NL-Figures.ipynb

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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib import cm\n",
    "import networkx as nx\n",
    "\n",
    "mpl.rcParams['xtick.labelsize'] = 20\n",
    "mpl.rcParams['ytick.labelsize'] = 20\n",
    "mpl.rcParams['font.size'] = 20\n",
    "mpl.rcParams['figure.autolayout'] = False\n",
    "mpl.rcParams['axes.linewidth'] = 3\n",
    "mpl.rcParams['axes.titlesize'] = 20\n",
    "mpl.rcParams['axes.labelsize'] = 20\n",
    "mpl.rcParams['lines.linewidth'] = 1\n",
    "mpl.rcParams['lines.markersize'] = 5\n",
    "mpl.rcParams['lines.markeredgewidth'] = 1.2\n",
    "mpl.rcParams['errorbar.capsize'] = 5\n",
    "mpl.rcParams['xtick.major.width'] = 3\n",
    "mpl.rcParams['xtick.major.size'] = 10\n",
    "mpl.rcParams['ytick.major.width'] = 3\n",
    "mpl.rcParams['ytick.major.size'] = 8\n",
    "mpl.rcParams['xtick.minor.width'] = 1.5\n",
    "mpl.rcParams['xtick.minor.size'] = 6\n",
    "mpl.rcParams['ytick.minor.width'] = 1.5\n",
    "mpl.rcParams['ytick.minor.size'] = 6\n",
    "mpl.rcParams['legend.fontsize'] = 14\n",
    "mpl.rcParams['mathtext.fontset'] = 'dejavusans'\n",
    "mpl.rcParams['font.family'] = 'sans-serif'\n",
    "cm_colors = plt.get_cmap('Set2')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "    (24, 25): np.float64(0.5),\n",
       "    (24, 31): np.float64(0.5),\n",
       "    (25, 26): np.float64(0.2),\n",
       "    (25, 32): np.float64(1.0),\n",
       "    (26, 27): np.float64(0.3),\n",
       "    (26, 33): np.float64(1.0),\n",
       "    (28, 29): np.float64(0.1),\n",
       "    (28, 35): np.float64(0.5),\n",
       "    (29, 30): np.float64(0.2),\n",
       "    (29, 36): np.float64(1.0),\n",
       "    (30, 31): np.float64(0.2),\n",
       "    (30, 37): np.float64(0.1),\n",
       "    (31, 38): np.float64(0.1),\n",
       "    (32, 33): np.float64(1.0),\n",
       "    (32, 39): np.float64(0.1),\n",
       "    (33, 34): np.float64(1.0),\n",
       "    (33, 40): np.float64(0.2),\n",
       "    (34, 41): np.float64(1.0),\n",
       "    (35, 36): np.float64(1.0),\n",
       "    (36, 37): np.float64(0.1),\n",
       "    (36, 42): np.float64(0.2),\n",
       "    (37, 38): np.float64(1.0),\n",
       "    (37, 43): np.float64(0.1),\n",
       "    (38, 39): np.float64(0.2),\n",
       "    (38, 44): np.float64(0.2),\n",
       "    (39, 40): np.float64(0.3),\n",
       "    (39, 45): np.float64(0.3),\n",
       "    (40, 41): np.float64(0.5),\n",
       "    (40, 46): np.float64(0.3),\n",
       "    (42, 48): np.float64(0.2),\n",
       "    (43, 44): np.float64(0.1),\n",
       "    (43, 49): np.float64(1.0),\n",
       "    (44, 45): np.float64(0.3),\n",
       "    (44, 50): np.float64(0.5),\n",
       "    (45, 46): np.float64(0.5),\n",
       "    (45, 51): np.float64(0.2),\n",
       "    (46, 52): np.float64(0.5),\n",
       "    (47, 48): np.float64(0.1),\n",
       "    (47, 54): np.float64(1.0),\n",
       "    (48, 49): np.float64(0.1),\n",
       "    (48, 55): np.float64(1.0),\n",
       "    (49, 50): np.float64(0.3),\n",
       "    (49, 56): np.float64(1.0),\n",
       "    (50, 51): np.float64(0.5),\n",
       "    (50, 57): np.float64(0.3),\n",
       "    (51, 52): np.float64(0.5),\n",
       "    (51, 58): np.float64(0.1),\n",
       "    (52, 53): np.float64(0.2),\n",
       "    (52, 59): np.float64(0.2),\n",
       "    (53, 60): np.float64(0.1),\n",
       "    (54, 55): np.float64(0.2),\n",
       "    (54, 61): np.float64(0.5),\n",
       "    (55, 56): np.float64(0.2),\n",
       "    (55, 62): np.float64(0.2),\n",
       "    (56, 57): np.float64(0.3),\n",
       "    (56, 63): np.float64(1.0),\n",
       "    (57, 58): np.float64(0.5),\n",
       "    (57, 64): np.float64(1.0),\n",
       "    (58, 59): np.float64(0.3),\n",
       "    (58, 65): np.float64(0.2),\n",
       "    (59, 60): np.float64(0.1),\n",
       "    (59, 66): np.float64(1.0),\n",
       "    (60, 67): np.float64(0.2),\n",
       "    (61, 62): np.float64(0.5),\n",
       "    (61, 68): np.float64(1.0),\n",
       "    (62, 63): np.float64(0.2),\n",
       "    (62, 69): np.float64(0.1),\n",
       "    (63, 64): np.float64(0.3),\n",
       "    (63, 70): np.float64(0.5),\n",
       "    (64, 71): np.float64(0.2),\n",
       "    (65, 66): np.float64(1.0),\n",
       "    (65, 72): np.float64(0.3),\n",
       "    (66, 67): np.float64(1.0),\n",
       "    (66, 73): np.float64(0.1),\n",
       "    (67, 74): np.float64(0.3),\n",
       "    (68, 69): np.float64(0.2),\n",
       "    (68, 75): np.float64(0.2),\n",
       "    (69, 70): np.float64(0.3),\n",
       "    (69, 76): np.float64(0.5),\n",
       "    (70, 71): np.float64(0.3),\n",
       "    (70, 77): np.float64(0.2),\n",
       "    (71, 72): np.float64(0.5),\n",
       "    (71, 78): np.float64(0.2),\n",
       "    (72, 73): np.float64(1.0),\n",
       "    (72, 79): np.float64(0.2),\n",
       "    (73, 74): np.float64(0.1),\n",
       "    (73, 80): np.float64(1.0),\n",
       "    (74, 81): np.float64(0.1),\n",
       "    (75, 76): np.float64(0.5),\n",
       "    (76, 77): np.float64(0.2),\n",
       "    (77, 78): np.float64(0.2),\n",
       "    (78, 79): np.float64(0.1),\n",
       "    (79, 80): np.float64(0.3),\n",
       "    (80, 81): np.float64(1.0)},\n",
       "   'max_cut': np.float64(59.500000000000036),\n",
       "   'r': np.float64(0.4598588235294115),\n",
       "   'probability': np.float64(0.0)}},\n",
       " 6: {0: {'results': array([[27.2       ,  0.45714286,  1.        ],\n",
       "          [30.9       ,  0.51932773,  1.        ],\n",
       "          [28.5       ,  0.4789916 ,  1.        ],\n",
       "          ...,\n",
       "          [26.2       ,  0.44033613,  1.        ],\n",
       "          [25.4       ,  0.42689076,  1.        ],\n",
       "          [22.        ,  0.3697479 ,  1.        ]], shape=(1000, 3)),\n",
       "   'G': <networkx.classes.graph.Graph at 0x146197f10>,\n",
       "   'weights': {(0, 1): np.float64(0.3),\n",
       "    (0, 7): np.float64(0.3),\n",
       "    (1, 2): np.float64(0.2),\n",
       "    (1, 8): np.float64(0.2),\n",
       "    (2, 3): np.float64(0.5),\n",
       "    (2, 9): np.float64(0.1),\n",
       "    (3, 4): np.float64(0.3),\n",
       "    (3, 10): np.float64(1.0),\n",
       "    (4, 5): np.float64(0.2),\n",
       "    (4, 11): np.float64(1.0),\n",
       "    (5, 6): np.float64(0.3),\n",
       "    (5, 12): np.float64(1.0),\n",
       "    (6, 13): np.float64(0.1),\n",
       "    (7, 8): np.float64(0.1),\n",
       "    (7, 14): np.float64(0.3),\n",
       "    (8, 9): np.float64(1.0),\n",
       "    (8, 15): np.float64(1.0),\n",
       "    (9, 10): np.float64(0.1),\n",
       "    (9, 16): np.float64(1.0),\n",
       "    (10, 11): np.float64(1.0),\n",
       "    (11, 12): np.float64(0.1),\n",
       "    (11, 18): np.float64(1.0),\n",
       "    (12, 13): np.float64(0.5),\n",
       "    (12, 19): np.float64(0.3),\n",
       "    (13, 20): np.float64(0.1),\n",
       "    (14, 15): np.float64(0.5),\n",
       "    (14, 21): np.float64(0.3),\n",
       "    (15, 16): np.float64(0.5),\n",
       "    (15, 22): np.float64(0.5),\n",
       "    (16, 17): np.float64(0.1),\n",
       "    (16, 23): np.float64(0.5),\n",
       "    (17, 18): np.float64(0.5),\n",
       "    (17, 24): np.float64(0.2),\n",
       "    (18, 19): np.float64(0.2),\n",
       "    (18, 25): np.float64(0.5),\n",
       "    (19, 20): np.float64(0.2),\n",
       "    (19, 26): np.float64(0.5),\n",
       "    (20, 27): np.float64(0.2),\n",
       "    (21, 22): np.float64(1.0),\n",
       "    (21, 28): np.float64(0.5),\n",
       "    (22, 23): np.float64(0.5),\n",
       "    (22, 29): np.float64(0.2),\n",
       "    (23, 24): np.float64(0.3),\n",
       "    (23, 30): np.float64(0.1),\n",
       "    (24, 25): np.float64(0.5),\n",
       "    (24, 31): np.float64(0.5),\n",
       "    (25, 26): np.float64(0.2),\n",
       "    (25, 32): np.float64(1.0),\n",
       "    (26, 27): np.float64(0.3),\n",
       "    (26, 33): np.float64(1.0),\n",
       "    (28, 29): np.float64(0.1),\n",
       "    (28, 35): np.float64(0.5),\n",
       "    (29, 30): np.float64(0.2),\n",
       "    (29, 36): np.float64(1.0),\n",
       "    (30, 31): np.float64(0.2),\n",
       "    (30, 37): np.float64(0.1),\n",
       "    (31, 38): np.float64(0.1),\n",
       "    (32, 33): np.float64(1.0),\n",
       "    (32, 39): np.float64(0.1),\n",
       "    (33, 34): np.float64(1.0),\n",
       "    (33, 40): np.float64(0.2),\n",
       "    (34, 41): np.float64(1.0),\n",
       "    (35, 36): np.float64(1.0),\n",
       "    (36, 37): np.float64(0.1),\n",
       "    (36, 42): np.float64(0.2),\n",
       "    (37, 38): np.float64(1.0),\n",
       "    (37, 43): np.float64(0.1),\n",
       "    (38, 39): np.float64(0.2),\n",
       "    (38, 44): np.float64(0.2),\n",
       "    (39, 40): np.float64(0.3),\n",
       "    (39, 45): np.float64(0.3),\n",
       "    (40, 41): np.float64(0.5),\n",
       "    (40, 46): np.float64(0.3),\n",
       "    (42, 48): np.float64(0.2),\n",
       "    (43, 44): np.float64(0.1),\n",
       "    (43, 49): np.float64(1.0),\n",
       "    (44, 45): np.float64(0.3),\n",
       "    (44, 50): np.float64(0.5),\n",
       "    (45, 46): np.float64(0.5),\n",
       "    (45, 51): np.float64(0.2),\n",
       "    (46, 52): np.float64(0.5),\n",
       "    (47, 48): np.float64(0.1),\n",
       "    (47, 54): np.float64(1.0),\n",
       "    (48, 49): np.float64(0.1),\n",
       "    (48, 55): np.float64(1.0),\n",
       "    (49, 50): np.float64(0.3),\n",
       "    (49, 56): np.float64(1.0),\n",
       "    (50, 51): np.float64(0.5),\n",
       "    (50, 57): np.float64(0.3),\n",
       "    (51, 52): np.float64(0.5),\n",
       "    (51, 58): np.float64(0.1),\n",
       "    (52, 53): np.float64(0.2),\n",
       "    (52, 59): np.float64(0.2),\n",
       "    (53, 60): np.float64(0.1),\n",
       "    (54, 55): np.float64(0.2),\n",
       "    (54, 61): np.float64(0.5),\n",
       "    (55, 56): np.float64(0.2),\n",
       "    (55, 62): np.float64(0.2),\n",
       "    (56, 57): np.float64(0.3),\n",
       "    (56, 63): np.float64(1.0),\n",
       "    (57, 58): np.float64(0.5),\n",
       "    (57, 64): np.float64(1.0),\n",
       "    (58, 59): np.float64(0.3),\n",
       "    (58, 65): np.float64(0.2),\n",
       "    (59, 60): np.float64(0.1),\n",
       "    (59, 66): np.float64(1.0),\n",
       "    (60, 67): np.float64(0.2),\n",
       "    (61, 62): np.float64(0.5),\n",
       "    (61, 68): np.float64(1.0),\n",
       "    (62, 63): np.float64(0.2),\n",
       "    (62, 69): np.float64(0.1),\n",
       "    (63, 64): np.float64(0.3),\n",
       "    (63, 70): np.float64(0.5),\n",
       "    (64, 71): np.float64(0.2),\n",
       "    (65, 66): np.float64(1.0),\n",
       "    (65, 72): np.float64(0.3),\n",
       "    (66, 67): np.float64(1.0),\n",
       "    (66, 73): np.float64(0.1),\n",
       "    (67, 74): np.float64(0.3),\n",
       "    (68, 69): np.float64(0.2),\n",
       "    (68, 75): np.float64(0.2),\n",
       "    (69, 70): np.float64(0.3),\n",
       "    (69, 76): np.float64(0.5),\n",
       "    (70, 71): np.float64(0.3),\n",
       "    (70, 77): np.float64(0.2),\n",
       "    (71, 72): np.float64(0.5),\n",
       "    (71, 78): np.float64(0.2),\n",
       "    (72, 73): np.float64(1.0),\n",
       "    (72, 79): np.float64(0.2),\n",
       "    (73, 74): np.float64(0.1),\n",
       "    (73, 80): np.float64(1.0),\n",
       "    (74, 81): np.float64(0.1),\n",
       "    (75, 76): np.float64(0.5),\n",
       "    (76, 77): np.float64(0.2),\n",
       "    (77, 78): np.float64(0.2),\n",
       "    (78, 79): np.float64(0.1),\n",
       "    (79, 80): np.float64(0.3),\n",
       "    (80, 81): np.float64(1.0)},\n",
       "   'max_cut': np.float64(59.500000000000036),\n",
       "   'r': np.float64(0.43651092436974764),\n",
       "   'probability': np.float64(0.0)}},\n",
       " 7: {0: {'results': array([[28.1       ,  0.47226891,  1.        ],\n",
       "          [24.3       ,  0.40840336,  1.        ],\n",
       "          [27.8       ,  0.46722689,  1.        ],\n",
       "          ...,\n",
       "          [26.9       ,  0.45210084,  1.        ],\n",
       "          [20.3       ,  0.34117647,  1.        ],\n",
       "          [26.7       ,  0.4487395 ,  1.        ]], shape=(1000, 3)),\n",
       "   'G': <networkx.classes.graph.Graph at 0x146197f10>,\n",
       "   'weights': {(0, 1): np.float64(0.3),\n",
       "    (0, 7): np.float64(0.3),\n",
       "    (1, 2): np.float64(0.2),\n",
       "    (1, 8): np.float64(0.2),\n",
       "    (2, 3): np.float64(0.5),\n",
       "    (2, 9): np.float64(0.1),\n",
       "    (3, 4): np.float64(0.3),\n",
       "    (3, 10): np.float64(1.0),\n",
       "    (4, 5): np.float64(0.2),\n",
       "    (4, 11): np.float64(1.0),\n",
       "    (5, 6): np.float64(0.3),\n",
       "    (5, 12): np.float64(1.0),\n",
       "    (6, 13): np.float64(0.1),\n",
       "    (7, 8): np.float64(0.1),\n",
       "    (7, 14): np.float64(0.3),\n",
       "    (8, 9): np.float64(1.0),\n",
       "    (8, 15): np.float64(1.0),\n",
       "    (9, 10): np.float64(0.1),\n",
       "    (9, 16): np.float64(1.0),\n",
       "    (10, 11): np.float64(1.0),\n",
       "    (11, 12): np.float64(0.1),\n",
       "    (11, 18): np.float64(1.0),\n",
       "    (12, 13): np.float64(0.5),\n",
       "    (12, 19): np.float64(0.3),\n",
       "    (13, 20): np.float64(0.1),\n",
       "    (14, 15): np.float64(0.5),\n",
       "    (14, 21): np.float64(0.3),\n",
       "    (15, 16): np.float64(0.5),\n",
       "    (15, 22): np.float64(0.5),\n",
       "    (16, 17): np.float64(0.1),\n",
       "    (16, 23): np.float64(0.5),\n",
       "    (17, 18): np.float64(0.5),\n",
       "    (17, 24): np.float64(0.2),\n",
       "    (18, 19): np.float64(0.2),\n",
       "    (18, 25): np.float64(0.5),\n",
       "    (19, 20): np.float64(0.2),\n",
       "    (19, 26): np.float64(0.5),\n",
       "    (20, 27): np.float64(0.2),\n",
       "    (21, 22): np.float64(1.0),\n",
       "    (21, 28): np.float64(0.5),\n",
       "    (22, 23): np.float64(0.5),\n",
       "    (22, 29): np.float64(0.2),\n",
       "    (23, 24): np.float64(0.3),\n",
       "    (23, 30): np.float64(0.1),\n",
       "    (24, 25): np.float64(0.5),\n",
       "    (24, 31): np.float64(0.5),\n",
       "    (25, 26): np.float64(0.2),\n",
       "    (25, 32): np.float64(1.0),\n",
       "    (26, 27): np.float64(0.3),\n",
       "    (26, 33): np.float64(1.0),\n",
       "    (28, 29): np.float64(0.1),\n",
       "    (28, 35): np.float64(0.5),\n",
       "    (29, 30): np.float64(0.2),\n",
       "    (29, 36): np.float64(1.0),\n",
       "    (30, 31): np.float64(0.2),\n",
       "    (30, 37): np.float64(0.1),\n",
       "    (31, 38): np.float64(0.1),\n",
       "    (32, 33): np.float64(1.0),\n",
       "    (32, 39): np.float64(0.1),\n",
       "    (33, 34): np.float64(1.0),\n",
       "    (33, 40): np.float64(0.2),\n",
       "    (34, 41): np.float64(1.0),\n",
       "    (35, 36): np.float64(1.0),\n",
       "    (36, 37): np.float64(0.1),\n",
       "    (36, 42): np.float64(0.2),\n",
       "    (37, 38): np.float64(1.0),\n",
       "    (37, 43): np.float64(0.1),\n",
       "    (38, 39): np.float64(0.2),\n",
       "    (38, 44): np.float64(0.2),\n",
       "    (39, 40): np.float64(0.3),\n",
       "    (39, 45): np.float64(0.3),\n",
       "    (40, 41): np.float64(0.5),\n",
       "    (40, 46): np.float64(0.3),\n",
       "    (42, 48): np.float64(0.2),\n",
       "    (43, 44): np.float64(0.1),\n",
       "    (43, 49): np.float64(1.0),\n",
       "    (44, 45): np.float64(0.3),\n",
       "    (44, 50): np.float64(0.5),\n",
       "    (45, 46): np.float64(0.5),\n",
       "    (45, 51): np.float64(0.2),\n",
       "    (46, 52): np.float64(0.5),\n",
       "    (47, 48): np.float64(0.1),\n",
       "    (47, 54): np.float64(1.0),\n",
       "    (48, 49): np.float64(0.1),\n",
       "    (48, 55): np.float64(1.0),\n",
       "    (49, 50): np.float64(0.3),\n",
       "    (49, 56): np.float64(1.0),\n",
       "    (50, 51): np.float64(0.5),\n",
       "    (50, 57): np.float64(0.3),\n",
       "    (51, 52): np.float64(0.5),\n",
       "    (51, 58): np.float64(0.1),\n",
       "    (52, 53): np.float64(0.2),\n",
       "    (52, 59): np.float64(0.2),\n",
       "    (53, 60): np.float64(0.1),\n",
       "    (54, 55): np.float64(0.2),\n",
       "    (54, 61): np.float64(0.5),\n",
       "    (55, 56): np.float64(0.2),\n",
       "    (55, 62): np.float64(0.2),\n",
       "    (56, 57): np.float64(0.3),\n",
       "    (56, 63): np.float64(1.0),\n",
       "    (57, 58): np.float64(0.5),\n",
       "    (57, 64): np.float64(1.0),\n",
       "    (58, 59): np.float64(0.3),\n",
       "    (58, 65): np.float64(0.2),\n",
       "    (59, 60): np.float64(0.1),\n",
       "    (59, 66): np.float64(1.0),\n",
       "    (60, 67): np.float64(0.2),\n",
       "    (61, 62): np.float64(0.5),\n",
       "    (61, 68): np.float64(1.0),\n",
       "    (62, 63): np.float64(0.2),\n",
       "    (62, 69): np.float64(0.1),\n",
       "    (63, 64): np.float64(0.3),\n",
       "    (63, 70): np.float64(0.5),\n",
       "    (64, 71): np.float64(0.2),\n",
       "    (65, 66): np.float64(1.0),\n",
       "    (65, 72): np.float64(0.3),\n",
       "    (66, 67): np.float64(1.0),\n",
       "    (66, 73): np.float64(0.1),\n",
       "    (67, 74): np.float64(0.3),\n",
       "    (68, 69): np.float64(0.2),\n",
       "    (68, 75): np.float64(0.2),\n",
       "    (69, 70): np.float64(0.3),\n",
       "    (69, 76): np.float64(0.5),\n",
       "    (70, 71): np.float64(0.3),\n",
       "    (70, 77): np.float64(0.2),\n",
       "    (71, 72): np.float64(0.5),\n",
       "    (71, 78): np.float64(0.2),\n",
       "    (72, 73): np.float64(1.0),\n",
       "    (72, 79): np.float64(0.2),\n",
       "    (73, 74): np.float64(0.1),\n",
       "    (73, 80): np.float64(1.0),\n",
       "    (74, 81): np.float64(0.1),\n",
       "    (75, 76): np.float64(0.5),\n",
       "    (76, 77): np.float64(0.2),\n",
       "    (77, 78): np.float64(0.2),\n",
       "    (78, 79): np.float64(0.1),\n",
       "    (79, 80): np.float64(0.3),\n",
       "    (80, 81): np.float64(1.0)},\n",
       "   'max_cut': np.float64(59.500000000000036),\n",
       "   'r': np.float64(0.4189025210084032),\n",
       "   'probability': np.float64(0.0)}},\n",
       " 8: {0: {'results': array([[23.3       ,  0.39159664,  1.        ],\n",
       "          [31.3       ,  0.52605042,  1.        ],\n",
       "          [18.3       ,  0.30756303,  1.        ],\n",
       "          ...,\n",
       "          [26.8       ,  0.45042017,  1.        ],\n",
       "          [25.7       ,  0.43193277,  1.        ],\n",
       "          [28.2       ,  0.47394958,  1.        ]], shape=(1000, 3)),\n",
       "   'G': <networkx.classes.graph.Graph at 0x146197f10>,\n",
       "   'weights': {(0, 1): np.float64(0.3),\n",
       "    (0, 7): np.float64(0.3),\n",
       "    (1, 2): np.float64(0.2),\n",
       "    (1, 8): np.float64(0.2),\n",
       "    (2, 3): np.float64(0.5),\n",
       "    (2, 9): np.float64(0.1),\n",
       "    (3, 4): np.float64(0.3),\n",
       "    (3, 10): np.float64(1.0),\n",
       "    (4, 5): np.float64(0.2),\n",
       "    (4, 11): np.float64(1.0),\n",
       "    (5, 6): np.float64(0.3),\n",
       "    (5, 12): np.float64(1.0),\n",
       "    (6, 13): np.float64(0.1),\n",
       "    (7, 8): np.float64(0.1),\n",
       "    (7, 14): np.float64(0.3),\n",
       "    (8, 9): np.float64(1.0),\n",
       "    (8, 15): np.float64(1.0),\n",
       "    (9, 10): np.float64(0.1),\n",
       "    (9, 16): np.float64(1.0),\n",
       "    (10, 11): np.float64(1.0),\n",
       "    (11, 12): np.float64(0.1),\n",
       "    (11, 18): np.float64(1.0),\n",
       "    (12, 13): np.float64(0.5),\n",
       "    (12, 19): np.float64(0.3),\n",
       "    (13, 20): np.float64(0.1),\n",
       "    (14, 15): np.float64(0.5),\n",
       "    (14, 21): np.float64(0.3),\n",
       "    (15, 16): np.float64(0.5),\n",
       "    (15, 22): np.float64(0.5),\n",
       "    (16, 17): np.float64(0.1),\n",
       "    (16, 23): np.float64(0.5),\n",
       "    (17, 18): np.float64(0.5),\n",
       "    (17, 24): np.float64(0.2),\n",
       "    (18, 19): np.float64(0.2),\n",
       "    (18, 25): np.float64(0.5),\n",
       "    (19, 20): np.float64(0.2),\n",
       "    (19, 26): np.float64(0.5),\n",
       "    (20, 27): np.float64(0.2),\n",
       "    (21, 22): np.float64(1.0),\n",
       "    (21, 28): np.float64(0.5),\n",
       "    (22, 23): np.float64(0.5),\n",
       "    (22, 29): np.float64(0.2),\n",
       "    (23, 24): np.float64(0.3),\n",
       "    (23, 30): np.float64(0.1),\n",
       "    (24, 25): np.float64(0.5),\n",
       "    (24, 31): np.float64(0.5),\n",
       "    (25, 26): np.float64(0.2),\n",
       "    (25, 32): np.float64(1.0),\n",
       "    (26, 27): np.float64(0.3),\n",
       "    (26, 33): np.float64(1.0),\n",
       "    (28, 29): np.float64(0.1),\n",
       "    (28, 35): np.float64(0.5),\n",
       "    (29, 30): np.float64(0.2),\n",
       "    (29, 36): np.float64(1.0),\n",
       "    (30, 31): np.float64(0.2),\n",
       "    (30, 37): np.float64(0.1),\n",
       "    (31, 38): np.float64(0.1),\n",
       "    (32, 33): np.float64(1.0),\n",
       "    (32, 39): np.float64(0.1),\n",
       "    (33, 34): np.float64(1.0),\n",
       "    (33, 40): np.float64(0.2),\n",
       "    (34, 41): np.float64(1.0),\n",
       "    (35, 36): np.float64(1.0),\n",
       "    (36, 37): np.float64(0.1),\n",
       "    (36, 42): np.float64(0.2),\n",
       "    (37, 38): np.float64(1.0),\n",
       "    (37, 43): np.float64(0.1),\n",
       "    (38, 39): np.float64(0.2),\n",
       "    (38, 44): np.float64(0.2),\n",
       "    (39, 40): np.float64(0.3),\n",
       "    (39, 45): np.float64(0.3),\n",
       "    (40, 41): np.float64(0.5),\n",
       "    (40, 46): np.float64(0.3),\n",
       "    (42, 48): np.float64(0.2),\n",
       "    (43, 44): np.float64(0.1),\n",
       "    (43, 49): np.float64(1.0),\n",
       "    (44, 45): np.float64(0.3),\n",
       "    (44, 50): np.float64(0.5),\n",
       "    (45, 46): np.float64(0.5),\n",
       "    (45, 51): np.float64(0.2),\n",
       "    (46, 52): np.float64(0.5),\n",
       "    (47, 48): np.float64(0.1),\n",
       "    (47, 54): np.float64(1.0),\n",
       "    (48, 49): np.float64(0.1),\n",
       "    (48, 55): np.float64(1.0),\n",
       "    (49, 50): np.float64(0.3),\n",
       "    (49, 56): np.float64(1.0),\n",
       "    (50, 51): np.float64(0.5),\n",
       "    (50, 57): np.float64(0.3),\n",
       "    (51, 52): np.float64(0.5),\n",
       "    (51, 58): np.float64(0.1),\n",
       "    (52, 53): np.float64(0.2),\n",
       "    (52, 59): np.float64(0.2),\n",
       "    (53, 60): np.float64(0.1),\n",
       "    (54, 55): np.float64(0.2),\n",
       "    (54, 61): np.float64(0.5),\n",
       "    (55, 56): np.float64(0.2),\n",
       "    (55, 62): np.float64(0.2),\n",
       "    (56, 57): np.float64(0.3),\n",
       "    (56, 63): np.float64(1.0),\n",
       "    (57, 58): np.float64(0.5),\n",
       "    (57, 64): np.float64(1.0),\n",
       "    (58, 59): np.float64(0.3),\n",
       "    (58, 65): np.float64(0.2),\n",
       "    (59, 60): np.float64(0.1),\n",
       "    (59, 66): np.float64(1.0),\n",
       "    (60, 67): np.float64(0.2),\n",
       "    (61, 62): np.float64(0.5),\n",
       "    (61, 68): np.float64(1.0),\n",
       "    (62, 63): np.float64(0.2),\n",
       "    (62, 69): np.float64(0.1),\n",
       "    (63, 64): np.float64(0.3),\n",
       "    (63, 70): np.float64(0.5),\n",
       "    (64, 71): np.float64(0.2),\n",
       "    (65, 66): np.float64(1.0),\n",
       "    (65, 72): np.float64(0.3),\n",
       "    (66, 67): np.float64(1.0),\n",
       "    (66, 73): np.float64(0.1),\n",
       "    (67, 74): np.float64(0.3),\n",
       "    (68, 69): np.float64(0.2),\n",
       "    (68, 75): np.float64(0.2),\n",
       "    (69, 70): np.float64(0.3),\n",
       "    (69, 76): np.float64(0.5),\n",
       "    (70, 71): np.float64(0.3),\n",
       "    (70, 77): np.float64(0.2),\n",
       "    (71, 72): np.float64(0.5),\n",
       "    (71, 78): np.float64(0.2),\n",
       "    (72, 73): np.float64(1.0),\n",
       "    (72, 79): np.float64(0.2),\n",
       "    (73, 74): np.float64(0.1),\n",
       "    (73, 80): np.float64(1.0),\n",
       "    (74, 81): np.float64(0.1),\n",
       "    (75, 76): np.float64(0.5),\n",
       "    (76, 77): np.float64(0.2),\n",
       "    (77, 78): np.float64(0.2),\n",
       "    (78, 79): np.float64(0.1),\n",
       "    (79, 80): np.float64(0.3),\n",
       "    (80, 81): np.float64(1.0)},\n",
       "   'max_cut': np.float64(59.500000000000036),\n",
       "   'r': np.float64(0.42580504201680647),\n",
       "   'probability': np.float64(0.0)}},\n",
       " 9: {0: {'results': array([[26.1       ,  0.43865546,  1.        ],\n",
       "          [28.1       ,  0.47226891,  1.        ],\n",
       "          [23.7       ,  0.39831933,  1.        ],\n",
       "          ...,\n",
       "          [21.2       ,  0.35630252,  1.        ],\n",
       "          [15.9       ,  0.26722689,  1.        ],\n",
       "          [20.8       ,  0.34957983,  1.        ]], shape=(1000, 3)),\n",
       "   'G': <networkx.classes.graph.Graph at 0x146197f10>,\n",
       "   'weights': {(0, 1): np.float64(0.3),\n",
       "    (0, 7): np.float64(0.3),\n",
       "    (1, 2): np.float64(0.2),\n",
       "    (1, 8): np.float64(0.2),\n",
       "    (2, 3): np.float64(0.5),\n",
       "    (2, 9): np.float64(0.1),\n",
       "    (3, 4): np.float64(0.3),\n",
       "    (3, 10): np.float64(1.0),\n",
       "    (4, 5): np.float64(0.2),\n",
       "    (4, 11): np.float64(1.0),\n",
       "    (5, 6): np.float64(0.3),\n",
       "    (5, 12): np.float64(1.0),\n",
       "    (6, 13): np.float64(0.1),\n",
       "    (7, 8): np.float64(0.1),\n",
       "    (7, 14): np.float64(0.3),\n",
       "    (8, 9): np.float64(1.0),\n",
       "    (8, 15): np.float64(1.0),\n",
       "    (9, 10): np.float64(0.1),\n",
       "    (9, 16): np.float64(1.0),\n",
       "    (10, 11): np.float64(1.0),\n",
       "    (11, 12): np.float64(0.1),\n",
       "    (11, 18): np.float64(1.0),\n",
       "    (12, 13): np.float64(0.5),\n",
       "    (12, 19): np.float64(0.3),\n",
       "    (13, 20): np.float64(0.1),\n",
       "    (14, 15): np.float64(0.5),\n",
       "    (14, 21): np.float64(0.3),\n",
       "    (15, 16): np.float64(0.5),\n",
       "    (15, 22): np.float64(0.5),\n",
       "    (16, 17): np.float64(0.1),\n",
       "    (16, 23): np.float64(0.5),\n",
       "    (17, 18): np.float64(0.5),\n",
       "    (17, 24): np.float64(0.2),\n",
       "    (18, 19): np.float64(0.2),\n",
       "    (18, 25): np.float64(0.5),\n",
       "    (19, 20): np.float64(0.2),\n",
       "    (19, 26): np.float64(0.5),\n",
       "    (20, 27): np.float64(0.2),\n",
       "    (21, 22): np.float64(1.0),\n",
       "    (21, 28): np.float64(0.5),\n",
       "    (22, 23): np.float64(0.5),\n",
       "    (22, 29): np.float64(0.2),\n",
       "    (23, 24): np.float64(0.3),\n",
       "    (23, 30): np.float64(0.1),\n",
       "    (24, 25): np.float64(0.5),\n",
       "    (24, 31): np.float64(0.5),\n",
       "    (25, 26): np.float64(0.2),\n",
       "    (25, 32): np.float64(1.0),\n",
       "    (26, 27): np.float64(0.3),\n",
       "    (26, 33): np.float64(1.0),\n",
       "    (28, 29): np.float64(0.1),\n",
       "    (28, 35): np.float64(0.5),\n",
       "    (29, 30): np.float64(0.2),\n",
       "    (29, 36): np.float64(1.0),\n",
       "    (30, 31): np.float64(0.2),\n",
       "    (30, 37): np.float64(0.1),\n",
       "    (31, 38): np.float64(0.1),\n",
       "    (32, 33): np.float64(1.0),\n",
       "    (32, 39): np.float64(0.1),\n",
       "    (33, 34): np.float64(1.0),\n",
       "    (33, 40): np.float64(0.2),\n",
       "    (34, 41): np.float64(1.0),\n",
       "    (35, 36): np.float64(1.0),\n",
       "    (36, 37): np.float64(0.1),\n",
       "    (36, 42): np.float64(0.2),\n",
       "    (37, 38): np.float64(1.0),\n",
       "    (37, 43): np.float64(0.1),\n",
       "    (38, 39): np.float64(0.2),\n",
       "    (38, 44): np.float64(0.2),\n",
       "    (39, 40): np.float64(0.3),\n",
       "    (39, 45): np.float64(0.3),\n",
       "    (40, 41): np.float64(0.5),\n",
       "    (40, 46): np.float64(0.3),\n",
       "    (42, 48): np.float64(0.2),\n",
       "    (43, 44): np.float64(0.1),\n",
       "    (43, 49): np.float64(1.0),\n",
       "    (44, 45): np.float64(0.3),\n",
       "    (44, 50): np.float64(0.5),\n",
       "    (45, 46): np.float64(0.5),\n",
       "    (45, 51): np.float64(0.2),\n",
       "    (46, 52): np.float64(0.5),\n",
       "    (47, 48): np.float64(0.1),\n",
       "    (47, 54): np.float64(1.0),\n",
       "    (48, 49): np.float64(0.1),\n",
       "    (48, 55): np.float64(1.0),\n",
       "    (49, 50): np.float64(0.3),\n",
       "    (49, 56): np.float64(1.0),\n",
       "    (50, 51): np.float64(0.5),\n",
       "    (50, 57): np.float64(0.3),\n",
       "    (51, 52): np.float64(0.5),\n",
       "    (51, 58): np.float64(0.1),\n",
       "    (52, 53): np.float64(0.2),\n",
       "    (52, 59): np.float64(0.2),\n",
       "    (53, 60): np.float64(0.1),\n",
       "    (54, 55): np.float64(0.2),\n",
       "    (54, 61): np.float64(0.5),\n",
       "    (55, 56): np.float64(0.2),\n",
       "    (55, 62): np.float64(0.2),\n",
       "    (56, 57): np.float64(0.3),\n",
       "    (56, 63): np.float64(1.0),\n",
       "    (57, 58): np.float64(0.5),\n",
       "    (57, 64): np.float64(1.0),\n",
       "    (58, 59): np.float64(0.3),\n",
       "    (58, 65): np.float64(0.2),\n",
       "    (59, 60): np.float64(0.1),\n",
       "    (59, 66): np.float64(1.0),\n",
       "    (60, 67): np.float64(0.2),\n",
       "    (61, 62): np.float64(0.5),\n",
       "    (61, 68): np.float64(1.0),\n",
       "    (62, 63): np.float64(0.2),\n",
       "    (62, 69): np.float64(0.1),\n",
       "    (63, 64): np.float64(0.3),\n",
       "    (63, 70): np.float64(0.5),\n",
       "    (64, 71): np.float64(0.2),\n",
       "    (65, 66): np.float64(1.0),\n",
       "    (65, 72): np.float64(0.3),\n",
       "    (66, 67): np.float64(1.0),\n",
       "    (66, 73): np.float64(0.1),\n",
       "    (67, 74): np.float64(0.3),\n",
       "    (68, 69): np.float64(0.2),\n",
       "    (68, 75): np.float64(0.2),\n",
       "    (69, 70): np.float64(0.3),\n",
       "    (69, 76): np.float64(0.5),\n",
       "    (70, 71): np.float64(0.3),\n",
       "    (70, 77): np.float64(0.2),\n",
       "    (71, 72): np.float64(0.5),\n",
       "    (71, 78): np.float64(0.2),\n",
       "    (72, 73): np.float64(1.0),\n",
       "    (72, 79): np.float64(0.2),\n",
       "    (73, 74): np.float64(0.1),\n",
       "    (73, 80): np.float64(1.0),\n",
       "    (74, 81): np.float64(0.1),\n",
       "    (75, 76): np.float64(0.5),\n",
       "    (76, 77): np.float64(0.2),\n",
       "    (77, 78): np.float64(0.2),\n",
       "    (78, 79): np.float64(0.1),\n",
       "    (79, 80): np.float64(0.3),\n",
       "    (80, 81): np.float64(1.0)},\n",
       "   'max_cut': np.float64(59.500000000000036),\n",
       "   'r': np.float64(0.40985546218487373),\n",
       "   'probability': np.float64(0.0)}},\n",
       " 10: {0: {'results': array([[22.        ,  0.3697479 ,  1.        ],\n",
       "          [21.8       ,  0.36638655,  1.        ],\n",
       "          [23.6       ,  0.39663866,  1.        ],\n",
       "          ...,\n",
       "          [29.6       ,  0.49747899,  1.        ],\n",
       "          [24.9       ,  0.41848739,  1.        ],\n",
       "          [22.        ,  0.3697479 ,  1.        ]], shape=(1000, 3)),\n",
       "   'G': <networkx.classes.graph.Graph at 0x146197f10>,\n",
       "   'weights': {(0, 1): np.float64(0.3),\n",
       "    (0, 7): np.float64(0.3),\n",
       "    (1, 2): np.float64(0.2),\n",
       "    (1, 8): np.float64(0.2),\n",
       "    (2, 3): np.float64(0.5),\n",
       "    (2, 9): np.float64(0.1),\n",
       "    (3, 4): np.float64(0.3),\n",
       "    (3, 10): np.float64(1.0),\n",
       "    (4, 5): np.float64(0.2),\n",
       "    (4, 11): np.float64(1.0),\n",
       "    (5, 6): np.float64(0.3),\n",
       "    (5, 12): np.float64(1.0),\n",
       "    (6, 13): np.float64(0.1),\n",
       "    (7, 8): np.float64(0.1),\n",
       "    (7, 14): np.float64(0.3),\n",
       "    (8, 9): np.float64(1.0),\n",
       "    (8, 15): np.float64(1.0),\n",
       "    (9, 10): np.float64(0.1),\n",
       "    (9, 16): np.float64(1.0),\n",
       "    (10, 11): np.float64(1.0),\n",
       "    (11, 12): np.float64(0.1),\n",
       "    (11, 18): np.float64(1.0),\n",
       "    (12, 13): np.float64(0.5),\n",
       "    (12, 19): np.float64(0.3),\n",
       "    (13, 20): np.float64(0.1),\n",
       "    (14, 15): np.float64(0.5),\n",
       "    (14, 21): np.float64(0.3),\n",
       "    (15, 16): np.float64(0.5),\n",
       "    (15, 22): np.float64(0.5),\n",
       "    (16, 17): np.float64(0.1),\n",
       "    (16, 23): np.float64(0.5),\n",
       "    (17, 18): np.float64(0.5),\n",
       "    (17, 24): np.float64(0.2),\n",
       "    (18, 19): np.float64(0.2),\n",
       "    (18, 25): np.float64(0.5),\n",
       "    (19, 20): np.float64(0.2),\n",
       "    (19, 26): np.float64(0.5),\n",
       "    (20, 27): np.float64(0.2),\n",
       "    (21, 22): np.float64(1.0),\n",
       "    (21, 28): np.float64(0.5),\n",
       "    (22, 23): np.float64(0.5),\n",
       "    (22, 29): np.float64(0.2),\n",
       "    (23, 24): np.float64(0.3),\n",
       "    (23, 30): np.float64(0.1),\n",
       "    (24, 25): np.float64(0.5),\n",
       "    (24, 31): np.float64(0.5),\n",
       "    (25, 26): np.float64(0.2),\n",
       "    (25, 32): np.float64(1.0),\n",
       "    (26, 27): np.float64(0.3),\n",
       "    (26, 33): np.float64(1.0),\n",
       "    (28, 29): np.float64(0.1),\n",
       "    (28, 35): np.float64(0.5),\n",
       "    (29, 30): np.float64(0.2),\n",
       "    (29, 36): np.float64(1.0),\n",
       "    (30, 31): np.float64(0.2),\n",
       "    (30, 37): np.float64(0.1),\n",
       "    (31, 38): np.float64(0.1),\n",
       "    (32, 33): np.float64(1.0),\n",
       "    (32, 39): np.float64(0.1),\n",
       "    (33, 34): np.float64(1.0),\n",
       "    (33, 40): np.float64(0.2),\n",
       "    (34, 41): np.float64(1.0),\n",
       "    (35, 36): np.float64(1.0),\n",
       "    (36, 37): np.float64(0.1),\n",
       "    (36, 42): np.float64(0.2),\n",
       "    (37, 38): np.float64(1.0),\n",
       "    (37, 43): np.float64(0.1),\n",
       "    (38, 39): np.float64(0.2),\n",
       "    (38, 44): np.float64(0.2),\n",
       "    (39, 40): np.float64(0.3),\n",
       "    (39, 45): np.float64(0.3),\n",
       "    (40, 41): np.float64(0.5),\n",
       "    (40, 46): np.float64(0.3),\n",
       "    (42, 48): np.float64(0.2),\n",
       "    (43, 44): np.float64(0.1),\n",
       "    (43, 49): np.float64(1.0),\n",
       "    (44, 45): np.float64(0.3),\n",
       "    (44, 50): np.float64(0.5),\n",
       "    (45, 46): np.float64(0.5),\n",
       "    (45, 51): np.float64(0.2),\n",
       "    (46, 52): np.float64(0.5),\n",
       "    (47, 48): np.float64(0.1),\n",
       "    (47, 54): np.float64(1.0),\n",
       "    (48, 49): np.float64(0.1),\n",
       "    (48, 55): np.float64(1.0),\n",
       "    (49, 50): np.float64(0.3),\n",
       "    (49, 56): np.float64(1.0),\n",
       "    (50, 51): np.float64(0.5),\n",
       "    (50, 57): np.float64(0.3),\n",
       "    (51, 52): np.float64(0.5),\n",
       "    (51, 58): np.float64(0.1),\n",
       "    (52, 53): np.float64(0.2),\n",
       "    (52, 59): np.float64(0.2),\n",
       "    (53, 60): np.float64(0.1),\n",
       "    (54, 55): np.float64(0.2),\n",
       "    (54, 61): np.float64(0.5),\n",
       "    (55, 56): np.float64(0.2),\n",
       "    (55, 62): np.float64(0.2),\n",
       "    (56, 57): np.float64(0.3),\n",
       "    (56, 63): np.float64(1.0),\n",
       "    (57, 58): np.float64(0.5),\n",
       "    (57, 64): np.float64(1.0),\n",
       "    (58, 59): np.float64(0.3),\n",
       "    (58, 65): np.float64(0.2),\n",
       "    (59, 60): np.float64(0.1),\n",
       "    (59, 66): np.float64(1.0),\n",
       "    (60, 67): np.float64(0.2),\n",
       "    (61, 62): np.float64(0.5),\n",
       "    (61, 68): np.float64(1.0),\n",
       "    (62, 63): np.float64(0.2),\n",
       "    (62, 69): np.float64(0.1),\n",
       "    (63, 64): np.float64(0.3),\n",
       "    (63, 70): np.float64(0.5),\n",
       "    (64, 71): np.float64(0.2),\n",
       "    (65, 66): np.float64(1.0),\n",
       "    (65, 72): np.float64(0.3),\n",
       "    (66, 67): np.float64(1.0),\n",
       "    (66, 73): np.float64(0.1),\n",
       "    (67, 74): np.float64(0.3),\n",
       "    (68, 69): np.float64(0.2),\n",
       "    (68, 75): np.float64(0.2),\n",
       "    (69, 70): np.float64(0.3),\n",
       "    (69, 76): np.float64(0.5),\n",
       "    (70, 71): np.float64(0.3),\n",
       "    (70, 77): np.float64(0.2),\n",
       "    (71, 72): np.float64(0.5),\n",
       "    (71, 78): np.float64(0.2),\n",
       "    (72, 73): np.float64(1.0),\n",
       "    (72, 79): np.float64(0.2),\n",
       "    (73, 74): np.float64(0.1),\n",
       "    (73, 80): np.float64(1.0),\n",
       "    (74, 81): np.float64(0.1),\n",
       "    (75, 76): np.float64(0.5),\n",
       "    (76, 77): np.float64(0.2),\n",
       "    (77, 78): np.float64(0.2),\n",
       "    (78, 79): np.float64(0.1),\n",
       "    (79, 80): np.float64(0.3),\n",
       "    (80, 81): np.float64(1.0)},\n",
       "   'max_cut': np.float64(59.500000000000036),\n",
       "   'r': np.float64(0.38923193277310897),\n",
       "   'probability': np.float64(0.0)}},\n",
       " 13: {0: {'results': array([[31.5       ,  0.52941176,  1.        ],\n",
       "          [22.1       ,  0.37142857,  1.        ],\n",
       "          [23.5       ,  0.39495798,  1.        ],\n",
       "          ...,\n",
       "          [23.7       ,  0.39831933,  1.        ],\n",
       "          [27.8       ,  0.46722689,  1.        ],\n",
       "          [18.        ,  0.30252101,  1.        ]], shape=(1000, 3)),\n",
       "   'G': <networkx.classes.graph.Graph at 0x146197f10>,\n",
       "   'weights': {(0, 1): np.float64(0.3),\n",
       "    (0, 7): np.float64(0.3),\n",
       "    (1, 2): np.float64(0.2),\n",
       "    (1, 8): np.float64(0.2),\n",
       "    (2, 3): np.float64(0.5),\n",
       "    (2, 9): np.float64(0.1),\n",
       "    (3, 4): np.float64(0.3),\n",
       "    (3, 10): np.float64(1.0),\n",
       "    (4, 5): np.float64(0.2),\n",
       "    (4, 11): np.float64(1.0),\n",
       "    (5, 6): np.float64(0.3),\n",
       "    (5, 12): np.float64(1.0),\n",
       "    (6, 13): np.float64(0.1),\n",
       "    (7, 8): np.float64(0.1),\n",
       "    (7, 14): np.float64(0.3),\n",
       "    (8, 9): np.float64(1.0),\n",
       "    (8, 15): np.float64(1.0),\n",
       "    (9, 10): np.float64(0.1),\n",
       "    (9, 16): np.float64(1.0),\n",
       "    (10, 11): np.float64(1.0),\n",
       "    (11, 12): np.float64(0.1),\n",
       "    (11, 18): np.float64(1.0),\n",
       "    (12, 13): np.float64(0.5),\n",
       "    (12, 19): np.float64(0.3),\n",
       "    (13, 20): np.float64(0.1),\n",
       "    (14, 15): np.float64(0.5),\n",
       "    (14, 21): np.float64(0.3),\n",
       "    (15, 16): np.float64(0.5),\n",
       "    (15, 22): np.float64(0.5),\n",
       "    (16, 17): np.float64(0.1),\n",
       "    (16, 23): np.float64(0.5),\n",
       "    (17, 18): np.float64(0.5),\n",
       "    (17, 24): np.float64(0.2),\n",
       "    (18, 19): np.float64(0.2),\n",
       "    (18, 25): np.float64(0.5),\n",
       "    (19, 20): np.float64(0.2),\n",
       "    (19, 26): np.float64(0.5),\n",
       "    (20, 27): np.float64(0.2),\n",
       "    (21, 22): np.float64(1.0),\n",
       "    (21, 28): np.float64(0.5),\n",
       "    (22, 23): np.float64(0.5),\n",
       "    (22, 29): np.float64(0.2),\n",
       "    (23, 24): np.float64(0.3),\n",
       "    (23, 30): np.float64(0.1),\n",
       "    (24, 25): np.float64(0.5),\n",
       "    (24, 31): np.float64(0.5),\n",
       "    (25, 26): np.float64(0.2),\n",
       "    (25, 32): np.float64(1.0),\n",
       "    (26, 27): np.float64(0.3),\n",
       "    (26, 33): np.float64(1.0),\n",
       "    (28, 29): np.float64(0.1),\n",
       "    (28, 35): np.float64(0.5),\n",
       "    (29, 30): np.float64(0.2),\n",
       "    (29, 36): np.float64(1.0),\n",
       "    (30, 31): np.float64(0.2),\n",
       "    (30, 37): np.float64(0.1),\n",
       "    (31, 38): np.float64(0.1),\n",
       "    (32, 33): np.float64(1.0),\n",
       "    (32, 39): np.float64(0.1),\n",
       "    (33, 34): np.float64(1.0),\n",
       "    (33, 40): np.float64(0.2),\n",
       "    (34, 41): np.float64(1.0),\n",
       "    (35, 36): np.float64(1.0),\n",
       "    (36, 37): np.float64(0.1),\n",
       "    (36, 42): np.float64(0.2),\n",
       "    (37, 38): np.float64(1.0),\n",
       "    (37, 43): np.float64(0.1),\n",
       "    (38, 39): np.float64(0.2),\n",
       "    (38, 44): np.float64(0.2),\n",
       "    (39, 40): np.float64(0.3),\n",
       "    (39, 45): np.float64(0.3),\n",
       "    (40, 41): np.float64(0.5),\n",
       "    (40, 46): np.float64(0.3),\n",
       "    (42, 48): np.float64(0.2),\n",
       "    (43, 44): np.float64(0.1),\n",
       "    (43, 49): np.float64(1.0),\n",
       "    (44, 45): np.float64(0.3),\n",
       "    (44, 50): np.float64(0.5),\n",
       "    (45, 46): np.float64(0.5),\n",
       "    (45, 51): np.float64(0.2),\n",
       "    (46, 52): np.float64(0.5),\n",
       "    (47, 48): np.float64(0.1),\n",
       "    (47, 54): np.float64(1.0),\n",
       "    (48, 49): np.float64(0.1),\n",
       "    (48, 55): np.float64(1.0),\n",
       "    (49, 50): np.float64(0.3),\n",
       "    (49, 56): np.float64(1.0),\n",
       "    (50, 51): np.float64(0.5),\n",
       "    (50, 57): np.float64(0.3),\n",
       "    (51, 52): np.float64(0.5),\n",
       "    (51, 58): np.float64(0.1),\n",
       "    (52, 53): np.float64(0.2),\n",
       "    (52, 59): np.float64(0.2),\n",
       "    (53, 60): np.float64(0.1),\n",
       "    (54, 55): np.float64(0.2),\n",
       "    (54, 61): np.float64(0.5),\n",
       "    (55, 56): np.float64(0.2),\n",
       "    (55, 62): np.float64(0.2),\n",
       "    (56, 57): np.float64(0.3),\n",
       "    (56, 63): np.float64(1.0),\n",
       "    (57, 58): np.float64(0.5),\n",
       "    (57, 64): np.float64(1.0),\n",
       "    (58, 59): np.float64(0.3),\n",
       "    (58, 65): np.float64(0.2),\n",
       "    (59, 60): np.float64(0.1),\n",
       "    (59, 66): np.float64(1.0),\n",
       "    (60, 67): np.float64(0.2),\n",
       "    (61, 62): np.float64(0.5),\n",
       "    (61, 68): np.float64(1.0),\n",
       "    (62, 63): np.float64(0.2),\n",
       "    (62, 69): np.float64(0.1),\n",
       "    (63, 64): np.float64(0.3),\n",
       "    (63, 70): np.float64(0.5),\n",
       "    (64, 71): np.float64(0.2),\n",
       "    (65, 66): np.float64(1.0),\n",
       "    (65, 72): np.float64(0.3),\n",
       "    (66, 67): np.float64(1.0),\n",
       "    (66, 73): np.float64(0.1),\n",
       "    (67, 74): np.float64(0.3),\n",
       "    (68, 69): np.float64(0.2),\n",
       "    (68, 75): np.float64(0.2),\n",
       "    (69, 70): np.float64(0.3),\n",
       "    (69, 76): np.float64(0.5),\n",
       "    (70, 71): np.float64(0.3),\n",
       "    (70, 77): np.float64(0.2),\n",
       "    (71, 72): np.float64(0.5),\n",
       "    (71, 78): np.float64(0.2),\n",
       "    (72, 73): np.float64(1.0),\n",
       "    (72, 79): np.float64(0.2),\n",
       "    (73, 74): np.float64(0.1),\n",
       "    (73, 80): np.float64(1.0),\n",
       "    (74, 81): np.float64(0.1),\n",
       "    (75, 76): np.float64(0.5),\n",
       "    (76, 77): np.float64(0.2),\n",
       "    (77, 78): np.float64(0.2),\n",
       "    (78, 79): np.float64(0.1),\n",
       "    (79, 80): np.float64(0.3),\n",
       "    (80, 81): np.float64(1.0)},\n",
       "   'max_cut': np.float64(59.500000000000036),\n",
       "   'r': np.float64(0.406250420168067),\n",
       "   'probability': np.float64(0.0)}},\n",
       " 15: {0: {'results': array([[25.3       ,  0.42521008,  1.        ],\n",
       "          [30.1       ,  0.50588235,  1.        ],\n",
       "          [25.7       ,  0.43193277,  1.        ],\n",
       "          ...,\n",
       "          [26.        ,  0.43697479,  1.        ],\n",
       "          [26.7       ,  0.4487395 ,  1.        ],\n",
       "          [23.3       ,  0.39159664,  1.        ]], shape=(1000, 3)),\n",
       "   'G': <networkx.classes.graph.Graph at 0x146197f10>,\n",
       "   'weights': {(0, 1): np.float64(0.3),\n",
       "    (0, 7): np.float64(0.3),\n",
       "    (1, 2): np.float64(0.2),\n",
       "    (1, 8): np.float64(0.2),\n",
       "    (2, 3): np.float64(0.5),\n",
       "    (2, 9): np.float64(0.1),\n",
       "    (3, 4): np.float64(0.3),\n",
       "    (3, 10): np.float64(1.0),\n",
       "    (4, 5): np.float64(0.2),\n",
       "    (4, 11): np.float64(1.0),\n",
       "    (5, 6): np.float64(0.3),\n",
       "    (5, 12): np.float64(1.0),\n",
       "    (6, 13): np.float64(0.1),\n",
       "    (7, 8): np.float64(0.1),\n",
       "    (7, 14): np.float64(0.3),\n",
       "    (8, 9): np.float64(1.0),\n",
       "    (8, 15): np.float64(1.0),\n",
       "    (9, 10): np.float64(0.1),\n",
       "    (9, 16): np.float64(1.0),\n",
       "    (10, 11): np.float64(1.0),\n",
       "    (11, 12): np.float64(0.1),\n",
       "    (11, 18): np.float64(1.0),\n",
       "    (12, 13): np.float64(0.5),\n",
       "    (12, 19): np.float64(0.3),\n",
       "    (13, 20): np.float64(0.1),\n",
       "    (14, 15): np.float64(0.5),\n",
       "    (14, 21): np.float64(0.3),\n",
       "    (15, 16): np.float64(0.5),\n",
       "    (15, 22): np.float64(0.5),\n",
       "    (16, 17): np.float64(0.1),\n",
       "    (16, 23): np.float64(0.5),\n",
       "    (17, 18): np.float64(0.5),\n",
       "    (17, 24): np.float64(0.2),\n",
       "    (18, 19): np.float64(0.2),\n",
       "    (18, 25): np.float64(0.5),\n",
       "    (19, 20): np.float64(0.2),\n",
       "    (19, 26): np.float64(0.5),\n",
       "    (20, 27): np.float64(0.2),\n",
       "    (21, 22): np.float64(1.0),\n",
       "    (21, 28): np.float64(0.5),\n",
       "    (22, 23): np.float64(0.5),\n",
       "    (22, 29): np.float64(0.2),\n",
       "    (23, 24): np.float64(0.3),\n",
       "    (23, 30): np.float64(0.1),\n",
       "    (24, 25): np.float64(0.5),\n",
       "    (24, 31): np.float64(0.5),\n",
       "    (25, 26): np.float64(0.2),\n",
       "    (25, 32): np.float64(1.0),\n",
       "    (26, 27): np.float64(0.3),\n",
       "    (26, 33): np.float64(1.0),\n",
       "    (28, 29): np.float64(0.1),\n",
       "    (28, 35): np.float64(0.5),\n",
       "    (29, 30): np.float64(0.2),\n",
       "    (29, 36): np.float64(1.0),\n",
       "    (30, 31): np.float64(0.2),\n",
       "    (30, 37): np.float64(0.1),\n",
       "    (31, 38): np.float64(0.1),\n",
       "    (32, 33): np.float64(1.0),\n",
       "    (32, 39): np.float64(0.1),\n",
       "    (33, 34): np.float64(1.0),\n",
       "    (33, 40): np.float64(0.2),\n",
       "    (34, 41): np.float64(1.0),\n",
       "    (35, 36): np.float64(1.0),\n",
       "    (36, 37): np.float64(0.1),\n",
       "    (36, 42): np.float64(0.2),\n",
       "    (37, 38): np.float64(1.0),\n",
       "    (37, 43): np.float64(0.1),\n",
       "    (38, 39): np.float64(0.2),\n",
       "    (38, 44): np.float64(0.2),\n",
       "    (39, 40): np.float64(0.3),\n",
       "    (39, 45): np.float64(0.3),\n",
       "    (40, 41): np.float64(0.5),\n",
       "    (40, 46): np.float64(0.3),\n",
       "    (42, 48): np.float64(0.2),\n",
       "    (43, 44): np.float64(0.1),\n",
       "    (43, 49): np.float64(1.0),\n",
       "    (44, 45): np.float64(0.3),\n",
       "    (44, 50): np.float64(0.5),\n",
       "    (45, 46): np.float64(0.5),\n",
       "    (45, 51): np.float64(0.2),\n",
       "    (46, 52): np.float64(0.5),\n",
       "    (47, 48): np.float64(0.1),\n",
       "    (47, 54): np.float64(1.0),\n",
       "    (48, 49): np.float64(0.1),\n",
       "    (48, 55): np.float64(1.0),\n",
       "    (49, 50): np.float64(0.3),\n",
       "    (49, 56): np.float64(1.0),\n",
       "    (50, 51): np.float64(0.5),\n",
       "    (50, 57): np.float64(0.3),\n",
       "    (51, 52): np.float64(0.5),\n",
       "    (51, 58): np.float64(0.1),\n",
       "    (52, 53): np.float64(0.2),\n",
       "    (52, 59): np.float64(0.2),\n",
       "    (53, 60): np.float64(0.1),\n",
       "    (54, 55): np.float64(0.2),\n",
       "    (54, 61): np.float64(0.5),\n",
       "    (55, 56): np.float64(0.2),\n",
       "    (55, 62): np.float64(0.2),\n",
       "    (56, 57): np.float64(0.3),\n",
       "    (56, 63): np.float64(1.0),\n",
       "    (57, 58): np.float64(0.5),\n",
       "    (57, 64): np.float64(1.0),\n",
       "    (58, 59): np.float64(0.3),\n",
       "    (58, 65): np.float64(0.2),\n",
       "    (59, 60): np.float64(0.1),\n",
       "    (59, 66): np.float64(1.0),\n",
       "    (60, 67): np.float64(0.2),\n",
       "    (61, 62): np.float64(0.5),\n",
       "    (61, 68): np.float64(1.0),\n",
       "    (62, 63): np.float64(0.2),\n",
       "    (62, 69): np.float64(0.1),\n",
       "    (63, 64): np.float64(0.3),\n",
       "    (63, 70): np.float64(0.5),\n",
       "    (64, 71): np.float64(0.2),\n",
       "    (65, 66): np.float64(1.0),\n",
       "    (65, 72): np.float64(0.3),\n",
       "    (66, 67): np.float64(1.0),\n",
       "    (66, 73): np.float64(0.1),\n",
       "    (67, 74): np.float64(0.3),\n",
       "    (68, 69): np.float64(0.2),\n",
       "    (68, 75): np.float64(0.2),\n",
       "    (69, 70): np.float64(0.3),\n",
       "    (69, 76): np.float64(0.5),\n",
       "    (70, 71): np.float64(0.3),\n",
       "    (70, 77): np.float64(0.2),\n",
       "    (71, 72): np.float64(0.5),\n",
       "    (71, 78): np.float64(0.2),\n",
       "    (72, 73): np.float64(1.0),\n",
       "    (72, 79): np.float64(0.2),\n",
       "    (73, 74): np.float64(0.1),\n",
       "    (73, 80): np.float64(1.0),\n",
       "    (74, 81): np.float64(0.1),\n",
       "    (75, 76): np.float64(0.5),\n",
       "    (76, 77): np.float64(0.2),\n",
       "    (77, 78): np.float64(0.2),\n",
       "    (78, 79): np.float64(0.1),\n",
       "    (79, 80): np.float64(0.3),\n",
       "    (80, 81): np.float64(1.0)},\n",
       "   'max_cut': np.float64(59.500000000000036),\n",
       "   'r': np.float64(0.4416873949579829),\n",
       "   'probability': np.float64(0.0)}},\n",
       " 20: {0: {'results': array([[20.9       ,  0.3512605 ,  1.        ],\n",
       "          [22.1       ,  0.37142857,  1.        ],\n",
       "          [24.9       ,  0.41848739,  1.        ],\n",
       "          ...,\n",
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       "          [23.        ,  0.38655462,  1.        ],\n",
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      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rigetti_ankaa_3[\"postprocessing\"+case][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.64365 0.6639772727272729 0.6775174603174602 0.724321928460342 0.7464046511627905 0.7506031331592691\n",
      "ibm_torino-v1 0.7464046511627905 6\n",
      "ibm_fez 0.7506031331592691 6\n",
      "ibm_fez-fractional 0.7713253012048192 10\n",
      "ibm_torino-fractional 0.7789720930232554 8\n",
      "ibm_marrakesh-fractional 0.7552637215528782 9\n",
      "ibm_garnet 0.6639772727272729 4\n"
     ]
    },
    {
     "data": {
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L+kG88MILWLFihTIppKmErVO5rbmD9YfQHnv37lWVmR8/npd+HYIjwAZ8TSOPzajnPo09vw7f3eaJDGmvXujraM7VUnW8rjLSlJEdZXtlrKsuOvq3ZPDgwdXOrwv47Ojo0CSasINgz/fv+PHjqrHev38/hgwZgtbIjqxUbExPrnWfcVFd0S84HAfzsvF7SnyDzk9hlqw5noCyygpc1bkXyisr8MWhnbUeF+LhhZtja3bXaSt10x562eyZ67LzzQ4x30maJrKzaj0YFBoa2uDr1be+1FYufk9oMk2zYx32c2z7OnyO/Ea0VtKLj+NYce3B1Tr5xiDUMwI5ZVk4WnAIg0PPUuuPFBzAnkNx8PHzwdhLx6h15142Bj6+3mr9npwd6Bc0GO6uHkgqPIpKrQKxAb3VnNtqw9PVC32Cqg7StcX6VNN52Y7YbqMQS9N/PciRDtsfmt/SvJn9TpodW/sZ2+5P+O7T/Lgm6Gajt2m20EWLrlh0ieGAra0Pq73r1dUfZpvGlKoM3EQB15b09HTLeRrbjtf3u2GvbaWLQkvmvG4aATc03CxANoDsnBzc8chjWLjkFxVQ6qsJl+Cq3rH46G9zBacWN8DDA/f9ulJpd78vAT546UUENfZhhYajKeGIC2HFsYe1Xb4t9vxl2Amta1tDA23xOFYKvnj0J+CILOHIF23yOdJkO4JVU7m5nloQNrZ0nqevAUfNqPWgDxx9IVh2PeAFtdu2ndTazq/DBp0Vlj5CdP4XHAWO7FVv+OrDSy+9h59/PgNTp36ELVtes7xz5rRBH6oG4eWX36d3WhOUsXmw997q6/RvQUvU8brKyGfN+mqvjHXVRUf/ltT2HPlu6WRlZal5Xf5C9M9trfQPjkCMf3Ct+/i7mTte3fyDEO7VD3MbcH6XfzUDIyOjLZ1ANxdXXB9Tc2ePGE9BIJHWUDftoVsM1FRP2GFne8j9rAXchtTbxtSX2spFDRHXcxBKh3XUtnNNzXBrFnDDvCIR6FF7fXJ38VDzQPdgeAee0Bh29o1Fr5je2LzmbyxfvFIJt5wXFhSp9b0C+8PoYo4/0MGnEzRl2co65qq21YbhFMSFbQ31qTHXtL0efV/pg8tAVbr/OzW9FNrefPNNu20O96nN97ymusk2iAPKtPykwMrYEhT82GZysIjWHPauV1d/WG/bOGDMqa62zdiIdry+3w17Aja1061ewDWw4WygdtQvNAIu3uaPwlkd2uHynjH4bvd+/Ocns1kAhVsGmfp6514s3ncIBi9v+HXrAYODBJnSTTg4KmMPjk45AuxA0smcTuEcxeHLzd909GZlpuN7fcrN9azYekP7+uuvq4r32WefVTMhvuuuu1Sn1B51Baag4z5HqFkB6fhP00FrcxOhpXBttHY0JmagGsmjac/8+X9gypTz1Pp585Zj+/bDeOyxx9C1a/NqeU4Wvv+2KTj0uuIo+QRZHmtTIr0BoqlcTZ3V1vwtqS96A83ODE3nnBFfN3c11Qcvo5uaGkOgu2cVoTfCq+W/za2hbtb2XtZUT2hZYL2fTnNHH7Uul63VBAc3uN66TGyrnS0qspuLu5rqA4VVXWAlnkYvPP3kbPzy86+YNuVx+Pj6oLCgUGndud7beGIg1sPVs8rf1XpbS9Fa61NDoDkw2wMGXGIwJmuhjhY/FHDtUVfdq2k7swlQuGXQMZp7W0OtLgXcxvSH9XpIM2x7gSSboh2vL46Y4aTF0gTxQXJEfcKECUqAZbTk+35ZgdCQEIQEByvNLddxG/dxtAjKepRk2+iDhKYO1iY8jgArHs0T6F/w+++/q3X2QogzQqItNKdITExEnz59LOYX9AsgttFN+ZIzkuvJwIrGDwGjPF544YXqeQqtG44McvRz5szPkZdXpCYuU/Ciz7qjY69e6Ots/TodqYyMDkzzpqYsY2v6lhDdrIvPQnA+HLluWlur2KKXzZ4rE+sI6wXNlK21t6eC2srF6KuMTGvPRFQ4AaMlU4iif/UZQ89Qc/5uDVGUHbk+NRV6m8NIy7YaS3v331TXs5cNoK7r1dYfZjtMIZfp8hqqETfUsx1vzbRoHlxbITezqBjvvveemjIKixxWuCUc4WKQE6bgsDV9YyAVa9v0loJmgPZyQuqjcbbpBwjTBvCerDuZFEDYQFvnTdNHdm0FfI5GMUXHycJRLgYLYOUXIbf1w48w/56pqdl4/vlv8dxz3yItzRwQoj4pLloaNjDW5llcZvoDNhJ6oJaWhqPO9KvR0fMBkppyHraFbwk7FfxeM/UJOwm2sGNgb6CyLZCRkISXxk+qdeI+jowj102m2dEFVnvvJTVi8+bNq2JeyHpCqxYOTJ1svW0MDEZFE0TWF2u/QH5PWC7SEuVqbVCYpeaOqdI4bw3CraPXp6aipjaH9ZCxIE7V9RjQjVrZxvaHWU8ZyIsDxw8//LBdIXfXrl0WS9PGtOOtmRbLg2sr5Oofdubg4pz54eiD5YjCrc67776r7OkZLZT5F/U8uBypo1N+c4wENQRqkZmfl/5tTOrMCG96fkk+26lTq/tN02SDgSSYX492+fw4c3SIub2s83TRdJANM00j+Dej2QNNO/755x81Kkazj5OFnWGWkyYTzNfF/Fv2orwJrQNG7GVQh9dfN48SUgvQWjpK9NOhD4x1bkAKk8xF5yiBiVhHaVlCfyKaMdEEi0Eo+A3Qy90WvyWMDknzLXYMmIuTufwYKIQdNnYM+J3mNfVAHG0F65yQtRESG1vvfVsCR66bfNdeeeUVlWea5WO9ZGeX+SY5sMdcnIxISisD1lvWE0ZvZXRS1jVG/j7V8G/NaMn0z2NAGtZJ6+8JBXPJbOC8OHJ9aipYtzh9++23OHbsmGqTaEJMDSbbnPqa+9YX1nfWKbZ7zGPLbwAD1bJNZLtqG+W4If1h+r6yraTJMdtKKt5oLZecnKxywvM6tF7iusa0462ZFhdwrYVcfVSCHQ9++PnHc1ThlvAjQBM6jmryZWNidkZV5jo2ai0t4PJjxLLR1IgvPrXKfKHp4M6Gk5XaFn7EaPf/xhtvqOAvHIFmmHCO6llHh6OpCpN7c2SJlZNmHnSE573rCeWbgmnTpqmKx/tgh5nP+VSbbAlNgzkt0BsqkBDhckNTXbQUbAhpKsTogxztpF8MGxQ9gbojwOfJgcGPPvpINdYMUkPfncb61DjTt4QBPtjQv/zyy2rEnOem4MsongyGQyGjreEs5miOXDc5qMLYA+zPvPrqq0rDwkEWdngJLdRYj6g14rvP1CrU9DFVC+tbS2lUWHe7deumtLiff/650t5S8OE93Hfffc3uByy0HI5cn5oKtjFMH8f+JfuUVEpRQcV+O+tsUwu4jDrMWBKPPvqoGsCidQbT9LHdo3VHfQTc2vrDjMxMKwFaTXFAggGrIiIilBDLAWQ98nNj2vHWjIHJcOvaiT4Xhw8fVi+6s6mwmwtqppgzjM+tNZimsCPMkSCOLtlLDyAITQXNcnRTOEEQBEEQBEFoSnnUITS4giC0HUSwFQRBEARBEJwyyJQgCIIgCIIgCIIgNBWiwW1l0EmcDuF1QbPo1hLARxCEE0gdFwTHROqmIDQdUp+E5kR8cFsZ8+fPV9Fo64KBLOzlsRMEwbGROi4IjonUTUFoOqQ+CQ2lIfKoCLiCIAiCIAiCIAiCw9IQeVR8cAVBEARBEARBEASnQARcQRAEQRAEQRAEwSkQAVcQBEEQBEEQBEFwCkTAFQRBEARBEARBEJwCEXAFQRAEQRAEQRAEp0AEXEEQBEEQBEEQBMEpEAFXEARBEARBEARBcApEwBUEQRAEQRAEQRCcAhFwBUEQBEEQBEEQBKdABNwmwmAwYPTo0WhNdO7cWU2OyFNPPaWe6apVq1q6KEIb4ciRI+qdu+WWWyzrWKe5TjiBPBOhpetla3oPDxw4gCuuuALt2rWDi4sLAgMDW7pIguCwOHK/VGhdiIDbiqHwxwaewqA92CHgdnYQhObnrbfewsUXX6w+zj4+PqojM2DAAPX3ycrKauniCU6K1POGU1lZibfffhv9+vWDl5cXwsLCMHHiRMTHx7d00QQnoqKiAuPHj8cvv/yi2oYnnngC06ZNa+liCYIgOD3Gli6As7Bnzx54e3ujNfHHH3+0dBGcirlz56r5qFGjEBkZiZKSEvz11194+umn8fHHH2PTpk1qfVsnKSlJCfzBwcHo0KEDHIX27durehwQENDSRXFoPv30UxQVFaE1c+edd+Kjjz5Cnz59cN999yElJQXffvstli1bho0bNyI2NhZtkUWLFqk5hTJHoTXXy8OHDyMuLg633347/ve//7V0cQRBENoMRkdqWNetW1dt/YgRI3D55ZfD0enZsydaGzExMS1dBKeCwqynp2e19bNmzcKzzz6LV199FS+//DLaKgsWLMDrr7+O9evXW9adddZZmDp1Kq6++mq0NG5ubq2yHp9qoqOj0ZpZuXKlEm7PPvts/P7773B3d1frJ02ahIsuugj33HMPli5dirbG1q1blSmtvjxw4MCWLlKrr5ccOCFRUVEtXRRBEIS2hVYPiouLtbi4ODVvDtLS0rSAAH+NxbGduJ7bW5KVK1eqsjz55JPaunXrtHHjxmkBAQFqnQ6XR40aVe3Yw4cPa9dcc40WFBSk+fj4aGeffba2evVqdS4ew3Pbwu2XXHKJFhISorm7u2vdunXTZs6cqRUWFlr20Y+3N/GanTp1srvNuozch1Nj2Ldvn/bII49ogwYN0oKDgzUPDw8tNjZWe+yxx7T8/Pxq+/O6vH5ZWZkqO6/Le+Mx7777brX9a3o+O3fu1Nq3b68FBgZqa9asaVRZtmzZot19991anz59NH9/f83T01Pr27ev9sILL6jyNTXbt29X9zJ+/HitrTJt2jT1DNw8PLTBl16gXXD37WrO31w/ffr0li6iqjcsy80331ztveW3j+9Tx44d1fvVs2dP7a233tIqKyurnGPevHlqf84XL16sDR06VPPy8tKioqK0xx9/XKuoqFD7zZ8/X+vfv79693jOl156qVFlrk89J2vXrtUuuugi9R1i+Xv06KE98cQTVb4pOvrxSUlJ2o033qhFRERoBoPBUhf1Z1LTfS9dulQ788wz1X2zPt50001aRkaG3fLzGY0ePdpSD/lMXn31Va28vLxe93/kyBFVtjFjxtjdzvrM72iHDh0sz37ixImqrPzO2sKycNvRo0e1tga/T/r7c8UVV2iOgr162ZrrJts2ndLSUvW+s+3y9vbWfH19tREjRmg//vij3T5IbROfkyA0RZ927ty52mWXXabeYdYpthvnnXeetmLFilrPtXnzZu3cc89V7zG/6fym1PReLlq0SBsyZIiqZ+Hh4dptt92mZWVl1dgvTU9P1+6//36tc+fOqu8YFhamTZgwQfUJbeG3gmU6dOiQ9vLLL6v+IK/Tq1cv7auvvrLUvRkzZljusV+/ftovv/zSBE9XaE4aIo86hAaXGq7c3Dzc+8JoDBje3rJ++7pkvD19lfJbef/999HSUPP0/PPPY8yYMbjjjjuQkJBQ6/7JyclKQ3Xs2DFccMEFGDRoEPbt24dx48bhnHPOsXsM7/Puu+9W/puXXnopwsPDsWXLFjz33HNK88CJGgcG2KDP3SeffKJMYq0DXPHYBx54APPnz8f27dtx//33WwJbNJXz/vfff69McvkseG36tNG0b86cOVi9ejX+/PNPNfJuC/3caKp74YUXwtXVVZkF8n65L824amPt2rXqmdC/dc2aNejbt2+jyvLhhx/ip59+Uhocamxobkl/5unTp2Pz5s1YuHAhmpIlS5aouV7etqi5ffHFF9F5QF/c8uYc+IUEW7Zd+tC9mHffY3jhhRcwePBgXHXVVXBErrnmGqXV0svHd4SmrayD1Mzb8sMPPyhzV5p6Dh8+XL0D1OJTfqSpJZdpmcL3led69NFHERERgZtuuqlB5apPPf/uu+9UvfPw8MC1116rviks2+zZs5Wmku++reVBZmYmzjzzTGVGft111ylze39//zrLs3jxYnWvrKf89rHu0aT50KFDqv5a89prr+Ghhx5S16D2lPWax3Md6zfrdV1BhDp16qTqMes5Td9tTd7p+8h7eeyxx1SAH8L75bX4d7Hl/PPPV9t5vhtvvBFtBb7btKIaf2Yn9Y7y/d22bZvDaHFba93kM7Rto/V5aWmp6hfwfeNznjJlCsrLy1V5eH36iNOaQK/PTz75ZLVrFBQUKKsYYs96SBAa06dln4zxQ84991wVn4B9WX4f+JvfZXtWlew7vfTSS+pcdAHRvyk7d+7Erl27qryfbBNuvvlm1abwO8t26+eff1bnLysrs1jV6KSnp6v2iO0I6w/bJJr/s2/B+sJ2jNaetjz44IPKso7tEfubX3/9tWprgoKCVP2i+wB949m+ffnll+q+6A4h1o1OQlNLzA1l69atagS+z9AobfGhu7Sf4v9jmfi7z+ntNBcXF23btm1aS2E9evrxxx/b3cee1uSGG25Q65977rkq6zk6pp/PWkO5e/duzWg0agMGDKim8aB2kfu/8sor1cplPSJsbxSrphG0k9HgUrvDETBbnn76aXXNzz//3O5o+xlnnKHl5uZa1u/du1fdMzVKtWlwOaLN0XbuZ6tdaWhZeLzJZKqyjqP9t956q9qf2q6T4YMPPlDlf/DBBy0aIY7Qc3SyLXLWWWcpTe1Tq5Zor+7cUG3iem4fPny4w2pw+d7l5ORY1nOZ6/jt4qi1rZbIzc1N27Rpk2V9Xl6eGqWmliYyMlKNLOskJCSoEWmOIDeG2uo56xpH5jlCTUsCHWqrrr32WnXc7Nmzqxyjf5smT55crZ5YPxNr9PtmXbauPzxerwMbNmywrD948KDal8+E969TUlKiNFjc/9NPP63X/X/00Udq/zlz5lTbdtVVV6ltu3btUr8LCgrUb1ps2GPBggVq+6xZs7S2qL39+63LtS1vXu5QWty6NLiOXDdra6OpPdLfNWttM8tDzRavm5ycXOO5WYdp6cVzUAssCE3Vp42Pj6+2LiUlRVk7UBta07m+/vrrKttoAcT1utZUb5Oo3aVFI63vrK1taOHI/W37pWyL7Fl6LVmyRK2nlaNugWHdJnbv3r2KBehff/2l1tMCkO0M2wOdb775Rm27995763x+QsvREHm0SaIoV1SWIbc0ocFTTslR3HPfXeocdzwxvNpoPX/f/sRwNbLK/bh/Y67DiWU8WU477TRMnjy5XvtydJaaE2pLqJGwhufo0aNHtWM++OADmEwmNbIUEhJSZRtHkTmS9tVXX8FRAn/YjrIRfcR5+fLldo+jps5aE8TnwFF0arbz8/PtHkPt7JVXXqkinlILZOsD2NCy8HiO5tm+axy1rK3s9YXBRBhYihoqjs6fd955+O2339SoYWumoLwMqcWFtU7FpnK1L+f8Ta0aR4n7nzemiubWGq7ndvrgc6Q4p6wE2aXFalulptV5zcySolNiZWId5IbLjz/+uPo2UUNjyw033IDTTz/9xD36+eGSSy5R1gL/+c9/0LVrV8u2jh07qtFnjiaz/jclP/74I3Jzc3Hrrbeif//+lvXUZnK03Wg0Kg2wLaxP3G5bT+qCo+PWmlEez5F6fYRfh6PlvFd+G3n/OtQy0/KC2CuXPei/Te3A559/XmV9Tk6O0gpQO8ZgUoTPgtQUsEj/Nun7tRa0khxouUdrn8rM31fO+Vvnn3W/W7S3A7uGYFBMCC4fFq00nVvX/GI+ttL8XmpF6dAKU83LWmXd18w3+6A2J62xbtLKiNZa1BSxrbDu+7A8tFqjJovasppg7AK+39SWUVMlNB20uvvnn3+qTNQYEmr7bLdx0mFfxnabnkWBmkjbbUwjpUfctndeavUJtZf8zbI1FTX1abt06VJtHdNc0UqC5T169MT3Q4eWNLQQsobtju23n9+avLw8ta179+6W9bSyo6WiLawH7PeyT8x6bQ0t8GgRefDgQbsxfGbOnKn6zTpDhw5V9ZttA69FSx4d3hvLQGsowTloEhPlgvLjWJPyYoOPO7wnA+vW/IV+w6LQtXeo3X1i+oSh7xntsPbPjfhsxaPo0quq4FdfRkZNQ4DHyQVHsW4U64IfOQq5Q4YMUZ02a9iY0XyP+1hDs1pCcwt7EY5Z+fbu3QtHgJ2HefPmqU4ozU/YIWSjbRtcwxaaodqimxXyo8PG3RqaX9FskaaDNBez/iA1tiz8YL7zzjvKXIXPk2ZeZsVV7WWvLzQpJxkZGdiwYYNKC8GGhOaS1kJGa2NHVio2pifXus+4qK7oFxyOg3nZ+D0lHudWmiOLh0WfEGLsEdrR/A7QnPSfikKUVVbgqs69UF5ZgS8O7az12BAPL9wcOwDNyciRI2tcR1MsW+yZdrKDUNs2dnBSU1PVgE1ToZfNXo5uDvSwsd+/f78aXLKue+zghIba/ybXRl31uz7loikaBVaad+pwoMg2JzafI81MKdBcdtllyt2BnROa1hEOMPIb3BZMjbWE1cCBxbXuY+h3MxB9NnB8K7Sdn8BwsTnq++zp96v5rIkn3stZEwfhx40JmP3Yf7Fw5lgYznkZ8AqGtuc7wFQCwxkPAqZSaGtn114w3ygYRj2D5qQ11k22/dnZ2Sr4FAVcWygIkZra+/fee0+lpWPnnm2Z0LRQ2WD7d7n++uvVIBoHbe195/Q+BNO26X05nc8++0wNrPAbpQ+863AAnH2+wsJCu+dNS0tTQhoHNOhaRVP1mlJDNlWflunSqIxYsWKFGnTmd9Qa9pHoHtKYb78uQNqrt/z2c9DVGtYBDirQ9NlephKuZ7BAthe256ypPvP+bLdxMJYKqZPt/wlOJuD6ukUqAbKhjGin4ZuRCVi/dhPi4zLsCrmHdqdj11/HMOLsYbjxnJcandidZTxZ6IdTXzhCRVhh6nsufZTP3iiWo0EfJzasHOFm55IfDV2QZ8Ng+0HUsefHp3/Q2Imwhb54hAKuPeG2MWWhxocNBUcPdZ9EDh7wI/zmm2/WWPaGQgGBvh/8kDLtCH2M6Q/SWukfHIEYf/taWB1/N7MmvZt/EMK9+qE8M1v9Tk9IrPW4jMQkNecobXRYiKWz4Obiiutj+tV6rLGR34SGYK++6uvsaftqe89r26aP1jcV+neopm8X6woFXO5nLeA25FvXmPpdW7n4jed6dqx0KNzadjipGdbT2VCIZeeRHVBdwGWnkp0WapV1dE1fTRpavVytLSWNIXoUEFGHv6zXv3U3chAMAZ0sAuCPa/ZYtLc6uhZ30Yaj2O5/FQZ6mP+uhl4TAO3fwUOjBwwjnqj9mi7V4zA0Na2xbupt/e7du9VUExR6bKE1ENu8Xr16qUEcW4FAOHmoFWdfwhrdAotC299//13jsRxot/276TER6C9OIc4a/bvL/o298+pxFTjYT8FWH4xprrpDbSg1nfwWUnhkH4b1glY/enwCe32k+n779Tppr2/M77Wt9WJ92jDr/epbppq2NXUbLLQcTfJldHVxb7R29J23/k+N/Pxv9jq88NVlVQRYdnI/nL1OreN+gZ5VR4xONQ0RrvXKw9E3e3A0uKZjbDubjgbv6d1331XaSGoorUfVjh8/bndEurHQPJkCP02w+PFjw34yZaGpDIVbCswMTmBtgslRVwq4TQ0Fb3ZGeG2awbW2fMk6vm7uaqoPXkY3NaGDj7JW2LxspQooZc9MOT8zCzuWrVSmrbYaEhcKO172BzZOJayvtqbxeh12ZGFI/6bY+97odcR6P53GDiQ2ply2mgB+97neukzs2NWmtWCwHt2FgybODJZCdwZqR6xzT7MTyQ4RzQ3Z6bI1wdbNBVtbHlyDZyDAqT77uvsBnKi9nT27mvbWVov7zOsf4fuRF5mP9T5h7mcwuAD/CsotSWusm/q7TbNIBsqpL7RQ4qAsA7Ox/XLU+2vt8BtRkyBJ6xJaZNWEPfczHX6jrE1mreG3qLbzNkfgI3vfeQrStC7Qtc7W3HXXXUrAPRn0d9Ze35jfZFpxWfcDGtuGCUKT+OCeDNRuUbO1e1MKfv92L1KT8iwTf+/efExFd9NH5VsL/MhRi8gROdvRLnbgKIzZcsYZZ6i5rXlLTeidM3uaz/psbyw07+A9MOKdrbCma1ybCo6a0ieWpt6MEmsrgDa0LPRjIYycZ9u5beqyW0O/GTYmDfVpdAZoWlVeWqqiJVOYtYa/uZ7buZ+jYu/d0NcxOnpLUls918tma95LEhMTVX2gmfKpHlCrrVy0cqBJWkMi+HLknZE1qfVlpPkvvvhCfRdsO2iEEW2pYbHns6Xnv6U/WVuKnGytvdWx9sW1Nhd3NBy5btYEBzzZIac7S301Ruzg01eY/Qn+3ez5SQrCyaL3kWwjJfN7au+b2VD0vry9est+sa2vO3Ngc1BBVxDYorchrSHiu9DGBFzyzDPPICDAX6UEuu3sLywTfwcGBlhGmVsTFG5pCstG6Y033qiyjSHS7fnW/Pe//1UdtXvvvdduCiKa0Fr7FHEUV++o2qOu7Y1F17gweJC1ryt9U5hqp6mhiQ59LOgvwtQL1s+zoWXR97dNWUIzMfqcnIwAa21Sad0oUPPE92Ds2LHV/LHbAqwH9EM+umMXnjv/Snw542kse3+umvM31/Nv5agpgvRvlLW5I5eZToSDFnoQpZaitnrOTgpHzOmjbm0KyfeSqXPYmaDP2KmGZsP81jEQm7XPE/3jWS7S0HLpvrbUPHCitvaKK66oth8HTPXgRLyezq+//moJCmerVXZGaPlC/tqXhn7//d7utGl/epV9HRFHrps1wXefAa0YrOfhhx+2K+RSW6truYqLi5XJLPf/+OOPlVWMIDQHNfWRmOqP7+TJwjaJgzt8j+keo8M6YBtESg94yDR3jGli20ejuT4HJbt162Y37ZvQtnEI5w2abMyf/4nd0SFGMKzJpMPRYWWk9pGde5p16HlwGfmQJnWsnHpuRj1PKoNHsOGjBpgR4miWwgAw1FTyHOz0/d///Z9lZItBKhgsiYITfUPYqFNAZqeWuXZfeeUV1aGj8MAOHz9eJxt0RY+mx6BP1KxScKMAx/visj4C2BxCLk2LqeljB53zhpaFviWc6K9HoXTYsGFqMIGBrKjVbYi5mDV6fmOej+aN9BfhB5mjlNzGv5MjdxJPRV3g34fmT+t++s2yno0S/46OLNwS+muzflrn2uQgCk3neV8tSW31nB0J5n1mB4EWIjRv5PeU3yVal7AuPPLII6e8zPyu0ZSYUZTpXkDfNJab7gOsL+wE2dO+1gYHwPjdZIRmdpZ4//b89ulXdtttt+Gjjz5SJoGs9/wWfPPNN2qwgFHs2wJ8BvYsiawJ8gWCIs37OiqOXDdrg+4zjIrLYFE0N6bVAP0SOVDK3KEMxsO/D9fxnWT+eHbkKRTYM9fn4K/urykIjYVmyBwQZX3id5k+sbQq5LvKbyXf1ZOBfVO+8+zL8ptNyxuuY5/Ny8vLrmk42wr2fzlwRWUG2zLmuaYPOi33WF7rvrQgKJo675AzUle+2Zry4Or5xCZMmKByUTLP3siRI7XVq1dr99xzjzqGeYBtYY6+6667TuUcY86+0NBQ7bTTTtOmTZum7dmzp8q+GzduVNf18/Oz5CKzzof50ksvqbxlPI9tGU8mD25+fr720EMPaZ07d1Y5NnmNZ555RuUys/cs7OXOrC2Pp20eXOscameeeWaVnMANLQvzojHnLZ+vp6enynH47rvvqr+VvXyL9eHYsWPao48+qvL8hoWFqRyf/Jvw78Y8h5mZmQ0+p7OSmJiocrIyf7EjUVseXH77+Pft2LGjyk/JPJtvvfVWlfyV1rk2Obelpne6Pjmr66K2ek7+/PNP7cILL1T5/1h+5gfke2mdB7Cub1l98uDau+/avp/Mb61/v1h3WReZ07O8vFxrDM8++6zlO7h06dIa92POxDfffFPr06ePum5ISIjKC8z8vIJjUVceXEeum3X1HZgnmnnTmQOcuUH5LkZHR2sXXHCB9v7771vqp16+2qbGfjuEtkdd7yW3853kd5ltxkUXXaT9/fffdutJbeeqqe6SH374QRs8eLB655mH+rbbbtOysrJq7Jemp6dr9913n9qm94uvvvpqbefOnQ2qs7X1RU+mTyycGhoijxr4X12yPv2hGJSDPh+0hRdOHmqmOTpLcypfX9+WLo4gCIIgCIIgCIJD0hB5VHT6zYy9pNxMZUFzbAZGEuFWEARBEARBEATBiXxwnRn6BtH3tnfv3iraKaNRMpAJo5bSb04QBEEQBEEQBEFoGkTAPQUO+wycwnQATE3BAC+MIMoIngwS5ShQ8GbqgbpgwvKWiLraEjBhOwMZ1MX48eMlRL3QZEhdFATHROqmIAhC60B8cAWLMDd58uQ692MOSXu5K52R0aNH1yupOSP4SWdGaCqkLgqCYyJ1UxAEoeVoiDwqAq4gCIIgCIIgCILgsEiQKUEQBEEQBEEQBKHNIQKuIAiCIAiCIAiC4BSIgCsIgiAIgiAIgiA4BSLgCoIgCIIgCIIgCE6BCLiCIAiCIAiCIAiCUyACriAIgiAIgiAIguAUiIArCIIgCIIgCIIgOAUi4AqCIAiCIAiCIAhOgQi49eDIkSMwGAy45ZZbWroogiCcwno+evRotU44gTwTwRHa39byHh44cABXXHEF2rVrBxcXFwQGBrZ0kfDWW2+hT58+8Pb2Vs/wjTfeaOkiCYIgNCki4Aqtmvnz56sGmnNBaIuw4886QEFAqJu0tDS88MILuPrqq9GlSxf17FqDoCS0PioqKjB+/Hj88ssvuPjii/HEE09g2rRpLVqmr7/+Gvfffz88PDzU/Mknn8SwYcNatEyCIAhNjREOwmWXXYZDhw5VWRcTE4PFixejpWnfvj327NmDgICAli6KILRqEhISMHXqVLX8+uuvIzo6Go6C1PP68emnn6KoqAitlbi4OMyYMUMJtbGxsUqL1ZrvpynZuHGjmjuSwNOa6+Xhw4fV+3b77bfjf//7HxyBn3/+2TKPiopq6eIIgiA4t4BL4ZamPFFRndTvlJSjcBTc3NzQs2fPli6GILRaSktL8eqrr+LZZ59FcXGxWvfrr79i1qxZePDBB5U2oaWRel4/HGlQojH06tULq1evxqBBg+Dn56f+5vv27UNbJysrCyNHjrRouYOCguAItOZ6mZKSouaOJEg6YpkEQRCc2kSZwu28D39Rky7oOroP7u7du3HJJZeojhJHmC+66CLs2rXLrtmgtTntTz/9hDPOOENpDzhCzY5+ZWWl2u+TTz7BgAED4OXlpTqTL7/88kmX/9prr0VwcDB8fX0xatQo/Pnnn3jqqadUeVatWmXZt6ysDG+//TbOP/98dOzYUQke4eHhuPLKK7F169Zq57a9p+HDh6tn0blzZ7Vdfw4cyabfDzsqPGenTp3w9NNPW+7Zlh9//BFjx45VnSxPT0/07dsXr7zyijL50uG5J0+erJY5100NxdzQsVi6dCn69euHmTNnwjciDHd88IaauExNGrdxH0eu5yUlJcq0kPWR7yOFJNYTTdOq7Heq6zjrGc9FrM1t6Z9ozbp165SJJL8BLD/rIU0T7Wku9eOTk5Nx0003ITIyUvkO6t8Je76P1ve9bNkynHXWWeq+Q0JCcPPNNyMzM9Nu+fmMxowZo76dfBZ8Jq+99hpMJlO97v/o0aOqbOecc47d7eXl5QgNDVXfMv3ZR0RE4Oyzz1bfKeEEixYtUs+dE5dbSwwMR66bbGsJ2zq9brLdtW5v+b6fdtpp8PHxUe8kBxlsLddY96zbN3tTXS4K+v2vXLlS/Zb2UhAEZ8boKGbJBw8eRLt2VTUDXMdACI5krmwNBVkKdAUFBUoApLnbpk2b1Do2kDXxww8/qE4gfXO475IlS5Rmiw0yO3pcvvzyy1VHcuHChXj00UdVp4ydzYbCTio7m8eOHcMFF1ygtBbUVowbN85up5Cj+A888IBqZCmsU8CMj49Xz54aNwrGp59+erXjvvvuO3VPFPb/+9//Ii8vr8r2Rx55RGlNuJ3CMztQbOjZwD/33HNV9p0+fTpefPFF1fHgc+UzWbNmjTrHX3/9pa5F+PxycnKUMMznNXDgwAY/H6H54Ps8ceJEfPPNN3D38sRF9/8Ho266DkZ3d7X94YWfYfWnX2H5B/PVu3ndddfhyy+/dMgO1zXXXKMGeK666ir1m/XyvvvuU51KaqZbqo6zrrLjun37duVPpwew0QeYCOsL/w4cWOJAFwesWLbZs2ergQV2nikYWEOB9Mwzz1QCMf8uFCL8/f3rLA+/E7zXSy+9VH13+L2gSTO/82vXrq2yLzv2Dz30kLrGpEmTVAefx3Md6/v3339f57vAgTIKq/y2JCUloUOHDlW20/eR9/LYY48pQVg4wf79+1XwIx22EUZX8zPi34ADijr8XrN9c0QcuW5u27ZNCcwUdPVBJ31OqxZ+91j/2HZNmTJFDciwPLw+hfR77rnHUp85IGUL+x509SC2ddgWXoPn4PeCA0P2zicIguA0aPWguLhYi4uLU/OmpHfv3pqbm5vWqVM3NZ057BxtxbL9auKyvp77cN+W4vDhwxwK1m6++eYq60eNGqXWf/7551XWT58+Xa3nxGN15s2bp9bxfjZt2mRZn5eXp4WHh2ve3t5aZGSkdujQIcu2hIQEzd3dXevXr1+jyn7DDTeoaz733HNV1s+dO9dSxpUrV1rWl5SUaElJSdXOs2vXLs3X11c799xzq6zX78nFxUX7/fffqx3HZ8btXbp00VJSUizr09PTtcDAQM3Pz08rLS21rF+2bJna//zzz9cKCgos6ysrK7W77rpLbVuwYEG163MuOB4XXHCB+vs8tOBT7dWdG+xOD373idrnwgsvbNGy2qvneh3v0aOHlpOTY1nPZa4zGAza5s2bW7SO63XM+lujk5ubqwUEBGgeHh7a9u3bLesrKiq0a6+9Vh03e/bsKsfo34XJkydrJpOp2jn1Z2KNft9Go1Fbu3atZT2PHz16tNq2YcMGy/qDBw+qfflMeP/W358RI0ao/T/99NN63f9HH32k9p8zZ061bVdddZXaxu9XTfDvWM+m0Kl4/fXXLX/r2Ch/rXd0oPbKlKFq4jLX6dvfeOMNh21/Hblusm3lNZ988slq22bMmKG2zZo1S7Vv1uUZMmSIum5ycnKN52YdvuSSS9Q5Xn311XqXyV79FQRBaA00RB5tkiHtiopK5OeVNHiqrNSqmCU/N/v/LOfksrW5MvdtzDX0iWVs6mA51Br0798f119/fZVtNLusLRXADTfcUEULSrMkajZpLvif//wHXbt2tWyjad2IESNUoIr6mu3pcISY2htqbDgibw1Nenv06FHtGGp5qDm1hZp0mhJSI8NRZls44nzuuefWWBaagDFNgg7NBnlMfn5+Ff+3d955R80ZkIMaHR1qcqjV5fyrr76q1/0LTUtBeRlSiwtrnYpN5neDc/6eMGGC+p0UV7OPY9Ie8zZdA5NTVoLsUrOfbqWm1XnNzJLmDxDE99c6yA2XH3/8caX10U2EW6KO1wWtG3Jzc3Hrrbeqb5UOtZkvvfQSjEaj3Qjk7u7uarurq2uDrkdNLLViOjyeJspk8+bNlvXU1PNe+V3i/Vt/f+bMmaOW6xsZndGQqb36/PPPq6yndQcD6VBzpVsCOStaSQ603KO1T2X55n3L8tXvu+++21I/B3QNxj9vjcfUK/qqictcRyaMvxj/uesO87FF6dAKU83LWmXd18w3+3s2J62xbtIc+v3331eWabr5snV5GG2Z1k20YqgJBuvj+33nnXeqOAaCIAhCE5soFxWWYctfiQ0+rri4vEH7NuYaOkPO6Ag//9pNeBoCTQIJGz9b6OfKTpW1b6s19kxpdeGvpm30PU1NTbUrfNYEBUcKuUOGDKkWxIcNKk0I7QVXoVkVO7c0KTx+/Hg1gTYjI6OKsEqGDh1aa1kGDx5cbZ1uTsiOqHUUTwq2H3/8sd3z0C9q7969tV5LaB52ZKViY3pyrfuMi+qKfsHhOJiXjd9T4nHL+PGqA7Z92QoMveIS++ddugIuRlece8lF6vea4wkoq6zAVZ17obyyAl8c2lnrNUM8vHBzbM0uAU2BHnzH3jp7vumnqo7XhV42W59cQv9CduZpqsqBJmufVPrzchCqodS3ntdWLppGU2Dld0iH31Lb7ymfI81MKdDQ3eXbb79V32XdPYSDe/z+3XjjjXB2tITVwIHaXXgM/W4Gos8Gjm+FtvMTuF08F1988QW0jD1YsHIXosO24OUp5u/49PmbsWDtEVw9ojM+uyUMbpUccPKCtuc7wFQCwxkPAqZSaGtn114w3ygYRj2D5qQ11k22u9nZ2SrQEwVcW9LT09W8prbuvffeUzEt6GqkDwrrWPv4WptLO0L+XUEQhFYl4Hr7uCsBsqF4ebnh0KGDmHy7uWMb1S7aosWd+cRdSDmWYImoHBPTrVHXsC5jU0KtCKF21B702akJe75s1KTUtc2e5rQ2dD/YhpRx/fr1Ft/c8847T/ldUWCnQEw/LHYg2Wmsz7msqe2+rANH0QeYI+X2Gn2dwsLCWq8lNA/9gyMQ42/W6tSEv5u5nnXzD0K4Vz8Ee/kozf7vy5ejKDcX3japPrjuwF9bMPqcsegQZn6HRkZGWwLEuLm44vqYfrVe03gKfHbtvd/6Ov1b0BJ1vL7fgJrqJzvvFHC5n7WAW1d9Ptl6Xlu5+K3hesYP0KFwa/tNoGaYAi6hEEsBl1pcXcD97LPPlAaZWmVnxxA9CoioIwaB1791N3IQDAGdLBGK35n7FRZ07Yf1cam4822zn/SuI9lq/u78hXAPDQE8zH9XQ68JgPavNZTRA4YRT9R+TRc3NDetsW6yndODVHJqSFv322+/KR9jBtPiII5ePh17bScDdImAKwhCW6JJBFxXV5dGaUdjY7vBxcVgCShlDYXbY8cS0K1bNyVk0ZSnKTWwJ4tuEsV0CvbgaG9LozfWDSkjAz5RgGWQF1vtNLWruubalqYKDMQy81zUEguOha+bu5rqg5fRTU2EZpDslO1asaaaFnfnij9RYTLhhuuug+u/QYAC3U/UcxcKO14nTNVbCtYV2/Q4ev1x5Pyc+jegpu8RLTSs99Np7kBf1uVioChrOLjB9dZlolbKnmZKh8F6wsLClPsCTZzpQkILFA7SMQq0s2PwDAQ41Wdfdz+A07/8sspsOr5xX7qarPllzY4q0YsN3mEnlg0uwL+CckvSGuum/m7TLWPBggUNCmypZ0RgMCp792cbPVoQBKEt0qJhJRkxUx/BpCBrC9fp2x0tgrKuJbCNDKpHNrQ2r2sp6GNL0+S///67mtaVjeCGDRuqHcNop2w8bYVb+if9888/zV5mpm5g1FPmRK4Puo+gtXZIcCyoZaOWYfErb+Gl8ZOqTD+98rbaRn9sR4YDPjWtY2TylqS2OqCXzZ67RGJioqrvNFM+1SlzaisXI6UzanNDoqLzHWK0Z2p9mQZFmd5qmvK3FGpHT9EVGhqitOCMes5lwoEpR8eR62ZNUPtKIXfLli311gxTaKevMNtyWlPRjUAQBEGwj0PlTaApMs2VOXHZkeGIMdNT7NixQ3WmrHn++eer+Ju1FBRuGYCFDeMbb7xRZRtTd9jz76E2hb5B1mZT7Dg//PDDFr+g5oSmV4RBcezlzqTGac+ePZbfFMb1zrrgmPBvREGjS4eOyl/WeuI6mpfqf0dH5Zlnnqli7shlphOhplMPotRS1FYHOHBALc+8efOq1GkKf0ydQ3eAmvKLNic0G6ZQylRBKSknAhExsA7LRRpaLt3XlqbJnOjLb50GR7APfTiZo3r37jhlbcG0O1xmsERb/05HxJHrZk3w3WdAK6brYdtqT8iltla3viouLlZ+5tyf8SkYP0MQBEFw0Dy41tAE2RrdLNmReffdd1XEUObH0/MEMg8uo4UyyIW9keVTzQsvvIDly5dj2rRpKuqzngeX0Rdp1scReuv8kPfee6/KEUgNLjs6DPZCLQs1IwwIU1PgrKaCZWJUTHZaqMHnbwrdFHZpxs5nys4LR8D1gDQMPEUBnoI5zRQJo2gKjgMFrNZM9+7d0bdv3yq5Npl3ldFLGcStJaHPPHOW3nHHHap8FOxYZyjwUUv04Ycfqjy4tI6geSPrCL8JtOxgcDjmlz7V8NtOU2JGUWZ0Z35rWO6ffvpJfZ8omDdU+8rIuLRaYYRmCgy8f+tI7NZYC8/M/2q7jt/Lnj17oi3AYGL8plrDuA22+ckdFUeum7VBX1laRTFYFM2NOWDO5862dufOncodiFZWXMecuOxbsE2kz7wEkhIEQWglAq6jmSDXBzaq69atUxoHCoo09aJgyHXscDqCgMs0B2wkWUYKrhRyGemUywxQQax93WgCRZ8gaqEZsMXb21t1oH/44QfMnl1HxMwmgtdhY8+G/48//lDa8JCQEGWSxYbdOi0TtVcsL9ezI8+RbiICrtCU0HTzySefVD6etIjgu8j385577mnpouHCCy9UUc/5/r/66qtKuBs1apRFo0mtHP1QOdjFtCN0N+jcubMaSOJ3gYNYLQEFEHbYqcXlt4baWworvAdacjTGD5j3rNf92gRke+ljrNdR2G0rAm5rx5HrZl0WVr/++ivmzp2rLKoomNP8mAGyevfujbvuugv9+pkD7LHOEg7y1hSAUQJJCYIgnMDAZLioA/pDHT58WDUcLdUZam2wsWGHic+NnUlHhMI4hV+adDFSsiAIgiAIgiAIgqPREHnUoXxwheZBN8GzhhoTapqZwkWEW0EQBEEQBEEQnAGHMVEWmteUmr63NHtixFVGeKYvLSOn0pRaEARBEARBEATBGRABt5VB4ZQBreqCZtF60BT68jB4C1MSMHE8g8wwiil98MTPTBBafx0XBKH5kbopCILQOhAf3FbG/PnzMXny5Dr3Y5CZ5o54LAhC0yN1XBAcE6mbgiAILUdD5FERcAVBEARBEARBEASHRYJMCYIgCIIgCIIgCG0OEXAFQRAEQRAEQRAEp0AEXEEQBEEQBEEQBKHtCbj1cNcVBEEQBEEQBEEQhCajIXJovQRc5k4l5eXljS+VIAiCIAiCIAiCIDQQXQ7V5dKTFnDd3Nzg4eGB3Nxc0eIKgiAIgiAIgiAIpwTKn5RDKY9SLq2LeqUJInl5eUhOToavry8CAgLUyQ0GQ1OUWRAEQRAEQRAEQRAsUEyl5pbCbUFBAdq3bw9/f380mYCrC7kZGRkoLS2t7yGCIAiCIAiCIAiC0CiouQ0NDa2XcNtgAVeHknRFRUVjyicIgiAIgiAIgiAIdUKf2/qYJZ+0gCsIgiAIgiAIgiAIjoaxpQsgCG0dk8mEAwcOVFkXHBwMFxdJUy0IgiAIjkplZSWysrKqrIuNjYXRKN1rQWhJpAYKQgtD4bZ3794tXQxBEARBEE6SuLg49OrVq6WLIQhtGlERCYIgCIIgCIIgCE6BCLiCIAiCIAiCIAiCUyACriAIgiAIgiAIguAUiA+uILQwDChlz4eH+b4EQRAEQXBMMjIyqsXQsNemC4JwahEBVxBaGHvRkinchoWFtUh5BEEQBEFoHJIBQRBaHqmFgiAIgiAIgiAIglMgAq4gCIIgCIIgCILgFIiAKwiCIAiCIAiCIDgFIuAKgiAIgiAIgiAIToEIuIIgCIIgCIIgCIJTIAKuIAiCIAiCIAiC4BSIgCsIgiAIgiAIgiA4BZIHVxCEenPZZZfh0KFD9do3JiYGixcvbvYyCYIgCIIgCIKOCLiCINQbCrf7DhxAaHSHWvfLSEg6ZWUSBEEQBEEQBB0RcAVBaBAUbh9d9KXl99x7H0FmYrJdYbhPnz5V1olWVxAEQRAEQWhORMAVBOGkoHBLja21VldfziwtbnKtrphJC4IgCIIgCDUhAq4gCE2u1bXHS+MnNcm1xExaEARBEARBqAkRcAVBcAjqq5nlPqdSoBYEQRAEQRBaDyLgCoLgENRXM1taWnrKyiQIgiAIgiC0LkTAFQSh2Tm6fRfKSkoAD69a96uPZvbR085u4tIJgiAIgiAIzoIIuIIg1NtkmL8DO0Q16DxFubl49+a7UFlZgXBf/yYupSAIgiAIgiCcQARcQRDqbTJsqqys8zjbtEHFefmoqKiwmzpIohwLgiAIgiAITYkIuIIg1NtkmIGbGJ3YOoCTbYqg1ENHkJmYBFc3N/W7sqICRleDWq6orFRCs1ouL6/Tn5amzaTTgL5NfGeCIAiCIAiCMyICriAI9SakY/tq6yioWlNaVGRZHxvlDzejC24d112t+/j3/Sg3VeJASp76XVBQoOZlFRUoqzRreW1Nm8lTq3+Bd4CYNwuCIAiCIAi1IwKuIAj1ZsrbL9sN+mSt1S3MybVsG9A1GJ8/PFoJueSeS3vjhldWKQHXw8cHfoGB+P7IXiQW5iK/vKzKeXeu+NNi2rxrxZ8YesUlzXx3giAIgiAIQmtHBFxBaIPUJ+dsfQNK0RTZUFGBkH8jJGcaDDD9u23B2iOIDtuCl6cMVb+nz9+s1l18dk+cf1oUDh0vhWdpJkaGd8FbmgFHExMtgnJeegaMrmbBePErb2HVJ19aNMPpR0/sVxMUun27dsWfx49iZEQ0DAazmbQgCIIgCILgvIiAKwhtkPrknK1PQCni6euD4uwcy+/Kykrlc6tp9LnVsD4uFXe+vVZt23UkW81nPDAe7fKOolOoF3DwY8DojcryYiW8ph46rEybQwJdceuEISdMm4szLabNBk2zCNQ1ERIbi9COHZT5swi3giAIgiAIbQMRcAWhjVJXztm6NKS6n2xeWrpajouLU4Jpzw7+yud20/50fLvmMDbuS1eTNW/tNmLV/I2ICfLD2l++APKS4OX7BwyG49A0rVbTZgqr3bt3x+7duxt0v3tzMtS8Z2Bog44TBEEQBEEQWg8i4AqCk1PffLa26X1soyPbi2pMP1lrrAXTiXNWqnUebq4oLa9Ap/59kJmQhIKcXOxbt1FtyykywRDWFwjri27d3lPr0lNTajRt9vf3R/v27dGtW7cGP4fEwjzszE7D0YJcjGnXGe6urg0+hyAIgiAIguDYiIArCE5OffPZUri1J9Raa2vfuelOZYIc1jkaLq6u//rJ0hxZg4urWxXB9O27zsSR1Hxs2p+BAeedg+tffBpFeXlY88W3OPvG6/De5P9WOb+eDzctLQ0RERF2TZsP7ItDeGT1SM714dyoLojy9sOKY4eRUpSPizvGItzLp1HnEgRBEARBEBwTEXAFoY3ms61rP9uct8V5+Uq4JelHEqz8ZE9X6/73217sTy6vIpi6/Ov7euXMh+HqZoRfSDAuus+c+qcmlixZoub2TJt/fvsBTH5qPgxuDRdMadrcJygM7bx9sSTxAL6K34WRkdEYFBwpPrqCIAiCIAhOggi4giDYxTckWAm4nEhlRQUY1JgyrmYnBZCftxvufHudXcH01atvgpe/X5V1PC8DQdmydOlSNQ8NDcF7772vtMN33/1fZGRkYum67bhlwxxg6FQYPIMsx2zcaDZ5HjZsWJ33FezhhYld+2JNagJWHTuKhII8nN++K7yMbnb3b8i5BUEQBEEQhJZFBFxBEOzSd/QIHNr0t1qmtpaCLINHVVRW4rlvtlfzk331+51q7urqgsjIdmo5NfU4TKYKoLSsWtRjCrcxMTHVrvvOO+8oH9v77rsP4eHhat3o0aPx5ptv4oHbJwKH5kNb/wIw9EEYfCORlZWFkSNHWsybg4JOCL41YXRxUX64nXwC8FvyIZWLd1JMX1x++eVV/JWZh3f//n1quXv3HnC18ttl2XWzakEQBEEQBMExEAFXEAS7DL/uahzZthPbl62opq2dOCoG0bd8U8Uc2c/TrAHdsWMnevfubRE4KZhOnToVoaH1i17M/Z599tkq6yjoPvfcc2pZi5gB7a/XoB1aAsOAKVi0aBFMJnPmXS5Pnjy53vfY1T8IN3r1R355qTJTPnjoEPZb+SvTLJvpjsixrEyLFlrXaguCIAiCIAiOhQi4giDYhT6zIyZNQGFOLhas/buKtva2N9eouT1z5E2bNlkEXGvBtKk4kJiBK/67kE61AF7DsWPHYKTtNICHHnoIr7zyimVfCryxdsygrfFzc1cTyS7Mr7KtvLTUcm4ue+GEmXVpaWmT3pcgCIIgCIJw8oiAKwhtFOsAUvpv6wjKjJr8f1PuUT6wxFpb+/fBTDUPCQ7G+//3f1X8ZH/77TfccsstzVbuX375BXF79lpMp9v5uWDmlUPU749/34/ynCSVL1ff9/7776/3uStKSlFRXo7UQ4etgmhZnbs403LugoKCZrg7QRAEQRAE4WQQAVcQ2iAhHaun2qFgZw1z3NIHVceetvaJJ5/EhAkTqvjJ0hy5Obn77ruxfv16fPfdd9VMp++5tDdueGWVEkKvHtEVd3Xbg8pl9wMuroDBVc0NI56Ewc0Llbu+AIyecOl5FbSyfGhbP8QXU8/E059vwrpdx2o9t4ePD4KDg5v1PgVBEARBEISGIwKuILRBprz9crV1jwwaWUWra85x66KCSlGJywBLvqEhyMvIBEMtmSoqlKDJYFDNZY5sDzc3N3zxxRdqmUKuten09PmbVfCrCRePwuevTYOb0cDwz4BWAe3fOVzMnz2DTzjg8m/kZCqpKewaDJh5/RC88+tBLPhzj91zM6fvsYPxklpIEARBEATBATFouv2hIAgtQnp6uiVasA6DM4WFhTXJ+fv06YN9VoGTaoJmuTrWUZMLS8rx9k9x8PNyw+FUs1kuy3b77bfbDR515MgRPPPMM2reuXNnzJo1S82bGj6jiIgIDOsRhr6dzZGTdx3JVlrmxj4/PqvM0mL8Z+47eGr0xXbPPXvVz3h3yr0qKvTu3bub/L4EQRCE1kFzt9+CIDQO0eAKgpNjm4qnpMKEQlN5tbQ9BT4+KnASIxLbmuc+NmGAMs+lgOvv74+hQ4cqbS2F2Mcee8wizDKC8fjx45GZmQlfPx+sWLFCpdLZvHlzkwu5S5YsqdF0mttOxg94z5/razy315IPcO+4Tvhk1fFGn18QBEEQBEFoHkTAFQQnxzZX6+/J8ThWVICbYvtX27e8vBzXX399zaa/EyYo82CaCVOoHTJkSBVhltsoJM/5+FmMvXQMli9eiWlTHlca3blz5zao3HVpgpcuXarmLi4uqKyshI+fDwrzC5Xp8MKFC09KwN23/i819w0KwJUzH1FBtH54/hUUZOfil3+OIdi9Ei4GKLPnQwW5qNA09AgIafT1BEEQBEEQhKZBBFxBaGOklxQhzNO72vo5c+ao+TvvvKMEXOuoyTTPJe+++64SbgmFTwq3tsKsj6+3+k3OvWwMfHx9lJDaEOwJzxTU125cgw7R7VGhVahy7t27F9u3b69WBmqZT4YrZjyE0I4dMOL6CfALMQeTijn9NKz54lucfeN1eG/yfxHq6QVt8xswwgf7IkYqAddUWYnkonx09PFX/rzWXHbZZTh06FC9te62AxOCIAiCIAhC3YiAKwhtiEpNQ0ZJEboHVI0AvGrVKkybNk0t52Rn1sv0l0IohU9rYdZoNKKwoEgJmvzNeWFBYYPNkx9/6nG7wvOjTzyCJ96crvYZFDIMISEhSnNbVaD2xuGkeKQWJcPHzR/eRh+4GMym1vWBgbYowJKdK1ZX275rxZ/mlErdY2EI64vofT+gY9FRaP63IsEYikUJ++BrdEevwFA1hf47mEDhtj6+0Dy3IAiCIAiC0DhEwBWENkRuWQlMWmU1De6DDz5oWf7uc7MpsdHVgGdvGqwiKM/4ZIuaz549G5999hmiO3WEu78RBfmFVYRZ+u96enr+q8n1UcKtf6AfcvNyMXbs2BqDThWU5yOtOAVd/LorE+NDhw/YFVxzU/LQK3AAjAYjDDCo81C7W1WgLkJUdDskFyVCQ6Xaj0Kuj9EPvm6c/OHm4l4vf+WaCImNRdeuMTB0PR8IHwBs/xjahjno3HksJnY6D3H5ediZnYbNGSkI9/RB78BQVEJTwu2ji76s9dzWuYkFQRAEQRCEhiECriC0IYwurhgW1h65CcnoM2G4WldSUoL4eKa9MafLSUjNhqe7C1xdXPDpHwfx/cyx+HVLItbsTsPhw4eRlp6qhEoKrgGBAUqY9fbxRlFhkYqo/MMPP2DevHlKwxsUHITfl/+OhQsWWkyNf1y8CD+u+gHB7QMQ5B6KSG/m5NVQrpXDpJngZnBDr5g+2PjnpmqCa0zXbkpY1aGwTFNea4GaZXjlmdfQKSQaRaYiFJryUWDKR05ZFtJKjiHKuyPaeXdEsakIeeU5CPOMgAtz5NrxV65PRGiDbyRw1jTg8O/Q9v2AiPSdiOx/K0ZHnobDBTmIy0nHn6kJyC4tOSV/Y0EQBEEQhLaMCLiC0Ibwc3PHWREd8cZXbyAuLk6t6xzuq/LdmioqlZaUgqTmakBJmQlxCTn4dUsSAn08VKAlW5NhBngiFG6ZJ5dRlE8//XSMGDFCrZ8yZQrycvKqHffqC6/h1fdfhpfRrEmmVrVHQF9LOZ944gn89NNP1QRXCpjWUNhkhOaahFAfN181haOd+l1eWWY5tshUgONFSQj3NG9LKIhXpsy6pjc5IcWuH7C9iNAGmkD/q83Vts+FtuFFGLqMQ0yvCejmH4xiUzneNrqp6NWCIAiCIAhC81F/xzRBEFo98XnZSC8pxN13342RI0eaVxqghFsKoasP/44X5z6rfpPOEb74cWMCNh/IgLdN8CgvHy+4urnhjg/eUFNwx/Z4+eWX0a9fP0uEY3t+uhRYc4/lo4tfLALczTlmbdEF11tvvRVnDD1DzWtKNcR1jND8xx9/qHlt/r40TdbNk0M8w9E/+HRlEk0qtQpklWYgPn8fdmRtwYOP32/xA14Vv0w9l4yMDCVM1wS1uYazpsPQ82qg0mQWfJlayOgGT1cZTxQEQRAEQWhupMclCG2IVcePIsYvCO9PfQxr1qxR646kFlSLfOzp7Yny0jK1LaNIQ0F+kdpmbTJcXFiMIZdfhLJis+ntwws/w+pPv8LyD+bjggsuwHXXXYdOnTrZ8ZGtX9ApXXBtTnThVl3PL1bNyypKlVlzalK6HT/guiNCK6E25gKOGyi0xLXQ8pPNJuA2HN2+S807DTihvRYEQRAEQRAaj2hwBaENcXNsfwwLb4/c3Nwq6/XIx4TzkqISVPyr1aX28tE5DylhkCbDo7qMU3Ojuzv6jxuD+Q9MU1Nq/BGMve1m3PTa8+o8v/76qzJRpmmx+bjz1NyeqbEj4e7qgSCPUPSM6aXy6lo/FwrnnTpFI6c0S5ls14uKUqCiTAXpsqYoNxfv3nyXmopy86psK6kwqZRDgiAIgiAIQsMwaPXupQmC0Bykp6cjPDy8yrq0tDSEhYU12zU//vhjJXzqGN2MMJWb4O3jhaLCYrWOJsl/Hv7dss+I6HMQ5OmCXh0DcfBYHhLSC1Vu2EOb/1Hb+4wZieh+vbH0vbmoNJnUOg8PD9x3333qfhITE2sM1OSIUFNLf2KaJVv7AS9b+yu0kHL0DRoMd1d3Jehaa4Jrok+fPjinfwBu/O+12BAwGOt//BXfPmEeDLh29kwMveKSKlGUn/75O5weGoV+weFwczEHwRIEQRDadvstCELdiIArCG2kgdyVnYZ9uZm4slNPrF69GmPGjEH7EG8kZxZh8t3XIT09B7//8DvKTBUWbSP9TnXTYmpfJ4+LxUf3j8TWQ5kYcv+Pap/xZ3ZSQh59dYm7lxfKis1Csru3F8qKihEbG4u3334b559/PloTNUVRLq0ogYerJyq1SuzJ2Y5A9xCEe0XWmH6IdOvWDbmZxxDm745KuCIppxSlhWbTbzcfH/iHhVry4EZHR+O9Vb9hT04GvFzdcFZEB/QPjjhl9y0IgiDUjQi4guCYiA+uILQRkgvzVTRfahs///xztY7BpLq398fU+67E/sOpiN+4Ua1Lzy1BXlG5EmoZTIr+tqEBnnj8uoHquEExIYgM8sLx7GLMmjhQCcQUcIM7RCErKaWK0Dti0tXY9P3PFr/cL7/8sl4aT0egJj9gCreEAq6/WyDSSlKQWpyMYI9QhHtFVUllpFNQUICMnCI1xUb5o1OgEbdOGKK2ffz7fpQXZ+JAitlUuaigABd06IZh4R2wOT0FFf+OOPDv52IwwEMCVgmCIAiCINhFekmC0EbIKC1CmKdZ8LrtttuUj2xKSgoemXg6Qg6sxRvv7UC6iz/SExJgcHGBgWaxlSYl3F4+LBqv3X4GOkf4qeOpwaVwS0F2YNcQtY776Fpca6E3NzUdF9x7Bxa//Jby/W0twm19MLoY0dG3i8qtm1GSitSSY8gsTYefWwAivKKU8KvfL1MOMbBXXl4eBnQNxucPj4ab0RwG4Z5Le+OGV1YpAdff31/tSwLdPTGufVfL9TamJ6tI2Ld2H1jlOV522WU4dOhQvcocExNTLd+vIAiCIAiCsyACriA4GTUJO1mlxfC2Slfj5+eHIF8PXHVGFJav348D+47CxdUVnn6+ynRW96OlELtw5tgq53rmq60WQVZnwsguSqCtJvT+sRo7/1itfvfo0QPOiKuLERHe7RHu1Q7ZZVlKm3swbw88Xb3Qxa+70uj+/PPPKC8vx/XXX4/vvvsO0WFb8PKUoer46fM3Y8HaI5hw9dX44ssv4ebmZvc69Mnt6mcWmotM5fg74xgGh7ZTf+99Bw4gNLpDreWk+bMgCIIgCIIzIwKuIDgZtQk7haZyNZUUFiI/PRMf3jcS3r0GIbMoB/5uh1Uwo5CO0WpfCmMHDhzAX/vS0O+/31vOUVxmwuHjBVUEWfLdmsPVhN5ZEwdZtLrkrbfeUv5KDz74oApA5WwwRRDNlIPcQ1SqobTiY3B3Md9nblm2EnTfeecdJeCuj0vFnW+vVdt2HclW83feeKlG4Zb4urmriaQWF2J7Viq2Zh5Xf1P+vR9d9GWt5dMDWAmCIAiCIDgrIuAKghNSm7CTtGcfXr/mFrXs7u6BMg9/dL/4bMTd+RyMLicyh23cuLFKpGWdjMTEaoIsTZZttbfWWPvkzpgxA/PmzWuVQafqCzWsvm7+aiKaVokj+QcR6hmOZUtWqHUb96WryZpffl+Nm68LAtK2Ax3PrtWcu4tfIKZ0H4StmcdQWmHWtguCIAiCILR1JA+uILQxfn37A8vyoC6+yCsoBwyhVYRbMmzYMOzevbvKxOBU+fn51QRZeybLtuupzSURMV1wNDFRBZ2aOHFi/fPJtnLNbp+ggcovd+nSpbhmylXw9TP7Q/v8O6cwu3DhQiB1G7T9PwImc4Tl2vAyGnFWREcEeXg1+z0IgiAIgiC0BkTAFYQ2BLW3e9dssPzecrQAqzKNiPUPrtfx7777rprrZsucut32rV3tra1Wl5GX6ZObeugwrpk9Q+3jbEGnasPo4qYmmign7ktGQX4h5nz8LFbHL1PpmCjoM8CUodNoGM6eDYObD7TSXGjHNtd57rbxBAVBEARBEOpGTJQFwclJP5KAeQ9MU8s5x1PV3OhqFol2uHfFed06YvxZZyshadGiRSpnbU0w+vKGDScEZJKdkqLmtr66iemFNfrkrpxnTlM0YcIEtDVCQ0Ph5e6tNLhjLx2j1jHXsI+vN44fP65+G9x9zTsnb4S251toUVth6Hu9EnoFQRAEQRCEmhEBVxCcnD1r1iutKWH+VbeQQNw6rrv63eHswTiwdRf2xMWp37/88gvuv//+Gs+lmy1bY+urW1FRgX379qllW62ursX9ceN+uLq64vLLL0dbhPl1V6xYgeWLVyrhlvPCgiKEtA9GRaVJRWVWdDkPBo8AaLu+gPbnk0D/yTCE9Wnp4guCIAiCIDgsIuAKgpMz/LqrcWTbTmxftqJK/tUKFzfs6NcDB9ebNbIjR47Ef//73waf357QGxERgbS0tGo+udZaXGoyg4PrZxrtbMyaNUvlop025XH4+PqgsKAQIaHBuHnqJMTlbEf3gD7wcPU0m2+3HwYEd4e2Yx60Ta9B63QODL2uhsG1cVGoc8tKEZ+fjS6+J3L0CoIgCIIgOAvigysITo6rmxGjbjGnh2Gu1RmfbFHLBX4RgMEFH87/Xf1es2YNbrrpppMO+rR161Yl3NYUUdnii5uaim3btqGtanA3b96MW2+9FWcMPUPNt2z+G+f2vxD+7oFwczGnAtIxeAXDMHQqDH0mAYlroa15GlpOfKOuTZl20dF9WHhkL9KLzWbkgiAIgiAIzoJocAWhDbDio08ty3r+1WHXTUCX0CwcPXQiT21TBH2yDURlj+yCUjV/bM7z+PDDDxHtG4C2KOTOnTu32vpOvjFqXmQqQFLhUXTx6w43FzcViRmdxwKhfaBt+wja+heAkU/B4Nde7Z+RkFRnnlvu0yM2FpdH98Cfx4/is0M70ScwDMMjOlry6wqCIAiCILRmRMAVBCfEWtgpLy1FVlKK0pwysrGef3V96kJ4frhE7TOoazC2xmfhjDPOOOlr64GoUlJSkJVabHcfLy8v9OoZjWsvHIiFR/ZgREQ0hoS2E5NZKyo1DS4GV7gaXKusN/hGAmdNB1K3WoTb0/tVDwxWVGROM+Tt7W1ZFxIbi5iYGMT4B6GzXwB2ZqVhQ1oS9uVm4vSwKPU3cHOpej1BEARBEITWhAi4guBkUICxJjkjS833JOaouYuLAZWVGo4eSkRhfqESKnf/u+3QoUMnfX17Prn20A78DC1pLTYGh2FNagKOFRfg/PZd4eFq/iwlJCRg6tSpavn1119HdHQ02hK+bn7o5tZTLReZCpFefBwdfDsrgddAIbTdELVNO/4PPr45EoazP4DBJ0Kty8rKUn7QhObiQUFB1c7vanDBwJBI9AoMxV/pydiUnozEglxc01WCWAmCIAiC0HoRH1xBcDIYvIgCpj799ttv8PX1RUlZBXx8PJRwa5t/tay8Uu1z9913VzlXUlISduzYoeb2fp8U3S6CYfjjOCsqBpdGd0dCQS6+it+FlNwcPP/88+jZsye+//57NXH5hRdeQGmp2bS5rVFaUYKs0nTsyd6OwvKCqhvD+sDQ9wbAO1z91MoKVLonk8mkJi7XBgcUzo7shFtiB2BkpHkQIau0GEcLzIMegiAIgiAIrQkRcAXByaFG9c0331TL3aLD4OvnXSX/qrev2YT1rbfespgoL1iwAMOHD0fHjh0xYMAANff396/ym9u5X2OhTynzvWolOYg58BVu6BCNXWs2YOCA/pg5cyZ8I8JwxwdvqInLM2bMQL9+/bB06VK0NYI8QtAraABcXVyxN3cnjhclWYKBMZrygeJw9O3bF316xKBvbDQefvABGF1d1PTQQw+hT58+lunAgQN2rxHg7ol23n5qeXd2OpYnH0aFVnlK71MQBEEQBOFkEQFXENoA7du3h6eHEX0iPFCQX6TyrhLOiwqKlJmynpN2+vTpmDBhAjb//TcGX3oBupw2QK0vLi1Vvy+4+3Y153buR8HzpKAQlX0Iqd8/hnfuuhe5aem46P7/4OGFn6HHWWeoicsX3X8XjiYm4oILLsDEiRNPOtpza8PT1Qs9Avoh0isKyUUJOJAXh7KKUkv+4ri4OMTtj0e5qQLtAlzx4i1D1NTOT0N5TpJ5e1yc2rcuRkR0xLVd+ygz5uzSYixPiUehqewU3KUgCIIgCMLJYdDaWi9REByM9PR0hIebzUt16DcZFhbWJOfX/TG1ygpsfuMynD19KQoKS+Dt44WiwmIYjUaMHz9e+bl+8803ePjhh9F5QF/c8uYcxP+9DZ8+NBOdBvTF5DfnwC/kRN7a/MwszLvvMRzdsUtpcq+66qpGl1HLOYyS1c9g4dp4bBt2K6J6dLe7X/Le/Xhtws248MIL6yWoOSv5Zbk4XHAAlVqlirrs6+KP66+/Ht999x2uHtHZkuuYlJsqccMrq1SKKA5IfPHFF3Bzc6v3tQ7lZeO35IMq6NXQ0PY4TQWikrFRQRCE5m6/BUFoHNJLEQQnR/fHrKjU8M9xI1as/BKXXXcheg0apPKv0nyZEY9pdkzh1ujupoRbCrN/fvY1jB7u1YRbwt+T35oDNw8PJRyfDIbALthQ0BOTRsdgSNbflvU7/1itJp2kPfvUnIJaW8bPPQC9AwfAz80f8fn7UelSoQRXPhfrXMdk+vzNat3VYwfg89enNUi4JYy4fGvsIPQLCseG9CTM278NcdnpbU6DLgiCIAhC60CiKAuCk7F//35cccUVlt/Hjh1TvpiAhkfeXYbIRXtQaKiEm0mDsbdRBZaikNrv3NHYuXwVBpw/VgmvOcfTcGTbTmWObCvc6nB9//PGYN1PvyE5OVmZQjeWgRfehqfvHYcnJw3Ez4XxWJVQjvkPTFPbHvzuE7Tv2R07lq5QGmfdnLotY3RxQ1e/HiiuKIKHqyc0Fw2vv/Oa0uLquY7JriPZav7OAxfCmB0HtD8NmqkEqCiFwaN++Ye9jEaMbtcZA4IjsTY1Ab8lH8LWzOM4u10ndPTxb9b7FARBEARBaAgi4AqCk6H7Y5LYKH+083PBzCvNKWU+W30YRTlJSEjJU78P7dtnMUfOz8hSAm5YdEe1rSjXvI/+uyZ8/01Bw2uejIAbHByMTVnh+ObPw7jy7FWY/d5Oy7bf/+9jXPP0dBz4awvOGzdO7SswUJcB3kYftZxechwplUcQGBJoyXVsza8ZPXDzRdeZfySug7ZvATD2NRjcvOp9vSAPTxXxOrkwD6uPH8V3h+NU7lxGYRYEQRAEQXAERMAVBCeDGtn169crTd6ArsFV/DGv+c8kxLlE4YLTrkFEVDtkZmRazJEryk1qn/SERDX3DvCv8tuW7ctWKBNmannJeeedh7POOkvlrr366qsbVXaa2N76nzvgGRyMv1dtxPgzOylT2B//WI3I7t1QYTK1efPkmgj1jMCrc15DTmYOXFxdUFlRCR8/H0uu44ULF+KWW24x79xhGAzeIUq41SpN0Da9DkPkYKD9mfUSeNv7+GNi177Yl5sJXzd3tS6ntAQerq7wMjbMBFoQBEEQBKEpER9cQXAy6GNZkz/mHi0M//y9F2eOPRPpaekYcP45FvPjwMhwdB7YTwmuDCBl+9uaJW+8p4JP0Se2KSMrM9iVqdKA65/+Sf2eNXEgZk0cpJb/+PATuBhd0WP08JN4Os6Li8EFj9zzmErjROHWNtcx0zzpGNx8YIgYaP5RVgi4eUOL+wraHw+hcudn0PLqznNMoblnYCg6/GuivOr4EfyYYPaRFgRBEARBaClEwBUEJxVy33nnHbWs+2M++EUcQjp3xupf/sSk6fej0mSqZn589o3XwVRapqIjU6i1/U0o8K6Y+5mKrPz40h8w6fknMe6uW9V85tLv0al/X7zwwgtKY9hQaHrMCMnFxWVKezuwawgGxYTg8mHRqrxDx4zB5qJsJBfmN9GTci5CQ0MREhICXz+fKrmOfXy9cfz4cbvHGDwD4DL4bhjGzAG6ngekboW25klUrn8RWvJf0CrK63Xt89vHYGxUV7WcXlKEvTkZEohKEARBEIRTjgi4guCkLFmyRM3pi/nR0v1I8YpCeVk51i7fgNx/jtg1Px5w3jk4Z8qNKvXPs+dfgd2r1qDLoAHm3+eNx5cznsbil9+E0b32yMrc/uyzzyIpqW5NoC2urq4W7a2OrsWN9PFFpJcvFifsQ05ZiVqXkJCgUhRx4nJbp3PnzijIL6yS67iwoAj+7XyRWpSMikqzKbotBq9guHQfD8M5L8Fw2n8AF1do2/4HbcWj0LIO1HldmiaHeXqr5YN5Wfgl6SC+it8tgxGCIAiCIJxSJA+uIDhpHr3rrrtO5bUNDQ3Be++9j8gBIdizezfuvPI+XHvttUhMTFQmxdS62gqqO35fiRUff47EXeZgVcTPzw/5+WZhhebI1NjW5ZdLGuKXu3XrVpx22mlKe7tw5tgq2658djl+3JiADZs3Ic7fDRXl5Tj2w1K8+PzzKC4uVvt4eXlh1qxZePDBB+Hh4YG2yJEjR3D66acjIyMDPr4+KCwoREhoCH5Y+R28Iz3hanBBpFcHRHrXHRBMy0+BlrAahthLYXD3hZawBvAOhSG0V53HJhTk4s/jR5FWUoRY/2CMjIhGoIdnE92lIAhCyyN5cAXBMRENriA4KTRRnjlzJnbvjsMVV10B72AvnNMlEFNvGInJD0/FlVdeifLS0irmxzpdThsAFxfz5+GVV15Rmti8vDwsW7asxsjKTeGXO3v27GraW1st7kvPvwD//QmYefFVeHLWLPhGhOGOD95QE5d5nX79+mHp0qVoqxrczZs3qxzHZww9Q823bN6CkX3HoF/QaSoYFf11SXllGYpNhTWey+AXBZc+E83CraZBS9kILcM86KGVF0Erq1k7G+0bgOtj+uGC9jE4VlSA+Qe3Y/WxoyipsK9BFgRBEARBaApEgysITj4CTI3e40/NxP74fejmVY4JV5yF55Ycwd8//2bZx+jhrsyTQzt2QEZiEnYsW6mE3+nTp+P555+37EdBt2PHjtU0uNTcUrilX66t6TKF5w/vehDJe/fhgw8+wB133NFg7a2tFpe4e3ni3DsmY9RN1ymTaGIqK8PqT7/C8g/mo6ykRGmxv/zySxUQSahOSmEC0kqOoX/wELgYzKbhtaGaC60CBhcjtAM/Qzv4E9DudBg6jQECu9b4nMsrK/BPxnFsykiGi8GAcVFd0T0gRG277LLLcOjQoXqVNyYmBosXL27gXQqCIDQPosEVBMdEBFxBcOIGksLtkCFDkJmZaUkZ4+3phq7Dz0SHnj2UD+62X5er9DvWDB8+XJkV06/VFm6zNW1++8Y7lOaWQaeshduGmCzfdtttmDt3LtoFeyHI1755cXZBKY5lFcPFAEz97lNE9Yi1u1/y3v14bcLNKmAV8wIL9qnUKlFSUQRvoy9MleU4lLcPYV6RCHIPqXNQQGlvmU/36EqgOAPwj4ah02ggahgMRvt/v8LyMmxIS0LvoDBEefuh2FSOIQMGYt+BAwiN7lDr9TISktAjNha7d+8+qXsWBEFoKkTAFQTHRPLgCoIT88wzzyjhliljGFWXAYemTXkc3v7+KvIxufShe5WZMgNJ0RyZWs/27Wv2z6RwSpNjHsOAUsyfSwGWWl1r4ZYmy4y2TO0wt9GsmQL15mUr1fG22mEKuBs2bKj1foK8yxHok46rhnfCgbi9FgF35x+r1bzf2FFqTmGbSM7c2qGpMoVbYtJMSqg9nL8fyS4eiPCKQohnOFxr0Owa3P2AmAvMkZfTdytBV9v5GbDnO2gdzoIherQycbbGx80d57Y3R1rm2Or3R/ei0FSuhNtHF31Za1lfGj+pye5bEARBEATnRQRcQXBiqMG1TRnj7euNrJRj1SIfP3f+lfjhhx/w0EMP1XpOal6nTZuGF198UR3TbdiQan651qmEbE2WdYGaqYQGDx5s0RIPGzasXtq5/H1/wOfgl3h5xXaeTQmz8x+YprY9+N0naN+zO3YsXQGj0YjLL7+8wc+sreLp6oXuAX1QZCpAanEKEgsPI6UoEWGekQj3ioSbi9kM3BYD/XnD+8EQ3g9aUYYKSoXENdCObwXOmaO2a1qleb8qxxkwIqIjPFxdxS9XEARBEIQmQ4JMCUIbSxlTVFCE4Kh2VfajANr/vDFYt24dkpOT6zwvhdMFCxZg6JAh2LN6XbWUQzRLpua2tlRCbh4eeP311xt8T349xuKbLfl4aHQ42mXsxe//97FlG5eLcnNx4K8tGDdunMqrKzQManS7+HVHv6DBCPEIQ1pJCnZm/Y2j+QeVGXNtGLxD4dLzKhjOeRmGoQ+YhVsKvUw1lHu02v6dfANhtBF8ydHtu9QkCIIgCILQUETAFQQnhilzgoKClFny2V3GqblPUCDG3Wk2T7aGAaYITZrrAzWva9euVemGBgwcoAJTMaBUzvE0ZbLMoFW2wm1jBWpbijuej583JSB62yLsWvGnCkx1+bBoZar85xffKZ/iS64Y3+DzCidwd/VAR98u6Bc0BFHeHVFUUWgJRFVSUWwOOFUDBlc3GPz+NXOnABt5GuATqX5qR1aoSMzU6tqDAxTv3nyXmopy85rj1gRBEARBcGJEwBVaJUePHlWmtD179oSPj4/S1DH358svv4yioqKTMuml6WRDJmpJHRWWbcmSJWrZw9sLoy47Fw989TGC21fV4BJGTyYhIebotvWlQ4cOeHzm45aUQ7om114qoZMRqK0ZP/4KTH5jHWbN22RJK6SnEfrjw0/gYnRFcd9uSCmqOY2NUD+MLkZEendAz4D+yme3rKIUu7O3Iqs0o17HG7yC4dJnkgo8RaFWS1oP7a9Xoa1+HFr8Mvh7VfXx3bniT1RUVKiJgxeCIAiCIAgNQXxwhVbHTz/9hBtuuEHlZdWhULtlyxY1ffTRR0qo69at2ykpT48ePeDInHnmmSpyMSMfX/zUE3B1c6u2DzWv1MAyQnJtAabq45f70X8erGaybI/GCtSEAxojx4xT7wK1twO7ms9BLS7TCF186aVoHxGO7w7H4fz2MegZGNrgawhV0aMq0xe3m39P+LoFqN/HipJgNBgR4hlWZ6oh5Yc7fCaQtR9awipoexfg/ckxuOSp5Xjt8mtgMhiRl54Bo6t57HXxK29h1SdfWqIo+0ZHo9hkQkpRntm8+d9czYIgCIIgCDrSOxBaFcyVeu211yrh1tfXF8899xzWr1+PP/74A7fffrvaZ//+/bj44ouRn99w7R2Fu507d9Y5TZp0IqLrzTffDEeHkY+fndQf7bP3o7SouJpwS80rNbDcr7HofrlnnH66+r196R/q3PY4WYGauLq6WrS3OroW191oxNWde6O7fwh+STqoUtNIRrSmE3QD3INVdGU+09KKYiQUxis/XebVLa/LT5eWDyE94DLoThjGvoynvo5DckYBkuMT4V+ciS6BrnjxliFq4jLXpR46jIrychQUFCChMBc/JuxH6b+BqY4XF1iWBUEQBEEQJA+u0Ko4++yzsWbNGhUh988//1TaSWtoovzoo4+q5SeffBJPPfVUk5eBppPR0dFISUmBn58fUlNT4eXl5dB59JQg8tNtWB88FLPeWYbSoiJlIkwtKgVNCre2aXtOhg8++AB33XUXOvXvqwJKWfvi6gI10xJRILaXa7c+Ax2nnXaa0t4unDm2yrYrn12utLjcZ8CAAfgrPRnr05LQIyBEaXNF69f0lFaUqMjLmSVpYIMS6hmGcK8oFZm5Lnr37o3DiQkoKSjE1SM64/OHR8PNaP4blZsqccMrq7Bg7RF4+Piga3Q04uLikFtWggB3T/Ve/2/fPyoKc7RPALr5ByPGPwjexupWCoIgCE2N5MEVBMdETJSFVsOmTZuUcEumTJlSTbgl9MudN28e9uzZgzfffBMzZ86Emx2T3JNh+fLlSrjVTXNPRrg9ZZTmwd0VyHcPQIRfAH745nvLJmpRqbltjKBZE3feeafyZ9ZTCTGglK1AffvU+xt9zdmzZ1fT3lprcSngcp/vv/8ew8I7INjDC7+nxCOrtBjhXj4nfX9CVTxcPRHt21UFo0ovOY604uNIL0lFoHsw2nl3sOTarUmjW1ZSqgRYCrLRYVvw8pShatv0+Zstwi3fGd1MmsKtfuzErn1xMC8LB/Ky1N94eQrQ3tsP3QKCEesXDD93j1P0FARBEARBcAREwBVaDYsWLbIsT5482e4+Li4uuOmmm5Q2MicnBytXrsR5553XpOX49NNPW5V5Mlm04Etoe4/CY2I43n7jDZw/+QbEGr3Ro33HRpsI18dkeciQISoV0LqffqsiUE+663bcef2NjTovNbN8F6x9b60ZFBOifHGZ03fbtm0YOHAgugeEoJNvADxcjajQKpFXVoogj1YwMNHKMLq4oZ13R0R4tUdmaTpSi5NRbCpSAq6p0qTMmnUhVScmJkbNTSaTci9YH5eKO99eq9btOpKt5sP7dUX39n5ILqv+9/Z398Bpoe3UVGQqx6G8bCXs/nk8AauOHUWfwDCc3yFGaXttry0IgiAIgvMhJspCqzNPZtRkCq80U7bHhg0bVFAl8sQTT+Dpp59usjLQrzcyMlIFtWKE4vj4+JPuNDe3iZNuzkv+2bwBg4YMw6kmKSkJWVlZKqCUtUCdWlyAIHcvuP/rT1sfbrvtNsydOxftgr0Q5GtfO5ddUIpjWcVq3w8//LDKto1pSfgn8zhu6z6oQdcVGo7evLCOxOftR4VmQmxAb7v70vLi1lurp68ic1+4F7f0KwSCe8DQ6xoYAuuOXE6/3MP5OcoknabL9NX9LekQrujUw6IBFgRBOBnERFkQHBPR4AqtBpodE0ZHrkm4JUwdZHtMU0GfUT0N0Y033lgv4ZYNYG1kZNQv3Upj0c15yTPPv4SvvvtWpXtxO4W+qEwlxMmasooKLDyyF4NCInFmeNVttUGhlYMYNVKaiyBfA4LCO6h9bTktpB3aefsp4dZaABOaHuvnGu7VTgm4eh7d7NJMhHlGKK0vWbp0qZqHhobgvffeV3+bu+/+LzIyMrFsWxom3zoD2p7voK17BlrUUBh6XAmDd82dSGrrraNnGw0u6ODtBz8386DI0qRD8HB1VcJvlLcfXOQdEARBEASnQDS4QqugpKTE4uvKCMk///xzrfszwnJhYSGGDRtWuzDUQMaMGYNVq1ap5QMHDtQrFVFjhKemGgG2DsbEqk7f1A+XLYFLpyjc2r26/+qphhrcUA9vuDahsK1VlENb9yxQWQHDyFkwuNbsg7ny2BFUahpGt+sEV6awEU4JWSXpOFJwCKwZIZ7hiPCKQn52Ad544w3cd999Fo0I6wF96ekjHhoaqvLoImkdtH2LgLJ8oPM5MHS7BAb3mn187cG6sOr4UezPzUShqRzerm4qOBWF3Wgf/yZ9HwVBcF5EgysIjolocIVWgXXKHwqvdUEzZgq4TCvSVCQkJGD16tVqmSbQpyrP7slgHYyJQ1kUcBe98wHe+fwTOAIRXr4WQdfdxbVJ/GINrm7AoDuhrX0GWtw3MPS7qcZ9Qzy8sCLlCHJKS3BxdCw8XeWTeCoI9gyDn3sA0ouPI62EAamOI8g9BNOfnIb05AwVRI5ByugGMGvWLCXcWvLodhwJtBsKHP4dWvyv0BLXAn2uh6FD9aBztQ06jWnXGaMjO+FYcYE5SFVuFnZmp8HDxRVd/AKV9rerX1CN57jssstw6NChel2PfsaLFy+ud/kEQRAEQWg80psTWo0GV8fd3b3O/T08zFq74uKqOV9Phs8//9xi0spAVo6OvWBMDL704+LFePbQEWBgy2twCZ/p0iT6MkNFxG2KND4Gvyigz3XQdn4KLbQ3DO2G2N2vf3AEAt098VPCfnwdvwvjo3si0MOzyqCGnhuYwbKYHkpoGtxc3BHlE41I7/bIKElDWvExrNi5FDeNuw05WTnw9fPBihUrlGC4efNmJezqGIweQOwlQPTZ0A78BHj4q/VaaT7g7mMWhOsp6NI8mdPIiGhklBYpQfdgfjb25mQoAZe+vAxcRe2utc82hdt9Bw4gNLp28/qMhKRGPyNBEARBEBqOCLhCq8DT84TQUVZWVuf+paWlat6UKXw+++wzi/B87bXXwtGxl0pHT6Ez88knsORHx9AoUci4oEMMvorfhTWpCUqz1iR0PBtI3w1txydAQGcYvE/4Y1oT7RuASTF98cPRffgyfhcui+6OMKMHXn31VTz77LOWQZJff/1VaRMffPBBywCKcPK4GFyVf26YZyRefPA1JdzO+fhZjL10DJYvXolpUx7HM888owKL2WLw8Ieh7/WW39rW/1PCrmHQnY16D8M8fdR0VkRHmCor1fpjRQX4LfkQbvPxVwJuSlE+/P/146Vw++iiL2s970vjJzW4LIIgCIIgNB5xNBJaBX5+fpbl+pgd0zy5vubM9c3Bu3fvXotpYmBgYL2PpT9ObVNcXByamppS6egpdH5Z/JNKoeMoMDft2ZGdsDXzOA7lZTXJOSmwGPrfDIR0p2NurfvSNHpi1z4I9fDCs1/MQ48+vVUOZd+IMNzxwRtq4vKMGTPQr18/S0Akoeng3+tY4jGluaVwS869bAx8fL0Rfzi+fufocQUMnc5Ry1p+CrTchEaXR7ck6OwXiLt6DlbpiAgjMf9v3z/ILTcPogmCIAiC4FiIgCu0Gg0uU8zoKWdqIzs72yLgduzYsclz3zbUPJnBJmqbdP/C5tbeWmtxrfdxFAYGRyDGLwhLk+OR/6/wwL/1jh076vyb14TBzQcuQ+6FwSfCHKCoFuh/u3D6bHxw5wM4lpKCi+7/Dx5e+Bl6nHWGmrh80f134WhiIi644AJMnDjRYrIuNA00Qy7IL1SaW8J5YUERunTuon7TjJkRmGvCENQNhuBYtawdXgZt7WxUbpsLrTjzpMrlbTRHeiYcCDm/fQxcVIgsQRAEQRAcDRFwhVZD797m/JkHDx6EyWRON2IPXdNKevXqddLXLS8vx9dff62WGS2Rwo0jU5P21laL+8MPPziUFpcavPPad1XpXJ7433s4a/hwNUAxYMAANR8+fLhK09QYtJJsaGuegpa5r9br5+XlqeV7P/sfxt52E4xW/t5cHnvbzbjnsw/U79zcXEkv1MToAaVoljyqy3lqzt/MZ11RacKxoiTszt6Kg3l7kF+eW+sAg6HvDWbz5fRd0FbNQCVTDJWbU3ydDF5GN/QJCoOfW92xAARBEARBOPWID67QahgxYgTWrFmjtLN///03zjjjDLv76ZGOCYWik2XJkiXIzDRrgCZNmlRrDl5H4N1331Xzv/alod9/v7e7T3ZBqWXfDz/8EI4ChYcdH32Od199HW4eHhh86QUIi+6I9IREbF62EhMmTMD06dPx/PPPN+zEHgFAUCzgfsLU3R48/2+//YakuH2I6mHWBNqStGefZV+h6TW4DChFn1vrKMp6gKl+wYORVZqO1OIU7M/dDW+jDyK82qsIzLaDDQYXI9BpDNB+GLT4pUD8UmiJa4DYS4Ho0eZo24IgCIIgOB2SB1doNdAPVhdq77zzTvzf//1ftX0qKyvRt29f7NmzR/nJ0sfVze3kOrJXXnml0nbq2tGBTRx9uKnz6G3cuFEFwTp+/LgKyOXq5gb/ULMmNy8jExXl5SoSdWRkJL799tsaBwpaAmpoKTh2GtAXk9+cA7+QYMu2/MwszLvvMRzdsUvtd9VVVzU6Ty5cjHa1r1lZWYiIiEC3Yafj9vdfs3v8h3dNxcG/tiA1NRXBwSfKJ5w62GzllecoQZeaXHcXd4R7RSHUIxyuFGztHVOSA+3AYiDhT8ArRPlnG0LNViGNoU+fPsgsLa5XkCmmo/p09XIEeXiik2+A5FwWBCdB8uAKgmMirazQahg6dChGjhyplhlRdcOGDdX2YeRbCrfk/vvvrybcrlq1yhx8yGDALbfcUuc1KfBQg0sYXKiphdvm6Pi/8cYbKr0NXF2UH+nzG5fj8WU/qInL9COFi4vah/s60hgXU/FQc2sr3BL+nvzWHLWd+zUGzVSi/DKRcELLbw0F1nPPPRcHNm5GUW5ute1cd+CvLRg3bpwIty0I62+AexC6B/RBr8AB8HULQFLhURSZzL739t5pg2cgXPrdBMPZswG/9oCr2cRYMzVdKrHaYNqhRUf34cO9W7Hy2BGkFhc6VN0TBEEQBGdBBFyhVfHmm2+q1D/0wT3vvPPwwgsvKI3lypUrlVb30UcfVft1794dDz300Elfj763elqim2++Ga2h40/f0NboR8pAUuvXr0f/88ZUE251uJ7b161bh+Tk5AZfw2D0BIK7Q4v7Glq+/cBV1CBXmEzYtWJNtW07V/yptol5suNAM+UufrHoHzwYvm7mfLj783Yrf92aciS7nH6fCkjFwGPaxleUf25zw1RUN8T0Q8/AEOzLzcQXh3bi04M7sCk92RJUTRAEQRCEk8exnQkFwYZBgwbhm2++wQ033KACAjFtiy0Ubql1tU4tdLLRk11dXXH99SfybToyrdWPlNpyQp/b2gjt2EHN6Rfdvn37Bl/H0PtaaFn7of3zATBiFgz/avJ0xo8frwZLFr/yFlZ9UtX8NC89A65Go0oVJTgWbi7/amQ1Tfnkerqac2AXmgpQXlmGALcgO4M5Bhi6XgB4BZmPzU8B3Lxg8DT/rouMhKQ689xyn5DYWHVtpsPSU2IdLchBXE4GNqQlYW1qIoaFtVf5dwVBEARBODlEwBVaHZdeeqlKHUNtLgVZav7oU9qtWzclsN1zzz3w9vY+6escOHAAf/31l1qmSSp9VlsDuoC2fdkKDL3iErv77Fi6QgXLuvzyy+Eo6Ca/DChVGxmJZs2cnjaqoSiB9rQ7oa19Ftqeb1W0XdtycACFPt+2+EVFIahnN+wsy8coLdhhtN/CCcyCZDvL7+zSDOWr6+HqhQivdgjxCIOLwdWyL6JOt+yr7f0OyNgLret5SvA1uJmFZHvExMTUqzwUbm33dTEY0MUvSE2lFSYcyMtCgLun2nY4Pxt7czMxLqqrJRevIAiCIAj1R4JMCYITBqm48MIL8fvy5Xhq1c/wDgio5kf61OhLcN64cfjll1/gSDDq9ea//8bMpd/bNVNmoKnnzr8SQ4cMwdq1a0/qWtrRVdB2fQbD4LthiDyt3sdtzzyOP44dwfDwjjgjvOEaZOHUU1Cep4TcnLIsGA1GhHlGIswr0qL11WEaIe3Qr8Dh3wGjJwyxlwHRZ5sjMp8i9udmqumS6O5KG70l4xi6+AUi1PPkB+0EQWhaJMiUIDgmMjwsCE5Ia/UjnTp1KspLS1W0ZAqz1uhRlLmd+5000aOAiEHQdsyDVlz1WrUxICQSZ4Z3wLq0ROzISj35cgjNDn1zY/x7om/QaQjyCFXC7s6sv3E0/yCKTSdy4xrcvOHS8yoYRj8PhPeHtvtLaH8+Ae3Y36csIFT3gBAl3JICUzk2Z6QoX93PD+7EPxnHUGQqPyXlEARBEITWimhwBcEJR4D1dDdu3l7wDwut5kdaXlTssGlumOf2xRdfVNGSGVCKPrc0S6bJtam0rHF5cGtAKyuAtuYpwDsMhmGPwFDP9C38bDIS7vasVFzcMVYJJULrwVRZjvSSVKQVH4OPmy+6+feyCLDWZudaXiK0vQuA9F1AYAwM/W6Ewb9mP1kGvCPDhg1rwrJW4nBBDvbkpCM+P0eVs7NfIHoHhiHGL0jMmAWhBRENriA4JiLgCoKTNpCTJ0+260dKeO477rgDkybVHiCnpVi4cKFKBcRoyTqDzhiK/teOx7N33I0OPuZouU2BlrkPyNoHdLuk3gKuOk7T8EvSQZRXVuLy6O7ij9sKqdQqUaGZlKlydmkmjhclqdRDtrl0tYw4Jega+k9WAq5WaapmtqwPKun1NyiofoGqGkKxqVyZLzM41bHiAni4uGJc+64ywCIILYQIuILgmIiAKwhtrIFkLuAxY8ZYlkeNGgVHhQHEKDgwoFRUVBS+it8Fk6apdCsM1NPUaBVl1aIq10ZFZaWau7q4oFLTmqVMwqmhoDwfOWWZ6ODTWQ1eZJamqWjMurDLdRzE0CorVC5lA31zO4+1HP/xxx9jypQplmUOMDUn2aXFStDtGRCKEE8v7MpOU+bLQ8PEL1wQThUi4AqCYyICriC0kQbys4M70DsgFFPGXYQ9e+LUut69+2DLli0qDVJr4FhRgRJyx7brrHxhmxItaT20A4thGPGE8sVsCGnFhfgl6QAuj+6BII+aI+8KrYMSUxHicrYrjX6oR7iKyuzhao5yTO0tDi/H/hx3XHnTvQB/u7ji2LHjyM8z56D28w9Au3YnIjkvWrQIsbH2U3Y1FRvTklBQXoZz23dFeWWFEn67+4fAyyjJEgShuRABVxAcE3HeEYQ2ADWN6SVF+GvDBmzfvh1Tp16GBx64DNu2bcP8+fPRWmjn7Ys+gWFYl5akzDWblODuMHQYAbh6NPhQPzd3tPPyg6erW9OWSWgRPI3e6Bc8GOGekcgsTceu7H8Qn7cPheX5yjTZEHMBfl2zA3FxcYjbux/lOclo56fhxVuGqInL5TlJ5u1xcackWvmw8A5KuNUHglakHMYH+/7G4oR9OJiXZbE2EARBEARnRzS4gtAGRoCzS0sw78A2fP3IE0jYugX79/+fWh8beycMBg/s378f/v5N59fanBSayjBv/3b0CgzF2KguzXINBp8yuPtatG96fuH6kFdWCjcXV9GcOQkVWgUyS9JUQKrSyhL4Gv0Q4RUFb4Ofypf83Xff4eoRnfH5w6PhZjSPGZebKnHDK6uwYO0RFa38iy++gJub2ymvJ/tyzP66aSWF8HQ1okdACHoHhiLSy1d8xgWhCRANriA4JtIDE4Q2wL4jh5Cy7yAObNuJV567Af7+ZhPc5567Abff/o6KSszIxa0BH6M7RkR0RHGFyeIXqfvqMip0hw4dTur8WtoOaFs/AM6aiW0HU3HFFVeo9Vu3bsXAgQNrP1bT8GPCPhgNLri6Sy8l6AqtG1eDqzJRZu5c5tFliqFD+fvQJ2iQElwJhdwA7/XKD/toagFSc4qxOyEHE664pEWEW72enBbaTk0ZJTS5TsfenEwV+TvI3VNF/w738jnl5RIEQRCE5kZMlAXBiVmwYAGGDx+OM3v2xatX34ic48cxd+7vWLDAHJ148uRzMWBAFxWxOD4+Hq2Fgf/momW0Zd5fx44dMWDAADXnb953ownpAXgGKSF39tNPWVbPnj27zkMpbI+L6oqM0iL8lLBfzEKdCP5tgzxC0DOwH/oEDoKnqxeMRiNmvfsYzhp7Bj5dcRDzfj+Av+JzlXDL/R8ZZYAxcUVLFx2hnt44O7ITbusxCFd17qVM/QPczab4WzJScCC3/nmgBUEQBMHREQFXEJwU5ouleeTmv//G4EsvwAV3367mW/6Jx4QJL2LGjE9VcKk33rgdZWVleOSRR9CamDZtmt3742+unzFjRqPOa3D1gGHQndi6YxcW/bgY48/shMuHReOHH35QPst1Eenti8uieyCxMA+/JR+y5FcVnAdPozmQWCUqkXz4GHb/s1eZJc/5+FmsPvw7Xpz7rPq7P/hVIhBh1vprh/9A5baPWvR9YJTvTr4BuKBDN3i4Gi3+upmlRWo5v6wU8fnZShMtCIIgCK0VMVEWBCeEGkyaHHce0Be3vDkHfiHBlm2XPnQv5t33KF544TsMHhyDkJDW4Xtre39z5sxBpwF9Mdnu/T2GF154AYMHD8ZVV13V4PMz1+mzP6Wq5VkTB4L9/R83Jigt7vfff1/n8RQiLuzQDT8nHoCXqxvGtOskPo9Oar48/7XPkZudC29fb4y91Jx+69zLxqjfhxJSYfAx58aFu4+yDFCphirKoB38GYb2Z8LgeyLacktwaXR3i9B9IC8Lq44fhbfRDT2Vv24Ywjy95d0VBEEQWhWiwRUEJ4Qmx24eHtWEW8Lfk996CW4e7njttR/xwAMfwt3dHS+//DJa2/3ZCrcn7m+O2s79GgP9bRctW6e0twO7hmBQTEiDtLike0AIzo3qgm1Zx/FXenKjyiE4Pu+8844yjy8qKMLyxSvVOs75+/Qxg3Ewdw+ySjOgRQ2FS89/B1sKUoCjq6CtfhyV656DxuVysxa1JdAF2EEhkSrHNIVb+ut+fmgnPj24A5vTU1QKIkEQBEFoDYgGVxCcDAZcWr9+vTLXtRX+dLi+/3nnYP1Pv6nfjz32GLp2NacYcZ77G4N1P/2G5ORktG/fvkHX0P1tqb3VmTVxUIO0uKR/cASKTOVYn5YEL1djk+fuFVqe0NBQFWn79NNPx7Qpj8PH1weFBYVq/axZs2DSynE4f7/S9oZ6RqCDT2cYAjoDY18F0rZDS1oHbdfnQNzX0CJPg6HDWUBob5WDtyUEXQae4jQyMhpHC3KxJycD69MSsTY1AdE+AbioY6xECBcEQRAcGmmlBMHJYDRhEhbdsdb9Qjuaow0HBwcof9Uvv/xS/Z40aRKc6f4yMzMbJOAq7e2iRRbtrY6tFreuiMo6Z4S1VxGfc8pL610GoXXRuXNnbN68Gc888wyOHDmiflO45ZyUmIpVPl1dU2qqLEdayTGERwyAsd0QaCU5QPJGaElroW36S5kyY/DdMAQ2Txqs+uBqcEFXvyA1lVaYsD83CwmFufB0NUcG35iWhC5+QYiQSMyCIAiCgyECriA4GUyVQ9ITEmvdLyMxSc2nT78K//yzEddff736TWFw1KhRcJb7Cwk5IaQ2Vnt7MlpcCjWjIztZfpdUmFRO0oSEBEydOlWtoyl1dHR0g8opOBYUZufOnVtjUKr2xhN/32JTEdJKjiPMy6zRL3R1gWeXsXDtej6Qexha0nrgX99dLX4p4BUCQ7shaCkYkKpfcLiaSFlFhcqvG+juqQTczBKzeXWIpzn9mCAIgiC0JOKDKwhOBvPAnnXWWdixbCXyM+2n/+D67Uv/gI+PN+6771I88MD9cHd3U9MDDzyAiooKtPb743amDGoK7a1OY3xxdSGX09GCHPzfzr/w+Oyn0bNnTyUkc+Iyg2KVloqWty3g5x6AAcFD4ObirgI8Hcrbhx1ZWxCfvw+5XoFAn0kwuJmFRS0nHsg3+3BrJdnQMuKgaS2bfsrd1RWTYwcoP3OyJeMYPjm4A18c3Il/Mo4ps3xBEARBaCkMmuSwEIQWJT09HeHhZs2ITlpaGsLCwk4qyjBT5XTq31cFXLL2VaXw9/F9jyJhx248/fTTiIqqxO23P43HHrtKRQt+6aWF+OijjzBlyhQ4KnXdH6MoH92xS+3XkCjKt912m9LCtQv2QpCvOU+oLdkFpTiWVaz2/fDDDxtU7iW//oL/3HMPEuMPI6xzNK6Y/qBa/8MLryH9SAJiY2Px9ttv4/zzz2/QeYXWTXllmQpElVmSjuKKQrgajAj2CEWIRxi8jb5qHxV9OX4ptD3fAl7BQPvhyl/X4FP129ESmCorcbggB3HZ6WrObkUXv0D0CgxDjF8QjC4yli44J83RfguCcPKIgCsITtpAMg8uUwUxmjADLtEnlWa7O5atQHlpGXx9fZV2MzHxKPz8PLF//wfquNjYO2EweGD//v3w93fcFEI1399KlJeW4rFp0/DiCy806JwbN26sW7Cn9szgogThYcOG1eu8/MxOnDgR33zzDdy9PHHuHZMx6qbrYHR3V9tNZWVY/elXWP7BfJSVlOC6665TPtGSnqXtUWwqVP66FHgp+Hq4eqGdV3uEeIab0/lkH1KBqXBsE2AqAYK7w9BhONBuCAxGz5YuPopN5diXm6lMmI8XF8DDxRU9AkMwtl0XeZ8Fp0MEXEFwTETAFQQnbiAXLlyIp556Crt27bKsi4oKRlmZO3x8fJGYGI/Kykp89NG9mDLlPLX9o4+W4vbb31GRlSlAOjK8P/qvrlu3zrJu6JnD0OeayzDpmmtxblTzRIamiai2/WMY/KJgiLmoXsdceOGF+O233/DQgk8R1SPW7j7Je/fjtQk3q31/+eWXJi610Jpg05xfnquEXV+jn/LXpe9uoSkfwR5hMFSWA8e3QktcC2TuBVzdgXaDYehN82YvOALZpcVK0KXJ8rj2XVGpaSrlUJ+gMPi6mQd3BKE1IwKuIDgmYjckCE7M+PHj4erqCjc3I4xGI3r3jkanTl3x7Zd/4tmn/091ogcM6IJbbhlrOWby5HPVOgqO8fHxcGRofrx27VokJiZi+/btKoXQX+s34IGbbkVKUQHKK5vHl1ilcPEOhbZ3ocphWh9oUk2S4vbVuE/Snn1V9hXaLtR2+rsHootfrCUYFQXelKJEGPjP1QNF4X1gOOMhGMa8CEPMhUBxNmA0m9ZriWugFdv3UT9VBHl4YXhERyXckszSYmzOSLH46KaXFKLYZGrRMgqCIAjOhwi4guDEzJs3Twl+Dz54uZri4hKwb1+C2va/j15SAu4bb9yuhGAdLnNdWVmZxVTW0WHgqf79+1sCSvULCsekrn3h5nLivpoaQ+zlQOexKoeplrKpXoMNHGTYvmxFjfvsWLpC7XP55Zc3cWkFZyDcqx36BA1Swi/Nl/fm7lTBqZIq81Hc5Ry4DHtYDb5oZQXQ4r4GsvafCE5lavkAZmGe3riz52A1J8uS4/G/fX9jccJ+HMzLQkVlywbPEgRBEJwDSRMkCE5KXl4eZs6cifBw5rm9Rq2bP385cnJyUFhYUK9zMLcn0wc5euogWygAGA0Glb5kY3oyzm8f0+SBbpQ/Ye/roJUXQtv2EWD0giG8X63pjc4991z8vnw5inJz4R0QUGU71x34awvOGzfOkgpJEGxxNZgHbYwGN/QM7I+sEvrrpqu8ul6u3sp8mZPb2FeBf/dVgalSt0NrdzoMHUcAQd1azB/Wzaoeju/UA/ty6K+broRcps/qERCC3oFhiPTyEZ9dQRAEoVGIBlcQnJTnnntO+QI9//xN8Pf3VtNzz92oNLNffP0+7rjtUbi5uWHq1I+qpAXi8v33/091LltL6qCaqNA05JSWqMA3zQG1ZYb+k4GwvtD+fg/avxqzmqDpcYXJhF0r1lTbtnPFn2rb6EsvbpayCs4F66eP0Rcdfbugf/AQxPj3hKerF1KKErAzewsOFsajsLLYvG+PKwHm2M3cC23Di9BWzYB24GdoxZkteg8+RnecFtoON3Trj5u69UffoDClyf0qfhfmH9iObZnHW7R8giAIQutEgkwJghMGqTh06BB69+6N3r07YMuW1ywmyBRSBw+eiri4JMz76Ff8vOQbfP3th3aDTJHWlDqoJviJUylW/p03yzUqyqFteh3IS4Bh2CMwBHSyu19WVhYiIiLg5u0F/7DQKtvy0jNQVlSM2auX4L7TR0sQHqFRmCpNyC7LRFZJGiK9OyDAPQgF5Xnq/fd181Nmy1riOuD4FqCiHAjtpbS6hqgz4AgwEFViYZ7S6vq5uWNERDQKTWU4nJ+jtLvN6XYgCA1FgkwJgmMiAq4gOGEDyeBL33//PVaufB6jR1c1m121aifGjJmBkSPOQ0lJCbZuW4/gYB8cOPA/tT0m5g5kZ+cjNDQA+/f/X6tKHVQTuWUl+DXpEC5oH4NAj+ZJpaKVF0P762XA1QOGYY/WKExPnjwZmzbZ99kdOnQoZr/zJjr6+JtTwuim0IJwEhzJP4DSilL0COyr3qvSihKoUFTHtphTDrl6wGXoVBUdHDmHgcCuDvXe7c/NxK9JB3FHj8HwMhqV6wEDWLk4UBmFtokIuILgmIgPriC0YdLSU2AymZCWlovnn/9WaWszMnLVtueeu0GZNevL1Oo+//zzDp86yB707aMW6OfEA7iua58m98clKjXL0KmAVrtQysBf9WF9WpKKAj26XecmLKXQFunk2w0VmjlacYEpH/tzd8Hb6IOQ0G4Iaj8MRn2YOyNOWSIYRjwJBERDqzTB4NLy3YTuASGI9g1Q9dhUWYmv43fD1cUFvQJC0TswFGFePi1dREEQBMGBEA2uILQxE+XTTnsAe/YkKxPlmU/cheTko6ioMMFodFXaHe7DNEFbtrxezbSZx+3ZswdduzZPftnmJK24UPn2McLyOVFdmvVaKmrt7i9h6HsTDB5+jTrH9szjMGkaBoe2a/LyCW2XSq0SuWXZyCxNQ25ZjloX4B6IEI8w+LsFwiUnHgiKVYM0lWufAdx9YegwAogYCIOrW0sXX32j0koKVX7dvTkZKK4wIdTDG72DQtEzIFRM+4VTimhwBcExkSBTguCExMTEqMBQ27bFY/78Pyzr581bjh07jsDLy0cJtykpR1VHNjy8HcrLTTCZKpQW9/XXa04d9Mgjj6A1Eu7lg1GRnbAtK1WZPDYrFWXmnKSmokafYkBIpEW43ZebgdIKyRcqnDwuBhcEeYSgm38vFZyqo09nlFeWIz5/P3Zm/40Ed1eUVZYqc2VD9NmAqQTa1v+D9seDqGRKrJzDFvP5loDfqwgvX4xp1xl39DwN46N7INjDE+tSE/Hhvn+w8MgeHMk3C+6CIAhC26TlbY8EQWgWmCJo/vz5mDnzc0yYMEKtmzHjM5Vntby8DMePJ8LFxQXlZeXw9PTGzGmvYsH387Fv/044KwOCI1QAG+bfpMAb6N48/rgGnwhg+ExzcCtTsUrXYnBtnGaJEaCXJx+Gv7sHruzUEz6ioRKaCDcXN5Vbl1OJqQiZpeaUQ5Fe7VWE8PzIgXBvfwbci3PMvrrJG6AdXQn4RgEdhgPtz4TBs2q6q1OJq8EFXf2D1FRSYVIDV3tyMpBbbs75m1VajILyMuXT7kg+xYIgCELzIibKguDEJk6MfHz77bdXiYb82MMv4PtF83HkaDyioszRfqPaReO52f+H5JQE3HzreejXrxP+/tu5TJR1qAn9/OBOeBqNuLZL8/jj6vDzqv31ijnw1OD/NtqfMb2kCN8f2QOjwQVXdu6pAuwIQnNgHW08LnsbvI2+6OxHH94KVFZWwMgozBR2j/8DRI+CS59J0CooULo4hAmz9T2sPZ6APbkZuK37IPU7r6xUDRTZ47LLLlOuHfW1kFm8eHETl1pojYiJsiA4JiLgCoITN5BmwXQw9uyJU7979uyFV+d8g3seuBIlJeWY9+Ev1Y7530cv15g66LHHHmuVQaZsSS0uUIFq+gdHKFPH5kRL3wVt81tA1OkwDJiiNGONgZ3z74/uQbHJhCs69USkt2+Tl1UQrKnUKpRg6+bijvSSVCQWxCPAPdjsr2twh0GrgMEjANrh36Ed+hWGMXMcRsgl7N4UmMpVuiHWn4/2b0WElw96B4aplEPexhNl7dOnD/YdOIDQ6A61njMjIQk9YmOxe/fuU3AHgqMjAq4gOCYi4AqCkzeQq1atwpgxY9Tyt9/+jLCg7rj7/vEoLCyzK+AWFhbgplvHwd29Avv3f+AUaYLssS3zOIpM5TgzvEOzmy9qKZuhbf0A6DQGhj6TLNdbtGiRmo8fP77e5sqLju5TGt1Lo7uji19gs5ZbEHTKK8uQVZqBzJI0FFcUwWgwIsgjFCEe4fAqyYch9zAMHc76Nyf0azBEDALaD4PBwzG+F4y+zFy6zK/LOensF6iiMHf1C8KAfv2QWVqMRxd9Wet5Xho/CSEeXiLgCgoRcAXBMREfXEFwckaPHo0vvvhCLffqfhrKyszBihhgavLtF1UxUSbPz3kYBQX5yk9XTx2UlpaDAQMGOI1wSwaGRFqWKzWtWXNqGqJOVwGntJ2fQnPzgaHHeGzduhVXXHGF2s7lgQMH1nkeL6Mbru7SC0sSD+LHo/twXvuu6B0kHSmh+aEWN8IrSk1FpkLlq5tZko70kuPwdPVCSHA0wior4FJeCLj7Qdu7ANi7AFp4Pxjorxvev0VTDtEVITYgWE0cKNqXm6mEXaYO83BxRaGpvMXKJgiCIDQtIuAKQhtg0qRJylxv3erDiOrgr3zIdA4ePFhl35RjCUq4pZbxtdd+BOW+gQO7IiCgeQIytTQ7slJVR3dC594qt2ZzYYgeBZQXqY6/5uaN2bPft2ybPXs2vv/++3qdx83FFZdFd8fylHisPH4EXfyC4GWUT7lw6mAOXU7tvTshrzxHCbupxcdUsCqDZyAK+l0Pr77Xw/XYFuWvq/39rlnopUa3w3AY/Du2aPk5UMQBLk4MRMXAVGWVFS1aJkEQBKHpkF6RILQRigrLUF5egcAg7yoBUuh7RnNla9zc3BEcHIrU1BT1++WXJ2PTpqP48ssvLQKzsxDu6YN8H7NA39wYYi6EVl6Ef35+D4sW/YjxZ3ZSAw8//PADtm3bVi8tLqG2eVxUV5wR1l4JtzS/dDUYJFKscErh+xbgHqQmlVbI4KL8dg/m7UE7746I7DwWldGjYchPVhGYkbxR+eti4O0wtB9WJaBVSxHs4YXhER0R5O6pTJQFQRCE1o8IuILQRsjOKlba2IDA6ppYmitffPlAmEwmpb0FDJbUQcTFxQQ/Pxdcf/316vc777yD9evXwxlgsCY9YFNZRQXcrfL/NgeGHlfimbufV8uzJg5UJuA/bkxokBb3hHDhqYSEJYkH4OlqxPkdzJr5hIQETJ06VS2//vrriI6Obqa7EQQzevA0F4Mr+gQNUvl2ybHiRGRVpCO40xkI7nYhvLLjgaBuapsW97USjF36mr8rgiAIgtAUiIArCE7MJZdcokyQme6ntMSkfE179+6On3/+2bKPbq5sTpGhoVOnbha/3LHnXKqW16x5A9//8Dvc3Y1KIKPPKCM062mEnIGDeVn4PTke18f0qzGVSFNATe2PK/5R2tuBXUPUusuHRTdYi2st6DIqLH0MS0tL8eqrr+LZZ59FcbFZG/Xrr79i1qxZePDBB+Hh0Xz3JQg67q4n3rNgj1BUapUqOFVqcQq83L0RUpGH4Ep3GP06AJo5JoBWnKW0u4aOw2Hgegcmr7wU2zOPo5t/sOSlFgRBcEBEwBUEJ6W8vBxr1qxRgo6e75aa2szMVLXNzc2cIkM3V9ZNle1GVs6PxPbth6vk050/fz6mTJkCZ6GDj7/yb2XQmWu7NJ8/LjW1uvZWZ9bEQY3S4uowcM7SpUtx3r334uCBAwjrHI0bpz+otv3wwmuYMWMG5s2bh7fffhvnn39+E96NINQO8+hG+/qig09n5JXlILM0HcmFR5FUeAT+ASEI8QxDED8qBceA5PXQDi+DFtDZHJgqaigM7o6ZDmvFsSP449gRdPD2U6nGwrx8WrpIgiAIwr80X0QVQRBaDAqwNCfOy8tTwi2FVk5c5jpu4z71gWmDPvz4TUREBGLGjGswc+Y1CAsLwMyZM9W5nAWa+F7csRvSiguxNjXRsj4pKQk7duxQ85OFmm+mBrLW3pJBMSFVtLgNgSbK1113HS644AIkJCXiovv/g4cXfoYeZ52hJi5fdP9dOJqYqPaZOHGiOkYQTiU0WQ70CEaMfw/0Dz4d0T5dVY7d40XJarshrA+KRj0NDP4v4BkILe4raH88hMp/3oeWtgOaAwWB8nfzwF09Bys/eA6E8dtBtjNgXXZ6SxdPEAShzSMaXEFwQrNkam4pfLrYid/Cdd99953S+I0cObKKubK91EFffPU+srMz8dFH98Lf31tte/75G3H77e/g+eefx4svvghnoZ23H0ZGRmP18aPY+ftKfPXBh1V8jc866yzl23r11Vc3mfb2ZLW4NFHOzc1Vy/d+9j9E9Yitst3o7o6xt92MniPOxGsTblb7tnRgH6FtY3QxIswrUk0Ucvk+FpsKsS8vDt2CeyEgcjAqSrLhkrLJHIV585uARwBw1nQYvJsvLVZGQpLKc1vXPiGxsSoSc7/gcDXppBYXwt3FBb0RhkJTmUpFFOsfAj8xYxYEQTiliIArCE5qlmztS6tjvUxhlvvq5sr2UgclpyRg4Q/zMWBAF9xyy1jL9smTz8U77yxRAYzuuOMOdO3aFc7CaSGReOGJJ7Hw/Q/h5uGBwZdegLDojkhPSMTmZSsxYcIETJ8+XQn3TaG9rUmL2xBfXJbpt99+Q1LcvmoCrk7Snn2WfQXBUXA1mP34PV290SOgL3yMZpPkw6XHYQpuh+B2/0FQaTFcj28FvMz1pnL3VzCE9oYhYkCTlcP6+1cbFG5r2pd5qXXriNSiQvx5PAGrjh1FlLcfuvubc/D6uYkfvCAIQnNj0MRWTRBalPT0dISHn9ACkLS0NISFNVxTkZ2djc6dOyMoKNyuL6011NRmZ6fhyJEjCAoKqrJN98eN7tgVa9Yuw8qVz2P06H5V9lm1aifGjJmBK6+8EgsXLoSzsGDBAiUEdhrQF5PfnAO/kGDLtvzMLMy77zEc3bFL7XfVVVfV+7y33XYb5s6di3bBXgjytd/JzS4oxbGsYrXvhx9+WO9zZ2VlISIiAt2GnY7b33/N7j4f3jUVB//agtTUVAQHn7gnQXBEskszkVmahtyyHNDegKmIgj3C4G/0g+Gf92FoNxSGDmdCy0sCSnMACrz/Rm52FEoqTIjPy8b+vEwcLchFhaahnZcvugeEKIHXrxmD2Qmtr/0WBKHpEA2uIDgRFFQ7dOhQLa9tTXBfW+HWWsObnn4MbQ1qpam5tRVuCX9PfmsOnjv/SrVfQwXcDRs21LpPkEchgnxzMeXiQQ0qMwXWc889F78vX46i3Fx4BwRU2c51+//ajDHnjBXhVmgVBHmEqKm8shzZpRlK2I3P3wdXgxHBvS5BiEc4GNZJY37d+N8AzyBo7c8yR2H2iYAjQN/c3kFhaiqtMOFQXjYO5GVhbWqCcoO4pktvFdyugnmsmymonSAIQltEBFxBcELs+dKSmU/chZRjCZZ9aFpsHVFZRzfBKysrU+mDHnjgQ/z99+uWtEBMEcR17u7uePnll+EsMJAUfW5plmwr3Opwff/zxmDdT78hOTkZ7du3r9e5hw0bht27d9e6Dw1qtN1fAgmroKXvgiGsb4PNlHetWIOhV1xSZdvOFX+i0lSBa6+5xnId8cMVWgNuLm4I92qnpmLT/7N3HuBR1Fsbf2drNr33hJBCILTQLKAiil0BpYgoChexYEWvonD13k/Fdq+9Kwo2RIoiiooiYAFReiiBJLRU0nvZbJnvOf8t2U2BEEiySc7veebZ2dlJMmk78845531rUKIvFE7MJHzj1H0hJ94AQ+hgaLL/Ao5vhHx4LWS/eIsLc9gISGodXAFtI7F7pLIMoTpLO/a3WenwVKkxNqL7jHowDMN0JixwGaYbQS3KtJBozc483ETEkLjNPJ4BhSSJdrnjx48LYUSmU44i1xYdRNx/30N4481XsGTJL5g163KxbfHi9dbYoHndav6WWn0Jmrk9GYFRlpzO4uLiVgvc1iB+X0lTIdcUQt75jsVUp5WZoBMmTMCdd96JNf97HZs+Xur0WkVhEVQqFSZefz3K6uvwY3YGroyMh6/G7awdO8O0NzqVOyJUvRDuHg2TNT+3wlCKI6YiDEyaAjX97+TvBLK3QN77CbD/C8hhwyENnulS7cskdvv5BtqfJ/kGihxrIremEhvzjqGPd4CY2eX/UYZhmNPHdd7xGYY5Yzw9PXHBBReI9Wv6xCLB37fJPjR0nxxqmRmqq6vDN998c9LYoP976kn4+QVgwYLPUFFRIxZaDwkJFPmqS5cuFUt3wNa+S4ZSJ6MoyxIZFBDQ1CzqTJEUSkhD7wTcg4R7rFxncUhuzbHfcsst6B0ZhQCtzmmhbdOnTxf7mGUZOqUaOmu0CcN0NehGkEphuSHnrfFDvHdfaJRaQKHCQZ0Wx/pejYrRTwIJ1wH0/yQpIMtmyBnfQ64rbfXX2bp1q1jaG5rJjfWyjIooJYWIIfqzIBsfpe3GZxl78XdhDsr0de1+HAzDMN0FNplimG5mUmHLwKWqrJdGjToZIv/W1pZsNhqFyKH2Yi+1CiMjw7Dm0GFRyf3888+btCsTTz7xXzz9zKOYN28i6B3jxRdXYdGiRxAXdxXGjLlE7LNp0yaMHj0aXZ1Ro0Zh244dWLDuq2bblMloimZwzxk+HH/88Ue7HYdcWwJ580JI8ddAirH8jM825fV1+P1EJsaExcCDo0yYLo5ZNqOgNk+0MNeZaqCS1PDXBiLALQhutRXAloWQzpkLyS8ectUJkbcrqdxOatxmez9uyaugvag3mXC0qgxp5cU4WlkGo2xGsJu7iB2iaCJ3VdP3aabjYZMphnFNWOAyTDc8QTqKXBskdiO9vaBTq7Azr0BsWzrpGkzoF4+py787qcg9eqQIl11+IbKyDovnSUmJ+Ouv/8M55zyJ1NQ067b+2L59u31Ot8u7KA8aIAylzpaLcluQ66sgaSxzeu1BTnUlvs1KEzO5V0TEIda7Yy/iGaY9oL/nWlMNiusKUKIvglE2wE2pg7/GH/7aEGhVbjBvXghU5gBhwyBFXgD4Jzi1MX/00UeYNWuWfX3mzJmd9v0YzCYhckns0uzu9PiB8NPqkFVVDk+1RqwznQMLXIZxTVjgMkw3PUGSyCWhRqKTzJACPdzx+lVjMKl/H3y4c6/YZ9bQgVixPw33f78BxTW1mDRpEt5///0m1YrqKj3ef3cFHnpkuni+adNGpKevxezZ/2tU1V1kvyjsylDO7fPPPy/clMlQimZuqS055aeNMOj1uPbOWVjx1rvCJbUjkDN/E3O5ir5nX1DXGA34KeewuHAe7B+Ci0J7Qc2Orkw3gS5xKgxlKK4rRFl9CUJ04YjwiIaxOh/I/RuK7C1ATQHSihSY+Mw6QO0OSErk5eWhssIyHuDl7YOwsDD756Q864SE5vOm2xuj2Wyf1/0sIwVBbh64IjJOVHyrjPXwZ7HbobDAZRjXhAUuw3TjEySJ3KqqKvz888+4Z84cFBUX453rxuL2YYPE64t2pODub9cjMCAAb739NqZYXXYbM27cOOzdexC1tVVQqRRi1jcvLws6nQZpae+JfRIS7oQkaZGWlgZvb290dSjbl6KANm/e7NS+fPs996AmOUFcSE6M6Qu1ov0r1vLR9ZCr8iANuKVd3I/pNLCnJF9El/ho3HBNZDyCdBTCwjDdB5PZCBmymN/Nq8lCQe0JDPQbBqksA68+92889L9lYr+EcG+oVQr847I+4vlHP6fBYDQjPbdCPH/11VfxwAMPoLOhym692QQPlQb7SwuxLuewmLm35ewGuLl39iF2e1jgMoxrwrfpGaYbQ63Gvr6+lviZk9zLOtV9LooKysk5Bjc3D6hUOqSnpwuzqYULp8Pb210sCxfegvz8fDz77LPoDlD7Mc3YZmVlYc+ePSJCiJ7PuOkmXN8rEYV11fguMx0m2dzuxyL1HmsXt7Lx7JvN0OdNDgjFzXEDhcP20iP7sKMo75R/FwzTlVAqVHZzKn9tEHp5xkGhUMDsG4exjz+CNz97BbGJvTE41h9fLbgU+zNL8f22LIxICEJ8uJf4OOqKmTNnDlwBurlG4pZI9AnAuOg+oqJL/7sfZ6Tg4/Q92JKfhaK6ms4+VIZhmA6FK7gM043vAFNk0OzZs0U1klqU37j6EkxMSrC3KFMld8W+Q7j/h40oqq5psUW5f//+qK6ux+IPvkdObiZunXkZBg2KaZKNO2zYXKSm5iA1NbVbxQc1x7HKMqzOPCTiPK6KjOuQXFm5KBXyrvcgnffPVscHtaUF8o/8TOwsPoGpsf0R7m65sGeY7orBXI+8mmyRsWuSTfht3R948s7/oKq6Fp5e7qiqrBH/31dddRW+/vg1qH2jIKm0cFXof/i4MKgqweHKUlHlpY6T66ISuKp7luEKLsO4JlzBZZhuDLUSU4WCILfk8X3jxMwttSXTQuKWTKboNYIu4uhjbG3JJGxpycjIsH/O9xe9KCp7r74628lQitZpW319PaZOndptooNaIsbLF1dFxuNgeRE2nTjeMdVO3xjh/Ho68UGnC833XRwWgxkJg4W4pe+LsjkZpruiVmgQ7RmLQf4jEGgOxRfvLRfi9oWPnsGmIz/j+Q+fEf8HPj4+UO16E/LPD8K8/S3I2X9CNrhedZT+h+O8/XFVVDzu6jsME6ITEabzhLfGIsp/O3EcKSX5nX2YDMMw7QYHITJMN29RJldkghyVyS15c1aumLmVDQZhLrVs78FmHZSpLZlakSliKCwsGuFh0a3+utu2bRMuzhEREd0iOqglqC2wzmjAL3nH4KdxE22+7Ymk0gHD7xfxQfL214HzH4VE+Z/tgM2shqJKVh8/JJxbqf2RYborCkmBDWt/xbbfd8Dd0x2XXjdGbB87bgzcPXTYvXs3as77AfrSdPhm/QXsWSQMqeTAfpBChwIhyZC0PnA1sUvu6I4O6QazGSbrDTlqX6abdNSJEuTm3iGdKAzDMO0NC1yG6WEil/hy8RKYCwtw0733nTQeiMQttSU7csftj2LrXxsxd+4ibN/+slOL8gMPvC8ukNRqy1vLgw8+2C2ig07G4IBQKKli4tUxETuSzh8YcT/kP1+AvGsRMOxup3iTs01vT19M6Z0kxC1Vscrq6ziWhOm2rFu3TjzWVNVg/ZqNQtzSY011Lerq6lCuAMq8AhEwagHMtSU4UbIXHkWH4LH/Cyj2fgq51xgoBtwMV+bS8N729RJ9rTCY+7swF74aN2FORSZVLHYZhunKsMBlmB4mckmkkFuy2WTEinfehCIopFlx2xIR4dGYeP0MLFv+AZYs+QWzZl0uti9evB4pKcfE+ty54+zRQUuWLOkW0UEnY4CfZQar0qBHYV0NYttZ7Eo+vYAhd0De/ibkgysh9ZvSfl9LkhDpYXHFPlRejB9zDuOCkCgMCwjjC2Cm2/Hmm28iMDAQXyz7Ao/N+hc8PD1QXVUNd3d3kX0d4dELYe6W+XeT1hOFbu7IixgAKWIgPE1meEMNb2M13CrygP1LIY24D5Kb6+ZLk5ilm3OZ1RUiZzeltAB/F+XCR6MVVd0+Pv4IdvPg/3WGYboUbDLFMD3IpMIWG2QzkSr64HV4KhRwm3Vvk31p9tbWokxQi/LCp94V6/Pm345du7fA398D6envi21xcXegtLQSgYE+SEt7t1tGB52KX/OOC1OX2+IHiapueyMf/RnygWWQBkyH1Ovidv96JrMZmwuysL0oD1Ee3rgyMh5eaouLK8N0J44dO4ann35aPMbExOCJJ54Qj42hS6haUw0q68tQYShHpaECMsxQQQGvmjL4h4yArzYA5j2LATcfSKHDAO9olxWM5AqfVVWBtIoSZFSUoM5kxPT4QaKiS5nZOqXKZY+9M2CTKYZxTbiCyzA9CKrSOjok+w9Mhnn9WjGPKzWq4MbFxdnXHU2miILCXBiNRhQUlOPZZ5eLam1RkcX0iOKCKDbItj579psiOuj5559Hd+eC0CgMDwzrEHEriBkLVBdATv0SCB3S7vN/9H1dFNoLMZ6++CE7A59mpOCy8Fgk+Pi369dlmI6GxOyHH354yv1I7LmrPMQSggiYZTOqDJWoNJShQqVDtbEaPho/1Kk1KDKVI3TrC1CpPSGHDrWIXT9yYHcdv0+lpBAGerRcGh6D3JoqBFpHElYdSxXGc9TibJZlkMxlscswjCvCFVyG6cF3gOX8PJje/R8U0++EIrZPi/s5xgQRM2dfjZyc4zCZjFCplKKKQTO4gwf3xvbtPTc6yEat0SAE4OjQXu0eyyGbTUBltqVt2YHVq1eLxwkTJrTb9/hz7hFkVJRigF8QLg6NgaYbz1ozTFug90YSgRX1ZcisOoJ+Zi2kE7uQK9VDUV8Nr9oyuHv1giL8XEgBiXDl7yOrugJapRIhOk8cKi/CbycyRRsz3eAil+aeKHa5gsswrgkLXIbpZDpV4JIwfek/kJLPgXLsNS3u17hdOTf3OL19wN8/EPn5ufb9Nm58FhdfPNDpYzdt2osxY+bjhhtuEHm8PQFq6/vyyH7oTSaRJWuL52hPZNkMOf1bSNGjsTv1KIYOHSq279q1C8nJye30NWXsKy3Exrxj8FRrMKFXot19mWGYljlSkYby+mKYIUNprIenoQ4+gcnwUmihLT0KBA2EpGydL0JnUFhbLeZ108tLUGMyiFGFBKtBVU8SuyxwGcY14RZlhunB0EWI1Dse8tH0k+5na1fOzMwUc7yUrUuPbm7uWPDYS1j51RIcStvbQUft+rgpVZgY0w/LjuwXbX03xvaHu6qdL1b1lUD2H4BXBJ56aqF981NPPYWvvvqq3f5+BvoHI9LDS1Rz2v17ZJhuQqx3H3GDqNpYKaq7FfWlyKw+Il7TGCvgW3UYUT59IZcfA9yDIanbtxPkdAnSeeBSXW+MCYtBDhlUVZTgYHkxdhafgKdKI8ypRgSFw0PFM/oMw3Q8rjP4wTBMp2CM7I3SIxmQ62rF89LSUiFeHVmzZg3279+P6OhomM1mhIZGISoqVhhPXXrJdfjX/FeE6H3wwQ9EW7INW3QQtSxfeOGF6ElQRZNELlVxvz5+EPUOP5f2QCIDm9HPYPcJpWhPnnB+L4w/Lxpff/21yO9sTyg2aHyvRCHsyUn6m+OHUGWob9evyTBdHbpB5Kn2RrhHNPr6DUay/zmI8+oLH89oyEqNpcNmx7s4lL0OlXsWQc78DTLdyHIhFJKEKE8fMZd7R+JQESkW7+0nRhdonpdILSvCidqqzj5UhmF6EFzBZZgeDAnZ6c/9D+b0A1h+251A4gDMnj1biNWWooMaZ+MuePIu5OZlwtPTG3v2HD1JdNBcLF++HFu2bEFPwU/rhhti+mL50QP4JvMQru/VF6p2NKCSlFpRsSWeuClZmH99szWzXau4jak2GoTbqu3ilmGY1qFUqOCr9ReLDdO5c6GpOARlcTbkvZ8gryQFNX4x8FZ6wssvETr3UJdpB1ZYI8VoGWOdPSaR/ndhDuK9/RGq8xQ3wMrr9cKsivZnGIZpD1jgMkwPFrc333wzVnzzjXi+/OMlYhbXcU62Nfm4JG5pJpequjU1VZg//xNMnnyBeG3+/E+hUEh20ymaB6Wqru15TyBY5yFmU6lV+fvsDFwblWC/sMvOzkZJSQn8/f0RGWnJ1jwT6Odrq94mxwaIbY5V3PaaxXWELmJp7pgubisN9dhWmIMLQqLZgIph2oDWIwSxHiFA2EWieqst2oMqcyVyNErItUehrkqDt3s4vNW+8NL4QK1wjZZgm+imx1vjB8Eom+1Z2jTO4KFSC9Hbx9sfER7eLHYZhjmrsMBlmJ4sbleswLjEOGzJysV9b7wHWbsEgR7uGBkZJl5rTuSSmCUXZYJalB2rumt/WI6XXvmXPTqosNASHfTII+PF8xdfXIUlS5Zg1qxZ6ElQRYOE7ZrMNKSU5CNj4x945ZVXnKrZI0eOFFXuSZMmtfnrOFZvbTxx05AOr+LaLm6L62qwv6wQR6vKcHVkAsLcPTvk6zNMd0TSeiEw4gIEUmW3vgpVRftQoXRDpbEaxfpCsY9O0iDRL1lUg20Ozp0NHYNastzgGhYQJqq3aeXFSK8owZ6SfDG7TwZVtNB7JYtdhmHOFHZRZpge5sLYWNwum3Itvk7NwM0r14rXl066BhP6xWPq8u+w5tBhTJ482S5yx40bh8OHD9uzccOsApcggUvV2bvuuR5Z2YfFxRW9uwQEeCIt7T2xT3z8HVAo3JCWlgZvb2/0NLKrK/DG08/ixRdegFqrxaDLxyAoOgqFmVlI+WkjDHo9Hn/8cZEb3JbqLTknU/V21YJLnV674Zn1QuS2p6NyS5Tq6/BDdjrya6sxMjhKGM/wBSzDnF3qKzJRkb8NtaHJiPKKgznlYxwM6gV/jT9C/AdY95JcQvDaoHNEXm2VcGJOqygWHR/uSjWmxQ3oEOf5swG7KDOMa8IVXIbpYVRVVYkLC2JLdh5Wp2Zgcv8+qNDrxbZJ/ftgxf40bM6yxP/QvvQxfn5+wmyqcTZu46quh7sX6usbTKoWLpwOb2+LA+izz07H7NlvCgH3/PPPo6ex9YefhLiNGTwAM157AV4BDbN21z18HxbfPw/PPfcchg0bhokTJ55x9bZJFfdfj+Cr735GR88hk4v01oIcbC7IwrGqMlwVGd9lLmAZpiug8Y5GoLflhqO4uahUw7/gANzLsyHX16E45kLk+obCS+MHb1rUvtAoO/d/kMQ2VXNpuSg0Gidqq8X7A0UOEWRWF+Pli8H+IZ16nAzDdD24gsswPfQOMBk+3TNnDoqKi/HOdWNx+7BBYvuiHSm4+9v1CAwIwFtvv40pU6acNBtXpVLDaDTCYKgXFyzR0XG45srJeG/RfzFgQBR27HjFPnNLFd5hw+YiNTUHqampiI2NRU9i1KhR2LZjBxas+8pJ3NqoLC7BwituwDnDh+OPP/44K9XbxlXcnT8uxpArZqCzKtg/ZGcIR2lyXe3rS82WDMO0VzY2Sg9DPrEDtaXpKPUMQKVvFGrc/UldQqvUwVvtY5nfVXuLtmZXgS5N/8jPEuI3ztsPx6vKxPxuH+8ARHl6u4yJHVdwGcY1cZ13M4ZhOgxL+7BlOdk+rcnGdYRycom9+3fAZDLi1VdnOxlK0TptGzNmPh555BEnQ6vuDhlK0cztsOuubFbcErSd2pY3f/sjcnJyEBER0arP/dZbb4nHvw4VYOCc5udsS6ssFfq3lm3Aok4SuDRfNz1+EH7JPSoMt3w0bk3mculviGaRCZpTpmgqhmFOH4lEoH8CJP8EuMsy3Csyhdg1Zq1HZUAfVEWdg/L6EhTWnRD79/EZIISuwWyASlJ1ajszfe0LQxv+9yluLauqAvtKC0UcWZyXH/r4BCD6JGLXcaTmVND5zLFDiWGYrg0LXIbpYVDOLUUBkbgkQ6kvJl+LiUkJonJLUCXXQ63Ggz9uwo033ihmdV988UXh8utoNtXcxYCtqltYmNeh31NXgNySCZq5PRmBURY35eLi4lYL3Ntvvx1//vnnSffx8wT8QoHZd90D2VANeftbkJJuhOTTCx0JXZxeHRmPZP9QIW7pRkppfR08oMBLL72EZ555BrW1lkzmH374AU888QQeeughaLXc0swwbUWIVZ9e4v9dk3gD/E31CKCs3cJ9qNvzGSpH3AN3pQfksmPIRBVMkoQ+Pv3F/6feXAetwq1TBS+JWTKhKqyrEQZVaRUlwsBOq1Qi3ssffXz8Ee3p4yR2SdweSk9HYPTJHeqLMrM74DtgGKYjYYHLMD0MT09PkXNLkFvy+L5xYuaW2pIJErc0l6t1yGt9+OGHoVKpThkbZKvq1tfXi4uLBx/8oEmLMm3TaDT473//i54ERQERZCh1MoqyLBdbAQGWmJ/WcN5552H//v2t3l+uKwVUWkDtgc6ALpQjPLzE+uHKUvz3i0+w/n9v4UhGBoJiojH98YfEa18/9zLmz5+PxYsX44033sAVV1zRKcfLMN0NSWmZc5WCBsDtgn9D5+Ynnpt3f4AQ2QiTXyxk3xOoCeyHg3XHoFFoRTuzl8ZXPKoU6o4/ZkkSsWu0jAqJQhGJ3YoSIXgPlBXizr7D4K5SiO1+GjfxMSRuH1299KSf98UJ0zroO2AYpqNggcswPQwSqCRUCarOklsyGUrRzK3ZbMbt3/wkZiRtwmnChAm45ZZb7B9/MpHrWNWdOeMeLPn4bSxZ8gtmzbpcbFu8eD327DmKefPm9bj5W6qAUxTQtp82CkOplmZwyU2ZZnVbW71tC5KbH6QRD4h12VADOe0bSH0mQFLr0JFQdWjBHXOw/MsvodG54eoH7sboW6dCpbFcfP9z1af49ZMvsP69JbjyyisxdepULF261KWcYBmmq0PvB/b18x+FR/5uyCd2Qj7wJbQSEBs2FJVBiaiUzSjSF4j93FUeYnaXFg+1FxQdPBNL7wFBOg+xjAyORLlBL+KGzLKMlUdT0d+PZ2AZpifjGlP6DMN0isilCCCKAiquqcVrr7+OPomJQtxSfFCAuxsy0tPxwP33i1Zm2kaCmCKGKGroVMx98BH4+QVgwYLPUFFRIxZaDwnxxfz5FnHV06DZUooCIrdkErOO0HPaTq/bZlA7hMocIHsz5M1PQ644eXW5PS5SK8otWcn3ffo+Lr39Vru4JWj90ttvw72fWmKmysvLWdwyTDsiaX0gRY+G4py5kC57FcpB/4CPyYTIlOXot/Vd9M/eh16e8XBT6lBUV4C0iv0oqLU47teb9Kg1Vp/Sv+GsH7MkwddasaV3h0m9+7HzMsP0cLiCyzA9FMdKLrUOr169Glu3bj1lNi6JXOJU7coRkUH4x4y5eOmVf+HZZ5eLTNyCgjIsWjQX3t40oxuGnsakSZPw2GOPiYgkcksmQymauaW2ZFsO7mWzb8P5V1kq3h0BGdDggicg73wH8uaFwIBbIEVd0GFfn26y/Pjjj8g+cAjhiQnN7pOdesi+L8MwHYOkdgcizoMUcR5kkx4o3A+tbIabWzACjGaYUxajbuidUGstLsJU3S2ozcNg/xHieXl9qaj0qhWajjtmSUKgmyWWjmGYngsLXIbpwdhELjn8Pvroo23Kxm0JL283XHn5RPy47ku88oqldTk5ORkzZtwL4ABN7ALwQU+Dcm6HDx8uHILJLdkGtSXf98ADwLAkfHXsICbG9GviMNxeSB4hwMj5kPcvhZyyGHJJOqQBN9vn9NoTaoG/8847seenDTjn+mub3Sdl3QYxAz5+/Ph2Px6GYZoiUWZu6FCHDRKkwCS4e4aL9wnz3k8RLCnhEzoQkM0w03x9xUHIkEW1V7Qza3zhSXFEUoOzPsMwTHvAApfpkhw/fhyvv/461q5di6ysLOGwSgZHlNl6zz33wN397N3BXb9+PT777DORS5qXlycutENCQjBo0CBceumlmD59ujBu6soit3fv3qIya8vGnbZyLcr1+mazcb9cvKTFbFxHNBolPDzd8Ni8pzD9tuvFtldffRVKZQz9BgEcBHAueiITJ04UC91YIHdlMpSyzdxSi/hXxw8is6q8wwQuQRep0qAZkP3iIe/7HHL5MWDYHIv4bWfzrbFjx+Ln9etRU14Odx/nmx60Le2vbTh39EXw9vVt12NhGKZ10PuCNHB6wwa1OxS5W6E7/gtktScQMhgDQoei0jsUlcYqlNYXo6AuDxIkMbNLgtdH4ycqvAzDMGcbFrhMl+Pbb78VpkcVFRX2bTU1Ndi+fbtYFi1aJIRvfHz8GcfpzJw5E998802T1+hrUxwORe2cf/75ojLZ1Tkb2biN8fbWIqnfMHsr9OjRo62v9AWwg5rayOcSPRUynqLFEY1SiUkx/aCyuljrTUZolR33Vi3ak31iIO98G/IfTwGjnoDkGdquX9PWprxvw+9Nqrh7N/wGs9GEXqNHYXNBFkaHdmysEcMwp0bRdyLkxBuAiuOQ83YCJ3ZAlb0Zfkot/IIHAeEjUB+YjApDOSrqy3CiNht1phr09uoDo9koBLCfJgAqBV+WMgxz5vA7CdMmPvnkE/GYmJiIc8/tuCrcrl27RDYr5WRS1fTxxx/HmDFjxPNly5bhgw8+QFpaGq655hohdr28LFEkpwuZ2Vx22WXYsYNEGHD99deL+UmqElPkDVWNf/31VyFwe0I27m3J/UW+4Pxf/hA/fxKs9LMODrbMXrWEl48bijKKMXXqTVAoHM2BSDD5Wqu4o6zWIIwNm7g9XFGCn3KOYFrcAPhYTVQ6Ask7UghbZP0OWCu4smyG1E5OqbY25TX/ex2bPnaO9KgoLBJdEwtvvwshgRZn1IyKEuGYGu7etv9vhmHaK2s3BpJPDND3BsiVuQC5MZ/YCWRvgVvoMGglNYKKDkMOGQqTyuLhUGOsQmbVYfj40TlBhRJ9kaj0eok4Ir5MZRjm9OF3DqZNzJgxQ5zMvvjiiw4VuA888IAQs3TB+9NPP4nqqY1LLrkECQkJYpaURO5LL72E//znP236Ovfdd58Qt9T6TG2748aNc3qdZihJ9NIcJRk0dfds3CW79iPcy0O8Rq7L9LOZM2eO+P2fzGjK29sNZrOMmup6eHppHV6RrFXcrQBO9EjDqdYQ4eGNYYFh8FY7/uw6BhEZFGsxu5Lz90DOWAuc86DFeKYd2pSpK+Pvv/9u8lpAZJR4j4kNa4hN2lOSDw8WuAzj0khe4YBXOKSEayGbrM775ccgpyyBdFEs1JpwyGVH4eXmi2T/c6C0itmiunxUGizu6h4qamf2EfO7HirP077JVpSZfcqcW9onIKF5gzuGYbomktzRfu5Mt4DMhahNl6qkQ4YM6ZCvSRe/NjFN1Z533323yT6U4zpgwACkpqbC19cXBQUFJxVgzUGzthdeeKFY/+9//4t//vOfaE8KCwubVELpuIOCOjbHj6J/KAKIZnHJSVkYSrnpUFlZiXqHWCDKxiW3ZVtr6cnclE0mM37bcBiJ/YIRHtmcodRfdP+empc5tewUHK8qg6dKiwC3js2qJeSyI5CzNluMpzo477LZ45FlGMxm0c69v7QQRXU1ODc4Am4d2MrNMEzbkPUVgMZL3CQ3//5/QEUm4BsLKXSYMLKSPIKhN9XZ25lJ7JpkIxSSEl5qbzG/G+QWesrIMLoxffjw4VYdE3VnOea4d7XzN8MwzvDVANMmyJRoz549orW1o6AYGxs0G9scVIW89dZbRetyWVkZNm7ciMsvP73IlTfffFM8+vj44N57yfG358UG2aKAzhs02B4d9EdmDmpMZqSnpYk2ZqrmnioySKlUwMNTg4qKOoQ365hMVdzfAVD+Ks9WnkzQ/ZGfhSpDPSb3ToK/tmNFrkQXn76xlmMp3GdxWe4zvtPELl3Ykrgl6s0mpJTmY39ZIc4PjsQg/2DRTs8wjGsiab0b1s/7J0AdItTKnLYaOLgCsncUNKFDEUiLVx97G7MQvIYyFOsLEawLE++L2dXHEOAWBHdVU0O+tghWhmG6B3wVwLQJas+lkwsZPnUUVFklPDw8MGzYsBb3azAyAjZv3nxaX6O+vt5uKkUzuG5ulrlHakOmudtjx46hrq4O3V3kksMvmR855uK+cc2lqKuvR3FJCV6/aozYRq+RyKXKL1WAm7uDPm365bj62gvRv39/sTi3e5PoDQeQRj/lDv1euxIk6K7v1VdUKFccPYBSfSf+DVadADLWQv7rZch6SxthZzIkIBQzE5IR7+2HjXnH8El6ipjR5eYkhnF9JLUHpMiRUAy/F9Jlr0EaejfgGQ75yDrIv/0b8t8vi/c/cl4O1UUg0WcA+voMFB9LVV0SvQaz5dxTqi8SgpeqvmaZgoqcofP3rFmzRPoBPdJzhmG6J1zBZdo8C/vRRx/hnXfewbXXXitOGO0NtR0T5I5MM7gt0bdv3yYf01qoKm0TsAMHDhRt2E8++SQ+/vhjUREmNBoNLrroIixYsAAXX3xxq1qYTkZRETkJu5bIffvtt4XpFMXYnEkuLrWHZWYeRXh4L1RX1yM3l+KBGpMIYBOAo/Tb7ZDvsStCpkrkrrz86AGsPHYAU3ondajxlA2p91jAKxLyrvcgU3vh0Lsg+VuqLJ2Fp1qDyyPihNj9NS8TazLTEOnhLRyXQ3QcQ8IwXQFJpQXChkMKG26Z2S0+CBgt52O5pgjyn88Dw++D5GPp9lEp1Ojv15BgUG+uR3FdIfJrcyFBAU9rHBHN7+ZnFWDEiBEoLi6Gh5cHNmzYICq827ZtQ0wMRdcxDNOd4Aou0ya8vb3x888/CzF55ZVX4o477sCmTZtEpmd7VE5IdNqEYONYlcaQyKIqL0FV19PhwIEDTvO8ZCb12muv2cWtrcpL2bhkavXCCy+c8nPSfM7JlqSkJLgadFxUyf7yyy/FHC7l4n64c6/IxaWF1m9euRaSzl3sQ1XcxuLWBonbxR98LxZabwr9rmh7Bv102/1768p4qDWiRZlacKmSW1FvueHQ0UiBfSFd+KRwWJa3/hfy4R9comIa5OaBiTF9MaFXImqMBnx+eC9Sy1zrBhLDMKdGUqohBQ+EFD7CukES87lwt8y7mvcvhTllCeSCFLuBVYguHIP8hyPJdzAiPKKFE3NuTRZSy/bgoSceEOL2hY+ewa9HfsLzHz4jrimefvrpzvw2GYZpJ1jgMm2ConIoImjv3r2ifffDDz8UVVwyVqDqKr3e0nKy6mtLkNGRo+PvqbAJXKoqng4k0G2QeKWsWxLwZHBFIpvMI6hqTfO5dEH/2GOPNZuT2x1oTS6uo4M0zWNTmzK1INvakTMySLQ6Q9uatiuTgyV9nab7M02rlZNj+omLNxK5lZ0lct38IJ37TyD2CsgHV0Le8SZkQ3WnHIvTcUkSYr38cGv8IIwN740YT4oeAXKqK1HfDRzPGaYnIukCoOg/zeLuTs813kDxIcjbXoO8/kGYd70POW87YNJDp/IQYjfBJwnJAeegj3d/FGZbKreXXjdGfPzYcWPg4enBbcoM003hFmWmTTQWPe1dvXGce6UW4VNB8T4ERQqdDtXV1U5fk+Zwv/vuOyHMCRLwd911l3BqpllfqvKSoRUJtVM5Ona3XFyKDtqVVyBycSlKiSrbNLNMApYWqtaGhUUjPCza/nlt603blen3RSZG5HjZG0DHOwV3Jbw0WlHJXX50P1YeSxViTmmNeepIJIUSUt9JkP3iIe/+EPKfLwIX/tslnJYVkoRB/pYMX5PZjG+z0pDkG4iLQtnMjGG6OhQ9hPhrgKoce9auvPMvQKGGHNQfUtgIpFf7C78QIjc3F9WV1Vi/ZqMQt/RYXVWNnTt3iputZGJJMYMMw3QPWOAybeLf//53h349m9kTQULqVOits6I6na7NX8dWxbWJW0cuuOAC3HDDDVi5cqWY86VK9qBBg9BdaE0u7l/Zefb9c3Jy7NFB1L5ua0tuzMKnGqKdZs6+utGrJHCPWw2nBrfTd9Z98LaK3IK6mk4Rt45IIclC2KL6hBC3stlIPYYuc9OHfj7TYgdAbf05HSwrgk6lRi/P5py9GYbpCoj3F69IsUgJ4yBXFzSI3ewt+P5PhX3sKCbYE1VKBR6b9S+4e+hQU10LlVIhxo9o+f7774W3CMMw3QMWuEyXELheXl729da0Hdsqsa1pZ27p61C19mQZv1dccYUQuAQZVbQkcKmt+WTQHJCrzeE2jgyauvw7YSgV4O+PyqoqIW7t0UFm2Sk6aM2hw/DzC27LV7W2Ku+3il0vLF26VLwybdq0s/wddg/IZIoW6qDYWXwC/XwDhRkVQQZh1HLv7+9/yrn1s4HkHgjQQh0dKUsApQbSwFvhSjcEbBwqL8bhylL09vTFRaHRCHBz79RjYxjmzKH8XMRdCSnuSnGT7Z6hMrb8uh4rVq/F8D6B+HH6MLywMgXH86sQFRSJgvIa/LA9R+S5z5kzp7MPn2GYswgLXKZLQJXVgIAAYRJBF+6naq+1CdyoqKjT+jqO+59KFDjuezKn5K4a+H6qXFyKCfo6NUOYTdXp9Vg66RpM6BePkBfeFu3HtgottSXbKrcLnrwLuXmZYp32adoSRi3MR6jGhk2bqkX8EBEREeEU/8Q4U2syYntRLnRKFQ788iteeeUVbNmyxf76yJEjMXfuXEyaNKlDjofMYQDXqN42x7joPkivKMHvJzLxSUYKBvoHY2RwlP3mAMMwXRtJoYJaAXy+/GvgphuxYtXXiA7yxKIHLhSv/3PRX/j4l3QhblvKcWcYpuvCApfpMlCV8/fffxfznUajsUWzqoMHD9rX+/Xrd1pfg2ZxmjNQag7H19tinNXVRC61fdvmn6ma21J0UJ3JBIPJjNLSgiaZwSRu8/IyRdQTidu4uLhGX5HawRNhMu3Agw/+CxqN5aLjwQcfxPbt25ttF2csEUK3JQzG//3rCTz//PNQa7UYdt2VCIqOQmFmFrb9tFFcyNG8+LPPPtvuxyOFn2tfN+/7HJJfHKSI8+BKrY19fAKEGdWeknxsLcjGwbJinBMUjqEBYVB1css3wzBn7xz25tvvCoG75UA+7nzjD7F937FS8fjWW2+xuGWYbkj3vCpnuiU090oCl6qzO3bswLnnNlxEO/Lrr7/a10eNGnVaX6NXr16Ijo5GZmamcFek1s+W5ggp49UGVRi7KzaRa8u5JUOpOXffLaKDyvV6ERtEkAEVzegGBgTgrbffxpQpU8QNAzKUcoQquju274abrqWLiggsXvwa9uzZh3nzJoL8y158cRWWLFmCWbNmdcB33DX57uvVQtz2GjwAM197AV4B/vbXrnv4Piy+fx6ee+45DBs2DBMnTuyQY5LNJsBYC3n3B5BL0iElTRXxH64CCdlhgWHCfGprYQ625GcLwTs+OhHBnJ/LMN2CtWvXisethwrF0vi1GTNmdNKRMQzTXvBtaqbLMGHCBPv64sWLm92HXI0/+eQTse7r64sxYyyRAKeD7eK/oqICv/zyS4v7ffXVV07iuztDIpfE7alig4jGr9valWkRzsmShMPpLWeTVlRUYsGCJQgJ8cX8+VOwYMEUBAf7YsGCBeJ3wjQPtSVT5baxuCXo+czXXxCv034d6rI8eJZlFjf7D8hbnoNc03I7f2dBhlNjwmJwW8IgRHv4wFdjMZurMnAuM8N0ddatWyceAwMDxA1aym6ndeLHH3/s5KNjGKY9YIHLdBnOOeccXHihZX6Gcnf//PPPJvu89NJLwtWYIEfExq1HmzZtEhVZWlq6a0vtsDY35YceeqhZUfXZZ5+Jz0Vcc801pz3r2xWh2WZqc506dSokfZ2IDpo1dKCo3NJClVyaw6XXKDqI9qVqOLUie3hoxELrfRLiUZBfhdKSmma/zsKFC1FQUISFC6fD29tdLAsX3oL8/PwOaa/titBcOs3cDrp8TBNxa4O20+ubN28Wrtcdhfh/ix4NaeR8SzX39/+DnL8broifVocrIuOgUSqFuP0obTcOlLmeIGcYpvW8+eab4gbp/v0HxHmJuotoff78+eI1hmG6HyxwmS7Fa6+9JqJ/aAb38ssvFy2XZHq0ceNG3HnnnXj00UfFfn369MHDDz/cpq9Bouypp54S6xT/Q8KaKsbUFk1f57777rOLY4rE6ciKmKtGB9GyYt8hYTJFr9mEzZo1a7B//36n5cd1a+Ht44b0Q4Uwm+Umbd+vvvoqkpNjMWPGpfbtM2eOxeDBvcXP+sgRMqFiHCG3ZIJmbk9GYJTFOI3M2joayacXpAueAAL6Qt7+BsypKyxxQi6Kh0qNyyJiEeflJ54frypHncl1j5dhmOYJDAzEM888g+DgBnd/WqebqfQawzDdD57BZboUFNtD7UW33HKLqKzSHdjGkLiluRrHyJ/T5ZFHHhGigXJwDx06hH/84x9N9qETZE8Kh28pOohmbmWTCfd/vwHL9h4UMUEnc6Yk4ZuQGIQdf2chN6cckVG+9tfoBgXlHL/yyu1OhlK0/uqrszFmzHzxu1m1alUHfdddA4oCIshQ6mQUZVkcyMmRvDOQ1O7AsHuAoz9BPrgKMmRI/aY02Y/+rxqPJXQ09HdKsUuE0WzGj9kZMMkyzguOwGD/ECgl5/vDNLdPTtUE3YihG2UMwzAMw3Q8XMFluhzXXXcdUlJSxMUkiVl3d3cxbzt8+HAhSHft2iVces8Uqg5TO+f06dMRExMDrVYLHx8fjBgxAk8//TTS0tJw/vnnoydhE7kkYEnIFtfUCkOpN995B0U1tacUtzaoghsW7o2jGcWorz+5W3VroLxcW2ZuT4QirSgKKOWnjagstlRzG0Pb6fWRo0Z1qimaaFmOvQLSeY+KR0I2Nrht0//v9ddfL5bdu3e7jBnVzXEDEe/tj1/zjuPj9BRkVJSIeXNyF6fW+b59+4q5fFpond4/6DWGYRiGYToWST6VYwzDMO0KZeg6tk4RBQUFLp2fazAYREYtvX1QNZceJ40ZDRTl4+3vf0JIdC/73C61Njcnduvrjfhr83EEh3ohsV+wvUWZ4qCSkiKxffvL9iouRTINGzYXqak5YsY6NjbW/nloFtpmJkbrPTUvd+XKleLmQq9BA4ShlOMsLolbclE+nrJP7HfxNVcjs7pcVCIVLbiEdxRyfaWYy5USJ0KKPF8IW1sFl9YdzdxcgcK6Gvx24rhoWS7anoJlC1/E0YzDCIqJxvWPPyT2+fq5l1F4LFN0d7zxxhu44gqLkGcYpnvRFc/fDNMTYIHLMJ1MVz1Bksi1RQcR+UcyMOe6qyGFRmDlLxuF6CXBRXO7LVV0szJLkXGoCMPPjYKXt8XYa968eXjxxRexaNF9mDXrcrFt0aJ1mD37TcybNwnPP/8GgFAH4TsMqakHxPOkpP49Oi+Xcm5tObhkKEUzt9SWTJVbg15vz8HdWZQn4nCmxw/q9MxXWTYDR34EIkZid+pRDB06FBPO7yX+fr7ZmikqusnJyXAl6NjGTZqI7776GhqdG8beMROjb50KlTUn2lhfj18/+QLr31uC+ro6YcxGHQYtRY4xDNM16arnb4bp7rDAZZhOpjucIG0VXarmEjQnTW8tdGFPtNS2TCZT27ZmQq1WYMjwSCEAaLaaKl+SZEBa2rtiv4SEOyFJGqSlfQlv72oAg6kxF4sWLcLs2bOd8nJpW0/Oy6X5ZJoBpfZ6xzxoaul3zL81mE1QK5Qo0ddib0kBzg+OFO7Bncn146/D6jXfYcfr48Xvc/gD37hkFZe46qqrRMTIwys/QXhi83P4OQfT8PLk28S+33//fYcfI8Mw7Ut3OH8zTHeETaYYhjlr4nZcYhy2ZOXinrvvIvsgBHq4C1dlm/BtLHIVCovh1J6dOSg4UYWQMC/hTE3uliRcn312uRA6BQVlQrh6e1P7cQqA3aioKBbRDyEhfiIvl1iy5BexjQQ1fZ6eCIlYWig6iIzSyFCquZlbErdEUV2NqOYeKi/GmPAYxHv5dUqlkSq1JG6pepscazHBGn9eNL7++msxi+tqVVz6GyOBm33gUIsCNzv1kH1fhmEYhmE6BjaZYhjmrInbZVOuxWtXX4KiklIUl5Tg9avGiG30Gu1D+9LHOOIf4I6gYA8cTi+C0WgW22bOnInBgwfjlVfW4NVX1whxY4lmIuE1CEAcFi58Wtwpp4xczstt3nhq0KBBpzSU6uMTgBkJgxGsc8e3mWn4JvMQKuo73hzJFs31xE0NQvaJm4ZYX/s/uBrk8KxSqbDnpw0t7pOybgOUKhWuvu7aDj02hmEYhunJsMBlGKbN0AyubcphS3YeVqdmYHL/PnjnurFioazcz/akijghglxlyXiqMXF9gmAwmJB5rMQhFuhV1NcbxELrDXO1Eg4f1liFL+flng28NVqMj07EdVF9UFBbgyXpe7CtMBcmmo/tqOrt6tVO1VtiSFyAtYq7Grs2fQ1Xi2YaO3Ys0rduQ015eZPXaVvaX9uQcN4IrCg4hvU5R3CituH/hWEYhmGY9oEFLsMwbYYMpqgySzO3cNNh2sq1+HDnXtw+bBBuS+6PCz9chjvW/ARJ545ly5aJ9uR77723SRVXp1MjOsYPmcfKUFtTL7ZdfPHFoqWZlsbOyJa8XEOLebmUpUt5uUzrEfnEPv6imjvIPxh/5Gfi84x9QpR1RvW2SRV3/lyY934CV4Jaj01GI/Zt+L3Ja3s3/Aaz0YR7b52BIQGhOFJZhqWH9+Gzw3uxu/gE6kzGTjlmhmEYhunu8AwuwzBnBFWkbIsNg8mEW1f9gJ15BeJ5XV0d/vzzT2GAZKPxPC4J3LzcCmSkFWFgcrjYNm3atA79XhgIo6mLw2LQzzcI63OPwGg2d0r1tnEVd/Wfx7HnhBJDBgKyvpIGuCGpPdDZbcp33nkn1vzvdWz62DmHuaKwSLQw3zRxkqj2konXsaoyYei1Me8YfjuRKbaNCLL8rTMMwzAMc3ZgF2WG6WS6sgsjtRuTGRQJVzKUeuPqSzAuMVZUbknc0uzt5qwclNXqYZZl+LvrMCoqHGsOHW7WWbkgvxL7U05g0JBwBAS2LF7akpfLnD50eqDKLj1+m5WG/r7BiPO2xEKdLW6//XZ8+OGHCPPXwc9T2+w+pVV65JXUin0/+OADmFM+BkrSIY1+CpLUuY1INC/+999/N/vaueeei48++qjJ9ipDPQ6UFSLQzR2xXn7IrakUC1V6lZ38/TAM0zPO3wzTneEKLsMwbcbT01Pk3BLklnx1QgwuWbLCLm7JYOrr1AzcvHKt2IcE8IR+8Zi6/LtmnZWDgj3h66fDhAnjUVKa5/S14uLisGbNGvv6gw8+KPJyyTnZlpe7ePF67NlzVGTpsrg9c2xuygazGQpI0Fh/1zbhezYg0UrV/ZPh5wn4hVr2FcfVZwJQnSfErVxXDpQfA4IHdYr78+LFi0/7YzzVGpwT1GD+VVBbjdSyIgwLCBPPSeyG6Tw5N5dhGIZh2gBXcBmmk+nqd4AdnZSvjI/BhqOZqDeZ4a9zw5vXXIpJ/fuIuVxi1tCBWLE/Dfd/vwHFNbWYNGkS3n//fTHLa6OqUo8BAwcgN/c4wsN7iW20Ttm4+/fvt+/Xcl4ukJa2Bd7e/Tr8Z9EToFPGqmMHEeHhhRGB4VBZRW+nHc+RdZBTlwOBSZD63QjJOxJdEepwUEiSyCUmky9vtQYD/ILR3y8IXurmK9sMw3QuXf38zTDdFa7gMgxzRlD1laqwhK0qS9XVjz78UJhOlev1wnSKWLQjBXd/ux6BAQH4cvESTJliya91xNNLC7VKKcTt4g++F9tmzr66yX4t5+X+G97e5KBM85zOFx7MmWOGjFB3D/xVkIODZUW4NLw3oj19Ou+Ael8OySNEiFz59/9Ajr4IUp/xkLSdeExtgMQt4adxw9TY/mJW9+/CXPxZkI0YL18M9AtGby9fbmFmGIZhmFPAApdhmLMqcs1mM8477zx8+knLjrfNNY6MGzdOzNYSmVlHERYW7fR6RkYG+vfv79SuTPOPb775psjLJSx5uQsA7AawA8B5JBnO6vfa0yGBdUFINPr6BOKX3KNYeSwV/XwCMTqsF9xVDfPUHYVo4w1JBoIGAMc3QU7/BnLuX0DcNUDvyyApO/6YzvT7CXf3EsvFYb1wqLwYe0sLsCYzDR4qNZJ8g5AcEMJVXYZhGIZpAb4VzDDMWRO5JDYpomfq1KmQ9HX4YvK1oi2ZKre0UCV36aRrxGs33nijMJqy5eKSuE1PT0d1db0Qt+EOApfWaRu9RvvYhHDzebkkaIZSjRcAmf+0f8xNT4QMkqb0TsLlEbE4WlWGxem7kVKS32k5r5JCBan3WEgXPwdEXgA5bTXkX/8FOX8PuipapQqD/ENwc9xATI8biARvf/EzrrbGbJXq69rd5ZphGIZhuhpcwWUY5qxBs7Rubm5206nxfePEzC21JRM+Wq0wmVq296BwUqZqFRlV2XBsS3Zk4VOWGdvm2pVteblEQ14uuSqPAEDmRX/R0VDabrt8zz0Z+v3RnGicl5+IvVmfe1SYJU2K6QdlJ83mShpPSP1vgtxrDOSDKwBTndgum02QFA2ZyV2NIJ0HLtH1xkWhvaC0tjN/n5UOX60brolKOKvGXwzDMAzTlWGByzBMu83jklvy5qxcMXNLF+D3/7DRLm4nT5rUJCaorTSfl6sBcA6AzdZK7vnWbczZRqdS44rIOCT5BSG3ulKIWzJNMslmqDtJVEqeoZCG32evKMu73oOsC4Ai6UZ0ZRxNva6OiofJ+v1lVJZie2GuuOGQ6BMg8owZhmEYpifCApdhmHY3nSJDKRIa1LpM4paclT+ZfWsTcUtuybYKLbUl2yq3C568C7l5mfZ9yD25dVDV9lwAWwBst67zhX97EeXhLRZib0k+thXl4db4QZ0qtmw5vlLQQEDtLrbJ+kpAqYaksnQbdFX8tA1dCe5KFbRKJX7OPYJNJ46JGWkypgrReXBll2EYhulRsMBlGKZdRS6JC3JLpkcSvJSb++nd/4Dyt/UwxydCEdvHbhzlaCjlCIlbEraxsXFC3Drue2q8rJXcrQB2AhjG9gMdQLSnL2g6lMQt/e6rjQaR/9oZCIEXfaH9uZz6JVB0AEi8HogcJfJ0uzoRHt64wcMbFfV67CstwL7SQmFOFeTmLqq6/XwD4abkUz7DMAzT/eEcXIbpZLpzjh5l5FZVVdlzbslQimZuVSolzJ99ADk/F8o7H4bkZan62SC3ZDKUcowJUioVWLLoeww7Nwru7m0RSgUAtgGgnFSKLeKqVkdBgmtj3jGMCo5CckCoPRKns5BriiAfWgXk/g14R0HqNxVSYF90J6hF/FhVmfjZH6koE63Nd/Yd2mkt4wzTHenO52+G6cqwwGWYTqanniDlqkqY3nsJUlAIFLfcCclhtpAELrklk+kUQdXb+PgEfPTBWlGNG3ZOpBDJp0+2NUIoHkD3EjSuTJ3JiM35WdhTko9gNw+MDe+NUPcGc7HOQi7NgHzgS6DsiIgakvpNEZm63Y1qQz1ya6uECzO5Lq84egCjQqI6N7+YYboBPfX8zTCuTtfvy2IYpksieXpBccMtkI8ehvy7xWXZBrUgUyuyh4dGLLQeHx+HQcnhqNcbcWCvJY6GsnNJDDsutK1lqHrbj5qgARxt9++RsUCtsZeG98ZNsf0hQ8bSI/uwIfco9CZjs/tnZ2cjJSVFPLYnkl88pJHzISXfAVRkQv71CZgPLINsqEZ3wkOtEeKWqDeb4KPRwkNl6YI4XFGCo5VlouLLMAzDMN0BHshhGKbTUPSOhzz6Mph//QnoFQtFDFVWgTVr1rT4MUkDQ5GyKxdHMort2bmOld5TQ/O7egD7KWmU7KzO2vfDnJwwdy+R6bqr+AS2FGQhvaIEY8JihPiiyvzKlSvxyiuvYMsWMgWzMHLkSMydOxeTJk1qv/nciHOB0CHA0Z8hZ6wFAvoBIYPRHXFXqXF1VINJG83qHq4shZdaI2Z1B/gGwUtD/xcMwzAM0zXhFmWG6WR6eouTbDbD/Om7kIsKobzrIUgeZAp1cjKPleJwehHuuncc9Hqj06wuVXz3799/qq9qbVXOtRpQ9YyftStRWa/HhrxjQlxdEhaDL//3Gp5//nmotVoMunwMgqKjUJiZhZSfNsKg1+Pxxx/Hs88+2+7HJddXAmpPIXzN+z6HFDoUUiBV/bsndAlworYKe0sLcai8SLQwx3j6YoB/MGK9fKHsBgZcDNNe9PTzN8O4KixwGaaT4RMkIFeWw/Tuy5DCIqC4+faTutpSCzJVbknYZmYeFdVbR4Gbl5eJ+Ph4e6tzy9Vgs9V0qsSakevbLt8bc3KOVJTirx9/wrQbpyJm8ADMeO0FeAVY2mmJyuISLL5/Ho6n7BMV3okTJ3bIccnGOsjb34AUc6kQubJs7hZuyyej3mTCofJi7C3Nx4naalHtHR4YhuGB3OXAMM3B52+GcU2699maYZgugeTlA8UN0yAfPgR580bhvkyOyzZonbYRtrZko9EsxC3l5dqg9bCwaOHATPvQvid/+6PIIKoY/w2gql2/R6Z5Yr398OZrr4vKbWNxS9Dzma+/IF6n9uWOgjJypXP/CYQMEc/lHW/BvPcTyPryFj9m9erVYumqUKTTQP9gTIsbiOnxA5HoEwDbLfAqQz1SyywVXoZhGIZxZXgGl2EYl0ARlwj5gkuh/3ktbnv+Zcg6d5GbS00ms2fPFvm5tmxdx6qtIwufete+TtXc1r0FUovyZqvIHUmWSE57LF26VDxOmzbtjL9HpilkJEUzt8Ouu7KJuLVB26ltefO3PyInJwcREREdcmxiPtfaxisF9IOc/g3k3L+AuGuA3pdBUqrt++7atQvXX3+9fT05ORldmSA3D4wJ87A/P15VJlrKqW1ZBYUQvJ2Va8wwDMMwJ4MFLsMwLoPpgktw62MLsHI7zccCy5cvF+Ji1apV9n3O/lQFXaSfC2CLVeRSu7JFuGzatAk333yzWCdRNXr06LP8tZmSEmoRh5i5PRmBUeSADRQXF3eYwHUSur3HAhHnQU7/FnLaaiBzE9B3EhA2Qrz+1FNP2fen9a+++grdif5+NJPrB61SJVqZF6fvRoBWh4F+IaLSS9VfhmEYhnEFWOAyDOMSUAvyLdNvFeJ2XGIctmTl4p45c4SgDfRwx8jIMFHR9fb2Rm1trb1CS23JtsrtgifvQm5ept1RmeKFWoe7tZL7J4DtYt1kAh588EFoNBaxS+vbt2+Hki/kzyr+/paqLRlKnYyiLEtkUEBAADoLSeMJqf9NkHuNgXxwBeRd7wHHfsEu4wDRmjzh/F7i7/Xrr7/G7t27u3wVtzE6leV/QamQcGVkPPaWFODn3CPYdOIYEn0CMdAvCKE6i0EXwzAMw3QWPIPLMIxLiFuqlJKAJXG7bMq1eO3qS1BUXIzikhK8ftUYsY1eq6iogE6ng7u7WhhK2QQtQeu0zZadSyZTrccbwAia+KUmUyxe/BH27NmDuXPH4cEHxwnBsmTJknb5/nsykZGRIgqI3JLJUKo5aPuenzYgZsgg7DbXoERfi85E8gyFYvh9kM59GDDW4al594rtT9yUjCdusszsOlZ0uxvkrEzRTjfE9MXtfYYIEypqYf7iyH58mrEXO4vzUGtsPuOYYRiGYdobFrgMw3Q6VVVV9tbjLdl5WJ2agcn9++Cd68aKZVL/Pvg6NQObsyjWB7j88suxefNmu1uyI2Qy9cvPf4qooJPl6TYPVROHoqLiCBYseAwhIX6YP38KFiyYguBgXyxYsEAIbObsQjm3FAVEbsmNRa7NRdmor8e999+PnOpKLEnfg8MVDSZknYUUmITdnuPwzdZMUb1Njg3AkLgAjD8v2l7F7e54a7Q4PzgSs/oMwfW9+sJX64bf8jJxpNLye9SbjO0wVsAwDMMwLcMtygzDdDp+fn6iekszt9SWPG3lWpTr9bh92CDx+qIdKbj72/UIDAjAl4uXYMqUKfaPpVZkW7syrUdH98bBA/lQa5QICGwwyWk9oVi4cCMKCkqwaNF98Pam9mVg4cJbMHv2myKLlfJambPHpEmT8Nhjj4mf68IrbhCGUjRzS23Jjjm4D8+YJVx8D5QVItqTKu4Qzr6hOg/4aXWdcuxPP/2MvXprg6q4JHqfWvBPrHrvKSBoACSt5Xi7KwpJQm8vX7FUG+uhUVguL8iYqtZowA0x3TdLmGEYhnEtWOAyDOMSUJXHtpxsH0catyBTW3JsbBz8AzywPyUPycMi4e3j7Ip8Kiha6NVXP0JycixmzLjUvn3mzLF48821IqrmjjvuQGxs7Gl9XubkPPfccxg+fLj4+ZJbso1Ro0aJCq8t/1alUGCQf4hYN8lm/FmQjX6+gaKKKNyOO3D+k9ySbbO3VL21Ya/ifv8Ldn3tieTYQMg+MUDwQEhBAwHfmG6dqeuhanBXHuQXDD0NtFPzv74Wv+dnYqBfMHp5+gpRzDAMwzBnG0nm3iGG6VQ4KN6Sc0tRQOSWTIZSb1x9CSYmJeDDnXvF61TJXbHvEO7/YSOKqmtExe/9998Xld/mMJnM2L0jB7U1Bgw9JxLu7q2PMyEhRQ64Gzc+i4svHuj02qZNezFmzHzccMMNTs7OzNmPDiJ3ZTKUOpVjMlV0ZchQK5TYkp+FCoMe5wZFwk97ejc22gLFApHA3fH6eCeBS+w6XIzhD3yD68dfi1VvPga5YC9QuB8w1gAaTyBwAKTggUDYcEjWamd3h9rLf8k7iqK6GnipNejvG4QBfsGizZlhuiJ8/mYY16RnnFUZhnFpPD09Rc4tQW7J4/vGYcX+NNGWTPhotZjQLx7L9h7EmkOHRZWOPobMqWh+1yZ0SSjTdrVajUHJ4di5LQt7duZi2IhIaLT8dteVjKdoaQ1U0bVBuax7SwtE23KSbxDODYoQM6EdWb1tUsX95jvs+c/TSB56F2SzCSg7YhW7KZALUiCFkbEZIGdvAXxjhYFVdyXCwwvT4wYiv7Za/J52FOdha2EOYjx9RNxQrLevMLBiGIZhmDOBK7gM08nwHeDmnZTJUErSuVvaTvV1QviSuJ00sB8+X/MtpIhosT+9Th9Dj5MnTxZC+fPPPxcit7bWgJ1/ZwlxO2R4JFQqRatalJOSkpCUFInt21+2xwKZTCYMGzYXqanZSE1NQWxs3w74qTCni8Fsxt6SfGwrykWN0WARusER8NWcXaF7++2348MPP0SYvw5+ns1XIEur9MgrqRX7fvDBB01elw01kNTukM1GyL88DClhHKSYSyFXZANVeUBQEiR1W+bIuwaUp5tWUSzihvJqq+CuVOPmuAHw4oou00Xg8zfDuCZc0mAYxiUgQUrClCDBSpChFAnXqVOnCnE7+YYb8PHlF8D02fu4bes+rFj7vdiPzKloP8e2YfpcOp0ag4dGYOf2bOxLyRNVXYXi5HN/NNdLmbcvvvgiliz5BbNmXS62L168Hnv2HMW8eZMRG3vcmp0bRfcJ2/GnwpwuaoUCQwPDMNA/BClWoSsqun6BoqLrc5aELonWP/+k3OSW8fME/EIt+zYHiVvxSC3Kl74EyGbLCwUpkA+tohcg+8ZZWplpdtc7qltlzGqUStGiTAu1LWdUlIgqPPFL7lERRRTt6dPZh8kwDMN0MbiCyzCdDN8Bbr6S21JlVtbX4eZLRmPltl2i0kuxQnDTNan00sfYKrmlJTWiVTk4xBP9BoScUiRQFBAZVkmSAWlp74ptCQl3QpK0SEvbC2/vbJooJAkDgOZ0u7dDblfGYDYhpaQA2wpzcV5wBJIDQjvcjKotyLUlQOFeSztz0QHApAe0vhZHZhK8ZFil1Hbb39nq44fE74pEbmFdDTnMIUjXfavZTNeEz98M45qwwGWYToZPkE1pabaWcGxjXjblWpGPe/PKteK1pZOuEbO6U5d/10TkFpyoxP69JxDVyw/xfQJPeQyLFi0Sxlfz5k2ka2u8+OIqsW3WrFnWPYoA7ANQDaA3gD7cFOPiokmCJGZ2qTqoUShxYWg0ugKyyQCUpltnd/eK9mVpzAuQ3AMhk/h184PkGYbuyo/ZGThQVoQQnYdwYO7rEyiqvwzT2fD5m2FcE74aYxjG5SBB6uiQ7Ch0bffkqHK7OjUDk/v3QYVeL7ZN6t9HmFPR/C5B+9qEcnCoF+rrTUg/VAitVimE7smYOXMm3nzzTbzyyhrxPDk5GTNmzHDYg0TyRQCOAEijFF4ASQBIaLh2dbAnQi7LNvw0bnZzKprTJSdmV3bylZRqIDAJUiD9fd0oqruSzl+8Jh/4EvCLhzRwOmR9JVCaAQT2g6RqfxfpjuKyiFjEe/sLYyq6OfHriePo4x2Agf7BCNN5unw1nmEYhulYuILLMJ0M3wE+fWjm9p45c1BUXIx3rhsrYoSIRTtShPNyYEAA3nr7bUyZMqXJxx5OL0LmsVIkDQxFSKjXSb/Opk2bMGbMGPv66NGjW9izBsB+APkA6Pc2gNJAT/l9LF26VDxOmzatFd810x78fiJTuPlSZfCcwPAuZ3AkqrumOkgaL8g5f0He/T4pYsC/T8PsrmdYtxGBlfV67C8rxL7SAlQY6hGg1WGwf4hoZ2aYjobP3wzjmnAFl2GYLgXdk7MtJ9unJWLjA6DXG5G6Lx8ajRJ+/hajn+a4+OKL7cZXLYtbgj4Hxb2csArdXwHEk2UVgOZbKUkwU7s1QVmvJ//8THtBxlNapRLbi/KEaCLDo3OCwuGl7hpCV1R3aaH1iHMB3xigYB9kiiE69DWQuhzQBUIWs7uDgMC+XXp2l25AnBcciXOCIpBZVS6qulnVFULgmmSzyNqN8vDuNoKeYRiGOX24gsswnQzfAW491KJMc7Hklhzo4Y43rr4EE5MS8OHOveJ1quSu2HcI9/+wEUXVNZg0aRLef/99p3ZnwmyWsXd3LsrL6jBkRCS8vM7mBb8RQAbVigHorCZUzr9LS+TQMKSmHhDPk5L6Y/v27fZIIqZzImt2l5wQQpfmdS1CNwJeVlffrohMxlTFhyyzuwUpQG0RpBEPCKErooiUakgeIejq2EzDjlSWCnOqW+MHIdDNXQheztVl2hM+fzOMa8IVXIZhugxkNEVuygS5JY/vGydmbqktmfDRaoXJ1LK9B4XJFF302sypHKGooP6DwrB7RzZSduZg6DlRIlLo7L2tUkZuhNWE6i/rXG6SVfBS5NBi7Nmzx8nAasmSJQ4GVkxHQ6ZFJGiT/UOxq/iEaFumiu751mphV0RUaoMHWQStPA2ozgd0AeI1Oe0bwFAJ6fzHIFM8EZlVUVuzsusJelu1trenr8jRJXFLonfp4X3wVmvFrG6Mpy8UXNVlGIbpEXAFl2E6Gb4D3LYYIZuTMhlKSTr3JjFBk5IH4PMf1kETGt7i56qvN2Ln39mg694hI6JEy/LZRbbGCVGl1gQgERUV/khISGwhgigN3t4cOeQK6E1G7C7Oh69Wi0SfQFQa9MKF2ZbT2hzZ2dkoKSmBv78/IiMj4crIxjpAXwHJIxhy2VHIm58BFBogINHSykwxRO5d9z3ILMsiB5lamClmyFOlwQC/IFGZb42hWGZmJubOnSvWX3nlFURHdw3HbaZj4fM3w7gmLHAZppPhE+SZiVziyy+/FAJ36tSp4vnk8ePx8ZgRUOvroLxxJqResS1+rtqaeuzYli0quMnDIqBUtkdLowHAQQDHMW/eUrz44hdYtOg+zJp1uXh10aJ1mD37TcybNw/PP/98O3x95kz5KecIsqsrMDNhcJP5zpUrVwoRtGXLFvu2kSNHCoFEbfKujrgMqMoTbcwyxRCVpFN/M+ARahG6ZFQlqrtnq8uhY7+3/Lpq7C0pwMHyIhjMZvTy9BGmYnFeflBaO0Js6PV6vPTSS3jmmWdQW1srtul0OjzxxBN46KGHoNV23fll5uzD52+GcU1Y4DJMJ8MnyDMTufQWRkKXHin3llqYyRhKZTTAvHwJ5KxjUIyfCsXAoS1+rsqKOuzang1fP3cMGBwmWpjbg8OHdyEp6VwkJUVh+/aX7TO3lpncuUhNzUFqaipiY1sW5EznVXRL9XUIdfdEeX2daGMeERiOZ578t7gpodZqMejyMQiKjkJhZhZSftoIg16Pxx9/HM8++yy6ErKhFihObcjdrSuF1HcypLgrIdeVArIZkrXVuavNWadVFAuxm1dbJQzGRoVE2Wd4161bh/vuuw/p6ekIionG9Y8/JD7u6+deRuGxTCQkJOCNN97AFVdc0dnfCuMi8PmbYVwTFrgM08nwCfLMRK4t59ZmQkUzt5SjS8gmI8zfroC8ZzsUF18J6aKxLbqrlhRXI2VXLkLDvJGYFIzx48fj8GEyimogLi4Oa9ZYcnHbwsSJE/HVV19h48ZncfHFZD7VwKZNezFmzHzccMMNwkSLcV2OVJTih+wM7Fi3Hosfmo+YwQMw47UX4BVgyaYlKotLsPj+eTiesk9UeOl33xURlwiV2YDGG5KbD8wHvwKyfoU09hVIkgJy+THAKxKSomtZehTV1Qj3bHLL/qsgGw/NugNbvvsBGp0bxt4xE6NvnQqVxtKObqyvx6+ffIH17y1BfV2d6BShiC92amb4/M0wrknXOiMxDMM4QELW0SG5sVuypFSJ6q3sHwjzxh8hlRZBcd1ksb0x/gEe6JsUgtT9+dBoVULcUiUnPLyXeD0393gHfEdMVyDW2w+zEofgvRl3Q6XVNBG3BD2f+foLWHjFDaJ9uasKXCHivKMansddCYQmW8StoQby5oWAUgM5IMmSu0stzW7O/4euCBlR2QjWeUCuqRPr9336PsITE5z2JaF76e23oe8F5+PlybehvLycxS3DMIwLw/75DMN0a+hCVHHRZVDccDPkfbtg/ux9yLU1ze4bGu6NuIRAHD9aAqPBLMTt4g++F4tN6J4JL774IjQaDebOXSTakm3Q+oMPfgCNRo3//veZM/46TPtTlHcCe7ftwODLL2kibm3Qdmpb3rx5M3JyyGys6yOp3SH5WlvoVW6QRv0LUuxVQH0F5L2fQP7lnzD/9m+YD66CTBFFZorNcm16e/nh9ptvEevZBw61uF92quU1GoVgGIZhXBcWuAzD9AhoBld5612Q8/Ng+vB1yKXFTfYZN24crrrmAtwx5zocPebcnkxkZGSgf//+YqF9TxdqcX7wwQexe/cRLFnyi3374sXrsWfPUcydez1iY49ZM3TNbfgumY6C3JIJmrk9GYFRFjfl4uKmf29dHariSj69ICVcC8XIxyFd9iqkIXdYKr5Zv0Pe+iLkfZ+JfWWjHnJdOVyVCRMmQKVSYc9PG1rcJ2XdBihVKgwac5F4XmcyorJeb2njZhiGYVwGFrgMw/QYpOhYKGfdT4OFMC16DXLWUafXbW3JBoNJVGzDwxqiQWg9LCwa1dX1Yp/G87mtZcGCBWJma8GCz1BRUSMWWg8JCcH8+W8BiLI6Lv9K01xn/D0z7QNFARFkKHUyirKyxWNAQIAwpvri8D78mnccaeXFqDLUozshaTwhhZ8LRfLtkMa+bKnu9h5reTF/F+RfHoasrxRP5ZpCS/6uC/0+x44di/St21BT3lSI07a0v7Yh+YKR8LNW7Ol3uChtF0xWgft3YQ7+KsgR24vramAyu873xzAM05PgGVyGYXoUUkCQELmmLxfD9PG7UEyYCsWAIfbXbW3JjVn4lCWzlpg5++o2f33KuV24cCFmz56NZ59dTlobBQVlWLRoEby9A6nmB4CE9X66ZKYJQQD9AXi0+WsyZx/KuaUooG0/bcR1D9/XbJsyGU2Rm/KoUaMQEREBfWWpyGAlJ98dxXliHy+1BuHuXgjTeSLM3QvBbu5Nomu6anUXvr0bNgQNgDT8XkhaL1HxpOouqKob1N8SQ0Svazs3A5paj3/88Ufs2/A7zrn+WqfX9m74DWajCffcOgOxXpYZ4zhvP/hq3KCy/r5O1FYhq7oCeuv4AU3p+mjcEKDVwU/rBn+tDkm+QVDw/C7DMEy7wi7KDNPJsAtj5yAbjTCv+RLy3p1QXHI1pAsuwYABA0SFtjmB6wgJXA8PDfbvJxF6+lhigYYhNfWAeJ6U1B/bt2+3xwZZjxAAiaBUCqmhSUEAZH7D9yVdBXJHJlHUa9AAYSh1Oi7KVL3NralEXk0V8morkV9bLSqBN8X2F0L3aGWZ2K+3ly+6G+Kyo+yINYYoBSgnAzcJ8IlpyN31jbGI5A5uO6dOCrW7Dt5BdKOpgYrCIhhqapGfn2+v3rf0vdWajCjR1zZa6kTU1Jx+w4UvwKpjqSKPd3hgOGqNBvH7JyHsqdawgVUXgs/fDOOasMBlmE6GT5CdB739mX/9CfKvP0Eacg4GPf6Uk3MytSXbKrcLnrwLuXmZdkdlysRsq8AlNm3ahDFjxtjXR48e3cKeVA3KsM7lUvxREh2ZtT7UMhRjQkybNv2ms18AAFi5SURBVK3Nx8icGsq5dczBpZlbaks+3Rxco9mMwrpqBLt5iAru2qx0sW18r0SR3/pz7pFuV+W1IWZzi/ZZBe9+wFgDBCZBce7DFjFsqBbtzx3BzJkz8fff1DnRlHPPPRcfffRRmz83/T5t1V5qZw7UugtH7oyKEqzJTBPb1QoF/DVU8dU5VX79NG7d6nfeXeDzN8O4JixwGaaT4RNk52Pesx3mNctx/Vc/4ojeKGqnhw4dQlRUrL2aS1XbnOxjiIiMgUIhITExAT/8sPaMvu7piVByfqaK7wkKRAIwgBogz1A8M2cDyi2mKCByS7ZBbclz5849o3ggg9ksBE+Zvg4/5mTYq7xKSUKoELsWwRuu84SH2pLZ2tWRzSZR3YXZACkwCXJFFuTf/w/SyPmQ/GIh11cB5OTcwdXd9sQsy6gw6JtUfOmRjKyI4YFhuCi0l9i2r7QA5wRFwE2pEnO+LHw7Dz5/M4xrwgKXYToZPkG6BuZjGTB/uQQGdw/ctuEvrFj9DXr1incSuMePZ+Duux/AP26di8rKeoSEeolYIa1bR7YNF1rnc6uss7p9AWhOs/2ZaQ+ys7NFmysZStHM7dnGVuXNpbbmmkrxWGW0GFUNDQjFxWExog22tL5OVIK7w6ynbKgG8nYCEedBUqph/vtVoOK4ZWY3aBAQlARJ3T3n0x3bnd1ValHJPV5Vhp9zjuK2hEFQK5Si1bmorkZUfP2t1V6qANMjzXdzu3P7wudvhnFNWOAyTCfDJ0jXoT4vGzdffRVW7t4HL40adbLFdMrWluwmAZX1BkyePAUv/+8dHDtSBrPZjJjYAERG+4rKbsdA7qwUJ0RtjfQ1E61iVyHMqsjAat68icLA6sUXV4lts2bN6qBjYzqSSoNezPF6Ws2qyMH3u6x0zE4cKgQOPSehS9VeD1XXr/LKJemQ83cDhXuByhxAmFnFQQomo6qBIqKoJ4m6IxWlwtzKVvUtra+1uzpT9d9Po8PEmL7QqdRiPzeFCr5at84+7G4Dn78ZxjVhgcswnQyfIF0Dg8GAm2++GStWrMC4xDhRLduTX4icCqqUAhHenhgcEiRm6NYcOizMhT5e8gmysyqRk1UGnU6NhL5B8A/oyGqS3hopRFE1XqioiEZCwnmQJAPS0iyzwwkJd0KStEhLSxMOzkz3xlLlrRGCllh+ZD+yayzRPD5qbUNbs7sXgtzcu3SVV64tEUJXLkgBilIBkx7wCIM0+mkhcmWTQVR9exKi3blej5J6S6tzqb4OY8N7i5/H54f3irnfKyLjRH7vL3nHGqq+1oXanpnWw+dvhnFN+J2MYRiGGn6rqiyGNgC2ZOfh9avGYHX/Pvhw516xbdbQgVixPw33f79BPKd96/S1SEgMQli4N9IOFmLPzlwEBXsgvk8Q3HQdcWGtBTAYAFWZ92HhwkfExdWiRffB29td7LFw4S2YPftNYXREZkhM94ZuwNjELTG5dxIqDfXCqdnW2pxWUSKEkEqy7Dsxpp8QumRmpelCreySzh+IHg0perQQsyhNB2pLLOKWIojWzwUGz4QUNsLyukJ1WtXd1atXi8cJEyagq0C/R6rQ0mKLM7Jxfa++MFmzhw2yWfwNpJWXiPlfG6IVWuMGfzcdYjx9Ee/tb39f7EmVcYZhujZcwWWYTobvALsWy5cvxz1z5qCouBjvXDcWtw8bJLYv2pGCu79dj8CAALz19tuYMmWK08fRW2nBiSpkpBfCaDCjV28/RPXyg1LZMQYwhw9nICkpCUlJUdi+/WX7zK1lJncuUlNzkJqaitjY2A45Hsa1q7xkWEWil+KKaHaX/n7fP7QTQwPCMCIoHNWGelQbDQjsolVe2VgHZG8BggdBcg+EOXU5kLutIYYosB8kVcuturt27cLQoUPt68nJyeiuGMwmYWRW7GhyVV+HWE9fXBAaLf5Wlh/dj5tiB4i/h8yqcmF+RRVfxxzgngifvxnGNeEKLsMwjBW6yLctLe5Tr4c5PxeybHZycqXqRkiYFwKCPHD8aAmOHSlBXm4lEhIDERjkHHEybtw4HD5MsT8NxMXFYc2aNW0+9kcfnYf6egNeeeV2J0MpWn/11dkYM2Y+HnnkEeH4y/RsSJBEeHiJxQb9xY8O7YUAN0vl/1BFMTblHRdznCE6T+HUbGlt9hTznK6OEK8xlzQ8DxlqcWimlubMX0U1V/ZLsMzuBg8CPEKdKpRPPfWU0/pXX32F7gqZVQXpPMTSHFTVPT84Et5q6hgB9pYW4FB5sVinn5gPVXwdTa60OmFy1pOFL8MwnQsLXIZhGAClpaXCnIkEYKCHO76YfC0mJiWIyi1BlVwfrVa0KN90/1ys/HAR3n3q3wi48BJIfgH2z6NSKYSzcmi4N9IPFmLv7jwEBLojPjEI7u4Wkx8St455u2Rg1TFUW+d2LReqDGODqrR9fQPtzwf6hSDEzcPS1lxbif1lhfi7KFe8RlU7ErpU7Q1uQRS5GpJ/vFiAmyBX5wMF+yAXpkA+9BVA1V1dIHDhk8KReefOHaI9ecL5vcTNrq+//hq7d+/u1lXck0FmZcMDKXvbwjVRCRgTFtMk1six3XlmwmDh7Ly9KFe0QlOsEf0sqV2e3Z0ZhmlvWOAyDMMA8PT0hMJacRgZGYbxfePEzC21JRMkbif0i8eyvQeFyZTk6QWPlO0w7doKKSYeUvIISEmDIFnzSD08NBg8NBxFhdVIP1SIv7dkIjrGF716W2baSNw6RhCd6bTIiy++iO+++w5z5y5q0qL84IMfQKNR47//nQyAvh9qn6MYm1Cq8Z7hT47pjlDlNsLDWywE/X2SeLHN8ZJzs9E6z0kihp5fF91HPKeoIq0LmxVJHiFA7xBIvS+FTMZUxYcglx2xxw099eAt4vGJm5KFE/k3WzO7fRX3dKGqLi2R1r8Px3ZnMraiqi5Bc922dzaKtFqUtkvMfjc2t+qIdufMzEyRTU1QbnV0NDnPMwzTHXHdMxDDMEwHolar8fnnn4t1clKeuvw7bM7KFTO3dHF//w8b7eKWHJRpX5Vshpy6F/Luv2Fe/QXw/VeQ+idDMWQEEBkjqhRBwZ7wD3DHFZdfbWlLliRkZR1BZGRvp69/6NAhMUNLH9OWdmX6mAcffFAI3SVLfsGsWZeL7YsXr8eePUcxb948xMZOB5BHia00WWg9BYRZxS5VobmqwjQP/V2SaKGln0Oll6DWVaOb1bzIbMY7B3fYHZvJrZkeA7SuOcsrKbWWOV1qUwZE9fab3w+K6m1yrKUzY/x50aKKu3PxHCQPTAJ0/pDc/AGdH0CPnmGQNM5jCD0Vand2rOqPDImyr5ND84ReiUIA2yq/x6vKRdYvQX8d9Ld1ZWS8mBM/WF6E3p6+8LDeNGwrer0eL730Ep555hnU1taKbT/88AOeeOIJPPTQQ9BquaOFYbobbDLFMJ0Mm1S4blwQ8eWXXwqBO3XqVPHcJm5JEDsilxbDvHsb5D3bgfJSICAIiuRzIA0eBsnLB/3793dqSw4Pi8bCpyxRPguevAu5eZn2duWEhATs37//tI+9oqJCfGzrYoKqrUI3B0ANAJ1V6EZSPbuNPz2mp0MCN6OiBLnWKm9hXbWo4GkUSoTqPMQcb7SnD6IaVf5cheuvv160J+94fbxd4O46XIzhD3yDCZcMxarnbhFOzagrAcjIii6k+t0IKfZyyAV7IaevgXTuw2IGWD6xi9yuLCKYHJ+1Pk5z+4yFWqNBiF0yudIp1Ujw8UdxXS0+ztiDG3v3F7Pi2wpzcbSytEnV91TtzuvWrcN9990n3nuDYqJx/eMPie1fP/cyCo9livfLN954A1dccUWbjp3P3wzjmrDAZZhOhk+Qrity6e2RhC49krClFubmxK0jZD4lH80QVV2q7sJkghTfFwOfeBY1JqW9LbklqF3Z3V2NAwcOtOnYFy1aJGaJ582bKNorX3xxldg2a9aslo6YJpCtYpequwZqyLYKXZq74+oG03aoZfUEOTbXNMQUkcClOU4SNr+dyMS5QREi1qazsTknU/V21YJLnV674Zn1olXZ0VFZNtRahK7aE5KbD+TSDMhZf0AaeJsQXebNzwJlDmZykhJw82kQvG5+kOKvhaR2h1xTBKjcuBLc6GaJQgKUkgKHyouQUVEqRHCpvhYm66Wrrd2Z5n3JCItELzk8KyFh+s03ixuUGp0bxt4xE6NvnQqVxlINNtbX49dPvsD695agvq5O3MBcunTpac8G8/mbYVwTblFmGIZpoV2ZsnEJuuj54IMPxJzuycStZV8FpNg+QGwfyHW1kPftgn77VuTk5MAvtHUzX7QviexTfa3mmDlzJt5880288oqlxZkuxmfMmHGyIwbgb1360+WZVewesC5BVrEbwvO6TJtaVqlaa6vY0s0iEi4ExRAV1FVT177g17zjKKqrES3NVOmlR2pr7Shszsk0e9uYJ24a0mQWV1LrADV1PViQ/OLFYn8+8nHAUN1Q8a0rhWxbry0Fyo4CfcaLfeWUJYDaHdKwOZD1FZB3vmtvgRZ5vw6iGGqPHmHSRHPgNhJ9AsVCkGlVpUFvFbt1dtFrY3N+luggKC8vF8/v+/R9hCcmOH1uErqX3n4b+l5wPl6efJvYtyf8TBmmp8ACl2EYphlIXPr5+dmfO663FslNB2n4SNTF9QMe+bdoP6YK7alalP18A1BWWoGg4AZ35tZiiQV6FWPGjBHPad0xNugUH22dyaWlno7GKnZ3OszrRlrFMF8MMqcPiQiN9e+RMlWnx1tmX4kANx1K62uxuyQfWwupdR6iIhemc5zl1bWLEKHKrM052daa7MiQuAD7LG5rHZXFcVJFlhYfy82tlo5c6n+TtZuCFBy1NfsANcVASTrkujJANjXsrNRADh0ORfIsyCYD5IzvIEWcD8kzFLKhxiq+LXFP3RGFwzw4GpKu7AzyD0aCtz8Mkyfjxx9/RPaBQ00Ero3s1EPikTp0GIbpPrDAZRiGaWdIHI8eM0ZcRGdnHxXOxo6QuD1+PANubjqEhUYKA6oDe0sRHGJARJQvvH1Or33z4osvthtmjR49uo1HTa18MdaFKtk5VrGbZZ3XJaFL1StuqWTODgP8gsVCVd6y+joxw5tba2ltPlBWKOTf3X2HQ6dSIa28GF5WI6uzwVtvvSUe/zpUgIFzmndLLq3S2/eljo6zieTlUAkmE6shdzqNPUBfbqn6iupviZjnFdRXAll/AAH9ABK4R34CMr6FTDnAjq3QVPm1VYJ9enXrVuggN4vJ1YQJE3DnnXdiz08bcM711za7b8q6DVCpVBg/3lJJZxime8ACl2EYpgP45ptvsHz5ctwzZw6KS0qa3cfDXYf//u95TJgwEXk55cjJLseJvEohcCOifBAc0hBldCqmTZt2Fo+eLoYTAVAMTIlV7B4FkE6pqA7zumfmdsowtsonzVTSkuQXZI+bKayrEeKW+KswR8zyksAlQ6IdxbkI03mJfF7/NlR5b7/9dvz5559O24xGo1hIANHi5wn4hVr27UiEMRUJVFoQ6/waieGxLzU8jzwf8AoXIli2tUJXZEEu2APoKyz7DL0bCBsOOfNXMTOsGLVAbJczfwMUKqswtnw9Sdl1/6f9/f0xduxY/Lx+PWrKy+HuY70pYIW2pf+1HZdfdpnYl2GY7gMLXIZhmHamtLRUGD+tWrUKgR7uiPD2dGpXpnXapq+rxY033ohJkybh/fffR3RMjMjRzckqQ+q+fGSkFSE8wgfhkd5wc2t+PnfcuHGWOCIH2hI71DwkGgKsC83r5lvF7n7rQmYrEWc8r0tmL2dfpDNdGWprJjddG7fEDYTRajRUazIII6v9pZYqr5Ycm20RRTqa5/U8ZS7veeedZ3cuX7lypchJ3bJli/31kSNHigxV+t90ZUTGLy3NtENTOzP0ZcIUS+AeBAT1b3j9yDqg+oTzx1Cl114J9het1CS45YpsQOsFyVZJdlEmW9uU9234vUkVd++G32AyGrk9mWG6IeyizDCdDLsw9qzooXGJcSLjcU9+IXIqLCZWJG4HhwRBpVCInN1JYy/B52t/gMbq+ElUV9UjJ7sMJ3IrYDbLCAzyFFVdXz/nalXjOKIziR1qPXqHeV0ydlE7zOtS1an11bRNmzbZ54dpve0t1kxPg6q8J2qr7BFFebVVwlGXuDS8Nwb7h6DKUA+9ySScd5ur8j7++ON4/vnnodZqMejyMQiKjkJhZhZSftoIg14vXn/22WfRXZFNeudWaDLGqiu1rJvqoDj/MbGfedN8IHgwFEk3Qi4/Dnnvp82YYllzgt18Oy0eqaSkBCEhIVC76+AdFIi6aopHA9w8PFBRWARDTS3y8/PbXMHl8zfDuCYscBmmk+ETZM8UuZuzciHp3MW8oaSvw8jIMIu4HXU+PrnkHGiSBkExbgqkRjOGRqMJJ3IrRVW3psYAD08NIqN8ERLmBaVSIQRudXW9PY6IqsQeHpp2FriOVDnk65KzqbtDvq5lNq4laDZ52LBhSE21RCQlJfXH9u3bT8Mki2EaoP+tUjHLW4lQnZcwsfq7MAd/F+ZiTr/hwqxoX2mBmOWljN5vv14tqnkxgwdgxmsvwCugQfRUFpdg8f3zcDxln6jwTpw4ET0ZueoEoFRD0gVArsy1VH9tLdH0SELZBonbmEuhSJoqxDLNCUu9LxNCWIhnugGm9W43EUzO8n///Tdqa2tx9CiNVgCxsbFwc3PDueeei48++qjNn5vP3wzjmrDAZZhOhk+QPVPkEpTRSG/BlMFI0MU1mUMpDx+Eec1yQKWCYsJNUFDsUHMX7yW1Qujeefd05OVlQqVWIjPzKMLCop0ELr0WHx9/ltuVT4Vsnde15esardXciBbndU8/w5dhTg+9ySgMrEJ0niJu5oNDO0VcEfHOrXfh+IGDWLDuKydx6yhyF15xA84ZPhx//PFHJxx910BcVhprG+KR6NEjGFJgkqXau+s9SOfMheQeBPPuD4GcLdaMYL+Gqi/NFou5Y38goA8k9clvjrWG66+/Xjhl29ZtcU9nAp+/GcY1YYHLMJ0MnyB7psilt14SuvRIwpbMo0jc2rJv5YpymFcvhXz0MKSRF0NxyZWQWpgjTEpKQkZGhr0t+WQRRO3frtwcJuu8LondQuu2YGtVlx6VqKioEMcmSQakpVmOPSHhTkiSFmlpafD2tuSoMszZhP7/SvR12JVxCJcNHIJh112Jac/+u8X9l87/P+z49kekHzsKn5Bg6JQqkdVL4wXM6SNX5wNVeVZTLGsrtEN7NMUjSaP+Bcm3N8wHVwHlx6E49yHLx6Z/C2i8rML45BnB5GA/dOhQEQNFv3PKNKZtrYl7Ohl8/mYY14RNphiGYToQErAkZKuqLPO3dDFGkSOenp52cSu2e/tAMf1OyFs2wbzhB5iOpkM58RZIAU0vnOhzkLi1VW0dsQldwmZq1fEorVVbWvTW9mVadljndcOxcOEH4sJw0aL74O1tyfBcuPAWzJ79pph5pLlIV4fNsboe9L9DrcvBZotApZnbkxEYRTdlgJ3HjyC7ssGQSa1QCKFLglenVMNNpUI/30DEevmhol6PnJpKkc1KQrjWaIBSUoiPaY9M367ESU2xRDxSJaCxVG8l/wRAZ8kols1Gi+szmWbRfjaUWsg2wesVIdqiiaf+9ah4fOKmZNEdQgL3qaeeOitVXIZhXA8WuEyX5Pjx43j99dexdu1aZGVlQavVitbLKVOm4J577oG7e9tD7pcsWSJmdlrD4sWLMWPGjDZ/LaZnQkKWsnFtOK47QjNp0qhLIPVOgGnVZzC99zIUV02AlHxOmy+M62qNOJJRBG8fHXx83KDWdPR8q9YadUJLpRC6hw//jVdffQfJybGYMeNS+54zZ47Fm2+uFY62d9xxh5ibc0XIuTolJUW8LxHz58/HoEGDOqgVnDkb2EyGyFDqZBRlURcCMLRXLC4ICkCd0Yhak1GYWZGbs+05tT2T6RVBplc/ZGcgrt8I8fz7rAwcry4XM8C2CrAQxyq1/fmwwDC4q9QiGokUWZDOIvKorZo+ridgiUdqcGmWggc1rCtUkC79L2SzyZIRTFVfawXYXgk2WAyldu7cidXfrxfV2+RYi0Aef140vv76a+zevfuMq7gMw7geLHCZLse3336LW265RbQ02qipqRFmNLTQzB4JX9vMIcN0daTwKCjvfAjmH1aL2Vwp4xAU104SJlU2HGOHTtaiHB3VG7k5FTh+lMxdAHcPNXx8dCJr18fXDe4emg6sKlHsS188+ugC1Ncb8cortzsZStH6q6/Oxpgx8/HII3dj1apFAHTkgWp9dA3zKWoPz8zMhFptOaXS+pncZGM6nsjISBEFtO2njbju4ftanMElN+VRo0YhvldMqz93ok8A4rz9oLKaKI0MicIAQxBqHcWxkR4NKDDohUhODrBUNf8qyBb7TO6dBIPZhDcObINWqbRUia0VY6oW2yrH3hqtqBwTxXU18FRrThmR1JWRFEpLtZYW27ZG+zz99NP26q2NJ24awlVchunGdN93PaZbQjMzlBNKbojU0kmRDRQpQs+XLVsmWj1pXu+aa64RYtfLqyE3sS2sW7cO4eHUVtnyRRHDdASSRgvl+Bthjk+E+dsVML37EpQ3TIPUK050LziKLUdI3NpMpmjGlfYddVFvUcktL69FeVkdKspqkZdruWGkUimsYlcnBK+Xt5vY1vlQNWZPo21qB7Hr+Oi43v6nubKyMjHX99BD4+3mWLSN6VpQzi3Nw5Nb8szXm3dRpqgg2u90oBtGajJRskK5vGFwdkdvCYo3MlpbcCVIuCw8VlSKhTAWotiI8no9ThgtkUg2gUt/j58d3ouLQnthSEAo0sqL8euJ49YKsRo6laqRQFYj2M1DtGtTjJlJNkOjUHb5Fmq6ZiBjKcfqLTEkLoCruAzTjWGTKaZLcdFFF+H333+HSqXCb7/9hvPPP9/p9f/+97949FHLrM2///1v/Oc//zmjFmWKFIiJaf2d+rbAJhXM6SKXl8L01VIg6yikCy6FYvTlkJRKYWAVGBgIP79gJxfl0tICFBUVOc34NobihyrK9aiwid7yOhiNlgtrTy+taGf29rUIXzc31Vm98D18+LAwykpKisT27S/bq7iW2KC5SE3NQWpqKmJjyUSrzho/VNdo3fZY34IIbk4It10EU1syHTcdY3p6GgIDvZGe/r54LT7+DhQXk2lWH/G9dJxzNXOmNOTgajDo8kvEzC21JXeVHFwROyZJ4pEygSkCiaq4BbXVSK8ocWqjtlWOaZtJlnF+cKRYjlaW4evjBzG7zxB4abRCGJ+oqbK2UduEsbNIDtZ5QK1Q2r++q2BzTt7x+ngngUvsOlyM4Q98c0aOynz+ZhjXhCu4TJeBcuxI3BIUG9JY3BIPP/ywmIuli+HXXnsNCxYsOOlFPcN0RSQfPyhvuxvyH7/AvOknmI6kwTzuRtxy7/2idZ86GmztytSWbHNudnRpboxKpYR/gLtYCLpQramuF2KXKr2lJTXIyS4Xr2k0SlHdFXO8vm5CAFMGb1shAfjggw/ixRdfxJIlv2DWrMvF9sWL12PPnqOYN2+ew/wtzSKeLDLEdBLxS8d/ohkRrGqh+uv46Hy6JHGbnp4Onc4DZrOMZ5+91W6O9eyz04U5Vl5ePmprLXOATNfgueeeE2675K1Absk2qH35oYcecvn8W5u4pMcw94YOJhKgtLQEtT/bCHJzxzVR8WIGmPBRa1GtNogWapoJtlWO9Q4fMz1+IILcPLAh75hojZ4S21+8h6zJTINGtFQ3iGGaNXZcJwHekdVbG1zFZZjuCwtcpstgy68jWjKBoqiVW2+9VdxlpxbBjRs34vLLLRfLDNOdkMiB9aLLIMX2Qd3yj3HrpWOwct9BhHi4o6imFtnZRy07ms1imy1792Qi1+nzSxI8PLViCY+0GL3U15ucKrxHDxcLcUf7enlrhdgV87y+btBqT+/0QjejXnnlZcyf/wkmT75AbKN1mmsl06bWozxNEdxYCFOrdoHV7dkRlV3syrIb5swZi9274/Hxxz+0aI514EA2goJaHnFgXJPy8nLh63D33VehttaAJUvW4x//+IfLi9szgaqvNkhwJvpY5niJ5IBQJDfVh6KNmYQuCV4fDf1vQDhFR3p42Q2xCHKRzjdVWyvHBpGQbcNbrcXtiUPE+mcZezHIPxiD/ENExXlvaUFDG7W9emw14lKpoD1FC/Vbb70lHv86VICBc5qv0JZWWf7P//nPf2L9+vWn8yNjGMaFYYHLdBn++OMP8ejh4YFhw4a1uN/o0aPt65s3b2aBy3RrjCHhuG3LHiFuxyXGYdmUa/F1agZuXrlWvL500jWY0C8eU5d/d9oitzFUuQ0M8hQLQeK2qpLamutQXlaLgvwqZB23zJ666VR2sUvtzSSUFYqWL0Yp5zYwMAh5eXl49tnlYpa1sLACYWFh7ZCB21QEU7XJYDDDUG8UQr6+3gCTqQay2SJ+FYo6KJR6qFR1UGsqMXnyufjll22iOn4yc6zERB889xzdkPuLbsFZLXAUDsvpPG/8Wmu22dZdp23UlaEOCLrZEhLih+eftzjkf//9drGN5nM5j7kBijryUGnEYiPas8H1WKlQYHyvRKePof+zerPJbrBlE8EEGXH5aalbAuI1cp+2mG8Z7XPIjgwNCMXFYTEorKvGhtxjuCoyXswgp5eXoKy+DhdPvgGfL12KvNI6GL2DoHD4H7Vh9jYBpVmi1ZhhmO4DC1ymy0BtxwSZ5dAMbkv07du3yce0FaoUHzp0SMwv0oUNfe2xY8fi7rvvRkRERKs+x6lOnPS5GaatUJ6ubHVn3ZKVi9WpGZjcvw8q9JbKxKT+fbBifxo2Z+XaLzDpY1qKJjodSLCSIRUtkdG+YltdncEqeC1V3oL8SiFWlUoJXmReZW1rpo9Rq5X2WVbb/4JGo8Urr1jmVWmdtvXv3188P91ZVnExXW+CQQhWI+r19GhZGoSsCfV6IwwGkzjOxt8fiXq1xhMaja9Y12iV0GhUuPSy83H4cNopj6GysgbFxZR5TBfXZuticFiXHdYbP7etnw1aEspnIrjb4/NJDkvHs3Dhwi6fx+zKUMWVXJ1psbxjNEDzvzZ6efpgenxDLJDBTNVig13wkgD21VDkGP3VKISwpVxhIrO6HAfLipCvNKKuthbDrrsS0579d4vHtHT+/4l29JycnFaf1xmGcW1Y4DJdgrq6OrsQPJVzMV24U5W3urpaZOSeCZs2bbKvFxcXi+Wvv/7CSy+9hFdffRV33nnnKT9HYwOKzqLKUIlqo/M8oJvSDT4aXxjNBhTri5t8TIguVDyW6IthMNNFeQPeam/oVO6oMVah0kAX8A1oFGr4aQNgls0orKN2T2cC3QKhlFQo05dAb3aeh/RUe8JD5Yk6Uw3K6xuioAiVpEKAm6V1rqA2H7JTsxvgrw2AWqFGRX0Zak3UctqAu8odXmpv1Jv0KK23ROTYUEgKBLlZfk90vHTcjvhp/KBRalFpqECNscbpNZ3SDd4aX/HzoZ+TI+R6GqyzxH0U1xXBKBudXvfReMNN6Y5qYxWqGv0MtQoNfLX+MMlGFNU1vQlCx0vHDXcz3vzkDdwy52YsWbwEH1ZWoTYnF7cPG4Q6Lx1WVFfiI2MdLpx4OWbMnIELR14IP61F3ObX0jyqMwHaAKgUapTXl6Gu0c/QQ+UBT7UX9KY6lNU7uwQrJSUC3YLg5qZGJUrh7W2GN9Qwm1RC9MpVWlSXmZBTVIDjJRbxrdGq4BXhDVW+EuX5tUjom4ThFw1DTo4lTzYishdqa6rx9y87xCzxwPMH4ET1CWGIRQZYtGgMOsgGlfgZ1plrLNtNZhgNZpj0ElDtBlkyQ/Kutf6uJShVSijVEjyUPvD21sKkM0FSK4VbNC1KlQJ+bj7w1NLfYS0qDLa/Q/p7M0KjkJCYmAAZZvQaEIVPVqcgcfggKKxzyH7qejzwwPsYMnIw+gzqj407yjFsjOV9y1PlAQ+1l/i85fWWmWYbKkmJALegZv6+LULXX+sLtUKFivpy1JpqrdstC81Leql1qDfVo7Se8oVtH2uGQpLFfCStF9VVwySbnD7WV0OCQ0KVwSDyWy3Q36oZbkoJPhoJRrMJxXpH4W05phCd5bFEL8PQSIt7q83QqWTUGCVUGpxntDUKGX5aM8xUqa9rWlkLdDOLv6kyvQJ6s2MVWoIn/e5UKtSZgPJ62UkkqyQgwI3aZRUoqK23/hQaPtZfq7O+R+hRK3JqG14rySsR7+sXjRmCq2+8Avm1lmO+ZuqVmL4z157H7BXu2aXeI0r1xahv9P7tpfaEu8oTtcYah79vC/TzoffS9nqPOPX7bDlqjLWNfoY6eGt8xN9jib4SWvEnY0B+bZX4GVL11vIzLMQAPx0G+EVhX2UFKCAoKDoKJ4OMxMTHFhezwGWYbgILXKZLUFlJF2wWKB7oVNgELlWq2gIZ2txwww3CyCoqynJyPHLkCFatWoWVK1cKwX3XXXeJu9F0wdMVSCk5jK2FzhcjfX0UuDrqHFQaKvH54WNNPuahARaBuy47DXm1zhWVKyP9kOSbiLTy49iQ1/D7IXp5AhNjAmAw1zf7ee/qa7m42nQiDUecPxSjQz0xLHAAMqty8V2W80VbsJsZt8RbBO4XR47AJDsf063xlguorYXp2FfaYIBCjAjU4sLQIcivzceKY5Zqpg1PlRl39LUI3K+PZaDK6HwxPjmmHlGe0dhdfBjbipxnMwf4KXF5xAhxsdf4e1VKMh7ob7l4/SE7DQV1zp/32qhA9PGJx8GyY/j1hPPfaqwXMKHXeeJCsbmf4T39fKFVumFDXjqO04cGheLCRx8Tr3kf3gnsPozjA+NxIqwvrr7e8jFUJ63JTsNNcRaDtuY+78wEDfy0/ticn4aD5c4XoOcFuWFkSDLyak7gq+POF74+GjNm9bFcvK48moFak/P3OjU2GrEx4diUl4ddxQ0X28PuuRejCo+gn3EE4FONXe51GOzwcWqFjLunBgrTrEvm3o2lR52PebBJBU2JH2ojirDPYBV19KW1QKw/MMqjN4zKGnyR53xTg7h/YB9xob78yJ/ILnP+W7osvBID3foho+I4fs51FqKR7rKoJNONodcP7BDbvnA4rKDUtUhJOYZPU9ahQPJ2+llfEOKOc4IGIac6D99kOt/8CdCacVuC5We4/OgR1Ath18DNcUpx02lbUSb2lDgLoaEBalwcNgxF+lwsO+L8veqUZtzdz1IF/yZzC8rrnX83N/QKQIxXDFJKdmNroe13YxGMDe8RJfi8mYr1QwPOE4/rsv9s5j3CB0m+vZFWnoYNec6ir5enGRNj+sBg1uPzw01vRN7V10+I9k0n8nCk0lkAjw41Y1igBzKrqvFdlvP3EuxmxC3xdCPPjC+OqJp5jyhAoJsJWwt12FdqmRu14VNyGPX19fjPK/fhi2POQ6ej7n8In77zFR555FZcsfAFVBmdj2lyTBaiPH2xu7gY2xppzQF+ClweMazz3yMcuCTMC8kB/XG0Kgs/Zjv/vYTp5E56jzAj3D0cO4oysNPhPYIY7K/CpeHDUaIvaXJMdLPk3iTLz/C7rHQU6y2ft6zSckO3MPPkN7rJJZsICGhm0JhhmC4JxwQxXQKqxEZHR4v16dOn45NPPjnp/rQvfQy1NDbOBW2NwQi1I7dkXvHdd98J8Uuzd+7u7qK9MjTUIgSboy2RCe0RM8AV3O5Zwc0sOo53PngX2/7eBi+tBrcN6Y9LtG5YumEr1IE+uOqS8/FXdh4+2b0flXoDBgUFYcHk6fA/bxQKVM5Vks6ozlCsV/bRE/jXA28BChPg5fw1xa+4wkMI3OGjk/HMsy9aKq1KBVRqBfzdfeGu9kC1oRJVjf6+tUotfDV+MJmNKNI3/RkGuwVDaqHCZfv7bq7CZfv7lmUzjpccF50cNGP88kuzxOsXDr0D+XmlOHriKFQ65/vIbavgWvDX+kOt0DhUcBtwV+ngpfY55d93UV2htYLbgK/GIoR65nuErQ1cxn8efwrvvvYBNv72X/QbbrkhYCN1/3GMGXE3brjhcrz7+XMwi58hLfQ/bYKfxgSN0ohKg1FUrB3RKWV4a8wwmKn6SPOq9DdBIkwJCZaIHYqzKq4zwCgEOb1OAloJH1G19EK1UY8qQ6O/7x5YwaWbpiRyW36fLYTR4e/7ukuuw+5de7Fg3VdOucaO+cYLr7gB5wwfbvf5OB04JohhXBMWuEyXwPEkcuONN2LZsmUn3T8kJEScZAYMGIC9e/ee9eN55pln8MQTT9jXyYDE1QUu0z2xRQCRgdSpTKbWHDqMSecOxydXXgQ1/V32ioWi/2BISYMgWZ1POxqar6W4nfBwyrgFwsOisfCpd8X6gifvQm5eplinFuWEhATs378frsbgwYORkpKCefMmijneF19cJbZR9AjTdWh9HrMtsqo5bIK53ip+Dc0sLW23vdYSJHzVDo+NF9puE9CNX1P2SKMx6rgic7BegwZg5usvOIlcEreL75+H4yn7xH5tcclmgcswrgm3KDNdAi+vhovv1rQdU3tya9uZ2wK1JT/55JPCxObXX389qcClk93JoNliuqhimLZAbsjkikyQyCUhS4ZSgQFUYZRx/w8bsWzvQSFu6UKP9lUZDZAP7oO8fzfMP6wGfvgaUkw8pP7JkPoNhOR+soidswt1Wdho3G1B4jYvL1OYu5G4ddzXldi5c6dwdreZY1Ge5vbt2zv7sJh2zWNuCckqJi1uwKeP3AoB7Cieqxu93pIpmXQSYewokFt6re05153JpEmT8NhjjwlzsGcun4DBV1wqZm6pLTnlpw0w6OtFrGB3joBimJ4IV3CZLkNgYKAwgThVZaS0tBT+/pa7tHRBv3z58nY5HrpDaxOnZ1JV4jvAzNmu5BJffvmlELhTp04Vz23itnE8kFxTBTl1L+T9eyAfy6CWA5GtK1Flt+9ASG5tvVBvWzW3uroeiz/4XjyntmQPD41LVm2bM6QbM2aMfd0xrozpWjFBdDNFkgxIS7N0EiQk3AlJ0iItLa0LxASZ2lg5tr3e0iWhog1VY0fRLHXq75S8NGprq4Vbug16L/zwww/F2FNb4fM3w7gmXMFlugwkJH///XdR5TEajS1GBR08eNC+3q9fv3Y7nra0HjNMR1RySdhOmTJFPJLgVSgULWbfSu6ekIadDww7H3JVJeTUFJj374b8zXLg25WQ4hMtld3E/pC0zqY87QG1IpOwta2T2OgKXHzxxfafP4vbrgsJWIoKmj17tj2PuaCgDIsWLeoC4hb22V3hsnbakLg1taJqbFtqGwnn1rRXn07VWHVW2qvp90kil6KfrrhiKEpKKrFu3U48+uiSLnHzjGGY04cruEyXYf78+XjuuefE+tatW3Huuec2ux+1IlHLEbFu3TpcfrmlzexsQndtac6X/n0oF/fnn38+o8/Fd4CZs1nJdcy5pY4GatVvTtyeDLmiHPKBPULsIvs4oFRBSuhnqez2SYJkzaA8mzhm4to43exbhjlTLDO3w5CaekA8T0rqL1rObTO5TEuYW6gSn6rlujXt1acjjhu2Hz6ciaSkAWc4V90yfP5mGNeEK7hMl2HChAl2gbt48eJmBa7ZbLY7LPv6+tpbBs8277//vhC3BFdrGFeChKxN3BKO66eD5O0D6byLoDjvIsjlpZZ5XWpjXvUZoNZA6kNiNxlSfD9IpymeW4KFLOMKkAiiPFzb+YPWWdy2BoW1XZmWtmBqRdXYttQ3M3/clEcffVZEP73yyu1Ov0PL73g2xoyZj0ceeUREADIM031ggct0Gc455xxceOGFok2Z5mZuu+02kVPryEsvvSTuxhIPPPBAk6qV45wcffySJUucXj927JioeA0ZMqTF46CYoKeeekqs63Q6zJw586x9jwzjikg+fpBGjoFi5BjIpcVWsbsb8vKPAY0WUmKSRezG9YXUwugAw3QluOW8M9ur3c7QnMtRFPu2w3EyDOPqcIsy06XYtWsXRo0ahdraWtF2SW3LJFjpOUUHUWWV6NOnj2gpc3Rfbo3Atb1Owvm6664Thla29qMjR46IKAFabP82b731FubMmXNG3xO3ODFdFbm4EPI+i9hF4QlA6wap7wCL2CWjqtOsep2t9mqGYZizF/3UMnz+ZhjXhG+1M10KqqySO+wtt9wiTCNI4DaGxO3atWubiNvT4c8//xRLS7i7u+OVV14RcUEM01ORAoIgjb4MitGXQS44Yanq0rJnO6Bzt7gwD0iGFBMHSaFslQu0zRiLHsno52QGWQzDMO0f/cQwTFeDK7hMl+T48eN47bXXhJDNzs6GRqMRWZkUhXLvvfcKAdocp6rgVlZWijlAErdUAc7LyxNRQOTaTFUlijG59NJLcfvttze5a9tW+A4w050Qp5T8PLvYRWkxQE7NSYMsBlXRsZAUirMSccQwDNOZ0U98/mYY14QFLsN0MnyCZLor4vSSlw3zvt3CkRnlpYCntxC7iv7JQFQvGI0mu7gdlxiHLdl5gJtOfKykr8PIyDCsOXSYRS7DMG2GYp6oI2TevIki+unFF1eJbbNmzTqjz8vnb4ZxTVjgMkwnwydIpicgTjXZxy2VXRK7lRUweHji1jW/YOXvm4W4XTblWnydmoGbV64VH7N00jWY0C8eU5d/xyKXYRiXi37i8zfDuCY8g8swDMO0O5IkAVExUEbFQL5iHJB5DGVb/4BcvlK8viUrF6tTMzC5fx9U6PVi26T+fbBifxo2Z+XaRbKjCRXDMExr4OgnhulZsMBlGIZhOhRJUgC9YhHYKxYrJt+CL99+A/cteALTVq5FuV6P24cNEvst2pGCu79dj8CAAHy5eAmmTJnS2YfOMEwXhaOfGKbnwAKXYboxq1evxubNm5tsv+CCCzB+/PhOOSaGcUKSIAWFQla13HYs19bA/NvPMAV6QYqIhhQZDcmj7S7pDMP0TKZNm9bZh8AwTAfAM7gM08m01wwPfd6EhHiUl1c0eY1cp8kJ2uY2zYKX6Qwo55aMX1atWoVAD3e8cfUlmJiUgA937hWvUyV3xb5DuP/7DSiqqcXEQUl456qL4adzA3z9LWLXKngRGgmJZ3MZhulAeAaXYVwTruAyTDfliSeeEOL2vucuxuBREagsq8P8aWtQW2VAfX093n77bfu+H3zwPtLTM/ikzHQonp6eIueWILfk8X3jxMwttSUTPlqtMJlatvegMJlSJPaHz8NPQlGQBzk7E3LOcciH9gFGI0CfJyTcKnh7iUcEBFraoRmGYRiG6TFwBZdhuuEd4N27d2Po0KFIGhGG574YJwx+3lrwK3784oBd8NrYszkHbzy+CXfddRfeeeedM/peGOZ0cczAJSdlMpSSdO6tjgmSTSbAUfDmZAJFBZYX3XSQIqKACIvgFYuHZ7t8D47mV1SZJvHObs8M073hCi7DuCYscBmmm50g6V+anCJ/++03vPrtJMQmBeLIgSI8eN0Kq+Adb3G0ddj/8anfIHVHPnbu3InBgwe36uvwfC/THiKX+PLLL8Xf5dSpU8Xz040HkutqLUI3h0RvphC/qKmyvOgX4NDa3AsIDYd0kvnf1h47HS8dPz3S8VJlmiONGKZ7wwKXYVwTblFmmG5GSkoKfv31Vww8L1yIW7rg/uBpixC948kLnMQtQc9nPzkKc8etwoMPPogNGzY02ae5k/qMGbc1O9/L7c7M6UIi0OZuSn+v5JZsE4xtEYoSVW7jEgFabBm8ZSV2sSsqval7ARO1NistItextdk/8JT/A80J8+XLl4uvRTPFNljkMgzDMEzHwgKXYboZgwYNEhEIVMGlyi1dp+/7K9cueBvz509HkbojDwGh7ti0aRNmzpyJsrIycYEfHx8v9klPT0dOTg4iIyPFtp9//tlpvrdxu/OTTz7J7c5Mm0QutfoS9Pf3wQcfnJVWXyFWqXLrFwAMGCK2ySRu86m12dLWLB9Jg7xts0NrczTgIHold4+TtlZvyc7DPXPmCIFLhlnUWm0TvixyGYZhGKbjYIHLMN0MupinEPthw4bh/ac249ml12HAueFC5JLgdRS55cW1eO2RDaiurLdv+/jjj4UodhxesD3fvn27fVv/EWG4bEpfp0qXl28RAkI98O6776Kmpgbl5eVOwthGT29j5vbu5iERaJtjJRzXzzaSUgWER0EKj3KKI3Jqbd62GfJvP1sPJsASTxTRC8bgMNzy6ONYsXKlELfLplyLr1MzcPPKtWLXpZOuEeZYU5d/xyKXYRiGYToYFrgM0w1JTk4W8Svvvfce1q84hBvuGIz92/Lw/lN/OM3gfvby30LcOlZiP/3fX/h1TYZ9m+PzQSPD8d8H1iN9TwHu+Ldzu7MQy49utIvlTz75pFlh3NPbmNu7vZvFc9shcyspvi9Ai621ubTYubX5QArKq6phTk0R+2zJysXq1AxM7t8HFXq92Dapfx/hBk2GWbbP42hCxTAMwzBM+8ECl2G6KU8//TSWLftCtAzb2P93Hn5eflAI18z0EqxbdgD9z2moxFKF97dvM+zbjqYWOz0/drAYabsLmm13biyWGwvl9mhj7opirnF809n8ufBs9NlF3MCheVz/QGDgULFNNhoRmJ+L5ddPFTO39733EaatXItyvV7k9hKLdqSIqKNAPz8se+stTLltRqtmehmGYRiGOXNY4DJMN4WEzJIlH9sFILUMf/jhh06Cl665bcZTjc2oiMbmVDF9A5ptd6Z1R7HcWBg7XtxfNsULG746hPfff19EEzXn2twa4doVK6EU30Tfd/9zwtv0c+lM8cxYkFQqMZsr2puP5UJeshSormnRzdm8dhVMeelAQBAkWgKDLeu2R7Wmw78HhmEYhunOcEwQw/SgmAGbaKPPTy3EVIl9dqlFrB1NLcL916ywb2v83AaJ2bnjVqLf8FDR7kzMn7YG+//OxavfTkbvfgFOz5sztjq8v1C4NpMZVmPXZvp5JCTENytcfXy87cKVRCC1YLdHrm9rj6E10FuswVyNakMhrr5sEv7avPMUP5eVGHZ+Xyz+5ilolO5QKXRQK3RQKdygVtieu9m3KyRVi9nHZxoFxTQP5dzSCAC5JZOh1BtXX4KJSQn4cOde8TpVclfsO4T7f9iIouoaTBxzEd69/Vb46muBosKGyCLCx08IX7voDQy2PPf25aovw7g4HBPEMK4JV3AZpgcxYcIEsZDgOX78uN1pmcRW4+osCdXmqrX0ePmN/fDjFwdEu7N/iLuTSzMJ45O5NhNx/YMw4Nww4dr83eZ3cM7QC+GtiYCb0q9VVcg777zTpSqhZtmEWmMJagyFqDEWodpYhBpDMWqMhagxFMEo14mfy9Y/drbi5xKO7VsO4q9dGxCd6AujuRYmucEErDEKSQ2V5IZH7/6SpCzueHLUSaOg7n/gXmzcsAkKisdxMbpKyzk5O1N8EUFuyeP7xomZW2pLJny0WmEytWzvQaw5dBiKwBD4TL4VKqvJFJlZoagAcnEBZBK8xYWQj2ZA3rEVMJssX4QquwGB1kqvRfTaq74abed98wzDMAzj4nAFl2F66B1gqviR07KlEmup+DWuzlKrseNzm3AiQ6k7L1nq5L5ML51eBXclks+LE9Vho1wrtmemVuHeaz9D0gjnr+dYhTyw/QSSBsfiwJ7Dp66EjuyHxav/D2qlG5SSFiqFFkpJA6VCC5VkWbds00Kp0Ihte1NSMXzYiFNUQk9g2S/PIDLRQwhaErckLsXPAQroVP5wVwXCXR3Y8KgMwPgrbsHvv2/Gq99OOq3KNgloEroGc531scbp+d6U/Rh/0X1Nqu2NmT/tG+zdmovX105BYv8YaJVe0Cq97Y+aRs/pkbYppPYXw2ezat4RNI4JIkMpMqmivxFJXyeEL4nbyZMnt9pBWSZxW1YKuajAKoALhQgWVd/qyoYdvX0gBQQ3rfr6UNXXIrwZhml/uILLMK4JC1yG6cEnyObafE/mouxYzfzq/d34/rP9okJKy6effmoXpi0J4+baZSm3t85UinJ9FsZfMR3btuw9qXB98LqVYr21Ym7RT3ehV6KfqIKazPUww9jix9CxtVacDz6vNz746jF4aILgrgqyi1mdyq9FQdjcTYWWfi6nU3mmjx0zZoyoyJ9KPJ9/wTB8/u3LMJiroDdXQG+qhN5keaw3VcBgttxsaECCRuFhFbvepxDFJIbb1hjUni3nHSFyiS+//FL8LqZOnSqen464PRU0z2sXvSSAbdXfkkLAZK36qtRW0RvUtOqrdTvjY2AYxhkWuAzjmnCLMsP0YJpzWiZIdzU2o2q8D+Hr64Off/5ZnMx1Op0QKDaX5guvjRPC2PbcUbBQZJFj+zBVPNP2Z+HvzXtb1dq876+8ZnN9HcUc7XPxxRfjH2PfdhKSVA01mfUwCsGrh0nWwyge67EnZS/2/fVuq9qId/95FJqC89BvkMU593Tjm1rzc2lr9nFz4vmDpzaLbW+/sQgxPi1/fpPZgHpzg+h1fKwXj+WoqM8S26iS3Bi1VQyfrCps266U1B1ivtVekHAlAWv7GU+ZMkU8kuClFuazmX0ruemAyF6QIns5bZfNZqDcueorxO/xI5CrHKq+Xt6Wqq+t2kuPJH656sswDMN0M7iCyzA9/A5wc3OP6enpQmTEx8fbn+fk5CAyMtK+rTlX48YtprYc3OaEcVpautP3eDpVSDJT2rVrV6dWQpszyDrTVtzmfi6uXAU1y0ar+HUUwI7CuGGdjLYaQyZZaoUXHr7xU+zZeqRNpmSuUsl1zLklEyqa0z1b4vaMqr6izblR1ZdEsMnayUCu0P4OVV9HAcxVX4Zx6fM3wzDNwwKXYTqZ7nSCbCyWWyOM29rCS0KtPcRce7URd4SZUnuK5zOFKucWAewsgvfsScGkix9pdcv5nj17RFs703bsVV8Suraqr9X0CpUOfzue3k7tzg1VXz9IVpMthunJdKfzN8N0J1jgMkwnwyfItlUhu1MltCc6EXdk1ZxpPbK+zlL1dWx3FtXfQsBorfoqqeprc3i2zPnaBTC1UjNMD4HP3wzjmrDAZZhOhk+QzpyOcO2JldDuSEdUzZkzQ5ap6lvWUO21il5R9a0ob9jRwwsIDBLzvo4CGL5U9XW9aCqGORP4/M0wrgmbTDEM41LQhcGSJR+3KFwdLxxsub6deQzMmdNe5lvM2UMYUfn6Q/L1B+ISnV6T6/XOVV96zM2EvHcnYLBGiSmVlqqvPd6IlhDLus69c74phmEYplvCFVyG6WT4DjDDcNW821Z9K8obqr2OjxVlDTu6e9rdnR3NruDn3ylVX1c1DWNcDz5/M4xrwhVchmEYptPhqnk3rfqSIZWPX/NV35Iip3gj+UQO5H27Gqq+JG79Axrme4XwtQpgd492zTa2xT3RI3UXnO3YJ4ZhGKb9YIHLMAzDuATt1XLOuB6SRguERkAKbWhHJ0RTWaVj1ZeMrvIh79sNuZyqvtamMxK4VPG1iV6rAIZfACRqhz4DcUvClli+fLk4nlWrVtn3YZHLMAzj+rDAZRiGYRjGJRAGY96+kLx9gdg+Tq/JBgNQYm1xLrIYXMkFeZAP7AGoIkxQfBGJXJvBlaMAplboVojbcYlx2JKdh3vmzBECN9DDHSMjw+zCl0UuwzCMa8MCl2EYhmEYl0ciURkSDikkvGnVt6rCWu11cHg+kAK5rLSh6ktmVvZYo2C727PByxu33HqbXdwum3Itvk7NwM0r14oPWzrpGkzoF4+py79jkcswDNMFYIHLMAzDMEzXrvp6+UDy8gF6xzdT9S2yxBnZq74nIKfuBSjzF0B5XT3M6alifUtWLlanZmBy/z6o0FuqwpP698GK/WnYnJVr+Zyy7GRCxTAMw7gWLHAZhmEYhunGVd8wSCFhTau+1ZVC9AYUFeDLi8dixdrvcd9Hn2HayrUo1+tx+7BBYt9FO1Jw97frEejri2VvvI4pM2c5ZTUzDMMwrgULXIZhGIZhehRCoHp6i0WKiROCVyqrg/z5SqC6ptmPkfV1MP+wGqaCo5DCIoGwSEjhkZZ1cnsm12iGYRim02GByzAMwzBMj4VybikKiNySyVDqi8nXYmJSgqjcElTJ9dFqcf8PG0V1d1VhBd6dfiN8D+yB/OcmyyfRukEKi7CIXiF8o1j0MgzDdBIscBmGYRiG6bF4enqKnFuC3JLH940TM7fUlkyQuCWTqWV7D2LNocNQBIfC59Y7oFKrIddUQc7LAXKzIedli9le+c9fLZ9Y62aJQnKs9AYEsuhlGIZpZyRZDKIwDNNZFBYWIjg42GlbQUEBgoKCOu2YGIZhehKNY4LIUErSuVtal/V1QviSuJ08efIpHZTlmmohdkGCl5bcbKCsxPIi5f+GUf6vo+gNgmQV2EzXgs/fDOOacAWXYRiGYZgeDQlWEq6ELQroy8VLhMCdOnVqq8UtIbl7QIpLBGixItfWWESvrdKbth/yX781iN7QcEul11rtBeX3suhlGIZpEyxwGYZhGIbp8TiKXBK2U6ZMEY8keKmF+Uyyb6kaLMX2AWhpLHptld60A5D/+t16MBqr6I1qqPRSfi+LXoZhmFPCLcoM08lwixPDMIxrtSs75tySCRXN6bZV3J4Ocl2tc6WX1kuKGoleh0qvEL3Kdj8upnn4/M0wrglXcBmGYRiGYayQkLWJW8Jxvb2R3HSQeicAtDiK3hMORlaHD0H++w/Liyp1g+gVld4oIIhFL8MwPRsWuAzDMAzDMC6KEL0x8QAtDpm8yMuxVnmzIB9Jg7xtC73iLHrJ0EqI3hBISha9DMP0DFjgMgzDMAzDdCEkiiCKiYMUE+csek/kCNdmIXyPpjuIXhUQ0qi9OSiURS/DMN0SFrgMwzAMwzDdQfT2ioPUq7HozbXP88rHDkPe/qdF9CpJ9IY55/QGk+jlS0OGYbo2/C7GMAzDMAzTbUVvLKResfZtcr2+odJLj5lHIe/cStbRAFV0G1d6WfQyDNPF4HcshmEYhmGYHoJEubvRsZCiG4ne/NyG9uasRqI3OKyR6A2DRG3PDMMwLgi/OzEMwzAMw/R00RvVG1JUb/s22VDv3N6cfRzyrr8B2QyQSzNVdh3bm6nyy6KXYRgXgN+JGIZhGIZhGCckyt2NioEUFWPfJhsM1kpvlkX05mZC3r3NKnoVDaI3LMpS6aUZX3J1ZhiG6UBY4DIMwzAMwzCnRFKrgchekCJ7NRW9QvBalz3bAbNV9AY1U+mlz8MwDNNOsMBlGIZhGIZhzp7oNZLozWsQvfSYYhW9ElV6QxxmeqMslV6qGLcTBoMBVVVV8PPzE89LS0vh6ekJNQtthumWsMBlGIZhGIZhzhqiLTkiGlJEtH2bbDQCBXkNgleI3p2A2WQRvUHBzkZWoRFnRfSSuL355pshyzJWrFghHmfPng2FQoHPP/+cRS7DdENY4DIMwzAMwzDtijCgCqfZ3Kimotex0rtvF2Ai0SsBgSH21mbR3hwabjHEOk1xS8KWWL58uRC4q1atsu/DIpdhuh8scBmGYRiGYZjOFb3DLNtkE4neE86VXifR20yltxnR6yhuxyXGYUt2Hu6ZM0cI3EAPd4yMDLMLXxa5DNO9YIHLMAzDMAzDuASSUgXYKrZoJHodK7379wC0HVbR61DpNQQG45aZ/7CL22VTrsXXqRm4eeVa8fmWTroGE/rFY+ry71jkMkw3hAUuwzAMwzAM0zVE71DLNpkquoWNKr0H9gBGI8pr9TBnHBT7bcnKxerUDEzu3wcVer3YNql/H6zYn4bNWbmWzyXLTiZUDMN0bVjgMgzDMAzDMF0KSam0tCeHRgA4t0H0FuUjIDcbX150CZav+Rb3f/olpq1ci3K9HrcPGyT2W7QjBXd/ux6BAQH4cvESTJkypZO/G4ZhziYscBmGYRiGYZjuIXopZzckHHLyCCgqDZBXrAFqapvdnyq3DMN0PxSdfQAMwzAMwzAMc7agnNvJkydj6tSpkPR1+GLytZg1dKCo3NJClVyaw6XXbrzxRrEvfQzDMN0DruAyDMMwDMMw3QZPT0+Rc0uQW/L4vnFi5pbakv+/vTuBjaL6Azj+a6lUKlDBAiIoIodyKBBbIypguYwBD+QSohwCgqCiMaCI4fAggoImRjlqReQQEQGLCgIGUBDl8kDAFrCoKDcop0DL/PN7/+xkObrdtnu+/X6S7c7uzM5MZ+bt7G/em98TEUlOTDRJpmZv+lWysndIXFyc+QwAOxDgAgAAwBqaDVmzIivNkqzZkjWhlN5zq82Sn1y03A1utfaWDMqAXQhwAQAAYHWQqzShlAa42nSZ4BawFwEuAAAArA5yNbDVbMn6rAGvNmEmuAXsRIALAAAAq4Nc7edW6f22GRkZ5p5bglvATgS4AAAAsJYGshUqVHBfew8DsA/dBAEAAAAArECACwAAAACwAgEuAAAAAMAKBLgAAAAAACsQ4AIAAAAArECACwAAAACwAt0EAWF29uzZC947cOBAWNYFAAD452Ln6oud0wGEFgEuEGaHDh264L369euHZV0AAEDJzulVqlQJ92oAMY0mygAAAAAAKxDgAgAAAACsQIALAAAAALBCnOM4TrhXAohleXl5sm3btnPeq1ixosTHxwckAcb59/Nu2bJFUlJSSjzvaFh+pGK72IX9aRf2p32CtU81odT5eTTq1KkjCQmkuAHCiRIIhJmeCOvVqxey5ekJvVKlSiFbXqQtP1KxXezC/rQL+9M+gdqnJJQCIg9NlAEAAAAAViDABQAAAABYgQAXAAAAAGAFAlwAAAAAgBUIcAEAAAAAViDABQAAAABYgQAXAAAAAGAFAlwAAAAAgBUIcAEAAAAAViDABQAAAABYIc5xHCfcKwEAAAAAQElRgwsAAAAAsAIBLgAAAADACgS4AAAAAAArEOACAAAAAKxAgAsAAAAAsAIBLmCRI0eOyOzZs+WZZ56RFi1aSO3atSU5OVlKly4tlStXljvvvFPGjRsnBw8eLPYy9u3bJ5999pmMGDFC7r77bklJSZG4uDjz6NWrV5Hnt2jRIunQoYNUr15dEhMTzbO+1vejxfr16+XFF1+Utm3buv9H2bJlpW7dutK7d29ZtWpVofN4//333e1Y2EOnRXD5uy+0TMXCMR7NdB/5uz89jxUrVpwzD8pn6ETiOSYvL08mTZokzZo1k0qVKkmZMmWkVq1a0r9/f9m8eXOR1wlAkGk3QQDssHTpUu32q9BHSkqKs3jx4mItw9d8e/bs6fd88vPznT59+vicX9++fc10kaxZs2Z+bfMePXo4p06dKnA+U6dO9Ws++tBpEVz+7osWLVpYf4xHO91H/u5PfcTHxzu7du06Zx6Uz9CJtHPM/v37nbS0tALnkZiY6GRkZATgPwcQKAnBDqABhNbVV18t6enpcvPNN5vhqlWrytmzZ2XXrl0yd+5cmTdvnhw4cEDuvfdeWbt2rTRq1KjYy7rmmmvkhhtukCVLlhT5s8OHD5fMzEwz3KRJExk6dKi5Ir5jxw5Ty/zDDz/Iu+++a66WjxkzRiLV33//bZ6vuuoq6dy5s7nCr9slPz9f1qxZI+PHj5e//vpLPvjgAzlz5ozMmjWr0Hl++eWXZn4F0RoIhMZjjz0mAwcOLHD8ZZddZv0xHu2mTp0qx48f9znNli1bpGvXrma4VatWUq1atQKnpXyGTrjPMfo9rrW969atM68feOAB6devn1SsWFG+//57efnll02Ns9bk6jGjNc4AIkDAQmUAYZeXl1foNPPnz3evPHfo0KHIyxgxYoSzcOFCZ8+ePeZ1bm5uka+uZ2dnOwkJCeYzqampzokTJ84Zf/z4cfO+jtfptm3b5kSqdu3aOR999FGB216v/tetW9fdRitXrrzodN41RLpNEV6efTFy5Mhifd6mYzwWDB061N3n06dPv2A85TN0Iukck5mZ6S574MCBF4zXz5UvX96Mr127tnPmzJli/c8AAot7cAGLlCpVqtBp7r//frn++uvN8DfffFPkZYwePVrat28vVapUkeJ68803zT1N6q233jL3M3lLSkoy7yud7o033pBIpfeKdenSpcBtr/ePaS2uh9aiw342HeO20xYuM2fONMN677zW0iF8Iukc8/rrr5tnrbF97bXXLhiveS6GDRtmhrdv3y7z588v9joDCBwCXCAGlStXzjz/999/IV+2Vo59+umnZlibnt16660XnU7f9wTiOv3/K9WikzYZ99DmcbBbLB7j0eyrr74ytxGoTp06meAH0StQ5S8nJ0e2bt1qhvUiZkHHhXfiKwJcIDIQ4AIxJjs7W3788Uf35B9qubm57n2rmunZF894/fG5c+dOiVanTp0qUi07olssHuPRTO+P9+jRo0dY1wWRU/68s9/7ms+VV15pMuar1atXl2jdAQQGAS4QA06cOCHbtm2TCRMmmBO1p+nWU089FfJ10WQuHoUF2N7jPVfSo9HKlSvd4Xr16hU6vXYtpElstHsnbeKsNQ0vvPCCW8uE0Pn444+lfv36pvZGWz7UqVNHevbsKcuXLy/wM7F4jEerY8eOubVuNWrU8KvbJ8pnZAtU+SvOfP78889CE5oBCD4CXMBS3v02aqZXvcKs/ePu3bvXjH/uueeke/fuIV8vzebsb7ZRzQLtoT8covX+vldffdV9rU3dCqN9cO7evdtkXdY+izVb5yuvvGLu95o8eXKQ1xjn/8jVH74nT540wZDeZ6c1fi1btjTZVf/991+J9WM8mn3yySduQPLQQw+Z78vCUD4jW6DKX3Hmo82cvT8HIDzoJgiIMY0bN5YpU6ZIWlpaWJZ/9OhRd1gTuvji3QWLBhfRSJOXaHdMSpPXaPdNBbnuuuvMNE2bNnV/MP3222/mR7gmp9J7pgcMGGB+hD/66KMh+x9ikdbYalda2mWM1s7osbp//35TGz9p0iQT2CxYsEDuu+8+Wbp0qVxyySUxe4zHSvNkymd0CFT5oxwD0YsAF7CUZktOTU01w1r7pMmN5syZY5rjdevWzWSZ1EyVoead2Eqb+PmSmJjoDuv/EG00GNKaclW5cmWZOHFigdNqbaA2fT2/BkkvRGj/nJqtWX9ca63R008/bYIvvfcLwaHNTS+//PIL3m/Tpo088cQTpr9L7UdT97Hu1yeffDImj/FopjVtWhurtJmx5z7Ki6F8Ro9AlT/KMRC9aKIMWEp/nDds2NA89EfYgw8+KPPmzTM1FlrroDVP2ow51C699FJ3+PTp034nZzq/m4dIt3nzZvOjWO931v9Z7+XUILcgycnJPptH6sWIESNGuPdUZ2ZmBmW98X8XC249tPsSrbHz1Np6uhuJtWM82s2YMcPcQqA0ePWF8hk9AlX+KMdA9CLABWLMww8/LJ07dzY/7B5//HE5dOhQWLoo8qcpl3eyjsKaiEVaFs+2bdvK4cOHTdbk2bNnS/PmzUs8X2326PmR7Z24CqGnzVW1NlfpfbmerK2xcozbYPr06W7tm9bClhTlMzIEqvxRjoHoRYALxCCtvfWclBcvXhzSZXsn6ygsGYd30g/vZCCRTAOd1q1bm2f9sfvee++527uktAb4iiuuMMNkbA0/za7s4b0/bD/GbbB+/Xo3S67WvlaoUKHE86R8RoZAlb/izEe/8wtLSAUg+AhwgRhUqVIld/j3338PW1Dw66+/+pzWe7w/3euE24EDB0ytnjYB9zRdDXS/mv5keUVoFLQvbD7GbUwuVVjz5KKgfIZfoMpfceajQbJ3wikA4UGAC8Qg79qFUDenqlmzpulD0p9mfF9//bV5rlatmlx77bUSybS7mLvuusutFdKugQYNGhTQZWgWXw2ilWcbIny8+8n03h+2HuO20ERQetuA52KfJgwLBMpnZAhU+bvjjjvcYV/z2bNnj+Tk5Jjh22+/vUTrDiAwCHCBGKQJjzxuvPHGkC5bazg8TXb1qvd333130en0fc9VcZ0+kmtGNKlMu3btZOPGjeb18OHD5dlnnw34crR7J+1nUbVo0SLg80fR7rPW7oFUrVq1zA9km49xmyxatMgEo0r7Ak9ICEyHEpTPyBCo8qdZtT21utoDgX7PX4x3skZNLAggAjgArDF16lTn5MmTPqeZMGGC/gIzj5o1azp5eXklWmZubq47v549e/r1mezsbKdUqVLmM6mpqc6JEyfOGa+v9X0dn5CQ4OTk5DiR6tSpU07btm3dbTB48OBibcONGzf6nGbhwoVO6dKlzTLKlCnj7Nq1qwRrDV+ysrKcM2fOFDh+z549TpMmTdx9Pn78eKuPcdt07NjR3XcbNmwodHrKZ3iF8xyTmZnpLnvQoEEXjN++fbtTvnx5M7527do+vzcAhE6c/gl3kA0gMLSJlXZO37FjR9O8SmuWtAmyvrdp0yaZOXOmrF692u3X7/PPPzcJkYpi1apVJmushzbJGzJkiNs8q2/fvudM36tXr4vOZ9iwYaYZr2rSpImp8dT11f56x44da/oY9Uw3ZswYiVS6rbX7JdWyZUvTv7Cvmjjd7uf3t6l9caanp0vTpk3lnnvukUaNGrldCun9vNoljT48X9dvv/22DBw4MKj/V6yXI23GqvtW94m+1q4/9FjXfTV58mS3KaqWs2XLlp3TD6Ztx7hNNLN51apVTbcu2oWafi8WhvIZWpF0jsnPzze18Z7zpn4n9OvXzyQlW7t2rbz00kuyb98+iY+PN30hB6q5O4ASCmEwDSDIatSo4V5t9vWoXr26s2TJkmItQ6+g+7MMz6Mg+fn5ziOPPOLzs3369DHTRbKibAt96D463/Lly/36bFJSkjN58uSw/J+xxN9ypDWBhw8ftv4Yt8nEiRPdbT9u3Di/PkP5DK1IO8fs37/fSUtLK3AeiYmJTkZGRhC2BIDiogYXsEh2drapldWrzXoFfO/evXLw4EFT+6Q1Do0bNzZdYnTp0kWSkpKKtQy9Wj5t2jS/py/sK+aLL74w966tW7fOXKlPSUmRtLQ06d+/f1RcDS/qfZM1atSQnTt3nvOe1rBnZWXJmjVrTPclu3fvNtsiLy/P1BQ0aNBAWrVqZWouPDVHCB5NKKMP3R9aQ6f74siRI6Y1hGZJve2220zmXa3R80e0H+M20RrAb7/91vRP/ccff/iVDIryGVqReI7RfZ2RkSGzZs2SrVu3mi729NjR/T548GBzDACIHAS4AAAAAAArkEUZAAAAAGAFAlwAAAAAgBUIcAEAAAAAViDABQAAAABYgQAXAAAAAGAFAlwAAAAAgBUIcAEAAAAAViDABQAAAABYgQAXAAAAAGAFAlwAAAAAgBUIcAEAAAAAViDABQAAAABYgQAXAAAAAGAFAlwAAAAAgBUIcAEAAAAAViDABQAAAABYgQAXAIAAGjVqlMTFxZmH+ueff2TkyJHSoEEDKVu2rFSsWFHS09Plww8/DPeqAgBgnYRwrwAAALbKzc2VNm3ayI4dO9z3jh8/LitWrDCPBQsWyMyZMyUhgdMxAACBQA0uAABB0rVrVxPkDhgwQJYtWybr1q2TzMxMqVu3rhk/Z84cGTJkSLhXEwAAa8Q5juOEeyUAALCpifLo0aPd17NmzZJu3bqdM83Ro0elWbNm8tNPP0l8fLx5btiwYRjWFgAAu1CDCwBAkLRv3/6C4FaVK1dOpkyZYobPnj0rkyZNCsPaAQBgHwJcAACCpHfv3gWOu+WWW0ziKaXNlwEAQMkR4AIAECRpaWk+x2uQq3JycuT06dMhWisAAOxFgAsAQJBUrlzZ5/gqVaqYZ02Hcfjw4RCtFQAA9iLABQAgSDx94QIAgNAgwAUAIEj27t3r13gNhCtUqBCitQIAwF4EuAAABIn2e+vP+Dp16kjp0qVDtFYAANiLABcAgCCZNm2az+D2l19+McOtW7cO4VoBAGAvAlwAAIIkKytL5syZc8H7x44dk/79+5vh+Ph4dxgAAJRMQgk/DwAACpCamirdu3eXlStXSqdOnaR8+fLy888/y9ixYyU7O9tMM2jQILnpppvCvaoAAFiBABcAgCDR2ttWrVrJO++8Yx7n69ixo0yYMCEs6wYAgI1oogwAQJDUrFlTNmzYIM8//7zUq1dPkpKSJDk5WZo3by4zZsyQuXPnSkIC15oBAAiUOEd7lwcAAAExatQoGT16tBnmFAsAQGhRgwsAAAAAsAIBLgAAAADACgS4AAAAAAArEOACAAAAAKxAgAsAAAAAsAJZlAEAAAAAVqAGFwAAAABgBQJcAAAAAIAVCHABAAAAAFYgwAUAAAAAWIEAFwAAAABgBQJcAAAAAIAVCHABAAAAAFYgwAUAAAAAWIEAFwAAAAAgNvgf1wbpOcrvug4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ibm_brisbane = np.load(\"./Data/ibm_brisbane/127_HE_day2.npy\", allow_pickle=True).item()\n",
    "ibm_torino = np.load(\"./Data/ibm_torino/133_HE.npy\", allow_pickle=True).item()\n",
    "ibm_torino_v1 = np.load(\"./Data/ibm_torino/133_HE_v1.npy\", allow_pickle=True).item()\n",
    "ibm_torino_fractional = np.load(\"./Data/ibm_torino/133_HE_fractional.npy\", allow_pickle=True).item()\n",
    "\n",
    "ibm_fez = np.load(\"./Data/ibm_fez/156_HE.npy\", allow_pickle=True).item()\n",
    "ibm_fez_fractional = np.load(\"./Data/ibm_fez/156_HE_fractional.npy\", allow_pickle=True).item()\n",
    "ibm_marrakesh_fractional = np.load(\"./Data/ibm_marrakesh/156_HE_fractional.npy\", allow_pickle=True).item()\n",
    "\n",
    "\n",
    "iqm_garnet = np.load(\"./Data/iqm_garnet/20_NL.npy\", allow_pickle=True).item()\n",
    "iqm_spark = np.load(\"./Data/iqm_spark/5_SQ.npy\", allow_pickle=True).item()\n",
    "\n",
    "\n",
    "rigetti_ankaa_3 = np.load(\"./Data/rigetti_ankaa_3/82_NL.npy\", allow_pickle=True).item()\n",
    "\n",
    "colors = plt.get_cmap(\"Set3\")\n",
    "delta = 1\n",
    "case = \"\"\n",
    "prop = \"r\"\n",
    "fig, ax = plt.subplots()\n",
    "ps = iqm_spark[\"ps\"][1:]\n",
    "ax.plot(ps, [iqm_spark[\"postprocessing\"+case][1][p][prop] for p in ps], label =\"iqm_spark \", markeredgecolor=\"black\", markersize=7, color=colors(3), marker=\"X\")\n",
    "\n",
    "ax.plot(rigetti_ankaa_3[\"ps\"], [rigetti_ankaa_3[\"postprocessing\"+case][1][p][0][prop] for p in rigetti_ankaa_3[\"ps\"]], label =\"rigetti_ankaa_3 \", markeredgecolor=\"black\", markersize=7, color=colors(6), marker=\"H\")\n",
    "\n",
    "ps = iqm_garnet[\"ps\"]\n",
    "ax.plot(ps, [iqm_garnet[\"postprocessing\"+case][1][p][0][prop] for p in ps], label =\"iqm_garnet \", markeredgecolor=\"black\", markersize=7, color=colors(2), marker=\"P\")\n",
    "ps = ibm_brisbane[\"ps\"]\n",
    "ax.plot(ps, [ibm_brisbane[\"postprocessing\"+case][1][p][prop] for p in ps], label =\"ibm_brisbane \", markeredgecolor=\"black\", markersize=7, color=colors(1), marker=\"d\")\n",
    "ax.plot(ps, len(ps)*[ibm_brisbane[\"random\"+case][prop]], linestyle=\"--\", color=colors(1))\n",
    "ps = ibm_torino[\"ps\"]\n",
    "ax.plot(ps, len(ps)*[ibm_torino[\"random\"+case][prop]], linestyle=\"--\", color=colors(0))\n",
    "ax.plot(ps, [ibm_torino[\"postprocessing\"+case][1][p][prop] for p in ps], marker=\"o\", label =\"ibm_torino-v0\", markeredgecolor=\"black\", markersize=7, color=colors(0), linestyle=\"-.\")\n",
    "ax.plot(ps, [ibm_torino_v1[\"postprocessing\"+case][1][p][prop] for p in ps], marker=\"p\", label =\"ibm_torino-v1\", markeredgecolor=\"black\", markersize=7, color=colors(0), linestyle=\"-.\")\n",
    "ax.plot(ps, [ibm_torino_fractional[\"postprocessing\"+case][0.75][p][prop] for p in ps], marker=\"s\", label =\"ibm_torino-f\", markeredgecolor=\"black\", markersize=7, color=colors(0), linestyle=\"-.\")\n",
    "\n",
    "ax.plot(ps, [ibm_fez[\"postprocessing\"+case][1][p][prop] for p in ps], label =\"ibm_fez \", markeredgecolor=\"black\", markersize=7, color=colors(5), linestyle=\"-.\", marker=\"^\")\n",
    "ax.plot(ps, [ibm_fez_fractional[\"postprocessing\"+case][0.75][p][prop] for p in ps], label =\"ibm_fez-f \", markeredgecolor=\"black\", markersize=7, color=colors(5), linestyle=\"-.\", marker=\"*\")\n",
    "\n",
    "ax.plot(ps, [ibm_marrakesh_fractional[\"postprocessing\"+case][0.75][p][prop] for p in ps], label =\"ibm_marrakesh-f \", markeredgecolor=\"black\", markersize=7, color=colors(10), linestyle=\"-.\", marker=\".\")\n",
    "\n",
    "print(max([iqm_spark[\"postprocessing\"+case][1][p][prop] for p in ps]),\n",
    "      max([iqm_garnet[\"postprocessing\"+case][1][p][0][prop] for p in iqm_garnet[\"ps\"]]),\n",
    "      max([ibm_brisbane[\"postprocessing\"+case][1][p][prop] for p in ps]),\n",
    "      max([ibm_torino[\"postprocessing\"+case][1][p][prop] for p in ps]),\n",
    "      max([ibm_torino_v1[\"postprocessing\"+case][1][p][prop] for p in ps]),\n",
    "      max([ibm_fez[\"postprocessing\"+case][1][p][prop] for p in ps]),\n",
    "      )\n",
    "print(\"ibm_torino-v1\", max([ibm_torino_v1[\"postprocessing\"+case][1][p][prop] for p in ps]), ps[np.argmax([ibm_torino_v1[\"postprocessing\"+case][1][p][prop] for p in ps])])\n",
    "print(\"ibm_fez\", max([ibm_fez[\"postprocessing\"+case][1][p][prop] for p in ps]), ps[np.argmax([ibm_fez[\"postprocessing\"+case][1][p][prop] for p in ps])])\n",
    "print(\"ibm_fez-fractional\", max([ibm_fez_fractional[\"postprocessing\"+case][0.75][p][prop] for p in ps]), ps[np.argmax([ibm_fez_fractional[\"postprocessing\"+case][0.75][p][prop] for p in ps])])\n",
    "print(\"ibm_torino-fractional\", max([ibm_torino_fractional[\"postprocessing\"+case][0.75][p][prop] for p in ps]), ps[np.argmax([ibm_torino_fractional[\"postprocessing\"+case][0.75][p][prop] for p in ps])])\n",
    "print(\"ibm_marrakesh-fractional\", max([ibm_marrakesh_fractional[\"postprocessing\"+case][0.75][p][prop] for p in ps]), ps[np.argmax([ibm_marrakesh_fractional[\"postprocessing\"+case][0.75][p][prop] for p in ps])])\n",
    "print(\"ibm_garnet\", max([iqm_garnet[\"postprocessing\"+case][1][p][0][prop] for p in iqm_garnet[\"ps\"]]), ps[np.argmax([iqm_garnet[\"postprocessing\"+case][1][p][0][prop] for p in iqm_garnet[\"ps\"]])])\n",
    "\n",
    "extra = \"fractional\"\n",
    "ax.plot([],[], label =\"random\" ,color=\"black\", linestyle=\"--\")\n",
    "ax.legend(loc=\"upper center\", bbox_to_anchor=(0.5,1.3), ncol=4)\n",
    "ax.set_ylabel(\"r\")\n",
    "ax.set_xlabel(\"p\")\n",
    "ax.set_xticks([3, 10, 25, 50, 75, 100])\n",
    "fig.savefig(f\"./Figures/NL/comparison_eagle_heron{case}_{prop}{extra}.pdf\", bbox_inches=\"tight\", transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ibm_torino 0.7729069767441857 8\n",
      "ibm_fez 0.7822663989290496 15\n",
      "ibm_marrakesh 0.7716532797858099 15\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ibm_torino = np.load(\"./Data/ibm_torino/133_HE_fractional_1000.npy\", allow_pickle=True).item()\n",
    "ibm_fez = np.load(\"./Data/ibm_fez/156_HE_fractional_1000.npy\", allow_pickle=True).item()\n",
    "ibm_marrakesh = np.load(\"./Data/ibm_marrakesh/156_HE_fractional_1000.npy\", allow_pickle=True).item()\n",
    "\n",
    "color = plt.get_cmap(\"Set3\")\n",
    "fig, ax = plt.subplots(figsize=(3,5))\n",
    "ps = ibm_fez[\"ps\"]\n",
    "# ax.plot(results[\"ps\"][:-2], results[\"r\"][10][\"ideal\"][:-2], color=\"black\", linewidth=2, linestyle=\"--\", label=r\"noiseless\")\n",
    "\n",
    "colors = plt.get_cmap(\"Set2\")\n",
    "delta = 1\n",
    "case = \"\"\n",
    "prop = \"r\"\n",
    "\n",
    "ps = ibm_torino[\"ps\"]\n",
    "n2q = ps\n",
    "n2q = np.array([150*i for i in ibm_torino[\"ps\"]])\n",
    "delta = 0.75\n",
    "ax.plot(ps, len(ps)*[ibm_torino[\"random\"+case][prop]], linestyle=\"--\", color=\"black\")\n",
    "\n",
    "ax.plot(ps, [ibm_torino[\"postprocessing\"+case][delta][p][prop] for p in ps], marker=\"o\", label =\"ibm_torino\", markeredgecolor=\"black\", markersize=7, color=colors(0), linestyle=\"-.\")\n",
    "ax.plot(ps, [ibm_fez[\"postprocessing\"+case][delta][p][prop] for p in ps], marker=\"p\", label =\"ibm_fez\", markeredgecolor=\"black\", markersize=7, color=colors(1), linestyle=\"-.\")\n",
    "ax.plot(ps, [ibm_marrakesh[\"postprocessing\"+case][0.75][p][prop] for p in ps], marker=\".\", label =\"ibm_marrakesh\", markeredgecolor=\"black\", markersize=7, color=colors(2), linestyle=\"-.\")\n",
    "ax.plot(ps, [max(ibm_torino[\"postprocessing\"+case][delta][p][\"results\"][:,1]) for p in ps], marker=\"o\", markeredgecolor=\"black\", markersize=7, color=colors(0), linestyle=\"-.\", alpha=0.3)\n",
    "ax.plot(ps, [max(ibm_fez[\"postprocessing\"+case][delta][p][\"results\"][:,1]) for p in ps], marker=\"p\", markeredgecolor=\"black\", markersize=7, color=colors(1), linestyle=\"-.\", alpha=0.3)\n",
    "ax.plot(ps, [max(ibm_marrakesh[\"postprocessing\"+case][delta][p][\"results\"][:,1]) for p in ps], marker=\".\", markeredgecolor=\"black\", markersize=7, color=colors(2), linestyle=\"-.\", alpha=0.3)\n",
    "\n",
    "ax.plot(ps, len(ps)*[max(ibm_torino[\"random\"+case][\"results\"][:,1])], linestyle=\"--\", color=\"black\", alpha=0.3)\n",
    "\n",
    "print(\"ibm_torino\", max([ibm_torino[\"postprocessing\"+case][delta][p][prop] for p in ps]), ps[np.argmax([ibm_torino[\"postprocessing\"+case][delta][p][prop] for p in ps])])\n",
    "print(\"ibm_fez\", max([ibm_fez[\"postprocessing\"+case][delta][p][prop] for p in ps]), ps[np.argmax([ibm_fez[\"postprocessing\"+case][delta][p][prop] for p in ps])])\n",
    "print(\"ibm_marrakesh\", max([ibm_marrakesh[\"postprocessing\"+case][delta][p][prop] for p in ps]), ps[np.argmax([ibm_fez[\"postprocessing\"+case][delta][p][prop] for p in ps])])\n",
    "\n",
    "extra = \"fractional\"\n",
    "ax.plot([],[], label =\"random\" ,color=\"black\", linestyle=\"--\")\n",
    "ax.legend(fontsize=11, loc=3)\n",
    "ax.set_ylabel(\"r\")\n",
    "ax.set_xlabel(\"p\")\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_xticks([10,100,1000])\n",
    "ax.set_xticklabels([10,100,1000])\n",
    "\n",
    "# ax.set_xticks([3, 10, 25, 50, 75, 100])\n",
    "fig.savefig(f\"./Figures/NL/Heron_p1000.pdf\", bbox_inches=\"tight\", transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ibm_torino-v1 0.7464046511627905 6\n",
      "ibm_torino-fractional 0.7789720930232554 8\n"
     ]
    },
    {
     "data": {
      "image/png": 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KnUZORGQtkgcnNcRkOamAgAA1EiHDlpJEL0OEMpRK9olBGZGZ4ABPTB3bFgUFBjXLyNaHXYnI8UgpCpmAIEsTySxNyQsbPHiwWhJKiuqS/WJQRhaT8Usn+TsSP283dR534gRiKimoK2y9BAgR2S5JrK8uKYtRXmkNsm8Mysi0hiSVolVeUNcaRXWJiKhhYlBGpoXEzTHQkJIZ1m4BERE5EgZlDqqyumGOPHxJRERkDQzKHFDpavu5+UUoKCiCj5eraVv6Jb1NFnwlIiJqqBiUOaDSswRjTqfiwqVsXNcjvNoFX+1lSSUiIiJbx6CMEN0iUJ3MsbxD9RQVcWEMIiKqGS7yRzifcgV5+UXWboZdMy7onl9QVCvLpRARkeNhUObg8vIL8d2vx3AqId3aTbFrxrUy12yLx7L1VSs4S0REZI7Dlw4uLTNPnQf4cYFjS6o72aFTVCMUFBb3mqVl5uLk2XR0ig6GqwsXOCcioooxKHNw6Zm56tzfh0FZaVWdwGA+2aFZE1/T9nPJV7Dpj0Rs338endsEo1u7xvD2dK32ZApjW5jnR0RC1uSVZZWuZTUAsm0Myhyc9JR5ujubht/oTzUNgjpENlJB2t6jSdh/PAV7jiShXctA9OgQikYBHiogO85lnIiqTIKQoUOH4q677sLnn39+zfsQ2SoGZQ4u7XIuAnzZS1ZXpPbbdT0j0KdzExyMuYg/jibh8IlLaBHmq4Y5uYwT2aIzZ87gySefVJfnz5+PZs2aWbtJRA6Bif4OLi0jF/5VCMrkS3rixInqJJepetxcndGzYyjuuaUTxgxsiSvZBcjOKbB2s4hKyMvLw9y5c9G2bVssWbJEneTya6+9pm4jorrFoMyBSemG9Mt5CPB1KzcImzBhAp555hmLX9KxsbFlAjUGbxVzctKjfesgTB/fHj7ef+aXEVnb6tWr0alTRzz//POI8HbFr/ePVie5PGvWLHWb7GOPWrRooU7p6el44IEHEBoaCnd3d3Tr1g3fffedxb+5ePEi7r//fjRu3Bienp7o1asXfvrpJzUkKjld5kOjp0+fVttmzJiBo0eP4uabb0ZQUJDaJrcJ+dtp06YhMjJS3Z+fnx8GDRqExYsXl9vuTz75BB07dlRtjYiIUN/FubnFecCWXL58GbNnz0aHDh3g4eEBf39/jB49Gps3b67R60f1h8OXDiwnr1DVJzMfvpSj4XfeeQevvvoqcnJyIGtyS9WtqGA/vDf9OrXPY0u2qS/pF55/HoarNbl+/fVXXHfdddi06Xfk5BR/aaxcuRIvvvgiZs6cCTe3soGfI5Mvaz1XPCcbOTiTYOGHH36Ap6sL5oztgScGd4Sbc3Ge6d6/TcD8jYcwZ+1+jBkzBlOnTsW3336r/g/bk/z8fIwYMQJXrlzB9OnTkZWVhYULF+K2225TAdijjz5q2lf2kUT6I0eOoH///uq7LSEhQT13CXLKExcXh759+6JTp04qQLt06RJcXYsPvp577jl1eeDAgWjSpAlSUlJU3uqkSZPw/vvvl3h88c9//hMvvfQSQkJCcN9998HFxUW9RxL0WZKamqraefjwYQwYMAAPPvggMjMzsXTpUpVj9+OPP6qDbLJtDMocmMGgqWT04AAPdV2Ogh999BHExsbBx81FbfNwccbzI7uYvqRXH/sz6bxVkA/ev6Uf9py9iH/+the//fYbohr54r3pg9XtT/y0XQVvCxZ8hg8++LDCLzMisg4JrjIyMtTlTY+ORZemQSVul8/9s8O7YEzbcPSct1Tta28BmTh//jyioqKwdetWU6Ak30/SW/b000/jlltuQVhYmNr+xhtvqIBMeso++ugj031IoCWBXXm2bNmiAql//OMfZW6TA9dWrVqV2CbBnwR9cvB6zz33qB40Y3D3yiuvqPb88ccfqrdOvPzyy+jdu7fFx5agTgKyjz/+GPfee69pu4xq9OzZUz0XCaql141sF4cvHZiUZxg9oAX8fNzUEaB8YBPj49WRct/mxV8Cmx4bh78P7wJXJz1u+2o9xn38G85lZqt99v1tAj7fGYMXV+6Bs15fvO3pmzGqTZg6yRH2q2N7ICH+tLpvORpntXuiuqNdSYeWFF/xKedy8b45l9V1MXnyZHW+N+FSufdtvE321dJToKUlFd+PwVD5Y146B1sg+XLGgEyEh4fj8ccfVyME33//vWn7119/rfaTwMjc8OHDMWrUqHLvX4ZFZfjXktIBmfD29laBngS6u3btMm2XnsjCwkI1ymAMyISvry9eeOGFMvcjPX3SizZs2LASAZmQv5egU3rm1q5dW27byTawp8yBpWbkwkmvU0FZ6SPlxt4eWBOTqL6I5bo6ms7Nv7rPONPRtGnbY39ua2hH2LZAhpkloOXrRxXRDmyEtr3iUi66kXdB1+k6aHF7oa35Ak4zP1XDWg/cfx8W7T+NGb2jLf7dogOn4OzsjJtuugmGTd8D+blwmjgTKMiD4ZuSwUsZQU3hdNc/YU3S9n79+pXZLnldYu/evepchvwkD6x9+/Zq6LA0GRqUUQFLunTpUiLoM5ecnIzXX39dpXXEx8er9BBz5879Gbju37+/RNsstdecBHRFRUUquJTetNIk/1ccO3YMN9xwg8X2kW1gUObAtuxNVMssTRrVRh39rlq1yhSE3dSpOf66aEuJL+lJnVti9bE/A7Xytpk7k3YF9/9QnGQqR3F0ba5k5+OHVccxvE8zBAcWD3EQlabrPBi61l0r3sk3sHjfyG7QNW6uLgcGBmLEkMFYu2EjUrPzEOhZMgdUtq2LPY+RI0epfbVBk6WLrPhGFzfob3+p4sd0Lk6HqCm9vnhwx2C4+tgWGG8z7mvUqFGjMtuEMfAyHphKUCbMe6gs7V+d2yTfSyYKyOQnCepkCFSS8J2cnLBv3z6V92U+u9XYFkttsPQYcv/G4VM5lUfy6Mi2MShzYEN7RyC/oPgLTB0pP/CAKQiTL+Xh0U2xNibR9CVtKVCztE3kFRZh3oZDmLt2H3IKihc7f+nFF1FQUMDE/2tYxqlVq9bIzSvEhl1nMXl0m3prH9kXnbc/IKeq7OvhA8jpqsm33YFV69Zj2aH4Mr1lSw/Go7DIgMlTiv+v6vyD/7wfCXRCioO7uiYzFoUk0JdHhvLM9zXfLgFb6cAsKSmpxP4yRGjs2bLEuL8l5fVkf/rppyogk+T90sOP0nsmQZk5Y1ukDc2bN6/08Y1tfuqpp/D222+X2z6yfcwpc/CcssCra16qI+URI7A29pwKwoy9YIUGTX1Jq31KBWrlbZPJAF3f+knlmkX4e/05td7Hze6n1tcmWTqpTXQUgvw9KjzJPm3bRKkyGtcPaqn+NjHpMo6fTmWOHtUaOTCTIb6nl+1E5zeXlDg988tO09ClNbVp00YND8pwneRcWbJt2zZ13rlz5xLbZX/jbeY2bdqkziXh3xjgSPkMSba3FJjJRIHqMi6nZun1Mz5+6WHQ8m6ztE164SQgtPT8yL4wKHNQWTkF+HXTSbVotpEMYcrRsDEIk14wZ72uxJf0gXOpJQI18+Bt6cHTpskAiVcnA+z9GxP/yyPT4WW2VFVOsq/UODOunRl3Jh17j5R/xE5UXXJgdscdd6BJy0iVA2Z+km1SRkL2sSaZOThlyhSVtC5le0o7ePCgqu3l4+OjaoWVJgeFUhrDSMpcvPfee6rnXiY7Gd1+++1qP6n5VXoJp2s5oDT2dpWuFyYJ/TIrszQp0yFDm/PmzSsRGMrQqqXnLRMM5HWRgPGtt96y+L26Y8cOZGdnV7vtVL90miP/Ktoo+cIpnUsgH8zg4D+HDGrqzPlMLPotBndP6IiAq71lkpcg+Qo+Lno08S3OW0rMyEZuQSEKDJqqSSaFD0+ciIO/u6tpnyKDATEpmfD3cEV+kQFZ+YXY89RNFnPMxL7ESyrx//rrr7f4hURVk19QBFcXJyQkXUb8uUz07tQELs48zmqopGjoqVOn0LJlS4cuayDfhVLrS5LXe/TooeqJyesRExOjDl7kJ+2bb74xzSgV0vMlQVbTpk1VGYrx48eb6pTJUGjpOmFShFVKT0hivDyWJNdLACf7y+zLX375BV988QXuvPNOtb9MDJD3pbz1NuVvpQisPLaU3pAgTZL5161bp3oopSj3ggUL1ExMI5n5KUGhfCdLwCU9lVJoVnoAly9fXmZBcvn+ltmhkqMmddJkUoPkrZ09exa7d+9Wr5eUBZEAjuruc1jT32/mlDmo9Mw8SPqDr49rmSPlnVs2AZmXAP/GCAv6879Inz598Nlnn+Huu+/Gzp07TdulxKRvfiKyc3Ohd5KE3sJyE/9LT62naycBmUhNz8XuQxdw7OQlDO3TDK3C/XHjjTeahkyqMoxa08XXieqL/ODJ8KWsyfnzzz+rOmIScEmwIYVY//a3v5mGIs3JsOeaNWvw7LPP4quvvlLV/WV1kg8++ED12puTnrbff/9dFXyVfC8JaqRKvlT/P3nypArKjHlcVSGlNzZu3Kgq8ktZChlK7d69u5rFKUGTBGWlSb0zCSLlecpzlOctvXkSrBnrmZmT72/pKfvwww9VeQwJTCWHTl4XGQ6VWmgy2YFsG3vKGnhPWXk/zrLuoiT5+5stsWT8cTZsXAjt2A7o73+72iUYjL1tIyJDsfw+y/V8hv1rBTadTMa4cePwf//3f1zsuJbKm/xvRzzOnL+MyGb++PtDNyI2Lk4teF7ZJALJWZMhUrJt7Cm7dtJTJoxLHtWEHLhKwCPFZdu1a1cLrSN7ksueMqoJCciOx8SW++N8KT3H9ONspJ09Bl2zttdUE8s0YWDNb2Wm1suMzDlr9mHTiSS1dJN0wUv3PZdiqjmZsDFxZDRiTqepGZqypqm858/MXVjh31U285PIUclQnyyHZE56u6TIrEw4YEBGdYFBmQOozo+zlnMFSD4DdL32mmLGmmfmU+tlRqYsuxR7MRORjXzV8kyCSzHVHgmi27QMRIswP7z2rJMqoUFE12bs2LFqUe+uXbvCy8tL9YzJ95ok4MuQJ1FdYFBGJSXEqCXIpafsWhlrnsmszXfWH1STBTLzCuDh4tRgFzu2JW6uTvDycGFQRlQDkrQvw5TSMyaJ/5I0LxMEJM9M8muJ6gKDMirJJwC6HqOh8732hFDThIGrkwGKsuKBvAJsfuwGLsVERPXuWnLJnnjiCXUiqk+cP08l6EJbQj+45nlGMr3bWGNLpptXZ7FjIiIiR8SgjP6kGWA4uh1a/p8FZWuzUrgsxVQe88WOiYiIHBGDMjI5e+Ys4r+dDxT8uTBubbC0hJPlxY5HWr1iuCNiURwiItvAoIxMLmdlocM7y/D6+/9CXl7tBmall3CyuNgxhy6tIuNyHs4lX7F2M4iIHB4T/cmkWYAX3J2d66REhfmMzHkbDpW47Xxmthq6vHHIALVEChP9a4fUnqusDpnsE9q0GX5YdUwt09S3SxM46XmsRkRkDQzKHEBVf5zbBHlh18yb6qRERekZmeaaBAE3de8K/1/mQ+s2HBg4kYFZDcnqDFUR5B+FVq1ao3/Xpti27zxOJ2ZgzMCWCPL3qPM2EhFRSQzKHPDH+cyZM2ph3FZBPnC/Wi8sKMhLXa/LEhUyI7Mihj/WQNvwPVBUCAy+lYFZDVzLWpZSdHblplOIO5POoIyIyAoYlDngj7MsKn7PPfdg1ogupor7tlCiQt99JAx6J2j/+wYoKgKGTYNOx6G0+hIS5IXbb2hnGr48cuISIkJ94OP156L1RERUd/iL54BsuUSFvusw6EbcCW3/emhrv4amGer18R2di7MT9HodCgqKsGVvIo6eLL+2HBER1S4GZQ7I1ktU6DsPhm70DGgHf4f22+fQDAbTsOvEiRPVSS5T3XFxccId49ujZ4dQdT02Pg25+Vy2iaimWrRooU62bMOGDSp95OWXX7Z2UxwOgzIHdS0lKhISEnDgwAF1XtG22qDvMBC66++FdmQrcpf/F3PmzEHbtm2xZMkSdZLLr732Wq2X7qA/ebg5q16zvPxCrNl2Gl8uPYwz5zOt3SyqAzfeeCM6dOhQpZPsa83lkiRYqOiUnp4Oa5oxY4Zqx7Us7USWff755+o1lfOGjjllDqoqJSqMQ5eLFi3C/PnzsXXrVtM+0dHR6kNy/Phx07b+/fvjySefxKRJk2qljfp2fbFyy048/ugcxJ5JQHSwH96dfp267YmfttdJ6Q4qy83VGXfc0B6rtpzGot9i0KN9CAZ0D4OzE4/pGooTJ07geEwsGjUOr3SWtq1MYJLZ3Ja4u7vDlq1bt87aTSAbxqDMQZUoUSGzHdOTAb9GgLOrKlExoU8ftc9zzz2H119/Hc5urogaPRB+4aE48b/tiImJgZOLs2lbRsIF7NywU/Wuyd/MnTu3Ru2TemXTpk3DDz/8AE9XF8wZ2wNPDO6oZoeKvX+bUCelO8gyX283TB4VjT+OJGHzH4k4fS4TYwe1RHCgp7WbRrVEArJn5i6scJ/KSuvUl8jISLsdWqtquRpyTDzUdWDGRcMPrV+JAy/chkPbNpkWEZcZmtJDJgFZSIcoTFv4LoY+/1f4N2+KtFMJaNwhCrctel9t637Xzepc9mncIVINKy5evLhGbZPgSspxiE2PjsXfh3cxBWTCWLpj0yPj1PXaLN1Blsnr26NDKG67oR3kpf5mxVHsOnQeBgPXaSLbHe765ZdfMGDAAPj4+JhyufLz8/HBBx+oHvaIiAi4ubmhcePGuOWWW7B3795y73fp0qUYNWoUgoKCVI+c3N/06dNx6FDxaINc/+KLL9Tlli1bmoZUhwwZUmlOWVZWFmbPnq1SM+S+5aB43Lhx2LJlS5l9JSCV+5XcLzkY7dq1Kzw8PNCkSRM8/vjjyMnJKbH/tT7f6pDh2ltvvVW129vbG4MHD8bvv/9eoq3X0p4ZM2bg7rvvVpfl3Hyo2tzly5fV6ydD7PJa+Pv7q/vfvHlzmbaeP39evU5RUVGmfdu1a4cHH3zQ9LtjLewpI+iatILTI/+neqfMqR4yVxeMmvskPAL81LaDC1fCydUFo822Gcn10XNn4rspT6jhTknIrwnpdVu1apUqz9GlaZDNlO5wdMEBnrhtXDts3XcOm/YkIv1yHkb2s+3EZXJMP/74I3777TfccMMNeOihh5CZWZwTmZqaiieeeAKDBg3C2LFjERAQgJMnT6oSQitXrlTBRK9evUrc11NPPYV58+apoEPSPySIOHv2LNauXYsePXqgY8eO6j4lENy/f7/60Zcfe1FZYn9ubi6GDRumRi66d++u7icpKUmNFKxevRrfffedxe+4Dz/8UH1HSqqJ/L1cfv/993Hx4kV88803pv2u5flWR2JiokpfkWBHRi66deumUltkspi0q7TqtGfChAkqT1ACYnmeEoBaur/rrrtOdShIAC7BlbzX8jdDhw5V/w/kfkR2drbaR4JICbBvvvlmFSSeOnUKX331Ff72t7/Bz6/kb1t9YlBGJsYjD2MP2Z49e9TwpDH4upJ8CUmHYktsK022txzSG1tWb1Yf1LCwsBrnvUnpjvLqqVmrdIejk3yy63qEo1WYH9zcinswc/MK4ebqxB5LK7qSnY+snIIK9/H1coWHuwtycguQmZWv6tNVlwTichAX4OuuekpT0rIr/f9SmwWJ4+LiLA5fSkDQt29f03UJUiSokdnm5iQIkBncpb+f5Edd/l7yVdesWWPavnz5chWQderUCevXr1c9ZUaFhYW4dKn44FACjX379qmgTC5XdZblm2++qQKy22+/XQUGxs/QY489ptpz//33q+cmvX3mJCCU7+k2bdqo6zIhSoKW77//Hm+99RaaNm16Tc+3up599lkVkMnjy32VrolZWnXaM8EsKJPL0nNW2qOPPqr+9uOPP8a9995r2i6jNj179jS9ftIDKTl9EoDJ+yOdB+akqLqLiwusiUEZwbBzBbSzMXCa+KQph0zyxYTkixnlXc4qs80S37AQdS5fVDUJykylO9b8psp0BHq6WS7dMWq0VUp3EBAeWvwjIT/MP64+juZhfipYI+s4EJOC7fvPV7jPyH7N0Sk6WK3csGZbPGbe1bPaj7NpTwLyC4owcWQ0CgoN+Gb50Qr3D/Jzx10TOqI2Jyb84x//KLNdeqbMgzI5WCsdkAkZLrP03SRDX9KzIoFcQUGB6Qf6X//6lzp/7733SgRkQg4KQ0KKv/OulQx5ymPJd6/5QY30ON11110q2Pj555/VUKk56Y0zBmRChuIkF1deGwnWjEFZdZ9vdcgMeOmJkp5D6U00J8ONEnCaTwir7fZcvHhR9ShKj5x5QCakTU8//bQKbiWAlR5T89eqNBl2tTYGZQRdo3BA71wih2zAzBlYcs/zKoHfyM2n+IjafJslmYlJ6rz0l1dNhjCldEfp3rLySndQ/ZPSGX26NIW/T3HgLD/ULs5MWa1vnaOD0TqieMisop4yEdnMH42Drm2ixqAe4aZ0B3mfZSWIitT2TF3JFZLvhcr07t273NukR0sCBsk5unDhggoCSv/YS46WkF4sCSQkT6q2yTCbDN1JTlN4eNkDGglSJCiT9pYOymTYtDTjfZQuDVKd51sdEnBJYCY9UvIamZMAU4Y1SwdltdmeXbt2oaioSLXBUu9pbGysOj927JgKymSYU+5XfuukR1O2yfsqr78t9PIzKCPoWnVRp/mPD1CzLI05ZCEdo3By/Q70feg2dd27cVCZbaXlpGWo26ULvXSOWp2U7nBy4tCljYhuHqDOi4oM+GHlMTQJ9lK9ZlKIluqHt6erOlWFDGHK6VoYg29jQH4tQ6D1obweLCnvY8x1krwiSfiWXhL5UZYeKfmxNq+BKMnf0rOjv7oEWW0y5rmV11ZjYGLcz5yvr2+ZbdJzJyRQudbney3tl14pSyw9r9psT2pqqjqXCRGWJkWYT6QQki+2fft2vPTSS2oSyK+//qq2y4QDGYaV3ENrYlBG0BJicCErT31QzPPFOk25Hmtfeh+rZ81TCfyy3bTtuXkY/VrxNqNjy9dj2wdfoyi/QB0FyX/ymtYuK1G6o5Qmrqm4KTwQAe78b2xL5Ee6U1QjbNydoIrNjhnYEk2CrT8sQI6nvJ4PyX2SH/1NmzZh4MCBJW6TH2wJCkoPi0pvjsFgqPXAzBhYSWK/JfK45vtdi+o+3+owtis5Odni7ZaeV222x/fq48vQ6dtvv12lv2nWrJmakCHvpxQ/l8kgMkHi4YcfVvluMgRsLfw1IxhWfAQ0blMmX6zVkD7oevt47PvmF3w7+XG0GtpH5YsFtAxH8pE4fDvpMbQa1ldtk94xKZVRF7XLpHSHJVpWBgxfvATD2q+gH/+QTXQ9U/EPYZe2jRHRxBcrN53E9yuPoW/npujTuYkK2Mg2SWHYyuqQyT5B/lGwd5KTJgd8pQMCmZn3xx9/WBwGlR6VjRs3quHEijg5OZXpqaosqGjVqpWavGBpcpSxlISlWYd19XyrQ3LaZNhSctgk0DIfwpTRkm3bttW4PU4VvKYyS1O+cyw9TmUkwJbXVU79+vVTQ5syA9SaQRmTPhycWvA7OxNuAcEW88V6PzAVI155HI3atETs6s3Y89liFXxJAcTI1q1LbJMaZXVZu6w0nZcf9COmA3F/QDu2o1bvm2ou0M8dU8e2VcHY9gPnVHCWlpFr7WaRBfJ5bhMdpWZIVnSSfRpC8dPmzZsjLS1Nzdgzkh98KYeQkpJSZn/pQTEm1huHy8xnX5r3BhknHUm5jKqSZH7JqZKDV/O0D+nFkR4dGXIzlnSoj+dbHRKEyUiIvAbvvvtuidu+/PJLlctV0/YEVvCahoaGYsqUKWqkR2acWkqb2bFjhwr4hDympd474zZrrwjBnjJHl5MFaAYEhDdXQ43Ss1U6X6zVkN7qdDH2NJY+OBvdunRVyZVC1ryURMnDR4+ahjjNyfWBM+9WfyeJlTWtXVaaLrondG16Q9u9Clrb3jh7NkENlwqZ7izd1GQ9Tno9+ncNQ8swP6zcdApfLT+CIb0iVEI62Q7pHXAkUkJBhqykp0Z+0OWHWHqkpKdKCr2aFzoVUktLAgYZHpP8J6ltJTlUsr+UWJDbpMSCkFwp2U/KMMj3nZeXlwpCSifpm3vmmWewYsUKVQ7j6NGjGD58uBoOlFmFEvRJon/pchh1+XyrSw66ZXaj5GRJb6KxTpmUEpFSFDIpw3zYt7rt6devn5otKUGfBHPBwcXfHy+88IJpdqw8nryO8hrK/jLkLEHc7t27VbK/lOzw9PRUpTZkRqbUKpPlAmVCmrFGmrTDGIBbjUY2Jzk5WUL9EifZVhcMyWe1wnf+ohkSY7Uff/xRPVbjDpHa9KX/0u7//RvTSa7Ldrl90aJFpr8/e/as2hY1emCJ/eU04pXHtJCOUSWeR8+ePdXj1OpzyLmi5WSkanPmzNE8PDxMjyWX586dq+Xm5tbq49G1yc8v1NZsPa1t2nPW2k2xSzk5OdqRI0fUuaM6deqU+myPHj26wv0WLFig9pPz8sj3WPfu3TVPT0+tUaNG2pQpU7QTJ05od911l/pbeazSFi9erA0dOlTz8/PT3NzctBYtWmjTp0/XDh06VGK/N998U4uKitJcXFzUfQ0ePNh0W/PmzdWptCtXrmgvvviiFh0drbm6umr+/v7a9ddfr23atKnMvrNnz1b3u379+io/9+o8X7lf2SaPU1UnT57UJk+erF4beYxBgwZpGzdu1B555BF1X3v37r3m9ogVK1ZovXr1KvEdby47O1u97j169NC8vLzUfi1bttQmTJigffnll1pBQYHaTz5Djz/+uNatWzctKChIvY+tWrVSj3v48GGtpp/Dmv5+6+Qf64aFVJp035aeySJHTcajg9qkxR+BYfE70N/zOnR+wZj5zNOY/9bbcHJ1RauhvVW+mJS4OLVhJwrz8svkhkn3epcuXdDznklqyNJo50ffq1w0qf4vuWjGHLNT63egML+gVtbHNJKaNo8++ghiY+OKFy2/ua9p0fKYlAxERUVy0XIbIl85kgOy71gyvDxcEHV11iah0qrvUvRSlu+x9hALUVVJb5jke8kMVluoA1bXn8Oa/n5z+NLBSbK8SEy7jNT48+g6cTTG+BUh49ft2Lb6zzXDpKtXhgVLDz8ax/rNc9FObtihAjKpd2a+RJOQoVGZzSnd3VJjpybDmVy03D7J6y/vXWLSZbXQOYMyIvsnw4Ol64p9/fXXqkyFlL1oCAFZfWBQ5uAObN+ElgVFiGj5Z/JuZLdOeG3WS+j7Q1+V1Cpj7uVV5pdChaVz0YzrY5YOyMzXx5TZnK+++ir69OljsWDitSxaXnp9TOOi5WPahqPnvKVctNyGyPsw9rpWMPbTH4xJQYCfOx6673Y1M6sqJOHc0XKhiGyVrP0puWTt27dXsyWlLJLkhkkuXFVLVRBnXzo0GUJc8/MinEvPUmUsZAhSzk8fOa7KWEjyZOfOnStdKkl60GRoU3rAZDKArI8pQ5aWistKL9pvz88vUctMeuFkNYFrYazmb1yY3BIuWm67gZmUyJBes+OnU7Fw1XEcPHwcx2NicSk9p8KT7FPV4I2I6p4sAi7DdDLjUhZKl8T72267TdWYlDVDqWqYU+agOWUSBEmQcv/4/hhxyzCsbd2uRFV+CbCSD8ep/aoyxGi+ZmZRQWGZHLMK88zKyVerCunJk4rRIyJDsfy+URb3GffxaqyLS1JTnrlGpm2StTN3H76AG68fKOPSeGbuwgr3l3paUqLBfEp9Q8ecMqKGn1PGnjIHJeUiZEmlnHvvKBGQmQ8xyu2yX1VIjpgEcFIuw1K9M/M8s9t+fK/WapmZFi2PPacWKC/NtGj5yJEMyGyY9Jj17tQE/t4l184jInIkDMockNQWk0J7LYf0RpSbHt4F+WX2kcBMbpckTakdUxXSoyb1yySBX2ZZSo+bUVXyzKoTBJqTHj9ZmFwWLS+Ni5bbFycn5vwRkeNiUOaAjBWpZfjwseN7MSDlnMX9pByGuHSp/HwtS6SAoJS9kCFQCcyuJF+qMM/sWoNAI6l0LYvwyqLlnd9cUuL0zC874azX48YBPat1n0RERPWNQZkDMi9jMb9Nd2xvVHIas5HUJxMy+7I6ZMkNCcwkJ+27KU9g87wFZdbVrM0g0LhoeZOWkUBQ0xKnJi1a446BXeC/7lNoqSWHVImIiGwJS2I4IPMyFieljIVn2fox0sMlCfgyM7Ky2ZeWSH5Yz5491XCk9H5ZyjOrrSCwokXLhZZzGYaFb8Kw6G3ob39RrZlJ9q+g0IAig0Et5URE1BDw28xBSRmL5t5uGLb8F7iWWgDWOPtSZkQa15G8FpJjtnnzZrX+mFT9P7V+Z4k8s9oMAiui8/CBfuJT0HUeDHhe+/pxZFsyr+Thk0UHsfmPBGRcLjvJg4jI3rCnzEHJEOPlg1sx3S8Dsx+cDbfunSwuqVQbC4hLz5wsHCvJ9hLslV64vLaCwIrovP2h6zteXdbOHAEaRUDHAM2u+fu4I7KZP/YdS8HOgxfQvKkvOkUHo3WEH3vPiMguNYigLD4+Hu+//z5WrFihemXc3NxUtW9ZfV5WfJeV4a/F6dOnVS2S6mjevLn6O3tw1+Sbof32OVq06Yjfq7CkUk0Y88yklpnkmUlSvzEIPLl+hyomW1tBYEW0wgIYVn0GXds+0F3HGZm26GJygqpDVtk+Qf5RGN63Oa7rGY6Y02k4EJOC5RtOwNPdGR0iG6Fr28bw8XKtt3YTEcHRg7JffvlFJXlnZmaatmVnZ2P37t3q9Mknn6hgLTIysl7a06ZNG9iNrEzAwxur1q9XZTJyMi9XuKRSTZXIMzMLApt2bosb77kDcx97HnVN5+wC/ZS/Az5cb9EWycFUVUhAZtzXxdlJBWFySknLxsGYiypAk140CcpkaNPb0wVOTuw9IyLbZtdB2d69e3HrrbciJydHLXYqPS1Dhw5V17///nt8/PHHiImJwbhx41SAJmtwVYcEJwcPHqxSsCELXYu77roLdkMWI/f0w//OHcOGC7F4q+8tdf6Q0hMmJwkCjetqnnbKxbakUygwFMFFX7yYeF3S+RdXVtYSY2H4Yw3019+ngjWyvpquZRkc4IlhfZphUI9wOF+tebZ84wkE+LqrtTaJ6kqLFi3Uub2MlJBtsuug7PHHH1cBmNSo+u2339CvXz/TbcOGDUNUVBSeeeYZFZi98847ePnll6t1/y4uLmqR1YoUFRWpRVeFBH0331xyaSGblp0BePkiPS8Hfq4e9frQkmdmXIi8iaahf0ir+l8s3FAEnDwAw4r/QH/DX6FzsuuPA5lxcf6zV2z0gJZqfU1x4kw6/jiahM6Se9bMH87sPSMiG2K330iyyOmmTZvU5XvuuadEQGb01FNPoV274iWE3nvvPRQUFNR6O9auXYtz586Z8qY8POo3uKkJLSsTOk8/pOfnwN/Neu3W63QqIDuWfgF7Us7U2+PqItpCf+PDwKmD0FZ9Bs1gqLfHpvrTKMADwYHFeaUuLnq1zuaK30/i4x8PYOPus0jLyLV2E4mI7Dso+/nnn02X7777bov76PV63Hnnnepyeno61q9fX+vt+PLLL02X7Wro0rynLD8b/q7XNhmiNu1IPo3tyafq9TF1LTtBP/Z+aDE7oa390tSjQg1Tsya+uPX6trjrpg5o1zoIh+MuYsHPh/Dj6uM4dvKSWpKLiMha7DYok/pXwsvLS621WJ7BgwebLhuLmNaWy5cvm4JDySe47rrrqvR3sop8RaeLFy+i3hL9vfysMnxpybTWPfFQ+6q9hrVJF90TulF3Qzu0CdrGHxiYOYAgfw8M6RWB+yd3wfWDioc3f910Cp8uPsjAzIZJqoj0qksqiqzfO2rUKPj7+5tSHz777DPcdNNN6vvY3d1drfYxevRoiwfk5vclOccjR45UKSh+fn4qDaW83LClS5eiV69ealQkJCQE9913H9LS0spts3yfP/HEE2omv1QGaNy4saoMcOjQoTL7zpgxQ7Xp5MmTePvttxEdHa0ep3379ipPWuTn5+P55583PcfOnTtj5cqVNXhVyZbYbRLN0aNH1bnMqpScsvK0bdu2zN/UlkWLFqmZnmL69OlVzomSD6W1yY+QfsJjMHgH4PLBNQiw4vClkevVnK7j6UnwdnFDmJd/vT22vsMAGAryoP3vG8DVHbr+E+rtscl6JKesXasgdUrNyMX5lCtqmwRmKzaeRJ/OTRDayMvazaRSJCCbO3eumth1//3348yZ4rQHKYEkhapHjBiB4OBgtY6uHDjL9SVLlqiArbRdu3bhzTffVPf1wAMPqAlk8jcyyUsCJwl8zEdGZETE19dXfedLQLh8+XJ1/xIsubqWLMEiB9mSWnPixAkMGTIEU6dOxalTp9Rvh1QFWL16NQYOHFimTTNnzsSOHTswfvx4ODk5qYDstttuQ0BAAD744AMcOXJETWDLzc1Vk8zkecnvW1VnL5PtssugTP4jGnuTjMni5ZH/xNKblpWVpWqY1SbzoUvjMKm9UAFkeDQy8rIg/UK20FMmDJoB38XtgpeLG/7WeUS9Jv/ruw6DIT8X2ubF0CK7Qde4eb09NllfoJ+7OonsnALkFRSZJgycOZ8JLw8X1cNmy86fP69Opb8DpZdGvjflx7y07t27q/Pjx4+r70lz0hsjvU0SXJT+/pReJZlMJZOd9u/fX+Z+O3XqpCZLSUCSkZGBJk2aqFNtWLNmjeoVK526Is+vdG1JeT2kFM/TTz9tMSj79ddfVdAjM/nNv8+/+uorFZxJICWk7NKjjz6qfk8kkJNeLDFnzhwVlMnjSJ1Kc3//+9/V85fKABJEmj+mBFXSfnndJdXGnARYBw4cUIGlkP369Omj2iKTzyRglHYI6QmUtkvetNTrJDun2aHk5GSJI9Tp1ltvrXT/xo0bq307duxYa22Ij4/XdDqdut/+/ftX62+Nba/OSZ5zbTKkXtCKNi7UTied1u7//RvtzOVUzVYcTj2n2rQz6ZRVHt9w4bRVHpds13crjmjvfL5L+/7Xo9rhuItafkFRmX3Gjx+vtW/fvkon2be6cnJytCNHjqjz8syePbvMd8ftt9+ubouNjbX43WLUt2/fMrd99dVX6rYPP/ywzG2jRo1St2VkZFT4nSXPVa5L22pq/fr16r66d+9erb979NFH1d+dPn26zH1dd9115T7OzJkzTdu++OILtU3uq7RNmzap25o3b27alpeXp7m7u2tBQUFaVlZWmb8ZOXKk+pvff//dtO2uu+5S2+SxSmvVqpW6bePGjSW2FxYWai4uLhafB9W+yj6H5vHJtfx+221PmVHp7mJLZBxfSPmM2vL111+bco/srZdMuZIOLXYP0LY3WvoE2cTwpVH7gCboGhSORaf2olNQGNyd6reGmC6k+GjXsGsl4OUPffuyM3vJsUwa3QYnzqbjYEwKVm0+hfU7z6B96yB0igpWszuF9Igcj4lFo8bhla5GUFdk+O3GG28s01NmHFXYs2dPuX/7+eefW+wpE5IDVXqGu7Huo/TYWLpfGdoTUixa8rZqq5dMSE6XJZKLJXUj//e//6mhy7y8kmuiykz50r1ZlnKSjSMwMkHMyNgbOGjQoDL7y2tTOo3m2LFj6rdKhkUtrSoj26XHb9++fWXus2vXrmX2l9dPnl/p22R4U1JijFUAyL7ZZVBmPsYv4/iVMX4wa7NchXRtGwM+827vqkhOTq7wdhmalcTOuqSLaAOne16HfD09a4PDdJNbdcfs3cux6uwRTGjRpd4fXwXcqReAgj//f0neinFtTvmhadasWb23i6xD8szatAhUp7TMXByKvahmbu49moymwV7o3iFU7ScB2TNzF1Z4X5UtIVUTFQ0RyvemcaiyuquRyDCacSitNAkKKrrfushzkgT70uLi4tC7d281zCgBj+RjSe6XDA1KUv/GjRvLBGlC9inNGGDJ0KyRDMGWlxMsr4EUwjZnXGXGUluF8X0yX42mKm0q77a6KPlE9c8ugzLzyvxXrlypdH/j0Z9U/a+tGmlyFCTkqNR4RFhV5X25WUN+USGc9U6qVpgtaeTujdHh7bE64QgGhLRCsEf9Lh6uctlGFc+Eki/yt1+fizlvvGXqbZXZTi+++KJKyDX2xJJjkNUBZMWA/l2bXu09u4iUS8UTfqh+WMo1lQMlmQUpB8yy9J65Bx98UAVlNSGzMss7qJbg7dKlSyWWqDMGT0lJSRbv78KFCyX2I7LbkhhyxGc8KpHleioiH1JjUBYREQFHT/A3Mqz7GkWL3sE3cbvwzoG1sEVjItrDx9UdP578w2pf/DI7qlPbaLzw8iuI8HbFr/ePVie5PGvWLHTq1FHtQ45H1tKMbhGIiaOi0b9bU2s3x+HJ8LEoncwvvd61UQ5JZnUKY9Fyc9u2bUNhYWGZmf/yWyWTAoyz9M0ZV4KxNFRJjssugzJhHN6TLuvSHwZzxh4tYazuXxPSRWysFyPd2GPGjIE90q6kAXonXNckEmObdYAtkhIZk1t2x/7URBxKPWcKwmVWUmXBeE3JF7nMdJL3N/HcecwZ2wN7/zYBo9qEqZNcfnVsDyTEn1b7TJs2jfXNHFi9LxFGZRhzxYw1LI1ef/11izXBqkuCPenVklmfsnSf+W/CCy+8UGZ/yXeW7wVJR5E8N3OrVq1SB3NS0mnAgAE1bhs1HHYblBlru0gvWEXJq+Zd1rXxn19qy0g3tZC6MRXVSLNpssSSly9a+wajQ4DtHuV3bxShesy2r1yr3j/p7ZQjVjmX61Lvp65+ZI05JJseHYu/D+8CN+c/F0uXy88O74JNj4xT12Vf/jATWY8MUUoJjokTJ6oirLLMnnxHvPLKK6r8RE3J8KWUnJDfHJloIJMqZG1l+T6SQuKWcvneeOMNtGrVCq+++iqGDx+uetfld0Py3ST5f8GCBWXKYZBjs9v/DRMm/FncU/5jW2IwGExDjZL3JcmfDr2s0tVkdfnSmvzOFzhzJR9rEo7iXNafM4xsjQQ6Oz/6AffdcRd27tmNqNED0fOeSepcrk+ePFl90dUFuW+xN6E4CLfEeJtxX6KqKCg0sGe1lnXr1g2//fabmnQghWKlR0u+92XoUuqU1Qb5zv/pp59UfbYvvvhCnSTwkzWQLVUCkPxhKQL72GOPqeFVqdIvMy7l90u2WyocS45NJ3UxYKdkWSMZ35feqt9//73MlO233npLHcmI2bNnq2nZpcf0jYGafNhkSnhFUlNT1dGQzPiUwogyjFYXpFBj6Rk+klxakwkCkqz+zjvvqCM2Y7K6h5srOt41Ae+88A8Mivhz5QNbIj1hEvCEdIjCqLlPwiOgONlW5KRlYPWseUg+HKf2k2CzNsn7LTOnRkSGYvl9oyzuM+7j1VgXl6SSeaXIJjmuDh064FJ6TpVnX771719ww+DWCLhasLYyUl5BqsFLcVTzGehEVH8q+xzW9PfbbnvKhFQwljIXklMma6DJuP327dvVOmfGrmUhlZelK7umJJfMWILDnnrJVLJ6p45qvbQSyeq+Htj134W4c8hYm01WlxlVzm6uZQIyIddHz52pbpf9apsEWVKpe23sOaRml51KL9vWxZ5Xa+YxIKPq8PV2U8GYt1dx78rpxAzkF/xZfoGIHJOdJkT92V39ww8/qOnPUuvF0jCWBGSSB2ZeRqOmQ5dSk+b222+HrZNOUEk0ldfI09VFJas/MbijKTdKktXnbzyEOWv3q2R1SWyXddRsJTdKkvlljTsZqiwdkBnJ9pZDemPL6s2qWKT5lPTaIL10kpS77FA8ZvQuXlbFaOnBeLVGIocuybwwbGV1yGSfIP8o1UsmJBj7ZcMJ9O3SFL06Ftc7IyLHZNdBmZCESRlGlF4zCb7kh1zG9mVWi/xYPvLIIxarKVdXbGysygEQ0jMSGmr7X56lk9W7NC1Z3NCYrD6mbTh6zltqc8nqMnwo/MIrfq19w4qLM5auE1QbJPdDel2fXrYT8zaUnMF1PjNbDZ1bWk+PHE9Vi6RKQGa+r6uLE2ZM6KjOxeY/EpCVU4CeHUJtfq1NIqpddh+UGadCz5s3T52qY8iQIVVOtpXETntMvzP29EhCeumgzNaT1Y1DghkJxUUWy5OZWFycsXRF7dpqg/TESsHg0poEATf16gn/M/ugBQy1qYCW6t+yZcuu+W99rg5jCm9PVxw5cQmH4y6hZbifCs7CQ2qn8DUR2bYGEZRR5T09i/afLjP8ZrTowCmb7PGR9ef69++PnRt2ou9Dt1kcwpRk/1MbdqoZULXdS1bZ7F6hnToIw/J/Q9esHRBYe2v7kePq2rYxOkU1wrFTqdh9OAk/rj6OkCBPdG8XCDs8LiSiarDrRH9q+MnqstZkYV6+mmUpAZg54+xLud24JmV907XsBP09r0PHgIxqebWADpGNcOeN7XHziCg1tLlhpwxr5iMvv/xi2URk39hT5gDsOVl90qRJePbZZ1VV7u+mPKGS+iWHTIYsT67fgaL8Ajz33HO1Xg6jOnSevtAK8qDt/BW6XtdD58pyBVQ7ZEi8ZZifOiVcSEXy+URohuLusqIiA6ADnFh8lKje1HUak13XKWuoartOmbHelo+LHk18Pcskq18uMNh8na3Fixershfma9iFdorG67Nm466pt8HatLQLMHz9CnTRPaEf/RdrN4caIFnOR5aVk2F6We4n80qemrkpkwGYz0hUP6TSg8z0l8mEsoJEbf9+s6fMAVSWrD6hTx+bDsiE9ITJSWbXSpDp6eeLDxN3wSs0ErZAFxAK3bDboa3+DIaIdtC3L1nImKim5AfAzc1NzZKWEj8yIUB6uSUgk16zy9n58HR3Mc3iJKLaJX1Y8vmTz6GlgKw2MChzEKWT1XOLCrArOR6dg8Lg52o/0+4l+V9OYrCWjvXnYnB9RAd4uZRd4qS+6dr3B84cgbbuK2hNWkEXUFyqg6i2NGrUSB2ly8GJrMUoPwy5RTrVY5aTnY/LV7Lh4qyHh5uzCs7Yg0ZUO8GY9FRLQHblypU6m1QmGJQ5qNTcLHwTtwvhXv52FZSZG960DbIK8lGo2UYldPUDOPwOaOdPwrDiI+inPgedc90cTZFjkmFLcfHiRRWcWVpTUwI06TnT63UqMJMgjcEZUc1JD5kxfaCuMKfMBtXF2peWFGkGyROGXsdE4dqkJcXD8N0c6LoOg37IVGs3hxooOXIvKrJ8QJJ8KQuH4i4i/txleLg5oV3rIDWb09mJn3WiayEr+VRlyJI5ZXTNnBpIMLbpfBzcnJzRu3EL2AJdSHPorpsMbcP30CLaQte6q7WbRA2Q/ECU9yPRLMwdzcKCkJaRi91HLiD2zBX07hyhesyycwrg6cEeXCJb1DB+lana1p87jn8d+R0NQVxmMuKvFC/JZCt03UYArbrAcGSrtZtCDkwWPR/ZrwVuG9dOBWQpqdn4748HkJB02dpNIyIL2FPmoBKzMpCWl4WG4K7oftDbWM6M/ADqx94HOLtZuylEKr9M+Pu4YVjfZmgS7KWu7zx4HsGBnmjR1Jd5Z0Q2gD1lDio9P9tuE/xLk4BM8uP+uHgWBhtKkdS5ekCn10NLiIHhj7XWbg4RXFyc0Dk6WBWcLTIYcOJsOn5aG4uvfjmCIycuFhekJSKrYVDmgGQ6/bFDR2BIbThDGKcvX8JHRzfhYGpxuYADBw6oc1ugnT0G7cReaAb+4JHtkMBs6vVtMXl0NHw8XbFq82l8uuQgdh+6gLx825jRTORoOHzpQBYtWqSq4m/d+mee0w/9+6t1I2U5I3vW2jcYWTuO4qZH5yB+/xHT9v428Px0fW6Ars841WtGZEtkyDIi1FedLqblYM/hC9i8NxHbD5xH5+hG6N4+RBWpJaL6wZIYDlISQ9aHlPUjnd1c1fqRfuGhyEi4gFMbdqoFveX2uXPnwl4Zn5+TqwtaDe1jk89POxcH7chW6IZPZ/4O2azLWfnYeywZB46nYNx1rdAy3E+tHMByGkR1//vNoMwBgjLpIZMFx0M6RGHU3CfhEeBnui0nLQOrZ81D8uE4tZ81F/a+Vvby/LST+2H4+X3ohkyFvvtIq7WDqCpkCNPVpbjw7OLfYtAowAODe0VYu1lEDfr3m4c+DkCGLKWHrHTAIuT66Lkz1e2ynz2yl+ena9UFuu4jof3+I7Sk01ZtC1Fl3FyLl2mS4/b2kUFo3rS4inlyajaOn0qFwcDjeaLaxqCsgZNkd8khkyHL0gGLkWyX27ds2WJx6RZbZm/PTzdoEhAcDsPyj6Dl5Vi1LURVIYFZu1ZBaBFW/Pk6eTYdK34/ic9+Ooi9R5NQUMBJAUS1hUFZA5eaWlxUVXKsKuIbVrx49qVLl2BP7O356ZycoR/7AJCTWbxwObMHyM707dIUt9/QDk2CvbFh11l8vPgAtuxNRFZOgbWbRmT3GJQ1cIGBgepckt4rkpmYpM6DgoJgT+zx+ekCQqAbcSe0YzugHdli7eYQVVtIkJeaBHDPLZ1UL9ofR5LwyaIDWLP1NFIzcq3dPCK7xaCsgQsPD1dlIWQWoiS9WyLb5fYBAwYgLCwM9sRen5++bR/oOgyEtu4baKnn1bYzZ86oiQhykstEts7X2w1DezfDfZM6o1/XpjiZkIH1O4r/77IXmKj6GJQ5AKnTJWUhZBZi6cDFODtRbpf97JG9Pj/dsNuAwFDknTupynW0bdsWS5YsUSe5/NprryEvL8/azSSqlLubM3p3aoJ7JnbCqAEt1LbTiZn47tejyM0rtHbziOwGS2I4aJ0yybGSIT1bquNVE+U9v5Prd6Aov8Bmn9+qlSvx2OOPITY2DtHBfnj35r5q+xM/bUdMSgaioiLxwQcfYvTo0dZuKlG1JCZfxrGTqRjWp5maLBBzOhUtw/3h4sy+AGq4UlinrOGpi6BMLF68WJWFkFmIRjKkJz1I9lifrCrPr1///nhq5kybe37ysZs2bRp++OEHeLq64PkRnfHE4I5wc3ZSt+cVFmH+xkOYs3Y/cvILMXXqVHz77bcsOkt2SfLMPv/5EDzcndGtbWN0adNYXSZqaFIYlDU8dRWUmZeRkFmLkvRuKzlWtan08ys0FGFr0ikMCG0FJ53tHKVff/31WLVqFfY8dRO6NLU8AWFf4iX0nLdU7fvrr7/WexuJaktaZq6aEHAo7qI6uOgY2Qg92ofAz8fN2k0jspnfbx6qOCBJjpeTozy/c9kZ+DZuJ4q0Igxt2ga2QlYhkKBsb8KlcoMyuc24L5E9C/B1x/C+zdWEgH3HkrHvWAr2H09GVPMA9OwQitBGXtZuIpHV2U63AVEdaeYdiAGhrbH09AFk5tvOdP0JEybA2dkZi/aXX91/0YFTap+bbrqpXttGVFc83V3Qv2sY7pvUSc3cTLqYjW9XHFU9aUSOjkEZOYSbW3SBXqfDktP7YEs11kaMGIG1seeQml12lqVsWxd7HiNHjjTVYyNqKFycndC1bWPcfXNHTB7dRvWkSTbNLxtO4Mz5TGs3j8gqGJSRQ/B2cceEFl2wLekkTmSmwFbIsGRhkQHLDsWXuW3pwXh1G4cuqSHT63WICPUxLYJeVGSAk754QktqRg7y8llSgxwHE/0dMNHfURk0A17b95s6Gp/VbTT0NpD0LxMSQkJC4OOiRxNfzxK3nc/MxuWCIiQlJbOnjBzSDyuPISUtG52igtG9fQh8vFyt3SSiOv39tv6vElE9kSDsttY9cTYrDRvPx8EWSLB1xx13oEnLSCCoaYlTk0aBuKNHJALy0q3dTCKrGDe4lRrilBmbny4+iJWbTiElNdvazSKqM+wps0HsKatbX8XuwB8Xz+CVHuPh4+oOW6UV5MOw+B0g7QL0U2epNTOJHFF+QREOxV7EniNJuJyVj+ZNfdWMzWZNfFi7j2wKe8qIriHpf0RYO7g52XZFGJ2LK/Q3PQq4e8Pw2wKuJUgOy9XFSQ1f/uWWjrh+UEtk5xRg8ZoYbN13ztpNI6pVtv2rRFRHSf/jmnU05ZnZQm5ZeXQe3tDf8iSgA3sEyOE56fVo1yoIbVsGqhmasiC6kCWcsnIK0K0de5PJvtnurxFRHdt8IQ5v7V+rAjNbpvNrBJ1vI2g5V2DYvARaEWejkWOTA5TmTf1UGQ2RkpaDc8lX1GWDQVMBGpE9Yk8ZOawwT3+09Q9Bkabh6gx823YxEdrhzdC17w8Ehlq7NUQ2Y0C3MNPwfmx8GlZtPoV2rYPQs0MIAv08rN08oipjor8NYqI/VZT8L7lmmmaAzoaHXYmsJTe/EAeOp2Dv0WTVY9Y6wl8FZ00bezMFgOoc174kqqE1CUdxMfcKpkX2gq1TAVlhAQxLP4Quqjv0nQdbu0lENsXd1Rm9OzVREwOOnUzFnsMX8MOq42gS7IUeHUIRGeGvCtYS2SIeapPDc9U7Y8P5WJzMvAi74OQMXUBjaOu+gnZyv7VbQ2STnJ306BjVCHfe1AEThkeqVQKWbziBr345rPLOiGwRgzJyeIOatEYz7wB8d2KXzSf9CxmC0Q2ZBrTuBsPy/0A7f9LaTSKy6c9Lq3B/TBnTFreNa6dmaEpPWUGhATsPnkdOHifOkO3g8CU5PCmJMbV1T7y5fw02nT+BwU2jYOt0ej30198Hw5J5MPz8HvRTn4MugMn/RBUJbeSlTiL5UpYKyqJbBMLDzRkFhUVqkXSjG2+8ESdOnKjS/bZu3RrLli2rs3aT42BPGZF8qfoGY0BIK/wcvx9XCnJhD1Rx2RsfATx8YFgyH1pWhrWbRGQ3wkJ88MDkLvD3cUORwYAvlx7GLxtO4HxKcWkNCciOx8TiUnpOhSfZp6rBG1Fl2FNGdNXNLbpi76Wz+On0fkyP6gN7YCwua/huDgw/vQf9lKehc2UJAKKqcHG52jOmAT07hmLP4SR89+sxhIV4o6DAgEaNw/HM3IUV3sebs6bUT2PJIbCnjOgqWQfzxuZdsPnCCZyyl6R/Ccx8g4qr/qcnQdvGIRSi6nJy0qNLm8aYMaEjxg9prSYCZGblWbtZ5IAYlBGZGdwkEhFeAVibeAz2RBccAf3kp6HrP8HaTSGyWzIBIKp5AKaNbQc/n+IlnIjqE4cviUol/T/U/jrVa2ZvdCEt1Ll26Ry00weh7zHa2k0isuuSGkT1jf/riEoJdPeCi94J57IycKXA/oYwtPjD0A5vhWaHbScicmQMyogsKDAUYd7BtViTeBT2RtdtBPTTnofOxU0txyTOnDmDiRMnqpNcJqLak5NbiCvZ+dZuBjUAHL4kskB6yh7pMARhXv6wN2p9P1mO6XIqcn6ch/kxOZjz/r+Rk5Ojbl+5ciVefPFFzJw5E25uzJshqikpQPvxogOIbBaALm2CERHqw3U26Zqwp4yoHC18glRwlpJzGQbN/pZlWb1pG7o+/x+88MY8RHi74tf7R6uTXJ41axY6deqI1atXW7uZRHYvwNcdQ3o1U3XLFv0Wgy+WHsYfR5PU4uhE1aHTNDv8tWngarrKPNWetLxsvLBrmar4P6hJJOyBfKSnTZuGH374AZ6uLnh+RGc8Mbgj3K5WK88rLML8jYcwZ+1+5OQXYurUqfj22295ZE9kpkOHDqowrNQqq8jF5AS0iY7C4cOH1WcvIekK9h9PRlx8Ony8XPGXWzrys+VAUmr4+83hS6IKBLh5oldwc1VQtlujCKQnpSA1NRWBgYEID6/4y9pa5AcgI6O4uv+mR8eiS9OgErdLcPbs8C4Y0zYcPectVfvyR4Oo7NJJVRHkH2XaVz5HMnQpJ8kxS83IVdvk8oqNJzGyf3ME+rG4M5WPQRlRJW5p2RU/LlqE7g//A7F7D5q29+/fH08++SQmTZoEWzN58mSsWrUKexMulQnKjOQ2475EVFJN17L09nRVJ5GXXwRXVyd4ebio63Fn0hAc4MlaaFQGc8qIKvHa7Ffw6wvzcPLwMUSNHoie90xS5zv37FYBjeRn2ZoJEybA2dkZi/afLnefRQdOqX1uuummem0bkaMJ8vfAzcOj4ObqrFYLWL/zLD5dchA/rYvFyYR0tY1IsKeMqAKLFi3C66+/jpAOURg190l4BPiZbuv70G1YPWseXnvtNfTo0UOVm7AVMrw6YsQIrF3zG1Kz8xDoWfKIXLatiz2PkaNGq32JqP5WDZgxoQOOn0rDvmPJ+HldHHy9XdE5OhgdoxrB0724N40cE3vKiCowf/58OLu5lgnIhFwfPXemul32szXSi1dYZMCyQ/Flblt6MF7dNunGcVZpG5Ejc3F2UgHY7Te0w7SxbREe4oNt+87h4x8PYOWmk0hOzbZ2E8lK2FNGVI6EhARs3bpVDVWWDsiMZHvLIb2xZfVmJCYmIiwsDLY0hPnAAw/g6WU7MW/DoRK3nc/MhrOzE8an7YaWEANdeLTV2knkqGQSQJNgb3Ua3DMCh+MuYv/xFESEZqNxoCeycgrg6qyHi0vxzGlq+BiUEZVDZlkKv/DQCvfzDQtR55cuXbKpoEyGJe+44w7s3LmzzG1NgoCbevdG0PXTgSatrNI+IvqTh7szenYMRY8OITAWqtq0JwEpadmYPr6DtZtH9haUffnll+q8TZs26NOnT23dLZHVGHOtMhIuVLhfZmKSOg8KsjzL0ZoWLFhQpf20xFhox3dBN+RW6PQ8KieyZu+ZsUJN/65NkXmlePmmtMxcrN0Wr1YMaN3MH056Zh81RLX2rs6YMQN333034uPL5q8Q2SOpQyZlL05t2ImctOK6X6XJdrl9wIABNtVLVl1axkVo+9fD8Mu/uJA5kY3w9XZDeKiPupxfUKRmaS7feBKfLDqIrfsScTmL6202NLUWlPn5FefcREVF1dZdElmd1CErzMtXsyxLB2ZyXbbL7bKfPdO37wf9hMeA+CMwLHobWs5lazeJiMyEBHnh1uvbYvr49mgd4Y89h5PwyeID+GXDCZw5n6lWEyD7V2vLLHXv3h379+/HmjVrMGzYsNq4S4fFZZZsy3PPPafKYsgsS0nqlxwyGbKUHjIJyP7+7LN4be7cBlEVX7twCoaf3gPcPaG/5Uno/Ph/jsgWSUHaoycvqSWdLqXnqvU3p4xpYypQS/b5+11rPWU333yzitR/+eWX2rpLIpsgdcikXlmfnr0Qu3oz9ny2WJ3L9e8XLoTfrUOw/lwMGgJdaEvop82SBTRh+G4utCSmIxDZIjdXJ3Rt2xh33tgBU0a3QatwP3i6F6eJ7zx4Xi2OTg7cU5aZmYkuXbrg/PnzWLFiBYYPH14bd+uQ2FNm22UyZFamJPUbc8h+OLEHv5+Pxd+7jkIz74ZRiFXLvgzDz+8Bl85BP/4h6Fp0tHaTiKgKcvIK8dWywxjSuxmimweodTfd3Zzh7MSJAQ7VU+br66uGLtu2bYsxY8bg/vvvx4YNG9QPGMe6qSEl/3fu3LlEUr+sjdnUyw8fH9uC3MICNAQ6Tx/oJz8NRLSFlmV5kgMR2R4PN2fcO7EzIiP81fV128/gvz8ewO+7zyI9M9fazaP66ilzcvpzGr3cZXXya2TfwsLC2mhGg8CeMvuTlJOJOXtXoWtQOO6O7tcg8stKf5a1M0eAiHYN5rkROQIppXHgeAoOxV1UeWgtwnzRpU1jtAzzU0s+UQPtKZMvb+Op9PWqnIjsWYiHL26P7IUdyaexPfkUGgpTQHb+JAyL3gESjlu7SURUDTIBYHCvCNw/uTNG9W+BnNxCLP1fHD5bclDlnmXnNIze/Yai1orHzp49u7buisgu9WncEsfSk/Bt3C609AlCqKflpZnska5JK+hve0FNBBCaoYhFZonscL1NOV24mIX9x5Kxbf85nE/Jwk3DIk2dI+wJbyDDl1R7OHxpv/KKCjF37yo46/X4e5dRcHVqeCuZGQ5thnZoE/Q3PQqdh7e1m0NENZgUIEOa/j5uiDuThq37zuHWMW3g5trwvrccbviSiAA3J2fc124A2gc0gb6BHnHqGoUBaUkwfP+aWgmAiOx3UoAEZMLb0xUtwvxUQCZ9Ndv3n2NZDStgT5kNYk9Zw+o5k0CtodEkKFvyLlCYB/3Nj0PXuLm1m0REtUSWb/pm+RFk5xaqZZ66cr3NKmNPGZGNOpiaiBd2LUNqbhYaGl1ACPTTngO8A2D44Q1o8Yet3SQiqiU+Xq64b1JnjLuuleo1M663uWUv19usa+wps0HsKWsYsgvzsS7xOMZEtIdLA02K1/JzYVjxH7Vmpm7UDOjb97d2k4ioll1My1HLOR05cQmFRQa19uaAbmEI8vewdtNsDnvKiGyUp7MrxjfvpAKyzPyGmZuhc3UvTvhv3x/aqk9h+GOttZtERLWsUYAHhvdtjgemdMGw3s2QfjnPNEsz6VIWcvNYZ7S2MCgjqmNnrqRi1q5lOJJ2Hg2RlMbQjbwLuv4TTCUziKjhcXVxQpe2jTF9fHsE+rmroc2Vm05hw66z6nYOvNUcgzKiOhbuFYAov8b47Pg2ZDTUHjOdDvq+46Fr2hpaUSEMO1ZAK2DuCVFDZOwlk/PJo9ugf9em6roMb8oEgcNxF1FQaLByK+0TgzKiOialMdTSSwAWHN8GQ0M/mryYCG33KiCl+OiZiBouLw8X+HoXl9Xw83ZTi5+v3nIa//1xPzbuOquWeaKqY6K/DWKif8N0NO0C3jv0P9zYvAvGNuuAhkzLy4bOzROawQBkZ0LnXbw4MhE53nqbzZv6oqustxne8NfbTGGiP5F9aBcQijERHfBL/AHEZaSgIZOATGg7lsPwzSvQks9Yu0lEVO/rbXbB6AEtVGC2dH0cvlp2mHlnlWBQRlSPZDZmS99G+OT4FmQV5KGh03UeXFzLbKHUMjti7eYQUT1ycdajQ2Qj3DaunTr16hSq8tAkSFu1+ZSaxUklMSgjqkdOOj3ubdNfVfr/Om4nGjqdlx/0k58GmkbB8NO7MBzZZu0mEZEVhDbyQvvWjdTlzCt5alF0p6tDmXI5v6DIyi20DQ1v/RciGxfo7oV72vSHh7MLHEFxLbNHoK39EtqqT2DISoeu5xjTDC4icizBgZ6466YO6jtAhjOXrY9TQVm7VkHo0qaxqovmqBiUEVlBx8DiKeRFmkGVyQh081LXExISkJqaisDAQISHh6Oh0Mn6n6PuBrwDoW1aBFxOA4ZMhY5r6RHB0ctqTBvbDgdiUnAo9iL2H09BWIi3mhgQKettOjnWdwSDMiIr+j5uN2IzktH+VDbee/c9bN261XRb//798eSTT2LSpEloCOTLVzdgAgw+AdDWfQW4uEI3qGE8NyKq2VqbsmxT385NEHc2XQVmK34/CU93Z3SKCkan6EamshsNHYMyIisaEd4Wm/79Nf4x/wM4u7kiavRA+IWHIiPhAnZu2InJkyfjueeew9y5c9FQ6DsPhuYTCDRqOD2BRFRzTk56tGkRqE6y3qb0nu09lgRXVyf06hiKgoIiODvrG3TqA+uU2SDWKXMcixYtUoFXSIcojJr7JDwC/Ey35aRlYPWseUg+HKf2mzhxIhoaLfsyDGs+h37oNOh8i5OAiYiMjBMAZIknKUZ7LuUKpl7f1mYDM9YpI7Jj8+fPVz1kpQMyIddHz52pbpf9GqS8bFVcFgYeGxJRWRKMyUlEtwhAt3YhKiDLzilQKwfIzM2GhMOXRFYiSf2SQyZDlqUDMiPZ3nJIb2xZvRmJiYkICwtDQ6ILCIF+6qziWVgSoF1MhC4sytrNIiIb1CTYG02udjhJjbMz5zPVOpshQZ5q1mabloGqNpo9Y1BGZCUyy1JIDllFfMNC1PmlS5caXFAmjMMQsl6mtmsVdKP/An27vmrbmTNn1GQHIb2FzZo1s2pbicg2NG3sjXtu6YTTiRnYdzwZv209jY27z6JjZCN0bhOsVhUwd+ONN+LEiRNVuu/WrVtj2bJlsAYGZURWImUvhCT1VyQzMUmdBwUFoSHT9b1RlcrQVn6MnNRkzNtwEHPmzEFOTo66feXKlXjxxRcxc+ZMuLk5xkwsIiqfrKPZKsJfndJlvU0pqxF3EXuOJKn1NmWJJ29PV7WvBGTHY2LRqHHFE4wuJifAmhiUEVmJ1CGTshcyy7LvQ7dZHMKUZP9TG3ZiwIABDbKXrEwts9F/weojp/H4hOmIvZiJ6GA/vDv9OnX7Ez9tx6xZs7BgwWf44IMPMXr0aGs3mYhshL+vO67rGYH+3cIQczoNsfFp8HQvLtB9/FQqDJqmArJn5i6s8H7enDUF1mTfg69Edk6G5grz8tUsSwnAzBlnX8rtxiG8hkwmgk+bNg1jn3oViZnZmDO2B/b+bQJGtQlTJ7n86tgeSIg/jTFjxqh9OXmciMw5O+nRvnUQbhoWqXrSZJ3NNdtOI7/AAHvAnjIiK5LCsM8++yxef/11fDflCZXULzlkMmR5cv0OFOUXqDplDbEchqXcsoyM4sB006Pj0KVpyeFaN2cnPDu8C8a0DUfPeUvVvrY6LZ6IbIObqxPundgZ/5rjhKxsa7emcuwpI7Ky1157TdUh69OzF2JXb8aezxar8+7du+P7hT80qMKxlZGabWJvwqVy9zHeZtyXiKgi7m7OsJfDNwZlRDZAesI2b96Ms2fPYv/+/apcxs5t23Hr5Ck4l5WOg6mJcAQTJkyAs7MzFu0/Xe4+iw6cUvvcdNNN9do2IqK6xuFLIhtL/i+9EPmaxGO4kJ2JjgFNG/xwncxIHTFiBNau+Q2p2XkI9Cw5y1K2rYs5j5GjR5tmrxIRNRTsKSOycVNb98QTnYY1+IDMSIYlC4sMWHYovsxtSw/Go9BgUPtoGReZ6E9EDQp7yohsnJuUigBUb9nmC3GY2LJbgw7QZAjzgQcewNPLdmLehkMlbjufma2GLm8cOQyGr2ZD17ITMHw6dO5eVmsvEVFtYVBGZCeScjLVUKa3ixvGRHRAQyXDknfccQd27txZ5rYmQcCEPn0QFN4chpF3QVv7FbQvZ0M/5i/QNWtvlfYSkX24mJxQaR0y2SfI33pLvTWIoCw+Ph7vv/8+VqxYoRKlpdq3LJMwZcoUPPzww/D09Ky1x1q7di2+/vprlZR9/vx5ddQeEhKCzp07Y/jw4Zg+fTq8vb1r7fGIjLoEhWNsRAf8fPoAmnkHon1AEzRUCxYsqHQffZve0JpGwrDqUxgWvQNd95HQDZwInXNxwUgiIiOJCapCArKq7lsXdJqdJ2X88ssv6qg6MzPT4u3R0dEqWIuMjKzR46SlpeHuu+/G0qVLK9xv79696Nq1a40eKyUlBY0bNy6xLTk5GcHBV1diJYdl0Az44PBGxF9OxfPdxiCIw3bQNAO0P9ZC27wY8G8M/dj7oQuOsHaziMgBpdTw99uuE/0lALr11ltVQCa9U7JO3tatW7Fu3Trcd999ap+YmBiMGzcOly9fvubHkSKVI0eONAVkN998M7755hts374du3btwpIlS/D444+XmTVHVNv0Oj3ubdMf7k7O+M/R35FfVAhHp9Ppoe8xCvrbXwR0ehi++ScM+zdYu1lERI7VU3bddddh06ZNagjx999/R79+/Urc/tZbb+GZZ55Rl2fPno2XX375mh7nzjvvxFdffaWGRRcuXKhWm7dEXsqioiLVnppgTxlV5syVVLy5fw16BTfHnVF9GnTif3VohQXQtv4MXUTb4kkARET1yGF7yiQJWAIycc8995QJyMRTTz2Fdu3aqcvvvfceCgoKqv04kjsmAZl49dVXyw3IhPww1jQgI6oKySm7PbIXtiadxKYLcdZujs2QfDL9dZNVQCYHSYYV/4UWs8vazSIiathB2c8//2y6LLleluj1etXLJdLT07F+/fpqP86HH36ozv38/PDII49cc3uJalu/kFYY0iQaVwryrd0U2yRDu3o94Oxq7ZYQEVWJ3XbrSA+W8PLyQo8ePcrdb/DgwabLW7ZswahRo6r8GPn5+aY8Mskpc3d3V5dliPLcuXPqPDQ01LS9Ot2bFbl48WK17o8c19TWPUxDlzIJQHLO6M9eM93196rL0mumrfkCuuie0LXoaO2mERFZZLff4EePHlXnMquyoiHDtm3blvmbqpI1CHNzc9XlTp06qQkFTzzxBBo1aoRmzZqhZcuWqgdNArYNG6qeWCzjzRWd2rdnvSWqGmNAtuFcDN47tB5FmsHaTbJNBXnQLl+CYcl8GP73LTT2LhKRDbLLoEwCJWNvUmUzHgMCAlRvmpAaZtVx5MgR02WDwYCePXuq3DQZCjXvTZPaZcOGDcMbb7xRzWdCVDuaevmjpU8jMN3fMp2rO/S3PAndkGnQDm6E4ZtXoCWVXcaJiMia7DIoMy9vUZVCrcag7MqVK9V6nNTUVNNlCbhiY2MxZswYNclAAkOZUfHvf/9b9ZbJ8Mizzz5baR0zoroQ7dcYE1p0UcOXWewFKr90RvcR0N8xG3ByhuG7OTDsXAHNwN5FIrINdhmUGYcUhatr5Um8UspC5OTkVOtxsrKySjymDFMuX74cvXr1UvcpU1wffPBBtU0mFYjnnnuOiyST1RxMTcTzu5biXNafvblUki6oKfS3vQBdj9HQNv8Ew49vQsuoOM+TiKg+2GVQZp5YL8OHlcnLy1PnHh4e1/w4xt4yJyenMvsNHDgQt9xyiylv7eDBgxXer/SwVXQyHzYlqo5ovxAEuHniP0c3IaeQPWbl0Tk5Qz9oIvRTngEup0Hb/ou1m0REZJ9BmY+Pj+lyVYYkjT1e1V2T0vxxpFesW7du5e47evRo02Wp8l8Rua+KTjKRgOhauDk548H2g5CRn4sFMdthYK9thXTh0dBPfxm6wVPVdS0hBlrOta/+QUTkcEGZ9GAFBQWpywkJCZWuWWkMyiIiqrcenvn+lU0oMN+3spIXRHUpxMMXf2nTD/svJWDV2T97XeWzcuDAgUo/M45G5+YBnbunyi0z/LYA2pafrN0kInJQdhmUCWPZiLi4OBQWlr/+37Fjx0yXjdX9q6pDhw6my1KTrCLmt7OqP1lbl6BwjIvoiGXx+zH/848xYMAAdeDQpUsXdS7XFy1aZO1m2hSdXg/9lL9DN6A4FUG7cBpaQXHqAxFRfbDboEzyuIT0gu3Zs6fc/TZu3Gi6LD9E1dG8eXNVj0ycPn26wgT+EydOmC6HhYVV63GI6sINzTvixBfLMfPu+7Fzz25EjR6InvdMUudyffLkyZg1a5a1m2lTdN7+0Hl4QysqhGHZhzB8/YoKzoiI6oPdBmUTJkwwXV6wYIHFfaS22Jdffqku+/v7Y+jQodV+nIkTJ6pzKRy7bt26cvdbsmRJmYCRyJqWLF6CtZ9+h5AOUZi28F0Mff6v6H7XzepcrjfuEInXXnsNixcvtnZTbXMiwKSnAFc3GL6fC8P2X6AZKu4tJyJy2KCsd+/eGDRokLr86aefYtu2bWX2eeedd0xV/B9//HG4uLiUuF2q8EtFdDnNmDHD4uNIBX/jLMyZM2eq4Ky0r7/+2lTRf9y4cdXOXSOqC/Pnz4ezmytGzX0SHgF+JW6T66PnzlS3y35Uli6wCfRTZ0HXawy0bUth+OENaOnJ1m4WETVgdhuUCamuL2UuJKdM1rSUo/7t27erhccfeOABPPPMM2q/6OhoPPXUU9f0GDJ8+corr6jLUupCgkHpmZMhU3mcRx991BTQ+fr68geObIIk82/duhUth/QuE5AZyXa5XdaETUxMrPc22k2P2YBbVK4ZsjNg+OplGA5uYi1CIqoTdp2RLiUqfvjhB9xxxx2qB8tSfowEZCtWrChR3qK6nn76aVXdX+qUHT9+HH/5y1/K7CNrVv7888+Iioq65schqi3G1Sj8wkMr3M83LESdX7p0ibmQFdCFRanSGdr676Gt+Rw4Fwfd6Lut3SwiamDsuqdMjB8/Xk3zf/LJJ1UA5unpqfLHZJ1KCaL27t2rFi2vKemFkx6F6dOno0WLFqqivyyvJNX9//nPfyImJgb9+vWrledEVFOBgYHqPCPhQoX7ZSYmqXNjiRkqn87VA/rRd0N/48PQRRbXLGSeGRHVJp3GfnibI3XOpOfNnFT6l8KyRFUls41llqUk9VsawsxJy8B3k59An169sHnzZqu00Z7JV6e24j+AX2O1OkBpZ86cUQeLQtIajDO5iajhSqnh77fd95QRkWUSEBTm5WP1rHkqADMn12V7YX4+uk8dhwL2+FybiHbQhbZQF43Ht7Ks29y5c9G2bVs1K1tOcll6241LvhERWcKeMhvEnjKqLc899xxef/11NctSkvolh0yGLE9t2KkCtjsefRC+UwajqZc/Hmg3EI3cq7cUGZn1mq38BKuPJ+Lxf32D2Lg4RAf74d2b+6rbn/hpO2JSMhAVFYkPPviwxLJsRNRwpNTw95tBmQ1iUEa1SeqQyfCZ5ESaD21KT5rU4TtzJRUfHd2E7MIC3NOmPzoGNrVqe+2RwVCEaaOGYOG6zfB0dcbzI7rgicEd4ebspG7PKyzC/I2HMGftfuTkF2Lq1Kn49ttvVTkeImo4Umr4+23Xsy+JqHISeMlJymTIrExJ6jefadnMOxCzul6PBTFb8eHhDRjbrCNuaNYReh2zG6pKr3dCpktxL+OmR8ehS9OSEyckOHt2eBeMaRuOnvOWIiMjgwEZEZXBb10iBxEeHo7OnTtbLH3h5eKKh9oPxo3NOyMmPRnsP68+WbZK7E24VO4+xtuM+xIRmWNQRkSKXqdTvWRPdh4GJ70e8ZdTcfpy+QEGlV36zdnZGYv2l79W5qIDp9Q+N910U722jYjsA4MyIirB6eqw5aqEw1h0ci+r11ejNtyIESOwNvYcUrPLzrKUbetiz2Pk0MGmOnJEROYYlBGRRX9p0x/3txuocp+ScjKRX1Ro7SbZPBmWLCwyYNmh+DK3LT0Yr26bGFTANTSJyCLOvrRBnH1JtsSgafjnH79CBx0eaD8QIR6+1m6SzZKJFCEhIfBx0aOJr2eJ285nZuNygQHnN/+KRn1GFpfR2LoUura9oQvijFeihoDFY4moznPN7m07AAVaEebuXY19F89au0k2S4YlZS3eJi0jAQm0zE6yTZZpk4BMuZIO7fBmGL54CYYV/4F2McHazSciK2NPmQ1iTxnZopzCAnwRsx17L53FqPB2mNCiiyn/jK6NVlgA7cgWaDt/BTIvAZHdoe97A3SNm1u7aUR0DVg8tgFiUEa2Sr4u1iQew0+n9iHSLxj3tR0AX1cPazfL7mlFhdCOboO241cgIxlo1QX6vuOhC21p7aYRUTVw+JKI6o0k/Usv2ZOdhuFCdiZe3bsKcRkp1m6W3dM5OUPfcRD0d78K3Zh7gLQkGL6dAy3zorWbRkT1iEEZEVVbtH8IXuh+PYLdvXH6CmuZ1Rad3gn69v2hv+uf0N/6d+h8G0EzFMGw+jNoKczlI2rouMwSEV0TP1cPzOw8HHoULxe0K/k0OgWGwd3ZxdpNs3s6vR4Iiyq+kpUBLfkMdAaDuqrlXAbcvblME1EDxJ4yIrpmkugvwUFmfg6+jtuFnSll63NRzeh8AqG/YzZ0Ic2haQYYfnwbhu9fg3bqIAv7EjUwDMqIqMYk2X92j7EYFNpaXT+XlW7tJjUof/aK6aAfNEn6y2D46V0Yvn0V2ol9DM6IGggOXxJRrQh081LnZ66kYu7eVRjaNBoTW3aDs97J2k1rWMFZy07Qt+gInDkKw/ZlMCz9AAiOgL7PDUBUd+hYpoTIbjEoI6JaFeEVgFtb98SPJ/9A/JVU3Nd2IALcSla3p1oIzpq3h1Pz9tDOHi8Ozpb/WxWp1Y+6G7omrazdRCK6BjykIqJaDxikl+xvnUcgNTcbc/auxLH0C9ZuVoOli2gDp8lPQz/1OcA3CPDwUdu1tCQ1c5OI7AeDMiKqE618G+H5bmMQ5uWPdw+ux6qzh9U6mlQ3dE0j4XTzE9D5B0MzGGBY9Da0jQut3SwiqgYOXxJRnfFxdcfjHYdiWfxB/HR6P05mXsSMNv3g6exq7aY1+JIa+pseAa6utqDF/QEtOxO69gOgY8kSIpvFoIyI6pRep1frZLbyaYTvT+zG5YJcBmX1wHz9TO3cCWi7V0Pbvhy6XtdD1+k6BmdENohrX9ogrn1JDVWRwQAnvR65RQU4nHoePYKbWbtJDkO7dA7ajhXQju8APH2h6zkGus6DoXNxs3bTiBqMFK59SUT2QgIysTslHl/EbkdGfo61m+QwdDIzc+x90M+YA12LjtB+/xGGT5+FYdcqaPm51m4eEXH4koisYWBoJNr6h6qlmvKLCnG5IA9B7sV1zqhu6QJCoBv9F2h9xkPb9Su0LUsAyTcbPMXaTSNyeAzKiMgqGrl7q/Nl8QewJekk7mnTHx0Dm1q7WQ5DZmnqRt4FTYrOOhX/FBgObAByrhQXoiWiesfhSyKyqusjOqC1byN8eHgDfok/AINWvPC2UUJCAg4cOKDOqfbpfIOg8/IrvpJ9WS2ALrTCguLFz4mo3jAoIyKr8nJxw0PtB+PG5p2x4swhfHB4I64U5GHRokUYMGAAIiIi0KVLF3Uu12U71Q193/HQD7tdXdYOb4Hhk7/D8PuP0K4GakRUtzh8SURWp9fpMLZZR7TwCcInx7Zi5L23YfuXS+Ds5oqo0QPhFx6KjIQL2LlhJyZPnoznnnsOc+fOtXazGzRdVA/g8iVoe9dB2/e/4pmaMmPT29/aTSNqsFgSwwaxJAY5ss+//Rp33z4dIR2iMGruk/AIuDq0BiAnLQOrZ81D8uE41WM2ceJEq7bVEWg5V6DtXatOKCwornEmtc58Aq3dNCKbw5IYRNSgfPx//1Y9ZKUDMiHXR8+dqW6fP3++1droSHQe3tD3nwD9vW9C1+cGaMd2FJfS2PiDtZtG1OBw+JKIbIYk82/dulUNWZYOyIxke8shvbFl9WYkJiYiLCys3tvpiHRuntD1HQ+t+who+9abtmt5OcUlNQJCrNo+ooaAPWVEZDNSU1PVueSQVcQ3rDgAuHTpUr20i/6kc/WAvvdYdRLagY0wfP2P4uCMiGqEPWVEZDMCA4vzlCSpvyKZiUnqPCgoqF7aReXTdR0GXWhL6Nw8oBXkQVv/HXTdR0DXKNzaTSOyO+wpIyKbER4ejv79++PUhp0qqd8S2X5y/Q606tYRHo04E9DadC6u0EW0Kb6Sngwt/jAMX85G0S//gpZ8xtrNI7IrDMqIyKY8+eSTKMzLV7MsSwdmxtmXRfkF6Dh5DF7eswL/SzxepuAsWYcuOAL6v7wG3cgZQPIZNaxZtPQDaBdOW7tpRHaBJTFsEEtikKOTOmSvv/66mmUpSf2SQyZDltKDJgGb3P7iKy/jp9P78fv5WDTzDsQdUb3VOdkGzVCkZmpqO5YDaUlAi07Q970BuqaR1m4akc3+fjMos0EMyoiAxYsXq7IXW7ZsMW2Tiv7Sk2Zen+xEZgq+jt2JC9mZGB7WBuObd4bb1bUcyfo0gwFazK7i4OxKOvT3vw2di5u1m0VUJxiUNUAMyohKlsmQWZmS1F9e+YsigwFrEo9hbeJRPNd1DILcveq9nVQxTYaY05KgC2yi1tQ0rPwU+qFToQuoeKYtkSP9fvNwkohsPvlfThVx0usxJqI9hjWNhquTM3ILC7Dk9D6Ma9YRfq4e9dZWKp9OpwcCmxRfycoECvKAq8GzlpEC+DaCTqezbiOJrIyJ/kTUYEhAJi7kZOJw2jkUGIqs3SSyQNcoDE63/h06Dx9o+TkwfP0KDN/OgXZyPzh4Q46MQRkRNTiysPkrPcejkbs3cosK8NHRTUjMSrd2s8gSF3fob3gQcHaG4ef3YfjmFWixe4qHO4kcDIcviahBcpLhMimdlZeDc1kZeHXvSowKb4dxER1NPWpkfWrIsnkH6Ju1BxKOw7D9Fxh++RcQFAadzNaM6gmdnv0H5BiY6G+DmOhPVLtkGPO3hCP49cxh+Lt54vbIXmgfcDW/iWyOlhirgjPEH1Z5aPrxD0EX1NTazSKq899vHn4QUYPnonfCuGad8FL3sQhy88J7h9bj02NbkJmfa+2mkQW6sCg4TZwJ/bTnoQtuBvgWL6elnT8JrajQ2s0jqjMMyojIYYR4+uLJTsMwI7ovDqddwOw9y7E9+ZS1m0Xl0DVpBf24+1VdMy03G4Yf34K2b521m0VUZ5hYQUQOl8PUL6QVOgU2xaKTe5FdkG/tJlEV6Nw9oZ82y9RrZtizGnByga7jIOicXazdPKJawaCMiBySt4s7ZrTpZyrBsOjkH/B2ccOYiA7WbhpVsLamSVoStIO/Q9uxArpeY6DrdB1XCiC7x6CMiODoPWcSmLk5uZiWZzJoGvQsZGrT9CPuhNZjlArKtI0Loe38Fbqeo6HrPAQ6V3drN4/omnD2pQ3i7Esi6/rhxB7kFBVgUstuqveMbJuWngJt5wpoR7YCrh7Q9RgFXddh0LlxNQeqX5x9SURUy8K8/LH/0lnM3r0c25JOssq8jdP5B0M/agb0f5kLXZue0LYvg3Z8h7WbRVRt7CmzQewpI7K+zPwcLDz5B3alxKOtfwhui+yFEA9fazeLqkC7nAZ4eKsJAIYN3wM+AdD3GG3tZpEDSGFPGRFR7fN19cC9bQfg0Q5DcDH3Cl7Z8ytWnDmEQq6nafN0PgF/zsiUc6fiy1rOZWjZmdZtHFEFmOhPRFSBjoFNMdtvHJafOYTl8QexK/k0Hu80DAFuntZuGlWBfuBE02U1KeDARui6DIGux2jovP2t2jai0hiUERFVQtbKvKVlV/Ru3Bwbz8XC7+rsPlm+SVYLIPug63ODWgBd27cW2r7/QddpcHE5DZ9AazeNSOHwJRFRFYV7BeD2qN7Q6/Q4kZmC53ctQ1IFw2EJCQk4cOCAOifr03l4Qz9gAvT3vKkCNO3oNhg+ew6GtV9Cy7hY4d+eOXMGEydOVCe5TFQXGJQREV0DWUOzX+OWCPbwVtezzFYGWLRoEQYMGICIiAh06dJFnct12U42sjpA3/HQ3/smdP1ugha7B4YFs2DYtrTMvnl5eZg7dy7atm2LJUuWqJNcfu2119RtRLWJsy9tEGdfEtmXU5cvYv6B/2Fss45Y/39f4s033oCzmytaDukNv/BQZCRcwKkNO1GYl4/nnntO/ciT7dAK8qDt3wCdbxB00T2Le82KCvHbrv149NFHEBsbh+hgP7x7c1+1/xM/bUdMSgaioiLxwQcfYvRozuyk2vn9ZlBmgxiUEdmX3KIC/BJ/EB9/8yXWvPQeQjpEYdTcJ+ER4GfaJyctA6tnzUPy4TjVYybDYGSbitZ+hdtmzcXCXcfg6eqC50d0xhODO8LNuTh/MK+wCPM3HsKctfuRk1+IqVOn4ttvv1WrQ5BjS2FJDCIi63J3csHkVt2RumIbnFxdygRkQq6PnjtT9aDNnz/fam2lyumHTEWmT6i6vOnRsfj78C6mgEzI5WeHd8GmR8ap6xkZGQzIqFYwKCMiqgWSzL9nx060GtqnTEBmJNtlSHPLli1ITEys9zZS1UiNs8m3T1eX9yZcKnc/422TJ0+ut7ZRw8agjIioFqSmpqpzySGriG9YiDq/dKn8H3uyvgkTJsDZ2RmL9p8ud59FB07B2ckJNw4bXK9to4aLQRkRUS0IDCyudSVJ/RXJTExS50FBQTiYmoiYjGQUGQz10kaq3vs5YsQIrI09h9TssrMsZdu6mPMY0SYcgd7FdesMW5fCsO4b0z7alTSum0rVwuKxRES1IDw8HP3798fODTvR96HbLA5hSrK/zMKU8hhhYWH4Zv8aVe/M3ckZbf1D0T6gCToENEEj9+IyG2RdMiy5atUqLDsUjxm9o0vctvRgPAoNBkx+6iUgsEnxRlkhwMVVXdSyMmD4798AT1+gcTPoQppD17g5ENIc8AliDhpZxNmXNoizL4nsk8yqlB/yxh0iVVJ/ZbMvDZqGs1fScDjtHA6nncfJyxfVtsYePmjv3wQdA5ugY0BT/oBbcUg6JCQEPi56NPEtuazW+cxsXC4wICkpydRLWrrMBuKPQEuKh5YcD8gpK6P4RncvoHFz6KJ7QN95iKk3je+z/avp7zd7yoiIasmkSZPw7LPP4vXXX8d3U55QSf2SQyZDluZ1yozlMPQ6HZr7BKqT1DjLKSzA8fQLKkCToc3jGUnoFBimfrQ3nI9F58AwBMkPOtULCbbuuOMO7Ny5s8xtTYKACX36WAzIhM7FDYjsBl1kN9M27Uq6Cs5MgZoxSEs9D8P3c6Gf/LTqTdMunQN0eiCgMXRyTg6DQRkRUS2SSu89e/ZUZS+2rN5s2i5Dlk8++WSF9ck8nF3QtVGEOkkgll1YvEpAZkEulpzai0A3TxWUxaQnIT0/B+0DQuHtUpzPRHVjwYIFtXZfagF0b3/oWnUpeYObB3Q9rwf8intTDFt+AuL+ACSwU0OfLYrPZfgzsAl0egZqDRWHL20Qhy+JGk6ZDBkCk6R+ySGrCVn8XAa3nPVOWHhyD9YlHlfXm3kHqjw0ObX0bQQn9qzYPS3nCpB8BlrS6avn8UBGcvGNzq5AcAT04/8KnXeAyl2T4VCdE/tYbAEr+jdADMqIqDLpedk4IkOdqedwNP0CsgrzVRHbtv4h6BDQFH0bt4Arf6gbDC03G0i5GqAln4Fu9N0qECta+CZ0Xr7Qj3sQWl42tJjdxT1qjcIYqFkBc8qIiByQv5sn+oe0UieDZkD8lVQcSTuv8tEWnfpDBWVi0/k4hHv5q140su9F1BHRFrqItiW26wdOBIzDmZfOQVv7ZfHEAb0T0ChczfpUkwrkXK47u1jnCVCVMCgjIrJzep0eLX0aqdO4Zp2QX1Soesnkx3ndueMqQJOgLDErXQVtMtTZ1NOPs/0aAF3T1maXI6F/5P+AlLPQks8ASaehXTgFHN4CzVCkAjVdn3HQ97upeIg07QIQ0oI9ajaE7wQRUQNjHLaUoGt297GqzIaQ8hvL4g9g8am98Hf1UHXR5NTOXyYMuFm51VQb1KzPppEqQDPSCguAiwlqxqfuak017cxRaCv+A/2D81UtNcO+/wGSt9i4WfGkAlcPKz4Lx8WgjIioAZPAzOlqj1jfkJboEdwMsRnJqsdMhju3Jp1UEwaa+wSpHrRBoZEIcCtZk4vsmxqyDG0JXWjLP7dJuY7pL0MnxW1FQgy0E3uhFRXKrcXlOGQIPERmfV6d/SlDqFSnGJQRETkQF72TqYdMpMmEgau5aOvPxaBno2YqKNuZfFoFdL2Cm1u7yVQH1JBlcITpuv6GB4sDstQL0JJPA0lnimupSaAmpVn0TmpoVAI8w7Ed0AWGFk8ooFrFoIyIyIFJADYgtLU6yYQBneo3g5rRKTlpEpRlFeRhxdlDqictyrcxZ3U26EAtHLrgcKBD8TZN1mWV3LP0ZNMkAW3LEqB9/+JCtxdOwbDz1xLLSJl636ja+MkiIiLThAGju6L7mnLRUnKv4I+Us6o2mvS0Rfk1VgGaLAXVxNOXEwYaMFWoNqhp8ekq/V9eAwplmBOA5KtJKY7dq1VJDsU7oDg4Mxa+DW0FnaePlZ6BfWGdMhvEOmVEZGvkp+J8dqZap1OGO2MyklGoGVRPmwRn1zWJRAufIGs3k6xEhRIZF4Hk09DU0KcMgcYDuVnQDZkGffcR0M6dgHbqAHR9xzfYGZ8prFNGRER1TXrDmnr5qdPI8Haq7EZsZjIOpxZPGJAlnyQok8snMy9iXLOO7EFzIOq99g9WJ110rz8DtcuXipeLkuvpSdDi9kLXf4K6LoVv4eRcYugTvo0c+v8NgzIiIqo2ySuTlQPkJIyDLheyM1Uv2g06ndr2TdwutPQJUhMLOKvTsajgyqxosb59f5WLZrq9WTu1lJR2ZBu0nb8Wb5T/I1eDNF1oC1OAV9vOnDmj1qIVsk5ts2bNYAsYlBERUY0ZezeGhbVRJyELqidkpWHzhThIyCYFa1UumkwY8Gus8tPIcen7jjddVmt4JhfP+JSlpLSYXdBO7ofT1aCsaNn/Qd9xEHStOhfXXZNCuNewMHteXh7eeecdvPrqq8jJyVHbVq5ciRdffBEzZ86Em5t16/Uxp8wGMaeMiBqSKwV5ajanDG3KKT0/RwVk0X6NMSCktaqdRlSaVpCniuFKEKat+gQ6CcpadIRh/3poGxcW105Tw55Xa6kFNYGugkB/9erVePTRRxAbG4foYD+8e3Nftf2Jn7YjJiUDUVGR+OCDDzF69GhcKy5I3gAxKCOihkp+cs5lZ5iK10b7hWBssw5qCaj/nTuOm1t0gbeLu7WbSTZMkzU+Tx24WkvtNJCWVHyDk4uqvaaTIK3z0OLSHlf/z02bNg0//PADPF1d8PyIznhicEe4ORcHcHmFRZi/8RDmrN2PnPxCTJ06Fd9+++015bYx0Z+IiOyG/NCFefmr06jwdqbtmfm5OHMlFe7ywwpg0cm98HB2VkOdzb0DS5TrIMemC2qqTkZaXg6QcubP9T4TYqCL7q1uM+xeBe30YWRkZKjrmx4diy5NS84SluDs2eFdMKZtOHrOW6r2tdZkAwZlRERkde0CQvF8wPWmno2M/GxsunAOy+IPwsvZTc3uVCsR+IfCnxMGyIzOzQMIbwNdeJuytwWFAQYDJk8OwKpVq7A34VKZoMxIbhOTJ0+GtXD40gZx+JKICCgyGHDy8kXTUKf0pMkPVpinP3oER2Bcs07WbiLZidTUVISEhGBEZCiW3zfK4j7jPl6NdXFJSEpKQmBg4DU9DocviYioQXLS69UsTTlNaNEFl/Nz1YQBCdLSZcgKQG5RAT49thU3teiMcK8AazeZbFRgYCBGjBiBtWt+Q2p2HgI9S86ylG3rYs9j5KjR1xyQ1QYO0hMRkV3wcXVH78YtcHebfrg9qjhn6HJ+HvKKCk3lNVafPYJvYndi38WzyJHSCURXybBkYZEByw7Fo7SlB+PVbdYcuhQcvrRBHL4kIro2EpRtTjqB5JzL0Ot0aO0TrHLRpD5ahHeA2natEhIS1DCY9KSEhxfP7CP7G8L0cdGjiW/JvMTzmdm4XGCo0dCl4PAlERHRVaMj2qtTSs4VlYd2OP08ViUcxtL4/fBxcUM7/1DcGd23WoVrFy1apKq+b9261bStf//+qiL8pEmT6uiZUG2TYOuOO+7Azp07y9zWJAiY0KePVYcuBXvKbBB7yoiIanfCwAk1YeAckrMv44H2g9T2fx35Hf0at0S3RhHl/u1zzz2H119/Hc5urmg5pDf8wkORkXABpzbsRGFevrp97ty59fhsyJaxp4yIiKiSCQOyeoCcjAoMRXB3coabU/HP4M7k09iVEm8a6mzs4aN6yCQgC+kQhVFzn4RHgJ/p7/s+dBtWz5qH1157DT169MDEiROt8tyoYWFPmQ1iTxkRUf3adykB6xKPIS4zBQZNQ7C7NxY++CJOHTqGaT++WyIgM8pJy8B3U55An569sHnzZqu0m2wLe8qIiIhqqGtQuDrlFhbgeEYSfj+yD7F/HEDU6IEWAzIh22VIc8vqzUhMTERYWFi9t5saFgZlREREV7k7u6BLUDh0fqnquuSQVcQ3LESd/9+O1WjXqQN8Xdzh6+oBP1d3+LpcPXf1MA2TElWE/0uIiIhKMc7Ck6T+imQmFi+G7envg7NX0tQanpkFuSjSDKZ9JJftqc4jUGgowvyD/8P45p3Q1j9U7R9/JdUUwPmqc3eVA0eOiUEZERFRKVKHTMpe7NywUyX1l5dTJrMwBwwYgBeG/Vl0VHLSsgvzrwZoOXC+uph6gcGARu5epl4zWZ1g8am9Ze7X29mtOEBzdceIsLboFBiGpOxMnLpyCb2Cm8NJp1fDrK5OzjWqu0a2h0EZERGRBVKHTCq8yyzL0XNnlgjMJCCT7VIWQ/YzJ4GSt4ubOjXFn3/j4eyCu9v0N10fFd4Ow5u2weWC4t61jPwcFchlXA3m5LIEYELy3L4/sQd9gluo6/MPrsOZrLQyw6USyBkvdw4KU/XYpIfOuRp12ch6OPvSBnH2JRGRbShdp0xyyGTI0hp1yvKLClXvmJCaaxdzspBxNXjLzM9BhgR3VwO7Qs2A9/pPhruTC947+D94ubjh3rYD1Pqh38TtKhG8FV/+c/iUAdy14+xLAPHx8Xj//fexYsUKnD17Fm5ubmjdujWmTJmChx9+GJ6eJZdTqI7PP/8cd999d5X2XbBgAWbMmHHNj0VERLZF6pD17NlTVfSXWZZGMmQpPWT1WZ/MGJCJDgFNgXLWX5e+luzCAhWQieFhbeF8NU9N6rPlGQpxIjNFBW/SS1e6Z6ZLYBge6jBY7ftlzHb19y18gtQQ6qW8rKvBm4fqCbTn4dMEG1w2y+6Dsl9++UUtm5CZmWnalp2djd27d6vTJ598ooK1yMhIq7aTiIjskwRecjL+iAcFBdl0+QudTgcvF1fT9Y6BTU2XA9298HjHoabrMiHhSkHe1WHTHDWM6uVc/Ld5RQVIz88xTVrYmRKP5WcOmv5WD51aJF5614yzTAeEtEakXzBS87JwMTcLkb7BNhe4LbLhZbPsOijbu3cvbr31VuTk5MDb21t1Iw8dOlRd//777/Hxxx8jJiYG48aNUwGaj49PjR5v9erVaNr0z//cpdlKpE1ERLVPvuMb2ve85KzJ0KWcIkp1vXm7uKtZo0ZjItqjX0hLNQRqHCpV+W9Xg7nz2RnIKcpX++67mIAlp/fhg/5T1PU3969BRn52iVmmplw4OXdxRzOfQFMOXX0MR0sNOuOyWTKhQ/IHrb1sll0HZY8//rgKwJydnfHbb7+hX79+ptuGDRuGqKgoPPPMMyowe+edd/Dyyy/X6PGio6PRokVxkiUREZEjkUkDjdy91akyA0Jbo1NgU9Vrp66HtEJyzmVTMHfq8iXVM3e5IA/a1QHUD/pPgZOTHv8+8juC3LwwpXUPXCnIxa9nDpsFcH/WfysePq16EGcPy2bZbaK/rPLep08fdfmBBx7Af/7znzL7GAwGdOzYEUePHoW/v79KtnNxKR5jv5acslOnTtVLUMZEfyIicgQGNXyar3Lbwrz81bYtF07A09lVLRR/ITsT/znyuwrmpMyIOZ0Mn7q4qZUYbo/qrSZCLD9zCP1DWiLU0w/pednILSpQAZ2HkwsGDhyInXt2Y9rCuls2y2ET/X/++WfT5fIS8fV6Pe68807VHZmeno7169dj1KhR9dhKIiIiKo/0dBlrspn3shmFevri5Z43qMsy8aB46NSsdEh+DoLcvdTtWYX52HPxjMqhk3UYNp6Pxa9nD6vbclPSVA6ZrS+bZbdBmTGK9fLyUl2N5Rk8eLDp8pYtW2wiKJNIuiIXL16st7YQERHZy/BpoLuXOlkS4OaJOb1uNF0f0jQa7QOaqADuj9x9+LIay2ZdunSJQVl1yJCkkFmVklNWnrZt25b5m2slPXLHjx9XQZOvr6967BEjRuCvf/1rtd680l2b1iBj+XIyJ93FkisgRyOSsFlaM+/iZUekOznfUFjiNhn/lzo4chSTlp9d4japXh3i4au6qROy0svcb5inv1pWJCXnMnKKCkrc5q9q53ggqyAfl/KulPmANvEsPuI5c6V4nTpzoR6+agr5pdwsZBXmlbjNx8VdfYClKnZy7uUSt0miqbEbPTErvcRyKaKxu49aHy8tL1t1uZvzcnZTR23SjX4hJ7Pc11BeX3mdS76G3mrGlBz5yYwnc9L1HuzhgyKDAYnZZV/DcC9/dcSZlJOJvKKS702Aq6eaIZVVkKems5tz1TurI9HyXkN5feV1vph7pczQgTE5OKewACmlXkOpYN706muYkJWmKpyXeA09fNR0fUuvoVQzDyznNZThigjv4mTkc1kZKNRKvoby/1f+H1v6/+3h5IpgD29VSPOchf/f4V4BapaYTPuXkgHmAt08VdKz5Lek5pX6/613Roin/P/W1HMtramnn6r7lJJzxZQEXfo1lNdWXuOSr6ETmnoV//+W5XiMeTel/3+n5mbhSnn/v4sKVB6POXmO8lyLX8N0Vc/KXLC7jypyyu8IfkfY+3dEVkG++r8ln/vOzVpWa9ksmWFrDXYZlOXm5pp6kyqbCRMQEKB607KyslQNs5rYsGGD6bJE0XLasWOHmkTw7rvvqtw2e/H7+Vg19m6ud3AL3NO2v/oQzNm7qszffDToNnX+ecw2laRp7u42/dC3cUvsvngG35/YXeK29v6heLzTMOQVFVm837f73KK+EBae/AMHUhNL3DapZTeMDG+HY+kX8N9jJcf4I7wC8EL369XlN/b9VubHZXb3sepDv+LMIWxJOlHitjHh7XFzy65q3bl5B9eV+ZJ/o8/N6vL7h9aX+fKb2Wk42viHYMO5GKxKOFLiNpkOfmd0H/UFVfq5ypfQ/w2cqi5/emwrzpb6Ab+/7UD0CG6GHcmnsajU0iudA8PwcIfB6kvP0mv4br/J8HDW4/u43TiSXvJLZ2rrnhjaNBoH085hwfFtJW5r6ROEZ7uOVpct3e8/e45XX45LTx/AzpTTJW67oVlHjG/eGScvp+D9Q39+NkSwuzdevXrEOv/A/8oEDc90GYnWvsFYk3gU6xKPl7htcJMo3BbZS33Zlm6Tu5Mz3rs6m0v+P5QODB5qf51aTHpr0kn8fHp/idu6N4rAA+0GqcRiS8/1wwG3Qq9zwtdxOxGTkVzitulRvTEwNBL7LiXgq9idJW4zrmsoP8yW7vf13hPUj/uS03vxx8WS30ETWnTB9REdEJuRjH8d+b3Mj93LPcapy28fWIPcUj+kz3cbo37A5f+gDNOYGx7WBlNa9VABg8x6K/2D9k6/4iRmecyUUsHgYx2HqBpY/I7gd0RD+44I6RiFU+urtmyWtUqe2GWiv3kinZTEkPIXFQkJCVGJdpL0f/DgnzVWqpro/89//hO33HKLmt0ZERGhtp88eRKLFy9WszmML+FHH32E+++/v9L7NM5GqY7aTvTnUTCPgu39KJg9ZewpE/yOKMbviMq/I1b8vAwPTb8bjTtElrtsVvLhOPW7fq2zL2ua6G+XQZn0eDVr1kxdnj59Or78UkaKyyf7yt9Ilf+4uLhqPVZGRoYaqiwvkFq+fLkK2AoKCtTKASdOnEBoaKjNB2VERESO5rk6XjarpkFZ3VZpqyPu7n/O0sjPLxmZW5KXV3wE5OHhUe3H8vPzqzCIuuGGG/DSSy+ZVhL49NNPK71PeYMqOh05UrK7m4iIiGpO6pBJT5iUvYhdvRl7PluszuW6bLdm4Vi7zSkzr8x/5UrJ7mpLJJ9MSNX/uiBDlhKYSafjxo0b8fzzz1e4P3u8iIiIrGOiDS+b5WyvPWXyIkqivbyoFUlLSzMFZcZ8sNomXZXSHpl8ILVNiIiIyLaF2+CyWXY5fCnat2+vziVHrLCwZNKiuWPHjpkut2vXrs7acy15YkRERER2H5TJcglCesH27NlT7n4ynGgk01zrgiT2GUt0VLRgOREREVGDC8omTJhgurxgwQKL+8jal8aZmbL25dChQ+ukLf/9739NZTHMVxAgIiIiavBBWe/evTFo0CB1WWY8bttWsuCdkKKuxir+jz/+eJnFyKUYrAw7ymnGjBll/v706dPYu7dkgT5LJTFeeeUV0+zO8tbhJCIiImpwif5G7733nhqSzMnJUWtazpo1S/WGyXUpKCs9WCI6OhpPPfVUte9fgjK5PykaO378eHTp0sVUf0SKx8r0WfPisW+//bbNzOAgIiIi+2LXQVm3bt3www8/4I477kBmZqYKykqTgGzFihUlymhUl/TCWeqJM5KisfPnz69SNX8iIiKiBheUCenBOnDggOo1k+BLSmS4urqqxcInT56MRx55RAVN16JHjx74+uuvVUC2e/dunD9/XiX0y2xPWVOzQ4cOGD58OO69916bWGSciIiI7JddLrPU0NV0mQYiIiKyv99vu+8pa4hk1mhpxpIbREREZJss/VZb+k0vD4MyGyTLPpRXLJeIiIjs6zc9JCSkYZfEICIiImpIGJQRERER2QAGZUREREQ2gLMvbZCU3IiNjS2xLTAwEHq93pRIWDrH7MiRI2jUqFG9tpNqD9/ThoXvZ8PC97NhuViH76ck9ZfOC4+KioKzc9VS+Jnob4PkzWvXrl21/kb+M7FkRsPC97Rh4fvZsPD9bFga1eL7WdWkfks4fElERERkAxiUEREREdkABmVERERENoBBGREREZENYFBGREREZAMYlBERERHZAAZlRERERDaAQRkRERGRDWBQRkRERGQDGJQRERER2QCufUlERERkA9hTRkRERGQDGJQRERER2QAGZUREREQ2gEEZERERkQ1gUEZERERkAxiU2aH4+Hg89dRTaNu2Lby8vBAYGIhevXrhrbfeQnZ2trWbRwB2796NV155BaNGjUJ4eDjc3Nzg7e2N6Oho3H333di8eXOl9/H5559Dp9NV6ST7Ut2p6vswZMiQSu9r5cqVuPnmm03/L+Rcrst2qh/yPlX1PTWeNmzYUOI++PmsP8nJyVi+fDleeuklXH/99WjUqJHptZ0xY0a17682PoOFhYX4z3/+g0GDBiE4OBgeHh5o3bo1HnjgARw+fBjXTEpikP1YtmyZ5uvrK2VMLJ6io6O12NhYazfToQ0aNKjc98f8dOedd2p5eXnl3s+CBQuqdD9ykn2p7lT1fRg8eHC591FUVKTdc889Ff79vffeq/ajuiXvU1XfUznp9XotISGhxH3w81l/UMFre9ddd1X5fmrrM5iSkqL16tWr3Ptwc3PTPv7442t6rs7XHs5Rfdu7dy9uvfVW5OTkqF6X5557DkOHDlXXv//+e3z88ceIiYnBuHHjVE+Nj4+PtZvskM6dO6fOmzZtismTJ6sjqWbNmqGoqAjbtm3DO++8g8TERHz55ZcoKCjAt99+W+l9rl69Wt1feeRIj+reX//6Vzz00EPl3i491+V5/vnn8emnn6rL3bp1wzPPPKOOrE+cOIE333xTfb4/+eQTddQ9d+7cOmk/FVuwYAGysrIq3OfIkSPq+1YMHz4cYWFh5e7Lz2f9adasmRol+u2336r9t7XxGZTvcelV27Vrl7p+yy234L777lMjVjt27MCrr76qevakx0z+z0jPXrVcUyhHVu2BcXZ21rZu3Vrm9jfffNMUqc+ePdsqbSRNGzdunPbDDz9ohYWF5R5lSY+m8b3auHGjxf3Mj8RPnTpVx62mitT0c3X8+HH1uZX76Nmzp5adnV3i9qysLLXd+Plmb7f1PfPMM6b3/auvvipzOz+f9eell17SfvnlF+3ChQvqurze1e0pq63P4Keffmp67IceeqjM7fJ3xtGsyMhIraCgoFrPlUGZndixY4fpP8IDDzxgcR/pcm3Xrp3ax9/fX8vPz6/3dlLVyBeM8f189NFHLe7DL/2GE5T99a9/Nd3Htm3bLO4j2yv6sqf6I9+lYWFh6r3w9vZWP9il8fNpPaeuISirrc+g8Tc2MDDQ4v8L8dprr5nuZ+HChdV4ZprGRH878fPPP5suS6K4JXq9Hnfeeae6nJ6ejvXr19db+6h6ZNjZSLrOqeGSmG7p0qXqsgy79O3b1+J+sr1NmzbqsuzPFfCsZ926dSrFQEyaNAmenp7WbhLZwGdQ0oOOHj2qLk+ZMqXc/xfmkw9++umnarWVQZmdMM7Wk5yVHj16lLvf4MGDTZe3bNlSL22j6svLyzNddnJysmpbqG6dOnXKlGdo/vm0xHi7BASnT5+ul/ZRWZLvaWQ80CX7daqWPoPms+Yrup/Q0FA10/5afocZlNkJY3QeGRkJZ+fy52fIUUDpvyHbs3HjRtPldu3aVbq/9I5KIrGrq6uaDi5HdC+88ILpaJ7qx48//oj27durI2SZSBMVFYW77rqrwl5pSRi39Pm0hJ9f67ty5Yqpd6N58+ZVKnPCz6dtO1JLn8FruZ+zZ89WOqnEHIMyO5Cbm4uLFy9WaRZPQECAaQaY/Gcg22MwGPD666+brks3eGWkRtL58+fVbM1Lly6pWT5z5sxRQfpHH31Uxy0m8y9l+aKWGc/y4x0XF6d6VYYNG6ZmZGVkZJT5m4SEBNPlyj6/ERERpsv8/FrH4sWLTT+id9xxh6qFVRl+Pm1bQi19Bq/lfmQI1PzvKsOSGHbg8uXLpstSCqMyEpTJl4r8aJDtmT9/Pnbu3GmaTl3RcHSrVq3UPv369TN9yE+ePKl+OBYtWqQC9gcffFD9cNx///319hwcjfSM3Xjjjao0ghwBy+cwJSVF9XhKAUn5IZa8z5tuuglr1qyBi4vLNX1+zUtq8PNr+0OX/Hzah8u19Bmsj88ygzI7IB9sI+ker4xUKBZyNE+2RX7En332WXW5cePG+Pe//13uvtLzIkNjpY/UZfUGqZ8kFa7lB0GOzp988kkVNEguA9U+GYby9/cvs33kyJF49NFHVS0iqXEk76+8p4899tg1fX6Nn13Bz2/9kx4NY+V+GYI05gVZws+n/citpc9gfXyWOXxpB9zd3U2X8/Pzq5xELss+kO2QpTfki1yW55D3VPKTJDArj5+fX4VDJzfccINadkTI8lrGoohU+ywFZEYhISGqV8TYO/bBBx9c8+fXfAIIP7/17+uvv1bpBUICrorw82k/3GvpM1gfn2UGZXbAvDJ/VbpBjfkQVRnqpPqb/SPrYKalpanZlrICw3XXXVfj+5UhEeMPg/nkAapfMowlvWZC8syMM72q+/k1Twjm57f+ffXVV6ZeDmM1/5rg59M2+NTSZ7A+PssMyuyAROdBQUHqcmUJg/Kjb/zPYJ6wSNYjP9AjRoxQ5/IF/dlnn6nco9ogPW3G/xuc6WVdMivTyPy9ME8Iruzza55YzM9v/ZKl6Yyz66SXSyZN1RQ/n7YhvJY+g9dyP/KdX51lthiU2dkXvhyFy/BXeY4dO1atUgtUt2TWrPSgSPKvcWirtuseVWV2GNW98t4H82DN/PNpCT+/tpHgX9nQZXXw82l97WvpM3gt9yOBXUVr4pbGoMxODBw4UJ1LL9iePXvK3c+8i3zAgAH10jayTMojjB492nT0LWUwHn744Vp9DJkBaCyXUtGCyFT3zGsYmb8XLVu2NF2vbAjr999/V+eykHGLFi3qrK1UkiTjS0qBkMWoq72IdDn4+bQNLWvpM2j8Ha7sfi5cuKCq/1/L7zCDMjsxYcIE0+UFCxZY3EcSVI1He5KYbL6UD9UvSewdN24c/vjjD3X9+eefx9///vdaf5z//ve/pqVAKqtUTXWbMyilMETr1q3VF7p5T4lxuFqOnrdv327xPmS78eha9mcPS/1ZuXKlCqDEbbfdVmGB7urg59M26GrpMyizcY29ZwsXLlTf85Z8/vnnpssyuataqrVSJlnVoEGDTCvYb926tcztb775Zo0XTqaay8vL00aNGmV6Lx5//PFrWnD3jz/+qHRRc1dXV/UYHh4eWkJCQg1aTeVZtmyZVlBQUO7tFy5c0Lp162Z6v995550y+xw/flxzcnJSt/fs2VPLzs4ucbtcl+3Gz3dMTEydPBeybOLEiab3b8+ePZXuz8+n/S1IfryWPoOffvqp6bEffvjhMrfHxcVpvr6+6vbIyMgKvzss0ck/1QvjyFqkDpJ0hUrNE5nNMWvWLNUbJtel612OyozRvCStms8UofozceJELFmyRF2WSu/vvvtuhb0eUu+mdD0kqZUk760UpRw/fjy6dOliKp8h+WlSgkFOxo/v//3f/+Ghhx6q0+flqGQIQ4a35H2V90OuyxR3GZaS90kqthuHqGR4Y+3atSVqFBk999xzppUcunXrpnpOpVdNFqR/44031OfbuN/cuXPr+Vk6Lpkc1aRJE1XCoGPHjjh48GClf8PPZ/3avHmzyqc2ks/b008/rS7Lb+K9995b7oLgtf0ZLCoqUr2exjUt5XvhvvvuUxNDpCj4P//5TyQnJ0Ov16taddUeCq9WCEc2cdRujMItnaKjo7XY2FhrN9OhlffelHdq3rx5mftYv359lf7W09NT++ijj6zyPB2FvD9VeS+ktyUtLa3c+ykqKtL+8pe/VHgf99xzj9qP6s+///1v0+svow1Vwc9n/brrrruq9Z1a15/BlJQUrVevXuXeh5ubm/bxxx9f03NlT5kdio+Px3vvvYcVK1aoabnS0yJrrE2ePBmPPPKIWhKGrKe6uUCy6PHp06fLLOexbNkybNu2TfV6yrp6cnQoM2/liKxDhw5qyR85QqyoAC3VnCT0ykneC+kFkfchMzNT9VbLzKr+/fur2XrSa1IVv/76q+rV3rVrl7ovWcBaqsA/8MADtZZgTlUnPS1bt25V9QPPnDlTpYR8fj7r14wZM/DFF19Uef/Kwpra+AzKe/3xxx/j22+/VevhyiQ8+b8j7/vjjz+u/g9cCwZlRERERDaAsy+JiIiIbACDMiIiIiIbwKCMiIiIyAYwKCMiIiKyAQzKiIiIiGwAgzIiIiIiG8CgjIiIiMgGMCgjIiIisgEMyoiIiIhsAIMyIiIiIhvAoIyIiIjIBjAoIyIiIrIBDMqIiIiIbACDMiIiIiIbwKCMiIiIyAYwKCMiIiKyAQzKiIiIiGwAgzIiojr08ssvQ6fTqZNIT0/H7Nmz0aFDB3h7eyMwMBBDhw7Fd999Z+2mEpGVOVu7AUREjuLUqVMYOXIkTpw4YdqWlZWFDRs2qNPPP/+Mb775Bs7O/GomckTsKSMiqie33nqrCswefPBBrF27Frt27cKnn36K6OhodfvChQvx9NNPW7uZRGQlOk3TNGs9OBGRIwxf/uMf/zBd//bbbzFt2rQS+1y+fBmDBg3C/v37odfr1XnHjh2t0Foisib2lBER1ZMbbrihTEAmfHx88N///lddNhgM+M9//mOF1hGRtTEoIyKqJ3fffXe5t/Xu3Vsl/wsZ2iQix8OgjIionvTq1avC2yUwEzExMcjPz6+nVhGRrWBQRkRUTxo3blzh7SEhIepcUn3T0tLqqVVEZCsYlBER1RNjrTIiIksYlBER1ZOkpKQq3S7BW0BAQD21iohsBYMyIqJ6InXJqnJ7VFQUXF1d66lVRGQrGJQREdWTL774osKA7NChQ+ryiBEj6rFVRGQrGJQREdWTZcuWqar9pV25cgUPPPCAuizFY42XicixcIE1IqJ60rNnT9x2223YuHEjJk2aBF9fXxw4cABvvPEGjh8/rvZ5+OGH0blzZ2s3lYisgEEZEVE9kV6y4cOH41//+pc6lTZx4kTMmzfPKm0jIuvj8CURUT1p2bIl9uzZg1mzZqFdu3bw9PSEn58frrvuOnz99ddYtGgRnJ15rEzkqLggORFRPS1Izq9bIqoIe8qIiIiIbACDMiIiIiIbwKCMiIiIyAYwKCMiIiKyAQzKiIiIiGwAZ18SERER2QD2lBERERHZAAZlRERERDaAQRkRERGRDWBQRkRERGQDGJQRERER2QAGZUREREQ2gEEZERERkQ1gUEZEREQE6/t/KtsUw8V1ceEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ibm_torino = np.load(\"./Data/ibm_torino/133_HE.npy\", allow_pickle=True).item()\n",
    "ibm_torino_v1 = np.load(\"./Data/ibm_torino/133_HE_v1.npy\", allow_pickle=True).item()\n",
    "ibm_torino_fractional = np.load(\"./Data/ibm_torino/133_HE_fractional.npy\", allow_pickle=True).item()\n",
    "case = \"same_brisbane\"\n",
    "name = \"raw\"\n",
    "results = np.load(f\"../TheMagicSchedule/Data/Noisy_sim/probabilities_{case}_{name}_depolarizing.npy\", allow_pickle=True).item()\n",
    "\n",
    "color = plt.get_cmap(\"Set3\")\n",
    "fig, ax = plt.subplots()\n",
    "ps = results[\"ps\"]\n",
    "# ax.plot(results[\"ps\"][:-2], results[\"r\"][10][\"ideal\"][:-2], color=\"black\", linewidth=2, linestyle=\"--\", label=r\"noiseless\")\n",
    "\n",
    "colors = plt.get_cmap(\"Set2\")\n",
    "delta = 1\n",
    "case = \"\"\n",
    "prop = \"r\"\n",
    "\n",
    "ps = ibm_torino[\"ps\"]\n",
    "n2q = ps\n",
    "n2q = np.array([150*i for i in ibm_torino[\"ps\"]])\n",
    "\n",
    "ax.plot(ps, len(ps)*[ibm_torino[\"random\"+case][prop]], linestyle=\"--\", color=colors(0))\n",
    "ax.plot(ps, [ibm_torino[\"postprocessing\"+case][1][p][prop] for p in ps], marker=\"o\", label =\"Pre-Upgrade\", markeredgecolor=\"black\", markersize=7, color=colors(0), linestyle=\"-.\")\n",
    "ax.plot(ps, [ibm_torino_v1[\"postprocessing\"+case][1][p][prop] for p in ps], marker=\"p\", label =\"Upgrade\", markeredgecolor=\"black\", markersize=7, color=colors(1), linestyle=\"-.\")\n",
    "ax.plot(ps, [ibm_torino_fractional[\"postprocessing\"+case][0.75][p][prop] for p in ps], marker=\"s\", label =\"Fractional gates\", markeredgecolor=\"black\", markersize=7, color=colors(2), linestyle=\"-.\")\n",
    "\n",
    "print(\"ibm_torino-v1\", max([ibm_torino_v1[\"postprocessing\"+case][1][p][prop] for p in ps]), ps[np.argmax([ibm_torino_v1[\"postprocessing\"+case][1][p][prop] for p in ps])])\n",
    "print(\"ibm_torino-fractional\", max([ibm_torino_fractional[\"postprocessing\"+case][0.75][p][prop] for p in ps]), ps[np.argmax([ibm_torino_fractional[\"postprocessing\"+case][0.75][p][prop] for p in ps])])\n",
    "extra = \"fractional\"\n",
    "ax.plot([],[], label =\"random\" ,color=\"black\", linestyle=\"--\")\n",
    "ax.legend()\n",
    "# ax.set_title(\"ibm_tornio performance\")\n",
    "ax.set_ylabel(\"r\")\n",
    "ax.set_xlabel(\"p\")\n",
    "# ax.set_xticks([3, 10, 25, 50, 75, 100])\n",
    "fig.savefig(f\"./Figures/NL/comparison_heron_torino.pdf\", bbox_inches=\"tight\", transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['mdl', 'G', 'sols', 'ising', 'nq', 'shots', 'ps', 'samples', 'postprocessing', 'postprocessing_mitig', 'random', 'random_mitig', 'Deltas'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[\"iqm_spark\"] = np.load(f\"./Data/iqm_spark/5_SQ.npy\", allow_pickle=True).item()\n",
    "results[\"iqm_spark\"].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QPU = iqm_garnet_old | delta = 1 | r = 0.632\n",
      "QPU = iqm_garnet_new | delta = 1 | r = 0.664\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "results = {}\n",
    "results[\"iqm_garnet_old\"] = np.load(f\"./Data/iqm_garnet/20_SQ.npy\", allow_pickle=True).item()\n",
    "results[\"iqm_garnet_new\"] = np.load(f\"./Data/iqm_garnet/20_NL.npy\", allow_pickle=True).item()\n",
    "iqm_spark = np.load(f\"./Data/iqm_spark/5_SQ.npy\", allow_pickle=True).item()\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "colors = plt.get_cmap(\"Set2\")\n",
    "# Iterate over each backend to plot performance results\n",
    "for i, name in enumerate(results.keys()):\n",
    "    delta = results[name][\"Deltas\"][0]  # Extract delta value\n",
    "    ps = results[name][\"ps\"]  # Extract QAOA layer depths\n",
    "    best_mean = 0  # Store the best mean result\n",
    "\n",
    "    # Iterate over all available sections of post-processed results\n",
    "    for sec_i in results[name][f\"postprocessing{case}\"][delta][ps[0]].keys():\n",
    "        res_i = [results[name][f\"postprocessing{case}\"][delta][p][sec_i][prop] for p in ps]\n",
    "\n",
    "        # Update best section based on the highest mean value\n",
    "        if np.mean(res_i) > best_mean:\n",
    "            best_sec = res_i\n",
    "            best_mean = np.mean(res_i)\n",
    "\n",
    "    # Print the best approximation ratio for this backend\n",
    "    print(f\"QPU = {name} | delta = {delta} | r = {round(max(best_sec), 3)}\")\n",
    "\n",
    "    # Plot the performance of this backend\n",
    "    ax.plot(results[name][\"ps\"], best_sec, label=name, color=colors(i), markeredgecolor=\"black\", markersize=7, marker=\"o\")\n",
    "ax.plot(ps, [iqm_spark[\"postprocessing\"+case][1][p][prop] for p in ps], label =\"iqm_spark \", markeredgecolor=\"black\", markersize=7, color=colors(3), marker=\"X\")\n",
    "ax.legend()\n",
    "\n",
    "ax.set_ylabel(\"r\")\n",
    "ax.set_xlabel(\"p\")\n",
    "\n",
    "fig.savefig(\"./Figures/NL_IQM.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphs = np.load(\"./Data/NL_regular_graph.npy\", allow_pickle=True).item()\n",
    "colors = plt.get_cmap(\"Set1\")\n",
    "fig, ax = plt.subplots()\n",
    "labels = {\"regular_graph_3\":\"3 Regular Graph\",\"regular_graph_4\":\"4 Regular Graph\",\"regular_graph_5\":\"5 Regular Graph\"}\n",
    "for i, graph_name in enumerate([\"regular_graph_3\", \"regular_graph_4\", \"regular_graph_5\"]):\n",
    "    for nq in [20]:\n",
    "        mean = []\n",
    "        for kk in graphs[graph_name][nq].keys():\n",
    "            mean.append([graphs[graph_name][nq][kk][p][\"r\"] for p in graphs[graph_name][nq][kk][\"ps\"]])\n",
    "        mean = np.array(mean)\n",
    "        ax.fill_between(graphs[graph_name][nq][kk][\"ps\"], mean.mean(axis=0)-mean.std(axis=0),  mean.mean(axis=0)+mean.std(axis=0), alpha=0.4, color=colors(i), label=labels[graph_name] + f\" Nq={nq}\")\n",
    "        ax.plot(graphs[graph_name][nq][kk][\"ps\"], mean.mean(axis=0), color=colors(i), linewidth=2)\n",
    "\n",
    "ax.plot(graphs[\"iqm_garnet\"][20][0][\"ps\"], [graphs[\"iqm_garnet\"][20][0][p][\"r\"] for p in graphs[\"iqm_garnet\"][20][0][\"ps\"]], label=\"NL-iqm_garnet\", color=\"black\", linewidth=2, linestyle=\"--\")\n",
    "\n",
    "ax.legend()\n",
    "fig.savefig(f\"./Figures/NL/graphs_nq{nq}.pdf\", bbox_inches=\"tight\", transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphs = np.load(\"./Data/NL_regular_graph.npy\", allow_pickle=True).item()\n",
    "colors = plt.get_cmap(\"Set2\")\n",
    "fig, ax = plt.subplots()\n",
    "labels = {\"regular_graph_3\":\"3 Regular Graph\",\"regular_graph_4\":\"4 Regular Graph\",\"regular_graph_5\":\"5 Regular Graph\"}\n",
    "for i, graph_name in enumerate([\"regular_graph_3\", \"regular_graph_4\", \"regular_graph_5\"]):\n",
    "    for nq in [10]:\n",
    "        mean = []\n",
    "        for kk in graphs[graph_name][nq].keys():\n",
    "            mean.append([graphs[graph_name][nq][kk][p][\"r\"] for p in graphs[graph_name][nq][kk][\"ps\"]])\n",
    "        mean = np.array(mean)\n",
    "        ax.fill_between(graphs[graph_name][nq][kk][\"ps\"], mean.mean(axis=0)-mean.std(axis=0),  mean.mean(axis=0)+mean.std(axis=0), alpha=0.4, color=colors(i), label=labels[graph_name] + f\" Nq={nq}\")\n",
    "        ax.plot(graphs[graph_name][nq][kk][\"ps\"], mean.mean(axis=0), color=colors(i), linewidth=2)\n",
    "\n",
    "ax.plot(graphs[\"iqm_garnet\"][20][0][\"ps\"], [graphs[\"iqm_garnet\"][20][0][p][\"r\"] for p in graphs[\"iqm_garnet\"][20][0][\"ps\"]], label=\"NL-iqm_garnet\", color=\"black\", linewidth=2, linestyle=\"--\")\n",
    "\n",
    "ax.legend()\n",
    "fig.savefig(f\"./Figures/NL/graphs_nq{nq}.pdf\", bbox_inches=\"tight\", transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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