJERGGKV6gwEog1iAJFz/BCRERhileoMGLUhc/oHn0YBSkiIqK6eIUKI3pt+PTa1XJWXSIiClMML2FEkiRowuQTYXYhIqJwFSaXSvIIh34vksQ+L0REFL54haIGdFoJ2nCbeIaIiOgPQQ0vJ0+exLhx42A2mxEfH4+7774blZWVfu0rhMDVV18NSZLwySefBLOaVI/ZxMnpiIgofAU1vIwbNw4//fQT1qxZg//85z/YuHEjJk6c6Ne+S5cuhcSOFyERy2UBiIgojAXtKrV7926sXr0aW7ZswcCBAwEAL774Iq655hosXLgQ6enpje67c+dOLFq0CFu3bkVaWlqwqkiNiDEwvBARUfgKWstLfn4+4uPjleACANnZ2dBoNNi8eXOj+1VXV+PWW2/Fyy+/jNTU1GZfx263w2q1ej3ozEkS2N+FiIjCWtDCS1FREZKTk7226XQ6JCYmoqioqNH9pk6diosvvhg33HCDX68zf/58WCwW5ZGRkdGqerd3ujCaa4aIiMiXFoeXGTNmQJKkJh979uw5o8r861//wrp167B06VK/95k5cybKy8uVx9GjR8/otalWlD58ZvklIiLypcWdGx555BHceeedTZbp2rUrUlNTUVJS4rXd5XLh5MmTjd4OWrduHfbv34/4+Hiv7aNGjcKll16KDRs2NNjHaDTCaDS25C1QE6LZ34WIiMJci69UHTt2RMeOHZstN3jwYJSVlWHbtm0YMGAAgNpwIssysrKyfO4zY8YM3HPPPV7b+vTpgyVLluC6665raVWphbQaCYlhtLI1ERGRL0H7Z3bPnj0xYsQITJgwAcuWLYPT6cTkyZMxduxYZaTR77//jmHDhuHvf/87Bg0ahNTUVJ+tMmeddRbOPvvsYFWV/hBn0rGzLhERhb2gzvPy7rvvokePHhg2bBiuueYaXHLJJVi+fLnyvNPpxN69e1FdXR3MaqiOXheaNY7MUZycjoiIwl9QOzgkJibivffea/T5zMxMCCGaPEZzz0cak14LvVaDCpsLNQ530F5HowFk+fTPOq2EOCP7uxARUfjj2kZhxtQGo32iDBp0jo9u8Loa3jIiIiIVYHgJM7F/tH7ogzjfSqolCpZoPeL+WAZAkoCOcRyxRURE6sD7BGHG02E2Sq+FtcYV8OMnxhqUgJSRGA2HS4YsBGJ4y4iIiFSCV6wwo5EkmHRauIPQ1yfaqEVKnRYWrUZClIGT0hERkbowvIQZrUaCJVqPakdgW11SzEYkxRrZr4WIiFSP4SVMaaTAhQyjXoNksylgxyMiIgoldtgNUya9FtHGwNzS6RjLzrhERBQ5GF7CWGwAOtEa9RpYOPkcERFFEIaXMGbSaRFlaN1HFGPUsZ8LERFFFIaXMGaJ1qNjrKlV6w3pGFyIiCjCMLyEOUu0vlXDmU06DoUmIqLIwvCiAgYdPyYiIiIPXhVVwDONPxERETG8qAJv/RAREZ3G8KICBp0GqRZOMkdERAQwvKhGUqwBnRKiEMCJd4mIiFSJnSlUQpIkJMYYoNdKOFZug90ph7pKREREIcHwojJxJj30Wg3sThlHTlaHujpERERtjreNVMik19ZOYBfHNYuIiKj9YXhRsRSzERp+gkRE1M7w0qdikiQh2sA7f0RE1L4wvKicmRPYERFRO8PwonImPSewIyKi9oXhhYiIiFSF4UXl9Fp+hERE1L7wyqdyeq0ErYbT7hIRUfvB8KJykiQhPlof6moQERG1GYaXCGCOYnghIqL2g+ElAph0Gi7YSERE7QbDSwTQaTWIMnDINBERtQ8MLxHC13wvkgSYoziJHRERRRaGlwhhidI3uHWk1UiQeD+JiIgiDMNLhIg16pAYY/DaxjlgiIgoEvHqFkFi661zZNLz4yUiosgTtKvbyZMnMW7cOJjNZsTHx+Puu+9GZWVls/vl5+fjyiuvRExMDMxmMy677DLU1NQEq5oRxWzyvnUUH21ovDAREZFKBS28jBs3Dj/99BPWrFmD//znP9i4cSMmTpzY5D75+fkYMWIEhg8fjoKCAmzZsgWTJ0+GRsMWBH9F/zHqSJJqbyURERFFGkkIIQJ90N27d6NXr17YsmULBg4cCABYvXo1rrnmGvz2229IT0/3ud+f/vQnXHXVVXjyySfP+LWtVissFgvKy8thNpvP+Dhq9dupapyqcqJjnBGpFlOoq0NEROSXlly/g9KkkZ+fj/j4eCW4AEB2djY0Gg02b97sc5+SkhJs3rwZycnJuPjii5GSkoKhQ4fiv//9b5OvZbfbYbVavR7tWVKsERpNw/4vREREkSIo4aWoqAjJycle23Q6HRITE1FUVORznwMHDgAAZs+ejQkTJmD16tW48MILMWzYMOzbt6/R15o/fz4sFovyyMjICNwbUSGTXouOcUaYdLzVRkREkalFV7gZM2ZAkqQmH3v27DmjisiyDAC49957kZubi/79+2PJkiXo3r07Vq5c2eh+M2fORHl5ufI4evToGb1+JDGb9NBxmDQREUWoFt1beOSRR3DnnXc2WaZr165ITU1FSUmJ13aXy4WTJ08iNTXV535paWkAgF69enlt79mzJ44cOdLo6xmNRhiNRj9q334YGFyIiCiCtSi8dOzYER07dmy23ODBg1FWVoZt27ZhwIABAIB169ZBlmVkZWX53CczMxPp6enYu3ev1/ZffvkFV199dUuq2e5pNJxVl4iIIldQ/ones2dPjBgxAhMmTEBBQQE2bdqEyZMnY+zYscpIo99//x09evRAQUEBAECSJDz66KN44YUX8NFHH+HXX3/F448/jj179uDuu+8ORjWJiIhIhYI2JOXdd9/F5MmTMWzYMGg0GowaNQovvPCC8rzT6cTevXtRXV2tbJsyZQpsNhumTp2KkydPom/fvlizZg26desWrGoSERGRygRlnpdQau/zvBAREalRyOd5ISIiIgoWhhciIiJSFYYXIiIiUhWGFyIiIlIVhhciIiJSFYYXIiIiUhWGFyIiIlIVhhciIiJSFYYXIiIiUhWGFyIiIlIVhhciIiJSFYYXIiIiUpWgrSodKp51Jq1Wa4hrQkRERP7yXLf9WS864sJLRUUFACAjIyPENSEiIqKWqqiogMViabKMJPyJOCoiyzIKCwsRFxcHSZJCXZ2wZLVakZGRgaNHjza77DjV4jlrOZ6zluH5ajmes5YJ9/MlhEBFRQXS09Oh0TTdqyXiWl40Gg06d+4c6mqogtlsDssvcDjjOWs5nrOW4flqOZ6zlgnn89Vci4sHO+wSERGRqjC8EBERkaowvLRDRqMRs2bNgtFoDHVVVIPnrOV4zlqG56vleM5aJpLOV8R12CUiIqLIxpYXIiIiUhWGFyIiIlIVhhciIiJSFYYXIiIiUhWGFxV6+eWXkZmZCZPJhKysLBQUFDRZ/sMPP0SPHj1gMpnQp08ffP75517PCyHwxBNPIC0tDVFRUcjOzsa+ffu8ymRmZkKSJK/HggULAv7egiXQ52zVqlUYPnw4OnToAEmSsHPnzgbHsNlsmDRpEjp06IDY2FiMGjUKxcXFgXxbQRWKc3b55Zc3+J7dd999gXxbQRPI8+V0OjF9+nT06dMHMTExSE9Px+23347CwkKvY5w8eRLjxo2D2WxGfHw87r77blRWVgbl/QVDKM4Z/5Z5/17Onj0bPXr0QExMDBISEpCdnY3Nmzd7lQnL75kgVXn//feFwWAQK1euFD/99JOYMGGCiI+PF8XFxT7Lb9q0SWi1WvHss8+Kn3/+WTz22GNCr9eLH374QSmzYMECYbFYxCeffCK+//57cf3114uzzz5b1NTUKGW6dOki5s6dK44dO6Y8Kisrg/5+AyEY5+zvf/+7mDNnjlixYoUAIHbs2NHgOPfdd5/IyMgQa9euFVu3bhV/+tOfxMUXXxystxlQoTpnQ4cOFRMmTPD6npWXlwfrbQZMoM9XWVmZyM7OFnl5eWLPnj0iPz9fDBo0SAwYMMDrOCNGjBB9+/YV3333nfjmm2/EOeecI2655Zagv99ACNU5498y79/Ld999V6xZs0bs379f/Pjjj+Luu+8WZrNZlJSUKGXC8XvG8KIygwYNEpMmTVJ+drvdIj09XcyfP99n+dGjR4trr73Wa1tWVpa49957hRBCyLIsUlNTxXPPPac8X1ZWJoxGo/jHP/6hbOvSpYtYsmRJAN9J2wn0Oavr4MGDPi/EZWVlQq/Xiw8//FDZtnv3bgFA5Ofnt+LdtI1QnDMhasPLww8/3Kq6h0Iwz5dHQUGBACAOHz4shBDi559/FgDEli1blDJffPGFkCRJ/P777615O20iFOdMCP4ta+6clZeXCwDi66+/FkKE7/eMt41UxOFwYNu2bcjOzla2aTQaZGdnIz8/3+c++fn5XuUBICcnRyl/8OBBFBUVeZWxWCzIyspqcMwFCxagQ4cO6N+/P5577jm4XK5AvbWgCcY588e2bdvgdDq9jtOjRw+cddZZLTpOKITqnHm8++67SEpKwvnnn4+ZM2eiurq6xcdoS211vsrLyyFJEuLj45VjxMfHY+DAgUqZ7OxsaDSaBs3+4SZU58yDf8t8l3c4HFi+fDksFgv69u2rHCMcv2cRtzBjJCstLYXb7UZKSorX9pSUFOzZs8fnPkVFRT7LFxUVKc97tjVWBgAeeughXHjhhUhMTMS3336LmTNn4tixY1i8eHGr31cwBeOc+aOoqAgGg6HBH82WHicUQnXOAODWW29Fly5dkJ6ejl27dmH69OnYu3cvVq1a1bI30Yba4nzZbDZMnz4dt9xyi7KgXlFREZKTk73K6XQ6JCYm8jsG3+cM4N8yX+fsP//5D8aOHYvq6mqkpaVhzZo1SEpKUo4Rjt8zhhfyy7Rp05T/v+CCC2AwGHDvvfdi/vz5ETHVNIWHiRMnKv/fp08fpKWlYdiwYdi/fz+6desWwpqFjtPpxOjRoyGEwKuvvhrq6qhCU+eMf8sauuKKK7Bz506UlpZixYoVGD16NDZv3twgtIQT3jZSkaSkJGi12gYjVoqLi5Gamupzn9TU1CbLe/7bkmMCQFZWFlwuFw4dOtTSt9GmgnHO/JGamgqHw4GysrJWHScUQnXOfMnKygIA/Prrr606TjAF83x5LsKHDx/GmjVrvFoQUlNTUVJS4lXe5XLh5MmT7fo71tQ584V/y4CYmBicc845+NOf/oTXX38dOp0Or7/+unKMcPyeMbyoiMFgwIABA7B27VplmyzLWLt2LQYPHuxzn8GDB3uVB4A1a9Yo5c8++2ykpqZ6lbFardi8eXOjxwSAnTt3QqPRhHUyB4JzzvwxYMAA6PV6r+Ps3bsXR44cadFxQiFU58wXz3DqtLS0Vh0nmIJ1vjwX4X379uHrr79Ghw4dGhyjrKwM27ZtU7atW7cOsiwroS9cheqc+cK/ZQ3Jsgy73a4cIyy/ZyHrKkxn5P333xdGo1G8+eab4ueffxYTJ04U8fHxoqioSAghxPjx48WMGTOU8ps2bRI6nU4sXLhQ7N69W8yaNcvnUOn4+Hjx6aefil27dokbbrjBa6j0t99+K5YsWSJ27twp9u/fL9555x3RsWNHcfvtt7ftmz9DwThnJ06cEDt27BCfffaZACDef/99sWPHDnHs2DGlzH333SfOOusssW7dOrF161YxePBgMXjw4LZ7460QinP266+/irlz54qtW7eKgwcPik8//VR07dpVXHbZZW375s9AoM+Xw+EQ119/vejcubPYuXOn17Beu92uHGfEiBGif//+YvPmzeK///2vOPfcc0M+hNVfoThn/Fvmfc4qKyvFzJkzRX5+vjh06JDYunWryM3NFUajUfz444/KccLxe8bwokIvvviiOOuss4TBYBCDBg0S3333nfLc0KFDxR133OFV/oMPPhDnnXeeMBgMonfv3uKzzz7zel6WZfH444+LlJQUYTQaxbBhw8TevXuV57dt2yaysrKExWIRJpNJ9OzZU8ybN0/YbLagvs9ACvQ5e+ONNwSABo9Zs2YpZWpqasQDDzwgEhISRHR0tPjzn//sFW7CXVufsyNHjojLLrtMJCYmCqPRKM455xzx6KOPqmKeFyECe748w8l9PdavX6+UO3HihLjllltEbGysMJvNIjc3V1RUVAT7rQZMW58z/i3zPmc1NTXiz3/+s0hPTxcGg0GkpaWJ66+/XhQUFHgdIxy/Z5IQQrRdOw8RERFR67DPCxEREakKwwsRERGpCsMLERERqQrDCxEREakKwwsRERGpCsMLERERqQrDCxEREakKwwsRhY0777wTN954Y6irQURhjuGFiNrcoUOHIEmSsnYREVFLMLwQERGRqjC8ELUDH330Efr06YOoqCh06NAB2dnZqKqqUm7TzJs3DykpKYiPj8fcuXPhcrnw6KOPIjExEZ07d8Ybb7zhdbwffvgBV155pXK8iRMnorKyUnlelmXMnTsXnTt3htFoRL9+/bB69Wrl+bPPPhsA0L9/f0iShMsvv9zr+AsXLkRaWho6dOiASZMmwel0Ks9lZmZi3rx5uOuuuxAXF4ezzjoLy5cv99r/6NGjGD16NOLj45GYmIgbbrgBhw4dUp7fsGEDBg0ahJiYGMTHx2PIkCE4fPgwAOD777/HFVdcgbi4OJjNZgwYMABbt25t9hwfPnwY1113HRISEhATE4PevXvj888/V57/8ccfcfXVVyM2NhYpKSkYP348SktLmz0uETXE8EIU4Y4dO4ZbbrkFd911F3bv3o0NGzZg5MiR8Cxrtm7dOhQWFmLjxo1YvHgxZs2ahf/5n/9BQkICNm/ejPvuuw/33nsvfvvtNwBAVVUVcnJykJCQgC1btuDDDz/E119/jcmTJyuv+fzzz2PRokVYuHAhdu3ahZycHFx//fXYt28fAKCgoAAA8PXXX+PYsWNYtWqVsu/69euxf/9+rF+/Hm+99RbefPNNvPnmm17vadGiRRg4cCB27NiBBx54APfffz/27t0LAHA6ncjJyUFcXBy++eYbbNq0CbGxsRgxYgQcDgdcLhduvPFGDB06FLt27UJ+fj4mTpwISZIAAOPGjUPnzp2xZcsWbNu2DTNmzIBer2/2PE+aNAl2ux0bN27EDz/8gGeeeQaxsbEAgLKyMlx55ZXo378/tm7ditWrV6O4uBijR48+k4+UiEK6LCQRBd22bdsEAHHo0KEGz91xxx2iS5cuwu12K9u6d+8uLr30UuVnl8slYmJixD/+8Q8hhBDLly8XCQkJorKyUinz2WefCY1GI4qKioQQQqSnp4unn37a67Uuuugi8cADDwghTq8AvGPHDp/1cblcyrabb75ZjBkzRvm5S5cu4rbbblN+lmVZJCcni1dffVUIIcTbb78tunfvLmRZVsrY7XYRFRUlvvzyS3HixAkBQGzYsMHn+YqLixNvvvmmz+ea0qdPHzF79myfzz355JNi+PDhXtuOHj0qAHit4E5E/mHLC1GE69u3L4YNG4Y+ffrg5ptvxooVK3Dq1Cnl+d69e0OjOf2nICUlBX369FF+1mq16NChA0pKSgAAu3fvRt++fRETE6OUGTJkCGRZxt69e2G1WlFYWIghQ4Z41WPIkCHYvXt3s/Xt3bs3tFqt8nNaWpry2h4XXHCB8v+SJCE1NVUp8/333+PXX39FXFwcYmNjERsbi8TERNhsNuzfvx+JiYm48847kZOTg+uuuw7PP/88jh07phxv2rRpuOeee5CdnY0FCxZg//79zdYZAB566CE89dRTGDJkCGbNmoVdu3Ypz33//fdYv369Up/Y2Fj06NEDAPw+PhGdxvBCFOG0Wi3WrFmDL774Ar169cKLL76I7t274+DBgwDQ4JaIJEk+t8my3Cb19ee1mypTWVmJAQMGYOfOnV6PX375BbfeeisA4I033kB+fj4uvvhi5OXl4bzzzsN3330HAJg9ezZ++uknXHvttVi3bh169eqFjz/+uNl633PPPThw4ADGjx+PH374AQMHDsSLL76o1Om6665rUKd9+/bhsssuO7MTRdSOMbwQtQOSJGHIkCGYM2cOduzYAYPB4NcF2ZeePXvi+++/R1VVlbJt06ZN0Gg06N69O8xmM9LT07Fp0yav/TZt2oRevXoBAAwGAwDA7Xaf4Ttq3IUXXoh9+/YhOTkZ55xzjtfDYrEo5fr374+ZM2fi22+/xfnnn4/33ntPee68887D1KlT8dVXX2HkyJENOiw3JiMjA/fddx9WrVqFRx55BCtWrFDq9NNPPyEzM7NBneq2YBGRfxheiCLc5s2bMW/ePGzduhVHjhzBqlWrcPz4cfTs2fOMjjdu3DiYTCbccccd+PHHH7F+/Xo8+OCDGD9+PFJSUgAAjz76KJ555hnk5eVh7969mDFjBnbu3ImHH34YAJCcnIyoqCil42p5eXnA3u+4ceOQlJSEG264Ad988w0OHjyIDRs24KGHHsJvv/2GgwcPYubMmcjPz8fhw4fx1VdfYd++fejZsydqamowefJkbNiwAYcPH8amTZuwZcsWv87VlClT8OWXX+LgwYPYvn071q9fr+w3adIknDx5Erfccgu2bNmC/fv348svv0Rubm5QAhxRpNOFugJEFFxmsxkbN27E0qVLYbVa0aVLFyxatAhXX3018vLyWny86OhofPnll3j44Ydx0UUXITo6GqNGjcLixYuVMg899BDKy8vxyCOPoKSkBL169cK//vUvnHvuuQAAnU6HF154AXPnzsUTTzyBSy+9FBs2bAjI+42OjsbGjRsxffp0jBw5EhUVFejUqROGDRsGs9mMmpoa7NmzB2+99RZOnDiBtLQ0TJo0Cffeey9cLhdOnDiB22+/HcXFxUhKSsLIkSMxZ86cZl/X7XZj0qRJ+O2332A2mzFixAgsWbIEAJSWqOnTp2P48OGw2+3o0qULRowY4dXfiIj8Iwnxx3hJIiIiIhVg5CciIiJVYXghIvKDZ3ZcX4958+aFunpE7QpvGxER+eH3339HTU2Nz+cSExORmJjYxjUiar8YXoiIiEhVeNuIiIiIVIXhhYiIiFSF4YWIiIhUheGFiIiIVIXhhYiIiFSF4YWIiIhUheGFiIiIVIXhhYiIiFTl/wMQokZWPgNhzAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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L4q2+ffviuuuuw9KlS1FeXo7ExERs3boVFovF7jitVotnnnkGM2fOxMiRI3H33XfbhkpnZWVh4cKFtmNnzZqFP/3pTxg9ejRmz56NkpISvPTSS+jbt69dR+X77rsP5eXluOWWW9C5c2ecPXsWa9aswcCBA239voDGvklHjx7FQw891KZ7JXIrGEOciNqjs2fPimnTponk5GSh1+tFt27dxEMPPWQ33LmgoEDccccdIj4+XhgMBjFkyBDx/vvv212nadjq22+/bbf9zJkzAoDYtGmT3fbPPvtM/PSnPxUxMTEiKipK9O/fX6xZs8a2//z58+LnP/+5iI+PF3FxceLOO+8UhYWFAoDT4cq7d+8WAIQkSeLcuXMO+50NlT558qQYMWKEiIiIEAAchk0bjUaRkJAg4uLi7Ibeeurll18WI0aMEJ06dRJ6vV50795d/OpXvxKVlZV2xxUXF4uHHnpIZGZmCq1WK9LS0sSoUaPE+vXrW30Po9EokpKSxPLlyx32ff3117bvocFgED179hSPP/643TFffvmlGD16tIiOjhaRkZHi5ptvFv/973/tjmkaqtxySL2z4c9FRUVi/PjxIiYmRgCwDUd2NVTa2VDylsOVhWj8GczJyRF6vV6kpqaK3/zmN7bPvPk1hRBi27Zt4pprrhF6vV4kJiaK3Nxccf78eYf3ef3110W3bt2ETqcTAwcOFP/+978d3nv79u3i1ltvFSkpKUKn04kuXbqI+++/X1y8eNHuWuvWrRORkZGiqqrK4X2I/IVrGxFRqywWCzIyMjBhwgT89a9/DXZxXFq+fDk2bdqEb775xmUHWFLWNddcg5tuugnPPfdcsItCYYx9XoioVe+++y5KS0sxbdq0YBfFrYULF6KmpgZbt24NdlE6pF27duGbb75pFwtjUnhjzQsRubR//34cPXoUy5cvR1JSksNEcSaTqdW+KHFxcXYT7RERtRU77BKRS+vWrcPrr7+OgQMHYvPmzQ77//vf/+Lmm292e41NmzZhxowZyhSQiDok1rwQkc8uXbqEQ4cOuT2mb9++SE9PD1CJiKgjYHghIiKikMIOu0RERBRSwq7PiyzLKCwsRExMDBcFIyIiChFCCFRXVyMjI6PVST/DLrwUFhb6tA4JERERBd+5c+fQuXNnt8eEXXhpmmr83LlzPq1HQkRERIFXVVWFzMxM23PcnbALL01NRbGxsQwvREREIcaTLh/ssEtEREQhheGFiIiIQgrDCxEREYUUhhciIiIKKQwvREREFFIYXoiIiCikMLwQERFRSGF4ISIiopDC8EJEREQhheGFiIiIQgrDCxEREYUUhhciIiIKKQwvREREFFIYXkKQECLYRSAiIgoahpcQZLLKDDBERNRhMbyEKLOV4YWIiDomhpcQJQeo5qWosgFWmUGJiIjaD4aXEFVnsvrtWu7CSa3Jgkt1Jr+9FxERUVsxvISoWqPFL9epMVpQVNXQ6nvJrH0hIqJ2guElRPmz1ai+lVqcqnoLakz+CUtERERtxfDSwZktMhrM1lZrVqobGF6IiKh9YHgJUVY/Vb0YLTKEAAor62G2yi6Pq6o3s+mIiIjaBYaXEFVrtLR5FJDJIqO02ggAuFRrRmFFPQDnk+BZrMJvgYmIiKgtGF5ClBBAvdl/I46Axr4t35bUuLyu0eK6ZoaIiChQGF5CmFWBierqTVbUuOjfUsEh00RE1A4wvIQwAQEhhM9LBbgKI65qXqobLGjwc20PERGRtxheQlhZjQn1ZitqTVaf5n2prDc73V5Vb8HFynqHDroWq0BFnfNziIiIAkUT7AKQ7+pNVpTXNtaeROk0iNJ7d767Drg/1JhQZ7LCaLbv53KpzoRO0Tpo1cy9REQUHAwvIe5SbWNNSJTOu4/SKgtY3PSZEQKoMzo2EVmsAlZZQKv2rpxERET+EpA/n9euXYusrCwYDAYMHToUBw4ccHnshg0bMHz4cCQkJCAhIQE5OTluj+8IWpsB1xdGi9XnWXpNbuaDISIiUpri4WXbtm1YtGgRli1bhi+//BIDBgzA6NGjUVJS4vT4vXv34u6778bHH3+MvLw8ZGZm4tZbb8WFCxeULmq7ZLLIqG5oX/1MKtnvhYiIgkgSvg5V8dDQoUPxk5/8BC+++CIAQJZlZGZm4uGHH8aSJUtaPd9qtSIhIQEvvvgipk2b1urxVVVViIuLQ2VlJWJjY9tc/mA7XVqDBrOMq1Kjofmxn4nRYsX/imrsjuucEIGEKJ3H1y2vNeHCpXqfy9U3IxYqleTz+URERM158/xWtObFZDLh0KFDyMnJufyGKhVycnKQl5fn0TXq6upgNpuRmJjodL/RaERVVZXdK1xYZYF6sxVW2f+z27oaaeSpgtKa1g8iIiJSgKLhpaysDFarFampqXbbU1NTUVRU5NE1Fi9ejIyMDLsA1NyKFSsQFxdne2VmZra53O3Fdz/UQv6xe0lblwJorrrB7HIiOk+ZrcKn4dlERERt1a7Hu65cuRJbt27FO++8A4PB4PSYpUuXorKy0vY6d+5cgEupjOoGs91on7aGjeZKflzPqC2aaoWIiIgCTdGh0klJSVCr1SguLrbbXlxcjLS0NLfnrlq1CitXrsSHH36I/v37uzxOr9dDr/dygpMQUN0irJTVmBAboYWhjWOUhRB2c7cIIWC0yNBrVJAk9mEhIqL2T9GaF51Oh0GDBmHPnj22bbIsY8+ePRg2bJjL85599lksX74cu3btwuDBg5UsYrtlarEIolUWfmk6Kq022q4jhMDiHUdx58t5WLzzmNfLDBRXNfi8NAEREZGvFG82WrRoETZs2IBXXnkF+fn5eOCBB1BbW4uZM2cCAKZNm4alS5fajn/mmWfw+OOPY+PGjcjKykJRURGKiopQU9NxOojKLppk2rqqs8Uqo7jqcpOR0SIjv6gaAJB/scrr68ty2zv+EhEReUvxGXanTp2K0tJSPPHEEygqKsLAgQOxa9cuWyfe77//HirV5Qy1bt06mEwm3HHHHXbXWbZsGZ588kmli9sumKyy09lvy2uNSPRiOHRzsixQVmO/EGPLShNfKlF+qDUhUqeBVi2x2YmIiAJC8XleAi0c5nkprTaiqLLBYbtWI6FXWqxP87yc/aEWVfWX+9EIIbBg2xGcLqu1bcuIM2DdPYOg8iGEXJka3eb+OERE1HG1m3leyHuyLFBc5RhcgMaaEV+ypski2wUXoLHJqHlwAYDCygYs3HbEp/coqTLiUq2p9QOJiIjaiOGlnakzu15zyGIVqPNhnaMGi+M5zd9j29zrkBHXOBT9dFktGsze962prDejhvO+EBFRADC8BJGz0UMNrcydUlJt9LpvimiRRYQQWLLzqO1rlSRh9dRrbF8v2XnUp9oXf42IIiIicofhJYiEELC0WKFZbiU01BotqPByYcSWq0A3bzLqlhQFvUYFg1aFbklRAHyvfalusKDWxNoXIiJSFsNLkNW1qGkxOxll1JwQ8HpmW4vsOoisnNQfktQ4UmjlpMuTAfpa+0JERKQ0hpcgk1s0s7RsNhJCoMFstQsSdV7UblysrEe9m34yzQcWOat9afnerbG2Er6IiIjaiuGlHRFC2M2s62oGXDcVKQ5qjRbUGlsGIufHtqx9Wbyz8b0XbDvSanNWk+Jq5yOliIiI/IXhJYBa1rIA9kHCKgu7yekazG2bAddilVFvsj+nZWfdlprXxJz5sV/M6bJan4dQExER+RvDSwA5Cx/mZp1prc3CQWshwxPO+s8466zbnF5zuemoOU878VqsAmd/qG31OCIiIl8xvARQeZ3jJG7NA03TMGNZNK5t1HISOU+bbtxpfommzrrNtWw6as6TTrxCNI464pwvRESkFIaXADFarDC2MkqozmSFLARuX7sPU9d/7rB/6vrPPQowQghU1Jkchi23rM1xtQqAQauyTVoHwLae0umyWo+aroSA0+UNiIiI/IHhJUCqGyxwFzvMVhlFlQ2oarFKc/MQAcBhvzMNZhnnyutxsaLBYbu7JqMmkiRh3T2DbF+vy7221fckIiIKFMVXlSbXmocZk0WGEICuWaBoWihxYbMFFPUa94sfXqo1OW2yaVnr4qzJqDmVJOFvD91gK9vl67h9e7v3IyIiUgJrXgKk1kmgaN5Z12yVIYTA0p3HbNtWT70GKjd9UFoqqmzAhYp6pzPwtqx1MWhb/+hVkuSwwrSnk9dZBZcKICIiZTC8BIizkTqWZg93qywcRgI1BYzm+cFVeDDLMspqnK97JAuBBdsO275urdalpeYjkDwddWS2CFQ3eLeMARERkScYXgLEWUdbY7MQ0HJv84DRMjw46zRb3WBxGlyEEFi47QgKf+xA27LWJUKnRnyk1m3ZfV064PylehRXseMuERH5F8NLgLiqEWna3jLcNK8YaRkenF3LbHVeG9K8NicjzoDnpg60q3WJ0KmRHKNvtfwtlw7wdNSRP4Z3ExERNcfwEkQWWdhWfLbKwm1n2Naajiwu1hRqflhTHxq766KxZqe1ViRPApQzP9SY8G1JNayycDrDMBERkbcYXoKsaSFGq+x+Rt3W+p24ajJyN6+LRi0hMUoHSZI86sDb/PwF2w57OOcMUG+ScaKwCmfL62C2yrbOyURERL5geAkii7VxeLTJIqPOaHU7B4sv/U48mdfFoG0cet05IRKRevfDsJsHqMLKBq/XO6ppsODkxWqcvFjtUadfIiIiZxhe2oGC0hpUNRuZ42o0kDf9TjyZ16Vp5tzGa6txRXyE23JKkoTnpg60TZznad8XZ8rrTByNREREPmF4CaKmhRMtVvv+Lq76n7hbd6glV8Oum4uLsB9lZNCqEWNwP2+hSpKweuo1HpXBnfIaE6oaLJBlwSYkIiLyCmfYDaCWHVZ9eWh7MT2LjauaHLXKcZvOxZIBrsrQltxRXmNCeY0JMQYNdBoVog0axBrcD9smIiJizUsAGS2y3ZDmQNU3OAs8EToVtGrHj9+T8NKcpx133alusOCHGhNKq42o9GDtJiIi6tgYXgJIiMZOq02COX1+U0fdluIitK3W7rS1464rdUYrymtNthFYREREzjC8BFjTkgBW2X7tH3fP/gid/z8mVws8atUqJEW7n7TOWcddf40eqmmwcFZeIiJyi+ElwJpqKKrqzbbA0nJkUEsJkTqX+3wVqXM9LNrTRRubd9xdsO0w6k1Wv9TANJhlWFzMGExERMTwEmBNM+rWmi43HzkbGRSpV0OvVaFLp0gkROqg1XjeU1cI4bbpRa2SEKV33Vfbk6YjwH7odmFlA6asz8PincfaHGBMFhllNaY2XYOIiMIXw0uAyT9WKLgKFysn9Ud8pA4xBg06RekQF6GFSiUh0cPaFyEEFu84ins3HnC6X5KAzMTW53PxRFPzUVOAAYD8i1U+z/3S3KU6E0x+uA4REYUfhpcAM1kbm0TqTc4fzJLU2KSTEmOwm0QuNsKzIcRGi4z8omrb173TY+1m1tVrVIjxYDiyp6OOGpuPBuK1WUM8Ot5TFqvA+Ut1fr0mERGFB87zEmD1JiuKq40u9+u0Klsfl+Y1IAZtYzOSNyNxXps15McmoMvXaR6I3OnaKRL/K6rx6NjGtZEu96Hx15xznLqOiIicYc1LEFyqbezPIQvhMEeKRiVB5WTyOADQOZmXpbmWfV0MWrVdcNFqJHRqZSRRE71GDZWPPx2erLvkiTojh0wTEZEj1rwEQJ3JAqt8OaioIEEWArev3QcA2Db3OtuxBhdDmAEgOUaPshrntTayEFi47Yit429LKhWQ3axviifUKslhVmBXmuZ+OV1Waxs6HeFmRBMREZGvWPMSIE1h5fa1+yALgapmM8k+sOVL27/dDWGO0muQHufY2VY4CS4t+7okRulczu3iSqcoz2ppAMd1l/wx8y4REZEzDC8BYLHKeOD1Q7avq1pMgV/+YzNSt6QoxEe670wbbXAMIA3my0OtM+IMeGvuMDwzqZ+tyUijlpASY/C63NF6jVdDtFsOnfbXzLtERETNMbwEQFmNCYWVl2eNdVUD8uwd/RGhc9+S13I9opYT3K2eeg0idPZ9XTLiI5wuwtiaCJ3a6fpHrjibedcfw6aJiIiaY3gJAlfTqGi9XBQRcD7BXXN6rarVjr7+1HLmXSIiIn9jh90AEAEa9LtyUn+7Ghe1SkL35Gj4UOnSJh7OcUdEROQT1rwoTAiBX77+pcN2Z5PAtTVktAwNOo0EtUryeMZcZ1JjDQwjRETUrjC8KKzebMU3xfaTvQkBLN15zMnR7S8lROs1SIrWezzjLhERkdL4RAoCo8XqMB9LRpwBER6s5hwMaXEGJMd4PmyaiIhISe3zadkBrbtnENS+TmkbAPERWp9GLDWYrRwuTUREfsUOu0G2be51MGjVUEkStOr212zURKWSoFIBVi9HPt+78QC6JUXhuakDoWLnGSIi8oP2+6d+B6GSJNtDPULbvqfTj9R6lnX1GhV6p8favj5dVuvzhHUl1Q2tH0RERB0Ka16CwNUjPErfvj+OLp0icfaHWlTVW9weJ0kSnpnUDw1mGQu2HUZhZQNOl9Wist7ssMp1a4orjTCaZbvJ8uIjtXarWBMRUcfCmpcgWLzjqNPt/n4gt2WItCueBixJkhChU9tNWHfvxgNYsO0I6k3e9YOpqDOjtNpoe3HWXiKijo3hJYCaps2/WOnYFKLxc38Xlcr9Io++6hSlg96LUVEGrWMT0pT1eVi885jPHXlrjBaU15psL3YIJiLqWBheAmjl5P52X3dLirKt/OzveVSSo/VOV6BuK0mSkBzt3WrTz0zqh7fmDrMt2ggA+RerUFlv9il4lNeYcOFSve11qc7c+klERBQ2GF4CqGXdSvPp/H1Zf8jdc9/V4o/+kBClQ3ZyVOsH/uhyE9JAvDZriG17UzOS3Maak1qjBVUNZjSYrW26DhERhQaGlwBq+Yhua5eUJTuPugwwWo2yw5LVPhRekiTERWgdmpEeeP0Q6k1Wn+eEqagz42xZHc5fqkdxVQNDDBFRmGvfw1vCTPOOphlxBluTEeB5s1GEVo3e6THIv1iN02W1MFqcP6iVnlMlQqdGYrQO5TUmr85zNhKpsLIBU9bnAUCb5oSpN1lRb7LCoFVzNBIRURhjzUsAxRouZ8V19wyyGw3k6cNakiRsuW9oK8fALhgpxeDjezQ1I627Z5BdPxigbXPCNKk3WVFjdD+cm4iIQhdrXgJIJUn420M32P5tv8/z67SWcyRJmWHSLcUYtJCkBrd9b9xRSRJWTx0Io0WGELCbE6bBLCPCx9FSpdVGlNeakBil+7GcmnY/hw4REXkuIDUva9euRVZWFgwGA4YOHYoDBw64Pf7tt99Gr169YDAY0K9fP/zzn/8MRDEDovmMus35sm5QS9F6DbomRaJrJ88707aFTqNCwo8BwVeSJMGgVTvMCdPYn8f32herLGzzwtSzDwwRUVhRPLxs27YNixYtwrJly/Dll19iwIABGD16NEpKSpwe/9///hd33303Zs+ejcOHD2PixImYOHEivv76a6WLGlRaH0YbtZQSq0esQYvoANYypMTocUVChF/mqTFoVbZmpKbaF3+4WNGAY+crcbywElZZ2F6yzPlhiIhCkeLh5U9/+hPmzJmDmTNnok+fPnjppZcQGRmJjRs3Oj3++eefx5gxY/CrX/0KvXv3xvLly3HttdfixRdfVLqoivD0AenrPC/Nrx6Ifi4tadUqJEbp/FJzJEkSVk66PBfOgm2H2zyMujlZBk4UVtle35TUoLLe3OqLo5eIiNoXRZ92JpMJhw4dQk5OzuU3VKmQk5ODvLw8p+fk5eXZHQ8Ao0ePdnm80WhEVVWV3as98efD15klzZYaUPkhQPiqS2IkUmI9n7zOlea1L4WVDW3uvOuOySLj+x/qWn1V1XMSPCKi9kTR8FJWVgar1YrU1FS77ampqSgqKnJ6TlFRkVfHr1ixAnFxcbZXZmamfwrvJ2arZw9eb4YGR2jVdg/49sCgVSNar2nz/DKSJOG5qQNtSyk0DgcP7lpGVQ1mnCuvc/uqbmDAISIKlJAfKr106VJUVlbaXufOnQt2kex4UvOiVkleNbu0bF5pL6L0GnSKanvtS+MopGtaPzBA6k0yKurMbl+V9WZUNTi+ajlkm4jI7xTt2ZmUlAS1Wo3i4mK77cXFxUhLS3N6TlpamlfH6/V66PVtf2AqxZM+L3GRWq+v27TgYf7FxmaygZnxiGgHE7N1itJBq5Zwrry+TdcJwEhvv7pUa8alWsfaF51GhbRYg9fXi9Cp/b7eFRFRuFA0vOh0OgwaNAh79uzBxIkTAQCyLGPPnj2YN2+e03OGDRuGPXv2YMGCBbZtu3fvxrBhw5QsqiKEEJi+6aDta1cPZG8WOrx8rcaZapuaVLonRwVkbpfWqFQSYgxaAG0LL+HCZJHxfXmd1+dlJkZAp3E/DL3OZHE6IisuQuuXDtRERO2V4mNqFy1ahOnTp2Pw4MEYMmQIVq9ejdraWsycORMAMG3aNFxxxRVYsWIFAGD+/PkYOXIk/vjHP2L8+PHYunUrvvjiC6xfv17povpdvdmKk0XVABqnvU+N1cNsFag12o9e8fUv7KY5Upr+3V6oVRIyEyPaXPvSkRVXGVHWytILZqsMi5M+VRIALWttAiJCq2ZQJAoCxcPL1KlTUVpaiieeeAJFRUUYOHAgdu3aZeuU+/3330Oluvw/2uuvvx5vvPEGfvvb3+I3v/kNrrzySrz77ru4+uqrlS6qotbmXoMr4iNRXN1gCy9KL54YTGzyaBtTGzopn7/E0Bgo2clRAZ1XiYgaBeS3bt68eS6bifbu3euw7c4778Sdd96pcKkCS69ROwxlTozUwWQN7kgaIvLdpVoTO2VTq+IitFws1s/4J0OARP+4KGPzIdExBi1qTfwfH1GoqqjjEHlqHVe69z+GlwBr3jVFrZKCMisuEREFkPB8tvVQEcxJUQGGl4CL0mkAGKFWSdCoJFjY2Y+IKKz5MuKwPYvQqdAjJSaoZeCf/QGi/rHKRa9RQZKArKRIqFQSInXhmR85AoOIiJTC8BIgTasua9QqxBq0YRtammhVKkTo2MZLRET+x/ASIM3rIeKjvJ9RN9So2J+HiIgUwqdLEBg0HaNGIjlGj8Ro97PEEhEReYvhJQg6ygRuBq0aiZE6pMa137WniIgo9HSMpygFTYROjfgI1r4QEZH/MLyQ4lQSEBuhYQdeIiLyC4YXUpxGrULXTlFIjmHzERERtV14j9eldiUuQot+neMAAN+WVKPe5Nm6TiK8JqYkIqI2Ys0LBYVKkuyWSnBnwbbDqDdZ0WBufAmmGSKiDo01LxQU3ZKjUVZjxMWKBqf79RoVuiVF4XRZLQorGzBlfZ5tX+/0WDwzqR8kT9MPERGFFda8UNBE6TRIizPAoHX8MZQkCc9NHYhuSVEO+/IvVqGy3syaGCKiDoo1LxQ0ETo1InRqaNUSGswy6kwW1Bqttv0qScLqqQNhtDT2jWkwW3HvxgMAYPsvAHRLisJzUwdCxZoYIqIOgTUvFHTxkTqkxRkQbXDM0pIkwaBVw6BVIy5Ci97psQ7HnC6rxcJtR1gDQ0TUQbDmhdqNuAgtrLJAWbXJ6X5JkvDMpH62mhghGjvzFlY24HRZLSrrzYiL0LIvDBFRmGPNC7Ubeo26ccVtvevJ7JrXxETo1Fg99Rrbvns3HsCCbUcgswaGiCisMbwoyGL1bB4TuixKr0FStOeT2Rm0KrumJDYhERGFP4YXBVnkyw9QjZrfaiU0NSW9NXcYMuIMAGBrQmKAISIKT3yiKojNF76J1mtwZWo0OidEeHS8JEkum5DqTRxKTUQUbhheFGS28KHpC7WqsV+LVuPdj6ezJqQp6/OweOcxBhgiojDC8KIgE/u8BFTzJqTmk9s1n9SOIYaIKPRxqDS1W2ofhjxfbkIaiMp6s8Okdt2SorByUn/bukp6jYpDq4mIQgzDC7VbETrXQ6ZbI0mSbVK7/ItVtu1NTUlNWoYZZxhwiIjaF4YXJbGFIqiaT2onBLBk51GcLqu1O6ZlmHGGC0ESEbUvDC8KapoJloKnaVI7AHbrJLkKM87kX6yC0SLbrkNERMHF8EIdRvMgA9iHGWeaLwTZYLa6PA5g0xIRUSAxvCjIIrPmpT1rGWbcab6KtTPu+s4w2BAR+RfDi0KEEHhk65FgF4PaQK9ROXT4dcVd3xn2mSEi8i+GF4XUm634tqQGANA7LQYR7C8RclquYu2MJ31nmuaZ8abPDGtriIhcY3gJgNdmD+WDKER50rTkqu9M8z4zrTU7tcQh3ERErjG8BACfL+HNVcDxptmpJU+GcLsKOAw1RBTuGF4UIjdbUZrPkY7Jk2anlrwZwu0q4LCPDRGFO4YXhVhkzlBH3o1oatLaEO7WAo4vfWzIN6zlIgoOhheidsbXfjZt6WNDvmEtF1FwcFVphdQaL09qxv+vkb81BZzmr6a1nChwmmZfJqLAYs2LQlqbkZXI33zpY0O+8Wb2ZSI2L/ofwwtRGPGljw21DZvoqDVsXvQ/hpcAkMAfWKJw0pZh8NTxhF0nekmgzmRBhFYdtEDG8KKQ5mONGLaJwgub6MgT4d6J/sTvRiNSF5wYwfCikBqjOdhFICIFsYmOWsMaOuUwvCjEYr1c96Ji1YvPkmJ0KKs2BbsYREReC9caOoNOhe7J0UFds4/hRQF1JgtEs3YjtYrhhYioIwrHGroIrSpozUVNOM+LAswWzq7rLwmROug0/DElIqLL+FRQQD3nffAbg1YNNX9KiYioGTYbUbvXOSEScrN2uKLKBrsZjImIqGNheFGAVbDZyJ9athezDxERUcfG8KIAwfCiqAit2q5DdI3RvoM0ERGFN4YXCjkpsQa7rwsr6vFDDYdTExF1FOwKSSFPw2YkIqIORdHwUl5ejtzcXMTGxiI+Ph6zZ89GTU2N2+Mffvhh9OzZExEREejSpQseeeQRVFZWKllMv2MTRmCpVRJ0GhX7whARdRCKhpfc3FwcP34cu3fvxvvvv49PP/0Uc+fOdXl8YWEhCgsLsWrVKnz99dfYvHkzdu3ahdmzZytZTL9Teqg0Z+y11ylaj55pMUiJ1Qe7KEREFACK9XnJz8/Hrl27cPDgQQwePBgAsGbNGowbNw6rVq1CRkaGwzlXX301duzYYfu6e/fuePrpp3HPPffAYrFAowmNLjpK17xoOfGJU/ERWkTpNLhYWc+h1EREYUyxp2BeXh7i4+NtwQUAcnJyoFKpsH//fo+vU1lZidjYWJfBxWg0oqqqyu4VTLIsYLYqv46FVsPal5Y0ahUidGrWTBERhTnFwktRURFSUlLstmk0GiQmJqKoqMija5SVlWH58uVum5pWrFiBuLg42yszM7NN5faHQPR5UfMB7VJKrB5pcYbWDyQiopDkdXhZsmQJJEly+zp58mSbC1ZVVYXx48ejT58+ePLJJ10et3TpUlRWVtpe586da/N7hwINm45citRpEGMIjSZGIiLyntf/h3/00UcxY8YMt8d069YNaWlpKCkpsdtusVhQXl6OtLQ0t+dXV1djzJgxiImJwTvvvAOtVuvyWL1eD72+/XXUVLr2hcOD3TNo1eibEQsAuFjVgHLOA0NEFDa8Di/JyclITk5u9bhhw4ahoqIChw4dwqBBgwAAH330EWRZxtChQ12eV1VVhdGjR0Ov1+O9996DwRBa1f8WWUAIgSU7jyr6PjEGDSrqzIq+R6hT/RjwOkXpEGvQQAA4W1YX3EIREVGbKdb20Lt3b4wZMwZz5szBgQMHsG/fPsybNw933XWXbaTRhQsX0KtXLxw4cABAY3C59dZbUVtbi7/+9a+oqqpCUVERioqKYLWGxugRAQGjRcbpsloAwJUp0YhosTaPP8RH6qDTsOnIEwatGjEGLaJ1bEoiIgoHiv7ffMuWLZg3bx5GjRoFlUqFyZMn44UXXrDtN5vNOHXqFOrqGv8a/vLLL20jkXr06GF3rTNnziArK0vJ4vpFy+ail+8dBEmhzrUatQSTRZFLhyVJgl1HXosso6yazUlERKFG0fCSmJiIN954w+X+rKwsu0UMb7rpppBf1NDUYpi0TsGOtRxx5B1JkpAcc7l/lMUqQ69pvVasqt6M6gamRCKi9oL16ApTq5ULGHqtCvVmCRar4NwmPtCoVUiM0rV6nNkqM7wQEbUj7DShMCVHBSVG6aD9MRxFc2gwERF1EAwvClOyRkSvUUOtUkGSAI6cJiKijoJ/rvtZyy47SrfmpMcZUFptVKxTMDWuJRWpd983xmSRYbGGdn8tIqJQwfDiZ0aL/ZBupTOFQavGFfERyr5JB5cYpWu1b0xhRT1+4ER4REQBwfDib0H441vFNqOgy4iPQLqT9ZTqzVYUlNQGoUREROGL4cXP6s3WgCzMSO2Ps6Y7tUryqTN1g9nKZigiIhcYXvys5dIAElgr0pHpNWpkJ0V5fd658jou/0BE5ALDi5+V15ptSwN0S4pClM7/SwNQ+OsUrUOswfWCpABQUW9CVT3nnyGijofhxY9kWUBu1ma0clJ/jgIin0TqNEAr8+fVmRlciKhjYnjxI6sQdv1dJMl5Pwgif4g1aKFROU7VVFpthFVmfxkiCl8MLwpjdCGlROk1iNI7/gpfqjMxvBBRWOMMu0RERBRSWPPiR87+2mWrEQVajEEDg4Y1L4FQY7SwlosoCBheFMY+LxRo6XGccTlQCkprUGe0tn4gEfkVm42IiIgopLDmxY9aVh+z0oUovEXpNFDzF51aUc8Zs/2O4cWPGrg0AFGHkuZkPSuils7+UMsJJf2MzUZ+1HJpACIiIvI/1rz4UWm1yW5pAIOW2ZCIqKOL0KrDqlZepwn+s43hxU/MVtluuvaVk/pDp+G6RkREHV1KLJsX/S348SlMWGUBWb78NfvwERERKYPhxU8snKiKiIgoIBhe/EQOpwZNIiKidozhxU+YXYiIiAKD4cVPuL4JERFRYDC8+Ilg1QsREVFAMLz4iZXhhYiIKCAYXvzFSXbRt4OJfIiIiMINn65+4qzmRc9J6oiIiPyO4cVPnLUaseaFiIjI//h09ROONiIiIgoMhhcFcYkAIiIi/2N48RNnNS9qFdMLERGRvzG8+Iml+aqMREREpBiGFz8xWhheiIiIAoHhxQ+EEHBW8cJmIyIiIv9jePEDVwONJDC8EBER+RvDi0LUKgk6zvNCRETkd3y6KkSlYrMRERGREhhe/EDmooxEREQBw/DiR80zjEbNby0REZES+IT1A1kICCGwZOdR27ZYvTaIJSIiIgpfDC9+UGe0wmiRcbqsFgDQLSkKBi2/tURERErgE1YBKyf1h8SFjYiIiBTB8OIHP9Sa7L5mbiEiIlIOw4sfmJwsDcA5XoiIiJTBJ6xCOMcLERGRMhhe2qjeZOU8L0RERAHE8NJGZlkGswsREVHgMLy0kdlJfxciIiJSDsNLG5msjuFFrZIQqVUHoTREREThj+GljSxWxzajCK0aKnbYJSIiUoSi4aW8vBy5ubmIjY1FfHw8Zs+ejZqaGo/OFUJg7NixkCQJ7777rpLFbJMGszXYRSAiIupQFA0vubm5OH78OHbv3o33338fn376KebOnevRuatXr273s9SarbLTZiMiIiJSjkapC+fn52PXrl04ePAgBg8eDABYs2YNxo0bh1WrViEjI8PluUeOHMEf//hHfPHFF0hPT1eqiG0mCwHZSXbhBHVERETKUewpm5eXh/j4eFtwAYCcnByoVCrs37/f5Xl1dXX4xS9+gbVr1yItLa3V9zEajaiqqrJ7BUqt8XKTUfPh0hr2dyEiIlKMYuGlqKgIKSkpdts0Gg0SExNRVFTk8ryFCxfi+uuvx+233+7R+6xYsQJxcXG2V2ZmZpvK7QshBJbsPBrw9yUiIuqIvA4vS5YsgSRJbl8nT570qTDvvfcePvroI6xevdrjc5YuXYrKykrb69y5cz69ty/qTBYAgNEi43RZLQCgW1IUDFo2GxERESnF6z4vjz76KGbMmOH2mG7duiEtLQ0lJSV22y0WC8rLy102B3300UcoKChAfHy83fbJkydj+PDh2Lt3r8M5er0eer3em1vwG2f9XVZO6g+NmuGFiIhIKV6Hl+TkZCQnJ7d63LBhw1BRUYFDhw5h0KBBABrDiSzLGDp0qNNzlixZgvvuu89uW79+/fDcc89hwoQJ3hY1KCQJ7X6UFBERUShTbLRR7969MWbMGMyZMwcvvfQSzGYz5s2bh7vuuss20ujChQsYNWoUXn31VQwZMgRpaWlOa2W6dOmC7OxspYrqd4wuREREylG0fWPLli3o1asXRo0ahXHjxuHGG2/E+vXrbfvNZjNOnTqFuro6JYuhGFdzvLDihYiISDmK1bwAQGJiIt544w2X+7OysiBaWZK5tf3BZOYEdURERAHHnqVEREQUUhhefGSxyk4XZQTYYZeIiEhJDC8+Kq8z2f7dsmWL87wQEREph0/ZNuLsukRERIHF8OKjijozAMfZdfVclJGIiEhRfNL6yCo79ndZOak/+7sQEREpjOHFjxpn1wVUDDBERESKYXjxgdkqO615aaLl2kZERESK4VPWBw1mq8MIoyasdSEiIlIWw4sP3M35q1YxvBARESmJ4YWIiIhCCsOLD2Q3/V1Y80JERKQshhcfmF0sCwAAGoYXIiIiRTG8+MDkZjVp9tclIiJSFsOLD0wW1+FFr1EHsCREREQdD8OLD6yy6/BCREREymJ48YGb/rpglxciIiJlMbz4wOKmw65ey2YjIiIiJTG8EBERUUhhePGSLAuIZnPstlwmIII1L0RERIpiePGSySqjqb+uEAJLdh61289J6oiIiJTF8OKlijqz7d9Gi4zTZbUAgG5JUTBo+e0kIiJSGp+2Xqo3W51uXzmpP9QqFWteiIiIFMbw4gWzVUZNg8XpPs6sS0REFBgML37EAENERKQ8hhc/UjG9EBERKY7hpQ1aDpMmIiIi5TG8+MjZMGkiIiJSHsOLj1oOk9ZrVJygjoiIKAAYXvxg5aT+kCQJKn43iYiIFMfHrR+wny4REVHgMLwQERFRSGF48aPEKF2wi0BERBT2GF78iPO8EBERKY/hxUct53iRJIYXIiKiQGB48YGzOV50GhV0Gn47iYiIlManrQ+czfFCREREgcGnbhvZ5nhhixEREVFAMLy0UVM3Fw1nqCMiIgoIPnH9hH11iYiIAoPhxU/YWZeIiCgw+MT1EwmseiEiIgoEhhciIiIKKQwvfsLRRkRERIHB8OKDlrPrAoDEHrtEREQBwfDiJWez6wKAmlUvREREAcHw4iXOrktERBRcfPK2QdPsukRERBQ4DC9t0Dy3MMIQEREFBsOLnxi06mAXgYiIqENgeCEiIqKQwvDiJwYtv5VERESBwCeun7DjLhERUWAoFl7Ky8uRm5uL2NhYxMfHY/bs2aipqWn1vLy8PNxyyy2IiopCbGwsRowYgfr6eqWK6TVnE9QRERFR4CgWXnJzc3H8+HHs3r0b77//Pj799FPMnTvX7Tl5eXkYM2YMbr31Vhw4cAAHDx7EvHnzoFK1jwoiVxPUsdKFiIgocDRKXDQ/Px+7du3CwYMHMXjwYADAmjVrMG7cOKxatQoZGRlOz1u4cCEeeeQRLFmyxLatZ8+eShTRJ/Vmq9MJ6hheiIiIAkeRKo28vDzEx8fbggsA5OTkQKVSYf/+/U7PKSkpwf79+5GSkoLrr78eqampGDlyJD777DO372U0GlFVVWX3CgROUEdERBQcioSXoqIipKSk2G3TaDRITExEUVGR03NOnz4NAHjyyScxZ84c7Nq1C9deey1GjRqFb775xuV7rVixAnFxcbZXZmam/27Ejea5RcUQQ0REFDBehZclS5ZAkiS3r5MnT/pUEFmWAQD3338/Zs6ciWuuuQbPPfccevbsiY0bN7o8b+nSpaisrLS9zp0759P7ExERUWjwqs/Lo48+ihkzZrg9plu3bkhLS0NJSYnddovFgvLycqSlpTk9Lz09HQDQp08fu+29e/fG999/7/L99Ho99Hq9B6VXDmteiIiIAser8JKcnIzk5ORWjxs2bBgqKipw6NAhDBo0CADw0UcfQZZlDB061Ok5WVlZyMjIwKlTp+y2/+9//8PYsWO9KWbAReq4NAAREVGgKNLnpXfv3hgzZgzmzJmDAwcOYN++fZg3bx7uuusu20ijCxcuoFevXjhw4ACAxknefvWrX+GFF17A9u3b8e233+Lxxx/HyZMnMXv2bCWK6TdRekUGbREREZETij11t2zZgnnz5mHUqFFQqVSYPHkyXnjhBdt+s9mMU6dOoa6uzrZtwYIFaGhowMKFC1FeXo4BAwZg9+7d6N69u1LF9ItYA8MLERFRoEhChNecsVVVVYiLi0NlZSViY2P9eu3KehMGPLUbAPD2/cNsK0n3To+BRt0+JtIjIiIKRd48v/nEbaNog4bBhYiIKID41G0jjjMiIiIKLIYXIiIiCikML14Ir95BREREoYnhxUNCCNy94XOH7WoVG46IiIgCieHFQ/VmK/IvVgOwX1G6acQRERERBQbDiw+aryitYc0LERFRQDG8+KD5UkYRXBqAiIgooDg1rIcitGp8teyn+F9Rja3JSK2SWPNCREQUYAwvHpIkCZE6jV0fF7VK4gR1REREAcYnLxEREYUUhpc2kNhiREREFHAML20Qyc66REREAcfwQkRERCGF4aUNInXs70xERBRoDC9toGanFyIiooBjeCEiIqKQwvDSBnotv31ERESBxqdvG3BFaSIiosBjeGkDLWfXJSIiCjg+fYmIiCikMLwQERFRSGF48VGnaF2wi0BERNQhMbz4iFO8EBERBQfDi4/YWZeIiCg4+AT2UVyENthFICIi6pAYXoiIiCikMLz4QJIAdnkhIiIKDoYXH+g0KmjY54WIiCgo+AT2AZcFICIiCh6GFx9oVfy2ERERBQufwj7gHC9ERETBw/BCREREIYXhxQecoI6IiCh4+BT2gVbNdiMiIqJgYXghIiKikMLw4gODVh3sIhAREXVYDC8+4GgjIiKi4GF4ISIiopDC8EJEREQhheGFiIiIQgrDCxEREYUUhhcfcJI6IiKi4OFTmIiIiEIKw4uXVCpAo+JYaSIiomBhePGSSpIgcaIXIiKioGF4ISIiopDC8EJEREQhheHFS2r2dyEiIgoqhhcvsbMuERFRcDG8EBERUUhhePGSXqsOdhGIiIg6NMXCS3l5OXJzcxEbG4v4+HjMnj0bNTU1bs8pKirCvffei7S0NERFReHaa6/Fjh07lCqiT2IMmmAXgYiIqENTLLzk5ubi+PHj2L17N95//318+umnmDt3rttzpk2bhlOnTuG9997DsWPHMGnSJEyZMgWHDx9WqphEREQUYiQhhPD3RfPz89GnTx8cPHgQgwcPBgDs2rUL48aNw/nz55GRkeH0vOjoaKxbtw733nuvbVunTp3wzDPP4L777vPovauqqhAXF4fKykrExsa2/WaasVhlmKwyInWsfSEiIvInb57fitS85OXlIT4+3hZcACAnJwcqlQr79+93ed7111+Pbdu2oby8HLIsY+vWrWhoaMBNN93k8hyj0Yiqqiq7l1I0ahWDCxERUZApEl6KioqQkpJit02j0SAxMRFFRUUuz3vrrbdgNpvRqVMn6PV63H///XjnnXfQo0cPl+esWLECcXFxtldmZqbf7oOIiIjaH6/Cy5IlSyD9uLaPq9fJkyd9Lszjjz+OiooKfPjhh/jiiy+waNEiTJkyBceOHXN5ztKlS1FZWWl7nTt3zuf3JyIiovbPqzaQRx99FDNmzHB7TLdu3ZCWloaSkhK77RaLBeXl5UhLS3N6XkFBAV588UV8/fXX6Nu3LwBgwIAB+M9//oO1a9fipZdecnqeXq+HXq/35jaIiIgohHkVXpKTk5GcnNzqccOGDUNFRQUOHTqEQYMGAQA++ugjyLKMoUOHOj2nrq4OAKBS2VcGqdVqyLLsTTGJiIgojCnS56V3794YM2YM5syZgwMHDmDfvn2YN28e7rrrLttIowsXLqBXr144cOAAAKBXr17o0aMH7r//fhw4cAAFBQX44x//iN27d2PixIlKFJOIiIhCkGLzvGzZsgW9evXCqFGjMG7cONx4441Yv369bb/ZbMapU6dsNS5arRb//Oc/kZycjAkTJqB///549dVX8corr2DcuHFKFZOIiIhCjCLzvASTkvO8EBERkTKCPs8LERERkVIYXoiIiCikMLwQERFRSGF4ISIiopDC8EJEREQhheGFiIiIQgrDCxEREYUUhhciIiIKKV6tbRQKmubcq6qqCnJJiIiIyFNNz21P5s4Nu/BSXV0NAMjMzAxySYiIiMhb1dXViIuLc3tM2C0PIMsyCgsLERMTA0mS/HrtqqoqZGZm4ty5c2G79ADvMTzwHsNDuN9juN8fwHv0hhAC1dXVyMjIgErlvldL2NW8qFQqdO7cWdH3iI2NDdsfwia8x/DAewwP4X6P4X5/AO/RU63VuDRhh10iIiIKKQwvREREFFIYXryg1+uxbNky6PX6YBdFMbzH8MB7DA/hfo/hfn8A71EpYddhl4iIiMIba16IiIgopDC8EBERUUhheCEiIqKQwvBCREREIaVDh5e1a9ciKysLBoMBQ4cOxYEDB9we//bbb6NXr14wGAzo168f/vnPf9rtF0LgiSeeQHp6OiIiIpCTk4NvvvlGyVtolb/vccaMGZAkye41ZswYJW+hVd7c4/HjxzF58mRkZWVBkiSsXr26zdcMBH/f45NPPunwOfbq1UvBO2idN/e4YcMGDB8+HAkJCUhISEBOTo7D8aH+++jJPYb67+POnTsxePBgxMfHIyoqCgMHDsRrr71md0yof46e3GOof47Nbd26FZIkYeLEiXbb/f45ig5q69atQqfTiY0bN4rjx4+LOXPmiPj4eFFcXOz0+H379gm1Wi2effZZceLECfHb3/5WaLVacezYMdsxK1euFHFxceLdd98VX331lbjttttEdna2qK+vD9Rt2VHiHqdPny7GjBkjLl68aHuVl5cH6pYceHuPBw4cEI899ph48803RVpamnjuuefafE2lKXGPy5YtE3379rX7HEtLSxW+E9e8vcdf/OIXYu3ateLw4cMiPz9fzJgxQ8TFxYnz58/bjgn130dP7jHUfx8//vhjsXPnTnHixAnx7bffitWrVwu1Wi127dplOybUP0dP7jHUP8cmZ86cEVdccYUYPny4uP322+32+ftz7LDhZciQIeKhhx6yfW21WkVGRoZYsWKF0+OnTJkixo8fb7dt6NCh4v777xdCCCHLskhLSxP/7//9P9v+iooKodfrxZtvvqnAHbTO3/coROMvWcsfymDy9h6b69q1q9MHe1uuqQQl7nHZsmViwIABfixl27T1e26xWERMTIx45ZVXhBDh8fvYUst7FCK8fh+bXHPNNeK3v/2tECI8P0ch7O9RiPD4HC0Wi7j++uvFX/7yF4f7UeJz7JDNRiaTCYcOHUJOTo5tm0qlQk5ODvLy8pyek5eXZ3c8AIwePdp2/JkzZ1BUVGR3TFxcHIYOHerymkpS4h6b7N27FykpKejZsyceeOAB/PDDD/6/AQ/4co/BuGZbKFmeb775BhkZGejWrRtyc3Px/ffft7W4PvHHPdbV1cFsNiMxMRFAePw+ttTyHpuEy++jEAJ79uzBqVOnMGLECADh9zk6u8cmof45/u53v0NKSgpmz57tsE+JzzHsFmb0RFlZGaxWK1JTU+22p6am4uTJk07PKSoqcnp8UVGRbX/TNlfHBJIS9wgAY8aMwaRJk5CdnY2CggL85je/wdixY5GXlwe1Wu3/G3HDl3sMxjXbQqnyDB06FJs3b0bPnj1x8eJFPPXUUxg+fDi+/vprxMTEtLXYXvHHPS5evBgZGRm2/zmGw+9jSy3vEQiP38fKykpcccUVMBqNUKvV+POf/4yf/vSnAMLnc3R3j0Dof46fffYZ/vrXv+LIkSNO9yvxOXbI8EK+u+uuu2z/7tevH/r374/u3btj7969GDVqVBBLRt4YO3as7d/9+/fH0KFD0bVrV7z11ltO/3Jqz1auXImtW7di7969MBgMwS6OIlzdYzj8PsbExODIkSOoqanBnj17sGjRInTr1g033XRTsIvmN63dYyh/jtXV1bj33nuxYcMGJCUlBex9O2SzUVJSEtRqNYqLi+22FxcXIy0tzek5aWlpbo9v+q8311SSEvfoTLdu3ZCUlIRvv/227YX2ki/3GIxrtkWgyhMfH4+rrroq5D7HVatWYeXKlfjggw/Qv39/2/Zw+H1s4uoenQnF30eVSoUePXpg4MCBePTRR3HHHXdgxYoVAMLnc3R3j86E0udYUFCA7777DhMmTIBGo4FGo8Grr76K9957DxqNBgUFBYp8jh0yvOh0OgwaNAh79uyxbZNlGXv27MGwYcOcnjNs2DC74wFg9+7dtuOzs7ORlpZmd0xVVRX279/v8ppKUuIenTl//jx++OEHpKen+6fgXvDlHoNxzbYIVHlqampQUFAQUp/js88+i+XLl2PXrl0YPHiw3b5w+H0E3N+jM+Hw+yjLMoxGI4Dw+Rxban6PzoTS59irVy8cO3YMR44csb1uu+023HzzzThy5AgyMzOV+Rx96uYbBrZu3Sr0er3YvHmzOHHihJg7d66Ij48XRUVFQggh7r33XrFkyRLb8fv27RMajUasWrVK5Ofni2XLljkdKh0fHy/+9re/iaNHj4rbb7896EP6/HmP1dXV4rHHHhN5eXnizJkz4sMPPxTXXnutuPLKK0VDQ0NI3KPRaBSHDx8Whw8fFunp6eKxxx4Thw8fFt98843H1ww0Je7x0UcfFXv37hVnzpwR+/btEzk5OSIpKUmUlJQE/P6E8P4eV65cKXQ6ndi+fbvd8NLq6mq7Y0L597G1ewyH38c//OEP4oMPPhAFBQXixIkTYtWqVUKj0YgNGzbYjgn1z7G1ewyHz7ElZ6On/P05dtjwIoQQa9asEV26dBE6nU4MGTJEfP7557Z9I0eOFNOnT7c7/q233hJXXXWV0Ol0om/fvuIf//iH3X5ZlsXjjz8uUlNThV6vF6NGjRKnTp0KxK245M97rKurE7feeqtITk4WWq1WdO3aVcyZMydoD/Um3tzjmTNnBACH18iRIz2+ZjD4+x6nTp0q0tPThU6nE1dccYWYOnWq+PbbbwN4R468uceuXbs6vcdly5bZjgn138fW7jEcfh//7//+T/To0UMYDAaRkJAghg0bJrZu3Wp3vVD/HFu7x3D4HFtyFl78/TlKQgjhW50NERERUeB1yD4vREREFLoYXoiIiCikMLwQERFRSGF4ISIiopDC8EJEREQhheGFiIiIQgrDCxEREYUUhhciCnmbN29GfHx8sItBRAHC8EJEIW/q1Kn43//+Z/v6ySefxMCBA4NXICJSlCbYBSAiaquIiAhEREQEuxhEFCCseSEiyLKMZ599Fj169IBer0eXLl3w9NNPAwCOHTuGW265BREREejUqRPmzp2Lmpoa27kzZszAxIkTsWrVKqSnp6NTp0546KGHYDabbccYjUYsXrwYmZmZ0Ov16NGjB/76178CAKxWK2bPno3s7GxERESgZ8+eeP75523nfvDBBzAYDKioqLAr8/z583HLLbcAsG822rx5M5566il89dVXkCQJkiRh8+bNmDVrFn72s5/ZXcNsNiMlJcVWFne2b9+Ofv362b4POTk5qK2tte3/y1/+gt69e8NgMKBXr17485//7MF3noh84vOqSEQUNn7961+LhIQEsXnzZvHtt9+K//znP2LDhg2ipqZGpKeni0mTJoljx46JPXv2iOzsbLtF2aZPny5iY2PFL3/5S5Gfny/+/ve/i8jISLF+/XrbMVOmTBGZmZli586doqCgQHz44Ye2xelMJpN44oknxMGDB8Xp06fF66+/LiIjI8W2bduEEEJYLBaRmpoq/vKXv9iu13Lbpk2bRFxcnBCicaG7Rx99VPTt29e2EnNdXZ3Yt2+fUKvVorCw0HadnTt3iqioKLvVqJ0pLCwUGo1G/OlPfxJnzpwRR48eFWvXrrWd9/rrr4v09HSxY8cOcfr0abFjxw6RmJgoNm/e7PuHQkQuMbwQdXBVVVVCr9eLDRs2OOxbv369SEhIEDU1NbZt//jHP4RKpbKtejt9+nTRtWtXYbFYbMfceeedYurUqUIIIU6dOiUAiN27d3tcpoceekhMnjzZ9vX8+fPFLbfcYvv63//+t9Dr9eLSpUtCCPvwIoQQy5YtEwMGDHC4bp8+fcQzzzxj+3rChAlixowZrZbn0KFDAoD47rvvnO7v3r27eOONN+y2LV++XAwbNqzVaxOR99hsRNTB5efnw2g0YtSoUU73DRgwAFFRUbZtN9xwA2RZxqlTp2zb+vbtC7Vabfs6PT0dJSUlAIAjR45ArVZj5MiRLsuwdu1aDBo0CMnJyYiOjsb69evx/fff2/bn5uZi7969KCwsBABs2bIF48eP93qE0X333YdNmzYBAIqLi/Gvf/0Ls2bNavW8AQMGYNSoUejXrx/uvPNObNiwAZcuXQIA1NbWoqCgALNnz0Z0dLTt9fvf/x4FBQVelY+IPMPwQtTB+aOjq1artftakiTIsuzR9bdu3YrHHnsMs2fPxgcffIAjR45g5syZMJlMtmN+8pOfoHv37ti6dSvq6+vxzjvvIDc31+tyTps2DadPn0ZeXh5ef/11ZGdnY/jw4a2ep1arsXv3bvzrX/9Cnz59sGbNGvTs2RNnzpyx9f/ZsGEDjhw5Ynt9/fXX+Pzzz70uIxG1juGFqIO78sorERERgT179jjs6927N7766iu7jqn79u2DSqVCz549Pbp+v379IMsyPvnkE6f79+3bh+uvvx4PPvggrrnmGvTo0cNpjUVubi62bNmCv//971CpVBg/frzL99TpdLBarQ7bO3XqhIkTJ2LTpk3YvHkzZs6c6dE9AI2B7IYbbsBTTz2Fw4cPQ6fT4Z133kFqaioyMjJw+vRp9OjRw+6VnZ3t8fWJyHMcKk3UwRkMBixevBi//vWvodPpcMMNN6C0tBTHjx9Hbm4uli1bhunTp+PJJ59EaWkpHn74Ydx7771ITU316PpZWVmYPn06Zs2ahRdeeAEDBgzA2bNnUVJSgilTpuDKK6/Eq6++in//+9/Izs7Ga6+9hoMHDzo8+HNzc/Hkk0/i6aefxh133AG9Xu/2Pc+cOYMjR46gc+fOiImJsR1/33334Wc/+xmsViumT5/u0T3s378fe/bswa233oqUlBTs378fpaWl6N27NwDgqaeewiOPPIK4uDiMGTMGRqMRX3zxBS5duoRFixZ59B5E5IVgd7ohouCzWq3i97//vejatavQarWiS5cu4g9/+IMQQoijR4+Km2++WRgMBpGYmCjmzJljNzpn+vTp4vbbb7e73vz588XIkSNtX9fX14uFCxeK9PR0odPpRI8ePcTGjRuFEEI0NDSIGTNmiLi4OBEfHy8eeOABsWTJEqcdbocMGSIAiI8++shue8sOuw0NDWLy5MkiPj5eABCbNm2y7ZNlWXTt2lWMGzfO4+/PiRMnxOjRo0VycrLQ6/XiqquuEmvWrLE7ZsuWLWLgwIFCp9OJhIQEMWLECLFz506P34OIPCcJIUSwAxQRUaDU1NTgiiuuwKZNmzBp0qRgF4eIfMBmIyLqEGRZRllZGf74xz8iPj4et912W7CLREQ+Ynghog7h+++/R3Z2Njp37ozNmzdDo9HY7evTp4/Lc0+cOIEuXboEophE5AE2GxFRh2exWPDdd9+53J+VlWUXdogouBheiIiIKKRwnhciIiIKKQwvREREFFIYXoiIiCikMLwQERFRSGF4ISIiopDC8EJEREQhheGFiIiIQgrDCxEREYWU/w9JoDMOpfPVYQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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dd8asTu1Nyd27d6+54IILTG5urpEUmSLb0fTdk08+uU25W0+hNcaYjz76yEybNs243W5TWlpqfvSjH5mVK1e2uaYxxqxYscKMHTvWuN1uU1hYaK688krzySeftLnPY489ZioqKozL5TJjxowxf/7zn9vc+8knnzTnnXeeKSkpMS6XywwaNMj8y7/8i/nss8+irrVkyRKTlZVlqqur29wH6CvsNQMgIX72s59p6dKl+vDDDzsc/In4Gjt2rKZOnRpZiA5IBMaIAEiIm2++WbW1tVq+fHmii5KRXnzxRX344YdJsekgMhstIkAChUKhqIGH7cnJyelyyXQASFUMVgUS6OOPP253EbGWFi5cqB//+Md9UyAA6GMEESCBysrKulyGu+VS5gCQbuiaAQAACcNgVQAAkDBJ3TUTDoe1Z88e5ebmsiETAAApwhijmpoalZeXy2brvM0jqYPInj17erVvBAAASLyPP/5YAwYM6PScpA4iubm5ko5WpDf7RwAAgL5XXV2tgQMHRj7HO5PUQaS5OyYvL48gAgBAiunOsAoGqwIAgIQhiAAAgIQhiAAAgIQhiAAAgIQhiAAAgIQhiAAAgIQhiAAAgIQhiAAAgIQhiAAAgIQhiAAAgIQhiAAAgIQhiAAAgIQhiAAAgIRJ6t13AQBIFaGwkTEm0cXoEcuyZLd1vUNuPBFEAABdav0BGwgZNQRC3X59UzCsOn8w1sVKKrX+oFIshyjP69DgftkJLQNBBACSiDGmyw8zfzCsplC4R9etbwr2OggEw0aBYIp9wiJlEEQAoAfCYaNQF0khFDaqbgj06vrVjUE1NHW/pQFIdQQRAOjEp5UNCoW+CB7+YEiNgZ61RgDoGEEEQNoKhsLyB6NDg5HatFaEwqbDro6GplDK9fsDqYQgAiAtNAZC+rSyoU23BiECSG4EEQBJKxQ2CnQxKLO6IaCQMaqsDygYInUAqYYgAiBmQmGjULjjMOAPhvRZVWOPrke4ANIbQQRAr1U3BtTYoiuEGR8AeoogAqDbqhoCagqGFTZGVQ0BBUJhhZlAAuAYEEQAdKmmMaBDtU1qCIToKgEQUwQRAJ2qagjoQI2fLhcAcUEQARClMRBSQ1NIgXBY/kBYlfW9WyEUALqDIAJADU0h1TcFVdlwdApsU5CBHwD6BkEEyGCNgZA+PlzPkuUAEoYgAmSQ1guE7TxUx66qABKKIAKkqVDYqLYxqJAxCn++znljIKQjdYz5AJA8CCJAGmkKhlXnDyoQCisQNjpc25ToIgFApwgiQBowxmhvdaMO1hA8AKQWggiQ4vZVNyoQCtPlAiAlEUSAFGA+H+PxaWVDZF0PtrcHkA4IIkCSMcbIHwxrf7VfkhQMh1XnZ1VTAOmJIAIkkUO1ftU0BlXTGEx0UQCgTxBEgARrCoa1+3CdGgNhulsAZByCCJBAobDRp5UNamhiZVMAmYkgAiRIdWNAlXUB1dINAyCDEUSAPlTVEFBjIKSwMapuCLK5HICMRxAB+kA4bLTrcD2tHwDQCkEEiLMP99WoKRRWmMYPAGiDIALEQThsVNlwdOGxQMgQQgCgAwQRIMb2Vzd+PhaE9AEAXbHF8+KLFi3SaaedptzcXJWUlOiiiy7S1q1b43lLICFq/UFVNwb0WVWD9lX7CSEA0E1xbRFZs2aNrr/+ep122mkKBoP60Y9+pPPOO0/vv/++srOz43lrIK4aAyHtrWpUQ+Do0uuhsGExMgDohbgGkRdffDHq8bJly1RSUqL169dr8uTJ8bw1EDfGGFXWB1iGHQBioE/HiFRVVUmSCgsL233e7/fL7/dHHldXV/dJuYCuBEJhHaw9+r15uK6JwacAECNxHSPSUjgc1k033aSzzz5bI0eObPecRYsWyefzRb4GDhzYV8UDOnSkrkkfH67XwZomHawhhABALFnG9E3P9nXXXacXXnhBr732mgYMGNDuOe21iAwcOFBVVVXKy8vri2ICEcFQWPtq/KpuCCgYYgAIgPST53VocL/Yj9msrq6Wz+fr1ud3n3TNzJs3T88995xeeeWVDkOIJLndbrnd7r4oEtBGYyAk/+ezXeqagqoigABA3MU1iBhjdMMNN+jpp5/W6tWrNXTo0HjeDuiV+qajg04P1Tapsj6Q4NIAQGaJaxC5/vrr9fvf/17PPvuscnNztXfvXkmSz+eT1+uN562Bbjlc16RPjzQkuhgAkLHiOkbEsqx2jy9dulRXX311l6/vSR8T0BONgZAO1vpVWR9g/Q8AGSvtx4j00ThYoNsaAyEdqPGrvimkpiDTXwAg0dhrBhkhEArrSF2TDtU1MQAVAJIIQQRpr6EppF2H6xQIEkAAINkQRJC2wmGj2qagdh2sT3RRAAAdIIggbe2patCROqbjAkAyI4gg7YTDRh/ur2UwKgCkAIII0kYwFFZd09FpuYQQAEgNBBGkhYamkLYfrGVDOgBIMX22+y4QTwdr/YQQAEhBBBGkvFDYqLqRQakAkIoIIkh59U1BWkMAIEUxRgQpqzEQkj8Y1mdVbFoHAKmKIIKUUt8U1P5qvySpjpYQAEh5BBGkhFDYqLohoIZASDWNwUQXBwAQIwQRpIRDdX7tq/InuhgAgBgjiCDpBUNhVdUzKwZIZcYY+VloMOk47Zbqm4LyOu2yLCshZSCIIOntOFinxgC/wIBEOdYQYYx021ObtP1gXQxLhVh6/6fTleVKTCQgiCBpVdUHdKDWz19RQAt93bJAiEC8EUSQdIKhsGoag9pb3ahgyCS6OECURHYxpHooqCjK1t2XjFKCegDQjlyPQ4P6ZcnrtCesDAQRJJVPKxtU7w/SFYM+1d1wkepB4FjEIkS4HbaEjUNA+7wue8K6ZJoRRJA0av1BHa5tSnQxkKJ621KRiuEiES0LhAjEC0EECdMYCCkYNgoEw9pXQzcMeq45fPR1mEh0FwOhAOmEIII+V9MYUH1TSDWNATU00QWTLlJ9EGVPwgVBAIgdggj6VDhsVFkfUCXrgqS01qEjWbo3jqWlgnABJAZBBH2qMRgihKSoRHWDdKVl+CBMAKmHIAKgQz0NHwyiBNBTBBEA7Qobo5tXbOwwfLQXOggFAHqKIAJAUvS4D2Okm1Zs0J6qxqhz6AYBEGsEESCDdafrpdzn0eKZYwkfAOKCIAJkqK66XqSjLSC/mjlGNsIHgDghiABpqrN1PbrT9SLRAgIg/ggi6FNN7KQbV72ZYkvXC4BEIoigz9T6g/q0siHRxUhLxhg1BsI9Xt+DrhcAiUYQQVx9cqReHqddwZDRgRp/oouTdroKIF2t60ELCIBEI4ggbg7W+nWkLiCJlVRjobvLqjPFFkAqIYggLg7W+rW31UBIdE97g0y7M+ajOYB4nIQPAKmDIIKYC4bC2l/tlzGJLklqOZZxHgQQAKmKIIKY+6yqUaEwKaS7ehJAWFYdQLohiCAmQmGjuqagquoD7K7bAx0tKtbRIFNCB4B0QxDBMQuHjXYeqlO9P5TooiS9rvZzoZsFQKYhiOCY7atpJIR0oavul+ZFxQggADINQQTHrLohmOgiJK3ujP9gUTEAmYwgAsRQ666Xrtb5kBj3ASCzEUSAGDHG6NY/btKWvTXtPs/4DwBoiyAC9FBHu9o2BkLthhACCAB0jCAC9EBH021be3T26fI47ZLoegGAzhBEgE50Nd22PSP658nndRI+AKAbCCJABzpr/Wiebtte1qAFBAC6jyACtCNsjK57bH27rR9MtwWA2CGIIKN1tNNtyy6Y1q0ftHgAQOwQRJCxujPwtNzn0ZKrxtP6AQBxQhBBRuqs66UZXTAAEH8EEWSU5iXXO+t6aUYXDADEH0EEGaGjPV/oegGAxCKIIO11NBaErhcASDyCCNJae2NBWHIdAJIHQQRpq3UIaR4LQgABgORBEEHa6WhAKmNBACD5EESQVowxuvWPm6J2wSWEAEDyIoggLTSvkNoYCEWFEAakAkByI4gg5bXXCiJJj84+nV1wASDJ2RJdAOBYNQbCbULIiP55hBAASAG0iCClGWN021ObIo8fnX26PE47q6ICQIogiCClNQbCkYXKKoqyaQUBgBRDEEFKajlFt9ndl4wihABAiiGI4JiEw0ZhY/rsfh3tGVNRlC2PkyFPAJBqCCI4Jv5gWMFQ/INIRwFE+mKKLq0hAJB64von5CuvvKILL7xQ5eXlsixLzzzzTDxvhwSoawrG/R5hY3TTio267JG1bVpB/ufaCVrMOiEAkLLi2iJSV1en0aNHa/bs2brkkkvieSskQH1TUPuqG7s+sZfaW6pdYtM6AEgncQ0i559/vs4///x43gIJEgob7alsUDgcn+uHjdHNKzZGtYCwaR0ApB/GiKBXqhoCamiKfQrprBWEpdoBIP0kVRDx+/3y+/2Rx9XV1QksDTpSVR/QnsqGmF+XVhAAyDxJNd9x0aJF8vl8ka+BAwcmukhopdYf1GfVDYrljF1jjBqaQrrusfVtBqMuuWq8vC47IQQA0lRStYjcfvvtmj9/fuRxdXU1YSSJ7K9u1P4af0xCSPNuucaozZRcWkEAIHMkVRBxu91yu92JLgY6UN0YOOYQ0tl6IBJjQQAg08Q1iNTW1mrbtm2Rxzt27NDGjRtVWFioQYMGxfPWSELGGN36x01tdsqVmJILpDuXw6Yslz3RxYirkjy3HLakGvHQpWT4bRvXILJu3Tp9+ctfjjxu7naZNWuWli1bFs9bIwn5g+GoENIcPixL7JYLxJllHf3qDZfdJo8zOkR4nHblebv/EWK3LDnsqfUhjb4R1yAydepUmT7chwSp49HZp7NTLtBLXpddbkfPPtRzPQ7lZ7niVCKg95JqjAgyh8fJTBikJofdktPe+feuzbLk7WU3hNdpl6uLkOGy22hdQNogiKDP0DiGZNE6JNhtVocf/tmtpo+7HW27KQD0HkEEcdM8Rffov6WbVmxIcImQjprHBroddnmcNtltX4QGj8OuPK8z6nxLks1GaxyQLAgiiIv2VkltVlGU3eP+baQHm03KdnX/147baVO+t/NxDcy0AlIbQQQxFzZG1z22PmqvmGbN64TwwZHammc6Zbsd8rbopvC67HJ2MnaB1ggArRFEEFPm85aQ5hDSvEpqc+5gmm5q8rqOvm85boecdptslpiBASAmCCKIqcZAONIdU+7zaMlV41klNYVZllSa51G/bBctGQDigiCCmAkbEzUgdfHMsYSQFGNZUo7boTyvU067JY+z864WADhWBBEcs+b9Y25asSHSJVNRlC2Pkw+wVOFx2uR12TWgICvRRQGQYQgiOCbtzY4p93kYkJoiLEvKz3KqKMfN2hgAEoIggl4z7YQQds9Nbi6HTbmeL37sC7NdBBAACUUQQa/5g9EDUxfPHMuaDknE5bCpf77n6HLgnw80tSwrasEvAEg0gghiYvHMsb3eWwOxYVmSz+tUnscpl8MmyxKtHQCSHkEEMUEjSGJZljSwIEu+LGfXJwNAEiGIACnE5bCpIMspu82KjMPxuuyfr3RK6weA1EMQAZJUcyuT0340fEhHB5ey/TuAdEIQAZKI3WYp221Xeb6XhcQAZASCCJAEnA5LboddJbluZbv5sQSQOfiNBySQw27puAKvct0Opj0DyEgEEaAP2W2W+uW4ZLcd3cfF7bDRBQMgoxFEgD5gWVJxrlsluW5aPgCgBYIIEEPNi4o57JaCISNJyvMenW6bw9gPAGiD34zAMWhu3Mhy2WWzLOV4HCrKcSe2UACQQggi6Jb6pqD8wXCii5EwlqXIAmIF2U5lux3KcTlkY98WADgmBBF0S1VDQOEMzCEF2U5luxxyO23KcvHjAgCxxm9WdCkUNjpc15ToYvS5gmynBhRkJboYAJDWCCLoUmMg1KY1xBijxkAoMQWKE6/LLo/z6FTabJdDuR5+PAAg3vhNi07tq27UwVp/1DFjjG794yZt2VuToFLFjsth09CibFmWZLcsxnwAQB8jiKBdgVBYnxxpUG1jsM1z/mA4KoSM6J8ntyO1FuXyOG0qznUrz+MkfABAAhFE0K69VY3thhBJMuaLfz86+3T5vM6UWaTLbrNUnOtWrschj9Oe6OIAQMYjiGSgxkBIxhwdE9HZOe0JG6ObVmyIPPY47SkTQrwuu0ryjraCAACSA0EkAx2o8cvjtHcYRJqCYTUG2s7VDRuj6x5brz1VjZKkiqLslOmSyc9yqjTPI1eKlBcAMgW/lTNQKGxU5++o28VoX3Vjm+OtQ0i5z6NfzRyTEq0hHqdNAwuzCCEAkIT4zZyB6pqCCrcc6NHCJ0caVFkfiDpmjNHNKzZGhZAlV42PrDSazBx2SwMLWQsEAJIVXTMZ5lCtv8MVUkNho6qGQJvj/mBY2w/WSUqtEJLltuv44pxEFwMA0AlaRDJMZTtBo1lNY0AdNJRELJ45NiVCiNdlU0VRdqKLAQDoAkEkgwRDYfnbGYTarLaDcSMtpUAGkWVJAwuzUmL8CgBkOrpmMshnVY0Khds2eYTDRofrm9qMDUlF2W67BhVmyWEnYwNAKiCIZIjGQKjDoPHJkYZ2x4akmkH9spTjdsjOSqkAkDIIIhmsMRDS/mp/lyGkq3EjyaDM51Gex0F3DACkGNqvM1j3QojRbU9t6qMS9Y5lSQVZqbPMPADgC7SIZKimUFj1Te0v495SY+CLqbvJupJqrsfBmBAASFEEkQwVCHbd39J6X5m7LxmVVK0OLodNNkvK97oSXRQAQC8RRNCGMUaNgbBuWrEhal8ZjzNxrQ42m+TzOmWzrMg6Jv1yXHLSEgIAKY0ggijGGN36x03asrcmciwZ9pXxeZ0aUMBS7QCQbggiiOIPhqNCSEVRtn41c0xCVlN1O23yOu0qzHYpq4OdggEAqY0ggigtp+o+Ovt0+byJmY3isFsqzHapKMfd5/cGAPQdgggiWg9O9TjtfR5CXA6bcjwO5Xudynbz7QkA6Y7f9JB0dGzIzSs2Rg1O7eupupYlDS/L7dN7AgASiyACSUfHhjSvF9LXg1NtNinX7VSpj24YAMg0BBFIih4bsnjm2LgOTnU6LDlslvplu5XjcciSWJAMADIUQQRtlnGPVwZx2C0V57pls44ORAUAgCCS4YwxqmoIxGUZd8uS+vs8ChvJYbOOLkjGzrgAgBYIIhks/PkA1eYQIsVuGXfLksrzvbR8AAA6RRDJUKGw0XWPr9dnn8+SkaQR/fNitox7mc9DCAEAdIkgkmGMMWoIhDTzkTcjx8p9Hi2eOVYepy0mrSEOu6WCLEIIAKBrBJEM0t4+Mtkuu5ZcNT6ms2TKfV7ZGQsCAOgGgkgGab2PjCQtuTJ2IcRus+Rx2uTLcsbkegCA9MfiDRmk5VohzbobGkry3PK6Ov52sdssFWQ7VVGc09viAQAyEC0iGaL1WiErrj1THqe9y9YQl8Om0jy38rNcqmkMtHuOZUkDC73K9dASAgDoGYJIhmgIhKLWCvF2Y0O7bLddQ/pld7n2R2G2ixACAOgVgkiGaNkt0521QgpzXDou39utazNNFwDQW4wRyQDGGF3127cij7szNjXH1f2M2kd74wEA0hBBJAM0BEKR2TLdXcLd1c1l3i1Lcd0gDwCQ3uiayTBddct4XTYV53i6vcKqx2mTk51zAQC9RBDJMF01Xgzul92jYJHjZpAqAKD3+FMWEZZ1dJfc7irKdak0zx3HEgEA0l2fBJEHHnhAQ4YMkcfj0RlnnKG//e1vfXFb9JDP6+z2XjM2m1SQ5YrJ3jQAgMwV9yCyYsUKzZ8/XwsXLtQ777yj0aNHa/r06dq/f3+8b51RAqHwMb3eZuvZNNwB+VnyOO3HdE8AAOIeRH75y19qzpw5uuaaa3TSSSfpoYceUlZWln73u9/F+9YZo6o+oL1VjR0+Hw63s7Z7Cw67pePyvcp2d2/IUJnPw34yAICYiGsQaWpq0vr16zVt2rQvbmizadq0aVq7dm2b8/1+v6qrq6O+0LV9NY3t7iPTrLoh2Onry/I8ys/qXmtIYY5LBYQQAECMxDWIHDx4UKFQSKWlpVHHS0tLtXfv3jbnL1q0SD6fL/I1cODAeBYvLfiDIfkDHXfLhMJGn1Y2dPi812VTjqd7LSHZbodKct1yMF0XABAjSfWJcvvtt6uqqiry9fHHHye6SEmvsr79jei6oyTPrWElud2erlua62HNEABATMV1HZGioiLZ7Xbt27cv6vi+fftUVlbW5ny32y23m+mgPdEYCHX6fKiD8SGWdXSWTE90tfkdAAA9Fdc/b10ul8aPH69Vq1ZFjoXDYa1atUoTJkyI560zQp0/qJrGzsd/1Prbf97rsjPrBQCQcHFfWXX+/PmaNWuWTj31VJ1++ulavHix6urqdM0118T71mlvX3Xng1SNMapqaL/rhrYNAEAyiHsQmTlzpg4cOKAFCxZo7969GjNmjF588cU2A1jRtVp/UDluh0Jho0N1ftX5O++WqWsKqbaLFhMAABKpT/aamTdvnubNm9cXt0pre6saFDZSUzDcaUuIJB2pa9Keqo5nywAAkAzY9C4F1DQGdKDGr4am7q+eWtcUVPjYFlsFACDuCCJJrjEQ0u7D9T0OFQ1NX3TbdNV6AgBAorAoRBJrDIT0cS9CSE1jQI2fL3JmjNFtT21qcw7rgQAAkgEtIknsQI0/Eii6Uh8IyhijplBYuw7VR443BsLafrBOklRRlC2342gA8bqYugsASDz+LE5Sxpgu1whpKRA0CoSM9lf7I10xrVtD7r5klCzLks0meVlDBACQBGgRSSLGGFnW0RU+6ppCHa6K2pGPj9SrvsWUXn8wujXE4zyaOwuyXN3eaRcAgHji0yiJHPl835iGQKjLpdvbU9/JuiLNrSH9clwqymEZfQBAciCIJJGqhkDcFiD7vKFF+VlOuRz0yAEAkgOfSBnEsiQXs2UAAEmET6U01nr9kFyPQw6CCAAgifCplKbaWz+EsSEAgGRDEEkigVDs1mRvvX5IntfBTBkAQNIhiCSJqvqA/N1cvKwr7a0fYmserQoAQBIhiCSBxkBIn1XHbqfc1q0hzeuHAACQbPiESgIHavwKBGOzM11Hq6kCAJCMCCJJoL6p54uXdaSj1VQBAEhGfEolWDAUjukg1ZZTdlu2hrDJHQAgGRFEEqw+EGqz3kdvte6Wae6RsSyp3OeNzU0AAIghgkiC1fljs6S7MUZVDYGobhm3wybLkoYUZctmY5wIACD5sLBEgtV1slFddxljdOsfN2nL3prIsZbdMjmsHwIASFK0iCTQ4bomNcRgoKo/GI4KISP65zFIFQCQEvhTOYH2VTfG/JqPzj5dPq+TKbsAgJTAn80J0hQMKxiK0SjVFjxOOyEEAJAyCCIJUhujQapdIZMAAJIZQSRBmoKxWzukM9kuet8AAMmLIJIg9U190yKS5WYhMwBA8iKIJEAobGIybbcrdpulfK8r7vcBAKC3CCIJ8MmR+j65T5bLLpeDtxgAkLwYQNCHjDFqDIRV3RD/bhmbTSrN88T9PgAAHAuCSB86VNekzypjv3ZIe/I8Tja6AwAkPdrt+1A8FjDrSK6HjAkASH4EkT7iD4YUjtOM3fZ27/U4aQ0BACQ/gkgficWeMu0JG6ObVmyIOpbndRBEAAApgSDSR+KxgJkxRjev2Kg9VUe7fCqKsuVx2tTf5435vQAAiAcGEsSZMUYNgZAO1Ppjfm1/MKztB+skSeU+j341c4wKc9xM2QUApAyCSByFwkY7DtbFrVum5diQxTPHKs/rVH+m7AIAUgh/OsdRrT8YxxBidNtTmyKPsz12DSjwymZjlzsAQOogiMTRgZrYd8c0a9ktU1GUrRyXQ047bycAILXwyRUnh2r9cWsNkaK7Ze6+ZJQsi5YQAEDqYYxIjIXDRgdr/dofx9aQ1t0yZBAAQKoiiMRYVUNA+6rjE0LCnzeDVDcEorpl3MySAQCkKIJIDIXDRofqmuJzbWP09Qdeb3OcbhkAQCrjT+kY2n24Pi7jQowx+t5j69scH9E/Tx4nbyEAIHXRIhIj/mBINY3BuFy7MRDWZ1VfbJhXmO3SQ1eOl8dpozUEAJDSCCIxEAyF9cmRhrhcu729ZJZefZpsBBAAQBqgXT8GDtc3qd5/bF0yYWMig1FbHrvusfWRvWSGFmXr2evPJoQAANIGQeQY+YMhHT7GAarNA1G//sDrkTDSOoSU+zxaPHMMIQQAkFYIIsegujGgHQfrFAiark/u7DoNgah/t95Vt9zn0ZKrxhNCAABphzEivdQUDGvXwfqYXKt1jGm9qy4hBACQrggivRAIhbWvurHrE7vh6GDUjZHHjcFw1POLZ44lhAAA0hZBpAeq6gPaU9Ugy9Ixd8dIinTBtBxjMue/10WdQwYBAKQzgkg31TcFta+mUcHQsQeQZi27YNozon8ey7cDANIaQaQLgVBYh2qbdLDWLxO7DNLGimvPbNMF43awYBkAIL0RRDoQDhvVB0LaW9WghqZw1y/ohZbBxmZZ8jjtcbkPAADJKmODiDFGobCRkVTn/2Jp9uZl2ivrAx28Mnb3v+2pTXG9BwAAyS5jg0h9U0jbD3Q8PiPeGgNfjA+pKMpmLAgAICPx6ZcArVtD7r5kFGNBAAAZiSCSAC1ny1QUZcvj5G0AAGQmPgETjNYQAEAmI4gkQMvZMmQQAEAmI4j0MWbLAADwBYJIH2s9PoTZMgCATManYAIxPgQAkOkIIn2M8SEAAHyBINKHGB8CAEA0gkgfMcaoqiHA+BAAAFqI2yfhXXfdpbPOOktZWVnKz8+P121SgjFGt/5xk779u79FjjE+BACAOAaRpqYmXXrppbruuuvidYuU4Q+GtWVvTeTxiP55rKYKAIDiuOndT37yE0nSsmXL4nWLlPTo7NPl8zppDQEAQEm2+67f75ff7488rq6uTmBp4sPjtBNCAAD4XFL1DyxatEg+ny/yNXDgwEQXKSZaTtkFAABf6FEQue2222RZVqdfH3zwQa8Lc/vtt6uqqiry9fHHH/f6WsnAGKOGppBuWrEh0UUBACAp9ahr5vvf/76uvvrqTs+pqKjodWHcbrfcbnevX59MmmfKtBykypRdAACi9SiIFBcXq7i4OF5lSSuNgXCbEPKrmWMYHwIAQAtxG6y6e/duHT58WLt371YoFNLGjRslScOGDVNOTk68bpsUWq+gykwZAADaF7cgsmDBAv3Xf/1X5PHYsWMlSS+//LKmTp0ar9smhcZA9A67fRFCHHZCDgAg9cRtwMKyZctkjGnzle4hpHVrSF+soJrltqs83xvXewAAEA+MnIyx1q0hfbGCalmeJ+73AAAgHggiMRQ2Jmqqbl+1hmS7k2pdOgAAuo0gEiPGGN28YqP2VDVK6rvWkBxCCAAghRFEYqRll0y5z9NnU3Vddt5CAEDq4lMsBloPUF08c6xsfRBCLEvK8dAiAgBIXQSRGPAH+36AqiQV5bjlpEUEAJDC+BSLgZab2h3LANWevizLbe/VfQAASBYEkWPUeqZMb3tkbDbJ4+x+sPC67Mp20S0DAEhtBJFjEDZG1z22PmqmTG83tSvIcslu616KyXLbVVGU3e3zAQBIVgSRXmodQo5lpozN1r1Bp5Z1dCn3AQVe2QghAIA0QBDphdZrhpT7PFpy1fhez5QZkJ+lPI+zy/Ny3A4VZLnkdjA2BACQHhhk0Aut1ww5lhAiSU5H915b5vP0aBwJAADJjiDSA8YYNQbCUYNTY7FmSHem4Jb63IQQAEDaIYh0kzFGt/5xk7bsrYkci8WaIW6nrcsgUp7vUb8c9zHdBwCAZMQYkW4wxqiqIdAmhMRiGXeft/OxIR6nTQVZrmO6BwAAyYoWkS6EPx+Y2jwmRJIenX26fF5nTPaSye1ktozNJg0szGKGDAAgbRFEOtByPEjz7BhJGtE/L2YhpCDbqawWi5K1vmJpHoNTAQDpjSDSgjFG/mBYxki3PbUpqhWk3OfR4plj5XHaYrarbn+fN+pxttuhmsagpKNjR/K76LYBACDVZWQQMcaovimoxkCoxbG24aNZ83iQWO6oa7dZbVZGLcpxyejoxjVep10ONrQDAKS5jAwiDYGQTrtrVZfnVRRl6+5LRsW0FaSZq52l4C3LUkmuJ6b3AQAgmWVkEOlMc/iwLMntiH0AadbbPWkAAEgnGRlEvE673r7jXO08WN/muXiGj5baaxEBACDTZGQQsSxLWS5HQmekMBsGAAAWNEuYHHdGZkAAAKIQRBIg221vM2MGAIBMRBBJgPJ8b9cnAQCQAQgifczrsjM+BACAzxFE+pDdZqkohw3sAABoxojJPlRRnE1rCAAALdAi0kcsiym7AAC0RhDpIyxgBgBAW3w69hEvrSEAALRBEOkj7C0DAEBbDFaNM6fDUkGWS/1y3IkuCgAASYcgEkeWJQ0rzpHDTmsIAADt4RMyjtwOGyEEAIBO8CkZR0zXBQCgcwSROGKHXQAAOkcQiZMcj0MF2SznDgBAZwgicWBZ0qDCrEQXAwCApEcQiYOSPLfsNivRxQAAIOkRRGLMsqSCLLpkAADoDoJIjHmcdjmZsgsAQLfwiRljPq8z0UUAACBlEERiyLIkF60hAAB0G5+aMVSa55EvixYRAAC6iyASI26nTcW5bGwHAEBPEERixMty7gAA9BhBJAZsNqnM50l0MQAASDkEkRjweZ1M2QUAoBf49DxGliX1y2ZsCAAAvcH2sMfA53Wq1OeW28H4EAAAeoMg0ksuh00DC72yLPaUAQCgt+ia6aVcj4MQAgDAMaJFpIcsSyrMdqksj1kyAAAcK4JIDzSHkPJ8b6KLAgBAWqBrpgd8XichBACAGCKIdFOW267CbFeiiwEAQFqha6YLbqdN5fle5bj5rwIAINZoEelCaa6HEAIAQJzwCdsBr8smr8uhXA//RQAAxEvGfspalmS3HV0HxNaiXSjf65JlSSW5btYJAQAgzjI2iGS5HDqpPC/RxQAAIKMxRgQAACQMQQQAACQMQQQAACRM3ILIzp079Z3vfEdDhw6V1+vV8ccfr4ULF6qpqSletwQAACkmboNVP/jgA4XDYT388MMaNmyYNm/erDlz5qiurk7//u//Hq/bAgCAFGIZY0xf3ezee+/VkiVLtH379m6dX11dLZ/Pp6qqKuXlMcMFAIBU0JPP7z4dI1JVVaXCwsK+vCUAAEhifbaOyLZt23T//fd32i3j9/vl9/sjj6urq/uiaAAAIEF63CJy2223ybKsTr8++OCDqNd8+umnmjFjhi699FLNmTOnw2svWrRIPp8v8jVw4MCe1wgAAKSMHo8ROXDggA4dOtTpORUVFXK5XJKkPXv2aOrUqTrzzDO1bNky2WwdZ5/2WkQGDhzIGBEAAFJIT8aI9Lhrpri4WMXFxd0699NPP9WXv/xljR8/XkuXLu00hEiS2+2W2+3uaZEAAECKitsYkU8//VRTp07V4MGD9e///u86cOBA5LmysrJ43RYAAKSQuAWRlStXatu2bdq2bZsGDBgQ9VwfzhgGAABJLG7Td6+++moZY9r9AgAAkNhrBgAAJFCfrSPSG82tJ6wnAgBA6mj+3O5OL0hSB5GamhpJYj0RAABSUE1NjXw+X6fn9OleMz0VDoe1Z88eGWM0aNAgffzxx2m5nkjzeinpWD/qlrrSuX7pXDcpveuXznWT0qd+xhjV1NSovLy8y6U7krpFxGazacCAAZEmnry8vJR+Y7qSzvWjbqkrneuXznWT0rt+6Vw3KT3q11VLSDMGqwIAgIQhiAAAgIRJiSDidru1cOHCtF3+PZ3rR91SVzrXL53rJqV3/dK5blL61689ST1YFQAApLeUaBEBAADpiSACAAAShiACAAAShiACAAASJiWCyAMPPKAhQ4bI4/HojDPO0N/+9rdEF6lLP/7xj2VZVtTXiSeeGHm+sbFR119/vfr166ecnBx94xvf0L59+6KusXv3bl1wwQXKyspSSUmJbrnlFgWDwb6uil555RVdeOGFKi8vl2VZeuaZZ6KeN8ZowYIF6t+/v7xer6ZNm6YPP/ww6pzDhw/ryiuvVF5envLz8/Wd73xHtbW1Ueds2rRJkyZNksfj0cCBA/WLX/wi3lXrsm5XX311m/dxxowZUecka90WLVqk0047Tbm5uSopKdFFF12krVu3Rp0Tq+/D1atXa9y4cXK73Ro2bJiWLVsW7+p1q35Tp05t8/5973vfizonGeu3ZMkSjRo1KrKo1YQJE/TCCy9Enk/l903qun6p+r615+6775ZlWbrpppsix1L9/Ys5k+SWL19uXC6X+d3vfmfee+89M2fOHJOfn2/27duX6KJ1auHChebkk082n332WeTrwIEDkee/973vmYEDB5pVq1aZdevWmTPPPNOcddZZkeeDwaAZOXKkmTZtmtmwYYP505/+ZIqKisztt9/e53X505/+ZO644w7z1FNPGUnm6aefjnr+7rvvNj6fzzzzzDPm73//u/na175mhg4dahoaGiLnzJgxw4wePdq8+eab5tVXXzXDhg0zV1xxReT5qqoqU1paaq688kqzefNm88QTTxiv12sefvjhhNZt1qxZZsaMGVHv4+HDh6POSda6TZ8+3SxdutRs3rzZbNy40fzTP/2TGTRokKmtrY2cE4vvw+3bt5usrCwzf/588/7775v777/f2O128+KLLya8flOmTDFz5syJev+qqqqSvn7/+7//a55//nnzj3/8w2zdutX86Ec/Mk6n02zevNkYk9rvW3fql6rvW2t/+9vfzJAhQ8yoUaPMjTfeGDme6u9frCV9EDn99NPN9ddfH3kcCoVMeXm5WbRoUQJL1bWFCxea0aNHt/tcZWWlcTqd5g9/+EPk2JYtW4wks3btWmPM0Q9Im81m9u7dGzlnyZIlJi8vz/j9/riWvTOtP6zD4bApKysz9957b+RYZWWlcbvd5oknnjDGGPP+++8bSebtt9+OnPPCCy8Yy7LMp59+aowx5sEHHzQFBQVRdbv11lvN8OHD41yjL3QURL7+9a93+JpUqZsxxuzfv99IMmvWrDHGxO778Ic//KE5+eSTo+41c+ZMM3369HhXKUrr+hlz9AOt5QdAa6lUv4KCAvPb3/427d63Zs31MyY93reamhpzwgknmJUrV0bVJ13fv2OR1F0zTU1NWr9+vaZNmxY5ZrPZNG3aNK1duzaBJeueDz/8UOXl5aqoqNCVV16p3bt3S5LWr1+vQCAQVa8TTzxRgwYNitRr7dq1OuWUU1RaWho5Z/r06aqurtZ7773XtxXpxI4dO7R3796ouvh8Pp1xxhlRdcnPz9epp54aOWfatGmy2Wx66623IudMnjxZLpcrcs706dO1detWHTlypI9q077Vq1erpKREw4cP13XXXadDhw5FnkululVVVUmSCgsLJcXu+3Dt2rVR12g+p69/RlvXr9njjz+uoqIijRw5Urfffrvq6+sjz6VC/UKhkJYvX666ujpNmDAh7d631vVrlurv2/XXX68LLrigTRnS7f2LhaTe9O7gwYMKhUJRb4YklZaW6oMPPkhQqbrnjDPO0LJlyzR8+HB99tln+slPfqJJkyZp8+bN2rt3r1wul/Lz86NeU1paqr1790qS9u7d2269m59LFs1laa+sLetSUlIS9bzD4VBhYWHUOUOHDm1zjebnCgoK4lL+rsyYMUOXXHKJhg4dqo8++kg/+tGPdP7552vt2rWy2+0pU7dwOKybbrpJZ599tkaOHBm5dyy+Dzs6p7q6Wg0NDfJ6vfGoUpT26idJ3/rWtzR48GCVl5dr06ZNuvXWW7V161Y99dRTnZa9+bnOzol3/d59911NmDBBjY2NysnJ0dNPP62TTjpJGzduTIv3raP6San9vknS8uXL9c477+jtt99u81w6/dzFSlIHkVR2/vnnR/49atQonXHGGRo8eLD+53/+J6W+QTLd5ZdfHvn3KaecolGjRun444/X6tWrde655yawZD1z/fXXa/PmzXrttdcSXZS46Kh+1157beTfp5xyivr3769zzz1XH330kY4//vi+LmaPDB8+XBs3blRVVZWefPJJzZo1S2vWrEl0sWKmo/qddNJJKf2+ffzxx7rxxhu1cuVKeTyeRBcnJSR110xRUZHsdnub0cT79u1TWVlZgkrVO/n5+frSl76kbdu2qaysTE1NTaqsrIw6p2W9ysrK2q1383PJorksnb1HZWVl2r9/f9TzwWBQhw8fTrn6VlRUqKioSNu2bZOUGnWbN2+ennvuOb388ssaMGBA5Hisvg87OicvL69PQndH9WvPGWecIUlR71+y1s/lcmnYsGEaP368Fi1apNGjR+u+++5Lm/eto/q1J5Xet/Xr12v//v0aN26cHA6HHA6H1qxZo//8z/+Uw+FQaWlpWrx/sZTUQcTlcmn8+PFatWpV5Fg4HNaqVaui+hJTQW1trT766CP1799f48ePl9PpjKrX1q1btXv37ki9JkyYoHfffTfqQ27lypXKy8uLNF8mg6FDh6qsrCyqLtXV1Xrrrbei6lJZWan169dHzvnrX/+qcDgc+QUzYcIEvfLKKwoEApFzVq5cqeHDhyesW6Y9n3zyiQ4dOqT+/ftLSu66GWM0b948Pf300/rrX//apnsoVt+HEyZMiLpG8znx/hntqn7t2bhxoyRFvX/JWr/WwuGw/H5/yr9vHWmuX3tS6X0799xz9e6772rjxo2Rr1NPPVVXXnll5N/p+P4dk0SPlu3K8uXLjdvtNsuWLTPvv/++ufbaa01+fn7UaOJk9P3vf9+sXr3a7Nixw7z++utm2rRppqioyOzfv98Yc3T61qBBg8xf//pXs27dOjNhwgQzYcKEyOubp2+dd955ZuPGjebFF180xcXFCZm+W1NTYzZs2GA2bNhgJJlf/vKXZsOGDWbXrl3GmKPTd/Pz882zzz5rNm3aZL7+9a+3O3137Nix5q233jKvvfaaOeGEE6KmuFZWVprS0lLz7W9/22zevNksX77cZGVlxX2Ka2d1q6mpMT/4wQ/M2rVrzY4dO8xf/vIXM27cOHPCCSeYxsbGpK/bddddZ3w+n1m9enXUNMj6+vrIObH4PmyeRnjLLbeYLVu2mAceeKBPphF2Vb9t27aZn/70p2bdunVmx44d5tlnnzUVFRVm8uTJSV+/2267zaxZs8bs2LHDbNq0ydx2223Gsizz0ksvGWNS+33rqn6p/L51pPUsoFR//2It6YOIMcbcf//9ZtCgQcblcpnTTz/dvPnmm4kuUpdmzpxp+vfvb1wulznuuOPMzJkzzbZt2yLPNzQ0mLlz55qCggKTlZVlLr74YvPZZ59FXWPnzp3m/PPPN16v1xQVFZnvf//7JhAI9HVVzMsvv2wktfmaNWuWMeboFN4777zTlJaWGrfbbc4991yzdevWqGscOnTIXHHFFSYnJ8fk5eWZa665xtTU1ESd8/e//91MnDjRuN1uc9xxx5m77747oXWrr6835513nikuLjZOp9MMHjzYzJkzp00ITta6tVcvSWbp0qWRc2L1ffjyyy+bMWPGGJfLZSoqKqLukaj67d6920yePNkUFhYat9tthg0bZm655Zao9SiStX6zZ882gwcPNi6XyxQXF5tzzz03EkKMSe33zZjO65fK71tHWgeRVH//Ys0yxpi+a38BAAD4QlKPEQEAAOmNIAIAABKGIAIAABKGIAIAABKGIAIAABKGIAIAABKGIAIAABKGIAIAABKGIAIg402dOlU33XRToosBZCSCCIAOtdyoL1U1NTUluggAOkEQATLIiy++qIkTJyo/P1/9+vXTV7/6VX300UeSpJ07d8qyLK1YsUJTpkyRx+PR448/Lkn67W9/qxEjRsjj8ejEE0/Ugw8+GHXdW2+9VV/60peUlZWliooK3Xnnnd0KMVVVVbLb7Vq3bp2kozuwFhYW6swzz4yc89hjj2ngwIGRx++++67OOecceb1e9evXT9dee61qa2sjz1999dW66KKLdNddd6m8vFzDhw+XJD344IM64YQT5PF4VFpaqm9+85uR89esWaP77rtPlmXJsizt3LmzF/+7AHrDkegCAOg7dXV1mj9/vkaNGqXa2lotWLBAF198cWSbdUm67bbb9B//8R8aO3ZsJIwsWLBAv/71rzV27Fht2LBBc+bMUXZ2tmbNmiVJys3N1bJly1ReXq53331Xc+bMUW5urn74wx92Wh6fz6cxY8Zo9erVOvXUU/Xuu+/Ksixt2LBBtbW1ysnJ0Zo1azRlypRI+adPn64JEybo7bff1v79+/Xd735X8+bN07JlyyLXXbVqlfLy8rRy5UpJ0rp16/Sv//qvevTRR3XWWWfp8OHDevXVVyVJ9913n/7xj39o5MiR+ulPfypJKi4ujtV/OYCuJHrXPQCJc+DAASPJvPvuu2bHjh1Gklm8eHHUOccff7z5/e9/H3XsZz/7WdS25a3de++9Zvz48d0qw/z5880FF1xgjDFm8eLFZubMmWb06NHmhRdeMMYYM2zYMPPII48YY4x55JFHTEFBgamtrY28/vnnnzc2my2yK/KsWbNMaWmp8fv9kXP++Mc/mry8PFNdXd1uGVrvjgqg79A1A2SQDz/8UFdccYUqKiqUl5enIUOGSJJ2794dOefUU0+N/Luurk4fffSRvvOd7ygnJyfy9fOf/zzSpSNJK1as0Nlnn62ysjLl5OTo//2//xd1zc5MmTJFr732mkKhkNasWaOpU6dq6tSpWr16tfbs2aNt27Zp6tSpkqQtW7Zo9OjRys7Ojrz+7LPPVjgc1tatWyPHTjnlFLlcrsjjr3zlKxo8eLAqKir07W9/W48//rjq6+t79H8HID4IIkAGufDCC3X48GH95je/0VtvvaW33npLUvSAzpYf8s1jL37zm99o48aNka/NmzfrzTfflCStXbtWV155pf7pn/5Jzz33nDZs2KA77rij24NEJ0+erJqaGr3zzjt65ZVXooLImjVrVF5erhNOOKFH9WxZB+lo19E777yjJ554Qv3799eCBQs0evRoVVZW9ui6AGKPMSJAhjh06JC2bt2q3/zmN5o0aZIk6bXXXuv0NaWlpSovL9f27dt15ZVXtnvOG2+8ocGDB+uOO+6IHNu1a1e3y5Wfn69Ro0bp17/+tZxOp0488USVlJRo5syZeu655yLjQyRpxIgRWrZsmerq6iJh4/XXX5fNZosMSu2Iw+HQtGnTNG3aNC1cuFD5+fn661//qksuuUQul0uhUKjbZQYQOwQRIEMUFBSoX79+euSRR9S/f3/t3r1bt912W5ev+8lPfqJ//dd/lc/n04wZM+T3+7Vu3TodOXJE8+fP1wknnKDdu3dr+fLlOu200/T888/r6aef7lHZpk6dqvvvvz8yk6WwsFAjRozQihUr9MADD0TOu/LKK7Vw4ULNmjVLP/7xj3XgwAHdcMMN+va3v63S0tIOr//cc89p+/btmjx5sgoKCvSnP/1J4XA4El6GDBmit956Szt37lROTo4KCwtls9FgDPQFftKADGGz2bR8+XKtX79eI0eO1M0336x77723y9d997vf1W9/+1stXbpUp5xyiqZMmaJly5Zp6NChkqSvfe1ruvnmmzVv3jyNGTNGb7zxhu68884elW3KlCkKhUKRsSDS0XDS+lhWVpb+/Oc/6/DhwzrttNP0zW9+U+eee65+/etfd3r9/Px8PfXUUzrnnHM0YsQIPfTQQ3riiSd08sknS5J+8IMfyG6366STTlJxcXG3x7cAOHaWMcYkuhAAACAz0SICAAAShiACIK5OPvnkqKm/Lb+aV24FkLnomgEQV7t27epwuffS0lLl5ub2cYkAJBOCCAAASBi6ZgAAQMIQRAAAQMIQRAAAQMIQRAAAQMIQRAAAQMIQRAAAQMIQRAAAQMIQRAAAQML8fyclje8I8p9lAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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plfzLTgiB119/3e68iIgI9O3bF6tWrcK5c+fsHiv53ICAAAAo80Hn6HWys7Pxzjvv1LoOSUlJUKlUWLp0qd31lyxZUutrV0apVOKee+7Bp59+6jD4XblypdrXNBgMsFgsdsc6duwIhUJRZgpuSbfffjv27t2L3bt3247l5eVh5cqViI+PR7t27apdlqro06cPzpw5g3Xr1tm6qRQKBXr16oXFixfDbDbbzZTq3r07IiMjsWLFCrv6fPPNNzh8+DAGDx5c6WtW9WcUEBBQ5dWyGzZsiLi4OPz66692xwcOHIigoCCkpKSgsLDQ7rHi95sz6lRa8WxGZ6/23bx5c2RnZ9t1B6WlpZWZ/XT77bcDKPvvaPHixQBgV6fmzZuXWXBv5cqVZVpuisc1FQsMDESLFi3KvK+zs7Nx8uRJ9OrVqxo1o7rGlhvyWG3atEHz5s0xffp0XLx4EXq9Hp9++qnDFo833ngDvXv3xg033IDx48ejadOmOHPmDL766ivb/lXdunUDULSE/fDhw6FSqXDnnXdi4MCBUKvVuPPOO/Gvf/0Lubm5ePPNNxEZGenwr7vqKF4jJCUlBXfccQduv/12HDhwAN98802dNGsvXLgQ27dvR0JCAsaNG4d27drh2rVr2L9/P7Zu3Ypr165V63rfffcdJk+ejHvvvRetWrWCxWLB+++/bwtS5Zk1axbWrFmD2267DVOmTEFYWBhWr16N06dP49NPP3XZwoXFweXo0aN48cUXbcf79u2Lb775BhqNBj169LAdV6lUeOmllzBmzBj069cP999/v23adHx8fJWm/lb1Z9StWzds3boVixcvtnWhVbRv0V133YXPPvvMbryHXq/Ha6+9hkceeQQ9evTAAw88gNDQUPz222/Iz8/H6tWrnVKn0nQ6Hdq1a4d169ahVatWCAsLQ4cOHWo17g0Ahg8fjpkzZ+Luu+/GlClTkJ+fj+XLl6NVq1Z2g987d+6MUaNGYeXKlcjKykK/fv2wd+9erF69GkOGDMHNN99sO/eRRx7Bo48+invuuQe33norfvvtN3z77bdl/v21a9cO/fv3R7du3RAWFoZff/0V//vf/zB58mS787Zu3QohBO66665a1ZVcrI5nZ1E9VtFU8ICAAIfP+fPPP0VSUpIIDAwU4eHhYty4cbYppCWnPAshxKFDh8Tdd98tQkJChFarFa1btxbPPvus3TkLFiwQDRs2FAqFwm5a+MaNG0WnTp2EVqsV8fHx4qWXXhKrVq0qM3W8ulPBhRDCarWK+fPni5iYGKHT6UT//v3FoUOHykxFLW8qePv27ctcs0mTJg6nrQIQkyZNsjuWkZEhJk2aJOLi4oRKpRLR0dFiwIABYuXKlWVeu/Tv5vTp03Y/61OnTomHH35YNG/eXGi1WhEWFiZuvvlmsXXr1jLlKz3N+eTJk+Kf//yn7ffTs2dP8eWXX9qdU9VyVEdkZKQAIDIyMmzHfvzxRwFA9OnTx+Fz1q1bJ7p27So0Go0ICwsTI0aMEBcuXLA7p7z3bVV/RkeOHBF9+/YVOp2uzLIAjuzfv7/MdPpiGzduFL169RI6nU7o9XrRs2dPsWbNGqfVydH07p9++kl069ZNqNVqu+nW5U0FL/2+FMLx+2Tz5s2iQ4cOQq1Wi9atW4sPPvjA4TXNZrOYP3++aNq0qVCpVCIuLk7Mnj1bFBYW2p1ntVrFzJkzRXh4uPD39xfJycnixIkTZV77+eefFz179hQhISFCp9OJNm3aiBdeeEGYTCa76w0bNkz07t27TF3Is3BvKSIiLzFgwADExsbi/fffd3dR6qX09HQ0bdoUa9euZcuNh2O4ISLyEnv27EGfPn1w/Phxt+8MXh/NmjUL3333Hfbu3evuolAlGG6IaujKlSvlTlEFALVajbCwsDosUf1QUFBQ6ZoxYWFhdosAElH9wnBDVEPx8fEVLhbWr18/u00wyTmK90mqyPbt220bsxJR/cNwQ1RDu3btcrg7ebHQ0FDbDC1ynrS0NIe7qpfUrVu3Kq8ATUS+h+GGiIiIfAoX8SMiIiKfUi8X8ZNlGZcuXUJQUBA3PyMiIvISQgjk5OQgNja2wsU/62W4uXTpUpkdlYmIiMg7nD9/Ho0aNSr38XoZboKCggAU/XD0er2bS0NERERVYTAYEBcXZ/seL0+9DDcl92VhuCEiIvIulQ0pcemA4p07d+LOO+9EbGwsJEnChg0bKjx//fr1uPXWWxEREQG9Xo/ExER8++23dufMmzcPkiTZ3dq0aePCWhAREZE3cWm4ycvLQ+fOnbFs2bIqnb9z507ceuut+Prrr7Fv3z7cfPPNuPPOO3HgwAG789q3b4+0tDTb7ccff3RF8YmIiMgLubRb6rbbbsNtt91W5fOXLFlid//FF1/E559/ji+++AJdu3a1Hffz80N0dLSziklEREQ+xKPXuZFlGTk5OWX25zl+/DhiY2PRrFkzjBgxAufOnavwOkajEQaDwe5GREREvsmjw82iRYuQm5uL++67z3YsISEB7777LjZt2oTly5fj9OnT6NOnD3Jycsq9TkpKCoKDg203TgMnIiLyXXW2/YIkSfjss88wZMiQKp3/0UcfYdy4cfj888+RlJRU7nlZWVlo0qQJFi9ejLFjxzo8x2g0wmg02u4XTyXLzs7mbCkiIiIvYTAYEBwcXOn3t0dOBV+7di0eeeQRfPLJJxUGGwAICQlBq1atcOLEiXLP0Wg00Gg0zi4mEREReSCP65Zas2YNxowZgzVr1mDw4MGVnp+bm4uTJ08iJiamDkpHREREns6lLTe5ubl2LSqnT59GamoqwsLC0LhxY8yePRsXL17Ee++9B6CoK2rUqFF4/fXXkZCQgPT0dACATqdDcHAwAGD69Om488470aRJE1y6dAlz586FUqnE/fff78qqEBERkZdwacvNr7/+iq5du9qmcU+bNg1du3bFnDlzAABpaWl2M51WrlwJi8WCSZMmISYmxnZ7/PHHbedcuHAB999/P1q3bo377rsPDRo0wM8//4yIiAhXVoWIiIi8RJ0NKPYkVR2QRERERJ6jqt/fHjfmhoiIiKg2GG6IiIjIpzDcEHkBqyxQaLbCYpXdXRQiIo/nkevcENHfLFYZF7MKYCiwQKNSoFVUkLuLRETk0RhuiDxYZq4RGYZCyH812AgB5BktUCokSBKg8VO6t4BERB6I4YbIAxkKzTBZZFw2GG3BBgDMVhmnM/MgBNAoVMdwQ0TkAMMNkQe6bChEgans+Jr6t3ADEVH1MdwQuZkQAkaLjHyTFbmFFgCA0cKBw0RENcVwQ+RmVlngeEauu4tBROQzOBWcyM1y/mqtISIi52C4IXIji1VGWnahu4tBRORTGG6I3MhsFbDKHCVMRORMDDdEbnQ93+TuIhAR+RyGGyI3ySk041oeww0RkbMx3BC5iQDXrSEicgWGGyI3sFhlGArM7i4GEZFPYrghcgOzVeB6HsMNEZErMNwQERGRT2G4ISIiIp/CcENEREQ+heGGiIiIfArDDREREfkUhhsiIiLyKQw3RERE5FMYboiIiMinMNwQERGRT2G4ISIiIp/CcENEREQ+heGGiIiIfArDDREREfkUhhsiIiLyKQw3RERE5FMYboiIiMinMNwQERGRT2G4ISIiIp/CcENEREQ+xaXhZufOnbjzzjsRGxsLSZKwYcOGSp+zY8cO3HDDDdBoNGjRogXefffdMucsW7YM8fHx0Gq1SEhIwN69e51feCIiIvJKLg03eXl56Ny5M5YtW1al80+fPo3Bgwfj5ptvRmpqKqZOnYpHHnkE3377re2cdevWYdq0aZg7dy7279+Pzp07Izk5GZcvX3ZVNYiIiMiLSEIIUScvJEn47LPPMGTIkHLPmTlzJr766iscOnTIdmz48OHIysrCpk2bAAAJCQno0aMH/vOf/wAAZFlGXFwcHnvsMcyaNatKZTEYDAgODkZ2djb0en3NK0VUQwUmK05czq3VNRqF6hAaoHZSiYiIPF9Vv789aszN7t27kZSUZHcsOTkZu3fvBgCYTCbs27fP7hyFQoGkpCTbOY4YjUYYDAa7GxEREfkmjwo36enpiIqKsjsWFRUFg8GAgoICZGZmwmq1OjwnPT293OumpKQgODjYdouLi3NJ+YmIiMj9PCrcuMrs2bORnZ1tu50/f97dRSIiIiIX8XN3AUqKjo5GRkaG3bGMjAzo9XrodDoolUoolUqH50RHR5d7XY1GA41G45IyExERkWfxqJabxMREbNu2ze7Yli1bkJiYCABQq9Xo1q2b3TmyLGPbtm22c4iIiKh+c2m4yc3NRWpqKlJTUwEUTfVOTU3FuXPnABR1F40cOdJ2/qOPPopTp07hqaeewpEjR/B///d/+Pjjj/HEE0/Yzpk2bRrefPNNrF69GocPH8aECROQl5eHMWPGuLIqRERE5CVc2i3166+/4uabb7bdnzZtGgBg1KhRePfdd5GWlmYLOgDQtGlTfPXVV3jiiSfw+uuvo1GjRnjrrbeQnJxsO2fYsGG4cuUK5syZg/T0dHTp0gWbNm0qM8iYiIiI6qc6W+fGk3CdG3I3rnNDRKVdyTEiu8Ds7mI4RUSgBsH+Kqdft6rf3x41oJiIiDyDEAL1709f9zJarCgwWd1dDKcwy7JbX5/hhoiIyriYVYDreb7RikD1j0fNliIiIiKqLbbcEBG5gBAC+V7cxWCV2SdF3ovhhojIBayywKkree4uBlG9xHBDRFQLJouM45dz3F0MIiqB4YaIqBYEBNw8MYSISuGAYiIiIvIpDDdERDUkhMCF6wXuLgYRlcJwQ0RUC/lG750RReSrGG6IiGrIUGBxdxGIyAGGGyKiGjJa2WpD5Ik4W4qIqBKFZisuG4xljhstDDdEnojhhoioElZZ+MxuzUT1AcMNEdVrQghUttOAzO2xibwKww0R1Wt5JitOc5sEIp/CcENEPslkkXHuWn6l57FVhsj3MNwQkU+ShUCBF+/KTUQ1x3BDRF7HKguISlpc2CJDVH8x3BCRx8opNCPPwQrAmblGMLsQUXkYbojIo+QZLSjOLdfzzJyCTUTVxnBDRG5nssjIMxZtZXAxq4CtMkRUKww3ROQ2sixwOccIk0VmCw0ROQ3DDRG5RWauEVn5Zs5oIiKnY7ghojp12VAIWQC5RguDDRG5BMMNEbmMLAtczTPZHbucw5lORORaDDdE5DKyEEjPLnR3MYionmG4ISKnEkLgwvUCdxeDiOoxhhsicrqsfM58IiL3Ubi7AETkW05lcodtInIvttwQkVNczzPBaJFhssjuLgoR1XMMN0RUK/kmCzJzTMgzWWCxchoUEbkfww0R1VhmrhFXcowMNUTkURhuiKhGzmTmwWiRGWyIyOMw3BBRtRWarcg1WrgYHxF5pDqZLbVs2TLEx8dDq9UiISEBe/fuLffc/v37Q5KkMrfBgwfbzhk9enSZxwcNGlQXVSGq94wWK85dy2ewISKP5fKWm3Xr1mHatGlYsWIFEhISsGTJEiQnJ+Po0aOIjIwsc/769ethMv29XPvVq1fRuXNn3HvvvXbnDRo0CO+8847tvkajcV0liOo5o8UKqyyQXVC00aXRzBlRROS5XB5uFi9ejHHjxmHMmDEAgBUrVuCrr77CqlWrMGvWrDLnh4WF2d1fu3Yt/P39y4QbjUaD6Oho1xWciJBTaIbRIuNanomBhoi8hku7pUwmE/bt24ekpKS/X1ChQFJSEnbv3l2la7z99tsYPnw4AgIC7I7v2LEDkZGRaN26NSZMmICrV686texEnu5yTtFMJVfIN1lwLCMH567lIy2rkMGGiLyKS1tuMjMzYbVaERUVZXc8KioKR44cqfT5e/fuxaFDh/D222/bHR80aBCGDh2Kpk2b4uTJk3j66adx2223Yffu3VAqlWWuYzQaYTT+/SVgMBhqWCMiz2GyyDBbnRM68k1Fg4MtVoF0QyFkITgLioi8lkfPlnr77bfRsWNH9OzZ0+748OHDbf/fsWNHdOrUCc2bN8eOHTswYMCAMtdJSUnB/PnzXV5eIm9wLc8EqyxgssoQf40Kvp7HvaCIyHe4tFsqPDwcSqUSGRkZdsczMjIqHS+Tl5eHtWvXYuzYsZW+TrNmzRAeHo4TJ044fHz27NnIzs623c6fP1/1ShD5mMs5hUjPLsS1XBOu55kZbIjI57g03KjVanTr1g3btm2zHZNlGdu2bUNiYmKFz/3kk09gNBrx4IMPVvo6Fy5cwNWrVxETE+PwcY1GA71eb3cj8nVWWcBosdpuxasJyxw+Q0Q+zuXdUtOmTcOoUaPQvXt39OzZE0uWLEFeXp5t9tTIkSPRsGFDpKSk2D3v7bffxpAhQ9CgQQO747m5uZg/fz7uueceREdH4+TJk3jqqafQokULJCcnu7o6RF7jaq4RGQbXDDgmIvJkLg83w4YNw5UrVzBnzhykp6ejS5cu2LRpk22Q8blz56BQ2DcgHT16FD/++CM2b95c5npKpRIHDx7E6tWrkZWVhdjYWAwcOBALFizgWjdU7528kmsbCGyVOSCYiOonSYj6t86owWBAcHAwsrOz2UVFblFgsuLE5dxaX0enViI2RAuLLHDZYESh2cqVg4nI7WJCtAgPdH6DQ1W/vz16thQRVazAZEWe0YpCsxUFJqu7i0NE5BHqZG8pInIdWRRti0BEREXYckPk5S5z0DCR1xNCwGjxnamMBSYrhBCQJMktr89wQ0REdnzti9bTCQHMWn8QpzLz3F0Up/rzuWT4q90TMxhuiIjIRgiBmZ8exOH0HHcXhajGGG6IiFzAW1s/Cs1WBhs3aRYegIVDO8FNPTlOFR2shU5Vdq/HusJwQ0TkZL7S+vH+wz2hdeMXVH2j8VO4bYyKs+nUSrfWheGGiKgWHLXQ+ELrR9sYPYJ1Kp/5sqX6heGGiKiGqtJC462tH77UikD1D8MNEVENFZrlCoMNWz+I3IPhhoioBoQQmLX+oO2+oxYatn4QuQfDDRFRDRgtsm1dkmbhAWyhIfIgDDdERJUob9BwsaLpuww2RJ6C4YbIi8iltvxW8AvV5aoyaJi/BiLPwnBD5OGKWw1kITBs5c92j30+6SYGnFqqbLG9yqZ1t43RQ+PHPYiJPAnDDZGHEkKg0CxXuOeMocCMEH91HZfMO1RlheDq7unDQcNE3oHhhsgDVXWFW42f962fUhdcsUIwp3UTeQ+GGyIPI4RAdoHZ7ou5eM8ZACi0WDFy1V4AHOtRHqOl4vVnSqvKnj5soSHyHgw3RG5WsvvEUTfJ+w/3tGsxqC/fr7XZeLLkTKaqrBDM4ELkWxhuiNyosu6T+toV4sxuJa1K6ZXbHxBRzTHcELlRecv3F3eTaFX1q0WhuLXGWRtPciYTUf3EcEPkJhUt319fukkq65IDarfxZH35ORKRPYYbIjcpNNff5furMs0dqL/dckRUOww3RG5gtlrtWm3qy/L9VQk1JWcuseWFiGqC4YbIDdKzjXatNlqVb44LqUq3U+lp2Aw0RFRbDDdEdUgIAUOhBUaL7266WBxoKlv9t74OmiYi12O4IapDZqvA2cw8TF2XajvmS9/rVZnCzVBDRK7GcENUhzIMhTBa7AcS+9JUZUdT29ntRER1jeGGqA5ZZWF331e6pIoHCk9dd8B2rHgKN8MMEdU1hhuiOmKxymW2E/CF73xZCDyxLtVubE19m9pORJ6F4YaojliFgKmGeyV5opKtNZeyC23Hm4UH4LVhXRhsiMhtGG6I6kh6iQDg7Ry11sQGa7FkWFcOFCYit2O4IXIxIQSu5plqvMO1p5GFwIQP9jlsrVEw1BCRB2C4IXIxWQBpWb7RaiP+arEpDjZsrSEiT8RwQ+RCVlmgwGyt/EQPVnKV4UKz1dYVFRusxfIHu7G1hog8DsMNkQvlmyw4k5nv7mLUSGX7QC0Z1pXBhog8Up2sHrZs2TLEx8dDq9UiISEBe/fuLffcd999F5Ik2d20Wq3dOUIIzJkzBzExMdDpdEhKSsLx48ddXQ2iaik0W3HJS7ujZCEwdV0q7lu522GwaRuj99n9sIjI+7m85WbdunWYNm0aVqxYgYSEBCxZsgTJyck4evQoIiMjHT5Hr9fj6NGjtvul+/JffvllvPHGG1i9ejWaNm2KZ599FsnJyfjzzz/LBCEid7BYZeQZLV4x9btkt1PRfTic3s1VhonIW7g83CxevBjjxo3DmDFjAAArVqzAV199hVWrVmHWrFkOnyNJEqKjox0+JoTAkiVL8Mwzz+Cuu+4CALz33nuIiorChg0bMHz4cNdUhKgaco0Wj2y1cRRkKtrckgOGicgbuTTcmEwm7Nu3D7Nnz7YdUygUSEpKwu7du8t9Xm5uLpo0aQJZlnHDDTfgxRdfRPv27QEAp0+fRnp6OpKSkmznBwcHIyEhAbt373YYboxGI4xGo+2+wWBwRvWIHLJYZVzNM7m7GNUOMqVxejcReSuXhpvMzExYrVZERUXZHY+KisKRI0ccPqd169ZYtWoVOnXqhOzsbCxatAi9evXCH3/8gUaNGiE9Pd12jdLXLH6stJSUFMyfP98JNSKqnFUI5BvrfoZUyTBTkyBTstsJYNcTEXkvj5stlZiYiMTERNv9Xr16oW3btvjvf/+LBQsW1Oias2fPxrRp02z3DQYD4uLial1WIkfMVlH5SU5SaLbadhWf+enBMjtyO8IgQ0S+zqXhJjw8HEqlEhkZGXbHMzIyyh1TU5pKpULXrl1x4sQJALA9LyMjAzExMXbX7NKli8NraDQaaDSaGtSAqPrOVLG1xBkeWrUXbWP0mHdnO4fBhkGGiOojl87lVKvV6NatG7Zt22Y7Jssytm3bZtc6UxGr1Yrff//dFmSaNm2K6Ohou2saDAbs2bOnytckcoV8kwXnr+VDuLjhRuOnQNsYve3+4TQDJny433b//Yd74pN/JeKTfyViybAu0KmV0Kr+vjHYEJGvc3m31LRp0zBq1Ch0794dPXv2xJIlS5CXl2ebPTVy5Eg0bNgQKSkpAIDnnnsON954I1q0aIGsrCy88sorOHv2LB555BEARTOppk6diueffx4tW7a0TQWPjY3FkCFDXF0dIoeu5ZlwKavA5cEGKPo38NLQjsguMOOhVXttrw8UtdQE61QMMERUr7k83AwbNgxXrlzBnDlzkJ6eji5dumDTpk22AcHnzp2DQvF3A9L169cxbtw4pKenIzQ0FN26dcNPP/2Edu3a2c556qmnkJeXh/HjxyMrKwu9e/fGpk2buMYN1TmLVUau0YKrucY6CTbFJEmCVqW0OxYbrMVrw7ow2BBRvScJUZcfyZ7BYDAgODgY2dnZ0Ov1lT+BqBz5JgtOXq7eGJtCsxX3/rdoKYRP/pVYJqRUlRACM9f/jsNpBk7bJiKPEhOiRXig88e6VvX72+NmSxF5A1kWyMgpRJ4bpnwXK+6eMlpkDhImIiqB4YaoBq7kGpGZ4/6F+hx1TxER1Xfc+Y6oBoxmz98zioiovmK4IaqBArP7uqOIiKhiDDdENSBQ78bhExF5DYYbomrKyjfBbGG4ISLyVAw3RNVksnK8DRGRJ+NsKSIiIg8gSfCZtarcXQ+GGyIiKkOpkKBU+MYXrbeI0msQ4q92dzF8AsMNERGVER2sRVgAv2jJOzHcEBHVksLB6EWlQkKryKC6L4yT+EjvCNVTDDdERLUQ4q9CXJi/u4tBRCVwthQRUQ2p/RQMNkQeiOGGiKiGgnUqdxeBiBxgtxQRUSVUfpLDwbX+an6EEnki/sskIqqESqlAZJDW3cUgoipiuCGies1PKSFQU/FHocaPPfhE3oThhoh8kkJRtTExGj8lIoI0dVAiIqorDDdE5JNUSgUahXImE1F9xHBDRF4nKlhT6d41ftw6gKjeYrghIo/VIFCNEP+yXUs6lRISl9AlonIw3BCRR2kUqrO1ymhUCmhVSjeXiIi8DcMNUR0Twt0l8Dx+Sgn+6qIQE6xTQcEuJSKqBYYbojokhMCs9QfdXQyPIElF2xe0ivLezSWJyDMx3BDVIaNFxqnMPABAs/CAerl+SkyIFgqpqKWGXU5E5AoMN0R1qGSX1MKhnXx2UKzKr6heSklC0/AAu8f8lPUv0BFR3WK4IaojshCYuu6A7b6P5hpIEtAmWu/uYhBRPcY/oYjqgBACT6xLxaXsQgC+3SVVuqWGiKiuseXGySxWmc3uVEbJsTaxwVq8NqyLz3RJKRRFey8FalTwU0pQ+2hoIyLvwXDjZIUWGYEMN1SBJcO6Vrq6rjcJ1PihSQO21hCR52C4cTKLVYYsC67TQXZKDiT2oVyDiCANIrnpJBF5GIYbJzNZZMhCQAEf+gajWik9kNhXtI/VQ5LgM91rROQ7GG6IXMgXBxJL0l9bJLB1kog8FMONC2TmmqCQAI1KiWBd2U3/qP4oNPvWQGKdumjgcIi/2t1FISIqF8ONC1zJMQIAQvxVDDf1WOnuKG8eSBzir4LGT4HQADVUHDBPRB6O4caFzFYZOYVmAIBSIcFfzR93feGoO0qr8r5QoFMroVYqEB6ogU7NrRKIyDvUyaftsmXLEB8fD61Wi4SEBOzdu7fcc99880306dMHoaGhCA0NRVJSUpnzR48eDUmS7G6DBg1ydTWqLc9oxZnMfJzJzMelrEJ3F4fqkLd3R6n9FIgJ0aJRqA6NG/gz2BCRV3F5uFm3bh2mTZuGuXPnYv/+/ejcuTOSk5Nx+fJlh+fv2LED999/P7Zv347du3cjLi4OAwcOxMWLF+3OGzRoENLS0my3NWvWuLoqtSRgssgwW2V3F4RcrPTO397QHRWo9UOg1g8h/iq0jApEfLg/wgM13NiSiLySJETJFTicLyEhAT169MB//vMfAIAsy4iLi8Njjz2GWbNmVfp8q9WK0NBQ/Oc//8HIkSMBFLXcZGVlYcOGDTUqk8FgQHBwMLKzs6HXO3cPnMuGQmQYjOU+LklAh4bBTn1NqluXcwqRkV3+77jAZMV9K3cDKOqOWuKmVpuiadp/348M0kKSAItVQEBAp1LaysWxYUTkDar6/e3SQSAmkwn79u3D7NmzbccUCgWSkpKwe/fuKl0jPz8fZrMZYWFhdsd37NiByMhIhIaG4pZbbsHzzz+PBg0aOLyG0WiE0fj3l5HBYKhBbYgqV3oQcV3v/K1TK4C/1lgKC1AjLICzmoio/nFpt1RmZiasViuioqLsjkdFRSE9Pb1K15g5cyZiY2ORlJRkOzZo0CC899572LZtG1566SV8//33uO2222C1Wh1eIyUlBcHBwbZbXFxczSvlBEIIuLjBjNxAFgITPtjntkHEkgQ0aRCAFpGBaBEZyGBDRPWWR0/fWbhwIdauXYsdO3ZAq9Xajg8fPtz2/x07dkSnTp3QvHlz7NixAwMGDChzndmzZ2PatGm2+waDwW0BRwjg0EUDVH4S4hsULejmTQNNybHSwaauBxGH+KsQF+ZfJ69FROTpXPpnZXh4OJRKJTIyMuyOZ2RkIDo6usLnLlq0CAsXLsTmzZvRqVOnCs9t1qwZwsPDceLECYePazQa6PV6u5u7mS0CxzNyYbRwgLG3cxRslj/YrU4HEbOVhojoby4NN2q1Gt26dcO2bdtsx2RZxrZt25CYmFju815++WUsWLAAmzZtQvfu3St9nQsXLuDq1auIiYlxSrmJqsoTgk2kXoMAjUc3whIR1SmXDwiYNm0a3nzzTaxevRqHDx/GhAkTkJeXhzFjxgAARo4caTfg+KWXXsKzzz6LVatWIT4+Hunp6UhPT0dubi4AIDc3FzNmzMDPP/+MM2fOYNu2bbjrrrvQokULJCcnu7o6Tme0FO0iTt6n9EJ97gg2If4qNGCrDRGRHZf/uTds2DBcuXIFc+bMQXp6Orp06YJNmzbZBhmfO3cOCsXfGWv58uUwmUz45z//aXeduXPnYt68eVAqlTh48CBWr16NrKwsxMbGYuDAgViwYAE0Go2rq+N0567mo0VkIBdJ8zJCCGQXmO0W6qvrYAMUrXztx+0QiIjsuHydG0/kznVuHGG48S7phgI8sHKPLdgAwMfjE+v8d6hTKxAWoOF4GyKqNzxinRsiXyOEwMi399oFm7YxerfsG9UgQINQBhsiojIYbjzAxawCtIgMdHcxqAoKzFYcyyga/xUbrMWSYV2hVXE6PxGRJ2G4IaqhJcO6sjuRiMgDcSSiRxCwcsaU12FjDRGRZ2K48QAFJhl/XjLAaHG8fQQRERFVHcMNUTVk55vdXQQiIqoEw40Hyc4323VPZReY2ZrjQSxWGTmFFncXg4iIKsEBxR4kw2BEvsmK6GAtzl/LR6FZRlyYDho/Dlp1t+x8M85dy0ehmWGTiMjTMdx4mFyjBWev5sP014aal7IKYbLIMMsCDUN0bi4dERGR52O4cSIhBApMVhSardD41WztEyFgCzYAYJUFMgxGSBIgAYhlwKlzQggUsMWGiMhrcMyNExWYrei3aAfu/e9uGEsEFGcoHXqo7sgCuJJTvS01iIjIfRhuXKTQXNSC48ytu6y1uJbZKuNqrtGp5SEiIvJE7JZykYdW7QVQtO/QS0M7OmV5fou1aLE/paL618ouMCMtqxBqPwWCtKpal6U+YYsZEZF3YcuNE+lUSnRuFGx37HCaAdkFZqe04pgsRYv9HU4zVLubpPgL2sCpzNVmlhluiIi8CVtunEiSJKx8qBvOXStAodlqa70p/m+z8AAsHNqp1hstWqwCGYZC6NRKBGqq9ivMNxWFmgKTBUIIbvRYDVYru/KIiLwJW26cTJIkaFVKBOtUaBujt3vsVGYe7lu5GzPX/17rVhwhgDOZeTAUVm3F3OIBzgUmGblGtt5UB39eRETehS03LiJJEl4a2hFGiwwhgFnrD+JUZh6Aoq4qo0WGVlW7xfmEAM5fy0eovxp+CgmReq3D87ILzCjZs5JvsnLcTSUuZhUgK98EvVbFaeBERF6GLTcuVNyKo1MrsWRYF7z/cE/bY86aSSXLwNVcEzIMRpy4nGMbWyOEgMlSNEPq/LV8u+fkVLG1pz6TZQFZBrLyzTCaOeaGiMibsOWmjhQHnWIPrdqLZuEBeG1YFyicNP6lwCTj3LV86HV+uJJjRHnjYAtMMowWK7d1KMe1PBNMVgYaIiJvxZabOqTxU9iNwzmVmYcn1qU6de2ZApMVGdnlBxuqWL7JgovXC5BvZFcUEZG3YripQ8XjcD4en4jY4KLxMacy81Dohm4PruXn2IXrBe4uAhER1RLDTR2TJOmvMThdbcdmrT/osPVGFgKyi1KIq67rzSxWmT8XIiIfwHDjJlqVAs3CAwAUtd6U3otKFgJ3LduFu5btcskXrlXml3hphkILzBb+XIiIvB3DjZtIkoSFQzuV+3h2wd8zmsa8+wsKTLWbXSWEsJuh5Y6uME8my4KzyIiIfARnS7lRyUlSJXOLLARmfnrQdv9angn3rdxd49lVshB4Yl0qTmXm2a6RlW9CRJCmtlXwGbIQMBRwsT4iIl/AlhsPMWv9QchCoMBkxdR1qUjLLixzTk1mV4kSwabkNQrNVmTns6Wi2NU8U52+Xoh/zRZRlCQgOliL2BAtArX824SIyBF+OrqRxq9o3M2pzDycyszD1HWpOP1XCAGA2GAtlj/YDUazjKnrDuBSdiFOZeYhu8AMrUoJjV/le1QZLbIt2BQrnqHFDSH/VmCq26nfNdnZHSgaq8UWNyKiirHlxo1Kj7s5XSqELBnWFQoHs6seWrUX9/63+ntUvTfm7xWSheCMqZIsdRT0JAkIDVAh1F9d7ef6a5RoFOrvglIREfkWhhs3KzlrqqS2MXpoVQq780pvxHk4zVDhwODiQcTFSjbyzFp/EBauwmtjqaPZY0qFVK2AolRIkCQgNkSL+AYBtd6PjIioPmC3lJtJkoTXhnWxjYtpGx2E+f/oAK3Kvsup5EachWYrHlq1FwAwdd0BLBnW1RZcNH5FgajQLNtt1ln0mNKuG+xSViGsMhAXxtaAuuqhC9RU/Z+cJAFNwwPKvBeIiKhiDDceQCFJWDKsC4wWucJxNMX7U5Ucq3MpuxD3rdxtO6fpX61Apbu4iluCFg7tZDtfiKIp51EWGWq/+tuIJ8uuWyyxJIUCVR4vExaohu6vTVeJiKh6GG48ROmNNSs7t2RrT0mlQ02z8AAsHNrJ9td/6dwkBFBgttbrcJNnstTJdhR+CkWFv+MgrZ9tLE6ARgk/Zf39nRAR1QbDjZcq2doDFIWUkt1QpUONI8Vf6FdyChGsq9nUZF9gKKyb9W3Ka7UJ1qnQMFQHCYCihrOoiIjobww3Xqx0a0/JsFOVaeKz1h/EkmFdUGCSYbHK9bKlQAiBAlPdhBtHrWNKhYSYEG2Np4YTEVFZ9e/bzIcVhx2tSllusCkerwPY72mVVWBGhqEQZ6/m1WqbB29isco4eSUXBSbXjiaWJKBjo2CHg4kVCkBVD0MlEZEr8VO1nilvT6v07EJk5hphKLAg1+j72xBczzPh5JU8lwcbpUKqcDXiIG397Q4kInKVOgk3y5YtQ3x8PLRaLRISErB3794Kz//kk0/Qpk0baLVadOzYEV9//bXd40IIzJkzBzExMdDpdEhKSsLx48ddWQWf4qhRR4i/p0Nn1YNtGQyFZpgsrp//7a92vPCen1JCsE4FNVttiIiczuWfrOvWrcO0adMwd+5c7N+/H507d0ZycjIuX77s8PyffvoJ999/P8aOHYsDBw5gyJAhGDJkCA4dOmQ75+WXX8Ybb7yBFStWYM+ePQgICEBycjIKC8vux0TkiNnq+q43nVqBRqE6h4+plArEhekQHlj9lYqJiKhiLg83ixcvxrhx4zBmzBi0a9cOK1asgL+/P1atWuXw/Ndffx2DBg3CjBkz0LZtWyxYsAA33HAD/vOf/wAoarVZsmQJnnnmGdx1113o1KkT3nvvPVy6dAkbNmxwdXW8mp+Sg1aBorE2rt5LSqmQEKDxq3CQdtHUfP5OiIiczaXhxmQyYd++fUhKSvr7BRUKJCUlYffu3Q6fs3v3brvzASA5Odl2/unTp5Genm53TnBwMBISEsq9ptFohMFgsLu5iiRJaBiqQ1yYDg086K/y0AAVWkcFldlJup6MHbZzKcv1LXxBWj/EBDtutSEiItdyabjJzMyE1WpFVFSU3fGoqCikp6c7fE56enqF5xf/tzrXTElJQXBwsO0WFxdXo/pURbBOhRCdCiH+aoQFqBERpEFEkKbCQaUVUfk55y/7II0KCoVUZj2bqesO1JsNNK2ywOWcQpcPmA4LVKNhCIMNEZG71IvRjLNnz0Z2drbtdv78eZe9ltpPYVuITatSIjpYW+ZWVf4aJWL0OgRp/RCkrfmSRJIE2/PDAtQIDVDZpoNfyi7EE+tS68X079xCCzKyjbC6aJNMlZ+E6GAtGobouBgfEZEbuTTchIeHQ6lUIiMjw+54RkYGoqOjHT4nOjq6wvOL/1uda2o0Guj1ertbXVMpFYgI0lRrAKlSkhDsr0J8eACaNPBHk3B/qPykai/4plUp7L5sY0N0WDK8C2L/ClqnMvMq3F3cV+QYXTsLLEavq/LeUURE5DouDTdqtRrdunXDtm3bbMdkWca2bduQmJjo8DmJiYl25wPAli1bbOc3bdoU0dHRducYDAbs2bOn3Gt6EkmS0DIqEPHhFe/ErVEp7HbrliQJeq0KbaL1aNLAv8INFSWpaIxNpF6DRqE6NAsPtHvcX+0HnUqJJcO62o7NWn/Q1nqTlW92+YDbulZotrp0irvKT6pV6xoRETmPyz+Np02bhlGjRqF79+7o2bMnlixZgry8PIwZMwYAMHLkSDRs2BApKSkAgMcffxz9+vXDq6++isGDB2Pt2rX49ddfsXLlSgBFX/JTp07F888/j5YtW6Jp06Z49tlnERsbiyFDhri6Ok6hVSmhUioQpdfgap4JFgfTkhuH+ZfbQhOg8UPziACcuZqPXAf7IsWG6BAWUHELkUqpgFb19+7ixasVV3XzTm8hywIZOYW4mmty6eBpdkUREXkOl4ebYcOG4cqVK5gzZw7S09PRpUsXbNq0yTYg+Ny5c1Ao/m5A6tWrFz766CM888wzePrpp9GyZUts2LABHTp0sJ3z1FNPIS8vD+PHj0dWVhZ69+6NTZs2Qaut+ngWd1MqJETqtcguMJcJN1WZHSxJEpqE+eNiVgFkIRDir0aByQqLLFcabICiTRxzjRYsHNoJ960smmXmi8NusgrMyMwxuez6OrUSLSIDKz+RiIjqjCTqw0jSUgwGA4KDg5Gdne2W8TclnbicW6YLqK6+MC9cz0daViHu/W9RuGkWHoAlw7pAkiSEB6m9firz0fQcmK2yU0Nbodlq+3n9+NTNCA/S+FxrFxGRp6rq93e9mC3lyVpEBkKn/vvXEKBRVmtGVW0oJKnMRprZBWYIIXA114RCs/eOuyk0W2GyODfYlKbykxhsiIg8EMONh4kO1jrcPdpVSm+k+dCqvZi6LhVWWcBQ6J17TMmywLU813VFFfNT8J8PEZEn4qezh3HHRopalQJtY/5u3juVmYcn1qWi0EtnTKUZigYQu1pFM9aIiMh9GG48QJS+qBsqUOtX7TVsnEGSJLw0tCM+Hp9ot/ZNVoF3tdzIskCh2YprLgw29W+EGhGR92G48QAqpQJBWj80CtW5bSNFSZKgU9uvfWM0y8hz8VYFziDLAicu5+JoRg4uZhW47HWEEJi1/qDLrk9ERM7BVcc8gFalRPxfg3rrksJBkCp96OzVfETpNZAFEOKvgsoN3WYVScsugKHAApOlaIVli9V1XWmFZhmnMvMAAO1i9NBxMDERkUdiuKnHgrR+yMw1VtjVYpWFbRft6/kmNA7zd8sMIVkWMFmLAkxx4LqSY4TRxTOibK8vBKauO2C7/8mjiW5rZSMiooox3NRjARo/xIX54/y1/CoFBKNZxtmr+WgVFei0L3ZZFhWu7CvLAqcyc2G2ClisApJUNO7l/DXXdT+VJoTAE+tScSm7KOS1igqEPwcTExF5LM/qY6A6F6xTIaAaU89NFhkFTlr/5rKhsNIxMhZZoMAk21ZxdseAXqPl7+6o2GAt3h3Tk602REQejOGGEKJTVev8q7km2xiXmsrONyPDYERWvtmr1tNZMqyrw7FKRETkORhuCHqdqkr7WRXLyjfjYlYBruQYYbHWLORkFfw9Xfv8tXwYLZ67pk7J1iLmGiIiz8cxNwSlQkKgxg85pXYYr6gLKLfQgtxCC4QQiNRXbbsIIQSu5BqhkCS715LloutpAj1vHAunfxMReR+23BAAOBwgO3XdAciVDHK5nGPElRxjlV6j0CwjI9uItKzCMsHpUlYhrtfBlgnVVXK8TbPwAGj8+E+GiMjT8ZOaAADqv760S26keSm7EE+sS0VFG8cLgSptsFlotuLC9fwKz8k1Wmo9lsfZSlZ94dBOHEhMROQFGG4IABDir4ZWpYAkSXhtWBe7bRiMlQSOnEJLhWNmhBC4mFWAQnPF18nKN+Noeg6OZeRAlt2/z0HpLinmGiIi78BwQzYh/moARSsXl9yGoTJWWcBQ4HibBqtcNM4m31j1AcNGs4wL1wtwJjMP5ytp7XEldkkREXknDigmm/BANdRKBS5k5du1UlRlbZnr+SZEBGkcHs/IrtqYnJKyPWDTTnZJERF5J/4pSjaSJCHYX4XYYJ3d8anrDqDAZEWh2Vru+BuTRXa4Xk1VxuN4GiEECkxWu+0WmGuIiLwHW27IoeKBxacy83ApuxD3rdwNAGgbo8dLQzuWacUQAjh3NR9NwwMQoPGDEAJGi4ysfPe3wFSHEAIzPz2Iw+k5tmPskiIi8i78xCaHigcWNyu1W/nhNEO5A4OFADIMhTh/LR/nrxXgdGaeW7ZLqI1Cs1wm2Lw2rAu7pIiIvAhbbqhcRQOLu8BokVFotuKhVXsBALPWH8SScr7w84xWAJV3RRW37ABFrUSeEB5K7/z9/sM9EaxTeUTZiIio6hhuqEKSJEGrUtp1U53KzEOhWYauhjtjy3/tsl1yJtJrw7q4dc+m0jt/NwsPYLAhIvJS7JaiMkIDita8KUmSJCwc2sl2v3iQcUUL/DkiSgUboGgtncoWC3S10jt/syuKiMh7MdyQQ46+17Uq+9WL71u5G1PXpVa6RUMxIQSyC8x2ISKmxGKBlS3y50olq8Cdv4mIvBvDDVWZo0HGpzLzMOGDfZUGnKLxLKm2cTtAUYh4vcRigbPWH3RL603psTbMNURE3o3hhhwK0qocHi8eZPzx+ETbFg2V7UHlqCuqbYweWpXCrjXIHa03jsbacNo3EZF346c4ORTiryq3BUOSJOjUSix/sFuV9qAqNNuPZ/l4fKJtrRxnjeWpKY61ISLyPQw35JDGT4kovbbCc6qyB1XpLp8lw7pCp1baBYjyxvJUtiqys3GsDRGRb2C4oXL5V2Gqd0VZwFGXT+lZWEXXcDyW576Vu3Hvf3dj5vrf6yTgMNcQEfkGhhsqV4DGD37Kqn/jl84f1enyKTmWx9GqyOV1eREREZXGcEMV0qmqvlBf6dlO1Z1eXTyWZ8mwLvjkX4l4/+Ge1S5vdXnb9hBERFQ5hhuqUHiQpsLHi1cuBuwHFddmenXxqsjaagSrmhBCYNb6gy59DSIiqnsMN1ShQI0fmkcGQO2ngE7teLxMydlOhWYrZCEw4YN9Hj+9umS3maeWkYiIqo97S1Gl/NV+aBUVCCGAk1dyy6xFU7JVpuQifYBnT68u2SW1cGgnjywjERFVH/9UpSqRJAkKhYTGDfzLdDFp/BRoG6Mv85zYYC2WP9jNI6dXl+6S8sAiEhFRDTHcULVo/JQI1tmvXixJEl4a2tFuplOz8IBKg41GpUCAxrXjaspTcmFBdkkREfkWl32iX7t2DSNGjIBer0dISAjGjh2L3NzcCs9/7LHH0Lp1a+h0OjRu3BhTpkxBdna23XnFq9qWvK1du9ZV1SAHwsrZNbzkTKclw7pUGGwUCqBxmD/iwvyhq8J6Os5UerAzu6SIiHyLy8bcjBgxAmlpadiyZQvMZjPGjBmD8ePH46OPPnJ4/qVLl3Dp0iUsWrQI7dq1w9mzZ/Hoo4/i0qVL+N///md37jvvvINBgwbZ7oeEhLiqGuRAgMYPWpUSRotcZip18UynimhUCjSPCIRSURQoGof541hGTp1My67qwoJEROS9XBJuDh8+jE2bNuGXX35B9+7dAQBLly7F7bffjkWLFiE2NrbMczp06IBPP/3Udr958+Z44YUX8OCDD8JiscDP7++ihoSEIDo62hVFpyqKC/NHrCyQYSjE1VxTlZ6j1/khUOOHEH+1LdgAgNpPgQCNH3ILLa4qrg33kiIi8n0u+ZN19+7dCAkJsQUbAEhKSoJCocCePXuqfJ3s7Gzo9Xq7YAMAkyZNQnh4OHr27IlVq1ZVujS/0WiEwWCwu1HtKRUSovTaKncrhejUaBCosQs2xcID1QgNcLwTuTNVd2FBIiLyPi5puUlPT0dkZKT9C/n5ISwsDOnp6VW6RmZmJhYsWIDx48fbHX/uuedwyy23wN/fH5s3b8bEiRORm5uLKVOmlHutlJQUzJ8/v/oVoUopFRIi9Rqczcyv9NyKBg8HaVUoMFudWbQyarOwIBEReY9qtdzMmjXL4YDekrcjR47UulAGgwGDBw9Gu3btMG/ePLvHnn32Wdx0003o2rUrZs6ciaeeegqvvPJKhdebPXs2srOzbbfz58/Xuoz0N62fstKgoFMrHbbYlKRRKqH2UyDEX/XXzuHOK6OjsTacIUVE5Juq1XLz5JNPYvTo0RWe06xZM0RHR+Py5ct2xy0WC65du1bpWJmcnBwMGjQIQUFB+Oyzz6BSVdxVkZCQgAULFsBoNEKjcbxVgEajKfcxqj11JSFBkoAovabSsS3B/ioE+xf9vmVZ4M9057XklJz6zbE2RES+rVrhJiIiAhEREZWel5iYiKysLOzbtw/dunUDAHz33XeQZRkJCQnlPs9gMCA5ORkajQYbN26EVqut9LVSU1MRGhrK8OJmKqUCpnJ27o7SaxGoqV4PqEIhldkdvKZKL9jHsTZERL7NJWNu2rZti0GDBmHcuHFYsWIFzGYzJk+ejOHDh9tmSl28eBEDBgzAe++9h549e8JgMGDgwIHIz8/HBx98YDfwNyIiAkqlEl988QUyMjJw4403QqvVYsuWLXjxxRcxffp0V1SDqqG8LqcQfxUiKtl809VKL9jHqd9ERL7NZevcfPjhh5g8eTIGDBgAhUKBe+65B2+88YbtcbPZjKNHjyI/v2gg6v79+20zqVq0aGF3rdOnTyM+Ph4qlQrLli3DE088ASEEWrRogcWLF2PcuHGuqgZVUYBGiQJT2W6kyrqsXI0L9hER1T8uCzdhYWHlLtgHAPHx8XZTuPv371/plO5BgwbZLd5HniNQ44fMnLLr3QRUszvKWYQQKDTLmLruABfsIyKqZ7grODmFrtSqxJIEBOtU1R5r4wxCCMz89CAOp+fYjnEQMRFR/cE/Y8kplAoJfsqi4CBJQGiAGg1DdG4pi9Ei2wWbqmziSUREvoMtN+QUkiQhLswfV3ONMFpktwUbwH4V4vcf7olgnYotNkRE9QjDDTlNoMYP/iolCi2uXWm4IqUHEGtVSgYbIqJ6ht1S5FQKhQR/tfsGEXMVYiIiYssNeT0hBIwWGYVmK1chJiIihhvyLsVB5u/7wKz1B22hppgrVyEO0vKfDRGRJ+OnNHmFQrO13CBTWtsYvcvWs1Eoyl+NmYiIPAPDDXmFh1btrfDxZuEBf60+DGj8FC7rjorWa6EttaYPERF5FoYb8lg6lRI3NA7F/nPX7Y6XDDLFXBloimlVCrdvJ0FERJVjuCGPJUkS1oxLwMEL2XbH6yLIOBLir0aQVlXnr0tERNXDcEMeTZIkdgMREVG1sI2diIiIfArDDVEVhAaoEBGkcXcxiIioChhuiCqhUACNQv3dXQwiIqoihhuiSug5iJiIyKsw3BBVgisSExF5F4YbIiIi8ikMN0QVCPFXceE+IiIvw/Z2ogpE6jXQ+HGdHSIib8I/SYmIiMinsOWGyIGGoTqE+qvcss0DERHVDltuiEqJ0mugUykZbIiIvBRbbohKaBSqQ2iA2t3FICKiWmDLDdFfAjRK6HVcsI+IyNsx3BD9RadWQqlgVxQRkbdjuCFCUatNTLDO3cUgIiInYLghAhDiz3E2RES+guGGCIC/mgv1ERH5CoYbIgB+HGtDROQzGG6IAK5pQ0TkQxhuqN5T+Ulgww0Rke9guKF6j6sRExH5FoYbqvc4U4qIyLcw3FC9plAAAZwpRUTkU1wWbq5du4YRI0ZAr9cjJCQEY8eORW5uboXP6d+/PyRJsrs9+uijduecO3cOgwcPhr+/PyIjIzFjxgxYLBZXVYN8XIDaD35KZnwiIl/iso0zR4wYgbS0NGzZsgVmsxljxozB+PHj8dFHH1X4vHHjxuG5556z3ff397f9v9VqxeDBgxEdHY2ffvoJaWlpGDlyJFQqFV588UVXVYV8lEIBBGq5dywRka9xySf74cOHsWnTJvzyyy/o3r07AGDp0qW4/fbbsWjRIsTGxpb7XH9/f0RHRzt8bPPmzfjzzz+xdetWREVFoUuXLliwYAFmzpyJefPmQa3m2AmqOq1KifBAjbuLQURETuaS9vjdu3cjJCTEFmwAICkpCQqFAnv27KnwuR9++CHCw8PRoUMHzJ49G/n5+XbX7dixI6KiomzHkpOTYTAY8Mcffzi/IuSzJAmI5V5SREQ+ySUtN+np6YiMjLR/IT8/hIWFIT09vdznPfDAA2jSpAliY2Nx8OBBzJw5E0ePHsX69ett1y0ZbADY7ld0XaPRCKPRaLtvMBiqXSfyLYEaP+g4kJiIyCdVK9zMmjULL730UoXnHD58uMaFGT9+vO3/O3bsiJiYGAwYMAAnT55E8+bNa3zdlJQUzJ8/v8bPJ98iSUCUXuvuYhARkYtUK9w8+eSTGD16dIXnNGvWDNHR0bh8+bLdcYvFgmvXrpU7nsaRhIQEAMCJEyfQvHlzREdHY+/evXbnZGRkAECF1509ezamTZtmu28wGBAXF1flcpBvUUgSW22IiHxYtcJNREQEIiIiKj0vMTERWVlZ2LdvH7p16wYA+O677yDLsi2wVEVqaioAICYmxnbdF154AZcvX7Z1e23ZsgV6vR7t2rUr9zoajQYaDQeOUtEMqSg93wtERL7MJQOK27Zti0GDBmHcuHHYu3cvdu3ahcmTJ2P48OG2mVIXL15EmzZtbC0xJ0+exIIFC7Bv3z6cOXMGGzduxMiRI9G3b1906tQJADBw4EC0a9cODz30EH777Td8++23eOaZZzBp0iSGF6qSBgEaNOAMKSIin+ay1cs+/PBDtGnTBgMGDMDtt9+O3r17Y+XKlbbHzWYzjh49apsNpVarsXXrVgwcOBBt2rTBk08+iXvuuQdffPGF7TlKpRJffvkllEolEhMT8eCDD2LkyJF26+IQlUenViA8kMsFEBH5OkkIIdxdiLpmMBgQHByM7Oxs6PV6dxeHKmCyyDianuOUawVq/dA0PMAp1yIiorpX1e9vrjtP9QbH2hAR1Q8MN1Qv6NRK6FScIUVEVB8w3JDPU/spEBGogSRJ7i4KERHVAe4aSD5N5SehdXSQu4tBRER1iOGGfFaDQDUigzjOhoiovmG3FPmsALUf/JR8ixMR1Tf85CefpPKToNexYZKIqD5iuCGfpJQkDiAmIqqnGG7IJ0UFc9dvIqL6iu325FNUfhIC1H7Q+nFNGyKi+orhhrxSkNYPaj8FsgvMsFgF9Do/WGUBvU6FcG6MSURUrzHckEdTKiQ0CtVBoZCgVEgwWWSo/RQI1BS9dYunenNWFBERFWO4IY+mVEgIDSixk3epRhmGGiIiKo3fDERERORTGG6IiIjIpzDcEBERkU9huCEiIiKfwnBDREREPoXhhoiIiHwKww0RERH5FIYbIiIi8ikMN0RERORTGG6IiIjIpzDcEBERkU9huCEiIiKfwnBDREREPoXhhoiIiHwKww0RERH5FD93F8AdhBAAAIPB4OaSEBERUVUVf28Xf4+Xp16Gm5ycHABAXFycm0tCRERE1ZWTk4Pg4OByH5dEZfHHB8myjEuXLiEoKAiSJLm7OACK0mhcXBzOnz8PvV7v7uK4FOvqu+pTfVlX38S6ejYhBHJychAbGwuFovyRNfWy5UahUKBRo0buLoZDer3ea95ktcW6+q76VF/W1Texrp6rohabYhxQTERERD6F4YaIiIh8CsONh9BoNJg7dy40Go27i+JyrKvvqk/1ZV19E+vqG+rlgGIiIiLyXWy5ISIiIp/CcENEREQ+heGGiIiIfArDDREREfkUhhsXWrZsGeLj46HVapGQkIC9e/dWeP4nn3yCNm3aQKvVomPHjvj666/LnHP48GH84x//QHBwMAICAtCjRw+cO3fOVVWoMmfXNTc3F5MnT0ajRo2g0+nQrl07rFixwpVVqLLq1PWPP/7APffcg/j4eEiShCVLltT6mnXJ2XVNSUlBjx49EBQUhMjISAwZMgRHjx51YQ2qzhW/12ILFy6EJEmYOnWqcwtdQ66o68WLF/Hggw+iQYMG0Ol06NixI3799VcX1aB6nF1fq9WKZ599Fk2bNoVOp0Pz5s2xYMGCSvc7qgvVqeubb76JPn36IDQ0FKGhoUhKSipzvhACc+bMQUxMDHQ6HZKSknD8+HFXV6P2BLnE2rVrhVqtFqtWrRJ//PGHGDdunAgJCREZGRkOz9+1a5dQKpXi5ZdfFn/++ad45plnhEqlEr///rvtnBMnToiwsDAxY8YMsX//fnHixAnx+eefl3vNuuKKuo4bN040b95cbN++XZw+fVr897//FUqlUnz++ed1VS2HqlvXvXv3iunTp4s1a9aI6Oho8dprr9X6mnXFFXVNTk4W77zzjjh06JBITU0Vt99+u2jcuLHIzc11cW0q5oq6ljw3Pj5edOrUSTz++OOuqUA1uKKu165dE02aNBGjR48We/bsEadOnRLffvutOHHihItrUzlX1PeFF14QDRo0EF9++aU4ffq0+OSTT0RgYKB4/fXXXVybilW3rg888IBYtmyZOHDggDh8+LAYPXq0CA4OFhcuXLCds3DhQhEcHCw2bNggfvvtN/GPf/xDNG3aVBQUFNRVtWqE4cZFevbsKSZNmmS7b7VaRWxsrEhJSXF4/n333ScGDx5sdywhIUH861//st0fNmyYePDBB11T4FpwRV3bt28vnnvuObtzbrjhBvHvf//biSWvvurWtaQmTZo4/KCszTVdyRV1Le3y5csCgPj+++9rU9Rac1Vdc3JyRMuWLcWWLVtEv379PCLcuKKuM2fOFL1793ZmMZ3GFfUdPHiwePjhh+2ODR06VIwYMaLW5a2N2n6WWCwWERQUJFavXi2EEEKWZREdHS1eeeUV2zlZWVlCo9GINWvWOLfwTsZuKRcwmUzYt28fkpKSbMcUCgWSkpKwe/duh8/ZvXu33fkAkJycbDtflmV89dVXaNWqFZKTkxEZGYmEhARs2LDBZfWoClfUFQB69eqFjRs34uLFixBCYPv27Th27BgGDhzomopUQU3q6o5rOkNdlSs7OxsAEBYW5rRrVpcr6zpp0iQMHjy4zPvdXVxV140bN6J79+649957ERkZia5du+LNN990RpFrxVX17dWrF7Zt24Zjx44BAH777Tf8+OOPuO2222pd5ppyRl3z8/NhNptt/x5Pnz6N9PR0u2sGBwcjISHBrZ9PVcFw4wKZmZmwWq2IioqyOx4VFYX09HSHz0lPT6/w/MuXLyM3NxcLFy7EoEGDsHnzZtx9990YOnQovv/+e9dUpApcUVcAWLp0Kdq1a4dGjRpBrVZj0KBBWLZsGfr27ev8SlRRTerqjms6Q12US5ZlTJ06FTfddBM6dOjglGvWhKvqunbtWuzfvx8pKSm1LaLTuKqup06dwvLly9GyZUt8++23mDBhAqZMmYLVq1fXtsi14qr6zpo1C8OHD0ebNm2gUqnQtWtXTJ06FSNGjKhtkWvMGXWdOXMmYmNjbWGm+Hme9vlUFfVyV3BvJMsyAOCuu+7CE088AQDo0qULfvrpJ6xYsQL9+vVzZ/GcbunSpfj555+xceNGNGnSBDt37sSkSZPs/uGRd5s0aRIOHTqEH3/80d1Fcbrz58/j8ccfx5YtW6DVat1dHJeTZRndu3fHiy++CADo2rUrDh06hBUrVmDUqFFuLp3zffzxx/jwww/x0UcfoX379khNTcXUqVMRGxvrtfVduHAh1q5dix07dvjEe5bhxgXCw8OhVCqRkZFhdzwjIwPR0dEOnxMdHV3h+eHh4fDz80O7du3szmnbtq1bvxxcUdeCggI8/fTT+OyzzzB48GAAQKdOnZCamopFixa5LdzUpK7uuKYzuLpckydPxpdffomdO3eiUaNGtb5ebbiirvv27cPly5dxww032I5ZrVbs3LkT//nPf2A0GqFUKmtV7ppw1e81JibG4WfTp59+WuNrOoOr6jtjxgxb6w0AdOzYEWfPnkVKSorbwk1t6rpo0SIsXLgQW7duRadOnWzHi5+XkZGBmJgYu2t26dLFeYV3AXZLuYBarUa3bt2wbds22zFZlrFt2zYkJiY6fE5iYqLd+QCwZcsW2/lqtRo9evQoM2322LFjaNKkiZNrUHWuqKvZbIbZbIZCYf/2VCqVthYsd6hJXd1xTWdwVbmEEJg8eTI+++wzfPfdd2jatKkzilsrrqjrgAED8PvvvyM1NdV26969O0aMGIHU1FS3BBvAdb/Xm266yeM+mwDX1Tc/P99nPp9efvllLFiwAJs2bUL37t3tHmvatCmio6PtrmkwGLBnzx63fj5VibtHNPuqtWvXCo1GI959913x559/ivHjx4uQkBCRnp4uhBDioYceErNmzbKdv2vXLuHn5ycWLVokDh8+LObOnVtmevT69euFSqUSK1euFMePHxdLly4VSqVS/PDDD3Vev5JcUdd+/fqJ9u3bi+3bt4tTp06Jd955R2i1WvF///d/dV6/kqpbV6PRKA4cOCAOHDggYmJixPTp08WBAwfE8ePHq3xNd3FFXSdMmCCCg4PFjh07RFpamu2Wn59f5/UryRV1Lc1TZku5oq579+4Vfn5+4oUXXhDHjx8XH374ofD39xcffPBBndevNFfUd9SoUaJhw4a2qeDr168X4eHh4qmnnqrz+pVU3bouXLhQqNVq8b///c/u32NOTo7dOSEhIeLzzz8XBw8eFHfddRengtd3S5cuFY0bNxZqtVr07NlT/Pzzz7bH+vXrJ0aNGmV3/scffyxatWol1Gq1aN++vfjqq6/KXPPtt98WLVq0EFqtVnTu3Fls2LDB1dWoEmfXNS0tTYwePVrExsYKrVYrWrduLV599VUhy3JdVKdC1anr6dOnBYAyt379+lX5mu7k7Lo6ehyAeOedd+quUuVwxe+1JE8JN0K4pq5ffPGF6NChg9BoNKJNmzZi5cqVdVSbyjm7vgaDQTz++OOicePGQqvVimbNmol///vfwmg01mGtHKtOXZs0aeKwrnPnzrWdI8uyePbZZ0VUVJTQaDRiwIAB4ujRo3VYo5qRhPCAJRWJiIiInIRjboiIiMinMNwQERGRT2G4ISIiIp/CcENEREQ+heGGiIiIfArDDREREfkUhhsiIiLyKQw3RB5CCIHx48cjLCwMkiQhNTXV3UWyM3r0aAwZMqTGz+/fvz+mTp1qux8fH48lS5bUulyudObMGY/8XRBRxbhxJpGH2LRpE959913s2LEDzZo1Q3h4uEtep3///ujSpYvbg8Uvv/yCgIAAt5ahMnFxcUhLS3PZ78IT7dixAzfffDOuX7+OkJAQdxeHqEYYbog8xMmTJxETE4NevXo5fNxkMkGtVtdxqVwnIiLC3UWolFKpdOvu7K5gNpuhUqncXQwil2K3FJEHGD16NB577DGcO3cOkiQhPj4e/fv3x+TJkzF16lSEh4cjOTkZALB48WJ07NgRAQEBiIuLw8SJE5Gbm2t3vV27dqF///7w9/dHaGgokpOTcf36dYwePRrff/89Xn/9dUiSBEmScObMGVitVowdOxZNmzaFTqdD69at8frrr9e4Pnl5eRg5ciQCAwMRExODV199tcw5pbulJEnCf//7X9xxxx3w9/dH27ZtsXv3bpw4cQL9+/dHQEAAevXqhZMnT9pd5/PPP8cNN9wArVaLZs2aYf78+bBYLHbXfeutt3D33XfD398fLVu2xMaNG22PX79+HSNGjEBERAR0Oh1atmyJd955B4Djbqnvv/8ePXv2hEajQUxMDGbNmmX3ev3798eUKVPw1FNPISwsDNHR0Zg3b16Vfm7Tp0/HHXfcYbu/ZMkSSJKETZs22Y61aNECb731FoCiXZ+fe+45NGrUCBqNBl26dLE7t7j869atQ79+/aDVavHhhx/i7NmzuPPOOxEaGoqAgAC0b98eX3/9Nc6cOYObb74ZABAaGgpJkjB69OgqlZ3Io7h5bysiEkJkZWWJ5557TjRq1EikpaWJy5cvi379+onAwEAxY8YMceTIEXHkyBEhhBCvvfaa+O6778Tp06fFtm3bROvWrcWECRNs1zpw4IDQaDRiwoQJIjU1VRw6dEgsXbpUXLlyRWRlZYnExEQxbtw42w7AFotFmEwmMWfOHPHLL7+IU6dOiQ8++ED4+/uLdevW2a47atQocdddd1WpPhMmTBCNGzcWW7duFQcPHhR33HGHCAoKsts4skmTJuK1116z3QcgGjZsKNatWyeOHj0qhgwZIuLj48Utt9wiNm3aJP78809x4403ikGDBtmes3PnTqHX68W7774rTp48KTZv3izi4+PFvHnz7K7bqFEj8dFHH4njx4+LKVOmiMDAQHH16lUhhBCTJk0SXbp0Eb/88os4ffq02LJli9i4caMQ4u9NFA8cOCCEEOLChQvC399fTJw4URw+fFh89tlnIjw83G6jwX79+gm9Xi/mzZsnjh07JlavXi0kSRKbN2+u9Oe2ceNGERwcLCwWixBCiCFDhojw8HAxc+ZM2+sDsO1QvXjxYqHX68WaNWvEkSNHxFNPPSVUKpU4duyYXfnj4+PFp59+Kk6dOiUuXbokBg8eLG699VZx8OBBcfLkSfHFF1+I77//XlgsFvHpp58KAOLo0aMiLS1NZGVlVel3TuRJGG6IPMRrr70mmjRpYrvfr18/0bVr10qf98knn4gGDRrY7t9///3ipptuKvf8qu5OPWnSJHHPPffY7lc13OTk5Ai1Wi0+/vhj27GrV68KnU5Xabh55plnbPd3794tAIi3337bdmzNmjVCq9Xa7g8YMEC8+OKLdq///vvvi5iYmHKvm5ubKwCIb775RgghxJ133inGjBnjsC6lw83TTz8tWrdubbc7/bJly0RgYKCwWq1CiKKfb+/eve2u06NHD1tAqcj169eFQqEQv/zyi5BlWYSFhYmUlBSRkJAghBDigw8+EA0bNrSdHxsbK1544YUyrzVx4kS78i9ZssTunI4dO9oFwJK2b98uAIjr169XWl4iT8VuKSIP1q1btzLHtm7digEDBqBhw4YICgrCQw89hKtXryI/Px8AkJqaigEDBlT7tZYtW4Zu3bohIiICgYGBWLlyJc6dO1ft65w8eRImkwkJCQm2Y2FhYWjdunWlz+3UqZPt/6OiogAAHTt2tDtWWFgIg8EAAPjtt9/w3HPPITAw0HYbN24c0tLSbD+P0tcNCAiAXq/H5cuXAQATJkzA2rVr0aVLFzz11FP46aefyi3f4cOHkZiYCEmSbMduuukm5Obm4sKFCw5fDwBiYmJsr1eRkJAQdO7cGTt27MDvv/8OtVqN8ePH48CBA8jNzcX333+Pfv36AQAMBgMuXbqEm266ye4aN910Ew4fPmx3rHv37nb3p0yZgueffx433XQT5s6di4MHD1ZaNiJvwnBD5MFKzyY6c+YM7rjjDnTq1Amffvop9u3bh2XLlgEoGnAMADqdrtqvs3btWkyfPh1jx47F5s2bkZqaijFjxtiuWVdKDnQtDhCOjsmyDADIzc3F/PnzkZqaarv9/vvvOH78OLRarcPrFl+n+Bq33XYbzp49iyeeeAKXLl3CgAEDMH36dKfVo/TrVaZ///7YsWOHLciEhYWhbdu2+PHHH+3CTXWUfh898sgjOHXqFB566CH8/vvv6N69O5YuXVrt6xJ5KoYbIi+yb98+yLKMV199FTfeeCNatWqFS5cu2Z3TqVMnbNu2rdxrqNVqWK1Wu2O7du1Cr169MHHiRHTt2hUtWrQoM3C3qpo3bw6VSoU9e/bYjl2/fh3Hjh2r0fUqcsMNN+Do0aNo0aJFmZtCUfWPt4iICIwaNQoffPABlixZgpUrVzo8r3iQsxDCdmzXrl0ICgpCo0aNal0fAOjXrx9+/PFHbNu2Df379wdQFHjWrFmDY8eO2Y7p9XrExsZi165dds/ftWsX2rVrV+nrxMXF4dFHH8X69evx5JNP4s033wQA24y80u8RIm/CcEPkRVq0aAGz2YylS5fi1KlTeP/997FixQq7c2bPno1ffvkFEydOxMGDB3HkyBEsX74cmZmZAIpmKe3ZswdnzpxBZmYmZFlGy5Yt8euvv+Lbb7/FsWPH8Oyzz+KXX36pURkDAwMxduxYzJgxA9999x0OHTqE0aNHVytsVNWcOXPw3nvvYf78+fjjjz9w+PBhrF27Fs8880y1rvH555/jxIkT+OOPP/Dll1+ibdu2Ds+dOHEizp8/j8ceewxHjhzB559/jrlz52LatGlOq1/fvn2Rk5ODL7/80i7cfPjhh4iJiUGrVq1s586YMQMvvfQS1q1bh6NHj2LWrFlITU3F448/XuFrTJ06Fd9++y1Onz6N/fv3Y/v27bY6N2nSBJIk4csvv8SVK1fKzMQj8gYMN0RepHPnzli8eDFeeukldOjQAR9++CFSUlLszmnVqhU2b96M3377DT179kRiYiI+//xz+PkVLWs1ffp0KJVKtGvXDhERETh37hz+9a9/YejQoRg2bBgSEhJw9epVTJw4scblfOWVV9CnTx/ceeedSEpKQu/evR2OH6qt5ORkfPnll9i8eTN69OiBG2+8Ea+99hqaNGlS5Wuo1WrMnj0bnTp1Qt++faFUKrF27VqH5zZs2BBff/019u7di86dO+PRRx/F2LFjqxWmKhMaGoqOHTsiIiICbdq0AVAUeGRZLtMlNWXKFEybNg1PPvkkOnbsiE2bNmHjxo1o2bJlha9htVoxadIktG3bFoMGDUKrVq3wf//3f7Y6zp8/H7NmzUJUVBQmT57stLoR1RVJlGxfJSIiIvJybLkhIiIin8JwQ0TVcu7cObup16VvNZk+Xl98+OGH5f7c2rdv7+7iEfkMdksRUbVYLBacOXOm3Mfj4+Nt43vIXk5ODjIyMhw+plKpqjVWiIjKx3BDREREPoXdUkRERORTGG6IiIjIpzDcEBERkU9huCEiIiKfwnBDREREPoXhhoiIiHwKww0RERH5FIYbIiIi8in/DzWn/2j9xhjlAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n_graphs = 0\n",
+    "for dataset_name in benchmark_utils.get_avaialble_datasets():\n",
+    "    ebm = benchmark_utils.get_ebm(dataset_name)\n",
+    "    _, _, _, _, feature_names = benchmark_utils.get_dataset(dataset_name)\n",
+    "    for feature_idx, feature_name in enumerate(feature_names):\n",
+    "        graph = graphs.extract_graph(ebm, feature_idx)\n",
+    "        #if graph.feature_type == \"continuous\":\n",
+    "        print(dataset_name, feature_name)\n",
+    "        graphs.plot_graph(graph)\n",
+    "        n_graphs += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "128"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# the total number of graphs\n",
+    "n_graphs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### extract graphs as text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: longitude\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(-124.35, -124.10499999999999)\": -50430.1, \"(-124.10499999999999, -124.08500000000001)\": -38925.6, \"(-124.08500000000001, -124.07499999999999)\": -23742.3, \"(-124.07499999999999, -123.3)\": -12526.0, \"(-123.3, -122.955)\": -1690.2, \"(-122.955, -122.66499999999999)\": 19040.8, \"(-122.66499999999999, -122.60499999999999)\": 29856.3, \"(-122.60499999999999, -122.58500000000001)\": 44315.6, \"(-122.58500000000001, -122.555)\": 75515.2, \"(-122.555, -122.455)\": 86444.1, \"(-122.455, -122.445)\": 99533.8, \"(-122.445, -122.42500000000001)\": 112351.5, \"(-122.42500000000001, -122.405)\": 89733.4, \"(-122.405, -122.39500000000001)\": 78586.0, \"(-122.39500000000001, -122.375)\": 46429.6, \"(-122.375, -122.36500000000001)\": 35622.6, \"(-122.36500000000001, -122.305)\": 20538.8, \"(-122.305, -122.155)\": 6386.6, \"(-122.155, -120.92500000000001)\": 24722.9, \"(-120.92500000000001, -120.91499999999999)\": 54457.6, \"(-120.91499999999999, -120.89500000000001)\": 34017.2, \"(-120.89500000000001, -120.86500000000001)\": 18216.5, \"(-120.86500000000001, -120.725)\": 6143.7, \"(-120.725, -120.63499999999999)\": 17429.8, \"(-120.63499999999999, -120.485)\": 407.5, \"(-120.485, -120.405)\": -15764.1, \"(-120.405, -120.10499999999999)\": 1041.6, \"(-120.10499999999999, -120.095)\": 24030.2, \"(-120.095, -119.91499999999999)\": -2161.3, \"(-119.91499999999999, -119.85499999999999)\": -20610.8, \"(-119.85499999999999, -119.795)\": -31705.4, \"(-119.795, -119.755)\": -20112.3, \"(-119.755, -119.525)\": -3774.8, \"(-119.525, -119.505)\": 10442.2, \"(-119.505, -119.295)\": -10555.7, \"(-119.295, -119.215)\": 3582.5, \"(-119.215, -118.905)\": -15819.3, \"(-118.905, -118.695)\": -2790.4, \"(-118.695, -118.57499999999999)\": 13581.3, \"(-118.57499999999999, -118.525)\": 26358.2, \"(-118.525, -118.495)\": 44919.9, \"(-118.495, -118.375)\": 60453.4, \"(-118.375, -118.35499999999999)\": 38572.6, \"(-118.35499999999999, -118.305)\": 21183.4, \"(-118.305, -118.265)\": -6755.4, \"(-118.265, -118.14500000000001)\": -17830.0, \"(-118.14500000000001, -117.985)\": -7071.0, \"(-117.985, -117.755)\": -26435.2, \"(-117.755, -117.725)\": -50667.3, \"(-117.725, -117.64500000000001)\": -63305.2, \"(-117.64500000000001, -117.57499999999999)\": -50999.4, \"(-117.57499999999999, -117.35499999999999)\": -38880.5, \"(-117.35499999999999, -117.285)\": -64800.9, \"(-117.285, -117.155)\": -47182.2, \"(-117.155, -117.13499999999999)\": -65749.6, \"(-117.13499999999999, -116.995)\": -77340.8, \"(-116.995, -116.795)\": -64524.7, \"(-116.795, -116.205)\": -53643.1, \"(-116.205, -116.1)\": -64388.1, \"(-116.1, -115.525)\": -75181.7, \"(-115.525, -115.1)\": -57014.4, \"(-115.1, -114.595)\": -74654.1, \"(-114.595, -114.31)\": -100620.1}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(-124.35, -124.10499999999999)\": -57749.3, \"(-124.10499999999999, -124.08500000000001)\": -46703.5, \"(-124.08500000000001, -124.07499999999999)\": -34644.5, \"(-124.07499999999999, -123.3)\": -20263.6, \"(-123.3, -122.955)\": -10198.1, \"(-122.955, -122.66499999999999)\": 10561.3, \"(-122.66499999999999, -122.60499999999999)\": 24254.2, \"(-122.60499999999999, -122.58500000000001)\": 34301.3, \"(-122.58500000000001, -122.555)\": 63992.2, \"(-122.555, -122.455)\": 76785.0, \"(-122.455, -122.445)\": 91169.4, \"(-122.445, -122.42500000000001)\": 103719.6, \"(-122.42500000000001, -122.405)\": 81277.9, \"(-122.405, -122.39500000000001)\": 66955.5, \"(-122.39500000000001, -122.375)\": 35631.1, \"(-122.375, -122.36500000000001)\": 24396.3, \"(-122.36500000000001, -122.305)\": 14520.6, \"(-122.305, -122.155)\": -1221.8, \"(-122.155, -120.92500000000001)\": 11630.1, \"(-120.92500000000001, -120.91499999999999)\": 11031.4, \"(-120.91499999999999, -120.89500000000001)\": 19105.3, \"(-120.89500000000001, -120.86500000000001)\": 4469.7, \"(-120.86500000000001, -120.725)\": -1198.1, \"(-120.725, -120.63499999999999)\": 7919.4, \"(-120.63499999999999, -120.485)\": -11276.9, \"(-120.485, -120.405)\": -23035.0, \"(-120.405, -120.10499999999999)\": -4074.0, \"(-120.10499999999999, -120.095)\": -10802.7, \"(-120.095, -119.91499999999999)\": -13216.2, \"(-119.91499999999999, -119.85499999999999)\": -33171.4, \"(-119.85499999999999, -119.795)\": -38315.5, \"(-119.795, -119.755)\": -24784.5, \"(-119.755, -119.525)\": -13160.2, \"(-119.525, -119.505)\": -4048.0, \"(-119.505, -119.295)\": -18789.9, \"(-119.295, -119.215)\": -8493.9, \"(-119.215, -118.905)\": -20485.4, \"(-118.905, -118.695)\": -7647.9, \"(-118.695, -118.57499999999999)\": 4745.1, \"(-118.57499999999999, -118.525)\": 17156.2, \"(-118.525, -118.495)\": 33913.3, \"(-118.495, -118.375)\": 52480.8, \"(-118.375, -118.35499999999999)\": 34068.9, \"(-118.35499999999999, -118.305)\": 14693.0, \"(-118.305, -118.265)\": -11878.1, \"(-118.265, -118.14500000000001)\": -21370.7, \"(-118.14500000000001, -117.985)\": -11803.1, \"(-117.985, -117.755)\": -35281.9, \"(-117.755, -117.725)\": -58041.5, \"(-117.725, -117.64500000000001)\": -72526.8, \"(-117.64500000000001, -117.57499999999999)\": -61627.3, \"(-117.57499999999999, -117.35499999999999)\": -45444.2, \"(-117.35499999999999, -117.285)\": -74287.3, \"(-117.285, -117.155)\": -55258.2, \"(-117.155, -117.13499999999999)\": -74456.9, \"(-117.13499999999999, -116.995)\": -86582.5, \"(-116.995, -116.795)\": -73433.2, \"(-116.795, -116.205)\": -69635.5, \"(-116.205, -116.1)\": -75131.9, \"(-116.1, -115.525)\": -97151.1, \"(-115.525, -115.1)\": -73988.5, \"(-115.1, -114.595)\": -91086.2, \"(-114.595, -114.31)\": -120109.7}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(-124.35, -124.10499999999999)\": -43110.8, \"(-124.10499999999999, -124.08500000000001)\": -31147.7, \"(-124.08500000000001, -124.07499999999999)\": -12840.0, \"(-124.07499999999999, -123.3)\": -4788.4, \"(-123.3, -122.955)\": 6817.8, \"(-122.955, -122.66499999999999)\": 27520.3, \"(-122.66499999999999, -122.60499999999999)\": 35458.4, \"(-122.60499999999999, -122.58500000000001)\": 54329.8, \"(-122.58500000000001, -122.555)\": 87038.2, \"(-122.555, -122.455)\": 96103.2, \"(-122.455, -122.445)\": 107898.1, \"(-122.445, -122.42500000000001)\": 120983.4, \"(-122.42500000000001, -122.405)\": 98188.9, \"(-122.405, -122.39500000000001)\": 90216.5, \"(-122.39500000000001, -122.375)\": 57228.1, \"(-122.375, -122.36500000000001)\": 46849.0, \"(-122.36500000000001, -122.305)\": 26556.9, \"(-122.305, -122.155)\": 13995.0, \"(-122.155, -120.92500000000001)\": 37815.7, \"(-120.92500000000001, -120.91499999999999)\": 97883.7, \"(-120.91499999999999, -120.89500000000001)\": 48929.0, \"(-120.89500000000001, -120.86500000000001)\": 31963.3, \"(-120.86500000000001, -120.725)\": 13485.5, \"(-120.725, -120.63499999999999)\": 26940.3, \"(-120.63499999999999, -120.485)\": 12092.0, \"(-120.485, -120.405)\": -8493.1, \"(-120.405, -120.10499999999999)\": 6157.2, \"(-120.10499999999999, -120.095)\": 58863.0, \"(-120.095, -119.91499999999999)\": 8893.5, \"(-119.91499999999999, -119.85499999999999)\": -8050.3, \"(-119.85499999999999, -119.795)\": -25095.3, \"(-119.795, -119.755)\": -15440.1, \"(-119.755, -119.525)\": 5610.6, \"(-119.525, -119.505)\": 24932.3, \"(-119.505, -119.295)\": -2321.4, \"(-119.295, -119.215)\": 15659.0, \"(-119.215, -118.905)\": -11153.1, \"(-118.905, -118.695)\": 2067.1, \"(-118.695, -118.57499999999999)\": 22417.4, \"(-118.57499999999999, -118.525)\": 35560.1, \"(-118.525, -118.495)\": 55926.4, \"(-118.495, -118.375)\": 68426.1, \"(-118.375, -118.35499999999999)\": 43076.4, \"(-118.35499999999999, -118.305)\": 27673.7, \"(-118.305, -118.265)\": -1632.7, \"(-118.265, -118.14500000000001)\": -14289.3, \"(-118.14500000000001, -117.985)\": -2338.9, \"(-117.985, -117.755)\": -17588.5, \"(-117.755, -117.725)\": -43293.2, \"(-117.725, -117.64500000000001)\": -54083.7, \"(-117.64500000000001, -117.57499999999999)\": -40371.5, \"(-117.57499999999999, -117.35499999999999)\": -32316.9, \"(-117.35499999999999, -117.285)\": -55314.6, \"(-117.285, -117.155)\": -39106.3, \"(-117.155, -117.13499999999999)\": -57042.4, \"(-117.13499999999999, -116.995)\": -68099.0, \"(-116.995, -116.795)\": -55616.1, \"(-116.795, -116.205)\": -37650.7, \"(-116.205, -116.1)\": -53644.2, \"(-116.1, -115.525)\": -53212.4, \"(-115.525, -115.1)\": -40040.3, \"(-115.1, -114.595)\": -58221.9, \"(-114.595, -114.31)\": -81130.6}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: latitude\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(32.54, 32.565)\": 23234.8, \"(32.565, 32.685)\": -3182.4, \"(32.685, 32.715)\": 7727.3, \"(32.715, 32.915)\": 17670.3, \"(32.915, 33.275000000000006)\": 34030.3, \"(33.275000000000006, 33.355000000000004)\": 55000.2, \"(33.355000000000004, 33.465)\": 64326.4, \"(33.465, 33.504999999999995)\": 81519.1, \"(33.504999999999995, 33.555)\": 94496.7, \"(33.555, 33.565)\": 63293.1, \"(33.565, 33.575)\": 51665.3, \"(33.575, 33.635000000000005)\": 66563.2, \"(33.635000000000005, 33.655)\": 47304.3, \"(33.655, 33.765)\": 29789.1, \"(33.765, 33.894999999999996)\": 15892.8, \"(33.894999999999996, 33.985)\": 2769.6, \"(33.985, 33.995000000000005)\": 17775.7, \"(33.995000000000005, 34.045)\": 28884.5, \"(34.045, 34.085)\": 55702.3, \"(34.085, 34.165)\": 46322.8, \"(34.165, 34.175)\": 33820.1, \"(34.175, 34.195)\": 7500.1, \"(34.195, 34.215)\": -4126.2, \"(34.215, 34.254999999999995)\": -16649.8, \"(34.254999999999995, 34.325)\": -27636.8, \"(34.325, 34.345)\": 17113.4, \"(34.345, 34.375)\": 28769.5, \"(34.375, 34.455)\": 43828.3, \"(34.455, 34.474999999999994)\": 57774.8, \"(34.474999999999994, 34.504999999999995)\": 33279.2, \"(34.504999999999995, 34.545)\": 19368.1, \"(34.545, 34.625)\": 5698.9, \"(34.625, 34.635000000000005)\": -19637.8, \"(34.635000000000005, 34.644999999999996)\": -39271.0, \"(34.644999999999996, 34.715)\": -26993.1, \"(34.715, 35.325)\": -17344.4, \"(35.325, 36.375)\": -37699.6, \"(36.375, 36.535)\": -27730.8, \"(36.535, 36.635000000000005)\": -14690.6, \"(36.635000000000005, 36.845)\": -25070.6, \"(36.845, 37.275000000000006)\": -15387.7, \"(37.275000000000006, 37.335)\": -3329.1, \"(37.335, 37.425)\": 7953.5, \"(37.425, 37.445)\": 34546.2, \"(37.445, 37.465)\": 45097.3, \"(37.465, 37.495000000000005)\": 30019.5, \"(37.495000000000005, 37.585)\": 16643.2, \"(37.585, 37.595)\": -3057.8, \"(37.595, 37.605000000000004)\": -32379.8, \"(37.605000000000004, 37.754999999999995)\": -42729.0, \"(37.754999999999995, 37.775000000000006)\": -17898.2, \"(37.775000000000006, 37.795)\": -3229.6, \"(37.795, 37.805)\": 8902.6, \"(37.805, 37.855000000000004)\": -13456.8, \"(37.855000000000004, 37.915)\": -1362.3, \"(37.915, 37.925)\": -19143.7, \"(37.925, 37.945)\": -38768.9, \"(37.945, 38.355000000000004)\": -48247.9, \"(38.355000000000004, 39.085)\": -38467.7, \"(39.085, 39.474999999999994)\": -47690.5, \"(39.474999999999994, 40.135000000000005)\": -56986.6, \"(40.135000000000005, 40.665)\": -66271.5, \"(40.665, 41.775000000000006)\": -75627.3, \"(41.775000000000006, 41.95)\": -85116.1}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(32.54, 32.565)\": 964.8, \"(32.565, 32.685)\": -13385.7, \"(32.685, 32.715)\": -6553.6, \"(32.715, 32.915)\": 6526.7, \"(32.915, 33.275000000000006)\": 15999.9, \"(33.275000000000006, 33.355000000000004)\": 42157.4, \"(33.355000000000004, 33.465)\": 51350.1, \"(33.465, 33.504999999999995)\": 60415.4, \"(33.504999999999995, 33.555)\": 76698.7, \"(33.555, 33.565)\": 39537.3, \"(33.565, 33.575)\": 41623.9, \"(33.575, 33.635000000000005)\": 54208.1, \"(33.635000000000005, 33.655)\": 37976.2, \"(33.655, 33.765)\": 22371.1, \"(33.765, 33.894999999999996)\": 10058.4, \"(33.894999999999996, 33.985)\": -876.4, \"(33.985, 33.995000000000005)\": 13047.1, \"(33.995000000000005, 34.045)\": 23199.9, \"(34.045, 34.085)\": 50112.0, \"(34.085, 34.165)\": 40162.5, \"(34.165, 34.175)\": 28146.4, \"(34.175, 34.195)\": 2466.5, \"(34.195, 34.215)\": -9561.1, \"(34.215, 34.254999999999995)\": -23153.3, \"(34.254999999999995, 34.325)\": -37515.4, \"(34.325, 34.345)\": -3758.1, \"(34.345, 34.375)\": 15207.7, \"(34.375, 34.455)\": 33875.3, \"(34.455, 34.474999999999994)\": 41591.3, \"(34.474999999999994, 34.504999999999995)\": 20304.7, \"(34.504999999999995, 34.545)\": 13245.7, \"(34.545, 34.625)\": -12771.5, \"(34.625, 34.635000000000005)\": -37375.2, \"(34.635000000000005, 34.644999999999996)\": -49797.4, \"(34.644999999999996, 34.715)\": -34913.5, \"(34.715, 35.325)\": -47411.7, \"(35.325, 36.375)\": -46798.2, \"(36.375, 36.535)\": -34852.7, \"(36.535, 36.635000000000005)\": -23680.1, \"(36.635000000000005, 36.845)\": -34287.5, \"(36.845, 37.275000000000006)\": -23625.3, \"(37.275000000000006, 37.335)\": -9268.7, \"(37.335, 37.425)\": -4329.5, \"(37.425, 37.445)\": 29053.6, \"(37.445, 37.465)\": 29188.3, \"(37.465, 37.495000000000005)\": 21566.7, \"(37.495000000000005, 37.585)\": 8469.3, \"(37.585, 37.595)\": -16791.2, \"(37.595, 37.605000000000004)\": -38739.3, \"(37.605000000000004, 37.754999999999995)\": -51675.5, \"(37.754999999999995, 37.775000000000006)\": -25033.4, \"(37.775000000000006, 37.795)\": -8688.7, \"(37.795, 37.805)\": 36.5, \"(37.805, 37.855000000000004)\": -20482.2, \"(37.855000000000004, 37.915)\": -9472.3, \"(37.915, 37.925)\": -25360.4, \"(37.925, 37.945)\": -46246.0, \"(37.945, 38.355000000000004)\": -55734.3, \"(38.355000000000004, 39.085)\": -48831.7, \"(39.085, 39.474999999999994)\": -57243.4, \"(39.474999999999994, 40.135000000000005)\": -65954.6, \"(40.135000000000005, 40.665)\": -75283.4, \"(40.665, 41.775000000000006)\": -84501.7, \"(41.775000000000006, 41.95)\": -93657.9}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(32.54, 32.565)\": 45504.8, \"(32.565, 32.685)\": 7020.9, \"(32.685, 32.715)\": 22008.3, \"(32.715, 32.915)\": 28813.8, \"(32.915, 33.275000000000006)\": 52060.7, \"(33.275000000000006, 33.355000000000004)\": 67843.0, \"(33.355000000000004, 33.465)\": 77302.7, \"(33.465, 33.504999999999995)\": 102622.7, \"(33.504999999999995, 33.555)\": 112294.7, \"(33.555, 33.565)\": 87049.0, \"(33.565, 33.575)\": 61706.7, \"(33.575, 33.635000000000005)\": 78918.4, \"(33.635000000000005, 33.655)\": 56632.4, \"(33.655, 33.765)\": 37207.2, \"(33.765, 33.894999999999996)\": 21727.2, \"(33.894999999999996, 33.985)\": 6415.7, \"(33.985, 33.995000000000005)\": 22504.3, \"(33.995000000000005, 34.045)\": 34569.1, \"(34.045, 34.085)\": 61292.7, \"(34.085, 34.165)\": 52483.1, \"(34.165, 34.175)\": 39493.7, \"(34.175, 34.195)\": 12533.7, \"(34.195, 34.215)\": 1308.7, \"(34.215, 34.254999999999995)\": -10146.2, \"(34.254999999999995, 34.325)\": -17758.2, \"(34.325, 34.345)\": 37984.8, \"(34.345, 34.375)\": 42331.3, \"(34.375, 34.455)\": 53781.2, \"(34.455, 34.474999999999994)\": 73958.2, \"(34.474999999999994, 34.504999999999995)\": 46253.8, \"(34.504999999999995, 34.545)\": 25490.4, \"(34.545, 34.625)\": 24169.2, \"(34.625, 34.635000000000005)\": -1900.3, \"(34.635000000000005, 34.644999999999996)\": -28744.6, \"(34.644999999999996, 34.715)\": -19072.7, \"(34.715, 35.325)\": 12722.9, \"(35.325, 36.375)\": -28601.0, \"(36.375, 36.535)\": -20608.8, \"(36.535, 36.635000000000005)\": -5701.1, \"(36.635000000000005, 36.845)\": -15853.6, \"(36.845, 37.275000000000006)\": -7150.2, \"(37.275000000000006, 37.335)\": 2610.4, \"(37.335, 37.425)\": 20236.5, \"(37.425, 37.445)\": 40038.7, \"(37.445, 37.465)\": 61006.3, \"(37.465, 37.495000000000005)\": 38472.2, \"(37.495000000000005, 37.585)\": 24817.1, \"(37.585, 37.595)\": 10675.7, \"(37.595, 37.605000000000004)\": -26020.2, \"(37.605000000000004, 37.754999999999995)\": -33782.5, \"(37.754999999999995, 37.775000000000006)\": -10763.1, \"(37.775000000000006, 37.795)\": 2229.6, \"(37.795, 37.805)\": 17768.7, \"(37.805, 37.855000000000004)\": -6431.3, \"(37.855000000000004, 37.915)\": 6747.6, \"(37.915, 37.925)\": -12927.0, \"(37.925, 37.945)\": -31291.8, \"(37.945, 38.355000000000004)\": -40761.5, \"(38.355000000000004, 39.085)\": -28103.6, \"(39.085, 39.474999999999994)\": -38137.5, \"(39.474999999999994, 40.135000000000005)\": -48018.6, \"(40.135000000000005, 40.665)\": -57259.6, \"(40.665, 41.775000000000006)\": -66753.0, \"(41.775000000000006, 41.95)\": -76574.4}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: housing_median_age\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(1.0, 4.5)\": -19998.0, \"(4.5, 7.5)\": -7788.2, \"(7.5, 16.5)\": -10680.2, \"(16.5, 18.5)\": -6304.4, \"(18.5, 27.5)\": -1760.6, \"(27.5, 34.5)\": 2164.8, \"(34.5, 38.5)\": -912.5, \"(38.5, 41.5)\": 4199.6, \"(41.5, 45.5)\": -497.4, \"(45.5, 47.5)\": -5189.8, \"(47.5, 48.5)\": 5201.0, \"(48.5, 49.5)\": 2159.0, \"(49.5, 50.5)\": 6135.7, \"(50.5, 51.5)\": 11513.8, \"(51.5, 52.0)\": 27549.7}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(1.0, 4.5)\": -26905.5, \"(4.5, 7.5)\": -11566.0, \"(7.5, 16.5)\": -12538.5, \"(16.5, 18.5)\": -7756.2, \"(18.5, 27.5)\": -3361.1, \"(27.5, 34.5)\": 124.5, \"(34.5, 38.5)\": -1933.4, \"(38.5, 41.5)\": 2260.6, \"(41.5, 45.5)\": -4429.7, \"(45.5, 47.5)\": -8697.7, \"(47.5, 48.5)\": 2180.3, \"(48.5, 49.5)\": -1981.1, \"(49.5, 50.5)\": 1581.5, \"(50.5, 51.5)\": 5647.5, \"(51.5, 52.0)\": 25827.1}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(1.0, 4.5)\": -13090.4, \"(4.5, 7.5)\": -4010.4, \"(7.5, 16.5)\": -8821.8, \"(16.5, 18.5)\": -4852.5, \"(18.5, 27.5)\": -160.0, \"(27.5, 34.5)\": 4205.0, \"(34.5, 38.5)\": 108.5, \"(38.5, 41.5)\": 6138.7, \"(41.5, 45.5)\": 3434.9, \"(45.5, 47.5)\": -1682.0, \"(47.5, 48.5)\": 8221.7, \"(48.5, 49.5)\": 6299.1, \"(49.5, 50.5)\": 10689.9, \"(50.5, 51.5)\": 17380.1, \"(51.5, 52.0)\": 29272.3}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: total_rooms\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(2.0, 23.0)\": -70808.9, \"(23.0, 38.5)\": -78966.6, \"(38.5, 48.5)\": -28602.1, \"(48.5, 119.0)\": -47079.6, \"(119.0, 163.0)\": -52692.3, \"(163.0, 186.5)\": -60093.0, \"(186.5, 223.5)\": -51150.5, \"(223.5, 239.5)\": -39728.1, \"(239.5, 248.5)\": -7038.8, \"(248.5, 265.5)\": -691.1, \"(265.5, 280.5)\": -14052.2, \"(280.5, 342.5)\": -35705.6, \"(342.5, 364.5)\": -24578.4, \"(364.5, 385.5)\": -34007.7, \"(385.5, 406.5)\": -46655.0, \"(406.5, 413.5)\": -17805.2, \"(413.5, 443.5)\": -12192.7, \"(443.5, 452.5)\": -22779.7, \"(452.5, 502.5)\": -30652.6, \"(502.5, 508.5)\": -25165.4, \"(508.5, 515.5)\": -12943.4, \"(515.5, 1152.5)\": -21645.3, \"(1152.5, 1239.5)\": -16264.4, \"(1239.5, 1245.5)\": -7023.2, \"(1245.5, 1619.5)\": -12855.2, \"(1619.5, 1944.5)\": -7415.6, \"(1944.5, 2330.5)\": -1233.9, \"(2330.5, 2710.5)\": 4370.8, \"(2710.5, 2834.5)\": 9739.0, \"(2834.5, 2838.5)\": 16667.1, \"(2838.5, 3577.5)\": 10096.4, \"(3577.5, 5401.0)\": 15549.4, \"(5401.0, 5535.5)\": 24928.2, \"(5535.5, 9961.0)\": 19069.3, \"(9961.0, 18662.0)\": 26262.6, \"(18662.0, 39320.0)\": 20736.3}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(2.0, 23.0)\": -91545.9, \"(23.0, 38.5)\": -102966.4, \"(38.5, 48.5)\": -57179.9, \"(48.5, 119.0)\": -64507.9, \"(119.0, 163.0)\": -67051.1, \"(163.0, 186.5)\": -74986.7, \"(186.5, 223.5)\": -62447.2, \"(223.5, 239.5)\": -55573.0, \"(239.5, 248.5)\": -34485.5, \"(248.5, 265.5)\": -18815.6, \"(265.5, 280.5)\": -35576.3, \"(280.5, 342.5)\": -44957.9, \"(342.5, 364.5)\": -36592.4, \"(364.5, 385.5)\": -39620.4, \"(385.5, 406.5)\": -54434.9, \"(406.5, 413.5)\": -28898.3, \"(413.5, 443.5)\": -21926.2, \"(443.5, 452.5)\": -34828.5, \"(452.5, 502.5)\": -40304.3, \"(502.5, 508.5)\": -35649.5, \"(508.5, 515.5)\": -27403.5, \"(515.5, 1152.5)\": -28456.5, \"(1152.5, 1239.5)\": -20918.2, \"(1239.5, 1245.5)\": -15907.4, \"(1245.5, 1619.5)\": -19943.7, \"(1619.5, 1944.5)\": -13063.6, \"(1944.5, 2330.5)\": -8595.8, \"(2330.5, 2710.5)\": 2936.6, \"(2710.5, 2834.5)\": 7069.8, \"(2834.5, 2838.5)\": 1263.0, \"(2838.5, 3577.5)\": 7025.1, \"(3577.5, 5401.0)\": 10287.4, \"(5401.0, 5535.5)\": 10519.1, \"(5535.5, 9961.0)\": 12536.6, \"(9961.0, 18662.0)\": 16596.5, \"(18662.0, 39320.0)\": 17189.5}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(2.0, 23.0)\": -50072.0, \"(23.0, 38.5)\": -54966.9, \"(38.5, 48.5)\": -24.3, \"(48.5, 119.0)\": -29651.4, \"(119.0, 163.0)\": -38333.5, \"(163.0, 186.5)\": -45199.3, \"(186.5, 223.5)\": -39853.9, \"(223.5, 239.5)\": -23883.2, \"(239.5, 248.5)\": 20408.0, \"(248.5, 265.5)\": 17433.4, \"(265.5, 280.5)\": 7471.9, \"(280.5, 342.5)\": -26453.2, \"(342.5, 364.5)\": -12564.3, \"(364.5, 385.5)\": -28395.1, \"(385.5, 406.5)\": -38875.1, \"(406.5, 413.5)\": -6712.1, \"(413.5, 443.5)\": -2459.1, \"(443.5, 452.5)\": -10730.8, \"(452.5, 502.5)\": -21000.9, \"(502.5, 508.5)\": -14681.3, \"(508.5, 515.5)\": 1516.8, \"(515.5, 1152.5)\": -14834.1, \"(1152.5, 1239.5)\": -11610.6, \"(1239.5, 1245.5)\": 1860.9, \"(1245.5, 1619.5)\": -5766.8, \"(1619.5, 1944.5)\": -1767.7, \"(1944.5, 2330.5)\": 6128.1, \"(2330.5, 2710.5)\": 5805.0, \"(2710.5, 2834.5)\": 12408.3, \"(2834.5, 2838.5)\": 32071.2, \"(2838.5, 3577.5)\": 13167.8, \"(3577.5, 5401.0)\": 20811.4, \"(5401.0, 5535.5)\": 39337.3, \"(5535.5, 9961.0)\": 25602.1, \"(9961.0, 18662.0)\": 35928.6, \"(18662.0, 39320.0)\": 24283.0}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: total_bedrooms\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(2.0, 4.5)\": -10633.3, \"(4.5, 9.5)\": -19829.1, \"(9.5, 12.5)\": -33356.0, \"(12.5, 14.5)\": -27510.0, \"(14.5, 17.5)\": -34141.4, \"(17.5, 20.5)\": -50740.7, \"(20.5, 22.5)\": -59049.5, \"(22.5, 25.5)\": -37177.7, \"(25.5, 29.5)\": -30710.5, \"(29.5, 111.5)\": -36287.1, \"(111.5, 112.5)\": -22540.1, \"(112.5, 176.5)\": -33870.1, \"(176.5, 245.5)\": -27701.3, \"(245.5, 265.5)\": -20526.0, \"(265.5, 268.5)\": -26170.7, \"(268.5, 317.5)\": -17267.5, \"(317.5, 424.5)\": -8013.2, \"(424.5, 463.5)\": -1894.5, \"(463.5, 512.5)\": 5095.6, \"(512.5, 513.5)\": 17024.1, \"(513.5, 655.5)\": 9371.5, \"(655.5, 697.5)\": 15515.9, \"(697.5, 776.5)\": 22859.4, \"(776.5, 779.5)\": 13774.7, \"(779.5, 1008.5)\": 22608.4, \"(1008.5, 1012.5)\": 37458.5, \"(1012.5, 1081.5)\": 30023.9, \"(1081.5, 1449.5)\": 37066.8, \"(1449.5, 1490.5)\": 51601.0, \"(1490.5, 1616.0)\": 42837.8, \"(1616.0, 2714.5)\": 49023.6, \"(2714.5, 2865.5)\": 40592.1, \"(2865.5, 6445.0)\": 51586.1}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(2.0, 4.5)\": -43650.4, \"(4.5, 9.5)\": -54645.6, \"(9.5, 12.5)\": -52929.5, \"(12.5, 14.5)\": -57181.8, \"(14.5, 17.5)\": -49207.2, \"(17.5, 20.5)\": -72519.5, \"(20.5, 22.5)\": -82934.2, \"(22.5, 25.5)\": -50942.7, \"(25.5, 29.5)\": -45748.1, \"(29.5, 111.5)\": -47452.5, \"(111.5, 112.5)\": -42457.2, \"(112.5, 176.5)\": -41599.3, \"(176.5, 245.5)\": -35478.0, \"(245.5, 265.5)\": -27520.5, \"(265.5, 268.5)\": -32234.3, \"(268.5, 317.5)\": -23732.7, \"(317.5, 424.5)\": -13237.9, \"(424.5, 463.5)\": -7023.7, \"(463.5, 512.5)\": -1510.7, \"(512.5, 513.5)\": 6820.8, \"(513.5, 655.5)\": 341.5, \"(655.5, 697.5)\": 12634.4, \"(697.5, 776.5)\": 15982.1, \"(776.5, 779.5)\": 221.5, \"(779.5, 1008.5)\": 18345.9, \"(1008.5, 1012.5)\": 20622.3, \"(1012.5, 1081.5)\": 21931.2, \"(1081.5, 1449.5)\": 22140.8, \"(1449.5, 1490.5)\": 39761.7, \"(1490.5, 1616.0)\": 35441.7, \"(1616.0, 2714.5)\": 37135.8, \"(2714.5, 2865.5)\": 32716.4, \"(2865.5, 6445.0)\": 42203.8}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(2.0, 4.5)\": 22383.8, \"(4.5, 9.5)\": 14987.3, \"(9.5, 12.5)\": -13782.5, \"(12.5, 14.5)\": 2161.9, \"(14.5, 17.5)\": -19075.5, \"(17.5, 20.5)\": -28961.9, \"(20.5, 22.5)\": -35164.8, \"(22.5, 25.5)\": -23412.7, \"(25.5, 29.5)\": -15672.9, \"(29.5, 111.5)\": -25121.6, \"(111.5, 112.5)\": -2622.9, \"(112.5, 176.5)\": -26141.0, \"(176.5, 245.5)\": -19924.6, \"(245.5, 265.5)\": -13531.5, \"(265.5, 268.5)\": -20107.0, \"(268.5, 317.5)\": -10802.3, \"(317.5, 424.5)\": -2788.6, \"(424.5, 463.5)\": 3234.7, \"(463.5, 512.5)\": 11701.8, \"(512.5, 513.5)\": 27227.4, \"(513.5, 655.5)\": 18401.4, \"(655.5, 697.5)\": 18397.4, \"(697.5, 776.5)\": 29736.8, \"(776.5, 779.5)\": 27327.8, \"(779.5, 1008.5)\": 26870.8, \"(1008.5, 1012.5)\": 54294.7, \"(1012.5, 1081.5)\": 38116.5, \"(1081.5, 1449.5)\": 51992.8, \"(1449.5, 1490.5)\": 63440.2, \"(1490.5, 1616.0)\": 50233.9, \"(1616.0, 2714.5)\": 60911.5, \"(2714.5, 2865.5)\": 48467.9, \"(2865.5, 6445.0)\": 60968.4}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: population\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(3.0, 14.5)\": 125210.2, \"(14.5, 25.5)\": 92452.9, \"(25.5, 65.5)\": 80407.9, \"(65.5, 138.5)\": 91917.4, \"(138.5, 151.5)\": 103409.9, \"(151.5, 301.5)\": 85121.7, \"(301.5, 490.5)\": 73106.0, \"(490.5, 657.5)\": 57994.5, \"(657.5, 761.5)\": 44760.8, \"(761.5, 837.5)\": 32058.9, \"(837.5, 1019.5)\": 20715.6, \"(1019.5, 1220.5)\": 6507.2, \"(1220.5, 1267.5)\": -6199.6, \"(1267.5, 1269.5)\": 9858.1, \"(1269.5, 1497.5)\": -9812.8, \"(1497.5, 1886.5)\": -25776.4, \"(1886.5, 2129.5)\": -36953.6, \"(2129.5, 2425.5)\": -48605.9, \"(2425.5, 2686.0)\": -59914.9, \"(2686.0, 2718.5)\": -46231.6, \"(2718.5, 3175.5)\": -61061.6, \"(3175.5, 3965.0)\": -76216.0, \"(3965.0, 35682.0)\": -91117.9}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(3.0, 14.5)\": 103123.1, \"(14.5, 25.5)\": 58681.0, \"(25.5, 65.5)\": 62309.7, \"(65.5, 138.5)\": 75243.8, \"(138.5, 151.5)\": 78950.4, \"(151.5, 301.5)\": 69535.1, \"(301.5, 490.5)\": 60924.6, \"(490.5, 657.5)\": 45395.6, \"(657.5, 761.5)\": 35273.5, \"(761.5, 837.5)\": 26626.5, \"(837.5, 1019.5)\": 8057.5, \"(1019.5, 1220.5)\": -10609.9, \"(1220.5, 1267.5)\": -14462.5, \"(1267.5, 1269.5)\": -5022.3, \"(1269.5, 1497.5)\": -22884.3, \"(1497.5, 1886.5)\": -37619.7, \"(1886.5, 2129.5)\": -51088.1, \"(2129.5, 2425.5)\": -56504.4, \"(2425.5, 2686.0)\": -64158.2, \"(2686.0, 2718.5)\": -69408.6, \"(2718.5, 3175.5)\": -68643.2, \"(3175.5, 3965.0)\": -84318.8, \"(3965.0, 35682.0)\": -101928.5}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(3.0, 14.5)\": 147297.2, \"(14.5, 25.5)\": 126224.8, \"(25.5, 65.5)\": 98506.2, \"(65.5, 138.5)\": 108591.1, \"(138.5, 151.5)\": 127869.3, \"(151.5, 301.5)\": 100708.2, \"(301.5, 490.5)\": 85287.5, \"(490.5, 657.5)\": 70593.3, \"(657.5, 761.5)\": 54248.0, \"(761.5, 837.5)\": 37491.3, \"(837.5, 1019.5)\": 33373.7, \"(1019.5, 1220.5)\": 23624.4, \"(1220.5, 1267.5)\": 2063.4, \"(1267.5, 1269.5)\": 24738.4, \"(1269.5, 1497.5)\": 3258.7, \"(1497.5, 1886.5)\": -13933.2, \"(1886.5, 2129.5)\": -22819.1, \"(2129.5, 2425.5)\": -40707.4, \"(2425.5, 2686.0)\": -55671.5, \"(2686.0, 2718.5)\": -23054.7, \"(2718.5, 3175.5)\": -53480.1, \"(3175.5, 3965.0)\": -68113.2, \"(3965.0, 35682.0)\": -80307.2}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: households\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(2.0, 4.5)\": -5401.6, \"(4.5, 6.5)\": -23687.9, \"(6.5, 8.5)\": -53732.5, \"(8.5, 9.5)\": -14617.2, \"(9.5, 12.5)\": 16225.5, \"(12.5, 13.5)\": 21846.0, \"(13.5, 14.5)\": 29456.0, \"(14.5, 15.5)\": 14293.2, \"(15.5, 20.5)\": -21670.3, \"(20.5, 21.5)\": 3195.8, \"(21.5, 55.5)\": -12458.9, \"(55.5, 155.5)\": -20063.6, \"(155.5, 156.5)\": -15642.0, \"(156.5, 157.5)\": -6390.8, \"(157.5, 186.5)\": -19320.2, \"(186.5, 196.5)\": -23743.0, \"(196.5, 198.5)\": -18377.6, \"(198.5, 223.5)\": -12744.1, \"(223.5, 230.5)\": -6336.7, \"(230.5, 295.5)\": -10855.3, \"(295.5, 394.5)\": -6355.5, \"(394.5, 535.5)\": -443.1, \"(535.5, 561.5)\": 3934.9, \"(561.5, 599.5)\": 9004.1, \"(599.5, 600.5)\": 13667.2, \"(600.5, 634.5)\": 8706.3, \"(634.5, 635.5)\": 25959.4, \"(635.5, 824.5)\": 13815.1, \"(824.5, 864.5)\": 18503.2, \"(864.5, 962.5)\": 26367.0, \"(962.5, 964.5)\": 14554.6, \"(964.5, 976.5)\": 23227.2, \"(976.5, 978.5)\": 18664.6, \"(978.5, 990.5)\": 26114.1, \"(990.5, 1000.5)\": 30854.6, \"(1000.5, 1088.5)\": 25473.5, \"(1088.5, 1092.5)\": 21095.0, \"(1092.5, 1130.5)\": 26497.2, \"(1130.5, 1272.5)\": 33562.7, \"(1272.5, 3516.0)\": 28522.2, \"(3516.0, 6082.0)\": 21556.0}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(2.0, 4.5)\": -30426.4, \"(4.5, 6.5)\": -41560.8, \"(6.5, 8.5)\": -83483.7, \"(8.5, 9.5)\": -68637.5, \"(9.5, 12.5)\": -15018.5, \"(12.5, 13.5)\": -5488.2, \"(13.5, 14.5)\": 1721.7, \"(14.5, 15.5)\": -25117.7, \"(15.5, 20.5)\": -41734.0, \"(20.5, 21.5)\": -26800.7, \"(21.5, 55.5)\": -26732.7, \"(55.5, 155.5)\": -27250.3, \"(155.5, 156.5)\": -25256.4, \"(156.5, 157.5)\": -28521.9, \"(157.5, 186.5)\": -26383.4, \"(186.5, 196.5)\": -29250.8, \"(196.5, 198.5)\": -25752.9, \"(198.5, 223.5)\": -20683.5, \"(223.5, 230.5)\": -15595.3, \"(230.5, 295.5)\": -18207.8, \"(295.5, 394.5)\": -15406.0, \"(394.5, 535.5)\": -9211.1, \"(535.5, 561.5)\": -5668.7, \"(561.5, 599.5)\": 904.9, \"(599.5, 600.5)\": -3740.6, \"(600.5, 634.5)\": 3782.4, \"(634.5, 635.5)\": 139.1, \"(635.5, 824.5)\": 6137.4, \"(824.5, 864.5)\": 11294.8, \"(864.5, 962.5)\": 17755.5, \"(962.5, 964.5)\": -5105.1, \"(964.5, 976.5)\": 14837.4, \"(976.5, 978.5)\": 5892.7, \"(978.5, 990.5)\": 18169.8, \"(990.5, 1000.5)\": 15738.6, \"(1000.5, 1088.5)\": 19888.5, \"(1088.5, 1092.5)\": 9478.6, \"(1092.5, 1130.5)\": 20925.9, \"(1130.5, 1272.5)\": 24768.1, \"(1272.5, 3516.0)\": 19419.3, \"(3516.0, 6082.0)\": 8532.3}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(2.0, 4.5)\": 19623.3, \"(4.5, 6.5)\": -5814.9, \"(6.5, 8.5)\": -23981.3, \"(8.5, 9.5)\": 39403.2, \"(9.5, 12.5)\": 47469.5, \"(12.5, 13.5)\": 49180.3, \"(13.5, 14.5)\": 57190.3, \"(14.5, 15.5)\": 53704.2, \"(15.5, 20.5)\": -1606.7, \"(20.5, 21.5)\": 33192.3, \"(21.5, 55.5)\": 1814.9, \"(55.5, 155.5)\": -12877.0, \"(155.5, 156.5)\": -6027.7, \"(156.5, 157.5)\": 15740.2, \"(157.5, 186.5)\": -12257.0, \"(186.5, 196.5)\": -18235.2, \"(196.5, 198.5)\": -11002.4, \"(198.5, 223.5)\": -4804.8, \"(223.5, 230.5)\": 2921.9, \"(230.5, 295.5)\": -3502.7, \"(295.5, 394.5)\": 2695.1, \"(394.5, 535.5)\": 8324.9, \"(535.5, 561.5)\": 13538.5, \"(561.5, 599.5)\": 17103.2, \"(599.5, 600.5)\": 31074.9, \"(600.5, 634.5)\": 13630.1, \"(634.5, 635.5)\": 51779.7, \"(635.5, 824.5)\": 21492.8, \"(824.5, 864.5)\": 25711.7, \"(864.5, 962.5)\": 34978.6, \"(962.5, 964.5)\": 34214.4, \"(964.5, 976.5)\": 31616.9, \"(976.5, 978.5)\": 31436.4, \"(978.5, 990.5)\": 34058.4, \"(990.5, 1000.5)\": 45970.6, \"(1000.5, 1088.5)\": 31058.5, \"(1088.5, 1092.5)\": 32711.5, \"(1092.5, 1130.5)\": 32068.4, \"(1130.5, 1272.5)\": 42357.3, \"(1272.5, 3516.0)\": 37625.1, \"(3516.0, 6082.0)\": 34579.6}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: median_income\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.4999, 0.5427500000000001)\": -16067.6, \"(0.5427500000000001, 1.4808)\": -55539.5, \"(1.4808, 2.1658999999999997)\": -71376.5, \"(2.1658999999999997, 2.6096)\": -56399.7, \"(2.6096, 3.2433)\": -40762.6, \"(3.2433, 3.66575)\": -25586.1, \"(3.66575, 4.3197)\": -8084.4, \"(4.3197, 4.691000000000001)\": 7391.3, \"(4.691000000000001, 5.1358)\": 22375.3, \"(5.1358, 5.59195)\": 40032.8, \"(5.59195, 5.8294)\": 56900.2, \"(5.8294, 6.29665)\": 75092.3, \"(6.29665, 6.3704)\": 96400.5, \"(6.3704, 6.874750000000001)\": 111491.7, \"(6.874750000000001, 7.6996)\": 135841.6, \"(7.6996, 7.8141)\": 151586.9, \"(7.8141, 8.3976)\": 170219.6, \"(8.3976, 9.046949999999999)\": 192482.3, \"(9.046949999999999, 15.00005)\": 214375.9, \"(15.00005, 15.0001)\": 193753.6}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.4999, 0.5427500000000001)\": -48216.1, \"(0.5427500000000001, 1.4808)\": -68098.8, \"(1.4808, 2.1658999999999997)\": -81907.3, \"(2.1658999999999997, 2.6096)\": -60824.9, \"(2.6096, 3.2433)\": -49299.1, \"(3.2433, 3.66575)\": -32546.2, \"(3.66575, 4.3197)\": -17048.9, \"(4.3197, 4.691000000000001)\": 1621.1, \"(4.691000000000001, 5.1358)\": 13670.4, \"(5.1358, 5.59195)\": 33628.4, \"(5.59195, 5.8294)\": 48173.8, \"(5.8294, 6.29665)\": 69358.1, \"(6.29665, 6.3704)\": 88897.2, \"(6.3704, 6.874750000000001)\": 105607.5, \"(6.874750000000001, 7.6996)\": 129446.9, \"(7.6996, 7.8141)\": 139775.0, \"(7.8141, 8.3976)\": 162646.8, \"(8.3976, 9.046949999999999)\": 184114.0, \"(9.046949999999999, 15.00005)\": 203670.8, \"(15.00005, 15.0001)\": 178950.1}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.4999, 0.5427500000000001)\": 16080.9, \"(0.5427500000000001, 1.4808)\": -42980.1, \"(1.4808, 2.1658999999999997)\": -60845.8, \"(2.1658999999999997, 2.6096)\": -51974.6, \"(2.6096, 3.2433)\": -32226.2, \"(3.2433, 3.66575)\": -18626.1, \"(3.66575, 4.3197)\": 880.1, \"(4.3197, 4.691000000000001)\": 13161.4, \"(4.691000000000001, 5.1358)\": 31080.1, \"(5.1358, 5.59195)\": 46437.2, \"(5.59195, 5.8294)\": 65626.7, \"(5.8294, 6.29665)\": 80826.5, \"(6.29665, 6.3704)\": 103903.7, \"(6.3704, 6.874750000000001)\": 117376.0, \"(6.874750000000001, 7.6996)\": 142236.4, \"(7.6996, 7.8141)\": 163398.8, \"(7.8141, 8.3976)\": 177792.4, \"(8.3976, 9.046949999999999)\": 200850.6, \"(9.046949999999999, 15.00005)\": 225081.0, \"(15.00005, 15.0001)\": 208557.1}\n",
+      "\n",
+      "This graph represents categorical feature. Each key represents a possible value that the feature can take.\n",
+      "\n",
+      "Feature Name: ocean_proximity\n",
+      "Feature Type: categorical\n",
+      "Means: {\"<1H OCEAN\": 8622.3, \"INLAND\": -35326.3, \"ISLAND\": 47394.2, \"NEAR BAY\": 13572.3, \"NEAR OCEAN\": 46487.3}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"<1H OCEAN\": 7208.5, \"INLAND\": -37567.4, \"ISLAND\": 31409.5, \"NEAR BAY\": 11900.7, \"NEAR OCEAN\": 44665.9}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"<1H OCEAN\": 10036.1, \"INLAND\": -33085.3, \"ISLAND\": 63378.8, \"NEAR BAY\": 15243.8, \"NEAR OCEAN\": 48308.8}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: Pregnancies\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": -0.1506, \"(0.5, 1.5)\": -0.2484, \"(1.5, 2.5)\": -0.1873, \"(2.5, 3.5)\": -0.0302, \"(3.5, 4.5)\": 0.0211, \"(4.5, 5.5)\": 0.1013, \"(5.5, 6.5)\": 0.1489, \"(6.5, 7.5)\": 0.264, \"(7.5, 8.5)\": 0.3553, \"(8.5, 9.5)\": 0.4117, \"(9.5, 13.5)\": 0.2996, \"(13.5, 14.0)\": 0.6729}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.2406, \"(0.5, 1.5)\": -0.3636, \"(1.5, 2.5)\": -0.242, \"(2.5, 3.5)\": -0.093, \"(3.5, 4.5)\": -0.038, \"(4.5, 5.5)\": 0.0314, \"(5.5, 6.5)\": 0.0909, \"(6.5, 7.5)\": 0.1609, \"(7.5, 8.5)\": 0.2075, \"(8.5, 9.5)\": 0.248, \"(9.5, 13.5)\": 0.0671, \"(13.5, 14.0)\": 0.084}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0606, \"(0.5, 1.5)\": -0.1333, \"(1.5, 2.5)\": -0.1326, \"(2.5, 3.5)\": 0.0326, \"(3.5, 4.5)\": 0.0802, \"(4.5, 5.5)\": 0.1712, \"(5.5, 6.5)\": 0.207, \"(6.5, 7.5)\": 0.3671, \"(7.5, 8.5)\": 0.5032, \"(8.5, 9.5)\": 0.5755, \"(9.5, 13.5)\": 0.5321, \"(13.5, 14.0)\": 1.2617}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: Glucose\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 22.0)\": -0.728, \"(22.0, 86.5)\": -1.069, \"(86.5, 94.5)\": -0.907, \"(94.5, 99.5)\": -0.729, \"(99.5, 105.5)\": -0.491, \"(105.5, 114.5)\": -0.326, \"(114.5, 123.5)\": -0.157, \"(123.5, 130.5)\": 0.045, \"(130.5, 139.5)\": 0.208, \"(139.5, 147.5)\": 0.37, \"(147.5, 154.5)\": 0.535, \"(154.5, 159.5)\": 0.724, \"(159.5, 165.5)\": 0.984, \"(165.5, 169.5)\": 1.342, \"(169.5, 178.5)\": 1.502, \"(178.5, 187.5)\": 1.691, \"(187.5, 198.5)\": 1.853, \"(198.5, 199.0)\": 2.022}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 22.0)\": -1.316, \"(22.0, 86.5)\": -1.535, \"(86.5, 94.5)\": -1.3, \"(94.5, 99.5)\": -1.042, \"(99.5, 105.5)\": -0.722, \"(105.5, 114.5)\": -0.428, \"(114.5, 123.5)\": -0.249, \"(123.5, 130.5)\": -0.151, \"(130.5, 139.5)\": 0.044, \"(139.5, 147.5)\": 0.215, \"(147.5, 154.5)\": 0.135, \"(154.5, 159.5)\": 0.451, \"(159.5, 165.5)\": 0.509, \"(165.5, 169.5)\": 0.633, \"(169.5, 178.5)\": 0.768, \"(178.5, 187.5)\": 0.987, \"(187.5, 198.5)\": 1.135, \"(198.5, 199.0)\": 1.3}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 22.0)\": -0.14, \"(22.0, 86.5)\": -0.602, \"(86.5, 94.5)\": -0.514, \"(94.5, 99.5)\": -0.417, \"(99.5, 105.5)\": -0.26, \"(105.5, 114.5)\": -0.223, \"(114.5, 123.5)\": -0.064, \"(123.5, 130.5)\": 0.242, \"(130.5, 139.5)\": 0.373, \"(139.5, 147.5)\": 0.525, \"(147.5, 154.5)\": 0.936, \"(154.5, 159.5)\": 0.997, \"(159.5, 165.5)\": 1.458, \"(165.5, 169.5)\": 2.051, \"(169.5, 178.5)\": 2.237, \"(178.5, 187.5)\": 2.394, \"(187.5, 198.5)\": 2.571, \"(198.5, 199.0)\": 2.744}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: BloodPressure\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 15.0)\": 0.236, \"(15.0, 37.0)\": 0.1532, \"(37.0, 45.0)\": -0.0296, \"(45.0, 47.0)\": -0.0891, \"(47.0, 54.5)\": -0.1348, \"(54.5, 60.5)\": -0.1774, \"(60.5, 61.5)\": -0.11, \"(61.5, 64.5)\": -0.0541, \"(64.5, 74.5)\": -0.0119, \"(74.5, 75.5)\": -0.058, \"(75.5, 83.0)\": -0.004, \"(83.0, 93.0)\": 0.0343, \"(93.0, 95.0)\": 0.0889, \"(95.0, 97.0)\": 0.1461, \"(97.0, 101.0)\": 0.183, \"(101.0, 103.0)\": 0.2699, \"(103.0, 107.0)\": 0.3158, \"(107.0, 109.0)\": 0.3837, \"(109.0, 110.0)\": 0.5269}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 15.0)\": -0.0274, \"(15.0, 37.0)\": -0.1145, \"(37.0, 45.0)\": -0.2191, \"(45.0, 47.0)\": -0.2854, \"(47.0, 54.5)\": -0.313, \"(54.5, 60.5)\": -0.2953, \"(60.5, 61.5)\": -0.1759, \"(61.5, 64.5)\": -0.1789, \"(64.5, 74.5)\": -0.1212, \"(74.5, 75.5)\": -0.3075, \"(75.5, 83.0)\": -0.0727, \"(83.0, 93.0)\": -0.1515, \"(93.0, 95.0)\": -0.0624, \"(95.0, 97.0)\": -0.0006, \"(97.0, 101.0)\": 0.0092, \"(101.0, 103.0)\": 0.085, \"(103.0, 107.0)\": 0.1217, \"(107.0, 109.0)\": 0.1853, \"(109.0, 110.0)\": 0.2653}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 15.0)\": 0.4994, \"(15.0, 37.0)\": 0.4208, \"(37.0, 45.0)\": 0.16, \"(45.0, 47.0)\": 0.1073, \"(47.0, 54.5)\": 0.0433, \"(54.5, 60.5)\": -0.0595, \"(60.5, 61.5)\": -0.0441, \"(61.5, 64.5)\": 0.0708, \"(64.5, 74.5)\": 0.0974, \"(74.5, 75.5)\": 0.1914, \"(75.5, 83.0)\": 0.0647, \"(83.0, 93.0)\": 0.2201, \"(93.0, 95.0)\": 0.2402, \"(95.0, 97.0)\": 0.2929, \"(97.0, 101.0)\": 0.3567, \"(101.0, 103.0)\": 0.4548, \"(103.0, 107.0)\": 0.51, \"(107.0, 109.0)\": 0.582, \"(109.0, 110.0)\": 0.7884}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: SkinThickness\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 3.5)\": 0.0121, \"(3.5, 7.5)\": -0.0407, \"(7.5, 9.0)\": -0.0873, \"(9.0, 11.5)\": -0.1192, \"(11.5, 13.5)\": -0.1587, \"(13.5, 20.5)\": -0.1856, \"(20.5, 22.5)\": -0.1532, \"(22.5, 24.5)\": -0.1123, \"(24.5, 26.5)\": -0.0708, \"(26.5, 28.5)\": -0.036, \"(28.5, 30.5)\": -0.0039, \"(30.5, 32.5)\": 0.0343, \"(32.5, 34.5)\": 0.0703, \"(34.5, 39.5)\": 0.1069, \"(39.5, 40.5)\": 0.143, \"(40.5, 41.5)\": 0.1769, \"(41.5, 43.5)\": 0.2279, \"(43.5, 47.5)\": 0.2859, \"(47.5, 49.5)\": 0.2453, \"(49.5, 51.0)\": -0.0169, \"(51.0, 55.0)\": -0.0754, \"(55.0, 77.5)\": 0.2174, \"(77.5, 99.0)\": 0.3109}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 3.5)\": -0.071, \"(3.5, 7.5)\": -0.1199, \"(7.5, 9.0)\": -0.1639, \"(9.0, 11.5)\": -0.1953, \"(11.5, 13.5)\": -0.2382, \"(13.5, 20.5)\": -0.2707, \"(20.5, 22.5)\": -0.2184, \"(22.5, 24.5)\": -0.1699, \"(24.5, 26.5)\": -0.1255, \"(26.5, 28.5)\": -0.0953, \"(28.5, 30.5)\": -0.0714, \"(30.5, 32.5)\": -0.0304, \"(32.5, 34.5)\": 0.0205, \"(34.5, 39.5)\": 0.0292, \"(39.5, 40.5)\": 0.0607, \"(40.5, 41.5)\": 0.0987, \"(41.5, 43.5)\": 0.0904, \"(43.5, 47.5)\": 0.0985, \"(47.5, 49.5)\": 0.0202, \"(49.5, 51.0)\": -0.3346, \"(51.0, 55.0)\": -0.5656, \"(55.0, 77.5)\": -0.4718, \"(77.5, 99.0)\": -0.4467}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 3.5)\": 0.0953, \"(3.5, 7.5)\": 0.0385, \"(7.5, 9.0)\": -0.0106, \"(9.0, 11.5)\": -0.0431, \"(11.5, 13.5)\": -0.0792, \"(13.5, 20.5)\": -0.1005, \"(20.5, 22.5)\": -0.088, \"(22.5, 24.5)\": -0.0547, \"(24.5, 26.5)\": -0.0161, \"(26.5, 28.5)\": 0.0233, \"(28.5, 30.5)\": 0.0636, \"(30.5, 32.5)\": 0.099, \"(32.5, 34.5)\": 0.12, \"(34.5, 39.5)\": 0.1847, \"(39.5, 40.5)\": 0.2253, \"(40.5, 41.5)\": 0.255, \"(41.5, 43.5)\": 0.3653, \"(43.5, 47.5)\": 0.4732, \"(47.5, 49.5)\": 0.4704, \"(49.5, 51.0)\": 0.3009, \"(51.0, 55.0)\": 0.4148, \"(55.0, 77.5)\": 0.9065, \"(77.5, 99.0)\": 1.0684}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: Insulin\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 20.0)\": 0.0422, \"(20.0, 36.5)\": -0.0027, \"(36.5, 40.5)\": -0.0554, \"(40.5, 45.5)\": -0.0967, \"(45.5, 48.5)\": -0.0409, \"(48.5, 55.5)\": -0.2263, \"(55.5, 80.5)\": -0.2661, \"(80.5, 87.5)\": -0.227, \"(87.5, 97.5)\": -0.1794, \"(97.5, 111.0)\": -0.1356, \"(111.0, 123.5)\": -0.0968, \"(123.5, 137.5)\": -0.0561, \"(137.5, 144.5)\": -0.0187, \"(144.5, 157.0)\": 0.0208, \"(157.0, 170.5)\": 0.0623, \"(170.5, 186.5)\": 0.0999, \"(186.5, 190.5)\": 0.0538, \"(190.5, 192.5)\": 0.1059, \"(192.5, 271.0)\": -0.0027, \"(271.0, 277.5)\": 0.035, \"(277.5, 292.0)\": 0.0732, \"(292.0, 311.0)\": 0.1129, \"(311.0, 365.0)\": 0.1551, \"(365.0, 397.0)\": 0.196, \"(397.0, 452.5)\": 0.2331, \"(452.5, 476.0)\": 0.2839, \"(476.0, 487.5)\": 0.346, \"(487.5, 526.5)\": 0.3915, \"(526.5, 680.0)\": 0.4346}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 20.0)\": -0.0556, \"(20.0, 36.5)\": -0.2244, \"(36.5, 40.5)\": -0.2184, \"(40.5, 45.5)\": -0.2543, \"(45.5, 48.5)\": -0.7961, \"(48.5, 55.5)\": -0.5056, \"(55.5, 80.5)\": -0.551, \"(80.5, 87.5)\": -0.3117, \"(87.5, 97.5)\": -0.251, \"(97.5, 111.0)\": -0.2086, \"(111.0, 123.5)\": -0.1731, \"(123.5, 137.5)\": -0.137, \"(137.5, 144.5)\": -0.1027, \"(144.5, 157.0)\": -0.0751, \"(157.0, 170.5)\": -0.0506, \"(170.5, 186.5)\": -0.0163, \"(186.5, 190.5)\": -0.2256, \"(190.5, 192.5)\": -0.2869, \"(192.5, 271.0)\": -0.3659, \"(271.0, 277.5)\": -0.245, \"(277.5, 292.0)\": -0.1491, \"(292.0, 311.0)\": -0.0995, \"(311.0, 365.0)\": -0.0355, \"(365.0, 397.0)\": -0.0134, \"(397.0, 452.5)\": 0.0212, \"(452.5, 476.0)\": 0.0711, \"(476.0, 487.5)\": 0.1139, \"(487.5, 526.5)\": 0.1534, \"(526.5, 680.0)\": 0.0241}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 20.0)\": 0.14, \"(20.0, 36.5)\": 0.2189, \"(36.5, 40.5)\": 0.1076, \"(40.5, 45.5)\": 0.0609, \"(45.5, 48.5)\": 0.7143, \"(48.5, 55.5)\": 0.053, \"(55.5, 80.5)\": 0.0187, \"(80.5, 87.5)\": -0.1422, \"(87.5, 97.5)\": -0.1078, \"(97.5, 111.0)\": -0.0625, \"(111.0, 123.5)\": -0.0206, \"(123.5, 137.5)\": 0.0247, \"(137.5, 144.5)\": 0.0654, \"(144.5, 157.0)\": 0.1166, \"(157.0, 170.5)\": 0.1751, \"(170.5, 186.5)\": 0.2162, \"(186.5, 190.5)\": 0.3332, \"(190.5, 192.5)\": 0.4987, \"(192.5, 271.0)\": 0.3605, \"(271.0, 277.5)\": 0.315, \"(277.5, 292.0)\": 0.2956, \"(292.0, 311.0)\": 0.3253, \"(311.0, 365.0)\": 0.3457, \"(365.0, 397.0)\": 0.4055, \"(397.0, 452.5)\": 0.445, \"(452.5, 476.0)\": 0.4967, \"(476.0, 487.5)\": 0.5782, \"(487.5, 526.5)\": 0.6295, \"(526.5, 680.0)\": 0.8452}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: BMI\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 9.1)\": -0.7, \"(9.1, 22.55)\": -0.961, \"(22.55, 23.65)\": -0.856, \"(23.65, 25.55)\": -0.762, \"(25.55, 26.35)\": -0.661, \"(26.35, 27.65)\": -0.24, \"(27.65, 28.45)\": -0.144, \"(28.45, 29.65)\": -0.051, \"(29.65, 30.45)\": 0.049, \"(30.45, 32.150000000000006)\": 0.153, \"(32.150000000000006, 37.650000000000006)\": 0.246, \"(37.650000000000006, 41.75)\": 0.34, \"(41.75, 42.849999999999994)\": 0.434, \"(42.849999999999994, 45.650000000000006)\": 0.529, \"(45.650000000000006, 48.349999999999994)\": 0.626, \"(48.349999999999994, 67.1)\": 0.784}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 9.1)\": -1.139, \"(9.1, 22.55)\": -1.349, \"(22.55, 23.65)\": -1.219, \"(23.65, 25.55)\": -1.281, \"(25.55, 26.35)\": -1.231, \"(26.35, 27.65)\": -0.568, \"(27.65, 28.45)\": -0.258, \"(28.45, 29.65)\": -0.157, \"(29.65, 30.45)\": -0.11, \"(30.45, 32.150000000000006)\": -0.086, \"(32.150000000000006, 37.650000000000006)\": 0.084, \"(37.650000000000006, 41.75)\": 0.189, \"(41.75, 42.849999999999994)\": 0.28, \"(42.849999999999994, 45.650000000000006)\": 0.348, \"(45.650000000000006, 48.349999999999994)\": 0.256, \"(48.349999999999994, 67.1)\": 0.265}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 9.1)\": -0.262, \"(9.1, 22.55)\": -0.573, \"(22.55, 23.65)\": -0.493, \"(23.65, 25.55)\": -0.243, \"(25.55, 26.35)\": -0.09, \"(26.35, 27.65)\": 0.088, \"(27.65, 28.45)\": -0.03, \"(28.45, 29.65)\": 0.054, \"(29.65, 30.45)\": 0.208, \"(30.45, 32.150000000000006)\": 0.392, \"(32.150000000000006, 37.650000000000006)\": 0.409, \"(37.650000000000006, 41.75)\": 0.491, \"(41.75, 42.849999999999994)\": 0.588, \"(42.849999999999994, 45.650000000000006)\": 0.709, \"(45.650000000000006, 48.349999999999994)\": 0.996, \"(48.349999999999994, 67.1)\": 1.303}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: DiabetesPedigreeFunction\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.078, 0.1265)\": -0.528, \"(0.1265, 0.128)\": -0.218, \"(0.128, 0.2185)\": -0.342, \"(0.2185, 0.3375)\": -0.168, \"(0.3375, 0.4215)\": -0.077, \"(0.4215, 0.4955)\": 0.015, \"(0.4955, 0.5874999999999999)\": 0.131, \"(0.5874999999999999, 0.7215)\": 0.223, \"(0.7215, 0.889)\": 0.316, \"(0.889, 1.0865)\": 0.407, \"(1.0865, 1.178)\": 0.498, \"(1.178, 1.275)\": 1.018, \"(1.275, 1.3925)\": 1.283, \"(1.3925, 1.4175)\": 1.168, \"(1.4175, 1.451)\": 0.065, \"(1.451, 1.837)\": -0.193, \"(1.837, 2.137)\": -0.092}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.078, 0.1265)\": -0.817, \"(0.1265, 0.128)\": -0.817, \"(0.128, 0.2185)\": -0.618, \"(0.2185, 0.3375)\": -0.533, \"(0.3375, 0.4215)\": -0.266, \"(0.4215, 0.4955)\": -0.104, \"(0.4955, 0.5874999999999999)\": -0.054, \"(0.5874999999999999, 0.7215)\": 0.138, \"(0.7215, 0.889)\": 0.186, \"(0.889, 1.0865)\": 0.263, \"(1.0865, 1.178)\": 0.35, \"(1.178, 1.275)\": 0.124, \"(1.275, 1.3925)\": 0.133, \"(1.3925, 1.4175)\": -0.063, \"(1.4175, 1.451)\": -1.163, \"(1.451, 1.837)\": -1.466, \"(1.837, 2.137)\": -1.112}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.078, 0.1265)\": -0.238, \"(0.1265, 0.128)\": 0.381, \"(0.128, 0.2185)\": -0.067, \"(0.2185, 0.3375)\": 0.197, \"(0.3375, 0.4215)\": 0.113, \"(0.4215, 0.4955)\": 0.135, \"(0.4955, 0.5874999999999999)\": 0.316, \"(0.5874999999999999, 0.7215)\": 0.308, \"(0.7215, 0.889)\": 0.445, \"(0.889, 1.0865)\": 0.552, \"(1.0865, 1.178)\": 0.646, \"(1.178, 1.275)\": 1.912, \"(1.275, 1.3925)\": 2.433, \"(1.3925, 1.4175)\": 2.398, \"(1.4175, 1.451)\": 1.293, \"(1.451, 1.837)\": 1.08, \"(1.837, 2.137)\": 0.928}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: Age\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(21.0, 21.5)\": -0.481, \"(21.5, 23.5)\": -0.377, \"(23.5, 24.5)\": -0.294, \"(24.5, 26.5)\": -0.205, \"(26.5, 28.5)\": -0.106, \"(28.5, 30.5)\": 0.056, \"(30.5, 34.5)\": 0.184, \"(34.5, 39.5)\": 0.286, \"(39.5, 44.5)\": 0.389, \"(44.5, 54.5)\": 0.476, \"(54.5, 56.5)\": 0.374, \"(56.5, 58.5)\": 0.224, \"(58.5, 60.5)\": 0.121, \"(60.5, 61.5)\": -0.053, \"(61.5, 62.5)\": -0.314, \"(62.5, 64.5)\": -0.437, \"(64.5, 66.5)\": -0.598, \"(66.5, 67.5)\": -0.714, \"(67.5, 68.5)\": -0.823, \"(68.5, 76.5)\": -0.922, \"(76.5, 81.0)\": -1.102}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(21.0, 21.5)\": -0.733, \"(21.5, 23.5)\": -0.545, \"(23.5, 24.5)\": -0.449, \"(24.5, 26.5)\": -0.316, \"(26.5, 28.5)\": -0.204, \"(28.5, 30.5)\": -0.094, \"(30.5, 34.5)\": 0.033, \"(34.5, 39.5)\": 0.131, \"(39.5, 44.5)\": 0.234, \"(44.5, 54.5)\": 0.292, \"(54.5, 56.5)\": 0.179, \"(56.5, 58.5)\": 0.067, \"(58.5, 60.5)\": -0.026, \"(60.5, 61.5)\": -0.314, \"(61.5, 62.5)\": -0.776, \"(62.5, 64.5)\": -0.923, \"(64.5, 66.5)\": -1.089, \"(66.5, 67.5)\": -1.205, \"(67.5, 68.5)\": -1.322, \"(68.5, 76.5)\": -1.445, \"(76.5, 81.0)\": -1.674}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(21.0, 21.5)\": -0.228, \"(21.5, 23.5)\": -0.21, \"(23.5, 24.5)\": -0.139, \"(24.5, 26.5)\": -0.094, \"(26.5, 28.5)\": -0.008, \"(28.5, 30.5)\": 0.206, \"(30.5, 34.5)\": 0.335, \"(34.5, 39.5)\": 0.441, \"(39.5, 44.5)\": 0.544, \"(44.5, 54.5)\": 0.66, \"(54.5, 56.5)\": 0.569, \"(56.5, 58.5)\": 0.382, \"(58.5, 60.5)\": 0.267, \"(60.5, 61.5)\": 0.208, \"(61.5, 62.5)\": 0.149, \"(62.5, 64.5)\": 0.05, \"(64.5, 66.5)\": -0.107, \"(66.5, 67.5)\": -0.222, \"(67.5, 68.5)\": -0.325, \"(68.5, 76.5)\": -0.399, \"(76.5, 81.0)\": -0.529}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: sepal_length\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(4.3, 4.55)\": 3.328, \"(4.55, 4.75)\": 2.995, \"(4.75, 4.85)\": 2.698, \"(4.85, 5.05)\": 1.665, \"(5.05, 5.25)\": 1.371, \"(5.25, 5.45)\": 1.085, \"(5.45, 5.55)\": 0.339, \"(5.55, 5.75)\": -0.057, \"(5.75, 5.85)\": -0.39, \"(5.85, 6.15)\": -0.757, \"(6.15, 6.45)\": -1.149, \"(6.45, 6.85)\": -1.436, \"(6.85, 7.7)\": -1.718}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(4.3, 4.55)\": 3.22, \"(4.55, 4.75)\": 2.846, \"(4.75, 4.85)\": 2.54, \"(4.85, 5.05)\": 1.185, \"(5.05, 5.25)\": 1.214, \"(5.25, 5.45)\": 0.892, \"(5.45, 5.55)\": -0.164, \"(5.55, 5.75)\": -0.32, \"(5.75, 5.85)\": -0.665, \"(5.85, 6.15)\": -0.888, \"(6.15, 6.45)\": -1.29, \"(6.45, 6.85)\": -1.575, \"(6.85, 7.7)\": -1.814}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(4.3, 4.55)\": 3.437, \"(4.55, 4.75)\": 3.144, \"(4.75, 4.85)\": 2.857, \"(4.85, 5.05)\": 2.145, \"(5.05, 5.25)\": 1.528, \"(5.25, 5.45)\": 1.277, \"(5.45, 5.55)\": 0.843, \"(5.55, 5.75)\": 0.206, \"(5.75, 5.85)\": -0.116, \"(5.85, 6.15)\": -0.627, \"(6.15, 6.45)\": -1.009, \"(6.45, 6.85)\": -1.298, \"(6.85, 7.7)\": -1.623}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: sepal_width\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(2.0, 2.25)\": -2.473, \"(2.25, 2.6500000000000004)\": -2.179, \"(2.6500000000000004, 2.8499999999999996)\": -1.736, \"(2.8499999999999996, 2.95)\": -0.945, \"(2.95, 3.05)\": 0.062, \"(3.05, 3.25)\": 0.509, \"(3.25, 3.3499999999999996)\": 1.373, \"(3.3499999999999996, 3.55)\": 1.669, \"(3.55, 3.75)\": 2.097, \"(3.75, 3.95)\": 2.489, \"(3.95, 4.1)\": 2.778}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(2.0, 2.25)\": -2.841, \"(2.25, 2.6500000000000004)\": -2.509, \"(2.6500000000000004, 2.8499999999999996)\": -1.936, \"(2.8499999999999996, 2.95)\": -1.506, \"(2.95, 3.05)\": -0.172, \"(3.05, 3.25)\": 0.23, \"(3.25, 3.3499999999999996)\": 1.091, \"(3.3499999999999996, 3.55)\": 1.492, \"(3.55, 3.75)\": 1.921, \"(3.75, 3.95)\": 2.293, \"(3.95, 4.1)\": 2.546}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(2.0, 2.25)\": -2.105, \"(2.25, 2.6500000000000004)\": -1.849, \"(2.6500000000000004, 2.8499999999999996)\": -1.537, \"(2.8499999999999996, 2.95)\": -0.384, \"(2.95, 3.05)\": 0.295, \"(3.05, 3.25)\": 0.789, \"(3.25, 3.3499999999999996)\": 1.656, \"(3.3499999999999996, 3.55)\": 1.846, \"(3.55, 3.75)\": 2.274, \"(3.75, 3.95)\": 2.685, \"(3.95, 4.1)\": 3.01}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: petal_length\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(1.1, 1.65)\": 8.05, \"(1.65, 2.45)\": 7.28, \"(2.45, 3.15)\": -1.17, \"(3.15, 3.8)\": -2.4, \"(3.8, 4.45)\": -3.03, \"(4.45, 5.65)\": -3.73, \"(5.65, 6.9)\": -4.38}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(1.1, 1.65)\": 7.87, \"(1.65, 2.45)\": 7.08, \"(2.45, 3.15)\": -4.92, \"(3.15, 3.8)\": -2.6, \"(3.8, 4.45)\": -3.19, \"(4.45, 5.65)\": -3.87, \"(5.65, 6.9)\": -4.55}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(1.1, 1.65)\": 8.24, \"(1.65, 2.45)\": 7.48, \"(2.45, 3.15)\": 2.57, \"(3.15, 3.8)\": -2.2, \"(3.8, 4.45)\": -2.86, \"(4.45, 5.65)\": -3.58, \"(5.65, 6.9)\": -4.2}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: petal_width\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.1, 0.35)\": 8.07, \"(0.35, 0.45)\": 7.27, \"(0.45, 0.75)\": 6.18, \"(0.75, 1.25)\": -2.64, \"(1.25, 1.75)\": -3.46, \"(1.75, 2.5)\": -4.19}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.1, 0.35)\": 7.9, \"(0.35, 0.45)\": 7.05, \"(0.45, 0.75)\": 3.08, \"(0.75, 1.25)\": -2.81, \"(1.25, 1.75)\": -3.62, \"(1.75, 2.5)\": -4.29}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.1, 0.35)\": 8.23, \"(0.35, 0.45)\": 7.49, \"(0.45, 0.75)\": 9.28, \"(0.75, 1.25)\": -2.47, \"(1.25, 1.75)\": -3.3, \"(1.75, 2.5)\": -4.08}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: Pclass\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(1.0, 1.5)\": -0.009, \"(1.5, 2.5)\": 0.534, \"(2.5, 3.0)\": -0.532}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -0.053, \"(1.5, 2.5)\": 0.174, \"(2.5, 3.0)\": -1.011}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": 0.035, \"(1.5, 2.5)\": 0.893, \"(2.5, 3.0)\": -0.052}\n",
+      "\n",
+      "This graph represents categorical feature. Each key represents a possible value that the feature can take.\n",
+      "\n",
+      "Feature Name: Sex\n",
+      "Feature Type: categorical\n",
+      "Means: {\"female\": 1.247, \"male\": -1.001}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"female\": 0.593, \"male\": -1.526}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"female\": 1.901, \"male\": -0.476}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: Age\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(2.0, 2.5)\": -0.503, \"(2.5, 5.0)\": 1.062, \"(5.0, 17.5)\": 1.188, \"(17.5, 24.5)\": 0.305, \"(24.5, 28.5)\": 0.438, \"(28.5, 31.5)\": 0.03, \"(31.5, 35.5)\": 0.337, \"(35.5, 36.25)\": 0.047, \"(36.25, 43.5)\": -0.09, \"(43.5, 44.5)\": -0.293, \"(44.5, 47.5)\": -0.611, \"(47.5, 49.5)\": -0.32, \"(49.5, 59.0)\": -0.561, \"(59.0, 60.5)\": -0.283, \"(60.5, 63.5)\": -0.939, \"(63.5, 70.5)\": -1.095, \"(70.5, 75.5)\": -0.598, \"(75.5, 80.0)\": -0.406}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(2.0, 2.5)\": -2.047, \"(2.5, 5.0)\": -0.63, \"(5.0, 17.5)\": -0.496, \"(17.5, 24.5)\": -0.053, \"(24.5, 28.5)\": -0.121, \"(28.5, 31.5)\": -0.759, \"(31.5, 35.5)\": -0.296, \"(35.5, 36.25)\": -0.141, \"(36.25, 43.5)\": -0.547, \"(43.5, 44.5)\": -0.684, \"(44.5, 47.5)\": -1.551, \"(47.5, 49.5)\": -0.563, \"(49.5, 59.0)\": -1.187, \"(59.0, 60.5)\": -1.123, \"(60.5, 63.5)\": -2.149, \"(63.5, 70.5)\": -2.327, \"(70.5, 75.5)\": -0.924, \"(75.5, 80.0)\": -0.696}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(2.0, 2.5)\": 1.042, \"(2.5, 5.0)\": 2.754, \"(5.0, 17.5)\": 2.872, \"(17.5, 24.5)\": 0.662, \"(24.5, 28.5)\": 0.998, \"(28.5, 31.5)\": 0.819, \"(31.5, 35.5)\": 0.969, \"(35.5, 36.25)\": 0.234, \"(36.25, 43.5)\": 0.367, \"(43.5, 44.5)\": 0.098, \"(44.5, 47.5)\": 0.329, \"(47.5, 49.5)\": -0.077, \"(49.5, 59.0)\": 0.064, \"(59.0, 60.5)\": 0.557, \"(60.5, 63.5)\": 0.271, \"(63.5, 70.5)\": 0.137, \"(70.5, 75.5)\": -0.271, \"(75.5, 80.0)\": -0.116}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: SibSp\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": -0.0751, \"(0.5, 2.5)\": 0.1633, \"(2.5, 3.0)\": -0.7301}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.1303, \"(0.5, 2.5)\": -0.2711, \"(2.5, 3.0)\": -2.435}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0198, \"(0.5, 2.5)\": 0.5976, \"(2.5, 3.0)\": 0.9748}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: Parch\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": 0.085, \"(0.5, 1.5)\": -0.055, \"(1.5, 3.0)\": -0.299, \"(3.0, 4.0)\": -1.704}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02, \"(0.5, 1.5)\": -0.269, \"(1.5, 3.0)\": -0.62, \"(3.0, 4.0)\": -3.014}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.19, \"(0.5, 1.5)\": 0.158, \"(1.5, 3.0)\": 0.022, \"(3.0, 4.0)\": -0.395}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: Fare\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 6.325)\": -1.425, \"(6.325, 7.8500000000000005)\": -1.303, \"(7.8500000000000005, 9.256250000000001)\": -0.472, \"(9.256250000000001, 10.48125)\": -0.602, \"(10.48125, 12.9375)\": -0.14, \"(12.9375, 25.79375)\": 0.225, \"(25.79375, 26.46875)\": 0.355, \"(26.46875, 27.7354)\": 0.207, \"(27.7354, 29.85)\": -0.238, \"(29.85, 31.6604)\": 0.051, \"(31.6604, 55.22085)\": -0.075, \"(55.22085, 89.5521)\": 0.041, \"(89.5521, 149.0354)\": 0.152, \"(149.0354, 387.6646)\": -0.029, \"(387.6646, 512.3292)\": 0.808}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 6.325)\": -3.39, \"(6.325, 7.8500000000000005)\": -3.252, \"(7.8500000000000005, 9.256250000000001)\": -1.321, \"(9.256250000000001, 10.48125)\": -1.756, \"(10.48125, 12.9375)\": -0.444, \"(12.9375, 25.79375)\": -0.464, \"(25.79375, 26.46875)\": -0.48, \"(26.46875, 27.7354)\": -0.42, \"(27.7354, 29.85)\": -1.008, \"(29.85, 31.6604)\": -0.616, \"(31.6604, 55.22085)\": -0.278, \"(55.22085, 89.5521)\": -0.095, \"(89.5521, 149.0354)\": -0.062, \"(149.0354, 387.6646)\": -0.493, \"(387.6646, 512.3292)\": -0.839}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 6.325)\": 0.54, \"(6.325, 7.8500000000000005)\": 0.645, \"(7.8500000000000005, 9.256250000000001)\": 0.377, \"(9.256250000000001, 10.48125)\": 0.553, \"(10.48125, 12.9375)\": 0.163, \"(12.9375, 25.79375)\": 0.913, \"(25.79375, 26.46875)\": 1.191, \"(26.46875, 27.7354)\": 0.833, \"(27.7354, 29.85)\": 0.533, \"(29.85, 31.6604)\": 0.718, \"(31.6604, 55.22085)\": 0.127, \"(55.22085, 89.5521)\": 0.176, \"(89.5521, 149.0354)\": 0.367, \"(149.0354, 387.6646)\": 0.436, \"(387.6646, 512.3292)\": 2.455}\n",
+      "\n",
+      "This graph represents categorical feature. Each key represents a possible value that the feature can take.\n",
+      "\n",
+      "Feature Name: Embarked\n",
+      "Feature Type: categorical\n",
+      "Means: {\"C\": 0.133, \"Q\": -2.234, \"S\": -0.061}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"C\": 0.032, \"Q\": -3.87, \"S\": -0.135}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"C\": 0.235, \"Q\": -0.599, \"S\": 0.013}\n",
+      "\n",
+      "This graph represents categorical feature. Each key represents a possible value that the feature can take.\n",
+      "\n",
+      "Feature Name: HomePlanet\n",
+      "Feature Type: categorical\n",
+      "Means: {\"Earth\": -0.3416, \"Europa\": 0.6582, \"Mars\": 0.1146}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"Earth\": -0.373, \"Europa\": 0.5826, \"Mars\": 0.0816}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"Earth\": -0.3102, \"Europa\": 0.7337, \"Mars\": 0.1476}\n",
+      "\n",
+      "This graph represents a boolean feature. The keys are 'True' and 'False', the two possible values of the feature.\n",
+      "\n",
+      "Feature Name: CryoSleep\n",
+      "Feature Type: boolean\n",
+      "Means: {\"False\": -0.438, \"True\": 0.784}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"False\": -0.46, \"True\": 0.744}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"False\": -0.416, \"True\": 0.824}\n",
+      "\n",
+      "This graph represents categorical feature. Each key represents a possible value that the feature can take.\n",
+      "\n",
+      "Feature Name: Cabin\n",
+      "Feature Type: categorical\n",
+      "Means: {\"A/P\": -0.321, \"A/S\": -0.213, \"B/P\": 0.227, \"B/S\": 1.082, \"C/P\": 0.854, \"C/S\": 1.948, \"D/P\": 0.115, \"D/S\": 0.018, \"E/P\": -0.187, \"E/S\": -0.23, \"F/P\": -0.126, \"F/S\": 0.15, \"G/P\": -0.775, \"G/S\": -0.248, \"T/P\": -0.374, \"T/S\": -0.606}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"A/P\": -0.509, \"A/S\": -0.395, \"B/P\": -0.013, \"B/S\": 0.853, \"C/P\": 0.713, \"C/S\": 1.448, \"D/P\": 0.05, \"D/S\": -0.038, \"E/P\": -0.302, \"E/S\": -0.345, \"F/P\": -0.167, \"F/S\": 0.06, \"G/P\": -0.862, \"G/S\": -0.307, \"T/P\": -0.552, \"T/S\": -0.926}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"A/P\": -0.134, \"A/S\": -0.031, \"B/P\": 0.467, \"B/S\": 1.31, \"C/P\": 0.995, \"C/S\": 2.448, \"D/P\": 0.18, \"D/S\": 0.073, \"E/P\": -0.073, \"E/S\": -0.114, \"F/P\": -0.085, \"F/S\": 0.24, \"G/P\": -0.688, \"G/S\": -0.189, \"T/P\": -0.197, \"T/S\": -0.285}\n",
+      "\n",
+      "This graph represents categorical feature. Each key represents a possible value that the feature can take.\n",
+      "\n",
+      "Feature Name: Destination\n",
+      "Feature Type: categorical\n",
+      "Means: {\"55 Cancri e\": 0.3722, \"PSO J318.5-22\": -0.129, \"TRAPPIST-1e\": -0.1027}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"55 Cancri e\": 0.3281, \"PSO J318.5-22\": -0.1764, \"TRAPPIST-1e\": -0.115}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"55 Cancri e\": 0.4163, \"PSO J318.5-22\": -0.0816, \"TRAPPIST-1e\": -0.0904}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: Age\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": 0.68, \"(0.5, 3.5)\": 0.36, \"(3.5, 4.5)\": 0.254, \"(4.5, 14.5)\": 0.09, \"(14.5, 23.5)\": 0.028, \"(23.5, 24.5)\": -0.027, \"(24.5, 25.5)\": -0.135, \"(25.5, 39.5)\": -0.05, \"(39.5, 44.5)\": 0.042, \"(44.5, 48.5)\": -0.025, \"(48.5, 54.5)\": -0.102, \"(54.5, 56.5)\": -0.012, \"(56.5, 63.5)\": 0.078, \"(63.5, 64.5)\": -0.028, \"(64.5, 65.5)\": -0.141, \"(65.5, 68.5)\": 0.058, \"(68.5, 69.5)\": -0.021, \"(69.5, 71.5)\": 0.037, \"(71.5, 73.5)\": -0.022, \"(73.5, 74.5)\": 0.413, \"(74.5, 77.5)\": 0.211, \"(77.5, 79.0)\": -0.412}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.461, \"(0.5, 3.5)\": 0.228, \"(3.5, 4.5)\": 0.097, \"(4.5, 14.5)\": -0.111, \"(14.5, 23.5)\": -0.031, \"(23.5, 24.5)\": -0.079, \"(24.5, 25.5)\": -0.32, \"(25.5, 39.5)\": -0.113, \"(39.5, 44.5)\": -0.088, \"(44.5, 48.5)\": -0.081, \"(48.5, 54.5)\": -0.336, \"(54.5, 56.5)\": -0.102, \"(56.5, 63.5)\": -0.123, \"(63.5, 64.5)\": -0.219, \"(64.5, 65.5)\": -0.706, \"(65.5, 68.5)\": -0.265, \"(68.5, 69.5)\": -0.416, \"(69.5, 71.5)\": -0.213, \"(71.5, 73.5)\": -0.172, \"(73.5, 74.5)\": -0.439, \"(74.5, 77.5)\": -0.317, \"(77.5, 79.0)\": -1.348}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.9, \"(0.5, 3.5)\": 0.491, \"(3.5, 4.5)\": 0.411, \"(4.5, 14.5)\": 0.29, \"(14.5, 23.5)\": 0.087, \"(23.5, 24.5)\": 0.024, \"(24.5, 25.5)\": 0.05, \"(25.5, 39.5)\": 0.012, \"(39.5, 44.5)\": 0.172, \"(44.5, 48.5)\": 0.031, \"(48.5, 54.5)\": 0.132, \"(54.5, 56.5)\": 0.077, \"(56.5, 63.5)\": 0.278, \"(63.5, 64.5)\": 0.163, \"(64.5, 65.5)\": 0.424, \"(65.5, 68.5)\": 0.382, \"(68.5, 69.5)\": 0.373, \"(69.5, 71.5)\": 0.287, \"(71.5, 73.5)\": 0.129, \"(73.5, 74.5)\": 1.265, \"(74.5, 77.5)\": 0.739, \"(77.5, 79.0)\": 0.524}\n",
+      "\n",
+      "This graph represents a boolean feature. The keys are 'True' and 'False', the two possible values of the feature.\n",
+      "\n",
+      "Feature Name: VIP\n",
+      "Feature Type: boolean\n",
+      "Means: {\"False\": 0.0017, \"True\": -0.3347}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"False\": -0.0028, \"True\": -0.4254}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"False\": 0.0062, \"True\": -0.244}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: RoomService\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 105.5)\": 0.328, \"(105.5, 296.5)\": 0.028, \"(296.5, 335.5)\": -0.208, \"(335.5, 340.0)\": 0.165, \"(340.0, 343.0)\": -0.1, \"(343.0, 596.5)\": -0.741, \"(596.5, 712.5)\": -0.978, \"(712.5, 734.0)\": -1.212, \"(734.0, 800.0)\": -1.446, \"(800.0, 816.0)\": -1.136, \"(816.0, 997.5)\": -1.454, \"(997.5, 1031.0)\": -1.106, \"(1031.0, 1041.0)\": -1.368, \"(1041.0, 2172.5)\": -1.866, \"(2172.5, 2283.5)\": -1.455, \"(2283.5, 2313.5)\": -1.171, \"(2313.5, 2336.5)\": -0.66, \"(2336.5, 2420.0)\": -2.559, \"(2420.0, 2992.5)\": -3.229, \"(2992.5, 3006.0)\": -2.708, \"(3006.0, 3196.5)\": -2.984, \"(3196.5, 3249.5)\": -2.709, \"(3249.5, 14327.0)\": -4.146}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 105.5)\": -0.06, \"(105.5, 296.5)\": -0.369, \"(296.5, 335.5)\": -1.022, \"(335.5, 340.0)\": -0.184, \"(340.0, 343.0)\": -1.038, \"(343.0, 596.5)\": -1.323, \"(596.5, 712.5)\": -1.547, \"(712.5, 734.0)\": -1.555, \"(734.0, 800.0)\": -1.8, \"(800.0, 816.0)\": -2.191, \"(816.0, 997.5)\": -1.824, \"(997.5, 1031.0)\": -1.706, \"(1031.0, 1041.0)\": -2.147, \"(1041.0, 2172.5)\": -2.244, \"(2172.5, 2283.5)\": -2.248, \"(2283.5, 2313.5)\": -1.568, \"(2313.5, 2336.5)\": -2.21, \"(2336.5, 2420.0)\": -3.537, \"(2420.0, 2992.5)\": -3.89, \"(2992.5, 3006.0)\": -3.955, \"(3006.0, 3196.5)\": -4.24, \"(3196.5, 3249.5)\": -3.98, \"(3249.5, 14327.0)\": -5.248}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 105.5)\": 0.716, \"(105.5, 296.5)\": 0.425, \"(296.5, 335.5)\": 0.607, \"(335.5, 340.0)\": 0.513, \"(340.0, 343.0)\": 0.837, \"(343.0, 596.5)\": -0.16, \"(596.5, 712.5)\": -0.409, \"(712.5, 734.0)\": -0.869, \"(734.0, 800.0)\": -1.092, \"(800.0, 816.0)\": -0.082, \"(816.0, 997.5)\": -1.083, \"(997.5, 1031.0)\": -0.506, \"(1031.0, 1041.0)\": -0.589, \"(1041.0, 2172.5)\": -1.488, \"(2172.5, 2283.5)\": -0.661, \"(2283.5, 2313.5)\": -0.774, \"(2313.5, 2336.5)\": 0.89, \"(2336.5, 2420.0)\": -1.582, \"(2420.0, 2992.5)\": -2.569, \"(2992.5, 3006.0)\": -1.461, \"(3006.0, 3196.5)\": -1.727, \"(3196.5, 3249.5)\": -1.438, \"(3249.5, 14327.0)\": -3.043}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: FoodCourt\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 593.5)\": -0.177, \"(593.5, 779.5)\": 0.043, \"(779.5, 1341.5)\": 0.27, \"(1341.5, 2175.5)\": 0.543, \"(2175.5, 3125.0)\": 0.863, \"(3125.0, 3637.0)\": 1.13, \"(3637.0, 4078.5)\": 1.479, \"(4078.5, 5218.5)\": 2.076, \"(5218.5, 6031.5)\": 1.81, \"(6031.5, 6171.5)\": 1.439, \"(6171.5, 8753.0)\": 2.236, \"(8753.0, 8824.0)\": 2.746, \"(8824.0, 10094.5)\": 3.43, \"(10094.5, 12683.5)\": 3.888, \"(12683.5, 27723.0)\": 4.131}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 593.5)\": -0.307, \"(593.5, 779.5)\": -0.11, \"(779.5, 1341.5)\": -0.04, \"(1341.5, 2175.5)\": -0.06, \"(2175.5, 3125.0)\": 0.404, \"(3125.0, 3637.0)\": 0.707, \"(3637.0, 4078.5)\": 0.742, \"(4078.5, 5218.5)\": 1.52, \"(5218.5, 6031.5)\": 1.485, \"(6031.5, 6171.5)\": 0.477, \"(6171.5, 8753.0)\": 1.548, \"(8753.0, 8824.0)\": 1.95, \"(8824.0, 10094.5)\": 2.626, \"(10094.5, 12683.5)\": 2.361, \"(12683.5, 27723.0)\": 2.558}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 593.5)\": -0.047, \"(593.5, 779.5)\": 0.196, \"(779.5, 1341.5)\": 0.58, \"(1341.5, 2175.5)\": 1.145, \"(2175.5, 3125.0)\": 1.322, \"(3125.0, 3637.0)\": 1.554, \"(3637.0, 4078.5)\": 2.216, \"(4078.5, 5218.5)\": 2.631, \"(5218.5, 6031.5)\": 2.135, \"(6031.5, 6171.5)\": 2.4, \"(6171.5, 8753.0)\": 2.925, \"(8753.0, 8824.0)\": 3.543, \"(8824.0, 10094.5)\": 4.234, \"(10094.5, 12683.5)\": 5.416, \"(12683.5, 27723.0)\": 5.705}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: ShoppingMall\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 125.5)\": -0.032, \"(125.5, 541.5)\": -0.211, \"(541.5, 808.5)\": 0.034, \"(808.5, 1082.0)\": 0.213, \"(1082.0, 1187.0)\": -0.042, \"(1187.0, 1434.5)\": 0.401, \"(1434.5, 1658.5)\": 0.585, \"(1658.5, 1968.5)\": 0.948, \"(1968.5, 3394.5)\": 1.235, \"(3394.5, 3460.0)\": 0.871, \"(3460.0, 3741.5)\": 1.066, \"(3741.5, 4803.5)\": 2.339, \"(4803.5, 5204.0)\": 2.909, \"(5204.0, 12253.0)\": 3.236}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 125.5)\": -0.092, \"(125.5, 541.5)\": -0.495, \"(541.5, 808.5)\": -0.379, \"(808.5, 1082.0)\": -0.05, \"(1082.0, 1187.0)\": -0.484, \"(1187.0, 1434.5)\": 0.131, \"(1434.5, 1658.5)\": 0.238, \"(1658.5, 1968.5)\": 0.465, \"(1968.5, 3394.5)\": 0.864, \"(3394.5, 3460.0)\": 0.262, \"(3460.0, 3741.5)\": -0.052, \"(3741.5, 4803.5)\": 1.632, \"(4803.5, 5204.0)\": 1.913, \"(5204.0, 12253.0)\": 2.114}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 125.5)\": 0.028, \"(125.5, 541.5)\": 0.073, \"(541.5, 808.5)\": 0.447, \"(808.5, 1082.0)\": 0.477, \"(1082.0, 1187.0)\": 0.4, \"(1187.0, 1434.5)\": 0.671, \"(1434.5, 1658.5)\": 0.931, \"(1658.5, 1968.5)\": 1.43, \"(1968.5, 3394.5)\": 1.605, \"(3394.5, 3460.0)\": 1.481, \"(3460.0, 3741.5)\": 2.183, \"(3741.5, 4803.5)\": 3.046, \"(4803.5, 5204.0)\": 3.906, \"(5204.0, 12253.0)\": 4.358}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: Spa\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 130.5)\": 0.521, \"(130.5, 278.5)\": 0.118, \"(278.5, 452.5)\": -0.285, \"(452.5, 754.5)\": -0.907, \"(754.5, 1209.5)\": -1.309, \"(1209.5, 1808.0)\": -1.712, \"(1808.0, 2204.5)\": -3.029, \"(2204.5, 2207.5)\": -2.456, \"(2207.5, 2428.0)\": -2.956, \"(2428.0, 2462.5)\": -2.512, \"(2462.5, 2714.5)\": -3.402, \"(2714.5, 2745.0)\": -2.902, \"(2745.0, 2993.5)\": -4.077, \"(2993.5, 3132.0)\": -4.481, \"(3132.0, 3705.5)\": -5.377, \"(3705.5, 3747.0)\": -4.36, \"(3747.0, 22408.0)\": -7.183}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 130.5)\": 0.36, \"(130.5, 278.5)\": -1.599, \"(278.5, 452.5)\": -1.362, \"(452.5, 754.5)\": -1.291, \"(754.5, 1209.5)\": -2.117, \"(1209.5, 1808.0)\": -2.592, \"(1808.0, 2204.5)\": -3.856, \"(2204.5, 2207.5)\": -3.562, \"(2207.5, 2428.0)\": -3.549, \"(2428.0, 2462.5)\": -3.455, \"(2462.5, 2714.5)\": -4.525, \"(2714.5, 2745.0)\": -4.721, \"(2745.0, 2993.5)\": -5.493, \"(2993.5, 3132.0)\": -6.214, \"(3132.0, 3705.5)\": -6.767, \"(3705.5, 3747.0)\": -6.498, \"(3747.0, 22408.0)\": -9.024}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 130.5)\": 0.682, \"(130.5, 278.5)\": 1.834, \"(278.5, 452.5)\": 0.791, \"(452.5, 754.5)\": -0.524, \"(754.5, 1209.5)\": -0.502, \"(1209.5, 1808.0)\": -0.831, \"(1808.0, 2204.5)\": -2.202, \"(2204.5, 2207.5)\": -1.35, \"(2207.5, 2428.0)\": -2.364, \"(2428.0, 2462.5)\": -1.569, \"(2462.5, 2714.5)\": -2.28, \"(2714.5, 2745.0)\": -1.083, \"(2745.0, 2993.5)\": -2.661, \"(2993.5, 3132.0)\": -2.749, \"(3132.0, 3705.5)\": -3.986, \"(3705.5, 3747.0)\": -2.222, \"(3747.0, 22408.0)\": -5.342}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: VRDeck\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 135.5)\": 0.445, \"(135.5, 215.5)\": 0.073, \"(215.5, 500.5)\": -0.294, \"(500.5, 727.5)\": -0.661, \"(727.5, 799.5)\": -1.026, \"(799.5, 831.5)\": -0.601, \"(831.5, 872.5)\": -1.156, \"(872.5, 993.5)\": -1.633, \"(993.5, 1430.5)\": -2.012, \"(1430.5, 1514.5)\": -1.512, \"(1514.5, 1796.0)\": -2.212, \"(1796.0, 1909.5)\": -1.699, \"(1909.5, 1970.0)\": -2.568, \"(1970.0, 2571.5)\": -3.006, \"(2571.5, 2582.0)\": -2.375, \"(2582.0, 2657.0)\": -2.964, \"(2657.0, 3710.5)\": -3.98, \"(3710.5, 4089.0)\": -4.347, \"(4089.0, 5089.5)\": -5.923, \"(5089.5, 24133.0)\": -6.634}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 135.5)\": -0.055, \"(135.5, 215.5)\": -0.275, \"(215.5, 500.5)\": -1.359, \"(500.5, 727.5)\": -0.968, \"(727.5, 799.5)\": -1.273, \"(799.5, 831.5)\": -1.285, \"(831.5, 872.5)\": -1.782, \"(872.5, 993.5)\": -2.358, \"(993.5, 1430.5)\": -2.589, \"(1430.5, 1514.5)\": -2.382, \"(1514.5, 1796.0)\": -2.87, \"(1796.0, 1909.5)\": -3.449, \"(1909.5, 1970.0)\": -3.46, \"(1970.0, 2571.5)\": -4.009, \"(2571.5, 2582.0)\": -4.195, \"(2582.0, 2657.0)\": -4.898, \"(2657.0, 3710.5)\": -5.152, \"(3710.5, 4089.0)\": -5.79, \"(4089.0, 5089.5)\": -7.804, \"(5089.5, 24133.0)\": -8.247}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 135.5)\": 0.945, \"(135.5, 215.5)\": 0.422, \"(215.5, 500.5)\": 0.772, \"(500.5, 727.5)\": -0.354, \"(727.5, 799.5)\": -0.779, \"(799.5, 831.5)\": 0.083, \"(831.5, 872.5)\": -0.529, \"(872.5, 993.5)\": -0.908, \"(993.5, 1430.5)\": -1.435, \"(1430.5, 1514.5)\": -0.643, \"(1514.5, 1796.0)\": -1.555, \"(1796.0, 1909.5)\": 0.051, \"(1909.5, 1970.0)\": -1.677, \"(1970.0, 2571.5)\": -2.002, \"(2571.5, 2582.0)\": -0.555, \"(2582.0, 2657.0)\": -1.03, \"(2657.0, 3710.5)\": -2.808, \"(3710.5, 4089.0)\": -2.905, \"(4089.0, 5089.5)\": -4.042, \"(5089.5, 24133.0)\": -5.02}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: Age\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(17.0, 18.5)\": -3.326, \"(18.5, 19.5)\": -2.358, \"(19.5, 20.5)\": -2.799, \"(20.5, 21.5)\": -2.354, \"(21.5, 22.5)\": -1.405, \"(22.5, 23.5)\": -1.633, \"(23.5, 24.5)\": -1.214, \"(24.5, 26.5)\": -0.789, \"(26.5, 27.5)\": -0.473, \"(27.5, 29.5)\": -0.216, \"(29.5, 33.5)\": 0.042, \"(33.5, 36.5)\": 0.351, \"(36.5, 44.5)\": 0.658, \"(44.5, 61.5)\": 0.897, \"(61.5, 66.5)\": 0.574, \"(66.5, 73.5)\": 0.099, \"(73.5, 74.5)\": 0.763, \"(74.5, 77.5)\": 0.502, \"(77.5, 79.5)\": 0.875, \"(79.5, 84.5)\": 0.065, \"(84.5, 90.0)\": -1.08}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(17.0, 18.5)\": -4.677, \"(18.5, 19.5)\": -3.672, \"(19.5, 20.5)\": -3.928, \"(20.5, 21.5)\": -2.706, \"(21.5, 22.5)\": -1.741, \"(22.5, 23.5)\": -1.856, \"(23.5, 24.5)\": -1.407, \"(24.5, 26.5)\": -0.941, \"(26.5, 27.5)\": -0.561, \"(27.5, 29.5)\": -0.322, \"(29.5, 33.5)\": -0.079, \"(33.5, 36.5)\": 0.229, \"(36.5, 44.5)\": 0.5, \"(44.5, 61.5)\": 0.753, \"(61.5, 66.5)\": 0.434, \"(66.5, 73.5)\": -0.37, \"(73.5, 74.5)\": 0.229, \"(74.5, 77.5)\": -0.136, \"(77.5, 79.5)\": 0.35, \"(79.5, 84.5)\": -0.573, \"(84.5, 90.0)\": -2.041}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(17.0, 18.5)\": -1.975, \"(18.5, 19.5)\": -1.044, \"(19.5, 20.5)\": -1.669, \"(20.5, 21.5)\": -2.002, \"(21.5, 22.5)\": -1.069, \"(22.5, 23.5)\": -1.41, \"(23.5, 24.5)\": -1.021, \"(24.5, 26.5)\": -0.637, \"(26.5, 27.5)\": -0.385, \"(27.5, 29.5)\": -0.11, \"(29.5, 33.5)\": 0.164, \"(33.5, 36.5)\": 0.473, \"(36.5, 44.5)\": 0.816, \"(44.5, 61.5)\": 1.04, \"(61.5, 66.5)\": 0.714, \"(66.5, 73.5)\": 0.567, \"(73.5, 74.5)\": 1.297, \"(74.5, 77.5)\": 1.141, \"(77.5, 79.5)\": 1.401, \"(79.5, 84.5)\": 0.702, \"(84.5, 90.0)\": -0.119}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: WorkClass\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": -0.013, \"(0.5, 1.5)\": 0.434, \"(1.5, 4.5)\": -0.066, \"(4.5, 5.5)\": 0.167, \"(5.5, 7.5)\": -0.464, \"(7.5, 8.0)\": -2.54}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.099, \"(0.5, 1.5)\": 0.319, \"(1.5, 4.5)\": -0.192, \"(4.5, 5.5)\": 0.106, \"(5.5, 7.5)\": -0.567, \"(7.5, 8.0)\": -4.038}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.074, \"(0.5, 1.5)\": 0.549, \"(1.5, 4.5)\": 0.059, \"(4.5, 5.5)\": 0.228, \"(5.5, 7.5)\": -0.362, \"(7.5, 8.0)\": -1.042}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: Education\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": -0.4028, \"(0.5, 1.5)\": -0.5397, \"(1.5, 3.5)\": -0.4851, \"(3.5, 4.5)\": -0.4021, \"(4.5, 5.5)\": -0.457, \"(5.5, 6.5)\": -0.2537, \"(6.5, 7.5)\": -0.0494, \"(7.5, 8.5)\": 0.0457, \"(8.5, 9.5)\": 0.1831, \"(9.5, 10.5)\": 0.1392, \"(10.5, 11.5)\": -0.0652, \"(11.5, 14.5)\": 0.1954, \"(14.5, 15.0)\": 0.1393}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.5596, \"(0.5, 1.5)\": -0.6499, \"(1.5, 3.5)\": -0.618, \"(3.5, 4.5)\": -0.5693, \"(4.5, 5.5)\": -0.5278, \"(5.5, 6.5)\": -0.3342, \"(6.5, 7.5)\": -0.0948, \"(7.5, 8.5)\": -0.0062, \"(8.5, 9.5)\": 0.1525, \"(9.5, 10.5)\": 0.1072, \"(10.5, 11.5)\": -0.0869, \"(11.5, 14.5)\": 0.1476, \"(14.5, 15.0)\": 0.1012}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.2459, \"(0.5, 1.5)\": -0.4295, \"(1.5, 3.5)\": -0.3523, \"(3.5, 4.5)\": -0.235, \"(4.5, 5.5)\": -0.3862, \"(5.5, 6.5)\": -0.1733, \"(6.5, 7.5)\": -0.0039, \"(7.5, 8.5)\": 0.0977, \"(8.5, 9.5)\": 0.2137, \"(9.5, 10.5)\": 0.1711, \"(10.5, 11.5)\": -0.0435, \"(11.5, 14.5)\": 0.2431, \"(14.5, 15.0)\": 0.1775}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: EducationNum\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(1.0, 1.5)\": -4.746, \"(1.5, 4.5)\": -1.252, \"(4.5, 6.5)\": -0.882, \"(6.5, 9.5)\": -0.483, \"(9.5, 11.5)\": -0.093, \"(11.5, 13.5)\": 0.276, \"(13.5, 14.5)\": 0.863, \"(14.5, 16.0)\": 1.487}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -6.411, \"(1.5, 4.5)\": -1.52, \"(4.5, 6.5)\": -0.99, \"(6.5, 9.5)\": -0.541, \"(9.5, 11.5)\": -0.138, \"(11.5, 13.5)\": 0.205, \"(13.5, 14.5)\": 0.788, \"(14.5, 16.0)\": 1.332}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -3.082, \"(1.5, 4.5)\": -0.984, \"(4.5, 6.5)\": -0.775, \"(6.5, 9.5)\": -0.425, \"(9.5, 11.5)\": -0.049, \"(11.5, 13.5)\": 0.347, \"(13.5, 14.5)\": 0.938, \"(14.5, 16.0)\": 1.641}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: MaritalStatus\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": -0.368, \"(0.5, 1.5)\": 0.724, \"(1.5, 2.5)\": 0.587, \"(2.5, 3.5)\": -0.221, \"(3.5, 4.5)\": -0.631, \"(4.5, 5.5)\": -0.545, \"(5.5, 6.0)\": 0.179}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.418, \"(0.5, 1.5)\": 0.02, \"(1.5, 2.5)\": 0.545, \"(2.5, 3.5)\": -0.336, \"(3.5, 4.5)\": -0.676, \"(4.5, 5.5)\": -0.688, \"(5.5, 6.0)\": 0.067}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.318, \"(0.5, 1.5)\": 1.428, \"(1.5, 2.5)\": 0.629, \"(2.5, 3.5)\": -0.106, \"(3.5, 4.5)\": -0.585, \"(4.5, 5.5)\": -0.403, \"(5.5, 6.0)\": 0.291}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: Occupation\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": -0.297, \"(0.5, 3.5)\": -0.074, \"(3.5, 4.5)\": 0.644, \"(4.5, 6.5)\": -0.723, \"(6.5, 7.5)\": -0.542, \"(7.5, 8.5)\": -0.665, \"(8.5, 9.5)\": -0.926, \"(9.5, 10.5)\": 0.423, \"(10.5, 11.5)\": 0.59, \"(11.5, 12.5)\": 0.27, \"(12.5, 13.5)\": 0.534, \"(13.5, 14.0)\": -0.133}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.409, \"(0.5, 3.5)\": -0.139, \"(3.5, 4.5)\": 0.592, \"(4.5, 6.5)\": -0.847, \"(6.5, 7.5)\": -0.624, \"(7.5, 8.5)\": -0.749, \"(8.5, 9.5)\": -1.549, \"(9.5, 10.5)\": 0.366, \"(10.5, 11.5)\": 0.452, \"(11.5, 12.5)\": 0.225, \"(12.5, 13.5)\": 0.445, \"(13.5, 14.0)\": -0.202}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.185, \"(0.5, 3.5)\": -0.01, \"(3.5, 4.5)\": 0.695, \"(4.5, 6.5)\": -0.598, \"(6.5, 7.5)\": -0.461, \"(7.5, 8.5)\": -0.581, \"(8.5, 9.5)\": -0.302, \"(9.5, 10.5)\": 0.48, \"(10.5, 11.5)\": 0.727, \"(11.5, 12.5)\": 0.315, \"(12.5, 13.5)\": 0.622, \"(13.5, 14.0)\": -0.064}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: Relationship\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": 0.511, \"(0.5, 1.5)\": -0.233, \"(1.5, 2.5)\": -0.666, \"(2.5, 3.5)\": -1.006, \"(3.5, 4.5)\": -0.529, \"(4.5, 5.0)\": 1.753}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.453, \"(0.5, 1.5)\": -0.278, \"(1.5, 2.5)\": -0.789, \"(2.5, 3.5)\": -1.092, \"(3.5, 4.5)\": -0.6, \"(4.5, 5.0)\": 1.664}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.568, \"(0.5, 1.5)\": -0.188, \"(1.5, 2.5)\": -0.543, \"(2.5, 3.5)\": -0.921, \"(3.5, 4.5)\": -0.458, \"(4.5, 5.0)\": 1.842}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: Race\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": -0.8604, \"(0.5, 1.5)\": -0.0173, \"(1.5, 2.5)\": -0.2499, \"(2.5, 3.5)\": -0.3026, \"(3.5, 4.0)\": 0.0414}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -1.0291, \"(0.5, 1.5)\": -0.1456, \"(1.5, 2.5)\": -0.3118, \"(2.5, 3.5)\": -0.4557, \"(3.5, 4.0)\": 0.0349}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.6918, \"(0.5, 1.5)\": 0.1111, \"(1.5, 2.5)\": -0.1879, \"(2.5, 3.5)\": -0.1496, \"(3.5, 4.0)\": 0.048}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: Gender\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": -0.4751, \"(0.5, 1.0)\": 0.2339}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.5571, \"(0.5, 1.0)\": 0.1936}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.3931, \"(0.5, 1.0)\": 0.2743}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: CapitalGain\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 57.0)\": -0.25, \"(57.0, 3048.0)\": -4.83, \"(3048.0, 3120.0)\": 2.57, \"(3120.0, 4243.5)\": -4.43, \"(4243.5, 4401.0)\": 1.45, \"(4401.0, 4668.5)\": -1.82, \"(4668.5, 4826.0)\": 3.79, \"(4826.0, 4898.0)\": 0.57, \"(4898.0, 4973.5)\": 2.25, \"(4973.5, 5119.0)\": -3.52, \"(5119.0, 5316.5)\": 4.26, \"(5316.5, 5505.5)\": 0.43, \"(5505.5, 6457.5)\": 2.15, \"(6457.5, 6505.5)\": -0.16, \"(6505.5, 6745.0)\": 0.81, \"(6745.0, 7073.5)\": -1.33, \"(7073.5, 7436.5)\": 5.76, \"(7436.5, 7565.5)\": 2.02, \"(7565.5, 7792.0)\": 6.56, \"(7792.0, 7937.0)\": 4.88, \"(7937.0, 8296.0)\": 3.84, \"(8296.0, 10543.0)\": 7.18, \"(10543.0, 10585.5)\": -1.48, \"(10585.5, 30961.5)\": 8.61, \"(30961.5, 70654.5)\": -0.66, \"(70654.5, 99999.0)\": 9.72}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 57.0)\": -0.27, \"(57.0, 3048.0)\": -6.42, \"(3048.0, 3120.0)\": 2.14, \"(3120.0, 4243.5)\": -5.31, \"(4243.5, 4401.0)\": 1.09, \"(4401.0, 4668.5)\": -2.65, \"(4668.5, 4826.0)\": 2.87, \"(4826.0, 4898.0)\": -0.25, \"(4898.0, 4973.5)\": 1.55, \"(4973.5, 5119.0)\": -6.13, \"(5119.0, 5316.5)\": 3.51, \"(5316.5, 5505.5)\": -0.29, \"(5505.5, 6457.5)\": 1.3, \"(6457.5, 6505.5)\": -0.94, \"(6505.5, 6745.0)\": 0.19, \"(6745.0, 7073.5)\": -2.33, \"(7073.5, 7436.5)\": 4.95, \"(7436.5, 7565.5)\": 0.42, \"(7565.5, 7792.0)\": 5.41, \"(7792.0, 7937.0)\": 2.59, \"(7937.0, 8296.0)\": 1.32, \"(8296.0, 10543.0)\": 6.05, \"(10543.0, 10585.5)\": -2.73, \"(10585.5, 30961.5)\": 7.51, \"(30961.5, 70654.5)\": -3.56, \"(70654.5, 99999.0)\": 8.19}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 57.0)\": -0.23, \"(57.0, 3048.0)\": -3.24, \"(3048.0, 3120.0)\": 3.0, \"(3120.0, 4243.5)\": -3.54, \"(4243.5, 4401.0)\": 1.81, \"(4401.0, 4668.5)\": -1.0, \"(4668.5, 4826.0)\": 4.71, \"(4826.0, 4898.0)\": 1.38, \"(4898.0, 4973.5)\": 2.95, \"(4973.5, 5119.0)\": -0.92, \"(5119.0, 5316.5)\": 5.0, \"(5316.5, 5505.5)\": 1.16, \"(5505.5, 6457.5)\": 3.0, \"(6457.5, 6505.5)\": 0.62, \"(6505.5, 6745.0)\": 1.44, \"(6745.0, 7073.5)\": -0.34, \"(7073.5, 7436.5)\": 6.58, \"(7436.5, 7565.5)\": 3.62, \"(7565.5, 7792.0)\": 7.72, \"(7792.0, 7937.0)\": 7.16, \"(7937.0, 8296.0)\": 6.36, \"(8296.0, 10543.0)\": 8.31, \"(10543.0, 10585.5)\": -0.22, \"(10585.5, 30961.5)\": 9.71, \"(30961.5, 70654.5)\": 2.23, \"(70654.5, 99999.0)\": 11.26}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: CapitalLoss\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 845.0)\": -0.044, \"(845.0, 1448.0)\": -1.147, \"(1448.0, 1551.5)\": 0.416, \"(1551.5, 1568.5)\": 3.928, \"(1568.5, 1748.0)\": -3.752, \"(1748.0, 1846.0)\": 1.139, \"(1846.0, 1862.0)\": 3.823, \"(1862.0, 1881.5)\": -1.36, \"(1881.5, 1894.5)\": 4.781, \"(1894.5, 1938.0)\": 3.172, \"(1938.0, 1975.5)\": 0.294, \"(1975.5, 1978.5)\": 4.013, \"(1978.5, 2139.0)\": -2.74, \"(2139.0, 2176.5)\": 0.361, \"(2176.5, 2190.0)\": -1.098, \"(2190.0, 2205.5)\": 1.259, \"(2205.5, 2262.5)\": 2.644, \"(2262.5, 2310.5)\": -0.616, \"(2310.5, 2364.5)\": -1.139, \"(2364.5, 2384.5)\": 1.07, \"(2384.5, 2450.5)\": 4.377, \"(2450.5, 2480.5)\": 1.517, \"(2480.5, 2553.0)\": 3.296, \"(2553.0, 2581.0)\": 5.5, \"(2581.0, 2678.5)\": -0.191, \"(2678.5, 2789.0)\": 0.326, \"(2789.0, 3343.5)\": 5.958, \"(3343.5, 3835.0)\": 2.152, \"(3835.0, 4356.0)\": -0.334}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 845.0)\": -0.934, \"(845.0, 1448.0)\": -2.192, \"(1448.0, 1551.5)\": 0.083, \"(1551.5, 1568.5)\": 2.921, \"(1568.5, 1748.0)\": -4.443, \"(1748.0, 1846.0)\": 0.39, \"(1846.0, 1862.0)\": 2.886, \"(1862.0, 1881.5)\": -2.359, \"(1881.5, 1894.5)\": 3.819, \"(1894.5, 1938.0)\": 2.82, \"(1938.0, 1975.5)\": -0.436, \"(1975.5, 1978.5)\": 3.487, \"(1978.5, 2139.0)\": -3.34, \"(2139.0, 2176.5)\": -0.308, \"(2176.5, 2190.0)\": -2.441, \"(2190.0, 2205.5)\": 0.899, \"(2205.5, 2262.5)\": 1.633, \"(2262.5, 2310.5)\": -2.272, \"(2310.5, 2364.5)\": -2.819, \"(2364.5, 2384.5)\": 0.659, \"(2384.5, 2450.5)\": 3.333, \"(2450.5, 2480.5)\": 0.001, \"(2480.5, 2553.0)\": 1.926, \"(2553.0, 2581.0)\": 4.074, \"(2581.0, 2678.5)\": -1.869, \"(2678.5, 2789.0)\": -1.325, \"(2789.0, 3343.5)\": 4.42, \"(3343.5, 3835.0)\": 0.138, \"(3835.0, 4356.0)\": -1.587}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 845.0)\": 0.845, \"(845.0, 1448.0)\": -0.101, \"(1448.0, 1551.5)\": 0.748, \"(1551.5, 1568.5)\": 4.935, \"(1568.5, 1748.0)\": -3.061, \"(1748.0, 1846.0)\": 1.889, \"(1846.0, 1862.0)\": 4.761, \"(1862.0, 1881.5)\": -0.361, \"(1881.5, 1894.5)\": 5.742, \"(1894.5, 1938.0)\": 3.524, \"(1938.0, 1975.5)\": 1.024, \"(1975.5, 1978.5)\": 4.539, \"(1978.5, 2139.0)\": -2.139, \"(2139.0, 2176.5)\": 1.029, \"(2176.5, 2190.0)\": 0.245, \"(2190.0, 2205.5)\": 1.619, \"(2205.5, 2262.5)\": 3.655, \"(2262.5, 2310.5)\": 1.041, \"(2310.5, 2364.5)\": 0.541, \"(2364.5, 2384.5)\": 1.481, \"(2384.5, 2450.5)\": 5.42, \"(2450.5, 2480.5)\": 3.034, \"(2480.5, 2553.0)\": 4.666, \"(2553.0, 2581.0)\": 6.926, \"(2581.0, 2678.5)\": 1.487, \"(2678.5, 2789.0)\": 1.978, \"(2789.0, 3343.5)\": 7.496, \"(3343.5, 3835.0)\": 4.167, \"(3835.0, 4356.0)\": 0.92}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: HoursPerWeek\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(1.0, 1.5)\": -0.765, \"(1.5, 2.5)\": -0.375, \"(2.5, 4.5)\": -1.909, \"(4.5, 6.5)\": -1.117, \"(6.5, 7.5)\": -0.618, \"(7.5, 14.5)\": -0.822, \"(14.5, 19.5)\": -1.132, \"(19.5, 29.5)\": -0.765, \"(29.5, 33.5)\": -0.6, \"(33.5, 34.5)\": -0.921, \"(34.5, 39.5)\": -0.155, \"(39.5, 41.5)\": 0.03, \"(41.5, 50.5)\": 0.392, \"(50.5, 51.5)\": 0.131, \"(51.5, 55.5)\": 0.457, \"(55.5, 59.5)\": 0.676, \"(59.5, 63.5)\": 0.416, \"(63.5, 64.5)\": 0.952, \"(64.5, 65.5)\": 0.516, \"(65.5, 71.0)\": 0.071, \"(71.0, 75.5)\": 0.43, \"(75.5, 77.5)\": 0.235, \"(77.5, 79.0)\": 0.742, \"(79.0, 83.0)\": 0.977, \"(83.0, 85.5)\": 1.287, \"(85.5, 90.5)\": 0.192, \"(90.5, 97.5)\": -0.071, \"(97.5, 98.5)\": 0.119, \"(98.5, 99.0)\": -0.139}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -2.672, \"(1.5, 2.5)\": -0.773, \"(2.5, 4.5)\": -2.709, \"(4.5, 6.5)\": -1.566, \"(6.5, 7.5)\": -1.241, \"(7.5, 14.5)\": -1.098, \"(14.5, 19.5)\": -1.535, \"(19.5, 29.5)\": -1.357, \"(29.5, 33.5)\": -1.248, \"(33.5, 34.5)\": -1.815, \"(34.5, 39.5)\": -0.223, \"(39.5, 41.5)\": -0.129, \"(41.5, 50.5)\": 0.212, \"(50.5, 51.5)\": -0.867, \"(51.5, 55.5)\": 0.357, \"(55.5, 59.5)\": 0.304, \"(59.5, 63.5)\": 0.014, \"(63.5, 64.5)\": 0.009, \"(64.5, 65.5)\": 0.379, \"(65.5, 71.0)\": -0.113, \"(71.0, 75.5)\": 0.054, \"(75.5, 77.5)\": -0.57, \"(77.5, 79.0)\": 0.234, \"(79.0, 83.0)\": 0.788, \"(83.0, 85.5)\": 0.721, \"(85.5, 90.5)\": -0.289, \"(90.5, 97.5)\": -0.504, \"(97.5, 98.5)\": -0.527, \"(98.5, 99.0)\": -0.548}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": 1.142, \"(1.5, 2.5)\": 0.023, \"(2.5, 4.5)\": -1.109, \"(4.5, 6.5)\": -0.668, \"(6.5, 7.5)\": 0.005, \"(7.5, 14.5)\": -0.546, \"(14.5, 19.5)\": -0.729, \"(19.5, 29.5)\": -0.172, \"(29.5, 33.5)\": 0.047, \"(33.5, 34.5)\": -0.027, \"(34.5, 39.5)\": -0.087, \"(39.5, 41.5)\": 0.19, \"(41.5, 50.5)\": 0.571, \"(50.5, 51.5)\": 1.13, \"(51.5, 55.5)\": 0.557, \"(55.5, 59.5)\": 1.048, \"(59.5, 63.5)\": 0.818, \"(63.5, 64.5)\": 1.896, \"(64.5, 65.5)\": 0.653, \"(65.5, 71.0)\": 0.254, \"(71.0, 75.5)\": 0.806, \"(75.5, 77.5)\": 1.04, \"(77.5, 79.0)\": 1.25, \"(79.0, 83.0)\": 1.166, \"(83.0, 85.5)\": 1.852, \"(85.5, 90.5)\": 0.673, \"(90.5, 97.5)\": 0.361, \"(97.5, 98.5)\": 0.765, \"(98.5, 99.0)\": 0.271}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: NativeCountry\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": -0.195, \"(0.5, 1.5)\": 1.333, \"(1.5, 2.5)\": -0.02, \"(2.5, 3.5)\": -0.402, \"(3.5, 4.5)\": -1.423, \"(4.5, 5.5)\": 0.086, \"(5.5, 7.5)\": -0.843, \"(7.5, 8.5)\": -0.246, \"(8.5, 11.5)\": 0.062, \"(11.5, 20.5)\": -0.315, \"(20.5, 21.5)\": 0.109, \"(21.5, 22.5)\": 0.476, \"(22.5, 24.5)\": 0.133, \"(24.5, 26.5)\": -0.35, \"(26.5, 29.5)\": -0.489, \"(29.5, 32.5)\": -0.108, \"(32.5, 33.5)\": -0.483, \"(33.5, 35.5)\": -0.664, \"(35.5, 38.5)\": -0.396, \"(38.5, 39.5)\": 0.028, \"(39.5, 40.5)\": -0.596, \"(40.5, 41.0)\": 1.112}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.344, \"(0.5, 1.5)\": 0.452, \"(1.5, 2.5)\": -0.269, \"(2.5, 3.5)\": -0.76, \"(3.5, 4.5)\": -2.688, \"(4.5, 5.5)\": -0.257, \"(5.5, 7.5)\": -1.727, \"(7.5, 8.5)\": -0.488, \"(8.5, 11.5)\": -0.121, \"(11.5, 20.5)\": -0.631, \"(20.5, 21.5)\": -0.319, \"(21.5, 22.5)\": 0.048, \"(22.5, 24.5)\": -0.066, \"(24.5, 26.5)\": -0.66, \"(26.5, 29.5)\": -1.067, \"(29.5, 32.5)\": -0.254, \"(32.5, 33.5)\": -0.844, \"(33.5, 35.5)\": -1.156, \"(35.5, 38.5)\": -0.997, \"(38.5, 39.5)\": 0.02, \"(39.5, 40.5)\": -1.452, \"(40.5, 41.0)\": 0.408}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.045, \"(0.5, 1.5)\": 2.213, \"(1.5, 2.5)\": 0.228, \"(2.5, 3.5)\": -0.043, \"(3.5, 4.5)\": -0.158, \"(4.5, 5.5)\": 0.429, \"(5.5, 7.5)\": 0.04, \"(7.5, 8.5)\": -0.004, \"(8.5, 11.5)\": 0.245, \"(11.5, 20.5)\": 0.001, \"(20.5, 21.5)\": 0.537, \"(21.5, 22.5)\": 0.904, \"(22.5, 24.5)\": 0.331, \"(24.5, 26.5)\": -0.04, \"(26.5, 29.5)\": 0.089, \"(29.5, 32.5)\": 0.038, \"(32.5, 33.5)\": -0.121, \"(33.5, 35.5)\": -0.172, \"(35.5, 38.5)\": 0.204, \"(38.5, 39.5)\": 0.036, \"(39.5, 40.5)\": 0.26, \"(40.5, 41.0)\": 1.816}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: id\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(91.0, 2307.0)\": 0.00838, \"(2307.0, 4713.5)\": 0.00964, \"(4713.5, 6928.5)\": 0.0038, \"(6928.5, 9761.5)\": 0.00118, \"(9761.5, 13120.0)\": -0.00051, \"(13120.0, 14826.0)\": -0.00127, \"(14826.0, 20043.5)\": 5e-05, \"(20043.5, 22448.0)\": 0.00075, \"(22448.0, 23794.0)\": -0.00133, \"(23794.0, 28014.5)\": -0.00281, \"(28014.5, 28671.0)\": -0.00155, \"(28671.0, 37439.5)\": -0.00049, \"(37439.5, 40007.0)\": 0.00015, \"(40007.0, 41128.5)\": 0.00473, \"(41128.5, 50305.5)\": -0.0009, \"(50305.5, 51818.5)\": -0.00193, \"(51818.5, 66668.0)\": -0.00104, \"(66668.0, 67776.0)\": 0.0019, \"(67776.0, 75664.5)\": 1e-05, \"(75664.5, 76606.0)\": 0.0007, \"(76606.0, 89235.5)\": 0.00161, \"(89235.5, 227800.5)\": -0.00038, \"(227800.5, 231707.5)\": 0.00024, \"(231707.5, 257871.0)\": -0.00045, \"(257871.0, 503283.0)\": 0.00017, \"(503283.0, 507804.5)\": -0.00061, \"(507804.5, 517795.0)\": -0.00125, \"(517795.0, 616121.0)\": -0.00038, \"(616121.0, 622616.5)\": 0.00042, \"(622616.5, 647046.0)\": -0.00022, \"(647046.0, 662956.5)\": 0.00117, \"(662956.5, 667208.5)\": -0.00102, \"(667208.5, 689123.0)\": 0.00021, \"(689123.0, 872554.5)\": -0.00065, \"(872554.5, 942666.5)\": 0.00032, \"(942666.5, 983736.5)\": -0.00052, \"(983736.5, 1025442.0)\": 0.00017, \"(1025442.0, 1029281.5)\": -0.00099, \"(1029281.5, 1040563.0)\": -0.00029, \"(1040563.0, 1103097.0)\": 0.00069, \"(1103097.0, 1103695.0)\": 0.00289, \"(1103695.0, 1104610.5)\": -0.00013, \"(1104610.5, 1109548.0)\": 0.00181, \"(1109548.0, 1113474.5)\": 1e-05, \"(1113474.5, 1114673.5)\": -0.00091, \"(1114673.5, 1116159.5)\": 0.00326, \"(1116159.5, 1117955.0)\": 0.00422}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(91.0, 2307.0)\": 0.00427, \"(2307.0, 4713.5)\": 0.00579, \"(4713.5, 6928.5)\": 0.00041, \"(6928.5, 9761.5)\": -0.0007, \"(9761.5, 13120.0)\": -0.00221, \"(13120.0, 14826.0)\": -0.00399, \"(14826.0, 20043.5)\": -0.00257, \"(20043.5, 22448.0)\": -0.00208, \"(22448.0, 23794.0)\": -0.00391, \"(23794.0, 28014.5)\": -0.0066, \"(28014.5, 28671.0)\": -0.00305, \"(28671.0, 37439.5)\": -0.00164, \"(37439.5, 40007.0)\": -0.00259, \"(40007.0, 41128.5)\": -0.00544, \"(41128.5, 50305.5)\": -0.00224, \"(50305.5, 51818.5)\": -0.00485, \"(51818.5, 66668.0)\": -0.00248, \"(66668.0, 67776.0)\": -0.00461, \"(67776.0, 75664.5)\": -0.0014, \"(75664.5, 76606.0)\": -0.0013, \"(76606.0, 89235.5)\": -0.00066, \"(89235.5, 227800.5)\": -0.00254, \"(227800.5, 231707.5)\": -0.00057, \"(231707.5, 257871.0)\": -0.00369, \"(257871.0, 503283.0)\": -0.0015, \"(503283.0, 507804.5)\": -0.00235, \"(507804.5, 517795.0)\": -0.00383, \"(517795.0, 616121.0)\": -0.00166, \"(616121.0, 622616.5)\": -0.00158, \"(622616.5, 647046.0)\": -0.00171, \"(647046.0, 662956.5)\": -0.00058, \"(662956.5, 667208.5)\": -0.00336, \"(667208.5, 689123.0)\": -0.00275, \"(689123.0, 872554.5)\": -0.00294, \"(872554.5, 942666.5)\": -0.00097, \"(942666.5, 983736.5)\": -0.00265, \"(983736.5, 1025442.0)\": -0.00252, \"(1025442.0, 1029281.5)\": -0.00403, \"(1029281.5, 1040563.0)\": -0.00272, \"(1040563.0, 1103097.0)\": -0.00112, \"(1103097.0, 1103695.0)\": -0.00196, \"(1103695.0, 1104610.5)\": -0.00237, \"(1104610.5, 1109548.0)\": -0.00097, \"(1109548.0, 1113474.5)\": -0.00207, \"(1113474.5, 1114673.5)\": -0.00422, \"(1114673.5, 1116159.5)\": -0.00105, \"(1116159.5, 1117955.0)\": 0.00066}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(91.0, 2307.0)\": 0.01249, \"(2307.0, 4713.5)\": 0.01349, \"(4713.5, 6928.5)\": 0.00719, \"(6928.5, 9761.5)\": 0.00306, \"(9761.5, 13120.0)\": 0.0012, \"(13120.0, 14826.0)\": 0.00144, \"(14826.0, 20043.5)\": 0.00267, \"(20043.5, 22448.0)\": 0.00358, \"(22448.0, 23794.0)\": 0.00124, \"(23794.0, 28014.5)\": 0.00098, \"(28014.5, 28671.0)\": -5e-05, \"(28671.0, 37439.5)\": 0.00065, \"(37439.5, 40007.0)\": 0.00288, \"(40007.0, 41128.5)\": 0.0149, \"(41128.5, 50305.5)\": 0.00044, \"(50305.5, 51818.5)\": 0.00099, \"(51818.5, 66668.0)\": 0.0004, \"(66668.0, 67776.0)\": 0.00841, \"(67776.0, 75664.5)\": 0.00142, \"(75664.5, 76606.0)\": 0.00269, \"(76606.0, 89235.5)\": 0.00388, \"(89235.5, 227800.5)\": 0.00178, \"(227800.5, 231707.5)\": 0.00106, \"(231707.5, 257871.0)\": 0.00278, \"(257871.0, 503283.0)\": 0.00185, \"(503283.0, 507804.5)\": 0.00113, \"(507804.5, 517795.0)\": 0.00134, \"(517795.0, 616121.0)\": 0.0009, \"(616121.0, 622616.5)\": 0.00241, \"(622616.5, 647046.0)\": 0.00128, \"(647046.0, 662956.5)\": 0.00293, \"(662956.5, 667208.5)\": 0.00132, \"(667208.5, 689123.0)\": 0.00317, \"(689123.0, 872554.5)\": 0.00165, \"(872554.5, 942666.5)\": 0.0016, \"(942666.5, 983736.5)\": 0.00161, \"(983736.5, 1025442.0)\": 0.00285, \"(1025442.0, 1029281.5)\": 0.00205, \"(1029281.5, 1040563.0)\": 0.00215, \"(1040563.0, 1103097.0)\": 0.0025, \"(1103097.0, 1103695.0)\": 0.00774, \"(1103695.0, 1104610.5)\": 0.00212, \"(1104610.5, 1109548.0)\": 0.00458, \"(1109548.0, 1113474.5)\": 0.00208, \"(1113474.5, 1114673.5)\": 0.00241, \"(1114673.5, 1116159.5)\": 0.00758, \"(1116159.5, 1117955.0)\": 0.00778}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: MonsoonIntensity\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 1.5)\": -0.02446, \"(1.5, 2.5)\": -0.01712, \"(2.5, 3.5)\": -0.00908, \"(3.5, 4.5)\": -0.00529, \"(4.5, 5.5)\": 0.0003, \"(5.5, 6.5)\": 0.00497, \"(6.5, 7.5)\": 0.01093, \"(7.5, 8.5)\": 0.01787, \"(8.5, 9.5)\": 0.02262, \"(9.5, 11.5)\": 0.02707, \"(11.5, 12.5)\": 0.03735, \"(12.5, 13.5)\": 0.043, \"(13.5, 15.0)\": 0.01734}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02705, \"(1.5, 2.5)\": -0.01788, \"(2.5, 3.5)\": -0.00955, \"(3.5, 4.5)\": -0.00566, \"(4.5, 5.5)\": 4e-05, \"(5.5, 6.5)\": 0.00451, \"(6.5, 7.5)\": 0.01051, \"(7.5, 8.5)\": 0.01741, \"(8.5, 9.5)\": 0.02167, \"(9.5, 11.5)\": 0.02561, \"(11.5, 12.5)\": 0.03439, \"(12.5, 13.5)\": 0.03822, \"(13.5, 15.0)\": -0.00028}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02187, \"(1.5, 2.5)\": -0.01637, \"(2.5, 3.5)\": -0.00861, \"(3.5, 4.5)\": -0.00492, \"(4.5, 5.5)\": 0.00056, \"(5.5, 6.5)\": 0.00544, \"(6.5, 7.5)\": 0.01134, \"(7.5, 8.5)\": 0.01833, \"(8.5, 9.5)\": 0.02358, \"(9.5, 11.5)\": 0.02853, \"(11.5, 12.5)\": 0.04032, \"(12.5, 13.5)\": 0.04778, \"(13.5, 15.0)\": 0.03495}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: TopographyDrainage\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": -0.0274, \"(0.5, 1.5)\": -0.02381, \"(1.5, 2.5)\": -0.01602, \"(2.5, 3.5)\": -0.01049, \"(3.5, 4.5)\": -0.00528, \"(4.5, 5.5)\": -0.00022, \"(5.5, 6.5)\": 0.00517, \"(6.5, 7.5)\": 0.01282, \"(7.5, 8.5)\": 0.01628, \"(8.5, 9.5)\": 0.02454, \"(9.5, 10.5)\": 0.02883, \"(10.5, 11.5)\": 0.03213, \"(11.5, 17.0)\": 0.03564}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.03013, \"(0.5, 1.5)\": -0.02484, \"(1.5, 2.5)\": -0.01655, \"(2.5, 3.5)\": -0.01088, \"(3.5, 4.5)\": -0.00574, \"(4.5, 5.5)\": -0.00046, \"(5.5, 6.5)\": 0.00473, \"(6.5, 7.5)\": 0.01242, \"(7.5, 8.5)\": 0.01574, \"(8.5, 9.5)\": 0.02354, \"(9.5, 10.5)\": 0.0277, \"(10.5, 11.5)\": 0.03039, \"(11.5, 17.0)\": 0.02281}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02466, \"(0.5, 1.5)\": -0.02278, \"(1.5, 2.5)\": -0.0155, \"(2.5, 3.5)\": -0.0101, \"(3.5, 4.5)\": -0.00482, \"(4.5, 5.5)\": 2e-05, \"(5.5, 6.5)\": 0.00561, \"(6.5, 7.5)\": 0.01323, \"(7.5, 8.5)\": 0.01683, \"(8.5, 9.5)\": 0.02554, \"(9.5, 10.5)\": 0.02996, \"(10.5, 11.5)\": 0.03386, \"(11.5, 17.0)\": 0.04848}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: RiverManagement\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": -0.0273, \"(0.5, 1.5)\": -0.02345, \"(1.5, 2.5)\": -0.01571, \"(2.5, 3.5)\": -0.01174, \"(3.5, 4.5)\": -0.00519, \"(4.5, 5.5)\": 0.00111, \"(5.5, 6.5)\": 0.00506, \"(6.5, 7.5)\": 0.01056, \"(7.5, 8.5)\": 0.01706, \"(8.5, 9.5)\": 0.02398, \"(9.5, 11.5)\": 0.02821, \"(11.5, 12.5)\": 0.03673, \"(12.5, 13.5)\": 0.01311, \"(13.5, 16.0)\": 0.03206}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02945, \"(0.5, 1.5)\": -0.02501, \"(1.5, 2.5)\": -0.01619, \"(2.5, 3.5)\": -0.0121, \"(3.5, 4.5)\": -0.00549, \"(4.5, 5.5)\": 0.00069, \"(5.5, 6.5)\": 0.00469, \"(6.5, 7.5)\": 0.00991, \"(7.5, 8.5)\": 0.01638, \"(8.5, 9.5)\": 0.02306, \"(9.5, 11.5)\": 0.0266, \"(11.5, 12.5)\": 0.02982, \"(12.5, 13.5)\": -0.01689, \"(13.5, 16.0)\": 0.01715}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02515, \"(0.5, 1.5)\": -0.0219, \"(1.5, 2.5)\": -0.01524, \"(2.5, 3.5)\": -0.01139, \"(3.5, 4.5)\": -0.0049, \"(4.5, 5.5)\": 0.00152, \"(5.5, 6.5)\": 0.00544, \"(6.5, 7.5)\": 0.01121, \"(7.5, 8.5)\": 0.01774, \"(8.5, 9.5)\": 0.0249, \"(9.5, 11.5)\": 0.02981, \"(11.5, 12.5)\": 0.04363, \"(12.5, 13.5)\": 0.04312, \"(13.5, 16.0)\": 0.04696}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: Deforestation\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": -0.02956, \"(0.5, 2.5)\": -0.02081, \"(2.5, 3.5)\": -0.00998, \"(3.5, 4.5)\": -0.00524, \"(4.5, 5.5)\": 0.00043, \"(5.5, 6.5)\": 0.00515, \"(6.5, 8.5)\": 0.01107, \"(8.5, 10.5)\": 0.02102, \"(10.5, 11.5)\": 0.02728, \"(11.5, 13.5)\": 0.0456, \"(13.5, 14.5)\": 0.05244, \"(14.5, 17.0)\": 0.06161}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.03241, \"(0.5, 2.5)\": -0.02172, \"(2.5, 3.5)\": -0.01056, \"(3.5, 4.5)\": -0.0057, \"(4.5, 5.5)\": 1e-05, \"(5.5, 6.5)\": 0.00474, \"(6.5, 8.5)\": 0.01043, \"(8.5, 10.5)\": 0.01957, \"(10.5, 11.5)\": 0.02542, \"(11.5, 13.5)\": 0.04264, \"(13.5, 14.5)\": 0.04883, \"(14.5, 17.0)\": 0.05758}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02672, \"(0.5, 2.5)\": -0.0199, \"(2.5, 3.5)\": -0.0094, \"(3.5, 4.5)\": -0.00479, \"(4.5, 5.5)\": 0.00085, \"(5.5, 6.5)\": 0.00557, \"(6.5, 8.5)\": 0.01172, \"(8.5, 10.5)\": 0.02247, \"(10.5, 11.5)\": 0.02915, \"(11.5, 13.5)\": 0.04855, \"(13.5, 14.5)\": 0.05605, \"(14.5, 17.0)\": 0.06565}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: Urbanization\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": -0.02565, \"(0.5, 1.5)\": -0.02133, \"(1.5, 2.5)\": -0.01683, \"(2.5, 3.5)\": -0.00993, \"(3.5, 4.5)\": -0.00473, \"(4.5, 5.5)\": -1e-05, \"(5.5, 6.5)\": 0.00511, \"(6.5, 7.5)\": 0.01148, \"(7.5, 8.5)\": 0.01621, \"(8.5, 9.5)\": 0.02476, \"(9.5, 11.5)\": 0.02962, \"(11.5, 12.5)\": 0.03469, \"(12.5, 13.5)\": 0.04866, \"(13.5, 16.0)\": 0.05902}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02758, \"(0.5, 1.5)\": -0.02272, \"(1.5, 2.5)\": -0.01769, \"(2.5, 3.5)\": -0.01036, \"(3.5, 4.5)\": -0.00514, \"(4.5, 5.5)\": -0.0004, \"(5.5, 6.5)\": 0.00453, \"(6.5, 7.5)\": 0.01098, \"(7.5, 8.5)\": 0.01535, \"(8.5, 9.5)\": 0.0239, \"(9.5, 11.5)\": 0.02772, \"(11.5, 12.5)\": 0.03206, \"(12.5, 13.5)\": 0.04307, \"(13.5, 16.0)\": 0.0546}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02372, \"(0.5, 1.5)\": -0.01994, \"(1.5, 2.5)\": -0.01596, \"(2.5, 3.5)\": -0.00951, \"(3.5, 4.5)\": -0.00432, \"(4.5, 5.5)\": 0.00037, \"(5.5, 6.5)\": 0.00568, \"(6.5, 7.5)\": 0.01199, \"(7.5, 8.5)\": 0.01706, \"(8.5, 9.5)\": 0.02562, \"(9.5, 11.5)\": 0.03152, \"(11.5, 12.5)\": 0.03732, \"(12.5, 13.5)\": 0.05424, \"(13.5, 16.0)\": 0.06343}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: ClimateChange\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 1.5)\": -0.02549, \"(1.5, 2.5)\": -0.01575, \"(2.5, 3.5)\": -0.01061, \"(3.5, 4.5)\": -0.0046, \"(4.5, 5.5)\": 0.00059, \"(5.5, 6.5)\": 0.00567, \"(6.5, 7.5)\": 0.01201, \"(7.5, 9.5)\": 0.01601, \"(9.5, 10.5)\": 0.02531, \"(10.5, 11.5)\": 0.02956, \"(11.5, 12.5)\": 0.04031, \"(12.5, 14.0)\": 0.04423}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02735, \"(1.5, 2.5)\": -0.01647, \"(2.5, 3.5)\": -0.01101, \"(3.5, 4.5)\": -0.00502, \"(4.5, 5.5)\": 0.00018, \"(5.5, 6.5)\": 0.00528, \"(6.5, 7.5)\": 0.01139, \"(7.5, 9.5)\": 0.01505, \"(9.5, 10.5)\": 0.0236, \"(10.5, 11.5)\": 0.02677, \"(11.5, 12.5)\": 0.03846, \"(12.5, 14.0)\": 0.03359}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02363, \"(1.5, 2.5)\": -0.01503, \"(2.5, 3.5)\": -0.0102, \"(3.5, 4.5)\": -0.00418, \"(4.5, 5.5)\": 0.00101, \"(5.5, 6.5)\": 0.00607, \"(6.5, 7.5)\": 0.01263, \"(7.5, 9.5)\": 0.01697, \"(9.5, 10.5)\": 0.02702, \"(10.5, 11.5)\": 0.03236, \"(11.5, 12.5)\": 0.04216, \"(12.5, 14.0)\": 0.05488}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: DamsQuality\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 1.5)\": -0.02325, \"(1.5, 2.5)\": -0.01532, \"(2.5, 3.5)\": -0.01073, \"(3.5, 4.5)\": -0.00482, \"(4.5, 5.5)\": -0.00032, \"(5.5, 6.5)\": 0.0063, \"(6.5, 7.5)\": 0.01228, \"(7.5, 8.5)\": 0.01637, \"(8.5, 10.5)\": 0.02537, \"(10.5, 12.5)\": 0.03189, \"(12.5, 13.5)\": 0.03961, \"(13.5, 14.0)\": 0.01644}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02598, \"(1.5, 2.5)\": -0.01586, \"(2.5, 3.5)\": -0.01113, \"(3.5, 4.5)\": -0.00525, \"(4.5, 5.5)\": -0.00072, \"(5.5, 6.5)\": 0.00574, \"(6.5, 7.5)\": 0.01173, \"(7.5, 8.5)\": 0.01585, \"(8.5, 10.5)\": 0.02412, \"(10.5, 12.5)\": 0.02908, \"(12.5, 13.5)\": 0.03687, \"(13.5, 14.0)\": 0.00331}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02052, \"(1.5, 2.5)\": -0.01477, \"(2.5, 3.5)\": -0.01034, \"(3.5, 4.5)\": -0.00438, \"(4.5, 5.5)\": 9e-05, \"(5.5, 6.5)\": 0.00686, \"(6.5, 7.5)\": 0.01282, \"(7.5, 8.5)\": 0.01689, \"(8.5, 10.5)\": 0.02662, \"(10.5, 12.5)\": 0.0347, \"(12.5, 13.5)\": 0.04234, \"(13.5, 14.0)\": 0.02957}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: Siltation\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 1.5)\": -0.02643, \"(1.5, 2.5)\": -0.01529, \"(2.5, 3.5)\": -0.01037, \"(3.5, 4.5)\": -0.00562, \"(4.5, 5.5)\": 0.00068, \"(5.5, 6.5)\": 0.00591, \"(6.5, 7.5)\": 0.01127, \"(7.5, 8.5)\": 0.01553, \"(8.5, 10.5)\": 0.02363, \"(10.5, 11.5)\": 0.03038, \"(11.5, 12.5)\": 0.03607, \"(12.5, 13.5)\": 0.04087, \"(13.5, 15.0)\": 0.04477}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02798, \"(1.5, 2.5)\": -0.01578, \"(2.5, 3.5)\": -0.01088, \"(3.5, 4.5)\": -0.00595, \"(4.5, 5.5)\": 0.0002, \"(5.5, 6.5)\": 0.0054, \"(6.5, 7.5)\": 0.0105, \"(7.5, 8.5)\": 0.01459, \"(8.5, 10.5)\": 0.02243, \"(10.5, 11.5)\": 0.0283, \"(11.5, 12.5)\": 0.03438, \"(12.5, 13.5)\": 0.03775, \"(13.5, 15.0)\": 0.03258}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02487, \"(1.5, 2.5)\": -0.0148, \"(2.5, 3.5)\": -0.00987, \"(3.5, 4.5)\": -0.00529, \"(4.5, 5.5)\": 0.00116, \"(5.5, 6.5)\": 0.00643, \"(6.5, 7.5)\": 0.01205, \"(7.5, 8.5)\": 0.01648, \"(8.5, 10.5)\": 0.02483, \"(10.5, 11.5)\": 0.03246, \"(11.5, 12.5)\": 0.03776, \"(12.5, 13.5)\": 0.044, \"(13.5, 15.0)\": 0.05697}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: AgriculturalPractices\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 1.5)\": -0.02463, \"(1.5, 2.5)\": -0.01694, \"(2.5, 3.5)\": -0.01147, \"(3.5, 4.5)\": -0.00533, \"(4.5, 5.5)\": 0.00036, \"(5.5, 6.5)\": 0.00641, \"(6.5, 7.5)\": 0.01086, \"(7.5, 8.5)\": 0.01753, \"(8.5, 9.5)\": 0.02391, \"(9.5, 11.5)\": 0.03162, \"(11.5, 14.0)\": 0.0391, \"(14.0, 15.0)\": 0.05506}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02721, \"(1.5, 2.5)\": -0.01778, \"(2.5, 3.5)\": -0.01182, \"(3.5, 4.5)\": -0.00574, \"(4.5, 5.5)\": -9e-05, \"(5.5, 6.5)\": 0.00587, \"(6.5, 7.5)\": 0.01028, \"(7.5, 8.5)\": 0.01669, \"(8.5, 9.5)\": 0.02306, \"(9.5, 11.5)\": 0.02986, \"(11.5, 14.0)\": 0.03465, \"(14.0, 15.0)\": 0.03109}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02205, \"(1.5, 2.5)\": -0.0161, \"(2.5, 3.5)\": -0.01113, \"(3.5, 4.5)\": -0.00492, \"(4.5, 5.5)\": 0.0008, \"(5.5, 6.5)\": 0.00696, \"(6.5, 7.5)\": 0.01144, \"(7.5, 8.5)\": 0.01837, \"(8.5, 9.5)\": 0.02477, \"(9.5, 11.5)\": 0.03339, \"(11.5, 14.0)\": 0.04355, \"(14.0, 15.0)\": 0.07902}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: Encroachments\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": -0.02484, \"(0.5, 1.5)\": -0.02089, \"(1.5, 2.5)\": -0.01739, \"(2.5, 3.5)\": -0.01124, \"(3.5, 4.5)\": -0.00474, \"(4.5, 5.5)\": 0.00077, \"(5.5, 6.5)\": 0.00574, \"(6.5, 7.5)\": 0.01068, \"(7.5, 8.5)\": 0.01599, \"(8.5, 9.5)\": 0.02231, \"(9.5, 10.5)\": 0.02667, \"(10.5, 13.5)\": 0.03305, \"(13.5, 16.0)\": 0.02016}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02637, \"(0.5, 1.5)\": -0.02217, \"(1.5, 2.5)\": -0.0179, \"(2.5, 3.5)\": -0.01163, \"(3.5, 4.5)\": -0.00519, \"(4.5, 5.5)\": 0.00046, \"(5.5, 6.5)\": 0.00525, \"(6.5, 7.5)\": 0.00992, \"(7.5, 8.5)\": 0.01538, \"(8.5, 9.5)\": 0.02115, \"(9.5, 10.5)\": 0.02528, \"(10.5, 13.5)\": 0.02547, \"(13.5, 16.0)\": 0.01297}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0233, \"(0.5, 1.5)\": -0.01962, \"(1.5, 2.5)\": -0.01689, \"(2.5, 3.5)\": -0.01085, \"(3.5, 4.5)\": -0.0043, \"(4.5, 5.5)\": 0.00109, \"(5.5, 6.5)\": 0.00623, \"(6.5, 7.5)\": 0.01144, \"(7.5, 8.5)\": 0.0166, \"(8.5, 9.5)\": 0.02348, \"(9.5, 10.5)\": 0.02807, \"(10.5, 13.5)\": 0.04062, \"(13.5, 16.0)\": 0.02734}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: IneffectiveDisasterPreparedness\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 1.5)\": -0.02526, \"(1.5, 2.5)\": -0.01738, \"(2.5, 3.5)\": -0.01172, \"(3.5, 4.5)\": -0.00537, \"(4.5, 5.5)\": -0.00021, \"(5.5, 6.5)\": 0.0066, \"(6.5, 7.5)\": 0.01026, \"(7.5, 8.5)\": 0.01717, \"(8.5, 9.5)\": 0.02426, \"(9.5, 10.5)\": 0.02823, \"(10.5, 11.5)\": 0.03325, \"(11.5, 13.5)\": 0.03915, \"(13.5, 15.0)\": 0.03572}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02806, \"(1.5, 2.5)\": -0.01811, \"(2.5, 3.5)\": -0.01241, \"(3.5, 4.5)\": -0.0056, \"(4.5, 5.5)\": -0.00057, \"(5.5, 6.5)\": 0.00621, \"(6.5, 7.5)\": 0.00967, \"(7.5, 8.5)\": 0.01672, \"(8.5, 9.5)\": 0.02334, \"(9.5, 10.5)\": 0.02687, \"(10.5, 11.5)\": 0.03182, \"(11.5, 13.5)\": 0.03364, \"(13.5, 15.0)\": 0.0307}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02245, \"(1.5, 2.5)\": -0.01664, \"(2.5, 3.5)\": -0.01102, \"(3.5, 4.5)\": -0.00514, \"(4.5, 5.5)\": 0.00016, \"(5.5, 6.5)\": 0.00699, \"(6.5, 7.5)\": 0.01085, \"(7.5, 8.5)\": 0.01761, \"(8.5, 9.5)\": 0.02519, \"(9.5, 10.5)\": 0.02958, \"(10.5, 11.5)\": 0.03468, \"(11.5, 13.5)\": 0.04466, \"(13.5, 15.0)\": 0.04073}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: DrainageSystems\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": -0.02593, \"(0.5, 1.5)\": -0.02211, \"(1.5, 2.5)\": -0.01611, \"(2.5, 3.5)\": -0.01125, \"(3.5, 4.5)\": -0.0047, \"(4.5, 5.5)\": 9e-05, \"(5.5, 6.5)\": 0.00652, \"(6.5, 8.5)\": 0.01219, \"(8.5, 10.5)\": 0.02253, \"(10.5, 11.5)\": 0.03412, \"(11.5, 12.5)\": 0.04015, \"(12.5, 14.0)\": 0.04564}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02795, \"(0.5, 1.5)\": -0.02324, \"(1.5, 2.5)\": -0.01672, \"(2.5, 3.5)\": -0.01177, \"(3.5, 4.5)\": -0.00504, \"(4.5, 5.5)\": -0.00021, \"(5.5, 6.5)\": 0.00613, \"(6.5, 8.5)\": 0.01137, \"(8.5, 10.5)\": 0.02139, \"(10.5, 11.5)\": 0.03184, \"(11.5, 12.5)\": 0.03703, \"(12.5, 14.0)\": 0.04222}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02391, \"(0.5, 1.5)\": -0.02097, \"(1.5, 2.5)\": -0.0155, \"(2.5, 3.5)\": -0.01073, \"(3.5, 4.5)\": -0.00435, \"(4.5, 5.5)\": 0.00039, \"(5.5, 6.5)\": 0.00691, \"(6.5, 8.5)\": 0.01301, \"(8.5, 10.5)\": 0.02367, \"(10.5, 11.5)\": 0.0364, \"(11.5, 12.5)\": 0.04328, \"(12.5, 14.0)\": 0.04907}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: CoastalVulnerability\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": -0.03259, \"(0.5, 1.5)\": -0.02272, \"(1.5, 2.5)\": -0.0157, \"(2.5, 3.5)\": -0.00983, \"(3.5, 4.5)\": -0.00444, \"(4.5, 5.5)\": -0.00035, \"(5.5, 6.5)\": 0.00575, \"(6.5, 7.5)\": 0.01126, \"(7.5, 8.5)\": 0.01651, \"(8.5, 9.5)\": 0.02143, \"(9.5, 12.5)\": 0.02903, \"(12.5, 13.5)\": 0.03437, \"(13.5, 15.0)\": 0.04826}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0359, \"(0.5, 1.5)\": -0.02356, \"(1.5, 2.5)\": -0.01657, \"(2.5, 3.5)\": -0.01034, \"(3.5, 4.5)\": -0.0048, \"(4.5, 5.5)\": -0.00077, \"(5.5, 6.5)\": 0.00528, \"(6.5, 7.5)\": 0.01081, \"(7.5, 8.5)\": 0.01566, \"(8.5, 9.5)\": 0.02049, \"(9.5, 12.5)\": 0.02706, \"(12.5, 13.5)\": 0.0298, \"(13.5, 15.0)\": 0.0329}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02927, \"(0.5, 1.5)\": -0.02189, \"(1.5, 2.5)\": -0.01482, \"(2.5, 3.5)\": -0.00931, \"(3.5, 4.5)\": -0.00409, \"(4.5, 5.5)\": 7e-05, \"(5.5, 6.5)\": 0.00622, \"(6.5, 7.5)\": 0.0117, \"(7.5, 8.5)\": 0.01736, \"(8.5, 9.5)\": 0.02236, \"(9.5, 12.5)\": 0.031, \"(12.5, 13.5)\": 0.03893, \"(13.5, 15.0)\": 0.06363}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: Landslides\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": -0.02593, \"(0.5, 1.5)\": -0.02172, \"(1.5, 2.5)\": -0.01544, \"(2.5, 3.5)\": -0.0098, \"(3.5, 4.5)\": -0.00541, \"(4.5, 5.5)\": 0.00066, \"(5.5, 6.5)\": 0.00575, \"(6.5, 7.5)\": 0.01201, \"(7.5, 8.5)\": 0.01649, \"(8.5, 9.5)\": 0.0215, \"(9.5, 10.5)\": 0.0267, \"(10.5, 11.5)\": 0.03057, \"(11.5, 13.5)\": 0.0366, \"(13.5, 14.0)\": 0.03003}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02743, \"(0.5, 1.5)\": -0.02261, \"(1.5, 2.5)\": -0.01616, \"(2.5, 3.5)\": -0.0102, \"(3.5, 4.5)\": -0.00579, \"(4.5, 5.5)\": 0.00027, \"(5.5, 6.5)\": 0.00544, \"(6.5, 7.5)\": 0.01146, \"(7.5, 8.5)\": 0.01601, \"(8.5, 9.5)\": 0.02065, \"(9.5, 10.5)\": 0.02512, \"(10.5, 11.5)\": 0.0285, \"(11.5, 13.5)\": 0.02931, \"(13.5, 14.0)\": 0.02233}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02443, \"(0.5, 1.5)\": -0.02083, \"(1.5, 2.5)\": -0.01472, \"(2.5, 3.5)\": -0.0094, \"(3.5, 4.5)\": -0.00504, \"(4.5, 5.5)\": 0.00105, \"(5.5, 6.5)\": 0.00606, \"(6.5, 7.5)\": 0.01257, \"(7.5, 8.5)\": 0.01698, \"(8.5, 9.5)\": 0.02234, \"(9.5, 10.5)\": 0.02828, \"(10.5, 11.5)\": 0.03265, \"(11.5, 13.5)\": 0.04389, \"(13.5, 14.0)\": 0.03772}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: Watersheds\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": -0.02526, \"(0.5, 1.5)\": -0.02147, \"(1.5, 2.5)\": -0.01542, \"(2.5, 3.5)\": -0.01026, \"(3.5, 4.5)\": -0.00466, \"(4.5, 5.5)\": 0.00049, \"(5.5, 6.5)\": 0.00555, \"(6.5, 8.5)\": 0.01133, \"(8.5, 10.5)\": 0.02234, \"(10.5, 11.5)\": 0.03241, \"(11.5, 12.5)\": 0.03775, \"(12.5, 13.5)\": 0.04216, \"(13.5, 14.0)\": 0.04656}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0274, \"(0.5, 1.5)\": -0.02237, \"(1.5, 2.5)\": -0.01633, \"(2.5, 3.5)\": -0.01068, \"(3.5, 4.5)\": -0.005, \"(4.5, 5.5)\": 0.00014, \"(5.5, 6.5)\": 0.00514, \"(6.5, 8.5)\": 0.01068, \"(8.5, 10.5)\": 0.02129, \"(10.5, 11.5)\": 0.03073, \"(11.5, 12.5)\": 0.03466, \"(12.5, 13.5)\": 0.038, \"(13.5, 14.0)\": 0.043}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02312, \"(0.5, 1.5)\": -0.02056, \"(1.5, 2.5)\": -0.01451, \"(2.5, 3.5)\": -0.00985, \"(3.5, 4.5)\": -0.00431, \"(4.5, 5.5)\": 0.00084, \"(5.5, 6.5)\": 0.00596, \"(6.5, 8.5)\": 0.01197, \"(8.5, 10.5)\": 0.0234, \"(10.5, 11.5)\": 0.03409, \"(11.5, 12.5)\": 0.04085, \"(12.5, 13.5)\": 0.04633, \"(13.5, 14.0)\": 0.05012}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: DeterioratingInfrastructure\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": -0.02508, \"(0.5, 1.5)\": -0.01897, \"(1.5, 2.5)\": -0.01452, \"(2.5, 3.5)\": -0.01085, \"(3.5, 4.5)\": -0.00475, \"(4.5, 5.5)\": 0.00054, \"(5.5, 6.5)\": 0.00555, \"(6.5, 7.5)\": 0.01137, \"(7.5, 8.5)\": 0.01653, \"(8.5, 9.5)\": 0.0237, \"(9.5, 10.5)\": 0.02782, \"(10.5, 11.5)\": 0.03175, \"(11.5, 12.5)\": 0.03686, \"(12.5, 15.0)\": 0.04451}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02677, \"(0.5, 1.5)\": -0.01971, \"(1.5, 2.5)\": -0.01507, \"(2.5, 3.5)\": -0.0113, \"(3.5, 4.5)\": -0.00523, \"(4.5, 5.5)\": 0.00016, \"(5.5, 6.5)\": 0.00517, \"(6.5, 7.5)\": 0.01094, \"(7.5, 8.5)\": 0.01606, \"(8.5, 9.5)\": 0.02309, \"(9.5, 10.5)\": 0.02666, \"(10.5, 11.5)\": 0.03007, \"(11.5, 12.5)\": 0.03455, \"(12.5, 15.0)\": 0.03295}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02339, \"(0.5, 1.5)\": -0.01822, \"(1.5, 2.5)\": -0.01398, \"(2.5, 3.5)\": -0.0104, \"(3.5, 4.5)\": -0.00428, \"(4.5, 5.5)\": 0.00091, \"(5.5, 6.5)\": 0.00594, \"(6.5, 7.5)\": 0.0118, \"(7.5, 8.5)\": 0.017, \"(8.5, 9.5)\": 0.0243, \"(9.5, 10.5)\": 0.02899, \"(10.5, 11.5)\": 0.03343, \"(11.5, 12.5)\": 0.03916, \"(12.5, 15.0)\": 0.05607}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: PopulationScore\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": -0.02443, \"(0.5, 1.5)\": -0.02088, \"(1.5, 2.5)\": -0.01613, \"(2.5, 3.5)\": -0.01086, \"(3.5, 4.5)\": -0.00583, \"(4.5, 5.5)\": 0.00139, \"(5.5, 6.5)\": 0.00556, \"(6.5, 7.5)\": 0.01145, \"(7.5, 8.5)\": 0.01748, \"(8.5, 10.5)\": 0.0242, \"(10.5, 11.5)\": 0.03351, \"(11.5, 13.5)\": 0.03691, \"(13.5, 15.0)\": 0.03345, \"(15.0, 16.0)\": 0.02926}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02582, \"(0.5, 1.5)\": -0.02181, \"(1.5, 2.5)\": -0.01706, \"(2.5, 3.5)\": -0.01143, \"(3.5, 4.5)\": -0.00626, \"(4.5, 5.5)\": 0.00099, \"(5.5, 6.5)\": 0.00524, \"(6.5, 7.5)\": 0.01084, \"(7.5, 8.5)\": 0.0167, \"(8.5, 10.5)\": 0.02302, \"(10.5, 11.5)\": 0.03159, \"(11.5, 13.5)\": 0.03427, \"(13.5, 15.0)\": 0.02849, \"(15.0, 16.0)\": 0.02539}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02304, \"(0.5, 1.5)\": -0.01995, \"(1.5, 2.5)\": -0.01521, \"(2.5, 3.5)\": -0.01028, \"(3.5, 4.5)\": -0.00541, \"(4.5, 5.5)\": 0.00178, \"(5.5, 6.5)\": 0.00588, \"(6.5, 7.5)\": 0.01205, \"(7.5, 8.5)\": 0.01826, \"(8.5, 10.5)\": 0.02538, \"(10.5, 11.5)\": 0.03543, \"(11.5, 13.5)\": 0.03955, \"(13.5, 15.0)\": 0.03841, \"(15.0, 16.0)\": 0.03313}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: WetlandLoss\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 1.5)\": -0.02419, \"(1.5, 2.5)\": -0.01693, \"(2.5, 3.5)\": -0.01069, \"(3.5, 4.5)\": -0.00585, \"(4.5, 5.5)\": 0.00051, \"(5.5, 6.5)\": 0.00676, \"(6.5, 8.5)\": 0.01245, \"(8.5, 10.5)\": 0.02257, \"(10.5, 11.5)\": 0.03265, \"(11.5, 13.5)\": 0.03889, \"(13.5, 14.5)\": 0.04912, \"(14.5, 16.0)\": 0.0585}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02604, \"(1.5, 2.5)\": -0.01758, \"(2.5, 3.5)\": -0.01104, \"(3.5, 4.5)\": -0.00622, \"(4.5, 5.5)\": 0.00022, \"(5.5, 6.5)\": 0.0063, \"(6.5, 8.5)\": 0.01194, \"(8.5, 10.5)\": 0.0215, \"(10.5, 11.5)\": 0.03022, \"(11.5, 13.5)\": 0.03581, \"(13.5, 14.5)\": 0.04439, \"(14.5, 16.0)\": 0.04645}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 1.5)\": -0.02235, \"(1.5, 2.5)\": -0.01628, \"(2.5, 3.5)\": -0.01034, \"(3.5, 4.5)\": -0.00547, \"(4.5, 5.5)\": 0.0008, \"(5.5, 6.5)\": 0.00723, \"(6.5, 8.5)\": 0.01295, \"(8.5, 10.5)\": 0.02363, \"(10.5, 11.5)\": 0.03508, \"(11.5, 13.5)\": 0.04198, \"(13.5, 14.5)\": 0.05386, \"(14.5, 16.0)\": 0.07055}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: InadequatePlanning\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": -0.02553, \"(0.5, 2.5)\": -0.02038, \"(2.5, 4.5)\": -0.0099, \"(4.5, 6.5)\": 0.00082, \"(6.5, 7.5)\": 0.01088, \"(7.5, 9.5)\": 0.0178, \"(9.5, 10.5)\": 0.02657, \"(10.5, 12.5)\": 0.0329, \"(12.5, 13.5)\": 0.03982, \"(13.5, 15.0)\": 0.05043, \"(15.0, 16.0)\": 0.06084}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02806, \"(0.5, 2.5)\": -0.02117, \"(2.5, 4.5)\": -0.01033, \"(4.5, 6.5)\": 0.00032, \"(6.5, 7.5)\": 0.01025, \"(7.5, 9.5)\": 0.01687, \"(9.5, 10.5)\": 0.02522, \"(10.5, 12.5)\": 0.02998, \"(12.5, 13.5)\": 0.03567, \"(13.5, 15.0)\": 0.03659, \"(15.0, 16.0)\": 0.04096}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.023, \"(0.5, 2.5)\": -0.01959, \"(2.5, 4.5)\": -0.00946, \"(4.5, 6.5)\": 0.00132, \"(6.5, 7.5)\": 0.0115, \"(7.5, 9.5)\": 0.01874, \"(9.5, 10.5)\": 0.02792, \"(10.5, 12.5)\": 0.03583, \"(12.5, 13.5)\": 0.04397, \"(13.5, 15.0)\": 0.06426, \"(15.0, 16.0)\": 0.08071}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: PoliticalFactors\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": -0.0263, \"(0.5, 1.5)\": -0.02126, \"(1.5, 2.5)\": -0.01709, \"(2.5, 3.5)\": -0.01038, \"(3.5, 4.5)\": -0.00633, \"(4.5, 5.5)\": 0.00068, \"(5.5, 6.5)\": 0.00618, \"(6.5, 7.5)\": 0.01223, \"(7.5, 8.5)\": 0.01761, \"(8.5, 9.5)\": 0.02318, \"(9.5, 10.5)\": 0.02782, \"(10.5, 11.5)\": 0.03238, \"(11.5, 13.5)\": 0.03978, \"(13.5, 15.0)\": 0.04468, \"(15.0, 16.0)\": 0.0529}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02939, \"(0.5, 1.5)\": -0.02258, \"(1.5, 2.5)\": -0.01777, \"(2.5, 3.5)\": -0.01075, \"(3.5, 4.5)\": -0.00677, \"(4.5, 5.5)\": 0.00038, \"(5.5, 6.5)\": 0.00571, \"(6.5, 7.5)\": 0.01182, \"(7.5, 8.5)\": 0.01718, \"(8.5, 9.5)\": 0.02223, \"(9.5, 10.5)\": 0.02645, \"(10.5, 11.5)\": 0.02946, \"(11.5, 13.5)\": 0.03697, \"(13.5, 15.0)\": 0.03459, \"(15.0, 16.0)\": 0.03844}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.02321, \"(0.5, 1.5)\": -0.01993, \"(1.5, 2.5)\": -0.01641, \"(2.5, 3.5)\": -0.01001, \"(3.5, 4.5)\": -0.00589, \"(4.5, 5.5)\": 0.00098, \"(5.5, 6.5)\": 0.00665, \"(6.5, 7.5)\": 0.01264, \"(7.5, 8.5)\": 0.01804, \"(8.5, 9.5)\": 0.02414, \"(9.5, 10.5)\": 0.02919, \"(10.5, 11.5)\": 0.0353, \"(11.5, 13.5)\": 0.04259, \"(13.5, 15.0)\": 0.05476, \"(15.0, 16.0)\": 0.06736}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: age\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(40.0, 41.5)\": -1.489, \"(41.5, 43.5)\": -0.895, \"(43.5, 44.5)\": -0.02, \"(44.5, 47.5)\": 0.701, \"(47.5, 48.5)\": 1.245, \"(48.5, 58.5)\": -0.923, \"(58.5, 59.5)\": 0.647, \"(59.5, 60.8335)\": -0.288, \"(60.8335, 64.5)\": -1.035, \"(64.5, 65.5)\": 0.0, \"(65.5, 67.5)\": -0.73, \"(67.5, 68.5)\": 0.19, \"(68.5, 70.5)\": 0.784, \"(70.5, 80.5)\": 1.169, \"(80.5, 81.5)\": 0.839, \"(81.5, 85.5)\": 2.112, \"(85.5, 86.5)\": 3.884, \"(86.5, 95.0)\": 4.517}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(40.0, 41.5)\": -2.719, \"(41.5, 43.5)\": -2.486, \"(43.5, 44.5)\": -0.761, \"(44.5, 47.5)\": 0.297, \"(47.5, 48.5)\": 0.199, \"(48.5, 58.5)\": -1.235, \"(58.5, 59.5)\": 0.291, \"(59.5, 60.8335)\": -0.805, \"(60.8335, 64.5)\": -1.655, \"(64.5, 65.5)\": -0.281, \"(65.5, 67.5)\": -2.122, \"(67.5, 68.5)\": -0.059, \"(68.5, 70.5)\": 0.513, \"(70.5, 80.5)\": 0.404, \"(80.5, 81.5)\": 0.173, \"(81.5, 85.5)\": 1.308, \"(85.5, 86.5)\": 2.758, \"(86.5, 95.0)\": 3.244}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(40.0, 41.5)\": -0.259, \"(41.5, 43.5)\": 0.696, \"(43.5, 44.5)\": 0.722, \"(44.5, 47.5)\": 1.105, \"(47.5, 48.5)\": 2.291, \"(48.5, 58.5)\": -0.612, \"(58.5, 59.5)\": 1.004, \"(59.5, 60.8335)\": 0.228, \"(60.8335, 64.5)\": -0.414, \"(64.5, 65.5)\": 0.281, \"(65.5, 67.5)\": 0.662, \"(67.5, 68.5)\": 0.44, \"(68.5, 70.5)\": 1.056, \"(70.5, 80.5)\": 1.934, \"(80.5, 81.5)\": 1.505, \"(81.5, 85.5)\": 2.916, \"(85.5, 86.5)\": 5.009, \"(86.5, 95.0)\": 5.79}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: anaemia\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": -0.0818, \"(0.5, 1.0)\": 0.0917}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.1464, \"(0.5, 1.0)\": 0.0194}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0173, \"(0.5, 1.0)\": 0.1641}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: creatinine_phosphokinase\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(23.0, 32.0)\": -0.48, \"(32.0, 49.5)\": 0.68, \"(49.5, 56.5)\": -4.31, \"(56.5, 59.5)\": -2.44, \"(59.5, 64.5)\": -1.82, \"(64.5, 85.0)\": -1.1, \"(85.0, 87.0)\": 0.42, \"(87.0, 93.5)\": -0.75, \"(93.5, 94.5)\": 0.47, \"(94.5, 103.5)\": -0.53, \"(103.5, 107.5)\": 0.12, \"(107.5, 120.0)\": -0.5, \"(120.0, 121.5)\": 0.24, \"(121.5, 126.0)\": 1.25, \"(126.0, 127.5)\": -3.14, \"(127.5, 145.5)\": 1.51, \"(145.5, 147.0)\": 0.91, \"(147.0, 150.0)\": -0.15, \"(150.0, 160.5)\": -1.08, \"(160.5, 189.5)\": -0.45, \"(189.5, 232.5)\": -1.26, \"(232.5, 254.5)\": -0.16, \"(254.5, 258.5)\": 2.88, \"(258.5, 280.5)\": 1.68, \"(280.5, 331.5)\": 1.11, \"(331.5, 370.0)\": 0.44, \"(370.0, 462.0)\": 1.1, \"(462.0, 597.5)\": 0.53, \"(597.5, 751.0)\": -1.87, \"(751.0, 766.5)\": 0.06, \"(766.5, 806.0)\": 2.64, \"(806.0, 873.5)\": 2.05, \"(873.5, 1036.0)\": 0.28, \"(1036.0, 1415.0)\": 0.85, \"(1415.0, 1649.0)\": 0.18, \"(1649.0, 1726.0)\": 2.26, \"(1726.0, 1886.0)\": 0.04, \"(1886.0, 2038.5)\": 7.0, \"(2038.5, 2307.5)\": 2.26, \"(2307.5, 2444.0)\": 5.81, \"(2444.0, 3440.5)\": -2.71, \"(3440.5, 4253.0)\": -1.47, \"(4253.0, 5548.5)\": 1.68, \"(5548.5, 7861.0)\": 3.47}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(23.0, 32.0)\": -1.51, \"(32.0, 49.5)\": -0.87, \"(49.5, 56.5)\": -5.69, \"(56.5, 59.5)\": -3.58, \"(59.5, 64.5)\": -2.64, \"(64.5, 85.0)\": -2.07, \"(85.0, 87.0)\": -2.37, \"(87.0, 93.5)\": -1.85, \"(93.5, 94.5)\": -0.56, \"(94.5, 103.5)\": -0.85, \"(103.5, 107.5)\": -0.45, \"(107.5, 120.0)\": -1.09, \"(120.0, 121.5)\": -0.48, \"(121.5, 126.0)\": 0.88, \"(126.0, 127.5)\": -5.59, \"(127.5, 145.5)\": 0.93, \"(145.5, 147.0)\": 0.57, \"(147.0, 150.0)\": -0.64, \"(150.0, 160.5)\": -2.38, \"(160.5, 189.5)\": -1.47, \"(189.5, 232.5)\": -2.02, \"(232.5, 254.5)\": -1.04, \"(254.5, 258.5)\": 1.73, \"(258.5, 280.5)\": 0.55, \"(280.5, 331.5)\": 0.09, \"(331.5, 370.0)\": -0.26, \"(370.0, 462.0)\": 0.18, \"(462.0, 597.5)\": 0.4, \"(597.5, 751.0)\": -3.59, \"(751.0, 766.5)\": -2.06, \"(766.5, 806.0)\": 1.02, \"(806.0, 873.5)\": 0.45, \"(873.5, 1036.0)\": -0.52, \"(1036.0, 1415.0)\": 0.33, \"(1415.0, 1649.0)\": -0.68, \"(1649.0, 1726.0)\": -0.23, \"(1726.0, 1886.0)\": -1.16, \"(1886.0, 2038.5)\": 5.88, \"(2038.5, 2307.5)\": 1.8, \"(2307.5, 2444.0)\": 4.43, \"(2444.0, 3440.5)\": -5.48, \"(3440.5, 4253.0)\": -2.15, \"(4253.0, 5548.5)\": 0.41, \"(5548.5, 7861.0)\": 2.17}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(23.0, 32.0)\": 0.54, \"(32.0, 49.5)\": 2.24, \"(49.5, 56.5)\": -2.93, \"(56.5, 59.5)\": -1.31, \"(59.5, 64.5)\": -1.0, \"(64.5, 85.0)\": -0.13, \"(85.0, 87.0)\": 3.22, \"(87.0, 93.5)\": 0.35, \"(93.5, 94.5)\": 1.51, \"(94.5, 103.5)\": -0.2, \"(103.5, 107.5)\": 0.69, \"(107.5, 120.0)\": 0.09, \"(120.0, 121.5)\": 0.97, \"(121.5, 126.0)\": 1.61, \"(126.0, 127.5)\": -0.68, \"(127.5, 145.5)\": 2.09, \"(145.5, 147.0)\": 1.25, \"(147.0, 150.0)\": 0.33, \"(150.0, 160.5)\": 0.22, \"(160.5, 189.5)\": 0.57, \"(189.5, 232.5)\": -0.49, \"(232.5, 254.5)\": 0.72, \"(254.5, 258.5)\": 4.03, \"(258.5, 280.5)\": 2.81, \"(280.5, 331.5)\": 2.12, \"(331.5, 370.0)\": 1.15, \"(370.0, 462.0)\": 2.02, \"(462.0, 597.5)\": 0.67, \"(597.5, 751.0)\": -0.15, \"(751.0, 766.5)\": 2.18, \"(766.5, 806.0)\": 4.25, \"(806.0, 873.5)\": 3.65, \"(873.5, 1036.0)\": 1.09, \"(1036.0, 1415.0)\": 1.38, \"(1415.0, 1649.0)\": 1.04, \"(1649.0, 1726.0)\": 4.75, \"(1726.0, 1886.0)\": 1.24, \"(1886.0, 2038.5)\": 8.11, \"(2038.5, 2307.5)\": 2.72, \"(2307.5, 2444.0)\": 7.19, \"(2444.0, 3440.5)\": 0.06, \"(3440.5, 4253.0)\": -0.79, \"(4253.0, 5548.5)\": 2.95, \"(5548.5, 7861.0)\": 4.78}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: diabetes\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": 0.3225, \"(0.5, 1.0)\": -0.415}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.1807, \"(0.5, 1.0)\": -0.5976}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.4643, \"(0.5, 1.0)\": -0.2325}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: ejection_fraction\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(14.0, 16.0)\": 4.55, \"(16.0, 22.5)\": 3.26, \"(22.5, 27.5)\": 1.89, \"(27.5, 32.5)\": -0.42, \"(32.5, 36.5)\": -1.76, \"(36.5, 39.0)\": 0.48, \"(39.0, 61.0)\": -0.83, \"(61.0, 67.5)\": 0.08, \"(67.5, 75.0)\": 0.8, \"(75.0, 80.0)\": -5.67}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(14.0, 16.0)\": 2.65, \"(16.0, 22.5)\": 2.42, \"(22.5, 27.5)\": 1.26, \"(27.5, 32.5)\": -0.83, \"(32.5, 36.5)\": -2.57, \"(36.5, 39.0)\": 0.17, \"(39.0, 61.0)\": -1.16, \"(61.0, 67.5)\": -0.39, \"(67.5, 75.0)\": 0.32, \"(75.0, 80.0)\": -8.05}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(14.0, 16.0)\": 6.45, \"(16.0, 22.5)\": 4.1, \"(22.5, 27.5)\": 2.51, \"(27.5, 32.5)\": -0.01, \"(32.5, 36.5)\": -0.95, \"(36.5, 39.0)\": 0.79, \"(39.0, 61.0)\": -0.49, \"(61.0, 67.5)\": 0.55, \"(67.5, 75.0)\": 1.28, \"(75.0, 80.0)\": -3.29}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: high_blood_pressure\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": -0.1077, \"(0.5, 1.0)\": 0.1864}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.1574, \"(0.5, 1.0)\": 0.1003}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.058, \"(0.5, 1.0)\": 0.2724}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: platelets\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(25100.0, 27700.0)\": -1.004, \"(27700.0, 34450.0)\": -0.687, \"(34450.0, 42200.0)\": 0.328, \"(42200.0, 56500.0)\": 1.717, \"(56500.0, 66050.0)\": 2.769, \"(66050.0, 74000.0)\": 2.195, \"(74000.0, 95500.0)\": 2.956, \"(95500.0, 104500.0)\": -0.265, \"(104500.0, 144000.0)\": -0.585, \"(144000.0, 150500.0)\": -0.895, \"(150500.0, 154000.0)\": 2.322, \"(154000.0, 169000.0)\": 0.469, \"(169000.0, 184500.0)\": -1.612, \"(184500.0, 195000.0)\": 1.111, \"(195000.0, 199000.0)\": 3.01, \"(199000.0, 200500.0)\": 1.837, \"(200500.0, 214000.0)\": 0.403, \"(214000.0, 217500.0)\": -0.825, \"(217500.0, 218500.0)\": -1.399, \"(218500.0, 220500.0)\": 0.341, \"(220500.0, 222500.0)\": 0.978, \"(222500.0, 226500.0)\": 1.584, \"(226500.0, 241500.0)\": 0.175, \"(241500.0, 242500.0)\": 0.642, \"(242500.0, 243500.0)\": 1.107, \"(243500.0, 244500.0)\": 1.516, \"(244500.0, 252500.0)\": -2.19, \"(252500.0, 261000.0)\": -0.878, \"(261000.0, 274500.0)\": -0.145, \"(274500.0, 283500.0)\": -0.968, \"(283500.0, 287500.0)\": 0.203, \"(287500.0, 289500.0)\": 1.032, \"(289500.0, 302500.0)\": -1.296, \"(302500.0, 305500.0)\": -2.984, \"(305500.0, 307000.0)\": 0.876, \"(307000.0, 332000.0)\": 0.368, \"(332000.0, 335000.0)\": 1.21, \"(335000.0, 343000.0)\": 0.8, \"(343000.0, 350500.0)\": -0.573, \"(350500.0, 354500.0)\": 3.0, \"(354500.0, 383500.0)\": -0.119, \"(383500.0, 449500.0)\": 0.655, \"(449500.0, 471000.0)\": 1.527, \"(471000.0, 500500.0)\": -2.247, \"(500500.0, 582000.0)\": -0.442, \"(582000.0, 675500.0)\": 2.645, \"(675500.0, 796000.0)\": 2.314, \"(796000.0, 850000.0)\": -0.709}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(25100.0, 27700.0)\": -1.75, \"(27700.0, 34450.0)\": -1.54, \"(34450.0, 42200.0)\": -0.532, \"(42200.0, 56500.0)\": 0.992, \"(56500.0, 66050.0)\": 1.538, \"(66050.0, 74000.0)\": 1.537, \"(74000.0, 95500.0)\": 1.91, \"(95500.0, 104500.0)\": -1.642, \"(104500.0, 144000.0)\": -1.428, \"(144000.0, 150500.0)\": -1.74, \"(150500.0, 154000.0)\": 1.125, \"(154000.0, 169000.0)\": 0.027, \"(169000.0, 184500.0)\": -2.523, \"(184500.0, 195000.0)\": 0.214, \"(195000.0, 199000.0)\": 0.239, \"(199000.0, 200500.0)\": 0.581, \"(200500.0, 214000.0)\": -0.252, \"(214000.0, 217500.0)\": -2.007, \"(217500.0, 218500.0)\": -3.583, \"(218500.0, 220500.0)\": 0.076, \"(220500.0, 222500.0)\": 0.244, \"(222500.0, 226500.0)\": -0.038, \"(226500.0, 241500.0)\": -0.123, \"(241500.0, 242500.0)\": 0.22, \"(242500.0, 243500.0)\": 0.116, \"(243500.0, 244500.0)\": 0.265, \"(244500.0, 252500.0)\": -4.008, \"(252500.0, 261000.0)\": -1.287, \"(261000.0, 274500.0)\": -0.465, \"(274500.0, 283500.0)\": -1.829, \"(283500.0, 287500.0)\": -1.587, \"(287500.0, 289500.0)\": -0.951, \"(289500.0, 302500.0)\": -1.857, \"(302500.0, 305500.0)\": -4.201, \"(305500.0, 307000.0)\": 0.125, \"(307000.0, 332000.0)\": -0.181, \"(332000.0, 335000.0)\": -0.179, \"(335000.0, 343000.0)\": 0.105, \"(343000.0, 350500.0)\": -1.469, \"(350500.0, 354500.0)\": 1.748, \"(354500.0, 383500.0)\": -0.848, \"(383500.0, 449500.0)\": 0.242, \"(449500.0, 471000.0)\": -2.033, \"(471000.0, 500500.0)\": -5.177, \"(500500.0, 582000.0)\": -1.795, \"(582000.0, 675500.0)\": 1.501, \"(675500.0, 796000.0)\": 0.104, \"(796000.0, 850000.0)\": -1.557}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(25100.0, 27700.0)\": -0.258, \"(27700.0, 34450.0)\": 0.165, \"(34450.0, 42200.0)\": 1.188, \"(42200.0, 56500.0)\": 2.441, \"(56500.0, 66050.0)\": 4.0, \"(66050.0, 74000.0)\": 2.853, \"(74000.0, 95500.0)\": 4.001, \"(95500.0, 104500.0)\": 1.113, \"(104500.0, 144000.0)\": 0.258, \"(144000.0, 150500.0)\": -0.049, \"(150500.0, 154000.0)\": 3.518, \"(154000.0, 169000.0)\": 0.911, \"(169000.0, 184500.0)\": -0.702, \"(184500.0, 195000.0)\": 2.008, \"(195000.0, 199000.0)\": 5.781, \"(199000.0, 200500.0)\": 3.093, \"(200500.0, 214000.0)\": 1.058, \"(214000.0, 217500.0)\": 0.356, \"(217500.0, 218500.0)\": 0.785, \"(218500.0, 220500.0)\": 0.606, \"(220500.0, 222500.0)\": 1.711, \"(222500.0, 226500.0)\": 3.206, \"(226500.0, 241500.0)\": 0.472, \"(241500.0, 242500.0)\": 1.064, \"(242500.0, 243500.0)\": 2.099, \"(243500.0, 244500.0)\": 2.766, \"(244500.0, 252500.0)\": -0.372, \"(252500.0, 261000.0)\": -0.468, \"(261000.0, 274500.0)\": 0.176, \"(274500.0, 283500.0)\": -0.106, \"(283500.0, 287500.0)\": 1.993, \"(287500.0, 289500.0)\": 3.014, \"(289500.0, 302500.0)\": -0.734, \"(302500.0, 305500.0)\": -1.767, \"(305500.0, 307000.0)\": 1.626, \"(307000.0, 332000.0)\": 0.917, \"(332000.0, 335000.0)\": 2.599, \"(335000.0, 343000.0)\": 1.496, \"(343000.0, 350500.0)\": 0.324, \"(350500.0, 354500.0)\": 4.251, \"(354500.0, 383500.0)\": 0.609, \"(383500.0, 449500.0)\": 1.068, \"(449500.0, 471000.0)\": 5.088, \"(471000.0, 500500.0)\": 0.684, \"(500500.0, 582000.0)\": 0.912, \"(582000.0, 675500.0)\": 3.789, \"(675500.0, 796000.0)\": 4.525, \"(796000.0, 850000.0)\": 0.138}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: serum_creatinine\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.5, 0.6499999999999999)\": -0.26, \"(0.6499999999999999, 0.725)\": -1.08, \"(0.725, 0.875)\": -3.77, \"(0.875, 0.95)\": -0.9, \"(0.95, 1.1400000000000001)\": -0.15, \"(1.1400000000000001, 1.35)\": -0.88, \"(1.35, 1.45)\": 0.2, \"(1.45, 1.55)\": 1.18, \"(1.55, 1.815)\": 2.18, \"(1.815, 2.05)\": 4.74, \"(2.05, 2.45)\": 1.14, \"(2.45, 2.6)\": 3.63, \"(2.6, 2.95)\": -0.36, \"(2.95, 3.1)\": 2.57, \"(3.1, 3.45)\": 0.36, \"(3.45, 3.6)\": 3.06, \"(3.6, 3.75)\": 6.76, \"(3.75, 3.9)\": 2.31, \"(3.9, 4.7)\": 2.92, \"(4.7, 5.949999999999999)\": 0.76, \"(5.949999999999999, 6.199999999999999)\": -0.43, \"(6.199999999999999, 6.55)\": 0.23, \"(6.55, 9.4)\": 6.97}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.5, 0.6499999999999999)\": -1.13, \"(0.6499999999999999, 0.725)\": -1.45, \"(0.725, 0.875)\": -5.7, \"(0.875, 0.95)\": -1.31, \"(0.95, 1.1400000000000001)\": -0.41, \"(1.1400000000000001, 1.35)\": -1.92, \"(1.35, 1.45)\": -0.14, \"(1.45, 1.55)\": 0.46, \"(1.55, 1.815)\": 1.68, \"(1.815, 2.05)\": 2.75, \"(2.05, 2.45)\": 0.72, \"(2.45, 2.6)\": 1.94, \"(2.6, 2.95)\": -2.5, \"(2.95, 3.1)\": 0.3, \"(3.1, 3.45)\": -0.49, \"(3.45, 3.6)\": 1.58, \"(3.6, 3.75)\": 4.55, \"(3.75, 3.9)\": 0.4, \"(3.9, 4.7)\": 0.8, \"(4.7, 5.949999999999999)\": -0.63, \"(5.949999999999999, 6.199999999999999)\": -1.75, \"(6.199999999999999, 6.55)\": -2.74, \"(6.55, 9.4)\": 5.07}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.5, 0.6499999999999999)\": 0.62, \"(0.6499999999999999, 0.725)\": -0.72, \"(0.725, 0.875)\": -1.84, \"(0.875, 0.95)\": -0.48, \"(0.95, 1.1400000000000001)\": 0.12, \"(1.1400000000000001, 1.35)\": 0.16, \"(1.35, 1.45)\": 0.53, \"(1.45, 1.55)\": 1.89, \"(1.55, 1.815)\": 2.68, \"(1.815, 2.05)\": 6.73, \"(2.05, 2.45)\": 1.56, \"(2.45, 2.6)\": 5.32, \"(2.6, 2.95)\": 1.77, \"(2.95, 3.1)\": 4.84, \"(3.1, 3.45)\": 1.2, \"(3.45, 3.6)\": 4.53, \"(3.6, 3.75)\": 8.97, \"(3.75, 3.9)\": 4.21, \"(3.9, 4.7)\": 5.04, \"(4.7, 5.949999999999999)\": 2.14, \"(5.949999999999999, 6.199999999999999)\": 0.9, \"(6.199999999999999, 6.55)\": 3.21, \"(6.55, 9.4)\": 8.88}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: serum_sodium\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(113.0, 114.5)\": -1.269, \"(114.5, 118.5)\": 0.283, \"(118.5, 124.5)\": 3.539, \"(124.5, 126.5)\": 2.46, \"(126.5, 127.5)\": 4.042, \"(127.5, 129.5)\": 3.553, \"(129.5, 130.5)\": 0.953, \"(130.5, 132.5)\": 1.22, \"(132.5, 133.5)\": -1.094, \"(133.5, 135.5)\": 0.587, \"(135.5, 138.5)\": -0.629, \"(138.5, 144.5)\": -0.233, \"(144.5, 148.0)\": 0.113}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(113.0, 114.5)\": -3.483, \"(114.5, 118.5)\": -4.768, \"(118.5, 124.5)\": 2.536, \"(124.5, 126.5)\": 1.699, \"(126.5, 127.5)\": 3.034, \"(127.5, 129.5)\": 2.614, \"(129.5, 130.5)\": 0.389, \"(130.5, 132.5)\": 0.304, \"(132.5, 133.5)\": -2.269, \"(133.5, 135.5)\": 0.366, \"(135.5, 138.5)\": -0.879, \"(138.5, 144.5)\": -0.845, \"(144.5, 148.0)\": -0.129}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(113.0, 114.5)\": 0.944, \"(114.5, 118.5)\": 5.334, \"(118.5, 124.5)\": 4.542, \"(124.5, 126.5)\": 3.222, \"(126.5, 127.5)\": 5.05, \"(127.5, 129.5)\": 4.492, \"(129.5, 130.5)\": 1.517, \"(130.5, 132.5)\": 2.136, \"(132.5, 133.5)\": 0.08, \"(133.5, 135.5)\": 0.808, \"(135.5, 138.5)\": -0.38, \"(138.5, 144.5)\": 0.38, \"(144.5, 148.0)\": 0.354}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: sex\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": 0.01719, \"(0.5, 1.0)\": -0.00954}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.08236, \"(0.5, 1.0)\": -0.06482}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.11675, \"(0.5, 1.0)\": 0.04573}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: smoking\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": 0.01522, \"(0.5, 1.0)\": -0.03391}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.0422, \"(0.5, 1.0)\": -0.16186}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.07264, \"(0.5, 1.0)\": 0.09404}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: time\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(4.0, 11.5)\": 10.73, \"(11.5, 12.5)\": 1.29, \"(12.5, 15.5)\": 3.88, \"(15.5, 18.0)\": 2.22, \"(18.0, 28.5)\": 6.17, \"(28.5, 30.5)\": 4.47, \"(30.5, 52.0)\": 5.56, \"(52.0, 54.5)\": 3.38, \"(54.5, 67.5)\": 4.79, \"(67.5, 73.5)\": 2.76, \"(73.5, 76.5)\": -3.15, \"(76.5, 78.5)\": 2.29, \"(78.5, 82.5)\": -0.16, \"(82.5, 87.5)\": -2.8, \"(87.5, 90.5)\": 0.19, \"(90.5, 92.5)\": -1.08, \"(92.5, 95.5)\": -2.7, \"(95.5, 108.5)\": -0.98, \"(108.5, 117.5)\": 0.02, \"(117.5, 124.5)\": -3.44, \"(124.5, 137.5)\": 0.64, \"(137.5, 149.0)\": -0.8, \"(149.0, 171.5)\": 5.06, \"(171.5, 173.0)\": 2.66, \"(173.0, 182.5)\": -0.84, \"(182.5, 192.5)\": -3.42, \"(192.5, 193.5)\": -1.01, \"(193.5, 253.0)\": -2.58, \"(253.0, 285.0)\": -8.42}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(4.0, 11.5)\": 8.45, \"(11.5, 12.5)\": 0.25, \"(12.5, 15.5)\": 2.94, \"(15.5, 18.0)\": -0.25, \"(18.0, 28.5)\": 4.04, \"(28.5, 30.5)\": 3.69, \"(30.5, 52.0)\": 4.21, \"(52.0, 54.5)\": 1.74, \"(54.5, 67.5)\": 3.17, \"(67.5, 73.5)\": 1.96, \"(73.5, 76.5)\": -4.69, \"(76.5, 78.5)\": 1.19, \"(78.5, 82.5)\": -1.25, \"(82.5, 87.5)\": -3.84, \"(87.5, 90.5)\": -0.35, \"(90.5, 92.5)\": -2.75, \"(92.5, 95.5)\": -4.6, \"(95.5, 108.5)\": -1.62, \"(108.5, 117.5)\": -0.66, \"(117.5, 124.5)\": -4.94, \"(124.5, 137.5)\": -0.24, \"(137.5, 149.0)\": -1.83, \"(149.0, 171.5)\": 3.59, \"(171.5, 173.0)\": 1.61, \"(173.0, 182.5)\": -1.86, \"(182.5, 192.5)\": -4.51, \"(192.5, 193.5)\": -1.89, \"(193.5, 253.0)\": -4.11, \"(253.0, 285.0)\": -10.7}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(4.0, 11.5)\": 13.0, \"(11.5, 12.5)\": 2.32, \"(12.5, 15.5)\": 4.82, \"(15.5, 18.0)\": 4.68, \"(18.0, 28.5)\": 8.31, \"(28.5, 30.5)\": 5.26, \"(30.5, 52.0)\": 6.91, \"(52.0, 54.5)\": 5.03, \"(54.5, 67.5)\": 6.41, \"(67.5, 73.5)\": 3.57, \"(73.5, 76.5)\": -1.61, \"(76.5, 78.5)\": 3.39, \"(78.5, 82.5)\": 0.92, \"(82.5, 87.5)\": -1.75, \"(87.5, 90.5)\": 0.72, \"(90.5, 92.5)\": 0.6, \"(92.5, 95.5)\": -0.81, \"(95.5, 108.5)\": -0.34, \"(108.5, 117.5)\": 0.7, \"(117.5, 124.5)\": -1.93, \"(124.5, 137.5)\": 1.53, \"(137.5, 149.0)\": 0.22, \"(149.0, 171.5)\": 6.52, \"(171.5, 173.0)\": 3.72, \"(173.0, 182.5)\": 0.18, \"(182.5, 192.5)\": -2.33, \"(192.5, 193.5)\": -0.13, \"(193.5, 253.0)\": -1.06, \"(253.0, 285.0)\": -6.14}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: id\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(8.0, 349.5)\": -0.1954, \"(349.5, 1899.5)\": -0.1448, \"(1899.5, 4908.5)\": -0.18, \"(4908.5, 5578.5)\": -0.2082, \"(5578.5, 5813.5)\": -0.25, \"(5813.5, 6004.5)\": -0.345, \"(6004.5, 7170.5)\": -0.1246, \"(7170.5, 7335.5)\": 0.0378, \"(7335.5, 8083.0)\": 0.1773, \"(8083.0, 8604.0)\": 0.1221, \"(8604.0, 8759.0)\": -0.0027, \"(8759.0, 45049.5)\": -0.0395, \"(45049.5, 45346.5)\": -0.3688, \"(45346.5, 46184.5)\": -0.0125, \"(46184.5, 54575.0)\": 0.0215, \"(54575.0, 55661.5)\": -0.0521, \"(55661.5, 66954.0)\": 0.0101, \"(66954.0, 67057.0)\": -0.0227, \"(67057.0, 68275.0)\": 0.0595, \"(68275.0, 97577.5)\": 0.0244, \"(97577.5, 110643.5)\": 0.0529, \"(110643.5, 146554.5)\": 0.0211, \"(146554.5, 146921.5)\": -0.0139, \"(146921.5, 147131.5)\": -0.0861, \"(147131.5, 161901.5)\": -0.0139, \"(161901.5, 162437.5)\": -0.0745, \"(162437.5, 164212.5)\": -0.0061, \"(164212.5, 164569.5)\": -0.057, \"(164569.5, 164786.5)\": 0.0766, \"(164786.5, 165030.0)\": 0.1394}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(8.0, 349.5)\": -0.513, \"(349.5, 1899.5)\": -0.2797, \"(1899.5, 4908.5)\": -0.4617, \"(4908.5, 5578.5)\": -0.3743, \"(5578.5, 5813.5)\": -0.5275, \"(5813.5, 6004.5)\": -0.9752, \"(6004.5, 7170.5)\": -0.3878, \"(7170.5, 7335.5)\": -0.2528, \"(7335.5, 8083.0)\": -0.2278, \"(8083.0, 8604.0)\": -0.157, \"(8604.0, 8759.0)\": -0.2655, \"(8759.0, 45049.5)\": -0.1283, \"(45049.5, 45346.5)\": -1.0587, \"(45346.5, 46184.5)\": -0.1339, \"(46184.5, 54575.0)\": -0.1154, \"(54575.0, 55661.5)\": -0.241, \"(55661.5, 66954.0)\": -0.0113, \"(66954.0, 67057.0)\": -0.3245, \"(67057.0, 68275.0)\": -0.2172, \"(68275.0, 97577.5)\": -0.0853, \"(97577.5, 110643.5)\": -0.0258, \"(110643.5, 146554.5)\": -0.1398, \"(146554.5, 146921.5)\": -0.0713, \"(146921.5, 147131.5)\": -0.6452, \"(147131.5, 161901.5)\": -0.1222, \"(161901.5, 162437.5)\": -0.3435, \"(162437.5, 164212.5)\": -0.1164, \"(164212.5, 164569.5)\": -0.2376, \"(164569.5, 164786.5)\": -0.3913, \"(164786.5, 165030.0)\": -0.3304}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(8.0, 349.5)\": 0.1221, \"(349.5, 1899.5)\": -0.0099, \"(1899.5, 4908.5)\": 0.1016, \"(4908.5, 5578.5)\": -0.0421, \"(5578.5, 5813.5)\": 0.0274, \"(5813.5, 6004.5)\": 0.2852, \"(6004.5, 7170.5)\": 0.1385, \"(7170.5, 7335.5)\": 0.3284, \"(7335.5, 8083.0)\": 0.5824, \"(8083.0, 8604.0)\": 0.4011, \"(8604.0, 8759.0)\": 0.2602, \"(8759.0, 45049.5)\": 0.0493, \"(45049.5, 45346.5)\": 0.321, \"(45346.5, 46184.5)\": 0.1088, \"(46184.5, 54575.0)\": 0.1583, \"(54575.0, 55661.5)\": 0.1369, \"(55661.5, 66954.0)\": 0.0316, \"(66954.0, 67057.0)\": 0.2791, \"(67057.0, 68275.0)\": 0.3361, \"(68275.0, 97577.5)\": 0.1341, \"(97577.5, 110643.5)\": 0.1316, \"(110643.5, 146554.5)\": 0.182, \"(146554.5, 146921.5)\": 0.0435, \"(146921.5, 147131.5)\": 0.4731, \"(147131.5, 161901.5)\": 0.0945, \"(161901.5, 162437.5)\": 0.1945, \"(162437.5, 164212.5)\": 0.1042, \"(164212.5, 164569.5)\": 0.1235, \"(164569.5, 164786.5)\": 0.5445, \"(164786.5, 165030.0)\": 0.6091}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: CustomerId\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(15565796.0, 15566519.0)\": -0.8769, \"(15566519.0, 15567333.5)\": -0.8241, \"(15567333.5, 15567844.5)\": -0.1763, \"(15567844.5, 15568343.5)\": 0.0021, \"(15568343.5, 15571612.0)\": -0.2283, \"(15571612.0, 15571858.5)\": -0.0522, \"(15571858.5, 15591260.5)\": -0.1299, \"(15591260.5, 15598058.0)\": -0.0821, \"(15598058.0, 15602525.5)\": -0.1509, \"(15602525.5, 15607288.0)\": -0.0818, \"(15607288.0, 15664896.0)\": -0.0316, \"(15664896.0, 15772587.0)\": 0.0162, \"(15772587.0, 15797097.0)\": 0.0757, \"(15797097.0, 15799214.0)\": 0.0081, \"(15799214.0, 15807559.5)\": 0.0581, \"(15807559.5, 15812616.5)\": -0.0049, \"(15812616.5, 15814479.0)\": -0.0569, \"(15814479.0, 15815247.5)\": -0.111, \"(15815247.5, 15815626.0)\": -0.0335}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(15565796.0, 15566519.0)\": -1.3796, \"(15566519.0, 15567333.5)\": -1.4199, \"(15567333.5, 15567844.5)\": -0.741, \"(15567844.5, 15568343.5)\": -0.4552, \"(15568343.5, 15571612.0)\": -0.4861, \"(15571612.0, 15571858.5)\": -0.3268, \"(15571858.5, 15591260.5)\": -0.2064, \"(15591260.5, 15598058.0)\": -0.1582, \"(15598058.0, 15602525.5)\": -0.5056, \"(15602525.5, 15607288.0)\": -0.1812, \"(15607288.0, 15664896.0)\": -0.056, \"(15664896.0, 15772587.0)\": -0.142, \"(15772587.0, 15797097.0)\": -0.0689, \"(15797097.0, 15799214.0)\": -0.206, \"(15799214.0, 15807559.5)\": -0.0544, \"(15807559.5, 15812616.5)\": -0.1396, \"(15812616.5, 15814479.0)\": -0.2475, \"(15814479.0, 15815247.5)\": -0.4076, \"(15815247.5, 15815626.0)\": -0.3716}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(15565796.0, 15566519.0)\": -0.3742, \"(15566519.0, 15567333.5)\": -0.2283, \"(15567333.5, 15567844.5)\": 0.3884, \"(15567844.5, 15568343.5)\": 0.4594, \"(15568343.5, 15571612.0)\": 0.0295, \"(15571612.0, 15571858.5)\": 0.2223, \"(15571858.5, 15591260.5)\": -0.0535, \"(15591260.5, 15598058.0)\": -0.0061, \"(15598058.0, 15602525.5)\": 0.2038, \"(15602525.5, 15607288.0)\": 0.0176, \"(15607288.0, 15664896.0)\": -0.0071, \"(15664896.0, 15772587.0)\": 0.1744, \"(15772587.0, 15797097.0)\": 0.2202, \"(15797097.0, 15799214.0)\": 0.2223, \"(15799214.0, 15807559.5)\": 0.1706, \"(15807559.5, 15812616.5)\": 0.1298, \"(15812616.5, 15814479.0)\": 0.1336, \"(15814479.0, 15815247.5)\": 0.1855, \"(15815247.5, 15815626.0)\": 0.3046}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: CreditScore\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(350.0, 416.5)\": 0.62, \"(416.5, 421.5)\": 0.5698, \"(421.5, 427.5)\": 0.3799, \"(427.5, 437.5)\": 0.2757, \"(437.5, 464.5)\": 0.3274, \"(464.5, 470.5)\": 0.2778, \"(470.5, 477.5)\": 0.4561, \"(477.5, 478.5)\": 0.0595, \"(478.5, 494.5)\": 0.1431, \"(494.5, 515.5)\": 0.0909, \"(515.5, 523.5)\": -0.3342, \"(523.5, 539.5)\": -0.2192, \"(539.5, 566.5)\": -0.1337, \"(566.5, 598.5)\": -0.0838, \"(598.5, 661.5)\": -0.0327, \"(661.5, 684.5)\": 0.0186, \"(684.5, 741.5)\": 0.0696, \"(741.5, 769.5)\": 0.0206, \"(769.5, 792.5)\": 0.0691, \"(792.5, 805.5)\": 0.2231, \"(805.5, 806.5)\": 0.1131, \"(806.5, 850.0)\": -0.1138}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(350.0, 416.5)\": -0.0945, \"(416.5, 421.5)\": -0.1033, \"(421.5, 427.5)\": -0.0186, \"(427.5, 437.5)\": -0.1491, \"(437.5, 464.5)\": 0.0705, \"(464.5, 470.5)\": -0.0008, \"(470.5, 477.5)\": -0.0519, \"(477.5, 478.5)\": -0.3016, \"(478.5, 494.5)\": 0.0126, \"(494.5, 515.5)\": -0.1354, \"(515.5, 523.5)\": -0.5637, \"(523.5, 539.5)\": -0.3225, \"(539.5, 566.5)\": -0.2064, \"(566.5, 598.5)\": -0.1252, \"(598.5, 661.5)\": -0.1126, \"(661.5, 684.5)\": -0.0289, \"(684.5, 741.5)\": -0.0156, \"(741.5, 769.5)\": -0.0756, \"(769.5, 792.5)\": -0.016, \"(792.5, 805.5)\": -0.0471, \"(805.5, 806.5)\": -0.2533, \"(806.5, 850.0)\": -0.3888}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(350.0, 416.5)\": 1.3346, \"(416.5, 421.5)\": 1.243, \"(421.5, 427.5)\": 0.7784, \"(427.5, 437.5)\": 0.7005, \"(437.5, 464.5)\": 0.5843, \"(464.5, 470.5)\": 0.5564, \"(470.5, 477.5)\": 0.9641, \"(477.5, 478.5)\": 0.4206, \"(478.5, 494.5)\": 0.2736, \"(494.5, 515.5)\": 0.3173, \"(515.5, 523.5)\": -0.1047, \"(523.5, 539.5)\": -0.1159, \"(539.5, 566.5)\": -0.061, \"(566.5, 598.5)\": -0.0424, \"(598.5, 661.5)\": 0.0472, \"(661.5, 684.5)\": 0.066, \"(684.5, 741.5)\": 0.1548, \"(741.5, 769.5)\": 0.1168, \"(769.5, 792.5)\": 0.1542, \"(792.5, 805.5)\": 0.4932, \"(805.5, 806.5)\": 0.4795, \"(806.5, 850.0)\": 0.1611}\n",
+      "\n",
+      "This graph represents categorical feature. Each key represents a possible value that the feature can take.\n",
+      "\n",
+      "Feature Name: Geography\n",
+      "Feature Type: categorical\n",
+      "Means: {\"France\": 0.2098, \"Germany\": -0.6619, \"Spain\": 0.1099}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"France\": 0.1772, \"Germany\": -0.7675, \"Spain\": 0.049}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"France\": 0.2423, \"Germany\": -0.5563, \"Spain\": 0.1707}\n",
+      "\n",
+      "This graph represents categorical feature. Each key represents a possible value that the feature can take.\n",
+      "\n",
+      "Feature Name: Gender\n",
+      "Feature Type: categorical\n",
+      "Means: {\"Female\": -0.3479, \"Male\": 0.2607}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"Female\": -0.3755, \"Male\": 0.2401}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"Female\": -0.3204, \"Male\": 0.2814}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: Age\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(18.0, 32.5)\": 0.83, \"(32.5, 34.5)\": 0.681, \"(34.5, 37.5)\": 0.423, \"(37.5, 38.5)\": 0.281, \"(38.5, 39.5)\": 0.054, \"(39.5, 40.5)\": -0.193, \"(40.5, 41.5)\": -0.354, \"(41.5, 42.5)\": -0.494, \"(42.5, 44.5)\": -0.781, \"(44.5, 46.5)\": -1.075, \"(46.5, 48.5)\": -1.546, \"(48.5, 54.5)\": -1.717, \"(54.5, 56.5)\": -1.858, \"(56.5, 64.5)\": -1.707, \"(64.5, 66.5)\": -1.27, \"(66.5, 69.5)\": -1.118, \"(69.5, 70.5)\": -0.888, \"(70.5, 72.5)\": -0.587, \"(72.5, 74.5)\": -0.31, \"(74.5, 81.0)\": -0.157}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(18.0, 32.5)\": 0.581, \"(32.5, 34.5)\": 0.529, \"(34.5, 37.5)\": 0.367, \"(37.5, 38.5)\": 0.229, \"(38.5, 39.5)\": -0.051, \"(39.5, 40.5)\": -0.305, \"(40.5, 41.5)\": -0.462, \"(41.5, 42.5)\": -0.607, \"(42.5, 44.5)\": -0.855, \"(44.5, 46.5)\": -1.16, \"(46.5, 48.5)\": -1.704, \"(48.5, 54.5)\": -1.885, \"(54.5, 56.5)\": -2.031, \"(56.5, 64.5)\": -1.913, \"(64.5, 66.5)\": -1.66, \"(66.5, 69.5)\": -1.33, \"(69.5, 70.5)\": -1.222, \"(70.5, 72.5)\": -1.257, \"(72.5, 74.5)\": -1.055, \"(74.5, 81.0)\": -0.939}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(18.0, 32.5)\": 1.079, \"(32.5, 34.5)\": 0.833, \"(34.5, 37.5)\": 0.48, \"(37.5, 38.5)\": 0.332, \"(38.5, 39.5)\": 0.159, \"(39.5, 40.5)\": -0.08, \"(40.5, 41.5)\": -0.246, \"(41.5, 42.5)\": -0.382, \"(42.5, 44.5)\": -0.706, \"(44.5, 46.5)\": -0.991, \"(46.5, 48.5)\": -1.387, \"(48.5, 54.5)\": -1.548, \"(54.5, 56.5)\": -1.684, \"(56.5, 64.5)\": -1.501, \"(64.5, 66.5)\": -0.88, \"(66.5, 69.5)\": -0.906, \"(69.5, 70.5)\": -0.554, \"(70.5, 72.5)\": 0.082, \"(72.5, 74.5)\": 0.436, \"(74.5, 81.0)\": 0.625}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: Tenure\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": -0.3765, \"(0.5, 1.5)\": -0.0692, \"(1.5, 4.5)\": -0.016, \"(4.5, 5.5)\": 0.0109, \"(5.5, 6.5)\": 0.0432, \"(6.5, 7.5)\": 0.0871, \"(7.5, 9.5)\": 0.0554, \"(9.5, 10.0)\": -0.0599}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.4596, \"(0.5, 1.5)\": -0.1046, \"(1.5, 4.5)\": -0.0506, \"(4.5, 5.5)\": -0.017, \"(5.5, 6.5)\": 0.014, \"(6.5, 7.5)\": 0.0581, \"(7.5, 9.5)\": 0.004, \"(9.5, 10.0)\": -0.1542}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.2933, \"(0.5, 1.5)\": -0.0338, \"(1.5, 4.5)\": 0.0185, \"(4.5, 5.5)\": 0.0387, \"(5.5, 6.5)\": 0.0724, \"(6.5, 7.5)\": 0.1161, \"(7.5, 9.5)\": 0.1067, \"(9.5, 10.0)\": 0.0343}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: Balance\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 50418.515)\": -0.132, \"(50418.515, 53570.93)\": -0.285, \"(53570.93, 54249.445)\": -0.826, \"(54249.445, 57428.56)\": -0.404, \"(57428.56, 60041.265)\": -0.005, \"(60041.265, 64897.8)\": 0.215, \"(64897.8, 72985.875)\": 0.086, \"(72985.875, 74989.08499999999)\": -0.012, \"(74989.08499999999, 76596.815)\": 0.247, \"(76596.815, 79953.185)\": 0.829, \"(79953.185, 83348.07)\": 0.564, \"(83348.07, 101890.23999999999)\": 0.414, \"(101890.23999999999, 114327.485)\": 0.248, \"(114327.485, 123946.3)\": 0.164, \"(123946.3, 141661.24)\": 0.075, \"(141661.24, 174920.08000000002)\": 0.173, \"(174920.08000000002, 181813.135)\": 0.059, \"(181813.135, 191993.675)\": -0.349, \"(191993.675, 200829.925)\": -0.459, \"(200829.925, 206951.87)\": -0.616, \"(206951.87, 216109.88)\": -0.256}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 50418.515)\": -0.192, \"(50418.515, 53570.93)\": -0.628, \"(53570.93, 54249.445)\": -1.999, \"(54249.445, 57428.56)\": -0.798, \"(57428.56, 60041.265)\": -0.322, \"(60041.265, 64897.8)\": -0.105, \"(64897.8, 72985.875)\": -0.195, \"(72985.875, 74989.08499999999)\": -0.418, \"(74989.08499999999, 76596.815)\": -0.231, \"(76596.815, 79953.185)\": 0.338, \"(79953.185, 83348.07)\": 0.321, \"(83348.07, 101890.23999999999)\": 0.247, \"(101890.23999999999, 114327.485)\": 0.097, \"(114327.485, 123946.3)\": 0.069, \"(123946.3, 141661.24)\": -0.23, \"(141661.24, 174920.08000000002)\": -0.272, \"(174920.08000000002, 181813.135)\": -0.147, \"(181813.135, 191993.675)\": -0.864, \"(191993.675, 200829.925)\": -0.991, \"(200829.925, 206951.87)\": -1.401, \"(206951.87, 216109.88)\": -0.862}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 50418.515)\": -0.072, \"(50418.515, 53570.93)\": 0.057, \"(53570.93, 54249.445)\": 0.347, \"(54249.445, 57428.56)\": -0.011, \"(57428.56, 60041.265)\": 0.312, \"(60041.265, 64897.8)\": 0.534, \"(64897.8, 72985.875)\": 0.367, \"(72985.875, 74989.08499999999)\": 0.395, \"(74989.08499999999, 76596.815)\": 0.725, \"(76596.815, 79953.185)\": 1.32, \"(79953.185, 83348.07)\": 0.806, \"(83348.07, 101890.23999999999)\": 0.582, \"(101890.23999999999, 114327.485)\": 0.398, \"(114327.485, 123946.3)\": 0.259, \"(123946.3, 141661.24)\": 0.379, \"(141661.24, 174920.08000000002)\": 0.618, \"(174920.08000000002, 181813.135)\": 0.264, \"(181813.135, 191993.675)\": 0.166, \"(191993.675, 200829.925)\": 0.073, \"(200829.925, 206951.87)\": 0.169, \"(206951.87, 216109.88)\": 0.35}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: NumOfProducts\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(1.0, 1.5)\": -0.918, \"(1.5, 2.5)\": 0.96, \"(2.5, 3.5)\": -3.104, \"(3.5, 4.0)\": -2.768}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -0.985, \"(1.5, 2.5)\": 0.893, \"(2.5, 3.5)\": -3.482, \"(3.5, 4.0)\": -3.159}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(1.0, 1.5)\": -0.852, \"(1.5, 2.5)\": 1.028, \"(2.5, 3.5)\": -2.727, \"(3.5, 4.0)\": -2.376}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: HasCrCard\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": -0.004421, \"(0.5, 1.0)\": 0.001379}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.037941, \"(0.5, 1.0)\": -0.009076}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": 0.0291, \"(0.5, 1.0)\": 0.011834}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: IsActiveMember\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.5)\": -0.555, \"(0.5, 1.0)\": 0.568}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.593, \"(0.5, 1.0)\": 0.529}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.5)\": -0.518, \"(0.5, 1.0)\": 0.606}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: EstimatedSalary\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(106.67, 780.2149999999999)\": 0.3865, \"(780.2149999999999, 4627.98)\": 0.3462, \"(4627.98, 6842.475)\": 0.0858, \"(6842.475, 7401.88)\": 0.157, \"(7401.88, 27330.43)\": 0.2048, \"(27330.43, 38816.375)\": 0.1737, \"(38816.375, 40348.645000000004)\": 0.1063, \"(40348.645000000004, 42807.509999999995)\": 0.0512, \"(42807.509999999995, 48226.81)\": 0.1098, \"(48226.81, 48498.15)\": -0.0771, \"(48498.15, 58535.68)\": 0.0187, \"(58535.68, 94498.98999999999)\": 0.0512, \"(94498.98999999999, 120892.955)\": 0.0186, \"(120892.955, 121151.28)\": -0.0263, \"(121151.28, 121482.61499999999)\": -0.0801, \"(121482.61499999999, 148569.97)\": -0.0388, \"(148569.97, 184522.325)\": -0.0796, \"(184522.325, 187947.635)\": -0.1332, \"(187947.635, 187985.865)\": -0.2342, \"(187985.865, 188452.565)\": -0.0632, \"(188452.565, 189006.61)\": -0.0053, \"(189006.61, 196418.97999999998)\": 0.0291, \"(196418.97999999998, 199505.41)\": -0.0098, \"(199505.41, 199992.48)\": 0.214}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(106.67, 780.2149999999999)\": 0.0871, \"(780.2149999999999, 4627.98)\": 0.1468, \"(4627.98, 6842.475)\": -0.2734, \"(6842.475, 7401.88)\": -0.01, \"(7401.88, 27330.43)\": 0.0941, \"(27330.43, 38816.375)\": 0.065, \"(38816.375, 40348.645000000004)\": -0.0568, \"(40348.645000000004, 42807.509999999995)\": -0.1427, \"(42807.509999999995, 48226.81)\": 0.0015, \"(48226.81, 48498.15)\": -0.404, \"(48498.15, 58535.68)\": -0.1286, \"(58535.68, 94498.98999999999)\": -0.003, \"(94498.98999999999, 120892.955)\": -0.0541, \"(120892.955, 121151.28)\": -0.186, \"(121151.28, 121482.61499999999)\": -0.2842, \"(121482.61499999999, 148569.97)\": -0.1593, \"(148569.97, 184522.325)\": -0.1401, \"(184522.325, 187947.635)\": -0.216, \"(187947.635, 187985.865)\": -0.7523, \"(187985.865, 188452.565)\": -0.2404, \"(188452.565, 189006.61)\": -0.1779, \"(189006.61, 196418.97999999998)\": -0.1285, \"(196418.97999999998, 199505.41)\": -0.2064, \"(199505.41, 199992.48)\": -0.3318}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(106.67, 780.2149999999999)\": 0.6859, \"(780.2149999999999, 4627.98)\": 0.5457, \"(4627.98, 6842.475)\": 0.445, \"(6842.475, 7401.88)\": 0.3239, \"(7401.88, 27330.43)\": 0.3154, \"(27330.43, 38816.375)\": 0.2823, \"(38816.375, 40348.645000000004)\": 0.2695, \"(40348.645000000004, 42807.509999999995)\": 0.2451, \"(42807.509999999995, 48226.81)\": 0.2181, \"(48226.81, 48498.15)\": 0.2497, \"(48498.15, 58535.68)\": 0.166, \"(58535.68, 94498.98999999999)\": 0.1054, \"(94498.98999999999, 120892.955)\": 0.0913, \"(120892.955, 121151.28)\": 0.1335, \"(121151.28, 121482.61499999999)\": 0.1239, \"(121482.61499999999, 148569.97)\": 0.0817, \"(148569.97, 184522.325)\": -0.019, \"(184522.325, 187947.635)\": -0.0504, \"(187947.635, 187985.865)\": 0.2839, \"(187985.865, 188452.565)\": 0.1139, \"(188452.565, 189006.61)\": 0.1673, \"(189006.61, 196418.97999999998)\": 0.1867, \"(196418.97999999998, 199505.41)\": 0.1868, \"(199505.41, 199992.48)\": 0.7597}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: id\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(8670.0, 90271.0)\": 0.342, \"(90271.0, 467526.5)\": 0.574, \"(467526.5, 853506.5)\": 0.657, \"(853506.5, 859643.0)\": 0.719, \"(859643.0, 864727.5)\": 0.655, \"(864727.5, 871421.0)\": 0.593, \"(871421.0, 874848.5)\": 0.528, \"(874848.5, 880845.5)\": 0.464, \"(880845.5, 882230.0)\": 0.399, \"(882230.0, 883266.5)\": 0.319, \"(883266.5, 889561.0)\": 0.171, \"(889561.0, 892521.0)\": 0.103, \"(892521.0, 894330.5)\": 0.039, \"(894330.5, 896851.5)\": -0.023, \"(896851.5, 899167.0)\": -0.107, \"(899167.0, 902138.0)\": -0.176, \"(902138.0, 905080.5)\": -0.241, \"(905080.5, 906551.5)\": -0.305, \"(906551.5, 911540.5)\": -0.368, \"(911540.5, 917896.5)\": -0.431, \"(917896.5, 8810615.5)\": -0.493, \"(8810615.5, 9112480.5)\": -0.386, \"(9112480.5, 89803401.5)\": -0.323, \"(89803401.5, 91544001.5)\": -0.259, \"(91544001.5, 91903901.5)\": -0.191, \"(91903901.5, 911320502.0)\": -0.121}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(8670.0, 90271.0)\": -0.06, \"(90271.0, 467526.5)\": 0.079, \"(467526.5, 853506.5)\": 0.101, \"(853506.5, 859643.0)\": 0.139, \"(859643.0, 864727.5)\": 0.076, \"(864727.5, 871421.0)\": 0.038, \"(871421.0, 874848.5)\": 0.005, \"(874848.5, 880845.5)\": -0.028, \"(880845.5, 882230.0)\": -0.061, \"(882230.0, 883266.5)\": -0.137, \"(883266.5, 889561.0)\": -0.14, \"(889561.0, 892521.0)\": -0.196, \"(892521.0, 894330.5)\": -0.267, \"(894330.5, 896851.5)\": -0.331, \"(896851.5, 899167.0)\": -0.426, \"(899167.0, 902138.0)\": -0.541, \"(902138.0, 905080.5)\": -0.608, \"(905080.5, 906551.5)\": -0.738, \"(906551.5, 911540.5)\": -0.807, \"(911540.5, 917896.5)\": -0.885, \"(917896.5, 8810615.5)\": -0.946, \"(8810615.5, 9112480.5)\": -0.664, \"(9112480.5, 89803401.5)\": -0.626, \"(89803401.5, 91544001.5)\": -0.554, \"(91544001.5, 91903901.5)\": -0.463, \"(91903901.5, 911320502.0)\": -0.414}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(8670.0, 90271.0)\": 0.744, \"(90271.0, 467526.5)\": 1.07, \"(467526.5, 853506.5)\": 1.212, \"(853506.5, 859643.0)\": 1.299, \"(859643.0, 864727.5)\": 1.234, \"(864727.5, 871421.0)\": 1.148, \"(871421.0, 874848.5)\": 1.051, \"(874848.5, 880845.5)\": 0.956, \"(880845.5, 882230.0)\": 0.86, \"(882230.0, 883266.5)\": 0.774, \"(883266.5, 889561.0)\": 0.482, \"(889561.0, 892521.0)\": 0.402, \"(892521.0, 894330.5)\": 0.345, \"(894330.5, 896851.5)\": 0.286, \"(896851.5, 899167.0)\": 0.212, \"(899167.0, 902138.0)\": 0.188, \"(902138.0, 905080.5)\": 0.127, \"(905080.5, 906551.5)\": 0.128, \"(906551.5, 911540.5)\": 0.07, \"(911540.5, 917896.5)\": 0.023, \"(917896.5, 8810615.5)\": -0.04, \"(8810615.5, 9112480.5)\": -0.107, \"(9112480.5, 89803401.5)\": -0.021, \"(89803401.5, 91544001.5)\": 0.036, \"(91544001.5, 91903901.5)\": 0.081, \"(91903901.5, 911320502.0)\": 0.171}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: radius_mean\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(6.981, 9.281500000000001)\": -0.762, \"(9.281500000000001, 9.7015)\": -0.659, \"(9.7015, 10.165)\": -0.56, \"(10.165, 10.655000000000001)\": -0.461, \"(10.655000000000001, 12.465)\": -0.36, \"(12.465, 13.39)\": -0.262, \"(13.39, 14.43)\": -0.163, \"(14.43, 14.934999999999999)\": -0.065, \"(14.934999999999999, 15.08)\": 0.037, \"(15.08, 15.815)\": 0.137, \"(15.815, 16.925)\": 0.235, \"(16.925, 17.385)\": 0.394, \"(17.385, 18.0)\": 0.494, \"(18.0, 18.735)\": 0.599, \"(18.735, 19.240000000000002)\": 0.695, \"(19.240000000000002, 19.990000000000002)\": 0.793, \"(19.990000000000002, 20.595)\": 0.891, \"(20.595, 23.240000000000002)\": 0.99, \"(23.240000000000002, 28.11)\": 1.093}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(6.981, 9.281500000000001)\": -1.01, \"(9.281500000000001, 9.7015)\": -0.884, \"(9.7015, 10.165)\": -0.748, \"(10.165, 10.655000000000001)\": -0.611, \"(10.655000000000001, 12.465)\": -0.536, \"(12.465, 13.39)\": -0.396, \"(13.39, 14.43)\": -0.269, \"(14.43, 14.934999999999999)\": -0.226, \"(14.934999999999999, 15.08)\": -0.156, \"(15.08, 15.815)\": -0.059, \"(15.815, 16.925)\": -0.127, \"(16.925, 17.385)\": 0.041, \"(17.385, 18.0)\": 0.136, \"(18.0, 18.735)\": 0.205, \"(18.735, 19.240000000000002)\": 0.283, \"(19.240000000000002, 19.990000000000002)\": 0.385, \"(19.990000000000002, 20.595)\": 0.462, \"(20.595, 23.240000000000002)\": 0.519, \"(23.240000000000002, 28.11)\": 0.611}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(6.981, 9.281500000000001)\": -0.515, \"(9.281500000000001, 9.7015)\": -0.435, \"(9.7015, 10.165)\": -0.373, \"(10.165, 10.655000000000001)\": -0.311, \"(10.655000000000001, 12.465)\": -0.184, \"(12.465, 13.39)\": -0.128, \"(13.39, 14.43)\": -0.057, \"(14.43, 14.934999999999999)\": 0.097, \"(14.934999999999999, 15.08)\": 0.231, \"(15.08, 15.815)\": 0.333, \"(15.815, 16.925)\": 0.597, \"(16.925, 17.385)\": 0.748, \"(17.385, 18.0)\": 0.853, \"(18.0, 18.735)\": 0.993, \"(18.735, 19.240000000000002)\": 1.107, \"(19.240000000000002, 19.990000000000002)\": 1.202, \"(19.990000000000002, 20.595)\": 1.32, \"(20.595, 23.240000000000002)\": 1.461, \"(23.240000000000002, 28.11)\": 1.575}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: texture_mean\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(9.71, 13.24)\": -1.121, \"(13.24, 14.075)\": -1.023, \"(14.075, 14.665)\": -0.921, \"(14.665, 15.010000000000002)\": -0.82, \"(15.010000000000002, 15.485)\": -0.718, \"(15.485, 15.774999999999999)\": -0.623, \"(15.774999999999999, 16.445)\": -0.523, \"(16.445, 17.045)\": -0.422, \"(17.045, 17.665)\": -0.324, \"(17.665, 18.335)\": -0.225, \"(18.335, 18.725)\": -0.129, \"(18.725, 19.075)\": -0.032, \"(19.075, 19.549999999999997)\": 0.063, \"(19.549999999999997, 19.915)\": 0.161, \"(19.915, 20.235)\": 0.26, \"(20.235, 20.8)\": 0.445, \"(20.8, 21.285)\": 0.549, \"(21.285, 33.81)\": 0.68}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(9.71, 13.24)\": -1.583, \"(13.24, 14.075)\": -1.428, \"(14.075, 14.665)\": -1.292, \"(14.665, 15.010000000000002)\": -1.127, \"(15.010000000000002, 15.485)\": -1.018, \"(15.485, 15.774999999999999)\": -0.932, \"(15.774999999999999, 16.445)\": -0.765, \"(16.445, 17.045)\": -0.657, \"(17.045, 17.665)\": -0.537, \"(17.665, 18.335)\": -0.404, \"(18.335, 18.725)\": -0.289, \"(18.725, 19.075)\": -0.203, \"(19.075, 19.549999999999997)\": -0.094, \"(19.549999999999997, 19.915)\": 0.017, \"(19.915, 20.235)\": 0.108, \"(20.235, 20.8)\": -0.11, \"(20.8, 21.285)\": -0.011, \"(21.285, 33.81)\": -0.0}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(9.71, 13.24)\": -0.658, \"(13.24, 14.075)\": -0.619, \"(14.075, 14.665)\": -0.55, \"(14.665, 15.010000000000002)\": -0.512, \"(15.010000000000002, 15.485)\": -0.417, \"(15.485, 15.774999999999999)\": -0.314, \"(15.774999999999999, 16.445)\": -0.282, \"(16.445, 17.045)\": -0.187, \"(17.045, 17.665)\": -0.112, \"(17.665, 18.335)\": -0.045, \"(18.335, 18.725)\": 0.031, \"(18.725, 19.075)\": 0.139, \"(19.075, 19.549999999999997)\": 0.22, \"(19.549999999999997, 19.915)\": 0.306, \"(19.915, 20.235)\": 0.412, \"(20.235, 20.8)\": 0.999, \"(20.8, 21.285)\": 1.109, \"(21.285, 33.81)\": 1.36}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: perimeter_mean\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(43.79, 60.035)\": -0.884, \"(60.035, 63.379999999999995)\": -0.783, \"(63.379999999999995, 66.67)\": -0.681, \"(66.67, 68.965)\": -0.581, \"(68.965, 71.275)\": -0.476, \"(71.275, 78.28)\": -0.369, \"(78.28, 84.015)\": -0.267, \"(84.015, 88.70500000000001)\": -0.166, \"(88.70500000000001, 94.68)\": -0.064, \"(94.68, 100.75)\": 0.035, \"(100.75, 106.75)\": 0.14, \"(106.75, 108.6)\": 0.249, \"(108.6, 112.6)\": 0.407, \"(112.6, 117.45)\": 0.518, \"(117.45, 121.7)\": 0.626, \"(121.7, 128.15)\": 0.73, \"(128.15, 133.25)\": 0.835, \"(133.25, 145.85000000000002)\": 0.936, \"(145.85000000000002, 188.5)\": 1.038}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(43.79, 60.035)\": -1.177, \"(60.035, 63.379999999999995)\": -1.04, \"(63.379999999999995, 66.67)\": -0.892, \"(66.67, 68.965)\": -0.75, \"(68.965, 71.275)\": -0.646, \"(71.275, 78.28)\": -0.532, \"(78.28, 84.015)\": -0.417, \"(84.015, 88.70500000000001)\": -0.316, \"(88.70500000000001, 94.68)\": -0.178, \"(94.68, 100.75)\": -0.201, \"(100.75, 106.75)\": -0.091, \"(106.75, 108.6)\": -0.055, \"(108.6, 112.6)\": 0.049, \"(112.6, 117.45)\": 0.15, \"(117.45, 121.7)\": 0.222, \"(121.7, 128.15)\": 0.282, \"(128.15, 133.25)\": 0.343, \"(133.25, 145.85000000000002)\": 0.427, \"(145.85000000000002, 188.5)\": 0.514}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(43.79, 60.035)\": -0.59, \"(60.035, 63.379999999999995)\": -0.526, \"(63.379999999999995, 66.67)\": -0.471, \"(66.67, 68.965)\": -0.411, \"(68.965, 71.275)\": -0.306, \"(71.275, 78.28)\": -0.206, \"(78.28, 84.015)\": -0.118, \"(84.015, 88.70500000000001)\": -0.017, \"(88.70500000000001, 94.68)\": 0.05, \"(94.68, 100.75)\": 0.271, \"(100.75, 106.75)\": 0.371, \"(106.75, 108.6)\": 0.553, \"(108.6, 112.6)\": 0.766, \"(112.6, 117.45)\": 0.887, \"(117.45, 121.7)\": 1.03, \"(121.7, 128.15)\": 1.179, \"(128.15, 133.25)\": 1.327, \"(133.25, 145.85000000000002)\": 1.444, \"(145.85000000000002, 188.5)\": 1.562}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: area_mean\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(143.5, 259.35)\": -0.759, \"(259.35, 289.4)\": -0.662, \"(289.4, 319.15)\": -0.567, \"(319.15, 348.3)\": -0.464, \"(348.3, 496.5)\": -0.368, \"(496.5, 548.75)\": -0.271, \"(548.75, 606.0)\": -0.173, \"(606.0, 696.25)\": -0.076, \"(696.25, 806.1500000000001)\": 0.309, \"(806.1500000000001, 901.8)\": 0.405, \"(901.8, 959.4000000000001)\": 0.51, \"(959.4000000000001, 1054.0)\": 0.607, \"(1054.0, 1150.0)\": 0.707, \"(1150.0, 1248.5)\": 0.806, \"(1248.5, 1341.0)\": 0.911, \"(1341.0, 1801.0)\": 1.01, \"(1801.0, 2501.0)\": 1.109}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(143.5, 259.35)\": -1.038, \"(259.35, 289.4)\": -0.892, \"(289.4, 319.15)\": -0.754, \"(319.15, 348.3)\": -0.634, \"(348.3, 496.5)\": -0.559, \"(496.5, 548.75)\": -0.436, \"(548.75, 606.0)\": -0.338, \"(606.0, 696.25)\": -0.727, \"(696.25, 806.1500000000001)\": -0.252, \"(806.1500000000001, 901.8)\": -0.022, \"(901.8, 959.4000000000001)\": 0.058, \"(959.4000000000001, 1054.0)\": 0.141, \"(1054.0, 1150.0)\": 0.243, \"(1150.0, 1248.5)\": 0.328, \"(1248.5, 1341.0)\": 0.393, \"(1341.0, 1801.0)\": 0.475, \"(1801.0, 2501.0)\": 0.574}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(143.5, 259.35)\": -0.48, \"(259.35, 289.4)\": -0.432, \"(289.4, 319.15)\": -0.38, \"(319.15, 348.3)\": -0.294, \"(348.3, 496.5)\": -0.177, \"(496.5, 548.75)\": -0.106, \"(548.75, 606.0)\": -0.007, \"(606.0, 696.25)\": 0.575, \"(696.25, 806.1500000000001)\": 0.871, \"(806.1500000000001, 901.8)\": 0.831, \"(901.8, 959.4000000000001)\": 0.962, \"(959.4000000000001, 1054.0)\": 1.074, \"(1054.0, 1150.0)\": 1.171, \"(1150.0, 1248.5)\": 1.285, \"(1248.5, 1341.0)\": 1.428, \"(1341.0, 1801.0)\": 1.544, \"(1801.0, 2501.0)\": 1.644}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: smoothness_mean\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.05263, 0.0706)\": -0.835, \"(0.0706, 0.07455500000000001)\": -0.769, \"(0.07455500000000001, 0.07589499999999999)\": -0.697, \"(0.07589499999999999, 0.07727500000000001)\": -0.632, \"(0.07727500000000001, 0.078275)\": -0.569, \"(0.078275, 0.07952000000000001)\": -0.506, \"(0.07952000000000001, 0.080315)\": -0.437, \"(0.080315, 0.081035)\": -0.368, \"(0.081035, 0.08308499999999999)\": -0.304, \"(0.08308499999999999, 0.085165)\": -0.242, \"(0.085165, 0.086795)\": -0.177, \"(0.086795, 0.087785)\": -0.111, \"(0.087785, 0.088615)\": -0.047, \"(0.088615, 0.08918999999999999)\": 0.065, \"(0.08918999999999999, 0.090335)\": 0.142, \"(0.090335, 0.09454)\": 0.211, \"(0.09454, 0.11525)\": 0.107, \"(0.11525, 0.11765)\": 0.171, \"(0.11765, 0.12455)\": 0.267, \"(0.12455, 0.13845000000000002)\": 0.334, \"(0.13845000000000002, 0.1634)\": 0.396}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.05263, 0.0706)\": -1.454, \"(0.0706, 0.07455500000000001)\": -1.359, \"(0.07455500000000001, 0.07589499999999999)\": -1.244, \"(0.07589499999999999, 0.07727500000000001)\": -1.162, \"(0.07727500000000001, 0.078275)\": -1.087, \"(0.078275, 0.07952000000000001)\": -1.006, \"(0.07952000000000001, 0.080315)\": -0.882, \"(0.080315, 0.081035)\": -0.622, \"(0.081035, 0.08308499999999999)\": -0.547, \"(0.08308499999999999, 0.085165)\": -0.444, \"(0.085165, 0.086795)\": -0.357, \"(0.086795, 0.087785)\": -0.296, \"(0.087785, 0.088615)\": -0.23, \"(0.088615, 0.08918999999999999)\": -0.16, \"(0.08918999999999999, 0.090335)\": -0.309, \"(0.090335, 0.09454)\": -0.264, \"(0.09454, 0.11525)\": -0.005, \"(0.11525, 0.11765)\": 0.07, \"(0.11765, 0.12455)\": 0.022, \"(0.12455, 0.13845000000000002)\": 0.077, \"(0.13845000000000002, 0.1634)\": 0.127}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.05263, 0.0706)\": -0.216, \"(0.0706, 0.07455500000000001)\": -0.178, \"(0.07455500000000001, 0.07589499999999999)\": -0.151, \"(0.07589499999999999, 0.07727500000000001)\": -0.102, \"(0.07727500000000001, 0.078275)\": -0.052, \"(0.078275, 0.07952000000000001)\": -0.006, \"(0.07952000000000001, 0.080315)\": 0.008, \"(0.080315, 0.081035)\": -0.113, \"(0.081035, 0.08308499999999999)\": -0.062, \"(0.08308499999999999, 0.085165)\": -0.04, \"(0.085165, 0.086795)\": 0.004, \"(0.086795, 0.087785)\": 0.075, \"(0.087785, 0.088615)\": 0.136, \"(0.088615, 0.08918999999999999)\": 0.291, \"(0.08918999999999999, 0.090335)\": 0.594, \"(0.090335, 0.09454)\": 0.685, \"(0.09454, 0.11525)\": 0.22, \"(0.11525, 0.11765)\": 0.273, \"(0.11765, 0.12455)\": 0.512, \"(0.12455, 0.13845000000000002)\": 0.591, \"(0.13845000000000002, 0.1634)\": 0.664}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: compactness_mean\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.01938, 0.03164)\": 0.0135, \"(0.03164, 0.035445000000000004)\": 0.0558, \"(0.035445000000000004, 0.03732)\": 0.0934, \"(0.03732, 0.038529999999999995)\": 0.1327, \"(0.038529999999999995, 0.040694999999999995)\": 0.1725, \"(0.040694999999999995, 0.042550000000000004)\": 0.2126, \"(0.042550000000000004, 0.044355000000000006)\": 0.2504, \"(0.044355000000000006, 0.045645000000000005)\": 0.299, \"(0.045645000000000005, 0.0498)\": 0.3373, \"(0.0498, 0.059495)\": 0.2969, \"(0.059495, 0.06042)\": 0.2605, \"(0.06042, 0.0618)\": 0.2247, \"(0.0618, 0.06289)\": 0.1851, \"(0.06289, 0.062985)\": 0.1459, \"(0.062985, 0.06375)\": 0.0823, \"(0.06375, 0.06615499999999999)\": 0.0446, \"(0.06615499999999999, 0.066575)\": 0.0084, \"(0.066575, 0.067345)\": -0.1354, \"(0.067345, 0.06788)\": -0.1923, \"(0.06788, 0.068945)\": -0.232, \"(0.068945, 0.07211999999999999)\": -0.2724, \"(0.07211999999999999, 0.07482)\": -0.309, \"(0.07482, 0.0785)\": -0.3463, \"(0.0785, 0.085875)\": -0.2755, \"(0.085875, 0.095275)\": -0.2297, \"(0.095275, 0.10439999999999999)\": -0.1927, \"(0.10439999999999999, 0.11305000000000001)\": -0.1576, \"(0.11305000000000001, 0.11465)\": -0.121, \"(0.11465, 0.1153)\": -0.0859, \"(0.1153, 0.119)\": -0.0125, \"(0.119, 0.12375)\": 0.024, \"(0.12375, 0.16655)\": 0.0599, \"(0.16655, 0.1923)\": 0.0956, \"(0.1923, 0.23235)\": 0.1316, \"(0.23235, 0.27165)\": 0.1705, \"(0.27165, 0.28075)\": 0.2103, \"(0.28075, 0.3114)\": 0.2453}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.01938, 0.03164)\": -0.4016, \"(0.03164, 0.035445000000000004)\": -0.3995, \"(0.035445000000000004, 0.03732)\": -0.3599, \"(0.03732, 0.038529999999999995)\": -0.3178, \"(0.038529999999999995, 0.040694999999999995)\": -0.2802, \"(0.040694999999999995, 0.042550000000000004)\": -0.2633, \"(0.042550000000000004, 0.044355000000000006)\": -0.2559, \"(0.044355000000000006, 0.045645000000000005)\": -0.2259, \"(0.045645000000000005, 0.0498)\": -0.1947, \"(0.0498, 0.059495)\": -0.2119, \"(0.059495, 0.06042)\": -0.1651, \"(0.06042, 0.0618)\": -0.1904, \"(0.0618, 0.06289)\": -0.2009, \"(0.06289, 0.062985)\": -0.2409, \"(0.062985, 0.06375)\": -0.1808, \"(0.06375, 0.06615499999999999)\": -0.2262, \"(0.06615499999999999, 0.066575)\": -0.2509, \"(0.066575, 0.067345)\": -0.7938, \"(0.067345, 0.06788)\": -0.7983, \"(0.06788, 0.068945)\": -0.838, \"(0.068945, 0.07211999999999999)\": -0.9135, \"(0.07211999999999999, 0.07482)\": -0.9538, \"(0.07482, 0.0785)\": -1.0103, \"(0.0785, 0.085875)\": -0.5241, \"(0.085875, 0.095275)\": -0.4606, \"(0.095275, 0.10439999999999999)\": -0.4301, \"(0.10439999999999999, 0.11305000000000001)\": -0.3863, \"(0.11305000000000001, 0.11465)\": -0.331, \"(0.11465, 0.1153)\": -0.2716, \"(0.1153, 0.119)\": -0.1247, \"(0.119, 0.12375)\": -0.0694, \"(0.12375, 0.16655)\": -0.0509, \"(0.16655, 0.1923)\": -0.025, \"(0.1923, 0.23235)\": 0.0179, \"(0.23235, 0.27165)\": 0.0681, \"(0.27165, 0.28075)\": 0.121, \"(0.28075, 0.3114)\": 0.148}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.01938, 0.03164)\": 0.4286, \"(0.03164, 0.035445000000000004)\": 0.5111, \"(0.035445000000000004, 0.03732)\": 0.5468, \"(0.03732, 0.038529999999999995)\": 0.5831, \"(0.038529999999999995, 0.040694999999999995)\": 0.6252, \"(0.040694999999999995, 0.042550000000000004)\": 0.6885, \"(0.042550000000000004, 0.044355000000000006)\": 0.7567, \"(0.044355000000000006, 0.045645000000000005)\": 0.8238, \"(0.045645000000000005, 0.0498)\": 0.8693, \"(0.0498, 0.059495)\": 0.8057, \"(0.059495, 0.06042)\": 0.6861, \"(0.06042, 0.0618)\": 0.6397, \"(0.0618, 0.06289)\": 0.5712, \"(0.06289, 0.062985)\": 0.5328, \"(0.062985, 0.06375)\": 0.3454, \"(0.06375, 0.06615499999999999)\": 0.3154, \"(0.06615499999999999, 0.066575)\": 0.2677, \"(0.066575, 0.067345)\": 0.5229, \"(0.067345, 0.06788)\": 0.4136, \"(0.06788, 0.068945)\": 0.3741, \"(0.068945, 0.07211999999999999)\": 0.3686, \"(0.07211999999999999, 0.07482)\": 0.3358, \"(0.07482, 0.0785)\": 0.3178, \"(0.0785, 0.085875)\": -0.0269, \"(0.085875, 0.095275)\": 0.0012, \"(0.095275, 0.10439999999999999)\": 0.0448, \"(0.10439999999999999, 0.11305000000000001)\": 0.0711, \"(0.11305000000000001, 0.11465)\": 0.0889, \"(0.11465, 0.1153)\": 0.0998, \"(0.1153, 0.119)\": 0.0997, \"(0.119, 0.12375)\": 0.1174, \"(0.12375, 0.16655)\": 0.1706, \"(0.16655, 0.1923)\": 0.2162, \"(0.1923, 0.23235)\": 0.2452, \"(0.23235, 0.27165)\": 0.273, \"(0.27165, 0.28075)\": 0.2996, \"(0.28075, 0.3114)\": 0.3427}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: concavity_mean\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.005855)\": -0.897, \"(0.005855, 0.011885)\": -0.811, \"(0.011885, 0.016545)\": -0.719, \"(0.016545, 0.02046)\": -0.631, \"(0.02046, 0.02373)\": -0.543, \"(0.02373, 0.02711)\": -0.458, \"(0.02711, 0.038885)\": -0.374, \"(0.038885, 0.044705)\": -0.29, \"(0.044705, 0.059585)\": -0.205, \"(0.059585, 0.06851)\": -0.121, \"(0.06851, 0.072265)\": -0.032, \"(0.072265, 0.092725)\": 0.14, \"(0.092725, 0.1015)\": 0.224, \"(0.1015, 0.11415)\": 0.309, \"(0.11415, 0.13)\": 0.397, \"(0.13, 0.14534999999999998)\": 0.486, \"(0.14534999999999998, 0.1525)\": 0.581, \"(0.1525, 0.1686)\": 0.665, \"(0.1686, 0.24280000000000002)\": 0.749, \"(0.24280000000000002, 0.29359999999999997)\": 0.657, \"(0.29359999999999997, 0.32699999999999996)\": 0.566, \"(0.32699999999999996, 0.4268)\": 0.48}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.005855)\": -1.183, \"(0.005855, 0.011885)\": -1.062, \"(0.011885, 0.016545)\": -0.961, \"(0.016545, 0.02046)\": -0.861, \"(0.02046, 0.02373)\": -0.749, \"(0.02373, 0.02711)\": -0.665, \"(0.02711, 0.038885)\": -0.545, \"(0.038885, 0.044705)\": -0.442, \"(0.044705, 0.059585)\": -0.43, \"(0.059585, 0.06851)\": -0.344, \"(0.06851, 0.072265)\": -0.246, \"(0.072265, 0.092725)\": -0.128, \"(0.092725, 0.1015)\": 0.093, \"(0.1015, 0.11415)\": 0.166, \"(0.11415, 0.13)\": 0.205, \"(0.13, 0.14534999999999998)\": 0.246, \"(0.14534999999999998, 0.1525)\": 0.264, \"(0.1525, 0.1686)\": 0.346, \"(0.1686, 0.24280000000000002)\": 0.435, \"(0.24280000000000002, 0.29359999999999997)\": 0.402, \"(0.29359999999999997, 0.32699999999999996)\": 0.316, \"(0.32699999999999996, 0.4268)\": 0.208}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.005855)\": -0.612, \"(0.005855, 0.011885)\": -0.559, \"(0.011885, 0.016545)\": -0.477, \"(0.016545, 0.02046)\": -0.4, \"(0.02046, 0.02373)\": -0.338, \"(0.02373, 0.02711)\": -0.252, \"(0.02711, 0.038885)\": -0.203, \"(0.038885, 0.044705)\": -0.138, \"(0.044705, 0.059585)\": 0.021, \"(0.059585, 0.06851)\": 0.103, \"(0.06851, 0.072265)\": 0.183, \"(0.072265, 0.092725)\": 0.409, \"(0.092725, 0.1015)\": 0.355, \"(0.1015, 0.11415)\": 0.452, \"(0.11415, 0.13)\": 0.589, \"(0.13, 0.14534999999999998)\": 0.726, \"(0.14534999999999998, 0.1525)\": 0.898, \"(0.1525, 0.1686)\": 0.984, \"(0.1686, 0.24280000000000002)\": 1.063, \"(0.24280000000000002, 0.29359999999999997)\": 0.912, \"(0.29359999999999997, 0.32699999999999996)\": 0.815, \"(0.32699999999999996, 0.4268)\": 0.752}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: concave points_mean\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.0074145)\": -1.054, \"(0.0074145, 0.011665)\": -0.937, \"(0.011665, 0.01503)\": -0.821, \"(0.01503, 0.017865)\": -0.705, \"(0.017865, 0.019315)\": -0.582, \"(0.019315, 0.023185)\": -0.466, \"(0.023185, 0.026115)\": -0.352, \"(0.026115, 0.042455)\": -0.235, \"(0.042455, 0.048235)\": -0.115, \"(0.048235, 0.048865)\": 0.04, \"(0.048865, 0.059615)\": 0.233, \"(0.059615, 0.070395)\": 0.35, \"(0.070395, 0.08221500000000001)\": 0.474, \"(0.08221500000000001, 0.087175)\": 0.592, \"(0.087175, 0.091445)\": 0.711, \"(0.091445, 0.1006)\": 0.832, \"(0.1006, 0.122)\": 0.949, \"(0.122, 0.16544999999999999)\": 1.068, \"(0.16544999999999999, 0.2012)\": 1.187}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.0074145)\": -1.411, \"(0.0074145, 0.011665)\": -1.253, \"(0.011665, 0.01503)\": -1.095, \"(0.01503, 0.017865)\": -0.965, \"(0.017865, 0.019315)\": -0.823, \"(0.019315, 0.023185)\": -0.72, \"(0.023185, 0.026115)\": -0.517, \"(0.026115, 0.042455)\": -0.743, \"(0.042455, 0.048235)\": -0.628, \"(0.048235, 0.048865)\": -0.409, \"(0.048865, 0.059615)\": -0.151, \"(0.059615, 0.070395)\": 0.09, \"(0.070395, 0.08221500000000001)\": 0.219, \"(0.08221500000000001, 0.087175)\": 0.306, \"(0.087175, 0.091445)\": 0.39, \"(0.091445, 0.1006)\": 0.481, \"(0.1006, 0.122)\": 0.562, \"(0.122, 0.16544999999999999)\": 0.634, \"(0.16544999999999999, 0.2012)\": 0.74}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.0074145)\": -0.697, \"(0.0074145, 0.011665)\": -0.62, \"(0.011665, 0.01503)\": -0.546, \"(0.01503, 0.017865)\": -0.445, \"(0.017865, 0.019315)\": -0.34, \"(0.019315, 0.023185)\": -0.212, \"(0.023185, 0.026115)\": -0.188, \"(0.026115, 0.042455)\": 0.274, \"(0.042455, 0.048235)\": 0.398, \"(0.048235, 0.048865)\": 0.489, \"(0.048865, 0.059615)\": 0.617, \"(0.059615, 0.070395)\": 0.611, \"(0.070395, 0.08221500000000001)\": 0.728, \"(0.08221500000000001, 0.087175)\": 0.878, \"(0.087175, 0.091445)\": 1.032, \"(0.091445, 0.1006)\": 1.182, \"(0.1006, 0.122)\": 1.336, \"(0.122, 0.16544999999999999)\": 1.503, \"(0.16544999999999999, 0.2012)\": 1.634}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: symmetry_mean\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.1167, 0.1384)\": -0.604, \"(0.1384, 0.14229999999999998)\": -0.55, \"(0.14229999999999998, 0.14565)\": -0.489, \"(0.14565, 0.1488)\": -0.428, \"(0.1488, 0.1507)\": -0.372, \"(0.1507, 0.15245)\": -0.316, \"(0.15245, 0.15375)\": -0.258, \"(0.15375, 0.15410000000000001)\": -0.087, \"(0.15410000000000001, 0.1545)\": -0.03, \"(0.1545, 0.15765)\": 0.279, \"(0.15765, 0.16625)\": 0.335, \"(0.16625, 0.16635)\": 0.258, \"(0.16635, 0.1684)\": 0.048, \"(0.1684, 0.17915)\": -0.007, \"(0.17915, 0.20355)\": -0.062, \"(0.20355, 0.20855)\": -0.005, \"(0.20855, 0.21105000000000002)\": 0.052, \"(0.21105000000000002, 0.21315)\": 0.107, \"(0.21315, 0.21705)\": 0.17, \"(0.21705, 0.22110000000000002)\": 0.234, \"(0.22110000000000002, 0.23020000000000002)\": 0.289, \"(0.23020000000000002, 0.2544)\": 0.347, \"(0.2544, 0.2626)\": 0.408, \"(0.2626, 0.304)\": 0.466}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.1167, 0.1384)\": -1.532, \"(0.1384, 0.14229999999999998)\": -1.437, \"(0.14229999999999998, 0.14565)\": -1.372, \"(0.14565, 0.1488)\": -1.313, \"(0.1488, 0.1507)\": -1.251, \"(0.1507, 0.15245)\": -1.195, \"(0.15245, 0.15375)\": -1.119, \"(0.15375, 0.15410000000000001)\": -0.701, \"(0.15410000000000001, 0.1545)\": -0.663, \"(0.1545, 0.15765)\": -0.664, \"(0.15765, 0.16625)\": -0.597, \"(0.16625, 0.16635)\": -0.613, \"(0.16635, 0.1684)\": -0.077, \"(0.1684, 0.17915)\": -0.11, \"(0.17915, 0.20355)\": -0.175, \"(0.20355, 0.20855)\": -0.112, \"(0.20855, 0.21105000000000002)\": -0.047, \"(0.21105000000000002, 0.21315)\": 0.003, \"(0.21315, 0.21705)\": 0.061, \"(0.21705, 0.22110000000000002)\": 0.118, \"(0.22110000000000002, 0.23020000000000002)\": 0.169, \"(0.23020000000000002, 0.2544)\": 0.191, \"(0.2544, 0.2626)\": 0.21, \"(0.2626, 0.304)\": 0.25}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.1167, 0.1384)\": 0.323, \"(0.1384, 0.14229999999999998)\": 0.338, \"(0.14229999999999998, 0.14565)\": 0.394, \"(0.14565, 0.1488)\": 0.457, \"(0.1488, 0.1507)\": 0.507, \"(0.1507, 0.15245)\": 0.564, \"(0.15245, 0.15375)\": 0.603, \"(0.15375, 0.15410000000000001)\": 0.527, \"(0.15410000000000001, 0.1545)\": 0.602, \"(0.1545, 0.15765)\": 1.221, \"(0.15765, 0.16625)\": 1.266, \"(0.16625, 0.16635)\": 1.129, \"(0.16635, 0.1684)\": 0.174, \"(0.1684, 0.17915)\": 0.095, \"(0.17915, 0.20355)\": 0.05, \"(0.20355, 0.20855)\": 0.102, \"(0.20855, 0.21105000000000002)\": 0.151, \"(0.21105000000000002, 0.21315)\": 0.211, \"(0.21315, 0.21705)\": 0.279, \"(0.21705, 0.22110000000000002)\": 0.351, \"(0.22110000000000002, 0.23020000000000002)\": 0.408, \"(0.23020000000000002, 0.2544)\": 0.503, \"(0.2544, 0.2626)\": 0.606, \"(0.2626, 0.304)\": 0.681}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: fractal_dimension_mean\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.04996, 0.05075)\": 0.5962, \"(0.05075, 0.052285)\": 0.5519, \"(0.052285, 0.05393)\": 0.5087, \"(0.05393, 0.05455)\": 0.4681, \"(0.05455, 0.05505)\": 0.4248, \"(0.05505, 0.055349999999999996)\": 0.3799, \"(0.055349999999999996, 0.055665)\": 0.337, \"(0.055665, 0.055895)\": 0.2922, \"(0.055895, 0.055935)\": 0.2475, \"(0.055935, 0.056365)\": 0.2007, \"(0.056365, 0.05655)\": 0.1163, \"(0.05655, 0.056720000000000007)\": 0.0704, \"(0.056720000000000007, 0.056995000000000004)\": 0.0288, \"(0.056995000000000004, 0.058145)\": -0.0168, \"(0.058145, 0.059715)\": -0.0575, \"(0.059715, 0.06078)\": -0.0163, \"(0.06078, 0.061385)\": -0.0618, \"(0.061385, 0.0622)\": -0.102, \"(0.0622, 0.063145)\": -0.1453, \"(0.063145, 0.065105)\": -0.1865, \"(0.065105, 0.06564)\": -0.1448, \"(0.06564, 0.067575)\": -0.1025, \"(0.067575, 0.09744)\": -0.0621}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.04996, 0.05075)\": 0.3734, \"(0.05075, 0.052285)\": 0.3548, \"(0.052285, 0.05393)\": 0.2212, \"(0.05393, 0.05455)\": 0.2141, \"(0.05455, 0.05505)\": 0.1669, \"(0.05505, 0.055349999999999996)\": 0.1178, \"(0.055349999999999996, 0.055665)\": 0.0712, \"(0.055665, 0.055895)\": 0.0309, \"(0.055895, 0.055935)\": -0.0117, \"(0.055935, 0.056365)\": -0.072, \"(0.056365, 0.05655)\": -0.0136, \"(0.05655, 0.056720000000000007)\": -0.0905, \"(0.056720000000000007, 0.056995000000000004)\": -0.1461, \"(0.056995000000000004, 0.058145)\": -0.1995, \"(0.058145, 0.059715)\": -0.2455, \"(0.059715, 0.06078)\": -0.1126, \"(0.06078, 0.061385)\": -0.1738, \"(0.061385, 0.0622)\": -0.1944, \"(0.0622, 0.063145)\": -0.2238, \"(0.063145, 0.065105)\": -0.2687, \"(0.065105, 0.06564)\": -0.2312, \"(0.06564, 0.067575)\": -0.191, \"(0.067575, 0.09744)\": -0.1891}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.04996, 0.05075)\": 0.819, \"(0.05075, 0.052285)\": 0.749, \"(0.052285, 0.05393)\": 0.7962, \"(0.05393, 0.05455)\": 0.722, \"(0.05455, 0.05505)\": 0.6828, \"(0.05505, 0.055349999999999996)\": 0.642, \"(0.055349999999999996, 0.055665)\": 0.6028, \"(0.055665, 0.055895)\": 0.5535, \"(0.055895, 0.055935)\": 0.5067, \"(0.055935, 0.056365)\": 0.4734, \"(0.056365, 0.05655)\": 0.2462, \"(0.05655, 0.056720000000000007)\": 0.2312, \"(0.056720000000000007, 0.056995000000000004)\": 0.2038, \"(0.056995000000000004, 0.058145)\": 0.1658, \"(0.058145, 0.059715)\": 0.1306, \"(0.059715, 0.06078)\": 0.0801, \"(0.06078, 0.061385)\": 0.0502, \"(0.061385, 0.0622)\": -0.0097, \"(0.0622, 0.063145)\": -0.0668, \"(0.063145, 0.065105)\": -0.1044, \"(0.065105, 0.06564)\": -0.0583, \"(0.06564, 0.067575)\": -0.0139, \"(0.067575, 0.09744)\": 0.0649}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: radius_se\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.1115, 0.15015)\": -0.773, \"(0.15015, 0.16904999999999998)\": -0.686, \"(0.16904999999999998, 0.1795)\": -0.589, \"(0.1795, 0.18535000000000001)\": -0.499, \"(0.18535000000000001, 0.19345)\": -0.412, \"(0.19345, 0.2103)\": -0.275, \"(0.2103, 0.2329)\": -0.187, \"(0.2329, 0.2939)\": -0.102, \"(0.2939, 0.368)\": -0.186, \"(0.368, 0.38585)\": -0.066, \"(0.38585, 0.42025)\": 0.064, \"(0.42025, 0.46775)\": 0.15, \"(0.46775, 0.54785)\": 0.239, \"(0.54785, 0.5881000000000001)\": 0.334, \"(0.5881000000000001, 0.66425)\": 0.422, \"(0.66425, 0.7562)\": 0.51, \"(0.7562, 0.9131)\": 0.594, \"(0.9131, 1.065)\": 0.683, \"(1.065, 1.2915)\": 0.774, \"(1.2915, 2.873)\": 0.866}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.1115, 0.15015)\": -1.244, \"(0.15015, 0.16904999999999998)\": -1.125, \"(0.16904999999999998, 0.1795)\": -1.008, \"(0.1795, 0.18535000000000001)\": -0.904, \"(0.18535000000000001, 0.19345)\": -0.8, \"(0.19345, 0.2103)\": -0.449, \"(0.2103, 0.2329)\": -0.273, \"(0.2329, 0.2939)\": -0.492, \"(0.2939, 0.368)\": -0.769, \"(0.368, 0.38585)\": -0.437, \"(0.38585, 0.42025)\": -0.188, \"(0.42025, 0.46775)\": -0.119, \"(0.46775, 0.54785)\": -0.037, \"(0.54785, 0.5881000000000001)\": -0.09, \"(0.5881000000000001, 0.66425)\": -0.016, \"(0.66425, 0.7562)\": 0.051, \"(0.7562, 0.9131)\": 0.051, \"(0.9131, 1.065)\": 0.113, \"(1.065, 1.2915)\": 0.123, \"(1.2915, 2.873)\": 0.198}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.1115, 0.15015)\": -0.302, \"(0.15015, 0.16904999999999998)\": -0.247, \"(0.16904999999999998, 0.1795)\": -0.169, \"(0.1795, 0.18535000000000001)\": -0.094, \"(0.18535000000000001, 0.19345)\": -0.024, \"(0.19345, 0.2103)\": -0.1, \"(0.2103, 0.2329)\": -0.101, \"(0.2329, 0.2939)\": 0.289, \"(0.2939, 0.368)\": 0.396, \"(0.368, 0.38585)\": 0.304, \"(0.38585, 0.42025)\": 0.315, \"(0.42025, 0.46775)\": 0.42, \"(0.46775, 0.54785)\": 0.514, \"(0.54785, 0.5881000000000001)\": 0.758, \"(0.5881000000000001, 0.66425)\": 0.86, \"(0.66425, 0.7562)\": 0.968, \"(0.7562, 0.9131)\": 1.137, \"(0.9131, 1.065)\": 1.253, \"(1.065, 1.2915)\": 1.425, \"(1.2915, 2.873)\": 1.533}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: texture_se\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.3602, 0.47535000000000005)\": -0.1353, \"(0.47535000000000005, 0.49585)\": -0.1099, \"(0.49585, 0.5344)\": -0.0872, \"(0.5344, 0.55835)\": -0.0633, \"(0.55835, 0.5779000000000001)\": -0.0358, \"(0.5779000000000001, 0.6065)\": -0.0122, \"(0.6065, 0.6938500000000001)\": 0.0114, \"(0.6938500000000001, 0.7878499999999999)\": -0.0151, \"(0.7878499999999999, 0.8181499999999999)\": 0.0089, \"(0.8181499999999999, 0.9497)\": 0.0332, \"(0.9497, 0.99)\": 0.0579, \"(0.99, 1.0579999999999998)\": 0.0811, \"(1.0579999999999998, 1.2845)\": 0.0581, \"(1.2845, 1.461)\": 0.0338, \"(1.461, 1.4785)\": 0.0097, \"(1.4785, 1.892)\": -0.0156, \"(1.892, 1.9255)\": -0.0438, \"(1.9255, 1.9945)\": -0.0684, \"(1.9945, 2.0999999999999996)\": -0.0943, \"(2.0999999999999996, 2.2295)\": -0.0299, \"(2.2295, 2.263)\": -0.2223, \"(2.263, 2.3085)\": -0.2461, \"(2.3085, 2.481)\": -0.2782, \"(2.481, 2.6235)\": -0.3069, \"(2.6235, 3.6075)\": -0.3341, \"(3.6075, 4.885)\": -0.3576}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.3602, 0.47535000000000005)\": -0.4063, \"(0.47535000000000005, 0.49585)\": -0.2824, \"(0.49585, 0.5344)\": -0.2408, \"(0.5344, 0.55835)\": -0.2114, \"(0.55835, 0.5779000000000001)\": -0.1606, \"(0.5779000000000001, 0.6065)\": -0.1405, \"(0.6065, 0.6938500000000001)\": -0.1139, \"(0.6938500000000001, 0.7878499999999999)\": -0.1541, \"(0.7878499999999999, 0.8181499999999999)\": -0.1144, \"(0.8181499999999999, 0.9497)\": -0.0897, \"(0.9497, 0.99)\": -0.0157, \"(0.99, 1.0579999999999998)\": 0.029, \"(1.0579999999999998, 1.2845)\": -0.012, \"(1.2845, 1.461)\": -0.0661, \"(1.461, 1.4785)\": -0.0888, \"(1.4785, 1.892)\": -0.2196, \"(1.892, 1.9255)\": -0.2461, \"(1.9255, 1.9945)\": -0.2824, \"(1.9945, 2.0999999999999996)\": -0.3214, \"(2.0999999999999996, 2.2295)\": -0.5019, \"(2.2295, 2.263)\": -0.6324, \"(2.263, 2.3085)\": -0.6746, \"(2.3085, 2.481)\": -0.7135, \"(2.481, 2.6235)\": -0.7339, \"(2.6235, 3.6075)\": -0.7611, \"(3.6075, 4.885)\": -0.7768}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.3602, 0.47535000000000005)\": 0.1358, \"(0.47535000000000005, 0.49585)\": 0.0627, \"(0.49585, 0.5344)\": 0.0665, \"(0.5344, 0.55835)\": 0.0849, \"(0.55835, 0.5779000000000001)\": 0.089, \"(0.5779000000000001, 0.6065)\": 0.1161, \"(0.6065, 0.6938500000000001)\": 0.1366, \"(0.6938500000000001, 0.7878499999999999)\": 0.124, \"(0.7878499999999999, 0.8181499999999999)\": 0.1322, \"(0.8181499999999999, 0.9497)\": 0.1561, \"(0.9497, 0.99)\": 0.1314, \"(0.99, 1.0579999999999998)\": 0.1333, \"(1.0579999999999998, 1.2845)\": 0.1282, \"(1.2845, 1.461)\": 0.1336, \"(1.461, 1.4785)\": 0.1082, \"(1.4785, 1.892)\": 0.1884, \"(1.892, 1.9255)\": 0.1585, \"(1.9255, 1.9945)\": 0.1456, \"(1.9945, 2.0999999999999996)\": 0.1329, \"(2.0999999999999996, 2.2295)\": 0.4421, \"(2.2295, 2.263)\": 0.1879, \"(2.263, 2.3085)\": 0.1825, \"(2.3085, 2.481)\": 0.1571, \"(2.481, 2.6235)\": 0.1201, \"(2.6235, 3.6075)\": 0.0929, \"(3.6075, 4.885)\": 0.0616}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: perimeter_se\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.7714, 1.0579999999999998)\": -0.698, \"(1.0579999999999998, 1.1345)\": -0.618, \"(1.1345, 1.197)\": -0.539, \"(1.197, 1.2365)\": -0.461, \"(1.2365, 1.326)\": -0.384, \"(1.326, 1.4435)\": -0.256, \"(1.4435, 1.5314999999999999)\": -0.176, \"(1.5314999999999999, 1.807)\": -0.099, \"(1.807, 2.107)\": -0.023, \"(2.107, 2.593)\": -0.098, \"(2.593, 2.878)\": -0.018, \"(2.878, 3.292)\": 0.065, \"(3.292, 4.095000000000001)\": 0.14, \"(4.095000000000001, 4.714)\": 0.219, \"(4.714, 4.885999999999999)\": 0.296, \"(4.885999999999999, 5.2844999999999995)\": 0.372, \"(5.2844999999999995, 5.8425)\": 0.451, \"(5.8425, 7.104)\": 0.536, \"(7.104, 7.7765)\": 0.611, \"(7.7765, 10.594999999999999)\": 0.701, \"(10.594999999999999, 21.98)\": 0.786}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.7714, 1.0579999999999998)\": -1.131, \"(1.0579999999999998, 1.1345)\": -1.029, \"(1.1345, 1.197)\": -0.923, \"(1.197, 1.2365)\": -0.835, \"(1.2365, 1.326)\": -0.754, \"(1.326, 1.4435)\": -0.43, \"(1.4435, 1.5314999999999999)\": -0.378, \"(1.5314999999999999, 1.807)\": -0.215, \"(1.807, 2.107)\": -0.131, \"(2.107, 2.593)\": -0.215, \"(2.593, 2.878)\": -0.124, \"(2.878, 3.292)\": -0.022, \"(3.292, 4.095000000000001)\": 0.04, \"(4.095000000000001, 4.714)\": 0.049, \"(4.714, 4.885999999999999)\": -0.063, \"(4.885999999999999, 5.2844999999999995)\": 0.007, \"(5.2844999999999995, 5.8425)\": 0.088, \"(5.8425, 7.104)\": 0.151, \"(7.104, 7.7765)\": 0.21, \"(7.7765, 10.594999999999999)\": 0.257, \"(10.594999999999999, 21.98)\": 0.341}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.7714, 1.0579999999999998)\": -0.265, \"(1.0579999999999998, 1.1345)\": -0.208, \"(1.1345, 1.197)\": -0.155, \"(1.197, 1.2365)\": -0.087, \"(1.2365, 1.326)\": -0.015, \"(1.326, 1.4435)\": -0.081, \"(1.4435, 1.5314999999999999)\": 0.026, \"(1.5314999999999999, 1.807)\": 0.016, \"(1.807, 2.107)\": 0.085, \"(2.107, 2.593)\": 0.019, \"(2.593, 2.878)\": 0.088, \"(2.878, 3.292)\": 0.152, \"(3.292, 4.095000000000001)\": 0.24, \"(4.095000000000001, 4.714)\": 0.388, \"(4.714, 4.885999999999999)\": 0.655, \"(4.885999999999999, 5.2844999999999995)\": 0.737, \"(5.2844999999999995, 5.8425)\": 0.814, \"(5.8425, 7.104)\": 0.921, \"(7.104, 7.7765)\": 1.012, \"(7.7765, 10.594999999999999)\": 1.146, \"(10.594999999999999, 21.98)\": 1.231}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: area_se\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(6.802, 11.184999999999999)\": -0.919, \"(11.184999999999999, 12.765)\": -0.814, \"(12.765, 13.350000000000001)\": -0.704, \"(13.350000000000001, 15.3)\": -0.596, \"(15.3, 16.955)\": -0.49, \"(16.955, 18.515)\": -0.367, \"(18.515, 20.905)\": -0.256, \"(20.905, 32.985)\": -0.151, \"(32.985, 34.730000000000004)\": 0.081, \"(34.730000000000004, 41.21)\": 0.188, \"(41.21, 50.405)\": 0.292, \"(50.405, 56.915)\": 0.417, \"(56.915, 67.5)\": 0.53, \"(67.5, 81.56)\": 0.638, \"(81.56, 94.00999999999999)\": 0.751, \"(94.00999999999999, 106.2)\": 0.862, \"(106.2, 153.25)\": 0.974, \"(153.25, 542.2)\": 1.082}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(6.802, 11.184999999999999)\": -1.305, \"(11.184999999999999, 12.765)\": -1.176, \"(12.765, 13.350000000000001)\": -1.036, \"(13.350000000000001, 15.3)\": -0.901, \"(15.3, 16.955)\": -0.696, \"(16.955, 18.515)\": -0.504, \"(18.515, 20.905)\": -0.392, \"(20.905, 32.985)\": -0.922, \"(32.985, 34.730000000000004)\": -0.261, \"(34.730000000000004, 41.21)\": -0.102, \"(41.21, 50.405)\": 0.02, \"(50.405, 56.915)\": 0.072, \"(56.915, 67.5)\": 0.147, \"(67.5, 81.56)\": 0.223, \"(81.56, 94.00999999999999)\": 0.326, \"(94.00999999999999, 106.2)\": 0.402, \"(106.2, 153.25)\": 0.501, \"(153.25, 542.2)\": 0.571}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(6.802, 11.184999999999999)\": -0.532, \"(11.184999999999999, 12.765)\": -0.452, \"(12.765, 13.350000000000001)\": -0.371, \"(13.350000000000001, 15.3)\": -0.291, \"(15.3, 16.955)\": -0.284, \"(16.955, 18.515)\": -0.23, \"(18.515, 20.905)\": -0.121, \"(20.905, 32.985)\": 0.62, \"(32.985, 34.730000000000004)\": 0.424, \"(34.730000000000004, 41.21)\": 0.479, \"(41.21, 50.405)\": 0.563, \"(50.405, 56.915)\": 0.762, \"(56.915, 67.5)\": 0.913, \"(67.5, 81.56)\": 1.052, \"(81.56, 94.00999999999999)\": 1.176, \"(94.00999999999999, 106.2)\": 1.323, \"(106.2, 153.25)\": 1.448, \"(153.25, 542.2)\": 1.593}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: smoothness_se\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.001713, 0.0031539999999999997)\": 0.2958, \"(0.0031539999999999997, 0.003299)\": 0.2615, \"(0.003299, 0.003384)\": 0.185, \"(0.003384, 0.0034675)\": -0.1523, \"(0.0034675, 0.0036699999999999997)\": -0.1838, \"(0.0036699999999999997, 0.0041069999999999995)\": -0.2174, \"(0.0041069999999999995, 0.004215)\": -0.2532, \"(0.004215, 0.004436)\": -0.2879, \"(0.004436, 0.0045775)\": -0.3223, \"(0.0045775, 0.004612)\": -0.2905, \"(0.004612, 0.0048915)\": -0.2425, \"(0.0048915, 0.0053335)\": -0.2106, \"(0.0053335, 0.005443)\": -0.1771, \"(0.005443, 0.00554)\": -0.1453, \"(0.00554, 0.005729)\": -0.1136, \"(0.005729, 0.0058625)\": -0.0812, \"(0.0058625, 0.0058955)\": -0.0495, \"(0.0058955, 0.0067525)\": 0.0229, \"(0.0067525, 0.00682)\": 0.0562, \"(0.00682, 0.007338)\": 0.1146, \"(0.007338, 0.0074805)\": 0.1474, \"(0.0074805, 0.007967)\": 0.1839, \"(0.007967, 0.009857000000000001)\": 0.219, \"(0.009857000000000001, 0.010665000000000001)\": 0.1863, \"(0.010665000000000001, 0.011054999999999999)\": 0.1538, \"(0.011054999999999999, 0.011915)\": 0.1219, \"(0.011915, 0.012885)\": 0.0873, \"(0.012885, 0.03113)\": 0.0542}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.001713, 0.0031539999999999997)\": -0.864, \"(0.0031539999999999997, 0.003299)\": -0.919, \"(0.003299, 0.003384)\": -1.0196, \"(0.003384, 0.0034675)\": -0.6905, \"(0.0034675, 0.0036699999999999997)\": -0.7233, \"(0.0036699999999999997, 0.0041069999999999995)\": -0.7618, \"(0.0041069999999999995, 0.004215)\": -0.7976, \"(0.004215, 0.004436)\": -0.8492, \"(0.004436, 0.0045775)\": -0.8863, \"(0.0045775, 0.004612)\": -0.8426, \"(0.004612, 0.0048915)\": -0.7021, \"(0.0048915, 0.0053335)\": -0.6905, \"(0.0053335, 0.005443)\": -0.6659, \"(0.005443, 0.00554)\": -0.624, \"(0.00554, 0.005729)\": -0.5761, \"(0.005729, 0.0058625)\": -0.538, \"(0.0058625, 0.0058955)\": -0.5073, \"(0.0058955, 0.0067525)\": -0.1186, \"(0.0067525, 0.00682)\": -0.0928, \"(0.00682, 0.007338)\": -0.288, \"(0.007338, 0.0074805)\": -0.2553, \"(0.0074805, 0.007967)\": -0.2176, \"(0.007967, 0.009857000000000001)\": -0.1787, \"(0.009857000000000001, 0.010665000000000001)\": -0.2012, \"(0.010665000000000001, 0.011054999999999999)\": -0.2344, \"(0.011054999999999999, 0.011915)\": -0.2614, \"(0.011915, 0.012885)\": -0.2838, \"(0.012885, 0.03113)\": -0.4136}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.001713, 0.0031539999999999997)\": 1.4555, \"(0.0031539999999999997, 0.003299)\": 1.442, \"(0.003299, 0.003384)\": 1.3896, \"(0.003384, 0.0034675)\": 0.386, \"(0.0034675, 0.0036699999999999997)\": 0.3557, \"(0.0036699999999999997, 0.0041069999999999995)\": 0.327, \"(0.0041069999999999995, 0.004215)\": 0.2913, \"(0.004215, 0.004436)\": 0.2734, \"(0.004436, 0.0045775)\": 0.2417, \"(0.0045775, 0.004612)\": 0.2615, \"(0.004612, 0.0048915)\": 0.2171, \"(0.0048915, 0.0053335)\": 0.2692, \"(0.0053335, 0.005443)\": 0.3117, \"(0.005443, 0.00554)\": 0.3335, \"(0.00554, 0.005729)\": 0.349, \"(0.005729, 0.0058625)\": 0.3757, \"(0.0058625, 0.0058955)\": 0.4082, \"(0.0058955, 0.0067525)\": 0.1644, \"(0.0067525, 0.00682)\": 0.2053, \"(0.00682, 0.007338)\": 0.5173, \"(0.007338, 0.0074805)\": 0.5502, \"(0.0074805, 0.007967)\": 0.5854, \"(0.007967, 0.009857000000000001)\": 0.6167, \"(0.009857000000000001, 0.010665000000000001)\": 0.5738, \"(0.010665000000000001, 0.011054999999999999)\": 0.5419, \"(0.011054999999999999, 0.011915)\": 0.5053, \"(0.011915, 0.012885)\": 0.4585, \"(0.012885, 0.03113)\": 0.522}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: compactness_se\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.002252, 0.0046765)\": -0.0693, \"(0.0046765, 0.005634)\": -0.0214, \"(0.005634, 0.006059500000000001)\": 0.0214, \"(0.006059500000000001, 0.006774499999999999)\": 0.0648, \"(0.006774499999999999, 0.0072375)\": 0.1132, \"(0.0072375, 0.008034)\": 0.1583, \"(0.008034, 0.0082145)\": 0.2045, \"(0.0082145, 0.0085705)\": 0.2482, \"(0.0085705, 0.0089915)\": 0.2969, \"(0.0089915, 0.01089)\": 0.3467, \"(0.01089, 0.011715)\": 0.3948, \"(0.011715, 0.012025000000000001)\": 0.3506, \"(0.012025000000000001, 0.012535000000000001)\": 0.2891, \"(0.012535000000000001, 0.013225)\": 0.244, \"(0.013225, 0.014275)\": 0.2001, \"(0.014275, 0.015615)\": 0.1571, \"(0.015615, 0.017669999999999998)\": 0.1142, \"(0.017669999999999998, 0.020155)\": 0.0681, \"(0.020155, 0.022855)\": 0.0256, \"(0.022855, 0.02586)\": -0.0272, \"(0.02586, 0.027540000000000002)\": -0.098, \"(0.027540000000000002, 0.038220000000000004)\": -0.1414, \"(0.038220000000000004, 0.039245)\": -0.1853, \"(0.039245, 0.040514999999999995)\": -0.2301, \"(0.040514999999999995, 0.04309)\": -0.2754, \"(0.04309, 0.04922)\": -0.3233, \"(0.04922, 0.068925)\": -0.3675, \"(0.068925, 0.1354)\": -0.4112}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.002252, 0.0046765)\": -0.2881, \"(0.0046765, 0.005634)\": -0.2345, \"(0.005634, 0.006059500000000001)\": -0.1933, \"(0.006059500000000001, 0.006774499999999999)\": -0.1451, \"(0.006774499999999999, 0.0072375)\": -0.0877, \"(0.0072375, 0.008034)\": -0.0418, \"(0.008034, 0.0082145)\": -0.0092, \"(0.0082145, 0.0085705)\": 0.0302, \"(0.0085705, 0.0089915)\": 0.0617, \"(0.0089915, 0.01089)\": 0.105, \"(0.01089, 0.011715)\": 0.1231, \"(0.011715, 0.012025000000000001)\": 0.0874, \"(0.012025000000000001, 0.012535000000000001)\": 0.097, \"(0.012535000000000001, 0.013225)\": 0.063, \"(0.013225, 0.014275)\": 0.031, \"(0.014275, 0.015615)\": 0.0018, \"(0.015615, 0.017669999999999998)\": -0.0333, \"(0.017669999999999998, 0.020155)\": -0.0326, \"(0.020155, 0.022855)\": -0.0543, \"(0.022855, 0.02586)\": -0.1745, \"(0.02586, 0.027540000000000002)\": -0.258, \"(0.027540000000000002, 0.038220000000000004)\": -0.3097, \"(0.038220000000000004, 0.039245)\": -0.326, \"(0.039245, 0.040514999999999995)\": -0.3788, \"(0.040514999999999995, 0.04309)\": -0.4514, \"(0.04309, 0.04922)\": -0.5306, \"(0.04922, 0.068925)\": -0.5903, \"(0.068925, 0.1354)\": -0.6732}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.002252, 0.0046765)\": 0.1496, \"(0.0046765, 0.005634)\": 0.1917, \"(0.005634, 0.006059500000000001)\": 0.2361, \"(0.006059500000000001, 0.006774499999999999)\": 0.2747, \"(0.006774499999999999, 0.0072375)\": 0.3141, \"(0.0072375, 0.008034)\": 0.3584, \"(0.008034, 0.0082145)\": 0.4182, \"(0.0082145, 0.0085705)\": 0.4662, \"(0.0085705, 0.0089915)\": 0.5321, \"(0.0089915, 0.01089)\": 0.5884, \"(0.01089, 0.011715)\": 0.6664, \"(0.011715, 0.012025000000000001)\": 0.6138, \"(0.012025000000000001, 0.012535000000000001)\": 0.4812, \"(0.012535000000000001, 0.013225)\": 0.4251, \"(0.013225, 0.014275)\": 0.3692, \"(0.014275, 0.015615)\": 0.3124, \"(0.015615, 0.017669999999999998)\": 0.2617, \"(0.017669999999999998, 0.020155)\": 0.1689, \"(0.020155, 0.022855)\": 0.1055, \"(0.022855, 0.02586)\": 0.1202, \"(0.02586, 0.027540000000000002)\": 0.062, \"(0.027540000000000002, 0.038220000000000004)\": 0.027, \"(0.038220000000000004, 0.039245)\": -0.0446, \"(0.039245, 0.040514999999999995)\": -0.0815, \"(0.040514999999999995, 0.04309)\": -0.0993, \"(0.04309, 0.04922)\": -0.1161, \"(0.04922, 0.068925)\": -0.1448, \"(0.068925, 0.1354)\": -0.1492}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: concavity_se\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.001156)\": -0.6445, \"(0.001156, 0.002325)\": -0.6016, \"(0.002325, 0.0037635)\": -0.5599, \"(0.0037635, 0.0053165)\": -0.5149, \"(0.0053165, 0.0058905)\": -0.4651, \"(0.0058905, 0.006987999999999999)\": -0.4227, \"(0.006987999999999999, 0.0077405)\": -0.3808, \"(0.0077405, 0.008344500000000001)\": -0.3373, \"(0.008344500000000001, 0.009263500000000001)\": -0.2906, \"(0.009263500000000001, 0.010215)\": -0.246, \"(0.010215, 0.010705)\": -0.2028, \"(0.010705, 0.01122)\": -0.1484, \"(0.01122, 0.011625)\": -0.1022, \"(0.011625, 0.01191)\": -0.0592, \"(0.01191, 0.012455)\": -0.0118, \"(0.012455, 0.0203)\": 0.0471, \"(0.0203, 0.022565)\": 0.0914, \"(0.022565, 0.02983)\": 0.1347, \"(0.02983, 0.032535)\": 0.0347, \"(0.032535, 0.0338)\": -0.0071, \"(0.0338, 0.038565)\": 0.0604, \"(0.038565, 0.04418)\": 0.1065, \"(0.04418, 0.059305)\": 0.1494, \"(0.059305, 0.065775)\": 0.1044, \"(0.065775, 0.07794000000000001)\": 0.0533, \"(0.07794000000000001, 0.08089)\": 0.0097, \"(0.08089, 0.096205)\": -0.0573, \"(0.096205, 0.22865000000000002)\": -0.1001, \"(0.22865000000000002, 0.396)\": -0.1471}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.001156)\": -0.9396, \"(0.001156, 0.002325)\": -0.8658, \"(0.002325, 0.0037635)\": -0.8192, \"(0.0037635, 0.0053165)\": -0.7681, \"(0.0053165, 0.0058905)\": -0.716, \"(0.0058905, 0.006987999999999999)\": -0.6675, \"(0.006987999999999999, 0.0077405)\": -0.6156, \"(0.0077405, 0.008344500000000001)\": -0.5789, \"(0.008344500000000001, 0.009263500000000001)\": -0.5182, \"(0.009263500000000001, 0.010215)\": -0.4535, \"(0.010215, 0.010705)\": -0.4164, \"(0.010705, 0.01122)\": -0.3446, \"(0.01122, 0.011625)\": -0.2792, \"(0.011625, 0.01191)\": -0.2184, \"(0.01191, 0.012455)\": -0.172, \"(0.012455, 0.0203)\": -0.1411, \"(0.0203, 0.022565)\": -0.0146, \"(0.022565, 0.02983)\": 0.021, \"(0.02983, 0.032535)\": -0.4149, \"(0.032535, 0.0338)\": -0.4515, \"(0.0338, 0.038565)\": -0.0686, \"(0.038565, 0.04418)\": -0.0006, \"(0.04418, 0.059305)\": -0.0422, \"(0.059305, 0.065775)\": -0.0685, \"(0.065775, 0.07794000000000001)\": -0.1246, \"(0.07794000000000001, 0.08089)\": -0.1713, \"(0.08089, 0.096205)\": -0.2715, \"(0.096205, 0.22865000000000002)\": -0.3432, \"(0.22865000000000002, 0.396)\": -0.3958}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.001156)\": -0.3494, \"(0.001156, 0.002325)\": -0.3374, \"(0.002325, 0.0037635)\": -0.3006, \"(0.0037635, 0.0053165)\": -0.2618, \"(0.0053165, 0.0058905)\": -0.2142, \"(0.0058905, 0.006987999999999999)\": -0.1779, \"(0.006987999999999999, 0.0077405)\": -0.1459, \"(0.0077405, 0.008344500000000001)\": -0.0957, \"(0.008344500000000001, 0.009263500000000001)\": -0.063, \"(0.009263500000000001, 0.010215)\": -0.0385, \"(0.010215, 0.010705)\": 0.0109, \"(0.010705, 0.01122)\": 0.0478, \"(0.01122, 0.011625)\": 0.0749, \"(0.011625, 0.01191)\": 0.0999, \"(0.01191, 0.012455)\": 0.1484, \"(0.012455, 0.0203)\": 0.2352, \"(0.0203, 0.022565)\": 0.1973, \"(0.022565, 0.02983)\": 0.2485, \"(0.02983, 0.032535)\": 0.4843, \"(0.032535, 0.0338)\": 0.4374, \"(0.0338, 0.038565)\": 0.1895, \"(0.038565, 0.04418)\": 0.2135, \"(0.04418, 0.059305)\": 0.3411, \"(0.059305, 0.065775)\": 0.2772, \"(0.065775, 0.07794000000000001)\": 0.2312, \"(0.07794000000000001, 0.08089)\": 0.1907, \"(0.08089, 0.096205)\": 0.1569, \"(0.096205, 0.22865000000000002)\": 0.1429, \"(0.22865000000000002, 0.396)\": 0.1015}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: concave points_se\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.002395)\": -0.0871, \"(0.002395, 0.0032875)\": -0.0609, \"(0.0032875, 0.0034045)\": -0.0373, \"(0.0034045, 0.0036125000000000003)\": -0.0134, \"(0.0036125000000000003, 0.004007999999999999)\": 0.015, \"(0.004007999999999999, 0.0044174999999999996)\": 0.0395, \"(0.0044174999999999996, 0.0048265)\": 0.0651, \"(0.0048265, 0.0049695)\": 0.092, \"(0.0049695, 0.005064)\": 0.1172, \"(0.005064, 0.0051675)\": 0.1498, \"(0.0051675, 0.0052465)\": 0.1811, \"(0.0052465, 0.0054895)\": 0.1545, \"(0.0054895, 0.00583)\": 0.1294, \"(0.00583, 0.006595999999999999)\": 0.1053, \"(0.006595999999999999, 0.006815)\": 0.1283, \"(0.006815, 0.00749)\": 0.151, \"(0.00749, 0.008282000000000001)\": 0.1286, \"(0.008282000000000001, 0.0088595)\": 0.1057, \"(0.0088595, 0.009246)\": 0.2195, \"(0.009246, 0.00954)\": 0.1918, \"(0.00954, 0.009698)\": 0.169, \"(0.009698, 0.00976)\": 0.1467, \"(0.00976, 0.009788999999999999)\": 0.1246, \"(0.009788999999999999, 0.009878999999999999)\": 0.0984, \"(0.009878999999999999, 0.0099215)\": -0.0268, \"(0.0099215, 0.010165)\": -0.0546, \"(0.010165, 0.010385)\": -0.0796, \"(0.010385, 0.010515)\": -0.1027, \"(0.010515, 0.010825)\": -0.1321, \"(0.010825, 0.011115)\": -0.1569, \"(0.011115, 0.011525)\": -0.181, \"(0.011525, 0.012580000000000001)\": -0.204, \"(0.012580000000000001, 0.012715)\": -0.1804, \"(0.012715, 0.012750000000000001)\": -0.1557, \"(0.012750000000000001, 0.01302)\": -0.1333, \"(0.01302, 0.013405)\": -0.1067, \"(0.013405, 0.01386)\": -0.0844, \"(0.01386, 0.014315)\": -0.061, \"(0.014315, 0.015605)\": -0.0317, \"(0.015605, 0.016655000000000003)\": -0.0097, \"(0.016655000000000003, 0.017509999999999998)\": 0.0142, \"(0.017509999999999998, 0.019655)\": 0.037, \"(0.019655, 0.021525)\": 0.0117, \"(0.021525, 0.02246)\": -0.018, \"(0.02246, 0.02611)\": -0.0625, \"(0.02611, 0.05279)\": -0.0866}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.002395)\": -0.2428, \"(0.002395, 0.0032875)\": -0.216, \"(0.0032875, 0.0034045)\": -0.1973, \"(0.0034045, 0.0036125000000000003)\": -0.1778, \"(0.0036125000000000003, 0.004007999999999999)\": -0.1423, \"(0.004007999999999999, 0.0044174999999999996)\": -0.1145, \"(0.0044174999999999996, 0.0048265)\": -0.0804, \"(0.0048265, 0.0049695)\": -0.0433, \"(0.0049695, 0.005064)\": -0.0289, \"(0.005064, 0.0051675)\": -0.0175, \"(0.0051675, 0.0052465)\": 0.0068, \"(0.0052465, 0.0054895)\": -0.0087, \"(0.0054895, 0.00583)\": -0.0088, \"(0.00583, 0.006595999999999999)\": -0.0253, \"(0.006595999999999999, 0.006815)\": 0.0061, \"(0.006815, 0.00749)\": 0.012, \"(0.00749, 0.008282000000000001)\": -0.0218, \"(0.008282000000000001, 0.0088595)\": -0.0694, \"(0.0088595, 0.009246)\": -0.313, \"(0.009246, 0.00954)\": -0.3369, \"(0.00954, 0.009698)\": -0.3531, \"(0.009698, 0.00976)\": -0.3635, \"(0.00976, 0.009788999999999999)\": -0.3663, \"(0.009788999999999999, 0.009878999999999999)\": -0.4105, \"(0.009878999999999999, 0.0099215)\": -0.1256, \"(0.0099215, 0.010165)\": -0.1624, \"(0.010165, 0.010385)\": -0.1986, \"(0.010385, 0.010515)\": -0.2385, \"(0.010515, 0.010825)\": -0.2937, \"(0.010825, 0.011115)\": -0.3279, \"(0.011115, 0.011525)\": -0.3717, \"(0.011525, 0.012580000000000001)\": -0.4177, \"(0.012580000000000001, 0.012715)\": -0.3676, \"(0.012715, 0.012750000000000001)\": -0.3439, \"(0.012750000000000001, 0.01302)\": -0.2873, \"(0.01302, 0.013405)\": -0.246, \"(0.013405, 0.01386)\": -0.2135, \"(0.01386, 0.014315)\": -0.1572, \"(0.014315, 0.015605)\": -0.1106, \"(0.015605, 0.016655000000000003)\": -0.0972, \"(0.016655000000000003, 0.017509999999999998)\": -0.0786, \"(0.017509999999999998, 0.019655)\": -0.0739, \"(0.019655, 0.021525)\": -0.099, \"(0.021525, 0.02246)\": -0.1362, \"(0.02246, 0.02611)\": -0.2114, \"(0.02611, 0.05279)\": -0.2994}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.002395)\": 0.0686, \"(0.002395, 0.0032875)\": 0.0943, \"(0.0032875, 0.0034045)\": 0.1227, \"(0.0034045, 0.0036125000000000003)\": 0.151, \"(0.0036125000000000003, 0.004007999999999999)\": 0.1722, \"(0.004007999999999999, 0.0044174999999999996)\": 0.1934, \"(0.0044174999999999996, 0.0048265)\": 0.2105, \"(0.0048265, 0.0049695)\": 0.2273, \"(0.0049695, 0.005064)\": 0.2632, \"(0.005064, 0.0051675)\": 0.3171, \"(0.0051675, 0.0052465)\": 0.3554, \"(0.0052465, 0.0054895)\": 0.3177, \"(0.0054895, 0.00583)\": 0.2677, \"(0.00583, 0.006595999999999999)\": 0.2359, \"(0.006595999999999999, 0.006815)\": 0.2505, \"(0.006815, 0.00749)\": 0.2901, \"(0.00749, 0.008282000000000001)\": 0.279, \"(0.008282000000000001, 0.0088595)\": 0.2808, \"(0.0088595, 0.009246)\": 0.752, \"(0.009246, 0.00954)\": 0.7204, \"(0.00954, 0.009698)\": 0.6912, \"(0.009698, 0.00976)\": 0.657, \"(0.00976, 0.009788999999999999)\": 0.6156, \"(0.009788999999999999, 0.009878999999999999)\": 0.6073, \"(0.009878999999999999, 0.0099215)\": 0.072, \"(0.0099215, 0.010165)\": 0.0531, \"(0.010165, 0.010385)\": 0.0395, \"(0.010385, 0.010515)\": 0.0331, \"(0.010515, 0.010825)\": 0.0294, \"(0.010825, 0.011115)\": 0.0142, \"(0.011115, 0.011525)\": 0.0097, \"(0.011525, 0.012580000000000001)\": 0.0096, \"(0.012580000000000001, 0.012715)\": 0.0069, \"(0.012715, 0.012750000000000001)\": 0.0325, \"(0.012750000000000001, 0.01302)\": 0.0207, \"(0.01302, 0.013405)\": 0.0325, \"(0.013405, 0.01386)\": 0.0446, \"(0.01386, 0.014315)\": 0.0352, \"(0.014315, 0.015605)\": 0.0471, \"(0.015605, 0.016655000000000003)\": 0.0777, \"(0.016655000000000003, 0.017509999999999998)\": 0.107, \"(0.017509999999999998, 0.019655)\": 0.1479, \"(0.019655, 0.021525)\": 0.1225, \"(0.021525, 0.02246)\": 0.1002, \"(0.02246, 0.02611)\": 0.0864, \"(0.02611, 0.05279)\": 0.1261}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: symmetry_se\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.007882, 0.010595)\": 0.771, \"(0.010595, 0.011365)\": 0.697, \"(0.011365, 0.012135)\": 0.635, \"(0.012135, 0.01279)\": 0.576, \"(0.01279, 0.01352)\": 0.513, \"(0.01352, 0.014105)\": 0.455, \"(0.014105, 0.014499999999999999)\": 0.393, \"(0.014499999999999999, 0.014525)\": 0.332, \"(0.014525, 0.01489)\": 0.227, \"(0.01489, 0.01532)\": 0.169, \"(0.01532, 0.015805)\": 0.109, \"(0.015805, 0.017215)\": 0.05, \"(0.017215, 0.017855)\": -0.008, \"(0.017855, 0.018165)\": -0.073, \"(0.018165, 0.018685)\": -0.131, \"(0.018685, 0.019545)\": -0.193, \"(0.019545, 0.02068)\": -0.252, \"(0.02068, 0.024730000000000002)\": -0.31, \"(0.024730000000000002, 0.026770000000000002)\": -0.376, \"(0.026770000000000002, 0.027435)\": -0.316, \"(0.027435, 0.028380000000000002)\": -0.252, \"(0.028380000000000002, 0.02966)\": -0.19, \"(0.02966, 0.031865)\": -0.092, \"(0.031865, 0.03651)\": -0.034, \"(0.03651, 0.041944999999999996)\": 0.024, \"(0.041944999999999996, 0.04665)\": 0.086, \"(0.04665, 0.054805)\": 0.152, \"(0.054805, 0.05963)\": 0.232}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.007882, 0.010595)\": 0.336, \"(0.010595, 0.011365)\": 0.284, \"(0.011365, 0.012135)\": 0.24, \"(0.012135, 0.01279)\": 0.211, \"(0.01279, 0.01352)\": 0.226, \"(0.01352, 0.014105)\": 0.178, \"(0.014105, 0.014499999999999999)\": 0.123, \"(0.014499999999999999, 0.014525)\": 0.09, \"(0.014525, 0.01489)\": -0.155, \"(0.01489, 0.01532)\": -0.203, \"(0.01532, 0.015805)\": -0.266, \"(0.015805, 0.017215)\": -0.317, \"(0.017215, 0.017855)\": -0.138, \"(0.017855, 0.018165)\": -0.193, \"(0.018165, 0.018685)\": -0.264, \"(0.018685, 0.019545)\": -0.327, \"(0.019545, 0.02068)\": -0.388, \"(0.02068, 0.024730000000000002)\": -0.457, \"(0.024730000000000002, 0.026770000000000002)\": -0.569, \"(0.026770000000000002, 0.027435)\": -0.507, \"(0.027435, 0.028380000000000002)\": -0.46, \"(0.028380000000000002, 0.02966)\": -0.393, \"(0.02966, 0.031865)\": -0.281, \"(0.031865, 0.03651)\": -0.265, \"(0.03651, 0.041944999999999996)\": -0.233, \"(0.041944999999999996, 0.04665)\": -0.174, \"(0.04665, 0.054805)\": -0.12, \"(0.054805, 0.05963)\": -0.058}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.007882, 0.010595)\": 1.206, \"(0.010595, 0.011365)\": 1.11, \"(0.011365, 0.012135)\": 1.031, \"(0.012135, 0.01279)\": 0.941, \"(0.01279, 0.01352)\": 0.8, \"(0.01352, 0.014105)\": 0.731, \"(0.014105, 0.014499999999999999)\": 0.662, \"(0.014499999999999999, 0.014525)\": 0.574, \"(0.014525, 0.01489)\": 0.609, \"(0.01489, 0.01532)\": 0.541, \"(0.01532, 0.015805)\": 0.484, \"(0.015805, 0.017215)\": 0.418, \"(0.017215, 0.017855)\": 0.123, \"(0.017855, 0.018165)\": 0.047, \"(0.018165, 0.018685)\": 0.002, \"(0.018685, 0.019545)\": -0.059, \"(0.019545, 0.02068)\": -0.116, \"(0.02068, 0.024730000000000002)\": -0.164, \"(0.024730000000000002, 0.026770000000000002)\": -0.182, \"(0.026770000000000002, 0.027435)\": -0.125, \"(0.027435, 0.028380000000000002)\": -0.043, \"(0.028380000000000002, 0.02966)\": 0.013, \"(0.02966, 0.031865)\": 0.097, \"(0.031865, 0.03651)\": 0.197, \"(0.03651, 0.041944999999999996)\": 0.281, \"(0.041944999999999996, 0.04665)\": 0.345, \"(0.04665, 0.054805)\": 0.424, \"(0.054805, 0.05963)\": 0.521}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: fractal_dimension_se\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0008948, 0.001092)\": 0.2818, \"(0.001092, 0.0014135)\": 0.3286, \"(0.0014135, 0.0015165)\": 0.2713, \"(0.0015165, 0.0017545)\": 0.2283, \"(0.0017545, 0.0017905)\": 0.144, \"(0.0017905, 0.0019039999999999999)\": 0.0956, \"(0.0019039999999999999, 0.0021525)\": 0.0526, \"(0.0021525, 0.002572)\": 0.0073, \"(0.002572, 0.002761)\": 0.1543, \"(0.002761, 0.003308)\": 0.1971, \"(0.003308, 0.0033604999999999998)\": 0.1525, \"(0.0033604999999999998, 0.0035329999999999997)\": 0.1049, \"(0.0035329999999999997, 0.003736)\": 0.0586, \"(0.003736, 0.003907)\": 0.0157, \"(0.003907, 0.004092500000000001)\": -0.029, \"(0.004092500000000001, 0.0045775)\": -0.0717, \"(0.0045775, 0.0045935)\": -0.1177, \"(0.0045935, 0.004644499999999999)\": -0.1739, \"(0.004644499999999999, 0.004809)\": -0.2208, \"(0.004809, 0.005856500000000001)\": -0.2666, \"(0.005856500000000001, 0.007497500000000001)\": -0.31, \"(0.007497500000000001, 0.009717)\": -0.356, \"(0.009717, 0.0127)\": -0.4, \"(0.0127, 0.02984)\": -0.4439}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0008948, 0.001092)\": 0.0382, \"(0.001092, 0.0014135)\": 0.0989, \"(0.0014135, 0.0015165)\": 0.1004, \"(0.0015165, 0.0017545)\": 0.0661, \"(0.0017545, 0.0017905)\": -0.1939, \"(0.0017905, 0.0019039999999999999)\": -0.2485, \"(0.0019039999999999999, 0.0021525)\": -0.2947, \"(0.0021525, 0.002572)\": -0.3301, \"(0.002572, 0.002761)\": -0.1655, \"(0.002761, 0.003308)\": -0.1517, \"(0.003308, 0.0033604999999999998)\": -0.1674, \"(0.0033604999999999998, 0.0035329999999999997)\": -0.1413, \"(0.0035329999999999997, 0.003736)\": -0.1763, \"(0.003736, 0.003907)\": -0.2066, \"(0.003907, 0.004092500000000001)\": -0.2479, \"(0.004092500000000001, 0.0045775)\": -0.2863, \"(0.0045775, 0.0045935)\": -0.3224, \"(0.0045935, 0.004644499999999999)\": -0.372, \"(0.004644499999999999, 0.004809)\": -0.418, \"(0.004809, 0.005856500000000001)\": -0.4726, \"(0.005856500000000001, 0.007497500000000001)\": -0.5133, \"(0.007497500000000001, 0.009717)\": -0.5704, \"(0.009717, 0.0127)\": -0.6199, \"(0.0127, 0.02984)\": -0.6593}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0008948, 0.001092)\": 0.5254, \"(0.001092, 0.0014135)\": 0.5584, \"(0.0014135, 0.0015165)\": 0.4422, \"(0.0015165, 0.0017545)\": 0.3904, \"(0.0017545, 0.0017905)\": 0.482, \"(0.0017905, 0.0019039999999999999)\": 0.4397, \"(0.0019039999999999999, 0.0021525)\": 0.3999, \"(0.0021525, 0.002572)\": 0.3448, \"(0.002572, 0.002761)\": 0.4742, \"(0.002761, 0.003308)\": 0.5459, \"(0.003308, 0.0033604999999999998)\": 0.4723, \"(0.0033604999999999998, 0.0035329999999999997)\": 0.3511, \"(0.0035329999999999997, 0.003736)\": 0.2936, \"(0.003736, 0.003907)\": 0.238, \"(0.003907, 0.004092500000000001)\": 0.1899, \"(0.004092500000000001, 0.0045775)\": 0.1429, \"(0.0045775, 0.0045935)\": 0.087, \"(0.0045935, 0.004644499999999999)\": 0.0243, \"(0.004644499999999999, 0.004809)\": -0.0237, \"(0.004809, 0.005856500000000001)\": -0.0605, \"(0.005856500000000001, 0.007497500000000001)\": -0.1068, \"(0.007497500000000001, 0.009717)\": -0.1417, \"(0.009717, 0.0127)\": -0.1801, \"(0.0127, 0.02984)\": -0.2284}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: radius_worst\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(7.93, 10.585)\": -1.149, \"(10.585, 11.305)\": -1.016, \"(11.305, 11.965)\": -0.883, \"(11.965, 12.54)\": -0.747, \"(12.54, 13.315000000000001)\": -0.616, \"(13.315000000000001, 14.184999999999999)\": -0.485, \"(14.184999999999999, 14.875)\": -0.349, \"(14.875, 15.485)\": -0.212, \"(15.485, 15.955)\": -0.078, \"(15.955, 16.54)\": 0.055, \"(16.54, 17.22)\": 0.19, \"(17.22, 17.78)\": 0.335, \"(17.78, 18.655)\": 0.469, \"(18.655, 19.785)\": 0.601, \"(19.785, 20.445)\": 0.734, \"(20.445, 21.935000000000002)\": 0.866, \"(21.935000000000002, 23.625)\": 0.997, \"(23.625, 25.335)\": 1.132, \"(25.335, 30.71)\": 1.274, \"(30.71, 36.04)\": 1.406}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(7.93, 10.585)\": -1.554, \"(10.585, 11.305)\": -1.397, \"(11.305, 11.965)\": -1.223, \"(11.965, 12.54)\": -1.048, \"(12.54, 13.315000000000001)\": -0.881, \"(13.315000000000001, 14.184999999999999)\": -0.698, \"(14.184999999999999, 14.875)\": -0.522, \"(14.875, 15.485)\": -0.332, \"(15.485, 15.955)\": -0.179, \"(15.955, 16.54)\": -0.216, \"(16.54, 17.22)\": -0.079, \"(17.22, 17.78)\": 0.047, \"(17.78, 18.655)\": 0.152, \"(18.655, 19.785)\": 0.262, \"(19.785, 20.445)\": 0.384, \"(20.445, 21.935000000000002)\": 0.494, \"(21.935000000000002, 23.625)\": 0.572, \"(23.625, 25.335)\": 0.664, \"(25.335, 30.71)\": 0.756, \"(30.71, 36.04)\": 0.872}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(7.93, 10.585)\": -0.745, \"(10.585, 11.305)\": -0.635, \"(11.305, 11.965)\": -0.542, \"(11.965, 12.54)\": -0.446, \"(12.54, 13.315000000000001)\": -0.351, \"(13.315000000000001, 14.184999999999999)\": -0.271, \"(14.184999999999999, 14.875)\": -0.176, \"(14.875, 15.485)\": -0.091, \"(15.485, 15.955)\": 0.022, \"(15.955, 16.54)\": 0.326, \"(16.54, 17.22)\": 0.459, \"(17.22, 17.78)\": 0.624, \"(17.78, 18.655)\": 0.785, \"(18.655, 19.785)\": 0.94, \"(19.785, 20.445)\": 1.085, \"(20.445, 21.935000000000002)\": 1.239, \"(21.935000000000002, 23.625)\": 1.422, \"(23.625, 25.335)\": 1.6, \"(25.335, 30.71)\": 1.792, \"(30.71, 36.04)\": 1.941}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: texture_worst\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(12.02, 16.935000000000002)\": -1.885, \"(16.935000000000002, 18.335)\": -1.717, \"(18.335, 19.505)\": -1.55, \"(19.505, 20.225)\": -0.851, \"(20.225, 21.955)\": -0.612, \"(21.955, 23.59)\": -0.44, \"(23.59, 24.795)\": -0.272, \"(24.795, 25.18)\": -0.1, \"(25.18, 25.83)\": 0.078, \"(25.83, 26.855)\": 0.279, \"(26.855, 27.994999999999997)\": 0.451, \"(27.994999999999997, 29.225)\": 0.619, \"(29.225, 31.515)\": 0.878, \"(31.515, 32.485)\": 1.044, \"(32.485, 35.05)\": 1.256, \"(35.05, 49.54)\": 1.423}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(12.02, 16.935000000000002)\": -4.342, \"(16.935000000000002, 18.335)\": -4.128, \"(18.335, 19.505)\": -3.934, \"(19.505, 20.225)\": -1.264, \"(20.225, 21.955)\": -0.945, \"(21.955, 23.59)\": -0.663, \"(23.59, 24.795)\": -0.468, \"(24.795, 25.18)\": -0.274, \"(25.18, 25.83)\": -0.503, \"(25.83, 26.855)\": -0.327, \"(26.855, 27.994999999999997)\": -0.163, \"(27.994999999999997, 29.225)\": -0.01, \"(29.225, 31.515)\": -0.206, \"(31.515, 32.485)\": -0.081, \"(32.485, 35.05)\": -0.18, \"(35.05, 49.54)\": -0.014}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(12.02, 16.935000000000002)\": 0.572, \"(16.935000000000002, 18.335)\": 0.695, \"(18.335, 19.505)\": 0.835, \"(19.505, 20.225)\": -0.437, \"(20.225, 21.955)\": -0.279, \"(21.955, 23.59)\": -0.218, \"(23.59, 24.795)\": -0.076, \"(24.795, 25.18)\": 0.073, \"(25.18, 25.83)\": 0.66, \"(25.83, 26.855)\": 0.884, \"(26.855, 27.994999999999997)\": 1.065, \"(27.994999999999997, 29.225)\": 1.248, \"(29.225, 31.515)\": 1.961, \"(31.515, 32.485)\": 2.17, \"(32.485, 35.05)\": 2.691, \"(35.05, 49.54)\": 2.861}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: perimeter_worst\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(50.41, 71.06)\": -1.379, \"(71.06, 76.52000000000001)\": -1.223, \"(76.52000000000001, 80.9)\": -1.069, \"(80.9, 84.035)\": -0.914, \"(84.035, 86.48500000000001)\": -0.755, \"(86.48500000000001, 87.3)\": -0.599, \"(87.3, 91.49000000000001)\": -0.447, \"(91.49000000000001, 95.66)\": -0.292, \"(95.66, 101.15)\": -0.446, \"(101.15, 102.05000000000001)\": -0.294, \"(102.05000000000001, 109.6)\": 0.197, \"(109.6, 116.25)\": 0.351, \"(116.25, 120.35)\": 0.507, \"(120.35, 127.0)\": 0.748, \"(127.0, 133.10000000000002)\": 0.902, \"(133.10000000000002, 145.10000000000002)\": 1.059, \"(145.10000000000002, 160.0)\": 1.215, \"(160.0, 178.85)\": 1.368, \"(178.85, 251.2)\": 1.523}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(50.41, 71.06)\": -2.45, \"(71.06, 76.52000000000001)\": -2.257, \"(76.52000000000001, 80.9)\": -2.023, \"(80.9, 84.035)\": -1.85, \"(84.035, 86.48500000000001)\": -1.682, \"(86.48500000000001, 87.3)\": -1.531, \"(87.3, 91.49000000000001)\": -1.053, \"(91.49000000000001, 95.66)\": -0.915, \"(95.66, 101.15)\": -1.829, \"(101.15, 102.05000000000001)\": -1.642, \"(102.05000000000001, 109.6)\": -0.387, \"(109.6, 116.25)\": -0.238, \"(116.25, 120.35)\": -0.074, \"(120.35, 127.0)\": -0.761, \"(127.0, 133.10000000000002)\": -0.623, \"(133.10000000000002, 145.10000000000002)\": -0.494, \"(145.10000000000002, 160.0)\": -0.379, \"(160.0, 178.85)\": -0.29, \"(178.85, 251.2)\": -0.162}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(50.41, 71.06)\": -0.307, \"(71.06, 76.52000000000001)\": -0.189, \"(76.52000000000001, 80.9)\": -0.114, \"(80.9, 84.035)\": 0.021, \"(84.035, 86.48500000000001)\": 0.172, \"(86.48500000000001, 87.3)\": 0.332, \"(87.3, 91.49000000000001)\": 0.159, \"(91.49000000000001, 95.66)\": 0.331, \"(95.66, 101.15)\": 0.936, \"(101.15, 102.05000000000001)\": 1.054, \"(102.05000000000001, 109.6)\": 0.782, \"(109.6, 116.25)\": 0.94, \"(116.25, 120.35)\": 1.088, \"(120.35, 127.0)\": 2.256, \"(127.0, 133.10000000000002)\": 2.428, \"(133.10000000000002, 145.10000000000002)\": 2.611, \"(145.10000000000002, 160.0)\": 2.809, \"(160.0, 178.85)\": 3.027, \"(178.85, 251.2)\": 3.208}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: area_worst\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(185.2, 357.5)\": -1.345, \"(357.5, 413.15)\": -1.192, \"(413.15, 471.9)\": -1.038, \"(471.9, 508.5)\": -0.878, \"(508.5, 633.9)\": -0.723, \"(633.9, 653.45)\": -0.565, \"(653.45, 710.2)\": -0.348, \"(710.2, 727.0999999999999)\": -0.165, \"(727.0999999999999, 805.95)\": 0.096, \"(805.95, 874.85)\": 0.253, \"(874.85, 928.5)\": 0.48, \"(928.5, 1033.5)\": 0.761, \"(1033.5, 1222.5)\": 0.932, \"(1222.5, 1346.5)\": 1.092, \"(1346.5, 1645.5)\": 1.245, \"(1645.5, 1979.0)\": 1.404, \"(1979.0, 4254.0)\": 1.557}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(185.2, 357.5)\": -2.413, \"(357.5, 413.15)\": -2.22, \"(413.15, 471.9)\": -2.004, \"(471.9, 508.5)\": -1.818, \"(508.5, 633.9)\": -1.868, \"(633.9, 653.45)\": -1.645, \"(653.45, 710.2)\": -0.767, \"(710.2, 727.0999999999999)\": -0.501, \"(727.0999999999999, 805.95)\": -0.573, \"(805.95, 874.85)\": -0.187, \"(874.85, 928.5)\": -0.49, \"(928.5, 1033.5)\": -0.484, \"(1033.5, 1222.5)\": -0.455, \"(1222.5, 1346.5)\": -0.298, \"(1346.5, 1645.5)\": -0.182, \"(1645.5, 1979.0)\": -0.049, \"(1979.0, 4254.0)\": 0.071}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(185.2, 357.5)\": -0.278, \"(357.5, 413.15)\": -0.164, \"(413.15, 471.9)\": -0.073, \"(471.9, 508.5)\": 0.062, \"(508.5, 633.9)\": 0.423, \"(633.9, 653.45)\": 0.516, \"(653.45, 710.2)\": 0.071, \"(710.2, 727.0999999999999)\": 0.17, \"(727.0999999999999, 805.95)\": 0.764, \"(805.95, 874.85)\": 0.693, \"(874.85, 928.5)\": 1.449, \"(928.5, 1033.5)\": 2.006, \"(1033.5, 1222.5)\": 2.319, \"(1222.5, 1346.5)\": 2.482, \"(1346.5, 1645.5)\": 2.672, \"(1645.5, 1979.0)\": 2.857, \"(1979.0, 4254.0)\": 3.043}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: smoothness_worst\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.07117, 0.09376000000000001)\": -1.298, \"(0.09376000000000001, 0.099705)\": -1.161, \"(0.099705, 0.10519999999999999)\": -1.024, \"(0.10519999999999999, 0.10825)\": -0.889, \"(0.10825, 0.11549999999999999)\": -0.527, \"(0.11549999999999999, 0.12345)\": -0.394, \"(0.12345, 0.13074999999999998)\": -0.26, \"(0.13074999999999998, 0.13585)\": -0.124, \"(0.13585, 0.13640000000000002)\": 0.011, \"(0.13640000000000002, 0.13845000000000002)\": 0.154, \"(0.13845000000000002, 0.14065)\": 0.288, \"(0.14065, 0.14635)\": 0.439, \"(0.14635, 0.15585)\": 0.574, \"(0.15585, 0.16885)\": 0.708, \"(0.16885, 0.17825)\": 0.846, \"(0.17825, 0.19574999999999998)\": 1.17, \"(0.19574999999999998, 0.2226)\": 1.304}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.07117, 0.09376000000000001)\": -2.26, \"(0.09376000000000001, 0.099705)\": -2.132, \"(0.099705, 0.10519999999999999)\": -2.009, \"(0.10519999999999999, 0.10825)\": -1.875, \"(0.10825, 0.11549999999999999)\": -0.765, \"(0.11549999999999999, 0.12345)\": -0.589, \"(0.12345, 0.13074999999999998)\": -0.435, \"(0.13074999999999998, 0.13585)\": -0.384, \"(0.13585, 0.13640000000000002)\": -0.325, \"(0.13640000000000002, 0.13845000000000002)\": -0.247, \"(0.13845000000000002, 0.14065)\": -0.076, \"(0.14065, 0.14635)\": -0.12, \"(0.14635, 0.15585)\": 0.165, \"(0.15585, 0.16885)\": 0.27, \"(0.16885, 0.17825)\": 0.402, \"(0.17825, 0.19574999999999998)\": -0.484, \"(0.19574999999999998, 0.2226)\": -0.349}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.07117, 0.09376000000000001)\": -0.336, \"(0.09376000000000001, 0.099705)\": -0.19, \"(0.099705, 0.10519999999999999)\": -0.039, \"(0.10519999999999999, 0.10825)\": 0.096, \"(0.10825, 0.11549999999999999)\": -0.289, \"(0.11549999999999999, 0.12345)\": -0.2, \"(0.12345, 0.13074999999999998)\": -0.086, \"(0.13074999999999998, 0.13585)\": 0.136, \"(0.13585, 0.13640000000000002)\": 0.348, \"(0.13640000000000002, 0.13845000000000002)\": 0.556, \"(0.13845000000000002, 0.14065)\": 0.652, \"(0.14065, 0.14635)\": 0.997, \"(0.14635, 0.15585)\": 0.983, \"(0.15585, 0.16885)\": 1.146, \"(0.16885, 0.17825)\": 1.289, \"(0.17825, 0.19574999999999998)\": 2.823, \"(0.19574999999999998, 0.2226)\": 2.957}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: compactness_worst\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.02729, 0.049945)\": -0.0578, \"(0.049945, 0.06971)\": -0.0099, \"(0.06971, 0.099305)\": -0.0565, \"(0.099305, 0.10635)\": -0.1408, \"(0.10635, 0.1243)\": -0.1882, \"(0.1243, 0.14795)\": -0.2357, \"(0.14795, 0.1507)\": -0.1883, \"(0.1507, 0.1861)\": -0.1381, \"(0.1861, 0.20124999999999998)\": -0.0918, \"(0.20124999999999998, 0.3358)\": -0.0443, \"(0.3358, 0.3456)\": 0.0027, \"(0.3456, 0.35755000000000003)\": 0.0649, \"(0.35755000000000003, 0.3703)\": 0.1151, \"(0.3703, 0.39235)\": 0.1642, \"(0.39235, 0.4087)\": 0.2124, \"(0.4087, 0.4229)\": 0.2605, \"(0.4229, 0.4486)\": 0.3109, \"(0.4486, 0.48865000000000003)\": 0.3586, \"(0.48865000000000003, 0.54825)\": 0.4132, \"(0.54825, 0.5892999999999999)\": 0.4651, \"(0.5892999999999999, 0.65835)\": 0.5154, \"(0.65835, 0.7680499999999999)\": 0.572, \"(0.7680499999999999, 0.99795)\": 0.6264, \"(0.99795, 1.058)\": 0.6748}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.02729, 0.049945)\": -0.8125, \"(0.049945, 0.06971)\": -0.7624, \"(0.06971, 0.099305)\": -0.6001, \"(0.099305, 0.10635)\": -0.4033, \"(0.10635, 0.1243)\": -0.4448, \"(0.1243, 0.14795)\": -0.4969, \"(0.14795, 0.1507)\": -0.4446, \"(0.1507, 0.1861)\": -0.2722, \"(0.1861, 0.20124999999999998)\": -0.1924, \"(0.20124999999999998, 0.3358)\": -0.2305, \"(0.3358, 0.3456)\": -0.1741, \"(0.3456, 0.35755000000000003)\": -0.068, \"(0.35755000000000003, 0.3703)\": 0.0047, \"(0.3703, 0.39235)\": 0.0473, \"(0.39235, 0.4087)\": 0.1107, \"(0.4087, 0.4229)\": 0.1686, \"(0.4229, 0.4486)\": 0.2243, \"(0.4486, 0.48865000000000003)\": 0.2736, \"(0.48865000000000003, 0.54825)\": 0.2405, \"(0.54825, 0.5892999999999999)\": 0.2819, \"(0.5892999999999999, 0.65835)\": 0.3155, \"(0.65835, 0.7680499999999999)\": 0.3513, \"(0.7680499999999999, 0.99795)\": 0.3892, \"(0.99795, 1.058)\": 0.4487}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.02729, 0.049945)\": 0.6969, \"(0.049945, 0.06971)\": 0.7425, \"(0.06971, 0.099305)\": 0.487, \"(0.099305, 0.10635)\": 0.1218, \"(0.10635, 0.1243)\": 0.0684, \"(0.1243, 0.14795)\": 0.0254, \"(0.14795, 0.1507)\": 0.068, \"(0.1507, 0.1861)\": -0.0039, \"(0.1861, 0.20124999999999998)\": 0.0087, \"(0.20124999999999998, 0.3358)\": 0.1418, \"(0.3358, 0.3456)\": 0.1794, \"(0.3456, 0.35755000000000003)\": 0.1979, \"(0.35755000000000003, 0.3703)\": 0.2255, \"(0.3703, 0.39235)\": 0.2811, \"(0.39235, 0.4087)\": 0.314, \"(0.4087, 0.4229)\": 0.3524, \"(0.4229, 0.4486)\": 0.3975, \"(0.4486, 0.48865000000000003)\": 0.4436, \"(0.48865000000000003, 0.54825)\": 0.5859, \"(0.54825, 0.5892999999999999)\": 0.6484, \"(0.5892999999999999, 0.65835)\": 0.7153, \"(0.65835, 0.7680499999999999)\": 0.7927, \"(0.7680499999999999, 0.99795)\": 0.8637, \"(0.99795, 1.058)\": 0.9008}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: concavity_worst\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.022775)\": -0.769, \"(0.022775, 0.024655)\": -0.671, \"(0.024655, 0.052095)\": -0.846, \"(0.052095, 0.10575)\": -0.943, \"(0.10575, 0.1313)\": -0.843, \"(0.1313, 0.14545000000000002)\": -0.745, \"(0.14545000000000002, 0.1694)\": -0.646, \"(0.1694, 0.1843)\": -0.54, \"(0.1843, 0.19235000000000002)\": -0.438, \"(0.19235000000000002, 0.1996)\": -0.332, \"(0.1996, 0.20695)\": -0.234, \"(0.20695, 0.20795)\": -0.081, \"(0.20795, 0.2539)\": 0.187, \"(0.2539, 0.273)\": 0.284, \"(0.273, 0.33975)\": 0.385, \"(0.33975, 0.3663)\": 0.486, \"(0.3663, 0.37695)\": 0.586, \"(0.37695, 0.39765)\": 0.698, \"(0.39765, 0.41025)\": 0.797, \"(0.41025, 1.252)\": 0.897}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.022775)\": -1.429, \"(0.022775, 0.024655)\": -1.337, \"(0.024655, 0.052095)\": -1.568, \"(0.052095, 0.10575)\": -1.701, \"(0.10575, 0.1313)\": -1.62, \"(0.1313, 0.14545000000000002)\": -1.521, \"(0.14545000000000002, 0.1694)\": -1.427, \"(0.1694, 0.1843)\": -1.324, \"(0.1843, 0.19235000000000002)\": -1.207, \"(0.19235000000000002, 0.1996)\": -1.093, \"(0.1996, 0.20695)\": -0.982, \"(0.20695, 0.20795)\": -0.814, \"(0.20795, 0.2539)\": -0.518, \"(0.2539, 0.273)\": -0.08, \"(0.273, 0.33975)\": 0.033, \"(0.33975, 0.3663)\": 0.265, \"(0.3663, 0.37695)\": 0.365, \"(0.37695, 0.39765)\": -0.026, \"(0.39765, 0.41025)\": -0.308, \"(0.41025, 1.252)\": -0.23}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.022775)\": -0.109, \"(0.022775, 0.024655)\": -0.005, \"(0.024655, 0.052095)\": -0.123, \"(0.052095, 0.10575)\": -0.186, \"(0.10575, 0.1313)\": -0.065, \"(0.1313, 0.14545000000000002)\": 0.031, \"(0.14545000000000002, 0.1694)\": 0.135, \"(0.1694, 0.1843)\": 0.244, \"(0.1843, 0.19235000000000002)\": 0.332, \"(0.19235000000000002, 0.1996)\": 0.428, \"(0.1996, 0.20695)\": 0.514, \"(0.20695, 0.20795)\": 0.653, \"(0.20795, 0.2539)\": 0.891, \"(0.2539, 0.273)\": 0.648, \"(0.273, 0.33975)\": 0.737, \"(0.33975, 0.3663)\": 0.708, \"(0.3663, 0.37695)\": 0.807, \"(0.37695, 0.39765)\": 1.423, \"(0.39765, 0.41025)\": 1.902, \"(0.41025, 1.252)\": 2.024}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: concave points_worst\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.0, 0.02814)\": -0.771, \"(0.02814, 0.08293)\": -0.653, \"(0.08293, 0.08555)\": -0.533, \"(0.08555, 0.093225)\": -0.403, \"(0.093225, 0.1055)\": -0.234, \"(0.1055, 0.11510000000000001)\": -0.117, \"(0.11510000000000001, 0.1346)\": 0.002, \"(0.1346, 0.14545000000000002)\": 0.121, \"(0.14545000000000002, 0.15175)\": 0.241, \"(0.15175, 0.1603)\": 0.365, \"(0.1603, 0.1722)\": 0.539, \"(0.1722, 0.17695)\": 0.661, \"(0.17695, 0.18359999999999999)\": 0.781, \"(0.18359999999999999, 0.194)\": 0.9, \"(0.194, 0.2019)\": 1.022, \"(0.2019, 0.21275)\": 1.14, \"(0.21275, 0.2383)\": 1.259, \"(0.2383, 0.26865)\": 1.378, \"(0.26865, 0.291)\": 1.494}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.0, 0.02814)\": -1.316, \"(0.02814, 0.08293)\": -1.204, \"(0.08293, 0.08555)\": -1.003, \"(0.08555, 0.093225)\": -0.715, \"(0.093225, 0.1055)\": -0.419, \"(0.1055, 0.11510000000000001)\": -0.299, \"(0.11510000000000001, 0.1346)\": -0.172, \"(0.1346, 0.14545000000000002)\": -0.125, \"(0.14545000000000002, 0.15175)\": 0.077, \"(0.15175, 0.1603)\": 0.185, \"(0.1603, 0.1722)\": 0.112, \"(0.1722, 0.17695)\": 0.052, \"(0.17695, 0.18359999999999999)\": 0.152, \"(0.18359999999999999, 0.194)\": 0.267, \"(0.194, 0.2019)\": 0.392, \"(0.2019, 0.21275)\": 0.477, \"(0.21275, 0.2383)\": 0.593, \"(0.2383, 0.26865)\": 0.693, \"(0.26865, 0.291)\": 0.803}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.0, 0.02814)\": -0.226, \"(0.02814, 0.08293)\": -0.101, \"(0.08293, 0.08555)\": -0.064, \"(0.08555, 0.093225)\": -0.091, \"(0.093225, 0.1055)\": -0.049, \"(0.1055, 0.11510000000000001)\": 0.065, \"(0.11510000000000001, 0.1346)\": 0.176, \"(0.1346, 0.14545000000000002)\": 0.367, \"(0.14545000000000002, 0.15175)\": 0.406, \"(0.15175, 0.1603)\": 0.545, \"(0.1603, 0.1722)\": 0.966, \"(0.1722, 0.17695)\": 1.27, \"(0.17695, 0.18359999999999999)\": 1.41, \"(0.18359999999999999, 0.194)\": 1.533, \"(0.194, 0.2019)\": 1.653, \"(0.2019, 0.21275)\": 1.803, \"(0.21275, 0.2383)\": 1.926, \"(0.2383, 0.26865)\": 2.063, \"(0.26865, 0.291)\": 2.186}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: symmetry_worst\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.1565, 0.165)\": -0.295, \"(0.165, 0.19055)\": -0.472, \"(0.19055, 0.24485)\": -0.549, \"(0.24485, 0.25225)\": -0.469, \"(0.25225, 0.2583)\": -0.392, \"(0.2583, 0.26635)\": -0.31, \"(0.26635, 0.26959999999999995)\": -0.23, \"(0.26959999999999995, 0.27495)\": -0.112, \"(0.27495, 0.28035)\": -0.034, \"(0.28035, 0.28815)\": 0.046, \"(0.28815, 0.2986)\": 0.125, \"(0.2986, 0.31745)\": 0.202, \"(0.31745, 0.32125000000000004)\": 0.281, \"(0.32125000000000004, 0.33065)\": 0.363, \"(0.33065, 0.35335)\": 0.444, \"(0.35335, 0.36085)\": 0.526, \"(0.36085, 0.3702)\": 0.624, \"(0.3702, 0.4223)\": 0.705, \"(0.4223, 0.4697)\": 0.785, \"(0.4697, 0.6638)\": 0.867}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.1565, 0.165)\": -0.839, \"(0.165, 0.19055)\": -0.743, \"(0.19055, 0.24485)\": -0.802, \"(0.24485, 0.25225)\": -0.663, \"(0.25225, 0.2583)\": -0.595, \"(0.2583, 0.26635)\": -0.479, \"(0.26635, 0.26959999999999995)\": -0.388, \"(0.26959999999999995, 0.27495)\": -0.253, \"(0.27495, 0.28035)\": -0.172, \"(0.28035, 0.28815)\": -0.1, \"(0.28815, 0.2986)\": -0.015, \"(0.2986, 0.31745)\": 0.043, \"(0.31745, 0.32125000000000004)\": 0.141, \"(0.32125000000000004, 0.33065)\": 0.21, \"(0.33065, 0.35335)\": 0.276, \"(0.35335, 0.36085)\": 0.357, \"(0.36085, 0.3702)\": 0.348, \"(0.3702, 0.4223)\": 0.395, \"(0.4223, 0.4697)\": 0.478, \"(0.4697, 0.6638)\": 0.538}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.1565, 0.165)\": 0.249, \"(0.165, 0.19055)\": -0.201, \"(0.19055, 0.24485)\": -0.296, \"(0.24485, 0.25225)\": -0.276, \"(0.25225, 0.2583)\": -0.188, \"(0.2583, 0.26635)\": -0.14, \"(0.26635, 0.26959999999999995)\": -0.072, \"(0.26959999999999995, 0.27495)\": 0.03, \"(0.27495, 0.28035)\": 0.104, \"(0.28035, 0.28815)\": 0.191, \"(0.28815, 0.2986)\": 0.264, \"(0.2986, 0.31745)\": 0.361, \"(0.31745, 0.32125000000000004)\": 0.42, \"(0.32125000000000004, 0.33065)\": 0.516, \"(0.33065, 0.35335)\": 0.612, \"(0.35335, 0.36085)\": 0.694, \"(0.36085, 0.3702)\": 0.901, \"(0.3702, 0.4223)\": 1.015, \"(0.4223, 0.4697)\": 1.092, \"(0.4697, 0.6638)\": 1.197}\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: fractal_dimension_worst\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(0.05504, 0.058984999999999996)\": 0.1558, \"(0.058984999999999996, 0.065905)\": 0.0938, \"(0.065905, 0.070015)\": 0.0421, \"(0.070015, 0.071645)\": -0.0137, \"(0.071645, 0.07281)\": -0.0856, \"(0.07281, 0.075845)\": -0.1383, \"(0.075845, 0.083565)\": -0.1924, \"(0.083565, 0.08926)\": -0.3371, \"(0.08926, 0.09129999999999999)\": -0.285, \"(0.09129999999999999, 0.09222)\": -0.2283, \"(0.09222, 0.094545)\": -0.1756, \"(0.094545, 0.095845)\": -0.1188, \"(0.095845, 0.09595500000000001)\": -0.0641, \"(0.09595500000000001, 0.09849)\": 0.188, \"(0.09849, 0.1008)\": 0.244, \"(0.1008, 0.1018)\": 0.3042, \"(0.1018, 0.10569999999999999)\": 0.5739, \"(0.10569999999999999, 0.1074)\": 0.6302, \"(0.1074, 0.11810000000000001)\": 0.4862, \"(0.11810000000000001, 0.12475)\": 0.5412, \"(0.12475, 0.14024999999999999)\": 0.5946, \"(0.14024999999999999, 0.2075)\": 0.6515}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(0.05504, 0.058984999999999996)\": -0.1488, \"(0.058984999999999996, 0.065905)\": -0.1634, \"(0.065905, 0.070015)\": -0.1018, \"(0.070015, 0.071645)\": -0.1659, \"(0.071645, 0.07281)\": -0.2246, \"(0.07281, 0.075845)\": -0.2831, \"(0.075845, 0.083565)\": -0.3593, \"(0.083565, 0.08926)\": -0.8641, \"(0.08926, 0.09129999999999999)\": -0.8008, \"(0.09129999999999999, 0.09222)\": -0.734, \"(0.09222, 0.094545)\": -0.6937, \"(0.094545, 0.095845)\": -0.6426, \"(0.095845, 0.09595500000000001)\": -0.5681, \"(0.09595500000000001, 0.09849)\": -0.3082, \"(0.09849, 0.1008)\": -0.2557, \"(0.1008, 0.1018)\": -0.2006, \"(0.1018, 0.10569999999999999)\": -0.5367, \"(0.10569999999999999, 0.1074)\": -0.4808, \"(0.1074, 0.11810000000000001)\": -0.0451, \"(0.11810000000000001, 0.12475)\": 0.0507, \"(0.12475, 0.14024999999999999)\": 0.0965, \"(0.14024999999999999, 0.2075)\": 0.119}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(0.05504, 0.058984999999999996)\": 0.4604, \"(0.058984999999999996, 0.065905)\": 0.3511, \"(0.065905, 0.070015)\": 0.186, \"(0.070015, 0.071645)\": 0.1385, \"(0.071645, 0.07281)\": 0.0533, \"(0.07281, 0.075845)\": 0.0064, \"(0.075845, 0.083565)\": -0.0255, \"(0.083565, 0.08926)\": 0.1899, \"(0.08926, 0.09129999999999999)\": 0.2307, \"(0.09129999999999999, 0.09222)\": 0.2773, \"(0.09222, 0.094545)\": 0.3426, \"(0.094545, 0.095845)\": 0.405, \"(0.095845, 0.09595500000000001)\": 0.4399, \"(0.09595500000000001, 0.09849)\": 0.6842, \"(0.09849, 0.1008)\": 0.7436, \"(0.1008, 0.1018)\": 0.8091, \"(0.1018, 0.10569999999999999)\": 1.6844, \"(0.10569999999999999, 0.1074)\": 1.7412, \"(0.1074, 0.11810000000000001)\": 1.0176, \"(0.11810000000000001, 0.12475)\": 1.0318, \"(0.12475, 0.14024999999999999)\": 1.0928, \"(0.14024999999999999, 0.2075)\": 1.184}\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "all_graphs = []\n",
+    "for dataset_name in benchmark_utils.get_avaialble_datasets():\n",
+    "    ebm = benchmark_utils.get_ebm(dataset_name)\n",
+    "    _, _, _, _, feature_names = benchmark_utils.get_dataset(dataset_name)\n",
+    "    for feature_idx, feature_name in enumerate(feature_names):\n",
+    "        graph = graphs.extract_graph(ebm, feature_idx)\n",
+    "        if graph.feature_type == \"continuous\":\n",
+    "            graph = graphs.simplify_graph(graph, 0.05)\n",
+    "        graph_as_text = graphs.graph_to_text(graph, max_tokens=10000)\n",
+    "        all_graphs.append((graph, graph_as_text))\n",
+    "        print(graph_as_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save the graphs (pickle)\n",
+    "import pickle\n",
+    "with open(\"all_graphs.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(all_graphs, f)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tmcd",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/benchmarks/notebooks/value.ipynb b/benchmarks/notebooks/value.ipynb
new file mode 100644
index 0000000..58123e8
--- /dev/null
+++ b/benchmarks/notebooks/value.ipynb
@@ -0,0 +1,1954 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Read Value Benchmark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add parent directory to path\n",
+    "import sys\n",
+    "sys.path.append('..')\n",
+    "\n",
+    "import t2ebm\n",
+    "from t2ebm import graphs\n",
+    "from t2ebm import prompts\n",
+    "\n",
+    "import json\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the benchmark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load graphs (pickle)\n",
+    "with open(\"all_graphs.pkl\", \"rb\") as f:\n",
+    "    all_graphs = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def graph_mean(graph, x_val):\n",
+    "    \"\"\"Returns the mean of the graph at x_val.\"\"\"\n",
+    "    # find the bin that x_val is in\n",
+    "    bin_index = 0\n",
+    "    for idx, x_bin in enumerate(graph.x_vals):\n",
+    "        if x_val >= x_bin[0] and x_val <= x_bin[1]:\n",
+    "            bin_index = idx\n",
+    "            break\n",
+    "    # return the mean of that bin\n",
+    "    return graph.scores[bin_index]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: longitude\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(-124.35, -124.10499999999999)\": -50430.1, \"(-124.10499999999999, -124.08500000000001)\": -38925.6, \"(-124.08500000000001, -124.07499999999999)\": -23742.3, \"(-124.07499999999999, -123.3)\": -12526.0, \"(-123.3, -122.955)\": -1690.2, \"(-122.955, -122.66499999999999)\": 19040.8, \"(-122.66499999999999, -122.60499999999999)\": 29856.3, \"(-122.60499999999999, -122.58500000000001)\": 44315.6, \"(-122.58500000000001, -122.555)\": 75515.2, \"(-122.555, -122.455)\": 86444.1, \"(-122.455, -122.445)\": 99533.8, \"(-122.445, -122.42500000000001)\": 112351.5, \"(-122.42500000000001, -122.405)\": 89733.4, \"(-122.405, -122.39500000000001)\": 78586.0, \"(-122.39500000000001, -122.375)\": 46429.6, \"(-122.375, -122.36500000000001)\": 35622.6, \"(-122.36500000000001, -122.305)\": 20538.8, \"(-122.305, -122.155)\": 6386.6, \"(-122.155, -120.92500000000001)\": 24722.9, \"(-120.92500000000001, -120.91499999999999)\": 54457.6, \"(-120.91499999999999, -120.89500000000001)\": 34017.2, \"(-120.89500000000001, -120.86500000000001)\": 18216.5, \"(-120.86500000000001, -120.725)\": 6143.7, \"(-120.725, -120.63499999999999)\": 17429.8, \"(-120.63499999999999, -120.485)\": 407.5, \"(-120.485, -120.405)\": -15764.1, \"(-120.405, -120.10499999999999)\": 1041.6, \"(-120.10499999999999, -120.095)\": 24030.2, \"(-120.095, -119.91499999999999)\": -2161.3, \"(-119.91499999999999, -119.85499999999999)\": -20610.8, \"(-119.85499999999999, -119.795)\": -31705.4, \"(-119.795, -119.755)\": -20112.3, \"(-119.755, -119.525)\": -3774.8, \"(-119.525, -119.505)\": 10442.2, \"(-119.505, -119.295)\": -10555.7, \"(-119.295, -119.215)\": 3582.5, \"(-119.215, -118.905)\": -15819.3, \"(-118.905, -118.695)\": -2790.4, \"(-118.695, -118.57499999999999)\": 13581.3, \"(-118.57499999999999, -118.525)\": 26358.2, \"(-118.525, -118.495)\": 44919.9, \"(-118.495, -118.375)\": 60453.4, \"(-118.375, -118.35499999999999)\": 38572.6, \"(-118.35499999999999, -118.305)\": 21183.4, \"(-118.305, -118.265)\": -6755.4, \"(-118.265, -118.14500000000001)\": -17830.0, \"(-118.14500000000001, -117.985)\": -7071.0, \"(-117.985, -117.755)\": -26435.2, \"(-117.755, -117.725)\": -50667.3, \"(-117.725, -117.64500000000001)\": -63305.2, \"(-117.64500000000001, -117.57499999999999)\": -50999.4, \"(-117.57499999999999, -117.35499999999999)\": -38880.5, \"(-117.35499999999999, -117.285)\": -64800.9, \"(-117.285, -117.155)\": -47182.2, \"(-117.155, -117.13499999999999)\": -65749.6, \"(-117.13499999999999, -116.995)\": -77340.8, \"(-116.995, -116.795)\": -64524.7, \"(-116.795, -116.205)\": -53643.1, \"(-116.205, -116.1)\": -64388.1, \"(-116.1, -115.525)\": -75181.7, \"(-115.525, -115.1)\": -57014.4, \"(-115.1, -114.595)\": -74654.1, \"(-114.595, -114.31)\": -100620.1}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(-124.35, -124.10499999999999)\": -57749.3, \"(-124.10499999999999, -124.08500000000001)\": -46703.5, \"(-124.08500000000001, -124.07499999999999)\": -34644.5, \"(-124.07499999999999, -123.3)\": -20263.6, \"(-123.3, -122.955)\": -10198.1, \"(-122.955, -122.66499999999999)\": 10561.3, \"(-122.66499999999999, -122.60499999999999)\": 24254.2, \"(-122.60499999999999, -122.58500000000001)\": 34301.3, \"(-122.58500000000001, -122.555)\": 63992.2, \"(-122.555, -122.455)\": 76785.0, \"(-122.455, -122.445)\": 91169.4, \"(-122.445, -122.42500000000001)\": 103719.6, \"(-122.42500000000001, -122.405)\": 81277.9, \"(-122.405, -122.39500000000001)\": 66955.5, \"(-122.39500000000001, -122.375)\": 35631.1, \"(-122.375, -122.36500000000001)\": 24396.3, \"(-122.36500000000001, -122.305)\": 14520.6, \"(-122.305, -122.155)\": -1221.8, \"(-122.155, -120.92500000000001)\": 11630.1, \"(-120.92500000000001, -120.91499999999999)\": 11031.4, \"(-120.91499999999999, -120.89500000000001)\": 19105.3, \"(-120.89500000000001, -120.86500000000001)\": 4469.7, \"(-120.86500000000001, -120.725)\": -1198.1, \"(-120.725, -120.63499999999999)\": 7919.4, \"(-120.63499999999999, -120.485)\": -11276.9, \"(-120.485, -120.405)\": -23035.0, \"(-120.405, -120.10499999999999)\": -4074.0, \"(-120.10499999999999, -120.095)\": -10802.7, \"(-120.095, -119.91499999999999)\": -13216.2, \"(-119.91499999999999, -119.85499999999999)\": -33171.4, \"(-119.85499999999999, -119.795)\": -38315.5, \"(-119.795, -119.755)\": -24784.5, \"(-119.755, -119.525)\": -13160.2, \"(-119.525, -119.505)\": -4048.0, \"(-119.505, -119.295)\": -18789.9, \"(-119.295, -119.215)\": -8493.9, \"(-119.215, -118.905)\": -20485.4, \"(-118.905, -118.695)\": -7647.9, \"(-118.695, -118.57499999999999)\": 4745.1, \"(-118.57499999999999, -118.525)\": 17156.2, \"(-118.525, -118.495)\": 33913.3, \"(-118.495, -118.375)\": 52480.8, \"(-118.375, -118.35499999999999)\": 34068.9, \"(-118.35499999999999, -118.305)\": 14693.0, \"(-118.305, -118.265)\": -11878.1, \"(-118.265, -118.14500000000001)\": -21370.7, \"(-118.14500000000001, -117.985)\": -11803.1, \"(-117.985, -117.755)\": -35281.9, \"(-117.755, -117.725)\": -58041.5, \"(-117.725, -117.64500000000001)\": -72526.8, \"(-117.64500000000001, -117.57499999999999)\": -61627.3, \"(-117.57499999999999, -117.35499999999999)\": -45444.2, \"(-117.35499999999999, -117.285)\": -74287.3, \"(-117.285, -117.155)\": -55258.2, \"(-117.155, -117.13499999999999)\": -74456.9, \"(-117.13499999999999, -116.995)\": -86582.5, \"(-116.995, -116.795)\": -73433.2, \"(-116.795, -116.205)\": -69635.5, \"(-116.205, -116.1)\": -75131.9, \"(-116.1, -115.525)\": -97151.1, \"(-115.525, -115.1)\": -73988.5, \"(-115.1, -114.595)\": -91086.2, \"(-114.595, -114.31)\": -120109.7}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(-124.35, -124.10499999999999)\": -43110.8, \"(-124.10499999999999, -124.08500000000001)\": -31147.7, \"(-124.08500000000001, -124.07499999999999)\": -12840.0, \"(-124.07499999999999, -123.3)\": -4788.4, \"(-123.3, -122.955)\": 6817.8, \"(-122.955, -122.66499999999999)\": 27520.3, \"(-122.66499999999999, -122.60499999999999)\": 35458.4, \"(-122.60499999999999, -122.58500000000001)\": 54329.8, \"(-122.58500000000001, -122.555)\": 87038.2, \"(-122.555, -122.455)\": 96103.2, \"(-122.455, -122.445)\": 107898.1, \"(-122.445, -122.42500000000001)\": 120983.4, \"(-122.42500000000001, -122.405)\": 98188.9, \"(-122.405, -122.39500000000001)\": 90216.5, \"(-122.39500000000001, -122.375)\": 57228.1, \"(-122.375, -122.36500000000001)\": 46849.0, \"(-122.36500000000001, -122.305)\": 26556.9, \"(-122.305, -122.155)\": 13995.0, \"(-122.155, -120.92500000000001)\": 37815.7, \"(-120.92500000000001, -120.91499999999999)\": 97883.7, \"(-120.91499999999999, -120.89500000000001)\": 48929.0, \"(-120.89500000000001, -120.86500000000001)\": 31963.3, \"(-120.86500000000001, -120.725)\": 13485.5, \"(-120.725, -120.63499999999999)\": 26940.3, \"(-120.63499999999999, -120.485)\": 12092.0, \"(-120.485, -120.405)\": -8493.1, \"(-120.405, -120.10499999999999)\": 6157.2, \"(-120.10499999999999, -120.095)\": 58863.0, \"(-120.095, -119.91499999999999)\": 8893.5, \"(-119.91499999999999, -119.85499999999999)\": -8050.3, \"(-119.85499999999999, -119.795)\": -25095.3, \"(-119.795, -119.755)\": -15440.1, \"(-119.755, -119.525)\": 5610.6, \"(-119.525, -119.505)\": 24932.3, \"(-119.505, -119.295)\": -2321.4, \"(-119.295, -119.215)\": 15659.0, \"(-119.215, -118.905)\": -11153.1, \"(-118.905, -118.695)\": 2067.1, \"(-118.695, -118.57499999999999)\": 22417.4, \"(-118.57499999999999, -118.525)\": 35560.1, \"(-118.525, -118.495)\": 55926.4, \"(-118.495, -118.375)\": 68426.1, \"(-118.375, -118.35499999999999)\": 43076.4, \"(-118.35499999999999, -118.305)\": 27673.7, \"(-118.305, -118.265)\": -1632.7, \"(-118.265, -118.14500000000001)\": -14289.3, \"(-118.14500000000001, -117.985)\": -2338.9, \"(-117.985, -117.755)\": -17588.5, \"(-117.755, -117.725)\": -43293.2, \"(-117.725, -117.64500000000001)\": -54083.7, \"(-117.64500000000001, -117.57499999999999)\": -40371.5, \"(-117.57499999999999, -117.35499999999999)\": -32316.9, \"(-117.35499999999999, -117.285)\": -55314.6, \"(-117.285, -117.155)\": -39106.3, \"(-117.155, -117.13499999999999)\": -57042.4, \"(-117.13499999999999, -116.995)\": -68099.0, \"(-116.995, -116.795)\": -55616.1, \"(-116.795, -116.205)\": -37650.7, \"(-116.205, -116.1)\": -53644.2, \"(-116.1, -115.525)\": -53212.4, \"(-115.525, -115.1)\": -40040.3, \"(-115.1, -114.595)\": -58221.9, \"(-114.595, -114.31)\": -81130.6}\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "-38925.6"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example_graph = all_graphs[0]\n",
+    "print(example_graph[1])\n",
+    "\n",
+    "graph_mean(graphs.text_to_graph(example_graph[1]), -124.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "questions = []\n",
+    "for graph, graph_as_text in all_graphs:\n",
+    "    # only continuous graphs\n",
+    "    if graph.feature_type != \"continuous\":\n",
+    "        continue\n",
+    "    graph = graphs.text_to_graph(graph_as_text)\n",
+    "    # choose a random bin and then sample a random value from within that bin\n",
+    "    num_bins = len(graph.x_vals)\n",
+    "    bin_index = np.random.randint(num_bins)\n",
+    "    bin = graph.x_vals[bin_index]\n",
+    "    x_val = np.random.uniform(bin[0], bin[1])\n",
+    "    # round to 2 decimal places\n",
+    "    x_val = round(x_val, 2)\n",
+    "    # the actual value of the graph at x_value\n",
+    "    y_val = graph_mean(graph, x_val)\n",
+    "    #print(x_val, y_val)\n",
+    "\n",
+    "    question = \"\"\"Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n",
+    "    \n",
+    "The graph is provided in the following format:\n",
+    "    - The name of the feature depicted in the graph\n",
+    "    - The type of the feature (continuous, categorical, or boolean)\n",
+    "    - Mean values\n",
+    "    - Lower bounds of confidence interval (optional)\n",
+    "    - Upper bounds of confidence interval (optional)\\n\\n\"\"\"\n",
+    "    question += f\"Here is the graph:\\n\\n{graph_as_text}\\n\\n\"\n",
+    "    question += f\"Your task is to provide the mean value of the graph at {x_val}. What is the mean value of the graph at {x_val}?\"\n",
+    "    questions.append((question, str(y_val)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# subset 100 random questions\n",
+    "random.shuffle(questions)\n",
+    "questions = questions[:100]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n",
+      "    \n",
+      "The graph is provided in the following format:\n",
+      "    - The name of the feature depicted in the graph\n",
+      "    - The type of the feature (continuous, categorical, or boolean)\n",
+      "    - Mean values\n",
+      "    - Lower bounds of confidence interval (optional)\n",
+      "    - Upper bounds of confidence interval (optional)\n",
+      "\n",
+      "Here is the graph:\n",
+      "\n",
+      "This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.\n",
+      "\n",
+      "Feature Name: ejection_fraction\n",
+      "Feature Type: continuous\n",
+      "Means: {\"(14.0, 16.0)\": 4.55, \"(16.0, 22.5)\": 3.26, \"(22.5, 27.5)\": 1.89, \"(27.5, 32.5)\": -0.42, \"(32.5, 36.5)\": -1.76, \"(36.5, 39.0)\": 0.48, \"(39.0, 61.0)\": -0.83, \"(61.0, 67.5)\": 0.08, \"(67.5, 75.0)\": 0.8, \"(75.0, 80.0)\": -5.67}\n",
+      "Lower Bounds (95%-Confidence Interval): {\"(14.0, 16.0)\": 2.65, \"(16.0, 22.5)\": 2.42, \"(22.5, 27.5)\": 1.26, \"(27.5, 32.5)\": -0.83, \"(32.5, 36.5)\": -2.57, \"(36.5, 39.0)\": 0.17, \"(39.0, 61.0)\": -1.16, \"(61.0, 67.5)\": -0.39, \"(67.5, 75.0)\": 0.32, \"(75.0, 80.0)\": -8.05}\n",
+      "Upper Bounds (95%-Confidence Interval): {\"(14.0, 16.0)\": 6.45, \"(16.0, 22.5)\": 4.1, \"(22.5, 27.5)\": 2.51, \"(27.5, 32.5)\": -0.01, \"(32.5, 36.5)\": -0.95, \"(36.5, 39.0)\": 0.79, \"(39.0, 61.0)\": -0.49, \"(61.0, 67.5)\": 0.55, \"(67.5, 75.0)\": 1.28, \"(75.0, 80.0)\": -3.29}\n",
+      "\n",
+      "\n",
+      "Your task is to provide the mean value of the graph at 64.63. What is the mean value of the graph at 64.63?\n",
+      "SOLUTION:  0.08\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print a random question\n",
+    "random.shuffle(questions)\n",
+    "print(questions[0][0])\n",
+    "print('SOLUTION: ', questions[0][1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save the questions to json\n",
+    "with open(\"../benchmark/read-value\", \"w\") as f:\n",
+    "    json.dump(questions, f, indent=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Run the benchmark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load the json file\n",
+    "with open(\"../benchmark/read-value.json\", \"r\") as f:\n",
+    "    questions = json.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LLM RESPONSE:  To find the mean value of the graph at 400.75, we need to identify the interval that includes this value in the \"total_rooms\" feature.\n",
+      "\n",
+      "From the given data, we can see that the intervals are defined as follows:\n",
+      "- \"(385.5, 406.5)\": -46655.0\n",
+      "- \"(406.5, 413.5)\": -17805.2\n",
+      "\n",
+      "Since 400.75 falls within the interval \"(385.5, 406.5)\", the mean value of the graph at 400.75 is -46655.0.\n",
+      "SOLUTION:  -46655.0\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 8.93, we need to look at the interval that includes 8.93. \n",
+      "\n",
+      "From the provided data, we can see that the feature \"DrainageSystems\" is a continuous feature with intervals ranging from 0.0 to 14.0. The mean values for each interval are given.\n",
+      "\n",
+      "The interval that includes 8.93 is \"(8.5, 10.5)\" with a mean value of 0.02253.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 8.93 is 0.02253.\n",
+      "SOLUTION:  0.02253\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 14.15, we need to look at the intervals provided in the graph and determine which interval includes the value 14.15.\n",
+      "\n",
+      "From the given intervals:\n",
+      "- \"(13.5, 15.0)\": 0.01734\n",
+      "\n",
+      "Since 14.15 falls within the interval \"(13.5, 15.0)\", the mean value of the graph at 14.15 is 0.01734.\n",
+      "SOLUTION:  0.01734\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 26.19, we need to identify the interval that includes the value 26.19. \n",
+      "\n",
+      "From the provided data, we can see that the intervals are defined as follows:\n",
+      "- \"(25.79375, 26.46875)\": 0.355\n",
+      "\n",
+      "Since 26.19 falls within the interval \"(25.79375, 26.46875)\", the mean value of the graph at 26.19 is 0.355.\n",
+      "SOLUTION:  0.355\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.32, we need to interpolate the value within the given intervals. \n",
+      "\n",
+      "From the provided graph:\n",
+      "- For the interval (0.0, 0.5), the mean value is -0.004421.\n",
+      "- For the interval (0.5, 1.0), the mean value is 0.001379.\n",
+      "\n",
+      "Since 0.32 falls within the interval (0.0, 0.5), the mean value of the graph at 0.32 is -0.004421.\n",
+      "SOLUTION:  -0.004421\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 76.3, we need to identify the interval that includes the value 76.3. \n",
+      "\n",
+      "From the provided data, we can see that the interval \"(71.275, 78.28)\" corresponds to the range where the function predicts the same value. The mean value for this interval is -0.369.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 76.3 is -0.369.\n",
+      "SOLUTION:  -0.369\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 1617.97, we need to identify the interval that contains this value. \n",
+      "\n",
+      "The interval ranges provided in the data are:\n",
+      "- \"(1434.5, 1658.5)\": 0.585\n",
+      "- \"(1658.5, 1968.5)\": 0.948\n",
+      "\n",
+      "Since 1617.97 falls within the interval \"(1434.5, 1658.5)\", the mean value of the graph at 1617.97 is 0.585.\n",
+      "SOLUTION:  0.585\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 34.34, we need to look at the interval that includes this value. In this case, the interval that includes 34.34 is \"(34.325, 34.345)\".\n",
+      "\n",
+      "From the provided data:\n",
+      "- Mean value at \"(34.325, 34.345)\": 17113.4\n",
+      "\n",
+      "Therefore, the mean value of the graph at 34.34 is 17113.4.\n",
+      "SOLUTION:  17113.4\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.93, we need to look at the intervals provided in the graph and determine which interval includes the value 0.93.\n",
+      "\n",
+      "From the given graph:\n",
+      "- For the feature \"anaemia,\" the intervals are \"(0.0, 0.5)\" and \"(0.5, 1.0)\".\n",
+      "- The mean values for these intervals are -0.0818 and 0.0917, respectively.\n",
+      "\n",
+      "Since 0.93 falls within the interval \"(0.5, 1.0),\" the mean value of the graph at 0.93 is 0.0917.\n",
+      "SOLUTION:  0.0917\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 150.77, we need to locate the interval that includes this value. \n",
+      "\n",
+      "From the given data, we can see that the intervals are defined as follows:\n",
+      "- \"(144.5, 157.0)\": 0.0208\n",
+      "- \"(157.0, 170.5)\": 0.0623\n",
+      "\n",
+      "Since 150.77 falls within the interval \"(144.5, 157.0)\", the mean value at 150.77 is 0.0208.\n",
+      "SOLUTION:  0.0208\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 4.58, we need to identify the interval in which 4.58 falls and then determine the corresponding mean value.\n",
+      "\n",
+      "From the given data, we can see that the intervals are defined as follows:\n",
+      "- \"(4.3, 4.55)\": 3.328\n",
+      "- \"(4.55, 4.75)\": 2.995\n",
+      "\n",
+      "Since 4.58 falls within the interval \"(4.55, 4.75)\", the mean value of the graph at 4.58 is 2.995.\n",
+      "SOLUTION:  2.995\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 2.78 for the continuous feature \"DamsQuality,\" we need to identify the interval that includes 2.78. \n",
+      "\n",
+      "From the given data, we can see that the intervals are defined as follows:\n",
+      "- \"(0.0, 1.5)\": -0.02325\n",
+      "- \"(1.5, 2.5)\": -0.01532\n",
+      "- \"(2.5, 3.5)\": -0.01073\n",
+      "\n",
+      "Since 2.78 falls within the interval \"(2.5, 3.5)\", the mean value of the graph at 2.78 is -0.01073.\n",
+      "SOLUTION:  -0.01073\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 3.86, we need to identify the interval that includes the value 3.86. \n",
+      "\n",
+      "From the provided data, we can see that the interval \"(3.5, 4.5)\" corresponds to values between 3.5 and 4.5. The mean value for this interval is 0.254.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 3.86 is 0.254.\n",
+      "SOLUTION:  0.254\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.45, we need to look at the interval that includes 0.45. In this case, the interval \"(0.0, 0.5)\" includes 0.45.\n",
+      "\n",
+      "From the provided data:\n",
+      "- Mean value for the interval \"(0.0, 0.5)\" is 0.085.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 0.45 is 0.085.\n",
+      "SOLUTION:  0.085\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 109.98, we need to look at the intervals provided in the graph and determine which interval includes the value 109.98.\n",
+      "\n",
+      "The interval that includes 109.98 is \"(109.0, 110.0)\". In this interval, the mean value is 0.5269.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 109.98 is 0.5269.\n",
+      "SOLUTION:  0.5269\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 2.52, we need to identify the interval that includes 2.52. \n",
+      "\n",
+      "From the given intervals:\n",
+      "- \"(2.5, 3.5)\": -0.3026\n",
+      "\n",
+      "Since 2.52 falls within the interval (2.5, 3.5), the mean value of the graph at 2.52 is -0.3026.\n",
+      "SOLUTION:  -0.3026\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 6.11, we need to identify the interval that includes the value 6.11. \n",
+      "\n",
+      "From the given data, we can see that the intervals are in increments of 1. Therefore, the interval that includes 6.11 is \"(5.5, 6.5)\".\n",
+      "\n",
+      "Looking at the mean values provided for each interval, the mean value for the interval \"(5.5, 6.5)\" is 0.00575.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 6.11 is 0.00575.\n",
+      "SOLUTION:  0.00575\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 1152.44, we need to identify the interval in which 1152.44 falls and then determine the corresponding mean value.\n",
+      "\n",
+      "From the provided data, we can see that the interval \"(1033.5, 1222.5)\" includes the value 1152.44. The mean value for this interval is 0.932.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 1152.44 is 0.932.\n",
+      "SOLUTION:  0.932\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.07, we need to determine which interval 0.07 falls into based on the keys provided in the graph.\n",
+      "\n",
+      "In this case, the intervals provided are \"(0.0, 0.5)\" and \"(0.5, 1.0)\". Since 0.07 falls within the range of 0.0 to 0.5, we will use the mean value associated with this interval.\n",
+      "\n",
+      "From the graph:\n",
+      "- For the interval \"(0.0, 0.5)\", the mean value is -0.1077.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 0.07 is -0.1077.\n",
+      "SOLUTION:  -0.1077\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 2.3, we need to identify the interval that includes the value 2.3. \n",
+      "\n",
+      "From the given data, we see that the intervals are defined as follows:\n",
+      "- \"(1.5, 2.5)\": -0.1873\n",
+      "- \"(2.5, 3.5)\": -0.0302\n",
+      "\n",
+      "Since 2.3 falls within the interval \"(1.5, 2.5)\", the mean value of the graph at 2.3 is -0.1873.\n",
+      "SOLUTION:  -0.1873\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.29, we need to look at the intervals provided in the graph and identify the interval that includes the value 0.29.\n",
+      "\n",
+      "From the given intervals:\n",
+      "- \"(0.273, 0.33975)\": 0.385\n",
+      "- \"(0.33975, 0.3663)\": 0.486\n",
+      "\n",
+      "Since 0.29 falls within the interval \"(0.273, 0.33975)\", the mean value of the graph at 0.29 is 0.385.\n",
+      "SOLUTION:  0.385\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 5.94, we need to identify the interval that includes 5.94. \n",
+      "\n",
+      "From the given data, we can see that the intervals are defined as follows:\n",
+      "- \"(5.5, 6.5)\": 0.00567\n",
+      "\n",
+      "Since 5.94 falls within the interval \"(5.5, 6.5)\", the mean value of the graph at 5.94 is 0.00567.\n",
+      "SOLUTION:  0.00567\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 141941.44, we need to identify the interval in which this value falls and then look up the corresponding mean value.\n",
+      "\n",
+      "From the provided data, we can see that the intervals are defined as follows:\n",
+      "- \"(121482.61499999999, 148569.97)\": -0.0388\n",
+      "- \"(148569.97, 184522.325)\": -0.0796\n",
+      "\n",
+      "Since 141941.44 falls within the interval \"(121482.61499999999, 148569.97)\", the mean value of the graph at 141941.44 is -0.0388.\n",
+      "SOLUTION:  -0.0388\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.48, we need to determine the interval that includes the value 0.48. \n",
+      "\n",
+      "From the provided graph, we see that the feature \"IsActiveMember\" is continuous and the intervals are \"(0.0, 0.5)\" and \"(0.5, 1.0)\".\n",
+      "\n",
+      "Since 0.48 falls within the interval \"(0.0, 0.5)\", we can use the mean value associated with this interval.\n",
+      "\n",
+      "The mean value for the interval \"(0.0, 0.5)\" is -0.555.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 0.48 is -0.555.\n",
+      "SOLUTION:  -0.555\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 75.26, we need to identify the interval that includes this value. \n",
+      "\n",
+      "The interval \"(71.06, 76.52000000000001)\" includes the value 75.26. \n",
+      "\n",
+      "In this interval, the mean value is -1.223.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 75.26 is -1.223.\n",
+      "SOLUTION:  -1.223\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 13.73, we need to identify the interval that includes the value 13.73. \n",
+      "\n",
+      "From the given data, we see that the intervals are defined as follows:\n",
+      "- \"(0.0, 0.5)\": -0.297\n",
+      "- \"(0.5, 3.5)\": -0.074\n",
+      "- \"(3.5, 4.5)\": 0.644\n",
+      "- \"(4.5, 6.5)\": -0.723\n",
+      "- \"(6.5, 7.5)\": -0.542\n",
+      "- \"(7.5, 8.5)\": -0.665\n",
+      "- \"(8.5, 9.5)\": -0.926\n",
+      "- \"(9.5, 10.5)\": 0.423\n",
+      "- \"(10.5, 11.5)\": 0.59\n",
+      "- \"(11.5, 12.5)\": 0.27\n",
+      "- \"(12.5, 13.5)\": 0.534\n",
+      "- \"(13.5, 14.0)\": -0.133\n",
+      "\n",
+      "Since 13.73 falls within the interval \"(13.5, 14.0)\", the mean value of the graph at 13.73 is -0.133.\n",
+      "SOLUTION:  -0.133\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.85, we need to look at the intervals provided in the graph and interpolate the value at 0.85.\n",
+      "\n",
+      "From the graph:\n",
+      "- For the interval (0.5, 1.0), the mean value is -0.03391.\n",
+      "\n",
+      "Since 0.85 falls within the interval (0.5, 1.0), the mean value of the graph at 0.85 is -0.03391.\n",
+      "SOLUTION:  -0.03391\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.01, we need to identify the interval in which 0.01 falls and then determine the corresponding mean value.\n",
+      "\n",
+      "From the provided data, we can see that the interval containing 0.01 is \"(0.0089915, 0.01089)\" with a mean value of 0.3467.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 0.01 is 0.3467.\n",
+      "SOLUTION:  0.3467\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.09, we need to identify the interval in which 0.09 falls and then determine the corresponding mean value.\n",
+      "\n",
+      "From the given data, we can see that the intervals are defined as follows:\n",
+      "- \"(0.0, 0.5)\": -0.02526\n",
+      "- \"(0.5, 1.5)\": -0.02147\n",
+      "\n",
+      "Since 0.09 falls within the interval \"(0.0, 0.5)\", the mean value of the graph at 0.09 is -0.02526.\n",
+      "SOLUTION:  -0.02526\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 6083.89, we need to identify the interval in which this value falls and then determine the corresponding mean value.\n",
+      "\n",
+      "From the given data, we can see that the intervals are defined as follows:\n",
+      "- \"(6031.5, 6171.5)\": 1.439\n",
+      "- \"(6171.5, 8753.0)\": 2.236\n",
+      "\n",
+      "Since 6083.89 falls within the interval \"(6031.5, 6171.5)\", the mean value of the graph at 6083.89 is 1.439.\n",
+      "SOLUTION:  1.439\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.02, we need to locate the interval that contains 0.02. \n",
+      "\n",
+      "From the given data, we can see that the interval \"(0.019545, 0.02068)\" contains 0.02. \n",
+      "\n",
+      "In this interval, the mean value is -0.252. \n",
+      "\n",
+      "Therefore, the mean value of the graph at 0.02 is -0.252.\n",
+      "SOLUTION:  -0.252\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 14.78, we need to determine the interval in which 14.78 falls and then extract the corresponding mean value from the provided data.\n",
+      "\n",
+      "The interval that includes 14.78 is \"(11.5, 14.0)\". From the given data:\n",
+      "- Mean value for \"(11.5, 14.0)\": 0.0391\n",
+      "\n",
+      "Therefore, the mean value of the graph at 14.78 is 0.0391.\n",
+      "SOLUTION:  0.05506\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 15.03, we need to identify the interval that includes the value 15.03. \n",
+      "\n",
+      "From the given data, we can see that the intervals are defined as follows:\n",
+      "- \"(14.665, 15.010000000000002)\": -0.82\n",
+      "- \"(15.010000000000002, 15.485)\": -0.718\n",
+      "\n",
+      "Since 15.03 falls within the interval \"(15.010000000000002, 15.485)\", the mean value of the graph at 15.03 is -0.718.\n",
+      "SOLUTION:  -0.718\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 31.93, we need to look at the intervals provided in the JSON object for the feature \"radius_worst\" and determine which interval includes the value 31.93.\n",
+      "\n",
+      "From the given intervals:\n",
+      "- \"(30.71, 36.04)\": 1.406\n",
+      "\n",
+      "The interval \"(30.71, 36.04)\" includes the value 31.93. The mean value for this interval is 1.406.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 31.93 is 1.406.\n",
+      "SOLUTION:  1.406\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.23, we need to determine which interval 0.23 falls into based on the keys provided in the graph.\n",
+      "\n",
+      "From the given intervals:\n",
+      "- \"(0.0, 0.5)\": -0.4751\n",
+      "- \"(0.5, 1.0)\": 0.2339\n",
+      "\n",
+      "Since 0.23 falls within the interval \"(0.0, 0.5)\", the mean value of the graph at 0.23 is -0.4751.\n",
+      "SOLUTION:  -0.4751\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 26.86, we need to look at the intervals provided in the graph for the feature \"NativeCountry\" and determine which interval contains the value 26.86.\n",
+      "\n",
+      "From the given intervals:\n",
+      "- \"(24.5, 26.5)\": -0.35\n",
+      "- \"(26.5, 29.5)\": -0.489\n",
+      "\n",
+      "Since 26.86 falls within the interval \"(26.5, 29.5)\", the mean value of the graph at 26.86 is -0.489.\n",
+      "SOLUTION:  -0.489\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.06, we need to look at the intervals provided in the graph and determine which interval contains the value 0.06.\n",
+      "\n",
+      "From the given intervals:\n",
+      "- \"(0.059715, 0.06078)\": -0.0163\n",
+      "- \"(0.06078, 0.061385)\": -0.0618\n",
+      "\n",
+      "Since 0.06 falls within the interval \"(0.059715, 0.06078)\", the mean value of the graph at 0.06 is -0.0163.\n",
+      "SOLUTION:  -0.0163\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.05, we need to identify the interval in which 0.05 falls and then determine the corresponding mean value.\n",
+      "\n",
+      "From the provided data, we can see that the interval containing 0.05 is \"(0.044705, 0.059585)\". In this interval, the mean value is -0.205.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 0.05 is -0.205.\n",
+      "SOLUTION:  -0.205\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.06, we need to look at the interval that includes 0.06. In this case, the interval that includes 0.06 is \"(0.059495, 0.06042)\".\n",
+      "\n",
+      "From the provided data:\n",
+      "- The mean value for the interval \"(0.059495, 0.06042)\" is 0.2605.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 0.06 is 0.2605.\n",
+      "SOLUTION:  0.2605\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.07, we need to determine which interval 0.07 falls into based on the keys provided in the graph.\n",
+      "\n",
+      "From the given intervals:\n",
+      "- \"(0.0, 0.5)\": 0.01719\n",
+      "- \"(0.5, 1.0)\": -0.00954\n",
+      "\n",
+      "Since 0.07 falls within the interval \"(0.0, 0.5)\", the mean value of the graph at 0.07 is 0.01719.\n",
+      "SOLUTION:  0.01719\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 1.95, we need to identify the interval in which 1.95 falls and then determine the corresponding mean value.\n",
+      "\n",
+      "From the given data:\n",
+      "- Interval \"(1.65, 2.45)\" corresponds to a mean value of 7.28.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 1.95 is 7.28.\n",
+      "SOLUTION:  7.28\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.0, we look at the provided JSON object for the feature \"concavity_se\" which is a continuous feature. In the JSON object, we see that the key \"(0.0, 0.001156)\" corresponds to the interval that includes 0.0.\n",
+      "\n",
+      "From the JSON object, the mean value for this interval is -0.6445. Therefore, the mean value of the graph at 0.0 for the feature \"concavity_se\" is -0.6445.\n",
+      "SOLUTION:  -0.6445\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 6.44, we need to identify the interval that includes the value 6.44. \n",
+      "\n",
+      "From the given data, we can see that the intervals are defined as follows:\n",
+      "- \"(1.0, 4.5)\": -19998.0\n",
+      "- \"(4.5, 7.5)\": -7788.2\n",
+      "- \"(7.5, 16.5)\": -10680.2\n",
+      "- \"(16.5, 18.5)\": -6304.4\n",
+      "- \"(18.5, 27.5)\": -1760.6\n",
+      "- \"(27.5, 34.5)\": 2164.8\n",
+      "- \"(34.5, 38.5)\": -912.5\n",
+      "- \"(38.5, 41.5)\": 4199.6\n",
+      "- \"(41.5, 45.5)\": -497.4\n",
+      "- \"(45.5, 47.5)\": -5189.8\n",
+      "- \"(47.5, 48.5)\": 5201.0\n",
+      "- \"(48.5, 49.5)\": 2159.0\n",
+      "- \"(49.5, 50.5)\": 6135.7\n",
+      "- \"(50.5, 51.5)\": 11513.8\n",
+      "- \"(51.5, 52.0)\": 27549.7\n",
+      "\n",
+      "Since 6.44 falls within the interval \"(4.5, 7.5)\", the mean value of the graph at 6.44 is -7788.2.\n",
+      "SOLUTION:  -7788.2\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 1.59, we need to interpolate between the intervals provided in the graph. \n",
+      "\n",
+      "The intervals given are:\n",
+      "- (1.0, 1.5): Mean = -0.009\n",
+      "- (1.5, 2.5): Mean = 0.534\n",
+      "\n",
+      "Since 1.59 falls within the (1.5, 2.5) interval, we can interpolate the mean value at 1.59 as follows:\n",
+      "\n",
+      "Mean at 1.59 = Mean at 1.5 + ((Mean at 2.5 - Mean at 1.5) / (2.5 - 1.5)) * (1.59 - 1.5)\n",
+      "Mean at 1.59 = 0.534 + ((0.534 - (-0.009)) / (2.5 - 1.5)) * (1.59 - 1.5)\n",
+      "Mean at 1.59 = 0.534 + (0.543 / 1) * 0.09\n",
+      "Mean at 1.59 = 0.534 + 0.04887\n",
+      "Mean at 1.59 ≈ 0.58287\n",
+      "\n",
+      "Therefore, the mean value of the graph at 1.59 is approximately 0.58287.\n",
+      "SOLUTION:  0.534\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.14, we need to look at the intervals provided in the graph and determine which interval includes the value 0.14.\n",
+      "\n",
+      "From the given intervals:\n",
+      "- \"(0.12455, 0.13845000000000002)\": 0.334\n",
+      "\n",
+      "Since 0.14 falls within the interval \"(0.12455, 0.13845000000000002)\", the mean value of the graph at 0.14 is 0.334.\n",
+      "SOLUTION:  0.396\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 34.1, we need to identify the interval that includes the value 34.1. \n",
+      "\n",
+      "From the given data, we can see that the intervals are defined as follows:\n",
+      "- \"(29.5, 33.5)\": 0.042\n",
+      "- \"(33.5, 36.5)\": 0.351\n",
+      "\n",
+      "Since 34.1 falls within the interval \"(33.5, 36.5)\", the mean value of the graph at 34.1 is 0.351.\n",
+      "SOLUTION:  0.351\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 27.13, we need to identify the interval that includes this value. \n",
+      "\n",
+      "From the provided data, we can see that the intervals are defined as follows:\n",
+      "- \"(26.5, 28.5)\": -0.036\n",
+      "- \"(28.5, 30.5)\": -0.0039\n",
+      "\n",
+      "Since 27.13 falls within the interval \"(26.5, 28.5)\", the mean value of the graph at 27.13 is -0.036.\n",
+      "SOLUTION:  -0.036\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.04, we need to look at the interval that includes 0.04. In this case, the interval \"(0.0, 0.5)\" includes 0.04.\n",
+      "\n",
+      "From the provided data:\n",
+      "- For the interval \"(0.0, 0.5)\", the mean value is 0.3225.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 0.04 is 0.3225.\n",
+      "SOLUTION:  0.3225\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 1.27 for the continuous feature \"NumOfProducts,\" we need to determine which interval 1.27 falls into. \n",
+      "\n",
+      "From the given data:\n",
+      "- \"(1.0, 1.5)\": -0.918\n",
+      "- \"(1.5, 2.5)\": 0.96\n",
+      "\n",
+      "Since 1.27 falls within the interval (1.0, 1.5), the mean value of the graph at 1.27 is -0.918.\n",
+      "SOLUTION:  -0.918\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 966.62, we need to identify the interval in which 966.62 falls and then determine the corresponding mean value from the provided data.\n",
+      "\n",
+      "From the given intervals:\n",
+      "- \"(964.5, 976.5)\": 23227.2\n",
+      "- \"(976.5, 978.5)\": 18664.6\n",
+      "\n",
+      "Since 966.62 falls within the interval \"(964.5, 976.5)\", the mean value of the graph at 966.62 is 23227.2.\n",
+      "SOLUTION:  23227.2\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 4568.36, we need to identify the interval in which this value falls. \n",
+      "\n",
+      "The intervals provided in the graph are:\n",
+      "- \"(4243.5, 4401.0)\": 1.45\n",
+      "- \"(4401.0, 4668.5)\": -1.82\n",
+      "\n",
+      "Since 4568.36 falls within the interval \"(4401.0, 4668.5)\", the mean value of the graph at 4568.36 is -1.82.\n",
+      "SOLUTION:  -1.82\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 14.65, we need to look at the intervals provided in the graph and determine which interval includes the value 14.65.\n",
+      "\n",
+      "From the given intervals:\n",
+      "- \"(11.5, 14.5)\": 0.1954\n",
+      "- \"(14.5, 15.0)\": 0.1393\n",
+      "\n",
+      "Since 14.65 falls within the interval \"(11.5, 14.5)\", the mean value of the graph at 14.65 is 0.1954.\n",
+      "SOLUTION:  0.1393\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 198.65, we need to identify the interval that includes this value. \n",
+      "\n",
+      "The interval that includes 198.65 is \"(187.5, 198.5)\". \n",
+      "\n",
+      "From the given data:\n",
+      "- Mean value for the interval \"(187.5, 198.5)\" is 1.853.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 198.65 is 1.853.\n",
+      "SOLUTION:  2.022\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 243849.53, we need to locate the interval that contains this value. \n",
+      "\n",
+      "The interval that contains 243849.53 is \"(242500.0, 243500.0)\".\n",
+      "\n",
+      "From the given data:\n",
+      "- The mean value for the interval \"(242500.0, 243500.0)\" is 1.107.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 243849.53 is 1.107.\n",
+      "SOLUTION:  1.516\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 18765.31, we need to locate the interval that includes this value in the keys of the means provided.\n",
+      "\n",
+      "The interval that includes 18765.31 is \"(0.0, 50418.515)\". The mean value for this interval is -0.132.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 18765.31 is -0.132.\n",
+      "SOLUTION:  -0.132\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 65.26, we need to identify the interval that includes this value. The interval \"(64.5, 65.5)\" includes 65.26. \n",
+      "\n",
+      "From the provided data:\n",
+      "- Mean value for the interval \"(64.5, 65.5)\" is 0.0.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 65.26 is 0.0.\n",
+      "SOLUTION:  0.0\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 24.6, we need to identify the interval in which 24.6 falls and then determine the corresponding mean value.\n",
+      "\n",
+      "From the provided data, we can see that the intervals are defined as follows:\n",
+      "- \"(23.59, 24.795)\": -0.272\n",
+      "- \"(24.795, 25.18)\": -0.1\n",
+      "\n",
+      "Since 24.6 falls within the interval \"(23.59, 24.795)\", the mean value of the graph at 24.6 is -0.272.\n",
+      "SOLUTION:  -0.272\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.49 for the continuous feature \"MaritalStatus,\" we need to look at the interval that includes 0.49. \n",
+      "\n",
+      "From the given data, we see that the intervals are defined as follows:\n",
+      "- \"(0.0, 0.5)\": -0.368\n",
+      "- \"(0.5, 1.5)\": 0.724\n",
+      "\n",
+      "Since 0.49 falls within the interval \"(0.0, 0.5)\", the mean value for this interval is -0.368.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 0.49 is -0.368.\n",
+      "SOLUTION:  -0.368\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 5.82, we look at the interval that contains 5.82 in the keys of the JSON object provided.\n",
+      "\n",
+      "From the given data, we see that the interval that contains 5.82 is \"(5.59195, 5.8294)\".\n",
+      "\n",
+      "In this interval, the mean value is 40032.8.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 5.82 is 40032.8.\n",
+      "SOLUTION:  56900.2\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 14.4, we need to look at the intervals provided in the graph and determine which interval contains the value 14.4.\n",
+      "\n",
+      "From the given intervals:\n",
+      "- \"(13.5, 16.0)\": 0.03206\n",
+      "\n",
+      "Since 14.4 falls within the interval \"(13.5, 16.0)\", the mean value of the graph at 14.4 is 0.03206.\n",
+      "SOLUTION:  0.03206\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.01, we need to locate the interval that contains 0.01 in the keys of the means provided.\n",
+      "\n",
+      "From the given data, we can see that the interval containing 0.01 is \"(0.009878999999999999, 0.0099215)\" with a mean value of -0.0268.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 0.01 is -0.0268.\n",
+      "SOLUTION:  -0.0546\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 1.47, we need to identify the interval that includes the value 1.47. \n",
+      "\n",
+      "From the given data, we see that the intervals are defined as follows:\n",
+      "- \"(0.0, 0.5)\": -0.03259\n",
+      "- \"(0.5, 1.5)\": -0.02272\n",
+      "- \"(1.5, 2.5)\": -0.0157\n",
+      "\n",
+      "Since 1.47 falls within the interval \"(0.5, 1.5)\", the mean value of the graph at 1.47 is -0.02272.\n",
+      "SOLUTION:  -0.02272\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.01, we need to locate the interval that contains 0.01. From the provided data, we can see that the intervals are not directly given in the data. However, we can infer the interval that contains 0.01 by looking at the neighboring intervals.\n",
+      "\n",
+      "The interval that contains 0.01 is likely \"(0.009857000000000001, 0.010665000000000001)\" based on the pattern of the intervals provided.\n",
+      "\n",
+      "From the mean values given for this interval:\n",
+      "- Mean value at (0.009857000000000001, 0.010665000000000001): 0.1863\n",
+      "\n",
+      "Therefore, the mean value of the graph at 0.01 is 0.1863.\n",
+      "SOLUTION:  0.1863\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 46.58, we need to identify the interval in which 46.58 falls and then determine the corresponding mean value.\n",
+      "\n",
+      "From the provided data, we can see that the interval \"(45.650000000000006, 48.349999999999994)\" includes the value 46.58. The mean value for this interval is 0.626.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 46.58 is 0.626.\n",
+      "SOLUTION:  0.626\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 1.27, we need to look at the interval that includes 1.27. \n",
+      "\n",
+      "From the provided data, we can see that the interval \"(1.275, 1.3925)\" includes 1.27. The mean value for this interval is 1.283.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 1.27 is 1.283.\n",
+      "SOLUTION:  1.018\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 4.75, we need to look at the interval that includes 4.75. In this case, 4.75 falls within the interval \"(4.5, 5.5)\".\n",
+      "\n",
+      "From the provided data:\n",
+      "- Mean value at \"(4.5, 5.5)\": 0.00051\n",
+      "\n",
+      "Therefore, the mean value of the graph at 4.75 is 0.00051.\n",
+      "SOLUTION:  0.00051\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 139172.54, we need to locate the interval that contains this value in the \"id\" feature.\n",
+      "\n",
+      "From the given data, we can see that the intervals are defined as follows:\n",
+      "- \"(147131.5, 161901.5)\": -0.0139\n",
+      "- \"(161901.5, 162437.5)\": -0.0745\n",
+      "\n",
+      "Since 139172.54 falls within the interval \"(147131.5, 161901.5)\", the mean value of the graph at 139172.54 is -0.0139.\n",
+      "SOLUTION:  0.0211\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.28 for the feature \"Tenure,\" we need to identify the interval that includes 0.28. \n",
+      "\n",
+      "From the provided intervals:\n",
+      "- \"(0.0, 0.5)\": -0.3765\n",
+      "- \"(0.5, 1.5)\": -0.0692\n",
+      "\n",
+      "Since 0.28 falls within the interval \"(0.0, 0.5)\", the mean value of the graph at 0.28 is -0.3765.\n",
+      "SOLUTION:  -0.3765\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.91, we need to look at the intervals provided in the data and determine which interval includes the value 0.91.\n",
+      "\n",
+      "From the given data, we can see that the intervals are defined as follows:\n",
+      "- \"(0.875, 0.95)\": -0.9\n",
+      "- \"(0.95, 1.1400000000000001)\": -0.15\n",
+      "\n",
+      "Since 0.91 falls within the interval \"(0.875, 0.95)\", the mean value of the graph at 0.91 is -0.9.\n",
+      "SOLUTION:  -0.9\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 3.38, we need to identify the interval in which 3.38 falls and then determine the corresponding mean value within that interval.\n",
+      "\n",
+      "Given the intervals provided in the graph:\n",
+      "- \"(2.5, 3.5)\": -0.01049\n",
+      "- \"(3.5, 4.5)\": -0.00528\n",
+      "\n",
+      "Since 3.38 falls within the interval \"(2.5, 3.5)\", the mean value of the graph at 3.38 is -0.01049.\n",
+      "SOLUTION:  -0.01049\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 130.05, we need to identify the interval that includes this value. \n",
+      "\n",
+      "The interval that includes 130.05 is \"(129.5, 130.5)\".\n",
+      "\n",
+      "From the given data:\n",
+      "- Mean value at \"(129.5, 130.5)\" is 0.953.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 130.05 is 0.953.\n",
+      "SOLUTION:  0.953\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 20.22, we need to locate the interval that includes the value 20.22. \n",
+      "\n",
+      "From the given data, we can see that the intervals are defined as follows:\n",
+      "- \"(17.5, 20.5)\": -50740.7\n",
+      "- \"(20.5, 22.5)\": -59049.5\n",
+      "\n",
+      "Since 20.22 falls within the interval \"(20.5, 22.5)\", the mean value of the graph at 20.22 is -59049.5.\n",
+      "SOLUTION:  -50740.7\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 2.8, we need to look at the intervals provided in the graph and determine which interval includes the value 2.8.\n",
+      "\n",
+      "From the given intervals:\n",
+      "- \"(0.0, 0.5)\": -0.02553\n",
+      "- \"(0.5, 2.5)\": -0.02038\n",
+      "- \"(2.5, 4.5)\": -0.0099\n",
+      "\n",
+      "The interval that includes 2.8 is \"(2.5, 4.5)\" with a mean value of -0.0099.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 2.8 is -0.0099.\n",
+      "SOLUTION:  -0.0099\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 1723.82, we need to identify the interval in which 1723.82 falls and then determine the corresponding mean value.\n",
+      "\n",
+      "From the given data, we can see that the intervals are as follows:\n",
+      "- \"(1209.5, 1808.0)\": -1.712\n",
+      "- \"(1808.0, 2204.5)\": -3.029\n",
+      "\n",
+      "Since 1723.82 falls within the interval \"(1209.5, 1808.0)\", the mean value of the graph at 1723.82 is -1.712.\n",
+      "SOLUTION:  -1.712\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 6176.67, we need to locate the interval that contains this value in the keys of the JSON object representing the graph.\n",
+      "\n",
+      "From the given data, we can see that the feature \"RoomService\" is a continuous feature with mean values provided for different intervals. The interval that contains 6176.67 is not explicitly listed in the JSON object, so we need to determine the mean value at this point by interpolation.\n",
+      "\n",
+      "The interval that contains 6176.67 falls between \"(3249.5, 14327.0)\" and \"(3006.0, 3196.5)\". To estimate the mean value at 6176.67, we can perform linear interpolation using the mean values at the boundaries of these intervals.\n",
+      "\n",
+      "Let's calculate the interpolated mean value at 6176.67:\n",
+      "- For the interval \"(3249.5, 14327.0)\":\n",
+      "  - Lower bound: 3249.5, Mean: -4.146\n",
+      "- For the interval \"(3006.0, 3196.5)\":\n",
+      "  - Upper bound: 3196.5, Mean: -2.984\n",
+      "\n",
+      "Now, we can interpolate the mean value at 6176.67 using linear interpolation:\n",
+      "\\[ \\text{Interpolated mean} = \\frac{(6176.67 - 3196.5) \\times (-4.146) + (3249.5 - 6176.67) \\times (-2.984)}{3249.5 - 3196.5} \\]\n",
+      "\n",
+      "Calculating the interpolated mean value at 6176.67:\n",
+      "\\[ \\text{Interpolated mean} = \\frac{(6176.67 - 3196.5) \\times (-4.146) + (3249.5 - 6176.67) \\times (-2.984)}{3249.5 - 3196.5} \\]\n",
+      "\\[ \\text{Interpolated mean} = \\frac{(2980.17) \\times (-4.146) + (-2927.17) \\times (-2.984)}{53} \\]\n",
+      "\\[ \\text{Interpolated mean} = \\frac{(-12380.77) + (8736.77)}{53} \\]\n",
+      "\\[ \\text{Interpolated mean} = \\frac{-3644}{53} \\]\n",
+      "\\[ \\text{Interpolated mean} \\approx -68.77 \\]\n",
+      "\n",
+      "Therefore, the estimated mean value of the graph at 6176.67 is approximately -68.77.\n",
+      "SOLUTION:  -4.146\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 4.16, we need to determine the interval in which 4.16 falls and then extract the corresponding mean value from the given data.\n",
+      "\n",
+      "The interval that contains 4.16 is \"(3.5, 4.5)\". In this interval, the mean value is -0.529.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 4.16 is -0.529.\n",
+      "SOLUTION:  -0.529\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 68251.52, we need to locate the interval that contains this value in the \"id\" feature.\n",
+      "\n",
+      "From the given data, we can see that the intervals are defined as follows:\n",
+      "- \"(66668.0, 67776.0)\": 0.0019\n",
+      "- \"(67776.0, 75664.5)\": 1e-05\n",
+      "\n",
+      "Since 68251.52 falls within the interval \"(66668.0, 67776.0)\", the mean value of the graph at 68251.52 is 0.0019.\n",
+      "SOLUTION:  1e-05\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 15804780.51, we need to identify the interval in which this value falls and then determine the corresponding mean value.\n",
+      "\n",
+      "The interval keys in the provided data represent ranges where the function predicts the same value. We need to locate the interval that contains the value 15804780.51.\n",
+      "\n",
+      "From the given data, we can see that the intervals are defined as follows:\n",
+      "- \"(15799214.0, 15807559.5)\": 0.0581\n",
+      "- \"(15807559.5, 15812616.5)\": -0.0049\n",
+      "\n",
+      "The value 15804780.51 falls within the interval \"(15799214.0, 15807559.5)\".\n",
+      "\n",
+      "Therefore, the mean value of the graph at 15804780.51 is 0.0581.\n",
+      "SOLUTION:  0.0581\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 1555.23, we need to identify the interval in which this value falls. \n",
+      "\n",
+      "The intervals provided in the graph are:\n",
+      "- (0.0, 135.5)\n",
+      "- (135.5, 215.5)\n",
+      "- (215.5, 500.5)\n",
+      "- (500.5, 727.5)\n",
+      "- (727.5, 799.5)\n",
+      "- (799.5, 831.5)\n",
+      "- (831.5, 872.5)\n",
+      "- (872.5, 993.5)\n",
+      "- (993.5, 1430.5)\n",
+      "- (1430.5, 1514.5)\n",
+      "- (1514.5, 1796.0)\n",
+      "- (1796.0, 1909.5)\n",
+      "- (1909.5, 1970.0)\n",
+      "- (1970.0, 2571.5)\n",
+      "- (2571.5, 2582.0)\n",
+      "- (2582.0, 2657.0)\n",
+      "- (2657.0, 3710.5)\n",
+      "- (3710.5, 4089.0)\n",
+      "- (4089.0, 5089.5)\n",
+      "- (5089.5, 24133.0)\n",
+      "\n",
+      "Since 1555.23 falls within the interval (1430.5, 1514.5), the mean value of the graph at 1555.23 is -1.512.\n",
+      "SOLUTION:  -2.212\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 41.25, we need to identify the interval that includes this value. In this case, 41.25 falls within the interval \"(40.5, 41.5)\".\n",
+      "\n",
+      "Looking at the provided data for the mean values, the mean value for the interval \"(40.5, 41.5)\" is -0.354.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 41.25 is -0.354.\n",
+      "SOLUTION:  -0.354\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.02, we need to look at the interval that contains 0.02. In this case, the interval that includes 0.02 is \"(0.019315, 0.023185)\".\n",
+      "\n",
+      "From the provided data:\n",
+      "- Mean value for the interval \"(0.019315, 0.023185)\" is -0.466.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 0.02 is -0.466.\n",
+      "SOLUTION:  -0.466\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 6.38, we need to identify the interval that includes 6.38. \n",
+      "\n",
+      "From the given data, we can see that the intervals are as follows:\n",
+      "- \"(5.5, 6.5)\": 0.00515\n",
+      "- \"(6.5, 8.5)\": 0.01107\n",
+      "\n",
+      "Since 6.38 falls within the interval \"(5.5, 6.5)\", the mean value of the graph at 6.38 is 0.00515.\n",
+      "SOLUTION:  0.00515\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 3.14, we need to identify the interval that includes 3.14 in the keys provided in the JSON object for the HoursPerWeek feature.\n",
+      "\n",
+      "From the given data, we can see that the interval \"(2.5, 4.5)\" includes 3.14. The mean value for this interval is -1.909.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 3.14 for the HoursPerWeek feature is -1.909.\n",
+      "SOLUTION:  -1.909\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 12.16, we need to identify the interval that includes the value 12.16. \n",
+      "\n",
+      "From the given data, we can see that the intervals are defined as follows:\n",
+      "- \"(3.0, 14.5)\": 125210.2\n",
+      "- \"(14.5, 25.5)\": 92452.9\n",
+      "\n",
+      "Since 12.16 falls within the interval \"(3.0, 14.5)\", the mean value of the graph at 12.16 is 125210.2.\n",
+      "SOLUTION:  125210.2\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 14.97, we need to locate the interval that includes this value in the \"Urbanization\" feature.\n",
+      "\n",
+      "From the given intervals:\n",
+      "- \"(13.5, 16.0)\": 0.05902\n",
+      "\n",
+      "Since 14.97 falls within the interval \"(13.5, 16.0)\", the mean value of the graph at 14.97 for the \"Urbanization\" feature is 0.05902.\n",
+      "SOLUTION:  0.05902\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.17, we need to look at the interval that includes 0.17. \n",
+      "\n",
+      "From the provided data, we see that the interval that includes 0.17 is \"(0.1603, 0.1722)\" with a mean value of 0.539. \n",
+      "\n",
+      "Therefore, the mean value of the graph at 0.17 is 0.539.\n",
+      "SOLUTION:  0.539\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at -114.37, we need to locate the interval that contains this value in the provided data.\n",
+      "\n",
+      "From the given data, we can see that the longitude feature is a continuous feature with intervals representing ranges where the function predicts the same value. The mean values are provided for each interval.\n",
+      "\n",
+      "The interval that contains -114.37 is \"(-114.595, -114.31)\" with a mean value of -100620.1.\n",
+      "\n",
+      "Therefore, the mean value of the graph at -114.37 is -100620.1.\n",
+      "SOLUTION:  -100620.1\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 239.62, we need to locate the interval that contains this value. \n",
+      "\n",
+      "From the given data, we can see that the feature \"creatinine_phosphokinase\" is a continuous feature with mean values provided for different intervals. We need to identify the interval that includes the value 239.62.\n",
+      "\n",
+      "Looking at the intervals provided, we can see that the interval \"(2307.5, 2444.0)\" contains the value 239.62. \n",
+      "\n",
+      "From the mean values provided for this interval:\n",
+      "- Lower bound: 2307.5\n",
+      "- Upper bound: 2444.0\n",
+      "- Mean value: 5.81\n",
+      "\n",
+      "Therefore, the mean value of the graph at 239.62 is 5.81.\n",
+      "SOLUTION:  -0.16\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 1305.91, we need to determine the interval in which 1305.91 falls and then extract the corresponding mean value from the provided data.\n",
+      "\n",
+      "The interval boundaries are as follows:\n",
+      "- \"(1248.5, 1341.0)\": 0.911\n",
+      "- \"(1341.0, 1801.0)\": 1.01\n",
+      "\n",
+      "Since 1305.91 falls within the interval \"(1248.5, 1341.0)\", the mean value of the graph at 1305.91 is 0.911.\n",
+      "SOLUTION:  0.911\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 1.26, we need to look at the interval that includes 1.26. In this case, the interval that includes 1.26 is \"(1.197, 1.2365)\" with a mean value of -0.461. \n",
+      "\n",
+      "Therefore, the mean value of the graph at 1.26 is -0.461.\n",
+      "SOLUTION:  -0.384\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.0, we need to locate the interval that includes 0.0 in the keys provided in the JSON object. In this case, the interval that includes 0.0 is \"(0.0008948, 0.001092)\".\n",
+      "\n",
+      "From the given data:\n",
+      "- Mean value for the interval \"(0.0008948, 0.001092)\" is 0.2818.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 0.0 is 0.2818.\n",
+      "SOLUTION:  0.2818\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 9.36, we need to determine the interval in which 9.36 falls and then extract the corresponding mean value from the provided data.\n",
+      "\n",
+      "Given that the intervals in the graph are in increments of 1 (e.g., \"(0.0, 0.5)\", \"(0.5, 1.5)\", etc.), we can see that 9.36 falls within the interval \"(9.5, 10.5)\".\n",
+      "\n",
+      "From the provided data:\n",
+      "- Mean value for the interval \"(9.5, 10.5)\": 0.02782\n",
+      "\n",
+      "Therefore, the mean value of the graph at 9.36 is 0.02782.\n",
+      "SOLUTION:  0.0237\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.37, we need to look at the interval that includes 0.37. In this case, the interval that includes 0.37 is \"(0.35, 0.45)\".\n",
+      "\n",
+      "From the given data:\n",
+      "- Mean value for the interval \"(0.35, 0.45)\" is 7.27.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 0.37 is 7.27.\n",
+      "SOLUTION:  7.27\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 1.58, we need to identify the interval that includes the value 1.58. \n",
+      "\n",
+      "From the given data, we can see that the intervals are defined as follows:\n",
+      "- \"(1.5, 2.5)\": -0.01613\n",
+      "\n",
+      "Since 1.58 falls within the interval \"(1.5, 2.5)\", the mean value of the graph at 1.58 is -0.01613.\n",
+      "SOLUTION:  -0.01613\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 37.49, we need to identify the interval that includes this value. \n",
+      "\n",
+      "The interval \"(36.5, 39.0)\" includes the value 37.49. \n",
+      "\n",
+      "From the provided data:\n",
+      "- Mean value for the interval \"(36.5, 39.0)\" is 0.48.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 37.49 is 0.48.\n",
+      "SOLUTION:  0.48\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 52.67, we need to identify the interval that includes the value 52.67. \n",
+      "\n",
+      "From the given data, we can see that the interval \"(30.5, 52.0)\" includes the value 52.67. \n",
+      "\n",
+      "In this interval, the mean value is 5.56.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 52.67 is 5.56.\n",
+      "SOLUTION:  3.38\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 34.12, we need to identify the interval in which 34.12 falls and then determine the corresponding mean value within that interval.\n",
+      "\n",
+      "From the provided data, we can see that the intervals are as follows:\n",
+      "- \"(32.985, 34.730000000000004)\": 0.081\n",
+      "- \"(34.730000000000004, 41.21)\": 0.188\n",
+      "\n",
+      "Since 34.12 falls within the interval \"(32.985, 34.730000000000004)\", the mean value of the graph at 34.12 is 0.081.\n",
+      "SOLUTION:  0.081\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 1886.32, we need to identify the interval in which 1886.32 falls and then determine the corresponding mean value.\n",
+      "\n",
+      "From the provided data, we can see that the intervals are defined as follows:\n",
+      "- \"(1846.0, 1862.0)\": 3.823\n",
+      "- \"(1862.0, 1881.5)\": -1.36\n",
+      "\n",
+      "Since 1886.32 falls between 1862.0 and 1881.5, the mean value of the graph at 1886.32 would be -1.36.\n",
+      "SOLUTION:  4.781\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.92, we need to look at the intervals provided in the JSON object for the feature \"radius_se\". \n",
+      "\n",
+      "The interval that includes 0.92 is \"(0.7562, 0.9131)\" with a mean value of 0.594. \n",
+      "\n",
+      "Therefore, the mean value of the graph at 0.92 is 0.594.\n",
+      "SOLUTION:  0.683\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  To find the mean value of the graph at 0.57, we need to look at the intervals provided in the JSON object for the feature \"compactness_worst.\"\n",
+      "\n",
+      "The interval that includes 0.57 is \"(0.54825, 0.5892999999999999)\", and the mean value for this interval is 0.4651.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 0.57 for the feature \"compactness_worst\" is 0.4651.\n",
+      "SOLUTION:  0.4651\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "system_msg = \"You are an expert statistician and data scientist. You interpret global explanations produced by a generalized additive model (GAM). You answer all questions to the best of your ability, combining the data contained in the graph, any data set description you are given, and your knowledge about the real world.\"\n",
+    "for question in questions:\n",
+    "    messages = [{\"role\": \"system\", \"content\": system_msg}, {\"role\": \"user\", \"content\": question[0]}]\n",
+    "    response = t2ebm.utils.openai_completion_query('gpt-3.5-turbo-0125', messages, temperature=0.0)\n",
+    "    print('LLM RESPONSE: ', response)\n",
+    "    print('SOLUTION: ', question[1])\n",
+    "    print('-'*80)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Gemini"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import google.generativeai as genai\n",
+    "\n",
+    "genai.configure(api_key=os.environ['GEMINI_API_KEY'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LLM RESPONSE:  The mean value at 400.75 is **-46655.0**. \n",
+      "\n",
+      "You can find this by looking at the \"Means\" data for the feature \"total_rooms\". The value 400.75 falls within the interval \"(385.5, 406.5)\", and the corresponding mean value for this interval is -46655.0. \n",
+      "\n",
+      "SOLUTION:  -46655.0\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The provided interval for 8.93 is \"(8.5, 10.5)\" which corresponds to a mean value of **0.02253**. \n",
+      "\n",
+      "SOLUTION:  0.02253\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value at 14.15 falls within the interval **\"(13.5, 15.0)\"**.  \n",
+      "\n",
+      "Therefore, the mean value of the graph at 14.15 is **0.01734**. \n",
+      "\n",
+      "SOLUTION:  0.01734\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value at 26.19 is **0.355**. \n",
+      "\n",
+      "Here's why:\n",
+      "\n",
+      "* The provided JSON object represents a piecewise function.  \n",
+      "* The value 26.19 falls within the interval \"(25.79375, 26.46875)\".\n",
+      "* The mean value associated with this interval is 0.355. \n",
+      "\n",
+      "SOLUTION:  0.355\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value at 0.32 is **-0.004421**. \n",
+      "\n",
+      "Here's why:\n",
+      "\n",
+      "* The provided JSON tells us the mean values for intervals.\n",
+      "* 0.32 falls within the interval \"(0.0, 0.5)\".\n",
+      "* The mean value for the interval \"(0.0, 0.5)\" is -0.004421. \n",
+      "\n",
+      "Therefore, the GAM predicts a mean value of -0.004421 for any input within the range of 0.0 to 0.5 for the feature \"HasCrCard\". \n",
+      "\n",
+      "SOLUTION:  -0.004421\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 76.3 is **-0.369**. \n",
+      "\n",
+      "You can find this by looking at the \"Means\" dictionary in the provided JSON. The value 76.3 falls within the interval \"(71.275, 78.28)\", and the corresponding mean value for this interval is -0.369. \n",
+      "\n",
+      "SOLUTION:  -0.369\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The value 1617.97 falls within the interval **(1658.5, 1968.5)**.  The mean value of the graph for this interval is **0.948**. \n",
+      "\n",
+      "SOLUTION:  0.585\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 34.34 falls within the interval **\"(34.325, 34.345)\"**  for which the mean value is **17113.4**. \n",
+      "\n",
+      "SOLUTION:  17113.4\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 0.93 is **0.0917**. \n",
+      "\n",
+      "Here's why:\n",
+      "\n",
+      "* The provided JSON object represents a continuous feature named \"anaemia\".\n",
+      "* The `Means` dictionary shows the predicted mean values for different ranges of the feature:\n",
+      "    * For values between 0.0 and 0.5 (exclusive), the mean value is -0.0818.\n",
+      "    * For values between 0.5 and 1.0 (exclusive), the mean value is **0.0917**.\n",
+      "\n",
+      "Since 0.93 falls within the interval \"(0.5, 1.0)\", the mean value at 0.93 is **0.0917**. \n",
+      "\n",
+      "SOLUTION:  0.0917\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 150.77 is **0.0208**. \n",
+      "\n",
+      "This is because 150.77 falls within the interval \"(144.5, 157.0)\" in the provided data, and the corresponding mean value for that interval is 0.0208. \n",
+      "\n",
+      "SOLUTION:  0.0208\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value at 4.58 is **2.995**. \n",
+      "\n",
+      "Here's why:\n",
+      "\n",
+      "* The provided JSON represents the graph as a series of intervals along the x-axis ('sepal_length') and their corresponding mean predicted values on the y-axis. \n",
+      "* The interval \"(4.55, 4.75)\" contains the value 4.58.\n",
+      "* The mean value associated with the interval \"(4.55, 4.75)\" is 2.995. \n",
+      "\n",
+      "SOLUTION:  2.995\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 2.78 is **-0.01073**. \n",
+      "\n",
+      "This is because the provided JSON object shows that the mean value is constant for the interval of 2.5 to 3.5, which includes the value of 2.78. \n",
+      "\n",
+      "SOLUTION:  -0.01073\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 3.86 is 0.254. \n",
+      "\n",
+      "This is because the provided JSON object shows that the mean value is the same for all ages between 3.5 and 4.5, which includes 3.86. \n",
+      "\n",
+      "SOLUTION:  0.254\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 0.45 is **0.085**. \n",
+      "\n",
+      "Here's why:\n",
+      "\n",
+      "* The provided JSON object shows the graph's behavior for the feature \"Parch\".\n",
+      "* The value 0.45 falls within the interval \"(0.0, 0.5)\".\n",
+      "* For the interval \"(0.0, 0.5)\", the mean value is given as 0.085. \n",
+      "\n",
+      "Therefore, the GAM predicts a mean value of 0.085 when \"Parch\" is 0.45. \n",
+      "\n",
+      "SOLUTION:  0.085\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 109.98 is **0.5269**. \n",
+      "\n",
+      "This is because the provided JSON object shows that the GAM predicts the same value for the interval \"(109.0, 110.0)\", which includes the value 109.98. The mean value for this interval is given as 0.5269. \n",
+      "\n",
+      "SOLUTION:  0.5269\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 2.52 is **-0.3026**. \n",
+      "\n",
+      "This is because the provided JSON object shows that the mean value is constant for the interval from 2.5 to 3.5. Since 2.52 falls within this interval, the mean value at 2.52 is the same as the mean value for the entire interval. \n",
+      "\n",
+      "SOLUTION:  -0.3026\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The provided interval for 6.11 falls within the \"(5.5, 6.5)\" range in the data.  \n",
+      "\n",
+      "Therefore, the mean value of the graph at 6.11 is **0.00575**. \n",
+      "\n",
+      "SOLUTION:  0.00575\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 1152.44 is **0.932**. \n",
+      "\n",
+      "Here's why:\n",
+      "\n",
+      "1. The provided JSON object represents a piecewise function where the mean value remains constant within each specified interval.\n",
+      "2. We need to find the interval that contains 1152.44.\n",
+      "3. Looking at the \"Means\" dictionary, we see that 1152.44 falls within the interval **\"(1033.5, 1222.5)\"**.\n",
+      "4. The corresponding mean value for this interval is **0.932**. \n",
+      "\n",
+      "SOLUTION:  0.932\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 0.07 is **-0.1077**. \n",
+      "\n",
+      "Here's why:\n",
+      "\n",
+      "* The provided JSON tells us that for the interval of **(0.0, 0.5)** on the x-axis (which includes 0.07), the mean predicted value is **-0.1077**. \n",
+      "\n",
+      "SOLUTION:  -0.1077\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value at 2.3 falls within the interval **\"(1.5, 2.5)\"**.  \n",
+      "\n",
+      "Therefore, the mean value of the graph at 2.3 is **-0.1873**. \n",
+      "\n",
+      "SOLUTION:  -0.1873\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 0.29 falls within the interval **(0.273, 0.33975)**.  The corresponding mean value for this interval is **0.385**. \n",
+      "\n",
+      "SOLUTION:  0.385\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 5.94 falls within the interval **(5.5, 6.5)**.  \n",
+      "\n",
+      "Therefore, the mean value at 5.94 is **0.00567**. \n",
+      "\n",
+      "SOLUTION:  0.00567\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 141941.44 is **-0.0796**. \n",
+      "\n",
+      "This is because 141941.44 falls within the interval **\"(148569.97, 184522.325)\"**  in the provided JSON object, and the corresponding mean value for this interval is -0.0796. \n",
+      "\n",
+      "SOLUTION:  -0.0388\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 0.48 is **-0.555**. \n",
+      "\n",
+      "Here's why:\n",
+      "\n",
+      "* The provided JSON object shows the graph is for the feature \"IsActiveMember\".\n",
+      "* The \"Means\" section tells us:\n",
+      "    * For values of IsActiveMember between 0.0 and 0.5 (inclusive of 0.0, exclusive of 0.5), the mean predicted value is -0.555.\n",
+      "    * For values of IsActiveMember between 0.5 and 1.0, the mean predicted value is 0.568.\n",
+      "\n",
+      "Since 0.48 falls within the interval (0.0, 0.5), the mean value at 0.48 is **-0.555**. \n",
+      "\n",
+      "SOLUTION:  -0.555\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 75.26 is **-1.223**. \n",
+      "\n",
+      "You can find this by looking at the \"Means\" dictionary in the JSON object. The key \"(71.06, 76.52000000000001)\" contains the value 75.26, and the corresponding value is -1.223. This means that for values of \"perimeter_worst\" between 71.06 and 76.52, the GAM predicts a mean value of -1.223. \n",
+      "\n",
+      "SOLUTION:  -1.223\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value at 13.73 is **-0.133**. \n",
+      "\n",
+      "Here's why:\n",
+      "\n",
+      "* The provided JSON object represents a continuous feature, meaning the model predicts the same value for a range of inputs.\n",
+      "* The value 13.73 falls within the interval \"(13.5, 14.0)\".\n",
+      "* The mean value associated with the interval \"(13.5, 14.0)\" is -0.133. \n",
+      "\n",
+      "SOLUTION:  -0.133\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 0.85 is **-0.03391**. \n",
+      "\n",
+      "Here's why:\n",
+      "\n",
+      "* The provided JSON tells us that the feature \"smoking\" is treated as a continuous variable in this GAM.\n",
+      "* The mean values are provided in intervals. Since 0.85 falls within the interval \"(0.5, 1.0)\", the corresponding mean value is used. \n",
+      "\n",
+      "SOLUTION:  -0.03391\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 0.01 is **0.3467**. \n",
+      "\n",
+      "This is because the provided JSON object shows that the mean value is constant for the interval **(0.0089915, 0.01089)** which includes the value of 0.01. \n",
+      "\n",
+      "SOLUTION:  0.3467\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 0.09 falls within the interval **\"(0.0, 0.5)\"**.  \n",
+      "\n",
+      "Therefore, the mean value at 0.09 is **-0.02526**. \n",
+      "\n",
+      "SOLUTION:  -0.02526\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 6083.89 is **1.81**. \n",
+      "\n",
+      "This is because the value 6083.89 falls within the interval \"(5218.5, 6171.5)\" in the provided data, and the corresponding mean value for this interval is 1.81. \n",
+      "\n",
+      "SOLUTION:  1.439\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 0.02 falls within the interval **(0.02068, 0.024730000000000002)**.  The corresponding mean value for this interval is **-0.31**. \n",
+      "\n",
+      "SOLUTION:  -0.252\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 14.78 falls within the interval **(14.0, 15.0)**.  \n",
+      "\n",
+      "Therefore, the mean value at 14.78 is **0.05506**. \n",
+      "\n",
+      "SOLUTION:  0.05506\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value at 15.03 is **-0.718**. \n",
+      "\n",
+      "This is because the provided JSON object shows that the mean value is -0.718 for the interval \"(15.010000000000002, 15.485)\", and 15.03 falls within this range. \n",
+      "\n",
+      "SOLUTION:  -0.718\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value at 31.93 is **1.274**. \n",
+      "\n",
+      "You can find this by looking at the \"Means\" dictionary. The value 31.93 falls within the interval \"(25.335, 30.71)\", and the corresponding mean value for this interval is 1.274. \n",
+      "\n",
+      "SOLUTION:  1.406\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The provided JSON object represents \"Gender\" as a **continuous** feature, which is illogical. Gender is a categorical variable, typically binary (Male/Female). \n",
+      "\n",
+      "Furthermore, the x-axis ranges (0.0 to 1.0) imply a continuous numerical scale, not categories. \n",
+      "\n",
+      "**This inconsistency makes it impossible to answer your question.** The data representation contradicts the nature of the feature it aims to describe. \n",
+      "\n",
+      "To provide a meaningful answer, the JSON object needs to be corrected. If \"Gender\" is indeed the intended feature, it should be represented as a categorical variable with appropriate category labels and corresponding values. \n",
+      "\n",
+      "SOLUTION:  -0.4751\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value at 26.86 is **-0.489**. \n",
+      "\n",
+      "This is because 26.86 falls within the interval \"(26.5, 29.5)\" in the provided data, and the mean value for that interval is -0.489. \n",
+      "\n",
+      "SOLUTION:  -0.489\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 0.06 falls within the interval **(0.059715, 0.06078)**.  \n",
+      "\n",
+      "The corresponding mean value for this interval is **-0.0163**. \n",
+      "\n",
+      "SOLUTION:  -0.0163\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 0.05 falls within the interval **\"(0.044705, 0.059585)\"**  for which the mean value is **-0.205**. \n",
+      "\n",
+      "SOLUTION:  -0.205\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 0.06 falls within the interval **\"(0.059495, 0.06042)\"**  which has a corresponding mean value of **0.2605**. \n",
+      "\n",
+      "SOLUTION:  0.2605\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The provided JSON represents \"sex\" as a continuous feature, which is unusual and likely incorrect. Sex is typically a categorical variable (male/female, or encoded as 0/1). \n",
+      "\n",
+      "However, focusing on the structure of the provided data:\n",
+      "\n",
+      "* **The key \"(0.0, 0.5)\" contains the value for 0.07**, because 0.07 falls within the interval between 0.0 and 0.5. \n",
+      "\n",
+      "* **The mean value associated with the key \"(0.0, 0.5)\" is 0.01719.**\n",
+      "\n",
+      "Therefore, according to the provided data, the mean value of the graph at 0.07 is **0.01719**. \n",
+      "\n",
+      "**Important Note:** It's crucial to review how \"sex\" is represented in the dataset and correct the feature type if needed.  The current representation as a continuous variable is likely flawed. \n",
+      "\n",
+      "SOLUTION:  0.01719\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 1.95 is **7.28**. \n",
+      "\n",
+      "Here's why:\n",
+      "\n",
+      "* The provided JSON data represents the graph in intervals.\n",
+      "* The value 1.95 falls within the interval **\"(1.65, 2.45)\"**.\n",
+      "* The mean value associated with the interval \"(1.65, 2.45)\" is **7.28**. \n",
+      "\n",
+      "SOLUTION:  7.28\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 0.0 is **-0.6445**. \n",
+      "\n",
+      "This is because the first interval in the \"Means\" dictionary is \"(0.0, 0.001156)\", which includes 0.0. The corresponding mean value for this interval is -0.6445. \n",
+      "\n",
+      "SOLUTION:  -0.6445\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value for housing_median_age = 6.44 can be found in the interval **(4.5, 7.5)**. \n",
+      "\n",
+      "The mean value for this interval is **-7788.2**. \n",
+      "\n",
+      "SOLUTION:  -7788.2\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 1.59 is **0.534**. \n",
+      "\n",
+      "Here's why:\n",
+      "\n",
+      "* The provided JSON object shows that the feature \"Pclass\" is treated as a continuous variable in this GAM, even though it might represent a categorical feature like passenger class in the real world.\n",
+      "* The interval \"(1.5, 2.5)\" includes the value 1.59.\n",
+      "* For the interval \"(1.5, 2.5)\", the mean value is 0.534. \n",
+      "\n",
+      "Therefore, the GAM predicts a mean value of 0.534 for any \"Pclass\" value falling within the interval (1.5, 2.5), including 1.59. \n",
+      "\n",
+      "SOLUTION:  0.534\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 0.14 is **0.396**. \n",
+      "\n",
+      "The provided JSON object shows that the value 0.14 falls within the interval \"(0.13845000000000002, 0.1634)\" for the feature \"smoothness_mean\".  The corresponding mean value for this interval is 0.396. \n",
+      "\n",
+      "SOLUTION:  0.396\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 34.1 is **0.042**. \n",
+      "\n",
+      "Here's why:\n",
+      "\n",
+      "* The provided JSON represents a piecewise function.  \n",
+      "* The value 34.1 falls within the interval \"(29.5, 33.5)\" in the \"Means\" dictionary.\n",
+      "* The associated mean value for this interval is 0.042. \n",
+      "\n",
+      "SOLUTION:  0.351\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 27.13 is **-0.036**. \n",
+      "\n",
+      "You can find this by looking at the \"Means\" dictionary in the JSON object. The value 27.13 falls within the interval \"(26.5, 28.5)\", and the corresponding mean value for this interval is -0.036. \n",
+      "\n",
+      "SOLUTION:  -0.036\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 0.04 is **0.3225**. \n",
+      "\n",
+      "Here's why:\n",
+      "\n",
+      "* The provided JSON tells us the mean values for intervals of the 'diabetes' feature.\n",
+      "* The value 0.04 falls within the interval \"(0.0, 0.5)\".\n",
+      "* The mean value for the interval \"(0.0, 0.5)\" is given as 0.3225. \n",
+      "\n",
+      "Therefore, the GAM predicts a mean value of 0.3225 when the 'diabetes' feature is 0.04. \n",
+      "\n",
+      "SOLUTION:  0.3225\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value at 1.27 is **-0.918**. \n",
+      "\n",
+      "This is because 1.27 falls within the interval \"(1.0, 1.5)\" in the provided data, and the mean value for that interval is -0.918. \n",
+      "\n",
+      "SOLUTION:  -0.918\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 966.62 falls within the interval **(964.5, 976.5)** on the x-axis. \n",
+      "\n",
+      "Therefore, the mean value at 966.62 is **23227.2**. \n",
+      "\n",
+      "SOLUTION:  23227.2\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 4568.36 is **-1.82**. \n",
+      "\n",
+      "This is because 4568.36 falls within the interval **\"(4401.0, 4668.5)\"**, for which the provided mean value is -1.82. \n",
+      "\n",
+      "SOLUTION:  -1.82\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value at 14.65 falls within the interval **\"(14.5, 15.0)\"** in the provided data. \n",
+      "\n",
+      "Therefore, the mean value of the graph at 14.65 is **0.1393**. \n",
+      "\n",
+      "SOLUTION:  0.1393\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 198.65 is **2.022**. \n",
+      "\n",
+      "This is because the provided JSON object places 198.65 within the interval \"(198.5, 199.0)\" for which the mean value is 2.022. \n",
+      "\n",
+      "SOLUTION:  2.022\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 243849.53 is **1.516**. \n",
+      "\n",
+      "This is because the value 243849.53 falls within the interval \"(243500.0, 244500.0)\" in the provided JSON object, and the corresponding mean value for this interval is 1.516. \n",
+      "\n",
+      "SOLUTION:  1.516\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 18765.31 is **-0.132**. \n",
+      "\n",
+      "Here's why:\n",
+      "\n",
+      "1. The provided JSON object represents a piecewise function where the mean value remains constant within each specified interval on the x-axis (Balance).\n",
+      "\n",
+      "2. We need to find the interval that contains the value 18765.31.\n",
+      "\n",
+      "3. Looking at the \"Means\" dictionary, we see that 18765.31 falls within the interval **\"(0.0, 50418.515)\"**.\n",
+      "\n",
+      "4. The corresponding mean value for this interval is **-0.132**. \n",
+      "\n",
+      "SOLUTION:  -0.132\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value at 65.26 is **-0.73**. \n",
+      "\n",
+      "You can find this by looking at the \"Means\" data for the \"age\" feature. The value 65.26 falls within the interval **\"(65.5, 67.5)\"**, for which the corresponding mean value is -0.73. \n",
+      "\n",
+      "SOLUTION:  0.0\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 24.6 is **-0.272**. \n",
+      "\n",
+      "You can find this by looking at the \"Means\" dictionary. The value 24.6 falls within the interval \"(23.59, 24.795)\", and the corresponding mean value for this interval is -0.272. \n",
+      "\n",
+      "SOLUTION:  -0.272\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 0.49 is **-0.368**. \n",
+      "\n",
+      "This is because 0.49 falls within the interval \"(0.0, 0.5)\" in the provided data, and the mean value for that interval is -0.368. \n",
+      "\n",
+      "SOLUTION:  -0.368\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 5.82 falls within the interval **(5.59195, 5.8294) **. \n",
+      "\n",
+      "Therefore, the mean value at 5.82 is **56900.2**. \n",
+      "\n",
+      "SOLUTION:  56900.2\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 14.4 is **0.03206**. \n",
+      "\n",
+      "This is because the provided JSON object groups the continuous variable \"River Management\" into intervals. The interval \"(13.5, 16.0)\" contains the value 14.4, and the corresponding mean value for this interval is 0.03206. \n",
+      "\n",
+      "SOLUTION:  0.03206\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The provided intervals for the feature 'concave points_se' are: \n",
+      "\n",
+      " \"(0.0099215, 0.010165)\", \"(0.010165, 0.010385)\" \n",
+      "\n",
+      "The value of 0.01 falls within the interval **(0.0099215, 0.010165)**.  \n",
+      "\n",
+      "The mean value associated with this interval is **-0.0546**. \n",
+      "\n",
+      "Therefore, the mean value of the graph at 0.01 is **-0.0546**. \n",
+      "\n",
+      "SOLUTION:  -0.0546\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of CoastalVulnerability at 1.47 is **-0.02272**. \n",
+      "\n",
+      "Here's why:\n",
+      "\n",
+      "* The provided JSON object represents the graph as a series of intervals.\n",
+      "* The value 1.47 falls within the interval \"(1.5, 2.5)\".\n",
+      "* The mean value associated with the interval \"(1.5, 2.5)\" is -0.02272. \n",
+      "\n",
+      "SOLUTION:  -0.02272\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 0.01 falls within the interval **(0.009857000000000001, 0.010665000000000001)**.  The corresponding mean value for this interval is **0.1863**. \n",
+      "\n",
+      "SOLUTION:  0.1863\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value at BMI 46.58 is **0.626**. \n",
+      "\n",
+      "This is because BMI 46.58 falls within the interval \"(45.650000000000006, 48.349999999999994)\" in the provided data.  The GAM model predicts the same mean value for all BMI values within that range. \n",
+      "\n",
+      "SOLUTION:  0.626\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The provided interval for 1.27 is \"(1.178, 1.275)\" which has a mean value of **1.018**. \n",
+      "\n",
+      "SOLUTION:  1.018\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 4.75 falls within the interval **\"(4.5, 5.5)\"**.  \n",
+      "\n",
+      "The JSON object provides the mean value for this interval as **0.00051**. \n",
+      "\n",
+      "Therefore, the mean value of the graph at 4.75 is **0.00051**. \n",
+      "\n",
+      "SOLUTION:  0.00051\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 139172.54 is **0.0211**. \n",
+      "\n",
+      "Here's why:\n",
+      "\n",
+      "1. The provided JSON object represents a piecewise function where the value within each interval on the x-axis (representing the 'id' feature) is constant.\n",
+      "2. We need to find the interval that contains the value 139172.54.\n",
+      "3. Looking at the \"Means\" dictionary, we find that 139172.54 falls within the interval **\"(110643.5, 146554.5)\"**.\n",
+      "4. The corresponding mean value for this interval is **0.0211**. \n",
+      "\n",
+      "SOLUTION:  0.0211\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 0.28 falls within the interval **\"(0.0, 0.5)\"**.  Therefore, the mean value at 0.28 is **-0.3765**. \n",
+      "\n",
+      "SOLUTION:  -0.3765\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 0.91 falls within the interval **(0.875, 0.95)**.  \n",
+      "\n",
+      "The corresponding mean value for this interval is **-0.9**. \n",
+      "\n",
+      "SOLUTION:  -0.9\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The provided interval for 3.38 is (2.5, 3.5). The mean value for the interval (2.5, 3.5) is -0.01049.\n",
+      "\n",
+      "Therefore, the mean value of the graph at 3.38 is **-0.01049**. \n",
+      "\n",
+      "SOLUTION:  -0.01049\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 130.05 is **0.953**. \n",
+      "\n",
+      "This is because the provided JSON object shows that the mean value is the same for any serum_sodium level within the interval of \"(129.5, 130.5)\", which includes 130.05. \n",
+      "\n",
+      "SOLUTION:  0.953\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value at 20.22 falls within the interval **\"(20.5, 22.5)\"**.  \n",
+      "\n",
+      "Therefore, the mean value of the graph at 20.22 is **-59049.5**. \n",
+      "\n",
+      "SOLUTION:  -50740.7\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The provided interval for the value 2.8 falls within the \"(2.5, 4.5)\" range on the x-axis.  \n",
+      "\n",
+      "Therefore, the mean value of the graph at 2.8 is **-0.0099**. \n",
+      "\n",
+      "SOLUTION:  -0.0099\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 1723.82 is **-1.712**. \n",
+      "\n",
+      "Here's why:\n",
+      "\n",
+      "1. The provided JSON object represents a piecewise function where the mean value remains constant within each specified interval on the x-axis (Spa values).\n",
+      "2. We need to find the interval that contains the value 1723.82. \n",
+      "3. Looking at the \"Means\" data, we see that 1723.82 falls within the interval **\"(1209.5, 1808.0)\"**.\n",
+      "4. The corresponding mean value for this interval is **-1.712**. \n",
+      "\n",
+      "SOLUTION:  -1.712\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 6176.67 is **-4.146**. \n",
+      "\n",
+      "Here's why:\n",
+      "\n",
+      "* The provided JSON object represents a piecewise function.  \n",
+      "* The value 6176.67 falls within the interval \"(3249.5, 14327.0)\" \n",
+      "* The corresponding mean value for this interval is -4.146. \n",
+      "\n",
+      "SOLUTION:  -4.146\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 4.16 is **-0.529**. \n",
+      "\n",
+      "This is because the provided JSON object shows that the feature \"Relationship\" is divided into intervals. The interval \"(3.5, 4.5)\" contains the value 4.16, and the corresponding mean value for this interval is -0.529. \n",
+      "\n",
+      "SOLUTION:  -0.529\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The value 68251.52 falls within the interval **(66668.0, 67776.0)**.  \n",
+      "\n",
+      "The mean value of the graph at that interval is **0.0019**. \n",
+      "\n",
+      "Therefore, the mean value of the graph at 68251.52 is **0.0019**. \n",
+      "\n",
+      "SOLUTION:  1e-05\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 15804780.51 is **0.0581**. \n",
+      "\n",
+      "Here's why:\n",
+      "\n",
+      "1. The provided JSON object represents a piecewise function where the intervals in the keys define regions with constant predicted values. \n",
+      "2. We need to find the interval that contains the value 15804780.51.\n",
+      "3. Looking at the \"Means\" dictionary, we see that 15804780.51 falls within the interval **(15799214.0, 15807559.5)**.\n",
+      "4. The corresponding mean value for this interval is **-0.0544**. \n",
+      "\n",
+      "Therefore, the mean value of the graph at 15804780.51 is **0.0581**. \n",
+      "\n",
+      "SOLUTION:  0.0581\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 1555.23 is **-2.212**. \n",
+      "\n",
+      "This is because 1555.23 falls within the interval \"(1514.5, 1796.0)\" in the provided data, and the corresponding mean value for this interval is -2.212. \n",
+      "\n",
+      "SOLUTION:  -2.212\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The age 41.25 falls within the interval **(41.5, 42.5)** on the provided graph. \n",
+      "\n",
+      "The mean value for this interval is **-0.494**. \n",
+      "\n",
+      "SOLUTION:  -0.354\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 0.02 falls within the interval **\"(0.019315, 0.023185)\"**.  \n",
+      "\n",
+      "The corresponding mean value for this interval is **-0.466**. \n",
+      "\n",
+      "SOLUTION:  -0.466\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value at 6.38 can be found by looking at the provided intervals for the \"Means\" data. \n",
+      "\n",
+      "The value 6.38 falls within the interval **\"(6.5, 8.5)\"**.  The corresponding mean value for this interval is **0.01107**. \n",
+      "\n",
+      "Therefore, the mean value of the graph at 6.38 is **0.01107**. \n",
+      "\n",
+      "SOLUTION:  0.00515\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 3.14 is **-1.909**. \n",
+      "\n",
+      "This is because 3.14 falls within the interval \"(2.5, 4.5)\" in the provided data, and the corresponding mean value for this interval is -1.909. \n",
+      "\n",
+      "SOLUTION:  -1.909\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 12.16 is 125210.2. \n",
+      "\n",
+      "This is because the value 12.16 falls within the interval \"(3.0, 14.5)\" on the x-axis.  The provided JSON data indicates that the mean value for this entire interval is 125210.2. \n",
+      "\n",
+      "SOLUTION:  125210.2\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value at 14.97 falls within the interval **\"(13.5, 16.0)\"**.  \n",
+      "\n",
+      "Therefore, the mean value of the graph at 14.97 is **0.05902**. \n",
+      "\n",
+      "SOLUTION:  0.05902\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 0.17 falls within the interval **(0.1603, 0.1722)**.  The corresponding mean value for this interval is **0.539**. \n",
+      "\n",
+      "SOLUTION:  0.539\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at longitude -114.37 falls within the interval **(-114.595, -114.31)**.  Therefore, the mean value at -114.37 is **-100620.1**. \n",
+      "\n",
+      "SOLUTION:  -100620.1\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 239.62 is **-0.16**. \n",
+      "\n",
+      "This is because the value 239.62 falls within the interval **\"(189.5, 232.5)\"**  in the provided JSON object. The corresponding mean value for this interval is **-0.16**. \n",
+      "\n",
+      "SOLUTION:  -0.16\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 1305.91 is **0.911**. \n",
+      "\n",
+      "This is because the value 1305.91 falls within the interval \"(1341.0, 1801.0)\" in the provided data, and the corresponding mean value for this interval is 0.911. \n",
+      "\n",
+      "SOLUTION:  0.911\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 1.26 is **-0.384**. \n",
+      "\n",
+      "You can find this by looking at the \"Means\" dictionary in the provided JSON. The value 1.26 falls within the interval \"(1.2365, 1.326)\", and the corresponding mean value for this interval is -0.384. \n",
+      "\n",
+      "SOLUTION:  -0.384\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The provided graph does not contain information for a value of 0.0 in its keys. Therefore, it's impossible to determine the mean value at 0.0 from the given data. \n",
+      "\n",
+      "**Important Note:** Extrapolating outside the range of data provided for a GAM can be misleading. The model's behavior outside the observed data range is not guaranteed. \n",
+      "\n",
+      "SOLUTION:  0.2818\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The provided interval for 9.36 falls within the \"(8.5, 9.5)\" range in the \"Means\" dictionary. \n",
+      "\n",
+      "Therefore, the mean value of the graph at 9.36 is **0.0237**. \n",
+      "\n",
+      "SOLUTION:  0.0237\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 0.37 is **7.27**. \n",
+      "\n",
+      "This is because 0.37 falls within the interval \"(0.35, 0.45)\" for the \"petal_width\" feature, and the corresponding mean value for this interval is 7.27. \n",
+      "\n",
+      "SOLUTION:  7.27\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 1.58 is **-0.01613**. \n",
+      "\n",
+      "This is because the value 1.58 falls within the interval \"(1.5, 2.5)\" in the provided data, and the mean value associated with that interval is -0.01613. \n",
+      "\n",
+      "SOLUTION:  -0.01613\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value at 37.49 is **0.48**. \n",
+      "\n",
+      "Here's why:\n",
+      "\n",
+      "* You provided a graph of a continuous feature, \"ejection_fraction\".\n",
+      "* The value 37.49 falls within the interval **\"(36.5, 39.0)\"**.\n",
+      "* The mean value associated with the interval \"(36.5, 39.0)\" is **0.48**. \n",
+      "\n",
+      "SOLUTION:  0.48\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 52.67 is **3.38**. \n",
+      "\n",
+      "This is because 52.67 falls within the interval **\"(52.0, 54.5)\"** in the provided JSON object, and the corresponding mean value for this interval is 3.38. \n",
+      "\n",
+      "SOLUTION:  3.38\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 34.12 is **0.081**. \n",
+      "\n",
+      "This is because 34.12 falls within the interval \"(32.985, 34.730000000000004)\" in the provided data, and the mean value associated with this interval is 0.081. \n",
+      "\n",
+      "SOLUTION:  0.081\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 1886.32 is **4.781**. \n",
+      "\n",
+      "You provided a graph where the x-axis represents the \"CapitalLoss\" feature. This appears to be bucketed into ranges.  The value 1886.32 falls within the interval **\"(1881.5, 1894.5)\"** for which you provided the mean value of **4.781**. \n",
+      "\n",
+      "SOLUTION:  4.781\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 0.92 is **0.594**. \n",
+      "\n",
+      "This is because 0.92 falls within the interval \"(0.7562, 0.9131)\" in the provided data, and the mean value associated with that interval is 0.594. \n",
+      "\n",
+      "SOLUTION:  0.683\n",
+      "--------------------------------------------------------------------------------\n",
+      "LLM RESPONSE:  The mean value of the graph at 0.57 falls within the interval **(0.54825, 0.5892999999999999)**.  The corresponding mean value for this interval is **0.4651**. \n",
+      "\n",
+      "SOLUTION:  0.4651\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "import time\n",
+    "\n",
+    "model = genai.GenerativeModel(model_name=\"gemini-1.5-pro-latest\")\n",
+    "\n",
+    "for question in questions:\n",
+    "    messages = [{'role':'user', 'parts': [question[0]]}]\n",
+    "    response = model.generate_content(\n",
+    "                messages,\n",
+    "                generation_config=genai.types.GenerationConfig(\n",
+    "                    candidate_count=1,\n",
+    "                    max_output_tokens=500,\n",
+    "                    temperature=0.2),\n",
+    "            )\n",
+    "    try:\n",
+    "        response = response.text\n",
+    "    except:\n",
+    "        print(f\"Gemini: Invalid response with parts {response.parts}.\")\n",
+    "        response = \"\"\n",
+    "    print('LLM RESPONSE: ', response)\n",
+    "    print('SOLUTION: ', question[1])\n",
+    "    print('-'*80)\n",
+    "    # sleep 20 sec to avoid rate limit\n",
+    "    time.sleep(20)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tmcd",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/benchmarks/results/confidence-gpt-4-turbo-2024-04-09.txt b/benchmarks/results/confidence-gpt-4-turbo-2024-04-09.txt
new file mode 100644
index 0000000..5c108d5
--- /dev/null
+++ b/benchmarks/results/confidence-gpt-4-turbo-2024-04-09.txt
@@ -0,0 +1,4011 @@
+LLM RESPONSE:  To find the x-axis interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds of the confidence interval for each interval and identify the interval with the maximum difference.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Difference**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Identify the Maximum Difference**: Compare the differences calculated in step 2 to find the maximum difference, which indicates the widest confidence interval.
+
+Let's perform these calculations:
+
+- For interval (0.05263, 0.0706): Difference = -0.216 - (-1.454) = 1.238
+- For interval (0.0706, 0.07455500000000001): Difference = -0.178 - (-1.359) = 1.181
+- For interval (0.07455500000000001, 0.07589499999999999): Difference = -0.151 - (-1.244) = 1.093
+- For interval (0.07589499999999999, 0.07727500000000001): Difference = -0.102 - (-1.162) = 1.060
+- For interval (0.07727500000000001, 0.078275): Difference = -0.052 - (-1.087) = 1.035
+- For interval (0.078275, 0.07952000000000001): Difference = -0.006 - (-1.006) = 1.000
+- For interval (0.07952000000000001, 0.080315): Difference = 0.008 - (-0.882) = 0.890
+- For interval (0.080315, 0.081035): Difference = -0.113 - (-0.622) = 0.509
+- For interval (0.081035, 0.08308499999999999): Difference = -0.062 - (-0.547) = 0.485
+- For interval (0.08308499999999999, 0.085165): Difference = -0.040 - (-0.444) = 0.404
+- For interval (0.085165, 0.086795): Difference = 0.004 - (-0.357) = 0.361
+- For interval (0.086795, 0.087785): Difference = 0.075 - (-0.296) = 0.371
+- For interval (0.087785, 0.088615): Difference = 0.136 - (-0.230) = 0.366
+- For interval (0.088615, 0.08918999999999999): Difference = 0.291 - (-0.160) = 0.451
+- For interval (0.08918999999999999, 0.090335): Difference = 0.594 - (-0.309) = 0.903
+- For interval (0.090335, 0.09454): Difference = 0.685 - (-0.264) = 0.949
+- For interval (0.09454, 0.11525): Difference = 0.220 - (-0.005) = 0.225
+- For interval (0.11525, 0.11765): Difference = 0.273 - 0.070 = 0.203
+- For interval (0.11765, 0.12455): Difference = 0.512 - 0.022 = 0.490
+- For interval (0.12455, 0.13845000000000002): Difference = 0.591 - 0.077 = 0.514
+- For interval (0.13845000000000002, 0.1634): Difference = 0.664 - 0.127 = 0.537
+
+From the calculations, the interval (0.05263, 0.0706) has the widest confidence bound with a difference of 1.238. This is the interval with the widest confidence interval.
+SOLUTION:  (0.05263, 0.0706)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, we have the lower and upper bounds of the confidence interval.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval with the maximum width.
+
+Let's calculate the width of the confidence interval for each interval:
+
+- **(0.0, 130.5)**: \(0.682 - 0.36 = 0.322\)
+- **(130.5, 278.5)**: \(1.834 - (-1.599) = 3.433\)
+- **(278.5, 452.5)**: \(0.791 - (-1.362) = 2.153\)
+- **(452.5, 754.5)**: \(-0.524 - (-1.291) = 0.767\)
+- **(754.5, 1209.5)**: \(-0.502 - (-2.117) = 1.615\)
+- **(1209.5, 1808.0)**: \(-0.831 - (-2.592) = 1.761\)
+- **(1808.0, 2204.5)**: \(-2.202 - (-3.856) = 1.654\)
+- **(2204.5, 2207.5)**: \(-1.35 - (-3.562) = 2.212\)
+- **(2207.5, 2428.0)**: \(-2.364 - (-3.549) = 1.185\)
+- **(2428.0, 2462.5)**: \(-1.569 - (-3.455) = 1.886\)
+- **(2462.5, 2714.5)**: \(-2.28 - (-4.525) = 2.245\)
+- **(2714.5, 2745.0)**: \(-1.083 - (-4.721) = 3.638\)
+- **(2745.0, 2993.5)**: \(-2.661 - (-5.493) = 2.832\)
+- **(2993.5, 3132.0)**: \(-2.749 - (-6.214) = 3.465\)
+- **(3132.0, 3705.5)**: \(-3.986 - (-6.767) = 2.781\)
+- **(3705.5, 3747.0)**: \(-2.222 - (-6.498) = 4.276\)
+- **(3747.0, 22408.0)**: \(-5.342 - (-9.024) = 3.682\)
+
+From the calculations above, the interval **(3705.5, 3747.0)** has the widest confidence bound with a width of \(4.276\). This is the interval with the widest confidence bound in the graph.
+SOLUTION:  (3705.5, 3747.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval with the largest difference.
+
+Here are the confidence bounds for each interval:
+1. Interval (0.0, 0.5): Lower bound = -0.418, Upper bound = -0.318
+   Difference = -0.318 - (-0.418) = 0.100
+
+2. Interval (0.5, 1.5): Lower bound = 0.02, Upper bound = 1.428
+   Difference = 1.428 - 0.02 = 1.408
+
+3. Interval (1.5, 2.5): Lower bound = 0.545, Upper bound = 0.629
+   Difference = 0.629 - 0.545 = 0.084
+
+4. Interval (2.5, 3.5): Lower bound = -0.336, Upper bound = -0.106
+   Difference = -0.106 - (-0.336) = 0.230
+
+5. Interval (3.5, 4.5): Lower bound = -0.676, Upper bound = -0.585
+   Difference = -0.585 - (-0.676) = 0.091
+
+6. Interval (4.5, 5.5): Lower bound = -0.688, Upper bound = -0.403
+   Difference = -0.403 - (-0.688) = 0.285
+
+7. Interval (5.5, 6.0): Lower bound = 0.067, Upper bound = 0.291
+   Difference = 0.291 - 0.067 = 0.224
+
+From these calculations, the interval (0.5, 1.5) has the widest confidence bound with a difference of 1.408. Therefore, the x-axis interval with the widest confidence bound is (0.5, 1.5).
+SOLUTION:  (0.5, 1.5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval with the maximum width.
+
+Let's perform these calculations:
+
+- For interval `(106.67, 780.2149999999999)`, width = `0.6859 - 0.0871 = 0.5988`
+- For interval `(780.2149999999999, 4627.98)`, width = `0.5457 - 0.1468 = 0.3989`
+- For interval `(4627.98, 6842.475)`, width = `0.445 - (-0.2734) = 0.7184`
+- For interval `(6842.475, 7401.88)`, width = `0.3239 - (-0.01) = 0.3339`
+- For interval `(7401.88, 27330.43)`, width = `0.3154 - 0.0941 = 0.2213`
+- For interval `(27330.43, 38816.375)`, width = `0.2823 - 0.065 = 0.2173`
+- For interval `(38816.375, 40348.645000000004)`, width = `0.2695 - (-0.0568) = 0.3263`
+- For interval `(40348.645000000004, 42807.509999999995)`, width = `0.2451 - (-0.1427) = 0.3878`
+- For interval `(42807.509999999995, 48226.81)`, width = `0.2181 - 0.0015 = 0.2166`
+- For interval `(48226.81, 48498.15)`, width = `0.2497 - (-0.404) = 0.6537`
+- For interval `(48498.15, 58535.68)`, width = `0.166 - (-0.1286) = 0.2946`
+- For interval `(58535.68, 94498.98999999999)`, width = `0.1054 - (-0.003) = 0.1084`
+- For interval `(94498.98999999999, 120892.955)`, width = `0.0913 - (-0.0541) = 0.1454`
+- For interval `(120892.955, 121151.28)`, width = `0.1335 - (-0.186) = 0.3195`
+- For interval `(121151.28, 121482.61499999999)`, width = `0.1239 - (-0.2842) = 0.4081`
+- For interval `(121482.61499999999, 148569.97)`, width = `0.0817 - (-0.1593) = 0.241`
+- For interval `(148569.97, 184522.325)`, width = `-0.019 - (-0.1401) = 0.1211`
+- For interval `(184522.325, 187947.635)`, width = `-0.0504 - (-0.216) = 0.1656`
+- For interval `(187947.635, 187985.865)`, width = `0.2839 - (-0.7523) = 1.0362`
+- For interval `(187985.865, 188452.565)`, width = `0.1139 - (-0.2404) = 0.3543`
+- For interval `(188452.565, 189006.61)`, width = `0.1673 - (-0.1779) = 0.3452`
+- For interval `(189006.61, 196418.97999999998)`, width = `0.1867 - (-0.1285) = 0.3152`
+- For interval `(196418.97999999998, 199505.41)`, width = `0.1868 - (-0.2064) = 0.3932`
+- For interval `(199505.41, 199992.48)`, width = `0.7597 - (-0.3318) = 1.0915`
+
+From these calculations, the interval `(199505.41, 199992.48)` has the widest confidence bound with a width of `1.0915`.
+SOLUTION:  (199505.41, 199992.48)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the calculations for each interval:
+
+1. Interval (0.0, 0.5): Upper - Lower = -0.02304 - (-0.02582) = 0.00278
+2. Interval (0.5, 1.5): Upper - Lower = -0.01995 - (-0.02181) = 0.00186
+3. Interval (1.5, 2.5): Upper - Lower = -0.01521 - (-0.01706) = 0.00185
+4. Interval (2.5, 3.5): Upper - Lower = -0.01028 - (-0.01143) = 0.00115
+5. Interval (3.5, 4.5): Upper - Lower = -0.00541 - (-0.00626) = 0.00085
+6. Interval (4.5, 5.5): Upper - Lower = 0.00178 - 0.00099 = 0.00079
+7. Interval (5.5, 6.5): Upper - Lower = 0.00588 - 0.00524 = 0.00064
+8. Interval (6.5, 7.5): Upper - Lower = 0.01205 - 0.01084 = 0.00121
+9. Interval (7.5, 8.5): Upper - Lower = 0.01826 - 0.0167 = 0.00156
+10. Interval (8.5, 10.5): Upper - Lower = 0.02538 - 0.02302 = 0.00236
+11. Interval (10.5, 11.5): Upper - Lower = 0.03543 - 0.03159 = 0.00384
+12. Interval (11.5, 13.5): Upper - Lower = 0.03955 - 0.03427 = 0.00528
+13. Interval (13.5, 15.0): Upper - Lower = 0.03841 - 0.02849 = 0.00992
+14. Interval (15.0, 16.0): Upper - Lower = 0.03313 - 0.02539 = 0.00774
+
+From these calculations, the interval (13.5, 15.0) has the widest confidence bound with a difference of 0.00992. Therefore, the x-axis interval with the widest confidence bound is (13.5, 15.0).
+SOLUTION:  (13.5, 15.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval with the largest difference.
+
+Here are the confidence bounds for each interval:
+1. Interval (0.0, 0.5): 
+   - Lower Bound: -1.0291
+   - Upper Bound: -0.6918
+   - Difference: -0.6918 - (-1.0291) = 0.3373
+
+2. Interval (0.5, 1.5): 
+   - Lower Bound: -0.1456
+   - Upper Bound: 0.1111
+   - Difference: 0.1111 - (-0.1456) = 0.2567
+
+3. Interval (1.5, 2.5): 
+   - Lower Bound: -0.3118
+   - Upper Bound: -0.1879
+   - Difference: -0.1879 - (-0.3118) = 0.1239
+
+4. Interval (2.5, 3.5): 
+   - Lower Bound: -0.4557
+   - Upper Bound: -0.1496
+   - Difference: -0.1496 - (-0.4557) = 0.3061
+
+5. Interval (3.5, 4.0): 
+   - Lower Bound: 0.0349
+   - Upper Bound: 0.048
+   - Difference: 0.048 - 0.0349 = 0.0131
+
+Comparing these differences:
+- (0.0, 0.5): 0.3373
+- (0.5, 1.5): 0.2567
+- (1.5, 2.5): 0.1239
+- (2.5, 3.5): 0.3061
+- (3.5, 4.0): 0.0131
+
+The interval (0.0, 0.5) has the widest confidence bound with a difference of 0.3373.
+SOLUTION:  (0.0, 0.5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis interval with the widest confidence bound in the graph, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval with the maximum difference.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Difference**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Identify the Maximum Difference**: Compare the differences calculated in step 2 to find the maximum difference, which indicates the widest confidence interval.
+
+Let's perform these calculations:
+
+- For interval "(0.0, 3.5)": Difference = 0.0953 - (-0.071) = 0.1663
+- For interval "(3.5, 7.5)": Difference = 0.0385 - (-0.1199) = 0.1584
+- For interval "(7.5, 9.0)": Difference = -0.0106 - (-0.1639) = 0.1533
+- For interval "(9.0, 11.5)": Difference = -0.0431 - (-0.1953) = 0.1522
+- For interval "(11.5, 13.5)": Difference = -0.0792 - (-0.2382) = 0.159
+- For interval "(13.5, 20.5)": Difference = -0.1005 - (-0.2707) = 0.1702
+- For interval "(20.5, 22.5)": Difference = -0.088 - (-0.2184) = 0.1304
+- For interval "(22.5, 24.5)": Difference = -0.0547 - (-0.1699) = 0.1152
+- For interval "(24.5, 26.5)": Difference = -0.0161 - (-0.1255) = 0.1094
+- For interval "(26.5, 28.5)": Difference = 0.0233 - (-0.0953) = 0.1186
+- For interval "(28.5, 30.5)": Difference = 0.0636 - (-0.0714) = 0.135
+- For interval "(30.5, 32.5)": Difference = 0.099 - (-0.0304) = 0.1294
+- For interval "(32.5, 34.5)": Difference = 0.12 - 0.0205 = 0.0995
+- For interval "(34.5, 39.5)": Difference = 0.1847 - 0.0292 = 0.1555
+- For interval "(39.5, 40.5)": Difference = 0.2253 - 0.0607 = 0.1646
+- For interval "(40.5, 41.5)": Difference = 0.255 - 0.0987 = 0.1563
+- For interval "(41.5, 43.5)": Difference = 0.3653 - 0.0904 = 0.2749
+- For interval "(43.5, 47.5)": Difference = 0.4732 - 0.0985 = 0.3747
+- For interval "(47.5, 49.5)": Difference = 0.4704 - 0.0202 = 0.4502
+- For interval "(49.5, 51.0)": Difference = 0.3009 - (-0.3346) = 0.6355
+- For interval "(51.0, 55.0)": Difference = 0.4148 - (-0.5656) = 0.9804
+- For interval "(55.0, 77.5)": Difference = 0.9065 - (-0.4718) = 1.3783
+- For interval "(77.5, 99.0)": Difference = 1.0684 - (-0.4467) = 1.5151
+
+From the calculations, the interval "(77.5, 99.0)" has the widest confidence bound with a difference of 1.5151.
+SOLUTION:  (77.5, 99.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the calculations for each interval:
+
+1. Interval (0.0, 1.5):
+   - Lower Bound: -0.02604
+   - Upper Bound: -0.02235
+   - Difference: -0.02235 - (-0.02604) = 0.00369
+
+2. Interval (1.5, 2.5):
+   - Lower Bound: -0.01758
+   - Upper Bound: -0.01628
+   - Difference: -0.01628 - (-0.01758) = 0.00130
+
+3. Interval (2.5, 3.5):
+   - Lower Bound: -0.01104
+   - Upper Bound: -0.01034
+   - Difference: -0.01034 - (-0.01104) = 0.00070
+
+4. Interval (3.5, 4.5):
+   - Lower Bound: -0.00622
+   - Upper Bound: -0.00547
+   - Difference: -0.00547 - (-0.00622) = 0.00075
+
+5. Interval (4.5, 5.5):
+   - Lower Bound: 0.00022
+   - Upper Bound: 0.00080
+   - Difference: 0.00080 - 0.00022 = 0.00058
+
+6. Interval (5.5, 6.5):
+   - Lower Bound: 0.00630
+   - Upper Bound: 0.00723
+   - Difference: 0.00723 - 0.00630 = 0.00093
+
+7. Interval (6.5, 8.5):
+   - Lower Bound: 0.01194
+   - Upper Bound: 0.01295
+   - Difference: 0.01295 - 0.01194 = 0.00101
+
+8. Interval (8.5, 10.5):
+   - Lower Bound: 0.02150
+   - Upper Bound: 0.02363
+   - Difference: 0.02363 - 0.02150 = 0.00213
+
+9. Interval (10.5, 11.5):
+   - Lower Bound: 0.03022
+   - Upper Bound: 0.03508
+   - Difference: 0.03508 - 0.03022 = 0.00486
+
+10. Interval (11.5, 13.5):
+    - Lower Bound: 0.03581
+    - Upper Bound: 0.04198
+    - Difference: 0.04198 - 0.03581 = 0.00617
+
+11. Interval (13.5, 14.5):
+    - Lower Bound: 0.04439
+    - Upper Bound: 0.05386
+    - Difference: 0.05386 - 0.04439 = 0.00947
+
+12. Interval (14.5, 16.0):
+    - Lower Bound: 0.04645
+    - Upper Bound: 0.07055
+    - Difference: 0.07055 - 0.04645 = 0.02410
+
+From these calculations, the interval (14.5, 16.0) has the widest confidence bound with a difference of 0.02410. This is the interval with the widest confidence bound in the graph.
+SOLUTION:  (14.5, 16.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds of the confidence interval for each interval and identify the interval with the largest difference.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: From the JSON object, extract the lower and upper bounds for each interval.
+
+2. **Calculate the Difference for Each Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Differences**: Identify the interval with the largest difference, which indicates the widest confidence bound.
+
+Let's perform these calculations:
+
+- For interval (0.0, 0.5): Upper - Lower = -0.02339 - (-0.02677) = 0.00338
+- For interval (0.5, 1.5): Upper - Lower = -0.01822 - (-0.01971) = 0.00149
+- For interval (1.5, 2.5): Upper - Lower = -0.01398 - (-0.01507) = 0.00109
+- For interval (2.5, 3.5): Upper - Lower = -0.0104 - (-0.0113) = 0.0009
+- For interval (3.5, 4.5): Upper - Lower = -0.00428 - (-0.00523) = 0.00095
+- For interval (4.5, 5.5): Upper - Lower = 0.00091 - 0.00016 = 0.00075
+- For interval (5.5, 6.5): Upper - Lower = 0.00594 - 0.00517 = 0.00077
+- For interval (6.5, 7.5): Upper - Lower = 0.0118 - 0.01094 = 0.00086
+- For interval (7.5, 8.5): Upper - Lower = 0.017 - 0.01606 = 0.00094
+- For interval (8.5, 9.5): Upper - Lower = 0.0243 - 0.02309 = 0.00121
+- For interval (9.5, 10.5): Upper - Lower = 0.02899 - 0.02666 = 0.00233
+- For interval (10.5, 11.5): Upper - Lower = 0.03343 - 0.03007 = 0.00336
+- For interval (11.5, 12.5): Upper - Lower = 0.03916 - 0.03455 = 0.00461
+- For interval (12.5, 15.0): Upper - Lower = 0.05607 - 0.03295 = 0.02312
+
+From these calculations, the interval (12.5, 15.0) has the widest confidence bound with a difference of 0.02312. This is the interval with the widest confidence bound.
+SOLUTION:  (12.5, 15.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the calculations for each interval:
+
+1. Interval (0.0, 0.5):
+   - Lower Bound: 0.453
+   - Upper Bound: 0.568
+   - Difference: 0.568 - 0.453 = 0.115
+
+2. Interval (0.5, 1.5):
+   - Lower Bound: -0.278
+   - Upper Bound: -0.188
+   - Difference: -0.188 - (-0.278) = 0.090
+
+3. Interval (1.5, 2.5):
+   - Lower Bound: -0.789
+   - Upper Bound: -0.543
+   - Difference: -0.543 - (-0.789) = 0.246
+
+4. Interval (2.5, 3.5):
+   - Lower Bound: -1.092
+   - Upper Bound: -0.921
+   - Difference: -0.921 - (-1.092) = 0.171
+
+5. Interval (3.5, 4.5):
+   - Lower Bound: -0.6
+   - Upper Bound: -0.458
+   - Difference: -0.458 - (-0.6) = 0.142
+
+6. Interval (4.5, 5.0):
+   - Lower Bound: 1.664
+   - Upper Bound: 1.842
+   - Difference: 1.842 - 1.664 = 0.178
+
+Comparing these differences:
+- (0.0, 0.5): 0.115
+- (0.5, 1.5): 0.090
+- (1.5, 2.5): 0.246
+- (2.5, 3.5): 0.171
+- (3.5, 4.5): 0.142
+- (4.5, 5.0): 0.178
+
+The interval (1.5, 2.5) has the widest confidence bound with a difference of 0.246.
+SOLUTION:  (1.5, 2.5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval with the largest width.
+
+Let's perform these calculations:
+
+- For interval (17.0, 18.5): Upper - Lower = -1.975 - (-4.677) = 2.702
+- For interval (18.5, 19.5): Upper - Lower = -1.044 - (-3.672) = 2.628
+- For interval (19.5, 20.5): Upper - Lower = -1.669 - (-3.928) = 2.259
+- For interval (20.5, 21.5): Upper - Lower = -2.002 - (-2.706) = 0.704
+- For interval (21.5, 22.5): Upper - Lower = -1.069 - (-1.741) = 0.672
+- For interval (22.5, 23.5): Upper - Lower = -1.41 - (-1.856) = 0.446
+- For interval (23.5, 24.5): Upper - Lower = -1.021 - (-1.407) = 0.386
+- For interval (24.5, 26.5): Upper - Lower = -0.637 - (-0.941) = 0.304
+- For interval (26.5, 27.5): Upper - Lower = -0.385 - (-0.561) = 0.176
+- For interval (27.5, 29.5): Upper - Lower = -0.11 - (-0.322) = 0.212
+- For interval (29.5, 33.5): Upper - Lower = 0.164 - (-0.079) = 0.243
+- For interval (33.5, 36.5): Upper - Lower = 0.473 - 0.229 = 0.244
+- For interval (36.5, 44.5): Upper - Lower = 0.816 - 0.5 = 0.316
+- For interval (44.5, 61.5): Upper - Lower = 1.04 - 0.753 = 0.287
+- For interval (61.5, 66.5): Upper - Lower = 0.714 - 0.434 = 0.28
+- For interval (66.5, 73.5): Upper - Lower = 0.567 - (-0.37) = 0.937
+- For interval (73.5, 74.5): Upper - Lower = 1.297 - 0.229 = 1.068
+- For interval (74.5, 77.5): Upper - Lower = 1.141 - (-0.136) = 1.277
+- For interval (77.5, 79.5): Upper - Lower = 1.401 - 0.35 = 1.051
+- For interval (79.5, 84.5): Upper - Lower = 0.702 - (-0.573) = 1.275
+- For interval (84.5, 90.0): Upper - Lower = -0.119 - (-2.041) = 1.922
+
+From these calculations, the interval (84.5, 90.0) has the widest confidence bound with a width of 1.922.
+SOLUTION:  (17.0, 18.5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval with the maximum width.
+
+Let's perform these calculations:
+
+- For interval (0.0, 0.0074145): Upper - Lower = -0.697 - (-1.411) = 0.714
+- For interval (0.0074145, 0.011665): Upper - Lower = -0.62 - (-1.253) = 0.633
+- For interval (0.011665, 0.01503): Upper - Lower = -0.546 - (-1.095) = 0.549
+- For interval (0.01503, 0.017865): Upper - Lower = -0.445 - (-0.965) = 0.52
+- For interval (0.017865, 0.019315): Upper - Lower = -0.34 - (-0.823) = 0.483
+- For interval (0.019315, 0.023185): Upper - Lower = -0.212 - (-0.72) = 0.508
+- For interval (0.023185, 0.026115): Upper - Lower = -0.188 - (-0.517) = 0.329
+- For interval (0.026115, 0.042455): Upper - Lower = 0.274 - (-0.743) = 1.017
+- For interval (0.042455, 0.048235): Upper - Lower = 0.398 - (-0.628) = 1.026
+- For interval (0.048235, 0.048865): Upper - Lower = 0.489 - (-0.409) = 0.898
+- For interval (0.048865, 0.059615): Upper - Lower = 0.617 - (-0.151) = 0.768
+- For interval (0.059615, 0.070395): Upper - Lower = 0.611 - 0.09 = 0.521
+- For interval (0.070395, 0.08221500000000001): Upper - Lower = 0.728 - 0.219 = 0.509
+- For interval (0.08221500000000001, 0.087175): Upper - Lower = 0.878 - 0.306 = 0.572
+- For interval (0.087175, 0.091445): Upper - Lower = 1.032 - 0.39 = 0.642
+- For interval (0.091445, 0.1006): Upper - Lower = 1.182 - 0.481 = 0.701
+- For interval (0.1006, 0.122): Upper - Lower = 1.336 - 0.562 = 0.774
+- For interval (0.122, 0.16544999999999999): Upper - Lower = 1.503 - 0.634 = 0.869
+- For interval (0.16544999999999999, 0.2012): Upper - Lower = 1.634 - 0.74 = 0.894
+
+From these calculations, the interval (0.042455, 0.048235) has the widest confidence bound with a width of 1.026. This is the interval with the widest confidence bound.
+SOLUTION:  (0.042455, 0.048235)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds of the confidence interval for each interval and identify the interval with the maximum difference.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Difference**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Identify the Maximum Difference**: Compare the differences calculated in step 2 to find the maximum difference, which indicates the widest confidence interval.
+
+Let's perform these calculations:
+
+- For interval (3.0, 14.5): 
+  - Lower Bound = 103123.1
+  - Upper Bound = 147297.2
+  - Difference = 147297.2 - 103123.1 = 44174.1
+
+- For interval (3965.0, 35682.0): 
+  - Lower Bound = -101928.5
+  - Upper Bound = -80307.2
+  - Difference = -80307.2 - (-101928.5) = 21621.3
+
+- For interval (3175.5, 3965.0): 
+  - Lower Bound = -84318.8
+  - Upper Bound = -68113.2
+  - Difference = -68113.2 - (-84318.8) = 16205.6
+
+- For interval (2686.0, 2718.5): 
+  - Lower Bound = -69408.6
+  - Upper Bound = -23054.7
+  - Difference = -23054.7 - (-69408.6) = 46353.9
+
+- For interval (2425.5, 2686.0): 
+  - Lower Bound = -64158.2
+  - Upper Bound = -55671.5
+  - Difference = -55671.5 - (-64158.2) = 8486.7
+
+- For interval (2129.5, 2425.5): 
+  - Lower Bound = -56504.4
+  - Upper Bound = -40707.4
+  - Difference = -40707.4 - (-56504.4) = 15797.0
+
+- For interval (1886.5, 2129.5): 
+  - Lower Bound = -51088.1
+  - Upper Bound = -22819.1
+  - Difference = -22819.1 - (-51088.1) = 28269.0
+
+- For interval (1497.5, 1886.5): 
+  - Lower Bound = -37619.7
+  - Upper Bound = -13933.2
+  - Difference = -13933.2 - (-37619.7) = 23686.5
+
+- For interval (1269.5, 1497.5): 
+  - Lower Bound = -22884.3
+  - Upper Bound = 3258.7
+  - Difference = 3258.7 - (-22884.3) = 26143.0
+
+- For interval (1220.5, 1267.5): 
+  - Lower Bound = -14462.5
+  - Upper Bound = 2063.4
+  - Difference = 2063.4 - (-14462.5) = 16525.9
+
+- For interval (1019.5, 1220.5): 
+  - Lower Bound = -10609.9
+  - Upper Bound = 23624.4
+  - Difference = 23624.4 - (-10609.9) = 34234.3
+
+- For interval (837.5, 1019.5): 
+  - Lower Bound = 8057.5
+  - Upper Bound = 33373.7
+  - Difference = 33373.7 - 8057.5 = 25316.2
+
+- For interval (761.5, 837.5): 
+  - Lower Bound = 26626.5
+  - Upper Bound = 37491.3
+  - Difference = 37491.3 - 26626.5 = 10864.8
+
+- For interval (657.5, 761.5): 
+  - Lower Bound = 35273.5
+  - Upper Bound = 54248.0
+  - Difference = 54248.0 - 35273.5 = 18974.5
+
+- For interval (490.5, 657.5): 
+  - Lower Bound = 45395.6
+  - Upper Bound = 70593.3
+  - Difference = 70593.3 - 45395.6 = 25197.7
+
+- For interval (301.5, 490.5): 
+  - Lower Bound = 60924.6
+  - Upper Bound = 85287.5
+  - Difference = 85287.5 - 60924.6 = 24362.9
+
+- For interval (151.5, 301.5): 
+  - Lower Bound = 69535.1
+  - Upper Bound = 100708.2
+  - Difference = 100708.2 - 69535.1 = 31173.1
+
+- For interval (138.5, 151.5): 
+  - Lower Bound = 78950.4
+  - Upper Bound = 127869.3
+  - Difference = 127869.3 - 78950.4 = 48918.9
+
+- For interval (65.5, 138.5): 
+  - Lower Bound = 75243.8
+  - Upper Bound = 108591.1
+  - Difference = 108591.1 - 75243.8 = 33347.3
+
+- For interval (25.5, 65.5): 
+  - Lower Bound = 62309.7
+  - Upper Bound = 98506.2
+  - Difference = 98506.2 - 62309.7 = 36196.5
+
+- For interval (14.5, 25.5): 
+  - Lower Bound = 58681.0
+  - Upper Bound = 126224.8
+  - Difference = 126224.8 - 58681.0 = 67543.8
+
+From the calculations, the interval with the widest confidence bound is (2686.0, 2718.5) with a difference of 46353.9.
+SOLUTION:  (14.5, 25.5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the width of the confidence interval for each interval provided in the graph. The width of the confidence interval can be calculated by subtracting the lower bound from the upper bound for each interval.
+
+Let's calculate the width of the confidence intervals for each interval:
+
+1. For the interval (0.0, 0.5):
+   - Lower Bound = -0.08236
+   - Upper Bound = 0.11675
+   - Width = 0.11675 - (-0.08236) = 0.11675 + 0.08236 = 0.19911
+
+2. For the interval (0.5, 1.0):
+   - Lower Bound = -0.06482
+   - Upper Bound = 0.04573
+   - Width = 0.04573 - (-0.06482) = 0.04573 + 0.06482 = 0.11055
+
+Comparing the widths:
+- Interval (0.0, 0.5) has a width of 0.19911
+- Interval (0.5, 1.0) has a width of 0.11055
+
+The interval (0.0, 0.5) has the widest confidence bound with a width of 0.19911.
+SOLUTION:  (0.0, 0.5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds of the confidence interval for each interval and identify the one with the largest difference.
+
+Here are the intervals and their corresponding confidence bounds:
+
+1. Interval (0.0, 0.5):
+   - Lower Bound: -0.099
+   - Upper Bound: 0.074
+   - Difference: 0.074 - (-0.099) = 0.173
+
+2. Interval (0.5, 1.5):
+   - Lower Bound: 0.319
+   - Upper Bound: 0.549
+   - Difference: 0.549 - 0.319 = 0.230
+
+3. Interval (1.5, 4.5):
+   - Lower Bound: -0.192
+   - Upper Bound: 0.059
+   - Difference: 0.059 - (-0.192) = 0.251
+
+4. Interval (4.5, 5.5):
+   - Lower Bound: 0.106
+   - Upper Bound: 0.228
+   - Difference: 0.228 - 0.106 = 0.122
+
+5. Interval (5.5, 7.5):
+   - Lower Bound: -0.567
+   - Upper Bound: -0.362
+   - Difference: -0.362 - (-0.567) = 0.205
+
+6. Interval (7.5, 8.0):
+   - Lower Bound: -4.038
+   - Upper Bound: -1.042
+   - Difference: -1.042 - (-4.038) = 2.996
+
+From the calculated differences, the interval (7.5, 8.0) has the widest confidence bound with a difference of 2.996. This is the interval with the widest confidence bound.
+SOLUTION:  (7.5, 8.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the calculations for each interval:
+
+1. Interval (0.0, 0.5):
+   - Lower Bound: -0.03013
+   - Upper Bound: -0.02466
+   - Difference: -0.02466 - (-0.03013) = 0.00547
+
+2. Interval (0.5, 1.5):
+   - Lower Bound: -0.02484
+   - Upper Bound: -0.02278
+   - Difference: -0.02278 - (-0.02484) = 0.00206
+
+3. Interval (1.5, 2.5):
+   - Lower Bound: -0.01655
+   - Upper Bound: -0.0155
+   - Difference: -0.0155 - (-0.01655) = 0.00105
+
+4. Interval (2.5, 3.5):
+   - Lower Bound: -0.01088
+   - Upper Bound: -0.0101
+   - Difference: -0.0101 - (-0.01088) = 0.00078
+
+5. Interval (3.5, 4.5):
+   - Lower Bound: -0.00574
+   - Upper Bound: -0.00482
+   - Difference: -0.00482 - (-0.00574) = 0.00092
+
+6. Interval (4.5, 5.5):
+   - Lower Bound: -0.00046
+   - Upper Bound: 0.00002
+   - Difference: 0.00002 - (-0.00046) = 0.00048
+
+7. Interval (5.5, 6.5):
+   - Lower Bound: 0.00473
+   - Upper Bound: 0.00561
+   - Difference: 0.00561 - 0.00473 = 0.00088
+
+8. Interval (6.5, 7.5):
+   - Lower Bound: 0.01242
+   - Upper Bound: 0.01323
+   - Difference: 0.01323 - 0.01242 = 0.00081
+
+9. Interval (7.5, 8.5):
+   - Lower Bound: 0.01574
+   - Upper Bound: 0.01683
+   - Difference: 0.01683 - 0.01574 = 0.00109
+
+10. Interval (8.5, 9.5):
+    - Lower Bound: 0.02354
+    - Upper Bound: 0.02554
+    - Difference: 0.02554 - 0.02354 = 0.002
+
+11. Interval (9.5, 10.5):
+    - Lower Bound: 0.0277
+    - Upper Bound: 0.02996
+    - Difference: 0.02996 - 0.0277 = 0.00226
+
+12. Interval (10.5, 11.5):
+    - Lower Bound: 0.03039
+    - Upper Bound: 0.03386
+    - Difference: 0.03386 - 0.03039 = 0.00347
+
+13. Interval (11.5, 17.0):
+    - Lower Bound: 0.02281
+    - Upper Bound: 0.04848
+    - Difference: 0.04848 - 0.02281 = 0.02567
+
+From these calculations, the interval (11.5, 17.0) has the widest confidence bound with a difference of 0.02567.
+SOLUTION:  (11.5, 17.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis interval with the widest confidence bound in the graph, we need to calculate the width of the confidence interval for each interval and then compare them to determine which one is the widest.
+
+The confidence interval width can be calculated by subtracting the lower bound from the upper bound for each interval. Let's calculate this for each interval:
+
+1. Interval (0.0, 0.5):
+   - Lower Bound: -0.1303
+   - Upper Bound: -0.0198
+   - Width: -0.0198 - (-0.1303) = 0.1105
+
+2. Interval (0.5, 2.5):
+   - Lower Bound: -0.2711
+   - Upper Bound: 0.5976
+   - Width: 0.5976 - (-0.2711) = 0.8687
+
+3. Interval (2.5, 3.0):
+   - Lower Bound: -2.435
+   - Upper Bound: 0.9748
+   - Width: 0.9748 - (-2.435) = 3.4098
+
+Comparing the widths:
+- Interval (0.0, 0.5): 0.1105
+- Interval (0.5, 2.5): 0.8687
+- Interval (2.5, 3.0): 3.4098
+
+The interval (2.5, 3.0) has the widest confidence bound with a width of 3.4098.
+SOLUTION:  (2.5, 3.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the calculations for each interval:
+
+1. Interval (0.0, 0.5): Upper - Lower = -0.185 - (-0.409) = 0.224
+2. Interval (0.5, 3.5): Upper - Lower = -0.01 - (-0.139) = 0.129
+3. Interval (3.5, 4.5): Upper - Lower = 0.695 - 0.592 = 0.103
+4. Interval (4.5, 6.5): Upper - Lower = -0.598 - (-0.847) = 0.249
+5. Interval (6.5, 7.5): Upper - Lower = -0.461 - (-0.624) = 0.163
+6. Interval (7.5, 8.5): Upper - Lower = -0.581 - (-0.749) = 0.168
+7. Interval (8.5, 9.5): Upper - Lower = -0.302 - (-1.549) = 1.247
+8. Interval (9.5, 10.5): Upper - Lower = 0.48 - 0.366 = 0.114
+9. Interval (10.5, 11.5): Upper - Lower = 0.727 - 0.452 = 0.275
+10. Interval (11.5, 12.5): Upper - Lower = 0.315 - 0.225 = 0.09
+11. Interval (12.5, 13.5): Upper - Lower = 0.622 - 0.445 = 0.177
+12. Interval (13.5, 14.0): Upper - Lower = -0.064 - (-0.202) = 0.138
+
+From these calculations, the interval (8.5, 9.5) has the widest confidence bound with a difference of 1.247. This is the interval with the largest difference between the upper and lower confidence bounds.
+SOLUTION:  (8.5, 9.5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval with the maximum difference.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Identify the Maximum Width**: Compare the widths of all intervals and identify the interval with the maximum width.
+
+Let's perform these calculations:
+
+- For interval (15565796.0, 15566519.0):
+  - Lower Bound = -1.3796
+  - Upper Bound = -0.3742
+  - Width = -0.3742 - (-1.3796) = 1.0054
+
+- For interval (15566519.0, 15567333.5):
+  - Lower Bound = -1.4199
+  - Upper Bound = -0.2283
+  - Width = -0.2283 - (-1.4199) = 1.1916
+
+- For interval (15567333.5, 15567844.5):
+  - Lower Bound = -0.741
+  - Upper Bound = 0.3884
+  - Width = 0.3884 - (-0.741) = 1.1294
+
+- For interval (15567844.5, 15568343.5):
+  - Lower Bound = -0.4552
+  - Upper Bound = 0.4594
+  - Width = 0.4594 - (-0.4552) = 0.9146
+
+- For interval (15568343.5, 15571612.0):
+  - Lower Bound = -0.4861
+  - Upper Bound = 0.0295
+  - Width = 0.0295 - (-0.4861) = 0.5156
+
+- For interval (15571612.0, 15571858.5):
+  - Lower Bound = -0.3268
+  - Upper Bound = 0.2223
+  - Width = 0.2223 - (-0.3268) = 0.5491
+
+- For interval (15571858.5, 15591260.5):
+  - Lower Bound = -0.2064
+  - Upper Bound = -0.0535
+  - Width = -0.0535 - (-0.2064) = 0.1529
+
+- For interval (15591260.5, 15598058.0):
+  - Lower Bound = -0.1582
+  - Upper Bound = -0.0061
+  - Width = -0.0061 - (-0.1582) = 0.1521
+
+- For interval (15598058.0, 15602525.5):
+  - Lower Bound = -0.5056
+  - Upper Bound = 0.2038
+  - Width = 0.2038 - (-0.5056) = 0.7094
+
+- For interval (15602525.5, 15607288.0):
+  - Lower Bound = -0.1812
+  - Upper Bound = 0.0176
+  - Width = 0.0176 - (-0.1812) = 0.1988
+
+- For interval (15607288.0, 15664896.0):
+  - Lower Bound = -0.056
+  - Upper Bound = -0.0071
+  - Width = -0.0071 - (-0.056) = 0.0489
+
+- For interval (15664896.0, 15772587.0):
+  - Lower Bound = -0.142
+  - Upper Bound = 0.1744
+  - Width = 0.1744 - (-0.142) = 0.3164
+
+- For interval (15772587.0, 15797097.0):
+  - Lower Bound = -0.0689
+  - Upper Bound = 0.2202
+  - Width = 0.2202 - (-0.0689) = 0.2891
+
+- For interval (15797097.0, 15799214.0):
+  - Lower Bound = -0.206
+  - Upper Bound = 0.2223
+  - Width = 0.2223 - (-0.206) = 0.4283
+
+- For interval (15799214.0, 15807559.5):
+  - Lower Bound = -0.0544
+  - Upper Bound = 0.1706
+  - Width = 0.1706 - (-0.0544) = 0.225
+
+- For interval (15807559.5, 15812616.5):
+  - Lower Bound = -0.1396
+  - Upper Bound = 0.1298
+  - Width = 0.1298 - (-0.1396) = 0.2694
+
+- For interval (15812616.5, 15814479.0):
+  - Lower Bound = -0.2475
+  - Upper Bound = 0.1336
+  - Width = 0.1336 - (-0.2475) = 0.3811
+
+- For interval (15814479.0, 15815247.5):
+  - Lower Bound = -0.4076
+  - Upper Bound = 0.1855
+  - Width = 0.1855 - (-0.4076) = 0.5931
+
+- For interval (15815247.5, 15815626.0):
+  - Lower Bound = -0.3716
+  - Upper Bound = 0.3046
+  - Width = 0.3046 - (-0.3716) = 0.6762
+
+From the calculations, the interval (15566519.0, 15567333.5) has the widest confidence bound with a width of 1.1916.
+SOLUTION:  (15566519.0, 15567333.5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Calculate the width of the confidence interval for each range**:
+   - For each interval, subtract the lower bound from the upper bound.
+
+2. **Identify the interval with the maximum width**:
+   - Compare the calculated widths and find the maximum.
+
+Let's calculate the width of the confidence interval for each interval:
+
+- **(0.0, 1.5)**: Upper - Lower = -0.02487 - (-0.02798) = 0.00311
+- **(1.5, 2.5)**: Upper - Lower = -0.0148 - (-0.01578) = 0.00098
+- **(2.5, 3.5)**: Upper - Lower = -0.00987 - (-0.01088) = 0.00101
+- **(3.5, 4.5)**: Upper - Lower = -0.00529 - (-0.00595) = 0.00066
+- **(4.5, 5.5)**: Upper - Lower = 0.00116 - 0.0002 = 0.00096
+- **(5.5, 6.5)**: Upper - Lower = 0.00643 - 0.0054 = 0.00103
+- **(6.5, 7.5)**: Upper - Lower = 0.01205 - 0.0105 = 0.00155
+- **(7.5, 8.5)**: Upper - Lower = 0.01648 - 0.01459 = 0.00189
+- **(8.5, 10.5)**: Upper - Lower = 0.02483 - 0.02243 = 0.0024
+- **(10.5, 11.5)**: Upper - Lower = 0.03246 - 0.0283 = 0.00416
+- **(11.5, 12.5)**: Upper - Lower = 0.03776 - 0.03438 = 0.00338
+- **(12.5, 13.5)**: Upper - Lower = 0.044 - 0.03775 = 0.00625
+- **(13.5, 15.0)**: Upper - Lower = 0.05697 - 0.03258 = 0.02439
+
+From the calculations, the interval **(13.5, 15.0)** has the widest confidence bound with a width of **0.02439**. This is the interval with the widest confidence bound in the graph.
+SOLUTION:  (13.5, 15.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the calculations for each interval:
+
+1. Interval (0.0, 0.5):
+   - Lower Bound: -0.0359
+   - Upper Bound: -0.02927
+   - Difference: -0.02927 - (-0.0359) = 0.00663
+
+2. Interval (0.5, 1.5):
+   - Lower Bound: -0.02356
+   - Upper Bound: -0.02189
+   - Difference: -0.02189 - (-0.02356) = 0.00167
+
+3. Interval (1.5, 2.5):
+   - Lower Bound: -0.01657
+   - Upper Bound: -0.01482
+   - Difference: -0.01482 - (-0.01657) = 0.00175
+
+4. Interval (2.5, 3.5):
+   - Lower Bound: -0.01034
+   - Upper Bound: -0.00931
+   - Difference: -0.00931 - (-0.01034) = 0.00103
+
+5. Interval (3.5, 4.5):
+   - Lower Bound: -0.0048
+   - Upper Bound: -0.00409
+   - Difference: -0.00409 - (-0.0048) = 0.00071
+
+6. Interval (4.5, 5.5):
+   - Lower Bound: -0.00077
+   - Upper Bound: 0.00007
+   - Difference: 0.00007 - (-0.00077) = 0.00084
+
+7. Interval (5.5, 6.5):
+   - Lower Bound: 0.00528
+   - Upper Bound: 0.00622
+   - Difference: 0.00622 - 0.00528 = 0.00094
+
+8. Interval (6.5, 7.5):
+   - Lower Bound: 0.01081
+   - Upper Bound: 0.0117
+   - Difference: 0.0117 - 0.01081 = 0.00089
+
+9. Interval (7.5, 8.5):
+   - Lower Bound: 0.01566
+   - Upper Bound: 0.01736
+   - Difference: 0.01736 - 0.01566 = 0.0017
+
+10. Interval (8.5, 9.5):
+    - Lower Bound: 0.02049
+    - Upper Bound: 0.02236
+    - Difference: 0.02236 - 0.02049 = 0.00187
+
+11. Interval (9.5, 12.5):
+    - Lower Bound: 0.02706
+    - Upper Bound: 0.031
+    - Difference: 0.031 - 0.02706 = 0.00394
+
+12. Interval (12.5, 13.5):
+    - Lower Bound: 0.0298
+    - Upper Bound: 0.03893
+    - Difference: 0.03893 - 0.0298 = 0.00913
+
+13. Interval (13.5, 15.0):
+    - Lower Bound: 0.0329
+    - Upper Bound: 0.06363
+    - Difference: 0.06363 - 0.0329 = 0.03073
+
+From these calculations, the interval (13.5, 15.0) has the widest confidence bound with a difference of 0.03073.
+SOLUTION:  (13.5, 15.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval with the largest width.
+
+Let's perform these calculations:
+
+- For interval (0.0, 0.5): Upper - Lower = 0.9 - 0.461 = 0.439
+- For interval (0.5, 3.5): Upper - Lower = 0.491 - 0.228 = 0.263
+- For interval (3.5, 4.5): Upper - Lower = 0.411 - 0.097 = 0.314
+- For interval (4.5, 14.5): Upper - Lower = 0.29 - (-0.111) = 0.401
+- For interval (14.5, 23.5): Upper - Lower = 0.087 - (-0.031) = 0.118
+- For interval (23.5, 24.5): Upper - Lower = 0.024 - (-0.079) = 0.103
+- For interval (24.5, 25.5): Upper - Lower = 0.05 - (-0.32) = 0.37
+- For interval (25.5, 39.5): Upper - Lower = 0.012 - (-0.113) = 0.125
+- For interval (39.5, 44.5): Upper - Lower = 0.172 - (-0.088) = 0.26
+- For interval (44.5, 48.5): Upper - Lower = 0.031 - (-0.081) = 0.112
+- For interval (48.5, 54.5): Upper - Lower = 0.132 - (-0.336) = 0.468
+- For interval (54.5, 56.5): Upper - Lower = 0.077 - (-0.102) = 0.179
+- For interval (56.5, 63.5): Upper - Lower = 0.278 - (-0.123) = 0.401
+- For interval (63.5, 64.5): Upper - Lower = 0.163 - (-0.219) = 0.382
+- For interval (64.5, 65.5): Upper - Lower = 0.424 - (-0.706) = 1.13
+- For interval (65.5, 68.5): Upper - Lower = 0.382 - (-0.265) = 0.647
+- For interval (68.5, 69.5): Upper - Lower = 0.373 - (-0.416) = 0.789
+- For interval (69.5, 71.5): Upper - Lower = 0.287 - (-0.213) = 0.5
+- For interval (71.5, 73.5): Upper - Lower = 0.129 - (-0.172) = 0.301
+- For interval (73.5, 74.5): Upper - Lower = 1.265 - (-0.439) = 1.704
+- For interval (74.5, 77.5): Upper - Lower = 0.739 - (-0.317) = 1.056
+- For interval (77.5, 79.0): Upper - Lower = 0.524 - (-1.348) = 1.872
+
+From the calculations, the interval (77.5, 79.0) has the widest confidence bound with a width of 1.872. This is the interval with the widest confidence bound.
+SOLUTION:  (77.5, 79.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds of the confidence interval for each interval and identify the one with the largest difference.
+
+Here are the intervals and their corresponding confidence bounds:
+
+1. Interval (0.0, 0.5):
+   - Lower Bound: -0.02
+   - Upper Bound: 0.19
+   - Difference: 0.19 - (-0.02) = 0.21
+
+2. Interval (0.5, 1.5):
+   - Lower Bound: -0.269
+   - Upper Bound: 0.158
+   - Difference: 0.158 - (-0.269) = 0.427
+
+3. Interval (1.5, 3.0):
+   - Lower Bound: -0.62
+   - Upper Bound: 0.022
+   - Difference: 0.022 - (-0.62) = 0.642
+
+4. Interval (3.0, 4.0):
+   - Lower Bound: -3.014
+   - Upper Bound: -0.395
+   - Difference: -0.395 - (-3.014) = 2.619
+
+Comparing the differences:
+- Interval (0.0, 0.5): 0.21
+- Interval (0.5, 1.5): 0.427
+- Interval (1.5, 3.0): 0.642
+- Interval (3.0, 4.0): 2.619
+
+The interval (3.0, 4.0) has the widest confidence bound with a difference of 2.619.
+SOLUTION:  (3.0, 4.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval with the maximum difference.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Difference**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Identify the Maximum Difference**: Compare the differences calculated in step 2 to find the maximum difference, which indicates the widest confidence interval.
+
+Let's perform these calculations:
+
+- For interval `(2.0, 4.5)`: Difference = \(22383.8 - (-43650.4)\) = \(66034.2\)
+- For interval `(4.5, 9.5)`: Difference = \(14987.3 - (-54645.6)\) = \(69632.9\)
+- For interval `(9.5, 12.5)`: Difference = \(-13782.5 - (-52929.5)\) = \(39147.0\)
+- For interval `(12.5, 14.5)`: Difference = \(2161.9 - (-57181.8)\) = \(59343.7\)
+- For interval `(14.5, 17.5)`: Difference = \(-19075.5 - (-49207.2)\) = \(30131.7\)
+- For interval `(17.5, 20.5)`: Difference = \(-28961.9 - (-72519.5)\) = \(43557.6\)
+- For interval `(20.5, 22.5)`: Difference = \(-35164.8 - (-82934.2)\) = \(47769.4\)
+- For interval `(22.5, 25.5)`: Difference = \(-23412.7 - (-50942.7)\) = \(27530.0\)
+- For interval `(25.5, 29.5)`: Difference = \(-15672.9 - (-45748.1)\) = \(30075.2\)
+- For interval `(29.5, 111.5)`: Difference = \(-25121.6 - (-47452.5)\) = \(22330.9\)
+- For interval `(111.5, 112.5)`: Difference = \(-2622.9 - (-42457.2)\) = \(39834.3\)
+- For interval `(112.5, 176.5)`: Difference = \(-26141.0 - (-41599.3)\) = \(15458.3\)
+- For interval `(176.5, 245.5)`: Difference = \(-19924.6 - (-35478.0)\) = \(15553.4\)
+- For interval `(245.5, 265.5)`: Difference = \(-13531.5 - (-27520.5)\) = \(13989.0\)
+- For interval `(265.5, 268.5)`: Difference = \(-20107.0 - (-32234.3)\) = \(12127.3\)
+- For interval `(268.5, 317.5)`: Difference = \(-10802.3 - (-23732.7)\) = \(12930.4\)
+- For interval `(317.5, 424.5)`: Difference = \(-2788.6 - (-13237.9)\) = \(10449.3\)
+- For interval `(424.5, 463.5)`: Difference = \(3234.7 - (-7023.7)\) = \(10258.4\)
+- For interval `(463.5, 512.5)`: Difference = \(11701.8 - (-1510.7)\) = \(13212.5\)
+- For interval `(512.5, 513.5)`: Difference = \(27227.4 - 6820.8\) = \(20406.6\)
+- For interval `(513.5, 655.5)`: Difference = \(18401.4 - 341.5\) = \(18059.9\)
+- For interval `(655.5, 697.5)`: Difference = \(18397.4 - 12634.4\) = \(5763.0\)
+- For interval `(697.5, 776.5)`: Difference = \(29736.8 - 15982.1\) = \(13754.7\)
+- For interval `(776.5, 779.5)`: Difference = \(27327.8 - 221.5\) = \(27106.3\)
+- For interval `(779.5, 1008.5)`: Difference = \(26870.8 - 18345.9\) = \(8524.9\)
+- For interval `(1008.5, 1012.5)`: Difference = \(54294.7 - 20622.3\) = \(33672.4\)
+- For interval `(1012.5, 1081.5)`: Difference = \(38116.5 - 21931.2\) = \(16185.3\)
+- For interval `(1081.5, 1449.5)`: Difference = \(51992.8 - 22140.8\) = \(29852.0\)
+- For interval `(1449.5, 1490.5)`: Difference = \(63440.2 - 39761.7\) = \(23678.5\)
+- For interval `(1490.5, 1616.0)`: Difference = \(50233.9 - 35441.7\) = \(14792.2\)
+- For interval `(1616.0, 2714.5)`: Difference = \(60911.5 - 37135.8\) = \(23775.7\)
+- For interval `(2714.5, 2865.5)`: Difference = \(48467.9 - 32716.4\) = \(15751.5\)
+- For interval `(2865.5, 6445.0)`: Difference = \(60968.4 - 42203.8\) = \(18764.6\)
+
+From the calculations, the interval `(4.5, 9.5)` has the widest confidence bound with a difference of \(69632.9\).
+SOLUTION:  (4.5, 9.5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds of the confidence interval for each interval and identify the interval with the maximum difference.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: From the JSON object, extract the lower and upper bounds for each interval.
+
+2. **Calculate the Difference for Each Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Identify the Maximum Difference**: Compare the differences calculated in the previous step and identify the interval with the maximum difference.
+
+Let's perform these calculations:
+
+- For interval `(0.0, 0.005855)`: Upper - Lower = -0.612 - (-1.183) = 0.571
+- For interval `(0.005855, 0.011885)`: Upper - Lower = -0.559 - (-1.062) = 0.503
+- For interval `(0.011885, 0.016545)`: Upper - Lower = -0.477 - (-0.961) = 0.484
+- For interval `(0.016545, 0.02046)`: Upper - Lower = -0.4 - (-0.861) = 0.461
+- For interval `(0.02046, 0.02373)`: Upper - Lower = -0.338 - (-0.749) = 0.411
+- For interval `(0.02373, 0.02711)`: Upper - Lower = -0.252 - (-0.665) = 0.413
+- For interval `(0.02711, 0.038885)`: Upper - Lower = -0.203 - (-0.545) = 0.342
+- For interval `(0.038885, 0.044705)`: Upper - Lower = -0.138 - (-0.442) = 0.304
+- For interval `(0.044705, 0.059585)`: Upper - Lower = 0.021 - (-0.43) = 0.451
+- For interval `(0.059585, 0.06851)`: Upper - Lower = 0.103 - (-0.344) = 0.447
+- For interval `(0.06851, 0.072265)`: Upper - Lower = 0.183 - (-0.246) = 0.429
+- For interval `(0.072265, 0.092725)`: Upper - Lower = 0.409 - (-0.128) = 0.537
+- For interval `(0.092725, 0.1015)`: Upper - Lower = 0.355 - 0.093 = 0.262
+- For interval `(0.1015, 0.11415)`: Upper - Lower = 0.452 - 0.166 = 0.286
+- For interval `(0.11415, 0.13)`: Upper - Lower = 0.589 - 0.205 = 0.384
+- For interval `(0.13, 0.14534999999999998)`: Upper - Lower = 0.726 - 0.246 = 0.480
+- For interval `(0.14534999999999998, 0.1525)`: Upper - Lower = 0.898 - 0.264 = 0.634
+- For interval `(0.1525, 0.1686)`: Upper - Lower = 0.984 - 0.346 = 0.638
+- For interval `(0.1686, 0.24280000000000002)`: Upper - Lower = 1.063 - 0.435 = 0.628
+- For interval `(0.24280000000000002, 0.29359999999999997)`: Upper - Lower = 0.912 - 0.402 = 0.510
+- For interval `(0.29359999999999997, 0.32699999999999996)`: Upper - Lower = 0.815 - 0.316 = 0.499
+- For interval `(0.32699999999999996, 0.4268)`: Upper - Lower = 0.752 - 0.208 = 0.544
+
+From these calculations, the interval `(0.1525, 0.1686)` has the widest confidence bound with a difference of 0.638.
+SOLUTION:  (0.1525, 0.1686)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis interval with the widest confidence bound in the graph, we need to calculate the difference between the upper and lower bounds of the confidence interval for each interval and identify the interval with the maximum difference.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Calculate the width of the confidence interval for each x-axis interval**:
+   - For each interval, subtract the lower bound from the upper bound.
+
+2. **Identify the interval with the maximum width**:
+   - Compare the calculated widths and find the interval with the largest value.
+
+Let's perform these calculations:
+
+- **Interval (0.0, 1.5)**: Upper - Lower = -0.02187 - (-0.02705) = 0.00518
+- **Interval (1.5, 2.5)**: Upper - Lower = -0.01637 - (-0.01788) = 0.00151
+- **Interval (2.5, 3.5)**: Upper - Lower = -0.00861 - (-0.00955) = 0.00094
+- **Interval (3.5, 4.5)**: Upper - Lower = -0.00492 - (-0.00566) = 0.00074
+- **Interval (4.5, 5.5)**: Upper - Lower = 0.00056 - 0.00004 = 0.00052
+- **Interval (5.5, 6.5)**: Upper - Lower = 0.00544 - 0.00451 = 0.00093
+- **Interval (6.5, 7.5)**: Upper - Lower = 0.01134 - 0.01051 = 0.00083
+- **Interval (7.5, 8.5)**: Upper - Lower = 0.01833 - 0.01741 = 0.00092
+- **Interval (8.5, 9.5)**: Upper - Lower = 0.02358 - 0.02167 = 0.00191
+- **Interval (9.5, 11.5)**: Upper - Lower = 0.02853 - 0.02561 = 0.00292
+- **Interval (11.5, 12.5)**: Upper - Lower = 0.04032 - 0.03439 = 0.00593
+- **Interval (12.5, 13.5)**: Upper - Lower = 0.04778 - 0.03822 = 0.00956
+- **Interval (13.5, 15.0)**: Upper - Lower = 0.03495 - (-0.00028) = 0.03523
+
+From the calculations, the interval **(13.5, 15.0)** has the widest confidence bound with a width of 0.03523. This is the interval with the maximum difference between the upper and lower bounds of the confidence interval.
+SOLUTION:  (13.5, 15.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval where this width (difference between upper and lower bounds) is the largest.
+
+Let's perform these calculations:
+
+- For interval (0.0, 0.02814): Upper - Lower = -0.226 - (-1.316) = 1.09
+- For interval (0.02814, 0.08293): Upper - Lower = -0.101 - (-1.204) = 1.103
+- For interval (0.08293, 0.08555): Upper - Lower = -0.064 - (-1.003) = 0.939
+- For interval (0.08555, 0.093225): Upper - Lower = -0.091 - (-0.715) = 0.624
+- For interval (0.093225, 0.1055): Upper - Lower = -0.049 - (-0.419) = 0.37
+- For interval (0.1055, 0.11510000000000001): Upper - Lower = 0.065 - (-0.299) = 0.364
+- For interval (0.11510000000000001, 0.1346): Upper - Lower = 0.176 - (-0.172) = 0.348
+- For interval (0.1346, 0.14545000000000002): Upper - Lower = 0.367 - (-0.125) = 0.492
+- For interval (0.14545000000000002, 0.15175): Upper - Lower = 0.406 - 0.077 = 0.329
+- For interval (0.15175, 0.1603): Upper - Lower = 0.545 - 0.185 = 0.36
+- For interval (0.1603, 0.1722): Upper - Lower = 0.966 - 0.112 = 0.854
+- For interval (0.1722, 0.17695): Upper - Lower = 1.27 - 0.052 = 1.218
+- For interval (0.17695, 0.18359999999999999): Upper - Lower = 1.41 - 0.152 = 1.258
+- For interval (0.18359999999999999, 0.194): Upper - Lower = 1.533 - 0.267 = 1.266
+- For interval (0.194, 0.2019): Upper - Lower = 1.653 - 0.392 = 1.261
+- For interval (0.2019, 0.21275): Upper - Lower = 1.803 - 0.477 = 1.326
+- For interval (0.21275, 0.2383): Upper - Lower = 1.926 - 0.593 = 1.333
+- For interval (0.2383, 0.26865): Upper - Lower = 2.063 - 0.693 = 1.37
+- For interval (0.26865, 0.291): Upper - Lower = 2.186 - 0.803 = 1.383
+
+From these calculations, the interval (0.26865, 0.291) has the widest confidence bound with a width of 1.383. This is the interval with the widest confidence bound.
+SOLUTION:  (0.26865, 0.291)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the width of the confidence interval for each interval and then compare them. The width of the confidence interval can be calculated by subtracting the lower bound from the upper bound for each interval.
+
+Here are the calculations for each interval:
+
+1. Interval (1.0, 1.5):
+   - Lower Bound: -0.053
+   - Upper Bound: 0.035
+   - Width: 0.035 - (-0.053) = 0.088
+
+2. Interval (1.5, 2.5):
+   - Lower Bound: 0.174
+   - Upper Bound: 0.893
+   - Width: 0.893 - 0.174 = 0.719
+
+3. Interval (2.5, 3.0):
+   - Lower Bound: -1.011
+   - Upper Bound: -0.052
+   - Width: -0.052 - (-1.011) = 0.959
+
+Comparing the widths:
+- Interval (1.0, 1.5): 0.088
+- Interval (1.5, 2.5): 0.719
+- Interval (2.5, 3.0): 0.959
+
+The interval (2.5, 3.0) has the widest confidence bound with a width of 0.959.
+SOLUTION:  (2.5, 3.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the width of the confidence interval for each interval provided in the graph. The width of the confidence interval can be calculated by subtracting the lower bound from the upper bound.
+
+Let's calculate the width of the confidence intervals for each interval:
+
+1. For the interval (0.0, 0.5):
+   - Lower Bound = 0.1807
+   - Upper Bound = 0.4643
+   - Width = Upper Bound - Lower Bound = 0.4643 - 0.1807 = 0.2836
+
+2. For the interval (0.5, 1.0):
+   - Lower Bound = -0.5976
+   - Upper Bound = -0.2325
+   - Width = Upper Bound - Lower Bound = -0.2325 - (-0.5976) = 0.3651
+
+Comparing the widths:
+- Interval (0.0, 0.5) has a width of 0.2836
+- Interval (0.5, 1.0) has a width of 0.3651
+
+The interval (0.5, 1.0) has the widest confidence bound with a width of 0.3651.
+SOLUTION:  (0.5, 1.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the calculations for each interval:
+
+1. Interval (0.0, 0.5):
+   - Lower Bound: -0.2406
+   - Upper Bound: -0.0606
+   - Difference: -0.0606 - (-0.2406) = 0.18
+
+2. Interval (0.5, 1.5):
+   - Lower Bound: -0.3636
+   - Upper Bound: -0.1333
+   - Difference: -0.1333 - (-0.3636) = 0.2303
+
+3. Interval (1.5, 2.5):
+   - Lower Bound: -0.242
+   - Upper Bound: -0.1326
+   - Difference: -0.1326 - (-0.242) = 0.1094
+
+4. Interval (2.5, 3.5):
+   - Lower Bound: -0.093
+   - Upper Bound: 0.0326
+   - Difference: 0.0326 - (-0.093) = 0.1256
+
+5. Interval (3.5, 4.5):
+   - Lower Bound: -0.038
+   - Upper Bound: 0.0802
+   - Difference: 0.0802 - (-0.038) = 0.1182
+
+6. Interval (4.5, 5.5):
+   - Lower Bound: 0.0314
+   - Upper Bound: 0.1712
+   - Difference: 0.1712 - 0.0314 = 0.1398
+
+7. Interval (5.5, 6.5):
+   - Lower Bound: 0.0909
+   - Upper Bound: 0.207
+   - Difference: 0.207 - 0.0909 = 0.1161
+
+8. Interval (6.5, 7.5):
+   - Lower Bound: 0.1609
+   - Upper Bound: 0.3671
+   - Difference: 0.3671 - 0.1609 = 0.2062
+
+9. Interval (7.5, 8.5):
+   - Lower Bound: 0.2075
+   - Upper Bound: 0.5032
+   - Difference: 0.5032 - 0.2075 = 0.2957
+
+10. Interval (8.5, 9.5):
+    - Lower Bound: 0.248
+    - Upper Bound: 0.5755
+    - Difference: 0.5755 - 0.248 = 0.3275
+
+11. Interval (9.5, 13.5):
+    - Lower Bound: 0.0671
+    - Upper Bound: 0.5321
+    - Difference: 0.5321 - 0.0671 = 0.465
+
+12. Interval (13.5, 14.0):
+    - Lower Bound: 0.084
+    - Upper Bound: 1.2617
+    - Difference: 1.2617 - 0.084 = 1.1777
+
+From these calculations, the interval (13.5, 14.0) has the widest confidence bound with a difference of 1.1777.
+SOLUTION:  (13.5, 14.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the calculations for each interval:
+
+1. Interval (0.0, 0.5): Upper - Lower = -0.02321 - (-0.02939) = 0.00618
+2. Interval (0.5, 1.5): Upper - Lower = -0.01993 - (-0.02258) = 0.00265
+3. Interval (1.5, 2.5): Upper - Lower = -0.01641 - (-0.01777) = 0.00136
+4. Interval (2.5, 3.5): Upper - Lower = -0.01001 - (-0.01075) = 0.00074
+5. Interval (3.5, 4.5): Upper - Lower = -0.00589 - (-0.00677) = 0.00088
+6. Interval (4.5, 5.5): Upper - Lower = 0.00098 - 0.00038 = 0.00060
+7. Interval (5.5, 6.5): Upper - Lower = 0.00665 - 0.00571 = 0.00094
+8. Interval (6.5, 7.5): Upper - Lower = 0.01264 - 0.01182 = 0.00082
+9. Interval (7.5, 8.5): Upper - Lower = 0.01804 - 0.01718 = 0.00086
+10. Interval (8.5, 9.5): Upper - Lower = 0.02414 - 0.02223 = 0.00191
+11. Interval (9.5, 10.5): Upper - Lower = 0.02919 - 0.02645 = 0.00274
+12. Interval (10.5, 11.5): Upper - Lower = 0.0353 - 0.02946 = 0.00584
+13. Interval (11.5, 13.5): Upper - Lower = 0.04259 - 0.03697 = 0.00562
+14. Interval (13.5, 15.0): Upper - Lower = 0.05476 - 0.03459 = 0.02017
+15. Interval (15.0, 16.0): Upper - Lower = 0.06736 - 0.03844 = 0.02892
+
+From these calculations, the interval (15.0, 16.0) has the widest confidence bound with a difference of 0.02892. Therefore, the x-axis interval with the widest confidence bound is (15.0, 16.0).
+SOLUTION:  (15.0, 16.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval where this width (difference between upper and lower bounds) is the largest.
+
+Let's perform these calculations:
+
+- For interval (40.0, 41.5): Upper - Lower = -0.259 - (-2.719) = 2.460
+- For interval (41.5, 43.5): Upper - Lower = 0.696 - (-2.486) = 3.182
+- For interval (43.5, 44.5): Upper - Lower = 0.722 - (-0.761) = 1.483
+- For interval (44.5, 47.5): Upper - Lower = 1.105 - 0.297 = 0.808
+- For interval (47.5, 48.5): Upper - Lower = 2.291 - 0.199 = 2.092
+- For interval (48.5, 58.5): Upper - Lower = -0.612 - (-1.235) = 0.623
+- For interval (58.5, 59.5): Upper - Lower = 1.004 - 0.291 = 0.713
+- For interval (59.5, 60.8335): Upper - Lower = 0.228 - (-0.805) = 1.033
+- For interval (60.8335, 64.5): Upper - Lower = -0.414 - (-1.655) = 1.241
+- For interval (64.5, 65.5): Upper - Lower = 0.281 - (-0.281) = 0.562
+- For interval (65.5, 67.5): Upper - Lower = 0.662 - (-2.122) = 2.784
+- For interval (67.5, 68.5): Upper - Lower = 0.44 - (-0.059) = 0.499
+- For interval (68.5, 70.5): Upper - Lower = 1.056 - 0.513 = 0.543
+- For interval (70.5, 80.5): Upper - Lower = 1.934 - 0.404 = 1.530
+- For interval (80.5, 81.5): Upper - Lower = 1.505 - 0.173 = 1.332
+- For interval (81.5, 85.5): Upper - Lower = 2.916 - 1.308 = 1.608
+- For interval (85.5, 86.5): Upper - Lower = 5.009 - 2.758 = 2.251
+- For interval (86.5, 95.0): Upper - Lower = 5.79 - 3.244 = 2.546
+
+From these calculations, the interval (86.5, 95.0) has the widest confidence bound with a width of 2.546.
+SOLUTION:  (41.5, 43.5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval with the maximum width.
+
+Let's perform these calculations:
+
+- For interval `(0.001713, 0.0031539999999999997)`, the width is `1.4555 - (-0.864) = 2.3195`.
+- For interval `(0.0031539999999999997, 0.003299)`, the width is `1.442 - (-0.919) = 2.361`.
+- For interval `(0.003299, 0.003384)`, the width is `1.3896 - (-1.0196) = 2.4092`.
+- For interval `(0.003384, 0.0034675)`, the width is `0.386 - (-0.6905) = 1.0765`.
+- For interval `(0.0034675, 0.0036699999999999997)`, the width is `0.3557 - (-0.7233) = 1.079`.
+- For interval `(0.0036699999999999997, 0.0041069999999999995)`, the width is `0.327 - (-0.7618) = 1.0888`.
+- For interval `(0.0041069999999999995, 0.004215)`, the width is `0.2913 - (-0.7976) = 1.0889`.
+- For interval `(0.004215, 0.004436)`, the width is `0.2734 - (-0.8492) = 1.1226`.
+- For interval `(0.004436, 0.0045775)`, the width is `0.2417 - (-0.8863) = 1.128`.
+- For interval `(0.0045775, 0.004612)`, the width is `0.2615 - (-0.8426) = 1.1041`.
+- For interval `(0.004612, 0.0048915)`, the width is `0.2171 - (-0.7021) = 0.9192`.
+- For interval `(0.0048915, 0.0053335)`, the width is `0.2692 - (-0.6905) = 0.9597`.
+- For interval `(0.0053335, 0.005443)`, the width is `0.3117 - (-0.6659) = 0.9776`.
+- For interval `(0.005443, 0.00554)`, the width is `0.3335 - (-0.624) = 0.9575`.
+- For interval `(0.00554, 0.005729)`, the width is `0.349 - (-0.5761) = 0.9251`.
+- For interval `(0.005729, 0.0058625)`, the width is `0.3757 - (-0.538) = 0.9137`.
+- For interval `(0.0058625, 0.0058955)`, the width is `0.4082 - (-0.5073) = 0.9155`.
+- For interval `(0.0058955, 0.0067525)`, the width is `0.1644 - (-0.1186) = 0.283`.
+- For interval `(0.0067525, 0.00682)`, the width is `0.2053 - (-0.0928) = 0.2981`.
+- For interval `(0.00682, 0.007338)`, the width is `0.5173 - (-0.288) = 0.8053`.
+- For interval `(0.007338, 0.0074805)`, the width is `0.5502 - (-0.2553) = 0.8055`.
+- For interval `(0.0074805, 0.007967)`, the width is `0.5854 - (-0.2176) = 0.803`.
+- For interval `(0.007967, 0.009857000000000001)`, the width is `0.6167 - (-0.1787) = 0.7954`.
+- For interval `(0.009857000000000001, 0.010665000000000001)`, the width is `0.5738 - (-0.2012) = 0.775`.
+- For interval `(0.010665000000000001, 0.011054999999999999)`, the width is `0.5419 - (-0.2344) = 0.7763`.
+- For interval `(0.011054999999999999, 0.011915)`, the width is `0.5053 - (-0.2614) = 0.7667`.
+- For interval `(0.011915, 0.012885)`, the width is `0.4585 - (-0.2838) = 0.7423`.
+- For interval `(0.012885, 0.03113)`, the width is `0.522 - (-0.4136) = 0.9356`.
+
+From the calculations, the interval `(0.003299, 0.003384)` has the widest confidence bound with a width of `2.4092`.
+SOLUTION:  (0.003299, 0.003384)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval with the maximum width.
+
+Let's perform these calculations:
+
+- For interval (0.5, 0.6499999999999999): Upper - Lower = 0.62 - (-1.13) = 1.75
+- For interval (0.6499999999999999, 0.725): Upper - Lower = -0.72 - (-1.45) = 0.73
+- For interval (0.725, 0.875): Upper - Lower = -1.84 - (-5.7) = 3.86
+- For interval (0.875, 0.95): Upper - Lower = -0.48 - (-1.31) = 0.83
+- For interval (0.95, 1.1400000000000001): Upper - Lower = 0.12 - (-0.41) = 0.53
+- For interval (1.1400000000000001, 1.35): Upper - Lower = 0.16 - (-1.92) = 2.08
+- For interval (1.35, 1.45): Upper - Lower = 0.53 - (-0.14) = 0.67
+- For interval (1.45, 1.55): Upper - Lower = 1.89 - 0.46 = 1.43
+- For interval (1.55, 1.815): Upper - Lower = 2.68 - 1.68 = 1.00
+- For interval (1.815, 2.05): Upper - Lower = 6.73 - 2.75 = 3.98
+- For interval (2.05, 2.45): Upper - Lower = 1.56 - 0.72 = 0.84
+- For interval (2.45, 2.6): Upper - Lower = 5.32 - 1.94 = 3.38
+- For interval (2.6, 2.95): Upper - Lower = 1.77 - (-2.5) = 4.27
+- For interval (2.95, 3.1): Upper - Lower = 4.84 - 0.3 = 4.54
+- For interval (3.1, 3.45): Upper - Lower = 1.2 - (-0.49) = 1.69
+- For interval (3.45, 3.6): Upper - Lower = 4.53 - 1.58 = 2.95
+- For interval (3.6, 3.75): Upper - Lower = 8.97 - 4.55 = 4.42
+- For interval (3.75, 3.9): Upper - Lower = 4.21 - 0.4 = 3.81
+- For interval (3.9, 4.7): Upper - Lower = 5.04 - 0.8 = 4.24
+- For interval (4.7, 5.949999999999999): Upper - Lower = 2.14 - (-0.63) = 2.77
+- For interval (5.949999999999999, 6.199999999999999): Upper - Lower = 0.9 - (-1.75) = 2.65
+- For interval (6.199999999999999, 6.55): Upper - Lower = 3.21 - (-2.74) = 5.95
+- For interval (6.55, 9.4): Upper - Lower = 8.88 - 5.07 = 3.81
+
+From the calculations, the interval (6.199999999999999, 6.55) has the widest confidence bound with a width of 5.95. This is the interval with the widest confidence bound in the graph.
+SOLUTION:  (6.199999999999999, 6.55)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the calculations for each interval:
+
+1. Interval (0.0, 1.5):
+   - Lower Bound: -0.02598
+   - Upper Bound: -0.02052
+   - Difference: -0.02052 - (-0.02598) = 0.00546
+
+2. Interval (1.5, 2.5):
+   - Lower Bound: -0.01586
+   - Upper Bound: -0.01477
+   - Difference: -0.01477 - (-0.01586) = 0.00109
+
+3. Interval (2.5, 3.5):
+   - Lower Bound: -0.01113
+   - Upper Bound: -0.01034
+   - Difference: -0.01034 - (-0.01113) = 0.00079
+
+4. Interval (3.5, 4.5):
+   - Lower Bound: -0.00525
+   - Upper Bound: -0.00438
+   - Difference: -0.00438 - (-0.00525) = 0.00087
+
+5. Interval (4.5, 5.5):
+   - Lower Bound: -0.00072
+   - Upper Bound: 0.00009
+   - Difference: 0.00009 - (-0.00072) = 0.00081
+
+6. Interval (5.5, 6.5):
+   - Lower Bound: 0.00574
+   - Upper Bound: 0.00686
+   - Difference: 0.00686 - 0.00574 = 0.00112
+
+7. Interval (6.5, 7.5):
+   - Lower Bound: 0.01173
+   - Upper Bound: 0.01282
+   - Difference: 0.01282 - 0.01173 = 0.00109
+
+8. Interval (7.5, 8.5):
+   - Lower Bound: 0.01585
+   - Upper Bound: 0.01689
+   - Difference: 0.01689 - 0.01585 = 0.00104
+
+9. Interval (8.5, 10.5):
+   - Lower Bound: 0.02412
+   - Upper Bound: 0.02662
+   - Difference: 0.02662 - 0.02412 = 0.00250
+
+10. Interval (10.5, 12.5):
+    - Lower Bound: 0.02908
+    - Upper Bound: 0.0347
+    - Difference: 0.0347 - 0.02908 = 0.00562
+
+11. Interval (12.5, 13.5):
+    - Lower Bound: 0.03687
+    - Upper Bound: 0.04234
+    - Difference: 0.04234 - 0.03687 = 0.00547
+
+12. Interval (13.5, 14.0):
+    - Lower Bound: 0.00331
+    - Upper Bound: 0.02957
+    - Difference: 0.02957 - 0.00331 = 0.02626
+
+From the above calculations, the interval (13.5, 14.0) has the widest confidence bound with a difference of 0.02626.
+SOLUTION:  (13.5, 14.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval where this width (difference between upper and lower bounds) is the largest.
+
+Let's perform these calculations:
+
+- For interval (0.7714, 1.0579999999999998): Upper - Lower = -0.265 - (-1.131) = 0.866
+- For interval (1.0579999999999998, 1.1345): Upper - Lower = -0.208 - (-1.029) = 0.821
+- For interval (1.1345, 1.197): Upper - Lower = -0.155 - (-0.923) = 0.768
+- For interval (1.197, 1.2365): Upper - Lower = -0.087 - (-0.835) = 0.748
+- For interval (1.2365, 1.326): Upper - Lower = -0.015 - (-0.754) = 0.739
+- For interval (1.326, 1.4435): Upper - Lower = -0.081 - (-0.43) = 0.349
+- For interval (1.4435, 1.5314999999999999): Upper - Lower = 0.026 - (-0.378) = 0.404
+- For interval (1.5314999999999999, 1.807): Upper - Lower = 0.016 - (-0.215) = 0.231
+- For interval (1.807, 2.107): Upper - Lower = 0.085 - (-0.131) = 0.216
+- For interval (2.107, 2.593): Upper - Lower = 0.019 - (-0.215) = 0.234
+- For interval (2.593, 2.878): Upper - Lower = 0.088 - (-0.124) = 0.212
+- For interval (2.878, 3.292): Upper - Lower = 0.152 - (-0.022) = 0.174
+- For interval (3.292, 4.095000000000001): Upper - Lower = 0.24 - 0.04 = 0.2
+- For interval (4.095000000000001, 4.714): Upper - Lower = 0.388 - 0.049 = 0.339
+- For interval (4.714, 4.885999999999999): Upper - Lower = 0.655 - (-0.063) = 0.718
+- For interval (4.885999999999999, 5.2844999999999995): Upper - Lower = 0.737 - 0.007 = 0.73
+- For interval (5.2844999999999995, 5.8425): Upper - Lower = 0.814 - 0.088 = 0.726
+- For interval (5.8425, 7.104): Upper - Lower = 0.921 - 0.151 = 0.77
+- For interval (7.104, 7.7765): Upper - Lower = 1.012 - 0.21 = 0.802
+- For interval (7.7765, 10.594999999999999): Upper - Lower = 1.146 - 0.257 = 0.889
+- For interval (10.594999999999999, 21.98): Upper - Lower = 1.231 - 0.341 = 0.89
+
+From these calculations, the interval (10.594999999999999, 21.98) has the widest confidence bound with a width of 0.89.
+SOLUTION:  (10.594999999999999, 21.98)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds of the confidence interval for each age range and identify the range with the maximum difference.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval with the maximum width.
+
+Let's perform these calculations:
+
+- For interval (21.0, 21.5): Width = -0.228 - (-0.733) = 0.505
+- For interval (21.5, 23.5): Width = -0.21 - (-0.545) = 0.335
+- For interval (23.5, 24.5): Width = -0.139 - (-0.449) = 0.310
+- For interval (24.5, 26.5): Width = -0.094 - (-0.316) = 0.222
+- For interval (26.5, 28.5): Width = -0.008 - (-0.204) = 0.196
+- For interval (28.5, 30.5): Width = 0.206 - (-0.094) = 0.300
+- For interval (30.5, 34.5): Width = 0.335 - 0.033 = 0.302
+- For interval (34.5, 39.5): Width = 0.441 - 0.131 = 0.310
+- For interval (39.5, 44.5): Width = 0.544 - 0.234 = 0.310
+- For interval (44.5, 54.5): Width = 0.66 - 0.292 = 0.368
+- For interval (54.5, 56.5): Width = 0.569 - 0.179 = 0.390
+- For interval (56.5, 58.5): Width = 0.382 - 0.067 = 0.315
+- For interval (58.5, 60.5): Width = 0.267 - (-0.026) = 0.293
+- For interval (60.5, 61.5): Width = 0.208 - (-0.314) = 0.522
+- For interval (61.5, 62.5): Width = 0.149 - (-0.776) = 0.925
+- For interval (62.5, 64.5): Width = 0.05 - (-0.923) = 0.973
+- For interval (64.5, 66.5): Width = -0.107 - (-1.089) = 0.982
+- For interval (66.5, 67.5): Width = -0.222 - (-1.205) = 0.983
+- For interval (67.5, 68.5): Width = -0.325 - (-1.322) = 0.997
+- For interval (68.5, 76.5): Width = -0.399 - (-1.445) = 1.046
+- For interval (76.5, 81.0): Width = -0.529 - (-1.674) = 1.145
+
+From these calculations, the interval (76.5, 81.0) has the widest confidence bound with a width of 1.145.
+SOLUTION:  (76.5, 81.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the calculations for each interval:
+
+1. Interval (0.0, 0.5): Upper - Lower = -0.02312 - (-0.0274) = 0.00428
+2. Interval (0.5, 1.5): Upper - Lower = -0.02056 - (-0.02237) = 0.00181
+3. Interval (1.5, 2.5): Upper - Lower = -0.01451 - (-0.01633) = 0.00182
+4. Interval (2.5, 3.5): Upper - Lower = -0.00985 - (-0.01068) = 0.00083
+5. Interval (3.5, 4.5): Upper - Lower = -0.00431 - (-0.005) = 0.00069
+6. Interval (4.5, 5.5): Upper - Lower = 0.00084 - 0.00014 = 0.00070
+7. Interval (5.5, 6.5): Upper - Lower = 0.00596 - 0.00514 = 0.00082
+8. Interval (6.5, 8.5): Upper - Lower = 0.01197 - 0.01068 = 0.00129
+9. Interval (8.5, 10.5): Upper - Lower = 0.0234 - 0.02129 = 0.00211
+10. Interval (10.5, 11.5): Upper - Lower = 0.03409 - 0.03073 = 0.00336
+11. Interval (11.5, 12.5): Upper - Lower = 0.04085 - 0.03466 = 0.00619
+12. Interval (12.5, 13.5): Upper - Lower = 0.04633 - 0.038 = 0.00833
+13. Interval (13.5, 14.0): Upper - Lower = 0.05012 - 0.043 = 0.00712
+
+From these calculations, the interval (12.5, 13.5) has the widest confidence bound with a difference of 0.00833. Therefore, the interval (12.5, 13.5) has the widest confidence bound in the graph.
+SOLUTION:  (12.5, 13.5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval with the maximum difference.
+
+Here are the calculations for each interval:
+
+1. Interval (0.0, 0.5): Upper - Lower = -0.02443 - (-0.02743) = 0.00300
+2. Interval (0.5, 1.5): Upper - Lower = -0.02083 - (-0.02261) = 0.00178
+3. Interval (1.5, 2.5): Upper - Lower = -0.01472 - (-0.01616) = 0.00144
+4. Interval (2.5, 3.5): Upper - Lower = -0.0094 - (-0.0102) = 0.00080
+5. Interval (3.5, 4.5): Upper - Lower = -0.00504 - (-0.00579) = 0.00075
+6. Interval (4.5, 5.5): Upper - Lower = 0.00105 - 0.00027 = 0.00078
+7. Interval (5.5, 6.5): Upper - Lower = 0.00606 - 0.00544 = 0.00062
+8. Interval (6.5, 7.5): Upper - Lower = 0.01257 - 0.01146 = 0.00111
+9. Interval (7.5, 8.5): Upper - Lower = 0.01698 - 0.01601 = 0.00097
+10. Interval (8.5, 9.5): Upper - Lower = 0.02234 - 0.02065 = 0.00169
+11. Interval (9.5, 10.5): Upper - Lower = 0.02828 - 0.02512 = 0.00316
+12. Interval (10.5, 11.5): Upper - Lower = 0.03265 - 0.0285 = 0.00415
+13. Interval (11.5, 13.5): Upper - Lower = 0.04389 - 0.02931 = 0.01458
+14. Interval (13.5, 14.0): Upper - Lower = 0.03772 - 0.02233 = 0.01539
+
+From these calculations, the interval (13.5, 14.0) has the widest confidence bound with a difference of 0.01539.
+SOLUTION:  (13.5, 14.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval where this width (difference between upper and lower bounds) is the largest.
+
+Let's perform these calculations using the provided data:
+
+- For interval `(0.3602, 0.47535000000000005)`, the width is `0.1358 - (-0.4063) = 0.5421`.
+- For interval `(0.47535000000000005, 0.49585)`, the width is `0.0627 - (-0.2824) = 0.3451`.
+- For interval `(0.49585, 0.5344)`, the width is `0.0665 - (-0.2408) = 0.3073`.
+- For interval `(0.5344, 0.55835)`, the width is `0.0849 - (-0.2114) = 0.2963`.
+- For interval `(0.55835, 0.5779000000000001)`, the width is `0.089 - (-0.1606) = 0.2496`.
+- For interval `(0.5779000000000001, 0.6065)`, the width is `0.1161 - (-0.1405) = 0.2566`.
+- For interval `(0.6065, 0.6938500000000001)`, the width is `0.1366 - (-0.1139) = 0.2505`.
+- For interval `(0.6938500000000001, 0.7878499999999999)`, the width is `0.124 - (-0.1541) = 0.2781`.
+- For interval `(0.7878499999999999, 0.8181499999999999)`, the width is `0.1322 - (-0.1144) = 0.2466`.
+- For interval `(0.8181499999999999, 0.9497)`, the width is `0.1561 - (-0.0897) = 0.2458`.
+- For interval `(0.9497, 0.99)`, the width is `0.1314 - (-0.0157) = 0.1471`.
+- For interval `(0.99, 1.0579999999999998)`, the width is `0.1333 - 0.029 = 0.1043`.
+- For interval `(1.0579999999999998, 1.2845)`, the width is `0.1282 - (-0.012) = 0.1402`.
+- For interval `(1.2845, 1.461)`, the width is `0.1336 - (-0.0661) = 0.1997`.
+- For interval `(1.461, 1.4785)`, the width is `0.1082 - (-0.0888) = 0.197`.
+- For interval `(1.4785, 1.892)`, the width is `0.1884 - (-0.2196) = 0.408`.
+- For interval `(1.892, 1.9255)`, the width is `0.1585 - (-0.2461) = 0.4046`.
+- For interval `(1.9255, 1.9945)`, the width is `0.1456 - (-0.2824) = 0.428`.
+- For interval `(1.9945, 2.0999999999999996)`, the width is `0.1329 - (-0.3214) = 0.4543`.
+- For interval `(2.0999999999999996, 2.2295)`, the width is `0.4421 - (-0.5019) = 0.944`.
+- For interval `(2.2295, 2.263)`, the width is `0.1879 - (-0.6324) = 0.8203`.
+- For interval `(2.263, 2.3085)`, the width is `0.1825 - (-0.6746) = 0.8571`.
+- For interval `(2.3085, 2.481)`, the width is `0.1571 - (-0.7135) = 0.8706`.
+- For interval `(2.481, 2.6235)`, the width is `0.1201 - (-0.7339) = 0.854`.
+- For interval `(2.6235, 3.6075)`, the width is `0.0929 - (-0.7611) = 0.854`.
+- For interval `(3.6075, 4.885)`, the width is `0.0616 - (-0.7768) = 0.8384`.
+
+From these calculations, the interval `(2.3085, 2.481)` has the widest confidence bound with a width of `0.8706`.
+SOLUTION:  (2.0999999999999996, 2.2295)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval with the maximum width.
+
+Let's perform these calculations:
+
+- For interval `(0.0, 0.022775)`: Width = `-0.109 - (-1.429) = 1.320`
+- For interval `(0.022775, 0.024655)`: Width = `-0.005 - (-1.337) = 1.332`
+- For interval `(0.024655, 0.052095)`: Width = `-0.123 - (-1.568) = 1.445`
+- For interval `(0.052095, 0.10575)`: Width = `-0.186 - (-1.701) = 1.515`
+- For interval `(0.10575, 0.1313)`: Width = `-0.065 - (-1.62) = 1.555`
+- For interval `(0.1313, 0.14545000000000002)`: Width = `0.031 - (-1.521) = 1.552`
+- For interval `(0.14545000000000002, 0.1694)`: Width = `0.135 - (-1.427) = 1.562`
+- For interval `(0.1694, 0.1843)`: Width = `0.244 - (-1.324) = 1.568`
+- For interval `(0.1843, 0.19235000000000002)`: Width = `0.332 - (-1.207) = 1.539`
+- For interval `(0.19235000000000002, 0.1996)`: Width = `0.428 - (-1.093) = 1.521`
+- For interval `(0.1996, 0.20695)`: Width = `0.514 - (-0.982) = 1.496`
+- For interval `(0.20695, 0.20795)`: Width = `0.653 - (-0.814) = 1.467`
+- For interval `(0.20795, 0.2539)`: Width = `0.891 - (-0.518) = 1.409`
+- For interval `(0.2539, 0.273)`: Width = `0.648 - (-0.08) = 0.728`
+- For interval `(0.273, 0.33975)`: Width = `0.737 - 0.033 = 0.704`
+- For interval `(0.33975, 0.3663)`: Width = `0.708 - 0.265 = 0.443`
+- For interval `(0.3663, 0.37695)`: Width = `0.807 - 0.365 = 0.442`
+- For interval `(0.37695, 0.39765)`: Width = `1.423 - (-0.026) = 1.449`
+- For interval `(0.39765, 0.41025)`: Width = `1.902 - (-0.308) = 2.210`
+- For interval `(0.41025, 1.252)`: Width = `2.024 - (-0.23) = 2.254`
+
+The interval with the widest confidence bound is `(0.41025, 1.252)` with a width of `2.254`.
+SOLUTION:  (0.41025, 1.252)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the calculations for each interval:
+
+1. Interval (0.0, 0.5): Upper - Lower = -0.02672 - (-0.03241) = 0.00569
+2. Interval (0.5, 2.5): Upper - Lower = -0.0199 - (-0.02172) = 0.00182
+3. Interval (2.5, 3.5): Upper - Lower = -0.0094 - (-0.01056) = 0.00116
+4. Interval (3.5, 4.5): Upper - Lower = -0.00479 - (-0.0057) = 0.00091
+5. Interval (4.5, 5.5): Upper - Lower = 0.00085 - 0.00001 = 0.00084
+6. Interval (5.5, 6.5): Upper - Lower = 0.00557 - 0.00474 = 0.00083
+7. Interval (6.5, 8.5): Upper - Lower = 0.01172 - 0.01043 = 0.00129
+8. Interval (8.5, 10.5): Upper - Lower = 0.02247 - 0.01957 = 0.0029
+9. Interval (10.5, 11.5): Upper - Lower = 0.02915 - 0.02542 = 0.00373
+10. Interval (11.5, 13.5): Upper - Lower = 0.04855 - 0.04264 = 0.00591
+11. Interval (13.5, 14.5): Upper - Lower = 0.05605 - 0.04883 = 0.00722
+12. Interval (14.5, 17.0): Upper - Lower = 0.06565 - 0.05758 = 0.00807
+
+From these calculations, the interval (14.5, 17.0) has the widest confidence bound with a difference of 0.00807. This is the interval with the widest confidence bound in the graph.
+SOLUTION:  (14.5, 17.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the calculations for each interval:
+
+1. Interval (0.0, 1.5):
+   - Lower Bound: -0.02735
+   - Upper Bound: -0.02363
+   - Difference: -0.02363 - (-0.02735) = 0.00372
+
+2. Interval (1.5, 2.5):
+   - Lower Bound: -0.01647
+   - Upper Bound: -0.01503
+   - Difference: -0.01503 - (-0.01647) = 0.00144
+
+3. Interval (2.5, 3.5):
+   - Lower Bound: -0.01101
+   - Upper Bound: -0.0102
+   - Difference: -0.0102 - (-0.01101) = 0.00081
+
+4. Interval (3.5, 4.5):
+   - Lower Bound: -0.00502
+   - Upper Bound: -0.00418
+   - Difference: -0.00418 - (-0.00502) = 0.00084
+
+5. Interval (4.5, 5.5):
+   - Lower Bound: 0.00018
+   - Upper Bound: 0.00101
+   - Difference: 0.00101 - 0.00018 = 0.00083
+
+6. Interval (5.5, 6.5):
+   - Lower Bound: 0.00528
+   - Upper Bound: 0.00607
+   - Difference: 0.00607 - 0.00528 = 0.00079
+
+7. Interval (6.5, 7.5):
+   - Lower Bound: 0.01139
+   - Upper Bound: 0.01263
+   - Difference: 0.01263 - 0.01139 = 0.00124
+
+8. Interval (7.5, 9.5):
+   - Lower Bound: 0.01505
+   - Upper Bound: 0.01697
+   - Difference: 0.01697 - 0.01505 = 0.00192
+
+9. Interval (9.5, 10.5):
+   - Lower Bound: 0.0236
+   - Upper Bound: 0.02702
+   - Difference: 0.02702 - 0.0236 = 0.00342
+
+10. Interval (10.5, 11.5):
+    - Lower Bound: 0.02677
+    - Upper Bound: 0.03236
+    - Difference: 0.03236 - 0.02677 = 0.00559
+
+11. Interval (11.5, 12.5):
+    - Lower Bound: 0.03846
+    - Upper Bound: 0.04216
+    - Difference: 0.04216 - 0.03846 = 0.0037
+
+12. Interval (12.5, 14.0):
+    - Lower Bound: 0.03359
+    - Upper Bound: 0.05488
+    - Difference: 0.05488 - 0.03359 = 0.02129
+
+From these calculations, the interval (12.5, 14.0) has the widest confidence bound with a difference of 0.02129.
+SOLUTION:  (12.5, 14.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval where this width (difference between upper and lower bounds) is the largest.
+
+Let's perform these calculations:
+
+- For interval (18.0, 32.5): Width = 1.079 - 0.581 = 0.498
+- For interval (32.5, 34.5): Width = 0.833 - 0.529 = 0.304
+- For interval (34.5, 37.5): Width = 0.48 - 0.367 = 0.113
+- For interval (37.5, 38.5): Width = 0.332 - 0.229 = 0.103
+- For interval (38.5, 39.5): Width = 0.159 - (-0.051) = 0.21
+- For interval (39.5, 40.5): Width = -0.08 - (-0.305) = 0.225
+- For interval (40.5, 41.5): Width = -0.246 - (-0.462) = 0.216
+- For interval (41.5, 42.5): Width = -0.382 - (-0.607) = 0.225
+- For interval (42.5, 44.5): Width = -0.706 - (-0.855) = 0.149
+- For interval (44.5, 46.5): Width = -0.991 - (-1.16) = 0.169
+- For interval (46.5, 48.5): Width = -1.387 - (-1.704) = 0.317
+- For interval (48.5, 54.5): Width = -1.548 - (-1.885) = 0.337
+- For interval (54.5, 56.5): Width = -1.684 - (-2.031) = 0.347
+- For interval (56.5, 64.5): Width = -1.501 - (-1.913) = 0.412
+- For interval (64.5, 66.5): Width = -0.88 - (-1.66) = 0.78
+- For interval (66.5, 69.5): Width = -0.906 - (-1.33) = 0.424
+- For interval (69.5, 70.5): Width = -0.554 - (-1.222) = 0.668
+- For interval (70.5, 72.5): Width = 0.082 - (-1.257) = 1.339
+- For interval (72.5, 74.5): Width = 0.436 - (-1.055) = 1.491
+- For interval (74.5, 81.0): Width = 0.625 - (-0.939) = 1.564
+
+From these calculations, the interval (74.5, 81.0) has the widest confidence bound with a width of 1.564.
+SOLUTION:  (74.5, 81.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the calculations for each interval:
+
+1. Interval (0.0, 1.5):
+   - Lower Bound: -0.02721
+   - Upper Bound: -0.02205
+   - Difference: -0.02205 - (-0.02721) = 0.00516
+
+2. Interval (1.5, 2.5):
+   - Lower Bound: -0.01778
+   - Upper Bound: -0.0161
+   - Difference: -0.0161 - (-0.01778) = 0.00168
+
+3. Interval (2.5, 3.5):
+   - Lower Bound: -0.01182
+   - Upper Bound: -0.01113
+   - Difference: -0.01113 - (-0.01182) = 0.00069
+
+4. Interval (3.5, 4.5):
+   - Lower Bound: -0.00574
+   - Upper Bound: -0.00492
+   - Difference: -0.00492 - (-0.00574) = 0.00082
+
+5. Interval (4.5, 5.5):
+   - Lower Bound: -0.00009
+   - Upper Bound: 0.0008
+   - Difference: 0.0008 - (-0.00009) = 0.00089
+
+6. Interval (5.5, 6.5):
+   - Lower Bound: 0.00587
+   - Upper Bound: 0.00696
+   - Difference: 0.00696 - 0.00587 = 0.00109
+
+7. Interval (6.5, 7.5):
+   - Lower Bound: 0.01028
+   - Upper Bound: 0.01144
+   - Difference: 0.01144 - 0.01028 = 0.00116
+
+8. Interval (7.5, 8.5):
+   - Lower Bound: 0.01669
+   - Upper Bound: 0.01837
+   - Difference: 0.01837 - 0.01669 = 0.00168
+
+9. Interval (8.5, 9.5):
+   - Lower Bound: 0.02306
+   - Upper Bound: 0.02477
+   - Difference: 0.02477 - 0.02306 = 0.00171
+
+10. Interval (9.5, 11.5):
+    - Lower Bound: 0.02986
+    - Upper Bound: 0.03339
+    - Difference: 0.03339 - 0.02986 = 0.00353
+
+11. Interval (11.5, 14.0):
+    - Lower Bound: 0.03465
+    - Upper Bound: 0.04355
+    - Difference: 0.04355 - 0.03465 = 0.0089
+
+12. Interval (14.0, 15.0):
+    - Lower Bound: 0.03109
+    - Upper Bound: 0.07902
+    - Difference: 0.07902 - 0.03109 = 0.04793
+
+From these calculations, the interval (14.0, 15.0) has the widest confidence bound with a difference of 0.04793.
+SOLUTION:  (14.0, 15.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the width of the confidence interval for each interval provided in the graph. The width of the confidence interval can be calculated by subtracting the lower bound from the upper bound for each interval.
+
+From the data provided:
+1. For the interval (0.0, 0.5):
+   - Lower Bound = -0.593
+   - Upper Bound = -0.518
+   - Width = -0.518 - (-0.593) = 0.075
+
+2. For the interval (0.5, 1.0):
+   - Lower Bound = 0.529
+   - Upper Bound = 0.606
+   - Width = 0.606 - 0.529 = 0.077
+
+Comparing the widths:
+- Interval (0.0, 0.5) has a width of 0.075
+- Interval (0.5, 1.0) has a width of 0.077
+
+The interval (0.5, 1.0) has the widest confidence bound with a width of 0.077.
+SOLUTION:  (0.5, 1.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval with the maximum width.
+
+Let's perform these calculations:
+
+- For interval `(0.02729, 0.049945)`: Width = 0.6969 - (-0.8125) = 1.5094
+- For interval `(0.049945, 0.06971)`: Width = 0.7425 - (-0.7624) = 1.5049
+- For interval `(0.06971, 0.099305)`: Width = 0.487 - (-0.6001) = 1.0871
+- For interval `(0.099305, 0.10635)`: Width = 0.1218 - (-0.4033) = 0.5251
+- For interval `(0.10635, 0.1243)`: Width = 0.0684 - (-0.4448) = 0.5132
+- For interval `(0.1243, 0.14795)`: Width = 0.0254 - (-0.4969) = 0.5223
+- For interval `(0.14795, 0.1507)`: Width = 0.068 - (-0.4446) = 0.5126
+- For interval `(0.1507, 0.1861)`: Width = -0.0039 - (-0.2722) = 0.2683
+- For interval `(0.1861, 0.20124999999999998)`: Width = 0.0087 - (-0.1924) = 0.2011
+- For interval `(0.20124999999999998, 0.3358)`: Width = 0.1418 - (-0.2305) = 0.3723
+- For interval `(0.3358, 0.3456)`: Width = 0.1794 - (-0.1741) = 0.3535
+- For interval `(0.3456, 0.35755000000000003)`: Width = 0.1979 - (-0.068) = 0.2659
+- For interval `(0.35755000000000003, 0.3703)`: Width = 0.2255 - 0.0047 = 0.2208
+- For interval `(0.3703, 0.39235)`: Width = 0.2811 - 0.0473 = 0.2338
+- For interval `(0.39235, 0.4087)`: Width = 0.314 - 0.1107 = 0.2033
+- For interval `(0.4087, 0.4229)`: Width = 0.3524 - 0.1686 = 0.1838
+- For interval `(0.4229, 0.4486)`: Width = 0.3975 - 0.2243 = 0.1732
+- For interval `(0.4486, 0.48865000000000003)`: Width = 0.4436 - 0.2736 = 0.1700
+- For interval `(0.48865000000000003, 0.54825)`: Width = 0.5859 - 0.2405 = 0.3454
+- For interval `(0.54825, 0.5892999999999999)`: Width = 0.6484 - 0.2819 = 0.3665
+- For interval `(0.5892999999999999, 0.65835)`: Width = 0.7153 - 0.3155 = 0.3998
+- For interval `(0.65835, 0.7680499999999999)`: Width = 0.7927 - 0.3513 = 0.4414
+- For interval `(0.7680499999999999, 0.99795)`: Width = 0.8637 - 0.3892 = 0.4745
+- For interval `(0.99795, 1.058)`: Width = 0.9008 - 0.4487 = 0.4521
+
+From these calculations, the interval `(0.02729, 0.049945)` has the widest confidence bound with a width of 1.5094.
+SOLUTION:  (0.02729, 0.049945)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Difference**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Differences**: Identify the interval where this difference (width of the confidence interval) is the largest.
+
+Let's calculate the differences for each interval:
+
+- **(0.0, 593.5)**: Upper - Lower = -0.047 - (-0.307) = 0.260
+- **(593.5, 779.5)**: Upper - Lower = 0.196 - (-0.110) = 0.306
+- **(779.5, 1341.5)**: Upper - Lower = 0.580 - (-0.040) = 0.620
+- **(1341.5, 2175.5)**: Upper - Lower = 1.145 - (-0.060) = 1.205
+- **(2175.5, 3125.0)**: Upper - Lower = 1.322 - 0.404 = 0.918
+- **(3125.0, 3637.0)**: Upper - Lower = 1.554 - 0.707 = 0.847
+- **(3637.0, 4078.5)**: Upper - Lower = 2.216 - 0.742 = 1.474
+- **(4078.5, 5218.5)**: Upper - Lower = 2.631 - 1.520 = 1.111
+- **(5218.5, 6031.5)**: Upper - Lower = 2.135 - 1.485 = 0.650
+- **(6031.5, 6171.5)**: Upper - Lower = 2.400 - 0.477 = 1.923
+- **(6171.5, 8753.0)**: Upper - Lower = 2.925 - 1.548 = 1.377
+- **(8753.0, 8824.0)**: Upper - Lower = 3.543 - 1.950 = 1.593
+- **(8824.0, 10094.5)**: Upper - Lower = 4.234 - 2.626 = 1.608
+- **(10094.5, 12683.5)**: Upper - Lower = 5.416 - 2.361 = 3.055
+- **(12683.5, 27723.0)**: Upper - Lower = 5.705 - 2.558 = 3.147
+
+From these calculations, the interval **(12683.5, 27723.0)** has the widest confidence bound with a difference of 3.147. This is the interval with the widest confidence bound.
+SOLUTION:  (12683.5, 27723.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval where this width (difference between upper and lower bounds) is the largest.
+
+Let's perform these calculations using the provided data:
+
+- For interval `(0.1167, 0.1384)`: Width = \(0.323 - (-1.532)\) = \(1.855\)
+- For interval `(0.1384, 0.14229999999999998)`: Width = \(0.338 - (-1.437)\) = \(1.775\)
+- For interval `(0.14229999999999998, 0.14565)`: Width = \(0.394 - (-1.372)\) = \(1.766\)
+- For interval `(0.14565, 0.1488)`: Width = \(0.457 - (-1.313)\) = \(1.770\)
+- For interval `(0.1488, 0.1507)`: Width = \(0.507 - (-1.251)\) = \(1.758\)
+- For interval `(0.1507, 0.15245)`: Width = \(0.564 - (-1.195)\) = \(1.759\)
+- For interval `(0.15245, 0.15375)`: Width = \(0.603 - (-1.119)\) = \(1.722\)
+- For interval `(0.15375, 0.15410000000000001)`: Width = \(0.527 - (-0.701)\) = \(1.228\)
+- For interval `(0.15410000000000001, 0.1545)`: Width = \(0.602 - (-0.663)\) = \(1.265\)
+- For interval `(0.1545, 0.15765)`: Width = \(1.221 - (-0.664)\) = \(1.885\)
+- For interval `(0.15765, 0.16625)`: Width = \(1.266 - (-0.597)\) = \(1.863\)
+- For interval `(0.16625, 0.16635)`: Width = \(1.129 - (-0.613)\) = \(1.742\)
+- For interval `(0.16635, 0.1684)`: Width = \(0.174 - (-0.077)\) = \(0.251\)
+- For interval `(0.1684, 0.17915)`: Width = \(0.095 - (-0.11)\) = \(0.205\)
+- For interval `(0.17915, 0.20355)`: Width = \(0.05 - (-0.175)\) = \(0.225\)
+- For interval `(0.20355, 0.20855)`: Width = \(0.102 - (-0.112)\) = \(0.214\)
+- For interval `(0.20855, 0.21105000000000002)`: Width = \(0.151 - (-0.047)\) = \(0.198\)
+- For interval `(0.21105000000000002, 0.21315)`: Width = \(0.211 - 0.003\) = \(0.208\)
+- For interval `(0.21315, 0.21705)`: Width = \(0.279 - 0.061\) = \(0.218\)
+- For interval `(0.21705, 0.22110000000000002)`: Width = \(0.351 - 0.118\) = \(0.233\)
+- For interval `(0.22110000000000002, 0.23020000000000002)`: Width = \(0.408 - 0.169\) = \(0.239\)
+- For interval `(0.23020000000000002, 0.2544)`: Width = \(0.503 - 0.191\) = \(0.312\)
+- For interval `(0.2544, 0.2626)`: Width = \(0.606 - 0.21\) = \(0.396\)
+- For interval `(0.2626, 0.304)`: Width = \(0.681 - 0.25\) = \(0.431\)
+
+From these calculations, the interval `(0.1545, 0.15765)` has the widest confidence bound with a width of \(1.885\). This is the interval with the widest confidence bound.
+SOLUTION:  (0.1545, 0.15765)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the width of the confidence interval for each interval provided in the graph. The width of the confidence interval can be calculated by subtracting the lower bound from the upper bound for each interval.
+
+Let's calculate the width of the confidence intervals for each interval:
+
+1. For the interval (0.0, 0.5):
+   - Lower Bound = -0.1574
+   - Upper Bound = -0.058
+   - Width = -0.058 - (-0.1574) = 0.0994
+
+2. For the interval (0.5, 1.0):
+   - Lower Bound = 0.1003
+   - Upper Bound = 0.2724
+   - Width = 0.2724 - 0.1003 = 0.1721
+
+Comparing the widths:
+- Interval (0.0, 0.5) has a width of 0.0994
+- Interval (0.5, 1.0) has a width of 0.1721
+
+The interval (0.5, 1.0) has the widest confidence bound with a width of 0.1721.
+SOLUTION:  (0.5, 1.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Difference**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Differences**: Identify the interval where this difference (width of the confidence interval) is the largest.
+
+Let's perform these calculations using the provided data:
+
+- For interval `(0.0, 0.001156)`, the difference is `-0.3494 - (-0.9396) = 0.5902`.
+- For interval `(0.001156, 0.002325)`, the difference is `-0.3374 - (-0.8658) = 0.5284`.
+- For interval `(0.002325, 0.0037635)`, the difference is `-0.3006 - (-0.8192) = 0.5186`.
+- For interval `(0.0037635, 0.0053165)`, the difference is `-0.2618 - (-0.7681) = 0.5063`.
+- For interval `(0.0053165, 0.0058905)`, the difference is `-0.2142 - (-0.716) = 0.5018`.
+- For interval `(0.0058905, 0.006987999999999999)`, the difference is `-0.1779 - (-0.6675) = 0.4896`.
+- For interval `(0.006987999999999999, 0.0077405)`, the difference is `-0.1459 - (-0.6156) = 0.4697`.
+- For interval `(0.0077405, 0.008344500000000001)`, the difference is `-0.0957 - (-0.5789) = 0.4832`.
+- For interval `(0.008344500000000001, 0.009263500000000001)`, the difference is `-0.063 - (-0.5182) = 0.4552`.
+- For interval `(0.009263500000000001, 0.010215)`, the difference is `-0.0385 - (-0.4535) = 0.415`.
+- For interval `(0.010215, 0.010705)`, the difference is `0.0109 - (-0.4164) = 0.4273`.
+- For interval `(0.010705, 0.01122)`, the difference is `0.0478 - (-0.3446) = 0.3924`.
+- For interval `(0.01122, 0.011625)`, the difference is `0.0749 - (-0.2792) = 0.3541`.
+- For interval `(0.011625, 0.01191)`, the difference is `0.0999 - (-0.2184) = 0.3183`.
+- For interval `(0.01191, 0.012455)`, the difference is `0.1484 - (-0.172) = 0.3204`.
+- For interval `(0.012455, 0.0203)`, the difference is `0.2352 - (-0.1411) = 0.3763`.
+- For interval `(0.0203, 0.022565)`, the difference is `0.1973 - (-0.0146) = 0.2119`.
+- For interval `(0.022565, 0.02983)`, the difference is `0.2485 - 0.021 = 0.2275`.
+- For interval `(0.02983, 0.032535)`, the difference is `0.4843 - (-0.4149) = 0.8992`.
+- For interval `(0.032535, 0.0338)`, the difference is `0.4374 - (-0.4515) = 0.8889`.
+- For interval `(0.0338, 0.038565)`, the difference is `0.1895 - (-0.0686) = 0.2581`.
+- For interval `(0.038565, 0.04418)`, the difference is `0.2135 - (-0.0006) = 0.2141`.
+- For interval `(0.04418, 0.059305)`, the difference is `0.3411 - (-0.0422) = 0.3833`.
+- For interval `(0.059305, 0.065775)`, the difference is `0.2772 - (-0.0685) = 0.3457`.
+- For interval `(0.065775, 0.07794000000000001)`, the difference is `0.2312 - (-0.1246) = 0.3558`.
+- For interval `(0.07794000000000001, 0.08089)`, the difference is `0.1907 - (-0.1713) = 0.362`.
+- For interval `(0.08089, 0.096205)`, the difference is `0.1569 - (-0.2715) = 0.4284`.
+- For interval `(0.096205, 0.22865000000000002)`, the difference is `0.1429 - (-0.3432) = 0.4861`.
+- For interval `(0.22865000000000002, 0.396)`, the difference is `0.1015 - (-0.3958) = 0.4973`.
+
+From the calculations, the interval `(0.02983, 0.032535)` has the widest confidence bound with a difference of `0.8992`.
+SOLUTION:  (0.02983, 0.032535)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the calculations for each interval:
+
+1. Interval (0.0, 0.5):
+   - Lower Bound: -0.02795
+   - Upper Bound: -0.02391
+   - Difference: -0.02391 - (-0.02795) = 0.00404
+
+2. Interval (0.5, 1.5):
+   - Lower Bound: -0.02324
+   - Upper Bound: -0.02097
+   - Difference: -0.02097 - (-0.02324) = 0.00227
+
+3. Interval (1.5, 2.5):
+   - Lower Bound: -0.01672
+   - Upper Bound: -0.0155
+   - Difference: -0.0155 - (-0.01672) = 0.00122
+
+4. Interval (2.5, 3.5):
+   - Lower Bound: -0.01177
+   - Upper Bound: -0.01073
+   - Difference: -0.01073 - (-0.01177) = 0.00104
+
+5. Interval (3.5, 4.5):
+   - Lower Bound: -0.00504
+   - Upper Bound: -0.00435
+   - Difference: -0.00435 - (-0.00504) = 0.00069
+
+6. Interval (4.5, 5.5):
+   - Lower Bound: -0.00021
+   - Upper Bound: 0.00039
+   - Difference: 0.00039 - (-0.00021) = 0.00060
+
+7. Interval (5.5, 6.5):
+   - Lower Bound: 0.00613
+   - Upper Bound: 0.00691
+   - Difference: 0.00691 - 0.00613 = 0.00078
+
+8. Interval (6.5, 8.5):
+   - Lower Bound: 0.01137
+   - Upper Bound: 0.01301
+   - Difference: 0.01301 - 0.01137 = 0.00164
+
+9. Interval (8.5, 10.5):
+   - Lower Bound: 0.02139
+   - Upper Bound: 0.02367
+   - Difference: 0.02367 - 0.02139 = 0.00228
+
+10. Interval (10.5, 11.5):
+    - Lower Bound: 0.03184
+    - Upper Bound: 0.0364
+    - Difference: 0.0364 - 0.03184 = 0.00456
+
+11. Interval (11.5, 12.5):
+    - Lower Bound: 0.03703
+    - Upper Bound: 0.04328
+    - Difference: 0.04328 - 0.03703 = 0.00625
+
+12. Interval (12.5, 14.0):
+    - Lower Bound: 0.04222
+    - Upper Bound: 0.04907
+    - Difference: 0.04907 - 0.04222 = 0.00685
+
+From the calculations above, the interval (12.5, 14.0) has the widest confidence bound with a difference of 0.00685.
+SOLUTION:  (12.5, 14.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the calculations for each interval:
+
+1. Interval (0.0, 0.5):
+   - Lower Bound: -0.02945
+   - Upper Bound: -0.02515
+   - Difference: -0.02515 - (-0.02945) = 0.0043
+
+2. Interval (0.5, 1.5):
+   - Lower Bound: -0.02501
+   - Upper Bound: -0.0219
+   - Difference: -0.0219 - (-0.02501) = 0.00311
+
+3. Interval (1.5, 2.5):
+   - Lower Bound: -0.01619
+   - Upper Bound: -0.01524
+   - Difference: -0.01524 - (-0.01619) = 0.00095
+
+4. Interval (2.5, 3.5):
+   - Lower Bound: -0.0121
+   - Upper Bound: -0.01139
+   - Difference: -0.01139 - (-0.0121) = 0.00071
+
+5. Interval (3.5, 4.5):
+   - Lower Bound: -0.00549
+   - Upper Bound: -0.0049
+   - Difference: -0.0049 - (-0.00549) = 0.00059
+
+6. Interval (4.5, 5.5):
+   - Lower Bound: 0.00069
+   - Upper Bound: 0.00152
+   - Difference: 0.00152 - 0.00069 = 0.00083
+
+7. Interval (5.5, 6.5):
+   - Lower Bound: 0.00469
+   - Upper Bound: 0.00544
+   - Difference: 0.00544 - 0.00469 = 0.00075
+
+8. Interval (6.5, 7.5):
+   - Lower Bound: 0.00991
+   - Upper Bound: 0.01121
+   - Difference: 0.01121 - 0.00991 = 0.0013
+
+9. Interval (7.5, 8.5):
+   - Lower Bound: 0.01638
+   - Upper Bound: 0.01774
+   - Difference: 0.01774 - 0.01638 = 0.00136
+
+10. Interval (8.5, 9.5):
+    - Lower Bound: 0.02306
+    - Upper Bound: 0.0249
+    - Difference: 0.0249 - 0.02306 = 0.00184
+
+11. Interval (9.5, 11.5):
+    - Lower Bound: 0.0266
+    - Upper Bound: 0.02981
+    - Difference: 0.02981 - 0.0266 = 0.00321
+
+12. Interval (11.5, 12.5):
+    - Lower Bound: 0.02982
+    - Upper Bound: 0.04363
+    - Difference: 0.04363 - 0.02982 = 0.01381
+
+13. Interval (12.5, 13.5):
+    - Lower Bound: -0.01689
+    - Upper Bound: 0.04312
+    - Difference: 0.04312 - (-0.01689) = 0.06001
+
+14. Interval (13.5, 16.0):
+    - Lower Bound: 0.01715
+    - Upper Bound: 0.04696
+    - Difference: 0.04696 - 0.01715 = 0.02981
+
+The interval with the widest confidence bound is (12.5, 13.5) with a difference of 0.06001.
+SOLUTION:  (12.5, 13.5)
+--------------------------------------------------------------------------------
+
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval with the maximum width.
+
+Let's perform these calculations:
+
+- For interval `(0.07117, 0.09376000000000001)`: Width = -0.336 - (-2.26) = 1.924
+- For interval `(0.09376000000000001, 0.099705)`: Width = -0.19 - (-2.132) = 1.942
+- For interval `(0.099705, 0.10519999999999999)`: Width = -0.039 - (-2.009) = 1.97
+- For interval `(0.10519999999999999, 0.10825)`: Width = 0.096 - (-1.875) = 1.971
+- For interval `(0.10825, 0.11549999999999999)`: Width = -0.289 - (-0.765) = 0.476
+- For interval `(0.11549999999999999, 0.12345)`: Width = -0.2 - (-0.589) = 0.389
+- For interval `(0.12345, 0.13074999999999998)`: Width = -0.086 - (-0.435) = 0.349
+- For interval `(0.13074999999999998, 0.13585)`: Width = 0.136 - (-0.384) = 0.52
+- For interval `(0.13585, 0.13640000000000002)`: Width = 0.348 - (-0.325) = 0.673
+- For interval `(0.13640000000000002, 0.13845000000000002)`: Width = 0.556 - (-0.247) = 0.803
+- For interval `(0.13845000000000002, 0.14065)`: Width = 0.652 - (-0.076) = 0.728
+- For interval `(0.14065, 0.14635)`: Width = 0.997 - (-0.12) = 1.117
+- For interval `(0.14635, 0.15585)`: Width = 0.983 - 0.165 = 0.818
+- For interval `(0.15585, 0.16885)`: Width = 1.146 - 0.27 = 0.876
+- For interval `(0.16885, 0.17825)`: Width = 1.289 - 0.402 = 0.887
+- For interval `(0.17825, 0.19574999999999998)`: Width = 2.823 - (-0.484) = 3.307
+- For interval `(0.19574999999999998, 0.2226)`: Width = 2.957 - (-0.349) = 3.306
+
+From the calculations, the interval `(0.17825, 0.19574999999999998)` has the widest confidence bound with a width of 3.307.
+SOLUTION:  (0.17825, 0.19574999999999998)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds of the confidence interval for each interval and identify the one with the largest difference.
+
+Here are the calculations for each interval:
+
+1. Interval (4.3, 4.55): Upper - Lower = 3.437 - 3.22 = 0.217
+2. Interval (4.55, 4.75): Upper - Lower = 3.144 - 2.846 = 0.298
+3. Interval (4.75, 4.85): Upper - Lower = 2.857 - 2.54 = 0.317
+4. Interval (4.85, 5.05): Upper - Lower = 2.145 - 1.185 = 0.96
+5. Interval (5.05, 5.25): Upper - Lower = 1.528 - 1.214 = 0.314
+6. Interval (5.25, 5.45): Upper - Lower = 1.277 - 0.892 = 0.385
+7. Interval (5.45, 5.55): Upper - Lower = 0.843 - (-0.164) = 1.007
+8. Interval (5.55, 5.75): Upper - Lower = 0.206 - (-0.32) = 0.526
+9. Interval (5.75, 5.85): Upper - Lower = -0.116 - (-0.665) = 0.549
+10. Interval (5.85, 6.15): Upper - Lower = -0.627 - (-0.888) = 0.261
+11. Interval (6.15, 6.45): Upper - Lower = -1.009 - (-1.29) = 0.281
+12. Interval (6.45, 6.85): Upper - Lower = -1.298 - (-1.575) = 0.277
+13. Interval (6.85, 7.7): Upper - Lower = -1.623 - (-1.814) = 0.191
+
+From these calculations, the interval (5.45, 5.55) has the widest confidence bound with a difference of 1.007. This interval therefore has the widest confidence interval among all the intervals listed.
+SOLUTION:  (5.45, 5.55)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval with the largest difference.
+
+Here are the calculations for each interval:
+
+1. Interval (1.0, 1.5):
+   - Lower Bound: -6.411
+   - Upper Bound: -3.082
+   - Difference: -3.082 - (-6.411) = 3.329
+
+2. Interval (1.5, 4.5):
+   - Lower Bound: -1.52
+   - Upper Bound: -0.984
+   - Difference: -0.984 - (-1.52) = 0.536
+
+3. Interval (4.5, 6.5):
+   - Lower Bound: -0.99
+   - Upper Bound: -0.775
+   - Difference: -0.775 - (-0.99) = 0.215
+
+4. Interval (6.5, 9.5):
+   - Lower Bound: -0.541
+   - Upper Bound: -0.425
+   - Difference: -0.425 - (-0.541) = 0.116
+
+5. Interval (9.5, 11.5):
+   - Lower Bound: -0.138
+   - Upper Bound: -0.049
+   - Difference: -0.049 - (-0.138) = 0.089
+
+6. Interval (11.5, 13.5):
+   - Lower Bound: 0.205
+   - Upper Bound: 0.347
+   - Difference: 0.347 - 0.205 = 0.142
+
+7. Interval (13.5, 14.5):
+   - Lower Bound: 0.788
+   - Upper Bound: 0.938
+   - Difference: 0.938 - 0.788 = 0.150
+
+8. Interval (14.5, 16.0):
+   - Lower Bound: 1.332
+   - Upper Bound: 1.641
+   - Difference: 1.641 - 1.332 = 0.309
+
+From these calculations, the interval (1.0, 1.5) has the widest confidence bound with a difference of 3.329. This is the interval with the largest difference between the upper and lower bounds of the confidence interval.
+SOLUTION:  (1.0, 1.5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds of the confidence interval for each interval and identify the one with the maximum difference.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Difference**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Identify the Maximum Difference**: Compare the differences calculated in step 2 to find the maximum difference, which indicates the widest confidence interval.
+
+Let's perform these calculations:
+
+- For interval `(2.0, 23.0)`: Difference = `-50072.0 - (-91545.9) = 41473.9`
+- For interval `(23.0, 38.5)`: Difference = `-54966.9 - (-102966.4) = 47999.5`
+- For interval `(38.5, 48.5)`: Difference = `-24.3 - (-57179.9) = 57155.6`
+- For interval `(48.5, 119.0)`: Difference = `-29651.4 - (-64507.9) = 34856.5`
+- For interval `(119.0, 163.0)`: Difference = `-38333.5 - (-67051.1) = 28717.6`
+- For interval `(163.0, 186.5)`: Difference = `-45199.3 - (-74986.7) = 29787.4`
+- For interval `(186.5, 223.5)`: Difference = `-39853.9 - (-62447.2) = 22593.3`
+- For interval `(223.5, 239.5)`: Difference = `-23883.2 - (-55573.0) = 31689.8`
+- For interval `(239.5, 248.5)`: Difference = `20408.0 - (-34485.5) = 54893.5`
+- For interval `(248.5, 265.5)`: Difference = `17433.4 - (-18815.6) = 36249.0`
+- For interval `(265.5, 280.5)`: Difference = `7471.9 - (-35576.3) = 43048.2`
+- For interval `(280.5, 342.5)`: Difference = `-26453.2 - (-44957.9) = 18504.7`
+- For interval `(342.5, 364.5)`: Difference = `-12564.3 - (-36592.4) = 24028.1`
+- For interval `(364.5, 385.5)`: Difference = `-28395.1 - (-39620.4) = 11225.3`
+- For interval `(385.5, 406.5)`: Difference = `-38875.1 - (-54434.9) = 15559.8`
+- For interval `(406.5, 413.5)`: Difference = `-6712.1 - (-28898.3) = 22186.2`
+- For interval `(413.5, 443.5)`: Difference = `-2459.1 - (-21926.2) = 19467.1`
+- For interval `(443.5, 452.5)`: Difference = `-10730.8 - (-34828.5) = 24097.7`
+- For interval `(452.5, 502.5)`: Difference = `-21000.9 - (-40304.3) = 19303.4`
+- For interval `(502.5, 508.5)`: Difference = `-14681.3 - (-35649.5) = 20968.2`
+- For interval `(508.5, 515.5)`: Difference = `1516.8 - (-27403.5) = 28920.3`
+- For interval `(515.5, 1152.5)`: Difference = `-14834.1 - (-28456.5) = 13622.4`
+- For interval `(1152.5, 1239.5)`: Difference = `-11610.6 - (-20918.2) = 9307.6`
+- For interval `(1239.5, 1245.5)`: Difference = `1860.9 - (-15907.4) = 17768.3`
+- For interval `(1245.5, 1619.5)`: Difference = `-5766.8 - (-19943.7) = 14176.9`
+- For interval `(1619.5, 1944.5)`: Difference = `-1767.7 - (-13063.6) = 11295.9`
+- For interval `(1944.5, 2330.5)`: Difference = `6128.1 - (-8595.8) = 14723.9`
+- For interval `(2330.5, 2710.5)`: Difference = `5805.0 - 2936.6 = 2868.4`
+- For interval `(2710.5, 2834.5)`: Difference = `12408.3 - 7069.8 = 5338.5`
+- For interval `(2834.5, 2838.5)`: Difference = `32071.2 - 1263.0 = 30808.2`
+- For interval `(2838.5, 3577.5)`: Difference = `13167.8 - 7025.1 = 6142.7`
+- For interval `(3577.5, 5401.0)`: Difference = `20811.4 - 10287.4 = 10524.0`
+- For interval `(5401.0, 5535.5)`: Difference = `39337.3 - 10519.1 = 28818.2`
+- For interval `(5535.5, 9961.0)`: Difference = `25602.1 - 12536.6 = 13065.5`
+- For interval `(9961.0, 18662.0)`: Difference = `35928.6 - 16596.5 = 19332.1`
+- For interval `(18662.0, 39320.0)`: Difference = `24283.0 - 17189.5 = 7093.5`
+
+From the calculations, the interval `(38.5, 48.5)` has the widest confidence bound with a difference of `57155.6`.
+SOLUTION:  (38.5, 48.5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: From the JSON object, extract the lower and upper bounds for each interval.
+
+2. **Calculate the Width of the Confidence Interval for Each Interval**: Subtract the lower bound from the upper bound for each interval.
+
+3. **Identify the Interval with the Maximum Width**: Compare the widths calculated in step 2 and identify the interval with the largest width.
+
+Let's perform these calculations:
+
+- For interval "(0.0, 6.325)": Width = 0.54 - (-3.39) = 3.93
+- For interval "(6.325, 7.8500000000000005)": Width = 0.645 - (-3.252) = 3.897
+- For interval "(7.8500000000000005, 9.256250000000001)": Width = 0.377 - (-1.321) = 1.698
+- For interval "(9.256250000000001, 10.48125)": Width = 0.553 - (-1.756) = 2.309
+- For interval "(10.48125, 12.9375)": Width = 0.163 - (-0.444) = 0.607
+- For interval "(12.9375, 25.79375)": Width = 0.913 - (-0.464) = 1.377
+- For interval "(25.79375, 26.46875)": Width = 1.191 - (-0.48) = 1.671
+- For interval "(26.46875, 27.7354)": Width = 0.833 - (-0.42) = 1.253
+- For interval "(27.7354, 29.85)": Width = 0.533 - (-1.008) = 1.541
+- For interval "(29.85, 31.6604)": Width = 0.718 - (-0.616) = 1.334
+- For interval "(31.6604, 55.22085)": Width = 0.127 - (-0.278) = 0.405
+- For interval "(55.22085, 89.5521)": Width = 0.176 - (-0.095) = 0.271
+- For interval "(89.5521, 149.0354)": Width = 0.367 - (-0.062) = 0.429
+- For interval "(149.0354, 387.6646)": Width = 0.436 - (-0.493) = 0.929
+- For interval "(387.6646, 512.3292)": Width = 2.455 - (-0.839) = 3.294
+
+From these calculations, the interval with the widest confidence bound is **"(0.0, 6.325)"** with a width of 3.93.
+SOLUTION:  (0.0, 6.325)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval with the maximum difference.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Identify the Maximum Width**: Compare the widths of all intervals and identify the interval with the maximum width.
+
+Let's perform these calculations using the provided data:
+
+- For interval `(0.002252, 0.0046765)`, the lower bound is `-0.2881` and the upper bound is `0.1496`. The width is `0.1496 - (-0.2881) = 0.4377`.
+- For interval `(0.0046765, 0.005634)`, the width is `0.1917 - (-0.2345) = 0.4262`.
+- Continue this for all intervals.
+
+After calculating the widths for all intervals, we find:
+
+- Interval `(0.068925, 0.1354)` has a lower bound of `-0.6732` and an upper bound of `-0.1492`. The width is `-0.1492 - (-0.6732) = 0.524`.
+
+By comparing all calculated widths, we find that the interval `(0.068925, 0.1354)` has the widest confidence interval with a width of `0.524`. This is the interval with the widest confidence bound.
+SOLUTION:  (0.01089, 0.011715)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval where this width (difference between upper and lower bounds) is the largest.
+
+Let's perform these calculations using the provided data:
+
+- For interval (0.1115, 0.15015): Upper - Lower = -0.302 - (-1.244) = 0.942
+- For interval (0.15015, 0.16904999999999998): Upper - Lower = -0.247 - (-1.125) = 0.878
+- For interval (0.16904999999999998, 0.1795): Upper - Lower = -0.169 - (-1.008) = 0.839
+- For interval (0.1795, 0.18535000000000001): Upper - Lower = -0.094 - (-0.904) = 0.810
+- For interval (0.18535000000000001, 0.19345): Upper - Lower = -0.024 - (-0.8) = 0.776
+- For interval (0.19345, 0.2103): Upper - Lower = -0.1 - (-0.449) = 0.349
+- For interval (0.2103, 0.2329): Upper - Lower = -0.101 - (-0.273) = 0.172
+- For interval (0.2329, 0.2939): Upper - Lower = 0.289 - (-0.492) = 0.781
+- For interval (0.2939, 0.368): Upper - Lower = 0.396 - (-0.769) = 1.165
+- For interval (0.368, 0.38585): Upper - Lower = 0.304 - (-0.437) = 0.741
+- For interval (0.38585, 0.42025): Upper - Lower = 0.315 - (-0.188) = 0.503
+- For interval (0.42025, 0.46775): Upper - Lower = 0.42 - (-0.119) = 0.539
+- For interval (0.46775, 0.54785): Upper - Lower = 0.514 - (-0.037) = 0.551
+- For interval (0.54785, 0.5881000000000001): Upper - Lower = 0.758 - (-0.09) = 0.848
+- For interval (0.5881000000000001, 0.66425): Upper - Lower = 0.86 - (-0.016) = 0.876
+- For interval (0.66425, 0.7562): Upper - Lower = 0.968 - 0.051 = 0.917
+- For interval (0.7562, 0.9131): Upper - Lower = 1.137 - 0.051 = 1.086
+- For interval (0.9131, 1.065): Upper - Lower = 1.253 - 0.113 = 1.140
+- For interval (1.065, 1.2915): Upper - Lower = 1.425 - 0.123 = 1.302
+- For interval (1.2915, 2.873): Upper - Lower = 1.533 - 0.198 = 1.335
+
+From these calculations, the interval (1.2915, 2.873) has the widest confidence bound with a width of 1.335.
+SOLUTION:  (1.2915, 2.873)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval where this width (difference between upper and lower bounds) is the largest.
+
+Let's calculate the width of the confidence interval for each interval:
+
+- **(143.5, 259.35)**: Upper - Lower = -0.48 - (-1.038) = 0.558
+- **(259.35, 289.4)**: Upper - Lower = -0.432 - (-0.892) = 0.46
+- **(289.4, 319.15)**: Upper - Lower = -0.38 - (-0.754) = 0.374
+- **(319.15, 348.3)**: Upper - Lower = -0.294 - (-0.634) = 0.34
+- **(348.3, 496.5)**: Upper - Lower = -0.177 - (-0.559) = 0.382
+- **(496.5, 548.75)**: Upper - Lower = -0.106 - (-0.436) = 0.33
+- **(548.75, 606.0)**: Upper - Lower = -0.007 - (-0.338) = 0.331
+- **(606.0, 696.25)**: Upper - Lower = 0.575 - (-0.727) = 1.302
+- **(696.25, 806.1500000000001)**: Upper - Lower = 0.871 - (-0.252) = 1.123
+- **(806.1500000000001, 901.8)**: Upper - Lower = 0.831 - (-0.022) = 0.853
+- **(901.8, 959.4000000000001)**: Upper - Lower = 0.962 - 0.058 = 0.904
+- **(959.4000000000001, 1054.0)**: Upper - Lower = 1.074 - 0.141 = 0.933
+- **(1054.0, 1150.0)**: Upper - Lower = 1.171 - 0.243 = 0.928
+- **(1150.0, 1248.5)**: Upper - Lower = 1.285 - 0.328 = 0.957
+- **(1248.5, 1341.0)**: Upper - Lower = 1.428 - 0.393 = 1.035
+- **(1341.0, 1801.0)**: Upper - Lower = 1.544 - 0.475 = 1.069
+- **(1801.0, 2501.0)**: Upper - Lower = 1.644 - 0.574 = 1.07
+
+From the calculations, the interval **(606.0, 696.25)** has the widest confidence interval with a width of 1.302. This is the interval with the widest confidence bound.
+SOLUTION:  (606.0, 696.25)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval with the maximum difference.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Difference**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Identify the Maximum Difference**: Compare the differences calculated in step 2 to find the maximum difference, which indicates the widest confidence interval.
+
+Let's perform these calculations:
+
+- For interval (0.0, 20.0): Upper - Lower = 0.14 - (-0.0556) = 0.1956
+- For interval (20.0, 36.5): Upper - Lower = 0.2189 - (-0.2244) = 0.4433
+- For interval (36.5, 40.5): Upper - Lower = 0.1076 - (-0.2184) = 0.326
+- For interval (40.5, 45.5): Upper - Lower = 0.0609 - (-0.2543) = 0.3152
+- For interval (45.5, 48.5): Upper - Lower = 0.7143 - (-0.7961) = 1.5104
+- For interval (48.5, 55.5): Upper - Lower = 0.053 - (-0.5056) = 0.5586
+- For interval (55.5, 80.5): Upper - Lower = 0.0187 - (-0.551) = 0.5697
+- For interval (80.5, 87.5): Upper - Lower = -0.1422 - (-0.3117) = 0.1695
+- For interval (87.5, 97.5): Upper - Lower = -0.1078 - (-0.251) = 0.1432
+- For interval (97.5, 111.0): Upper - Lower = -0.0625 - (-0.2086) = 0.1461
+- For interval (111.0, 123.5): Upper - Lower = -0.0206 - (-0.1731) = 0.1525
+- For interval (123.5, 137.5): Upper - Lower = 0.0247 - (-0.137) = 0.1617
+- For interval (137.5, 144.5): Upper - Lower = 0.0654 - (-0.1027) = 0.1681
+- For interval (144.5, 157.0): Upper - Lower = 0.1166 - (-0.0751) = 0.1917
+- For interval (157.0, 170.5): Upper - Lower = 0.1751 - (-0.0506) = 0.2257
+- For interval (170.5, 186.5): Upper - Lower = 0.2162 - (-0.0163) = 0.2325
+- For interval (186.5, 190.5): Upper - Lower = 0.3332 - (-0.2256) = 0.5588
+- For interval (190.5, 192.5): Upper - Lower = 0.4987 - (-0.2869) = 0.7856
+- For interval (192.5, 271.0): Upper - Lower = 0.3605 - (-0.3659) = 0.7264
+- For interval (271.0, 277.5): Upper - Lower = 0.315 - (-0.245) = 0.56
+- For interval (277.5, 292.0): Upper - Lower = 0.2956 - (-0.1491) = 0.4447
+- For interval (292.0, 311.0): Upper - Lower = 0.3253 - (-0.0995) = 0.4248
+- For interval (311.0, 365.0): Upper - Lower = 0.3457 - (-0.0355) = 0.3812
+- For interval (365.0, 397.0): Upper - Lower = 0.4055 - (-0.0134) = 0.4189
+- For interval (397.0, 452.5): Upper - Lower = 0.445 - 0.0212 = 0.4238
+- For interval (452.5, 476.0): Upper - Lower = 0.4967 - 0.0711 = 0.4256
+- For interval (476.0, 487.5): Upper - Lower = 0.5782 - 0.1139 = 0.4643
+- For interval (487.5, 526.5): Upper - Lower = 0.6295 - 0.1534 = 0.4761
+- For interval (526.5, 680.0): Upper - Lower = 0.8452 - 0.0241 = 0.8211
+
+From the calculations, the interval (526.5, 680.0) has the widest confidence bound with a difference of 0.8211.
+SOLUTION:  (45.5, 48.5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds of the confidence interval for each interval and identify the one with the largest difference.
+
+Here are the intervals and their corresponding confidence bounds:
+
+1. Interval (1.0, 1.5):
+   - Lower Bound: -0.985
+   - Upper Bound: -0.852
+   - Difference: -0.852 - (-0.985) = 0.133
+
+2. Interval (1.5, 2.5):
+   - Lower Bound: 0.893
+   - Upper Bound: 1.028
+   - Difference: 1.028 - 0.893 = 0.135
+
+3. Interval (2.5, 3.5):
+   - Lower Bound: -3.482
+   - Upper Bound: -2.727
+   - Difference: -2.727 - (-3.482) = 0.755
+
+4. Interval (3.5, 4.0):
+   - Lower Bound: -3.159
+   - Upper Bound: -2.376
+   - Difference: -2.376 - (-3.159) = 0.783
+
+Comparing the differences:
+- (1.0, 1.5): 0.133
+- (1.5, 2.5): 0.135
+- (2.5, 3.5): 0.755
+- (3.5, 4.0): 0.783
+
+The interval (3.5, 4.0) has the widest confidence bound with a difference of 0.783.
+SOLUTION:  (3.5, 4.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval with the maximum width.
+
+Let's perform these calculations:
+
+- For interval `(0.01938, 0.03164)`: 
+  - Lower bound = -0.4016
+  - Upper bound = 0.4286
+  - Width = 0.4286 - (-0.4016) = 0.8302
+
+- For interval `(0.066575, 0.067345)`: 
+  - Lower bound = -0.7938
+  - Upper bound = 0.5229
+  - Width = 0.5229 - (-0.7938) = 1.3167
+
+- For interval `(0.07211999999999999, 0.07482)`: 
+  - Lower bound = -0.9538
+  - Upper bound = 0.3358
+  - Width = 0.3358 - (-0.9538) = 1.2896
+
+- For interval `(0.067345, 0.06788)`: 
+  - Lower bound = -0.7983
+  - Upper bound = 0.4136
+  - Width = 0.4136 - (-0.7983) = 1.2119
+
+- For interval `(0.068945, 0.07211999999999999)`: 
+  - Lower bound = -0.9135
+  - Upper bound = 0.3686
+  - Width = 0.3686 - (-0.9135) = 1.2821
+
+From these calculations, the interval `(0.066575, 0.067345)` has the widest confidence interval with a width of 1.3167. This is the interval with the widest confidence bound.
+SOLUTION:  (0.07482, 0.0785)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval with the largest width, which indicates the widest confidence interval.
+
+Let's perform these calculations:
+
+- For interval (9.71, 13.24): Width = -0.658 - (-1.583) = 0.925
+- For interval (13.24, 14.075): Width = -0.619 - (-1.428) = 0.809
+- For interval (14.075, 14.665): Width = -0.55 - (-1.292) = 0.742
+- For interval (14.665, 15.010000000000002): Width = -0.512 - (-1.127) = 0.615
+- For interval (15.010000000000002, 15.485): Width = -0.417 - (-1.018) = 0.601
+- For interval (15.485, 15.774999999999999): Width = -0.314 - (-0.932) = 0.618
+- For interval (15.774999999999999, 16.445): Width = -0.282 - (-0.765) = 0.483
+- For interval (16.445, 17.045): Width = -0.187 - (-0.657) = 0.47
+- For interval (17.045, 17.665): Width = -0.112 - (-0.537) = 0.425
+- For interval (17.665, 18.335): Width = -0.045 - (-0.404) = 0.359
+- For interval (18.335, 18.725): Width = 0.031 - (-0.289) = 0.32
+- For interval (18.725, 19.075): Width = 0.139 - (-0.203) = 0.342
+- For interval (19.075, 19.549999999999997): Width = 0.22 - (-0.094) = 0.314
+- For interval (19.549999999999997, 19.915): Width = 0.306 - 0.017 = 0.289
+- For interval (19.915, 20.235): Width = 0.412 - 0.108 = 0.304
+- For interval (20.235, 20.8): Width = 0.999 - (-0.11) = 1.109
+- For interval (20.8, 21.285): Width = 1.109 - (-0.011) = 1.12
+- For interval (21.285, 33.81): Width = 1.36 - (-0.0) = 1.36
+
+From these calculations, the interval (21.285, 33.81) has the widest confidence interval with a width of 1.36.
+SOLUTION:  (21.285, 33.81)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: From the JSON object, extract the lower and upper bounds for each interval.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval with the maximum width.
+
+Let's perform these calculations:
+
+- For interval `(0.007882, 0.010595)`, the width is `1.206 - 0.336 = 0.87`.
+- For interval `(0.010595, 0.011365)`, the width is `1.11 - 0.284 = 0.826`.
+- For interval `(0.011365, 0.012135)`, the width is `1.031 - 0.24 = 0.791`.
+- For interval `(0.012135, 0.01279)`, the width is `0.941 - 0.211 = 0.73`.
+- For interval `(0.01279, 0.01352)`, the width is `0.8 - 0.226 = 0.574`.
+- For interval `(0.01352, 0.014105)`, the width is `0.731 - 0.178 = 0.553`.
+- For interval `(0.014105, 0.014499999999999999)`, the width is `0.662 - 0.123 = 0.539`.
+- For interval `(0.014499999999999999, 0.014525)`, the width is `0.574 - 0.09 = 0.484`.
+- For interval `(0.014525, 0.01489)`, the width is `0.609 - (-0.155) = 0.764`.
+- For interval `(0.01489, 0.01532)`, the width is `0.541 - (-0.203) = 0.744`.
+- For interval `(0.01532, 0.015805)`, the width is `0.484 - (-0.266) = 0.75`.
+- For interval `(0.015805, 0.017215)`, the width is `0.418 - (-0.317) = 0.735`.
+- For interval `(0.017215, 0.017855)`, the width is `0.123 - (-0.138) = 0.261`.
+- For interval `(0.017855, 0.018165)`, the width is `0.047 - (-0.193) = 0.24`.
+- For interval `(0.018165, 0.018685)`, the width is `0.002 - (-0.264) = 0.266`.
+- For interval `(0.018685, 0.019545)`, the width is `-0.059 - (-0.327) = 0.268`.
+- For interval `(0.019545, 0.02068)`, the width is `-0.116 - (-0.388) = 0.272`.
+- For interval `(0.02068, 0.024730000000000002)`, the width is `-0.164 - (-0.457) = 0.293`.
+- For interval `(0.024730000000000002, 0.026770000000000002)`, the width is `-0.182 - (-0.569) = 0.387`.
+- For interval `(0.026770000000000002, 0.027435)`, the width is `-0.125 - (-0.507) = 0.382`.
+- For interval `(0.027435, 0.028380000000000002)`, the width is `-0.043 - (-0.46) = 0.417`.
+- For interval `(0.028380000000000002, 0.02966)`, the width is `0.013 - (-0.393) = 0.406`.
+- For interval `(0.02966, 0.031865)`, the width is `0.097 - (-0.281) = 0.378`.
+- For interval `(0.031865, 0.03651)`, the width is `0.197 - (-0.265) = 0.462`.
+- For interval `(0.03651, 0.041944999999999996)`, the width is `0.281 - (-0.233) = 0.514`.
+- For interval `(0.041944999999999996, 0.04665)`, the width is `0.345 - (-0.174) = 0.519`.
+- For interval `(0.04665, 0.054805)`, the width is `0.424 - (-0.12) = 0.544`.
+- For interval `(0.054805, 0.05963)`, the width is `0.521 - (-0.058) = 0.579`.
+
+The interval with the widest confidence bound is `(0.054805, 0.05963)` with a width of `0.579`.
+SOLUTION:  (0.007882, 0.010595)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, we have a lower bound and an upper bound.
+
+2. **Calculate the Difference**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Differences**: Identify the interval where this difference (width of the confidence interval) is the largest.
+
+Let's calculate the differences for each interval:
+
+- **(6.981, 9.281500000000001)**: Upper - Lower = -0.515 - (-1.01) = 0.495
+- **(9.281500000000001, 9.7015)**: Upper - Lower = -0.435 - (-0.884) = 0.449
+- **(9.7015, 10.165)**: Upper - Lower = -0.373 - (-0.748) = 0.375
+- **(10.165, 10.655000000000001)**: Upper - Lower = -0.311 - (-0.611) = 0.300
+- **(10.655000000000001, 12.465)**: Upper - Lower = -0.184 - (-0.536) = 0.352
+- **(12.465, 13.39)**: Upper - Lower = -0.128 - (-0.396) = 0.268
+- **(13.39, 14.43)**: Upper - Lower = -0.057 - (-0.269) = 0.212
+- **(14.43, 14.934999999999999)**: Upper - Lower = 0.097 - (-0.226) = 0.323
+- **(14.934999999999999, 15.08)**: Upper - Lower = 0.231 - (-0.156) = 0.387
+- **(15.08, 15.815)**: Upper - Lower = 0.333 - (-0.059) = 0.392
+- **(15.815, 16.925)**: Upper - Lower = 0.597 - (-0.127) = 0.724
+- **(16.925, 17.385)**: Upper - Lower = 0.748 - 0.041 = 0.707
+- **(17.385, 18.0)**: Upper - Lower = 0.853 - 0.136 = 0.717
+- **(18.0, 18.735)**: Upper - Lower = 0.993 - 0.205 = 0.788
+- **(18.735, 19.240000000000002)**: Upper - Lower = 1.107 - 0.283 = 0.824
+- **(19.240000000000002, 19.990000000000002)**: Upper - Lower = 1.202 - 0.385 = 0.817
+- **(19.990000000000002, 20.595)**: Upper - Lower = 1.32 - 0.462 = 0.858
+- **(20.595, 23.240000000000002)**: Upper - Lower = 1.461 - 0.519 = 0.942
+- **(23.240000000000002, 28.11)**: Upper - Lower = 1.575 - 0.611 = 0.964
+
+From these calculations, the interval **(23.240000000000002, 28.11)** has the widest confidence bound with a difference of 0.964.
+SOLUTION:  (23.240000000000002, 28.11)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds of the confidence interval for each interval and identify the interval with the maximum difference.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds of the confidence interval from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Identify the Maximum Width**: Compare the widths of all intervals and identify the interval with the maximum width.
+
+Let's perform these calculations:
+
+- For interval (50.41, 71.06): Width = -0.307 - (-2.45) = 2.143
+- For interval (71.06, 76.52000000000001): Width = -0.189 - (-2.257) = 2.068
+- For interval (76.52000000000001, 80.9): Width = -0.114 - (-2.023) = 1.909
+- For interval (80.9, 84.035): Width = 0.021 - (-1.85) = 1.871
+- For interval (84.035, 86.48500000000001): Width = 0.172 - (-1.682) = 1.854
+- For interval (86.48500000000001, 87.3): Width = 0.332 - (-1.531) = 1.863
+- For interval (87.3, 91.49000000000001): Width = 0.159 - (-1.053) = 1.212
+- For interval (91.49000000000001, 95.66): Width = 0.331 - (-0.915) = 1.246
+- For interval (95.66, 101.15): Width = 0.936 - (-1.829) = 2.765
+- For interval (101.15, 102.05000000000001): Width = 1.054 - (-1.642) = 2.696
+- For interval (102.05000000000001, 109.6): Width = 0.782 - (-0.387) = 1.169
+- For interval (109.6, 116.25): Width = 0.94 - (-0.238) = 1.178
+- For interval (116.25, 120.35): Width = 1.088 - (-0.074) = 1.162
+- For interval (120.35, 127.0): Width = 2.256 - (-0.761) = 3.017
+- For interval (127.0, 133.10000000000002): Width = 2.428 - (-0.623) = 3.051
+- For interval (133.10000000000002, 145.10000000000002): Width = 2.611 - (-0.494) = 3.105
+- For interval (145.10000000000002, 160.0): Width = 2.809 - (-0.379) = 3.188
+- For interval (160.0, 178.85): Width = 3.027 - (-0.29) = 3.317
+- For interval (178.85, 251.2): Width = 3.208 - (-0.162) = 3.37
+
+From these calculations, the interval with the widest confidence bound is (178.85, 251.2) with a width of 3.37.
+SOLUTION:  (178.85, 251.2)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds of the confidence interval for each interval and identify the one with the maximum difference.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: From the JSON object, extract the lower and upper bounds for each interval.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Identify the Maximum Width**: Compare the widths of all intervals and identify the interval with the maximum width.
+
+Let's perform these calculations:
+
+- For interval `(0.4999, 0.5427500000000001)`: 
+  - Lower Bound = -48216.1
+  - Upper Bound = 16080.9
+  - Width = 16080.9 - (-48216.1) = 64297.0
+
+- For interval `(0.5427500000000001, 1.4808)`: 
+  - Lower Bound = -68098.8
+  - Upper Bound = -42980.1
+  - Width = -42980.1 - (-68098.8) = 25118.7
+
+- Continue this for all intervals...
+
+- For interval `(9.046949999999999, 15.00005)`: 
+  - Lower Bound = 203670.8
+  - Upper Bound = 225081.0
+  - Width = 225081.0 - 203670.8 = 21410.2
+
+- For interval `(15.00005, 15.0001)`: 
+  - Lower Bound = 178950.1
+  - Upper Bound = 208557.1
+  - Width = 208557.1 - 178950.1 = 29607.0
+
+After calculating the widths for all intervals, we find that the interval with the widest confidence bound is `(0.4999, 0.5427500000000001)` with a width of 64297.0.
+SOLUTION:  (0.4999, 0.5427500000000001)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the calculations for each interval:
+
+1. Interval (113.0, 114.5):
+   - Lower Bound: -3.483
+   - Upper Bound: 0.944
+   - Difference: 0.944 - (-3.483) = 4.427
+
+2. Interval (114.5, 118.5):
+   - Lower Bound: -4.768
+   - Upper Bound: 5.334
+   - Difference: 5.334 - (-4.768) = 10.102
+
+3. Interval (118.5, 124.5):
+   - Lower Bound: 2.536
+   - Upper Bound: 4.542
+   - Difference: 4.542 - 2.536 = 2.006
+
+4. Interval (124.5, 126.5):
+   - Lower Bound: 1.699
+   - Upper Bound: 3.222
+   - Difference: 3.222 - 1.699 = 1.523
+
+5. Interval (126.5, 127.5):
+   - Lower Bound: 3.034
+   - Upper Bound: 5.05
+   - Difference: 5.05 - 3.034 = 2.016
+
+6. Interval (127.5, 129.5):
+   - Lower Bound: 2.614
+   - Upper Bound: 4.492
+   - Difference: 4.492 - 2.614 = 1.878
+
+7. Interval (129.5, 130.5):
+   - Lower Bound: 0.389
+   - Upper Bound: 1.517
+   - Difference: 1.517 - 0.389 = 1.128
+
+8. Interval (130.5, 132.5):
+   - Lower Bound: 0.304
+   - Upper Bound: 2.136
+   - Difference: 2.136 - 0.304 = 1.832
+
+9. Interval (132.5, 133.5):
+   - Lower Bound: -2.269
+   - Upper Bound: 0.08
+   - Difference: 0.08 - (-2.269) = 2.349
+
+10. Interval (133.5, 135.5):
+    - Lower Bound: 0.366
+    - Upper Bound: 0.808
+    - Difference: 0.808 - 0.366 = 0.442
+
+11. Interval (135.5, 138.5):
+    - Lower Bound: -0.879
+    - Upper Bound: -0.38
+    - Difference: -0.38 - (-0.879) = 0.499
+
+12. Interval (138.5, 144.5):
+    - Lower Bound: -0.845
+    - Upper Bound: 0.38
+    - Difference: 0.38 - (-0.845) = 1.225
+
+13. Interval (144.5, 148.0):
+    - Lower Bound: -0.129
+    - Upper Bound: 0.354
+    - Difference: 0.354 - (-0.129) = 0.483
+
+From these calculations, the interval (114.5, 118.5) has the widest confidence bound with a difference of 10.102. This is the interval with the widest confidence bound.
+SOLUTION:  (114.5, 118.5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the width of the confidence interval for each interval and compare them. The confidence interval width is calculated as the difference between the upper bound and the lower bound of the confidence interval for each interval.
+
+Let's calculate the width of the confidence intervals for each interval:
+
+1. For the interval (0.0, 0.5):
+   - Lower Bound = -0.037941
+   - Upper Bound = 0.0291
+   - Width = Upper Bound - Lower Bound = 0.0291 - (-0.037941) = 0.0291 + 0.037941 = 0.067041
+
+2. For the interval (0.5, 1.0):
+   - Lower Bound = -0.009076
+   - Upper Bound = 0.011834
+   - Width = Upper Bound - Lower Bound = 0.011834 - (-0.009076) = 0.011834 + 0.009076 = 0.02091
+
+Comparing the widths:
+- Interval (0.0, 0.5) has a width of 0.067041
+- Interval (0.5, 1.0) has a width of 0.02091
+
+The interval (0.0, 0.5) has the widest confidence bound with a width of 0.067041.
+SOLUTION:  (0.0, 0.5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval with the maximum difference.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Difference**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Identify the Maximum Difference**: Compare the differences calculated in step 2 to find the maximum difference, which indicates the widest confidence interval.
+
+Let's perform these calculations:
+
+- For interval (8670.0, 90271.0): 
+  - Lower Bound = -0.06
+  - Upper Bound = 0.744
+  - Difference = 0.744 - (-0.06) = 0.804
+
+- For interval (90271.0, 467526.5): 
+  - Lower Bound = 0.079
+  - Upper Bound = 1.07
+  - Difference = 1.07 - 0.079 = 0.991
+
+- For interval (467526.5, 853506.5): 
+  - Lower Bound = 0.101
+  - Upper Bound = 1.212
+  - Difference = 1.212 - 0.101 = 1.111
+
+- For interval (853506.5, 859643.0): 
+  - Lower Bound = 0.139
+  - Upper Bound = 1.299
+  - Difference = 1.299 - 0.139 = 1.16
+
+- For interval (859643.0, 864727.5): 
+  - Lower Bound = 0.076
+  - Upper Bound = 1.234
+  - Difference = 1.234 - 0.076 = 1.158
+
+- For interval (864727.5, 871421.0): 
+  - Lower Bound = 0.038
+  - Upper Bound = 1.148
+  - Difference = 1.148 - 0.038 = 1.11
+
+- For interval (871421.0, 874848.5): 
+  - Lower Bound = 0.005
+  - Upper Bound = 1.051
+  - Difference = 1.051 - 0.005 = 1.046
+
+- For interval (874848.5, 880845.5): 
+  - Lower Bound = -0.028
+  - Upper Bound = 0.956
+  - Difference = 0.956 - (-0.028) = 0.984
+
+- For interval (880845.5, 882230.0): 
+  - Lower Bound = -0.061
+  - Upper Bound = 0.86
+  - Difference = 0.86 - (-0.061) = 0.921
+
+- For interval (882230.0, 883266.5): 
+  - Lower Bound = -0.137
+  - Upper Bound = 0.774
+  - Difference = 0.774 - (-0.137) = 0.911
+
+Continue this calculation for all intervals. From the calculations above, the interval (853506.5, 859643.0) has the widest confidence bound with a difference of 1.16. This is the interval with the widest confidence bound based on the data provided.
+SOLUTION:  (853506.5, 859643.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here's how we can do this step by step:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, we have a lower bound and an upper bound.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval where this width (difference between upper and lower bounds) is the largest.
+
+Let's perform these calculations using the provided data:
+
+- For interval `(0.0088595, 0.009246)`, the lower bound is `-0.313` and the upper bound is `0.752`. The width of the confidence interval is `0.752 - (-0.313) = 1.065`.
+
+We can compare this width with the widths of other intervals, but given the values provided, it's clear that this interval has a significantly larger width than the others, which generally have smaller differences.
+
+Thus, the interval `(0.0088595, 0.009246)` has the widest confidence bound in the graph.
+SOLUTION:  (0.0088595, 0.009246)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: From the JSON object, extract the lower and upper bounds for each interval.
+
+2. **Calculate the Width of the Confidence Interval for Each Interval**: Subtract the lower bound from the upper bound for each interval.
+
+3. **Identify the Interval with the Maximum Width**: Compare the widths calculated in step 2 and identify the interval with the largest width.
+
+Let's perform these calculations:
+
+- For interval `(0.1565, 0.165)`: Width = 0.249 - (-0.839) = 1.088
+- For interval `(0.165, 0.19055)`: Width = -0.201 - (-0.743) = 0.542
+- For interval `(0.19055, 0.24485)`: Width = -0.296 - (-0.802) = 0.506
+- For interval `(0.24485, 0.25225)`: Width = -0.276 - (-0.663) = 0.387
+- For interval `(0.25225, 0.2583)`: Width = -0.188 - (-0.595) = 0.407
+- For interval `(0.2583, 0.26635)`: Width = -0.140 - (-0.479) = 0.339
+- For interval `(0.26635, 0.26959999999999995)`: Width = -0.072 - (-0.388) = 0.316
+- For interval `(0.26959999999999995, 0.27495)`: Width = 0.030 - (-0.253) = 0.283
+- For interval `(0.27495, 0.28035)`: Width = 0.104 - (-0.172) = 0.276
+- For interval `(0.28035, 0.28815)`: Width = 0.191 - (-0.100) = 0.291
+- For interval `(0.28815, 0.2986)`: Width = 0.264 - (-0.015) = 0.279
+- For interval `(0.2986, 0.31745)`: Width = 0.361 - 0.043 = 0.318
+- For interval `(0.31745, 0.32125000000000004)`: Width = 0.420 - 0.141 = 0.279
+- For interval `(0.32125000000000004, 0.33065)`: Width = 0.516 - 0.210 = 0.306
+- For interval `(0.33065, 0.35335)`: Width = 0.612 - 0.276 = 0.336
+- For interval `(0.35335, 0.36085)`: Width = 0.694 - 0.357 = 0.337
+- For interval `(0.36085, 0.3702)`: Width = 0.901 - 0.348 = 0.553
+- For interval `(0.3702, 0.4223)`: Width = 1.015 - 0.395 = 0.620
+- For interval `(0.4223, 0.4697)`: Width = 1.092 - 0.478 = 0.614
+- For interval `(0.4697, 0.6638)`: Width = 1.197 - 0.538 = 0.659
+
+From these calculations, the interval `(0.4697, 0.6638)` has the widest confidence bound with a width of 0.659.
+SOLUTION:  (0.1565, 0.165)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval with the maximum width.
+
+Let's perform these calculations:
+
+- For interval (0.0, 9.1): Width = -0.262 - (-1.139) = 0.877
+- For interval (9.1, 22.55): Width = -0.573 - (-1.349) = 0.776
+- For interval (22.55, 23.65): Width = -0.493 - (-1.219) = 0.726
+- For interval (23.65, 25.55): Width = -0.243 - (-1.281) = 1.038
+- For interval (25.55, 26.35): Width = -0.09 - (-1.231) = 1.141
+- For interval (26.35, 27.65): Width = 0.088 - (-0.568) = 0.656
+- For interval (27.65, 28.45): Width = -0.03 - (-0.258) = 0.228
+- For interval (28.45, 29.65): Width = 0.054 - (-0.157) = 0.211
+- For interval (29.65, 30.45): Width = 0.208 - (-0.11) = 0.318
+- For interval (30.45, 32.150000000000006): Width = 0.392 - (-0.086) = 0.478
+- For interval (32.150000000000006, 37.650000000000006): Width = 0.409 - 0.084 = 0.325
+- For interval (37.650000000000006, 41.75): Width = 0.491 - 0.189 = 0.302
+- For interval (41.75, 42.849999999999994): Width = 0.588 - 0.28 = 0.308
+- For interval (42.849999999999994, 45.650000000000006): Width = 0.709 - 0.348 = 0.361
+- For interval (45.650000000000006, 48.349999999999994): Width = 0.996 - 0.256 = 0.74
+- For interval (48.349999999999994, 67.1): Width = 1.303 - 0.265 = 1.038
+
+From the calculations, the interval with the widest confidence bound is (25.55, 26.35) with a width of 1.141. This interval has the largest difference between the upper and lower confidence bounds.
+SOLUTION:  (25.55, 26.35)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Difference**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Differences**: Identify the interval where this difference (width of the confidence interval) is the largest.
+
+Let's perform these calculations using the provided data:
+
+- For interval `(4.0, 11.5)`, the difference is `13.0 - 8.45 = 4.55`.
+- For interval `(11.5, 12.5)`, the difference is `2.32 - 0.25 = 2.07`.
+- For interval `(12.5, 15.5)`, the difference is `4.82 - 2.94 = 1.88`.
+- For interval `(15.5, 18.0)`, the difference is `4.68 - (-0.25) = 4.93`.
+- For interval `(18.0, 28.5)`, the difference is `8.31 - 4.04 = 4.27`.
+- For interval `(28.5, 30.5)`, the difference is `5.26 - 3.69 = 1.57`.
+- For interval `(30.5, 52.0)`, the difference is `6.91 - 4.21 = 2.7`.
+- For interval `(52.0, 54.5)`, the difference is `5.03 - 1.74 = 3.29`.
+- For interval `(54.5, 67.5)`, the difference is `6.41 - 3.17 = 3.24`.
+- For interval `(67.5, 73.5)`, the difference is `3.57 - 1.96 = 1.61`.
+- For interval `(73.5, 76.5)`, the difference is `(-1.61) - (-4.69) = 3.08`.
+- For interval `(76.5, 78.5)`, the difference is `3.39 - 1.19 = 2.2`.
+- For interval `(78.5, 82.5)`, the difference is `0.92 - (-1.25) = 2.17`.
+- For interval `(82.5, 87.5)`, the difference is `(-1.75) - (-3.84) = 2.09`.
+- For interval `(87.5, 90.5)`, the difference is `0.72 - (-0.35) = 1.07`.
+- For interval `(90.5, 92.5)`, the difference is `0.6 - (-2.75) = 3.35`.
+- For interval `(92.5, 95.5)`, the difference is `(-0.81) - (-4.6) = 3.79`.
+- For interval `(95.5, 108.5)`, the difference is `(-0.34) - (-1.62) = 1.28`.
+- For interval `(108.5, 117.5)`, the difference is `0.7 - (-0.66) = 1.36`.
+- For interval `(117.5, 124.5)`, the difference is `(-1.93) - (-4.94) = 3.01`.
+- For interval `(124.5, 137.5)`, the difference is `1.53 - (-0.24) = 1.77`.
+- For interval `(137.5, 149.0)`, the difference is `0.22 - (-1.83) = 2.05`.
+- For interval `(149.0, 171.5)`, the difference is `6.52 - 3.59 = 2.93`.
+- For interval `(171.5, 173.0)`, the difference is `3.72 - 1.61 = 2.11`.
+- For interval `(173.0, 182.5)`, the difference is `0.18 - (-1.86) = 2.04`.
+- For interval `(182.5, 192.5)`, the difference is `(-2.33) - (-4.51) = 2.18`.
+- For interval `(192.5, 193.5)`, the difference is `(-0.13) - (-1.89) = 1.76`.
+- For interval `(193.5, 253.0)`, the difference is `(-1.06) - (-4.11) = 3.05`.
+- For interval `(253.0, 285.0)`, the difference is `(-6.14) - (-10.7) = 4.56`.
+
+From the calculations, the interval `(253.0, 285.0)` has the widest confidence bound with a difference of `4.56`.
+SOLUTION:  (15.5, 18.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the calculations for each interval:
+
+1. Interval (14.0, 16.0):
+   - Lower Bound: 2.65
+   - Upper Bound: 6.45
+   - Difference: 6.45 - 2.65 = 3.8
+
+2. Interval (16.0, 22.5):
+   - Lower Bound: 2.42
+   - Upper Bound: 4.1
+   - Difference: 4.1 - 2.42 = 1.68
+
+3. Interval (22.5, 27.5):
+   - Lower Bound: 1.26
+   - Upper Bound: 2.51
+   - Difference: 2.51 - 1.26 = 1.25
+
+4. Interval (27.5, 32.5):
+   - Lower Bound: -0.83
+   - Upper Bound: -0.01
+   - Difference: -0.01 - (-0.83) = 0.82
+
+5. Interval (32.5, 36.5):
+   - Lower Bound: -2.57
+   - Upper Bound: -0.95
+   - Difference: -0.95 - (-2.57) = 1.62
+
+6. Interval (36.5, 39.0):
+   - Lower Bound: 0.17
+   - Upper Bound: 0.79
+   - Difference: 0.79 - 0.17 = 0.62
+
+7. Interval (39.0, 61.0):
+   - Lower Bound: -1.16
+   - Upper Bound: -0.49
+   - Difference: -0.49 - (-1.16) = 0.67
+
+8. Interval (61.0, 67.5):
+   - Lower Bound: -0.39
+   - Upper Bound: 0.55
+   - Difference: 0.55 - (-0.39) = 0.94
+
+9. Interval (67.5, 75.0):
+   - Lower Bound: 0.32
+   - Upper Bound: 1.28
+   - Difference: 1.28 - 0.32 = 0.96
+
+10. Interval (75.0, 80.0):
+    - Lower Bound: -8.05
+    - Upper Bound: -3.29
+    - Difference: -3.29 - (-8.05) = 4.76
+
+From these calculations, the interval (75.0, 80.0) has the widest confidence bound with a difference of 4.76.
+SOLUTION:  (75.0, 80.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds of the confidence interval for each interval and identify the one with the largest difference.
+
+Here are the intervals and their corresponding confidence bounds:
+
+1. Interval (0.1, 0.35):
+   - Lower Bound: 7.9
+   - Upper Bound: 8.23
+   - Difference: 8.23 - 7.9 = 0.33
+
+2. Interval (0.35, 0.45):
+   - Lower Bound: 7.05
+   - Upper Bound: 7.49
+   - Difference: 7.49 - 7.05 = 0.44
+
+3. Interval (0.45, 0.75):
+   - Lower Bound: 3.08
+   - Upper Bound: 9.28
+   - Difference: 9.28 - 3.08 = 6.2
+
+4. Interval (0.75, 1.25):
+   - Lower Bound: -2.81
+   - Upper Bound: -2.47
+   - Difference: -2.47 - (-2.81) = 0.34
+
+5. Interval (1.25, 1.75):
+   - Lower Bound: -3.62
+   - Upper Bound: -3.3
+   - Difference: -3.3 - (-3.62) = 0.32
+
+6. Interval (1.75, 2.5):
+   - Lower Bound: -4.29
+   - Upper Bound: -4.08
+   - Difference: -4.08 - (-4.29) = 0.21
+
+From the calculated differences, the interval (0.45, 0.75) has the widest confidence bound with a difference of 6.2. This is the interval with the widest confidence bound.
+SOLUTION:  (0.45, 0.75)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the calculations for each interval:
+
+1. Interval (0.0, 1.5): Upper - Lower = -0.02245 - (-0.02806) = 0.00561
+2. Interval (1.5, 2.5): Upper - Lower = -0.01664 - (-0.01811) = 0.00147
+3. Interval (2.5, 3.5): Upper - Lower = -0.01102 - (-0.01241) = 0.00139
+4. Interval (3.5, 4.5): Upper - Lower = -0.00514 - (-0.0056) = 0.00046
+5. Interval (4.5, 5.5): Upper - Lower = 0.00016 - (-0.00057) = 0.00073
+6. Interval (5.5, 6.5): Upper - Lower = 0.00699 - 0.00621 = 0.00078
+7. Interval (6.5, 7.5): Upper - Lower = 0.01085 - 0.00967 = 0.00118
+8. Interval (7.5, 8.5): Upper - Lower = 0.01761 - 0.01672 = 0.00089
+9. Interval (8.5, 9.5): Upper - Lower = 0.02519 - 0.02334 = 0.00185
+10. Interval (9.5, 10.5): Upper - Lower = 0.02958 - 0.02687 = 0.00271
+11. Interval (10.5, 11.5): Upper - Lower = 0.03468 - 0.03182 = 0.00286
+12. Interval (11.5, 13.5): Upper - Lower = 0.04466 - 0.03364 = 0.01102
+13. Interval (13.5, 15.0): Upper - Lower = 0.04073 - 0.0307 = 0.01003
+
+From these calculations, the interval (11.5, 13.5) has the widest confidence bound with a difference of 0.01102. Therefore, the x-axis interval with the widest confidence bound is (11.5, 13.5).
+SOLUTION:  (11.5, 13.5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval with the maximum difference.
+
+Here are the calculations for each interval:
+
+1. Interval (1.0, 4.5):
+   - Lower Bound: -26905.5
+   - Upper Bound: -13090.4
+   - Difference: -13090.4 - (-26905.5) = 13815.1
+
+2. Interval (4.5, 7.5):
+   - Lower Bound: -11566.0
+   - Upper Bound: -4010.4
+   - Difference: -4010.4 - (-11566.0) = 7555.6
+
+3. Interval (7.5, 16.5):
+   - Lower Bound: -12538.5
+   - Upper Bound: -8821.8
+   - Difference: -8821.8 - (-12538.5) = 3716.7
+
+4. Interval (16.5, 18.5):
+   - Lower Bound: -7756.2
+   - Upper Bound: -4852.5
+   - Difference: -4852.5 - (-7756.2) = 2903.7
+
+5. Interval (18.5, 27.5):
+   - Lower Bound: -3361.1
+   - Upper Bound: -160.0
+   - Difference: -160.0 - (-3361.1) = 3201.1
+
+6. Interval (27.5, 34.5):
+   - Lower Bound: 124.5
+   - Upper Bound: 4205.0
+   - Difference: 4205.0 - 124.5 = 4080.5
+
+7. Interval (34.5, 38.5):
+   - Lower Bound: -1933.4
+   - Upper Bound: 108.5
+   - Difference: 108.5 - (-1933.4) = 2041.9
+
+8. Interval (38.5, 41.5):
+   - Lower Bound: 2260.6
+   - Upper Bound: 6138.7
+   - Difference: 6138.7 - 2260.6 = 3878.1
+
+9. Interval (41.5, 45.5):
+   - Lower Bound: -4429.7
+   - Upper Bound: 3434.9
+   - Difference: 3434.9 - (-4429.7) = 7864.6
+
+10. Interval (45.5, 47.5):
+    - Lower Bound: -8697.7
+    - Upper Bound: -1682.0
+    - Difference: -1682.0 - (-8697.7) = 7015.7
+
+11. Interval (47.5, 48.5):
+    - Lower Bound: 2180.3
+    - Upper Bound: 8221.7
+    - Difference: 8221.7 - 2180.3 = 6041.4
+
+12. Interval (48.5, 49.5):
+    - Lower Bound: -1981.1
+    - Upper Bound: 6299.1
+    - Difference: 6299.1 - (-1981.1) = 8280.2
+
+13. Interval (49.5, 50.5):
+    - Lower Bound: 1581.5
+    - Upper Bound: 10689.9
+    - Difference: 10689.9 - 1581.5 = 9108.4
+
+14. Interval (50.5, 51.5):
+    - Lower Bound: 5647.5
+    - Upper Bound: 17380.1
+    - Difference: 17380.1 - 5647.5 = 11732.6
+
+15. Interval (51.5, 52.0):
+    - Lower Bound: 25827.1
+    - Upper Bound: 29272.3
+    - Difference: 29272.3 - 25827.1 = 3445.2
+
+From the above calculations, the interval (1.0, 4.5) has the widest confidence bound with a difference of 13815.1.
+SOLUTION:  (1.0, 4.5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the width of the confidence interval for each interval and compare them. The confidence interval width is calculated as the difference between the upper bound and the lower bound of the confidence interval.
+
+Let's calculate the width of the confidence intervals for each interval:
+
+1. For the interval (0.0, 0.5):
+   - Lower Bound = -0.1464
+   - Upper Bound = -0.0173
+   - Width = Upper Bound - Lower Bound = -0.0173 - (-0.1464) = 0.1291
+
+2. For the interval (0.5, 1.0):
+   - Lower Bound = 0.0194
+   - Upper Bound = 0.1641
+   - Width = Upper Bound - Lower Bound = 0.1641 - 0.0194 = 0.1447
+
+Comparing the widths:
+- Interval (0.0, 0.5) has a width of 0.1291
+- Interval (0.5, 1.0) has a width of 0.1447
+
+The interval (0.5, 1.0) has the widest confidence bound with a width of 0.1447.
+SOLUTION:  (0.5, 1.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval where this width (difference between upper and lower bounds) is the largest.
+
+Let's calculate the width of the confidence interval for each interval:
+
+- **(12.02, 16.935000000000002)**: \(0.572 - (-4.342) = 4.914\)
+- **(16.935000000000002, 18.335)**: \(0.695 - (-4.128) = 4.823\)
+- **(18.335, 19.505)**: \(0.835 - (-3.934) = 4.769\)
+- **(19.505, 20.225)**: \(-0.437 - (-1.264) = 0.827\)
+- **(20.225, 21.955)**: \(-0.279 - (-0.945) = 0.666\)
+- **(21.955, 23.59)**: \(-0.218 - (-0.663) = 0.445\)
+- **(23.59, 24.795)**: \(-0.076 - (-0.468) = 0.392\)
+- **(24.795, 25.18)**: \(0.073 - (-0.274) = 0.347\)
+- **(25.18, 25.83)**: \(0.66 - (-0.503) = 1.163\)
+- **(25.83, 26.855)**: \(0.884 - (-0.327) = 1.211\)
+- **(26.855, 27.994999999999997)**: \(1.065 - (-0.163) = 1.228\)
+- **(27.994999999999997, 29.225)**: \(1.248 - (-0.01) = 1.258\)
+- **(29.225, 31.515)**: \(1.961 - (-0.206) = 2.167\)
+- **(31.515, 32.485)**: \(2.17 - (-0.081) = 2.251\)
+- **(32.485, 35.05)**: \(2.691 - (-0.18) = 2.871\)
+- **(35.05, 49.54)**: \(2.861 - (-0.014) = 2.875\)
+
+From the calculations above, the interval **(35.05, 49.54)** has the widest confidence bound with a width of \(2.875\). This is the interval with the widest confidence bound.
+SOLUTION:  (12.02, 16.935000000000002)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval with the maximum width of the confidence interval.
+
+Let's perform these calculations:
+
+- For interval (23.0, 32.0), the width is \(0.54 - (-1.51) = 2.05\).
+- For interval (32.0, 49.5), the width is \(2.24 - (-0.87) = 3.11\).
+- Continue this for all intervals...
+
+After calculating the widths for all intervals, we find:
+
+- For interval (1886.0, 2038.5), the width is \(8.11 - 5.88 = 2.23\).
+- For interval (2038.5, 2307.5), the width is \(2.72 - 1.8 = 0.92\).
+- For interval (2307.5, 2444.0), the width is \(7.19 - 4.43 = 2.76\).
+- For interval (2444.0, 3440.5), the width is \(0.06 - (-5.48) = 5.54\).
+- For interval (3440.5, 4253.0), the width is \(-0.79 - (-2.15) = 1.36\).
+- For interval (4253.0, 5548.5), the width is \(2.95 - 0.41 = 2.54\).
+- For interval (5548.5, 7861.0), the width is \(4.78 - 2.17 = 2.61\).
+
+From these calculations, the interval with the widest confidence bound is (2444.0, 3440.5) with a width of 5.54. This interval has the largest difference between the upper and lower confidence bounds among all the intervals provided.
+SOLUTION:  (85.0, 87.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval with the largest width.
+
+Let's perform these calculations:
+
+- For interval (0.0, 0.5): Upper - Lower = -0.045 - (-0.344) = 0.299
+- For interval (0.5, 1.5): Upper - Lower = 2.213 - 0.452 = 1.761
+- For interval (1.5, 2.5): Upper - Lower = 0.228 - (-0.269) = 0.497
+- For interval (2.5, 3.5): Upper - Lower = -0.043 - (-0.76) = 0.717
+- For interval (3.5, 4.5): Upper - Lower = -0.158 - (-2.688) = 2.53
+- For interval (4.5, 5.5): Upper - Lower = 0.429 - (-0.257) = 0.686
+- For interval (5.5, 7.5): Upper - Lower = 0.04 - (-1.727) = 1.767
+- For interval (7.5, 8.5): Upper - Lower = -0.004 - (-0.488) = 0.484
+- For interval (8.5, 11.5): Upper - Lower = 0.245 - (-0.121) = 0.366
+- For interval (11.5, 20.5): Upper - Lower = 0.001 - (-0.631) = 0.632
+- For interval (20.5, 21.5): Upper - Lower = 0.537 - (-0.319) = 0.856
+- For interval (21.5, 22.5): Upper - Lower = 0.904 - 0.048 = 0.856
+- For interval (22.5, 24.5): Upper - Lower = 0.331 - (-0.066) = 0.397
+- For interval (24.5, 26.5): Upper - Lower = -0.04 - (-0.66) = 0.62
+- For interval (26.5, 29.5): Upper - Lower = 0.089 - (-1.067) = 1.156
+- For interval (29.5, 32.5): Upper - Lower = 0.038 - (-0.254) = 0.292
+- For interval (32.5, 33.5): Upper - Lower = -0.121 - (-0.844) = 0.723
+- For interval (33.5, 35.5): Upper - Lower = -0.172 - (-1.156) = 0.984
+- For interval (35.5, 38.5): Upper - Lower = 0.204 - (-0.997) = 1.201
+- For interval (38.5, 39.5): Upper - Lower = 0.036 - 0.02 = 0.016
+- For interval (39.5, 40.5): Upper - Lower = 0.26 - (-1.452) = 1.712
+- For interval (40.5, 41.0): Upper - Lower = 1.816 - 0.408 = 1.408
+
+From these calculations, the interval (0.5, 1.5) has the widest confidence bound with a width of 1.761.
+SOLUTION:  (3.5, 4.5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval where this width (difference between upper and lower bounds) is the largest.
+
+Let's perform these calculations using the provided data:
+
+- For interval `(0.05504, 0.058984999999999996)`, the width is `0.4604 - (-0.1488) = 0.6092`.
+- For interval `(0.058984999999999996, 0.065905)`, the width is `0.3511 - (-0.1634) = 0.5145`.
+- For interval `(0.065905, 0.070015)`, the width is `0.186 - (-0.1018) = 0.2878`.
+- For interval `(0.070015, 0.071645)`, the width is `0.1385 - (-0.1659) = 0.3044`.
+- For interval `(0.071645, 0.07281)`, the width is `0.0533 - (-0.2246) = 0.2779`.
+- For interval `(0.07281, 0.075845)`, the width is `0.0064 - (-0.2831) = 0.2895`.
+- For interval `(0.075845, 0.083565)`, the width is `-0.0255 - (-0.3593) = 0.3338`.
+- For interval `(0.083565, 0.08926)`, the width is `0.1899 - (-0.8641) = 1.054`.
+- For interval `(0.08926, 0.09129999999999999)`, the width is `0.2307 - (-0.8008) = 1.0315`.
+- For interval `(0.09129999999999999, 0.09222)`, the width is `0.2773 - (-0.734) = 1.0113`.
+- For interval `(0.09222, 0.094545)`, the width is `0.3426 - (-0.6937) = 1.0363`.
+- For interval `(0.094545, 0.095845)`, the width is `0.405 - (-0.6426) = 1.0476`.
+- For interval `(0.095845, 0.09595500000000001)`, the width is `0.4399 - (-0.5681) = 1.008`.
+- For interval `(0.09595500000000001, 0.09849)`, the width is `0.6842 - (-0.3082) = 0.9924`.
+- For interval `(0.09849, 0.1008)`, the width is `0.7436 - (-0.2557) = 0.9993`.
+- For interval `(0.1008, 0.1018)`, the width is `0.8091 - (-0.2006) = 1.0097`.
+- For interval `(0.1018, 0.10569999999999999)`, the width is `1.6844 - (-0.5367) = 2.2211`.
+- For interval `(0.10569999999999999, 0.1074)`, the width is `1.7412 - (-0.4808) = 2.222`.
+- For interval `(0.1074, 0.11810000000000001)`, the width is `1.0176 - (-0.0451) = 1.0627`.
+- For interval `(0.11810000000000001, 0.12475)`, the width is `1.0318 - 0.0507 = 0.9811`.
+- For interval `(0.12475, 0.14024999999999999)`, the width is `1.0928 - 0.0965 = 0.9963`.
+- For interval `(0.14024999999999999, 0.2075)`, the width is `1.184 - 0.119 = 1.065`.
+
+From these calculations, the interval `(0.10569999999999999, 0.1074)` has the widest confidence bound with a width of `2.222`.
+SOLUTION:  (0.10569999999999999, 0.1074)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the width of the confidence interval for each interval provided in the graph. The width of the confidence interval can be calculated by subtracting the lower bound from the upper bound for each interval.
+
+From the data provided:
+1. For the interval (0.0, 0.5):
+   - Lower Bound = -0.5571
+   - Upper Bound = -0.3931
+   - Width = -0.3931 - (-0.5571) = 0.164
+
+2. For the interval (0.5, 1.0):
+   - Lower Bound = 0.1936
+   - Upper Bound = 0.2743
+   - Width = 0.2743 - 0.1936 = 0.0807
+
+Comparing the widths:
+- Interval (0.0, 0.5) has a width of 0.164
+- Interval (0.5, 1.0) has a width of 0.0807
+
+The interval (0.0, 0.5) has the widest confidence bound with a width of 0.164.
+SOLUTION:  (0.0, 0.5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Difference**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Differences**: Identify the interval where this difference (width of the confidence interval) is the largest.
+
+Let's perform these calculations using the provided data:
+
+- **Interval (6.802, 11.184999999999999)**:
+  - Lower Bound: -1.305
+  - Upper Bound: -0.532
+  - Difference: -0.532 - (-1.305) = 0.773
+
+- **Interval (11.184999999999999, 12.765)**:
+  - Lower Bound: -1.176
+  - Upper Bound: -0.452
+  - Difference: -0.452 - (-1.176) = 0.724
+
+- **Interval (12.765, 13.350000000000001)**:
+  - Lower Bound: -1.036
+  - Upper Bound: -0.371
+  - Difference: -0.371 - (-1.036) = 0.665
+
+- **Interval (13.350000000000001, 15.3)**:
+  - Lower Bound: -0.901
+  - Upper Bound: -0.291
+  - Difference: -0.291 - (-0.901) = 0.610
+
+- **Interval (15.3, 16.955)**:
+  - Lower Bound: -0.696
+  - Upper Bound: -0.284
+  - Difference: -0.284 - (-0.696) = 0.412
+
+- **Interval (16.955, 18.515)**:
+  - Lower Bound: -0.504
+  - Upper Bound: -0.23
+  - Difference: -0.23 - (-0.504) = 0.274
+
+- **Interval (18.515, 20.905)**:
+  - Lower Bound: -0.392
+  - Upper Bound: -0.121
+  - Difference: -0.121 - (-0.392) = 0.271
+
+- **Interval (20.905, 32.985)**:
+  - Lower Bound: -0.922
+  - Upper Bound: 0.62
+  - Difference: 0.62 - (-0.922) = 1.542
+
+- **Interval (32.985, 34.730000000000004)**:
+  - Lower Bound: -0.261
+  - Upper Bound: 0.424
+  - Difference: 0.424 - (-0.261) = 0.685
+
+- **Interval (34.730000000000004, 41.21)**:
+  - Lower Bound: -0.102
+  - Upper Bound: 0.479
+  - Difference: 0.479 - (-0.102) = 0.581
+
+- **Interval (41.21, 50.405)**:
+  - Lower Bound: 0.02
+  - Upper Bound: 0.563
+  - Difference: 0.563 - 0.02 = 0.543
+
+- **Interval (50.405, 56.915)**:
+  - Lower Bound: 0.072
+  - Upper Bound: 0.762
+  - Difference: 0.762 - 0.072 = 0.690
+
+- **Interval (56.915, 67.5)**:
+  - Lower Bound: 0.147
+  - Upper Bound: 0.913
+  - Difference: 0.913 - 0.147 = 0.766
+
+- **Interval (67.5, 81.56)**:
+  - Lower Bound: 0.223
+  - Upper Bound: 1.052
+  - Difference: 1.052 - 0.223 = 0.829
+
+- **Interval (81.56, 94.00999999999999)**:
+  - Lower Bound: 0.326
+  - Upper Bound: 1.176
+  - Difference: 1.176 - 0.326 = 0.850
+
+- **Interval (94.00999999999999, 106.2)**:
+  - Lower Bound: 0.402
+  - Upper Bound: 1.323
+  - Difference: 1.323 - 0.402 = 0.921
+
+- **Interval (106.2, 153.25)**:
+  - Lower Bound: 0.501
+  - Upper Bound: 1.448
+  - Difference: 1.448 - 0.501 = 0.947
+
+- **Interval (153.25, 542.2)**:
+  - Lower Bound: 0.571
+  - Upper Bound: 1.593
+  - Difference: 1.593 - 0.571 = 1.022
+
+From the calculations, the interval **(153.25, 542.2)** has the widest confidence bound with a difference of 1.022.
+SOLUTION:  (20.905, 32.985)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the calculations for each interval:
+
+1. Interval (0.0, 0.5):
+   - Lower Bound: -0.02637
+   - Upper Bound: -0.0233
+   - Difference: -0.0233 - (-0.02637) = 0.00307
+
+2. Interval (0.5, 1.5):
+   - Lower Bound: -0.02217
+   - Upper Bound: -0.01962
+   - Difference: -0.01962 - (-0.02217) = 0.00255
+
+3. Interval (1.5, 2.5):
+   - Lower Bound: -0.0179
+   - Upper Bound: -0.01689
+   - Difference: -0.01689 - (-0.0179) = 0.00101
+
+4. Interval (2.5, 3.5):
+   - Lower Bound: -0.01163
+   - Upper Bound: -0.01085
+   - Difference: -0.01085 - (-0.01163) = 0.00078
+
+5. Interval (3.5, 4.5):
+   - Lower Bound: -0.00519
+   - Upper Bound: -0.0043
+   - Difference: -0.0043 - (-0.00519) = 0.00089
+
+6. Interval (4.5, 5.5):
+   - Lower Bound: 0.00046
+   - Upper Bound: 0.00109
+   - Difference: 0.00109 - 0.00046 = 0.00063
+
+7. Interval (5.5, 6.5):
+   - Lower Bound: 0.00525
+   - Upper Bound: 0.00623
+   - Difference: 0.00623 - 0.00525 = 0.00098
+
+8. Interval (6.5, 7.5):
+   - Lower Bound: 0.00992
+   - Upper Bound: 0.01144
+   - Difference: 0.01144 - 0.00992 = 0.00152
+
+9. Interval (7.5, 8.5):
+   - Lower Bound: 0.01538
+   - Upper Bound: 0.0166
+   - Difference: 0.0166 - 0.01538 = 0.00122
+
+10. Interval (8.5, 9.5):
+    - Lower Bound: 0.02115
+    - Upper Bound: 0.02348
+    - Difference: 0.02348 - 0.02115 = 0.00233
+
+11. Interval (9.5, 10.5):
+    - Lower Bound: 0.02528
+    - Upper Bound: 0.02807
+    - Difference: 0.02807 - 0.02528 = 0.00279
+
+12. Interval (10.5, 13.5):
+    - Lower Bound: 0.02547
+    - Upper Bound: 0.04062
+    - Difference: 0.04062 - 0.02547 = 0.01515
+
+13. Interval (13.5, 16.0):
+    - Lower Bound: 0.01297
+    - Upper Bound: 0.02734
+    - Difference: 0.02734 - 0.01297 = 0.01437
+
+From these calculations, the interval (10.5, 13.5) has the widest confidence bound with a difference of 0.01515.
+SOLUTION:  (10.5, 13.5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the calculations for each interval:
+
+1. Interval (0.0, 0.5): Upper - Lower = -0.2459 - (-0.5596) = 0.3137
+2. Interval (0.5, 1.5): Upper - Lower = -0.4295 - (-0.6499) = 0.2204
+3. Interval (1.5, 3.5): Upper - Lower = -0.3523 - (-0.618) = 0.2657
+4. Interval (3.5, 4.5): Upper - Lower = -0.235 - (-0.5693) = 0.3343
+5. Interval (4.5, 5.5): Upper - Lower = -0.3862 - (-0.5278) = 0.1416
+6. Interval (5.5, 6.5): Upper - Lower = -0.1733 - (-0.3342) = 0.1609
+7. Interval (6.5, 7.5): Upper - Lower = -0.0039 - (-0.0948) = 0.0909
+8. Interval (7.5, 8.5): Upper - Lower = 0.0977 - (-0.0062) = 0.1039
+9. Interval (8.5, 9.5): Upper - Lower = 0.2137 - 0.1525 = 0.0612
+10. Interval (9.5, 10.5): Upper - Lower = 0.1711 - 0.1072 = 0.0639
+11. Interval (10.5, 11.5): Upper - Lower = -0.0435 - (-0.0869) = 0.0434
+12. Interval (11.5, 14.5): Upper - Lower = 0.2431 - 0.1476 = 0.0955
+13. Interval (14.5, 15.0): Upper - Lower = 0.1775 - 0.1012 = 0.0763
+
+From these calculations, the interval (3.5, 4.5) has the widest confidence bound with a difference of 0.3343. Therefore, the x-axis interval with the widest confidence bound is (3.5, 4.5).
+SOLUTION:  (3.5, 4.5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval with the maximum width.
+
+Let's perform these calculations:
+
+- For interval (0.0, 50418.515): Width = -0.072 - (-0.192) = 0.12
+- For interval (50418.515, 53570.93): Width = 0.057 - (-0.628) = 0.685
+- For interval (53570.93, 54249.445): Width = 0.347 - (-1.999) = 2.346
+- For interval (54249.445, 57428.56): Width = -0.011 - (-0.798) = 0.787
+- For interval (57428.56, 60041.265): Width = 0.312 - (-0.322) = 0.634
+- For interval (60041.265, 64897.8): Width = 0.534 - (-0.105) = 0.639
+- For interval (64897.8, 72985.875): Width = 0.367 - (-0.195) = 0.562
+- For interval (72985.875, 74989.08499999999): Width = 0.395 - (-0.418) = 0.813
+- For interval (74989.08499999999, 76596.815): Width = 0.725 - (-0.231) = 0.956
+- For interval (76596.815, 79953.185): Width = 1.32 - 0.338 = 0.982
+- For interval (79953.185, 83348.07): Width = 0.806 - 0.321 = 0.485
+- For interval (83348.07, 101890.23999999999): Width = 0.582 - 0.247 = 0.335
+- For interval (101890.23999999999, 114327.485): Width = 0.398 - 0.097 = 0.301
+- For interval (114327.485, 123946.3): Width = 0.259 - 0.069 = 0.19
+- For interval (123946.3, 141661.24): Width = 0.379 - (-0.23) = 0.609
+- For interval (141661.24, 174920.08000000002): Width = 0.618 - (-0.272) = 0.89
+- For interval (174920.08000000002, 181813.135): Width = 0.264 - (-0.147) = 0.411
+- For interval (181813.135, 191993.675): Width = 0.166 - (-0.864) = 1.03
+- For interval (191993.675, 200829.925): Width = 0.073 - (-0.991) = 1.064
+- For interval (200829.925, 206951.87): Width = 0.169 - (-1.401) = 1.57
+- For interval (206951.87, 216109.88): Width = 0.35 - (-0.862) = 1.212
+
+From the calculations, the interval with the widest confidence bound is (200829.925, 206951.87) with a width of 1.57.
+SOLUTION:  (53570.93, 54249.445)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds of the confidence interval for each interval and identify the one with the maximum difference.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Identify the Maximum Width**: Compare the widths of all intervals and identify the interval with the maximum width.
+
+Let's perform these calculations:
+
+- For interval (0.0, 105.5): Upper - Lower = 0.716 - (-0.06) = 0.776
+- For interval (105.5, 296.5): Upper - Lower = 0.425 - (-0.369) = 0.794
+- For interval (296.5, 335.5): Upper - Lower = 0.607 - (-1.022) = 1.629
+- For interval (335.5, 340.0): Upper - Lower = 0.513 - (-0.184) = 0.697
+- For interval (340.0, 343.0): Upper - Lower = 0.837 - (-1.038) = 1.875
+- For interval (343.0, 596.5): Upper - Lower = -0.16 - (-1.323) = 1.163
+- For interval (596.5, 712.5): Upper - Lower = -0.409 - (-1.547) = 1.138
+- For interval (712.5, 734.0): Upper - Lower = -0.869 - (-1.555) = 0.686
+- For interval (734.0, 800.0): Upper - Lower = -1.092 - (-1.8) = 0.708
+- For interval (800.0, 816.0): Upper - Lower = -0.082 - (-2.191) = 2.109
+- For interval (816.0, 997.5): Upper - Lower = -1.083 - (-1.824) = 0.741
+- For interval (997.5, 1031.0): Upper - Lower = -0.506 - (-1.706) = 1.2
+- For interval (1031.0, 1041.0): Upper - Lower = -0.589 - (-2.147) = 1.558
+- For interval (1041.0, 2172.5): Upper - Lower = -1.488 - (-2.244) = 0.756
+- For interval (2172.5, 2283.5): Upper - Lower = -0.661 - (-2.248) = 1.587
+- For interval (2283.5, 2313.5): Upper - Lower = -0.774 - (-1.568) = 0.794
+- For interval (2313.5, 2336.5): Upper - Lower = 0.89 - (-2.21) = 3.1
+- For interval (2336.5, 2420.0): Upper - Lower = -1.582 - (-3.537) = 1.955
+- For interval (2420.0, 2992.5): Upper - Lower = -2.569 - (-3.89) = 1.321
+- For interval (2992.5, 3006.0): Upper - Lower = -1.461 - (-3.955) = 2.494
+- For interval (3006.0, 3196.5): Upper - Lower = -1.727 - (-4.24) = 2.513
+- For interval (3196.5, 3249.5): Upper - Lower = -1.438 - (-3.98) = 2.542
+- For interval (3249.5, 14327.0): Upper - Lower = -3.043 - (-5.248) = 2.205
+
+From the calculations, the interval with the widest confidence bound is (2313.5, 2336.5) with a width of 3.1.
+SOLUTION:  (2313.5, 2336.5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval where this width (difference between upper and lower bounds) is the largest.
+
+Let's perform these calculations:
+
+- For interval (185.2, 357.5): Width = -0.278 - (-2.413) = 2.135
+- For interval (357.5, 413.15): Width = -0.164 - (-2.22) = 2.056
+- For interval (413.15, 471.9): Width = -0.073 - (-2.004) = 1.931
+- For interval (471.9, 508.5): Width = 0.062 - (-1.818) = 1.88
+- For interval (508.5, 633.9): Width = 0.423 - (-1.868) = 2.291
+- For interval (633.9, 653.45): Width = 0.516 - (-1.645) = 2.161
+- For interval (653.45, 710.2): Width = 0.071 - (-0.767) = 0.838
+- For interval (710.2, 727.0999999999999): Width = 0.17 - (-0.501) = 0.671
+- For interval (727.0999999999999, 805.95): Width = 0.764 - (-0.573) = 1.337
+- For interval (805.95, 874.85): Width = 0.693 - (-0.187) = 0.88
+- For interval (874.85, 928.5): Width = 1.449 - (-0.49) = 1.939
+- For interval (928.5, 1033.5): Width = 2.006 - (-0.484) = 2.49
+- For interval (1033.5, 1222.5): Width = 2.319 - (-0.455) = 2.774
+- For interval (1222.5, 1346.5): Width = 2.482 - (-0.298) = 2.78
+- For interval (1346.5, 1645.5): Width = 2.672 - (-0.182) = 2.854
+- For interval (1645.5, 1979.0): Width = 2.857 - (-0.049) = 2.906
+- For interval (1979.0, 4254.0): Width = 3.043 - 0.071 = 2.972
+
+From these calculations, the interval (1979.0, 4254.0) has the widest confidence bound with a width of 2.972. This is the interval with the widest confidence interval in the graph.
+SOLUTION:  (1979.0, 4254.0)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval with the maximum difference.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Difference**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Identify the Maximum Difference**: Compare the differences calculated in step 2 to find the maximum difference, which indicates the widest confidence interval.
+
+Let's perform these calculations:
+
+- For interval (350.0, 416.5): Upper - Lower = 1.3346 - (-0.0945) = 1.4291
+- For interval (416.5, 421.5): Upper - Lower = 1.243 - (-0.1033) = 1.3463
+- For interval (421.5, 427.5): Upper - Lower = 0.7784 - (-0.0186) = 0.7970
+- For interval (427.5, 437.5): Upper - Lower = 0.7005 - (-0.1491) = 0.8496
+- For interval (437.5, 464.5): Upper - Lower = 0.5843 - 0.0705 = 0.5138
+- For interval (464.5, 470.5): Upper - Lower = 0.5564 - (-0.0008) = 0.5572
+- For interval (470.5, 477.5): Upper - Lower = 0.9641 - (-0.0519) = 1.0160
+- For interval (477.5, 478.5): Upper - Lower = 0.4206 - (-0.3016) = 0.7222
+- For interval (478.5, 494.5): Upper - Lower = 0.2736 - 0.0126 = 0.2610
+- For interval (494.5, 515.5): Upper - Lower = 0.3173 - (-0.1354) = 0.4527
+- For interval (515.5, 523.5): Upper - Lower = -0.1047 - (-0.5637) = 0.4590
+- For interval (523.5, 539.5): Upper - Lower = -0.1159 - (-0.3225) = 0.2066
+- For interval (539.5, 566.5): Upper - Lower = -0.061 - (-0.2064) = 0.1454
+- For interval (566.5, 598.5): Upper - Lower = -0.0424 - (-0.1252) = 0.0828
+- For interval (598.5, 661.5): Upper - Lower = 0.0472 - (-0.1126) = 0.1598
+- For interval (661.5, 684.5): Upper - Lower = 0.066 - (-0.0289) = 0.0949
+- For interval (684.5, 741.5): Upper - Lower = 0.1548 - (-0.0156) = 0.1704
+- For interval (741.5, 769.5): Upper - Lower = 0.1168 - (-0.0756) = 0.1924
+- For interval (769.5, 792.5): Upper - Lower = 0.1542 - (-0.016) = 0.1702
+- For interval (792.5, 805.5): Upper - Lower = 0.4932 - (-0.0471) = 0.5403
+- For interval (805.5, 806.5): Upper - Lower = 0.4795 - (-0.2533) = 0.7328
+- For interval (806.5, 850.0): Upper - Lower = 0.1611 - (-0.3888) = 0.5499
+
+From the calculations above, the interval with the widest confidence bound is (350.0, 416.5) with a difference of 1.4291.
+SOLUTION:  (350.0, 416.5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval with the maximum difference.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Identify the Maximum Width**: Compare the widths of all intervals and identify the interval with the maximum width.
+
+Let's perform these calculations:
+
+For example, for the interval `(2.0, 4.5)`, the lower bound is `-30426.4` and the upper bound is `19623.3`. The width of the confidence interval is:
+\[ \text{Width} = 19623.3 - (-30426.4) = 19623.3 + 30426.4 = 50049.7 \]
+
+We need to perform similar calculations for all intervals and find the maximum width. Here are a few more calculations:
+
+- For `(4.5, 6.5)`: Width = `-5814.9 - (-41560.8) = 35745.9`
+- For `(6.5, 8.5)`: Width = `-23981.3 - (-83483.7) = 59502.4`
+- For `(8.5, 9.5)`: Width = `39403.2 - (-68637.5) = 108040.7`
+- ...
+
+From the few calculations above, we can see that the interval `(8.5, 9.5)` has a very wide confidence interval with a width of `108040.7`. We would continue this process for all intervals, but based on the calculations shown, it appears that the interval `(8.5, 9.5)` might have the widest confidence interval.
+
+Thus, the x-axis interval with the widest confidence bound is `(8.5, 9.5)`.
+SOLUTION:  (8.5, 9.5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval with the maximum width.
+
+Let's perform these calculations:
+
+- For interval (0.078, 0.1265): Upper - Lower = -0.238 - (-0.817) = 0.579
+- For interval (0.1265, 0.128): Upper - Lower = 0.381 - (-0.817) = 1.198
+- For interval (0.128, 0.2185): Upper - Lower = -0.067 - (-0.618) = 0.551
+- For interval (0.2185, 0.3375): Upper - Lower = 0.197 - (-0.533) = 0.730
+- For interval (0.3375, 0.4215): Upper - Lower = 0.113 - (-0.266) = 0.379
+- For interval (0.4215, 0.4955): Upper - Lower = 0.135 - (-0.104) = 0.239
+- For interval (0.4955, 0.5874999999999999): Upper - Lower = 0.316 - (-0.054) = 0.370
+- For interval (0.5874999999999999, 0.7215): Upper - Lower = 0.308 - 0.138 = 0.170
+- For interval (0.7215, 0.889): Upper - Lower = 0.445 - 0.186 = 0.259
+- For interval (0.889, 1.0865): Upper - Lower = 0.552 - 0.263 = 0.289
+- For interval (1.0865, 1.178): Upper - Lower = 0.646 - 0.350 = 0.296
+- For interval (1.178, 1.275): Upper - Lower = 1.912 - 0.124 = 1.788
+- For interval (1.275, 1.3925): Upper - Lower = 2.433 - 0.133 = 2.300
+- For interval (1.3925, 1.4175): Upper - Lower = 2.398 - (-0.063) = 2.461
+- For interval (1.4175, 1.451): Upper - Lower = 1.293 - (-1.163) = 2.456
+- For interval (1.451, 1.837): Upper - Lower = 1.080 - (-1.466) = 2.546
+- For interval (1.837, 2.137): Upper - Lower = 0.928 - (-1.112) = 2.040
+
+From these calculations, the interval (1.451, 1.837) has the widest confidence bound with a width of 2.546. This is the interval with the widest confidence bound in the graph.
+SOLUTION:  (1.451, 1.837)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+The confidence bound width for each interval can be calculated as:
+\[ \text{Width} = \text{Upper Bound} - \text{Lower Bound} \]
+
+Let's calculate the width for each interval using the provided data:
+
+1. For the interval (32.54, 32.565):
+   - Lower Bound = 964.8
+   - Upper Bound = 45504.8
+   - Width = 45504.8 - 964.8 = 44540.0
+
+2. For the interval (32.565, 32.685):
+   - Lower Bound = -13385.7
+   - Upper Bound = 7020.9
+   - Width = 7020.9 - (-13385.7) = 20406.6
+
+... (similar calculations for other intervals) ...
+
+To find the interval with the widest confidence bound, we need to compare the calculated widths for all intervals. The interval with the highest width value will have the widest confidence bound.
+
+After calculating the widths for all intervals, we find that the interval with the widest confidence bound is (32.54, 32.565) with a width of 44540.0.
+SOLUTION:  (34.715, 35.325)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the calculations for each interval:
+
+1. Interval (0.0, 0.5): Upper - Lower = -0.02372 - (-0.02758) = 0.00386
+2. Interval (0.5, 1.5): Upper - Lower = -0.01994 - (-0.02272) = 0.00278
+3. Interval (1.5, 2.5): Upper - Lower = -0.01596 - (-0.01769) = 0.00173
+4. Interval (2.5, 3.5): Upper - Lower = -0.00951 - (-0.01036) = 0.00085
+5. Interval (3.5, 4.5): Upper - Lower = -0.00432 - (-0.00514) = 0.00082
+6. Interval (4.5, 5.5): Upper - Lower = 0.00037 - (-0.0004) = 0.00077
+7. Interval (5.5, 6.5): Upper - Lower = 0.00568 - 0.00453 = 0.00115
+8. Interval (6.5, 7.5): Upper - Lower = 0.01199 - 0.01098 = 0.00101
+9. Interval (7.5, 8.5): Upper - Lower = 0.01706 - 0.01535 = 0.00171
+10. Interval (8.5, 9.5): Upper - Lower = 0.02562 - 0.0239 = 0.00172
+11. Interval (9.5, 11.5): Upper - Lower = 0.03152 - 0.02772 = 0.0038
+12. Interval (11.5, 12.5): Upper - Lower = 0.03732 - 0.03206 = 0.00526
+13. Interval (12.5, 13.5): Upper - Lower = 0.05424 - 0.04307 = 0.01117
+14. Interval (13.5, 16.0): Upper - Lower = 0.06343 - 0.0546 = 0.00883
+
+From these calculations, the interval (12.5, 13.5) has the widest confidence bound with a difference of 0.01117. Therefore, the x-axis interval with the widest confidence bound is (12.5, 13.5).
+SOLUTION:  (12.5, 13.5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Width of the Confidence Interval**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Widths**: Identify the interval with the maximum width.
+
+Let's perform these calculations:
+
+- For interval (1.0, 1.5): Upper - Lower = 1.142 - (-2.672) = 3.814
+- For interval (1.5, 2.5): Upper - Lower = 0.023 - (-0.773) = 0.796
+- For interval (2.5, 4.5): Upper - Lower = -1.109 - (-2.709) = 1.600
+- For interval (4.5, 6.5): Upper - Lower = -0.668 - (-1.566) = 0.898
+- For interval (6.5, 7.5): Upper - Lower = 0.005 - (-1.241) = 1.246
+- For interval (7.5, 14.5): Upper - Lower = -0.546 - (-1.098) = 0.552
+- For interval (14.5, 19.5): Upper - Lower = -0.729 - (-1.535) = 0.806
+- For interval (19.5, 29.5): Upper - Lower = -0.172 - (-1.357) = 1.185
+- For interval (29.5, 33.5): Upper - Lower = 0.047 - (-1.248) = 1.295
+- For interval (33.5, 34.5): Upper - Lower = -0.027 - (-1.815) = 1.788
+- For interval (34.5, 39.5): Upper - Lower = -0.087 - (-0.223) = 0.136
+- For interval (39.5, 41.5): Upper - Lower = 0.19 - (-0.129) = 0.319
+- For interval (41.5, 50.5): Upper - Lower = 0.571 - 0.212 = 0.359
+- For interval (50.5, 51.5): Upper - Lower = 1.13 - (-0.867) = 1.997
+- For interval (51.5, 55.5): Upper - Lower = 0.557 - 0.357 = 0.200
+- For interval (55.5, 59.5): Upper - Lower = 1.048 - 0.304 = 0.744
+- For interval (59.5, 63.5): Upper - Lower = 0.818 - 0.014 = 0.804
+- For interval (63.5, 64.5): Upper - Lower = 1.896 - 0.009 = 1.887
+- For interval (64.5, 65.5): Upper - Lower = 0.653 - 0.379 = 0.274
+- For interval (65.5, 71.0): Upper - Lower = 0.254 - (-0.113) = 0.367
+- For interval (71.0, 75.5): Upper - Lower = 0.806 - 0.054 = 0.752
+- For interval (75.5, 77.5): Upper - Lower = 1.04 - (-0.57) = 1.61
+- For interval (77.5, 79.0): Upper - Lower = 1.25 - 0.234 = 1.016
+- For interval (79.0, 83.0): Upper - Lower = 1.166 - 0.788 = 0.378
+- For interval (83.0, 85.5): Upper - Lower = 1.852 - 0.721 = 1.131
+- For interval (85.5, 90.5): Upper - Lower = 0.673 - (-0.289) = 0.962
+- For interval (90.5, 97.5): Upper - Lower = 0.361 - (-0.504) = 0.865
+- For interval (97.5, 98.5): Upper - Lower = 0.765 - (-0.527) = 1.292
+- For interval (98.5, 99.0): Upper - Lower = 0.271 - (-0.548) = 0.819
+
+From these calculations, the interval (1.0, 1.5) has the widest confidence bound with a width of 3.814.
+SOLUTION:  (1.0, 1.5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the interval with the widest confidence bound, we need to calculate the difference between the upper and lower bounds for each interval and identify the interval where this difference is the largest.
+
+Here are the steps to find the interval with the widest confidence bound:
+
+1. **Extract the Lower and Upper Bounds**: For each interval, retrieve the lower and upper bounds from the provided data.
+
+2. **Calculate the Difference**: For each interval, subtract the lower bound from the upper bound to get the width of the confidence interval.
+
+3. **Compare the Differences**: Identify the interval with the largest difference, which indicates the widest confidence bound.
+
+Let's perform these calculations:
+
+- For interval "(0.0, 57.0)": Difference = -0.23 - (-0.27) = 0.04
+- For interval "(57.0, 3048.0)": Difference = -3.24 - (-6.42) = 3.18
+- For interval "(3048.0, 3120.0)": Difference = 3.0 - 2.14 = 0.86
+- For interval "(3120.0, 4243.5)": Difference = -3.54 - (-5.31) = 1.77
+- For interval "(4243.5, 4401.0)": Difference = 1.81 - 1.09 = 0.72
+- For interval "(4401.0, 4668.5)": Difference = -1.0 - (-2.65) = 1.65
+- For interval "(4668.5, 4826.0)": Difference = 4.71 - 2.87 = 1.84
+- For interval "(4826.0, 4898.0)": Difference = 1.38 - (-0.25) = 1.63
+- For interval "(4898.0, 4973.5)": Difference = 2.95 - 1.55 = 1.40
+- For interval "(4973.5, 5119.0)": Difference = -0.92 - (-6.13) = 5.21
+- For interval "(5119.0, 5316.5)": Difference = 5.0 - 3.51 = 1.49
+- For interval "(5316.5, 5505.5)": Difference = 1.16 - (-0.29) = 1.45
+- For interval "(5505.5, 6457.5)": Difference = 3.0 - 1.3 = 1.70
+- For interval "(6457.5, 6505.5)": Difference = 0.62 - (-0.94) = 1.56
+- For interval "(6505.5, 6745.0)": Difference = 1.44 - 0.19 = 1.25
+- For interval "(6745.0, 7073.5)": Difference = -0.34 - (-2.33) = 1.99
+- For interval "(7073.5, 7436.5)": Difference = 6.58 - 4.95 = 1.63
+- For interval "(7436.5, 7565.5)": Difference = 3.62 - 0.42 = 3.20
+- For interval "(7565.5, 7792.0)": Difference = 7.72 - 5.41 = 2.31
+- For interval "(7792.0, 7937.0)": Difference = 7.16 - 2.59 = 4.57
+- For interval "(7937.0, 8296.0)": Difference = 6.36 - 1.32 = 5.04
+- For interval "(8296.0, 10543.0)": Difference = 8.31 - 6.05 = 2.26
+- For interval "(10543.0, 10585.5)": Difference = -0.22 - (-2.73) = 2.51
+- For interval "(10585.5, 30961.5)": Difference = 9.71 - 7.51 = 2.20
+- For interval "(30961.5, 70654.5)": Difference = 2.23 - (-3.56) = 5.79
+- For interval "(70654.5, 99999.0)": Difference = 11.26 - 8.19 = 3.07
+
+From the calculations, the interval "(30961.5, 70654.5)" has the widest confidence bound with a difference of 5.79.
+SOLUTION:  (30961.5, 70654.5)
+--------------------------------------------------------------------------------
diff --git a/benchmarks/results/function-recognition-gemini-1.5.txt b/benchmarks/results/function-recognition-gemini-1.5.txt
new file mode 100644
index 0000000..ae480cb
--- /dev/null
+++ b/benchmarks/results/function-recognition-gemini-1.5.txt
@@ -0,0 +1,1260 @@
+Graph x
+
+LLM RESPONSE:  Here's how to determine the function depicted in the graph:
+
+**1. Analyze the Graph's Shape:**
+
+* **Symmetry:** The graph appears to be roughly symmetrical about the origin (0,0). This suggests it could be an odd function. Odd functions satisfy the condition f(-x) = -f(x).
+* **End Behavior:** As x approaches negative infinity, y approaches positive infinity. As x approaches positive infinity, y approaches negative infinity. This behavior eliminates options c) and d) as they have opposite end behaviors. 
+* **General Trend:** The graph has a single inflection point around the origin and resembles a rotated "S" shape.
+
+**2. Evaluate the Functions:**
+
+* **a) f(x) = x:** This is a straight line and doesn't match the graph's shape.
+* **b) f(x) = -sinh(x):** The hyperbolic sine function grows exponentially, which is not consistent with the graph's behavior around x = 0.
+* **c) f(x) = -3*x^3:**  While an odd function, its shape is more curved than the graph and doesn't have the characteristic "S" shape.
+* **d) f(x) = -x^5:**  Similar to option c), it's an odd function but lacks the "S" shape.
+* **e) f(x) = -sin(x):** This is an odd function with a sinusoidal shape.  While the standard sine function oscillates between -1 and 1, this one appears stretched and shifted, which can be achieved with transformations.
+
+**3. Conclusion:**
+
+The graph most closely resembles a transformed version of the sine function. Therefore, the function depicted in the graph is most likely **e) f(x) = -sin(x)**. 
+
+SOLUTION: x
+--------------------------------------------------------------------------------
+Graph -2*x+5
+
+LLM RESPONSE:  Here's how to determine the function from the graph:
+
+**1. Analyze the Graph's Shape**
+
+* The graph is generally decreasing as x increases. This eliminates options (c) and (e), which represent increasing functions.
+* The graph appears to be curved rather than a straight line. This eliminates option (b), which is a linear function.
+
+**2. Consider the Function Behavior**
+
+* **Option (a) f(x) = -x^5:**  A negative odd power function would have a similar downward curve. This is a strong possibility.
+* **Option (d) f(x) = sign(x+3):** The sign function produces only three possible outputs (-1, 0, 1) and would result in a horizontal line with a single step. This doesn't match the graph.
+
+**3. Focus on Key Points**
+
+* Observe that the graph crosses the y-axis (x=0) at a positive value.  The function f(x) = -x^5 would equal 0 at x=0. This eliminates option (a).
+
+**Conclusion**
+
+None of the provided functions perfectly match the graph. While option (a) had the closest shape, it doesn't align with the y-intercept. 
+
+**It's likely that the actual function represented by the graph is a variation or combination of the provided options.**  For example, a function like  f(x) = -0.2x^5 + 5  could potentially produce a similar shape with a positive y-intercept. 
+
+SOLUTION: -2*x+5
+--------------------------------------------------------------------------------
+Graph x^2
+
+LLM RESPONSE:  Here's how to determine the function:
+
+1. **Analyze the graph's shape:** The graph is symmetric, with the lowest point around x = 0. It increases as we move away from x = 0 in both directions.
+
+2. **Eliminate unlikely options:**
+    * **a) f(x) = |x|:** This function has a sharp point at x=0, while our graph is smoother.
+    * **b) f(x) = -|-x|:** This function is always negative and has a peak at x=0, unlike our graph. 
+    * **d) f(x) = -sin(x):** This is a periodic wave, and our graph doesn't show repeating patterns.
+    * **e) f(x) = x^5:** This function doesn't have the same symmetry as our graph. It increases rapidly for positive x and decreases rapidly for negative x.
+
+3. **Focus on the remaining option:**
+    * **c) f(x) = x^2:** This function is symmetric around x=0, always positive, and its slope increases as we move away from x=0. This aligns perfectly with our graph.
+
+**Therefore, the function depicted in the graph is most likely (c) f(x) = x^2.** 
+
+SOLUTION: x^2
+--------------------------------------------------------------------------------
+Graph -2*x^2
+
+LLM RESPONSE:  Let's analyze the graph and each function to find the match:
+
+**1. Analyzing the Graph:**
+
+* **Overall Shape:** The graph has a downward-facing parabolic shape. 
+* **Symmetry:** It appears somewhat symmetrical around x = 2.
+* **Negative Values:** The graph primarily exists in the negative y-value region.
+
+**2. Evaluating the Functions:**
+
+* **a) f(x) = 1/2*cos(x-2):** This is a cosine function, which has a repeating wave pattern. The graph doesn't match this.
+* **b) f(x) = -2*x^2:** This is a downward-facing parabola, which seems promising. It's symmetrical around x = 0. We'll come back to this.
+* **c) f(x) = (x-2)^2:** This is an upward-facing parabola. The graph doesn't match this.
+* **d) f(x) = sqrt(x+10):** This is a square root function, which has a continuously increasing curve. The graph doesn't match this.
+* **e) f(x) = -sign(x):** This function returns -1 for positive x, 1 for negative x, and 0 for x=0. This is a horizontal line with a jump at x=0. The graph doesn't match this.
+
+**3. Focusing on the Most Likely Candidate:**
+
+Function **b) f(x) = -2*x^2** is the closest match so far, but its symmetry is off.  We need to shift it to the right by 2 units to match the graph's symmetry.
+
+**4. Adjusting for Symmetry:**
+
+To shift a function horizontally, we modify the x-value inside the function. To shift 2 units to the right, we replace 'x' with '(x-2)':
+
+ **f(x) = -2*(x-2)^2**
+
+**Conclusion:**
+
+The graph most closely depicts the function **f(x) = -2*(x-2)^2**. 
+
+SOLUTION: -2*x^2
+--------------------------------------------------------------------------------
+Graph (x-2)^2
+
+LLM RESPONSE:  Let's analyze the graph and the function options step-by-step:
+
+**1. Observing the Graph's Shape:**
+
+* The graph is symmetrical, with a minimum point around x = 2. 
+* It has increasing values as we move away from the minimum point in both directions.
+
+**2. Eliminating Unlikely Options:**
+
+* **b) f(x) = sign(x):** This function only outputs -1, 0, or 1, and wouldn't create the smooth curve seen in the graph.
+* **d) f(x) = x^4:** This function has a minimum at x=0 and increases much more rapidly than the graph.
+* **e) f(x) = x:** This is a straight line and doesn't match the graph's curvature.
+
+**3. Analyzing the Remaining Options:**
+
+* **a) f(x) = 1/2*cos(x-2):** 
+    * The cosine function is symmetrical and oscillates. 
+    * The (x-2) shifts the graph 2 units to the right, aligning with the graph's minimum.
+    * The 1/2 scales the amplitude, making the oscillations smaller.
+* **c) f(x) = (x-2)^2:**
+    * This is a parabola with a minimum at x=2, matching the graph's shape.
+
+**4. Determining the Best Fit:**
+
+While both options (a) and (c) have characteristics that resemble the graph, the graph doesn't show the oscillating behavior of a cosine function. 
+
+**Conclusion:**
+
+The graph most closely depicts the function **c) f(x) = (x-2)^2**. 
+
+SOLUTION: (x-2)^2
+--------------------------------------------------------------------------------
+Graph (x-1)*(x+1)
+
+LLM RESPONSE:  Here's how to determine the function depicted in the graph:
+
+**1. Analyze the Graph's Shape:**
+
+* The graph is generally increasing from left to right.
+* It has a distinct "jump" around x = 0.
+
+**2. Eliminate Unlikely Candidates:**
+
+* **a) f(x) = (x-1)*(x+1):** This is a parabola opening upwards. The graph doesn't resemble a parabola. **Eliminate.**
+* **c) f(x) = -3*x^3:** This is a cubic function with a single inflection point. The graph doesn't have this shape. **Eliminate.**
+* **e) f(x) = -sqrt(x+10):** This is a square root function reflected over the x-axis. The graph doesn't have this smooth, curved shape. **Eliminate.**
+
+**3. Focus on Remaining Options:**
+
+* **b) f(x) = sign(x-1):** The sign function returns -1 for negative values, 0 for 0, and 1 for positive values. This function would jump from -1 to 1 at x=1. 
+* **d) f(x) = sign(x):** This is similar to option b, but the jump occurs at x=0.
+
+**4. Determine the Jump Location:**
+
+* The graph clearly shows a jump around x = 0, not x = 1.
+
+**Conclusion:**
+
+The graph most closely depicts the function **d) f(x) = sign(x)**. 
+
+SOLUTION: (x-1)*(x+1)
+--------------------------------------------------------------------------------
+Graph x^2+3*x-1
+
+LLM RESPONSE:  Here's how to determine the function depicted in the graph:
+
+**1. Analyze the Graph's Shape**
+
+* The graph is generally increasing from left to right.
+* It has a steeper increase towards the right end, suggesting an exponential or higher-order polynomial function.
+* It doesn't show symmetry like a parabola (x^2), ruling out options (c) and (d).
+
+**2. Consider the Function Behavior**
+
+* **Option (a) f(x) = x^3:**  A cubic function would have both positive and negative values, but our graph seems to have primarily positive y-values.
+* **Option (b) f(x) = 2^x:** An exponential function with a base greater than 1 will always be positive and exhibit increasing growth, matching our graph's behavior.
+* **Option (e) f(x) = -3*x^3:** A negative cubic function would decrease as x increases, contradicting our graph.
+
+**3. Test a Point**
+
+Let's test a point from the graph to confirm our suspicion about option (b).  Take the interval "(2.0, 2.18)": 9.1.  This means an x-value within that range should produce a y-value around 9.1. Let's try x = 2.1:
+
+* f(2.1) = 2^(2.1) ≈ 4.28 
+
+This value is significantly lower than 9.1. This discrepancy suggests that while the graph's shape resembles an exponential function, a simple 2^x doesn't fit the data precisely.
+
+**Conclusion**
+
+None of the provided functions perfectly match the graph. While the shape most closely resembles an exponential function like **f(x) = 2^x**, the actual values don't align. It's likely that the graph represents a more complex function or a transformation of an exponential function (e.g., with a vertical stretch or shift). 
+
+SOLUTION: x^2+3*x-1
+--------------------------------------------------------------------------------
+Graph x^3
+
+LLM RESPONSE:  Here's how to determine the function from the graph:
+
+1. **Analyze the graph's overall shape:**
+    - The graph is not symmetrical about the y-axis, ruling out even functions like x^2 or cos(x).
+    - The graph has a steep increase as x becomes positive, suggesting exponential growth or a positive power function.
+    - The graph has a steep decrease as x becomes negative, suggesting a negative power function or a function with a similar behavior in the negative domain.
+
+2. **Eliminate unlikely candidates:**
+    - **a) f(x) = -x^5:**  While this has a similar shape, the negative sign would flip the graph, making it increase for negative x and decrease for positive x. This doesn't match our graph.
+    - **b) f(x) = -sinh(x):**  The hyperbolic sine function is symmetrical about the origin. Our graph isn't, so this is incorrect.
+    - **c) f(x) = x:** This is a straight line, not matching our curved graph.
+
+3. **Compare remaining options:**
+    - **d) f(x) = x^3:** This cubic function has the correct shape: increasing rapidly for positive x and decreasing rapidly for negative x.
+    - **e) f(x) = exp(x):** The exponential function also increases rapidly for positive x. However, it approaches 0 for negative x, not decreasing rapidly like our graph.
+
+**Conclusion:**
+
+Based on the analysis, the function depicted in the graph is most likely **(d) f(x) = x^3**. 
+
+SOLUTION: x^3
+--------------------------------------------------------------------------------
+Graph -3*x^3
+
+LLM RESPONSE:  Let's analyze the graph step-by-step to determine the correct function:
+
+1. **Observe the overall shape:** The graph is not symmetrical around the y-axis, ruling out options (c) and (d). It also doesn't resemble a straight line, eliminating option (e).
+
+2. **Analyze the behavior for positive and negative x:**
+    - For negative x values, the function produces large positive outputs.
+    - For positive x values, the function produces large negative outputs. 
+
+3. **Consider the growth rate:** The magnitude of the output increases rapidly as the magnitude of x increases. This rapid growth suggests a higher-order polynomial function.
+
+4. **Compare with remaining options:**
+    - Option (a), f(x) = -3*x^3, exhibits the observed behavior: large positive outputs for negative x, large negative outputs for positive x, and rapid growth due to the cubic term.
+    - Option (b), f(x) = -sign(-x), only produces outputs of -1 or 1, not matching the graph.
+
+**Therefore, the function depicted in the graph is most likely (a) f(x) = -3*x^3.** 
+
+SOLUTION: -3*x^3
+--------------------------------------------------------------------------------
+Graph x^4
+
+LLM RESPONSE:  Here's how to determine the function:
+
+1. **Analyze the graph's shape:** The graph has low values for negative x, increases rapidly around x=0, and then continues to increase at a slower rate for positive x. This shape suggests an exponential function.
+
+2. **Eliminate unlikely options:**
+    * **a) f(x) = x^4:**  This function would be symmetrical around the y-axis, and our graph is not.
+    * **b) f(x) = cos(x):** This function is periodic and oscillates between -1 and 1. Our graph is always increasing.
+    * **d) f(x) = x:** This is a straight line, not the curved shape we see.
+    * **e) f(x) = x^3:** While this function increases, it doesn't have the same rapid growth around x=0 that the graph shows.
+
+3. **Conclusion:** The graph most closely resembles the behavior of an exponential function. 
+
+**Therefore, the function depicted in the graph is most likely (c) f(x) = exp(x).** 
+
+SOLUTION: x^4
+--------------------------------------------------------------------------------
+Graph -(x + 4)^4
+
+LLM RESPONSE:  Here's how to determine the function depicted in the graph:
+
+**1. Analyze the Graph's Shape:**
+
+* The graph is always **decreasing** as x increases.
+* The rate of decrease seems to be **slowing down** as x increases. 
+* The graph appears to be **asymptotically approaching negative infinity** as x approaches positive infinity.
+
+**2. Eliminate Options Based on Shape:**
+
+* **a) f(x) = sin(x) and b) f(x) = -sin(x):**  Sine functions oscillate between positive and negative values. This graph does not oscillate. **Eliminate a) and b).**
+* **d) f(x) = -|x|:** The absolute value function has a sharp point at its minimum and decreases at a constant rate on either side. This graph is curved. **Eliminate d).**
+
+**3. Compare Remaining Options:**
+
+* **c) f(x) = -exp(x):** The negative exponential function decreases rapidly at first, then slows down as x increases, approaching zero but never reaching it.
+* **e) f(x) = -(x + 4)^4:** This function is a fourth-degree polynomial with a negative leading coefficient. It will decrease as x approaches both positive and negative infinity.
+
+**4. Determine the Best Fit:**
+
+The graph's shape more closely resembles the behavior of a **negative exponential function**. The slowing rate of decrease and the asymptotic behavior point towards this conclusion.
+
+**Therefore, the function depicted in the graph is most likely c) f(x) = -exp(x).** 
+
+SOLUTION: -(x + 4)^4
+--------------------------------------------------------------------------------
+Graph x^5
+
+LLM RESPONSE:  Let's analyze the graph and compare it to the function options:
+
+**1. Observing the Graph's Behavior:**
+
+* **Symmetry:** The graph doesn't seem symmetrical around the y-axis (like even functions) or the origin (like odd functions). This might rule out some options.
+* **Trends:** The graph has a steep negative slope for negative x values, becomes close to zero around x=0, and then has a steep positive slope for positive x values.
+* **Sharp Changes:** There's no abrupt jump or discontinuity in the graph.
+
+**2. Eliminating Options:**
+
+* **a) f(x) = sqrt(x+10):** This function is only defined for x >= -10 and would have a gradual increase, not a steep change like the graph shows. **Ruled out.**
+* **b) f(x) = sqrt(x ** 2 + 3*x +5):** This function would have a more symmetrical shape due to the x**2 term. **Ruled out.**
+* **c) f(x) = -sign(-x):** This function would be a step function with a value of -1 for x < 0 and 1 for x > 0. It wouldn't have the steep slopes seen in the graph. **Ruled out.**
+* **e) f(x) = x:** This is a simple linear function. The graph shows a much steeper increase for both positive and negative x values. **Ruled out.**
+
+**3. The Best Fit:**
+
+* **d) f(x) = x^5:** This function aligns well with our observations:
+    * **Steep Slopes:** x^5 creates very steep slopes as x moves away from zero.
+    * **Near Zero:** The function is close to zero for small values of x.
+    * **No Discontinuities:** The function is smooth and continuous.
+
+**Conclusion:**
+
+The graph most likely depicts the function **d) f(x) = x^5**. 
+
+SOLUTION: x^5
+--------------------------------------------------------------------------------
+Graph -x^5
+
+Gemini: Invalid response with parts [].
+LLM RESPONSE:  
+SOLUTION: -x^5
+--------------------------------------------------------------------------------
+Graph sign(x)
+
+LLM RESPONSE:  Let's analyze the graph and each function to determine the best fit.
+
+**Graph Analysis:**
+
+* **Symmetry:** The graph appears mostly symmetrical around x=0, with slight variations.
+* **Sign Change:** The function changes sign around x=0, going from negative to positive.
+* **Magnitude:** The function's output is close to -1 for negative x values near 0 and close to 1 for positive x values near 0. 
+
+**Function Analysis:**
+
+a) **f(x) = sign(x):** This function outputs -1 for x < 0, 0 for x = 0, and 1 for x > 0. It matches the sign change and approximate magnitudes near 0 but is a step function, unlike our graph.
+
+b) **f(x) = x^2 + 3x - 1:** This quadratic function doesn't exhibit symmetry around x=0 and wouldn't have the near-constant outputs observed in the graph for intervals near 0.
+
+c) **f(x) = x:** This linear function matches the sign change but doesn't have the near-constant outputs observed in the graph for intervals near 0.
+
+d) **f(x) = |2x + 4|:** This absolute value function is always non-negative, contradicting the negative outputs in the graph for x < 0.
+
+e) **f(x) = x^2:** This quadratic function is always non-negative, contradicting the negative outputs in the graph for x < 0.
+
+**Conclusion:**
+
+None of the functions perfectly match the graph. However, **f(x) = sign(x)** is the closest approximation. The graph likely represents a smoothed or slightly modified version of the sign function. 
+
+SOLUTION: sign(x)
+--------------------------------------------------------------------------------
+Graph -sign(x)
+
+LLM RESPONSE:  Let's analyze the graph step-by-step to determine the function:
+
+1. **Sign Changes:** The graph has positive y-values for negative x-values and negative y-values for positive x-values. This eliminates options a), d), and e) as they don't exhibit this sign change behavior.
+
+2. **Constant Values within Intervals:** The graph shows constant y-values within each specified interval of x. This behavior aligns with the sign function (sign(x)), which outputs a constant value (-1, 0, or 1) depending on the sign of x.
+
+3. **Matching Values:**  
+    - For negative x values, the graph shows positive y-values close to 1. This matches the output of `sign(x)` for negative x (which is -1).
+    - For positive x values, the graph shows negative y-values close to -1. This matches the output of `-sign(x)` for positive x (which is -1).
+
+**Therefore, the graph most closely depicts the function c) f(x) = -sign(x).** 
+
+SOLUTION: -sign(x)
+--------------------------------------------------------------------------------
+Graph -sign(-x)
+
+LLM RESPONSE:  Let's analyze the graph and the function options step-by-step:
+
+1. **Analyze the graph:**
+    * The graph has negative y-values for negative x-values and positive y-values for positive x-values. 
+    * The graph seems to have a jump discontinuity around x=0.
+    * For x values further away from zero, the graph seems to approach constant values (around -1 for negative x and around 1 for positive x).
+
+2. **Eliminate unlikely functions:**
+    * **a) f(x) = x^3:** This function is always increasing and doesn't have a jump discontinuity. **Eliminate.**
+    * **b) f(x) = sin(x+2)+2:** This function is periodic and oscillates. The graph doesn't show oscillations. **Eliminate.**
+    * **d) f(x) = sin(x):** Similar to (b), this function is periodic and oscillates. **Eliminate.**
+    * **e) f(x) = -cosh(x):** This function is always negative and symmetrical around the y-axis. The graph has both positive and negative values. **Eliminate.**
+
+3. **Focus on the remaining function:**
+    * **c) f(x) = -sign(-x):** The sign function returns -1 for negative values, 0 for 0, and 1 for positive values.  Let's break down this function:
+        * `-x`: This inverts the sign of x.
+        * `sign(-x)`: This returns -1 for positive x, 0 for x=0, and 1 for negative x.
+        * `-sign(-x)`: This finally returns 1 for positive x, 0 for x=0, and -1 for negative x.
+
+4. **Conclusion:**
+    The function **c) f(x) = -sign(-x)** accurately represents the graph. It has a jump discontinuity at x=0 and approaches -1 for negative x and 1 for positive x, matching the graph's behavior. 
+
+SOLUTION: -sign(-x)
+--------------------------------------------------------------------------------
+Graph sign(x+3)
+
+LLM RESPONSE:  Here's how to determine the function represented by the graph:
+
+**1. Analyze the Graph's Characteristics:**
+
+* **Piecewise Constant:** The graph shows distinct horizontal segments, meaning the function's output remains constant within certain x-value intervals.
+* **Sign Change:** The function takes on both positive and negative values.
+* **Discontinuity:** There's likely a jump discontinuity where the function changes signs.
+
+**2. Eliminate Unlikely Candidates:**
+
+* **a) f(x) = 2^(x-5):** Exponential functions are continuous and always increasing or decreasing. This doesn't match the piecewise constant nature of the graph.
+* **c) f(x) = x:** A linear function would be a straight line, not horizontal segments.
+* **d) f(x) = |x|:** The absolute value function has a V-shape, not horizontal segments.
+* **e) f(x) = -(x + 4)^4:** This function is a transformed even-degree polynomial. It would have a smooth, continuous curve, not the abrupt changes seen in the graph.
+
+**3. Focus on the Most Likely Candidate:**
+
+* **b) f(x) = sign(x+3):** The sign function (also called signum function) returns:
+    * -1 if the input is negative
+    * 0 if the input is zero
+    * +1 if the input is positive
+
+This behavior aligns perfectly with the graph's characteristics: piecewise constant, a sign change, and a likely jump discontinuity at x = -3.
+
+**Conclusion:**
+
+The graph most closely depicts the function **b) f(x) = sign(x+3)**. 
+
+SOLUTION: sign(x+3)
+--------------------------------------------------------------------------------
+Graph sign(x-1)
+
+LLM RESPONSE:  Let's analyze the graph and each function to find the best match:
+
+**1. Analyzing the Graph:**
+
+* **Negative to Positive:** The graph transitions from negative y-values to positive y-values around x = 1.
+* **Steep Change:** The change around x = 1 appears quite steep.
+* **Relatively Flat:**  The graph seems relatively flat for x-values significantly less than 1 and significantly greater than 1.
+
+**2. Evaluating the Functions:**
+
+* **a) f(x) = -|-x|:** This function is always negative or zero, which doesn't match our graph.
+* **b) f(x) = sign(x-1):** This function has a sharp jump from -1 to 1 at x = 1, which aligns well with the graph's behavior.
+* **c) f(x) = exp(-x):** This function is always positive and decays exponentially. It doesn't match the graph.
+* **d) f(x) = -(x + 4)^4:** This function is always negative and doesn't have the sharp transition seen in the graph.
+* **e) f(x) = 2^(x-5):** This function is always positive and increases exponentially. It doesn't match the graph.
+
+**Conclusion:**
+
+Based on the analysis, the function that best matches the graph is **b) f(x) = sign(x-1)**. 
+
+SOLUTION: sign(x-1)
+--------------------------------------------------------------------------------
+Graph |x|
+
+LLM RESPONSE:  Let's analyze the graph and the function options step-by-step:
+
+**1. Observing the Graph's Behavior:**
+
+* **Overall Trend:** The graph generally increases as x increases. 
+* **Curvature:** The rate of increase seems to slow down as x increases.
+* **Negative x-values:** The graph exists for negative x-values.
+
+**2. Eliminating Function Options based on Observations:**
+
+* **d) f(x) = |x|:** This function has a sharp turn at x=0 and is symmetrical. Our graph doesn't have a sharp turn. **Eliminated.**
+* **e) f(x) = x:** This is a straight line and doesn't match the curvature of our graph. **Eliminated.**
+* **a) f(x) = 1/2*cos(x-2):** Cosine functions are periodic (repeating). Our graph doesn't show a repeating pattern. **Eliminated.**
+
+**3. Analyzing Remaining Options:**
+
+* **b) f(x) = log(x+10):** Logarithmic functions are defined only for positive values of the argument (inside the logarithm). Our graph exists for negative x-values.  **Eliminated.**
+* **c) f(x) = -sqrt(x+10):**  Square root functions with a negative sign in front will start at a certain x-value (where the term inside the square root becomes zero) and then decrease as x increases. This matches our graph's overall trend and curvature.
+
+**4. Verifying the Fit:**
+
+The function f(x) = -sqrt(x+10) is defined for x >= -10, which aligns with the graph's domain. The general shape and values in the graph also seem consistent with a negative square root function.
+
+**Conclusion:**
+
+The graph most likely depicts the function **c) f(x) = -sqrt(x+10)**. 
+
+SOLUTION: |x|
+--------------------------------------------------------------------------------
+Graph -|x|
+
+LLM RESPONSE:  Let's analyze the graph step-by-step to determine the correct function:
+
+1. **Symmetry:** The graph appears to be symmetric around the y-axis. This means the function is likely an even function, where f(-x) = f(x). This eliminates options a) and e).
+
+2. **Behavior around x=0:** The graph has a sharp point (non-differentiable) at x=0. This suggests an absolute value function. This eliminates option b).
+
+3. **Positive vs. Negative:** The graph has negative y-values for all x-values. This eliminates option d).
+
+4. **Matching the shape:** Option c), f(x) = -|x|, represents a downward-pointing V shape, which closely matches the provided graph.
+
+**Therefore, the function depicted in the graph is most likely c) f(x) = -|x|.** 
+
+SOLUTION: -|x|
+--------------------------------------------------------------------------------
+Graph -|-x|
+
+LLM RESPONSE:  Let's analyze the graph and each function option to determine the best fit.
+
+**Graph Observations:**
+
+* **Symmetry:** The graph appears roughly symmetrical around the point (0,0). 
+* **Negative Slope (mostly):**  The graph generally has a negative slope for x < 0 and a positive slope for x > 0.
+* **Constant Values:** The function takes on constant values within each specified interval.
+
+Now let's consider each function:
+
+* **a) f(x) = -sign(-x):** This function returns -1 for x > 0 and 1 for x < 0. It doesn't match the graph's shape.
+* **b) f(x) = sign(x-1):** This function returns -1 for x < 1 and 1 for x > 1.  It doesn't have the negative slope for x < 0.
+* **c) f(x) = -|-x|:** This function represents a negative absolute value, creating a V-shape with the vertex pointing downwards at (0,0). While it has symmetry, it doesn't match the graph's slopes.
+* **d) f(x) = (x-1)*(x+1):** This simplifies to f(x) = x² - 1, a parabola opening upwards with the vertex at (0,-1). This doesn't match the graph's shape.
+* **e) f(x) = sinh(x):** The hyperbolic sine function is odd (symmetric about the origin) and has a negative slope for x < 0 and a positive slope for x > 0. This aligns well with our observations.
+
+**Conclusion:**
+
+The graph most closely resembles the function **e) f(x) = sinh(x)**. While the graph is a piecewise constant approximation, the overall shape, symmetry, and slope behavior strongly suggest the hyperbolic sine function. 
+
+SOLUTION: -|-x|
+--------------------------------------------------------------------------------
+Graph |2*x+4|
+
+LLM RESPONSE:  Let's analyze the graph and each function to determine the best fit.
+
+**1. Analyzing the Graph:**
+
+* **Overall Shape:** The graph has a generally increasing trend. It starts with low values, gradually increases, and then seems to level off at higher x-values.
+* **Symmetry:** The graph doesn't appear symmetrical around the y-axis or the origin.
+* **Key Features:**  There's a noticeable "bend" or change in slope around x = -2 and x = 0.
+
+**2. Evaluating the Functions:**
+
+* **a) f(x) = 1/(1+exp(-x)) (Sigmoid Function):** This function is always positive, increasing, and has a characteristic "S" shape. It doesn't match the graph's bend around x = -2.
+* **b) f(x) = -sin(x) (Sine Function):** This function is periodic and oscillates between -1 and 1. It doesn't match the overall increasing trend of the graph.
+* **c) f(x) = |2*x+4| (Absolute Value Function):** This function has a "V" shape with its vertex at x = -2. This aligns with the bend in the graph. The function is also always positive and generally increasing, matching the graph's characteristics.
+* **d) f(x) = sign(x) (Sign Function):** This function only takes on values of -1, 0, and 1. It doesn't match the continuous and gradually increasing nature of the graph.
+* **e) f(x) = log(x+10) (Logarithmic Function):** This function is only defined for x > -10 and increases rapidly at first, then slows down. It doesn't match the graph's behavior for x < 0.
+
+**Conclusion:**
+
+Based on the analysis, the function that most closely resembles the graph is **c) f(x) = |2*x+4|**. The graph's shape, bend at x = -2, and overall increasing trend align well with the characteristics of an absolute value function. 
+
+SOLUTION: |2*x+4|
+--------------------------------------------------------------------------------
+Graph |x^3|
+
+LLM RESPONSE:  Let's analyze the graph and the function options step-by-step:
+
+1. **Symmetry:** The graph appears to be roughly symmetrical around the y-axis. This eliminates options b) and e) which are linear and don't exhibit this symmetry.
+
+2. **Behavior at Extremes:** As x goes to negative infinity, the y values increase rapidly. This eliminates options c) and d). Option c) approaches zero as x goes to negative infinity, and option d) oscillates within a bounded range.
+
+3. **Shape:** The graph resembles a "V" shape, especially for values close to zero. This strongly suggests an absolute value function.
+
+4. **Option a):** The function f(x) = |x^3| would indeed create a symmetrical "V" shape with rapidly increasing y values as x moves away from zero. 
+
+**Therefore, the function depicted in the graph is most likely (a) f(x) = |x^3|.** 
+
+SOLUTION: |x^3|
+--------------------------------------------------------------------------------
+Graph sqrt(x+10)
+
+LLM RESPONSE:  Here's how to determine the function depicted in the graph:
+
+**1. Analyze the Graph's Behavior**
+
+* **Increasing:** The y-values increase as x increases.
+* **Continuous (approximately):** The graph shows a smooth transition between intervals, suggesting a continuous function.
+* **Positive for x > -10:**  The y-values are positive for x-values greater than approximately -10.
+
+**2. Eliminate Options Based on Behavior**
+
+* **a) f(x) = sign(x):** This function is discontinuous, taking only values of -1, 0, and 1. Eliminate.
+* **b) f(x) = -sign(x):**  Similar to (a), this is discontinuous. Eliminate.
+* **c) f(x) = -sinh(x):** The hyperbolic sine function increases exponentially for positive x, and this graph does not. Eliminate.
+* **d) f(x) = -|-x|:** This function is always negative or zero, but our graph has positive values. Eliminate.
+
+**3. Focus on the Remaining Option**
+
+* **e) f(x) = sqrt(x+10):** 
+    * This function is defined only for x >= -10, matching our graph.
+    * It's a square root function, which increases steadily as x increases, aligning with the graph's behavior.
+
+**Conclusion**
+
+The graph most closely depicts the function **e) f(x) = sqrt(x+10)**. 
+
+SOLUTION: sqrt(x+10)
+--------------------------------------------------------------------------------
+Graph -sqrt(x+10)
+
+LLM RESPONSE:  Here's how to determine the function:
+
+**1. Analyze the Graph's Shape:**
+
+* **Negative x-values:** The graph has negative y-values for negative x-values. This eliminates options a) and d) since even powers always result in positive outputs for negative inputs.
+* **Decreasing then Increasing:** The graph decreases as x approaches zero from the negative side and then increases as x becomes positive. This eliminates option b) as x^3 increases consistently.
+* **Curve Shape:** The graph has a curve that resembles a square root function flipped horizontally.
+
+**2. Consider the Remaining Options:**
+
+* **Option c) f(x) = -sqrt(x+10):** This function is a square root function shifted 10 units to the left and then flipped vertically. This matches the observed shape and behavior of the graph.
+* **Option e) f(x) = (x-1)*(x+1):** This is a quadratic function (parabola) that opens upwards. The graph clearly doesn't fit this shape.
+
+**3. Conclusion:**
+
+Based on the analysis, the graph most closely depicts the function **c) f(x) = -sqrt(x+10)**. 
+
+SOLUTION: -sqrt(x+10)
+--------------------------------------------------------------------------------
+Graph sqrt(x ** 2 + 3*x +5)
+
+LLM RESPONSE:  Let's analyze the graph and the function options step-by-step:
+
+**1. Observing the Graph's Behavior:**
+
+* **General Trend:** The graph has a generally increasing trend. It starts with lower values for negative x and increases as x becomes positive.
+* **Shape:** The graph seems to flatten out for very negative values of x and approaches a limit as x increases. 
+* **Symmetry:** The graph doesn't appear to be symmetrical about the y-axis or the origin.
+
+**2. Eliminating Function Options Based on Observations:**
+
+* **a) f(x) = sign(x):** This function only takes on values of -1, 0, and 1. The graph clearly has a wider range, so this option is incorrect.
+* **d) f(x) = -exp(x):** This function is always negative and decreases exponentially. Our graph is mostly positive and increasing, so this option is incorrect.
+* **e) f(x) = cos(x):** This function is periodic and oscillates between -1 and 1. Our graph doesn't show this behavior, so this option is incorrect.
+
+**3. Analyzing the Remaining Options:**
+
+* **b) f(x) = sqrt(x ** 2 + 3*x +5):** This function represents a parabola that opens upwards. While it's always positive, it doesn't exhibit the flattening behavior for negative x values seen in the graph.
+* **c) f(x) = 1/(1+exp(-x)):** This is the sigmoid function. It has a characteristic 'S' shape, starts to flatten for very negative x, approaches 1 as x increases, and is always positive. This closely matches our graph's behavior.
+
+**Conclusion:**
+
+Based on the step-by-step analysis, the graph most likely depicts the function **c) f(x) = 1/(1+exp(-x))**, the sigmoid function. 
+
+SOLUTION: sqrt(x ** 2 + 3*x +5)
+--------------------------------------------------------------------------------
+Graph exp(x)
+
+LLM RESPONSE:  Here's how to determine the function:
+
+1. **Analyze the graph's behavior:**
+    * The y-values are always positive.
+    * The y-values increase as x increases.
+    * The rate of increase seems to accelerate as x increases.
+
+2. **Eliminate unlikely candidates based on the behavior:**
+    * **c) f(x) = -sign(x):** This function only takes on values of -1, 0, or 1, and doesn't match the graph's behavior.
+    * **d) f(x) = -cosh(x):** The hyperbolic cosine function is always positive, and its negative would be always negative, not matching the graph.
+    * **e) f(x) = sign(x+3):** Similar to option c, this only takes on values of -1, 0, or 1.
+
+3. **Compare the remaining options:**
+    * **a) f(x) = exp(x):** The exponential function matches the observed behavior of always positive values and accelerating increase.
+    * **b) f(x) = sqrt(x+10):** While this function is always positive and increasing, its rate of increase slows down as x increases, unlike the graph.
+
+4. **Conclusion:**
+
+The graph most closely resembles the behavior of the exponential function. Therefore, the function depicted in the graph is most likely **a) f(x) = exp(x)**. 
+
+SOLUTION: exp(x)
+--------------------------------------------------------------------------------
+Graph -exp(x)
+
+LLM RESPONSE:  Let's analyze the graph and the function options step-by-step:
+
+**1. Observing the Graph:**
+
+* **Negative Values:** The graph shows only negative y-values.
+* **Sharp Change:** There's a sharp change in the y-value around x = 5.31, transitioning from a relatively small negative value to a much larger one.
+* **Continuously Decreasing:** After the sharp change, the function continues to decrease as x increases.
+
+**2. Eliminating Options:**
+
+* **a) f(x) = -exp(x):**  The exponential function is always negative but decreases smoothly. The graph shows a sharp change, making this option unlikely.
+* **b) f(x) = -sign(x):** The sign function only outputs -1, 0, or 1. It doesn't match the continuous and decreasing nature of the graph.
+* **d) f(x) = x and e) f(x) = tanh(x):** Both of these functions can have positive and negative values, unlike the graph which only has negative values.
+
+**3. Focusing on the Remaining Option:**
+
+* **c) f(x) = x^2:** The square function is always positive. However, we can modify it to fit the graph: **f(x) = -x^2**. This modified function is always negative and has a continuously increasing slope in the negative direction, matching the graph's behavior.
+
+**4. The Sharp Change:**
+
+The sharp change around x = 5.31 suggests there might be a piecewise function involved. The graph could be a combination of two functions, one for x < 5.31 and another (resembling -x^2) for x >= 5.31.
+
+**Conclusion:**
+
+While none of the options perfectly match the graph, **the graph most closely resembles a modified version of option (c): f(x) = -x^2**, potentially as part of a piecewise function to account for the sharp change. 
+
+SOLUTION: -exp(x)
+--------------------------------------------------------------------------------
+Graph exp(-x)
+
+LLM RESPONSE:  Here's how to determine the function from the graph:
+
+**1. Analyze the Graph's Shape:**
+
+* The graph shows a generally decreasing trend as x increases.
+* The decrease is very sharp for negative values of x and becomes much slower as x approaches 0 and beyond. 
+
+**2. Eliminate Unlikely Options:**
+
+* **a) f(x) = -|-x|:** This function would be a straight line decreasing at a constant rate for x < 0 and then increasing at a constant rate for x > 0. This doesn't match our graph.
+* **b) f(x) = log(x+10):**  The logarithmic function increases slowly and is only defined for x > -10. This doesn't match the sharp decrease in our graph.
+* **c) f(x) = -|x|:** Similar to option (a), this is a V-shaped graph with a constant rate of decrease and increase. It doesn't match the curvature of our graph.
+* **d) f(x) = x^2:** This is a parabola opening upwards. Our graph is clearly not a parabola.
+
+**3. Focus on the Remaining Option:**
+
+* **e) f(x) = exp(-x):** This is an exponential decay function. It starts with a rapid decrease for negative x values and then the decrease slows down as x approaches 0 and becomes positive. This closely matches the shape of our graph.
+
+**Conclusion:**
+
+The graph most likely depicts the function **f(x) = exp(-x)**. 
+
+SOLUTION: exp(-x)
+--------------------------------------------------------------------------------
+Graph exp(-x^2)
+
+LLM RESPONSE:  Here's how to determine the function depicted in the graph:
+
+**1. Analyze the Graph's Shape:**
+
+* **The graph is not symmetrical about the y-axis**, ruling out options (d) and (e) which are even functions.
+* **The graph has a steep increase as x approaches zero from the negative side**, and then **decreases more gradually as x becomes positive**. This suggests an exponential function with a negative exponent.
+
+**2. Consider the Exponential Options:**
+
+* **Option (a) f(x) = 3^x+1:** This function would be increasing for all x, not decreasing as our graph does for positive x values.
+* **Option (b) f(x) = 2^x:**  Similar to option (a), this function would also be increasing for all x.
+* **Option (c) f(x) = exp(-x):** This function has the desired behavior: a steep increase for negative x, reaching a maximum at x=0, and then a gradual decrease for positive x.
+
+**3. Conclusion:**
+
+The graph most closely resembles the function **(c) f(x) = exp(-x)**. 
+
+SOLUTION: exp(-x^2)
+--------------------------------------------------------------------------------
+Graph 2^x
+
+LLM RESPONSE:  Let's analyze the graph and the function options step-by-step:
+
+**1. Observing the Graph:**
+
+* **Positive Trend:** The graph generally shows an increasing trend as x increases.
+* **Curvature:** The rate of increase seems to accelerate as x gets larger. This suggests a possible exponential or polynomial function with a degree greater than 1. 
+* **No Negative Values:** All y-values are positive.
+
+**2. Eliminating Options based on Observations:**
+
+* **a) f(x) = sign(x):** This function only outputs -1, 0, or 1, and wouldn't match the increasing trend of the graph. **Eliminated.**
+* **c) f(x) = x:** This is a straight line and doesn't show the accelerating increase. **Eliminated.**
+* **d) f(x) = -|-x|:** This function is always negative or zero, contradicting the positive y-values in the graph. **Eliminated.**
+* **e) f(x) = -3*x^3:** While this is a cubic function and could show some curvature, it would have negative values for positive x, which the graph doesn't. **Eliminated.**
+
+**3. Remaining Option:**
+
+* **b) f(x) = 2^x:** This exponential function has a positive trend, accelerates as x increases, and always produces positive y-values. This aligns well with our observations of the graph.
+
+**Conclusion:**
+
+The graph most likely depicts the function **b) f(x) = 2^x**. 
+
+SOLUTION: 2^x
+--------------------------------------------------------------------------------
+Graph 3^x+1
+
+LLM RESPONSE:  Let's analyze the graph and the function options step-by-step:
+
+**1. Observing the Graph's Behavior:**
+
+* **Increasing:** The y-values are consistently increasing as x increases.
+* **Sharp Rise:** The graph shows a very rapid increase in y-values within a relatively small range of x-values.
+* **Positive Values:** All y-values are positive.
+
+**2. Eliminating Function Options Based on Observations:**
+
+* **a) f(x) = 1/(1+exp(-x)) (Logistic Function):**  This function is always bounded between 0 and 1, while our graph has values far exceeding 1. **Eliminated.**
+* **b) f(x) = cosh(x) (Hyperbolic Cosine):** This function is always greater than or equal to 1 and has a more symmetrical shape. Our graph seems to rise much more sharply. **Eliminated.**
+* **c) f(x) = -sqrt(x+10):** This function produces only negative y-values, while our graph has positive values. **Eliminated.**
+* **e) f(x) = -|x|:** This function is always negative or zero, while our graph has positive values. **Eliminated.**
+
+**3. Analyzing the Remaining Option:**
+
+* **d) f(x) = 3^x+1 (Exponential Function):** Exponential functions are known for their rapid growth. The "+1" shifts the graph upwards, ensuring all y-values are positive. This aligns well with our observations.
+
+**Conclusion:**
+
+The graph most likely depicts the function **d) f(x) = 3^x + 1**. 
+
+SOLUTION: 3^x+1
+--------------------------------------------------------------------------------
+Graph 2^(x-5)
+
+LLM RESPONSE:  Here's how to determine the function:
+
+1. **Analyze the graph's shape:** The graph shows an increasing trend, starting with low values and becoming very steep as x increases. This eliminates options c, d, and e:
+    * **c) f(x) = -|x|:** This is a V-shaped graph, decreasing then increasing.
+    * **d) f(x) = -exp(x):** This is a rapidly decreasing exponential function.
+    * **e) f(x) = -sign(x):** This is a horizontal line at -1 for negative x and +1 for positive x.
+
+2. **Compare remaining options:** We're left with:
+    * **a) f(x) = 2^(x-5)**: An exponential function shifted 5 units to the right.
+    * **b) f(x) = x:** A straight line with a slope of 1.
+
+3. **Exponential growth:** The graph's steep increase suggests exponential growth, making option **a) f(x) = 2^(x-5)** the most likely candidate. 
+
+**Therefore, the graph approximately depicts the function f(x) = 2^(x-5).** 
+
+SOLUTION: 2^(x-5)
+--------------------------------------------------------------------------------
+Graph log(x+10)
+
+LLM RESPONSE:  Let's analyze the graph and the function options step-by-step:
+
+**1. Analyzing the Graph:**
+
+* **Shape:** The graph is smooth and S-shaped, symmetric around the origin (approximately). 
+* **Domain:** The x-values span both negative and positive ranges.
+* **Range:** The y-values are also both negative and positive.
+
+**2. Eliminating Options based on Shape and Domain:**
+
+* **b) f(x) = log(exp(x)) = x:** This is a linear function, not S-shaped. **Eliminated.**
+* **d) f(x) = log(x+10) and e) f(x) = -log(x+10):** Logarithmic functions have a vertical asymptote at x = -10 and are not defined for x <= -10. The graph provided has data points for x < -10. **Eliminated.**
+
+**3. Comparing Remaining Options:**
+
+We are left with:
+
+* **a) f(x) = arcsinh(x)**
+* **c) f(x) = tanh(x)**
+
+Both of these functions are S-shaped and defined for all real numbers. Let's consider their behavior around the origin:
+
+* **arcsinh(x):**  The inverse hyperbolic sine function has a steeper slope near the origin compared to tanh(x).
+* **tanh(x):** The hyperbolic tangent function has a more gradual slope near the origin and approaches -1 as x goes to negative infinity and +1 as x goes to positive infinity.
+
+**4. Matching Behavior to the Graph:**
+
+The provided graph has a more gradual slope near the origin, suggesting it's more likely to be **tanh(x)**. Additionally, the y-values in the graph seem to approach -3 and +3 as x goes towards negative and positive infinity, respectively, which aligns with the behavior of **tanh(x)**.
+
+**Conclusion:**
+
+Based on the shape, domain, range, and behavior around the origin, the function depicted in the graph is most likely **(c) f(x) = tanh(x)**. 
+
+SOLUTION: log(x+10)
+--------------------------------------------------------------------------------
+Graph -log(x+10)
+
+LLM RESPONSE:  Let's analyze the graph and compare it to the function options:
+
+**1. Overall Shape and Trend:**
+
+* The graph is generally decreasing as x increases. 
+* It has a steeper decline in the middle and flattens out towards the edges.
+
+This eliminates:
+* **b) f(x) = x:** This is a straight line increasing at a constant rate.
+* **e) f(x) = -2*x^2:** This is a parabola opening downwards, symmetric around the y-axis.
+
+**2. Symmetry:**
+
+* The graph doesn't seem perfectly symmetrical around any vertical line.
+
+This eliminates:
+* **d) f(x) = -|x|:** This function is symmetrical around the y-axis.
+
+**3. Remaining Options:**
+
+* **a) f(x) = -sin(x):** The sine function oscillates between -1 and 1. While our graph has a somewhat wave-like pattern, it doesn't repeat and keeps decreasing overall.
+* **c) f(x) = -log(x+10):** The negative logarithmic function has a vertical asymptote at x = -10 and decreases rapidly initially, then flattens out.
+
+**4. Best Fit:**
+
+Comparing the remaining options, **c) f(x) = -log(x+10)** seems to fit the graph best. The graph's shape, the steep decline in the beginning, and the flattening trend align well with the characteristics of a negative logarithmic function.
+
+**Therefore, the function depicted in the graph is most likely (c) f(x) = -log(x+10).** 
+
+SOLUTION: -log(x+10)
+--------------------------------------------------------------------------------
+Graph log(exp(x))
+
+LLM RESPONSE:  Here's how to determine the function depicted in the graph:
+
+**1. Analyze the Graph's Shape:**
+
+* The graph is generally increasing.
+* It appears to have a slight curve, suggesting it's not perfectly linear.
+* The curve seems to be getting less steep as x increases.
+
+**2. Eliminate Options Based on Shape:**
+
+* **b) f(x) = x^2:** This is a parabola opening upwards, not matching our graph.
+* **d) f(x) = log(exp(x))**: This simplifies to f(x) = x, a straight line, not matching our graph.
+* **e) f(x) = -(x + 4)^4:** This is a steep curve opening downwards, not matching our graph.
+
+**3.  Consider Remaining Options:**
+
+* **a) f(x) = -log(x+10):**  Logarithmic functions tend to increase at a decreasing rate, which aligns with our graph's shape. The negative sign indicates a reflection across the x-axis, which also seems plausible.
+* **c) f(x) = -sqrt(x+10):** Square root functions also increase at a decreasing rate. The negative sign indicates a reflection across the x-axis.
+
+**4. Test a Point:**
+
+Let's test a point from the graph to differentiate between options (a) and (c).  A point around x = -5 seems convenient.
+
+* **From the graph:** When x is approximately -5, y is approximately -5.
+* **a) f(-5) = -log(-5 + 10) = -log(5):** This value is negative and closer to -5.
+* **c) f(-5) = -sqrt(-5 + 10) = -sqrt(5):** This value is also negative but further from -5.
+
+**Conclusion:**
+
+The graph most closely resembles the function **f(x) = -log(x+10)**. 
+
+SOLUTION: log(exp(x))
+--------------------------------------------------------------------------------
+Graph sin(x)
+
+LLM RESPONSE:  Here's how to determine the function depicted in the graph:
+
+**1. Analyze the graph's behavior:**
+
+* **Overall Shape:** The graph has a wave-like pattern, increasing and decreasing periodically. 
+* **Symmetry:** The graph appears roughly symmetrical around the y-axis.
+* **Range:** The y-values oscillate between approximately -1 and 1.
+
+**2. Eliminate unlikely options:**
+
+* **a) f(x) = log(exp(x))**: This simplifies to f(x) = x, which is a straight line, not a wave. **Eliminate.**
+* **b) f(x) = x**:  A straight line, not a wave. **Eliminate.**
+* **c) f(x) = sign(x-1)**: This function would be a step function with a value of -1 for x < 1 and 1 for x > 1.  **Eliminate.**
+* **e) f(x) = -sign(x)**: This is a step function with a value of 1 for x < 0 and -1 for x > 0. **Eliminate.**
+
+**3. Focus on the remaining option:**
+
+* **d) f(x) = sin(x)**: The sine function is known for its wave-like pattern, symmetry around the y-axis, and a range between -1 and 1.
+
+**Conclusion:**
+
+The graph most closely resembles the function **d) f(x) = sin(x)**. 
+
+SOLUTION: sin(x)
+--------------------------------------------------------------------------------
+Graph -sin(x)
+
+LLM RESPONSE:  Let's analyze the graph and compare it to the function options:
+
+**1. General Shape and Trends**
+
+* The graph is symmetrical, with a central point around x = 0. 
+* It has a peak around x = 0 and descends on both sides.
+* The descent appears steeper than a simple linear decrease.
+
+**2. Eliminating Options**
+
+* **a) f(x) = x^2+3*x-1:** This is a parabola, which opens upwards. Our graph has a peak, not a bottom point, so this is incorrect.
+* **b) f(x) = sign(x):** This function only outputs -1, 0, or 1. Our graph has a continuous range of values, so this is incorrect.
+* **d) f(x) = -(x + 4)^4:** This function is always negative and has a single peak at x = -4. Our graph has a peak near x = 0 and both positive and negative values, so this is incorrect.
+* **e) f(x) = x:** This is a straight line. Our graph has curves, so this is incorrect.
+
+**3. Analyzing the Remaining Option**
+
+* **c) f(x) = -sin(x):** This is a sine wave flipped vertically. It has a peak at x = 0, is symmetrical, and oscillates between -1 and 1, similar to our graph.
+
+**Conclusion**
+
+Based on the shape, symmetry, and value range, the graph most closely resembles the function **c) f(x) = -sin(x)**. 
+
+SOLUTION: -sin(x)
+--------------------------------------------------------------------------------
+Graph -sin(-x)
+
+LLM RESPONSE:  Let's analyze the graph step-by-step to determine the function:
+
+1. **Symmetry:** The graph appears to be symmetrical around the y-axis. This means the function is likely even. An even function satisfies f(x) = f(-x).
+
+2. **Behavior at x=0:** The graph seems to have a minimum point at x=0 with a positive y-value.
+
+3. **Behavior as x approaches positive/negative infinity:**  As x moves away from zero in either direction, the y-values seem to increase.
+
+Now let's consider the options:
+
+* **a) f(x) = x:** This is a linear function and doesn't exhibit symmetry around the y-axis. **Ruled out.**
+* **b) f(x) = |x^3|:** This function is symmetrical around the y-axis and increases as x moves away from zero. However, it has a sharp turn at x=0, while the graph appears smoother. **Possible, but let's investigate further.**
+* **c) f(x) = cosh(x):** This function is symmetrical around the y-axis, has a minimum at x=0, and increases as x moves away from zero. It also has a smooth curve. **This is a strong candidate.**
+* **d) f(x) = x:** This is the same as option a) and is already ruled out.
+* **e) f(x) = -sin(-x):** This function simplifies to f(x) = sin(x), which is an odd function (symmetrical about the origin) and doesn't match the graph's symmetry. **Ruled out.**
+
+**Conclusion:**
+
+Comparing the characteristics, the graph most closely resembles the function **c) f(x) = cosh(x)**. 
+
+SOLUTION: -sin(-x)
+--------------------------------------------------------------------------------
+Graph sin(x+2)+2
+
+LLM RESPONSE:  Here's how to determine the function depicted in the graph:
+
+**1. Analyze the Graph's Shape:**
+
+* The graph is not always increasing or decreasing. It has a peak around x = -8 and a trough around x = 6. This eliminates options (a) and (d) which are strictly decreasing and increasing respectively. 
+
+* The graph appears to have some periodicity, meaning it repeats a similar pattern. This makes option (c), a sine function, a likely candidate.
+
+**2. Consider Key Features:**
+
+* **Option (b) arcsinh(x):** The arcsinh function is always increasing and has a much gentler slope than the graph presented. This option is unlikely.
+
+* **Option (c) sin(x+2)+2:**  Sine functions oscillate between -1 and 1. Adding 2 shifts the graph up, making it oscillate between 1 and 3. The graph's y-values generally fall within this range. The horizontal shift of +2 also seems consistent with the graph's peaks and troughs.
+
+* **Option (e) (x-2)^2:** This is a parabola opening upwards. While it has a minimum point, it doesn't exhibit the periodic behavior seen in the graph.
+
+**3. Conclusion:**
+
+Based on the shape, periodicity, and y-value range, the graph most closely resembles a sine function shifted upwards by 2 units. 
+
+**Therefore, the function depicted in the graph is most likely (c) f(x) = sin(x+2)+2.** 
+
+SOLUTION: sin(x+2)+2
+--------------------------------------------------------------------------------
+Graph cos(x)
+
+LLM RESPONSE:  Here's how to determine the function depicted in the graph:
+
+**1. Analyze the Graph's Shape:**
+
+* The graph is generally increasing.
+* It has a smooth, S-like shape.
+* It seems to level off as x approaches negative and positive infinity.
+
+**2. Eliminate Unlikely Options:**
+
+* **a) f(x) = cos(x):**  Cosine is periodic and oscillates between -1 and 1. The graph doesn't show this behavior.
+* **b) f(x) = -sign(-x):** This function is a step function with a single jump from -1 to 1 at x=0. The graph is smooth.
+* **c) f(x) = sign(x+3):** This is also a step function, jumping from -1 to 1 at x=-3. The graph is smooth.
+* **e) f(x) = x^3:**  A cubic function increases without bound as x goes to infinity and decreases without bound as x goes to negative infinity. The graph levels off.
+
+**3. Focus on the Remaining Option:**
+
+* **d) f(x) = tanh(x):** The hyperbolic tangent function has the following properties:
+    * It's smooth and increasing.
+    * It asymptotically approaches -1 as x goes to negative infinity.
+    * It asymptotically approaches 1 as x goes to positive infinity.
+
+**Conclusion:**
+
+The graph closely resembles the characteristics of the hyperbolic tangent function. 
+
+**Therefore, the function depicted in the graph is most likely (d) f(x) = tanh(x).** 
+
+SOLUTION: cos(x)
+--------------------------------------------------------------------------------
+Graph 1/2*cos(x-2)
+
+LLM RESPONSE:  Let's analyze the graph and each function to determine the best match.
+
+**Graph Observations:**
+
+* **Shape:** The graph has a sigmoid shape, meaning it starts with a low slope, increases in slope in the middle, and then flattens out again. 
+* **Symmetry:** The graph appears roughly symmetrical around the point (0,0).
+* **Range:** The y-values are bounded, staying within a specific range (approximately -0.5 to 0.5).
+
+**Analyzing the Functions:**
+
+* **a) f(x) = 1/2*cos(x-2):** This is a cosine function, which has a periodic, wave-like shape. This doesn't match our graph.
+* **b) f(x) = |2*x+4|:** This is an absolute value function, resulting in a V-shaped graph. This doesn't match our graph.
+* **c) f(x) = -|-x|:** This is a negated absolute value function, creating an upside-down V-shape. This doesn't match our graph.
+* **d) f(x) = sqrt(x ** 2 + 3*x +5):** This function involves a square root, which typically produces a curve that starts at a point and increases. It doesn't have the sigmoid shape or symmetry we're looking for.
+* **e) f(x) = tanh(x):** The hyperbolic tangent function (tanh) is known for its sigmoid shape, symmetry around (0,0), and bounded output (between -1 and 1). This closely aligns with our graph's characteristics.
+
+**Conclusion:**
+
+Based on the analysis, the function depicted in the graph is most likely **(e) f(x) = tanh(x)**. 
+
+SOLUTION: 1/2*cos(x-2)
+--------------------------------------------------------------------------------
+Graph sinh(x)
+
+LLM RESPONSE:  Let's analyze the graph and the function options step-by-step:
+
+**1. Observing the Graph's Behavior:**
+
+* **General Trend:** The graph has a steep negative slope for negative x values, flattens out near zero, and then has a steep positive slope for positive x values. 
+* **Symmetry:**  The graph appears somewhat symmetrical, with the negative and positive x regions mirroring each other to some extent.
+
+**2. Eliminating Unlikely Functions:**
+
+* **a) f(x) = -x^5:** This function would have a similar general trend but wouldn't flatten out near zero. It would continue to have a steep slope.
+* **b) f(x) = sqrt(x+10):** This function is only defined for x >= -10 and wouldn't have the negative x portion seen in the graph.
+* **e) f(x) = x:** This is a simple linear function and doesn't match the curvature of the graph.
+
+**3. Focusing on Potential Matches:**
+
+* **c) f(x) = exp(x):** The exponential function has a very steep positive slope for positive x and approaches zero for negative x. This matches a portion of the graph but not the steep negative slope for negative x.
+* **d) f(x) = sinh(x):** The hyperbolic sine function is symmetric around the origin. It has a steep positive slope for positive x, flattens out near zero, and has a steep negative slope for negative x. This closely matches the observed behavior of the graph.
+
+**Conclusion:**
+
+Based on the step-by-step analysis, the function that is most likely depicted in the graph is **d) f(x) = sinh(x)**. 
+
+SOLUTION: sinh(x)
+--------------------------------------------------------------------------------
+Graph -sinh(x)
+
+LLM RESPONSE:  Here's a step-by-step analysis to determine the function:
+
+1. **Analyze the graph's behavior:**
+    * The graph is generally decreasing.
+    * It seems to have a very steep decrease around x = 9.
+    * It appears to approach positive infinity as x approaches -10.
+
+2. **Eliminate unlikely candidates based on behavior:**
+    * **d) f(x) = (x-1)*(x+1):** This is a parabola, not a continuously decreasing function. **Eliminated.**
+    * **c) f(x) = arctan(x):** Arctangent has horizontal asymptotes at y = π/2 and y = -π/2, and wouldn't show such a steep decrease. **Eliminated.**
+
+3. **Examine remaining options:**
+    * **a) f(x) = -log(x+10):**  Logarithms have a vertical asymptote at x = -10 and decrease as x increases. This aligns with the graph's behavior.
+    * **b) f(x) = -sinh(x):** Hyperbolic sine is an odd function, symmetric about the origin. The graph doesn't show this symmetry. **Eliminated.**
+    * **e) f(x) = -exp(x):**  Negative exponential decay decreases but approaches zero as x approaches negative infinity. The graph doesn't approach zero on the left side. **Eliminated.**
+
+4. **Conclusion:**
+
+The graph's behavior most closely matches the function **a) f(x) = -log(x+10)**. 
+
+SOLUTION: -sinh(x)
+--------------------------------------------------------------------------------
+Graph cosh(x)
+
+LLM RESPONSE:  Let's analyze the graph step-by-step to determine the function:
+
+1. **Symmetry:** The graph appears to be symmetric around the y-axis. This eliminates options c) and d) which are not symmetrical.
+
+2. **Behavior as x approaches positive/negative infinity:** As x goes to both positive and negative infinity, the y-value increases rapidly. This eliminates option b) which approaches zero as x goes to positive infinity.
+
+3. **Shape:** The graph has a parabolic shape, opening upwards. This eliminates option a) which has a U-shape.
+
+4. **Remaining Option:** The only remaining option is **e) f(x) = x^2**, which is symmetrical, increases rapidly as x moves away from zero, and has a parabolic shape.
+
+**Therefore, the graph approximately depicts the function f(x) = x^2.** 
+
+SOLUTION: cosh(x)
+--------------------------------------------------------------------------------
+Graph -cosh(x)
+
+LLM RESPONSE:  Here's how to determine the function:
+
+1. **Analyze the graph's shape:** The graph is symmetric, with a minimum point and increasing values as we move away from the minimum in either direction. This eliminates options a) and d), which are always increasing, and option b) which is always decreasing or constant.
+
+2. **Consider the remaining options:** We are left with:
+    * c) f(x) = exp(x) - Exponential function
+    * e) f(x) = -cosh(x) - Negative hyperbolic cosine function
+
+3. **Differentiate between exponential and hyperbolic cosine:** 
+    * Exponential functions grow very rapidly. 
+    * Hyperbolic cosine functions have a more rounded bottom and grow slightly less rapidly than exponential functions.
+
+4. **Compare growth rates:** Looking at the graph, the function's values increase rapidly but not as explosively as a pure exponential function. The change in y-values seems more consistent with a hyperbolic cosine function.
+
+5. **Symmetry and minimum point:** The graph is symmetrical and has a minimum point, which aligns perfectly with the properties of a negative hyperbolic cosine function.
+
+**Therefore, the function depicted in the graph is most likely (e) f(x) = -cosh(x).** 
+
+SOLUTION: -cosh(x)
+--------------------------------------------------------------------------------
+Graph tanh(x)
+
+LLM RESPONSE:  Here's how to determine the function from the graph:
+
+**1. Analyze the Graph's Shape:**
+
+* The graph is **symmetric** around the y-axis (approximately). This eliminates options (a) and (e) as they are not symmetric.
+* The graph has **horizontal asymptotes** as x approaches positive and negative infinity. This eliminates options (b) and (d). Option (b) goes to negative infinity as x increases, and option (d) oscillates.
+
+**2. Consider the Remaining Option:**
+
+* **Option (c): f(x) = tanh(x)** 
+    * The hyperbolic tangent function (tanh) is symmetric around the origin.
+    * It has horizontal asymptotes at y = 1 and y = -1 as x approaches positive and negative infinity, respectively.
+
+**Conclusion:**
+
+The graph most closely resembles the function **f(x) = tanh(x)**. 
+
+SOLUTION: tanh(x)
+--------------------------------------------------------------------------------
+Graph arcsinh(x)
+
+LLM RESPONSE:  Here's how to determine the function:
+
+1. **Analyze the graph's shape:** The graph is generally increasing. It has a steep slope in the middle that flattens out towards the edges. This shape suggests a function with a sigmoid-like curve.
+
+2. **Eliminate unlikely options:**
+    * **a) f(x) = sin(x):**  Sine is periodic and oscillates between -1 and 1. The graph doesn't show this behavior.
+    * **b) f(x) = x^2 + 3x - 1:** This is a parabola, which has a U-shape. The graph doesn't match.
+    * **d) f(x) = x:** This is a straight line, not a curve like the graph.
+    * **e) f(x) = sign(x):** This function only outputs -1, 0, or 1. The graph has a continuous range.
+
+3. **Focus on the remaining option:**
+    * **c) f(x) = arcsinh(x):** The inverse hyperbolic sine function (arcsinh) has a sigmoid-like shape, increasing more slowly at its extremes, which aligns with the graph.
+
+**Therefore, the function depicted in the graph is most likely (c) f(x) = arcsinh(x).** 
+
+SOLUTION: arcsinh(x)
+--------------------------------------------------------------------------------
+Graph arctan(x)
+
+LLM RESPONSE:  Let's analyze the graph and function options step-by-step:
+
+**1. Analyzing the Graph:**
+
+* **Overall Shape:** The graph is smooth and has a single inflection point, suggesting it might represent an inverse trigonometric function or a sigmoid function like arctangent or hyperbolic tangent. 
+* **Symmetry:** The graph appears roughly symmetric around the point (0,0), which is a characteristic of odd functions like arctangent and hyperbolic sine.
+* **Asymptotes:** The graph seems to approach certain y-values as x goes to positive and negative infinity, hinting at horizontal asymptotes.
+
+**2. Eliminating Options:**
+
+* **a) f(x) = 3^x+1:** Exponential functions grow rapidly and don't have the same shape or symmetry as the graph. **Eliminated.**
+* **b) f(x) = x^4:** This function is always positive and has a different shape than the graph. **Eliminated.**
+* **c) f(x) = 1/2*cos(x-2):** Cosine functions are periodic and oscillate between fixed values. The graph doesn't show this behavior. **Eliminated.**
+* **e) f(x) = sinh(x):** Hyperbolic sine grows exponentially for large positive and negative x-values. While the graph is symmetric, it doesn't exhibit this rapid growth. **Eliminated.**
+
+**3. The Best Fit:**
+
+* **d) f(x) = arctan(x):**  The arctangent function has all the characteristics observed in the graph:
+    * **Shape:**  The smooth, increasing shape with a single inflection point matches.
+    * **Symmetry:** Arctangent is an odd function, symmetric around the origin.
+    * **Asymptotes:** It has horizontal asymptotes at y = π/2 and y = -π/2, which the graph seems to approach.
+
+**Conclusion:**
+
+Based on the step-by-step analysis, the graph most likely depicts the function **d) f(x) = arctan(x)**. 
+
+SOLUTION: arctan(x)
+--------------------------------------------------------------------------------
+Graph 1/(1+exp(-x))
+
+LLM RESPONSE:  Let's analyze the graph and each function to find the match:
+
+**1. Analyzing the Graph:**
+
+* **Shape:** The graph is roughly symmetrical, with a steep increase in the middle and flattening out towards both ends. 
+* **Range:** The y-values are always positive and seem to approach 0 on the left and 1 on the right.
+
+**2. Eliminating Options:**
+
+* **b) f(x) = -x^5:** This function is always decreasing and becomes very large and negative for positive x. It doesn't match the graph.
+* **d) f(x) = exp(-x^2):** This is a Gaussian function, symmetrical around x=0, and always positive. While it shares some similarities with the graph, it doesn't flatten out towards 1 on the right side.
+* **e) f(x) = |x|:** This function has a sharp V-shape and doesn't match the smooth curve of the graph.
+
+**3. Comparing the Remaining Options:**
+
+* **a) f(x) = sinh(x):** The hyperbolic sine function is symmetrical but increases exponentially for both positive and negative x. It doesn't match the flattening behavior of the graph.
+* **c) f(x) = 1/(1+exp(-x)):** This is the sigmoid function. It's symmetrical around (0, 0.5), increases monotonically, and asymptotically approaches 0 for negative x and 1 for positive x. This closely matches our graph's characteristics.
+
+**Conclusion:**
+
+The graph most closely depicts the function **c) f(x) = 1/(1+exp(-x))**, the sigmoid function. 
+
+SOLUTION: 1/(1+exp(-x))
+--------------------------------------------------------------------------------
\ No newline at end of file
diff --git a/benchmarks/results/function-recognition-gpt-4-turbo-2024-04-09.txt b/benchmarks/results/function-recognition-gpt-4-turbo-2024-04-09.txt
new file mode 100644
index 0000000..ab08971
--- /dev/null
+++ b/benchmarks/results/function-recognition-gpt-4-turbo-2024-04-09.txt
@@ -0,0 +1,1342 @@
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given intervals and compare them with the expected behavior of each of the candidate functions. Let's examine each function:
+
+a) **f(x) = x**
+   - This is a linear function with a direct proportionality between x and f(x). As x increases, f(x) should increase linearly at the same rate.
+
+b) **f(x) = -sinh(x)**
+   - The hyperbolic sine function, sinh(x), grows exponentially for positive x and decreases exponentially for negative x. The negative sign would invert this behavior, so -sinh(x) decreases exponentially for positive x and increases exponentially for negative x.
+
+c) **f(x) = -3*x^3**
+   - This cubic function will have a steep decrease as x becomes more negative and a steep increase as x becomes more positive, due to the cubic power and the negative coefficient.
+
+d) **f(x) = -x^5**
+   - Similar to the cubic function but even more extreme, this quintic function will decrease very sharply as x becomes negative and increase sharply as x becomes positive, with the changes being more pronounced due to the higher power.
+
+e) **f(x) = -sin(x)**
+   - The sine function oscillates between -1 and 1. With a negative sign, it would oscillate between 1 and -1. This function would not show a consistent increase or decrease but rather periodic peaks and troughs.
+
+Now, let's analyze the provided data:
+- The values start at -9.99 when x is near -10 and increase towards 9.93 as x approaches 10.
+- The increase appears to be roughly linear, without the oscillations that would be characteristic of a sine function, nor the sharp increases or decreases that would be expected from the exponential or higher power functions.
+
+Given this analysis, the function that best matches the behavior of the data is **a) f(x) = x**. This function shows a consistent, linear increase in y as x increases from negative to positive, which aligns with the changes observed in the graph data.
+SOLUTION: x
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the y-values as the x-values change. Let's consider each function option and see which one matches the pattern observed in the graph:
+
+### a) \( f(x) = -x^5 \)
+This function involves a fifth power, which would result in very rapid increases or decreases as x moves away from zero. The negative sign would make the function decrease as x increases, but the rate of change would be very steep due to the fifth power.
+
+### b) \( f(x) = -2x + 5 \)
+This is a linear function with a negative slope. As x increases, the function value decreases linearly.
+
+### c) \( f(x) = x \)
+This is a simple linear function with a positive slope. As x increases, the function value also increases linearly.
+
+### d) \( f(x) = \text{sign}(x+3) \)
+The sign function returns -1 for negative inputs, 0 for zero, and 1 for positive inputs. The transformation \( x+3 \) shifts the "switching point" from 0 to -3.
+
+### e) \( f(x) = x^3 \)
+This function involves a cubic term. It results in a symmetric curve around the y-axis where the function decreases as x decreases and increases as x increases, with the rate of change accelerating as x moves away from zero.
+
+### Analysis of the Graph Data:
+The graph data shows a symmetric behavior around x = 0, where y-values decrease as x increases or decreases from zero. The rate of change appears to be accelerating as x moves away from zero, which is characteristic of a polynomial function rather than a linear one. The symmetry and the nature of change (increasing rate of change as x moves away from zero) suggest a cubic function rather than a quintic one, as the latter would show even more extreme changes.
+
+### Conclusion:
+Given the symmetry and the pattern of change, the most likely function represented by the graph is:
+**e) \( f(x) = x^3 \)**
+
+This conclusion is based on the observed behavior that as x increases or decreases from zero, the y-values change in a manner consistent with a cubic function, showing increasing rates of change as x moves further from zero.
+SOLUTION: -2*x+5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given x-axis intervals and compare them with the expected behavior of each of the listed functions. Let's consider each function option:
+
+a) \( f(x) = |x| \)
+- This function represents the absolute value of \( x \). It is symmetric around the y-axis, with values increasing linearly as \( x \) moves away from zero in either direction.
+
+b) \( f(x) = -|-x| \)
+- This is the negative of the absolute value of \( x \), which would decrease linearly from zero and be symmetric around the y-axis.
+
+c) \( f(x) = x^2 \)
+- This function represents a parabola opening upwards, symmetric around the y-axis, with values increasing as \( x \) moves away from zero.
+
+d) \( f(x) = -\sin(x) \)
+- This function is the negative sine of \( x \), which oscillates between -1 and 1, with periodic peaks and troughs.
+
+e) \( f(x) = x^5 \)
+- This function is an odd-powered polynomial, asymmetric, with values decreasing in the negative x-direction and increasing in the positive x-direction, passing through zero at \( x = 0 \).
+
+Now, let's analyze the provided data:
+- The values are symmetric around \( x = 0 \), suggesting a function symmetric around the y-axis.
+- The values increase as \( x \) moves away from zero in both directions, reaching a maximum near the ends of the range and a minimum near \( x = 0 \).
+
+Given these observations:
+- Options (b) and (d) can be eliminated because they do not show symmetry around the y-axis in the manner described.
+- Option (e) can be eliminated because \( x^5 \) would not be symmetric around the y-axis.
+- Option (a), \( f(x) = |x| \), and option (c), \( f(x) = x^2 \), both show symmetry and increase from the center outwards. However, the quadratic nature of \( x^2 \) typically results in a more pronounced increase as \( x \) moves away from zero compared to the linear increase of \( |x| \).
+
+Given the data points, which show a gradual and symmetric increase from a central minimum, the function \( f(x) = x^2 \) (option c) is the most likely candidate. The values increase in a manner consistent with a parabolic shape, which is characteristic of a squared function.
+SOLUTION: x^2
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given x-axis intervals and compare them with the expected behavior of each of the listed functions. Let's examine each function option:
+
+a) \( f(x) = \frac{1}{2}\cos(x-2) \)
+- The cosine function oscillates between -1 and 1, and multiplying by 1/2 would limit its range between -0.5 and 0.5. The values in the graph range from about -200 to -1.7, which is far outside this range.
+
+b) \( f(x) = -2x^2 \)
+- This is a downward-opening parabola. As \( x \) increases or decreases from zero, \( f(x) \) should decrease (become more negative). We need to check if the values in the graph consistently become more negative as \( x \) moves away from zero in both directions.
+
+c) \( f(x) = (x-2)^2 \)
+- This is an upward-opening parabola with a vertex at \( x = 2 \). The function value should be smallest (zero) at \( x = 2 \) and increase as \( x \) moves away from 2.
+
+d) \( f(x) = \sqrt{x+10} \)
+- The square root function is defined for \( x \geq -10 \) and increases as \( x \) increases. The function starts from zero at \( x = -10 \) and increases thereafter.
+
+e) \( f(x) = -\text{sign}(x) \)
+- The sign function returns -1 for negative \( x \), 0 for \( x = 0 \), and 1 for positive \( x \). Multiplying by -1 would flip these values. This function would only take values -1, 0, and 1, which does not match the range seen in the graph.
+
+Analyzing the graph:
+- The values start very negative and increase (become less negative) as \( x \) increases from -10 towards 0, then decrease again after passing through a less negative peak near \( x = 0 \). This behavior suggests a parabolic shape, specifically a downward-opening parabola centered near \( x = 0 \).
+
+Given this analysis, option (b) \( f(x) = -2x^2 \) seems to be the best fit. The values in the graph decrease (become more negative) as \( x \) moves away from zero, consistent with a downward-opening parabola. The function \( -2x^2 \) would indeed produce increasingly negative values as \( |x| \) increases from zero, matching the observed behavior in the graph.
+SOLUTION: -2*x^2
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the y-values as the x-values change, based on the given JSON object. Let's consider each function option and compare it to the pattern observed in the graph:
+
+### a) \( f(x) = \frac{1}{2} \cos(x-2) \)
+- The cosine function oscillates between -1 and 1. Given the factor of \(\frac{1}{2}\), this function would oscillate between -0.5 and 0.5. The graph values, however, range from 0.4 to 144.0, which is far outside this range. Thus, this function is unlikely.
+
+### b) \( f(x) = \text{sign}(x) \)
+- The sign function returns -1 for negative x, 0 for x=0, and 1 for positive x. The graph values show a continuous change and a much wider range, which does not match the behavior of the sign function. Thus, this function is also unlikely.
+
+### c) \( f(x) = (x-2)^2 \)
+- This function is a parabola opening upwards, centered at x=2. As x moves away from 2, the function value should increase quadratically. We need to check if the graph shows a minimum around x=2 and increases as x moves away from 2 on both sides.
+
+### d) \( f(x) = x^4 \)
+- This function also describes a parabola but grows much faster than \( (x-2)^2 \) as x moves away from 0. It is symmetric around x=0. We need to check if the graph shows a minimum around x=0 and if the increase rate matches the steepness expected from a quartic function.
+
+### e) \( f(x) = x \)
+- This is a linear function. If the graph represented this function, we would expect a straight line, which is not indicated by the provided values.
+
+### Analysis:
+- Observing the provided graph values, the y-values decrease as x increases from -10 towards 0, reach a minimum, and then increase as x moves from 0 towards 10. This suggests a symmetric behavior around a central x-value, which is characteristic of both options c) and d).
+
+- To differentiate between c) and d), we note that the values near x=0 are not extremely small (or approaching zero), which might be expected with a quartic function due to its rapid increase. Instead, the values decrease and increase in a manner that suggests a less steep curve, more indicative of a quadratic function.
+
+### Conclusion:
+Based on the analysis, the graph most likely represents the function \( f(x) = (x-2)^2 \) (option c). This function has a minimum at x=2, and the values increase quadratically as x moves away from 2, which aligns with the observed behavior in the graph.
+SOLUTION: (x-2)^2
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given x-axis intervals and compare them with the expected behavior of each function option. Let's consider each function:
+
+a) \( f(x) = (x-1)(x+1) \)  
+This is a quadratic function with roots at \( x = 1 \) and \( x = -1 \). The function decreases for \( x < -1 \), increases between \( -1 \) and \( 1 \), and increases for \( x > 1 \). The function should have a minimum value at \( x = 0 \).
+
+b) \( f(x) = \text{sign}(x-1) \)  
+This function returns -1 for \( x < 1 \) and 1 for \( x > 1 \), with a discontinuity at \( x = 1 \).
+
+c) \( f(x) = -3x^3 \)  
+This is a cubic function that decreases as \( x \) increases or decreases from zero, skewed by the negative coefficient.
+
+d) \( f(x) = \text{sign}(x) \)  
+This function returns -1 for \( x < 0 \) and 1 for \( x > 0 \), with a discontinuity at \( x = 0 \).
+
+e) \( f(x) = -\sqrt{x+10} \)  
+This function is defined for \( x \geq -10 \) and decreases as \( x \) increases. The function values are negative due to the negative sign outside the square root.
+
+Now, let's analyze the JSON data:
+- The function values decrease as \( x \) increases from -9.97 to about -0.86, where it reaches a minimum and then starts increasing.
+- The function values are negative throughout, and there is a symmetry around \( x = 0 \).
+
+Given these observations, option (e) \( f(x) = -\sqrt{x+10} \) seems to be the most likely candidate:
+- The function is defined and decreasing for \( x \) values close to -10, which aligns with the start of the graph.
+- The function reaches its minimum (most negative) around \( x = 0 \) and then starts increasing (becoming less negative), which matches the behavior in the graph.
+- The symmetry around \( x = 0 \) and the continuous decrease and increase in function values also support this choice.
+
+Therefore, the graph most likely represents the function \( f(x) = -\sqrt{x+10} \).
+SOLUTION: (x-1)*(x+1)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given x-axis intervals and compare this behavior with the expected behavior of each of the candidate functions. Let's consider each function:
+
+a) \( f(x) = x^3 \)
+- This function is a cubic function with positive leading coefficient. It decreases as \( x \) becomes more negative and increases as \( x \) becomes more positive, passing through zero at \( x = 0 \). The rate of increase/decrease accelerates as \( |x| \) increases.
+
+b) \( f(x) = 2^x \)
+- This is an exponential growth function. It increases rapidly as \( x \) increases, and approaches zero but never becomes negative as \( x \) decreases.
+
+c) \( f(x) = x^2 + 3x - 1 \)
+- This is a quadratic function with a positive leading coefficient (upward parabola). It has a minimum point (vertex), and the function values decrease on one side of the vertex and increase on the other side.
+
+d) \( f(x) = -2x^2 \)
+- This is a downward-opening parabolic function. It has a maximum point at \( x = 0 \) and decreases symmetrically as \( x \) moves away from zero in both directions.
+
+e) \( f(x) = -3x^3 \)
+- This is a cubic function with a negative leading coefficient. It increases as \( x \) becomes more negative and decreases as \( x \) becomes more positive, passing through zero at \( x = 0 \). The rate of increase/decrease accelerates as \( |x| \) increases.
+
+Now, let's analyze the behavior of the function values in the graph:
+- The function values decrease as \( x \) moves from negative to zero and increase as \( x \) moves from zero to positive. This behavior is symmetric around \( x = 0 \).
+- The function values are negative for \( x \) values close to zero, suggesting the function passes through zero and changes sign.
+
+Given this analysis, the behavior matches closely with option (e) \( f(x) = -3x^3 \). This function decreases as \( x \) increases from negative to zero and increases as \( x \) increases from zero to positive, with values passing through zero at \( x = 0 \). The symmetry and the rate of change also align with the cubic nature of the function. Thus, the graph likely represents the function \( f(x) = -3x^3 \).
+SOLUTION: x^2+3*x-1
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given x-axis intervals and compare them with the expected behavior of each of the candidate functions. Let's consider each function:
+
+a) \( f(x) = -x^5 \)
+- This function is an odd function and has a very steep increase or decrease as \( x \) moves away from zero. The negative sign would invert the typical shape of \( x^5 \), making it decrease steeply as \( x \) increases.
+
+b) \( f(x) = -\sinh(x) \)
+- The hyperbolic sine function, \( \sinh(x) \), grows exponentially for positive \( x \) and decreases exponentially for negative \( x \). The negative sign would invert this behavior.
+
+c) \( f(x) = x \)
+- This is a simple linear function with a direct proportionality between \( x \) and \( f(x) \).
+
+d) \( f(x) = x^3 \)
+- This function is an odd function and increases as \( x \) moves away from zero, but at a rate slower than \( x^5 \).
+
+e) \( f(x) = \exp(x) \)
+- The exponential function grows very rapidly as \( x \) increases and approaches zero as \( x \) decreases.
+
+Now, let's analyze the behavior of the function values in the JSON object:
+- For negative \( x \) values, the function values are negative and decrease in magnitude as \( x \) becomes more negative.
+- For positive \( x \) values, the function values are positive and increase in magnitude as \( x \) increases.
+- The function values near \( x = 0 \) are close to zero.
+
+This behavior suggests a function that is symmetric around the y-axis and has a steep increase/decrease as \( x \) moves away from zero. Among the options, \( f(x) = -x^5 \) and \( f(x) = x^3 \) are symmetric and have steep changes. However, \( f(x) = -x^5 \) would show a much steeper and more pronounced change due to the higher power, and the negative sign would make the function decrease as \( x \) increases, which matches the observed behavior in the graph.
+
+Thus, the function that best matches the graph is:
+a) \( f(x) = -x^5 \)
+SOLUTION: x^3
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the y-values as the x-values change across the given intervals. Let's consider each function option and compare it to the pattern observed in the JSON data:
+
+1. **f(x) = -3*x^3**: This function is a cubic function with a negative coefficient. It should show a rapid decrease as x moves away from zero in both the positive and negative directions. The values should be symmetric around x = 0 if the function is purely cubic.
+
+2. **f(x) = -sign(-x)**: This function returns -1 for x > 0 and 1 for x < 0. It should show a constant value of 1 for negative x and -1 for positive x, with a discontinuity at x = 0.
+
+3. **f(x) = |x|**: This function represents the absolute value of x, which increases linearly from zero as x moves away from zero in both directions, forming a V-shape with the vertex at x = 0.
+
+4. **f(x) = 1/2*cos(x-2)**: This function is a cosine wave shifted horizontally by 2 units, scaled vertically by 1/2. It would oscillate between -1/2 and 1/2, showing periodic peaks and troughs.
+
+5. **f(x) = x**: This is a linear function with a slope of 1. It should increase linearly as x increases.
+
+Now, let's analyze the JSON data:
+- The y-values decrease as x-values move from negative to positive, reaching a minimum around x = 0, and then start increasing as x becomes more positive.
+- The function appears to be symmetric around x = 0, suggesting a function that has similar behavior in both the positive and negative domains of x.
+
+Given this analysis:
+- **f(x) = -3*x^3** seems to be a plausible match because it shows rapid decrease and increase as x moves away from zero, and it is symmetric around x = 0.
+- **f(x) = -sign(-x)** does not match because it should show only two constant values, which is not observed.
+- **f(x) = |x|** could initially seem plausible due to symmetry, but the rate of change in the JSON data suggests a more rapid change than a linear increase/decrease.
+- **f(x) = 1/2*cos(x-2)** does not match as the data does not show periodic behavior.
+- **f(x) = x** does not match because it should show a linear increase, not the rapid changes observed.
+
+Thus, the best match for the given data is **f(x) = -3*x^3**.
+SOLUTION: -3*x^3
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given x-axis intervals. Let's consider each function option and how it behaves:
+
+a) \( f(x) = x^4 \)
+- This function is a quartic function, which means it grows rapidly as \( x \) moves away from zero, both in the positive and negative directions. The function is symmetric around the y-axis because raising a negative number to an even power results in a positive number.
+
+b) \( f(x) = \cos(x) \)
+- The cosine function oscillates between -1 and 1 with a periodicity of \( 2\pi \). It would not produce values in the thousands or show a trend of rapid increase or decrease over a large range of x-values.
+
+c) \( f(x) = \exp(x) \)
+- The exponential function grows very rapidly as \( x \) increases and approaches zero as \( x \) decreases. It is not symmetric around the y-axis.
+
+d) \( f(x) = x \)
+- This is a linear function with direct proportionality. It increases and decreases linearly with \( x \) and passes through the origin.
+
+e) \( f(x) = x^3 \)
+- This cubic function has an odd symmetry, meaning it behaves similarly in shape but opposite in sign in the positive and negative directions of the x-axis. It grows and decreases rapidly but not as sharply as the quartic function.
+
+Given the JSON object, we observe that the function values increase as \( x \) moves away from zero in both the negative and positive directions, suggesting a symmetric behavior around the y-axis. The values are also very large, indicating a rapid growth rate as \( x \) moves away from zero.
+
+Comparing this behavior with the options:
+- The cosine function (option b) and the exponential function (option c) can be ruled out due to the lack of symmetry and the value range, respectively.
+- The linear function (option d) does not fit because it would not return large values far from zero and is not symmetric.
+- Between the quartic (option a) and cubic (option e) functions, the quartic function is more plausible because it grows faster and remains positive, matching the large positive values for both negative and positive x-values in the graph.
+
+Thus, the most likely function that the graph depicts is:
+a) \( f(x) = x^4 \)
+SOLUTION: x^4
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the y-values as x increases, based on the intervals and corresponding values provided in the JSON object. Here's a step-by-step analysis:
+
+1. **Examine the Range and Behavior**: The x-values range from -10.0 to 9.98, and the y-values start at 1274.2, decrease to a minimum around the interval (-7.28, 0.69) with a value of 111.5, and then increase significantly as x increases further.
+
+2. **Behavior Analysis**:
+   - **f(x) = sin(x)**: This function oscillates between -1 and 1. The provided y-values do not fit this range or behavior (oscillation with a fixed amplitude).
+   - **f(x) = -sin(x)**: Similar to sin(x), but inverted. It also oscillates between -1 and 1, which does not match the large and increasing values in the data.
+   - **f(x) = -exp(x)**: This function decreases exponentially as x increases. The graph data shows an increase as x increases, especially in positive x-values, which contradicts the behavior of -exp(x).
+   - **f(x) = -|x|**: This function decreases linearly as x moves away from zero in both directions, reaching more negative values. The graph data shows an increase in y-values as x increases, especially in the positive direction, which is inconsistent with -|x|.
+   - **f(x) = -(x + 4)^4**: This function involves a fourth power, which can lead to rapid increases or decreases. The negative sign suggests the function values should be negative or decrease as x increases, but the addition of 4 shifts the function horizontally. We need to consider how this shift affects the function's graph.
+
+3. **Detailed Consideration of -(x + 4)^4**:
+   - When x = -4, the function -(x + 4)^4 = -0^4 = 0.
+   - For x < -4, (x + 4) is negative, and raising it to the fourth power results in a positive value; the negative sign then makes these values negative, but they decrease in magnitude as x decreases (becomes more negative).
+   - For x > -4, (x + 4) is positive, and similarly, the fourth power is positive, and the negative sign makes these values negative, but they decrease in magnitude as x increases.
+
+4. **Matching with Graph Data**:
+   - The minimum value around x = -7.28 to 0.69 suggests a turning point or minimum, which aligns with the expected behavior near x = -4 for the function -(x + 4)^4.
+   - The rapid increase in y-values as x moves away from -4 in both directions, especially on the positive side, aligns with the fourth power causing rapid increases in the magnitude of y-values.
+
+Based on this analysis, the function that best matches the behavior depicted in the graph is **e) f(x) = -(x + 4)^4**. This function explains the observed minimum and the rapid increase in y-values as x increases, particularly in the positive direction.
+SOLUTION: -(x + 4)^4
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given x-axis intervals and compare them with the expected behavior of each of the candidate functions. Let's examine each function option:
+
+a) \( f(x) = \sqrt{x+10} \)
+- This function is defined for \( x \geq -10 \) and increases as \( x \) increases. The square root function grows slower as \( x \) gets larger.
+
+b) \( f(x) = \sqrt{x^2 + 3x + 5} \)
+- This function is always defined as the expression under the square root is always positive. It resembles a shifted and scaled parabola and will increase as \( |x| \) increases, due to the \( x^2 \) term dominating.
+
+c) \( f(x) = -\text{sign}(-x) \)
+- This function returns -1 for \( x > 0 \) and 1 for \( x < 0 \), with a possible discontinuity or different behavior at \( x = 0 \). It's a step function with only two values.
+
+d) \( f(x) = x^5 \)
+- This function is an odd-powered polynomial. It decreases rapidly as \( x \) becomes more negative and increases rapidly as \( x \) becomes more positive, passing through zero at \( x = 0 \).
+
+e) \( f(x) = x \)
+- This is a linear function with a direct proportionality between \( x \) and \( f(x) \).
+
+Now, let's analyze the behavior of the function values from the JSON object:
+- The function values are very negative for large negative \( x \) and very positive for large positive \( x \).
+- The function values increase as \( x \) moves from negative to positive, showing a symmetric behavior around \( x = 0 \).
+
+Given this analysis:
+- Option (a) is unlikely because the function should not become negative, and the increase should be more gradual.
+- Option (b) is a strong candidate because it captures the symmetric increase and decrease around \( x = 0 \), typical of functions involving \( x^2 \).
+- Option (c) is not possible because the function values are not constant at -1 or 1.
+- Option (d) is also a strong candidate due to the rapid increase and decrease, typical of high-degree polynomials.
+- Option (e) is not possible because the function values do not linearly correlate with \( x \).
+
+Between options (b) and (d), the behavior of \( f(x) = \sqrt{x^2 + 3x + 5} \) (option b) is more plausible because it always remains positive and grows as \( |x| \) increases, which matches the observed behavior in the graph more closely than the more extreme behavior of the fifth power in option (d). Thus, the graph most likely represents option (b): \( f(x) = \sqrt{x^2 + 3x + 5} \).
+SOLUTION: x^5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given x-axis intervals and compare them with the expected behavior of each of the candidate functions. Let's consider each function:
+
+a) \( f(x) = -x^5 \)
+- This function is a negative quintic function. As \( x \) increases, \( f(x) \) should decrease sharply due to the power of 5, especially as \( x \) moves away from zero.
+
+b) \( f(x) = 2^{(x-5)} \)
+- This is an exponential function shifted to the right by 5 units. For \( x < 5 \), the function should rapidly approach zero, and for \( x > 5 \), the function should increase exponentially.
+
+c) \( f(x) = -\sqrt{x+10} \)
+- This function involves a square root, which is defined for \( x \geq -10 \). The negative sign indicates that the function values should be negative and decreasing as \( x \) increases.
+
+d) \( f(x) = \sqrt{x+10} \)
+- Similar to the previous function but without the negative sign, this function should produce non-negative values that increase as \( x \) increases.
+
+e) \( f(x) = \tanh(x) \)
+- The hyperbolic tangent function increases from -1 to 1 as \( x \) moves from negative to positive infinity. The increase is most rapid near \( x = 0 \) and saturates towards the extremes.
+
+Now, let's analyze the given data:
+- The values start very high and positive when \( x \) is around -10 and decrease as \( x \) increases.
+- The values become negative as \( x \) surpasses a certain threshold, indicating a function that changes sign.
+- The decrease is not symmetrical around \( x = 0 \); it seems to be more gradual on the negative side and sharper on the positive side.
+
+Comparing these observations:
+- \( f(x) = -x^5 \) would not change signs; it would remain negative for all \( x \).
+- \( f(x) = 2^{(x-5)} \) would not become negative; it would approach zero as \( x \) decreases from 5 and increase as \( x \) increases from 5.
+- \( f(x) = -\sqrt{x+10} \) and \( f(x) = \sqrt{x+10} \) do not fit well because they do not change signs across the domain.
+- \( f(x) = \tanh(x) \) fits the behavior well, as it increases from -1 to 1, changing sign around \( x = 0 \), and the rate of increase/decrease changes as \( x \) moves away from zero.
+
+Therefore, the function that best matches the behavior described in the graph is:
+e) \( f(x) = \tanh(x) \)
+SOLUTION: -x^5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, let's analyze the behavior of the function values across the different intervals provided in the JSON object:
+
+1. **Interval (-9.98, -0.1)**: The function value is approximately -1.003. This suggests that for negative values of x, the function is close to -1.
+
+2. **Interval (-0.1, -0.01)**: The function value is approximately -0.981. This is still close to -1, indicating that as x approaches zero from the negative side, the function value remains near -1.
+
+3. **Interval (-0.01, -0.0)**: The function value is approximately -0.903. This is still negative and close to -1, but slightly increasing as x approaches zero.
+
+4. **Interval (-0.0, 0.01)**: The function value is approximately 0.828. This is a significant change, as the function value jumps from negative to positive as x crosses zero.
+
+5. **Interval (0.01, 0.02)**: The function value is approximately 0.938. This indicates that the function value is positive and close to 1 for small positive x.
+
+6. **Interval (0.02, 0.55)**: The function value is approximately 0.979. The function remains positive and close to 1.
+
+7. **Interval (0.55, 9.97)**: The function value is approximately 0.999. The function value remains positive and very close to 1.
+
+From this analysis, we can see that the function values are close to -1 for negative x and close to 1 for positive x, with a sharp transition around x = 0. Let's compare this behavior with the given options:
+
+a) **f(x) = sign(x)**: This function returns -1 for negative x, 0 for x = 0, and 1 for positive x. The behavior of the function in the graph closely matches this, except the graph does not show a value of 0 at x = 0. However, the transition from negative to positive around zero is very sharp, similar to the sign function.
+
+b) **f(x) = x^2 + 3*x - 1**: This quadratic function does not consistently yield values close to -1 or 1 across the ranges of x given. It also does not exhibit a sharp transition around x = 0.
+
+c) **f(x) = x**: This linear function would not yield values close to -1 or 1 across such a wide range of x values.
+
+d) **f(x) = |2*x + 4|**: This absolute value function would not switch signs around x = 0.
+
+e) **f(x) = x^2**: This function would not be negative for any x and would not exhibit a sharp transition around x = 0.
+
+Based on this analysis, the function depicted in the graph most closely resembles **option (a) f(x) = sign(x)**, despite the graph not showing a value of 0 at x = 0. This might be due to the way the intervals are defined or due to an approximation in the graph representation.
+SOLUTION: sign(x)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the specified intervals. Let's examine each interval and the corresponding function value:
+
+1. **Interval (-9.99, -0.24)**: The function value is approximately 1.004. This suggests that for negative x values in this range, the function outputs a value close to 1.
+
+2. **Interval (-0.24, -0.02)**: The function value is approximately 0.984. This is also close to 1, indicating a similar behavior for negative x values, albeit closer to zero.
+
+3. **Interval (-0.02, -0.0)**: The function value is approximately 0.924. This is still close to 1, but slightly less, indicating a decrease as x approaches zero from the left.
+
+4. **Interval (-0.0, 0.02)**: The function value is approximately -0.664. This is a significant change, indicating a switch to a negative value as x crosses zero.
+
+5. **Interval (0.02, 0.3)**: The function value is approximately -0.974. This suggests that for positive x values close to zero, the function outputs a value close to -1.
+
+6. **Interval (0.3, 9.97)**: The function value is approximately -0.996. This indicates that for larger positive x values, the function consistently outputs a value close to -1.
+
+Given these observations, we can analyze the options:
+
+- **a) f(x) = x^3**: This function would not consistently output values close to 1 or -1 across such wide ranges of x. It would vary significantly as x changes.
+- **b) f(x) = sign(x)**: This function outputs 1 for x > 0 and -1 for x < 0. The graph, however, shows negative values for positive x and positive values for negative x, which is the opposite.
+- **c) f(x) = -sign(x)**: This function outputs -1 for x > 0 and 1 for x < 0, which matches the behavior observed in the graph.
+- **d) f(x) = -3*x^3**: Similar to option a, this cubic function would not maintain values close to 1 or -1 across wide ranges of x.
+- **e) f(x) = x^5**: Like the cubic function, a quintic function would also not maintain values close to 1 or -1 across wide ranges of x.
+
+Based on the analysis, the function depicted in the graph is most likely **c) f(x) = -sign(x)**, as it consistently outputs -1 for positive x values and 1 for negative x values, matching the behavior shown in the graph.
+SOLUTION: -sign(x)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given intervals and compare them with the expected behavior of each function option. Let's examine each function and its characteristics:
+
+1. **f(x) = x^3**
+   - This is a cubic function, which is symmetric around the origin and changes from negative to positive as x crosses zero, with increasing magnitude as |x| increases.
+
+2. **f(x) = sin(x+2) + 2**
+   - This function is a sine wave shifted horizontally by -2 and vertically by +2. The sine function oscillates between -1 and 1, so this function oscillates between 1 and 3.
+
+3. **f(x) = -sign(-x)**
+   - The sign function returns -1 for negative x, 0 for x=0, and 1 for positive x. The negative sign in front of the function inverts these values, so it returns 1 for negative x and -1 for positive x.
+
+4. **f(x) = sin(x)**
+   - The sine function oscillates between -1 and 1 with a period of 2π, crossing the x-axis at multiples of π.
+
+5. **f(x) = -cosh(x)**
+   - The hyperbolic cosine function, cosh(x), is always positive and symmetric about the y-axis, increasing exponentially as |x| increases. The negative sign inverts all values to be negative.
+
+Now, let's analyze the JSON object:
+- For negative x values, the function values are negative and seem to approach -1 as x becomes more negative.
+- There is a sudden change around x = 0, where the function value jumps from negative to positive.
+- For positive x values, the function values are positive and close to 1.
+
+Given this behavior:
+- **f(x) = x^3** does not fit because it would not produce a sudden jump around x = 0.
+- **f(x) = sin(x+2) + 2** does not fit because it would not produce values close to -1 or 1, and it would oscillate between 1 and 3.
+- **f(x) = sin(x)** does not fit because it would oscillate more frequently and not produce the observed behavior around x = 0.
+- **f(x) = -cosh(x)** does not fit because it would not produce positive values.
+
+The function **f(x) = -sign(-x)** fits the observed behavior:
+- It returns 1 for negative x (matching the negative values close to -1 in the JSON object due to the negative sign in front of the function).
+- It returns -1 for positive x (matching the positive values close to 1 in the JSON object).
+- The sudden change around x = 0 is consistent with the behavior of the sign function, which changes abruptly at x = 0.
+
+Therefore, the function depicted in the graph is most likely **c) f(x) = -sign(-x)**.
+SOLUTION: -sign(-x)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, let's analyze the behavior of the function values across the given intervals:
+
+1. **Interval (-9.96, -3.18)**: The function value is approximately -1.007.
+2. **Interval (-3.18, -3.09)**: The function value is approximately -0.986.
+3. **Interval (-3.09, -3.05)**: The function value is approximately -0.941.
+4. **Interval (-3.05, -2.99)**: The function value is approximately -0.839.
+5. **Interval (-2.99, -2.56)**: The function value is approximately 0.969.
+6. **Interval (-2.56, 9.97)**: The function value is approximately 0.99.
+
+Let's evaluate each function option:
+
+a) **f(x) = 2^(x-5)**: This function is an exponential function that increases as x increases. The values would not match the pattern of the graph, which shows a sudden change around x = -3.
+
+b) **f(x) = sign(x+3)**: The sign function returns -1 if the argument is negative, 0 if the argument is zero, and 1 if the argument is positive. Given the intervals and values:
+   - For x < -3, x + 3 is negative, so sign(x+3) = -1.
+   - For x > -3, x + 3 is positive, so sign(x+3) = 1.
+   This matches the pattern where the function value changes from negative to positive around x = -3.
+
+c) **f(x) = x**: This linear function would continuously increase or decrease, which does not match the sudden change in function values around x = -3.
+
+d) **f(x) = |x|**: The absolute value function would not produce negative values, and it would continuously increase as x moves away from zero, which does not match the observed pattern.
+
+e) **f(x) = -(x + 4)^4**: This function would be a downward-opening quartic function shifted to the left by 4 units. The values would continuously decrease or increase in a smooth curve, which does not match the observed pattern.
+
+Given this analysis, the function that best matches the graph is **b) f(x) = sign(x+3)**. This function shows a change from -1 to 1 around x = -3, which aligns with the intervals and values provided in the graph.
+SOLUTION: sign(x+3)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, let's analyze the behavior of the function values across the given intervals and compare them with the characteristics of each option provided.
+
+1. **Graph Analysis**:
+   - For x in (-10.0, 0.81), y ≈ -1.002
+   - For x in (0.81, 0.97), y ≈ -0.981
+   - For x in (0.97, 0.98), y ≈ -0.928
+   - For x in (0.98, 1.0), y ≈ -0.816
+   - For x in (1.0, 1.01), y ≈ 0.877
+   - For x in (1.01, 1.01), y ≈ 0.914
+   - For x in (1.01, 1.04), y ≈ 0.944
+   - For x in (1.04, 1.4), y ≈ 0.98
+   - For x in (1.4, 9.88), y ≈ 1.001
+
+   The function values are negative and close to -1 for x < 1, and positive and close to 1 for x > 1. There is a sharp transition around x = 1.
+
+2. **Function Options**:
+   - **a) f(x) = -|-x|**: This function is symmetric around x = 0 and always negative. It does not match the behavior of the graph where the function becomes positive.
+   - **b) f(x) = sign(x-1)**: The sign function returns -1 for values less than 0, 0 for exactly 0, and 1 for values greater than 0. This function would be -1 for x < 1 and 1 for x > 1, which matches the sharp transition and values observed in the graph.
+   - **c) f(x) = exp(-x)**: This exponential decay function is always positive and decreases towards 0 as x increases, which does not match the graph.
+   - **d) f(x) = -(x + 4)^4**: This function is always negative and does not exhibit a transition from negative to positive values as seen in the graph.
+   - **e) f(x) = 2^(x-5)**: This exponential growth function is always positive and increases as x increases, which does not match the graph.
+
+Given this analysis, the function that best matches the graph is **b) f(x) = sign(x-1)**. This function shows a clear transition from -1 to 1 at x = 1, which aligns with the behavior observed in the graph.
+SOLUTION: sign(x-1)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given intervals and compare them with the expected behavior of each of the listed functions. Let's consider each function option:
+
+a) \( f(x) = \frac{1}{2}\cos(x-2) \)
+- This function is a cosine function, which typically oscillates between -1 and 1, scaled by 1/2, so it oscillates between -0.5 and 0.5. The graph values in the JSON object range from approximately 0 to 10, which is outside the range of this function.
+
+b) \( f(x) = \log(x+10) \)
+- The logarithmic function increases as x increases, but the rate of increase slows down as x gets larger. The function is defined for \( x > -10 \) and would start from a value close to zero (as \( \log(0) \) is undefined but approaches negative infinity as x approaches -10 from the right). This function could potentially match if the values start near zero and increase.
+
+c) \( f(x) = -\sqrt{x+10} \)
+- This function involves a square root, which is only defined for \( x+10 \geq 0 \) or \( x \geq -10 \). The negative sign indicates the function values should be non-positive (i.e., zero or negative). This does not match the positive values in the graph.
+
+d) \( f(x) = |x| \)
+- The absolute value function is zero at x = 0 and increases linearly as x moves away from zero in either direction. This function does not match the behavior in the JSON object, where values increase and then decrease, suggesting a non-linear relationship.
+
+e) \( f(x) = x \)
+- This is a linear function increasing as x increases. The values in the JSON object do not suggest a simple linear relationship as they increase to a peak and then decrease.
+
+Given these considerations, option (b) \( f(x) = \log(x+10) \) seems the most plausible. The logarithmic function would start from values close to zero (as x approaches -10) and increase as x increases, which is consistent with the values provided in the JSON object. The values increase, but the rate of increase diminishes, which is characteristic of a logarithmic function. Thus, the graph likely represents the function \( f(x) = \log(x+10) \).
+SOLUTION: |x|
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given intervals and compare them with the expected behavior of each of the candidate functions. Let's examine each function:
+
+a) \( f(x) = 2^{(x-5)} \)
+- This function is an exponential function that decreases as \( x \) moves from negative to positive values, reaching its minimum at \( x = 5 \) and then increasing. The function is always positive.
+
+b) \( f(x) = |x^3| \)
+- This function represents the absolute value of the cube of \( x \). It is symmetric around \( x = 0 \), increases as \( x \) moves away from zero in both directions, and is always non-negative.
+
+c) \( f(x) = -|x| \)
+- This function is the negative of the absolute value of \( x \). It decreases linearly as \( x \) moves away from zero in both directions, reaching its maximum at \( x = 0 \) (which is 0) and becoming more negative as \( |x| \) increases.
+
+d) \( f(x) = |x| \)
+- This function is the absolute value of \( x \). It increases linearly as \( x \) moves away from zero in both directions, and is always non-negative.
+
+e) \( f(x) = \frac{1}{2}\cos(x-2) \)
+- This function is a cosine wave shifted horizontally by 2 units, scaled vertically by a factor of \( \frac{1}{2} \). It oscillates between -0.5 and 0.5.
+
+Now, let's analyze the provided data:
+- The function values are negative across the entire range.
+- The values decrease as \( x \) moves from negative to positive, reaching a minimum around \( x = 0 \), and then increase as \( x \) moves further positive.
+
+From this behavior, we can see that the function values are symmetric around \( x = 0 \) and decrease as \( |x| \) increases, which matches the description of function \( c) f(x) = -|x| \). This function is the only one among the options that is always negative, reaches its least negative value at \( x = 0 \), and increases in negativity as \( |x| \) increases in both directions from zero.
+
+Therefore, the graph approximately depicts the function:
+**c) \( f(x) = -|x| \)**
+SOLUTION: -|x|
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, let's analyze the given options and the nature of the graph:
+
+1. **f(x) = -sign(-x)**: The sign function returns -1 for negative inputs, 0 for zero, and 1 for positive inputs. The negative sign outside would flip these values. This would result in a function that is 1 for negative x, -1 for positive x, and 0 at x = 0. This does not match the continuous and varying values in the graph.
+
+2. **f(x) = sign(x-1)**: This function would return -1 for x < 1, 0 at x = 1, and 1 for x > 1. Again, this does not match the continuous and varying values in the graph.
+
+3. **f(x) = -|-x|**: This function represents the negative of the absolute value of x, which would be a V-shaped graph opening downwards. This is a potential candidate as it continuously decreases and increases, but we need to check the exact shape and behavior at x = 0.
+
+4. **f(x) = (x-1)*(x+1)**: This simplifies to \(x^2 - 1\), a parabola opening upwards, shifted down by 1. This does not match the graph, which does not show a minimum point at x = 0.
+
+5. **f(x) = sinh(x)**: The hyperbolic sine function, which increases exponentially for positive x and decreases exponentially for negative x, passing through the origin (0,0). This does not match the graph, which should show symmetric behavior around the y-axis if it were sinh(x).
+
+Given these considerations, let's focus on option 3, **f(x) = -|-x|**. This function should produce a graph that decreases towards zero as x approaches 0 from both sides and then becomes less negative as x moves away from zero, which seems to match the description of the graph provided. The values in the JSON object show a decrease towards zero and then an increase away from zero, consistent with the negative absolute value function.
+
+Thus, the graph most likely represents the function **f(x) = -|-x|**.
+SOLUTION: -|-x|
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given x-axis intervals and compare them with the expected behavior of each of the listed functions. Let's consider each function:
+
+a) **f(x) = 1/(1+exp(-x))** (Logistic function)
+   - This function is known for its S-shaped curve (sigmoid function). It approaches 0 as x approaches negative infinity and approaches 1 as x approaches positive infinity.
+
+b) **f(x) = -sin(x)**
+   - This function oscillates between -1 and 1 with a periodicity of \(2\pi\). It should show a repeating pattern of peaks and troughs.
+
+c) **f(x) = |2*x+4|**
+   - This is an absolute value function, which is V-shaped. It decreases linearly, hits a minimum, and then increases linearly.
+
+d) **f(x) = sign(x)**
+   - This function returns -1 for negative x, 0 for x=0, and 1 for positive x. It should show a jump from -1 to 1 at x=0.
+
+e) **f(x) = log(x+10)**
+   - This logarithmic function gradually increases as x increases. It is defined for x > -10 and increases without bound as x increases, but the rate of increase slows down.
+
+Now, let's analyze the provided data:
+- The values start at 14.94 when x is near -10 and increase steadily to 24.82 as x approaches 10.
+- The function values are continuously increasing as x increases from -10 to 10.
+
+Considering the behavior:
+- **Option a (Logistic function)**: The values in the data range from about 15 to 25, which does not fit the range [0, 1] typical of a logistic function.
+- **Option b (-sin(x))**: There is no oscillatory pattern in the data, so this is unlikely.
+- **Option c (|2*x+4|)**: The data does not show a V-shaped pattern, which rules out the absolute value function.
+- **Option d (sign(x))**: The data does not show a jump from -1 to 1 around x=0, so this is not likely.
+- **Option e (log(x+10))**: This function increases gradually and is defined and increasing in the range given. The behavior of the data, continuously increasing over the range of x, matches well with a logarithmic function's characteristics.
+
+Based on this analysis, the graph most likely represents **Option e, f(x) = log(x+10)**. This function would continuously increase over the range of x provided and does not have bounds like the logistic function, nor does it show oscillatory or piecewise behavior like the other options.
+SOLUTION: |2*x+4|
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given x-axis intervals and compare them with the expected behavior of each of the listed functions. Let's consider each function option:
+
+a) \( f(x) = |x^3| \)
+- This function represents the absolute value of the cube of x. It should be symmetric around the y-axis, with values increasing as x moves away from zero in either direction. The function should have a minimum value at x = 0.
+
+b) \( f(x) = -2x + 5 \)
+- This is a linear function with a negative slope, decreasing as x increases.
+
+c) \( f(x) = 2^{(x-5)} \)
+- This exponential function shifts the standard \( 2^x \) graph to the right by 5 units. It grows exponentially as x increases, especially beyond the x = 5 point.
+
+d) \( f(x) = \sin(x+2) + 2 \)
+- This function represents a sine wave shifted to the left by 2 units and vertically shifted upwards by 2 units. It should oscillate between 1 and 3, with periodic peaks and troughs.
+
+e) \( f(x) = x \)
+- This is a simple linear function with a positive slope, increasing as x increases.
+
+Now, let's analyze the provided JSON data:
+- The function values start high at the leftmost interval and decrease as x increases from negative to zero, then increase again as x moves from zero to positive values. This suggests a symmetric behavior around x = 0.
+
+Given this analysis:
+- Option (a) \( f(x) = |x^3| \) seems plausible because it would decrease as x approaches zero from either side and then increase again, reflecting the symmetric and cubic nature of the function.
+- Option (b) does not fit because the function should consistently decrease, which does not match the symmetric increase and decrease around x = 0.
+- Option (c) does not fit because the exponential growth should become very pronounced as x increases beyond 5, which is not symmetric around x = 0.
+- Option (d) does not fit because the values should oscillate between 1 and 3, which is not observed in the data.
+- Option (e) does not fit because the function should consistently increase, which does not match the symmetric behavior around x = 0.
+
+Thus, the best match for the given data is:
+**a) \( f(x) = |x^3| \)**
+SOLUTION: |x^3|
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, let's analyze the given options and the characteristics of the graph:
+
+1. **f(x) = sign(x)**: This function returns -1 for x < 0, 0 for x = 0, and 1 for x > 0. The graph should show a jump from -1 to 1 at x = 0.
+
+2. **f(x) = -sign(x)**: This function returns 1 for x < 0, 0 for x = 0, and -1 for x > 0. The graph should show a jump from 1 to -1 at x = 0.
+
+3. **f(x) = -sinh(x)**: The hyperbolic sine function, sinh(x), grows exponentially for large positive x and decreases exponentially for large negative x. The negative of this function, -sinh(x), would mirror this behavior, decreasing for positive x and increasing for negative x.
+
+4. **f(x) = -|-x|**: This function is the negative of the absolute value of x. It would be a V-shaped graph opening downwards, centered at x = 0.
+
+5. **f(x) = sqrt(x+10)**: This function involves a square root, which is only defined for non-negative arguments. Thus, the function is only defined for x ≥ -10. The function would start from 0 at x = -10 and increase as x increases.
+
+Now, let's analyze the JSON object:
+- The graph starts at x = -9.96 and ends at x = 10.0.
+- The y-values start from a positive value (0.217) and consistently increase as x increases, reaching a value of 4.47 at x = 10.0.
+
+Given this analysis:
+- Options a) and b) are not likely because the graph does not show a jump discontinuity at x = 0.
+- Option c) is not likely because -sinh(x) would decrease as x increases, which is opposite to the trend shown in the graph.
+- Option d) is not likely because the graph does not show a V-shape centered at x = 0.
+
+Option e) **f(x) = sqrt(x+10)** seems the most plausible:
+- The function starts being defined at x = -10, close to the starting point of the graph at x = -9.96.
+- The function value starts from 0 at x = -10 and increases as x increases, which matches the trend observed in the graph.
+
+Therefore, the graph most likely represents the function **f(x) = sqrt(x+10)**.
+SOLUTION: sqrt(x+10)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given x intervals and compare them with the expected behavior of each function option. Let's consider each function:
+
+a) \( f(x) = x^2 \)
+- This function is a parabola opening upwards. The values should increase as \( x \) moves away from zero in both the positive and negative directions.
+
+b) \( f(x) = x^3 \)
+- This cubic function has a point of inflection at \( x = 0 \) and changes from decreasing to increasing at this point. Negative values of \( x \) yield negative function values, and positive values of \( x \) yield positive function values.
+
+c) \( f(x) = -\sqrt{x+10} \)
+- This function involves a square root, which is only defined for \( x+10 \geq 0 \) (i.e., \( x \geq -10 \)). The negative sign in front of the square root indicates the function values should be negative and decreasing as \( x \) increases.
+
+d) \( f(x) = x^4 \)
+- This function is similar to \( x^2 \) but grows faster as \( x \) moves away from zero. It is always positive or zero.
+
+e) \( f(x) = (x-1)(x+1) = x^2 - 1 \)
+- This function is a downward-shifted parabola, similar to \( x^2 \) but shifted downward by 1 unit.
+
+Now, let's analyze the provided data:
+- The function values are all negative.
+- The function values decrease (become more negative) as \( x \) increases from a negative value towards zero, and continue to decrease as \( x \) moves into positive values.
+
+Given these observations:
+- Options a), d), and e) are unlikely because they involve functions that are either always positive or symmetric around the y-axis with positive and negative values.
+- Option b) is also unlikely because the function values for \( x^3 \) should change sign as \( x \) crosses zero, which is not observed in the data.
+- Option c), \( f(x) = -\sqrt{x+10} \), fits the behavior we see: the function is defined and negative for \( x \geq -10 \), and the values decrease as \( x \) increases.
+
+Therefore, the function that best matches the graph is:
+c) \( f(x) = -\sqrt{x+10} \)
+SOLUTION: -sqrt(x+10)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, let's analyze the characteristics of each function and compare them with the behavior of the graph as described in the JSON object:
+
+a) **f(x) = sign(x)**: This function returns -1 for negative x, 0 for x=0, and 1 for positive x. The graph should show a jump from -1 to 1 at x=0, which is not consistent with the gradual changes in the y-values provided.
+
+b) **f(x) = sqrt(x^2 + 3x + 5)**: This function is a square root of a quadratic expression. The graph of this function would be smooth and continuously increasing, as the expression under the square root is always positive and increases with x. This is a potential candidate based on the smooth and increasing nature of the values.
+
+c) **f(x) = 1/(1 + exp(-x))**: This is the logistic function, typically used in logistic regression for modeling probabilities. It has an S-shaped curve, starting near 0 for large negative x, approaching 0.5 at x=0, and approaching 1 for large positive x. This function could potentially match if the graph shows an S-shaped curve.
+
+d) **f(x) = -exp(x)**: This function is an exponential decay function for all x. It should rapidly decrease as x increases, which does not match the increasing trend in the y-values provided.
+
+e) **f(x) = cos(x)**: This trigonometric function oscillates between -1 and 1 with a period of 2π. The graph should show periodic peaks and troughs, which is not indicated in the provided data.
+
+Given the descriptions and the nature of the graph values which increase smoothly and continuously over the range of x values, the most likely candidates are:
+
+- **b) f(x) = sqrt(x^2 + 3x + 5)**: This function would continuously increase as x increases, matching the trend in the data.
+- **c) f(x) = 1/(1 + exp(-x))**: This function could also be a candidate if the data shows an S-shaped curve transitioning from values near 0 to values near 1 as x increases from negative to positive.
+
+To further narrow it down, we can look at the behavior at extreme values:
+- For very negative x, **f(x) = sqrt(x^2 + 3x + 5)** will behave like sqrt(x^2), which increases as |x| increases, consistent with the increasing values in the negative x range of the data.
+- For **f(x) = 1/(1 + exp(-x))**, the values should start near 0 for large negative x and approach 1 for large positive x. The provided data starts at values around 8.651 for large negative x and increases beyond 1 (up to around 11.49), which does not perfectly fit the expected range (0 to 1) of the logistic function.
+
+Based on this analysis, the most likely function represented by the graph is:
+**b) f(x) = sqrt(x^2 + 3x + 5)**, as it matches the increasing trend and the behavior at extreme values of x.
+SOLUTION: sqrt(x ** 2 + 3*x +5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the y-values as the x-values increase, based on the intervals and corresponding values provided in the JSON object. Let's consider each function option:
+
+a) \( f(x) = \exp(x) \)
+- This function is the exponential function, which increases very rapidly as x increases. The values should grow exponentially, which means each successive increase should be larger than the previous one.
+
+b) \( f(x) = \sqrt{x+10} \)
+- This function represents a square root transformation, which increases as x increases but at a decreasing rate. The growth in y-values should slow down as x increases.
+
+c) \( f(x) = -\text{sign}(x) \)
+- The sign function returns -1 for negative x, 0 for x=0, and 1 for positive x. Multiplying by -1 would flip these values. This function would only have three possible y-values: 1, 0, and -1, which does not match the continuous and varied y-values in the graph.
+
+d) \( f(x) = -\cosh(x) \)
+- The hyperbolic cosine function, \(\cosh(x)\), generally increases as x moves away from zero in either direction, but it is symmetric around y-axis. The negative would flip it upside down. This function does not match the behavior of increasing y-values as x increases.
+
+e) \( f(x) = \text{sign}(x+3) \)
+- This function shifts the sign function to the left by 3 units. It would have only three possible y-values: -1, 0, and 1, similar to option c, which does not fit the data.
+
+Given these considerations, let's look at the data:
+- The y-values start at 4.3 and increase to 21618.4 as x increases from -9.92 to 9.99.
+- The increase in y-values accelerates as x increases, suggesting an exponential growth pattern.
+
+Based on this analysis, the function that best matches the behavior of the y-values in relation to the x-values in the graph is:
+a) \( f(x) = \exp(x) \)
+
+This function shows the characteristic rapid and accelerating increase in values as x increases, which aligns with the data provided in the JSON object.
+SOLUTION: exp(x)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the y-values as the x-values increase, based on the intervals and corresponding values provided in the JSON object. Let's examine the characteristics of each function option and compare them with the graph data:
+
+1. **f(x) = -exp(x)**: This function would show a rapid decrease as x increases, with y-values becoming more negative exponentially.
+
+2. **f(x) = -sign(x)**: This function would be -1 for all x > 0 and 1 for all x < 0, with a discontinuity at x = 0.
+
+3. **f(x) = x^2**: This function would show a parabolic increase as x increases, with y-values becoming more positive and growing quadratically.
+
+4. **f(x) = x**: This linear function would increase or decrease steadily with x.
+
+5. **f(x) = tanh(x)**: This function would approach -1 as x decreases and 1 as x increases, with a smooth transition around x = 0.
+
+Now, let's analyze the provided data:
+- The y-values are all negative and decrease (become more negative) as x increases.
+- The decrease in y-values accelerates as x increases, suggesting an exponential growth in the absolute values of y.
+
+Given these observations, the behavior of the y-values aligns most closely with **f(x) = -exp(x)**. This function would result in increasingly negative outputs as x increases, matching the pattern seen in the graph where y-values drop sharply and increasingly as x moves from -10.0 to 10.0. The other functions do not match this pattern:
+- **f(x) = -sign(x)** would not show a variable decrease.
+- **f(x) = x^2** and **f(x) = x** would not produce negative values across the entire range for positive x.
+- **f(x) = tanh(x)** would not produce values that continue to decrease beyond -1.
+
+Therefore, the best match for the graph based on the provided data is **a) f(x) = -exp(x)**.
+SOLUTION: -exp(x)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given x-axis intervals and compare this behavior to the expected behavior of each of the candidate functions. Let's examine each function option:
+
+a) \( f(x) = -|-x| \)
+   - This function is the negative of the absolute value of \( -x \), which simplifies to \( -|x| \). This function decreases as \( x \) moves away from zero in both directions, reaching more negative values.
+
+b) \( f(x) = \log(x+10) \)
+   - This function is defined for \( x > -10 \) and increases as \( x \) increases. The logarithmic growth is slower compared to linear or exponential functions.
+
+c) \( f(x) = -|x| \)
+   - This function is the negative of the absolute value of \( x \), which decreases linearly as \( x \) moves away from zero in both directions, reaching more negative values.
+
+d) \( f(x) = x^2 \)
+   - This function is a parabola opening upwards, increasing as \( x \) moves away from zero in both directions.
+
+e) \( f(x) = \exp(-x) \)
+   - This exponential decay function decreases as \( x \) increases, approaching zero but never becoming negative.
+
+Now, let's analyze the behavior of the function values in the JSON object:
+- The function values start high and decrease as \( x \) increases from approximately -10 to 10.
+- The decrease is not symmetric around zero, suggesting it's not a simple absolute value or quadratic function.
+- The values decrease significantly as \( x \) approaches zero from the negative side and continue to decrease past zero, though the rate of decrease changes.
+
+Given these observations, the function \( f(x) = \log(x+10) \) seems to be the most likely candidate:
+- The function is defined and real-valued for \( x > -10 \), which matches the domain in the JSON object.
+- The logarithmic function increases as \( x \) increases but at a decreasing rate, which aligns with the observed behavior where values are high near \( x = -10 \) and decrease as \( x \) increases.
+- The function \( \log(x+10) \) would not symmetrically decrease around zero like \( -|x| \) or \( x^2 \) would, and it does not approach zero as \( \exp(-x) \) does.
+
+Therefore, the graph most likely represents the function \( f(x) = \log(x+10) \).
+SOLUTION: exp(-x)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given intervals and compare this behavior with the characteristics of each function option provided. Let's examine each function and its typical behavior:
+
+a) \( f(x) = 3^x + 1 \)
+   - This function grows exponentially as \( x \) increases. The addition of 1 shifts the graph upwards by 1 unit.
+
+b) \( f(x) = 2^x \)
+   - This function also grows exponentially as \( x \) increases, but at a slower rate compared to \( 3^x \).
+
+c) \( f(x) = \exp(-x) \)
+   - This function decreases exponentially as \( x \) increases, approaching zero but never becoming negative.
+
+d) \( f(x) = \exp(-x^2) \)
+   - This is a Gaussian or normal distribution curve, which is symmetric around \( x = 0 \) and has its maximum at \( x = 0 \), decreasing rapidly as \( x \) moves away from zero in both directions.
+
+e) \( f(x) = |x| \)
+   - This function is linear with a V-shape, having its vertex at \( x = 0 \) and increasing linearly as \( x \) moves away from zero in both positive and negative directions.
+
+Now, let's analyze the behavior of the function values in the JSON object:
+- The function values increase as \( x \) moves from negative to zero, reaching a peak around \( x = 0 \), and then decrease as \( x \) moves from zero to positive values.
+- The function is symmetric around \( x = 0 \), with similar values for negative and positive \( x \) of similar magnitude.
+
+Given this behavior:
+- Options a) and b) are not suitable as they do not decrease after reaching a peak.
+- Option c) is not suitable as it does not increase before reaching \( x = 0 \) and should not have symmetry around \( x = 0 \).
+- Option e) is not suitable as it should continuously increase from \( x = 0 \) in both directions without a peak.
+
+Option d) \( f(x) = \exp(-x^2) \) is the most likely candidate:
+- It shows a peak at \( x = 0 \) and symmetric decrease on both sides, matching the behavior of a Gaussian curve, which is exactly what \( \exp(-x^2) \) represents.
+
+Thus, the graph most likely depicts the function \( f(x) = \exp(-x^2) \).
+SOLUTION: exp(-x^2)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the y-values as the x-values increase based on the intervals provided in the JSON object. Let's examine the characteristics of each function option and compare them with the trend observed in the graph:
+
+a) **f(x) = sign(x)**: This function returns -1 for negative x, 0 for x=0, and 1 for positive x. It does not produce a continuous increase or a wide range of values.
+
+b) **f(x) = 2^x**: This is an exponential function where y-values increase exponentially as x increases. This function starts very small for negative x and increases very rapidly for positive x.
+
+c) **f(x) = x**: This is a linear function with a direct proportionality between x and y. The increase is steady and linear.
+
+d) **f(x) = -|-x|**: This function decreases as x moves away from zero, whether x is positive or negative. It is symmetric around y-axis and always negative or zero.
+
+e) **f(x) = -3*x^3**: This cubic function decreases as x increases if x is positive, due to the negative coefficient. For negative x, the function values would be positive and decrease as x becomes more negative.
+
+Now, let's analyze the provided graph data:
+- The y-values start from a lower value (0.2) and increase as the x-values increase.
+- The increase in y-values accelerates, suggesting a faster-than-linear growth rate.
+
+Given these observations:
+- The linear growth (option c) is ruled out because the rate of increase in y-values is not constant.
+- The sign function (option a) and the negative absolute value function (option d) do not fit because they do not show a continuous and broad range of increasing values.
+- The cubic function (option e) does not fit as it would not consistently increase for increasing positive x-values.
+
+The exponential function (option b, f(x) = 2^x) is the most likely candidate:
+- It starts with smaller values for negative or small x and increases rapidly for larger x-values.
+- The provided y-values in the graph show a rapid increase as x increases, which is characteristic of exponential growth.
+
+Thus, the graph most likely represents the function **f(x) = 2^x**.
+SOLUTION: 2^x
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the y-values as x increases, based on the given intervals and corresponding y-values. Let's consider each function option and see which one matches the pattern observed in the graph:
+
+### a) Logistic Function: \( f(x) = \frac{1}{1 + e^{-x}} \)
+- This function is an S-shaped curve that starts near 0, approaches 0.5 at \( x = 0 \), and asymptotically approaches 1 as \( x \) increases. The values in the graph, however, start from 16 and increase to 57606.1, which doesn't match the bounded nature of the logistic function.
+
+### b) Hyperbolic Cosine: \( f(x) = \cosh(x) \)
+- The hyperbolic cosine function starts from 1 at \( x = 0 \) and increases exponentially as \( |x| \) increases. This function is symmetric around \( x = 0 \). The values in the graph consistently increase as \( x \) increases, which is a potential match, especially considering the exponential growth.
+
+### c) Negative Square Root: \( f(x) = -\sqrt{x + 10} \)
+- This function is defined for \( x \geq -10 \) and would decrease as \( x \) increases, moving towards zero from negative values. The graph values are positive and increasing, which contradicts this behavior.
+
+### d) Exponential Function: \( f(x) = 3^x + 1 \)
+- This function starts from 2 at \( x = 0 \) and increases exponentially as \( x \) increases. This matches the pattern of rapid increase in the graph values, similar to the hyperbolic cosine but without symmetry around \( x = 0 \).
+
+### e) Negative Absolute Value: \( f(x) = -|x| \)
+- This function decreases linearly as \( |x| \) increases, which does not match the increasing pattern of the graph values.
+
+### Analysis:
+Both options b) \( \cosh(x) \) and d) \( 3^x + 1 \) show exponential growth, which aligns with the increasing pattern of the graph. However, the exponential growth of \( 3^x + 1 \) is generally faster than that of \( \cosh(x) \), which might better match the rapid increase in y-values observed in the graph. Additionally, \( \cosh(x) \) is symmetric around \( x = 0 \), which doesn't seem to be a feature of the graph based on the provided intervals.
+
+### Conclusion:
+Given the rapid and consistent increase in y-values, the graph most likely represents the function:
+**d) \( f(x) = 3^x + 1 \)**.
+SOLUTION: 3^x+1
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given intervals and compare them with the expected behavior of each of the listed functions. Let's break down each function and compare:
+
+a) \( f(x) = 2^{(x-5)} \)
+- This function is an exponential function where the base is 2. The function value increases exponentially as \( x \) increases. The function value is 1 when \( x = 5 \).
+
+b) \( f(x) = x \)
+- This is a linear function with a constant rate of increase. The function value is equal to \( x \).
+
+c) \( f(x) = -|x| \)
+- This function represents the negative of the absolute value of \( x \). It decreases as \( x \) moves away from zero in either direction, reaching a minimum at \( x = 0 \).
+
+d) \( f(x) = -\exp(x) \)
+- This function is an exponential decay function, where the function values become more negative as \( x \) increases.
+
+e) \( f(x) = -\text{sign}(x) \)
+- This function returns -1 for all positive \( x \), 1 for all negative \( x \), and 0 at \( x = 0 \).
+
+Now, let's analyze the provided JSON object:
+- The function values start very low near 0.01 when \( x \) is around -9.99 to 3.32 and increase as \( x \) increases, reaching values as high as 30.89 when \( x \) is around 9.94 to 9.98.
+
+This behavior suggests an exponential increase as \( x \) increases. The function value is around 1 when \( x \) is near 5, which is a key characteristic of the function \( f(x) = 2^{(x-5)} \) where \( f(5) = 2^{(5-5)} = 2^0 = 1 \). As \( x \) increases from 5, the function values increase exponentially, which matches the behavior of \( f(x) = 2^{(x-5)} \).
+
+Comparing this with the other functions:
+- \( f(x) = x \) would not fit as it would linearly increase, not exponentially.
+- \( f(x) = -|x| \) would decrease as \( x \) increases, which does not match.
+- \( f(x) = -\exp(x) \) would also decrease (become more negative), which does not match.
+- \( f(x) = -\text{sign}(x) \) would be constant for all positive \( x \), which does not match.
+
+Therefore, the function that best matches the graph described in the JSON object is:
+**a) \( f(x) = 2^{(x-5)} \)**.
+SOLUTION: 2^(x-5)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of each function across the given range and compare it to the values and trends in the JSON object. Let's consider each function:
+
+a) **f(x) = arcsinh(x)**: The arcsinh function, or hyperbolic arcsine, grows slowly at first and then more rapidly, but it does not have an upper or lower bound. It is defined for all real numbers and is an odd function, meaning it is symmetric about the origin.
+
+b) **f(x) = log(exp(x))**: This simplifies to f(x) = x because the logarithm and exponential functions are inverses of each other. This function is a straight line with a slope of 1.
+
+c) **f(x) = tanh(x)**: The hyperbolic tangent function smoothly transitions from -1 to 1 as x moves from negative to positive infinity. It has horizontal asymptotes at y = -1 and y = 1.
+
+d) **f(x) = log(x+10)**: This logarithmic function is defined for x > -10. It increases slowly and does not have an upper bound, but the growth rate decreases as x increases.
+
+e) **f(x) = -log(x+10)**: This is the negative of the logarithmic function in d). It decreases as x increases, starting from a high value when x is close to -10 and approaching negative infinity as x approaches infinity.
+
+Now, let's analyze the JSON data:
+- The values start at -3.425 when x is close to -10 and increase as x increases, reaching a maximum of 2.962 near x = 10.
+- The function value increases throughout the range, suggesting a function that is always increasing within the given domain.
+
+Comparing these observations with the characteristics of the listed functions:
+- **f(x) = arcsinh(x)**: This could be a candidate as it is always increasing and unbounded. However, the range of values in the JSON object doesn't fit well with typical values of arcsinh, which would not reach such high values over the given domain.
+- **f(x) = log(exp(x)) = x**: This function increases linearly, which doesn't match the non-linear increase in the JSON data.
+- **f(x) = tanh(x)**: This function is bounded between -1 and 1, which does not match the range of values in the JSON data.
+- **f(x) = log(x+10)**: This function increases and fits the unbounded increase as x increases. The values in the JSON object suggest a logarithmic increase, starting from a negative value when x is just above -10 and increasing as x increases.
+- **f(x) = -log(x+10)**: This function decreases as x increases, which contradicts the increasing trend in the JSON data.
+
+Based on this analysis, the most likely function represented by the graph is **d) f(x) = log(x+10)**. This function matches the increasing trend and the behavior of starting from a lower value and increasing as x moves away from -10.
+SOLUTION: log(x+10)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given x-axis intervals and compare them with the expected behavior of each of the candidate functions. Let's examine each function option:
+
+a) **f(x) = -sin(x)**
+   - The sine function oscillates between -1 and 1. The negative sine function would oscillate between -1 and 1 as well, but with inverted peaks and troughs. The values in the JSON object, however, show a consistent decrease rather than oscillation, so this is unlikely to be the correct function.
+
+b) **f(x) = x**
+   - A linear function with a positive slope would increase as x increases. The values in the JSON object consistently decrease as x increases, which suggests this is not a simple linear function with a positive slope.
+
+c) **f(x) = -log(x+10)**
+   - The logarithmic function grows slowly as x increases, and the negative logarithm would decrease as x increases. Given that x+10 shifts the domain to avoid negative values inside the logarithm, this function could potentially match the behavior if the decrease is gradual and consistent.
+
+d) **f(x) = -|x|**
+   - The absolute value function |x| increases linearly from zero as x moves away from zero in either direction. The negative of this function would decrease from zero, becoming more negative as x moves away from zero. This function would show symmetry around x = 0, which we need to check against the data.
+
+e) **f(x) = -2*x^2**
+   - The function x^2 grows quadratically as x moves away from zero, and -2*x^2 would decrease quadratically. This function would also show symmetry around x = 0, with values decreasing more steeply as x moves away from zero.
+
+To further analyze, let's look at the behavior near x = 0 and the symmetry:
+- The values near x = 0 in the JSON object are `(-0.63, 0.07)": -2.234` and `(0.07, 0.85)": -2.306`. This indicates a decrease as x moves away from zero, which is consistent with both d) and e).
+
+However, the rate of decrease and the values across the range need to be considered. The quadratic function would show a steeper decrease compared to the linear decrease of the absolute value function. Observing the values from the JSON:
+- As x moves from -10 to 0 and from 0 to 10, the decrease in y values is quite consistent and becomes more negative rapidly, which suggests a quadratic rather than a linear relationship.
+
+Given this analysis, the most likely function represented by the graph is:
+**e) f(x) = -2*x^2**. This function matches the observed rapid and symmetric decrease in y values as x moves away from zero.
+SOLUTION: -log(x+10)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given intervals and compare them with the expected behavior of each of the candidate functions. Let's consider each function:
+
+a) \( f(x) = -\log(x+10) \)
+- This function is defined for \( x > -10 \) and will decrease as \( x \) increases, approaching negative infinity as \( x \) approaches -10 from the right and approaching 0 as \( x \) increases indefinitely.
+
+b) \( f(x) = x^2 \)
+- This is a simple quadratic function that decreases as \( x \) moves away from 0 in either direction, reaching a minimum at \( x = 0 \).
+
+c) \( f(x) = -\sqrt{x+10} \)
+- This function is defined for \( x \geq -10 \) and will decrease as \( x \) increases, starting from 0 when \( x = -10 \) and becoming more negative as \( x \) increases.
+
+d) \( f(x) = \log(\exp(x)) \)
+- This function simplifies to \( f(x) = x \), which is a linear function increasing as \( x \) increases.
+
+e) \( f(x) = -(x + 4)^4 \)
+- This function is a quartic function shifted to the left by 4 units and reflected about the x-axis. It has a maximum at \( x = -4 \) and decreases sharply as \( x \) moves away from -4 in either direction.
+
+Now, let's analyze the behavior of the function values in the JSON object:
+- The function values start at -9.99 when \( x \) is close to -10 and increase as \( x \) increases, passing through 0 and becoming positive, continuing to increase.
+
+From this behavior, we can eliminate:
+- \( f(x) = x^2 \) and \( f(x) = -(x + 4)^4 \) because both are symmetric around their respective axes or points and do not pass through zero in the manner described.
+- \( f(x) = -\log(x+10) \) and \( f(x) = -\sqrt{x+10} \) because both should only decrease as \( x \) increases, and neither should cross from negative to positive values.
+
+The remaining function:
+- \( f(x) = \log(\exp(x)) \) or \( f(x) = x \), which increases linearly as \( x \) increases, matches the behavior described in the JSON object where the function value starts negative, increases through zero, and continues increasing positively as \( x \) increases further.
+
+Therefore, the function depicted in the graph is:
+d) \( f(x) = \log(\exp(x)) \) or equivalently \( f(x) = x \).
+SOLUTION: log(exp(x))
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, let's analyze the behavior of the function values across the given x-axis intervals and compare them with the expected behavior of each of the listed functions.
+
+1. **f(x) = log(exp(x))**:
+   - This function simplifies to \( f(x) = x \). Therefore, it should be a straight line with a slope of 1.
+
+2. **f(x) = x**:
+   - This is a linear function with a slope of 1, increasing uniformly as x increases.
+
+3. **f(x) = sign(x-1)**:
+   - This function returns -1 for x < 1, 0 for x = 1, and 1 for x > 1. It should show a jump from -1 to 1 at x = 1.
+
+4. **f(x) = sin(x)**:
+   - The sine function oscillates between -1 and 1 with a periodicity of \( 2\pi \). It should show a repeating pattern of peaks and troughs.
+
+5. **f(x) = -sign(x)**:
+   - This function returns 1 for x < 0, 0 for x = 0, and -1 for x > 0. It should show a jump from 1 to -1 at x = 0.
+
+Now, let's analyze the JSON data:
+- The values start at a positive value, decrease to negative, and then increase back to positive as x increases from -10 to 10.
+- The function values are not bounded between -1 and 1, which rules out the sine function.
+- There is no abrupt jump from -1 to 1 or 1 to -1 at specific points (like x = 1 or x = 0), which rules out the sign functions.
+- The function values do not maintain a constant rate of increase or decrease, which suggests it's not a simple linear function.
+
+Given the behavior of the function values in the JSON data, none of the options (a, b, c, d, e) perfectly match the described behavior. However, the closest match in terms of general shape (increasing, then decreasing, then increasing) might be the sine function, despite the values not being strictly bounded between -1 and 1. This could suggest a scaled or shifted sine function, or possibly a different function not listed among the options.
+
+If we strictly adhere to the given options and their standard mathematical definitions, none of the options perfectly fits the data provided. However, if we consider potential transformations or misinterpretations of the function forms, **f(x) = sin(x)** might be a plausible guess due to its oscillatory nature, albeit not strictly matching the bounded range.
+SOLUTION: sin(x)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given intervals and compare them with the expected behavior of each of the listed functions. Let's consider each function option:
+
+a) \( f(x) = x^2 + 3x - 1 \)
+- This is a quadratic function with a positive leading coefficient, meaning it has a parabolic shape opening upwards. The function should decrease to a minimum point and then increase.
+
+b) \( f(x) = \text{sign}(x) \)
+- This function returns -1 for negative x, 0 for x=0, and 1 for positive x. It should show a step-like behavior with jumps at x=0.
+
+c) \( f(x) = -\sin(x) \)
+- This function is the negative sine function, which oscillates between -1 and 1, with periodic peaks and troughs.
+
+d) \( f(x) = -(x + 4)^4 \)
+- This function is a quartic function shifted to the left by 4 units and reflected about the x-axis. It should decrease sharply as x moves away from -4 in either direction, with a minimum value at x = -4.
+
+e) \( f(x) = x \)
+- This is a linear function with a slope of 1, increasing steadily as x increases.
+
+Now, let's analyze the behavior of the function values in the JSON object:
+- The values start negative, increase to positive, and then decrease back to negative as x increases from -9.98 to 9.98.
+- The function values appear to have a symmetric behavior around some central point, suggesting a function that has a central peak or trough.
+
+Considering the descriptions and the behavior of the function values:
+- Option (a) could be plausible if the minimum point of the parabola is near the center of the x-range, but the quadratic nature typically does not lead to symmetry about a central point unless specifically designed.
+- Option (b) is not plausible as the function values do not show a step-like behavior.
+- Option (c) is not plausible as the function values do not oscillate in a typical sinusoidal pattern.
+- Option (d) seems plausible because the function values increase to a peak and then symmetrically decrease, which is characteristic of a quartic function with a single minimum or maximum, especially since it is reflected and shifted.
+- Option (e) is not plausible as the function values do not show a linear increase or decrease.
+
+Given the symmetry and the sharp increase to a peak followed by a decrease, the most likely function represented by the graph is:
+**d) \( f(x) = -(x + 4)^4 \)**. This function would have a sharp peak at x = -4 and show symmetric behavior around this point, consistent with the observed values in the JSON object.
+SOLUTION: -sin(x)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given x-axis intervals and compare them to the typical behaviors of the functions listed in the options. Let's break down each function and compare:
+
+a) **f(x) = x**: This function is a simple linear function with a direct proportionality between x and f(x). As x increases, f(x) increases linearly without any bounds.
+
+b) **f(x) = |x^3|**: This function is the absolute value of the cube of x. It results in a graph that is symmetric about the y-axis, with values increasing steeply as x moves away from zero in either direction.
+
+c) **f(x) = cosh(x)**: The hyperbolic cosine function, cosh(x), is symmetric about the y-axis and increases exponentially as x moves away from zero in either direction. It starts from 1 at x = 0 and grows exponentially.
+
+d) **f(x) = x**: This is a duplicate of option a) and has the same characteristics.
+
+e) **f(x) = -sin(-x)**: This function simplifies to -(-sin(x)) = sin(x) due to the even property of the sine function. The sine function oscillates between -1 and 1 with a period of 2π, and it crosses the x-axis at multiples of π.
+
+Now, let's analyze the JSON data:
+- The values start negative, reach a minimum, then increase, pass through zero, become positive, reach a maximum, and then decrease again. This behavior suggests a function that is symmetric around the y-axis and has a single peak and trough within the observed range.
+
+Given the descriptions and the behavior in the data:
+- **f(x) = x** and **f(x) = x** (duplicates) do not fit because they would not return to negative values after becoming positive.
+- **f(x) = |x^3|** fits the description of a function that increases steeply as x moves away from zero, but it should not cross the x-axis or become negative once it becomes positive.
+- **f(x) = cosh(x)** fits well because it is symmetric, starts from a positive value (greater than zero), and increases exponentially as x moves away from zero. However, the values in the JSON object cross zero and become negative, which does not happen with cosh(x).
+- **f(x) = sin(x)** fits the observed behavior of crossing zero, having a single peak and trough, and being periodic (though the period isn't clear from the data, the behavior over the range suggests a wave-like pattern).
+
+Thus, the best match from the given options, considering the periodic and oscillating nature of the data, is:
+e) **f(x) = -sin(-x)**, which simplifies to **sin(x)**.
+SOLUTION: -sin(-x)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given x-axis intervals and compare them with the expected behavior of each of the listed functions. Let's consider each function:
+
+a) \( f(x) = \exp(-x) \)
+- This function is an exponential decay function. As \( x \) increases, \( f(x) \) should decrease towards zero.
+
+b) \( f(x) = \text{arcsinh}(x) \)
+- The arcsinh function, or inverse hyperbolic sine, behaves linearly for large values of \( x \) but grows slower than a linear function. It passes through the origin (0,0) and is odd symmetric.
+
+c) \( f(x) = \sin(x+2)+2 \)
+- This function is a sinusoidal wave shifted horizontally by -2 units and vertically by +2 units. It should oscillate between 1 and 3 periodically.
+
+d) \( f(x) = -(x + 4)^4 \)
+- This function is a quartic polynomial mirrored vertically (due to the negative sign) and shifted left by 4 units. It should decrease steeply as \( x \) moves away from -4, with a minimum point at \( x = -4 \).
+
+e) \( f(x) = (x-2)^2 \)
+- This is a parabolic function with a vertex at \( x = 2 \). As \( x \) moves away from 2, \( f(x) \) should increase quadratically.
+
+Now, let's analyze the behavior of the function values from the JSON object:
+- The function values start at 1.0 when \( x \) is around -9.96 and increase as \( x \) increases, reaching a peak around \( x = 2.99 \) with \( f(x) = 3.07 \), and then decrease again as \( x \) increases further.
+
+This behavior suggests a function that has a single peak (or minimum if inverted) and is symmetric around that peak. Among the given options, the parabolic function \( f(x) = (x-2)^2 \) fits this description best, as it has a vertex at \( x = 2 \) and symmetrically increases as \( x \) moves away from 2 in either direction.
+
+Therefore, the graph most likely represents the function:
+e) \( f(x) = (x-2)^2 \)
+SOLUTION: sin(x+2)+2
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given x-axis intervals and compare them to the typical behaviors of the functions listed (a through e).
+
+1. **Cosine Function (f(x) = cos(x))**: This function oscillates between -1 and 1 with a periodic pattern. The cosine function has zeros at odd multiples of π/2 and maximums and minimums at multiples of π.
+
+2. **Sign Function (f(x) = -sign(-x) and f(x) = sign(x+3))**: The sign function outputs -1 for negative inputs, 0 at zero, and 1 for positive inputs. The function f(x) = -sign(-x) would effectively be the same as sign(x), flipping at x = 0. The function f(x) = sign(x+3) would flip at x = -3.
+
+3. **Hyperbolic Tangent (f(x) = tanh(x))**: This function approaches -1 as x approaches negative infinity and 1 as x approaches positive infinity, with a smooth S-shaped curve passing through the origin (0,0).
+
+4. **Cubic Function (f(x) = x^3)**: This function decreases as x decreases and increases as x increases, passing through the origin with a slope change at x = 0.
+
+Now, let's analyze the JSON data:
+- The values start at -0.847 near x = -10 and gradually increase as x approaches 0, reaching a maximum near 0.995 at x = 6.17. After this peak, the values decrease again as x increases, approaching -0.867 near x = 10.
+- The function values are bounded between -1 and 1.
+- The function appears to be symmetric around a central peak, suggesting a smooth, continuous change rather than abrupt flips or oscillations.
+
+Given this analysis:
+- **Cosine Function**: The behavior doesn't match a periodic oscillation.
+- **Sign Functions**: There is no abrupt change from -1 to 1 or vice versa at any specific point (like x = 0 or x = -3), which rules out the sign functions.
+- **Cubic Function**: The behavior doesn't match the unbounded increase and decrease expected of a cubic function.
+- **Hyperbolic Tangent**: The gradual approach towards -1 and 1 as x moves away from zero, the smooth S-shaped curve, and the symmetry about a central peak are all characteristic of the hyperbolic tangent function.
+
+Thus, the function that best matches the graph described in the JSON data is:
+**d) f(x) = tanh(x)**.
+SOLUTION: cos(x)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the characteristics of each function and compare them to the behavior shown in the JSON object. Let's break down each function:
+
+a) \( f(x) = \frac{1}{2}\cos(x-2) \)
+- This function is a cosine wave, scaled by 1/2, and shifted to the right by 2 units. It oscillates between -0.5 and 0.5, with periodic zeros and peaks.
+
+b) \( f(x) = |2x+4| \)
+- This is an absolute value function, which is linear with a vertex (turning point) at \( x = -2 \). It decreases to 0 at \( x = -2 \) and then increases linearly.
+
+c) \( f(x) = -|-x| \)
+- This function represents a downward-opening V-shape centered at \( x = 0 \).
+
+d) \( f(x) = \sqrt{x^2 + 3x + 5} \)
+- This function is a square root of a quadratic expression. It is always positive and gradually increases as \( x \) moves away from the vertex of the parabola \( x^2 + 3x + 5 \).
+
+e) \( f(x) = \tanh(x) \)
+- The hyperbolic tangent function smoothly transitions from -1 to 1 as \( x \) moves from negative to positive infinity, with a smooth S-shaped curve passing through the origin (0,0).
+
+Now, let's analyze the JSON data:
+- The values start negative, increase to a peak, decrease back to negative, and then increase again. This suggests a periodic function or a function with symmetry.
+- The values are bounded between approximately -0.5 and 0.5.
+
+Given these observations:
+- Option (a), \( f(x) = \frac{1}{2}\cos(x-2) \), fits well because it is periodic and oscillates between -0.5 and 0.5, matching the range of y-values in the JSON data.
+- Option (b), the absolute value function, does not fit because it only increases after reaching zero, which does not match the periodic nature of the data.
+- Option (c), the negative absolute value function, does not fit because it only has one peak and does not show periodic behavior.
+- Option (d), the square root of a quadratic, does not fit because it should only increase or decrease, not oscillate.
+- Option (e), \( \tanh(x) \), could be considered due to the bounded nature of the output, but it does not show the clear periodicity or the exact shape of the cosine function.
+
+Therefore, the best match for the data provided in the JSON object is:
+**a) \( f(x) = \frac{1}{2}\cos(x-2) \)**.
+SOLUTION: 1/2*cos(x-2)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given x-axis intervals and compare them with the expected behavior of each of the candidate functions. Let's consider each function:
+
+a) \( f(x) = -x^5 \)
+- This function is a polynomial with a negative leading coefficient and an odd power. It should exhibit rapid growth or decay as \( x \) moves away from zero, with symmetry about the y-axis (i.e., similar behavior in positive and negative x-values but with opposite signs).
+
+b) \( f(x) = \sqrt{x+10} \)
+- This function is defined for \( x \geq -10 \) and increases slowly as \( x \) increases. It starts from zero (at \( x = -10 \)) and gradually increases.
+
+c) \( f(x) = \exp(x) \)
+- The exponential function grows very rapidly as \( x \) increases and approaches zero as \( x \) decreases towards negative infinity.
+
+d) \( f(x) = \sinh(x) \)
+- The hyperbolic sine function, similar to the exponential function, grows rapidly for positive \( x \) and decreases (approaching zero) rapidly for negative \( x \).
+
+e) \( f(x) = x \)
+- A linear function with direct proportionality between \( x \) and \( y \).
+
+Now, let's analyze the behavior of the function values from the JSON object:
+- For large negative \( x \) values, the function values are large negative numbers, and as \( x \) increases, the function values increase, crossing zero, and becoming positive, continuing to increase as \( x \) becomes more positive.
+
+This behavior matches closely with the hyperbolic sine function (\( \sinh(x) \)), which exhibits rapid growth and decay symmetrically around the origin, crossing zero at \( x = 0 \). The exponential function (\( \exp(x) \)) does not cross zero and remains positive, which does not match the behavior in the JSON data. The polynomial \( -x^5 \) would not cross zero and change signs in the manner described. The square root function starts at zero and increases but does not become negative, which is not consistent with the data. The linear function would not fit the rapid increase and decrease observed.
+
+Therefore, the best match among the given options is:
+d) \( f(x) = \sinh(x) \)
+SOLUTION: sinh(x)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given x-axis intervals and compare them with the expected behavior of each of the listed functions. Let's break down each function and consider its behavior:
+
+a) \( f(x) = -\log(x+10) \)
+- This function involves a logarithm shifted to the right by 10 units and negated. As \( x \) approaches -10 from the right, \( \log(x+10) \) approaches negative infinity, and \( -\log(x+10) \) approaches positive infinity. As \( x \) increases, \( -\log(x+10) \) decreases.
+
+b) \( f(x) = -\sinh(x) \)
+- The hyperbolic sine function, \( \sinh(x) \), grows exponentially in the positive direction and decreases exponentially in the negative direction. Negating it, \( -\sinh(x) \), flips this behavior: it decreases exponentially as \( x \) increases and increases exponentially as \( x \) decreases.
+
+c) \( f(x) = \arctan(x) \)
+- The arctan function increases gradually and has horizontal asymptotes as \( x \) approaches positive and negative infinity. It is bounded between \( -\frac{\pi}{2} \) and \( \frac{\pi}{2} \).
+
+d) \( f(x) = (x-1)(x+1) = x^2 - 1 \)
+- This is a simple parabola opening upwards, shifted down by 1 unit. It has a minimum value of -1 at \( x = 0 \) and increases as \( x \) moves away from zero in either direction.
+
+e) \( f(x) = -\exp(x) \)
+- This function is the exponential function negated. It rapidly decreases as \( x \) increases and approaches zero from below as \( x \) decreases.
+
+Now, let's analyze the behavior of the function values in the JSON object:
+- The function values start very high and positive when \( x \) is close to -10 and decrease as \( x \) increases.
+- The function values become negative and large in magnitude as \( x \) becomes positive and large.
+
+This behavior matches closely with option (a) \( f(x) = -\log(x+10) \). As \( x \) approaches -10 from the right, \( -\log(x+10) \) should approach positive infinity, and as \( x \) increases past -10, \( -\log(x+10) \) should decrease, crossing zero and becoming negative, continuing to decrease as \( x \) increases further. This matches the trend observed in the JSON data, where the function values start very high and positive and decrease, crossing zero, and becoming increasingly negative as \( x \) increases.
+
+Therefore, the graph approximately depicts the function \( f(x) = -\log(x+10) \).
+SOLUTION: -sinh(x)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the y-values as x changes, based on the provided JSON object. Let's consider each function option and how it behaves:
+
+a) \( f(x) = \cosh(x) \) - The hyperbolic cosine function is symmetric around the y-axis and increases exponentially as \( |x| \) increases. It has a minimum value at \( x = 0 \).
+
+b) \( f(x) = -\exp(x) \) - This function is always decreasing as \( x \) increases, and it is not symmetric around the y-axis.
+
+c) \( f(x) = x \) - This is a linear function with a slope of 1, increasing as \( x \) increases, and passing through the origin.
+
+d) \( f(x) = -2x + 5 \) - This is a linear function with a negative slope, decreasing as \( x \) increases.
+
+e) \( f(x) = x^2 \) - This function is a parabola opening upwards, symmetric around the y-axis, and has a minimum at \( x = 0 \).
+
+Given the JSON data, we observe that the y-values are symmetric around \( x = 0 \) and increase as \( |x| \) increases, suggesting an exponential growth on both sides of the y-axis. This behavior matches the description of the hyperbolic cosine function, \( \cosh(x) \), which is known for its characteristic symmetric exponential growth as \( |x| \) increases, with a minimum at \( x = 0 \).
+
+Thus, the function that the graph most likely represents is:
+**a) \( f(x) = \cosh(x) \)**.
+SOLUTION: cosh(x)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the y-values as x changes, based on the given options. Let's consider each function and its typical behavior:
+
+a) \( f(x) = 2^x \) - This function grows exponentially as x increases. The y-values should increase rapidly as x moves from negative to positive.
+
+b) \( f(x) = -|-x| \) - This function is a downward-opening V-shaped graph, symmetric around the y-axis, with its vertex at the origin.
+
+c) \( f(x) = \exp(x) \) - Similar to \( 2^x \), this function also grows exponentially as x increases, but with a different base (e instead of 2).
+
+d) \( f(x) = 2^{(x-5)} \) - This function is a horizontal shift of \( 2^x \), shifted 5 units to the right. It behaves like \( 2^x \) but starts increasing significantly only when x is near 5.
+
+e) \( f(x) = -\cosh(x) \) - The hyperbolic cosine function \( \cosh(x) \) is symmetric and has a minimum at x = 0, increasing exponentially as x moves away from zero in both directions. The negative sign would flip it upside down.
+
+Now, let's analyze the provided data:
+- The y-values are negative across the entire range of x-values provided.
+- The y-values decrease (become more negative) as x moves from a large negative towards zero, reach a minimum around x = 0, and then decrease again (become less negative) as x moves from zero to positive values.
+
+This behavior suggests a symmetric function around x = 0, where the function has a maximum negative value at x = 0 and decreases as x moves away from zero in both directions. Among the options, the function \( -\cosh(x) \) fits this description. The hyperbolic cosine function is symmetric and has its minimum value at x = 0, increasing on either side. The negative sign would invert this to have a maximum negative value at x = 0, decreasing (becoming less negative) on either side.
+
+Therefore, the function that the graph most likely represents is:
+e) \( f(x) = -\cosh(x) \)
+SOLUTION: -cosh(x)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given intervals and compare them with the expected behavior of each of the listed functions. Let's consider each function option:
+
+a) \( f(x) = x^2 + 3x - 1 \)
+- This is a quadratic function with a positive leading coefficient, meaning it has a parabolic shape opening upwards. The function should decrease to a minimum point and then increase.
+
+b) \( f(x) = -2x^2 \)
+- This is a quadratic function with a negative leading coefficient, meaning it has a parabolic shape opening downwards. The function should reach a maximum at \( x = 0 \) and decrease as \( x \) moves away from zero in both directions.
+
+c) \( f(x) = \tanh(x) \)
+- The hyperbolic tangent function, \( \tanh(x) \), increases from -1 to 1 as \( x \) moves from negative to positive infinity, passing through 0 at \( x = 0 \). It has horizontal asymptotes at \( y = -1 \) and \( y = 1 \).
+
+d) \( f(x) = \sin(x+2) + 2 \)
+- This function is a sine wave shifted horizontally and vertically. The sine function oscillates between -1 and 1, so this function oscillates between 1 and 3.
+
+e) \( f(x) = -x^5 \)
+- This is a quintic function with a negative leading coefficient. It decreases as \( x \) increases in the positive direction and increases as \( x \) increases in the negative direction, passing through 0 at \( x = 0 \).
+
+Now, let's analyze the JSON data:
+- The function values are symmetric around \( x = 0 \) and increase from negative to positive as \( x \) increases from negative to positive values.
+- The function values are bounded between approximately -1 and 1.
+
+Given this analysis, the behavior of the function values in the JSON object most closely matches the description of \( f(x) = \tanh(x) \). The function increases smoothly from a value near -1, crosses 0 around \( x = 0 \), and approaches 1 as \( x \) increases further, which is characteristic of the hyperbolic tangent function.
+
+Therefore, the correct answer is:
+c) \( f(x) = \tanh(x) \)
+SOLUTION: tanh(x)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given intervals and compare them with the expected behavior of each function option. Let's examine each function:
+
+a) **f(x) = sin(x)**: This function oscillates between -1 and 1 with a periodicity of \(2\pi\). The values in the JSON object range from approximately -3 to 2.943, which is outside the range of the sine function and lacks periodic behavior.
+
+b) **f(x) = x^2 + 3x - 1**: This is a quadratic function, which should exhibit parabolic growth or decay. The values should continuously increase or decrease at an increasing rate as \(x\) moves away from the vertex of the parabola. The JSON data shows a symmetric pattern around \(x = 0\), which is not typical for a simple quadratic function unless centered at the origin, but the values do not fit the expected quadratic growth.
+
+c) **f(x) = arcsinh(x)**: The arcsinh function, or inverse hyperbolic sine, behaves similarly to a linear function for large absolute values of \(x\), but it is always increasing and passes through the origin (0,0). The values in the JSON object show symmetry around \(x = 0\) and increase as \(x\) moves away from zero in both directions, which is consistent with the behavior of the arcsinh function.
+
+d) **f(x) = x**: This linear function is simply the identity function, increasing linearly without bounds as \(x\) increases. The JSON data does not support this as the values are not strictly proportional to \(x\).
+
+e) **f(x) = sign(x)**: This function returns -1 for negative \(x\), 0 for \(x = 0\), and 1 for positive \(x\). The values in the JSON object do not fit this pattern as they are not confined to -1, 0, and 1.
+
+Based on this analysis, the most likely function represented by the graph is **c) f(x) = arcsinh(x)**. The values show a symmetric increase around zero and resemble the growth pattern of the arcsinh function, which increases logarithmically for large absolute values of \(x\) and is linear near the origin.
+SOLUTION: arcsinh(x)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the function values across the given intervals and compare them with the expected behavior of each of the candidate functions. Let's consider each function:
+
+a) \( f(x) = 3^x + 1 \)
+- This function is an exponential function, which would increase very rapidly as \( x \) increases. The values should be significantly higher as \( x \) moves from negative to positive, and the rate of increase should be exponential.
+
+b) \( f(x) = x^4 \)
+- This function is a polynomial of degree 4. It should produce symmetric values around \( x = 0 \) (since all powers are even), and the values should increase rapidly as \( |x| \) increases, both in the positive and negative directions.
+
+c) \( f(x) = \frac{1}{2} \cos(x - 2) \)
+- This function is a cosine function shifted to the right by 2 units. It would oscillate between -0.5 and 0.5, with periodic peaks and troughs.
+
+d) \( f(x) = \arctan(x) \)
+- The arctangent function increases as \( x \) increases, but it has horizontal asymptotes at \( y = -\frac{\pi}{2} \) and \( y = \frac{\pi}{2} \). The increase is most rapid near \( x = 0 \) and slows down as \( x \) moves away from zero.
+
+e) \( f(x) = \sinh(x) \)
+- The hyperbolic sine function, \( \sinh(x) \), grows exponentially as \( x \) increases and decreases exponentially (in the negative direction) as \( x \) decreases. It is an odd function, so it is symmetric about the origin, but with opposite signs in the positive and negative directions.
+
+Now, let's analyze the given data:
+- The values in the JSON object start negative, increase through zero, and continue increasing as \( x \) moves from negative to positive. The increase is not symmetric around zero, and the values do not oscillate, which rules out the cosine function (c).
+- The values do not show the rapid increase expected from the exponential function \( 3^x + 1 \) (a) or the polynomial \( x^4 \) (b), which would show much higher values for larger \( |x| \).
+- The values do not remain bounded within a small range, which rules out the cosine function (c).
+- The values increase steadily and do not show the rapid exponential growth or decline expected from \( \sinh(x) \) (e).
+
+Given this analysis, the most likely candidate is:
+d) \( f(x) = \arctan(x) \)
+- The values increase steadily and remain within a range that is typical for the arctangent function, which increases with \( x \) but is bounded by its horizontal asymptotes. The behavior of the function in the JSON object matches well with the expected behavior of \( \arctan(x) \).
+SOLUTION: arctan(x)
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine which function the graph represents, we can analyze the behavior of the y-values as x changes, based on the given intervals and corresponding values. Let's consider each function's characteristics and compare them with the graph's behavior:
+
+a) **f(x) = sinh(x)**: The hyperbolic sine function, sinh(x), is an increasing function that behaves similarly to the exponential function for large positive x and like the negative of an exponential function for large negative x. It is symmetric around the y-axis.
+
+b) **f(x) = -x^5**: This function is an odd function (symmetric with respect to the origin) and has a steep decrease as x becomes more negative and a steep increase as x becomes more positive. The rate of change increases dramatically as |x| increases.
+
+c) **f(x) = 1/(1+exp(-x))**: This is the logistic function, typically used in logistic regression and models a probability curve. It has an S-shaped curve (sigmoid function), where y approaches 0 as x approaches negative infinity and y approaches 1 as x approaches positive infinity. The transition (inflection point) occurs around x = 0.
+
+d) **f(x) = exp(-x^2)**: This is a Gaussian function, which is not symmetric around the y-axis but is symmetric around x = 0. It peaks at x = 0 and approaches 0 as x moves away from 0 in both directions.
+
+e) **f(x) = |x|**: The absolute value function is V-shaped, linearly increasing from x = 0 in both the positive and negative directions.
+
+Now, let's analyze the given JSON object:
+- The y-values start very close to 0 for large negative x-values and gradually increase as x increases.
+- The y-values approach 1 as x becomes large and positive.
+- The function appears to be smooth and continuous, with a gradual, consistent increase as x moves from negative to positive.
+
+Given these observations, the behavior matches closely with the logistic function, **f(x) = 1/(1+exp(-x))**. This function starts near 0 for large negative x, transitions around x = 0, and approaches 1 for large positive x, which aligns with the pattern in the provided data. The other functions do not match this pattern:
+- **sinh(x)** would not level off at y-values near 1.
+- **-x^5** would not have a smooth, gradual transition but rather a sharp change at x = 0.
+- **exp(-x^2)** would peak at x = 0 and decrease symmetrically, which is not observed.
+- **|x|** would not have the sigmoidal shape and would not approach a limit as x increases.
+
+Therefore, the correct answer is:
+c) **f(x) = 1/(1+exp(-x))**.
+SOLUTION: 1/(1+exp(-x))
+--------------------------------------------------------------------------------
\ No newline at end of file
diff --git a/benchmarks/results/jump-gpt-3.5-turbo-0125.txt b/benchmarks/results/jump-gpt-3.5-turbo-0125.txt
new file mode 100644
index 0000000..7b5e4e2
--- /dev/null
+++ b/benchmarks/results/jump-gpt-3.5-turbo-0125.txt
@@ -0,0 +1,2782 @@
+LLM RESPONSE:  To identify the position of the largest jump in the graph, we need to calculate the absolute differences in mean values between adjacent intervals. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences in mean values between adjacent intervals:
+
+1. Absolute difference between intervals (1.0, 1.5) and (1.5, 2.5):
+   Absolute difference = |0.534 - (-0.009)| = 0.543
+
+2. Absolute difference between intervals (1.5, 2.5) and (2.5, 3.0):
+   Absolute difference = |(-0.532) - 0.534| = 1.066
+
+Therefore, the largest absolute difference occurs between the intervals (1.5, 2.5) and (2.5, 3.0) with a value of 1.066.
+
+This means that the largest jump in the graph occurs between the Pclass values of 1.5 and 2.5.
+SOLUTION:  2.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute difference in mean values between adjacent intervals. Since the graph represents a continuous feature with intervals, we will compare the mean values within each interval to identify the largest jump.
+
+Given the mean values provided:
+- For the interval (0.0, 0.5): Mean = 0.01522
+- For the interval (0.5, 1.0): Mean = -0.03391
+
+The absolute difference in mean values between these two intervals is:
+|0.01522 - (-0.03391)| = 0.04913
+
+Therefore, the largest jump in the graph occurs between the intervals (0.0, 0.5) and (0.5, 1.0) with an absolute difference of 0.04913 on the y-axis.
+
+As the x-axis represents the feature "smoking" in this case, the x-axis position of the largest jump corresponds to the midpoint between the two intervals, which is 0.25.
+SOLUTION:  0.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals and identify the interval where this difference is the largest.
+
+1. Calculate the absolute differences between consecutive mean values:
+   - For example, the absolute difference between the mean values of the intervals "(143.5, 259.35)" and "(259.35, 289.4)" is |-0.759 - (-0.662)| = 0.097.
+   - Repeat this calculation for all consecutive intervals.
+
+2. Identify the interval with the largest absolute difference:
+   - Compare all the absolute differences calculated in step 1 and find the interval where this difference is the largest.
+
+3. Determine the x-axis position of the largest jump:
+   - The x-axis position of the largest jump corresponds to the boundary between the two intervals that have the largest absolute difference in mean values.
+
+By following these steps, you can determine the x-axis position of the largest jump in the graph.
+SOLUTION:  696.25
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between intervals (0.0, 0.5) and (0.5, 1.5):
+   |-0.0274 - (-0.02381)| = 0.00359
+
+2. Absolute difference between intervals (0.5, 1.5) and (1.5, 2.5):
+   |-0.02381 - (-0.01602)| = 0.00779
+
+3. Absolute difference between intervals (1.5, 2.5) and (2.5, 3.5):
+   |-0.01602 - (-0.01049)| = 0.00553
+
+4. Absolute difference between intervals (2.5, 3.5) and (3.5, 4.5):
+   |-0.01049 - (-0.00528)| = 0.00521
+
+5. Absolute difference between intervals (3.5, 4.5) and (4.5, 5.5):
+   |-0.00528 - (-0.00022)| = 0.00506
+
+6. Absolute difference between intervals (4.5, 5.5) and (5.5, 6.5):
+   |-0.00022 - 0.00517| = 0.00539
+
+7. Absolute difference between intervals (5.5, 6.5) and (6.5, 7.5):
+   |0.00517 - 0.01282| = 0.00765
+
+8. Absolute difference between intervals (6.5, 7.5) and (7.5, 8.5):
+   |0.01282 - 0.01628| = 0.00346
+
+9. Absolute difference between intervals (7.5, 8.5) and (8.5, 9.5):
+   |0.01628 - 0.02454| = 0.00826
+
+10. Absolute difference between intervals (8.5, 9.5) and (9.5, 10.5):
+    |0.02454 - 0.02883| = 0.00429
+
+11. Absolute difference between intervals (9.5, 10.5) and (10.5, 11.5):
+    |0.02883 - 0.03213| = 0.0033
+
+12. Absolute difference between intervals (10.5, 11.5) and (11.5, 17.0):
+    |0.03213 - 0.03564| = 0.00351
+
+The largest absolute difference is 0.00826 between the intervals (7.5, 8.5) and (8.5, 9.5). Therefore, the x-axis position of the largest jump in the graph is within the interval (7.5, 8.5).
+SOLUTION:  8.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to identify the intervals where the absolute difference between consecutive mean values is the largest.
+
+1. Calculate the absolute differences between consecutive mean values:
+   - Absolute difference between (-1.345) and (-1.192) = 0.153
+   - Absolute difference between (-1.192) and (-1.038) = 0.154
+   - Absolute difference between (-1.038) and (-0.878) = 0.16
+   - Absolute difference between (-0.878) and (-0.723) = 0.155
+   - Absolute difference between (-0.723) and (-0.565) = 0.158
+   - Absolute difference between (-0.565) and (-0.348) = 0.217
+   - Absolute difference between (-0.348) and (-0.165) = 0.183
+   - Absolute difference between (-0.165) and (0.096) = 0.261
+   - Absolute difference between (0.096) and (0.253) = 0.157
+   - Absolute difference between (0.253) and (0.48) = 0.227
+   - Absolute difference between (0.48) and (0.761) = 0.281
+   - Absolute difference between (0.761) and (0.932) = 0.171
+   - Absolute difference between (0.932) and (1.092) = 0.16
+   - Absolute difference between (1.092) and (1.245) = 0.153
+   - Absolute difference between (1.245) and (1.404) = 0.159
+   - Absolute difference between (1.404) and (1.557) = 0.153
+
+2. The largest absolute difference is 0.281, which occurs between the intervals "(874.85, 928.5)" and "(928.5, 1033.5)".
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval "(874.85, 928.5)".
+SOLUTION:  928.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between "(2.0, 2.5)" and "(2.5, 5.0)": |1.062 - (-0.503)| = 1.565
+2. Absolute difference between "(2.5, 5.0)" and "(5.0, 17.5)": |1.188 - 1.062| = 0.126
+3. Absolute difference between "(5.0, 17.5)" and "(17.5, 24.5)": |0.305 - 1.188| = 0.883
+4. Absolute difference between "(17.5, 24.5)" and "(24.5, 28.5)": |0.438 - 0.305| = 0.133
+5. Absolute difference between "(24.5, 28.5)" and "(28.5, 31.5)": |0.03 - 0.438| = 0.408
+6. Absolute difference between "(28.5, 31.5)" and "(31.5, 35.5)": |0.337 - 0.03| = 0.307
+7. Absolute difference between "(31.5, 35.5)" and "(35.5, 36.25)": |0.047 - 0.337| = 0.29
+8. Absolute difference between "(35.5, 36.25)" and "(36.25, 43.5)": |-0.09 - 0.047| = 0.137
+9. Absolute difference between "(36.25, 43.5)" and "(43.5, 44.5)": |-0.293 - (-0.09)| = 0.203
+10. Absolute difference between "(43.5, 44.5)" and "(44.5, 47.5)": |-0.611 - (-0.293)| = 0.318
+11. Absolute difference between "(44.5, 47.5)" and "(47.5, 49.5)": |-0.32 - (-0.611)| = 0.291
+12. Absolute difference between "(47.5, 49.5)" and "(49.5, 59.0)": |-0.561 - (-0.32)| = 0.241
+13. Absolute difference between "(49.5, 59.0)" and "(59.0, 60.5)": |-0.283 - (-0.561)| = 0.278
+14. Absolute difference between "(59.0, 60.5)" and "(60.5, 63.5)": |-0.939 - (-0.283)| = 0.656
+15. Absolute difference between "(60.5, 63.5)" and "(63.5, 70.5)": |-1.095 - (-0.939)| = 0.156
+16. Absolute difference between "(63.5, 70.5)" and "(70.5, 75.5)": |-0.598 - (-1.095)| = 0.497
+17. Absolute difference between "(70.5, 75.5)" and "(75.5, 80.0)": |-0.406 - (-0.598)| = 0.192
+
+The largest absolute difference is 1.565, which occurs between the intervals "(2.0, 2.5)" and "(2.5, 5.0)".
+
+Therefore, the x-axis position of the largest jump in the graph is between the ages of 2.0 and 2.5.
+SOLUTION:  2.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences in mean values between adjacent intervals and identify the interval where this difference is the largest.
+
+Let's calculate the absolute differences in mean values for each adjacent interval:
+
+1. Calculate the absolute differences in mean values:
+   - Between "(0.0, 0.001156)" and "(0.001156, 0.002325)": |-0.6445 - (-0.6016)| = 0.0429
+   - Between "(0.001156, 0.002325)" and "(0.002325, 0.0037635)": |-0.6016 - (-0.5599)| = 0.0417
+   - Continue this calculation for all adjacent intervals.
+
+2. Identify the interval with the largest absolute difference in mean values. This interval will correspond to the largest jump in the graph.
+
+3. Report the x-axis position of this interval as the position of the largest jump in the graph.
+
+By following these steps, you can determine the x-axis position of the largest jump in the graph for the given continuous feature "concavity_se."
+SOLUTION:  0.02983
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Calculate the absolute differences between consecutive mean values for each interval.
+2. Identify the interval with the largest absolute difference.
+
+Let's start by calculating the absolute differences between consecutive mean values for each interval:
+
+- Absolute difference for interval "(2.0, 23.0)": |-78966.6 - (-70808.9)| = 8157.7
+- Absolute difference for interval "(23.0, 38.5)": |-28602.1 - (-78966.6)| = 50364.5
+- Absolute difference for interval "(38.5, 48.5)": |-47079.6 - (-28602.1)| = 18477.5
+- Absolute difference for interval "(48.5, 119.0)": |-52692.3 - (-47079.6)| = 5612.7
+- Absolute difference for interval "(119.0, 163.0)": |-60093.0 - (-52692.3)| = 7400.7
+- Absolute difference for interval "(163.0, 186.5)": |-51150.5 - (-60093.0)| = 8942.5
+- Absolute difference for interval "(186.5, 223.5)": |-39728.1 - (-51150.5)| = 11422.4
+- Absolute difference for interval "(223.5, 239.5)": |-7038.8 - (-39728.1)| = 32689.3
+- Absolute difference for interval "(239.5, 248.5)": |-691.1 - (-7038.8)| = 6347.7
+- Absolute difference for interval "(248.5, 265.5)": |-14052.2 - (-691.1)| = 13361.1
+- Absolute difference for interval "(265.5, 280.5)": |-35705.6 - (-14052.2)| = 21653.4
+- Absolute difference for interval "(280.5, 342.5)": |-24578.4 - (-35705.6)| = 11127.2
+- Absolute difference for interval "(342.5, 364.5)": |-34007.7 - (-24578.4)| = 9429.3
+- Absolute difference for interval "(364.5, 385.5)": |-46655.0 - (-34007.7)| = 12647.3
+- Absolute difference for interval "(385.5, 406.5)": |-17805.2 - (-46655.0)| = 28849.8
+- Absolute difference for interval "(406.5, 413.5)": |-12192.7 - (-17805.2)| = 5612.5
+- Absolute difference for interval "(413.5, 443.5)": |-22779.7 - (-12192.7)| = 10587.0
+- Absolute difference for interval "(443.5, 452.5)": |-30652.6 - (-22779.7)| = 7872.9
+- Absolute difference for interval "(452.5, 502.5)": |-25165.4 - (-30652.6)| = 5487.2
+- Absolute difference for interval "(502.5, 508.5)": |-12943.4 - (-25165.4)| = 12222.0
+- Absolute difference for interval "(508.5, 515.5)": |-21645.3 - (-12943.4)| = 8701.9
+- Absolute difference for interval "(515.5, 1152.5)": |-16264.4 - (-21645.3)| = 5380.9
+- Absolute difference for interval "(1152.5, 1239.5)": |-7023.2 - (-16264.4)| = 9241.2
+- Absolute difference for interval "(1239.5, 1245.5)": |-12855.2 - (-7023.2)| = 5832.0
+- Absolute difference for interval "(1245.5, 1619.5)": |-7415.6 - (-12855.2)| = 5440.4
+- Absolute difference for interval "(1619.5, 1944.5)": |-1233.9 - (-7415.6)| = 6181.7
+- Absolute difference for interval "(1944.5, 2330.5)": |4370.8 - (-1233.9)| = 5604.7
+- Absolute difference for interval "(2330.5, 2710.5)": |9739.0 - 4370.8| = 5368.2
+- Absolute difference for interval "(2710.5, 2834.5)": |16667.1 - 9739.0| = 6928.1
+- Absolute difference for interval "(2834.5, 2838.5)": |10096.4 - 16667.1| = 6570.7
+- Absolute difference for interval "(2838.5, 3577.5)": |15549.4 - 10096.4| = 5453.0
+- Absolute difference for interval "(3577.5, 5401.0)": |24928.2 - 15549.4| = 9378.8
+- Absolute difference for interval "(5401.0, 5535.5)": |19069.3 - 24928.2| = 5858.9
+- Absolute difference for interval "(5535.5, 9961.0)": |26262.6 - 19069.3| = 7193.3
+- Absolute difference for interval "(9961.0, 18662.0)": |20736.3 - 26262.6| = 5526.3
+- Absolute difference for interval "(18662.0, 39320.0)": |4370.8 - 20736.3| = 16365.5
+
+The largest absolute difference is 50364.5, which occurs in the interval "(23.0, 38.5)".
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval "(23.0, 38.5)".
+SOLUTION:  38.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences in mean values between adjacent intervals. The largest jump will be the interval boundary where the absolute difference is the greatest.
+
+Let's calculate the absolute differences in mean values for each adjacent interval:
+
+1. Absolute difference between "(106.67, 780.2149999999999)" and "(780.2149999999999, 4627.98)": |0.3865 - 0.3462| = 0.0403
+2. Absolute difference between "(780.2149999999999, 4627.98)" and "(4627.98, 6842.475)": |0.3462 - 0.0858| = 0.2604
+3. Absolute difference between "(4627.98, 6842.475)" and "(6842.475, 7401.88)": |0.0858 - 0.157| = 0.0712
+4. Absolute difference between "(6842.475, 7401.88)" and "(7401.88, 27330.43)": |0.157 - 0.2048| = 0.0478
+5. Absolute difference between "(7401.88, 27330.43)" and "(27330.43, 38816.375)": |0.2048 - 0.1737| = 0.0311
+6. Absolute difference between "(27330.43, 38816.375)" and "(38816.375, 40348.645000000004)": |0.1737 - 0.1063| = 0.0674
+7. Absolute difference between "(38816.375, 40348.645000000004)" and "(40348.645000000004, 42807.509999999995)": |0.1063 - 0.0512| = 0.0551
+8. Absolute difference between "(40348.645000000004, 42807.509999999995)" and "(42807.509999999995, 48226.81)": |0.0512 - 0.1098| = 0.0586
+9. Absolute difference between "(42807.509999999995, 48226.81)" and "(48226.81, 48498.15)": |0.1098 - (-0.0771)| = 0.1869
+10. Absolute difference between "(48226.81, 48498.15)" and "(48498.15, 58535.68)": |-0.0771 - 0.0187| = 0.0958
+11. Absolute difference between "(48498.15, 58535.68)" and "(58535.68, 94498.98999999999)": |0.0187 - 0.0512| = 0.0325
+12. Absolute difference between "(58535.68, 94498.98999999999)" and "(94498.98999999999, 120892.955)": |0.0512 - 0.0186| = 0.0326
+13. Absolute difference between "(94498.98999999999, 120892.955)" and "(120892.955, 121151.28)": |0.0186 - (-0.0263)| = 0.0449
+14. Absolute difference between "(120892.955, 121151.28)" and "(121151.28, 121482.61499999999)": |-0.0263 - (-0.0801)| = 0.0538
+15. Absolute difference between "(121151.28, 121482.61499999999)" and "(121482.61499999999, 148569.97)": |-0.0801 - (-0.0388)| = 0.0413
+16. Absolute difference between "(121482.61499999999, 148569.97)" and "(148569.97, 184522.325)": |-0.0388 - (-0.0796)| = 0.0408
+17. Absolute difference between "(148569.97, 184522.325)" and "(184522.325, 187947.635)": |-0.0796 - (-0.1332)| = 0.0536
+18. Absolute difference between "(184522.325, 187947.635)" and "(187947.635, 187985.865)": |-0.1332 - (-0.2342)| = 0.101
+19. Absolute difference between "(187947.635, 187985.865)" and "(187985.865, 188452.565)": |-0.2342 - (-0.0632)| = 0.171
+20. Absolute difference between "(187985.865, 188452.565)" and "(188452.565, 189006.61)": |-0.0632 - (-0.0053)| = 0.0579
+21. Absolute difference between "(188452.565, 189006.61)" and "(189006.61, 196418.97999999998)": |-0.0053 - 0.0291| = 0.0338
+22. Absolute difference between "(189006.61, 196418.97999999998)" and "(196418.97999999998, 199505.41)": |0.0291 - (-0.0098)| = 0.0389
+23. Absolute difference between "(196418.97999999998, 199505.41)" and "(199505.41, 199992.48)": |-0.0098 - 0.214| = 0.2238
+
+The largest absolute difference is 0.2238, which occurs between the intervals "(196418.97999999998, 199505.41)" and "(199505.41, 199992.48)".
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval (196418.97999999998, 199505.41).
+SOLUTION:  4627.98
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals and identify the interval where this difference is the largest.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between "(0.0, 0.5)" and "(0.5, 1.5)": |-0.02443 - (-0.02088)| = 0.00355
+2. Absolute difference between "(0.5, 1.5)" and "(1.5, 2.5)": |-0.02088 - (-0.01613)| = 0.00475
+3. Absolute difference between "(1.5, 2.5)" and "(2.5, 3.5)": |-0.01613 - (-0.01086)| = 0.00527
+4. Absolute difference between "(2.5, 3.5)" and "(3.5, 4.5)": |-0.01086 - (-0.00583)| = 0.00503
+5. Absolute difference between "(3.5, 4.5)" and "(4.5, 5.5)": |-0.00583 - 0.00139| = 0.00722
+6. Absolute difference between "(4.5, 5.5)" and "(5.5, 6.5)": |0.00139 - 0.00556| = 0.00417
+7. Absolute difference between "(5.5, 6.5)" and "(6.5, 7.5)": |0.00556 - 0.01145| = 0.00589
+8. Absolute difference between "(6.5, 7.5)" and "(7.5, 8.5)": |0.01145 - 0.01748| = 0.00603
+9. Absolute difference between "(7.5, 8.5)" and "(8.5, 10.5)": |0.01748 - 0.0242| = 0.00672
+10. Absolute difference between "(8.5, 10.5)" and "(10.5, 11.5)": |0.0242 - 0.03351| = 0.00931
+11. Absolute difference between "(10.5, 11.5)" and "(11.5, 13.5)": |0.03351 - 0.03691| = 0.0034
+12. Absolute difference between "(11.5, 13.5)" and "(13.5, 15.0)": |0.03691 - 0.03345| = 0.00346
+13. Absolute difference between "(13.5, 15.0)" and "(15.0, 16.0)": |0.03345 - 0.02926| = 0.00419
+
+The largest absolute difference is 0.00931, which occurs between the intervals "(8.5, 10.5)" and "(10.5, 11.5)".
+
+Therefore, the x-axis position of the largest jump in the graph is between 8.5 and 10.5 on the PopulationScore feature.
+SOLUTION:  10.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between intervals (1.0, 4.5) and (4.5, 7.5):
+   Absolute difference = |-7788.2 - (-19998.0)| = 12209.8
+
+2. Absolute difference between intervals (4.5, 7.5) and (7.5, 16.5):
+   Absolute difference = |-10680.2 - (-7788.2)| = 2892.0
+
+3. Absolute difference between intervals (7.5, 16.5) and (16.5, 18.5):
+   Absolute difference = |-6304.4 - (-10680.2)| = 4375.8
+
+4. Absolute difference between intervals (16.5, 18.5) and (18.5, 27.5):
+   Absolute difference = |-1760.6 - (-6304.4)| = 4543.8
+
+5. Absolute difference between intervals (18.5, 27.5) and (27.5, 34.5):
+   Absolute difference = |2164.8 - (-1760.6)| = 3925.4
+
+6. Absolute difference between intervals (27.5, 34.5) and (34.5, 38.5):
+   Absolute difference = |-912.5 - 2164.8| = 3077.3
+
+7. Absolute difference between intervals (34.5, 38.5) and (38.5, 41.5):
+   Absolute difference = |4199.6 - (-912.5)| = 5112.1
+
+8. Absolute difference between intervals (38.5, 41.5) and (41.5, 45.5):
+   Absolute difference = |-497.4 - 4199.6| = 4697.0
+
+9. Absolute difference between intervals (41.5, 45.5) and (45.5, 47.5):
+   Absolute difference = |-5189.8 - (-497.4)| = 4692.4
+
+10. Absolute difference between intervals (45.5, 47.5) and (47.5, 48.5):
+    Absolute difference = |5201.0 - (-5189.8)| = 10390.8
+
+11. Absolute difference between intervals (47.5, 48.5) and (48.5, 49.5):
+    Absolute difference = |2159.0 - 5201.0| = 3042.0
+
+12. Absolute difference between intervals (48.5, 49.5) and (49.5, 50.5):
+    Absolute difference = |6135.7 - 2159.0| = 3976.7
+
+13. Absolute difference between intervals (49.5, 50.5) and (50.5, 51.5):
+    Absolute difference = |11513.8 - 6135.7| = 5380.1
+
+14. Absolute difference between intervals (50.5, 51.5) and (51.5, 52.0):
+    Absolute difference = |27549.7 - 11513.8| = 16035.9
+
+The largest absolute difference is 16035.9, which occurs between the intervals (50.5, 51.5) and (51.5, 52.0). Therefore, the x-axis position of the largest jump in the graph is within the interval (50.5, 51.5).
+SOLUTION:  51.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between (0.0, 0.5) and (0.5, 2.5):
+   |-0.02038 - (-0.02553)| = 0.00515
+
+2. Absolute difference between (0.5, 2.5) and (2.5, 4.5):
+   |-0.0099 - (-0.02038)| = 0.01048
+
+3. Absolute difference between (2.5, 4.5) and (4.5, 6.5):
+   |0.00082 - (-0.0099)| = 0.01072
+
+4. Absolute difference between (4.5, 6.5) and (6.5, 7.5):
+   |0.01088 - 0.00082| = 0.01006
+
+5. Absolute difference between (6.5, 7.5) and (7.5, 9.5):
+   |0.0178 - 0.01088| = 0.00692
+
+6. Absolute difference between (7.5, 9.5) and (9.5, 10.5):
+   |0.02657 - 0.0178| = 0.00877
+
+7. Absolute difference between (9.5, 10.5) and (10.5, 12.5):
+   |0.0329 - 0.02657| = 0.00633
+
+8. Absolute difference between (10.5, 12.5) and (12.5, 13.5):
+   |0.03982 - 0.0329| = 0.00692
+
+9. Absolute difference between (12.5, 13.5) and (13.5, 15.0):
+   |0.05043 - 0.03982| = 0.01061
+
+10. Absolute difference between (13.5, 15.0) and (15.0, 16.0):
+    |0.06084 - 0.05043| = 0.01041
+
+The largest absolute difference is 0.01072, which occurs between the intervals (2.5, 4.5) and (4.5, 6.5). Therefore, the x-axis position of the largest jump in the graph is around 3.5.
+SOLUTION:  4.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to identify the intervals where the absolute difference between consecutive mean values is the largest.
+
+1. Calculate the absolute differences between consecutive mean values in the given intervals:
+   - Absolute difference for each interval:
+     - (0.0, 3.5): |-0.0407 - 0.0121| = 0.0528
+     - (3.5, 7.5): |-0.0873 - (-0.0407)| = 0.0466
+     - (7.5, 9.0): |-0.1192 - (-0.0873)| = 0.0319
+     - ...
+     - (77.5, 99.0): |0.3109 - 0.2174| = 0.0935
+
+2. Identify the interval with the largest absolute difference. In this case, the interval with the largest jump is:
+   - (55.0, 77.5): 0.2174 to 0.3109 with an absolute difference of 0.0935
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval (55.0, 77.5).
+SOLUTION:  55.0
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between (0.0, 0.5) and (0.5, 1.5):
+   Absolute difference = |0.724 - (-0.368)| = 1.092
+
+2. Absolute difference between (0.5, 1.5) and (1.5, 2.5):
+   Absolute difference = |0.587 - 0.724| = 0.137
+
+3. Absolute difference between (1.5, 2.5) and (2.5, 3.5):
+   Absolute difference = |-0.221 - 0.587| = 0.808
+
+4. Absolute difference between (2.5, 3.5) and (3.5, 4.5):
+   Absolute difference = |-0.631 - (-0.221)| = 0.41
+
+5. Absolute difference between (3.5, 4.5) and (4.5, 5.5):
+   Absolute difference = |-0.545 - (-0.631)| = 0.086
+
+6. Absolute difference between (4.5, 5.5) and (5.5, 6.0):
+   Absolute difference = |0.179 - (-0.545)| = 0.724
+
+The largest absolute difference is 1.092, which occurs between the intervals (0.0, 0.5) and (0.5, 1.5). Therefore, the x-axis position of the largest jump in the graph is between 0.0 and 0.5 on the MaritalStatus feature.
+SOLUTION:  0.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals and identify the interval where the largest absolute difference occurs.
+
+1. Calculate the absolute differences between consecutive mean values:
+   - Absolute difference between "(0.0, 1.5)" and "(1.5, 2.5)": |-0.02463 - (-0.01694)| = 0.00769
+   - Absolute difference between "(1.5, 2.5)" and "(2.5, 3.5)": |-0.01694 - (-0.01147)| = 0.00547
+   - Absolute difference between "(2.5, 3.5)" and "(3.5, 4.5)": |-0.01147 - (-0.00533)| = 0.00614
+   - Absolute difference between "(3.5, 4.5)" and "(4.5, 5.5)": |-0.00533 - 0.00036| = 0.00569
+   - Absolute difference between "(4.5, 5.5)" and "(5.5, 6.5)": |0.00036 - 0.00641| = 0.00605
+   - Absolute difference between "(5.5, 6.5)" and "(6.5, 7.5)": |0.00641 - 0.01086| = 0.00445
+   - Absolute difference between "(6.5, 7.5)" and "(7.5, 8.5)": |0.01086 - 0.01753| = 0.00667
+   - Absolute difference between "(7.5, 8.5)" and "(8.5, 9.5)": |0.01753 - 0.02391| = 0.00638
+   - Absolute difference between "(8.5, 9.5)" and "(9.5, 11.5)": |0.02391 - 0.03162| = 0.00771
+   - Absolute difference between "(9.5, 11.5)" and "(11.5, 14.0)": |0.03162 - 0.0391| = 0.00748
+   - Absolute difference between "(11.5, 14.0)" and "(14.0, 15.0)": |0.0391 - 0.05506| = 0.01596
+
+2. Identify the interval with the largest absolute difference:
+   - The largest absolute difference is 0.01596, which occurs between the intervals "(11.5, 14.0)" and "(14.0, 15.0)".
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval "(11.5, 14.0)".
+SOLUTION:  14.0
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals and identify the interval where this difference is the largest.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between (-0.177) and 0.043 = 0.22
+2. Absolute difference between 0.043 and 0.27 = 0.227
+3. Absolute difference between 0.27 and 0.543 = 0.273
+4. Absolute difference between 0.543 and 0.863 = 0.32
+5. Absolute difference between 0.863 and 1.13 = 0.267
+6. Absolute difference between 1.13 and 1.479 = 0.349
+7. Absolute difference between 1.479 and 2.076 = 0.597
+8. Absolute difference between 2.076 and 1.81 = 0.266
+9. Absolute difference between 1.81 and 1.439 = 0.371
+10. Absolute difference between 1.439 and 2.236 = 0.797
+11. Absolute difference between 2.236 and 2.746 = 0.51
+12. Absolute difference between 2.746 and 3.43 = 0.684
+13. Absolute difference between 3.43 and 3.888 = 0.458
+14. Absolute difference between 3.888 and 4.131 = 0.243
+
+The largest absolute difference is 0.797, which occurs between the intervals "(6031.5, 6171.5)" and "(6171.5, 8753.0)".
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval "(6031.5, 6171.5)".
+SOLUTION:  6171.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to identify the intervals where the absolute difference between consecutive mean values is the greatest.
+
+1. Calculate the absolute differences between consecutive mean values in the given intervals:
+   - Absolute difference between (-3.326) and (-2.358) = 0.968
+   - Absolute difference between (-2.358) and (-2.799) = 0.441
+   - Absolute difference between (-2.799) and (-2.354) = 0.445
+   - Absolute difference between (-2.354) and (-1.405) = 0.949
+   - Absolute difference between (-1.405) and (-1.633) = 0.228
+   - Absolute difference between (-1.633) and (-1.214) = 0.419
+   - Absolute difference between (-1.214) and (-0.789) = 0.425
+   - Absolute difference between (-0.789) and (-0.473) = 0.316
+   - Absolute difference between (-0.473) and (-0.216) = 0.257
+   - Absolute difference between (-0.216) and (0.042) = 0.258
+   - Absolute difference between (0.042) and (0.351) = 0.309
+   - Absolute difference between (0.351) and (0.658) = 0.307
+   - Absolute difference between (0.658) and (0.897) = 0.239
+   - Absolute difference between (0.897) and (0.574) = 0.323
+   - Absolute difference between (0.574) and (0.099) = 0.475
+   - Absolute difference between (0.099) and (0.763) = 0.664
+   - Absolute difference between (0.763) and (0.502) = 0.261
+   - Absolute difference between (0.502) and (0.875) = 0.373
+   - Absolute difference between (0.875) and (0.065) = 0.81
+   - Absolute difference between (0.065) and (-1.08) = 1.145
+
+2. The largest absolute difference is 1.145, which occurs between the intervals (79.5, 84.5) and (84.5, 90.0).
+
+Therefore, the x-axis position of the largest jump in the graph is between the ages of 79.5 and 84.5.
+SOLUTION:  84.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals and identify the interval where this difference is the largest.
+
+1. Calculate the absolute differences between consecutive mean values:
+   - Absolute difference between (0.0, 0.5) and (0.5, 1.5): |-0.0263 - (-0.02126)| = 0.00504
+   - Absolute difference between (0.5, 1.5) and (1.5, 2.5): |-0.02126 - (-0.01709)| = 0.00417
+   - Absolute difference between (1.5, 2.5) and (2.5, 3.5): |-0.01709 - (-0.01038)| = 0.00671
+   - Absolute difference between (2.5, 3.5) and (3.5, 4.5): |-0.01038 - (-0.00633)| = 0.00405
+   - Absolute difference between (3.5, 4.5) and (4.5, 5.5): |-0.00633 - 0.00068| = 0.00701
+   - Absolute difference between (4.5, 5.5) and (5.5, 6.5): |0.00068 - 0.00618| = 0.0055
+   - Absolute difference between (5.5, 6.5) and (6.5, 7.5): |0.00618 - 0.01223| = 0.00605
+   - Absolute difference between (6.5, 7.5) and (7.5, 8.5): |0.01223 - 0.01761| = 0.00538
+   - Absolute difference between (7.5, 8.5) and (8.5, 9.5): |0.01761 - 0.02318| = 0.00557
+   - Absolute difference between (8.5, 9.5) and (9.5, 10.5): |0.02318 - 0.02782| = 0.00464
+   - Absolute difference between (9.5, 10.5) and (10.5, 11.5): |0.02782 - 0.03238| = 0.00456
+   - Absolute difference between (10.5, 11.5) and (11.5, 13.5): |0.03238 - 0.03978| = 0.0074
+   - Absolute difference between (11.5, 13.5) and (13.5, 15.0): |0.03978 - 0.04468| = 0.0049
+   - Absolute difference between (13.5, 15.0) and (15.0, 16.0): |0.04468 - 0.0529| = 0.00822
+
+2. Identify the largest absolute difference:
+   The largest absolute difference is 0.00822, which occurs between the intervals (13.5, 15.0) and (15.0, 16.0).
+
+Therefore, the x-axis position of the largest jump in the graph is between the intervals 13.5 and 15.0.
+SOLUTION:  15.0
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between (0.0, 135.5) and (135.5, 215.5):
+   Absolute difference = |0.445 - 0.073| = 0.372
+
+2. Absolute difference between (135.5, 215.5) and (215.5, 500.5):
+   Absolute difference = |0.073 - (-0.294)| = 0.367
+
+3. Absolute difference between (215.5, 500.5) and (500.5, 727.5):
+   Absolute difference = |-0.294 - (-0.661)| = 0.367
+
+4. Absolute difference between (500.5, 727.5) and (727.5, 799.5):
+   Absolute difference = |-0.661 - (-1.026)| = 0.365
+
+5. Absolute difference between (727.5, 799.5) and (799.5, 831.5):
+   Absolute difference = |-1.026 - (-0.601)| = 0.425
+
+6. Absolute difference between (799.5, 831.5) and (831.5, 872.5):
+   Absolute difference = |-0.601 - (-1.156)| = 0.555
+
+7. Absolute difference between (831.5, 872.5) and (872.5, 993.5):
+   Absolute difference = |-1.156 - (-1.633)| = 0.477
+
+8. Absolute difference between (872.5, 993.5) and (993.5, 1430.5):
+   Absolute difference = |-1.633 - (-2.012)| = 0.379
+
+9. Absolute difference between (993.5, 1430.5) and (1430.5, 1514.5):
+   Absolute difference = |-2.012 - (-1.512)| = 0.5
+
+10. Absolute difference between (1430.5, 1514.5) and (1514.5, 1796.0):
+    Absolute difference = |-1.512 - (-2.212)| = 0.7
+
+11. Absolute difference between (1514.5, 1796.0) and (1796.0, 1909.5):
+    Absolute difference = |-2.212 - (-1.699)| = 0.513
+
+12. Absolute difference between (1796.0, 1909.5) and (1909.5, 1970.0):
+    Absolute difference = |-1.699 - (-2.568)| = 0.869
+
+13. Absolute difference between (1909.5, 1970.0) and (1970.0, 2571.5):
+    Absolute difference = |-2.568 - (-3.006)| = 0.438
+
+14. Absolute difference between (1970.0, 2571.5) and (2571.5, 2582.0):
+    Absolute difference = |-3.006 - (-2.375)| = 0.631
+
+15. Absolute difference between (2571.5, 2582.0) and (2582.0, 2657.0):
+    Absolute difference = |-2.375 - (-2.964)| = 0.589
+
+16. Absolute difference between (2582.0, 2657.0) and (2657.0, 3710.5):
+    Absolute difference = |-2.964 - (-3.98)| = 1.016
+
+17. Absolute difference between (2657.0, 3710.5) and (3710.5, 4089.0):
+    Absolute difference = |-3.98 - (-4.347)| = 0.367
+
+18. Absolute difference between (3710.5, 4089.0) and (4089.0, 5089.5):
+    Absolute difference = |-4.347 - (-5.923)| = 1.576
+
+19. Absolute difference between (4089.0, 5089.5) and (5089.5, 24133.0):
+    Absolute difference = |-5.923 - (-6.634)| = 0.711
+
+The largest absolute difference is 1.576, which occurs between the intervals (4089.0, 5089.5) and (5089.5, 24133.0).
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval (4089.0, 5089.5).
+SOLUTION:  4089.0
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences in mean values between adjacent intervals. The largest jump will be the interval boundary where the absolute difference is the greatest.
+
+Let's calculate the absolute differences in mean values between adjacent intervals:
+
+1. Calculate the absolute differences for each adjacent interval:
+   - (130.5, 278.5): |0.118 - 0.521| = 0.403
+   - (278.5, 452.5): |-0.285 - 0.118| = 0.403
+   - (452.5, 754.5): |-0.907 - (-0.285)| = 0.622
+   - (754.5, 1209.5): |-1.309 - (-0.907)| = 0.402
+   - (1209.5, 1808.0): |-1.712 - (-1.309)| = 0.403
+   - (1808.0, 2204.5): |-3.029 - (-1.712)| = 1.317
+   - (2204.5, 2207.5): |-2.456 - (-3.029)| = 0.573
+   - (2207.5, 2428.0): |-2.956 - (-2.456)| = 0.5
+   - (2428.0, 2462.5): |-2.512 - (-2.956)| = 0.444
+   - (2462.5, 2714.5): |-3.402 - (-2.512)| = 0.89
+   - (2714.5, 2745.0): |-2.902 - (-3.402)| = 0.5
+   - (2745.0, 2993.5): |-4.077 - (-2.902)| = 1.175
+   - (2993.5, 3132.0): |-4.481 - (-4.077)| = 0.404
+   - (3132.0, 3705.5): |-5.377 - (-4.481)| = 0.896
+   - (3705.5, 3747.0): |-4.36 - (-5.377)| = 1.017
+   - (3747.0, 22408.0): |-7.183 - (-4.36)| = 2.823
+
+2. Identify the interval with the largest absolute difference:
+   - The largest absolute difference is 2.823, which occurs between the intervals (3747.0, 22408.0).
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between the intervals 3747.0 and 22408.0 for the feature "Spa."
+SOLUTION:  3747.0
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences in mean values between adjacent intervals. The largest jump will be the interval boundary where the absolute difference is the greatest.
+
+Let's calculate the absolute differences in mean values between adjacent intervals:
+
+1. Calculate the absolute differences in mean values:
+   - Between (12.02, 16.935000000000002) and (16.935000000000002, 18.335): |-1.885 - (-1.717)| = 0.168
+   - Between (16.935000000000002, 18.335) and (18.335, 19.505): |-1.717 - (-1.55)| = 0.167
+   - Continue this calculation for all adjacent intervals.
+
+2. Identify the interval with the largest absolute difference. This interval will represent the largest jump in the graph.
+
+3. Report the x-axis position of the boundary between the intervals with the largest absolute difference as the position of the largest jump in the graph.
+
+By following these steps, you can determine the x-axis position of the largest jump in the graph.
+SOLUTION:  19.505
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute difference in mean values between adjacent intervals. The largest jump will be the interval boundary where this absolute difference is the greatest.
+
+Given the means provided:
+- For the interval (0.0, 0.5): Mean = -0.1077
+- For the interval (0.5, 1.0): Mean = 0.1864
+
+The absolute difference between these means is |0.1864 - (-0.1077)| = 0.2941.
+
+Therefore, the largest jump in the graph occurs at the boundary between the intervals (0.0, 0.5) and (0.5, 1.0). The x-axis position of this largest jump is at the boundary value of 0.5.
+SOLUTION:  0.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To identify the position of the largest jump in the graph, we need to compare the mean values within adjacent intervals. The largest jump will occur where there is the greatest absolute difference in mean values between two adjacent intervals.
+
+Let's calculate the absolute differences in mean values between adjacent intervals:
+
+1. Between intervals (1.0, 1.5) and (1.5, 2.5):
+   Absolute difference = |0.96 - (-0.918)| = 1.878
+
+2. Between intervals (1.5, 2.5) and (2.5, 3.5):
+   Absolute difference = |-3.104 - 0.96| = 4.064
+
+3. Between intervals (2.5, 3.5) and (3.5, 4.0):
+   Absolute difference = |-2.768 - (-3.104)| = 0.336
+
+The largest absolute jump occurs between the intervals (1.5, 2.5) and (2.5, 3.5) with a magnitude of 4.064.
+
+Therefore, the x-axis position of the largest jump in the graph is between 1.5 and 2.5 in the feature "NumOfProducts".
+SOLUTION:  2.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the graph. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Calculate the absolute differences between consecutive mean values:
+   - Absolute difference between "(0.02729, 0.049945)" and "(0.049945, 0.06971)": |-0.0578 - (-0.0099)| = 0.0479
+   - Absolute difference between "(0.049945, 0.06971)" and "(0.06971, 0.099305)": |-0.0099 - (-0.0565)| = 0.0466
+   - Continue this calculation for all consecutive intervals.
+
+2. Identify the largest absolute difference to find the position of the largest jump in the graph.
+
+Let's perform these calculations to determine the x-axis position of the largest jump in the graph.
+SOLUTION:  0.099305
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals and identify the interval where this difference is the largest.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between -0.8769 and -0.8241 = 0.0528
+2. Absolute difference between -0.8241 and -0.1763 = 0.6478
+3. Absolute difference between -0.1763 and 0.0021 = 0.1784
+4. Absolute difference between 0.0021 and -0.2283 = 0.2304
+5. Absolute difference between -0.2283 and -0.0522 = 0.1761
+6. Absolute difference between -0.0522 and -0.1299 = 0.0777
+7. Absolute difference between -0.1299 and -0.0821 = 0.0478
+8. Absolute difference between -0.0821 and -0.1509 = 0.0688
+9. Absolute difference between -0.1509 and -0.0818 = 0.0691
+10. Absolute difference between -0.0818 and -0.0316 = 0.0502
+11. Absolute difference between -0.0316 and 0.0162 = 0.0478
+12. Absolute difference between 0.0162 and 0.0757 = 0.0595
+13. Absolute difference between 0.0757 and 0.0081 = 0.0676
+14. Absolute difference between 0.0081 and 0.0581 = 0.05
+15. Absolute difference between 0.0581 and -0.0049 = 0.063
+16. Absolute difference between -0.0049 and -0.0569 = 0.052
+17. Absolute difference between -0.0569 and -0.111 = 0.0541
+18. Absolute difference between -0.111 and -0.0335 = 0.0775
+
+The largest absolute difference is 0.6478, which occurs between the intervals "(15566519.0, 15567333.5)" and "(15567333.5, 15567844.5)".
+
+Therefore, the x-axis position of the largest jump in the graph is between the values 15567333.5 and 15567844.5 on the CustomerId feature.
+SOLUTION:  15567333.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between -0.773 and -0.686: 0.087
+2. Absolute difference between -0.686 and -0.589: 0.097
+3. Absolute difference between -0.589 and -0.499: 0.09
+4. Absolute difference between -0.499 and -0.412: 0.087
+5. Absolute difference between -0.412 and -0.275: 0.137
+6. Absolute difference between -0.275 and -0.187: 0.088
+7. Absolute difference between -0.187 and -0.102: 0.085
+8. Absolute difference between -0.102 and -0.186: 0.084
+9. Absolute difference between -0.186 and -0.066: 0.12
+10. Absolute difference between -0.066 and 0.064: 0.13
+11. Absolute difference between 0.064 and 0.15: 0.086
+12. Absolute difference between 0.15 and 0.239: 0.089
+13. Absolute difference between 0.239 and 0.334: 0.095
+14. Absolute difference between 0.334 and 0.422: 0.088
+15. Absolute difference between 0.422 and 0.51: 0.088
+16. Absolute difference between 0.51 and 0.594: 0.084
+17. Absolute difference between 0.594 and 0.683: 0.089
+18. Absolute difference between 0.683 and 0.774: 0.091
+19. Absolute difference between 0.774 and 0.866: 0.092
+
+The largest absolute difference is 0.137, which occurs between the intervals (0.412, 0.275) and (0.275, 0.187). Therefore, the x-axis position of the largest jump in the graph is within the interval (0.412, 0.275).
+SOLUTION:  0.19345
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between "(1.0, 1.5)" and "(1.5, 4.5)": |-4.746 - (-1.252)| = 3.494
+2. Absolute difference between "(1.5, 4.5)" and "(4.5, 6.5)": |-1.252 - (-0.882)| = 0.37
+3. Absolute difference between "(4.5, 6.5)" and "(6.5, 9.5)": |-0.882 - (-0.483)| = 0.399
+4. Absolute difference between "(6.5, 9.5)" and "(9.5, 11.5)": |-0.483 - (-0.093)| = 0.39
+5. Absolute difference between "(9.5, 11.5)" and "(11.5, 13.5)": |-0.093 - 0.276| = 0.369
+6. Absolute difference between "(11.5, 13.5)" and "(13.5, 14.5)": |0.276 - 0.863| = 0.587
+7. Absolute difference between "(13.5, 14.5)" and "(14.5, 16.0)": |0.863 - 1.487| = 0.624
+
+The largest absolute difference is 3.494, which occurs between the intervals "(1.0, 1.5)" and "(1.5, 4.5)".
+
+Therefore, the x-axis position of the largest jump in the graph is between the EducationNum values of 1.0 and 1.5.
+SOLUTION:  1.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between "(4.3, 4.55)" and "(4.55, 4.75)": |3.328 - 2.995| = 0.333
+2. Absolute difference between "(4.55, 4.75)" and "(4.75, 4.85)": |2.995 - 2.698| = 0.297
+3. Absolute difference between "(4.75, 4.85)" and "(4.85, 5.05)": |2.698 - 1.665| = 1.033
+4. Absolute difference between "(4.85, 5.05)" and "(5.05, 5.25)": |1.665 - 1.371| = 0.294
+5. Absolute difference between "(5.05, 5.25)" and "(5.25, 5.45)": |1.371 - 1.085| = 0.286
+6. Absolute difference between "(5.25, 5.45)" and "(5.45, 5.55)": |1.085 - 0.339| = 0.746
+7. Absolute difference between "(5.45, 5.55)" and "(5.55, 5.75)": |0.339 - (-0.057)| = 0.396
+8. Absolute difference between "(5.55, 5.75)" and "(5.75, 5.85)": |-0.057 - (-0.39)| = 0.333
+9. Absolute difference between "(5.75, 5.85)" and "(5.85, 6.15)": |-0.39 - (-0.757)| = 0.367
+10. Absolute difference between "(5.85, 6.15)" and "(6.15, 6.45)": |-0.757 - (-1.149)| = 0.392
+11. Absolute difference between "(6.15, 6.45)" and "(6.45, 6.85)": |-1.149 - (-1.436)| = 0.287
+12. Absolute difference between "(6.45, 6.85)" and "(6.85, 7.7)": |-1.436 - (-1.718)| = 0.282
+
+The largest absolute difference is 1.033, which occurs between the intervals "(4.75, 4.85)" and "(4.85, 5.05)".
+
+Therefore, the x-axis position of the largest jump in the graph is between the values 4.75 and 4.85 on the sepal_length feature.
+SOLUTION:  4.85
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the given intervals. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Calculate the absolute differences between consecutive mean values for each interval.
+2. Identify the interval with the largest absolute difference.
+
+Let's start by calculating the absolute differences between consecutive mean values for each interval:
+
+- Absolute difference between "(32.54, 32.565)" and "(32.565, 32.685)": 23234.8 - (-3182.4) = 26417.2
+- Absolute difference between "(32.565, 32.685)" and "(32.685, 32.715)": -3182.4 - 7727.3 = 10909.7
+- Absolute difference between "(32.685, 32.715)" and "(32.715, 32.915)": 7727.3 - 17670.3 = 9943.0
+- Continue this calculation for all intervals.
+
+After calculating the absolute differences for all intervals, we will identify the interval with the largest absolute difference. The x-axis position of this interval will correspond to the position of the largest jump in the graph.
+
+Let's perform these calculations to find the x-axis position of the largest jump in the graph.
+SOLUTION:  34.325
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest jump will be the interval where the absolute difference between mean values is the greatest.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between -0.762 and -0.659 = 0.103
+2. Absolute difference between -0.659 and -0.56 = 0.099
+3. Absolute difference between -0.56 and -0.461 = 0.099
+4. Absolute difference between -0.461 and -0.36 = 0.101
+5. Absolute difference between -0.36 and -0.262 = 0.098
+6. Absolute difference between -0.262 and -0.163 = 0.099
+7. Absolute difference between -0.163 and -0.065 = 0.098
+8. Absolute difference between -0.065 and 0.037 = 0.102
+9. Absolute difference between 0.037 and 0.137 = 0.1
+10. Absolute difference between 0.137 and 0.235 = 0.098
+11. Absolute difference between 0.235 and 0.394 = 0.159
+12. Absolute difference between 0.394 and 0.494 = 0.1
+13. Absolute difference between 0.494 and 0.599 = 0.105
+14. Absolute difference between 0.599 and 0.695 = 0.096
+15. Absolute difference between 0.695 and 0.793 = 0.098
+16. Absolute difference between 0.793 and 0.891 = 0.098
+17. Absolute difference between 0.891 and 0.99 = 0.099
+18. Absolute difference between 0.99 and 1.093 = 0.103
+
+The largest absolute difference is 0.159, which occurs between the intervals (0.235, 0.394) and (0.394, 0.494).
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval (16.925, 17.385).
+SOLUTION:  16.925
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To identify the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between -0.835 and -0.769: 0.066
+2. Absolute difference between -0.769 and -0.697: 0.072
+3. Absolute difference between -0.697 and -0.632: 0.065
+4. Absolute difference between -0.632 and -0.569: 0.063
+5. Absolute difference between -0.569 and -0.506: 0.063
+6. Absolute difference between -0.506 and -0.437: 0.069
+7. Absolute difference between -0.437 and -0.368: 0.069
+8. Absolute difference between -0.368 and -0.304: 0.064
+9. Absolute difference between -0.304 and -0.242: 0.062
+10. Absolute difference between -0.242 and -0.177: 0.065
+11. Absolute difference between -0.177 and -0.111: 0.066
+12. Absolute difference between -0.111 and -0.047: 0.064
+13. Absolute difference between -0.047 and 0.065: 0.112
+14. Absolute difference between 0.065 and 0.142: 0.077
+15. Absolute difference between 0.142 and 0.211: 0.069
+16. Absolute difference between 0.211 and 0.107: 0.104
+17. Absolute difference between 0.107 and 0.171: 0.064
+18. Absolute difference between 0.171 and 0.267: 0.096
+19. Absolute difference between 0.267 and 0.334: 0.067
+20. Absolute difference between 0.334 and 0.396: 0.062
+
+The largest absolute difference is 0.112, which occurs between the intervals (0.088615, 0.08918999999999999) and (0.08918999999999999, 0.090335).
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval (0.088615, 0.08918999999999999).
+SOLUTION:  0.088615
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the graph. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Calculate the absolute differences between consecutive mean values:
+   - Absolute difference between (-0.698) and (-0.618) = 0.08
+   - Absolute difference between (-0.618) and (-0.539) = 0.079
+   - Absolute difference between (-0.539) and (-0.461) = 0.078
+   - Absolute difference between (-0.461) and (-0.384) = 0.077
+   - Absolute difference between (-0.384) and (-0.256) = 0.128
+   - Absolute difference between (-0.256) and (-0.176) = 0.08
+   - Absolute difference between (-0.176) and (-0.099) = 0.077
+   - Absolute difference between (-0.099) and (-0.023) = 0.076
+   - Absolute difference between (-0.023) and (-0.098) = 0.075
+   - Absolute difference between (-0.098) and (-0.018) = 0.08
+   - Absolute difference between (-0.018) and (0.065) = 0.083
+   - Absolute difference between (0.065) and (0.14) = 0.075
+   - Absolute difference between (0.14) and (0.219) = 0.079
+   - Absolute difference between (0.219) and (0.296) = 0.077
+   - Absolute difference between (0.296) and (0.372) = 0.076
+   - Absolute difference between (0.372) and (0.451) = 0.079
+   - Absolute difference between (0.451) and (0.536) = 0.085
+   - Absolute difference between (0.536) and (0.611) = 0.075
+   - Absolute difference between (0.611) and (0.701) = 0.09
+   - Absolute difference between (0.701) and (0.786) = 0.085
+
+2. Identify the largest absolute difference:
+   - The largest absolute difference is 0.085, which occurs between the intervals "(0.536, 0.611)" and "(0.611, 0.701)".
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval "(0.536, 0.611)".
+SOLUTION:  1.326
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to identify the intervals where the absolute difference between consecutive mean values is the greatest.
+
+1. Calculate the absolute differences between consecutive mean values for each interval:
+   - For example, for the interval "(0.002252, 0.0046765)", the absolute difference is |-0.0214 - (-0.0693)| = 0.0479.
+   - Repeat this calculation for all intervals.
+
+2. Identify the interval with the largest absolute difference. This interval represents the largest jump in the graph.
+
+3. Report the x-axis position of this interval. The x-axis position is typically the midpoint of the interval.
+
+Let's calculate the absolute differences between consecutive mean values for each interval to identify the largest jump in the graph.
+SOLUTION:  0.02586
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the Education feature. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between (0.0, 0.5) and (0.5, 1.5): |-0.4028 - (-0.5397)| = 0.1369
+2. Absolute difference between (0.5, 1.5) and (1.5, 3.5): |-0.5397 - (-0.4851)| = 0.0546
+3. Absolute difference between (1.5, 3.5) and (3.5, 4.5): |-0.4851 - (-0.4021)| = 0.083
+4. Absolute difference between (3.5, 4.5) and (4.5, 5.5): |-0.4021 - (-0.457)| = 0.0549
+5. Absolute difference between (4.5, 5.5) and (5.5, 6.5): |-0.457 - (-0.2537)| = 0.2033
+6. Absolute difference between (5.5, 6.5) and (6.5, 7.5): |-0.2537 - (-0.0494)| = 0.2043
+7. Absolute difference between (6.5, 7.5) and (7.5, 8.5): |-0.0494 - 0.0457| = 0.0951
+8. Absolute difference between (7.5, 8.5) and (8.5, 9.5): |0.0457 - 0.1831| = 0.1374
+9. Absolute difference between (8.5, 9.5) and (9.5, 10.5): |0.1831 - 0.1392| = 0.0439
+10. Absolute difference between (9.5, 10.5) and (10.5, 11.5): |0.1392 - (-0.0652)| = 0.2044
+11. Absolute difference between (10.5, 11.5) and (11.5, 14.5): |-0.0652 - 0.1954| = 0.2606
+12. Absolute difference between (11.5, 14.5) and (14.5, 15.0): |0.1954 - 0.1393| = 0.0561
+
+The largest absolute difference is 0.2606, which occurs between the intervals (10.5, 11.5) and (11.5, 14.5).
+
+Therefore, the x-axis position of the largest jump in the graph is between the Education levels of 10.5 and 11.5.
+SOLUTION:  11.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals and identify the interval where the largest absolute difference occurs.
+
+1. Calculate the absolute differences between consecutive mean values:
+   - For each interval, subtract the mean value of the next interval from the mean value of the current interval.
+   - Take the absolute value of each difference to consider both positive and negative jumps.
+
+2. Identify the interval with the largest absolute difference:
+   - Find the interval where the absolute difference calculated in step 1 is the largest.
+
+Let's perform these calculations step by step:
+
+1. Calculate the absolute differences between consecutive mean values:
+
+| Interval        | Absolute Difference |
+|-----------------|----------------------|
+| (2.0, 4.5)      | |-5401.6 - (-23687.9)| = 18286.3 |
+| (4.5, 6.5)      | |-23687.9 - (-53732.5)| = 30044.6 |
+| (6.5, 8.5)      | |-53732.5 - (-14617.2)| = 39115.3 |
+| ...             | ...                  |
+| (1272.5, 3516.0)| |28522.2 - 21556.0| = 6966.2 |
+| (3516.0, 6082.0)| |21556.0 - 0| = 21556.0 |
+
+2. Identify the interval with the largest absolute difference:
+   - The largest absolute difference is 39115.3 in the interval (6.5, 8.5).
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval (6.5, 8.5) for the feature "households."
+SOLUTION:  8.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals and identify the interval where this difference is the largest.
+
+1. Calculate the absolute differences between consecutive mean values:
+   - For each consecutive pair of intervals, calculate the absolute difference in mean values.
+   - Keep track of the interval where the largest absolute difference occurs.
+
+2. Identify the interval with the largest absolute difference:
+   - Compare the absolute differences calculated in step 1 to find the largest absolute difference.
+   - Note the x-axis position associated with this interval.
+
+Let's perform these calculations step by step:
+
+1. Calculate the absolute differences between consecutive mean values:
+   - Calculate the absolute differences between consecutive mean values for all intervals:
+     - Absolute difference between "(8.0, 349.5)" and "(349.5, 1899.5)": |-0.1954 - (-0.1448)| = 0.0506
+     - Absolute difference between "(349.5, 1899.5)" and "(1899.5, 4908.5)": |-0.1448 - (-0.18)| = 0.0352
+     - Continue this calculation for all consecutive intervals.
+
+2. Identify the interval with the largest absolute difference:
+   - Compare all absolute differences calculated in step 1 to find the largest absolute difference.
+   - Note the x-axis position associated with this interval.
+
+By following these steps, you can determine the x-axis position of the largest jump in the graph.
+SOLUTION:  45346.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between intervals (0.0, 1.5) and (1.5, 2.5):
+   Absolute difference = |-0.01738 - (-0.02526)| = 0.00788
+
+2. Absolute difference between intervals (1.5, 2.5) and (2.5, 3.5):
+   Absolute difference = |-0.01172 - (-0.01738)| = 0.00566
+
+3. Absolute difference between intervals (2.5, 3.5) and (3.5, 4.5):
+   Absolute difference = |-0.00537 - (-0.01172)| = 0.00635
+
+4. Absolute difference between intervals (3.5, 4.5) and (4.5, 5.5):
+   Absolute difference = |-0.00021 - (-0.00537)| = 0.00516
+
+5. Absolute difference between intervals (4.5, 5.5) and (5.5, 6.5):
+   Absolute difference = |0.0066 - (-0.00021)| = 0.00681
+
+6. Absolute difference between intervals (5.5, 6.5) and (6.5, 7.5):
+   Absolute difference = |0.01026 - 0.0066| = 0.00366
+
+7. Absolute difference between intervals (6.5, 7.5) and (7.5, 8.5):
+   Absolute difference = |0.01717 - 0.01026| = 0.00691
+
+8. Absolute difference between intervals (7.5, 8.5) and (8.5, 9.5):
+   Absolute difference = |0.02426 - 0.01717| = 0.00709
+
+9. Absolute difference between intervals (8.5, 9.5) and (9.5, 10.5):
+   Absolute difference = |0.02823 - 0.02426| = 0.00397
+
+10. Absolute difference between intervals (9.5, 10.5) and (10.5, 11.5):
+    Absolute difference = |0.03325 - 0.02823| = 0.00502
+
+11. Absolute difference between intervals (10.5, 11.5) and (11.5, 13.5):
+    Absolute difference = |0.03915 - 0.03325| = 0.0059
+
+12. Absolute difference between intervals (11.5, 13.5) and (13.5, 15.0):
+    Absolute difference = |0.03572 - 0.03915| = 0.00343
+
+The largest absolute difference is 0.00788 between intervals (0.0, 1.5) and (1.5, 2.5). This indicates the largest jump in the graph occurs between these two intervals.
+
+Therefore, the x-axis position of the largest jump in the graph is between 0.0 and 1.5.
+SOLUTION:  1.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the provided intervals. The largest jump will be the interval where the absolute difference between mean values is the greatest.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Calculate the absolute differences between consecutive mean values for each interval:
+   - (0.0, 20.0): abs(0.0422 - (-0.0027)) = 0.0449
+   - (20.0, 36.5): abs(-0.0027 - (-0.0554)) = 0.0527
+   - (36.5, 40.5): abs(-0.0554 - (-0.0967)) = 0.0413
+   - (40.5, 45.5): abs(-0.0967 - (-0.0409)) = 0.0558
+   - (45.5, 48.5): abs(-0.0409 - (-0.2263)) = 0.1854
+   - (48.5, 55.5): abs(-0.2263 - (-0.2661)) = 0.0398
+   - ...
+   - Continue calculating the absolute differences for all intervals
+
+2. Identify the interval with the largest absolute difference. This interval represents the position of the largest jump in the graph.
+
+3. Report the x-axis position of this interval, which corresponds to the feature value where the largest jump occurs.
+
+By following these steps, we can determine the x-axis position of the largest jump in the graph for both positive and negative jumps.
+SOLUTION:  48.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences in mean values between adjacent intervals. The largest jump will be the interval where the absolute difference is the greatest.
+
+Let's calculate the absolute differences in mean values between adjacent intervals:
+
+1. Absolute difference between intervals (0.0, 6.325) and (6.325, 7.8500000000000005):
+   Absolute difference = |-1.303 - (-1.425)| = 0.122
+
+2. Absolute difference between intervals (6.325, 7.8500000000000005) and (7.8500000000000005, 9.256250000000001):
+   Absolute difference = |-0.472 - (-1.303)| = 0.831
+
+3. Absolute difference between intervals (7.8500000000000005, 9.256250000000001) and (9.256250000000001, 10.48125):
+   Absolute difference = |-0.602 - (-0.472)| = 0.13
+
+4. Absolute difference between intervals (9.256250000000001, 10.48125) and (10.48125, 12.9375):
+   Absolute difference = |-0.14 - (-0.602)| = 0.462
+
+5. Absolute difference between intervals (10.48125, 12.9375) and (12.9375, 25.79375):
+   Absolute difference = |0.225 - (-0.14)| = 0.365
+
+6. Absolute difference between intervals (12.9375, 25.79375) and (25.79375, 26.46875):
+   Absolute difference = |0.355 - 0.225| = 0.13
+
+7. Absolute difference between intervals (25.79375, 26.46875) and (26.46875, 27.7354):
+   Absolute difference = |0.207 - 0.355| = 0.148
+
+8. Absolute difference between intervals (26.46875, 27.7354) and (27.7354, 29.85):
+   Absolute difference = |-0.238 - 0.207| = 0.445
+
+9. Absolute difference between intervals (27.7354, 29.85) and (29.85, 31.6604):
+   Absolute difference = |0.051 - (-0.238)| = 0.289
+
+10. Absolute difference between intervals (29.85, 31.6604) and (31.6604, 55.22085):
+    Absolute difference = |-0.075 - 0.051| = 0.126
+
+11. Absolute difference between intervals (31.6604, 55.22085) and (55.22085, 89.5521):
+    Absolute difference = |0.041 - (-0.075)| = 0.116
+
+12. Absolute difference between intervals (55.22085, 89.5521) and (89.5521, 149.0354):
+    Absolute difference = |0.152 - 0.041| = 0.111
+
+13. Absolute difference between intervals (89.5521, 149.0354) and (149.0354, 387.6646):
+    Absolute difference = |-0.029 - 0.152| = 0.181
+
+14. Absolute difference between intervals (149.0354, 387.6646) and (387.6646, 512.3292):
+    Absolute difference = |0.808 - (-0.029)| = 0.837
+
+The largest absolute jump in mean values occurs between the intervals (387.6646, 512.3292) and (149.0354, 387.6646) with an absolute difference of 0.837. This represents the largest jump in the graph.
+SOLUTION:  387.6646
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between (0.0, 0.5) and (0.5, 1.5):
+   Absolute difference = |-0.03259 - (-0.02272)| = 0.00987
+
+2. Absolute difference between (0.5, 1.5) and (1.5, 2.5):
+   Absolute difference = |-0.02272 - (-0.0157)| = 0.00702
+
+3. Absolute difference between (1.5, 2.5) and (2.5, 3.5):
+   Absolute difference = |-0.0157 - (-0.00983)| = 0.00587
+
+4. Absolute difference between (2.5, 3.5) and (3.5, 4.5):
+   Absolute difference = |-0.00983 - (-0.00444)| = 0.00539
+
+5. Absolute difference between (3.5, 4.5) and (4.5, 5.5):
+   Absolute difference = |-0.00444 - (-0.00035)| = 0.00409
+
+6. Absolute difference between (4.5, 5.5) and (5.5, 6.5):
+   Absolute difference = |-0.00035 - 0.00575| = 0.0061
+
+7. Absolute difference between (5.5, 6.5) and (6.5, 7.5):
+   Absolute difference = |0.00575 - 0.01126| = 0.00551
+
+8. Absolute difference between (6.5, 7.5) and (7.5, 8.5):
+   Absolute difference = |0.01126 - 0.01651| = 0.00525
+
+9. Absolute difference between (7.5, 8.5) and (8.5, 9.5):
+   Absolute difference = |0.01651 - 0.02143| = 0.00492
+
+10. Absolute difference between (8.5, 9.5) and (9.5, 12.5):
+    Absolute difference = |0.02143 - 0.02903| = 0.0076
+
+11. Absolute difference between (9.5, 12.5) and (12.5, 13.5):
+    Absolute difference = |0.02903 - 0.03437| = 0.00534
+
+12. Absolute difference between (12.5, 13.5) and (13.5, 15.0):
+    Absolute difference = |0.03437 - 0.04826| = 0.01389
+
+The largest absolute difference is 0.01389, which occurs between the intervals (12.5, 13.5) and (13.5, 15.0).
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval (12.5, 13.5).
+SOLUTION:  13.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals and identify the interval where this difference is the largest.
+
+1. Calculate the absolute differences between consecutive mean values:
+   - For each interval, subtract the mean value of the next interval from the mean value of the current interval.
+   - Take the absolute value of each difference to consider both positive and negative jumps.
+
+2. Identify the interval with the largest absolute difference:
+   - Find the interval where the absolute difference calculated in step 1 is the largest.
+
+Let's perform these calculations step by step:
+
+1. Calculate the absolute differences between consecutive mean values:
+
+| Interval             | Absolute Difference |
+|----------------------|---------------------|
+| (23.0, 32.0)         | 0.54 - (-0.48) = 1.02 |
+| (32.0, 49.5)         | -2.93 - 0.68 = 3.61 |
+| (49.5, 56.5)         | -1.31 - (-4.31) = 3.00 |
+| ...                  | ...                 |
+| (5548.5, 7861.0)     | 4.78 - 3.47 = 1.31 |
+
+2. Identify the interval with the largest absolute difference:
+   - The largest absolute difference is 3.61, which occurs in the interval (32.0, 49.5).
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval (32.0, 49.5).
+SOLUTION:  2444.0
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To identify the position of the largest jump in the graph, we need to compare the mean values within each interval and calculate the absolute difference between them. The largest jump will occur where this absolute difference is the greatest.
+
+Let's calculate the absolute differences for each interval:
+
+1. For the interval "(0.0, 0.5)":
+   Absolute difference = |Mean(0.5, 1.0) - Mean(0.0, 0.5)|
+                      = |0.0917 - (-0.0818)|
+                      = 0.1735
+
+2. For the interval "(0.5, 1.0)":
+   Absolute difference = |Mean(0.5, 1.0) - Mean(0.0, 0.5)|
+                      = |0.0917 - (-0.0818)|
+                      = 0.1735
+
+The absolute differences are the same for both intervals, so the largest jump occurs at the boundary between the intervals. In this case, the x-axis position of the largest jump is at the boundary between the intervals, which is at 0.5.
+
+Therefore, the x-axis position of the largest jump in the graph is 0.5.
+SOLUTION:  0.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to identify the intervals where the absolute difference between the mean values of adjacent intervals is the largest.
+
+1. Calculate the absolute differences between the mean values of adjacent intervals:
+   - For example, the absolute difference between the mean value of the interval "(25100.0, 27700.0)" (-1.004) and the mean value of the interval "(27700.0, 34450.0)" (-0.687) is |(-1.004) - (-0.687)| = 0.317.
+
+2. Identify the interval with the largest absolute difference:
+   - Compare the absolute differences calculated in step 1 and find the interval with the largest absolute difference. This interval represents the largest jump in the graph.
+
+3. Determine the x-axis position of the largest jump:
+   - The x-axis position of the largest jump corresponds to the boundary between the two intervals with the largest absolute difference in mean values.
+
+By following these steps, you can identify the x-axis position of the largest jump in the graph.
+SOLUTION:  305500.0
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between -0.132 and -0.285 = 0.153
+2. Absolute difference between -0.285 and -0.826 = 0.541
+3. Absolute difference between -0.826 and -0.404 = 0.422
+4. Absolute difference between -0.404 and -0.005 = 0.399
+5. Absolute difference between -0.005 and 0.215 = 0.22
+6. Absolute difference between 0.215 and 0.086 = 0.129
+7. Absolute difference between 0.086 and -0.012 = 0.098
+8. Absolute difference between -0.012 and 0.247 = 0.259
+9. Absolute difference between 0.247 and 0.829 = 0.582
+10. Absolute difference between 0.829 and 0.564 = 0.265
+11. Absolute difference between 0.564 and 0.414 = 0.15
+12. Absolute difference between 0.414 and 0.248 = 0.166
+13. Absolute difference between 0.248 and 0.164 = 0.084
+14. Absolute difference between 0.164 and 0.075 = 0.089
+15. Absolute difference between 0.075 and 0.173 = 0.098
+16. Absolute difference between 0.173 and 0.059 = 0.114
+17. Absolute difference between 0.059 and -0.349 = 0.408
+18. Absolute difference between -0.349 and -0.459 = 0.11
+19. Absolute difference between -0.459 and -0.616 = 0.157
+20. Absolute difference between -0.616 and -0.256 = 0.36
+
+The largest absolute difference is 0.582, which occurs between the intervals "(79953.185, 83348.07)" and "(83348.07, 101890.23999999999)".
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval "(79953.185, 83348.07)".
+SOLUTION:  76596.815
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between (0.0, 1.5) and (1.5, 2.5):
+   Absolute difference = |-0.02325 - (-0.01532)| = 0.00793
+
+2. Absolute difference between (1.5, 2.5) and (2.5, 3.5):
+   Absolute difference = |-0.01532 - (-0.01073)| = 0.00459
+
+3. Absolute difference between (2.5, 3.5) and (3.5, 4.5):
+   Absolute difference = |-0.01073 - (-0.00482)| = 0.00591
+
+4. Absolute difference between (3.5, 4.5) and (4.5, 5.5):
+   Absolute difference = |-0.00482 - (-0.00032)| = 0.0045
+
+5. Absolute difference between (4.5, 5.5) and (5.5, 6.5):
+   Absolute difference = |-0.00032 - 0.0063| = 0.00662
+
+6. Absolute difference between (5.5, 6.5) and (6.5, 7.5):
+   Absolute difference = |0.0063 - 0.01228| = 0.00598
+
+7. Absolute difference between (6.5, 7.5) and (7.5, 8.5):
+   Absolute difference = |0.01228 - 0.01637| = 0.00409
+
+8. Absolute difference between (7.5, 8.5) and (8.5, 10.5):
+   Absolute difference = |0.01637 - 0.02537| = 0.009
+
+9. Absolute difference between (8.5, 10.5) and (10.5, 12.5):
+   Absolute difference = |0.02537 - 0.03189| = 0.00652
+
+10. Absolute difference between (10.5, 12.5) and (12.5, 13.5):
+    Absolute difference = |0.03189 - 0.03961| = 0.00772
+
+11. Absolute difference between (12.5, 13.5) and (13.5, 14.0):
+    Absolute difference = |0.03961 - 0.01644| = 0.02317
+
+The largest absolute difference is 0.02317, which occurs between the intervals (12.5, 13.5) and (13.5, 14.0). Therefore, the x-axis position of the largest jump in the graph is within the interval (12.5, 13.5).
+SOLUTION:  13.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals and identify the interval where the absolute difference is the largest.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between "(0.0, 1.5)" and "(1.5, 2.5)": |-0.02446 - (-0.01712)| = 0.00734
+2. Absolute difference between "(1.5, 2.5)" and "(2.5, 3.5)": |-0.01712 - (-0.00908)| = 0.00804
+3. Absolute difference between "(2.5, 3.5)" and "(3.5, 4.5)": |-0.00908 - (-0.00529)| = 0.00379
+4. Absolute difference between "(3.5, 4.5)" and "(4.5, 5.5)": |-0.00529 - 0.0003| = 0.00559
+5. Absolute difference between "(4.5, 5.5)" and "(5.5, 6.5)": |0.0003 - 0.00497| = 0.00467
+6. Absolute difference between "(5.5, 6.5)" and "(6.5, 7.5)": |0.00497 - 0.01093| = 0.00596
+7. Absolute difference between "(6.5, 7.5)" and "(7.5, 8.5)": |0.01093 - 0.01787| = 0.00694
+8. Absolute difference between "(7.5, 8.5)" and "(8.5, 9.5)": |0.01787 - 0.02262| = 0.00475
+9. Absolute difference between "(8.5, 9.5)" and "(9.5, 11.5)": |0.02262 - 0.02707| = 0.00445
+10. Absolute difference between "(9.5, 11.5)" and "(11.5, 12.5)": |0.02707 - 0.03735| = 0.01028
+11. Absolute difference between "(11.5, 12.5)" and "(12.5, 13.5)": |0.03735 - 0.043| = 0.00565
+12. Absolute difference between "(12.5, 13.5)" and "(13.5, 15.0)": |0.043 - 0.01734| = 0.02566
+
+The largest absolute difference is 0.02566, which occurs between the intervals "(12.5, 13.5)" and "(13.5, 15.0)".
+
+Therefore, the x-axis position of the largest jump in the graph is between 12.5 and 13.5 on the MonsoonIntensity feature.
+SOLUTION:  13.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values within the intervals provided for the feature "Parch". 
+
+1. Calculate the absolute differences between consecutive mean values:
+   - Absolute difference between (0.0, 0.5) and (0.5, 1.5): |-0.055 - 0.085| = 0.14
+   - Absolute difference between (0.5, 1.5) and (1.5, 3.0): |-0.299 - (-0.055)| = 0.244
+   - Absolute difference between (1.5, 3.0) and (3.0, 4.0): |-1.704 - (-0.299)| = 1.405
+
+2. Identify the largest absolute difference, which represents the largest jump in the graph:
+   - The largest absolute difference is 1.405, which occurs between the intervals (1.5, 3.0) and (3.0, 4.0).
+
+Therefore, the x-axis position of the largest jump in the graph for the feature "Parch" is between 1.5 and 3.0.
+SOLUTION:  3.0
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between "(113.0, 114.5)" and "(114.5, 118.5)": |0.283 - (-1.269)| = 1.552
+2. Absolute difference between "(114.5, 118.5)" and "(118.5, 124.5)": |3.539 - 0.283| = 3.256
+3. Absolute difference between "(118.5, 124.5)" and "(124.5, 126.5)": |2.46 - 3.539| = 1.079
+4. Absolute difference between "(124.5, 126.5)" and "(126.5, 127.5)": |4.042 - 2.46| = 1.582
+5. Absolute difference between "(126.5, 127.5)" and "(127.5, 129.5)": |3.553 - 4.042| = 0.489
+6. Absolute difference between "(127.5, 129.5)" and "(129.5, 130.5)": |0.953 - 3.553| = 2.6
+7. Absolute difference between "(129.5, 130.5)" and "(130.5, 132.5)": |1.22 - 0.953| = 0.267
+8. Absolute difference between "(130.5, 132.5)" and "(132.5, 133.5)": |-1.094 - 1.22| = 2.314
+9. Absolute difference between "(132.5, 133.5)" and "(133.5, 135.5)": |0.587 - (-1.094)| = 1.681
+10. Absolute difference between "(133.5, 135.5)" and "(135.5, 138.5)": |-0.629 - 0.587| = 1.216
+11. Absolute difference between "(135.5, 138.5)" and "(138.5, 144.5)": |-0.233 - (-0.629)| = 0.396
+12. Absolute difference between "(138.5, 144.5)" and "(144.5, 148.0)": |0.113 - (-0.233)| = 0.346
+
+The largest absolute difference is 3.256, which occurs between the intervals "(114.5, 118.5)" and "(118.5, 124.5)".
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval range of 114.5 to 118.5 for the feature "serum_sodium".
+SOLUTION:  118.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between -1.149 and -1.016: 0.133
+2. Absolute difference between -1.016 and -0.883: 0.133
+3. Absolute difference between -0.883 and -0.747: 0.136
+4. Absolute difference between -0.747 and -0.616: 0.131
+5. Absolute difference between -0.616 and -0.485: 0.131
+6. Absolute difference between -0.485 and -0.349: 0.136
+7. Absolute difference between -0.349 and -0.212: 0.137
+8. Absolute difference between -0.212 and -0.078: 0.134
+9. Absolute difference between -0.078 and 0.055: 0.133
+10. Absolute difference between 0.055 and 0.19: 0.135
+11. Absolute difference between 0.19 and 0.335: 0.145
+12. Absolute difference between 0.335 and 0.469: 0.134
+13. Absolute difference between 0.469 and 0.601: 0.132
+14. Absolute difference between 0.601 and 0.734: 0.133
+15. Absolute difference between 0.734 and 0.866: 0.132
+16. Absolute difference between 0.866 and 0.997: 0.131
+17. Absolute difference between 0.997 and 1.132: 0.135
+18. Absolute difference between 1.132 and 1.274: 0.142
+19. Absolute difference between 1.274 and 1.406: 0.132
+
+The largest absolute difference is 0.145, which occurs between the intervals (17.22, 17.78) and (17.78, 18.655).
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval (17.22, 17.78).
+SOLUTION:  17.22
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Calculate the absolute differences between consecutive mean values for each interval.
+2. Identify the interval with the largest absolute difference.
+
+Let's perform the calculations:
+
+- Absolute difference between mean values for each interval:
+    - (0.01938, 0.03164): Absolute difference = |0.0558 - 0.0135| = 0.0423
+    - (0.03164, 0.035445000000000004): Absolute difference = |0.0934 - 0.0558| = 0.0376
+    - (0.035445000000000004, 0.03732): Absolute difference = |0.1327 - 0.0934| = 0.0393
+    - ...
+    - (0.28075, 0.3114): Absolute difference = |0.3427 - 0.2996| = 0.0431
+
+The largest absolute difference is 0.0431, which occurs in the interval (0.28075, 0.3114).
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval (0.28075, 0.3114).
+SOLUTION:  0.066575
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to identify the intervals where the absolute difference between consecutive mean values is the greatest.
+
+1. Calculate the absolute differences between consecutive mean values in the provided intervals:
+   - Absolute difference between (18.0, 32.5) and (32.5, 34.5): |0.83 - 0.681| = 0.149
+   - Absolute difference between (32.5, 34.5) and (34.5, 37.5): |0.681 - 0.423| = 0.258
+   - Absolute difference between (34.5, 37.5) and (37.5, 38.5): |0.423 - 0.281| = 0.142
+   - Absolute difference between (37.5, 38.5) and (38.5, 39.5): |0.281 - 0.054| = 0.227
+   - Absolute difference between (38.5, 39.5) and (39.5, 40.5): |0.054 - (-0.193)| = 0.247
+   - Absolute difference between (39.5, 40.5) and (40.5, 41.5): |-0.193 - (-0.354)| = 0.161
+   - Absolute difference between (40.5, 41.5) and (41.5, 42.5): |-0.354 - (-0.494)| = 0.14
+   - Absolute difference between (41.5, 42.5) and (42.5, 44.5): |-0.494 - (-0.781)| = 0.287
+   - Absolute difference between (42.5, 44.5) and (44.5, 46.5): |-0.781 - (-1.075)| = 0.294
+   - Absolute difference between (44.5, 46.5) and (46.5, 48.5): |-1.075 - (-1.546)| = 0.471
+   - Absolute difference between (46.5, 48.5) and (48.5, 54.5): |-1.546 - (-1.717)| = 0.171
+   - Absolute difference between (48.5, 54.5) and (54.5, 56.5): |-1.717 - (-1.858)| = 0.141
+   - Absolute difference between (54.5, 56.5) and (56.5, 64.5): |-1.858 - (-1.707)| = 0.151
+   - Absolute difference between (56.5, 64.5) and (64.5, 66.5): |-1.707 - (-1.27)| = 0.437
+   - Absolute difference between (64.5, 66.5) and (66.5, 69.5): |-1.27 - (-1.118)| = 0.152
+   - Absolute difference between (66.5, 69.5) and (69.5, 70.5): |-1.118 - (-0.888)| = 0.23
+   - Absolute difference between (69.5, 70.5) and (70.5, 72.5): |-0.888 - (-0.587)| = 0.301
+   - Absolute difference between (70.5, 72.5) and (72.5, 74.5): |-0.587 - (-0.31)| = 0.277
+   - Absolute difference between (72.5, 74.5) and (74.5, 81.0): |-0.31 - (-0.157)| = 0.153
+
+2. Identify the interval with the largest absolute difference:
+   - The largest absolute difference is 0.471, which occurs between the intervals (44.5, 46.5) and (46.5, 48.5).
+
+Therefore, the x-axis position of the largest jump in the graph is between the ages of 44.5 and 46.5.
+SOLUTION:  46.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between "(0.0, 1.5)" and "(1.5, 2.5)":  
+   |-0.02549 - (-0.01575)| = 0.00974
+
+2. Absolute difference between "(1.5, 2.5)" and "(2.5, 3.5)":  
+   |-0.01575 - (-0.01061)| = 0.00514
+
+3. Absolute difference between "(2.5, 3.5)" and "(3.5, 4.5)":  
+   |-0.01061 - (-0.0046)| = 0.00601
+
+4. Absolute difference between "(3.5, 4.5)" and "(4.5, 5.5)":  
+   |-0.0046 - 0.00059| = 0.00519
+
+5. Absolute difference between "(4.5, 5.5)" and "(5.5, 6.5)":  
+   |0.00059 - 0.00567| = 0.00508
+
+6. Absolute difference between "(5.5, 6.5)" and "(6.5, 7.5)":  
+   |0.00567 - 0.01201| = 0.00634
+
+7. Absolute difference between "(6.5, 7.5)" and "(7.5, 9.5)":  
+   |0.01201 - 0.01601| = 0.004
+
+8. Absolute difference between "(7.5, 9.5)" and "(9.5, 10.5)":  
+   |0.01601 - 0.02531| = 0.0093
+
+9. Absolute difference between "(9.5, 10.5)" and "(10.5, 11.5)":  
+   |0.02531 - 0.02956| = 0.00425
+
+10. Absolute difference between "(10.5, 11.5)" and "(11.5, 12.5)":  
+    |0.02956 - 0.04031| = 0.01075
+
+11. Absolute difference between "(11.5, 12.5)" and "(12.5, 14.0)":  
+    |0.04031 - 0.04423| = 0.00392
+
+The largest absolute difference is 0.01075, which occurs between the intervals "(10.5, 11.5)" and "(11.5, 12.5)".
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval (10.5, 11.5).
+SOLUTION:  11.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals and identify the interval where this difference is the largest.
+
+1. Calculate the absolute differences between consecutive mean values:
+   - Absolute difference between "(0.0, 125.5)" and "(125.5, 541.5)": |-0.211 - (-0.032)| = 0.179
+   - Absolute difference between "(125.5, 541.5)" and "(541.5, 808.5)": |0.034 - (-0.211)| = 0.245
+   - Absolute difference between "(541.5, 808.5)" and "(808.5, 1082.0)": |0.213 - 0.034| = 0.179
+   - Absolute difference between "(808.5, 1082.0)" and "(1082.0, 1187.0)": |-0.042 - 0.213| = 0.255
+   - Absolute difference between "(1082.0, 1187.0)" and "(1187.0, 1434.5)": |0.401 - (-0.042)| = 0.443
+   - Absolute difference between "(1187.0, 1434.5)" and "(1434.5, 1658.5)": |0.585 - 0.401| = 0.184
+   - Absolute difference between "(1434.5, 1658.5)" and "(1658.5, 1968.5)": |0.948 - 0.585| = 0.363
+   - Absolute difference between "(1658.5, 1968.5)" and "(1968.5, 3394.5)": |1.235 - 0.948| = 0.287
+   - Absolute difference between "(1968.5, 3394.5)" and "(3394.5, 3460.0)": |0.871 - 1.235| = 0.364
+   - Absolute difference between "(3394.5, 3460.0)" and "(3460.0, 3741.5)": |1.066 - 0.871| = 0.195
+   - Absolute difference between "(3460.0, 3741.5)" and "(3741.5, 4803.5)": |2.339 - 1.066| = 1.273
+   - Absolute difference between "(3741.5, 4803.5)" and "(4803.5, 5204.0)": |2.909 - 2.339| = 0.570
+   - Absolute difference between "(4803.5, 5204.0)" and "(5204.0, 12253.0)": |3.236 - 2.909| = 0.327
+
+2. Identify the interval with the largest absolute difference:
+   - The largest absolute difference is 1.273, which occurs between the intervals "(3460.0, 3741.5)" and "(3741.5, 4803.5)".
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval "(3460.0, 3741.5)".
+SOLUTION:  3741.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the provided intervals. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between "(3.0, 14.5)" and "(14.5, 25.5)":
+   Absolute difference = |125210.2 - 92452.9| = 32757.3
+
+2. Absolute difference between "(14.5, 25.5)" and "(25.5, 65.5)":
+   Absolute difference = |92452.9 - 80407.9| = 12045.0
+
+3. Absolute difference between "(25.5, 65.5)" and "(65.5, 138.5)":
+   Absolute difference = |80407.9 - 91917.4| = 11509.5
+
+4. Continue calculating the absolute differences between consecutive mean values for all intervals.
+
+After calculating all absolute differences, we will identify the interval with the largest absolute difference. The x-axis position of this interval will correspond to the position of the largest jump in the graph.
+
+Let's perform the calculations to determine the x-axis position of the largest jump.
+SOLUTION:  14.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals and identify the interval where this difference is the largest.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between -1.054 and -0.937 = 0.117
+2. Absolute difference between -0.937 and -0.821 = 0.116
+3. Absolute difference between -0.821 and -0.705 = 0.116
+4. Absolute difference between -0.705 and -0.582 = 0.123
+5. Absolute difference between -0.582 and -0.466 = 0.116
+6. Absolute difference between -0.466 and -0.352 = 0.114
+7. Absolute difference between -0.352 and -0.235 = 0.117
+8. Absolute difference between -0.235 and -0.115 = 0.12
+9. Absolute difference between -0.115 and 0.04 = 0.155
+10. Absolute difference between 0.04 and 0.233 = 0.193
+11. Absolute difference between 0.233 and 0.35 = 0.117
+12. Absolute difference between 0.35 and 0.474 = 0.124
+13. Absolute difference between 0.474 and 0.592 = 0.118
+14. Absolute difference between 0.592 and 0.711 = 0.119
+15. Absolute difference between 0.711 and 0.832 = 0.121
+16. Absolute difference between 0.832 and 0.949 = 0.117
+17. Absolute difference between 0.949 and 1.068 = 0.119
+18. Absolute difference between 1.068 and 1.187 = 0.119
+
+The largest absolute difference is 0.193, which occurs between the intervals (0.048865, 0.059615) and (0.059615, 0.070395).
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval (0.048865, 0.059615).
+SOLUTION:  0.048865
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To identify the position of the largest jump in the graph, we need to compare the mean values within each interval and calculate the absolute differences between adjacent intervals. The largest jump will occur where the absolute difference between mean values is the greatest.
+
+Let's calculate the absolute differences between mean values for each adjacent interval:
+
+1. Between (0.0, 0.5) and (0.5, 1.5):
+   Absolute difference = |0.511 - (-0.233)| = 0.744
+
+2. Between (0.5, 1.5) and (1.5, 2.5):
+   Absolute difference = |-0.233 - (-0.666)| = 0.433
+
+3. Between (1.5, 2.5) and (2.5, 3.5):
+   Absolute difference = |-0.666 - (-1.006)| = 0.34
+
+4. Between (2.5, 3.5) and (3.5, 4.5):
+   Absolute difference = |-1.006 - (-0.529)| = 0.477
+
+5. Between (3.5, 4.5) and (4.5, 5.0):
+   Absolute difference = |-0.529 - 1.753| = 2.282
+
+The largest absolute jump occurs between the intervals (3.5, 4.5) and (4.5, 5.0) with a magnitude of 2.282.
+
+Therefore, the x-axis position of the largest jump in the graph is between 3.5 and 4.5.
+SOLUTION:  4.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to identify the intervals where the absolute difference between consecutive mean values is the largest.
+
+1. Calculate the absolute differences between consecutive mean values for each interval:
+   - For example, for the interval "(2.0, 4.5)", the absolute difference is |-19829.1 - (-10633.3)| = 9185.8
+   - Repeat this calculation for all intervals.
+
+2. Identify the interval with the largest absolute difference. This interval represents the largest jump in the graph.
+
+Let's calculate the absolute differences and find the interval with the largest jump:
+
+- Absolute differences:
+  - "(2.0, 4.5)": 9185.8
+  - "(4.5, 9.5)": 13525.9
+  - "(9.5, 12.5)": 3458.9
+  - "(12.5, 14.5)": 5826.0
+  - "(14.5, 17.5)": 6629.4
+  - "(17.5, 20.5)": 16699.3
+  - "(20.5, 22.5)": 8308.8
+  - "(22.5, 25.5)": 19471.8
+  - "(25.5, 29.5)": 6357.2
+  - "(29.5, 111.5)": 556.6
+  - "(111.5, 112.5)": 13717.0
+  - "(112.5, 176.5)": 1629.0
+  - "(176.5, 245.5)": 6097.2
+  - "(245.5, 265.5)": 6995.3
+  - "(265.5, 268.5)": 5636.7
+  - "(268.5, 317.5)": 8913.2
+  - "(317.5, 424.5)": 5894.3
+  - "(424.5, 463.5)": 7149.2
+  - "(463.5, 512.5)": 11890.9
+  - "(512.5, 513.5)": 10153.5
+  - "(513.5, 655.5)": 4330.1
+  - "(655.5, 697.5)": 7256.5
+  - "(697.5, 776.5)": 7064.5
+  - "(776.5, 779.5)": 8985.1
+  - "(779.5, 1008.5)": 3842.7
+  - "(1008.5, 1012.5)": 16836.2
+  - "(1012.5, 1081.5)": 7416.4
+  - "(1081.5, 1449.5)": 6943.9
+  - "(1449.5, 1490.5)": 14739.2
+  - "(1490.5, 1616.0)": 7386.8
+  - "(1616.0, 2714.5)": 11887.8
+  - "(2714.5, 2865.5)": 7876.4
+  - "(2865.5, 6445.0)": 9298.0
+
+The largest absolute difference is 19471.8 for the interval "(22.5, 25.5)". Therefore, the x-axis position of the largest jump in the graph is within the interval "(22.5, 25.5)".
+SOLUTION:  22.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the BloodPressure feature. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between (0.0, 15.0) and (15.0, 37.0):
+   Absolute difference = |0.236 - 0.1532| = 0.0828
+
+2. Absolute difference between (15.0, 37.0) and (37.0, 45.0):
+   Absolute difference = |0.1532 - (-0.0296)| = 0.1828
+
+3. Absolute difference between (37.0, 45.0) and (45.0, 47.0):
+   Absolute difference = |-0.0296 - (-0.0891)| = 0.0595
+
+4. Absolute difference between (45.0, 47.0) and (47.0, 54.5):
+   Absolute difference = |-0.0891 - (-0.1348)| = 0.0457
+
+5. Absolute difference between (47.0, 54.5) and (54.5, 60.5):
+   Absolute difference = |-0.1348 - (-0.1774)| = 0.0426
+
+6. Absolute difference between (54.5, 60.5) and (60.5, 61.5):
+   Absolute difference = |-0.1774 - (-0.11)| = 0.0674
+
+7. Absolute difference between (60.5, 61.5) and (61.5, 64.5):
+   Absolute difference = |-0.11 - (-0.0541)| = 0.0559
+
+8. Absolute difference between (61.5, 64.5) and (64.5, 74.5):
+   Absolute difference = |-0.0541 - (-0.0119)| = 0.0422
+
+9. Absolute difference between (64.5, 74.5) and (74.5, 75.5):
+   Absolute difference = |-0.0119 - (-0.058)| = 0.0461
+
+10. Absolute difference between (74.5, 75.5) and (75.5, 83.0):
+    Absolute difference = |-0.058 - (-0.004)| = 0.054
+
+11. Absolute difference between (75.5, 83.0) and (83.0, 93.0):
+    Absolute difference = |-0.004 - 0.0343| = 0.0383
+
+12. Absolute difference between (83.0, 93.0) and (93.0, 95.0):
+    Absolute difference = |0.0343 - 0.0889| = 0.0546
+
+13. Absolute difference between (93.0, 95.0) and (95.0, 97.0):
+    Absolute difference = |0.0889 - 0.1461| = 0.0572
+
+14. Absolute difference between (95.0, 97.0) and (97.0, 101.0):
+    Absolute difference = |0.1461 - 0.183| = 0.0369
+
+15. Absolute difference between (97.0, 101.0) and (101.0, 103.0):
+    Absolute difference = |0.183 - 0.2699| = 0.0869
+
+16. Absolute difference between (101.0, 103.0) and (103.0, 107.0):
+    Absolute difference = |0.2699 - 0.3158| = 0.0459
+
+17. Absolute difference between (103.0, 107.0) and (107.0, 109.0):
+    Absolute difference = |0.3158 - 0.3837| = 0.0679
+
+18. Absolute difference between (107.0, 109.0) and (109.0, 110.0):
+    Absolute difference = |0.3837 - 0.5269| = 0.1432
+
+The largest absolute difference is 0.1828 between the intervals (15.0, 37.0) and (37.0, 45.0). Therefore, the x-axis position of the largest jump in the graph is within the interval (15.0, 37.0).
+SOLUTION:  37.0
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences in mean values between adjacent intervals. The largest jump will be the interval where this absolute difference is the greatest.
+
+Let's calculate the absolute differences in mean values between adjacent intervals:
+
+1. Calculate the absolute differences in mean values:
+   - Between (0.0, 105.5) and (105.5, 296.5): |0.328 - 0.028| = 0.3
+   - Between (105.5, 296.5) and (296.5, 335.5): |0.028 - (-0.208)| = 0.236
+   - Between (296.5, 335.5) and (335.5, 340.0): |-0.208 - 0.165| = 0.373
+   - Continue this calculation for all adjacent intervals.
+
+2. Identify the interval with the largest absolute difference:
+   - The largest absolute difference is between (3249.5, 14327.0) and (2336.5, 2420.0) with an absolute difference of |(-4.146) - (-2.559)| = 1.587
+
+Therefore, the x-axis position of the largest jump in the graph is between the intervals (3249.5, 14327.0) and (2336.5, 2420.0).
+SOLUTION:  2336.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between (14.0, 16.0) and (16.0, 22.5):
+   Absolute difference = |4.55 - 3.26| = 1.29
+
+2. Absolute difference between (16.0, 22.5) and (22.5, 27.5):
+   Absolute difference = |3.26 - 1.89| = 1.37
+
+3. Absolute difference between (22.5, 27.5) and (27.5, 32.5):
+   Absolute difference = |1.89 - (-0.42)| = 2.31
+
+4. Absolute difference between (27.5, 32.5) and (32.5, 36.5):
+   Absolute difference = |-0.42 - (-1.76)| = 1.34
+
+5. Absolute difference between (32.5, 36.5) and (36.5, 39.0):
+   Absolute difference = |-1.76 - 0.48| = 2.24
+
+6. Absolute difference between (36.5, 39.0) and (39.0, 61.0):
+   Absolute difference = |0.48 - (-0.83)| = 1.31
+
+7. Absolute difference between (39.0, 61.0) and (61.0, 67.5):
+   Absolute difference = |-0.83 - 0.08| = 0.91
+
+8. Absolute difference between (61.0, 67.5) and (67.5, 75.0):
+   Absolute difference = |0.08 - 0.8| = 0.72
+
+9. Absolute difference between (67.5, 75.0) and (75.0, 80.0):
+   Absolute difference = |0.8 - (-5.67)| = 6.47
+
+The largest absolute difference is 6.47, which occurs between the intervals (67.5, 75.0) and (75.0, 80.0). Therefore, the x-axis position of the largest jump in the graph is within the interval (67.5, 75.0).
+SOLUTION:  75.0
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences in mean values between adjacent intervals. The largest jump will be the interval where this absolute difference is the greatest.
+
+Let's calculate the absolute differences in mean values between adjacent intervals:
+
+1. Absolute difference between "(9.71, 13.24)" and "(13.24, 14.075)":
+   Absolute difference = |-1.121 - (-1.023)| = 0.098
+
+2. Absolute difference between "(13.24, 14.075)" and "(14.075, 14.665)":
+   Absolute difference = |-1.023 - (-0.921)| = 0.102
+
+3. Absolute difference between "(14.075, 14.665)" and "(14.665, 15.010000000000002)":
+   Absolute difference = |-0.921 - (-0.82)| = 0.101
+
+4. Continue calculating the absolute differences for all adjacent intervals.
+
+After calculating the absolute differences for all intervals, we will identify the interval with the largest absolute difference. The x-axis position of this interval will correspond to the position of the largest jump in the graph.
+
+Let's perform these calculations to determine the x-axis position of the largest jump in the graph.
+SOLUTION:  20.235
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences in mean values between adjacent intervals. The largest jump will be the interval where the absolute difference is the greatest.
+
+Let's calculate the absolute differences in mean values between adjacent intervals:
+
+1. Absolute difference between "(0.0, 0.5)" and "(0.5, 1.5)": 
+   Absolute difference = |-0.02484 - (-0.02089)| = 0.00395
+
+2. Absolute difference between "(0.5, 1.5)" and "(1.5, 2.5)":
+   Absolute difference = |-0.02089 - (-0.01739)| = 0.0035
+
+3. Absolute difference between "(1.5, 2.5)" and "(2.5, 3.5)":
+   Absolute difference = |-0.01739 - (-0.01124)| = 0.00615
+
+4. Absolute difference between "(2.5, 3.5)" and "(3.5, 4.5)":
+   Absolute difference = |-0.01124 - (-0.00474)| = 0.0065
+
+5. Absolute difference between "(3.5, 4.5)" and "(4.5, 5.5)":
+   Absolute difference = |-0.00474 - 0.00077| = 0.00551
+
+6. Absolute difference between "(4.5, 5.5)" and "(5.5, 6.5)":
+   Absolute difference = |0.00077 - 0.00574| = 0.00497
+
+7. Absolute difference between "(5.5, 6.5)" and "(6.5, 7.5)":
+   Absolute difference = |0.00574 - 0.01068| = 0.00494
+
+8. Absolute difference between "(6.5, 7.5)" and "(7.5, 8.5)":
+   Absolute difference = |0.01068 - 0.01599| = 0.00531
+
+9. Absolute difference between "(7.5, 8.5)" and "(8.5, 9.5)":
+   Absolute difference = |0.01599 - 0.02231| = 0.00632
+
+10. Absolute difference between "(8.5, 9.5)" and "(9.5, 10.5)":
+    Absolute difference = |0.02231 - 0.02667| = 0.00436
+
+11. Absolute difference between "(9.5, 10.5)" and "(10.5, 13.5)":
+    Absolute difference = |0.02667 - 0.03305| = 0.00638
+
+12. Absolute difference between "(10.5, 13.5)" and "(13.5, 16.0)":
+    Absolute difference = |0.03305 - 0.02016| = 0.01289
+
+The largest absolute difference occurs between the intervals "(10.5, 13.5)" and "(13.5, 16.0)" with a value of 0.01289. This represents the largest jump in the graph.
+
+Therefore, the x-axis position of the largest jump in the graph is between 10.5 and 13.5.
+SOLUTION:  13.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to identify the intervals where the absolute difference between consecutive mean values is the greatest.
+
+1. Calculate the absolute differences between consecutive mean values in the provided intervals:
+   - Absolute difference between (-0.7) and (-0.961) = 0.261
+   - Absolute difference between (-0.961) and (-0.856) = 0.105
+   - Absolute difference between (-0.856) and (-0.762) = 0.094
+   - Absolute difference between (-0.762) and (-0.661) = 0.101
+   - Absolute difference between (-0.661) and (-0.24) = 0.421
+   - Absolute difference between (-0.24) and (-0.144) = 0.096
+   - Absolute difference between (-0.144) and (-0.051) = 0.093
+   - Absolute difference between (-0.051) and (0.049) = 0.1
+   - Absolute difference between (0.049) and (0.153) = 0.104
+   - Absolute difference between (0.153) and (0.246) = 0.093
+   - Absolute difference between (0.246) and (0.34) = 0.094
+   - Absolute difference between (0.34) and (0.434) = 0.094
+   - Absolute difference between (0.434) and (0.529) = 0.095
+   - Absolute difference between (0.529) and (0.626) = 0.097
+   - Absolute difference between (0.626) and (0.784) = 0.158
+
+2. Identify the interval with the largest absolute difference:
+   - The largest absolute difference is 0.421, which occurs between the intervals "(25.55, 26.35)" and "(26.35, 27.65)".
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval "(25.55, 26.35)".
+SOLUTION:  26.35
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals and identify the interval where the absolute difference is the largest.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between "(0.0, 0.5)" and "(0.5, 1.5)": |-0.02565 - (-0.02133)| = 0.00432
+2. Absolute difference between "(0.5, 1.5)" and "(1.5, 2.5)": |-0.02133 - (-0.01683)| = 0.0045
+3. Absolute difference between "(1.5, 2.5)" and "(2.5, 3.5)": |-0.01683 - (-0.00993)| = 0.0069
+4. Absolute difference between "(2.5, 3.5)" and "(3.5, 4.5)": |-0.00993 - (-0.00473)| = 0.0052
+5. Absolute difference between "(3.5, 4.5)" and "(4.5, 5.5)": |-0.00473 - (-1e-05)| = 0.00472
+6. Absolute difference between "(4.5, 5.5)" and "(5.5, 6.5)": |-1e-05 - 0.00511| = 0.00512
+7. Absolute difference between "(5.5, 6.5)" and "(6.5, 7.5)": |0.00511 - 0.01148| = 0.00637
+8. Absolute difference between "(6.5, 7.5)" and "(7.5, 8.5)": |0.01148 - 0.01621| = 0.00473
+9. Absolute difference between "(7.5, 8.5)" and "(8.5, 9.5)": |0.01621 - 0.02476| = 0.00855
+10. Absolute difference between "(8.5, 9.5)" and "(9.5, 11.5)": |0.02476 - 0.02962| = 0.00486
+11. Absolute difference between "(9.5, 11.5)" and "(11.5, 12.5)": |0.02962 - 0.03469| = 0.00507
+12. Absolute difference between "(11.5, 12.5)" and "(12.5, 13.5)": |0.03469 - 0.04866| = 0.01397
+13. Absolute difference between "(12.5, 13.5)" and "(13.5, 16.0)": |0.04866 - 0.05902| = 0.01036
+
+The largest absolute difference is 0.01397, which occurs between the intervals "(11.5, 12.5)" and "(12.5, 13.5)".
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval "(11.5, 12.5)".
+SOLUTION:  12.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals and identify the interval where this difference is the largest.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Calculate the absolute differences between consecutive mean values:
+   - Absolute difference between "(1.0, 1.5)" and "(1.5, 2.5)": |-0.765 - (-0.375)| = 0.39
+   - Absolute difference between "(1.5, 2.5)" and "(2.5, 4.5)": |-0.375 - (-1.909)| = 1.534
+   - Continue this calculation for all consecutive intervals.
+
+2. Identify the interval with the largest absolute difference. This interval will correspond to the position of the largest jump in the graph.
+
+Let's perform these calculations: 
+
+- Absolute differences between consecutive mean values:
+   - |0.39|, |1.534|, |0.792|, |0.491|, |0.499|, |0.314|, |0.31|, |0.765|, |0.321|, |0.266|, |0.338|, |0.185|, |0.261|, |0.261|, |0.219|, |0.259|, |0.56|, |0.436|, |0.436|, |0.587|, |0.373|, |0.805|, |0.235|, |0.514|, |0.665|, |0.481|, |0.263|, |0.41|
+
+The largest absolute difference is 0.805, which occurs between the intervals "(79.0, 83.0)" and "(83.0, 85.5)".
+
+Therefore, the x-axis position of the largest jump in the graph is between 79.0 and 83.0 on the HoursPerWeek feature.
+SOLUTION:  2.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To identify the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between "(0.0, 0.5)" and "(0.5, 2.5)":  
+   |-0.02081 - (-0.02956)| = 0.00875
+
+2. Absolute difference between "(0.5, 2.5)" and "(2.5, 3.5)":  
+   |-0.00998 - (-0.02081)| = 0.01083
+
+3. Absolute difference between "(2.5, 3.5)" and "(3.5, 4.5)":  
+   |-0.00524 - (-0.00998)| = 0.00474
+
+4. Absolute difference between "(3.5, 4.5)" and "(4.5, 5.5)":  
+   |0.00043 - (-0.00524)| = 0.00567
+
+5. Absolute difference between "(4.5, 5.5)" and "(5.5, 6.5)":  
+   |0.00515 - 0.00043| = 0.00472
+
+6. Absolute difference between "(5.5, 6.5)" and "(6.5, 8.5)":  
+   |0.01107 - 0.00515| = 0.00592
+
+7. Absolute difference between "(6.5, 8.5)" and "(8.5, 10.5)":  
+   |0.02102 - 0.01107| = 0.00995
+
+8. Absolute difference between "(8.5, 10.5)" and "(10.5, 11.5)":  
+   |0.02728 - 0.02102| = 0.00626
+
+9. Absolute difference between "(10.5, 11.5)" and "(11.5, 13.5)":  
+   |0.0456 - 0.02728| = 0.01832
+
+10. Absolute difference between "(11.5, 13.5)" and "(13.5, 14.5)":  
+    |0.05244 - 0.0456| = 0.00684
+
+11. Absolute difference between "(13.5, 14.5)" and "(14.5, 17.0)":  
+    |0.06161 - 0.05244| = 0.00917
+
+The largest absolute difference is 0.01832, which occurs between the intervals "(10.5, 11.5)" and "(11.5, 13.5)". Therefore, the x-axis position of the largest jump in the graph is within the interval (10.5, 11.5).
+SOLUTION:  11.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals and identify the interval where this difference is the largest.
+
+1. Calculate the absolute differences between consecutive mean values:
+   - Absolute difference between (0.0, 1.5) and (1.5, 2.5): |-0.02643 - (-0.01529)| = 0.01114
+   - Absolute difference between (1.5, 2.5) and (2.5, 3.5): |-0.01529 - (-0.01037)| = 0.00492
+   - Absolute difference between (2.5, 3.5) and (3.5, 4.5): |-0.01037 - (-0.00562)| = 0.00475
+   - Absolute difference between (3.5, 4.5) and (4.5, 5.5): |-0.00562 - 0.00068| = 0.00630
+   - Absolute difference between (4.5, 5.5) and (5.5, 6.5): |0.00068 - 0.00591| = 0.00523
+   - Absolute difference between (5.5, 6.5) and (6.5, 7.5): |0.00591 - 0.01127| = 0.00536
+   - Absolute difference between (6.5, 7.5) and (7.5, 8.5): |0.01127 - 0.01553| = 0.00426
+   - Absolute difference between (7.5, 8.5) and (8.5, 10.5): |0.01553 - 0.02363| = 0.00810
+   - Absolute difference between (8.5, 10.5) and (10.5, 11.5): |0.02363 - 0.03038| = 0.00675
+   - Absolute difference between (10.5, 11.5) and (11.5, 12.5): |0.03038 - 0.03607| = 0.00569
+   - Absolute difference between (11.5, 12.5) and (12.5, 13.5): |0.03607 - 0.04087| = 0.00480
+   - Absolute difference between (12.5, 13.5) and (13.5, 15.0): |0.04087 - 0.04477| = 0.00390
+
+2. Identify the interval with the largest absolute difference:
+   - The largest absolute difference is 0.01114 between the intervals (0.0, 1.5) and (1.5, 2.5).
+
+Therefore, the x-axis position of the largest jump in the graph is between 0.0 and 1.5 in the feature "Siltation".
+SOLUTION:  1.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to identify the intervals where the absolute difference between consecutive mean values is the greatest.
+
+1. Calculate the absolute differences between consecutive mean values in each interval:
+   - Absolute difference for each interval:
+     - (0.0008948, 0.001092): |0.3286 - 0.2818| = 0.0468
+     - (0.001092, 0.0014135): |0.2713 - 0.3286| = 0.0573
+     - (0.0014135, 0.0015165): |0.2283 - 0.2713| = 0.043
+     - (0.0015165, 0.0017545): |0.144 - 0.2283| = 0.0843
+     - (0.0017545, 0.0017905): |0.0956 - 0.144| = 0.0484
+     - (0.0017905, 0.0019039999999999999): |0.0526 - 0.0956| = 0.043
+     - (0.0019039999999999999, 0.0021525): |0.0073 - 0.0526| = 0.0453
+     - (0.0021525, 0.002572): |0.1543 - 0.0073| = 0.147
+     - (0.002572, 0.002761): |0.1971 - 0.1543| = 0.0428
+     - (0.002761, 0.003308): |0.1525 - 0.1971| = 0.0446
+     - (0.003308, 0.0033604999999999998): |0.1049 - 0.1525| = 0.0476
+     - (0.0033604999999999998, 0.0035329999999999997): |0.0586 - 0.1049| = 0.0463
+     - (0.0035329999999999997, 0.003736): |0.0157 - 0.0586| = 0.0429
+     - (0.003736, 0.003907): |-0.029 - 0.0157| = 0.0447
+     - (0.003907, 0.004092500000000001): |-0.0717 - (-0.029)| = 0.0427
+     - (0.004092500000000001, 0.0045775): |-0.1177 - (-0.0717)| = 0.046
+     - (0.0045775, 0.0045935): |-0.1739 - (-0.1177)| = 0.0562
+     - (0.0045935, 0.004644499999999999): |-0.2208 - (-0.1739)| = 0.0469
+     - (0.004644499999999999, 0.004809): |-0.2666 - (-0.2208)| = 0.0458
+     - (0.004809, 0.005856500000000001): |-0.31 - (-0.2666)| = 0.0434
+     - (0.005856500000000001, 0.007497500000000001): |-0.356 - (-0.31)| = 0.046
+     - (0.007497500000000001, 0.009717): |-0.4 - (-0.356)| = 0.044
+     - (0.009717, 0.0127): |-0.4439 - (-0.4)| = 0.0439
+     - (0.0127, 0.02984): |-0.4 - (-0.4439)| = 0.0439
+
+2. Identify the interval with the largest absolute difference:
+   - The largest absolute difference is 0.0843 in the interval (0.0015165, 0.0017545).
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval (0.0015165, 0.0017545).
+SOLUTION:  0.002572
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the graph. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between (-0.295) and (-0.472) = 0.177
+2. Absolute difference between (-0.472) and (-0.549) = 0.077
+3. Absolute difference between (-0.549) and (-0.469) = 0.08
+4. Absolute difference between (-0.469) and (-0.392) = 0.077
+5. Absolute difference between (-0.392) and (-0.31) = 0.082
+6. Absolute difference between (-0.31) and (-0.23) = 0.08
+7. Absolute difference between (-0.23) and (-0.112) = 0.118
+8. Absolute difference between (-0.112) and (-0.034) = 0.078
+9. Absolute difference between (-0.034) and (0.046) = 0.08
+10. Absolute difference between (0.046) and (0.125) = 0.079
+11. Absolute difference between (0.125) and (0.202) = 0.077
+12. Absolute difference between (0.202) and (0.281) = 0.079
+13. Absolute difference between (0.281) and (0.363) = 0.082
+14. Absolute difference between (0.363) and (0.444) = 0.081
+15. Absolute difference between (0.444) and (0.526) = 0.082
+16. Absolute difference between (0.526) and (0.624) = 0.098
+17. Absolute difference between (0.624) and (0.705) = 0.081
+18. Absolute difference between (0.705) and (0.785) = 0.08
+19. Absolute difference between (0.785) and (0.867) = 0.082
+
+The largest absolute difference is 0.118, which occurs between the intervals (0.26959999999999995, 0.27495) and (0.27495, 0.28035).
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval (0.26959999999999995, 0.27495).
+SOLUTION:  0.165
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the provided intervals and identify the interval where this difference is the largest.
+
+1. Calculate the absolute differences between consecutive mean values:
+   - For example, the absolute difference between the mean values for the intervals "(-124.35, -124.10499999999999)" and "(-124.10499999999999, -124.08500000000001)" is abs(-50430.1 - (-38925.6)) = 11504.5.
+   - Repeat this calculation for all consecutive intervals.
+
+2. Identify the interval with the largest absolute difference:
+   - Compare all the absolute differences calculated in step 1 and find the interval where this difference is the largest.
+
+3. Determine the x-axis position of the largest jump:
+   - The x-axis position of the largest jump corresponds to the boundary between the two intervals with the largest absolute difference in mean values.
+
+By following these steps, you can pinpoint the x-axis position where the largest jump in the graph occurs.
+SOLUTION:  -122.39500000000001
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals and identify the interval where this difference is the largest.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between -0.26 and -1.08 = 0.82
+2. Absolute difference between -1.08 and -3.77 = 2.69
+3. Absolute difference between -3.77 and -0.9 = 2.87
+4. Absolute difference between -0.9 and -0.15 = 0.75
+5. Absolute difference between -0.15 and -0.88 = 0.73
+6. Absolute difference between -0.88 and 0.2 = 1.08
+7. Absolute difference between 0.2 and 1.18 = 0.98
+8. Absolute difference between 1.18 and 2.18 = 1.00
+9. Absolute difference between 2.18 and 4.74 = 2.56
+10. Absolute difference between 4.74 and 1.14 = 3.60
+11. Absolute difference between 1.14 and 3.63 = 2.49
+12. Absolute difference between 3.63 and -0.36 = 3.99
+13. Absolute difference between -0.36 and 2.57 = 2.93
+14. Absolute difference between 2.57 and 0.36 = 2.21
+15. Absolute difference between 0.36 and 3.06 = 2.70
+16. Absolute difference between 3.06 and 6.76 = 3.70
+17. Absolute difference between 6.76 and 2.31 = 4.45
+18. Absolute difference between 2.31 and 2.92 = 0.61
+19. Absolute difference between 2.92 and 0.76 = 2.16
+20. Absolute difference between 0.76 and -0.43 = 1.19
+21. Absolute difference between -0.43 and 0.23 = 0.66
+22. Absolute difference between 0.23 and 6.97 = 6.74
+
+The largest absolute difference is 6.74, which occurs between the intervals (0.23, 6.97). Therefore, the x-axis position of the largest jump in the graph is within the interval (0.23, 6.97).
+SOLUTION:  6.55
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals and identify the interval where this difference is the largest.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between (0.0, 0.5) and (0.5, 1.5): |-0.02593 - (-0.02172)| = 0.00421
+2. Absolute difference between (0.5, 1.5) and (1.5, 2.5): |-0.02172 - (-0.01544)| = 0.00628
+3. Absolute difference between (1.5, 2.5) and (2.5, 3.5): |-0.01544 - (-0.0098)| = 0.00564
+4. Absolute difference between (2.5, 3.5) and (3.5, 4.5): |-0.0098 - (-0.00541)| = 0.00439
+5. Absolute difference between (3.5, 4.5) and (4.5, 5.5): |-0.00541 - 0.00066| = 0.00607
+6. Absolute difference between (4.5, 5.5) and (5.5, 6.5): |0.00066 - 0.00575| = 0.00509
+7. Absolute difference between (5.5, 6.5) and (6.5, 7.5): |0.00575 - 0.01201| = 0.00626
+8. Absolute difference between (6.5, 7.5) and (7.5, 8.5): |0.01201 - 0.01649| = 0.00448
+9. Absolute difference between (7.5, 8.5) and (8.5, 9.5): |0.01649 - 0.0215| = 0.00501
+10. Absolute difference between (8.5, 9.5) and (9.5, 10.5): |0.0215 - 0.0267| = 0.0052
+11. Absolute difference between (9.5, 10.5) and (10.5, 11.5): |0.0267 - 0.03057| = 0.00387
+12. Absolute difference between (10.5, 11.5) and (11.5, 13.5): |0.03057 - 0.0366| = 0.00603
+13. Absolute difference between (11.5, 13.5) and (13.5, 14.0): |0.0366 - 0.03003| = 0.00657
+
+The largest absolute difference is 0.00657, which occurs between the intervals (11.5, 13.5) and (13.5, 14.0).
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval (11.5, 13.5).
+SOLUTION:  13.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between -0.884 and -0.783 = 0.101
+2. Absolute difference between -0.783 and -0.681 = 0.102
+3. Absolute difference between -0.681 and -0.581 = 0.1
+4. Absolute difference between -0.581 and -0.476 = 0.105
+5. Absolute difference between -0.476 and -0.369 = 0.107
+6. Absolute difference between -0.369 and -0.267 = 0.102
+7. Absolute difference between -0.267 and -0.166 = 0.101
+8. Absolute difference between -0.166 and -0.064 = 0.102
+9. Absolute difference between -0.064 and 0.035 = 0.099
+10. Absolute difference between 0.035 and 0.14 = 0.105
+11. Absolute difference between 0.14 and 0.249 = 0.109
+12. Absolute difference between 0.249 and 0.407 = 0.158
+13. Absolute difference between 0.407 and 0.518 = 0.111
+14. Absolute difference between 0.518 and 0.626 = 0.108
+15. Absolute difference between 0.626 and 0.73 = 0.104
+16. Absolute difference between 0.73 and 0.835 = 0.105
+17. Absolute difference between 0.835 and 0.936 = 0.101
+18. Absolute difference between 0.936 and 1.038 = 0.102
+
+The largest absolute difference is 0.158, which occurs between the intervals "(108.6, 112.6)" and "(112.6, 117.45)".
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval (108.6, 112.6).
+SOLUTION:  108.6
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals and identify the interval where this difference is the largest.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between (0.0, 0.5) and (0.5, 1.5): |-0.0273 - (-0.02345)| = 0.00385
+2. Absolute difference between (0.5, 1.5) and (1.5, 2.5): |-0.02345 - (-0.01571)| = 0.00774
+3. Absolute difference between (1.5, 2.5) and (2.5, 3.5): |-0.01571 - (-0.01174)| = 0.00397
+4. Absolute difference between (2.5, 3.5) and (3.5, 4.5): |-0.01174 - (-0.00519)| = 0.00655
+5. Absolute difference between (3.5, 4.5) and (4.5, 5.5): |-0.00519 - 0.00111| = 0.0063
+6. Absolute difference between (4.5, 5.5) and (5.5, 6.5): |0.00111 - 0.00506| = 0.00395
+7. Absolute difference between (5.5, 6.5) and (6.5, 7.5): |0.00506 - 0.01056| = 0.0055
+8. Absolute difference between (6.5, 7.5) and (7.5, 8.5): |0.01056 - 0.01706| = 0.0065
+9. Absolute difference between (7.5, 8.5) and (8.5, 9.5): |0.01706 - 0.02398| = 0.00692
+10. Absolute difference between (8.5, 9.5) and (9.5, 11.5): |0.02398 - 0.02821| = 0.00423
+11. Absolute difference between (9.5, 11.5) and (11.5, 12.5): |0.02821 - 0.03673| = 0.00852
+12. Absolute difference between (11.5, 12.5) and (12.5, 13.5): |0.03673 - 0.01311| = 0.02362
+13. Absolute difference between (12.5, 13.5) and (13.5, 16.0): |0.01311 - 0.03206| = 0.01895
+
+The largest absolute difference is 0.02362, which occurs between the intervals (11.5, 12.5) and (12.5, 13.5).
+
+Therefore, the x-axis position of the largest jump in the graph is between 11.5 and 12.5.
+SOLUTION:  12.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to identify the intervals where the absolute difference between consecutive mean values is the greatest.
+
+1. Calculate the absolute differences between consecutive mean values for each interval:
+   - For example, for the interval "(0.4999, 0.5427500000000001)", the absolute difference is |-55539.5 - (-16067.6)| = 39471.9.
+   - Repeat this calculation for all intervals.
+
+2. Identify the interval with the largest absolute difference. This interval represents the largest jump in the graph.
+
+3. Determine the midpoint of this interval to find the x-axis position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values for each interval:
+
+- Absolute difference for "(0.4999, 0.5427500000000001)": 39471.9
+- Absolute difference for "(0.5427500000000001, 1.4808)": 15736.9
+- Absolute difference for "(1.4808, 2.1658999999999997)": 15877.0
+- Absolute difference for "(2.1658999999999997, 2.6096)": 15577.2
+- Absolute difference for "(2.6096, 3.2433)": 15637.1
+- Absolute difference for "(3.2433, 3.66575)": 15176.5
+- Absolute difference for "(3.66575, 4.3197)": 17478.2
+- Absolute difference for "(4.3197, 4.691000000000001)": 14775.7
+- Absolute difference for "(4.691000000000001, 5.1358)": 17657.0
+- Absolute difference for "(5.1358, 5.59195)": 16867.5
+- Absolute difference for "(5.59195, 5.8294)": 16967.4
+- Absolute difference for "(5.8294, 6.29665)": 18192.1
+- Absolute difference for "(6.29665, 6.3704)": 27608.2
+- Absolute difference for "(6.3704, 6.874750000000001)": 24190.2
+- Absolute difference for "(6.874750000000001, 7.6996)": 24149.9
+- Absolute difference for "(7.6996, 7.8141)": 15845.3
+- Absolute difference for "(7.8141, 8.3976)": 18932.7
+- Absolute difference for "(8.3976, 9.046949999999999)": 18463.3
+- Absolute difference for "(9.046949999999999, 15.00005)": 10805.6
+- Absolute difference for "(15.00005, 15.0001)": 14625.9
+
+The largest absolute difference is 27608.2 for the interval "(6.29665, 6.3704)".
+
+The midpoint of this interval is (6.29665 + 6.3704) / 2 = 6.333525.
+
+Therefore, the x-axis position of the largest jump in the graph is approximately 6.333525.
+SOLUTION:  0.5427500000000001
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To identify the position of the largest jump in the graph, we need to compare the mean values within the intervals provided for the continuous feature "diabetes". Since the mean values are different in the intervals, we can calculate the absolute difference between the mean values of adjacent intervals to find the largest jump.
+
+1. Calculate the absolute differences between the mean values of adjacent intervals:
+   - Absolute difference between (0.0, 0.5) and (0.5, 1.0):
+     Absolute difference = |0.3225 - (-0.415)| = 0.7375
+
+2. Compare the absolute differences to find the largest jump:
+   - Largest jump magnitude = 0.7375
+
+3. Determine the x-axis position of the largest jump:
+   - The largest jump occurs between the intervals (0.0, 0.5) and (0.5, 1.0) on the x-axis.
+
+Therefore, the x-axis position of the largest jump in the graph for the continuous feature "diabetes" is between 0.0 and 0.5.
+SOLUTION:  0.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals and identify the interval where this difference is the largest.
+
+1. Calculate the absolute differences between consecutive mean values:
+   - For example, the absolute difference between -0.897 and -0.811 is |(-0.897) - (-0.811)| = 0.086.
+   - Repeat this calculation for all consecutive mean values in the intervals.
+
+2. Identify the interval with the largest absolute difference:
+   - Compare all the absolute differences calculated in step 1 and find the interval where this difference is the largest.
+
+3. Determine the x-axis position corresponding to this interval:
+   - The x-axis position for the largest jump will be the midpoint of the interval where the largest absolute difference occurs.
+
+Let's perform these calculations step by step:
+
+1. Calculate the absolute differences between consecutive mean values:
+   - Absolute differences:
+     - 0.086, 0.092, 0.088, 0.088, 0.088, 0.085, 0.084, 0.085, 0.084, 0.084, 0.109, 0.084, 0.115, 0.095, 0.088, 0.095, 0.095, 0.084, 0.174, 0.091, 0.149, 0.072
+
+2. Identify the interval with the largest absolute difference:
+   - The largest absolute difference is 0.174, which occurs in the interval "(0.1686, 0.2428)".
+
+3. Determine the x-axis position corresponding to this interval:
+   - The midpoint of the interval "(0.1686, 0.2428)" is (0.1686 + 0.2428) / 2 = 0.2057.
+
+Therefore, the x-axis position of the largest jump in the graph is approximately 0.2057.
+SOLUTION:  0.072265
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals and identify the interval where the absolute difference is the largest.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between "(0.0, 0.5)" and "(0.5, 1.5)": |-0.02593 - (-0.02211)| = 0.00382
+2. Absolute difference between "(0.5, 1.5)" and "(1.5, 2.5)": |-0.02211 - (-0.01611)| = 0.006
+3. Absolute difference between "(1.5, 2.5)" and "(2.5, 3.5)": |-0.01611 - (-0.01125)| = 0.00486
+4. Absolute difference between "(2.5, 3.5)" and "(3.5, 4.5)": |-0.01125 - (-0.0047)| = 0.00655
+5. Absolute difference between "(3.5, 4.5)" and "(4.5, 5.5)": |-0.0047 - 9e-05| = 0.00461
+6. Absolute difference between "(4.5, 5.5)" and "(5.5, 6.5)": |9e-05 - 0.00652| = 0.00643
+7. Absolute difference between "(5.5, 6.5)" and "(6.5, 8.5)": |0.00652 - 0.01219| = 0.00567
+8. Absolute difference between "(6.5, 8.5)" and "(8.5, 10.5)": |0.01219 - 0.02253| = 0.01034
+9. Absolute difference between "(8.5, 10.5)" and "(10.5, 11.5)": |0.02253 - 0.03412| = 0.01159
+10. Absolute difference between "(10.5, 11.5)" and "(11.5, 12.5)": |0.03412 - 0.04015| = 0.00603
+11. Absolute difference between "(11.5, 12.5)" and "(12.5, 14.0)": |0.04015 - 0.04564| = 0.00549
+
+The largest absolute difference is 0.01159, which occurs between the intervals "(8.5, 10.5)" and "(10.5, 11.5)".
+
+Therefore, the x-axis position of the largest jump in the graph is between 8.5 and 10.5.
+SOLUTION:  10.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences in mean values between adjacent intervals. The largest jump will be the interval where this absolute difference is the greatest.
+
+Let's calculate the absolute differences in mean values between adjacent intervals:
+
+1. Calculate the absolute differences in mean values:
+   - Between (0.0, 22.0) and (22.0, 86.5): |-0.728 - (-1.069)| = 0.341
+   - Between (22.0, 86.5) and (86.5, 94.5): |-1.069 - (-0.907)| = 0.162
+   - Between (86.5, 94.5) and (94.5, 99.5): |-0.907 - (-0.729)| = 0.178
+   - Between (94.5, 99.5) and (99.5, 105.5): |-0.729 - (-0.491)| = 0.238
+   - Between (99.5, 105.5) and (105.5, 114.5): |-0.491 - (-0.326)| = 0.165
+   - Between (105.5, 114.5) and (114.5, 123.5): |-0.326 - (-0.157)| = 0.169
+   - Between (114.5, 123.5) and (123.5, 130.5): |-0.157 - 0.045| = 0.202
+   - Between (123.5, 130.5) and (130.5, 139.5): |0.045 - 0.208| = 0.163
+   - Between (130.5, 139.5) and (139.5, 147.5): |0.208 - 0.37| = 0.162
+   - Between (139.5, 147.5) and (147.5, 154.5): |0.37 - 0.535| = 0.165
+   - Between (147.5, 154.5) and (154.5, 159.5): |0.535 - 0.724| = 0.189
+   - Between (154.5, 159.5) and (159.5, 165.5): |0.724 - 0.984| = 0.26
+   - Between (159.5, 165.5) and (165.5, 169.5): |0.984 - 1.342| = 0.358
+   - Between (165.5, 169.5) and (169.5, 178.5): |1.342 - 1.502| = 0.16
+   - Between (169.5, 178.5) and (178.5, 187.5): |1.502 - 1.691| = 0.189
+   - Between (178.5, 187.5) and (187.5, 198.5): |1.691 - 1.853| = 0.162
+   - Between (187.5, 198.5) and (198.5, 199.0): |1.853 - 2.022| = 0.169
+
+2. Identify the largest absolute difference:
+   The largest absolute difference is 0.358, which occurs between the intervals (159.5, 165.5) and (165.5, 169.5).
+
+Therefore, the x-axis position of the largest jump in the graph is between the values 159.5 and 165.5 on the Glucose feature.
+SOLUTION:  165.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between (0.0, 1.5) and (1.5, 2.5):
+   |-0.02419 - (-0.01693)| = 0.00726
+
+2. Absolute difference between (1.5, 2.5) and (2.5, 3.5):
+   |-0.01693 - (-0.01069)| = 0.00624
+
+3. Absolute difference between (2.5, 3.5) and (3.5, 4.5):
+   |-0.01069 - (-0.00585)| = 0.00484
+
+4. Absolute difference between (3.5, 4.5) and (4.5, 5.5):
+   |-0.00585 - 0.00051| = 0.00636
+
+5. Absolute difference between (4.5, 5.5) and (5.5, 6.5):
+   |0.00051 - 0.00676| = 0.00625
+
+6. Absolute difference between (5.5, 6.5) and (6.5, 8.5):
+   |0.00676 - 0.01245| = 0.00569
+
+7. Absolute difference between (6.5, 8.5) and (8.5, 10.5):
+   |0.01245 - 0.02257| = 0.01012
+
+8. Absolute difference between (8.5, 10.5) and (10.5, 11.5):
+   |0.02257 - 0.03265| = 0.01008
+
+9. Absolute difference between (10.5, 11.5) and (11.5, 13.5):
+   |0.03265 - 0.03889| = 0.00624
+
+10. Absolute difference between (11.5, 13.5) and (13.5, 14.5):
+    |0.03889 - 0.04912| = 0.01023
+
+11. Absolute difference between (13.5, 14.5) and (14.5, 16.0):
+    |0.04912 - 0.0585| = 0.00938
+
+The largest absolute difference is 0.01023, which occurs between the intervals (11.5, 13.5) and (13.5, 14.5).
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval (11.5, 13.5).
+SOLUTION:  13.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences in mean values between adjacent intervals. The largest jump will be the interval boundary where the absolute difference is the greatest.
+
+Let's calculate the absolute differences in mean values between adjacent intervals:
+
+1. Calculate the absolute differences in mean values for each adjacent pair of intervals.
+2. Identify the interval boundary where the absolute difference is the greatest.
+
+Let's calculate the absolute differences in mean values:
+
+- Absolute difference between "(0.1167, 0.1384)" and "(0.1384, 0.14229999999999998)": |-0.604 - (-0.55)| = 0.054
+- Absolute difference between "(0.1384, 0.14229999999999998)" and "(0.14229999999999998, 0.14565)": |-0.55 - (-0.489)| = 0.061
+- Absolute difference between "(0.14229999999999998, 0.14565)" and "(0.14565, 0.1488)": |-0.489 - (-0.428)| = 0.061
+- Continue calculating the absolute differences for all adjacent intervals.
+
+After calculating all absolute differences, identify the interval boundary where the absolute difference is the greatest. The x-axis position of this boundary will correspond to the largest jump in the graph.
+
+Please perform these calculations to determine the x-axis position of the largest jump in the graph. Let me know if you need further assistance.
+SOLUTION:  0.1545
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between -1.379 and -1.223: 0.156
+2. Absolute difference between -1.223 and -1.069: 0.154
+3. Absolute difference between -1.069 and -0.914: 0.155
+4. Absolute difference between -0.914 and -0.755: 0.159
+5. Absolute difference between -0.755 and -0.599: 0.156
+6. Absolute difference between -0.599 and -0.447: 0.152
+7. Absolute difference between -0.447 and -0.292: 0.155
+8. Absolute difference between -0.292 and -0.446: 0.154
+9. Absolute difference between -0.446 and -0.294: 0.152
+10. Absolute difference between -0.294 and 0.197: 0.491
+11. Absolute difference between 0.197 and 0.351: 0.154
+12. Absolute difference between 0.351 and 0.507: 0.156
+13. Absolute difference between 0.507 and 0.748: 0.241
+14. Absolute difference between 0.748 and 0.902: 0.154
+15. Absolute difference between 0.902 and 1.059: 0.157
+16. Absolute difference between 1.059 and 1.215: 0.156
+17. Absolute difference between 1.215 and 1.368: 0.153
+18. Absolute difference between 1.368 and 1.523: 0.155
+
+The largest absolute difference is 0.491, which occurs between the intervals (102.05000000000001, 109.6) and (109.6, 116.25).
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval (102.05000000000001, 109.6).
+SOLUTION:  102.05000000000001
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between -0.919 and -0.814 = 0.105
+2. Absolute difference between -0.814 and -0.704 = 0.11
+3. Absolute difference between -0.704 and -0.596 = 0.108
+4. Absolute difference between -0.596 and -0.49 = 0.106
+5. Absolute difference between -0.49 and -0.367 = 0.123
+6. Absolute difference between -0.367 and -0.256 = 0.111
+7. Absolute difference between -0.256 and -0.151 = 0.105
+8. Absolute difference between -0.151 and 0.081 = 0.232
+9. Absolute difference between 0.081 and 0.188 = 0.107
+10. Absolute difference between 0.188 and 0.292 = 0.104
+11. Absolute difference between 0.292 and 0.417 = 0.125
+12. Absolute difference between 0.417 and 0.53 = 0.113
+13. Absolute difference between 0.53 and 0.638 = 0.108
+14. Absolute difference between 0.638 and 0.751 = 0.113
+15. Absolute difference between 0.751 and 0.862 = 0.111
+16. Absolute difference between 0.862 and 0.974 = 0.112
+17. Absolute difference between 0.974 and 1.082 = 0.108
+
+The largest absolute difference is 0.232, which occurs between the intervals "(20.905, 32.985)" and "(32.985, 34.730000000000004)".
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval "(20.905, 32.985)".
+SOLUTION:  32.985
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to identify the intervals where the absolute difference between consecutive mean values is the largest.
+
+1. Calculate the absolute differences between consecutive mean values in the given intervals:
+   - Absolute difference between (-1.489) and (-0.895) = 0.594
+   - Absolute difference between (-0.895) and (-0.02) = 0.875
+   - Absolute difference between (-0.02) and (0.701) = 0.721
+   - Absolute difference between (0.701) and (1.245) = 0.544
+   - Absolute difference between (1.245) and (-0.923) = 2.168
+   - Absolute difference between (-0.923) and (0.647) = 1.57
+   - Absolute difference between (0.647) and (-0.288) = 0.935
+   - Absolute difference between (-0.288) and (-1.035) = 0.747
+   - Absolute difference between (-1.035) and (0.0) = 1.035
+   - Absolute difference between (0.0) and (-0.73) = 0.73
+   - Absolute difference between (-0.73) and (0.19) = 0.92
+   - Absolute difference between (0.19) and (0.784) = 0.594
+   - Absolute difference between (0.784) and (1.169) = 0.385
+   - Absolute difference between (1.169) and (0.839) = 0.33
+   - Absolute difference between (0.839) and (2.112) = 1.273
+   - Absolute difference between (2.112) and (3.884) = 1.772
+   - Absolute difference between (3.884) and (4.517) = 0.633
+
+2. The largest absolute difference between consecutive mean values is 2.168, which occurs between the intervals "(1.245)" and "(-0.923)".
+
+Therefore, the x-axis position of the largest jump in the graph is between the ages of 47.5 and 48.5.
+SOLUTION:  48.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals and identify the interval where this difference is the largest.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between mean values for intervals (0.0, 0.5) and (0.5, 3.5):
+   Absolute difference = |-0.074 - (-0.297)| = 0.223
+
+2. Absolute difference between mean values for intervals (0.5, 3.5) and (3.5, 4.5):
+   Absolute difference = |0.644 - (-0.074)| = 0.718
+
+3. Absolute difference between mean values for intervals (3.5, 4.5) and (4.5, 6.5):
+   Absolute difference = |-0.723 - 0.644| = 1.367
+
+4. Absolute difference between mean values for intervals (4.5, 6.5) and (6.5, 7.5):
+   Absolute difference = |-0.542 - (-0.723)| = 0.181
+
+5. Absolute difference between mean values for intervals (6.5, 7.5) and (7.5, 8.5):
+   Absolute difference = |-0.665 - (-0.542)| = 0.123
+
+6. Absolute difference between mean values for intervals (7.5, 8.5) and (8.5, 9.5):
+   Absolute difference = |-0.926 - (-0.665)| = 0.261
+
+7. Absolute difference between mean values for intervals (8.5, 9.5) and (9.5, 10.5):
+   Absolute difference = |0.423 - (-0.926)| = 1.349
+
+8. Absolute difference between mean values for intervals (9.5, 10.5) and (10.5, 11.5):
+   Absolute difference = |0.59 - 0.423| = 0.167
+
+9. Absolute difference between mean values for intervals (10.5, 11.5) and (11.5, 12.5):
+   Absolute difference = |0.27 - 0.59| = 0.32
+
+10. Absolute difference between mean values for intervals (11.5, 12.5) and (12.5, 13.5):
+    Absolute difference = |0.534 - 0.27| = 0.264
+
+11. Absolute difference between mean values for intervals (12.5, 13.5) and (13.5, 14.0):
+    Absolute difference = |-0.133 - 0.534| = 0.667
+
+The largest absolute difference is 1.367, which occurs between the intervals (3.5, 4.5) and (4.5, 6.5).
+
+Therefore, the x-axis position of the largest jump in the graph is between the values 3.5 and 4.5 on the Occupation feature.
+SOLUTION:  4.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between intervals (0.0, 0.5) and (0.5, 1.5):
+   Absolute difference = |1.333 - (-0.195)| = 1.528
+
+2. Absolute difference between intervals (0.5, 1.5) and (1.5, 2.5):
+   Absolute difference = |(-0.02) - 1.333| = 1.353
+
+3. Absolute difference between intervals (1.5, 2.5) and (2.5, 3.5):
+   Absolute difference = |(-0.402) - (-0.02)| = 0.382
+
+4. Absolute difference between intervals (2.5, 3.5) and (3.5, 4.5):
+   Absolute difference = |(-1.423) - (-0.402)| = 1.021
+
+5. Absolute difference between intervals (3.5, 4.5) and (4.5, 5.5):
+   Absolute difference = |0.086 - (-1.423)| = 1.509
+
+6. Absolute difference between intervals (4.5, 5.5) and (5.5, 7.5):
+   Absolute difference = |-0.843 - 0.086| = 0.929
+
+7. Absolute difference between intervals (5.5, 7.5) and (7.5, 8.5):
+   Absolute difference = |-0.246 - (-0.843)| = 0.597
+
+8. Absolute difference between intervals (7.5, 8.5) and (8.5, 11.5):
+   Absolute difference = |0.062 - (-0.246)| = 0.308
+
+9. Absolute difference between intervals (8.5, 11.5) and (11.5, 20.5):
+   Absolute difference = |-0.315 - 0.062| = 0.377
+
+10. Absolute difference between intervals (11.5, 20.5) and (20.5, 21.5):
+    Absolute difference = |0.109 - (-0.315)| = 0.424
+
+11. Absolute difference between intervals (20.5, 21.5) and (21.5, 22.5):
+    Absolute difference = |0.476 - 0.109| = 0.367
+
+12. Absolute difference between intervals (21.5, 22.5) and (22.5, 24.5):
+    Absolute difference = |0.133 - 0.476| = 0.343
+
+13. Absolute difference between intervals (22.5, 24.5) and (24.5, 26.5):
+    Absolute difference = |-0.35 - 0.133| = 0.483
+
+14. Absolute difference between intervals (24.5, 26.5) and (26.5, 29.5):
+    Absolute difference = |-0.489 - (-0.35)| = 0.139
+
+15. Absolute difference between intervals (26.5, 29.5) and (29.5, 32.5):
+    Absolute difference = |-0.108 - (-0.489)| = 0.381
+
+16. Absolute difference between intervals (29.5, 32.5) and (32.5, 33.5):
+    Absolute difference = |-0.483 - (-0.108)| = 0.375
+
+17. Absolute difference between intervals (32.5, 33.5) and (33.5, 35.5):
+    Absolute difference = |-0.664 - (-0.483)| = 0.181
+
+18. Absolute difference between intervals (33.5, 35.5) and (35.5, 38.5):
+    Absolute difference = |-0.396 - (-0.664)| = 0.268
+
+19. Absolute difference between intervals (35.5, 38.5) and (38.5, 39.5):
+    Absolute difference = |0.028 - (-0.396)| = 0.424
+
+20. Absolute difference between intervals (38.5, 39.5) and (39.5, 40.5):
+    Absolute difference = |-0.596 - 0.028| = 0.624
+
+21. Absolute difference between intervals (39.5, 40.5) and (40.5, 41.0):
+    Absolute difference = |1.112 - (-0.596)| = 1.708
+
+The largest absolute difference is 1.708, which occurs between the intervals (39.5, 40.5) and (40.5, 41.0). Therefore, the x-axis position of the largest jump in the graph is in the interval (39.5, 40.5).
+SOLUTION:  40.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values within the intervals provided.
+
+1. Calculate the absolute differences between consecutive mean values:
+   - Absolute difference between (0.0, 0.5) and (0.5, 2.5): |0.1633 - (-0.0751)| = 0.2384
+   - Absolute difference between (0.5, 2.5) and (2.5, 3.0): |(-0.7301) - 0.1633| = 0.8934
+
+2. Identify the largest absolute difference:
+   - The largest absolute difference is 0.8934, which corresponds to the jump between the intervals (0.5, 2.5) and (2.5, 3.0).
+
+Therefore, the x-axis position of the largest jump in the graph is between 0.5 and 2.5 on the SibSp feature.
+SOLUTION:  2.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To identify the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided for the feature "petal_width". The largest jump will be the interval where the absolute difference between mean values is the greatest.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between (0.1, 0.35) and (0.35, 0.45):
+   |8.07 - 7.27| = 0.8
+
+2. Absolute difference between (0.35, 0.45) and (0.45, 0.75):
+   |7.27 - 6.18| = 1.09
+
+3. Absolute difference between (0.45, 0.75) and (0.75, 1.25):
+   |6.18 - (-2.64)| = 8.82
+
+4. Absolute difference between (0.75, 1.25) and (1.25, 1.75):
+   |-2.64 - (-3.46)| = 0.82
+
+5. Absolute difference between (1.25, 1.75) and (1.75, 2.5):
+   |-3.46 - (-4.19)| = 0.73
+
+The largest absolute difference occurs between the intervals (0.45, 0.75) and (0.75, 1.25) with a value of 8.82. This represents the largest jump in the graph.
+
+Therefore, the x-axis position of the largest jump in the graph for the feature "petal_width" is between 0.45 and 0.75.
+SOLUTION:  0.75
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To identify the position of the largest jump in the graph, we need to compare the mean values within the intervals provided for the continuous feature "HasCrCard". Since the mean values are different in adjacent intervals, we can calculate the absolute difference between these mean values to determine the size of the jump.
+
+Let's calculate the absolute differences between the mean values in the intervals:
+
+1. Absolute difference between the mean values in the interval "(0.0, 0.5)":
+   - Mean value for "(0.0, 0.5)": -0.004421
+   - Mean value for "(0.5, 1.0)": 0.001379
+   - Absolute difference: |(-0.004421) - 0.001379| = 0.0058
+
+2. Absolute difference between the mean values in the interval "(0.5, 1.0)":
+   - Mean value for "(0.5, 1.0)": 0.001379
+   - Mean value for "(0.0, 0.5)": -0.004421
+   - Absolute difference: |0.001379 - (-0.004421)| = 0.0058
+
+The absolute differences are the same for both intervals, indicating that the jump in the graph is consistent across the entire feature range. Therefore, the x-axis position of the largest jump in the graph for the continuous feature "HasCrCard" is at the boundary between the intervals, specifically at the point where the feature transitions from 0.5 to 1.0.
+SOLUTION:  0.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between (1.1, 1.65) and (1.65, 2.45):
+   Absolute difference = |8.05 - 7.28| = 0.77
+
+2. Absolute difference between (1.65, 2.45) and (2.45, 3.15):
+   Absolute difference = |7.28 - (-1.17)| = 8.45
+
+3. Absolute difference between (2.45, 3.15) and (3.15, 3.8):
+   Absolute difference = |-1.17 - (-2.4)| = 1.23
+
+4. Absolute difference between (3.15, 3.8) and (3.8, 4.45):
+   Absolute difference = |-2.4 - (-3.03)| = 0.63
+
+5. Absolute difference between (3.8, 4.45) and (4.45, 5.65):
+   Absolute difference = |-3.03 - (-3.73)| = 0.70
+
+6. Absolute difference between (4.45, 5.65) and (5.65, 6.9):
+   Absolute difference = |-3.73 - (-4.38)| = 0.65
+
+The largest absolute difference is 8.45, which occurs between the intervals (1.65, 2.45) and (2.45, 3.15). Therefore, the x-axis position of the largest jump in the graph is within the range of petal_length from 1.65 to 2.45.
+SOLUTION:  2.45
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to identify the intervals where the absolute difference between consecutive mean values is the greatest.
+
+1. Calculate the absolute differences between consecutive mean values in the graph:
+   - Absolute difference between (-0.528) and (-0.218) = 0.31
+   - Absolute difference between (-0.218) and (-0.342) = 0.124
+   - Absolute difference between (-0.342) and (-0.168) = 0.174
+   - Absolute difference between (-0.168) and (-0.077) = 0.091
+   - Absolute difference between (-0.077) and (0.015) = 0.092
+   - Absolute difference between (0.015) and (0.131) = 0.116
+   - Absolute difference between (0.131) and (0.223) = 0.092
+   - Absolute difference between (0.223) and (0.316) = 0.093
+   - Absolute difference between (0.316) and (0.407) = 0.091
+   - Absolute difference between (0.407) and (0.498) = 0.091
+   - Absolute difference between (0.498) and (1.018) = 0.52
+   - Absolute difference between (1.018) and (1.283) = 0.265
+   - Absolute difference between (1.283) and (1.168) = 0.115
+   - Absolute difference between (1.168) and (0.065) = 1.103
+   - Absolute difference between (0.065) and (-0.193) = 0.258
+   - Absolute difference between (-0.193) and (-0.092) = 0.101
+
+2. The largest absolute difference between consecutive mean values is 1.103, which occurs between the intervals (1.4175, 1.451) and (1.451, 1.837).
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval (1.4175, 1.451).
+SOLUTION:  1.4175
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to identify the intervals where the absolute difference between consecutive mean values is the greatest.
+
+1. Calculate the absolute differences between consecutive mean values for each interval:
+   - For example, for the interval "(0.0, 57.0)", the absolute difference is |-4.83 - (-0.25)| = 4.58.
+   - Repeat this calculation for all intervals.
+
+2. Identify the interval with the largest absolute difference. This interval represents the largest jump in the graph.
+
+3. Determine the midpoint of the interval with the largest jump. This midpoint corresponds to the x-axis position of the largest jump in the graph.
+
+Let's calculate the absolute differences between consecutive mean values for each interval and identify the interval with the largest jump: 
+
+- Absolute differences:
+  - "(0.0, 57.0)": |-4.83 - (-0.25)| = 4.58
+  - "(57.0, 3048.0)": |-4.83 - 2.57| = 7.4
+  - "(3048.0, 3120.0)": |2.57 - (-4.43)| = 7
+  - "(3120.0, 4243.5)": |-4.43 - 1.45| = 5.88
+  - "(4243.5, 4401.0)": |1.45 - (-1.82)| = 3.27
+  - "(4401.0, 4668.5)": |-1.82 - 3.79| = 5.61
+  - "(4668.5, 4826.0)": |3.79 - 0.57| = 3.22
+  - "(4826.0, 4898.0)": |0.57 - 2.25| = 1.68
+  - "(4898.0, 4973.5)": |2.25 - (-3.52)| = 5.77
+  - "(4973.5, 5119.0)": |-3.52 - 4.26| = 7.78
+  - "(5119.0, 5316.5)": |4.26 - 0.43| = 3.83
+  - "(5316.5, 5505.5)": |0.43 - 2.15| = 1.72
+  - "(5505.5, 6457.5)": |2.15 - (-0.16)| = 2.31
+  - "(6457.5, 6505.5)": |-0.16 - 0.81| = 0.97
+  - "(6505.5, 6745.0)": |0.81 - (-1.33)| = 2.14
+  - "(6745.0, 7073.5)": |-1.33 - 5.76| = 7.09
+  - "(7073.5, 7436.5)": |5.76 - 2.02| = 3.74
+  - "(7436.5, 7565.5)": |2.02 - 6.56| = 4.54
+  - "(7565.5, 7792.0)": |6.56 - 4.88| = 1.68
+  - "(7792.0, 7937.0)": |4.88 - 3.84| = 1.04
+  - "(7937.0, 8296.0)": |3.84 - 7.18| = 3.34
+  - "(8296.0, 10543.0)": |7.18 - (-1.48)| = 8.66
+  - "(10543.0, 10585.5)": |-1.48 - 8.61| = 10.09
+  - "(10585.5, 30961.5)": |8.61 - (-0.66)| = 9.27
+  - "(30961.5, 70654.5)": |-0.66 - 9.72| = 10.38
+  - "(70654.5, 99999.0)": |9.72 - 0| = 9.72
+
+The largest absolute difference is 10.38, which occurs in the interval "(30961.5, 70654.5)".
+
+Therefore, the x-axis position of the largest jump in the graph is the midpoint of the interval "(30961.5, 70654.5)", which is (30961.5 + 70654.5) / 2 = 50808.0.
+SOLUTION:  70654.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between mean values for intervals (2.0, 2.25) and (2.25, 2.6500000000000004):
+   Absolute difference = |-2.473 - (-2.179)| = 0.294
+
+2. Absolute difference between mean values for intervals (2.25, 2.6500000000000004) and (2.6500000000000004, 2.8499999999999996):
+   Absolute difference = |-2.179 - (-1.736)| = 0.443
+
+3. Absolute difference between mean values for intervals (2.6500000000000004, 2.8499999999999996) and (2.8499999999999996, 2.95):
+   Absolute difference = |-1.736 - (-0.945)| = 0.791
+
+4. Absolute difference between mean values for intervals (2.8499999999999996, 2.95) and (2.95, 3.05):
+   Absolute difference = |-0.945 - 0.062| = 1.007
+
+5. Absolute difference between mean values for intervals (2.95, 3.05) and (3.05, 3.25):
+   Absolute difference = |0.062 - 0.509| = 0.447
+
+6. Absolute difference between mean values for intervals (3.05, 3.25) and (3.25, 3.3499999999999996):
+   Absolute difference = |0.509 - 1.373| = 0.864
+
+7. Absolute difference between mean values for intervals (3.25, 3.3499999999999996) and (3.3499999999999996, 3.55):
+   Absolute difference = |1.373 - 1.669| = 0.296
+
+8. Absolute difference between mean values for intervals (3.3499999999999996, 3.55) and (3.55, 3.75):
+   Absolute difference = |1.669 - 2.097| = 0.428
+
+9. Absolute difference between mean values for intervals (3.55, 3.75) and (3.75, 3.95):
+   Absolute difference = |2.097 - 2.489| = 0.392
+
+10. Absolute difference between mean values for intervals (3.75, 3.95) and (3.95, 4.1):
+    Absolute difference = |2.489 - 2.778| = 0.289
+
+The largest absolute difference is 1.007, which occurs between the intervals (2.8499999999999996, 2.95) and (2.95, 3.05).
+
+Therefore, the x-axis position of the largest jump in the graph is in the interval (2.8499999999999996, 2.95).
+SOLUTION:  2.95
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between mean values in the interval "(0.04996, 0.05075)":  
+   Absolute difference = |0.5962 - 0.5519| = 0.0443
+
+2. Absolute difference between mean values in the interval "(0.05075, 0.052285)":  
+   Absolute difference = |0.5519 - 0.5087| = 0.0432
+
+3. Absolute difference between mean values in the interval "(0.052285, 0.05393)":  
+   Absolute difference = |0.5087 - 0.4681| = 0.0406
+
+4. Continue calculating the absolute differences between consecutive mean values for all intervals.
+
+After calculating the absolute differences, we will identify the interval with the largest absolute difference. The x-axis position of this interval will correspond to the position of the largest jump in the graph.
+
+Let's perform the calculations and identify the x-axis position of the largest jump.
+SOLUTION:  0.056365
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals provided. The largest absolute difference will indicate the position of the largest jump.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between "(4.0, 11.5)" and "(11.5, 12.5)": |10.73 - 1.29| = 9.44
+2. Absolute difference between "(11.5, 12.5)" and "(12.5, 15.5)": |1.29 - 3.88| = 2.59
+3. Absolute difference between "(12.5, 15.5)" and "(15.5, 18.0)": |3.88 - 2.22| = 1.66
+4. Absolute difference between "(15.5, 18.0)" and "(18.0, 28.5)": |2.22 - 6.17| = 3.95
+5. Absolute difference between "(18.0, 28.5)" and "(28.5, 30.5)": |6.17 - 4.47| = 1.70
+6. Absolute difference between "(28.5, 30.5)" and "(30.5, 52.0)": |4.47 - 5.56| = 1.09
+7. Absolute difference between "(30.5, 52.0)" and "(52.0, 54.5)": |5.56 - 3.38| = 2.18
+8. Absolute difference between "(52.0, 54.5)" and "(54.5, 67.5)": |3.38 - 4.79| = 1.41
+9. Absolute difference between "(54.5, 67.5)" and "(67.5, 73.5)": |4.79 - 2.76| = 2.03
+10. Absolute difference between "(67.5, 73.5)" and "(73.5, 76.5)": |2.76 - (-3.15)| = 5.91
+11. Absolute difference between "(73.5, 76.5)" and "(76.5, 78.5)": |-3.15 - 2.29| = 5.44
+12. Absolute difference between "(76.5, 78.5)" and "(78.5, 82.5)": |2.29 - (-0.16)| = 2.45
+13. Absolute difference between "(78.5, 82.5)" and "(82.5, 87.5)": |-0.16 - (-2.8)| = 2.64
+14. Absolute difference between "(82.5, 87.5)" and "(87.5, 90.5)": |-2.8 - 0.19| = 2.99
+15. Absolute difference between "(87.5, 90.5)" and "(90.5, 92.5)": |0.19 - (-1.08)| = 1.27
+16. Absolute difference between "(90.5, 92.5)" and "(92.5, 95.5)": |-1.08 - (-2.7)| = 1.62
+17. Absolute difference between "(92.5, 95.5)" and "(95.5, 108.5)": |-2.7 - (-0.98)| = 1.72
+18. Absolute difference between "(95.5, 108.5)" and "(108.5, 117.5)": |-0.98 - 0.02| = 1.00
+19. Absolute difference between "(108.5, 117.5)" and "(117.5, 124.5)": |0.02 - (-3.44)| = 3.46
+20. Absolute difference between "(117.5, 124.5)" and "(124.5, 137.5)": |-3.44 - 0.64| = 4.08
+21. Absolute difference between "(124.5, 137.5)" and "(137.5, 149.0)": |0.64 - (-0.8)| = 1.44
+22. Absolute difference between "(137.5, 149.0)" and "(149.0, 171.5)": |-0.8 - 5.06| = 5.86
+23. Absolute difference between "(149.0, 171.5)" and "(171.5, 173.0)": |5.06 - 2.66| = 2.40
+24. Absolute difference between "(171.5, 173.0)" and "(173.0, 182.5)": |2.66 - (-0.84)| = 3.50
+25. Absolute difference between "(173.0, 182.5)" and "(182.5, 192.5)": |-0.84 - (-3.42)| = 2.58
+26. Absolute difference between "(182.5, 192.5)" and "(192.5, 193.5)": |-3.42 - (-1.01)| = 2.41
+27. Absolute difference between "(192.5, 193.5)" and "(193.5, 253.0)": |-1.01 - (-2.58)| = 1.57
+28. Absolute difference between "(193.5, 253.0)" and "(253.0, 285.0)": |-2.58 - (-8.42)| = 5.84
+
+The largest absolute difference is 5.91, which occurs between the intervals "(67.5, 73.5)" and "(73.5, 76.5)".
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval (67.5, 73.5).
+SOLUTION:  11.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences in mean values between adjacent intervals and identify the interval where this difference is the largest.
+
+1. Calculate the absolute differences in mean values between adjacent intervals:
+   - For each adjacent pair of intervals, calculate the absolute difference in mean values.
+   - Keep track of the interval where the largest absolute difference occurs.
+
+2. Identify the interval with the largest absolute difference:
+   - Compare the absolute differences calculated in step 1 to find the interval with the largest jump.
+
+Let's perform these calculations:
+
+- Absolute differences in mean values:
+  - (0.001713, 0.0031539999999999997): |0.2958 - 0.2615| = 0.0343
+  - (0.0031539999999999997, 0.003299): |0.2615 - 0.185| = 0.0765
+  - (0.003299, 0.003384): |0.185 - (-0.1523)| = 0.3373
+  - (0.003384, 0.0034675): |-0.1523 - (-0.1838)| = 0.0315
+  - (0.0034675, 0.0036699999999999997): |-0.1838 - (-0.2174)| = 0.0336
+  - ...
+  - Continue calculating absolute differences for all intervals.
+
+The largest absolute difference is 0.3373, which occurs between the intervals (0.003299, 0.003384).
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval (0.003299, 0.003384).
+SOLUTION:  0.003384
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals and identify the interval where this difference is the largest.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between -0.769 and -0.671 = 0.098
+2. Absolute difference between -0.671 and -0.846 = 0.175
+3. Absolute difference between -0.846 and -0.943 = 0.097
+4. Absolute difference between -0.943 and -0.843 = 0.1
+5. Absolute difference between -0.843 and -0.745 = 0.098
+6. Absolute difference between -0.745 and -0.646 = 0.099
+7. Absolute difference between -0.646 and -0.54 = 0.106
+8. Absolute difference between -0.54 and -0.438 = 0.102
+9. Absolute difference between -0.438 and -0.332 = 0.106
+10. Absolute difference between -0.332 and -0.234 = 0.098
+11. Absolute difference between -0.234 and -0.081 = 0.153
+12. Absolute difference between -0.081 and 0.187 = 0.268
+13. Absolute difference between 0.187 and 0.284 = 0.097
+14. Absolute difference between 0.284 and 0.385 = 0.101
+15. Absolute difference between 0.385 and 0.486 = 0.101
+16. Absolute difference between 0.486 and 0.586 = 0.1
+17. Absolute difference between 0.586 and 0.698 = 0.112
+18. Absolute difference between 0.698 and 0.797 = 0.099
+19. Absolute difference between 0.797 and 0.897 = 0.1
+
+The largest absolute difference is 0.268, which occurs between the intervals (0.20695, 0.20795) and (0.20795, 0.2539).
+
+Therefore, the x-axis position of the largest jump in the graph is in the interval (0.20695, 0.20795).
+SOLUTION:  0.20795
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To identify the position of the largest jump in the graph, we need to compare the mean values within the intervals provided for the continuous feature "sex". The mean values within each interval are as follows:
+
+- For the interval (0.0, 0.5): Mean = 0.01719
+- For the interval (0.5, 1.0): Mean = -0.00954
+
+To find the largest jump, we calculate the absolute difference between these mean values:
+
+Absolute Jump = |0.01719 - (-0.00954)| = 0.02673
+
+Therefore, the largest jump in the graph occurs between the intervals (0.0, 0.5) and (0.5, 1.0) for the feature "sex".
+SOLUTION:  0.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals and identify the interval where this difference is the largest.
+
+Let's calculate the absolute differences between consecutive mean values:
+
+1. Absolute difference between (0.0, 0.5) and (0.5, 1.5):
+   |-0.0692 - (-0.3765)| = 0.3073
+
+2. Absolute difference between (0.5, 1.5) and (1.5, 4.5):
+   |-0.016 - (-0.0692)| = 0.0532
+
+3. Absolute difference between (1.5, 4.5) and (4.5, 5.5):
+   |0.0109 - (-0.016)| = 0.0269
+
+4. Absolute difference between (4.5, 5.5) and (5.5, 6.5):
+   |0.0432 - 0.0109| = 0.0323
+
+5. Absolute difference between (5.5, 6.5) and (6.5, 7.5):
+   |0.0871 - 0.0432| = 0.0439
+
+6. Absolute difference between (6.5, 7.5) and (7.5, 9.5):
+   |0.0554 - 0.0871| = 0.0317
+
+7. Absolute difference between (7.5, 9.5) and (9.5, 10.0):
+   |-0.0599 - 0.0554| = 0.1153
+
+The largest absolute difference is 0.3073 between the intervals (0.0, 0.5) and (0.5, 1.5).
+
+Therefore, the x-axis position of the largest jump in the graph occurs between 0.0 and 0.5 on the Tenure feature.
+SOLUTION:  0.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the x-axis position of the largest jump in the graph, we need to calculate the absolute differences between consecutive mean values in the intervals and identify the interval where this difference is the largest.
+
+1. Calculate the absolute differences between consecutive mean values:
+   - For each consecutive pair of intervals, calculate the absolute difference in mean values.
+   - Keep track of the interval where the absolute difference is the largest.
+
+2. Identify the interval with the largest absolute difference:
+   - Compare the absolute differences calculated in step 1 to find the largest absolute difference.
+   - Note the x-axis position associated with this interval.
+
+Let's perform these calculations:
+
+- Absolute differences between consecutive mean values:
+  - Absolute difference between "(0.0, 845.0)" and "(845.0, 1448.0)": |-0.044 - (-1.147)| = 1.103
+  - Absolute difference between "(845.0, 1448.0)" and "(1448.0, 1551.5)": |-1.147 - 0.416| = 1.563
+  - Absolute difference between "(1448.0, 1551.5)" and "(1551.5, 1568.5)": |0.416 - 3.928| = 3.512
+  - Continue calculating absolute differences for all consecutive intervals.
+
+- The largest absolute difference is found to be between the intervals "(2553.0, 2581.0)" and "(2581.0, 2678.5)" with a value of |5.5 - (-0.191)| = 5.691.
+
+Therefore, the x-axis position of the largest jump in the graph is within the interval "(2553.0, 2581.0)".
+SOLUTION:  1568.5
+--------------------------------------------------------------------------------
\ No newline at end of file
diff --git a/benchmarks/results/jump-gpt-4-turbo-2024-04-09.txt b/benchmarks/results/jump-gpt-4-turbo-2024-04-09.txt
new file mode 100644
index 0000000..d5141d9
--- /dev/null
+++ b/benchmarks/results/jump-gpt-4-turbo-2024-04-09.txt
@@ -0,0 +1,3987 @@
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between adjacent intervals. The intervals given in the graph are (1.0, 1.5), (1.5, 2.5), and (2.5, 3.0). We will calculate the differences in mean values at the boundaries of these intervals.
+
+1. **Calculate the difference between the intervals (1.0, 1.5) and (1.5, 2.5):**
+   - Mean value at the end of interval (1.0, 1.5): -0.009
+   - Mean value at the start of interval (1.5, 2.5): 0.534
+   - Difference: \(0.534 - (-0.009) = 0.543\)
+
+2. **Calculate the difference between the intervals (1.5, 2.5) and (2.5, 3.0):**
+   - Mean value at the end of interval (1.5, 2.5): 0.534
+   - Mean value at the start of interval (2.5, 3.0): -0.532
+   - Difference: \(-0.532 - 0.534 = -1.066\)
+
+The absolute values of these differences are:
+- \(|0.543| = 0.543\)
+- \(| -1.066| = 1.066\)
+
+The largest jump in absolute terms is 1.066, which occurs between the intervals (1.5, 2.5) and (2.5, 3.0). The x-axis position of this largest jump is at the boundary between these intervals, which is at 2.5.
+SOLUTION:  2.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph for the feature "smoking," we need to compare the mean values at the boundaries of the intervals provided. The intervals given are (0.0, 0.5) and (0.5, 1.0), and the corresponding mean values are 0.01522 and -0.03391 respectively.
+
+Step-by-step process:
+
+1. **Identify the intervals and their corresponding mean values**:
+   - Interval (0.0, 0.5) has a mean value of 0.01522.
+   - Interval (0.5, 1.0) has a mean value of -0.03391.
+
+2. **Calculate the jump between the intervals**:
+   - The jump occurs at the boundary between the two intervals, which is at x = 0.5.
+   - To find the magnitude of the jump, calculate the difference in mean values across this boundary:
+     \[
+     \text{Jump magnitude} = |\text{mean at upper end of first interval} - \text{mean at lower end of second interval}|
+     \]
+     \[
+     = |0.01522 - (-0.03391)| = |0.01522 + 0.03391| = 0.04913
+     \]
+
+3. **Conclusion**:
+   - The largest jump occurs at x = 0.5, with a magnitude of 0.04913.
+
+Thus, the x-axis position of the largest jump in the graph is at x = 0.5.
+SOLUTION:  0.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (143.5, 259.35): -0.759
+- (259.35, 289.4): -0.662
+- (289.4, 319.15): -0.567
+- (319.15, 348.3): -0.464
+- (348.3, 496.5): -0.368
+- (496.5, 548.75): -0.271
+- (548.75, 606.0): -0.173
+- (606.0, 696.25): -0.076
+- (696.25, 806.1500000000001): 0.309
+- (806.1500000000001, 901.8): 0.405
+- (901.8, 959.4000000000001): 0.51
+- (959.4000000000001, 1054.0): 0.607
+- (1054.0, 1150.0): 0.707
+- (1150.0, 1248.5): 0.806
+- (1248.5, 1341.0): 0.911
+- (1341.0, 1801.0): 1.01
+- (1801.0, 2501.0): 1.109
+
+Now, let's calculate the differences between consecutive intervals:
+- Difference between (259.35, 289.4) and (143.5, 259.35): -0.662 - (-0.759) = 0.097
+- Difference between (289.4, 319.15) and (259.35, 289.4): -0.567 - (-0.662) = 0.095
+- Difference between (319.15, 348.3) and (289.4, 319.15): -0.464 - (-0.567) = 0.103
+- Difference between (348.3, 496.5) and (319.15, 348.3): -0.368 - (-0.464) = 0.096
+- Difference between (496.5, 548.75) and (348.3, 496.5): -0.271 - (-0.368) = 0.097
+- Difference between (548.75, 606.0) and (496.5, 548.75): -0.173 - (-0.271) = 0.098
+- Difference between (606.0, 696.25) and (548.75, 606.0): -0.076 - (-0.173) = 0.097
+- Difference between (696.25, 806.1500000000001) and (606.0, 696.25): 0.309 - (-0.076) = 0.385
+- Difference between (806.1500000000001, 901.8) and (696.25, 806.1500000000001): 0.405 - 0.309 = 0.096
+- Difference between (901.8, 959.4000000000001) and (806.1500000000001, 901.8): 0.51 - 0.405 = 0.105
+- Difference between (959.4000000000001, 1054.0) and (901.8, 959.4000000000001): 0.607 - 0.51 = 0.097
+- Difference between (1054.0, 1150.0) and (959.4000000000001, 1054.0): 0.707 - 0.607 = 0.1
+- Difference between (1150.0, 1248.5) and (1054.0, 1150.0): 0.806 - 0.707 = 0.099
+- Difference between (1248.5, 1341.0) and (1150.0, 1248.5): 0.911 - 0.806 = 0.105
+- Difference between (1341.0, 1801.0) and (1248.5, 1341.0): 1.01 - 0.911 = 0.099
+- Difference between (1801.0, 2501.0) and (1341.0, 1801.0): 1.109 - 1.01 = 0.099
+
+The largest jump in absolute terms is between the intervals (606.0, 696.25) and (696.25, 806.1500000000001) with a difference of 0.385. Therefore, the x-axis position of the largest jump is at the boundary between these two intervals, which is 696.25.
+SOLUTION:  696.25
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.0274
+- (0.5, 1.5): -0.02381
+- (1.5, 2.5): -0.01602
+- (2.5, 3.5): -0.01049
+- (3.5, 4.5): -0.00528
+- (4.5, 5.5): -0.00022
+- (5.5, 6.5): 0.00517
+- (6.5, 7.5): 0.01282
+- (7.5, 8.5): 0.01628
+- (8.5, 9.5): 0.02454
+- (9.5, 10.5): 0.02883
+- (10.5, 11.5): 0.03213
+- (11.5, 17.0): 0.03564
+
+Now, let's calculate the differences (jumps) between consecutive intervals:
+- Jump from (0.0, 0.5) to (0.5, 1.5): |-0.02381 - (-0.0274)| = 0.00359
+- Jump from (0.5, 1.5) to (1.5, 2.5): |-0.01602 - (-0.02381)| = 0.00779
+- Jump from (1.5, 2.5) to (2.5, 3.5): |-0.01049 - (-0.01602)| = 0.00553
+- Jump from (2.5, 3.5) to (3.5, 4.5): |-0.00528 - (-0.01049)| = 0.00521
+- Jump from (3.5, 4.5) to (4.5, 5.5): |-0.00022 - (-0.00528)| = 0.00506
+- Jump from (4.5, 5.5) to (5.5, 6.5): |0.00517 - (-0.00022)| = 0.00539
+- Jump from (5.5, 6.5) to (6.5, 7.5): |0.01282 - 0.00517| = 0.00765
+- Jump from (6.5, 7.5) to (7.5, 8.5): |0.01628 - 0.01282| = 0.00346
+- Jump from (7.5, 8.5) to (8.5, 9.5): |0.02454 - 0.01628| = 0.00826
+- Jump from (8.5, 9.5) to (9.5, 10.5): |0.02883 - 0.02454| = 0.00429
+- Jump from (9.5, 10.5) to (10.5, 11.5): |0.03213 - 0.02883| = 0.00330
+- Jump from (10.5, 11.5) to (11.5, 17.0): |0.03564 - 0.03213| = 0.00351
+
+The largest jump in absolute terms is from (7.5, 8.5) to (8.5, 9.5) with a difference of 0.00826. Therefore, the x-axis position of the largest jump is at the boundary between these intervals, which is 8.5.
+SOLUTION:  8.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+1. (185.2, 357.5): -1.345
+2. (357.5, 413.15): -1.192
+3. (413.15, 471.9): -1.038
+4. (471.9, 508.5): -0.878
+5. (508.5, 633.9): -0.723
+6. (633.9, 653.45): -0.565
+7. (653.45, 710.2): -0.348
+8. (710.2, 727.0999999999999): -0.165
+9. (727.0999999999999, 805.95): 0.096
+10. (805.95, 874.85): 0.253
+11. (874.85, 928.5): 0.48
+12. (928.5, 1033.5): 0.761
+13. (1033.5, 1222.5): 0.932
+14. (1222.5, 1346.5): 1.092
+15. (1346.5, 1645.5): 1.245
+16. (1645.5, 1979.0): 1.404
+17. (1979.0, 4254.0): 1.557
+
+Now, let's calculate the differences between consecutive intervals:
+- Jump from (357.5, 413.15) to (413.15, 471.9): |-1.192 - (-1.038)| = 0.154
+- Jump from (413.15, 471.9) to (471.9, 508.5): |-1.038 - (-0.878)| = 0.160
+- Jump from (471.9, 508.5) to (508.5, 633.9): |-0.878 - (-0.723)| = 0.155
+- Jump from (508.5, 633.9) to (633.9, 653.45): |-0.723 - (-0.565)| = 0.158
+- Jump from (633.9, 653.45) to (653.45, 710.2): |-0.565 - (-0.348)| = 0.217
+- Jump from (653.45, 710.2) to (710.2, 727.0999999999999): |-0.348 - (-0.165)| = 0.183
+- Jump from (710.2, 727.0999999999999) to (727.0999999999999, 805.95): |-0.165 - 0.096| = 0.261
+- Jump from (727.0999999999999, 805.95) to (805.95, 874.85): |0.096 - 0.253| = 0.157
+- Jump from (805.95, 874.85) to (874.85, 928.5): |0.253 - 0.48| = 0.227
+- Jump from (874.85, 928.5) to (928.5, 1033.5): |0.48 - 0.761| = 0.281
+- Jump from (928.5, 1033.5) to (1033.5, 1222.5): |0.761 - 0.932| = 0.171
+- Jump from (1033.5, 1222.5) to (1222.5, 1346.5): |0.932 - 1.092| = 0.160
+- Jump from (1222.5, 1346.5) to (1346.5, 1645.5): |1.092 - 1.245| = 0.153
+- Jump from (1346.5, 1645.5) to (1645.5, 1979.0): |1.245 - 1.404| = 0.159
+- Jump from (1645.5, 1979.0) to (1979.0, 4254.0): |1.404 - 1.557| = 0.153
+
+The largest jump in absolute terms is from (874.85, 928.5) to (928.5, 1033.5) with a difference of 0.281. Therefore, the x-axis position of the largest jump is at the boundary between these intervals, which is 928.5.
+SOLUTION:  928.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (2.0, 2.5): -0.503
+- (2.5, 5.0): 1.062
+- (5.0, 17.5): 1.188
+- (17.5, 24.5): 0.305
+- (24.5, 28.5): 0.438
+- (28.5, 31.5): 0.03
+- (31.5, 35.5): 0.337
+- (35.5, 36.25): 0.047
+- (36.25, 43.5): -0.09
+- (43.5, 44.5): -0.293
+- (44.5, 47.5): -0.611
+- (47.5, 49.5): -0.32
+- (49.5, 59.0): -0.561
+- (59.0, 60.5): -0.283
+- (60.5, 63.5): -0.939
+- (63.5, 70.5): -1.095
+- (70.5, 75.5): -0.598
+- (75.5, 80.0): -0.406
+
+Now, let's calculate the absolute differences between consecutive intervals:
+- Jump from (2.0, 2.5) to (2.5, 5.0): |1.062 - (-0.503)| = 1.565
+- Jump from (2.5, 5.0) to (5.0, 17.5): |1.188 - 1.062| = 0.126
+- Jump from (5.0, 17.5) to (17.5, 24.5): |0.305 - 1.188| = 0.883
+- Jump from (17.5, 24.5) to (24.5, 28.5): |0.438 - 0.305| = 0.133
+- Jump from (24.5, 28.5) to (28.5, 31.5): |0.03 - 0.438| = 0.408
+- Jump from (28.5, 31.5) to (31.5, 35.5): |0.337 - 0.03| = 0.307
+- Jump from (31.5, 35.5) to (35.5, 36.25): |0.047 - 0.337| = 0.290
+- Jump from (35.5, 36.25) to (36.25, 43.5): |-0.09 - 0.047| = 0.137
+- Jump from (36.25, 43.5) to (43.5, 44.5): |-0.293 - (-0.09)| = 0.203
+- Jump from (43.5, 44.5) to (44.5, 47.5): |-0.611 - (-0.293)| = 0.318
+- Jump from (44.5, 47.5) to (47.5, 49.5): |-0.32 - (-0.611)| = 0.291
+- Jump from (47.5, 49.5) to (49.5, 59.0): |-0.561 - (-0.32)| = 0.241
+- Jump from (49.5, 59.0) to (59.0, 60.5): |-0.283 - (-0.561)| = 0.278
+- Jump from (59.0, 60.5) to (60.5, 63.5): |-0.939 - (-0.283)| = 0.656
+- Jump from (60.5, 63.5) to (63.5, 70.5): |-1.095 - (-0.939)| = 0.156
+- Jump from (63.5, 70.5) to (70.5, 75.5): |-0.598 - (-1.095)| = 0.497
+- Jump from (70.5, 75.5) to (75.5, 80.0): |-0.406 - (-0.598)| = 0.192
+
+The largest jump in absolute terms is from the interval (2.0, 2.5) to (2.5, 5.0) with a magnitude of 1.565. Therefore, the x-axis position of the largest jump is at the boundary between these intervals, which is 2.5.
+SOLUTION:  2.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the steps to find the largest jump:
+
+1. **Extract the mean values for each interval**: We have the mean values provided in the JSON object under "Means".
+
+2. **Calculate the differences between consecutive intervals**: We will subtract the mean value of one interval from the mean value of the next interval.
+
+3. **Identify the largest absolute difference**: The largest jump will be where the absolute difference between consecutive intervals is the highest.
+
+Let's perform these calculations using the provided mean values:
+
+- From "(0.01191, 0.012455)" to "(0.012455, 0.0203)": Difference = 0.0471 - (-0.0118) = 0.0589
+- From "(0.02983, 0.032535)" to "(0.032535, 0.0338)": Difference = -0.0071 - 0.0347 = -0.0418
+- From "(0.032535, 0.0338)" to "(0.0338, 0.038565)": Difference = 0.0604 - (-0.0071) = 0.0675
+- From "(0.038565, 0.04418)" to "(0.04418, 0.059305)": Difference = 0.1494 - 0.1065 = 0.0429
+- From "(0.059305, 0.065775)" to "(0.065775, 0.07794000000000001)": Difference = 0.0533 - 0.1044 = -0.0511
+- From "(0.07794000000000001, 0.08089)" to "(0.08089, 0.096205)": Difference = -0.0573 - 0.0097 = -0.067
+- From "(0.096205, 0.22865000000000002)" to "(0.22865000000000002, 0.396)": Difference = -0.1471 - (-0.1001) = -0.047
+
+From these calculations, the largest absolute jump is between the intervals "(0.032535, 0.0338)" and "(0.0338, 0.038565)" with a difference of 0.0675.
+
+**Conclusion**: The x-axis position of the largest jump in the graph is at the boundary between the intervals "(0.032535, 0.0338)" and "(0.0338, 0.038565)".
+SOLUTION:  0.02983
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms. Here's how we can proceed:
+
+1. **Extract the mean values and their corresponding intervals** from the JSON object provided. The intervals are keys, and the mean values are the corresponding values.
+
+2. **Calculate the differences in mean values between consecutive intervals**. This involves subtracting the mean value of one interval from the mean value of the next interval.
+
+3. **Identify the interval where the absolute difference is the largest**. This will indicate the largest jump.
+
+Let's perform these calculations using the provided data:
+
+- **Intervals and Mean Values**:
+  - (2.0, 23.0): -70808.9
+  - (23.0, 38.5): -78966.6
+  - (38.5, 48.5): -28602.1
+  - (48.5, 119.0): -47079.6
+  - (119.0, 163.0): -52692.3
+  - (163.0, 186.5): -60093.0
+  - (186.5, 223.5): -51150.5
+  - (223.5, 239.5): -39728.1
+  - (239.5, 248.5): -7038.8
+  - (248.5, 265.5): -691.1
+  - (265.5, 280.5): -14052.2
+  - (280.5, 342.5): -35705.6
+  - (342.5, 364.5): -24578.4
+  - (364.5, 385.5): -34007.7
+  - (385.5, 406.5): -46655.0
+  - (406.5, 413.5): -17805.2
+  - (413.5, 443.5): -12192.7
+  - (443.5, 452.5): -22779.7
+  - (452.5, 502.5): -30652.6
+  - (502.5, 508.5): -25165.4
+  - (508.5, 515.5): -12943.4
+  - (515.5, 1152.5): -21645.3
+  - (1152.5, 1239.5): -16264.4
+  - (1239.5, 1245.5): -7023.2
+  - (1245.5, 1619.5): -12855.2
+  - (1619.5, 1944.5): -7415.6
+  - (1944.5, 2330.5): -1233.9
+  - (2330.5, 2710.5): 4370.8
+  - (2710.5, 2834.5): 9739.0
+  - (2834.5, 2838.5): 16667.1
+  - (2838.5, 3577.5): 10096.4
+  - (3577.5, 5401.0): 15549.4
+  - (5401.0, 5535.5): 24928.2
+  - (5535.5, 9961.0): 19069.3
+  - (9961.0, 18662.0): 26262.6
+  - (18662.0, 39320.0): 20736.3
+
+4. **Calculate Differences**:
+   - Difference between (2834.5, 2838.5) and (2838.5, 3577.5): |16667.1 - 10096.4| = 6570.7
+   - Other differences can be calculated similarly.
+
+5. **Identify Largest Jump**:
+   - From the calculations, the largest jump in absolute terms is between the intervals (2834.5, 2838.5) and (2838.5, 3577.5) with a difference of 6570.7.
+
+Thus, the x-axis position of the largest jump in the graph is at the boundary between the intervals (2834.5, 2838.5) and (2838.5, 3577.5).
+SOLUTION:  38.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+1. (106.67, 780.2149999999999): 0.3865
+2. (780.2149999999999, 4627.98): 0.3462
+3. (4627.98, 6842.475): 0.0858
+4. (6842.475, 7401.88): 0.157
+5. (7401.88, 27330.43): 0.2048
+6. (27330.43, 38816.375): 0.1737
+7. (38816.375, 40348.645000000004): 0.1063
+8. (40348.645000000004, 42807.509999999995): 0.0512
+9. (42807.509999999995, 48226.81): 0.1098
+10. (48226.81, 48498.15): -0.0771
+11. (48498.15, 58535.68): 0.0187
+12. (58535.68, 94498.98999999999): 0.0512
+13. (94498.98999999999, 120892.955): 0.0186
+14. (120892.955, 121151.28): -0.0263
+15. (121151.28, 121482.61499999999): -0.0801
+16. (121482.61499999999, 148569.97): -0.0388
+17. (148569.97, 184522.325): -0.0796
+18. (184522.325, 187947.635): -0.1332
+19. (187947.635, 187985.865): -0.2342
+20. (187985.865, 188452.565): -0.0632
+21. (188452.565, 189006.61): -0.0053
+22. (189006.61, 196418.97999999998): 0.0291
+23. (196418.97999999998, 199505.41): -0.0098
+24. (199505.41, 199992.48): 0.214
+
+Now, let's calculate the absolute differences between consecutive intervals:
+- |0.3462 - 0.3865| = 0.0403
+- |0.0858 - 0.3462| = 0.2604
+- |0.157 - 0.0858| = 0.0712
+- |0.2048 - 0.157| = 0.0478
+- |0.1737 - 0.2048| = 0.0311
+- |0.1063 - 0.1737| = 0.0674
+- |0.0512 - 0.1063| = 0.0551
+- |0.1098 - 0.0512| = 0.0586
+- |-0.0771 - 0.1098| = 0.1869
+- |0.0187 - (-0.0771)| = 0.0958
+- |0.0512 - 0.0187| = 0.0325
+- |0.0186 - 0.0512| = 0.0326
+- |-0.0263 - 0.0186| = 0.0449
+- |-0.0801 - (-0.0263)| = 0.0538
+- |-0.0388 - (-0.0801)| = 0.0413
+- |-0.0796 - (-0.0388)| = 0.0408
+- |-0.1332 - (-0.0796)| = 0.0536
+- |-0.2342 - (-0.1332)| = 0.101
+- |-0.0632 - (-0.2342)| = 0.171
+- |-0.0053 - (-0.0632)| = 0.0579
+- |0.0291 - (-0.0053)| = 0.0344
+- |-0.0098 - 0.0291| = 0.0389
+- |0.214 - (-0.0098)| = 0.2238
+
+The largest jump in absolute terms is between the intervals (199505.41, 199992.48) and (196418.97999999998, 199505.41) with a difference of 0.2238.
+
+Thus, the x-axis position of the largest jump is at the boundary between these intervals, which is 199505.41.
+SOLUTION:  4627.98
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.02443
+- (0.5, 1.5): -0.02088
+- (1.5, 2.5): -0.01613
+- (2.5, 3.5): -0.01086
+- (3.5, 4.5): -0.00583
+- (4.5, 5.5): 0.00139
+- (5.5, 6.5): 0.00556
+- (6.5, 7.5): 0.01145
+- (7.5, 8.5): 0.01748
+- (8.5, 10.5): 0.0242
+- (10.5, 11.5): 0.03351
+- (11.5, 13.5): 0.03691
+- (13.5, 15.0): 0.03345
+- (15.0, 16.0): 0.02926
+
+Now, let's calculate the differences (jumps) between consecutive intervals:
+- Jump from (0.0, 0.5) to (0.5, 1.5): |-0.02088 - (-0.02443)| = 0.00355
+- Jump from (0.5, 1.5) to (1.5, 2.5): |-0.01613 - (-0.02088)| = 0.00475
+- Jump from (1.5, 2.5) to (2.5, 3.5): |-0.01086 - (-0.01613)| = 0.00527
+- Jump from (2.5, 3.5) to (3.5, 4.5): |-0.00583 - (-0.01086)| = 0.00503
+- Jump from (3.5, 4.5) to (4.5, 5.5): |0.00139 - (-0.00583)| = 0.00722
+- Jump from (4.5, 5.5) to (5.5, 6.5): |0.00556 - 0.00139| = 0.00417
+- Jump from (5.5, 6.5) to (6.5, 7.5): |0.01145 - 0.00556| = 0.00589
+- Jump from (6.5, 7.5) to (7.5, 8.5): |0.01748 - 0.01145| = 0.00603
+- Jump from (7.5, 8.5) to (8.5, 10.5): |0.0242 - 0.01748| = 0.00672
+- Jump from (8.5, 10.5) to (10.5, 11.5): |0.03351 - 0.0242| = 0.00931
+- Jump from (10.5, 11.5) to (11.5, 13.5): |0.03691 - 0.03351| = 0.00340
+- Jump from (11.5, 13.5) to (13.5, 15.0): |0.03345 - 0.03691| = 0.00346
+- Jump from (13.5, 15.0) to (15.0, 16.0): |0.02926 - 0.03345| = 0.00419
+
+The largest jump in absolute terms is from (8.5, 10.5) to (10.5, 11.5) with a difference of 0.00931.
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between the intervals (8.5, 10.5) and (10.5, 11.5), which is at 10.5.
+SOLUTION:  10.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (1.0, 4.5): -19998.0
+- (4.5, 7.5): -7788.2
+- (7.5, 16.5): -10680.2
+- (16.5, 18.5): -6304.4
+- (18.5, 27.5): -1760.6
+- (27.5, 34.5): 2164.8
+- (34.5, 38.5): -912.5
+- (38.5, 41.5): 4199.6
+- (41.5, 45.5): -497.4
+- (45.5, 47.5): -5189.8
+- (47.5, 48.5): 5201.0
+- (48.5, 49.5): 2159.0
+- (49.5, 50.5): 6135.7
+- (50.5, 51.5): 11513.8
+- (51.5, 52.0): 27549.7
+
+Now, let's calculate the differences between consecutive intervals:
+- Jump from (4.5, 7.5) to (7.5, 16.5): |-7788.2 - (-10680.2)| = 2892.0
+- Jump from (7.5, 16.5) to (16.5, 18.5): |-10680.2 - (-6304.4)| = 4375.8
+- Jump from (16.5, 18.5) to (18.5, 27.5): |-6304.4 - (-1760.6)| = 4543.8
+- Jump from (18.5, 27.5) to (27.5, 34.5): |-1760.6 - 2164.8| = 3925.4
+- Jump from (27.5, 34.5) to (34.5, 38.5): |2164.8 - (-912.5)| = 3077.3
+- Jump from (34.5, 38.5) to (38.5, 41.5): |-912.5 - 4199.6| = 5112.1
+- Jump from (38.5, 41.5) to (41.5, 45.5): |4199.6 - (-497.4)| = 4697.0
+- Jump from (41.5, 45.5) to (45.5, 47.5): |-497.4 - (-5189.8)| = 4692.4
+- Jump from (45.5, 47.5) to (47.5, 48.5): |-5189.8 - 5201.0| = 10390.8
+- Jump from (47.5, 48.5) to (48.5, 49.5): |5201.0 - 2159.0| = 3042.0
+- Jump from (48.5, 49.5) to (49.5, 50.5): |2159.0 - 6135.7| = 3976.7
+- Jump from (49.5, 50.5) to (50.5, 51.5): |6135.7 - 11513.8| = 5378.1
+- Jump from (50.5, 51.5) to (51.5, 52.0): |11513.8 - 27549.7| = 16035.9
+
+The largest jump in absolute terms is from (45.5, 47.5) to (47.5, 48.5) with a difference of 10390.8. Therefore, the x-axis position of the largest jump in the graph is at the boundary between these intervals, which is at 47.5.
+SOLUTION:  51.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.02553
+- (0.5, 2.5): -0.02038
+- (2.5, 4.5): -0.0099
+- (4.5, 6.5): 0.00082
+- (6.5, 7.5): 0.01088
+- (7.5, 9.5): 0.0178
+- (9.5, 10.5): 0.02657
+- (10.5, 12.5): 0.0329
+- (12.5, 13.5): 0.03982
+- (13.5, 15.0): 0.05043
+- (15.0, 16.0): 0.06084
+
+Now, let's calculate the differences (jumps) between consecutive intervals:
+- Jump from (0.0, 0.5) to (0.5, 2.5): |-0.02038 - (-0.02553)| = 0.00515
+- Jump from (0.5, 2.5) to (2.5, 4.5): |-0.0099 - (-0.02038)| = 0.01048
+- Jump from (2.5, 4.5) to (4.5, 6.5): |0.00082 - (-0.0099)| = 0.01072
+- Jump from (4.5, 6.5) to (6.5, 7.5): |0.01088 - 0.00082| = 0.01006
+- Jump from (6.5, 7.5) to (7.5, 9.5): |0.0178 - 0.01088| = 0.00692
+- Jump from (7.5, 9.5) to (9.5, 10.5): |0.02657 - 0.0178| = 0.00877
+- Jump from (9.5, 10.5) to (10.5, 12.5): |0.0329 - 0.02657| = 0.00633
+- Jump from (10.5, 12.5) to (12.5, 13.5): |0.03982 - 0.0329| = 0.00692
+- Jump from (12.5, 13.5) to (13.5, 15.0): |0.05043 - 0.03982| = 0.01061
+- Jump from (13.5, 15.0) to (15.0, 16.0): |0.06084 - 0.05043| = 0.01041
+
+The largest jump in absolute terms is from (2.5, 4.5) to (4.5, 6.5) with a difference of 0.01072. Therefore, the x-axis position of the largest jump in the graph is at the upper boundary of the interval (2.5, 4.5), which is 4.5.
+SOLUTION:  4.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 3.5): 0.0121
+- (3.5, 7.5): -0.0407
+- (7.5, 9.0): -0.0873
+- (9.0, 11.5): -0.1192
+- (11.5, 13.5): -0.1587
+- (13.5, 20.5): -0.1856
+- (20.5, 22.5): -0.1532
+- (22.5, 24.5): -0.1123
+- (24.5, 26.5): -0.0708
+- (26.5, 28.5): -0.036
+- (28.5, 30.5): -0.0039
+- (30.5, 32.5): 0.0343
+- (32.5, 34.5): 0.0703
+- (34.5, 39.5): 0.1069
+- (39.5, 40.5): 0.143
+- (40.5, 41.5): 0.1769
+- (41.5, 43.5): 0.2279
+- (43.5, 47.5): 0.2859
+- (47.5, 49.5): 0.2453
+- (49.5, 51.0): -0.0169
+- (51.0, 55.0): -0.0754
+- (55.0, 77.5): 0.2174
+- (77.5, 99.0): 0.3109
+
+Now, let's calculate the absolute differences between consecutive intervals:
+- |0.0121 - (-0.0407)| = 0.0528
+- |-0.0407 - (-0.0873)| = 0.0466
+- |-0.0873 - (-0.1192)| = 0.0319
+- |-0.1192 - (-0.1587)| = 0.0395
+- |-0.1587 - (-0.1856)| = 0.0269
+- |-0.1856 - (-0.1532)| = 0.0324
+- |-0.1532 - (-0.1123)| = 0.0409
+- |-0.1123 - (-0.0708)| = 0.0415
+- |-0.0708 - (-0.036)| = 0.0348
+- |-0.036 - (-0.0039)| = 0.0321
+- |-0.0039 - 0.0343| = 0.0382
+- |0.0343 - 0.0703| = 0.036
+- |0.0703 - 0.1069| = 0.0366
+- |0.1069 - 0.143| = 0.0361
+- |0.143 - 0.1769| = 0.0339
+- |0.1769 - 0.2279| = 0.051
+- |0.2279 - 0.2859| = 0.058
+- |0.2859 - 0.2453| = 0.0406
+- |0.2453 - (-0.0169)| = 0.2622
+- |-0.0169 - (-0.0754)| = 0.0585
+- |-0.0754 - 0.2174| = 0.2928
+- |0.2174 - 0.3109| = 0.0935
+
+The largest jump in absolute terms is between the intervals (51.0, 55.0) and (55.0, 77.5) with a difference of 0.2928. Therefore, the x-axis position of the largest jump is at the boundary between these intervals, which is 55.0.
+SOLUTION:  55.0
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.368
+- (0.5, 1.5): 0.724
+- (1.5, 2.5): 0.587
+- (2.5, 3.5): -0.221
+- (3.5, 4.5): -0.631
+- (4.5, 5.5): -0.545
+- (5.5, 6.0): 0.179
+
+Now, let's calculate the differences between consecutive intervals:
+1. Difference between (0.5, 1.5) and (0.0, 0.5): \(0.724 - (-0.368) = 1.092\)
+2. Difference between (1.5, 2.5) and (0.5, 1.5): \(0.587 - 0.724 = -0.137\)
+3. Difference between (2.5, 3.5) and (1.5, 2.5): \(-0.221 - 0.587 = -0.808\)
+4. Difference between (3.5, 4.5) and (2.5, 3.5): \(-0.631 - (-0.221) = -0.410\)
+5. Difference between (4.5, 5.5) and (3.5, 4.5): \(-0.545 - (-0.631) = 0.086\)
+6. Difference between (5.5, 6.0) and (4.5, 5.5): \(0.179 - (-0.545) = 0.724\)
+
+Now, we look for the largest absolute difference:
+- \(|1.092|\)
+- \(|0.137|\)
+- \(|0.808|\)
+- \(|0.410|\)
+- \(|0.086|\)
+- \(|0.724|\)
+
+The largest absolute difference is \(1.092\), which occurs between the intervals (0.0, 0.5) and (0.5, 1.5).
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between the intervals (0.0, 0.5) and (0.5, 1.5), which is at \(x = 0.5\).
+SOLUTION:  0.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 1.5): -0.02463
+- (1.5, 2.5): -0.01694
+- (2.5, 3.5): -0.01147
+- (3.5, 4.5): -0.00533
+- (4.5, 5.5): 0.00036
+- (5.5, 6.5): 0.00641
+- (6.5, 7.5): 0.01086
+- (7.5, 8.5): 0.01753
+- (8.5, 9.5): 0.02391
+- (9.5, 11.5): 0.03162
+- (11.5, 14.0): 0.0391
+- (14.0, 15.0): 0.05506
+
+Now, let's calculate the differences (jumps) between consecutive intervals:
+- Jump from (0.0, 1.5) to (1.5, 2.5): |-0.01694 - (-0.02463)| = 0.00769
+- Jump from (1.5, 2.5) to (2.5, 3.5): |-0.01147 - (-0.01694)| = 0.00547
+- Jump from (2.5, 3.5) to (3.5, 4.5): |-0.00533 - (-0.01147)| = 0.00614
+- Jump from (3.5, 4.5) to (4.5, 5.5): |0.00036 - (-0.00533)| = 0.00569
+- Jump from (4.5, 5.5) to (5.5, 6.5): |0.00641 - 0.00036| = 0.00605
+- Jump from (5.5, 6.5) to (6.5, 7.5): |0.01086 - 0.00641| = 0.00445
+- Jump from (6.5, 7.5) to (7.5, 8.5): |0.01753 - 0.01086| = 0.00667
+- Jump from (7.5, 8.5) to (8.5, 9.5): |0.02391 - 0.01753| = 0.00638
+- Jump from (8.5, 9.5) to (9.5, 11.5): |0.03162 - 0.02391| = 0.00771
+- Jump from (9.5, 11.5) to (11.5, 14.0): |0.0391 - 0.03162| = 0.00748
+- Jump from (11.5, 14.0) to (14.0, 15.0): |0.05506 - 0.0391| = 0.01596
+
+The largest jump in absolute terms is from (11.5, 14.0) to (14.0, 15.0) with a magnitude of 0.01596.
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between the intervals (11.5, 14.0) and (14.0, 15.0), which is at 14.0.
+SOLUTION:  14.0
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest. Here are the steps to find this:
+
+1. **Extract the mean values for each interval**:
+   - (0.0, 593.5): -0.177
+   - (593.5, 779.5): 0.043
+   - (779.5, 1341.5): 0.27
+   - (1341.5, 2175.5): 0.543
+   - (2175.5, 3125.0): 0.863
+   - (3125.0, 3637.0): 1.13
+   - (3637.0, 4078.5): 1.479
+   - (4078.5, 5218.5): 2.076
+   - (5218.5, 6031.5): 1.81
+   - (6031.5, 6171.5): 1.439
+   - (6171.5, 8753.0): 2.236
+   - (8753.0, 8824.0): 2.746
+   - (8824.0, 10094.5): 3.43
+   - (10094.5, 12683.5): 3.888
+   - (12683.5, 27723.0): 4.131
+
+2. **Calculate the differences (jumps) between consecutive mean values**:
+   - Jump from (593.5, 779.5) to (779.5, 1341.5): |0.27 - 0.043| = 0.227
+   - Jump from (779.5, 1341.5) to (1341.5, 2175.5): |0.543 - 0.27| = 0.273
+   - Jump from (1341.5, 2175.5) to (2175.5, 3125.0): |0.863 - 0.543| = 0.32
+   - Jump from (2175.5, 3125.0) to (3125.0, 3637.0): |1.13 - 0.863| = 0.267
+   - Jump from (3125.0, 3637.0) to (3637.0, 4078.5): |1.479 - 1.13| = 0.349
+   - Jump from (3637.0, 4078.5) to (4078.5, 5218.5): |2.076 - 1.479| = 0.597
+   - Jump from (4078.5, 5218.5) to (5218.5, 6031.5): |1.81 - 2.076| = 0.266
+   - Jump from (5218.5, 6031.5) to (6031.5, 6171.5): |1.439 - 1.81| = 0.371
+   - Jump from (6031.5, 6171.5) to (6171.5, 8753.0): |2.236 - 1.439| = 0.797
+   - Jump from (6171.5, 8753.0) to (8753.0, 8824.0): |2.746 - 2.236| = 0.51
+   - Jump from (8753.0, 8824.0) to (8824.0, 10094.5): |3.43 - 2.746| = 0.684
+   - Jump from (8824.0, 10094.5) to (10094.5, 12683.5): |3.888 - 3.43| = 0.458
+   - Jump from (10094.5, 12683.5) to (12683.5, 27723.0): |4.131 - 3.888| = 0.243
+
+3. **Identify the largest jump**:
+   The largest jump is from (6031.5, 6171.5) to (6171.5, 8753.0) with a magnitude of 0.797.
+
+4. **Position of the largest jump**:
+   The x-axis position of the largest jump is at the upper bound of the first interval and the lower bound of the second interval, which is 6171.5.
+SOLUTION:  6171.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive age intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (17.0, 18.5): -3.326
+- (18.5, 19.5): -2.358
+- (19.5, 20.5): -2.799
+- (20.5, 21.5): -2.354
+- (21.5, 22.5): -1.405
+- (22.5, 23.5): -1.633
+- (23.5, 24.5): -1.214
+- (24.5, 26.5): -0.789
+- (26.5, 27.5): -0.473
+- (27.5, 29.5): -0.216
+- (29.5, 33.5): 0.042
+- (33.5, 36.5): 0.351
+- (36.5, 44.5): 0.658
+- (44.5, 61.5): 0.897
+- (61.5, 66.5): 0.574
+- (66.5, 73.5): 0.099
+- (73.5, 74.5): 0.763
+- (74.5, 77.5): 0.502
+- (77.5, 79.5): 0.875
+- (79.5, 84.5): 0.065
+- (84.5, 90.0): -1.08
+
+Now, let's calculate the absolute differences between consecutive intervals:
+- |(-2.358) - (-3.326)| = 0.968
+- |(-2.799) - (-2.358)| = 0.441
+- |(-2.354) - (-2.799)| = 0.445
+- |(-1.405) - (-2.354)| = 0.949
+- |(-1.633) - (-1.405)| = 0.228
+- |(-1.214) - (-1.633)| = 0.419
+- |(-0.789) - (-1.214)| = 0.425
+- |(-0.473) - (-0.789)| = 0.316
+- |(-0.216) - (-0.473)| = 0.257
+- |(0.042) - (-0.216)| = 0.258
+- |(0.351) - (0.042)| = 0.309
+- |(0.658) - (0.351)| = 0.307
+- |(0.897) - (0.658)| = 0.239
+- |(0.574) - (0.897)| = 0.323
+- |(0.099) - (0.574)| = 0.475
+- |(0.763) - (0.099)| = 0.664
+- |(0.502) - (0.763)| = 0.261
+- |(0.875) - (0.502)| = 0.373
+- |(0.065) - (0.875)| = 0.810
+- |(-1.08) - (0.065)| = 1.145
+
+The largest jump in absolute terms is 1.145, occurring between the intervals (79.5, 84.5) and (84.5, 90.0).
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between the intervals (79.5, 84.5) and (84.5, 90.0), which is at age 84.5.
+SOLUTION:  84.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values provided for each interval:
+- (0.0, 0.5): -0.0263
+- (0.5, 1.5): -0.02126
+- (1.5, 2.5): -0.01709
+- (2.5, 3.5): -0.01038
+- (3.5, 4.5): -0.00633
+- (4.5, 5.5): 0.00068
+- (5.5, 6.5): 0.00618
+- (6.5, 7.5): 0.01223
+- (7.5, 8.5): 0.01761
+- (8.5, 9.5): 0.02318
+- (9.5, 10.5): 0.02782
+- (10.5, 11.5): 0.03238
+- (11.5, 13.5): 0.03978
+- (13.5, 15.0): 0.04468
+- (15.0, 16.0): 0.0529
+
+Now, let's calculate the differences between consecutive intervals:
+- Jump from (0.5, 1.5) to (1.5, 2.5): |-0.02126 - (-0.01709)| = 0.00417
+- Jump from (1.5, 2.5) to (2.5, 3.5): |-0.01709 - (-0.01038)| = 0.00671
+- Jump from (2.5, 3.5) to (3.5, 4.5): |-0.01038 - (-0.00633)| = 0.00405
+- Jump from (3.5, 4.5) to (4.5, 5.5): |-0.00633 - 0.00068| = 0.00701
+- Jump from (4.5, 5.5) to (5.5, 6.5): |0.00068 - 0.00618| = 0.0055
+- Jump from (5.5, 6.5) to (6.5, 7.5): |0.00618 - 0.01223| = 0.00605
+- Jump from (6.5, 7.5) to (7.5, 8.5): |0.01223 - 0.01761| = 0.00538
+- Jump from (7.5, 8.5) to (8.5, 9.5): |0.01761 - 0.02318| = 0.00557
+- Jump from (8.5, 9.5) to (9.5, 10.5): |0.02318 - 0.02782| = 0.00464
+- Jump from (9.5, 10.5) to (10.5, 11.5): |0.02782 - 0.03238| = 0.00456
+- Jump from (10.5, 11.5) to (11.5, 13.5): |0.03238 - 0.03978| = 0.0074
+- Jump from (11.5, 13.5) to (13.5, 15.0): |0.03978 - 0.04468| = 0.0049
+- Jump from (13.5, 15.0) to (15.0, 16.0): |0.04468 - 0.0529| = 0.00822
+
+The largest jump in absolute terms is from (13.5, 15.0) to (15.0, 16.0) with a difference of 0.00822.
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between the intervals (13.5, 15.0) and (15.0, 16.0), which is at 15.0.
+SOLUTION:  15.0
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 135.5): 0.445
+- (135.5, 215.5): 0.073
+- (215.5, 500.5): -0.294
+- (500.5, 727.5): -0.661
+- (727.5, 799.5): -1.026
+- (799.5, 831.5): -0.601
+- (831.5, 872.5): -1.156
+- (872.5, 993.5): -1.633
+- (993.5, 1430.5): -2.012
+- (1430.5, 1514.5): -1.512
+- (1514.5, 1796.0): -2.212
+- (1796.0, 1909.5): -1.699
+- (1909.5, 1970.0): -2.568
+- (1970.0, 2571.5): -3.006
+- (2571.5, 2582.0): -2.375
+- (2582.0, 2657.0): -2.964
+- (2657.0, 3710.5): -3.98
+- (3710.5, 4089.0): -4.347
+- (4089.0, 5089.5): -5.923
+- (5089.5, 24133.0): -6.634
+
+Now, let's calculate the absolute differences between consecutive intervals:
+- |0.073 - 0.445| = 0.372
+- |-0.294 - 0.073| = 0.367
+- |-0.661 - (-0.294)| = 0.367
+- |-1.026 - (-0.661)| = 0.365
+- |-0.601 - (-1.026)| = 0.425
+- |-1.156 - (-0.601)| = 0.555
+- |-1.633 - (-1.156)| = 0.477
+- |-2.012 - (-1.633)| = 0.379
+- |-1.512 - (-2.012)| = 0.500
+- |-2.212 - (-1.512)| = 0.700
+- |-1.699 - (-2.212)| = 0.513
+- |-2.568 - (-1.699)| = 0.869
+- |-3.006 - (-2.568)| = 0.438
+- |-2.375 - (-3.006)| = 0.631
+- |-2.964 - (-2.375)| = 0.589
+- |-3.98 - (-2.964)| = 1.016
+- |-4.347 - (-3.98)| = 0.367
+- |-5.923 - (-4.347)| = 1.576
+- |-6.634 - (-5.923)| = 0.711
+
+The largest jump in absolute terms is between the intervals (4089.0, 5089.5) and (5089.5, 24133.0), with a difference of 1.576. Therefore, the x-axis position of the largest jump in the graph is at 5089.5.
+SOLUTION:  4089.0
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 130.5): 0.521
+- (130.5, 278.5): 0.118
+- (278.5, 452.5): -0.285
+- (452.5, 754.5): -0.907
+- (754.5, 1209.5): -1.309
+- (1209.5, 1808.0): -1.712
+- (1808.0, 2204.5): -3.029
+- (2204.5, 2207.5): -2.456
+- (2207.5, 2428.0): -2.956
+- (2428.0, 2462.5): -2.512
+- (2462.5, 2714.5): -3.402
+- (2714.5, 2745.0): -2.902
+- (2745.0, 2993.5): -4.077
+- (2993.5, 3132.0): -4.481
+- (3132.0, 3705.5): -5.377
+- (3705.5, 3747.0): -4.36
+- (3747.0, 22408.0): -7.183
+
+Now, let's calculate the absolute differences between consecutive intervals:
+- |0.118 - 0.521| = 0.403
+- |-0.285 - 0.118| = 0.403
+- |-0.907 + 0.285| = 0.622
+- |-1.309 + 0.907| = 0.402
+- |-1.712 + 1.309| = 0.403
+- |-3.029 + 1.712| = 1.317
+- |-2.456 + 3.029| = 0.573
+- |-2.956 + 2.456| = 0.500
+- |-2.512 + 2.956| = 0.444
+- |-3.402 + 2.512| = 0.890
+- |-2.902 + 3.402| = 0.500
+- |-4.077 + 2.902| = 1.175
+- |-4.481 + 4.077| = 0.404
+- |-5.377 + 4.481| = 0.896
+- |-4.36 + 5.377| = 1.017
+- |-7.183 + 4.36| = 2.823
+
+The largest jump in absolute terms is 2.823, which occurs between the intervals (3747.0, 22408.0) and (3705.5, 3747.0). Therefore, the x-axis position of the largest jump is at 3747.0.
+SOLUTION:  3747.0
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values provided for each interval:
+- (12.02, 16.935000000000002): -1.885
+- (16.935000000000002, 18.335): -1.717
+- (18.335, 19.505): -1.55
+- (19.505, 20.225): -0.851
+- (20.225, 21.955): -0.612
+- (21.955, 23.59): -0.44
+- (23.59, 24.795): -0.272
+- (24.795, 25.18): -0.1
+- (25.18, 25.83): 0.078
+- (25.83, 26.855): 0.279
+- (26.855, 27.994999999999997): 0.451
+- (27.994999999999997, 29.225): 0.619
+- (29.225, 31.515): 0.878
+- (31.515, 32.485): 1.044
+- (32.485, 35.05): 1.256
+- (35.05, 49.54): 1.423
+
+Now, let's calculate the differences between consecutive intervals:
+- Jump from (16.935000000000002, 18.335) to (18.335, 19.505): |-1.717 - (-1.55)| = 0.167
+- Jump from (18.335, 19.505) to (19.505, 20.225): |-1.55 - (-0.851)| = 0.699
+- Jump from (19.505, 20.225) to (20.225, 21.955): |-0.851 - (-0.612)| = 0.239
+- Jump from (20.225, 21.955) to (21.955, 23.59): |-0.612 - (-0.44)| = 0.172
+- Jump from (21.955, 23.59) to (23.59, 24.795): |-0.44 - (-0.272)| = 0.168
+- Jump from (23.59, 24.795) to (24.795, 25.18): |-0.272 - (-0.1)| = 0.172
+- Jump from (24.795, 25.18) to (25.18, 25.83): |-0.1 - 0.078| = 0.178
+- Jump from (25.18, 25.83) to (25.83, 26.855): |0.078 - 0.279| = 0.201
+- Jump from (25.83, 26.855) to (26.855, 27.994999999999997): |0.279 - 0.451| = 0.172
+- Jump from (26.855, 27.994999999999997) to (27.994999999999997, 29.225): |0.451 - 0.619| = 0.168
+- Jump from (27.994999999999997, 29.225) to (29.225, 31.515): |0.619 - 0.878| = 0.259
+- Jump from (29.225, 31.515) to (31.515, 32.485): |0.878 - 1.044| = 0.166
+- Jump from (31.515, 32.485) to (32.485, 35.05): |1.044 - 1.256| = 0.212
+- Jump from (32.485, 35.05) to (35.05, 49.54): |1.256 - 1.423| = 0.167
+
+The largest jump in absolute terms is from (18.335, 19.505) to (19.505, 20.225) with a difference of 0.699.
+
+Therefore, the x-axis position of the largest jump in the graph is at the upper bound of the interval (18.335, 19.505), which is 19.505.
+SOLUTION:  19.505
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to compare the mean values at the boundaries of the intervals provided for the feature "high_blood_pressure". The intervals given are "(0.0, 0.5)" and "(0.5, 1.0)", and the corresponding mean values are -0.1077 and 0.1864 respectively.
+
+Step-by-step analysis:
+
+1. **Identify the intervals and their corresponding mean values**:
+   - Interval "(0.0, 0.5)": Mean = -0.1077
+   - Interval "(0.5, 1.0)": Mean = 0.1864
+
+2. **Calculate the jump between the intervals**:
+   - The jump occurs at the boundary between these two intervals, which is at x = 0.5.
+   - To find the magnitude of the jump, calculate the difference in mean values across this boundary:
+     \[
+     \text{Jump magnitude} = |0.1864 - (-0.1077)| = |0.1864 + 0.1077| = 0.2941
+     \]
+
+3. **Conclusion**:
+   - The largest jump occurs at x = 0.5, with a magnitude of 0.2941.
+
+Thus, the x-axis position of the largest jump in the graph is at x = 0.5.
+SOLUTION:  0.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- Interval (1.0, 1.5): Mean = -0.918
+- Interval (1.5, 2.5): Mean = 0.96
+- Interval (2.5, 3.5): Mean = -3.104
+- Interval (3.5, 4.0): Mean = -2.768
+
+Now, let's calculate the differences between consecutive intervals:
+1. Difference between (1.5, 2.5) and (1.0, 1.5): \(0.96 - (-0.918) = 0.96 + 0.918 = 1.878\)
+2. Difference between (2.5, 3.5) and (1.5, 2.5): \(-3.104 - 0.96 = -4.064\)
+3. Difference between (3.5, 4.0) and (2.5, 3.5): \(-2.768 - (-3.104) = -2.768 + 3.104 = 0.336\)
+
+The absolute values of these differences are:
+1. \(|1.878|\) = 1.878
+2. \(|4.064|\) = 4.064
+3. \(|0.336|\) = 0.336
+
+The largest jump in absolute terms is 4.064, which occurs between the intervals (1.5, 2.5) and (2.5, 3.5).
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between the intervals (1.5, 2.5) and (2.5, 3.5), which is at 2.5.
+SOLUTION:  2.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.02729, 0.049945): -0.0578
+- (0.049945, 0.06971): -0.0099
+- (0.06971, 0.099305): -0.0565
+- (0.099305, 0.10635): -0.1408
+- (0.10635, 0.1243): -0.1882
+- (0.1243, 0.14795): -0.2357
+- (0.14795, 0.1507): -0.1883
+- (0.1507, 0.1861): -0.1381
+- (0.1861, 0.20124999999999998): -0.0918
+- (0.20124999999999998, 0.3358): -0.0443
+- (0.3358, 0.3456): 0.0027
+- (0.3456, 0.35755000000000003): 0.0649
+- (0.35755000000000003, 0.3703): 0.1151
+- (0.3703, 0.39235): 0.1642
+- (0.39235, 0.4087): 0.2124
+- (0.4087, 0.4229): 0.2605
+- (0.4229, 0.4486): 0.3109
+- (0.4486, 0.48865000000000003): 0.3586
+- (0.48865000000000003, 0.54825): 0.4132
+- (0.54825, 0.5892999999999999): 0.4651
+- (0.5892999999999999, 0.65835): 0.5154
+- (0.65835, 0.7680499999999999): 0.572
+- (0.7680499999999999, 0.99795): 0.6264
+- (0.99795, 1.058): 0.6748
+
+Now, let's calculate the differences between consecutive intervals:
+- (-0.0099) - (-0.0578) = 0.0479
+- (-0.0565) - (-0.0099) = -0.0466
+- (-0.1408) - (-0.0565) = -0.0843
+- (-0.1882) - (-0.1408) = -0.0474
+- (-0.2357) - (-0.1882) = -0.0475
+- (-0.1883) - (-0.2357) = 0.0474
+- (-0.1381) - (-0.1883) = 0.0502
+- (-0.0918) - (-0.1381) = 0.0463
+- (-0.0443) - (-0.0918) = 0.0475
+- (0.0027) - (-0.0443) = 0.047
+- (0.0649) - (0.0027) = 0.0622
+- (0.1151) - (0.0649) = 0.0502
+- (0.1642) - (0.1151) = 0.0491
+- (0.2124) - (0.1642) = 0.0482
+- (0.2605) - (0.2124) = 0.0481
+- (0.3109) - (0.2605) = 0.0504
+- (0.3586) - (0.3109) = 0.0477
+- (0.4132) - (0.3586) = 0.0546
+- (0.4651) - (0.4132) = 0.0519
+- (0.5154) - (0.4651) = 0.0503
+- (0.572) - (0.5154) = 0.0566
+- (0.6264) - (0.572) = 0.0544
+- (0.6748) - (0.6264) = 0.0484
+
+The largest jump in absolute terms is -0.0843, which occurs between the intervals (0.06971, 0.099305) and (0.099305, 0.10635). Therefore, the x-axis position of the largest jump is at the upper bound of the first interval, which is 0.099305.
+SOLUTION:  0.099305
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms. Here's how we can do it step by step:
+
+1. **Extract the Mean Values**: We have the mean values for each interval as provided in the JSON object under "Means".
+
+2. **Calculate Differences Between Consecutive Intervals**: We need to compute the difference in mean values between the end of one interval and the beginning of the next.
+
+3. **Identify the Largest Jump**: We look for the largest absolute difference from the calculations in step 2.
+
+Let's perform these calculations using the provided mean values:
+
+- **Intervals and Mean Values**:
+  - (15565796.0, 15566519.0): -0.8769
+  - (15566519.0, 15567333.5): -0.8241
+  - (15567333.5, 15567844.5): -0.1763
+  - (15567844.5, 15568343.5): 0.0021
+  - (15568343.5, 15571612.0): -0.2283
+  - (15571612.0, 15571858.5): -0.0522
+  - (15571858.5, 15591260.5): -0.1299
+  - (15591260.5, 15598058.0): -0.0821
+  - (15598058.0, 15602525.5): -0.1509
+  - (15602525.5, 15607288.0): -0.0818
+  - (15607288.0, 15664896.0): -0.0316
+  - (15664896.0, 15772587.0): 0.0162
+  - (15772587.0, 15797097.0): 0.0757
+  - (15797097.0, 15799214.0): 0.0081
+  - (15799214.0, 15807559.5): 0.0581
+  - (15807559.5, 15812616.5): -0.0049
+  - (15812616.5, 15814479.0): -0.0569
+  - (15814479.0, 15815247.5): -0.111
+  - (15815247.5, 15815626.0): -0.0335
+
+- **Differences Between Consecutive Intervals**:
+  - |(-0.8241) - (-0.8769)| = 0.0528
+  - |(-0.1763) - (-0.8241)| = 0.6478
+  - |0.0021 - (-0.1763)| = 0.1784
+  - |-0.2283 - 0.0021| = 0.2304
+  - |-0.0522 - (-0.2283)| = 0.1761
+  - |-0.1299 - (-0.0522)| = 0.0777
+  - |-0.0821 - (-0.1299)| = 0.0478
+  - |-0.1509 - (-0.0821)| = 0.0688
+  - |-0.0818 - (-0.1509)| = 0.0691
+  - |-0.0316 - (-0.0818)| = 0.0502
+  - |0.0162 - (-0.0316)| = 0.0478
+  - |0.0757 - 0.0162| = 0.0595
+  - |0.0081 - 0.0757| = 0.0676
+  - |0.0581 - 0.0081| = 0.0500
+  - |-0.0049 - 0.0581| = 0.0630
+  - |-0.0569 - (-0.0049)| = 0.0520
+  - |-0.111 - (-0.0569)| = 0.0541
+  - |-0.0335 - (-0.111)| = 0.0775
+
+The largest jump is 0.6478, which occurs between the intervals (15566519.0, 15567333.5) and (15567333.5, 15567844.5). Therefore, the x-axis position of the largest jump is at the end of the interval (15566519.0, 15567333.5), which is 15567333.5.
+SOLUTION:  15567333.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values provided for each interval:
+- "(0.1115, 0.15015)": -0.773
+- "(0.15015, 0.16904999999999998)": -0.686
+- "(0.16904999999999998, 0.1795)": -0.589
+- "(0.1795, 0.18535000000000001)": -0.499
+- "(0.18535000000000001, 0.19345)": -0.412
+- "(0.19345, 0.2103)": -0.275
+- "(0.2103, 0.2329)": -0.187
+- "(0.2329, 0.2939)": -0.102
+- "(0.2939, 0.368)": -0.186
+- "(0.368, 0.38585)": -0.066
+- "(0.38585, 0.42025)": 0.064
+- "(0.42025, 0.46775)": 0.15
+- "(0.46775, 0.54785)": 0.239
+- "(0.54785, 0.5881000000000001)": 0.334
+- "(0.5881000000000001, 0.66425)": 0.422
+- "(0.66425, 0.7562)": 0.51
+- "(0.7562, 0.9131)": 0.594
+- "(0.9131, 1.065)": 0.683
+- "(1.065, 1.2915)": 0.774
+- "(1.2915, 2.873)": 0.866
+
+Now, let's calculate the differences between consecutive intervals:
+- Jump from "(0.1115, 0.15015)" to "(0.15015, 0.16904999999999998)": |-0.773 - (-0.686)| = 0.087
+- Jump from "(0.15015, 0.16904999999999998)" to "(0.16904999999999998, 0.1795)": |-0.686 - (-0.589)| = 0.097
+- Jump from "(0.16904999999999998, 0.1795)" to "(0.1795, 0.18535000000000001)": |-0.589 - (-0.499)| = 0.09
+- Jump from "(0.1795, 0.18535000000000001)" to "(0.18535000000000001, 0.19345)": |-0.499 - (-0.412)| = 0.087
+- Jump from "(0.18535000000000001, 0.19345)" to "(0.19345, 0.2103)": |-0.412 - (-0.275)| = 0.137
+- Jump from "(0.19345, 0.2103)" to "(0.2103, 0.2329)": |-0.275 - (-0.187)| = 0.088
+- Jump from "(0.2103, 0.2329)" to "(0.2329, 0.2939)": |-0.187 - (-0.102)| = 0.085
+- Jump from "(0.2329, 0.2939)" to "(0.2939, 0.368)": |-0.102 - (-0.186)| = 0.084
+- Jump from "(0.2939, 0.368)" to "(0.368, 0.38585)": |-0.186 - (-0.066)| = 0.12
+- Jump from "(0.368, 0.38585)" to "(0.38585, 0.42025)": |-0.066 - 0.064| = 0.13
+- Jump from "(0.38585, 0.42025)" to "(0.42025, 0.46775)": |0.064 - 0.15| = 0.086
+- Jump from "(0.42025, 0.46775)" to "(0.46775, 0.54785)": |0.15 - 0.239| = 0.089
+- Jump from "(0.46775, 0.54785)" to "(0.54785, 0.5881000000000001)": |0.239 - 0.334| = 0.095
+- Jump from "(0.54785, 0.5881000000000001)" to "(0.5881000000000001, 0.66425)": |0.334 - 0.422| = 0.088
+- Jump from "(0.5881000000000001, 0.66425)" to "(0.66425, 0.7562)": |0.422 - 0.51| = 0.088
+- Jump from "(0.66425, 0.7562)" to "(0.7562, 0.9131)": |0.51 - 0.594| = 0.084
+- Jump from "(0.7562, 0.9131)" to "(0.9131, 1.065)": |0.594 - 0.683| = 0.089
+- Jump from "(0.9131, 1.065)" to "(1.065, 1.2915)": |0.683 - 0.774| = 0.091
+- Jump from "(1.065, 1.2915)" to "(1.2915, 2.873)": |0.774 - 0.866| = 0.092
+
+The largest jump in absolute terms is from "(0.368, 0.38585)" to "(0.38585, 0.42025)" with a magnitude of 0.13. Therefore, the x-axis position of the largest jump is at the boundary between these intervals, which is 0.38585.
+SOLUTION:  0.19345
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (1.0, 1.5): -4.746
+- (1.5, 4.5): -1.252
+- (4.5, 6.5): -0.882
+- (6.5, 9.5): -0.483
+- (9.5, 11.5): -0.093
+- (11.5, 13.5): 0.276
+- (13.5, 14.5): 0.863
+- (14.5, 16.0): 1.487
+
+Now, let's calculate the differences between consecutive intervals:
+- Jump from (1.0, 1.5) to (1.5, 4.5): |-4.746 - (-1.252)| = 3.494
+- Jump from (1.5, 4.5) to (4.5, 6.5): |-1.252 - (-0.882)| = 0.370
+- Jump from (4.5, 6.5) to (6.5, 9.5): |-0.882 - (-0.483)| = 0.399
+- Jump from (6.5, 9.5) to (9.5, 11.5): |-0.483 - (-0.093)| = 0.390
+- Jump from (9.5, 11.5) to (11.5, 13.5): |-0.093 - 0.276| = 0.369
+- Jump from (11.5, 13.5) to (13.5, 14.5): |0.276 - 0.863| = 0.587
+- Jump from (13.5, 14.5) to (14.5, 16.0): |0.863 - 1.487| = 0.624
+
+The largest jump in absolute terms is from the interval (1.0, 1.5) to (1.5, 4.5) with a magnitude of 3.494.
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between the intervals (1.0, 1.5) and (1.5, 4.5). This position is 1.5.
+SOLUTION:  1.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (4.3, 4.55): 3.328
+- (4.55, 4.75): 2.995
+- (4.75, 4.85): 2.698
+- (4.85, 5.05): 1.665
+- (5.05, 5.25): 1.371
+- (5.25, 5.45): 1.085
+- (5.45, 5.55): 0.339
+- (5.55, 5.75): -0.057
+- (5.75, 5.85): -0.39
+- (5.85, 6.15): -0.757
+- (6.15, 6.45): -1.149
+- (6.45, 6.85): -1.436
+- (6.85, 7.7): -1.718
+
+Now, let's calculate the absolute differences between consecutive intervals:
+- |2.995 - 3.328| = 0.333
+- |2.698 - 2.995| = 0.297
+- |1.665 - 2.698| = 1.033
+- |1.371 - 1.665| = 0.294
+- |1.085 - 1.371| = 0.286
+- |0.339 - 1.085| = 0.746
+- |-0.057 - 0.339| = 0.396
+- |-0.39 - (-0.057)| = 0.333
+- |-0.757 - (-0.39)| = 0.367
+- |-1.149 - (-0.757)| = 0.392
+- |-1.436 - (-1.149)| = 0.287
+- |-1.718 - (-1.436)| = 0.282
+
+The largest jump in absolute terms is between the intervals (4.75, 4.85) and (4.85, 5.05) with a difference of 1.033.
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between the intervals (4.75, 4.85) and (4.85, 5.05), which is at 4.85.
+SOLUTION:  4.85
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms. Here's how we can proceed:
+
+1. **Extract the mean values and their corresponding intervals**: We have a dictionary of mean values where the keys are the intervals and the values are the mean predictions for those intervals.
+
+2. **Calculate the differences between consecutive intervals**: We need to compute the difference in mean values between the end of one interval and the beginning of the next.
+
+3. **Identify the largest jump**: We look for the maximum absolute difference from the differences calculated in the previous step.
+
+Let's perform these steps using the provided data:
+
+- **Extract and sort the intervals**: The intervals are given as keys in the dictionary. We need to sort these keys because dictionary keys do not necessarily maintain any order.
+
+- **Calculate differences**: For each consecutive pair of intervals, calculate the difference in their mean values.
+
+- **Find the maximum absolute difference**.
+
+Here are the sorted intervals and their corresponding mean values:
+- (32.54, 32.565): 23234.8
+- (32.565, 32.685): -3182.4
+- ...
+- (41.775000000000006, 41.95): -85116.1
+
+**Calculating differences**:
+- Difference between (32.565, 32.685) and (32.54, 32.565): |-3182.4 - 23234.8| = 26417.2
+- Continue this for all consecutive intervals.
+
+**Identifying the largest jump**:
+- We continue calculating as above and find the maximum of these values.
+
+After performing these calculations, the largest jump is found between the intervals (37.595, 37.605000000000004) and (37.605000000000004, 37.754999999999995) with the mean values changing from -32379.8 to -42729.0, giving a jump of |(-42729.0) - (-32379.8)| = 10349.2.
+
+Thus, the position of the largest jump in the graph is at the boundary between the intervals (37.595, 37.605000000000004) and (37.605000000000004, 37.754999999999995).
+SOLUTION:  34.325
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values provided for each interval:
+- (6.981, 9.281500000000001): -0.762
+- (9.281500000000001, 9.7015): -0.659
+- (9.7015, 10.165): -0.56
+- (10.165, 10.655000000000001): -0.461
+- (10.655000000000001, 12.465): -0.36
+- (12.465, 13.39): -0.262
+- (13.39, 14.43): -0.163
+- (14.43, 14.934999999999999): -0.065
+- (14.934999999999999, 15.08): 0.037
+- (15.08, 15.815): 0.137
+- (15.815, 16.925): 0.235
+- (16.925, 17.385): 0.394
+- (17.385, 18.0): 0.494
+- (18.0, 18.735): 0.599
+- (18.735, 19.240000000000002): 0.695
+- (19.240000000000002, 19.990000000000002): 0.793
+- (19.990000000000002, 20.595): 0.891
+- (20.595, 23.240000000000002): 0.99
+- (23.240000000000002, 28.11): 1.093
+
+Now, let's calculate the differences between consecutive mean values:
+- Jump from (9.281500000000001, 9.7015) to (9.7015, 10.165): -0.56 - (-0.659) = 0.099
+- Continue this calculation for all consecutive intervals.
+
+After calculating all differences:
+- (6.981, 9.281500000000001) to (9.281500000000001, 9.7015): 0.103
+- (9.281500000000001, 9.7015) to (9.7015, 10.165): 0.099
+- (9.7015, 10.165) to (10.165, 10.655000000000001): 0.099
+- (10.165, 10.655000000000001) to (10.655000000000001, 12.465): 0.101
+- (10.655000000000001, 12.465) to (12.465, 13.39): 0.098
+- (12.465, 13.39) to (13.39, 14.43): 0.099
+- (13.39, 14.43) to (14.43, 14.934999999999999): 0.098
+- (14.43, 14.934999999999999) to (14.934999999999999, 15.08): 0.102
+- (14.934999999999999, 15.08) to (15.08, 15.815): 0.100
+- (15.08, 15.815) to (15.815, 16.925): 0.098
+- (15.815, 16.925) to (16.925, 17.385): 0.159
+- (16.925, 17.385) to (17.385, 18.0): 0.100
+- (17.385, 18.0) to (18.0, 18.735): 0.105
+- (18.0, 18.735) to (18.735, 19.240000000000002): 0.096
+- (18.735, 19.240000000000002) to (19.240000000000002, 19.990000000000002): 0.098
+- (19.240000000000002, 19.990000000000002) to (19.990000000000002, 20.595): 0.098
+- (19.990000000000002, 20.595) to (20.595, 23.240000000000002): 0.099
+- (20.595, 23.240000000000002) to (23.240000000000002, 28.11): 0.103
+
+The largest jump in absolute terms is from (15.815, 16.925) to (16.925, 17.385) with a difference of 0.159.
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between the intervals (15.815, 16.925) and (16.925, 17.385).
+SOLUTION:  16.925
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values provided in the graph:
+
+- "(0.05263, 0.0706)": -0.835
+- "(0.0706, 0.07455500000000001)": -0.769
+- "(0.07455500000000001, 0.07589499999999999)": -0.697
+- "(0.07589499999999999, 0.07727500000000001)": -0.632
+- "(0.07727500000000001, 0.078275)": -0.569
+- "(0.078275, 0.07952000000000001)": -0.506
+- "(0.07952000000000001, 0.080315)": -0.437
+- "(0.080315, 0.081035)": -0.368
+- "(0.081035, 0.08308499999999999)": -0.304
+- "(0.08308499999999999, 0.085165)": -0.242
+- "(0.085165, 0.086795)": -0.177
+- "(0.086795, 0.087785)": -0.111
+- "(0.087785, 0.088615)": -0.047
+- "(0.088615, 0.08918999999999999)": 0.065
+- "(0.08918999999999999, 0.090335)": 0.142
+- "(0.090335, 0.09454)": 0.211
+- "(0.09454, 0.11525)": 0.107
+- "(0.11525, 0.11765)": 0.171
+- "(0.11765, 0.12455)": 0.267
+- "(0.12455, 0.13845000000000002)": 0.334
+- "(0.13845000000000002, 0.1634)": 0.396
+
+Now, let's calculate the differences between consecutive intervals:
+
+- Jump from "(0.088615, 0.08918999999999999)" to "(0.08918999999999999, 0.090335)": 0.142 - 0.065 = 0.077
+- Jump from "(0.09454, 0.11525)" to "(0.11525, 0.11765)": 0.171 - 0.107 = 0.064
+- Other jumps are smaller.
+
+The largest jump in absolute terms is from the interval "(0.088615, 0.08918999999999999)" to "(0.08918999999999999, 0.090335)" with a magnitude of 0.077. Therefore, the x-axis position of the largest jump is at the boundary between these two intervals, which is at 0.08918999999999999.
+SOLUTION:  0.088615
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values provided for each interval:
+- (0.7714, 1.0579999999999998): -0.698
+- (1.0579999999999998, 1.1345): -0.618
+- (1.1345, 1.197): -0.539
+- (1.197, 1.2365): -0.461
+- (1.2365, 1.326): -0.384
+- (1.326, 1.4435): -0.256
+- (1.4435, 1.5314999999999999): -0.176
+- (1.5314999999999999, 1.807): -0.099
+- (1.807, 2.107): -0.023
+- (2.107, 2.593): -0.098
+- (2.593, 2.878): -0.018
+- (2.878, 3.292): 0.065
+- (3.292, 4.095000000000001): 0.14
+- (4.095000000000001, 4.714): 0.219
+- (4.714, 4.885999999999999): 0.296
+- (4.885999999999999, 5.2844999999999995): 0.372
+- (5.2844999999999995, 5.8425): 0.451
+- (5.8425, 7.104): 0.536
+- (7.104, 7.7765): 0.611
+- (7.7765, 10.594999999999999): 0.701
+- (10.594999999999999, 21.98): 0.786
+
+Now, let's calculate the differences between consecutive mean values:
+- Jump from (1.0579999999999998, 1.1345) to (1.1345, 1.197): |-0.618 - (-0.539)| = 0.079
+- Continue this for all intervals...
+
+After calculating all differences:
+- The largest jump in absolute terms is from (4.714, 4.885999999999999) to (4.885999999999999, 5.2844999999999995): |0.296 - 0.372| = 0.076
+
+Thus, the largest jump occurs at the upper boundary of the interval (4.714, 4.885999999999999), which is 4.885999999999999. This is the position on the x-axis where the largest jump in the graph occurs.
+SOLUTION:  1.326
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms. Here's how we can proceed step by step:
+
+1. **Extract the Mean Values**: We have a dictionary of mean values where each key is an interval and each value is the mean prediction for that interval.
+
+2. **Calculate Differences Between Consecutive Intervals**: We need to compute the difference in mean values between the end of one interval and the beginning of the next.
+
+3. **Identify the Largest Jump**: We look for the maximum absolute difference calculated in the previous step.
+
+Let's perform these steps using the provided mean values:
+
+```json
+Means: {
+    "(0.002252, 0.0046765)": -0.0693, 
+    "(0.0046765, 0.005634)": -0.0214, 
+    "(0.005634, 0.006059500000000001)": 0.0214, 
+    "(0.006059500000000001, 0.006774499999999999)": 0.0648, 
+    "(0.006774499999999999, 0.0072375)": 0.1132, 
+    "(0.0072375, 0.008034)": 0.1583, 
+    "(0.008034, 0.0082145)": 0.2045, 
+    "(0.0082145, 0.0085705)": 0.2482, 
+    "(0.0085705, 0.0089915)": 0.2969, 
+    "(0.0089915, 0.01089)": 0.3467, 
+    "(0.01089, 0.011715)": 0.3948, 
+    "(0.011715, 0.012025000000000001)": 0.3506, 
+    "(0.012025000000000001, 0.012535000000000001)": 0.2891, 
+    "(0.012535000000000001, 0.013225)": 0.244, 
+    "(0.013225, 0.014275)": 0.2001, 
+    "(0.014275, 0.015615)": 0.1571, 
+    "(0.015615, 0.017669999999999998)": 0.1142, 
+    "(0.017669999999999998, 0.020155)": 0.0681, 
+    "(0.020155, 0.022855)": 0.0256, 
+    "(0.022855, 0.02586)": -0.0272, 
+    "(0.02586, 0.027540000000000002)": -0.098, 
+    "(0.027540000000000002, 0.038220000000000004)": -0.1414, 
+    "(0.038220000000000004, 0.039245)": -0.1853, 
+    "(0.039245, 0.040514999999999995)": -0.2301, 
+    "(0.040514999999999995, 0.04309)": -0.2754, 
+    "(0.04309, 0.04922)": -0.3233, 
+    "(0.04922, 0.068925)": -0.3675, 
+    "(0.068925, 0.1354)": -0.4112
+}
+```
+
+Calculating the differences between consecutive intervals:
+- From `0.011715` to `0.012025000000000001`: \(0.3506 - 0.3948 = -0.0442\)
+- From `0.022855` to `0.02586`: \(-0.0272 - 0.0256 = -0.0528\)
+- From `0.02586` to `0.027540000000000002`: \(-0.098 - (-0.0272) = -0.0708\)
+
+The largest jump in absolute terms is from `0.02586` to `0.027540000000000002` with a difference of -0.0708.
+
+**Conclusion**: The x-axis position of the largest jump in the graph is between the intervals `(0.02586, 0.027540000000000002)`.
+SOLUTION:  0.02586
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.4028
+- (0.5, 1.5): -0.5397
+- (1.5, 3.5): -0.4851
+- (3.5, 4.5): -0.4021
+- (4.5, 5.5): -0.457
+- (5.5, 6.5): -0.2537
+- (6.5, 7.5): -0.0494
+- (7.5, 8.5): 0.0457
+- (8.5, 9.5): 0.1831
+- (9.5, 10.5): 0.1392
+- (10.5, 11.5): -0.0652
+- (11.5, 14.5): 0.1954
+- (14.5, 15.0): 0.1393
+
+Now, let's calculate the differences between consecutive intervals:
+- Jump from (0.0, 0.5) to (0.5, 1.5): |-0.5397 - (-0.4028)| = 0.1369
+- Jump from (0.5, 1.5) to (1.5, 3.5): |-0.4851 - (-0.5397)| = 0.0546
+- Jump from (1.5, 3.5) to (3.5, 4.5): |-0.4021 - (-0.4851)| = 0.083
+- Jump from (3.5, 4.5) to (4.5, 5.5): |-0.457 - (-0.4021)| = 0.0549
+- Jump from (4.5, 5.5) to (5.5, 6.5): |-0.2537 - (-0.457)| = 0.2033
+- Jump from (5.5, 6.5) to (6.5, 7.5): |-0.0494 - (-0.2537)| = 0.2043
+- Jump from (6.5, 7.5) to (7.5, 8.5): |0.0457 - (-0.0494)| = 0.0951
+- Jump from (7.5, 8.5) to (8.5, 9.5): |0.1831 - 0.0457| = 0.1374
+- Jump from (8.5, 9.5) to (9.5, 10.5): |0.1392 - 0.1831| = 0.0439
+- Jump from (9.5, 10.5) to (10.5, 11.5): |-0.0652 - 0.1392| = 0.2044
+- Jump from (10.5, 11.5) to (11.5, 14.5): |0.1954 - (-0.0652)| = 0.2606
+- Jump from (11.5, 14.5) to (14.5, 15.0): |0.1393 - 0.1954| = 0.0561
+
+The largest jump in absolute terms is from (10.5, 11.5) to (11.5, 14.5) with a difference of 0.2606.
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between the intervals (10.5, 11.5) and (11.5, 14.5).
+SOLUTION:  11.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms. Here's how we can proceed:
+
+1. **Extract the mean values and intervals**: We have the mean values for each interval as provided in the JSON object. We need to parse these intervals and mean values.
+
+2. **Calculate the differences between consecutive intervals**: For each pair of consecutive intervals, calculate the difference in mean values.
+
+3. **Identify the largest jump**: Find the interval where the absolute difference between consecutive mean values is the largest.
+
+Let's perform these steps using the provided data:
+
+- **Means**:
+  - (2.0, 4.5): -5401.6
+  - (4.5, 6.5): -23687.9
+  - (6.5, 8.5): -53732.5
+  - (8.5, 9.5): -14617.2
+  - (9.5, 12.5): 16225.5
+  - (12.5, 13.5): 21846.0
+  - (13.5, 14.5): 29456.0
+  - (14.5, 15.5): 14293.2
+  - (15.5, 20.5): -21670.3
+  - (20.5, 21.5): 3195.8
+  - (21.5, 55.5): -12458.9
+  - (55.5, 155.5): -20063.6
+  - (155.5, 156.5): -15642.0
+  - (156.5, 157.5): -6390.8
+  - (157.5, 186.5): -19320.2
+  - (186.5, 196.5): -23743.0
+  - (196.5, 198.5): -18377.6
+  - (198.5, 223.5): -12744.1
+  - (223.5, 230.5): -6336.7
+  - (230.5, 295.5): -10855.3
+  - (295.5, 394.5): -6355.5
+  - (394.5, 535.5): -443.1
+  - (535.5, 561.5): 3934.9
+  - (561.5, 599.5): 9004.1
+  - (599.5, 600.5): 13667.2
+  - (600.5, 634.5): 8706.3
+  - (634.5, 635.5): 25959.4
+  - (635.5, 824.5): 13815.1
+  - (824.5, 864.5): 18503.2
+  - (864.5, 962.5): 26367.0
+  - (962.5, 964.5): 14554.6
+  - (964.5, 976.5): 23227.2
+  - (976.5, 978.5): 18664.6
+  - (978.5, 990.5): 26114.1
+  - (990.5, 1000.5): 30854.6
+  - (1000.5, 1088.5): 25473.5
+  - (1088.5, 1092.5): 21095.0
+  - (1092.5, 1130.5): 26497.2
+  - (1130.5, 1272.5): 33562.7
+  - (1272.5, 3516.0): 28522.2
+  - (3516.0, 6082.0): 21556.0
+
+**Calculating Differences**:
+- Difference between (2.0, 4.5) and (4.5, 6.5): |-5401.6 - (-23687.9)| = 18286.3
+- Difference between (4.5, 6.5) and (6.5, 8.5): |-23687.9 - (-53732.5)| = 30044.6
+- Continue this for all intervals...
+
+**Identifying the Largest Jump**:
+- After calculating all differences, the largest difference will be the largest jump.
+
+From the calculations, the largest jump is between (6.5, 8.5) and (8.5, 9.5) with a difference of 39115.3. Thus, the x-axis position of the largest jump is at the boundary between these intervals, which is 8.5.
+SOLUTION:  8.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (8.0, 349.5): -0.1954
+- (349.5, 1899.5): -0.1448
+- (1899.5, 4908.5): -0.18
+- (4908.5, 5578.5): -0.2082
+- (5578.5, 5813.5): -0.25
+- (5813.5, 6004.5): -0.345
+- (6004.5, 7170.5): -0.1246
+- (7170.5, 7335.5): 0.0378
+- (7335.5, 8083.0): 0.1773
+- (8083.0, 8604.0): 0.1221
+- (8604.0, 8759.0): -0.0027
+- (8759.0, 45049.5): -0.0395
+- (45049.5, 45346.5): -0.3688
+- (45346.5, 46184.5): -0.0125
+- (46184.5, 54575.0): 0.0215
+- (54575.0, 55661.5): -0.0521
+- (55661.5, 66954.0): 0.0101
+- (66954.0, 67057.0): -0.0227
+- (67057.0, 68275.0): 0.0595
+- (68275.0, 97577.5): 0.0244
+- (97577.5, 110643.5): 0.0529
+- (110643.5, 146554.5): 0.0211
+- (146554.5, 146921.5): -0.0139
+- (146921.5, 147131.5): -0.0861
+- (147131.5, 161901.5): -0.0139
+- (161901.5, 162437.5): -0.0745
+- (162437.5, 164212.5): -0.0061
+- (164212.5, 164569.5): -0.057
+- (164569.5, 164786.5): 0.0766
+- (164786.5, 165030.0): 0.1394
+
+Now, calculate the absolute differences between consecutive intervals:
+- |(-0.1448) - (-0.1954)| = 0.0506
+- |(-0.18) - (-0.1448)| = 0.0352
+- |(-0.2082) - (-0.18)| = 0.0282
+- |(-0.25) - (-0.2082)| = 0.0418
+- |(-0.345) - (-0.25)| = 0.095
+- |(-0.1246) - (-0.345)| = 0.2204
+- |0.0378 - (-0.1246)| = 0.1624
+- |0.1773 - 0.0378| = 0.1395
+- |0.1221 - 0.1773| = 0.0552
+- |(-0.0027) - 0.1221| = 0.1248
+- |(-0.0395) - (-0.0027)| = 0.0368
+- |(-0.3688) - (-0.0395)| = 0.3293
+- |(-0.0125) - (-0.3688)| = 0.3563
+- |0.0215 - (-0.0125)| = 0.034
+- |(-0.0521) - 0.0215| = 0.0736
+- |0.0101 - (-0.0521)| = 0.0622
+- |(-0.0227) - 0.0101| = 0.0328
+- |0.0595 - (-0.0227)| = 0.0822
+- |0.0244 - 0.0595| = 0.0351
+- |0.0529 - 0.0244| = 0.0285
+- |0.0211 - 0.0529| = 0.0318
+- |(-0.0139) - 0.0211| = 0.035
+- |(-0.0861) - (-0.0139)| = 0.0722
+- |(-0.0139) - (-0.0861)| = 0.0722
+- |(-0.0745) - (-0.0139)| = 0.0606
+- |(-0.0061) - (-0.0745)| = 0.0684
+- |(-0.057) - (-0.0061)| = 0.0509
+- |0.0766 - (-0.057)| = 0.1336
+- |0.1394 - 0.0766| = 0.0628
+
+The largest jump in absolute terms is between the intervals (45049.5, 45346.5) and (45346.5, 46184.5) with a difference of 0.3563. Therefore, the x-axis position of the largest jump is at the boundary between these intervals, which is 45346.5.
+SOLUTION:  45346.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values provided for each interval:
+- (0.0, 1.5): -0.02526
+- (1.5, 2.5): -0.01738
+- (2.5, 3.5): -0.01172
+- (3.5, 4.5): -0.00537
+- (4.5, 5.5): -0.00021
+- (5.5, 6.5): 0.0066
+- (6.5, 7.5): 0.01026
+- (7.5, 8.5): 0.01717
+- (8.5, 9.5): 0.02426
+- (9.5, 10.5): 0.02823
+- (10.5, 11.5): 0.03325
+- (11.5, 13.5): 0.03915
+- (13.5, 15.0): 0.03572
+
+Now, let's calculate the differences between consecutive intervals:
+- Difference between (1.5, 2.5) and (0.0, 1.5): -0.01738 - (-0.02526) = 0.00788
+- Difference between (2.5, 3.5) and (1.5, 2.5): -0.01172 - (-0.01738) = 0.00566
+- Difference between (3.5, 4.5) and (2.5, 3.5): -0.00537 - (-0.01172) = 0.00635
+- Difference between (4.5, 5.5) and (3.5, 4.5): -0.00021 - (-0.00537) = 0.00516
+- Difference between (5.5, 6.5) and (4.5, 5.5): 0.0066 - (-0.00021) = 0.00681
+- Difference between (6.5, 7.5) and (5.5, 6.5): 0.01026 - 0.0066 = 0.00366
+- Difference between (7.5, 8.5) and (6.5, 7.5): 0.01717 - 0.01026 = 0.00691
+- Difference between (8.5, 9.5) and (7.5, 8.5): 0.02426 - 0.01717 = 0.00709
+- Difference between (9.5, 10.5) and (8.5, 9.5): 0.02823 - 0.02426 = 0.00397
+- Difference between (10.5, 11.5) and (9.5, 10.5): 0.03325 - 0.02823 = 0.00502
+- Difference between (11.5, 13.5) and (10.5, 11.5): 0.03915 - 0.03325 = 0.00590
+- Difference between (13.5, 15.0) and (11.5, 13.5): 0.03572 - 0.03915 = -0.00343
+
+The largest absolute jump is 0.00709, which occurs between the intervals (7.5, 8.5) and (8.5, 9.5).
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between 8.5 and 9.5.
+SOLUTION:  1.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 20.0): 0.0422
+- (20.0, 36.5): -0.0027
+- (36.5, 40.5): -0.0554
+- (40.5, 45.5): -0.0967
+- (45.5, 48.5): -0.0409
+- (48.5, 55.5): -0.2263
+- (55.5, 80.5): -0.2661
+- (80.5, 87.5): -0.227
+- (87.5, 97.5): -0.1794
+- (97.5, 111.0): -0.1356
+- (111.0, 123.5): -0.0968
+- (123.5, 137.5): -0.0561
+- (137.5, 144.5): -0.0187
+- (144.5, 157.0): 0.0208
+- (157.0, 170.5): 0.0623
+- (170.5, 186.5): 0.0999
+- (186.5, 190.5): 0.0538
+- (190.5, 192.5): 0.1059
+- (192.5, 271.0): -0.0027
+- (271.0, 277.5): 0.035
+- (277.5, 292.0): 0.0732
+- (292.0, 311.0): 0.1129
+- (311.0, 365.0): 0.1551
+- (365.0, 397.0): 0.196
+- (397.0, 452.5): 0.2331
+- (452.5, 476.0): 0.2839
+- (476.0, 487.5): 0.346
+- (487.5, 526.5): 0.3915
+- (526.5, 680.0): 0.4346
+
+Now, let's calculate the absolute differences between consecutive intervals:
+- |0.0422 - (-0.0027)| = 0.0449
+- |-0.0027 - (-0.0554)| = 0.0527
+- |-0.0554 - (-0.0967)| = 0.0413
+- |-0.0967 - (-0.0409)| = 0.0558
+- |-0.0409 - (-0.2263)| = 0.1854
+- |-0.2263 - (-0.2661)| = 0.0398
+- |-0.2661 - (-0.227)| = 0.0391
+- |-0.227 - (-0.1794)| = 0.0476
+- |-0.1794 - (-0.1356)| = 0.0438
+- |-0.1356 - (-0.0968)| = 0.0388
+- |-0.0968 - (-0.0561)| = 0.0407
+- |-0.0561 - (-0.0187)| = 0.0374
+- |-0.0187 - 0.0208| = 0.0395
+- |0.0208 - 0.0623| = 0.0415
+- |0.0623 - 0.0999| = 0.0376
+- |0.0999 - 0.0538| = 0.0461
+- |0.0538 - 0.1059| = 0.0521
+- |0.1059 - (-0.0027)| = 0.1086
+- |-0.0027 - 0.035| = 0.0377
+- |0.035 - 0.0732| = 0.0382
+- |0.0732 - 0.1129| = 0.0397
+- |0.1129 - 0.1551| = 0.0422
+- |0.1551 - 0.196| = 0.0409
+- |0.196 - 0.2331| = 0.0371
+- |0.2331 - 0.2839| = 0.0508
+- |0.2839 - 0.346| = 0.0621
+- |0.346 - 0.3915| = 0.0455
+- |0.3915 - 0.4346| = 0.0431
+
+The largest jump is between the intervals (190.5, 192.5) and (192.5, 271.0) with a difference of 0.1086. Thus, the x-axis position of the largest jump is at 192.5.
+SOLUTION:  48.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+1. (0.0, 6.325): -1.425
+2. (6.325, 7.8500000000000005): -1.303
+3. (7.8500000000000005, 9.256250000000001): -0.472
+4. (9.256250000000001, 10.48125): -0.602
+5. (10.48125, 12.9375): -0.14
+6. (12.9375, 25.79375): 0.225
+7. (25.79375, 26.46875): 0.355
+8. (26.46875, 27.7354): 0.207
+9. (27.7354, 29.85): -0.238
+10. (29.85, 31.6604): 0.051
+11. (31.6604, 55.22085): -0.075
+12. (55.22085, 89.5521): 0.041
+13. (89.5521, 149.0354): 0.152
+14. (149.0354, 387.6646): -0.029
+15. (387.6646, 512.3292): 0.808
+
+Now, let's calculate the differences between consecutive intervals:
+- Jump from (0.0, 6.325) to (6.325, 7.8500000000000005): |-1.303 - (-1.425)| = 0.122
+- Jump from (6.325, 7.8500000000000005) to (7.8500000000000005, 9.256250000000001): |-0.472 - (-1.303)| = 0.831
+- Jump from (7.8500000000000005, 9.256250000000001) to (9.256250000000001, 10.48125): |-0.602 - (-0.472)| = 0.13
+- Jump from (9.256250000000001, 10.48125) to (10.48125, 12.9375): |-0.14 - (-0.602)| = 0.462
+- Jump from (10.48125, 12.9375) to (12.9375, 25.79375): |0.225 - (-0.14)| = 0.365
+- Jump from (12.9375, 25.79375) to (25.79375, 26.46875): |0.355 - 0.225| = 0.13
+- Jump from (25.79375, 26.46875) to (26.46875, 27.7354): |0.207 - 0.355| = 0.148
+- Jump from (26.46875, 27.7354) to (27.7354, 29.85): |-0.238 - 0.207| = 0.445
+- Jump from (27.7354, 29.85) to (29.85, 31.6604): |0.051 - (-0.238)| = 0.289
+- Jump from (29.85, 31.6604) to (31.6604, 55.22085): |-0.075 - 0.051| = 0.126
+- Jump from (31.6604, 55.22085) to (55.22085, 89.5521): |0.041 - (-0.075)| = 0.116
+- Jump from (55.22085, 89.5521) to (89.5521, 149.0354): |0.152 - 0.041| = 0.111
+- Jump from (89.5521, 149.0354) to (149.0354, 387.6646): |-0.029 - 0.152| = 0.181
+- Jump from (149.0354, 387.6646) to (387.6646, 512.3292): |0.808 - (-0.029)| = 0.837
+
+The largest jump in absolute terms is from (149.0354, 387.6646) to (387.6646, 512.3292) with a difference of 0.837.
+
+Therefore, the x-axis position of the largest jump in the graph is at the interval boundary between 387.6646 and 512.3292.
+SOLUTION:  387.6646
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.03259
+- (0.5, 1.5): -0.02272
+- (1.5, 2.5): -0.0157
+- (2.5, 3.5): -0.00983
+- (3.5, 4.5): -0.00444
+- (4.5, 5.5): -0.00035
+- (5.5, 6.5): 0.00575
+- (6.5, 7.5): 0.01126
+- (7.5, 8.5): 0.01651
+- (8.5, 9.5): 0.02143
+- (9.5, 12.5): 0.02903
+- (12.5, 13.5): 0.03437
+- (13.5, 15.0): 0.04826
+
+Now, let's calculate the differences between consecutive intervals:
+- Jump from (0.5, 1.5) to (1.5, 2.5): |-0.02272 - (-0.0157)| = 0.00702
+- Jump from (1.5, 2.5) to (2.5, 3.5): |-0.0157 - (-0.00983)| = 0.00587
+- Jump from (2.5, 3.5) to (3.5, 4.5): |-0.00983 - (-0.00444)| = 0.00539
+- Jump from (3.5, 4.5) to (4.5, 5.5): |-0.00444 - (-0.00035)| = 0.00409
+- Jump from (4.5, 5.5) to (5.5, 6.5): |-0.00035 - 0.00575| = 0.0061
+- Jump from (5.5, 6.5) to (6.5, 7.5): |0.00575 - 0.01126| = 0.00551
+- Jump from (6.5, 7.5) to (7.5, 8.5): |0.01126 - 0.01651| = 0.00525
+- Jump from (7.5, 8.5) to (8.5, 9.5): |0.01651 - 0.02143| = 0.00492
+- Jump from (8.5, 9.5) to (9.5, 12.5): |0.02143 - 0.02903| = 0.0076
+- Jump from (9.5, 12.5) to (12.5, 13.5): |0.02903 - 0.03437| = 0.00534
+- Jump from (12.5, 13.5) to (13.5, 15.0): |0.03437 - 0.04826| = 0.01389
+
+The largest jump in absolute terms is from (12.5, 13.5) to (13.5, 15.0) with a difference of 0.01389. Therefore, the x-axis position of the largest jump in the graph is at the boundary between these two intervals, which is 13.5.
+SOLUTION:  13.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the steps to find the largest jump:
+
+1. **Extract the mean values for each interval**: These values are given in the "Means" dictionary.
+
+2. **Calculate the differences between consecutive mean values**: This involves subtracting the mean value of one interval from the mean value of the next interval.
+
+3. **Identify the largest absolute difference**: The largest jump will be where the absolute value of the difference between consecutive mean values is the highest.
+
+Let's perform these calculations using the provided mean values:
+
+- From "(23.0, 32.0)": -0.48 to "(32.0, 49.5)": 0.68, the change is \(0.68 - (-0.48) = 1.16\)
+- From "(32.0, 49.5)": 0.68 to "(49.5, 56.5)": -4.31, the change is \(-4.31 - 0.68 = -4.99\)
+- Continue this for all intervals...
+
+After calculating these differences for all consecutive intervals, we find the largest absolute difference. From the calculations:
+
+- The largest absolute difference is between "(1886.0, 2038.5)": 7.0 and "(2038.5, 2307.5)": 2.26, which is \(|7.0 - 2.26| = 4.74\).
+
+Thus, the largest jump in the graph occurs at the boundary between the intervals "(1886.0, 2038.5)" and "(2038.5, 2307.5)". The x-axis position of this jump is at 2038.5.
+SOLUTION:  2444.0
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to compare the mean values at the boundaries of the intervals provided for the feature "anaemia". The intervals given are (0.0, 0.5) and (0.5, 1.0), and the corresponding mean values are -0.0818 and 0.0917 respectively.
+
+The jump occurs at the boundary between these two intervals, which is at x = 0.5. To determine the magnitude of the jump, we calculate the difference in mean values across this boundary:
+
+Jump at x = 0.5:
+Difference = Mean[(0.5, 1.0)] - Mean[(0.0, 0.5)]
+           = 0.0917 - (-0.0818)
+           = 0.0917 + 0.0818
+           = 0.1735
+
+Since this is the only jump in the graph (as there are only two intervals), it is also the largest jump. Therefore, the x-axis position of the largest jump in the graph is at x = 0.5.
+SOLUTION:  0.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms. Here are the steps to achieve this:
+
+1. **Extract the Mean Values**: We have the mean values for each interval as provided in the JSON object. We will use these values to calculate the differences between consecutive intervals.
+
+2. **Calculate Differences**: For each consecutive pair of intervals, calculate the difference in their mean values. Since we are interested in the magnitude of the jump, we will take the absolute value of these differences.
+
+3. **Identify the Largest Jump**: Compare all the calculated differences to find the maximum value. This will give us the largest jump.
+
+Let's perform these calculations using the provided mean values:
+
+- **Extracted Mean Values**:
+  - (25100.0, 27700.0): -1.004
+  - (27700.0, 34450.0): -0.687
+  - (34450.0, 42200.0): 0.328
+  - (42200.0, 56500.0): 1.717
+  - (56500.0, 66050.0): 2.769
+  - (66050.0, 74000.0): 2.195
+  - (74000.0, 95500.0): 2.956
+  - (95500.0, 104500.0): -0.265
+  - (104500.0, 144000.0): -0.585
+  - (144000.0, 150500.0): -0.895
+  - (150500.0, 154000.0): 2.322
+  - (154000.0, 169000.0): 0.469
+  - (169000.0, 184500.0): -1.612
+  - (184500.0, 195000.0): 1.111
+  - (195000.0, 199000.0): 3.01
+  - (199000.0, 200500.0): 1.837
+  - (200500.0, 214000.0): 0.403
+  - (214000.0, 217500.0): -0.825
+  - (217500.0, 218500.0): -1.399
+  - (218500.0, 220500.0): 0.341
+  - (220500.0, 222500.0): 0.978
+  - (222500.0, 226500.0): 1.584
+  - (226500.0, 241500.0): 0.175
+  - (241500.0, 242500.0): 0.642
+  - (242500.0, 243500.0): 1.107
+  - (243500.0, 244500.0): 1.516
+  - (244500.0, 252500.0): -2.19
+  - (252500.0, 261000.0): -0.878
+  - (261000.0, 274500.0): -0.145
+  - (274500.0, 283500.0): -0.968
+  - (283500.0, 287500.0): 0.203
+  - (287500.0, 289500.0): 1.032
+  - (289500.0, 302500.0): -1.296
+  - (302500.0, 305500.0): -2.984
+  - (305500.0, 307000.0): 0.876
+  - (307000.0, 332000.0): 0.368
+  - (332000.0, 335000.0): 1.21
+  - (335000.0, 343000.0): 0.8
+  - (343000.0, 350500.0): -0.573
+  - (350500.0, 354500.0): 3.0
+  - (354500.0, 383500.0): -0.119
+  - (383500.0, 449500.0): 0.655
+  - (449500.0, 471000.0): 1.527
+  - (471000.0, 500500.0): -2.247
+  - (500500.0, 582000.0): -0.442
+  - (582000.0, 675500.0): 2.645
+  - (675500.0, 796000.0): 2.314
+  - (796000.0, 850000.0): -0.709
+
+4. **Calculate Absolute Differences**:
+   - |(-0.687) - (-1.004)| = 0.317
+   - |0.328 - (-0.687)| = 1.015
+   - |1.717 - 0.328| = 1.389
+   - |2.769 - 1.717| = 1.052
+   - |2.195 - 2.769| = 0.574
+   - |2.956 - 2.195| = 0.761
+   - |(-0.265) - 2.956| = 3.221
+   - |(-0.585) - (-0.265)| = 0.32
+   - |(-0.895) - (-0.585)| = 0.31
+   - |2.322 - (-0.895)| = 3.217
+   - |0.469 - 2.322| = 1.853
+   - |(-1.612) - 0.469| = 2.081
+   - |1.111 - (-1.612)| = 2.723
+   - |3.01 - 1.111| = 1.899
+   - |1.837 - 3.01| = 1.173
+   - |0.403 - 1.837| = 1.434
+   - |(-0.825) - 0.403| = 1.228
+   - |(-1.399) - (-0.825)| = 0.574
+   - |0.341 - (-1.399)| = 1.74
+   - |0.978 - 0.341| = 0.637
+   - |1.584 - 0.978| = 0.606
+   - |0.175 - 1.584| = 1.409
+   - |0.642 - 0.175| = 0.467
+   - |1.107 - 0.642| = 0.465
+   - |1.516 - 1.107| = 0.409
+   - |(-2.19) - 1.516| = 3.706
+   - |(-0.878) - (-2.19)| = 1.312
+   - |(-0.145) - (-0.878)| = 0.733
+   - |(-0.968) - (-0.145)| = 0.823
+   - |0.203 - (-0.968)| = 1.171
+   - |1.032 - 0.203| = 0.829
+   - |(-1.296) - 1.032| = 2.328
+   - |(-2.984) - (-1.296)| = 1.688
+   - |0.876 - (-2.984)| = 3.86
+   - |0.368 - 0.876| = 0.508
+   - |1.21 - 0.368| = 0.842
+   - |0.8 - 1.21| = 0.41
+   - |(-0.573) - 0.8| = 1.373
+   - |3.0 - (-0.573)| = 3.573
+   - |(-0.119) - 3.0| = 3.119
+   - |0.655 - (-0.119)| = 0.774
+   - |1.527 - 0.655| = 0.872
+   - |(-2.247) - 1.527| = 3.774
+   - |(-0.442) - (-2.247)| = 1.805
+   - |2.645 - (-0.442)| = 3.087
+   - |2.314 - 2.645| = 0.331
+   - |(-0.709) - 2.314| = 3.023
+
+5. **Identify the Largest Jump**:
+   - The largest jump in absolute terms is between the intervals (244500.0, 252500.0) and (252500.0, 261000.0) with a jump of 3.706.
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between the intervals (244500.0, 252500.0) and (252500.0, 261000.0), which is at 252500.0.
+SOLUTION:  305500.0
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+1. (0.0, 50418.515): -0.132
+2. (50418.515, 53570.93): -0.285
+3. (53570.93, 54249.445): -0.826
+4. (54249.445, 57428.56): -0.404
+5. (57428.56, 60041.265): -0.005
+6. (60041.265, 64897.8): 0.215
+7. (64897.8, 72985.875): 0.086
+8. (72985.875, 74989.08499999999): -0.012
+9. (74989.08499999999, 76596.815): 0.247
+10. (76596.815, 79953.185): 0.829
+11. (79953.185, 83348.07): 0.564
+12. (83348.07, 101890.23999999999): 0.414
+13. (101890.23999999999, 114327.485): 0.248
+14. (114327.485, 123946.3): 0.164
+15. (123946.3, 141661.24): 0.075
+16. (141661.24, 174920.08000000002): 0.173
+17. (174920.08000000002, 181813.135): 0.059
+18. (181813.135, 191993.675): -0.349
+19. (191993.675, 200829.925): -0.459
+20. (200829.925, 206951.87): -0.616
+21. (206951.87, 216109.88): -0.256
+
+Now, let's calculate the absolute differences between consecutive intervals:
+- |(-0.285) - (-0.132)| = 0.153
+- |(-0.826) - (-0.285)| = 0.541
+- |(-0.404) - (-0.826)| = 0.422
+- |(-0.005) - (-0.404)| = 0.399
+- |0.215 - (-0.005)| = 0.220
+- |0.086 - 0.215| = 0.129
+- |(-0.012) - 0.086| = 0.098
+- |0.247 - (-0.012)| = 0.259
+- |0.829 - 0.247| = 0.582
+- |0.564 - 0.829| = 0.265
+- |0.414 - 0.564| = 0.150
+- |0.248 - 0.414| = 0.166
+- |0.164 - 0.248| = 0.084
+- |0.075 - 0.164| = 0.089
+- |0.173 - 0.075| = 0.098
+- |0.059 - 0.173| = 0.114
+- |(-0.349) - 0.059| = 0.408
+- |(-0.459) - (-0.349)| = 0.110
+- |(-0.616) - (-0.459)| = 0.157
+- |(-0.256) - (-0.616)| = 0.360
+
+The largest jump in absolute terms is 0.582, which occurs between the intervals (74989.08499999999, 76596.815) and (76596.815, 79953.185).
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between these intervals, which is at 76596.815.
+SOLUTION:  76596.815
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 1.5): -0.02325
+- (1.5, 2.5): -0.01532
+- (2.5, 3.5): -0.01073
+- (3.5, 4.5): -0.00482
+- (4.5, 5.5): -0.00032
+- (5.5, 6.5): 0.0063
+- (6.5, 7.5): 0.01228
+- (7.5, 8.5): 0.01637
+- (8.5, 10.5): 0.02537
+- (10.5, 12.5): 0.03189
+- (12.5, 13.5): 0.03961
+- (13.5, 14.0): 0.01644
+
+Now, let's calculate the differences (jumps) between consecutive intervals:
+- Jump from (0.0, 1.5) to (1.5, 2.5): |-0.01532 - (-0.02325)| = 0.00793
+- Jump from (1.5, 2.5) to (2.5, 3.5): |-0.01073 - (-0.01532)| = 0.00459
+- Jump from (2.5, 3.5) to (3.5, 4.5): |-0.00482 - (-0.01073)| = 0.00591
+- Jump from (3.5, 4.5) to (4.5, 5.5): |-0.00032 - (-0.00482)| = 0.0045
+- Jump from (4.5, 5.5) to (5.5, 6.5): |0.0063 - (-0.00032)| = 0.00662
+- Jump from (5.5, 6.5) to (6.5, 7.5): |0.01228 - 0.0063| = 0.00598
+- Jump from (6.5, 7.5) to (7.5, 8.5): |0.01637 - 0.01228| = 0.00409
+- Jump from (7.5, 8.5) to (8.5, 10.5): |0.02537 - 0.01637| = 0.009
+- Jump from (8.5, 10.5) to (10.5, 12.5): |0.03189 - 0.02537| = 0.00652
+- Jump from (10.5, 12.5) to (12.5, 13.5): |0.03961 - 0.03189| = 0.00772
+- Jump from (12.5, 13.5) to (13.5, 14.0): |0.01644 - 0.03961| = 0.02317
+
+The largest jump in absolute terms is from (12.5, 13.5) to (13.5, 14.0) with a magnitude of 0.02317. Therefore, the x-axis position of the largest jump in the graph is at the boundary between these intervals, which is at 13.5.
+SOLUTION:  13.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 1.5): -0.02446
+- (1.5, 2.5): -0.01712
+- (2.5, 3.5): -0.00908
+- (3.5, 4.5): -0.00529
+- (4.5, 5.5): 0.0003
+- (5.5, 6.5): 0.00497
+- (6.5, 7.5): 0.01093
+- (7.5, 8.5): 0.01787
+- (8.5, 9.5): 0.02262
+- (9.5, 11.5): 0.02707
+- (11.5, 12.5): 0.03735
+- (12.5, 13.5): 0.043
+- (13.5, 15.0): 0.01734
+
+Now, let's calculate the differences (jumps) between consecutive intervals:
+- Jump from (1.5, 2.5) to (2.5, 3.5): |-0.01712 - (-0.00908)| = 0.00804
+- Jump from (2.5, 3.5) to (3.5, 4.5): |-0.00908 - (-0.00529)| = 0.00379
+- Jump from (3.5, 4.5) to (4.5, 5.5): |-0.00529 - 0.0003| = 0.00559
+- Jump from (4.5, 5.5) to (5.5, 6.5): |0.0003 - 0.00497| = 0.00467
+- Jump from (5.5, 6.5) to (6.5, 7.5): |0.00497 - 0.01093| = 0.00596
+- Jump from (6.5, 7.5) to (7.5, 8.5): |0.01093 - 0.01787| = 0.00694
+- Jump from (7.5, 8.5) to (8.5, 9.5): |0.01787 - 0.02262| = 0.00475
+- Jump from (8.5, 9.5) to (9.5, 11.5): |0.02262 - 0.02707| = 0.00445
+- Jump from (9.5, 11.5) to (11.5, 12.5): |0.02707 - 0.03735| = 0.01028
+- Jump from (11.5, 12.5) to (12.5, 13.5): |0.03735 - 0.043| = 0.00565
+- Jump from (12.5, 13.5) to (13.5, 15.0): |0.043 - 0.01734| = 0.02566
+
+The largest jump in absolute terms is from (12.5, 13.5) to (13.5, 15.0) with a magnitude of 0.02566. Therefore, the x-axis position of the largest jump in the graph is at the boundary between these intervals, which is 13.5.
+SOLUTION:  13.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+1. \( (0.0, 0.5) \): 0.085
+2. \( (0.5, 1.5) \): -0.055
+3. \( (1.5, 3.0) \): -0.299
+4. \( (3.0, 4.0) \): -1.704
+
+Now, let's calculate the differences between consecutive intervals:
+- Jump from \( (0.0, 0.5) \) to \( (0.5, 1.5) \): \( -0.055 - 0.085 = -0.14 \)
+- Jump from \( (0.5, 1.5) \) to \( (1.5, 3.0) \): \( -0.299 - (-0.055) = -0.244 \)
+- Jump from \( (1.5, 3.0) \) to \( (3.0, 4.0) \): \( -1.704 - (-0.299) = -1.405 \)
+
+The absolute values of these jumps are:
+- \( |-0.14| = 0.14 \)
+- \( |-0.244| = 0.244 \)
+- \( |-1.405| = 1.405 \)
+
+The largest jump in absolute terms is \( 1.405 \), which occurs between the intervals \( (1.5, 3.0) \) and \( (3.0, 4.0) \).
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between \( (1.5, 3.0) \) and \( (3.0, 4.0) \), which is at \( x = 3.0 \).
+SOLUTION:  3.0
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (113.0, 114.5): -1.269
+- (114.5, 118.5): 0.283
+- (118.5, 124.5): 3.539
+- (124.5, 126.5): 2.46
+- (126.5, 127.5): 4.042
+- (127.5, 129.5): 3.553
+- (129.5, 130.5): 0.953
+- (130.5, 132.5): 1.22
+- (132.5, 133.5): -1.094
+- (133.5, 135.5): 0.587
+- (135.5, 138.5): -0.629
+- (138.5, 144.5): -0.233
+- (144.5, 148.0): 0.113
+
+Now, let's calculate the differences between consecutive intervals:
+- Jump from (113.0, 114.5) to (114.5, 118.5): |0.283 - (-1.269)| = 1.552
+- Jump from (114.5, 118.5) to (118.5, 124.5): |3.539 - 0.283| = 3.256
+- Jump from (118.5, 124.5) to (124.5, 126.5): |2.46 - 3.539| = 1.079
+- Jump from (124.5, 126.5) to (126.5, 127.5): |4.042 - 2.46| = 1.582
+- Jump from (126.5, 127.5) to (127.5, 129.5): |3.553 - 4.042| = 0.489
+- Jump from (127.5, 129.5) to (129.5, 130.5): |0.953 - 3.553| = 2.6
+- Jump from (129.5, 130.5) to (130.5, 132.5): |1.22 - 0.953| = 0.267
+- Jump from (130.5, 132.5) to (132.5, 133.5): |(-1.094) - 1.22| = 2.314
+- Jump from (132.5, 133.5) to (133.5, 135.5): |0.587 - (-1.094)| = 1.681
+- Jump from (133.5, 135.5) to (135.5, 138.5): |(-0.629) - 0.587| = 1.216
+- Jump from (135.5, 138.5) to (138.5, 144.5): |(-0.233) - (-0.629)| = 0.396
+- Jump from (138.5, 144.5) to (144.5, 148.0): |0.113 - (-0.233)| = 0.346
+
+The largest jump in absolute terms is from (114.5, 118.5) to (118.5, 124.5) with a difference of 3.256. Therefore, the x-axis position of the largest jump in the graph is at the boundary between these intervals, which is 118.5.
+SOLUTION:  118.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values provided for each interval:
+- (7.93, 10.585): -1.149
+- (10.585, 11.305): -1.016
+- (11.305, 11.965): -0.883
+- (11.965, 12.54): -0.747
+- (12.54, 13.315000000000001): -0.616
+- (13.315000000000001, 14.184999999999999): -0.485
+- (14.184999999999999, 14.875): -0.349
+- (14.875, 15.485): -0.212
+- (15.485, 15.955): -0.078
+- (15.955, 16.54): 0.055
+- (16.54, 17.22): 0.19
+- (17.22, 17.78): 0.335
+- (17.78, 18.655): 0.469
+- (18.655, 19.785): 0.601
+- (19.785, 20.445): 0.734
+- (20.445, 21.935000000000002): 0.866
+- (21.935000000000002, 23.625): 0.997
+- (23.625, 25.335): 1.132
+- (25.335, 30.71): 1.274
+- (30.71, 36.04): 1.406
+
+Now, let's calculate the differences between consecutive mean values:
+- Jump from (10.585, 11.305) to (11.305, 11.965): |-1.016 - (-0.883)| = 0.133
+- Jump from (11.305, 11.965) to (11.965, 12.54): |-0.883 - (-0.747)| = 0.136
+- Jump from (11.965, 12.54) to (12.54, 13.315000000000001): |-0.747 - (-0.616)| = 0.131
+- Jump from (12.54, 13.315000000000001) to (13.315000000000001, 14.184999999999999): |-0.616 - (-0.485)| = 0.131
+- Jump from (13.315000000000001, 14.184999999999999) to (14.184999999999999, 14.875): |-0.485 - (-0.349)| = 0.136
+- Jump from (14.184999999999999, 14.875) to (14.875, 15.485): |-0.349 - (-0.212)| = 0.137
+- Jump from (14.875, 15.485) to (15.485, 15.955): |-0.212 - (-0.078)| = 0.134
+- Jump from (15.485, 15.955) to (15.955, 16.54): |-0.078 - 0.055| = 0.133
+- Jump from (15.955, 16.54) to (16.54, 17.22): |0.055 - 0.19| = 0.135
+- Jump from (16.54, 17.22) to (17.22, 17.78): |0.19 - 0.335| = 0.145
+- Jump from (17.22, 17.78) to (17.78, 18.655): |0.335 - 0.469| = 0.134
+- Jump from (17.78, 18.655) to (18.655, 19.785): |0.469 - 0.601| = 0.132
+- Jump from (18.655, 19.785) to (19.785, 20.445): |0.601 - 0.734| = 0.133
+- Jump from (19.785, 20.445) to (20.445, 21.935000000000002): |0.734 - 0.866| = 0.132
+- Jump from (20.445, 21.935000000000002) to (21.935000000000002, 23.625): |0.866 - 0.997| = 0.131
+- Jump from (21.935000000000002, 23.625) to (23.625, 25.335): |0.997 - 1.132| = 0.135
+- Jump from (23.625, 25.335) to (25.335, 30.71): |1.132 - 1.274| = 0.142
+- Jump from (25.335, 30.71) to (30.71, 36.04): |1.274 - 1.406| = 0.132
+
+The largest jump is from (23.625, 25.335) to (25.335, 30.71) with a difference of 0.142. Therefore, the x-axis position of the largest jump in the graph is at the boundary between these intervals, which is 25.335.
+SOLUTION:  17.22
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms. Here's how we can proceed:
+
+1. **Extract the mean values and their corresponding intervals** from the JSON object provided. The intervals are keys, and the mean values are the corresponding values in the "Means" dictionary.
+
+2. **Calculate the differences in mean values between consecutive intervals**. This involves subtracting the mean value of one interval from the mean value of the next interval.
+
+3. **Identify the interval where the absolute difference is the largest**. This will indicate the largest jump.
+
+Let's perform these steps using the data provided:
+
+- **Extracted Mean Values**:
+  - (0.01938, 0.03164): 0.0135
+  - (0.03164, 0.035445000000000004): 0.0558
+  - ...
+  - (0.066575, 0.067345): -0.1354
+  - (0.067345, 0.06788): -0.1923
+  - ...
+  - (0.28075, 0.3114): 0.2453
+
+- **Calculate Differences**:
+  - Difference between (0.03164, 0.035445000000000004) and (0.01938, 0.03164): 0.0558 - 0.0135 = 0.0423
+  - ...
+  - Difference between (0.067345, 0.06788) and (0.066575, 0.067345): -0.1923 - (-0.1354) = -0.0569
+  - ...
+
+- **Identify Largest Jump**:
+  - We need to find the maximum of these absolute differences.
+
+From the calculated differences, we can see that the largest jump occurs between the intervals (0.066575, 0.067345) and (0.067345, 0.06788), where the mean value jumps from -0.1354 to -0.1923, resulting in a difference of -0.0569 (in absolute terms, 0.0569).
+
+Therefore, the **x-axis position of the largest jump** in the graph is at the boundary between these two intervals, which is **0.067345**.
+SOLUTION:  0.066575
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (18.0, 32.5): 0.83
+- (32.5, 34.5): 0.681
+- (34.5, 37.5): 0.423
+- (37.5, 38.5): 0.281
+- (38.5, 39.5): 0.054
+- (39.5, 40.5): -0.193
+- (40.5, 41.5): -0.354
+- (41.5, 42.5): -0.494
+- (42.5, 44.5): -0.781
+- (44.5, 46.5): -1.075
+- (46.5, 48.5): -1.546
+- (48.5, 54.5): -1.717
+- (54.5, 56.5): -1.858
+- (56.5, 64.5): -1.707
+- (64.5, 66.5): -1.27
+- (66.5, 69.5): -1.118
+- (69.5, 70.5): -0.888
+- (70.5, 72.5): -0.587
+- (72.5, 74.5): -0.31
+- (74.5, 81.0): -0.157
+
+Now, let's calculate the differences between consecutive intervals:
+- Difference between (32.5, 34.5) and (34.5, 37.5): |0.681 - 0.423| = 0.258
+- Difference between (34.5, 37.5) and (37.5, 38.5): |0.423 - 0.281| = 0.142
+- Difference between (37.5, 38.5) and (38.5, 39.5): |0.281 - 0.054| = 0.227
+- Difference between (38.5, 39.5) and (39.5, 40.5): |0.054 - (-0.193)| = 0.247
+- Difference between (39.5, 40.5) and (40.5, 41.5): |-0.193 - (-0.354)| = 0.161
+- Difference between (40.5, 41.5) and (41.5, 42.5): |-0.354 - (-0.494)| = 0.14
+- Difference between (41.5, 42.5) and (42.5, 44.5): |-0.494 - (-0.781)| = 0.287
+- Difference between (42.5, 44.5) and (44.5, 46.5): |-0.781 - (-1.075)| = 0.294
+- Difference between (44.5, 46.5) and (46.5, 48.5): |-1.075 - (-1.546)| = 0.471
+- Difference between (46.5, 48.5) and (48.5, 54.5): |-1.546 - (-1.717)| = 0.171
+- Difference between (48.5, 54.5) and (54.5, 56.5): |-1.717 - (-1.858)| = 0.141
+- Difference between (54.5, 56.5) and (56.5, 64.5): |-1.858 - (-1.707)| = 0.151
+- Difference between (56.5, 64.5) and (64.5, 66.5): |-1.707 - (-1.27)| = 0.437
+- Difference between (64.5, 66.5) and (66.5, 69.5): |-1.27 - (-1.118)| = 0.152
+- Difference between (66.5, 69.5) and (69.5, 70.5): |-1.118 - (-0.888)| = 0.23
+- Difference between (69.5, 70.5) and (70.5, 72.5): |-0.888 - (-0.587)| = 0.301
+- Difference between (70.5, 72.5) and (72.5, 74.5): |-0.587 - (-0.31)| = 0.277
+- Difference between (72.5, 74.5) and (74.5, 81.0): |-0.31 - (-0.157)| = 0.153
+
+The largest jump in absolute terms is between the intervals (44.5, 46.5) and (46.5, 48.5) with a difference of 0.471. Therefore, the x-axis position of the largest jump is at the boundary between these intervals, which is at age 46.5.
+SOLUTION:  46.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 1.5): -0.02549
+- (1.5, 2.5): -0.01575
+- (2.5, 3.5): -0.01061
+- (3.5, 4.5): -0.0046
+- (4.5, 5.5): 0.00059
+- (5.5, 6.5): 0.00567
+- (6.5, 7.5): 0.01201
+- (7.5, 9.5): 0.01601
+- (9.5, 10.5): 0.02531
+- (10.5, 11.5): 0.02956
+- (11.5, 12.5): 0.04031
+- (12.5, 14.0): 0.04423
+
+Now, let's calculate the differences (jumps) between consecutive intervals:
+- Jump from (0.0, 1.5) to (1.5, 2.5): |-0.01575 - (-0.02549)| = 0.00974
+- Jump from (1.5, 2.5) to (2.5, 3.5): |-0.01061 - (-0.01575)| = 0.00514
+- Jump from (2.5, 3.5) to (3.5, 4.5): |-0.0046 - (-0.01061)| = 0.00601
+- Jump from (3.5, 4.5) to (4.5, 5.5): |0.00059 - (-0.0046)| = 0.00519
+- Jump from (4.5, 5.5) to (5.5, 6.5): |0.00567 - 0.00059| = 0.00508
+- Jump from (5.5, 6.5) to (6.5, 7.5): |0.01201 - 0.00567| = 0.00634
+- Jump from (6.5, 7.5) to (7.5, 9.5): |0.01601 - 0.01201| = 0.004
+- Jump from (7.5, 9.5) to (9.5, 10.5): |0.02531 - 0.01601| = 0.0093
+- Jump from (9.5, 10.5) to (10.5, 11.5): |0.02956 - 0.02531| = 0.00425
+- Jump from (10.5, 11.5) to (11.5, 12.5): |0.04031 - 0.02956| = 0.01075
+- Jump from (11.5, 12.5) to (12.5, 14.0): |0.04423 - 0.04031| = 0.00392
+
+The largest jump in absolute terms is from (10.5, 11.5) to (11.5, 12.5) with a magnitude of 0.01075.
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between the intervals (10.5, 11.5) and (11.5, 12.5), which is at 11.5.
+SOLUTION:  11.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+1. (0.0, 125.5): -0.032
+2. (125.5, 541.5): -0.211
+3. (541.5, 808.5): 0.034
+4. (808.5, 1082.0): 0.213
+5. (1082.0, 1187.0): -0.042
+6. (1187.0, 1434.5): 0.401
+7. (1434.5, 1658.5): 0.585
+8. (1658.5, 1968.5): 0.948
+9. (1968.5, 3394.5): 1.235
+10. (3394.5, 3460.0): 0.871
+11. (3460.0, 3741.5): 1.066
+12. (3741.5, 4803.5): 2.339
+13. (4803.5, 5204.0): 2.909
+14. (5204.0, 12253.0): 3.236
+
+Now, let's calculate the differences (jumps) between consecutive intervals:
+- Jump from (0.0, 125.5) to (125.5, 541.5): |-0.211 - (-0.032)| = 0.179
+- Jump from (125.5, 541.5) to (541.5, 808.5): |0.034 - (-0.211)| = 0.245
+- Jump from (541.5, 808.5) to (808.5, 1082.0): |0.213 - 0.034| = 0.179
+- Jump from (808.5, 1082.0) to (1082.0, 1187.0): |-0.042 - 0.213| = 0.255
+- Jump from (1082.0, 1187.0) to (1187.0, 1434.5): |0.401 - (-0.042)| = 0.443
+- Jump from (1187.0, 1434.5) to (1434.5, 1658.5): |0.585 - 0.401| = 0.184
+- Jump from (1434.5, 1658.5) to (1658.5, 1968.5): |0.948 - 0.585| = 0.363
+- Jump from (1658.5, 1968.5) to (1968.5, 3394.5): |1.235 - 0.948| = 0.287
+- Jump from (1968.5, 3394.5) to (3394.5, 3460.0): |0.871 - 1.235| = 0.364
+- Jump from (3394.5, 3460.0) to (3460.0, 3741.5): |1.066 - 0.871| = 0.195
+- Jump from (3460.0, 3741.5) to (3741.5, 4803.5): |2.339 - 1.066| = 1.273
+- Jump from (3741.5, 4803.5) to (4803.5, 5204.0): |2.909 - 2.339| = 0.570
+- Jump from (4803.5, 5204.0) to (5204.0, 12253.0): |3.236 - 2.909| = 0.327
+
+The largest jump in absolute terms is from (3460.0, 3741.5) to (3741.5, 4803.5) with a difference of 1.273. Therefore, the x-axis position of the largest jump in the graph is at the end of the interval (3460.0, 3741.5), which is 3741.5.
+SOLUTION:  3741.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (3.0, 14.5): 125210.2
+- (14.5, 25.5): 92452.9
+- (25.5, 65.5): 80407.9
+- (65.5, 138.5): 91917.4
+- (138.5, 151.5): 103409.9
+- (151.5, 301.5): 85121.7
+- (301.5, 490.5): 73106.0
+- (490.5, 657.5): 57994.5
+- (657.5, 761.5): 44760.8
+- (761.5, 837.5): 32058.9
+- (837.5, 1019.5): 20715.6
+- (1019.5, 1220.5): 6507.2
+- (1220.5, 1267.5): -6199.6
+- (1267.5, 1269.5): 9858.1
+- (1269.5, 1497.5): -9812.8
+- (1497.5, 1886.5): -25776.4
+- (1886.5, 2129.5): -36953.6
+- (2129.5, 2425.5): -48605.9
+- (2425.5, 2686.0): -59914.9
+- (2686.0, 2718.5): -46231.6
+- (2718.5, 3175.5): -61061.6
+- (3175.5, 3965.0): -76216.0
+- (3965.0, 35682.0): -91117.9
+
+Now, let's calculate the absolute differences between consecutive intervals:
+- |92452.9 - 125210.2| = 32757.3
+- |80407.9 - 92452.9| = 12045.0
+- |91917.4 - 80407.9| = 11509.5
+- |103409.9 - 91917.4| = 11492.5
+- |85121.7 - 103409.9| = 18288.2
+- |73106.0 - 85121.7| = 12015.7
+- |57994.5 - 73106.0| = 15111.5
+- |44760.8 - 57994.5| = 13233.7
+- |32058.9 - 44760.8| = 12701.9
+- |20715.6 - 32058.9| = 11343.3
+- |6507.2 - 20715.6| = 14208.4
+- |-6199.6 - 6507.2| = 12706.8
+- |9858.1 + 6199.6| = 16057.7
+- |-9812.8 - 9858.1| = 19670.9
+- |-25776.4 + 9812.8| = 15963.6
+- |-36953.6 + 25776.4| = 11177.2
+- |-48605.9 + 36953.6| = 11652.3
+- |-59914.9 + 48605.9| = 11309.0
+- |-46231.6 + 59914.9| = 13683.3
+- |-61061.6 + 46231.6| = 14830.0
+- |-76216.0 + 61061.6| = 15154.4
+- |-91117.9 + 76216.0| = 14901.9
+
+The largest jump in absolute terms is between the intervals (3.0, 14.5) and (14.5, 25.5) with a difference of 32757.3.
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between these intervals, which is 14.5.
+SOLUTION:  14.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values provided for each interval:
+- (0.0, 0.0074145): -1.054
+- (0.0074145, 0.011665): -0.937
+- (0.011665, 0.01503): -0.821
+- (0.01503, 0.017865): -0.705
+- (0.017865, 0.019315): -0.582
+- (0.019315, 0.023185): -0.466
+- (0.023185, 0.026115): -0.352
+- (0.026115, 0.042455): -0.235
+- (0.042455, 0.048235): -0.115
+- (0.048235, 0.048865): 0.04
+- (0.048865, 0.059615): 0.233
+- (0.059615, 0.070395): 0.35
+- (0.070395, 0.08221500000000001): 0.474
+- (0.08221500000000001, 0.087175): 0.592
+- (0.087175, 0.091445): 0.711
+- (0.091445, 0.1006): 0.832
+- (0.1006, 0.122): 0.949
+- (0.122, 0.16544999999999999): 1.068
+- (0.16544999999999999, 0.2012): 1.187
+
+Now, let's calculate the differences between consecutive mean values:
+- Difference between -0.937 and -1.054 = 0.117
+- Difference between -0.821 and -0.937 = 0.116
+- Difference between -0.705 and -0.821 = 0.116
+- Difference between -0.582 and -0.705 = 0.123
+- Difference between -0.466 and -0.582 = 0.116
+- Difference between -0.352 and -0.466 = 0.114
+- Difference between -0.235 and -0.352 = 0.117
+- Difference between -0.115 and -0.235 = 0.120
+- Difference between 0.04 and -0.115 = 0.155
+- Difference between 0.233 and 0.04 = 0.193
+- Difference between 0.35 and 0.233 = 0.117
+- Difference between 0.474 and 0.35 = 0.124
+- Difference between 0.592 and 0.474 = 0.118
+- Difference between 0.711 and 0.592 = 0.119
+- Difference between 0.832 and 0.711 = 0.121
+- Difference between 0.949 and 0.832 = 0.117
+- Difference between 1.068 and 0.949 = 0.119
+- Difference between 1.187 and 1.068 = 0.119
+
+The largest jump in absolute terms is between the intervals (0.048235, 0.048865) and (0.048865, 0.059615), with a difference of 0.193. Therefore, the x-axis position of the largest jump is at the upper boundary of the interval (0.048235, 0.048865), which is 0.048865.
+SOLUTION:  0.048865
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 0.5): 0.511
+- (0.5, 1.5): -0.233
+- (1.5, 2.5): -0.666
+- (2.5, 3.5): -1.006
+- (3.5, 4.5): -0.529
+- (4.5, 5.0): 1.753
+
+Now, let's calculate the differences between consecutive intervals:
+1. Difference between (0.5, 1.5) and (0.0, 0.5): |-0.233 - 0.511| = 0.744
+2. Difference between (1.5, 2.5) and (0.5, 1.5): |-0.666 - (-0.233)| = 0.433
+3. Difference between (2.5, 3.5) and (1.5, 2.5): |-1.006 - (-0.666)| = 0.340
+4. Difference between (3.5, 4.5) and (2.5, 3.5): |-0.529 - (-1.006)| = 0.477
+5. Difference between (4.5, 5.0) and (3.5, 4.5): |1.753 - (-0.529)| = 2.282
+
+The largest jump in absolute terms is between the intervals (3.5, 4.5) and (4.5, 5.0), with a difference of 2.282.
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between the intervals (3.5, 4.5) and (4.5, 5.0), which is at 4.5.
+SOLUTION:  4.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the steps to find the largest jump:
+
+1. **Extract the mean values for each interval**: We have the mean values provided in the JSON object under "Means".
+
+2. **Calculate the differences between consecutive intervals**: We need to subtract the mean value of one interval from the mean value of the next interval.
+
+3. **Identify the largest absolute difference**: The largest jump will be where the absolute value of the difference between consecutive intervals is the highest.
+
+Let's calculate the differences:
+
+- From "(2.0, 4.5)" to "(4.5, 9.5)": \( -19829.1 - (-10633.3) = -9195.8 \)
+- From "(4.5, 9.5)" to "(9.5, 12.5)": \( -33356.0 - (-19829.1) = -13526.9 \)
+- From "(9.5, 12.5)" to "(12.5, 14.5)": \( -27510.0 - (-33356.0) = 5846.0 \)
+- From "(12.5, 14.5)" to "(14.5, 17.5)": \( -34141.4 - (-27510.0) = -6631.4 \)
+- From "(14.5, 17.5)" to "(17.5, 20.5)": \( -50740.7 - (-34141.4) = -16599.3 \)
+- From "(17.5, 20.5)" to "(20.5, 22.5)": \( -59049.5 - (-50740.7) = -8308.8 \)
+- From "(20.5, 22.5)" to "(22.5, 25.5)": \( -37177.7 - (-59049.5) = 21871.8 \)
+- Continue this process for all intervals.
+
+After calculating these differences, we find the largest absolute difference:
+
+- The largest absolute difference is \( 21871.8 \) between the intervals "(20.5, 22.5)" and "(22.5, 25.5)".
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between the intervals \( 22.5 \). This is where the function predicts a significant change in the mean value, indicating a large jump in the effect of the 'total_bedrooms' feature on the model's output.
+SOLUTION:  22.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 15.0): 0.236
+- (15.0, 37.0): 0.1532
+- (37.0, 45.0): -0.0296
+- (45.0, 47.0): -0.0891
+- (47.0, 54.5): -0.1348
+- (54.5, 60.5): -0.1774
+- (60.5, 61.5): -0.11
+- (61.5, 64.5): -0.0541
+- (64.5, 74.5): -0.0119
+- (74.5, 75.5): -0.058
+- (75.5, 83.0): -0.004
+- (83.0, 93.0): 0.0343
+- (93.0, 95.0): 0.0889
+- (95.0, 97.0): 0.1461
+- (97.0, 101.0): 0.183
+- (101.0, 103.0): 0.2699
+- (103.0, 107.0): 0.3158
+- (107.0, 109.0): 0.3837
+- (109.0, 110.0): 0.5269
+
+Now, let's calculate the differences between consecutive intervals:
+- Jump from (15.0, 37.0) to (37.0, 45.0): |0.1532 - (-0.0296)| = 0.1828
+- Jump from (45.0, 47.0) to (47.0, 54.5): |-0.0891 - (-0.1348)| = 0.0457
+- Jump from (54.5, 60.5) to (60.5, 61.5): |-0.1774 - (-0.11)| = 0.0674
+- Jump from (60.5, 61.5) to (61.5, 64.5): |-0.11 - (-0.0541)| = 0.0559
+- Jump from (64.5, 74.5) to (74.5, 75.5): |-0.0119 - (-0.058)| = 0.0461
+- Jump from (75.5, 83.0) to (83.0, 93.0): |-0.004 - 0.0343| = 0.0383
+- Jump from (93.0, 95.0) to (95.0, 97.0): |0.0889 - 0.1461| = 0.0572
+- Jump from (97.0, 101.0) to (101.0, 103.0): |0.183 - 0.2699| = 0.0869
+- Jump from (103.0, 107.0) to (107.0, 109.0): |0.3158 - 0.3837| = 0.0679
+- Jump from (107.0, 109.0) to (109.0, 110.0): |0.3837 - 0.5269| = 0.1432
+
+The largest jump in absolute terms is from (107.0, 109.0) to (109.0, 110.0) with a difference of 0.1432. Therefore, the x-axis position of the largest jump in the graph is at 109.0.
+SOLUTION:  37.0
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+1. (0.0, 105.5): 0.328
+2. (105.5, 296.5): 0.028
+3. (296.5, 335.5): -0.208
+4. (335.5, 340.0): 0.165
+5. (340.0, 343.0): -0.1
+6. (343.0, 596.5): -0.741
+7. (596.5, 712.5): -0.978
+8. (712.5, 734.0): -1.212
+9. (734.0, 800.0): -1.446
+10. (800.0, 816.0): -1.136
+11. (816.0, 997.5): -1.454
+12. (997.5, 1031.0): -1.106
+13. (1031.0, 1041.0): -1.368
+14. (1041.0, 2172.5): -1.866
+15. (2172.5, 2283.5): -1.455
+16. (2283.5, 2313.5): -1.171
+17. (2313.5, 2336.5): -0.66
+18. (2336.5, 2420.0): -2.559
+19. (2420.0, 2992.5): -3.229
+20. (2992.5, 3006.0): -2.708
+21. (3006.0, 3196.5): -2.984
+22. (3196.5, 3249.5): -2.709
+23. (3249.5, 14327.0): -4.146
+
+Now, let's calculate the absolute differences between consecutive intervals:
+- |0.328 - 0.028| = 0.3
+- |0.028 - (-0.208)| = 0.236
+- |-0.208 - 0.165| = 0.373
+- |0.165 - (-0.1)| = 0.265
+- |-0.1 - (-0.741)| = 0.641
+- |-0.741 - (-0.978)| = 0.237
+- |-0.978 - (-1.212)| = 0.234
+- |-1.212 - (-1.446)| = 0.234
+- |-1.446 - (-1.136)| = 0.31
+- |-1.136 - (-1.454)| = 0.318
+- |-1.454 - (-1.106)| = 0.348
+- |-1.106 - (-1.368)| = 0.262
+- |-1.368 - (-1.866)| = 0.498
+- |-1.866 - (-1.455)| = 0.411
+- |-1.455 - (-1.171)| = 0.284
+- |-1.171 - (-0.66)| = 0.511
+- |-0.66 - (-2.559)| = 1.899
+- |-2.559 - (-3.229)| = 0.67
+- |-3.229 - (-2.708)| = 0.521
+- |-2.708 - (-2.984)| = 0.276
+- |-2.984 - (-2.709)| = 0.275
+- |-2.709 - (-4.146)| = 1.437
+
+The largest jump in absolute terms is between the intervals (2313.5, 2336.5) and (2336.5, 2420.0) with a difference of 1.899. Therefore, the x-axis position of the largest jump in the graph is at 2336.5.
+SOLUTION:  2336.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (14.0, 16.0): 4.55
+- (16.0, 22.5): 3.26
+- (22.5, 27.5): 1.89
+- (27.5, 32.5): -0.42
+- (32.5, 36.5): -1.76
+- (36.5, 39.0): 0.48
+- (39.0, 61.0): -0.83
+- (61.0, 67.5): 0.08
+- (67.5, 75.0): 0.8
+- (75.0, 80.0): -5.67
+
+Now, let's calculate the differences between consecutive intervals:
+- Jump from (16.0, 22.5) to (22.5, 27.5): |3.26 - 1.89| = 1.37
+- Jump from (22.5, 27.5) to (27.5, 32.5): |1.89 - (-0.42)| = 2.31
+- Jump from (27.5, 32.5) to (32.5, 36.5): |-0.42 - (-1.76)| = 1.34
+- Jump from (32.5, 36.5) to (36.5, 39.0): |-1.76 - 0.48| = 2.24
+- Jump from (36.5, 39.0) to (39.0, 61.0): |0.48 - (-0.83)| = 1.31
+- Jump from (39.0, 61.0) to (61.0, 67.5): |-0.83 - 0.08| = 0.91
+- Jump from (61.0, 67.5) to (67.5, 75.0): |0.08 - 0.8| = 0.72
+- Jump from (67.5, 75.0) to (75.0, 80.0): |0.8 - (-5.67)| = 6.47
+
+The largest jump in absolute terms is from (67.5, 75.0) to (75.0, 80.0) with a difference of 6.47.
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between the intervals (67.5, 75.0) and (75.0, 80.0), which is at 75.0.
+SOLUTION:  75.0
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values provided for each interval:
+- (9.71, 13.24): -1.121
+- (13.24, 14.075): -1.023
+- (14.075, 14.665): -0.921
+- (14.665, 15.010000000000002): -0.82
+- (15.010000000000002, 15.485): -0.718
+- (15.485, 15.774999999999999): -0.623
+- (15.774999999999999, 16.445): -0.523
+- (16.445, 17.045): -0.422
+- (17.045, 17.665): -0.324
+- (17.665, 18.335): -0.225
+- (18.335, 18.725): -0.129
+- (18.725, 19.075): -0.032
+- (19.075, 19.549999999999997): 0.063
+- (19.549999999999997, 19.915): 0.161
+- (19.915, 20.235): 0.26
+- (20.235, 20.8): 0.445
+- (20.8, 21.285): 0.549
+- (21.285, 33.81): 0.68
+
+Now, let's calculate the differences between consecutive intervals:
+- Jump from (13.24, 14.075) to (14.075, 14.665): |-1.023 - (-0.921)| = 0.102
+- Jump from (14.075, 14.665) to (14.665, 15.010000000000002): |-0.921 - (-0.82)| = 0.101
+- Jump from (14.665, 15.010000000000002) to (15.010000000000002, 15.485): |-0.82 - (-0.718)| = 0.102
+- Jump from (15.010000000000002, 15.485) to (15.485, 15.774999999999999): |-0.718 - (-0.623)| = 0.095
+- Jump from (15.485, 15.774999999999999) to (15.774999999999999, 16.445): |-0.623 - (-0.523)| = 0.1
+- Jump from (15.774999999999999, 16.445) to (16.445, 17.045): |-0.523 - (-0.422)| = 0.101
+- Jump from (16.445, 17.045) to (17.045, 17.665): |-0.422 - (-0.324)| = 0.098
+- Jump from (17.045, 17.665) to (17.665, 18.335): |-0.324 - (-0.225)| = 0.099
+- Jump from (17.665, 18.335) to (18.335, 18.725): |-0.225 - (-0.129)| = 0.096
+- Jump from (18.335, 18.725) to (18.725, 19.075): |-0.129 - (-0.032)| = 0.097
+- Jump from (18.725, 19.075) to (19.075, 19.549999999999997): |-0.032 - 0.063| = 0.095
+- Jump from (19.075, 19.549999999999997) to (19.549999999999997, 19.915): |0.063 - 0.161| = 0.098
+- Jump from (19.549999999999997, 19.915) to (19.915, 20.235): |0.161 - 0.26| = 0.099
+- Jump from (19.915, 20.235) to (20.235, 20.8): |0.26 - 0.445| = 0.185
+- Jump from (20.235, 20.8) to (20.8, 21.285): |0.445 - 0.549| = 0.104
+- Jump from (20.8, 21.285) to (21.285, 33.81): |0.549 - 0.68| = 0.131
+
+The largest jump in absolute terms is from (19.915, 20.235) to (20.235, 20.8) with a difference of 0.185. Therefore, the x-axis position of the largest jump is at the boundary between these intervals, which is 20.235.
+SOLUTION:  20.235
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.02484
+- (0.5, 1.5): -0.02089
+- (1.5, 2.5): -0.01739
+- (2.5, 3.5): -0.01124
+- (3.5, 4.5): -0.00474
+- (4.5, 5.5): 0.00077
+- (5.5, 6.5): 0.00574
+- (6.5, 7.5): 0.01068
+- (7.5, 8.5): 0.01599
+- (8.5, 9.5): 0.02231
+- (9.5, 10.5): 0.02667
+- (10.5, 13.5): 0.03305
+- (13.5, 16.0): 0.02016
+
+Now, let's calculate the differences (jumps) between consecutive intervals:
+- Jump from (0.0, 0.5) to (0.5, 1.5): |-0.02089 - (-0.02484)| = 0.00395
+- Jump from (0.5, 1.5) to (1.5, 2.5): |-0.01739 - (-0.02089)| = 0.00350
+- Jump from (1.5, 2.5) to (2.5, 3.5): |-0.01124 - (-0.01739)| = 0.00615
+- Jump from (2.5, 3.5) to (3.5, 4.5): |-0.00474 - (-0.01124)| = 0.00650
+- Jump from (3.5, 4.5) to (4.5, 5.5): |0.00077 - (-0.00474)| = 0.00551
+- Jump from (4.5, 5.5) to (5.5, 6.5): |0.00574 - 0.00077| = 0.00497
+- Jump from (5.5, 6.5) to (6.5, 7.5): |0.01068 - 0.00574| = 0.00494
+- Jump from (6.5, 7.5) to (7.5, 8.5): |0.01599 - 0.01068| = 0.00531
+- Jump from (7.5, 8.5) to (8.5, 9.5): |0.02231 - 0.01599| = 0.00632
+- Jump from (8.5, 9.5) to (9.5, 10.5): |0.02667 - 0.02231| = 0.00436
+- Jump from (9.5, 10.5) to (10.5, 13.5): |0.03305 - 0.02667| = 0.00638
+- Jump from (10.5, 13.5) to (13.5, 16.0): |0.02016 - 0.03305| = 0.01289
+
+The largest jump in absolute terms is from (10.5, 13.5) to (13.5, 16.0) with a magnitude of 0.01289. Therefore, the x-axis position of the largest jump is at the boundary between these two intervals, which is at 13.5.
+SOLUTION:  13.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 9.1): -0.7
+- (9.1, 22.55): -0.961
+- (22.55, 23.65): -0.856
+- (23.65, 25.55): -0.762
+- (25.55, 26.35): -0.661
+- (26.35, 27.65): -0.24
+- (27.65, 28.45): -0.144
+- (28.45, 29.65): -0.051
+- (29.65, 30.45): 0.049
+- (30.45, 32.150000000000006): 0.153
+- (32.150000000000006, 37.650000000000006): 0.246
+- (37.650000000000006, 41.75): 0.34
+- (41.75, 42.849999999999994): 0.434
+- (42.849999999999994, 45.650000000000006): 0.529
+- (45.650000000000006, 48.349999999999994): 0.626
+- (48.349999999999994, 67.1): 0.784
+
+Now, let's calculate the differences between consecutive intervals:
+- Jump from (0.0, 9.1) to (9.1, 22.55): |-0.961 - (-0.7)| = 0.261
+- Jump from (9.1, 22.55) to (22.55, 23.65): |-0.856 - (-0.961)| = 0.105
+- Jump from (22.55, 23.65) to (23.65, 25.55): |-0.762 - (-0.856)| = 0.094
+- Jump from (23.65, 25.55) to (25.55, 26.35): |-0.661 - (-0.762)| = 0.101
+- Jump from (25.55, 26.35) to (26.35, 27.65): |-0.24 - (-0.661)| = 0.421
+- Jump from (26.35, 27.65) to (27.65, 28.45): |-0.144 - (-0.24)| = 0.096
+- Jump from (27.65, 28.45) to (28.45, 29.65): |-0.051 - (-0.144)| = 0.093
+- Jump from (28.45, 29.65) to (29.65, 30.45): |0.049 - (-0.051)| = 0.1
+- Jump from (29.65, 30.45) to (30.45, 32.150000000000006): |0.153 - 0.049| = 0.104
+- Jump from (30.45, 32.150000000000006) to (32.150000000000006, 37.650000000000006): |0.246 - 0.153| = 0.093
+- Jump from (32.150000000000006, 37.650000000000006) to (37.650000000000006, 41.75): |0.34 - 0.246| = 0.094
+- Jump from (37.650000000000006, 41.75) to (41.75, 42.849999999999994): |0.434 - 0.34| = 0.094
+- Jump from (41.75, 42.849999999999994) to (42.849999999999994, 45.650000000000006): |0.529 - 0.434| = 0.095
+- Jump from (42.849999999999994, 45.650000000000006) to (45.650000000000006, 48.349999999999994): |0.626 - 0.529| = 0.097
+- Jump from (45.650000000000006, 48.349999999999994) to (48.349999999999994, 67.1): |0.784 - 0.626| = 0.158
+
+The largest jump in absolute terms is from the interval (25.55, 26.35) to (26.35, 27.65) with a difference of 0.421.
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between the intervals (25.55, 26.35) and (26.35, 27.65), which is 26.35.
+SOLUTION:  26.35
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.02565
+- (0.5, 1.5): -0.02133
+- (1.5, 2.5): -0.01683
+- (2.5, 3.5): -0.00993
+- (3.5, 4.5): -0.00473
+- (4.5, 5.5): -1e-05
+- (5.5, 6.5): 0.00511
+- (6.5, 7.5): 0.01148
+- (7.5, 8.5): 0.01621
+- (8.5, 9.5): 0.02476
+- (9.5, 11.5): 0.02962
+- (11.5, 12.5): 0.03469
+- (12.5, 13.5): 0.04866
+- (13.5, 16.0): 0.05902
+
+Now, let's calculate the differences between consecutive intervals:
+- Jump from (0.0, 0.5) to (0.5, 1.5): |-0.02133 - (-0.02565)| = 0.00432
+- Jump from (0.5, 1.5) to (1.5, 2.5): |-0.01683 - (-0.02133)| = 0.00450
+- Jump from (1.5, 2.5) to (2.5, 3.5): |-0.00993 - (-0.01683)| = 0.00690
+- Jump from (2.5, 3.5) to (3.5, 4.5): |-0.00473 - (-0.00993)| = 0.00520
+- Jump from (3.5, 4.5) to (4.5, 5.5): |-1e-05 - (-0.00473)| = 0.00472
+- Jump from (4.5, 5.5) to (5.5, 6.5): |0.00511 - (-1e-05)| = 0.00512
+- Jump from (5.5, 6.5) to (6.5, 7.5): |0.01148 - 0.00511| = 0.00637
+- Jump from (6.5, 7.5) to (7.5, 8.5): |0.01621 - 0.01148| = 0.00473
+- Jump from (7.5, 8.5) to (8.5, 9.5): |0.02476 - 0.01621| = 0.00855
+- Jump from (8.5, 9.5) to (9.5, 11.5): |0.02962 - 0.02476| = 0.00486
+- Jump from (9.5, 11.5) to (11.5, 12.5): |0.03469 - 0.02962| = 0.00507
+- Jump from (11.5, 12.5) to (12.5, 13.5): |0.04866 - 0.03469| = 0.01397
+- Jump from (12.5, 13.5) to (13.5, 16.0): |0.05902 - 0.04866| = 0.01036
+
+The largest jump in absolute terms is from (11.5, 12.5) to (12.5, 13.5) with a difference of 0.01397. Therefore, the x-axis position of the largest jump is at the boundary between these intervals, which is 12.5.
+SOLUTION:  12.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (1.0, 1.5): -0.765
+- (1.5, 2.5): -0.375
+- (2.5, 4.5): -1.909
+- (4.5, 6.5): -1.117
+- (6.5, 7.5): -0.618
+- (7.5, 14.5): -0.822
+- (14.5, 19.5): -1.132
+- (19.5, 29.5): -0.765
+- (29.5, 33.5): -0.6
+- (33.5, 34.5): -0.921
+- (34.5, 39.5): -0.155
+- (39.5, 41.5): 0.03
+- (41.5, 50.5): 0.392
+- (50.5, 51.5): 0.131
+- (51.5, 55.5): 0.457
+- (55.5, 59.5): 0.676
+- (59.5, 63.5): 0.416
+- (63.5, 64.5): 0.952
+- (64.5, 65.5): 0.516
+- (65.5, 71.0): 0.071
+- (71.0, 75.5): 0.43
+- (75.5, 77.5): 0.235
+- (77.5, 79.0): 0.742
+- (79.0, 83.0): 0.977
+- (83.0, 85.5): 1.287
+- (85.5, 90.5): 0.192
+- (90.5, 97.5): -0.071
+- (97.5, 98.5): 0.119
+- (98.5, 99.0): -0.139
+
+Now, let's calculate the absolute differences between consecutive intervals:
+- |(-0.375) - (-0.765)| = 0.39
+- |(-1.909) - (-0.375)| = 1.534
+- |(-1.117) - (-1.909)| = 0.792
+- |(-0.618) - (-1.117)| = 0.499
+- |(-0.822) - (-0.618)| = 0.204
+- |(-1.132) - (-0.822)| = 0.31
+- |(-0.765) - (-1.132)| = 0.367
+- |(-0.6) - (-0.765)| = 0.165
+- |(-0.921) - (-0.6)| = 0.321
+- |(-0.155) - (-0.921)| = 0.766
+- |(0.03) - (-0.155)| = 0.185
+- |(0.392) - (0.03)| = 0.362
+- |(0.131) - (0.392)| = 0.261
+- |(0.457) - (0.131)| = 0.326
+- |(0.676) - (0.457)| = 0.219
+- |(0.416) - (0.676)| = 0.26
+- |(0.952) - (0.416)| = 0.536
+- |(0.516) - (0.952)| = 0.436
+- |(0.071) - (0.516)| = 0.445
+- |(0.43) - (0.071)| = 0.359
+- |(0.235) - (0.43)| = 0.195
+- |(0.742) - (0.235)| = 0.507
+- |(0.977) - (0.742)| = 0.235
+- |(1.287) - (0.977)| = 0.31
+- |(0.192) - (1.287)| = 1.095
+- |(-0.071) - (0.192)| = 0.263
+- |(0.119) - (-0.071)| = 0.19
+- |(-0.139) - (0.119)| = 0.258
+
+The largest jump is between the intervals (2.5, 4.5) and (4.5, 6.5) with a difference of 1.534. Therefore, the x-axis position of the largest jump is at the boundary between these intervals, which is 4.5.
+SOLUTION:  2.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.02956
+- (0.5, 2.5): -0.02081
+- (2.5, 3.5): -0.00998
+- (3.5, 4.5): -0.00524
+- (4.5, 5.5): 0.00043
+- (5.5, 6.5): 0.00515
+- (6.5, 8.5): 0.01107
+- (8.5, 10.5): 0.02102
+- (10.5, 11.5): 0.02728
+- (11.5, 13.5): 0.0456
+- (13.5, 14.5): 0.05244
+- (14.5, 17.0): 0.06161
+
+Now, calculate the differences (jumps) between consecutive intervals:
+- Jump from (0.0, 0.5) to (0.5, 2.5): |-0.02081 - (-0.02956)| = 0.00875
+- Jump from (0.5, 2.5) to (2.5, 3.5): |-0.00998 - (-0.02081)| = 0.01083
+- Jump from (2.5, 3.5) to (3.5, 4.5): |-0.00524 - (-0.00998)| = 0.00474
+- Jump from (3.5, 4.5) to (4.5, 5.5): |0.00043 - (-0.00524)| = 0.00567
+- Jump from (4.5, 5.5) to (5.5, 6.5): |0.00515 - 0.00043| = 0.00472
+- Jump from (5.5, 6.5) to (6.5, 8.5): |0.01107 - 0.00515| = 0.00592
+- Jump from (6.5, 8.5) to (8.5, 10.5): |0.02102 - 0.01107| = 0.00995
+- Jump from (8.5, 10.5) to (10.5, 11.5): |0.02728 - 0.02102| = 0.00626
+- Jump from (10.5, 11.5) to (11.5, 13.5): |0.0456 - 0.02728| = 0.01832
+- Jump from (11.5, 13.5) to (13.5, 14.5): |0.05244 - 0.0456| = 0.00684
+- Jump from (13.5, 14.5) to (14.5, 17.0): |0.06161 - 0.05244| = 0.00917
+
+The largest jump in absolute terms is from (10.5, 11.5) to (11.5, 13.5) with a difference of 0.01832. Therefore, the x-axis position of the largest jump in the graph is at the boundary between these intervals, which is at 11.5.
+SOLUTION:  11.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 1.5): -0.02643
+- (1.5, 2.5): -0.01529
+- (2.5, 3.5): -0.01037
+- (3.5, 4.5): -0.00562
+- (4.5, 5.5): 0.00068
+- (5.5, 6.5): 0.00591
+- (6.5, 7.5): 0.01127
+- (7.5, 8.5): 0.01553
+- (8.5, 10.5): 0.02363
+- (10.5, 11.5): 0.03038
+- (11.5, 12.5): 0.03607
+- (12.5, 13.5): 0.04087
+- (13.5, 15.0): 0.04477
+
+Now, let's calculate the differences between consecutive intervals:
+- Difference between (1.5, 2.5) and (0.0, 1.5): -0.01529 - (-0.02643) = 0.01114
+- Difference between (2.5, 3.5) and (1.5, 2.5): -0.01037 - (-0.01529) = 0.00492
+- Difference between (3.5, 4.5) and (2.5, 3.5): -0.00562 - (-0.01037) = 0.00475
+- Difference between (4.5, 5.5) and (3.5, 4.5): 0.00068 - (-0.00562) = 0.00630
+- Difference between (5.5, 6.5) and (4.5, 5.5): 0.00591 - 0.00068 = 0.00523
+- Difference between (6.5, 7.5) and (5.5, 6.5): 0.01127 - 0.00591 = 0.00536
+- Difference between (7.5, 8.5) and (6.5, 7.5): 0.01553 - 0.01127 = 0.00426
+- Difference between (8.5, 10.5) and (7.5, 8.5): 0.02363 - 0.01553 = 0.00810
+- Difference between (10.5, 11.5) and (8.5, 10.5): 0.03038 - 0.02363 = 0.00675
+- Difference between (11.5, 12.5) and (10.5, 11.5): 0.03607 - 0.03038 = 0.00569
+- Difference between (12.5, 13.5) and (11.5, 12.5): 0.04087 - 0.03607 = 0.00480
+- Difference between (13.5, 15.0) and (12.5, 13.5): 0.04477 - 0.04087 = 0.00390
+
+The largest jump in absolute terms is between the intervals (1.5, 2.5) and (0.0, 1.5) with a difference of 0.01114.
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between the intervals (0.0, 1.5) and (1.5, 2.5), which is at 1.5.
+SOLUTION:  1.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms. Here's how we can proceed:
+
+1. **Extract the intervals and corresponding mean values**: We have the intervals as keys and the mean values as values in the "Means" dictionary.
+
+2. **Calculate the differences between consecutive mean values**: We need to compute the difference in mean values between the end of one interval and the beginning of the next.
+
+3. **Identify the largest absolute difference**: The largest jump will be where the absolute value of these differences is the maximum.
+
+Let's perform these calculations using the provided data:
+
+- **Intervals and Mean Values**:
+  - (0.0008948, 0.001092): 0.2818
+  - (0.001092, 0.0014135): 0.3286
+  - (0.0014135, 0.0015165): 0.2713
+  - (0.0015165, 0.0017545): 0.2283
+  - (0.0017545, 0.0017905): 0.144
+  - (0.0017905, 0.0019039999999999999): 0.0956
+  - (0.0019039999999999999, 0.0021525): 0.0526
+  - (0.0021525, 0.002572): 0.0073
+  - (0.002572, 0.002761): 0.1543
+  - (0.002761, 0.003308): 0.1971
+  - (0.003308, 0.0033604999999999998): 0.1525
+  - (0.0033604999999999998, 0.0035329999999999997): 0.1049
+  - (0.0035329999999999997, 0.003736): 0.0586
+  - (0.003736, 0.003907): 0.0157
+  - (0.003907, 0.004092500000000001): -0.029
+  - (0.004092500000000001, 0.0045775): -0.0717
+  - (0.0045775, 0.0045935): -0.1177
+  - (0.0045935, 0.004644499999999999): -0.1739
+  - (0.004644499999999999, 0.004809): -0.2208
+  - (0.004809, 0.005856500000000001): -0.2666
+  - (0.005856500000000001, 0.007497500000000001): -0.31
+  - (0.007497500000000001, 0.009717): -0.356
+  - (0.009717, 0.0127): -0.4
+  - (0.0127, 0.02984): -0.4439
+
+- **Calculate Differences**:
+  - Difference between 0.2818 and 0.3286 = 0.0468
+  - Difference between 0.3286 and 0.2713 = -0.0573
+  - ...
+  - Difference between -0.4 and -0.4439 = -0.0439
+
+- **Identify Largest Absolute Difference**:
+  - The largest absolute difference is -0.0573 (from 0.3286 to 0.2713).
+
+Thus, the largest jump occurs at the position between the intervals (0.001092, 0.0014135) and (0.0014135, 0.0015165). The x-axis position of this largest jump is at the end of the interval (0.001092, 0.0014135), which is 0.0014135.
+SOLUTION:  0.002572
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.1565, 0.165): -0.295
+- (0.165, 0.19055): -0.472
+- (0.19055, 0.24485): -0.549
+- (0.24485, 0.25225): -0.469
+- (0.25225, 0.2583): -0.392
+- (0.2583, 0.26635): -0.31
+- (0.26635, 0.26959999999999995): -0.23
+- (0.26959999999999995, 0.27495): -0.112
+- (0.27495, 0.28035): -0.034
+- (0.28035, 0.28815): 0.046
+- (0.28815, 0.2986): 0.125
+- (0.2986, 0.31745): 0.202
+- (0.31745, 0.32125000000000004): 0.281
+- (0.32125000000000004, 0.33065): 0.363
+- (0.33065, 0.35335): 0.444
+- (0.35335, 0.36085): 0.526
+- (0.36085, 0.3702): 0.624
+- (0.3702, 0.4223): 0.705
+- (0.4223, 0.4697): 0.785
+- (0.4697, 0.6638): 0.867
+
+Now, let's calculate the absolute differences between consecutive intervals:
+- |(-0.472) - (-0.295)| = 0.177
+- |(-0.549) - (-0.472)| = 0.077
+- |(-0.469) - (-0.549)| = 0.080
+- |(-0.392) - (-0.469)| = 0.077
+- |(-0.31) - (-0.392)| = 0.082
+- |(-0.23) - (-0.31)| = 0.080
+- |(-0.112) - (-0.23)| = 0.118
+- |(-0.034) - (-0.112)| = 0.078
+- |0.046 - (-0.034)| = 0.080
+- |0.125 - 0.046| = 0.079
+- |0.202 - 0.125| = 0.077
+- |0.281 - 0.202| = 0.079
+- |0.363 - 0.281| = 0.082
+- |0.444 - 0.363| = 0.081
+- |0.526 - 0.444| = 0.082
+- |0.624 - 0.526| = 0.098
+- |0.705 - 0.624| = 0.081
+- |0.785 - 0.705| = 0.080
+- |0.867 - 0.785| = 0.082
+
+The largest jump in absolute terms is 0.177, which occurs between the intervals (0.1565, 0.165) and (0.165, 0.19055). Therefore, the x-axis position of the largest jump in the graph is at the boundary between these two intervals, which is 0.165.
+SOLUTION:  0.165
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms. Here's how we can proceed:
+
+1. **Extract the intervals and mean values**: We have the intervals as keys and the mean values as values in the "Means" dictionary.
+
+2. **Sort the intervals**: Since the intervals are in string format and represent ranges, we need to sort them based on their starting point to ensure we are calculating consecutive differences.
+
+3. **Calculate differences between consecutive intervals**: For each pair of consecutive intervals, calculate the difference in their mean values.
+
+4. **Identify the largest jump**: Find the maximum absolute difference from the differences calculated in the previous step.
+
+Let's perform these steps using the provided data:
+
+### Step 1: Extract and Sort Intervals
+Extract the intervals and corresponding mean values from the JSON object. The intervals are already provided in a sorted manner from the JSON object, so we can use them directly.
+
+### Step 2: Calculate Differences
+Calculate the differences in mean values between consecutive intervals. We will iterate through the sorted keys and compute the difference between each consecutive pair.
+
+### Step 3: Identify the Largest Jump
+Find the interval where the absolute difference is the largest.
+
+Here are the calculations for the differences between consecutive intervals:
+
+- Difference between `(-124.10499999999999, -124.08500000000001)` and `(-124.08500000000001, -124.07499999999999)` = -38925.6 - (-23742.3) = -15183.3
+- Continue this for all consecutive intervals...
+
+After calculating these differences, we find the maximum absolute difference:
+
+- The largest jump in absolute terms is found between the intervals `(-122.42500000000001, -122.405)` and `(-122.405, -122.39500000000001)` with mean values 112351.5 and 89733.4 respectively. The difference is 112351.5 - 89733.4 = 22618.1.
+
+### Conclusion
+The largest jump occurs at the boundary between the intervals `(-122.42500000000001, -122.405)` and `(-122.405, -122.39500000000001)`. This is the position of the largest jump on the x-axis.
+SOLUTION:  -122.39500000000001
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.5, 0.6499999999999999): -0.26
+- (0.6499999999999999, 0.725): -1.08
+- (0.725, 0.875): -3.77
+- (0.875, 0.95): -0.9
+- (0.95, 1.1400000000000001): -0.15
+- (1.1400000000000001, 1.35): -0.88
+- (1.35, 1.45): 0.2
+- (1.45, 1.55): 1.18
+- (1.55, 1.815): 2.18
+- (1.815, 2.05): 4.74
+- (2.05, 2.45): 1.14
+- (2.45, 2.6): 3.63
+- (2.6, 2.95): -0.36
+- (2.95, 3.1): 2.57
+- (3.1, 3.45): 0.36
+- (3.45, 3.6): 3.06
+- (3.6, 3.75): 6.76
+- (3.75, 3.9): 2.31
+- (3.9, 4.7): 2.92
+- (4.7, 5.949999999999999): 0.76
+- (5.949999999999999, 6.199999999999999): -0.43
+- (6.199999999999999, 6.55): 0.23
+- (6.55, 9.4): 6.97
+
+Now, let's calculate the absolute differences between consecutive intervals:
+- |-1.08 - (-0.26)| = 0.82
+- |-3.77 - (-1.08)| = 2.69
+- |-0.9 - (-3.77)| = 2.87
+- |-0.15 - (-0.9)| = 0.75
+- |-0.88 - (-0.15)| = 0.73
+- |0.2 - (-0.88)| = 1.08
+- |1.18 - 0.2| = 0.98
+- |2.18 - 1.18| = 1.00
+- |4.74 - 2.18| = 2.56
+- |1.14 - 4.74| = 3.60
+- |3.63 - 1.14| = 2.49
+- |-0.36 - 3.63| = 3.99
+- |2.57 - (-0.36)| = 2.93
+- |0.36 - 2.57| = 2.21
+- |3.06 - 0.36| = 2.70
+- |6.76 - 3.06| = 3.70
+- |2.31 - 6.76| = 4.45
+- |2.92 - 2.31| = 0.61
+- |0.76 - 2.92| = 2.16
+- |-0.43 - 0.76| = 1.19
+- |0.23 - (-0.43)| = 0.66
+- |6.97 - 0.23| = 6.74
+
+The largest jump in absolute terms is between the intervals (3.75, 3.9) and (3.9, 4.7) with a difference of 4.45. Therefore, the x-axis position of the largest jump is at the boundary between these intervals, which is 3.9.
+SOLUTION:  6.55
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values provided for each interval:
+- (0.0, 0.5): -0.02593
+- (0.5, 1.5): -0.02172
+- (1.5, 2.5): -0.01544
+- (2.5, 3.5): -0.0098
+- (3.5, 4.5): -0.00541
+- (4.5, 5.5): 0.00066
+- (5.5, 6.5): 0.00575
+- (6.5, 7.5): 0.01201
+- (7.5, 8.5): 0.01649
+- (8.5, 9.5): 0.0215
+- (9.5, 10.5): 0.0267
+- (10.5, 11.5): 0.03057
+- (11.5, 13.5): 0.0366
+- (13.5, 14.0): 0.03003
+
+Now, let's calculate the differences (jumps) between consecutive intervals:
+- Jump from (0.5, 1.5) to (1.5, 2.5): |-0.02172 - (-0.01544)| = 0.00628
+- Jump from (1.5, 2.5) to (2.5, 3.5): |-0.01544 - (-0.0098)| = 0.00564
+- Jump from (2.5, 3.5) to (3.5, 4.5): |-0.0098 - (-0.00541)| = 0.00439
+- Jump from (3.5, 4.5) to (4.5, 5.5): |-0.00541 - 0.00066| = 0.00607
+- Jump from (4.5, 5.5) to (5.5, 6.5): |0.00066 - 0.00575| = 0.00509
+- Jump from (5.5, 6.5) to (6.5, 7.5): |0.00575 - 0.01201| = 0.00626
+- Jump from (6.5, 7.5) to (7.5, 8.5): |0.01201 - 0.01649| = 0.00448
+- Jump from (7.5, 8.5) to (8.5, 9.5): |0.01649 - 0.0215| = 0.00501
+- Jump from (8.5, 9.5) to (9.5, 10.5): |0.0215 - 0.0267| = 0.0052
+- Jump from (9.5, 10.5) to (10.5, 11.5): |0.0267 - 0.03057| = 0.00387
+- Jump from (10.5, 11.5) to (11.5, 13.5): |0.03057 - 0.0366| = 0.00603
+- Jump from (11.5, 13.5) to (13.5, 14.0): |0.0366 - 0.03003| = 0.00657
+
+The largest jump in absolute terms is from (11.5, 13.5) to (13.5, 14.0) with a magnitude of 0.00657.
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between the intervals (11.5, 13.5) and (13.5, 14.0), which is at 13.5.
+SOLUTION:  13.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+1. (43.79, 60.035): -0.884
+2. (60.035, 63.379999999999995): -0.783
+3. (63.379999999999995, 66.67): -0.681
+4. (66.67, 68.965): -0.581
+5. (68.965, 71.275): -0.476
+6. (71.275, 78.28): -0.369
+7. (78.28, 84.015): -0.267
+8. (84.015, 88.70500000000001): -0.166
+9. (88.70500000000001, 94.68): -0.064
+10. (94.68, 100.75): 0.035
+11. (100.75, 106.75): 0.14
+12. (106.75, 108.6): 0.249
+13. (108.6, 112.6): 0.407
+14. (112.6, 117.45): 0.518
+15. (117.45, 121.7): 0.626
+16. (121.7, 128.15): 0.73
+17. (128.15, 133.25): 0.835
+18. (133.25, 145.85000000000002): 0.936
+19. (145.85000000000002, 188.5): 1.038
+
+Now, let's calculate the differences between consecutive intervals:
+1. -0.783 - (-0.884) = 0.101
+2. -0.681 - (-0.783) = 0.102
+3. -0.581 - (-0.681) = 0.100
+4. -0.476 - (-0.581) = 0.105
+5. -0.369 - (-0.476) = 0.107
+6. -0.267 - (-0.369) = 0.102
+7. -0.166 - (-0.267) = 0.101
+8. -0.064 - (-0.166) = 0.102
+9. 0.035 - (-0.064) = 0.099
+10. 0.14 - 0.035 = 0.105
+11. 0.249 - 0.14 = 0.109
+12. 0.407 - 0.249 = 0.158
+13. 0.518 - 0.407 = 0.111
+14. 0.626 - 0.518 = 0.108
+15. 0.73 - 0.626 = 0.104
+16. 0.835 - 0.73 = 0.105
+17. 0.936 - 0.835 = 0.101
+18. 1.038 - 0.936 = 0.102
+
+The largest jump in mean values is 0.158, which occurs between the intervals (106.75, 108.6) and (108.6, 112.6). Therefore, the x-axis position of the largest jump in the graph is at the boundary between these intervals, which is 108.6.
+SOLUTION:  108.6
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.0273
+- (0.5, 1.5): -0.02345
+- (1.5, 2.5): -0.01571
+- (2.5, 3.5): -0.01174
+- (3.5, 4.5): -0.00519
+- (4.5, 5.5): 0.00111
+- (5.5, 6.5): 0.00506
+- (6.5, 7.5): 0.01056
+- (7.5, 8.5): 0.01706
+- (8.5, 9.5): 0.02398
+- (9.5, 11.5): 0.02821
+- (11.5, 12.5): 0.03673
+- (12.5, 13.5): 0.01311
+- (13.5, 16.0): 0.03206
+
+Now, let's calculate the differences between consecutive intervals:
+- Jump from (0.5, 1.5) to (1.5, 2.5): |-0.02345 - (-0.01571)| = 0.00774
+- Jump from (1.5, 2.5) to (2.5, 3.5): |-0.01571 - (-0.01174)| = 0.00397
+- Jump from (2.5, 3.5) to (3.5, 4.5): |-0.01174 - (-0.00519)| = 0.00655
+- Jump from (3.5, 4.5) to (4.5, 5.5): |-0.00519 - 0.00111| = 0.0063
+- Jump from (4.5, 5.5) to (5.5, 6.5): |0.00111 - 0.00506| = 0.00395
+- Jump from (5.5, 6.5) to (6.5, 7.5): |0.00506 - 0.01056| = 0.0055
+- Jump from (6.5, 7.5) to (7.5, 8.5): |0.01056 - 0.01706| = 0.0065
+- Jump from (7.5, 8.5) to (8.5, 9.5): |0.01706 - 0.02398| = 0.00692
+- Jump from (8.5, 9.5) to (9.5, 11.5): |0.02398 - 0.02821| = 0.00423
+- Jump from (9.5, 11.5) to (11.5, 12.5): |0.02821 - 0.03673| = 0.00852
+- Jump from (11.5, 12.5) to (12.5, 13.5): |0.03673 - 0.01311| = 0.02362
+- Jump from (12.5, 13.5) to (13.5, 16.0): |0.01311 - 0.03206| = 0.01895
+
+The largest jump in absolute terms is from the interval (11.5, 12.5) to (12.5, 13.5) with a difference of 0.02362. Therefore, the x-axis position of the largest jump is at the boundary between these intervals, which is 12.5.
+SOLUTION:  12.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+1. (0.4999, 0.5427500000000001): -16067.6
+2. (0.5427500000000001, 1.4808): -55539.5
+3. (1.4808, 2.1658999999999997): -71376.5
+4. (2.1658999999999997, 2.6096): -56399.7
+5. (2.6096, 3.2433): -40762.6
+6. (3.2433, 3.66575): -25586.1
+7. (3.66575, 4.3197): -8084.4
+8. (4.3197, 4.691000000000001): 7391.3
+9. (4.691000000000001, 5.1358): 22375.3
+10. (5.1358, 5.59195): 40032.8
+11. (5.59195, 5.8294): 56900.2
+12. (5.8294, 6.29665): 75092.3
+13. (6.29665, 6.3704): 96400.5
+14. (6.3704, 6.874750000000001): 111491.7
+15. (6.874750000000001, 7.6996): 135841.6
+16. (7.6996, 7.8141): 151586.9
+17. (7.8141, 8.3976): 170219.6
+18. (8.3976, 9.046949999999999): 192482.3
+19. (9.046949999999999, 15.00005): 214375.9
+20. (15.00005, 15.0001): 193753.6
+
+Now, let's calculate the differences between consecutive intervals:
+- Difference between 1 and 2: |-55539.5 - (-16067.6)| = 39471.9
+- Difference between 2 and 3: |-71376.5 - (-55539.5)| = 15837.0
+- Difference between 3 and 4: |-56399.7 - (-71376.5)| = 14976.8
+- Difference between 4 and 5: |-40762.6 - (-56399.7)| = 15637.1
+- Difference between 5 and 6: |-25586.1 - (-40762.6)| = 15176.5
+- Difference between 6 and 7: |-8084.4 - (-25586.1)| = 17501.7
+- Difference between 7 and 8: |7391.3 - (-8084.4)| = 15475.7
+- Difference between 8 and 9: |22375.3 - 7391.3| = 14984.0
+- Difference between 9 and 10: |40032.8 - 22375.3| = 17657.5
+- Difference between 10 and 11: |56900.2 - 40032.8| = 16867.4
+- Difference between 11 and 12: |75092.3 - 56900.2| = 18192.1
+- Difference between 12 and 13: |96400.5 - 75092.3| = 21308.2
+- Difference between 13 and 14: |111491.7 - 96400.5| = 15091.2
+- Difference between 14 and 15: |135841.6 - 111491.7| = 24349.9
+- Difference between 15 and 16: |151586.9 - 135841.6| = 15745.3
+- Difference between 16 and 17: |170219.6 - 151586.9| = 18632.7
+- Difference between 17 and 18: |192482.3 - 170219.6| = 22262.7
+- Difference between 18 and 19: |214375.9 - 192482.3| = 21893.6
+- Difference between 19 and 20: |193753.6 - 214375.9| = 20622.3
+
+The largest jump in absolute terms is between intervals 14 and 15, with a difference of 24349.9. Therefore, the x-axis position of the largest jump is at the boundary between the intervals (6.874750000000001, 7.6996) and (7.6996, 7.8141).
+SOLUTION:  0.5427500000000001
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to examine the changes in the mean values between the specified intervals. The graph provided has two intervals for the feature "diabetes" with corresponding mean values.
+
+Here are the intervals and their mean values:
+1. Interval (0.0, 0.5) has a mean value of 0.3225.
+2. Interval (0.5, 1.0) has a mean value of -0.415.
+
+To find the jump between these intervals, we calculate the difference in mean values between the end of the first interval and the start of the second interval. Since we are interested in the absolute magnitude of the jump, we take the absolute value of the difference:
+
+\[ \text{Jump} = | \text{Mean value at end of first interval} - \text{Mean value at start of second interval} | \]
+\[ \text{Jump} = | 0.3225 - (-0.415) | \]
+\[ \text{Jump} = | 0.3225 + 0.415 | \]
+\[ \text{Jump} = 0.7375 \]
+
+The x-axis position of this jump is at the point where the first interval ends and the second interval begins, which is at 0.5. This is the boundary between the two intervals, and it is where the function value changes most significantly.
+
+Therefore, the position of the largest jump in the graph is at x = 0.5.
+SOLUTION:  0.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values provided for each interval:
+- (0.0, 0.005855): -0.897
+- (0.005855, 0.011885): -0.811
+- (0.011885, 0.016545): -0.719
+- (0.016545, 0.02046): -0.631
+- (0.02046, 0.02373): -0.543
+- (0.02373, 0.02711): -0.458
+- (0.02711, 0.038885): -0.374
+- (0.038885, 0.044705): -0.29
+- (0.044705, 0.059585): -0.205
+- (0.059585, 0.06851): -0.121
+- (0.06851, 0.072265): -0.032
+- (0.072265, 0.092725): 0.14
+- (0.092725, 0.1015): 0.224
+- (0.1015, 0.11415): 0.309
+- (0.11415, 0.13): 0.397
+- (0.13, 0.14534999999999998): 0.486
+- (0.14534999999999998, 0.1525): 0.581
+- (0.1525, 0.1686): 0.665
+- (0.1686, 0.24280000000000002): 0.749
+- (0.24280000000000002, 0.29359999999999997): 0.657
+- (0.29359999999999997, 0.32699999999999996): 0.566
+- (0.32699999999999996, 0.4268): 0.48
+
+Now, let's calculate the differences between consecutive intervals:
+- Jump from (0.06851, 0.072265) to (0.072265, 0.092725): |0.14 - (-0.032)| = 0.172
+- Other jumps can be calculated similarly, but this one stands out as potentially the largest.
+
+Checking the other jumps:
+- From (0.1686, 0.24280000000000002) to (0.24280000000000002, 0.29359999999999997): |0.657 - 0.749| = 0.092
+- From (0.24280000000000002, 0.29359999999999997) to (0.29359999999999997, 0.32699999999999996): |0.566 - 0.657| = 0.091
+- From (0.32699999999999996, 0.4268) to (0.29359999999999997, 0.32699999999999996): |0.48 - 0.566| = 0.086
+
+The largest jump in absolute terms is indeed from (0.06851, 0.072265) to (0.072265, 0.092725) with a magnitude of 0.172. Therefore, the x-axis position of the largest jump is at the upper bound of the interval (0.06851, 0.072265), which is 0.072265.
+SOLUTION:  0.072265
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.02593
+- (0.5, 1.5): -0.02211
+- (1.5, 2.5): -0.01611
+- (2.5, 3.5): -0.01125
+- (3.5, 4.5): -0.0047
+- (4.5, 5.5): 0.00009
+- (5.5, 6.5): 0.00652
+- (6.5, 8.5): 0.01219
+- (8.5, 10.5): 0.02253
+- (10.5, 11.5): 0.03412
+- (11.5, 12.5): 0.04015
+- (12.5, 14.0): 0.04564
+
+Now, let's calculate the differences between consecutive intervals:
+- Jump from (0.5, 1.5) to (1.5, 2.5): |-0.02211 - (-0.01611)| = 0.006
+- Jump from (1.5, 2.5) to (2.5, 3.5): |-0.01611 - (-0.01125)| = 0.00486
+- Jump from (2.5, 3.5) to (3.5, 4.5): |-0.01125 - (-0.0047)| = 0.00655
+- Jump from (3.5, 4.5) to (4.5, 5.5): |-0.0047 - 0.00009| = 0.00479
+- Jump from (4.5, 5.5) to (5.5, 6.5): |0.00009 - 0.00652| = 0.00643
+- Jump from (5.5, 6.5) to (6.5, 8.5): |0.00652 - 0.01219| = 0.00567
+- Jump from (6.5, 8.5) to (8.5, 10.5): |0.01219 - 0.02253| = 0.01034
+- Jump from (8.5, 10.5) to (10.5, 11.5): |0.02253 - 0.03412| = 0.01159
+- Jump from (10.5, 11.5) to (11.5, 12.5): |0.03412 - 0.04015| = 0.00603
+- Jump from (11.5, 12.5) to (12.5, 14.0): |0.04015 - 0.04564| = 0.00549
+
+The largest jump in absolute terms is from (8.5, 10.5) to (10.5, 11.5) with a difference of 0.01159.
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between the intervals (8.5, 10.5) and (10.5, 11.5), which is at 10.5.
+SOLUTION:  10.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 22.0): -0.728
+- (22.0, 86.5): -1.069
+- (86.5, 94.5): -0.907
+- (94.5, 99.5): -0.729
+- (99.5, 105.5): -0.491
+- (105.5, 114.5): -0.326
+- (114.5, 123.5): -0.157
+- (123.5, 130.5): 0.045
+- (130.5, 139.5): 0.208
+- (139.5, 147.5): 0.37
+- (147.5, 154.5): 0.535
+- (154.5, 159.5): 0.724
+- (159.5, 165.5): 0.984
+- (165.5, 169.5): 1.342
+- (169.5, 178.5): 1.502
+- (178.5, 187.5): 1.691
+- (187.5, 198.5): 1.853
+- (198.5, 199.0): 2.022
+
+Now, let's calculate the differences between consecutive intervals:
+- Difference between (22.0, 86.5) and (0.0, 22.0): |-1.069 - (-0.728)| = 0.341
+- Difference between (86.5, 94.5) and (22.0, 86.5): |-0.907 - (-1.069)| = 0.162
+- Difference between (94.5, 99.5) and (86.5, 94.5): |-0.729 - (-0.907)| = 0.178
+- Difference between (99.5, 105.5) and (94.5, 99.5): |-0.491 - (-0.729)| = 0.238
+- Difference between (105.5, 114.5) and (99.5, 105.5): |-0.326 - (-0.491)| = 0.165
+- Difference between (114.5, 123.5) and (105.5, 114.5): |-0.157 - (-0.326)| = 0.169
+- Difference between (123.5, 130.5) and (114.5, 123.5): |0.045 - (-0.157)| = 0.202
+- Difference between (130.5, 139.5) and (123.5, 130.5): |0.208 - 0.045| = 0.163
+- Difference between (139.5, 147.5) and (130.5, 139.5): |0.37 - 0.208| = 0.162
+- Difference between (147.5, 154.5) and (139.5, 147.5): |0.535 - 0.37| = 0.165
+- Difference between (154.5, 159.5) and (147.5, 154.5): |0.724 - 0.535| = 0.189
+- Difference between (159.5, 165.5) and (154.5, 159.5): |0.984 - 0.724| = 0.26
+- Difference between (165.5, 169.5) and (159.5, 165.5): |1.342 - 0.984| = 0.358
+- Difference between (169.5, 178.5) and (165.5, 169.5): |1.502 - 1.342| = 0.16
+- Difference between (178.5, 187.5) and (169.5, 178.5): |1.691 - 1.502| = 0.189
+- Difference between (187.5, 198.5) and (178.5, 187.5): |1.853 - 1.691| = 0.162
+- Difference between (198.5, 199.0) and (187.5, 198.5): |2.022 - 1.853| = 0.169
+
+The largest jump in absolute terms is between the intervals (165.5, 169.5) and (159.5, 165.5), with a difference of 0.358. Therefore, the x-axis position of the largest jump is at the boundary between these intervals, which is 165.5.
+SOLUTION:  165.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values provided for each interval:
+- (0.0, 1.5): -0.02419
+- (1.5, 2.5): -0.01693
+- (2.5, 3.5): -0.01069
+- (3.5, 4.5): -0.00585
+- (4.5, 5.5): 0.00051
+- (5.5, 6.5): 0.00676
+- (6.5, 8.5): 0.01245
+- (8.5, 10.5): 0.02257
+- (10.5, 11.5): 0.03265
+- (11.5, 13.5): 0.03889
+- (13.5, 14.5): 0.04912
+- (14.5, 16.0): 0.0585
+
+Now, let's calculate the differences (jumps) between consecutive intervals:
+- Jump from (1.5, 2.5) to (2.5, 3.5): |-0.01693 - (-0.01069)| = 0.00624
+- Jump from (2.5, 3.5) to (3.5, 4.5): |-0.01069 - (-0.00585)| = 0.00484
+- Jump from (3.5, 4.5) to (4.5, 5.5): |-0.00585 - 0.00051| = 0.00636
+- Jump from (4.5, 5.5) to (5.5, 6.5): |0.00051 - 0.00676| = 0.00625
+- Jump from (5.5, 6.5) to (6.5, 8.5): |0.00676 - 0.01245| = 0.00569
+- Jump from (6.5, 8.5) to (8.5, 10.5): |0.01245 - 0.02257| = 0.01012
+- Jump from (8.5, 10.5) to (10.5, 11.5): |0.02257 - 0.03265| = 0.01008
+- Jump from (10.5, 11.5) to (11.5, 13.5): |0.03265 - 0.03889| = 0.00624
+- Jump from (11.5, 13.5) to (13.5, 14.5): |0.03889 - 0.04912| = 0.01023
+- Jump from (13.5, 14.5) to (14.5, 16.0): |0.04912 - 0.0585| = 0.00938
+
+The largest jump in absolute terms is from (11.5, 13.5) to (13.5, 14.5) with a difference of 0.01023.
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between the intervals (11.5, 13.5) and (13.5, 14.5), which is at 13.5.
+SOLUTION:  13.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values provided in the graph:
+
+1. (0.1167, 0.1384): -0.604
+2. (0.1384, 0.14229999999999998): -0.55
+3. (0.14229999999999998, 0.14565): -0.489
+4. (0.14565, 0.1488): -0.428
+5. (0.1488, 0.1507): -0.372
+6. (0.1507, 0.15245): -0.316
+7. (0.15245, 0.15375): -0.258
+8. (0.15375, 0.15410000000000001): -0.087
+9. (0.15410000000000001, 0.1545): -0.03
+10. (0.1545, 0.15765): 0.279
+11. (0.15765, 0.16625): 0.335
+12. (0.16625, 0.16635): 0.258
+13. (0.16635, 0.1684): 0.048
+14. (0.1684, 0.17915): -0.007
+15. (0.17915, 0.20355): -0.062
+16. (0.20355, 0.20855): -0.005
+17. (0.20855, 0.21105000000000002): 0.052
+18. (0.21105000000000002, 0.21315): 0.107
+19. (0.21315, 0.21705): 0.17
+20. (0.21705, 0.22110000000000002): 0.234
+21. (0.22110000000000002, 0.23020000000000002): 0.289
+22. (0.23020000000000002, 0.2544): 0.347
+23. (0.2544, 0.2626): 0.408
+24. (0.2626, 0.304): 0.466
+
+Now, let's calculate the differences between consecutive mean values:
+
+1. -0.55 - (-0.604) = 0.054
+2. -0.489 - (-0.55) = 0.061
+3. -0.428 - (-0.489) = 0.061
+4. -0.372 - (-0.428) = 0.056
+5. -0.316 - (-0.372) = 0.056
+6. -0.258 - (-0.316) = 0.058
+7. -0.087 - (-0.258) = 0.171
+8. -0.03 - (-0.087) = 0.057
+9. 0.279 - (-0.03) = 0.309
+10. 0.335 - 0.279 = 0.056
+11. 0.258 - 0.335 = -0.077
+12. 0.048 - 0.258 = -0.210
+13. -0.007 - 0.048 = -0.055
+14. -0.062 - (-0.007) = -0.055
+15. -0.005 - (-0.062) = 0.057
+16. 0.052 - (-0.005) = 0.057
+17. 0.107 - 0.052 = 0.055
+18. 0.17 - 0.107 = 0.063
+19. 0.234 - 0.17 = 0.064
+20. 0.289 - 0.234 = 0.055
+21. 0.347 - 0.289 = 0.058
+22. 0.408 - 0.347 = 0.061
+23. 0.466 - 0.408 = 0.058
+
+The largest jump in absolute terms is 0.309, which occurs between the intervals (0.15410000000000001, 0.1545) and (0.1545, 0.15765). The x-axis position of this largest jump is at the boundary of these intervals, which is 0.1545.
+SOLUTION:  0.1545
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values provided for each interval:
+- (50.41, 71.06): -1.379
+- (71.06, 76.52000000000001): -1.223
+- (76.52000000000001, 80.9): -1.069
+- (80.9, 84.035): -0.914
+- (84.035, 86.48500000000001): -0.755
+- (86.48500000000001, 87.3): -0.599
+- (87.3, 91.49000000000001): -0.447
+- (91.49000000000001, 95.66): -0.292
+- (95.66, 101.15): -0.446
+- (101.15, 102.05000000000001): -0.294
+- (102.05000000000001, 109.6): 0.197
+- (109.6, 116.25): 0.351
+- (116.25, 120.35): 0.507
+- (120.35, 127.0): 0.748
+- (127.0, 133.10000000000002): 0.902
+- (133.10000000000002, 145.10000000000002): 1.059
+- (145.10000000000002, 160.0): 1.215
+- (160.0, 178.85): 1.368
+- (178.85, 251.2): 1.523
+
+Now, let's calculate the differences between consecutive intervals:
+- Jump from (71.06, 76.52000000000001) to (76.52000000000001, 80.9): |-1.223 - (-1.069)| = 0.154
+- Continue this calculation for all consecutive intervals.
+
+After calculating all differences:
+- The largest jump occurs between (101.15, 102.05000000000001) and (102.05000000000001, 109.6): |-0.294 - 0.197| = 0.491
+
+Thus, the largest jump in the graph occurs at the boundary between the intervals (101.15, 102.05000000000001) and (102.05000000000001, 109.6). The x-axis position of this largest jump is at 102.05000000000001.
+SOLUTION:  102.05000000000001
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values provided for each interval:
+- (6.802, 11.184999999999999): -0.919
+- (11.184999999999999, 12.765): -0.814
+- (12.765, 13.350000000000001): -0.704
+- (13.350000000000001, 15.3): -0.596
+- (15.3, 16.955): -0.49
+- (16.955, 18.515): -0.367
+- (18.515, 20.905): -0.256
+- (20.905, 32.985): -0.151
+- (32.985, 34.730000000000004): 0.081
+- (34.730000000000004, 41.21): 0.188
+- (41.21, 50.405): 0.292
+- (50.405, 56.915): 0.417
+- (56.915, 67.5): 0.53
+- (67.5, 81.56): 0.638
+- (81.56, 94.00999999999999): 0.751
+- (94.00999999999999, 106.2): 0.862
+- (106.2, 153.25): 0.974
+- (153.25, 542.2): 1.082
+
+Now, let's calculate the differences between consecutive intervals:
+- Jump from (11.184999999999999, 12.765) to (12.765, 13.350000000000001): |-0.814 - (-0.704)| = 0.11
+- Jump from (12.765, 13.350000000000001) to (13.350000000000001, 15.3): |-0.704 - (-0.596)| = 0.108
+- Jump from (13.350000000000001, 15.3) to (15.3, 16.955): |-0.596 - (-0.49)| = 0.106
+- Jump from (15.3, 16.955) to (16.955, 18.515): |-0.49 - (-0.367)| = 0.123
+- Jump from (16.955, 18.515) to (18.515, 20.905): |-0.367 - (-0.256)| = 0.111
+- Jump from (18.515, 20.905) to (20.905, 32.985): |-0.256 - (-0.151)| = 0.105
+- Jump from (20.905, 32.985) to (32.985, 34.730000000000004): |-0.151 - 0.081| = 0.232
+- Jump from (32.985, 34.730000000000004) to (34.730000000000004, 41.21): |0.081 - 0.188| = 0.107
+- Jump from (34.730000000000004, 41.21) to (41.21, 50.405): |0.188 - 0.292| = 0.104
+- Jump from (41.21, 50.405) to (50.405, 56.915): |0.292 - 0.417| = 0.125
+- Jump from (50.405, 56.915) to (56.915, 67.5): |0.417 - 0.53| = 0.113
+- Jump from (56.915, 67.5) to (67.5, 81.56): |0.53 - 0.638| = 0.108
+- Jump from (67.5, 81.56) to (81.56, 94.00999999999999): |0.638 - 0.751| = 0.113
+- Jump from (81.56, 94.00999999999999) to (94.00999999999999, 106.2): |0.751 - 0.862| = 0.111
+- Jump from (94.00999999999999, 106.2) to (106.2, 153.25): |0.862 - 0.974| = 0.112
+- Jump from (106.2, 153.25) to (153.25, 542.2): |0.974 - 1.082| = 0.108
+
+The largest jump in absolute terms is from (20.905, 32.985) to (32.985, 34.730000000000004) with a difference of 0.232. Therefore, the x-axis position of the largest jump is at the boundary between these two intervals, which is 32.985.
+SOLUTION:  32.985
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (40.0, 41.5): -1.489
+- (41.5, 43.5): -0.895
+- (43.5, 44.5): -0.02
+- (44.5, 47.5): 0.701
+- (47.5, 48.5): 1.245
+- (48.5, 58.5): -0.923
+- (58.5, 59.5): 0.647
+- (59.5, 60.8335): -0.288
+- (60.8335, 64.5): -1.035
+- (64.5, 65.5): 0.0
+- (65.5, 67.5): -0.73
+- (67.5, 68.5): 0.19
+- (68.5, 70.5): 0.784
+- (70.5, 80.5): 1.169
+- (80.5, 81.5): 0.839
+- (81.5, 85.5): 2.112
+- (85.5, 86.5): 3.884
+- (86.5, 95.0): 4.517
+
+Now, let's calculate the differences between consecutive intervals:
+- Jump from (41.5, 43.5) to (43.5, 44.5): |-0.895 - (-0.02)| = 0.875
+- Jump from (43.5, 44.5) to (44.5, 47.5): |-0.02 - 0.701| = 0.721
+- Jump from (44.5, 47.5) to (47.5, 48.5): |0.701 - 1.245| = 0.544
+- Jump from (47.5, 48.5) to (48.5, 58.5): |1.245 - (-0.923)| = 2.168
+- Jump from (48.5, 58.5) to (58.5, 59.5): |-0.923 - 0.647| = 1.57
+- Jump from (58.5, 59.5) to (59.5, 60.8335): |0.647 - (-0.288)| = 0.935
+- Jump from (59.5, 60.8335) to (60.8335, 64.5): |-0.288 - (-1.035)| = 0.747
+- Jump from (60.8335, 64.5) to (64.5, 65.5): |-1.035 - 0.0| = 1.035
+- Jump from (64.5, 65.5) to (65.5, 67.5): |0.0 - (-0.73)| = 0.73
+- Jump from (65.5, 67.5) to (67.5, 68.5): |-0.73 - 0.19| = 0.92
+- Jump from (67.5, 68.5) to (68.5, 70.5): |0.19 - 0.784| = 0.594
+- Jump from (68.5, 70.5) to (70.5, 80.5): |0.784 - 1.169| = 0.385
+- Jump from (70.5, 80.5) to (80.5, 81.5): |1.169 - 0.839| = 0.33
+- Jump from (80.5, 81.5) to (81.5, 85.5): |0.839 - 2.112| = 1.273
+- Jump from (81.5, 85.5) to (85.5, 86.5): |2.112 - 3.884| = 1.772
+- Jump from (85.5, 86.5) to (86.5, 95.0): |3.884 - 4.517| = 0.633
+
+The largest jump in absolute terms is from (47.5, 48.5) to (48.5, 58.5) with a difference of 2.168. Therefore, the x-axis position of the largest jump is at the boundary between these intervals, which is at age 48.5.
+SOLUTION:  48.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.297
+- (0.5, 3.5): -0.074
+- (3.5, 4.5): 0.644
+- (4.5, 6.5): -0.723
+- (6.5, 7.5): -0.542
+- (7.5, 8.5): -0.665
+- (8.5, 9.5): -0.926
+- (9.5, 10.5): 0.423
+- (10.5, 11.5): 0.59
+- (11.5, 12.5): 0.27
+- (12.5, 13.5): 0.534
+- (13.5, 14.0): -0.133
+
+Now, let's calculate the differences between consecutive intervals:
+- Jump from (0.0, 0.5) to (0.5, 3.5): |-0.074 - (-0.297)| = 0.223
+- Jump from (0.5, 3.5) to (3.5, 4.5): |0.644 - (-0.074)| = 0.718
+- Jump from (3.5, 4.5) to (4.5, 6.5): |-0.723 - 0.644| = 1.367
+- Jump from (4.5, 6.5) to (6.5, 7.5): |-0.542 - (-0.723)| = 0.181
+- Jump from (6.5, 7.5) to (7.5, 8.5): |-0.665 - (-0.542)| = 0.123
+- Jump from (7.5, 8.5) to (8.5, 9.5): |-0.926 - (-0.665)| = 0.261
+- Jump from (8.5, 9.5) to (9.5, 10.5): |0.423 - (-0.926)| = 1.349
+- Jump from (9.5, 10.5) to (10.5, 11.5): |0.59 - 0.423| = 0.167
+- Jump from (10.5, 11.5) to (11.5, 12.5): |0.27 - 0.59| = 0.32
+- Jump from (11.5, 12.5) to (12.5, 13.5): |0.534 - 0.27| = 0.264
+- Jump from (12.5, 13.5) to (13.5, 14.0): |-0.133 - 0.534| = 0.667
+
+The largest jump in absolute terms is from (3.5, 4.5) to (4.5, 6.5) with a difference of 1.367.
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between the intervals (3.5, 4.5) and (4.5, 6.5), which is at 4.5.
+SOLUTION:  4.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.195
+- (0.5, 1.5): 1.333
+- (1.5, 2.5): -0.02
+- (2.5, 3.5): -0.402
+- (3.5, 4.5): -1.423
+- (4.5, 5.5): 0.086
+- (5.5, 7.5): -0.843
+- (7.5, 8.5): -0.246
+- (8.5, 11.5): 0.062
+- (11.5, 20.5): -0.315
+- (20.5, 21.5): 0.109
+- (21.5, 22.5): 0.476
+- (22.5, 24.5): 0.133
+- (24.5, 26.5): -0.35
+- (26.5, 29.5): -0.489
+- (29.5, 32.5): -0.108
+- (32.5, 33.5): -0.483
+- (33.5, 35.5): -0.664
+- (35.5, 38.5): -0.396
+- (38.5, 39.5): 0.028
+- (39.5, 40.5): -0.596
+- (40.5, 41.0): 1.112
+
+Now, let's calculate the absolute differences between consecutive intervals:
+- Jump from (0.0, 0.5) to (0.5, 1.5): |1.333 - (-0.195)| = 1.528
+- Jump from (0.5, 1.5) to (1.5, 2.5): |1.333 - (-0.02)| = 1.353
+- Jump from (1.5, 2.5) to (2.5, 3.5): |-0.02 - (-0.402)| = 0.382
+- Jump from (2.5, 3.5) to (3.5, 4.5): |-0.402 - (-1.423)| = 1.021
+- Jump from (3.5, 4.5) to (4.5, 5.5): |-1.423 - 0.086| = 1.509
+- Jump from (4.5, 5.5) to (5.5, 7.5): |0.086 - (-0.843)| = 0.929
+- Jump from (5.5, 7.5) to (7.5, 8.5): |-0.843 - (-0.246)| = 0.597
+- Jump from (7.5, 8.5) to (8.5, 11.5): |-0.246 - 0.062| = 0.308
+- Jump from (8.5, 11.5) to (11.5, 20.5): |0.062 - (-0.315)| = 0.377
+- Jump from (11.5, 20.5) to (20.5, 21.5): |-0.315 - 0.109| = 0.424
+- Jump from (20.5, 21.5) to (21.5, 22.5): |0.109 - 0.476| = 0.367
+- Jump from (21.5, 22.5) to (22.5, 24.5): |0.476 - 0.133| = 0.343
+- Jump from (22.5, 24.5) to (24.5, 26.5): |0.133 - (-0.35)| = 0.483
+- Jump from (24.5, 26.5) to (26.5, 29.5): |-0.35 - (-0.489)| = 0.139
+- Jump from (26.5, 29.5) to (29.5, 32.5): |-0.489 - (-0.108)| = 0.381
+- Jump from (29.5, 32.5) to (32.5, 33.5): |-0.108 - (-0.483)| = 0.375
+- Jump from (32.5, 33.5) to (33.5, 35.5): |-0.483 - (-0.664)| = 0.181
+- Jump from (33.5, 35.5) to (35.5, 38.5): |-0.664 - (-0.396)| = 0.268
+- Jump from (35.5, 38.5) to (38.5, 39.5): |-0.396 - 0.028| = 0.424
+- Jump from (38.5, 39.5) to (39.5, 40.5): |0.028 - (-0.596)| = 0.624
+- Jump from (39.5, 40.5) to (40.5, 41.0): |-0.596 - 1.112| = 1.708
+
+The largest jump in absolute terms is from (39.5, 40.5) to (40.5, 41.0) with a magnitude of 1.708. Therefore, the x-axis position of the largest jump is at the boundary between these intervals, which is 40.5.
+SOLUTION:  40.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval boundary where the largest absolute difference occurs.
+
+From the data provided:
+- Means: {"(0.0, 0.5)": -0.0751, "(0.5, 2.5)": 0.1633, "(2.5, 3.0)": -0.7301}
+
+Let's calculate the differences between consecutive intervals:
+1. Between "(0.0, 0.5)" and "(0.5, 2.5)": \(0.1633 - (-0.0751) = 0.1633 + 0.0751 = 0.2384\)
+2. Between "(0.5, 2.5)" and "(2.5, 3.0)": \(-0.7301 - 0.1633 = -0.8934\)
+
+Now, we find the absolute values of these differences:
+1. \(|0.2384| = 0.2384\)
+2. \(|-0.8934| = 0.8934\)
+
+The largest jump in absolute terms is 0.8934, which occurs between the intervals "(0.5, 2.5)" and "(2.5, 3.0)".
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between these intervals, which is at 2.5.
+SOLUTION:  2.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+1. (0.1, 0.35): 8.07
+2. (0.35, 0.45): 7.27
+3. (0.45, 0.75): 6.18
+4. (0.75, 1.25): -2.64
+5. (1.25, 1.75): -3.46
+6. (1.75, 2.5): -4.19
+
+Now, let's calculate the differences between consecutive intervals:
+- Jump from (0.1, 0.35) to (0.35, 0.45): \( |8.07 - 7.27| = 0.8 \)
+- Jump from (0.35, 0.45) to (0.45, 0.75): \( |7.27 - 6.18| = 1.09 \)
+- Jump from (0.45, 0.75) to (0.75, 1.25): \( |6.18 - (-2.64)| = 8.82 \)
+- Jump from (0.75, 1.25) to (1.25, 1.75): \( |-2.64 - (-3.46)| = 0.82 \)
+- Jump from (1.25, 1.75) to (1.75, 2.5): \( |-3.46 - (-4.19)| = 0.73 \)
+
+The largest jump in absolute terms is from the interval (0.45, 0.75) to (0.75, 1.25) with a magnitude of 8.82.
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between the intervals (0.45, 0.75) and (0.75, 1.25), which is at 0.75.
+SOLUTION:  0.75
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to compare the mean values at the boundaries of the intervals provided for the continuous feature "HasCrCard". The intervals given are "(0.0, 0.5)" and "(0.5, 1.0)", and the corresponding mean values are -0.004421 and 0.001379 respectively.
+
+Step-by-step process:
+
+1. **Identify the intervals and their corresponding mean values**:
+   - Interval "(0.0, 0.5)": Mean = -0.004421
+   - Interval "(0.5, 1.0)": Mean = 0.001379
+
+2. **Calculate the difference in mean values at the boundary between the two intervals**:
+   - The boundary between these intervals is at x = 0.5.
+   - The difference in mean values at x = 0.5 is:
+     \[ \text{Difference} = 0.001379 - (-0.004421) = 0.001379 + 0.004421 = 0.0058 \]
+
+3. **Determine the magnitude of the jump**:
+   - The magnitude of the jump at x = 0.5 is 0.0058.
+
+Since there are only two intervals provided and thus only one boundary between them, the position of the largest jump in the graph is at **x = 0.5**. This is the only jump, and hence it is both the largest positive and negative jump in terms of absolute magnitude.
+SOLUTION:  0.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+1. (1.1, 1.65): 8.05
+2. (1.65, 2.45): 7.28
+3. (2.45, 3.15): -1.17
+4. (3.15, 3.8): -2.4
+5. (3.8, 4.45): -3.03
+6. (4.45, 5.65): -3.73
+7. (5.65, 6.9): -4.38
+
+Now, let's calculate the differences between consecutive intervals:
+- Jump from (1.65, 2.45) to (2.45, 3.15): \( |7.28 - (-1.17)| = 8.45 \)
+- Jump from (2.45, 3.15) to (3.15, 3.8): \( |-1.17 - (-2.4)| = 1.23 \)
+- Jump from (3.15, 3.8) to (3.8, 4.45): \( |-2.4 - (-3.03)| = 0.63 \)
+- Jump from (3.8, 4.45) to (4.45, 5.65): \( |-3.03 - (-3.73)| = 0.7 \)
+- Jump from (4.45, 5.65) to (5.65, 6.9): \( |-3.73 - (-4.38)| = 0.65 \)
+
+The largest jump in absolute terms is from the interval (1.65, 2.45) to (2.45, 3.15) with a magnitude of 8.45.
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between the intervals (1.65, 2.45) and (2.45, 3.15), which is at 2.45.
+SOLUTION:  2.45
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.078, 0.1265): -0.528
+- (0.1265, 0.128): -0.218
+- (0.128, 0.2185): -0.342
+- (0.2185, 0.3375): -0.168
+- (0.3375, 0.4215): -0.077
+- (0.4215, 0.4955): 0.015
+- (0.4955, 0.5875): 0.131
+- (0.5875, 0.7215): 0.223
+- (0.7215, 0.889): 0.316
+- (0.889, 1.0865): 0.407
+- (1.0865, 1.178): 0.498
+- (1.178, 1.275): 1.018
+- (1.275, 1.3925): 1.283
+- (1.3925, 1.4175): 1.168
+- (1.4175, 1.451): 0.065
+- (1.451, 1.837): -0.193
+- (1.837, 2.137): -0.092
+
+Now, let's calculate the differences between consecutive intervals:
+- Jump from (0.1265, 0.128) to (0.128, 0.2185): |-0.218 - (-0.342)| = 0.124
+- Jump from (0.128, 0.2185) to (0.2185, 0.3375): |-0.342 - (-0.168)| = 0.174
+- Jump from (0.2185, 0.3375) to (0.3375, 0.4215): |-0.168 - (-0.077)| = 0.091
+- Jump from (0.3375, 0.4215) to (0.4215, 0.4955): |-0.077 - 0.015| = 0.092
+- Jump from (0.4215, 0.4955) to (0.4955, 0.5875): |0.015 - 0.131| = 0.116
+- Jump from (0.4955, 0.5875) to (0.5875, 0.7215): |0.131 - 0.223| = 0.092
+- Jump from (0.5875, 0.7215) to (0.7215, 0.889): |0.223 - 0.316| = 0.093
+- Jump from (0.7215, 0.889) to (0.889, 1.0865): |0.316 - 0.407| = 0.091
+- Jump from (0.889, 1.0865) to (1.0865, 1.178): |0.407 - 0.498| = 0.091
+- Jump from (1.0865, 1.178) to (1.178, 1.275): |0.498 - 1.018| = 0.520
+- Jump from (1.178, 1.275) to (1.275, 1.3925): |1.018 - 1.283| = 0.265
+- Jump from (1.275, 1.3925) to (1.3925, 1.4175): |1.283 - 1.168| = 0.115
+- Jump from (1.3925, 1.4175) to (1.4175, 1.451): |1.168 - 0.065| = 1.103
+- Jump from (1.4175, 1.451) to (1.451, 1.837): |0.065 - (-0.193)| = 0.258
+- Jump from (1.451, 1.837) to (1.837, 2.137): |-0.193 - (-0.092)| = 0.101
+
+The largest jump in absolute terms is from (1.3925, 1.4175) to (1.4175, 1.451) with a difference of 1.103. Therefore, the x-axis position of the largest jump is at the boundary between these two intervals, which is 1.4175.
+SOLUTION:  1.4175
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 57.0): -0.25
+- (57.0, 3048.0): -4.83
+- (3048.0, 3120.0): 2.57
+- (3120.0, 4243.5): -4.43
+- (4243.5, 4401.0): 1.45
+- (4401.0, 4668.5): -1.82
+- (4668.5, 4826.0): 3.79
+- (4826.0, 4898.0): 0.57
+- (4898.0, 4973.5): 2.25
+- (4973.5, 5119.0): -3.52
+- (5119.0, 5316.5): 4.26
+- (5316.5, 5505.5): 0.43
+- (5505.5, 6457.5): 2.15
+- (6457.5, 6505.5): -0.16
+- (6505.5, 6745.0): 0.81
+- (6745.0, 7073.5): -1.33
+- (7073.5, 7436.5): 5.76
+- (7436.5, 7565.5): 2.02
+- (7565.5, 7792.0): 6.56
+- (7792.0, 7937.0): 4.88
+- (7937.0, 8296.0): 3.84
+- (8296.0, 10543.0): 7.18
+- (10543.0, 10585.5): -1.48
+- (10585.5, 30961.5): 8.61
+- (30961.5, 70654.5): -0.66
+- (70654.5, 99999.0): 9.72
+
+Now, let's calculate the absolute differences between consecutive intervals:
+- Jump from (57.0, 3048.0) to (3048.0, 3120.0): |2.57 - (-4.83)| = 7.4
+- Continue this calculation for all consecutive intervals.
+
+After calculating all the differences:
+- The largest jump is from (10585.5, 30961.5) to (30961.5, 70654.5): |8.61 - (-0.66)| = 9.27
+
+Thus, the x-axis position of the largest jump in the graph is at the boundary between the intervals (10585.5, 30961.5) and (30961.5, 70654.5), which is at 30961.5.
+SOLUTION:  70654.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (2.0, 2.25): -2.473
+- (2.25, 2.6500000000000004): -2.179
+- (2.6500000000000004, 2.8499999999999996): -1.736
+- (2.8499999999999996, 2.95): -0.945
+- (2.95, 3.05): 0.062
+- (3.05, 3.25): 0.509
+- (3.25, 3.3499999999999996): 1.373
+- (3.3499999999999996, 3.55): 1.669
+- (3.55, 3.75): 2.097
+- (3.75, 3.95): 2.489
+- (3.95, 4.1): 2.778
+
+Now, let's calculate the differences between consecutive intervals:
+- Difference between (2.25, 2.6500000000000004) and (2.0, 2.25): |-2.179 - (-2.473)| = 0.294
+- Difference between (2.6500000000000004, 2.8499999999999996) and (2.25, 2.6500000000000004): |-1.736 - (-2.179)| = 0.443
+- Difference between (2.8499999999999996, 2.95) and (2.6500000000000004, 2.8499999999999996): |-0.945 - (-1.736)| = 0.791
+- Difference between (2.95, 3.05) and (2.8499999999999996, 2.95): |0.062 - (-0.945)| = 1.007
+- Difference between (3.05, 3.25) and (2.95, 3.05): |0.509 - 0.062| = 0.447
+- Difference between (3.25, 3.3499999999999996) and (3.05, 3.25): |1.373 - 0.509| = 0.864
+- Difference between (3.3499999999999996, 3.55) and (3.25, 3.3499999999999996): |1.669 - 1.373| = 0.296
+- Difference between (3.55, 3.75) and (3.3499999999999996, 3.55): |2.097 - 1.669| = 0.428
+- Difference between (3.75, 3.95) and (3.55, 3.75): |2.489 - 2.097| = 0.392
+- Difference between (3.95, 4.1) and (3.75, 3.95): |2.778 - 2.489| = 0.289
+
+The largest jump in absolute terms is between the intervals (2.95, 3.05) and (2.8499999999999996, 2.95) with a difference of 1.007.
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between the intervals (2.8499999999999996, 2.95) and (2.95, 3.05). This position is 2.95.
+SOLUTION:  2.95
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values provided in the graph:
+
+- "(0.04996, 0.05075)": 0.5962
+- "(0.05075, 0.052285)": 0.5519
+- "(0.052285, 0.05393)": 0.5087
+- "(0.05393, 0.05455)": 0.4681
+- "(0.05455, 0.05505)": 0.4248
+- "(0.05505, 0.055349999999999996)": 0.3799
+- "(0.055349999999999996, 0.055665)": 0.337
+- "(0.055665, 0.055895)": 0.2922
+- "(0.055895, 0.055935)": 0.2475
+- "(0.055935, 0.056365)": 0.2007
+- "(0.056365, 0.05655)": 0.1163
+- "(0.05655, 0.056720000000000007)": 0.0704
+- "(0.056720000000000007, 0.056995000000000004)": 0.0288
+- "(0.056995000000000004, 0.058145)": -0.0168
+- "(0.058145, 0.059715)": -0.0575
+- "(0.059715, 0.06078)": -0.0163
+- "(0.06078, 0.061385)": -0.0618
+- "(0.061385, 0.0622)": -0.102
+- "(0.0622, 0.063145)": -0.1453
+- "(0.063145, 0.065105)": -0.1865
+- "(0.065105, 0.06564)": -0.1448
+- "(0.06564, 0.067575)": -0.1025
+- "(0.067575, 0.09744)": -0.0621
+
+Now, let's calculate the absolute differences between consecutive intervals:
+
+- |0.5519 - 0.5962| = 0.0443
+- |0.5087 - 0.5519| = 0.0432
+- |0.4681 - 0.5087| = 0.0406
+- |0.4248 - 0.4681| = 0.0433
+- |0.3799 - 0.4248| = 0.0449
+- |0.337 - 0.3799| = 0.0429
+- |0.2922 - 0.337| = 0.0448
+- |0.2475 - 0.2922| = 0.0447
+- |0.2007 - 0.2475| = 0.0468
+- |0.1163 - 0.2007| = 0.0844
+- |0.0704 - 0.1163| = 0.0459
+- |0.0288 - 0.0704| = 0.0416
+- |-0.0168 - 0.0288| = 0.0456
+- |-0.0575 - -0.0168| = 0.0407
+- |-0.0163 - -0.0575| = 0.0412
+- |-0.0618 - -0.0163| = 0.0455
+- |-0.102 - -0.0618| = 0.0402
+- |-0.1453 - -0.102| = 0.0433
+- |-0.1865 - -0.1453| = 0.0412
+- |-0.1448 - -0.1865| = 0.0417
+- |-0.1025 - -0.1448| = 0.0423
+- |-0.0621 - -0.1025| = 0.0404
+
+The largest jump is 0.0844 between the intervals "(0.055935, 0.056365)" and "(0.056365, 0.05655)". Therefore, the x-axis position of the largest jump in the graph is at the boundary between these two intervals, which is 0.056365.
+SOLUTION:  0.056365
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms. Here's how we can do it step by step:
+
+1. **Extract the mean values for each interval**:
+   - (4.0, 11.5): 10.73
+   - (11.5, 12.5): 1.29
+   - (12.5, 15.5): 3.88
+   - (15.5, 18.0): 2.22
+   - (18.0, 28.5): 6.17
+   - (28.5, 30.5): 4.47
+   - (30.5, 52.0): 5.56
+   - (52.0, 54.5): 3.38
+   - (54.5, 67.5): 4.79
+   - (67.5, 73.5): 2.76
+   - (73.5, 76.5): -3.15
+   - (76.5, 78.5): 2.29
+   - (78.5, 82.5): -0.16
+   - (82.5, 87.5): -2.8
+   - (87.5, 90.5): 0.19
+   - (90.5, 92.5): -1.08
+   - (92.5, 95.5): -2.7
+   - (95.5, 108.5): -0.98
+   - (108.5, 117.5): 0.02
+   - (117.5, 124.5): -3.44
+   - (124.5, 137.5): 0.64
+   - (137.5, 149.0): -0.8
+   - (149.0, 171.5): 5.06
+   - (171.5, 173.0): 2.66
+   - (173.0, 182.5): -0.84
+   - (182.5, 192.5): -3.42
+   - (192.5, 193.5): -1.01
+   - (193.5, 253.0): -2.58
+   - (253.0, 285.0): -8.42
+
+2. **Calculate the differences between consecutive intervals**:
+   - Difference between (11.5, 12.5) and (4.0, 11.5): |1.29 - 10.73| = 9.44
+   - Continue this for all intervals...
+
+3. **Identify the largest jump**:
+   - From the calculations, the largest jump is between (73.5, 76.5) and (76.5, 78.5): |-3.15 - 2.29| = 5.44
+
+4. **Find the x-axis position of this jump**:
+   - The jump occurs at the boundary between these intervals, which is at x = 76.5.
+
+Therefore, the x-axis position of the largest jump in the graph is at 76.5.
+SOLUTION:  11.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the steps to find the largest jump:
+
+1. **Extract the mean values for each interval**: We have the mean values provided in the JSON object under "Means".
+
+2. **Calculate the differences between consecutive intervals**: We need to compute the difference in mean values between the end of one interval and the beginning of the next.
+
+3. **Identify the largest absolute difference**: The largest jump will be where the absolute value of the difference between consecutive mean values is the highest.
+
+Let's perform these calculations using the provided mean values:
+
+- From `(0.001713, 0.0031539999999999997)` to `(0.0031539999999999997, 0.003299)`: \(0.2615 - 0.2958 = -0.0343\)
+- From `(0.0031539999999999997, 0.003299)` to `(0.003299, 0.003384)`: \(0.185 - 0.2615 = -0.0765\)
+- From `(0.003299, 0.003384)` to `(0.003384, 0.0034675)`: \(-0.1523 - 0.185 = -0.3373\)
+- From `(0.003384, 0.0034675)` to `(0.0034675, 0.0036699999999999997)`: \(-0.1838 - (-0.1523) = -0.0315\)
+- Continue this for all intervals...
+
+After calculating these differences, we find the largest absolute difference. From the calculations above, the largest jump appears to be between `(0.003299, 0.003384)` and `(0.003384, 0.0034675)` with a difference of \(-0.3373\).
+
+**Conclusion**: The largest jump in the graph occurs at the position between the intervals `(0.003299, 0.003384)` and `(0.003384, 0.0034675)`. This is where the mean value changes most abruptly, indicating a significant change in the model's prediction as the feature value crosses this threshold.
+SOLUTION:  0.003384
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values provided for each interval:
+- (0.0, 0.022775): -0.769
+- (0.022775, 0.024655): -0.671
+- (0.024655, 0.052095): -0.846
+- (0.052095, 0.10575): -0.943
+- (0.10575, 0.1313): -0.843
+- (0.1313, 0.14545000000000002): -0.745
+- (0.14545000000000002, 0.1694): -0.646
+- (0.1694, 0.1843): -0.54
+- (0.1843, 0.19235000000000002): -0.438
+- (0.19235000000000002, 0.1996): -0.332
+- (0.1996, 0.20695): -0.234
+- (0.20695, 0.20795): -0.081
+- (0.20795, 0.2539): 0.187
+- (0.2539, 0.273): 0.284
+- (0.273, 0.33975): 0.385
+- (0.33975, 0.3663): 0.486
+- (0.3663, 0.37695): 0.586
+- (0.37695, 0.39765): 0.698
+- (0.39765, 0.41025): 0.797
+- (0.41025, 1.252): 0.897
+
+Now, let's calculate the differences between consecutive mean values:
+- Jump from (0.022775, 0.024655) to (0.024655, 0.052095): |-0.671 - (-0.846)| = 0.175
+- Jump from (0.024655, 0.052095) to (0.052095, 0.10575): |-0.846 - (-0.943)| = 0.097
+- Jump from (0.052095, 0.10575) to (0.10575, 0.1313): |-0.943 - (-0.843)| = 0.100
+- Jump from (0.10575, 0.1313) to (0.1313, 0.14545000000000002): |-0.843 - (-0.745)| = 0.098
+- Jump from (0.1313, 0.14545000000000002) to (0.14545000000000002, 0.1694): |-0.745 - (-0.646)| = 0.099
+- Jump from (0.14545000000000002, 0.1694) to (0.1694, 0.1843): |-0.646 - (-0.54)| = 0.106
+- Jump from (0.1694, 0.1843) to (0.1843, 0.19235000000000002): |-0.54 - (-0.438)| = 0.102
+- Jump from (0.1843, 0.19235000000000002) to (0.19235000000000002, 0.1996): |-0.438 - (-0.332)| = 0.106
+- Jump from (0.19235000000000002, 0.1996) to (0.1996, 0.20695): |-0.332 - (-0.234)| = 0.098
+- Jump from (0.1996, 0.20695) to (0.20695, 0.20795): |-0.234 - (-0.081)| = 0.153
+- Jump from (0.20695, 0.20795) to (0.20795, 0.2539): |-0.081 - 0.187| = 0.268
+- Jump from (0.20795, 0.2539) to (0.2539, 0.273): |0.187 - 0.284| = 0.097
+- Jump from (0.2539, 0.273) to (0.273, 0.33975): |0.284 - 0.385| = 0.101
+- Jump from (0.273, 0.33975) to (0.33975, 0.3663): |0.385 - 0.486| = 0.101
+- Jump from (0.33975, 0.3663) to (0.3663, 0.37695): |0.486 - 0.586| = 0.100
+- Jump from (0.3663, 0.37695) to (0.37695, 0.39765): |0.586 - 0.698| = 0.112
+- Jump from (0.37695, 0.39765) to (0.39765, 0.41025): |0.698 - 0.797| = 0.099
+- Jump from (0.39765, 0.41025) to (0.41025, 1.252): |0.797 - 0.897| = 0.100
+
+The largest jump in absolute terms is from (0.20695, 0.20795) to (0.20795, 0.2539) with a difference of 0.268. Therefore, the x-axis position of the largest jump is at the upper boundary of the interval (0.20695, 0.20795), which is 0.20795.
+SOLUTION:  0.20795
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to compare the mean values at the boundaries of the intervals provided. The feature is continuous, but the intervals are treated as discrete steps in this model.
+
+From the data provided:
+- The mean value for the interval (0.0, 0.5) is 0.01719.
+- The mean value for the interval (0.5, 1.0) is -0.00954.
+
+The jump occurs at the boundary between these two intervals, which is at x = 0.5. To find the magnitude of the jump, we calculate the difference in mean values across this boundary:
+
+Magnitude of jump = |Mean value at upper end of first interval - Mean value at lower end of second interval|
+                   = |0.01719 - (-0.00954)|
+                   = |0.01719 + 0.00954|
+                   = 0.02673
+
+Thus, the largest jump in the graph occurs at x = 0.5, with a magnitude of 0.02673.
+SOLUTION:  0.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the difference in mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.3765
+- (0.5, 1.5): -0.0692
+- (1.5, 4.5): -0.016
+- (4.5, 5.5): 0.0109
+- (5.5, 6.5): 0.0432
+- (6.5, 7.5): 0.0871
+- (7.5, 9.5): 0.0554
+- (9.5, 10.0): -0.0599
+
+Now, let's calculate the absolute differences between consecutive intervals:
+- Jump from (0.0, 0.5) to (0.5, 1.5): | -0.0692 - (-0.3765) | = 0.3073
+- Jump from (0.5, 1.5) to (1.5, 4.5): | -0.016 - (-0.0692) | = 0.0532
+- Jump from (1.5, 4.5) to (4.5, 5.5): | 0.0109 - (-0.016) | = 0.0269
+- Jump from (4.5, 5.5) to (5.5, 6.5): | 0.0432 - 0.0109 | = 0.0323
+- Jump from (5.5, 6.5) to (6.5, 7.5): | 0.0871 - 0.0432 | = 0.0439
+- Jump from (6.5, 7.5) to (7.5, 9.5): | 0.0554 - 0.0871 | = 0.0317
+- Jump from (7.5, 9.5) to (9.5, 10.0): | -0.0599 - 0.0554 | = 0.1153
+
+The largest jump in absolute terms is from the interval (7.5, 9.5) to (9.5, 10.0) with a difference of 0.1153.
+
+Therefore, the x-axis position of the largest jump in the graph is at the boundary between the intervals (7.5, 9.5) and (9.5, 10.0), which is at 9.5.
+SOLUTION:  0.5
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the position of the largest jump in the graph, we need to calculate the differences in the mean values between consecutive intervals and identify the interval where this difference is the largest in absolute terms.
+
+Here are the mean values for each interval:
+1. (0.0, 845.0): -0.044
+2. (845.0, 1448.0): -1.147
+3. (1448.0, 1551.5): 0.416
+4. (1551.5, 1568.5): 3.928
+5. (1568.5, 1748.0): -3.752
+6. (1748.0, 1846.0): 1.139
+7. (1846.0, 1862.0): 3.823
+8. (1862.0, 1881.5): -1.36
+9. (1881.5, 1894.5): 4.781
+10. (1894.5, 1938.0): 3.172
+11. (1938.0, 1975.5): 0.294
+12. (1975.5, 1978.5): 4.013
+13. (1978.5, 2139.0): -2.74
+14. (2139.0, 2176.5): 0.361
+15. (2176.5, 2190.0): -1.098
+16. (2190.0, 2205.5): 1.259
+17. (2205.5, 2262.5): 2.644
+18. (2262.5, 2310.5): -0.616
+19. (2310.5, 2364.5): -1.139
+20. (2364.5, 2384.5): 1.07
+21. (2384.5, 2450.5): 4.377
+22. (2450.5, 2480.5): 1.517
+23. (2480.5, 2553.0): 3.296
+24. (2553.0, 2581.0): 5.5
+25. (2581.0, 2678.5): -0.191
+26. (2678.5, 2789.0): 0.326
+27. (2789.0, 3343.5): 5.958
+28. (3343.5, 3835.0): 2.152
+29. (3835.0, 4356.0): -0.334
+
+Now, let's calculate the absolute differences between consecutive intervals:
+1. |-1.147 - (-0.044)| = 1.103
+2. |0.416 - (-1.147)| = 1.563
+3. |3.928 - 0.416| = 3.512
+4. |-3.752 - 3.928| = 7.68
+5. |1.139 - (-3.752)| = 4.891
+6. |3.823 - 1.139| = 2.684
+7. |-1.36 - 3.823| = 5.183
+8. |4.781 - (-1.36)| = 6.141
+9. |3.172 - 4.781| = 1.609
+10. |0.294 - 3.172| = 2.878
+11. |4.013 - 0.294| = 3.719
+12. |-2.74 - 4.013| = 6.753
+13. |0.361 - (-2.74)| = 3.101
+14. |-1.098 - 0.361| = 1.459
+15. |1.259 - (-1.098)| = 2.357
+16. |2.644 - 1.259| = 1.385
+17. |-0.616 - 2.644| = 3.26
+18. |-1.139 - (-0.616)| = 0.523
+19. |1.07 - (-1.139)| = 2.209
+20. |4.377 - 1.07| = 3.307
+21. |1.517 - 4.377| = 2.86
+22. |3.296 - 1.517| = 1.779
+23. |5.5 - 3.296| = 2.204
+24. |-0.191 - 5.5| = 5.691
+25. |0.326 - (-0.191)| = 0.517
+26. |5.958 - 0.326| = 5.632
+27. |2.152 - 5.958| = 3.806
+28. |-0.334 - 2.152| = 2.486
+
+The largest jump is 7.68, occurring between the intervals (1551.5, 1568.5) and (1568.5, 1748.0). Therefore, the x-axis position of the largest jump is at the boundary between these intervals, which is 1568.5.
+SOLUTION:  1568.5
+--------------------------------------------------------------------------------
\ No newline at end of file
diff --git a/benchmarks/results/monotonicity-gemini-1.5.txt b/benchmarks/results/monotonicity-gemini-1.5.txt
new file mode 100644
index 0000000..ab82298
--- /dev/null
+++ b/benchmarks/results/monotonicity-gemini-1.5.txt
@@ -0,0 +1,1999 @@
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+2. **Analyze the "Means" data:**  We need to see if the mean values consistently increase or decrease as the input intervals increase. Here's a breakdown:
+
+   | Interval        | Mean     | 
+   |-----------------|----------|
+   | (0.0, 593.5)     | -0.177   | 
+   | (593.5, 779.5)    | 0.043    | 
+   | (779.5, 1341.5)   | 0.27     |
+   | (1341.5, 2175.5)  | 0.543    |
+   | ...             | ...      | 
+
+3. **Identify the Trend:** As you move from left to right across the intervals, the mean values are generally increasing. There's a small dip between the intervals (5218.5, 6031.5) and (6031.5, 6171.5), but the overall trend is upwards.
+
+**Conclusion:**
+
+The graph is **not monotone** because while it's mostly increasing, there's a small dip in the mean values, breaking the strict definition of monotone increasing. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. 
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Analyze the Graph Data:**
+   - We are given a series of age ranges and the corresponding mean output of the GAM.
+   -  Let's look at the trend of the mean values:
+      - Starts around -1.489, increases to 1.245, then decreases, then increases again, and so on.
+
+3. **Determine Monotonicity:**
+   - The mean output value does not consistently increase or decrease as age increases. There are both upward and downward trends within the data.
+
+**Therefore, the correct answer is (c) not monotone.** 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone increasing:**  A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+   - **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+2. **Analyze the Mean Values:**
+   - Look at the mean values provided in the JSON object.  We need to see if these values consistently increase or decrease as the "Spa" feature range increases.
+   - The mean values are: 0.521, 0.118, -0.285, -0.907, -1.309, -1.712, -3.029, -2.456, -2.956, -2.512, -3.402, -2.902, -4.077, -4.481, -5.377, -4.36, -7.183
+
+3. **Identify the Trend:**
+   - As the "Spa" range increases, the mean values predominantly decrease. There are a couple of instances where the decrease isn't strictly maintained (e.g., between -3.029 and -2.456), but the overall trend is clearly downward.
+
+**Conclusion:**
+
+The graph is **not monotone** because while it mostly decreases, there are small intervals where it doesn't strictly follow a decreasing trend. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. 
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Analyze the "Means" data:**
+   -  Look at the values in the "Means" dictionary. They represent the average predicted value of the output for each interval of the input feature "id".
+   - We need to see if these values consistently increase or decrease as the "id" intervals increase.
+
+3. **Identify the Trend:**
+    - Initially, the mean values increase as "id" increases.
+    - However, around the interval "(859643.0, 864727.5)", the mean values start to decrease.
+    - This switch from increasing to decreasing means the function is not monotone.
+
+**Answer:** The graph is **c) not monotone**. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+2. **Analyze the "Means" data:**  We need to see if the mean values consistently increase or decrease as the intervals of "Watersheds" increase.
+
+   - (0.0, 0.5): -0.02526
+   - (0.5, 1.5): -0.02147 
+   - (1.5, 2.5): -0.01542
+   - ... and so on
+
+   Notice that as the intervals of "Watersheds" increase, the mean values are consistently increasing.
+
+3. **Consider Confidence Intervals:** While the means show an increasing trend, it's important to briefly check if the confidence intervals significantly overlap, which might introduce ambiguity. However, in this case, the confidence intervals are relatively tight around the means and don't suggest a deviation from the increasing trend.
+
+**Conclusion:**
+
+The graph represents a **monotone increasing** function. As the value of "Watersheds" increases, the predicted output of the GAM also increases. 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+**1. Understand Monotonicity:**
+
+* **Monotone Increasing:**  A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+* **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+**2. Analyze the "Means" data:**
+
+* Look at the mean values provided for each interval of "Deforestation." 
+* We want to see if these values consistently increase or decrease as the deforestation interval increases.
+
+**3.  Observations:**
+
+*  As deforestation increases from the interval "(0.0, 0.5)" to "(14.5, 17.0)", the mean values generally increase. There are a few instances where the increase is very small, but there's no clear decreasing trend.
+
+**Conclusion:**
+
+The graph is **(a) monotone increasing**.  While the rate of increase might vary, the general trend of the mean values is upwards as deforestation increases. 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+2. **Analyze the "Means" data:**  We need to see if the mean values consistently increase or decrease as the input intervals increase.
+
+   -  Let's list the intervals and their corresponding mean values:
+
+     ```
+     Interval               | Mean
+     -----------------------|-------
+     (6.802, 11.184...)      | -0.919
+     (11.184..., 12.765)     | -0.814
+     (12.765, 13.35...)     | -0.704
+     (13.35..., 15.3)       | -0.596
+     ...                     | ...
+     (153.25, 542.2)        | 1.082 
+     ```
+
+   -  As we move from left to right (increasing input intervals), we can see that the mean values are consistently **increasing**.
+
+3. **Consider Confidence Intervals:** While the means show an increasing trend, it's important to briefly check if the confidence intervals significantly overlap, which might introduce ambiguity. In this case, while there's some overlap, the overall trend is still clearly increasing.
+
+**Conclusion:**
+
+The graph represents a **monotone increasing** function. 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+2. **Analyze the Provided Data:**
+   - We have a continuous feature "IsActiveMember" with two intervals: (0.0, 0.5) and (0.5, 1.0).
+   - The mean value for the interval (0.0, 0.5) is -0.555.
+   - The mean value for the interval (0.5, 1.0) is 0.568.
+
+3. **Determine Monotonicity:**
+   - As we move from the interval (0.0, 0.5) to (0.5, 1.0), the mean value increases from -0.555 to 0.568. 
+
+**Conclusion:**
+
+Since the output increases as the input increases, the graph represents a **monotone increasing** function. 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. 
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Analyze the Mean Values:**
+   - Look at the mean values provided for each interval of the "Relationship" feature:
+      - (0.0, 0.5): 0.511
+      - (0.5, 1.5): -0.233 
+      - (1.5, 2.5): -0.666
+      - (2.5, 3.5): -1.006
+      - (3.5, 4.5): -0.529
+      - (4.5, 5.0): 1.753
+
+3. **Identify the Pattern:**
+   - As the "Relationship" feature increases from 0.0 to 3.5, the mean value decreases.
+   - However, from 3.5 to 5.0, the mean value increases.
+
+**Conclusion:**
+
+Since the function does not strictly increase or decrease as the input increases, the graph is **c) not monotone**. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. 
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Analyze the Means:**
+   - Look at the "Means" values in the JSON data. We need to see if these values generally increase or decrease as the intervals of "ShoppingMall" increase.
+   - The values are: -0.032, -0.211, 0.034, 0.213, -0.042, 0.401, 0.585, 0.948, 1.235, 0.871, 1.066, 2.339, 2.909, 3.236
+
+3. **Identify the Pattern:**
+   - While the values fluctuate slightly, there's a clear overall trend: as the "ShoppingMall" interval increases, the mean values generally increase as well. 
+   - Although there are a few small dips (e.g., from 1.235 to 0.871), the overall trend is upwards.
+
+**Conclusion:**
+
+The graph is **not monotone** because there are minor dips in the mean values as the input increases. However, it's important to note that the graph exhibits a generally increasing trend. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+2. **Analyze the Mean Values:**
+   - Look at the "Means" values in the JSON object.  We want to see if these values consistently increase or decrease as the interval for "Siltation" increases.
+   -  Here's the trend:
+      - (0.0, 1.5): -0.02643
+      - (1.5, 2.5): -0.01529
+      - (2.5, 3.5): -0.01037
+      - ... and so on, with the values generally increasing.
+
+3. **Consider Confidence Intervals:**
+   - While the means show an increasing trend, it's important to briefly check if the confidence intervals significantly overlap.  Significant overlap might introduce ambiguity.
+   - In this case, while there's some overlap, the overall trend across intervals is still consistently increasing.
+
+**Conclusion:**
+
+The graph representing the "Siltation" feature is **a) monotone increasing**. As the value of "Siltation" increases, the predicted output of the GAM model also generally increases. 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone increasing:**  A function is monotone increasing if, as the input increases, the output never decreases. 
+   - **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Examine the "Means" values:**
+   -  We need to see if the mean values consistently increase or decrease as the "area_worst" intervals increase.
+   -  Let's list the intervals and their corresponding means:
+
+     | Interval               | Mean    |
+     |------------------------|---------|
+     | (185.2, 357.5)        | -1.345  |
+     | (357.5, 413.15)       | -1.192  |
+     | (413.15, 471.9)      | -1.038  |
+     | ...                   | ...     |
+     | (1979.0, 4254.0)     |  1.557  |
+
+3. **Observe the Trend:** As we move from the lowest "area_worst" interval to the highest, the mean values are consistently increasing. 
+
+**Conclusion:** The graph represents a **monotone increasing** function. 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+2. **Analyze the "Means" data:**  We'll focus on the mean values as they represent the general trend of the function. Look at the y-values (means) as the x-value intervals increase:
+
+   - (0.0, 0.5): -0.02484
+   - (0.5, 1.5): -0.02089
+   - (1.5, 2.5): -0.01739
+   - ...
+   - (10.5, 13.5): 0.03305
+   - (13.5, 16.0): 0.02016 
+
+3. **Identify the Trend:**  We can see that as the x-value intervals increase, the mean values generally increase. However, there's a dip at the end between the intervals (10.5, 13.5) and (13.5, 16.0).
+
+**Conclusion:**
+
+Since the function increases for most of the range but decreases at the end, it is **not monotone**. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here is the solution:
+
+1. **Understanding Monotonicity:**
+    - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. 
+    - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Analyzing the GAM Graph:**
+    - We need to look at the 'Means' values across the intervals of the 'RiverManagement' feature. 
+    - Observe how the mean value changes as we move from the lowest interval to the highest:
+        - "(0.0, 0.5)": -0.0273
+        - "(0.5, 1.5)": -0.02345 
+        - ...
+        - "(11.5, 12.5)": 0.03673
+        - "(12.5, 13.5)": 0.01311 
+        - "(13.5, 16.0)": 0.03206
+
+3. **Identifying the Pattern:**
+    - As we move from left to right across the intervals, the mean values generally increase. 
+    - However, there's a dip in the mean value between the intervals "(11.5, 12.5)" and "(12.5, 13.5)". 
+
+4. **Conclusion:**
+    - Since the mean value does not strictly increase across the entire range of 'RiverManagement', the graph is **not monotone**.
+
+**Therefore, the correct answer is (c) not monotone.** 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+2. **Analyze the "Means" data:**  We'll look at the mean values across the BMI intervals:
+
+   - (0.0, 9.1): -0.7 
+   - (9.1, 22.55): -0.961 
+   - (22.55, 23.65): -0.856 
+   - ... (and so on)
+
+3. **Look for Violations of Monotonicity:**
+   - Initially, the mean values decrease (from -0.7 to -0.961). 
+   - However, as BMI increases, the mean values start to increase (from -0.961 to -0.856 and onwards).
+
+**Conclusion:** Since the mean values do not consistently increase or decrease as BMI increases, the graph is **not monotone**. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. 
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Examine the "Means" Values:**
+   -  Look at the mean values provided for each interval of "texture_worst".  We need to see if these values consistently increase or decrease as the intervals progress from lower to higher values.
+   - The mean values are: -1.885, -1.717, -1.55, -0.851, -0.612, -0.44, -0.272, -0.1, 0.078, 0.279, 0.451, 0.619, 0.878, 1.044, 1.256, 1.423
+
+3. **Observe the Trend:**
+    - As the intervals of "texture_worst" increase, the mean values consistently increase. 
+
+**Conclusion:**
+
+The graph is **a) monotone increasing**.  The mean predicted value increases as the value of the "texture_worst" feature increases. 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+Gemini: Invalid response with parts [].
+LLM RESPONSE:  
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+    - **Monotone increasing:**  A function is monotone increasing if, as the input increases, the output never decreases.
+    - **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Analyze the Means:**
+    - Observe how the mean values change as the petal_width intervals increase:
+        - (0.1, 0.35): 8.07
+        - (0.35, 0.45): 7.27 
+        - (0.45, 0.75): 6.18
+        - (0.75, 1.25): -2.64
+        - (1.25, 1.75): -3.46
+        - (1.75, 2.5): -4.19
+
+3. **Identify the Trend:**
+    - Initially, as petal_width increases, the mean value decreases.
+    - After a certain point (around petal_width 0.75), the mean value continues to decrease.
+
+4. **Conclusion:**
+    - The graph is **not monotone increasing** because the mean values decrease as petal_width increases.
+    - The graph is **not monotone decreasing** because while the mean values initially decrease, they don't decrease for the entire range of petal_width.
+
+**Therefore, the correct answer is (c) not monotone.** 
+
+SOLUTION:  Decreasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone increasing:**  A function is monotone increasing if, as the input increases, the output never decreases. 
+   - **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Analyze the Intervals:**
+   - **(0.0, 0.5):** Mean = -0.0751
+   - **(0.5, 2.5):** Mean = 0.1633 
+   - **(2.5, 3.0):** Mean = -0.7301
+
+3. **Compare Mean Values:**
+   - From interval (0.0, 0.5) to (0.5, 2.5), the mean value increases.
+   - From interval (0.5, 2.5) to (2.5, 3.0), the mean value decreases.
+
+**Conclusion:**
+
+Since the mean value increases in one interval and then decreases in the next, the graph is **not monotone**. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+2. **Analyze the "Means" data:**
+   -  Look for instances where the mean value decreases and then increases again as you move from left to right across the x-axis intervals. 
+   -  We see this happening: the mean decreases from the interval "(0.0, 3.5)" to "(28.5, 30.5)" and then increases afterwards.
+
+3. **Conclusion:**
+   Since the mean values do not consistently increase or decrease, the graph is **not monotone**. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. 
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Analyze the "Means" data:**  We'll focus on the "Means" values as they represent the model's prediction for each interval. Look at how the mean value changes as the input (DiabetesPedigreeFunction) increases:
+
+   - Starts at -0.528 in the interval (0.078, 0.1265)
+   - Jumps to -0.218, then -0.342, then -0.168... there's some fluctuation here.
+   - Continues to increase up to 1.283 in the interval (1.275, 1.3925)
+   - Drops sharply to 1.168, then to 0.065, and continues decreasing.
+
+3. **Conclusion:** Since the mean value both increases and decreases as the input increases, the graph is **not monotone**. 
+
+**Therefore, the correct answer is (c).** 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here is the solution:
+
+1. **Understanding Monotonicity:**
+    - **Monotone increasing:** A function is monotone increasing if, as the input increases, the output never decreases. 
+    - **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Analyzing the GAM Graph:**
+    - We need to look at the "Means" values across the different intervals of the "Occupation" feature. 
+    - Observe the trend of these mean values as the interval (representing the input) increases.
+
+3. **Observations:**
+    -  "(0.0, 0.5)": -0.297
+    -  "(0.5, 3.5)": -0.074  -> Increase from previous interval
+    -  "(3.5, 4.5)": 0.644   -> Increase from previous interval
+    -  "(4.5, 6.5)": -0.723  -> Decrease from previous interval 
+
+    We can stop here. We've found instances where the mean value increases and then decreases as the input interval increases.
+
+4. **Conclusion:**
+    Since the graph shows both increasing and decreasing trends in mean values as the input increases, the GAM graph is **not monotone**. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here is the step-by-step reasoning:
+
+1. **Understanding Monotonicity:**
+    - **Monotone increasing:**  A function is monotone increasing if, as the input increases, the output never decreases. 
+    - **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Analyzing the GAM Graph:**
+    - Look at the "Means" values across the intervals of the 'ejection_fraction'.
+    - From (14.0, 16.0) to (32.5, 36.5), the mean values decrease (4.55 -> 3.26 -> 1.89 -> -0.42 -> -1.76).
+    - There's an increase from (32.5, 36.5) to (36.5, 39.0) (-1.76 -> 0.48).
+    - Further on, there are both increases and decreases in the mean values.
+
+3. **Conclusion:**
+    Since the graph shows both increasing and decreasing trends in mean values as the 'ejection_fraction' increases, the function is **not monotone**. 
+
+**Therefore, the correct answer is (c).** 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The correct answer is **(c) not monotone**. Here's why:
+
+* **Monotone increasing** means the graph's y-values only increase or stay the same as the x-values increase. 
+* **Monotone decreasing** means the graph's y-values only decrease or stay the same as the x-values increase.
+
+Let's look at the provided means:
+
+* The mean starts at -0.8769 in the interval (15565796.0, 15566519.0).
+* It then increases to -0.1763 in the interval (15567333.5, 15567844.5). 
+* However, it decreases again to -0.2283 in the interval (15568343.5, 15571612.0).
+
+This up-and-down pattern continues throughout the graph.  Since the y-values (means) don't consistently increase or decrease, the graph is **not monotone**. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+2. **Analyze the "Means" data:**
+   - Look at the mean values provided for each interval of "WetlandLoss". 
+   -  We need to see if these values consistently increase or decrease as the intervals progress.
+
+3. **The Trend:**
+    - (0.0, 1.5): -0.02419
+    - (1.5, 2.5): -0.01693 
+    - (2.5, 3.5): -0.01069
+    - ... and so on
+    - Notice that as the "WetlandLoss" intervals increase, the mean values are also increasing.
+
+4. **Consider Confidence Intervals:**
+   - While the mean values show an increasing trend, it's important to briefly check if the confidence intervals significantly overlap.  Significant overlap could make the increasing trend less certain. In this case, the overlap is not substantial enough to change the overall increasing pattern.
+
+**Conclusion:**
+
+The graph represents a **monotone increasing** relationship between "WetlandLoss" and the target variable. As wetland loss increases, the model's prediction also tends to increase. 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:**  A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+2. **Analyze the "Means" data:**
+   - Look at the y-values (means) across the x-axis intervals.  
+   - We need to see if these values consistently increase or decrease.
+
+3. **The Data:**
+    - (0.0, 22.0): -0.728
+    - (22.0, 86.5): -1.069 
+    - (86.5, 94.5): -0.907 
+    - ... and so on
+
+4. **Observation:**
+    - Initially, the mean decreases from -0.728 to -1.069. 
+    - Then, it starts increasing and continues to increase as the x-value intervals increase.
+
+5. **Conclusion:** Since the mean values do not consistently increase or decrease, the graph is **not monotone**. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input value increases, the output value either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input value increases, the output value either decreases or stays the same. It never increases.
+
+2. **Analyze the "Means" data:**
+   - Look at the mean values provided for each interval of the "ClimateChange" feature. 
+   -  We need to see if these values consistently increase or decrease as the intervals progress.
+
+3. **Observations:**
+    - "(0.0, 1.5)": -0.02549
+    - "(1.5, 2.5)": -0.01575 
+    - "(2.5, 3.5)": -0.01061
+    - ... and so on, with the final mean being 0.04423 for the "(12.5, 14.0)" interval.
+
+    Notice that the mean values are generally increasing as the intervals of "ClimateChange" increase. While there might be slight fluctuations, the overall trend is upwards.
+
+4. **Consider Confidence Intervals:**
+    - While the means show an increasing trend, it's essential to glance at the confidence intervals to ensure there's no significant overlap that might contradict the trend. 
+    - In this case, the confidence intervals are relatively tight around the means, and they don't suggest a reversal of the increasing trend.
+
+**Conclusion:**
+
+The graph representing the "ClimateChange" feature is **a) monotone increasing**.  As the value of "ClimateChange" increases, the predicted output of the GAM generally increases as well. 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+    - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. 
+    - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Analyze the Mean Values:**
+    - Look at the mean values provided for each interval of "Pregnancies".  
+    - The values are: -0.1506, -0.2484, -0.1873, -0.0302, 0.0211, 0.1013, 0.1489, 0.264, 0.3553, 0.4117, 0.2996, 0.6729
+
+3. **Identify the Trend:**
+    - Initially, the mean values decrease (from -0.1506 to -0.2484).
+    - Then, the mean values generally increase (from -0.2484 to 0.6729) with a small dip at the end.
+
+4. **Conclusion:**
+    - Since the mean values both increase and decrease over the range of "Pregnancies", the graph is **not monotone**. 
+
+**Therefore, the correct answer is (c) not monotone.** 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The correct answer is **c) not monotone**. Here's why:
+
+* **Monotone increasing** means the graph's y-values only increase or stay the same as the x-values increase. 
+* **Monotone decreasing** means the graph's y-values only decrease or stay the same as the x-values increase.
+
+Let's look at the provided data:
+
+* The mean value for the interval (32.0, 49.5) is 0.68.
+* The mean value for the interval (49.5, 56.5) is -4.31. 
+
+This shows a **decrease** in the mean value as the x-value increases. However, if we look at other intervals, we see increases as well. For example:
+
+* The mean value for the interval (121.5, 126.0) is 1.25.
+* The mean value for the interval (127.5, 145.5) is 1.51.
+
+This shows an **increase** in the mean value as the x-value increases.
+
+**Therefore, the graph is not monotone because it exhibits both increasing and decreasing trends.** 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. 
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Analyze the Means:**
+   - Look at the mean values provided for each interval of "EducationNum." 
+   - The means are: -4.746, -1.252, -0.882, -0.483, -0.093, 0.276, 0.863, 1.487
+
+3. **Observe the Trend:**
+    - As the value of "EducationNum" increases across the intervals, the mean value consistently increases.
+
+**Conclusion:**
+
+Since the mean values of the function consistently increase as "EducationNum" increases, the graph is **a) monotone increasing**. 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The correct answer is **c) not monotone**. Here's why:
+
+* **Monotone increasing** means the function's output (y-value) always increases or stays the same as the input (x-value) increases. 
+* **Monotone decreasing** means the function's output always decreases or stays the same as the input increases.
+
+Looking at the provided data:
+
+1. **Initial Increase:** The mean value increases from -0.195 for the interval (0.0, 0.5) to 1.333 for (0.5, 1.5). This suggests an increase.
+
+2. **Subsequent Decrease:** However, the mean value then decreases to -0.02 for the interval (1.5, 2.5). This breaks the pattern of a strictly increasing function.
+
+3. **Fluctuations:**  As we continue across the intervals, we see further fluctuations with both increases and decreases in the mean value.
+
+**Conclusion:**  Since the function does not consistently increase or decrease across the entire domain of the input feature "NativeCountry", it is **not monotone**. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. 
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Examine the Mean Values:**
+   - Look at the mean values provided in the JSON object.  
+   - Notice that as the intervals for "PoliticalFactors" increase (e.g., from "(0.0, 0.5)" to "(0.5, 1.5)" and so on), the corresponding mean values generally increase as well. 
+
+3. **Consider Confidence Intervals:**
+   - While the general trend is increasing, it's important to check if the confidence intervals overlap. Overlapping confidence intervals might indicate that the relationship is not strictly monotonic.
+   - In this case, there might be slight overlaps between some confidence intervals. However, the overall trend of the means is still clearly increasing.
+
+**Conclusion:**
+
+The graph is **not strictly monotone increasing** due to the potential slight overlaps in confidence intervals. However, the graph exhibits a **general upward trend**, indicating a strong positive association between "PoliticalFactors" and the target variable. 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. 
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Analyze the "Means" data:**  We need to see if the mean values consistently increase or decrease as the "Balance" intervals increase.
+
+   - Looking at the initial values:
+      - (0.0, 50418.515): -0.132
+      - (50418.515, 53570.93): -0.285  
+      - (53570.93, 54249.445): -0.826 
+      ... and so on
+
+   - We see fluctuations. The mean value decreases, then increases again later in the intervals.
+
+3. **Conclusion:** Since the mean values don't consistently increase or decrease, the graph is **not monotone**. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+2. **Analyze the Mean Values:**
+   - Look at the mean values provided in the JSON object.  We want to see if these values consistently increase or decrease as the interval for "DrainageSystems" increases.
+   -  Here's the trend of the mean values:
+      - Starts at -0.02593
+      - Generally increases as the "DrainageSystems" interval increases.
+      - Ends at 0.04564 
+
+3. **Consider Confidence Intervals:**
+   - While the means show an increasing trend, it's important to briefly check if the confidence intervals significantly overlap.  Significant overlap could mean the trend is not definitively increasing.
+   - In this case, while there's some overlap, it's not substantial enough to negate the clear increasing trend of the means.
+
+**Conclusion:**
+
+The graph represents a function that is **a) monotone increasing**. As the value of "DrainageSystems" increases, the predicted output generally increases. 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+2. **Analyze the "Means" data:**
+   - Look at the y-values (means) across the x-axis intervals. 
+   -  We need to see if the values consistently increase or decrease as we move from left to right on the x-axis.
+
+3. **The Data:**
+    - (0.0, 0.5): -0.02565
+    - (0.5, 1.5): -0.02133 
+    - (1.5, 2.5): -0.01683
+    - ... and so on
+
+4. **Observation:** As the x-values increase, the mean values are consistently increasing. 
+
+**Conclusion:** The graph represents a **monotone increasing** function. 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+2. **Analyze the Mean Values:**
+   - Look at the mean values provided in the JSON object.  We want to see if these values generally increase or decrease as the intervals of "Landslides" increase.
+   -  Here's a simplified view of the means:
+      - (0.0, 0.5): -0.02593
+      - (0.5, 1.5): -0.02172
+      - (1.5, 2.5): -0.01544
+      - ... and so on, with the last interval having a positive mean.
+
+3. **Observe the Trend:**  Notice that as the intervals of "Landslides" increase, the mean values are also increasing. 
+
+**Conclusion:**
+
+The graph represents a function that is **a) monotone increasing**. Even though the increase is slight in some intervals, the overall trend of the mean values is upwards as the value of "Landslides" increases. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:**  A function is monotone increasing if, as the input increases, the output never decreases. 
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Examine the Mean Values:**
+   - Look at the mean values provided in the JSON object.  We want to see if these values consistently increase or decrease as the intervals of the feature increase.
+   -  Notice that the mean values generally increase as we move from the interval "(0.0, 1.5)" to "(14.0, 15.0)":
+      - (0.0, 1.5): -0.02463
+      - (1.5, 2.5): -0.01694
+      - ...
+      - (14.0, 15.0): 0.05506
+
+3. **Consider Confidence Intervals:**
+   - While the mean values suggest an increasing trend, it's essential to look at the confidence intervals.  There might be overlap in the confidence intervals, which could mean the relationship isn't strictly increasing.
+   - In this case, there is some minor overlap between the confidence intervals of consecutive intervals, particularly in the lower range of the feature. However, the overall trend still points towards an increasing relationship.
+
+**Conclusion:**
+
+The graph is **not strictly monotone increasing** due to the slight overlap in confidence intervals. However, the general trend of the mean values strongly suggests that the relationship between the feature "AgriculturalPractices" and the output of the GAM is **predominantly increasing**. 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand the data representation:** The graph represents the effect of the "diabetes" feature on the model's prediction.  The x-axis represents different ranges of the "diabetes" feature, and the y-axis represents the corresponding effect on the prediction.
+
+2. **Analyze the mean values:**
+    - For values of "diabetes" between 0.0 and 0.5, the mean effect is 0.3225.
+    - For values of "diabetes" between 0.5 and 1.0, the mean effect is -0.415.
+
+3. **Compare the mean values:** The mean effect decreases from 0.3225 to -0.415 as the value of "diabetes" increases.
+
+4. **Consider the confidence intervals:**  Notice that the confidence intervals for both ranges don't overlap and stay entirely within their respective positive and negative regions. This strengthens our conclusion.
+
+**Conclusion:** Since the mean effect decreases as the value of the "diabetes" feature increases, the graph is **b) monotone decreasing**. 
+
+SOLUTION:  Decreasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+**Understanding Monotonicity**
+
+* **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. It can stay flat or increase.
+* **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases. It can stay flat or decrease.
+* **Not Monotone:** If the function sometimes increases and sometimes decreases as the input increases, it's not monotone.
+
+**Analyzing the GAM Graph**
+
+1. **Focus on the Means:**  We'll primarily look at the "Means" values of the graph, as they represent the general trend of the function.
+
+2. **Trace the Trend:**  Examine the mean values as the 'id' intervals increase:
+    * The values initially fluctuate slightly around -0.1 and -0.2.
+    * There's a jump to a positive value (0.0378) in the interval (7170.5, 7335.5).
+    * The values then increase further, reaching 0.1773.
+    * After this, there's a general downward trend, but with some minor fluctuations.
+
+**Conclusion**
+
+The graph shows that the function does not consistently increase or decrease as the 'id' feature increases. There are both increasing and decreasing sections. 
+
+**Therefore, the correct answer is (c) not monotone.** 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. 
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Examine the Mean Values:**
+   -  Look at the "Means" values in the JSON object. We need to see if the values consistently increase or decrease as the interval of the input feature increases.
+   -  The values initially decrease, then increase as the input "smoothness_mean" increases.
+
+3. **Conclusion:**
+    Since the mean values do not consistently increase or decrease, the graph is **not monotone**. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The provided graph and description indicate a potential inconsistency. Let's break down why:
+
+* **The Problem:** The description states that "Race" is a **continuous** feature. However, the provided intervals and the very nature of race suggest it should be a **categorical** feature. Continuous variables can take on any value within a range (like height or temperature), while categorical variables represent distinct groups.
+
+* **Analyzing the Data:**  Even if we treat the "Race" feature as continuous for the sake of this example, the trend is not clearly monotone:
+
+    * It decreases from the first interval to the second  (-0.8604 to -0.0173).
+    * Then it fluctuates, decreasing again, then increasing (-0.0173 to -0.2499 to -0.3026 to 0.0414).
+
+* **Conclusion:** Due to the likely misclassification of the "Race" feature and the fluctuating values, the graph is **c) not monotone**.
+
+**Important Note:**  It's crucial to use appropriate data types for features in any model. Misrepresenting categorical data as continuous can lead to inaccurate and potentially harmful interpretations. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here is the step-by-step reasoning:
+
+1. **Understanding Monotonicity:** A monotone function is a function that either never decreases (monotone increasing) or never increases (monotone decreasing). 
+
+2. **Analyzing the Means:**  We need to look at the mean values of the function for each interval and see if they consistently increase or decrease. Here's the breakdown:
+
+    * (0.0, 0.5): -0.013
+    * (0.5, 1.5): 0.434  (Increases from previous)
+    * (1.5, 4.5): -0.066 (Decreases from previous)
+    * (4.5, 5.5): 0.167  (Increases from previous)
+    * (5.5, 7.5): -0.464 (Decreases from previous)
+    * (7.5, 8.0): -2.54 (Decreases from previous)
+
+3. **Conclusion:** Since the mean values sometimes increase and sometimes decrease, the function is **not monotone**. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. 
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Analyze the Graph Data:**
+   - Look at the "Means" values. We need to see if these values generally increase or decrease as the Age intervals increase.
+   -  - The means initially increase (from age 2 to around age 17.5).
+      - Then the means fluctuate a bit between ages 17.5 and 35.5.
+      - Finally, the means generally decrease from age 35.5 onwards.
+
+3. **Conclusion:**
+   Since the graph initially increases and then decreases, it is **not monotone**. 
+
+**Therefore, the correct answer is (c).** 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+2. **Analyze the "Means" data:**  We'll focus on the mean values as they represent the general trend of the function. Look at the mean values in the provided JSON and see if they consistently increase or decrease as the input intervals increase:
+
+   -  The values initially decrease (from -5401.6 to -53732.5), then increase (up to 30854.6), and then generally decrease again.
+
+3. **Conclusion:** Since the mean values do not show a consistent increase or decrease as the input intervals increase, the graph is **not monotone**. 
+
+**Therefore, the correct answer is (c).** 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand the data:** The graph represents the effect of the feature "anaemia" on the model's output. The x-axis represents the value of anaemia (ranging from 0.0 to 1.0), divided into two intervals: (0.0, 0.5) and (0.5, 1.0). The y-axis represents the model's prediction (mean, lower bound, and upper bound).
+
+2. **Compare the means:**
+    - For the interval (0.0, 0.5), the mean is -0.0818.
+    - For the interval (0.5, 1.0), the mean is 0.0917.
+    - The mean is higher for the interval (0.5, 1.0) compared to (0.0, 0.5).
+
+3. **Consider the confidence intervals:**
+    - While the means suggest an increase, it's crucial to check if the confidence intervals overlap. 
+    - The upper bound of the confidence interval for (0.0, 0.5) is -0.0173, which is lower than the lower bound of the confidence interval for (0.5, 1.0), which is 0.0194. This indicates a clear separation and strengthens the observation that the effect is likely increasing.
+
+**Conclusion:**
+
+Since the mean value of the function increases as the value of the "anaemia" feature increases, and the confidence intervals don't overlap, the graph is **a) monotone increasing**. 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here is the solution:
+
+1. **Understanding the graph:** The graph represents the effect of the "smoking" feature on the model's prediction. The x-axis represents the value of the "smoking" feature, and the y-axis represents the contribution of that value to the prediction.
+
+2. **Analyzing the means:**  
+    - For the interval "(0.0, 0.5)", the mean effect is 0.01522.
+    - For the interval "(0.5, 1.0)", the mean effect is -0.03391.
+
+3. **Determining monotonicity:**  Since the mean effect decreases as the value of the "smoking" feature increases, the graph is **monotone decreasing**.
+
+**Therefore, the correct answer is (b).** 
+
+SOLUTION:  Decreasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone increasing:**  A function is monotone increasing if, as the input (x-value) increases, the output (y-value) never decreases. It can stay the same or go up.
+   - **Monotone decreasing:** A function is monotone decreasing if, as the input (x-value) increases, the output (y-value) never increases. It can stay the same or go down.
+
+2. **Analyze the Graph Data:**
+   - Look at the "Means" values in the JSON data. These represent the predicted output of the GAM for different ranges of the 'platelets' feature. 
+   - We need to see if these values consistently increase, consistently decrease, or fluctuate.
+
+3. **Observe the Trend:**
+   - As the platelet count increases initially, the mean values increase (going from -1.004 to 2.956).
+   - However, the mean values then start to decrease and fluctuate up and down across the entire range of platelet counts.
+
+**Conclusion:**
+
+The graph is **c) not monotone**. The relationship between platelet count and the output of the GAM is not consistently increasing or decreasing. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+2. **Analyze the Mean Values:**
+   - Look at the mean values provided in the JSON object.  We need to see if these values consistently increase or decrease as the input intervals increase.
+   -  Here's a breakdown:
+      - (0.0, 0.5): -0.02443
+      - (0.5, 1.5): -0.02088 
+      - (1.5, 2.5): -0.01613
+      - ... (and so on)
+      - (13.5, 15.0): 0.03345
+      - (15.0, 16.0): 0.02926
+
+3. **Identify the Trend:**
+   - As we move from the lowest interval to the highest interval, we can see that the mean values are generally increasing. However, there's a slight decrease from (13.5, 15.0) to (15.0, 16.0).
+
+4. **Conclusion:**
+   - Since there's a small decrease in the mean values near the end, the graph is **not monotone**. Even a single decrease means it doesn't strictly adhere to the rules of monotone increasing or decreasing. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. 
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Examine the "Means" data:**  We need to see if the mean values consistently increase or decrease as the input intervals increase.
+
+    -  Let's list the intervals and their corresponding mean values:
+
+       | Interval                   | Mean Value |
+       |----------------------------|------------|
+       | (0.0, 0.02814)           | -0.771     |
+       | (0.02814, 0.08293)        | -0.653     |
+       | (0.08293, 0.08555)        | -0.533     |
+       | (0.08555, 0.093225)       | -0.403     |
+       | ...                       | ...         |
+       | (0.26865, 0.291)          | 1.494      | 
+
+3. **Analyze the Trend:** As we move from left to right across the intervals, the mean values are consistently increasing. 
+
+**Conclusion:** The graph represents a **monotone increasing** function. 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The correct answer is **c) not monotone**. Here's why:
+
+* **Monotone Increasing:** A monotone increasing function means that as the x-value increases, the y-value never decreases. 
+* **Monotone Decreasing:** A monotone decreasing function means that as the x-value increases, the y-value never increases.
+
+Looking at the provided data, we can see multiple instances where the predicted value (mean) increases and then decreases as the CapitalLoss value increases. For example:
+
+* The mean is -1.147 for the interval (845.0, 1448.0) and 0.416 for the interval (1448.0, 1551.5). The mean increased.
+* The mean is 3.928 for the interval (1551.5, 1568.5) and -3.752 for the interval (1568.5, 1748.0). The mean decreased.
+
+**Therefore, the graph is not monotone.** 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:**  A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+2. **Analyze the Mean Values:**
+   - Look at the mean values provided in the JSON object.  
+   - Do the values generally increase or decrease as the intervals of "MonsoonIntensity" increase?
+     - We see the mean values generally increase as MonsoonIntensity increases (with a small dip at the end).
+
+3. **Consider Confidence Intervals:**
+   - While the means show a generally increasing trend, it's important to consider the confidence intervals. 
+   - Notice that the confidence intervals are quite narrow, and even with the intervals, the trend remains generally increasing. The dip at the end is within the margin of error.
+
+**Conclusion:**
+
+The graph is **not strictly** monotone increasing due to the slight dip in mean value at the high end of the "MonsoonIntensity" range. However, considering the confidence intervals and the overall trend, it's more accurate to say the graph demonstrates a **generally increasing** relationship between "MonsoonIntensity" and the response variable. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone increasing:**  A function is monotone increasing if, as the input increases, the output never decreases. 
+   - **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Analyze the Provided Data:**
+   - We have three intervals for the feature "Pclass". Let's arrange them in increasing order of input:
+      - (1.0, 1.5): Mean = -0.009
+      - (1.5, 2.5): Mean = 0.534
+      - (2.5, 3.0): Mean = -0.532
+
+3. **Check for Monotonicity:**
+   - From (1.0, 1.5) to (1.5, 2.5) the mean value increases.
+   - From (1.5, 2.5) to (2.5, 3.0) the mean value decreases.
+
+**Conclusion:**
+
+Since the function does not consistently increase or decrease as the input increases, the graph is **c) not monotone**. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The provided information indicates that the feature "sex" is a **continuous** variable. However, the provided data represents the feature with intervals "(0.0, 0.5)" and "(0.5, 1.0)", which implies a binary or categorical nature. 
+
+This presents a contradiction:
+
+1. **Continuous Variable:**  A continuous variable would typically have a smooth curve representing the relationship with the output, not distinct intervals like these.
+2. **Categorical Representation:** The way the data is presented suggests a categorical variable (perhaps representing two genders) rather than a continuous one.
+
+**Due to this inconsistency, it's impossible to determine the monotonicity of the graph.** 
+
+Here's why we need clarification:
+
+* **Continuous "sex" variable:**  This doesn't make logical sense. We need to understand what this continuous variable represents.
+* **Categorical "sex" variable:** If it's categorical, the intervals are unusual. We'd typically expect distinct categories like "Male" and "Female".
+
+**To determine monotonicity, we need a clear understanding of the feature and its representation.** 
+
+SOLUTION:  Decreasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+    - **Monotone increasing:**  A function is monotone increasing if, as the input increases, the output never decreases. 
+    - **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Analyze the Means:**
+    -  Let's look at the mean values provided in the JSON:
+        - (1.0, 1.5): -0.918
+        - (1.5, 2.5): 0.96 
+        - (2.5, 3.5): -3.104
+        - (3.5, 4.0): -2.768
+
+    - As the input (NumOfProducts) increases from (1.0, 1.5) to (1.5, 2.5), the mean output increases. However, as the input increases further to (2.5, 3.5), the mean output decreases.  
+
+3. **Conclusion:** Since the mean output does not consistently increase or decrease as the input increases, the graph is **not monotone**. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Let's analyze the graph step-by-step to determine its monotonicity:
+
+1. **Understanding Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. 
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Examining the "Means" data:**  We'll focus on the mean values as they represent the general trend of the function. Here's a simplified look at the means:
+
+   ```
+   total_bedrooms:  (2, 4.5] -> -10633   (4.5, 9.5] -> -19829  (9.5, 12.5] -> -33356 ...  (2865.5, 6445] -> 51586 
+   ```
+
+3. **Initial Trend:**  The function initially shows a decreasing trend. As the number of bedrooms increases from 2 to around 12.5, the mean value decreases.
+
+4. **Shift in Trend:**  However, as the number of bedrooms continues to increase beyond 12.5, the mean values start to increase. This shift is evident as we move towards the higher end of the 'total_bedrooms' range.
+
+5. **Conclusion:** Since the function initially decreases and then increases, it is **not monotone**. 
+
+**Therefore, the correct answer is (c) not monotone.** 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand the data representation:** The graph represents the effect of the "high_blood_pressure" feature on the model's prediction.  The x-axis represents the value of "high_blood_pressure" (divided into two intervals: 0.0-0.5 and 0.5-1.0). The y-axis represents the model's prediction (mean, lower bound, and upper bound).
+
+2. **Compare the means:**
+    - For the interval (0.0, 0.5), the mean is -0.1077.
+    - For the interval (0.5, 1.0), the mean is 0.1864.
+
+3. **Analyze the trend:** The mean increases as the value of "high_blood_pressure" increases. 
+
+4. **Consider the confidence intervals:** Notice that the confidence intervals for both intervals are entirely above (for the second interval) or below (for the first interval) zero, indicating a statistically significant difference between the two groups.
+
+**Conclusion:** Since the mean increases as the feature value increases, the graph is **a) monotone increasing**. 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:**  A function is monotone increasing if, as the input increases, the output never decreases. 
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Analyze the Means:**
+   - Look at the "Means" values in the JSON data.  
+   - Notice that as the intervals of `sepal_length` increase, the corresponding mean values generally decrease:
+      - (4.3, 4.55): 3.328 
+      - (4.55, 4.75): 2.995
+      - ...
+      - (6.85, 7.7): -1.718
+
+3. **Consider Confidence Intervals:**
+   - While the general trend is decreasing, it's important to check if the confidence intervals overlap significantly. Significant overlap could indicate that the relationship might not be strictly monotone.
+   - In this case, while there's some overlap between adjacent intervals, the overall trend of the means and the confidence intervals suggests a decreasing relationship.
+
+**Conclusion:**
+
+The graph is **b) monotone decreasing**.  The mean values of the function decrease as the `sepal_length` increases, and the confidence intervals, while showing some overlap, support this overall trend. 
+
+SOLUTION:  Decreasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+2. **Analyze the "Means" data:**  We need to see if the mean values consistently increase or decrease as the area_mean intervals increase.
+
+   -  Let's list the intervals and their corresponding mean values:
+
+     ```
+     Interval            | Mean
+     ---------------------|-------
+     (143.5, 259.35)     | -0.759
+     (259.35, 289.4)    | -0.662 
+     (289.4, 319.15)    | -0.567
+     ...                 | ...
+     (1801.0, 2501.0)    | 1.109 
+     ```
+
+3. **Observe the Trend:** As we move from the lowest area_mean interval to the highest, the mean values are consistently increasing. 
+
+**Conclusion:** The graph represents a **monotone increasing** function. 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+    - **Monotone Increasing:** A function is monotone increasing if, as the x-value increases, the y-value never decreases. It can stay the same or increase.
+    - **Monotone Decreasing:** A function is monotone decreasing if, as the x-value increases, the y-value never increases. It can stay the same or decrease.
+
+2. **Analyze the Mean Values:**
+    - Look at the mean values provided in the JSON object.  We need to see if the values generally increase or decrease as the serum_sodium intervals increase.
+    - The values initially increase, then decrease, then increase again. This tells us the graph is not strictly increasing or decreasing.
+
+3. **Consider Confidence Intervals:**
+    - Even though the mean values show some fluctuation, we need to consider the confidence intervals. If the confidence intervals for neighboring intervals overlap significantly, it makes it harder to definitively say the relationship is purely increasing or decreasing.
+
+**Conclusion:**
+
+The graph is **c) not monotone**. The mean values do not show a consistent increasing or decreasing trend, and the confidence intervals likely overlap, further indicating the relationship between serum_sodium and the target variable is not strictly monotone. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The correct answer is **c) not monotone**. Here's why:
+
+1. **Understanding Monotonicity:** A monotone increasing function always has a positive slope (or 0), meaning the output (y-value) increases or stays the same as the input (x-value) increases. Conversely, a monotone decreasing function always has a negative slope (or 0), meaning the output decreases or stays the same as the input increases.
+
+2. **Analyzing the Graph Data:** Let's look at the mean values of the graph:
+
+   * (0.0, 0.5): -0.368
+   * (0.5, 1.5): 0.724  
+   * (1.5, 2.5): 0.587 
+   * (2.5, 3.5): -0.221 
+   * (3.5, 4.5): -0.631
+   * (4.5, 5.5): -0.545
+   * (5.5, 6.0): 0.179
+
+   We can see that the function increases from the first interval to the second, then decreases, then increases again towards the end. This up-and-down pattern clearly indicates the function is not always increasing or always decreasing.
+
+3. **Conclusion:** Since the graph doesn't exhibit a consistently increasing or decreasing trend, it is **not monotone**. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. 
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Analyze the Mean Values:**
+   -  Look at the "Means" data in the JSON.  We need to see if the values generally increase or decrease as the population range increases.
+   - The values initially decrease as population increases (e.g., from the interval "(3.0, 14.5)" to "(837.5, 1019.5)").
+   - Then there's a fluctuation: an increase followed by a decrease around the intervals centered on population "1268".
+   - After that, the values generally continue to decrease as the population increases.
+
+3. **Consider Confidence Intervals:**
+   - While the means show some fluctuations, it's important to look at the confidence intervals. There are overlaps between the confidence intervals of some consecutive intervals, suggesting that the fluctuations might not be statistically significant. However, the general trend is still not strictly increasing or decreasing.
+
+**Conclusion:**
+
+The graph is **c) not monotone**. While it shows a general trend of decreasing values as population increases, the fluctuations and overlapping confidence intervals indicate that it's not strictly monotone decreasing. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the x-value increases, the y-value either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the x-value increases, the y-value either decreases or stays the same. It never increases.
+
+2. **Examine the "Means" values:**  We'll focus on the mean values since they represent the function's overall trend. Look at the y-values (means) as the x-value intervals increase:
+
+   - (43.79, 60.035): -0.884
+   - (60.035, 63.379999999999995): -0.783
+   - (63.379999999999995, 66.67): -0.681
+   - ... and so on
+
+   Notice that as the x-value intervals increase, the corresponding y-values are also increasing. 
+
+3. **Check for Violations:**  Scan through all the mean values. Is there any instance where the y-value decreases as the x-value interval increases? No.
+
+**Conclusion:**
+
+Since the mean values of the function consistently increase as the x-value intervals increase, the graph represents a **monotone increasing** function. 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. 
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Analyze the "Means" data:**  We'll focus on the mean values as they represent the general trend of the function. Look at the mean values in each interval and see if they consistently increase or decrease as the serum_creatinine level (the input) increases.
+
+3. **Observations:**
+   -  Initially, the mean values decrease (from -0.26 to -3.77).
+   - Then, they generally increase (from -3.77 to 6.97) with some minor fluctuations.
+
+**Conclusion:** Since the mean values do not consistently increase or decrease as serum_creatinine increases, the graph is **not monotone**. 
+
+**Therefore, the correct answer is (c).** 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases (it can stay the same or increase).
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases (it can stay the same or decrease).
+
+2. **Analyze the "Means" Values:**
+   - Look at the mean values provided for each interval of the "CoastalVulnerability" feature. 
+   -  Notice that as the intervals increase (0.0-0.5, 0.5-1.5, 1.5-2.5, etc.), the corresponding mean values also increase:
+      - -0.03259, -0.02272, -0.0157, -0.00983,... 0.02903, 0.03437, 0.04826 
+
+3. **Conclusion:**
+   Since the mean values consistently increase as the input intervals increase, the graph represents a **monotone increasing** function. 
+
+**Therefore, the correct answer is (a).** 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The correct answer is **c) not monotone**. Here's why:
+
+* **Monotone Increasing:** A monotone increasing function means that as the x-value increases, the y-value either increases or stays the same. It never decreases. 
+* **Monotone Decreasing:** A monotone decreasing function means that as the x-value increases, the y-value either decreases or stays the same. It never increases.
+
+Looking at the provided data, we can see several instances where the function changes direction:
+
+1. **Initial Increase, then Decrease:** The function initially increases from the interval "(0.0, 57.0)" to "(3048.0, 3120.0)" but then decreases in the interval "(3120.0, 4243.5)".
+
+2. **Fluctuations:**  The function continues to fluctuate throughout the rest of the intervals, with periods of both increasing and decreasing values.
+
+**Therefore, because the function does not consistently increase or decrease, it is not monotone.** 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+2. **Analyze the Mean Values:**
+   - Observe the mean values provided in the JSON object.  We need to see if there's a consistent trend of increasing or decreasing as the tenure range increases.
+   -  Let's list the mean values in order of increasing tenure:
+      - (0.0, 0.5): -0.3765
+      - (0.5, 1.5): -0.0692
+      - (1.5, 4.5): -0.016
+      - (4.5, 5.5): 0.0109
+      - (5.5, 6.5): 0.0432
+      - (6.5, 7.5): 0.0871
+      - (7.5, 9.5): 0.0554
+      - (9.5, 10.0): -0.0599 
+
+3. **Identify the Trend:**
+   - Initially, the mean values increase as tenure increases. 
+   - However, there's a drop in the mean value at the end, from the interval (7.5, 9.5) to (9.5, 10.0).
+
+**Conclusion:**
+
+Since the mean values do not strictly increase or decrease, the graph is **not monotone**. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone increasing:**  A function is monotone increasing if, as the input increases, the output never decreases. It can stay flat or increase.
+   - **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output never increases. It can stay flat or decrease.
+
+2. **Examine the "Means" data:**  We'll focus on the mean values as they represent the general trend of the GAM function. Look at the y-values (means) as the x-values (intervals) increase:
+
+   -  Interval (9.71, 13.24): -1.121
+   -  Interval (13.24, 14.075): -1.023 
+   -  ...
+   -  Interval (21.285, 33.81): 0.68
+
+   Notice that as the intervals increase, the mean values generally increase as well. There are a few instances where the increase is very small, but there's no point where the mean value decreases as the interval increases.
+
+3. **Consider Confidence Intervals:** While the means show a generally increasing trend, the confidence intervals do overlap slightly in some areas. However, the overall trend still points towards an increasing relationship.
+
+**Conclusion:**
+
+The graph is **not strictly monotone increasing** due to the slight overlaps in the confidence intervals. However, it exhibits a **generally increasing trend**. 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone increasing:**  A function is monotone increasing if, as the input increases, the output never decreases. It can stay the same or increase.
+   - **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output never increases. It can stay the same or decrease.
+
+2. **Analyze the Graph Data:**
+   - We are given a series of latitude ranges and their corresponding mean values. 
+   - We need to check if these mean values consistently increase or decrease as latitude increases.
+
+3. **Look for Violations:**
+   - Starting from the lowest latitude range, examine the mean values. Are there any instances where the mean value increases after a decrease, or decreases after an increase?
+   - **Example:**  The mean value for the latitude range (33.555, 33.565) is lower than the mean value for the preceding range (33.504999999999995, 33.555). This indicates the function does not strictly increase. Similarly, there are other instances where the mean value increases after a decrease.
+
+**Conclusion:**
+
+The graph is **c) not monotone**. The mean values do not consistently increase or decrease as latitude increases. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:**  A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+2. **Analyze the Mean Values:**
+   - Look at the mean values provided in the JSON object.  We want to see if these values generally increase or decrease as the intervals of "DamsQuality" increase.
+   -  Here's the trend:
+      - (0.0, 1.5): -0.02325
+      - (1.5, 2.5): -0.01532 
+      - (2.5, 3.5): -0.01073
+      - ...
+      - (12.5, 13.5): 0.03961
+      - (13.5, 14.0): 0.01644 
+
+3. **Identify the Pattern:**
+   - We can see that the mean values generally increase as the "DamsQuality" intervals increase. However, there's a dip at the end between (12.5, 13.5) and (13.5, 14.0).
+
+4. **Consider Confidence Intervals:**
+   - While there's a dip in the mean at the end, notice that the confidence intervals for the (12.5, 13.5) and (13.5, 14.0) intervals overlap significantly. This overlap suggests that the dip might not be statistically significant.
+
+**Conclusion:**
+
+The graph is **not strictly monotone increasing** due to the dip at the end. However, considering the confidence intervals, the relationship is **almost monotone increasing**. The dip at the end could be due to random variation in the data or a slight non-linear effect within that range. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases (it can stay the same or increase).
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases (it can stay the same or decrease).
+
+2. **Analyze the "Means" data:**
+   - Look at the mean values provided for each interval of the feature "IneffectiveDisasterPreparedness." 
+   -  As the intervals increase (0.0-1.5, 1.5-2.5, 2.5-3.5, etc.), observe the corresponding mean values.
+
+3. **Observe the Trend:**
+   - We see that as the "IneffectiveDisasterPreparedness" value increases, the mean value consistently increases as well. 
+
+**Conclusion:**
+
+Since the mean values consistently increase as the input feature increases, the graph represents a **monotone increasing** function. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here is the step-by-step reasoning:
+
+1. **Understanding the graph:** The graph represents the effect of the feature "HasCrCard" on the model's prediction. The x-axis represents the value of "HasCrCard", and the y-axis represents the contribution to the prediction.
+
+2. **Interpreting the intervals:** The feature is continuous, and the JSON object provides intervals.  This means the model predicts the same effect for any value of "HasCrCard" within a given interval.
+
+3. **Analyzing the means:**
+    - For the interval "(0.0, 0.5)", the mean effect is -0.004421.
+    - For the interval "(0.5, 1.0)", the mean effect is 0.001379.
+
+4. **Monotonicity:**  Since the mean effect increases as the value of "HasCrCard" increases (from -0.004421 to 0.001379), the graph is **monotone increasing**.
+
+**Therefore, the correct answer is (a) monotone increasing.** 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+Gemini: Invalid response with parts [].
+LLM RESPONSE:  
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The correct answer is **c) not monotone**. Here's why:
+
+* **Monotone increasing** means the function's output (y-value) either increases or stays the same as the input (x-value) increases. 
+* **Monotone decreasing** means the function's output (y-value) either decreases or stays the same as the input (x-value) increases.
+
+Looking at the provided data, we can see:
+
+1. **Initial Increase:** The mean value increases from the first interval "(4.0, 11.5)" to "(18.0, 28.5)".
+2. **Decrease:** Then, the mean value decreases from "(18.0, 28.5)" to "(73.5, 76.5)".
+3. **Fluctuations:**  The mean value continues to fluctuate up and down throughout the rest of the intervals.
+
+**Therefore, the graph is not consistently increasing or decreasing, making it not monotone.** 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+Gemini: Invalid response with parts [].
+LLM RESPONSE:  
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here is the solution:
+
+1. **Understanding the graph:** The graph represents the effect of the "Gender" feature on the model's prediction.  The feature is continuous, which is unusual for a variable like gender. This suggests that "Gender" might be coded as a numerical variable (e.g., on a scale from 0 to 1).
+
+2. **Analyzing the means:** The mean effect for the interval "(0.0, 0.5)" is -0.4751, while the mean effect for the interval "(0.5, 1.0)" is 0.2339.  
+
+3. **Monotonicity:** Since the mean effect increases as the value of the "Gender" feature increases, the graph is **monotone increasing**.
+
+**Important Note:** While the graph shows a monotone increasing trend, it's crucial to remember that labeling "Gender" as a continuous variable is unusual. Gender is typically a categorical variable (male/female/other). Representing it as continuous might indicate an underlying coding scheme that needs further investigation. 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone increasing:**  A function is monotone increasing if, as the input increases, the output never decreases. 
+   - **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Analyze the "Means" data:**
+   - Look at the mean values provided for each interval of "Fare".  We need to see if these values consistently increase or decrease as the Fare intervals increase.
+   -  Here's a simplified look at the means:
+      - -1.425, -1.303, -0.472, -0.602, -0.14, 0.225, 0.355, 0.207, -0.238, 0.051, -0.075,...
+
+3. **Identify Non-Monotonic Behavior:**
+    - We can see that the mean values do not consistently increase or decrease. For example, the mean increases from -1.425 to -0.472, then decreases to -0.602. This up-and-down pattern continues throughout the data.
+
+**Conclusion:**
+
+The graph is **c) not monotone**. The relationship between "Fare" and the target variable is not strictly increasing or decreasing. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The correct answer is **c) not monotone**. Here's why:
+
+* **Monotone increasing** means the graph's y-values only increase or stay the same as the x-values increase. 
+* **Monotone decreasing** means the graph's y-values only decrease or stay the same as the x-values increase.
+
+Let's look at the provided data:
+
+1. **Initial Increase:**  The mean values initially increase, starting from 0.3865 and rising to 0.3462, 0.2048, and so on.
+
+2. **Subsequent Decrease:** However, as the 'EstimatedSalary' (x-values) continues to increase, we see the mean values start to decrease. For example, from the interval starting at 48226.81, the mean value is -0.0771, and it continues to fluctuate between negative and small positive values.
+
+3. **Fluctuations:** The presence of both increasing and decreasing trends in the mean values across the different salary ranges indicates that the graph is not strictly going up or down. 
+
+**Therefore, the graph is not monotone.** The GAM model captures a non-linear relationship between 'EstimatedSalary' and the target variable. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the x-value increases, the y-value either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the x-value increases, the y-value either decreases or stays the same. It never increases.
+
+2. **Analyze the "Means" data:**  We'll focus on the mean values as they represent the general trend of the function. Look at the y-values (means) as the x-value intervals increase:
+
+   - (7.93, 10.585): -1.149
+   - (10.585, 11.305): -1.016
+   - (11.305, 11.965): -0.883
+   - ... and so on
+
+   Notice that as the x-value intervals increase, the corresponding y-values are also increasing. 
+
+3. **Check for Violations:**  Scan through all the mean values. Is there any instance where the y-value decreases as the x-value interval increases? No.
+
+**Conclusion:**
+
+Since the mean values of the function consistently increase as the x-value intervals increase, the graph represents a **monotone increasing** function. 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+2. **Analyze the "Means" data:**
+   - Look at the mean values provided for each interval of "Education".  We need to see if these values generally increase or decrease as the education level goes up.
+   -  Here's the trend:
+      - Starts around -0.40
+      - Decreases to around -0.54
+      - Fluctuates slightly around -0.48 to -0.40
+      - Increases from -0.45 to a peak around 0.18
+      - Dips slightly, then increases again to 0.19
+
+3. **Conclusion:**
+   The function is not strictly increasing or decreasing. It initially decreases, then generally increases, showing a somewhat curved relationship. 
+
+**Therefore, the correct answer is (c) not monotone.** 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. It can stay flat or increase.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases. It can stay flat or decrease.
+
+2. **Analyze the Graph Data:**  We need to look at the "Means" values in the JSON object. Since the x-axis represents "longitude" (a continuous variable), we need to see if the mean values consistently increase or decrease as longitude increases.
+
+3. **Step through the Intervals:**
+   -  Let's look at a few consecutive intervals:
+      - `(-124.35, -124.10499999999999)`: -50430.1
+      - `(-124.10499999999999, -124.08500000000001)`: -38925.6 
+      - `(-124.08500000000001, -124.07499999999999)`: -23742.3
+      - ... and so on
+
+    - In these initial intervals, as longitude increases, the mean value is also increasing. 
+
+    - However, if we continue down the list, we'll find places where the trend doesn't hold. For example:
+      - `(-122.42500000000001, -122.405)`: 89733.4
+      - `(-122.405, -122.39500000000001)`: 78586.0
+
+      - Here, the longitude increases, but the mean value decreases.
+
+4. **Conclusion:** Since the mean values do not consistently increase or decrease as longitude increases, the graph is **not monotone**. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The correct answer is **c) not monotone**. Here's why:
+
+**Understanding Monotonicity**
+
+* **Monotone increasing:**  A function is monotone increasing if, as the input increases, the output never decreases. It can stay the same or go up.
+* **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output never increases. It can stay the same or go down.
+
+**Analyzing the GAM Graph**
+
+Let's look at the provided means for the "Age" feature:
+
+* The mean output is higher for the interval "(0.0, 0.5)" than for "(0.5, 3.5)". This suggests a decrease.
+* The mean output is higher for the interval "(73.5, 74.5)" than for "(74.5, 77.5)". This suggests a decrease.
+* However, there are intervals where the mean output increases as "Age" increases, for example, between "(25.5, 39.5)" and "(39.5, 44.5)".
+
+**Conclusion**
+
+Since the graph shows both increasing and decreasing trends in the mean output as "Age" increases, the function represented by this GAM is **not monotone**. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+2. **Analyze the "Means" data:**  We'll focus on the "Means" values since they represent the function's output for each interval of 'radius_mean'. 
+   -  Observe how the mean values change as we move from the leftmost interval to the rightmost interval:
+      - (-0.762, -0.659, -0.56, ..., 0.891, 0.99, 1.093)
+
+3. **Identify the Trend:** The mean values consistently *increase* as the 'radius_mean' increases. 
+
+**Conclusion:** The graph represents a **monotone increasing** function. 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. 
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Analyze the Means:**
+   - Look at the "Means" values in the JSON data. We need to see if these values generally increase or decrease as the input intervals increase.
+   - Initially, the mean values decrease (e.g., from -70808.9 to -78966.6).
+   - However, as the input intervals grow larger, the mean values eventually start to increase (e.g., from -1233.9 to 4370.8 and onwards).
+
+3. **Conclusion:**
+   Since the mean values initially decrease and then increase, the graph is **not monotone**. 
+
+**Therefore, the correct answer is (c).** 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone increasing:**  A function is monotone increasing if, as the input increases, the output never decreases. 
+   - **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Analyze the Means:**
+   - Look at the mean values provided for each interval of the "Parch" feature:
+      - (0.0, 0.5): 0.085
+      - (0.5, 1.5): -0.055
+      - (1.5, 3.0): -0.299
+      - (3.0, 4.0): -1.704
+
+   - As the value of "Parch" increases, the mean value of the function generally decreases. 
+
+3. **Consider Confidence Intervals:**
+   - While the means show a decreasing trend, it's essential to check if the confidence intervals overlap.  Overlapping confidence intervals might indicate that we cannot be certain about the direction of the relationship.
+   - In this case, there's a slight overlap between the confidence intervals of (0.0, 0.5) and (0.5, 1.5). However, the overall trend across all intervals is still downward.
+
+**Conclusion:**
+
+The graph is **b) monotone decreasing**. Despite a minor overlap in confidence intervals, the general trend shows that as "Parch" increases, the output of the GAM model decreases. 
+
+SOLUTION:  Decreasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. 
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Analyze the Mean Values:**
+   - Look at the mean values provided in the JSON object.  We want to see if these values consistently increase or decrease as the interval of "InadequatePlanning" increases.
+   -  Here's a breakdown:
+      - (0.0, 0.5): -0.02553
+      - (0.5, 2.5): -0.02038 
+      - (2.5, 4.5): -0.0099
+      - (4.5, 6.5): 0.00082
+      - (6.5, 7.5): 0.01088
+      - ... and so on
+
+   - Notice that the mean values are consistently increasing as we move from lower intervals of "InadequatePlanning" to higher intervals.
+
+3. **Consider Confidence Intervals:**
+   - While the mean values show an increasing trend, it's important to briefly consider the confidence intervals. There's a tiny overlap between the upper bound of (13.5, 15.0) and the lower bound of (15.0, 16.0). However, this overlap is very small and doesn't change the overall increasing trend.
+
+**Conclusion:**
+
+The graph is **(a) monotone increasing**. As the value of "InadequatePlanning" increases, the predicted output of the GAM generally increases as well. 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The correct answer is **c) not monotone**. Here's why:
+
+* **Monotone Increasing:** A monotone increasing graph means the y-value never decreases as the x-value increases.  Looking at the "Means" values, we see the graph initially decreases (from -0.765 to -1.909), then increases (from -1.909 to 0.977), and then decreases again. This up-and-down pattern means it's not monotone increasing.
+
+* **Monotone Decreasing:** A monotone decreasing graph means the y-value never increases as the x-value increases.  As we already established, the graph both increases and decreases, so it's not monotone decreasing either.
+
+**Therefore, the graph is not monotone.** 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here is the step-by-step reasoning:
+
+1. **Understanding Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+2. **Analyzing the GAM Graph:**
+   - We need to look at the "Means" values of the graph. These represent the average predicted value of the output for each interval of the input feature (Insulin).
+   - Observe the trend of the mean values as Insulin increases. 
+
+3. **Observations:**
+   - Initially, from "(0.0, 20.0)" to "(87.5, 97.5)", the mean values generally decrease.
+   - Then, from "(87.5, 97.5)" to "(190.5, 192.5)", there's a slight increase.
+   - After "(190.5, 192.5)", the mean values consistently increase until "(526.5, 680.0)".
+
+4. **Conclusion:**
+   Since the mean values do not show a strictly increasing or decreasing pattern across the entire range of Insulin values, the graph is **not monotone**.
+
+**Therefore, the correct answer is (c).** 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+2. **Analyze the Mean Values:**
+   -  Look at the mean values provided in the JSON object. We need to see if there's a consistent trend of increasing or decreasing values as the housing_median_age increases.
+   -  Here's a rough trend of the mean values:
+      - Starts negative, becomes less negative.
+      - Becomes positive and generally increases.
+      - Dips back negative briefly around "(41.5, 45.5)" and "(45.5, 47.5)".
+      - Then increases again significantly.
+
+3. **Conclusion:**
+    - The function is not strictly increasing or decreasing. There are dips and rises in the mean values as the housing_median_age increases. 
+
+**Therefore, the correct answer is (c) not monotone.** 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+Gemini: Invalid response with parts [].
+LLM RESPONSE:  
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. 
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Analyze the Means:**
+   -  Look at the mean values provided for each interval of 'petal_length'.  
+   - The means are: 8.05, 7.28, -1.17, -2.4, -3.03, -3.73, -4.38
+
+3. **Identify the Trend:**
+    -  As 'petal_length' increases, the mean values initially decrease (from 8.05 to -1.17) and then continue to decrease.
+
+**Conclusion:**
+
+Since the mean values consistently decrease as 'petal_length' increases, the graph is **b) monotone decreasing**. 
+
+SOLUTION:  Decreasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases (it can stay the same or increase).
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases (it can stay the same or decrease).
+
+2. **Analyze the "Means" data:**  We need to see if the mean values consistently increase or decrease as the input intervals increase. 
+
+   -  Let's look at the first few mean values:
+      - (0.07117, 0.09376000000000001): -1.298
+      - (0.09376000000000001, 0.099705): -1.161
+      - (0.099705, 0.10519999999999999): -1.024 
+      - ...
+
+   - We can see that the mean values are increasing as the intervals increase.
+
+3. **Check for Violations:** Continue examining the mean values. Notice that as the intervals progress, the mean values continue to increase. There are no instances where the mean value decreases as the interval increases.
+
+**Conclusion:**
+
+The graph represents a **monotone increasing** function. 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here is the solution:
+
+1. **Understanding Monotonicity:**
+    - **Monotone increasing:** A function is monotone increasing if, as the input increases, the output never decreases. 
+    - **Monotone decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Analyzing the Graph Data:**
+    - We are given a series of intervals on the x-axis ("concave points_mean") and the corresponding mean predicted value on the y-axis.
+    - We need to check if the mean values consistently increase or decrease as we move from left to right across the intervals.
+
+3. **Examining the Mean Values:**
+    - Initially, the mean values increase as the "concave points_mean" increases. For example, the mean value for the interval (0.0, 0.0074145) is -1.054, and it increases for subsequent intervals.
+    - However, after the interval (0.026115, 0.042455), where the mean is -0.235, the mean value for the next interval (0.042455, 0.048235) jumps to -0.115. This indicates a change from increasing to decreasing.
+
+4. **Conclusion:**
+    - Since the mean values do not consistently increase or decrease across all intervals, the graph is **not monotone**.
+
+**Therefore, the correct answer is (c).** 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. 
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Analyze the "Means" data:**  We need to see if the mean values consistently increase or decrease as the RoomService value increases. Here's a breakdown:
+
+   - The mean starts at 0.328 for the interval "(0.0, 105.5)".
+   - It then decreases to 0.028 for "(105.5, 296.5)". 
+   - It continues to decrease as the RoomService value increases.
+   - However, there's a small increase from -1.446 to -1.136 between the intervals "(734.0, 800.0)" and "(800.0, 816.0)". 
+
+3. **Consider Confidence Intervals:** While there's a slight increase in the mean value in one section, notice that the confidence intervals in that area still largely overlap with the intervals surrounding it. This overlap suggests the slight increase might not be statistically significant.
+
+**Conclusion:**
+
+The graph is **not strictly monotone** due to the small increase in mean value. However, it demonstrates a predominantly decreasing trend with increasing RoomService values. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The correct answer is **c) not monotone**. Here's why:
+
+* **Monotone Increasing:** A monotone increasing graph means the y-value never decreases as the x-value increases.  We can see this is not true, for example, the mean y-value for the interval (22448.0, 23794.0) is lower than the mean y-value for the interval (20043.5, 22448.0).
+
+* **Monotone Decreasing:** A monotone decreasing graph means the y-value never increases as the x-value increases. This is also not true. For example, the mean y-value for the interval (40007.0, 41128.5) is higher than the mean y-value for the interval (37439.5, 40007.0).
+
+* **Not Monotone:** Since the graph is neither consistently increasing nor consistently decreasing, it is not monotone. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+2. **Analyze the Mean Values:**
+   - Look at the "Means" values in the JSON data.  
+   - As the intervals of "TopographyDrainage" increase (0.0-0.5, 0.5-1.5, 1.5-2.5, ...), observe the corresponding mean values.
+   - We see that the mean values are consistently increasing: -0.0274, -0.02381, -0.01602,... 0.03564
+
+3. **Consider Confidence Intervals:**
+   - While the means show an increasing trend, it's important to check if the confidence intervals overlap significantly. Significant overlap might suggest the trend isn't strictly increasing.
+   - In this case, while there's some overlap, the overall trend within the confidence intervals remains largely increasing.
+
+**Conclusion:**
+
+Based on the analysis of mean values and considering the confidence intervals, the graph representing the "TopographyDrainage" feature is **a) monotone increasing**. 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+    - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. 
+    - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Examine the Means:**
+    - Look at the mean values provided in the JSON object.  
+    - Notice that as the intervals for "DeterioratingInfrastructure" increase (0.0-0.5, 0.5-1.5, 1.5-2.5, etc.), the corresponding mean values also increase:
+        - -0.02508, -0.01897, -0.01452,... 0.02782, 0.03175, 0.03686, 0.04451 
+
+3. **Consider Confidence Intervals:**
+    - While the means show an increasing trend, it's important to briefly check if the confidence intervals significantly contradict this. In this case, the confidence intervals are relatively narrow and don't suggest a deviation from the increasing pattern.
+
+**Conclusion:**
+
+The graph representing "DeterioratingInfrastructure" is **a) monotone increasing**. As the value of "DeterioratingInfrastructure" increases, the model's prediction also tends to increase. 
+
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+2. **Analyze the "Means" data:**  We'll focus on the "Means" values as they represent the general trend of the GAM function for the "Age" feature. 
+
+   -  Look for instances where the mean value decreases and then subsequently increases as age increases. This would indicate the function is not monotone.
+
+3. **Identify Key Intervals:**
+    - From the "Means" data:
+        - The mean decreases from age 17 to around 29.5.
+        - The mean generally increases from age 29.5 to 61.5.
+        - There's a fluctuation between 61.5 and 79.5, with means increasing and decreasing.
+
+**Conclusion:**
+
+Since the mean values do not consistently increase or decrease as age increases, the graph is **not monotone**. 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:**  A function is monotone increasing if, as the input increases, the output never decreases. It can stay flat or go up.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases. It can stay flat or go down.
+
+2. **Analyze the "Means" data:**
+   -  Look for instances where the mean value decreases as the age interval increases. 
+   - We see that the mean value increases from age 21.0 to around 54.5.  
+   - After 54.5, the mean value starts to decrease.
+
+3. **Conclusion:**
+   - Since the mean value both increases and decreases over the range of ages, the graph is **not monotone**. 
+
+**Therefore, the correct answer is (c).** 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output never decreases. 
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output never increases.
+
+2. **Analyze the "Means" data:**  We need to see if the mean values consistently increase or decrease as the CreditScore intervals increase.
+
+   -  Let's look at the initial values:
+      - (350.0, 416.5): 0.62
+      - (416.5, 421.5): 0.5698
+      - (421.5, 427.5): 0.3799 
+      - ... and so on
+
+   - We see that the mean values initially *decrease*. 
+
+   - However, as we move further down:
+      - ...
+      - (661.5, 684.5): 0.0186
+      - (684.5, 741.5): 0.0696
+      - ...
+      - (806.5, 850.0): -0.1138 
+
+    - We see fluctuations - the mean values increase and then decrease again.
+
+3. **Conclusion:**  Since the mean values do not consistently increase or decrease as the CreditScore increases, the graph is **not monotone**. 
+
+**Therefore, the correct answer is (c).** 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  Here's how to determine the monotonicity of the graph:
+
+1. **Understand Monotonicity:**
+   - **Monotone Increasing:** A function is monotone increasing if, as the input increases, the output either increases or stays the same. It never decreases.
+   - **Monotone Decreasing:** A function is monotone decreasing if, as the input increases, the output either decreases or stays the same. It never increases.
+
+2. **Analyze the "Means" data:**  We'll focus on the mean values as they represent the general trend of the function. Look at the mean values in each interval:
+
+   - (0.0, 135.5): 0.445
+   - (135.5, 215.5): 0.073 
+   - (215.5, 500.5): -0.294
+   - ... (and so on)
+
+3. **Identify the Trend:**  As the input (VRDeck value) increases, the mean output (y-axis value) generally decreases. There might be small fluctuations, but the overall trend is downwards.
+
+**Therefore, the correct answer is (b) monotone decreasing.** 
+
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
\ No newline at end of file
diff --git a/benchmarks/results/monotonicity-gpt-4-turbo-2024-04-09.txt b/benchmarks/results/monotonicity-gpt-4-turbo-2024-04-09.txt
new file mode 100644
index 0000000..56f6148
--- /dev/null
+++ b/benchmarks/results/monotonicity-gpt-4-turbo-2024-04-09.txt
@@ -0,0 +1,2990 @@
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "FoodCourt".
+
+Here are the mean values for each interval:
+- (0.0, 593.5): -0.177
+- (593.5, 779.5): 0.043
+- (779.5, 1341.5): 0.27
+- (1341.5, 2175.5): 0.543
+- (2175.5, 3125.0): 0.863
+- (3125.0, 3637.0): 1.13
+- (3637.0, 4078.5): 1.479
+- (4078.5, 5218.5): 2.076
+- (5218.5, 6031.5): 1.81
+- (6031.5, 6171.5): 1.439
+- (6171.5, 8753.0): 2.236
+- (8753.0, 8824.0): 2.746
+- (8824.0, 10094.5): 3.43
+- (10094.5, 12683.5): 3.888
+- (12683.5, 27723.0): 4.131
+
+Observing the trend:
+- From (0.0, 593.5) to (4078.5, 5218.5), the mean values are increasing.
+- However, there is a decrease from (4078.5, 5218.5) to (5218.5, 6031.5) and again from (5218.5, 6031.5) to (6031.5, 6171.5).
+- After this decrease, the values start increasing again from (6031.5, 6171.5) onwards.
+
+Since there are intervals where the mean values decrease before increasing again, the graph is not monotone increasing throughout its entire range. Similarly, it is not monotone decreasing as it mostly shows an increasing trend. Therefore, the correct answer is:
+
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the age intervals. Let's examine the mean values step by step:
+
+1. From age (40.0, 41.5) to (41.5, 43.5): -1.489 to -0.895 (increasing)
+2. From age (41.5, 43.5) to (43.5, 44.5): -0.895 to -0.02 (increasing)
+3. From age (43.5, 44.5) to (44.5, 47.5): -0.02 to 0.701 (increasing)
+4. From age (44.5, 47.5) to (47.5, 48.5): 0.701 to 1.245 (increasing)
+5. From age (47.5, 48.5) to (48.5, 58.5): 1.245 to -0.923 (decreasing)
+6. From age (48.5, 58.5) to (58.5, 59.5): -0.923 to 0.647 (increasing)
+7. From age (58.5, 59.5) to (59.5, 60.8335): 0.647 to -0.288 (decreasing)
+8. From age (59.5, 60.8335) to (60.8335, 64.5): -0.288 to -1.035 (decreasing)
+9. From age (60.8335, 64.5) to (64.5, 65.5): -1.035 to 0.0 (increasing)
+10. From age (64.5, 65.5) to (65.5, 67.5): 0.0 to -0.73 (decreasing)
+11. From age (65.5, 67.5) to (67.5, 68.5): -0.73 to 0.19 (increasing)
+12. From age (67.5, 68.5) to (68.5, 70.5): 0.19 to 0.784 (increasing)
+13. From age (68.5, 70.5) to (70.5, 80.5): 0.784 to 1.169 (increasing)
+14. From age (70.5, 80.5) to (80.5, 81.5): 1.169 to 0.839 (decreasing)
+15. From age (80.5, 81.5) to (81.5, 85.5): 0.839 to 2.112 (increasing)
+16. From age (81.5, 85.5) to (85.5, 86.5): 2.112 to 3.884 (increasing)
+17. From age (85.5, 86.5) to (86.5, 95.0): 3.884 to 4.517 (increasing)
+
+The mean values show both increases and decreases as age progresses through the intervals. Therefore, the graph is not monotone increasing or monotone decreasing; it exhibits both increasing and decreasing trends at different intervals.
+
+The correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "Spa".
+
+Here are the mean values for each interval:
+- (0.0, 130.5): 0.521
+- (130.5, 278.5): 0.118
+- (278.5, 452.5): -0.285
+- (452.5, 754.5): -0.907
+- (754.5, 1209.5): -1.309
+- (1209.5, 1808.0): -1.712
+- (1808.0, 2204.5): -3.029
+- (2204.5, 2207.5): -2.456
+- (2207.5, 2428.0): -2.956
+- (2428.0, 2462.5): -2.512
+- (2462.5, 2714.5): -3.402
+- (2714.5, 2745.0): -2.902
+- (2745.0, 2993.5): -4.077
+- (2993.5, 3132.0): -4.481
+- (3132.0, 3705.5): -5.377
+- (3705.5, 3747.0): -4.36
+- (3747.0, 22408.0): -7.183
+
+Observing the trend:
+- From (0.0, 130.5) to (130.5, 278.5), the value decreases from 0.521 to 0.118.
+- This decreasing trend continues consistently through the intervals up to (3132.0, 3705.5), where the value reaches -5.377.
+- There is a slight increase from (3132.0, 3705.5) to (3705.5, 3747.0), where the value increases to -4.36.
+- After this, the value decreases again from (3705.5, 3747.0) to (3747.0, 22408.0), reaching -7.183.
+
+The overall trend is decreasing, but there is a slight increase in the interval from (3132.0, 3705.5) to (3705.5, 3747.0). Therefore, the graph is not strictly monotone decreasing due to this increase.
+
+Given this analysis, the correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "id".
+
+Here are the mean values for each interval:
+- (8670.0, 90271.0): 0.342
+- (90271.0, 467526.5): 0.574
+- (467526.5, 853506.5): 0.657
+- (853506.5, 859643.0): 0.719
+- (859643.0, 864727.5): 0.655
+- (864727.5, 871421.0): 0.593
+- (871421.0, 874848.5): 0.528
+- (874848.5, 880845.5): 0.464
+- (880845.5, 882230.0): 0.399
+- (882230.0, 883266.5): 0.319
+- (883266.5, 889561.0): 0.171
+- (889561.0, 892521.0): 0.103
+- (892521.0, 894330.5): 0.039
+- (894330.5, 896851.5): -0.023
+- (896851.5, 899167.0): -0.107
+- (899167.0, 902138.0): -0.176
+- (902138.0, 905080.5): -0.241
+- (905080.5, 906551.5): -0.305
+- (906551.5, 911540.5): -0.368
+- (911540.5, 917896.5): -0.431
+- (917896.5, 8810615.5): -0.493
+- (8810615.5, 9112480.5): -0.386
+- (9112480.5, 89803401.5): -0.323
+- (89803401.5, 91544001.5): -0.259
+- (91544001.5, 91903901.5): -0.191
+- (91903901.5, 911320502.0): -0.121
+
+Observing the trend:
+- From (8670.0, 90271.0) to (853506.5, 859643.0), the values increase.
+- From (859643.0, 864727.5) onwards, the values generally decrease, with a few minor increases towards the end.
+
+Since the values initially increase and then decrease, the function is not monotone increasing or monotone decreasing. Therefore, the correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "Watersheds".
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.02526
+- (0.5, 1.5): -0.02147
+- (1.5, 2.5): -0.01542
+- (2.5, 3.5): -0.01026
+- (3.5, 4.5): -0.00466
+- (4.5, 5.5): 0.00049
+- (5.5, 6.5): 0.00555
+- (6.5, 8.5): 0.01133
+- (8.5, 10.5): 0.02234
+- (10.5, 11.5): 0.03241
+- (11.5, 12.5): 0.03775
+- (12.5, 13.5): 0.04216
+- (13.5, 14.0): 0.04656
+
+Observing these values, we see that each subsequent interval has a higher mean value than the previous one. This indicates that as the value of the "Watersheds" feature increases, the mean value of the function also increases.
+
+Since the mean values consistently increase as we move from one interval to the next, the graph is **monotone increasing**.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "Deforestation."
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.02956
+- (0.5, 2.5): -0.02081
+- (2.5, 3.5): -0.00998
+- (3.5, 4.5): -0.00524
+- (4.5, 5.5): 0.00043
+- (5.5, 6.5): 0.00515
+- (6.5, 8.5): 0.01107
+- (8.5, 10.5): 0.02102
+- (10.5, 11.5): 0.02728
+- (11.5, 13.5): 0.0456
+- (13.5, 14.5): 0.05244
+- (14.5, 17.0): 0.06161
+
+Observing the trend:
+- From (0.0, 0.5) to (0.5, 2.5), the value increases from -0.02956 to -0.02081.
+- This increasing trend continues consistently through all subsequent intervals, with each interval's mean value being higher than the previous one.
+
+Since the mean values consistently increase as the intervals progress, the graph is **monotone increasing**.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "area_se".
+
+Here are the mean values for each interval:
+- (6.802, 11.184999999999999): -0.919
+- (11.184999999999999, 12.765): -0.814
+- (12.765, 13.350000000000001): -0.704
+- (13.350000000000001, 15.3): -0.596
+- (15.3, 16.955): -0.49
+- (16.955, 18.515): -0.367
+- (18.515, 20.905): -0.256
+- (20.905, 32.985): -0.151
+- (32.985, 34.730000000000004): 0.081
+- (34.730000000000004, 41.21): 0.188
+- (41.21, 50.405): 0.292
+- (50.405, 56.915): 0.417
+- (56.915, 67.5): 0.53
+- (67.5, 81.56): 0.638
+- (81.56, 94.00999999999999): 0.751
+- (94.00999999999999, 106.2): 0.862
+- (106.2, 153.25): 0.974
+- (153.25, 542.2): 1.082
+
+Observing the mean values, we see that they consistently increase as the intervals progress from the smallest to the largest. Each subsequent interval has a higher mean value than the previous one.
+
+Therefore, the graph is **monotone increasing**.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph of the generalized additive model (GAM) for the feature "IsActiveMember" is monotone increasing, monotone decreasing, or not monotone, we need to analyze the mean values across the specified intervals of the feature.
+
+The feature "IsActiveMember" is continuous, and the graph provides mean values for two intervals:
+1. Interval (0.0, 0.5) has a mean value of -0.555.
+2. Interval (0.5, 1.0) has a mean value of 0.568.
+
+Step-by-step analysis:
+- In the first interval (0.0, 0.5), the mean value is -0.555.
+- In the second interval (0.5, 1.0), the mean value is 0.568.
+
+Observing the mean values, we see that the mean value increases as we move from the first interval to the second interval. Specifically, it goes from a negative value (-0.555) to a positive value (0.568).
+
+Since the mean value increases as the feature value increases, the graph is monotone increasing. Therefore, the correct answer is:
+a) monotone increasing.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine whether the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the specified intervals of the "Relationship" feature.
+
+Here are the mean values for each interval:
+- (0.0, 0.5): 0.511
+- (0.5, 1.5): -0.233
+- (1.5, 2.5): -0.666
+- (2.5, 3.5): -1.006
+- (3.5, 4.5): -0.529
+- (4.5, 5.0): 1.753
+
+Now, let's examine the trend:
+1. From (0.0, 0.5) to (0.5, 1.5), the mean value decreases from 0.511 to -0.233.
+2. From (0.5, 1.5) to (1.5, 2.5), the mean value decreases further from -0.233 to -0.666.
+3. From (1.5, 2.5) to (2.5, 3.5), the mean value continues to decrease from -0.666 to -1.006.
+4. From (2.5, 3.5) to (3.5, 4.5), the mean value increases from -1.006 to -0.529.
+5. From (3.5, 4.5) to (4.5, 5.0), the mean value increases significantly from -0.529 to 1.753.
+
+The trend in the mean values is not consistent throughout the intervals. Initially, there is a decreasing trend until the interval (2.5, 3.5), after which the trend reverses and starts increasing. This change in direction from decreasing to increasing indicates that the function is not monotone increasing or monotone decreasing.
+
+Therefore, the correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "ShoppingMall".
+
+Here are the mean values for each interval:
+- (0.0, 125.5): -0.032
+- (125.5, 541.5): -0.211
+- (541.5, 808.5): 0.034
+- (808.5, 1082.0): 0.213
+- (1082.0, 1187.0): -0.042
+- (1187.0, 1434.5): 0.401
+- (1434.5, 1658.5): 0.585
+- (1658.5, 1968.5): 0.948
+- (1968.5, 3394.5): 1.235
+- (3394.5, 3460.0): 0.871
+- (3460.0, 3741.5): 1.066
+- (3741.5, 4803.5): 2.339
+- (4803.5, 5204.0): 2.909
+- (5204.0, 12253.0): 3.236
+
+Analyzing the trend:
+- From (0.0, 125.5) to (125.5, 541.5), the mean decreases from -0.032 to -0.211.
+- From (125.5, 541.5) to (541.5, 808.5), the mean increases from -0.211 to 0.034.
+- From (541.5, 808.5) to (808.5, 1082.0), the mean increases from 0.034 to 0.213.
+- From (808.5, 1082.0) to (1082.0, 1187.0), the mean decreases from 0.213 to -0.042.
+- From (1082.0, 1187.0) to (1187.0, 1434.5), the mean increases from -0.042 to 0.401.
+- From (1187.0, 1434.5) to (1434.5, 1658.5), the mean increases from 0.401 to 0.585.
+- From (1434.5, 1658.5) to (1658.5, 1968.5), the mean increases from 0.585 to 0.948.
+- From (1658.5, 1968.5) to (1968.5, 3394.5), the mean increases from 0.948 to 1.235.
+- From (1968.5, 3394.5) to (3394.5, 3460.0), the mean decreases from 1.235 to 0.871.
+- From (3394.5, 3460.0) to (3460.0, 3741.5), the mean increases from 0.871 to 1.066.
+- From (3460.0, 3741.5) to (3741.5, 4803.5), the mean increases from 1.066 to 2.339.
+- From (3741.5, 4803.5) to (4803.5, 5204.0), the mean increases from 2.339 to 2.909.
+- From (4803.5, 5204.0) to (5204.0, 12253.0), the mean increases from 2.909 to 3.236.
+
+The trend is not consistently increasing or decreasing across all intervals. There are instances where the mean value decreases from one interval to the next, followed by increases in subsequent intervals.
+
+Therefore, the correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "Siltation."
+
+Here are the mean values for each interval:
+- (0.0, 1.5): -0.02643
+- (1.5, 2.5): -0.01529
+- (2.5, 3.5): -0.01037
+- (3.5, 4.5): -0.00562
+- (4.5, 5.5): 0.00068
+- (5.5, 6.5): 0.00591
+- (6.5, 7.5): 0.01127
+- (7.5, 8.5): 0.01553
+- (8.5, 10.5): 0.02363
+- (10.5, 11.5): 0.03038
+- (11.5, 12.5): 0.03607
+- (12.5, 13.5): 0.04087
+- (13.5, 15.0): 0.04477
+
+Observing the trend:
+- From (0.0, 1.5) to (1.5, 2.5), the value increases from -0.02643 to -0.01529.
+- This increasing trend continues consistently through all subsequent intervals, with each interval showing a higher mean value than the previous one.
+
+Since the mean values consistently increase as the intervals progress, the graph represents a monotone increasing function. Therefore, the correct answer is:
+a) monotone increasing.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "area_worst".
+
+Here are the mean values for each interval:
+- (185.2, 357.5): -1.345
+- (357.5, 413.15): -1.192
+- (413.15, 471.9): -1.038
+- (471.9, 508.5): -0.878
+- (508.5, 633.9): -0.723
+- (633.9, 653.45): -0.565
+- (653.45, 710.2): -0.348
+- (710.2, 727.0999999999999): -0.165
+- (727.0999999999999, 805.95): 0.096
+- (805.95, 874.85): 0.253
+- (874.85, 928.5): 0.48
+- (928.5, 1033.5): 0.761
+- (1033.5, 1222.5): 0.932
+- (1222.5, 1346.5): 1.092
+- (1346.5, 1645.5): 1.245
+- (1645.5, 1979.0): 1.404
+- (1979.0, 4254.0): 1.557
+
+Observing the mean values, we see that each subsequent interval has a higher mean value than the previous one. This indicates that as the "area_worst" increases, the corresponding mean value also increases.
+
+Therefore, the graph is **monotone increasing**.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "Encroachments."
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.02484
+- (0.5, 1.5): -0.02089
+- (1.5, 2.5): -0.01739
+- (2.5, 3.5): -0.01124
+- (3.5, 4.5): -0.00474
+- (4.5, 5.5): 0.00077
+- (5.5, 6.5): 0.00574
+- (6.5, 7.5): 0.01068
+- (7.5, 8.5): 0.01599
+- (8.5, 9.5): 0.02231
+- (9.5, 10.5): 0.02667
+- (10.5, 13.5): 0.03305
+- (13.5, 16.0): 0.02016
+
+Observing the trend:
+- From (0.0, 0.5) to (10.5, 13.5), the mean values are increasing, indicating a positive trend.
+- However, from (10.5, 13.5) to (13.5, 16.0), there is a decrease in the mean value from 0.03305 to 0.02016.
+
+Since the values initially increase and then decrease, the function is not strictly monotone increasing or decreasing throughout its entire range. Therefore, the correct answer is:
+
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine whether the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "RiverManagement."
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.0273
+- (0.5, 1.5): -0.02345
+- (1.5, 2.5): -0.01571
+- (2.5, 3.5): -0.01174
+- (3.5, 4.5): -0.00519
+- (4.5, 5.5): 0.00111
+- (5.5, 6.5): 0.00506
+- (6.5, 7.5): 0.01056
+- (7.5, 8.5): 0.01706
+- (8.5, 9.5): 0.02398
+- (9.5, 11.5): 0.02821
+- (11.5, 12.5): 0.03673
+- (12.5, 13.5): 0.01311
+- (13.5, 16.0): 0.03206
+
+Observing the trend:
+- From (0.0, 0.5) to (11.5, 12.5), the mean values are increasing, indicating a monotone increasing trend.
+- However, there is a decrease in the mean value from (11.5, 12.5) at 0.03673 to (12.5, 13.5) at 0.01311.
+- After this decrease, the mean value increases again from (12.5, 13.5) to (13.5, 16.0).
+
+Since there is a decrease in the sequence of mean values at the interval (12.5, 13.5), the function is not strictly monotone increasing across the entire range. Similarly, it is not monotone decreasing as the general trend is upwards.
+
+Therefore, the correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine whether the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the BMI intervals.
+
+Here are the mean values for each BMI interval:
+- (0.0, 9.1): -0.7
+- (9.1, 22.55): -0.961
+- (22.55, 23.65): -0.856
+- (23.65, 25.55): -0.762
+- (25.55, 26.35): -0.661
+- (26.35, 27.65): -0.24
+- (27.65, 28.45): -0.144
+- (28.45, 29.65): -0.051
+- (29.65, 30.45): 0.049
+- (30.45, 32.150000000000006): 0.153
+- (32.150000000000006, 37.650000000000006): 0.246
+- (37.650000000000006, 41.75): 0.34
+- (41.75, 42.849999999999994): 0.434
+- (42.849999999999994, 45.650000000000006): 0.529
+- (45.650000000000006, 48.349999999999994): 0.626
+- (48.349999999999994, 67.1): 0.784
+
+Observing the trend:
+- From (0.0, 9.1) to (9.1, 22.55), the value decreases from -0.7 to -0.961.
+- From (9.1, 22.55) to (22.55, 23.65), the value increases from -0.961 to -0.856.
+- From (22.55, 23.65) to (25.55, 26.35), the values continue to increase, reaching -0.661.
+- From (25.55, 26.35) onwards, the values consistently increase through all subsequent intervals, ending at 0.784 for the interval (48.349999999999994, 67.1).
+
+The graph shows an initial decrease followed by a consistent increase. Therefore, the graph is not monotone decreasing (as it does not consistently decrease) nor is it monotone increasing (as it does not consistently increase from the very beginning). 
+
+Thus, the correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "texture_worst".
+
+Here are the mean values for each interval:
+- (12.02, 16.935000000000002): -1.885
+- (16.935000000000002, 18.335): -1.717
+- (18.335, 19.505): -1.55
+- (19.505, 20.225): -0.851
+- (20.225, 21.955): -0.612
+- (21.955, 23.59): -0.44
+- (23.59, 24.795): -0.272
+- (24.795, 25.18): -0.1
+- (25.18, 25.83): 0.078
+- (25.83, 26.855): 0.279
+- (26.855, 27.994999999999997): 0.451
+- (27.994999999999997, 29.225): 0.619
+- (29.225, 31.515): 0.878
+- (31.515, 32.485): 1.044
+- (32.485, 35.05): 1.256
+- (35.05, 49.54): 1.423
+
+Observing the mean values, we see that they start at -1.885 and consistently increase as the intervals progress, ending at 1.423. There is no interval where the mean value decreases relative to the previous interval.
+
+Therefore, the graph is **monotone increasing**.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the age intervals.
+
+Here are the mean values for each age interval:
+- (18.0, 32.5): 0.83
+- (32.5, 34.5): 0.681
+- (34.5, 37.5): 0.423
+- (37.5, 38.5): 0.281
+- (38.5, 39.5): 0.054
+- (39.5, 40.5): -0.193
+- (40.5, 41.5): -0.354
+- (41.5, 42.5): -0.494
+- (42.5, 44.5): -0.781
+- (44.5, 46.5): -1.075
+- (46.5, 48.5): -1.546
+- (48.5, 54.5): -1.717
+- (54.5, 56.5): -1.858
+- (56.5, 64.5): -1.707
+- (64.5, 66.5): -1.27
+- (66.5, 69.5): -1.118
+- (69.5, 70.5): -0.888
+- (70.5, 72.5): -0.587
+- (72.5, 74.5): -0.31
+- (74.5, 81.0): -0.157
+
+Observing the trend:
+- From (18.0, 32.5) to (48.5, 54.5), the mean values consistently decrease.
+- From (54.5, 56.5) to (56.5, 64.5), there is a slight increase followed by a decrease.
+- From (64.5, 66.5) to (74.5, 81.0), the mean values consistently increase.
+
+Since the values first decrease, then increase, and then decrease again, the function is not consistently moving in one direction (either always increasing or always decreasing) across the entire range of the feature. Therefore, the graph is **not monotone**.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the petal_width feature.
+
+Here are the mean values for each interval:
+- (0.1, 0.35): 8.07
+- (0.35, 0.45): 7.27
+- (0.45, 0.75): 6.18
+- (0.75, 1.25): -2.64
+- (1.25, 1.75): -3.46
+- (1.75, 2.5): -4.19
+
+Now, let's examine the trend:
+- From (0.1, 0.35) to (0.35, 0.45), the mean value decreases from 8.07 to 7.27.
+- From (0.35, 0.45) to (0.45, 0.75), the mean value decreases further from 7.27 to 6.18.
+- From (0.45, 0.75) to (0.75, 1.25), there is a significant drop from 6.18 to -2.64.
+- From (0.75, 1.25) to (1.25, 1.75), the mean value decreases again from -2.64 to -3.46.
+- From (1.25, 1.75) to (1.75, 2.5), the mean value continues to decrease from -3.46 to -4.19.
+
+In each interval, the mean value consistently decreases. Therefore, the graph is monotone decreasing.
+SOLUTION:  Decreasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the mean values of the function across the specified intervals and observe the trend.
+
+1. **Intervals and Mean Values:**
+   - Interval (0.0, 0.5): Mean = -0.0751
+   - Interval (0.5, 2.5): Mean = 0.1633
+   - Interval (2.5, 3.0): Mean = -0.7301
+
+2. **Analysis of Trends:**
+   - From Interval (0.0, 0.5) to Interval (0.5, 2.5), the mean value increases from -0.0751 to 0.1633. This indicates an increasing trend.
+   - From Interval (0.5, 2.5) to Interval (2.5, 3.0), the mean value decreases significantly from 0.1633 to -0.7301. This indicates a decreasing trend.
+
+3. **Conclusion:**
+   - The function first increases and then decreases. Therefore, it is not monotone increasing or monotone decreasing.
+
+Based on this analysis, the correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "SkinThickness".
+
+Here are the mean values for each interval:
+- (0.0, 3.5): 0.0121
+- (3.5, 7.5): -0.0407
+- (7.5, 9.0): -0.0873
+- (9.0, 11.5): -0.1192
+- (11.5, 13.5): -0.1587
+- (13.5, 20.5): -0.1856
+- (20.5, 22.5): -0.1532
+- (22.5, 24.5): -0.1123
+- (24.5, 26.5): -0.0708
+- (26.5, 28.5): -0.036
+- (28.5, 30.5): -0.0039
+- (30.5, 32.5): 0.0343
+- (32.5, 34.5): 0.0703
+- (34.5, 39.5): 0.1069
+- (39.5, 40.5): 0.143
+- (40.5, 41.5): 0.1769
+- (41.5, 43.5): 0.2279
+- (43.5, 47.5): 0.2859
+- (47.5, 49.5): 0.2453
+- (49.5, 51.0): -0.0169
+- (51.0, 55.0): -0.0754
+- (55.0, 77.5): 0.2174
+- (77.5, 99.0): 0.3109
+
+Analyzing the trend:
+- From (0.0, 3.5) to (13.5, 20.5), the values decrease consistently, suggesting a monotone decreasing trend.
+- From (20.5, 22.5) to (43.5, 47.5), the values increase consistently, suggesting a monotone increasing trend.
+- However, there is a decrease from (47.5, 49.5) to (51.0, 55.0), followed by an increase again from (55.0, 77.5) to (77.5, 99.0).
+
+Given that the values first decrease, then increase, decrease again, and finally increase, the graph is not monotone. The values do not consistently increase or decrease throughout the entire range of the feature "SkinThickness".
+
+Therefore, the correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the DiabetesPedigreeFunction.
+
+Here are the mean values for each interval:
+- (0.078, 0.1265): -0.528
+- (0.1265, 0.128): -0.218
+- (0.128, 0.2185): -0.342
+- (0.2185, 0.3375): -0.168
+- (0.3375, 0.4215): -0.077
+- (0.4215, 0.4955): 0.015
+- (0.4955, 0.5874999999999999): 0.131
+- (0.5874999999999999, 0.7215): 0.223
+- (0.7215, 0.889): 0.316
+- (0.889, 1.0865): 0.407
+- (1.0865, 1.178): 0.498
+- (1.178, 1.275): 1.018
+- (1.275, 1.3925): 1.283
+- (1.3925, 1.4175): 1.168
+- (1.4175, 1.451): 0.065
+- (1.451, 1.837): -0.193
+- (1.837, 2.137): -0.092
+
+Analyzing the trend:
+- From (0.078, 0.1265) to (0.4215, 0.4955), the values generally increase, though there is a slight decrease from (0.1265, 0.128) to (0.128, 0.2185).
+- From (0.4215, 0.4955) to (1.3925, 1.4175), there is a consistent increase.
+- However, from (1.3925, 1.4175) to (1.451, 1.837), there is a significant decrease in the mean values.
+- The value slightly increases again from (1.451, 1.837) to (1.837, 2.137).
+
+Given that there are intervals where the function decreases after previously increasing, the graph is not monotone increasing. Similarly, it is not monotone decreasing as it does not consistently decrease across all intervals. Therefore, the correct answer is:
+
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the specified intervals. Let's examine the mean values in the order of the intervals:
+
+1. (0.0, 0.5): -0.297
+2. (0.5, 3.5): -0.074
+3. (3.5, 4.5): 0.644
+4. (4.5, 6.5): -0.723
+5. (6.5, 7.5): -0.542
+6. (7.5, 8.5): -0.665
+7. (8.5, 9.5): -0.926
+8. (9.5, 10.5): 0.423
+9. (10.5, 11.5): 0.59
+10. (11.5, 12.5): 0.27
+11. (12.5, 13.5): 0.534
+12. (13.5, 14.0): -0.133
+
+Analyzing the trend:
+- From (0.0, 0.5) to (0.5, 3.5), the value increases from -0.297 to -0.074.
+- From (0.5, 3.5) to (3.5, 4.5), the value increases from -0.074 to 0.644.
+- From (3.5, 4.5) to (4.5, 6.5), the value decreases significantly from 0.644 to -0.723.
+- From (4.5, 6.5) to (6.5, 7.5), the value increases slightly from -0.723 to -0.542.
+- From (6.5, 7.5) to (7.5, 8.5), the value decreases from -0.542 to -0.665.
+- From (7.5, 8.5) to (8.5, 9.5), the value decreases from -0.665 to -0.926.
+- From (8.5, 9.5) to (9.5, 10.5), the value increases from -0.926 to 0.423.
+- From (9.5, 10.5) to (10.5, 11.5), the value increases from 0.423 to 0.59.
+- From (10.5, 11.5) to (11.5, 12.5), the value decreases from 0.59 to 0.27.
+- From (11.5, 12.5) to (12.5, 13.5), the value increases from 0.27 to 0.534.
+- From (12.5, 13.5) to (13.5, 14.0), the value decreases from 0.534 to -0.133.
+
+The values do not consistently increase or decrease across the intervals. There are multiple points where the trend changes from increasing to decreasing and vice versa.
+
+Therefore, the correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the ejection_fraction feature.
+
+Here are the mean values for each interval:
+- (14.0, 16.0): 4.55
+- (16.0, 22.5): 3.26
+- (22.5, 27.5): 1.89
+- (27.5, 32.5): -0.42
+- (32.5, 36.5): -1.76
+- (36.5, 39.0): 0.48
+- (39.0, 61.0): -0.83
+- (61.0, 67.5): 0.08
+- (67.5, 75.0): 0.8
+- (75.0, 80.0): -5.67
+
+Now, let's analyze the trend:
+- From (14.0, 16.0) to (16.0, 22.5) to (22.5, 27.5) to (27.5, 32.5) to (32.5, 36.5), the values decrease: 4.55 → 3.26 → 1.89 → -0.42 → -1.76.
+- However, from (32.5, 36.5) to (36.5, 39.0), there is an increase: -1.76 → 0.48.
+- Then, from (36.5, 39.0) to (39.0, 61.0), it decreases again: 0.48 → -0.83.
+- From (39.0, 61.0) to (61.0, 67.5) to (67.5, 75.0), it increases: -0.83 → 0.08 → 0.8.
+- Finally, from (67.5, 75.0) to (75.0, 80.0), there is a significant decrease: 0.8 → -5.67.
+
+Given this analysis, the values first generally decrease, then increase, decrease again, increase again, and finally decrease significantly. This pattern of alternating increases and decreases indicates that the function is not consistently moving in one direction (neither consistently increasing nor consistently decreasing).
+
+Therefore, the correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the CustomerId feature.
+
+Here are the mean values for each interval:
+1. (-0.8769)
+2. (-0.8241)
+3. (-0.1763)
+4. (0.0021)
+5. (-0.2283)
+6. (-0.0522)
+7. (-0.1299)
+8. (-0.0821)
+9. (-0.1509)
+10. (-0.0818)
+11. (-0.0316)
+12. (0.0162)
+13. (0.0757)
+14. (0.0081)
+15. (0.0581)
+16. (-0.0049)
+17. (-0.0569)
+18. (-0.111)
+19. (-0.0335)
+
+Analyzing the sequence:
+- The values start at -0.8769 and increase to -0.8241.
+- There is a significant increase to -0.1763, followed by a slight increase to 0.0021.
+- The value then decreases to -0.2283, increases slightly to -0.0522, and then decreases again to -0.1299.
+- The values fluctuate around this range, with minor increases and decreases, until reaching a peak at 0.0757.
+- After this peak, the values decrease to 0.0081, increase to 0.0581, decrease again to -0.0049, and continue to decrease to -0.111 before slightly increasing to -0.0335.
+
+From this analysis, the graph is not monotone increasing as it does not consistently increase throughout the intervals. It is also not monotone decreasing as it does not consistently decrease throughout the intervals. The values fluctuate, showing both increases and decreases at different intervals.
+
+Therefore, the correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine whether the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "WetlandLoss."
+
+Here are the mean values for each interval:
+- (0.0, 1.5): -0.02419
+- (1.5, 2.5): -0.01693
+- (2.5, 3.5): -0.01069
+- (3.5, 4.5): -0.00585
+- (4.5, 5.5): 0.00051
+- (5.5, 6.5): 0.00676
+- (6.5, 8.5): 0.01245
+- (8.5, 10.5): 0.02257
+- (10.5, 11.5): 0.03265
+- (11.5, 13.5): 0.03889
+- (13.5, 14.5): 0.04912
+- (14.5, 16.0): 0.0585
+
+Observing the trend:
+- From (0.0, 1.5) to (1.5, 2.5), the value increases from -0.02419 to -0.01693.
+- This increasing trend continues consistently through all subsequent intervals, with each interval showing a higher mean value than the previous one.
+
+Since the mean values consistently increase as the intervals progress, the graph represents a monotone increasing function. Therefore, the correct answer is:
+a) monotone increasing
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the glucose intervals.
+
+Here are the mean values for each interval:
+- (0.0, 22.0): -0.728
+- (22.0, 86.5): -1.069
+- (86.5, 94.5): -0.907
+- (94.5, 99.5): -0.729
+- (99.5, 105.5): -0.491
+- (105.5, 114.5): -0.326
+- (114.5, 123.5): -0.157
+- (123.5, 130.5): 0.045
+- (130.5, 139.5): 0.208
+- (139.5, 147.5): 0.37
+- (147.5, 154.5): 0.535
+- (154.5, 159.5): 0.724
+- (159.5, 165.5): 0.984
+- (165.5, 169.5): 1.342
+- (169.5, 178.5): 1.502
+- (178.5, 187.5): 1.691
+- (187.5, 198.5): 1.853
+- (198.5, 199.0): 2.022
+
+Observing the trend:
+- From (0.0, 22.0) to (22.0, 86.5), the value decreases from -0.728 to -1.069.
+- From (22.0, 86.5) onwards, the values consistently increase up to the last interval (198.5, 199.0) where it reaches 2.022.
+
+Since the values initially decrease and then consistently increase, the graph is not monotone increasing throughout the entire range nor is it monotone decreasing. Therefore, the correct answer is:
+
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "ClimateChange".
+
+Here are the mean values for each interval:
+- (0.0, 1.5): -0.02549
+- (1.5, 2.5): -0.01575
+- (2.5, 3.5): -0.01061
+- (3.5, 4.5): -0.0046
+- (4.5, 5.5): 0.00059
+- (5.5, 6.5): 0.00567
+- (6.5, 7.5): 0.01201
+- (7.5, 9.5): 0.01601
+- (9.5, 10.5): 0.02531
+- (10.5, 11.5): 0.02956
+- (11.5, 12.5): 0.04031
+- (12.5, 14.0): 0.04423
+
+Observing the trend:
+- From (0.0, 1.5) to (1.5, 2.5), the value increases from -0.02549 to -0.01575.
+- From (1.5, 2.5) to (2.5, 3.5), the value increases from -0.01575 to -0.01061.
+- From (2.5, 3.5) to (3.5, 4.5), the value increases from -0.01061 to -0.0046.
+- From (3.5, 4.5) to (4.5, 5.5), the value increases from -0.0046 to 0.00059.
+- From (4.5, 5.5) to (5.5, 6.5), the value increases from 0.00059 to 0.00567.
+- From (5.5, 6.5) to (6.5, 7.5), the value increases from 0.00567 to 0.01201.
+- From (6.5, 7.5) to (7.5, 9.5), the value increases from 0.01201 to 0.01601.
+- From (7.5, 9.5) to (9.5, 10.5), the value increases from 0.01601 to 0.02531.
+- From (9.5, 10.5) to (10.5, 11.5), the value increases from 0.02531 to 0.02956.
+- From (10.5, 11.5) to (11.5, 12.5), the value increases from 0.02956 to 0.04031.
+- From (11.5, 12.5) to (12.5, 14.0), the value increases from 0.04031 to 0.04423.
+
+Since the mean values consistently increase as we move from one interval to the next, the graph is **monotone increasing**.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "Pregnancies".
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.1506
+- (0.5, 1.5): -0.2484
+- (1.5, 2.5): -0.1873
+- (2.5, 3.5): -0.0302
+- (3.5, 4.5): 0.0211
+- (4.5, 5.5): 0.1013
+- (5.5, 6.5): 0.1489
+- (6.5, 7.5): 0.264
+- (7.5, 8.5): 0.3553
+- (8.5, 9.5): 0.4117
+- (9.5, 13.5): 0.2996
+- (13.5, 14.0): 0.6729
+
+Step-by-step analysis:
+1. From (0.0, 0.5) to (0.5, 1.5), the mean value decreases from -0.1506 to -0.2484.
+2. From (0.5, 1.5) to (1.5, 2.5), the mean value increases from -0.2484 to -0.1873.
+3. From (1.5, 2.5) to (2.5, 3.5), the mean value increases from -0.1873 to -0.0302.
+4. From (2.5, 3.5) to (3.5, 4.5), the mean value increases from -0.0302 to 0.0211.
+5. From (3.5, 4.5) to (4.5, 5.5), the mean value increases from 0.0211 to 0.1013.
+6. From (4.5, 5.5) to (5.5, 6.5), the mean value increases from 0.1013 to 0.1489.
+7. From (5.5, 6.5) to (6.5, 7.5), the mean value increases from 0.1489 to 0.264.
+8. From (6.5, 7.5) to (7.5, 8.5), the mean value increases from 0.264 to 0.3553.
+9. From (7.5, 8.5) to (8.5, 9.5), the mean value increases from 0.3553 to 0.4117.
+10. From (8.5, 9.5) to (9.5, 13.5), the mean value decreases from 0.4117 to 0.2996.
+11. From (9.5, 13.5) to (13.5, 14.0), the mean value increases from 0.2996 to 0.6729.
+
+From this analysis, we observe that the mean values generally increase as the number of pregnancies increases, except for a decrease between the intervals (0.5, 1.5) to (1.5, 2.5) and (8.5, 9.5) to (9.5, 13.5). Therefore, the graph is not strictly monotone increasing or decreasing throughout all intervals.
+
+The correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph of the generalized additive model (GAM) for the feature "creatinine_phosphokinase" is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the specified intervals.
+
+Here's a step-by-step analysis of the mean values:
+
+1. **(23.0, 32.0)**: -0.48
+2. **(32.0, 49.5)**: 0.68
+3. **(49.5, 56.5)**: -4.31
+4. **(56.5, 59.5)**: -2.44
+5. **(59.5, 64.5)**: -1.82
+6. **(64.5, 85.0)**: -1.1
+7. **(85.0, 87.0)**: 0.42
+8. **(87.0, 93.5)**: -0.75
+9. **(93.5, 94.5)**: 0.47
+10. **(94.5, 103.5)**: -0.53
+11. **(103.5, 107.5)**: 0.12
+12. **(107.5, 120.0)**: -0.5
+13. **(120.0, 121.5)**: 0.24
+14. **(121.5, 126.0)**: 1.25
+15. **(126.0, 127.5)**: -3.14
+16. **(127.5, 145.5)**: 1.51
+17. **(145.5, 147.0)**: 0.91
+18. **(147.0, 150.0)**: -0.15
+19. **(150.0, 160.5)**: -1.08
+20. **(160.5, 189.5)**: -0.45
+21. **(189.5, 232.5)**: -1.26
+22. **(232.5, 254.5)**: -0.16
+23. **(254.5, 258.5)**: 2.88
+24. **(258.5, 280.5)**: 1.68
+25. **(280.5, 331.5)**: 1.11
+26. **(331.5, 370.0)**: 0.44
+27. **(370.0, 462.0)**: 1.1
+28. **(462.0, 597.5)**: 0.53
+29. **(597.5, 751.0)**: -1.87
+30. **(751.0, 766.5)**: 0.06
+31. **(766.5, 806.0)**: 2.64
+32. **(806.0, 873.5)**: 2.05
+33. **(873.5, 1036.0)**: 0.28
+34. **(1036.0, 1415.0)**: 0.85
+35. **(1415.0, 1649.0)**: 0.18
+36. **(1649.0, 1726.0)**: 2.26
+37. **(1726.0, 1886.0)**: 0.04
+38. **(1886.0, 2038.5)**: 7.0
+39. **(2038.5, 2307.5)**: 2.26
+40. **(2307.5, 2444.0)**: 5.81
+41. **(2444.0, 3440.5)**: -2.71
+42. **(3440.5, 4253.0)**: -1.47
+43. **(4253.0, 5548.5)**: 1.68
+44. **(5548.5, 7861.0)**: 3.47
+
+From the analysis, it is evident that the mean values do not consistently increase or decrease across the intervals. There are several points where the trend reverses, showing increases followed by decreases and vice versa.
+
+Therefore, the correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "EducationNum."
+
+Here are the mean values for each interval:
+- (1.0, 1.5): -4.746
+- (1.5, 4.5): -1.252
+- (4.5, 6.5): -0.882
+- (6.5, 9.5): -0.483
+- (9.5, 11.5): -0.093
+- (11.5, 13.5): 0.276
+- (13.5, 14.5): 0.863
+- (14.5, 16.0): 1.487
+
+Observing the mean values:
+- From (1.0, 1.5) to (1.5, 4.5), the value increases from -4.746 to -1.252.
+- From (1.5, 4.5) to (4.5, 6.5), the value increases from -1.252 to -0.882.
+- From (4.5, 6.5) to (6.5, 9.5), the value increases from -0.882 to -0.483.
+- From (6.5, 9.5) to (9.5, 11.5), the value increases from -0.483 to -0.093.
+- From (9.5, 11.5) to (11.5, 13.5), the value increases from -0.093 to 0.276.
+- From (11.5, 13.5) to (13.5, 14.5), the value increases from 0.276 to 0.863.
+- From (13.5, 14.5) to (14.5, 16.0), the value increases from 0.863 to 1.487.
+
+Since the mean values consistently increase as the intervals progress from lower to higher education numbers, the graph is monotone increasing. 
+
+Therefore, the correct answer is:
+a) monotone increasing
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals.
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.195
+- (0.5, 1.5): 1.333
+- (1.5, 2.5): -0.02
+- (2.5, 3.5): -0.402
+- (3.5, 4.5): -1.423
+- (4.5, 5.5): 0.086
+- (5.5, 7.5): -0.843
+- (7.5, 8.5): -0.246
+- (8.5, 11.5): 0.062
+- (11.5, 20.5): -0.315
+- (20.5, 21.5): 0.109
+- (21.5, 22.5): 0.476
+- (22.5, 24.5): 0.133
+- (24.5, 26.5): -0.35
+- (26.5, 29.5): -0.489
+- (29.5, 32.5): -0.108
+- (32.5, 33.5): -0.483
+- (33.5, 35.5): -0.664
+- (35.5, 38.5): -0.396
+- (38.5, 39.5): 0.028
+- (39.5, 40.5): -0.596
+- (40.5, 41.0): 1.112
+
+Analyzing these values:
+- The values do not consistently increase or decrease. For example, the value increases from (0.0, 0.5) to (0.5, 1.5), then decreases to (1.5, 2.5), and continues to fluctuate throughout the intervals.
+- There are several peaks and troughs, such as a peak at (0.5, 1.5) and a trough at (3.5, 4.5), followed by another peak at (21.5, 22.5) and a trough at (33.5, 35.5).
+
+Given this fluctuation in values, the graph is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine whether the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "PoliticalFactors."
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.0263
+- (0.5, 1.5): -0.02126
+- (1.5, 2.5): -0.01709
+- (2.5, 3.5): -0.01038
+- (3.5, 4.5): -0.00633
+- (4.5, 5.5): 0.00068
+- (5.5, 6.5): 0.00618
+- (6.5, 7.5): 0.01223
+- (7.5, 8.5): 0.01761
+- (8.5, 9.5): 0.02318
+- (9.5, 10.5): 0.02782
+- (10.5, 11.5): 0.03238
+- (11.5, 13.5): 0.03978
+- (13.5, 15.0): 0.04468
+- (15.0, 16.0): 0.0529
+
+Observing the trend:
+- The values start from a negative value and increase to positive values as the intervals increase.
+- Each subsequent interval has a higher mean value than the previous one.
+
+Since the mean values consistently increase as the intervals increase, the graph is **monotone increasing**.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the balance intervals.
+
+Here are the mean values for each interval:
+1. (0.0, 50418.515): -0.132
+2. (50418.515, 53570.93): -0.285
+3. (53570.93, 54249.445): -0.826
+4. (54249.445, 57428.56): -0.404
+5. (57428.56, 60041.265): -0.005
+6. (60041.265, 64897.8): 0.215
+7. (64897.8, 72985.875): 0.086
+8. (72985.875, 74989.08499999999): -0.012
+9. (74989.08499999999, 76596.815): 0.247
+10. (76596.815, 79953.185): 0.829
+11. (79953.185, 83348.07): 0.564
+12. (83348.07, 101890.23999999999): 0.414
+13. (101890.23999999999, 114327.485): 0.248
+14. (114327.485, 123946.3): 0.164
+15. (123946.3, 141661.24): 0.075
+16. (141661.24, 174920.08000000002): 0.173
+17. (174920.08000000002, 181813.135): 0.059
+18. (181813.135, 191993.675): -0.349
+19. (191993.675, 200829.925): -0.459
+20. (200829.925, 206951.87): -0.616
+21. (206951.87, 216109.88): -0.256
+
+Analyzing the trend:
+- From the first interval to the third, the values decrease (-0.132 to -0.826).
+- From the third interval to the sixth, the values increase (-0.826 to 0.215).
+- There is a slight decrease from the sixth to the eighth interval (0.215 to -0.012).
+- From the eighth to the tenth interval, there is an increase again (-0.012 to 0.829).
+- From the tenth to the seventeenth interval, the values generally decrease (0.829 to 0.059), though not strictly in every step.
+- From the seventeenth to the twentieth interval, there is a clear decrease (0.059 to -0.616).
+- The last interval shows a slight increase (-0.616 to -0.256).
+
+Given these observations, the graph is not monotone increasing as it does not always increase, nor is it monotone decreasing as it does not always decrease. The graph shows fluctuations in both directions.
+
+Therefore, the correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "DrainageSystems."
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.02593
+- (0.5, 1.5): -0.02211
+- (1.5, 2.5): -0.01611
+- (2.5, 3.5): -0.01125
+- (3.5, 4.5): -0.0047
+- (4.5, 5.5): 0.00009
+- (5.5, 6.5): 0.00652
+- (6.5, 8.5): 0.01219
+- (8.5, 10.5): 0.02253
+- (10.5, 11.5): 0.03412
+- (11.5, 12.5): 0.04015
+- (12.5, 14.0): 0.04564
+
+Observing the trend:
+- From (0.0, 0.5) to (3.5, 4.5), the values are negative but increasing towards zero.
+- From (4.5, 5.5) onwards, the values are positive and continue to increase.
+
+Since the values consistently increase from one interval to the next across the entire range of the feature, the graph is **monotone increasing**.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "Urbanization."
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.02565
+- (0.5, 1.5): -0.02133
+- (1.5, 2.5): -0.01683
+- (2.5, 3.5): -0.00993
+- (3.5, 4.5): -0.00473
+- (4.5, 5.5): -1e-05
+- (5.5, 6.5): 0.00511
+- (6.5, 7.5): 0.01148
+- (7.5, 8.5): 0.01621
+- (8.5, 9.5): 0.02476
+- (9.5, 11.5): 0.02962
+- (11.5, 12.5): 0.03469
+- (12.5, 13.5): 0.04866
+- (13.5, 16.0): 0.05902
+
+Observing these values, we see that each subsequent interval has a higher mean value than the previous one. This indicates that as the level of urbanization increases, the predicted value from the model also increases.
+
+Since the values consistently increase as we move from one interval to the next, the graph is **monotone increasing**.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "Landslides".
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.02593
+- (0.5, 1.5): -0.02172
+- (1.5, 2.5): -0.01544
+- (2.5, 3.5): -0.0098
+- (3.5, 4.5): -0.00541
+- (4.5, 5.5): 0.00066
+- (5.5, 6.5): 0.00575
+- (6.5, 7.5): 0.01201
+- (7.5, 8.5): 0.01649
+- (8.5, 9.5): 0.0215
+- (9.5, 10.5): 0.0267
+- (10.5, 11.5): 0.03057
+- (11.5, 13.5): 0.0366
+- (13.5, 14.0): 0.03003
+
+Observing the trend:
+- From (0.0, 0.5) to (11.5, 13.5), the mean values are increasing.
+- However, from (11.5, 13.5) to (13.5, 14.0), there is a decrease in the mean value (from 0.0366 to 0.03003).
+
+Since the values increase initially and then decrease, the function is not strictly monotone increasing or decreasing throughout its entire range. Therefore, the correct answer is:
+
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine whether the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "AgriculturalPractices".
+
+Here are the mean values for each interval:
+- (0.0, 1.5): -0.02463
+- (1.5, 2.5): -0.01694
+- (2.5, 3.5): -0.01147
+- (3.5, 4.5): -0.00533
+- (4.5, 5.5): 0.00036
+- (5.5, 6.5): 0.00641
+- (6.5, 7.5): 0.01086
+- (7.5, 8.5): 0.01753
+- (8.5, 9.5): 0.02391
+- (9.5, 11.5): 0.03162
+- (11.5, 14.0): 0.0391
+- (14.0, 15.0): 0.05506
+
+Observing these values, we see that each subsequent interval has a higher mean value than the previous one. This pattern indicates that as the value of the feature "AgriculturalPractices" increases, the corresponding mean value also increases.
+
+Since the mean values consistently increase as we move from the lower to the higher intervals, the graph is **monotone increasing**.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the mean values of the function across the specified intervals and observe the trend.
+
+1. **Understanding the Intervals and Values**:
+   - The feature "diabetes" is continuous and the graph provides mean values for two intervals:
+     - Interval (0.0, 0.5) has a mean value of 0.3225.
+     - Interval (0.5, 1.0) has a mean value of -0.415.
+
+2. **Analyzing the Trend**:
+   - From the interval (0.0, 0.5) to (0.5, 1.0), the mean value decreases from 0.3225 to -0.415.
+
+3. **Monotonicity**:
+   - A function is **monotone increasing** if the function values never decrease as the input increases.
+   - A function is **monotone decreasing** if the function values never increase as the input increases.
+   - In this case, the mean value decreases as we move from the first interval to the second. This indicates a decrease in the function value with increasing input.
+
+4. **Conclusion**:
+   - Since the function value decreases as the input increases from the first interval to the second, the graph represents a **monotone decreasing** function.
+
+Therefore, the correct answer is:
+b) monotone decreasing.
+SOLUTION:  Decreasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "id".
+
+Here are the mean values for each interval:
+- (8.0, 349.5): -0.1954
+- (349.5, 1899.5): -0.1448
+- (1899.5, 4908.5): -0.18
+- (4908.5, 5578.5): -0.2082
+- (5578.5, 5813.5): -0.25
+- (5813.5, 6004.5): -0.345
+- (6004.5, 7170.5): -0.1246
+- (7170.5, 7335.5): 0.0378
+- (7335.5, 8083.0): 0.1773
+- (8083.0, 8604.0): 0.1221
+- (8604.0, 8759.0): -0.0027
+- (8759.0, 45049.5): -0.0395
+- (45049.5, 45346.5): -0.3688
+- (45346.5, 46184.5): -0.0125
+- (46184.5, 54575.0): 0.0215
+- (54575.0, 55661.5): -0.0521
+- (55661.5, 66954.0): 0.0101
+- (66954.0, 67057.0): -0.0227
+- (67057.0, 68275.0): 0.0595
+- (68275.0, 97577.5): 0.0244
+- (97577.5, 110643.5): 0.0529
+- (110643.5, 146554.5): 0.0211
+- (146554.5, 146921.5): -0.0139
+- (146921.5, 147131.5): -0.0861
+- (147131.5, 161901.5): -0.0139
+- (161901.5, 162437.5): -0.0745
+- (162437.5, 164212.5): -0.0061
+- (164212.5, 164569.5): -0.057
+- (164569.5, 164786.5): 0.0766
+- (164786.5, 165030.0): 0.1394
+
+Analyzing the sequence of mean values:
+- The values initially decrease from -0.1954 to -0.345, then increase to 0.1773, decrease slightly, and then fluctuate with both increases and decreases throughout the sequence.
+
+Since the values do not consistently increase or decrease but instead show a mix of upward and downward movements, the graph is **not monotone**.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "smoothness_mean".
+
+Here are the mean values extracted from the JSON object:
+- (0.05263, 0.0706): -0.835
+- (0.0706, 0.07455500000000001): -0.769
+- (0.07455500000000001, 0.07589499999999999): -0.697
+- (0.07589499999999999, 0.07727500000000001): -0.632
+- (0.07727500000000001, 0.078275): -0.569
+- (0.078275, 0.07952000000000001): -0.506
+- (0.07952000000000001, 0.080315): -0.437
+- (0.080315, 0.081035): -0.368
+- (0.081035, 0.08308499999999999): -0.304
+- (0.08308499999999999, 0.085165): -0.242
+- (0.085165, 0.086795): -0.177
+- (0.086795, 0.087785): -0.111
+- (0.087785, 0.088615): -0.047
+- (0.088615, 0.08918999999999999): 0.065
+- (0.08918999999999999, 0.090335): 0.142
+- (0.090335, 0.09454): 0.211
+- (0.09454, 0.11525): 0.107
+- (0.11525, 0.11765): 0.171
+- (0.11765, 0.12455): 0.267
+- (0.12455, 0.13845000000000002): 0.334
+- (0.13845000000000002, 0.1634): 0.396
+
+Observing the trend:
+- From (0.05263, 0.0706) to (0.090335, 0.09454), the mean values increase consistently, indicating a monotone increasing trend.
+- However, at (0.09454, 0.11525), there is a decrease in the mean value to 0.107 from the previous interval's value of 0.211.
+- After this decrease, the mean values continue to increase again from (0.11525, 0.11765) onwards.
+
+Since there is a decrease in the mean value at one point in the sequence, the function is not strictly monotone increasing across the entire range. Therefore, the graph is not monotone increasing.
+
+Similarly, since the function mostly increases and only decreases once, it is not monotone decreasing either.
+
+Conclusion:
+c) The graph is not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the specified intervals.
+
+Here are the mean values for each interval:
+- Interval (0.0, 0.5): -0.8604
+- Interval (0.5, 1.5): -0.0173
+- Interval (1.5, 2.5): -0.2499
+- Interval (2.5, 3.5): -0.3026
+- Interval (3.5, 4.0): 0.0414
+
+Now, let's examine the trend:
+1. From interval (0.0, 0.5) to (0.5, 1.5), the mean value increases from -0.8604 to -0.0173.
+2. From interval (0.5, 1.5) to (1.5, 2.5), the mean value decreases from -0.0173 to -0.2499.
+3. From interval (1.5, 2.5) to (2.5, 3.5), the mean value decreases slightly from -0.2499 to -0.3026.
+4. From interval (2.5, 3.5) to (3.5, 4.0), the mean value increases from -0.3026 to 0.0414.
+
+Given this analysis, the mean values first increase, then decrease, and finally increase again. This pattern of changes (increase, decrease, increase) indicates that the function is not consistently moving in one direction (either always increasing or always decreasing).
+
+Therefore, the correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the specified intervals. Let's examine the mean values for each interval:
+
+1. Interval (0.0, 0.5): Mean = -0.013
+2. Interval (0.5, 1.5): Mean = 0.434
+3. Interval (1.5, 4.5): Mean = -0.066
+4. Interval (4.5, 5.5): Mean = 0.167
+5. Interval (5.5, 7.5): Mean = -0.464
+6. Interval (7.5, 8.0): Mean = -2.54
+
+Now, let's analyze the trend:
+- From (0.0, 0.5) to (0.5, 1.5), the mean increases from -0.013 to 0.434.
+- From (0.5, 1.5) to (1.5, 4.5), the mean decreases from 0.434 to -0.066.
+- From (1.5, 4.5) to (4.5, 5.5), the mean increases from -0.066 to 0.167.
+- From (4.5, 5.5) to (5.5, 7.5), the mean decreases from 0.167 to -0.464.
+- From (5.5, 7.5) to (7.5, 8.0), the mean decreases further from -0.464 to -2.54.
+
+The mean values do not consistently increase or decrease across the intervals; they increase at some points and decrease at others. Therefore, the graph is not monotone increasing or monotone decreasing.
+
+The correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the age intervals.
+
+Here are the mean values for each age interval:
+- (2.0, 2.5): -0.503
+- (2.5, 5.0): 1.062
+- (5.0, 17.5): 1.188
+- (17.5, 24.5): 0.305
+- (24.5, 28.5): 0.438
+- (28.5, 31.5): 0.03
+- (31.5, 35.5): 0.337
+- (35.5, 36.25): 0.047
+- (36.25, 43.5): -0.09
+- (43.5, 44.5): -0.293
+- (44.5, 47.5): -0.611
+- (47.5, 49.5): -0.32
+- (49.5, 59.0): -0.561
+- (59.0, 60.5): -0.283
+- (60.5, 63.5): -0.939
+- (63.5, 70.5): -1.095
+- (70.5, 75.5): -0.598
+- (75.5, 80.0): -0.406
+
+Analyzing the trend:
+- From (2.0, 2.5) to (5.0, 17.5), the values increase.
+- From (5.0, 17.5) to (17.5, 24.5), there is a decrease.
+- From (17.5, 24.5) to (24.5, 28.5), there is an increase.
+- From (24.5, 28.5) to (28.5, 31.5), there is a decrease.
+- From (28.5, 31.5) to (31.5, 35.5), there is an increase.
+- From (31.5, 35.5) to (36.25, 43.5), there is a decrease.
+- From (36.25, 43.5) onwards, the values generally decrease, with minor fluctuations.
+
+The pattern of the mean values shows increases and decreases at different intervals. Therefore, the graph is not monotone increasing or monotone decreasing, as it does not consistently move in one direction (either always increasing or always decreasing).
+
+The correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "households."
+
+Here are the mean values for each interval:
+- (2.0, 4.5): -5401.6
+- (4.5, 6.5): -23687.9
+- (6.5, 8.5): -53732.5
+- (8.5, 9.5): -14617.2
+- (9.5, 12.5): 16225.5
+- (12.5, 13.5): 21846.0
+- (13.5, 14.5): 29456.0
+- (14.5, 15.5): 14293.2
+- (15.5, 20.5): -21670.3
+- (20.5, 21.5): 3195.8
+- (21.5, 55.5): -12458.9
+- (55.5, 155.5): -20063.6
+- (155.5, 156.5): -15642.0
+- (156.5, 157.5): -6390.8
+- (157.5, 186.5): -19320.2
+- (186.5, 196.5): -23743.0
+- (196.5, 198.5): -18377.6
+- (198.5, 223.5): -12744.1
+- (223.5, 230.5): -6336.7
+- (230.5, 295.5): -10855.3
+- (295.5, 394.5): -6355.5
+- (394.5, 535.5): -443.1
+- (535.5, 561.5): 3934.9
+- (561.5, 599.5): 9004.1
+- (599.5, 600.5): 13667.2
+- (600.5, 634.5): 8706.3
+- (634.5, 635.5): 25959.4
+- (635.5, 824.5): 13815.1
+- (824.5, 864.5): 18503.2
+- (864.5, 962.5): 26367.0
+- (962.5, 964.5): 14554.6
+- (964.5, 976.5): 23227.2
+- (976.5, 978.5): 18664.6
+- (978.5, 990.5): 26114.1
+- (990.5, 1000.5): 30854.6
+- (1000.5, 1088.5): 25473.5
+- (1088.5, 1092.5): 21095.0
+- (1092.5, 1130.5): 26497.2
+- (1130.5, 1272.5): 33562.7
+- (1272.5, 3516.0): 28522.2
+- (3516.0, 6082.0): 21556.0
+
+Analyzing the trend:
+- The values start negative, decrease further, then start increasing, occasionally dropping, but generally increasing towards the end.
+- There are several intervals where the mean value decreases from one interval to the next, followed by intervals where it increases.
+
+Conclusion:
+The graph is **not monotone** because it does not consistently increase or decrease throughout all intervals. Instead, it shows a mix of increasing and decreasing trends across different intervals.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the mean values of the feature "anaemia" across the specified intervals and observe the trend.
+
+From the provided data:
+- For the interval (0.0, 0.5), the mean value is -0.0818.
+- For the interval (0.5, 1.0), the mean value is 0.0917.
+
+The mean value increases as we move from the first interval (0.0, 0.5) to the second interval (0.5, 1.0). Specifically, it goes from a negative value (-0.0818) to a positive value (0.0917). This indicates that the function value is increasing as the feature value increases.
+
+Therefore, the graph is:
+a) monotone increasing.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the mean values of the function across the specified intervals of the feature "smoking".
+
+From the data provided:
+- For the interval (0.0, 0.5), the mean value is 0.01522.
+- For the interval (0.5, 1.0), the mean value is -0.03391.
+
+To check for monotonicity:
+- A function is monotone increasing if the values do not decrease as the input increases.
+- A function is monotone decreasing if the values do not increase as the input increases.
+
+In this case:
+- The mean value decreases from 0.01522 to -0.03391 as we move from the interval (0.0, 0.5) to (0.5, 1.0).
+
+Therefore, the function is monotone decreasing across the specified intervals. Hence, the correct answer is:
+b) monotone decreasing.
+SOLUTION:  Decreasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the platelet counts.
+
+Here's a step-by-step analysis of the mean values:
+
+1. From (25100.0, 27700.0) to (27700.0, 34450.0): -1.004 to -0.687 (increasing)
+2. From (27700.0, 34450.0) to (34450.0, 42200.0): -0.687 to 0.328 (increasing)
+3. From (34450.0, 42200.0) to (42200.0, 56500.0): 0.328 to 1.717 (increasing)
+4. From (42200.0, 56500.0) to (56500.0, 66050.0): 1.717 to 2.769 (increasing)
+5. From (56500.0, 66050.0) to (66050.0, 74000.0): 2.769 to 2.195 (decreasing)
+6. From (66050.0, 74000.0) to (74000.0, 95500.0): 2.195 to 2.956 (increasing)
+7. From (74000.0, 95500.0) to (95500.0, 104500.0): 2.956 to -0.265 (decreasing)
+8. From (95500.0, 104500.0) to (104500.0, 144000.0): -0.265 to -0.585 (decreasing)
+9. From (104500.0, 144000.0) to (144000.0, 150500.0): -0.585 to -0.895 (decreasing)
+10. From (144000.0, 150500.0) to (150500.0, 154000.0): -0.895 to 2.322 (increasing)
+11. From (150500.0, 154000.0) to (154000.0, 169000.0): 2.322 to 0.469 (decreasing)
+12. From (154000.0, 169000.0) to (169000.0, 184500.0): 0.469 to -1.612 (decreasing)
+13. From (169000.0, 184500.0) to (184500.0, 195000.0): -1.612 to 1.111 (increasing)
+14. From (184500.0, 195000.0) to (195000.0, 199000.0): 1.111 to 3.01 (increasing)
+15. From (195000.0, 199000.0) to (199000.0, 200500.0): 3.01 to 1.837 (decreasing)
+16. From (199000.0, 200500.0) to (200500.0, 214000.0): 1.837 to 0.403 (decreasing)
+17. From (200500.0, 214000.0) to (214000.0, 217500.0): 0.403 to -0.825 (decreasing)
+18. From (214000.0, 217500.0) to (217500.0, 218500.0): -0.825 to -1.399 (decreasing)
+19. From (217500.0, 218500.0) to (218500.0, 220500.0): -1.399 to 0.341 (increasing)
+20. From (218500.0, 220500.0) to (220500.0, 222500.0): 0.341 to 0.978 (increasing)
+21. From (220500.0, 222500.0) to (222500.0, 226500.0): 0.978 to 1.584 (increasing)
+22. From (222500.0, 226500.0) to (226500.0, 241500.0): 1.584 to 0.175 (decreasing)
+23. From (226500.0, 241500.0) to (241500.0, 242500.0): 0.175 to 0.642 (increasing)
+24. From (241500.0, 242500.0) to (242500.0, 243500.0): 0.642 to 1.107 (increasing)
+25. From (242500.0, 243500.0) to (243500.0, 244500.0): 1.107 to 1.516 (increasing)
+26. From (243500.0, 244500.0) to (244500.0, 252500.0): 1.516 to -2.19 (decreasing)
+27. From (244500.0, 252500.0) to (252500.0, 261000.0): -2.19 to -0.878 (increasing)
+28. From (252500.0, 261000.0) to (261000.0, 274500.0): -0.878 to -0.145 (increasing)
+29. From (261000.0, 274500.0) to (274500.0, 283500.0): -0.145 to -0.968 (decreasing)
+30. From (274500.0, 283500.0) to (283500.0, 287500.0): -0.968 to 0.203 (increasing)
+31. From (283500.0, 287500.0) to (287500.0, 289500.0): 0.203 to 1.032 (increasing)
+32. From (287500.0, 289500.0) to (289500.0, 302500.0): 1.032 to -1.296 (decreasing)
+33. From (289500.0, 302500.0) to (302500.0, 305500.0): -1.296 to -2.984 (decreasing)
+34. From (302500.0, 305500.0) to (305500.0, 307000.0): -2.984 to 0.876 (increasing)
+35. From (305500.0, 307000.0) to (307000.0, 332000.0): 0.876 to 0.368 (decreasing)
+36. From (307000.0, 332000.0) to (332000.0, 335000.0): 0.368 to 1.21 (increasing)
+37. From (332000.0, 335000.0) to (335000.0, 343000.0): 1.21 to 0.8 (decreasing)
+38. From (335000.0, 343000.0) to (343000.0, 350500.0): 0.8 to -0.573 (decreasing)
+39. From (343000.0, 350500.0) to (350500.0, 354500.0): -0.573 to 3.0 (increasing)
+40. From (350500.0, 354500.0) to (354500.0, 383500.0): 3.0 to -0.119 (decreasing)
+41. From (354500.0, 383500.0) to (383500.0, 449500.0): -0.119 to 0.655 (increasing)
+42. From (383500.0, 449500.0) to (449500.0, 471000.0): 0.655 to 1.527 (increasing)
+43. From (449500.0, 471000.0) to (471000.0, 500500.0): 1.527 to -2.247 (decreasing)
+44. From (471000.0, 500500.0) to (500500.0, 582000.0): -2.247 to -0.442 (increasing)
+45. From (500500.0, 582000.0) to (582000.0, 675500.0): -0.442 to 2.645 (increasing)
+46. From (582000.0, 675500.0) to (675500.0, 796000.0): 2.645 to 2.314 (decreasing)
+47. From (675500.0, 796000.0) to (796000.0, 850000.0): 2.314 to -0.709 (decreasing)
+
+The mean values fluctuate between increasing and decreasing across the intervals, showing no consistent pattern of either strictly increasing or strictly decreasing values.
+
+Therefore, the correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the PopulationScore feature.
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.02443
+- (0.5, 1.5): -0.02088
+- (1.5, 2.5): -0.01613
+- (2.5, 3.5): -0.01086
+- (3.5, 4.5): -0.00583
+- (4.5, 5.5): 0.00139
+- (5.5, 6.5): 0.00556
+- (6.5, 7.5): 0.01145
+- (7.5, 8.5): 0.01748
+- (8.5, 10.5): 0.0242
+- (10.5, 11.5): 0.03351
+- (11.5, 13.5): 0.03691
+- (13.5, 15.0): 0.03345
+- (15.0, 16.0): 0.02926
+
+Observing the trend:
+- From (0.0, 0.5) to (11.5, 13.5), the mean values are increasing.
+- However, from (11.5, 13.5) to (15.0, 16.0), there is a slight decrease in the mean values.
+
+Since the values initially increase and then decrease, the function is not strictly monotone increasing or decreasing. Therefore, the correct answer is:
+
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "concave points_worst".
+
+Here are the mean values provided in the graph:
+- (0.0, 0.02814): -0.771
+- (0.02814, 0.08293): -0.653
+- (0.08293, 0.08555): -0.533
+- (0.08555, 0.093225): -0.403
+- (0.093225, 0.1055): -0.234
+- (0.1055, 0.11510000000000001): -0.117
+- (0.11510000000000001, 0.1346): 0.002
+- (0.1346, 0.14545000000000002): 0.121
+- (0.14545000000000002, 0.15175): 0.241
+- (0.15175, 0.1603): 0.365
+- (0.1603, 0.1722): 0.539
+- (0.1722, 0.17695): 0.661
+- (0.17695, 0.18359999999999999): 0.781
+- (0.18359999999999999, 0.194): 0.9
+- (0.194, 0.2019): 1.022
+- (0.2019, 0.21275): 1.14
+- (0.21275, 0.2383): 1.259
+- (0.2383, 0.26865): 1.378
+- (0.26865, 0.291): 1.494
+
+Observing the mean values, we see that each subsequent interval has a higher mean value than the previous one. This indicates that as the value of "concave points_worst" increases, the predicted value from the model also increases.
+
+Since the mean values consistently increase as we move from one interval to the next, the graph is **monotone increasing**.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the CapitalLoss feature.
+
+Here are the mean values for each interval:
+- (0.0, 845.0): -0.044
+- (845.0, 1448.0): -1.147
+- (1448.0, 1551.5): 0.416
+- (1551.5, 1568.5): 3.928
+- (1568.5, 1748.0): -3.752
+- (1748.0, 1846.0): 1.139
+- (1846.0, 1862.0): 3.823
+- (1862.0, 1881.5): -1.36
+- (1881.5, 1894.5): 4.781
+- (1894.5, 1938.0): 3.172
+- (1938.0, 1975.5): 0.294
+- (1975.5, 1978.5): 4.013
+- (1978.5, 2139.0): -2.74
+- (2139.0, 2176.5): 0.361
+- (2176.5, 2190.0): -1.098
+- (2190.0, 2205.5): 1.259
+- (2205.5, 2262.5): 2.644
+- (2262.5, 2310.5): -0.616
+- (2310.5, 2364.5): -1.139
+- (2364.5, 2384.5): 1.07
+- (2384.5, 2450.5): 4.377
+- (2450.5, 2480.5): 1.517
+- (2480.5, 2553.0): 3.296
+- (2553.0, 2581.0): 5.5
+- (2581.0, 2678.5): -0.191
+- (2678.5, 2789.0): 0.326
+- (2789.0, 3343.5): 5.958
+- (3343.5, 3835.0): 2.152
+- (3835.0, 4356.0): -0.334
+
+Analyzing these values, we observe that the mean values do not consistently increase or decrease across the intervals. For example:
+- The value decreases from -0.044 to -1.147 between the first two intervals, then increases to 0.416, and jumps to 3.928 before dropping significantly to -3.752.
+- This pattern of fluctuating values continues throughout the intervals, with no consistent direction in the changes.
+
+Given this fluctuation in values, the graph is **not monotone**. It neither consistently increases nor consistently decreases across the intervals of the CapitalLoss feature.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the MonsoonIntensity feature.
+
+Here are the mean values for each interval:
+- (0.0, 1.5): -0.02446
+- (1.5, 2.5): -0.01712
+- (2.5, 3.5): -0.00908
+- (3.5, 4.5): -0.00529
+- (4.5, 5.5): 0.0003
+- (5.5, 6.5): 0.00497
+- (6.5, 7.5): 0.01093
+- (7.5, 8.5): 0.01787
+- (8.5, 9.5): 0.02262
+- (9.5, 11.5): 0.02707
+- (11.5, 12.5): 0.03735
+- (12.5, 13.5): 0.043
+- (13.5, 15.0): 0.01734
+
+Observing the trend:
+- From (0.0, 1.5) to (12.5, 13.5), the mean values are increasing, indicating a positive trend.
+- However, from (12.5, 13.5) to (13.5, 15.0), there is a decrease in the mean value (from 0.043 to 0.01734).
+
+Since the values increase initially and then decrease, the function is not strictly monotone increasing or decreasing throughout the entire range. Therefore, the correct answer is:
+
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the mean values of the function across the specified intervals and observe the trend.
+
+Here are the intervals and their corresponding mean values:
+1. Interval (1.0, 1.5): Mean = -0.009
+2. Interval (1.5, 2.5): Mean = 0.534
+3. Interval (2.5, 3.0): Mean = -0.532
+
+Now, let's analyze the trend:
+- From the first interval (1.0, 1.5) to the second interval (1.5, 2.5), the mean value increases from -0.009 to 0.534.
+- From the second interval (1.5, 2.5) to the third interval (2.5, 3.0), the mean value decreases from 0.534 to -0.532.
+
+Since the mean values first increase and then decrease, the function is not monotone increasing or monotone decreasing. Therefore, the function is not monotone. 
+
+The correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the mean values of the function across the specified intervals and observe the trend.
+
+From the provided data:
+- The intervals and their corresponding mean values are:
+  - Interval (0.0, 0.5): Mean = 0.01719
+  - Interval (0.5, 1.0): Mean = -0.00954
+
+Step-by-step analysis:
+1. **Order of Intervals**: The intervals are ordered from (0.0, 0.5) to (0.5, 1.0).
+2. **Comparison of Means**:
+   - For the first interval (0.0, 0.5), the mean is 0.01719.
+   - For the second interval (0.5, 1.0), the mean is -0.00954.
+
+   Observing the means, the value decreases as we move from the first interval to the second interval.
+
+3. **Monotonicity**:
+   - A function is **monotone increasing** if the function values never decrease as the input increases.
+   - A function is **monotone decreasing** if the function values never increase as the input increases.
+   - If the function values increase in some places and decrease in others as the input increases, it is **not monotone**.
+
+Given that the mean value decreases from the first interval to the second, the function is **monotone decreasing** over the range of the input values provided.
+SOLUTION:  Decreasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine whether the function represented by the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the specified intervals of the feature "NumOfProducts."
+
+Here are the mean values for each interval:
+- Interval (1.0, 1.5): Mean = -0.918
+- Interval (1.5, 2.5): Mean = 0.96
+- Interval (2.5, 3.5): Mean = -3.104
+- Interval (3.5, 4.0): Mean = -2.768
+
+Now, let's examine the trend:
+1. From the first interval (1.0, 1.5) to the second interval (1.5, 2.5), the mean value increases from -0.918 to 0.96.
+2. From the second interval (1.5, 2.5) to the third interval (2.5, 3.5), the mean value decreases significantly from 0.96 to -3.104.
+3. From the third interval (2.5, 3.5) to the fourth interval (3.5, 4.0), the mean value increases slightly from -3.104 to -2.768.
+
+Given this analysis, the function first increases, then decreases, and finally increases again. This pattern indicates that the function is not consistently moving in one direction (either always increasing or always decreasing) across the intervals.
+
+Therefore, the correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "total_bedrooms."
+
+Here are the mean values for each interval:
+- (2.0, 4.5): -10633.3
+- (4.5, 9.5): -19829.1
+- (9.5, 12.5): -33356.0
+- (12.5, 14.5): -27510.0
+- (14.5, 17.5): -34141.4
+- (17.5, 20.5): -50740.7
+- (20.5, 22.5): -59049.5
+- (22.5, 25.5): -37177.7
+- (25.5, 29.5): -30710.5
+- (29.5, 111.5): -36287.1
+- (111.5, 112.5): -22540.1
+- (112.5, 176.5): -33870.1
+- (176.5, 245.5): -27701.3
+- (245.5, 265.5): -20526.0
+- (265.5, 268.5): -26170.7
+- (268.5, 317.5): -17267.5
+- (317.5, 424.5): -8013.2
+- (424.5, 463.5): -1894.5
+- (463.5, 512.5): 5095.6
+- (512.5, 513.5): 17024.1
+- (513.5, 655.5): 9371.5
+- (655.5, 697.5): 15515.9
+- (697.5, 776.5): 22859.4
+- (776.5, 779.5): 13774.7
+- (779.5, 1008.5): 22608.4
+- (1008.5, 1012.5): 37458.5
+- (1012.5, 1081.5): 30023.9
+- (1081.5, 1449.5): 37066.8
+- (1449.5, 1490.5): 51601.0
+- (1490.5, 1616.0): 42837.8
+- (1616.0, 2714.5): 49023.6
+- (2714.5, 2865.5): 40592.1
+- (2865.5, 6445.0): 51586.1
+
+Observing the trend:
+- From (2.0, 4.5) to (20.5, 22.5), the values are generally decreasing.
+- From (22.5, 25.5) onwards, the values start to increase, with some fluctuations but generally moving towards higher values.
+
+Since the values initially decrease and then increase, the function is not monotone increasing or monotone decreasing. Therefore, the correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the mean values of the function across the specified intervals of the feature "high_blood_pressure".
+
+From the data provided:
+- For the interval (0.0, 0.5), the mean value is -0.1077.
+- For the interval (0.5, 1.0), the mean value is 0.1864.
+
+The mean value increases as we move from the first interval (0.0, 0.5) to the second interval (0.5, 1.0). This indicates that the function value is higher for higher intervals of the feature "high_blood_pressure".
+
+To confirm monotonicity, we also need to consider the confidence intervals:
+- For the interval (0.0, 0.5), the confidence interval is from -0.1574 to -0.058.
+- For the interval (0.5, 1.0), the confidence interval is from 0.1003 to 0.2724.
+
+The confidence intervals do not overlap, which supports the observation that the mean values are indeed increasing from the first interval to the second interval.
+
+Based on this analysis, the graph is:
+a) monotone increasing
+
+This conclusion is drawn from the fact that the mean values increase from the first interval to the second, and the confidence intervals support this increase without overlapping.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine whether the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the sepal_length feature.
+
+Here are the mean values for each interval:
+- (4.3, 4.55): 3.328
+- (4.55, 4.75): 2.995
+- (4.75, 4.85): 2.698
+- (4.85, 5.05): 1.665
+- (5.05, 5.25): 1.371
+- (5.25, 5.45): 1.085
+- (5.45, 5.55): 0.339
+- (5.55, 5.75): -0.057
+- (5.75, 5.85): -0.39
+- (5.85, 6.15): -0.757
+- (6.15, 6.45): -1.149
+- (6.45, 6.85): -1.436
+- (6.85, 7.7): -1.718
+
+Observing the trend:
+- From (4.3, 4.55) to (4.55, 4.75) the value decreases from 3.328 to 2.995.
+- This decreasing trend continues consistently through all subsequent intervals, with each interval's mean value being lower than the previous.
+
+Since the mean values consistently decrease as the sepal_length increases, the graph represents a monotone decreasing function. There are no intervals where the mean value increases relative to the previous interval, which would indicate a non-monotone behavior.
+
+Therefore, the correct answer is:
+b) monotone decreasing.
+SOLUTION:  Decreasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "area_mean".
+
+Here are the mean values for each interval:
+- (143.5, 259.35): -0.759
+- (259.35, 289.4): -0.662
+- (289.4, 319.15): -0.567
+- (319.15, 348.3): -0.464
+- (348.3, 496.5): -0.368
+- (496.5, 548.75): -0.271
+- (548.75, 606.0): -0.173
+- (606.0, 696.25): -0.076
+- (696.25, 806.1500000000001): 0.309
+- (806.1500000000001, 901.8): 0.405
+- (901.8, 959.4000000000001): 0.51
+- (959.4000000000001, 1054.0): 0.607
+- (1054.0, 1150.0): 0.707
+- (1150.0, 1248.5): 0.806
+- (1248.5, 1341.0): 0.911
+- (1341.0, 1801.0): 1.01
+- (1801.0, 2501.0): 1.109
+
+Observing the mean values, we see that they consistently increase as the intervals progress from lower to higher values of "area_mean". Each subsequent interval has a higher mean value than the previous one.
+
+Therefore, the graph is **monotone increasing**.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the serum sodium feature.
+
+Here are the mean values for each interval:
+- (113.0, 114.5): -1.269
+- (114.5, 118.5): 0.283
+- (118.5, 124.5): 3.539
+- (124.5, 126.5): 2.46
+- (126.5, 127.5): 4.042
+- (127.5, 129.5): 3.553
+- (129.5, 130.5): 0.953
+- (130.5, 132.5): 1.22
+- (132.5, 133.5): -1.094
+- (133.5, 135.5): 0.587
+- (135.5, 138.5): -0.629
+- (138.5, 144.5): -0.233
+- (144.5, 148.0): 0.113
+
+To determine monotonicity, we need to check if the values either consistently increase or consistently decrease as the intervals progress.
+
+- From (113.0, 114.5) to (114.5, 118.5), the value increases from -1.269 to 0.283.
+- From (114.5, 118.5) to (118.5, 124.5), the value increases from 0.283 to 3.539.
+- From (118.5, 124.5) to (124.5, 126.5), the value decreases from 3.539 to 2.46.
+- From (124.5, 126.5) to (126.5, 127.5), the value increases from 2.46 to 4.042.
+- From (126.5, 127.5) to (127.5, 129.5), the value decreases from 4.042 to 3.553.
+- From (127.5, 129.5) to (129.5, 130.5), the value decreases from 3.553 to 0.953.
+- From (129.5, 130.5) to (130.5, 132.5), the value increases from 0.953 to 1.22.
+- From (130.5, 132.5) to (132.5, 133.5), the value decreases from 1.22 to -1.094.
+- From (132.5, 133.5) to (133.5, 135.5), the value increases from -1.094 to 0.587.
+- From (133.5, 135.5) to (135.5, 138.5), the value decreases from 0.587 to -0.629.
+- From (135.5, 138.5) to (138.5, 144.5), the value increases from -0.629 to -0.233.
+- From (138.5, 144.5) to (144.5, 148.0), the value increases from -0.233 to 0.113.
+
+The values do not consistently increase or decrease; they fluctuate between increasing and decreasing as the intervals progress. Therefore, the graph is **not monotone**.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the specified intervals. Let's examine the mean values provided in the JSON object:
+
+- Interval (0.0, 0.5): Mean = -0.368
+- Interval (0.5, 1.5): Mean = 0.724
+- Interval (1.5, 2.5): Mean = 0.587
+- Interval (2.5, 3.5): Mean = -0.221
+- Interval (3.5, 4.5): Mean = -0.631
+- Interval (4.5, 5.5): Mean = -0.545
+- Interval (5.5, 6.0): Mean = 0.179
+
+Now, let's analyze the trend:
+- From (0.0, 0.5) to (0.5, 1.5), the mean increases from -0.368 to 0.724.
+- From (0.5, 1.5) to (1.5, 2.5), the mean decreases from 0.724 to 0.587.
+- From (1.5, 2.5) to (2.5, 3.5), the mean decreases further from 0.587 to -0.221.
+- From (2.5, 3.5) to (3.5, 4.5), the mean decreases again from -0.221 to -0.631.
+- From (3.5, 4.5) to (4.5, 5.5), the mean slightly increases from -0.631 to -0.545.
+- From (4.5, 5.5) to (5.5, 6.0), the mean increases from -0.545 to 0.179.
+
+Given this analysis, the mean values do not consistently increase or decrease across the intervals. Instead, they increase at some points and decrease at others. Therefore, the graph is **not monotone**.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the population intervals.
+
+Here are the mean values for each interval:
+- (3.0, 14.5): 125210.2
+- (14.5, 25.5): 92452.9
+- (25.5, 65.5): 80407.9
+- (65.5, 138.5): 91917.4
+- (138.5, 151.5): 103409.9
+- (151.5, 301.5): 85121.7
+- (301.5, 490.5): 73106.0
+- (490.5, 657.5): 57994.5
+- (657.5, 761.5): 44760.8
+- (761.5, 837.5): 32058.9
+- (837.5, 1019.5): 20715.6
+- (1019.5, 1220.5): 6507.2
+- (1220.5, 1267.5): -6199.6
+- (1267.5, 1269.5): 9858.1
+- (1269.5, 1497.5): -9812.8
+- (1497.5, 1886.5): -25776.4
+- (1886.5, 2129.5): -36953.6
+- (2129.5, 2425.5): -48605.9
+- (2425.5, 2686.0): -59914.9
+- (2686.0, 2718.5): -46231.6
+- (2718.5, 3175.5): -61061.6
+- (3175.5, 3965.0): -76216.0
+- (3965.0, 35682.0): -91117.9
+
+Analyzing the trend:
+- From (3.0, 14.5) to (14.5, 25.5), the value decreases.
+- From (14.5, 25.5) to (25.5, 65.5), the value decreases.
+- From (25.5, 65.5) to (65.5, 138.5), the value increases.
+- From (65.5, 138.5) to (138.5, 151.5), the value increases.
+- From (138.5, 151.5) to (151.5, 301.5), the value decreases.
+- From (151.5, 301.5) onwards, the values generally decrease, with a few exceptions where there are minor increases (e.g., from (1220.5, 1267.5) to (1267.5, 1269.5)).
+
+Overall, the general trend is decreasing, especially in the larger intervals. However, there are intervals where the value increases. Therefore, the graph is not strictly monotone increasing or decreasing.
+
+The correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the perimeter_mean feature.
+
+Here are the mean values for each interval:
+- (43.79, 60.035): -0.884
+- (60.035, 63.379999999999995): -0.783
+- (63.379999999999995, 66.67): -0.681
+- (66.67, 68.965): -0.581
+- (68.965, 71.275): -0.476
+- (71.275, 78.28): -0.369
+- (78.28, 84.015): -0.267
+- (84.015, 88.70500000000001): -0.166
+- (88.70500000000001, 94.68): -0.064
+- (94.68, 100.75): 0.035
+- (100.75, 106.75): 0.14
+- (106.75, 108.6): 0.249
+- (108.6, 112.6): 0.407
+- (112.6, 117.45): 0.518
+- (117.45, 121.7): 0.626
+- (121.7, 128.15): 0.73
+- (128.15, 133.25): 0.835
+- (133.25, 145.85000000000002): 0.936
+- (145.85000000000002, 188.5): 1.038
+
+Observing the mean values, we see that they consistently increase as the perimeter_mean increases. Each subsequent interval has a higher mean value than the previous one, starting from a negative value and moving into positive values.
+
+Given this consistent increase in mean values across the intervals, the graph is:
+a) monotone increasing
+
+This indicates that as the perimeter_mean increases, the predicted value from the GAM also increases.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the serum creatinine levels.
+
+Here are the mean values for each interval:
+- (0.5, 0.65): -0.26
+- (0.65, 0.725): -1.08
+- (0.725, 0.875): -3.77
+- (0.875, 0.95): -0.9
+- (0.95, 1.14): -0.15
+- (1.14, 1.35): -0.88
+- (1.35, 1.45): 0.2
+- (1.45, 1.55): 1.18
+- (1.55, 1.815): 2.18
+- (1.815, 2.05): 4.74
+- (2.05, 2.45): 1.14
+- (2.45, 2.6): 3.63
+- (2.6, 2.95): -0.36
+- (2.95, 3.1): 2.57
+- (3.1, 3.45): 0.36
+- (3.45, 3.6): 3.06
+- (3.6, 3.75): 6.76
+- (3.75, 3.9): 2.31
+- (3.9, 4.7): 2.92
+- (4.7, 5.95): 0.76
+- (5.95, 6.2): -0.43
+- (6.2, 6.55): 0.23
+- (6.55, 9.4): 6.97
+
+Analyzing the trend:
+- From (0.5, 0.65) to (0.725, 0.875), the values decrease significantly.
+- From (0.725, 0.875) to (1.815, 2.05), the values generally increase, though not strictly in every step.
+- From (1.815, 2.05) to (2.6, 2.95), there is a decrease.
+- From (2.6, 2.95) to (3.6, 3.75), there is an increase.
+- From (3.6, 3.75) to (5.95, 6.2), there is a decrease.
+- From (5.95, 6.2) to (6.55, 9.4), there is an increase.
+
+Given these observations, the graph shows fluctuations in the mean values as serum creatinine levels increase. The values do not consistently increase or decrease across the entire range of serum creatinine levels. Therefore, the graph is **not monotone**.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine whether the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "CoastalVulnerability".
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.03259
+- (0.5, 1.5): -0.02272
+- (1.5, 2.5): -0.0157
+- (2.5, 3.5): -0.00983
+- (3.5, 4.5): -0.00444
+- (4.5, 5.5): -0.00035
+- (5.5, 6.5): 0.00575
+- (6.5, 7.5): 0.01126
+- (7.5, 8.5): 0.01651
+- (8.5, 9.5): 0.02143
+- (9.5, 12.5): 0.02903
+- (12.5, 13.5): 0.03437
+- (13.5, 15.0): 0.04826
+
+Observing the trend:
+- From the interval (0.0, 0.5) to (13.5, 15.0), the mean values consistently increase as the intervals progress.
+
+Since the mean values increase from one interval to the next without any decrease at any point, the graph is **monotone increasing**.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the CapitalGain feature.
+
+Here are the mean values for each interval:
+- (0.0, 57.0): -0.25
+- (57.0, 3048.0): -4.83
+- (3048.0, 3120.0): 2.57
+- (3120.0, 4243.5): -4.43
+- (4243.5, 4401.0): 1.45
+- (4401.0, 4668.5): -1.82
+- (4668.5, 4826.0): 3.79
+- (4826.0, 4898.0): 0.57
+- (4898.0, 4973.5): 2.25
+- (4973.5, 5119.0): -3.52
+- (5119.0, 5316.5): 4.26
+- (5316.5, 5505.5): 0.43
+- (5505.5, 6457.5): 2.15
+- (6457.5, 6505.5): -0.16
+- (6505.5, 6745.0): 0.81
+- (6745.0, 7073.5): -1.33
+- (7073.5, 7436.5): 5.76
+- (7436.5, 7565.5): 2.02
+- (7565.5, 7792.0): 6.56
+- (7792.0, 7937.0): 4.88
+- (7937.0, 8296.0): 3.84
+- (8296.0, 10543.0): 7.18
+- (10543.0, 10585.5): -1.48
+- (10585.5, 30961.5): 8.61
+- (30961.5, 70654.5): -0.66
+- (70654.5, 99999.0): 9.72
+
+Analyzing the sequence of mean values:
+- The values do not consistently increase or decrease. They fluctuate, showing increases and decreases as the intervals change.
+- For example, the value decreases from -0.25 to -4.83 between the first two intervals, then increases to 2.57, and decreases again to -4.43.
+- This pattern of fluctuation continues throughout the intervals.
+
+Since the values neither consistently increase nor consistently decrease across the intervals, the graph is **not monotone**.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "Tenure".
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.3765
+- (0.5, 1.5): -0.0692
+- (1.5, 4.5): -0.016
+- (4.5, 5.5): 0.0109
+- (5.5, 6.5): 0.0432
+- (6.5, 7.5): 0.0871
+- (7.5, 9.5): 0.0554
+- (9.5, 10.0): -0.0599
+
+Analyzing the trend:
+- From (0.0, 0.5) to (6.5, 7.5), the mean values increase, indicating a positive trend.
+- However, from (6.5, 7.5) to (7.5, 9.5), there is a decrease from 0.0871 to 0.0554.
+- From (7.5, 9.5) to (9.5, 10.0), there is a further decrease from 0.0554 to -0.0599.
+
+Since the values initially increase and then decrease, the function is not consistently moving in one direction (either always increasing or always decreasing) across the entire range of the feature. Therefore, the graph is **not monotone**.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine whether the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "texture_mean".
+
+Here are the mean values for each interval:
+- (9.71, 13.24): -1.121
+- (13.24, 14.075): -1.023
+- (14.075, 14.665): -0.921
+- (14.665, 15.010000000000002): -0.82
+- (15.010000000000002, 15.485): -0.718
+- (15.485, 15.774999999999999): -0.623
+- (15.774999999999999, 16.445): -0.523
+- (16.445, 17.045): -0.422
+- (17.045, 17.665): -0.324
+- (17.665, 18.335): -0.225
+- (18.335, 18.725): -0.129
+- (18.725, 19.075): -0.032
+- (19.075, 19.549999999999997): 0.063
+- (19.549999999999997, 19.915): 0.161
+- (19.915, 20.235): 0.26
+- (20.235, 20.8): 0.445
+- (20.8, 21.285): 0.549
+- (21.285, 33.81): 0.68
+
+Observing the trend:
+- The values start at -1.121 and consistently increase as the intervals progress, ending at 0.68.
+
+Since the mean values consistently increase from one interval to the next without any decrease, the graph represents a monotone increasing function. Therefore, the correct answer is:
+a) monotone increasing.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the latitude intervals.
+
+1. **Monotone Increasing**: A function is monotone increasing if for any two values, if the first value is less than the second value, then the function's output at the first value is less than or equal to the function's output at the second value.
+
+2. **Monotone Decreasing**: A function is monotone decreasing if for any two values, if the first value is less than the second value, then the function's output at the first value is greater than or equal to the function's output at the second value.
+
+3. **Not Monotone**: A function is not monotone if it neither strictly increases nor decreases.
+
+Let's analyze the mean values from the JSON object:
+
+- Starting from the first interval `(32.54, 32.565)` with a mean of `23234.8`, the mean value decreases in the next interval `(32.565, 32.685)` to `-3182.4`.
+- The mean then increases to `7727.3` in the interval `(32.685, 32.715)`.
+- It continues to increase to `17670.3` in `(32.715, 32.915)` and further to `34030.3` in `(32.915, 33.275000000000006)`.
+- The trend of increasing continues up to the interval `(33.504999999999995, 33.555)` with a mean of `94496.7`.
+- After this peak, the mean decreases to `63293.1` in `(33.555, 33.565)` and then to `51665.3` in `(33.565, 33.575)`.
+- The mean increases again to `66563.2` in `(33.575, 33.635000000000005)` and then decreases to `47304.3` in `(33.635000000000005, 33.655)`.
+
+This pattern of increasing and decreasing continues throughout the dataset, showing multiple peaks and troughs.
+
+Given this analysis, the function represented by the graph is **not monotone** because it does not consistently increase or decrease but instead shows multiple increases and decreases across different intervals of latitude.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "DamsQuality".
+
+Here are the mean values for each interval:
+- (0.0, 1.5): -0.02325
+- (1.5, 2.5): -0.01532
+- (2.5, 3.5): -0.01073
+- (3.5, 4.5): -0.00482
+- (4.5, 5.5): -0.00032
+- (5.5, 6.5): 0.0063
+- (6.5, 7.5): 0.01228
+- (7.5, 8.5): 0.01637
+- (8.5, 10.5): 0.02537
+- (10.5, 12.5): 0.03189
+- (12.5, 13.5): 0.03961
+- (13.5, 14.0): 0.01644
+
+Observing the trend:
+- From (0.0, 1.5) to (12.5, 13.5), the mean values are increasing, indicating a positive trend.
+- However, from (12.5, 13.5) to (13.5, 14.0), there is a decrease in the mean value (from 0.03961 to 0.01644).
+
+Since the values increase consistently until the last interval, where there is a decrease, the function is not strictly monotone increasing throughout the entire range. Similarly, it is not monotone decreasing as the majority of the function shows an increasing trend.
+
+Therefore, the correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "IneffectiveDisasterPreparedness".
+
+Here are the mean values for each interval:
+- (0.0, 1.5): -0.02526
+- (1.5, 2.5): -0.01738
+- (2.5, 3.5): -0.01172
+- (3.5, 4.5): -0.00537
+- (4.5, 5.5): -0.00021
+- (5.5, 6.5): 0.0066
+- (6.5, 7.5): 0.01026
+- (7.5, 8.5): 0.01717
+- (8.5, 9.5): 0.02426
+- (9.5, 10.5): 0.02823
+- (10.5, 11.5): 0.03325
+- (11.5, 13.5): 0.03915
+- (13.5, 15.0): 0.03572
+
+Observing the trend:
+- From (0.0, 1.5) to (11.5, 13.5), the mean values are increasing.
+- However, from (11.5, 13.5) to (13.5, 15.0), there is a slight decrease from 0.03915 to 0.03572.
+
+Since the values increase consistently until the last interval, where there is a slight decrease, the function is not strictly monotone increasing throughout the entire range. Similarly, it is not monotone decreasing as the majority of the function is increasing. Therefore, the correct answer is:
+
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine whether the function represented by the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the mean values across the specified intervals and observe how they change.
+
+1. **Understanding the Intervals and Values**:
+   - The feature "HasCrCard" is described as continuous in this context, with two intervals: (0.0, 0.5) and (0.5, 1.0).
+   - The mean values for these intervals are:
+     - For (0.0, 0.5): -0.004421
+     - For (0.5, 1.0): 0.001379
+
+2. **Analyzing the Change in Mean Values**:
+   - The mean value increases from -0.004421 in the interval (0.0, 0.5) to 0.001379 in the interval (0.5, 1.0).
+
+3. **Monotonicity**:
+   - A function is **monotone increasing** if the function values never decrease as the input increases.
+   - A function is **monotone decreasing** if the function values never increase as the input increases.
+   - In this case, the mean value increases as we move from the first interval to the second. This suggests that the function is monotone increasing over the range provided.
+
+4. **Conclusion**:
+   - Based on the increase in mean values from the first interval to the second, the function represented by this GAM graph is **monotone increasing**.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the sepal width feature.
+
+Here are the mean values for each interval:
+- (2.0, 2.25): -2.473
+- (2.25, 2.6500000000000004): -2.179
+- (2.6500000000000004, 2.8499999999999996): -1.736
+- (2.8499999999999996, 2.95): -0.945
+- (2.95, 3.05): 0.062
+- (3.05, 3.25): 0.509
+- (3.25, 3.3499999999999996): 1.373
+- (3.3499999999999996, 3.55): 1.669
+- (3.55, 3.75): 2.097
+- (3.75, 3.95): 2.489
+- (3.95, 4.1): 2.778
+
+Observing the trend:
+- From (2.0, 2.25) to (2.25, 2.6500000000000004), the value increases from -2.473 to -2.179.
+- This increasing trend continues consistently through each subsequent interval, all the way to the last interval (3.95, 4.1) where the value is 2.778.
+
+Since the mean values consistently increase as the intervals progress from lower to higher sepal widths, the graph is **monotone increasing**.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "time."
+
+Here are the mean values for each interval:
+- (4.0, 11.5): 10.73
+- (11.5, 12.5): 1.29
+- (12.5, 15.5): 3.88
+- (15.5, 18.0): 2.22
+- (18.0, 28.5): 6.17
+- (28.5, 30.5): 4.47
+- (30.5, 52.0): 5.56
+- (52.0, 54.5): 3.38
+- (54.5, 67.5): 4.79
+- (67.5, 73.5): 2.76
+- (73.5, 76.5): -3.15
+- (76.5, 78.5): 2.29
+- (78.5, 82.5): -0.16
+- (82.5, 87.5): -2.8
+- (87.5, 90.5): 0.19
+- (90.5, 92.5): -1.08
+- (92.5, 95.5): -2.7
+- (95.5, 108.5): -0.98
+- (108.5, 117.5): 0.02
+- (117.5, 124.5): -3.44
+- (124.5, 137.5): 0.64
+- (137.5, 149.0): -0.8
+- (149.0, 171.5): 5.06
+- (171.5, 173.0): 2.66
+- (173.0, 182.5): -0.84
+- (182.5, 192.5): -3.42
+- (192.5, 193.5): -1.01
+- (193.5, 253.0): -2.58
+- (253.0, 285.0): -8.42
+
+Analyzing these values:
+- The values start high at 10.73, then drop significantly to 1.29, and fluctuate throughout the intervals.
+- There are several intervals where the mean value increases from one interval to the next (e.g., from (11.5, 12.5) at 1.29 to (12.5, 15.5) at 3.88).
+- There are also intervals where the mean value decreases from one interval to the next (e.g., from (73.5, 76.5) at -3.15 to (76.5, 78.5) at 2.29, then drops again).
+- The pattern of increase and decrease continues throughout the intervals.
+
+Given this fluctuation in values, where the mean values increase in some intervals and decrease in others, the graph is **not monotone**. It neither consistently increases nor consistently decreases across the entire range of the feature "time." Thus, the correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the median_income feature.
+
+Here are the mean values for each interval:
+- (0.4999, 0.5427500000000001): -16067.6
+- (0.5427500000000001, 1.4808): -55539.5
+- (1.4808, 2.1658999999999997): -71376.5
+- (2.1658999999999997, 2.6096): -56399.7
+- (2.6096, 3.2433): -40762.6
+- (3.2433, 3.66575): -25586.1
+- (3.66575, 4.3197): -8084.4
+- (4.3197, 4.691000000000001): 7391.3
+- (4.691000000000001, 5.1358): 22375.3
+- (5.1358, 5.59195): 40032.8
+- (5.59195, 5.8294): 56900.2
+- (5.8294, 6.29665): 75092.3
+- (6.29665, 6.3704): 96400.5
+- (6.3704, 6.874750000000001): 111491.7
+- (6.874750000000001, 7.6996): 135841.6
+- (7.6996, 7.8141): 151586.9
+- (7.8141, 8.3976): 170219.6
+- (8.3976, 9.046949999999999): 192482.3
+- (9.046949999999999, 15.00005): 214375.9
+- (15.00005, 15.0001): 193753.6
+
+Observing the trend:
+- The values start negative and increase as the intervals progress.
+- The values transition from negative to positive between the intervals (3.66575, 4.3197) and (4.3197, 4.691000000000001).
+- From there, the values continue to increase consistently until the last interval.
+
+The only exception to the increasing trend is the slight decrease in the last interval from 214375.9 to 193753.6. However, this is a minor deviation in the context of the overall trend from significantly negative to positive values.
+
+Based on this analysis, the graph is predominantly monotone increasing, with a minor deviation at the end. Therefore, the correct answer is:
+a) monotone increasing.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the mean values of the function across the specified intervals and observe how they change.
+
+From the provided data:
+- The feature is named "Gender" and is described as a continuous feature, which is unusual since gender is typically a categorical variable. However, for the purpose of this analysis, we will treat it as described.
+- The intervals and corresponding mean values are:
+  - Interval (0.0, 0.5): Mean = -0.4751
+  - Interval (0.5, 1.0): Mean = 0.2339
+
+Step-by-step analysis:
+1. **Comparing the Means**: The mean value for the interval (0.0, 0.5) is -0.4751, and for the interval (0.5, 1.0) it is 0.2339.
+2. **Direction of Change**: The mean value increases as we move from the first interval (0.0, 0.5) to the second interval (0.5, 1.0).
+
+Since the mean value increases from the first interval to the second, the function represented by this graph is **monotone increasing**.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the fare ranges.
+
+Here are the mean values for each fare range:
+- (0.0, 6.325): -1.425
+- (6.325, 7.8500000000000005): -1.303
+- (7.8500000000000005, 9.256250000000001): -0.472
+- (9.256250000000001, 10.48125): -0.602
+- (10.48125, 12.9375): -0.14
+- (12.9375, 25.79375): 0.225
+- (25.79375, 26.46875): 0.355
+- (26.46875, 27.7354): 0.207
+- (27.7354, 29.85): -0.238
+- (29.85, 31.6604): 0.051
+- (31.6604, 55.22085): -0.075
+- (55.22085, 89.5521): 0.041
+- (89.5521, 149.0354): 0.152
+- (149.0354, 387.6646): -0.029
+- (387.6646, 512.3292): 0.808
+
+Analyzing the trend:
+- From (0.0, 6.325) to (6.325, 7.8500000000000005), the value increases from -1.425 to -1.303.
+- From (6.325, 7.8500000000000005) to (7.8500000000000005, 9.256250000000001), the value increases from -1.303 to -0.472.
+- From (7.8500000000000005, 9.256250000000001) to (9.256250000000001, 10.48125), the value decreases from -0.472 to -0.602.
+- From (9.256250000000001, 10.48125) to (10.48125, 12.9375), the value increases from -0.602 to -0.14.
+- From (10.48125, 12.9375) to (12.9375, 25.79375), the value increases from -0.14 to 0.225.
+- From (12.9375, 25.79375) to (25.79375, 26.46875), the value increases from 0.225 to 0.355.
+- From (25.79375, 26.46875) to (26.46875, 27.7354), the value decreases from 0.355 to 0.207.
+- From (26.46875, 27.7354) to (27.7354, 29.85), the value decreases from 0.207 to -0.238.
+- From (27.7354, 29.85) to (29.85, 31.6604), the value increases from -0.238 to 0.051.
+- From (29.85, 31.6604) to (31.6604, 55.22085), the value decreases from 0.051 to -0.075.
+- From (31.6604, 55.22085) to (55.22085, 89.5521), the value increases from -0.075 to 0.041.
+- From (55.22085, 89.5521) to (89.5521, 149.0354), the value increases from 0.041 to 0.152.
+- From (89.5521, 149.0354) to (149.0354, 387.6646), the value decreases from 0.152 to -0.029.
+- From (149.0354, 387.6646) to (387.6646, 512.3292), the value increases from -0.029 to 0.808.
+
+The values do not consistently increase or decrease across the entire range of fares. There are several intervals where the mean value increases followed by intervals where it decreases. 
+
+Therefore, the correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the salary ranges. Let's examine the mean values for each interval:
+
+1. (106.67, 780.2149999999999): 0.3865
+2. (780.2149999999999, 4627.98): 0.3462
+3. (4627.98, 6842.475): 0.0858
+4. (6842.475, 7401.88): 0.157
+5. (7401.88, 27330.43): 0.2048
+6. (27330.43, 38816.375): 0.1737
+7. (38816.375, 40348.645000000004): 0.1063
+8. (40348.645000000004, 42807.509999999995): 0.0512
+9. (42807.509999999995, 48226.81): 0.1098
+10. (48226.81, 48498.15): -0.0771
+11. (48498.15, 58535.68): 0.0187
+12. (58535.68, 94498.98999999999): 0.0512
+13. (94498.98999999999, 120892.955): 0.0186
+14. (120892.955, 121151.28): -0.0263
+15. (121151.28, 121482.61499999999): -0.0801
+16. (121482.61499999999, 148569.97): -0.0388
+17. (148569.97, 184522.325): -0.0796
+18. (184522.325, 187947.635): -0.1332
+19. (187947.635, 187985.865): -0.2342
+20. (187985.865, 188452.565): -0.0632
+21. (188452.565, 189006.61): -0.0053
+22. (189006.61, 196418.97999999998): 0.0291
+23. (196418.97999999998, 199505.41): -0.0098
+24. (199505.41, 199992.48): 0.214
+
+Analyzing the trend:
+- From the first interval to the second, the mean value decreases from 0.3865 to 0.3462.
+- The mean value continues to decrease until the fourth interval, where it increases to 0.157.
+- The mean value fluctuates, increasing and decreasing across subsequent intervals.
+
+The mean values do not consistently increase or decrease across the entire range of salary values. Instead, they exhibit both increases and decreases at different points. This behavior indicates that the function is not monotone increasing or decreasing.
+
+Therefore, the correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "radius_worst".
+
+Here are the mean values provided in the JSON object:
+- (7.93, 10.585): -1.149
+- (10.585, 11.305): -1.016
+- (11.305, 11.965): -0.883
+- (11.965, 12.54): -0.747
+- (12.54, 13.315000000000001): -0.616
+- (13.315000000000001, 14.184999999999999): -0.485
+- (14.184999999999999, 14.875): -0.349
+- (14.875, 15.485): -0.212
+- (15.485, 15.955): -0.078
+- (15.955, 16.54): 0.055
+- (16.54, 17.22): 0.19
+- (17.22, 17.78): 0.335
+- (17.78, 18.655): 0.469
+- (18.655, 19.785): 0.601
+- (19.785, 20.445): 0.734
+- (20.445, 21.935000000000002): 0.866
+- (21.935000000000002, 23.625): 0.997
+- (23.625, 25.335): 1.132
+- (25.335, 30.71): 1.274
+- (30.71, 36.04): 1.406
+
+Observing these values, we see that each subsequent interval has a higher mean value than the previous one. This indicates that as the "radius_worst" increases, the mean value also increases consistently across all intervals.
+
+Therefore, the graph is:
+a) monotone increasing
+
+This conclusion is based on the consistent increase in mean values as the feature "radius_worst" increases, without any decrease at any point in the intervals provided.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the "Education" feature.
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.4028
+- (0.5, 1.5): -0.5397
+- (1.5, 3.5): -0.4851
+- (3.5, 4.5): -0.4021
+- (4.5, 5.5): -0.457
+- (5.5, 6.5): -0.2537
+- (6.5, 7.5): -0.0494
+- (7.5, 8.5): 0.0457
+- (8.5, 9.5): 0.1831
+- (9.5, 10.5): 0.1392
+- (10.5, 11.5): -0.0652
+- (11.5, 14.5): 0.1954
+- (14.5, 15.0): 0.1393
+
+Now, let's analyze the trend:
+- From (0.0, 0.5) to (0.5, 1.5), the value decreases.
+- From (0.5, 1.5) to (1.5, 3.5), the value increases.
+- From (1.5, 3.5) to (3.5, 4.5), the value increases.
+- From (3.5, 4.5) to (4.5, 5.5), the value decreases.
+- From (4.5, 5.5) to (5.5, 6.5), the value increases.
+- From (5.5, 6.5) to (6.5, 7.5), the value increases.
+- From (6.5, 7.5) to (7.5, 8.5), the value increases.
+- From (7.5, 8.5) to (8.5, 9.5), the value increases.
+- From (8.5, 9.5) to (9.5, 10.5), the value decreases.
+- From (9.5, 10.5) to (10.5, 11.5), the value decreases.
+- From (10.5, 11.5) to (11.5, 14.5), the value increases.
+- From (11.5, 14.5) to (14.5, 15.0), the value decreases.
+
+The trend is not consistently increasing or decreasing across all intervals. There are several points where the trend changes from increasing to decreasing and vice versa.
+
+Therefore, the correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine whether the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the longitude intervals. Monotonicity in this context means that the function either consistently increases or decreases as longitude changes.
+
+Let's examine the mean values provided in the JSON object:
+
+1. Starting from the first interval `(-124.35, -124.10499999999999)` with a mean of `-50430.1`, we observe the following trend:
+   - The mean increases from `-50430.1` to `-38925.6` in the next interval.
+   - It continues to increase to `-23742.3`, then to `-12526.0`, and further to `-1690.2`.
+   - The trend of increasing mean values continues significantly as we move to higher intervals, reaching a peak at `112351.5` in the interval `(-122.445, -122.42500000000001)`.
+
+2. After reaching this peak, the mean value starts to decrease:
+   - It decreases to `89733.4` in the interval `(-122.42500000000001, -122.405)`.
+   - It further decreases to `78586.0`, then to `46429.6`, and continues decreasing across several intervals.
+
+3. The mean values then show fluctuations:
+   - They increase again in some intervals, such as `26358.2` in `(-118.57499999999999, -118.525)`, and then decrease in subsequent intervals.
+
+4. Towards the end of the longitude range, the mean values continue to show a mix of increases and decreases:
+   - For example, it decreases to `-100620.1` in the interval `(-114.595, -114.31)`.
+
+From this analysis, it is clear that the mean values do not consistently increase or decrease across all longitude intervals. Instead, they show a pattern of increasing to a certain point, then decreasing, and then fluctuating. This behavior indicates that the function represented by the graph is **not monotone**.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the age intervals.
+
+Here are the mean values for each age interval:
+- (0.0, 0.5): 0.68
+- (0.5, 3.5): 0.36
+- (3.5, 4.5): 0.254
+- (4.5, 14.5): 0.09
+- (14.5, 23.5): 0.028
+- (23.5, 24.5): -0.027
+- (24.5, 25.5): -0.135
+- (25.5, 39.5): -0.05
+- (39.5, 44.5): 0.042
+- (44.5, 48.5): -0.025
+- (48.5, 54.5): -0.102
+- (54.5, 56.5): -0.012
+- (56.5, 63.5): 0.078
+- (63.5, 64.5): -0.028
+- (64.5, 65.5): -0.141
+- (65.5, 68.5): 0.058
+- (68.5, 69.5): -0.021
+- (69.5, 71.5): 0.037
+- (71.5, 73.5): -0.022
+- (73.5, 74.5): 0.413
+- (74.5, 77.5): 0.211
+- (77.5, 79.0): -0.412
+
+Analyzing these values:
+- From (0.0, 0.5) to (24.5, 25.5), the values generally decrease.
+- From (24.5, 25.5) to (39.5, 44.5), there is a slight increase followed by a decrease.
+- From (39.5, 44.5) to (56.5, 63.5), the values fluctuate but generally increase.
+- From (56.5, 63.5) to (65.5, 68.5), the values fluctuate with both increases and decreases.
+- From (65.5, 68.5) to (77.5, 79.0), the values again fluctuate with significant increases and then a sharp decrease.
+
+Given these observations, the graph is not monotone increasing as it does not consistently increase throughout the intervals. It is also not monotone decreasing as it does not consistently decrease throughout the intervals. The values fluctuate with both increases and decreases across different age ranges.
+
+Therefore, the correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "radius_mean".
+
+Here are the mean values for each interval:
+- (6.981, 9.281500000000001): -0.762
+- (9.281500000000001, 9.7015): -0.659
+- (9.7015, 10.165): -0.56
+- (10.165, 10.655000000000001): -0.461
+- (10.655000000000001, 12.465): -0.36
+- (12.465, 13.39): -0.262
+- (13.39, 14.43): -0.163
+- (14.43, 14.934999999999999): -0.065
+- (14.934999999999999, 15.08): 0.037
+- (15.08, 15.815): 0.137
+- (15.815, 16.925): 0.235
+- (16.925, 17.385): 0.394
+- (17.385, 18.0): 0.494
+- (18.0, 18.735): 0.599
+- (18.735, 19.240000000000002): 0.695
+- (19.240000000000002, 19.990000000000002): 0.793
+- (19.990000000000002, 20.595): 0.891
+- (20.595, 23.240000000000002): 0.99
+- (23.240000000000002, 28.11): 1.093
+
+Observing the mean values, we see that each subsequent interval has a higher mean value than the previous one. This indicates that as the "radius_mean" increases, the corresponding mean value of the function also increases.
+
+Therefore, the graph is **monotone increasing**.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the "total_rooms" feature.
+
+Here are the key steps and observations:
+
+1. **Understanding the Data**: The data provided shows mean values for different intervals of the "total_rooms" feature. Each interval represents a range of total rooms, and the corresponding mean value indicates the model's prediction for that range.
+
+2. **Analyzing Trends**:
+   - From the smallest interval "(2.0, 23.0)" with a mean of -70808.9 to the next interval "(23.0, 38.5)" with a mean of -78966.6, the mean decreases.
+   - From "(23.0, 38.5)" to "(38.5, 48.5)", the mean increases to -28602.1.
+   - The trend continues with fluctuations in the mean values as we move through the intervals. For example, from "(38.5, 48.5)" to "(48.5, 119.0)", the mean decreases to -47079.6, and from "(239.5, 248.5)" to "(248.5, 265.5)", the mean increases from -7038.8 to -691.1.
+   - Notably, in the higher intervals, such as from "(2330.5, 2710.5)" with a mean of 4370.8 to "(2710.5, 2834.5)" with a mean of 9739.0, and further increasing to "(2834.5, 2838.5)" with a mean of 16667.1, the trend is clearly increasing.
+
+3. **Conclusion**:
+   - The graph is **not monotone** because the mean values do not consistently increase or decrease across all intervals. There are several points where the trend reverses, indicating increases and decreases at different intervals.
+
+Therefore, the correct answer is **c) not monotone**.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the specified intervals of the feature "Parch".
+
+Here are the mean values for each interval:
+- Interval (0.0, 0.5): Mean = 0.085
+- Interval (0.5, 1.5): Mean = -0.055
+- Interval (1.5, 3.0): Mean = -0.299
+- Interval (3.0, 4.0): Mean = -1.704
+
+Now, let's examine the trend:
+1. From the first interval (0.0, 0.5) to the second interval (0.5, 1.5), the mean value decreases from 0.085 to -0.055.
+2. From the second interval (0.5, 1.5) to the third interval (1.5, 3.0), the mean value further decreases from -0.055 to -0.299.
+3. From the third interval (1.5, 3.0) to the fourth interval (3.0, 4.0), the mean value continues to decrease from -0.299 to -1.704.
+
+Since the mean values consistently decrease as we move from one interval to the next, the graph is monotone decreasing. Therefore, the correct answer is:
+b) monotone decreasing.
+SOLUTION:  Decreasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "InadequatePlanning".
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.02553
+- (0.5, 2.5): -0.02038
+- (2.5, 4.5): -0.0099
+- (4.5, 6.5): 0.00082
+- (6.5, 7.5): 0.01088
+- (7.5, 9.5): 0.0178
+- (9.5, 10.5): 0.02657
+- (10.5, 12.5): 0.0329
+- (12.5, 13.5): 0.03982
+- (13.5, 15.0): 0.05043
+- (15.0, 16.0): 0.06084
+
+Observing these values, we see that each subsequent interval has a higher mean value than the previous one. This indicates that as the value of the feature "InadequatePlanning" increases, the mean value of the function also increases consistently across all intervals.
+
+Therefore, the graph is **monotone increasing**.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the "HoursPerWeek" feature.
+
+Here are the mean values for each interval:
+- (1.0, 1.5): -0.765
+- (1.5, 2.5): -0.375
+- (2.5, 4.5): -1.909
+- (4.5, 6.5): -1.117
+- (6.5, 7.5): -0.618
+- (7.5, 14.5): -0.822
+- (14.5, 19.5): -1.132
+- (19.5, 29.5): -0.765
+- (29.5, 33.5): -0.6
+- (33.5, 34.5): -0.921
+- (34.5, 39.5): -0.155
+- (39.5, 41.5): 0.03
+- (41.5, 50.5): 0.392
+- (50.5, 51.5): 0.131
+- (51.5, 55.5): 0.457
+- (55.5, 59.5): 0.676
+- (59.5, 63.5): 0.416
+- (63.5, 64.5): 0.952
+- (64.5, 65.5): 0.516
+- (65.5, 71.0): 0.071
+- (71.0, 75.5): 0.43
+- (75.5, 77.5): 0.235
+- (77.5, 79.0): 0.742
+- (79.0, 83.0): 0.977
+- (83.0, 85.5): 1.287
+- (85.5, 90.5): 0.192
+- (90.5, 97.5): -0.071
+- (97.5, 98.5): 0.119
+- (98.5, 99.0): -0.139
+
+Analyzing the trend:
+- From (1.0, 1.5) to (2.5, 4.5), the values decrease from -0.765 to -1.909.
+- From (2.5, 4.5) to (6.5, 7.5), the values increase from -1.909 to -0.618.
+- From (6.5, 7.5) to (14.5, 19.5), the values decrease again to -1.132.
+- From (14.5, 19.5) to (34.5, 39.5), the values increase to -0.155.
+- From (34.5, 39.5) onwards, the values generally increase, reaching a peak at (83.0, 85.5) with 1.287.
+- After (85.5, 90.5), the values decrease to -0.071 at (90.5, 97.5), then slightly increase and decrease again.
+
+The graph shows both increases and decreases in the mean values across different intervals. Therefore, the graph is **not monotone** as it does not consistently increase or decrease throughout the entire range of the feature.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the insulin levels.
+
+Here are the mean values for each interval:
+- (0.0, 20.0): 0.0422
+- (20.0, 36.5): -0.0027
+- (36.5, 40.5): -0.0554
+- (40.5, 45.5): -0.0967
+- (45.5, 48.5): -0.0409
+- (48.5, 55.5): -0.2263
+- (55.5, 80.5): -0.2661
+- (80.5, 87.5): -0.227
+- (87.5, 97.5): -0.1794
+- (97.5, 111.0): -0.1356
+- (111.0, 123.5): -0.0968
+- (123.5, 137.5): -0.0561
+- (137.5, 144.5): -0.0187
+- (144.5, 157.0): 0.0208
+- (157.0, 170.5): 0.0623
+- (170.5, 186.5): 0.0999
+- (186.5, 190.5): 0.0538
+- (190.5, 192.5): 0.1059
+- (192.5, 271.0): -0.0027
+- (271.0, 277.5): 0.035
+- (277.5, 292.0): 0.0732
+- (292.0, 311.0): 0.1129
+- (311.0, 365.0): 0.1551
+- (365.0, 397.0): 0.196
+- (397.0, 452.5): 0.2331
+- (452.5, 476.0): 0.2839
+- (476.0, 487.5): 0.346
+- (487.5, 526.5): 0.3915
+- (526.5, 680.0): 0.4346
+
+Analyzing the trend:
+- From (0.0, 20.0) to (55.5, 80.5), the values generally decrease.
+- From (55.5, 80.5) to (170.5, 186.5), the values increase.
+- There is a slight decrease at (186.5, 190.5) followed by an increase at (190.5, 192.5).
+- A notable decrease occurs again at (192.5, 271.0).
+- From (271.0, 277.5) onwards, the values increase consistently up to the highest interval (526.5, 680.0).
+
+Given this analysis, the graph is not monotone increasing throughout because there are intervals where the function decreases. Similarly, it is not monotone decreasing as there are many intervals where the function increases. Therefore, the graph is not monotone. 
+
+The correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the housing_median_age feature.
+
+Here are the mean values for each interval:
+- (1.0, 4.5): -19998.0
+- (4.5, 7.5): -7788.2
+- (7.5, 16.5): -10680.2
+- (16.5, 18.5): -6304.4
+- (18.5, 27.5): -1760.6
+- (27.5, 34.5): 2164.8
+- (34.5, 38.5): -912.5
+- (38.5, 41.5): 4199.6
+- (41.5, 45.5): -497.4
+- (45.5, 47.5): -5189.8
+- (47.5, 48.5): 5201.0
+- (48.5, 49.5): 2159.0
+- (49.5, 50.5): 6135.7
+- (50.5, 51.5): 11513.8
+- (51.5, 52.0): 27549.7
+
+Analyzing the trend:
+- From (1.0, 4.5) to (4.5, 7.5), the value increases.
+- From (4.5, 7.5) to (7.5, 16.5), the value decreases.
+- From (7.5, 16.5) to (16.5, 18.5), the value increases.
+- From (16.5, 18.5) to (18.5, 27.5), the value increases.
+- From (18.5, 27.5) to (27.5, 34.5), the value increases.
+- From (27.5, 34.5) to (34.5, 38.5), the value decreases.
+- From (34.5, 38.5) to (38.5, 41.5), the value increases.
+- From (38.5, 41.5) to (41.5, 45.5), the value decreases.
+- From (41.5, 45.5) to (45.5, 47.5), the value decreases.
+- From (45.5, 47.5) to (47.5, 48.5), the value increases.
+- From (47.5, 48.5) to (48.5, 49.5), the value decreases.
+- From (48.5, 49.5) to (49.5, 50.5), the value increases.
+- From (49.5, 50.5) to (50.5, 51.5), the value increases.
+- From (50.5, 51.5) to (51.5, 52.0), the value increases.
+
+The values do not consistently increase or decrease across the intervals. Instead, they fluctuate, showing both increases and decreases.
+
+Therefore, the correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the specified intervals of the Blood Pressure feature.
+
+Here are the mean values for each interval:
+- (0.0, 15.0): 0.236
+- (15.0, 37.0): 0.1532
+- (37.0, 45.0): -0.0296
+- (45.0, 47.0): -0.0891
+- (47.0, 54.5): -0.1348
+- (54.5, 60.5): -0.1774
+- (60.5, 61.5): -0.11
+- (61.5, 64.5): -0.0541
+- (64.5, 74.5): -0.0119
+- (74.5, 75.5): -0.058
+- (75.5, 83.0): -0.004
+- (83.0, 93.0): 0.0343
+- (93.0, 95.0): 0.0889
+- (95.0, 97.0): 0.1461
+- (97.0, 101.0): 0.183
+- (101.0, 103.0): 0.2699
+- (103.0, 107.0): 0.3158
+- (107.0, 109.0): 0.3837
+- (109.0, 110.0): 0.5269
+
+Analyzing the trend:
+- From (0.0, 15.0) to (54.5, 60.5), the values generally decrease.
+- From (60.5, 61.5) to (110.0), the values generally increase.
+
+Since the values first decrease and then increase, the function is not monotone increasing or monotone decreasing throughout its entire range. Therefore, the correct answer is:
+
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the petal_length feature.
+
+Here are the mean values for each interval:
+- (1.1, 1.65): 8.05
+- (1.65, 2.45): 7.28
+- (2.45, 3.15): -1.17
+- (3.15, 3.8): -2.4
+- (3.8, 4.45): -3.03
+- (4.45, 5.65): -3.73
+- (5.65, 6.9): -4.38
+
+Analyzing the trend:
+- From (1.1, 1.65) to (1.65, 2.45), the mean value decreases from 8.05 to 7.28.
+- From (1.65, 2.45) to (2.45, 3.15), the mean value decreases significantly from 7.28 to -1.17.
+- From (2.45, 3.15) to (3.15, 3.8), the mean value decreases from -1.17 to -2.4.
+- From (3.15, 3.8) to (3.8, 4.45), the mean value decreases from -2.4 to -3.03.
+- From (3.8, 4.45) to (4.45, 5.65), the mean value decreases from -3.03 to -3.73.
+- From (4.45, 5.65) to (5.65, 6.9), the mean value decreases from -3.73 to -4.38.
+
+Since the mean values consistently decrease as the petal_length increases, the graph is monotone decreasing. 
+
+Therefore, the correct answer is:
+b) monotone decreasing.
+SOLUTION:  Decreasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "smoothness_worst".
+
+Here are the mean values for each interval:
+- (0.07117, 0.09376000000000001): -1.298
+- (0.09376000000000001, 0.099705): -1.161
+- (0.099705, 0.10519999999999999): -1.024
+- (0.10519999999999999, 0.10825): -0.889
+- (0.10825, 0.11549999999999999): -0.527
+- (0.11549999999999999, 0.12345): -0.394
+- (0.12345, 0.13074999999999998): -0.26
+- (0.13074999999999998, 0.13585): -0.124
+- (0.13585, 0.13640000000000002): 0.011
+- (0.13640000000000002, 0.13845000000000002): 0.154
+- (0.13845000000000002, 0.14065): 0.288
+- (0.14065, 0.14635): 0.439
+- (0.14635, 0.15585): 0.574
+- (0.15585, 0.16885): 0.708
+- (0.16885, 0.17825): 0.846
+- (0.17825, 0.19574999999999998): 1.17
+- (0.19574999999999998, 0.2226): 1.304
+
+Observing the mean values, we see that they consistently increase as the intervals progress from the lowest range (0.07117, 0.09376000000000001) to the highest range (0.19574999999999998, 0.2226). Each subsequent interval has a higher mean value than the previous one.
+
+Therefore, the graph is **monotone increasing**.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "concave points_mean".
+
+Here are the mean values provided in the JSON object:
+- (0.0, 0.0074145): -1.054
+- (0.0074145, 0.011665): -0.937
+- (0.011665, 0.01503): -0.821
+- (0.01503, 0.017865): -0.705
+- (0.017865, 0.019315): -0.582
+- (0.019315, 0.023185): -0.466
+- (0.023185, 0.026115): -0.352
+- (0.026115, 0.042455): -0.235
+- (0.042455, 0.048235): -0.115
+- (0.048235, 0.048865): 0.04
+- (0.048865, 0.059615): 0.233
+- (0.059615, 0.070395): 0.35
+- (0.070395, 0.08221500000000001): 0.474
+- (0.08221500000000001, 0.087175): 0.592
+- (0.087175, 0.091445): 0.711
+- (0.091445, 0.1006): 0.832
+- (0.1006, 0.122): 0.949
+- (0.122, 0.16544999999999999): 1.068
+- (0.16544999999999999, 0.2012): 1.187
+
+Observing these values, we see that each subsequent mean value is higher than the previous one. This indicates that as the value of "concave points_mean" increases, the predicted value also increases consistently across all intervals.
+
+Therefore, the graph is **monotone increasing**.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the RoomService feature.
+
+Here are the mean values for each interval:
+- (0.0, 105.5): 0.328
+- (105.5, 296.5): 0.028
+- (296.5, 335.5): -0.208
+- (335.5, 340.0): 0.165
+- (340.0, 343.0): -0.1
+- (343.0, 596.5): -0.741
+- (596.5, 712.5): -0.978
+- (712.5, 734.0): -1.212
+- (734.0, 800.0): -1.446
+- (800.0, 816.0): -1.136
+- (816.0, 997.5): -1.454
+- (997.5, 1031.0): -1.106
+- (1031.0, 1041.0): -1.368
+- (1041.0, 2172.5): -1.866
+- (2172.5, 2283.5): -1.455
+- (2283.5, 2313.5): -1.171
+- (2313.5, 2336.5): -0.66
+- (2336.5, 2420.0): -2.559
+- (2420.0, 2992.5): -3.229
+- (2992.5, 3006.0): -2.708
+- (3006.0, 3196.5): -2.984
+- (3196.5, 3249.5): -2.709
+- (3249.5, 14327.0): -4.146
+
+Analyzing the trend:
+- From (0.0, 105.5) to (105.5, 296.5), the mean decreases from 0.328 to 0.028.
+- From (105.5, 296.5) to (296.5, 335.5), it further decreases to -0.208.
+- There is a temporary increase from (296.5, 335.5) to (335.5, 340.0) where it rises to 0.165.
+- It then decreases again from (335.5, 340.0) to (340.0, 343.0) and continues to decrease or fluctuate with a general downward trend through the remaining intervals.
+
+The graph shows a general downward trend with some fluctuations (temporary increases). However, the overall pattern is a decrease in mean values as the RoomService value increases, especially noticeable in the larger intervals.
+
+Conclusion:
+The graph is **not monotone** because it does not consistently increase or decrease throughout all intervals. There are instances of increase within the general downward trend, which disrupts a purely monotone decreasing pattern.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "id".
+
+Here are the mean values for each interval:
+- (91.0, 2307.0): 0.00838
+- (2307.0, 4713.5): 0.00964
+- (4713.5, 6928.5): 0.0038
+- (6928.5, 9761.5): 0.00118
+- (9761.5, 13120.0): -0.00051
+- (13120.0, 14826.0): -0.00127
+- (14826.0, 20043.5): 5e-05
+- (20043.5, 22448.0): 0.00075
+- (22448.0, 23794.0): -0.00133
+- (23794.0, 28014.5): -0.00281
+- (28014.5, 28671.0): -0.00155
+- (28671.0, 37439.5): -0.00049
+- (37439.5, 40007.0): 0.00015
+- (40007.0, 41128.5): 0.00473
+- (41128.5, 50305.5): -0.0009
+- (50305.5, 51818.5): -0.00193
+- (51818.5, 66668.0): -0.00104
+- (66668.0, 67776.0): 0.0019
+- (67776.0, 75664.5): 1e-05
+- (75664.5, 76606.0): 0.0007
+- (76606.0, 89235.5): 0.00161
+- (89235.5, 227800.5): -0.00038
+- (227800.5, 231707.5): 0.00024
+- (231707.5, 257871.0): -0.00045
+- (257871.0, 503283.0): 0.00017
+- (503283.0, 507804.5): -0.00061
+- (507804.5, 517795.0): -0.00125
+- (517795.0, 616121.0): -0.00038
+- (616121.0, 622616.5): 0.00042
+- (622616.5, 647046.0): -0.00022
+- (647046.0, 662956.5): 0.00117
+- (662956.5, 667208.5): -0.00102
+- (667208.5, 689123.0): 0.00021
+- (689123.0, 872554.5): -0.00065
+- (872554.5, 942666.5): 0.00032
+- (942666.5, 983736.5): -0.00052
+- (983736.5, 1025442.0): 0.00017
+- (1025442.0, 1029281.5): -0.00099
+- (1029281.5, 1040563.0): -0.00029
+- (1040563.0, 1103097.0): 0.00069
+- (1103097.0, 1103695.0): 0.00289
+- (1103695.0, 1104610.5): -0.00013
+- (1104610.5, 1109548.0): 0.00181
+- (1109548.0, 1113474.5): 1e-05
+- (1113474.5, 1114673.5): -0.00091
+- (1114673.5, 1116159.5): 0.00326
+- (1116159.5, 1117955.0): 0.00422
+
+Analyzing these values, we see that the means do not consistently increase or decrease across the intervals. There are several points where the trend changes from increasing to decreasing and vice versa. For example, the mean increases from (91.0, 2307.0) to (2307.0, 4713.5) but then decreases significantly by the interval (4713.5, 6928.5). This pattern of fluctuation continues throughout the intervals.
+
+Therefore, the graph is **not monotone**.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "TopographyDrainage".
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.0274
+- (0.5, 1.5): -0.02381
+- (1.5, 2.5): -0.01602
+- (2.5, 3.5): -0.01049
+- (3.5, 4.5): -0.00528
+- (4.5, 5.5): -0.00022
+- (5.5, 6.5): 0.00517
+- (6.5, 7.5): 0.01282
+- (7.5, 8.5): 0.01628
+- (8.5, 9.5): 0.02454
+- (9.5, 10.5): 0.02883
+- (10.5, 11.5): 0.03213
+- (11.5, 17.0): 0.03564
+
+Observing the trend:
+- From (0.0, 0.5) to (11.5, 17.0), the mean values consistently increase as the intervals progress.
+
+Since the mean values increase from the lowest interval to the highest interval without any decrease at any point, the graph is **monotone increasing**.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the feature "DeterioratingInfrastructure".
+
+Here are the mean values for each interval:
+- (0.0, 0.5): -0.02508
+- (0.5, 1.5): -0.01897
+- (1.5, 2.5): -0.01452
+- (2.5, 3.5): -0.01085
+- (3.5, 4.5): -0.00475
+- (4.5, 5.5): 0.00054
+- (5.5, 6.5): 0.00555
+- (6.5, 7.5): 0.01137
+- (7.5, 8.5): 0.01653
+- (8.5, 9.5): 0.0237
+- (9.5, 10.5): 0.02782
+- (10.5, 11.5): 0.03175
+- (11.5, 12.5): 0.03686
+- (12.5, 15.0): 0.04451
+
+Observing the mean values, we see that they start from a negative value and gradually increase, eventually becoming positive and continuing to increase. Each subsequent interval has a higher mean value than the previous one.
+
+This pattern indicates that the graph is monotone increasing, as there is a consistent increase in the mean values from one interval to the next across the entire range of the feature.
+
+Therefore, the correct answer is:
+a) monotone increasing.
+SOLUTION:  Increasing
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the age intervals. Let's examine the mean values step by step:
+
+1. From age interval (17.0, 18.5) to (18.5, 19.5): -3.326 to -2.358 (increasing)
+2. From age interval (18.5, 19.5) to (19.5, 20.5): -2.358 to -2.799 (decreasing)
+3. From age interval (19.5, 20.5) to (20.5, 21.5): -2.799 to -2.354 (increasing)
+4. From age interval (20.5, 21.5) to (21.5, 22.5): -2.354 to -1.405 (increasing)
+5. From age interval (21.5, 22.5) to (22.5, 23.5): -1.405 to -1.633 (decreasing)
+6. From age interval (22.5, 23.5) to (23.5, 24.5): -1.633 to -1.214 (increasing)
+7. From age interval (23.5, 24.5) to (24.5, 26.5): -1.214 to -0.789 (increasing)
+8. From age interval (24.5, 26.5) to (26.5, 27.5): -0.789 to -0.473 (increasing)
+9. From age interval (26.5, 27.5) to (27.5, 29.5): -0.473 to -0.216 (increasing)
+10. From age interval (27.5, 29.5) to (29.5, 33.5): -0.216 to 0.042 (increasing)
+11. From age interval (29.5, 33.5) to (33.5, 36.5): 0.042 to 0.351 (increasing)
+12. From age interval (33.5, 36.5) to (36.5, 44.5): 0.351 to 0.658 (increasing)
+13. From age interval (36.5, 44.5) to (44.5, 61.5): 0.658 to 0.897 (increasing)
+14. From age interval (44.5, 61.5) to (61.5, 66.5): 0.897 to 0.574 (decreasing)
+15. From age interval (61.5, 66.5) to (66.5, 73.5): 0.574 to 0.099 (decreasing)
+16. From age interval (66.5, 73.5) to (73.5, 74.5): 0.099 to 0.763 (increasing)
+17. From age interval (73.5, 74.5) to (74.5, 77.5): 0.763 to 0.502 (decreasing)
+18. From age interval (74.5, 77.5) to (77.5, 79.5): 0.502 to 0.875 (increasing)
+19. From age interval (77.5, 79.5) to (79.5, 84.5): 0.875 to 0.065 (decreasing)
+20. From age interval (79.5, 84.5) to (84.5, 90.0): 0.065 to -1.08 (decreasing)
+
+The mean values fluctuate between increasing and decreasing across different age intervals. Therefore, the graph is not monotone increasing or monotone decreasing. 
+
+The correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the age intervals.
+
+Here are the mean values for each age interval:
+- (21.0, 21.5): -0.481
+- (21.5, 23.5): -0.377
+- (23.5, 24.5): -0.294
+- (24.5, 26.5): -0.205
+- (26.5, 28.5): -0.106
+- (28.5, 30.5): 0.056
+- (30.5, 34.5): 0.184
+- (34.5, 39.5): 0.286
+- (39.5, 44.5): 0.389
+- (44.5, 54.5): 0.476
+- (54.5, 56.5): 0.374
+- (56.5, 58.5): 0.224
+- (58.5, 60.5): 0.121
+- (60.5, 61.5): -0.053
+- (61.5, 62.5): -0.314
+- (62.5, 64.5): -0.437
+- (64.5, 66.5): -0.598
+- (66.5, 67.5): -0.714
+- (67.5, 68.5): -0.823
+- (68.5, 76.5): -0.922
+- (76.5, 81.0): -1.102
+
+Analyzing the trend:
+- From (21.0, 21.5) to (44.5, 54.5), the mean values increase, indicating a positive trend.
+- From (44.5, 54.5) to (76.5, 81.0), the mean values decrease, indicating a negative trend.
+
+Since the function first increases and then decreases, it is neither strictly increasing nor strictly decreasing throughout the entire range. Therefore, the graph is **not monotone**.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the CreditScore feature.
+
+Here are the mean values for each interval:
+- (350.0, 416.5): 0.62
+- (416.5, 421.5): 0.5698
+- (421.5, 427.5): 0.3799
+- (427.5, 437.5): 0.2757
+- (437.5, 464.5): 0.3274
+- (464.5, 470.5): 0.2778
+- (470.5, 477.5): 0.4561
+- (477.5, 478.5): 0.0595
+- (478.5, 494.5): 0.1431
+- (494.5, 515.5): 0.0909
+- (515.5, 523.5): -0.3342
+- (523.5, 539.5): -0.2192
+- (539.5, 566.5): -0.1337
+- (566.5, 598.5): -0.0838
+- (598.5, 661.5): -0.0327
+- (661.5, 684.5): 0.0186
+- (684.5, 741.5): 0.0696
+- (741.5, 769.5): 0.0206
+- (769.5, 792.5): 0.0691
+- (792.5, 805.5): 0.2231
+- (805.5, 806.5): 0.1131
+- (806.5, 850.0): -0.1138
+
+Analyzing the trend:
+- From (350.0, 416.5) to (427.5, 437.5), the values generally decrease.
+- From (437.5, 464.5) to (470.5, 477.5), there is a slight increase followed by a decrease.
+- From (477.5, 478.5) to (515.5, 523.5), there is a general decrease.
+- From (523.5, 539.5) to (598.5, 661.5), the values continue to decrease.
+- From (661.5, 684.5) to (792.5, 805.5), there is an increasing trend.
+- From (805.5, 806.5) to (806.5, 850.0), there is a decrease.
+
+Given these observations, the values do not consistently increase or decrease across the entire range of CreditScore. Instead, they fluctuate, showing both increases and decreases at different intervals.
+
+Therefore, the correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To determine if the graph is monotone increasing, monotone decreasing, or not monotone, we need to analyze the trend of the mean values across the intervals of the VRDeck feature.
+
+Here are the mean values for each interval:
+- (0.0, 135.5): 0.445
+- (135.5, 215.5): 0.073
+- (215.5, 500.5): -0.294
+- (500.5, 727.5): -0.661
+- (727.5, 799.5): -1.026
+- (799.5, 831.5): -0.601
+- (831.5, 872.5): -1.156
+- (872.5, 993.5): -1.633
+- (993.5, 1430.5): -2.012
+- (1430.5, 1514.5): -1.512
+- (1514.5, 1796.0): -2.212
+- (1796.0, 1909.5): -1.699
+- (1909.5, 1970.0): -2.568
+- (1970.0, 2571.5): -3.006
+- (2571.5, 2582.0): -2.375
+- (2582.0, 2657.0): -2.964
+- (2657.0, 3710.5): -3.98
+- (3710.5, 4089.0): -4.347
+- (4089.0, 5089.5): -5.923
+- (5089.5, 24133.0): -6.634
+
+Observing the trend:
+- The values start at 0.445 and generally decrease as the VRDeck value increases.
+- There are a few intervals where the mean value increases slightly compared to the previous interval (e.g., from -1.026 to -0.601 between intervals (727.5, 799.5) and (799.5, 831.5), and from -2.212 to -1.699 between intervals (1514.5, 1796.0) and (1796.0, 1909.5)).
+- However, the overall trend from the beginning to the end of the range is a decrease.
+
+Conclusion:
+The graph is not strictly monotone decreasing due to the few intervals where the mean value increases. However, it is predominantly decreasing. Therefore, the correct answer is:
+c) not monotone.
+SOLUTION:  Not monotone
+--------------------------------------------------------------------------------
\ No newline at end of file
diff --git a/benchmarks/results/read-value-gemini-1.5.txt b/benchmarks/results/read-value-gemini-1.5.txt
new file mode 100644
index 0000000..60378ca
--- /dev/null
+++ b/benchmarks/results/read-value-gemini-1.5.txt
@@ -0,0 +1,703 @@
+LLM RESPONSE:  The mean value at 400.75 is **-46655.0**. 
+
+You can find this by looking at the "Means" data for the feature "total_rooms". The value 400.75 falls within the interval "(385.5, 406.5)", and the corresponding mean value for this interval is -46655.0. 
+
+SOLUTION:  -46655.0
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The provided interval for 8.93 is "(8.5, 10.5)" which corresponds to a mean value of **0.02253**. 
+
+SOLUTION:  0.02253
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value at 14.15 falls within the interval **"(13.5, 15.0)"**.  
+
+Therefore, the mean value of the graph at 14.15 is **0.01734**. 
+
+SOLUTION:  0.01734
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value at 26.19 is **0.355**. 
+
+Here's why:
+
+* The provided JSON object represents a piecewise function.  
+* The value 26.19 falls within the interval "(25.79375, 26.46875)".
+* The mean value associated with this interval is 0.355. 
+
+SOLUTION:  0.355
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value at 0.32 is **-0.004421**. 
+
+Here's why:
+
+* The provided JSON tells us the mean values for intervals.
+* 0.32 falls within the interval "(0.0, 0.5)".
+* The mean value for the interval "(0.0, 0.5)" is -0.004421. 
+
+Therefore, the GAM predicts a mean value of -0.004421 for any input within the range of 0.0 to 0.5 for the feature "HasCrCard". 
+
+SOLUTION:  -0.004421
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 76.3 is **-0.369**. 
+
+You can find this by looking at the "Means" dictionary in the provided JSON. The value 76.3 falls within the interval "(71.275, 78.28)", and the corresponding mean value for this interval is -0.369. 
+
+SOLUTION:  -0.369
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The value 1617.97 falls within the interval **(1658.5, 1968.5)**.  The mean value of the graph for this interval is **0.948**. 
+
+SOLUTION:  0.585
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 34.34 falls within the interval **"(34.325, 34.345)"**  for which the mean value is **17113.4**. 
+
+SOLUTION:  17113.4
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 0.93 is **0.0917**. 
+
+Here's why:
+
+* The provided JSON object represents a continuous feature named "anaemia".
+* The `Means` dictionary shows the predicted mean values for different ranges of the feature:
+    * For values between 0.0 and 0.5 (exclusive), the mean value is -0.0818.
+    * For values between 0.5 and 1.0 (exclusive), the mean value is **0.0917**.
+
+Since 0.93 falls within the interval "(0.5, 1.0)", the mean value at 0.93 is **0.0917**. 
+
+SOLUTION:  0.0917
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 150.77 is **0.0208**. 
+
+This is because 150.77 falls within the interval "(144.5, 157.0)" in the provided data, and the corresponding mean value for that interval is 0.0208. 
+
+SOLUTION:  0.0208
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value at 4.58 is **2.995**. 
+
+Here's why:
+
+* The provided JSON represents the graph as a series of intervals along the x-axis ('sepal_length') and their corresponding mean predicted values on the y-axis. 
+* The interval "(4.55, 4.75)" contains the value 4.58.
+* The mean value associated with the interval "(4.55, 4.75)" is 2.995. 
+
+SOLUTION:  2.995
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 2.78 is **-0.01073**. 
+
+This is because the provided JSON object shows that the mean value is constant for the interval of 2.5 to 3.5, which includes the value of 2.78. 
+
+SOLUTION:  -0.01073
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 3.86 is 0.254. 
+
+This is because the provided JSON object shows that the mean value is the same for all ages between 3.5 and 4.5, which includes 3.86. 
+
+SOLUTION:  0.254
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 0.45 is **0.085**. 
+
+Here's why:
+
+* The provided JSON object shows the graph's behavior for the feature "Parch".
+* The value 0.45 falls within the interval "(0.0, 0.5)".
+* For the interval "(0.0, 0.5)", the mean value is given as 0.085. 
+
+Therefore, the GAM predicts a mean value of 0.085 when "Parch" is 0.45. 
+
+SOLUTION:  0.085
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 109.98 is **0.5269**. 
+
+This is because the provided JSON object shows that the GAM predicts the same value for the interval "(109.0, 110.0)", which includes the value 109.98. The mean value for this interval is given as 0.5269. 
+
+SOLUTION:  0.5269
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 2.52 is **-0.3026**. 
+
+This is because the provided JSON object shows that the mean value is constant for the interval from 2.5 to 3.5. Since 2.52 falls within this interval, the mean value at 2.52 is the same as the mean value for the entire interval. 
+
+SOLUTION:  -0.3026
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The provided interval for 6.11 falls within the "(5.5, 6.5)" range in the data.  
+
+Therefore, the mean value of the graph at 6.11 is **0.00575**. 
+
+SOLUTION:  0.00575
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 1152.44 is **0.932**. 
+
+Here's why:
+
+1. The provided JSON object represents a piecewise function where the mean value remains constant within each specified interval.
+2. We need to find the interval that contains 1152.44.
+3. Looking at the "Means" dictionary, we see that 1152.44 falls within the interval **"(1033.5, 1222.5)"**.
+4. The corresponding mean value for this interval is **0.932**. 
+
+SOLUTION:  0.932
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 0.07 is **-0.1077**. 
+
+Here's why:
+
+* The provided JSON tells us that for the interval of **(0.0, 0.5)** on the x-axis (which includes 0.07), the mean predicted value is **-0.1077**. 
+
+SOLUTION:  -0.1077
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value at 2.3 falls within the interval **"(1.5, 2.5)"**.  
+
+Therefore, the mean value of the graph at 2.3 is **-0.1873**. 
+
+SOLUTION:  -0.1873
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 0.29 falls within the interval **(0.273, 0.33975)**.  The corresponding mean value for this interval is **0.385**. 
+
+SOLUTION:  0.385
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 5.94 falls within the interval **(5.5, 6.5)**.  
+
+Therefore, the mean value at 5.94 is **0.00567**. 
+
+SOLUTION:  0.00567
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 141941.44 is **-0.0796**. 
+
+This is because 141941.44 falls within the interval **"(148569.97, 184522.325)"**  in the provided JSON object, and the corresponding mean value for this interval is -0.0796. 
+
+SOLUTION:  -0.0388
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 0.48 is **-0.555**. 
+
+Here's why:
+
+* The provided JSON object shows the graph is for the feature "IsActiveMember".
+* The "Means" section tells us:
+    * For values of IsActiveMember between 0.0 and 0.5 (inclusive of 0.0, exclusive of 0.5), the mean predicted value is -0.555.
+    * For values of IsActiveMember between 0.5 and 1.0, the mean predicted value is 0.568.
+
+Since 0.48 falls within the interval (0.0, 0.5), the mean value at 0.48 is **-0.555**. 
+
+SOLUTION:  -0.555
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 75.26 is **-1.223**. 
+
+You can find this by looking at the "Means" dictionary in the JSON object. The key "(71.06, 76.52000000000001)" contains the value 75.26, and the corresponding value is -1.223. This means that for values of "perimeter_worst" between 71.06 and 76.52, the GAM predicts a mean value of -1.223. 
+
+SOLUTION:  -1.223
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value at 13.73 is **-0.133**. 
+
+Here's why:
+
+* The provided JSON object represents a continuous feature, meaning the model predicts the same value for a range of inputs.
+* The value 13.73 falls within the interval "(13.5, 14.0)".
+* The mean value associated with the interval "(13.5, 14.0)" is -0.133. 
+
+SOLUTION:  -0.133
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 0.85 is **-0.03391**. 
+
+Here's why:
+
+* The provided JSON tells us that the feature "smoking" is treated as a continuous variable in this GAM.
+* The mean values are provided in intervals. Since 0.85 falls within the interval "(0.5, 1.0)", the corresponding mean value is used. 
+
+SOLUTION:  -0.03391
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 0.01 is **0.3467**. 
+
+This is because the provided JSON object shows that the mean value is constant for the interval **(0.0089915, 0.01089)** which includes the value of 0.01. 
+
+SOLUTION:  0.3467
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 0.09 falls within the interval **"(0.0, 0.5)"**.  
+
+Therefore, the mean value at 0.09 is **-0.02526**. 
+
+SOLUTION:  -0.02526
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 6083.89 is **1.81**. 
+
+This is because the value 6083.89 falls within the interval "(5218.5, 6171.5)" in the provided data, and the corresponding mean value for this interval is 1.81. 
+
+SOLUTION:  1.439
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 0.02 falls within the interval **(0.02068, 0.024730000000000002)**.  The corresponding mean value for this interval is **-0.31**. 
+
+SOLUTION:  -0.252
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 14.78 falls within the interval **(14.0, 15.0)**.  
+
+Therefore, the mean value at 14.78 is **0.05506**. 
+
+SOLUTION:  0.05506
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value at 15.03 is **-0.718**. 
+
+This is because the provided JSON object shows that the mean value is -0.718 for the interval "(15.010000000000002, 15.485)", and 15.03 falls within this range. 
+
+SOLUTION:  -0.718
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value at 31.93 is **1.274**. 
+
+You can find this by looking at the "Means" dictionary. The value 31.93 falls within the interval "(25.335, 30.71)", and the corresponding mean value for this interval is 1.274. 
+
+SOLUTION:  1.406
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The provided JSON object represents "Gender" as a **continuous** feature, which is illogical. Gender is a categorical variable, typically binary (Male/Female). 
+
+Furthermore, the x-axis ranges (0.0 to 1.0) imply a continuous numerical scale, not categories. 
+
+**This inconsistency makes it impossible to answer your question.** The data representation contradicts the nature of the feature it aims to describe. 
+
+To provide a meaningful answer, the JSON object needs to be corrected. If "Gender" is indeed the intended feature, it should be represented as a categorical variable with appropriate category labels and corresponding values. 
+
+SOLUTION:  -0.4751
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value at 26.86 is **-0.489**. 
+
+This is because 26.86 falls within the interval "(26.5, 29.5)" in the provided data, and the mean value for that interval is -0.489. 
+
+SOLUTION:  -0.489
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 0.06 falls within the interval **(0.059715, 0.06078)**.  
+
+The corresponding mean value for this interval is **-0.0163**. 
+
+SOLUTION:  -0.0163
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 0.05 falls within the interval **"(0.044705, 0.059585)"**  for which the mean value is **-0.205**. 
+
+SOLUTION:  -0.205
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 0.06 falls within the interval **"(0.059495, 0.06042)"**  which has a corresponding mean value of **0.2605**. 
+
+SOLUTION:  0.2605
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The provided JSON represents "sex" as a continuous feature, which is unusual and likely incorrect. Sex is typically a categorical variable (male/female, or encoded as 0/1). 
+
+However, focusing on the structure of the provided data:
+
+* **The key "(0.0, 0.5)" contains the value for 0.07**, because 0.07 falls within the interval between 0.0 and 0.5. 
+
+* **The mean value associated with the key "(0.0, 0.5)" is 0.01719.**
+
+Therefore, according to the provided data, the mean value of the graph at 0.07 is **0.01719**. 
+
+**Important Note:** It's crucial to review how "sex" is represented in the dataset and correct the feature type if needed.  The current representation as a continuous variable is likely flawed. 
+
+SOLUTION:  0.01719
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 1.95 is **7.28**. 
+
+Here's why:
+
+* The provided JSON data represents the graph in intervals.
+* The value 1.95 falls within the interval **"(1.65, 2.45)"**.
+* The mean value associated with the interval "(1.65, 2.45)" is **7.28**. 
+
+SOLUTION:  7.28
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 0.0 is **-0.6445**. 
+
+This is because the first interval in the "Means" dictionary is "(0.0, 0.001156)", which includes 0.0. The corresponding mean value for this interval is -0.6445. 
+
+SOLUTION:  -0.6445
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value for housing_median_age = 6.44 can be found in the interval **(4.5, 7.5)**. 
+
+The mean value for this interval is **-7788.2**. 
+
+SOLUTION:  -7788.2
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 1.59 is **0.534**. 
+
+Here's why:
+
+* The provided JSON object shows that the feature "Pclass" is treated as a continuous variable in this GAM, even though it might represent a categorical feature like passenger class in the real world.
+* The interval "(1.5, 2.5)" includes the value 1.59.
+* For the interval "(1.5, 2.5)", the mean value is 0.534. 
+
+Therefore, the GAM predicts a mean value of 0.534 for any "Pclass" value falling within the interval (1.5, 2.5), including 1.59. 
+
+SOLUTION:  0.534
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 0.14 is **0.396**. 
+
+The provided JSON object shows that the value 0.14 falls within the interval "(0.13845000000000002, 0.1634)" for the feature "smoothness_mean".  The corresponding mean value for this interval is 0.396. 
+
+SOLUTION:  0.396
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 34.1 is **0.042**. 
+
+Here's why:
+
+* The provided JSON represents a piecewise function.  
+* The value 34.1 falls within the interval "(29.5, 33.5)" in the "Means" dictionary.
+* The associated mean value for this interval is 0.042. 
+
+SOLUTION:  0.351
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 27.13 is **-0.036**. 
+
+You can find this by looking at the "Means" dictionary in the JSON object. The value 27.13 falls within the interval "(26.5, 28.5)", and the corresponding mean value for this interval is -0.036. 
+
+SOLUTION:  -0.036
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 0.04 is **0.3225**. 
+
+Here's why:
+
+* The provided JSON tells us the mean values for intervals of the 'diabetes' feature.
+* The value 0.04 falls within the interval "(0.0, 0.5)".
+* The mean value for the interval "(0.0, 0.5)" is given as 0.3225. 
+
+Therefore, the GAM predicts a mean value of 0.3225 when the 'diabetes' feature is 0.04. 
+
+SOLUTION:  0.3225
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value at 1.27 is **-0.918**. 
+
+This is because 1.27 falls within the interval "(1.0, 1.5)" in the provided data, and the mean value for that interval is -0.918. 
+
+SOLUTION:  -0.918
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 966.62 falls within the interval **(964.5, 976.5)** on the x-axis. 
+
+Therefore, the mean value at 966.62 is **23227.2**. 
+
+SOLUTION:  23227.2
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 4568.36 is **-1.82**. 
+
+This is because 4568.36 falls within the interval **"(4401.0, 4668.5)"**, for which the provided mean value is -1.82. 
+
+SOLUTION:  -1.82
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value at 14.65 falls within the interval **"(14.5, 15.0)"** in the provided data. 
+
+Therefore, the mean value of the graph at 14.65 is **0.1393**. 
+
+SOLUTION:  0.1393
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 198.65 is **2.022**. 
+
+This is because the provided JSON object places 198.65 within the interval "(198.5, 199.0)" for which the mean value is 2.022. 
+
+SOLUTION:  2.022
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 243849.53 is **1.516**. 
+
+This is because the value 243849.53 falls within the interval "(243500.0, 244500.0)" in the provided JSON object, and the corresponding mean value for this interval is 1.516. 
+
+SOLUTION:  1.516
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 18765.31 is **-0.132**. 
+
+Here's why:
+
+1. The provided JSON object represents a piecewise function where the mean value remains constant within each specified interval on the x-axis (Balance).
+
+2. We need to find the interval that contains the value 18765.31.
+
+3. Looking at the "Means" dictionary, we see that 18765.31 falls within the interval **"(0.0, 50418.515)"**.
+
+4. The corresponding mean value for this interval is **-0.132**. 
+
+SOLUTION:  -0.132
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value at 65.26 is **-0.73**. 
+
+You can find this by looking at the "Means" data for the "age" feature. The value 65.26 falls within the interval **"(65.5, 67.5)"**, for which the corresponding mean value is -0.73. 
+
+SOLUTION:  0.0
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 24.6 is **-0.272**. 
+
+You can find this by looking at the "Means" dictionary. The value 24.6 falls within the interval "(23.59, 24.795)", and the corresponding mean value for this interval is -0.272. 
+
+SOLUTION:  -0.272
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 0.49 is **-0.368**. 
+
+This is because 0.49 falls within the interval "(0.0, 0.5)" in the provided data, and the mean value for that interval is -0.368. 
+
+SOLUTION:  -0.368
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 5.82 falls within the interval **(5.59195, 5.8294) **. 
+
+Therefore, the mean value at 5.82 is **56900.2**. 
+
+SOLUTION:  56900.2
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 14.4 is **0.03206**. 
+
+This is because the provided JSON object groups the continuous variable "River Management" into intervals. The interval "(13.5, 16.0)" contains the value 14.4, and the corresponding mean value for this interval is 0.03206. 
+
+SOLUTION:  0.03206
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The provided intervals for the feature 'concave points_se' are: 
+
+ "(0.0099215, 0.010165)", "(0.010165, 0.010385)" 
+
+The value of 0.01 falls within the interval **(0.0099215, 0.010165)**.  
+
+The mean value associated with this interval is **-0.0546**. 
+
+Therefore, the mean value of the graph at 0.01 is **-0.0546**. 
+
+SOLUTION:  -0.0546
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of CoastalVulnerability at 1.47 is **-0.02272**. 
+
+Here's why:
+
+* The provided JSON object represents the graph as a series of intervals.
+* The value 1.47 falls within the interval "(1.5, 2.5)".
+* The mean value associated with the interval "(1.5, 2.5)" is -0.02272. 
+
+SOLUTION:  -0.02272
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 0.01 falls within the interval **(0.009857000000000001, 0.010665000000000001)**.  The corresponding mean value for this interval is **0.1863**. 
+
+SOLUTION:  0.1863
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value at BMI 46.58 is **0.626**. 
+
+This is because BMI 46.58 falls within the interval "(45.650000000000006, 48.349999999999994)" in the provided data.  The GAM model predicts the same mean value for all BMI values within that range. 
+
+SOLUTION:  0.626
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The provided interval for 1.27 is "(1.178, 1.275)" which has a mean value of **1.018**. 
+
+SOLUTION:  1.018
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 4.75 falls within the interval **"(4.5, 5.5)"**.  
+
+The JSON object provides the mean value for this interval as **0.00051**. 
+
+Therefore, the mean value of the graph at 4.75 is **0.00051**. 
+
+SOLUTION:  0.00051
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 139172.54 is **0.0211**. 
+
+Here's why:
+
+1. The provided JSON object represents a piecewise function where the value within each interval on the x-axis (representing the 'id' feature) is constant.
+2. We need to find the interval that contains the value 139172.54.
+3. Looking at the "Means" dictionary, we find that 139172.54 falls within the interval **"(110643.5, 146554.5)"**.
+4. The corresponding mean value for this interval is **0.0211**. 
+
+SOLUTION:  0.0211
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 0.28 falls within the interval **"(0.0, 0.5)"**.  Therefore, the mean value at 0.28 is **-0.3765**. 
+
+SOLUTION:  -0.3765
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 0.91 falls within the interval **(0.875, 0.95)**.  
+
+The corresponding mean value for this interval is **-0.9**. 
+
+SOLUTION:  -0.9
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The provided interval for 3.38 is (2.5, 3.5). The mean value for the interval (2.5, 3.5) is -0.01049.
+
+Therefore, the mean value of the graph at 3.38 is **-0.01049**. 
+
+SOLUTION:  -0.01049
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 130.05 is **0.953**. 
+
+This is because the provided JSON object shows that the mean value is the same for any serum_sodium level within the interval of "(129.5, 130.5)", which includes 130.05. 
+
+SOLUTION:  0.953
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value at 20.22 falls within the interval **"(20.5, 22.5)"**.  
+
+Therefore, the mean value of the graph at 20.22 is **-59049.5**. 
+
+SOLUTION:  -50740.7
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The provided interval for the value 2.8 falls within the "(2.5, 4.5)" range on the x-axis.  
+
+Therefore, the mean value of the graph at 2.8 is **-0.0099**. 
+
+SOLUTION:  -0.0099
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 1723.82 is **-1.712**. 
+
+Here's why:
+
+1. The provided JSON object represents a piecewise function where the mean value remains constant within each specified interval on the x-axis (Spa values).
+2. We need to find the interval that contains the value 1723.82. 
+3. Looking at the "Means" data, we see that 1723.82 falls within the interval **"(1209.5, 1808.0)"**.
+4. The corresponding mean value for this interval is **-1.712**. 
+
+SOLUTION:  -1.712
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 6176.67 is **-4.146**. 
+
+Here's why:
+
+* The provided JSON object represents a piecewise function.  
+* The value 6176.67 falls within the interval "(3249.5, 14327.0)" 
+* The corresponding mean value for this interval is -4.146. 
+
+SOLUTION:  -4.146
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 4.16 is **-0.529**. 
+
+This is because the provided JSON object shows that the feature "Relationship" is divided into intervals. The interval "(3.5, 4.5)" contains the value 4.16, and the corresponding mean value for this interval is -0.529. 
+
+SOLUTION:  -0.529
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The value 68251.52 falls within the interval **(66668.0, 67776.0)**.  
+
+The mean value of the graph at that interval is **0.0019**. 
+
+Therefore, the mean value of the graph at 68251.52 is **0.0019**. 
+
+SOLUTION:  1e-05
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 15804780.51 is **0.0581**. 
+
+Here's why:
+
+1. The provided JSON object represents a piecewise function where the intervals in the keys define regions with constant predicted values. 
+2. We need to find the interval that contains the value 15804780.51.
+3. Looking at the "Means" dictionary, we see that 15804780.51 falls within the interval **(15799214.0, 15807559.5)**.
+4. The corresponding mean value for this interval is **-0.0544**. 
+
+Therefore, the mean value of the graph at 15804780.51 is **0.0581**. 
+
+SOLUTION:  0.0581
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 1555.23 is **-2.212**. 
+
+This is because 1555.23 falls within the interval "(1514.5, 1796.0)" in the provided data, and the corresponding mean value for this interval is -2.212. 
+
+SOLUTION:  -2.212
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The age 41.25 falls within the interval **(41.5, 42.5)** on the provided graph. 
+
+The mean value for this interval is **-0.494**. 
+
+SOLUTION:  -0.354
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 0.02 falls within the interval **"(0.019315, 0.023185)"**.  
+
+The corresponding mean value for this interval is **-0.466**. 
+
+SOLUTION:  -0.466
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value at 6.38 can be found by looking at the provided intervals for the "Means" data. 
+
+The value 6.38 falls within the interval **"(6.5, 8.5)"**.  The corresponding mean value for this interval is **0.01107**. 
+
+Therefore, the mean value of the graph at 6.38 is **0.01107**. 
+
+SOLUTION:  0.00515
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 3.14 is **-1.909**. 
+
+This is because 3.14 falls within the interval "(2.5, 4.5)" in the provided data, and the corresponding mean value for this interval is -1.909. 
+
+SOLUTION:  -1.909
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 12.16 is 125210.2. 
+
+This is because the value 12.16 falls within the interval "(3.0, 14.5)" on the x-axis.  The provided JSON data indicates that the mean value for this entire interval is 125210.2. 
+
+SOLUTION:  125210.2
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value at 14.97 falls within the interval **"(13.5, 16.0)"**.  
+
+Therefore, the mean value of the graph at 14.97 is **0.05902**. 
+
+SOLUTION:  0.05902
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 0.17 falls within the interval **(0.1603, 0.1722)**.  The corresponding mean value for this interval is **0.539**. 
+
+SOLUTION:  0.539
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at longitude -114.37 falls within the interval **(-114.595, -114.31)**.  Therefore, the mean value at -114.37 is **-100620.1**. 
+
+SOLUTION:  -100620.1
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 239.62 is **-0.16**. 
+
+This is because the value 239.62 falls within the interval **"(189.5, 232.5)"**  in the provided JSON object. The corresponding mean value for this interval is **-0.16**. 
+
+SOLUTION:  -0.16
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 1305.91 is **0.911**. 
+
+This is because the value 1305.91 falls within the interval "(1341.0, 1801.0)" in the provided data, and the corresponding mean value for this interval is 0.911. 
+
+SOLUTION:  0.911
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 1.26 is **-0.384**. 
+
+You can find this by looking at the "Means" dictionary in the provided JSON. The value 1.26 falls within the interval "(1.2365, 1.326)", and the corresponding mean value for this interval is -0.384. 
+
+SOLUTION:  -0.384
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The provided graph does not contain information for a value of 0.0 in its keys. Therefore, it's impossible to determine the mean value at 0.0 from the given data. 
+
+**Important Note:** Extrapolating outside the range of data provided for a GAM can be misleading. The model's behavior outside the observed data range is not guaranteed. 
+
+SOLUTION:  0.2818
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The provided interval for 9.36 falls within the "(8.5, 9.5)" range in the "Means" dictionary. 
+
+Therefore, the mean value of the graph at 9.36 is **0.0237**. 
+
+SOLUTION:  0.0237
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 0.37 is **7.27**. 
+
+This is because 0.37 falls within the interval "(0.35, 0.45)" for the "petal_width" feature, and the corresponding mean value for this interval is 7.27. 
+
+SOLUTION:  7.27
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 1.58 is **-0.01613**. 
+
+This is because the value 1.58 falls within the interval "(1.5, 2.5)" in the provided data, and the mean value associated with that interval is -0.01613. 
+
+SOLUTION:  -0.01613
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value at 37.49 is **0.48**. 
+
+Here's why:
+
+* You provided a graph of a continuous feature, "ejection_fraction".
+* The value 37.49 falls within the interval **"(36.5, 39.0)"**.
+* The mean value associated with the interval "(36.5, 39.0)" is **0.48**. 
+
+SOLUTION:  0.48
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 52.67 is **3.38**. 
+
+This is because 52.67 falls within the interval **"(52.0, 54.5)"** in the provided JSON object, and the corresponding mean value for this interval is 3.38. 
+
+SOLUTION:  3.38
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 34.12 is **0.081**. 
+
+This is because 34.12 falls within the interval "(32.985, 34.730000000000004)" in the provided data, and the mean value associated with this interval is 0.081. 
+
+SOLUTION:  0.081
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 1886.32 is **4.781**. 
+
+You provided a graph where the x-axis represents the "CapitalLoss" feature. This appears to be bucketed into ranges.  The value 1886.32 falls within the interval **"(1881.5, 1894.5)"** for which you provided the mean value of **4.781**. 
+
+SOLUTION:  4.781
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 0.92 is **0.594**. 
+
+This is because 0.92 falls within the interval "(0.7562, 0.9131)" in the provided data, and the mean value associated with that interval is 0.594. 
+
+SOLUTION:  0.683
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 0.57 falls within the interval **(0.54825, 0.5892999999999999)**.  The corresponding mean value for this interval is **0.4651**. 
+
+SOLUTION:  0.4651
+--------------------------------------------------------------------------------
\ No newline at end of file
diff --git a/benchmarks/results/read-value-gpt-3.5-turbo-0125.txt b/benchmarks/results/read-value-gpt-3.5-turbo-0125.txt
new file mode 100644
index 0000000..24b1fcf
--- /dev/null
+++ b/benchmarks/results/read-value-gpt-3.5-turbo-0125.txt
@@ -0,0 +1,931 @@
+LLM RESPONSE:  To find the mean value of the graph at 400.75, we need to identify the interval that includes this value in the "total_rooms" feature.
+
+From the given data, we can see that the intervals are defined as follows:
+- "(385.5, 406.5)": -46655.0
+- "(406.5, 413.5)": -17805.2
+
+Since 400.75 falls within the interval "(385.5, 406.5)", the mean value of the graph at 400.75 is -46655.0.
+SOLUTION:  -46655.0
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 8.93, we need to look at the interval that includes 8.93. 
+
+From the provided data, we can see that the feature "DrainageSystems" is a continuous feature with intervals ranging from 0.0 to 14.0. The mean values for each interval are given.
+
+The interval that includes 8.93 is "(8.5, 10.5)" with a mean value of 0.02253.
+
+Therefore, the mean value of the graph at 8.93 is 0.02253.
+SOLUTION:  0.02253
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 14.15, we need to look at the intervals provided in the graph and determine which interval includes the value 14.15.
+
+From the given intervals:
+- "(13.5, 15.0)": 0.01734
+
+Since 14.15 falls within the interval "(13.5, 15.0)", the mean value of the graph at 14.15 is 0.01734.
+SOLUTION:  0.01734
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 26.19, we need to identify the interval that includes the value 26.19. 
+
+From the provided data, we can see that the intervals are defined as follows:
+- "(25.79375, 26.46875)": 0.355
+
+Since 26.19 falls within the interval "(25.79375, 26.46875)", the mean value of the graph at 26.19 is 0.355.
+SOLUTION:  0.355
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.32, we need to interpolate the value within the given intervals. 
+
+From the provided graph:
+- For the interval (0.0, 0.5), the mean value is -0.004421.
+- For the interval (0.5, 1.0), the mean value is 0.001379.
+
+Since 0.32 falls within the interval (0.0, 0.5), the mean value of the graph at 0.32 is -0.004421.
+SOLUTION:  -0.004421
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 76.3, we need to identify the interval that includes the value 76.3. 
+
+From the provided data, we can see that the interval "(71.275, 78.28)" corresponds to the range where the function predicts the same value. The mean value for this interval is -0.369.
+
+Therefore, the mean value of the graph at 76.3 is -0.369.
+SOLUTION:  -0.369
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 1617.97, we need to identify the interval that contains this value. 
+
+The interval ranges provided in the data are:
+- "(1434.5, 1658.5)": 0.585
+- "(1658.5, 1968.5)": 0.948
+
+Since 1617.97 falls within the interval "(1434.5, 1658.5)", the mean value of the graph at 1617.97 is 0.585.
+SOLUTION:  0.585
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 34.34, we need to look at the interval that includes this value. In this case, the interval that includes 34.34 is "(34.325, 34.345)".
+
+From the provided data:
+- Mean value at "(34.325, 34.345)": 17113.4
+
+Therefore, the mean value of the graph at 34.34 is 17113.4.
+SOLUTION:  17113.4
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.93, we need to look at the intervals provided in the graph and determine which interval includes the value 0.93.
+
+From the given graph:
+- For the feature "anaemia," the intervals are "(0.0, 0.5)" and "(0.5, 1.0)".
+- The mean values for these intervals are -0.0818 and 0.0917, respectively.
+
+Since 0.93 falls within the interval "(0.5, 1.0)," the mean value of the graph at 0.93 is 0.0917.
+SOLUTION:  0.0917
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 150.77, we need to locate the interval that includes this value. 
+
+From the given data, we can see that the intervals are defined as follows:
+- "(144.5, 157.0)": 0.0208
+- "(157.0, 170.5)": 0.0623
+
+Since 150.77 falls within the interval "(144.5, 157.0)", the mean value at 150.77 is 0.0208.
+SOLUTION:  0.0208
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 4.58, we need to identify the interval in which 4.58 falls and then determine the corresponding mean value.
+
+From the given data, we can see that the intervals are defined as follows:
+- "(4.3, 4.55)": 3.328
+- "(4.55, 4.75)": 2.995
+
+Since 4.58 falls within the interval "(4.55, 4.75)", the mean value of the graph at 4.58 is 2.995.
+SOLUTION:  2.995
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 2.78 for the continuous feature "DamsQuality," we need to identify the interval that includes 2.78. 
+
+From the given data, we can see that the intervals are defined as follows:
+- "(0.0, 1.5)": -0.02325
+- "(1.5, 2.5)": -0.01532
+- "(2.5, 3.5)": -0.01073
+
+Since 2.78 falls within the interval "(2.5, 3.5)", the mean value of the graph at 2.78 is -0.01073.
+SOLUTION:  -0.01073
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 3.86, we need to identify the interval that includes the value 3.86. 
+
+From the provided data, we can see that the interval "(3.5, 4.5)" corresponds to values between 3.5 and 4.5. The mean value for this interval is 0.254.
+
+Therefore, the mean value of the graph at 3.86 is 0.254.
+SOLUTION:  0.254
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.45, we need to look at the interval that includes 0.45. In this case, the interval "(0.0, 0.5)" includes 0.45.
+
+From the provided data:
+- Mean value for the interval "(0.0, 0.5)" is 0.085.
+
+Therefore, the mean value of the graph at 0.45 is 0.085.
+SOLUTION:  0.085
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 109.98, we need to look at the intervals provided in the graph and determine which interval includes the value 109.98.
+
+The interval that includes 109.98 is "(109.0, 110.0)". In this interval, the mean value is 0.5269.
+
+Therefore, the mean value of the graph at 109.98 is 0.5269.
+SOLUTION:  0.5269
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 2.52, we need to identify the interval that includes 2.52. 
+
+From the given intervals:
+- "(2.5, 3.5)": -0.3026
+
+Since 2.52 falls within the interval (2.5, 3.5), the mean value of the graph at 2.52 is -0.3026.
+SOLUTION:  -0.3026
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 6.11, we need to identify the interval that includes the value 6.11. 
+
+From the given data, we can see that the intervals are in increments of 1. Therefore, the interval that includes 6.11 is "(5.5, 6.5)".
+
+Looking at the mean values provided for each interval, the mean value for the interval "(5.5, 6.5)" is 0.00575.
+
+Therefore, the mean value of the graph at 6.11 is 0.00575.
+SOLUTION:  0.00575
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 1152.44, we need to identify the interval in which 1152.44 falls and then determine the corresponding mean value.
+
+From the provided data, we can see that the interval "(1033.5, 1222.5)" includes the value 1152.44. The mean value for this interval is 0.932.
+
+Therefore, the mean value of the graph at 1152.44 is 0.932.
+SOLUTION:  0.932
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.07, we need to determine which interval 0.07 falls into based on the keys provided in the graph.
+
+In this case, the intervals provided are "(0.0, 0.5)" and "(0.5, 1.0)". Since 0.07 falls within the range of 0.0 to 0.5, we will use the mean value associated with this interval.
+
+From the graph:
+- For the interval "(0.0, 0.5)", the mean value is -0.1077.
+
+Therefore, the mean value of the graph at 0.07 is -0.1077.
+SOLUTION:  -0.1077
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 2.3, we need to identify the interval that includes the value 2.3. 
+
+From the given data, we see that the intervals are defined as follows:
+- "(1.5, 2.5)": -0.1873
+- "(2.5, 3.5)": -0.0302
+
+Since 2.3 falls within the interval "(1.5, 2.5)", the mean value of the graph at 2.3 is -0.1873.
+SOLUTION:  -0.1873
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.29, we need to look at the intervals provided in the graph and identify the interval that includes the value 0.29.
+
+From the given intervals:
+- "(0.273, 0.33975)": 0.385
+- "(0.33975, 0.3663)": 0.486
+
+Since 0.29 falls within the interval "(0.273, 0.33975)", the mean value of the graph at 0.29 is 0.385.
+SOLUTION:  0.385
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 5.94, we need to identify the interval that includes 5.94. 
+
+From the given data, we can see that the intervals are defined as follows:
+- "(5.5, 6.5)": 0.00567
+
+Since 5.94 falls within the interval "(5.5, 6.5)", the mean value of the graph at 5.94 is 0.00567.
+SOLUTION:  0.00567
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 141941.44, we need to identify the interval in which this value falls and then look up the corresponding mean value.
+
+From the provided data, we can see that the intervals are defined as follows:
+- "(121482.61499999999, 148569.97)": -0.0388
+- "(148569.97, 184522.325)": -0.0796
+
+Since 141941.44 falls within the interval "(121482.61499999999, 148569.97)", the mean value of the graph at 141941.44 is -0.0388.
+SOLUTION:  -0.0388
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.48, we need to determine the interval that includes the value 0.48. 
+
+From the provided graph, we see that the feature "IsActiveMember" is continuous and the intervals are "(0.0, 0.5)" and "(0.5, 1.0)".
+
+Since 0.48 falls within the interval "(0.0, 0.5)", we can use the mean value associated with this interval.
+
+The mean value for the interval "(0.0, 0.5)" is -0.555.
+
+Therefore, the mean value of the graph at 0.48 is -0.555.
+SOLUTION:  -0.555
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 75.26, we need to identify the interval that includes this value. 
+
+The interval "(71.06, 76.52000000000001)" includes the value 75.26. 
+
+In this interval, the mean value is -1.223.
+
+Therefore, the mean value of the graph at 75.26 is -1.223.
+SOLUTION:  -1.223
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 13.73, we need to identify the interval that includes the value 13.73. 
+
+From the given data, we see that the intervals are defined as follows:
+- "(0.0, 0.5)": -0.297
+- "(0.5, 3.5)": -0.074
+- "(3.5, 4.5)": 0.644
+- "(4.5, 6.5)": -0.723
+- "(6.5, 7.5)": -0.542
+- "(7.5, 8.5)": -0.665
+- "(8.5, 9.5)": -0.926
+- "(9.5, 10.5)": 0.423
+- "(10.5, 11.5)": 0.59
+- "(11.5, 12.5)": 0.27
+- "(12.5, 13.5)": 0.534
+- "(13.5, 14.0)": -0.133
+
+Since 13.73 falls within the interval "(13.5, 14.0)", the mean value of the graph at 13.73 is -0.133.
+SOLUTION:  -0.133
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.85, we need to look at the intervals provided in the graph and interpolate the value at 0.85.
+
+From the graph:
+- For the interval (0.5, 1.0), the mean value is -0.03391.
+
+Since 0.85 falls within the interval (0.5, 1.0), the mean value of the graph at 0.85 is -0.03391.
+SOLUTION:  -0.03391
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.01, we need to identify the interval in which 0.01 falls and then determine the corresponding mean value.
+
+From the provided data, we can see that the interval containing 0.01 is "(0.0089915, 0.01089)" with a mean value of 0.3467.
+
+Therefore, the mean value of the graph at 0.01 is 0.3467.
+SOLUTION:  0.3467
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.09, we need to identify the interval in which 0.09 falls and then determine the corresponding mean value.
+
+From the given data, we can see that the intervals are defined as follows:
+- "(0.0, 0.5)": -0.02526
+- "(0.5, 1.5)": -0.02147
+
+Since 0.09 falls within the interval "(0.0, 0.5)", the mean value of the graph at 0.09 is -0.02526.
+SOLUTION:  -0.02526
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 6083.89, we need to identify the interval in which this value falls and then determine the corresponding mean value.
+
+From the given data, we can see that the intervals are defined as follows:
+- "(6031.5, 6171.5)": 1.439
+- "(6171.5, 8753.0)": 2.236
+
+Since 6083.89 falls within the interval "(6031.5, 6171.5)", the mean value of the graph at 6083.89 is 1.439.
+SOLUTION:  1.439
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.02, we need to locate the interval that contains 0.02. 
+
+From the given data, we can see that the interval "(0.019545, 0.02068)" contains 0.02. 
+
+In this interval, the mean value is -0.252. 
+
+Therefore, the mean value of the graph at 0.02 is -0.252.
+SOLUTION:  -0.252
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 14.78, we need to determine the interval in which 14.78 falls and then extract the corresponding mean value from the provided data.
+
+The interval that includes 14.78 is "(11.5, 14.0)". From the given data:
+- Mean value for "(11.5, 14.0)": 0.0391
+
+Therefore, the mean value of the graph at 14.78 is 0.0391.
+SOLUTION:  0.05506
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 15.03, we need to identify the interval that includes the value 15.03. 
+
+From the given data, we can see that the intervals are defined as follows:
+- "(14.665, 15.010000000000002)": -0.82
+- "(15.010000000000002, 15.485)": -0.718
+
+Since 15.03 falls within the interval "(15.010000000000002, 15.485)", the mean value of the graph at 15.03 is -0.718.
+SOLUTION:  -0.718
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 31.93, we need to look at the intervals provided in the JSON object for the feature "radius_worst" and determine which interval includes the value 31.93.
+
+From the given intervals:
+- "(30.71, 36.04)": 1.406
+
+The interval "(30.71, 36.04)" includes the value 31.93. The mean value for this interval is 1.406.
+
+Therefore, the mean value of the graph at 31.93 is 1.406.
+SOLUTION:  1.406
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.23, we need to determine which interval 0.23 falls into based on the keys provided in the graph.
+
+From the given intervals:
+- "(0.0, 0.5)": -0.4751
+- "(0.5, 1.0)": 0.2339
+
+Since 0.23 falls within the interval "(0.0, 0.5)", the mean value of the graph at 0.23 is -0.4751.
+SOLUTION:  -0.4751
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 26.86, we need to look at the intervals provided in the graph for the feature "NativeCountry" and determine which interval contains the value 26.86.
+
+From the given intervals:
+- "(24.5, 26.5)": -0.35
+- "(26.5, 29.5)": -0.489
+
+Since 26.86 falls within the interval "(26.5, 29.5)", the mean value of the graph at 26.86 is -0.489.
+SOLUTION:  -0.489
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.06, we need to look at the intervals provided in the graph and determine which interval contains the value 0.06.
+
+From the given intervals:
+- "(0.059715, 0.06078)": -0.0163
+- "(0.06078, 0.061385)": -0.0618
+
+Since 0.06 falls within the interval "(0.059715, 0.06078)", the mean value of the graph at 0.06 is -0.0163.
+SOLUTION:  -0.0163
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.05, we need to identify the interval in which 0.05 falls and then determine the corresponding mean value.
+
+From the provided data, we can see that the interval containing 0.05 is "(0.044705, 0.059585)". In this interval, the mean value is -0.205.
+
+Therefore, the mean value of the graph at 0.05 is -0.205.
+SOLUTION:  -0.205
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.06, we need to look at the interval that includes 0.06. In this case, the interval that includes 0.06 is "(0.059495, 0.06042)".
+
+From the provided data:
+- The mean value for the interval "(0.059495, 0.06042)" is 0.2605.
+
+Therefore, the mean value of the graph at 0.06 is 0.2605.
+SOLUTION:  0.2605
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.07, we need to determine which interval 0.07 falls into based on the keys provided in the graph.
+
+From the given intervals:
+- "(0.0, 0.5)": 0.01719
+- "(0.5, 1.0)": -0.00954
+
+Since 0.07 falls within the interval "(0.0, 0.5)", the mean value of the graph at 0.07 is 0.01719.
+SOLUTION:  0.01719
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 1.95, we need to identify the interval in which 1.95 falls and then determine the corresponding mean value.
+
+From the given data:
+- Interval "(1.65, 2.45)" corresponds to a mean value of 7.28.
+
+Therefore, the mean value of the graph at 1.95 is 7.28.
+SOLUTION:  7.28
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.0, we look at the provided JSON object for the feature "concavity_se" which is a continuous feature. In the JSON object, we see that the key "(0.0, 0.001156)" corresponds to the interval that includes 0.0.
+
+From the JSON object, the mean value for this interval is -0.6445. Therefore, the mean value of the graph at 0.0 for the feature "concavity_se" is -0.6445.
+SOLUTION:  -0.6445
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 6.44, we need to identify the interval that includes the value 6.44. 
+
+From the given data, we can see that the intervals are defined as follows:
+- "(1.0, 4.5)": -19998.0
+- "(4.5, 7.5)": -7788.2
+- "(7.5, 16.5)": -10680.2
+- "(16.5, 18.5)": -6304.4
+- "(18.5, 27.5)": -1760.6
+- "(27.5, 34.5)": 2164.8
+- "(34.5, 38.5)": -912.5
+- "(38.5, 41.5)": 4199.6
+- "(41.5, 45.5)": -497.4
+- "(45.5, 47.5)": -5189.8
+- "(47.5, 48.5)": 5201.0
+- "(48.5, 49.5)": 2159.0
+- "(49.5, 50.5)": 6135.7
+- "(50.5, 51.5)": 11513.8
+- "(51.5, 52.0)": 27549.7
+
+Since 6.44 falls within the interval "(4.5, 7.5)", the mean value of the graph at 6.44 is -7788.2.
+SOLUTION:  -7788.2
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 1.59, we need to interpolate between the intervals provided in the graph. 
+
+The intervals given are:
+- (1.0, 1.5): Mean = -0.009
+- (1.5, 2.5): Mean = 0.534
+
+Since 1.59 falls within the (1.5, 2.5) interval, we can interpolate the mean value at 1.59 as follows:
+
+Mean at 1.59 = Mean at 1.5 + ((Mean at 2.5 - Mean at 1.5) / (2.5 - 1.5)) * (1.59 - 1.5)
+Mean at 1.59 = 0.534 + ((0.534 - (-0.009)) / (2.5 - 1.5)) * (1.59 - 1.5)
+Mean at 1.59 = 0.534 + (0.543 / 1) * 0.09
+Mean at 1.59 = 0.534 + 0.04887
+Mean at 1.59 ≈ 0.58287
+
+Therefore, the mean value of the graph at 1.59 is approximately 0.58287.
+SOLUTION:  0.534
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.14, we need to look at the intervals provided in the graph and determine which interval includes the value 0.14.
+
+From the given intervals:
+- "(0.12455, 0.13845000000000002)": 0.334
+
+Since 0.14 falls within the interval "(0.12455, 0.13845000000000002)", the mean value of the graph at 0.14 is 0.334.
+SOLUTION:  0.396
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 34.1, we need to identify the interval that includes the value 34.1. 
+
+From the given data, we can see that the intervals are defined as follows:
+- "(29.5, 33.5)": 0.042
+- "(33.5, 36.5)": 0.351
+
+Since 34.1 falls within the interval "(33.5, 36.5)", the mean value of the graph at 34.1 is 0.351.
+SOLUTION:  0.351
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 27.13, we need to identify the interval that includes this value. 
+
+From the provided data, we can see that the intervals are defined as follows:
+- "(26.5, 28.5)": -0.036
+- "(28.5, 30.5)": -0.0039
+
+Since 27.13 falls within the interval "(26.5, 28.5)", the mean value of the graph at 27.13 is -0.036.
+SOLUTION:  -0.036
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.04, we need to look at the interval that includes 0.04. In this case, the interval "(0.0, 0.5)" includes 0.04.
+
+From the provided data:
+- For the interval "(0.0, 0.5)", the mean value is 0.3225.
+
+Therefore, the mean value of the graph at 0.04 is 0.3225.
+SOLUTION:  0.3225
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 1.27 for the continuous feature "NumOfProducts," we need to determine which interval 1.27 falls into. 
+
+From the given data:
+- "(1.0, 1.5)": -0.918
+- "(1.5, 2.5)": 0.96
+
+Since 1.27 falls within the interval (1.0, 1.5), the mean value of the graph at 1.27 is -0.918.
+SOLUTION:  -0.918
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 966.62, we need to identify the interval in which 966.62 falls and then determine the corresponding mean value from the provided data.
+
+From the given intervals:
+- "(964.5, 976.5)": 23227.2
+- "(976.5, 978.5)": 18664.6
+
+Since 966.62 falls within the interval "(964.5, 976.5)", the mean value of the graph at 966.62 is 23227.2.
+SOLUTION:  23227.2
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 4568.36, we need to identify the interval in which this value falls. 
+
+The intervals provided in the graph are:
+- "(4243.5, 4401.0)": 1.45
+- "(4401.0, 4668.5)": -1.82
+
+Since 4568.36 falls within the interval "(4401.0, 4668.5)", the mean value of the graph at 4568.36 is -1.82.
+SOLUTION:  -1.82
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 14.65, we need to look at the intervals provided in the graph and determine which interval includes the value 14.65.
+
+From the given intervals:
+- "(11.5, 14.5)": 0.1954
+- "(14.5, 15.0)": 0.1393
+
+Since 14.65 falls within the interval "(11.5, 14.5)", the mean value of the graph at 14.65 is 0.1954.
+SOLUTION:  0.1393
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 198.65, we need to identify the interval that includes this value. 
+
+The interval that includes 198.65 is "(187.5, 198.5)". 
+
+From the given data:
+- Mean value for the interval "(187.5, 198.5)" is 1.853.
+
+Therefore, the mean value of the graph at 198.65 is 1.853.
+SOLUTION:  2.022
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 243849.53, we need to locate the interval that contains this value. 
+
+The interval that contains 243849.53 is "(242500.0, 243500.0)".
+
+From the given data:
+- The mean value for the interval "(242500.0, 243500.0)" is 1.107.
+
+Therefore, the mean value of the graph at 243849.53 is 1.107.
+SOLUTION:  1.516
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 18765.31, we need to locate the interval that includes this value in the keys of the means provided.
+
+The interval that includes 18765.31 is "(0.0, 50418.515)". The mean value for this interval is -0.132.
+
+Therefore, the mean value of the graph at 18765.31 is -0.132.
+SOLUTION:  -0.132
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 65.26, we need to identify the interval that includes this value. The interval "(64.5, 65.5)" includes 65.26. 
+
+From the provided data:
+- Mean value for the interval "(64.5, 65.5)" is 0.0.
+
+Therefore, the mean value of the graph at 65.26 is 0.0.
+SOLUTION:  0.0
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 24.6, we need to identify the interval in which 24.6 falls and then determine the corresponding mean value.
+
+From the provided data, we can see that the intervals are defined as follows:
+- "(23.59, 24.795)": -0.272
+- "(24.795, 25.18)": -0.1
+
+Since 24.6 falls within the interval "(23.59, 24.795)", the mean value of the graph at 24.6 is -0.272.
+SOLUTION:  -0.272
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.49 for the continuous feature "MaritalStatus," we need to look at the interval that includes 0.49. 
+
+From the given data, we see that the intervals are defined as follows:
+- "(0.0, 0.5)": -0.368
+- "(0.5, 1.5)": 0.724
+
+Since 0.49 falls within the interval "(0.0, 0.5)", the mean value for this interval is -0.368.
+
+Therefore, the mean value of the graph at 0.49 is -0.368.
+SOLUTION:  -0.368
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 5.82, we look at the interval that contains 5.82 in the keys of the JSON object provided.
+
+From the given data, we see that the interval that contains 5.82 is "(5.59195, 5.8294)".
+
+In this interval, the mean value is 40032.8.
+
+Therefore, the mean value of the graph at 5.82 is 40032.8.
+SOLUTION:  56900.2
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 14.4, we need to look at the intervals provided in the graph and determine which interval contains the value 14.4.
+
+From the given intervals:
+- "(13.5, 16.0)": 0.03206
+
+Since 14.4 falls within the interval "(13.5, 16.0)", the mean value of the graph at 14.4 is 0.03206.
+SOLUTION:  0.03206
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.01, we need to locate the interval that contains 0.01 in the keys of the means provided.
+
+From the given data, we can see that the interval containing 0.01 is "(0.009878999999999999, 0.0099215)" with a mean value of -0.0268.
+
+Therefore, the mean value of the graph at 0.01 is -0.0268.
+SOLUTION:  -0.0546
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 1.47, we need to identify the interval that includes the value 1.47. 
+
+From the given data, we see that the intervals are defined as follows:
+- "(0.0, 0.5)": -0.03259
+- "(0.5, 1.5)": -0.02272
+- "(1.5, 2.5)": -0.0157
+
+Since 1.47 falls within the interval "(0.5, 1.5)", the mean value of the graph at 1.47 is -0.02272.
+SOLUTION:  -0.02272
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.01, we need to locate the interval that contains 0.01. From the provided data, we can see that the intervals are not directly given in the data. However, we can infer the interval that contains 0.01 by looking at the neighboring intervals.
+
+The interval that contains 0.01 is likely "(0.009857000000000001, 0.010665000000000001)" based on the pattern of the intervals provided.
+
+From the mean values given for this interval:
+- Mean value at (0.009857000000000001, 0.010665000000000001): 0.1863
+
+Therefore, the mean value of the graph at 0.01 is 0.1863.
+SOLUTION:  0.1863
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 46.58, we need to identify the interval in which 46.58 falls and then determine the corresponding mean value.
+
+From the provided data, we can see that the interval "(45.650000000000006, 48.349999999999994)" includes the value 46.58. The mean value for this interval is 0.626.
+
+Therefore, the mean value of the graph at 46.58 is 0.626.
+SOLUTION:  0.626
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 1.27, we need to look at the interval that includes 1.27. 
+
+From the provided data, we can see that the interval "(1.275, 1.3925)" includes 1.27. The mean value for this interval is 1.283.
+
+Therefore, the mean value of the graph at 1.27 is 1.283.
+SOLUTION:  1.018
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 4.75, we need to look at the interval that includes 4.75. In this case, 4.75 falls within the interval "(4.5, 5.5)".
+
+From the provided data:
+- Mean value at "(4.5, 5.5)": 0.00051
+
+Therefore, the mean value of the graph at 4.75 is 0.00051.
+SOLUTION:  0.00051
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 139172.54, we need to locate the interval that contains this value in the "id" feature.
+
+From the given data, we can see that the intervals are defined as follows:
+- "(147131.5, 161901.5)": -0.0139
+- "(161901.5, 162437.5)": -0.0745
+
+Since 139172.54 falls within the interval "(147131.5, 161901.5)", the mean value of the graph at 139172.54 is -0.0139.
+SOLUTION:  0.0211
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.28 for the feature "Tenure," we need to identify the interval that includes 0.28. 
+
+From the provided intervals:
+- "(0.0, 0.5)": -0.3765
+- "(0.5, 1.5)": -0.0692
+
+Since 0.28 falls within the interval "(0.0, 0.5)", the mean value of the graph at 0.28 is -0.3765.
+SOLUTION:  -0.3765
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.91, we need to look at the intervals provided in the data and determine which interval includes the value 0.91.
+
+From the given data, we can see that the intervals are defined as follows:
+- "(0.875, 0.95)": -0.9
+- "(0.95, 1.1400000000000001)": -0.15
+
+Since 0.91 falls within the interval "(0.875, 0.95)", the mean value of the graph at 0.91 is -0.9.
+SOLUTION:  -0.9
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 3.38, we need to identify the interval in which 3.38 falls and then determine the corresponding mean value within that interval.
+
+Given the intervals provided in the graph:
+- "(2.5, 3.5)": -0.01049
+- "(3.5, 4.5)": -0.00528
+
+Since 3.38 falls within the interval "(2.5, 3.5)", the mean value of the graph at 3.38 is -0.01049.
+SOLUTION:  -0.01049
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 130.05, we need to identify the interval that includes this value. 
+
+The interval that includes 130.05 is "(129.5, 130.5)".
+
+From the given data:
+- Mean value at "(129.5, 130.5)" is 0.953.
+
+Therefore, the mean value of the graph at 130.05 is 0.953.
+SOLUTION:  0.953
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 20.22, we need to locate the interval that includes the value 20.22. 
+
+From the given data, we can see that the intervals are defined as follows:
+- "(17.5, 20.5)": -50740.7
+- "(20.5, 22.5)": -59049.5
+
+Since 20.22 falls within the interval "(20.5, 22.5)", the mean value of the graph at 20.22 is -59049.5.
+SOLUTION:  -50740.7
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 2.8, we need to look at the intervals provided in the graph and determine which interval includes the value 2.8.
+
+From the given intervals:
+- "(0.0, 0.5)": -0.02553
+- "(0.5, 2.5)": -0.02038
+- "(2.5, 4.5)": -0.0099
+
+The interval that includes 2.8 is "(2.5, 4.5)" with a mean value of -0.0099.
+
+Therefore, the mean value of the graph at 2.8 is -0.0099.
+SOLUTION:  -0.0099
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 1723.82, we need to identify the interval in which 1723.82 falls and then determine the corresponding mean value.
+
+From the given data, we can see that the intervals are as follows:
+- "(1209.5, 1808.0)": -1.712
+- "(1808.0, 2204.5)": -3.029
+
+Since 1723.82 falls within the interval "(1209.5, 1808.0)", the mean value of the graph at 1723.82 is -1.712.
+SOLUTION:  -1.712
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 6176.67, we need to locate the interval that contains this value in the keys of the JSON object representing the graph.
+
+From the given data, we can see that the feature "RoomService" is a continuous feature with mean values provided for different intervals. The interval that contains 6176.67 is not explicitly listed in the JSON object, so we need to determine the mean value at this point by interpolation.
+
+The interval that contains 6176.67 falls between "(3249.5, 14327.0)" and "(3006.0, 3196.5)". To estimate the mean value at 6176.67, we can perform linear interpolation using the mean values at the boundaries of these intervals.
+
+Let's calculate the interpolated mean value at 6176.67:
+- For the interval "(3249.5, 14327.0)":
+  - Lower bound: 3249.5, Mean: -4.146
+- For the interval "(3006.0, 3196.5)":
+  - Upper bound: 3196.5, Mean: -2.984
+
+Now, we can interpolate the mean value at 6176.67 using linear interpolation:
+\[ \text{Interpolated mean} = \frac{(6176.67 - 3196.5) \times (-4.146) + (3249.5 - 6176.67) \times (-2.984)}{3249.5 - 3196.5} \]
+
+Calculating the interpolated mean value at 6176.67:
+\[ \text{Interpolated mean} = \frac{(6176.67 - 3196.5) \times (-4.146) + (3249.5 - 6176.67) \times (-2.984)}{3249.5 - 3196.5} \]
+\[ \text{Interpolated mean} = \frac{(2980.17) \times (-4.146) + (-2927.17) \times (-2.984)}{53} \]
+\[ \text{Interpolated mean} = \frac{(-12380.77) + (8736.77)}{53} \]
+\[ \text{Interpolated mean} = \frac{-3644}{53} \]
+\[ \text{Interpolated mean} \approx -68.77 \]
+
+Therefore, the estimated mean value of the graph at 6176.67 is approximately -68.77.
+SOLUTION:  -4.146
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 4.16, we need to determine the interval in which 4.16 falls and then extract the corresponding mean value from the given data.
+
+The interval that contains 4.16 is "(3.5, 4.5)". In this interval, the mean value is -0.529.
+
+Therefore, the mean value of the graph at 4.16 is -0.529.
+SOLUTION:  -0.529
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 68251.52, we need to locate the interval that contains this value in the "id" feature.
+
+From the given data, we can see that the intervals are defined as follows:
+- "(66668.0, 67776.0)": 0.0019
+- "(67776.0, 75664.5)": 1e-05
+
+Since 68251.52 falls within the interval "(66668.0, 67776.0)", the mean value of the graph at 68251.52 is 0.0019.
+SOLUTION:  1e-05
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 15804780.51, we need to identify the interval in which this value falls and then determine the corresponding mean value.
+
+The interval keys in the provided data represent ranges where the function predicts the same value. We need to locate the interval that contains the value 15804780.51.
+
+From the given data, we can see that the intervals are defined as follows:
+- "(15799214.0, 15807559.5)": 0.0581
+- "(15807559.5, 15812616.5)": -0.0049
+
+The value 15804780.51 falls within the interval "(15799214.0, 15807559.5)".
+
+Therefore, the mean value of the graph at 15804780.51 is 0.0581.
+SOLUTION:  0.0581
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 1555.23, we need to identify the interval in which this value falls. 
+
+The intervals provided in the graph are:
+- (0.0, 135.5)
+- (135.5, 215.5)
+- (215.5, 500.5)
+- (500.5, 727.5)
+- (727.5, 799.5)
+- (799.5, 831.5)
+- (831.5, 872.5)
+- (872.5, 993.5)
+- (993.5, 1430.5)
+- (1430.5, 1514.5)
+- (1514.5, 1796.0)
+- (1796.0, 1909.5)
+- (1909.5, 1970.0)
+- (1970.0, 2571.5)
+- (2571.5, 2582.0)
+- (2582.0, 2657.0)
+- (2657.0, 3710.5)
+- (3710.5, 4089.0)
+- (4089.0, 5089.5)
+- (5089.5, 24133.0)
+
+Since 1555.23 falls within the interval (1430.5, 1514.5), the mean value of the graph at 1555.23 is -1.512.
+SOLUTION:  -2.212
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 41.25, we need to identify the interval that includes this value. In this case, 41.25 falls within the interval "(40.5, 41.5)".
+
+Looking at the provided data for the mean values, the mean value for the interval "(40.5, 41.5)" is -0.354.
+
+Therefore, the mean value of the graph at 41.25 is -0.354.
+SOLUTION:  -0.354
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.02, we need to look at the interval that contains 0.02. In this case, the interval that includes 0.02 is "(0.019315, 0.023185)".
+
+From the provided data:
+- Mean value for the interval "(0.019315, 0.023185)" is -0.466.
+
+Therefore, the mean value of the graph at 0.02 is -0.466.
+SOLUTION:  -0.466
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 6.38, we need to identify the interval that includes 6.38. 
+
+From the given data, we can see that the intervals are as follows:
+- "(5.5, 6.5)": 0.00515
+- "(6.5, 8.5)": 0.01107
+
+Since 6.38 falls within the interval "(5.5, 6.5)", the mean value of the graph at 6.38 is 0.00515.
+SOLUTION:  0.00515
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 3.14, we need to identify the interval that includes 3.14 in the keys provided in the JSON object for the HoursPerWeek feature.
+
+From the given data, we can see that the interval "(2.5, 4.5)" includes 3.14. The mean value for this interval is -1.909.
+
+Therefore, the mean value of the graph at 3.14 for the HoursPerWeek feature is -1.909.
+SOLUTION:  -1.909
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 12.16, we need to identify the interval that includes the value 12.16. 
+
+From the given data, we can see that the intervals are defined as follows:
+- "(3.0, 14.5)": 125210.2
+- "(14.5, 25.5)": 92452.9
+
+Since 12.16 falls within the interval "(3.0, 14.5)", the mean value of the graph at 12.16 is 125210.2.
+SOLUTION:  125210.2
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 14.97, we need to locate the interval that includes this value in the "Urbanization" feature.
+
+From the given intervals:
+- "(13.5, 16.0)": 0.05902
+
+Since 14.97 falls within the interval "(13.5, 16.0)", the mean value of the graph at 14.97 for the "Urbanization" feature is 0.05902.
+SOLUTION:  0.05902
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.17, we need to look at the interval that includes 0.17. 
+
+From the provided data, we see that the interval that includes 0.17 is "(0.1603, 0.1722)" with a mean value of 0.539. 
+
+Therefore, the mean value of the graph at 0.17 is 0.539.
+SOLUTION:  0.539
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at -114.37, we need to locate the interval that contains this value in the provided data.
+
+From the given data, we can see that the longitude feature is a continuous feature with intervals representing ranges where the function predicts the same value. The mean values are provided for each interval.
+
+The interval that contains -114.37 is "(-114.595, -114.31)" with a mean value of -100620.1.
+
+Therefore, the mean value of the graph at -114.37 is -100620.1.
+SOLUTION:  -100620.1
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 239.62, we need to locate the interval that contains this value. 
+
+From the given data, we can see that the feature "creatinine_phosphokinase" is a continuous feature with mean values provided for different intervals. We need to identify the interval that includes the value 239.62.
+
+Looking at the intervals provided, we can see that the interval "(2307.5, 2444.0)" contains the value 239.62. 
+
+From the mean values provided for this interval:
+- Lower bound: 2307.5
+- Upper bound: 2444.0
+- Mean value: 5.81
+
+Therefore, the mean value of the graph at 239.62 is 5.81.
+SOLUTION:  -0.16
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 1305.91, we need to determine the interval in which 1305.91 falls and then extract the corresponding mean value from the provided data.
+
+The interval boundaries are as follows:
+- "(1248.5, 1341.0)": 0.911
+- "(1341.0, 1801.0)": 1.01
+
+Since 1305.91 falls within the interval "(1248.5, 1341.0)", the mean value of the graph at 1305.91 is 0.911.
+SOLUTION:  0.911
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 1.26, we need to look at the interval that includes 1.26. In this case, the interval that includes 1.26 is "(1.197, 1.2365)" with a mean value of -0.461. 
+
+Therefore, the mean value of the graph at 1.26 is -0.461.
+SOLUTION:  -0.384
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.0, we need to locate the interval that includes 0.0 in the keys provided in the JSON object. In this case, the interval that includes 0.0 is "(0.0008948, 0.001092)".
+
+From the given data:
+- Mean value for the interval "(0.0008948, 0.001092)" is 0.2818.
+
+Therefore, the mean value of the graph at 0.0 is 0.2818.
+SOLUTION:  0.2818
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 9.36, we need to determine the interval in which 9.36 falls and then extract the corresponding mean value from the provided data.
+
+Given that the intervals in the graph are in increments of 1 (e.g., "(0.0, 0.5)", "(0.5, 1.5)", etc.), we can see that 9.36 falls within the interval "(9.5, 10.5)".
+
+From the provided data:
+- Mean value for the interval "(9.5, 10.5)": 0.02782
+
+Therefore, the mean value of the graph at 9.36 is 0.02782.
+SOLUTION:  0.0237
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.37, we need to look at the interval that includes 0.37. In this case, the interval that includes 0.37 is "(0.35, 0.45)".
+
+From the given data:
+- Mean value for the interval "(0.35, 0.45)" is 7.27.
+
+Therefore, the mean value of the graph at 0.37 is 7.27.
+SOLUTION:  7.27
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 1.58, we need to identify the interval that includes the value 1.58. 
+
+From the given data, we can see that the intervals are defined as follows:
+- "(1.5, 2.5)": -0.01613
+
+Since 1.58 falls within the interval "(1.5, 2.5)", the mean value of the graph at 1.58 is -0.01613.
+SOLUTION:  -0.01613
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 37.49, we need to identify the interval that includes this value. 
+
+The interval "(36.5, 39.0)" includes the value 37.49. 
+
+From the provided data:
+- Mean value for the interval "(36.5, 39.0)" is 0.48.
+
+Therefore, the mean value of the graph at 37.49 is 0.48.
+SOLUTION:  0.48
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 52.67, we need to identify the interval that includes the value 52.67. 
+
+From the given data, we can see that the interval "(30.5, 52.0)" includes the value 52.67. 
+
+In this interval, the mean value is 5.56.
+
+Therefore, the mean value of the graph at 52.67 is 5.56.
+SOLUTION:  3.38
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 34.12, we need to identify the interval in which 34.12 falls and then determine the corresponding mean value within that interval.
+
+From the provided data, we can see that the intervals are as follows:
+- "(32.985, 34.730000000000004)": 0.081
+- "(34.730000000000004, 41.21)": 0.188
+
+Since 34.12 falls within the interval "(32.985, 34.730000000000004)", the mean value of the graph at 34.12 is 0.081.
+SOLUTION:  0.081
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 1886.32, we need to identify the interval in which 1886.32 falls and then determine the corresponding mean value.
+
+From the provided data, we can see that the intervals are defined as follows:
+- "(1846.0, 1862.0)": 3.823
+- "(1862.0, 1881.5)": -1.36
+
+Since 1886.32 falls between 1862.0 and 1881.5, the mean value of the graph at 1886.32 would be -1.36.
+SOLUTION:  4.781
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.92, we need to look at the intervals provided in the JSON object for the feature "radius_se". 
+
+The interval that includes 0.92 is "(0.7562, 0.9131)" with a mean value of 0.594. 
+
+Therefore, the mean value of the graph at 0.92 is 0.594.
+SOLUTION:  0.683
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.57, we need to look at the intervals provided in the JSON object for the feature "compactness_worst."
+
+The interval that includes 0.57 is "(0.54825, 0.5892999999999999)", and the mean value for this interval is 0.4651.
+
+Therefore, the mean value of the graph at 0.57 for the feature "compactness_worst" is 0.4651.
+SOLUTION:  0.4651
+--------------------------------------------------------------------------------
\ No newline at end of file
diff --git a/benchmarks/results/read-value-gpt-4-turbo-2024-04-09.txt b/benchmarks/results/read-value-gpt-4-turbo-2024-04-09.txt
new file mode 100644
index 0000000..65e8ac9
--- /dev/null
+++ b/benchmarks/results/read-value-gpt-4-turbo-2024-04-09.txt
@@ -0,0 +1,878 @@
+LLM RESPONSE:  To find the mean value of the graph at 400.75, we need to determine which interval the value 400.75 falls into based on the provided keys in the "Means" dictionary.
+
+From the provided intervals, we can see that the value 400.75 falls within the interval "(385.5, 406.5)". 
+
+The mean value for this interval, as provided in the "Means" dictionary, is -46655.0.
+
+Therefore, the mean value of the graph at 400.75 is -46655.0.
+SOLUTION:  -46655.0
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 8.93, we need to determine which interval the value 8.93 falls into. According to the provided intervals in the "Means" data:
+
+- "(8.5, 10.5)": 0.02253
+
+The value 8.93 falls within the interval (8.5, 10.5). Therefore, the mean value of the graph at 8.93 is 0.02253.
+SOLUTION:  0.02253
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 14.15, we need to determine which interval the value 14.15 falls into. From the provided data, the intervals are:
+
+- (0.0, 1.5)
+- (1.5, 2.5)
+- (2.5, 3.5)
+- (3.5, 4.5)
+- (4.5, 5.5)
+- (5.5, 6.5)
+- (6.5, 7.5)
+- (7.5, 8.5)
+- (8.5, 9.5)
+- (9.5, 11.5)
+- (11.5, 12.5)
+- (12.5, 13.5)
+- (13.5, 15.0)
+
+The value 14.15 falls within the interval (13.5, 15.0). According to the data provided for the means:
+
+- "(13.5, 15.0)": 0.01734
+
+Therefore, the mean value of the graph at 14.15 is 0.01734.
+SOLUTION:  0.01734
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 26.19, we need to determine which interval the value 26.19 falls into. From the provided intervals in the "Means" data, we can see that 26.19 falls within the interval "(25.79375, 26.46875)".
+
+The mean value for the interval "(25.79375, 26.46875)" is given as 0.355.
+
+Therefore, the mean value of the graph at 26.19 is 0.355.
+SOLUTION:  0.355
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The value 0.32 falls within the interval (0.0, 0.5). According to the provided data, the mean value for this interval is -0.004421. Therefore, the mean value of the graph at 0.32 is -0.004421.
+SOLUTION:  -0.004421
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 76.3, we need to determine which interval the value 76.3 falls into. From the provided intervals in the "Means" data, we can see that 76.3 falls within the interval "(71.275, 78.28)".
+
+The mean value for the interval "(71.275, 78.28)" is -0.369.
+
+Therefore, the mean value of the graph at 76.3 is -0.369.
+SOLUTION:  -0.369
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 1617.97, we need to determine which interval this value falls into based on the provided ranges in the "Means" data.
+
+From the given intervals:
+- "(0.0, 125.5)": -0.032
+- "(125.5, 541.5)": -0.211
+- "(541.5, 808.5)": 0.034
+- "(808.5, 1082.0)": 0.213
+- "(1082.0, 1187.0)": -0.042
+- "(1187.0, 1434.5)": 0.401
+- "(1434.5, 1658.5)": 0.585
+- "(1658.5, 1968.5)": 0.948
+- "(1968.5, 3394.5)": 1.235
+- "(3394.5, 3460.0)": 0.871
+- "(3460.0, 3741.5)": 1.066
+- "(3741.5, 4803.5)": 2.339
+- "(4803.5, 5204.0)": 2.909
+- "(5204.0, 12253.0)": 3.236
+
+The value 1617.97 falls within the interval "(1434.5, 1658.5)". According to the data, the mean value for this interval is 0.585.
+
+Therefore, the mean value of the graph at 1617.97 is 0.585.
+SOLUTION:  0.585
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at a latitude of 34.34, we need to locate the interval that includes this value and then retrieve the corresponding mean value from the data provided.
+
+From the intervals given in the "Means" data, the interval that includes 34.34 is "(34.325, 34.345)". The mean value for this interval is 17113.4.
+
+Therefore, the mean value of the graph at a latitude of 34.34 is 17113.4.
+SOLUTION:  17113.4
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.93, we need to determine which interval the value 0.93 falls into. According to the provided data, the intervals are:
+
+- (0.0, 0.5)
+- (0.5, 1.0)
+
+The value 0.93 falls into the interval (0.5, 1.0). The mean value for this interval is given as 0.0917.
+
+Therefore, the mean value of the graph at 0.93 is 0.0917.
+SOLUTION:  0.0917
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 150.77, we need to determine which interval the value 150.77 falls into based on the provided ranges in the "Means" data.
+
+From the provided intervals in the "Means" data:
+- "(144.5, 157.0)": 0.0208
+
+The value 150.77 falls within the interval (144.5, 157.0). Therefore, the mean value of the graph at 150.77 is 0.0208.
+SOLUTION:  0.0208
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 4.58, we need to determine which interval the value 4.58 falls into based on the keys provided in the JSON object for the means.
+
+From the intervals provided:
+- "(4.3, 4.55)"
+- "(4.55, 4.75)"
+- "(4.75, 4.85)"
+- ...
+
+The value 4.58 falls within the interval "(4.55, 4.75)". 
+
+Looking up the mean value for this interval in the Means JSON object:
+- Means: {"(4.55, 4.75)": 2.995}
+
+Therefore, the mean value of the graph at 4.58 is 2.995.
+SOLUTION:  2.995
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 2.78, we need to determine which interval the value 2.78 falls into. According to the provided intervals in the "Means" data:
+
+- "(0.0, 1.5)": -0.02325
+- "(1.5, 2.5)": -0.01532
+- "(2.5, 3.5)": -0.01073
+- ...
+
+The value 2.78 falls within the interval "(2.5, 3.5)". Therefore, the mean value of the graph at 2.78 is -0.01073.
+SOLUTION:  -0.01073
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at the age of 3.86, we need to determine which interval the age 3.86 falls into based on the provided keys in the JSON object.
+
+From the given intervals:
+- "(0.0, 0.5)"
+- "(0.5, 3.5)"
+- "(3.5, 4.5)"
+- ...
+
+The age 3.86 falls within the interval "(3.5, 4.5)".
+
+Looking up the mean value for this interval in the provided JSON object under "Means":
+- "(3.5, 4.5)": 0.254
+
+Therefore, the mean value of the graph at the age of 3.86 is 0.254.
+SOLUTION:  0.254
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 0.45 can be determined by identifying the interval in which the value 0.45 falls. According to the provided data, the intervals and their corresponding mean values are:
+
+- "(0.0, 0.5)": 0.085
+- "(0.5, 1.5)": -0.055
+- "(1.5, 3.0)": -0.299
+- "(3.0, 4.0)": -1.704
+
+The value 0.45 falls within the interval "(0.0, 0.5)". Therefore, the mean value of the graph at 0.45 is 0.085.
+SOLUTION:  0.085
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 109.98, we need to determine which interval this value falls into based on the provided keys in the "Means" dictionary. 
+
+Looking at the intervals, the value 109.98 falls within the interval "(109.0, 110.0)". According to the "Means" dictionary, the mean value for this interval is 0.5269.
+
+Therefore, the mean value of the graph at 109.98 is 0.5269.
+SOLUTION:  0.5269
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 2.52, we need to determine which interval the value 2.52 falls into based on the keys provided in the JSON object. The intervals given are:
+
+- (0.0, 0.5)
+- (0.5, 1.5)
+- (1.5, 2.5)
+- (2.5, 3.5)
+- (3.5, 4.0)
+
+The value 2.52 falls within the interval (2.5, 3.5). According to the JSON object, the mean value for this interval is -0.3026. Therefore, the mean value of the graph at 2.52 is -0.3026.
+SOLUTION:  -0.3026
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 6.11, we need to determine which interval the value 6.11 falls into. From the provided data, the intervals are given as keys in the "Means" dictionary.
+
+Looking at the intervals, 6.11 falls within the interval "(5.5, 6.5)". The mean value for this interval is provided in the "Means" dictionary:
+
+Means: {"(5.5, 6.5)": 0.00575}
+
+Therefore, the mean value of the graph at 6.11 is 0.00575.
+SOLUTION:  0.00575
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 1152.44, we need to determine which interval this value falls into based on the provided ranges in the "Means" data.
+
+From the given intervals:
+- "(1033.5, 1222.5)": 0.932
+
+The value 1152.44 falls within the interval (1033.5, 1222.5). Therefore, the mean value of the graph at 1152.44 is 0.932.
+SOLUTION:  0.932
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.07 for the feature "high_blood_pressure," we need to determine which interval the value 0.07 falls into. According to the provided data, the intervals are "(0.0, 0.5)" and "(0.5, 1.0)."
+
+Since 0.07 is between 0.0 and 0.5, it falls into the interval "(0.0, 0.5)." The mean value for this interval is given as -0.1077.
+
+Therefore, the mean value of the graph at 0.07 is -0.1077.
+SOLUTION:  -0.1077
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 2.3, we need to determine which interval the value 2.3 falls into. According to the intervals provided in the "Means" data:
+
+- "(1.5, 2.5)": -0.1873
+
+Since 2.3 is between 1.5 and 2.5, the mean value of the graph at 2.3 is -0.1873.
+SOLUTION:  -0.1873
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.29, we need to locate the interval that includes the value 0.29. From the provided intervals in the "Means" data:
+
+- "(0.273, 0.33975)": 0.385
+
+The interval (0.273, 0.33975) includes the value 0.29. Therefore, the mean value of the graph at 0.29 is 0.385.
+SOLUTION:  0.385
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 5.94, we need to determine which interval the value 5.94 falls into based on the keys provided in the JSON object. 
+
+From the intervals given:
+- "(5.5, 6.5)": 0.00567
+
+The value 5.94 falls within the interval (5.5, 6.5). Therefore, the mean value of the graph at 5.94 is 0.00567.
+SOLUTION:  0.00567
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 141941.44, we need to determine which interval this value falls into based on the keys provided in the "Means" object.
+
+From the intervals given:
+- "(121482.61499999999, 148569.97)": -0.0388
+
+The value 141941.44 falls within the interval (121482.61499999999, 148569.97). Therefore, the mean value of the graph at 141941.44 is -0.0388.
+SOLUTION:  -0.0388
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.48, we need to determine which interval the value 0.48 falls into. According to the provided data, the intervals are "(0.0, 0.5)" and "(0.5, 1.0)".
+
+Since 0.48 is less than 0.5, it falls within the interval "(0.0, 0.5)". The mean value for this interval is given as -0.555.
+
+Therefore, the mean value of the graph at 0.48 is -0.555.
+SOLUTION:  -0.555
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 75.26, we need to determine which interval the value 75.26 falls into. From the provided intervals in the "Means" data, we can see that 75.26 falls within the interval "(71.06, 76.52000000000001)".
+
+The mean value for the interval "(71.06, 76.52000000000001)" is given as -1.223.
+
+Therefore, the mean value of the graph at 75.26 is -1.223.
+SOLUTION:  -1.223
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 13.73, we need to determine which interval the value 13.73 falls into based on the provided keys in the JSON object. 
+
+From the given intervals:
+- "(12.5, 13.5)": 0.534
+- "(13.5, 14.0)": -0.133
+
+The value 13.73 falls within the interval "(13.5, 14.0)". Therefore, the mean value of the graph at 13.73 is -0.133.
+SOLUTION:  -0.133
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 0.85 falls within the interval (0.5, 1.0). According to the data provided, the mean value for this interval is -0.03391. Therefore, the mean value of the graph at 0.85 is -0.03391.
+SOLUTION:  -0.03391
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.01 for the feature "compactness_se," we need to locate the interval that includes the value 0.01. From the provided JSON object for the means, we can see that the interval "(0.0089915, 0.01089)" includes the value 0.01.
+
+The mean value for the interval "(0.0089915, 0.01089)" is given as 0.3467.
+
+Therefore, the mean value of the graph at 0.01 for the feature "compactness_se" is 0.3467.
+SOLUTION:  0.3467
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.09, we need to determine which interval the value 0.09 falls into. According to the provided intervals in the "Means" data:
+
+- "(0.0, 0.5)": -0.02526
+
+Since 0.09 is between 0.0 and 0.5, it falls within this interval. Therefore, the mean value of the graph at 0.09 is -0.02526.
+SOLUTION:  -0.02526
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 6083.89, we need to determine which interval this value falls into based on the keys provided in the "Means" dictionary.
+
+From the provided intervals:
+- "(6031.5, 6171.5)": 1.439
+
+The value 6083.89 falls within the interval (6031.5, 6171.5). Therefore, the mean value of the graph at 6083.89 is 1.439.
+SOLUTION:  1.439
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.02 for the feature "symmetry_se", we need to locate the interval that includes the value 0.02. 
+
+From the provided intervals in the "Means" data, the interval that includes 0.02 is "(0.019545, 0.02068)". The mean value for this interval is -0.252.
+
+Therefore, the mean value of the graph at 0.02 is -0.252.
+SOLUTION:  -0.252
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 14.78, we need to determine which interval this value falls into based on the provided keys in the "Means" dictionary. 
+
+From the intervals given:
+- "(14.0, 15.0)": 0.05506
+
+The value 14.78 falls within the interval (14.0, 15.0). Therefore, the mean value of the graph at 14.78 is 0.05506.
+SOLUTION:  0.05506
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 15.03, we need to locate the interval that includes the value 15.03. From the provided intervals in the "Means" data, we can see that the value 15.03 falls within the interval "(15.010000000000002, 15.485)".
+
+The mean value for this interval is given as -0.718.
+
+Therefore, the mean value of the graph at 15.03 is -0.718.
+SOLUTION:  -0.718
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at a specific point, in this case, 31.93, we need to identify the interval in which this value falls and then look up the corresponding mean value from the provided data.
+
+From the given intervals in the "Means" data:
+- "(25.335, 30.71)": 1.274
+- "(30.71, 36.04)": 1.406
+
+The value 31.93 falls within the interval "(30.71, 36.04)". Therefore, the mean value of the graph at 31.93 is 1.406.
+SOLUTION:  1.406
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The value 0.23 falls within the interval (0.0, 0.5). According to the provided data, the mean value for this interval is -0.4751. Therefore, the mean value of the graph at 0.23 is -0.4751.
+SOLUTION:  -0.4751
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 26.86, we need to determine which interval this value falls into based on the provided keys in the "Means" object.
+
+From the given intervals, the value 26.86 falls within the interval "(26.5, 29.5)". According to the "Means" object, the mean value for this interval is -0.489.
+
+Therefore, the mean value of the graph at 26.86 is -0.489.
+SOLUTION:  -0.489
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.06 for the feature "fractal_dimension_mean," we need to locate the interval that includes the value 0.06. 
+
+From the provided data, the interval that includes 0.06 is "(0.059715, 0.06078)". The mean value for this interval is given as -0.0163.
+
+Therefore, the mean value of the graph at 0.06 is -0.0163.
+SOLUTION:  -0.0163
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.05 for the feature "concavity_mean," we need to locate the interval that includes the value 0.05. From the provided intervals and their corresponding mean values, we can see that the value 0.05 falls within the interval "(0.044705, 0.059585)".
+
+The mean value for the interval "(0.044705, 0.059585)" is -0.205.
+
+Therefore, the mean value of the graph at 0.05 is -0.205.
+SOLUTION:  -0.205
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.06, we need to locate the interval that includes the value 0.06. From the provided data, the interval that includes 0.06 is "(0.059495, 0.06042)". 
+
+The mean value for this interval is given as 0.2605.
+
+Therefore, the mean value of the graph at 0.06 is 0.2605.
+SOLUTION:  0.2605
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The value 0.07 falls within the interval (0.0, 0.5). According to the provided data, the mean value for this interval is 0.01719. Therefore, the mean value of the graph at 0.07 is 0.01719.
+SOLUTION:  0.01719
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 1.95, we need to determine which interval the value 1.95 falls into based on the provided keys in the JSON object. 
+
+From the given intervals:
+- "(1.1, 1.65)"
+- "(1.65, 2.45)"
+- "(2.45, 3.15)"
+- "(3.15, 3.8)"
+- "(3.8, 4.45)"
+- "(4.45, 5.65)"
+- "(5.65, 6.9)"
+
+The value 1.95 falls within the interval "(1.65, 2.45)". 
+
+According to the JSON object for Means:
+- Means: {"(1.1, 1.65)": 8.05, "(1.65, 2.45)": 7.28, "(2.45, 3.15)": -1.17, "(3.15, 3.8)": -2.4, "(3.8, 4.45)": -3.03, "(4.45, 5.65)": -3.73, "(5.65, 6.9)": -4.38}
+
+The mean value at the interval "(1.65, 2.45)" is 7.28.
+
+Therefore, the mean value of the graph at 1.95 is 7.28.
+SOLUTION:  7.28
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The mean value of the graph at 0.0 for the feature "concavity_se" is -0.6445. This value corresponds to the interval "(0.0, 0.001156)" in the provided data.
+SOLUTION:  -0.6445
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 6.44, we need to determine which interval the value 6.44 falls into based on the keys provided in the JSON object for the means.
+
+From the provided intervals:
+- "(1.0, 4.5)": -19998.0
+- "(4.5, 7.5)": -7788.2
+- ...
+
+The value 6.44 falls within the interval "(4.5, 7.5)". Therefore, the mean value of the graph at 6.44 is -7788.2.
+SOLUTION:  -7788.2
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 1.59, we need to determine which interval the value 1.59 falls into based on the keys provided in the JSON object. The intervals given are:
+
+- (1.0, 1.5)
+- (1.5, 2.5)
+- (2.5, 3.0)
+
+The value 1.59 falls within the interval (1.5, 2.5). According to the JSON object for the means, the mean value for this interval is:
+
+Means: {"(1.5, 2.5)": 0.534}
+
+Therefore, the mean value of the graph at 1.59 is 0.534.
+SOLUTION:  0.534
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.14, we need to locate the interval that includes the value 0.14. From the provided data, the interval that includes 0.14 is "(0.12455, 0.13845000000000002)".
+
+The mean value for this interval is given as 0.334.
+
+Therefore, the mean value of the graph at 0.14 is 0.334.
+SOLUTION:  0.396
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at age 34.1, we need to identify the interval that includes this age. From the provided data, the interval "(29.5, 33.5)" includes ages from 29.5 to 33.5, and the next interval "(33.5, 36.5)" includes ages from 33.5 to 36.5. Since 34.1 falls within the interval "(33.5, 36.5)", we look at the mean value associated with this interval.
+
+The mean value for the interval "(33.5, 36.5)" is 0.351.
+
+Therefore, the mean value of the graph at age 34.1 is 0.351.
+SOLUTION:  0.351
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 27.13, we need to determine which interval the value 27.13 falls into based on the provided ranges in the "Means" data.
+
+From the given intervals in the "Means" data:
+- "(24.5, 26.5)": -0.0708
+- "(26.5, 28.5)": -0.036
+
+The value 27.13 falls within the interval "(26.5, 28.5)". Therefore, the mean value of the graph at 27.13 is -0.036.
+SOLUTION:  -0.036
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.04, we need to determine which interval the value 0.04 falls into. According to the provided data, the intervals are "(0.0, 0.5)" and "(0.5, 1.0)". The value 0.04 falls within the interval "(0.0, 0.5)".
+
+From the data provided:
+Means: {"(0.0, 0.5)": 0.3225, "(0.5, 1.0)": -0.415}
+
+The mean value of the graph at 0.04, which is within the interval "(0.0, 0.5)", is 0.3225.
+SOLUTION:  0.3225
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 1.27, we need to determine which interval the value 1.27 falls into based on the keys provided in the "Means" dictionary.
+
+The intervals given are:
+- (1.0, 1.5)
+- (1.5, 2.5)
+- (2.5, 3.5)
+- (3.5, 4.0)
+
+The value 1.27 falls within the interval (1.0, 1.5). According to the "Means" dictionary, the mean value for this interval is -0.918.
+
+Therefore, the mean value of the graph at 1.27 is -0.918.
+SOLUTION:  -0.918
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 966.62, we need to locate the interval that includes this value. From the provided data, the interval that contains 966.62 is "(962.5, 964.5)".
+
+The mean value for this interval is given as 14554.6.
+
+Therefore, the mean value of the graph at 966.62 is 14554.6.
+SOLUTION:  23227.2
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 4568.36, we need to determine which interval this value falls into based on the provided ranges in the "Means" data.
+
+Looking at the intervals provided:
+- "(4401.0, 4668.5)": -1.82
+- "(4668.5, 4826.0)": 3.79
+
+The value 4568.36 falls within the interval "(4401.0, 4668.5)". Therefore, the mean value of the graph at 4568.36 is -1.82.
+SOLUTION:  -1.82
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 14.65, we need to determine which interval this value falls into based on the provided keys in the "Means" dictionary.
+
+From the given intervals, 14.65 falls within the interval "(14.5, 15.0)". According to the "Means" dictionary, the mean value for this interval is 0.1393.
+
+Therefore, the mean value of the graph at 14.65 is 0.1393.
+SOLUTION:  0.1393
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 198.65, we need to determine which interval this value falls into based on the provided ranges. 
+
+From the given data, the interval for the glucose value of 198.65 is "(198.5, 199.0)", as 198.65 falls between 198.5 and 199.0.
+
+The mean value for this interval is given as 2.022.
+
+Therefore, the mean value of the graph at 198.65 is 2.022.
+SOLUTION:  2.022
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 243849.53, we need to determine which interval this value falls into based on the provided ranges in the "Means" data.
+
+Looking at the intervals provided:
+- "(243500.0, 244500.0)": 1.516
+
+The value 243849.53 falls within the interval (243500.0, 244500.0). Therefore, the mean value of the graph at 243849.53 is 1.516.
+SOLUTION:  1.516
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 18765.31, we need to determine which interval the value 18765.31 falls into. From the provided intervals in the "Means" data, we can see that 18765.31 falls within the interval "(0.0, 50418.515)".
+
+The mean value for the interval "(0.0, 50418.515)" is given as -0.132.
+
+Therefore, the mean value of the graph at 18765.31 is -0.132.
+SOLUTION:  -0.132
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at age 65.26, we need to determine which interval the age 65.26 falls into based on the provided keys in the JSON object.
+
+From the given intervals, age 65.26 falls within the interval "(64.5, 65.5)". The mean value for this interval is provided in the JSON object under "Means".
+
+The mean value for the interval "(64.5, 65.5)" is 0.0.
+
+Therefore, the mean value of the graph at age 65.26 is 0.0.
+SOLUTION:  0.0
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 24.6, we need to determine which interval the value 24.6 falls into based on the provided keys in the "Means" dictionary.
+
+From the given intervals:
+- "(23.59, 24.795)": -0.272
+- "(24.795, 25.18)": -0.1
+
+The value 24.6 falls within the interval "(23.59, 24.795)". Therefore, the mean value of the graph at 24.6 is -0.272.
+SOLUTION:  -0.272
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.49, we need to determine which interval the value 0.49 falls into based on the keys provided in the JSON object. The intervals are given as:
+
+- "(0.0, 0.5)"
+- "(0.5, 1.5)"
+- "(1.5, 2.5)"
+- "(2.5, 3.5)"
+- "(3.5, 4.5)"
+- "(4.5, 5.5)"
+- "(5.5, 6.0)"
+
+The value 0.49 falls within the interval "(0.0, 0.5)". According to the JSON object for Means, the mean value for this interval is -0.368.
+
+Therefore, the mean value of the graph at 0.49 is -0.368.
+SOLUTION:  -0.368
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 5.82, we need to locate the interval that includes the value 5.82. From the provided intervals in the "Means" data, the interval that includes 5.82 is "(5.59195, 5.8294)".
+
+The mean value for this interval is given as 56900.2.
+
+Therefore, the mean value of the graph at 5.82 is 56900.2.
+SOLUTION:  56900.2
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 14.4, we need to determine which interval the value 14.4 falls into based on the provided ranges in the graph data.
+
+From the given intervals:
+- "(13.5, 16.0)": 0.03206
+
+The value 14.4 falls within the interval (13.5, 16.0). Therefore, the mean value of the graph at 14.4 is 0.03206.
+SOLUTION:  0.03206
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.01 for the feature "concave points_se," we need to locate the interval that includes the value 0.01. From the provided data, the interval that includes 0.01 is "(0.0099215, 0.010165)".
+
+The mean value for this interval is given as -0.0546.
+
+Therefore, the mean value of the graph at 0.01 for the feature "concave points_se" is -0.0546.
+SOLUTION:  -0.0546
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 1.47, we need to determine which interval the value 1.47 falls into. According to the provided intervals in the "Means" data:
+
+- "(0.0, 0.5)": -0.03259
+- "(0.5, 1.5)": -0.02272
+- "(1.5, 2.5)": -0.0157
+- ...
+
+The value 1.47 falls within the interval "(0.5, 1.5)". Therefore, the mean value of the graph at 1.47 is -0.02272.
+SOLUTION:  -0.02272
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.01 for the feature "smoothness_se", we need to locate the interval that includes the value 0.01. From the provided data, the interval that includes 0.01 is "(0.009857000000000001, 0.010665000000000001)".
+
+The mean value for this interval is given as 0.1863.
+
+Therefore, the mean value of the graph at 0.01 for the feature "smoothness_se" is 0.1863.
+SOLUTION:  0.1863
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at BMI 46.58, we need to determine which interval this value falls into based on the provided ranges in the "Means" data.
+
+From the given intervals:
+- "(45.650000000000006, 48.349999999999994)": 0.626
+
+BMI 46.58 falls within the interval (45.650000000000006, 48.349999999999994). Therefore, the mean value of the graph at BMI 46.58 is 0.626.
+SOLUTION:  0.626
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 1.27 for the feature "DiabetesPedigreeFunction," we need to identify the interval that includes the value 1.27. 
+
+From the provided intervals and mean values:
+- "(1.275, 1.3925)": 1.283
+
+Since 1.27 is slightly less than 1.275, it falls into the previous interval:
+- "(1.178, 1.275)": 1.018
+
+Therefore, the mean value of the graph at 1.27 is 1.018.
+SOLUTION:  1.018
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 4.75, we need to determine which interval 4.75 falls into based on the keys provided in the "Means" dictionary. 
+
+From the intervals given:
+- "(4.5, 5.5)": 0.00051
+
+The value 4.75 falls within the interval (4.5, 5.5). Therefore, the mean value of the graph at 4.75 is 0.00051.
+SOLUTION:  0.00051
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 139172.54, we need to locate which interval this value falls into based on the provided ranges in the "Means" data.
+
+From the provided intervals in the "Means" data, the value 139172.54 falls within the interval "(110643.5, 146554.5)".
+
+The mean value for this interval is given as 0.0211.
+
+Therefore, the mean value of the graph at 139172.54 is 0.0211.
+SOLUTION:  0.0211
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.28, we need to determine which interval the value 0.28 falls into. According to the provided data, the intervals are specified as keys in the "Means" dictionary.
+
+The interval that includes 0.28 is "(0.0, 0.5)". The mean value for this interval is given in the "Means" dictionary as -0.3765.
+
+Therefore, the mean value of the graph at 0.28 is -0.3765.
+SOLUTION:  -0.3765
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.91, we need to locate the interval that includes the value 0.91. From the provided data, the interval that includes 0.91 is "(0.875, 0.95)". 
+
+The mean value for this interval is given as -0.9.
+
+Therefore, the mean value of the graph at 0.91 is -0.9.
+SOLUTION:  -0.9
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 3.38, we need to determine which interval the value 3.38 falls into. According to the provided intervals in the "Means" data:
+
+- "(2.5, 3.5)": -0.01049
+- "(3.5, 4.5)": -0.00528
+
+The value 3.38 falls within the interval (2.5, 3.5). Therefore, the mean value of the graph at 3.38 is -0.01049.
+SOLUTION:  -0.01049
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 130.05, we need to determine which interval the value 130.05 falls into based on the keys provided in the "Means" dictionary.
+
+From the provided intervals:
+- "(129.5, 130.5)": 0.953
+
+The value 130.05 falls within the interval (129.5, 130.5). Therefore, the mean value of the graph at 130.05 is 0.953.
+SOLUTION:  0.953
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 20.22, we need to locate the interval that includes the value 20.22. From the provided intervals in the "Means" data, the interval that includes 20.22 is "(17.5, 20.5)".
+
+The mean value for the interval "(17.5, 20.5)" is -50740.7.
+
+Therefore, the mean value of the graph at 20.22 is -50740.7.
+SOLUTION:  -50740.7
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 2.8, we need to identify which interval the value 2.8 falls into based on the keys provided in the JSON object for the feature "InadequatePlanning".
+
+From the intervals provided:
+- "(0.0, 0.5)"
+- "(0.5, 2.5)"
+- "(2.5, 4.5)"
+- "(4.5, 6.5)"
+- "(6.5, 7.5)"
+- "(7.5, 9.5)"
+- "(9.5, 10.5)"
+- "(10.5, 12.5)"
+- "(12.5, 13.5)"
+- "(13.5, 15.0)"
+- "(15.0, 16.0)"
+
+The value 2.8 falls within the interval "(2.5, 4.5)". According to the JSON object for the means:
+- Means: {"(2.5, 4.5)": -0.0099}
+
+Therefore, the mean value of the graph at 2.8 is -0.0099.
+SOLUTION:  -0.0099
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 1723.82, we need to determine which interval this value falls into based on the provided ranges in the "Means" data.
+
+From the given intervals:
+- "(1209.5, 1808.0)": -1.712
+
+The value 1723.82 falls within the interval (1209.5, 1808.0). Therefore, the mean value of the graph at 1723.82 is -1.712.
+SOLUTION:  -1.712
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 6176.67, we need to determine which interval this value falls into based on the provided ranges in the "Means" data.
+
+From the provided intervals in the "Means" data, the value 6176.67 falls within the interval "(3249.5, 14327.0)". The mean value for this interval is given as -4.146.
+
+Therefore, the mean value of the graph at 6176.67 is -4.146.
+SOLUTION:  -4.146
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 4.16, we need to determine which interval the value 4.16 falls into based on the keys provided in the JSON object. The intervals are given as:
+
+- (0.0, 0.5)
+- (0.5, 1.5)
+- (1.5, 2.5)
+- (2.5, 3.5)
+- (3.5, 4.5)
+- (4.5, 5.0)
+
+The value 4.16 falls within the interval (3.5, 4.5). According to the JSON object, the mean value for this interval is -0.529.
+
+Therefore, the mean value of the graph at 4.16 is -0.529.
+SOLUTION:  -0.529
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 68251.52, we need to locate the interval that includes this value and then retrieve the corresponding mean value from the data provided.
+
+From the intervals given in the "Means" data, the value 68251.52 falls within the interval "(66668.0, 67776.0)". The mean value for this interval is 0.0019.
+
+Therefore, the mean value of the graph at 68251.52 is 0.0019.
+SOLUTION:  1e-05
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 15804780.51, we need to determine which interval this value falls into based on the provided keys in the "Means" dictionary.
+
+From the provided intervals:
+- "(15799214.0, 15807559.5)"
+- "(15807559.5, 15812616.5)"
+
+The value 15804780.51 falls within the interval "(15799214.0, 15807559.5)".
+
+According to the "Means" dictionary, the mean value for the interval "(15799214.0, 15807559.5)" is 0.0581.
+
+Therefore, the mean value of the graph at 15804780.51 is 0.0581.
+SOLUTION:  0.0581
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 1555.23, we need to determine which interval this value falls into based on the provided ranges in the "Means" data.
+
+From the given intervals:
+- "(1430.5, 1514.5)": -1.512
+- "(1514.5, 1796.0)": -2.212
+
+The value 1555.23 falls within the interval "(1514.5, 1796.0)". Therefore, the mean value of the graph at 1555.23 is -2.212.
+SOLUTION:  -2.212
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 41.25, we need to determine which interval the age 41.25 falls into. According to the provided intervals in the "Means" data:
+
+- "(40.5, 41.5)": -0.354
+
+The age 41.25 falls within the interval (40.5, 41.5). Therefore, the mean value of the graph at 41.25 is -0.354.
+SOLUTION:  -0.354
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.02 for the feature "concave points_mean", we need to locate the interval that includes the value 0.02. 
+
+From the provided intervals and mean values:
+- "(0.019315, 0.023185)": -0.466
+
+The value 0.02 falls within the interval (0.019315, 0.023185). Therefore, the mean value of the graph at 0.02 is -0.466.
+SOLUTION:  -0.466
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 6.38, we need to determine which interval the value 6.38 falls into. According to the provided intervals in the "Means" data:
+
+- "(0.0, 0.5)": -0.02956
+- "(0.5, 2.5)": -0.02081
+- "(2.5, 3.5)": -0.00998
+- "(3.5, 4.5)": -0.00524
+- "(4.5, 5.5)": 0.00043
+- "(5.5, 6.5)": 0.00515
+- "(6.5, 8.5)": 0.01107
+- "(8.5, 10.5)": 0.02102
+- "(10.5, 11.5)": 0.02728
+- "(11.5, 13.5)": 0.0456
+- "(13.5, 14.5)": 0.05244
+- "(14.5, 17.0)": 0.06161
+
+The value 6.38 falls within the interval "(6.5, 8.5)". The mean value for this interval is 0.01107. Therefore, the mean value of the graph at 6.38 is 0.01107.
+SOLUTION:  0.00515
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 3.14, we need to determine which interval the value 3.14 falls into. From the provided intervals in the "Means" data, we can see that the value 3.14 falls within the interval "(2.5, 4.5)".
+
+The mean value for the interval "(2.5, 4.5)" is given as -1.909.
+
+Therefore, the mean value of the graph at 3.14 is -1.909.
+SOLUTION:  -1.909
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 12.16, we need to determine which interval the value 12.16 falls into. From the provided intervals in the "Means" data, we can see that 12.16 falls within the interval "(3.0, 14.5)".
+
+The mean value for the interval "(3.0, 14.5)" is given as 125210.2.
+
+Therefore, the mean value of the graph at 12.16 is 125210.2.
+SOLUTION:  125210.2
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 14.97, we need to determine which interval the value 14.97 falls into based on the provided intervals in the "Means" data.
+
+From the intervals given:
+- "(13.5, 16.0)": 0.05902
+
+The value 14.97 falls within the interval (13.5, 16.0). Therefore, the mean value of the graph at 14.97 is 0.05902.
+SOLUTION:  0.05902
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.17, we need to locate the interval that includes the value 0.17. From the provided intervals in the "Means" data:
+
+- "(0.1603, 0.1722)": 0.539
+
+The value 0.17 falls within the interval (0.1603, 0.1722). Therefore, the mean value of the graph at 0.17 is 0.539.
+SOLUTION:  0.539
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at a longitude of -114.37, we need to locate the interval that includes this value and then look up the corresponding mean value.
+
+From the provided data, the interval that includes -114.37 is "(-114.595, -114.31)". The mean value for this interval is -100620.1.
+
+Therefore, the mean value of the graph at a longitude of -114.37 is -100620.1.
+SOLUTION:  -100620.1
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 239.62 for the feature "creatinine_phosphokinase," we need to identify the interval that includes the value 239.62. 
+
+From the provided intervals in the "Means" data, the interval that includes 239.62 is "(189.5, 232.5)". The mean value for this interval is given as -1.26.
+
+Therefore, the mean value of the graph at 239.62 is -1.26.
+SOLUTION:  -0.16
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 1305.91, we need to determine which interval this value falls into based on the provided keys in the "Means" dictionary.
+
+From the intervals provided:
+- "(1248.5, 1341.0)": 0.911
+- "(1341.0, 1801.0)": 1.01
+
+The value 1305.91 falls within the interval "(1248.5, 1341.0)". Therefore, the mean value of the graph at 1305.91 is 0.911.
+SOLUTION:  0.911
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 1.26 for the feature "perimeter_se", we need to locate the interval that includes the value 1.26. From the provided intervals in the "Means" data:
+
+- "(1.2365, 1.326)": -0.384
+
+The value 1.26 falls within the interval (1.2365, 1.326). Therefore, the mean value of the graph at 1.26 is -0.384.
+SOLUTION:  -0.384
+--------------------------------------------------------------------------------
+LLM RESPONSE:  The graph provided represents the feature "fractal_dimension_se" and is a continuous feature with specified intervals for the x-axis values. The intervals define the range of "fractal_dimension_se" values and the corresponding mean values of the model's output for those ranges.
+
+Since the intervals are specified, we can only determine the mean values for the ranges provided. The smallest interval provided in the data starts at 0.0008948. There is no information or data provided for the value 0.0 specifically, as it falls outside the smallest interval range given in the dataset.
+
+Therefore, it is not possible to provide a mean value of the graph at 0.0 based on the data provided. The mean value can only be determined for intervals explicitly listed in the dataset.
+SOLUTION:  0.2818
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 9.36, we need to determine which interval the value 9.36 falls into. According to the provided intervals in the "Means" data, 9.36 falls within the interval "(8.5, 9.5)".
+
+The mean value for the interval "(8.5, 9.5)" is given as 0.0237.
+
+Therefore, the mean value of the graph at 9.36 is 0.0237.
+SOLUTION:  0.0237
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.37, we need to determine which interval the value 0.37 falls into based on the provided keys in the JSON object. The intervals are given as strings, and we need to identify the correct interval that includes 0.37.
+
+From the provided intervals:
+- "(0.1, 0.35)"
+- "(0.35, 0.45)"
+- "(0.45, 0.75)"
+- "(0.75, 1.25)"
+- "(1.25, 1.75)"
+- "(1.75, 2.5)"
+
+The value 0.37 falls within the interval "(0.35, 0.45)". 
+
+Looking up the mean value for this interval in the "Means" dictionary:
+- Means: {"(0.1, 0.35)": 8.07, "(0.35, 0.45)": 7.27, "(0.45, 0.75)": 6.18, "(0.75, 1.25)": -2.64, "(1.25, 1.75)": -3.46, "(1.75, 2.5)": -4.19}
+
+The mean value at 0.37, which is within the interval "(0.35, 0.45)", is 7.27.
+SOLUTION:  7.27
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 1.58, we need to determine which interval the value 1.58 falls into. According to the provided intervals in the "Means" data, the value 1.58 falls within the interval "(1.5, 2.5)".
+
+From the "Means" data:
+- The mean value for the interval "(1.5, 2.5)" is -0.01613.
+
+Therefore, the mean value of the graph at 1.58 is -0.01613.
+SOLUTION:  -0.01613
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 37.49, we need to determine which interval the value 37.49 falls into based on the provided ranges in the "Means" data.
+
+From the given intervals:
+- "(36.5, 39.0)": 0.48
+
+The value 37.49 falls within the interval (36.5, 39.0). Therefore, the mean value of the graph at 37.49 is 0.48.
+SOLUTION:  0.48
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 52.67, we need to determine which interval this value falls into based on the provided keys in the JSON object. 
+
+From the given intervals, the value 52.67 falls within the interval "(52.0, 54.5)". According to the JSON object for the Means, the mean value for this interval is 3.38.
+
+Therefore, the mean value of the graph at 52.67 is 3.38.
+SOLUTION:  3.38
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 34.12, we need to determine which interval the value 34.12 falls into. From the provided intervals in the "Means" data, we can see that 34.12 falls within the interval "(32.985, 34.730000000000004)".
+
+The mean value for this interval is given as 0.081.
+
+Therefore, the mean value of the graph at 34.12 is 0.081.
+SOLUTION:  0.081
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 1886.32, we need to determine which interval this value falls into based on the provided ranges in the "Means" data.
+
+From the provided data, the interval that includes 1886.32 is "(1862.0, 1881.5)". The mean value for this interval is given as -1.36.
+
+Therefore, the mean value of the graph at 1886.32 is -1.36.
+SOLUTION:  4.781
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.92, we need to locate the interval that includes the value 0.92. From the provided intervals in the "Means" data, the interval that includes 0.92 is "(0.9131, 1.065)". 
+
+The mean value for this interval is given as 0.683.
+
+Therefore, the mean value of the graph at 0.92 is 0.683.
+SOLUTION:  0.683
+--------------------------------------------------------------------------------
+LLM RESPONSE:  To find the mean value of the graph at 0.57, we need to locate the interval that includes the value 0.57. From the provided intervals in the "Means" data, we can see that the value 0.57 falls within the interval "(0.54825, 0.5892999999999999)".
+
+The mean value for this interval is given as 0.4651.
+
+Therefore, the mean value of the graph at 0.57 is 0.4651.
+SOLUTION:  0.4651
+--------------------------------------------------------------------------------
\ No newline at end of file
diff --git a/notebooks/Spaceship Titanic.ipynb b/notebooks/Spaceship Titanic.ipynb
index 32d5ae2..bdc56c1 100644
--- a/notebooks/Spaceship Titanic.ipynb	
+++ b/notebooks/Spaceship Titanic.ipynb	
@@ -23,7 +23,7 @@
        "        require.undef(\"plotly\");\n",
        "        requirejs.config({\n",
        "            paths: {\n",
-       "                'plotly': ['https://cdn.plot.ly/plotly-2.24.1.min']\n",
+       "                'plotly': ['https://cdn.plot.ly/plotly-2.32.0.min']\n",
        "            }\n",
        "        });\n",
        "        require(['plotly'], function(Plotly) {\n",
@@ -64,7 +64,19 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "module 'guidance' has no attribute 'llms'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[2], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m openai\u001b[38;5;241m.\u001b[39morganization \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m      3\u001b[0m openai\u001b[38;5;241m.\u001b[39mapi_key \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39menviron[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOPENAI_API_KEY\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m----> 5\u001b[0m llm \u001b[38;5;241m=\u001b[39m \u001b[43mguidance\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mllms\u001b[49m\u001b[38;5;241m.\u001b[39mOpenAI(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgpt-3.5-turbo-16k\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;66;03m# prompts require ~6k tokens\u001b[39;00m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: module 'guidance' has no attribute 'llms'"
+     ]
+    }
+   ],
    "source": [
     "# this notebook works with any LLM supported by guidance. we obtained the best results with GPT-4\n",
     "openai.organization = \"\"\n",
@@ -73,6 +85,42 @@
     "llm = guidance.llms.OpenAI(\"gpt-3.5-turbo-16k\") # prompts require ~6k tokens"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SYSTEM: You are {an expert statisticial and data scientist}. You interpret global explanations produced by a generalized additive model (GAM). You answer all questions to the best of your ability, combining the data contained in the graph, any data set description you are given, and your knowledge about the real world.\n",
+      "USER: Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.\n",
+      "    \n",
+      "The graph is provided in the following format:\n",
+      "    - The name of the feature depicted in the graph\n",
+      "    - The type of the feature (continuous, categorical, or boolean)\n",
+      "    - Mean values\n",
+      "    - Lower bounds of confidence interval (optional)\n",
+      "    - Upper bounds of confidence interval (optional)\n",
+      "\n",
+      "{y-axis description (optional)}\n",
+      "\n",
+      "Here is the graph:\n",
+      "\n",
+      "{the graph}\n",
+      "\n",
+      "Please describe the general pattern of the graph. {special task description (optional)}\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "messages = t2ebm.prompts.describe_graph(\"{the graph}\", \"{an expert statistician and data scientist}\", \"{y-axis description (optional)}\", \"{dataset description (optional)}\", \"{special task description (optional)}\")\n",
+    "print(\"SYSTEM:\", messages[0][\"content\"])\n",
+    "print(\"USER:\", messages[1][\"content\"])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -681,7 +729,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.17"
+   "version": "3.12.2"
   },
   "orig_nbformat": 4
  },
diff --git a/t2ebm/graphs.py b/t2ebm/graphs.py
index b1121df..2d1b0cb 100644
--- a/t2ebm/graphs.py
+++ b/t2ebm/graphs.py
@@ -4,6 +4,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import scipy
+import json
 
 from interpret.glassbox._ebm._utils import convert_to_intervals
 
@@ -335,3 +336,49 @@ def graph_to_text(
                 )
             else:
                 return prompt
+
+
+def parse_str_tuple_to_float_tuple(str_tuple: str):
+    """Parse a string tuple to a float tuple"""
+    return tuple(float(x) for x in str_tuple[1:-1].split(","))
+
+
+def text_to_graph(graph: str):
+    """Convert  the textual representation of a graph back to an EBMGraph."""
+    split_graph = graph.split("\n")
+
+    # find the line that starts with "Feature Name:"
+    start_idx = 0
+    while not split_graph[start_idx].startswith("Feature Name:"):
+        start_idx = start_idx + 1
+
+    feature_name = split_graph[start_idx][13:].strip()
+    feature_type = split_graph[start_idx + 1][13:].strip()
+    assert (
+        feature_type == "continuous"
+    ), "currently only continuous features are supported to convert back to graph"
+
+    # parse json
+    means_json = split_graph[start_idx + 2][6:]
+    means_json = json.loads(means_json)
+
+    lower_bounds_json = split_graph[start_idx + 3]
+    lower_bounds_json = lower_bounds_json[lower_bounds_json.find("):") + 2 :]
+    lower_bounds_json = json.loads(lower_bounds_json)
+
+    upper_bounds_json = split_graph[start_idx + 4]
+    upper_bounds_json = upper_bounds_json[upper_bounds_json.find("):") + 2 :]
+    upper_bounds_json = json.loads(upper_bounds_json)
+
+    # json to EBMGraph format
+    x_vals = [parse_str_tuple_to_float_tuple(k) for k, _ in means_json.items()]
+    scores = [v for _, v in means_json.items()]
+    lower_bounds = [v for _, v in lower_bounds_json.items()]
+    upper_bounds = [v for _, v in upper_bounds_json.items()]
+
+    # heuristically determine the stds from the lower and upper bounds
+    confidence_level = 0.95  # assume 95% confidence interval TODO: infer from the text
+    factor = scipy.stats.norm.interval(confidence_level, loc=0, scale=1)[1]
+    stds = [(u - l) / 2 / factor for u, l in zip(upper_bounds, lower_bounds)]
+
+    return EBMGraph(feature_name, feature_type, x_vals, scores, stds)
diff --git a/t2ebm/prompts.py b/t2ebm/prompts.py
index 7465468..f6492bb 100644
--- a/t2ebm/prompts.py
+++ b/t2ebm/prompts.py
@@ -1,16 +1,14 @@
 """
 Prompts that ask the LLM to perform tasks with Graphs and EBMs.
-We use guidance: https://github.com/microsoft/guidance
 """
 
 
 def describe_graph(
     graph: str,
-    expert_description="an expert statistician and data scientist.",
+    expert_description="an expert statistician and data scientist",
     y_axis_description="",
-    special_task_description="",
     dataset_description="",
-    include_assistant_response=True,
+    special_task_description="",
 ):
     """Prompt the LLM to describe a graph. This will often be the very first prompt in a conversation.
 
@@ -20,53 +18,34 @@ def describe_graph(
     :param expertise_desc: description of the desired expertise of the LLM
     :return:
     """
-
-    # the system prompt
-    prompt = (
-        "{{#system~}}\n"
-        + f"""You are {expert_description}
+    # a general system prompt that instructs the LLM
+    # the system prompt does not contain any specific information about the data, so it could be omitted for a model that does not support a system prompt
+    system_msg = f"You are {expert_description}. You interpret global explanations produced by a generalized additive model (GAM). You answer all questions to the best of your ability, combining the data contained in the graph, any data set description you are given, and your knowledge about the real world."
+    # the user message begins with an introduction to the task
+    user_msg = """Below is the graph of a generalized additive model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.
     
-You interpret global explanations produced by a generalized additive model (GAM). GAMs produce explanations in the form of graphs that contain the effect of a specific input feature.
-
-{'You will be given graphs from the model, and the user will ask you questions about the graphs.'  if dataset_description is None or dataset_description == '' else 'The user will first provide a general description of the dataset. Then you will be given graphs from the model, and the user will ask you questions about the graphs.'} 
-    
-Answer all questions to the best of your ability, combining both the data contained in the graph{', the data set description you were given, and your knowledge about the real world.' if dataset_description is not None and len(dataset_description) > 0 else ' and your knowledge about the real world.'}
-
-Graphs will be presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take. {y_axis_description if y_axis_description is not None and len(dataset_description) > 0 else ''} 
-    
-The user will provide graphs in the following format:
+The graph is provided in the following format:
     - The name of the feature depicted in the graph
     - The type of the feature (continuous, categorical, or boolean)
     - Mean values
-    - Lower bounds of confidence interval
-    - Upper bounds of confidence interval
-
-{special_task_description}\n"""
-        + "{{~/system}}\n"
-    )
-
-    # a user-assistant interaction where the user describes the data set
-    if dataset_description is not None and len(dataset_description) > 0:
-        prompt += (
-            "\n{{#user~}}\n"
-            + dataset_description
-            + "\n{{~/user}}\n\n{{#assistant~}}\nThanks for this general description of"
-            " the data set. Please continue and provide more information, for example"
-            " about the graphs from the model.\n{{~/assistant}}\n"
-        )
-
-    # the user provides the graph and asks for a description of the patterns in the graph
-    prompt += (
-        "\n{{#user~}}\nConsider the following graph from the model. "
-        + graph
-        + "\nPlease describe the general pattern of the graph.\n{{~/user}}\n\n"
-    )
-
-    # the assistant responds
-    if include_assistant_response:
-        prompt += """{{#assistant~}}{{gen 'graph_description' temperature=0.7 max_tokens=2000}}{{~/assistant}}"""
-
-    return prompt
+    - Lower bounds of confidence interval (optional)
+    - Upper bounds of confidence interval (optional)\n\n"""
+    # optional y axis description
+    if y_axis_description is not None and len(y_axis_description) > 0:
+        user_msg += f"{y_axis_description}\n\n"
+    # the graph
+    user_msg += f"Here is the graph:\n\n{graph}\n\n"
+
+    # the task is to describe the graph, optionally with a special task description
+    user_msg += "Please describe the general pattern of the graph."
+    if special_task_description is not None and len(special_task_description) > 0:
+        user_msg += f" {special_task_description}\n"
+
+    # return in the openai message format
+    return [
+        {"role": "system", "content": system_msg},
+        {"role": "user", "content": user_msg},
+    ]
 
 
 def describe_graph_cot(graph, num_sentences=7, **kwargs):